From 8a090a8a085a16eec6bf1f29e4eb839504c0afc3 Mon Sep 17 00:00:00 2001 From: husein zolkepli Date: Sat, 1 Jun 2019 13:22:18 +0800 Subject: [PATCH] release version 2.4 --- README.rst | 11 +- docs/Api.rst | 9 +- docs/Dataset.rst | 254 +- docs/Relevancy.rst | 9 + docs/index.rst | 1 + docs/load-emotion.rst | 472 +- docs/load-emotion_files/load-emotion_14_0.png | Bin 8314 -> 0 bytes docs/load-emotion_files/load-emotion_16_0.png | Bin 8515 -> 0 bytes docs/load-emotion_files/load-emotion_17_0.png | Bin 0 -> 8294 bytes docs/load-emotion_files/load-emotion_18_0.png | Bin 10418 -> 0 bytes docs/load-emotion_files/load-emotion_20_0.png | Bin 0 -> 86214 bytes docs/load-relevancy.rst | 368 + .../load-relevancy_13_0.png | Bin 0 -> 34705 bytes .../load-relevancy_16_0.png | Bin 0 -> 131914 bytes docs/load-sentiment.rst | 310 +- .../load-sentiment_15_0.png | Bin 21455 -> 0 bytes .../load-sentiment_17_0.png | Bin 20654 -> 0 bytes .../load-sentiment_18_0.png | Bin 0 -> 20233 bytes 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.../load-subjectivity_20_0.png | Bin 0 -> 99961 bytes example/toxicity/README.rst | 227 +- example/toxicity/load-toxic.ipynb | 281 +- .../load-toxic_files/load-toxic_23_0.png | Bin 8715 -> 0 bytes .../load-toxic_files/load-toxic_24_0.png | Bin 0 -> 8392 bytes .../load-toxic_files/load-toxic_25_0.png | Bin 9096 -> 0 bytes .../load-toxic_files/load-toxic_27_0.png | Bin 10057 -> 90124 bytes malaya/__init__.py | 5 +- malaya/_models/_sklearn_model.py | 263 +- malaya/_models/_tensorflow_model.py | 633 +- malaya/_utils/_html.py | 314 + malaya/_utils/_paths.py | 309 +- malaya/_utils/_server.py | 113 + malaya/_utils/_softmax_class.py | 202 +- malaya/_utils/_utils.py | 10 + malaya/_utils/web/index.html | 164 + malaya/_utils/web/index_emotion.html | 208 + malaya/_utils/web/index_toxic.html | 208 + .../web/static/admin-materialize.min.css | 9925 +++++++++++++++++ malaya/_utils/web/static/dashboard.min.js | 25 + malaya/_utils/web/static/echarts.min.js | 2002 ++++ malaya/_utils/web/static/jquery.min.js | 4 + malaya/emotion.py | 30 +- malaya/preprocessing.py | 45 + malaya/relevancy.py | 53 + malaya/sentiment.py | 49 +- malaya/subjective.py | 33 +- malaya/toxic.py | 109 +- session/sentiment/bahdanau.ipynb | 1111 +- session/sentiment/bert.ipynb | 1946 ---- session/sentiment/bert_model.py | 1097 -- session/sentiment/bidirectional.ipynb | 1563 --- session/sentiment/entity-network.ipynb | 1101 -- session/sentiment/entity_network.py | 317 - session/sentiment/fast-text.ipynb | 1390 --- session/sentiment/hierarchical.ipynb | 1491 --- session/sentiment/luong.ipynb | 1118 +- session/sentiment/recurrent-char.ipynb | 1170 -- session/sentiment/self-attention.ipynb | 1260 +++ session/sentiment/tokenization.ipynb | 362 + session/toxic/bahdanau.ipynb | 721 +- session/toxic/entity-network.ipynb | 776 -- session/toxic/fast-text.ipynb | 1216 -- session/toxic/hierarchical.ipynb | 873 -- session/toxic/luong.ipynb | 724 +- session/toxic/self-attention.ipynb | 955 ++ session/toxic/tokenization.ipynb | 332 + setup-gpu.py | 9 +- setup.py | 9 +- 104 files changed, 22223 insertions(+), 17876 deletions(-) create mode 100644 docs/Relevancy.rst delete mode 100644 docs/load-emotion_files/load-emotion_14_0.png delete mode 100644 docs/load-emotion_files/load-emotion_16_0.png create mode 100644 docs/load-emotion_files/load-emotion_17_0.png delete mode 100644 docs/load-emotion_files/load-emotion_18_0.png create mode 100644 docs/load-emotion_files/load-emotion_20_0.png create mode 100644 docs/load-relevancy.rst create mode 100644 docs/load-relevancy_files/load-relevancy_13_0.png create mode 100644 docs/load-relevancy_files/load-relevancy_16_0.png delete mode 100644 docs/load-sentiment_files/load-sentiment_15_0.png delete mode 100644 docs/load-sentiment_files/load-sentiment_17_0.png create mode 100644 docs/load-sentiment_files/load-sentiment_18_0.png delete mode 100644 docs/load-sentiment_files/load-sentiment_19_0.png create mode 100644 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delete mode 100644 session/sentiment/fast-text.ipynb delete mode 100644 session/sentiment/hierarchical.ipynb delete mode 100644 session/sentiment/recurrent-char.ipynb create mode 100644 session/sentiment/self-attention.ipynb create mode 100644 session/sentiment/tokenization.ipynb delete mode 100644 session/toxic/entity-network.ipynb delete mode 100644 session/toxic/fast-text.ipynb delete mode 100644 session/toxic/hierarchical.ipynb create mode 100644 session/toxic/self-attention.ipynb create mode 100644 session/toxic/tokenization.ipynb diff --git a/README.rst b/README.rst index 56167ccd..2f4d6d9e 100644 --- a/README.rst +++ b/README.rst @@ -42,7 +42,7 @@ Features - **Emotion Analysis** - From BERT, Fast-Text, Dynamic-Memory Network, Sparse Tensorflow, Attention Neural Network to build deep emotion analysis models. + From Attention-Recurrent model, Sparse Tensorflow, Self-Attention to build deep emotion analysis models. - **Entities Recognition** Latest state-of-art CRF deep learning models to do Naming Entity Recognition. @@ -66,16 +66,19 @@ Features - **ELMO (biLM)** Provide pretrained bahasa wikipedia and bahasa news ELMO, with easy interface and visualization. +- **Relevancy Analysis** + + From Dilated Convolutional Neural Network and Self-Attention to build deep relevancy analysis models. - **Sentiment Analysis** - From BERT, Fast-Text, Dynamic-Memory Network, Sparse Tensorflow, Attention Neural Network to build deep sentiment analysis models. + From Attention-Recurrent model, Sparse Tensorflow and Self-Attention to build deep sentiment analysis models. - **Spell Correction** Using local Malaysia NLP researches to auto-correct any bahasa words. - Stemmer - **Subjectivity Analysis** - From BERT, Fast-Text, Dynamic-Memory Network, Sparse Tensorflow, Attention Neural Network to build deep subjectivity analysis models. + From Attention-Recurrent model, Sparse Tensorflow and Self-Attention to build deep subjectivity analysis models. - **Summarization** Using skip-thought with attention state-of-art to give precise unsupervised summarization. @@ -84,7 +87,7 @@ Features Provide LDA2Vec, LDA, NMF and LSA interface for easy topic modelling with topics visualization. - **Toxicity Analysis** - From BERT, Fast-Text, Dynamic-Memory Network, Attention Neural Network to build deep toxicity analysis models. + From Attention-Recurrent model, Self-Attention to build deep toxicity analysis models. - **Word2Vec** Provide pretrained bahasa wikipedia and bahasa news Word2Vec, with easy interface and visualization. diff --git a/docs/Api.rst b/docs/Api.rst index 109f9390..8eccc823 100644 --- a/docs/Api.rst +++ b/docs/Api.rst @@ -69,9 +69,6 @@ malaya.normalize .. automodule:: malaya.normalize :members: -.. autoclass:: malaya.normalize._DEEP_NORMALIZER() - :members: - .. autoclass:: malaya.normalize._SPELL_NORMALIZE() :members: @@ -96,6 +93,12 @@ malaya.preprocessing .. automodule:: malaya.preprocessing :members: +malaya.relevancy +------------------ + +.. automodule:: malaya.relevancy + :members: + malaya.sentiment ----------------- diff --git a/docs/Dataset.rst b/docs/Dataset.rst index f0b373dd..69dfe8b1 100644 --- a/docs/Dataset.rst +++ b/docs/Dataset.rst @@ -1,6 +1,14 @@ Dataset ======= +.. raw:: html + +

+ + logo + +

+ We want to make sure not just the code we open-sourced, but also goes to dataset, so everyone can validate. @@ -8,252 +16,6 @@ You can check in `Malaya-Dataset `__ for our open dataset. -`Article `__ --------------------------------------------------------------------------------- - -Total size: 3.1 MB - -1. Filem -2. Kerajaan -3. Pembelajaran -4. Pendidikan -5. Sekolah - -`Dependency `__ -------------------------------------------------------------------------------------- - -`Dictionary, 24550 unique words `__ ---------------------------------------------------------------------------------------------------------- - -`Emotion `__ -------------------------------------------------------------------------------- - -Total size: 8.5 MB - -1. Anger -2. Fear -3. Joy -4. Love -5. Sadness -6. Surprise - -`Gender `__ ------------------------------------------------------------------------------ - -Total size: 2.2 MB - -1. Unknown -2. Male -3. Female -4. Brand - -`Irony `__ ---------------------------------------------------------------------------- - -Total size: 100 KB - -1. Positive -2. Negative - -`Entities, JSON `__ ---------------------------------------------------------------------------------------- - -Total size: 1.1 MB - -1. OTHER - Other -2. law - law, regulation, related law documents, documents, etc -3. location - location, place -4. organization - organization, company, government, facilities, etc -5. person - person, group of people, believes, etc -6. quantity - numbers, quantity -7. time - date, day, time, etc -8. event - unique event happened, etc - -`Karangan sekolah `__ -------------------------------------------------------------------------------------------------- - -Total size: 221 KB - -`Language-detection, Wikipedia `__ ----------------------------------------------------------------------------------------------------------------- - -`News, crawled `__ ----------------------------------------------------------------------------------- - -Total size: 28.9 MB - -.. raw:: html - -
- -Complete list (51 news) - -1. Cuti sekolah -2. isu 1MDB -3. isu agama -4. isu agong -5. isu agrikultur -6. isu air -7. isu anwar ibrahim -8. isu artis -9. isu astro -10. isu bahasa melayu -11. isu barisan nasional -12. isu cikgu -13. isu cukai -14. isu cyberjaya -15. isu dunia -16. isu ekonomi -17. isu gst -18. isu harakah -19. isu harga -20. isu icerd -21. isu imigren -22. isu kapitalis -23. isu kerajaan -24. isu kesihatan -25. isu kuala lumpur -26. isu lgbt -27. isu mahathir -28. isu makanan -29. isu malaysia airlines -30. isu malaysia -31. isu minyak -32. isu isu najib razak -33. isu pelajar -34. isu pelakon -35. isu pembangkang -36. isu perkauman -37. isu permainan -38. isu pertanian -39. isu politik -40. isu rosmah -41. isu sabah -42. isu sarawak -43. isu sosial media -44. isu sultan melayu -45. isu teknologi -46. isu TM -47. isu ubat -48. isu universiti -49. isu wan azizah -50. peluang pekerjaan -51. perkahwinan - -.. raw:: html - -
- -`Sentiment News `__ ---------------------------------------------------------------------------------------------- - -Total size: 496 KB - -1. Positive -2. Negative - -`Sentiment Twitter `__ ---------------------------------------------------------------------------------------------------- - -Total size: 50.6 MB - -1. Positive -2. Negative - -`Sentiment Multidomain `__ ------------------------------------------------------------------------------------------------------------ - -159 KB - -1. Amazon review, Positive and Negative -2. IMDB review, Positive and Negative -3. Yelp review, Positive and Negative - -`Part-of-Speech `__ ---------------------------------------------------------------------------------------------- - -Total size: 3.1 MB - -1. ADJ - Adjective, kata sifat -2. ADP - Adposition -3. ADV - Adverb, kata keterangan -4. ADX - Auxiliary verb, kata kerja tambahan -5. CCONJ - Coordinating conjuction, kata hubung -6. DET - Determiner, kata penentu -7. NOUN - Noun, kata nama -8. NUM - Number, nombor -9. PART - Particle -10. PRON - Pronoun, kata ganti -11. PROPN - Proper noun, kata ganti nama khas -12. SCONJ - Subordinating conjunction -13. SYM - Symbol -14. VERB - Verb, kata kerja -15. X - Other - -`Polarity `__ ---------------------------------------------------------------------------------- - -Total size: 1.3 MB - -1. Positive -2. Negative - -`Political landscape `__ -------------------------------------------------------------------------------------------------------- - -Total size: 2 MB - -1. Kerajaan -2. Pembangkang - -`Sarcastic news-headline `__ ---------------------------------------------------------------------------------------------------------------- - -1. Positive -2. Negative - -`Stemmer `__ -------------------------------------------------------------------------------- - -Total size: 6.5 MB - -1. News stemming -2. Wikipedia stemming - -`Subjectivity `__ ------------------------------------------------------------------------------------------ - -Total size: 1.4 MB - -1. Positive -2. Negative - -`Toxicity `__ ------------------------------------------------------------------------------------------ - -Total size: 70 MB - -Toxicity is multilabel, prefer to use sigmoid based. - -1. toxic -2. severe toxic -3. obscene -4. threat -5. insult -6. identity hate - -`Subtitle `__ ---------------------------------------------------------------------------------- - -Total size: 1.5 MB - -Suggestion ----------- - -1. Always apply text augmentation, like swapping based words using - synonyms or thesaurus. I still waiting respond from third-party to - open source Bahasa thesaurus. - Citation -------- diff --git a/docs/Relevancy.rst b/docs/Relevancy.rst new file mode 100644 index 00000000..187b804f --- /dev/null +++ b/docs/Relevancy.rst @@ -0,0 +1,9 @@ +Relevancy Analysis +=================== + +.. note:: + + This tutorial is available as an IPython notebook + `here `_. + +.. include:: load-relevancy.rst diff --git a/docs/index.rst b/docs/index.rst index b6fa8f16..b611eed4 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -34,6 +34,7 @@ Contents: Num2word Pos Preprocessing + Relevancy Sentiment Similarity Spell diff --git a/docs/load-emotion.rst b/docs/load-emotion.rst index a232b936..9f0361e4 100644 --- a/docs/load-emotion.rst +++ b/docs/load-emotion.rst @@ -7,23 +7,26 @@ .. parsed-literal:: - CPU times: user 11.5 s, sys: 819 ms, total: 12.3 s - Wall time: 12.5 s + CPU times: user 10.1 s, sys: 769 ms, total: 10.9 s + Wall time: 11 s .. code:: python - anger_text = 'aku cukup tak suka budak gemuk tu' - fear_text = 'saya takut dengan hantu' - joy_text = 'gembiranya hari ni, dapat jumpa crush' - love_text = 'saya terlalu cintakan dia' - sadness_text = 'kawan rapat aku putuskan hubungan' - surprise_text = 'terharu aku harini, semua orang cakap selamat hari jadi' + anger_text = 'babi la company ni, aku dah la penat datang dari jauh' + fear_text = 'takut doh tengok cerita hantu tadi' + joy_text = 'bestnya dapat tidur harini, tak payah pergi kerja' + love_text = 'aku sayang sgt dia dah doh' + sadness_text = 'kecewa tengok kerajaan baru ni, janji ape pun tak dapat' + surprise_text = 'sakit jantung aku, terkejut dengan cerita hantu tadi' All models got ``get_proba`` parameters. If True, it will returned probability every classes. Else, it will return highest probability class. **Default is True.** +I suggest you to use deep learning models to understand emotion, these +models are better for detecting emotions from social media. + Load multinomial model ---------------------- @@ -42,15 +45,15 @@ Load multinomial model .. parsed-literal:: - anger - {'anger': 0.30367763926253094, 'fear': 0.16709964152193366, 'joy': 0.17026521921403184, 'love': 0.18405977732934192, 'sadness': 0.1388341895665479, 'surprise': 0.03606353310561458} + joy + {'anger': 0.11733952772643724, 'fear': 0.15199690452609127, 'joy': 0.2589124946219144, 'love': 0.2024854510784686, 'sadness': 0.2211704631949802, 'surprise': 0.048095158852106114} .. parsed-literal:: - ['anger', 'fear', 'joy', 'joy', 'sadness', 'fear'] + ['joy', 'fear', 'anger', 'love', 'anger', 'fear'] @@ -72,50 +75,50 @@ Load xgb model .. parsed-literal:: - love - {'anger': 0.22918181, 'fear': 0.089252785, 'joy': 0.1318236, 'love': 0.46476611, 'sadness': 0.07200217, 'surprise': 0.012973559} + anger + {'anger': 0.37050414, 'fear': 0.17237191, 'joy': 0.21709572, 'love': 0.040401105, 'sadness': 0.16886166, 'surprise': 0.030765474} .. parsed-literal:: - [{'anger': 0.22918181, - 'fear': 0.089252785, - 'joy': 0.1318236, - 'love': 0.46476611, - 'sadness': 0.07200217, - 'surprise': 0.012973559}, - {'anger': 0.013483193, - 'fear': 0.939588, - 'joy': 0.01674833, - 'love': 0.003220023, - 'sadness': 0.022906518, - 'surprise': 0.0040539484}, - {'anger': 0.10506946, - 'fear': 0.025150253, - 'joy': 0.725915, - 'love': 0.05211037, - 'sadness': 0.078554265, - 'surprise': 0.013200594}, - {'anger': 0.11640434, - 'fear': 0.097485565, - 'joy': 0.24893147, - 'love': 0.25440857, - 'sadness': 0.2650988, - 'surprise': 0.01767122}, - {'anger': 0.27124837, - 'fear': 0.15662362, - 'joy': 0.131251, - 'love': 0.022184724, - 'sadness': 0.41255626, - 'surprise': 0.006135965}, - {'anger': 0.07513438, - 'fear': 0.2525073, - 'joy': 0.024355419, - 'love': 0.002638406, - 'sadness': 0.0059716892, - 'surprise': 0.6393928}] + [{'anger': 0.37050414, + 'fear': 0.17237191, + 'joy': 0.21709572, + 'love': 0.040401105, + 'sadness': 0.16886166, + 'surprise': 0.030765474}, + {'anger': 0.024576988, + 'fear': 0.9346102, + 'joy': 0.017333886, + 'love': 0.00388243, + 'sadness': 0.015722778, + 'surprise': 0.0038737422}, + {'anger': 0.6121963, + 'fear': 0.07615826, + 'joy': 0.12938385, + 'love': 0.021234306, + 'sadness': 0.13893352, + 'surprise': 0.022093719}, + {'anger': 0.078999326, + 'fear': 0.05360329, + 'joy': 0.05482416, + 'love': 0.7758813, + 'sadness': 0.03182298, + 'surprise': 0.004868941}, + {'anger': 0.7553716, + 'fear': 0.060115993, + 'joy': 0.01417792, + 'love': 0.005369766, + 'sadness': 0.15161343, + 'surprise': 0.01335139}, + {'anger': 0.07156373, + 'fear': 0.241878, + 'joy': 0.0074376594, + 'love': 0.0042003845, + 'sadness': 0.06661975, + 'surprise': 0.6083005}] @@ -131,148 +134,253 @@ List available deep learning models .. parsed-literal:: - ['fast-text', - 'hierarchical', - 'bahdanau', - 'luong', - 'bidirectional', - 'bert', - 'entity-network'] + ['self-attention', 'bahdanau', 'luong'] + +Load deep learning models +------------------------- + +Good thing about deep learning models from Malaya, it returns +``Attention`` result, means, which part of words give the high impact to +the results. But to get ``Attention``, you need to set +``get_proba=True``. .. code:: python - for i in malaya.emotion.available_deep_model(): - print('Testing %s model'%(i)) - model = malaya.emotion.deep_model(i) - print(model.predict(anger_text)) - print(model.predict_batch([anger_text, - fear_text, - joy_text, - love_text, - sadness_text, - surprise_text])) - print(model.predict_batch([anger_text, - fear_text, - joy_text, - love_text, - sadness_text, - surprise_text], get_proba = True)) - print() + import matplotlib.pyplot as plt + import seaborn as sns + sns.set() + +Load bahdanau model +~~~~~~~~~~~~~~~~~~~ + +.. code:: python + + model = malaya.emotion.deep_model('bahdanau') + +Predict single string +^^^^^^^^^^^^^^^^^^^^^ + +.. code:: python + + model.predict(anger_text) + + .. parsed-literal:: - Testing fast-text model - love - ['love', 'fear', 'joy', 'love', 'sadness', 'surprise'] - [{'anger': 2.538603e-07, 'fear': 4.1372344e-13, 'joy': 1.0892472e-08, 'love': 0.99999976, 'sadness': 3.8994935e-16, 'surprise': 2.439655e-08}, {'anger': 4.4489467e-24, 'fear': 1.0, 'joy': 1.3903143e-28, 'love': 1.7920514e-33, 'sadness': 1.01771616e-26, 'surprise': 6.799581e-18}, {'anger': 9.583714e-26, 'fear': 1.5029816e-24, 'joy': 1.0, 'love': 3.7527533e-13, 'sadness': 8.348174e-24, 'surprise': 2.080897e-16}, {'anger': 1.7409228e-13, 'fear': 3.2279754e-12, 'joy': 0.0005876841, 'love': 0.9994123, 'sadness': 1.8902605e-11, 'surprise': 9.9256076e-11}, {'anger': 1.2737708e-11, 'fear': 5.882562e-10, 'joy': 9.112171e-13, 'love': 7.7659496e-20, 'sadness': 1.0, 'surprise': 1.6035637e-16}, {'anger': 5.5730725e-37, 'fear': 0.16033638, 'joy': 1.2999706e-30, 'love': 0.0, 'sadness': 0.0, 'surprise': 0.8396636}] + 'anger' - Testing hierarchical model - anger - ['anger', 'fear', 'joy', 'joy', 'sadness', 'joy'] - [{'anger': 0.2683842, 'fear': 0.3206215, 'joy': 0.17552154, 'love': 0.041160323, 'sadness': 0.1729689, 'surprise': 0.021343619}, {'anger': 0.006326744, 'fear': 0.97895914, 'joy': 0.01049677, 'love': 0.0004076402, 'sadness': 0.0026508307, 'surprise': 0.0011588057}, {'anger': 0.018994415, 'fear': 0.025846783, 'joy': 0.87117314, 'love': 0.039761506, 'sadness': 0.01727374, 'surprise': 0.026950512}, {'anger': 0.030335624, 'fear': 0.071699485, 'joy': 0.5445254, 'love': 0.24373391, 'sadness': 0.08821325, 'surprise': 0.021492302}, {'anger': 0.022141613, 'fear': 0.01158712, 'joy': 0.0046922215, 'love': 0.0013928033, 'sadness': 0.95863473, 'surprise': 0.0015515403}, {'anger': 0.028982855, 'fear': 0.15079749, 'joy': 0.4619698, 'love': 0.18604991, 'sadness': 0.009463493, 'surprise': 0.16273652}] - Testing bahdanau model - love - ['anger', 'fear', 'joy', 'love', 'sadness', 'surprise'] - [{'anger': 0.4797056, 'fear': 0.168805, 'joy': 0.01420066, 'love': 0.25597632, 'sadness': 0.07191573, 'surprise': 0.009396704}, {'anger': 0.0008658771, 'fear': 0.9851621, 'joy': 0.0026717593, 'love': 0.00049689104, 'sadness': 0.0037894566, 'surprise': 0.007013949}, {'anger': 0.047901724, 'fear': 0.0058520515, 'joy': 0.6818599, 'love': 0.22279568, 'sadness': 0.010295693, 'surprise': 0.03129497}, {'anger': 0.0136362715, 'fear': 0.025698097, 'joy': 0.13118966, 'love': 0.75916684, 'sadness': 0.016278507, 'surprise': 0.05403061}, {'anger': 0.010733987, 'fear': 0.023202764, 'joy': 0.005261726, 'love': 0.00054325344, 'sadness': 0.9586973, 'surprise': 0.0015610264}, {'anger': 0.0028718843, 'fear': 0.21446393, 'joy': 0.034736242, 'love': 0.004341282, 'sadness': 0.001976919, 'surprise': 0.7416098}] - Testing luong model - love - ['love', 'fear', 'joy', 'love', 'sadness', 'surprise'] - [{'anger': 0.0027926113, 'fear': 0.0042426162, 'joy': 0.6864444, 'love': 0.2944655, 'sadness': 0.0010767934, 'surprise': 0.010978072}, {'anger': 0.0057431706, 'fear': 0.9705748, 'joy': 0.0043815267, 'love': 0.0008089542, 'sadness': 0.009230111, 'surprise': 0.009261452}, {'anger': 0.0016454123, 'fear': 0.0012319328, 'joy': 0.87483215, 'love': 0.0678572, 'sadness': 0.010661957, 'surprise': 0.043771397}, {'anger': 0.016030224, 'fear': 0.005272452, 'joy': 0.04561528, 'love': 0.31633487, 'sadness': 0.59368426, 'surprise': 0.023062926}, {'anger': 0.0010896003, 'fear': 0.00057456765, 'joy': 4.035251e-05, 'love': 6.2987274e-05, 'sadness': 0.9982096, 'surprise': 2.283629e-05}, {'anger': 0.00042841653, 'fear': 0.33654872, 'joy': 0.00097602647, 'love': 0.00029422223, 'sadness': 0.00032926636, 'surprise': 0.6614233}] +.. code:: python + + model.predict(fear_text) - Testing bidirectional model - love - ['love', 'fear', 'anger', 'joy', 'sadness', 'surprise'] - [{'anger': 0.04017815, 'fear': 0.40091494, 'joy': 0.005757856, 'love': 0.44828272, 'sadness': 0.00045654154, 'surprise': 0.10440978}, {'anger': 0.0035579149, 'fear': 0.97265065, 'joy': 0.020677356, 'love': 0.000660097, 'sadness': 0.0010607935, 'surprise': 0.0013932855}, {'anger': 0.48268953, 'fear': 0.35196006, 'joy': 0.029008513, 'love': 0.014688243, 'sadness': 0.018093651, 'surprise': 0.10355998}, {'anger': 0.029928548, 'fear': 0.05067155, 'joy': 0.65202534, 'love': 0.10342105, 'sadness': 0.059337158, 'surprise': 0.10461627}, {'anger': 0.06666384, 'fear': 0.057405185, 'joy': 0.06833278, 'love': 0.0036288379, 'sadness': 0.79270375, 'surprise': 0.011265635}, {'anger': 2.1909349e-05, 'fear': 0.0034499653, 'joy': 1.994826e-06, 'love': 0.005034323, 'sadness': 1.7297366e-06, 'surprise': 0.99149007}] - Testing bert model - anger - ['anger', 'anger', 'anger', 'anger', 'anger', 'anger'] - [{'anger': 0.79530007, 'fear': 0.043149363, 'joy': 0.050191555, 'love': 0.0028053583, 'sadness': 0.1083552, 'surprise': 0.0001983959}, {'anger': 0.7761929, 'fear': 0.02267685, 'joy': 0.08533038, 'love': 0.019361326, 'sadness': 0.09622978, 'surprise': 0.00020885638}, {'anger': 0.724599, 'fear': 0.021534633, 'joy': 0.14938025, 'love': 0.009412263, 'sadness': 0.09488238, 'surprise': 0.0001914676}, {'anger': 0.8217926, 'fear': 0.009756618, 'joy': 0.061514165, 'love': 0.03527268, 'sadness': 0.07142815, 'surprise': 0.00023569519}, {'anger': 0.9093987, 'fear': 0.00811897, 'joy': 0.024754424, 'love': 0.003218321, 'sadness': 0.054415427, 'surprise': 9.422473e-05}, {'anger': 0.9215124, 'fear': 0.009484482, 'joy': 0.023237498, 'love': 0.0027847919, 'sadness': 0.042906344, 'surprise': 7.447611e-05}] - Testing entity-network model - joy - ['joy', 'sadness', 'joy', 'sadness', 'sadness', 'joy'] - [{'anger': 0.11245817, 'fear': 0.09678851, 'joy': 0.29964533, 'love': 0.07372399, 'sadness': 0.26239878, 'surprise': 0.15498528}, {'anger': 0.12070423, 'fear': 0.13202831, 'joy': 0.22073878, 'love': 0.031163175, 'sadness': 0.3202514, 'surprise': 0.175114}, {'anger': 0.11448454, 'fear': 0.10408847, 'joy': 0.2848294, 'love': 0.059466686, 'sadness': 0.27815202, 'surprise': 0.1589789}, {'anger': 0.12346853, 'fear': 0.15664044, 'joy': 0.17575133, 'love': 0.019622162, 'sadness': 0.33732292, 'surprise': 0.18719462}, {'anger': 0.117459856, 'fear': 0.115517266, 'joy': 0.25831792, 'love': 0.044844825, 'sadness': 0.2980614, 'surprise': 0.16579871}, {'anger': 0.11082334, 'fear': 0.09062623, 'joy': 0.30381778, 'love': 0.097978726, 'sadness': 0.24158238, 'surprise': 0.15517157}] +.. parsed-literal:: + + 'fear' -Unsupervised important words learning -------------------------------------- .. code:: python - import matplotlib.pyplot as plt - import seaborn as sns - sns.set() # i just really like seaborn colors + result = model.predict(sadness_text,get_proba=True) + result + + + + +.. parsed-literal:: + + {'anger': 0.38946533, + 'fear': 0.0006788928, + 'joy': 4.502521e-05, + 'love': 0.00035775406, + 'sadness': 0.58941084, + 'surprise': 0.020042183, + 'attention': {'kecewa': 0.9982584, + 'tengok': 0.00024031276, + 'kerajaan': 0.00020539697, + 'baru': 0.00023952134, + 'ni': 0.00016858104, + ',': 0.0, + 'janji': 0.00020889708, + 'ape': 0.00017103164, + 'pun': 0.00016638139, + 'tak': 0.00017561934, + 'dapat': 0.00016583406}} -We need to set ``get_proba`` become True to get the ‘attention’. -Visualizing bahdanau model -^^^^^^^^^^^^^^^^^^^^^^^^^^ .. code:: python - model = malaya.emotion.deep_model('bahdanau') - result = model.predict(surprise_text, get_proba = True)['attention'] - - plt.figure(figsize = (15, 7)) - labels = [r[0] for r in result] - val = [r[1] for r in result] - aranged = [i for i in range(len(labels))] - plt.bar(aranged, val) - plt.xticks(aranged, labels, rotation = 'vertical') + plt.figure(figsize = (15, 5)) + keys = result['attention'].keys() + values = result['attention'].values() + aranged = [i for i in range(len(keys))] + plt.bar(aranged, values) + plt.xticks(aranged, keys, rotation = 'vertical') plt.show() -.. image:: load-emotion_files/load-emotion_14_0.png +.. image:: load-emotion_files/load-emotion_17_0.png -Visualizing luong model -^^^^^^^^^^^^^^^^^^^^^^^ +Open emotion visualization dashboard +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Default when you call ``predict_words`` it will open a browser with +visualization dashboard, you can disable by ``visualization=False``. .. code:: python - model = malaya.emotion.deep_model('luong') - result = model.predict(surprise_text, get_proba = True)['attention'] + model.predict_words(sadness_text) - plt.figure(figsize = (15, 7)) - labels = [r[0] for r in result] - val = [r[1] for r in result] - aranged = [i for i in range(len(labels))] - plt.bar(aranged, val) - plt.xticks(aranged, labels, rotation = 'vertical') - plt.show() + +.. parsed-literal:: + + Serving to http://127.0.0.1:8889/ [Ctrl-C to exit] + + +.. parsed-literal:: + + 127.0.0.1 - - [01/Jun/2019 12:09:32] "GET / HTTP/1.1" 200 - + 127.0.0.1 - - [01/Jun/2019 12:09:33] "GET /static/admin-materialize.min.css HTTP/1.1" 200 - + 127.0.0.1 - - [01/Jun/2019 12:09:33] "GET /static/echarts.min.js HTTP/1.1" 200 - + 127.0.0.1 - - [01/Jun/2019 12:09:33] "GET /favicon.ico HTTP/1.1" 200 - + ---------------------------------------- + Exception happened during processing of request from ('127.0.0.1', 61764) + Traceback (most recent call last): + File "/usr/local/Cellar/python/3.6.5_1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/socketserver.py", line 317, in _handle_request_noblock + self.process_request(request, client_address) + File "/usr/local/Cellar/python/3.6.5_1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/socketserver.py", line 348, in process_request + self.finish_request(request, client_address) + File "/usr/local/Cellar/python/3.6.5_1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/socketserver.py", line 361, in finish_request + self.RequestHandlerClass(request, client_address, self) + File "/usr/local/Cellar/python/3.6.5_1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/socketserver.py", line 696, in __init__ + self.handle() + File "/usr/local/Cellar/python/3.6.5_1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/http/server.py", line 418, in handle + self.handle_one_request() + File "/usr/local/Cellar/python/3.6.5_1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/http/server.py", line 406, in handle_one_request + method() + File "/Users/huseinzol/Documents/Malaya/malaya/_utils/_server.py", line 32, in do_GET + with open(filepath, 'rb') as fh: + FileNotFoundError: [Errno 2] No such file or directory: '/Users/huseinzol/Documents/Malaya/malaya/_utils/web/favicon.ico' + ---------------------------------------- +.. parsed-literal:: -.. image:: load-emotion_files/load-emotion_16_0.png + stopping Server... -Visualizing hierarchical model -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. code:: python - model = malaya.emotion.deep_model('hierarchical') - result = model.predict(surprise_text, get_proba=True)['attention'] + from IPython.core.display import Image, display - plt.figure(figsize = (15, 7)) - labels = [r[0] for r in result] - val = [r[1] for r in result] - aranged = [i for i in range(len(labels))] - plt.bar(aranged, val) - plt.xticks(aranged, labels, rotation = 'vertical') - plt.show() + display(Image('emotion-bahdanau.png', width=800)) + + + +.. image:: load-emotion_files/load-emotion_20_0.png + :width: 800px + + +I tried to put the html and javascript inside a notebook cell, pretty +hard you know and a lot of weird bugs. Let stick to HTTP serving ya. + +``predict_words`` only accept a single string. You can’t predict +multiple texts. +Predict batch of strings +^^^^^^^^^^^^^^^^^^^^^^^^ + +.. code:: python + + model.predict_batch([anger_text, fear_text, joy_text, + love_text, sadness_text, surprise_text],get_proba=True) + + + + +.. parsed-literal:: + + [{'anger': 0.9470582, + 'fear': 0.00033167106, + 'joy': 9.066894e-06, + 'love': 0.00016375592, + 'sadness': 0.0035682153, + 'surprise': 0.04886904}, + {'anger': 0.013118468, + 'fear': 0.9863961, + 'joy': 5.042084e-05, + 'love': 0.0001933256, + 'sadness': 7.211378e-05, + 'surprise': 0.00016965455}, + {'anger': 0.87134695, + 'fear': 0.008704395, + 'joy': 0.00010600241, + 'love': 0.00063341705, + 'sadness': 0.10522701, + 'surprise': 0.013982114}, + {'anger': 0.007687589, + 'fear': 0.00057056156, + 'joy': 0.0033636452, + 'love': 0.98015594, + 'sadness': 0.003200713, + 'surprise': 0.0050216294}, + {'anger': 0.3216797, + 'fear': 0.00076514384, + 'joy': 3.4114866e-05, + 'love': 0.00025139845, + 'sadness': 0.6674833, + 'surprise': 0.009786241}, + {'anger': 0.028422995, + 'fear': 0.0025764697, + 'joy': 0.00012938023, + 'love': 0.00037871674, + 'sadness': 0.36064965, + 'surprise': 0.60784286}] + + + +**You might want to try ``luong`` and ``self-attention`` by yourself.** + +Stacking models +--------------- + +More information, you can read at +https://malaya.readthedocs.io/en/latest/Stack.html + +.. code:: python + + multinomial = malaya.emotion.multinomial() + xgb = malaya.emotion.xgb() + +.. code:: python + + malaya.stack.predict_stack([multinomial, xgb, model], anger_text) + + + + +.. parsed-literal:: + {'anger': 0.34863618616181175, + 'fear': 0.022930197531217115, + 'joy': 0.006847226031582371, + 'love': 0.014764885318498622, + 'sadness': 0.030968564413978033, + 'surprise': 0.032697431654758996} -.. image:: load-emotion_files/load-emotion_18_0.png Load Sparse deep learning models @@ -324,14 +432,14 @@ will try to evolve it. .. code:: python - sparse_model.predict(sadness_text) + sparse_model.predict(love_text) .. parsed-literal:: - 'sadness' + 'love' @@ -349,7 +457,7 @@ will try to evolve it. .. parsed-literal:: - ['love', 'fear', 'joy', 'love', 'sadness', 'fear'] + ['joy', 'fear', 'sadness', 'love', 'anger', 'surprise'] @@ -367,39 +475,39 @@ will try to evolve it. .. parsed-literal:: - [{'anger': 0.07479232, - 'fear': 0.012134718, - 'joy': 0.034137156, - 'love': 0.85221285, - 'sadness': 0.006336733, - 'surprise': 0.020386234}, - {'anger': 1.6892743e-08, - 'fear': 0.99999964, - 'joy': 6.260633e-08, - 'love': 3.2111713e-10, - 'sadness': 3.542872e-08, - 'surprise': 2.2207877e-07}, - {'anger': 0.00012469916, - 'fear': 9.6892345e-06, - 'joy': 0.9917463, - 'love': 0.006561422, - 'sadness': 0.00040069615, - 'surprise': 0.0011572224}, - {'anger': 5.0021445e-05, - 'fear': 0.0010109642, - 'joy': 0.049688663, - 'love': 0.94577587, - 'sadness': 0.0032941191, - 'surprise': 0.00018034693}, - {'anger': 0.0010146926, - 'fear': 0.00020020001, - 'joy': 5.2909185e-05, - 'love': 2.640257e-06, - 'sadness': 0.99870074, - 'surprise': 2.8823646e-05}, - {'anger': 0.0057854424, - 'fear': 0.8317998, - 'joy': 0.017287944, - 'love': 0.008883897, - 'sadness': 0.0070799366, - 'surprise': 0.12916291}] + [{'anger': 0.04526143, + 'fear': 0.09979316, + 'joy': 0.4658971, + 'love': 0.020664953, + 'sadness': 0.30431935, + 'surprise': 0.064064085}, + {'anger': 6.130409e-06, + 'fear': 0.9997508, + 'joy': 2.2800703e-05, + 'love': 3.8201404e-07, + 'sadness': 4.3520595e-06, + 'surprise': 0.00021553691}, + {'anger': 0.20740777, + 'fear': 0.22583336, + 'joy': 0.017892161, + 'love': 0.0037904498, + 'sadness': 0.5115712, + 'surprise': 0.033505023}, + {'anger': 5.2901927e-05, + 'fear': 6.0754595e-05, + 'joy': 1.1822221e-05, + 'love': 0.9991229, + 'sadness': 0.00072059815, + 'surprise': 3.093863e-05}, + {'anger': 0.48188186, + 'fear': 0.33322, + 'joy': 0.018766977, + 'love': 0.00092354603, + 'sadness': 0.078747146, + 'surprise': 0.086460434}, + {'anger': 0.007138084, + 'fear': 0.2971508, + 'joy': 0.0024597635, + 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Can be a positive or negative +sentiment. + +Negative relevancy: The article or piece of text is not relevant, +tendency is high to become a fake news. Can be a positive or negative +sentiment. + +Right now relevancy module only support deep learning model. + +.. code:: python + + negative_text = 'Roti Massimo Mengandungi DNA Babi. Roti produk Massimo keluaran Syarikat The Italian Baker mengandungi DNA babi. Para pengguna dinasihatkan supaya tidak memakan produk massimo. Terdapat pelbagai produk roti keluaran syarikat lain yang boleh dimakan dan halal. Mari kita sebarkan berita ini supaya semua rakyat Malaysia sedar dengan apa yang mereka makna setiap hari. Roti tidak halal ada DNA babi jangan makan ok.' + positive_text = 'Jabatan Kemajuan Islam Malaysia memperjelaskan dakwaan sebuah mesej yang dikitar semula, yang mendakwa kononnya kod E dikaitkan dengan kandungan lemak babi sepertimana yang tular di media sosial. . Tular: November 2017 . Tular: Mei 2014 JAKIM ingin memaklumkan kepada masyarakat berhubung maklumat yang telah disebarkan secara meluas khasnya melalui media sosial berhubung kod E yang dikaitkan mempunyai lemak babi. Untuk makluman, KOD E ialah kod untuk bahan tambah (aditif) dan ianya selalu digunakan pada label makanan di negara Kesatuan Eropah. Menurut JAKIM, tidak semua nombor E yang digunakan untuk membuat sesuatu produk makanan berasaskan dari sumber yang haram. Sehubungan itu, sekiranya sesuatu produk merupakan produk tempatan dan mendapat sijil Pengesahan Halal Malaysia, maka ia boleh digunakan tanpa was-was sekalipun mempunyai kod E-kod. Tetapi sekiranya produk tersebut bukan produk tempatan serta tidak mendapat sijil pengesahan halal Malaysia walaupun menggunakan e-kod yang sama, pengguna dinasihatkan agar berhati-hati dalam memilih produk tersebut.' + +List available deep learning models +----------------------------------- + +.. code:: python + + malaya.relevancy.available_deep_model() + + + + +.. parsed-literal:: + + ['self-attention', 'dilated-cnn'] + + + +As you can see, we are not using recurrent architecture for relevancy +classification. Article or news can be really a long text, and when +talking about recurrent on long text dependency, we might have a problem +about gradient vanishing or long term dependency, plus it is very +expensive to calculate. + +Load deep learning models +------------------------- + +Good thing about deep learning models from Malaya, it returns +``Attention`` result, means, which part of words give the high impact to +the results. But to get ``Attention``, you need to set +``get_proba=True``. + +.. code:: python + + import matplotlib.pyplot as plt + import seaborn as sns + sns.set() + +Load dilated-cnn model +~~~~~~~~~~~~~~~~~~~~~~ + +.. code:: python + + model = malaya.relevancy.deep_model('dilated-cnn') + + +.. parsed-literal:: + + downloading frozen /Users/huseinzol/Malaya/relevancy/self-attention model + + +.. parsed-literal:: + + 94.0MB [00:33, 3.73MB/s] + 0%| | 0.00/7.17 [00:00W4v)6W0193+3da7T;H7Yo8Madm8r2F`{_%k zX=rHJ^>6E#)6mcd)6mdHo?@c*{48z?r8W%yH}&tHqW*-RdK5?fo5kn0wLc9F>)DfE z+Wv{iLu#i=fbRVO3vWmO^r4>%jpxGv9}n*U4>zaFK`ws&Zr)y(!P3{HgZ4hZm3 zm67@P6QsTUTxFD_)yipTF4O4i+_)Q@u{;sdBy=yNc%v~LlJ#_0{L`t&n<||InqmM8 z?Mvk|@&tzW#OTh8Mt2dH7#Yp(zH!J??&Ll-EeSeZ{QYs;Qave^soRZ*lb&tc5J_9yk-s6hSAO6hAGY;%^+E!L6OjWrrg)%?Q)#*4 zi%UGcPo6{r0b{g!^qjf!><(uDuC8-J3K15B6%;*~1O#jV2;8W!Xeiwp29v1MDG_5u zCzF1~QHfsC5$ROUu^#>95DrOmEBwbt!xK06JDP^AM}_6OmQTKqN+y-5(Op3KqZah4@?J<_$D%QwnPs=KAcjbF;@4Wk8b5BAx#TWI)NF5jTob(z;oxQj 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a/docs/load-sentiment.rst +++ b/docs/load-sentiment.rst @@ -7,19 +7,23 @@ .. parsed-literal:: - CPU times: user 12.3 s, sys: 1.5 s, total: 13.8 s - Wall time: 17.7 s + CPU times: user 12.8 s, sys: 1.72 s, total: 14.5 s + Wall time: 19.2 s .. code:: python - positive_text = 'Kerajaan negeri Kelantan mempersoalkan motif kenyataan Menteri Kewangan Lim Guan Eng yang hanya menyebut Kelantan penerima terbesar bantuan kewangan dari Kerajaan Persekutuan. Sedangkan menurut Timbalan Menteri Besarnya, Datuk Mohd Amar Nik Abdullah, negeri lain yang lebih maju dari Kelantan turut mendapat pembiayaan dan pinjaman.' + positive_text = 'Kerajaan negeri Kelantan mempersoalkan motif kenyataan Menteri Kewangan Lim Guan Eng yang hanya menyebut Kelantan penerima terbesar bantuan kewangan dari Kerajaan Persekutuan sebanyak RM50 juta. Sedangkan menurut Timbalan Menteri Besarnya, Datuk Mohd Amar Nik Abdullah, negeri lain yang lebih maju dari Kelantan turut mendapat pembiayaan dan pinjaman.' negative_text = 'kerajaan sebenarnya sangat bencikan rakyatnya, minyak naik dan segalanya' -All models got ``get_proba`` parameters. If True, it will returned +All models have ``get_proba`` parameters. If True, it will returned probability every classes. Else, it will return highest probability class. **Default is False.** +All models have ``add_neutral`` parameters. If True, it will add +``neutral`` probability, Else, default probabilities. **Default is +True.** + Load multinomial model ---------------------- @@ -33,17 +37,35 @@ Load multinomial model .. parsed-literal:: - {'negative': 0.2422829560944563, 'positive': 0.7577170439055456} - {'negative': 0.7385102541701198, 'positive': 0.26148974582987783} + {'negative': 0.003559988321312934, 'positive': 0.6440011678687021, 'neutral': 0.352438843809985} + {'negative': 0.4770205083402397, 'positive': 0.005229794916597557, 'neutral': 0.5177496967431627} .. parsed-literal:: - [{'negative': 0.7385102541701198, 'positive': 0.26148974582987783}, - {'negative': 0.7385102541701198, 'positive': 0.26148974582987783}] + [{'negative': 0.4770205083402397, + 'positive': 0.005229794916597557, + 'neutral': 0.5177496967431627}, + {'negative': 0.4770205083402397, + 'positive': 0.005229794916597557, + 'neutral': 0.5177496967431627}] + + + +Disable ``neutral`` probability, + +.. code:: python + + print(model.predict(negative_text,get_proba=True,add_neutral=True)) + print(model.predict(negative_text,get_proba=True,add_neutral=False)) + + +.. parsed-literal:: + {'negative': 0.4770205083402397, 'positive': 0.005229794916597557, 'neutral': 0.5177496967431627} + {'negative': 0.7385102541701198, 'positive': 0.26148974582987783} Load xgb model @@ -59,16 +81,16 @@ Load xgb model .. parsed-literal:: - {'negative': 0.24086821, 'positive': 0.7591318} - {'negative': 0.844284, 'positive': 0.15571605} + {'negative': 0.0045786616, 'positive': 0.5421338, 'neutral': 0.45328754} + {'negative': 0.688568, 'positive': 0.0031143208, 'neutral': 0.30831766} .. parsed-literal:: - [{'negative': 0.844284, 'positive': 0.15571605}, - {'negative': 0.844284, 'positive': 0.15571605}] + [{'negative': 0.688568, 'positive': 0.0031143208, 'neutral': 0.30831766}, + {'negative': 0.688568, 'positive': 0.0031143208, 'neutral': 0.30831766}] @@ -84,141 +106,220 @@ List available deep learning models .. parsed-literal:: - ['fast-text', - 'hierarchical', - 'bahdanau', - 'luong', - 'bidirectional', - 'bert', - 'entity-network'] + ['self-attention', 'bahdanau', 'luong'] Load deep learning models ------------------------- +Good thing about deep learning models from Malaya, it returns +``Attention`` result, means, which part of words give the high impact to +the results. But to get ``Attention``, you need to set +``get_proba=True``. + .. code:: python - for i in malaya.sentiment.available_deep_model(): - print('Testing %s model'%(i)) - model = malaya.sentiment.deep_model(i) - print(model.predict(negative_text)) - print(model.predict_batch([negative_text, positive_text])) - print(model.predict_batch([negative_text, positive_text],get_proba=True)) - print() + import matplotlib.pyplot as plt + import seaborn as sns + sns.set() +Load bahdanau model +~~~~~~~~~~~~~~~~~~~ -.. parsed-literal:: +.. code:: python - Testing fast-text model - negative - ['negative', 'positive'] - [{'negative': 0.8405276, 'positive': 0.15947239}, {'negative': 1.8619101e-05, 'positive': 0.9999814}] - - Testing hierarchical model - negative - ['negative', 'positive'] - [{'negative': 0.9504666, 'positive': 0.049533408}, {'negative': 0.041675426, 'positive': 0.9583246}] - - Testing bahdanau model - negative - ['negative', 'positive'] - [{'negative': 0.9993631, 'positive': 0.0006369345}, {'negative': 0.10564381, 'positive': 0.89435613}] - - Testing luong model - negative - ['negative', 'positive'] - [{'negative': 0.8851047, 'positive': 0.11489531}, {'negative': 0.0025337301, 'positive': 0.9974663}] - - Testing bidirectional model - negative - ['negative', 'positive'] - [{'negative': 0.97722447, 'positive': 0.02277552}, {'negative': 0.007992058, 'positive': 0.992008}] - - Testing bert model - positive - ['positive', 'negative'] - [{'negative': 0.37042966, 'positive': 0.62957036}, {'negative': 0.84760416, 'positive': 0.15239581}] + model = malaya.sentiment.deep_model('bahdanau') + +Predict single string +^^^^^^^^^^^^^^^^^^^^^ + +.. code:: python - Testing entity-network model - positive - ['positive', 'positive'] - [{'negative': 0.44306344, 'positive': 0.55693656}, {'negative': 0.32117522, 'positive': 0.6788247}] + model.predict(positive_text) -Unsupervised important words learning -------------------------------------- + +.. parsed-literal:: + + 'neutral' + + .. code:: python - import matplotlib.pyplot as plt - import seaborn as sns - sns.set() # i just really like seaborn colors + result = model.predict(positive_text,get_proba=True,add_neutral=False) + result + + + + +.. parsed-literal:: + + {'negative': 0.29423502, + 'positive': 0.70576495, + 'attention': {'Kerajaan': 0.0019730187, + 'negeri': 0.0016380441, + 'Kelantan': 0.52261657, + 'mempersoalkan': 0.0041695302, + 'motif': 0.009157478, + 'kenyataan': 0.0020427739, + 'Menteri': 0.0026452087, + 'Kewangan': 0.0017612759, + 'Lim': 0.046150286, + 'Guan': 0.046651356, + 'Eng': 0.014238223, + 'yang': 0.0014762171, + 'hanya': 0.0030002387, + 'menyebut': 0.0025070142, + 'penerima': 0.001477954, + 'terbesar': 0.0014683361, + 'bantuan': 0.0020200813, + 'kewangan': 0.0015684298, + 'dari': 0.001558458, + 'Persekutuan': 0.0021011133, + 'sebanyak': 0.001435061, + 'RM50': 0.037767526, + 'juta': 0.0031749196, + '.': 0.0, + 'Sedangkan': 0.0015534447, + 'menurut': 0.0014812354, + 'Timbalan': 0.0020608688, + 'Besarnya': 0.001435703, + ',': 0.0, + 'Datuk': 0.0014482451, + 'Mohd': 0.0014422016, + 'Amar': 0.0014641153, + 'Nik': 0.0015784851, + 'Abdullah': 0.0014410047, + 'lain': 0.0016714201, + 'lebih': 0.0037415246, + 'maju': 0.019784313, + 'turut': 0.011382608, + 'mendapat': 0.0025349073, + 'pembiayaan': 0.0020161376, + 'dan': 0.0, + 'pinjaman': 0.009653877}} -We need to set ``get_proba`` become True to get the ‘attention’. -Visualizing bahdanau model -^^^^^^^^^^^^^^^^^^^^^^^^^^ .. code:: python - model = malaya.sentiment.deep_model('bahdanau') - result = model.predict(positive_text,get_proba=True)['attention'] - - plt.figure(figsize = (15, 7)) - labels = [r[0] for r in result] - val = [r[1] for r in result] - aranged = [i for i in range(len(labels))] - plt.bar(aranged, val) - plt.xticks(aranged, labels, rotation = 'vertical') + plt.figure(figsize = (15, 5)) + keys = result['attention'].keys() + values = result['attention'].values() + aranged = [i for i in range(len(keys))] + plt.bar(aranged, values) + plt.xticks(aranged, keys, rotation = 'vertical') plt.show() -.. image:: load-sentiment_files/load-sentiment_15_0.png +.. image:: load-sentiment_files/load-sentiment_18_0.png -Visualizing luong model -^^^^^^^^^^^^^^^^^^^^^^^ +Open sentiment visualization dashboard +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Default when you call ``predict_words`` it will open a browser with +visualization dashboard, you can disable by ``visualization=False``. .. code:: python - model = malaya.sentiment.deep_model('luong') - result = model.predict(positive_text,get_proba=True)['attention'] + model.predict_words(positive_text) - plt.figure(figsize = (15, 7)) - labels = [r[0] for r in result] - val = [r[1] for r in result] - aranged = [i for i in range(len(labels))] - plt.bar(aranged, val) - plt.xticks(aranged, labels, rotation = 'vertical') - plt.show() +.. parsed-literal:: + + Serving to http://127.0.0.1:8889/ [Ctrl-C to exit] -.. image:: load-sentiment_files/load-sentiment_17_0.png +.. parsed-literal:: + 127.0.0.1 - - [30/May/2019 11:13:59] "GET / HTTP/1.1" 200 - + 127.0.0.1 - - [30/May/2019 11:13:59] "GET /static/admin-materialize.min.css HTTP/1.1" 200 - + 127.0.0.1 - - [30/May/2019 11:13:59] "GET /static/echarts.min.js HTTP/1.1" 200 - + + +.. parsed-literal:: + + + stopping Server... -Visualizing hierarchical model -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. code:: python - model = malaya.sentiment.deep_model('hierarchical') - result = model.predict(positive_text,get_proba=True)['attention'] + from IPython.core.display import Image, display - plt.figure(figsize = (15, 7)) - labels = [r[0] for r in result] - val = [r[1] for r in result] - aranged = [i for i in range(len(labels))] - plt.bar(aranged, val) - plt.xticks(aranged, labels, rotation = 'vertical') - plt.show() + display(Image('sentiment-visualization.png', width=800)) + + + +.. image:: load-sentiment_files/load-sentiment_21_0.png + :width: 800px + + +I tried to put the html and javascript inside a notebook cell, pretty +hard you know and a lot of weird bugs. Let stick to HTTP serving ya. + +.. code:: python + + display(Image('sentiment-negative.png', width=800)) + + + +.. image:: load-sentiment_files/load-sentiment_23_0.png + :width: 800px + + +``predict_words`` only accept a single string. You can’t predict +multiple texts. + +Predict batch of strings +^^^^^^^^^^^^^^^^^^^^^^^^ + +.. code:: python + + model.predict_batch([negative_text, positive_text],get_proba=True) -.. image:: load-sentiment_files/load-sentiment_19_0.png + +.. parsed-literal:: + + [{'negative': 0.94391596, 'positive': 0.000560839, 'neutral': 0.055523217}, + {'negative': 0.004329388, 'positive': 0.5670612, 'neutral': 0.42860943}] + + + +**You might want to try ``luong`` and ``self-attention`` by yourself.** + +Stacking models +--------------- + +More information, you can read at +https://malaya.readthedocs.io/en/latest/Stack.html + +.. code:: python + + multinomial = malaya.sentiment.multinomial() + xgb = malaya.sentiment.xgb() + bahdanau = malaya.sentiment.deep_model('bahdanau') + +.. code:: python + + malaya.stack.predict_stack([multinomial, xgb, bahdanau], positive_text) + + + + +.. parsed-literal:: + + {'negative': 0.0037063136821626594, + 'positive': 0.6215181632979583, + 'neutral': 0.3669251238766725} + Load Sparse deep learning models @@ -303,10 +404,9 @@ will try to evolve it. .. parsed-literal:: - [{'negative': 0.41368636, 'positive': 0.58631366}, + [{'negative': 0.42412993, 'positive': 0.5758701}, {'negative': 0.6855174, 'positive': 0.31448266}] -**Not bad huh, but the polarity is not really high as word-based models. -Word-based models can get negative / positive value really near to 1.0** +Right now sparse models does not have ``neutral`` class. diff --git a/docs/load-sentiment_files/load-sentiment_15_0.png b/docs/load-sentiment_files/load-sentiment_15_0.png deleted file mode 100644 index 12ce9d07d190fd9c18aeae175aa577fef2952fc0..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 21455 zcmeFZcT`jFwk{k%KtRBVpmYsgni6_d2_PU{dQmAsI?{WwK$NBsgiuAA^xj((qy~`Q zA%XX_XRdyXI7rmXf;)x*ATU=7^!E*lX?G4etX7 zV#O;+Li;Ocd`w6hlR-iItujUxaJc(ec9psJ%=?i7mzos5k{<0IR8IXvQ>mdx2^k-t 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'positive': 0.13927052233855727} + {'negative': 0.009240767162200498, 'positive': 0.0759232837799535, 'neutral': 0.914835949057846} + {'negative': 0.7214589553228845, 'positive': 0.0027854104467711456, 'neutral': 0.2757556342303443} .. parsed-literal:: - [{'negative': 0.8607294776614423, 'positive': 0.13927052233855727}, - {'negative': 0.8607294776614423, 'positive': 0.13927052233855727}] + [{'negative': 0.7214589553228845, + 'positive': 0.0027854104467711456, + 'neutral': 0.2757556342303443}, + {'negative': 0.7214589553228845, + 'positive': 0.0027854104467711456, + 'neutral': 0.2757556342303443}] @@ -68,16 +72,16 @@ Load xgb model .. parsed-literal:: - {'negative': 0.4284472, 'positive': 0.5715528} - {'negative': 0.9249991, 'positive': 0.07500088} + {'negative': 0.0085689435, 'positive': 0.14310563, 'neutral': 0.84832543} + {'negative': 0.84999824, 'positive': 0.0015000176, 'neutral': 0.14850175} .. parsed-literal:: - [{'negative': 0.9249991, 'positive': 0.07500088}, - {'negative': 0.9249991, 'positive': 0.07500088}] + [{'negative': 0.84999824, 'positive': 0.0015000176, 'neutral': 0.14850175}, + {'negative': 0.84999824, 'positive': 0.0015000176, 'neutral': 0.14850175}] @@ -93,112 +97,101 @@ List available deep learning models .. parsed-literal:: - ['fast-text', - 'hierarchical', - 'bahdanau', - 'luong', - 'bidirectional', - 'bert', - 'entity-network'] + ['self-attention', 'bahdanau', 'luong'] +Load deep learning models +------------------------- + +Good thing about deep learning models from Malaya, it returns +``Attention`` result, means, which part of words give the high impact to +the results. But to get ``Attention``, you need to set +``get_proba=True``. + .. code:: python - for i in malaya.subjective.available_deep_model(): - print('Testing %s model'%(i)) - model = malaya.subjective.deep_model(i) - print(model.predict(negative_text)) - print(model.predict_batch([negative_text, positive_text])) - print(model.predict_batch([negative_text, positive_text],get_proba=True)) - print() + import matplotlib.pyplot as plt + import seaborn as sns + sns.set() + +Load bahdanau model +~~~~~~~~~~~~~~~~~~~ + +.. code:: python + + model = malaya.subjective.deep_model('bahdanau') .. parsed-literal:: - Testing fast-text model - negative - ['negative', 'positive'] - [{'negative': 1.0, 'positive': 5.600171e-08}, {'negative': 0.25236478, 'positive': 0.74763525}] + downloading frozen /Users/huseinzol/Malaya/subjective/bahdanau model - Testing hierarchical model - negative - ['negative', 'positive'] - [{'negative': 0.9999939, 'positive': 6.036344e-06}, {'negative': 0.23363505, 'positive': 0.766365}] - Testing bahdanau model - negative - ['negative', 'negative'] - [{'negative': 0.9999505, 'positive': 4.9492166e-05}, {'negative': 0.6453213, 'positive': 0.35467872}] +.. parsed-literal:: - Testing luong model - positive - ['positive', 'negative'] - [{'negative': 0.08237837, 'positive': 0.9176216}, {'negative': 0.64468145, 'positive': 0.35531852}] + 20.0MB [00:07, 2.85MB/s] + 0%| | 0.00/0.45 [00:00q_Bs3PbIx7op1syxch6d^FHC0MnR(}}&&-!_U2WBi7g#Tlk&#_g zSA*)4kx{ynk&*vCM+4N**G#Vg4=Rrb>W|L>CGear68QhTtD30?85!N>vp@2I>EI)v zQpQu+#M8h9?&d^a-ebZN`1DY)i{WiC(DUj#-FLY2CQP(IsTzz+A_yTiTiIK)t z2kJuoZ`BS3EUO3s(8sHfg?DSPopfg85>^-ZA8b*)sQdVS&eG59YB3E$%$FQ$CAN5M zvv$)%TRXmmSe$@UJZa6+QIvt$!FyyORijpYRtN>ecN~sB{+4=t&QFuZ8lfBNz~|+T zd)jAJH2=F#_+-h^;Upy^c`(_LgV3n*8PVEmr_kxpKJvQipn(yFUz`+yz&H2*ep5>s zrc)QR3DgFYZO(Jewl{h~%o_yP)+)QTi!B4P7c~7WnGrIBkCW|%>{6F2O}f!}jhg0X z72nKy?+(Kzut0_IhUr3olW`f9UAxl*yp74+66UWNHgi~^?J&> z+2Hx&Ny^d z3^Ii3E{*X)h_0UeqY8A_=19g|0mlyoj1JnW|0%Dol&d 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But to get ``Attention``, you need to set +``get_proba=True``. + +.. code:: python + + import matplotlib.pyplot as plt + import seaborn as sns + sns.set() + +Load bahdanau model +~~~~~~~~~~~~~~~~~~~ .. code:: python - for model in malaya.toxic.available_deep_model(): - print('Testing %s model'%(model)) - deep_model = malaya.toxic.deep_model(model = model) - print(deep_model.predict(string)) - print(deep_model.predict_batch([string, another_string])) - print(deep_model.predict_batch([string, another_string],get_proba=True)) - print() + model = malaya.toxic.deep_model(model = 'bahdanau') + +Predict single string +^^^^^^^^^^^^^^^^^^^^^ + +.. code:: python + + model.predict(string) + + .. parsed-literal:: - Testing bahdanau model [] - [[], ['toxic']] - [{'toxic': 0.004403565, 'severe_toxic': 3.365281e-05, 'obscene': 0.0006261515, 'threat': 7.532223e-05, 'insult': 0.00030458395, 'identity_hate': 5.894399e-05}, {'toxic': 0.8340975, 'severe_toxic': 0.0020475034, 'obscene': 0.034258205, 'threat': 0.0003628814, 'insult': 0.0734416, 'identity_hate': 0.0614648}] - Testing hierarchical model - [] - [[], ['toxic']] - [{'toxic': 0.54248875, 'severe_toxic': 0.0065838215, 'obscene': 0.17371807, 'threat': 0.0148023935, 'insult': 0.12660918, 'identity_hate': 0.055212382}, {'toxic': 0.6858065, 'severe_toxic': 0.0032611815, 'obscene': 0.041103873, 'threat': 0.0025343157, 'insult': 0.28004634, 'identity_hate': 0.055198606}] - Testing luong model - [] - [[], ['toxic']] - [{'toxic': 0.13619128, 'severe_toxic': 0.0055254013, 'obscene': 0.032115582, 'threat': 0.017055651, 'insult': 0.039386936, 'identity_hate': 0.012571168}, {'toxic': 0.95763326, 'severe_toxic': 0.018214434, 'obscene': 0.2505002, 'threat': 0.010534671, 'insult': 0.37684646, 'identity_hate': 0.11548582}] - Testing fast-text model - ['identity_hate'] - [[], []] - [{'toxic': 1.04480705e-05, 'severe_toxic': 3.0439846e-06, 'obscene': 1.0294245e-05, 'threat': 7.333487e-05, 'insult': 3.3929928e-06, 'identity_hate': 0.0013527799}, {'toxic': 0.3151637, 'severe_toxic': 0.0074781813, 'obscene': 0.014187617, 'threat': 0.0036906046, 'insult': 0.087501705, 'identity_hate': 0.049142193}] +.. code:: python - Testing entity-network model - [] - [['toxic', 'obscene', 'insult'], []] - [{'toxic': 0.77070284, 'severe_toxic': 0.18705268, 'obscene': 0.5038762, 'threat': 0.16385102, 'insult': 0.6224154, 'identity_hate': 0.23296514}, {'toxic': 0.20222966, 'severe_toxic': 0.007314409, 'obscene': 0.13172735, 'threat': 0.027272865, 'insult': 0.10753201, 'identity_hate': 0.016206132}] + result = model.predict(another_string, get_proba = True) + result -Unsupervised important words learning -------------------------------------- -.. code:: python +.. parsed-literal:: - import matplotlib.pyplot as plt - import seaborn as sns - sns.set() # i just really like seaborn colors + {'toxic': 0.75407517, + 'severe_toxic': 0.16274202, + 'obscene': 0.5291958, + 'threat': 0.10058941, + 'insult': 0.75971705, + 'identity_hate': 0.8826025, + 'attention': {'bodoh': 0.10320988, + ',': 0.0, + 'dah': 0.027506806, + 'la': 0.021144494, + 'gay': 0.4988079, + 'sokong': 0.06969115, + 'lgbt': 0.20489135, + 'lagi': 0.018106166, + 'memang': 0.02190801, + 'tak': 0.017407918, + 'guna': 0.017326297}} -We need to set ``get_proba`` become True to get the ‘attention’. -Visualizing bahdanau model -^^^^^^^^^^^^^^^^^^^^^^^^^^ .. code:: python - model = malaya.toxic.deep_model('bahdanau') - result = model.predict(another_string,get_proba=True)['attention'] - - plt.figure(figsize = (15, 7)) - labels = [r[0] for r in result] - val = [r[1] for r in result] - aranged = [i for i in range(len(labels))] - plt.bar(aranged, val) - plt.xticks(aranged, labels, rotation = 'vertical') + plt.figure(figsize = (15, 5)) + keys = result['attention'].keys() + values = result['attention'].values() + aranged = [i for i in range(len(keys))] + plt.bar(aranged, values) + plt.xticks(aranged, keys, rotation = 'vertical') plt.show() -.. image:: load-toxic_files/load-toxic_23_0.png +.. image:: load-toxic_files/load-toxic_24_0.png -Visualizing luong model -^^^^^^^^^^^^^^^^^^^^^^^ +Open toxicity visualization dashboard +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Default when you call ``predict_words`` it will open a browser with +visualization dashboard, you can disable by ``visualization=False``. .. code:: python - model = malaya.toxic.deep_model('luong') - result = model.predict(another_string,get_proba=True)['attention'] + model.predict_words(another_string) - plt.figure(figsize = (15, 7)) - labels = [r[0] for r in result] - val = [r[1] for r in result] - aranged = [i for i in range(len(labels))] - plt.bar(aranged, val) - plt.xticks(aranged, labels, rotation = 'vertical') - plt.show() + +.. parsed-literal:: + + Serving to http://127.0.0.1:8889/ [Ctrl-C to exit] +.. parsed-literal:: + + 127.0.0.1 - - [01/Jun/2019 12:12:10] "GET / HTTP/1.1" 200 - + 127.0.0.1 - - [01/Jun/2019 12:12:10] "GET /static/admin-materialize.min.css HTTP/1.1" 200 - + 127.0.0.1 - - [01/Jun/2019 12:12:10] "GET /static/echarts.min.js HTTP/1.1" 200 - + 127.0.0.1 - - [01/Jun/2019 12:12:11] "GET /favicon.ico HTTP/1.1" 200 - + ---------------------------------------- + Exception happened during processing of request from ('127.0.0.1', 61873) + Traceback (most recent call last): + File "/usr/local/Cellar/python/3.6.5_1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/socketserver.py", line 317, in _handle_request_noblock + self.process_request(request, client_address) + File "/usr/local/Cellar/python/3.6.5_1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/socketserver.py", line 348, in process_request + self.finish_request(request, client_address) + File "/usr/local/Cellar/python/3.6.5_1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/socketserver.py", line 361, in finish_request + self.RequestHandlerClass(request, client_address, self) + File "/usr/local/Cellar/python/3.6.5_1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/socketserver.py", line 696, in __init__ + self.handle() + File "/usr/local/Cellar/python/3.6.5_1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/http/server.py", line 418, in handle + self.handle_one_request() + File "/usr/local/Cellar/python/3.6.5_1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/http/server.py", line 406, in handle_one_request + method() + File "/Users/huseinzol/Documents/Malaya/malaya/_utils/_server.py", line 32, in do_GET + with open(filepath, 'rb') as fh: + FileNotFoundError: [Errno 2] No such file or directory: '/Users/huseinzol/Documents/Malaya/malaya/_utils/web/favicon.ico' + ---------------------------------------- -.. image:: load-toxic_files/load-toxic_25_0.png + +.. parsed-literal:: -Visualizing hierarchical model -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + stopping Server... + .. code:: python - model = malaya.toxic.deep_model('hierarchical') - result = model.predict(another_string,get_proba=True)['attention'] + from IPython.core.display import Image, display - plt.figure(figsize = (15, 7)) - labels = [r[0] for r in result] - val = [r[1] for r in result] - aranged = [i for i in range(len(labels))] - plt.bar(aranged, val) - plt.xticks(aranged, labels, rotation = 'vertical') - plt.show() + display(Image('toxicity.png', width=800)) .. image:: load-toxic_files/load-toxic_27_0.png + :width: 800px + + +Predict batch of strings +^^^^^^^^^^^^^^^^^^^^^^^^ + +.. code:: python + + model.predict_batch([string, another_string],get_proba=True) + + + + +.. parsed-literal:: + + [{'toxic': 0.21699297, + 'severe_toxic': 0.021610105, + 'obscene': 0.110013016, + 'threat': 0.010765828, + 'insult': 0.10372056, + 'identity_hate': 0.03946534}, + {'toxic': 0.93524134, + 'severe_toxic': 0.33446547, + 'obscene': 0.7178085, + 'threat': 0.56429744, + 'insult': 0.8922918, + 'identity_hate': 0.9618688}] + + + +**You might want to try ``luong`` and ``self-attention`` by yourself.** + +Stacking models +--------------- + +More information, you can read at +https://malaya.readthedocs.io/en/latest/Stack.html + +.. code:: python + + multinomial = malaya.toxic.multinomial() + logistics = malaya.toxic.logistic() + bahdanau = malaya.toxic.deep_model('bahdanau') + +.. code:: python + + malaya.stack.predict_stack([multinomial, logistics, bahdanau], another_string) diff --git a/docs/load-toxic_files/load-toxic_23_0.png b/docs/load-toxic_files/load-toxic_23_0.png deleted file mode 100644 index cd8b0958ed38824f7e82647c13352747f7d3eeea..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 8715 zcmeHNc~}$Yx{rzntyQ$N(pCi20Zyxcg@T6ls6`ppRveIBD~rgI5|%VVh}EOGfjE&( zz|^H8!)6H(L)Zkj2!d52OBhfYKogi~Fi9Z>?nlo(_n!7HPkW!|{#TxdFHdIP`R1GV z`~BYc_r94EUwZFY`p(DiP$-n8u-g|qDU=0t3T58yMSlTD_!;dB!G})bR(RJU@RznI z{5bf&_#3x?L<(iedvpKu9^vV4!9i-0>+U39T4WMqFFk@1w>RmVSXxqS^w%aS5%k1p 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If True, it will returned probability every classes. Else, it will return highest probability class. **Default is True.** +I suggest you to use deep learning models to understand emotion, these +models are better for detecting emotions from social media. + Load multinomial model ---------------------- @@ -42,15 +45,15 @@ Load multinomial model .. parsed-literal:: - anger - {'anger': 0.30367763926253094, 'fear': 0.16709964152193366, 'joy': 0.17026521921403184, 'love': 0.18405977732934192, 'sadness': 0.1388341895665479, 'surprise': 0.03606353310561458} + joy + {'anger': 0.11733952772643724, 'fear': 0.15199690452609127, 'joy': 0.2589124946219144, 'love': 0.2024854510784686, 'sadness': 0.2211704631949802, 'surprise': 0.048095158852106114} .. parsed-literal:: - ['anger', 'fear', 'joy', 'joy', 'sadness', 'fear'] + ['joy', 'fear', 'anger', 'love', 'anger', 'fear'] @@ -72,50 +75,50 @@ Load xgb model .. parsed-literal:: - love - {'anger': 0.22918181, 'fear': 0.089252785, 'joy': 0.1318236, 'love': 0.46476611, 'sadness': 0.07200217, 'surprise': 0.012973559} + anger + {'anger': 0.37050414, 'fear': 0.17237191, 'joy': 0.21709572, 'love': 0.040401105, 'sadness': 0.16886166, 'surprise': 0.030765474} .. parsed-literal:: - [{'anger': 0.22918181, - 'fear': 0.089252785, - 'joy': 0.1318236, - 'love': 0.46476611, - 'sadness': 0.07200217, - 'surprise': 0.012973559}, - {'anger': 0.013483193, - 'fear': 0.939588, - 'joy': 0.01674833, - 'love': 0.003220023, - 'sadness': 0.022906518, - 'surprise': 0.0040539484}, - {'anger': 0.10506946, - 'fear': 0.025150253, - 'joy': 0.725915, - 'love': 0.05211037, - 'sadness': 0.078554265, - 'surprise': 0.013200594}, - {'anger': 0.11640434, - 'fear': 0.097485565, - 'joy': 0.24893147, - 'love': 0.25440857, - 'sadness': 0.2650988, - 'surprise': 0.01767122}, - {'anger': 0.27124837, - 'fear': 0.15662362, - 'joy': 0.131251, - 'love': 0.022184724, - 'sadness': 0.41255626, - 'surprise': 0.006135965}, - {'anger': 0.07513438, - 'fear': 0.2525073, - 'joy': 0.024355419, - 'love': 0.002638406, - 'sadness': 0.0059716892, - 'surprise': 0.6393928}] + [{'anger': 0.37050414, + 'fear': 0.17237191, + 'joy': 0.21709572, + 'love': 0.040401105, + 'sadness': 0.16886166, + 'surprise': 0.030765474}, + {'anger': 0.024576988, + 'fear': 0.9346102, + 'joy': 0.017333886, + 'love': 0.00388243, + 'sadness': 0.015722778, + 'surprise': 0.0038737422}, + {'anger': 0.6121963, + 'fear': 0.07615826, + 'joy': 0.12938385, + 'love': 0.021234306, + 'sadness': 0.13893352, + 'surprise': 0.022093719}, + {'anger': 0.078999326, + 'fear': 0.05360329, + 'joy': 0.05482416, + 'love': 0.7758813, + 'sadness': 0.03182298, + 'surprise': 0.004868941}, + {'anger': 0.7553716, + 'fear': 0.060115993, + 'joy': 0.01417792, + 'love': 0.005369766, + 'sadness': 0.15161343, + 'surprise': 0.01335139}, + {'anger': 0.07156373, + 'fear': 0.241878, + 'joy': 0.0074376594, + 'love': 0.0042003845, + 'sadness': 0.06661975, + 'surprise': 0.6083005}] @@ -131,148 +134,253 @@ List available deep learning models .. parsed-literal:: - ['fast-text', - 'hierarchical', - 'bahdanau', - 'luong', - 'bidirectional', - 'bert', - 'entity-network'] + ['self-attention', 'bahdanau', 'luong'] +Load deep learning models +------------------------- + +Good thing about deep learning models from Malaya, it returns +``Attention`` result, means, which part of words give the high impact to +the results. But to get ``Attention``, you need to set +``get_proba=True``. + .. code:: ipython3 - for i in malaya.emotion.available_deep_model(): - print('Testing %s model'%(i)) - model = malaya.emotion.deep_model(i) - print(model.predict(anger_text)) - print(model.predict_batch([anger_text, - fear_text, - joy_text, - love_text, - sadness_text, - surprise_text])) - print(model.predict_batch([anger_text, - fear_text, - joy_text, - love_text, - sadness_text, - surprise_text], get_proba = True)) - print() + import matplotlib.pyplot as plt + import seaborn as sns + sns.set() + +Load bahdanau model +~~~~~~~~~~~~~~~~~~~ + +.. code:: ipython3 + + model = malaya.emotion.deep_model('bahdanau') + +Predict single string +^^^^^^^^^^^^^^^^^^^^^ + +.. code:: ipython3 + + model.predict(anger_text) + + .. parsed-literal:: - Testing fast-text model - love - ['love', 'fear', 'joy', 'love', 'sadness', 'surprise'] - [{'anger': 2.538603e-07, 'fear': 4.1372344e-13, 'joy': 1.0892472e-08, 'love': 0.99999976, 'sadness': 3.8994935e-16, 'surprise': 2.439655e-08}, {'anger': 4.4489467e-24, 'fear': 1.0, 'joy': 1.3903143e-28, 'love': 1.7920514e-33, 'sadness': 1.01771616e-26, 'surprise': 6.799581e-18}, {'anger': 9.583714e-26, 'fear': 1.5029816e-24, 'joy': 1.0, 'love': 3.7527533e-13, 'sadness': 8.348174e-24, 'surprise': 2.080897e-16}, {'anger': 1.7409228e-13, 'fear': 3.2279754e-12, 'joy': 0.0005876841, 'love': 0.9994123, 'sadness': 1.8902605e-11, 'surprise': 9.9256076e-11}, {'anger': 1.2737708e-11, 'fear': 5.882562e-10, 'joy': 9.112171e-13, 'love': 7.7659496e-20, 'sadness': 1.0, 'surprise': 1.6035637e-16}, {'anger': 5.5730725e-37, 'fear': 0.16033638, 'joy': 1.2999706e-30, 'love': 0.0, 'sadness': 0.0, 'surprise': 0.8396636}] - - Testing hierarchical model - anger - ['anger', 'fear', 'joy', 'joy', 'sadness', 'joy'] - [{'anger': 0.2683842, 'fear': 0.3206215, 'joy': 0.17552154, 'love': 0.041160323, 'sadness': 0.1729689, 'surprise': 0.021343619}, {'anger': 0.006326744, 'fear': 0.97895914, 'joy': 0.01049677, 'love': 0.0004076402, 'sadness': 0.0026508307, 'surprise': 0.0011588057}, {'anger': 0.018994415, 'fear': 0.025846783, 'joy': 0.87117314, 'love': 0.039761506, 'sadness': 0.01727374, 'surprise': 0.026950512}, {'anger': 0.030335624, 'fear': 0.071699485, 'joy': 0.5445254, 'love': 0.24373391, 'sadness': 0.08821325, 'surprise': 0.021492302}, {'anger': 0.022141613, 'fear': 0.01158712, 'joy': 0.0046922215, 'love': 0.0013928033, 'sadness': 0.95863473, 'surprise': 0.0015515403}, {'anger': 0.028982855, 'fear': 0.15079749, 'joy': 0.4619698, 'love': 0.18604991, 'sadness': 0.009463493, 'surprise': 0.16273652}] - - Testing bahdanau model - love - ['anger', 'fear', 'joy', 'love', 'sadness', 'surprise'] - [{'anger': 0.4797056, 'fear': 0.168805, 'joy': 0.01420066, 'love': 0.25597632, 'sadness': 0.07191573, 'surprise': 0.009396704}, {'anger': 0.0008658771, 'fear': 0.9851621, 'joy': 0.0026717593, 'love': 0.00049689104, 'sadness': 0.0037894566, 'surprise': 0.007013949}, {'anger': 0.047901724, 'fear': 0.0058520515, 'joy': 0.6818599, 'love': 0.22279568, 'sadness': 0.010295693, 'surprise': 0.03129497}, {'anger': 0.0136362715, 'fear': 0.025698097, 'joy': 0.13118966, 'love': 0.75916684, 'sadness': 0.016278507, 'surprise': 0.05403061}, {'anger': 0.010733987, 'fear': 0.023202764, 'joy': 0.005261726, 'love': 0.00054325344, 'sadness': 0.9586973, 'surprise': 0.0015610264}, {'anger': 0.0028718843, 'fear': 0.21446393, 'joy': 0.034736242, 'love': 0.004341282, 'sadness': 0.001976919, 'surprise': 0.7416098}] - - Testing luong model - love - ['love', 'fear', 'joy', 'love', 'sadness', 'surprise'] - [{'anger': 0.0027926113, 'fear': 0.0042426162, 'joy': 0.6864444, 'love': 0.2944655, 'sadness': 0.0010767934, 'surprise': 0.010978072}, {'anger': 0.0057431706, 'fear': 0.9705748, 'joy': 0.0043815267, 'love': 0.0008089542, 'sadness': 0.009230111, 'surprise': 0.009261452}, {'anger': 0.0016454123, 'fear': 0.0012319328, 'joy': 0.87483215, 'love': 0.0678572, 'sadness': 0.010661957, 'surprise': 0.043771397}, {'anger': 0.016030224, 'fear': 0.005272452, 'joy': 0.04561528, 'love': 0.31633487, 'sadness': 0.59368426, 'surprise': 0.023062926}, {'anger': 0.0010896003, 'fear': 0.00057456765, 'joy': 4.035251e-05, 'love': 6.2987274e-05, 'sadness': 0.9982096, 'surprise': 2.283629e-05}, {'anger': 0.00042841653, 'fear': 0.33654872, 'joy': 0.00097602647, 'love': 0.00029422223, 'sadness': 0.00032926636, 'surprise': 0.6614233}] - - Testing bidirectional model - love - ['love', 'fear', 'anger', 'joy', 'sadness', 'surprise'] - [{'anger': 0.04017815, 'fear': 0.40091494, 'joy': 0.005757856, 'love': 0.44828272, 'sadness': 0.00045654154, 'surprise': 0.10440978}, {'anger': 0.0035579149, 'fear': 0.97265065, 'joy': 0.020677356, 'love': 0.000660097, 'sadness': 0.0010607935, 'surprise': 0.0013932855}, {'anger': 0.48268953, 'fear': 0.35196006, 'joy': 0.029008513, 'love': 0.014688243, 'sadness': 0.018093651, 'surprise': 0.10355998}, {'anger': 0.029928548, 'fear': 0.05067155, 'joy': 0.65202534, 'love': 0.10342105, 'sadness': 0.059337158, 'surprise': 0.10461627}, {'anger': 0.06666384, 'fear': 0.057405185, 'joy': 0.06833278, 'love': 0.0036288379, 'sadness': 0.79270375, 'surprise': 0.011265635}, {'anger': 2.1909349e-05, 'fear': 0.0034499653, 'joy': 1.994826e-06, 'love': 0.005034323, 'sadness': 1.7297366e-06, 'surprise': 0.99149007}] - - Testing bert model - anger - ['anger', 'anger', 'anger', 'anger', 'anger', 'anger'] - [{'anger': 0.79530007, 'fear': 0.043149363, 'joy': 0.050191555, 'love': 0.0028053583, 'sadness': 0.1083552, 'surprise': 0.0001983959}, {'anger': 0.7761929, 'fear': 0.02267685, 'joy': 0.08533038, 'love': 0.019361326, 'sadness': 0.09622978, 'surprise': 0.00020885638}, {'anger': 0.724599, 'fear': 0.021534633, 'joy': 0.14938025, 'love': 0.009412263, 'sadness': 0.09488238, 'surprise': 0.0001914676}, {'anger': 0.8217926, 'fear': 0.009756618, 'joy': 0.061514165, 'love': 0.03527268, 'sadness': 0.07142815, 'surprise': 0.00023569519}, {'anger': 0.9093987, 'fear': 0.00811897, 'joy': 0.024754424, 'love': 0.003218321, 'sadness': 0.054415427, 'surprise': 9.422473e-05}, {'anger': 0.9215124, 'fear': 0.009484482, 'joy': 0.023237498, 'love': 0.0027847919, 'sadness': 0.042906344, 'surprise': 7.447611e-05}] - - Testing entity-network model - joy - ['joy', 'sadness', 'joy', 'sadness', 'sadness', 'joy'] - [{'anger': 0.11245817, 'fear': 0.09678851, 'joy': 0.29964533, 'love': 0.07372399, 'sadness': 0.26239878, 'surprise': 0.15498528}, {'anger': 0.12070423, 'fear': 0.13202831, 'joy': 0.22073878, 'love': 0.031163175, 'sadness': 0.3202514, 'surprise': 0.175114}, {'anger': 0.11448454, 'fear': 0.10408847, 'joy': 0.2848294, 'love': 0.059466686, 'sadness': 0.27815202, 'surprise': 0.1589789}, {'anger': 0.12346853, 'fear': 0.15664044, 'joy': 0.17575133, 'love': 0.019622162, 'sadness': 0.33732292, 'surprise': 0.18719462}, {'anger': 0.117459856, 'fear': 0.115517266, 'joy': 0.25831792, 'love': 0.044844825, 'sadness': 0.2980614, 'surprise': 0.16579871}, {'anger': 0.11082334, 'fear': 0.09062623, 'joy': 0.30381778, 'love': 0.097978726, 'sadness': 0.24158238, 'surprise': 0.15517157}] - + 'anger' -Unsupervised important words learning -------------------------------------- .. code:: ipython3 - import matplotlib.pyplot as plt - import seaborn as sns - sns.set() # i just really like seaborn colors + model.predict(fear_text) + + + + +.. parsed-literal:: + + 'fear' -We need to set ``get_proba`` become True to get the ‘attention’. -Visualizing bahdanau model -^^^^^^^^^^^^^^^^^^^^^^^^^^ .. code:: ipython3 - model = malaya.emotion.deep_model('bahdanau') - result = model.predict(surprise_text, get_proba = True)['attention'] - - plt.figure(figsize = (15, 7)) - labels = [r[0] for r in result] - val = [r[1] for r in result] - aranged = [i for i in range(len(labels))] - plt.bar(aranged, val) - plt.xticks(aranged, labels, rotation = 'vertical') - plt.show() + result = model.predict(sadness_text,get_proba=True) + result -.. image:: load-emotion_files/load-emotion_14_0.png + +.. parsed-literal:: + + {'anger': 0.38946533, + 'fear': 0.0006788928, + 'joy': 4.502521e-05, + 'love': 0.00035775406, + 'sadness': 0.58941084, + 'surprise': 0.020042183, + 'attention': {'kecewa': 0.9982584, + 'tengok': 0.00024031276, + 'kerajaan': 0.00020539697, + 'baru': 0.00023952134, + 'ni': 0.00016858104, + ',': 0.0, + 'janji': 0.00020889708, + 'ape': 0.00017103164, + 'pun': 0.00016638139, + 'tak': 0.00017561934, + 'dapat': 0.00016583406}} -Visualizing luong model -^^^^^^^^^^^^^^^^^^^^^^^ .. code:: ipython3 - model = malaya.emotion.deep_model('luong') - result = model.predict(surprise_text, get_proba = True)['attention'] - - plt.figure(figsize = (15, 7)) - labels = [r[0] for r in result] - val = [r[1] for r in result] - aranged = [i for i in range(len(labels))] - plt.bar(aranged, val) - plt.xticks(aranged, labels, rotation = 'vertical') + plt.figure(figsize = (15, 5)) + keys = result['attention'].keys() + values = result['attention'].values() + aranged = [i for i in range(len(keys))] + plt.bar(aranged, values) + plt.xticks(aranged, keys, rotation = 'vertical') plt.show() -.. image:: load-emotion_files/load-emotion_16_0.png +.. image:: load-emotion_files/load-emotion_17_0.png + + +Open emotion visualization dashboard +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Default when you call ``predict_words`` it will open a browser with +visualization dashboard, you can disable by ``visualization=False``. + +.. code:: ipython3 + + model.predict_words(sadness_text) + + +.. parsed-literal:: + + Serving to http://127.0.0.1:8889/ [Ctrl-C to exit] + + +.. parsed-literal:: + + 127.0.0.1 - - [01/Jun/2019 12:09:32] "GET / HTTP/1.1" 200 - + 127.0.0.1 - - [01/Jun/2019 12:09:33] "GET /static/admin-materialize.min.css HTTP/1.1" 200 - + 127.0.0.1 - - [01/Jun/2019 12:09:33] "GET /static/echarts.min.js HTTP/1.1" 200 - + 127.0.0.1 - - [01/Jun/2019 12:09:33] "GET /favicon.ico HTTP/1.1" 200 - + ---------------------------------------- + Exception happened during processing of request from ('127.0.0.1', 61764) + Traceback (most recent call last): + File "/usr/local/Cellar/python/3.6.5_1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/socketserver.py", line 317, in _handle_request_noblock + self.process_request(request, client_address) + File "/usr/local/Cellar/python/3.6.5_1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/socketserver.py", line 348, in process_request + self.finish_request(request, client_address) + File "/usr/local/Cellar/python/3.6.5_1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/socketserver.py", line 361, in finish_request + self.RequestHandlerClass(request, client_address, self) + File "/usr/local/Cellar/python/3.6.5_1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/socketserver.py", line 696, in __init__ + self.handle() + File "/usr/local/Cellar/python/3.6.5_1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/http/server.py", line 418, in handle + self.handle_one_request() + File "/usr/local/Cellar/python/3.6.5_1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/http/server.py", line 406, in handle_one_request + method() + File "/Users/huseinzol/Documents/Malaya/malaya/_utils/_server.py", line 32, in do_GET + with open(filepath, 'rb') as fh: + FileNotFoundError: [Errno 2] No such file or directory: '/Users/huseinzol/Documents/Malaya/malaya/_utils/web/favicon.ico' + ---------------------------------------- + +.. parsed-literal:: + + + stopping Server... -Visualizing hierarchical model -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. code:: ipython3 - model = malaya.emotion.deep_model('hierarchical') - result = model.predict(surprise_text, get_proba=True)['attention'] + from IPython.core.display import Image, display - plt.figure(figsize = (15, 7)) - labels = [r[0] for r in result] - val = [r[1] for r in result] - aranged = [i for i in range(len(labels))] - plt.bar(aranged, val) - plt.xticks(aranged, labels, rotation = 'vertical') - plt.show() + display(Image('emotion-bahdanau.png', width=800)) + + +.. image:: load-emotion_files/load-emotion_20_0.png + :width: 800px + + +I tried to put the html and javascript inside a notebook cell, pretty +hard you know and a lot of weird bugs. Let stick to HTTP serving ya. + +``predict_words`` only accept a single string. You can’t predict +multiple texts. + +Predict batch of strings +^^^^^^^^^^^^^^^^^^^^^^^^ + +.. code:: ipython3 + + model.predict_batch([anger_text, fear_text, joy_text, + love_text, sadness_text, surprise_text],get_proba=True) + + + + +.. parsed-literal:: + + [{'anger': 0.9470582, + 'fear': 0.00033167106, + 'joy': 9.066894e-06, + 'love': 0.00016375592, + 'sadness': 0.0035682153, + 'surprise': 0.04886904}, + {'anger': 0.013118468, + 'fear': 0.9863961, + 'joy': 5.042084e-05, + 'love': 0.0001933256, + 'sadness': 7.211378e-05, + 'surprise': 0.00016965455}, + {'anger': 0.87134695, + 'fear': 0.008704395, + 'joy': 0.00010600241, + 'love': 0.00063341705, + 'sadness': 0.10522701, + 'surprise': 0.013982114}, + {'anger': 0.007687589, + 'fear': 0.00057056156, + 'joy': 0.0033636452, + 'love': 0.98015594, + 'sadness': 0.003200713, + 'surprise': 0.0050216294}, + {'anger': 0.3216797, + 'fear': 0.00076514384, + 'joy': 3.4114866e-05, + 'love': 0.00025139845, + 'sadness': 0.6674833, + 'surprise': 0.009786241}, + {'anger': 0.028422995, + 'fear': 0.0025764697, + 'joy': 0.00012938023, + 'love': 0.00037871674, + 'sadness': 0.36064965, + 'surprise': 0.60784286}] + + + +**You might want to try ``luong`` and ``self-attention`` by yourself.** + +Stacking models +--------------- + +More information, you can read at +https://malaya.readthedocs.io/en/latest/Stack.html + +.. code:: ipython3 + + multinomial = malaya.emotion.multinomial() + xgb = malaya.emotion.xgb() + +.. code:: ipython3 + + malaya.stack.predict_stack([multinomial, xgb, model], anger_text) + + + + +.. parsed-literal:: + {'anger': 0.34863618616181175, + 'fear': 0.022930197531217115, + 'joy': 0.006847226031582371, + 'love': 0.014764885318498622, + 'sadness': 0.030968564413978033, + 'surprise': 0.032697431654758996} -.. image:: load-emotion_files/load-emotion_18_0.png Load Sparse deep learning models @@ -324,14 +432,14 @@ will try to evolve it. .. code:: ipython3 - sparse_model.predict(sadness_text) + sparse_model.predict(love_text) .. parsed-literal:: - 'sadness' + 'love' @@ -349,7 +457,7 @@ will try to evolve it. .. parsed-literal:: - ['love', 'fear', 'joy', 'love', 'sadness', 'fear'] + ['joy', 'fear', 'sadness', 'love', 'anger', 'surprise'] @@ -367,41 +475,41 @@ will try to evolve it. .. parsed-literal:: - [{'anger': 0.07479232, - 'fear': 0.012134718, - 'joy': 0.034137156, - 'love': 0.85221285, - 'sadness': 0.006336733, - 'surprise': 0.020386234}, - {'anger': 1.6892743e-08, - 'fear': 0.99999964, - 'joy': 6.260633e-08, - 'love': 3.2111713e-10, - 'sadness': 3.542872e-08, - 'surprise': 2.2207877e-07}, - {'anger': 0.00012469916, - 'fear': 9.6892345e-06, - 'joy': 0.9917463, - 'love': 0.006561422, - 'sadness': 0.00040069615, - 'surprise': 0.0011572224}, - {'anger': 5.0021445e-05, - 'fear': 0.0010109642, - 'joy': 0.049688663, - 'love': 0.94577587, - 'sadness': 0.0032941191, - 'surprise': 0.00018034693}, - {'anger': 0.0010146926, - 'fear': 0.00020020001, - 'joy': 5.2909185e-05, - 'love': 2.640257e-06, - 'sadness': 0.99870074, - 'surprise': 2.8823646e-05}, - {'anger': 0.0057854424, - 'fear': 0.8317998, - 'joy': 0.017287944, - 'love': 0.008883897, - 'sadness': 0.0070799366, - 'surprise': 0.12916291}] + [{'anger': 0.04526143, + 'fear': 0.09979316, + 'joy': 0.4658971, + 'love': 0.020664953, + 'sadness': 0.30431935, + 'surprise': 0.064064085}, + {'anger': 6.130409e-06, + 'fear': 0.9997508, + 'joy': 2.2800703e-05, + 'love': 3.8201404e-07, + 'sadness': 4.3520595e-06, + 'surprise': 0.00021553691}, + {'anger': 0.20740777, + 'fear': 0.22583336, + 'joy': 0.017892161, + 'love': 0.0037904498, + 'sadness': 0.5115712, + 'surprise': 0.033505023}, + {'anger': 5.2901927e-05, + 'fear': 6.0754595e-05, + 'joy': 1.1822221e-05, + 'love': 0.9991229, + 'sadness': 0.00072059815, + 'surprise': 3.093863e-05}, + {'anger': 0.48188186, + 'fear': 0.33322, + 'joy': 0.018766977, + 'love': 0.00092354603, + 'sadness': 0.078747146, + 'surprise': 0.086460434}, + {'anger': 0.007138084, + 'fear': 0.2971508, + 'joy': 0.0024597635, + 'love': 0.00037835565, + 'sadness': 0.031262815, + 'surprise': 0.6616101}] diff --git a/example/emotion/load-emotion.ipynb b/example/emotion/load-emotion.ipynb index d8a3240a..4013b6d7 100644 --- a/example/emotion/load-emotion.ipynb +++ b/example/emotion/load-emotion.ipynb @@ -9,8 +9,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 11.5 s, sys: 819 ms, total: 12.3 s\n", - "Wall time: 12.5 s\n" + "CPU times: user 10.1 s, sys: 769 ms, total: 10.9 s\n", + "Wall time: 11 s\n" ] } ], @@ -25,12 +25,12 @@ "metadata": {}, "outputs": [], "source": [ - "anger_text = 'aku cukup tak suka budak gemuk tu'\n", - "fear_text = 'saya takut dengan hantu'\n", - "joy_text = 'gembiranya hari ni, dapat jumpa crush'\n", - "love_text = 'saya terlalu cintakan dia'\n", - "sadness_text = 'kawan rapat aku putuskan hubungan'\n", - "surprise_text = 'terharu aku harini, semua orang cakap selamat hari jadi'" + "anger_text = 'babi la company ni, aku dah la penat datang dari jauh'\n", + "fear_text = 'takut doh tengok cerita hantu tadi'\n", + "joy_text = 'bestnya dapat tidur harini, tak payah pergi kerja'\n", + "love_text = 'aku sayang sgt dia dah doh'\n", + "sadness_text = 'kecewa tengok kerajaan baru ni, janji ape pun tak dapat'\n", + "surprise_text = 'sakit jantung aku, terkejut dengan cerita hantu tadi'" ] }, { @@ -38,7 +38,9 @@ "metadata": {}, "source": [ "All models got `get_proba` parameters.\n", - "If True, it will returned probability every classes. Else, it will return highest probability class. **Default is True.**" + "If True, it will returned probability every classes. Else, it will return highest probability class. **Default is True.**\n", + "\n", + "I suggest you to use deep learning models to understand emotion, these models are better for detecting emotions from social media." ] }, { @@ -57,14 +59,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "anger\n", - "{'anger': 0.30367763926253094, 'fear': 0.16709964152193366, 'joy': 0.17026521921403184, 'love': 0.18405977732934192, 'sadness': 0.1388341895665479, 'surprise': 0.03606353310561458}\n" + "joy\n", + "{'anger': 0.11733952772643724, 'fear': 0.15199690452609127, 'joy': 0.2589124946219144, 'love': 0.2024854510784686, 'sadness': 0.2211704631949802, 'surprise': 0.048095158852106114}\n" ] }, { "data": { "text/plain": [ - "['anger', 'fear', 'joy', 'joy', 'sadness', 'fear']" + "['joy', 'fear', 'anger', 'love', 'anger', 'fear']" ] }, "execution_count": 3, @@ -100,49 +102,49 @@ "name": "stdout", "output_type": "stream", "text": [ - "love\n", - "{'anger': 0.22918181, 'fear': 0.089252785, 'joy': 0.1318236, 'love': 0.46476611, 'sadness': 0.07200217, 'surprise': 0.012973559}\n" + "anger\n", + "{'anger': 0.37050414, 'fear': 0.17237191, 'joy': 0.21709572, 'love': 0.040401105, 'sadness': 0.16886166, 'surprise': 0.030765474}\n" ] }, { "data": { "text/plain": [ - "[{'anger': 0.22918181,\n", - " 'fear': 0.089252785,\n", - " 'joy': 0.1318236,\n", - " 'love': 0.46476611,\n", - " 'sadness': 0.07200217,\n", - " 'surprise': 0.012973559},\n", - " {'anger': 0.013483193,\n", - " 'fear': 0.939588,\n", - " 'joy': 0.01674833,\n", - " 'love': 0.003220023,\n", - " 'sadness': 0.022906518,\n", - " 'surprise': 0.0040539484},\n", - " {'anger': 0.10506946,\n", - " 'fear': 0.025150253,\n", - " 'joy': 0.725915,\n", - " 'love': 0.05211037,\n", - " 'sadness': 0.078554265,\n", - " 'surprise': 0.013200594},\n", - " {'anger': 0.11640434,\n", - " 'fear': 0.097485565,\n", - " 'joy': 0.24893147,\n", - " 'love': 0.25440857,\n", - " 'sadness': 0.2650988,\n", - " 'surprise': 0.01767122},\n", - " {'anger': 0.27124837,\n", - " 'fear': 0.15662362,\n", - " 'joy': 0.131251,\n", - " 'love': 0.022184724,\n", - " 'sadness': 0.41255626,\n", - " 'surprise': 0.006135965},\n", - " {'anger': 0.07513438,\n", - " 'fear': 0.2525073,\n", - " 'joy': 0.024355419,\n", - " 'love': 0.002638406,\n", - " 'sadness': 0.0059716892,\n", - " 'surprise': 0.6393928}]" + "[{'anger': 0.37050414,\n", + " 'fear': 0.17237191,\n", + " 'joy': 0.21709572,\n", + " 'love': 0.040401105,\n", + " 'sadness': 0.16886166,\n", + " 'surprise': 0.030765474},\n", + " {'anger': 0.024576988,\n", + " 'fear': 0.9346102,\n", + " 'joy': 0.017333886,\n", + " 'love': 0.00388243,\n", + " 'sadness': 0.015722778,\n", + " 'surprise': 0.0038737422},\n", + " {'anger': 0.6121963,\n", + " 'fear': 0.07615826,\n", + " 'joy': 0.12938385,\n", + " 'love': 0.021234306,\n", + " 'sadness': 0.13893352,\n", + " 'surprise': 0.022093719},\n", + " {'anger': 0.078999326,\n", + " 'fear': 0.05360329,\n", + " 'joy': 0.05482416,\n", + " 'love': 0.7758813,\n", + " 'sadness': 0.03182298,\n", + " 'surprise': 0.004868941},\n", + " {'anger': 0.7553716,\n", + " 'fear': 0.060115993,\n", + " 'joy': 0.01417792,\n", + " 'love': 0.005369766,\n", + " 'sadness': 0.15161343,\n", + " 'surprise': 0.01335139},\n", + " {'anger': 0.07156373,\n", + " 'fear': 0.241878,\n", + " 'joy': 0.0074376594,\n", + " 'love': 0.0042003845,\n", + " 'sadness': 0.06661975,\n", + " 'surprise': 0.6083005}]" ] }, "execution_count": 4, @@ -177,13 +179,7 @@ { "data": { "text/plain": [ - "['fast-text',\n", - " 'hierarchical',\n", - " 'bahdanau',\n", - " 'luong',\n", - " 'bidirectional',\n", - " 'bert',\n", - " 'entity-network']" + "['self-attention', 'bahdanau', 'luong']" ] }, "execution_count": 5, @@ -195,78 +191,31 @@ "malaya.emotion.available_deep_model()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load deep learning models\n", + "\n", + "Good thing about deep learning models from Malaya, it returns `Attention` result, means, which part of words give the high impact to the results. But to get `Attention`, you need to set `get_proba=True`." + ] + }, { "cell_type": "code", "execution_count": 6, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Testing fast-text model\n", - "love\n", - "['love', 'fear', 'joy', 'love', 'sadness', 'surprise']\n", - "[{'anger': 2.538603e-07, 'fear': 4.1372344e-13, 'joy': 1.0892472e-08, 'love': 0.99999976, 'sadness': 3.8994935e-16, 'surprise': 2.439655e-08}, {'anger': 4.4489467e-24, 'fear': 1.0, 'joy': 1.3903143e-28, 'love': 1.7920514e-33, 'sadness': 1.01771616e-26, 'surprise': 6.799581e-18}, {'anger': 9.583714e-26, 'fear': 1.5029816e-24, 'joy': 1.0, 'love': 3.7527533e-13, 'sadness': 8.348174e-24, 'surprise': 2.080897e-16}, {'anger': 1.7409228e-13, 'fear': 3.2279754e-12, 'joy': 0.0005876841, 'love': 0.9994123, 'sadness': 1.8902605e-11, 'surprise': 9.9256076e-11}, {'anger': 1.2737708e-11, 'fear': 5.882562e-10, 'joy': 9.112171e-13, 'love': 7.7659496e-20, 'sadness': 1.0, 'surprise': 1.6035637e-16}, {'anger': 5.5730725e-37, 'fear': 0.16033638, 'joy': 1.2999706e-30, 'love': 0.0, 'sadness': 0.0, 'surprise': 0.8396636}]\n", - "\n", - "Testing hierarchical model\n", - "anger\n", - "['anger', 'fear', 'joy', 'joy', 'sadness', 'joy']\n", - "[{'anger': 0.2683842, 'fear': 0.3206215, 'joy': 0.17552154, 'love': 0.041160323, 'sadness': 0.1729689, 'surprise': 0.021343619}, {'anger': 0.006326744, 'fear': 0.97895914, 'joy': 0.01049677, 'love': 0.0004076402, 'sadness': 0.0026508307, 'surprise': 0.0011588057}, {'anger': 0.018994415, 'fear': 0.025846783, 'joy': 0.87117314, 'love': 0.039761506, 'sadness': 0.01727374, 'surprise': 0.026950512}, {'anger': 0.030335624, 'fear': 0.071699485, 'joy': 0.5445254, 'love': 0.24373391, 'sadness': 0.08821325, 'surprise': 0.021492302}, {'anger': 0.022141613, 'fear': 0.01158712, 'joy': 0.0046922215, 'love': 0.0013928033, 'sadness': 0.95863473, 'surprise': 0.0015515403}, {'anger': 0.028982855, 'fear': 0.15079749, 'joy': 0.4619698, 'love': 0.18604991, 'sadness': 0.009463493, 'surprise': 0.16273652}]\n", - "\n", - "Testing bahdanau model\n", - "love\n", - "['anger', 'fear', 'joy', 'love', 'sadness', 'surprise']\n", - "[{'anger': 0.4797056, 'fear': 0.168805, 'joy': 0.01420066, 'love': 0.25597632, 'sadness': 0.07191573, 'surprise': 0.009396704}, {'anger': 0.0008658771, 'fear': 0.9851621, 'joy': 0.0026717593, 'love': 0.00049689104, 'sadness': 0.0037894566, 'surprise': 0.007013949}, {'anger': 0.047901724, 'fear': 0.0058520515, 'joy': 0.6818599, 'love': 0.22279568, 'sadness': 0.010295693, 'surprise': 0.03129497}, {'anger': 0.0136362715, 'fear': 0.025698097, 'joy': 0.13118966, 'love': 0.75916684, 'sadness': 0.016278507, 'surprise': 0.05403061}, {'anger': 0.010733987, 'fear': 0.023202764, 'joy': 0.005261726, 'love': 0.00054325344, 'sadness': 0.9586973, 'surprise': 0.0015610264}, {'anger': 0.0028718843, 'fear': 0.21446393, 'joy': 0.034736242, 'love': 0.004341282, 'sadness': 0.001976919, 'surprise': 0.7416098}]\n", - "\n", - "Testing luong model\n", - "love\n", - "['love', 'fear', 'joy', 'love', 'sadness', 'surprise']\n", - "[{'anger': 0.0027926113, 'fear': 0.0042426162, 'joy': 0.6864444, 'love': 0.2944655, 'sadness': 0.0010767934, 'surprise': 0.010978072}, {'anger': 0.0057431706, 'fear': 0.9705748, 'joy': 0.0043815267, 'love': 0.0008089542, 'sadness': 0.009230111, 'surprise': 0.009261452}, {'anger': 0.0016454123, 'fear': 0.0012319328, 'joy': 0.87483215, 'love': 0.0678572, 'sadness': 0.010661957, 'surprise': 0.043771397}, {'anger': 0.016030224, 'fear': 0.005272452, 'joy': 0.04561528, 'love': 0.31633487, 'sadness': 0.59368426, 'surprise': 0.023062926}, {'anger': 0.0010896003, 'fear': 0.00057456765, 'joy': 4.035251e-05, 'love': 6.2987274e-05, 'sadness': 0.9982096, 'surprise': 2.283629e-05}, {'anger': 0.00042841653, 'fear': 0.33654872, 'joy': 0.00097602647, 'love': 0.00029422223, 'sadness': 0.00032926636, 'surprise': 0.6614233}]\n", - "\n", - "Testing bidirectional model\n", - "love\n", - "['love', 'fear', 'anger', 'joy', 'sadness', 'surprise']\n", - "[{'anger': 0.04017815, 'fear': 0.40091494, 'joy': 0.005757856, 'love': 0.44828272, 'sadness': 0.00045654154, 'surprise': 0.10440978}, {'anger': 0.0035579149, 'fear': 0.97265065, 'joy': 0.020677356, 'love': 0.000660097, 'sadness': 0.0010607935, 'surprise': 0.0013932855}, {'anger': 0.48268953, 'fear': 0.35196006, 'joy': 0.029008513, 'love': 0.014688243, 'sadness': 0.018093651, 'surprise': 0.10355998}, {'anger': 0.029928548, 'fear': 0.05067155, 'joy': 0.65202534, 'love': 0.10342105, 'sadness': 0.059337158, 'surprise': 0.10461627}, {'anger': 0.06666384, 'fear': 0.057405185, 'joy': 0.06833278, 'love': 0.0036288379, 'sadness': 0.79270375, 'surprise': 0.011265635}, {'anger': 2.1909349e-05, 'fear': 0.0034499653, 'joy': 1.994826e-06, 'love': 0.005034323, 'sadness': 1.7297366e-06, 'surprise': 0.99149007}]\n", - "\n", - "Testing bert model\n", - "anger\n", - "['anger', 'anger', 'anger', 'anger', 'anger', 'anger']\n", - "[{'anger': 0.79530007, 'fear': 0.043149363, 'joy': 0.050191555, 'love': 0.0028053583, 'sadness': 0.1083552, 'surprise': 0.0001983959}, {'anger': 0.7761929, 'fear': 0.02267685, 'joy': 0.08533038, 'love': 0.019361326, 'sadness': 0.09622978, 'surprise': 0.00020885638}, {'anger': 0.724599, 'fear': 0.021534633, 'joy': 0.14938025, 'love': 0.009412263, 'sadness': 0.09488238, 'surprise': 0.0001914676}, {'anger': 0.8217926, 'fear': 0.009756618, 'joy': 0.061514165, 'love': 0.03527268, 'sadness': 0.07142815, 'surprise': 0.00023569519}, {'anger': 0.9093987, 'fear': 0.00811897, 'joy': 0.024754424, 'love': 0.003218321, 'sadness': 0.054415427, 'surprise': 9.422473e-05}, {'anger': 0.9215124, 'fear': 0.009484482, 'joy': 0.023237498, 'love': 0.0027847919, 'sadness': 0.042906344, 'surprise': 7.447611e-05}]\n", - "\n", - "Testing entity-network model\n", - "joy\n", - "['joy', 'sadness', 'joy', 'sadness', 'sadness', 'joy']\n", - "[{'anger': 0.11245817, 'fear': 0.09678851, 'joy': 0.29964533, 'love': 0.07372399, 'sadness': 0.26239878, 'surprise': 0.15498528}, {'anger': 0.12070423, 'fear': 0.13202831, 'joy': 0.22073878, 'love': 0.031163175, 'sadness': 0.3202514, 'surprise': 0.175114}, {'anger': 0.11448454, 'fear': 0.10408847, 'joy': 0.2848294, 'love': 0.059466686, 'sadness': 0.27815202, 'surprise': 0.1589789}, {'anger': 0.12346853, 'fear': 0.15664044, 'joy': 0.17575133, 'love': 0.019622162, 'sadness': 0.33732292, 'surprise': 0.18719462}, {'anger': 0.117459856, 'fear': 0.115517266, 'joy': 0.25831792, 'love': 0.044844825, 'sadness': 0.2980614, 'surprise': 0.16579871}, {'anger': 0.11082334, 'fear': 0.09062623, 'joy': 0.30381778, 'love': 0.097978726, 'sadness': 0.24158238, 'surprise': 0.15517157}]\n", - "\n" - ] - } - ], + "outputs": [], "source": [ - "for i in malaya.emotion.available_deep_model():\n", - " print('Testing %s model'%(i))\n", - " model = malaya.emotion.deep_model(i)\n", - " print(model.predict(anger_text))\n", - " print(model.predict_batch([anger_text,\n", - " fear_text,\n", - " joy_text,\n", - " love_text,\n", - " sadness_text,\n", - " surprise_text]))\n", - " print(model.predict_batch([anger_text,\n", - " fear_text,\n", - " joy_text,\n", - " love_text,\n", - " sadness_text,\n", - " surprise_text], get_proba = True))\n", - " print()" + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "sns.set()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Unsupervised important words learning" + "### Load bahdanau model" ] }, { @@ -275,35 +224,103 @@ "metadata": {}, "outputs": [], "source": [ - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "sns.set() # i just really like seaborn colors" + "model = malaya.emotion.deep_model('bahdanau')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We need to set `get_proba` become True to get the 'attention'." + "#### Predict single string" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 10, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'anger'" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#### Visualizing bahdanau model" + "model.predict(anger_text)" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'fear'" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.predict(fear_text)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'anger': 0.38946533,\n", + " 'fear': 0.0006788928,\n", + " 'joy': 4.502521e-05,\n", + " 'love': 0.00035775406,\n", + " 'sadness': 0.58941084,\n", + " 'surprise': 0.020042183,\n", + " 'attention': {'kecewa': 0.9982584,\n", + " 'tengok': 0.00024031276,\n", + " 'kerajaan': 0.00020539697,\n", + " 'baru': 0.00023952134,\n", + " 'ni': 0.00016858104,\n", + " ',': 0.0,\n", + " 'janji': 0.00020889708,\n", + " 'ape': 0.00017103164,\n", + " 'pun': 0.00016638139,\n", + " 'tak': 0.00017561934,\n", + " 'dapat': 0.00016583406}}" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result = model.predict(sadness_text,get_proba=True)\n", + "result" + ] + }, + { + "cell_type": "code", + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" + "
" ] }, "metadata": { @@ -313,15 +330,12 @@ } ], "source": [ - "model = malaya.emotion.deep_model('bahdanau')\n", - "result = model.predict(surprise_text, get_proba = True)['attention']\n", - "\n", - "plt.figure(figsize = (15, 7))\n", - "labels = [r[0] for r in result]\n", - "val = [r[1] for r in result]\n", - "aranged = [i for i in range(len(labels))]\n", - "plt.bar(aranged, val)\n", - "plt.xticks(aranged, labels, rotation = 'vertical')\n", + "plt.figure(figsize = (15, 5))\n", + "keys = result['attention'].keys()\n", + "values = result['attention'].values()\n", + "aranged = [i for i in range(len(keys))]\n", + "plt.bar(aranged, values)\n", + "plt.xticks(aranged, keys, rotation = 'vertical')\n", "plt.show()" ] }, @@ -329,76 +343,212 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Visualizing luong model" + "#### Open emotion visualization dashboard\n", + "\n", + "Default when you call `predict_words` it will open a browser with visualization dashboard, you can disable by `visualization=False`." ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Serving to http://127.0.0.1:8889/ [Ctrl-C to exit]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "127.0.0.1 - - [01/Jun/2019 12:09:32] \"GET / HTTP/1.1\" 200 -\n", + "127.0.0.1 - - [01/Jun/2019 12:09:33] \"GET /static/admin-materialize.min.css HTTP/1.1\" 200 -\n", + "127.0.0.1 - - [01/Jun/2019 12:09:33] \"GET /static/echarts.min.js HTTP/1.1\" 200 -\n", + "127.0.0.1 - - [01/Jun/2019 12:09:33] \"GET /favicon.ico HTTP/1.1\" 200 -\n", + "----------------------------------------\n", + "Exception happened during processing of request from ('127.0.0.1', 61764)\n", + "Traceback (most recent call last):\n", + " File \"/usr/local/Cellar/python/3.6.5_1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/socketserver.py\", line 317, in _handle_request_noblock\n", + " self.process_request(request, client_address)\n", + " File \"/usr/local/Cellar/python/3.6.5_1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/socketserver.py\", line 348, in process_request\n", + " self.finish_request(request, client_address)\n", + " File \"/usr/local/Cellar/python/3.6.5_1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/socketserver.py\", line 361, in finish_request\n", + " self.RequestHandlerClass(request, client_address, self)\n", + " File \"/usr/local/Cellar/python/3.6.5_1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/socketserver.py\", line 696, in __init__\n", + " self.handle()\n", + " File \"/usr/local/Cellar/python/3.6.5_1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/http/server.py\", line 418, in handle\n", + " self.handle_one_request()\n", + " File \"/usr/local/Cellar/python/3.6.5_1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/http/server.py\", line 406, in handle_one_request\n", + " method()\n", + " File \"/Users/huseinzol/Documents/Malaya/malaya/_utils/_server.py\", line 32, in do_GET\n", + " with open(filepath, 'rb') as fh:\n", + "FileNotFoundError: [Errno 2] No such file or directory: '/Users/huseinzol/Documents/Malaya/malaya/_utils/web/favicon.ico'\n", + "----------------------------------------\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "stopping Server...\n" + ] + } + ], + "source": [ + "model.predict_words(sadness_text)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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N2uU6ubrxej76FgYKhpxGCKP3xs9Mrpjmm0WbO1moWLJZrli9sb5nU2ubb5dkH+jg6KjvJO8s4EJyxbouu310H/Ho9rzjVb/zrPexXYd8kn2pfhSyr79HgFOgfZBNsCXFMsQsVDNMNlwggmU3TSQicjXqB/Vn9FosOo4YL5SgsccpMTqpMLll71QKzX6+VJk07QM26X4H4zMOH6rMbMsazv6ey3xY4Yh9XsTRw/l1BY+OvTu+XshZpFxsdzK05GDpmVPNp3vLZs78OsdcLnFeu8LuArkytupQdTG8z3XXzV0iXlasd7wSdTXvWl1D5/WRG1+aMM0cNyVvady2aHG7E9ga05ZyN7X9QMfBexmdh+5nPsh+mPvocNfhx4efHO7OfZrzLKvnUG96X2r/3udxf0UN7B6MfBEzlPTy4PCxV+UjDa8fjL5882kcvCVOCL6Tn9SZMp/2mzn7/tNH5dmkT62ff81rLsR9ufz13SL7kuX3lB8NP6dXuFcd1rJ/dW6PvzFCH7kD+RnVjj6EccSKYxdwN/EZBAcabppR2vN04fTqDAiGdsZ0JgsiA7GXdJTZloWB5SlrNpsJO8TezBHBKcQ5xJXDrcP9iaeU14z3G18Zvxn/Z4HjghqCI0J7hfmFW0W8RVZFi8WUxLrFA8RXJY5JSkm2STlKTUmnyojIDMnm7jDY8U2uSt5TgU6hTTFSSUCpXzlNRUFlXDVPTVvtk3qphrnGouZ5LXutX9p1Ou66WN0bemR9ov5dg0hDfsNeozRjJeNpkxJTW/jecds8ykLK4p1lmZWHNav1c5sCWwc7kt2g/UkHb0dhxw9OV50TXYxdGVyH3Srdoz0MPGk9B73O7Az2VvBe2XXfJ9/Xy0/Cb4nc6X8swDdQMQgVNBhcS0kJcQqVDkOHvQm/FVG8Oz7SNUqDyhuNip6NGYhtj6uPL0vI25OaGJ8Umuy/d+c+txSn/Q6p9mn2BxzSnQ66Z+w8FJAZmhWdnZKTmVtwuOxITV7j0Xv5fQWjxz6fQBVKFHkVHz15v2TllOxpv7ITZx6fXS1XOB9QUXKhpwpVrVUTX1tf9/GS5OWQ+torc9dUGvZf727kaApr7rzFdzul5W2rVVtLu3zHxU6p+1cfGjwafpzQzfe0t+dwn9Nz0QEw+HHo3fCH1+CNyPiuidop9EziR/CpYp78VXdJ7afzavHG+G/93rdRMCoAnDgEwMbvOQ41AOReBEBsDwBscO5pRweAkzpACJgCaLEDQBbaf84PCE48CYAEeIAkUANmcH4ZBueURaAedIEJsAaxQ8qQIxQNnYCaoddwzieNcEGkIuoRo0gGpAEyHnkJOQVnaV6oUtQrOBPzQV9Af8KoYFIxz7A82HBsG46Eo+Da8Zz4WHw/QZFQRFilIdM8pVWnraZjp8ulR9An0X9niGVYYkxkgpiyiazECpIaqZc5hAXLcoHVmHWKLZNdir2XI4aTg7OVy5+bhvsajwcvkvcSnyecEfQJ5AnaCjEJPRcuEvEUFRCdErsoHi2hIQlJdknlS3vCs3Netn9Hi1ylfIHCPkWKkqOyhgqfKqQ6ptaiflwjRFNbi15rRLtGJ0ZXVw+n16ffYHDdsMnolnGryT3TLrMe80GLUctpqwXrFVucHau9qIOao5UT2TnZpdi11W3Gg+Sp7xW5s9x7wIfgq++XRG72/x6oFpQU3B5CCHUOqwhf3G0WWRY1F60VkxM7Gq+UcHTPQpJr8oN92imtqZZpE+lZGdqZIKsv58rhU3kF+WbHkMfvF+YXB5QYnpIuEzwrUq5UYVMZVV1a++QSqFe9atPgfiO4KfnmidvX7vS3LXXwdpo9iHl09vGz7rUemb6dz48M3B0iDZNHLo3OjnNPqE3qTcu/p//wYvbI5x1z7QtmXzq/KSyWLC3/sP95YXlhVWMt5dfdzf1ja/yJ8PhLAFVgAlxBCNgHjoM60AlGwQ+IBMlBNlAEdBRqgF4iAEICzvLTEFcRb+E83gqZjmxDrqC0UQdQ3Wh2dCC6EYPHeGMasYzYMOwTnDQuD7eI98I/IMgSimiQNFE047TOtI/pDOla6bXo78BZ7CNGe8ZROE9dJx4nyZKeMkfAmWczqy8bDVszeyAHK8dDzj1c0lzj3EU8trw43g6+/fwGAhiBp4KFQr7CssKrIt2iZWJR4sYSXBJfJB9LnZdOkfGU1dwhIccuj5dfVZhTnFB6ofxY5bbqRbUS9UMaVE1PLUNtSR1GnUXdYb1W/XqDq4YNRk3Gt03aTDvNHpv3WrywfGM1bb1gs2KHs2d1EHNUd7J29nfZ61ridtN92GPNS3CnhXfMrrM+PX4QWcU/IqAmcCpYlBIScjV0Odw0onD3TJQWdW90Wywqziq+KGEqUT3pSPL0PuOU6lT6tD0HpuH9pDfTIuthjllu9xGHvLH8lGO8x+8WBhbTn2wu9T9NKntwdm+5yvkvF65WxdZo1WEuDly+cCX5mtd1lUb6pomb128fuGPTxn53vKOmk/pA6xG2a/BJzdP9PV59Os9FBpgGHw05v5x8lfiaefTamNP46kT1pPs0w0zXh8xZy8+Mcy8Wzn4NWVT5jvjRs1y6GvRLcXv8kQADaDd3AHGgAs8ANxAODoIz4DYYhte/IGQBxUIV0CCCBmEEr/wOJA5pjzyD/IKyQFWh8Wgq+g3GCV7tNtgBHBn3E19IUCdM0pyk1aMdoUui56fvYohnlGScYDpD9CNJkL4zP2QpY01i82TX45DiZOei4UZwr/Is867yAwEsfAPlEZYV0RZ1EAsS3y9xUvIGnHfPyzLuUJBzld+nUKHYo7SiIqHqrlag3q/JrOWuXaEzp6etf9jgjZGicY7JuJmWeaHFFys760u2tHZh9o8dJZ1ynD+4WrjVeuA9KV4PvUV3HfSZ9DMkVwYgA/2D7lFEQzJCZ8KtIuojWaISqGMxRrGX49kT9u35mOQGr1OVlKpUjrQj6aiDyRlfMj2yrmav5zodrjqyfNQx//IxwnHKiQdFUsW5J+dKXU/dKRM9kw/v/f7nuy9oVlZVM9Uk1k5ddLzUUi96Je/qUoP39QeNMk1Hm+dv2d++fIfQGtjW2k7sCLjXeB/1wO5h6aOJxxJPKN2VT8d7OHvt+w72X3/+doAwKPfCYYj68shwzat7IwOvp0YX3qyOQ2+xE5h3mEkwuTz1aXp05un75g/lHzNnIz5Zf5aaw869mW9eyPri8VXi65dvLYtpS0bfMd87f6T81Py5sHxhxWOVsNq4Rv5F9+vauvvG+EcHKipsHh8QrQEA6NH19a+iAGALAFjLX19fKV9fXzsPJxsjANwN2/oPafOsYQTgLNcG6rsy+frf/+X8D78U1VXQOwEbAAABnmlUWHRYTUw6Y29tLmFkb2JlLnhtcAAAAAAAPHg6eG1wbWV0YSB4bWxuczp4PSJhZG9iZTpuczptZXRhLyIgeDp4bXB0az0iWE1QIENvcmUgNS40LjAiPgogICA8cmRmOlJERiB4bWxuczpyZGY9Imh0dHA6Ly93d3cudzMub3JnLzE5OTkvMDIvMjItcmRmLXN5bnRheC1ucyMiPgogICAgICA8cmRmOkRlc2NyaXB0aW9uIHJkZjphYm91dD0iIgogICAgICAgICAgICB4bWxuczpleGlmPSJodHRwOi8vbnMuYWRvYmUuY29tL2V4aWYvMS4wLyI+CiAgICAgICAgIDxleGlmOlBpeGVsWERpbWVuc2lvbj4xMjQwPC9leGlmOlBpeGVsWERpbWVuc2lvbj4KICAgICAgICAgPGV4aWY6UGl4ZWxZRGltZW5zaW9uPjYzNTwvZXhpZjpQaXhlbFlEaW1lbnNpb24+CiAgICAgIDwvcmRmOkRlc2NyaXB0aW9uPgogICA8L3JkZjpSREY+CjwveDp4bXBtZXRhPgqzFMNfAABAAElEQVR4AezdB5wU5f3H8d/R7o6mdEGKCAfYC2ADC9aAEQV7STFGjcSYaCIRG5oYTfyLGmM0RmMsUaMQS0zEghgRFRBNBEXgOBRQlI6CcvT/fAeeYXZvd2/3bvduy+d5vY6d8szMM+/ZnWV++5SirV4yEgIIIIAAAggggAACCCCAAAIIIIAAAgjUSKBBjbZiIwQQQAABBBBAAAEEEEAAAQQQQAABBBDwBQiw8UZAAAEEEEAAAQQQQAABBBBAAAEEEECgFgIE2GqBx6YIIIAAAggggAACCCCAAAIIIIAAAggQYOM9gAACCCCAAAIIIIAAAggggAACCCCAQC0ECLDVAo9NEUAAAQQQQAABBBBAAAEEEEAAAQQQIMDGewABBBBAAAEEEEAAAQQQQAABBBBAAIFaCBBgqwUemyKAAAIIIIAAAggggAACCCCAAAIIIECAjfcAAggggAACCCCAAAIIIIAAAggggAACtRAgwFYLPDZFAAEEEEAAAQQQQAABBBBAAAEEEECAABvvAQQQQAABBBBAAAEEEEAAAQQQQAABBGohQICtFnhsigACCCCAAAIIIIAAAggggAACCCCAAAE23gMIIIAAAggggAACCCCAAAIIIIAAAgjUQoAAWy3w2BQBBBBAAAEEEEAAAQQQQAABBBBAAAECbLwHEEAAAQQQQAABBBBAAAEEEEAAAQQQqIUAAbZa4LEpAggggAACCCCAAAIIIIAAAggggAACBNh4DyCAAAIIIIAAAggggAACCCCAAAIIIFALAQJstcBjUwQQQAABBBBAAAEEEEAAAQQQQAABBBpBgAACCCBQeAKVlett6fIV9tWatVZaWmKtd97JdmrZwho04HeXwns3cMYIIIAAAggggAACCCBQW4GcCrDpQfCWO+6xTZs2R5z3j84/x3p07xaxrLqZWbPL7a+Pj4vIdnC//e20oYMjlqVrZuvWrTZy9G+D3Z128mA7uO/+wTwTCMQS2LJliy367HPr1mXXKqsffuIf9sFHc/3lPXfvZhd//5wqeQp5wUsTJ9mE/7zpE7Ro0cyuv/KyvOdI5j2he9Hj4/5pb7/zXhWPvvvvYw2KinL2fTV/wSLbvVuXKuf15tTp9uy/X/GXK4D4uxt+WSUPCxBAAAEEEEAAAQQQQACB2gjkVIDt62++sTVrv65yvlOm/y/lANub0961dZWVEfta+/U3EfPpngkfb/36DenePfvLM4H5nyz0g8DNmjW1q376oypnp4Cze09pmhQp8M036wKfLVu3RK7M07lk3hP/HD8hZnBNJJ12aW9z530cuOXK+2rV6i+9oOFz9tHcCrv71hurXF3V1nOflSorWYAAAggggAACCCCAAAIIpEEgL9oCTf/vDFOtjGTTxk2bbOasOclmJx8CdS4wfsJ/7PZ7/mIKHJAQSJfAZ58vsVf+Mznu7jp36hh3XbaumDNvvo3+7Z1+cC1by0i5EEAAAQQQQAABBBBAIP8FcqoGW7zLoYDZvPkLrKzHbvGyRCyf+eFsU9M7EgLZKrBg0WfVFk39ZrlEv1lOorBfq3tPfLxgYQTQwEP62RmnnOj3u+Z+pAg3Hc2F99WSpcurvZ+XlBQH550L5xQUlgkEEEAAAQQQQAABBBDIGYG8CLBJe+q7/0s6wDZl+n9z5gJRUATiCfzwO2fGW8XyAhWo7j3x9dfrImSGffuEYFCDIq/vNaXq9hGxgxyZGXBwP9MfCQEEEEAAAQQQQAABBBDIlEBONxEN10R4938zk2omun7DBps1Z16mPNkvAgggkLUCmzZvCsqm+2dxkybBPBMIIIAAAggggAACCCCAAAI1F8jpGmwH7re3qf81JTUTLZ//ifXq0T2hxv9mzgrWt2vT2lZ/+ZW/bbCwmgl1lv35kqX+n5ommVfro1nTUtutS2fr1nXXtD2wqrnWipWrbPEX246lwR2Ki5tY+7ZtbPfduprKHp02b94c0WdX09JSa+qVrbq0YtVq27q9yWyrnXeyhg0bxtzky6/W2BdLl9nnXpm0TSMv3847tbTu3qh9nTvtEtSEiblxDRbKYOnyFf4omp96I2mqs/9unXe1zrvuYjq3REmds2/wgqlKbVq38i7Ttto56vhefTZ9svBT36ZD+3a2zx69qpyzjv3Z4i9s1tx5pmve0ev8vWvnTtahXdtEh41Y567fYq/fq3Xr11vnjh28snf09+HKE7GBN6M+13Qdw4NgbNy40ZavWOlnbd6smbnmbmvWrg3yNW/uLS/e0Qwuer+ar0l5tF34faWgTOtWO2uxH9DW+6Hi4wW2bPlK6+Sdnz4H7du1Cbz9jGn4Z5P3+Z5dPt87zgrfSOfb3rsWXbz3na5vOlI6Ptt632jU1yWey5JlK/ymixo4QDZ678T7bEWXX9f7U+99s3TZctPgK9p2V28feh8mCorFek+oOfxK7/OqFK7B1tC7lu59pXUtWjT39x1rH1ofL+neVF7xsS337ldrvWm9P3Sf0v2wWdOm8TaLWF4T+7Vff+1/NlXecHLn1MQLHrb0zklJ+1d+pWLvc9LCe/8kSvohRve5xV8s8a7lcmvZsoV18T67us+lcu8Jfy5VBg1cUuE109U11GdF+ww3601UJtYhgAACCCCAAAIIIIBAdgvkdICtl9fn2sxZs4Mgw1RvNNHqAmxvTXsvuCKHHnSgjX/lP8F8oon/ffCRPfn08zFHMQ1vN3TwsXbskQNqHGzSQ+A//vmivbM9cBjed3i60y4d7AfnnW67eAEilxSUueF3v3eztlvXzvaLSy8M5mNNLPMe5G8MbXPu6Sfbof0PDLIquDLhP2/av195LWE/R6UlJfa9s0+1vb1gVTrS5Cnv2FPPvhD3mArqqSnbTt6Db6z018fG+gFXrTvvjFOse9cu9qe/PmY63+jUuFEju3zEBX4ATeteenVS3PM96MD9/D6rXJArel+aX/jpYnv0yWf8IGys9QpSDfea5h018JAqq2+9674q7zE94Lvr2q3LrnblTy7yt3vo8X/4wULNlO2+m/30R+dX2Z8W1KY82l4d46tcSrK64+brvPfEZPvni6/GvD56L/zoB+daDy8QXNukQMcz/3rZ3vJG/Y3Xb6Lec6cNHWxtYwSdkzl+uj7baqb+9PMvmUY7jpdU1tO9Ps/abA9SRufTtfrbU8/6gZ3odW6+Y4f2dsawE/1r7pa511jvCQXX3PvH5dOrfpQILz/3NO+z790TY+0jvJ2b1mdp7HMv2KzZ5W5RxKve54O89/jgY48KgsIRGbyZ2tjHu0+Gz+kPv7vBD/a+OXW6PfPvl/3Dq1x3/XZ0dFH8eQVyX/Q+/y+++nrM9VqoQPsF3r0n3jUM33v0vtx/nz3tjw88Gvd+cHDf/e3sU0+yRt5ni4QAAggggAACCCCAAAK5K5DTTURVC6jf/vsE+u+9/0HCZqLfrFvn17ZxG+jBprqkANPv//RXe+CRv1cJfMTa9p/jJ9htd98fa1W1yz74aK5d/evbqg2uaUeqWXHTbXdHjJyn2mq9e+4eHEe1tFSTK1F6c8r0iNX77b1HMK9aatf+Zow9/1LsQEqQ0ZtYV1npB7CUtzZJAca77nvI/v70v+IGVLT/jxcssutuvt3+O+PDag+nATB+e+e9MYNr2liBhj/8+WE/MPKs9xCe6Hynvfe+PfTEuLjHVMBBwSjVcoyXFCga98/x9n9/+LNfgzJevljLN2zYGGuxbfBqucVKmSjP08+/aM++8Erc66P3wh3eCKgKitUmKYDz6//7gynYGi+4pv3rc/MrL59fozSFA6bzs62atAqqJgquubLe5JU1VqBX5de9Q5/tREnvLd2T/huqjRsrf7z3RKy88ZbF24fMdW3iBde0P12zVye9Zb/7/Z+qvD/TaR+v7FquGoXRKd57Sf7X3DQmYXBN+1IQVD9K6H1ZXVINz9+M+WPC+4ECs7/27uUKJpMQQAABBBBAAAEEEEAgdwVy/idzBcnenLrtQd5vJlrhNRPtGbuZqAJwLqmpXrzaTy6PXrVvNT0NJwWh1LRHTZDU7G6GNyqpmva5pAewOV5ztt5lO4Jdbl28Vz30PfzEPyICCWp+2ddrBruT96rmRXrwDjdx1b6e+ddLtscVI4LdHjng4KBWkxbq4e24owYG68MTevh8+50dAz7s2btnRPOnsc/+OyKoqNpLB/Xdz2+eusXbVkGC6e/NiGhi+8prk+3YIwbUuNnTn/76uN98M1xONQVW8zg1lZOrC0DI7C9/e8quvuLHpmZ48VJ4UAudg66Lrp1Gk9V7RklBoVG/+r/AX7VcenvvoxbNm9tcr/mbmhK7pOCCgpeqIRhOsn79zanhRdahfVvr07OH1xy1xN/mo7kVwXqNFKqaLVd71881GVUNudVeU9zZXj4XrFHT4L336O1vt4u3v2RTOsoTfSx5TXzj7WCxDFSjStckeuTTf3iBuEP6HVCj2pwuABN21zXZZ8/efg0iBX7e8j6bap6opPfC7V5Q78ZRP6u2qawrfLo+2/rsP+R9dsPpAK/WkmobymvmrDl+UMat17IXJ7xu3zlzmFvkv97154eC958WqJbmXn3KrHHjxt77vsJvIhsODo3zgrk6TnVJzRtVK1VmC733XDi41zf0A4Xeq8kkNcFUbdBwUlNQ1c5TU1jdEyd7gXtXVh3vgUeftBE/OC/YJB32KrvuQ64pp9t5+Jzc58qti/eqst738OPBZ0759H6Tv2qs6X2ovjvdfV759SNA61atTPfNeEkOLsmo5+7d/GbCuo+5z7fWy+xtr3Z1rFqtbnteEUAAAQQQQAABBBBAILsFcj7ApodQNUlTgERJQYV4AbZwMOnQ/gckdWVefu2NIJ+CM9f8/MdVmqKpGdBLEyfZv16aGOR9bfLbKQXYVBPLnYN2csShB/nNwIIdbp/QA164GaECG0u8fppc32B6yFU5XeBIQYh4AbZ5Xt9Z4Ye8w71juqT+1hRIcklBlJE/vdjft1umV527ave5wJEePKf993078rCDw9mSmlagUoErl/TAf8n551bxVhBLNbNcemzss0GzSbcs1usJxxxh3z7+6CCYpXNULUDn7gICeqC++PvnRARg/+41Dw4/LH84e25EgE1BhMfGPhdx2NNPHmIKeIaT+tT74wOPmI6tpNpIqunlRjjUqI5KCmA4/3Zef1bnn3OavzzZf9JVnnjHU79nF3//bC+w2SHIotqSqtXkPNVkWQFhBUhTTeO9AFQ4uKY+By+98LsR/a0N8Zoe3u+995yT3suqSTbwkP5JHS5dn+0Po5pIjrr8Ei8gvEtQBjWRVNmu9WpHuc+lmoCfc9rQoE82Ba3ce0IbnjX8JO88+gX7OOaIw/yA1Zg/PhAEMpVfQU0F8hKlJl6ATk2/lf798kSTrZLuE6m+r7TdXx+PrMGpJpDf9YKFClq7NOS4o+yWO+4Nzkk13fSedH1HpsNe9zr9TXprmtec/N/u0DU6JzWBX+r1meeS7nc//uF3/D4m3TL9IPGcV3NzwutvukVercWn7aZrfh5cx2BF1MS3Tzjajh90eESw+QWve4IXvOO6pP0SYHMavCKAAAIIIIAAAgggkHsCOd1EVNyqodD/gH0D+XjNRNURdriGTbhpabBx1ISCRuGH/DO8vpNi9fOkMnzrmCMjAjIrVm7rVDxql3FnX5q4I5Cnmg6nnzIkZl4NQqD+u8IpfCzVugj3oaaHWjVTipXCASM9bKu2hksTvaZd4aQ+tZQnOqmz7u+cOTxi8YoVqyLmk5lRjaVwgErH+pnXp1gsbwWthp14fLBbXVf15ZQoqZbJSSccEwTXlFc1GNVfXjjJ7xLvXKNrN5457Nv+IBMurzqwDyf1SeYCdFo+/KRvVQmuablq2l31sx9FPGiPe268VXqDIKQzZbo8Iy44LyK4prK3bNHcD4KFz2O+15S3JilcE1DX5OdeX4LRgxlowIAfnHdGxHWZvL02a3XHTOdnO1zDVZ/PcHDNlUOfaQVZwunjBTuCyRVe5/fh1O+AfcKz/rQczvYCb+H0kTcIR10mBQLD91EFwdUXYji4pvJoMA69R8JJNbSU0mkf3n9Np3WPV7+LLqnGqH5MUA3icNJ9/hTvvnPEYTt+iFANSvXZligpAKnvB12/cFIQskf3bsEilUN9wJEQQAABBBBAAAEEEEAgNwWqRkxy8DwO7re/TXp7ml9y1RBRk75wX2Ra8c5/ZwZnpocaPfBWl1p5D1h6KFbfPHr46RvjoTe8DwVPXC2UeH0XhfOHp48aeLBXm0kjDy63fb1mcImaNu3qjWQXTtEPZQMP7R94KJ8GdogOyskp3H+ZOjcPPwD26dXT73Rb5dEoqfE69Nb+FVhRQMzVztmwMfWHxE+9ETvDtelOPH6Q3zxT+4+VVNNDNQbdMWd4Abb9Q/3HRW9z7umnRC/y57tG1f5RcFLNQqOTrodqtbgadm5URpdv7ryP3aRfo/LI0EN4sGL7hPZ/9OGHBjVhdA6LPv3cynrsFp21xvOZLE8/L6DtakxGF1C1qcLvhXXrttUsjc6XaF6jcLpacMqna93cG0E2VlLtrMO9GmuqnbpLh3beKLOdYmWrsiydn20F1VxSDVPVqAoHYdw6nUd3fwTgVlXeY6122rEP5VfzQ9U6iw5qq2n7yMsu9kfUbe0dN9F9wh03na/vRfV5eNrQ2D8E6JgKNKo2qIJQHb1r07ZNK78o6bRPx7mpSX84nTzkuCru4fWqBRtuAjvzozmm+1W8pJqs8ZK6G9AovC6t87oCaNE8L76W3SnxigACCCCAAAIIIIBAwQjkxf/k9VCvgJkL0Ex79/0qAbYpob7GDvOCSckkPbB/q8ORCbOq1pKa+anPLAWJXArXZnLLEr0edlDfRKv9dTq/cq/D/vejOjdX7a9wUqBPTbFUe01JNUeiA2xquhcuY3Szuj169TD9JUoKIqo2i5p/bfYcXNq8JbI8bnmi1+hadgrwJUqqvaTgmHs4jd4+vK2aEEfXSHPrd2oZGUxL1JdbaUmx2yzCTp2TO2tlOOzgvtU2GVMAJtzUTO+hdAXYMl2eLlEB3gBl+0QzLxjman66AGh0nkTz7pq6PAd7/f4lSqpVpL9UUjo/2+oXTjUGXVJzRTWBPGDfvfw+43p6AX0Fr/We3d1r0h4r9eje1c/jPpNq6qoRkvfds4/tu1cf28OrgVlSvO39p6BVfSU1K3dJ51TdPULBwOiUTvvofddk3vXp6LYdUM29WIPJ7OddEzfIxLLlkbVZ3X70KqN49x6t38n7cSKcou/l4XVMI4AAAggggAACCCCAQHYL5EWATcT9D9zX/jN5iq+tZqLnnXFKULtDtUrCD1FqslOT9NnnX3ijV37qB9KWeM0uFdRxnazXZH/xttGABmp2ppoVqkGmTrzVNCuVYIWaUWqkSiXVBtJImupg26U33t4xAp6CcYkCS3ror/h4oVeezzzHpf55q7+icC0jt9+avn7h1d4LJ436mUpatnxbMDHWNi1aNIu12F/WoCiy2VZ0M8TwhkVRTbzcunCTOS1r37a1WxX3NVzrSZnkmq6U6fK08d4viVJ0ratEeWOt0yAP4dR6553DsxmZrs1nu7s30INqa2kfLinA+Jo3GIT+FGRRkE190eneE6s2noJvquEWbpqtPuzUV5v+lNTfnYJ2aj7q+jJzx6ur1y+/3HFtEgWOUilPbexTOU68vOHPnoLxuhbVpfbtdgwIoeukoLaay0en6ow0gEU4xRr1NLyeaQQQQAABBBBAAAEEEMhegbwJsB3ijSbqAmwKRIWbiU577/3gCqgvrlgPQkGGGBNqSvn8S69GdIIdI1utF6l56fgJ//GbdLqaLDXdqUb7dAE27ePNqdODAJv6o5sf6vPp8DjNGdX0VCNGamTQdAbTYp1TohposfJHL1P5VPsj1sOxHpqTTU2aRD7wJrPdpqgmsS1btKh2MzXtC9e6TFQLptqdRWXIdHmia91EHb7Ws2u8wRJcUnCqtDT56+e2S/Y1HZ9tlXHkZRdFDLgQPr4+y7of6U+DZagZ8lnDv13lvapapi2bN7NnvY70YyX9SKA/DVSgWrsaiEPNs+syrf1626itOmbrVrULfKbDPh3nvjRUAy1W8DPWMdp6g3yEkwL8nWPU7Izuxy28jabruolv9PGZRwABBBBAAAEEEEAAgfQJRFbfSd9+63xP6puohfdw6tLU6f9zkxZuHhoeACDIkGDiVW9kt7/87amEwTXVXtEof7WpVaIHV9XaCvftE10sBWTUHE0dZleXmpaW+iPsuXzverX6XPOjKSEbrY/VBE81Ke576HH75/gJcYNrqqmk/uzUZ1FtU7iJqfalfaf6F70PVyYFQDKZGnllDadkH5o3bd7RV13jxpH7CO8v1elMlyfZ80u13C5/+HrVNtDs9hnrNZ2fbQV2f+SNePvjH37XDvBqqYXPIfrYb7/znvdZ/5OFg1Uuz7FHDfRHKtYovIn6iVQtxRtv/X3EgANuH5l8DV/72lybdNrX9ny3btka7KKoQVEwnWgiujZxOj+/iY7LOgQQQAABBBBAAAEEEMhegfQ91WfBOR7k1WLTg5uSakd858xhtmLlqqB/LD30KkCVbJo1Z5498++XI7Jr1Lz9997Tr0GiZpVqUugeOlW7xPXFlWpTn7vueziiuanKquZkatbZpVNH6+gdy/XBpOO8+OrrQbniHeuIQw+yDz6a6+fTw/CHXl9p6s9JD/guqQ+lWA/yz3rnrdH+wkkDR/Tx8nfdtZNfnnDtmRdefm1HE9Ydz6vhzRNO79K+nd+Xm8t026+vrlLDx63Lttd2UU1CowdAiFVeBTvVtMylFmmsiZRt5XHnmOzrLt5nLJzU92Cs92g4T6rTmfpsu74L9ZlUE++Zs+bYjA9nRzRRV1nV596bU961E445okrRNZiGRhHWn+4nH3qfYe1D/Z+Fg1p6/zz9r5fs8kt+UGUfmVqgwS1c/3pqel+TlCn7mpRF2+ie7pr3ukFqqtvXcu97JZzCP+6ElzONAAIIIIAAAggggAAChSOQVwG2g0MBNtUwKK/4xO/LzF1OdUwdXbvHrYv1OmX6fyMWa0TRRLXHVof6J4oX9IrY4fYZPbAqaOaS+u254sc/jDty55df7WhCp23CD91uH3pVx+jFxU2CQM70/830A4PqP82lw70gXKwUruWmYN8lPzgvbofmChaFa3TEK0+s47hl0UEV9YvUxauVmCipCatiebXt8yvRMZJZF90MTP3mVZei+4zbKYlmpdXt063PtvK4ciX7Gu7fStusXPVlwgCbRnZ94NEn/eaS7dq2sZMHH1tt88VMfLb1OW7qNWdt4vXFpaC7mnHqT/cNBQlf9ppaux8AdF4zvEEMogNs33yzzrZs3eL107atNq5qxapvNv3pc6b+2B4f98/gM68BIbQ8VtNoHSPdSYHwOfPm+7v9KtSUN95xXn9zqj/gQ1vvPPQZP/vUoZYJ+3jHT2Z5eERcBS03eP2p6RomStFN2lVjmIQAAggggAACCCCAAAKFLZDZtnN1bKsaZeHggvpeCz/Mpdo8dE75tgdJnYZq0CQKrmlgAtVKcSmVINPs8siaYnogb5Ogf6MPPprjDuO/xmsaqYf8AQf3C/LO9GrBaPRQlxSY2nuPXm42eFWgwI3IqoWqSZdotMDomm6pnLs7qB7cw2nylHfCs1WmFcAc/ds77fKrf+3/3Xb3/VXy1NUCOYffd29Nfdfv9DzR8V/5z+SI1WW77xYxH54JN2ELL483nenyxDtuupa3b9cmYldqNp0o6T2t96xqjL3rBZGT6XMvXZ/tyvXr7cbf/d4uu+pGu/Y3Y/wm3rHKqvvHMG+kU9UCdSk8GMX9j/zdfx+PvOG39vAT/3BZIl4VRDuk3wF+ADG8Ijx6cXh5Jqbbt99xbfQ5/98HHyU8zFveCMaqFaZA4Nx5H/t502Wf8MAprIwO7r866e2EW6vmnkZOdkmDT5AQQAABBBBAAAEEEEAAgbwKsOlyqhabSwquuWZMCiapeWMqSTUZXFLNlHhJNanu+vNDEau3hPr1iVgRY0a1VsKpRfP4HZeryZlqhYTTls1bwrMR04cf2j+YVy2zfzz/YjB/sPewHquvqG/WRZVne22aYMPQhJrg/vXxsaElZvECfhGZoma6du4UERh50wtSJQocvOEF4FxzLp1Xbfq/iypKjWb7HbBvsJ3K8+KEHU14gxXbJ9Tsb+q7O/oI1HuzrMduEdnCNWjC78OITAlm0l2eBIdK+ypdy/DoixqgQwNzxEr67Oi94NJu3oieyQyKEDatzWdbzbbXfv1NUKNMo/MmCjBv2LjRFTXi/a6FrhboHC8QpYBhvLQhalCN5qG+J+Ntk67le+8R2cT+Oa8pebzauhXeQCqu6aWO796T6bJ35xRdgzVs7PIketX3Qvg+qIFmou/J4e2juw2I1YdlOD/TCCCAAAIIIIAAAgggUBgCeRdgO+jA/WJeub777xPxEBUzU9RC9XvmkoIisWpr6GHxD/c/4teecXn1msqom1284FI4PeeNIqigXXRSH2oaeCA6hWubRa9TsCJcwyL88B8OvoW3i65NpgCGAmnRacWq1XbrXX8OmqC69V97AYdUk5runnP6yRGbjfFqpanvqeikZnJPPfPviMXHH314xHxdzww57qiIgIlqqGkU17C3yqRaPDeP+WNE8QYdfmiV92Z4pFu997Sd9hXrfRGxs+0z6S5PrGNkapmCHeeeFvleuPn2e7xm1DtqiOrYa9Z+bfc8+LeI999hBx2YVLHS+dnuHwqu6lo95NVAWx8KzqtACkJplOOPFywKyterZ/dgOvzDgK6zAvax+vKb722vAJBLqq3Xeued3GzGX1WzVu9Xl3S+d933kEUH5T/97HP704OPuWz+q3NKp712rGbw4SRnBStdwDK8Lta0q13o1sn/xlvvqhLgV+DuwcfG2nvegDEuKbh3cL8dP+q45bwigAACCCCAAAIIIIBA4QnkVR9sunzqsLqV98Dpaq65S6qmVammvvvtHRE4e8BrxqWmkj295nyqtTLbGwQg3Cw0vH89pKn5mBuYILwuerqbF2AL95WmfY761f/ZoV6woHmzpjbfqwlSPv+TiEBCeB/LV64Oz1aZPnLAQfbEP56PWK7BGXbtGLtpkwIcasrm+lrSuag5pgzl+8WSZf66eLVsojsAjzhwghmNvqgaSOpTS0kPyH9++Am/dlo3b7keZudWfFwl2KcHfnUMX5+pSePGXv9SJ/kP4K4cetDXaLbdd+tizb0mgp8s+rTKaLQqt5oER6c2rXeOWORqSKoz9VuuHxmxLtZMussT6xiZXLZnnzJ/hFo1LVRSMO3m2/9oqum4a6ddbI3X/5c6y9d70yV9Ng87qK+bTfiazs/2MUcN8AcOcQEdBWDUHFs1oxp774uG3udJn9/w50WfsfB138s7X322lizd1n+f+km8/pY7/M+hauQpkKbaYGoGG04awVdNgusyqdyTvZp67nx1btf8+jbr3q2LqQ889U/mrpsr1/Bvn2Cu6W867bV/3cvCSSMf60/p11df4X8fhNfHmlYfdxPfeDv43tCPFhrVWT9O6D236ssvbf7HC4Nzdvv4wXlnBP3luWW8IoAAAggggAACCCCAQGEK5F0NNl3G6L7W9HDao3vXlK/w0Ucc5ndSHt5Q/Y09/+Kr9pr3MBYOrukYp588JJzV6w9qR02HiBVRM2oOePH3zolYqhpwEye95T8oaiTQ8IiTOj89zLqkfqcSJTXNCjeBUt4jDjso0Sb2/XNOqzJ4gJrcqnadmjeGgwWdvcEIjhxwcLA/1XaLVeMtyJBg4scXfMcUaAsn1ZKZ7tVa0+in0ftVjcVTT/pWOHu9TR/oBWTP9Wrhha11HdVfk/oDDA8uoUL26N7NHwEynN8V/oB99orYj1uuQJMLbLhl8V7TWZ54x8jk8ou/f3aVPgIVYHrb69dLn4lwcE01NX/43bOSLk46P9uq1XXJBedFHFvXSM25FWxTjcvw50U1pkZdfklEUFj9q/304vMjakFqhwpyq485vfejg2uq5TfwkB19LEYUIIMzql058qcXRwS2dL4Kfqs5b3RwTZ9RebuUTnvts7MXcA33geiOo9foWo/hdeFpBSl/cemF/mcyvFyDz+i+p37jwp87fWZ/cO7pKY1KHd4v0wgggAACCCCAAAIIIJB/AnkZYDvowB39YemS6QEvmVoexU0aR1xhbfOzS35gg489MmawQ5n1sDz42KPspmt/7getwv0Bvf/BrIj9JZpRc7Erf3KRV6tsl7jZdt+tq10x4gI/iKNaIC4piBMO9rnl7lUPxHt6I4qGU7hJWni5m1ZNqRuu+pkpSBMvKajxnTOH2S8vu9gOP2RHX2/KH6tpZ7z9hJerts4F3znTLvre2XEfmpVfNUt+/MPv2nfPGh7ePOXpJlHXPOUdRG2g4OevRl3u1zyKFThTdtXkOee0oX5wrWnT2KMPqjbTpRd+N+bomcuWbxsFNpmyp6s8UaeZcDbcf5xq0tU0aWTGH51/rn3Pu8b6nMVK+rwpsH39yMss3KxWecPliN423Z/tXj2623W/+Imp5l28pFqq++29h43+5WURwTWXv2WL5naj95lTbap47x0t18ikV/30R34NV7ete03mPeHyxhtROZl9qObl9Vf+xA+che95bt96VdDrJxd+r8pnNN32Ck7q3hmrFusSrzadUuMkPufq9+9y736vkU7jvd/kr+8TnXuie6N/0CT+iX7PxrsmSeyKLAgggAACCCCAAAIIIFDPAkVe30Bb67kMOXF49bW2dPlKrxbScr9/tV28B0w1sUymCWiqJ6jmrcu8Yy1bscKaeoGFLl4NDTWDSiZIGO9Yt951X1ADpnfZ7v6Db7y80cvVXEq1r5ZuD+woCNixQzvTg22mk/ocU5PUz7yaJEoK6rVr29oSDQSR6TIlu399tFTjbtHiL0z90rX2ajp18IJr0U3aqtvfcq8Gn2rP6JOqpqPRD+XVbe/Wp6s8bn91/aom1595lp977wcFozRqcG0/FzqHdH+2da0+9/qLU9BbZW7XurV17dIppaaEqp2ne4Cahao5upqKK7CW6nunrq7RV16TXfW7tnL1au8z2sZ28e4P4YEq4pUj3faq4an7p4KEMmueYICWeGVyy/332+dLvPvPUv8+39a79+i+lyhw67blFQEEEEAAAQQQQAABBApPgABbAVxzdZau/pxcUq2gvffo5WZ5RQABBBBAAAEEEEAAAQQQQAABBBCohUBeNhGthUdebqo+41xSX3HqUJ2EAAIIIIAAAggggAACCCCAAAIIIJAegbwbRTQ9LLm9F3XI3aBhA9u4YaNN9TrXDw+CcKw34mFtmprmtgylRwABBBBAAAEEEEAAAQQQQAABBNIvQIAt/ab1vscHHxtr6jctOmngAnWgTkIAAQQQQAABBBBAAAEEEEAAAQQQSJ8ATUTTZ5k1e1JH+NFJo99pRNSadpAfvT/mEUAAAQQQQAABBBBAAAEEEEAAAQS2CVCDLQ/fCV07dwpGDNXpde/WxU4Zcpw3gmXbPDxbTgkBBBBAAAEEEEAAAQQQQAABBBCoXwFGEa1f/4wdff2GDbZ8xUo/qNaoEXHUjEGzYwQQQAABBBBAAAEEEEAAAQQQKHgBAmwF/xYAAAEEEEAAAQQQQAABBBBAAAEEEECgNgL0wVYbPbZFAAEEEEAAAQQQQAABBBBAAAEEECh4AQJsBf8WAAABBBBAAAEEEEAAAQQQQAABBBBAoDYCBNhqo8e2CCCAAAIIIIAAAggggAACCCCAAAIFL0CAreDfAgAggAACCCCAAAIIIIAAAggggAACCNRGgABbbfTYFgEEEEAAAQQQQAABBBBAAAEEEECg4AUIsBX8WwAABBBAAAEEEEAAAQQQQAABBBBAAIHaCBBgq40e2yKAAAIIIIAAAggggAACCCCAAAIIFLwAAbaCfwsAgAACCCCAAAIIIIAAAggggAACCCBQGwECbLXRY1sEEEAAAQQQQAABBBBAAAEEEEAAgYIXIMBW8G8BABBAAAEEEEAAAQQQQAABBBBAAAEEaiNAgK02emyLAAIIIIAAAggggAACCCCAAAIIIFDwAgTYCv4tAAACCCCAAAIIIIAAAggggAACCCCAQG0ECLDVRo9tEUAAAQQQQAABBBBAAAEEEEAAAQQKXoAAW8G/BQBAAAEEEEAAAQQQQAABBBBAAAEEEKiNAAG22uixLQIIIIAAAggggAACCCCAAAIIIIBAwQsQYCv4twAACCCAAAIIIIAAAggggAACCCCAAAK1ESDAVhs9tkUAAQQQQAABBBBAAAEEEEAAAQQQKHgBAmwF/xYAAAEEEEAAAQQQQAABBBBAAAEEEECgNgIE2Gqjx7YIIIAAAggggAACCCCAAAIIIIAAAgUvQICt4N8CACCAAAIIIIAAAggggAACCCCAAAII1EaAAFtt9NgWAQQQQAABBBBAAAEEEEAAAQQQQKDgBQiwFfxbAAAEEEAAAQQQQAABBBBAAAEEEEAAgdoIEGCrjR7bIoAAAggggAACCCCAAAIIIIAAAggUvECjQhT4+uuvbdGiRda2bVv/rxANOGcEEEAAAQSSFVi7dq1t2bLFGjZsaM2aNYu7WbL54u4gAyuysUwZOE12iQACCNS7APfber8EFAABBOpZoGirl+q5DHV2+ClTptitt95qCxcuDI7ZtGlTO+KII+zKK6+0nXbaKVh+8skn22effWZ9+vSxv/3tb8HyfJzYvHmzHXzwwf6pDRkyxH71q19l1WnW9losW7bMVq1a5Z9Tjx49/AfErDpBCoMAAggkEMjEPTrVfR5//PG2cuVK23333e2pp57ySxvr3hwrXybuwe7Yhx56qP3hD39IoGcWq0wJNwitzETZQ7tnEgEEEAgEysvL7eyzzw7mr7vuOtO9LjrFuy/FWx69fTrm4x2rNvfbdJSLfSCAAAL1LVAwTUQffPBBu/TSSyOCa8L/5ptv7MUXX7Tvfve7Nn/+/OB6bNy40Z/WQ0i+p3CMVTUUsi3V9lo89thjds455/h/X375ZbadHuVBAAEEEgpk4h6djn0me2/OxD143bp1vpm+wzOZMlH2TJaXfSOAQO4K6HkknP71r3+FZ4PpePeleMuDDdM4UZfHSmOx2RUCCCCQcYGCaCI6d+5cu+eeewLMU0891QYMGGCVlZV+7bRZs2b5tdV+97vf2X333RfkYwIBBBBAAAEEzK666ir/O7N58+YJOZLNl3AnaV6ZjWVK8ymyOwQQyHEB/aD//PPPR5zFf//7X1u8eLF16tQpYnk2z3C/zearQ9kQQKAuBAoiwDZmzJjA8qKLLjL9uXTUUUfZt7/9bb/py7vvvuv3zdalSxe3usrrv//9b5s4caLNmzfPWrRoYQceeKDtu+++dvTRR1uDBtsqBKoWmPI9++yzpureu+66qx100EHWt29fO/zww62oqMjfb7L5lPm9996zv//97zZnzhy/ieP+++9vp5xyin9sV8hU9ue2ifU6YcIEGz9+vH388cfWu3dvO+200/yyh/Mm43DDDTfY+vXrrX///ta1a1ffQ7UObrvtNrv66qv93R122GHWqlUre/nll+2DDz6wXr16+TXNZFpd0n867r//fvvwww/92odlZWW255572sUXX2zt27f3N7/33nvtrbfeCnalpkQqj5rCkhBAAIFUBVTzK5X7V7z7oPteSuY+Fl3GdN2jw/utbp/vvPOOrV692jp06OB3qxDeNjwdnS/ePVjfZUuXLvU3vfzyy4N7tpod3X777f5yfR+cf/754d3HnFb3D48++qj/ndW9e3cbNmyYHXvssUHe6DJpRTLfl/HK7r4/Url206ZNs6effto++ugjv+/XESNGmH7c05+6qlBTMKXq3i/JfPfeeOONptqFBxxwgLVp08Zeeukl//8OhxxyiN/8TMf7y1/+YjNmzLDGjRvbSSedZMOHD/ePzz8IIFA/AnoGUTN8JX1GXe1c3ZvVysalePelBQsWJPz/bjLPEepG56uvvvL/L63uAF544QX//+b6f/ORRx5puofoWSdeGXRvjHW/VdmTvV8mWwbnwSsCCCCQbQJ53wdbuJ8Z4Ss41rJly4jr8P777/sBMy3s16+fdevWzQ/A6D//Cto88cQTfn4FZx5++OGIbd2MmiBeccUV/uwdd9xhqjodK4UDfMnmU383+sKJlbRcwT2lZPcXvZ9Nmzb5X5rRy8PzN998s9+PjZYl6yBLJQUY1Z+dkgJgMtSXtVL4PxH+gu3/qDbhMccc48/pCzv6Wuia/eQnPwn+AxLeVvtUTcQ99tgj6HsnvF4PE6NHjw4vYhoBBBBISkABtlTuX/Hug4888oglex/LxD061X3G6lcn1r05Op+bD+PqHty6devg+/Saa67xg2LKowCSuz+PHDnSzjjjjPCmwXSs/QYrvQntQ8dRcnnD/ccl833ptvN3sv0f9/2R7LXTZnpAVq2O6OS+//Q6adIkf3Wi90uq371u/+Hj6scupXBftJpXP7RnnnmmJkkIIFAPAur/+J///Kd/5Lvuussuu+wyf1qfWQXnXYp3X3rzzTeDAJ3L6+5XyT5HuH3Hundon+6e7PK54+jVHcutC99vU7lfuu2rK0P42EwjgAAC2SSwrcpVNpUozWVZsmRJsEcNWBAdXNPK/fbbz9RsVH8KrsVKGzZssLFjx/qrFDBSDYbrr78+yKraanrw0i8/Lrim/arvN31p6mFCSQMmKOiXbL4vvvgiCK7py0YBugsuuMAPTGl/+rJTrbBk96dtqkv6Mv/e974XUWtNtc7UpDZZh/AxXHBNBhpkIJz0C508dTw30ILWK8Dm+tgJ59e0/H7zm98EwTU5//znP/cHpNB67fOWW27xr4dqBaiWnEu6bqeffrqb5RUBBBCosUAq96/o+2Aq97HoAqb7Hq39J9pn9PFTmY93D9ZDlEuvv/66mzQ9JLo0aNAgN5nwVTWWf/zjH/s1tlzGJ5980k1WeU32+zJe2VO5dqrFHf6BTLVA9H8NJVdDpUoBvQXR75eafPdq/7quQ4cODQ6hwJr+LrzwQv//Pm5FIi+Xh1cEEMiMgP6/64Jrup9p8Bb9Kenzqhq/LsW7L8VbnuxzhNu/XnXv0A/i4f9ba7krY7xjKU90SuV+Gd62ujKE8zKNAAIIZJNA3jcRVXMTl1yzQTefyquaxqippNLAgQP9pqGq7qygkap064tAX5DK59Lnn39un376qak5p5oDLVq0yF+lphvJ5nO/amtDNaNR0xelhg0b2p///Gd/WnlUW8ulRMfVdomSgnhqdqlmJfpSVEBPvzzpHNUUU81nk3HQfsIpXAMu3Lm28j3wwAPWrl07v8nO97//fb/JjI6nvvMUPItOKo8bkEI1SVRVXUkjL+lPzXLV7Gb27Nl+k1xVu3fNRNUk2AU7o/fLPAIIIJCKQKr3r/B9UM11kr2PqSa1S+m6Rzdp0sTt0v/BJtF9X90b1DSpW4R492AFf/TwOHnyZP/7U2XStJJ+cNH3QjJJNZb13aRmVO6HGtfUKtb2yX7/xit7KtdOA+u4sqi2uQu2aWTAcLOvWOUMv19UizvV7159140bN85v0tW5c+egL1p1o6AAm15dbRFdBxICCNSPwBtvvBEcWDWD1ZWM7hdvv/22v1zNvNVli1K8+5LWxbrXulGftT7Rc8QJJ5ygLEFSjdmddtrJr9mq/zvrOcfdJxKVIdjB9olU/s8efpbR5onKEH0c5hFAAIFsEcj7Gmz6cnBp1apVbjLlVwXnzjvvPP8//H/961/9Pmj0q7D7j7PboR4YVFNOSf8hVi035bv22mv9/sjU7LGkpMT/VTmZfArQuaQmLfrPsP5ccE3r9JCW7HHdvuK9Khio4JqSgnFHHHFEkFW/qCfrEGzkTeiBMFxbIbxOx3MPUerXQV/aLrlf8N28e/3kk0/cZPALn1sQrq0WzufW84oAAgikSyCV+1f0fTB8f3I1FVy5Et3HMnGPrm6frlzpfg3XrJo+fbr/I44e4pT0kJlM0neS6zdV31lqlqS0du3auJvX9vsylWsX/g5X7TWX9P2f6Mee6PdLTb571R+d6xu2Y8eO7tCBkRZovyQEEKhfAfV15pL6bNZ9UH0ouqTRRPWjd01S+B6U6DkivG/dR93zk+4humcquftzOG9106ncL8P7SmcZwvtlGgEEEMi0QN4H2ML/qayoqPCDXNGo6pftzjvv9P9U+ylWUiBNnS2rJpp+UdKXjH4pV/PG6KQmoeHAlNYrWKRmjWre6VIy+dasWeOy+69qpqk//efb/bkaYcnsL2JnMWYU/Aun0tLSYFadQqfi4DYM175wy9yrC+a5+fAIdfH+MxFuOrrzzju7Tf3X8Lya1JAQQACBTAmkcv+Kvg/W9D6WiXt0dfvMlN9xxx0X7Fo116ZOnRrMR3+HBiuiJsLf8VpVXS1tt3ltvi9TuXb6vnapuLjYTfqBr/D3a7Bi+0T0+6Um373hfYZdXNAtvJ5pBBCoH4EVK1YENXdVAg2Aovufq7GqZfr8q3ZaTVIqzxFu/xrELV0plftl+JjpLEN4v0wjgAACmRbI+yai+g+tfilWc0EFxdRc4txzzw1cdePXqF3uVxkNVhAr6T//rkaVOgL+6U9/amrOolpt4aQmqV9//bUfjPvFL37hN1fU6GEaelvHUDk0cpceCpLJ536Z1zE06lf0f7rdsZM9bnWjc2o0NgW23H/GNRqQSxomPFkHt41eGzWK/zaLDmiq6Y1L8YYld7+kKZ9qPYRrQcjapVjBT7eOVwQQQKC2Aqncv6LvgzW9j2XiHl3dPmvrFG973aPVDYCaEL322mtBbSqNAJrJh6vafl+mcu3U35tL+n5zo5uqqZX7P4VbH36Nfr/U5Ls3vD+mEUAgOwVeffXVpAr24osvmmq3pZqSfY5Idb/J5k/lfpnsPsmHAAIIZLNA/MhHNpc6xbJdeumlpj8lVY/WL0HqR01NSNTvjAuuqVlOvOYS4f8IqzmPgmsKPilg5pJqkql2m34ZV1LwTX2YqVmIBlfQsZT0a5WqTCeTzzUj1Xbqi0Adiyr4dfvtt5u+bJXuueceUyemyezP3yDBP7K46aab7KyzzvJ/LdNDj0sqS/gXtEQObpvqXuUnFzUh1X8ywseLHhDB7WufffZxk/4Q4nvvvbdv/PLLLwf9Vah2X3RfDtpID1aJmuUEO2YCAQQQqEagJvcvt8ua3scycY+ubp+uzOl4jb4Hn3jiiUE/n/puVvrWt76VjkPF3Uey39PRO3BlT+Xauf9faF/qC6lVq1Z+Nwx///vfo3efcD7Z/4Mk3AkrEUAg6wTcwAEqmEbzdd2maF6DpOj//UrKp4HNomscu/uSnyn0j1ue7HNETYJ37nDuWG4+/JrK/TK8HdMIIIBArgoURIDtkEMO8YePVi0ypYcfftj/C180BV3Co4KG12k6HOz52c9+5vdH4Dr7dHnVFETHUnBH/6nWiKH6077dg4PW6Rf7TZs2JZVP/xlXVXENZKCO+gcPHuwO579qX/pSVH9vyRw3YuM4M3JyVi6LOkRWkDBZh2bNmrlNq31VB9X6Cyc1pQ039wyv03JdAzXrVVKn0a7jaJdP610Zwv9ZUe3F4cOH+6PAury8IoAAAjUVSPX+5Y6Tyn1M3xfhlI57dLi5ovadaJ/hY9dkOtE9WJ1nqzN/l/Q9Ft0nnVuXrtdkv6d1vHhlT/Y7SN9DGkzAfce511TPJRPfvamWgfwIIJBegY8//jj4oV4/8J9xxhn+AAfho6jljWr5Kqkmq2rBxrsvxVo+atSopJ4jwsdMZjrWsa6++uoqm6byXVdlYxYggAACOSiQ932wuWsyevRoP4Cm/7xHJ43U88gjj1jbtm2DVY0bN/anXVNJfaGpVpfbXsE11YJTv2wujR8/3q8Bp8CP62hZ61xwTctUC00BN32RJpNPIwndeOON/rHdcdyran2pRp5Ssvtz24ZfdQyXBg0aZBqZM5w00pkCbErJOoS3T9Tfi2oNyjGcvve97wXH0/Loa6Flqh14zTXXaLJKUk0+BdFc0sAS4SrqGsWVhAACCNRWIJn7lztGrPtgsvexTNyjU9mnziG6yaKWxbo3x8qX6B6s78Pwd4D6ZYsO/ulY8VIs1+i80WVK5fsyXtmTvXYqi74/9eDp/l+gGiVXXHFFMB9dI0XbRJ9XOr97w9c++jg6NgkBBOpGINw8VLV5w59NVwJ99l3SaKJK8e5LsZZrn8k8R7hj6DX6vuDm3TOQ8sQ6lpZH32+1LJX7pfIruWNum9sxHy6DW8crAgggkE0CRV6zxq3ZVKBMl0Ud9as5pZpo6iatwIv+g59sUsf5aha02267+TW64m2n46hJh6pNi1j/oVd/M9FfGMnm03HUUemCBQtM28Q7fir7i1d2d6zFixdbt27dqlRH1/pkHWIdQx4uiKemQGqSqn5qNNKRRj1L1PFz9P5Us2PRokW2ZMkSv1+7zp07B/3HRefVMVTLUDUKXO226DzMI4AAAokE0nn/csdJ5T7mttH3Qbrv0dXt0x27pq/x7sHqX/Tee+/1d6suA8Kj59X0WMlsl8r3ZbyyV3ft9F2p7yclfbepVrp+uFNfpxooSUl9qz7xxBP+dHX/1Oa7t7p9sx4BBHJLIN59Kd7yZJ4jUhWId6xY+6nufhlrG5YhgAACuSZQcAG2XLtA+VjeWA+o+XienBMCCOSfAPev9F3T+fPn28yZM/3RudWtgn6EevbZZ2PW4kjfUet2T/oBaNiwYf5B9aPeVVddZR06dLDnnnvO70NUK0499VRTMy4SAggggAACCCCAQG4LFEQfbLl9iSg9AggggAAC+SeggWkeeOCB4MTU92asJlJBhhyc0Ah+qqmtQYkURIzu61W16NUtAgkBBBBAAAEEEEAg9wUIsOX+NczJM1ATUTXPUZNQEgIIIJBLAty/0nO1dtppJ7/Wmvo/HTBggKkPonxM6v9IQbb//e9//kjc6pe1V69e/t8555xDlwX5eNE5JwQQQAABBBAoSAGaiBbkZeekEUAAAQQQQAABBBBAAAEEEEAAAQTSJVAwo4imC4z9IIAAAggggAACCCCAAAIIIIAAAgggEBYgwBbWYBoBBBBAAAEEEEAAAQQQQAABBBBAAIEUBQiwpQhGdgQQQAABBBBAAAEEEEAAAQQQQAABBMICBNjCGkwjgAACCCCAAAIIIIAAAggggAACCCCQogABthTByI4AAggggAACCCCAAAIIIIAAAggggEBYgABbWINpBBBAAAEEEEAAAQQQQAABBBBAAAEEUhQgwJYiGNkRQAABBBBAAAEEEEAAAQQQQAABBBAICxBgC2swjQACCCCAAAIIIIAAAggggAACCCCAQIoCBNhSBCM7AgjkuMCmFWafjTCb3dGs/ECzVQ/l+AlRfAQQQAABBBBAAAEEEEAAgfoWIMBW31eA4yOAQN0KLDrHbPVfzXY6z6zhTmafnm9W+d+6LQNHQwABBBBAAAEEEEAAAQQQyCsBAmx5dTk5GQQQqFbgm8lmzQaZdfw/s+4vetmLvFpsj1e7GRkQQAABBBBAAAEEEEAAAQQQiCfQKN4KliOAAAJ5KdB0oNmal8wWnmG2bnvNtTYX5uWpclIIIIAAAggggAACCCCAAAJ1I0ANtrpx5igIIJAtAs0GeCXZYvblOLMN88walHrzTbKldJQDAQQQQAABBBBAAAEEEEAgBwUIsOXgRaPICCBQQ4GtG82W3GDW9GCzvb4xK/NqsG1ZZ/bFNTXcIZshgAACCCCAAAIIIIAAAggg4NXdAAEBBBAoGIFvpnqnutWs9Y+9u1+JWcn+Zo13HcxLawAAQABJREFU85qKvlMwBJwoAggggAACCCCAAAIIIIBA+gUIsKXflD0igEC2CpR6ATWlZbeabfzM7Ju3vddPzHY+01/MPwgggAACCCCAAAIIIIAAAgjURKBoq5dqsiHbIIAAAjkpsOT6bQG2reu3Fb9JL7OeXg22hi1z8nQoNAIIIIAAAggggAACCCCAQP0LEGCr/2tACRBAoD4Evn7DrLiPWaN29XF0jokAAggggAACCCCAAAIIIJBHAgTY8uhicioIIIAAAggggAACCCCAAAIIIIAAAnUvQB9sdW/OERFAAAEEEEAAAQQQQAABBBBAAAEE8kiAAFseXUxOBQEEEEAAAQQQQAABBBBAAAEEEECg7gUIsNW9OUdEAAEEEEAAAQQQQAABBBBAAAEEEMgjAQJseXQxORUEEEAAAQQQQAABBBBAAAEEEEAAgboXIMBW9+YcEQEEEEAAAQQQQAABBBBAAAEEEEAgjwQa5dG51MmpTJkyxV555RXbuHGjDRo0yI488khr0IA4ZZ3gcxAEEEAgRwSmTZtm48eP978rjjnmGP/7IlbRJ0yYYK+++qpt3rzZvvWtb9mAAQOsuLg4VlaWIYAAAgggkFBg5cqVdsMNN9itt95qJSUlCfOyEgEEEEAg/QJEhlIwnTFjho0cOdL2228/69u3r40ePdomTZqUwh7IigACCCCQ7wKzZs2yESNGWGlpqR8w03fFxIkTq5z25MmT7aqrrrL27dv7Abjf//739pe//KVKPhYggAACCCCQSKCystJeeOEFu+666+ytt96yTZs2JcrOOgQQQACBDAlQgy0F2Ndff90Prg0dOtTfSsE11VI46qijUtgLWRFAAAEE8llg3LhxNmzYMP8HGZ3n7NmzbezYsXb00UdHnLa+P04//XS7/PLL/eVr1qyxBx54wA/ORWRkBgEEEEAAgQQC+v544403EuRgFQIIIIBAXQgQYEtB+fDDD7eHH37Yr2GwZcsWU8DtnnvuSWEPZEUAAQQQyHeBBQsW2EknnRScZteuXf3mosGC7ROnnHKKNW/ePFj8wQcf2IEHHhjMM4EAAggggEAyAu3atbNbbrnF5s2bZ2eddVYym5AHAQQQQCADAjQRTQG1Y8eOflOee++91+677z5r2rSptWrVqto9KC8JAQQQQKAwBBYtWhQROOvQoYOpX5zotPvuu/vfKV9//bX9+te/9pv3nHPOOdHZqszznVKFhAUIIIAAAjUQqM33yYKLf2Rr/vOfGhyVTRBAAIH8FaAGWwrX9o477rBdd93V1PynqKjIRo0aZQ8++KD/i5Hbzc0332xPP/20mw1eL7744mCaCQQQqIHAwlPNvqz62arBnjK3yW7jzVp8K3P7Z885IaC+11avXh2UVX3j9OnTJ5gPT8yZM8d+8pOf+IE2fbfstttu4dXGd0oEBzM1EJh73NFW+dGcpLfc5eqrrf2IHyedn4wIIJD9AsuWLbPBgwfHLGhNnlG2fLXGvvz387Zx6RJrQVc5MV1ZiAAChSlAgC2F665BDs4991y/5po2U386d999d8Qervb+Y6q/cOrXr194lmkEEEAAgTwW6NSpky1evDg4w4ULF1pZWVkw7ybmz59vF154of3whz+08847L+aI1HynOC1eEUAAAQRqKqAmpNOnT6+yeU2fUYq8H5KU1pfPsa1etzlFDWgUVQWXBQggUJAC3A1TuOy9evWyCRMm2JIlS0ydiWo63q9BKeyWrAgggAACeSQwaNAgv6bz0qVLbebMmf60Rp9W0g81L730kj/9xBNPWO/eve3II480NStV3216JSGAAAIIIJDNAkWNG1mjdm1t8+ovbX3FvGwuKmVDAAEE6lSAGmwpcGukt9/+9rd24okn+lsNHDjQTjvttBT2QFYEEEAAgXwXGD58uE2dOtWGDBnin+qxxx5rbvTpKVOmmEagPuGEE+zdd9811W479VSv+fP2pL49tZ6EAAIIIIBAqgLqwkbJvaa6fSr5S3r3sbXLJtuG8gorKeuVyqbkRQABBPJWgABbCpe2W7dupgEO1LfO5s2brU2bNilsTVYEEEAAgUIQaNSokY0ZM8ZUg61hw4YR3xUXXXSR6U8pVn+dheDDOSKAAAIIZEagR48eMZuCZuJoxV7fomsnT7Z1FeXW0mL375aJ47JPBBBAIJsFCLDV4OrsvPPONdiKTRBAAAEECkmgffv2hXS6nCsCCCCAQAEJlHpdHCitn1deQGfNqSKAAAKJBeiDLbEPaxFAAAEEEEAAAQQQQAABBEICaiKqtH4uAbYQC5MIIFDgAgTYCvwNwOkjgAACCCCAAAIIIIAAAqkIlPTeNjr2+ooK21pZmcqm5EUAAQTyVoAAW95eWk4MAQQQQAABBBBAAAEEEEi/QINmLaxxl8625ZtvbH05tdjSL8weEUAgFwUIsOXiVaPMCCCAAAIIIIAAAggggEA9CpSUbWsmuq5iXj2WgkMjgAAC2SNAgC17rgUlQQABBBBAAAEEEEAAAQRyQqC0Ty+/nBvmEWDLiQtGIRFAIOMCBNgyTswBEEAAAQQQQAABBBBAAIH8EmjSa/tAB+UV+XVinA0CCCBQQwECbDWEYzMEEEAAAQQQQAABBBBAoFAFXA22dRVzCpWA80YAAQQiBAiwRXAwgwACCCCAAAIIIIAAAgggUJ1ASa/efpYNXg22zV9+VV121iOAAAJ5L0CALe8vMSeIAAIIIIAAAggggAACCKRXoKhJEyvp0dO2bt5slRWMJJpeXfaGAAK5KECALRevGmVGAAEEEEAAAQQQQAABBOpZoEnvMr8EG8oZ6KCeLwWHRwCBLBAgwJYFF4EiIIAAAggggAACCCCAAAK5JlDSew+/yJXzqcGWa9eO8iKAQPoFCLCl35Q9IoAAAggggAACCCCAAAJ5L+AGOqicSw22vL/YnCACCFQrQICtWiIyIIAAAggggAACCCCAAAIIRAsU9+7jL6KJaLQM8wggUIgCBNgK8apzzggggAACCCCAAAIIIIBALQVKenp9sBU1tPWffGwbly+r5d7YHAEEEMhtAQJsuX39KD0CCCCAAAIIIIAAAgggUG8CJb17+cfeMJd+2OrtInBgBBDICgECbFlxGSgEAggggAACCCCAAAIIIJB7AqV9tjUTrZxfkXuFp8QIIIBAGgUIsKURk10hgAACCCCAAAIIIIAAAoUkUNynt3+668sZ6KCQrjvnigACVQUIsFU1YQkCCCCAAAIIIIAAAggggEASAsW9tgfYGEk0CS2yIIBAPgsQYMvnq8u5IYAAAggggAACCCCAAAIZFCjdPpJo5fy5GTwKu0YAAQSyX4AAW/ZfI0qIAAIIIIAAAggggAACCGSlQJNuXa2otNQ2frbYNn76WVaWkUIhgAACdSFAgK0ulDkGAggggAACCCCAAAIIIJCnAsFABxX0w5anl5jTQgCBJAQIsCWBRBYEEEAAAQQQQAABBBBAAIHYAsW9yvwV68tpJhpbiKUIIFAIAgTYCuEqc44IIIAAAggggAACCCCAQIYESvvs4e+5khpsGRJmtwggkAsCBNhy4SpRRgQQQAABBBBAAAEEEEAgSwWK+/TyS7Z+XkWWlpBiIYAAApkXIMCWeWOOgAACCCCAAAIIIIAAAgjkrUBJ2fYAWzl9sOXtRebEEECgWoFG1eYgAwIIIIAAAggggAACCCCAQJ0LTJs2zcaPH28bN260Y445xgYNGhSzDFOmTLFXXnnFz6c8Rx55pDVoUHd1KRrv0skattzZNi1fZus/+diKd+ses5wsRAABBPJZoO7uuvmsyLkhgAACCCCAAAIIIIAAAmkUmDVrlo0YMcJKS0ttwIABNnr0aJs4cWKVI8yYMcNGjhxp++23n/Xt29fPN2nSpCr5Mr2gZHsz0Q1zyjN9KPaPAAIIZKUAAbasvCwUCgEEEEAAAQQQQAABBApZYNy4cTZs2DA/eDZ48GB/euzYsVVIXn/9dT+4NnToUDv55JOtf//+pppvdZ1Ktg90sK6CAFtd23M8BBDIDgECbNlxHSgFAggggAACCCCAAAIIIBAILFiwwPbaa69gvmvXrlZRUXUQgcMPP9zefvtt+8tf/mL333+/KeB21FFHBdvV1URJbwY6qCtrjoMAAtkpQB9s2XldKBUCCCCAAAIIIIAAAggUsMCiRYusefPmgUCHDh1s5cqVwbyb6Nixo7Vv397uvfdef1HTpk2tVatWbnWdvZb06u0fa0P53Do7JgdCAAEEskmAAFs2XQ3KggACCCCAAAIIIIAAAgh4Aup7bfXq1YFFZWWl9enTJ5h3E3fccYftuuuupialRUVFNmrUKHvwwQftlltu8bMsW7bM1MQ006l4+0ii6yoYSTTT1uwfAQSyU4AAW3ZeF0qFAAIIIIAAAggggAACBSzQqVMnW7x4cSCwcOFCKysrC+bdhAY5OPfcc00115SOPvpou/vuu91qa9eunU2fPj2YdxP9+vVzk2l5bdS2jTXq0N42LVlqlXNmW0nvqsHAtByInSCAAAJZKkAfbFl6YSgWAggggAACCCCAAAIIFK7AoEGD/FppS5cutZkzZ/rTGilUSUG1l156yZ/u1auXTZgwwZYsWWJr1qzxp+uixpp/8Kh/SrcH1dbPY6CDKBpmEUCgAAQIsBXAReYUEUAAAQQQQAABBBBAILcEhg8f7o8IOmTIEDv//PNt3333NY0UqjRlyhR79NFH/enLL7/cSkpK7MQTTzQF5Ro0aGCnnXaav66u/ynuva2GXeVcAmx1bc/xEECg/gVoIpriNZgzZ44999xz/gg+GtXnggsusGbNmqW4F7IjgAACCOS6gEZ3e+aZZ0ydUPft29fOPPNMa9iwYczT2rp1q910002mhyU3ItzmzZvt5ptvjsivB6OBAwdGLGMGAQQQQKAwBRo1amRjxowx1WDT90ubNm0CiIsuusj0p9StWzd/gAP116bvlnC+YIM6mijps4d/pPXzq452WkdF4DAIIIBAvQlQgy0F+uXLl9uFF15omzZt8h+kNBz2448/nsIeyIoAAgggkA8CX331lV1yySVWXl7u1xh44YUXTJ1Mx0qTJ0+23/72t/6PM19++WWQ5YsvvrBXXnnFD7gp6Ka/tm3bBuuZQAABBBBAQAIaITSZoNnOO++cVL5Mqrp+19aXE2DLpDP7RgCB7BSgBlsK10U1Ffr3729XX321v1X37t39mgsp7IKsCCCAAAJ5IKCgmdIf//hH/1W1DK644gq79NJL/WY6/sLt/7z++uu2du3a8CJ/+rPPPrPevXv7tdqqrGQBAggggAACOShQsntPv9SVc+eYbdliXnvVHDwLiowAAgjUTIAAWwpuagbUuXNnu/76680Nd3388censAeyIoAAAgjkg8Cnn35qBx54YHAqu+66qz+t74YuXboEyzVxzTXX+PNvvvlmxHIF2PSnrgZcfzka+a1x48YR+ZhBAAEEEEAgVwQatGxhjTt3sY2fLrJ1H35gpfvsmytFp5wIIIBArQX4SSEFwk8++cQee+wxa968uR1wwAF22223+f3vpLALsiKAAAII5IGAAmwtW7YMzqRdu3b+9Lp164Jl1U1oH+py4NRTT/X7XfvNb37j96FT3XasRwABBBBAIJsFSvr08ou3bg4DHWTzdaJsCCCQfgFqsKVgusWr5qwmoiNHjvS3Ul8I48aNs7PPPjvYizqsfvrpp4N5JhBAAAEE8k+gtLTUVq5cGZzY+vXr/emOHTsGy6qbUJ+e+tPIb0pqRqpBdC677LJgU75TAgomEEAAAQRyRKC09x62ZsKrtmH+vBwpMcVEAAEE0iNAgC0FRz04tWrVKthC0ytWrDCNDldUVOQvV/9sro82l7Ffv35uklcEEEAAgTwQ6NChg3344YfBmaipZ+vWra1FixbBsuomNFBOjx49rGvXrn5WfcdEd2LNd0p1iqxHAAEEEMg2gR0DHVCDLduuDeVBAIHMCtBENAXfgQMH2osvvmhz5syxVatW2bPPPmtDhw4Ngmsp7IqsCCCAAAI5LHD44Yfb7NmzbcqUKbZ06VK7//777aCDDvLP6PPPP/dHmN64cWPCM3z//fftl7/8pW3YsMEWLFjg136mX8+EZKxEAAEEEMgBgeJebqADAmw5cLkoIgIIpFGAGmwpYA4ZMsSmTp1q5557rr+Vah2MGjUqhT2QFQEEEEAgHwTKysr8EUM1aqiSvg9uuukmf1oD4tx+++3+DzCJBiw455xz7L333rPDDjvM327PPfe0k046yZ/mHwQQQAABBHJVoKRnmV/09fMrbKvXN2mR160CCQEEECgEAQJsKVxlPSipP5wrrrjCKisrrVOnTv7IbynsgqwIIIAAAnki8P3vf99OO+00v0ZzeORQ1WSbPn16lbOcNGlSxLL27dvbI4884o9KrW4G2rZtG7GeGQQQQAABBHJRoMjrW7S4Zw9bP6/CvvG6U2hGdzm5eBkpMwII1ECAAFsN0HgIqgEamyCAAAJ5KKBRpfVXm+RGIK3NPtgWAQQQQACBbBIo7t3HD7CtL59LgC2bLgxlQQCBjArQB1tGedk5AggggAACCCCAAAIIIFBYAiV9+vgnvL6ckUQL68pztggUtgABtsK+/pw9AggggAACCCCAAAIIIJBWgdKyXv7+1lUQYEsrLDtDAIGsFiDAltWXh8IhgAACCCCAAAIIIIAAArklUNxr20AHG+bNza2CU1oEEECgFgIE2GqBx6YIIIAAAggggAACCCCAAAKRAsUaSbRBA9uwYJFt/mp15ErmEEAAgTwVIMCWpxeW00IAAQQQQAABBBBAAAEE6kOgyAuuuX7Y1s38sD6KwDERQACBOhcgwFbn5BwQAQQQQAABBBBAAAEEEMhvgZLee/gnWMlAB/l9oTk7BBAIBAiwBRRMIIAAAggggAACCCCAAAIIpEOgtPe2gQ7WV9APWzo82QcCCGS/AAG27L9GlBABBBBAAAEEEEAAAQQQyCmB4t69/fKupwZbTl03CosAAjUXIMBWczu2RAABBBBAAAEEEEAAAQQQiCFQXNbTX0qALQYOixBAIC8FCLDl5WXlpBBAAAEEEEAAAQQQQACB+hMo3q27NWja1DYu+cI2LltWfwXhyAgggEAdCRBgqyNoDoMAAggggAACCCCAAAIIFJJASZ9t/bCt+5CRRAvpunOuCBSqAAG2Qr3ynDcCCCCAAAIIIIAAAgggkEGB4t59/L1vKGeggwwys2sEEMgSAQJsWXIhKAYCCCCAAAIIIIAAAgggkE8CpWXbAmyVc8vz6bQ4FwQQQCCmAAG2mCwsRAABBBBAAAEEEEAAAQQQqI1Ace9tTUQr5xNgq40j2yKAQG4IEGDLjetEKRFAAAEEEEAAAQQQQACBnBIo7tHDL++G8nk5VW4KiwACCNREgABbTdTYBgEEEEAAAQQQQAABBBBAIKFAk86dreHOO9mmlats46JPE+ZlJQIIIJDrAo1y/QQoPwIIIIAAAggggAACCCCQjwLTpk2z8ePH28aNG+2YY46xQYMGxTzNOXPm2HPPPWcVFRW211572QUXXGDNmjWLmbeuF5Z4Ax18PXWqaSTRxl061/XhOR4CCCBQZwLUYKszag6EAAIIIIAAAggggAACCCQnMGvWLBsxYoSVlpbagAEDbPTo0TZx4sQqGy9fvtwuvPBC27Rpk5155pn29ttv2+OPP14lX30tKOmzh3/odeVz6qsIHBcBBBCoEwFqsNUJMwdBAAEEEEAAAQQQQAABBJIXGDdunA0bNsxGjhzpbzR79mwbO3asHX300RE7eeaZZ6x///529dVX+8u7d+9uixYtishTnzMl2wc6WF9BP2z1eR04NgIIZF6AGmyZN+YICCCAAAIIIIAAAggggEBKAgsWLPCbe7qNunbt6jcBdfPuVcG0zl5fZ9dff71dcsklNnPmTDvooIPc6np/Le7R0y/DhvKKei8LBUAAAQQyKUCALZO67BsBBBBAAAEEEEAAAQQQqIGAAmfNmzcPtuzQoYOtXLkymHcTn3zyiT322GN+3gMOOMBuu+02U622bEklZb38olTOLc+WIlEOBBBAICMCNBHNCCs7RQABBBBAAAEEEEAAAQRqLqC+11avXh3soLKy0vr06RPMu4ktW7b4TURdU9I2bdqYmpeeffbZfpZly5bZ4MGDXfY6f23Uvp012qWDbfpiia2fX2HFu/eo8zJwQAQQQKAuBAiw1YUyx0AAAQQQQAABBBBAAAEEUhDo1KmTLV68ONhi4cKFVlZWFsy7iY4dO1qrVq3crD+9YsUK27p1qxUVFVm7du1s+vTpwXo30a9fPzeZ8ddSLzC4xguwrZv1IQG2jGtzAAQQqC8BmojWlzzHRQABBBBAAAEEEEAAAQTiCAwaNMivibZ06VK/XzXVSttvv/383DNmzLCXXnrJnx44cKC9+OKLNmfOHFu1apU9++yzNnToUD+4FmfXdb64uOf2ZqJzaCZa5/gcEAEE6kyAGmx1Rs2BEEAAAQQQQAABBBBAAIHkBIYPH25Tp061IUOG+Bsce+yxfuBMM1OmTLFJkybZCSec4K9XvnPPPdfPp8EQRo0a5U9nyz8lvXr7RdlQPjdbikQ5EEAAgbQLEGBLOyk7RAABBBBAAAEEEEAAAQRqJ9CoUSMbM2aMqQZbw4YNTX2ruXTRRReZ/pQaN25sN998s11xxRWmftrUtLRBg+xqqFTca1vT1kqvDzYSAgggkK8CBNjy9cpyXggggAACCCCAAAIIIJDzAu3bt0/qHNq2bZtUvvrIVNJzd/+wleU0Ea0Pf46JAAJ1I5BdP23UzTlzFAQQQAABBBBAAAEEEEAAgToSaLhza2vSpavZxk1W+dHsOjoqh0EAAQTqVoAAW916czQEEEAAAQQQQAABBBBAoOAEivts64dt3axZBXfunDACCBSGAAG2wrjOnCUCCCCAAAIIIIAAAgggUG8Cpb37+MeunEMNtnq7CBwYAQQyKkCALaO87BwBBBBAAAEEEEAAAQQQQKC4rJePsL5iHhgIIIBAXgoQYMvLy8pJIYAAAggggAACCCCAAALZI1BS1sMvzPpyAmzZc1UoCQIIpFOAAFs6NdkXAggggAACCCCAAAIIIIBAFYHi3Xv6y9bPr7Ct3mAHJAQQQCDfBAiw5dsV5XwQQAABBBBAAAEEEEAAgSwTaNC8mRX32hZkq/zowywrHcVBAAEEai9AgK2GhhMmTLBRo0bVcGs2QwABBBDIZ4Fp06bZjTfeaNdee6299tprCU915cqVdtlll1llZWXCfKxEAAEEEEAg1wVKy/bwT6GSkURz/VJSfgQQiCFAgC0GSnWLFi9ebFdddZXNmTOnuqysRwABBBAoMIFZ3kPDiBEjrLS01AYMGGCjR4+2iRMnVlFQQO2FF16w6667zt566y3btInmMlWQWIAAAgggkFcCTXqX+eezjueovLqunAwCCGwTaAREagKbN2/2H4aOPfZYmzt3bmobkxsBBBBAIO8Fxo0bZ8OGDbORI0f65zp79mwbO3asHX300RHnvmbNGnvjjTciljGDAAIIIIBAPguUbh9JdEN5eT6fJueGAAIFKkANthQv/EMPPWQdO3a0448/PsUtyY4AAgggUAgCCxYssL322is41a5du1pFRUUw7ybatWtnt9xyi11++eVuEa8IIIAAAgjktUBx2bY+2NZVMJJoXl9oTg6BAhUgwJbChZ85c6Y9+eSTduWVV6awFVkRQAABBApJYNGiRda8efPglDt06GDqZ42EAAIIIIBAoQs08UYSbdCokW1c9KltWft1oXNw/gggkGcCNBFN8oJu3brV76z6uOOOs3KvSvO8efNs7dq1Nn36dNt///2tkfdFoXTzzTfb008/neReyYYAAgggkG8C6ntt9erVwWmpr7U+ffoE86lM8J2SilbivGu95rjzzz4rcaaotfvM/9iKmjTxl87qe4BtWrI0Kkf82c633W6tzzrTz/D5b35jy+69J37mqDXNDzvMdn9qrL9UHYHPPf64qByJZ/u8+6416bBL4kxJrF16zx/tC+//Ncmmkj16W69XtvU3uGHJFza7b99kN/Xz9Xr5FSvZc09/ev4Zp9tar2/CZFO7S0ZYx2uuSTZ7xvIt/NEltvpf/0x6/63OONO63H570vnJiECuCzRo0tia9O5jlR9+YOu8kUSb9T8o10+J8iOAAAKBAAG2gCLxhAJsxcXF9s477/h/enhSjYRbb73VHnjgAWvZsqW/g6uvvtr0F079+vULzzKNAAIIIJDHAp06dTINhuPSwoULraxsW6fOblmyr3ynJCtFPgQQQACBXBEo6dPLD7DpBwQCbLly1SgnAggkI0CALRklL0+DBg3sqaeeCnJPmjTJ7rzzzohlwUomEMg3gZnfmI3+1Gx+pdlxO5td2N6sV0m+nSXng0BaBAYNGmR33323nXXWWbZkyRLToAcXXXSRv+8ZM2bY559/bieccEJajsVOEEAAAQQQyDWBkl69/SJXzmHAuFy7dpQXAQQSC9AHW2If1iKAwIatZgd+YLZ4g9lPOpq9ucbspNm4IIBAHIHhw4db//79bciQIXb++efbvvvua0OHDvVzT5kyxR599NGILYuKivx59xqxkhkEEEAAAQTyTKCkZw//jCrnMZJonl1aTgeBghegBlsN3wJHHHGE6Y+EQN4LTPUCapu8INtkb1TERl4goEtjsxPmmC3daNbemyYhgECEgPrkHDNmjC1dutQaNmxobdq0CdarJpurzeYW9ujRw+/P083zigACCCCAQD4LNOnRyz+99eXUYMvn68y5IVCIAgTYCvGqc84IpCIw0OtfcKPXAe26Ldtqr41caNa9CcG1VAzJW5AC7dt7TalJCCCAAAIIIBAhoBpsDZo3s03LltumVautUSuv+xESAgggkAcCNBHNg4vIKSCQUQG1XlPNtQ+8ftgGezXXZq4z6998W622jB6YnSOAAAIIIIAAAgjko0Bp2bZabJXeSKIkBBBAIF8ECLDly5XkPBDItMChLbbVZPtbT7OnVpq98mWmj8j+EUAAAQQQQAABBPJQoLjPHv5ZaSRREgIIIJAvAgTY8uVKch4IZErg/qVmR2z/z49qsp3j9Sel1zneiKIkBBBAAAEEEEAAAQRSFCjpVeZvse4jAmwp0pEdAQSyWIAAWxZfHIqGQFYIHO7VXHvDG+jggWVm33j9sP1xybbmoWft6Lg9K8pJIRBAAAEEEEAAAQRyQqDJ7ttGEl1fXpET5aWQCCCAQDICDHKQjBJ5EChkgT6lZkO9zmcvnL/tbycvLn9zF7NdGEG0kN8WnDsCCCCAAAIIIFBTAQ10oLR+PgG2mhqyHQIIZJ8AAbbsuyaUCIHsE3iut9niDWarNpnt1TT7ykeJEEAAAQQQQAABBHJGoEm33ayhN3roZm8U0U1fLLFGu3TImbJTUAQQQCCeAE1E48mwHAEEIgU6NSG4FinCHAIIIIAAAggggEANBUq2D3SwbhYjidaQkM0QQCDLBAiwZdkFoTgIIIAAAggggAACCCCAgASmTZtmN954o1177bX22muvVYsyYcIEGzVqVLX5siFDae9efjEYSTQbrgZlQACBdAgQYEuHIvtAAAEEEEAAAQQQQAABBNIoMGvWLBsxYoSVlpbagAEDbPTo0TZx4sS4R1i8eLFdddVVNmfOnLh5smlFk923BdjWzc6N8maTHWVBAIHsFKAPtuy8LpQKAQQQQAABBBBAAAEEClhg3LhxNmzYMBs5cqSvMHv2bBs7dqwdffTRVVQ2b95s1113nR177LE2d+7cKuuzcUFxmRvoIDfKm42GlAkBBLJLgBps2XU9KA0CCCCAAAIIIIAAAgggYAsWLLC99torkOjatatVVMQedfOhhx6yjh072vHHHx/kz/aJ0rKefhHXl8/L9qJSPgQQQCApAQJsSTGRCQEEEEAAAQQQQAABBBCoO4FFixZZ8+bNgwN26NDBVq5cGcy7iZkzZ9qTTz5pV155pVuUE6+NdulojTp0tC3rKm3DooU5UWYKiQACCCQSoIloIh3WIYAAAggggAACCCCAAAL1IKC+11avXh0cubKy0vr06RPMa2Lr1q3+AAjHHXeclZeX27x582zt2rU2ffp023///a1Ro0a2bNkyGzx4cMR22TJT2qeXrVnyuVV6I4k26dI1W4pFORBAAIEaCRBgqxEbGyGAAAIIIIAAAggggAACmRPo1KmTaeAClxYuXGhlZWVu1n9VgK24uNjeeecd/08BOdVyu/XWW+2BBx6wli1bWrt27fyAW8SG3ky/fv2iF9X5fHGZF2B7/XVb98Esa3lCdgYB6xyFAyKAQM4KEGDL2UtHwRFAAAEEEEAAAQQQQCBfBQYNGmR33323nXXWWbZkyRLToAcXXXSRf7ozZsywzz//3E444QR76qmnAoJJkybZnXfeGbEsWJmFE8XbA4aVc2dnYekoEgIIIJCaAAG21LzIjQACCCCAAAIIIIAAAghkXGD48OE2depUGzJkiH8sjRA6dOhQf3rKlCmmYJoCbLmcdgx0EHvwhlw+N8qOAAKFJ0CArfCuOWdcSAI3fJr9Z3tpB7O2jbO/nJQQAQQQQAABBBCoQwH1nzZmzBhbunSpNWzY0Nq0aRMcXTXZXG22YKE3ccQRR/h/4WXZPN1k955+8SorGEk0m68TZUMAgeQECLAl50QuBHJT4KFlZgs2ZHfZFWAjIYAAAggggAACCMQUaN++fczl+bCwUds21qRbF9uwYJFVzquwkp498uG0OAcEEChQgQYFet6cNgIZF3jx68128MfrrGv5OvvZFxtswcatGT8mByg8ga1bfmFbNnX1/na1rVtGesOJrSo8BM4YAQQQQAABBHJWoKT3Hn7ZNZIoCQEEEMhlAQJsuXz1KHvWCszbsNUGL1hvqzabXdq6sT27ZrMdMH9d1paXguWmwNatv/KCamOsqKidFTU4xZu+07Zu/nZungylRgABBBBAAIGCFCjxRhJVqpw1qyDPn5NGAIH8EaCJaP5cS84kiwSeX7vJSoq22tyeTf1S9SstsmM+WW/T122xfqXEtbPoUuV2UbY8Y1a0txU1fHf7eSz3gmzjrCi3z4rSI4AAAggggEABCbhmoetmE2AroMvOqSKQlwIE2PLysnJS9S1wYrNGdmC3HYG0l9duNs3tXULoo76vTV4dv8FN3vsq1Ifdlmne6XXMq1PkZBBAAAEEEEAgvwWa9CjzT3D9PAY6yO8rzdkhkP8CBNjy/xpzhvUg0Ku46P/ZOw8wqYqsDZ+BgRmyAoIiCpIVc1qzophQUYxrzqi46uK/q666q65h1dU1rDlnzBldRcSAARUTQRRBMogICAIDA9N/fXXn9vQ0PTPdE3q6p996npmb6ladeuv27b7fPXXKelljW+3Crin+2p2LVtvgto2dVxsCWz10R4OtMi/voNK2zbaSNXu79WnOm+3eBtteGgYBCEAAAhCAQMMjUNCjh2/UqmnTG17jaBEEIJBTBMpcbHKq2TQWAnVPYO7qEus0ebndu7jY7tigid27fkHdV0oNOUcgUvKMn+TAIrOcuPami8c2OOcY0GAIQAACEIAABLKXQOPWrayAOGzZ24FYDgEIRAkgsEVRsAKB2iPwi3Nd6zulyNZr3Mhm92xh567bpPYKpyQIlBKIRF5zMdeOdaLagdYof7FbHgAbCEAAAhCAAAQgkHUEmvXu421e8R0ziWZd52EwBCAQJcAQ0SgKViBQewTudkNCf3MziD7dualNLV7j/oKytyxoZM0bMUy09kjndkmRkr+6SQ5amTU6zyKRdz2MPHNibp6Gi5IgAAEIQAACEIBAdhBo2jMYJlo03k10cER22IyVEIAABOIJILDFE2EbArVAYNTyNVbiytl/elG50kZ2LbC9mzcut48NCFSbQOQnd+oqi6wp81yLuDlEG+Xr6iNBAAIQgAAEIACB7CAQziRa9P2k7DAYKyEAAQgkIIDAlgAKuyBQUwKjuhTWtAjOh0CVBBrlr6wyDxkgAAEIQAACEIBAphMo6N7dm1j0AzOJZnpfYR8EIFAxAWKwVcyGIxCAAAQgAAEIQAACEIAABCBQxwQKe/S0vKZNrHjeHIusWlXHtVE8BCAAgbohgMBWN1wpFQIQgAAEIAABCEAAAhCAAASSIJBXWGgFvUonOpjARAdJICMLBCCQgQQQ2DKwUzAJAhCAAAQgAAEIQAACEIBALhEo7N3TN7eImURzqdtpKwQaFAEEtgbVnTQGAhCAAAQgAAEIQAACEIBA9hHQMFGloolMdJB9vYfFEICACCCwcR1AAAIQgAAEIAABCEAAAhCAQL0SKOjWw9e/fCJDROu1I6gcAhCoNgEEtmqj40QIQAACEIAABCAAAQhAAAIQqA0CTXsGM4mumjqlNoqjDAhAAAJpJ5Cf9hqzvMJ33nnHRo4caWvWrLEDDjjAdt11VysoKMjyVmE+BCAAAQikSmD69On20ksv2cyZM2277bazY445xho3brxWMRXl0/fIddddVy5/v379bLfddiu3jw0IQAACEIBALhAo7NHDGrVoYasX/Goly5b59VxoN22EAAQaDgE82FLoy9GjR9sll1xiHTp0MD0E3Xbbbfbggw+mUAJZIQABCECgIRBYsmSJnXPOOTZ58mQ76KCD7I033rBbbrllraZVlm/evHk2YsQI69u3b/Svffv2a5XBDghAAAIQgEAuEMhr1NgKewcziS5nJtFc6HLaCIEGRwAPthS69LPPPrOjjjrKhg4d6s9aunSpPfDAAzZkyJAUSiErBCAAAQhkOwG9cFG68847/TI/P98uvPBC+9Of/mSFhYV+n/5Vlm/27NnWu3dvO/zww6P5WYEABCAAAQjkMoECN5Po8i/HWtH4idZyxx1zGQVthwAEspAAAlsKnXbYYYdZy5Yto2eMHz/ett122+g2KxCAAAQgkBsEZs2aVe7+v+GGG/qG//LLL7bRRhtFIVSWTwKb/k4//XRr1KiRHXnkkbb33ntbkyZNouezAgEIQAACEMglAoXdgjhsRd8x0UEu9TtthUBDIYDAlkJPduvWzede5mIC/Oc///FDgh566KEUSiArBCAAAQg0BAISzlq3bh1tynrrrefXV6xYEd2nlcry6djq1avtiCOOMAlz1157rX3//fd2/vnnlyuDDQhAAAIQgECuEGjao6dvatGk73KlybQTAhBoQAQQ2FLsTD38nHfeeT4O2/PPP29du3YtV4ICVr/44ovl9rGRPQQWrI7Yej+Uf0DONOu7NMmzaT2bZZpZ2JMCgciaay0SuTyFM9KfNS/vGstrfFn6K86SGps1a2YLFy6MWrty5Uq/vsEGG0T3aaWyfGeeeabpLxxS+vvvv9srr7xSTmDjO6UcTjYgAAEIQKCBEyjs2cO3cOXkHxp4S2keBCDQEAkwyUEKvTp16lT/MHTCCSfYY489tpa4pqIuvfRS++KLL8r9pVAFWSEAAQhAIAsIdOzY0XunhaZqqGfbtm2tVatW4S6/rCzfJ598YvPnz4/mlzjXrl276LZW+E4ph4MNCEAAAhBo4AQKum5i+e3b2Zrfl9nqmBdZDbzZNA8CEGggBBDYUujIYcOG+YDUe+65p82cOdOmT5/ulykUQdYkCBQtLrIPLxtla4pWJ5GbLBCAAATST2D33Xe3SZMm2aeffupFsvvvv992LA3GPHfuXHvqqaesuLjYKsv3zTff2MUXX2yrVq3y3yfyft5vv/3S3xhqhAAEIAABCGQQgYJevb01K5hJNIN6BVMgAIFkCDBENBlKpXnGjh1rM2bM8PFywtOaN29uH3zwQbjJsgYEJKjN/HCGTR85zZbOXGIlq0uscQ3K41QIQAACdUWgZ8+efsZQzRqqtPHGG9s111zj1/UCRnE6Bw4caJXlO+644+zLL7+0XXbZxZ+32Wab2SGHHOLX+QcBCEAAAhDIVQKa6GDZxx9b0XcTrZV7oUWCAAQgkC0EENhS6Cliq6UAqxpZV/2+yuZ8NrsaZ3IKBCAAgfQTOOWUU/zMn4sWLSo3c6g82RQqIEwV5evQoYMPN6AJDvLy8qx9+/bhKSwhAAEIQAACnsBnn31mb775pveK3meffaxfv34Jybzzzjs2cuRIW7NmjR1wwAG26667WkFBQcK8mb6zaWkcthUTmUk00/sK+yAAgfIEGCJangdb9UigWfvmtstlu9tWg7etRyuoGgIQgEDyBFq2bFlOXKvozMryaQZSxLWKyLEfAhCAQO4SmDhxog0ZMsRPmCPB7IorrrB33313LSCjR4+2Sy65xE/CJgHutttuswcffHCtfNmyo1nPXt7UoonMJJotfYadEIBAQAAPNq4ECEAAAhCAAAQgAAEIQAACGUbg+eeft0GDBtlFF13kLVPsz+eee8723nvvcpbKy+2oo46yoUOH+v1Lly61Bx54wItz5TJmyUaBGyKqtHLKlCyxGDMhAAEIBATwYONKgAAEIAABCEAAAhCAAAQgkGEENKFa3759o1Yp3ueUBKLTYYcdZqeeemo03/jx423bbbN3REiTzhtak/U7WWTlSls9b260XaxAAAIQyHQCCGyZ3kPYBwEIQAACEIAABCAAAQjkHAFNmqMQA2Hq2LGjLVy4MNyMLrt16+aHhy5btsyuvvpqe+ONN0wT6WRzKiwdJrqcOGzZ3I3YDoGcI8AQ0ZzrchoMAQhAAAIQgAAEIAABCGQ6gWbNmtnixYujZhYVFVmfPn2i27Er33//vZ133nleaNPQ0q5du0YPazKdAw88MLqdDStNe7phoh++Z0XjJ1jrvftng8nYCAEIQMAQ2LgIMpdAXuaahmUQgAAEIAABCEAAAhCoSwKdOnWyOXPmRKuYMWOG9ezZM7odrkydOtXOPPNMO+OMM+yEE06wRo3KD1LSZDqxs1uH522//fbhasYtC3sF7SyaOCnjbMMgCEAAAhURKH/3rSgX+yGQRgLrdFvHDn7yMGvSomkaa6UqCEAAAhCAAAQgAAEIZA4BzQgqb7T58+fbuHHj/PpWW23lDfz222/trbfe8uvDhg2z3r1725577mkaVqrYbVpmcyosnehgxaQJ2dwMbIcABHKMAB5sOdbhNBcCEIAABCAAAQhAAAIQyHwChx9+uI0ZM8YGDBjgje3fv78NHDjQr3/66af2wQcf2P77729jx441ebcdccQR0UY1b97cH4/uyLKVpqWeeit/ZCbRLOs6zIVAThNAYMvp7qfxEIAABCAAAQhAAAIQgEAmEsjPz7ebb77Ze7A1btzY2rVrFzVz8ODBpj+lF198Mbq/oaw0ccNam3btYqumTbdV02dY0y4bN5Sm0Q4IQKABE2CIaAPuXJoGAQhAAAIQgAAEIAABCGQ3gQ4dOpQT17K7NclbX9Cjh8+8gplEk4dGTghAoF4JILDVK34qhwAEIAABCEAAAhCAAAQgAIF4AoU9e/tdKyZOjD/ENgQgAIGMJIDAlpHdglEQgAAEIAABCEAAAhCAAARyl0A40UERHmy5exHQcghkGQEEtizrMMyFAAQgAAEIQAACEIAABCDQ0Ak07Vk6RHQCHmwNva9pHwQaCgEEtobSk7QDAhCAAAQgAAEIQAACEIBAAyFQ2KObb0nxrJkNpEU0AwIQaOgEENgaeg/TPghAAAIQgAAEIAABCEAAAllGoPE6ba2gZy9vddGPk7PMesyFAARykQACWy72Om2GAAQgAAEIQAACEIAABCCQ4QQKe5UOEx0/IcMtxTwIQAACZghsXAUQgAAEIAABCEAAAhCAAAQgkHEEyiY6IA5bxnUOBkEAAmsRyF9rDzsgkCKB+d/8bIsnL4yelZeXZ5FIJLpd1Up8/vjtqs5P9nivIzdNNiv5IAABCEAAAhCAAAQgAIF6JtC0R+kQUWYSreeeoHoIQCAZAghsyVAiT5UEfnjp+yrz1GeGDXboVJ/VUzcEIAABCEAAAhCAAAQgkCKBgp7d/RkrvsODLUV0ZIcABOqBAAJbPUCv9SrfecfsySfNVq0yO/JIs0GDar0KCoQABCAAAQhAAAIQgAAEIJBOAoXdelheQYGt/nm+uSEyZm6kDAkCEIBAphIgBlum9kyydn3+udm++5q1bGk2YIDZySebvfBCsmeTDwIQgAAEIAABCEAAAhCAQEYSaNSyhRWGM4kyTDQj+wijIACBMgIIbGUssnPtnnvMzjzT7L//NTv++GD9rruysy1YDQEIQAACEIAABCAAAQhAIIZAdJjoBGYSjcHCKgQgkIEEENgysFNSMul7F/tsxx3LTunZ02z8+LJt1iAAAQhAAAIQgAAEIAABCGQpgYJuYRw2BLYs7ULMhkDOEEBgy/aunjzZrE2bslZstJHZfBejgAQBCEAAAhCAAAQgAAEIQCDLCRT07OFbUDRxUpa3BPMhAIGGTgCBLdt7WLHXFiwoa8Xy5Wbbblu2zRoEIAABCEAAAhCAAAQgAIEsJVC4iRuh49KKCcwkmqVdiNkQyBkCCGzZ3tVdu5pNm1bWCnm0bbll2TZrEIAABCAAAQhAAAIQgAAEspRAQa+e1qhVS1uzeJGVrFyVpa3AbAhAIBcIILBley8ffrjZ3XebzZpl9umnwfquu2Z7q7AfAhCAAAQgAAEIQAACEICA5TXJdzOJlg4TZaIDrggIQCCDCSCwZXDnJGXa4MFme+9tpthrO+8c/J16alKnkgkCEIAABCAAAQhAAAIQgECmEyjo1subuAKBLdO7CvsgkNME8nO69Q2h8U2amL38stns2WaNG5utv35DaBVtgAAEIAABCEAAAhCAAAQg4AkU9AxmEi36jjhsXBIQgEDmEkBgy9y+Sc2yDTdMLT+5IQABCEAAAhCAAAQgAAEIZAGBwh6BwLZi4oQssBYTIQCBXCXAENFc7XnaDQEIQAACEIAABCAAAQhAIAsIFPTo7a0smogHWxZ0FyZCIGcJILDlbNfTcAhAAAIQgAAEIAABCEAAAplPoKB7N8tv385Kli+3NUuWZr7BWAgBCOQkAQS2nOx2Gg0BCEAAAhCAAAQgAAEIQCB7CBR0Iw5b9vQWlkIgNwkgsOVmv9NqCEAAAhCAAAQgAAEIQAACWUOgoEdPb+vyCeOzxmYMhQAEcosAAltu9TethQAEIAABCEAAAhCAAAQgkHUECnr28DYzk2jWdR0GQyBnCDCLaIpd/dlnn9mbb75pxcXFts8++1i/fv1SLIHsEIAABCAAgYAA3ylcCRCAAAQgUBsEcuH7pLB7qcA2nplEa+OaoQwIQKD2CeDBlgLTiW7WmiFDhlizZs1s1113tSuuuMLefffdFEogKwQgAAEIQCAgwHcKVwIEIAABCNQGgVz5Pol6sDGTaG1cNpQBAQjUAQE82FKA+vzzz9ugQYPsoosu8mdNmjTJnnvuOdt7771TKIWsEIAABCAAATO+U7gKIAABCECgNgjkyvdJ0402tvyOG9jqn+fa6l8XWH679rWBjzIgAAEI1BoBPNhSQDl9+nTr27dv9IyNN97YpkyZEt1mBQIQgAAEIJAsAb5TkiVFPghAAAIQqIxALn2fFPYsnUmUYaKVXRIcgwAE6okAHmwpgJ85c6a1bNkyekbHjh1t4cKF0W1WIAABCEAAAskS4DslWVLkgwAEIACByghk4/fJvffea2eddVZlzUp4rKkEttGjberxxyU8zk4IQCA7CTTfamvr/sILlldYmHIDqns/SbmiJE7Ii7iURD6yOAKHHnqonXjiiXbkkUd6Hu+884498sgj9sQTT0T5XHfddfbiiy9Gt1mBAAQg0JAIaJKX9dZbryE1qd7awndKvaGnYghAIEMI8J1SOx1R1ffJL7/8YgceeGDtVFbPpXQuidjZSxdbpxVF9WwJ1UMAArVJ4KcWzeyGVuvYqmoWminfJ3iwpdCBnTp1sjlz5kTPmDFjhvXs2TO6rZVLL73U/8Xu3H777e2LL76I3ZW16w2pLeqEhtQe2pKZH6uG1i+Ia7V3nVX3O6X2LKCkZAg0pM9wMu0lT/YR4BrNvj6rbYur+j7Rd3eiZ5H6vHZyse5cbLOu9fpqd33VW59tzua6t3TGH6oGVCOprzPlGYUYbCl0YL9+/XxQ6vnz59u4ceP8+lZbbZVCCWSFAAQgAAEIBAT4TuFKgAAEIACB2iDA90ltUKQMCEAAAjUngAdbCgwPP/xwGzNmjA0YMMCf1b9/fxs4cGAKJZAVAhCAAAQgEBDgO4UrAQIQgAAEaoMA3ye1QZEyIAABCNScAAJbCgzz8/Pt5ptvNnmwNW7c2Nq1a5fC2WSFAAQgAAEIlBHgO6WMBWsQgAAEIFB9AnyfVJ8dZ0IAAhCoTQIMEa0GzQ4dOqQkriWKeVCNais8JTYuXIWZaulAXbfl559/tpKSklqytupi6rI9y5Ytq9qAWsxRl22pRTOTKoq2JIUp7ZkaUr+kHV4lFab6nVJJURyqAwJc93UAlSJrlQDXaK3izOrCUv0+OfPMM+utvblYdy62WRdYfbW7vuqtzzbnat312ddiHpuYRTSWRpatr1ixwiZPnmynnXaaaUbTddZZJ8taUGauRDXNcKRZWq+88krbZZddyg5m4Zr6Zv/997cX3FTDmRJwsToYNcnwTz/9ZK1atcrqdoRt1zU2bdo0U+zEpk2bhruzcjlv3jxbtWqVde7c2Ro14l1JVnYiRkMAAhCAAAQgAAEIQAACDYYAQ0SzuCvPOuss++233+zPf/5zVotr6oJ77rnH3nrrLdt8881tp512yuJeCUxv1qyZFRYWenEqWwW2pUuX2rnnnmsSchYuXGhHHnmkXXTRRVkp5kjAvemmm+z111+39ddf3/r06WP//Oc/s/I6U1tuuOEG01TUSprJWEPXs1lgz8qOwGgIQAACEIAABCAAAQhAAAIxBHB7iIGR6avyJpo6dar39JKtJ510ks2ePdt742S67YnsmzJlimlIqNJhhx3m27L11ltnrYAjz7t7773X1E9K2267rf34449+PRv//f3vf/eCp4TPF1980c+a+/zzz2djU7x4K29PeXo+9dRT9re//S0r2yGjZf+ECRPs7bff9n/Lly+36667Lmvbg+EQgAAEIAABCEAAAhCAAAQaAgEEtizpRXkTnXzyyXb22WfbgQceaNdff71pSu5tttnGiwZZ0gxvpiaJOOGEE+y8886zgw46yN577z3r1KmTDR482AsG6YzBVlNuGgr63Xff+aF6EgmffPJJu+aaa2zNmjW22Wab+SG8Na0jned/8MEH9tBDD/kqR48ebYceeqh98803dumll9ree+9tvXv39m1Np03VrUvi5vjx4/3p8ib89ddf7ZVXXrFrr73W/vjHP9qxxx5r06dPr27xaT1PQ0FD4fbbb7/1sxfLq/Cqq67yduja0zYJAhCAAAQgAAEIQAACEIAABOqHAAJb/XBPudZE3kSK7/WXv/zFizqKK5Ut6U9/+pPtt99+9sYbb9iQIUPsH//4h0lAVPw1iW/Dhw/PuKZ8+eWXNnHixHJ2PfHEE7b77rvbBRdc4AUPxcGSd9FXX31l//d//2ddunSxSZMmlTsn0zfE/+uvv/ZmbrjhhqbrTt5RGoZ84403es+vWbNmZXQzlixZYqeffrofznrhhRfaq6++an/4wx+8IP3JJ5/YRhtt5IUpDa2Ud14mpc8++6zcJB9qi7ztFJNQIprYr7vuuv4zf8opp/h26TocOXKkF6ozqS3YAgEIQAACEIAABCAAAQhAIJcIILBlcG9rGFiYKvIm2mSTTeyII46w//znP2HWjF9qmKs8oyQmaMihYmI98sgjprhlEgz//e9/2++//55R7ZDod/XVV0fFD3l6adikhhxqqN7BBx9sEg7btGnj21JcXOzjZNSvRoUAAEAASURBVGlYorzZMjXNnDnTHnjggahXWq9evaKioGKuqa8U72v77be3zz//3PdLpseU++9//2saaqz+0Z/6pnnz5t5jUmKVJgXZcsstTX2kv0xJGup5+eWXR73uZJfE265du5quN9mu2cHUHg0NlweoRLdFixbZhx9+6PNlSluwAwIQgAAEIAABCEAAAhCAQK4RQGDLwB6XsPbMM8/4IaESoZQq8ybSZAcKcF5UVFSvrZEXmoSA2CSBRh5Q8lL79NNP/SF5FKmNEhNuueUW7x316KOPeqFq11139bNvZtpwN3naSSx77bXXfBs09FCTMYSB5SWutWzZ0rehdevWdtttt3nvNmUO48z5EzPsnwQmtUlijfqvR48efqihBM7jjjvOdtttN38dqv/klSeRRzOKZlLS8En1TSjKSqjVzKfDhg2zK664wvbaay9vt4So888/3/9JPFy5cqX3dMuUtkgEfPnll61Fixa2bNkyL8yOGzfOD8vVMGRdc+oH9Zkmn9DwUG0rFqOGiyvmHwkCEIAABCAAAQhAAAIQgAAE6odAnovrE0Rkr5/6qTWOgDy6FANLninymiooKPBCgYYeSrR57rnnTF5r8iaS4KE89S14yENLszNqJsNNN93U7rrrLmvcuLH3ftIwtjPOOMNvS0yTN9Q+++zjh4hecsklPq7Xs88+62dElECo4xtvvHEclczYfPzxx+3+++/3zDW8VSKoPKTCpPZJ/NBMm2GS+KHhlZkkfowaNcr0pz46/vjjrX379nbxxRd7T6g777zTJNgqjpxiyOn2oGttwYIFtt1221nHjh3DpmXEUsKahoNK3NTwVs0MKu81XYOK5acYhZoxVN6H8o7ULLUaFtq5c2fvlZeXl5cR7ZAR8mDT8FzNqLvjjjt6j0gN/7z11lu955rETvXVfffdZ2PGjPGfLwlvap88D0kQ0AsZzS6r+5DusxJeSRBIFwENaVfoCr0Y0O8UhX0IX0LF2qC4q/K6jU164ab7sbzZ5VkdJpWjmK0kCEAgcwjot6Feln/00Uc+bMXhhx/uXwam00L9Bld8XY2ISVfSqBWF5NBzzwEHHGByCtBzWl0n/T58+OGH/QgThTfRd/sWW2xR19WWK18vpfWsoOeGHXbYodyxutqoz++D77//3sdt1oR8ffv29c8aegFel0nfoXrWj0969lKc8rpOetbTqCw5xOhZSte4YqSnI+lZ86WXXvLODwrrI0cIhV7K1pS9lmcr8Urs1s1LkxdoiJs+SA8++KC/0HTBZao3keKN6cevhBn9QJY4IOFG6bHHHvPB5CUySTCQcBbGvFIcLH05q42aGOD222/3QmKmimtqzzHHHON/SOhLrn///jZjxgyTOKikHxtz5861du3a+W39C4eGrl69Orqvvlfk1SWBRt53TZs29f0jb8E77rjD30w1kYZ+LEwrjemnBx713YABAzJOXNMPDolruu4keIaekvLEk0A4dOhQO+qoo3wsPH221A/yEhs0aJD/cZAp4pquHU0koc+APDklBIY/KvTZkpim9mmiCU3OoKQvYf3IGjhwIOJafX+oMqR+xYiUp608OPWjX96b7777boZYhxkNnYDuY3qRIS9cfT/Kc/ucc86JhlWIbb883fX9qIeW8C+8H+tlon7Qh/sVy5QEAQhkFgH99tVLdT2E6+H/zDPP9DFi02GlYhvrd7jCyeh3eLqSQvXIMUChOiRwSQjRM0w6kl4eq37Fr27SpImdeuqp/ndgOuoO69BznmzQb+x0pfr6PpDQpGtazw169lP8ZtlS1yk/P9+PJNJoovBPdadrJJR+N+oFmUJPyYFBjjzpmHjwl19+8b8X9H2v3w9yNtKzajan/Gw2Pttt10O/ZjVU4PKjjz46Ks7oAUlJ3iry/tKXiG6q8pAKvYk0RKy+vYnk0aUvGHk86U213iQp6YezRDZ5pOlGLK+0jz/+2Hty6Ue41vXFLEFB4o68wurbC88bXsU/CVK62Shwvt7WaTbKyy67zA/dU0wsiTd6sxMmiVl6UFD8snQl8dXb/0RCpYZSiruuOQ05lmimWVB1XUnUlYAjm5VHXy6ZnsRcIptuxhrmqjboR4feeGiIqMRCCQ1jx471X5CaKCATk8QyDdFVkveHYtzprZE819QXepskMVo/6J5++mn/pa+JDkgQiCWgHyQSj0MPWj2EyONZs/+SIFDXBObMmeM9o3Uv3mCDDfy9V16UmvVYD+GxSbM3SwyWV3Rs0m8G/SbQ7wl935IgAIHMJCBhSSFDDjzwQG/giBEj/G8Vxbit66R7yg8//GBt27at66rKlS8Pcb201ctbJT3fKIax7mV1mfRMJc85PXNplJB+y4u/XsxqorV0JD23abI3PTukK9Xn94EcW+Slp+ciJXlSx3pW1xUDPUcee+yx0eJ1zenZ+q9//Wt0X12t6HrWC32J1/KOVHx0PdPqO1l6RF0mvQzeaqutvJ6gevQsq2egs88+uy6rrdOy8WCrU7wVFy5hQ662umnJ/VSCmZRyXWCxww4l0EhEkEdLpnkT7bHHHr6B8qQJxbX333/fi0/68tOwSD3gKSaZ3nZJINBNS+KNho0ofpRuGnoblC1JbdaQPA3D2n///X27tK03HZpoIuSg9sjrUB6J6XRxlRgjr6fFixevhTR8CxHGKlMGCbt6OxLu041d158ecOoz6W1GfNIXjTjLa00Pc5pQQkleXe+5IUf6waG3LjquL0O9bdIX5N133+33xZdXn9vy4JCwrB8saoc8PfQ511BXJQlu+iypzbrO5C4tDyWJuxK0SRCIJyDRQl4/YZLIru8WEgTSQSB8ySRxTUn3NT0sJHoo0Ztx/abRiyq94NG1qyQvcCWJxPou0hD53377ze/jHwQgkBkEQuFDYk+YurrJmBJ91sPjtbnU79Z//etfaQ+9otA9eokbJsXFTUf4FzksSPgQb8WvlieZkp4X05EksFx55ZX+LxyhlI566/P7QNeyQsnoOVW/z+XVpdE86UwSvDRCR4Ju7LNlXdkgRxc9a0jY0nPgjTfe6K+xuhbX1B7FnY4dfitRWddd+GxaV22uy3IR2OqQrgQNDYmMD/yv2CMSAhQnRxeyvMD0JkZvQiS06YenPIk0QYBiSUkwkXdCJg01FDa5cmr4pz6EuunL9r///e9ezJBr60EHHeQ9qRRXat68eX7IqN64KOh8NiX9mJBYo/5Sksgm7xClbt26eSFNQy7lth2b0imshfXKWyWM0xXuC5e6QesLWSJcmNRPSmpjmPRgHg7XCfelc6m3c/p8yPNMSWKb1vUFrwcyeXgprp1EWnml6XOhIaEadizvifBHn74clb979+7pNL/SunQNye1Zop++wDUUXG8lFWtA3ENxXd6p+lK/6aab/JucU045xbdfcdhIEEhEQNdTKNDquK4h/UAhQSAdBPQdH/9DXPc0vUyMTXpo0MsEvTyU54d+5+h+rxiaEumU5PUdvuzRy4bw5VBsOaxDAAL1Q0CfVaXYB2IJ69n8MJwMSf3el0OAxAANh1foDr1IT0cKhS2JLQqro9/y8vKp66Q69Pta9+PweaGu6wzLr8/vA4XJEWf9plIsZ/0Wl4NIOpPqk8dgOn/3K/a2ntOlTWgp4TwdSc+ucjjSCKsvvvjCPxepXv1WyNrkPjykOiLg3rxG3BAI/+fikUVrcQJCxMVOiriYUdF9TqDy+dzQtohzBfbHnStyxHmA+Tz77rtvxHmFRfNnyoob3hZxLsredicURrQdm9wNMuK81CKy38WWysg2xNqbaN19mUacG3zEeQ9FXFBV314nmibKmhH7XNBZ3x/OS2Ate9wQY3/MCYYR9Y3zsIu4GVDXylffO2Sf80yLOPEs4oZ+epvdjdeb5YZW+8+H826IuB96vm+UV/2jazFRu+u7PbqG3IQE/nPghvFF3EOnN8m9lfRt+/HHHyNhv/3666/+mPK4eEaeQX3bT/2ZT0DfKeH3hax1Q3Yizr0/8w3HwgZBwL1M9Pff2MboXudeKMbuijixLOI8rKP7wt9Jbhh8xL2AKPcbIrwnujhL0fysQAAC9UvAxYNa6zemCwUTcWFf0mqYGwWU9jrdy3X/O07frXpeq4+k+6F+6zqnjDqv3omIvi430iWi5wc9C917770R53Vc53XX5/eB+tcNT4y20Tm5+GeS6I46XlHb9dys33HpStIYpFm4EUG+SjeCJvp8kg4b3Kgef63p2nax4HzdzpMtHVXXSR14sNWhNKqhkfJMk0uxvNT0xkPeOfJ00tBIxVsKPQykGitguYYZKm6JVFx5r8mzJfSAy8Rgv3IpVVwyJb3hiI+lprfU8nCT15RiyWk4abYlDXPRJADuE+jjrUnZdzeAjG2GvLr0xkPxu+KTPAPUH/KaCgPm681MJiV5pG255ZbmhCYfe0yx1NQHoZddGAtPQ4v0Vk9eX/qMHXLIIf6NYrrfslXFTm9gFMdBMUo0nFhBTEN3b7mc6+2YjoX9polClJRHs2OFby6rqofjuU1A91oNnQ6Tgj9n4/02tJ9ldhGQZ4fudYopqaT7ta7B+Dfgukb1hjpM8hDQ/V1DSjUrsnvZEB7yMWC0EeuZGT3ICgQgUC8EwpmB5bUaJic2+REd4XZDXMrbVmFKNKpIk7jF39vqqs3yTtdojNCbR6M1FF9Y++s6yQNZsbj0bKpnB3kvakTVqFGj6rrqev0+kEemOIdJcY/1TKLnwHQkhe6RPpBO7zWFblAKn28VYkcahmZTreuk3wV77rmn1zsUHkc2SBepj5FgtdVWBLbaIllBOXqg1odUrqYaGqphlIopog+NHqjdm4DomRoSplkoNfmBJj6Q67EC0Ws4meJLZWrQ34MPPtg/yEkQbKhJruH6gtFMPumIuVBTjnIj14QYulGFSUMtr7rqKj+kUq7tOqY4gOmYYjy0oaqlbNRwIU2MoSQRTV9oij2mIdShyKZhuooVIDFKQpQmAdHQ40x7ENNU1xKeFZNQw6nVDv1IC4cYq426nkJhRMcJSi8qpFQJKMalfvjqB7DihWg9XTFaUrWV/A2PwOabb+6FMoW30IOgfrfox7mEX8VTUZgFDfnR/VrfO3rppiHzuk6VXy8bFi1a5B9g9ftHQp1eBOk+z6QuDe96oUXZS0DPIvqdos+6BBjFJNbvzTA8R/a2rHLL1d7evXt7IUDilmJHpkPkUrgT/fZV+CANT5Xg4bx7/e/eyi2u+VEJe4qhHf7ppZ1mUtWL77pO9fl9oGf0//3vf5617NDs2HKCSVf4HH2e9JsufBlf16xVfujEM3z4cP+cqCGbEvkUAqmuk4bk6llJ9ek58NFHH/Xxp+u63rosP09+cXVZQa6XrZky5ZGjB2fFY9OskxpTLY82JXmoSXzTTTs+6UenPtjpnLUl3oZktxWsXfGk9KGIDbSd7Pnkq30Cuvb0BaGZJ+XlJS88zU7phoRGPQNqv9aalShvSHl4anII3Wg1g8zOO+9s559/vn9rKO9OzayrpBuyJsxI5yytybZOP4T0UCjxWV8aioelWU6VJBpKbFegWrVVQrraqR8yJAhUl4C+ZyRchKK6Ztd1YQiy+g1gdVlwXv0QkGdaOOuXvNL0HaQZRPXSUB4X2taLRQlnmuQl9MiQh6/uf4q1pt9I8uhVkkAnD2t5NJMgAIHMIaCHYP0WC+Nk6RknXfHIQgqaxVTPR/pNm46ke5S8cmOT7nPhKKPY/bW9rvjCekGupDr121K/l9M9wkG/Z+XFl44XwfX5faCXP26YYjRmteKJ6jd7OIlPbfdvfHnirD7WLJ7pTA899JB/ZtTzl75/Ff85HZ9rvYTT5EYSFvW7QN5s8phM9/Vdm6wR2GqTZoKyXFwR/5ZHqr9mOlRwdrl+akYY/XCUp4He+uhHZ7Yn3fzlUiqvO1L9E5AHgG7QulnJk1LDkjPNo0W2yS1ZD1B6MyQvL83UFIpN+nxo+Ke8HHTDl4irtysSrDI5acppTcrw+uuv+zdg8k6V16CGUcX2i34oHXPMMd6zNZPbg23ZQ0AebPpRIm9oEgTSTUAPJnrolud+ZT+OJQgrnx5Y4r3zFSxdnv4amlRZGeluG/VBAAJlBOSfIQ8uTTzVrFmzsgOs1QkB/V7WsFx5GuXSfbE+vw/0El/eg/LEzubhiqlckBI2JSRLVEx3m/W9r98Q8RMmpWJ/puRFYKvjnvj222/ttNNO8943p59+uo9RJqVWXiy6aWjWzVy6UdYxboqPI6BhOPIe0Iyu6b5Rxpmy1qaGDIVDPDXTp7w69fZkypQpPl6fTtBnRUOEJEDL+0Fv8fRmQ/EN6yvpDYs80CQMamiqC+TtXZo1nbZEc8Uh1A9PiYESz/VmV+KmZtyS6KYkj0INedUynS7gvnL+QQACEIAABCAAAQhAAAIQgECtEyAGW60jLV9gOKZZAeXDCQAkqLkZNf3DNuJaeV5s1S4BCUAK/l9f4prEKMWukwdX7BTXGsbmZgH1cQ00oYfyaSir3M4VPFWxLpTk7SmvO71JkVCottSnuCZBUEOYJJZp6Oq1115rEtHlLaRYJA8//LC3W954GtYqEVHBfy+44ALvzSaPPCVNXqCg3jpOggAEIAABCEAAAhCAAAQgAIHsJ4AHWxr6UCKHPFcawjDQNOCiigZCQGKUPLQ0aYGGD8gDTR5qGg6qgKkKZK14PYq3pnhqEydO9MFy3dTQPvaBhlPKy1NxD7bYYot6pzJ69Gg/G6iEQQl+SrIvnFghHM6q2YFDYV1DXRWgVm1X3KFNNtkkOivQ2LFjvdee4rXVlwBa71AxAAIQgAAEIAABCEAAAhCAQAMhkN9A2pHRzdBUsxrTTIJArhCQGCXvrFgxSmJTKEZpsgINjZRnl4ZRyvNLsdY0dFpToI8cOdLHAOjRo0fGxPaYO3euny03FNfksSbvNQ1pVYxFzbSj+IM333yz3Xrrrd7jTkNcNdOUYg2deOKJ5bp/u+22M8S1ckjYgAAEIAABCEAAAhCAAAQgkLUE8GBLQ9dpaFtBQUEaaqIKCGQGgeeee87PYPrggw96gyRGffXVV+XEKE0EoJlN5eWmIKIS2DRkWrPNaUhppqUff/zRNNRb03dLMNcU1krdunWzqVOn+niK8lLVrD+aVELBaDVrq6YzZyh4pvUm9kAAAhCAAAQgAAEIQAACEKhdAghstcuT0iAAAUcgGTFK3mkSn4YOHWoS2/bff387+uijM5qfJjeQd51myNt66619fLj8/Hw/YYFmCZbHnjzW3nrrLZPnqsQ4EgQgAAEIQAACEIAABCAAAQg0fAIIbA2/j2khBOqFQDJilGY5HTFihPXr188Pr6wXQ2uh0meeecaGDx9ujz32WC2URhEQgAAEIAABCEAAAhCAAAQgkG0EiMGWbT2GvRDIEgK777676S8+aTbdMKi/JgDRXzYlxZeTeKiZgCORiPdW01BYTcZAggAEIAABCEAAAhCAAAQgAIHcJIDAlpv9TqshkBYCDVGM0iQHo0aN8jOeLl++3E9woBlTFYuNBAEIQAACEIAABCAAAQhAAAK5SYAhornZ77QaAmkhMGPGDDvjjDP8JAahGHXttddmvRi1atUq+/rrr61z587WqVOntLCkEghAAAIQgAAEIAABCEAAAhDIXAIIbJnbN1gGgQZBADGqQXQjjYAABCAAAQhAAAIQgAAEIACBSgggsFUCh0MQgAAEIAABCEAAAhCAAAQgAIFMJfDzzz/buuuua02bNs1UEzPOrpKSEm9TGBc64wzEoKwl0ChrLcdwCEAAAhCAAAQgAAEIQAACEIBADhJ49tln7eijj7aDDjrIdtllF/u///s/mzZtWrVJaNTJq6++6kO7VLuQGp740EMP2T/+8Y8alpL4dMVQnjVrlj94//33m8LWkCBQ2wQQ2GqbKOVBAAIQgAAEIAABCEAAAhCAAATqiIBiAd94443217/+1caMGWNPPvmkrVmzpkbiVFFRkf3zn/+0pUuX1pHVyRUbepcllzv5XPfdd59NmjQp+RPICYFqEGAW0WpA4xQIQAACEIAABCAAAQhAAAIQgEB9EJg5c6avtnfv3ta4cWPT8s9//rONGDEias5rr71mTz31lGmisQEDBviJxyTCaQKyI444wotyK1eutFNPPdUOO+wwL9bp5PPPP9/uuusuk9B100032dixY61nz55+v+qZMGGCPfHEE37Sspdeesnatm1rF198sW2xxRZe5HvwwQft+eeft3XWWceOPPJI72WnchPZI9srSr/++mvK9ctmecG98cYbtskmm1jfvn1Nw0BXrFhhkydPtv/85z/WunVrX6XK/9vf/ubb16dPH7v66qutTZs2FZnDfggkRQAPtqQwkQkCEIAABCAAAQhAAAIQgAAEIFD/BLbaaitvxCmnnGIa7igRTDPbn3nmmX7/xx9/bFdddZUXzi677DJ75ZVXvPAkAWrixIn26KOP2oUXXmjbb7+9XXPNNabhoRLalAYPHmwtWrSwoUOH2pIlS/zxTTfd1I4//njv3bZs2TIv5P3www925ZVXWkFBgd1+++3+XNUj8U3DPGWbvOzmzJljFdnjT0rwLxKJVKv+0aNH22OPPWZDhgwx2Syh8Mcff/QC44YbbmiDBg2yXr16+RqVd6ONNvLioPJIFCRBoKYE8GCrKUHOhwAEIAABCEAAAhCAAAQgAAEIpInAxhtvbIrBJkFLXmT33nuvNW/e3ItFiskmsejQQw+1Y445xlt09tln2yOPPGInnnii37700ku9uLblllv6MiSCbbbZZv7Y5ptvblOmTPFCnGKySbjbYYcdfHy2zz//3Fq2bOnzScBTnatXr7ZLLrnE73v55Zd9HbvttpvflufYvHnzKrQnFAR95ph/GsopITDV+j/66CM766yzrH///r608ePH+2WXLl28rfJqk2edkkRKCXFKquunn37y6/yDQE0IILDVhB7nQgACEIAABCAAAQhAAAIQgAAE0khAwz47d+7svbzkaTZ16lTvyXbFFVfYzjvv7Cc7+OCDD7x4FpolMSxMHTt29KvhPnmwxabZs2f7zYEDB8butkWLFnmBTcNCw3MluMkeJU2ycNJJJ0XP0VBUJXnJVWZP9ITSlerWr4kMYidJkIAoNomSvNfCJI89DZclQaCmBBDYakqQ8yEAAQhAAAIQgAAEIAABCEAAAmkicP3111t+fn5UTOrWrZudd955fujmzz//7GOJnXzyydEhoxrqqZhjYaos9pnyhF5qb7/9dlRIk1C1wQYb+FhmqjtRUoy2X375JXro3XfftfXXX79Ke6InlK5Ut/5mzZrZ3Llzo8UtWLAguh6/UhWD+PxsQyAZAsRgS4YSeSAAAQhAAAIQgAAEIAABCEAAAhlAYLvttosO2VRcNXmgDR8+3Fum4ZA77rijyZtLApO8y2644QZ7/PHHK7U8Ly/PH9csogr6r6QyJUR98cUX3jNNHmyVpX79+vlzNORUgtxFF13kxbpU7alu/eKiyRRU/3fffWevv/561Fy1Q0IjCQJ1SSCx9FyXNVI2BCAAAQhAAAIQgAAEIAABCEAAAtUicMghh5jilJ1zzjnR8yVK3XLLLd7j7IQTTrBvv/3WT3KgDIqvpgkHKkoS11q1auXjkh199NH25ptv2nXXXWeK1Xbbbbf5084991w/c2hlXmEHHHCAn8FTQ0s1hPT00083xYtLxR7N+qk4adWpXzHVNOmD6tcwVs1+2qRJE2+/hs6qzHAW0XgWocAYv59tCKRCIM/N0BFJ5QTyQgACEIAABCAAAQhAAAIQgAAEIFC/BOSRpSGRGroZLxzpMV/HiouLvciVrICkMsOyNEmBvMHWW2+96L5kWjx//nxbd911o+KWzqmOPanW/80333ihUPHpmjZt6mc57d69e3RyB7VNw08l4pEgUBcEENjqgiplQgACEIAABCAAAQhAAAIQgAAEIJA2AooZd+utt9pxxx1nGuo6bNgwP3uqYtSRIJAOAghs6aBMHRCAAAQgAAEIQAACEIAABCAAAQjUGQHFo/voo49s7Nix3uNujz32sB49etRZfRQMgXgCCGzxRNiGAAQgAAEIQAACEIAABCAAAQhAAAIQgEAKBBh8nAIsskIAAhCAAAQgAAEIQAACEIAABCAAAQhAIJ4AAls8EbYhAAEIQAACEIAABCAAAQhAAAIQgAAEIJACAQS2FGCRFQIQgAAEIAABCEAAAhCAAAQgAAEIQAAC8QQQ2OKJsA0BCEAAAhCAAAQgAAEIQAACEIAABCAAgRQIILClAIusEIAABCAAAQhAAAIQgAAEIAABCEAAAhCIJ4DAFk+EbQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIpEAAgS0FWGSFAAQgAAEIQAACEIAABCAAAQhAAAIQgEA8AQS2eCJsQwACEIAABCAAAQhAAAIQgAAEIAABCEAgBQIIbCnAIisEIAABCEAAAhCAAAQgAAEIQAACEIAABOIJILDFE2EbAhCAAAQgAAEIQAACEIAABCAAAQhAAAIpEEBgSwEWWSEAAQhAAAIQgAAEIAABCEAAAhCAAAQgEE8AgS2eCNsQgAAEIAABCEAAAhCAAAQgAAEIQAACEEiBAAJbCrDICgEIQAACEIAABCAAAQhAAAIQgAAEIACBeAIIbPFE2IYABCAAAQhAAAIQgAAEIAABCEAAAhCAQAoEENhSgEVWCEAAAhCAAAQgAAEIQAACEIAABCAAAQjEE0BgiyfCNgQgAAEIQAACEIAABCAAAQhAAAIQgAAEUiCAwJYCLLJCAAIQgAAEIAABCEAAAhCAAAQgAAEIQCCeAAJbPBG2IQABCEAAAhCAAAQgAAEIQAACEIAABCCQAgEEthRgkRUCEIAABCAAAQhAAAIQgAAEIAABCEAAAvEEENjiibANAQhAAAIQgAAEIAABCEAAAhCAAAQgAIEUCCCwpQCLrBCAAAQgAAEIQAACEIAABCAAAQhAAAIQiCeAwBZPhG0IQAACEIAABCAAAQhAAAIQgAAEIAABCKRAAIEtBVhkhQAEIAABCEAAAhCAAAQgAAEIQAACEIBAPAEEtngibEMAAhCAAAQgAAEIQAACEIAABCAAAQhAIAUCCGwpwCIrBCAAAQhAAAIQgAAEIAABCEAAAhCAAATiCSCwxRNhGwIQgAAEIAABCEAAAhCAAAQgAAEIQAACKRBAYEsBFlkhAAEIQAACEIAABCAAAQhAAAIQgAAEIBBPAIEtngjbEIAABCAAAQhAAAIQgAAEIAABCEAAAhBIgQACWwqwyAoBCEAAAhCAAAQgAAEIQAACEIAABCAAgXgCCGzxRNiGAAQgAAEIQAACEIAABCAAAQhAAAIQgEAKBBDYUoBFVghAAAIQgAAEIAABCEAAAhCAAAQgAAEIxBNAYIsnwjYEIAABCEAAAhCAAAQgAAEIQAACEIAABFIggMCWAiyyQgACEIAABCAAAQhAAAIQgAAEIAABCEAgngACWzwRtiEAAQhAAAIQgAAEIAABCEAAAhCAAAQgkAIBBLYUYJEVAhCAAAQgAAEIQAACEIAABCAAAQhAAALxBPLjd2TidsnqvEw0q05tapQfWav8hsohUVttXO71+VodnmjHFmtfFxnHqjZtTFRWIi6J9tX3NZTI9vqyKddtyaT2J7pWk92XqB3Jnks+CEAAAhCAAAQgAAEIQKBOCeDBVqd4KRwCEIAABCAAAQhAAAIQgAAEIAABCECgoRNAYGvoPUz7IAABCEAAAhCAAAQgAAEIQAACEIAABOqUQNYIbI8/bta4idkzz9Qpj6wo/PsfzJ58MitM9UZWt+8ee80sb0uzp/9X/2196KXAlhfeqX9b4i3IZNviba1quzb7vK65xJdfm7ZXxakhHv9+mtkTr6festrmfsew4LP+v49St6W2z1i0xOymR2q7VMqDAAQgAAEIQAACEIAABOqCQNYIbCUJQk/VBZBML3PePLPN+pqNHJXplpbZV92+KykpK6O+15o3M+vQ1qywaX1bsnb9Id9M4rW2lcntaV5o1rmjWQvHu6aprvssvvzatL2mbc+28+ctMOsz0N3XxqRueW1f95HS75pwmbpFtXfGbieb/fuR2iuPkiAAAQhAAAIQgAAEIACBuiOQFZMc1F3zs6/k1auzz+aGYPEfDzDTH6luCRy5r5n+aiPVdZ/Fl1+bttdG+7OpjNVrssna9NlatDJ9dVETBCAAAQhAAAIQgAAEIFAzAlnjwZaomS+8YLbHnmZHHWO2YkWQo7jY7F/Xm+20czCkdIcdg+14YWqle3C5/gaz/vsF+TbdzOzGf5vp/DBVVdazzwb1/y9mCKPO2ad/8LdqVViSmfLI1hEjgn0/TTM762yzbbYN6l/HeUcdOshswsSyc+LXvvzKbNDhwd4XXwzKm/hdsF2Vrcr1xhvBOV+MNRtyrlnPXmaqd9ARZlOmBuWE/+Up948rAvs22NDshJMC29WG2PYuWWJ2+d/NtnBDOTWEV7yvudYstu1hmbHL8n1X2nmxGSpYf97xk1fHkRe6Pi99+Cx2ouN1D5jteFwwtGs7dz1oO/ah/dX3gvOGf2C21ZFmGzkR54aHgkp+mm02+Kpgv4aktnbXzsDzXF9MKTPi9feD89/7vGxfMucpdzL5ZJfa9cUEs3OuMes+ILDjsAtc38wsq7OytTm/mB3z1+C83oeYDb3RbFkc2mRsqYjVSyMDGz9y12FsCvvk029j95ZfT7Z9b44O6njn0/LnV2erun2WrK3x5SdrezJ9ENogDkf/JehTXdf/vMds8dLyNJK5/sufEWwlW0ey/R6Wl+o1/OWXX9qh5wc2aQi2PgcTSz97ybBK1Lbwmoy9TyTKJ8+5S2830+el417BfUDDMuNTMnbM+jmwXUNW/++m4DOs+8wF7ntm0k/lS1zpvhuuuS/Ir/uN6u53upk+e0rq0z1OMZs6y2z+wiDfsDf9If5BAAIQgAAEIAABCEAAAplKIJIFaU2xRR560CKOYeSpJyyi7VdfDrY7dLDI+HHBvuKVFunXL9ivvCecYJEttwi299vPIqtXBfm01DHl0fFTT7GIytH2XXcmX9annwTnnHF6cI7s+mh0sE9laV379HfyycH+n6ZYZN6csjw7/SGwJay/VSuLLFlsCXvlm68tMmBAcK7yH3WURSZ9Z5Fk2/3A/WX1yr699rRIr55l5a1YFtiq5a67BPt33y2wXXbpHP2pL9SmZUvL+IrjmWeUlafzV64I8lXVdwkb+61FHr46qG/YjY6H237tv8G2G6oZmej6X/vWOCZ771hm24mHOJt6Bdv7OxtKvgny3fuPsjxhOy4+zSLz3y/bv9OWFtH5Kl95WrWwyO9jgvPvvzLY9+xNwXay5yWb78GryuxQ3Xvt4Fh2CfbJnpVjg3oTsQptC20+/XCLbLtpcK6WxV+lZnNFrO64NCjzldtLbfk2WN5+SbD/9TsqtjHZ9sX3eaL2Jrsv5JJqnyVra3z5ydhenetB/X/hSWXX+aB9yq7rZK//RMxi21lZHcn2e2x5qVzD48aNixy0R9m1fvT+Fvnh9eQ/m/HcE90nErV/1ZfuPrdNUO/u27r73MDgMy/b9ffGXal9bia9GpwXnn/20UGZ2nbDniO/flj2udHnW/u7dbbIKYdaZLPuZed+4r7jZJs4hGVp3X/uEjWEfRCAAAQgAAEIQAACEIBARhDISg+2kc6TZuBhLiZWB7NR75pt2sc9hrg0zAWnHjXKeaU5D7LF7q3/ow+bff6Z83A7yuztt81efjnI99RTLpj2E84Lynky6bgTnuz778yciGQXX2Imr6xkytp2m+CcV53HQphUf5g+/DBYW7PGTB5nToSyjTc2e8xN2KB0ycVmToTzds52XkpOzLKlzjvlM2dTorR5X7O77wyOHHigC/7v2tGzR3K2xpYnblN+dPGOnLfIOOd1pHrnzzf75JMg1623Obs+NrtwqNl7rj0PPWD21ZdBW2PLueVWs2/HmZ3kvNvE8Z67zb523k0DBwbnP/xIbO5gvaK+Wztn+T3y5DnkvCAO2vuuXzftFhx/ynnlvevq3td5gSxx9j/mvOfGPuO8fvY3e8u1Qd43scmJZvaT8yZc6LhfdKpj/0pw9G9nuPY/EZw/111T7oHbli4zG+Palygle16y+cI6nNDh7Rv1oPOgc9er7JAHy8dfhzkqXqptTni0B650drtr46j9zL501/Uzrr1KqdoSzyoopWb/a9K+mtWcevvrwtbq9MFXz5nd7LzY3nH3KScC+Wv6yeEBjVSv/0QM1c+V1ZHonMr2pcpt8803t3v+HpQ4YHd3vf7b3dfcfTJVViqhovtEIntvf9Ldp9z9auiJZh88YvbINe7e6z438ak6doxw3ml3Xx6U6YRHk3fbRbcEJeueIk/YHTY3+9H1oxMIbcJLZlecExx/6yOzJvkBByfA+diPYjJwr3jL2IYABCAAAQhAAAIQgAAEMolA1glsI94x2++AQOyRuNandxnO518M1odeUCYG5bsHlXPODva/VvpQOqZUwPqHe6jTcaXWrd2D6wtm991jJkEsmbIaN3ZxuY4JxCnN7Kn01ltuEoJNg/rfLRXbNLRTwtkRRwR5Dj/cDQVyQshFfw229V9DKrfcMtj+/fey/cmsJWNrbDni0bVLsEftd15xPi1cGCxHvRcsL78sWOr/Jl3dsKcLtVaW3nwzWL/WPSCGHAsK3NDbfwX7Xy4Vr8IzKuu7ME+i5YhPnIA22DF1QoDEtT6blOXSUDAlPSTruFK+65chrl+UXns/WIb/tb9rJ7N1XX+3beP6xA3hch4v5rzZomlVsRsuWnpd/b48urvcSrLnJZsvLDy0T9tqh/Ps8Wnhb8Gysv+XnhFMEKA8Ovdyx0zpjQ+DZXVtCVkFpdTsf03aV7OaU+/rurA11T645HSzTusFLc/LM5MQrPRRqeCa6vUfnF3+f1V1lM9d9VZtcUuVVWX3iURWf/hlsDdkqi3dW2R/bErVDudRa/13KivhpIGBSDaq9HtH4qEEuKeud0PaXZ8qaaKG3l39qi1ZFiz5DwEIQAACEIAABCAAAQhkF4FSeSl7jH74kcBWCVYtSwWV0Ho3XNKnPx5n1qxZuLcsPtv3k4J9Y8cGy969yvJozQ0vjaZkyzrIiVP3Ow+v994z29AJNx+ONvvL/zlPImeLYp4p1pu855RCIWuTrmYFTZ2Y5zxSxrqHvAnjg/zKoySBL5WUrK1hmV1KxbVwWx5tSmHcNHmydXaeE22cABWbtnAeF7FJXm46t5Nrd2xyw059Gu/aFZsefiTYStR3sfni1x96qfQ89+DZsnn5o99NDbYVe6xZQdmxMD5bfOwjPdzGpk02DPri3udcX0w0G/9jWfwn5auoL5I9L9l8oU1d4ljKG0hp1epgWdn/UBQM8/TuGqyFDFK1JZ5VWG5NljVpX03q1bmptr8ubE3Vhm36lG91942CbV2rSqle/8FZ5f9XVUf53FVv1Ra3VFlVdp9IZLUYSpRfb93yR7eOY56qHTs6T+XYJLFbn00JgEvcyxMJ+7ts7TyQ33RxP90Lg3GTzWLjF9b2rKixtrAOAQhAAAIQgAAEIAABCNQdgawT2DSM8/jj3HDEe83OPc/slVLxRYgWl3r5yEOscQLfvI4dA5Chp1ajBHlC1MmWtddewRlvjzDr5rwflPbcIxCdJLBJQHvttUCI2nqr4Lg86HbZNVjXfwl7gwaZTZtu9qQbtpRqStbWsNxmheHa2stIJPC2ixfXlDP0tgjPUl8kSmG+pk3KH62s78rnLL+lh+ATDnJDrp513iXXOO8/53EWpsVOaFU6or/rc/cgG586lgpU4f51WodrwVLDtXY6vmyfvE8O38f1xRw3jPj1sv3xa8mel2y+sPxYkTDcl+xSom1siueRqi3xrMKydY3EplDMjN1X0XpN2ldRmcnuT7X9dWFrqjbEf4bCz1Yo/KZ6/SdiVVUd4TnJ9nttcUuVVWX3ibANscvfnNiVyNZ4ET9VO+I/h6qzUV5Q8xrnqbZgsdkf3HeYJjBQ0lBRec1JeNPEByQIQAACEIAABCAAAQhAIDsJZJ3AdtutgcCmOGWvOwHk6afdMM0/BvA1NFOxxE46wSwUvnREXmTjxruZI51XllIP52H1g/Ma0EyZsd5Xw4c7rzPnVTB4cDDMM5myJBodcEAQ361H96D8XXYx26BTsP7MMy4+2RdODBziHrJKBb3LLg+O3euGo55yctnwyj8PDfan6sGQbLuD0iv/rwd4xYpTbDXFotPQ2TB9V+oBGG737OFifH1ltny5WfPm4V6zn38O1jWjaGyqrO9i88WvuyD6dsLBQTw0DfnUbHrHHhjkcsHBfYyykw5xQmVMfZqlT54hG60fX1r57UtvC7bvu8Ls1MOCoZXac8ENwf6SODEp2OtmHkzyvGTzheXWZDnHXfuxaboTCZXcZAk+1dQWeeIoaQhtbNIMi9mQatr+2mhjqjbMjuvTWe6epbR932BZ0+tfpVRVR331e6qsKrtPBLTK/99tGzPN/KqZdls0KzsWfm7CPanaEX++ytGMxG6iAz80/dr7A3HttEFmsjms+8V3gholwpEgAAEIQAACEIAABCAAgewjUIkPV2Y2ptANA1S8r/tK3/Sf7YSrUNDZbbfA5seeKG/7DTc6j4GdzG66Odi/s1tXevgRv/D/5J1xyd8CzzgJccmWpZMHOnFHSeXvsL3ZOusEIpXEtzvuDI4d4gSiME2YEKzpvDB2mcS+l14O9hfHCRjheVqGIp1ErTClYmt4TmVLTf6gdOO/g6X+y77bbi/b1truuwfbd99Tfv9/7wi2Q4+98GhlfRfmSbT05zlx54GrgqNn/dP1+a/BuiYBUHrstWAZ/r/+QdcXx5r9+5FwT+KlhoQqHdqvTFybt8BNSlH6sFu8Ojge/z/Z85LNF19+dbbve94s1t47nw5KcTMW+lRTW9q461kpjF2ldXnjvO5Ez3SnFStWmP5SSTVtfyp1VZQ3VRvuGOaGKccILvLiVJLXk1JNr3+VUVUd6ej36H2tSBYFKVVWld0nwjJjl27WYJ80PDxMRe5lzP0vhFvBMlU7HnnF3S/dPSRMwz8IJjnYY7tgz/fTguXBe5SJa6vXuAkR3HlKsZ9hiZupeIgGJfAfAhCAAAQgAAEIQAACEKgPAk6qys60zdbBJAESgS4YGsyoOfTPgQj06KNBm/bey80M+WkgmilWmCY1UPrTuYF49A/ntaThopttZvase8hS3LQr/uGG6rhhhcmWpfL231//g7TffsFSw/O0/3kneiiFIpjWd3IC36uvOo+p092sgCe6hzHn8XX33e4hbJaOmv3qbKoohUM3n3P2tl3X7LzzUrO1onJj98vbTvHh/nV94H3XvVsg/smjLzZdfJGbYfRh1w8Xm82Y4URM50H2vnuYfMCJW/KqO/202Nxl6/F992ypaFCWI/GaYkVpMoIbHjI739n2jOv7C08yu9UJquHDqYZ4fvJNMJxU8cuuODtxWeHend2w3VdGOU/Cy4MZGuc54e6up4MHYuX51QlIiVKy5yWbL1Edqe6T8HXIn5xH54EBAwlumoVQnnlKNbUlFHP++5SZBGkF3xd3zZCYzuTmX3Yek819lSXO3TMvHDdZhRE1bX8VxSd1OFUbPneetwe5z+PxBwVxuu56xnnnOsFUHptKNb3+VUZVdaSj39u0lCXuPvxWMFTy/OOqf70muk8EpZf/f/7xbkj+cDd5y02BYN+3h7tvOL7h0M0wd6p9pvN2dfclTZ6wYJFb3hZMcnC9+35S2m4zs8fdC4FLbnX3l98CYV/x2DTrsdIvMfd/DRv9YbqZYkzqGhi4RZCH/xCAAAQgAAEIQAACEIBA5hFolHkmJbYojGETe1SzXHZ2AoLEpv/9L/Ac+8qJDP37m0lkO/lU9wDlxIBjnSfTKy8HM3vqfA17/OJz5/2xmxNn3MPP4LPM3nnHDUM8wYl15wc1yAstmbKUWzNySlBSUvy1MO2zd7Amj7DYSRdu/Y+z7WQ3E+BHzjb3kDf0Qufx5oQezYqq9NbbwTLR/xYtymYfvdd58WlCgmRtDb1EwmVYfqhPhDG7VN4Xn7mHePeQOGmSm+1uWBAn7sorgjNaBNqGKabdp67+XXcJPPVOdG2SuHbYYY7nCLN27YL8VfXdm+F0pKFBpct4O7X7766vNNRKD+Ia3rVOK7NvnJC0786B2HOSuyaecA/Nxw1wse+cJ53iMimFbYy35TYnDp5yqNnor5ww5QTDP98QBCTXbKVKb7k+SpSSPS/ZfGFbw2VYZ2h3opiCYZ6wTVf/yezjb5yg5oRkiWti8qH7HIQxtpK1JawzLDesZ8MOjrsTI8RUXk+X3u4CxDsR8+GrgxzxtofnaRkeC5fhsbCusH3xx8N8tbFMtv2hDeEyrDve1nB/uIzPH+6PXSZrQ3jOHw8I+lTXtcS1Qfs40f7msj5N9voPy0u0rKqOZPs9bH+4DOuqipvyaZhkOJPvPc+6Nn/tXpYk+dmMr0/lxd8ntC8+id3wO92LEHf/0mQDJzvGEovDWUVDu5O1IyxfgqTiuJ15ZSCu7bpNcC8Kh6ufc3TQ1uXOW0959Hn9ZZG7lz8R3NtecN9FoRebmOjzpvvdc5V8L4R1s4QABCAAAQhAAAIQgAAE6o9AnvMGidRf9cnVXLI6L7mMMbk0hHLGTLPu3cyaNIk5ELeqGS1nzgris1UUtD/ZsuKKrnJTs3b+NM2sy8ZmhYXlszfKX7tbYjnIbsWWk4gVPgiqhNqwdbQTlYqdbRoCGg5hVdny+Lv2OieeuQc9TcwQm2TP7Dkuvl338ufE5qloPVFbbVw1+tw9sM6Y6/p8I9fn+RXVlni/4ooplliXDVxfFKydRw/951xj9oITRw/vX3a8qvPCnMnmC/NXuNxi7esilpXi98kDp/26gfiYqJya2qI6xGpdJ1TLw2atVIWNa+WvbEeislz+j5w6/UcXfHHmTPchryDd8/e8GvVZBcVGd1d0TUQzJLK99Lquqg80I+bp7vP29I1mR+0X9OmGTlhOFJQ/rE+CTYXXfwJbHvpnXkp1VNnvoSFVLRPYEl7DS5e5+5r7LLZz11V4X6uKVVXV+eOV1KnjmvBg0RL3sqRTxaVVZcf308z6DHT3CSeg3XV5ENtOEx60dy8sEiV98/7oLl952oYefInyibuGYuuzlr91gs9/opPYBwEIQAACEIAABCAAAQiknUCKMkTa7at2hRpB1qd31adLVAu9zyrKnWxZFZ1f0f6m7uGrd6+Kjla+X3YnEgRrw1bNeqp4cnc5747BZwYPuuMnBOKarNrWeWjEJ9mSDO/482pzu7kTKftsUr0S5eXVu2vF54aB/ONn1qzqvLDEZPOF+au7lDdPj40rP7umtqgOiZj1lYqKiuz888+3gw8+uFITatpnlRbuDlZUflXn6XgqfZBMn6rMmlz/ydSRjn6Xt5a7lZRLqbAqd2IKGxK4KhO5VFSqdsjzr7IkAbFnFZ9VnS/uEuFIEIAABCAAAQhAAAIQgEBmE3A/3UkQKE9Aw1eVhpzr4mx1dg+BTgTcautg32uvuAfRRF5LweEG9//L78w67hUMIVPjFN+JVL8ECp2751/+8he77bbbEhry5ZdfuuHLHf2wP2Wo7T7jmkiInZ0QgAAEIAABCEAAAhCAAARymkCD9WDL6V6tYePl0TfXDQF87z0X284NB120yHmyDQ5i22mCglxKmlVQQcnlWTPkmGBYZC61P1PbeqwCK1aQ5OG23XbbWauSN+ukz9JxTWze08VaPLX6HpkVoCm3Ox11lKuwgW+0c0NB1Wd/2KKBN5TmQQACEIAABCAAAQhAAAIJCWRFDLaElrMTAhCAAAQgAAEIQAACEIAABCAAAQhAAAIZQIAhohnQCZgAAQhAAAIQgAAEIAABCEAAAhCAAAQgkL0EENiyt++wHAIQgAAEIAABCEAAAhCAAAQgAAEIQCADCCCwZUAnYAIEIAABCEAAAhCAAAQgAAEIQAACEIBA9hJAYMvevsNyCEAAAhCAAAQgAAEIQAACEIAABCAAgQwggMCWAZ2ACRCAAAQgAAEIQAACEIAABCAAAQhAAALZSwCBLXv7DsshAAEIQAACEIAABCAAAQhAAAIQgAAEMoAAAlsGdAImQAACEIAABCAAAQhAAAIQgAAEIAABCGQvAQS27O07LIcABCAAAQhAAAIQgAAEIAABCEAAAhDIAAIIbBnQCZgAAQhAAAIQgAAEIAABCEAAAhCAAAQgkL0E8rPXdCyHAAQaIoFIJNIQm0Wb6oBAXl5eHZRKkRCAAAQgAAEIQAACEIAABFIngMCWOjPOgAAEaplAvKgWv13L1VFcAyAgcS32OkFsawCdShMgAAEIQAACEIAABCCQxQQQ2LK48zAdAtlOIFYg0Xq4HS6zvX3YX3cEQkFNy1ixLdxfdzVTMgQgAAEIQAACEIAABCAAgbUJILCtzYQ9EIBAGgiEIloorCVapsEMqshCAqGolmip6wiRLQs7FZMhAAEIQAACEIAABCCQ5QQQ2LK8AzEfAtlMIBTVSkpKLPYv3K8lCQKxBGJFtUaNGlnsn/IhrsXSYh0CEIAABCAAAQhAAAIQSBcBBLZ0kaYeCEAgSiAUzrSUsLZmzRp/rLCwEIEkSomVqgjo+lm5cmX0+pHYJoFN+xHaqqLHcQhAAAIQgAAEIAABCECgNgk0qs3CKAsCEIBAsgQkgoQCW3FxsTVt2hRRJFl45PMEJKLputH1I6E2vKbAAwEIQAACEIAABCAAAQhAIN0EENjSTZz6IAABTyAUQ+S9Ji8keR+RIJAqAV03oRdbeE2lWgb5IQABCEAAAhCAAAQgAAEI1JQAT7Q1Jcj5EIBAtQlIEJHAJg8kEgSqS0DXj64jXU8kCEAAAhCAAAQgAAEIQAAC9UEAga0+qFMnBHKYQOhlFC41tG/16tU5TISm15SArp/YIaLhtVXTcjkfAhCAAAQgAAEIQAACEIBAsgQQ2JIlRT4IQKDWCYQeR+Gy1iugwJwgEF4/4TInGk0jIQABCEAAAhCAAAQgAIGMIoDAllHdgTEQyD0CEkUQRnKv32uzxVxDtUmTsiAAAQhAAAIQgAAEIACB6hBAYKsONc6BAAQgAAEIQAACEIAABCAAAQhAAAIQgEApAQQ2LgUIQAACEIAABCAAAQhAAAIQgAAEIAABCNSAAAJbDeBxKgQgAAEIQAACEIAABCAAAQhAAAIQgAAEENi4BiAAAQhAAAIQgAAEIAABCEAAAhCAAAQgUAMC+TU4l1MhAAEIQCAVApE1ZkWTzJa+b7ZklNmKb83W/BaU0LiNWbMtzVr3M2u1p1lhH7O8xqmUTl4IQAACEIAABCAAAQhAAAIQqCcCCGz1BJ5qIQCBHCIgYS1SZDbrcrPf3jArWeEar30lZRDWLDFb9rH7G2P2821mbQaYdb7GiWyFCG1llFiDAAQgAAEIQAACEIAABCCQkQQYIpqR3YJREIBAgyEQWW22/CuzyYPMFj3ndDUnpEVWuT8nsFkkpplu3Qtx7pjyKK/O0bkqgwQBCEAAAhCAAAQgAAEIQAACGUsAgS1juwbDIACBrCcgwWz512bTznTDQceVCmWxolpFLZTY5kQ1naNzVYYX5CrKX7a/pCTGK65sd4VrkUjE9FdZSrXMysriGAQgAAEIQAACEIAABCAAgYZIgCGiDbFXaRMEGjiB/97/qH0/eaoNOnh/22ePXTK3tX5Y6KVmxfOcjZWLWIkb4c7RubNcGT1fckNFWyTO5vb+8ONP9tpbI+2n6TOtRfPmdsA+e1i/3XeuMP/i35bY8BGj7Mtvxltx8Wrr2a2rnXr8UdayRfPoOR98/JmN+vAT++WBsZapAABAAElEQVTXhdau7bp24tGDrEe3Lv74v26521RGonTOacdb1407JzrEPghAAAIQgAAEIAABCEAAAg2SAB5sDbJbaRQEGi6B35ct8+KaWijxJ2OTPM4Uc61oojOxOuJa2DJ3rspQWRV4sRWtXGl3P/SE/f77MjvxmEHWudP69sJr//PiWVhK/PL5V9+0MV98bbvttIP133NXmzx1mv3nrgei2abNmGXPvjzcOnZob8cfdaitWbPGbr3nIVu0OJiUYePOnazLxhuW+1u2fLnpr3mzZtFyWIEABCAAAQhAAAIQgAAEIJALBPBgy4Vepo0QaEAEPv7sS9+afZwoNPL9j+zHqdOjXlUZ1UzNFqoJDSoQxVKyVWWorPXOcDON9l3r1P+N/MCKV6+28wafbG3XXcd22GZLu+yam+wdx2fbrTZfK/+vCxfZ1+Mm2l677WSDDtrPH1+5apW9/9EY75W2TpvWNuyF17wn3FmnHGd5eXnWu0c3+8e/brHRn35hhxywjxfdYgseN/F7mzhpsqlfOqzXLvYQ6xCAAAQgAAEIQAACEIAABBo8AQS2Bt/FNBACDYuAhi22atnC9t0rENhGfvBROYHt1TffsXnzf/FDGj/+bKwf/rhJl43s1OOONAlHSvLCeual123i9z9akyb5tvfuu9gXX31rB/Tf0/6w3dY+Jtmb77zvBSd5ZOk8iUo6pvTy8Lf9sMkVK4rsx5+m2x677GhHDjzQH4v+W/p+6WyhNfFeC0tzZWjmUZWZQGCb7rzNJGpJXFNq1KiRbbf1Fvbe6E9N8dO0HZtauGGgQ047wTbqvEF097LlmtnU6YGl8djm/jzfdnfebRLXlFS2vNkkpIlFbJI499jTL9q667SxgXHHYvOxDgEIQAACEIAABCAAAQhAoKESQGBrqD1LuyDQAAlMnznbe1jtv/ceLlZYC+u+SRcv+Py+bHk0dthCJ559O2GS977aZ49draioyN51Q0mffO5lO/eMk2xVcbEbCvmg/bZkqRPWdrZVq4rtrXc/8EKUhlgqafikvLk237SXbdanp43+5At7/JmXvFAl7zDV8c3473wdW2zW2zbouN7atJeMcvs0U6jZkqUl9s2EInNanrVv28g6bdDECpo2ssaNA/HKZ3L/1pREbOXKEpszp9gWLCpxXmlmW/UttNatJJC5slRmhyFh9uhSMdI6rd8huq2V9i5mmtKSpb9HhUW/w/0rLCjw7dL2dz9M8eKiBMatNt/Ui2QSFSXMtW8XlBGepzhs02fMDjejS4maKxznc8840bWpcXQ/KxCAAAQgAAEIQAACEIAABHKFAAJbrvQ07YRAAyAg7zWl3Xba3i/32vUPNsV5kH3khi3u74L6x6a/nHemrdeurd/1k/PwmjFrrl+f8N0P3oPt2CMOsV3/EJTTpnUrP0GAMhQVrfTimoL0n33q8f6cPXbe0Q+5fOWNEX74pd/p/g095zRbP5G4pgwrvnXuYMGMnot/W2NvjVxmq9dEXCwzc8Kc2REHt7A+vZo7D7pAZCsujrjYcsvt+deWuThm5oQqs3wnwHXp3CQQ2FSWykyQJKL1cJMUxKZmzQr95lInGoaee7HHw/WXXn/L5sz72W922WhDL6wtWhxMXtC8eflYai1cbDUJabFptRua+vGYsU7g68jEBrFgWIcABCAAAQhAAAIQgAAEcooAAltOdTeNhUD2EpCQ87nzstJwx1Fu6KOSAu8ryUNtv713jw5nVJ5QXNNxDW8MRSSJbUpb9u3jl/q39RabRQW2n39Z4Pf/PH+BXX/bPdE8y90QSsU5k2eXkuqoUFxThjXBZABa3diJZFdc1NaWL4/YsBcX2YTvS+zhYb/bjtsW2x8PD4Z1vvDab/bZlyu911rf3o3s2MPXtebN85wAF3qEuWGiMWWq3DBpmGux88yLTZoZVKlFnEgWm0frl144xIuKr/1vpMkTTfl7uXhrSmIem+T9V9C0aewuG6tZSF2+/frtVm4/GxCAAAQgAAEIQAACEIAABHKJAAJbLvU2bYVAFhP46tsJXtySt9k4NwQ0TM0KC/3MlZMmT7VNe3X3uyU4xaZGpXHEtG91VHhybmQJkuKJKSneWKxXWJ+eQdmhwBYvNCUoqtwuCWWtW0dsyrSIa4fTytxffqiduZz5zmTt0zHlad26cVQwLFdQgo3WLVuaPNViU7ituGiJkrzeFMtOMdYKCwts4ID+9v7HY/zkBztsu5U/JSwjPF/ntIsbNvruB59YQUHThJMphOexhAAEIAABCEAAAhCAAAQg0NAJlH8KbeitpX0QgEDWEnjPxUST19gVF51vTWO8qOb9/Itdc/Md9q6b7CAU2CprZOcN1/eHJ0+ZZr17Bp5a304sE+w6rtfeH2/iFK/DBuwbLerNd95zwyNXOiEsydtmYydsrdFQS+d5VpokZrkmOFHLrKkbGnpg/2DSBR3W+pixC5xXXqQ0T/n4bO4sN260LH9YppYbde7khTF5kslupXGuTaGA5nfE/Pv0i6/siWdfthOPGRSduGHxb8Gw0AIXn61pkyY+vpwmNDhgnz39mfJeUwy87WJmJZXYOHvuPOeJt5Xvm5gqWIUABCAAAQhAAAIQgAAEIJBTBBQ5mwQBCEAgowlo1k+JO5pQIFZck9EaprlBxw4+WL8mLqgqSQyS19udDz5uij/23CtvmIZHhkkechLqJk+d5o+p3jdGvGfD3x7lZxwN81W5bLal08TWvsUedmAzH1Ntmy3cRAcFZSJaQdM80z5NaKA8ayWVpTITpANc/DmJXQ8/+Zxp9s+3Rn7gYs7NscMO2i+a+95HnjLNjKrUt08vL4i9+Npb9rWbrEFC2gOPPeOPaUZUJcW0U9s1AYTKVNmq48D+e/nj+jf/l1/9eucNy2YjjR5kBQIQgAAEIAABCEAAAhCAQA4RSNIVI4eI0FQIQCDjCHz82Zfept122iGhbXvttpMNe+FV+8gF2y+TrBJm9R5ofxt6jj381PM+lpuGev5h+63tE1dHE+e5pXTqcUfZY8+86Cc70Gyi8grT7KEH7dsvcaGJ9rZ2eZeNcUeCOHFhlh22a+mGU7b02pu82UrDyHmvtSMPXcfc3AvWOOGd2Y0nVZkJkiYYOHrQQfbsS8P9DKqyd083AcQftts6mlsi2vIVRU4g29N7tl1w1il236NPO2HtaZ9H55x87BHWq/smfntPJ7TNmj3Xi48SIOUNJ4+3Duu1i5Y5d/4vfn2jTghsUSisQAACEIAABCAAAQhAAAI5SSAv4lJOtpxGQwAC9UJAtxz9yRtKQfSL3KyUv/32m3Xp0iUt9syZN98mTvrBdt5xWz8MUpV+98MUu/OBx+yMk/5oW2++adQODbn8zQ2dbNd23aTjoUVPXjHB7MfD1xomquOu6bZq1Rr7afpKmx84gVkHp1tt0sUNz2za2Itt0XL8Sl4wPLTHi86LrW/5QzFbYvrrosXWzk3qoOG0VSX1g4aG6jxNBKEhrPFplYtJp1hsYpCpafr06damTRsXS67QC6hqu9qSqD2Z2gbsggAEIAABCEAAAhCAAASym0BCP4nsbhLWQwACEKiYgESll98YYd9NnmID3HDHJU48et4NE5UHV69uXcudqH3t27Utty/pjUI3S2mbAWaLnnNh2Mpm4ywpidjHny2zEe8ttwULy7/faN/2d9t3r+a2y44tnEAWI3blOe81laUyK0kSlmJnT60kqz8kAaqiSRDCczUkt13b8jOHhsdYQgACEIAABCAAAQhAAAIQgEBAAA82rgQIQCCtBOrbg02NHfnBx/be6E9Nsd2U5J01+ORjbcMNOvrtWvtX4mb2nDzIbMU4V2Qgpk2dtsIeHrbUmhU0ss6d8qztOkFtCxebzZoTsRUrS+zUY1tZt65hHDYntDXbwqznS2aNWtSaaQ2pIDzYGlJv0hYIQAACEIAABCAAAQhkJwEEtuzsN6yGQNYSyASBLYSnoY8FTZusNXFCeLzGy4iLv7b8K7NpZ5oVz3PFRWzCpCIrdJMbrN8h35o6kS0clekc62yVE9fmzXfDZldG3EQEhS6/E9eauFlPu95v1nwbt+k82UhrEUBgWwsJOyAAAQhAAAIQgAAEIACBNBNgiGiagVMdBCCQOQQUuL9OkwSx5m6iAQlksy41K5roZigt9KJaKKzF1t8kv7HzXGvsYtS5vXnu9ly4mVnn64IyENdiUbEOAQhAAAIQgAAEIAABCEAgowhUHQU7o8zFGAhAAAJZRkBCmbzPNMRz3aOsUZM2bgZRF9PMC2Yxcdbkreb26ZjyKK8/x3uu8S4ky3odcyEAAQhAAAIQgAAEIACBHCPAU1uOdTjNhQAE6oGAxLQ85y230U1m651htvR9syWjXGy2b90so0EcOGvc2sVa29KsdT+zVnsGExrgtVYPnUWVEIAABCAAAQhAAAIQgAAEUieAwJY6M86AAAQgsBYBDev8vThiC5aX2IIVa2yJi6NW7GYMVWriZgRt7eKutW/W2No338xartfX8joMWasMdkAAAhCAAAQgAAEIQAACEIBAdhJAYMvOfsNqCEAgQwhIQltTYjbp12L7eVmJrXFKm8S2QFoLjJTQtnBFxBYVldjUxXnWsUUj69OuiTV2g/RjB4lmSJMwAwIQgAAEIAABCEAAAhCAAARSJIDAliIwskMAAhAICUhI+815qk1csMqWOu81bVeUdEjHS9y/2b/Lw63ENmvf1No4z7ZEEx5UVA77IQABCEAAAhCAAAQgAAEIQCDzCCCwZV6fYBEEIJAFBCSYSVz7+udVVrR6ja1e+bst/3WaFS9fbM3W3dCat+9erhXLF0yxFYtmW5Pm61jzdl1tSUlLf+7WHZ3IVuhEtnK5E2+UlJRYo0bJz00TcWJeXhXqXTJlrioutib5+VWWldhq9kIAAhCAAAQgAAEIQAACEGj4BBDYGn4f00IINAgCY8Z+bY8/42birCDd9M9LrbCwoIKjtb9bw0LlubbCiU9Fv821ccP+ZL9N/9oJbcus+76DrddB15WrdNaYB23KiPssv6CFtemytW1x7B32/+ydB5hU5dn+79nZ3ju7sPTeO9JBUIqKiCIWbLHERGPy9/viZxKTfKZ/MfYklqgoCQQCgiIiRXoVBKT3trDUBbb3nZ3/87yzZ5jZnYUFQWbhfq5r5pzztvOe35lrr4ubpzhjUmUNoFf9EASeRzdbvnodlqxYg8wzZ5EQH4cHx41Bi2aNvdb3vDiblY0Zs+dh647dEoYagK6d2uOBcXe4xTmHw2H6N2zahoLCQjRplIYnHroXMdFRnsuY82079+CdDyfj588+hQap9ar1s4EESIAESIAESIAESIAESIAESACgwMZfAQmQQJ0goN5Yap07tEVSQny1PQcFfXd/znQrmnMtr9SBrAOrsXHCeITGNUT7e15CfPMBCIlOqba/lrf8Ho36/whn96/AgUVvYNVf+qLbo5MR0KK/Wat9YpDPUNFDhzMw7dM56NC2FYYNGYA5C5bg9Xcm4He/+C/ExcZUu482TJw6EwfTj2Ds6JHIPH3WiHN2ux3j7x5txi9cugoq2t08uL8R1WZ+Ph8v/+09s6bngvsPHcY/Jk7xbOI5CZAACZAACZAACZAACZAACZCADwLf3b9IfdycTSRAAiRwsQQG9+uNls2bXOy0yzpeq4VqQYOy4nxsmfIwIpKaoMeTnyEwNApBthIEOc/A7iyUsE+Hua8TdjkLR1B0DEK63IWkNsOw/t3bzdz+z23ByYBoNI5xIiq4eqDolBmzEREejicfud+EaLZu0Qy//tNrWPnVeowaMbTac+3ZdxD7D6Zj/NjR6NOrm+nPyc2DegDeLYKb5oCb8+USdOvcAaNvudn0q+ffpGmfYu+BQ2jZrIlp+3z+YsxbtMyc84sESIAESIAESIAESIAESIAESOD8BCiwnZ8Pe0mABOoYgeMnT2Hy9Fk4nHHMhEe2btnMeG5FRUaaJzl56jSmzpwN9QwrKy9HVGQEhgzsa7y5dMCncxaYUMyiomLsE6FqYN9eGHv7SC8Kpwtd1UIDgkLR9eGpiIhLQ0LwSSTnvYuYiq8QZs9BsK0QASg38yrEWbjUGY4iRwxyAnrjVMQd6Pn4VBRkZcAua2jlUV0zKtjudR+90OcZ0LunO/9ZfFws6iUnmvBPXwLboSMZZo1uXTq417qhexds3LxNvNoyEBYWCs271qtbZ3e/hpCqwKbhoJbAtmDJCrRr09J4zk37ZI57LE9IgARIgARIgARIgARIgARIgASqE6DAVp0JW0iABPyYQMax47DbvROW1U+pZ/Kv5ebl40+vvY2w0FDcevONyM3Px4o1X+PPb7yL3/78WSMs/d/rbyM4OBh33DoMWllg6YqvMOuLL9GqeVM0btgAZ7NzsHnbTuM11rFda6TWS6pG43SRw1QEDbTb0KhBHBqWvIeA9Bn44J+HpVSoA9+7NwJpaWHueSqbncwowodTC+SeS/DYQ5PRMPUuHGlwP/KkU0NOdc2msd4Cm+ZHUzEsMSHOvZaeaB629MNHvdqsCw0J1YIEIfKMliWIKKeWnZtrcq7peUK8q03PdazOyc7J1Utjv37uGblvvBHmrDYeSYAESIAESIAESIAESIAESIAEfBOgwOabC1tJgAT8lIAm769qP/jeeONpNX/xciNIPfnIfWjWpJEZ1rRxQ3z074+xaesONKifgratW2Bw/95GUNMBKs698c6HOHEq0whs1trP/vBRpPgQ17Q/V6qH2sQ7rV75arQvehY2RxYWb8uTfGelcErxg3WbykVgs1ZyHbXtVKYDtgAHtm87jiEJE5Hi+Azbw17DycB+smaQ9wS5ysp2CV7h4efEOh0UERaGouLiauO1QQscqIDoaeq1pqahogGVVUXDZQ1P0zDRXOm3TMU1GgmQAAmQAAmQAAmQAAmQAAmQQO0IUGCrHSeOIgES8BMC4+641VS99NyOhkyqHTl63BynzfrCHPWrTKp8qh09ftLkHdNqml9v3IK16zeZ8MuMYydMf3m5K1+aXgRI5c2axDXtLxOvstTyVWhb9BwCnWdNprUKVw0G6KHCutDBlSZTTJ9mWdNzdVvTuboGwv6C44EDraHuY0iISygrl1BWTyuVZ/L0UPPsCw4OMiKjZ5vFIDIiHOVSQVSt6pr6/NpPIwESIAESIAESIAESIAESIAESuHgCFNgunhlnkAAJXEUCqfWS0Sitvs8dqPCk1qZlc6/+ju1gPNqyJPzzty+9aXKvaTiohoX26NIRWkXTJX+5ptUkXlmLRlZkoF3RT6WYwWnTFCCqWZsWdqnWaZNcaU60b+0d6qmD2rcOgNQZEF3NJvuzi4jnWi3YmWnWyouYKQ0tXY2V3zHRUeYsL19CSz1MQ2ETqoSNWt3xsbHi3bbbujTH/IJCc0xKSEBxSYm7TUNN1bRCq3rEJSUmmGt+kQAJkAAJkAAJkAAJkAAJkAAJXBwBCmwXx4ujSYAE/JhAanISMsSLrbtUyGzYINXs9MTJTGjoqIpmX63/xohrGv7ZvGlj079+01ZzdBi3sto9XGPHbPE+y5HBLrc1jbpsmBaKX/00xOR1Cw1RPzVva9UiHL/8qYRlypSwMBXirH71ZMuBrgn8l9VojsFBQSYX3NYduzFi6CDTpiJi+pGj5hm9BldeNExzPbdWErWecetOl+CWnJQAR6UHm66pIqPa/oOSO07M8gQ0F/wiARIgARIgARIgARIgARIgARKoNQHvTOG1nsaBJEACJOB/BIYO6mc2NWHSNJOcf/e+A3j7w8km/1pqShJSRIBT2yLikuYq2yTFDCZL9Uy10pJSc6zNV1rgNhHINM7znAWIG1tERIAIYgFShMFmvMLKy50Siuk059qmfTpGx3qarqVr+rLhQwcaQU1FQq0o+uHk6SYEdORNg83wk5mn8daESdiyfZe57tm1EzS09KMpM0wlVX3GL5esRHfx1FOPOK1C2rZVc2iVUJ2jYt3EqTMQGxNtvPl87YFtJEACJEACJEACJEACJEACJEAC5ydAD7bz82EvCZCAnxCwWS5f3tqU1+7SpIjBYw+Mw6Tpn2KCCFFqWsTgiYfvM55gXSRWtEPbVhLKuQaLlq0yuda02ui8RcuwZ/9B3DS4v9d6NV3YonoA+Qt9dmtF0JISBw4dKsL2Pa7QzPatwtGkSZgIX3YPzzXP6QEwa3o2VZ4P6tvLeOXNnrcI+omKjMCD94yBeqOpFUj4545de9Fcijp0at9GxD07nn78Ibz9wSS89Oa7ZkzrFs1w352jzLl+PTBuDN6S/n9MnGLaNOxW19S5Vc3ibuGv2s9rEiABEiABEiABEiABEiABEiABCWaS3DuuGCfSIAESIIHvgID+ydFPhYRkaqL9Ysn9lZMjIZKNG1/Wu2fn5EJDLKtW4NSb6H21X3OQWQLSRd28aDuw707AoVU+vf+EOhxOfLHgDBatcEhBAdeqgaJbDR1gxy3DEox3m/e9RDG0RwMtJAdbWHvvLo+r0tJSaC42K2+aR1eNp+qlFyGFC2rKKae52fQ9REdF1rhGXehIT09HTEwMQkNDERgYaIRTfa+X9G7rwgNzjyRAAiRAAiRAAiRAAiRAAn5HgCGifvdKuCESIIHLQUBDHn2Ja7q2ijCJCfGXLsCEtgFiRsp/UVT3+MrIKMDKdQ7xNAPSUlwfPdc27atmuoaupWuex4Ilh9zFiGu6lIaD1iSuab9WDa3r4po+B40ESIAESIAESIAESIAESIAErjYBhohe7TfA+5MACdQ9AiqKpf0BUE+2Is2dds6LLSKsAj95PBxJyaKqWeGs0p15Kl886rzztrkqIrR1reVDrKt7YLhjEiABEiABEiABEiABEiABErg+CVBguz7fO5+aBEjg2xKwhYow9ifg0GNA2SlZzSWyJSRFmZWrhiem1q8ahinqW1Cyaw1di0YCJEACJEACJEACJEACJEACJFBnCTBEtM6+Om6cBEjgqhJQj7PwLkCTDyR3WgdxRtP/r3Dl/aoqruk+z+UEE2FNx+ocnatr0Hvtqr5K3pwESIAESIAESIAESIAESIAEvi0BCmzfliDnkwAJXL8EVCgL7wq0/BSIG+sqVmALrhTMrPhQxaOimghy2qcFDXSsztG5Rpi7fhHyyUmABEiABEiABEiABEiABEjgWiDAENFr4S3yGUiABK4eASOcRQANXwGSngDylgG5iyU322apMprn2pddwkPDOgPRQ4CoQa6CBvRau3rvjHcmARIgARIgARIgARIgARIggctMgALbZQbK5UiABK5hAo4coHgXkLMAyF8p53uAiiLXA9vjXB5p0TdKXrXfASEtxDst6BqGwUcjARIgARIgARIgARIgARIgARKwCFBgs0jwSAIkQAI+CUjxAqdU/8xbDpx8TUS1nSKqFbraPKqHwpEtY5aK8CbjAhOBiBuA+r8EghvIqozG94mWjSRAAiRAAiRAAiRAAiRAAiRwjRCgwHaNvEg+BgmQwJUgIOKaIx84/b6Ia39zCWueoprXLbWKaKUYV3YCyJ4tYaLbgAbizRbV35V/zWs8L0iABEiABEiABEiABEiABEiABK4VAnSruFbeJJ+DBEjgMhOoFNcyngdOvCriWoGsryJabU283kr2A+lPS0jpfJkk1zQSIAESIAESIAESIAESIAESIIFrkgAFtmvytfKhSIAEvjUBDQtVzzX1RHOW1X45ZxURTkNHj/xUQkfXyRrnF9mcVefW4q4VFedf80L9eovajKnFVjiEBEiABEiABEiABEiABEiABK5bAgwRvW5fPR+cBOo2gb+//0/sO5CO1/74qyvzICbnmoSFVopr5Q4nNn6Th+VflaFXt1AM6BMBm8371g4Z8/aEswgLBW4eHI60BqEI0P/GcOQCRyUfW7N/AUGp3pPk6mxWNmbMnoetO3bDLhO6dmqPB8bdIXNr/j+Q5avXYcmKNcg8cxYJ8XF4cNwYtGjW2L32nn0HMXv+IhxMP4KI8HCMGDoQNw7o4+53OBzmnhs2bUNBYSGaNErDEw/di5joKPcYnpAACZAACZAACZAACZAACZAACdSOQM3/eqvdfI4iARIggatCoEK8vRwX8N665I1ptVAtaCDFDNSp7NDhErzzwRnMnFMsYhUQH2v3ubTqbQ0bBOJsdgX++n4eZs3NQV6+Q1plkeLdruqjPmZOnDrTiGtjR49E/z49sW7jZkyZIZ5zNdihwxmY9ukc1EtOxPi7R0PFstffmYCsbNm3WHFJiQh9k5CfX4AH7xmDtPopRkzbuFlywlXawqWroCJd317dMPb2kTiccQwv/+09q5tHEiABEiABEiABEiABEiABEiCBiyBAD7aLgMWhJEAC1wmB4l2mWqiGbJ45U4p/TMxBoPy1fOKhKDRMC0FwkO//mwiw23D7yGgUFzuxYVM+Zs0rxpGj2fj+Q3EIDZUw08x3gcSHvSCqp9n+g+kYP3Y0+ojYpZaTm4e1GzbhbhHcgoODvcbrhYpv6pX25CP3ixedDa1bNMOv//QaVn61HqNGDMW8RctRVl6OZ77/MOLjYtGzaye88PuXsXDZKnTr3MEIcHO+XGLOR99ys1k/NDQEk6Z9ir0HDqFlsyamjV8kQAIkQAIkQAIkQAIkQAIkQAK1I+D7X4m1m8tRJEACJOA3BNR7650PJ+PZX/wOP/qf/8UfX30LByQ8Uk29s375h1ewftNWr/1Onj4Lr709wavNXOQsMN5r+QUOTJyai+ioAPzkyTg0bxpWo7hmLaKCV1hYAPr1jsLYUREisDmwel2+8YRDqWs/1lg9HjqSYS67dengbr6hexeTF+1guqvP3VF5cvzkKfTo0tGIa9qkIpp6s2mIqVq6eLglJyWYdr3WUNPuMl45aL61U5lnzLFXt87abUzDUtW27dxjjvwiARIgARIgARIgARIgARIgARKoPQEKbLVnxZEkQAJ+SkBDJF996wPs2L0PPcRb67bhQ0y45Kt/fx/HTpySsM1UFBUXY/Hy1e4nKCwqwpqvN6JxWn13m/skf6VEdVbg8JEyZJxw4LZhYYiNkdDPs2VYsDQfq9bmo6zM6RLNKidptOrxEyWYtzhfQjzzRcCCiFrh6NcrCPOXFLvGOjVc1NsyT59FkLjHhXh4qiWIYKaWnSu526qY5ktTkSwxIc6rR/OwqeebmuZlS5RrT7Ouc/PycVr61RLiXffRc72/7iM7p/o9tZ9GAiRAAiRAAiRAAiRAAiRAAiRQMwEJeqKRAAmQQN0moLnF1INNwx1vHtzfPEzXju3xu5f/itnzFppQyn69umOxFAXQcXGxMfh64xYzbkDfXtUfvli9uJxISrTj1pvD0bZ1OLZsL8SMzwuRl+eq2rlkRaEUFohGo4ZS0UDGfrUuD7MXFKOwSPKtSTK2FWuKpT8Wg/tHITKiSJqk3Xy8b6cFDqqGgYZplQQxSzDznJGV7RLAwsPDPJsRERZmRERtVBGtRZUwT2vNPMnLpvdUC5c5nqZhormVIp1nO89JgARIgARIgARIgARIgARIgATOT4AebOfnw14SIIE6QCDj2Amzyy4d27l3qyGTUZERsPoGVgppa77+xoxZtnot6qfUQ1JCvHuO+6SiyJwmJwVhyKAYHC9piH+c+bWEftrRs7MN3TraUFgMLFzuGnf8RBk+lgIIibJUry42dG1vw7GCJMw4eT8io8MwdHA0bAFaAkFFNm8LDg4yHmmerWVlkq9NLFIrKlSxkBBXTrZyybHmaaUyx/KCCwoKFA871xrWmLIy1/gIEeaCa1ijvNzh857WGjySAAmQAAmQAAmQAAmQAAmQAAn4JkAPNt9c2EoCJOBHBDQEdM/+QyavmBU+6axwwi65xdTKpV8tOCjIHK0vu92OQPmoJYqQ1iA1RfKhbUB3SfSvecjuu2uUNdTn0Sl+ZzuKb8Cbmb/HiYb18VDP3egT+bkZq/c/k+W6b1xcIJ5/Jk72F2jyoum8v2f+BV/k3IqsE0Pxw+RfIc6eKfOqC2zxsbHieebKnWZtIr+g0JwmJSRYTe5jTHSUOVdPNE9Tr7WEyrDR6MhIqV7q3W9dq/deXEyMmar30dBSNS3ooGG0SYnV72kG8IsESIAESIAESIAESIAESIAESKBGAvRgqxENO0iABPyFgENyjv39/X9i8bJzOdRMqGecSyhSbzW1rTuk+melab/mE2vSKM1qknDNG0zbZxI2qon/NV+bT7PHiRQWgMOl7fDS8VdwtLQhHE47vs7vLRU4IRU6nRB9TYoIiHgnwlRoiE2EqUCTd61UcrPlFUdgae7NMsaO1fmD8GHmr1DuVPGv+p/chmmpZgtaSdSyrTtdgpsWKqhqKiJqBVGroIH2q/da+pGjSE1OMsMbSl65Q1LoQCuJWqZs1KNPizA0SK1nmj3X2H/wsGmzWFrzeCQBEiABEiABEiABEiABEiABErgwAXqwXZgRR5AACVxlAioqNW/a2HifqZdWjghnmshfq22q9eraGZ/Mno9ZXyyEw1FhqmfOnrfI1Vc5Ri+0kuaUGbOxedtOdBMvNiuk0gz0/ArvitzsbXjr1Is4U54oYpuGdwJfHWqAghlFiA4tlfxlFTLfNUk0K62JIGGjNhQUBeBEWBsU9w4x88TPDovzhiEu8DmMT3wdlVNcE+W7p4h802d9gY+mzMATD92LsyIMfrlkpdmr5a02Z8FiHD+ZiccfvMfMGz50IGbOnof5i5ejU/s2+GzuQhNmOvKmwaZ/hPRrXroPJ0/HqBFDsWXbLlNB9MF7xph+rTratlVzLFiyAo1EjNP7TJw6Qwo5RJvqpGYQv0iABEiABEiABEiABEiABEiABGpNwP6iWK1HcyAJkAAJXCYCGpKo1TA1l1hJSQliJVTyfKYVL9d/swXbd+3BwfQjaNywAR59YJwJAQ2U6pftWreUQgS7sEGEpQ2btsraTikycIcRoKx1NWRUQ0OPnTiJcWNuhRVuavVbR0d5Ht47OBRf5fc1nmxWe0h4DIa1aQTYE5FTGI2zBdHIKYpHblkK8krrISi2E1JbjkBJqwdwEI1lmkuYU4EuvbQlOobvRHLandZy5qiedC2bN8VXkhtu2aq1RhhrJdcP33sX9LnUVEDbs/8gLAFNRTEtVLBEijasWPM1ioqKcc+Y29C6RTMzPkpCRCPFW23xcle/8hrQpyeG3TjA9OuXjt21Zz+WrvzKCJcaNvro+LtNAQj3oDpykpOTg9DQUMNLeaqXnvWpI4/AbZIACZAACZAACZAACZAACdRxAjb5R271pEB1/KG4fRIgAf8loH9yPMW1Ysn7pQJJ48YqSJ3fVJA7I8JSVESECCohPgdrXjHN2WZ5f1Ud9M6Hk3Hk6HH84Zc/rdrlvj5wNhe/WlGKrFLve4Tanfhp6jr0vuFGEXBEyHHPOJddrbi4EK+vOIqVOSluzzdrWM/4o/jN0DbWZbWjimYRUtigRs+6KjNKS0tNrjUrj1qVbiNgKi8VElV48mXKS7lGR0X66q4Tbenp6YgRgdCXyFYnHoCbJAESIAESIAESIAESIAESqPMEGCJa518hH4AErh8CKhL5rPrpgcBX5U3t1nxjWlF02849uPO24R4zqp+eLg5HXrmnfOYaUyKVOP/1yhNYlhSNPsO/h8ZteiMswuV5l3UqHTvWz8WahZOQ0eW3QMvx4sDmvcbmnAbVb+bRoqGbF2PBwcFSpKBq0Om5Fb4Nr3Or8IwESIAESIAESIAESIAESIAESOBCBCiwXYgQ+0mABK4JApqvTBP/ay63wf17n/eZQgJtCAywoVwrGXiYrbwQTkcJjh7YhhnvPmfCEK0wUC124JREbBXi1xZQkiWzvOfqMjFSDIFGAiRAAiRAAiRAAiRAAiRAAiRw7RGgwHbtvVM+EQmQgA8CP37yEZOrrKbQUc8pDaMC0Cg6APuyHKZaqPYFwIHW9q+RmFiAU8crUFYmYpqHhqbOavqJCAtA2/C5WG5/BAUVkSaHm8pqgRKhObKZVhKlkQAJkAAJkAAJkAAJkAAJkAAJXGsEKLBda2+Uz0MCJOCTgFYi1U9tLD7Mhv/XIxSTt5fg6xPlsDtLMCBqIcbFvYn4JiHYuSdSiikUY//BcuQXuoQ1KW6Kjm2D0bVTOBo22I++pc9j8tlnsKe4LSKDA3Br8yDc1rzmcM7a7ItjSIAESIAESIAESIAESIAESIAE/JMAixz453vhrkjgmiXwbYocfJdQ1DlNS8BI4CdsBethO/oCULwLNmepq93De83al5VyTY/OACkaED0cFWn/B5s9ygwxXm7WYB4vGwEWObhsKLkQCZAACZAACZAACZAACZDAJRKgB9slguM0EiCBa5uAhnW6BDM5i+wONJsI5CwAMt+FrfSICG0OGWFkuEoQMk4qi8ImXnJRA2FLfEziRbvDHhBxbYPi05EACZAACZAACZAACZAACZAACYACG38EJEACJHBBAiKcBaUCiQ+7PjWNd1SgfM0WFD37Lhw7/oigjhGIeGwv0DrTNWN3Ego+aImyrQWwt2uGsN88icA+nSAqXE0rsp0ESIAESIAESIAESIAESIAESKAOEGCIaB14SdwiCVxLBOpKiGhtmTslCZtDRLWK7HwE9mqPvAGPo+L4aaCiQisjICChAjEfuQS2nEeSUHFGGqULAQEISE1E1Ir3Ub5uOwJiI2EXsc0WGV7bW3NcJQGGiPKnQAIkQAIkQAIkQAIkQAIkcLUJ0IPtar8B3p8ESKDOEnDsOIDCJ/4Ax+Y9RlCzhYWgIivPlbxNn0qENGcOULbbbp5Rz1FembytQiqUZpxCXrfxcBaVGMHN3rkVwt97wXi3mQn8IgESIAESIAESIAESIAESIAESqBMEGJdUJ14TN0kCJOCPBEo/mIXy9TvgLCgyIlnF2dxz4pq1YRXZCuRTKDna1HPN06SKgs5RgU3X0LV0zQtZhXrHXYRdaLx6FV7IdA2HQ/PO0UiABEiABEiABEiABEiABEiABKoSoAdbVSK8JgES8EsCs+cvwvxFy/H7F/4bsTHRfrFHxze7Acm7dl6T/8awmahPEbEu9F8aspZZ08eCKm7NmD0PGzZtQ0FhIZo0SsMTD92LmGhXhVIfU7Bn30Eot4PpRxARHo4RQwfixgF93EPPZmWbNbfu2C1p4ALQtVN7PDDuDole9d6oimv/9/o7iIqKwDNPSB46GgmQAAmQAAmQAAmQAAmQAAmQgBcB739FeXXxggRIgAT8h4Cz4sJeVt/1bgO6tBLRTDzTzmdSVNTe0Gk+kPPzmqxl1vQxaOHSVVi+eh369uqGsbePxOGMY3j5b+/5GOlqKi4pwdsTJiE/vwAP3jMGafVTjJi2cfM295yJU2dCxbWxo0eif5+eWLdxM6bMmO3u15Py8nJMmDwdx06c9GrnBQmQAAmQAAmQAAmQAAmQAAmQwDkCFNjOseAZCZAACVwUgdDv9UNAojgC16SxyV9Ye6IDAY1KzEfPa/RikzV0LV2zqqlYNufLJejWuQNG33IzBvfvjfvH3o6s7BzsPXCo6nBzPU+8/cpEHHvm+w/jhu5d8PTjDyIqMgILl60y/erdtv9gOu67cxQG9umFu0aNMOuv3bAJpaWlZszJzNP4zUtvYtPWHT7vwUYSIAESIAESIAESIAESIAESIAEXAQps/CWQAAlcMwRUcHrnw8l49he/w4/+53/xx1ffwgEJj1RbsGQFfvmHV1BYWOR+XhWS/vf/Xse8RctM2/GTp4xX2I9/9huzhq6Vl5/vHl/1JCBpAyJfPoXA5hIm6qpj4DXEFuJExG+l6EGQeN/JR8+1rZrJXF1D19I1q9qpzDNSlLQCvbp1dndpOKfatp1SYMGHpR/OQHJSAuLjYk2vhn1279LReL7pWoeOZJj2bl06uGerEKd9B9Ndfd9s2Y7CoiI8cv9YvwnLdW+WJyRAAiRAAiRAAiRAAiRAAiTgRwQosPnRy+BWSIAELp2A5ih79a0PsGP3PvTo2gm3DR9iPLxe/fv7Et54Cq1bNEN2Ti6+/maL+yYbJFzyzNkstJK+3Lx8/Om1t5F5+ixuvflG9L2hu1nrz2+8a0Qn9yTPk/zFsLcpQORLZxE6qhgBCRWwaRio/GXVY8go8VxrVeieoefa5jlG54TeVmzW0LUga1a102fOmqaEeJdYphchwcEICgw0z1R1vF5nypzE+DivLutan1WfU+frOpYlVIpx2blSrEGsd4+ueOnFn6GHCHM0EiABEiABEiABEiABEiABEiCBmgmwyEHNbNhDAiRQhwhobjH1YNMQypsH9zc779qxPX738l8xe95CPPnI/YiLjcHKr9ZjUL8bTL+ea5GAZo0bYvqsL4yQ9uQj96FZk0amv6m0f/Tvj02IpIZnVrOizSKmiajWtBhhvyhBaI4d5XNjUTw/CPZ6Ipw9nQUEe3isybm2OU8nwnEyAKHDyxA4Mhu2GAkdVS83jTXVNauYFiNQCw8L8+oJDQ1Bbq54yPkwFdFaNGvi1RMWFmqu8yQvm64Z7CGuaYfVn1O5pr8Uk/B6CF6QAAmQAAmQAAmQAAmQAAmQgB8SoMDmhy+FWyIBErh4AhnHTphJXTq2c0+ul5xo8o5ZfYNFWPtkzgKoR5jdbkf6kaMYNWKoGX/k6HFznCZCm2VlZWXm9OjxkyY/mdXuPjo8xC0RyGyJ5Qh68LR83COqndiiHQh/paaCASKyea5ZOTs4xOVlpgUHPK283IHICFOi1LPZnAcFBcLav9VZVuaaHxEeJuJaUDXPPGt8TWta6/BIAiRAAiRAAiRAAiRAAiRAAiTgTYACmzcPXpEACdRRAuUSIqoWHORdqlOFtED5qPXu2dUIbCvXbjDhkdrWt1d3PaC0Ukxr07K5uba+VK+zPNqstu/6GBcTY26ZX1CIhMqwT6fTiaLiYiQlJvjcTnRkpOSPk5BTD7Ou1ZMvPjZW5u/26JXoVFlfLSnB95peg3lBAiRAAiRAAiRAAiRAAiRAAiTgJsAcbG4UPCEBEqjLBNRbTW3rjl3ux9CQUc271qRRmmmLCA9HuzYt8fXGzVjz9UaTl00ra6qlJieZY3cJBb1Dwkz101uS/ufIfM88ZWaQ9WWPkrNzJURF80J5uVMKKZShuLhCRLsKOMorUOFwffRc27RPx+hYnXPOZC2z5rkWPWuQWs80bN1xThDbf/CwabOe21x4fDVMq49DUuhAK4lapmz0eW02GxqmpZpmrSRq2dadrvW1OAKNBEiABEiABEiABEiABEiABEig9gTowVZ7VhxJAiTgBwSWrVpbLSyybeuW6NW1Mz6ZPR+zvlgIhwhaWj1z9rxFZse9RCizbHC/3njrg3+Zy3F33Go1Y+igfqYAwoRJ00zYaISEXv77489MjrOxo0e6x3mdhElVz4I1gNPlPadi2YrV+fhicREiJXJT0rshQtKmBboc6CARnSiQIqaa4ixfnMVuGRKGQf2jRPCqXNUm/+eha1YxfZa2rZqbSqiNRDjTvHETp84wlT2tAgTfbN1hRMNxo29BYkI8RgwdCM1L9+Hk6eZ5tmzbZSqIPnjPGLN6TykEoXnnPpoyA088dC/Oihj55ZKVptKork8jARIgARIgARIgARIgARIgARKoPQEKbLVnxZEkQAJXkYB6Xal9uXRltV0ESVjowL698OxTj+G9f041wpEOUo+1R8ffbcQpa5IKVWGhoahwVqBD21ZWM9Lqp+CxB8Zh0vRPMUFEKbX6KfXwxMP3mXXcAz1PooeIYrZOWlwCmyhtkJIHKClRLzbg1GnPwd7nGrWqY3XOOS84adQ1fdgD48aIMDgJ/5g4xfSm1kuGimUaAqt2TPLE7di1F4XDis217n3cmFsx7ZM52LJ9lwmJ1eION1SKjTrv6ccfwtuy5ktvvmvmaKXV++4cZc6rfin/ALcSWLWX1yRAAiRAAiRAAiRAAiRAAiRwfROwSR4frwCl6xsHn54ESOBKE9A/OfqpqKiQEMlyCZcsljDMHDRu3Piy3VpziTkkJ9ulemJpWKnmcguXYgDntaLtwL47RV/LlWGucM+TJwvw0t8KJBS0avjnuZVUpwoOsuH5ZyKQnKwhm9onX/ZooMVM8WJrf25wlTN9NmUXHRVZpcf3pY49IxVDE8QLLiBAPOR8mFYUVY+9GkNhfczxp6b09HTESJ66UBFOAwMDzXOqIGiJsv60V+6FBEiABEiABEiABEiABEjg2iTg+19b1+az8qlIgASuEwJaBfNSxTVFFBsTfWFxTQeGtgFiJHzU5vIiU6EsJSUCd48KES85HeDbtE/H1KtniWsyTtfQtXTN85g+W23FNV1GRbUkCRmtSVzTMRqCWlfFNd0/jQRIgARIgARIgARIgARIgASuNgGGiF7tN8D7kwAJ1F0CKoql/QFQT7aibfIcLofgXj2iIUU8MXt+CU6fdaKk1PWIIcGQ/Gg2jBoWIsUWxFvNbaLMhbZ1rVUp1rm7eEICJEACJEACJEACJEACJEACJOD3BCiw+f0r4gZJgAT8moBN3NHS/gQcegwoOyVbdUpeNJsR0Jo2LpfwTCfy81x50SKjQiVU04aw8EDYA0xcqIyXY1Cyaw1di0YCJEACJEACJEACJEACJEACJFDnCFBgq3OvjBsmARLwKwLqcRYuVUqbfABk/Bwo3ikam0NENogXW5DxZJOMaz62LMKazjWeayLQ6Rr0XvPBiU0kQAIkQAIkQAIkQAIkQAIk4P8EmIPN/98Rd0gCJODvBGzyfxXhXYGWnwJxY13FCmwiqhnBzPJU04eoFNW0Twsa6Fido3N1DRoJkAAJkAAJkAAJkAAJkAAJkECdJMB/0dXJ18ZNkwAJ+B0BFdNsEUDDV4CkJ4C8ZUDuYsnNtlmqjOa5tmuXxGxhnYHoIUDUIFdBA3qt+d2r5IZIgARIgARIgARIgARIgARI4GIJUGC7WGIcTwIkQALnI6CCWVh71yf5qfON/HZ9jhwJR90F5CwA8lfI+V6gosi1pj3O5RUXfaMIef2BkBYi/gV9u/txNgmQAAmQAAmQAAmQAAmQAAmQQI0EKLDViIYdJEACJOBvBKRKqbNCvOOWAydfc+V7qyh0tVVWMDU7dmTLmKUivMm4wEQg4gag/i8lFVwD6WZmAH97q9wPCZAACZAACZAACZAACZBA3SdAga3uv0M+AQmQwHVBQMQ1Rz5w+n0R1/4m3moirHmKal4MZKz2qRhXdgLIni2hqtuABr9zebRpDjgaCZAACZAACZAACZAACZAACZDAZSNAV4bLhpILkQAJkMCVIlAprmU8D5x4VcS1ArmRimi1NRHaSvYD6U9LSOl8mSTXNBIgARIgARIgARIgARIgARIggctGgALbZUPJhUiABL4rAiWlpTiVeQYVFdeJUKSeaOq5pp5ozrJLx6yho0d+KqGj62SN87NzOi9GwKvdlmrzvmozpnZ34ygSIAESIAESIAESIAESIAES+O4IMET0u2PNO5EACXxLAvsOpOOjKR8jOyfXvVJag1T84JH7ERsT7W672JPTZ87ixT+/gYfvvRM9u0mVT38zk3NNwkK/jbhmnkk94YTdUcnH1uxfQFBqtSc9m5WNGbPnYeuO3bAHBKBrp/Z4YNwdCJBzX+ZwOPDLP7wiYqe3INe/dw+MGjHUTFm+Zh1mz12EouJi1EtOxE0D+6FPr27u5TKF/6Rpn+Jg+hEjmuq7HDv6FnTp0NY9hickQAIkQAIkQAIkQAIkQAIk4M8EfP+LyZ93zL2RAAlclwRycvPw+jsTUFBQaMSZcWNuRYe2rZBx9Dh+9/JfUVZefslcLGmo4gp4bV3ypqyJWi1UCxqYnGtWo2htqpU5nKiia7kHaL86+OkY78eSjuLdruqj7tHnTiZOnWnEtbGjR6J/n55Yt3EzpswQz7ka7NiJU8jLL0BcXAwaN2rg/sTHxZoZm7btxLRP5pjzUcOHIjQkBJM/noXdew+YNvVYe+OdD424dkP3LkaUKysrx/v/nIqjxyV/HI0ESIAESIAESIAESIAESIAE6gABerDVgZfELZIACQDbdu4xGB667y507djOnA/s0wufzFmARctWYc++g2jfpuW1h6p4l6taqEfONRXMNm8rwKdzizC4bzAG9o0SDzOb17OXlDjw4b+zUFgE8fCLQXh4EGzWEPWEy3wXSHzYa44y3H8wHePHjnZ7mKmwuXbDJtwtgltwcPXiCEeOHjNrPPHgPUiIj/NaTy++WLDEtP36f36MqMgIDB86EL/646uYMHk6/vzi8zicccx4JA4Z0Ad3jhphxrZs3hSv/v19rNuwGWNuSzFt/CIBEiABEiABEiABEiABEiABfyZAgc2f3w73RgIk4CZQXumh5qjiqXbToH4IDLQjMiLcjNWQxS8WLsXa9ZvcoaRNGzc04Z+JCfFmTFZ2DqbMnI1de/YjLDQUPbp0dN9HT157ewJaNGuMw0eOYreIThoq2aNrJ9wjXnOBga4/m1u27zKhlGfOZpk1+vTsitG33Ay73W7WWrZqLb74cikKCgsRER6O7p07YMyo4QiS+Rri+sGkaUiX9dXqJSXi3jtvQ/Omjc2111fOgmrea+qV9vmCQpw+U4Evlxajd48oyGN42Zr1Rdix22Fkuemz8vDgvfGwWwKbjiw94jVeLw4dyTBt3bp0cPepV9nGzdvEwywDrVs2c7dbJyqQafioimsaXhoh7yHEQ4jLPH0GjdLqG3HNmqMC6eIVa8z44OAgDOp7A/r37ml1IznR9Z4Ki4rdbTwhARIgARIgARIgARIgARIgAX8mQIHNn98O90YCJOAm0EVEmU/FW+2jKTOwVMSrTu3bGI+1+in1oKGHli1avhrzFy2HCkNtWzU3wtCy1WvxL8nx9ewPH0VpWZmEmn4IFdmGDOxrBDDt97RTmaeNJ5euPebWYdi5Zx/WfL0RqSnJUE8r9ab7x8QpSK2XjHF33CpC1l4jGOVL+OpDksft0OEMTJ/1hdmjCmt7DxyC5iGLFA+uW24ebMQ1DW29Szy2goICMUe8vN549yO88vsXjADnuRfkr5B4UO+CBOrBdvqMK/QzJ8+J8nItfODtXXY261z46JFjFbKErOGZR83p8LqNXmSePmvu7ymQJVSGembnSu42H6YCm9oLv38Z6u2m1llypykHXUcFydy8fNNufZ0RIU4tW8Y3E/Hz7jtusbrMUcVJNV+CnungFwmQAAmQAAmQAAmQAAmQAAn4GQEKbH72QrgdEiAB3wRioqOgYYb/+eRzEbT2GRHrs7kLjSA0/u7RxsNMZ2r1y949uprE/HqtnmcHDx/BiZOZemm81tTr7AffG29yuGlbw/qpmPapK0+YXqupZ9vP/t8PjHfW4P698ZOf/9bcU/tmfj7PtD/3zBMmbHJg3174138+MaGUd4qX2kkR6NQ6tm2NbiKwdRcPuZTkJCPIabsWVdBE/tqnYZMNG9THPhHhykrLqgtsxXtlhihqHmaT7JnxcTacOu1EdKRNRKwgj17XaVyMzehpKsbVTwkw+/Ue5L2m9qkHWtUw0LAwl2ucJZ55rwEclxxsaiocxsbEYIUIiZsl79p/Zn5uRLa2rVsYD7gFS1ZggHip7d5/0OR40zklJaV68LJvtu7A3IXL0KRRWjXPQq+BvCABEiABEiABEiABEiABEiABPyJAgc2PXga3QgIkcH4CcbExRhjTxPjqJbZdPMeWrvzKeLWVS2ioCmvDhww0HmOffD4fx09l4tjxkyYkUwUzNcvjytM7SoslVBXYGkp1Uqtypk2Sl+n80lKXIHT6TJZZ61UJJbUsp7Ky6XER8tq3aWXCQjWZv66r9+opQl+rFk3N8KHiOffpF1/i5799CclJCVIts52ESPaQPGlh1nLnjhWSRK2K2SXf2sihYZj6SaGEV4aIKFZlgFz2pTfKwQAANllJREFU6RmKjVsKxUMPuOeOqHP519xDqwtsGq6pbD2tTDz+1KwQXM8+Pf/jr56DQ+ZY/ZoH7/kX/4z1m7YagU1zt2l1UBVD9aPWrEkjHDh0WJiGmGvrS8NGZ0oFU/UM/NHjD1nNPJIACZAACZAACZAACZAACZCA3xOgwOb3r4gbJAESUAIffzbXhByOGjHUCF8q0ujnpoH98Pxv/ozV6zYagW2mCGuLJUxU855pHrUbxftsrSTL15BQNavaaKFk/1evOM82c1H5pWKTp1lFBFSA0o96nrVp2dxziDmPjow0YtNvfvb/sHbjJmzZtst43GlYqX4eliINNw3uj2aSb23DN1uxeftOqHfXQinU8Itnn0JKvaRqa1Zt0GIFPbtFmk/VPus6LCwQP/1RsnVZq2N8bCyKtMKoh2nYq1pSQoJH67nTkJBg4zV4rgVo1bwJ1BOtRATJKOHx4vM/MVVDc/PzoQUMtu3YbQQ29eKzzHpvGtb7/Ufur+7JZw3kkQRIgARIgARIgARIgARIgAT8kAAFNj98KdwSCZBAdQIavqiFBfrd0F3CI2PdA1QI0yIEoeINpcKXerRpeOF/P/24eG3ZjPizRNoqKvOYacEDta07drkT66vgU1tTrzYV14qLSyT32xB3UQMVlHbs2mv2oftUD64Hxt1hEvhrgYY/v/EuNkixAA1n1fxsKg5q7jH96HjN6aahldUENrtU5nRozrLqHme13bPvcZ4VD1wjGqalmhOtJGoVXNi608VGPe2qmopvPxNxU0NdHx1/t+nWEN0dkrNOhTfNwTZ/8XIcFS9Cq18HaVVSFUAtgU29/JavXgcNxdW8dPreaCRAAiRAAiRAAiRAAiRAAiRQlwhQYKtLb4t7JYHrmMCN/fsYIerFP79hCg1oCKcKPBpWqF5pmt9LxS/1StNk/RoKGhQUZAQerdpphXu2adHMiDuzvlhoEvCrIDRPiiJcjN184wATyvjOh5Mx7MaByMnLwz+nzkRa/RRzfxWOtPKmmlY51fufzc421UI16f+pzDNYJ151WnE0RSqIqsCmps9UzcK7AnlLpbm6wKb51UpLK7DvYLE8r8OEg0r0KFR/bNUiWMS6YBEfaxCrNJFbFdMwVhX/tJDEEw/dK3vOwZdLVpoccpa335wFi6FhsI8/eI/x1NNiEiqYabEC3f+XS1ea3Gq3j7zJrK7hnrPnLZKwzyRT/GDV2g3m3TxdGQK6X0JFVVzTENw44abeh5bp3HYSckojARIgARIgARIgARIgARIgAX8nQIHN398Q90cCJGAItJSww+ee+T4mTJ5uwiktLOpN9qSEFHZs19o03TPmNkydORt/+es/zHWD1BTxeuuBVWvXizB0yuT3+q+nHoOKY5Oksqia5mDT8M0AD8+p83lRadhpgYh76p21c89+I961kJDP8eKxptYorb4k/b/RJPy3hLakhHjZ532m/967RuHDf3+Mj+SjpuLfiKGDoAUBqln0jUC+CIBVKok6KpzIOluKf07LRXqGVgk9J8HpY9jthejTPRC3DouR3G6B1XOw2bxDYPW+Kvip8PX2B5Pw0pvvmq20FkHyvjtHubelz2vlsdNGFdKOnjhpQnj1Wp9Fn32YiJBqWu1V+S9YvMJUS1XPtTvFS01DQdVWi+CmVlRcjE+kSqyn6VwKbJ5EeE4CJEACJEACJEACJEACJOCvBGzivVHdLcJfd8t9kQAJ1HkC+idHPxrOqaGTxSKs5OTkoHHjxrV+Np13SrzU4qXogYaG+jLNuaYVMENDfPfrnNy8fASLl1tNa/ha17NNn0MrkqrHmnqm+TL1XtNQSasap+eYgsJCFBYVIzE+ruawyOKdwH4R5spOuKfqX+1vpIDBtFn54i3mRJOGgSLq2REZDgmFlWqg2U7xaisXT74KNG0UKHnfYsSrrYrIFjMMaPpP95pVTzQkNyIi3Oy9ap+va81ppx6FSYnxPp9F33eWsEjwCO/1tc6ltKWnpyNGKpiGihecvgcV+VQgPZ9Iein34RwSIAESIAESIAESIAESIAESqIkABbaayLCdBEjgihC4HALbFdmYvy7qlCqe6T8CsmfLDl0VPk+eKsXbH2ZLMQE7bh4chfj4YFQNBFXO+w4UYu7CIjhEdPvhI3HiyWZ3PWWAKHFNPwSiBvnrU1/UviiwXRQuDiYBEiABEiABEiABEiABErgCBHy7XFyBG3FJEiABEiCBSyCgoZz1fykxlJLTrWS/WcAmHlqP3BeLtAZS4MEunlo+l7VJHrYIyYsWKiGd5R7eXJJ7LWY4ENHd5yw2kgAJkAAJkAAJkAAJkAAJkAAJXDwB+ZcWjQRIgARIwK8JBDcAGvxOkqRJRVGR05ITA6VSajACaxTXXE+judjUa61NqxAJUZU/9zbxYAtrD6T9SZKlRfj1I3NzJEACJEACJEACJEACJEACJFCXCFBgq0tvi3slARK4TgnIn+qo/lJm9CUR2aKFgW+ftfPDkTVC2wGN/y5rxFziGue/A3tJgARIgARIgARIgARIgARI4HolQIHten3zfG4SIIG6RcAWDMTeKrnTJrq80HxUAa3xgTTnWtxooMV0EdlayrBLEehqXJ0dJEACJEACJEACJEACJEACJHDdE2AOtuv+J0AAJEACdYeA/J9IZC+g2b+AnAVA5rtA6RHA6ZBHkEoG5qNPIwKaTcaqCBc1EEh8zJVzzYSFUlxTQjQSIAESIAESIAESIAESIAESuJwEKLBdTppciwRIgASuJAEV0op3AXnLgNzFQHmmaGpSZVTNHiWebZ2B6CGu6qChbURgk5xrNBIgARIgARIgARIgARIgARIggStOgALbFUfMG5AACZDAtySgwpqzGMh4QTzX5gIVRbKgtlWcW9iRDxSskc864OQbUil0pBQz+IOIbKEU2s5R4hkJkAAJkAAJkAAJkAAJkAAJXBECFNiuCFYuSgIkQAKXiYCzHCjcJOLaz+Es2oHysnJUiK5mF+e0wEDPcE+n6XM4yhEQUIJAx3TYira7KoaGdxGRjX/uL9Mb4TIkQAIkQAIkQAIkQAIkQAIkUI0A/8VVDQkbSIAE/J1ASWkpcnLykJgQJ2LSNVyrRT3XVFw7+BhKCo5h/8ECrF5XgsNHgQF9wnDz4EivV7VkZQFWrClCowZA314haN50E0LKJP9a0w+A8K618mSrEPXuYpg6nU7YbJ5Cn9eWoP1q5xtzsff0vgOvSIAESIAESIAESIAESIAESODqE6DAdvXfAXdAAiRQSwL7DqTjoykfIzsn1z0jrUEqfvDI/YiNiXa3fVcnL/z+ZTTU+39v/JW5pYaFHvk5CnOO4t8zsrBrXwVaNbPhrlsC0KRJSLV79uoaguS4Yqz9pgIT/l2ENi1KcP9dToTLGmj1qahcEdXmWA3LV6/DkhVrkHnmLBLi4/DguDFo0ayx1V3teDYrGzNmz8PWHbthF5Gza6f2eGDcHW5xTt/RxCkzsPfAIQQFBqJdm5a4987bEBV5ThRcvmYdZs9dhKLiYtRLTsRNA/uhT69u1e7FBhIgARIgARIgARIgARIgARLwdwLXsOuHv6Pn/kiABC6GQE5uHl5/ZwIKCgqNCDNuzK3o0LYVMo4ex+9e/ivKyiWU8js2FddSkpOuzF3Ve01yrpXnb8Mnc85if3oFHr03BI8/lITOnRMREyMVQquYtmmfjtGxOkfn6homf5uu6cMOHc7AtE/nGJFr/N2j4XA4DOus7Bwfo11NE6fONOLa2NEj0b9PT6zbuBlTZsw2neq19trbE4y4pu9ocP/eZuzbEya7Pdo2bduJaZ/MMeNHDR+K0JAQTP54FnbvPVDjPdlBAiRAAiRAAiRAAiRAAiRAAv5KgAKbv74Z7osESMCLwLade8z1Q/fdhfFjR2Ngn17Gc2zooH4oKSnFnn0HvcZ/FxfquXbHrcOuzK20WqgWNJBiBiVlgXj6e2Fo0yZGPMRqDse0NqJjdKzOKSlXR2UJ09S1dE0fpsJYRHg4nhRPwD49u+G/npKwUrGVX633MRqG9f6D6bjvzlHmPdw1agS6de6AtRs2oVTCd/VdnDmbZdZSRqNvuRnfu38sDmccw1frvzFrfrFgiTn++n9+jOFDB+K5Z76PuNgYTJg83ec92UgCJEACJEACJEACJEACJEAC/kyAIaL+/Ha4NxIgATeB8koPNUcVT7WbRGALDLQjMiLcjJ35+XwcTD+C/376cffcjz+biyPi6fbsDx9FXn4+/vzGu7ihRxcsXrYa4eFh4vF1Dz741zT0u6GH5DjbAPXcatywAW4fcRNat2xm1nnl7++jaaM0bNi8DYWFRSYcUgWo+qn1cPfoW0zY6geTpiH9iCRIE6uXlGhCIps3dYVZFhYVYerMz7F1+y7jbdcgNQXqLdYorb4ZX+0rb5mpFmq3B+Dh++JMwdDN24ux70AxRg6NwK69hdgrmuJ9d8aaqVNmZqNlU6BNy3DMXVQg4Z2h6Ng2Cg/fCxhNTiuP6pph7avd6vjJUxjQu6c7T1p8XKzxZtPwz1EjhlYbf+hIhmnr1qWDu++G7l2wUdgcTM/A0RMnTXuv7p3d/e3Fk01tx669RnjLPH3GPHtU5Lmw1a4d22GxhKlq+KnugUYCJEACJEACJEACJEACJEACdYUAPdjqypviPkngOifQRcQXzeX1keT1evlv72HBkhU4evyEEdY0xFAFMTX1nDqVecaLlradOJlp2srLHUYMm79oOVq2aIrkxATEREWZtjkLFiM1JRn33TUKGpL69w/+hROnXPN0TRV/1NOrWdNGRkDTPl1bTcU1DVdVby7NNaaC2hvvfmTENA2ZVIFu09YdRlxSjy7NUfbSm+/ijIhJPi13sTQ7RPSSiqGikNntNoQEVeCbrWX4zydZ+HRuKcJDzoXF6rm2aZ+O0bE6R+e6ahBIeKhZ0/tuBYWFUpW0whSM8OzRPGzKwJdlnj5r3kVIcLC7O6FSEMvOzUVIsCt8NS8v392fW7lWVmX+vEB5l7ke/TrQYpFdw33di/GEBEiABEiABEiABEiABEiABPyMAD3Y/OyFcDskQAK+CcRER0HDCf/zyefYsXsfNG/YZ3MXGqFHPcF6dO3ke2INrZp0/6lHHzC9lpCUWi/Z3dayWVP89i9vYunKr0QwG2XGhYQE4/mfPCnClb3aqqelOIAWWtBQSfXKatigvnibHUJZaRl2px/AyVOnMfKmwbh12I1mrnp8/eJ3f8G8hcuMJ1u1BYs2S2RnhVdz65ZhGNynAJ8vBFo3d+KmG895eel5xvEz2LwDuO0m6ZexXqZr6ZpVLCvbVTBCPfk8LSIszBQf8GyzztXDLNhDXNP2sLBQ060steCB2ux5i0S4izc81ItQrVR4qLVt3cJ4vKlQqt5zu/cfNHnatE9DfmkkQAIkQAIkQAIkQAIkQAIkUJcIUGCrS2+LeyWB65yA5ujSnF7qcaUC2/bde40Apl5t5ZKYv3ePrrUm1Lxxo2pju3Rs625LTkow3mqHKkM+tSOtfqpPcU37hg7si0+/+BI//+1L0LldOrRD/949TAhqxrHjOsTkH9u6c7c5t77UC8+nOap7j23fVYSFK4BuEpm55wBEYMwWb7tEM13PxYHO9OmYlJQidGx3LvzS5GHzsaaKhmpWCK65kK/SMvGCqyKiWX3B4qGm78DTymS8mobqJomodudtw6Hhuuqlp6YCqXr/Wfe7W4ojaCiviqT6UWvWpBEOHDqMsNDqFVLNAH6RAAmQAAmQAAmQAAmQAAmQgJ8SoMDmpy+G2yIBEvAmoB5QKvhoTrCAgAAjxqggc9PAfnj+N3+W3Gkb3QJbVfHH8pryXDGiMmebZ1u4eG15mopCzgopEFBpVp4369rzeNPg/hI62hgbvtmKzdt3mhDWhctW4RfPPuX2yGrVvCmioiLd09q0bI448XqrrWmuuRv7h8gnXLzjinDg8LmZERHBuP8uSO61MCxZWWjy0p3rrflMn1EtL7/Aa5CGbyYkxHm1WRfxsbHi3eYtFOZLdVe1pIQEcxwigmPH9m2MF5/mU2su7+q5X//JnVstKjISLz7/E1M1NFfy4rUUNtsk55sKbOoJSCMBEiABEiABEiABEiABEiCBukSAAltdelvcKwlcxwQ0LHGLFAjod0N3t0ijONSbyi6CW2il11NQUBBKpJKl5j2zuZKPufOoXQifViq9cUAfM0zX0Bxr/aXwwYVMvb+mz/rCiH5333EL9KN7/cfEKdi8bSdS6iWZJeolJ2LYjQPMeZnM+Y8UPUhOjPe9vF2EL4fmMHMJfPoobVqGyifEPFfbNoGSQ84pBRdcnmTDh0SKqGaTggY2DB+iApWVe81aXpO5nRP3rNZg4aWeZVrQYMTQQaZZvde0WEN3CXf1ZQ3TUk2zVhK1ijhYnnnqvadFIv45dSaGDRlocs7p4L0HJFxWnrltq+Zm7vzFy8Xj7iQeHX+3udYvrUKqe6HA5kbCExIgARIgARIgARIgARIggTpCgAJbHXlR3CYJXO8Ebuzfx4hWL/75DQwREaxhg1So15QWHlDhRvN4qdWXIgXrv6kwglf3Lh2xcs3XJlm/CjcXst37DmD2/EXQ8NHP5KiecH17dbvQNBG2Ak1hhXUbNpsQ0hSpIKoCm5rus0Wzxvh41lzM/XKpEQNbNGuCuQuXQgU9zcXm08KkAmfBGtHXpDhBpYmOKCZCmVh2Vhlmzs42lUT1upUUOx1zWywS4oPdwqK2u80mk3VNHzZ86EBZax5U9OokXmcasqnPrjnj1E5mnsYM6VexUft7Sr47FRQ1NPeJh+7FWRHUvlyyEsrb8ojTIg7//ngWHrnvLgk3LccEKQKRJiys59V8d5qjLVXEx84d2mLV2g04nHEMTz/+kLknv0iABEiABEiABEiABEiABEigLhGgwFaX3hb3SgLXMYGWzZvguWe+jwmTp0NDLy3TggJPPnK/5BtrbZoG9ullvLGWr14H/ag3lFYgVW8rNcurrdK5zbRZXyr6aHVRNRXkvv/wfUYUsvqrHjVU1bJ7pfLoh//+GB/JR0371CNMk/nrPZ996lG8N3EqPpmzwPSrEDXm1mESGtnEXFf7ih4iAts6aT4nsFljHA4nVqzOwZadThHCXK1a3CAxPgejRiaa6qHW2HNHO6Br+rBBfXuZCqgqeOlHmT54zxiTS06HF4iQuWPXXhPmqQKbFnlQIeztDya5c6y1btEM91UWg9A5D8h89dB77e0Jemk83b53/1g3f12nnwh2CxavwJwFSwzvO6UCq+XhZibxiwRIgARIgARIgARIgARIgATqCAGbhFGdSzBURzbNbZIACdRdAvonRz/qIaWhlcXFxcjJyUHjxo1r/VA679Tps4iXogdWaGjVyYWFRcazzfKoqtrvea2VL1/4/csYd8etxmNN849p3rBLsYLCQhQWFYvYFecWkzzXKSwqMjnZtGDDea1oO7DvTtHXtMqn95/p4uIK/PG10ziT5d2eEGeTnG+JwuSc8Oe6h4aHSthoi5nixeaq8Onr3qUSFqu52BJk77U1Dd3VfHY1FURQtlq0oGrVUWt9/R1kibdbwiXy1nXS09MRE6O/hVDjTajipoqalphq3YtHEiABEiABEiABEiABEiABErhSBOjBdqXIcl0SIIErRkBDMjUU9HwWHu5dsOB8Yz37dO1LFdd0HfV8O184qhZSqFpMwfP+7vPQNlJ6cySQJR5xznJ3s55o3QXRD6uZtlWId1s1s4n3mq6la57HVATTENOLsQuxupDAqWLYtxHXLmavHEsCJEACJEACJEACJEACJEACV4pAVTeHK3UfrksCJEACfkvA8nQKCHDlN/OLjaoolvYHEcXayna896Xb9KUfaluA3Xusmatr6Fq6Jo0ESIAESIAESIAESIAESIAESOCyE6DAdtmRckESIIG6RiA6KhJ/e+k36F9ZKMFv9m8LFWHsT0CQeuudE86Cgmzo1lEqhnr8BddzbdO+cybnOlfX0LVoJEACJEACJEACJEACJEACJEACV4SAxz/Prsj6XJQESIAESOBSCajHWbhUGW3ygeRO6yAimUb120wRgwF9Y9CprU3CUV2fzu1s0Da78WATYU3H6hydq2vQe+1S3wLnkQAJkAAJkAAJkAAJkAAJkMAFCbDIwQURcQAJkMDlJHA5ihxczv3UibWcUknUWQxkvADkzJVEa0VSKKIcjvIKlJa6yogGBwfAHqjJ/UVYC5BYUc25ZsJCxXPtGhfXWOSgTvyKuUkSIAESIAESIAESIAESuKYJsMjBNf16+XAkQALXBAEVyGwRQMNXgKQngLxlsOUuRmDRZqmamed6RHuUeKx1BqKHAFGDXAUNrnFh7Zp4t3wIEiABEiABEiABEiABEiCBa4IABbZr4jXyIUiABK4LAiqYhbV3fZKfui4emQ9JAiRAAiRAAiRAAiRAAiRAAnWBAAW2uvCWuEcSIAESuBABZymQPds1KnaUeLwFX2gG+0mABEiABEiABEiABEiABEiABC4TAQpslwkklyEBEiCBq0dA8rCVnZYcbb90bSGiDxCcIuesY3P13gnvTAIkQAIkQAIkQAIkQAIkcD0R4L++rqe3zWclARK4NglUSAGEo78AHNmuj55rG40ESIAESIAESIAESIAESIAESOA7IUCB7TvBzJuQAAmQwJUiIN5rBV+bwgdSalRuIh8pgmDa4Kow+m3uXFFxcWvUZvyFxlyo/9s8D+eSAAmQAAmQAAmQAAmQAAmQwJUgwBDRK0GVa5IACVxRAn99byJ27z2AMbcNx9CBfb3u5XA4MHfhMgzs2wvRUZGmb8OmrQgLC0O71i28xl7qRdV7fP3NFkycMgO/eu4Z1EtKvNRlL21eRRlw5j+ocBRDHt2Y3V6MAGlDRG+JEg256HX1+WbMnocNm7ahoLAQTRql4YmH7kVMtFQqrcH27DuI2fMX4WD6EUSEh2PE0IG4cYCEqlZabdZcvnodlqxYg8wzZ5EQH4cHx41Bi2aNrSV4JAESIAESIAESIAESIAESIAG/JUAPNr99NdwYCZCALwL5BQVGXNM+FWOq2noR0+YtWoayMhGexLJzcvHhvz9GVraET14mq3qPyIgIJCclIDgo6DLd4SKWyV8N5H6JnDwbnns9BD97M8Scaxu07xJs4dJVULGrb69uGHv7SBzOOIaX//ZejSsVl5Tg7QmTkJ9fgAfvGYO0+ilGoNu4eZt7zoXWPHQ4A9M+nYN6yYkYf/doEQsdeP2dCfLectxr8IQESIAESIAESIAESIAESIAE/JUABTZ/fTPcFwmQgE8Cq9dtNO1DB/Uz4tm+A+le45xODZO8slb1Hm1bNcevn/sx4mJjruyNq65eUQicfEO81wqweU8Q5q0OwJyVAeZc27QPOuYiTMWyOV8uQbfOHTD6lpsxuH9v3D/2diN07T1wyOdK8xYtR1l5OZ75/sO4oXsXPP34g4iKjMDCZavM+NqsOWXGbOP59uQj96NPz274r6ceM3NXfrXe5z3ZSAIkQAIkQAIkQAIkQAIkQAL+RIAhov70NrgXEiCBCxJQzyoVb24e3A+LRMBZtHyVO4xw5579mD7rC7PGS2/+AyNvGoz5iyUfmdjMz+dL+GIGHhh3BwqLijB15ufYun2XEYYapKYYr6lGafXN2NfenmDWPHzkKHZL6KM9IAA9unbCPWNuxV4R9DzvcfvIm0zo5NSZs/GsiEIJcbFGjPrPJ58bTzsVnuqn1MO9d41Cs8YNzfqfzV2IE6cyTRjk6nUbxNuuHE2l73v3j0VsTLQZU6uvwm+A4u1wOivkI/UNKkNE9VzbtA86JrJfrZbTQacyz0BzoPXq1tk9p2un9pg07VNs27kHLZs1cbdbJ+nifaYefPHy7GoBwqt7l45YuvIrs1Zt1jx+8hQG9O4Jm81m1tC11Jtt647dGDViqGnjFwmQAAmQAAmQAAmQAAmQAAn4KwF6sPnrm+G+SIAEqhFIF8FLQz779uoODcts3rSxEWDyC1xeWikiyHRu39bMGzqoL1o1b4KbBvc31x3atkav7p1FeHLilb+/j01bdxhPKfXS0jVfevNdnMlyhZGeyjyN+eKVlZObjzG3DkNLWWfN1xuxfM3XqHqP5k0aoUgEO12jXIQyDW189a0PsGP3PiPK3TZ8iBHcXpV7HjtxyuzlrIQ9bhFxb92GzZJDrh8G97sB+w+mY/L0T6s9s+8GUdDUM+34H8/voeY1pnaefacl/5laQrxLLNPzkOBgBAUGmmfU66qmOdMSJWeap1nXuXn5uNCamudNRb3EBO81NA9bTm6e57I8JwESIAESIAESIAESIAESIAG/JECBzS9fCzdFAiTgi4B6r6n1793DHFWYUltVGUaoIZqtWjQ1bd0lxLF+aj3oUU3FtlbNm2L7rr04eeo0hg8ZiHHikXazCHAv/PfTZsw8KY5gWVhoKH72/35gEvX/8NEHjFeW5gmreo+UeknWFHPUvGOaN0y9rjSX2Iihg/DTHz1h+mbPW+g19qfPPIFbbh6MO0eNMB5shzOOe/XXeKHeaTnzxTttq7iqVbqt+RqsfTpGx+qcWtjZSpExXIpCeFpoaAhyaxC7VEQLl8IGnhYWFmou8yQv24XWzMrONWPDw73vGSF7KCou9lyW5yRAAiRAAiRAAiRAAiRAAiTglwQYIuqXr4WbIgESqEqgXEIttVqnhh8ukdBDNfUWU1ssxQ6GDRngDi80jTV8ZRxziVhfrf8GW3fu9hp19PgJ93XDBqnmXtqgYYsquJWWlrr7azrJOOZao0vHdu4hGuqoYa1Wn3bocyQlxLvHaEjksRMn3dfnPXFKAYczE0U0Kz/vMNOpY3RszEh5EPsFxweHBJsxytvTyssd4jXoLaJZ/UFBge6iElabhr2qRYhodqE1Q2q4Z6kUqlDvORoJkAAJkAAJkAAJkAAJkAAJ+DsBCmz+/oa4PxIgAUPgmy3bTRhhTHSUyZ1mYVHhS0MMd+09AC02cCErKXGJZOrNFhUV6R7epmVzxHnkPwsO9q4IGhBgc48930l5pehXtaKo3W5HoHwsU1HK0wIqc495tvk8V8EsZ65UCF0r3bUJ+xTPNR2rc2JHicjmfd+q94iLcRVq0LBbDdFU07Ba9SRLSkyoOtxcR0dGQj3VPM26Vo+/C62p71TNmmOto55xCVXCRq0+HkmABEiABEiABEiABEiABEjAnwic/19a/rRT7oUESOC6JrB01Vrj9fW///NjBHt4NZ04mYnfv/I3LJZiByqwWUnyq1b6tK6tkE71Kht24wDDVAsR/EeKHiQnnvMoOx/smu6hc3Rdta07dkkoa09zriGjmqOtpxRK+NbmyJLqoK/IMrUR16y7yVidE9UfCPQOabVGWMcGElarpsUFGjdsYM73HzxsjtazmQuPr4ZSHEJz2ilHzdWmps+vXnvK6kJrqhgZISGmek8NqVVT7zXNuWeF+JpGfpEACZAACZAACZAACZAACZCAnxJgDjY/fTHcFgmQwDkCKlCp2NKxXWsvcU1HqGCWWi8ZWkFUE+JbAs/KtRtMpU7Lk+wbEYD2SQXQrhK6qV5vc79caqqQ6rof/Os/0JDRpATfHlrnduI6q3oPz/5eXTubPcz6YiGWiSiootHbEyabIb26d/EcevHn6r2WuxQoOeI115dvXbU2nZO77IJhpRqqqkLlgiUrTCEG5TNx6gxT3bSHVAZVOylFIN6aMMn06/WIoQONd+GHk6dDq4FqgYjDGcdwhxSIUKvNmsNlDb3X/MXLzRq6lhY+0EqwNBIgARIgARIgARIgARIgARLwdwL0YPP3N8T9kQAJYPW6jYaC5RFWFcng/r0xZcZnWCWi2oA+PY031MKlK01S/ofuvdMUN9gtIaTq7faHX/4Uzz71KN6bOBWfzFlgltIQRataqLW25aVmXXseW0p4qXpcWfdo27qFq1tULS0G8OxTj+G9f07F9FlfmHYd++j4u90hrNXEL8/Fz3eu3mun3hSRrMRrlEaXNq3vQKMU1/+Z6Hm1iFOdc+oNIFo8xC7gxfbAuDF464NJ+MfEKeY+KmA+eM8YaJirWoGEj+6QYhFaQbVT+zaon1LPFIyY9skcI7qpADlIClDc4CEoXmjNQX17IePoccyet8h81PtN75mcVDvR02yMXyRAAiRAAiRAAiRAAiRAAiRwlQjYJGzqYuKMrtI2eVsSIIFrhYD+ydGPeidpIv1iye2Vk5ODxo0bX7ZH1PXzCwqMCKbFBNSKS0qMQGR5n2lbYVERNCeb5gm7WPN1j6praB4zLcRg5Rir2n9x15JL7eTfgBN/EYFNihxUMa1JcOKM61lTEipQGanpPcomeeVSnpM41h9J+4UdmHX/+p6iPXLVeS/ofaVjz0gV0gTxgrO4e4+QdHAXWFMLSWguNiv/W9X5vq7T09MRI7njQsUzMVAeXO+tAun5RFJf67CNBEiABEiABEiABEiABEiABC6VgC0rK4sC26XS4zwSIIGLJuApsKn4pAJbbm4uOnZ0hR9e9ILXywRHHrBvDFC0zecTn84KwB8muAozvPBoGRLjRJDzZWEdgBafAPYoX711sm3r1q2Ijo42Apt62VFgq5OvkZsmARIgARIgARIgARIggTpN4MIuDHX68bh5EiABErgWCMj/g2S+J254e3w+TLkD+Gx5IOauCjCfT5faoW0+TdfQtS6qSILPldhIAiRAAiRAAiRAAiRAAiRAAiRQSYACG38KJEACJODvBEoOA6c/8hkaqlsPkBxsvTtUIE6c0vTTt5PTtPl8LA0v1bV0TRoJkAAJkAAJkAAJkAAJkAAJkMBlIcAiB5cFIxchARIggStIIOdzwJEjN/Ad0a9p5lo1LseEF139LRs6JEyypv3IGF1L10x+uqZBbCcBEiABEiABEiABEiABEiABErgIAhTYLgIWh5IACZDAVSFQtBOwaQXPkBpvHyjp19o2s7ov8Kdd19I1aSRAAiRAAiRAAiRAAiRAAiRAApeFwAX+FXZZ7sFFSIAESIAEvg2BlP8GYm4WBzYpFXo5zCZ/+sM6XY6VuAYJkAAJkAAJkAAJkAAJkAAJkIAQoMDGnwEJkAAJ+DuBkKbivCYfGgmQAAmQAAmQAAmQAAmQAAmQgF8SqDFLj1/ulpsiARIgARIgARIgARIgARIgARIgARIgARIgAT8jQIHNz14It0MCJEACJEACJEACJEACJEACJEACJEACJFC3CFBgq1vvi7slARIgARIgARIgARIgARIgARIgARIgARLwMwIU2PzshXA7JEACJEACJEACJEACJEACJEACJEACJEACdYsABba69b64WxIgARIgARIgARIgARIgARIgARIgARIgAT8jQIHNz14It0MC1xsBm80G/dBI4FIJ8Dd0qeQ4jwRIgARIgARIgARIgARI4HIRoMB2uUhyHRIggYsi4CmqeZ5f1CIcTAJCwPP343lOOCRAAiRAAiRAAiRAAiRAAiTwXRGgwPZdkeZ9SIAEDAEVQCwRxDrXY1lZGQmRwEUT0N+N5+9IF7CuL3oxTiABEiABEiABEiABEiABEiCBSyRAge0SwXEaCZDAtyNgiSABAQGw2+1IT09HSUnJt1uUs68rAvp7OXz4sPn96O/I+k1dVxD4sCRAAiRAAiRAAiRAAiRAAn5BINAvdsFNkAAJXJcEVBBRYSQwMBDZ2dnYvXs3cnJyUFFRAafT6f5cl3D40NUIWAKa9buJiYlB/fr1ER0dbX5H2k4jARIgARIgARIgARIgARIggatBgALb1aDOe5IACbi9jdR7LTg4GBEREYiPj0dISIiXwEZUJOBJwBLZVJjV30x4eLj5/ejvyOrzHM9zEiABEiABEiABEiABEiABEvguCFBg+y4o8x4kQAJeBFQIUQ81PVoCm56ruOZwOIzAphN0DI0EPAno70TNCi0OCgqCfiyBTfusMXpOIwESIAESIAESIAESIAESIIHvgkCdEdjy8guQX1CAsNBQxMZEV2Nz+kwWysrLkJSYgEDxZDifFRYWIVfWS0lOPN+wK9pXKom5D2ccQ6gICg3qp+BqBTYVFhUjNy9PWCRd8HkvZuwFF+MAEhAClhCiIaIqmKgnmxUeSkAkcD4C+tvR34zn0fo9nW8e+0iABEiABEiABEiABEiABEjgShCoMwLb0lVrsXbDZgkJCsfPf/KkF4vS0jK8/u5Hpu3px8YjtV6yV3/Vi/Wbt2HBkpX4/S+erdp1xa9VJJw0/TNkHDvhvpeKC316dMHwIQPcbd/VyZbtu/D5giW1YrF5207M+XJpjWNPZZ7BjM/n44ffu///t3fnQVJVVxzHz4AOi+zINsDAMMAMw7CvYZFFZAsICoJL0Gg0xixlmbLyR/JP/jX/pCqYpCohWBWRVFBMDAQEUfZ93xl2hnWGZUAFZmAcc38XX9PT9CwUxat0871V0N3v3fduv0/PX6fOuSesr886CSygYEiQxab3QXAkyFoLXhP4Efnq90kg+FuJfdVywbH7tDS3RQABBBBAAAEEEEAAAQTiCiRMgC349levXrMz5wotreXtINqeAweD0//Xr5eKLtufZs915UwP2U9++Jx/htLSb2xv3iGbv2CJ20uojg0d2DfUZ8jt0tlaRVney+J5R47Z6bMF93ILrn3ABIJgSBBo0+PrWPTnB4yEx62mQPC3E0yP/Rwc5xUBBBBAAAEEEEAAAQQQCEMgoQJsyvRKdcGpbbv2lguwbdm+25o2bmQXXQArGEeO59viZavswqUiKy0t9SWQ0yaPs+auhDR2VDRX9/z9n9+z/n162GPf6xe57G9zPnRlnS1s7MjHbN2mbf6fSk41sjpm2NSJY91eUqmR+cGbFWs3WXFJiSnLrnGjhv5wamoN69Utx1S2Guw2pdeVLmNP9y4uuWFN3NyJY0ZaZka6v+bdWXOsc2Z723vgkBVd+dIy0tvYhNEjXHlsE5s7f4Hbw6rMZkybFCxry1aus6POY8qTY23W+/OsZ24XW+vu3dHdr2dujn36xSr71S9e8/PPFZ63/y5dYSdc+WpjV4qbk9XRDhw6am++/lK5+23YssNK3V5ZXbM72pQJY+zwsRP2+ar1fs47f/iLvfL8VP99IhfxBoFKBKKDIwTXKoHiVDmB6L+bcif4gAACCCCAAAIIIIAAAgiELFAj5PXuebkeLji0Y/f+yH2uusBU/umz1qt7TuRYiQtKvTd3vpV9W+YDU8MG9bfCCxft44VLI3OCN5XN1abZCoSt37w9mG7a6+1Y/ilr16a1DyotWrbS0lq1sKcnjLbuOVm2/+ARW7Nxa2R+9JsTJ09bg/r1IsG16HODB/SxIe6fxqp1m3xQTMHAMSOGWMkN9zz/mG8KfmkoaLhq/WZr1KiBjR81zGeNzZ77kd28WWpt3H5ueYePmvas01Cwbt3mbT5LTed1fPWGLZbdqYO1aPaoXbvu9qP76ms/VxazP/jIzhact1HDBln9eo/4udGBS01UcG34kAHWxd1j554D3qe5u1fHjHb+Pk8MH2z16tX17/kPgbsVUNCEfxhU52/gbv+2mI8AAggggAACCCCAAAII3C+BhAuwKftKWWBqEKChfcHq1qntA0sBUtGVK9apQ3t7YeqT1qdHV1PAp0vnTFOJZuyoam6/Xt19UOpc4QV/6fbd+0yZdJ1dptq1a8U+w+vZp77vs8KmugwxNS24cPFS7DL+8/XiYp+NFvdk1MHV67dYs6ZN7NUZ00yBtzd/fCt77IvVGyKzFPx6+bkpNtDt3fbMpLH+OyqLTNlwGjvd3moax0+cMu1R17t7V/9Z/yng+PyUiXfs+aZSVTUymDF9ss/Y0/oKCMaOHzwzyQcDp7nnlsWZgkJr1KC+tWub5qdqLTWjYCCAAAIIIIAAAggggAACCCCAAAIPgkBClYjqB2ntssUUUFOgK71NmqlhQU8XVHL5HpHfSx0xp04cY9td8O20ayagckdlaaWmPhyZE7ypam5OVqbvVKf1xj3+mC9Pzc3uZDVd97ruXbNcFlhTU+BLTQv0T8E/lU7GG8qGu+xKOisbKhXVPfr16haZVrt2LWvu1in4LsinE50zMyLnM9Lb+vdnzhX4QKKy2FRGq4y4rc5HwTi5BUFCnY83tH+auvK1bd0qclqZblt27Il81hvdS0Piev6rV6/7z/yHAAIIIIAAAggggAACCCCAAAIIPIgCCZfBph+pe9dsn6Gl/cfUubJ3VHmozitT7Z2Zf/WdQhVYU9ab9imLN6qaqwytrm4fsh0uWKcSTZVY9u15K/i1e/9BmznrfV+CWaNGii+bVPCvoqHA1uUvv3KlqyrcLD+0T5rKQG+6/eI0Yvdwe9h9DwW/ghG9jgKHOldWduu+yrqTi4J5e9w+bX165AaX+Vc1U4g3lOlW2+0dV8OV6AUjes3gmJo0MBBAAAEEEEAAAQQQQAABBBBAAAEEbgncjtgkkIjKIBUMWrhkuc/OUhZa9FDGVVlZmb39sx/Zay9Ot9FuHzNtnB5vVGduX5dNpu6lylRTCWi79Nb+VqvWbfYllL/55U/txelP2aD+vX1TgiDQFbtekHW26LMV5U4paLfCNTU4f+GSNXSllgpqqbFAMJQRp+yytFa3O6cePpYfnPaNDvS8QWZaN9cZVEP7w6nBQ2wAMnJhzJuWzbUnW3FkTzadzjt8LGYWHxFAAAEEEEAAAQQQQAABBBBAAAEEogUSMhUpKBPVZv5DB/aNfh7/PujQeTz/tGW2b2tbXbnkcddgIF42VnXmdmjX1peX7ss77INoQX5XY9dk4Lzbb+28a6BQywXeFruAlgJdCmrFG+owquCgmgQoQKgOnAUu0yxooqBOoBrKONu8fZd9tmKtL/lcvmajP56bfStwpg9n3b5nCsqlt06z/3z6uQ/8BVl6ymjL6tjB9H21l1sT1w21OqO3269uyfI19sfZH9iIwQNcE4d8K7p8Ja5bvPspy05D+79lu/Vjs/DiXcMxBBBAAAEEEEAAAQQQQAABBBBAINEFEirApjLMYKhMVIGqYFP/4Lh2BlNJ6HYXVJv3ySJ/WHuQDejTwzZu3em7gKo7XTCqmvto08a+o6HW0fV9opoFjBgy0O+L9u6sOf526W7vssyMdDvpuppWNCaPH+UCVil28Mhxv0+a5ikgNsXtGZfjSlE1xj4+1JeirnTdRPVP5xV8U6OGYKipgMpKNfR8r7ww1bRXWzD69sz13UT1GozgqYPX4HjwqsYEr7/0rH34yWJbuHS5b1zQvm1rO+Wy5zSi3YJrol/VRVQltbpejR9yv8uki57DewQQQAABBBBAAAEEEEAAAQQQQCDZBFKKiori104mwZOWlNzwe5rVe6RulU9T1dx5/17kyzTfeuPlO+6lEs9aqalxmyjcMTnqwNeu7FSlqwqQxRs3b5ba1WvXrFHDBuVO//Z3M22wK0cdOXSgO389bqdPNWWYv2CJ/fqtN3xTiHI3qOCDsuJUEjrEZQU+VLOmn/X3f/7L7+f29s9freCq8oeVwSfLOpXsRVf+Cj4hgAACCCCAAAIIIIAAAggggAACiS2QUBlsd0utEsXqlilWNFcBpyPH823Xvjx7esLouF+hogBZ3MlRB6sK/KmZQGxwLepyq+mCYA3q14s+ZIWuXHXX3jzfeKFbTla1g2u6ibapU1bcydPnrEduts/EU6bdE8MHl1ujsg8qwyW4VpkQ5xBAAAEEEEAAAQQQQAABBBBAINkEErLJQZg/gvZY27Rtl6kzZ++o8tAwv0PsWmktmlUYeCt22WNrNm61Vq7xw+Rxo2IvrfRzWsvmNm3yePvGNVX4eOFSO3ripI0ZOdSGDepf6XWcRAABBBBAAAEEEEAAAQQQQAABBB5kgaQuEX2Qf1ieHQEEEEAAAQQQQAABBBBAAAEEEEAgHAEy2MJxZhUEEEAAAQQQQAABBBBAAAEEEEAAgSQVIMCWpD8sj4UAAggggAACCCCAAAIIIIAAAgggEI4AAbZwnFkFAQQQQAABBBBAAAEEEEAAAQQQQCBJBQiwJekPy2MhgAACCCCAAAIIIIAAAggggAACCIQjQIAtHGdWQQABBBBAAAEEEEAAAQQQQAABBBBIUgECbEn6w/JYCCCAAAIIIIAAAggggAACCCCAAALhCBBgC8eZVRBAAAEEEEAAAQQQQAABBBBAAAEEklSAAFuS/rA8FgIIIIAAAggggAACCCCAAAIIIIBAOAIE2MJxZhUEEEAAAQQQQAABBBBAAAEEEEAAgSQVIMCWpD8sj4UAAggggAACCCCAAAIIIIAAAgggEI4AAbZwnFkFAQQQQAABBBBAAAEEEEAAAQQQQCBJBQiwJekPy2MhgAACCCCAAAIIIIAAAggggAACCIQjQIAtHGdWQQABBBBAAAEEEEAAAQQQQAABBBBIUgECbEn6w/JYCCCAAAIIIIAAAggggAACCCCAAALhCBBgC8eZVRBAAAEEEEAAAQQQQAABBBBAAAEEklSAAFuS/rA8FgIIIIAAAggggAACCCCAAAIIIIBAOAIp37oRzlKJuQo8ifm78a0RQAABBBBAAAEEEEAAAQQQQACBlJSUUBD+B76aGJ+nxcl/AAAAAElFTkSuQmCC\n", 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" + "" ] }, "metadata": { - "needs_background": "light" + "image/png": { + "width": 800 + } }, "output_type": "display_data" } ], "source": [ - "model = malaya.emotion.deep_model('luong')\n", - "result = model.predict(surprise_text, get_proba = True)['attention']\n", + "from IPython.core.display import Image, display\n", "\n", - "plt.figure(figsize = (15, 7))\n", - "labels = [r[0] for r in result]\n", - "val = [r[1] for r in result]\n", - "aranged = [i for i in range(len(labels))]\n", - "plt.bar(aranged, val)\n", - "plt.xticks(aranged, labels, rotation = 'vertical')\n", - "plt.show()" + "display(Image('emotion-bahdanau.png', width=800))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### Visualizing hierarchical model" + "I tried to put the html and javascript inside a notebook cell, pretty hard you know and a lot of weird bugs. Let stick to HTTP serving ya.\n", + "\n", + "`predict_words` only accept a single string. You can't predict multiple texts." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Predict batch of strings" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", "text/plain": [ - "
" + "[{'anger': 0.9470582,\n", + " 'fear': 0.00033167106,\n", + " 'joy': 9.066894e-06,\n", + " 'love': 0.00016375592,\n", + " 'sadness': 0.0035682153,\n", + " 'surprise': 0.04886904},\n", + " {'anger': 0.013118468,\n", + " 'fear': 0.9863961,\n", + " 'joy': 5.042084e-05,\n", + " 'love': 0.0001933256,\n", + " 'sadness': 7.211378e-05,\n", + " 'surprise': 0.00016965455},\n", + " {'anger': 0.87134695,\n", + " 'fear': 0.008704395,\n", + " 'joy': 0.00010600241,\n", + " 'love': 0.00063341705,\n", + " 'sadness': 0.10522701,\n", + " 'surprise': 0.013982114},\n", + " {'anger': 0.007687589,\n", + " 'fear': 0.00057056156,\n", + " 'joy': 0.0033636452,\n", + " 'love': 0.98015594,\n", + " 'sadness': 0.003200713,\n", + " 'surprise': 0.0050216294},\n", + " {'anger': 0.3216797,\n", + " 'fear': 0.00076514384,\n", + " 'joy': 3.4114866e-05,\n", + " 'love': 0.00025139845,\n", + " 'sadness': 0.6674833,\n", + " 'surprise': 0.009786241},\n", + " {'anger': 0.028422995,\n", + " 'fear': 0.0025764697,\n", + " 'joy': 0.00012938023,\n", + " 'love': 0.00037871674,\n", + " 'sadness': 0.36064965,\n", + " 'surprise': 0.60784286}]" ] }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "model = malaya.emotion.deep_model('hierarchical')\n", - "result = model.predict(surprise_text, get_proba=True)['attention']\n", + "model.predict_batch([anger_text, fear_text, joy_text,\n", + " love_text, sadness_text, surprise_text],get_proba=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**You might want to try `luong` and `self-attention` by yourself.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Stacking models\n", "\n", - "plt.figure(figsize = (15, 7))\n", - "labels = [r[0] for r in result]\n", - "val = [r[1] for r in result]\n", - "aranged = [i for i in range(len(labels))]\n", - "plt.bar(aranged, val)\n", - "plt.xticks(aranged, labels, rotation = 'vertical')\n", - "plt.show()" + "More information, you can read at [https://malaya.readthedocs.io/en/latest/Stack.html](https://malaya.readthedocs.io/en/latest/Stack.html)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "multinomial = malaya.emotion.multinomial()\n", + "xgb = malaya.emotion.xgb()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'anger': 0.34863618616181175,\n", + " 'fear': 0.022930197531217115,\n", + " 'joy': 0.006847226031582371,\n", + " 'love': 0.014764885318498622,\n", + " 'sadness': 0.030968564413978033,\n", + " 'surprise': 0.032697431654758996}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "malaya.stack.predict_stack([multinomial, xgb, model], anger_text)" ] }, { @@ -425,7 +575,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -434,7 +584,7 @@ "['fast-text-char']" ] }, - "execution_count": 11, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -452,7 +602,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -469,36 +619,36 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'sadness'" + "'love'" ] }, - "execution_count": 13, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "sparse_model.predict(sadness_text)" + "sparse_model.predict(love_text)" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['love', 'fear', 'joy', 'love', 'sadness', 'fear']" + "['joy', 'fear', 'sadness', 'love', 'anger', 'surprise']" ] }, - "execution_count": 14, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -514,51 +664,51 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[{'anger': 0.07479232,\n", - " 'fear': 0.012134718,\n", - " 'joy': 0.034137156,\n", - " 'love': 0.85221285,\n", - " 'sadness': 0.006336733,\n", - " 'surprise': 0.020386234},\n", - " {'anger': 1.6892743e-08,\n", - " 'fear': 0.99999964,\n", - " 'joy': 6.260633e-08,\n", - " 'love': 3.2111713e-10,\n", - " 'sadness': 3.542872e-08,\n", - " 'surprise': 2.2207877e-07},\n", - " {'anger': 0.00012469916,\n", - " 'fear': 9.6892345e-06,\n", - " 'joy': 0.9917463,\n", - " 'love': 0.006561422,\n", - " 'sadness': 0.00040069615,\n", - " 'surprise': 0.0011572224},\n", - " {'anger': 5.0021445e-05,\n", - " 'fear': 0.0010109642,\n", - " 'joy': 0.049688663,\n", - " 'love': 0.94577587,\n", - " 'sadness': 0.0032941191,\n", - " 'surprise': 0.00018034693},\n", - " {'anger': 0.0010146926,\n", - " 'fear': 0.00020020001,\n", - " 'joy': 5.2909185e-05,\n", - " 'love': 2.640257e-06,\n", - " 'sadness': 0.99870074,\n", - " 'surprise': 2.8823646e-05},\n", - " {'anger': 0.0057854424,\n", - " 'fear': 0.8317998,\n", - " 'joy': 0.017287944,\n", - " 'love': 0.008883897,\n", - " 'sadness': 0.0070799366,\n", - " 'surprise': 0.12916291}]" + "[{'anger': 0.04526143,\n", + " 'fear': 0.09979316,\n", + " 'joy': 0.4658971,\n", + " 'love': 0.020664953,\n", + " 'sadness': 0.30431935,\n", + " 'surprise': 0.064064085},\n", + " {'anger': 6.130409e-06,\n", + " 'fear': 0.9997508,\n", + " 'joy': 2.2800703e-05,\n", + " 'love': 3.8201404e-07,\n", + " 'sadness': 4.3520595e-06,\n", + " 'surprise': 0.00021553691},\n", + " {'anger': 0.20740777,\n", + " 'fear': 0.22583336,\n", + " 'joy': 0.017892161,\n", + " 'love': 0.0037904498,\n", + " 'sadness': 0.5115712,\n", + " 'surprise': 0.033505023},\n", + " {'anger': 5.2901927e-05,\n", + " 'fear': 6.0754595e-05,\n", + " 'joy': 1.1822221e-05,\n", + " 'love': 0.9991229,\n", + " 'sadness': 0.00072059815,\n", + " 'surprise': 3.093863e-05},\n", + " {'anger': 0.48188186,\n", + " 'fear': 0.33322,\n", + " 'joy': 0.018766977,\n", + " 'love': 0.00092354603,\n", + " 'sadness': 0.078747146,\n", + " 'surprise': 0.086460434},\n", + " {'anger': 0.007138084,\n", + " 'fear': 0.2971508,\n", + " 'joy': 0.0024597635,\n", + " 'love': 0.00037835565,\n", + " 'sadness': 0.031262815,\n", + " 'surprise': 0.6616101}]" ] }, - "execution_count": 15, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } diff --git a/example/emotion/load-emotion_files/load-emotion_14_0.png b/example/emotion/load-emotion_files/load-emotion_14_0.png deleted file mode 100644 index b7737f2d1e1b94203a43f0ab864834c07965e43b..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 8314 zcmd^E2~-p5mX5nNEwtK7cLRc25W7)Ok$r2oh7tlQ6o`NU5l~s8VTmCSY&S$@u_Bv+ 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Can be a positive or negative +sentiment. + +Negative relevancy: The article or piece of text is not relevant, +tendency is high to become a fake news. Can be a positive or negative +sentiment. + +Right now relevancy module only support deep learning model. + +.. code:: ipython3 + + negative_text = 'Roti Massimo Mengandungi DNA Babi. Roti produk Massimo keluaran Syarikat The Italian Baker mengandungi DNA babi. Para pengguna dinasihatkan supaya tidak memakan produk massimo. Terdapat pelbagai produk roti keluaran syarikat lain yang boleh dimakan dan halal. Mari kita sebarkan berita ini supaya semua rakyat Malaysia sedar dengan apa yang mereka makna setiap hari. Roti tidak halal ada DNA babi jangan makan ok.' + positive_text = 'Jabatan Kemajuan Islam Malaysia memperjelaskan dakwaan sebuah mesej yang dikitar semula, yang mendakwa kononnya kod E dikaitkan dengan kandungan lemak babi sepertimana yang tular di media sosial. . Tular: November 2017 . Tular: Mei 2014 JAKIM ingin memaklumkan kepada masyarakat berhubung maklumat yang telah disebarkan secara meluas khasnya melalui media sosial berhubung kod E yang dikaitkan mempunyai lemak babi. Untuk makluman, KOD E ialah kod untuk bahan tambah (aditif) dan ianya selalu digunakan pada label makanan di negara Kesatuan Eropah. Menurut JAKIM, tidak semua nombor E yang digunakan untuk membuat sesuatu produk makanan berasaskan dari sumber yang haram. Sehubungan itu, sekiranya sesuatu produk merupakan produk tempatan dan mendapat sijil Pengesahan Halal Malaysia, maka ia boleh digunakan tanpa was-was sekalipun mempunyai kod E-kod. Tetapi sekiranya produk tersebut bukan produk tempatan serta tidak mendapat sijil pengesahan halal Malaysia walaupun menggunakan e-kod yang sama, pengguna dinasihatkan agar berhati-hati dalam memilih produk tersebut.' + +List available deep learning models +----------------------------------- + +.. code:: ipython3 + + malaya.relevancy.available_deep_model() + + + + +.. parsed-literal:: + + ['self-attention', 'dilated-cnn'] + + + +As you can see, we are not using recurrent architecture for relevancy +classification. Article or news can be really a long text, and when +talking about recurrent on long text dependency, we might have a problem +about gradient vanishing or long term dependency, plus it is very +expensive to calculate. + +Load deep learning models +------------------------- + +Good thing about deep learning models from Malaya, it returns +``Attention`` result, means, which part of words give the high impact to +the results. But to get ``Attention``, you need to set +``get_proba=True``. + +.. code:: ipython3 + + import matplotlib.pyplot as plt + import seaborn as sns + sns.set() + +Load dilated-cnn model +~~~~~~~~~~~~~~~~~~~~~~ + +.. code:: ipython3 + + model = malaya.relevancy.deep_model('dilated-cnn') + + +.. parsed-literal:: + + downloading frozen /Users/huseinzol/Malaya/relevancy/self-attention model + + +.. parsed-literal:: + + 94.0MB [00:33, 3.73MB/s] + 0%| | 0.00/7.17 [00:00" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize = (15, 5))\n", + "keys = result['attention'].keys()\n", + "values = result['attention'].values()\n", + "aranged = [i for i in range(len(keys))]\n", + "plt.bar(aranged, values)\n", + "plt.xticks(aranged, keys, rotation = 'vertical')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Open relevancy visualization dashboard\n", + "\n", + "Default when you call `predict_words` it will open a browser with visualization dashboard, you can disable by `visualization=False`." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Serving to http://127.0.0.1:8889/ [Ctrl-C to exit]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "127.0.0.1 - - [01/Jun/2019 12:40:51] \"GET / HTTP/1.1\" 200 -\n", + "127.0.0.1 - - [01/Jun/2019 12:40:52] \"GET /static/admin-materialize.min.css HTTP/1.1\" 200 -\n", + "127.0.0.1 - - [01/Jun/2019 12:40:52] \"GET /static/echarts.min.js HTTP/1.1\" 200 -\n", + "127.0.0.1 - - [01/Jun/2019 12:40:52] \"GET /favicon.ico HTTP/1.1\" 200 -\n", + "----------------------------------------\n", + "Exception happened during processing of request from ('127.0.0.1', 62487)\n", + "Traceback (most recent call last):\n", + " File \"/usr/local/Cellar/python/3.6.5_1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/socketserver.py\", line 317, in _handle_request_noblock\n", + " self.process_request(request, client_address)\n", + " File \"/usr/local/Cellar/python/3.6.5_1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/socketserver.py\", line 348, in process_request\n", + " self.finish_request(request, client_address)\n", + " File \"/usr/local/Cellar/python/3.6.5_1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/socketserver.py\", line 361, in finish_request\n", + " self.RequestHandlerClass(request, client_address, self)\n", + " File \"/usr/local/Cellar/python/3.6.5_1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/socketserver.py\", line 696, in __init__\n", + " self.handle()\n", + " File \"/usr/local/Cellar/python/3.6.5_1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/http/server.py\", line 418, in handle\n", + " self.handle_one_request()\n", + " File \"/usr/local/Cellar/python/3.6.5_1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/http/server.py\", line 406, in handle_one_request\n", + " method()\n", + " File \"/Users/huseinzol/Documents/Malaya/malaya/_utils/_server.py\", line 32, in do_GET\n", + " with open(filepath, 'rb') as fh:\n", + "FileNotFoundError: [Errno 2] No such file or directory: '/Users/huseinzol/Documents/Malaya/malaya/_utils/web/favicon.ico'\n", + "----------------------------------------\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "stopping Server...\n" + ] + } + ], + "source": [ + "model.predict_words(positive_text)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "image/png": { + "width": 800 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "from IPython.core.display import Image, display\n", + "\n", + "display(Image('relevancy-dilated-cnn.png', width=800))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "I tried to put the html and javascript inside a notebook cell, pretty hard you know and a lot of weird bugs. Let stick to HTTP serving ya.\n", + "\n", + "`predict_words` only accept a single string. You can't predict multiple texts." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Predict batch of strings" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'positive': 0.43126237, 'negative': 0.5687377},\n", + " {'positive': 0.9965766, 'negative': 0.003423397}]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.predict_batch([negative_text, positive_text],get_proba=True, add_neutral=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**You might want to try `self-attention` by yourself.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Stacking models\n", + "\n", + "More information, you can read at [https://malaya.readthedocs.io/en/latest/Stack.html](https://malaya.readthedocs.io/en/latest/Stack.html)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "downloading frozen /Users/huseinzol/Malaya/relevancy/self-attention model\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "180MB [00:56, 4.70MB/s] \n" + ] + } + ], + "source": [ + "cnn = malaya.relevancy.deep_model('dilated-cnn')\n", + "attention = malaya.relevancy.deep_model('self-attention')" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'positive': 0.6821273, 'negative': 0.002180755, 'neutral': 0.21589473}" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "malaya.stack.predict_stack([cnn, attention], positive_text)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + 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a/example/sentiment/README.rst +++ b/example/sentiment/README.rst @@ -7,19 +7,23 @@ .. parsed-literal:: - CPU times: user 12.3 s, sys: 1.5 s, total: 13.8 s - Wall time: 17.7 s + CPU times: user 12.8 s, sys: 1.72 s, total: 14.5 s + Wall time: 19.2 s .. code:: ipython3 - positive_text = 'Kerajaan negeri Kelantan mempersoalkan motif kenyataan Menteri Kewangan Lim Guan Eng yang hanya menyebut Kelantan penerima terbesar bantuan kewangan dari Kerajaan Persekutuan. Sedangkan menurut Timbalan Menteri Besarnya, Datuk Mohd Amar Nik Abdullah, negeri lain yang lebih maju dari Kelantan turut mendapat pembiayaan dan pinjaman.' + positive_text = 'Kerajaan negeri Kelantan mempersoalkan motif kenyataan Menteri Kewangan Lim Guan Eng yang hanya menyebut Kelantan penerima terbesar bantuan kewangan dari Kerajaan Persekutuan sebanyak RM50 juta. Sedangkan menurut Timbalan Menteri Besarnya, Datuk Mohd Amar Nik Abdullah, negeri lain yang lebih maju dari Kelantan turut mendapat pembiayaan dan pinjaman.' negative_text = 'kerajaan sebenarnya sangat bencikan rakyatnya, minyak naik dan segalanya' -All models got ``get_proba`` parameters. If True, it will returned +All models have ``get_proba`` parameters. If True, it will returned probability every classes. Else, it will return highest probability class. **Default is False.** +All models have ``add_neutral`` parameters. If True, it will add +``neutral`` probability, Else, default probabilities. **Default is +True.** + Load multinomial model ---------------------- @@ -33,17 +37,35 @@ Load multinomial model .. parsed-literal:: - {'negative': 0.2422829560944563, 'positive': 0.7577170439055456} - {'negative': 0.7385102541701198, 'positive': 0.26148974582987783} + {'negative': 0.003559988321312934, 'positive': 0.6440011678687021, 'neutral': 0.352438843809985} + {'negative': 0.4770205083402397, 'positive': 0.005229794916597557, 'neutral': 0.5177496967431627} .. parsed-literal:: - [{'negative': 0.7385102541701198, 'positive': 0.26148974582987783}, - {'negative': 0.7385102541701198, 'positive': 0.26148974582987783}] + [{'negative': 0.4770205083402397, + 'positive': 0.005229794916597557, + 'neutral': 0.5177496967431627}, + {'negative': 0.4770205083402397, + 'positive': 0.005229794916597557, + 'neutral': 0.5177496967431627}] + + + +Disable ``neutral`` probability, + +.. code:: ipython3 + + print(model.predict(negative_text,get_proba=True,add_neutral=True)) + print(model.predict(negative_text,get_proba=True,add_neutral=False)) + +.. parsed-literal:: + + {'negative': 0.4770205083402397, 'positive': 0.005229794916597557, 'neutral': 0.5177496967431627} + {'negative': 0.7385102541701198, 'positive': 0.26148974582987783} Load xgb model @@ -59,16 +81,16 @@ Load xgb model .. parsed-literal:: - {'negative': 0.24086821, 'positive': 0.7591318} - {'negative': 0.844284, 'positive': 0.15571605} + {'negative': 0.0045786616, 'positive': 0.5421338, 'neutral': 0.45328754} + {'negative': 0.688568, 'positive': 0.0031143208, 'neutral': 0.30831766} .. parsed-literal:: - [{'negative': 0.844284, 'positive': 0.15571605}, - {'negative': 0.844284, 'positive': 0.15571605}] + [{'negative': 0.688568, 'positive': 0.0031143208, 'neutral': 0.30831766}, + {'negative': 0.688568, 'positive': 0.0031143208, 'neutral': 0.30831766}] @@ -84,141 +106,220 @@ List available deep learning models .. parsed-literal:: - ['fast-text', - 'hierarchical', - 'bahdanau', - 'luong', - 'bidirectional', - 'bert', - 'entity-network'] + ['self-attention', 'bahdanau', 'luong'] Load deep learning models ------------------------- +Good thing about deep learning models from Malaya, it returns +``Attention`` result, means, which part of words give the high impact to +the results. But to get ``Attention``, you need to set +``get_proba=True``. + +.. code:: ipython3 + + import matplotlib.pyplot as plt + import seaborn as sns + sns.set() + +Load bahdanau model +~~~~~~~~~~~~~~~~~~~ + +.. code:: ipython3 + + model = malaya.sentiment.deep_model('bahdanau') + +Predict single string +^^^^^^^^^^^^^^^^^^^^^ + .. code:: ipython3 - for i in malaya.sentiment.available_deep_model(): - print('Testing %s model'%(i)) - model = malaya.sentiment.deep_model(i) - print(model.predict(negative_text)) - print(model.predict_batch([negative_text, positive_text])) - print(model.predict_batch([negative_text, positive_text],get_proba=True)) - print() + model.predict(positive_text) + + .. parsed-literal:: - Testing fast-text model - negative - ['negative', 'positive'] - [{'negative': 0.8405276, 'positive': 0.15947239}, {'negative': 1.8619101e-05, 'positive': 0.9999814}] - - Testing hierarchical model - negative - ['negative', 'positive'] - [{'negative': 0.9504666, 'positive': 0.049533408}, {'negative': 0.041675426, 'positive': 0.9583246}] - - Testing bahdanau model - negative - ['negative', 'positive'] - [{'negative': 0.9993631, 'positive': 0.0006369345}, {'negative': 0.10564381, 'positive': 0.89435613}] - - Testing luong model - negative - ['negative', 'positive'] - [{'negative': 0.8851047, 'positive': 0.11489531}, {'negative': 0.0025337301, 'positive': 0.9974663}] - - Testing bidirectional model - negative - ['negative', 'positive'] - [{'negative': 0.97722447, 'positive': 0.02277552}, {'negative': 0.007992058, 'positive': 0.992008}] - - Testing bert model - positive - ['positive', 'negative'] - [{'negative': 0.37042966, 'positive': 0.62957036}, {'negative': 0.84760416, 'positive': 0.15239581}] - - Testing entity-network model - positive - ['positive', 'positive'] - [{'negative': 0.44306344, 'positive': 0.55693656}, {'negative': 0.32117522, 'positive': 0.6788247}] - + 'neutral' -Unsupervised important words learning -------------------------------------- .. code:: ipython3 - import matplotlib.pyplot as plt - import seaborn as sns - sns.set() # i just really like seaborn colors + result = model.predict(positive_text,get_proba=True,add_neutral=False) + result + + + + +.. parsed-literal:: + + {'negative': 0.29423502, + 'positive': 0.70576495, + 'attention': {'Kerajaan': 0.0019730187, + 'negeri': 0.0016380441, + 'Kelantan': 0.52261657, + 'mempersoalkan': 0.0041695302, + 'motif': 0.009157478, + 'kenyataan': 0.0020427739, + 'Menteri': 0.0026452087, + 'Kewangan': 0.0017612759, + 'Lim': 0.046150286, + 'Guan': 0.046651356, + 'Eng': 0.014238223, + 'yang': 0.0014762171, + 'hanya': 0.0030002387, + 'menyebut': 0.0025070142, + 'penerima': 0.001477954, + 'terbesar': 0.0014683361, + 'bantuan': 0.0020200813, + 'kewangan': 0.0015684298, + 'dari': 0.001558458, + 'Persekutuan': 0.0021011133, + 'sebanyak': 0.001435061, + 'RM50': 0.037767526, + 'juta': 0.0031749196, + '.': 0.0, + 'Sedangkan': 0.0015534447, + 'menurut': 0.0014812354, + 'Timbalan': 0.0020608688, + 'Besarnya': 0.001435703, + ',': 0.0, + 'Datuk': 0.0014482451, + 'Mohd': 0.0014422016, + 'Amar': 0.0014641153, + 'Nik': 0.0015784851, + 'Abdullah': 0.0014410047, + 'lain': 0.0016714201, + 'lebih': 0.0037415246, + 'maju': 0.019784313, + 'turut': 0.011382608, + 'mendapat': 0.0025349073, + 'pembiayaan': 0.0020161376, + 'dan': 0.0, + 'pinjaman': 0.009653877}} -We need to set ``get_proba`` become True to get the ‘attention’. -Visualizing bahdanau model -^^^^^^^^^^^^^^^^^^^^^^^^^^ .. code:: ipython3 - model = malaya.sentiment.deep_model('bahdanau') - result = model.predict(positive_text,get_proba=True)['attention'] - - plt.figure(figsize = (15, 7)) - labels = [r[0] for r in result] - val = [r[1] for r in result] - aranged = [i for i in range(len(labels))] - plt.bar(aranged, val) - plt.xticks(aranged, labels, rotation = 'vertical') + plt.figure(figsize = (15, 5)) + keys = result['attention'].keys() + values = result['attention'].values() + aranged = [i for i in range(len(keys))] + plt.bar(aranged, values) + plt.xticks(aranged, keys, rotation = 'vertical') plt.show() -.. image:: load-sentiment_files/load-sentiment_15_0.png +.. image:: load-sentiment_files/load-sentiment_18_0.png + +Open sentiment visualization dashboard +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -Visualizing luong model -^^^^^^^^^^^^^^^^^^^^^^^ +Default when you call ``predict_words`` it will open a browser with +visualization dashboard, you can disable by ``visualization=False``. .. code:: ipython3 - model = malaya.sentiment.deep_model('luong') - result = model.predict(positive_text,get_proba=True)['attention'] - - plt.figure(figsize = (15, 7)) - labels = [r[0] for r in result] - val = [r[1] for r in result] - aranged = [i for i in range(len(labels))] - plt.bar(aranged, val) - plt.xticks(aranged, labels, rotation = 'vertical') - plt.show() + model.predict_words(positive_text) +.. parsed-literal:: -.. image:: load-sentiment_files/load-sentiment_17_0.png + Serving to http://127.0.0.1:8889/ [Ctrl-C to exit] -Visualizing hierarchical model -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +.. parsed-literal:: + + 127.0.0.1 - - [30/May/2019 11:13:59] "GET / HTTP/1.1" 200 - + 127.0.0.1 - - [30/May/2019 11:13:59] "GET /static/admin-materialize.min.css HTTP/1.1" 200 - + 127.0.0.1 - - [30/May/2019 11:13:59] "GET /static/echarts.min.js HTTP/1.1" 200 - + + +.. parsed-literal:: + + + stopping Server... + .. code:: ipython3 - model = malaya.sentiment.deep_model('hierarchical') - result = model.predict(positive_text,get_proba=True)['attention'] + from IPython.core.display import Image, display - plt.figure(figsize = (15, 7)) - labels = [r[0] for r in result] - val = [r[1] for r in result] - aranged = [i for i in range(len(labels))] - plt.bar(aranged, val) - plt.xticks(aranged, labels, rotation = 'vertical') - plt.show() + display(Image('sentiment-visualization.png', width=800)) + + + +.. image:: load-sentiment_files/load-sentiment_21_0.png + :width: 800px +I tried to put the html and javascript inside a notebook cell, pretty +hard you know and a lot of weird bugs. Let stick to HTTP serving ya. + +.. code:: ipython3 + + display(Image('sentiment-negative.png', width=800)) + + + +.. image:: load-sentiment_files/load-sentiment_23_0.png + :width: 800px + + +``predict_words`` only accept a single string. You can’t predict +multiple texts. + +Predict batch of strings +^^^^^^^^^^^^^^^^^^^^^^^^ + +.. code:: ipython3 + + model.predict_batch([negative_text, positive_text],get_proba=True) + + + + +.. parsed-literal:: + + [{'negative': 0.94391596, 'positive': 0.000560839, 'neutral': 0.055523217}, + {'negative': 0.004329388, 'positive': 0.5670612, 'neutral': 0.42860943}] + + + +**You might want to try ``luong`` and ``self-attention`` by yourself.** + +Stacking models +--------------- + +More information, you can read at +https://malaya.readthedocs.io/en/latest/Stack.html + +.. code:: ipython3 + + multinomial = malaya.sentiment.multinomial() + xgb = malaya.sentiment.xgb() + bahdanau = malaya.sentiment.deep_model('bahdanau') + +.. code:: ipython3 + + malaya.stack.predict_stack([multinomial, xgb, bahdanau], positive_text) + + + + +.. parsed-literal:: + + {'negative': 0.0037063136821626594, + 'positive': 0.6215181632979583, + 'neutral': 0.3669251238766725} -.. image:: load-sentiment_files/load-sentiment_19_0.png Load Sparse deep learning models @@ -303,10 +404,9 @@ will try to evolve it. .. parsed-literal:: - [{'negative': 0.41368636, 'positive': 0.58631366}, + [{'negative': 0.42412993, 'positive': 0.5758701}, {'negative': 0.6855174, 'positive': 0.31448266}] -**Not bad huh, but the polarity is not really high as word-based models. -Word-based models can get negative / positive value really near to 1.0** +Right now sparse models does not have ``neutral`` class. diff --git a/example/sentiment/load-sentiment.ipynb b/example/sentiment/load-sentiment.ipynb index 5ab8519f..85a506e4 100644 --- a/example/sentiment/load-sentiment.ipynb +++ b/example/sentiment/load-sentiment.ipynb @@ -9,8 +9,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 12.3 s, sys: 1.5 s, total: 13.8 s\n", - "Wall time: 17.7 s\n" + "CPU times: user 12.8 s, sys: 1.72 s, total: 14.5 s\n", + "Wall time: 19.2 s\n" ] } ], @@ -25,7 +25,7 @@ "metadata": {}, "outputs": [], "source": [ - "positive_text = 'Kerajaan negeri Kelantan mempersoalkan motif kenyataan Menteri Kewangan Lim Guan Eng yang hanya menyebut Kelantan penerima terbesar bantuan kewangan dari Kerajaan Persekutuan. Sedangkan menurut Timbalan Menteri Besarnya, Datuk Mohd Amar Nik Abdullah, negeri lain yang lebih maju dari Kelantan turut mendapat pembiayaan dan pinjaman.'\n", + "positive_text = 'Kerajaan negeri Kelantan mempersoalkan motif kenyataan Menteri Kewangan Lim Guan Eng yang hanya menyebut Kelantan penerima terbesar bantuan kewangan dari Kerajaan Persekutuan sebanyak RM50 juta. Sedangkan menurut Timbalan Menteri Besarnya, Datuk Mohd Amar Nik Abdullah, negeri lain yang lebih maju dari Kelantan turut mendapat pembiayaan dan pinjaman.'\n", "negative_text = 'kerajaan sebenarnya sangat bencikan rakyatnya, minyak naik dan segalanya'" ] }, @@ -33,8 +33,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "All models got `get_proba` parameters.\n", - "If True, it will returned probability every classes. Else, it will return highest probability class. **Default is False.**" + "All models have `get_proba` parameters.\n", + "If True, it will returned probability every classes. Else, it will return highest probability class. **Default is False.**\n", + "\n", + "All models have `add_neutral` parameters.\n", + "If True, it will add `neutral` probability, Else, default probabilities. **Default is True.**" ] }, { @@ -53,15 +56,19 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'negative': 0.2422829560944563, 'positive': 0.7577170439055456}\n", - "{'negative': 0.7385102541701198, 'positive': 0.26148974582987783}\n" + "{'negative': 0.003559988321312934, 'positive': 0.6440011678687021, 'neutral': 0.352438843809985}\n", + "{'negative': 0.4770205083402397, 'positive': 0.005229794916597557, 'neutral': 0.5177496967431627}\n" ] }, { "data": { "text/plain": [ - "[{'negative': 0.7385102541701198, 'positive': 0.26148974582987783},\n", - " {'negative': 0.7385102541701198, 'positive': 0.26148974582987783}]" + "[{'negative': 0.4770205083402397,\n", + " 'positive': 0.005229794916597557,\n", + " 'neutral': 0.5177496967431627},\n", + " {'negative': 0.4770205083402397,\n", + " 'positive': 0.005229794916597557,\n", + " 'neutral': 0.5177496967431627}]" ] }, "execution_count": 3, @@ -80,7 +87,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Load xgb model" + "Disable `neutral` probability," ] }, { @@ -92,18 +99,44 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'negative': 0.24086821, 'positive': 0.7591318}\n", - "{'negative': 0.844284, 'positive': 0.15571605}\n" + "{'negative': 0.4770205083402397, 'positive': 0.005229794916597557, 'neutral': 0.5177496967431627}\n", + "{'negative': 0.7385102541701198, 'positive': 0.26148974582987783}\n" + ] + } + ], + "source": [ + "print(model.predict(negative_text,get_proba=True,add_neutral=True))\n", + "print(model.predict(negative_text,get_proba=True,add_neutral=False))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load xgb model" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'negative': 0.0045786616, 'positive': 0.5421338, 'neutral': 0.45328754}\n", + "{'negative': 0.688568, 'positive': 0.0031143208, 'neutral': 0.30831766}\n" ] }, { "data": { "text/plain": [ - "[{'negative': 0.844284, 'positive': 0.15571605},\n", - " {'negative': 0.844284, 'positive': 0.15571605}]" + "[{'negative': 0.688568, 'positive': 0.0031143208, 'neutral': 0.30831766},\n", + " {'negative': 0.688568, 'positive': 0.0031143208, 'neutral': 0.30831766}]" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -124,22 +157,16 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['fast-text',\n", - " 'hierarchical',\n", - " 'bahdanau',\n", - " 'luong',\n", - " 'bidirectional',\n", - " 'bert',\n", - " 'entity-network']" + "['self-attention', 'bahdanau', 'luong']" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -152,108 +179,139 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Load deep learning models" + "## Load deep learning models\n", + "\n", + "Good thing about deep learning models from Malaya, it returns `Attention` result, means, which part of words give the high impact to the results. But to get `Attention`, you need to set `get_proba=True`." ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Testing fast-text model\n", - "negative\n", - "['negative', 'positive']\n", - "[{'negative': 0.8405276, 'positive': 0.15947239}, {'negative': 1.8619101e-05, 'positive': 0.9999814}]\n", - "\n", - "Testing hierarchical model\n", - "negative\n", - "['negative', 'positive']\n", - "[{'negative': 0.9504666, 'positive': 0.049533408}, {'negative': 0.041675426, 'positive': 0.9583246}]\n", - "\n", - "Testing bahdanau model\n", - "negative\n", - "['negative', 'positive']\n", - "[{'negative': 0.9993631, 'positive': 0.0006369345}, {'negative': 0.10564381, 'positive': 0.89435613}]\n", - "\n", - "Testing luong model\n", - "negative\n", - "['negative', 'positive']\n", - "[{'negative': 0.8851047, 'positive': 0.11489531}, {'negative': 0.0025337301, 'positive': 0.9974663}]\n", - "\n", - "Testing bidirectional model\n", - "negative\n", - "['negative', 'positive']\n", - "[{'negative': 0.97722447, 'positive': 0.02277552}, {'negative': 0.007992058, 'positive': 0.992008}]\n", - "\n", - "Testing bert model\n", - "positive\n", - "['positive', 'negative']\n", - "[{'negative': 0.37042966, 'positive': 0.62957036}, {'negative': 0.84760416, 'positive': 0.15239581}]\n", - "\n", - "Testing entity-network model\n", - "positive\n", - "['positive', 'positive']\n", - "[{'negative': 0.44306344, 'positive': 0.55693656}, {'negative': 0.32117522, 'positive': 0.6788247}]\n", - "\n" - ] - } - ], + "outputs": [], "source": [ - "for i in malaya.sentiment.available_deep_model():\n", - " print('Testing %s model'%(i))\n", - " model = malaya.sentiment.deep_model(i)\n", - " print(model.predict(negative_text))\n", - " print(model.predict_batch([negative_text, positive_text]))\n", - " print(model.predict_batch([negative_text, positive_text],get_proba=True))\n", - " print()" + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "sns.set()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Unsupervised important words learning" + "### Load bahdanau model" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "sns.set() # i just really like seaborn colors" + "model = malaya.sentiment.deep_model('bahdanau')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We need to set `get_proba` become True to get the 'attention'." + "#### Predict single string" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 9, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'neutral'" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#### Visualizing bahdanau model" + "model.predict(positive_text)" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'negative': 0.29423502,\n", + " 'positive': 0.70576495,\n", + " 'attention': {'Kerajaan': 0.0019730187,\n", + " 'negeri': 0.0016380441,\n", + " 'Kelantan': 0.52261657,\n", + " 'mempersoalkan': 0.0041695302,\n", + " 'motif': 0.009157478,\n", + " 'kenyataan': 0.0020427739,\n", + " 'Menteri': 0.0026452087,\n", + " 'Kewangan': 0.0017612759,\n", + " 'Lim': 0.046150286,\n", + " 'Guan': 0.046651356,\n", + " 'Eng': 0.014238223,\n", + " 'yang': 0.0014762171,\n", + " 'hanya': 0.0030002387,\n", + " 'menyebut': 0.0025070142,\n", + " 'penerima': 0.001477954,\n", + " 'terbesar': 0.0014683361,\n", + " 'bantuan': 0.0020200813,\n", + " 'kewangan': 0.0015684298,\n", + " 'dari': 0.001558458,\n", + " 'Persekutuan': 0.0021011133,\n", + " 'sebanyak': 0.001435061,\n", + " 'RM50': 0.037767526,\n", + " 'juta': 0.0031749196,\n", + " '.': 0.0,\n", + " 'Sedangkan': 0.0015534447,\n", + " 'menurut': 0.0014812354,\n", + " 'Timbalan': 0.0020608688,\n", + " 'Besarnya': 0.001435703,\n", + " ',': 0.0,\n", + " 'Datuk': 0.0014482451,\n", + " 'Mohd': 0.0014422016,\n", + " 'Amar': 0.0014641153,\n", + " 'Nik': 0.0015784851,\n", + " 'Abdullah': 0.0014410047,\n", + " 'lain': 0.0016714201,\n", + " 'lebih': 0.0037415246,\n", + " 'maju': 0.019784313,\n", + " 'turut': 0.011382608,\n", + " 'mendapat': 0.0025349073,\n", + " 'pembiayaan': 0.0020161376,\n", + " 'dan': 0.0,\n", + " 'pinjaman': 0.009653877}}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result = model.predict(positive_text,get_proba=True,add_neutral=False)\n", + "result" + ] + }, + { + "cell_type": "code", + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" + "
" ] }, "metadata": { @@ -263,15 +321,12 @@ } ], "source": [ - "model = malaya.sentiment.deep_model('bahdanau')\n", - "result = model.predict(positive_text,get_proba=True)['attention']\n", - "\n", - "plt.figure(figsize = (15, 7))\n", - "labels = [r[0] for r in result]\n", - "val = [r[1] for r in result]\n", - "aranged = [i for i in range(len(labels))]\n", - "plt.bar(aranged, val)\n", - "plt.xticks(aranged, labels, rotation = 'vertical')\n", + "plt.figure(figsize = (15, 5))\n", + "keys = result['attention'].keys()\n", + "values = result['attention'].values()\n", + "aranged = [i for i in range(len(keys))]\n", + "plt.bar(aranged, values)\n", + "plt.xticks(aranged, keys, rotation = 'vertical')\n", "plt.show()" ] }, @@ -279,76 +334,184 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Visualizing luong model" + "#### Open sentiment visualization dashboard\n", + "\n", + "Default when you call `predict_words` it will open a browser with visualization dashboard, you can disable by `visualization=False`." ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Serving to http://127.0.0.1:8889/ [Ctrl-C to exit]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "127.0.0.1 - - [30/May/2019 11:13:59] \"GET / HTTP/1.1\" 200 -\n", + "127.0.0.1 - - [30/May/2019 11:13:59] \"GET /static/admin-materialize.min.css HTTP/1.1\" 200 -\n", + "127.0.0.1 - - [30/May/2019 11:13:59] \"GET /static/echarts.min.js HTTP/1.1\" 200 -\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "stopping Server...\n" + ] + } + ], + "source": [ + "model.predict_words(positive_text)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" + "" ] }, "metadata": { - "needs_background": "light" + "image/png": { + "width": 800 + } }, "output_type": "display_data" } ], "source": [ - "model = malaya.sentiment.deep_model('luong')\n", - "result = model.predict(positive_text,get_proba=True)['attention']\n", + "from IPython.core.display import Image, display\n", "\n", - "plt.figure(figsize = (15, 7))\n", - "labels = [r[0] for r in result]\n", - "val = [r[1] for r in result]\n", - "aranged = [i for i in range(len(labels))]\n", - "plt.bar(aranged, val)\n", - "plt.xticks(aranged, labels, rotation = 'vertical')\n", - "plt.show()" + "display(Image('sentiment-visualization.png', width=800))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### Visualizing hierarchical model" + "I tried to put the html and javascript inside a notebook cell, pretty hard you know and a lot of weird bugs. Let stick to HTTP serving ya." ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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N2uU6ubrxej76FgYKhpxGCKP3xs9Mrpjmm0WbO1moWLJZrli9sb5nU2ubb5dkH+jg6KjvJO8s4EJyxbouu310H/Ho9rzjVb/zrPexXYd8kn2pfhSyr79HgFOgfZBNsCXFMsQsVDNMNlwggmU3TSQicjXqB/Vn9FosOo4YL5SgsccpMTqpMLll71QKzX6+VJk07QM26X4H4zMOH6rMbMsazv6ey3xY4Yh9XsTRw/l1BY+OvTu+XshZpFxsdzK05GDpmVPNp3vLZs78OsdcLnFeu8LuArkytupQdTG8z3XXzV0iXlasd7wSdTXvWl1D5/WRG1+aMM0cNyVvady2aHG7E9ga05ZyN7X9QMfBexmdh+5nPsh+mPvocNfhx4efHO7OfZrzLKvnUG96X2r/3udxf0UN7B6MfBEzlPTy4PCxV+UjDa8fjL5882kcvCVOCL6Tn9SZMp/2mzn7/tNH5dmkT62ff81rLsR9ufz13SL7kuX3lB8NP6dXuFcd1rJ/dW6PvzFCH7kD+RnVjj6EccSKYxdwN/EZBAcabppR2vN04fTqDAiGdsZ0JgsiA7GXdJTZloWB5SlrNpsJO8TezBHBKcQ5xJXDrcP9iaeU14z3G18Zvxn/Z4HjghqCI0J7hfmFW0W8RVZFi8WUxLrFA8RXJY5JSkm2STlKTUmnyojIDMnm7jDY8U2uSt5TgU6hTTFSSUCpXzlNRUFlXDVPTVvtk3qphrnGouZ5LXutX9p1Ou66WN0bemR9ov5dg0hDfsNeozRjJeNpkxJTW/jecds8ykLK4p1lmZWHNav1c5sCWwc7kt2g/UkHb0dhxw9OV50TXYxdGVyH3Srdoz0MPGk9B73O7Az2VvBe2XXfJ9/Xy0/Cb4nc6X8swDdQMQgVNBhcS0kJcQqVDkOHvQm/FVG8Oz7SNUqDyhuNip6NGYhtj6uPL0vI25OaGJ8Umuy/d+c+txSn/Q6p9mn2BxzSnQ66Z+w8FJAZmhWdnZKTmVtwuOxITV7j0Xv5fQWjxz6fQBVKFHkVHz15v2TllOxpv7ITZx6fXS1XOB9QUXKhpwpVrVUTX1tf9/GS5OWQ+torc9dUGvZf727kaApr7rzFdzul5W2rVVtLu3zHxU6p+1cfGjwafpzQzfe0t+dwn9Nz0QEw+HHo3fCH1+CNyPiuidop9EziR/CpYp78VXdJ7afzavHG+G/93rdRMCoAnDgEwMbvOQ41AOReBEBsDwBscO5pRweAkzpACJgCaLEDQBbaf84PCE48CYAEeIAkUANmcH4ZBueURaAedIEJsAaxQ8qQIxQNnYCaoddwzieNcEGkIuoRo0gGpAEyHnkJOQVnaV6oUtQrOBPzQV9Af8KoYFIxz7A82HBsG46Eo+Da8Zz4WHw/QZFQRFilIdM8pVWnraZjp8ulR9An0X9niGVYYkxkgpiyiazECpIaqZc5hAXLcoHVmHWKLZNdir2XI4aTg7OVy5+bhvsajwcvkvcSnyecEfQJ5AnaCjEJPRcuEvEUFRCdErsoHi2hIQlJdknlS3vCs3Netn9Hi1ylfIHCPkWKkqOyhgqfKqQ6ptaiflwjRFNbi15rRLtGJ0ZXVw+n16ffYHDdsMnolnGryT3TLrMe80GLUctpqwXrFVucHau9qIOao5UT2TnZpdi11W3Gg+Sp7xW5s9x7wIfgq++XRG72/x6oFpQU3B5CCHUOqwhf3G0WWRY1F60VkxM7Gq+UcHTPQpJr8oN92imtqZZpE+lZGdqZIKsv58rhU3kF+WbHkMfvF+YXB5QYnpIuEzwrUq5UYVMZVV1a++QSqFe9atPgfiO4KfnmidvX7vS3LXXwdpo9iHl09vGz7rUemb6dz48M3B0iDZNHLo3OjnNPqE3qTcu/p//wYvbI5x1z7QtmXzq/KSyWLC3/sP95YXlhVWMt5dfdzf1ja/yJ8PhLAFVgAlxBCNgHjoM60AlGwQ+IBMlBNlAEdBRqgF4iAEICzvLTEFcRb+E83gqZjmxDrqC0UQdQ3Wh2dCC6EYPHeGMasYzYMOwTnDQuD7eI98I/IMgSimiQNFE047TOtI/pDOla6bXo78BZ7CNGe8ZROE9dJx4nyZKeMkfAmWczqy8bDVszeyAHK8dDzj1c0lzj3EU8trw43g6+/fwGAhiBp4KFQr7CssKrIt2iZWJR4sYSXBJfJB9LnZdOkfGU1dwhIccuj5dfVZhTnFB6ofxY5bbqRbUS9UMaVE1PLUNtSR1GnUXdYb1W/XqDq4YNRk3Gt03aTDvNHpv3WrywfGM1bb1gs2KHs2d1EHNUd7J29nfZ61ridtN92GPNS3CnhXfMrrM+PX4QWcU/IqAmcCpYlBIScjV0Odw0onD3TJQWdW90Wywqziq+KGEqUT3pSPL0PuOU6lT6tD0HpuH9pDfTIuthjllu9xGHvLH8lGO8x+8WBhbTn2wu9T9NKntwdm+5yvkvF65WxdZo1WEuDly+cCX5mtd1lUb6pomb128fuGPTxn53vKOmk/pA6xG2a/BJzdP9PV59Os9FBpgGHw05v5x8lfiaefTamNP46kT1pPs0w0zXh8xZy8+Mcy8Wzn4NWVT5jvjRs1y6GvRLcXv8kQADaDd3AHGgAs8ANxAODoIz4DYYhte/IGQBxUIV0CCCBmEEr/wOJA5pjzyD/IKyQFWh8Wgq+g3GCV7tNtgBHBn3E19IUCdM0pyk1aMdoUui56fvYohnlGScYDpD9CNJkL4zP2QpY01i82TX45DiZOei4UZwr/Is867yAwEsfAPlEZYV0RZ1EAsS3y9xUvIGnHfPyzLuUJBzld+nUKHYo7SiIqHqrlag3q/JrOWuXaEzp6etf9jgjZGicY7JuJmWeaHFFys760u2tHZh9o8dJZ1ynD+4WrjVeuA9KV4PvUV3HfSZ9DMkVwYgA/2D7lFEQzJCZ8KtIuojWaISqGMxRrGX49kT9u35mOQGr1OVlKpUjrQj6aiDyRlfMj2yrmav5zodrjqyfNQx//IxwnHKiQdFUsW5J+dKXU/dKRM9kw/v/f7nuy9oVlZVM9Uk1k5ddLzUUi96Je/qUoP39QeNMk1Hm+dv2d++fIfQGtjW2k7sCLjXeB/1wO5h6aOJxxJPKN2VT8d7OHvt+w72X3/+doAwKPfCYYj68shwzat7IwOvp0YX3qyOQ2+xE5h3mEkwuTz1aXp05un75g/lHzNnIz5Zf5aaw869mW9eyPri8VXi65dvLYtpS0bfMd87f6T81Py5sHxhxWOVsNq4Rv5F9+vauvvG+EcHKipsHh8QrQEA6NH19a+iAGALAFjLX19fKV9fXzsPJxsjANwN2/oPafOsYQTgLNcG6rsy+frf/+X8D78U1VXQOwEbAAABnmlUWHRYTUw6Y29tLmFkb2JlLnhtcAAAAAAAPHg6eG1wbWV0YSB4bWxuczp4PSJhZG9iZTpuczptZXRhLyIgeDp4bXB0az0iWE1QIENvcmUgNS40LjAiPgogICA8cmRmOlJERiB4bWxuczpyZGY9Imh0dHA6Ly93d3cudzMub3JnLzE5OTkvMDIvMjItcmRmLXN5bnRheC1ucyMiPgogICAgICA8cmRmOkRlc2NyaXB0aW9uIHJkZjphYm91dD0iIgogICAgICAgICAgICB4bWxuczpleGlmPSJodHRwOi8vbnMuYWRvYmUuY29tL2V4aWYvMS4wLyI+CiAgICAgICAgIDxleGlmOlBpeGVsWERpbWVuc2lvbj4xMjMzPC9leGlmOlBpeGVsWERpbWVuc2lvbj4KICAgICAgICAgPGV4aWY6UGl4ZWxZRGltZW5zaW9uPjUxMjwvZXhpZjpQaXhlbFlEaW1lbnNpb24+CiAgICAgIDwvcmRmOkRlc2NyaXB0aW9uPgogICA8L3JkZjpSREY+CjwveDp4bXBtZXRhPgoc8tDvAABAAElEQVR4AezdB5wU9f3/8c/R7w4L0gQRRAVRlA4WsKCoASMKVjSJGn9qJJpiIlGioima+NdojD/9GbtGjUIUYxRsGFFpYqOjoDRBqqjAHf0/7zm+w+zeltm7vbqvr49zp3znOzPP2Z1lPvsteTu9ZCQEEEAAAQQQQAABBBBAAAEEEEAAAQQQSCpQJ+kaViCAAAIIIIAAAggggAACCCCAAAIIIICAL0AQjTcCAggggAACCCCAAAIIIIAAAggggAACaQQIoqUBYjUCCCCAAAIIIIAAAggggAACCCCAAAIE0XgPIIAAAggggAACCCCAAAIIIIAAAgggkEaAIFoaIFYjgAACCCCAAAIIIIAAAggggAACCCBAEI33AAIIIIAAAggggAACCCCAAAIIIIAAAmkECKKlAWI1AggggAACCCCAAAIIIIAAAggggAACBNF4DyCAAAIIIIAAAggggAACCCCAAAIIIJBGgCBaGiBWI4AAAggggAACCCCAAAIIIIAAAgggQBCN9wACCCCAAAIIIIAAAggggAACCCCAAAJpBAiipQFiNQIIIIAAAggggAACCCCAAAIIIIAAAgTReA8ggAACCCCAAAIIIIAAAggggAACCCCQRoAgWhogViOAAAIIIIAAAggggAACCCCAAAIIIEAQjfcAAggggAACCCCAAAIIIIAAAggggAACaQQIoqUBYjUCCCCAAAIIIIAAAggggAACCCCAAAIE0XgPIIAAAggggAACCCCAAAIIIIAAAgggkEaAIFoaIFYjgAACCCCAAAIIIIAAAggggAACCCBAEI33AAIIIIAAAggggAACCCCAAAIIIIAAAmkECKKlAWI1AggggAACCCCAAAIIIIAAAggggAACBNF4DyCAAAIIIIAAAggggAACCCCAAAIIIJBGgCBaGiBWI4AAAggggAACCCCAAAIIIIAAAgggUA8CBBBAAAEEsiHwzbff2Zp1X1tRUbHtuUdj23uvPW2PxoWWl5eXjeIpAwEEEEAAAQQQQAABBBCoUoFqGUT79rsNdttd99m2bdtjcH5yyQV2UPt2McvSzcyZ95k9+vSYmGxH9upmZw8eGLMsWzM7d+60EaP+FBR39hkD7cie3YJ5JnJHYPXadZbfqKE1LiyMOemvVq22O+99KFh29eUXWds2rYN5JhILfL54qR3Ybv/EKythaS5+tqO+Vzdu2mT3P/KULVqyrNSV+OF5Q+w/r06w4uLN/rqBJ59gJx57dKl81XHBjh07bOmXK6zd/vuVOrzHn/mXzZr7qb/84APb2RUXX1AqDwsQQAABBBBAAAEEEECgdglUyyCaHsi+27CxlPSU6R9nHER7b9oHVlRcHFPWho2bYuazPRPe3+bNW7JdPOVVc4EtW7f6QYMJEyfZz6642Doe1D7miLdv3x7znty6bVvMemZiBb5e/409PeZFm/vpQrv39ltiV1byXK59tqO8VxVouu/hf9jipV8mvBqtWrYw1VBTPqWioqKE+arbws8XLfF/gCksLLDrfv6TUoenH3vc+0HTJAQQQAABBBBAAAEEEKj9AtUyiJaMffpHM+yCswdHbhqk4MTMOfOTFcdyBLIusGHjRrvlz/cED9dZ30GOFTh/wef2vw89GQRgcuz0a8Tp/ve9qUkDaDqBfVs2rxHnET7IcW/8115+7S1/kYJoJAQQQAABBBBAAAEEEEBAAjUqiKag2ILPF1uHgw6IdPVmzp7Hw3ckKTJlS2DTpuK0AbSGDRrE7I7eomI4YmZWrlrDZzhGpHJnorxXF3y+KOagfnzhOdaj6+H+MtU+q1OnjtesuZGphrGS5qt7SlarLnzc+fmNgtmacE7BwTKBAAIIIIAAAggggAACZRaoUUE0neXUDz6OHESbMv2jMsOwIQIVJdCs6T5V3iyxos6NcmuXQJT36sZNu5tnNvfe2y6AJgkXXPrzzb+pXTDe2fzPD8+rdefECSGAAAIIIIAAAggggEBqgepfJcA7fvcgplP54OOZpg6+06XNW7bYnPkL0mVjPQIIIIBAOQS2hfr026fJ3uUoiU0RQAABBBBAAAEEEEAAgeotUCNqoqlmg/pDU1KTzs+85kPxnbXHM388c06wSLUj1n/zrb9tsDDNhEaSW7Fylf+nJmVeR2xWWJBvB+zfxtq13c/imzmlKS7pagUE16772pZ/VbIvDajQsGEDa9GsqR14QFvTsccndfatztZdKsjPtwLv2NKltV+vt527OvdusvdeVrdu3YSbqBNwjcq3wjsmbVPPy7f3Xntae29kxDat940JaiYsIIOFa7wRLJetWGmrVq8xDfjQsnkz269VS2u1b4uMjOW4as1afyS9Zd5oeurHqF2b/azNfvuafFIldQq+xQu6KjVuXGiNGjb0p/UeUOfiCxcv8Y9F137//VpZuBmXn9H7n4K233nlhK+L1n3jve90jnleE7amuwIM4eunAHF84EH7Vd9qSnvu0dga7Gr+qWDFQu94Pl34heV5/zVr2sSOOOwQ731Zus8mXbd5ny7wXNdai+ZNrfW+Lf3r5xca4X86xi+967L0y+W20itD+5Knrk29eqlvG8neZ/oMLvhisS1dttz22Wdv/7Ok91Oi96HOXw7fbYjtsF2WSjKRTXmSPmufeZZrvM/fBm9a10GfO32+E5mWZV/Z+CzpnqdRL/UZWbWmZMRXXU9dCx1znndvSpfKWkay96p7v2u/W7ZsDXav/bhrpIU6Pr3Hw+8JtyzYKG5Cn2WNxLriq5VeWV/777eW3nt4X2+AAn3+oqay2Ovzq3MODwiz1RsoxJ2TRtpt5I24q6T3pssXvm8kOz53n1/ufa6KNm+2Nt71a+Odj+55qa5hsmsgJ92nF3qfqdXe+6K1V57uUfq8pyov2fGxHAEEEEAAAQQQQAABBNILpH4aTr99peTo6PWBNnPOvOCBZao3Sme6INqkaR8Gx3Z0nx427vX/BvOpJj6eNdeeff6lhKODhrcbPHCADTi+b5kDSgoS/Ovf4+39XcHBcNnhaT0s//gH59i+LXZ3zq0Ht5v//Ncg2wFt29ivr7osmE80sdoLPtwS2ubCc86wo3v3CLLqQe2N/75nL7/+Vso+qNS30UXDzrLDD+0YbFuWiSVeIOUfz431gocrk26uUf3OHXKadTjwgKR5tOLdKe/bc2NfSXrcCv6p6dVee+6RsJxHnxrtB2a18uzBA63bEYf5ndkriJooHdmzmw076/SYYJKCtk8++0Kp7I//8/lg2S9+8mM7+MB2/oPvbXfdHyz/jTfyXzg4MHHyNPv3uDf89Qog61r9/fFnbP5nnwfbhCdkdNzRffxFGsHykX88l7BfNj2w//iCc/yH7PD24WkFQca8+Iq9N/WD8OKY6X5H9facvhdz/uEMep+5kRhv+NVV/mdJx+9GMgzn1fS5Z3rHf0zJ8bt1yT4b4ff93/58c5mCBfosjPbOcc68z9zuYl4V9Onf7ygbOOCEIGASkyHNTLY+SwqSjH35NXvr3SmBZ/yudawn9D3Svn/qiUGwNZynvGUoSJPovfr+hzPsn959Mj4p6By+Rr8feY0pYB9+T+jeeUr/Y+M39effemeyjX9zYtB/Wnymli2a2Xlnft86Hhw74q3LV1772+95oNS9Xz+iuHNqt/9+du3Vl/u7e+zpf5kGvlDSPernP7nEn47/n+51ujcku5/oGg79/ql2gveeS5QUzNZxKdX3Ath33Xqjd69+1/49/s2E7wvdo3/y4wvtIO9HGBICCCCAAAIIIIAAAghkV6BGNOfUr+q9uh0RnPmHn8xK2aRzU1GR/+u820BBj3RJD19//b9H7aEn/lnqISrRtgpy3HHvg4lWpV02a+6nNvL3d6QNoKkgBZn+cMe9puCIS6p1dsjBB7pZv5aKalOlSu9NmR6zuuvhhwbzqiVywx/vtJdeTfxQFmT0JhQI+b9Hn/LzhpdnMq2HUtmlCqCpPD106pp8FKpVGN6PApH3PPCY9zD/n4QPky7vF16tlhtv/Yt9NGO2W5T0VUGDP975v0kfeLWh+uX7vXdNVBsnk7TFq9GSKCVbrryqGfPnv/5f0gCa8jz3wst+zR2NRPu/Dz2RNFilGnp3/u9DFm5+p+1d0vqb/3R3ygCa8ipoqaBCuuunvB/NnG1/feDRpMekPM+Nfdkee+ZfmswoKUCUadJn7/f/729JA2gqTwHANydO8t1TXZtE+87mZ+mp0S/6x+ECkon2p3UTvMDT//vbg34Nqvg82SgjXGamHuFt3bRqdsUnfZb03v3XS+OTBtC0je4d9/z9saBmcricbNqHyw1Ph2vdxSxPcE5ar2CtAmDJAmjKo2s45t/jvGv4d7/GtJalSs97RmNfeT3pPU/36Lvue9gmTUseCE9VPusQQAABBBBAAAEEEEAguUCNqImmw1cgzNWOUW2ZzxZ6TTqT1EZQkM0l1b5JVgPJ5dGrylYz0XBSoEk1hNR0TE1xZnijfYab66mGgWoHHdJhd0ArvH2iaT0wPe4FDMIPxmoq2dOrcbSX96ombApOhJujqpwX/vOqHXrN8KDI473aJ64WhBYqsHPyCf2C9eEJBRsmv797kIXDDjk4ponjaC+IoaZtLqm2Q5+eXf2mpDu8bVVzZ7pX80TuLr3+1rs24Li+CZs2ujzJXvUQHD5/1RTr3KmD1a9f3/NcaPM80/B61Yzq7tUOi0//9+jTfgAxvFw1t9TMTU1DdW1coEflPezV0Bp5zU+9po0twpvETL8bCjaqSZ9qjqm5ocpyowtqA70fJnu1HV3tEdX66OkFevUAG67hdFD7dn5TWG3TZO899ZJRUgAwnGSlJoe69moe6dJfvOBYOKl2omov6j3qDLRe5/DG2+/Z9046Ppzdf98pwBa+xqrRolp5akqnJp2z5s4P3ifa9133PWK33XRt0hpp2sHLr70V7EfNzNQkVMegz294X2qufeqJx5pqHyrJUu89NSkOH7+Wu5RpkzU1yVMAOJx0jVWrUk3hdE11/d17T+/7h5581ob/+AfhTVJOZ+uzpBqq4YFR9JlUjVo1717nBVZ1L9LxuqQgjYLN4R8bslGGKz/+VTWv3LXQ+8I1a5Rnp44HBdkbNKgfTKeaeOZfL8X8UKC82seB7dtag3r1bYZXEzk8YqaCri28ZpBt27QOis2GfZ8eXW39t995TaEXBp93Na8//NBD/P3s69WEi5p0T377vakx2VWTrtPBB3nN7xv5967wjyM6v/996EnvHjU8aQ1LfWYUNHVJn3N9ZvQZCftovQKSR/XqXuba0m4fvCKAAAIIIIAAAggggMBugRoTRFPwQA/1rkmYHlCSBdHCAaOje3fffbYppl57651grR5Yf/urn3r9QMX2R6amfq9OmGj/eXVCkPetdydnFERTbSh3DipEzfDUHC8+KVgXblqkh6SVXp9I6j9HSQ/+Ok4XiJjkBQGTBdHUD1U4AHTsrqZ/Kkf9Bql2jkt6IBvx8yv8st0yvercVUvPPfQp0DDto0/s+GOODGdLO61Ahvbp0vlDT7d+R/Vys3bSccf4QQwFdNxDofJrWk2pXFIQQf1EuaSH0ysvubDUNdNDrGqDuPTU6LFBcyy3LNGrmsepyZmaWrn0itck+BWvuatLCka5IJreK5dccLbfB9nvQs0ETzulf9qmx668VK/qH+/qyy8KrotqTt7z98djalxqe31Ghl/6g5j+zxRcedgLBrmkGmvxQTTVbHHvJeVzzUjDff+pBtuTXhNcDe6hpPexmt7JKlXS+/Qiz6ZbqPaj3j93e7UM1fzPpQkTJ/tNVzWv97f+Jk6a5tdUc3lkXNb06NNjYjZVgPBH5w2JaQY56OQT/OaL7j2qgKiCaYn6JowpzJvJ5mcpHETXjwA3Xnt10Fef9nvW6d/zA5Gq5eeSrMJBtGyU4cqOf9XnzV0L1aByn1X1J+eWx2+TbH7B54tjapbp/XL5xRfYoaFg3KknHecHj1QLy6Xxb75tl180zJ/Nlv0Qr1mlkoKt7r7Y3AtaZ3pOes+oFmA4nXPGINOPH+GkvjBVA8+93xQMVQ2yvkfuvieG87vppvs0sSsuHub3deiWqTayalm67xcFNvUe0GeZhAACCCCAAAIIIIAAAtkR2B0hyE55FVaKap307t4lKD9Zk0519uwe6JQ5/FAZbBw3ocBQuFaP+miKD6BpEx2Dgg/hmm1r162PKy317KsTdgfrVGvjnDMHJdxA/Qipn5xwCu9LwZ1wn2Z6aFNTxEQpXLtKD6iq9eXSBK/ZWjipLx3liU8KpvzwvKExi9d6nX5nmtQxfjj16r67ZpFbrnMb5gXXwmmu10m+SwoghR9Qdby/8PojSnTN9NA65LRT3Kb+e0P93qVKCq7oOocDaMqvAItqlrmk90yyppEuTzZe9T5RgDB8XVQ7Lv79oX2pvzoFnMNJtfgOC11zddYeTmoipyaaLu3Xal8/aBAOoGmdBhRQMCHclFiBDBcAcNvHv57tBQ/CATStl+1P/+eHMef0uTeAQ0UlBW/D9wUFgdRPnhu0we1XHccrCBlOqnEYJWXzs6TAkksKJrrBLtwyvXY46ICYa6EBG8Lvx2yUEd5fRU3rh4hwuuxH58cE0Ny6E489OuZ8FeRyA4Jk097trzyv6rPM1WhUOUO9oGd8AE3LVSv2ul/8JOZeM+bFcVbsDTyQKuk9qv4yw2nPPRrbVZf9KLzIb+Yds4AZBBBAAAEEEEAAAQQQKJdA6WhJuYqr2I2P7NXN1Om6kmrNaJTC8AO9lr//UUktGU0r4KEARLrUxGtGqdo0CiYoMNIzQWAnXIYefFzgINM+gk7od6TXP85qv1ZZF29kxVRN0vbzRi4Mp/ADspb3O7p34KF5DaYQH1iRU7gvMDUJCweHOnU82A+OqJabRh91I0iqvPikhzQFclyNpS1bt8VnSTvfZK+9YvKoPzN1nB8OECmDmuGO+NkV/iib+3gBxbDTsuVfxdSsU22vPRonH6lRtcVUe9Ad9wwviBYf1AkflGqMJEtq4qvR8Fwq8prf7tG4Yj9GQ08/NWGz2fBgBDoeBV4VcEmU1IzSNTMN10pUXvVbFk4/9Gpnhb3D6zStoOSfvH7aXNLnMBzgdsv1uoc32ukx3nsuUVKQTs1lXe1GNWWuqPRhXH94Zw9Ofo0VRFQzQTVxbtWyuRecbRLpsLL5WVITb3edpn3wiamZYTiA6w7oYi+oqXuWasq5USPdumyU4cqqqFcFmlQz0iXVhA0HfN1y96qaqouWLvNr5CqvPn8KhGbT3u2rPK+fLvgi2Fy1Q4+PGzgjWOlN6N6lAKFqtirpPrV02Qo/SOoviPtfL+/HJFcjOW6VX1s3fI8uKiqOz8I8AggggAACCCCAAAIIlEOgYp/+y3FgiTZVcz4FxcIPl/FBtCmhvr+SPbzHl72v96D8vZbHxy+OmdfDnpraqK8cBXFcCtc2cMtSvR7Tp2eq1f46nd9nXk2UT+I61FcNrHBSME8Pz6qFpqQaM/FBNDXnCR+jRlYMJzWZCjebCq9z0woUqhaPgjDbPQeXtu+IPR63PNXrQV4fRwriuWNSX1gaebXLYZ2sS+dOdqjXX5urdRPu7yhcZnyNOz1Ap0qqtdXWe++44Ff89uFtdWzhmobhdZreywskhlP8NQmvy9b0waHab+EydazhB2b1BZcs5TdqmGyVV4NxTbBOZaYqRxkV3A1fw1Seqi2TKiC31567+4nbti3z91Nw4Gkm1KTZJR17uve8AriZpmx+lrp0PsS+XFFyn1FQ5a77H/EDy90PP8wO94Lv7hopSKm/RCkbZSQqN5vLVnl97bl7gco9yvuhJFVSgO3O3/+2VJZs2pcqPMMFGiTB3ZO16TFH9vT7VUxVjEandUE05dN3jWoaJkr7x/24Ep+nsLAgqFntfjiIz8M8AggggAACCCCAAAIIlE2gRgXRdIq9e3Sx/747xT9bNen8wblnBg/p6kcs3Am5muWVJenh9YvFy/xg2UqviaSCBOGO98tSZqJtVPNGgxmo83fVBFMn6mp2lsmDj5oIaWQ3JfWFoyZcqt3j0juTdzfTU8AtVaf6ephd+MUS73i+9BxX+eeth1zXx44rszyvCmipZli4+ZX67lEn6PpTUuCle5fOpqaeifqi+sqryRdOf7r7/vBs2unVa0qCjokypgqgKb8GPwinsowQGd4+yvSeXp9YUZL6SUqW5J4shYNgeg9c/Zubk2VNuDz+eoQzJWpiG14fteP58DZlmf7mm++CzdJd4yBjOSbK+1lSh/DqQN512K9D0eip+tMouursvnOnjn5zdQ0Uoqa28SkbZcSXme35+FGF90nxHo667/LaR91PsnzhZsPK06JZbN+aibZTLdJw0v03WWrq3cdTpfhavanysg4BBBBAAAEEEEAAAQQyEyj95JXZ9pWe+yhvlE4XRFOwKdykc9qHnwTHowfL+D6dgpVJJtTsUQ+oChxVZFJT0HFv/NdvfqkHvvIkjaLpgmgq572p04MgmvqHC3fcfmySJkVqJqoHdo24mc2AWbLzUm25Pb3aM+rMPlFSIFR/L782wW+edIXXybiakroUDvq4ZZm86hxVgyxRYElN4FKlVLWqUm1XnnVRH4obxAX4ou5z9Zryvd9TfV723it1ADDP8qIeZrnybdi4e/TZfZrsXa6yUm2crc+SAqI3/vpq0wAb4RGB3b4VXNOPCPpTzbpzvb4V42uZZqMMt7+Kev32u93BTe1DTbfLmrJlX9b9u+22xTVz33OP1J8Bbaf7SriWdarPZHxtWLdfXhFAAAEEEEAAAQQQQKDiBepU/C6yuwf1lRVuvjR1+sfBDsJNOcOd7gcZUky86fVH8/A/nksZQFNfSeqTJ1HtqBRFx6zSw7xqTqmz/2QBND1MHeE12YofQTGmoF0zBfn5Mf1gfeA9VLsmhlNCNsp+pBdwi0+qSfXAY0/bv8e9kTSApiCO+mM6Y9DJ8ZuXeX7ACf38EVA1umeqfutUq+OW2/8a0yl8uEmpDkDHl+lffBllPpFasOGO7bGB3Ewt4/viiiWpnCBZ7D5Lz4WDn8k+d6W3ymxJtj9LCujeNOJnfp+BifpDc0en81Hfgk/883mLrxmZjTLcfiriVQHAcIo//vC6VNPZtk+1r3Tr4msFht97qbbdtn13H5P16yf/fStqean2xToEEEAAAQQQQAABBBAom0Dyf6mXrbxK2aqPVxtNQS8l1R5TR+hr130d9EOjBzMFoaKmOfMX2AsvvxaTXaP3dfP6H1I/bGoCqVod7uFFtaRcnzeZPvTd88DjMU1DdaxqdqommPu3bmWtvH25PsG0H41+6FKyfR13dB/TSHVKeqCe7fVdpv7FJr//odvU7wMqUbBqrHfermN3l1n9zHXy+kpru19r/3jCtcBeee2t3c1Nd7otyvaqjsE1Oqn+5DnbO4cZs+eZ+q8KBzpU6+b5/7xqv7zyx/6O9m3RPOgkXwvu+P3IhLXKynZUubeVmlyqDyYlBYqv/+WVtQ5BHbG7EXgT1ezKxglXxGdJAU39IKA/9U342cJF/ufkk9lzg8FN3LGrJq6ad+ueFU7ZKCNcXjandZ8Np3Vfr7cD2rYJL4o0XRH2kXacIFPzuOabOqd0ST98hJvu7hGqeZtuW9YjgAACCCCAAAIIIIBA5QnUyCDakaEgmpp06sFSfYu51NULIMXXBnDrEr1Omf5RzGKN1JmqFtj6UP9KyQJbMQXumtFDvAJjLqlvpmt++j9JR8T85tsNLqv/Gg4shVeoM371keQewqZ/PNN/kA43szvWC7QlSuHaagroXfnjHyTtdF0PeuH+2pIdT6L9xC/btKnIduzcYY0LSzpFV+0+9ZWmP+1H/aM9PebfQTBNgwJouZpg7hv34K3+g+JHqozfn5p6KeangAIpVkCeLojmXmNzlJ5T5+myjK9JVDpn9ViiwOv8BZ/7BxPfD1eiI3z7van22lvveCNz7uO/34adNThRtphl2f4s6fOlAIzrV07NdTt7Hevr79whp/mjCT839uXgvHQwGukyHETLRhkxJ5nlmfhavWu8H0NSJd0Dbv7zX/0fGhSs0n1Ngwpk2z7VMaRbF98kXP1dpkvx/TTuFaEJaLoyWY8AAggggAACCCCAAALZF6iREQXVDNODiqtZohoY7gFZRJk25Zz/WcnDtbZVba1UATQNBhAONGQSSJr32ULtIkgK1jVN0T/TrLnzg7yaSNYEUTXk+h7ZK+isf6ZXm+ugA9oG2yrYcfihHYN5NyE/N9KplqlGXKpRC+NrrGVy7m6fDz7xT78WmYJx2tdP/+dHblXwqkCZOkXfuHFTTA1BjYqqAIECIuH07pT3LVWQQ4HOUX+626+5IwuNLvnrqy4LF1Fx0+WsrVdxB7a75H29GoFe9MVfoGv6kTfdPcWgHGu9wM6o2+7y8+c3amRH9+lRalTY3aVXj6kWLZoGB6Jz/HjWXK+m6aHBsviJSd5It+q7UH/fen/pUjY/Sxoc5NGnRwc1zUZeM9wfbCP+GFSL66f/80P71Q1/DILb6iNSKRtlxO+vIuYbNGgQ8wPAe1M/sFP6H5t0V6ql6moS6j6se3027RPteOeOzD7Euh+Hv58meec0eOCAlH10vv7fd2N23eHAA2LmmUEAAQQQQAABBBBAAIHqIRDbIU31OKZIR6HaaC6pJpl7sFKQRE0RM0lbvFo1LhXkN3KTpV5Vm+mevz8Ws3xHBg9Yqn0VTns03t1Zfni5plWjRLVhwim+76rwumOP7h3MKkD1r5fGB/NHegGpRDWGNhXFHc+uWmHBhqEJNZfVg304JQvqhfMkmna12eYv+CIIhCbKtyWug+7G3mAESm3btDYFb1zSg7cCbMnSO16QTcEQJe07vvZLsu3Ksjy+LyPV2KruSSOhhtPTo1+08GcivE7TY158JVikQRr294KSFZX0eQ4nNWksSzr80Njm3S96zZiT1SJduGiJaYRel3p17+Imk75m87OkwRjc+1U7nDhpWtL96hzCn0PX9DobZSTdaZZXdDmsU1Ci7jNq0p0s/efVCcEq3dM0gEw27V3hCu65lOqz4PLEv4bfM7rnjH9jd7P8+Lxqyj71g919e+o93+GgA+KzMY8AAggggAACCCCAAALVQKDGBtH69CjdSb48e3Y7ImHAKJW1+iFzSQ80qqUSn/Qg9bcHn7Aly5bHrMpkNMv9veBPOL3ojU6pwFx8Up9m6uw/PoVrjcWvU2Co9b4tg8XhWmLhAFuQwZuIr9GlYJMeYuOTah7dfs/fg+aibr1qimWawsFPHaOCkon6DPp88VJ/BFNXvoJmbuQ+NdW94Jwz3Cr/9c57H0z48K1moc+98HJM3lNOTF7TJSZjGWYaNKgfs5VMFTzVg3SyoE3MBlUwo5qdfY/sGexZ7+k//uU++2rl6mCZJtSUbvTYV/wAr1uhZsT6zFVUUvnhpJF5Zam/TJJqfPY/9uhgE33O73ngsVIBmGVfrrD/e+SpIJ8mekcIomXzs6Tmm+FArwYhUUA9/v2jgOKjT48JmjzrWDt1KPkBIRtlqLzKSENP/17MPfuhJ5/1+7oM79t/73nB2y+8+4JLarZf0rw7tmZqNu5j4ZGd9V751Av4636V6H7tjif8OujkE2IC/appplGUw/dl5Ve5t975v+FN/fdpoh89YjIxgwACCCCAAAIIIIAAAlUiEFvNo0oOoWw7VVOmJnvvFdRAc6WoGWCmqWfXw2OCYw95TQ7V1PBgr0nNBi9QNO/ThTFNOMPl66GoePPmYDCA8Lr46XZeEC3cd5maI13/u//nN4drXFhgn3s1YNS3m+vbLH77NetSd1B9fN8+9sy/XorZTAMi7Ndqd3AtvFIPahpEwDWF1bmo2aMM5asgita5ZrPhbTWdrv+i+PyaV39OKnvlqpJ+gtRv201e00AdR75XC1DBMtUCig9WamRQN7CDylFzQ3VAvmjJMs36QZW/P/6MH3xo5y1XbQ41bYsPCiqQogENKipptFS5uoflOV5AdMTNf/J3pwEwwkHEijqGspR75qBTbNoHnwTBKbn94c57/Vp/av6q9+TseZ+Wem9edP5ZMQGQsuw71TZ6/4aTRpHVn9LvR17j3wPC61NNq/n0u5PfD85Rn7Xf/v4Oa99uf2verKl9tWq1qe+9cBr6/VOtRfOm4UUJp7P9WTrjtFNM9yGXRnsBJPXR5kbp1PtLA3GEg4nqY/Ho3rvvf9kow+2/Il812vJpp/S3l8a/6e9G56aRkhVI1GdcNe3U5D78I4Ka3Q87u6Sfumzb6yCa7rN3zCm7Gsg61ttuGhGzLtGM+q8bdtbp9shTu2vvKgCs0aTbH7C/NfaOf9HSZaVGg9a9Se9TEgIIIIAAAggggAACCFRPgRpbE02c8X2fKQBzUPvdfYFFJT/xuGNiOuPWdur/Sw91b70zOSaApn2cc8agmKI/+HhWzHyyGTURuuKiC2JWq9bPhImT/MCARtgMB9B0fnrAd+kDb8CAVElNiPRAGU7HHdMnPFtq+uILzi7V2b6ax6qWnJoYhQNobfZr5Y/+5wpRoCU+SOXWJXtVzZGfX3FJTC0N5VWw7mOvLy6NKBofQLvw7DOs31G9ShX500t/WKrvLtUame7VPlM58cem2otnebVeKjIp0KegbKK01KvlVF2TApjX/eJKP8AZPkZdi8le/2AffjIr5r2pPAoKahTYikxtvABefEftbn8aUCKTpNpFI35+hT/SrttOQSgFW9+bOr1UAE3vF90boqZsfpbUX9vAASfE7FpNPHUd9KfPSjiApvvEDb+6KmaU2myUEXMAFTijftDiz1efZdUk1fmGA2gKkP/siotMAWuXsmmvMrsf0bnUvVTLv9uwMcZdy5KlHt594EKvxmz4nqz7vQLr6sczPPCLylCAVCMQh/MnK5vlCCCAAAIIIIAAAgggUDUCsRGXqjmGMu+1T4/Yvor00BuurZSs4IZxTe60zS+8h5eBA45P+gCjmg96yPvDDb8yBabCfTV9MmtOsl2VWt7x4PZ27dWXe7XDkvcjdaA3KMA1wy/1H8DCARk9gIUHNYgvXEEC9REUTulqPqlmxc3X/cL0wJcsqUaIAia/+dkVduxRu/teU/5U/RclK0/9Nt3i7VMjcSZ7YNRyDSJw3c9/4tfUS1SWAj+X/vA8u/yiYUkDLdpOzVw1gMGPzh+aqJiMloWbeWnDRKPAykqDNMSnlV5NJ6Vwf0vxeTKdT7T/TMtw+VVDUIEY1YSJb0bp8uhVgbMbf311VmrV1Q99FuP7k9O+FHTV5yVR7UHnqXxRk8q56dqr/eBY+DMc3l5Bu6svuyjj90u2P0uqnTXcCxSnqgmn2mcDTujn3y/0eYhP5S0j0/dqQ68GVqKU6NqG8+kerGP9jfd51/swWern3X9UEyz+/pltex3DVZf9yB9oJv5YVq9Z6y+Kb7odn0/z+iHkd9f/0q9pm+xep+t7gVerTgG0goLdgcFE5UVZFr5mqhFHQgABBBBAAAEEEEAAgewJ5Hn97GQ29Fj29l0tS1LfZ6vWrPNqCawxBa00cqGaQzZq2DDrx6vBEFZ7+1q9dq338FTgd9Cu5mtRAoHJDub2ex4IanId0uFAPxiQLG/8ctX2UO2IVbseEvWg2qpl85jaLfHblHdeTbdkoCacajqrZq0KnsU344uyH/VXpCaoX3610s+u4F/zZvtYqgEcopRb1jx6L63wjkfvHQWlFPAoz7Ut63GUdbuiomJb7lmqmaNqYKrJozwr4rMQ5RhVC0ifGQUv9D5pnGIgjCjlKc+3320w9YO2bv16r/lgU+/z3ty/TlG3T5Yv258ldZ7/5YqV/udz584d1soLDGtAh3DAJNmxuOXZKMOVVdGv6gNNn51ly1dYPS+QqvNt6QWbogSNs22/xqsRp1p/+qZUM8/4QHpUC33VqnbsUm8QFPUnuY/XT5/OqSz3uqj7JB8CCCCAAAIIIIAAAghkV4AgWnY9q7Q0ddCv/sVc+sklF9rhh3Z0s7wigAACCCCAAAIIIIAAAggggAACCJRRoEY35yzjOdfazVzH3DpB1RxSJ/4kBBBAAAEEEEAAAQQQQAABBBBAAIHyC9TY0TnLf+o1vwSNWFenbh3bumWrTfU6qg4PPDDghL41qulgzb8anAECCCCAAAIIIIAAAggggAACCNRmAYJoNfjqPvLU6JhR69ypqJNtddpPQgABBBBAAAEEEEAAAQQQQAABBBDIjgDNObPjWCWlqJPr+KQR4DTSaFk7v44vj3kEEEAAAQQQQAABBBBAAAEEEEAAATNqotXgd0HbNq2DkTh1Gu3b7W9nDjrZG/GtWQ0+Kw4dAQQQQAABBBBAAAEEEEAAAQQQqH4CjM5Z/a5JRke0ecsWW7N2nR84q1ePmGhGeGRGAAEEEEAAAQQQQAABBBBAAAEEIgoQRIsIRTYEEEAAAQQQQAABBBBAAAEEEEAAgdwVoE+03L32nDkCCCCAAAIIIIAAAggggAACCCCAQEQBgmgRociGAAIIIIAAAggggAACCCCAAAIIIJC7AgTRcvfac+YIIIAAAggggAACCCCAAAIIIIAAAhEFCKJFhCIbAggggAACCCCAAAIIIIAAAggggEDuChBEy91rz5kjgAACCCCAAAIIIIAAAggggAACCEQUIIgWEYpsCCCAAAIIIIAAAggggAACCCCAAAK5K0AQLXevPWeOAAIIIIAAAggggAACCCCAAAIIIBBRgCBaRCiyIYAAAggggAACCCCAAAIIIIAAAgjkrgBBtNy99pw5AggggAACCCCAAAIIIIAAAggggEBEAYJoEaHIhgACCCCAAAIIIIAAAggggAACCCCQuwIE0XL32nPmCCCAAAIIIIAAAggggAACCCCAAAIRBQiiRYQiGwIIIIAAAggggAACCCCAAAIIIIBA7goQRMvda8+ZI4AAAggggAACCCCAAAIIIIAAAghEFCCIFhGKbAgggAACCCCAAAIIIIAAAggggAACuStAEC13rz1njgACCCCAAAIIIIAAAggggAACCCAQUYAgWkQosiGAAAIIIIAAAggggAACCCCAAAII5K4AQbTcvfacOQIIIIAAAggggAACCCCAAAIIIIBARAGCaBGhyIYAAggggAACCCCAAAIIIIAAAgggkLsCBNFy99pz5ggggAACCCCAAAIIIIAAAggggAACEQUIokWEIhsCCCCAAAIIIIAAAggggAACCCCAQO4KEETL3WvPmSOAAAIIIIAAAggggAACCCCAAAIIRBQgiBYRimwIIIAAAggggAACCCCAAAIIIIAAArkrQBAtd689Z44AAggggAACCCCAAAIIIIAAAgggEFGgXsR8NTLbxo0bbenSpdasWTP/r0aeBAeNAAIIIIBAJQls2LDBduzYYXXr1rXCwsKke42aL2kBFbCiOh5TBZwmRSKAAAJVLsD9tsovAQeAAAJVKJC300tVuP8K2fWUKVPs9ttvtyVLlgTlFxQU2HHHHWfXXnut7bXXXsHyM844w7788kvr1KmT/eMf/wiW18aJ7du325FHHumf2qBBg+x3v/tdtTrN8l6L1atX29dff+2f00EHHeQ/BFarE+RgEEAAgRQCFXGPzrTMU045xdatW2cHHnigPffcc/7RJro3J8pXEfdgt++jjz7a/va3v6XQM0t0TCk3CK2siGMPFc8kAgggEAh89tlnNmzYsGD+xhtvNN3r4lOy+1Ky5fHbZ2M+2b7Kc7/NxnFRBgIIIFCVArWuOecjjzxiV111VUwATcCbNm2y8ePH249+9CP7/PPPA/OtW7f603rQqO0pHC9VTYPqlsp7LZ566im74IIL/L9vvvmmup0ex4MAAgikFKiIe3Q2yox6b66Ie3BRUZFvpu/wikwVcewVebyUjQACNVdAzyPh9J///Cc8G0wnuy8lWx5smMWJytxXFg+bohBAAIEKFahVzTk//fRTu++++wKws846y/r27WvFxcV+LbM5c+b4tc7+/Oc/2wMPPBDkYwIBBBBAAAEEzK677jr/O7Nx48YpOaLmS1lIlldWx2PK8ilSHAII1HAB/Wj/0ksvxZzFRx99ZMuXL7fWrVvHLK/OM9xvq/PV4dgQQKCiBWpVEO3OO+8MvC6//HLTn0snnHCCff/73/ebqXzwwQd+X2n777+/W13q9eWXX7YJEybYggULbI899rAePXpYly5d7MQTT7Q6dUoq8Kk2l/KNHTvWVDV7v/32sz59+ljPnj3t2GOPtby8PL/cqPmU+cMPP7R//vOfNn/+fL85Yrdu3ezMM8/09+0OMpPy3DaJXt944w0bN26cffHFF3bIIYfY2Wef7R97OG8Uh5tvvtk2b95svXv3trZt2/oeqj1wxx132MiRI/3ijjnmGGvSpIm99tprNmvWLOvYsaNfY0ym6ZL+YfHggw/a7Nmz/VqEHTp0sMMOO8yuuOIKa9Gihb/5/fffb5MmTQqKUrMfHY+arZIQQACBTAVUgyuT+1ey+6D7XopyH4s/xmzdo8Plpivz/ffft/Xr11vLli39LhDC24an4/Mluwfru2zVqlX+pr/85S+De7aaCP3lL3/xl+v74JJLLgkXn3BaXTU8+eST/ndW+/btbciQITZgwIAgb/wxaUWU78tkx+6+PzK5dtOmTbPnn3/e5s6d6/fFOnz4cNMPePpTtxJqtqWU7v0S5bv3lltuMdUS7N69uzVt2tReffVV/98ORx11lN9UTPt7+OGHbcaMGVa/fn07/fTTbejQof7++R8CCFSNgJ5B1GReSZ9RV8tW92a1lnEp2X1p8eLFKf+9G+U5Ql3efPvtt/6/pdV0/5VXXvH/ba5/Nx9//PGme4iedZIdg+6Nie63Ovao98uox+A8eEUAAQSqk0Ct6RMt3O+LgBUA23PPPWOsP/nkEz8opoW9evWydu3a+UEW/QNfgZlnnnnGz68AzOOPPx6zrZtRc8FrrrnGn73rrrtM1ZwTpXAQL2o+9T+jL5VEScsVwFOKWl58Odu2bfO/GOOXh+dvvfVWv18ZLYvqIEslBRHVv5ySglwy1BeyUvgfCv6CXf9TrcCTTjrJn9OXcvy10DW7+uqrg39khLdVmapReOihhwZ94YTX64Fh1KhR4UVMI4AAApEEFETL5P6V7D74xBNPWNT7WEXcozMtM1E/N4nuzfH53HwYV/fgffbZJ/g+/e1vf+sHvpRHQSJ3fx4xYoSde+654U2D6UTlBiu9CZWh/Si5vOH+3KJ8X7rt/EJ2/c99f0S9dtpMD8GqnRGf3PefXidOnOivTvV+yfS715Uf3q9+0FIK9w2refULe95552mShAACVSCg/oj//e9/+3u+55577Gc/+5k/rc+sAvAuJbsvvffee0EQzuV196uozxGu7ET3DpXp7skun9uPXt2+3Lrw/TaT+6XbPt0xhPfNNAIIIFBdBEqqVFWXoynHcaxcuTLYWoMExAfQtLJr166mJp76UwAtUdqyZYuNHj3aX6WgkGoi3HTTTUFW1TrTw5V+wXEBNJWrvtj0xagHBiUNUqDAXtR8X331VRBA0xeKgnCXXnqpH3xSefpCU+2uqOVpm3RJX9gXXXRRTO0z1R5T89eoDuF9uACaDNSxfzjplzZ5an9ucAOtVxDN9XkTzq9p+f3xj38MAmhy/tWvfuUPAqH1KvO2227zr4d+3VdtN5d03c455xw3yysCCCBQZoFM7l/x98FM7mPxB5jte7TKT1Vm/P4zmU92D9aDkktvv/22mzQ9CLrUv39/N5nyVTWPf/rTn/o1r1zGZ5991k2Weo36fZns2DO5dqqNHf4RTLU59G8NJVfTpNQBegvi3y9l+e5V+bqugwcPDnah4Jn+LrvsMv/fPm5FKi+Xh1cEEKgYAf171wXQdD/TgCn6U9LnVTV3XUp2X0q2POpzhCtfr7p36Efv8L+ttdwdY7J9KU98yuR+Gd423TGE8zKNAAIIVBeBWtOcU01DXHJN/Nx8Jq9qxqJmjUr9+vXzm3GqarICQ6p+rZu9vgSVz6UVK1bYsmXLTE0v1XRn6dKl/io1s4iaz/06rQ3V5EXNVJTq1q1rf//73/1p5VGtK5dS7VfbpUoK1KmJpJqA6ItPQTv9gqRzVLNJNXWN4qBywilcky3cobXyPfTQQ9a8eXO/ec3FF1/sN2/R/tSXnQJk8UnH4waBUI0QVStX0ohG+lMTWjWRmTdvnt98VlXkXZNONd91Ac34cplHAAEEMhHI9P4Vvg+qaU3U+5hqRLuUrXt0gwYNXJH+jzKp7vvqiqCsSV0YJLsHK8CjB8R3333X//7UMWlaST+q6HshSlLNY303qcmT+zHGNYtKtH3U799kx57JtdNgNu5YVGvcBdQ04l64iVai4wy/X1QbO9PvXn3XjRkzxm9+1aZNm6BvWHV5oCCaXl2tD10HEgIIVI3AO++8E+xYNXzV7YvuF5MnT/aXq0m2uldRSnZf0rpE91o3mrLWp3qOOPXUU5UlSKr5utdee/k1VPVvZz3nuPtEqmMICtg1kcm/2cPPMto81THE74d5BBBAoDoI1JqaaPoCcOnrr792kxm/KgD3gx/8wP9H/aOPPur3CaNfd90/jl2BeihQjTcl/aNXtdWU74YbbvD7B1MTxUaNGvm/DkfJpyCcS2p+on/w6s8F0LROD2JR9+vKSvaqgJ8CaEoKuB133HFBVv0yHtUh2Mib0ENfuNZBeJ325x6U1M+Cvphdcr/Eu3n3umjRIjcZ/FLnFoRrnYXzufW8IoAAAtkSyOT+FX8fDN+fXI0Dd1yp7mMVcY9OV6Y7rmy/hmtITZ8+3f+hRg9qSnqQjJL0neT6MdV3lpoQKW3YsCHp5uX9vszk2oW/w1ULzSV9/6f6QSf+/VKW7171D+f6am3VqpXbdWCkBSqXhAACVSugvsdcUh/Kug+qT0OXNEqnftguSwrfg1I9R4TL1n3UPT/pHqJ7ppK7P4fzppvO5H4ZLiubxxAul2kEEECgIgVqTRAt/A/HhQsX+oGseDj1k3b33Xf7f6rFlCgpWKYOjlWjTL8M6YtEv3irKWJ8UvPNcPBJ6xUQUhNENcV0KUq+7777zmX3X9WkUn/6B7b7czW7opQXU1iCGQX4wik/Pz+YVUfMmTi4DcO1KNwy9+oCdm4+PPJbsn8whJt57r333m5T/zU8r+YvJAQQQKCiBDK5f8XfB8t6H6uIe3S6MivK7+STTw6KVg20qVOnBvPx36HBiriJ8He8VqWrbe02L8/3ZSbXTt/XLjVs2NBN+sGt8PdrsGLXRPz7pSzfveEywy4usBZezzQCCFSNwNq1a4MauDoCDTqi+5+reapl+vyrlllZUibPEa58DZyWrZTJ/TK8z2weQ7hcphFAAIGKFKg1zTn1j1b94qumfQp8qWnDhRdeGNjp5q7RsNyvKxogIFHSP/BdzSh1vvvzn//c1PREtdPCSc1HN27c6Afcfv3rX/tNCzUql4at1j50HBoRS//wj5LP/cKufWg0rfh/WLt9R91vulEvNcqZglfuH9waZcclDbEd1cFto9d69ZK/neKDlmom41KyIb3dL2LKp9oL4doMsnYpUYDTreMVAQQQKK9AJvev+PtgWe9jFXGPTldmeZ2Sba97tJrsq7nPW2+9FdSK0siaFfkAVd7vy0yunfpfc0nfb27UUDWLcv+mcOvDr/Hvl7J894bLYxoBBKqnwJtvvhnpwMaPH2+qpZZpivockWm5UfNncr+MWib5EEAAgeoqkDzqUV2POMVxXXXVVaY/JVVl1i866tdMzT3UD4wLoKkJTbKmDeF/7KrpjQJoCjApKOaSaoSplpp+4VZSgE19iqkJhwY00L6U9KuTqjdHyeeafGo79Q2gzjwV4PrLX/5i+kJVuu+++0wdh0Ypz98gxf9k8Yc//MHOP/98/1cvPdi4pGMJ/xKWysFtk+5VfnJRc0/9QyK8v/hBCFxZRxxxhJv0h98+/PDDfePXXnst6D9CtfTi+1bQRnp4StWEJiiYCQQQQCCNQFnuX67Ist7HKuIena5Md8zZeI2/B5922mlBv5v6blb63ve+l41dJS0j6vd0fAHu2DO5du7fFypLfRM1adLE7zLhn//8Z3zxKeej/hskZSGsRACBaifgOuvXgWmUXNfFieY1MIn+3a+kfBpMLL7msLsv+ZlC/3PLoz5HlCVA53bn9uXmw6+Z3C/D2zGNAAII1ESBWhVEO+qoo/yhl1UbTOnxxx/3/8IXRoGV8Gib4XWaDgd0fvGLX/j9A7gONl1eNdvQvhTA0T+cNRKn/lS2ezjQOv3yvm3btkj59A9uVevW4AHqHH/gwIFud/6rytIXn/pfi7LfmI2TzMjJWbks6oRYgcCoDoWFhW7TtK/qFFp/4aRmr+GmmeF1Wq5roCa4Suqo2XXW7PJpvTuG8D9IVAtx6NCh/uiqLi+vCCCAQFkFMr1/uf1kch/T90U4ZeMeHW5aqLJTlRned1mmU92D1WG1OtB3Sd9j8X3EuXXZeo36Pa39JTv2qN9B+h5SB/7uO869ZnouFfHdm+kxkB8BBLIr8MUXXwQ/xutH/HPPPdcfVCC8F7WgUW1dJdVIVW3WZPelRMuvv/76SM8R4X1GmU60r5EjR5baNJPvulIbswABBBCoYQK1pk805z5q1Cg/SKZ/oMcnjYDzxBNPWLNmzYJV9evX96dds0Z9aal2ltteATTVZlM/aS6NGzfOr8mm4I7r3FjrXABNy1SbTEE1fVlGyacRem655RZ/324/7lW1t1SzTilqeW7b8Kv24VL//v1NI16Gk0YQUxBNKapDePtU/a+o9p8cw+miiy4K9qfl8ddCy1TL77e//a0mSyXVyFOgzCUN5hCuTq7RUUkIIIBAeQWi3L/cPhLdB6PexyriHp1JmTqH+OaFWpbo3pwoX6p7sL4Pw98B6ictPsCnfSVLiVzj88YfUybfl8mOPeq107Ho+1MPl+7fBaoZcs011wTz8TVLtE38eWXzuzd87eP3o32TEECgcgTCTTlVKzf82XRHoM++SxqlUynZfSnRcpUZ5TnC7UOv8fcFN++egZQn0b60PP5+q2WZ3C+VX8nts2Ru93z4GNw6XhFAAIHqIpDnNU3cWV0OJpvHoc7x1fRRzSl1I1ZwRf+Ij5rUWb2a8BxwwAF+zaxk22k/an6hKs6i1D/a1f9L/JdC1HzajzoHXbx4sWmbZPvPpLxkx+72tXz5cmvXrl2pquNaH9Uh0T7k4QJ1araj5qPqN0YjCGk0sVSdLceXpxoaS5cutZUrV/r9zLVp0ybozy0+r/ah2oKqGeBqqcXnYR4BBBBIJZDN+5fbTyb3MbeNvg+yfY9OV6bbd1lfk92D1d/n/fff7xer5v3hUenKuq8o22XyfZns2NNdO31X6vtJSd9tql2uH+fU96gGJ1JSX6fPPPOMP53uf+X57k1XNusRQKBmCSS7LyVbHuU5IlOBZPtKVE66+2WibViGAAII1CSBWhtEq0kXobYea6KH0Np6rpwXAgjULgHuX9m7np9//rnNnDnTH/VaXSDoh6axY8cmrI2Rvb1Wbkn6kWfIkCH+TvXD3XXXXWctW7a0F1980e/TUyvOOussU5MrEgIIIIAAAggggEDNFahVfaLV3MvAkSOAAAIIIFA7BTQYzEMPPRScnPrCTNScKchQAyc0Mp5qXGsgIAUK4/teVW14dWFAQgABBBBAAAEEEKjZAgTRavb1q/ZHr+acakqj5pskBBBAoCYJcP/KztXaa6+9/Npn6o+0b9++pj6BamNSf0QKpH388cf+CNfqJ7Vjx47+3wUXXED3ArXxonNOCCCAAAIIIJBzAjTnzLlLzgkjgAACCCCAAAIIIIAAAggggAACCGQqUOtG58wUgPwIIIAAAggggAACCCCAYWT1ewAAQABJREFUAAIIIIAAAgikEyCIlk6I9QgggAACCCCAAAIIIIAAAggggAACOS9AEC3n3wIAIIAAAggggAACCCCAAAIIIIAAAgikEyCIlk6I9QgggAACCCCAAAIIIIAAAggggAACOS9AEC3n3wIAIIAAAggggAACCCCAAAIIIIAAAgikEyCIlk6I9QgggAACCCCAAAIIIIAAAggggAACOS9AEC3n3wIAIIAAAggggAACCCCAAAIIIIAAAgikEyCIlk6I9QgggAACCCCAAAIIIIAAAggggAACOS9QL+cFAEAAAQTKI7D4M7Mpb5qtXm7WqbvZ0SebFTYuT4lsiwACCCCAAAIIIIAAAgggUA0FqIlWDS8Kh4QAAjVEYNNGs3/81aye93tEj+PMPn7PbOyjNeTgOUwEEEAAAQQQQAABBBBAAIFMBKiJlokWeRFAAIGwwLyPzHbuNDvjIi+QVt/smzVmM6aEczCNAAIIIIAAAggggAACCCBQSwSoiVZLLiSngQACVSCg5pt5eWaP3WE2/lmvJtoks4MPr4IDYZcIIIAAAggggAACCCCAAAIVLUBNtIoWpnwEEKi9AvXqmu21j9lXy8xWfllSK61F69p7vpwZAggggAACCCCAAAIIIJDDAtREy+GLz6kjgEA5Bd56yWz9WrMrbjC7/h6zzj3NJr5itmN7OQtmcwQQQAABBBBAAAEEEEAAgeomQBCtul0RjgcBBGqOwPJFZns3NWveyqyOdzvt07+kNtqqFTXnHDhSBBBAAAEEEEAAAQQQQACBSAIE0SIxkQkBBBBIINDca7qpmmgL55ht22o25U2zBo3M9m2TIDOLEEAAAQQQQAABBBBAAAEEarJA3k4v1eQT4NgRQACBKhPYUmT2xN0lfaLpIBrlmw0YatbtmCo7JHaMAAIIIIAAAggggAACCCBQMQIE0SrGlVIRQCCXBLYUm61ZZda6bS6dNeeKAAIIIIAAAggggAACCOSUAEG0nLrcnCwCCCCAAAIIIIAAAggggAACCCCAQFkE6BOtLGpsgwACCCCAAAIIIIAAAggggAACCCCQUwIE0XLqcnOyCCCAAAIIIIAAAggggAACCCCAAAJlESCIVhY1tkEAAQQQQAABBBBAAAEEEEAAAQQQyCkBgmg5dbk5WQQQQAABBBBAAAEEEEAAAQQQQACBsggQRCuLGtsggAACCCCAAAIIIIAAAggggAACCOSUQL2cOtsMTnbKlCn2+uuv29atW61///52/PHHW506xBwzICQrAgggUOsFpk2bZuPGjfO/K0466ST/+yLRSc+fP99efPFFW7hwoXXu3NkuvfRSKywsTJSVZQgggAACCKQUWLdund188812++23W6NGjVLmZSUCCCCAQHYFiAol8JwxY4aNGDHCunbtaj179rRRo0bZxIkTE+RkEQIIIIBArgrMmTPHhg8fbvn5+da3b1//u2LChAmlONasWWOXXXaZbdu2zc477zybPHmyPf3006XysQABBBBAAIFUAsXFxfbKK6/YjTfeaJMmTfK/V1LlZx0CCCCAQPYFqImWwPTtt9/2A2iDBw/21yqAptoGJ5xwQoLcLEIAAQQQyEWBMWPG2JAhQ/wfXXT+8+bNs9GjR9uJJ54Yw/HCCy9Y7969beTIkf7y9u3b29KlS2PyMIMAAggggEA6ge+++87eeeeddNlYjwACCCBQgQLUREuAe+yxx/o1BR5++GF78MEHTUE1AmgJoFiEAAII5LDA4sWL/aaZjqBt27Z+c003714VMGvTpo3ddNNNduWVV9rMmTOtT58+bjWvCCCAAAIIRBJo3ry53XbbbfbLX/4yUn4yIYAAAghkX4AgWgLTVq1aWYsWLez++++3Bx54wAoKCqxJkyYJcsYuUl4SAggggEBuCCg41rhx4+BkW7ZsaeqnJj4tWrTInnrqKT9v9+7d7Y477jDVTkuX+E5JJ8R6BBBAAIEoAnyfRFEiDwIIIBBNgOacCZzuuusu22+//UxNdfLy8uz666+3Rx55xP/lx2W/9dZb7fnnn3ezwesVV1wRTDOBAAJZEnjjX2ZTSvc1laXSc6eYMy4yO4IaUNm64OoLbf369UFx6qumU6dOwbyb2LFjh9+cU31tKjVt2tT/fhk2bJjLYnynBBRMIIAAAgiUUWD16tU2cODAhFvzjJKQhYUIIIBAxgIE0RKQaWCBCy+80K+BptXq3+bee++Nyam+bVz/Nm5Fr1693CSvCCCAAAK1XKB169a2fPny4CyXLFliHTp0CObdhGo3h2sza3rt2rW2c+dO/4ca5eM7xWnxigACCCBQVgE195w+fXqpzXlGKUXCAgQQQKDMAjTnTEDXsWNHe+ONN2zlypWmDjw1nexXnQSbswgBBBBAIAcE+vfv79coW7Vqld/PmWova1RnJf0Y8+qrr/rT/fr1s/Hjx9v8+fPt66+/trFjx5oGrlFNZxICCCCAAAIIIIAAAgjUHAGCaAmulTrrbNSokZ122mmmh6Q6derY2WefnSAnixBAAAEEclVg6NChfjPNQYMG2SWXXGJdunTxg2PymDJlij355JM+jdYrkKYazieffLItW7bMzjrrrFxl47wRQAABBMop4H6Eca/lLI7NEUAAAQQyEMjzmpPszCB/TmVVXzfbt2/3+6+JcuKqKp2oCnWUbcmDAAIpBOgTLQVOBqvoEy0DrOhZVROtbt26ab8r1qxZY+o3Tc1A9eNMusR3Sjoh1iOAAAIIRBHg+ySKEnkQQACBaAL0iZbCae+9906xllUIIIAAAgiYP5pzFIdmzZpFyUYeBBBAAAEEEEAAAQQQqKYC6X8Kr6YHzmEhgAACCCCAAAIIIIAAAggggAACCCBQWQIE0SpLmv0ggAACCCCAAAIIIIAAAggggAACCNRYAZpz1thLx4EjgAACCCCAAAIIIBBdYPUjD0fPvCtn8x9fmvE2bIAAAggggEBtFSCIVluvLOeFAAIIIIAAAggggEBIYMvCz23t44+FlqSe3GPASUYQLbURaxFAAAEEckuAIFpuXW/OFgEEEEAAAQQQQAABBBBAIIcFNkyaFJx942OOCaaZQACB9AL0iZbeiBwIIIAAAggggAACCCCAAAII1AqBorlz7PNzz7Fl1//GdhQV1Ypz4iQQqCwBgmiVJc1+EEAAAQQQQAABBBBAAAEEEKhigeI5s/0jUBPvotkl01V8SOwegRojQBCtxlwqDhQBBBBAAAEEEEAAAQQQQACB8gkUezXRXCqeSRDNWfCKQBQBgmhRlMiDAAIIIIAAAggggAACCCCAQA0XKJ47z6t9NtfyGjX0z6Ro1swafkYcPgKVK0AQrXK92RsCCCCAAAIIIIAAAggggAACVSJQNMerhbZ9uxV07+Hvv2guQbQquRDstMYKEESrsZeOA0cAAQQQQAABBBBAAAEEEEAguoBrylnYu7fl1a1rRV5zTvpFi+5HTgQIovEeQAABBBBAAAEEEEAAAQQQQCAHBDQyp1JBj55W0LOn2c6dVjx7dx9pOUDAKSJQLgGCaOXiY2MEEEAAAQQQQAABBBBAAAEEqr/A1pWrrGhWyUACBT16eEG0XU066Ret+l88jrDaCBBEqzaXggNBAAEEEEAAAQQQQAABBBBAoGIE1JRz+9o11rBTJ6u3zz5W2LO3vyMGF6gYb0qtnQIE0WrndeWsEEAAAQQQQAABBBBAAAEEEAgEimbP8qcLvaacSvlqzukl9Yu2edEX/jT/QwCB1AIE0VL7sBYBBBBAAAEEEEAAAQQQQACBGi9QrJE5vaT+0JTqN29ujQ47zHYUbfSaec70l/E/BBBILUAQLbUPaxFAAAEEEEAAAQQQQAABBBCo0QI7t2zZ3R9ar5K+0HRCrlZa8SwGF6jRF5iDrzQBgmiVRs2OEEAAAQQQQAABBBBAAAEEEKh8AY3KuXnhAqvXzKt9dnCH4AAKeu9q0klNtMCECQRSCRBES6XDOgQQQAABBBBAAAEEEEAAAQRquEDQlHPXiJzudAq6uxE6Z9u2r1a6xbwigEASgXpJlrMYAQQQQAABBNIITJs2zcaNG2dbt261k046yfr3719qi+3bt9utt94as1z5+vXrF7OMGQQQQAABBBBAoKIENs3ZNajArsEE3H4aHniQNWi3v21ZvNQ2zZlpe+7b0q3iFQEEEghQEy0BCosQQAABBBBIJzDH65x3+PDhlp+fb3379rVRo0bZhAkTSm321Vdf2euvv26dO3cO/po1a1YqHwsQQAABBBBAAIGKEnB9nhX07FVqFwU9+vjLimbNLrWOBQggECtATbRYD+YQQAABBBCIJDBmzBgbMmSIjRgxws8/b948Gz16tJ144okx23/55Zd2yCGH2NChQ2OWM4MAAggggAACCFSGwJbFS7xBBWZZXp06lr+r+WZ4v2rSuf6FfzFCZxiFaQSSCFATLQkMixFAAAEEEEglsHjxYr9mmcvTtm1bW7hwoZsNXhVE09+ll15ql112mb366qt+888gAxMIIIAAAggggEAFChR7gwrsLCqy/B49rE6D+qX2VNi7pF+04tlzbcemTaXWswABBHYLEETbbcEUAggggAACkQWWLl1qjRs3DvK3bNnS1q1bF8y7iWXLltm2bdvsrLPO8vtB++Mf/2j333+/W80rAggggAACCCBQoQJFc0qaabpBBOJ3ln9EF2/UzqZev2iLrOjjj+NXM48AAiEBmnOGMJhEAAEEEEAgqoD6Qlu/fn2Qvbi42Dp16hTMuwnVPtNfo0aN/EUbNmywF1980X72s5+5LP7AA88//3wwzwQCCCCAAAIIIJAtgSKvhplSYdygAuHy1czzO68PV/WLVnjMMeFVTCOAQEiAIFoIg0kEEEAAAQSiCrRu3dqWL18eZF+yZIl16NAhmHcTkydPtoMOOsjU3FOpVatW1rRpU7fafx05cqTpL5x69Srd8W94PdMIIIAAArVHIMpozzrbKVOm+IPVaFRojfR8/PHHWx2vnysSAskEtn/7nRXN3jUyZ6+eybL5ATYF0TRCJwkBBJILcMdNbsMaBBBAAAEEkgro4UWDC6xatcpmzpzpT3ft2tXPP2PGDL/vM8188skn9pvf/Ma2bNli6kdNNc5OOeWUpOWyAgEEEEAgtwSijvas7xYNZqPvmp5ejSKNCj1x4sTcwuJsMxZQf2hbly21Bu0OsHr7tkq6fWGPkgBb0SyCaEmRWIGAJ0BNNN4GCCCAAAIIlEFAo21OnTrVBg0a5G89YMAAGzx4sD+tmgJ6sDn11FPtggsusA8//NCO2dU04rDDDrPTTz+9DHtkEwQQQACB2igQdbTnt99+2w+gue8afc+oBtsJJ5xQG1k4pywJBP2h9U5eC027KvAGHchr2NA2z/vU7xctv1u3LB0BxSBQuwQIotWu68nZIIAAAghUkkC9evXszjvv9Gui1a1bN6aJ5uWXX276U2rRooU98cQTtnr1asvLy7NmzZpV0hGyGwQQQACBmiCgWsrhH1fU/H/cuHGlDv3YY4+1xx9/3B5++GHbsWOHKah23333lcrHAgTCAsWz5vizrqZZeF14Os/ru7XAq+G4cdIkv180gmhhHaYR2C1AEG23BVMIIIAAAghkLKAgWZTUvHnzKNnIgwACCCCQYwJRR3tWn5r6znEjPBcUFFiTJk1yTIvTzVSgeO6ukTm9mmbpkkbvLAmi0aQznRXrc1eAIFruXnvOHAEEEEAAAQQQQAABBKpYIOpoz3fddZftt99+fh+cqtl8/fXX2yOPPGK33Xabfwaq8Txw4MAqPht2X50Eij6db5u8vvTq7r235R9+RNpDU000pSIGF0hrRYbcFSCIlrvXnjNHAAEEEEAAAQQQQACBKhaIOtqzBha48MILTTXQlE488US79957g6NXjefp06cH826C0Z6dRO69Fs/Z1ZQzYv9mhT1Laqtt8pqAFn/2qTXq0DH30DhjBNIIMDpnGiBWI4AAAggggAACCCCAAAIVJRB1tOeOHTvaG2+8YStXrrTvvvvOn6bmWUVdldpRbvHskqacjSI05dQZ12vazPK7HG7ekOJ+v2i1Q4GzQCC7AgTRsutJaQgggAACCCCAAAIIIIBAZAGN9ty7d29/tOdLLrnEunTpEjPa85NPPumX9ctf/tIaeZ2/n3baaabAW506dezss8+OvB8y5p5AUBMtzcicYRk3AEHxTPpFC7swjYAToDmnk4h7nT9/vr344ou2cOFC69y5s1166aVWWFgYl4tZBBBAAAEEEEAAAQQQQKDsAlFHe27Xrp0/qMD69ett+/btMaNCl33vbFlbBbatWWWbPvnYP73CHr0jn2Z+Ty/vY497NdEIokVGI2NOCVATLcHlXrNmjV122WW2bds2O++882zy5Mn29NNPJ8jJIgQQQAABBBBAAAEEEECg/AIaebNp06ZpC9rb6yQ+Sr60BZGhVgsUzZ5r29d/4zfPrJNBZZBCb4ROpSKvX7RtX62s1UacHAJlEaAmWgK1F154wa9SPXLkSH9t+/btTUNPkxBAAAEEEEAAAQQQQAABBBCo7gLFXhBMyTXPjHq8DQ5oZw0PaG+bF31hGz/+wPb63qCom5IPgZwQoCZagsusgFmbNm3spptusiuvvNJmeu3B+/TpkyAnixBAAAEEEEAAAQQQQAABBBCoXgJFc0uCaPnde2V8YAW7+lBTbTYSAgjEChBEi/Xw5xYtWmRPPfWUNW7c2Lp372533HGHqXYaCQEEEEAAAQQQQAABBBBAAIHqLLDT6zOvaOYM/xALe/bM+FALdjXpLJ5VUkbGBbABArVYgOacCS7ujh07/OacI0aM8Neqz4ExY8bYsGHDgty33nqrPf/888E8EwgggAACCCCAAAIIIIAAAghUtUDxnNm2eeECq79fa1PzzEyTC7wVzZxjO4qKrE5+fqZFkB+BWitATbQEl7ZVq1a2//77B2uaNGlia9eutZ07dwbL1F/a9OnTY/6ClUwggAACCCCAAAIIIIAAAgggUAUCrj+0gm6Z10LT4TbqfLjVbdbUtn613DZOn1oFZ8AuEai+AgTRElybfv362fjx423+/Pn29ddf29ixY23w4MGWl5eXIDeLEEAAAQQQQAABBBBAAAEEEKgeApu8mmhKrkZZWY6qsGdJn+BFM0vKKksZbINAbRQgiJbgqg4aNMgUSLvwwgvt5JNPtmXLltlZZ52VICeLEEAAAQQQQAABBBBAAAEEEKg+AmrOqVTQq2w10bRtYa8eerHi2QTRfAj+h8AuAfpES/BWqF+/vqnPs2uuucaKi4utdevWVqcO8cYEVCxCAAEEEEAAAQQQQAABBBCoJgJbli21TV63Q3UKCqygR0kgrCyHVtCjJABXPIsgWln82Kb2ChAZSnFtmzVrZm3atCGAlsKIVQgggAACCCCAAAIIIIAAAtVDQP2haXTO/G7dynVABT26WZ43oECxN0DBRi8oR0IAgRIBaqLxTkAAAQQQQAABBBBAoJIEvnntNdv47rsZ7a31736XUX4yI4BA7goUzS2pOVa4qyZZWSXy6jf0+1Tb4N2vimbM8pp39iprUWyHQK0SIIhWqy4nJ4MAAggggAACCCBQ3QXWPPJw5EPMq1vXCKJF5iIjAjkvULSrD7P8cjTldIgF3XuYH0SbO9Mt4hWBnBegOWfOvwUAQAABBBBAAAEEEEAAAQQQqOkCOzZ+Z0UffeyfRnlroqmQgt4l/aIVzSKIVtPfGxx/9gQIomXPkpIQQAABBBBAAAEEEEAAAQQQqBKB4lnzbOvKr6xhp05Wr1nTch9DQbeSgQk2e/2sFS/4rNzlUQACtUGAIFptuIqcAwIIIIBAlQhMmzbNbrnlFrvhhhvsrbfeSnsMb7zxhl1//fVp85EBAQQQQAABBBDIVKBozhx/k8Ku3TPdNGH+evvs4w9QsHPnTiv64MOEeViIQK4JEETLtSvO+SKAAAIIZEVgjvcP1eHDh1u+N3JV3759bdSoUTZhwoSkZS9fvtyuu+46mz9/ftI8rEAAAQQQQAABBMoq4AYVKOhT0gyzrOWEtyvYNUDBplmzwouZRiBnBQii5eyl58QRQAABBMojMGbMGBsyZIiNGDHCBg4c6E+PHj06YZHbvaHmb7zxRhswYEDC9SxEAAEEEEAAAQTKK1A0Y4ZfhAt8lbc8bV/Yk37RsuFIGbVHgCBa7bmWnAkCCCCAQCUKLF682Dp37hzssW3btrZw4cJgPjzx2GOPWatWreyUU04JL2YaAQQQQAABBBDIikDxpwtMAwDUa9nCGnXomJUyVUhBj5KmoUVzZnv9ra3KWrkUhEBNFSCIVlOvHMeNAAIIIFClAkuXLrXGjRsHx9CyZUtbt25dMO8mZs6cac8++6xde+21bhGvCCCAAAIIIIBAVgWK5872yyvo2i2r5TbYv601PPgg27lxk22aNjWrZVMYAjVRgCBaTbxqHDMCCCCAQJULqC+09evXB8dRXFxsnbzRsMJJHfFq0IGTTz7ZPvvsM1uwYIFt2LDBpk+fbtu2bQuy3nrrrdarV6+Yv2AlEwgggAACCCCAQBqB4l2DChT0KBlRM032jFbvbtJJv2gZwZG5VgrUq5VnxUkhgAACCCBQwQKtW7c2DRbg0pIlS6xDhw5u1n9VEK1hw4b2/vvv+38Kuqm22u23324PPfSQ7bnnnn6+kSNHmv7CSUE1EgIIIIAAAgggEEWgaHZJgKtw10AAUbaJmie/h/dvkmef85uLRt2GfAjUVgGCaLX1ynJeCCCAAAIVKtC/f3+799577fzzz7eVK1eaBhq4/PLL/X3O8Dr2XbFihZ166qn23HPPBccxceJEu/vuu2OWBSuZQAABBBBAAAEEyiCwdfUa2zj1fctrUN8KemdvZE53KIU9S2q3bZo123Zs3mJ1GjZwq3hFIOcEaM6Zc5ecE0YAAQQQyIbA0KFDrXfv3jZo0CC75JJLrEuXLjZ48GC/6ClTptiTTz6Zjd1QBgIIIIAAAgggkFKgeO4c21G00fK7dLW8+g1T5i3LykadDrV6LVra9rVrbMOk98pSBNsgUGsEqIlWay4lJ4IAAgggUJkC9erVszvvvNNWrVpldevWtaZNmwa7V400VystWOhNHHfccf5feBnTCCCAAAIIIIBAeQSKvZEzldxImuUpK9m2hV4Nt29efsWKvdr2e3q18UkI5KoANdFy9cpz3ggggAACWRFo0aJFTAAtK4VSCAIIIIAAAgggEFGgyKuJpuQGAIi4WUbZXF9rRbNmZrQdmRGobQIE0WrbFeV8EEAAAQQQQAABBBBAAAEEckNgxw4r+mC6f64F3StuUKJG3bv7+yjy+kUjIZDLAjTnzOWrz7kjgAACCCCAAAIIIIBAlQtMmzbNxo0bZ1u3brWTTjrJNHhNojR//nx78cUXbeHChda5c2e79NJLrbCwMFFWluWIQPHsubZ50RJrdNDBVr91qwo764JuPayO917bsnSJbfrwQ6/paMlgAxW2QwpGoJoKUBOtml4YDgsBBBBAAAEEEEAAAQRqv8CcOXNs+PDhlp+fb3379rVRo0bZhAkTSp34mjVr7LLLLrNt27bZeeedZ5MnT7ann366VD4W5JZA0ZxZ/gk36talQk+8jjfyZ2HvkppuG3fVfKvQHVI4AtVUgJpo1fTCcFgIIIAAAggggAACCCBQ+wXGjBljQ4YMsREjRvgnO2/ePBs9erSdeOKJMSf/wgsv+KNCjxw50l/evn17W7p0aUweZnJPYNOu/tAqsimnU83v3tO+++/bVjy7JHDnlvOKQC4JUBMtl64254oAAggggAACCCCAAALVSmDx4sV+00x3UG3btvWba7p596qAWZs2beymm26yK6+80mbOnGl9+vRxq3nNUYFNH33kn7nr+L8iGVwTzqKZ9ItWkc6UXb0FCKJV7+vD0SGAAAIIIIAAAggggEAtFlBwrHHjxsEZtmzZ0tatWxfMu4lFixbZU0895eft7nXyfscdd5hqp5FyV2Drsi+9QQU+sLpN9rb8LodXOERBt5LBBYrnz7PiBQsrfH/sAIHqKEBzzup4VTgmBBBAAAEEEEAAAQQQyAkB9YW2fv364FyLi4utU6dOwbyb2OGNwti7d++g2WfTpk1NTUGHDRvmZ1m9erUNHDjQZec1BwSKXFPOrt0q5WzrKVjXvYcVffShbfT65Gt08EGVsl92gkB1EiCIVp2uBseCAAIIIIAAAggggAACOSXQunVrW758eXDOS5YssQ4dOgTzbqJVq1bWpEkTN+tPr1271nbu3Gl5eXnWvHlzmz59erDeTfTqVdIZvJvntfYIFM0uaVbpmllWxpkV9u7pB9GK5sysjN2xDwSqnQDNOavdJeGAEEAAAQQQQAABBBBAIFcE+vfv79coW7Vqld/PmWqXde3a1T/9GTNm2KuvvupP9+vXz8aPH2/z58+3r7/+2saOHWuDBw/2A2i5YsV5xgoUzSzp4D+/R8/YFRU45wJ2xbPnVOBeKBqB6itATbTqe204MgQQQAABBBBAAAEEEKjlAkOHDrWpU6faoEGD/DMdMGCAHxzTzJQpU2zixIl26qmn+uuV78ILL/TzaQCC66+/3p/mf7knsGPTJtv0/hT/xCtjUAEn7PpFK5o107atWWP1mjVzq3hFICcECKLlxGXmJBFAAAEEEEAAAQQQQKA6CtSrV8/uvPNOU020unXrmvo6c+nyyy83/SnVr1/fbr31VrvmmmtM/aapGWidOjQscla59lo0Z45tW/e1N6BAV6u75x6VdvoN2uxvDTseYps/nW8b3nvP9j7jjErbNztCoDoIcNetDleBY0AAAQQQQAABBBBAAIGcFmjRokVMAC0ZRjOv5k+bNm0IoCUDypHlxXN29YfWrXIGFQizFvYsaT666eOPw4uZRiAnBAii5cRl5iQRQAABBBBAAAEEEEAAAQRqi0CRC6JVYn9ozq6gVw9/Uk06SQjkmgBBtFy74pwvAggggAACCCCAAAIIIIBAjRbYOK1kJFaNllnZqaBLd3+XalK6Y/Pmyt49+0OgSgUIolUpPztHAAEEEEAAAQQQQAABBBBAILrA5oUL/D7JGuzf1hq0OyD6hlnK2ejQTla/dSvb8c03tnHy5CyVSjEI1AwBgmg14zpxlAgggAACCCCAAAIIIIAAAghY0dx5voIGFaiqVOD6RZv+flUdAvtFoEoECKJVCTs7RQABBBBAAAEEEEAAAQQQQCBzATeoQOGuvskyL6H8WxT06O0Xsol+0cqPSQk1SoAgWo26XBwsAggggAACCCCAAAIIIIBALgtsfL+k9ld+95IO/qvCoqB7yaigRTPnVMXu2ScCVSZAEK3K6NkxAggggAACCCCAAAIIIIAAAtEFtq1bZxs/+MDqNi60wl1NKqNvnb2cBd27W9299rRtK1fYpg8/yl7BlIRANReoV82Pr8oP74033rA333zTbrvttio/Fg4AAQQQQKB6CUybNs3GjRtnW7dutZNOOsn69+9f6gB37txpY8aMsclex7tNmjSxvn372oknnlgqHwsQQAABBBBAAIF0AsXeiJi2ZYvl9+pllpeXLnuFrc+rW9cKe/S0b996yzZOmWQFPUpG7KywHVIwAtVEgJpoKS7E8uXL7brrrrP58+enyMUqBBBAAIFcFJjj/SN2+PDhlp+f7wfGRo0aZRMmTChFoQDagw8+aN26dbOuXbvaiBEjbPr0kmHpS2VmAQIIIIAAAgggkEKgeG5J88n8blUftMrvUdKctIh+0VJcMVbVNgFqoiW5otu3b7cbb7zRBgwYYJ9++mmSXCxGAAEEEMhVAQXHhgwZ4gfFZDBv3jwbPXp0qVpmqoF28cUX2wUXXOBTvfrqqzZp0iTrpV+QSQgggAACCCCAQAYCmz7+2M+tWmBVnQp2HUPRLPpFq+prwf4rT4CaaEmsH3vsMWvVqpWdcsopSXKwGAEEEEAglwUWL15snTt3Dgjatm1rCxcuDObdxB133GHDhg2zJUuW2EsvvWRTp061o446yq3mFQEEEEAAAQQQiCywYdquQQWqcGROd7AF3btaXp06tvnzhVa8cIFbzCsCtVqAIFqCyztz5kx79tln7dprr02wlkUIIIAAAgiYLV261Bo3bhxQtGzZ0tZ5nf3GpzrePy7zvD5LHnroIbvlllusoKDA7xstPh/zCCCAAAIIIIBAKoGi2bNt24rl1uiww6x+s+apslbKurp77m35PUtq1m+aNKVS9slOEKhqAYJocVdAHUDfcMMNdvLJJ9tnn31mCxYssA0bNvj912zbti3Ifeutt/pNcdQcx/0FK5lAAAEEEKj1AuoLbf369cF5FhcXW6dOnYL5+Inf/e539vrrr9sRRxxhqp0WTnynhDWYRgABBBBAAIFEAkVzZ/uL87t0TbS6Spa5EUI3ffJhleyfnSJQ2QIE0eLEFURr2LChvf/++3b77bf7I6qpZoGmN23aFOQeOXKkH1hT59DuL1jJBAIIIIBArRdo3bq1aQAal9Rcs0OHDm42eL3kkkvsww9L/mGp0Tk1iqdqsYUT3ylhDaYRQAABBBBAIJFA8cySIFphj26JVlfJsoJdx0K/aFXCz06rQICBBeLQ1ezmueeeC5ZOnDjR7r777phlwUomEEAAAQRyVqB///5277332vnnn28rV670f3S5/PLLfY8ZM2bYihUr7NRTT7X27dvbP/7xD9t3332tXr169vLLL9vgwYNz1o0TRwABBBBAAIGyCWycNtXfsGBXE8qylZLdrfK7lAT0NELntrVrrF7TZtndAaUhUM0EqIlWzS4Ih4MAAgggUDMEhg4dar1797ZBgwaZapt16dIlCI5NmTLFnnzySf9Ezj33XJs1a5a/TnmbN29uZ555Zs04SY4SAQQQQAABBKqFwLYVX1qR13d3/X1bW6NDkncfUdkH26BNG2vU6VB/txvem1TZu2d/CFS6ADXR0pAfd9xxpj8SAggggAACYQHVKrvzzjtt1apVVrduXWvatGmwWjXSXK009ZOm2mdq+tmsWTMrLCwM8jGBAAIIIIAAAghEEdg4Z56fLb9r9ekPzR13Ye+eVjxvrm2YNs32pra9Y+G1lgpQE62WXlhOCwEEEECgcgRatGgRE0BLtNf69etbu3btCKAlwmEZAggggAACCKQVKJ5T0h9aQdcj0uat7AwF3Xr4u9w8Z05l75r9IVDpAgTRKp2cHSKAAAIIIIAAAggggAACCCAQXWCTN6CdUqHXlUR1S/ndSmrHqV+0HVu2VrfD43gQyKoAQbSsclIYAggggAACCCCAAAIIIIAAAtkT2FlcbBpUIK9BAyvoXlLrK3ull78k9dFWv83+tmPTJts0ZXL5C6QEBKqxAEG0anxxODQEEEAAAQQQQAABBBBAAIHcFijymknu+G6DFXTrZnmNGlVLjMKePf3j2vDee9Xy+DgoBLIlQBAtW5KUgwACCCCAAAIIIIAAAggggECWBYrnlvQ15ppNZrn4rBRX0LOkhtymmTOyUh6FIFBdBQiiVdcrw3EhgAACCCCAAAIIIIAAAgj8f/buA06q6vz/+LMsbRdUFAFFQaRb6FixoaiIigVbbNFE1GBMYhIRiTUak5hoor80Y/RvixWxBqIiGqUXlSKCAtJEKSLK7lIX/vd7Zu8wOzs7c2fZ3ZnZ+zm8lpm59Zz3uTN35rnnnBt6geKZHzgDfwD/bAQp8FrJKW0uC/hlYx7JEwLVIUAQrToU2QYCCCCAAAIIIIAAAggggAACNSBQMm2a22qT3pEukzWwi13eZGHPXlZ/z2a2dc1aK/nww13eHhtAIFsFCKJla82QLwQQQAABBBBAAAEEEEAAgVALbF7yuemvUYeO3uD9+2WtRV69elbgj4s2eVLW5pOMIbCrAgTRdlWQ9RFAAAEEEEAAAQQQQACBXRCY5rU0uvPOO+2WW26xd955J+WWxo0bZzfffHPK5Vgg9wU2zfvYFaKwd6S7ZDaXqLCspVzJB5Hup9mcV/KGQFUFCKJVVY71EEAAAQQQQAABBBBAAIFdFJjn3Xlx2LBhVlBQYP369bPbb7/dxo8fX+lWV65caSNGjLAFCxZUugwz6o7Axo9mucL4Y45lc8kKu/dw2ds4NxL4y+a8kjcEqipAEK2qcqyHAAIIIIAAAggggAACCOyiwKhRo+ycc86x4cOH22mnneaev/DCCwm3WlpaarfeeqsNGDAg4Xwm1j2BoukzXKEKe0XufpnNJVQe8xo2sK0rlrsuqNmcV/KGQFUFCKJVVY71EEAAAQQQQAABBBBAAIFdFFi6dKkdcsgh0a20bdvWFi1aFH0d++Sxxx6zfffd10455ZTYyTyvowKl69dZyfRplt98b/NbeWVzUfP32N38YF/xBMZFy+a6Im9VFyCIVnU71kQAAQQQQAABBBBAAAEEdklg+fLl1rRp0+g2WrVqZevWrYu+9p/MmTPHnnvuObvxxhv9STzWcYGNn3ziSljQM9JNMheKW9g70mKuaPqUXMgueUQgbYH6aa/BCggggAACCCCAAAIIIIAAAtUioLHQ1q9fH93Wpk2brGvXrtHXerJjxw5304GTTz7ZPvvsM1u4cKEVFRXZjBkzrGfPnla/fn1bs2aN6w5abkVe5LSAP7ZYkx45FETrGQmibZzDuGg5ffCR+UoFCKJVSsMMBBBAAAEEEEAAAQQQQKBmBVq3bm26WYCfli1bZp06dfJfukcF0Ro1amTTp093fwq6qbXavffea//6179s9913txYtWrigWrkVvRd9+/aNn8TrHBEo8e7aqlTYs1eO5NjLa69IXjd/usC2rf3a6u/dPGfyTkYRCCJAd84gSiyDAAIIIIAAAggggAACCNSAQP/+/U03F1i9erWpy6ae9yhreTR79mx74403rF69evb8889H/2655RbT2GmapgAaqW4KFE2bbnle3Tfp3SdnCtig9b5WcGg3l9+iKRNzJt9kFIGgAgTRgkqxHAIIIIAAAggggAACCCBQzQLnnnuuHXbYYTZo0CC78sorrXv37jZ48GC3lylTptiTTz5ZzXtkc7kgsGn+J1b69Vor6Nnd6jXbIxeyHM2jPy5a8STGRYui8KTOCNCds85UJQVBAAEEEEAAAQQQQACBXBPQeGb33Xefa4mWn59vzZvv7P529dVXm/7i03HHHWf6I9VdgY1z57nCFfTomXOFLPRuhPD1E2Yb58zOubyTYQRSCRBESyXEfAQQQAABBBBAAAEEEECghgVatmxZw3tg87kkUDJjustuQQ7dVMD3LSgbF61k7hzbUVpqeV5wmIRAXRGgO2ddqUnKgQACCCCAAAIIIIAAAgggUCcEimdGbiqQS+Oh+fCNO3W2hu0OMNu6zYonTPAn84hAnRCgJVqdqEYKgQACCCCQCYFp3l2zxo4da1u3brWTTjrJNDh0ojRu3Dh7++23rdS7Gjtw4EDr16+fu8taomWZhgACCCCAAALhFtiy6ivb9MkCa3RgO2vUvkNOYhT26WNbliy1osmTrenxx+dkGcg0AokEaImWSIVpCCCAAAIIpBCYN2+eDRs2zAoKClxQ7Pbbb7fx48dXWGuCdwV2xIgRpm46CrI98MAD9sgjj1RYjgkIIIAAAggggIAENs392EEUdO+RsyCFPXu5vJd8+EHOloGMI5BIgJZoiVSYhgACCCCAQAqBUaNG2TnnnGPDhw93S86fP99eeOEFO/HEE8utqdZq559/vt1www1u+oYNG+xf//qXC8CVW5AXCCCAAAIIIICAJ1Aye5ZzKOyZezcV8CuwsEckiLbRGxeNhEBdEqAlWl2qTcqCAAIIIFBrAkuXLrVDDjkkur+2bdvaokWLoq/9J2effbZdeeWV/kubO3eu9e7dO/qaJwgggAACCCCAQKxAydSp7mVBLgfRevW0/OZ7W+m331nJrI9ii8dzBHJagCBaTlcfmUcAAQQQyJTA8uXLrWnTptHdt2rVytatWxd97T9p376968pZXFxsd911l40ZM8YuvvhifzaPCCCAAAIIIIBAVGDH1s1W7LViz2/WzAr79I1Oz7kneXnWpG8fl+3iSVNyLvtkGIHKBAiiVSbDdAQQQAABBJIIaCy09evXR5fYtGmTde3aNfo69smCBQtc1089qhto9+7dY2fbPffcY3379i33V24BXiCAAAIIIIBAKAQ2zp1nO7ZssULvu0Jevdz+uV7YIzKmW9HkiaGoOwoZDgHGRAtHPVNKBBBAAIFqFmjdurWtXLkyutVly5ZZp06doq/9J4sXL7ahQ4faVVddZZdeeqnVS/CFeOTIkaa/2KSgGgkBBBBAAAEEwiVQMicyHlou31TAr7HCXpHhKzZ9PM+fxCMCOS+Q26HtnOenAAgggAACuSqgO22qVdnq1attzpw57nmPsiuus2fPtjfeeMMV7ZlnnrEuXbrY8d7t3dUFVGOp6ZGEAAIIIIAAAgjEC2ycNsNNyuXx0PwyFfToaXmNG9nWVV/ZlqVL/Mk8IpDTArREy+nqI/MIIIAAApkSOPfcc22qN/DvoEGDXBYGDBhggwcPds+nTJli7733np166qk2c+ZMUyu1IUOGRLNaWFjo5kcn8AQBBBBAAAEEEPAEiqZHbipQ2Ccynlguo+TvvpupNVrx5MlWNHGC7XVAu1wuDnlHwAkQRONAQAABBBBAoAoC9evXt/vuu8+1RMvPz7fmzZtHt3L11Veb/pRGjx4dnc4TBBBAAAEEEECgMoEtS5ba1i9WWkH3Q61BixaVLZZT05v07lMWRJtse118aU7lncwikEiAIFoiFaYhgAACCCAQUKBly5YBl2QxBBBAAAEEEECgcoGNH3/sZtaF8dD8Uhb26uWebpwVGevNn84jArkqwJhouVpz5BsBBBBAAAEEEEAAAQQQQKDOCBTPnObKojtz1pVU0DNSls1LPrdt69bVlWJRjhALEEQLceVTdAQQQAABBBBAAAEEEEAAgewQKJk63WWksEfuj4fmizbYp7X5QcHiKZP8yTwikLMCBNFyturIOAIIIIAAAggggAACCCCAQF0QKP32OyuZ9ZE12K+1NT7koLpQpGgZCntHunQWTZwcncYTBHJVgCBartYc+UYAAQQQQAABBBBAAAEEEKgTApvmRcZDK+zZs06UJ7YQBWXjohXPmBE7mecI5KQANxaopNrGjRtnb7/9tpWWltrAgQOtX79+1qhRo0qWZjICCCCAAAIIIIAAAggggAACVRMo/mCmW7Eu3VTAlyjoFmmJtunjubZj+3bLq0dbHt+Gx9wT4OhNUGcTJkywESNGmO641r9/f3vggQfskUceSbAkkxBAAAEEEEAAAQQQQAABBBDYNYGSaWU3FehR91qiNe7c0Rq17+CAiidO3DUo1kYgwwIE0RJUwDTvA+z888+3G264wU477TS75JJL7OWXX06wJJMQQAABBBBAAAEEEEAAAQQQ2DWB4hnTrV5hoTXpU3duKhArUtinbFy0yRNiJ/McgZwToDtngio7++yzrWnTptE5c+fOtd69e0df8wQBBBBAAAEEEEAAAQQQQACB6hDYtGC+6cYCTY46yvIKCqpjk1m3DXXp/OaFUVY0OdLiLusySIYQCChAEC0BVPv27d3U4uJiu//++23MmDH26KOPJliSSQgggAACCCCAAAIIIIAAAghUXWDjR7PcyoV1sCunr1LYq4d7unHuHH8SjwjkpABBtEqqbcGCBXb99de7cdFGjRpl7dq1K7fkPffcY6NHjy43jRcIIIAAAggggAACCCCAAAIIpCNQNCPSOquge/d0VsupZQu9O3TWb9XStq1abRvnzLaCbnW3rDlVMWQ2bQHGREtAtnjxYhs6dKhdeuml9sQTT1QIoGmVkSNH2gzvFr2xfwk2xSQEEEAAAQQQQAABBBBAAAEEKhXYOG2Gm1fYOzJuWKUL5viMJr36uhIUcXOBHK/JcGefIFqC+n/mmWesS5cudvzxx9vy5ctt6dKl7jHBokxCAAEEEEAAAQQQQAABBBBAoEoCW72WWZsWLbRGXbtaw/3bVGkbubJSQc9I67OiyZNzJcvkE4EKAnTnrEBiNnPmTFu2bJkNGTIkOrfQu1PKe++9F33NEwQQQAABBBBAAAEEEEAAAQR2RWDjx7Pd6oXdu+3KZnJi3cKekZZ2G2d9lBP5JZMIJBIgiJZAhbHOEqAwCQEEEEAAAQQQQAABBGpEYNq0aTZ27FjbunWrnXTSSda/f/+E+xk3bpy9/fbbVlpaagMHDrR+/fpZo0aNEi7LxNwQKPG7ctbhmwr4NaFx0eo1bWLb1n5tW5YstYbtDvBn8YhAzgjQnTNnqoqMIoAAAggggAACCCCAQF0TmDdvng0bNswKCgpcUOz222+38ePHVyjmhAkTbMSIEe7GZwqyPfDAA/bII49UWI4JuSVQNG2qy3BBz565lfEq5LZekybWpFdvt2bR5IlV2AKrIJB5AYJoma8DcoAAAggggAACCCCAAAIhFRg1apSdc845Nnz4cDvttNPc8xdeeKGChlqrnX/++XbDDTe45S655BJ7+eWXKyzHhNwR2LF1m230blanu1YWhqAlmmqmoKycRZOm5E5FkVMEYgTozhmDwVMEEEAAAQQQQAABBBBAoDYFdBOzM888M7rLtm3buq6d0QllT84++2xr2rRpdPLcuXOtd+9Iq57oRJ7klMDGeXNtx/btVtC9R07le1cyW9grUtaS6dN2ZTOsi0DGBAiiZYyeHSOAAAIIIIAAAggggEDYBZYvX14uONaqVStbt25dBZb27du7acXFxXb//ffbmDFj7NFHH62wHBNyR6DYa12oFJZWaJGyRgK/W1Yst9L131p+sz2cAf8hkCsCBNFypabIJwIIIIAAAggggAACCNQ5AY2Ftn79+mi5Nm3aZF27do2+jn2yYMECu/766924aOoG2q5du+jsNWvWuG6e0Qk8yXqBkpkzXR4Lu3fP+rxWVwbr79PKdIOBkg8/tKKJE22P0wdV16bZDgK1IkAQrVaY2QkCCCCAQF0UCHo3NZVdrQruuOMOu/fee61x48Z1kYMyIYAAAghUQaB169a2cuXK6JrLli2zTp06RV/7TxYvXmxDhw61q666yi699FKrV6/88NYtWrSwGd74WvGpb9++8ZN4nSUCxVOnWF7DBlbYu+7fVCCWvKBnDxdE2zBlAkG0WBie54RA+U/enMgymUQAAQQQQCDzAkHvpqYWBepyc+utt9qkSZNs27Ztmc88OUAAAQQQyBoB3WlTrcpWr15tc+bMcc979IiMGzV79mx74403XF6feeYZ69Klix1//PGmLqAaS02PpNwU2LJ8mW1bs9aNh5bfbK/cLEQVc13YI9Kls2QiNxeoIiGrZVCAlmgZxGfXCCCAAAK5KxB7NzWVYv78+aa7qZ144onlCrVhwwZ7//33y03jBQIIIIAAAr7Aueeea1OnTrVBgyLd2gYMGGCDBw92s6dMmWLvvfeenXrqqTbT6/qnVmpDhgzxV7XCwkI3PzqBJzkjUPLBBy6vYbqpgF856s6ptOnTBe7GCnlxrSr95XhEIBsFCKJlY62QJwQQQACBrBcIejc1da/57W9/awsXLrSLLroo68tFBhFAAAEEalegfv36dt9997mWaPn5+da8efNoBq6++mrTn9Lo0aOj03mS+wLF06a7QoTppgJ+rTXq0N4adehomxcttGKvlX7TY47xZ/GIQNYL0J0z66uIDCKAAAIIZKNA0LupZWPeyRMCCCCAQPYJtGzZslwALftySI6qU6B4etmdOXuGazw037CwV6RLp4JoJARySYCWaLlUW+QVAQQQQCBrBNK5m1qqTN9zzz20MEiFxHwEEEAAAQTqiMD2DUW2ad48a+y1xlKrrDCmwl497JtRz9uGyZOtVRgBKHPOChBEy9mqI+MIIIAAApkUCHo3tSB5HDlypOkvNnE3tVgNniOAAAIIIFB3BEpmfeQK07h75AYSdadkwUvij4u28YOZwVdiSQSyQIDunFlQCWQBAQQQQCD3BILeTS33SkaOEUAAAQQQQKAmBfyunAXdD63J3WT1tnVDhQb7tLYdpaW2cfbcrM4rmUMgVoAgWqwGzxFAAAEEEAgooLupHXbYYe5ualdeeaV179693N3UnnzyyXJbysvLc6/9x3IzeYEAAggggAACoREonjHDlbUwpOOh+RWtLp1KRVMm+pN4RCDrBejOmfVVRAYRQAABBLJRIOjd1Py8d+jQwWaUfWn2p/GIAAIIIIAAAuETKJk21fL3bGaFvfuEr/AxJVaXzm/HjrWiCROsxdXXxMzhKQLZK0AQLXvrhpwhgAACCOSAgO6mRkIAAQQQQAABBIIIbJ7/iW3fuMl2O+JIy8vPD7JKnV3GHxeteMb0OltGClb3BOjOWffqlBIhgAACCCCAAAIIIIAAAghkoUCR35UzxDcV8KuloFsPy99tN9v+3QbbsnSJP5lHBLJagCBaVlcPmUMAAQQQQAABBBBAAAEEEKgrAjtvKhDeO3P6dVmvaRMr6N3bvSyeNMWfzCMCWS1AEC2rq4fMIYAAAggggAACCCCAAAII1BWB4qmRrothv6mAX59NvBszKW3g5gI+CY9ZLsCYaFleQbWdvbW21h6xx2ypLbcWtrdda0NtX9untrPB/hIJbHnFbNub3pxtZvWPNWt4aaKlmIYAAggggAACCCCAAAJZKLB17RrbumK5FXhdOevv0yoLc1j7WSrsGWmJVjRhcu3vnD0iUAUBWqJVAa2urrLddtgddrfNs/l2mPWx7+xb+5XdaqXeP1KGBbb9z2zL414mGprl9/Wev+T9PZHhTLF7BBBAAAEEEEAAAQQQCCqwccZMtyit0HaKNe7Z0ywvz7at+tJKv1u/cwbPEMhSAYJoWVoxmcjWp/aZrfcCZ7fYCLvSLrebbLht8Vo9vWvvZSI77DNWYMvzXvCstVnhn8waDTWrd6DZ1nGxS/AcAQQQQAABBBBAAAEEsligaOpUlzu1RCNFBBq0amkFPXu5F0UTaY3GcZH9AgTRsr+Oai2Hi2yxt68862Qd3T73sZbu9ee2xL3mvwwK7PjGq5rOOzNQb3+zHcU7X/MMAQQQQAABBBBAAAEEslqgZOY0l78mBNHK1VOTXpEg2oaJE8tN5wUC2ShAEC0bayVDeVppK62+lT8kGlgDK/H+kTIssGOz1/ps952ZyN/Xe75j52ueIYAAAggggAACCCCAQNYK7CgttZIPPrKGbdpao4O7Zm0+M5Gxgh5el04vlUymJVom/NlnegLlIybprcvSdUygkTXyOm9uL1eqHd54aG3Na/VEyrCA91bd8fXOPOzY6LVMa7TzNc8QQAABBBBAAAEEEEAgawVKPvzQ5Y2unBWrqLBXpHvrpgXzbccOGgpUFGJKNgkQRMum2shwXvYzb8wtr3WT7tCpVOT92+YF0boYV0ocSCb/yysw2756Zw62L/WCaHvtfM0zBBBAAAEEEEAAAQQQyFqBkumRrpzcVKBiFTVq38Eade7iZpRMoTVaRSGmZJMAQbRsqo0M5+VYO9rLQZ79wx72bjCw3n5nf/Re5VnnsjHSMpy9cO++fnez0oVeIM372/q22bY5XvfOyIkm3DCUHgEEEEAAAQQQQACB7Bconj7dZbKge7fsz2wGcui3RitiXLQM6LPLdAQIoqWjVceXbeh157zULrL59qn92G6wJbbMrrDLLN/7R8qwQMPrvaDZHt5AAcPNNv818rzxsAxnit0jgAACCCCAAAIIIIBAEIHiadMtv2kTKywb/yvIOmFaxnfh5gJhqvXcLGv93Mw2ua4pgYF2ivW347xA2md2iNeNs753YwFSFgho/LPCR72WaIu8zHh1Uq9tFmSKLCCAAAIIIIAAAggggEAqgW4D8g8AAEAASURBVM2fL7HS9d9Y06OPtnpeII1UUaCw7A6dJWUt9iouwRQEskOAlmjZUQ9ZlYtG1th6WDcCaFlVK2WZqdeBAFo21gt5QgABBBBAAAEEEECgEoGNMxgPrRKa6OSCbt2twX4ao9ts41xv6BoSAlkqQBAtSyuGbCGAAAIIIIAAAggggAACCOS+QJHXlVOJO3Mmr8vCXr3dAkWTublAcinmZlKAIFom9dk3AggggAACCCCAAAIIIIBAnRYonhlpiVbQs0edLueuFq6wW0+3ieJJE3d1U6yPQI0JEESrMVo2jAACCCCAAAIIIIAAAgggEGaB7cXFtvnThdb44IOt4f5twkyRsuzRO3ROoCVaSiwWyJgANxbIGD07RgABBBBAAAEEEEAAAQQQqMsCRdPKWqF1pxVaqnrWzQXymzXzbsKw3r4YOdIadmhvjTt2skbeIwHIVHrMry0Bgmi1Jc1+EEAAAQQQQAABBBBAAAEEQiWwccZUV94mPSJdFUNV+DQLm1dQYE37HWvf/uc1+/qJxyuunZ9vjdp3cEE1BdcadvSCbAd2sAZeC78GrVpWXJ4pCNSAAEG0SlCneVcMxo4da1u3brWTTjrJ+vfvX8mSTEYAAQQQQCC5AOeU5D7MRQABBBAIJsD5JJhTNi1VNG2Gy05Bz+7ZlK2szcsBD/3DC6INti1fLLcty1fY1i+8P+9xs/d6+3cbbPNnn7q/7+y/FcqQ32zPstZrCrR1tIYHtLVGbdq6IFv9vfaqsDwTEKiKAEG0BGrz5s2zYcOG2QUXXGCHH3643X777bZjxw478cQTEyzNJAQQQAABBCoX4JxSuQ1zEEAAAQSCC3A+CW6VTUuWeI0zGuzT2gq6EUQLWi97nD4o4aJbViig5gXXVq6wLcv0+IULsCnQptel67+xjTNnur9EG2h4QDsXZGvU7kBr6AXXGrXdz3s8wAXZ8nffLdEqTEOgggBBtAokZqNGjbJzzjnHhg8f7ubOnz/fXnjhBYJoCayYhAACCCCQXIBzSnIf5iKAAAIIBBPgfBLMKZuW2jh7ru0oLbWCHoyHVh310nD//b2x0fa3Jgk2tmPLFtdybcvyZV5Abalt9QJuW1Yst83LvUCbF3Tbtmq1bVm6xP0VJVhfXUnVeq3RAW2igbVGbfa3Bm3buNZseY0bJ1iLSWEUIIiWoNaXLl1qZ555ZnRO27ZtXdfO6ASeIIAAAgggEFCAc0pAKBZDAAEEEEgqkOnzyZaly2x+v6Msr14926Gc5tfznueb12XHe55ved5rzdB8vdYLPc/znm/foUdNyouso+em9SPTd7j53kRvVS0T2Z6eR7YVv25kea2f7+3F24/2l+etl6WpoDut0Gq6avIaNnRjpekmBInS9g1FXpDNC7AtX+qCbZu941nP/e6ipUXFtmnuHPeXaP38vVta4zb7mXnBtqxM3vtQAVvvHWY7tpd6z7d7b4ntZqXe28h7bXma7+XcW8a86Tufe29abzG3btn0Hdu9CZoYt249N9/btpvvvd+8bbnn3lPtL7LtyP5N+/e2ovmdx//PGnfumJVsVckUQbQEasu9JqJNmzaNzmnVqpWtW7cu+ponCCCAAAIIBBXgnBJUiuUQQAABBJIJZMv5JPID2sup9+N4h22LZtkF1qKveBIrUNirem4q8NBDD9k111wTu+mseZ6tefPzVW+3ptb44IPdXyK0rWvXeEG15bZV3URda7ayR++5ppWuXW3F3l+Yk2Jw6aTq/kzw6zKdPNTEsgTREqgWeNHl9d5tdf20adMm69q1q//SPd5zzz02evToctP0om/fvhWmMQEBBBDICoGXI3eHCpIX3VilRYsWQRZlmRQCnFNSADEbgTAK7LNveqWuzu+X6ex77jx9uU0vrwmW5pySAKUKk1KdT9asWWOnnXZawi1X228U7/hR6xL9qdGY/1yPSv5rN0+t0tw0r6VY/LyY197TivOj68bOi2zH32/59bx51f2LXTuopvTJz39eTVsye/jhh6ttW9W9oWzN2y7nq2Ur29tr6dXCtcCqbrXq2Z4acOodoreB/1bQo9qURabtnOcvo3V2zi+/nr9M/PzoPuLWVSn8dfQYu55dfJFmV0s699xzM/4bhSBagqps3bq1rVy5Mjpn2bJl1qlTp+hrPRk5cqT7i52ok9OMGZG7r8RO53nmBaibzNdBohxQL4lUMj9N9UIArfrqoarnlOrLAVsKIsDnURAllsmkAMdoJvWzY9+pzic6dyf6LZLNx0625o18pX/MY4ZZ+gLpraFjLBt+oyiQT4oT6N+/v7u5wOrVq23OnDnueQ8Gg4xT4iUCCCCAQBABzilBlFgGAQQQQCCVAOeTVELMRwABBGpegJZoCYzVRHDq1Kk2aFDk1roDBgywwYMHJ1iSSQgggAACCCQX4JyS3Ie5CCCAAALBBDifBHNiKQQQQKAmBQiiJdCtX7++3XfffaaWaPnenV6aN2+eYCkmIYAAAgggkFqAc0pqI5ZAAAEEEEgtwPkktRFLIIAAAjUtQBAtiXDLli2TzK04K9EYBBWXYkomBKibTKin3if1ktooE0tQLzWjnu45pWZywVYrE+C4r0yG6dkiwDGaLTWR+Xykez4ZOnRo5jNdSQ6yNW/kq5IKSzIZsyQ4lczCrBKYSiZni1feDi9VkkcmI4AAAggggAACCCCAAAIIIIAAAggggIAnwI0FOAwQQAABBBBAAAEEEEAAAQQQQAABBBBIIUAQLQUQsxFAAAEEEEAAAQQQQAABBBBAAAEEECCIxjGAAAIIIIAAAggggAACCCCAAAIIIIBACgGCaCmAmI0AAggggAACCCCAAAIIIIAAAggggABBNI4BBBBAAAEEEEAAAQQQQAABBBBAAAEEUggQREsBxGwEEEAAAQQQQAABBBBAAAEEEEAAAQTqQ1A3BP75z3+a/vxUWFhop556ql1++eXWpk0bf3Lgx02bNtkxxxxjL730krVq1cr++9//2imnnOLW96dXZbuBM1DHFhwzZozddtttdvvtt9uZZ54ZLd0tt9xi++67r1133XXRaek8+fLLL+3jjz+2AQMGpLOaPfroo7ZkyRL79a9/ndZ6ub5wTdVDMpc5c+bY9u3brUePHu49unLlSrvjjjuSrcI8BJIKTJs2zcaOHWtbt261k046yfr37590eWYiUJ0C3333nb344oumz7YDDzzQLrvsMmvWrFmFXbz77rv2/vvvl5uuc15eXp499thjtnz58ug8befSSy+NvuYJAghkn0DQ936mcr5u3Tr3/eree++1xo0bZyob5fY7f/58e+GFF2z16tV2/PHHu3P2nnvuWW6ZTLwoKSmx//f//p8pf506dXLfI7p165aJrFS6z3Hjxtnbb79tv/3tbytdpjZnJDun1WY+Eu1rwYIF9sorr9iiRYvskEMOsR/+8IfWpEmTRIvW2rT//Oc/9sEHH1TY3/nnn29du3atMD3XJtASLddqrJL87tixw/1If/PNN01/+oI6a9Yse/jhhytZI/nkhg0buh/8LVq0MAXUFGzZsGGDxU5PvgXmxgqofpT+8Ic/2Lfffhs7y/x55SYGfPHZZ5/Z//3f/wVcuvxiCuyELfnW1V0PyRz1BUBBaKUzzjjDrrjiCvec/xCoisC8efNs2LBhVlBQYP369XOB+fHjx1dlU6yDQNoC+gy966677OWXX3YXb1atWmU/+tGP3IWC+I299957Vlpa6r7Q60u9/hRAU3r66aetdevW0XkHHHBA/Oq8RgCBLBJI571f29nW7xRdJL311ltt0qRJtm3bttrOQsL9rV271l0cUPDxrLPOMn0m/vSnP024bG1P1O+6CRMmuAYSDRo0sCuvvNKUz2xJuuA8YsQIU3AoW1Kyc1om86jjbOjQoe64v/DCC23y5MnuHJvJPGnfiiF07Ngx+teoUSMX6NNjXUi0RKsLtVhWBl112WuvvdwrPZ533nn2l7/8xQVpioqKXABHUXRdAdEP+XPOOcct+84779izzz5rn3zyiZ1wwgl20003mT5Q//73v9tvfvMbd1LSgj/5yU/sgQceiE7Xh5tapw0aNMhtRycwnbzuvvtu1zrqz3/+sy1dutSOPPJI+8UvfmF77LGHWy6s/7Vt29bZ/+1vf7Obb765AsPXX39tf/zjH23mzJnuqpC8u3TpYrNnz3ZB0fvvv9+to+Dov//9b/vZz35m9913n33xxRc2fPhwd9XhkUcesUMPPdS1UnnmmWfsf//7nz311FPRevjxj39sLVu2rLDvME1IVQ9q3fOPf/zDdAVst912c605/VaYClaoFZ9OACeeeKLNnTs3eoVMV/TUOkhf3tRaU/Uj/9GjRzveffbZx7XWUD3rqt8TTzzh9pOfn+9+aCowcs0115iuBFa2/zDVE2VNLDBq1Cj32a33vJJ/lVvHIwmBmhbQDxt9Z3jttddcK2oFctUaUuepnj17ltu9zv/6XOvTp0+56frBqxYjasGmC3MkBBDIfoF03vu1XRpd5I9v9VrbeUi0P7XW3W+//dzvL83XxYLvfe97rhVuJnvz6OKGvuM++eSTdtBBB9mWLVtMvx/0++LYY49NVJRanab8KSCqXjaffvppre472c4qO6clW6c25qnX2GGHHWYjR450u1PL7tiW3rWRh0T7OPzww01/Smq4oQtud955p2vBnmj5XJtGS7Rcq7Ek+dWPd3051Z9aKL3xxht23HHHuSu/v//9790PfgW49KVWwbEpU6bY+vXr7cYbbzQ1rVQAR+vpCrMO9g8//NBtS1cnlK6++mrX+sGfrmCEmmr6Sc1IdYLQl+Pvf//71rlzZ/vd737nrmz88pe/9BcL7WP9+vXdVRW/G0wshK7w3XDDDc5KdaST2iWXXOJa/ykAqh/KftKXBV2ZUaD07LPPdo8/+MEPTE2zFeRR/elKhIJBqueTTz7ZFNBU8EYBtbCnZPUgGz+ApSCmvuzopKTApt4rClwooKYrio8//rhNnDjRcS5cuNAUtFS3XHVXUsszBdAUENOPTJ3cFOT46quvbInXjVb1q/eRgnBK+uKifah5c2X7dwvyX+gF9CVOLXr8pM9hNd8nIVAbArpoo+EiNAyBki6O6XWiL+z6PvGY1yr+3HPPtT/96U/uYo7W0TAESvo81WeszkvxLbTdAvyHAAJZI5DOe7+2M60WL+ryp+/R2ZR0Udu/AK58qSV57OdnpvKqi7dTp05130U1JMxf//pXlxUNO5INSecNnWP8C9jZkCflobJzWqbzp/Pv/vvv74YNUqBKwVs/eJXpvPn7V2Od4uJiO+200/xJOf9IS7Scr8KdBdCPcLWA8ZMCaAqA6aDVj3q1SlOrMCVdsVHApV27du61Al9HH320+6K7efNmN83/7+CDD3ZPdTJQCzU/6QqBumSo+a8CeNq/Ag7al1o7KXCmrhvNmze3IUOGuCCOnoc5qQXSxRdfbPfcc0+5gJaCZDq5vvrqq66Li4Iuej59+vRKx3VQy8MOHTq4wKaCLwrKKKm1oK5wqV7UWlDBm2+++cYFOHWyJJlrCZaoHhQ8VnBMzdzVukJJXzT0XlEQUu8vjTOoJF9/HEJ9IVEQWu8RdW9SqzOdbDVWlcYU1Hsq9qrj3nvvbUcccYRr2q8vLWohqhadqtPK9h/fmsNlgv9CJ6AvS02bNo2WW8eXPr9JCNSGgC4E6PMrNimQu3HjxthJ7gKQLuyoy6YuOugim74HqMW6fowr9e3b13bffXcXaPOHoahXj2u75SB5gUCWCAR972dJdrMiGwru6U/fLdWKXGO1/fznPzddzM100vdWJQUe9R1C30X9IU8ymTcFgJ577jk3jpx+V2ZLUgOGys5pme7ho4vzOrdecMEFLpim3yP67a+LVNmQ9P1ADQR0/Nelc3zm38XZULt1JA9q9aKxSpR0pcPv2rl48WI3Ta1f/KQf9GrNpB/76namA1t/ChLoAz5IUsBAHxxq0abWbwrkqCWaPvw0gKYCQbFJLXnCHkSTh1r0KdCoFml+8n9UDB482J/kHhX88q/4+zPUzLmypHr3gzUKyKiO77jjDvfBr7qK31Zl2wnD9ET1oECZkm4CoT8/qTWfjvP27dv7k8q1BpK7unOqRaCeK8UGtKMrxTwZOHCg/etf/3Kt13Q8qJ6S7T9mVZ6GWEBjoemz1E/+Z6//mkcEalJA5xWNvxKb/B8WsdMU6NV4kP4wDroqrgs6uiihK9HqEqru8koKyqnlr86D/vkrdls8RwCBzAsEfe9nPqfZlQNdcFWgShfA1CI3G7pLxgrpAobypt4vCvRpQPpMJQXx1JtD37l1IVq9PNQbZ8aMGW64gEwGH5Od02JvGJcJOwVp9ZvbH+ZDv7VVl9kSRNNvHJ3n/YY8mTCqiX0SRKsJ1QxtUz/e1ZwzPulKsJICW/4X2s8//9xddVAXCt0tRtFrjWny0EMPudY1+hBLlRRNPv30011rGn3I+QEgfTFW6zW/lY5aqam7ka5Wk8y1ItEYcepqqZZpCmT6LUt0MvODMAp+Kuilsepk6CddDQyS1NVQYxxozAONraagnT7ISBEBmVdWD2p+7zeFVssyHesah05jgvgptvWP5ulLiD9OkFoApkp632lsgNdff90FoXUC9Os50f5TbY/54RDQ53nscbhs2TL3ORKO0lPKTAvoYoyCZvphqFZkCuLqGPRbtfv50zGqFtZ+i1593urcpu8g/jmtV69ebnGdA5X886B7wX8IIJBVAkHf+1mV6QxnRq2BNBaxxkVTL5Fs+YzT91Xd4EBd6fW5rIsXGnpE0zOZFETTmMPqhaM/XTDUd2018tBFZ51zMpWSndMylSd/v/qtGHvHVz3XRXl5+jfz8ZfNxOPzzz8fjRFkYv81tU/azdeUbBZtV1eP9INd/ZEV7FIwTTcAUJBAY5Po6sOaNWtc1wp1r9Ayscl/A6opa3xSl04FZnSHF3VdU9IXY3VN1J/W1VhRGnetLjXhjHdI97X6+SuwpbGwlPxb/WqMOTWx1lUXdRtUSzQ1BddJRLcJ1gkltgWbTNVMNlHrNH3gq+WUxqbTeupOo6sVpJ0C8fWgVj5q0akbBGhMORlqfAGdzLt37+7eNwo26+Sklmd+0o9ItfTUiUw/HPX+8q1Vn7Eth/x19GVA7xl1HdVVLHWVTrZ/fz0ewy2gY0ZXGPU5rm4Pep4t45iEu2bCUXq1QNePLp3XFUzTDYjU6l3BXZ2HNMSDWpTpe4duUqQLQ/os1XGq5fW9Q+c13UlsxYoVLhinCz3q3h77IyAcmpQSgdwRSPbez51S1G5OdTdHdbW74oor3PdGjWmqPw3kn8mkBhe6APKYN/aYAn3qtaIL75keg0y/aRRw8f/UoEMNMPQ6kwE01VWyc1om61L7Vs8X/RZXPer8qrGx1bDF//2eyfwpP2pVqCGj6lqiJVodqdFUbxQFZK6//noXSFGRNUaJgjj6wNKBrdf6IqyrJOpW5idtVy3L9CNNrdX0xlTy96dt6ANOV1kU7FFSc00F5tRdTknbVasrv/+9mxjy/+Sn1kq6AYBSs2bN3DhpGlNOV6uUNEi9gmD6YaIfGPLUjxe1WFITZyX5K+nGELHdDzVNJ0OdePzgpupPJ0z/bpEENSPHcWw9yE11oCt0vpse/ZORfhxqrD8FNY866igXyNA6F110kWtGrVZl+iF4xhlnuKtm6r6koLLGOdN0tcKIdT/11FNdtyY9+qmy/fvzeQy3gAZpV5c4/67IupDhtwIOtwylrw0BdadRS9lrr73WHn74YXdOevDBB93nmsZ+1Lx23lir+l6hz1HdKEefaUp61A8RfeFXdx3dGEdJ3xE0hgsJAQSyVyDZez9bcu3/NvEfM50v3TxKFw/0myg26SKEeqJkKsnHv1Oi7jiv3xb63qrfGqTEAuoiWdk5LfEatTdV3wf1vdA/zvS7/Oabb669DCTZky72KmkM77qW8rymfjvqWqEoT2IBXfnQlV99EPjdOv0l1SJNV4uTdbn0u2/466R6VPRZwQY1E+Y29qm0IvPVqkytnxSQjL/qotZPmhZ7cwetpSCbfrzoJBif9PZWnauVgIKYamWo9dVcmlS5gEzVrF2m6sKgpKuHGguoZ8+eLoisVoNvvfWW6Qekkt5fmq/WaPqCouPfD5rpSp/cg74PEu3f7YT/ECgTUEs0vacZZ5JDIhMC+r7gj2GW7AKZuqhrOX0uxn/+6XykISXUnTPZNjJRPvaJAAKJBYK+9xOvzdRsElCAT0PEaDxrPoOD1Uyyc1qwLdTcUvoNohaG+s0Xe8G+5vYY7i0TRAt3/VN6BBAIKKDgplr8aNBVtRxUd07dlMNvERRwMyyGAAIIIIAAAggggAACCCCQowIE0XK04sg2AgjUvoDG+dO4EbrSo261de1OM7Uvyh4RQAABBBBAAAEEEEAAgdwRIIiWO3VFThFAAAEEEEAAAQQQQAABBBBAAAEEMiTA3TkzBM9uEUAAAQQQQAABBBBAAAEEEEAAAQRyR4AgWu7UFTlFAAEEEEAAAQQQQAABBBBAAAEEEMiQAEG0DMGzWwQQQAABBBBAAAEEEEAAAQQQQACB3BEgiJY7dUVOEUAAAQQQQAABBBBAAAEEEEAAAQQyJEAQLUPw7BYBBBBAAAEEEEAAAQQQQAABBBBAIHcECKLlTl2RUwQQQAABBBBAAAEEEEAAAQQQQACBDAkQRMsQPLtFAAEEEEAAAQQQQAABBBBAAAEEEMgdAYJouVNX5BQBBBBAAAEEEEAAAQQQQAABBBBAIEMCBNEyBM9uEUAAAQQQQAABBBBAAAEEEAgisGrVKtuyZUuQRVmmTGD79u2mPxIC1SlAEK06NdkWAggggAACCCCAAAIIIIAAAtUk8Pzzz9sFF1xgp59+uh199NH2i1/8wpYsWVLlrSsQ9+qrr9qmTZuqvI1dXfHRRx+12267bVc3k3D9d955x1asWOHmPfzww/ab3/wm4XJMRKCqAgTRqirHeggggAACCCCAAAIIIIAAAgjUkMBHH31k9957r9144402depU+/e//22lpaW7FIBS8OzXv/61bdiwoYZyHWyzNdVC7J///KfNnz8/WCZYCoEqCNSvwjqsggACCCCAAAIIIIAAAggggAACNSiwfPlyt/UuXbpYfn6+6fFnP/uZvfXWW9G9vvbaa/b0009bSUmJDRo0yK666ioXaNPjkCFDXOBt8+bNduWVV9rZZ5/tAnJa+Sc/+Yn97W9/c90d//jHP9rMmTOtU6dObrr28/HHH9tTTz1l7du3t5deesn22msvu+mmm6xbt25u+4888oiNGjXKmjVrZuedd55rLaftJsqP8l5Z+vrrry3d/SsAp9ZsY8aMsQMPPNAOOeQQq1evnm3cuNE+++wzu//++2333Xd3u9T2b775Zle+rl272l133WV77LFHZdlhOgIpBWiJlpKIBRBAAAEEEEAAAQQQQAABBBCoXYEePXq4HV5xxRWmrokKdLVu3dqGDh3qpk+aNMnuvPNOFxz71a9+Za+88ooLLinING/ePHv88cft5z//ufXt29fuvvtuN6aagmlKV199tTVp0sRuuOEG++6779z8gw46yC655BLXSq24uNgF6z799FO74447rFGjRvbggw+6dbUfBdjUJVN5U2u5lStXWmX5cSsl+G/Hjh1V2v+ECRPsiSeesGHDhpnyrGDgwoULXRBxv/32s3POOcc6d+7s9qhl27Rp4wKAWkaBPxICuyJAS7Rd0WNdBBBAAAEEEEAAAQQQQAABBGpAoG3btqYx0RS0Umuwhx56yAoLC11ASGOkKSB01lln2YUXXuj2fu2119pjjz1ml112mXs9cuRIF0Dr3r2724YCXQcffLCbd+ihh9qiRYtcsE1jpCk4d9hhh7nx0qZPn25NmzZ1yylIp31u27bNRowY4aa9/PLLbh/HHHOMe60WYF999VWl+fGDfm7hmP/U7VLBvnT3P3HiRLvmmmtswIABbmtz5851jwcccIDLq1qnqYWckgKRCrYpaV+ff/65e85/CFRVgCBaVeVYDwEEEEAAAQQQQAABBBBAAIEaElAXzf3339+11lKLscWLF7sWabfffrsdddRR7gYD7733nguQ+VlQwMtPrVq1ck/9afF39/ziiy/c/MGDB/uruMdvvvnGBdHUhdNfV0E15UdJNza4/PLL3XP9p26jSmrtliw/bqGY/6q6f908IPbGBAoSyiZRUis0P6nlnbq2khDYFQGCaLuix7oIIIAAAggggAACCCCAAAII1IDA7373O6tfv340YKTxya6//nrXzXLVqlVubK/vf//70e6d6papMcD8lGwsMi3jtzZ78803o8EyBaP23XdfN7aY9p0oacy0NWvWRGeNHz/e9tlnn5T5ia5Q9qSq+y8oKLAvv/wyurm1a9dGn8c/SWUQvzyvEUglwJhoqYSYjwACCCCAAAIIIIAAAggggEAtC/Tp0yfavVLjnKkl2X/+8x+XC3VdPPzww02tshREUiux3//+9/bkk08mzWVeXp6br7tzaqB9JW1TwaYZM2a4FmZqiZYs9e/f362j7qEKug0fPtwF5NLNT1X3LxfdwED7/+STT+z111+PZlflUDCRhEBNCSQOLdfU3tguAggggAACCCCAAAIIIIAAAgikFDjzzDNN44b96Ec/ii6rwNOf/vQn13Ls0ksvtdmzZ7sbC2gBjXemQf4rSwqg7bbbbm6csAsuuMDGjh1r99xzj2nstAceeMCtdt1117k7ciZr3TVw4EB3Z0x1A1V3zx/+8Iem8dvSyY/upqlxy6qyf41xphstaP/qcqq7ijZo0MDlX91ctU3/7pzxFn4QMX46rxEIKpDn3RFjR9CFWQ4BBBBAAAEEEEAAAQQQQAABBGpPQC2r1H1R3Szjg0P6Oa95W7dudYGsoEEibdPflm4MoFZdLVq0iE4LUrrVq1fbnnvuGQ1gaZ2q5Cfd/c+aNcsFAzVeXMOGDd3dQzt06BC9oYLKpq6iCtSREKhuAYJo1S3K9hBAAAEEEEAAAQQQQAABBBBAoEYENIbbn//8Z7v44otN3VKfeeYZd1dSjRlHQqCmBQii1bQw20cAAQQQQAABBBBAAAEEEEAAgWoR0PhwEydOtJkzZ7qWc8cdd5x17NixWrbNRhBIJUAQLZUQ8xFAAAEEEEAAAQQQQAABBBBAAAEEQi9AJ+HQHwIAIIAAAggggAACCCCAAAIIIIAAAgikEiCIlkqI+QgggAACCCCAAAIIIIAAAggggAACoRcgiBb6QwAABBBAAAEEEEAAAQQQQAABBBBAAIFUAgTRUgkxHwEEEEAAAQQQQAABBBBAAAEEEEAg9AIE0UJ/CACAAAIIIIAAAggggAACCCCAAAIIIJBKgCBaKiHmI4AAAggggAACCCCAAAIIIIAAAgiEXoAgWugPAQAQQAABBBBAAAEEEEAAAQQQQAABBFIJEERLJcR8BBBAAAEEEEAAAQQQQAABBBBAAIHQCxBEC/0hAAACCCCAAAIIIIAAAggggAACCCCAQCoBgmiphJiPAAIIIIAAAggggAACCCCAAAIIIBB6AYJooT8EAEAAAQQQQAABBBBAAAEEEEAAAQQQSCVAEC2VEPMRQAABBBBAAAEEEEAAAQQQQAABBEIvQBAt9IcAAAgggAACCCCAAAIIIIAAAggggAACqQQIoqUSYj4CCCCAAAIIIIAAAggggAACCCCAQOgFCKKF/hAAAAEEEEAAAQQQQAABBBBAAAEEEEAglQBBtFRCzEcAAQQQQAABBBBAAAEEEEAAAQQQCL0AQbTQHwIAIIAAAggggAACCCCAAAIIIIAAAgikEiCIlkqI+QgggAACCCCAAAIIIIAAAggggAACoRcgiBb6QwAABBBAAAEEEEAAAQQQQAABBBBAAIFUAgTRUgkxHwEEEEAAAQQQQAABBBBAAAEEEEAg9AIE0UJ/CACAAAIIIIAAAggggAACCCCAAAIIIJBKgCBaKiHmI4AAAggggAACCCCAAAIIIIAAAgiEXoAgWugPAQAQQAABBBBAAAEEEEAAAQQQQAABBFIJEERLJcR8BBBAAAEEEEAAAQQQQAABBBBAAIHQCxBEC/0hAAACCCCAAAIIIIAAAggggAACCCCAQCoBgmiphJiPAAIIIIAAAggggAACCCCAAAIIIBB6AYJooT8EAEAAAQQQQAABBBBAAAEEEEAAAQQQSCVAEC2VEPMRQAABBBBAAAEEEEAAAQQQQAABBEIvQBAt9IcAAAgggAACCCCAAAIIIIAAAggggAACqQQIoqUSYj4CCCCAAAIIIIAAAggggAACCCCAQOgFCKKF/hAAAAEEEEAAAQQQQAABBBBAAAEEEEAglQBBtFRCzEcAAQQQQAABBBBAAAEEEEAAAQQQCL0AQbTQHwIAIIAAAggggAACCCCAAAIIIIAAAgikEiCIlkqI+QgggAACCCCAAAIIIIAAAggggAACoRcgiBb6QwAABBBAAAEEEEAAAQQQQAABBBBAAIFUAvVTLVCr8+++rlZ3lxM7u+WvFbOJU0WT2poSpvoIU1mr6/jBrLokzRJZFp1bcftNR1ecFobPyEQ+2VDuRPlKVG8Va636p4T12Kh+yYpbTFTPFZdiCgIIIIAAAgggUOcEaIlW56qUAiGAAAIIIIAAAggggAACCCCAAAIIVLcAQbTqFmV7CCCAAAIIIIAAAggggAACCCCAAAJ1TiDrgmhPfLTA8m79mz0757OMYz/6wScuLy9+vCjjeanJDISlnDVpWBe2vWDtentq1qdJi5LNx0r8Z0f866QFy+KZmShHkGMhi8l2KWuZ8I7P8KJ137pzz49e/V/8rFp9HZ+P+Ne7mpkPZ623vN1espF3fLyrm6oT689Z9bWr91/+d1KdKA+FQAABBBBAAAEE6qJA1gXRtu/YkTXOhQ3qW8smBda4fnYNHVfdQL759uyhr+4isr0UAl8VlVjXB562txetSLpkNh8rer/uv3tTa9KggSuDn9ekBcqBmbVdjqDHQg7QVSmL8cdRlTayiytly0dx/Xr13Htqr8LGrkQ1la8sOu3vYs3t2uq+ww6rKeldyx9rI4AAAggggAACCJjV7ejQLtbwRd06mf5ICNR1gW3bt+d8Ec87pIPpj7RrAnXhWNgVAY6jnXoHNNvNlt94+c4JPEMAAQQQQAABBBBAIOQCORFEG+V1p/zzpNm2z26F9uSQk6zAa3GytXS7/WHCh/byJ5/b9C9WW+/WLWzIwe1t+LG9TFfPlV6dv8Tuff9Du/m43jZy3BRbV7LZfnxkN7vJW+bzb76z3773gU1dscpmf/W17daogZ1w4H7225OPtENa7uXWf33BEvvdex/a3QMOd/M0Mch6QZf7z4KlLg9/HtTPHpn5ib25cLmtKdloJ7bf3+4beLR12GsPl4/K/nt4xjz7x/SP7YOVa1xrgcP2b2m/PvFwO7RVJP9aL4iTv/2VG4rtwufetLGfLbV9d2tigzofYHefdLg1aRhp2RN0e+mUK4hnZfV4qFdPqsNkfvPXfGNXvfyuHd12H7v31KP8orpHdRn+y5S50foNkpdyG4h5EaQugmw/HTu1GPrr1Lnecf65fbWhxAZ02N++36ur/fqdGfar4/vYaZ3buhym2q+On6GvvOuWfXHeIvvs62/tn2edYAfHlC/+aSaOlc3bSu3+SbNs3KLlNn7xF9Z572b2g94H2c+P7mEN8uvZ2E+X2W/+N9PuOPEwZxGfZ71O9FmSykfrpVMvWj42VXb8Bv0cit2W/zy2HLd4df3j19+vlmO8smPBN8qG95pvkOxxxXdFdtFzb9lPj+pun3ifAc/N/czWFm9yx8UfB/az7zZvsV+Nm+p95i6zFoUFdu3hh9iNx/Rym4w/joLUvVoLDnz8dbf+mMtPj56DNGHe6m/sau/9dd4h7e1n3rGq41jnrv9+ttxmr1prBV5L54O9z7IbvHmDu7Zz20j0n8ox7LX33Kx/DD7euiRYKLbci71z3PNzF3rv5/XWc5+97bb+5d8XQfKh9/kFz75pZx90oP3ymJ4J9mjON1W+tOK2bTvswb8vspdfX2kfzf7Wjuu3t1103v4Vtrl5s3duf+Az++9b3rl57rdWUJBvB3fdzW74cUcbPGhft/yKLzbaRVdMt58O62CLlxTb86O/sM8WFlnP7nvYbSO62oD+LStsN3bCxGVf2p3jZ9iUFV+5yYft19KuP7K7K2fsci/MXeSdY+e67xg6Tk7p2MbuGnCE7V3WMk/LKuj8lylz7DXv+4K+ixy5/z72a++8OfyNSTawU1sb6b0/lYJ4uwXj/guyXpB6r41jNC7rvEQAAQQQQAABBOqkQNZ154xXViDr/GffcD8E7vK+mCqA5r4MPvGa+xGkL62X9ezivsjqR9EZT44xv0uEggz6snzGU/9xgTJ90fxm4yZbU7zR2t//lCnwoa47Wl8/ZF7zgm5H/fNFK96y1WXDX3+N9+NLKeh6QZdbVRzJ32H/GOWCYW29q/4KXr3iBQaP/udo2+IFCitLCgDph9mKb4tc/jvvvYe9NG+xdfvLs6ZyKgV18vfxszETXADtgkM7WlMvcPZnL2Bx3CMvO9t0the0XEGd/HqIr8cg++nUvJk7dvSjdf2mzX5R3eMDk2e746OH9wMzaF7KbaDsRZC6CLr9IGXSbnVsnPfMG3b3uzNsj0YNXcBTP+JOffw1VyZtRynIfhvm53vHXaFbXu+D/XZv4oJSbkIl/9X2saL3tAJ9I9+aYmtLNrng2fqNm23Em5PtEW/sQiXfbq0XiE6UEn2WBPGJ3XZV3quVHb9B9x1flvhydGvVvNqO8cqOBd82Wflr470Wb1HZ6+It29z74ILn3rDbx0+zfZpGju+nZ39mZ/17jB33r5dMY10ec8C+pmDT8Dcmm1yV/LL6x5H/OlnZ6+XluYs8b3kB3nc/X+m24//3jPdZrfOQAmVKA5943W59e5p96QWohhzcwfZu0thb5wuXrynLV/mrlXvUWGQneJ/FWu66Iw61Ll4AOVGKLbfeG5u2bXMBtPeXfmknP/aqacwtPwXJR8nWiKP2nygFzZfWve3uefaLkXNs1erNdsG5+9ksL0B22dAZFTY78JyJdutd8+zLrzbZkLNa297NG9q776+1sy6cYlOmr3PLF5eU2sQpX9sFl0+zEbd9bJs2lboA2vuTvraTB0+0OR9/V2G7/gRdJDjm4ZdMdaULVgp0KSh/ztNjbbR3DvWTLkbo+NE8LdelRTN3nj7kwWfcZ5C/3C3e944bxk60Jd9scC3XZaLvETL/1Atg+imIt79s7GOQ9YLUe00fo7F55jkCCCCAAAIIIFCXBbK6Jdo4b3ymM58a48Yl+98Pz7auLfZ0daEfQvpie3KHNvbi9wa6VmS6GnzJC+PclfeXPlls53qt0vykVmazf3yRCzZopJFHvVZfSmqhdo/X8kxJASf9SNEX36krVntfmvdz02P/e/zDBe5lqvWCLudvW+OuTb32PGvnBdFUjhMffcXlY5L3w+sEf6G4x2fnLHRT/PX04i4voHKb9+NMLSeuOewQq4rTvJ98z7VqUz4ufuEt05X457x9XdKjc9rbS1our9Vfuk7x9fiyV89KqfYjC9koSKqAqdJC74eOfrAqYLhnQSP744SP3PRUdesWivsvSF2kW9ZUZfqTF+DUD/Nf9Otpf/RaLSrd1r+v9fjrc7ZhcyQIrGlB96uWLW3+8IQLxv2/c0/UqkmT6qI2j5V/z/7UnvRuOnL+oR3s6fNPdi191Jpo/z887lp8XNw9ebfryj5Lgvr4GKnqxV8u0WP88VuVz6HKylGdx3iiY+H9pZHAUKryV2c+EhlWZdob3z/TtSBSi54Wv3vUtRY602vxpeNIFwv0GecHS87o0q7SXaQq++XeZ4uO0We885NahSrpvPJPr7Ww1tU5RS2fFQhTy6ep15xnXuzNpTvGT7c735lub3gt445s0yoysez/Zd6FkhMeecVWexd/Rl10qg0J2GV5zGVnRFuj6rygz8CX5n1uCrpWJR/lMuW9SCdfU2d8Y7+971M78rC97O3Xj7HCwnz7bsM2O/aU91xrM3/bWk4Bs8P67GlT3zlhp889n9idv51vb4zzWnp524hNY1482k47JWJ2292f2F2/n28vvbbSuh2ye+xi0ef//WyZe/6E16rdPx/874iV7vyvc52+O+j8oOCr3rMfX/89a7NHU7fOX6bOseu9Vp+yfGDQMTZp2Vf2e6+1e7+2+9qbV5zpLsop8HjKY5GLGf5Oq+pdlfWS1XtNHaN+OXlEAAEEEEAAAQTCIFAvWwv51sIVpivn+hIbG0BTftWVSUndXzRfSV04h3lX6JUULIlNww4/1AWoFCzZy/sb4nWree3SQV63zt7RxdSyRy2SlIrKWqJFZ5Y9Cbpe0OX87SvfCqApqRynl/2QW+e1tKks+d1Jfu91Z1TrArXUUXmKbh3qAmhaL12nkcf1cQE0rat83HJ8Xz21MZ8udY/pbi9VudJ2iqtHlynvv1T7+V5ZgOXfs3be8VU/lpT0o0Ip3by4lcr+C1IX6W4/VZne8YLISreeEKkjPT9wz93tl/0iXdL0Wind/UbWSv1/bR8rU8ta6NzudUnTsam0u9cC7+WLB9nDZ/W30iR3xUj2WZKuT6p6SSa3q59DycpR08e4X65U5a+tfPj5SfWobv7qgqfUqH6+a3Wk5z/xuu4pgKbUd78W7vFLr0t0spSq7Ccc2NoFy3QH241eIEVpohdkUfDrit5d3XGr1npvXTHYnr7g5GiASIE2v2WZAsOxaem3G6y/d3FHrYvTCaApqON359b21NpK6WuvJbZSuvlwK8X8l26+ps2ItCAb8YvOLoCmTe2+W327ZXj5TqmdOjSxt17tZ08/ethOH++93aVT5Pz43YadFwi0jX5HNo8G0PR64MmRYNrX68o7ap6f9D1ASS3R31uy0g15cHy71rZu5A/tuQtPcfN0IUrpuiO6RQNoen2F111e6VkvUKo0wbvopqQLGGrVrqTHu7whIGJTVb3TXS9VvdfEMRpbTp4jgAACCCCAAAJhEMjalmj6IaKkVjVNywJlfoVobBilC59/w3XD9Kdv9LquKM1fG5nvT9cX0dikYIN+UD3ktRCY6Y0HNXf1127cGn+ZUq8VVqIUdL2gy/n70ODNsUmtFpS2lJbGTi73XF/uNd6NxkTTn+5KeJ7XSkdBoV77RoKB6Tr5QUR/R/4Pu/lrI11S0t1eqnKl6xRfj34+U+3nIK8Fo1p+qJWHuta1alLoWiMqAHty2Q/sdPPi71uPQeoi3e2nKtOk5V+6Ot+jccPYrHitTMq30kh3v+U2luRFbR8rM1audrnxj0k/a4lajPrz/MdknyXp+qSqF3+fiR7jj990952sHDV9jPvlSVX+2sqHn59Ujx3jxpXc2xvXSkldlv20mxeMVVIwK1lKVXYFd3/Y5yA3TuMb3viWGkdMrdKU/JaSuoij8RnVBVzjdeoCSGwXzvhYsMZn85POWUFTp+blx9Ns4Z9TvNZ4SunmI36/6eZr1pxId9Cuncuf6w49uHxrsb32bGhHH9Hcnn1xhd37p09dt0y/C6fyEH9qVtAtNrXYO1KXW7YkPodr2dO9sT4VXFWr8+O9AKXOA6d3bue1tu5kfkvEhV6XT6XfeRep/BajbkLZfwqMqsXZh1+udVPiP5f8cVX9darqne56qeq9Jo5Rv4w8IoAAAggggAACYRHI2iCavthe2qOL/X3aXDeY8quXDIrWicZCUtJ4Mvn1yvrDROeatSob/8af1Cwu0KAuEkc+9KI/23WzOdfb1hJvbJynZn0anR7/JOh6QZfzt69xqNJNCpSpO92TH33qxkLT2HAaw0x/6uKqLinpOsX/SIu3TXd7qcqVrlN8Pfpmqfaj5X7g/biVkcaNU0BN4yCpJWNDb0B6pXTz4lYq+y9IXaS7/WRl0m99BZf3aBRpURGblzy/f1jZxHT3G7utZM9r+1jxW2VqXJ90U7LPknR9ktVLqnzFH7/p7jtZObTvmjzG/bIFKX9t5MPPT6rH5jEDwMcuW5XjKEjZL+7e2QXRnva6H5/mtf56atYCbyy0PaOtnDWe3xHeGJj6/FHSZ5FaKO7l5VPjGyZK6mKuG6j88KV3bMHPLrZmjSu+7+PX09ihyVJV8hG/vXTytXFTJKhV1og0uqkmXrfO2LT26y12xAnvupsFaLq6dQ4b2t722rOB3X1vZDiF2OV104F0k1qivecND6HhDjT0gwKCCmrqz+8ev2FLpCWbgvQKdidK6h5cvDXSMi7+czf+U6qq3umul6reVY6aOEYT+TANAQQQQAABBBCoqwLJv2lnsNQPnn6sF0Tr7IIb6p6pK/p+VyEN0LzaG1fm8l5drL83tpaf9KVWV/b98Uv86fGPGpxcSXcgvLKsm41e/9QbWF+pshYJQdcLupzbWRX/W7J+g33t/SDTDxn9qYXV/3l3CLvHuzuhAmkKoqXrpDuxxaal3j6UOpe15Et3e7HbSvS8Npz8/Z7vjSP0o1f/57q4ykrJP570fFfyEqQudmX7yl9sUhyp+z7N3c0y1P1L3Rr95LcW9F9X5379beqxto8VtbD41GsRqbpr7d18w08aDH6M9yP4msMO9idVeEz2WVJTPhUykWBCuvtOVg5tviaP8QTZr3RStuSj0gzGzdiRogVa3OJJX+rOyGrlpHHWLvECagp2/7D3zmNTrZ8VQNNdZR88/ZjonY/9Ae3jW0Gr+6DG7dzstUq+f+Isu8m7AcJD3nlrV1O6+YjfX7r56uXdNfPp55fb0mUl1qlDZHwxbXPFykj3Un/7Dz36uQug/eCyA+zBP/YwP8g2+tWVbpHS0uStBf3tJHvUDT004L/O/UP7HuyGb1D3zYuef9Pum/iRu7Omf2fs49vt57pqxm5Prc8UEFcw02/5pc+mtmXjpmnZ2BsK6HVVvau6nvZZWaruY7Sy/TAdAQQQQAABBBCoqwKRZjhZWLrGXtcVdT3419n9Xe6uefVdW1UW/DjWu6Oa0hNlA/27F95/v3v/A9Md1P5QNki8Pz3+ce6qyPgsZ3ndbfzxlfTjfPTHi92iW+P7jJRtIOh6QZeLz1fQ1/rNd/qTr1vfv79gfqBLd5+71htAX2lTWZeddJ00APbWmDuC/nXqXLc9jaOilO723EpJ/qtpp9hdq0WKBqXXDSk0KH9n7+52agXip6rmRT/Ag9RFVbfv5y/+UYEKJQ1q7Scdwwqgxqag+/Vb5qiLUpBU28fKUW32cdmK7Vql98FN3h0I1Vq1zR7lu4nFliHZZ0lQn9jtVdfzdPedrBzKU3Ud4+keC/Ee1ZWP+O36r9VVr8S7O6PqPxuTunQq6e7JShd26+ge9d+Csq7xZ3Q5IBpA001cHvtwvlsm9vNXE1TnSnf0P9x13/6nN47X24tXuGm78l+6+YjfV7r56tMrMqTCg39fVK7e/vHI5+U2veCzIvf6jNP2iQbQtm3bYY89tdRN37q18m6a5TaU5MXd3oUm3Z3zxbLzvcbG07mhfVmLM12M8z9vNG6a6sdPOn/0/tvzdp53x3BdbDur64Fu1m+8bfrnXY2vqtexqareVV0vdt+JnlfnMZpo+0xDAAEEEEAAAQTqskDWtkTz0dVV7qZje7lgwU/+M8EN/Pvzfj3sz5NnRX94qMvFZG/gcf2Y1nhiGnw8WTrKG5PmlU8+tytGv23f79nVtW75m7euBm9W+rok8YD+QdcLulyyPCabp5ZI9ww40s5+eqyd8vhrbrDjAu/H1pNlXVHPKwuwpOukMWJ0N9SLune0yd6A2PrBph8WV3qtJpTS3V6yMmheTTvF7/8yr3uwWoiodcivjo+UyV+mqnlRN54gdVHV7fv5i3/88ZHdvNYN81zLw+ne3WQ77LW7qTWLxuqJTUH3u0dZazaNs6dxeH5yVHcrLxS7VXPjCdXmsXK9V97fe0HyW727DKprp7rIPT9nkRvL8I4TD3N5Lp/Diq8SfZYE9am4tV2fUtV9JyqHn5vqOMYTHQv+9oM+Vkc+KtvX4AuneHdpXGUTxx3vjZ9VfgzAytapzekKcF/32nvuvXhqx7blxl/r47VS0x08R3itoTXIvy7iPDt7oRuvUXlc47UuTpTUlVd3TT3jqf/YFS+Ot09++j3b2Z4r0RrJp1U1H/FbDZqvE45tYZde1Maeena5Dblkqp07uLW9OX61PfnMzjHftO0+PZu5aSNu+9h0c4D69T2fUStcfWv+mrWJz82aFzTp+8SDk2fb0FfecV38NZ7ZWO+OnWoheLoX3FR3T91dVXWncTR1Z9Sr+h5kn3vz/zHtY7ebh73WgBru4DjvhgTneK2+NUxAn78/byceuL+N/3xFdIzVPPNO1l6qqndV13M7TfJfTRyjSXbHLAQQQAABBBBAoE4JZF1LNL8VRKyy7kCogfP1A1/jl6gbxazrLrSTO7RxgbTLX3zbjT2jwZtfu/T06B07I19fzeK3qVvTqzuK7qylLhw/87px9vC6x+kuoEr64pwoBV0v6HJ+vvxHf5/+sE/5eZVXz2DvCriChdu8q97qFnbD2In2mddF5a6TDrdfHtPTbSqok79/rasB668cPd4F0OT7/tBzouOGpbs9f7uVlSuoU2X16G/ff6xsP/70U70xivSjT+mimNYheh00L1o2PgWpi6Db98viP/r7ij8mVBcfDDvfvu91aVYXzn97YzCd2H5/u/PEyF3hmjSIlDPofpt4rTH041JJN6qY5AVREyU/X7V9rKjL6gfDLnCtIdWScOjL79pbi5bbZd6NNH52VA+XVT9vfr7jX2t6/GdJUB9/W/6jv4/4evGnxz5Wdvymu+/YbcaXw59XHcd4omPBL7f/6O+vsvJXRz78fVTl0R/P0bf3t5FflmF/vqb7ZapfNr6m/9pfx3/tP/rTKyu7BvFXK2clvT9j04+88c/0PivZss0dw/qsXVOy0SZfPcSd41707jyt1mjx+dY2FOC54NCO7mJPZeOn+eWKX9/Pq1+GquYjfrtB86X9P/RAL7vikrY27p3VdtnQGS5Ydu/dhzoeP38/uupAu+mGzlaysdSG/vhDu/LamS5wNnn88bb/fgX24ive3TS91mj5+ZGcVBiLrGxD9RKMlep25P2n7uCPDznJvVT3TbUYVBBM3x+eveAUfzEb9b1T7cfeDXwmLoucE3/9zgw3tt0z3p1V1WXXT897d/TU3TmV/jJ1jnchrzC6ff8OsIG9y4D94FvQ9YLWu5/n6jhG/W3xiAACCCCAAAIIhE0gz+uOlj2dYu6+Lm1/dT9b5o3dpTFMGpQNEh90I+p2oavLuuua3z0ldl0FEzSOlj9Qvz8v1XrpLucvn/Dxlr9WnBzntHDdt65FwwFelzb/x0j8SkGd1EVFV+T39ro/KlBTWQq6vcrWj50e1DN2nao8X79ps+35m0fcjSTevvKshJtImZcU9ZGqLlJuP2GuKk5UAFjbOq7dvtEuyVpKLbX0A1vli71zZdD9qpWexl9qXtDY8m5Nfuxl4lhRGZXH5d8WubEP/aCopu9KCuqTch8pjo9E61fbvr2NV8sxXpbJcsdCouhJosKUTauWfCSyLDrXNm/ebo33fsUWzz3VDjyg0Kzp6Io5ifuMrLhAzU5Rd0EFXzbcOtT8QErsHnXW1WeFWk7H32E3drmkzxP5pFnuaslHfCYT5curNz+pO+7nS0usw4FNvJZmiQ8sl6/FRdayRSPbY/fIBQF//bQekxwb6qb55YYSd5dNteT1h3aI376W07iXLbw7u8bX1bzV37jvELrjqlqw+elN7+6sp3qtxNVKNrZlfFW9q7qen59Ej7t8jCaq50Q7YhoCCCDRJQq0AAANmklEQVSAAAIIIFDHBLK+O2cq70LvLmRdW+yZarGE83Vnxvhb08cuqACbUnwwKdV6/jaCLucvX9XHjl4AMVUK6qSWCtW5vVT50vyadtIYN4oU6w53Srq6X1na1bykstvV7fv5fnX+Em/svw/t714Xr2v6HuKCpxpjy2+hom5AsSnofhWU2s2C/WjNxLGiMimP6s5ZnSmoT3Xu099Wdey7Jo7xdI4Fvyw1kQ9/2/7j8Fvn2sFdd4sE0PyJWfCoYIuCHbrZhQJoasWUKICmrOpihz8ofSaznol8NGzonXc7Je+M6vIVcwOCmjBS0CzVTYi0Xy1X2ef6Ii8QOvjfY9xNkB4950R3Ie8br7v5jW9Mclnu1zYyfquf/6p6V3U9f7/+Yy4eo37eeUQAAQQQQAABBLJFIOeDaDUB+cHKNXbaE69Hx5fSGESk3BX45X8nuW42KoFujnB2WVer3C2Rue7ICqKppeTtXusz/VhXC0K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"text/plain": [ - "
" + "" ] }, "metadata": { - "needs_background": "light" + "image/png": { + "width": 800 + } }, "output_type": "display_data" } ], "source": [ - "model = malaya.sentiment.deep_model('hierarchical')\n", - "result = model.predict(positive_text,get_proba=True)['attention']\n", + "display(Image('sentiment-negative.png', width=800))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`predict_words` only accept a single string. You can't predict multiple texts." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Predict batch of strings" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'negative': 0.94391596, 'positive': 0.000560839, 'neutral': 0.055523217},\n", + " {'negative': 0.004329388, 'positive': 0.5670612, 'neutral': 0.42860943}]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.predict_batch([negative_text, positive_text],get_proba=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**You might want to try `luong` and `self-attention` by yourself.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Stacking models\n", "\n", - "plt.figure(figsize = (15, 7))\n", - "labels = [r[0] for r in result]\n", - "val = [r[1] for r in result]\n", - "aranged = [i for i in range(len(labels))]\n", - "plt.bar(aranged, val)\n", - "plt.xticks(aranged, labels, rotation = 'vertical')\n", - "plt.show()" + "More information, you can read at [https://malaya.readthedocs.io/en/latest/Stack.html](https://malaya.readthedocs.io/en/latest/Stack.html)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "multinomial = malaya.sentiment.multinomial()\n", + "xgb = malaya.sentiment.xgb()\n", + "bahdanau = malaya.sentiment.deep_model('bahdanau')" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'negative': 0.0037063136821626594,\n", + " 'positive': 0.6215181632979583,\n", + " 'neutral': 0.3669251238766725}" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "malaya.stack.predict_stack([multinomial, xgb, bahdanau], positive_text)" ] }, { @@ -375,7 +538,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -384,7 +547,7 @@ "['fast-text-char']" ] }, - "execution_count": 11, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -402,7 +565,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -419,7 +582,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -428,7 +591,7 @@ "'positive'" ] }, - "execution_count": 13, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -439,7 +602,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -448,7 +611,7 @@ "['positive', 'negative']" ] }, - "execution_count": 14, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -459,17 +622,17 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[{'negative': 0.41368636, 'positive': 0.58631366},\n", + "[{'negative': 0.42412993, 'positive': 0.5758701},\n", " {'negative': 0.6855174, 'positive': 0.31448266}]" ] }, - "execution_count": 15, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -482,8 +645,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "**Not bad huh, but the polarity is not really high as word-based models. Word-based models can get negative / positive value really near to 1.0**" + "Right now sparse models does not have `neutral` class." ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/example/sentiment/load-sentiment_files/load-sentiment_15_0.png b/example/sentiment/load-sentiment_files/load-sentiment_15_0.png deleted file mode 100644 index 12ce9d07d190fd9c18aeae175aa577fef2952fc0..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 21455 zcmeFZcT`jFwk{k%KtRBVpmYsgni6_d2_PU{dQmAsI?{WwK$NBsgiuAA^xj((qy~`Q zA%XX_XRdyXI7rmXf;)x*ATU=7^!E*lX?G4etX7 zV#O;+Li;Ocd`w6hlR-iItujUxaJc(ec9psJ%=?i7mzos5k{<0IR8IXvQ>mdx2^k-t z8lI?Xx12m7MM2x76%nV!hkE8=CViqKHj~}JX~P1xddBr06nl03G~e5loirRmyoJ$8 zV$g_NXp;O9Z^S>Hf2k_PF2AQT9=I)|{rNZ8@t@yz`1~7&41n8a!=;jqa`_y%jo$;$ zn-zK+9ryoqJMQLgc=y~?_D0o1UXBgDVTvh>Jqf)B$66cp;Zx2{?iu-2s*HbTkLi_D 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zGkwa||HX;{UwZp-z{_~IE=qauXoh{ST+W+`4>%Uxg qTesce6<)a=^8W#)EVg3+ literal 0 HcmV?d00001 diff --git a/example/subjectivity/README.rst b/example/subjectivity/README.rst index 04bd1f1e..8261c69b 100644 --- a/example/subjectivity/README.rst +++ b/example/subjectivity/README.rst @@ -7,8 +7,8 @@ .. parsed-literal:: - CPU times: user 10.3 s, sys: 818 ms, total: 11.1 s - Wall time: 11.4 s + CPU times: user 10.9 s, sys: 915 ms, total: 11.9 s + Wall time: 12.2 s Explanation @@ -42,16 +42,20 @@ Load multinomial model .. parsed-literal:: - {'negative': 0.46203835811002486, 'positive': 0.5379616418899767} - {'negative': 0.8607294776614423, 'positive': 0.13927052233855727} + {'negative': 0.009240767162200498, 'positive': 0.0759232837799535, 'neutral': 0.914835949057846} + {'negative': 0.7214589553228845, 'positive': 0.0027854104467711456, 'neutral': 0.2757556342303443} .. parsed-literal:: - [{'negative': 0.8607294776614423, 'positive': 0.13927052233855727}, - {'negative': 0.8607294776614423, 'positive': 0.13927052233855727}] + [{'negative': 0.7214589553228845, + 'positive': 0.0027854104467711456, + 'neutral': 0.2757556342303443}, + {'negative': 0.7214589553228845, + 'positive': 0.0027854104467711456, + 'neutral': 0.2757556342303443}] @@ -68,16 +72,16 @@ Load xgb model .. parsed-literal:: - {'negative': 0.4284472, 'positive': 0.5715528} - {'negative': 0.9249991, 'positive': 0.07500088} + {'negative': 0.0085689435, 'positive': 0.14310563, 'neutral': 0.84832543} + {'negative': 0.84999824, 'positive': 0.0015000176, 'neutral': 0.14850175} .. parsed-literal:: - [{'negative': 0.9249991, 'positive': 0.07500088}, - {'negative': 0.9249991, 'positive': 0.07500088}] + [{'negative': 0.84999824, 'positive': 0.0015000176, 'neutral': 0.14850175}, + {'negative': 0.84999824, 'positive': 0.0015000176, 'neutral': 0.14850175}] @@ -93,112 +97,101 @@ List available deep learning models .. parsed-literal:: - ['fast-text', - 'hierarchical', - 'bahdanau', - 'luong', - 'bidirectional', - 'bert', - 'entity-network'] + ['self-attention', 'bahdanau', 'luong'] +Load deep learning models +------------------------- + +Good thing about deep learning models from Malaya, it returns +``Attention`` result, means, which part of words give the high impact to +the results. But to get ``Attention``, you need to set +``get_proba=True``. + +.. code:: ipython3 + + import matplotlib.pyplot as plt + import seaborn as sns + sns.set() + +Load bahdanau model +~~~~~~~~~~~~~~~~~~~ + .. code:: ipython3 - for i in malaya.subjective.available_deep_model(): - print('Testing %s model'%(i)) - model = malaya.subjective.deep_model(i) - print(model.predict(negative_text)) - print(model.predict_batch([negative_text, positive_text])) - print(model.predict_batch([negative_text, positive_text],get_proba=True)) - print() + model = malaya.subjective.deep_model('bahdanau') .. parsed-literal:: - Testing fast-text model - negative - ['negative', 'positive'] - [{'negative': 1.0, 'positive': 5.600171e-08}, {'negative': 0.25236478, 'positive': 0.74763525}] - - Testing hierarchical model - negative - ['negative', 'positive'] - [{'negative': 0.9999939, 'positive': 6.036344e-06}, {'negative': 0.23363505, 'positive': 0.766365}] - - Testing bahdanau model - negative - ['negative', 'negative'] - [{'negative': 0.9999505, 'positive': 4.9492166e-05}, {'negative': 0.6453213, 'positive': 0.35467872}] - - Testing luong model - positive - ['positive', 'negative'] - [{'negative': 0.08237837, 'positive': 0.9176216}, {'negative': 0.64468145, 'positive': 0.35531852}] - - Testing bidirectional model - negative - ['negative', 'positive'] - [{'negative': 0.9999932, 'positive': 6.7871633e-06}, {'negative': 0.11161333, 'positive': 0.88838667}] - - Testing bert model - negative - ['negative', 'negative'] - [{'negative': 0.98487025, 'positive': 0.015129704}, {'negative': 0.98668575, 'positive': 0.013314218}] - - Testing entity-network model - negative - ['negative', 'negative'] - [{'negative': 0.6470482, 'positive': 0.35295185}, {'negative': 0.65467215, 'positive': 0.34532788}] - + downloading frozen /Users/huseinzol/Malaya/subjective/bahdanau model + +.. parsed-literal:: + + 20.0MB [00:07, 2.85MB/s] + 0%| | 0.00/0.45 [00:00" + "
" ] }, "metadata": { @@ -267,15 +301,12 @@ } ], "source": [ - "model = malaya.subjective.deep_model('bahdanau')\n", - "result = model.predict(negative_text, get_proba=True)['attention']\n", - "\n", - "plt.figure(figsize = (15, 7))\n", - "labels = [r[0] for r in result]\n", - "val = [r[1] for r in result]\n", - "aranged = [i for i in range(len(labels))]\n", - "plt.bar(aranged, val)\n", - "plt.xticks(aranged, labels, rotation = 'vertical')\n", + "plt.figure(figsize = (15, 5))\n", + "keys = result['attention'].keys()\n", + "values = result['attention'].values()\n", + "aranged = [i for i in range(len(keys))]\n", + "plt.bar(aranged, values)\n", + "plt.xticks(aranged, keys, rotation = 'vertical')\n", "plt.show()" ] }, @@ -283,76 +314,175 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Visualizing luong model" + "#### Open subjectivity visualization dashboard\n", + "\n", + "Default when you call `predict_words` it will open a browser with visualization dashboard, you can disable by `visualization=False`." ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Serving to http://127.0.0.1:8889/ [Ctrl-C to exit]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "127.0.0.1 - - [01/Jun/2019 12:16:49] \"GET / HTTP/1.1\" 200 -\n", + "127.0.0.1 - - [01/Jun/2019 12:16:49] \"GET /static/admin-materialize.min.css HTTP/1.1\" 200 -\n", + "127.0.0.1 - - [01/Jun/2019 12:16:49] \"GET /static/echarts.min.js HTTP/1.1\" 200 -\n", + "127.0.0.1 - - [01/Jun/2019 12:16:49] \"GET /favicon.ico HTTP/1.1\" 200 -\n", + "----------------------------------------\n", + "Exception happened during processing of request from ('127.0.0.1', 61989)\n", + "Traceback (most recent call last):\n", + " File \"/usr/local/Cellar/python/3.6.5_1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/socketserver.py\", line 317, in _handle_request_noblock\n", + " self.process_request(request, client_address)\n", + " File \"/usr/local/Cellar/python/3.6.5_1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/socketserver.py\", line 348, in process_request\n", + " self.finish_request(request, client_address)\n", + " File \"/usr/local/Cellar/python/3.6.5_1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/socketserver.py\", line 361, in finish_request\n", + " self.RequestHandlerClass(request, client_address, self)\n", + " File \"/usr/local/Cellar/python/3.6.5_1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/socketserver.py\", line 696, in __init__\n", + " self.handle()\n", + " File \"/usr/local/Cellar/python/3.6.5_1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/http/server.py\", line 418, in handle\n", + " self.handle_one_request()\n", + " File \"/usr/local/Cellar/python/3.6.5_1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/http/server.py\", line 406, in handle_one_request\n", + " method()\n", + " File \"/Users/huseinzol/Documents/Malaya/malaya/_utils/_server.py\", line 32, in do_GET\n", + " with open(filepath, 'rb') as fh:\n", + "FileNotFoundError: [Errno 2] No such file or directory: '/Users/huseinzol/Documents/Malaya/malaya/_utils/web/favicon.ico'\n", + "----------------------------------------\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "stopping Server...\n" + ] + } + ], + "source": [ + "model.predict_words(negative_text)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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N2uU6ubrxej76FgYKhpxGCKP3xs9Mrpjmm0WbO1moWLJZrli9sb5nU2ubb5dkH+jg6KjvJO8s4EJyxbouu310H/Ho9rzjVb/zrPexXYd8kn2pfhSyr79HgFOgfZBNsCXFMsQsVDNMNlwggmU3TSQicjXqB/Vn9FosOo4YL5SgsccpMTqpMLll71QKzX6+VJk07QM26X4H4zMOH6rMbMsazv6ey3xY4Yh9XsTRw/l1BY+OvTu+XshZpFxsdzK05GDpmVPNp3vLZs78OsdcLnFeu8LuArkytupQdTG8z3XXzV0iXlasd7wSdTXvWl1D5/WRG1+aMM0cNyVvady2aHG7E9ga05ZyN7X9QMfBexmdh+5nPsh+mPvocNfhx4efHO7OfZrzLKvnUG96X2r/3udxf0UN7B6MfBEzlPTy4PCxV+UjDa8fjL5882kcvCVOCL6Tn9SZMp/2mzn7/tNH5dmkT62ff81rLsR9ufz13SL7kuX3lB8NP6dXuFcd1rJ/dW6PvzFCH7kD+RnVjj6EccSKYxdwN/EZBAcabppR2vN04fTqDAiGdsZ0JgsiA7GXdJTZloWB5SlrNpsJO8TezBHBKcQ5xJXDrcP9iaeU14z3G18Zvxn/Z4HjghqCI0J7hfmFW0W8RVZFi8WUxLrFA8RXJY5JSkm2STlKTUmnyojIDMnm7jDY8U2uSt5TgU6hTTFSSUCpXzlNRUFlXDVPTVvtk3qphrnGouZ5LXutX9p1Ou66WN0bemR9ov5dg0hDfsNeozRjJeNpkxJTW/jecds8ykLK4p1lmZWHNav1c5sCWwc7kt2g/UkHb0dhxw9OV50TXYxdGVyH3Srdoz0MPGk9B73O7Az2VvBe2XXfJ9/Xy0/Cb4nc6X8swDdQMQgVNBhcS0kJcQqVDkOHvQm/FVG8Oz7SNUqDyhuNip6NGYhtj6uPL0vI25OaGJ8Umuy/d+c+txSn/Q6p9mn2BxzSnQ66Z+w8FJAZmhWdnZKTmVtwuOxITV7j0Xv5fQWjxz6fQBVKFHkVHz15v2TllOxpv7ITZx6fXS1XOB9QUXKhpwpVrVUTX1tf9/GS5OWQ+torc9dUGvZf727kaApr7rzFdzul5W2rVVtLu3zHxU6p+1cfGjwafpzQzfe0t+dwn9Nz0QEw+HHo3fCH1+CNyPiuidop9EziR/CpYp78VXdJ7afzavHG+G/93rdRMCoAnDgEwMbvOQ41AOReBEBsDwBscO5pRweAkzpACJgCaLEDQBbaf84PCE48CYAEeIAkUANmcH4ZBueURaAedIEJsAaxQ8qQIxQNnYCaoddwzieNcEGkIuoRo0gGpAEyHnkJOQVnaV6oUtQrOBPzQV9Af8KoYFIxz7A82HBsG46Eo+Da8Zz4WHw/QZFQRFilIdM8pVWnraZjp8ulR9An0X9niGVYYkxkgpiyiazECpIaqZc5hAXLcoHVmHWKLZNdir2XI4aTg7OVy5+bhvsajwcvkvcSnyecEfQJ5AnaCjEJPRcuEvEUFRCdErsoHi2hIQlJdknlS3vCs3Netn9Hi1ylfIHCPkWKkqOyhgqfKqQ6ptaiflwjRFNbi15rRLtGJ0ZXVw+n16ffYHDdsMnolnGryT3TLrMe80GLUctpqwXrFVucHau9qIOao5UT2TnZpdi11W3Gg+Sp7xW5s9x7wIfgq++XRG72/x6oFpQU3B5CCHUOqwhf3G0WWRY1F60VkxM7Gq+UcHTPQpJr8oN92imtqZZpE+lZGdqZIKsv58rhU3kF+WbHkMfvF+YXB5QYnpIuEzwrUq5UYVMZVV1a++QSqFe9atPgfiO4KfnmidvX7vS3LXXwdpo9iHl09vGz7rUemb6dz48M3B0iDZNHLo3OjnNPqE3qTcu/p//wYvbI5x1z7QtmXzq/KSyWLC3/sP95YXlhVWMt5dfdzf1ja/yJ8PhLAFVgAlxBCNgHjoM60AlGwQ+IBMlBNlAEdBRqgF4iAEICzvLTEFcRb+E83gqZjmxDrqC0UQdQ3Wh2dCC6EYPHeGMasYzYMOwTnDQuD7eI98I/IMgSimiQNFE047TOtI/pDOla6bXo78BZ7CNGe8ZROE9dJx4nyZKeMkfAmWczqy8bDVszeyAHK8dDzj1c0lzj3EU8trw43g6+/fwGAhiBp4KFQr7CssKrIt2iZWJR4sYSXBJfJB9LnZdOkfGU1dwhIccuj5dfVZhTnFB6ofxY5bbqRbUS9UMaVE1PLUNtSR1GnUXdYb1W/XqDq4YNRk3Gt03aTDvNHpv3WrywfGM1bb1gs2KHs2d1EHNUd7J29nfZ61ridtN92GPNS3CnhXfMrrM+PX4QWcU/IqAmcCpYlBIScjV0Odw0onD3TJQWdW90Wywqziq+KGEqUT3pSPL0PuOU6lT6tD0HpuH9pDfTIuthjllu9xGHvLH8lGO8x+8WBhbTn2wu9T9NKntwdm+5yvkvF65WxdZo1WEuDly+cCX5mtd1lUb6pomb128fuGPTxn53vKOmk/pA6xG2a/BJzdP9PV59Os9FBpgGHw05v5x8lfiaefTamNP46kT1pPs0w0zXh8xZy8+Mcy8Wzn4NWVT5jvjRs1y6GvRLcXv8kQADaDd3AHGgAs8ANxAODoIz4DYYhte/IGQBxUIV0CCCBmEEr/wOJA5pjzyD/IKyQFWh8Wgq+g3GCV7tNtgBHBn3E19IUCdM0pyk1aMdoUui56fvYohnlGScYDpD9CNJkL4zP2QpY01i82TX45DiZOei4UZwr/Is867yAwEsfAPlEZYV0RZ1EAsS3y9xUvIGnHfPyzLuUJBzld+nUKHYo7SiIqHqrlag3q/JrOWuXaEzp6etf9jgjZGicY7JuJmWeaHFFys760u2tHZh9o8dJZ1ynD+4WrjVeuA9KV4PvUV3HfSZ9DMkVwYgA/2D7lFEQzJCZ8KtIuojWaISqGMxRrGX49kT9u35mOQGr1OVlKpUjrQj6aiDyRlfMj2yrmav5zodrjqyfNQx//IxwnHKiQdFUsW5J+dKXU/dKRM9kw/v/f7nuy9oVlZVM9Uk1k5ddLzUUi96Je/qUoP39QeNMk1Hm+dv2d++fIfQGtjW2k7sCLjXeB/1wO5h6aOJxxJPKN2VT8d7OHvt+w72X3/+doAwKPfCYYj68shwzat7IwOvp0YX3qyOQ2+xE5h3mEkwuTz1aXp05un75g/lHzNnIz5Zf5aaw869mW9eyPri8VXi65dvLYtpS0bfMd87f6T81Py5sHxhxWOVsNq4Rv5F9+vauvvG+EcHKipsHh8QrQEA6NH19a+iAGALAFjLX19fKV9fXzsPJxsjANwN2/oPafOsYQTgLNcG6rsy+frf/+X8D78U1VXQOwEbAAABnmlUWHRYTUw6Y29tLmFkb2JlLnhtcAAAAAAAPHg6eG1wbWV0YSB4bWxuczp4PSJhZG9iZTpuczptZXRhLyIgeDp4bXB0az0iWE1QIENvcmUgNS40LjAiPgogICA8cmRmOlJERiB4bWxuczpyZGY9Imh0dHA6Ly93d3cudzMub3JnLzE5OTkvMDIvMjItcmRmLXN5bnRheC1ucyMiPgogICAgICA8cmRmOkRlc2NyaXB0aW9uIHJkZjphYm91dD0iIgogICAgICAgICAgICB4bWxuczpleGlmPSJodHRwOi8vbnMuYWRvYmUuY29tL2V4aWYvMS4wLyI+CiAgICAgICAgIDxleGlmOlBpeGVsWERpbWVuc2lvbj4xMjg0PC9leGlmOlBpeGVsWERpbWVuc2lvbj4KICAgICAgICAgPGV4aWY6UGl4ZWxZRGltZW5zaW9uPjU2NzwvZXhpZjpQaXhlbFlEaW1lbnNpb24+CiAgICAgIDwvcmRmOkRlc2NyaXB0aW9uPgogICA8L3JkZjpSREY+CjwveDp4bXBtZXRhPgowWdaJAABAAElEQVR4AezdB5wU5f3H8R+9CtKlqwRRwGgAUSkiWMFYKDbkHzEGjQY1YizBJMZEjbFgmprEkr8mESMGNSp285eggIIRBQRRlCYIAop0OPjPd85neHZud2/3bu+4u/08r9feTn1m5j2zsze/fUq13UEyEgIIIIAAAggggAACCCCAAAIIIIAAAgjkhUD1vDhKDhIBBBBAAAEEEEAAAQQQQAABBBBAAAEEQgECglwICCCAAAIIIIAAAggggAACCCCAAAII5JEAAcE8OtkcKgIIIIAAAggggAACCCCAAAIIIIAAAgQEuQYQQAABBBBAAAEEEEAAAQQQQAABBBDIIwECgnl0sjlUBBBAAAEEEEAAAQQQQAABBBBAAAEECAhyDSCAAAIIIIAAAggggAACCCCAAAIIIJBHAgQE8+hkc6gIIIAAAggggAACCCCAAAIIIIAAAggQEOQaQAABBBBAAAEEEEAAAQQQQAABBBBAII8ECAjm0cnmUBFAAAEEEEAAAQQQQAABBBBAAAEEECAgyDWAAAIIIIAAAggggAACCCCAAAIIIIBAHgkQEMyjk82hIoAAAggggAACCCCAAAIIIIAAAgggQECQawABBBBAAAEEEEAAAQQQQAABBBBAAIE8EiAgmEcnm0NFAAEEEEAAAQQQQAABBBBAAAEEEECAgCDXAAIIIIAAAggggAACCCCAAAIIIIAAAnkkQEAwj042h4oAAggggAACCCCAAAIIIIAAAggggAABQa4BBBBAAAEEEEAAAQQQQAABBBBAAAEE8kiAgGAenWwOFQEEEEAAAQQQQAABBBBAAAEEEEAAAQKCXAMIIIAAAggggAACCCCAAAIIIIAAAgjkkUDNPDpWDhUBBBBAoBIIrF6z1tZ/+aUV7CywffdtZPs2bmT169WrBHvOLiKAAAIIIIAAAggggAAClUOgSgUEN3y10X511z22M3iI9NP3LxhpnQ7o6E8qdnj+gkX2l0ceT1juyF6H24jTBidMy9XI7t277Zobbo2yG3H6YDuy5+HROAMVT+D+v/7DFi5aHO7YIV2+Yd8978wS72Qu8yrxTpRwxcVLltmBHdsXWfv1mbPsyWdfCqdXr17dfv3za4ssk48TPvp4if3xL49Eh37dlZdYsyb7RuNVcWDV6jV25x/ujw7tsovOtw7t2kTjbuCTpcvtvocftS83fOUmRe9XXHyB/fmhidF4qjyiBSrQwJYtW8Nj2q9ViyJ79dNbJtjWrdvC6YNPONYG9T+6yDJMQAABBBBAAAEEEEAAAQRyLVClAoKbNm+2rzZuKmI0Y9Y7WQcEX39ztm3ZujUhr42bNieM53rE3962bdtznT355VhAQQt3zr4KgtGlSbnMqzT7kc2667/40h55/Cl7/4OP7A+33VhkVQU5nE+RmXk8YfuOnQkuKgVX1VNBQUHCMe/YubPIIesz8Pv7HrJk9z4FlOvXr1tsHkUyrQATpr/5tj325LN2bL+j7PQhJxTZIx33rl27wulbtmwpMp8JCCCAAAIIIIAAAggggEBZCFSpgGAqoFn/fddGjjjNqlWrlmqRhOl6WH1v/sKEaYwggMAegYUfLra77/9rFMjYM4chBEom8LfHnkgZDGzRvGnJMt3La0245wFb/MnSvbwXbB4BBBBAAAEEEEAAAQQQKCqQFwFBBfg+XLzEOnfav6hAkinvzVtAoCOJC5PKTqB27Vpll3kZ5PzZ6s+L/YzUrVsn2rJKeJHyV6BO7doJB5/spxlVPXepVs2ads0VF1vrVi3DSSpBt279F252+J4sj4QFKsDIipWrit2LenXrmkq3K/E5KZaLBRBAAAEEEEAAAQQQQCBHAnkREJTVzNnvZBwQnDHrvzniJRsEMhMY+73vZLZgJVqq75G9TC8SAs2bNU1ardyX2RFUpXbp6CN6RMFATVOgLJM83PqV6Z22NSvT2WJfEUAAAQQQQAABBBCoOgJVutiOX9pi9jvvmTruKC5t277d5i/8sLjFmI9ATgVUnd1/5TRzMkOgEgi4dvS0q02bNK4Ee8wuIoAAAggggAACCCCAAAKVV6BKlxDscVh3U/uBSqo2vGjxJ3ZQpwPSnq133psfzW8RlGr54ssN4brRxGIG1JHCys9Why9VqwyiPNagfj3bv30769ihrcWrzhWTXcrZCm6uXbfePl1VuC11plKnTm1r2byZHbh/B9O+x5Ma9ldHEC7Vr1cvaKi/nhtN+b42qKq3++tG75vs29hq1KiRdFk1jq/eRFcG+6R1agbL7du4kR0Q9EDbrs1+Oa0O9/nadbZ85We2es3nps5eWrVobm1bt7LW+7VMa6x93LFjR7j/jfZpaLVjVRn9A9u6bZtt9DqpUQml4pLOy8rP1tiHH38SnJ8vrH3b1mEPvE2L6UVWPWRvD4LRSvsE+1XcdaJzuSI4/mUrPrXP1qwNSk81sY7t2oYGNYPqltkkXTuLPvrYPg+uJx2v9lXXka7XBvXrJ2S1cdOmsEfUrzYmdqKi86EkT7kq6bOg5ZXq1Klj+zRsEA7750D7qmukuOS2q+XqBlUsGzZI3C+3vvxXf742cFlpy4NXg2A5ubRru5/pei+LpM/5J8uWmwzUYYjsWrYIPofBdZ/qs5LNfpTks54sf1VLXbJ0Reij6rf7Bp9lfWbaBJ+ZfRoWnrNk6/nTSpqHf+/RDzXu86Brb1vwOYunLcG1464pGeq+kyqP+LpufGdwz18Q9AK+JrgedN9rGFx/LYP7RPvgXtSsaRO3WNr3ktj71Zt3FRR2FqKNbA46DHHH5N9H/furXPwfsuI7JwN93j9d9Vl4n5WN7jF6Ffc5Svw81o7OufJcsvzToFmNT8I2HDu2bxv2AF1cfvF9YxwBBBBAAAEEEEAAAQQql0B2kYPKdWxB8G//oHOQBVFD9TOD3oaLCwi+EfQI6dLRvXvYcy/9nxtN+/7O3PftH5OfTtrLsb/iaYOPt+MH9E370OcvHx9WYOSf/3re3vo60Bmf78bb7NfKvjvqTNuvZQs3KXT4+a9/G43v36Gd/WjsmGg82cCaIMhxo7fOeWeebqrO55IeJl/+v9ft2Zf+nbZNObWTdf65w637IQe5VUv0vjR4cP3bY0+GD8SpMlC7Y2cNPcU6H7h/kUXUEYYeppUUMP7ueWcWWcZNePPtOfbYE8+6UfvdrTekPW8KQP32T/+b0BOqW1nHf9Hoc5Puk5a57+FH7eOv21DrenBnu/S7o9yqCe8KbD/+1BR7febshOn+SL+jjrARp51sxQUGdW4nBXnNX7DIXz0aVmBiYNAz6uDjjw2CcIXtAaa69vzr6ve//nlY2vH1mbPsiWdfDPNTXvJT+vukJxNK4d5+44+tXr264bxUf279zR/D4Lzm69oeP+7SIotOm/FW0JvrlJTXoQLT3/ufs61xo32KrFuSCepd+YlnXoiup3geOufqVbbvkT0z7tDIz6M0n/V4Pk8882LYbII/3R92+9rvqORVvLUvpclDPxT86q57o01ee8X3wyDWo8E9c05w74ynF16danopKSitarWp8oivq1Le2tc3gp7i/VKH/nK6D404bXBYDdmf7oZLY68gp/95cHnqM+s+tycdd4ydetJx4SzdX91+6vvhxIH93SoJ7/px6x/B/ShVz91yuvB/zkr5HTd1+pv2r+deDvPUPebi88+1vzzyuPk/gvkbbNt6P7vku+cVG2j012EYAQQQQAABBBBAAAEEKo9Ala4yrCqYvQ4/NDobb8+Zm7basEpwfPTxkmj5I3seHg2nGlBA7Ld//IvdHwR09CBYXNID2R1/uK+4xZLOn/v+Bzb+l3cUGwzUygp63XTHH0xBC5dUGrDLNw50o/bJ0uWmkmnp0uszZiXMPqz7IdG4Srb85OY77ekXXokeaKOZsQE9xP7xL38Pl43NynhUJbFk5wJ6qVZUCU2dk/96pT2TLetK5CWbl+00lT699bd/TPmwruPXPrkgR7r8d2wvLMEYX0YBx5/f+psoqBCf78YVGFNAIp2TrqVf3v77lMFA5aUgxStT37BfB8e1/etSlW4b6d5VqiqeXMBD0xWw9NNb7xSW4vWn+cPLP10VBQM1/cieh/mzw1KIvwsCsY9OfibtdaiA609vmWD/fXdewvolGXn2xVeDXpYfTmusc66AlwLY2abSftbd9nSN3/mH+9MGA7Ws29fHntwTAM9lHi4v957N9eTWib8ny0NBbl3X+gz411x8Xfn+IlhO95R4ypV9PF9/PNVn3JVe9pfVcd72uz/Z/078Z8r7i5ZX6U19Dib+81+m0pHpkkoLqgfkVMFArasOURSs1H2HhAACCCCAAAIIIIAAAlVPoEqXENTpUlDPlcoIqw1/FFQb/kbyasMKGLrULqiClUlJIuWtYJCfFDRTFS5Vn1S13neDXov9qroq5bYwqMrWpfOe4Jy/frJhPdw+FDwQ+g+5qtLVMyjl1jh41wOeAkDxBzyVYDrEK001oO+RtvDDxdEm1NnKCcf2i8b9AQV2pr+1p4OVrl2+kVDtclIQPPCDoOoZtHcQrFF15V3Buno4n/X2uwlVrl/69zQ7/pi+xZYI8/fDDf/uz/+bcPwq8dUtKOlSq1atwPOjsHqg76OSdN86tKtbvdzeVR3xm90OttrBfr3/wYem8+3S08+/EvqodGI2Sef3zrvvT7BUqa7Dg+NrFVRPVTXCue8vjM6Hqrrfdc+D9qufXV2kpKCqLSo46yeVLlKpqTZBFVJds9OCQLCz1Hm8/6//CEst9gwC7Dq3qhbuBxw13SUF4tMlbUfXij6PSq/PmG3HHN075SraFz8d2etb/mhwLI+EwW1/onxVHVbVyfVZc/uqY3rgb48FJQx/EFaV9dfJdFifmedefi1h8VYtm9vBnTtZ06Bq68dBoN3/HGp53Q+ODUpbZpK0j7n4rGtbCpLq/Lmke0bvHoeF1bqXLFsRXDMfJASZpr7xpp006JiEe18u8nDbj78fEeyLK8mqdl5dUilfXYtKrgq6m5fq3f04o2vfJZVMPbRrl7AKrAJrbwT3a3fPkrOCYjf++IdWN6jSrpQLe33u3efBPybdF1QqW+mAoFmHTNPfJz2VcA/Reir9rLy0vx8GP2LpXLqk7yQ1OTDs1JPdpCLviz9ZGk2TUZfgO7Fxo0bhD2L+9aLP6L+CH3xSlViOMmEAAQQQQAABBBBAAAEEKp1AlQ8IKmikwIlKwCjp4TxVQNAPfh19RGLQIdWZffHf/4lmKchx/VU/KFINTVXTVDLsmRdejZb997TpWQUEVarJHYMyUQBF1WLjSYFHlSZxD70KhHwWtLOnNvaU4sEYPSCnCgjqQVOlTlzq7wVt1A6cggku6QH+misuDgM9bpredewqPelKKuoB9s3/zrEBfY70Fyt2WEEsbdOlc4adGpQ021O98bhj+oQPxwqauYdjLa9htYlVXknHNeL0wVEV0VNPPs5ee31mWDXX7cPkIEj7rW92i5Zx09O9PznlpSiApuUU8FL1bb+tQZUK+mtQGs0FIXS9PP/KVPv2SYMSslY1QT8pqPids4cmtKc45IRjwyqezlzVihUo0PWjlwJHfmmyC0aO8LNMO6wARJ+gGq1clFQSSdet2lWLJ10vM71evxVE99sPVLBdJV1dUmDukgvOK/IZjJ8DVVu++rKL3GoZv6sU8aP/fDpheVULjn+GtE9+SeBnX/y3KRhfXLBUGefqs6683pu/UG9h0g8VY75zjhuN3hWk9kuuTn/rbTv5uAHR/FzkEWUWGzg82Ce9lNx1q2FVs840gKrllRSk9YOB+mFi7JjvJLQXOCSo/q7q+e7epfubquK6Uqu5sFf1d/d5UJB+27bCtkF7BJ95XSvZJDUN4buondhLLhhl3ziwY0I22m99rvV5UXr1P9PDz5jfZETCCl+P6LM86qyhCZ+pD4L2RFXS0CV99lWSPNPArFuPdwQQQAABBBBAAAEEEKjYAtUr9u6Vfu/0AH7Et74ZZZSq2rA6SXCBJC3sVzWOVo4NKMjlP4CedcYpRQIRWkX7oAdsv8ShOpzIJr3w6p7Ao0pznXnGkKSrK6gy7NsnJczzt6VgjN8GoII8apsrWfJLZinYqdJ4Lr0aVCX10/eDtqa0TDwpYPU/Zw9LmLx27fqE8UxGPvJKtGj5Xt/aUyLNra9jOzcIFPpJJfTKK+nhWuclHvRRIEgvl3TNuM5u3LR076rWqCqQLqltLwUc/GCg5qmklab71cKff+W1hECqAqv+da4AmtrVi3eu0rBBA7v0wlFuk+H7dK99zYQZJRjpf3RitWE/GO9np8+YK0mo6X4gWSXCVHrKJV1/P/z+BUk/g/IfesqJbtHQQO1+ZpvenjMvYX8G9j+6SDBQear0lr89BWc/XLwko83l6rOu68z/EaGXdx/0d0Tt2SnQ5JJKVLqUizxcXmX97gLM2o7uBVcF7aPGOw9RJxzfHXVWwvFOC34UcSlX9i6/0r67Nv9cPuMuvbBIMFDz9AODPsd+Kq6qujr5UYDYD7BrfbWzqx8y/KROekgIIIAAAggggAACCCBQtQSKRnCq1vGFR3Nkr8NNDaorKbigEhB+0ETT3/rvnupqnQ7oWKR3VS0TT02C6ncqfaWAjR6ceyYJUvnrqDdPV+IqWftX/rLx4WP7HRn2XqvSft8MqsDFg07+8m2DXjT9FG9Pql8QjHEeWk4dqcSDiHJSqROX1MGKHrJdOvigb4QBKO2PelFulqYXXZUsUbDGBXbUE2u2qUnjxNJjqsaoEnLxIKSqel9z+cVh77KqvpnOKdt9KG75obFArL+8AsJ+wELBKFWXzCT9970950HL/09Qmi/dcSkQpfYMXdL17oLib3vnVPNHnJY8sKx5Cjx2aNcmLG3aulWLINDWRJNzklRyScFIfXaU1AGESiXGkx8I1blW0NUltS3ol2A95cSBUc+pbhn/XSXOVErXXYfvBufAlU7zl0s37JfW0ufh5CCYlir16d3Tprz8f2GnDCpBW716+qrULp9cfdbVw7Kfngmqfh4QBCrjvceqiuu1l38/LF2mc+yq8GrdXOTh70NZDStg5Qc/da7jgS63bR1v/6AdSwWh9wuu647BNe5SruxdfqV5VzMBflMTqvqsz2SqpGYKNF8lbpVUSlUlBv37tr/uGcF9IlUv2Id2PdhUctSlLUHJWBICCCCAAAIIIIAAAghULYG8CAiqyqhK1bngwZuz5xQJCM7w2srrEwS/Mkl6mDy51Z6qdcnW0QOZOrlYEJR0UgDDJVe1y40X967gQnFJx7coKIU0J9aZhkpS+UmBSVWnc21FqeRXPCCoNtD8fXRV6lw+hxzUyfRKlxT0VGk0VTkr+Loqm5Yv2JW4P+nycPM6HdAhfLB1+6QSdupB+pvBg6sehA8J2jd07YApiFXeST3fumrZybat0jgHBu2Guba7UpXKTLbuqq+DZpqnh3u1jZcuKSCs5ZyVvy1VA3dJyxR3DhVcLauk0n6u2rEC6vp8tPOC2eot1q+uqs+AH9zwj0v7qCB1uqTgR4fgXuA6Doqvn25dN8+vnqx23HRfSZVUdfTOX16fanbK6bn6rCuAqnufKxGq4Ks6AdI5V/Xh7od0iYKDLYN2KJOlXOSRLN9cT3Pn1OUb73jGTXfvCobpFU+5so/nW5Jx/7Oq9Y/te1Sx2Qzo29seefxf0XKfB6WxU51b/7MWrfD1gH7E8dPO2HeIP49hBBBAAAEEEEAAAQQQqJwCeREQ1Kk5osc37f+mzQjPkqoNjzrrjKiUlUphuE4HtIDaVCtJUsmMj5csDwMbnwXVcBVwcG35lSS/VOuo5Ig6MlFnFSqhpw4eVBXUlXxKtZ4/XVUoH//Xc+EkV53Rb5fqP9P3VFFV8FBBxFRJgaePPl4a7M+KwHF1eNyrg04u/BI7qdbNdLqCOSr141dVVttcamNLLyUF5VR1TtWJtc/lmVo0L357zYNOBVxAcM3nezp6KG4//cCVrC+79ufFrZIwf9Vne6qEf/nlV9E8vwp7NLEcB9QBjQsIarMqJahq9y75pfE0LV7N2D8uzb/1N/fqLeOUzTlQpupkx/+M5bLEZKqdLu1nXR2E/PmhiQnZqxq2XmbPhAHBw7odEnSC0d3U3mqykqe5yCNhB8pg5AuvfVFl33TffUu9ldLal3YH4kHOTK63Fs0TA7u6d6QKCDZrkrrEb+1aif8a7N61u7SHw/oIIIAAAggggAACCCBQwQQS/+uvYDuXy905Kuht2AUE9VDvVxt+8+050abUk268bbZoZooBVa19OqiOpyBYWSZVN34uqIKoKr6u9FdJt6dgjAsIKo/XZ86K2qZSe4oucKV5/fsk7wFWVZHVeL16Ds5l8E/bTJZUirFRUNJOHWwkSwrq6vXsi6+GJaMuHj2y3BrC36dhYomaZPvX1KtWrfOnEnCZXGtrPi/ddeVflxs3bYp2zd+faGI5DqhEp0qqzfm6LT91+HPm6XvaYPTbsFRQQyVy/eQHSv3pmQ7rmlXp2VTVJuP5qMdiP+Ui6OTn5w/n6rOu0rNXXHyB/f6+h5LeM1Qy87U3ZoYvlWK96Pxzw8Cgvy+5yMPPryyGvwo6vXBJpUhVOrOkKVf2Jd2+Wy9eKi9ZpztuWfeuHx38tDr4wShVqlu3TqpZavg29TzmIIAAAggggAACCCCAQJUQ2NMoXJU4nNQHobbl9MDr0sxZ77hB86sL+x1uRAukGXjltdftgb89ljYYqHad1AtuaUqtKZCjElAKkqQKBqr6otqZ8nsITbXr9evVS2iPbXZQatJVLZ7h2Wj9ZNXvVFrqT//7iKnR+1TBQFU3VHuM2fasmWqfNf34Y/uFPTmrumm66pqqJnnjbb+NqkumyzPVvGxKxdSuXStVNtH0+AO+2jLLJO0qKOw51C0r12xe/oO/XwIs1XXktlMe78d4wWaV+Pzgw4/DzSpQpRKwLvmdibhpfjV0TcvGxC0bz8Plney9RhBo8tOu4DNQFinXn/XOnfa3W376Izsj6OFWgdVUSaWZ1Uv3614nG27ZXOTh8iqLd78qeWmu61zbl+ZY44Fq3XOLSzti7bPWyuC+VFyezEcAAQQQQAABBBBAAIGqKZA3JQR1+noHpQQVwFNSqT51zrB23fqoLT09VCqglmmav/BDe+LZFxMWV0cJh3fvGpZQUzVb9XLpgjAqveba7cvk4c7P+Hd/eiih+rH2VVWbVc23fZvW1jrYlmtDT9tR77IupdrWMUf3trnvfxAupofoeUFbfyoNNP2tt92qYXtjyQJvTwbHXVjtMFo0bJfx4KB9sg5t24T747dDNeXFf++pbln8c+2eTJMMqYMG9earlzznBcfw7rwFpja3/GCAAkyTn3nBrrzku0lysYRlky2wecvWZJOTTvNLKCVdIJi4NthXl1Rd110Xblqq9+ZB9We1Q6mk4PKPr7wk1aLFTlc7hwq2KfkdFhS7YhktoB5NdX259j1fD6oNd+l8YNjhg7/J3kk6YFHHJGqf0qU7fjk+49J+bp1s3usHnefoc+eusXXrs+spPNNtlcVnXb1GK5iul6x171L7jPMWfGD6nPjpH088E/RE/q2E9ho1Pxd5+NvJ5fB+wX3XTzrGZPctf5lkw2Vhn2w7mUxrFav+q97i0wV0lefa2DXZKIOSy5nsC8sggAACCCCAAAIIIIBA1RPIq4DgkV5AUNWGF330SdgWnzuthwXBML+HTTc91fuMWf9NmKUeh9OVzvvCa78tVZAuIcOvRxTAUZDPJQWTxv3geyl79v1yw57qc1rHBTDc+u5dHXHUqVM7CgjMeue9MJDpVzHtHwQNkyW/FKGCJJd8d1TKDipU8tBvey3V/iTbTnza5s1bbNfuXWFwQvNU6lJtC+ql7ag9QTWq77ahdrj8aqF+EC5emia+LRc4i09PNr46g2q9n3lVyhXkyzQp2OECgu69uHVVHVml4HRu/KQg2sIPF4eTNnjVLP1l/GH1jPziv/8T9DDc1LQf5w4/zZ9d6mGdj75H9gy3oczeCwK7Onf+Z0s9CyerAhoPAqn9yvZBSeB0SdXcFY+WTUmSetN2QX2121lcuv+v/zD1gNsyaGNSnXio7c50qSw+6+rcZ1NQ3dlVOVWgTL1Ou56nVSrz4X9MjgLF8v94ybKwdK/b11zk4fIqi/eWQaDbT+vWf5k2IKjOYXRu9KOF2t07ffDx4WelLO6z/n5lM6wfl/yke0xxAUG1Xesn/0cZfzrDCCCAAAIIIIAAAggggEDJnoorqZtK7O3buFH04Ku2A11wRIeUbXXhhYsKAytaVw/Z6YKBaqDeD+a4gJXWLS4tWKQOAPYkBR4VmEiV5r6/MGFWqmqRhcGYXlFHHQrGdAp6wnVJQRMFY+JJQQtXokvzVFIxXW+18ZKE2Ry72/Z9Dz8algZTYFHb+sH3vuNmRe+qYndUr2+FwQ+/5KZ6r1Vvq0o1a9aIli8u4Bd3j1ZMMqAqyqpuqFJUyZI6nPGDDQrMZZr2C0pEBsW5wsVl999g+FtpOr5RKaEbfnVXuHy9unXt6KDXbNeLdMuWe6qMKq93gvb7Dg/a8UuV1F6l2lTTa0PwKovU76heUUBQ51edi6jkrkvHBNXDk6W44bQZb6UNWCoIf8OtvwmPRde2emP+0dgxybJOOU1BGhcQVMc+CqqmCrroWNRbt5KOp2XzxABPso3Er7nSfNZVGvr5V6aGVfp1vBNu/knSUqkHfeOAIKB/nv3qrj2dsujYVN0/F3kkO85cT4sHytS0wrnDT025GZ0Xff71UtX0c4Z+2+bMez9h+dLYJ2T09Ui2BaPjQc4XXp2a9H7stqXr++WvS8C7aWoqg4QAAggggAACCCCAAAIIJBNILD6UbIkqNk2lBF1SKSRXbVIPzKrumk3aHpTCcql+mkbsVSrpd3/+X7do+L4ri14bVSrOT+k6sFA1QJXq8lO8DTp/nt9zqwIY/3z6+Wj2kUFwLV7CTDM3b4ntT4ogmJZVIOQvj0zSYJRSBSijBVIMaP+UFgYlmtIF87bH2tFq6LUd6bcjqcCOAh/J0rNBFWc/KJVsmfi0x/+1xy4+75+xecf0OSK+SMpx9Zzsp0cmPWX+tefP0/DjT02JJql9x/ZB4MsllVLz01NB1e9UpVU/+mSpKZDpUq+gVJlL+rz4SSXISprUuYkL2CqPRyc/E2WlgGaqYHOHdm1M811S23cK/qZK/wkChgpsKulaKkmbnj0P656Q/fMv76manzAjGHn5/wqbJ3DT1ZNvcSmXn/U6Qactrn1PHe+cIOCfKsVLy7p7TC7ySLXNXE7XufR7zVYnSeocKVmSsa4Fl/bv0C4sgZpLe5d3zRp7Pic7tmf3GWnYoH7YRIDLS6U21RlWqqS2cP37YpdvHJhRp0Wp8mM6AggggAACCCCAAAIIVG2BvAsIJmuLTKe45+GHJg1+pTv9arfPJQWXVNoqnhS4+f19Dyd0kKBl3IN6fPlk4+2DwIefngp62VWQMZ7UBqA6+ognvzRffJ4epNvs1yqa7Jfe84OF0QLBQLxklh6ukwXPVFLttt/9OaqS7PJQ9cVskx/I1T4qwJqsDbfFwUOzemJ2SQGjpvs2dqPWtUvnaFgDDwYdwvjtbqmqrUpF+XkkrJBmZFZQXfmxJ55NWEKBGJ0T/0G+RxBUUluAmSaVbFW1Wpd07dw84R5b9dkaNyl8V9XoSU9OCduGczNUJVzXtksqWTqw/9FuNCzt9rs//W+RIO/yoJrrHx/8e7ScBlwVUw0rXz+pB28dq14lSX7nIv76fYLSjX41b3+eqvePPPN0f5Ld+Yf7wvYkEyYGI6pKHj83Jw7qH1+s2PEjgrYM1YalS1Onvxm2IxoPqqr6vXq7dkkliA/o2N6NpnzP5We9RyyQrGtdVejjSUFSdYzkJ5UaVMpFHn6+ZTWsHy7OG5F4LdwSfEZUjdxP6jjlngf/lnBP0jWmlEt7t81atfYEBN9+d66pmrk+p/591i2b7P075wxNmPyH4LtE13I8vTL1Dfv7408lTD5p0DEJ44wggAACCCCAAAIIIIAAAr7AnqcVf2oVHlaVP7Wl5UoGukNVVdNsk0oL+T2h3h9Ua1Vppm8cuH9QfXSzLfjgo4Rqwn7+eiDcum1b1BGIPy8+3DEICPpt/anq8Y9/cXtYFVSlSBYHJblU0i3eOYDL5/OgMfp0aUDf3jbxn08nLKLOUNq23hMo9Gfq4VulT1x1ax2LqmLKUL4KVGmeX1rFX/9zrzqoPz3dcLeDO4d5f7b683AxtXP4s6BarPZD7csp8KfSbP750ILq4dgPKB3V63B7/F/PRQ/kChCoeq0CNuopOH5dpNunZPMUIFKnLOoYQyl+XmQ34rTByVZNO+2MISfam7PnRAE3BWBvuvMPplJyqvqqc5+sg4jzzxleJNCtqpDTpr8V5aV9vP6Xd4QBK7WntipohyweOFKVY79apq4PP6m3ab2Ufjl+XNRenb9MumEFSf8elHyMB0pSBaVdXqo6rRJenwRtwikpIPnnhyaGpf86BtNVklHB2HjAWkFRP7Dn8svkfdRZZ9jtv/9ztKgCyCqd1emADqbeuxcvWVqk1/FLLxyVcB1GK8cGcvlZVyco6jhI16SSbO+698EwGL1fqxZWPWi/UdXc41X6DwuqkLvSk7nII3aIZTbaNbhHqJqzu3b12b5lwt3RZ0Qd/6gzFf8a0/26T+/CYHsu7d1BqvSruw9qf37+69+Gs1TadvS5w91iKd/1w4GasnAdPWnfH5r4z/CzpgCzgou69l3JV5eR2qp0QV03jXcEEEAAAQQQQAABBBBAwBfIuxKCOvh4W4EKJulhPts06Jg+CVUdtb4erp9+/hX793+mJwQDtY0zTx+SsInZ78xNGE81Urt2bbv4/JEJs1VK7NWgVIiCMOop2A8G6vj80kizg9JK6ZIeTuNVg1OV2HL5jB45okjHDKqCrdKLM2e/Ez0Ea3m1Y+V3pqDgTDxA4/JN9a72Aa+4+IKEKqJaVoFHtQemB+Z4MFAlhtQ+nZ9kmaxtMZWi9IOBCiR9/4Lz/FXTDncOgsDOUEEpnZP4eVF15Wsuvzhlm3PpNqCg53U/vCQMivrL6ZinB+38vT1nbsI1oGXUi7Z6jY6nOoHBNVdcHPaA7eZpnxU4U1VLF1Bx81SqVte6n9oFQUi1x5ksxUtlJVsmPk3evbySjJqvYEgmna/84ML/KdKmokrsqsSmrov4tabjGX7qyfFdyHhc1ZsvOv/chOtf1496utZnwO+UR5nqOvSrRKfbUK4/6+qJ278XaNsKnOueoJJm8WCgSqJeOOqshF3MRR4JGZbhyMWjzy3Szp77jOjz6AcDFfT83nfOifYm1/bK2C/ZG20oGFi24lN/NO3w2UNPSbh/amHdq/SZnxOUSo8HA48LPqulub7T7gwzEUAAAQQQQAABBBBAoMoI5GVAsHePPW2h6UwqQOCXIkt1dusEJcj8pHV+eMl3bfDxA6JgkD9fwyp5Nvj4Y+2mn1xlCrL5ba/NmTs/vnjKcZX2uPqyi9JWNT0w6BBk3KUX2nlBNUq/rTMFD/0OTeIbUYCoa9DjsJ/8Krr+dDes4NbPr/uhqWRXqqQHbgWlrg2CYP2PSmwzT8GTbJM6b7gx2KZ6FHbBt3gemq7gy3VXfD8sQRmfr3EFTBVcc6Wg4suo1N21wfqdO+2fMCt+jdT1qs2qROB1P/x+QpDNrax9Uscr8lIgraRJpS9/ctVYUwm/eJVdP08FAX/6o8ss3TlU6bifXX1ZGOjzr0k/HwX8Lhtzvn3nnGH+5HBYAVpdj8lK2bmeTmvFPi9FMolNiAeh4+OxxaNRBUsv/J+zwyBdqiClFlbVeHVGk+x4oswyHJDxL4KSkCqVlupaVJD4hmuvKHIdxu8j8U3m8rOua/bK4B6loJ5+lEiV9Fk4PzjP6kU6fjy5yEPBtmySSuvGUyZ5qISmAvk6Ft17kyVd7/px5mfXXF6kjb1c2mvb+gyeEZRSjtuv80pt+9WKk+2vqsZrf9WzfKp7ltY7qNMB4Y8mQ4PSvPFzmCzfdNNqBPcsPyU7H/58hhFAAAEEEEAAAQQQQKDyCVQL2r7KtvPDyneU5bDHaitw9efrgtJBn4ftA6pnWFW5rRs07J/rpNIha4JtrVm71uoHD73qNEJVOOMBq2y2e9vv/hSVsFNwS4GgTJNKR6lU1OrP14arqGRX66BKooJGZZVU0kcGKu2k6tmqOq1AYLwqa3HbV5uBKkGkqsg6X8qjtA/TW7ZsNXXIoU4NOrZvF1oUd27uCNq+U9U/JQWSrvj+BcXtumk76rlYVXwVcFB13xbNm5bomlNvuWo3cN0XXwRBh2amKqV+Jw3pdkZVIXVNKmig85Cqp+V0eeRyntrXVLX1FYGNkoIocnEdZeRyW8pLt1BVg18WXEcqqdsqsGsTfP7r1i39Zz/Xn3VVX12x8rOg3cz1VrtWLWvftk14fWZzzecij1yfg1T5qVmGFUFHMyuD60E/KKg9zkzvlbm2131KVbR1X1S7piW9P6qasD7z6kBH156OR/fbvf25S3UOmI4AAggggAACCCCAAAIVU4CAYMU8L+W6V+qcQ+3xuaQSNt0POciN8l4OAjffeXdUilP22VRXLofdYxMIIIAAAggggAACCCCAAAIIIFCFBBLrBVWhA+NQMhdQm4cuqaSZOvAgla+ASui5REkfJ8E7AggggAACCCCAAAIIIIAAAgiUhUDe9TJcFoiVLc+FixZb9RrVbcf2HTbz7TlhBwPuGI4/tm+pqh67fHgvXkDVlVVtUx08qNq1S6raSEIAAQQQQAABBBBAAAEEEEAAAQTKSoCAYFnJVuB8H/z7pIQAlNtVdRSiDjtIZS+gtsSuu/G2pBtK1jNw0gWZiAACCCCAAAIIIIAAAggggAACCJRAgCrDJUCr7Ks0a7pvkUNQpwLqMVk9DpPKXmDxJ8uSbkQ94e7foV3SeUxEAAEEEEAAAQQQQAABBBBAAAEEciFACcFcKFayPDq0axP1KKxdP6BjeztjyAnWqkXzSnYklXd31ZGLnxSQVWcio0eO8CczjAACCCCAAAIIIIAAAggggAACCORcgF6Gc05aOTJU+3Wfr10XBgFr1iQuvDfOWkFBQXAO1odtNrZo3pS2G/fGSWCbCCCAAAIIIIAAAggggAACCOShAAHBPDzpHDICCCCAAAIIIIAAAggggAACCCCAQP4K0IZg/p57jhwBBBBAAAEEEEAAAQQQQAABBBBAIA8FCAjm4UnnkBFAAAEEEEAAAQQQQAABBBBAAAEE8leAgGD+nnuOHAEEEEAAAQQQQAABBBBAAAEEEEAgDwUICObhSeeQEUAAAQQQQAABBBBAAAEEEEAAAQTyV4CAYP6ee44cAQQQQAABBBBAAAEEEEAAAQQQQCAPBQgI5uFJ55ARQAABBBBAAAEEEEAAAQQQQAABBPJXgIBg/p57jhwBBBBAAAEEEEAAAQQQQAABBBBAIA8FCAjm4UnnkBFAAAEEEEAAAQQQQAABBBBAAAEE8leAgGD+nnuOHAEEEEAAAQQQQAABBBBAAAEEEEAgDwUICObhSeeQEUAAAQQQQAABBBBAAAEEEEAAAQTyV4CAYP6ee44cAQQQQAABBBBAAAEEEEAAAQQQQCAPBQgI5uFJ55ARQAABBBBAAAEEEEAAAQQQQAABBPJXgIBg/p57jhwBBBBAAAEEEEAAAQQQQAABBBBAIA8FCAjm4UnnkBFAAAEEEEAAAQQQQAABBBBAAAEE8leAgGD+nnuOHAEEEEAAAQQQQAABBBBAAAEEEEAgDwUICObhSeeQEUAAAQQQQAABBBBAAAEEEEAAAQTyV4CAYP6ee44cAQQQQAABBBBAAAEEEEAAAQQQQCAPBQgI5uFJ55ARQAABBBBAAAEEEEAAAQQQQAABBPJXgIBg/p57jhwBBBBAAAEEEEAAAQQQQAABBBBAIA8FCAjm4UnnkBFAAAEEEEAAAQQQQAABBBBAAAEE8leAgGD+nnuOHAEEEEAAAQQQQAABBBBAAAEEEEAgDwUICObhSeeQEUAAAQQQQAABBBBAAAEEEEAAAQTyV4CAYP6ee44cAQQQQAABBBBAAAEEEEAAAQQQQCAPBWrm4TEXe8ibNm2yZcuWWfPmzcNXsSuwAAIIIIAAAnkssHHjRtu1a5fVqFHDGjRokFIi0+VSZlAGMyriPpXBYZIlAgggsNcFuN/u9VPADiCAAAIJAtV2BylhSh6PzJgxw2677TZbunRppFC/fn075phj7Oqrr7bGjRtH008//XRbsWKFHXzwwfa3v/0tml4VBwoKCuzII48MD23IkCH2i1/8okIdZmnPxZo1a2z9+vXhMXXq1Cl8oK1QB8jOIIAAAmkEyuIenW2eJ554oq1bt84OPPBAe+yxx8K9TXZvTrZcWdyD3baPPvpo+/3vf59GzyzZPqVdwZtZFvvuZc8gAgggEAksWrTIzj333Gj8pz/9qeleF0+p7kuppsfXz8V4qm2V5n6bi/0iDwQQQACBRAGqDH/t8eCDD9rYsWMTgoGatXnzZnv++eftO9/5ji1evDjS27FjRzish6aqnvyYsUqAVLRU2nPx97//3UaOHBm+vvzyy4p2eOwPAgggkFagLO7Rucgz03tzWdyDt2zZEprpO7wsU1nse1nuL3kjgEDlFdDziJ+eeeYZfzQaTnVfSjU9WjGHA+W5rRzuNlkhgAACeSdAleHglH/wwQd2zz33RCd/+PDh1rdvX9u6dWtY+m/+/PlhacBf//rX9qc//SlajgEEEEAAAQQQMLvuuuvC78yGDRum5ch0ubSZ5HhmRdynHB8i2SGAQCUXUAGEp59+OuEo/vvf/9qnn35qbdq0SZhekUe431bks8O+IYBAPgoQEAzO+p133hmd+4suusj0cunYY4+1b3/722FVqNmzZ4dtC7Zv397NLvL+7LPP2quvvmoffvih7bPPPtajRw/75je/aYMGDbLq1QsLZKqUnZZ78sknTcX/27Zta71797aePXta//79rVq1amG+mS6nhd9++2179NFHbeHChWGV18MPP9zOOOOMcNtuJ7PJz62T7P3ll1+25557zj7++GPr0qWLjRgxItx3f9lMHH7+85/btm3b7IgjjrAOHTqEHirVcccdd9j48ePD7Pr06WNNmjSxF1980ebOnWsHHXRQWJJPpsUl/ZN033332bx588LSnZ07d7auXbvaxRdfbC1btgxXv/fee+2NN96IslLVMu2PqkaTEEAAgWwFVLIum/tXqvug+17K5D4W38dc3aP9fIvL86233rIvvvjCWrVqFTaz4a/rD8eXS3UP1nfZ6tWrw1WvvPLK6J6tamgTJkwIp+v74IILLvCzTzqs5kD++te/ht9ZBxxwgA0dOtSOP/74aNn4PmlGJt+XqfbdfX9kc+7efPNNmzx5sr3//vth28WXXnqp6cdIvdR0iaoGKhV3vWTy3XvjjTeaSm9+61vfsmbNmtkLL7wQ/u9w1FFHhdURtb0HHnjA3n33XatVq5adeuqpNmzYsHD7/EEAgb0joGcQNcugpM+oK/2se7NqMbmU6r60ZMmStP/vZvIcoWaVNmzYEP4vreYhpkyZEv5vrv+bBwwYYLqH6Fkn1T7o3pjsfqt9z/R+mek+OA/eEUAAAQTSC+R9G4J+O0miUjCvUaNGCWpz5swJA3ya2KtXL+vYsWMYMNLDioJMEydODJdXMOmhhx5KWNeNqErquHHjwtG77rrLVJQ+WfIDkpkup/aa9AWZLGm6gpFKmeYXz2fnzp3hl3x8uj9+yy23hO0waVqmDrJUUkBU7TEqKWAnQ/1zoeT/0xNO+PqPSmsed9xx4Zj+wYifC52zyy67LPqHyV9Xeaqk5yGHHBK1HeXP18PPDTfc4E9iGAEEEMhIQAHBbO5fqe6DDz/8sGV6HyuLe3S2eSZrFyrZvTm+nBv3cXUPbtq0afR9ev3114dBPC2jgJe7P19zzTV21lln+atGw8nyjWYGA8pD21Fyy/rtH2byfenWCzP5+o/7/sj03Gk1PdCr1Ew8ue8/vU+dOjWcne56yfa71+Xvb1c/zin5bSlrXO0on3322RokIYDAXhBQ+93/+te/wi3/7ne/s8svvzwc1mdWPya4lOq+9Prrr0cBRbesu19l+hzh8k5271Ce7p7slnPb0bvblpvn32+zuV+69YvbB3/bDCOAAAIIpBYoLLKWen6Vn/PZZ59Fx6gOQuLBQM087LDDTNWI9VIwMFnavn27TZo0KZylAJdKiPzsZz+LFlVpQD0o6pc1FwxUvmq7UF/yevhRUgclClJmutyqVauiYKC+HBVQvPDCC8NAmvLTl7NK3WWan9YpLumfj/PPPz+hVKBK9amKdaYO/jZcMFAG6tTDT/oFVJ7anuvYRPMVEHRtRPnLa1h+N998cxQMlPNVV10VdgCj+crzV7/6VXg+VOpCpRBd0nk788wz3SjvCCCAQIkFsrl/xe+D2dzH4juY63u08k+XZ3z72Yynugfroc+l1157zQ2aHmpdGjhwoBtM+64S4T/4wQ/CEnFuwX/84x9usMh7pt+XqfY9m3OnUvL+D3oqZaP/NZRcCaAiOxhMiF8vJfnuVf46r6eddlq0CQUC9RozZkz4v4+bkc7LLcM7AgiUjYD+33XBQN3P1FmSXkr6vKpEtUup7kuppmf6HOHy17vuHfoB3//fWtPdPqbalpaJp2zul/66xe2DvyzDCCCAAAKpBfK+yrCqH7nkqpG68WzeVVVKVWeV+vXrF1YVVvF3BblUxF9fXPpC13IurVy50pYvX26q3qvqYcuWLQtnqSpPpsu5UgNaUdWqVBVKqUaNGvbnP/85HNYyKg3nUrrtar10SUFHVcNVNSN9iSsAqV/2dIyqmqvq1Jk4KB8/+SUM/cbstdz9999vLVq0CKtwjR49OqxCpe2p7UcF++JJ++M6gFFJHVVdUFLPbHqpmraqYS1YsCCsoq1qGK7asKqIu+BsPF/GEUAAgWwEsr1/+fdBVd/K9D6mkuou5eoeXbt2bZdl+ANTuvu+mrsoaVIzGanuwQpW6WF32rRp4fen9knDSvqBSN8LmSSVCNd3k6rVuR+WXNW7ZOtn+v2bat+zOXfqyMrti0rzu+Cgeg71qwEm20//elEp+Wy/e/Vd9/jjj4dV/Nq1axe1paxmNRQQ1LsrjaPzQEIAgb0j8J///CfasEpeq2kh3S+mT58eTle1fzXho5TqvqR5ye61rld4zU/3HHHSSSdpkSipRHLjxo3DksP631nPOe4+kW4fogy+Hsjmf3b/WUarp9uH+HYYRwABBBBILpD3JQT1ZebS+vXr3WDW7womjho1KnxA+ctf/hK2oaRf3d0/+i5DPeCoJKKS/oFXKUIt95Of/CRsT0/VYOvWrRv+ap/JcgoouqQqTvrnXS8XDNQ8PVRmul2XV6p3BS8VDFRS8PCYY46JFlWJhUwdopWCAT3A+qVB/HnannvoU7sk+ifDJVdCwo27908++cQNRr+gugl+aUB/OTefdwQQQCBXAtncv+L3Qf/+5EqCuP1Kdx8ri3t0cXm6/cr1u19ybdasWeGPTnroVNJDcSZJ30mu3V99Z6mamtLGjRtTrl7a78tszp3/Ha7SgS7p+z/dj1Px66Uk371qT9G1bdy6dWu36chIE5QvCQEE9q6A2upzSW2O6z6oNkBdUm/D+pG+JMm/B6V7jvDz1n3UPT/pHqJ7ppK7P/vLFjeczf3SzyuX++DnyzACCCCQbwJ5HxD0/wn+6KOPwqBc/CJQu4K/+c1vwpdKlyVLCvypcXOV9NMvdvpSVEkEVXeNJ1UR9gNpmq/glqq5qrqvS5ks99VXX7nFw3dV29VLDwvu5UrcZZJfQmZJRhSs9FO9evWiUTXCno2DW9Ev3eKmuXcXfHTjfg+Wqf758asS77vvvm7V8N0fVxUrEgIIIFBWAtncv+L3wZLex8riHl1cnmXld8IJJ0RZq2TgzJkzo/H4d2g0Izbgf8drVnGl4N3qpfm+zObc6fvapTp16rjBMFDnf79GM74eiF8vJfnu9fP0XVyQ0J/PMAII7B2BtWvXRiWjtQfqcEj3P1ciWNP0+Vfpv5KkbJ4jXP7qNDFXKZv7pb/NXO6Dny/DCCCAQL4J5H2VYf0Drl/iVX1UQTxVnznvvPOi60BfVOrVz/3qpc5BkiU9rLgSa2p4+4orrjBVb1KpQT+pivKmTZvC4OGPfvSjsPqqehd8+umnw21oP9Sznx5iMlnOlXzQNtQrYPwhwW070+0W13uvemtUIM49PKi3MJfatGkT/tOSiYNbR+81a6a+DOMBWFXFcknbS5bcL5Wap1IlfikTWbuULFjr5vGOAAIIlFYgm/tX/D5Y0vtYWdyji8uztE6p1tc9Ws1CqErZv//976i0mnoILsuHwdJ+X2Zz7tReoUv6fnO9H6vqnfsudfP99/j1kun/IH4eDCOAQMUXeOWVVzLayeeff95UejDblOlzRLb5Zrp8NvfLTPNkOQQQQACBzAVSR2Iyz6PSLzl27FjTS0nF5fVLm9oBVJUitZvkgoGqppWq+oz/j7uqdykYqGCZAnwuqaSeSg+q5IGSgoVqg0/VhNSZibalpF8DVYQ+k+VctWKtp7Y01JCvgnUTJkww/XOgdM8995gaDc4kv3CFNH9kcdNNN9k555wT/hqphzSXtC/+L5TpHNw6xb3LTy6qUqx/ivztxTsgcXkdeuihbtBUzaJ79+6h8Ysvvhi1t6LSk/G2SLSSHgTTVdOKMmYAAQQQKEagJPcvl2VJ72NlcY8uLk+3z7l4j9+DTznllKidWn03K5188sm52FTKPDL9no5n4PY9m3Pn/r9QXmrLq0mTJmGzHI8++mg8+7Tjmf4PkjYTZiKAQIUTcB11aMfU27drRkfj6pRI//craTl1JBgv0e3uS+FC3h83PdPniJIEG93m3LbcuP+ezf3SX49hBBBAAIHcCBAQDByPOuooO/XUU8NSemJ96KGHwpdPrCCR32uwP0/DfnDqhz/8Ydiehmtc1y2rqkHaloJReghQj8J6KW/3oKN5KhGxc+fOjJbTw4OqDqjjEHWMMXjwYLe58F156Utc7RVmst2ElVOMqDSjXn5SA+QKambq0KBBA3/1tMNqEF4vP6lqtV/915+n6ToHquatpEbaXUPtbjnNd/vg/3Ol0qHDhg0Le4l2y/KOAAIIlFQg2/uX20429zF9X/gpF/dov/qq8k6Xp7/tkgynuwersXp1nuGSvsfibSq6ebl6z/R7WttLte+Zfgfpe0idd7jvOPee7bGUxXdvtvvA8gggkFuBjz/+OCpYoAIJZ511VtihiL8V1WxSKWollRRWKeNU96Vk03/84x9n9BzhbzOT4WTbGj9+fJFVs/muK7IyExBAAAEESi2Q920IOsEbbrghDPjpYSOe1JPXww8/bM2bN49m1apVKxx2VWf1BaxSc259BQNVylDtCrr03HPPhSUMFahyDZtrngsGappK+SlAqC/+TJZTT2M33nhjuG23HfeuUnUq8aiUaX5uXf9d23Bp4MCBpp57/aSeEBUQVMrUwV8/XXtFKpUpRz+df/750fY0PX4uNE2lL6+//noNFkkqKamgn0vqyMWvsqBenkkIIIBAaQUyuX+5bSS7D2Z6HyuLe3Q2eeoY4lVYNS3ZvTnZcunuwfo+9L8D1K5gPFipbaVKyVzjy8b3KZvvy1T7num5077o+1MPyu7/ApXYGTduXDQeL/GjdeLHlcvvXv/cx7ejbZMQQKB8BPzqwiot7X823R7os++SehtWSnVfSjZdeWbyHOG2off4fcGNu2cgLZNsW5oev99qWjb3Sy2v5LZZOLZn3N8HN493BBBAAIHUAtWCUAo/ggAAQABJREFUaqy7U8/OvznqGEPVa1VlV18qChTpgSTTpI4qVE1s//33D0vMpVpP21EVHxWj1ynQA4jaS4p/wWW6nLajhoGXLFliWifV9rPJL9W+u219+umn1rFjxyLVEzQ/U4dk25CHCzqqapiqKKudJfWEpl4R0zW0Hs9PJWeWLVtmn332WdguY7t27aL2D+PLahsqxakSG670YHwZxhFAAIF0Arm8f7ntZHMfc+vo+yDX9+ji8nTbLul7qnuw2se99957w2zVhITfu2ZJt5XJetl8X6ba9+LOnb4r9f2kpO82lfrXD41qq1cdkympbeCJEyeGw8X9Kc13b3F5Mx8BBCqXQKr7UqrpmTxHZCuQalvJ8inufplsHaYhgAACCJROgIBg6fxYuwwEkj1Ql8FmyBIBBBDIuQD3r9yRLl682N577z278847w2Y29KPZk08+mbSUTO62Wr456QeroUOHhhvVj5DXXXedtWrVyp566qmwDVzNGD58uKlaHwkBBBBAAAEEEEAAgVwK0IZgLjXJCwEEEEAAAQRyIqCOoO6///4oL7Udm6zKXLRAJRxQD58qCa9OwNS2cLytYtVSUDMZJAQQQAABBBBAAAEEci1AQDDXouSXEwFVGVZ1LVURJiGAAAKVSYD7V27OVuPGjcOmNNR+b9++fU1taFXFpPa7FBR85513bPbs2WG7wgcddJDpNXLkSJqwqIonnWNCAAEEEEAAAQQqgABVhivASWAXEEAAAQQQQAABBBBAAAEEEEAAAQQQKC8BehkuL2m2gwACCCCAAAIIIIAAAggggAACCCCAQAUQICBYAU4Cu4AAAggggAACCCCAAAIIIIAAAggggEB5CRAQLC9ptoMAAggggAACCCCAAAIIIIAAAggggEAFECAgWAFOAruAAAIIIIAAAggggAACCCCAAAIIIIBAeQkQECwvabaDAAIIIIAAAggggAACCCCAAAIIIIBABRAgIFgBTgK7gAACCCCAAAIIIIAAAggggAACCCCAQHkJEBAsL2m2gwACCCCAAAIIIIAAAggggAACCCCAQAUQICBYAU4Cu4AAAggggAACCCCAAAIIIIAAAggggEB5CRAQLC9ptoMAAggggAACCCCAAAIIIIAAAggggEAFECAgWAFOAruAAAIIIIAAAggggAACCCCAAAIIIIBAeQkQECwvabaDAAIIIIAAAggggAACCCCAAAIIIIBABRAgIFgBTgK7gAACCCCAAAIIIIAAAggggAACCCCAQHkJEBAsL2m2gwACCCCAAAIIIIAAAggggAACCCCAQAUQICBYAU4Cu4AAAggggAACCCCAAAIIIIAAAggggEB5CRAQLC9ptoMAAggggAACCCCAAAIIIIAAAggggEAFECAgWAFOAruAAAIIIIAAAggggAACCCCAAAIIIIBAeQkQECwvabaDAAIIIIAAAggggAACCCCAAAIIIIBABRAgIFgBTgK7gAACCCCAAAIIIIAAAggggAACCCCAQHkJEBAsL2m2gwACCCCAAAIIIIAAAggggAACCCCAQAUQICBYAU4Cu4AAAggggAACCCCAAAIIIIAAAggggEB5CRAQLC9ptoMAAggggAACCCCAAAIIIIAAAggggEAFECAgWAFOAruAAAIIIIAAAggggAACCCCAAAIIIIBAeQkQECwvabaDAAIIIIAAAggggAACCCCAAAIIIIBABRAgIFgBTgK7gAACCCCAAAIIIIAAAggggAACCCCAQHkJ1CyvDVWl7cyYMcOeeeYZ2759u/Xp08dOOOEEa9CgQXiIb775pj333HO2Y8cOO+6442zgwIFV6dA5FgQQQACBDAWWLFliTzzxhC1btsx69uxpZ599ttWoUaPI2gsWLLBJkybZ6tWrbcCAAeF3R5MmTaygoMBuueWWhOX1ndKvX7+EaYwggAACCOSvQCbPHrt377bHH3/cpk+fbvp+6du3rw0aNCh/0ThyBBBAAIFQgBKCWV4ICxcutLFjx9q+++5rJ554oj3yyCPhS9nMnz/fLr30UqtXr174RXvDDTfYq6++muUWWBwBBBBAoLILbNiwwS655BJbtGiRnXLKKTZlyhS76667ihzW559/bqNGjTItf/rpp9vUqVPtiiuuCJdbtWqVvfTSS9atW7fo1bx58yJ5MAEBBBBAID8FMn32UDDwvvvus8MPP9wOO+wwu+aaa2zWrFn5icZRI4AAAghEApQQjCgyG5g9e7Ydc8wx9qMf/ShcQSU/XnzxRRszZkz4y9vQoUPDL1nNdKU++AUuM1uWQgABBKqKwLRp08JDufvuu8P3mjVr2rhx48IflOrWrRsd5nvvvWdt27a122+/PZzWsWNHO/fcc8NShStXrrQuXbrYsGHDouUZQAABBBBAwAko0JfJs4dKBo4ePdpGjhwZrvrCCy/YG2+8Yb169XJZ8Y4AAgggkIcClBDM8qSfc845dscdd9jatWtNVYcnTpwYVvFSNqoeppIcLnXo0ME++ugjN8o7AggggECeCCxfvtx69OgRHa2Cfkpr1qyJpmmge/fuNmHChGiaSnvUr1/fWrdubStWrAhfF154Yfijkx7g1BwFCQEEEEAAAQlk+uyhZxf92LR06VJ7+umnbebMmXbUUUeBiAACCCCQ5wIEBLO8AKpXr256vfbaa2FJj3Xr1tmBBx4Y5qLSgg0bNoxybNWqlWl+celPf/pTcYswHwEEEECgEgkoINioUaNoj1u0aBEOb9myJZqmAU3v1KmT7dq1yx577DH75S9/ad///vdNJQqVx86dO2348OFhu4E333yz3XvvvQnrJxvhOyWZCtMQQACBqieQ6bOHnl2qVatm999/v914443hD09qS7C4xPdJcULMRwABBCq3AFWGS3j+VIVryJAh9uCDD9r48ePDasRqO/CLL76Icty6dasdfPDB0bgG1ED85MmTE6Zp5OKLLy4yjQkIIIAAApVTQN8H/g9C27ZtCw9EJf/iSe0HXnnllWE1YbUz2L9//3ARNUWhl6tivHHjRnvqqafs8ssvj7LgOyWiYAABBBDIO4FMnj18lF/84hfh981PfvKTsMaTC/ip9PrgwYP9RaNhnlEiCgYQQACBKidACcEsT6mqdv3tb38L19JD2sknnxwO68GvTZs29umnn0Y5qlh+586do3ENKHioRnz9V8ICjCCAAAIIVHoBlRBXCT+XVP23adOmts8++7hJ4fumTZvC0uYtW7YMfyxywUDNVJtP6nnYJQUTmzVr5kbDd75TEjgYQQABBPJKIJNnD4FccMEF9vbbb4c2Khl43HHHhT9COSyVVvefTdywm887AggggEDVFCAgmOV5VYm/SZMmmRqC14OcSvsdeuihYTBw4MCBYccieoDTfDX0q568SAgggAAC+SWgwJ46llJbs/pOUO+OvXv3DhHUWYh6qFd7gOpV+JNPPgkbe1fbtGoPSq/t27fbnDlz7Nprrw2HNU3fN+rdnoQAAggggIAE0j17vPvuu6a2Z5UOOOCAsECDCi7oO+nZZ5+10047LZzHHwQQQACB/BWgynCW575fv3726KOPhr+0aVUF/MaOHRvmomrEaqRXVYmVjj/+eL5sQwn+IIBAKPDGy2bvzjDbvcvs8L5mPYNX7a97nH3r/8zeft1se1C19MhjzXoPDFapFq7Gn8onoNLh+m5w3w/qZOqmm24KD0RtPqm0uR7G5s6da5s3b7bzzjsv4SDVYZV6g1SJjj59+oTzunbtaqeeemrCcowggAACCOSvQLpnD/0gpR+dTjrpJDvrrLPC5iZcEFDPKGeccUb+wnHkCCCAAAKhQLXdQcIiOwE1/r5q1SqrVatW2CB8fG398lajRo0iVbviy7nxXr16hcX03TjvCCBQBQVm/8fsuUeDn+kPNuvQyez14Ff7A7qanR20Hzpnutkzfw+6nD3CrNG+Zm+8ZHba+WaHBuOkSi2gdv/Wr19v7du3L/FxqG0nNQbfvHnzjPLgOyUjJhZCAAEEqoxAJs8eKpWuEoL6LmnQoEFGx873SUZMLIQAAghUWgFKCJbg1KmnLrXZkSqpLSgSAgggkCDwftB2T4dvmJ13WeHkzZvMZk8tHH71KbMe/cwGn1M43jzoeKJe/YTVGamcAup53u99viRH4XooLsm6rIMAAgggUPUFMnn2UEGGjh07Vn0MjhABBBBAIGMBAoIZU7EgAgggUAqBAd8OgnzeL/JLFpk1aVGY4eaNZgVBNeI//MysRnBbPnJQUDqwsL25UmyRVRFAAAEEEEAAAQQQQAABBBBIKkCnIklZmIgAAgjkWKB9UE24+X5mm4Lg319uN1u9wuzYoD24LUFJQbXc8E7QfmDbA8zq1jObMtHso/k53gGyQwABBBBAAAEEEEAAAQQQQKBQgBKCXAkIIIBAeQksft/s0XuCjkTqmI2+yqzdgUGA8KvCrXfvZXbGBYXDf7zJbOarZp2CNgZJCCCAAAIIIIAAAggggAACCORYgIBgjkHJDgEEEEgqsPRDs4l3m33zKLNTRpoFbZGGqcE+he8qPehSw2DalqAkIQkBBBBAAAEEEEAAAQQQQACBMhCgynAZoJIlAgggUETglSfN6jcMOg/pa7ZyidmKj80+XVq4WMu2Zm+9ZrZutZkCh3qpkxESAggggAACCCCAAAIIIIAAAmUgQAnBMkAlSwQQQKCIwOrlZjt2BO0H3rFnVrVqZtf/wWxoUFVY0++5sXCeOhs59Mg9yzGEAAIIIIAAAggggAACCCCAQA4FCAjmEJOsEEAAgZQC1/4m5Sxr0drs6qCjkVVBRyN16+7pfTj1GsxBAAEEEEAAAQQQQAABBBBAoMQCBARLTMeKCCCAQA4FqgUtOLRun8MMyQoBBBBAAAEEEEAAAQQQQACB5AK0IZjchakIIIAAAggggAACCCCAAAIIIIAAAghUSQECglXytHJQCCCAAAIIIIAAAggggAACCCCAAAIIJBcgIJjchakIIIAAAggggAACCCCAAAIIIIAAAghUSQECglXytHJQCCCAAAIIIIAAAggggAACCCCAAAIIJBegU5HkLkxFAAEEEEAAAQTKRWDlzTfbmnvvyXhbDfv0sQMfm5Tx8iyIAAIIIIAAAggggEBcgBKCcRHGEUAAAQQQQAABBBBAAAEEEEAAAQQQqMICBASr8Mnl0BBAAAEEEEAAAQQQQAABBBBAAAEEEIgLEBCMizCOAAIIIIAAAggggAACCCCAAAIIIIBAFRYgIFiFTy6HhgACCCCAAAIIIIAAAggggAACCCCAQFyAgGBchHEEEEAAAQQQQAABBBBAAAEEEEAAAQSqsAC9DFfhk8uhIZD3Ai//02zGq3nPUGqA0883O7R3qbMhAwQQQAABBBBAAAEEEEBAAtuXL7Mdy5ZbrfbtrHa79qDsBQECgiVAX7BggU2aNMlWr15tAwYMsOOOO86aNGliBQUFdssttyTkOHDgQOvXr1/CNEYQQAABBBBAAAEEEEAAgUwElixZYk888YQtW7bMevbsaWeffbbVqFGjyKo8oxQhYQICCFRggXWKqdx5pzU66STb/4EHK/CeVt1do8pwluf2888/t1GjRtmGDRvs9NNPt6lTp9oVV1wR5rJq1Sp76aWXrFu3btGrefPmWW6BxRFAAAEEEEAAAQQQQAABC585LrnkElu0aJGdcsopNmXKFLvrrruK0PCMUoSECQggUEkECoLYCmnvCFBCMEv39957z9q2bWu33357uGbHjh3t3HPPDX+xW7lypXXp0sWGDRuWZa4sjgACCCCAAAIIIIAAAggkCkybNi2ccPfdd4fvNWvWtHHjxtnYsWOtbt260cI8o0QUDCCAQCUR2LFkWSXZ06q7m5QQzPLcdu/e3SZMmBCtNX/+fKtfv761bt3aVqxYEb4uvPBCGzNmjL3wwgu2Y8eOaFkGEEAAAQQQQAABBBBAAIFMBZYvX249evSIFlfBBKU1a9ZE0zTAM0oCByMIIFAJBLavICC4t08TJQSzPAMtWrQwvXbt2mWPP/643XbbbeGvdPq1Tl/YO3futOHDh4df0jfffLMtXLjQLr/88iy3wuIIIIAAAggggAACCCCQ7wJ6vmjUqFHEoOcQpS1btkTTNMAzSgIHIwgggAACGQgQEMwAKb6I2g+88sorw2rCasOjf//+4SIqFaiXK76/ceNGe+qppxICgup0ZPLkyfEsGUcAAQQQQAABBBBAAAEEEgTq1atn69ati6Zt27YtHFbtpHgq6TOKShsOHjw4nh3jCCCAQLkIFGz4qly2w0aKChAQLGqSdsqmTZvCNjtUXP+3v/2tNWzYMFp++vTp1qlTJ+vQoUM4TV/UzZo1i+ZrYPz48eHLn9irVy9/lGEEEEAAgSou8Oabb9pzzz0XNiuhnurVI32y9PLLL9srr7wS9mJ/8sknW9++fa1OnTrJFmUaAggggEAVFGjVqpXNmzcvOjI1UdS0aVPbZ599omkaKM0zikoXzpo1KyE/jfCMUoSECQggkEOBTUH8RGnrvLk5zJWsshGgDcFstIJl1avwJ598YqNHj7a1a9fakiVLwtf27dttzpw5du2115qGNV0lAU888cQst8DiCCCAAAJVWUBtz1566aWmUh8K8N1www326quvFjlkNSR/3XXXWcuWLcOAoX6EeuCBB4osxwQEEEAAgaoroJpICxYssBkzZtjq1avtvvvus969e4cHrA4NH3nkkfDHJZ5Rqu41wJEhgAACZSVACcEsZefOnWubN2+28847L2HNiRMn2siRI+3tt9+2Pn36hPO6du1qp556asJyjCCAAAII5LeA2p8dOnSoXXPNNSGEHvQmTZpkgwYNSoBRKcIzzzwzbKJCM7766iu7//77w2BiwoKMIIAAAghUWYHOnTuHtZPUq7CSaiLddNNN4fCyZcvCzg5PO+004xklJOEPAggggEAWAgQEs8DSoldffXX4SrXaww8/HHYoUq1aNWvevHmqxZiOAAIIIJCnAipB7v9YpIc7VR+OpzPOOCOhWQo97Pk9TcaXZxwBBBBAoGoKqGbSiBEjbP369da+ffvoIFVS0FX15RklYmEAAQQqgcD25fQwXBFOEwHBMjgLrvevMsiaLBFAAAEEKrmASnT47c+qfSi/wXh3eAceeGA4qHahJkyYYFOmTLEHH3zQzeYdAQQQQCCPBPS94X93lOTQeUYpiRrrIIBAWQjsWEpAsCxcs82TgGC2YiyPAAIIIIBAKQTUduAXX3wR5bB161Y7+OCDo3F/YOHChXbZZZeF7QiqqvH+++/vzzZ6rk/gyMuRrUGblB+ceEJWx37w7NlWu9V+Wa3DwggggAACCCCAQFkJbHrjDWvwddNrZbUN8i0qQECwqAlTEEAAAQQQKDOBNm3a2Keffhrlv3TpUlMbUfG0ePFiGzNmjH3ve9+zUaNGWfXqRfsBo+f6uBrjCCCAAAIIIIAAAhVdoOCrLyv6LubF/hV9usiLw+YgEUAAAQQQ2DsCAwcONJX2U2+R7733Xjh82GGHhTvz7rvv2gsvvBAOq7OqLl262IABA0zVjNX2oN5JCCCAAAIIIIAAAghUZoHN89+vzLtfZfadEoJV5lRyIAgggAAClUFg2LBhNnPmTBsyZEi4u8cff7yph0ilGTNm2NSpU+2kk06y2UG1TpUeHD58eDhPf+rXrx/OjyYwgAACCCCAAAIIIIAAAgiUQICAYAnQWAUBBBBAAIGSCtSsWdPuvPPOsIRgjRo1rFmzZlFWF110kemlNHny5Gg6AwgggAACCCCAAAIIIIBALgWoMpxLTfJCAAEEEEAgQ4GWLVsmBAMzXI3FEEAAAQQQQAABBBCo1AJb584N979u167h++Z58yv18VTWnScgWFnPHPuNAAIIIIAAAggggAACCCCAAAIIVDKBgg0bwj2u0bhx+F6wsXC8kh1Gpd9dAoKV/hRyAAgggAACCCCAAAIIIIAAAggggAACCGQuQEAwcyuWRAABBBBAAAEEEEAAAQQQQAABBBAohcCOZcvDtWu3bV+KXFi1tAIEBEsryPoIIIAAAggggAACCCCAAAIIIIAAAhkJbF++LFyuVkcCghmBldFCBATLCJZsEUAAAQQQQAABBBBAAAEEEEAAAQSSC9Rp2zacsWNJYYAw+VJMLSsBAoJlJUu+CCCAAAIIIIAAAggggAACCCCAAAJJBWq3KywhuH0FAcGkQGU8kYBgGQOTPQIIIIAAAggggAACCCCAAAIIIICA2aY33ggZGhx9NBx7WYCA4F4+AWweAQQQQAABBBBAAAEEEEAAAQQQQACB8hQgIFie2mwLAQQQQAABBBBAAAEEEEAAAQQQQACBvSxAQHAvnwA2jwACCCCAAAIIIIAAAggggAACCOSDwOZ588PDrNe1m9Vo3CgcLtjwVT4ceoU7RgKCFe6UsEMIIIAAAggggAACCCCAAAIIIIBA1RMo2LghPKjq+za2ut26h8Nb582tegdaCY6IgGAlOEnsIgIIIIAAAggggAACCCCAAAIIIIAAArkSICCYK0nyQQABBBBAAAEEEEAAAQQQQAABBBBIKbDriy/DeTUaFlYXTrkgM8pcoGaZb6EKbmDBggU2adIkW716tQ0YMMCOO+44a9KkSXikb775pj333HO2Y8eOcPrAgQOroACHhAACCCCAAAIIIIAAAntbINNnj5dfftleeeUVKygosJNPPtn69u1rderU2du7z/YRQCAPBbbMnxcedf1uXfPw6CvWIVNCMMvz8fnnn9uoUaNsw4YNdvrpp9vUqVPtiiuuCHOZP3++XXrppVavXr3wS/aGG26wV199NcstsDgCCCCAAAIIIIAAAgggkF4g02ePadOm2XXXXWctW7Y0FVb47W9/aw888ED6zJmLAAIIIFDlBSghmOUpfu+996xt27Z2++23h2t27NjRzj33XFu2bJk9/vjjNnToULvmmmvCea4k4aBBg7LcCosjkAOBbcE1uruuWd3LCjPbdq/ZrsJfYxJyr3W2Wc3+CZMYQQABBBBAAAEEEKjYApk+e6gU4ZlnnmlXXnlleEBfffWV3X///WFBhop9hOwdAgjkg0CDo4+2TdOn26Y33rAGffrkwyFXmGOkhGCWp6J79+42YcKEaC39Mle/fn1r3bq1LVmyxLp16xbN69Chg3300UfROAMIlIvAzv+Ybb3RbMf0ICC4bM8mq7Uxq9Zuz2t3LbOCT4PxBnuWYQgBBBBAAAEEEECgUghk+uxxxhln2AUXXBAd09y5c61Hjx7ROAMIIIBAeQoo+Kfkehguz22zrUQBSggmehQ71qJFC9Nr165dYYnA2267zcaNG2c1a9YMSwk2bNgwyqNVq1a2bt26aJwBBMpFoGBGUBJwRbCpaombq316MK6X0k6zTaPNap1gVoN/CEMS/iCAAAIIIIAAApVIQDWUMnn2OPDAA8Oj2rRpU1iwYcqUKfbggw9WoiNlVxFAoCoK1GhMpyJ7+7wSECzBGVD7gSpyry/hu+66y/r3L6xuqbYDv/jiiyjHrVu32sEHHxyNa+CWW26xyZMnJ0xjBIGcCtS5ujC7zRemznbrncG8ArM6F6VehjkIIIAAAggggAACFVYgk2cPt/MLFy60yy67LGxHUFWN999/fzfL1qxZY4MHD47GGUAAAQQQyA8BAoJZnmf9sjZ27NiwHUE1yOv/KtemTRv79NOgCubXaenSpda5c2c3Gr6PHz/e9PJTr169/FGGEShbgd0bggKCb5rVHhVsp0bZbovcEUAAAQQQQAABBMpEIJNnD2148eLFNmbMGPve974Xdo5YvXpiq1Gq/TRr1qwi+8gzShESJiCAQCkFti8vbNKqVvugKSvSXhdI/DbY67tT8XdAvQp/8sknNnr0aFu7dm3YbqDa79i+fXvYa5d+cVu9erWp8xENH3bYYRX/oNjD/BLY9pfgeIOPfu2h+XXcHC0CCCCAAAIIIFCFBNRjcKpnj3fffddeeOGF8GgnTpxoXbp0sQEDBoQ1nPTsoppOJAQQQKC8BXYsLbz31G7XPtp0va6F/TBsnjc/msZA+QhQQjBLZzXCu3nzZjvvvPMS1tQX7bBhw2zmzJk2ZMiQcN7xxx9vp512WsJyjCCw1wUKgkZca35zr+8GO4AAAggggAACCCBQcoF0zx4zZswwFWQ46aSTbPbs2aaaS8OHD482pk4RNZ+EAAII7G2B6vs2DnehYGNQk41UrgIEBLPkvvrqq02vVOnOO+8MSwjWqFHDmjVrlmoxpiOwdwTU2cju7UFAcNDe2T5bRQABBBBAAAEEEMiJgDo1TPXscdFFF5leSrRfnhNuMkEAgRwIFHz1ZQ5yIYtcCRAQzJWkl0/Lli29MQYR2FsCNYINx1oF2Pla4c7UOGJv7RTbRQABBBBAAAEEEMihAM8eOcQkKwQQKFOBzfPfD/Nv0KdPmW6HzDMTICCYmRNLIVD5BOr/ueg+1x4ZtB0YvEgIIIAAAggggAACCCCAAAIIIJC3AgQE8/bUc+AIIIAAAmUpoEbbn3jiibDh9p49e9rZZ59tak4iWdq9e7fddNNNYVu03boVNqxcUFBgt9xyS8LiakC+X79+CdMYQQABBBBAAAEEEECgsgrUads23PUdS+jsqLzPYaw+YXlvnu0hgAACCCBQ9QQ2bNhgl1xyiS1atMhOOeUUmzJlit11111JD3TatGl266232lNPPWVffrmnXZVVq1bZSy+9ZAoQulfz5s2T5sFEBBBAAAEEEEAAAQQqusDWoJNWpfpdD4l21fU4vH0FAcEIpZwGKCFYTtBsBgEEEEAgfwQU5FO6++67w3c1/D5u3DgbO3as1a1bN5zm/rz22mu2ceNGNxq9r1ixwrp06RKWGowmMoAAAggggAACCCCAQCUVKAh+NFeqsU9hz8KV9DCqzG4TEKwyp5IDQQABBBCoKALLly+3Hj16RLvT9uuqEGvWrLH27dtH0zVw/fXXh+Ovv/56wnQFBPW68MILrXr16jZixAgbNGiQ1apVK2E5RhBAAAEEEEAAAQQQQACBbAWoMpytGMsjgAACCCBQjIACgo0aNYqWatGiRTi8ZcuWaFpxA8pj586dNnz48LDdwJtvvtnuvffe4lZjPgIIIIAAAggggAACFVJgx7Ll4X7VaLzn/+QKuaN5slOUEMyTE81hIoAAAgiUn0C9evVs3bp10Qa3bdsWDrdu3TqaVtzAmDFjTC9XxVjVitXO4OWXXx6tqk5HJk+eHI0zgAACCCCAAAIIIIBARRXYvrywncC63bpHu+iCgwUbvoqmMVA+ApQQLB9ntoIAAgggkEcCrVq1MpXwc0lVf5s2bWr77LOPm1Ts+/Tp02316tXRcgomNmvWLBrXwPjx423WrFkJr4QFGEEAAQQQQAABBBBAoAILuODg1nmFHY5U4F2tcrtGQLDKnVIOCAEEEEBgbwv079/fFixYYDNmzAiDevfdd5/17t073K2VK1faI488Yjt27Ei7m3PmzLFrr73Wtm/fbkuWLAlLAp544olp12EmAggggAACCCCAAAIIIJCJAAHBTJRYBgEEEEAAgSwEOnfuHPYorF6FhwwZYgoCXnXVVWEOy5YtswkTJpirRpwq25EjR4YdiPTp0ydsR1Adi5x66qmpFmc6AggggAACCCCAAAIVVmDTG2+E+9bg6KMr7D7m247RhmC+nXGOFwEEEECgXARGjx4d9gy8fv36hJ6FVVJQ1XzjaerUqQmTWrZsaQ8//LCpZ+Jq1apZ8+bNE+YzggACCCCAAAIIIIAAAgiUVICAYEnlWA8BBBBAAIFiBBo2bGh6lSa5HopLkwfrIoAAAggggAACCCCAAAK+AAFBX4NhBEoqsOlcs92FvYiWNIu8X69aHbMGE/OeAQAEEEAAAQQQQAABBBBAoKoJuB6Ga7dtX+TQVI14U9ChnqoVNwiayyGVjwBtCJaPM1tBAAEEEEAAAQQQQAABBBBAAAEE8lJg24oV4XHX6lg0IJiXIBXgoAkIVoCTwC4ggAACCCCAAAIIIIAAAggggAACCCBQXgIEBMtLmu0ggAACCCCAAAIIIIAAAggggAACeSiw64svw6Ou0bBRHh59xTxkAoIV87ywVwgggAACCCCAAAIIIIAAAggggECVENgyf154HPW7da0Sx1MVDoKAYFU4ixwDAggggAACCCCAAAIIIIAAAgggUAkF6nXtFu715nnzK+HeV95dJiBYec8de44AAggggAACCCCAAAIIIIAAAghUaoHq+zYO979g44ZKfRyVbedrVrYdrij7u3v3brvpppts2LBh1q1bYTS7oKDAbrnlloRdHDhwoPXr1y9hGiMIIIAAAggggAACCCCAQCYCS5YssSeeeMKWLVtmPXv2tLPPPttq1KiRdFWeUZKyMBEBBCqAwNavqwzX7da9AuwNuyABSgiW4DqYNm2a3XrrrfbUU0/Zl18WNoypbFatWmUvvfRSGCBUkFCv5s2bl2ALrIIAAggggAACCCCAAAL5LrBhwwa75JJLbNGiRXbKKafYlClT7K677krKwjNKUhYmIoBABREo+LKw9F+NxnQqUkFOiVFCsARn4rXXXrONGzcWWXPFihXWpUuXsNRgkZlMQAABBBBAAAEEEEAAAQSyEFCQT+nuu+8O32vWrGnjxo2zsWPHWt26dcNp7g/PKE6CdwQQQACBTAQICGaiFFvm+uuvD6e8/vrrCXMUENTrwgsvtOrVq9uIESNs0KBBVqtWrYTlGEEAAQQQQAABBBBAAAEEihNYvny59ejRI1qsbdu24fCaNWusffv20XQN8IySwMEIAghUIIHty5eFe1Orfbuke1Xn63vbjiWFyyVdiIk5F6DKcA5J9YW9c+dOGz58eNhu4M0332z33ntvDrdAVggggAACCCCAAAIIIJAvAnq+aNRoT/W6Fi1ahIe+ZcuWjAl4RsmYigURQKCMBHYsLQz01W6X+EOG25ybvn0FAUFnUh7vlBDMofKYMWNML1d8X9WK1c7g5ZdfHm1FnY5Mnjw5GmcAAQQQQAABBBBAAAEEEEgmUK9ePVu3bl00a9u2beFw69ato2nFDRT3jKLShoMHDy4uG+YjgAACCFQxAQKCOTyh06dPt06dOlmHDh3CXPVF3axZs4QtjB8/3vTyU69evfxRhhFAAAEEEEAAAQQQQAABa9Wqlc2bNy+SUPNETZs2tX322SeaVtxAcc8oKnU4a9asItnwjFKEhAkIIIBAlRKgynAOT+ecOXPs2muvte3bt9uSJUvCkoAnnnhiDrdAVggggAACCCCAAAIIIJAvAv3797cFCxbYjBkzbPXq1XbfffdZ7969w8NfuXKlPfLII7Zjx460HDyjpOVhJgIIlIPAVzNnhFtp0KdPOWyNTWQqQEAwU6kMlhs5cmTYgUif4CJXO4LqWOTUU0/NYE0WQQABBBBAAAEEEEAAAQQSBTp37hz2KKxehYcMGWIKAl511VXhQsuWLbMJEyaYq0acuOaeMZ5R9lgwhAACFVOgRuPCtlILNnxVMXewiu4VVYZLcWKnTp2asHbLli3t4YcfNrXDUa1aNWvevHnCfEYQQAABBBBAAAEEEEAAgWwERo8ebSNGjLD169cn9CyskoLJqvryjJKNLssigEBFEKjbrXu4G1vnza0Iu5M3+0BAsAxOtev9qwyyJksEEEAAAQQQQAABBBDIM4GGDRuaXqVJPKOURo91EUCgNAJb5xYG+up3PaQ02bBujgWoMpxjULJDAAEEEEAAAQQQQAABBBBAAAEEECgUKNiwIRyosU9jSCqQAAHBCnQy2BUEEEAAAQQQQAABBBBAAAEEEEAAAQTKWoCAYFkLkz8CCCCAAAIIIIAAAggggAACCCDw/+ydB5wURdrGX1hyWjKSBSQuiAqSBBVEEFSQIBgRRfHEk09Q8cSAnIp6Jxju9BTMWUEQURBQUCQLKmEXFJCw5AxLDvL1U7M129Pb09Mz27PMzjwvv2U6VPxXd1fVW29VJSgBvVmI3jwkQTHEXLapEIy5ImGCSIAESIAESIAESIAESIAESIAESIAESCA+COjNQvTmIXa5Kt66tbp8eP58u9u8FgUCVAhGASqDJAESIAESIAESIAESIAESIAESIAESIAESIIFYJUCFYKyWDNNFAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAlEgQIVgFKAySBIgARIgARIgARIgARIgARIgARIgARJIdAJ6CnCRRo0SHUXM5Z8KwZgrEiaIBEiABEgg3gksXrxYRo4cKY899pjMnj3bMbt79+6VwYMHy7Fjxxzd8SYJkAAJkAAJkAAJkAAJxCqBpOTkWE1awqaLCsGELXpmnARIgARI4GwQSEtLk0GDBknRokXlkksukREjRsisWbOyJQUKwKlTp8rjjz8u843FlU+dOpXNDS+QAAmQAAmQAAmQAAmQQDwQKNooRWXjSGpaPGQnT+ShQJ5IJRNJAiRAAiRAAnFCYMKECdKjRw8ZNmyYytHq1atl/Pjx0qFDh4AcZmRkyE8//RRwjSckQAIkQAIkQAIkQAIkkJcInNicrpJbqGp1x2TnL+2zIDx96KCjO970jgAtBL1jyZBIgARIgARIICSBjRs3SkqKbwQUjmvUqCHr1q3L5q9ChQry7LPPypAhQ7Ld4wUSIAESIAESIAESIAESyAsEjm/ZopJZsKazQjAv5CXe0kiFYLyVKPNDAiRAAiQQ0wTS09OlRIkS/jRWqlRJsE4ghQRIgARIgARIgARIgARIgARyiwCnDOcWacZDAiRAAiRAAgYBrB24f/9+PwusFdigQQP/eTgHo0aNkokTJ4bjhW6DEDhkTM/+88Ybgty1v9zkz/WSr1AhdTOt2YVyasdOe4c2V6u9MEbK3tDX5g4vkQAJkAAJkAAJkED8EPhr/4H4yUyc5YQKwTgrUGaHBEiABEggtglUqVJFtm7d6k/kpk2bpG7duv7zcA6GDx8u+DNL8+bNzac8JgESIAESIAESIAESIIGzRuBoWqqKu2TLVo5pSCpRSt0/udG35qCjY970hACnDHuCkYGQAAmQAAmQgDsC7du3F2wssnPnTlmxYoU6btq0qfK8fPlymT59uruA6IoESIAESIAESIAESIAE4oRAsZRGKicntlAhmFtFSgvB3CLNeEiABEiABEjAINCzZ09ZtGiRdO3aVfHo2LGjdOvWTR0vXLhQ5syZI507d/azypcvnzrWv/4bPCABEiABEiABEiABEiABEiCBCAlQIRghOHojARIgARIggUgIFChQQEaPHq0sBJOSkqRcuXL+YAYOHCj4M0udOnVkyZIl5ks8JgESIAESIAESIAESIIE8QeBY5pThIimN80R6EymRVAgmUmkzryRAAiRAAjFDoGLFijGTFiaEBEiABEiABEiABEiABKJB4PSBgyrYpGTfGoHRiINhRkaAawhGxo2+SIAESIAESIAESIAESIAESIAESIAESIAEPCRw+mCGh6ExKCcCVAg60XG4d+bMGXnqqackNdW3Y452unjxYhk5cqQ89thjMnv2bH2ZvyRAAiRAAiRAAiRAAiRAAiTgKYFw+h579+6VwYMHy7FjxzxNAwMjARIggWAETmz2bRCSVLJkMCf+68XbtFHHx1JX+q/xILoEqBCMgO/cuXPlueeek8mTJ8uBAwf8IaSlpcmgQYOkaNGicskll8iIESNk1qxZ/vs8IAESIAESIAESIAESIAESIAEvCLjte0ABOHXqVHn88cdl/vz5curUKS+iZxgkQAIkEJLAyU0+hWCRxlw/MCSss+CAawhGAP3HH3+UQ4cOZfM5YcIE6dGjhwwbNkzdW716tYwfP146dOiQzS0vkAAJkAAJkAAJkAAJkAAJkECkBNz2PTIyMuSnn36KNBr6IwESIAESiFMCtBCMoGAfffRRefbZZ6VYsWIBvjdu3CgpKSn+azVq1JB169b5z3lAAiRAAiRAAiRAAiRAAiRAAl4QcNv3qFChguq7DBkyxItoGQYJkAAJkECcEKBC0MOCTE9PlxIlSvhDrFSpkmCtDgoJkAAJkAAJkAAJkAAJkAAJeEmAfQ8vaTIsEiCBaBDIWLRQBavXB4xGHAwzcgKcMhw5u2w+sXbg/v37/dexXkeDBg385zgYNWqUTJw4MeAaT0iABEiABEiABEiABEiABEggHAJu+h5uwtu1a5d06dLFjVO6IQESIIGoEijSqJEcM/ZmOGysd0olYlRRq8CpEPSQcZUqVWTr1q3+EDdt2iR169b1n+Ng+PDh6s98sXnz5uZTHpMACZAACZAACZAACZAACZCAIwE3fQ/HADJvYkrxkiVLsjllHyUbEl4gARKIMoGk5OQox8DgzQQ4ZdhMI4fH7du3Fyzuu3PnTlmxYoU6btq0aQ5DpXcSIAESIAESIAESIAESIAESCCTg1PdYvny5TJ8+PdADz0iABEgglwnA0g9SsmWrXI6Z0bkhQAtBN5RcuunZs6csWrRIunbtqnx07NhRunXr5tI3nZEACZAACZAACZAACZAACZCAOwJOfY+FCxfKnDlzpHPnzv7A8uXLp471r/8GD0iABEggSgROH8yIUsgM1gsCVAjmgCIqWbMUKFBARo8erSwEk5KSpFy5cubbPCYBEiABEiABEiABEiABEiABTwg49T0GDhwo+DNLnTp1bKcGm93wmARIgAS8JHAsdaUKjusBeknVu7A4Zdg7lv6QKlasSGWgnwYPSIAESIAESIAESIAESIAEokWAfY9okWW4JEACOSFw+sBB5T2pZEnXwRSqWl25PZKa5toPHUZOgBaCkbOjTxIgARIgARIgARJIWAI7X3tVto8a5Tr/RRrWl3ozZyn3J3Zsl9XNmrn2C4f1ZswU7D5IIQESIAESIAESiH0C2jqwSOPGrhNbsKZPIXj6kE+Z6NojHUZEgBaCEWGjJxIgARIgARIgARIgARIgARIgARIgARIgATsCJzanq8tJpUrZ3ea1GCBAhWAMFAKTQAIkQAIkQAIkQAIkQAIkQAIkQAIkQALxQuD4li0qK+FYCMZL3vNKPqgQzCslxXSSAAmQAAmQAAmQAAmQAAmQAAmQAAmQQB4gcHKjz0KwcNWqeSC1iZlEKgQTs9yZaxIgARIgARIgARIgARIgARIgARIgARKICoETW3wKwULVfOsCuokkqYRverFWJrrxQzeRE6BCMHJ29EkCJEACJEACJEACJEACJEACJEACJEACJGAhcDJ9s7pSsIZ7hWCxFN/mYVqZaAmSpx4ToELQY6AMjgRIgARIgARIgARIgARIgARIgARIgAQSmYDeVCQcC8FE5nU28k6F4NmgzjhJgARIgARIgARIgARIgARIgARIgARIIA4JHEtdqXJVsHq1OMxd/GSJCsH4KUvmhARIgARIgARIgARIgARIgARIgARIgATOKoHTBw6q+GkdeFaLIWTkVAiGREQHJEACJEACJEACJEACJEACJEACJEACJEACbggcSU1Tzoo2SnHjPJub0wczsl3jBe8JUCHoPVOGSAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIJSeD0IZ+FYP7SyWHlv3ibNsq9nnIclmc6DpsAFYJhI6MHEiABEiABEiABEiABEiABEiABEiABEiABOwKH589Xl4s1amh3m9dihAAVgjFSEEwGCZAACZAACZAACZAACZAACZAACZAACcQLgaSS4VkIxku+80o+CuSVhDKdJEACJEACJJCXCGzcuFEmTZok6enp0qxZM+nbt68kJSVly0Iwd6dPn5ZRo0YFuG/fvr20bds24BpPSIAESIAESIAESIAESCCWCBxesEAlR08B9jJt2LDkWFqqL/zWrb0MOuHCooVgwhU5M0wCJEACJBBtAgcPHpR77rlH1qxZI1dffbVMnTpVXnzxxWzROrnbvn27zJw5U1JSUvx/5cuXzxYGL5AACZAACZAACZAACZBArBDQOwxHKz1YX3Dd9b3VX7TiSJRwaSGYKCXNfJIACZAACeQagblz56q4Xn31VfVboEABGTp0qPz973+XIkWK+NPh5G7Lli1Sv3596dmzp989D0iABEiABEiABEiABEgglgnoDUGKR2i9V6RRI8MCME2wDqGdhWHGooX+7Adz43fAA0cCtBB0xMObJEACJEACJBA+gc2bN8tFF13k91i1alV1vGvXLv81HDi5g0IQfwMGDJC77rpLpk+fLidPngzwzxMSIAESIAESIAESIAESiCUCpzMO5Cg5ScnO6w4eW7nSH/6R1DT/MQ/CJ0CFYPjM6IMESIAESIAEHAlA0VeqVCm/mwoVKqjjo0eP+q/hwMkd7p06dUp69eql1g185pln5H//+1+Af56QAAmQAAmQAAmQAAmQQCwROJK2SiXHzrrPi3QeMykBj6X61hL0ItxEDINThhOx1JlnEiABEiCBqBIoWrSo7N271x/H8ePH1XHlypX913Dg5A5WgfjTU4wPHTokkydPlsGDB/vDwKYjEydO9J/zgARIILYJbPrbPbL/669cJ7JMn75SfcwY5f7AjBmy8Y7bXfvNZ2xi1GTjJtfu6ZAESIAESIAEvCDw136fhWBSiVJeBBcQBtYnPLE53X/t8ELf5iX+CzwIiwAVgmHhcnbMHSGd+fAuCZAACSQKgUqVKkmqacQSU3/Lli0rJUuWDEDg5G727NlSp04dqVGjhvIDZWK5cuUC/A8fPlzwZ5bmzZubT3lMAiRAAiSQxwkE243emq1g7thHsZLiOQmQQDQJHM3cAbhYSiPPozGvT4idjKEchJIwKdl75aPniY/BADll2MNC4Y6QHsJkUCRAAiSQhwm0a9dOVq9eLQsXLpSdO3fKuHHjpEWLFipH27Ztk48//litB+jkbtmyZfLwww/LiRMnBJ08WAJ26tQpD1Nh0kmABEiABMIl4LQbvTksJ3fso5hJ8ZgESCDaBE6mb1ZRRKqkK1S1uvJvtgTUadYbihRtlCJ60xKtJNRu+OueAC0E3bMK6ZI7QoZERAckQAIkkBAE6tatq3YUxq7CEFj5Pf300+o4PT1dxhhTALt16yZO7m666Sb55ZdfpE2bNspfI2PHtWuvvVYd8z8SIAESIIHEIOC0G71eUgIknNyxj5IYzwpzSQKxQkAr8oqkNI4oSQVr+hSCx40ZNlbRG4oUzbQ+hJUglITRWq/QGn+8nVMh6GGJorLFH3aEzJ8/v/Tu3Vs6dOggBQsW9DAWBkUCJEACJJAXCPTv31/VA/v27ZPq1X0NG6QbloJLlizxZyGYu4oVK8r7778v2Jk4X758Ur58eb8fHpAACZAACSQGAafd6M11i5M79lES41lhLkkgFghoZWDB6tWikhy9oUjRlBR/+FpJ6L/AA9cEqBB0jSq0Q/OOkOjAYUfI33//PWAB+NCh0AUJkAAJkEC8EChRooTgL5Q4udM7FIcKg/dJgARIgATijwD6F17uWs8+Svw9I8wRCcQSgZObfBt+FKqWNRjuVfrMG4rA+jB/crIKWisJvYonkcKhQtDD0uaOkB7CZFAkQAIkQAIkQAIkQAIkkOAEnHajN6NxcheqjwIlYZcuXczB8ZgESIAEIiJwJDVN+dPrAEYUSBBPeq1AvXYglI5JxoZ9sErEXzSUkEGSEjeXqRD0sCgXGPPXuSOkh0AZFAmQAAmQAAmQAAmQAAkkMAGn3ejNWJzchdq1Hpbo5qUsdLjctV6T4C8JkIBbAqcPHVRO9TqAbv2Z3SWV8O0Y/Nf+A+bLaq1AXDCvF1ikcWPBOoJQFlIhGIDL1Ql3GXaFyZ0j7gjpjhNdkQAJkAAJkAAJkAAJkAAJhCbgtBs9d60PzY8uSIAEcpeAXs+vWKOGEUdcLHPDkKNpqQFh2IWtlYNH0lYFuOWJOwJUCLrj5MoVdoTEBiLYEbJXr15qY5FE2xHyJfmvvCFvuuJFRyRAAiRAAiRAAiRAAiRAAsEJmHej79q1q0AJ+MADDygPetf648ePB+xab3XHPkpwvryTWAR2jXtT1vW5XvZ9/pmnGT/47XQ1ZdXTQPNoYKcP+iwEk0r61vfzMhuHFy5QwZl3L9aKx8Pz53sZVcKExSnDHhZ1Iu8IuUAWyhyZKyskVepIbQ+pMigSIAESIAESIAESIAESSFwCwXaj5671iftMMOfhE8CGFDtfGi34xcYXZfr0DT8Qkw+sWbf7rXGGcnG8ChO3GixcmPDTVo9lWvWZlXYmbBEfgjfKDmsGmqcG63h0vBFHkKAeqRCMQsEn4o6QP8tS2Wr8y2f8o5AACZAACZAACZAACZAACXhHwGk3enMsTu4SsY9iZsPjxCaw6+03/Yo7KJew5pxWJoVDBtaAe8ePl4PTp/m9QUl1OiNDdrwwRqq/9KL/eiIeQGkHSUr2rQPoFQO9oQjWDDQLlIMFq1eTk+mbubGIGYzLY04ZdgmKzpwJDJZ75WUZI6XFe9Ng55h5lwRIgARIgARIgARIgARIgARIgATsCUABuHP0aHWzVOfO6nevYdkXjmCa8epWrWTDnXf4lYFleveRetOnS92ZM1VQ+yZ8ntBTh/W03SKNGoWD1pVbvUagXjPQ7KlooxR1quM33+OxMwEqBJ358C4JkAAJkAAJkAAJkAAJkAAJkAAJkEAeJQDLPQgUeOcMHaqODxqKPLcCRVO64Q+KRVijVR4xUlJSVylrQFgZwkoNYUN0XG7Djkd3Sck5MxLSSj/sHqxFK/v0moH6On611eDR1DTzZR67IECFoAtIdEICJEACJEACJEACJEACJEACJEACJJC3CECRBMs9TOut9OBQNU0YSj09bdhNbrQ1YfkBd0rDBYukwl13ZpsSi7ARB+LS01vdhB1PbjIWLVTZ0Qo9L/Om1wi0m+ZdsmUrFZV1V2Iv44/XsKgQjNeSZb5IgARIgARIgARIgARIgARIgARIIIEJbM9c06/cwIH+zSiSO12liGhFnxMerIkHJR+kvKEIDCawEkQckC0jRwZz5vo6dkNeXq2qpN8/xLWfs+3wr/0HopIEKG/tNhTRkWklodmiUN/jrzMBKgSd+fAuCZAACZAACZAACZAACZAACZAACZBAHiMA60D8wXKvwh1ZyryyhrIN4mba8N7PfcpArD1o3t3WDgXiQFw6Xjs3bq6Z/ecli0Ntoact9tzk1Y0bbXGppwZb/WADE1h9QrRbqxue2xOgQtCeC69GSCC/scswH6oI4dEbCZAACZAACZAACZAACZAACZCAJwSw7h+k4tAHA6b4wqLM7bThPW+9qcLQSkR1EuQ/KKa0laC2TAzi1PGy9quVXF5YHDpG6NHN0wczVEhe7zDstKGITnqJlm3U4dHUVH2Jvy4IFHDhhk5IwDUB7DRMIQESIAESIAESyBsE/riygxxb9bvrxJ4zfLhUHHSva/ex6HDvp5/JZmOtJ7dSoFJFabT0V+X8zIkTsqJ2Lbdelbvan3wqJdq1C8tPNByvbFhf/so45DroGq++JqW7d1futzz6qOx5713Xfkt2vEJqvfu+a/d0SAIkQAJeE8CuwHoTEKz5ZxVMG95tKPswbbjKyMbW2+oclno6jFKdu9i6sV6EleCesWP9VoLhrqenrQNhaVhn/ARZc+WVKqyD06eJ2zRY05Rb59o6T0/hzUm82Kn4WFqasvgDE4jdhiI6jiIpxk7DE0QOzV8oZfr01Zf5G4IAjblCAOJtEiABEiABEiABEiABEiABEiCB8AlAmcJ1vcLnRh85I4D15raOfFIFUvVJ3681RG3x5zRteFemdWA4CqacWgmarRoxRRnWjZCtxs7GsSx41yFQZHoheqdilKXThiI6rmIpjdTh0VXcaVgzcfNLhaAbSnRDAiRAAiRAAiRAAiRAAiRAAiTgmgCshdZ07iTrru8t21/kLCLX4OgwxwR2vYAUDpAAAEAASURBVP2m2oSieOvWQa3qQk0bhoJLKwvLXu9bc9BtwiJdS9DOqhHWjXp6865xvunLbtORm+5ObvIpBIOt8xdpWlAOUAqCgdMajtoSU1spRhpfovmjQjDRSpz5JQESIAESIAESIAESIAESIIEoEkAHfsOAO1VHHtHsHD1aTXt0GyWUAHuNnV3ZuXdLjO40ATw7e94cp07PGeK8PITTbsN7x49XYZTp3cdREaXjNf9GaiW4Y8yLtunWVo47Xxrtf6fM8cXCMbhDkkqV8jQ5R1N9Fn9FGxlTgkMIphlD9BTjEM552yBAhSAfAxIgARIgARIgARIgARIgARIgAU8IQBm4ztjFFQoCdNDLG4pBCKZC4l4ogZs/e18vm+8fosKhUjAUMd7XBMyKaOwKrK3G9H3rr9O04f2f+XYX1m6sfkOdQxkJqzYop2D5F0rM1oHWKcpYOxDWjsgfrB9jUY5v2aKS5bWFoN652E24RRv51oI8kqlEjEVOsZYmKgRjrUSYHhIgARIgARIgARIgARIgARLIowSwdhuUeFCG1Bn/hbFhw0ilGISCUK+PFixrUHhoZSLWItPnsaoUhLIH6f3DUD65UfoEyzev55yA+VnBs4PnLpSYpw1j0w4tOMbzimc4lFJR+7H71RaKsPxD+pwkmHWg9uO3EjSsbbU1nr4XC78nN/osBAtXrepJcgpVra7C0WuQlmzZKmS4Jdr43BzjTsMhWWkHVAhqEvwlARIgARIgARIgARIgARIgARKImMDWESOUYgwKmVpvviWYOgk59+231WYDULQ4rYO24a4BSpkIy8IGCxcrRSIUKZuGPhBSoRJxoiPwqBWBUAbiGApLrQiNIDh6ySEBqzKwzoQJrqf56mnDB6bN8KcCOw9Dyt9xl/9aJAew9INSEQq81W1aGmsSZikdzeE5WQdqd1BeYvoyZMcLubsmJ/hC6b28WtWgiu8TW3wKQad1/nRe3PwWrOlTCGq3bnYuLoqdhg05vHCB9sbfEASoEAwBiLdJgARIgARIgARIgARIgARIgAScCUCpsTtzV9Zz33pbzB14KAmqv+RbHw3roNlZ/MF6EMo1KBNrvPiiUibCwhDKQbiH8i2UlZVzCp3vYkdZrHu4w7DAOvjtdFsrLKsiEGmt+MADSlGjlVLRTKNzDhL3Lp4NPCMoDygDzc9eKCp6SvDBmd8qp1De+TcT6eNTwIUKw+l+nfET/NN98XxtHflEwHOM50VbB2orwGDhVXpwqMrjPmN9TTyLuSH6udbvLN5TO2tYvRNwwRqBijwv0gilqh5ccApPlzvKkO+hE6mse1QIZrHgEQmQAAmQAAmQAAmQAAmQAAmQQJgEoJzQ04GrjxljO80S66DBwgkddVj8mQW7EEPJYFXoQAkQbaUg0r66VSvZZlg3woJrh5GWDXfeoa7BIgrKJuQNv/iDe60IhBUjpoVC2anXeIMbN8oIhAO3VgWRmUtuHiPNenpmbsab07hQNpEqAxE3lEhQOCH/KH/zZiJulFCh0g9lOJSClUeMVM/N7nFvqd23Uf4QrAkIBZbTjsg6DoRVbuBAdbo9U8Gu70XjF0zwjIIvFPNQfkPslIL6mffKQtCcHzcbimj34AjRCkx9nb/2BKgQtOfCqyRAAiRAAiRAAiRAAiRAAiRAAgYBKMygHNMKMigJYEmnrekw1ReCDUSsGyKoG5n/YV03KF/QWcf0YggUgdiFGALFmrbyUReM/7RSUPvTikd9P9JfKGFgsYW84BgKDyhtoPSAUgHxQaC4QRrtFIFmhdG5bxpWkZnWjKHSCG6IF2FaFUSh8oO0wj8sGrUSJpSfUPexozOmtK67vrcqa5znBdGKKasiOdy0m6cN6x2KteVguGEFc1/hrjul7syZ6hlBGaL8oQzW8en1BoP519cr3HGnUizi2cFz4NUzoMPXvzqNWhkIxbxSfhsKf4hmj2OtfMPzHw1xs6GIjlcrDzMWLdSX+OtAoIDDPd4iARIgARIgARIgARIgARIgARJIcAJQjkFBAIEiwvyrToz/YP0XaiMHKNCwtiDWI1PTi/OfUQoxhAHLQlgR2on2t653b2XFBWUE3EcqUKTsNiyzoExR1n5DHxQobPxiWP1pQX71rqVljSmkZiWgdoNfXMdUZ51GKDytPBAflKeaIRSohxYYaxCmpSkFUXnjXqX7H7SNQykCjc0pzNM1d44ZLRUNa8sKRjiRCCzithpKUF22YIFj7PC8z1hH7xzjNyebaiC/BzKn4hY2Noko0ijFNm+RpF0rpHKqDETcUP7hecRUXAgUWznJtwrE5j9Yz9WbMVNgEQslOJTBECig3caH5wyKczW93QgHz3F5Q0lY/s67PGMLBR8Ulig/sIAyUD/3WuEP/vg7lZEhxRr6FIFJyck2uY7sUlIJ3/qj8O1mQxEdS9EUX1qOrVypL/HXgQAVgg5weIsESIAESIAESIAESIAESIAEEp0ApjxCoCCAsuB0xgE5krZKXYNyC1Y5VuWXumnzHywAYYm3beQIv0JErcNnbMDgJPCH9eGgcINSTCvGrIoUKF0KOex0um/8eL8CrJShmKw+5iW/ssMufoRvjcPOHa7pNGqFJ5QTWoECTlAGgiGUWFhnUYdrVhAdNDa3ME+7tlMEQvmKTRwwxRdTnfeMezPAT7D06etIC6acasUkFL5Yvw4KWXBFenBvnfGHNFY14rBabuqw7H7hd69RVlA4Ir9WQZhJJZON56ahYPOIEq1au9oEBPk9MGOagBG4eKEMRNqQNzA4mb5ZJbXCnZEpWK35DHYOS7vSnTrJpiFDjGcvWbEP5tbuOsqpjqGwVeVkMME0dygGcb3S0CGuWNqFi2tOykDtRz/TUAji+dPKQm2dp93l5LdYpmIPYYTz7OmNRY6lpuUk+oTxS4VgHirqubJAXpexeSjFsZnULtJJbpYbYzNxTBUJkAAJkAAJkAAJkAAJxCgBdPy1EstvzWeypnObbFjjHZzxrVJoQbnldrqkVritv3OAX3mjlVo67sP6wOEXyp8ao411/wzFlNeCNEKhpy2ooKA4YGxSAqUNBNZgmF6slSi4ZlYQma0FTx845Fd8wh1YYWMJvU4bFG5bDEXeiXTfFFTkB3Hr+/ADUYrctFR1vMuw0IQ/CBRqUORqBQ+u4Rhli7Xt9owdqxSDUHDievE2rQSWfhC9Vps6Mf6Dgm7fZ5+LWeGq3SWVKiWnD2atUajLTKcD7tSz1eoSpSQsYeQD4SPdPiXg9GzKRZQhrE3DURYhnmCCacOwEgQT/7MdzLEH15FuWAtGKijrOsYfWGL6ODZC0YpylFVyp86KaThWmW6UgTq9+pnBc66VvvlLe2chqONBOZvfFX092C+4ws/p/fuDOeF1EwEqBE0wvDhcvHixTJs2TU6ePClXXHGFtG/f3otgGQYJkAAJkEACEmCdkoCFziyTAAmQQBQIxGJ9oq0Ow80uOvwNFyzye9PKJX0B03tPH8pulabvYypiwPRgfcPDXyhLjhrpgIIJyjQtsIQMpvzUCqJdhrXfzjEv+K0n4deqCNThQXGFP1iKaeUd1nvUis6Tm9L91pDaD36h9MLmFGo9OkPJaxUoYJBO3IdiENNbtbLJ6hbKR+wsay4HKGTAoOz119srJw0rUygQj2/ZIpjaCb9QKkFBiD+tPLXGhXChuMMUX/DyUioZU6+TjbKC9Ws4Cigv0xBJWChr/ClL0heMzXmMac92ZaXLSSuL4R5i94xYpwkHSxfKGKzSjanlp42pw7B69EqQp3qGkjMSifTbEklced0PFYIelmCasfbDoEGDpI+xtkSLFi1khGE+e+bMGenQoYOHsTAoEiABEiCBRCDAOiURSpl5JAESIIHoE4j3+kQrvzRJ67m+ntu/sLyD0gWWW1DAmacIO6UFysrkLp3VpiuYVmu2CAzmTyvvsG4hFEJm5Rz8IH69MQP42Cnq7MLWikG4x+67JzcaCkZjqjJE70iMPGrlEhSXyV06OVrYIUykobglQoSBdEORqtdVhBMop8pcb1i8GUy0Msvi1ZNTnS5PAjsLgYAN1hbE84Ky0s8AFK5Q1ulycrKg1daReHbBw41AIV13ZmPjGUt27cdNuHATqdI3ms+J27TnFXdUCHpYUhOMdRJ69Oghw4YNU6GuXr1axhsvIxWCHkJmUCRAAiSQIARYpyRIQTObJEACJBBlAqxPogzYIXgoATENs2C1GmEpS6DQgN9wBAocrRCC1RcElnteKEcQRjDLRrWmpGHdB+WNWyWSXb4QRyHD4qyM3U1ec03AX1aWqfy6nLTyVj8XKLNIFW86UTosfc7fvEOACkEPy2rjxo1y7bXX+kOsUaOGmj7sv8ADEiABEiABEnBJgHWKS1B0RgIkQAIk4EiA9YkjnqjfzKmyJdwEKsWaoVzLLcnt/OVWvuItHl1OVsvMeMsn8xMegfzhOadrJwLpxmKuJUqU8DupVKmS7N2713/OAxIgARIgARJwS4B1iltSdEcCJEACJOBEILfrkzfeeMMpORHfi0a40QgTGYxGuNEIMy+lNdHzn5fKKq+lFelNVMlnrHF3JlEz73W+u3fvLrfeeqv07t1bBf3dd9/Ju+++Kx9++KE/qlGjRsnEiRP95zwgARIggXgigE2VKlSoEE9ZOmt5YZ1y1tAzYhIggRghwDrFm4IIVZ/s2rVLunTp4k1kDIUESIAEYpAA6xP7QuGUYXsuEV2tUqWKbN261e9306ZNUrduXf85DoYPH67+zBebN28uS5YsMV/icYwQYNnESEFYksFysQCJkVOUC5WB3hVGpHWKdylgSG4I8HvkhhLdnE0CfEbPJv3YiDtUfYK628u+SLSeuWiEG40wUerRCDcaYealtCZ6/vNSWeW1tCK9iSqcMuxhybdv316waO/OnTtlxYoV6rhp06YexsCgSIAESIAEEoUA65REKWnmkwRIgASiS4D1SXT5MnQSIAESyKsEaCHoYcn17NlTFi1aJF27dlWhduzYUbp16+ZhDAyKBEiABEggUQiwTkmUkmY+SYAESCC6BFifRJcvQycBEiCBvEqACkEPS65AgQIyevRoZSGYlJQk5cqV8zB0BkUCJEACJJBIBFinJFJpM68kQAIkED0CrE+ix5YhkwAJkEBeJpD0pCF5OQOxmPbixYtLsWLFXCdt4MCBrt3SYe4SYNnkLm+3sbFc3JLKXXcsl+jwDrdOiU4qGGowAnzug5Hh9VghwGc0Vkri7Kcjt+oT7FmJ9d68lmiEG40wke9ohBuNMPNSWhM9/3mprPJaWpHeRBXuMpyoJc98kwAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJJCQBbiqSkMXOTJMACZAACZAACZAACZAACZAACZAACZAACSQqASoEE7XkmW8SIAESIAESIAESIAESIAESIAESIAESIIGEJECFYEIWOzNNAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiSQqASoEEzUkme+SYAESIAESIAESIAESIAESIAESIAESIAEEpIAFYIJWezMNAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQQKISoEIwUUue+SYBEiABEiABEiABEiABEiABEiABEiABEkhIAklPGpKQOWemFYGxY8fK3XffLfjF34cffihbt26VWrVqSXJyctiUjh07Jq1atZIuXbpI0aJF5ZtvvpGaNWvKqVOn/NcjCTfshMSJh6lTp8qNN94olStXlvr16/tz9dhjj8mqVaukRYsW/mvhHGzbtk0WLVoktWvXDsebvP322zJp0iRp3759WP7yuuNolYMTlxUrVsj27dvlnHPOUe/mlClT5PLLL3fywnsk4Ehg8eLF8uabb8p3330n+fLlU995Rw+8SQIeEjh48KB8/PHH8sEHH8gff/yh6rQiRYpki+GHH35QbubMmSP679JLL1XP7Lvvviv4Furr6enpcv7552cLgxdIgAS8JRCN+mPhwoXy+uuvy7fffiuHDx+W6tWrS6FChXKccHwfxo0bJ19++aXgG3Puueeq70dOAv7555/l1VdfFbQHDxw4IA0bNow4zDNnzsjTTz8t5cuXl4oVK/qThboZ6Z4xY4YkJSVJlSpVpECBAv77oQ6WLl2q+nGXXHKJ36nT99TvyOHALq3o6yGNKLvU1FQpWbJkQD4cgpN9+/bJ//73P3n//fdVfxMMSpcu7fdiF5//psOBG3b/+c9/VN3TtGlTh5CybgVL63//+1+ZPn26vx7S9dHFF18sBQsWzArA5ujIkSOqTY96cO3atVKsWDGpVKmScum2jrQJVoK9S+A5YcIEeeuttwTv8OnTp123/ZzSin49wn3jjTdkwYIFqq9SoUIFu6RluxYsrYgP6XzvvfckLS1NEF65cuWy+Xe6cPz4cXnwwQeVv6pVqyqnq1evVs8c0os40KeGjoKSRYAWglksEvIIHwp8GPFhxx8a28uWLVMVUiRAUJFDsYiXGBXGP//5T8nIyFAVvL4eSbiJ6gflA/n3v/+tGiBmDvqe+Zrb4zVr1ggqxkjkr7/+isRbnvajWXtdDk5Qvv/+e9VIhptrrrlG+vfv7+Sc90jAkQAaV4MGDVKNIHQWRowYIbNmzXL0w5sk4BUBfEOfeuop1UHv2LGj7NixQ+655x6xq0/QuUKnJSUlxf8HBTYECkV0kvU9DDhSSIAEoksgGvXH77//Ln//+9+VMqhTp07q3cb7nVOB0gOD5uedd57gW4O6DoqynMiuXbvU9wrfG4QJxQL6NJHI3Llz5bnnnpPJkycHtOtx/R//+IdSrGHQ/eWXX1bKETdx7Ny5U8aPH6/6XMuXLw/w4vQ9DXBocxIsrZ988okatOnatauUKFFCtU/ByI3cd999AuXqtddeK4cOHZJevXoJFGGQYPGFCtcNu9mzZytFEwba3UqwtOI5wPOl/8D/119/daW8Rb8Y6cUzD+Xh7bffrvIfTh1pTb/Tu4RnFUrmCy64QPX3hw0bJkuWLLEGYXseLK1wDAORjz76SK666iqpVq2a3Hrrrao8bQMyXXRKKxStYIM+D/QJAwYMELANR6C0RxjQPUB2794tt9xyi2LcvXt3pcT9v//7v3CCTAi37ocdEgJHYmYSo2dly5ZVmcdv7969BS8lPk74WEMJghGmMmXKqI9+jx49lFt8XD/99FNlqQbLpYcfflh93DDy88wzz8jjjz+u3A0ePFhVbPo6Kjx8CFGRQDDaNn/+fDVahpGml156STZu3KgsCh944IGILBVVwHHyX40aNRT71157TR555JFsudqzZ4+88MILqsFTt25dAW9YE6JRAAXvmDFjlB8oevHxvv/++2X06NGyZcsWQcWADy5GZBo3bizTpk0TVPQ//vijGmXU5YBGm3kUM1siEuBCqHI4efKkGi3FKCVGS/v166eec6CB4gWVZ+HChaVDhw6ycuVKefbZZxW1d955R3HHaFvbtm1V+YD/xIkT1X1YCGL0FOWM8sWoKkZlMXqMTjOUPLDybdKkSdD4VUD8L6EJoFGIbzfeeQhGTNGBwPNIIYFoE8DMA7QZYN2H0Xkopa+44gpVT6GjYhbUO/iuNWvWzHxZDTLu3btXdTy8sCIKCJwnJEACQQlEo/6Akg6Wv7DmgcDaF4YJd911V9B0uLkB5ce9994rffv2Vc7xvYEVVk4EbTgYT6CtBUH/CH2Vv/3tb2EHi/Yd+lZWgSLz+uuvlyFDhqhbUGjAoh/fwlCyYcMG+eWXX2zzGex7GipM3A+WVgxYYyYYvuHo/2F2GfocOHcSfL/R9kBfA4YjUCbBihPxQEEYLD6nMHEvFDsoldCXbdOmTaig/PdDpVU7RNsc7XgoiEPVS2izo48A60BYmJ44cUL1v9A/w4wtt3Wkjlv/Or1LsN7rbxgU3HTTTco5LBvR527evLn2bvvrlFbMTkN+0afUs9egFIT1HRTEThIsrUjj119/rfqzCB96AlgOQ3l89dVXOwXpv4d84T3QloG4gdlWOEf5Q6DMxcw7fG9gkUzxEaCFIJ8ENZ0X1nz4g+UYPhZ6es7zzz+vlBcwbUelBEUfTH33798vDz30kKq8oIyCP3zUMdqPURKEhVEPyMCBA5VVir4OxQqmEmvBKBleUHx8b7vtNqlXr54aPcOIkW4oaLeJ+IvpAlCifvHFF+rDZmaARgkaD2CFMkIFc/PNN6uRETQ4UPFqQeMCIzNQ+l533XXq94477lAfcDR2UH5oQEGxhXK+8sorVYMHlR0q+0QXp3IAGyjpUNFDIYvKZvjw4UpJi3cFShhUbhidgin8vHnzFE5MF4ACFo1XjGij8kODCMo9dJgx/QAKG4xoosGH8sV7BIUiBI0IVK4NGjQIGr9yyP8SngA6BbCq0oLv8Lp16/Qpf0kgqgQwAIVOOTrnECwdgnM0yq2C9gQGs3r27CkvvviiGiCEGyx1AcH3FN9Y1EuYukchARKILoFo1B833HCD6vyjjYl+BdpCl112WY4zgnYuBlgx+I1vBdq0jRo1ylG4mM5cvHhxfxhQlqDPYqfY8zsKcvDoo4+qAWGrkhLtct1vgle08y666KIgoQRehgIFg8ywtrNKsO+p1Z3debC0QvGH7+/nn3+u+h7wax3AsQsPBigYHNdTSzdt2qT6IGjDQoLFZxeW+ZoTO/RLR44cqRTNaPe4lVBp1eGgvwSLNkxLDyUYyMdyTWjLwwAG1mwQKJvDqSOt8Ti9S+ijo74EawzIIX4s7RVKnNKqrSyxdBUU+P/617+UwYIbw5FgacU7BYWinsqL/i3e4z///DNUUtV9vI9YBQ9/SLsWGLtowxhcg7WzuS2i3SX6LxWCif4EGPmHQgGWSfjDRwMNdVRKqAChoECFCgVh586d1WgOlEdQ+EHwAuJlQ6Md/s2iK2DcN6+pAHN7fJCgxIJ/xK9HAvAxgRIQlSAUXVB+oLGQ6ALLMIzujBo1SlmFaR5Q+OHjhkoUDQIolqDww4hKMEElV6dOHfXR1ZUw3GJ6AirVo0ePKgVknz59VOcNylpUXBRRFZ5dOaDBAUUfRo/RUMJoFixg8a5g5BLvBiwGu3Xrpn41S1RaqKzbtWunWMMaEI03rKmCNUXwPphHsHC9ZcuWyuQdYcByF/GgTIPFr+Pib2ITgOLFPHKL5wvfXwoJ5AYBdCDw/TILOmeob8yCgSt0CjAtGIOQ6AygkwsLD3SYILBsQFsFVtSY0mU37dgcJo9JgARyRiAa9Uf+/PkFfxgExSwU1EfhrmttzRW+HfhDnwRri2K2BgZpzYPjVj9uzjEwC+sjGDBguiUsvCCIyytB3tHmQ98Lyytg9pS26oo0DqfvaaRhwh/6JCgvzCaDkgnfcpRlKIEiRq/5irYxDEbQPkZ4OREndlA0Q9C/CUfcpBXWZ5iSjfa9W9HKKvRxYWEHZSCUX27rSLt4nN4l3MOSG7A2hWIU+cKMPzcSLK16cA5T/PFurF+/Xs02g8VjKAmWVvRjYDEKC9/ffvtN9WnwjLlRuoMfjGLQLsA0brNA+Yw+L9oJUGDj3YJlbzhrc5rDi9fj0G9vvOac+fITgDUSrMPwB3N9aNLx8mCNHwhGMrRAOYH5+FBcYPQNowJQFmI9DL3Gj3Yb7BcKQlR6GBHEPH8opaB02rx5s2r0wyoKDX490gULK4rP0hLKUVgKatEdJCiawAzs8AHFQrhWwehLMEEFoRVP+ChjhBXlCitBKJ0oWQTQgLGWg1ZaP/HEE6ocUBZozKEs8JybG7lmKy1wx1QcKHNhnQkrwFCCChOWiKjcoLDHuVP8ocLj/cQggFFX87cUgzrmAYHEoMBcni0CqFfQdjCLVvyZr0FpjeloWB4Dg4ewvoBoqwZMqcJ6QKjzMHiIATFdD5rD4TEJkIB3BKJZf8ASGH0BzFjBzAptcBBJ6nU7F4MJCA9LF6Edi7ZSTgTWX1AiYLmdoUOH+i0OrYMcOYkDftH2xtIe+EXbUCvPIg3X6XsaaZhoe6KcMKML32NM/4X12cyZM10FiTLC7DOUERQ4YOqF2LFDvxLKYRi0wPgEiiwo3twaOYRK62effaZmypk3RXGbF/S3sUkjjABQ1m7rSKfwnd4lrAeIMkKfH4YI4Yg1rbDcg+A5QBlqS0f0d9yKXVphbQgl9p133qmeLaTVjeUl3m8o6qGvwC++IZiBhecSAgMkhA2lKJ6HnCra3eYxL7mjQjAvlVaU0gqlBOb+40+vJYioMEIPMS/oiVEAjGZgmg5M+zFihqmSGNFyu8AuRgdgQYVRFVhQoWEPwUgerArRMMAfFFFY2y4cE28VUJz+h4odaypiRE6PzmiLH3ysNTesMafXBdMfbSDRJt6h8GA6K7hj5yiM4JmnMITymwj3ncoBynRdDlDcosGDNUWwfpYWKAm1YHQQI+9610w365vgvUMHGGttoNKDElg/B3bx67j4m9gE8D03P4doKOV0VD6xiTL34RDAICAUgGiYQ/DtwjNobezjGTUveI5vG9oomLmA6Ulo5GvBwCREf//0df6SAAl4SyAa9QfaK3o5Gm0dhFSb20jh5gL9CIh5DTHsUprTpQXwXULbC/0WWDRiVgf6K+jPeCWwhobSAgMeaMdbv42RxOP0PY0kPPhBXwLfcsyGgQUZLLAuv/xy10o2bPICCzC0kdG/0FZokaYH/oKxg3UZBuTxnMGABbOn8IclKdyIU1oxwApFFNZSdCto70MRpi1LYYiB5YFw3W0daReX07sExlhXDwLLQJQb4gslTmnVU751HYz3AMpxKPNCSbC0wrABf+jjoozwDuC9hcFQKMFMA6QFYaOcobeAkhUKa+gn9Dr4mFWAd5eSnYB3X7LsYfNKHieAChoVIDYOwUcVLxgUgLBmgkIKa9VhVylYQ+HPatarLQbtPhAY+ceHFMoT7KYFufDCC9VoP0b84Rdm3lin0MsKN48XiZpajQVcsXYcRFv4YE1GVKroSMF0HRaC+GCjYYWKABWX2bIQTPEB1aOpZi5oQKACxVqO8IcpEpySZSYk2coBo+cYycJIKdarAUPsoIlKDSO8eG+w4DIqOyw+rAUdYoxoYV0tTGmBO80a5Wm26NJ+SpUqpd4ZjPZhEWZMx3eKX/vjb2ITwHcWDSR8xzHNBccY3KGQQG4QwMwAKPZQr6MzhE3GMAAJRQPqIUw9wkAH2h3YoAyDXPiW4jmFe7Q7UK+hwwyrDygWMW0PSyi4nf6UG/lkHCQQjwSiUX+g/YqNrVAfodOOzjraUdoYIVKOWEYF3w30T6AowrcEVoI5EczegCUU2tQIF0u0wOrMS8G3Ee179LugjMG6jW4UN05pcPqeOvlzuqeVQRjIRrmhPwgDDigFQwncou+H7zgEecSfHigK5T/Y/WDs8Ixhmqj+gzIO7Wa9wUSw8HA9VFrRD0OdhrrNrcDwBoNhUEiCHawaYYSBZbOc6shQ4Tu9S7Vq1VIKUfRL0P5Df1Eb4jiF65RWPUUb9TbqZyjK0Z9xo2wLllYo8bFRD9aYxBRgzFqEXgHfhFCCctVljF8MdmPtfVixIm14f/v376/6YPqZczO9OVS88XS/QDxlhnkJn4BW2gXzCeUS1uiBUgiCTRFQYUGhBGsmnKNRjxF6LOSpBeFipA4dTqxFhxcbouNDGLD8wwdAVy5Y5BRKRkzJhCBcTBfyYvRIBRgH/4EfPnJ69zSYqWNdQZhtYw1ACNYRxMcanSx0lsATlRYsybR1BfhDsKMZprmaBRUTPqhaUYvyQ+Wld72lgtb3HJvLAfxQBtjKXnPDLypdlBk6upjehsZk69atVaUMP1hcF2t0wtoPnVosTAyTdlh4QkGORieuwzrGzB0NUYx8mRukweJHPBQSQIMJ0y717u4YlHHTKCQ5EvCCANbrweg9pt1hjSDUSa+88or6rh0/flzdO9eYlod2Bb6jWA8I3zQIftGxxVpT6NjrtaDQRgh36pMXeWEYJJBoBKJRf+B9hsGBnoWC/gIseXIqmJmB9dm05RYUbGj/5kTQlkY4+PZAAYJjtN+8FExphVJFL5eEsPGdhELDraC9aW4rwjoy2PfUbZhWdxiExrccllzgAEFf0M1GFbDyhqDdaxYMAqE/Eql4wc4ad6i0wtgC692beVvDsJ6jfGAsgLX8oPhC+aLdj+cLfd1gdaQ1HOu507uEPhzW0dTtPbT9dB1qDcd8HiqtMErA9HkoBSHQFcBgIZQ4pRWKYmyOA2UzFKew9tNWv6HCDXYfm/PgnYV+wSxQInOWTBaRfIYW9kzWKY9IIDsBaNExIo+KBYoJs2AEBaP4TtN6MfLj5iOhw4UVABQnMKUOtYW79pPov7D2w+gPlKtW1rBKwzXzxi7gBYUhOmKokKyCzwLKHCO1qKQwSgP/hQsXtjrluYkAmGJEF0xh/g/BaBTWzrrggguUchCjc1jHA51hCN4v3IeVICpgPP9aAYgRRHB3+x7Yxa8i4X8kkEkAI8R4p/E9p5BAbhNAewEDJKjfnQb7sNwF3OG7aP3+oT7CVCJMEXIKI7fzxvhIIN4JeF1/YEYEpqCinaONA7xgiHDRJsZAgpfr/OG7g2+Yl2F6kd9QYTh9T0P5dbqPbzQGZjBLheKeABRUeO4xHdZah7mtI62xOb1LCBPvA55b827Z1jDszt2kFe9uOOE6pRX30I+ChaKVjV36eM0bAlQIesORoZAACZCALQFUwhiZGzBggMCiE1OGsSGPttSy9cSLJEACJEACJEACJEACJEACJEACJBBFAlQIRhEugyYBEiABEMC6mFgnRG8C4mZqBcmRAAmQAAmQAAmQAAmQAAmQAAmQQLQIUCEYLbIMlwRIgARIgARIgARIgARIgARIgARIgARIgARikAB3GY7BQmGSSIAESIAESIAESIAESIAESIAESIAESIAESCBaBKgQjBZZhksCJEACJEACJEACJEACJEACJEACJEACJEACMUiACsEYLBQmiQRIgARIgARIgARIgARIgARIgARIgARIgASiRYAKwWiRZbgkQAIkQAIkQAIkQAIkQAIkQAIkQAIkQAIkEIMEqBCMwUJhkkiABEiABEiABEiABEiABEiABEiABEiABEggWgSoEIwWWYZLAiRAAiRAAiRAAiRAAiRAAiRAAiRAAiRAAjFIgArBGCwUJokESIAESIAESIAESIAESIAESIAESIAESIAEokWACsFokWW4JEACJEACJEACJEACJEACJEACJEACJEACJBCDBKgQjMFCYZJIgARIgARIgARIgARIgARIgARIIKcEduzYISdOnMhpMAnl/6+//hL8UUgg3glQIRjvJcz8kQAJkAAJkAAJkAAJkAAJkAAJJBSBzz//XPr06SNXX321tGnTRh544AHZsGFDxAygVPzqq6/k2LFjEYeRU49vv/22PPHEEzkNxtb/7NmzZfPmzereuHHj5JlnnrF1x4skEE8EqBCMp9JkXkiABEiABEiABEiABEiABEiABBKawG+//Sb/+te/5KGHHpJFixbJRx99JKdPn86RMg2KwH/+85+SkZFxVtlGy3Jv7Nixsnr16rOaN0ZOArlNoEBuR8j4SIAESIAESIAESIAESIAESIAESIAEokMgPT1dBVy/fn1JSkoS/N5///0yc+ZMf4RTpkyRjz/+WI4cOSJdu3aVO++8UykN8durVy+lRDx+/Ljcfvvtct111ynlIjwPHjxYXnvtNTWl9oUXXpClS5dK3bp11XXEk5qaKh9++KHUrl1bJk2aJGXLlpWHH35YmjRposJ/6623ZMKECVK6dGnp3bu3smJEuHbpQdqDyZ49eyTc+KFMhJXh1KlTpVatWpKSkiL58+eXo0ePypo1a2TMmDFSqlQpFSXCf+SRR1T+GjRoIE899ZQkJycHSw6vk0CeJEALwTxZbEw0CZAACZAACZAACZAACZAACZAACWQn0LRpU3Wxf//+gumvUNpVqVJF7rrrLnV9/vz5MnLkSKXoe/TRR2Xy5MlKUQaFWVpamrz33nsydOhQad68uTz99NNqDUIoBiEDBw6U4sWLy5AhQ+TgwYPqfsOGDeXmm29W1oOHDx9Wisc//vhDnnzySSlcuLC88soryi/igbIQ036RNlgxbt26VYKlR3my+e/MmTMRxT937lx5//33ZdCgQYI0Q7G5du1apRCtWrWq9OjRQ+rVq6dihNvq1asrZSbcQIlJIYF4I0ALwXgrUeaHBEiABEiABEiABEiABEiABEggYQnUqFFDsIYgFHCw0nvjjTekWLFiSrmFNQWh3Orevbv07dtXMfrb3/4m7777rtx6663qfPjw4UoZeP7556swoLRr1KiRute4cWNZt26dUhxiTUEoGi+++GK1vuDPP/8sJUqUUO6gcEScp06dkn/84x/q2pdffqniaNu2rTqHZd727duDpkcrMJVj03+Y2gvFZbjxz5s3T+6++27p2LGjCm3lypXqt2bNmiqtsBqE5SIESlUoDiGIa/369eqY/5FAPBGgQjCeSpN5IQESIAESIAESIAESIAESIAESSGgCmAZcrVo1ZUUHS74///xTWQqOGDFCWrdurTYXmTNnjlL2aVBQ3mmpVKmSOtTXrLsUb9myRd3v1q2b9qJ+9+3bpxSCmCas/UJBiPRAsKlJv3791DH+w9RkCKwQndKjHJn+izR+bBxi3pQECk+wsRNYB2qBRSSmT1NIIN4IUCEYbyXK/JAACZAACZAACZAACZAACZAACSQsgeeee04KFCjgV35hPb/77rtPTeXdsWOHWgvvtttu808hxtRfrJmnxWntPrjRVoAzZszwK/6gWKtcubJaiw9x2wnWGNy1a5f/1qxZs+Scc84JmR6/h8yDSOMvWrSobNu2zR/c7t27/cfWg1AMrO55TgJ5kQDXEMyLpcY0kwAJkAAJkAAJkAAJkAAJkAAJkIANgWbNmvmn8GJdQFj4ffPNN8olpse2aNFCYC0HhRis955//nn54IMPbELKupQvXz51gl2GsckGBGFCcbZkyRJl+QcLQSdp37698oMpyFAgDhs2TCkXw01PpPGDCzYvQfyrVq2Sr7/+2p9c5AOKUQoJJBIBe9V9IhFgXkmABEiABEiABEiABEiABEiABEggTghce+21gnX27rnnHn+OoER78cUXlUXfLbfcIsuXL1ebisAB1gfEBh/BBMrAkiVLqnX1+vTpI9OmTZNRo0YJ1hp8+eWXlbd7771X7SzsZHV31VVXqR1+MdUYU4oHDBggWO8wnPRgV2Cs8xdJ/FgTEJusIH5Ma8buyAULFlTpx1RqhKl3Gbay0ApR63Wek0BeJpDP2KHnTF7OANNOAiRAAiRAAiRAAiRAAiRAAiRAAiQQSAAWb5gii6m8VkUX1AC4d/LkSaWUc6vwQpg6LGwKAmu7ChUq+K8FpsD+bOfOnVKmTBm/Mg6uIklPuPEvW7ZMKTaxvmKhQoXULsh16tTxb6aCvGE6MpSOFBJIBAJUCCZCKTOPJEACJEACJEACJEACJEACJEACJJDABLDm4UsvvSQ33XSTYOrzJ598onZXxhqLFBJIRAJUCCZiqTPPJEACJEACJEACJEACJEACJEACJJBABLCe4rx582Tp0qXKovHSSy+V8847L4EIMKskEEiACsFAHjwjARIgARIgARIgARIgARIgARIgARIgARIggbgmwMnxcV28zBwJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJBBKgQjCQB89IgARIgARIgARIgARIgARIgARIgARIgARIIK4JUCEY18XLzJEACZAACZAACZAACZAACZAACZAACZAACZBAIAEqBAN58IwESIAESIAESIAESIAESIAESIAESIAESIAE4poAFYJxXbzMHAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAkEEqBCMJAHz0iABEiABEiABEiABEiABEiABEiABEiABEggrglQIRjXxcvMkQAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkEAgASoEA3nwjARIgARIgARIgARIgARIgARIgARIgARIgATimgAVgnFdvMwcCZAACZAACZAACZAACZAACZAACZAACZAACQQSoEIwkAfPSIAESIAESIAESIAESIAESIAESIAESIAESCCuCVAhGNfFy8yRAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQQCABKgQDefCMBEiABEiABEiABEiABEiABEiABEiABEiABOKaABWCcV28zBwJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJBBKgQjCQB89IgARIgARIgARIgARIgARIgARIgARIgARIIK4JUCEY18XLzJEACZAACZAACZAACZAACZAACZAACZAACZBAIAEqBAN58IwESIAESIAESIAESIAESIAESIAESIAESIAE4poAFYJxXbzMHAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAkEEqBCMJAHz0iABEiABEiABEiABEiABEiABEiABEiABEggrglQIRjXxcvMkQAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkEAgASoEA3nwjARIgARIgARIgARIgARIgARIgARIgARIgATimgAVgnFdvMwcCZAACZAACZAACZAACZAACZAACZAACZAACQQSoEIwkAfPSIAESIAESIAESIAESIAESIAESIAESIAESCCuCVAhGNfFy8yRAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQQCABKgQDefCMBEiABEiABEiABEiABEiABEiABEiABEiABOKaABWCcV28zBwJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJBBKgQjCQB89IgARIgARIgARIgARIgARIgARIgARIgARIIK4JUCEY18XLzJEACZAACZAACZAACZAACZAACZAACZAACZBAIAEqBAN58IwESIAESIAESIAESIAESIAESIAESIAESIAE4poAFYJxXbzMHAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAkEEqBCMJAHz0iABEiABEiABEiABEiABEiABEiABEiABEggrglQIRjXxcvMkQAJkAAJkAAJkAAJkAAJkAAJkAAJkAAJkEAgASoEA3nwjARIgARIgARIgARIgARIgARIgARIgARIgATimgAVgnFdvMwcCZAACZAACZAACZAACZAACZAACZAACZAACQQSoEIwkAfPSIAESIAESIAESIAESIAESIAESIAESIAESCCuCVAhGNfFy8yRAAmQAAmQAAmQAAmQAAmQAAmQAAmQAAmQQCCBAoGnefTsUM88mvBcSnaJidkjevre7Nfy4pXHXs2e6njJW/aceXPFjhnfIW/Y5tVQ7L4RsfxM5LX05rXnwo5von1X7b6TicYgN57bWOEcK+nICfNYzEMspsmJcV5Lr1NezPfs8hXLdbw57bF4bFdHkqf7kiK/4KzIxp6NHRd7l7waAQFaCEYAjV5IgARIgARIgARIgARIgARIgARIgARIgARIIK8SiA8LwbxKn+kmARIgARJwReDUqTPyx9pDUqxYklSrd0oKFGD15QocHZEACXhG4PCJk7I144ic/usvOa9cshTIz3F1z+AyIBIgARIgARIggVwnEDctmR9+2i35Sk6SQUN+ywbx5Mm/5OYBS9T9Ok1myLbtx7K5idaFtz/YqOL9YvLWaEWRcOG+/9vvku/x1+TTFWuC5v2H9VuUm0FT5mRzc/K08TyMn6nu1xnzoWwzGvdu5X+LVyp/U1ZvcOslbHe/794vHy77I2x/9JA3CfzvzfXqGzFl2naVgfc/3qTOP52wOW9mKMxU/77mkHz4abrfl13+v/x6mxQs86WkXPyd1EqZLuvXr/e71wd2/vS9vPJrZRFL6T5lKEDw3cVfpefeMRQiZ2yTt3LHXr+7MfOW2brx4uK+o8flhbnZ6/tIw16394BK9z1f/Rg0iOOnTis3qDfs5NVFvvoBjOZsYJ1vx8jNtVCcdRhuyky7zenvTxu3yVXvfS0lnhon9V76SBq+8okUHPG6tHh9gnz/Z2x/q0s9Pc7/Tup32Pr7wLfzcooobv2vzfw23GvTnozbTOfhjKEeRX9w8EPLPcsFBiTRVtu567hnYcZSQJVqTxX0j8+2RIMz8lb/wplnO2s5jv9s5SMaZZJjGAzAcwJxY2LxV2bn5Iylj3Ls2Gm56Y4lMmnKVrnogtIybWIbqVihsOcggwVYrGiSiq9IkbjRvQbLaq5dL1awgFQrVUKKFywYNE7dVz1jeSCOGR26mwxl4KS0P+WiKhVkWr9rpGLxokHDsd7wh2u94dH59kNHpMHLH0v/CxvILU3reRQqg4llAlnfLt/HSz9jsZxmr9K2fccxaXDRTOl/cw255YbqKlhlAVi1qBQvnlU93T34V3Vv5KMN5ZxKRaRWrVrZkpDXudmxyJbJGLmw8/BRmZ++XdrVrJwtReNT1/qvnRFLhey/k/ODtm9OlN2Hj8mDbS/IeWBGCLD0Qr1StliRoOE55eb5n36Vf8xYICULF5Tv+neXFtUqBg2HN5wJOHE2+3RTZmb3kR5/uWq99Ph4mvKOerlRxbKCdkjazr0ydkmadHznK3mmY0sZflmzSKPIFX8DmzcKGk+LqpWC3kv0G3rww+1zmei8znb+dbtf/3qRnmGPr5QX/7tWOl1RMVf7kF6k3U0Ytc8tLgUL5nPjNKpuosH56NHTUjo5eH8xqhnyMPCzlY9olImHWBiURwSyelweBRhLwRw5clp637pIps3YIR0uqyCTPmklpUrmbpZv6F1N8EfxjkDvlDqCv3DlyMlT0vvTb2XaH5ukQ+2qMummLlKqcKFwg4mqe1jhUEggUQhg5NEqva+rKvjTcvz4X2pUvtXFZeWJfzTwXY7D6cJ2LDSDWPz9fMXabApBjL+8vXR1riT32MnTnsZTs3RJSX+oX0RhPjnrZxk5+2c1uPTjgOukQYUyEYVDT+ERyEmZuY3pi9R1RrthuqEELCOTb+4q55VNDvD66OXN5ZKxE+XR7xbJjefXlVplSgXcj6WTN7pfHkvJYVpIIM8QgDImnmXBrMtiInvxzjkmIIeZCJZJmMDyqPO4NVvLOHRKru49XykDr+9RVb6Z0DqbMhBTiUe98Lu0uPwHZV7erN1sdW7umH01dZu0vXKOfDN9uzRtPUuqN/hWnn/RN51z/cYjMvC+X9V1mKeXqjxFuvVZIKmrDvofh6+/3a78Y0qzFjf+4NaNO6QL6Vvyyz655/7flMk30nHdDQtl3frDOkrb329+3yhtx02SJVt2CqZJYRoUppZcZ4yEYyqOWTDNdtSPS9X0GEw1afa/8ercqsCChdvj3y+Wpq9+pqaVYWrujLXpKh4o4rS4Ce+r1RuUP6QT4VX/9/sCKwyEg3R/t26zDi7kb8bxk3L1B98ov9c3riPf3HpNNmWgmzTZRbR+30EZOPkHlUawAcNuH02VVMN6QIsb1r9s3SXdP/JZIXyRtk7lMW3nPhWEV3Ho9Fh/9XO08Oe9cue9v4iePvDgoysF7wPuX3HNXPWe4D2Akt0sbt6lnMSh/X43e6f06bdYvWt4X//53GrZf+CkOSniJi1O7/W4dzcIwsY7jfe9502LZGVa1juNyA5mnJJHR6apKaxwB/dPPb9aTpwIVOi6eYcDEh/kZMKXW9R73vuWRaIrZzdha24RfR8yvy2RPhOhGP3y237pbnynIFhSAd+xtNUZ6tnCMcoa5XR5l5+UG3xXcX3sOxvUuZv/7Li5eT7ccFv9R4ZKz7DHVmZLCqZ7I636ux+qrIKxQMCh/OrI3bjbvOWoStf4SVtUPYbnFvXFpZ19vHVYTr/nn1NO6pUvLe/9tlqs3/9Fm3fI5oOH5Or6NW2DGL9ynVzxzmT1jUR9g3pn95GsJTzgF992uMO3HvUMvqeXvpn1vcd3Gud/Gt9dWCrC/SfLs5aPCBXHxv1GuRl+sCwDvtMIv98X38sGIzxcD2ca8hlDATps+gKlDASTJfdcb6sMdErT6l37VLzPzvklgNmQafPU9V+3ZbUdtmYcVtf+ZbCBYGrt0z8sUdeQD0zlbv/2ZEHdqcUNU+0W5fnS/GX+Mur07hRZmL5D8Ub9r8XreHW44f5qHrrMzHn95+wlkvKfT/ztECwNguU4oNwDKzx//57r4+gU78sLfNMOP+x9ZTZlIPzVSC4ho65sKZfXqiobjGcLgvYTniVMITfLmj2+61h6REussNTpMTMM9g5qt26fF+0ev38ZzxieK/xZvx9o74AbnkGI12wiSa9KSOZ/WDcSTNAexTPU9YOvs7VDvUrzX8bHxUtO5nzoY3N9gLYUluRA2w9LLGFpJUy9RZsDdQSmk/775azvrA4DdQnahtoN+iG795zQt8UcB/pOdnUOZkl06j5P/Zn7XwgEbQLUpS+9mmV5HipOHTnaSv94IlWlHflC2/HPDVn9IvSREParY//UXtTvmnVGPWRcxxRhyH0PLpOPPvMta4Iw7Op85dD4D30vxKnbkeACnjO+36md+H+XrTig3CNt+IPf5SsD+164hrbm5G+2+TmjDf7ya+sE9Y9Z3ISHPuqIZ1bJ40+lqTLr2mu+Kme0c/vettgfHOJ94ulVgrYM2gfIB9zOW7hH0L++d+gy1T5GutEPRnvPLPB3VY/5Kl/wi342nh9r+Zr9OHHOSd50HO9+tMn//OF5QxvTKm7i2ZR+RPoNXOrPW+sOP6r2lDVvbhjYlsfvv1uTFXDuJh9u4nbzbAUrEzwLt/8tq02gEwiu6EOaxau0mMPksfcEctdczvv024a4b/9J6dpzvqAje8+dteSVfzc1FqAPNIVGBYSP1awfd6kwbr2xhuBDgA/vnHl71NTifIaX7TuOqw/gNb0X+OPat++k7Np9XGo3nq6uwXIF/qd/t0OwDhg6gdvWdZXixuL3mAaGDyjcQ9z6c+tux05f+i6+7AcV/uXtyqu8ovJYsHivpK++SoLZwO04fETmbdomFxtr4EDQoC2QlF8mG9NjFmzablhL3CaFjHM0TK56f4rM+nOLcnfrBfVl2fbdakR8zoZtatotWJ0wOmq9P5muwsRUsosqVxAotj7O7KwhPojb8KBcRPqu+fAb5Q//7Tt6THS6dx856r/udIC1ptBwQ8fmnhaN5ZWr22ZbCNxtmqzx7DI6pLWNjgWkVfVKAjbT12wSrDGIdQy3DesvxQsV9KfZmXWSVC5ZTIVV1LCAqlrKMOE3+Hsbh/0YgH6OULGVLFFA2rQqp57n0a+sUcowPNvVjGmceL7wfKMy2Lqmi1Q+p4jRwHf3LuUkDu33ym7z1HSNu24/V35bfkA1bPD7xUctBc+g27QEe69RcaFxg2UF8E5v2XpULTeAJQfwLoEBliFo12mOarid3zhZBhppARM0nqZ/t1N+mNZOvYNu32FV4A7/YVDh+lsXqzSN+++FUtRYhsBt2Jqb4/ehkPfPhBtGhYx4KxvTfyHIU9UqRdSUlR3GGj34ZqJTUb5cIfWMaTxwg+fTjdhxc/t8uOFWt04JQccBaR3+UP2AKSlorKP+adok2VVZBWPhtpzdujtsWM0jvfiDNGpQUi44P1l+mr9H8G4tX3iFNEkJbeF0qzFtEgM/Pxrr5F1RO8sC/rOVvk5bj4a1BYMgZoGCZsQsX4eje8NaRn1xWl7/OVUmGss3pA6+UcobU3UPnzilvvn47kNglXXBOeUF67dd+e5XsvzvfaVB+TLGd7K4P2h8J/GNhUQSB/xh8OaUUc8h3iaVyuJSSEGdcd/XP8lrhnIH3/6vb7laytlMNw6VptqGxdnyHbsFispHLr1IxQulglaKzDQG1C6sXF5dxyAY0nhvy8bq/Kr3v1b1TG3DKq1XozqyeMsOdY66Z8HAXipdbpg2qVROhfeYYeUGhQfCu6FJXfneiK/12C/Uvdpls54Lr+NVEUQ2oVkRAABAAElEQVTwH6z+zWVmzStmAmBaOdogUMaBMZTIXerVUIODUOY2NKw5r6l/rm3sKAc8e1hWRJeBnUPU+/jTotPRsnrgtPFDxoYkSG/bmudop0bbytsy9Acc4YFOO9IJsXsHw31ezEnJb0zNP8do53xgrAX9w/qt0rFO1vfjE2NdaMT7RPvmyovXbNw+3+b0mo8xPRyC9m11QxGMwWn84bukmXiZZi85mfOhj631AWZSod79+PN0Vbdt3HREWed36VRJDdRBEdawfkm55irf8wslIpRLkO5XV5YTJ/+S199aLxO/2iqpP3dU9bc1jmB1DpYC+eCTTUZbapd0bJ/13nwyPl3VV3p2gJs4df7eet9XB93Up7pkZJwUKBK/nblDflvQQTBF9vBh4/th1IUtLw606D5kKLxwvW1r33exXNlCkmxMOYUirEL5wlKmTLBelah+F/pfmsmll5SXt97boJgunH25tGzuiwvKKNS5kIublZH6dUuotZTh98dv2wn8QdCP0+GhXdq2TTlVFvc/vFwKF84vfxtQS7lzGx7aqnq9anjE4P4H4wqp/JYontW2MseL5baqVyuq3M412goNjGfg56X75MoOFVW/GcpPTMn919O+egmD42gPo62GZwfrLiJe+IE89H911a/1v2Ccc5o3xIMN6aDAwjPeq3sVgVJt5qydMvHjltLj2ioqKW7igdIP5Ybw2hllUadWcZlqMIQSeOu2Y/Lyv85XYbllYFse5X1lrwKy/OcmH27jNpdxsGcrWJnguUH/xypgiudFi5dp0WHyNzoE7HuC0YkrV0LFqFbHa+eqzhgi7HRFpWzKQFz/+PPNShmID9rBbdfK+2ObydKf2kufnlWVIgQKALPgw7Y+tbPsTb9Ghg2pK+8ZHxPIIw/UE5haw/+2tV3UBwKVxiKjM2gnbv25dafjwMuM9M2e2k5VxPhQ4SM8f5F9OrQ//KKxu/6BW2X2Hd0l9b4bVGNHrRGV2SBEYxrKwCvrVJeDj90l7/e6QpYalhB9Gp8n09dukkmrfKNrLxqjumjMPXDJBTLnzh7yTs8OsuzevmpNJXN8bsPTfrAmE9K3d/gAGdbO12HS90L9bjOUih2NjiSUgZBO51XPpgzE9XDTBD+Q9371jeSgI4cOGNhse7i/YgirxEWbA0egnFg3Njqir3e7TIXbtV5N+axvJ6lr7GLoZRwqcIf/8JyvWtpRvp3URpYZjSYIlIFPP9FI1q/0PV+D7qqtri9a4qvcI3mXwo1DRWj8h/T9Oq+DjB7VRL6b0lZuM9aew7uqR28jSYv5vYZCELLoh8vVO/39123ln481VNdgNQYZY6wjg1FcxI1vxhuvXKgUKWgQoxGJjYQg4b7DypPlPzRQrr1+gVIGopGIxjgk3LBz8n2I5Jlww6hxo1Ly+su+9d+6Go3Gz95rIVCymQUdg4/e8nUO0RCGmxuvz+pAmt2aj4NxC/f5cOKWlJRP7r7D1xifMtXXAUAa1v55WNU/qEvKlC7oqqyCsXBbzm7dmRlN/aKNqivmTL9UHn/YNxXbWu+Z3ZuPe2Uu2YBpw1pgffPW0jS5yZg2aV2KAYvyQxmIb/mmB/vJl8ZyDVMNK+3/XNNOKWieMqzcrIL7qffdqOqSx41pmZBJaevVIAm+jVBa4XuK424NzpVI4oAS79iIu42poF2s0TueY/BrwKTZShkIhy2qVbJVBrpJEwbdrjUUUrDM0pb5PxtW+1r0QBzOtZIV9RisMaH4u7hqRVk75BZV34LXiPYXK6+om60SjCnczTcGAaEMvKRGZVlhtAPGdr9c/eLcLF7Haw7by+Ppt10r39/eXT1veO7AtKWhuM14/C717H3et7OKzszXGv+KHT7FOfxFQ3KbJSxug/3tNQZOreLF82INE+f9MpWnZsteKNjHGgMEeKehyPWajdvn2y695mtTbumqvkl414a0aapuYWAc4nWao8VJJdby3/QvLxG0dzatukq1s6C8gfIqY/u1grri8/dbKB/aiAL1HJSBaB/Az5eftlLu/vNCU9X3gCLAKk51Tr8bfWsIfzLe1waDXwzgYUYA6mEociKJE/lCG+Krz1sbxiHnK6Xec6N9s7ys6Qt2/uTwhn4l6Ktjmqp+XzC3+jrqVDB58bkmqo+I67C2hGCWAizsIGCy2GhvfjCuufwyt726dt+Dy+X06UDzvxGPNJAtfxj1puF+5leXKHdQ3EIiCQ/pO7G3u6xd3kmg9AkmcId27srFHQWz7dC/haIYZT5j8iWyZI4vzbp9jKW6/v2Sz5J0zbJOqs2Gfina65DJxgZxwcSOs5d5e2xYffWMv/N6M/n5x8tVMh4c7psF5TaeFakHlDLwjltrCtpOCGv1L1eq9wD9BygMI2EQUB7lfIroYJyc8hFJ3E7Pll2ZBEuX9brXabGGz3NvCeT3NrizHxpGUjD9qnNHXwOu311LlJWPNWWYSgYZcu956kXGMawItbLD3MHDPVw/t0Yx1cEra4wO9TLWuJoyvrU8PLQebivBdEFYhEAOGSNPduLWn1t3Og6dPpwjH1dnjuDt3Zdluq/dWn8HGdYG5xrrJ0EKGKO3VxudE4huIE4w1tCBoPGDhjUE7uAPonfcnZ1pQag7b7iH9XQevORCHPrFbXjawyDDqg/pK1O0sJQ1/sIRWDtiKm7n82oob/2++E62HMyaMqDDCjdN2l+vlNqCBuLDJkUlOotNDasWCCwCzBKKtdmtPs6NOHRceI6qVimqTpukJPvfDbwn2sq2dQufBY3ebS2SdyncOHT6/mEo4KtU9lmVwSLwkQfqq1va4imStJjfa1ikQZ4f84esSD2opmQ8PKSeHNrRza/8mTp9h3IzakSKnwlGap9/yvc+fPn1VnU/3HdYeTL9h5E2jESiwQ1lYIN6vncUTsINO0ffhwieCbeMTNn17NCJW6TPBxJn913VysmPjAEmLZ994Tvud5PvmxNuWelw8OvWr1t3OuxLDAtgjNxruepK3/GevaHrC/iBVRUsh2DRg+8dBNY+GATpa1iWWUUrsu5t2URZ1uj72DwJ8qkx6GQWKKFgxaXlqrq+4z2GhXgwCTcObCqFqc2FCySpzUSChWt3Hcq7d39drZRnuP+KMa30W8My3Cpu06Qt1MAQopVUsDyEYg+WauD8+cq1ypIfloh1y5WWmf27ycd9rlTW0fAHpUp9Y+oy5ODxwLIMxXSuYQkHgXUWNsyA4Pepjj5FgLpg/Od1vDpcL39RtlCaQlC++vkZ3Op8KZFpTdq8agV1H1OJgwksCiHlivrqHLM7vcQKpo7qPyyREo7kNss1e/YblpL2f9bpu149L3Y8Lq9VRSn+3v5llRw1rDwh8wyFNAai+1/UQLUvvWbj9vm2S6++BuW7fldxDZbOkD8MphCv0xwNTiqhlv9g0YPNMiBoy+j6YPA9dURbjTW/yGfZBqMLyDfGzAXIvQNrK+sxdWL81/8W37daD67q66HqnMvbVVCKPwyo6mVR5i3cq5SL2HQM9W+4ccLgQ+cL6YA1HdpT1rTpNHr5C8MRLSkNS6lN0zB9Gn9oW8LSC5aB5nr4wqal1SwcDDhjCRCzoB2q2+BgBcFMBkgk4T0w+DxjRkZ+ZeGmAgnyH9xB0N5uf6kvXnCExSAEv5g5k775qDrHQOk0w6Bg3neXSaWKWf21mkb/GQKFYjjiZd50fwHx43mGghNTyDHTw208pUv7+giYgYL25IGDJ5VeYJMxgwhGQSijSBi4LQ+k3SkfkcTt9GwhvkglltISaR4SyZ+v1RdnOR7+YH15ZkQj6W+YB8NyAnP9MZKBh1PLqt99DT2smYDpalp0RbT6j0P6kvqte16g5UqtmsWksDHl7Y23N8jSX/epaZVY60KLdXRHX3frz607Ha7+2OpzjKhBrGua6fvmXyzMbRaM0EIwpQuyyljjCNL38+mCqaxajp7yfdhX7/bdn5++TXWskov4PpjanXUKltvwtH80snIi2PkPOwD2n/i9sraDUnDGbd0kKb/peXCZR2s6oPBEh+MNY2R7qaF4XLlzj7HzoI8H3GLNGbOEYm12q49zIw4d17nGc60FDQBMj4Bg51ctJTM35tE7uIX7LkUSh477wvMDnwWY60OWGoMAkHDTYn2v0bj9fOIWNe0FU1/Q0MEGFxi9RmMNAuUj3i+tmFQXjf/qZX4j0LCAhPsOK0+m/7SlIRpQulGub4cbdk6+D5GUl1tGOj9e/jpxC/f5CMUNFpto1MOKFstDVKpYRN42pimh03Fl5rSncMvKzMKtX7fudNh162RNucW1CuV932w39YUO49am9eWRmQvVtFIo77S1z5XGFMCvLdOF1xrTNSHPGevkvb10lQ7C/wtFAKZ/aoFltFkq6DrJUIwFk3DjaGQoNXMisGKaYlgYQkl3+8RZ0uez6bLm/pulUomsb6jbNOlpk1hvd0CzhoJfrNV4fcp5yroddYtW2FyXqYTA4FibGufIp4ZSFmsKwppNW8IjX4ZxTYCEYqrXKtQKRe05xdhR1yxex2sO26tj68Yf5Yv52jSYXq6lZOaGYlCiBpPzMwf2MBXbKjWMdlOJzAHSY8Zz+YexPuEBixLW6sd6ntssMcPDrXj1vNjFhwFlPOdYN3O68azjmdbfD1gYQ7xm4/b5tkuvvmbdLAjrR0IOZw78ep3maHDSeTH/nlc7673AdT0wqgducQ11GgRWe5C1f/r6SM8Zg6e6zlU3Mv/DgDEshLSEqnOgSBnQr6Y8a1jvTTfW27vumsqC6cIQTPmFhBtnM9PURfiHAkwvkWFdexr3vRK0CaxtNigFIWgfHj7iq+tghYk1+MyiB9o3bDwsulzQ3jT3U8EK1/SadXpdRLfhoSyTSxU0R2t7bHVXtozPD9rFZsFmnQcy1/KGQhlGA9OMqdlDH1mhphRjDW6dr1OnAvtE5nDsjr3KG8rE3I9BXJiZgWnkiEP3/UMxxKwVPKeYjo6lfCBQPOt+QpEiSUqpHg4DK2cVaJD/QuUDbdJw4g71bAVJhqvL4T4L0UyLqwQnuKMs7U6cgMC0PSgDIS89f75a8wAm7pjCZl63QFcGWEvArCjUGNCxM4t1y3JMl2zV/ge/E5iz9+xWRTYYptQffuqrxPw3TQdu/bl1p4MuWiRyY0+zkk+HZ/7dnzmVBGsUmZVo2g06QGhTwzokuXDWiJC+nw+aJZO4Cc/kXEpbFIzme6GOMYILZSDkpa5t1XovsL4YM/83eahtluViuGnS8WKKSKs3vtCnaqpLT4MTFqjHovVWCcXa6h7nuRGHjhdTHK1ibohY7+E83Hcpkjh0vFhrzSz60dIK+HDTYn2vofRLW9JRPjDeYUyfROPgpVfXqj+sU4h3XDeMzenAsU5LIaPRCQn3HVaeTP8hnltuqK4Wth405Dc15UXfDjfsnHwfIikvt4x0frz8deIW7vPhhhumjuA5mTTFWI/1otKqcTnk7+eJflbDLSszC7d+3brTYYd6p7U7p98ejWorhSAUYpcZFj+w9rnjooZSNNO6zOw344TPWg1KNAxw2Ams4LTYhaHvBfsNNw5YnEcqsJSHMhAWdLBy/NKwRIc1+oAvZ8uUm6/2fwvcpgkKT1hkTVuzUVnmY+mNh9tdqLgijVjH7sAxnzUIlpOAYDOWlsb6v9hcBQLrJVjTlzWsB7HRiFVCMT180mfNbq2vA2tv7+O1ptOLc7u1HBFufv2RdhkJFIsoawzyQWGtLSfhHUusaEEZwGLQKlZdo/kZh1uvy9Aaf07OvXpegqXhpvPrKYXgx8v/kC6GBfCHy35XVsd6doXXbNw+38HSi+vaujSYG6/TjHi85mSX9mBTRvObBs2t/rSlF/o+GJCyk+OmTdbc1DlQ/EEhiKmwXQyrdfSlsOagnnkVbpxQzlhFXzN/CrK9p6Z0W/27OddtQLNbzRIDEIcO++o6DCJjeSc7KWVS2FmVi1b34YaHtb/diDYIsLq16zNrN2iL33j7z0rRhmvII2bstWlZVm18qd25/fUqb3ZlAgUxBPVCOPGM++9F0s3QNUCZiCnQmJWCv3c+2Ggs29VWKZ7DYeC2PJDWUPkIl3+oZwtxBhOrclf3w7T73EyLjpO/kROIO4Wg+cVCZ//jty9WawpiMdz2xoYI2uwdlQxGLDCtS5tBA+Px438ZIzgHDDNo+wpOox7+ZKo6HPufC+X2W2r6Tbn/b9hydd1iGKa9iVt/bt35A47iQSPDQmCnsVZRvwvrS3tj4xEtaNzCKgGLK6NyhUXD8u171FQl8xpS2iJQ+3MTnnab09/KJmuN0kUKq+lVHd/5Su0Mibw0NzpRkEjTNNywkIFgvaXbM6e64Pz/ps7Fj5rCpQ5y8F9uxBFO8qyNp5y+S3ZxW+PQbrDJh1kw/QLS/EKf9V5O0wKF/h5jUW2sDYo/WH395/U/1e7jUAxCIQirQixLgNFv84ijnr7RInPR6Jy+w68Ya/Hc0tdYK8wYfMAi0FhbR09RzWnYZoZeHFvLyy0jL+K2huHELafPhzUunGPaCXZWxPQRPC+QG3tXU7/4Lydl5davW3f+RHlwAEsyfPOxcZRWUvVtcp5tyHUMxQrksnOr+jcM0A5huYNBH3yfYSkYqbiNw2mKqNu4KxgWZ2blENZ+nfXnZrXG36uLV8jfjanRELdpgltYSUER+J+FvjYEWDU1+EIhhc09tmQcUrs7a8stWKVDEQUlLDbK0hurYJMWiNU6XV10+E+HC0s3bfkE53o6pPbqdbw63Nz+1RbuTvGiXdPB2DQHyl5Ysz11RQsn5/57euAUO2KbZcN+n/JWX8vLLN0+Lzqv1l+smYyp3diB+2ZDOYgB5QEX+Qbz4dZrNjlNrzX9dudepxlx5DYnu3zhmvV90bMzLmtbXvSGH9rvr8v2C6ZWog+mLcP0PadfWGxh+jIULTcbbR8oAAf0O9fvxW2cO3b66uH0zYHTbtFOwSwBDBrCQk4rtrB+nFk2WKbrmu+5OcYUVKuszbzWxMijng6MKbVvvnpRgFOskwhFCzY9cStaIetVeG7jtXOHdetRfijHrz5r5V+CCO1jtJO0VaOdX7trXuVto7EzsFX+WOOb1YeZILotH4ohNslZbTxDbVqWk25dK6v8YHOO/ncvVWtHo70O5a+XDMzpDpUPr/mb4zYf4x2ylqVeTkC7y6206Pj4mzMCgeY2OQsrJn1f8f/sXQV4FNcWPiFAcHd3d4prcSnFpS2Fugt1f311f+/VhbaUAhVaoEVaaIsWKe7u7g4BAoF3/rt7NrOT2d3ZZCPAOV+yY9fmnzty/3ukdUEawv7PIP0HL/T59mvR1OPj7dvvdvi1G+rviMiJ8OjBBCrQEGgkil8HDAYRXQtif8GYnfzjNp/bdFJuSi4RUQ3yrTeAhtT15t9LTITid2YvM7v6eh3NwzG5CCIFS7RE2ee2PEkfySUiYg7xOoLu/+MfPh9/SW3Tqv2eoC3QRIR5BwTnPHa1Z1B2PhAzHOCkRIPBaj4X6ToCVJ3k3cm9l8Kp+KMvtvg5W/70y60mO9ToIclpCz4Wu/aeS9e0nG6cJqM8RL6TKG5nebIAItHfPhnqucZmJ/988OlmswqTFEhy7+EsbHqBZ8uXH3k0We9+aKnPZ0xyyzYNTMEftxjJrHnsmQTtsOQ2KxhuyekfgdoF7QqQgtBE/y9romNGXPoj8ri9Vk5YuM3rNl2gc0jq/oEcbdgM5H+ZZogr64SRtcwmJYuYzaEcpVNMX7ED2tr1PhlNfX6YEvbkSUYOxiFuK1BWStSBct1IEZ54Gt6rrUmKyMOYGIOE0ybxc/fvaQtNXpgD453Sgf3f/rl5p9FS6+N9xyLBeibuINexH0QhA4EtfBtC7GSU2Rnkp3uVsuboazMXE0xgIfBbiG2rRLpea9npcf31do1N34bWJSJGW/sv2otAMI/+Psev6TIhOmv7Hl/6eDa1/MEShAcZLmcs3fYXP2BsGzAbhtz16wyztE4oRBqbSLTXNDLIT6TbLFWlJk5SZ6il+JJGhFkrMYD3YL3m000ADTEvDlWW9TjMMSF3PegZR/TvnaCEEG6dcP+CyK8i8DGPSWTx25crp8ciZtacw75zgFaTk49BGeeJaamU6bQECYoorCIYGwInkCilSmajmtU9WvJ/c8ReEIAiyFev2TSqWv8vOnjIoxEux4ItI11esLpCHRMyFN+AVpPz4d6xtrWvOJVlxzlS54br/gebootgG5GGYaYKotltPXAPgzHCv15dY4pCe6HlCa4BAkWB5GJgCgrwE+o8UqJu+zVB06A9Cj+Y4jsS+3C/WSUl2mItX9cji8AVpyHoBM9rHCH1N44Qis47hDX4MCPz6IMVjBkgHggQqL2D5QfBgAcEou4EE7yY8HKBn8LBrGW4b/85AkGAmxUSyDm723xu0wVrY6SOPdqsNv1v3nLfQANmX/M4au+nC1YZx9AS1fCBxjV5VncNvc4DiIUcXbd8vlwEbQW71ofb8iLVfns5MCH+bcN24/NnCGvyfdnjWkpqm5rwoA3aA/BPOLhOFUMGfsK4wOk85HCs+5c60uf2+jWCGR780TzUpBZFug7UE0lJ7r0UTltgmtm1zzy6qV8Jno07au651qz5O+iGUqaY5LQF2iCv/7s69RjwD3XoPoedY5cmmIzCfBgCHyGQpzmQ0FfDt9ETrHW8gx0pIxLfzNmHzAcfNNDuGFzGpIvUPQwzZjj9feu/G+ihJ5abqG2RKts0NAV+3GKUmzUIIPiQyJd3GcGJeaTECbfk9I9g7bqZ+x9mhKHR8Jx3QCPp3V4rJyzc5nWbTtoUqSXMhp+cMs+QgtCKy8QknZPARx4COyFARuuvfqU7rqlKW1m77bMFHk37oaxhLVpVTvmd9uH5CG02TOzcxMQkIg1Hug6negPtAxY3c/TUEcvWU98fp9DS+/pROOcNTaASuXKYdweCiYgv3rb8vh3jDezV1WsujDbUZ+0q1PU0a6kj2ArIwx9WbDIY4/hBNikOR1qWKUY4h3H8zq7/6WhqU7YETdu6y+cTN4r4AckS6XqDtREakB2+meCY5JPrWzruj/ROBM+ZfUcvav7lWBMpG9Gy4TOzWM7sJnKxkL+Iei1RZ3EdK7EGLY71+O53gyWCzoDYtUpqYol6A2GJY0VyZqNve3tIbWyHErf9JVg5mES+f8Is842Ie9fq4zHS2ESivcHOBcci3WapLzVxkjpDLeFLDaagIEhad/6b7rilDG1lX2zwvQwZytZTooEXqizrcUyuIQIvyDGUbyWVwq0T7+Nru/xtlEKOHouj517ykDgvP+/RRIUvPEzgrVh13Hz3YRw4mc8H5p92yesNJnHPw8uMf8PHn7On8N/uwpPLCDQHlysS1VgiNSMw5ZsvV6en/7WamrWbaTQsQZTBFyPajEjN1vP2LznxVqTLS1yD+z11anomxPGNXKRwjNF0BEks/QJEJybfPW+TxOUmwvnhihHDqmOPOeYbH5O4EgX73ddqGJ9/MTz2cnNNelyXhWrVyG24Akxkg/iERqwQvtCYXbfBo3kYEoNAICSGxW9PsPNwjX8YdTtdky4dC9NHn2+h6/rOo5vZtRHOGX4VrZISbbGWr+uRRcD56z2ydaRKaaJdgUG9XaAKPOLLa8xudFgQeVBlX/5PW+MMFKQgAo/AXwV8WCB6MGZyIFIef2v7yftv1yJEvprNMzwDbllIQ55aYfxcIBooBC9JJ3Gbz206OW9ZSp3iByjYC1m00WSZkNezFh3lOWmYci2/vz+1L1/SkIKDxkw1/l7g/Bl+lCTyMNItua8vDWbTYpgJj2LfMDC3ealNQ1Ng9kyewb/b8uRS2ttn35Z2W5fi+kRwsB6DqdeI3u3Mrq/YwT0IPbdt8pXrLfB99ksIH1KIYDdg9B8EghFmXjNv72FSYAAMkTbL0uzkH+lfgjU0PeA7CvIZm4TN5ch7ka7DFG77kf4jSzksM0OyjaXcC5LW7b0k6WUpZbqpQ9IOYFPMuRzUA/crCPie3YrRzyMb+fy1uW2L4C7nIuXDBACTAfg4gxnmI0+vpI08kfDKC9XocW+0NZgULJjZmhA178PPNtPAOxaZjwG0ZdqkFiT+eMK9h6WvSh+TNmH5wtNVTIATEGeYeQ63bDvmUlfQ54O3Ifa8bq6XW4yy87MZZCcEH4wwMbCfv+9ayYpJnfjHng8p7Li57R9yzrKU2gLh1pEjNMo7Y0DvBHNh5HN7rZywcJvXbTq53nIe9vOyn68cD7SEjzWY/UH61kggc+VSCYmE4z/f0NGY0sIsFkE4oG0Ff2Hfc5RcKUNIQXn2Ix9EyrM+PxHZHe8eTJ78xP8QN3VIWdG2m99epynQ9hMqDZ7VCMoFovKFqfNNbjdtkmoEQ4mQi/0gMiA414YlCpl1/NzL/gLxroiNu0B3/jLDYHow9gzNu6u3IRZBIkJLMBxMR/fv4DPp/mj+Sj4X1nz0kkTiPy0l6vWdlHfFijNINKd/aKZa0yFroHON9l50OY600pcyOj04kMAiMI0HKdivhsfa5PcNOwjfDiD8cOytDk1o9UM3ECwQIKjup/4dDSmISNOPTZ5jvommDO5mKTVlrqFfBbYNJxxl33TW1oUIRnZs5b4R3JDWTX9BukAC35kSqRffjVZJiX6W1PYGxsSDkmCSEm0GJpHAyYqtrAd6H8h+WSK9vBsyZkwYDP08siE9cHc5E2jtVlaOePnNdWYc9P2wBsZkFPmkDLfvHGgewfIKAmULu7ipU+q6785ylIPHcvDBDDIQZrjzprWiyhU9wWDMfTqioSEFJ7HSyGPPrjQmxVN+aWav1riXQn4ETMN5BhN8C/TrVZyeemGV0XTEN9Pwz+v7ojcjL3zaf/LfOiaYxQOPLTdjSOx/+fmqPqsUbLuVcMpz+oZzU4/0AVlKHmufgKnwULZqgdsYEJ79Bi0wlnMjhl5jxswgPBGIM5DAjZcd5+SeG/xXQosP43t818PaBkFQvv60HmEyV8RNPTh3WO1g8n/4qB10+31LDDEGq5Alc9oY0+NwMXB7PdycR7h1y7kHWzpdk38/W5W6dS5iyHQoR2Bs8tF7tc24SMpKibZI2bqMPAJR7BeCufrLXE71StYJQMV3B/uagNqwOBl1WyCiMsIfROmSWUmc1VrzYpALvwkSkECOhcoXbjpJ77jMMTbx7lfvT7zPxR6Ysu44dtL4RrJrg4AUg4lRyzJsRm0ZbL0wdQHB3Gbqrd1N0A1rNcHKs6YLuP78x4kPJfHcpKCktAnnDY0XRBHOwlGHkysY7JzjKM/5s2bxDYYjVocTZsm8h+R8k3MvSRlOS8yc4sX7wzcNjHnmlm2xVLxoFr/Ia/Z8yW0LTDnwoi7NJh4yGLLXgY8b+DWsUC6Hz3WAPY3be92ez812xMp2ekZEqE+4wQhp4L8VZGogrP3wiEB7k9s/rO1BsJK8JSYaTfOpE5tbD/nW3V4rJyzc5nWbzteoQCtO+CbzuSpVweRyG79D4IdPtODkWLhLOGiHI39oC1rfORGpw+k5mQwMItImB4DwBbeJzVZBRCYHTwTOwDsMpsrWgCuIeNxx+AT6d5sGJNYAaEak6qUI4+wAkbtdLtsB38l72S0IiFhYQcSEeN/DZyVch1j9MtobdDli6bq/hMC1+dBxxnfmyRfudAzaESlsItVe+7Vz2o5Um61lJxsnp+sQoXc8JlDhfxnR6t1Er7Wel9N68/azDPF2cl+3RNF6JX04dcJsGEPdYFp38H8GV08w6Q0msP7KxgRT1oK/Jk7GeCJqMAI1bF7ZwQTcw7dBGfZRF0jQV/bsPcPfPVFUjL9pkyuRLi857YHFHAg0x/Ny+saw9Ecfzoy1SCTO7fiJ82yOHWcC4QhZLeXL0k09SANtx0Pscxw+CDGh6yRBMXDKgH0hsEESN+eRpLpReABxuiYnTl4g3DuIiB0ITxQXkbY44RKgrbo7fASuCpPhULBAg7BKpZyhkjkeRyRJmW1ySgAVegg0U6wSKp+kdZtO0qf0Etp1VQp6/LXZ6xq/bhu9M3spfcoO1u++proZ2MP/nUQ8hDmFXYKVZ0+bWttJaVNmNpWDk/1ICbRBcpKtz0S4jki11VpOcu4laznB1vGBgZdPKEluW9zUgZngUM+OlLyHU7LsUPi6Pe4GI6TJ6Zm0d1tsstMlt3+gASAxMdB44931pj333lE2YLvcXisnLNzmdZsuYCNT4QCIO2gWRkKgmQMizC6RrMNedlK3U6pNINAlYEJS24Z88IV3/ajfCH4hv+7Zxph/Hz1zjp6YMtcUiyjIVolUvdYyL4d1EIBleOLPrRRlU9xQcjliGW5/sWIAchwD6onrtxkyEO4GRAPVmg7rkcImOe21tynUdqTanBY4hTo3p+OYOHXzveSUV/aB4MO7dOLkfYYMhOZhsAio4dTpSEhJxd6lNSCl7ZDfpliA+O0MsIFxn33sZ0+KvhKMqLSnD7Ud6fJC1RfsOEyykypOOEfi3EBYhyKt3dSDNHAvhv9gkhwMgpXr5jwiXbfTNcmVMyPlcvHxHum2BMNGjyUNASUEk4ZbyFyIQtq511xfhC34s7rSBaazIATvHT+TXmStQHzgwQcQZCKbFidHe+FKx07PTxFQBC4/BB5/bqUxF0HLYZLS4zqPeefldyba4qsdgWvZVyH85Y1cvsH8wxRW/OM93/oa4w/xasdIzz8BgeT0l1/YTUtfDiQEweTnUy09blISSo/8WnLaG/nWuCsxLXBy17LIp/pl4h7qe/MCUzAmxcSVSORr0hIVAUVAEVAE7AgoIWhHJELbiEhav24e41cKfizgWPZKFwwm9j99K8EXDRxoHz17ju5uUJ3aVyhJdYt6ojpf6Rjo+aUMAjWq5aInh1QMqY2XMrVrqYqAMwId2hbmiHKnqXaNXPTEkEoBzcadc+teRSD9IIAJvIX39DX+cGEmvObgEQ7UUpaacoRoBNFQUQSsCCSnv9QsnJ+6VSljtHsf5GB0CMKS0pKc9qZ02wKVnxY4BWpLSu+vWT238UkGjasH7ylvfCandJ0pUT40G2NinM1HU6I+LVMRUAQUgUggcGX4EIwEElqGIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCJwFSCQEC7qKjhZPUVFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQBBQBReBqR0AJwau9B+j5KwKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCFxVCCgheFVdbj1ZRUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUgasdASUEr/YeoOevCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCioAicFUhoITgVXW59WQVAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAErnYElBC82nuAnr8ioAgoAoqAIqAIKAKKgCKgCCgCioAioAgoAorAVYVAxivibE/1uiJOI81PIsfYxE149f7E+1Jjz/MfJ65Fr3NiTNLDHqd+43StnNKlVf9KD7ildhv0ngqMuPbNwNgEO+LUp/SeDoZY4mNOGF4Nz0+n874c+87lfh5p2f60rDvxnZi+96QHrNJDG9xepcuprTiny6G9l0Mb3faP9JJOMU0vVyJxO5yujdO3WeKckd/jNEaJcC2qIRhhQLU4RUARUAQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUUgPSOghGB6vjraNkXgKkPgcOxZWrX/CJ05f+EqO3M9XUVAEVAEFAFFQBFQBBQBRUARUAQUAUUg9RC4MkyGGa9vv9tBg+9eTN8Pa0AD+pRIPQSv4po+mr+SHpz4N/0+6DrqVLFUspH4dtl6GjxmKn3frz0NCFDa8pXHqU7TaY5HCxWMoT49itOD95SjKpVyOqZJy52R7qPrN56ihYuP0sABJdPytJJd94WLF+mtv5fSB/NW0IHTZ3zlVSuUl15p24h6VSvn25fclUj32aS2x6+v16yY1GI0XxIROHrsPH01fBs9/nBg7HO9OpROnjsfsob3OjXj/ruEcmTORJsfHRgyfaAEl7wHirw5jPJkjaF1D98YKGlY+3PzeZTNm4uW3d8/rHyuEl+SVvunbvXlOPp7+17/nbzVrFRRalyyML3YpoHBK1GCq3GHE4ZOsFrSRUVF+ZDq9f1kGrdmi29bVsrxNW9aqgg90bwu1SqSX3aHtcSzeeiiNdS7enkqlD1rWHkLcz9Oyj3RdtivNG3LblPX2oduoCoF8yaqF+3K9/pX5v7sWrk0TRzYNVGaYDuS2jYpM+qFT6hB8UK04J4+sivo8oeVG+mG0X+aNAefuY0KZMsSNH16OHj9qN9owrpt9FSLuvRmhyaJmrR07yGq98lourdhDfqkW0tz3I6rfTtRIS52uMEO9eCZuT6Jz8xwr6c0G+8IPFuXe5+t9m1JF2qZHJzSok77+SSnDW6u261jp9E3S9fRzicGUYlcOeiWsVNp+NL1tPvJwVQsZ3Z7c9J8exCPY0bweGbvU7dQkRzZkt2ePzbtpGyZMlLz0kWTXVa4BaRl3eG2NVh6eU+eeP5OyhmTKVjSFD92uWHqtj/b79MUBzKdVdDrxvk0bsIeOrG3G+XMcfnRa5dfiwN0gItOH9AB0uruyCAg4xNZJrdUvPDwss+eKfDD+qL3QpconpW6dCjsq/LU6XhateYEfTJ0C434fgctmNk63ZGCkeyj+/afpSr1/qRbbip12ROCA3igNGb1ZqpXrCA90LgmxURH09yd+3hQuIt682D3tXaN6NlW9X3XOjkr0ldlmZyykpP3Ylo3IDmNvwLyNm8/kw4djgtKCN55TTU6ZSEEf9uwg3adOEXdq5alwhZypEbhfFQuXy7KlCF8hXvz2kr4IayeuXCB8lAMXYpQHxFSM1Ll+V1+pzbyPnnW9WeyOxd/fOO8zl6IpwW79tN7c5bR7xu30z939absTKJe9RIAw0S4eNOBDLReS3mW9OSJk4JeoukMY73lyAkauXyD+f+8e2u6i/tzuPLklHn037nLqUOFkmETguHW5ZR+DBOdzzk8+2ds3eOKrHcqMy32gVQV+ZaJjEeb1ZbNdLuUfoXJuuurlDXkslNjrX0xqc9Bp3Jlnxvs5JkpeVJrKc9Wqc++LftTcpnUOiN5rZLaBrfXrSC/bzE2iI7yvGPl/eL06ExJrN2WnSdLZvO8zGCZuHGb157u17Vbqcd3v9Pw3m1TnRBMy7rtOCR3W55nl8zXSHJLS3r+yxHTeJ6AgwiGgc7efp8GSnel7hd+Ir0+l0LhfsUQgqFOVI+nfwT6sBYC/t1Ik4b56PMP6volxc342LOr6H8fb6JPv9xK779dy+/4lbRx4YJhES77UwLBAjIQ2ixz7+xFMRmjfee07uBRqvrB9/TcX/NpSNPaZobUd1BXFIFkIHD2rOcDJ1gR0PyzSv8f/6DRqzbR80xQXMPaQVYBYRKOJHCAns9T2Za7GstQH1/h1hfJ8nx1ez8UfdtY4a8hOY83OzTmgVyCBgc0uwb9PJV+4nv+t407qHcEtX/92nA5bThh6NQ9velEOxDLKMuX50usdVmzsL8m4KT12+mGn/6gu3+dQdVZ4xoamuFIWrtugCa1EyEIrbHLRTYfOW40Hh/kya4fV26ij9my4uEmtSg6Q4KWZ3o/l5t//otWPNA/JIE/j0n+SMqVgF0k8YhkWZG+VpFsm72stzs2IfxfLvJB1xaE/0gISNO0krSsO63OOaXrvZIxvdzu05S+1pdb+Vc0IfjzL7uZHNpMRQrH0Iih11DWrNF0/vxFeuf9jfTLxL3G3LJenTzUu3sxenJIJcqYMYp+HLOLPvyMZ6WfqEydvRpoyNOh+xxzbaf80owyZ/bMUv3+x3567Z319K+nq1CHtoVo6/ZYeuPd9TR/0VFaseq4URlt3aIAvfFSdapeNZfJP2nKPk6zgf73Vk366tvt9MfUA3Tw0Dlq06ogvfdGTSpfNmHwBC2wj7/YQuN/20dYb3dtIRp8Yyl6+c11fu2LdL1OnXjfqVhj0okZ+2NnzhktGczY2WXr0RP0xqwlNJ81QVbsO2xUs1uXLU5vtG/MA5J8Jvn4ddvobZ51fqZlPXr2r3/oSOw5oxlWiwczr81cTP/mgU07e8EutjPwB3av64sZQnDx0mO+HKGuORKO/20vvf3fjfTM45Xo2X+voSNH4+iBu8vRU49UoqHfbKPPvtpKS5YdI2gmNqiXl15+virVqOa5psjvpg6ks4u9j07+izVoPthEb71SnZo1ThjcSbp3X69BmTNloDsfXGqKGvPrHtq4+TR98WFdWr/xZMi8jRt4rkFq9Bn7uTptHzx91uzOz5otVjIQO2Eq9tF1LWnn8ZME34LZcnv62/l4vodnL6VfeOZ04e4DRrMQxMKTbNqU0aKlFck+i4E1+vX/ujSjrxavJaj8H4w9Q23KlaD3OjWl8vly+04P9X48fxWNX7eV9p2MpXblS9DgulXo5emLzOC2c6VSvrTWlZ+ZJPnf3BVUJGc2GsGzwVlZY9bN/RRO26z1YV2eR+hXX3I/n/D7PsqRPSP15PvoTX5uTZm6n/7z4SaaNvMg1aqR2+yT5yLyu+n3UkeoZ97mradp8F2L6Ya+Jej+uxLMxDduPkW33rOEbupfku69oyxt3xFLN92+iO65vSyNHruLZvx9iHp0K0bPPFaJ7nxgadD8dwwuTW2vm01btp1G86l5+1mmrhtuNZtJ/oE5CjQEf+zfwZSBGX08z2D2/gn3hWX7DpnZfZAbNQsXoGf+mMf9YxvFxcdTtypl6C02yYMZi4/fYUZt2JJ19MmCVYRBccPihVmrqA73t+J+bfyJCcoRyzaY8s/ywKFSfn6fVS9HDzau5XcvoOALolbBJbjJ1+/HKVSV78FrihXidqykxXsOUpk8OWkQ9+X7G9Yko/zgNFjhekRrCDPL1nqJogyZCkIQ97T12Ir9h+jVGYvpn537zTnCtPiF1tf4kVw7j5+if09bSH9u3skahxdM+6C9BILFeu+7Ob8+P0ym2mxKC1hA0sC89u2OTel5noAIdd7P83tr7o799DGbSiKtCAjPXt9NpgLZs9DXPduY3SHb4oThBRsjCLA5nSEBQQTKP7ed/wIKzGm/7d2OenJ/hKbX+JsSCEGQat8sWU9L9x40WqlVCuSlvjXK0yM8+QIs4Q5k1IoNpux+TIa35WcdPvi7jJjIGrLZaFgvz/lJ5R2+mUCl8uSgL3tcK7v8ltAiuv2XabTnRKzRkOvldzTxRr8aFQwBj+8Iq8lzLPuXxTMY54Znn11mbdtDr89cwlrmeylrxozUhK/rv7gfQQPdLvItgr6H75OBtSvT/Y1q+CWDOfYI1rT8i/tcLb538Z4JR6ClCcFkZ+4sMdzHF9FU1n63TyIEe2ag3qctzwxoKb/L751cMZl9TQl1PZEQZsB1ixYwff79ecvNM+m/fR+iypUr+8qxr9xYqyJ9t2Ijf6vNp/e7NLcf9tu2Pwf9DvJGuH3ALXZSD8xKP/xnJW08fIwalyhirhXev1Zxcz2T2set9ci6m+siad30R0kbbOmmTvu1Qv/DPdCQJ7s++GeF+baCOfRt9arSQ/w+Mc/7YJXaji3nd96L/Kyet2OfOYL78OU2Df3uZckS6rq9zuMCaOiPvbGTn6by6gNH6N4JM2nG1t1UlE2Hu1QqbcYaWSwTy1KHLHH/zd6+j15v34he5nVYouDZd0/D6nRr3apGg33E8vXm26tpyaL0ftfmVLlAHslObrB9hcudsnEnjR/YhfKxKfu/pi4wkwK/Derqd88+++c/NGvbXpoy+DpKGPn5qjLfAGgv5CXGEt8E02/r7q481r7HN+c/u/bRHfWr0cOTZlOhHFnNNcD4DG4ZgrVn9KrN5lnlV7dT1FVOkFxMD7KrIIMRX8e9J09Tff7mwHPYqiAidfynczN6fup88zwumI0txXDNeeIR7ilElvC3yucLV5sxAt7FN/Nz3WlCFG5NoAGPfrTh0DHTt+Ci6DUer+K6QVDvdG7Xy20bmjZizIF+0bNaWda6r+53X4QqD9cv0fUMgqnbesPBL6n9Hljg2wzP18V7Dph7ZlBdfl/KtyAfd7pPQ2GCSTKUiet9A79rrAILhbn8/JD7KFRZktdNunD6k5RrXYIP+PzrrYZLKpA/M918QykSDUFrur/nHqb/frSJVq89QRs2nSK4NgNP8dqL1ShfXs/7+9W319PseYfpP8wBPf/yGvpr+gEqWCCGunQsbPgjjMlSQ1KnltQ4E1sdEyfvo743LzDgD/2oriEDcbE69ZxrBrdIjgsIn3TPvbSGZs05TL+PbUrlymSnOf8cNnbgMvBdzBceg07IkuX8oeElVTAYRdoqlXIYUq9cjSkmDY6j7ClM7mCAjbx7N3eh7Nmiaf+BcyZPg1YzTFoQhiAif520l+YtOEI713UyhGNc3EXqM3CBSduiaX6qV7swgfz5bvROk2//wXNmCTIxYvWaEhP/gIDp8/0UmrNjL7VgHxZNeKCGl6LdTAAPpXL/GWkKwGDu5jqV+aW4w/ihwQt775O3mBlmECYo67qRk3yVHT1zlvaf9uw/xERLUuXPaQdM1tKlPH473FxzfOjs2++5Ltf1meer+ujR8/TDz7voLibfcBPjmu7ec8b0DfgJwLUCQei2Dl/B3hWnPrpn71lzzQ8fifNLvndfwv7SJbNR0cIeH0QguYsXy0KZMkWRm7woNKJ9xkuO+zU2jA2QJiBC8JLFB+kd9atSqzLFfT4+7IMzvNQ7fTvBfMygGvQxfHhCixAfVvBniesZ6T4rfbPBZz+bswPJnTE6A0H9Hx+8O58YTJl5O852r9QrWpDGrNlsBlTIiHKcZOL6bdT3hynmg2Roj9aGDHR7P7ltm1O98jxq0mammcBoyiQ0nlvvfbDRmOBjHX0czyk8x7r0nkt7NnamokWyuO73UkeoZ97p0xdM32/UIIFgQZtPnfLsb97EQ5Cfjo036fDsFcHLFuQk9oXKj7aL4N7Jnj1BK1X2h7ucwx+W1g9S9An0DQiIiJJMZv/OA5rZnA6DD/T3duVL8qTJIUNuYGD/Cn9wgtzBPwa0d7FWV8syxej6ymUNMQMSYWSfdoZARLmYVMEESg6+f9pzWSDXZ2/fY4g7EG0gyURAL4F8hLjN9w8TXvAhBsGHN57pf27aRU9MnmsIo9vqVeFC/Z9TJjHXI8Tm+YsXffXi2MbDx2noYo/5JN4l0iaYgHZnsgICvCoywf4jk52o/ze+p5vxYBJkGwbqm7iMpiWLGN9dIOZB4EHTGKQqxO35zWTiyEooYQD3WbfM5Oa8y7KJ+HtzltMPTJQ817q+qRc/GGSBrITfPpybq7Y4YXgevcAqlygq83kfEZiBCTvzD6C9Se05JHdnr3/fpXs83zDYjwErBmB49nauWNr4bsU7Gv0SgvZjkiZ3TIx5z+P65/UOkNCPrQSdycA/OG8n0g3HQeKBjEIdIBx7Vk0g/CW/fdm/pocQHMuEnLU+fFNABrA5uvX6YR/Sws0EBEQW7gNo9eJehJ9BkIgiW3jyEn0OpD0GoGgbBjxZM0UbEgTpcK4gToBT3+oVaM3BI75+KuUEW6LPYkIA/hehnYklBiIg+u2EoJtnBu5zvO9gRpuHyUXRmnJzPdFOnKPc09jG+Y0oUACrAQUDYeACH789mIi8lt99gcT+HLSmC7cPhIMd6sGAHj6sMGnSu1p543MOfXLsDZ148O7pb26vZ1L6uPVcZd3tdUF6N/1Ryg22dFun/VpZ+x/6KfzVAYchv802k7X3NKgerFq/Y39t3kXtvxlv9sHXJgg1kLu4D2fe3sO81ySDm+uG9wbGDOfYFYJVOg6fYO4pvCfxLEBZmGj6lidUA8kGLmvKph1mwgDPtzpFChh/twvHHSBMyuKc5Z2NdDf+9CctvrevKc4ttrBuQXsxaUXs/mONdxvfpVbx7efnhJPgmwLPYfQN+FoUf4S+fCHKW8v14nzwD8F7EmbMbvIHqtupncnBdA8TgHU/Hm3eQVAywXNWvofhuxQ+TCFSR+Mv9pp3EibW8T0Fv+DwPS6TsZg0bP31LyYNJpXwDfIME692sfZRlAXtelgrfcZE4nZWQvjt5utMFtSL9/q0LeNMv4DPVCgj3DN+pnGDIt9ZbsoLF1M39YaLHybKwu33gh3erZX4Xr6B370g6DFpCNckIGkh9vvUDSZ1eIIK90r8pYt+hOBRVjyC4ge+O0HOuikLbXCbzm1/Qpl22bnrDLXu/Ded5LFJv17FeexxiZ55cbU9mSH22l/vUSYDn1Szei7D40DBaPvOWPptTFOTB0QhxlqNefyCMpF2NhOJH32+hQ4w1/Pj8IaJyk6JHVckIQh2tVvfeYbEmTm5hc+X3HejdxkysH2bQjRmVCMzAIbp5U23L2RNk92G6OnetajZD4IOWlcQaMeIgDgE4Rcff8lcWGjNlGKC5l3WOoRAS+X1f3tenCCK0GnAEM9feMRoAUo5IJjmz2hNZZi4QhvadPWkmzv/iBl8/5fNXjG4feyhivTuazVMtn89U5VqN55qOoyUM3yU50EfkXo7S6n+S8wW4obFh7zc+E+3qGfMOa0p4eQXAs2/13mGBQICp/VXv5gX7vxdB/w0XPCRveKBAfxwymzGNNCociunmDyAphAEA6GjrNEHzUyYC0N6ditqlm6uOdh6ETgCXTG/LeXOBW2dS3TbvUvMIblW2HjlrXX0r1fXGu2qu28ryyRt6H5lrQNlBOqjOBZKoJn42ft1qGSVycaP4rDPPANSaJu6kYj2GSaKkiMwm5p5e0/qxB92+FgUEgWEGwayGBSWZq0kEWgq4CWJgdEY/tBHH8LA4aaf/jIDv3Frt5ggJCnVZ/GhPJ8dyUNTCvW2+fpX07fn8v2BNmO2EffKY6zNBQ0OyL+uvYZqf/xjIgJdzgkvsG4jfzMftvhYFif64d5Podom9Tkt0e/XLm7H5HJWo91cu8k084J69V/VjJYsJi3uf3S58dGJ+6zHdUXD7vehnnlO7Qq1b+LPTahd60KG5IZWbyjJxNq1eLkuWvIH4RmS0i9aaLhhQA3epv/oKayZt5m2HztJWx+7mYqzRgPWXz+emV545x3Knt9DeJ58+YtEp+GZZvHf/Spv4j+UXHplqF8St/kO2PL5FRJsg/PNe8U5AZ5UGz9JfOx63nXJdpI/JE5GG1/7ymGv/y6353cywPkdeO9b/wIdtu7nffi3Sw/ecWlUwl63bUnI4V3L5RlMJ9rv3YF307lz5/gjGubZeAOy8AJrPB/iJ9C8xkALg0GQMhB8aEM2DrmJCnud3oNwbjdsPD+DtxlCEJr6+3nyDgOkj7u18NOCNpld/mAQD21MIQNhiu9G2wiEBJ5pcM7/4rUNfHm+58lI7G/FJIBVjp+NM8EGsA9BHoRExICt2dCxZgC3YciNZrJF8kHbT4hkaI6B/MNAB1pRwAr3LATfKXjm43sGA0GrXzspy2mJZzsGq/CBi3cdnu0gSPCew71vfbdJ/kDPjB2PDzITCxjwlnr3W5q0gS0tWGMT7XRzPaV8LFEH/nfwwDe/97ljPW5dz8Y+nb/tw/7Lho4z5v6rHuRvNvaRFo4kpQ8kBbvn+ZwwsQLBZCIm8B7nCQxoYWOyLrnXM5xzTsp1CdYf3dSdlDrt5eJee54nOaAlLIPr75jMc0sIgvS6f+IsUyxIFbGIgIY7gtA8OOlvWnJvPz+T+WDXDe0IJCDZ/7r1eoJGIBQNir71DZM1Wzh5YEJQysIkwJ+3dDPnCYuOB7jNIM5QHrSh0V/qfvIjQdsMk7PwdxvufSZ1JXUJjW1DlLCfbTzHBvEEeFIE38vQrgRZWpxdeHy1ZG3IYpJSd7iYwuccJqbwjMTzCN/KuN5oZ0O+d6EVjYkdTKiK4L3wfd8O5hm0HtDTKAAAQABJREFUly1wKr8/ynz7f8Ma67CqgcYolFWsEwEgo+H2wCr/mbvMbFqDYh47e47y8jcG+gF8Hls1TTHRgP6M9yksPfBsAVmOSaYKPIHpprykYBqq3nDxS8o1EtzQlkmMAXCRa4QxD3y/w+2TXdxggskCkH6YdALxLeX86uUDbmGLFIibstAut+mkrUH7kySyLe8dsszwMGO/a8Rcg+c7ZOQPO+nmOxf5pfzPRx4+AspmndoXNseOHT9PeUtMJFiYnj3LfSxLgkJCc1b+QmBc8A9QAqpc90/DTX3zWTxlTbgF/OqI5EbgJ20ka0nFsqAhBkYWA1wrGYgmwOwS8sj9FcxxrGOge9+d5bBKE9hsNDo6ypicgZVFFFfI5D/3U7UqOU0eIQehLgomt08PT2fozdFtJ/zEsxmPVjJ58AMtv9o1PaaEGHxaBXWCDISgDV07FTHrMqidPuuQ2X7hqYQXQNnS2RI5wY90vaZS2w/UbyEg+kTwYXsfz5RYBaZqEwZ24RmdhHR4qdbmGTjIqbjz1uQmPz6yoXkg6tl+CYJs4GYqU32K+S/Ly3rNp5PcpDD1RbRhiJtrbq1GrkvePJmMOi9UgSFv/WcDrVx9wgzsYUZ8av/1BDIQEm4dwfqoKTCFf1Kjz4RzCjBhwiDjw+taGKIPeTF4fIo/Bsq8N8JPowEzuBCQ0yADIfiAuM9r5iXaDynWZ7ke9Fmpt2vlMmb9CM9mQaYzWQnBx40IzG4eb+ZsZgaNK8yk41ysZCDyhn0/hWibtMdpiX4PMhBSs3pu3/MRz0o8nyDw2wnBsxESbr+Xewt5nZ552B+OwN1D145FKCYmg9FiDCdvaqUFMQwBASKaNXezpgX86oFcKMwmPO+8/TblDTEoT632aj2XBwIwGc6cOTNrQidozPiIQYdTgPYyBAOeaM6LQdAc9tkqZCCOCTl10kljEQmSIHjn38AaNhhgwYeeWzIQVeG5flPtSmaQsITNmiFoP0j1gXUq+REKOAbNOQwEH2hU00cGYj9MwaFJDkJ0xf4ErWIcs/on7OjVpFzpTbOetY1QHt418syHdg2IUrfytXfgjUGtyG2sBQ+RY7Jflk7PDJgzyqAYSxC8IAYhSbmeqCMT9wmrqwup32kJ4gV+fIHhE1PmOiUJuC+pfUDwCQc76zcq/LxiAI5BJjRXInE9A56kw4GkXJdg/dGhikS7klKnvRBoZOHeg7Qu6xnf7GfCxq3g/oGmHohvIQORF995mDSFC4Ctx074FRfsuvkltG0ALyFtoD2H8nHPYiIjlDzJWtByni3LeBQI0M9BBkLwzGzCmugQaN9HAltTWBr84BkG7TQ84+WcU6IZ4WLKn0DGQgJtAeEpbYNW5C2wQGDBRJVV8C0tExJF2b0OzHchJ87FmW8qaIpC61u0gnHsplqVjGYb1kVea9fYjFc7eZ/72I/2iJb7adt49SU2dxe3RrDowLlCFnm16sMtz2R28ROs3qTgF+41sjbxlbaNfPcbrtFjzT3ft1N54stJ3GKCCTjIz/xuF8FEIKS3N66A27LcppN6gvUnSWNdQtELbpDACQkZiONwaVSpgj9r9xorVIAXEjIQ6Uwf47ELBNZOVnmcFcBABkJgyQSLLciJk/78kdmZAj8ZU6DMNC3y6xHbTf0g6+x212vXnzTH+g9eYEyIpaFnznguyroNng8skHNfDNtG02cdNOaY0PB74uGKtGbdSdMRzp27yL61PNpYGJBCQNbFsPnk519vo8VLjxpzO6QXgUahVcSkVfZBewYCEhEyl7UDYaonncPs5B+onFol0vVay5b1xbsPGsICszlWgaqvVUB84IEJ3w3wN7XqwGFac+CoL4lEKpIdFdnfVVIF2HT2Mu4oAwQeMAVpUbd2Qrlur7m0o6LthoYvM2iPQsUX/6gXZOOgG0r66gm3jmB9VNqRksvU6DPhth99CwM5/GMAAb8RX7BZFFT4YWq25L5+5oMSJhAQzPTDP5SIOOpdd8hzPKX6rAyapV5oqkDE9BHq+BisyQeLpKvJkWidRAY9+IiF6adVwr2fQrXNWrZ9vQw/v0RAXsF/BSQbuzkQyZnTg7f4hwu334d65kk9bpd4IadnAclr7Qf5smYxzcUMPT4KoN11jn3FFQhhspeez1HblnYIwGQY/Qd9ySfYwA1sEWgygxSBwPcfNNUw0AVJ9+jvcwyRtmr/EaOlgTRIHymB5gc07yBZ+Hlta1rIauBDChoImAiqz6bkMuEDszK7bGAze4gM6KzH6/G3CgYYMDdvVMIzU49nt9UHXzbWLsGzOzbO8/ENv1IQ8X0s5RVjzV6ZjJJ9TkucO8hLyEfsI0lkr5eswHk927K+b5CJ44GeGWiXVXJlyUTHWUMUgm+ucK6nvQ5rucHWoXmHARu0I2HyDV+3biQpfSAp2IGAwjW0So1C+ekn2mzM2o4ymQxJ6vW0lutmPdzrEqo/pkSd9jLRBmhZiYCgwb4LPLHvVuRZA/cDhd8c5pcN1xWy7ehJo1WF9VDXzeqjFemtUiG/R+FC9pVnVw6YTEaU9VAiBDvSybu5NPtAtYo8HzDZEu71tJaT1uuIKJ0aEi6m0OoUqcYBBK0ifQUmqVaBn1qrwC8iBKbB0LqGwFWPVfDeuYbfHyCqRUBQQ+DHcRU/61fzO1D6Lvbj+8wqonEu+6RfLmeCG+4rwi1Pygm1DFYv3CuJuMUv3Gsk5WNpD1pWjRWDIPBz7SRuMcF7Hi5yhi9bZ/y+YuIJlmAgdQsw8QhxW5bbdNLeYP1J0liX23d6nmFQSLCK6WMcXwDmvyIeLuIYxyVYbTih1WtP+vyXI42dF4K1qVWEF4I7pNSQhCd/atSWCnVAM3DggJImyux9jyyj8aOb+GqFqiYEQUSgCWiXwoU8He/alp6HCUww4VMQ0opNI4sVzWIIQfgURBAKXKw6tTwvJJjRNb52hkmLHwQJgZnoNjZrhSqpXbJm8czA2fdjG88hEJq5c/sTBDgGzQCrRLJea7nWdXx4WskXOYYZJ6vAUW3jz8f4dkG9uBf7ctnGgxFxDu07yCt5wjQ7seYF8Scm3db99nW311zy5bFhjht6zaJ2NIKvIfwGLlzsMUuGaTLMznGNw60jWB+VdtjeRXTGRVRUt3lTo89IW0ItMWCCU3tolQp5gn4F/0r4hzo8/LZMZr9ReNAjoA0EPoKcojSKxktK9Vmn+0DO0dy3TOzBP4dd7PetHMfgDI7sP2WfUvdNmMVO/7vIIROYJ5z7KVjbfIUGWAGhbhf4pwwm4fb7YM88az32vn/OO0liTYP1vHk82rv2/W7z2/NFeht+15wE/RYDDEyQ4CNWRRFIKgLoPzJskaW9rM3eARVMcdD34nmGG5GHhayCTyBox0GT7l42h3UjdtIQZQaSO6+pZkxkYW4HrWch5AKlt+5HWhASIzlwzmvtGtGo5RsNaYf9e0+dtiY15Ch2ZORztEumaM+zLLN3ieP27xd7HomwDK1Auzg94+1pvmf3FiIwu7YLJoEQRAJabCLBnhmSxr4M93oi+EJSBHgNZ99sbYf9akyzR/Vt77qYcPtAUrCzXltpGLQgIbiG4V7PcPq41GddhntdQvVHa9mB1sOt015OJNoglkB4rsBPrJMI0YZjoa6bU37ZZyeAZb+bpfhEtaaN9mpGWvfJenKxRTn2p+TZ86GJS6nfaem2PPm2tpfhNr89X6DtcDE9fd4zJsd3MMz6nQT9yCrBvnNh5gtx+t629234HoQPPAjeMY3YbPV+VkgAKQUtVrtgQssq8l6ApQck3PKsZQVbD1ZvUvAL9xpZ2ybauLJPnq+ybV+6xQRtkiBi0DCGqwKI1UTebVlu00lbg/UnSWNdwswX4tjHsvuPmeAD8MHHl5v04IsaXZPXBDEc/t0O45rJHLD8uB0jWbJEdNW/h0e06LQp7IN3a9NAVt0E6YGAHt//tMuYAKM10CiBudugG0uRkH7YD42/lauPU8kSHnYWhA2cOoIAqlDO8/GEiK/FvM7oEWgCxBBMUxHZFgIGGAKS6taBpX1mdg8/ucLsD2fch+9P+CZEpGKoiubyauagINHKMYXyTyTrlTLtS3EuDBVq+NEQkdkY2cZMC+SL7q3pVlb3FvXvh9nPAkQenGYjlX7cXvNAzQGhe/hwnPENCT+NiPaMKNSvczRpkIIgBMOtI1gfFfNM0RSVdm31RkWVbael27yp0Wec2ue0D7PIGChiFgyzbHbBIBUCHy6QahwF7wDPACO6lZhgYj98FOFFIrNfadFnzX3L54GPCZgvWD96RbMRbbXKB11bMCFYyZB/0H7BQEgibaW3+8lOtIXb763n7bQukzT22bBtHL3djSQ3v5s6IpUGWIIKtA86I1W+lnN1IID+4zMV5j7lGZrwx6rl9MXvVR8v8TR35z5DBkKTDhMQ0FiFwMwOhKA18rOlGN8qBm/2NHZyThJjoPX59a3pOnatgAAet42bZvyHQdvGjYDARIR2nAM0GuHkH/74HDg6n1mvVctD6tjk1WCwa4fJcaelaGZss5k3wkcaNBhgrhZMEDgEAn+hJW0afpM2bDd4fMj+ma2EYLDyAh1L7vUMVK7TfkzywpccCE4E8HAjSekDScHO/j2KtolGELTn82XzTNS5uZ7h9PFAGKTmdZE2pEWdUrcsYdkAKcz3vj3iOO5DaBuWY59rIqGum6RL62VysIW5MUQsSeRcEDwsKZLc8pKbPyltdsojz0VMjsA/vfWb+RD7EdzNz9lyeRP6ilMZ1n0VvRqjdq1CpNnKWqkiIA6FDJx3V2/jw06Ofb7IM5a3j1d3sr9Vq4sF0YpDUJqklCf1hVoGqzfS+IVqC957cn8jrW+y0XI/SxnhYgLyDwHAMA6CH188g8Wc221ZbtNJG5OyrFjeo6G6Zav/hCTK2moZq4A4FDJw3rRWvmC0SIfoxBCYH6cnCaymlp5aGUZbsrA/KRAjX37kse+/+6GlJrIvimjR1KMi/C2zs1Z5k/3DIQLmO97AIDh2fRfPzBb2Naifl6A5BpIOZOGHn2022bt1Tpj9WrXGo9aMoCRCzIA8Gjt+j0lrH+SanUF++vb0qALDd50IygMJZZVI12stW9Ybl/CQMp8vXCO7zAPQ7lgbZkeQ7hyJTshAqISPXb3F7E8LbZhwrrlppOUHg/auHFX1mpbTfQFMinB033tuL2tSnWUiGRJuHcH6qJiIw0xd5BATkohIbBUhomO95u445jZvavQZtAcPRDHHx7aTiI+ZhyfN9kW4lHRwEv+/uR5CXSIyyozzt94ANpL2zb+XGCe/78xeZnalVZ/t6/V38RZHgBXBPfA/Ng9zEsy44V6RD+e7x8/w+b9Jj/eT9RzC7ffWvE7ruXJ6JhsQuAmBliBQqccEjBsJJz+e0aH6pps6k5rGnB3/eM4yqaVovqsdgWD9B2QhnOV/tXitgQnaDxAZhCIyp5CB2C9BjKzmgfIeP2PRZIEWG4gW8WGHvKNXer6JsG4VaGVgLHw9a3/AFxBciOBZHY708kaIhVYjpJ9Fo85ajrgwQVRf+BoUAdEpz99KBdwPLmvw5BPkswWrzQSPlOdk7SDHZAkH6cAIPs3gfxDEpvUfAx0MeODrVp7zkjfcZTjXM9yyndIj8idIPgwO3Ui4fSCp2KE9iDougu1vlq4zbYUpaTjXM5w+LvXZl6l9XVB/WtRpP28xK0TfFiIeaWACiqAiVdk0VCZ4sT/UdUOa9CDJwVZ8pCOSswgsqpwmL+S4LKOjPMN0BIwRSU55KMNtfqe6pQ2RWGJiCAElIIjcKwIyru8PU6gORx+GCbhbwfsKfiAR2A/KBiKI+oyI4yJCQsPXoNSPY8gnkwj2yVrreBfHoIkGgeVSOOWFi2mweiONnzmhID9fer8lkAQTY+9z5HlIU6+vTbPh/QkHE2TBGA/vRJSJ6zC4ThWfv0K3ZblNZ21nuOsYO0BBDEFfoRgmsm7DSUJ8ABExLYbiBALRiiCfmBXLOEeOpfXyitMQFEBh6ongD2/9dwM99MRyE03y0QcrGELtm1EeQhBmvfMWHDHmxVDnfPGZKpLdzwlkx7aFzH5ooMA55E/jPA+oFhwRRgQmrIhMfMs9i2kwayDu23/OROTctduj2XT4SJwkdbWE9iFYZGiiLVxylMqXzW7IRXHoL4VEul4p17p8qElNGrViAz02eQ7tPx1rfLDAxNH+MmvC2lyInnfL2KnmZgYRgple+XA8HJvwkW4tPyXXw7nm9nZgIIOI0T0G/EMdus+hW1jzEyq9MB+GSOCSpNbh1EelT4F0hq+2YkWz0jcjt5P0I2mjmJPDv2G+vMvooXvLMzHp6Y+h8qZGn0E7sxYcb5obe+B6P5+dcg5YguC7nZ2sY9CKqGJ4OcP/B2YH8SGAj0hESMPACvJos9r0v3nLzYc+tqGxMI8HX+iPGKQgQh4krfosom2BOH995mJayFG1MRAZyz60xB+KaZzDDz4q4MwbROJDTI7+2L8Dpcf7ydr0pPZ7axnWdfjnhFNeaEbjnsPzefJf+/1estb09vVw8ufLm9m8lOFPFs6Ar+9nLy3lt6HZJf4YU742reFKRMDaf+78dQblzeLRgtp98pSf/97v+7WnUt5IjdBogHy1eA3BET9Miaex43Yxaz0Ye8a4LcH7T8yLEFm3B0/0Pc5OxLtUKm0GQ9eNnEQ3s7sD+G0V0jEYxh90bW58wv572kLjg65WsMSWYw1LFDJmwviOgPmYBCmzJDGrIDehPYhnbyN2XYLAGTDLf32mh4CE5YIQnPa8TtuwhkAe+DZq8eU4GtKktonKi/aHkuFMREFu5uAnToKABfc0qGE0H4fydXi/S3OnZK72hXM9XRUYIhHMD7/p1Za6jJgYImXiw276QHKw6zh8Ar3evjHB4f0r0xeZBrzbqanx/xZD0a6vZ1L7uPWMU/u6oO60qNN6zlgH2QTSGBFim30xliPHNjCa8PCXDE0wBI+zTkQgT7DrhuPpQZKDbdvyJTxatawhDX91MF8XLdhQ5ybP4NdmLDZ+7t5jTbrklIf63OZPVPeTcSaYVag2h3McgaYQBX7wmKnG1yd8uuGbGd//8CEXyJQ4UB3oXyCe4doAQTDwrLU/szFJA/IJE1QPcdRrWBsh4NR7czwKBSj7CI9Xrb5b8W1+mv3L4n00gl1YYOIC0bERYBMWSm7LCxfTYPWinZHGD2UGErxbT7CiRv3iBc0EIiJwIyBIDQcf6eFiDPPju66p7rsGYimFtrgtCxGL3V6HQOfoZv+H79YygUzbXjebXnmhGt8T3MdeX+uXFUFjoUCGWBIPPbGCrVIL8NjmBL33QYIrEQSRxbglvcgVoyHotdz1w/WFp6sYsEGaICottPyW/9OW2rcpRCAFB9212Pj3u7FfSRMJBhdPBBcTzC6kVXPPBzTW27YuiAVBg8/qY+v9t2vRLTeVotms2TXgloU05KkVJsIwIh1DpvDAFiKaXbI0O/lH7NHF7A1tXTL7WhrMZcJMeNSPO80A+aXnqpos2bN5TG4iXa+0x7rMw4OMSTd3pY4VStHb/FDEg3vX8dO+qMNebXjzUYsQ4bN5ZnDA6D9MSPbabEKJ6KkQmPtAeJxhRHwweDeNnxdZD7QU3KTOQOlkv9trLuXxBJOfQFMURDGYfJjaPvL0StrITkPxEHj8oQomrds63PRRRHod/W1D8yCB/wHUiQAPwz6rb+oSzHD9QXhDEOxk7vwjJkqsm7yp0WdMw7w/gq11n6yLdhxMZzHYw0sWH0pQHYdvGZBk6HviuwJ9cfn9/Q1JiNn/QdwXRy5fT4hEOGFgV/MyQNmR7rOCuyyl/XJuMuuHepfc15fN3CoTzIRBpLfhqHWIFAbJnsmjBWcvB8cQmRgfIDh3mMdhkOjmfpKyZImyIPa2efb6/8r9JEs5KlrOso2l3BuS1nW/93Z8ySdl2p95aO9PIxoaUhBRvB57dqV59k35pZlkMUvfedn8wLrNj0IQDR7Pe7wbZILHrxKHDfFNZsfZIanjLsknS8dEulMRCBMB6U+IdggtCPxjoIPnKZ4fC+/p4+eOAabCQ3u0JgT1wqC9349TzABsRJ92Jj0G7Yv3eGa64ZoBhCFm7F+e4SHCEGUXgzS4RkDEWTyvPrqupdHMCNZ0BOP49PpWJsmtPDAOJdbvhJtqVzTJb2b3CiJRvi8J2UP8nG1A73RsarQ87mYiD35ZEYADGtjwYxeuIA/IDQwOYe6MgSWi7QKTQHL27FkfudqDg28EEriKgHzAGhHi386eVq6tLOW4ldgM53oivzzHpKxAS6nT6bsFmv3oWxB5jgcqx7o/VB9IKnbwA4XJRXwHwNUGrj38CH/ds42JMC1tcHs93fZxDD6tYt0O97pYywlnPS3qtLfP2gYce4InDj7p1pKDe1ygBybOMmMB7H+ZA9PA5FzE7XWT9PLulz4p23JclvLskG3r0imvlCPHJH3C/igTrMjtc1PyyxKTKYikjGcrniEgeeAzDd+3EKdnmeSF2xxMfGNCBFppW9knu9vy5HzkXpYy3eZPVPfWBC0+KQtLqUfwwj5Zl2PYB0nY77lKqGP6bd3N++ql6Qvp9nHTzWQ6otJ/xOSeiJQj+WW/mD/LOWJyfSKPBWDaOoTdVeEdAFdD4pIIWEOzDmnwHPuQAz71+n6yuS4P86SPvKPs0Y3v4ncB8MeY459d+7iP1zXvG7QjnPLcYirnF6xepEkOfoKlYCt1Juz3XCPxrQmzbmAAdxEgbPEuxH1uF+QPBxPJLxHl8X6VCN845rYst+lQppyznCv2Qez9ybPX/xfKPBN/bkJlOcYEuB7EqwBfNKBPCZPQnD9bqyIN3M9BSafXjfMNafjwfeXp0//VMemmzjholjK2sr9LvS5wfbyRfysivxXFM8zBrE4iX2NKlHiqV9ilxnK45x27Yo3mXaZMNhYo7NISMsD3G+zIS5fMSlmyeEi7hKPu12bPO2wiDrdsVsBngozcL7yyhl59ez1NndjcEIRSYkTqzTFWiktYvnp/wjqvwYwT0drA2AeSOFYlxksLfluEyAmUNuD+5z9OfCgJ19laSHKv+aYtp821KM2RgOwPEaknuXVIOfAtgH6EYA/QZgokCD4DH5j582X2tclt3oj0GTTMqd84XSundLb+BYfU8P+BCIbQ+AsmMKHYwVHF4NcjmHPbVOuz3FiQ4ej/Lcuw6wB5yvP+Fzg4yqscHGXqrd3Nx12w87Ifi8j9hEJT4J6Stkaq30t5e/edJbhZsEfdkuOhlm7y4z6BOT7ur4x5xiUu0tY3EycIbw9etHjdwhwGPtjO8mAJfbP0u9+GV5CmVgS8CGx/fJAJxgTH4yB6MCjCR6XnMz40TBhkIg+ImmBymDW24cDfGoEUflL3now10UJhDhu2OD2PInTP4R7bcfwUB0OLJgk0FXb7LBnwlbz56HGDU6JABil4HpYmuFp1ez39CkvL9qdA3XimQsu1bB5PEB2/c/VuBL2elgzJ7uPespJ0XSztMKthYhWROpPZBuC85+Rp820a6hnj5rrZmxN0O0y8gpZlOxgK2/4//mEmSvY9dYvf80e+5aCtbX2WmuJDtBdWM5n4m9IaICRoebY2O226ze+r+9WhiYuJ0DMbBR/loIGHz5yl8uw3MNAYK3EDAu/ZfeK0IZMkUq1TSriXwPutTIDnBQhARKjf9cRgE/F2G485KvCYI9A7L1R50oZQmIZbL8qNNH7SVusS2pAY35dh0s7t+N4tJiBh2w0bbwKIQdvfSdyW5TadUx2+fU73pG1su3vPGYqJiaYC+QOP0xGAEVaiUDQTpS9fHW5XnMbPbvO6TJegEucyw5WSLBtrWFWpFJjUSup5QnW0ckX/sOhJKWs8mx/DfyGY5LtvK2sejvD9BjIQUr9uHr9iI1WvX6EOG3gZWV9IDkmMijZUd9ObJPeaS4CZYOeV3DqkbGhTwUw8lEDLKaetu7nNm1p9JtQ5WI/D35A4c7fud1rHwAzq+qEkNfssokbCAT5mGe9m9Xd81MBHFMhASH3WzglXYPKQHu8n63lEqt9LmUW9AZxkO9ylm/y4T+AqQkURuFoRsJpEBcMAJph2gQN4qxN4+/G03AbJGWzSMty24TmOQWB6F7fXM72fR3La5+Z97/Z6RqqPp8V1SYs67dcNONvNg+1pZNvNdZO0ab0Mhi0mI8R3Ym6vCwdpb3K+5ZxIreSUhza5ze9Ut5xTJJcwpxWT2kiU66bvwaoH/24EGmihvsXdlhcOpm7qRfsjjZ8TJmiLm3GXNW8oTBAMBFryL3ndcSCQWCAJVZbkc5tO0id1Cau+UAJrKvynd7lqCcH0fmHgrw6E4L1DltGLr62lHNkz0hZvpFmooUoAifR+Hto+ReBqQgBmVCAEEa3zRdYKBMEpvjZhohCKTL+asNJzVQQMAttZC/3YBKJs9dgBVV/WOGYT8cwl2bYoTA37i7EcyYgnzI5P5qnyXziE8z6iWtsVZEVAEVAEFAFFIMURgLsbkBrwGd2geCHXGlQp3jCtQBFIxwjU/vhHXzAXaAa6IXLT8elctk1TQjCdXjrYo+/f0oWmzzpIk//cT0ePnae7by9L7a8tSLBfV1EEFIH0hwCilu1/+laavmU3Td64w5jX380+c9pzBC34NlFRBBQBGwLn9xIdPk10aAbRyQVEWdgPS+5ORIUfZIcu+TgxjK05ojvszyQuM1RPCK4+vO4+znIQh71vEJ1eyv/7mRQ8z9Pl2fi4iiKgCCgCioAikPIIxLDfawQ9QmAMBFNRufwR6MJ+BouyCyO7n8yUPrO0qjelz8up/MF1KtNs9lPclv2tI3iXStogcGX4EEwb7LRWRUARUAQUAUXAFQLwH2h8CHLk0wvsPxAO9I8fP06lS5d2lf+KTbSqFdHcuUzkXWDv0bAxYxOMIuwHITcHgYDG4FmO3naGCb84ju5+8YwHhoxMFMaUY/KwMnOCbM56cBiTgCeJdvP/QSYDOQgUVWSXC91OXbGw4cS2b99OuXPnZn/FWdi/bUZ2Pp2B3RR4/Ahe0SeuJ6cIKAKKgCKgCCgCioAiEBEEVEMwIjBqIYqAIqAIKAKKgCIQPgKsyccKgEZKsmPmuHgmB88y4cdE4J6XnYuDViH+T83xHOcgLUbLMDubGZ/h/Ef5n3lBFUVAEVAEFAFFQBFQBBQBRUARCIyAEoKBsdEjioAioAgoAoqAIpCSCGRlf4ESpTYHa/vl4X9YBIcjyF+Wgz6AGDx2jHMyIZjFa04cpJyLrK0JrTq3Eio9NEChoaeiCCgCioAioAgoAoqAIqAIXA4IKCF4OVwlbaMioAgoAorAVYnA7r376I3/fpro3GM4ymvt6lWpb/culDUrk2jJlG9/GEvLV6+l9155zpS0eNlKLjcrVatcgU6eOkXPvPwO9e3RhVo1bZTMmmzZc9QgysqafdAMzMTL5PBpyMuR1+lIHAcpcY7+Fh8fT2MmTKbFy1bR6dhYKlOqBN05aAAH6sppa1jC5oZNW2nClKm0dftOyp4tG3Vq25KubdHEl+DI0WOmzJVr1lM0E4x1a1Wngf16OJKNBw8foVfe+ZDatGxKPbq095WhK4qAIqAIKAKKgCKgCCgCikBqI+B+ajy1W6b1KQKKgCKgCCgCVzkCJpYGY1C2dElq16qZ+W/ZpCHlyZ2LFixZTl+PGh0RhAoVzE8lihU1ZR07foKGffczB7OCth3H9sgQTTieK2dg0swkTMpPtppEhdhnYKkYouwJc5Q473g2Jca/YGAt3u+4HIB2Xmk2QS6SiYnB/LLXb/nXjDk0a+4CatqwHvW5vjPt2LWH3v1oqF8a68bZc+fo069H0qlTp+nm/j0ZoyKG/FuyfJUv2XAmU0EG9unemZo3aWCuy/djOHKyTaBB+MU33xM0DbGuoggoAoqAIqAIKAKKgCKgCKQlAglf32nZCq1bEVAEFAFFQBFQBAIiAK2zNhatNJBKr/3nY1q7YTPFnT9PmTMxCZYM6dS2FWu+tXIsIRtr2/3riYccjyV7ZyaOKlyc/8/vYe3ABPXA46eiacjHBahWufP0UO+jlDHan0A7cDQTPfBBfurR7Azd0O54QlaO9EiV8nJAkuaJmgZyb9Kf06le7RrU3audlyVLDI0c/Qtt3LKNKpYrkyjP5Kmz6DwHgXnwrsGUL28ealC3Fj336rv018w5phxoD27eup1u6tOdmjDJCDl+4iTNX7yMtTc7U+bM7BfRK7/9OYP27j8gm7pUBBQBRUARUAQUAUVAEVAE0hQBJQTTFH6tXBFQBBQBRUARCB8B+L4rV7oU7T9wiI4cOUZFChekLdt20I/jJhHMjDNx1NmKFcrSjb2vN9qEqGHmnPkEUgqmsjB9rc/EWM9uHU3aCZOn0ur1G2nI3bfSW+9/Zho0duIUNpPdRT2v62DMlnt27UCH2TwWGnbPPnIfgSiExMXFMTn5CTVpUNeQiiC9Rv30q9G+gwlt5Yrl6Ka+3SlnDtYEtEtG1uTLmIcjA3OQEIvMX5eFlmzKRBt2Z6K7uh2jHFn9CcEJ87LTmh0Z6dCJnNS/zQnKYCUM4VMwW21LaZ7VAwcPG+28hvUSjoFoBSG4au0GR0Jw+45dRjsSZCAEuNevU5NmzP7HlLVt5y6zv14dNn32SqP6dQgahMAO5w7ZtXsv/f7XDOrWsa0xP/Ym1YUioAgoAoqAIqAIKAKKgCKQZghcGYTgq/enGYCXTcXPf3zZNFUbqggoAoqAIhAcAZj1LlnhMVstkD8vwTfd/z4bRpkyZaQu7a81WmrzFi4xRN6rzz9Gu/fso59+/Y1qVa9iiEBoxM2at4By5MjO6VsT/ODt33/Q5G/XujmNYzKwRtXK1LB+bYpnu13Ud+p0LFWuUI7G//4XLVy6glo18/gTXMzk1+EjR6kSHztx8pSpM2uWLNSV23GC/Q/+PW8hk4yf08vPPJLYr17cTiYDE2vNnYjl3Rc4RsgpDhx8LgMTghKK2IPLoeNsSsy7DhxlQvJ8BtYgZB+EVjk+hU2R/b8NDjFGkPz5POQe1mNYgw/kKc7PSYBrsSKF/A4VyMcaiCw414OHjpj8KEckv5c8PHbCU+YFPpHPvvmOihctQh3atFBCUIDSpSKgCCgCioAioAgoAopAmiJwZRCCaQqhVq4IKAKKgCKgCKQsAlPZRFX81oF8O8k+7SBdO7ShjExo/fbHdKOx9sg9t1GJ4h5fgCCyQAKCGBTCqiaTfDCZhZZbkUIFqWhhf7Irmk1uoTkIQrBS+TL8X9YQX3J2pUsWp7x5ctPsfxb5CEGsIyhHOfZziPpgznz3LTdQuTKlTDb4P/yGfRIuW7nG1C1lmWXsUnYUyOyeTXKy8iFzm5SN46VkjfEnA5E0Xy5o6xEVyE1sLp34OJ1ZYSuRY40w6QnJxsFSrAKz4RNs5uskIP0q2EyJJYgLrgHKtJoFoww5DtNhCDQtUc6j992uUYgNIvqjCCgCioAioAgoAoqAIpAeEFBCMD1cBW2DIqAIKAKKgCIQBIEo9q8Hsg5SskQxKpgvH9WpVc1n5rp7735C5GEhA5Gudo2qhqCDdmC3Tu2MmfCon3+l0b9MMqas8IdXic2Kw5XWrBk4btIfBI07tGn7zt1cfltTzE42jYWMZmJQ5Dz7OISgjSAj/cQQd/Ad6B/jrEGVOCpTJJ7qVYijmExsLmwshr0+BqMu0XWNY+nH6dlpQOtYJgax33tMCof6oE0yMz4QaOxZ5cKFeMqRnYOROAg0LqX9cvj8eU/+7GwynTlzJkOAyjEsJT3K3LiZNTHZxBom02J2bE2r64qAIqAIKAKKgCKgCCgCikBaIXDVEYLfLltPg8dMpe/7tacBNSu6xv3rJWvp9nHT6ecBHal39fKu82lCRUARUAQUAUUguQi0adnUL6iIvbwL8RfYbNb/lQ7NQQj2g5x66ekhNH/JMlqxah2tWb/J+M2D77zBN/S2Fxd0uzH7CgQhOHv+YmMui8RNG9Y3eRDgBFKlov97smY18mkMmgTyk6EkUe6bmPATko7bfOks5Y9ZSb++uoZTecjAS3vz0qVYz/lE5T9LxQqcpKnv7WYekIOpZLuWT7IYp2VS8RKbDht+MHGQlby5WZ2QBabP+b1mv4j2e+bsWSpYIL85Zv/JxX4PRRtTjsk2NCXz5cnD+dfLIbNE+ZCC+fPT1FlzzPrP438n/ItM/3se/bNwKb3176dkly4VAUVAEVAEFAFFQBFQBBSBVEXAf/SQqlWnTWXZeLa/RK4clD3MiIzIVyh7VsriHWClTeu1VkVAEVAEFAFFIDEChQsWoJUH1xutvQL585kEq9Z6iKoypYrTitXraNGylTSwXw9q1bSR0ZKDXz/4/wtECIIscxIEJKlWpSItXLLcmMDCr2BO9kUIKcpmyAigAbPjkl7T5X3sm3DKtFk+s2W/Mo+NYpU6aBVyXVHQEmQtyOjC/M/lRYEcZIIxLoriPm/O6fgYawdG5YijzE+yj0AEEgEheJHNp09O5LS8BCFoygIr+Ab/J0jxolwuy8o16wmmz5DNW3eYZeFCBczS/gNtTJg6I9IwfA1CVq5ZZ84XWpslS3jMsxFpuHzZ0p7jXtwLFcxviFKpyxzkn0ls3l2iWBFCQBMVRUARUAQUAUVAEVAEFAFFIK0QuOoIwT6s3Yf/cAXahOFoFIZbvqZXBBQBRUARUASSikD7a1sYouvTYaOoe+f27LPuJE2YPM2QWAj2AX924oOwXatmJojGkWPHCESiXTJ7J8yWMhFWpFAhE2XXnqZ1s8b0yVcjzO5+Pbr6DrflshFw5OuRo40ZcXbWTPzu5/HGR1+f7p196RJWmPCL5iAfMWxKnAk+B9nU15B6IPaY1DvHWoLMA2YoEEsX9+TkY1GUodJhQwxSFB/I2piP8zlkKsP72CT4EmvnxW3mfOsSqvCuwWS3aqXy9Mf0v6kUE33wezj8hzEmCvM17FMRgnOGz8V+3bsQiNVObVsa3IaN+smcD7Qrd+zaQzf372nSw+wafhO/+X4M3TloAB05dpz+nD7b+GhE+Qjign+rgBCsyL4Z23PwFhVFQBFQBBQBRUARUAQUAUUgrRC4YgjBSeu30xuzltC/2zSgLxatpskbd1DF/Hmoe5Wy9FCTmpSHnYZDft+wg16budika1e+BEm+/3VpRl8tXkt/bNpJB2PPUJtyJei9Tk2pfD6PidHE9dvozVlL6dV2Dal1WY9mwdajJ0yd83ftpxX7DlPOmEzm2BvtG1P1Qh4NDSn/XS7ry8VraMK6bZSDfQ71rFaO3uzQmKZs3En/mbuMpm3ZTbWK5Kc32zehzpUwKPJIOHWEOgcpU5eKgCKgCCgClwcCrIRmxLsI2GgE9ABJ9cOYCfTF8O9NOmioPXLfbYb4AjmF6MN/c2RhIQYLMuGF4B8QaLtliPb48cvGvvEQTGT9xi0E7b6nh9xj0ljd9IFYQyThi+yrr0bVSp7j/AvNt9sH9qORP/1CXzOJBilWpDDdOfgG48PQ7LD+ZG3EZGA9orPLWMtvEp2LO0Vn4zJQTMaLTGbGc7u4bdys6O7L6eIP9Sm69DGK7rHIuBS8FB9PF0/MonMXoondAFLObJcoQ2bW0svehv+vtdbiWx/YrycTmSN9GCGoCnAT/4x72M/hmnUbKbbDWZMHbe/XsyuNHjfJaFlCSxDRlRvVr2OOI9/9dwyiT7nMtz/43OyDxuQNvbr56nRaCXU9nfLoPkVAEVAEFAFFQBFQBBQBRSCSCESxSRDb3Fzm8ur9JD7+cCYw7R1YpxIt23vIEG0g38YM6GQGFt8sXUe3jp3m8yFozYe8IPv2nDxNGw4dM+XsfGIwZeZBEsi8O3+ZQaP7d6S+NcrTwdNnqNCbw5CFGpcsbMjHKUxCHuD9IAb3PnkLZWfiz1o+9jctWZSmbPKYKHWsUMqsw4S5Qv7cNGPrblPeHs5bNGe2JNWBAhzP4cVPTdn6owgoAoqAIpD6COBVi39E4EVQi7Pst+748eNUujQTWBEWRL5FJF1Ez3WSY8dPGPNdiYbrlAb7zp47Z4gyMZUNlC7QftQDbUMQjAFl18tER0dzpOFDTC4SzViWjbbE3UXxZzbSuaOLqFyRM1S+2HmOJnyRMvMUZhSbDJ88k4H2Hs5AG3fF8Du3EOUq1Jg1+7bSu7f8zfXhk4Y1BzOXJ6o2K2C18POHa5ErZ46AaawHkPYw45qftQwzILyxgwB3aERKRGeHJBHdtX37dsrNfhGzMDELf5FoF4hd/KsoAoqAIqAIKAKKgCKgCCgCoRC4YjQE5URBui29vx8Vy5mdB19Et46bSsOXrqdRKzbQwNoJWgySXpYgEeff04fK5MlJF/jDv83Xv9Lf2/fS3B17fRqBkhZLlAl5pmU9ep01AiEXucLWX/1i8s3fdYC1DD2ahDiGdq196EYqniu70Sas/fGPhgx8tV0jeqpFXcrIH/L3T5hFnyxYRdA47FG1bNh1BD0HNEJFEVAEFAFF4IpHIFQ02zy5c7nCIEuMM6HoKjMnclVPDPvgu3jCFAkaCxGFs2YpSh373UOHDx+mLVs20y8rVtCOHTs4eMcZky5fnnxUpUplqtK4CjVlQjVz5sy0dccTZtLPJCBWF4zf41kN8BsoqnCA5IZsg0ZlMAmFe7C8ekwRUAQUAUVAEVAEFAFFQBFIbQSuOELw6Rb1DBkIIDFJ/kzL+oZYm8PkXjBC8L5GNQwZiHwg57pWLmOIvSNnzmFXIuldvRxVKZiHWpRGZEOPxMVfpNpFCph8p+LYL5JF7mtYw5CB2FWzcH5DEJ48d54eaVrb1If9TUoVMYQgtAwhYdcR5jmYSvRHEVAEFAFFQBFIKwTysGnt8UlEpxdwCy5StdJx9NWXI+nvuUtoy7btHLykMjVu3Jiu79adCrE/Q2hXbuP9Cxctoi+HfUsn2Vdi8WKFqEnJ5RSdAdqBLBmysKnAQ551/VUEFAFFQBFQBBQBRUARUAQUAUcErjhCsG7Rgn4nWj6fRxNi8Z6DfvvtG6VZM9Aq0LaDxLGPIicpmzcX+ziKps8XriaUverAYVpz4KgvaTxrGVqlDKcXAVFZ0GtChejFIjnZxBgiVtzh1hHuOUi9ulQEFAFFQBFQBNIEgUxFiEp/RrT1Foo6s5Ly5oynJ3ttpPs+OEz7j2agXbt20V/TZjDZx+aw/A+B+W48v5svsf/CjLwrV9Qq6tzzMGvx8cs1A2s15uvPL9k70uR0tFJFQBFQBBQBRUARUAQUAUXgckEggY26XFocop2ZMTqwCHvTMVvxsB8OIlnZ/044ArPexp+P8WWBeXCvauVpGwcaGbl8g2+/rOR18OUUqs5w6whVnrRFl4qAIqAIKAKKQLpBAKRgmaFEu54hOjGNKpc6R588fJg+GJuXZq3MSJcuxtN5/rcK3uxZmfu7rVMsDWx3nHLn4Em4DNmJijxKVOB2DzFozaDrioAioAgoAoqAIqAIKAKKgCLgh0B4LJhf1vS5sfvEab+G7TpxymxfU8xfc9AvURI2nv3zH5Pri+6t6dZ6VXxmvw//Ntvshz/BcMWeIyXqCLdNml4RUAQUAUVAEUhxBDKXICr7NdH+Dynq8CgmBffRhw/to407Y+iPRTlo1bZoOhPneUvmy5mBmlQ7Sx0bxDIReJ41A1m7PksVoqJPs7pgW26q/8RgirddK1AEFAFFQBFQBBQBRUARUAQuQwSuOELwo39W0k21KrF5kUcz8FMO0gFpUKJQRC/Pqv1HTHndOfgHfA5C9p2KpbGrt5j18zaTYbMzzJ/UqCPMJmlyRUARUAQUAUUgBRDgd3YUE3uFH+RoJNdT1MnpFH3kR6pceiNVLHWctQThhsM7bcZ+NzJEwYQ4M0WBAISJcLb6RJkKcBolA1Pg4miRioAioAgoAoqAIqAIKAJXIAJXHCG4cPcB6jpiEt1UuyL9s3O/CdLRumxxGlSHtQciKAgA8uvarXTL2Kk0mMsGGYgIwaKReDjWORhJOE1IjTrCaY+mVQQUAUVAEVAEIoJAPPvchYlvVGZLcSAFeTtLRc9/wbuM049oS4pEq5fi2KkgWwZE5010SHcoAoqAIqAIKAKKgCKgCCgCikBgBK44QnBAzYo0acM2mrJphznrntXK0VA2680c7dEayICIHhaRbVnKIUkWzVoITvJ+l+YEv4Bj1mym3zd46upbo7ypq9VXv5j67+eov1KuLKWsjN72yDaWkkaWya0j1DlY69Z1RUARUAQUAUUgVRC4sJ9oZSeijOwEsMitrOE3gAk9BN7yfz8HbgtrCsafIDryA6vmDyO6wBNwNSdzeYUDZ9EjioAioAgoAoqAIqAIKAKKgCLgh0AUR7T12uD47b+8Nl69n75espZuHzedfujXgUDMbeHgHsVzZqeslii+yTmpzzia8L3jZ9KYGzpx8JByvqLi4i/SVq4LEX6zcNThlJCI1PH8xynRNC1TEVAEFAFFwAUCeNXiHxFyL1y4QGfPnqXjx49T6dKlXeS+wpJwNGEa3YounT9BUaVzExWvzlGBbyLKP5BnxrLyyTpPxLEqIP+dITo8kujgKKLdq+nS9uNsacxkYr+ZHGWk5hUGVPDT2b59O+XOnZuyZMlCGTkwWgZEYuaZQPyrKAKKgCKgCCgCioAioAgoAqEQuOI0BHHC0LCrkI8HGREUkH6QPLZowdA8rFwgTwRrSlxUatSRuFbdowgoAoqAIqAIpAACUfzpAYL0RDxdWnuUMlxcRBS3mejkXKISr7GmH5v/xnNAMJB/EJCE0TlYE5DNjHc9x5GI/+LgI0fp4vqzvO8SReXjeU2UGURAxkaaKAO5CxJORRFQBBQBRUARUAQUAUVAEbgcEQj+BX05nlGE27xkz0Hq/O1EOnDaMzCpWxROy1UUAUVAEVAEFIGUR2Dn7r301vufUbEihemZR+71I7VOnDxFz77yDnXv0p7at24ekcacPHWa/po5h3p27ZDk8ubMX0zfjxlPrzz7KOXN4zA5F82TaHn584P5vahsrFl/4TyTgSe5vj+Jtu7zEIAXeHke+1gy5WSSsIiHIIR2IZ838iDvpZMXPGWhTAc5cvQYjZkwmVauWc/BxjJQ3VrVaWC/Hq6IvIOHj9Ar73xIbVo2pR6MscjW7TtpyrRZtGb9JsrEVgiN6tWhvj26+K7N5q3b6YvhbM5sk8EDelG1KuwfUUURUAQUAUVAEVAEFAFFQBFIBwhcMYRgjUL56ckWdalKQedBQVKxPnshnuoXL0g5M2em+9gnYN6s7PNIRRFQBBQBRUARSAUExKvHnn37adrf86gtk1MickyWsj85y5/H/05r1m1MFiHoiwYcqCEZCxEVLc7k32FOymbARfJ53QfyeuwyonM8AReThc2CmRBkvo+KMCEYt4b3saYgzGGz83sY/3uPeLZRFsp0kOE/jCUQeH26d6aDh47QdMYwOjqaburb3SF1wi5g+sU33xsTbyu+0Ar8bNh3JiGI2F1M2M6at4AjHkdR3+5dzP7N23bQ6dhYqla5gqd93mKzZYM5tIoioAgoAoqAIqAIKAKKgCKQPhC4MghB9o/XkPHEf6QFQ6/fIl2olqcIKAKKgCKgCISJwLiJU6h29SpUID8TaCkkVvIrhapgkoy1Aov1oagca1kbEIwfSwavD154Nd4VS1TaMvkWzzuxrxwIQW/a+HjOw5s5MpmyTJkoxyIbNm0laOvd1Kc7NWlYzxw5fuIkzV+8jMm7zpSZJ/oCyW9/zqC9+w8kOoy8IPvuvW0gVfdq+23ftZsWLV3pIwR37tpDWdmv332335wov+5QBBQBRUARUAQUAUVAEVAE0gsCVwYhmF7Q1HYoAoqAIqAIKAIpgED9OjVp8bKV9OXI0fTUQ3f7zFPtVa1Yvc6YyB4+ctSQUk0a1DUmxdCKW7ZqLf3862/0JOfPlZN98rEsWb6KxjLR+PSQe2jmnAW0bOUaoxX3/GvvGUILpr9lS5WgxZwuNvaMMbetVKEs/fTr77R+4xZDjsGP3jXcvv69rqOYICSbX1sL3EhRxT9gc18m9rhtPgHhl521A0+d4BWvfz6sY581VgbniarGFgFHOS+X5STbdu4yu+vVqeE73Kh+HXPOW7fvosoVy/n2W1eg9ff7XzOoW8e2NGHKVOshqlqpAt0xaIBH+4+PgEA9c4Z9GVpkB+cvVaKYOXaIr0P+vHlcmShbitBVRUARUAQUAUVAEVAEFAFFIMURUEIwxSHWChQBRUARUAQUgeQhUIO10TIyCQYNtVlzF1CrZo0SFbhq7Qb2Xfc9FS1ciPr16Mo+7jYaM+NTp2NpEPuvO336NB07foLioV3nlVOs7YZ9F9g9Rs1qlQmEIjTjunVqS3ly5aQDBw8bk1v4MCxSuCAVLliAvh75E23cso26driW8jHZBaJywZLl5ljHti2l6ODLTPmJynJ04TOsJWgVmATHMBF4kv0KShAvrOdgjUF79NwY1g4sW5V9DHJZDgIT4UwcfddKUoKcgxw7AcIxsSAC9GfffEfFixahDm1aJCIE8+TORXX4HzJn/iKjGQi/i8AbApNikLEgTx997lU6z+VBruvYhjq1bWXW9UcRUAQUAUVAEVAEFAFFQBFIDwgoIZgeroK2QRFQBBQBRUARCIFAv55dCaQfgmSAvIPWn1XGTpxsNNGeePBOYw7bsmlDGvHjOEMi9urW0ZrUcR1abYULFSAE4oAmnUhMTGZ66uG7TX0gvECK9e7Wia5t0cQkqV+7Bg159hXa42BiK2UkWsYzIXeeNfisWn+SKCvvvMSkoPCW2Xkd++yCXSgDZUUnDl6C87CbBWfNypqGLDAddhJoSyJYy6P33R5QCxP5zsXF0Y/jJhkCEKSjBE/Zd+CQKTYLk5l9u/ekM2fPmgAkE6dMo/z58lKDurWcqtV9ioAioAgoAoqAIqAIKAKKQKojoIRgqkOuFSoCioAioAgoAuEjAE23O27uT+9/Poy+HvUT3cmmq1Y5dJjD9rL859OvfbuPs/YfZO/+g7594a6UKFbURz7CPPjm/j1pyYrV9MPYiUabcM/e/aZIq+ZhyDpO/MXqdHF+yeI5pghdiqKoLFkpA/+zLS6nYf+B2XOTORTPDGDUJY4WbMmGMlBWgVstOz2rmTNnMoSd9cD586xtyJIjezbrbrO+cfM2o32JgCPQfAwmuBYfvPmiIU+/YjPuz1mr8KWnh3A06EL02vOPs7l2jI+MrFCuDL34xn9p9j+LlBAMBqoeUwQUAUVAEVAEFAFFQBFIVQSsn9WpWrFWpggoAoqAIqAIKALhIVCxfBlq1uga2rZjl4luK7mhuYf/7BzJtkrF8r7/RtfUpXb/b+8+wKwqr4WPrzO9DzBD7wxVCFVQEJEiCHYx9iimaGIM3msSv8Tvxpt2vfczft7E+HmjxlhiFzUaQEVEEUGUIiIKAiK9DH1mmN6+td4z+3CmF+YMB+b/Pp45u75779+e53GexXrfNXG8pCT55wy04+04rxUX+QNk3npN38HBMzv3vgcflaeef0XnENwi6Zr1ZtmHFihsVEGS3JV6qYqCIhUX/dXjnWXcHV3kzeWa7WfZf1aVN0kDd7ps22yfHVO5aR+ur8pbba1dmzYuQy94jw2fttY+rfow40VLlrl9Vmn5Z/fc6z62wSoT/+I397l99vyWQeg1CxxeOuN8t7pW52i0Zu8gOlqHM1c0G6ZsRUaya8lK9I7jGwEEEEAAAQQQQACBlhQgQ7AltbkWAggggAACJyhw5aXTda6/DbJg0ZJATxaQS05KlIKCQi2GMTmQ0bdGi4Ss/2qz2BDWKB3aai1X57ez4avWdldk97mVih9l5ccDhsHbbXn7zt16zj43H57Ni2ftmM5N+NycNyoFGt2Oun7kb9RswMrX+fCLCCkq8m0AjScAACwASURBVMn67ZFy8bjKJ9s222fHVGrWh/VVQ+vezR88tErDGb17uiPWbfAf26F99YDguDGjpGf3rpV6mv/O+9KtSycZMVTnO9T21AuvuqIkv7v7zkAWYeYB/zDh2NhYNwejzeNoWYZjR49052TqMGIbOpzRu4db5wcCCCCAAAIIIIAAAuEgQEAwHN4C94AAAggggEADBWI0+8yGC//3//yt0hlTJ50rr+n8go88+ZxMmzRBsnJy5O8vvuYCWqlaIMSKjVh7+fX5csVF01yxECsGEtxsKGxhYZF8vGqNm6cweJ8te4FEKypigTDLuHv+lTfcYUUNyDYM9FdqQ5h1OHBQe/iOo7JpV6SMH1K5aq8dcuPUY9K7c4n07+ZNLOidqH0E+rK0wuPN5uubo1WVLYhnXoePZsnC95eKVWw2D2sWMF2+8lO5+rILZejgge5zvAcRCwj2y+gtUzXL0poF+awysw3ZNkMrwDL3rUWueMmwIQM1OzDBzbH4+vyFWhMl1gVmX3/zHXfujPMnum9+IIAAAggggAACCCAQDgIEBMPhLXAPCCCAAAII1CDg8yrret8Vx/Tp1cNVGv5g2SeB4heTxp+tlYTzXBGLDZu2uGG8fTUz7oarL3dnWdEQq05s5/xR5xm0YhhWGMSGxHrXGTNymCtC8uzLr8us666sdkcpyUlaXXiyvPvBUvn9/Q+5/RZgS4iPl2+27agYNlw5MFetE5sRMLKLDgW2Ycwx+tFhy+UlMmxAngzrq/Mg6rKUaiZgvr8AiMQXSOe0Irlqop2nmY0R6Xqu/fliw3KLdF2r/pZrYLCKkRVduf0HN8lf/vas/OHPj+qxIgP69pHrZl7ilu2HzX9oGZR506oHIb2Dgp9mUP8MV1HYhhWboTUrKHLrrJt1XsJEt/7d67+t1Z5fFJtb0JoVZfnRd2+oln3odvIDAQQQQAABBBBAAIGTJODTOX8q/xP9SboRLosAAggggMDpKmD/q7WPzUFXUlKiQ3sLJCsrS3r27Nnsj2zXOXT4iMtU84YJB1+kWK9vxUYs288LBAbvt+IgNsTVst1q2m/HetewYFjVasfBfdW4XFaoY5U1mGhz/5XrsgX3ojRAGKXDeHNe1akFdQjuioFStLS7Oz1m/E6RMV/pfg0EJmuQsuSQfvb4A4e+WJ20b7RI1zs0MGjBxZqbVRxO1EIilgHZHM3e44GDhyVB5wu0odo1taNqbE5eBeKajjmRbdu3b5fU1FQdDh7nhoPbsHF7X7W9sxO5FucigAACCCCAAAIInH4CVSbjOf0ekCdCAAEEEECgNQlYQCg9rV1gzsCqz26Zgba/tsCRBfgs2622/dafd41GBwPt5AgN4iV00uS+VVrhRIN/ERoQ9Gn2X5kW60g4Vw/Q9XZaHbk40v+xZdtm++wYO9bOsXOtD+urjmCgXdKKfzRXMND6s+Bbxw7ptQYD7Zg2qSkhCwZa/zQEEEAAAQQQQAABBE5EQP+ipiGAAAIIIIAAAi0loAMTkqeJdNQhv4VbRPJWixQs0Yy/w/rRYJ9PMyl775N9EzpJYUmU9OmVKZE+HUac8w/dp/+O6WunI437aR8X6njcDP2epDdugx2CB/e21LNwHQQQQAABBBBAAAEETk0BAoKn5nvjrhFAAAEEEDg1BcpLdY7AHJ18b4rIwa81y2+NZvvlS3FptOw83EdiIovlk+29JM+XKFFxpbLys/NkQsYWDQ5GS9e2OyQuWguSFGmmYIpW8bU+SrM0HqhBQjev4KlJwl0jgAACCCCAAAIIINDSAgQEW1qc6yGAAAIIINCaBSxwF99HBTSrr+O/6Gjg9iKHn9FMwb2yJztRDuXGSp+0TBnUeatE6PDgjZm9ZfWujpIcVyxd2+kcgHHddQzwjVry+HotMpKg/ejcg2QHtubfKJ4dAQQQQAABBBBAoAkCFBVpAhqnIIAAAggg0BgBKy5hn5YoKtKY+wqPY3WYsGUMHnpeP09rwRCrNGyVhzWT0JovUj86vDhKKwynzaoIBCbrjtY9DTJFRdxvBz8QQAABBBBAAAEEmihAhmAT4TgNAQQQQAABBJpDQAN7kakiHW4TSb9ZhwPv0M8uDRJaMRFtkSk6Z2A3/fTQGGC8fxs/EUAAAQQQQAABBBBA4IQEWvc/r58QHScjgAACCCCAQLMKWMAvboDOD6hzAxZtE9mhQ4ozHxLJ1WrCxZnHswYbc9GyPB2OvEZk73+JrD9L5POejTmbYxFAAAEEEEAAAQQQOC0FyBA8LV8rD4UAAggggMApLpDzoZQfyhXfoQ+0wvAKDRRqlmDqdJ13cLZmDWoREVdZWIcb61Bs/7J++azSsP1bZ8W/dxZ85Q8E5mpAME+LkRwt1EIkNu8gDQEEEEAAAQQQQACB1i1AQLB1v3+eHgEEEEAAgfAUKDgk8nW2lOWWiC8uS3xdtZpwJx1OrIFCaXeVSMEGkXwN+BXtdFWK3UNEaaAwto8/yzAiTuTAkyLZOVK+Wz/7dV7CknKJ6FcxN2F4PjV3hQACCCCAAAIIIIBAiwgQEGwRZi6CAAIIIIAAAo0T0Ew+TQB0rVuMlBdqYDBHh//GaCBwz+9q7qp4rw4t1s+xZf79ZVrM5cAx8SVoYZLkUik/rMFAjQvSEEAAAQQQQAABBBBo7QIEBFv7bwDPjwACCCCAQDgKxHfXocE2BFhHAidrtl83/fhXG363ET7x9WmjgUUdVnz0qJ6nAcG4iuHEtfRi1aB9buhxLQc0YXN9fda3vwmX5BQEEEAAAQQQQAABBOoUICBYJw87EUAAAQQQOHkCO3fvlfsefES6dOood995W6VAVXbOMfnfv79fLrtwqkydOL5FbrK0tFTeevcDmTBujKQkJ8nKNZ/L0y+8KvfcNVs6tk9v3ntIGqLBO83sKyyVsshIyTkWJTn50dK+bYFEakwvMsICdzVfskwzC0vLfFJc4pPD2bHSPrVAopO0r8N6fELNlYoPHzkqr859W9at36h9R8iIoYPlO1dfLhG6XFMzi1/d+4CUWbAxqI0/+0y5ZPoUtyXzwEF58vlXZJe+x/i4OBkzapjbFxcb6/Y39ppBl2ERAQQQQAABBBBAAIETEiAgeEJ8nIwAAggggEDoBCxzzNqefZny3ofLZcqEcYGLefu878COEC6s+mydvL3oAxk7eoS7SlJionRonyYx0dHNf9WEb4mvQ5IWEsmTNXtS5bYH22qQT6cI1EsN7lUiE4cXyJQRedK+TbEG7fyXLyn1yfrtsbLo0wRZ9kWcbN2nGYK6q0eHUvnrT33SrrhEJCmtxnt9+sXXZOv2nfLty2bIgYOH5X31jtRA5A1XXVbj8Xv27dcgZa5069rZBUe9g9q11YxEbQWFhXLvAw9LdHSUzLxkumzbsUs+WPaJJCclyvQp57ljGntNdxI/EEAAAQQQQAABBBBoBgECgs2ASBcIIIAAAgiEWuAf8xbIsMEDJT3NKuyenFY1+Diof4b8+113hOZmorWqcFf9FO2RwaX58uzdZZIYXyLLv0yWe59PkI/0+7F5ifLXnx2Sft20erC2hSuT5LfPpEhOnk/OHFAqj92ZLZ3aFUh+UbS0SdGMwpS2WpCkejblpq+3ypat2+WGb18mY8eMdH1laTGST1Z/JldpgDAmJsZtC/6xc/cet3rLjddIWjvtt0r7cPlKzR4skx/Oul769+3t9ubm5snCxUvlgskTZPOWbY2+ZpVLsIoAAggggAACCCCAQJMFCAg2mY4TEUAAAQQQaBmBUcO/Jas1O+/xZ1+WX9zxw0pDh4Pv4PMvv3LDXg8dPuKGqFomnw0ptkw3axu//kbmvr3IZaulpiSLDW9d9slquePWm12mX86xYzLnjbdk4+ZvJDcvzw2XPVOvfc3Mi+WbbTt135uunz/8+TG5dMb5Yn28+NpcufPH35c5r8+XYs3Am33rLHeM/diybYc8+dwcuemamS4oVt/9BU60hSjN5ItqI76SvRKrw4P7dS9wu5MSinQocKJY7uT4IYUSFelVHtHRwHFl0jW9VDbsiJIdmZEyrG+u7rcjNTPQNc0XTBhWsXz8a9vOXW5l5HAdplzRzho1XD5d+4VmDe6SAf36eJsD3zt27XE+Fgy0ob+JiQkSGxQ4XLF6rTO1YKAXSL1l1rVSrkOMbY7CplwzcHEWEEAAAQQQQAABBBA4QQECgicIyOkIIIAAAgiEWmDIwH4a2Ip0GWtLPloh551zVrVLfrFhkzz29AvSuWMHufryi2T9xs1umPExzUq76dqZsm//AXn48WckUefQu+LiC1xwcP4777t+ikv8pXefeHaObP5mm1w0bZLY0FcLQq74dK2bH9Dmvxs2eJBbn3LeOMno1UMsS+5oVraUaCCwd4/uMnfBInedTh3au35tiKzNddi7V3ep7/6qPVDRTo3j7a+2OVsLDev0fS4geMfMbDdk2Dvo3GG5suzLaB02nCT7tYZIcUmEuunBwS1rgUiH24O3uCHC0VFRlQJ6aRVDf49mZ1c61luxgKC1f/uP/yuWTWht2JBBztoCg+bSu2c3efyZl8QCoW6/Znher1mI1mxYcmOv6U7kBwIIIIAAAggggAACzSBQ80zZzdAxXSCAAAIIIIBA8wlcfcVFGsxLcBmAlpFWtb02722XsXbX7Ftc0Y8fffcGsSw3C+gdy82VRUs+ckNYf/6TW9xchD/+3ncko3fPQDc2vLVNaopcqfPdzTh/ojv31lnXuf17MvdL2zapgaGvo4YNkU4d/UE/r4NxZ41yix9pxqG1ouJi+Wzdehk9YqgLfNV3f+6k4B95azTylxW8xS0na00QnZZP2uj0gglxpZUKi2hRYUnXocFWdKR9arkedzx7MNBR/ueBRW/BPKsOC46P16rG2rxgn3es971X5xC0duHUiXKbWg4Z1F/WfrFBXnptnssIzC8o0KDs125ewoumTpKBOrx6jXpYgNBaU67pTuQHAggggAACCCCAAALNIECGYDMg0gUCCCCAAAKhFrCssx/ofHUPPvqkPKHDcG+56dpKlzx46Ihb/++/PBHYnqVZatb2Zh6QbTr01QpaBM93N1Ir6drcedasmu6N11whn37+pQ4Dnqfn7Jc9ezPdPquoW1+zvgf07SPLV65xGYhrtB8LMnrZjPXdX78+iZUv4QJ3GuGTyv92OXpgkfTqVCoj+xZpgREdDmwjgl3pEPsql4vH5stLi5Pk2ok25NnOt09QK68eJIyJiXb3GnSUDn/2Z00m6VDgmtp/3nOXFjkpE2//YM3i/MVv7hMrvGIZmV775b/+SN01eqntob8+7YZjW9ZkU67p9ck3AggggAACCCCAAAInKkBA8EQFOR8BBBBAAIEWEuiX0UvOOcvm/VslS5avCFzVAm/2saDcwH4Zge3eQooGpEpKSrQabrK3qdq3nX/fg4/K7r37pL0WLumjQ4LPPnO4vPDq3MAceNVOqrLBgn82bNkKZlhRDcsq7NGti7u3+u6vSlcaB+whknK9bq4IRvq0vHB5vqTFrpM3/mO9bvcHA8v3tpXyPP+fM760AumSniOLHtitcUA9PmGyzkPYWZc1qFju9VP9T592bdpIfsHGSrdgQ62ttU+ruSpxbGxMNZf++n4sC7CwqMhlcyYlJQSCgdbX0DMGuoDgkaNZ0pRrWh80BBBAAAEEEEAAAQSaQ6D6X8XN0St9IIAAAggggEBIBK68dLrOSbdBFixaEujfsvssGFhQUCiXXDA5UETEglPrv9oscXGx0q1LJ1mrc9nl5edLQryOu9VmQ1y9tn3nbhcMnD7lPLlY+7BmQ42fm/NGIHvOimFY84pkuJWgHzZs1gJlCxd/6AqXXHHRNLe3IfcX1I1/8ejfdRJAy1DUwJ8F9ET/ZInsoIFCzbbz6XK5ZvAVlUvRo1o1uFiLpmh2oC+pSGL+l84R6AqJ6DFluSI58/RYDe6VW2ER68ue4f/o53jr3k2DhtosW9IbRr1ugz9A2KF99YCgBQt/+dv7ZKQOnf7eDVe5c81k/aav3fNbNqd5f6NFVYo1EGtzBVqz+Rmtddbh1o29pjuRHwgggAACCCCAAAIINJOA/YVNQwABBBBAAIFTRCAmOrracGG79amTznXBp0eefM5l6NnQVavwaxl/Vg14hs51Z1l6v7//IXl38VKx4zZt2Rp4am8osQWtMg8cdBWC/1gx/LioyD981gtsLdV5Aq1ISdVmgb9zxoySDZu2uF1na5Vjr9V3f95xx781oy8yVbP8NOCXosOjUzTwlnCOSNxgnUSwr/+wqAiJSLdgn66W+SSi/yEXGBSfBgjjx+px3UWSplecf4VI7FA9MOb4JSqWbJ5DC2Q+9cKrYsVCPtNA6cL3l4pVdzY7a/PfeS8w/58NE/aqEC9e+rELJD761PNSWFgkF0ye4I63wiwWDPzL354VC7YueG+JKy4ySOcStPkKG3JN1xE/EEAAAQQQQAABBBAIgYD/n6xD0DFdIoAAAggggMCJCXgZeZUqZ2iXNpzXhudaFV/vmEnjz5ZczVyzwJMF5Cw411eLhtxw9eXuJrp06iizb5ml8wPOldffXOgKiAzVqrdWATc6KlqHEydpdeHJ8u4HS13Q0E6ygJhlE1qmm2XA9cvo7YbCWkAxWyvrDhpQEZgLmqZvwrgxrrrxGTqnnhVB8Vp99+cdF/iO10rKsSNFCrQIyLG3NOBn8yHq/H8W/PP/cNMLRl2+VopfHKUjjI9K1OWr/AFBGx6ct1iP0xtzGYH675/RvUUSJ/k/1kVQi9QKzrf/4CYXvPvDnx91e2w+xOtmXhI4yky9ysK28dIZ58vufZnyyj/13rSZ94VaPGSaBmat2TuyORktw/L+hx5z2/qr3y0VhVoack13Ej8QQAABBBBAAAEEEAiBgE//wHd/Woegb7pEAAEEEEAAARWw/9XaxzL0bC6/Aq1Am5WVJT179mx2H7vOocNHXMAvqmKoql1k3fqNkp2T4+Yg9C76j3kLXPXh+397t3hVdb3zbf4/C1pVbbbfhhJbsM+CYI1tXv9W0Tj4/qr1s+t3Ikde1ikED1bb5W0Ijg162ypGNXurVb71eWL6iJzxYZXtx1et+m+iZgDasN+GtLy8fPXIk/bp7QLB2eDzvOe1gGvVSsbecY29pp23fft2SU1N1eHgcc7R3oUFh70Asdc33wgggAACCCCAAAII1CRAhmBNKmxDAAEEEEDgFBWwgFC6FgWp2vYfPCQWADxw8LCM0OrCO3fvdcHAnt27BoKBdk5t53v92X6vaq63rTHf9fUf6Cu2c0VWYGBLYMECgYWFEbJqU7w8OjdF1m6JkLhYn0wdUSQ3z8iSjC6FGqwMHB60oJmDpXuD1qsvtmvbpvrGOrYkJMSLfWprDXnexl6ztmuxHQEEEEAAAQQQQACBhgoQEGyoFMchgAACCCBwCgtM1CHGe/ft1wrFq3VY8DL3JP369JIf3myVfMOwtdHhulnzRXK1mnK5DhWuaJqgKPlFkfKbp9rJwtUxUlLqH6+cXygy9+MYWb4hTW44P09umpalmXNaaMQ70b4j4kQ63BG8hWUEEEAAAQQQQAABBFqlAEOGW+Vr56ERQAABBFpSwIaN2qclhgw35LmOHM1yxTKaMuS3If032zHF+0S23qwRwHUaFNTsPm25BZHyi8fS5evdOkdilzIZllEoPTuV6vZy2bonTj79OlIOHI2Q2y/PlUvGZmvGo52lPyJiRdpdI9Ll17p8fG5D23sqNoYMn4pvjXtGAAEEEEAAAQTCR4AMwfB5F9wJAggggAACLSJg8wOeEi26k0ivv4rsulsk+z29ZQuqlmsGYJYGA0skPbWkIuDnPc0xrewbIbsORGmA0MsNtGBgokinn4qkf98fGPQO5xsBBBBAAAEEEEAAgVYqQECwlb54HhsBBBBAAIFTQiCmm0jvJ0QyHxI59JwkJ+yTswcVVAkEHn+S6Kgy6dW5yGKH4ovQP3PiBoh0/qVIyhQ9qMaJBY+fzBICCCCAAAIIIIAAAq1EgIBgK3nRPCYCCCCAAAKnpoBm+PmiRTrOFmlzqUjO++I7/JJIwWZ9HB1G7OYXtDIj1jTg57Nqu3p88nn+IcIJo0Si0/373DH8QAABBBBAAAEEEEAAAQKC/A4ggAACCCCAQJgLWFAwRrP9+vk/7W89fr/lmg14dK5/3QqR2HE0BBBAAAEEEEAAAQQQqFOAgGCdPOxEAAEEEEAAgfAV0OrDxQd0jsFf+W8x8WyRmM66zNDg8H1n3BkCCCCAAAIIIIBAOAjwF3M4vAXuAQEEEEAAAQQaL1BWILJbC46UHvV/bNm20RBAAAEEEEAAAQQQQKBOAQKCdfKwEwEEEEAAAQTCU0CzA3NX6JyCH+rt2RyC+rFl2ya6r4mtvNybj7DhHZSVNf16Db8KRyKAAAIIIIAAAggg0HwCDBluPkt6QgABBBBAIGQCi5Z8JP+Yt0AG9Osjs2+ZVe06qz9bJ/Hx8XLGgL5uX9X1aic0YUNwnyvXfC5Pv/Cq3HPXbOnY3op2tHArKxbR4iLlpQVSorVFrEVFavVhKziSOFZHDcf6Nzbw5+EjR+XVuW/LuvUbJTIiQkYMHSzfufpyidDl2tqSj1bI+x8ulwOHDktau7Zy49VXSN8+PasdXlpaKr+570FJ0Pdz9523VdtvG1Z+ulaefvE1mX3rLBnQt0+Nx7ARAQQQQAABBBBAAIHmEqj9r9zmugL9IIAAAggggMAJCyxe+rHrY+PmbyQrO6dSf0ezsuXJ51+RI0d16Ky2quuVDm7iStU+kxITpUP7NImJ1oq+J6MdWyaS9a5k50TIL/6UKHf/OcEt2zaxfY1sFoyzYOC3L5sh48eOlhUaoHvh1YpiJTX0tW3HLnn59fnSsUO63HDVZWJBvz898oS+g6xqR7+mgVzbXlv2odk+8/Lr/vMan6BY7XpsQAABBBBAAAEEEECgPgECgvUJsR8BBBBAAIGTLLBz914XUJpy3jnuTpYst2GxJ7cN6p8h/37XHdK2TWrL30hZnkjmg1JWmiufb4qTNz8qk3lLy92ybbN9Ysc0sG36eqts2bpdrpt5iUwYO0auvGS6jBw2RD5Z/ZkUFWkV4xqaBQsTExLkhzdfL2NHj5Sf/vj77qilH6+qdPTX32yXD5Z9Umlb1ZXHn3lJGHZcVYV1BBBAAAEEEEAAgVAKMGQ4lLr0jQACCCCAQDMI2NBUa5PGny2frv1ClixbIRdPmyw+n08KCgrlvgcfcfstE80CUF9t3hJY37p9lxv6mpefLy++Nk/WffmVFJeUSNfOnVxmW49uXdyxf/zLE264646du2WjBshs2OyZI4bKNVdcJCU6Jjf4Gtbn8G+dof3NlTs1EJbWto3rc+5b78rylWskv6BA2qSmyIzzJ8o5Z41y/f9T9+3bf8ANrf1oxWopLi6R3j27y3ev/7Y71h3U0B+5n4oUrNdpA8vtP83O85/opv+zH7bPjkke36Aet+3c5Y4bOXxI4PizRg131vasNky7atubuV/OPXu0ewe2r50aWLagZRleMn2KO7xQg4l//fuLktG7p9hyWWn1uQZtyLFlG158wWSZt+C9qpdhHQEEEEAAAQQQQACBkAiQIRgSVjpFAAEEEECgeQRsKKplqlngzoJs488+0wXcLPBkLTo6Ss6f6A98DRk0QM46c3il9TGjhrmhqg88/Lh8tm69y2a77MKpbljxH/78qBzSufOs7T9wUBYsWqLDkY/JFRdNk34ZvTS496ksWb6y2jWsz3wNMNpQ1xIN7Fmz+QTf0+CW3edVl10oMTHROuT2n+IFMw/rkNnPNRi5YvVamTLhHJl4zlkuK++5ORVDZV0v9f3QYJ9l/u39z7ozACsdU/8Y3AMHD0t0VJTExsQEbsCCnNaOZmcHtnkLuXl5LqMvPa2tt8l92zyCwcO5LYvQMgx/cOPVlY7zVvYfOOTmLbTMz4xePb3NfCOAAAIIIIAAAgggEHIBAoIhJ+YCCCCAAAIINF1gjQbxbDjpeRpAszZujD/jzoqMWIuMjJRROrzVWn8N4g3sl1FpvX9Gb/nyq82Suf+gXDB5glytGX9TNYD4bz+73Z3z9rsfuG/7ER8XJ7/81x/JpHPHym3f+44rqGHZa1WvYX0Gt+ycYy7YaNutKIbd6z0/ny2xsTHyxlsLgw+Vn8++RS6cOlFm6rBcyxDcsWtvpf11rpRrhl3W2yL5X2h2YEVaYE0n2D47xo61c+ppVlAkJigYaIfHx8e5s4IDfF43R476g4QJCfHeJvedqEVDLDvSmgU/V2nhlZuuu1KSk5LctuAf9k4fffp5aZ/WTi6bcX7wLpYRQAABBBBAAAEEEAi5AEOGQ07MBRBAAAEEEGi6gDf/nA1d3b0303Vk2Ww2550Fsmyoan1t1x5/0O3jVWtk3QZ/ZqF3zu69+7xF6d61c6Cqrg1HtgBhbXPoBU7SBQsaWhs+9Az3bT/s/KFnDBSrRpyf7w+SWcVeC4B5ze59zz7/M3nb6vwu18rCh/6uQT5/VmLdx+oxdmzqhXozkXUeatmMVefwKy7Wa2lLSkyodq4FOq2V6NDr4Fak51iW4bHcPHnyuTluHsIROrS6pvbmwsUuSPurn/8kYF7TcWxDAAEEEEAAAQQQQCAUAgQEQ6FKnwgggAACCDSDgA3J3bp9p8u021gxL6B1m6hBKtu3WItVzLz4gnqvVFjoL4xhGXzJycez1SybsK0OQ/aaBcaCW0SEL3i11mUL9FkLHnJr61EauLQWEenfb8Obg1uEBg0b3CwImP2WVhC2Ah31DwPWMcX+Y+2c1Es0KFj52sHXbdemjWb2VQ6UWlDPWvu0tOBD3XJqSrL7zjmmBUyCmmVKpukwYpvn0eZptCHaP7vnXneE9w5s/c7bvi/vL13utt//0GPu2+ZUtPbw356Rb50xQG656Vq3zg8EEEAAAQQQQAABBEIhUPtfx6G4Gn0igAACCCCAQIMFln2yyh1rlWyDh+laNttdv/4v+VCLjVyu8wF6rdxV1fDWNGxWsd6pY3u30YpeTJt0rlu2gNVLWmSkQ/rxjL3jZ9a+5PUZfIT1a23tFxvEinF4zYbNJiclVgsUevsb9V16RGTfA3pKQ4KBXs96rJ2TNF6jk34Db0/wd/dund2qZV1aARBrXiZlh/bVA4Ix0dGuwrDN4zh9ynnueMsO3K4FWWz4dkbvHnLRtEluu/dj8dJPXBbi5AljJUWDslYUpqCw0NutczgectmUI4YOlmGDBwa2s4AAAggggAACCCCAQCgECAiGQpU+EUAAAQQQaAaBpR+vcsN2+/XpVak3y8g756wz5T2dR3DN51/KoP593X6bb7BThw7SpVOHSus2bPWVN96St3SYqlUP7qv9vfXuYvliw6ZKAbxKF6myYkEwa941gnfbMGCbD9ACgFbp2DLcPtRiJFZ8w+YrPOFm2YE5H4gU7mx8V3aOndvm8lqzBEdrNeU5b7wpT2lhFMvMswIoC99fKqOGf0u8bMD577wnezMPaIGQa9w9XDBlgrw2921Z8N4SGaoBPKuibIFaq6xsQUSr4hzc1qqNVRm2/dZsnsbgtnnLNhcQHDd6ZI1VjYOPZRkBBBBAAAEEEEAAgRMV8I/hOdFeOB8BBBBAAAEEmlXgm207xIakjhsz0s3HV7XzCWNHu03vL/1YrLiFZRBu3PyNPPn8nGrrVjDjzh9/z1Up/sf8d8SGqe7cvTdQTdjr2+b9q61VvYbelP/Qiq9bZ13r7sGClA8+8qSs0wDYlAnjxCoaW6u9Z383df502YF/0uTA4xl1rk9fufTsUiQ9NPZmH1v26bZKzc7Zp+daH7U0K5py+w9ukoKCQrHKy4///UWxIOx1M3WocUXbsGmLC3h66+eNGyNjRg6TuW8vknsfeNjNo3jjNVe4YKB3TPC3DY+uyzcAdEJQwVdkGQEEEEAAAQQQQACB2gV8OvSnyl/OtR/MHgQQQAABBBBovID9r9Y+lkFmhSgKtBJtVlaW9OzpH57a+B5rPsOGoFpwy4qOWKu6btvy8vPF5rNr2ybVVhvdauozuBMrQuLm0mvXtu4AWPBJdS7rXICZ/0+DevdrQNBf6CP4cKvrse9QpNvUKa1U5y0M3lux7NPsxk53iXT8iW6o+99CrVCLzdFYdT7EGnp1m+x5LXCbps/bkm379u2SmpoqcVr4xeZqtKxRCzjWGXRsyRvkWggggAACCCCAAAJhLVD3X8VhfevcHAIIIIAAAggEC8TFxgaCgba96rptS4iPb3IwsLY+bbvXLBsxXYcQN1tgqjRX5Og/awwG2jWzcyLlj8/GuY8t19gskGh9WF/1NKt83NBgoHVlz9vSwcB6HoHdCCCAAAIIIIAAAgjUK0BAsF4iDkAAAQQQQACBkyOggxgOaBXegk01Xr6k1CdzlyTI/GVl7vPG4gSxbTU268P6alRRkhp7YiMCCCCAAAIIIIAAAqe8AAHBU/4V8gAIIIAAAgicpgKF20UOPlVrdmCEzhd49pBCaZvsc59xQwvFttXYLEvQ+rI+aQgggAACCCCAAAIItHKBmmbaaeUkPD4CCCCAAAIIhIVA1jwd5putt1JzkE+nzZOMnkXy1K/9d5vRo0jn0qvtzrUP68v67GBzCdIQQAABBBBAAAEEEGi9AgQEW++758kRQAABBBAIb4GCr7Q8sc0LGFvrfUZpvZABGd7u2o9zR1hf1icNAQQQQAABBBBAAIFWLkBAsJX/AvD4CCCAAAIIhK1Ax5+JpEzVBEEtJdwczad/9sQPbY6e6AMBBBBAAAEEEEAAgVNagIDgKf36uHkEEEAAAQROY4HY3pocqB8aAggggAACCCCAAAIINKtArTPtNOtV6AwBBBBAAAEEEEAAAQQQQAABBBBAAAEEwkKAgGBYvAZuAgEEEEAAAQQQQAABBBBAAAEEEEAAgZYRICDYMs5cBQEEEEAAAQQQQAABBBBAAAEEEEAAgbAQICAYFq+Bm0AAAQQQQAABBBBAAAEEEEAAAQQQQKBlBAgItowzV0EAAQQQQAABBBBAAAEEEEAAAQQQQCAsBAgIhsVr4CYQQAABBFqTgM/nE/vQEGiqAL9DTZXjPAQQQAABBBBAAAETICDI7wECCCCAAAItKOAFAr3vFrw0lzqNBLzfH+/7NHo0HgUBBBBAAAEEEECgBQQICLYAMpdAAAEEEGjdAha0Cf5ERESIfUpKSlo3DE/fJAH7vfF+h4J/rwgONomTkxBAAAEEEEAAgVYpQECwVb52HhoBBBBA4GQJWNDGC+bs27dPioqKTtatcN1TUMB+XzIzMwO/QwQBT8GXyC0jgAACCCCAAAJhIOAr1xYG98EtIIAAAgggcFoL2P9u7VNWVibFxcWSl5cnhw8flt27d0t2dna1bEH+93xa/zo06OGqBvuioqIkJSVFunXrJm3btpWEhASJjo52wUE7turxDboIByGAAAIIIIAAAgi0SoGoVvnUPDQCCCCAAAInQcAL2kRGRkpMTIwkJiZKenq6xMfHS2lpqbsjAoEn4cWE+SW9QJ/93tjvjH3s98fWvd+pMH8Ebg8BBBBAAAEEEEAgzATIEAyzF8LtIIAAAgicvgJelqB92zxwliloH1sO3nf6CvBkTRHwgn72bVmClhVoH1sO3teUvjkHAQQQQAABBBBAoHUKEBBsne+dp0YAAQQQOAkCXvafF/yz4cP28da9/Sfh1rhkmAsEB/68OSi9bXbrtkxDAAEEEEAAAQQQQKChAgQEGyrFcQgggAACCDSDQHDQzwsEWrfB25vhMnRxGgp4Qb/gQKA9prf9NHxkHgkBBBBAAAEEEEAgRAIEBEMES7cIIIAAAgjUJVA1AFh1va5z2dc6BaoG/qqut04VnhoBBBBAAAEEEECgKQIEBJuixjkIIIAAAgg0owDBwGbEPM27Igh4mr9gHg8BBBBAAAEEEGghAaoMtxA0l0EAAQQQQKA2AYI8tcmwHQEEEEAAAQQQQAABBEIhEBGKTukTAQQQQAABBBBAAAEEEEAAAQQQQAABBMJTgIBgeL4X7goBBBBAAAEEEEAAAQQQQAABBBBAAIGQCBAQDAkrnSKAAAIIIIAAAggggAACCCCAAAIIIBCeAgQEw/O9cFcIIIAAAggggAACCCCAAAIIIIAAAgiERICAYEhY6RQBBBBAAAEEEEAAAQQQQAABBBBAAIHwFCAgGJ7vhbtCAAEEEEAAAQQQQAABBBBAAAEEEEAgJAIEBEPCSqcIIIAAAggggAACCCCAAAIIIIAAAgiEpwABwfB8L9wVAggggAACCCCAAAIIIIAAAggggAACIREgIBgSVjpFAAEEEEAAAQQQQAABBBBAAAEEEEAgPAUICIbne+GuEEAAAQQQQAABBBBAAAEEEEAAAQQQCIkAAcGQsNIpAggggAACCCCAAAIIIIAAAggggAAC4SlAQDA83wt3hQACCCCAAAIIIIAAAggggAACCCCAQEgECAiGhJVOEUAAAQQQQAABBBBAAAEEEEAAAQQQCE8BAoLh+V64KwQQQAABBBBAAAEEEEAAAQQQQAABBEIiQEAwJKx0igACCCCAAAIIIIAAAggggAACCCCAQHgKEBAMz/fCXSGAAAIIIIAAAggggAACCCCAAAIIIBASAQKCIWGlUwQQQAABBBBAAAEEEEAAAQQQQAABBMJTgIBgeL4X7goBBBBAAAEEEEAAAQQQQAABBBBAAIGQCBAQDAkrnSKAAAIIIIAAAggggAACCCCAAAIIIBCeAgQEw/O9cFcIIIAAAggggAACCCCAAAIIIIAAAgiERICAYEhY6RQBBBBAAAEEEEAAAQQQQAABBBBAAIHwFCAgGJ7vhbtCAAEEEEAAAQQQQAABBBBAAAEEEEAgJAIEBEPCSqcIIIAAAggggAACCCCAAAIIIIAAAgiEpwABwfB8L9wVAggggAACCCCAAAIIIIAAAggggAACIREgIBgSVjpFAAEEEEAAAQQQQAABBBBAAAEEEEAgPAUICIbne+GuEEAAAQQQQAABBBBAAAEEEEAAAQQQCInA/wekvWvzEUNiOgAAAABJRU5ErkJggg==\n", 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" + "" ] }, "metadata": { - "needs_background": "light" + "image/png": { + "width": 800 + } }, "output_type": "display_data" } ], "source": [ - "model = malaya.subjective.deep_model('luong')\n", - "result = model.predict(negative_text, get_proba=True)['attention']\n", + "from IPython.core.display import Image, display\n", "\n", - "plt.figure(figsize = (15, 7))\n", - "labels = [r[0] for r in result]\n", - "val = [r[1] for r in result]\n", - "aranged = [i for i in range(len(labels))]\n", - "plt.bar(aranged, val)\n", - "plt.xticks(aranged, labels, rotation = 'vertical')\n", - "plt.show()" + "display(Image('subjective-bahdanau.png', width=800))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "I tried to put the html and javascript inside a notebook cell, pretty hard you know and a lot of weird bugs. Let stick to HTTP serving ya.\n", + "\n", + "`predict_words` only accept a single string. You can't predict multiple texts." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### Visualizing hierarchical model" + "#### Predict batch of strings" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", "text/plain": [ - "
" + "[{'negative': 0.83364284, 'positive': 0.0016635716, 'neutral': 0.1646936},\n", + " {'negative': 0.003325577, 'positive': 0.6674423, 'neutral': 0.3292321}]" ] }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "model = malaya.subjective.deep_model('hierarchical')\n", - "result = model.predict(negative_text, get_proba=True)['attention']\n", + "model.predict_batch([negative_text, positive_text],get_proba=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**You might want to try `luong` and `self-attention` by yourself.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Stacking models\n", "\n", - "plt.figure(figsize = (15, 7))\n", - "labels = [r[0] for r in result]\n", - "val = [r[1] for r in result]\n", - "aranged = [i for i in range(len(labels))]\n", - "plt.bar(aranged, val)\n", - "plt.xticks(aranged, labels, rotation = 'vertical')\n", - "plt.show()" + "More information, you can read at [https://malaya.readthedocs.io/en/latest/Stack.html](https://malaya.readthedocs.io/en/latest/Stack.html)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "multinomial = malaya.subjective.multinomial()\n", + "xgb = malaya.subjective.xgb()\n", + "bahdanau = malaya.subjective.deep_model('bahdanau')" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'negative': 0.008627402242055781,\n", + " 'positive': 0.12711225500695544,\n", + " 'neutral': 0.8541128287159148}" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "malaya.stack.predict_stack([multinomial, xgb, bahdanau], positive_text)" ] }, { @@ -379,7 +509,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -388,7 +518,7 @@ "['fast-text-char']" ] }, - "execution_count": 11, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -406,7 +536,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -423,7 +553,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -432,7 +562,7 @@ "'positive'" ] }, - "execution_count": 13, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -443,7 +573,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -452,7 +582,7 @@ "['positive', 'negative']" ] }, - "execution_count": 14, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -463,7 +593,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -473,7 +603,7 @@ " {'negative': 0.95071983, 'positive': 0.04928014}]" ] }, - "execution_count": 15, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -481,6 +611,20 @@ "source": [ "sparse_model.predict_batch([positive_text, negative_text], get_proba=True)" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Right now sparse models does not have `neutral` class." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/example/subjectivity/load-subjectivity_files/load-subjectivity_15_0.png b/example/subjectivity/load-subjectivity_files/load-subjectivity_15_0.png deleted file mode 100644 index 531e1759cc93b7be685b95ddf44d8db0f645c689..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 20257 zcmeIa2UL?myDmyc1;L1dQZ-0N5F))q_Bs3PbIx7op1syxch6d^FHC0MnR(}}&&-!_U2WBi7g#Tlk&#_g 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But to get ``Attention``, you need to set +``get_proba=True``. .. code:: ipython3 - for model in malaya.toxic.available_deep_model(): - print('Testing %s model'%(model)) - deep_model = malaya.toxic.deep_model(model = model) - print(deep_model.predict(string)) - print(deep_model.predict_batch([string, another_string])) - print(deep_model.predict_batch([string, another_string],get_proba=True)) - print() + import matplotlib.pyplot as plt + import seaborn as sns + sns.set() + +Load bahdanau model +~~~~~~~~~~~~~~~~~~~ + +.. code:: ipython3 + + model = malaya.toxic.deep_model(model = 'bahdanau') + +Predict single string +^^^^^^^^^^^^^^^^^^^^^ + +.. code:: ipython3 + + model.predict(string) + + .. parsed-literal:: - Testing bahdanau model - [] - [[], ['toxic']] - [{'toxic': 0.004403565, 'severe_toxic': 3.365281e-05, 'obscene': 0.0006261515, 'threat': 7.532223e-05, 'insult': 0.00030458395, 'identity_hate': 5.894399e-05}, {'toxic': 0.8340975, 'severe_toxic': 0.0020475034, 'obscene': 0.034258205, 'threat': 0.0003628814, 'insult': 0.0734416, 'identity_hate': 0.0614648}] - - Testing hierarchical model [] - [[], ['toxic']] - [{'toxic': 0.54248875, 'severe_toxic': 0.0065838215, 'obscene': 0.17371807, 'threat': 0.0148023935, 'insult': 0.12660918, 'identity_hate': 0.055212382}, {'toxic': 0.6858065, 'severe_toxic': 0.0032611815, 'obscene': 0.041103873, 'threat': 0.0025343157, 'insult': 0.28004634, 'identity_hate': 0.055198606}] - - Testing luong model - [] - [[], ['toxic']] - [{'toxic': 0.13619128, 'severe_toxic': 0.0055254013, 'obscene': 0.032115582, 'threat': 0.017055651, 'insult': 0.039386936, 'identity_hate': 0.012571168}, {'toxic': 0.95763326, 'severe_toxic': 0.018214434, 'obscene': 0.2505002, 'threat': 0.010534671, 'insult': 0.37684646, 'identity_hate': 0.11548582}] - - Testing fast-text model - ['identity_hate'] - [[], []] - [{'toxic': 1.04480705e-05, 'severe_toxic': 3.0439846e-06, 'obscene': 1.0294245e-05, 'threat': 7.333487e-05, 'insult': 3.3929928e-06, 'identity_hate': 0.0013527799}, {'toxic': 0.3151637, 'severe_toxic': 0.0074781813, 'obscene': 0.014187617, 'threat': 0.0036906046, 'insult': 0.087501705, 'identity_hate': 0.049142193}] - - Testing entity-network model - [] - [['toxic', 'obscene', 'insult'], []] - [{'toxic': 0.77070284, 'severe_toxic': 0.18705268, 'obscene': 0.5038762, 'threat': 0.16385102, 'insult': 0.6224154, 'identity_hate': 0.23296514}, {'toxic': 0.20222966, 'severe_toxic': 0.007314409, 'obscene': 0.13172735, 'threat': 0.027272865, 'insult': 0.10753201, 'identity_hate': 0.016206132}] - -Unsupervised important words learning -------------------------------------- .. code:: ipython3 - import matplotlib.pyplot as plt - import seaborn as sns - sns.set() # i just really like seaborn colors + result = model.predict(another_string, get_proba = True) + result + + + + +.. parsed-literal:: + + {'toxic': 0.75407517, + 'severe_toxic': 0.16274202, + 'obscene': 0.5291958, + 'threat': 0.10058941, + 'insult': 0.75971705, + 'identity_hate': 0.8826025, + 'attention': {'bodoh': 0.10320988, + ',': 0.0, + 'dah': 0.027506806, + 'la': 0.021144494, + 'gay': 0.4988079, + 'sokong': 0.06969115, + 'lgbt': 0.20489135, + 'lagi': 0.018106166, + 'memang': 0.02190801, + 'tak': 0.017407918, + 'guna': 0.017326297}} -We need to set ``get_proba`` become True to get the ‘attention’. -Visualizing bahdanau model -^^^^^^^^^^^^^^^^^^^^^^^^^^ .. code:: ipython3 - model = malaya.toxic.deep_model('bahdanau') - result = model.predict(another_string,get_proba=True)['attention'] - - plt.figure(figsize = (15, 7)) - labels = [r[0] for r in result] - val = [r[1] for r in result] - aranged = [i for i in range(len(labels))] - plt.bar(aranged, val) - plt.xticks(aranged, labels, rotation = 'vertical') + plt.figure(figsize = (15, 5)) + keys = result['attention'].keys() + values = result['attention'].values() + aranged = [i for i in range(len(keys))] + plt.bar(aranged, values) + plt.xticks(aranged, keys, rotation = 'vertical') plt.show() -.. image:: load-toxic_files/load-toxic_23_0.png +.. image:: load-toxic_files/load-toxic_24_0.png -Visualizing luong model -^^^^^^^^^^^^^^^^^^^^^^^ +Open toxicity visualization dashboard +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Default when you call ``predict_words`` it will open a browser with +visualization dashboard, you can disable by ``visualization=False``. .. code:: ipython3 - model = malaya.toxic.deep_model('luong') - result = model.predict(another_string,get_proba=True)['attention'] - - plt.figure(figsize = (15, 7)) - labels = [r[0] for r in result] - val = [r[1] for r in result] - aranged = [i for i in range(len(labels))] - plt.bar(aranged, val) - plt.xticks(aranged, labels, rotation = 'vertical') - plt.show() + model.predict_words(another_string) + + +.. parsed-literal:: + + Serving to http://127.0.0.1:8889/ [Ctrl-C to exit] + + +.. parsed-literal:: + 127.0.0.1 - - [01/Jun/2019 12:12:10] "GET / HTTP/1.1" 200 - + 127.0.0.1 - - [01/Jun/2019 12:12:10] "GET /static/admin-materialize.min.css HTTP/1.1" 200 - + 127.0.0.1 - - [01/Jun/2019 12:12:10] "GET /static/echarts.min.js HTTP/1.1" 200 - + 127.0.0.1 - - [01/Jun/2019 12:12:11] "GET /favicon.ico HTTP/1.1" 200 - + ---------------------------------------- + Exception happened during processing of request from ('127.0.0.1', 61873) + Traceback (most recent call last): + File "/usr/local/Cellar/python/3.6.5_1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/socketserver.py", line 317, in _handle_request_noblock + self.process_request(request, client_address) + File "/usr/local/Cellar/python/3.6.5_1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/socketserver.py", line 348, in process_request + self.finish_request(request, client_address) + File "/usr/local/Cellar/python/3.6.5_1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/socketserver.py", line 361, in finish_request + self.RequestHandlerClass(request, client_address, self) + File "/usr/local/Cellar/python/3.6.5_1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/socketserver.py", line 696, in __init__ + self.handle() + File "/usr/local/Cellar/python/3.6.5_1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/http/server.py", line 418, in handle + self.handle_one_request() + File "/usr/local/Cellar/python/3.6.5_1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/http/server.py", line 406, in handle_one_request + method() + File "/Users/huseinzol/Documents/Malaya/malaya/_utils/_server.py", line 32, in do_GET + with open(filepath, 'rb') as fh: + FileNotFoundError: [Errno 2] No such file or directory: '/Users/huseinzol/Documents/Malaya/malaya/_utils/web/favicon.ico' + ---------------------------------------- -.. image:: load-toxic_files/load-toxic_25_0.png +.. parsed-literal:: + + stopping Server... -Visualizing hierarchical model -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. code:: ipython3 - model = malaya.toxic.deep_model('hierarchical') - result = model.predict(another_string,get_proba=True)['attention'] + from IPython.core.display import Image, display - plt.figure(figsize = (15, 7)) - labels = [r[0] for r in result] - val = [r[1] for r in result] - aranged = [i for i in range(len(labels))] - plt.bar(aranged, val) - plt.xticks(aranged, labels, rotation = 'vertical') - plt.show() + display(Image('toxicity.png', width=800)) .. image:: load-toxic_files/load-toxic_27_0.png + :width: 800px + + +Predict batch of strings +^^^^^^^^^^^^^^^^^^^^^^^^ + +.. code:: ipython3 + + model.predict_batch([string, another_string],get_proba=True) + + + + +.. parsed-literal:: + + [{'toxic': 0.21699297, + 'severe_toxic': 0.021610105, + 'obscene': 0.110013016, + 'threat': 0.010765828, + 'insult': 0.10372056, + 'identity_hate': 0.03946534}, + {'toxic': 0.93524134, + 'severe_toxic': 0.33446547, + 'obscene': 0.7178085, + 'threat': 0.56429744, + 'insult': 0.8922918, + 'identity_hate': 0.9618688}] + + + +**You might want to try ``luong`` and ``self-attention`` by yourself.** + +Stacking models +--------------- + +More information, you can read at +https://malaya.readthedocs.io/en/latest/Stack.html + +.. code:: ipython3 + + multinomial = malaya.toxic.multinomial() + logistics = malaya.toxic.logistic() + bahdanau = malaya.toxic.deep_model('bahdanau') + +.. code:: ipython3 + malaya.stack.predict_stack([multinomial, logistics, bahdanau], another_string) diff --git a/example/toxicity/load-toxic.ipynb b/example/toxicity/load-toxic.ipynb index 33e4729e..cca71113 100644 --- a/example/toxicity/load-toxic.ipynb +++ b/example/toxicity/load-toxic.ipynb @@ -9,8 +9,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 11.2 s, sys: 1.38 s, total: 12.6 s\n", - "Wall time: 16.1 s\n" + "CPU times: user 12.6 s, sys: 1.5 s, total: 14.1 s\n", + "Wall time: 17.9 s\n" ] } ], @@ -276,7 +276,7 @@ { "data": { "text/plain": [ - "['bahdanau', 'hierarchical', 'luong', 'fast-text', 'entity-network']" + "['self-attention', 'bahdanau', 'luong']" ] }, "execution_count": 13, @@ -292,61 +292,27 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Load deep learning model" + "## Load deep learning models\n", + "\n", + "Good thing about deep learning models from Malaya, it returns `Attention` result, means, which part of words give the high impact to the results. But to get `Attention`, you need to set `get_proba=True`." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Testing bahdanau model\n", - "[]\n", - "[[], ['toxic']]\n", - "[{'toxic': 0.004403565, 'severe_toxic': 3.365281e-05, 'obscene': 0.0006261515, 'threat': 7.532223e-05, 'insult': 0.00030458395, 'identity_hate': 5.894399e-05}, {'toxic': 0.8340975, 'severe_toxic': 0.0020475034, 'obscene': 0.034258205, 'threat': 0.0003628814, 'insult': 0.0734416, 'identity_hate': 0.0614648}]\n", - "\n", - "Testing hierarchical model\n", - "[]\n", - "[[], ['toxic']]\n", - "[{'toxic': 0.54248875, 'severe_toxic': 0.0065838215, 'obscene': 0.17371807, 'threat': 0.0148023935, 'insult': 0.12660918, 'identity_hate': 0.055212382}, {'toxic': 0.6858065, 'severe_toxic': 0.0032611815, 'obscene': 0.041103873, 'threat': 0.0025343157, 'insult': 0.28004634, 'identity_hate': 0.055198606}]\n", - "\n", - "Testing luong model\n", - "[]\n", - "[[], ['toxic']]\n", - "[{'toxic': 0.13619128, 'severe_toxic': 0.0055254013, 'obscene': 0.032115582, 'threat': 0.017055651, 'insult': 0.039386936, 'identity_hate': 0.012571168}, {'toxic': 0.95763326, 'severe_toxic': 0.018214434, 'obscene': 0.2505002, 'threat': 0.010534671, 'insult': 0.37684646, 'identity_hate': 0.11548582}]\n", - "\n", - "Testing fast-text model\n", - "['identity_hate']\n", - "[[], []]\n", - "[{'toxic': 1.04480705e-05, 'severe_toxic': 3.0439846e-06, 'obscene': 1.0294245e-05, 'threat': 7.333487e-05, 'insult': 3.3929928e-06, 'identity_hate': 0.0013527799}, {'toxic': 0.3151637, 'severe_toxic': 0.0074781813, 'obscene': 0.014187617, 'threat': 0.0036906046, 'insult': 0.087501705, 'identity_hate': 0.049142193}]\n", - "\n", - "Testing entity-network model\n", - "[]\n", - "[['toxic', 'obscene', 'insult'], []]\n", - "[{'toxic': 0.77070284, 'severe_toxic': 0.18705268, 'obscene': 0.5038762, 'threat': 0.16385102, 'insult': 0.6224154, 'identity_hate': 0.23296514}, {'toxic': 0.20222966, 'severe_toxic': 0.007314409, 'obscene': 0.13172735, 'threat': 0.027272865, 'insult': 0.10753201, 'identity_hate': 0.016206132}]\n", - "\n" - ] - } - ], + "outputs": [], "source": [ - "for model in malaya.toxic.available_deep_model():\n", - " print('Testing %s model'%(model))\n", - " deep_model = malaya.toxic.deep_model(model = model)\n", - " print(deep_model.predict(string))\n", - " print(deep_model.predict_batch([string, another_string]))\n", - " print(deep_model.predict_batch([string, another_string],get_proba=True))\n", - " print()" + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "sns.set()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Unsupervised important words learning" + "### Load bahdanau model" ] }, { @@ -355,35 +321,83 @@ "metadata": {}, "outputs": [], "source": [ - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "sns.set() # i just really like seaborn colors" + "model = malaya.toxic.deep_model(model = 'bahdanau')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We need to set `get_proba` become True to get the 'attention'." + "#### Predict single string" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.predict(string)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'toxic': 0.75407517,\n", + " 'severe_toxic': 0.16274202,\n", + " 'obscene': 0.5291958,\n", + " 'threat': 0.10058941,\n", + " 'insult': 0.75971705,\n", + " 'identity_hate': 0.8826025,\n", + " 'attention': {'bodoh': 0.10320988,\n", + " ',': 0.0,\n", + " 'dah': 0.027506806,\n", + " 'la': 0.021144494,\n", + " 'gay': 0.4988079,\n", + " 'sokong': 0.06969115,\n", + " 'lgbt': 0.20489135,\n", + " 'lagi': 0.018106166,\n", + " 'memang': 0.02190801,\n", + " 'tak': 0.017407918,\n", + " 'guna': 0.017326297}}" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#### Visualizing bahdanau model" + "result = model.predict(another_string, get_proba = True)\n", + "result" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" + "
" ] }, "metadata": { @@ -393,15 +407,12 @@ } ], "source": [ - "model = malaya.toxic.deep_model('bahdanau')\n", - "result = model.predict(another_string,get_proba=True)['attention']\n", - "\n", - "plt.figure(figsize = (15, 7))\n", - "labels = [r[0] for r in result]\n", - "val = [r[1] for r in result]\n", - "aranged = [i for i in range(len(labels))]\n", - "plt.bar(aranged, val)\n", - "plt.xticks(aranged, labels, rotation = 'vertical')\n", + "plt.figure(figsize = (15, 5))\n", + "keys = result['attention'].keys()\n", + "values = result['attention'].values()\n", + "aranged = [i for i in range(len(keys))]\n", + "plt.bar(aranged, values)\n", + "plt.xticks(aranged, keys, rotation = 'vertical')\n", "plt.show()" ] }, @@ -409,76 +420,143 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Visualizing luong model" + "#### Open toxicity visualization dashboard\n", + "\n", + "Default when you call `predict_words` it will open a browser with visualization dashboard, you can disable by `visualization=False`." ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Serving to http://127.0.0.1:8889/ [Ctrl-C to exit]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "127.0.0.1 - - [01/Jun/2019 12:12:10] \"GET / HTTP/1.1\" 200 -\n", + "127.0.0.1 - - [01/Jun/2019 12:12:10] \"GET /static/admin-materialize.min.css HTTP/1.1\" 200 -\n", + "127.0.0.1 - - [01/Jun/2019 12:12:10] \"GET /static/echarts.min.js HTTP/1.1\" 200 -\n", + "127.0.0.1 - - [01/Jun/2019 12:12:11] \"GET /favicon.ico HTTP/1.1\" 200 -\n", + "----------------------------------------\n", + "Exception happened during processing of request from ('127.0.0.1', 61873)\n", + "Traceback (most recent call last):\n", + " File \"/usr/local/Cellar/python/3.6.5_1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/socketserver.py\", line 317, in _handle_request_noblock\n", + " self.process_request(request, client_address)\n", + " File \"/usr/local/Cellar/python/3.6.5_1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/socketserver.py\", line 348, in process_request\n", + " self.finish_request(request, client_address)\n", + " File \"/usr/local/Cellar/python/3.6.5_1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/socketserver.py\", line 361, in finish_request\n", + " self.RequestHandlerClass(request, client_address, self)\n", + " File \"/usr/local/Cellar/python/3.6.5_1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/socketserver.py\", line 696, in __init__\n", + " self.handle()\n", + " File \"/usr/local/Cellar/python/3.6.5_1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/http/server.py\", line 418, in handle\n", + " self.handle_one_request()\n", + " File \"/usr/local/Cellar/python/3.6.5_1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/http/server.py\", line 406, in handle_one_request\n", + " method()\n", + " File \"/Users/huseinzol/Documents/Malaya/malaya/_utils/_server.py\", line 32, in do_GET\n", + " with open(filepath, 'rb') as fh:\n", + "FileNotFoundError: [Errno 2] No such file or directory: '/Users/huseinzol/Documents/Malaya/malaya/_utils/web/favicon.ico'\n", + "----------------------------------------\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "stopping Server...\n" + ] + } + ], + "source": [ + "model.predict_words(another_string)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ - "
" + "" ] }, "metadata": { - "needs_background": "light" + "image/png": { + "width": 800 + } }, "output_type": "display_data" } ], "source": [ - "model = malaya.toxic.deep_model('luong')\n", - "result = model.predict(another_string,get_proba=True)['attention']\n", + "from IPython.core.display import Image, display\n", "\n", - "plt.figure(figsize = (15, 7))\n", - "labels = [r[0] for r in result]\n", - "val = [r[1] for r in result]\n", - "aranged = [i for i in range(len(labels))]\n", - "plt.bar(aranged, val)\n", - "plt.xticks(aranged, labels, rotation = 'vertical')\n", - "plt.show()" + "display(Image('toxicity.png', width=800))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### Visualizing hierarchical model" + "#### Predict batch of strings" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { - "image/png": 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AAACDEPAAAAAGsdAkKwBwT9l+1BE5/LCN8+dpI6zDddvtO3LTjbdOXQYAm8DG+QsKAEkOP2xbnnb2JVOXsaFc+tpTYhlgABZhiCYAAMAgBDwAAIBBCHgAAACDEPAAAAAGIeABAAAMQsADAAAYhIAHAAAwCAEPAABgEAIeAADAIAQ8AACAQQh4AAAAgxDwAAAABiHgAQAADELAAwAAGISABwAAMAgBDwAAYBACHgAAwCAEPAAAgEEIeAAAAIMQ8AAAAAYh4AEAAAxCwAMAABiEgAcAADAIAQ8AAGAQAh4AAMAgBDwAAIBBCHgAAACDEPAAAAAGIeABAAAMQsADAAAYhIAHAAAwCAEPAABgEAIeAADAIAQ8AACAQQh4AAAAgxDwAAAABiHgAQAADELAAwAAGISABwAAMAgBDwAAYBACHgAAwCAEPAAAgEEIeAAAAIMQ8AAAAAYh4AEAAAxCwAMAABjEtkUOqqrTk5yb5F5JLujuN9zFcW9PcmV3v22+/eAkFya5f5JOckZ337wOdQMAALCbvV7Bq6oHJjk/yclJTkhyVlU9Yrdjjq+qS5M8Y7fT35jkjd39vUk+neRl61I1AAAAd7LIEM2nJLmiu2/o7luSvDvJabsdc0aSS5K8a7Whqg5N8sT58Unyttw5AAIAALBOFhmieXySa9dsX5vksWsP6O7XJElVnbym+X5JbuzuHWvOe9C+FHfssUfuy+EHpZWV7VOXMCT9ujz6djn06/j8jpdH3y6Hfl2e0fp2tMcztUUC3pY9tO1c4nl/6/rrb87Onbv25ZSDysrK9lx33U1TlzEc/bo8+nY5RutXf+j3bD1+x/p2zw60b/XrnnnOLs9or/kjPZ71tHXrlv264LXIEM1rkjxgzfZxSb62wHnXJTmqqg7Zx/MAAADYD4sEvA8leXJVrVTVvZOcmuQDezupu7+V5Kokz5o3nZnk8v0tFAAAgLu314DX3dckOSfJlUk+k+Si7v5kVV1WVSfu5fQXZDbr5ueTPCGzpRYAAABYgoXWwevui5JctFvbU/dw3HN22/5ykiftf3kAAAAsapEhmgAAAGwCAh4AAMAgBDwAAIBBCHgAAACDEPAAAAAGIeABAAAMQsADAAAYhIAHAAAwCAEPAABgEAIeAADAIAQ8AACAQQh4AAAAgxDwAAAABiHgAQAADELAAwAAGISABwAAMAgBDwAAYBACHgAAwCAEPAAAgEEIeAAAAIMQ8AAAAAYh4AEAAAxCwAMAABiEgAcAADAIAQ8AAGAQAh4AAMAgBDwAAIBBCHgAAACDEPAAAAAGIeABAAAMQsADAAAYhIAHAAAwCAEPAABgEAIeAADAIAQ8AACAQQh4AAAAgxDwAAAABiHgAQAADELAAwAAGISABwAAMAgBDwAAYBACHgAAwCAEPAAAgEEIeAAAAIMQ8AAAAAYh4AEAAAxCwAMAABiEgAcAADAIAQ8AAGAQAh4AAMAgBDwAAIBBbFvkoKo6Pcm5Se6V5ILufsNu+x+V5C1Jjk7y0SQ/3d07qurMJK9K8vX5oe/v7nPWq3gAAAC+ba9X8KrqgUnOT3JykhOSnFVVj9jtsAuTvKi7H55kS5Lnz9tPSvLi7n7U/Eu4AwAAWJJFhmg+JckV3X1Dd9+S5N1JTlvdWVUPSXJEd39i3vS2JM+Y/3xSkjOr6uqqurCqjlm/0gEAAFhrkSGaxye5ds32tUkeu5f9D1rz868l+WSSX03y+iRn7G+xAADAgdl+1BE5/LCF7tS6R6ysbJ+6hNx2+47cdOOtU5exLhb5zW7ZQ9vORfZ399NXG6rq1Um+uC/FHXvskfty+EFpI/yDGJF+XR59uxz6dXx+x8ujb5dDvy7PevTt086+ZB0qGcelrz0lhw/ynF0k4F2T5Alrto9L8rXd9j9g9/1VdXSSn+ruC+btW5J8a1+Ku/76m7Nz5659OeWgsrKyPdddd9PUZQxHvy6Pvl2O0frVm8I9W4/fsb7dswPtW/26Z56zy+M5uxwb7W/p1q1b9uuC1yL34H0oyZOraqWq7p3k1CQfWN3Z3V9OcltVPX7edGaSy5PcnOQlVfWD8/YXJnnvPlcIAADAQvYa8Lr7miTnJLkyyWeSXNTdn6yqy6rqxPlhZyS5oKr+PMl9kryuu+9I8swkb5q3PybJS5bxIAAAAFhwHbzuvijJRbu1PXXNz1fnOydeWW2/KsmjD7BGAAAAFrDIEE0AAAA2AQEPAABgEAIeAADAIAQ8AACAQQh4AAAAgxDwAAAABiHgAQAADELAAwAAGISABwAAMIhtUxcAsFltP+qIHH7YxngZXVnZPnUJSZLbbt+Rm268deoyAOCgtTHemQBsQocfti1PO/uSqcvYUC597Sm5aeoiAOAgZogmAADAIAQ8AACAQQh4AAAAgxDwAAAABiHgAQAADELAAwAAGISABwAAMAgBDwAAYBACHgAAwCAEPAAAgEEIeAAAAIMQ8AAAAAYh4AEAAAxCwAMAABiEgAcAADAIAQ8AAGAQAh4AAMAgBDwAAIBBCHgAAACDEPAAAAAGsW3qAoDl2n7UETn8sI3zT31lZfvUJeS223fkphtvnboMAIB1t3He9QFLcfhh2/K0sy+ZuowN5dLXnpKbpi4CAGAJDNEEAAAYhIAHAAAwCAEPAABgEAIeAADAIAQ8AACAQQh4AAAAgxDwAAAABiHgAQAADELAAwAAGISABwAAMAgBDwAAYBACHgAAwCC2TV0ArNp+1BE5/LCN8ZRcWdk+dQlJkttu35Gbbrx16jIAANgkNsa76U1kI4WQZGMEkfUKIYcfti1PO/uSdahoHJe+9pTcNHURAABsGhsnqWwSQsidCSEAALAxuAcPAABgEAIeAADAIAQ8AACAQQh4AAAAg1hokpWqOj3JuUnuleSC7n7DbvsfleQtSY5O8tEkP93dO6rqwUkuTHL/JJ3kjO6+eR3rBwAAYG6vV/Cq6oFJzk9ycpITkpxVVY/Y7bALk7youx+eZEuS58/b35jkjd39vUk+neRl61U4AAAA32mRK3hPSXJFd9+QJFX17iSnJfmV+fZDkhzR3Z+YH/+2JK+oqrcmeWKSn1jT/pEkv7jA//OQJNm6dctCD+Kedv9jjpi6hA1nvX5X+vbO1qNv9eudec4uj+fscnjOLo/n7HJ4zi6P5+xybLTssaaeQ/blvC27du262wOq6qVJ7tPd5863n5fksd191nz7h5K8prtPnm9/T5LLkvxwkk9194Pm7duS/E1332uBuk5OctW+PBAAAIABPSHJxxY9eJEreHuKsjsX2L+38+7OpzJ7INcmuWPBcwAAAEZxSJLjMstGC1sk4F2TWdhadVySr+22/wF72H9dkqOq6pDuvmMP592d27MPKRUAAGBA/2NfT1hkmYQPJXlyVa1U1b2TnJrkA6s7u/vLSW6rqsfPm85Mcnl3fyuzYZbPWtu+rwUCAACwmL0GvO6+Jsk5Sa5M8pkkF3X3J6vqsqo6cX7YGUkuqKo/T3KfJK+bt78gs1k3P5/ZVcBz1/sBAAAAMLPXSVYAAADYHBYZogkAAMAmIOABAAAMQsADAAAYhIAHAAAwCAEPAABgEAIeAADAILZNXQAwrqr6hSS/093/c+paYBFV9Zvd/aLd2t7e3c+eqqbNrqrO6u7fqqqX72l/d//KPV3TSKrqibs17Upya5IvdPdfT1DSEKrqYd39xd3aXtTdvzlVTbAoAW+TqarnJzk/ybHzpi1JdnX3IdNVtflV1cOS/Osk98usT5Mk3f1TkxU1hiOSfKSqvpDkbUku7u5vTVvS5ldVv53Zm7hVq2/o/jzJW7r7m5MUtolV1VuTPCzJiVX1D9bsOjTJ0dNUNYwtu31nfb08yYlJ/iizPn5Skr9MclRVvay73zldaZva5VX14939hap6ZJK3Jrk5iYB3gKrq5CS/kOTIzJ6zhyR5SHd/95R1jUTA23x+OcmPdPd/nbqQwbwnyYeSXJXvfOPMAZh/Mv8r8xfz05O8oqquSPLW7v7MtNVtajuS3DfJ2+fb/zzJ9iR3JPk/k/hgYt+9Msl3J/mNJK9Y074js+DMfuruN8+/v2Jvx7JftiT5/u7+SpJU1fFJfjuzoPfhJALe/nlukt+vqj9McmqSl3b370xc0yjemuRVSZ6T5HVJfizJn05Z0GgEvM3nfwl3S7Glu39h6iJGVFX3TvLQzK6O7ExyQ5LfqKqPd/dLJy1u8/qB7j5xdaOqLk3yx939zKq6esK6Nq3u/svMrnqcUFX3T/KDSb6V5JPdfcOEpQ2jqr6a5Pgkq8MG/8785y8meb4Pffbb8avhLkm6+2tVdVx331hVrprup+7+eFX9ZJLLk5ze3R+euKSR3Nrdv11V353kG0men+RPpi1pLALeJlFVZ85//HJVXZLkksw+WU6SdPf/PUlh4/h4VT09ySXdvXPqYkZRVe9I8uQk70/yyu7+2Lz9sCTXJhHw9s99quoBa+5tvH9mw2ETr+sHpKpOy+wT5Y9nNhHZb83vIfvAtJUN4SNJ3t3dFydJVf1Ykmdm1t9vSPL4CWvbzP6fqrooyTsye87+8yT/uap+PLMhheyDqtqZb4/kWQ3IV1RV4paY9XJbVd03SSd5XHdfUVX3mbqokXgjsHn8yPz7LfOvJ6zZtyuJgLcf1ryQb0ny0/O21d1eyA/cHyU5q7tvWdvY3bdX1SMmqmkE/zbJn1TVxzO7d+HEJD9bVecl+cMpCxvAy5I8pruvTZKqekiS308i4B247+vuf7G60d2XV9Uru/vPquqIuzuRu/XTSX4myVmZDdP+wyRvSfKPkvzLCevalLrbDPPL9++T/G6Sf5bkU1V1RlzBW1dbdu1yu9FmU1WHJqnMAvrnunvHXk6BScyHup2R77yR+qHdfebdnsheVdX9Mvug544kH+/uv6qq+xpOeGCq6tNJTuruXWva/qS7HzNhWUOoqg9ndj/YhZldaTojs3ubXpTkP3b3o6arbnOrqqMymwxo7SRhX7nrM9gbf7+Wq6q2dPeu+ZW7hyf5zNrXXQ6MK3ibTFU9JrMJQa7P7A/k362qp3f3H09b2ebmhXxpfi/J/0jyuCQXZ/aJsnvEDlBV/Z0kz8psopUtSR5VVaabPwBrhsF/KcmlVfX2zIbB/2Q8Z9fLGZlNYvPqzD6Y+GCSM5OcluSXJqxrU6uqX86s/67Pt0ek7Mrsvmf2n79fSzIfGfHC+TDNtfeJmiBsnQh4m8/rkjxrNdBV1eMym7L3sZNWtfl5IV+O+3X3yVX165n18a9mNlspB+Y/Jfn/knwuZn1dL6vD4G+efz11vn1LTO+/Lrr7mszC3N+qqiOsK3bA/lWSv9fd101dyGD8/Vqed2U2a7mZy5dEwNt8jlx7ta67P1FVh09Z0CC8kC/HN+bfO8kJ3f3HVXWvKQsaxAO6+x9OXcRIuvu5U9cwuqo6NbM129aOlLh3kpUp6xrAVzKbnZj15e/X8hza3T8/dREjE/A2nxuq6pTuviRJquonMhuWwYHxQr4cf1RV/ynJzyf5YFU9OsnfTFzTCP6sqr6/uz87dSGjqaq/yCx4rFq7iPzPd/eXJylsDK9O8rwkZyc5P8k/TnK/SSsaw18k+VhVXZnkttVGQ7YPmL9fy/Oxqnpakj/o7m9OXcyIBLzN518n+Z2q+g+ZfQL6hZglaz14IV+Ov8hsjasfTvLmJPfJbErv7+vuz01a2eb2fZmFvK9n9oZuS2azvrrn5sBdntlz9j/Mt89IclKSS5P8X0meMlFdI/hGd19ZVY9PcnR3n1dVZs47cNfMvxLDidfT/5bkF7r7y/P18H44yd+fuKZRnJbkhYmZy5fFLJqb1HzWoa3dfdPUtWxmayZWSGYfeOzI7BPlnUmO6O7/Y5LCBlFV70nyqMzWbUySf5rZG5Ejk1zU3RdMVdtmNr9B/U5cXTpwVfWn3f3o3do+3d0n7mkfi6uqqzK7X+yRmYXmlyf5fHd/z6SFDWa+uPlDu/uLU9eyGVXVe5OckOT4JF9bs+vQJF/u7pMnKQz2gSt4m8R86MWd0vjqJx/d/aP3dE2DWJ1Y4WFJviezT+93JPknSf7rVEUN5AGZrSn210kyX6ft0iQ/lNmaNwLe/vlKZmtfPTn1tIN3AAAGmklEQVSz1/Erkrx+0orGcUdV/ePu/oMkqap/kuSbVfV3M3uDx/47J8krk/yLJL+Y2YiUt05a0QCq6oWZ3Te+dqHoL2X2N4199+zMZij+jST/+5r2HUm+PklFg6mql++p3bDi9SPgbR7nzb8/P7P7QdZO4W2B2P20OrHCPECf0N1/Nd8+JrPZNDkwK0nWXmW+Ncl9u3tHVRk+sP9endlQodWh2s9N8tAk/2bKogbxnCRvr6oL8+1h8M/ObBHpX5+wrk1rtw8ot2S2aPwtmX1QYX3BA3d2Zleczk/yy0melMQkTPupu29McmOSU6auZWBrhxIfmtmH6pb7WkcC3ibR3R9Jkqr69e4+ac2uT8wX5uXAHJ/vnIXsliTHTVTLSN6T5Iqqeldm6zaemuTi+dDYayetbHP7R0l+oLt3JklVvT/Jf4mAt992CyE3ZTY0647MPpR4k1ESB+S8qQsY3P/q7i9V1WeTPLK73za/qgcbUne/Yu12Vf27zNbFZJ0IeJvPEVX18O7+70lSVY+MYUPr4f1J/rCqfi+zIPKMJL87bUmbX3e/tKr+aWafJt+R5FXdffl8/cbTp61uU9s2//rmmu07pitnCOdNXcCoVj+gZGluqaofSfLZJD9RVZ9KcszENcG+ODLJg6cuYiQC3ubz4iQfrqprMpvKeyWzYZocgO5+8XyNpidl9in+r3f3709b1Ri6+31J3rdb2ycmKmcU78jsdeCd8+2fTPLOuzmevRBC2MRelG8vP/GvMlvu57wpC4K7U1VfyncO2z4myWumq2g8ZtHchObrsz0ys5ke/0t375i4JOAeVlU/luRHM7vifEV3v3/ikgBgr6rq2ZkFvC1JHpLkLzNbmuq/WUJpfQh4m0xVrWQ2W95TMruCd0WSn+luMzvBQaKqntbdl67ZPi7J67v71AnLAiZQVacleWl2G5ZpXUw2qjVLKF2cWcizhNI62zp1AeyzNyf5VGYz5j0kyScyW3wXOHj8alU9PUmq6gVJPjP/Ag4+r81sgqUf2e0LNqrVJZTO7u4XJzkxs0zyQ5nNZMwBcg/e5vOw7v5na7ZfXVX/crJqgCk8Ocn7quplSa5L8vju/sLENQHT+EKSj63OqgubgCWUlkzA23x2VdV3dfdXk6SqHpzkWxPXBNwDquqJazZfmdkV/bcnOb6qju/uj05TGTCh1ya5sqo+ktn6uEksGs2GZgmlJRPwNp+XJfnPVbW6IOTjMluAFxjfK3bb/m9JfmL+tSuzSVeAg8v5Sf4ss6VStuzlWJicJZSWzyQrm0xVHZrZzdQ/l9kbul/LbEp/v0g4yFTV9iSHdPdfT10LMI2q+lx3f9/UdQAbh4C3yVTVbyXZnuSizD6pe3aSr3b3z01aGHCPqaqHJfmPSf5eZq8DX07yzO7+i0kLA+5xVfVrSf7fJB9I8s3V9u7+ymRFAZMyRHPzeVx3f//qRlW9L8nVE9YD3PPenOTV3f3uJKmqZyZ5S5InTVkUMIlnzb+fvaZtVxLLJMBBSsDbfK6pqod19xfn28fHDalwsLnfarhLku5+V1WdO2VBwDS6+6FT1wBsLALeJlFVV2b2idxKkqur6qOZzZb1hCSfm7I24B53e1U9urv/NEmq6sQkfzNxTcAEquqYJK/ObMj2M5K8JsmL3ZsLBy8Bb/M47y7a//09WQSwIfxskvdU1Q3z7WPz7WFawMHlLUk+mOSxma0tdm2SdyT58SmLAqYj4G0S3f2RqWsANoxdSd6Y5LIkv5nkoUkelOSP7+4kYEgP7e7fqqqf6e5vJjmnqtybDwexrVMXAMA+e11mYe6EJDfOv//SpBUBU9lRVUdn9sFPqurvJ9k5bUnAlAQ8gM1na3d/NLMhWO/p7q/GiAw4WP3bJB9O8uCqujjJx5KYdAkOYgIewObzN1V1dpIfTfK+qvrZzO69AQ4+f5LkvUm+lOTBSX4vyWMmrQiYlIAHsPmckeQ+SU7t7m9ktlzK6dOWBEzksiQPSfK+JJck+Z9JtkxaETCpLbt27Zq6BgAA9kNVfaq7T5q6DmDjEPAAADapqjonydeTXJHZ+rhJku7+ymRFAZNyUz4AwOZ1dGaz6P7VmrZdSR42TTnA1AQ8AIDN69Qk9+/uW6cuBNgYTLICALB5fTHJMVMXAWwcruABAGxeu5J8vqo+l+Sbq43d/aPTlQRMScADANi8zp+6AGBjMYsmAADAINyDBwAAMAgBDwAAYBACHgAAwCAEPAAAgEEIeAAAAIP4/wFqIyXGg4m4ugAAAABJRU5ErkJggg==\n", 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" + "[{'toxic': 0.21699297,\n", + " 'severe_toxic': 0.021610105,\n", + " 'obscene': 0.110013016,\n", + " 'threat': 0.010765828,\n", + " 'insult': 0.10372056,\n", + " 'identity_hate': 0.03946534},\n", + " {'toxic': 0.93524134,\n", + " 'severe_toxic': 0.33446547,\n", + " 'obscene': 0.7178085,\n", + " 'threat': 0.56429744,\n", + " 'insult': 0.8922918,\n", + " 'identity_hate': 0.9618688}]" ] }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "model = malaya.toxic.deep_model('hierarchical')\n", - "result = model.predict(another_string,get_proba=True)['attention']\n", + "model.predict_batch([string, another_string],get_proba=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**You might want to try `luong` and `self-attention` by yourself.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Stacking models\n", "\n", - "plt.figure(figsize = (15, 7))\n", - "labels = [r[0] for r in result]\n", - "val = [r[1] for r in result]\n", - "aranged = [i for i in range(len(labels))]\n", - "plt.bar(aranged, val)\n", - "plt.xticks(aranged, labels, rotation = 'vertical')\n", - "plt.show()" + "More information, you can read at [https://malaya.readthedocs.io/en/latest/Stack.html](https://malaya.readthedocs.io/en/latest/Stack.html)" ] }, { @@ -486,7 +564,20 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "multinomial = malaya.toxic.multinomial()\n", + "logistics = malaya.toxic.logistic()\n", + "bahdanau = malaya.toxic.deep_model('bahdanau')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "malaya.stack.predict_stack([multinomial, logistics, bahdanau], another_string)" + ] } ], "metadata": { diff --git a/example/toxicity/load-toxic_files/load-toxic_23_0.png b/example/toxicity/load-toxic_files/load-toxic_23_0.png deleted file mode 100644 index cd8b0958ed38824f7e82647c13352747f7d3eeea..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 8715 zcmeHNc~}$Yx{rzntyQ$N(pCi20Zyxcg@T6ls6`ppRveIBD~rgI5|%VVh}EOGfjE&( zz|^H8!)6H(L)Zkj2!d52OBhfYKogi~Fi9Z>?nlo(_n!7HPkW!|{#TxdFHdIP`R1GV z`~BYc_r94EUwZFY`p(DiP$-n8u-g|qDU=0t3T58yMSlTD_!;dB!G})bR(RJU@RznI z{5bf&_#3x?L<(iedvpKu9^vV4!9i-0>+U39T4WMqFFk@1w>RmVSXxqS^w%aS5%k1p zTD*yk_cnbM-OfIirKkw&+hrp=k3bfvXFXw@wF`h@2|DI)<*wJ_!4(R*K_m24eb^l{eIDX zBOT9PvufJf>38qhU;HWm?`ux1$xrGs=DUAd>^+^rz~7Ronn`Mjx0og#I zIN>>A1P^YKi@>qfF#YC2gQ&625dFOBhAijl<3)@?SKquJ>2Hz~-lTl?+fjwZ4K-Qp zSe@J$?zHMbjMRj|M8^jN9@&y5+JJXiA%=}1|7FC>SoPwLaP!|TJd#W$FzGbh>8-eo zYmeetxUWmdW`Ybd@5?2A3i34q zQwj?i`$sNw-tI)J#?!yY;?EMV$n@Fi$~<%CtK40{i!C`Fwl&p4;~WU7Eor+udNRgn z+>Op#Q28$Kr&IsQWTI|j?@%XeN_Xz@E&jT%wxVJ+HF@!RILu%N&0yPaNB$E(9YG>G z5dK*8Szp`ojR7Et}DO}#vsMlo2DK@T@@JHjot`#4V`=r&fchIeH_?_ z^9SB2wRSA+35d~GUp;sHQ(eazz>PyOx&I2Apda5-uIpqq%sdlx?rooJeq&w!!DOa~ zRr}Gf=5VPsg;F=5cq`U5%$6CZ>^FM#^Cr)bJ3E<=U;K`z0h`BlQ02*V?jAt}|!yjgNDlRF9!@L!bac!+d)J|BeYJhy?EP~X% zH7rlGDz?8V4)v(l9Fa+^+!ckIowt}{A?J!67!&Q=mVFu%vtx81(Wq^apuV!HbEnU&x;Sb3`OfEJ=Du@A)w!0mDqdKq?S%GN&k?h%3xu+ld&vkP4d1 zMp1@J0JuRJt?n`D!N(w`JIX`T97Qi>+x|Kfh?pm-7s5to30p)df?NuTvrEc9qEMcG zZ`Wf_$5{u_#|#Oh*(;2DmN;Euv2-qC6h!Se1@n1)@qY`{f0E(%kl_z_Y4jn}8^bsp zM_W-X$B{1#yCHe)uDAs4Fr7Mpa>vOfbvOLLep%lId89rXeT)t`iC)9rrI;kvzR5zg zit{#wVaz4=!53=s=^*3eN9`YBp+$Z87(;y<4lN|Ec-@uf6VJ(X$+U`Q%G?Vk+kKpQ z%+IJW{O;9>)i7Bdtr4PWwOHs`Vi`PRDcZ*`-wKQ>_gpaH$LVQONEPkv9#k$T_58&& zvneffCZB)&@)_r^2YvMd0ouJoTHPP@i1`Oxh7nDA?ulPzj`#}Z;Q=a4{ur&vhr;@5 zv|@<+lGLlBHQV!^r98|?=D zU!~>70}?(>Qne&#a$udh6H-@VwOD;Ts?TYNd;FTmDM!SwWfDC%#aULR4nVwM-1c6w znhIY)iE_x;hklU3zK#w4LLF1s=p12+DAh2TL%cq<;bTCZJ-?w2+r+V zkL$WWK!iW693v}D{1J11aY>n!hbTS#69=_r(#atIUu2aeCc6wPG^1LGaE>9DIEU3g zFzVE(pf0#NuIv=R#nYfs!CD3vib_#W;j9F-$BY`SH`e&M$cA6Nc@F-u7fcwj=<32c z1|;Yk)yJg%Xxir@4Zrepa8^Y;q;~c_{!&!DDr}cHZZ2#O_E|iAAbWB7OfNTM^{yvR zvWn{eo^z$Dj5Y^i)o($J2TuMX!}b9g1~&l;-jr#ai->+Ws{&J}s7LPxS4?&a|LVXX z&#T`Y>Fn=6OH~gnDY4u)QXUxAxS8z3w5R=FY68p-3O}5#-~8`ms<@6eR2ODYH@cWE z_oN>{Doh2Nd1bVcSi^BL3Sw?YcZn@H4KZO`fhMPDBCFK@JvBDe|833%`!B4gza$@X zikc)%nu%u(R*+!C?|o3|v|M;lYcl7h_HBQrk>6e9{C|?+AMEM4_+Leso~NP%lAvkM zqIE*e;r~&E6=sFGh>Xny?73{hfxScAYQg@3+T%Am?CNljyW$ki>YP@Ya|Y64+5wmL zv^j2Df5xjCfH@1O`ra4hB--E%W71cUkGtY9>yzhmw8(p`=l;9WMV-^QM4-vS>bFFG zN9yC+lVE~|Nz414#X{A{8r>YA))Gm9fp^HA`vJ902NMQ(h~}fJM{1tuuS&jOioLjI z%h)-cSOjhA`-gaQzKCVPib06$rnttcoIjT#9wfSw#jtk;Rtp)JQ$G`ZvP!p1nhnvs z73azHCtI<(aN+WgG=eD4K~WKPZ(BTsnj?tv3FPCW068?9$j18fbw=9oiTiWq?);A0 ztY8NB0`Ue{;WTZ#-rd%@5~%0Hd7y0jeI@b#KU)60Lq=!fPNcYqD1|GF2?4Hc#j}Qf zUBy2S1e)re;g)|EE$;MalvgG#CuiPVeH>aqe1&Ugu!8~U4)dut*Z0%BsEX-~G5d@M zIf+#(egXT|ri7Off>*LU&kzte4d0BypQ>ScB!kO^5M@7PZaS5zoCt!b4Z@-3^qF>! 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zvu)<^P#}Pi-F0_fuY?`4@XGA#E2AP%fSd1@pPEnv&}z0sn^r<4S)Hc9y;=ATNSKXZ zfET6Xsd54ea55Yk?>h>AAZVsG#aK_S;grhjz=NoBh^2(=A&x=T%|iXt%byu|bbbR) z?UZ-iF`1m)X%hk0rBAX;YzhTO%WF7GyyQO+hCYP-9nx02}RWByJnbasY^Y8y2MFcVM7q0!^<^%}P(4QlZXE!yRC^A%YfE z?fdA5){IFm;Fb+xg#a9&s0-n0m))PNDjDxYbGYDfGvy{E9^<*^aAyU)8g~V(O0e~0 zk(lk9HlB#!H1EUa(mfnVb$#H#pYul7mQziE7g@R5)I9?RdScVSGe{*Te2UE{b<7=q zo~a@x{wvW}9!3V?3(7!S7w*SH;{=#PCZ1ag?CDFb=O($pbrVS){N_R~DIoBxQy-28 idXoOjhq>PC4fZcJd60DS78f**BzUvi*JT?6zWslT$`auK diff --git a/malaya/__init__.py b/malaya/__init__.py index 8fbd66b4..4129d3e2 100644 --- a/malaya/__init__.py +++ b/malaya/__init__.py @@ -17,8 +17,8 @@ from pathlib import Path home = os.path.join(str(Path.home()), 'Malaya') -version = '2.3' -bump_version = '2.3.5' +version = '2.4' +bump_version = '2.4.0' version_path = os.path.join(home, 'version') @@ -301,6 +301,7 @@ def describe_dependency(): from . import num2word from . import pos from . import preprocessing +from . import relevancy from . import sentiment from . import similarity from . import spell diff --git a/malaya/_models/_sklearn_model.py b/malaya/_models/_sklearn_model.py index 22c1a8b4..309e6716 100644 --- a/malaya/_models/_sklearn_model.py +++ b/malaya/_models/_sklearn_model.py @@ -8,6 +8,7 @@ entities_textcleaning, language_detection_textcleaning, ) +from .._utils._utils import add_neutral as neutral from .._utils._parse_dependency import DependencyGraph from ..texts.vectorizer import features_crf, features_crf_dependency @@ -195,13 +196,212 @@ def print_transitions_index(self, top_k = 10): ) -class USER_XGB: +class XGB: def __init__(self, xgb, label, vectorize, cleaning = simple_textcleaning): - self.xgb = xgb - self.label = label - self.vectorize = vectorize + self._xgb = xgb + self._label = label + self._vectorize = vectorize + self._cleaning = cleaning + + +class BAYES: + def __init__( + self, multinomial, label, vectorize, cleaning = simple_textcleaning + ): + self._multinomial = multinomial + self._label = label + self._vectorize = vectorize self._cleaning = cleaning + +class BINARY_XGB(XGB): + def __init__(self, xgb, label, vectorize, cleaning = simple_textcleaning): + XGB.__init__(self, xgb, label, vectorize, cleaning) + + def predict(self, string, get_proba = False, add_neutral = True): + """ + Classify a string + + Parameters + ---------- + string : str + get_proba: bool, optional (default=False) + If True, it will return probability of classes. + add_neutral: bool, optional (default=True) + if True, it will add neutral probability. + + + Returns + ------- + string: result + """ + if not isinstance(string, str): + raise ValueError('input must be a string') + if not isinstance(get_proba, bool): + raise ValueError('get_proba must be a boolean') + if not isinstance(add_neutral, bool): + raise ValueError('add_neutral must be a boolean') + + if add_neutral: + label = self._label + ['neutral'] + else: + label = self._label + + vectors = self._vectorize.transform([self._cleaning(string)]) + result = self._xgb.predict( + xgb.DMatrix(vectors), ntree_limit = self._xgb.best_ntree_limit + ) + if add_neutral: + result = neutral(result) + result = result[0] + if get_proba: + return {label[i]: result[i] for i in range(len(result))} + else: + return label[np.argmax(result)] + + def predict_batch(self, strings, get_proba = False, add_neutral = True): + """ + Classify a list of strings + + Parameters + ---------- + strings: list + get_proba: bool, optional (default=False) + If True, it will return probability of classes. + add_neutral: bool, optional (default=True) + if True, it will add neutral probability. + + Returns + ------- + string: list of results + """ + if not isinstance(strings, list): + raise ValueError('input must be a list') + if not isinstance(strings[0], str): + raise ValueError('input must be list of strings') + if not isinstance(get_proba, bool): + raise ValueError('get_proba must be a boolean') + if not isinstance(add_neutral, bool): + raise ValueError('add_neutral must be a boolean') + + if add_neutral: + label = self._label + ['neutral'] + else: + label = self._label + + strings = [self._cleaning(string) for string in strings] + vectors = self._vectorize.transform(strings) + results = self._xgb.predict( + xgb.DMatrix(vectors), ntree_limit = self._xgb.best_ntree_limit + ) + if add_neutral: + results = neutral(results) + if get_proba: + outputs = [] + for result in results: + outputs.append( + {label[i]: result[i] for i in range(len(result))} + ) + return outputs + else: + return [label[i] for i in np.argmax(results, axis = 1)] + + +class BINARY_BAYES(BAYES): + def __init__( + self, multinomial, label, vectorize, cleaning = simple_textcleaning + ): + BAYES.__init__(self, multinomial, label, vectorize, cleaning) + + def predict(self, string, get_proba = False, add_neutral = True): + """ + Classify a string + + Parameters + ---------- + string : str + get_proba: bool, optional (default=False) + If True, it will return probability of classes. + add_neutral: bool, optional (default=True) + if True, it will add neutral probability. + + Returns + ------- + string: result + """ + if not isinstance(string, str): + raise ValueError('input must be a string') + if not isinstance(get_proba, bool): + raise ValueError('get_proba must be a boolean') + if not isinstance(add_neutral, bool): + raise ValueError('add_neutral must be a boolean') + + if add_neutral: + label = self._label + ['neutral'] + else: + label = self._label + vectors = self._vectorize.transform([self._cleaning(string)]) + result = self._multinomial.predict_proba(vectors) + if add_neutral: + result = neutral(result) + result = result[0] + if get_proba: + return {label[i]: result[i] for i in range(len(result))} + else: + return label[np.argmax(result)] + + def predict_batch(self, strings, get_proba = False, add_neutral = True): + """ + Classify a list of strings + + Parameters + ---------- + strings: list + get_proba: bool, optional (default=False) + If True, it will return probability of classes. + add_neutral: bool, optional (default=True) + if True, it will add neutral probability. + + Returns + ------- + string: list of results + """ + if not isinstance(strings, list): + raise ValueError('input must be a list') + if not isinstance(strings[0], str): + raise ValueError('input must be list of strings') + if not isinstance(get_proba, bool): + raise ValueError('get_proba must be a boolean') + if not isinstance(add_neutral, bool): + raise ValueError('add_neutral must be a boolean') + + if add_neutral: + label = self._label + ['neutral'] + else: + label = self._label + + strings = [self._cleaning(string) for string in strings] + vectors = self._vectorize.transform(strings) + results = self._multinomial.predict_proba(vectors) + + if add_neutral: + results = neutral(results) + + if get_proba: + outputs = [] + for result in results: + outputs.append( + {label[i]: result[i] for i in range(len(result))} + ) + return outputs + else: + return [label[result] for result in np.argmax(results, axis = 1)] + + +class MULTICLASS_XGB(XGB): + def __init__(self, xgb, label, vectorize, cleaning = simple_textcleaning): + XGB.__init__(self, xgb, label, vectorize, cleaning) + def predict(self, string, get_proba = False): """ Classify a string @@ -218,14 +418,17 @@ def predict(self, string, get_proba = False): """ if not isinstance(string, str): raise ValueError('input must be a string') - vectors = self.vectorize.transform([self._cleaning(string)]) - result = self.xgb.predict( - xgb.DMatrix(vectors), ntree_limit = self.xgb.best_ntree_limit + if not isinstance(get_proba, bool): + raise ValueError('get_proba must be a boolean') + + vectors = self._vectorize.transform([self._cleaning(string)]) + result = self._xgb.predict( + xgb.DMatrix(vectors), ntree_limit = self._xgb.best_ntree_limit )[0] if get_proba: - return {self.label[i]: result[i] for i in range(len(result))} + return {self._label[i]: result[i] for i in range(len(result))} else: - return self.label[np.argmax(result)] + return self._label[np.argmax(result)] def predict_batch(self, strings, get_proba = False): """ @@ -245,30 +448,30 @@ def predict_batch(self, strings, get_proba = False): raise ValueError('input must be a list') if not isinstance(strings[0], str): raise ValueError('input must be list of strings') + if not isinstance(get_proba, bool): + raise ValueError('get_proba must be a boolean') + strings = [self._cleaning(string) for string in strings] - vectors = self.vectorize.transform(strings) - results = self.xgb.predict( - xgb.DMatrix(vectors), ntree_limit = self.xgb.best_ntree_limit + vectors = self._vectorize.transform(strings) + results = self._xgb.predict( + xgb.DMatrix(vectors), ntree_limit = self._xgb.best_ntree_limit ) if get_proba: outputs = [] for result in results: outputs.append( - {self.label[i]: result[i] for i in range(len(result))} + {self._label[i]: result[i] for i in range(len(result))} ) return outputs else: - return [self.label[i] for i in np.argmax(results, axis = 1)] + return [self._label[i] for i in np.argmax(results, axis = 1)] -class USER_BAYES: +class MULTICLASS_BAYES(BAYES): def __init__( self, multinomial, label, vectorize, cleaning = simple_textcleaning ): - self.multinomial = multinomial - self.label = label - self.vectorize = vectorize - self._cleaning = cleaning + BAYES.__init__(self, multinomial, label, vectorize, cleaning) def predict(self, string, get_proba = False): """ @@ -286,12 +489,15 @@ def predict(self, string, get_proba = False): """ if not isinstance(string, str): raise ValueError('input must be a string') - vectors = self.vectorize.transform([self._cleaning(string)]) + if not isinstance(get_proba, bool): + raise ValueError('get_proba must be a boolean') + vectors = self._vectorize.transform([self._cleaning(string)]) + result = self._multinomial.predict_proba(vectors)[0] if get_proba: - result = self.multinomial.predict_proba(vectors)[0] - return {self.label[i]: result[i] for i in range(len(result))} + + return {self._label[i]: result[i] for i in range(len(result))} else: - return self.label[self.multinomial.predict(vectors)[0]] + return self._label[np.argmax(result)] def predict_batch(self, strings, get_proba = False): """ @@ -311,20 +517,21 @@ def predict_batch(self, strings, get_proba = False): raise ValueError('input must be a list') if not isinstance(strings[0], str): raise ValueError('input must be list of strings') + if not isinstance(get_proba, bool): + raise ValueError('get_proba must be a boolean') strings = [self._cleaning(string) for string in strings] - vectors = self.vectorize.transform(strings) + vectors = self._vectorize.transform(strings) + results = self._multinomial.predict_proba(vectors) if get_proba: - results = self.multinomial.predict_proba(vectors) outputs = [] for result in results: outputs.append( - {self.label[i]: result[i] for i in range(len(result))} + {self._label[i]: result[i] for i in range(len(result))} ) return outputs else: return [ - self.label[result] - for result in self.multinomial.predict(vectors) + self._label[result] for result in np.argmax(results, axis = 1) ] diff --git a/malaya/_models/_tensorflow_model.py b/malaya/_models/_tensorflow_model.py index 9b5d4eab..b7612b4e 100644 --- a/malaya/_models/_tensorflow_model.py +++ b/malaya/_models/_tensorflow_model.py @@ -14,7 +14,10 @@ language_detection_textcleaning, ) from .._utils._parse_dependency import DependencyGraph +from ..preprocessing import preprocessing_classification_index from ..stem import _classification_textcleaning_stemmer +from .._utils._utils import add_neutral as neutral +from .._utils._html import _render_binary, _render_toxic, _render_emotion def _convert_sparse_matrix_to_sparse_tensor(X, got_limit = True, limit = 5): @@ -428,42 +431,58 @@ def __init__( self, X, logits, + logits_seq, + alphas, sess, - mode, dictionary, - alphas = None, - input_mask = None, - segment_ids = None, - is_training = None, - dropout_keep_prob = None, - story = None, - maxlen = 80, + class_name, label = ['negative', 'positive'], ): self._X = X self._logits = logits + self._logits_seq = logits_seq + self._alphas = alphas self._sess = sess - self._mode = mode self._dictionary = dictionary - self._alphas = alphas - self._input_mask = input_mask - self._segment_ids = segment_ids - self._is_training = is_training - self._dropout_keep_prob = dropout_keep_prob - self._story = story - self._maxlen = maxlen self._label = label + self._class_name = class_name - def get_dictionary(self): - return self._dictionary - def predict(self, string, get_proba = False): +class BINARY_SOFTMAX(SOFTMAX): + def __init__( + self, + X, + logits, + logits_seq, + alphas, + sess, + dictionary, + class_name, + label = ['negative', 'positive'], + ): + SOFTMAX.__init__( + self, + X, + logits, + logits_seq, + alphas, + sess, + dictionary, + class_name, + label, + ) + + def predict(self, string, get_proba = False, add_neutral = True): """ classify a string. Parameters ---------- string : str + get_proba: bool, optional (default=False) + If True, it will return probability of classes. + add_neutral: bool, optional (default=True) + if True, it will add neutral probability. Returns ------- @@ -471,70 +490,123 @@ def predict(self, string, get_proba = False): """ if not isinstance(string, str): raise ValueError('input must be a string') - string = _classification_textcleaning_stemmer(string, attention = True) - splitted = string[1].split() - if self._mode in ['entity-network', 'bert']: - batch_x = str_idx( - [string[0]], self._dictionary, self._maxlen, UNK = 3 - ) + if not isinstance(get_proba, bool): + raise ValueError('get_proba must be a boolean') + if not isinstance(add_neutral, bool): + raise ValueError('add_neutral must be a boolean') + + if add_neutral: + label = self._label + ['neutral'] else: - batch_x = str_idx( - [string[0]], self._dictionary, len(splitted), UNK = 3 - ) - if self._mode in ['luong', 'bahdanau', 'hierarchical']: - probs, alphas = self._sess.run( - [tf.nn.softmax(self._logits), self._alphas], - feed_dict = {self._X: batch_x}, - ) - if self._mode == 'hierarchical': - alphas = alphas[0] - words = [] - for i in range(alphas.shape[0]): - words.append([splitted[i], alphas[i]]) - if self._mode in ['bidirectional', 'fast-text']: - probs = self._sess.run( - tf.nn.softmax(self._logits), feed_dict = {self._X: batch_x} - ) - if self._mode == 'bert': - np_mask = np.ones((1, self._maxlen), dtype = np.int32) - np_segment = np.ones((1, self._maxlen), dtype = np.int32) - probs = self._sess.run( - tf.nn.softmax(self._logits), - feed_dict = { - self._X: batch_x, - self._input_mask: np_mask, - self._segment_ids: np_segment, - self._is_training: False, - }, - ) - if self._mode == 'entity-network': - batch_x_expand = np.expand_dims(batch_x, axis = 1) - probs = self._sess.run( - tf.nn.softmax(self._logits), - feed_dict = { - self._X: batch_x, - self._story: batch_x_expand, - self._dropout_keep_prob: 1.0, - }, - ) + label = self._label + + tokenized_indices, splitted = preprocessing_classification_index(string) + batch_x = str_idx( + [' '.join(splitted)], self._dictionary, len(splitted), UNK = 3 + ) + result, alphas = self._sess.run( + [tf.nn.softmax(self._logits), self._alphas], + feed_dict = {self._X: batch_x}, + ) + if add_neutral: + result = neutral(result) + result = result[0] if get_proba: - dict_result = {} - for no, label in enumerate(self._label): - dict_result[label] = probs[0, no] - if self._mode in ['luong', 'bahdanau', 'hierarchical']: - dict_result['attention'] = words + dict_result = {label[i]: result[i] for i in range(len(result))} + dict_result['attention'] = { + k: alphas[v] if v > -1 else 0.0 + for k, v in tokenized_indices.items() + } return dict_result else: - return self._label[np.argmax(probs[0])] + return label[np.argmax(result)] - def predict_batch(self, strings, get_proba = False): + def predict_words(self, string, visualization = True): + """ + classify words. + + Parameters + ---------- + string : str + visualization: bool, optional (default=True) + If True, it will open the visualization dashboard. + + Returns + ------- + dictionary: results + """ + + if not isinstance(string, str): + raise ValueError('input must be a string') + if not isinstance(visualization, bool): + raise ValueError('visualization must be a boolean') + + label = self._label + ['neutral'] + + tokenized_indices, splitted = preprocessing_classification_index(string) + batch_x = str_idx( + [' '.join(splitted)], self._dictionary, len(splitted), UNK = 3 + ) + result, alphas, words = self._sess.run( + [ + tf.nn.softmax(self._logits), + self._alphas, + tf.nn.softmax(self._logits_seq), + ], + feed_dict = {self._X: batch_x}, + ) + result = neutral(result) + result = result[0] + words = neutral(words[0]) + distribution_words = words[:, np.argmax(words.sum(axis = 0))] + y_histogram, x_histogram = np.histogram( + distribution_words, bins = np.arange(0, 1, 0.05) + ) + y_histogram = y_histogram / y_histogram.sum() + x_attention = np.arange(len(alphas)) + neutral_word = [0.0 for _ in range(len(self._label))] + neutral_word.append(1.0) + left, right = np.unique( + np.argmax(words, axis = 1), return_counts = True + ) + left = left.tolist() + y_barplot = [] + for i in range(len(label)): + if i not in left: + y_barplot.append(i) + else: + y_barplot.append(right[left.index(i)]) + + dict_result = {label[i]: result[i] for i in range(len(result))} + dict_result['alphas'] = { + k: alphas[v] if v > -1 else 0.0 + for k, v in tokenized_indices.items() + } + dict_result['word'] = { + k: words[v] if v > -1 else neutral_word + for k, v in tokenized_indices.items() + } + dict_result['histogram'] = {'x': x_histogram, 'y': y_histogram} + dict_result['attention'] = {'x': x_attention, 'y': alphas} + dict_result['barplot'] = {'x': label, 'y': y_barplot} + dict_result['class_name'] = self._class_name + if visualization: + _render_binary(dict_result) + else: + return dict_result + + def predict_batch(self, strings, get_proba = False, add_neutral = True): """ classify list of strings Parameters ---------- strings : list + get_proba: bool, optional (default=False) + If True, it will return probability of classes. + add_neutral: bool, optional (default=True) + if True, it will add neutral probability. Returns ------- @@ -542,84 +614,222 @@ def predict_batch(self, strings, get_proba = False): """ if not isinstance(strings, list): raise ValueError('input must be a list') - if not isinstance(strings[0], str): - raise ValueError('input must be list of strings') - strings = [_classification_textcleaning_stemmer(i) for i in strings] - maxlen = max([len(i.split()) for i in strings]) - if self._mode in ['entity-network', 'bert']: - batch_x = str_idx(strings, self._dictionary, self._maxlen, UNK = 3) + if not isinstance(get_proba, bool): + raise ValueError('get_proba must be a boolean') + if not isinstance(add_neutral, bool): + raise ValueError('add_neutral must be a boolean') + + if add_neutral: + label = self._label + ['neutral'] else: - batch_x = str_idx(strings, self._dictionary, maxlen, UNK = 3) + label = self._label - if self._mode not in ['bert', 'entity-network']: - probs = self._sess.run( - tf.nn.softmax(self._logits), feed_dict = {self._X: batch_x} - ) - if self._mode == 'bert': - np_mask = np.ones((len(batch_x), self._maxlen), dtype = np.int32) - np_segment = np.ones((len(batch_x), self._maxlen), dtype = np.int32) - probs = self._sess.run( - tf.nn.softmax(self._logits), - feed_dict = { - self._X: batch_x, - self._input_mask: np_mask, - self._segment_ids: np_segment, - self._is_training: False, - }, - ) - if self._mode == 'entity-network': - batch_x_expand = np.expand_dims(batch_x, axis = 1) - probs = self._sess.run( - tf.nn.softmax(self._logits), - feed_dict = { - self._X: batch_x, - self._story: batch_x_expand, - self._dropout_keep_prob: 1.0, - }, - ) + strings = [ + ' '.join(preprocessing_classification_index(i)[1]) for i in strings + ] + maxlen = max([len(i.split()) for i in strings]) + batch_x = str_idx(strings, self._dictionary, maxlen, UNK = 3) + results = self._sess.run( + tf.nn.softmax(self._logits), feed_dict = {self._X: batch_x} + ) + if add_neutral: + results = neutral(results) - results = [] if get_proba: - for prob in probs: - dict_result = {} - for no, label in enumerate(self._label): - dict_result[label] = prob[no] - results.append(dict_result) + outputs = [] + for result in results: + outputs.append( + {label[i]: result[i] for i in range(len(result))} + ) + return outputs else: - probs = np.argmax(probs, 1) - for prob in probs: - results.append(self._label[prob]) - return results + return [label[result] for result in np.argmax(results, axis = 1)] -class SIGMOID: +class MULTICLASS_SOFTMAX(SOFTMAX): def __init__( self, X, logits, + logits_seq, + alphas, sess, - mode, dictionary, - alphas = None, - input_mask = None, - segment_ids = None, - is_training = None, - dropout_keep_prob = None, - story = None, - maxlen = 80, + class_name, + label = ['negative', 'positive'], ): + SOFTMAX.__init__( + self, + X, + logits, + logits_seq, + alphas, + sess, + dictionary, + class_name, + label, + ) + + def predict(self, string, get_proba = False): + """ + classify a string. + + Parameters + ---------- + string : str + get_proba: bool, optional (default=False) + If True, it will return probability of classes. + + Returns + ------- + dictionary: results + """ + if not isinstance(string, str): + raise ValueError('input must be a string') + if not isinstance(get_proba, bool): + raise ValueError('get_proba must be a boolean') + + tokenized_indices, splitted = preprocessing_classification_index(string) + batch_x = str_idx( + [' '.join(splitted)], self._dictionary, len(splitted), UNK = 3 + ) + result, alphas = self._sess.run( + [tf.nn.softmax(self._logits), self._alphas], + feed_dict = {self._X: batch_x}, + ) + result = result[0] + + if get_proba: + dict_result = { + self._label[i]: result[i] for i in range(len(result)) + } + dict_result['attention'] = { + k: alphas[v] if v > -1 else 0.0 + for k, v in tokenized_indices.items() + } + return dict_result + else: + return self._label[np.argmax(result)] + + def predict_words(self, string, visualization = True): + """ + classify words. + + Parameters + ---------- + string : str + visualization: bool, optional (default=True) + If True, it will open the visualization dashboard. + + Returns + ------- + dictionary: results + """ + + if not isinstance(string, str): + raise ValueError('input must be a string') + if not isinstance(visualization, bool): + raise ValueError('visualization must be a boolean') + + tokenized_indices, splitted = preprocessing_classification_index(string) + batch_x = str_idx( + [' '.join(splitted)], self._dictionary, len(splitted), UNK = 3 + ) + result, alphas, words = self._sess.run( + [ + tf.nn.softmax(self._logits), + self._alphas, + tf.nn.softmax(self._logits_seq), + ], + feed_dict = {self._X: batch_x}, + ) + result = result[0] + words = words[0] + distribution_words = words[:, np.argmax(words.sum(axis = 0))] + y_histogram, x_histogram = np.histogram( + distribution_words, bins = np.arange(0, 1, 0.05) + ) + y_histogram = y_histogram / y_histogram.sum() + x_attention = np.arange(len(alphas)) + neutral_word = [0.0 for _ in range(len(self._label))] + left, right = np.unique( + np.argmax(words, axis = 1), return_counts = True + ) + left = left.tolist() + y_barplot = [] + for i in range(len(self._label)): + if i not in left: + y_barplot.append(i) + else: + y_barplot.append(right[left.index(i)]) + + dict_result = {self._label[i]: result[i] for i in range(len(result))} + dict_result['alphas'] = { + k: alphas[v] if v > -1 else 0.0 + for k, v in tokenized_indices.items() + } + dict_result['word'] = { + k: words[v] if v > -1 else neutral_word + for k, v in tokenized_indices.items() + } + dict_result['histogram'] = {'x': x_histogram, 'y': y_histogram} + dict_result['attention'] = {'x': x_attention, 'y': alphas} + dict_result['barplot'] = {'x': self._label, 'y': y_barplot} + dict_result['class_name'] = self._class_name + if visualization: + _render_emotion(dict_result) + else: + return dict_result + + def predict_batch(self, strings, get_proba = False): + """ + classify list of strings + + Parameters + ---------- + strings : list + get_proba: bool, optional (default=False) + If True, it will return probability of classes. + + Returns + ------- + list_dictionaries: list of results + """ + if not isinstance(strings, list): + raise ValueError('input must be a list') + if not isinstance(get_proba, bool): + raise ValueError('get_proba must be a boolean') + + strings = [ + ' '.join(preprocessing_classification_index(i)[1]) for i in strings + ] + maxlen = max([len(i.split()) for i in strings]) + batch_x = str_idx(strings, self._dictionary, maxlen, UNK = 3) + results = self._sess.run( + tf.nn.softmax(self._logits), feed_dict = {self._X: batch_x} + ) + + if get_proba: + outputs = [] + for result in results: + outputs.append( + {self._label[i]: result[i] for i in range(len(result))} + ) + return outputs + else: + return [ + self._label[result] for result in np.argmax(results, axis = 1) + ] + + +class SIGMOID: + def __init__(self, X, logits, logits_seq, alphas, sess, dictionary): self._X = X self._logits = logits + self._logits_seq = logits_seq + self._alphas = alphas self._sess = sess - self._mode = mode self._dictionary = dictionary - self._alphas = alphas - self._input_mask = input_mask - self._segment_ids = segment_ids - self._is_training = is_training - self._dropout_keep_prob = dropout_keep_prob - self._story = story - self._maxlen = maxlen self._label = [ 'toxic', 'severe_toxic', @@ -628,9 +838,7 @@ def __init__( 'insult', 'identity_hate', ] - - def get_dictionary(self): - return self._dictionary + self._class_name = 'toxicity' def predict(self, string, get_proba = False): """ @@ -646,54 +854,93 @@ def predict(self, string, get_proba = False): """ if not isinstance(string, str): raise ValueError('input must be a string') - string = _classification_textcleaning_stemmer(string, attention = True) - splitted = string[1].split() - if self._mode in ['entity-network', 'bert']: - batch_x = str_idx( - [string[0]], self._dictionary, self._maxlen, UNK = 3 - ) - else: - batch_x = str_idx( - [string[0]], self._dictionary, len(splitted), UNK = 3 - ) - if self._mode in ['luong', 'bahdanau', 'hierarchical']: - probs, alphas = self._sess.run( - [tf.nn.sigmoid(self._logits), self._alphas], - feed_dict = {self._X: batch_x}, - ) - if self._mode == 'hierarchical': - alphas = alphas[0] - words = [] - for i in range(alphas.shape[0]): - words.append([splitted[i], alphas[i]]) - if self._mode in ['fast-text']: - probs = self._sess.run( - tf.nn.softmax(self._logits), feed_dict = {self._X: batch_x} - ) - if self._mode == 'entity-network': - batch_x_expand = np.expand_dims(batch_x, axis = 1) - probs = self._sess.run( - tf.nn.softmax(self._logits), - feed_dict = { - self._X: batch_x, - self._story: batch_x_expand, - self._dropout_keep_prob: 1.0, - }, - ) + if not isinstance(get_proba, bool): + raise ValueError('get_proba must be a boolean') + + tokenized_indices, splitted = preprocessing_classification_index(string) + batch_x = str_idx( + [' '.join(splitted)], self._dictionary, len(splitted), UNK = 3 + ) + result, alphas = self._sess.run( + [tf.nn.sigmoid(self._logits), self._alphas], + feed_dict = {self._X: batch_x}, + ) + result = result[0] + if get_proba: - dict_result = {} - for no, label in enumerate(self._label): - dict_result[label] = probs[0, no] - if self._mode in ['luong', 'bahdanau', 'hierarchical']: - dict_result['attention'] = words + dict_result = { + label: result[no] for no, label in enumerate(self._label) + } + dict_result['attention'] = { + k: alphas[v] if v > -1 else 0.0 + for k, v in tokenized_indices.items() + } return dict_result else: - result = [] - probs = np.around(probs[0]) - for no, label in enumerate(self._label): - if probs[no]: - result.append(label) - return result + probs = np.around(result) + return [label for no, label in enumerate(self._label) if probs[no]] + + def predict_words(self, string, visualization = True): + """ + classify words. + + Parameters + ---------- + string : str + visualization: bool, optional (default=True) + If True, it will open the visualization dashboard. + + Returns + ------- + dictionary: results + """ + + if not isinstance(string, str): + raise ValueError('input must be a string') + if not isinstance(visualization, bool): + raise ValueError('visualization must be a boolean') + + tokenized_indices, splitted = preprocessing_classification_index(string) + batch_x = str_idx( + [' '.join(splitted)], self._dictionary, len(splitted), UNK = 3 + ) + result, alphas, words = self._sess.run( + [ + tf.nn.sigmoid(self._logits), + self._alphas, + tf.nn.sigmoid(self._logits_seq), + ], + feed_dict = {self._X: batch_x}, + ) + result = result[0] + words = words[0] + distribution_words = words[:, np.argmax(words.sum(axis = 0))] + y_histogram, x_histogram = np.histogram( + distribution_words, bins = np.arange(0, 1, 0.05) + ) + y_histogram = y_histogram / y_histogram.sum() + x_attention = np.arange(len(alphas)) + neutral_word = [0.0 for _ in range(len(self._label))] + around_words = np.around(words) + y_barplot = np.sum(around_words, axis = 0).tolist() + + dict_result = {self._label[i]: result[i] for i in range(len(result))} + dict_result['alphas'] = { + k: alphas[v] if v > -1 else 0.0 + for k, v in tokenized_indices.items() + } + dict_result['word'] = { + k: words[v] if v > -1 else neutral_word + for k, v in tokenized_indices.items() + } + dict_result['histogram'] = {'x': x_histogram, 'y': y_histogram} + dict_result['attention'] = {'x': x_attention, 'y': alphas} + dict_result['barplot'] = {'x': self._label, 'y': y_barplot} + dict_result['class_name'] = self._class_name + if visualization: + _render_toxic(dict_result) + else: + return dict_result def predict_batch(self, strings, get_proba = False): """ @@ -711,29 +958,16 @@ def predict_batch(self, strings, get_proba = False): raise ValueError('input must be a list') if not isinstance(strings[0], str): raise ValueError('input must be list of strings') + if not isinstance(get_proba, bool): + raise ValueError('get_proba must be a boolean') strings = [ - _classification_textcleaning_stemmer(i, attention = True)[0] - for i in strings + ' '.join(preprocessing_classification_index(i)[1]) for i in strings ] maxlen = max([len(i.split()) for i in strings]) - if self._mode in ['entity-network']: - batch_x = str_idx(strings, self._dictionary, self._maxlen, UNK = 3) - else: - batch_x = str_idx(strings, self._dictionary, maxlen, UNK = 3) - if self._mode not in ['entity-network']: - probs = self._sess.run( - tf.nn.sigmoid(self._logits), feed_dict = {self._X: batch_x} - ) - if self._mode == 'entity-network': - batch_x_expand = np.expand_dims(batch_x, axis = 1) - probs = self._sess.run( - tf.nn.sigmoid(self._logits), - feed_dict = { - self._X: batch_x, - self._story: batch_x_expand, - self._dropout_keep_prob: 1.0, - }, - ) + batch_x = str_idx(strings, self._dictionary, maxlen, UNK = 3) + probs = self._sess.run( + tf.nn.sigmoid(self._logits), feed_dict = {self._X: batch_x} + ) results = [] if get_proba: for prob in probs: @@ -749,7 +983,6 @@ def predict_batch(self, strings, get_proba = False): if prob[no]: list_result.append(label) results.append(list_result) - return results diff --git a/malaya/_utils/_html.py b/malaya/_utils/_html.py new file mode 100644 index 00000000..96f19975 --- /dev/null +++ b/malaya/_utils/_html.py @@ -0,0 +1,314 @@ +import string +import os +import json +import numpy as np +import shutil +from ._server import serve + +try: + from html import escape +except ImportError: + from cgi import escape + +_color_sentiment = { + 'positive': 'rgb(143, 255, 113)', + 'neutral': 'rgb(255, 238, 109)', + 'negative': 'rgb(255, 139, 118)', +} +_color_emotion = { + 'anger': 'rgb(254, 0, 20)', + 'fear': 'rgb(168, 103, 172)', + 'joy': 'rgb(0, 211, 239)', + 'love': 'rgb(255, 70, 198)', + 'sadness': 'rgb(255, 206, 0)', + 'surprise': 'rgb(255, 244, 0)', + 'neutral': 'rgb(255, 255, 255)', +} +_color_toxic = { + 'toxic': 'rgb(0, 217, 0)', + 'severe_toxic': 'rgb(244, 248, 0)', + 'obscene': 'rgb(248, 34, 13)', + 'threat': 'rgb(0, 238, 241)', + 'insult': 'rgb(248, 95, 34)', + 'identity_hate': 'rgb(230, 183, 0)', + 'neutral': 'rgb(255, 255, 255)', +} + + +def _sentiment_mark(text, negative, positive, neutral, attention, label): + return ( + " %.3f
Neutral %.3f
Negative %.3f
Attention %.3f\">%s" + % ( + _color_sentiment[label], + positive, + neutral, + negative, + attention, + text, + ) + ) + + +def _toxic_mark( + text, + toxic, + severe_toxic, + obscene, + threat, + insult, + identity_hate, + attention, + label, +): + return ( + " %.3f
Severe Toxic %.3f
Obscene %.3f
Threat %.3f
Insult %.3f
Identity hate %.3f
Attention %.3f
\">%s" + % ( + _color_toxic[label], + toxic, + severe_toxic, + obscene, + threat, + insult, + identity_hate, + attention, + text, + ) + ) + + +def _emotion_mark( + text, anger, fear, joy, love, sadness, surprise, attention, label +): + return ( + " %.3f
Fear %.3f
Joy %.3f
Love %.3f
Sadness %.3f
Surprise %.3f
Attention %.3f
\">%s" + % ( + _color_emotion[label], + anger, + fear, + joy, + love, + sadness, + surprise, + attention, + text, + ) + ) + + +def _render_binary(data, notebook_mode = False): + index_negative = data['barplot']['x'].index('negative') + index_positive = data['barplot']['x'].index('positive') + index_neutral = data['barplot']['x'].index('neutral') + sentiment_mark = [] + for k, v in data['word'].items(): + sentiment_mark.append( + _sentiment_mark( + k, + v[index_negative], + v[index_positive], + v[index_neutral], + data['alphas'][k], + data['barplot']['x'][np.argmax(v)], + ) + ) + sentiment_mark = ' '.join(sentiment_mark) + this_dir = os.path.dirname(__file__) + + if notebook_mode: + js_location, css_location = _upload_jupyter() + else: + js_location = 'static/echarts.min.js' + css_location = 'static/admin-materialize.min.css' + + with open(os.path.join(this_dir, 'web', 'index.html')) as _file: + template = string.Template(_file.read()) + + template = template.substitute( + label = escape(data['class_name']), + p = sentiment_mark, + barplot_positive = escape( + json.dumps(int(data['barplot']['y'][index_positive])) + ), + barplot_neutral = escape( + json.dumps(int(data['barplot']['y'][index_neutral])) + ), + barplot_negative = escape( + json.dumps(int(data['barplot']['y'][index_negative])) + ), + histogram_x = escape(json.dumps(data['histogram']['x'].tolist())), + histogram_y = escape(json.dumps(data['histogram']['y'].tolist())), + attention_x = escape(json.dumps(data['attention']['x'].tolist())), + attention_y = escape(json.dumps(data['attention']['y'].tolist())), + css_location = css_location, + js_location = js_location, + ) + if notebook_mode: + from IPython.display import display, HTML + + display(HTML(template)) + else: + serve(template) + + +def _render_toxic(data, notebook_mode = False): + index_toxic = data['barplot']['x'].index('toxic') + index_severe_toxic = data['barplot']['x'].index('severe_toxic') + index_obscene = data['barplot']['x'].index('obscene') + index_threat = data['barplot']['x'].index('threat') + index_insult = data['barplot']['x'].index('insult') + index_identity_hate = data['barplot']['x'].index('identity_hate') + toxic_mark = [] + for k, v in data['word'].items(): + where = np.where(np.array(v) >= 0.5)[0].shape[0] + if where: + where = data['barplot']['x'][np.argmax(v)] + else: + where = 'neutral' + toxic_mark.append( + _toxic_mark( + k, + v[index_toxic], + v[index_severe_toxic], + v[index_obscene], + v[index_threat], + v[index_insult], + v[index_identity_hate], + data['alphas'][k], + where, + ) + ) + toxic_mark = ' '.join(toxic_mark) + this_dir = os.path.dirname(__file__) + + if notebook_mode: + js_location, css_location = _upload_jupyter() + else: + js_location = 'static/echarts.min.js' + css_location = 'static/admin-materialize.min.css' + + with open(os.path.join(this_dir, 'web', 'index_toxic.html')) as _file: + template = string.Template(_file.read()) + + template = template.substitute( + label = escape(data['class_name']), + p = toxic_mark, + barplot_toxic = escape( + json.dumps(int(data['barplot']['y'][index_toxic])) + ), + barplot_severe_toxic = escape( + json.dumps(int(data['barplot']['y'][index_severe_toxic])) + ), + barplot_obscene = escape( + json.dumps(int(data['barplot']['y'][index_obscene])) + ), + barplot_threat = escape( + json.dumps(int(data['barplot']['y'][index_threat])) + ), + barplot_insult = escape( + json.dumps(int(data['barplot']['y'][index_insult])) + ), + barplot_identity_hate = escape( + json.dumps(int(data['barplot']['y'][index_identity_hate])) + ), + histogram_x = escape(json.dumps(data['histogram']['x'].tolist())), + histogram_y = escape(json.dumps(data['histogram']['y'].tolist())), + attention_x = escape(json.dumps(data['attention']['x'].tolist())), + attention_y = escape(json.dumps(data['attention']['y'].tolist())), + css_location = css_location, + js_location = js_location, + ) + if notebook_mode: + from IPython.display import display, HTML + + display(HTML(template)) + else: + serve(template) + + +def _render_emotion(data, notebook_mode = False): + index_anger = data['barplot']['x'].index('anger') + index_fear = data['barplot']['x'].index('fear') + index_joy = data['barplot']['x'].index('joy') + index_love = data['barplot']['x'].index('love') + index_sadness = data['barplot']['x'].index('sadness') + index_surprise = data['barplot']['x'].index('surprise') + emotion_mark = [] + for k, v in data['word'].items(): + where = np.where(np.array(v) >= 0.3)[0].shape[0] + if where: + where = data['barplot']['x'][np.argmax(v)] + else: + where = 'neutral' + emotion_mark.append( + _emotion_mark( + k, + v[index_anger], + v[index_fear], + v[index_joy], + v[index_love], + v[index_sadness], + v[index_surprise], + data['alphas'][k], + where, + ) + ) + emotion_mark = ' '.join(emotion_mark) + this_dir = os.path.dirname(__file__) + + if notebook_mode: + js_location, css_location = _upload_jupyter() + else: + js_location = 'static/echarts.min.js' + css_location = 'static/admin-materialize.min.css' + + with open(os.path.join(this_dir, 'web', 'index_emotion.html')) as _file: + template = string.Template(_file.read()) + + template = template.substitute( + label = escape(data['class_name']), + p = emotion_mark, + barplot_anger = escape( + json.dumps(int(data['barplot']['y'][index_anger])) + ), + barplot_fear = escape( + json.dumps(int(data['barplot']['y'][index_fear])) + ), + barplot_joy = escape(json.dumps(int(data['barplot']['y'][index_joy]))), + barplot_love = escape( + json.dumps(int(data['barplot']['y'][index_love])) + ), + barplot_sadness = escape( + json.dumps(int(data['barplot']['y'][index_sadness])) + ), + barplot_surprise = escape( + json.dumps(int(data['barplot']['y'][index_surprise])) + ), + histogram_x = escape(json.dumps(data['histogram']['x'].tolist())), + histogram_y = escape(json.dumps(data['histogram']['y'].tolist())), + attention_x = escape(json.dumps(data['attention']['x'].tolist())), + attention_y = escape(json.dumps(data['attention']['y'].tolist())), + css_location = css_location, + js_location = js_location, + ) + if notebook_mode: + from IPython.display import display, HTML + + display(HTML(template)) + else: + serve(template) + + +def _upload_jupyter(): + location = os.getcwd() + + this_dir = os.path.dirname(__file__) + + js_location = os.path.join(this_dir, 'web', 'static', 'echarts.min.js') + css_location = os.path.join( + this_dir, 'web', 'static', 'admin-materialize.min.css' + ) + + shutil.copyfile(js_location, './echarts.min.js') + shutil.copyfile(css_location, './admin-materialize.min.css') + + return 'echarts.min.js', 'admin-materialize.min.css' diff --git a/malaya/_utils/_paths.py b/malaya/_utils/_paths.py index 1246d2fc..9433abbb 100644 --- a/malaya/_utils/_paths.py +++ b/malaya/_utils/_paths.py @@ -116,28 +116,18 @@ }, 'luong': { 'model': home + '/toxic/luong/luong-toxic.pb', - 'setting': home + '/toxic/luong/luong-toxic.json', - 'version': 'v8', + 'setting': home + '/toxic/toxicity-dictionary.json', + 'version': 'v24', }, 'bahdanau': { 'model': home + '/toxic/bahdanau/bahdanau-toxic.pb', - 'setting': home + '/toxic/bahdanau/bahdanau-toxic.json', - 'version': 'v8', + 'setting': home + '/toxic/toxicity-dictionary.json', + 'version': 'v24', }, - 'hierarchical': { - 'model': home + '/toxic/hierarchical/hierarchical-toxic.pb', - 'setting': home + '/toxic/hierarchical/hierarchical-toxic.json', - 'version': 'v8', - }, - 'fast-text': { - 'model': home + '/toxic/fast-text/fasttext-toxic.pb', - 'setting': home + '/toxic/fast-text/fasttext-toxic.json', - 'version': 'v17', - }, - 'entity-network': { - 'model': home + '/toxic/entity-network/entity-toxic.pb', - 'setting': home + '/toxic/entity-network/entity-toxic.json', - 'version': 'v8', + 'self-attention': { + 'model': home + '/toxic/self-attention/self-attention-toxic.pb', + 'setting': home + '/toxic/toxicity-dictionary.json', + 'version': 'v24', }, } @@ -151,24 +141,16 @@ 'vector': 'v6/vectorizer-toxic.pkl', }, 'luong': { - 'model': 'v8/toxic/luong-toxic.pb', - 'setting': 'v8/toxic/luong-toxic.json', + 'model': 'v24/toxicity/luong-toxic.pb', + 'setting': 'v24/toxicity/toxicity-dictionary.json', }, 'bahdanau': { - 'model': 'v8/toxic/bahdanau-toxic.pb', - 'setting': 'v8/toxic/bahdanau-toxic.json', - }, - 'hierarchical': { - 'model': 'v8/toxic/hierarchical-toxic.pb', - 'setting': 'v8/toxic/hierarchical-toxic.json', + 'model': 'v24/toxicity/bahdanau-toxic.pb', + 'setting': 'v24/toxicity/toxicity-dictionary.json', }, - 'fast-text': { - 'model': 'v17/toxic/fasttext-toxic.pb', - 'setting': 'v17/toxic/fasttext-toxic.json', - }, - 'entity-network': { - 'model': 'v8/toxic/entity-toxic.pb', - 'setting': 'v8/toxic/entity-toxic.json', + 'self-attention': { + 'model': 'v24/toxicity/self-attention-toxic.pb', + 'setting': 'v24/toxicity/toxicity-dictionary.json', }, } @@ -225,39 +207,6 @@ }, } -PATH_NORMALIZER = { - 'lstm': { - 'model': home + '/normalizer/lstm/lstm-normalizer.pb', - 'setting': home + '/normalizer/lstm/lstm-normalizer.json', - 'version': 'v18', - }, - 'bahdanau': { - 'model': home + '/normalizer/bahdanau/bahdanau-normalizer.pb', - 'setting': home + '/normalizer/bahdanau/bahdanau-normalizer.json', - 'version': 'v18', - }, - 'luong': { - 'model': home + '/normalizer/luong/luong-normalizer.pb', - 'setting': home + '/normalizer/luong/luong-normalizer.json', - 'version': 'v18', - }, -} - -S3_PATH_NORMALIZER = { - 'lstm': { - 'model': 'v18/normalizer/lstm-normalizer.pb', - 'setting': 'v18/normalizer/lstm-normalizer.json', - }, - 'bahdanau': { - 'model': 'v18/normalizer/bahdanau-normalizer.pb', - 'setting': 'v18/normalizer/bahdanau-normalizer.json', - }, - 'luong': { - 'model': 'v18/normalizer/luong-normalizer.pb', - 'setting': 'v18/normalizer/luong-normalizer.json', - }, -} - PATH_LANG_DETECTION = { 'multinomial': { 'model': home @@ -420,42 +369,21 @@ }, } -PATH_SENTIMENTS = { - 'bert': { - 'model': home + '/sentiment/bert/bert-sentiment.pb', - 'setting': home + '/sentiment/bert/bert-sentiment.json', - 'version': 'v17', - }, - 'entity-network': { - 'model': home + '/sentiment/entity-network/entity-sentiment.pb', - 'setting': home + '/sentiment/entity-network/entity-sentiment.json', - 'version': 'v17', - }, - 'hierarchical': { - 'model': home + '/sentiment/hierarchical/hierarchical-sentiment.pb', - 'setting': home + '/sentiment/hierarchical/hierarchical-sentiment.json', - 'version': 'v17', - }, +PATH_SENTIMENT = { 'bahdanau': { 'model': home + '/sentiment/bahdanau/bahdanau-sentiment.pb', - 'setting': home + '/sentiment/bahdanau/bahdanau-sentiment.json', - 'version': 'v17', + 'setting': home + '/sentiment/sentiment-dictionary.json', + 'version': 'v24', }, 'luong': { 'model': home + '/sentiment/luong/luong-sentiment.pb', - 'setting': home + '/sentiment/luong/luong-sentiment.json', - 'version': 'v17', + 'setting': home + '/sentiment/sentiment-dictionary.json', + 'version': 'v24', }, - 'bidirectional': { - 'model': home + '/sentiment/bidirectional/bidirectional-sentiment.pb', - 'setting': home - + '/sentiment/bidirectional/bidirectional-sentiment.json', - 'version': 'v17', - }, - 'fast-text': { - 'model': home + '/sentiment/fast-text/fasttext-sentiment.pb', - 'setting': home + '/sentiment/fast-text/fasttext-sentiment.json', - 'version': 'v17', + 'self-attention': { + 'model': home + '/sentiment/self-attention/self-attention-sentiment.pb', + 'setting': home + '/sentiment/sentiment-dictionary.json', + 'version': 'v24', }, 'multinomial': { 'model': home + '/sentiment/multinomial/multinomial-sentiment.pkl', @@ -479,34 +407,18 @@ }, } -S3_PATH_SENTIMENTS = { - 'bert': { - 'model': 'v17/sentiment/bert-sentiment.pb', - 'setting': 'v17/sentiment/bert-sentiment.json', - }, - 'entity-network': { - 'model': 'v17/sentiment/entity-sentiment.pb', - 'setting': 'v17/sentiment/entity-sentiment.json', - }, - 'hierarchical': { - 'model': 'v17/sentiment/hierarchical-sentiment.pb', - 'setting': 'v17/sentiment/hierarchical-sentiment.json', - }, +S3_PATH_SENTIMENT = { 'bahdanau': { - 'model': 'v17/sentiment/bahdanau-sentiment.pb', - 'setting': 'v17/sentiment/bahdanau-sentiment.json', + 'model': 'v24/sentiment/bahdanau-sentiment.pb', + 'setting': 'v24/sentiment/sentiment-dictionary.json', }, 'luong': { - 'model': 'v17/sentiment/luong-sentiment.pb', - 'setting': 'v17/sentiment/luong-sentiment.json', + 'model': 'v24/sentiment/luong-sentiment.pb', + 'setting': 'v24/sentiment/sentiment-dictionary.json', }, - 'bidirectional': { - 'model': 'v17/sentiment/bidirectional-sentiment.pb', - 'setting': 'v17/sentiment/bidirectional-sentiment.json', - }, - 'fast-text': { - 'model': 'v17/sentiment/fasttext-sentiment.pb', - 'setting': 'v17/sentiment/fasttext-sentiment.json', + 'self-attention': { + 'model': 'v24/sentiment/self-attention-sentiment.pb', + 'setting': 'v24/sentiment/sentiment-dictionary.json', }, 'multinomial': { 'model': 'v17/sentiment/multinomial-sentiment.pkl', @@ -525,42 +437,20 @@ } PATH_SUBJECTIVE = { - 'bert': { - 'model': home + '/subjective/bert/bert-subjective.pb', - 'setting': home + '/subjective/bert/bert-subjective.json', - 'version': 'v10', - }, - 'entity-network': { - 'model': home + '/subjective/entity-network/entity-subjective.pb', - 'setting': home + '/subjective/entity-network/entity-subjective.json', - 'version': 'v10', - }, - 'hierarchical': { - 'model': home + '/subjective/hierarchical/hierarchical-subjective.pb', - 'setting': home - + '/subjective/hierarchical/hierarchical-subjective.json', - 'version': 'v10', - }, 'bahdanau': { 'model': home + '/subjective/bahdanau/bahdanau-subjective.pb', - 'setting': home + '/subjective/bahdanau/bahdanau-subjective.json', - 'version': 'v10', + 'setting': home + '/subjective/subjective-dictionary.json', + 'version': 'v24', }, 'luong': { 'model': home + '/subjective/luong/luong-subjective.pb', - 'setting': home + '/subjective/luong/luong-subjective.json', - 'version': 'v10', + 'setting': home + '/subjective/subjective-dictionary.json', + 'version': 'v24', }, - 'bidirectional': { - 'model': home + '/subjective/bidirectional/bidirectional-subjective.pb', - 'setting': home - + '/subjective/bidirectional/bidirectional-subjective.json', - 'version': 'v10', - }, - 'fast-text': { - 'model': home + '/subjective/fast-text/fasttext-subjective.pb', - 'setting': home + '/subjective/fast-text/fasttext-subjective.json', - 'version': 'v17', + 'self-attention': { + 'model': home + '/subjective/luong/luong-subjective.pb', + 'setting': home + '/subjective/subjective-dictionary.json', + 'version': 'v24', }, 'multinomial': { 'model': home + '/subjective/multinomial/multinomial-subjective.pkl', @@ -585,33 +475,17 @@ } S3_PATH_SUBJECTIVE = { - 'bert': { - 'model': 'v10/subjective/bert-subjective.pb', - 'setting': 'v10/subjective/bert-subjective.json', - }, - 'entity-network': { - 'model': 'v10/subjective/entity-subjective.pb', - 'setting': 'v10/subjective/entity-subjective.json', - }, - 'hierarchical': { - 'model': 'v10/subjective/hierarchical-subjective.pb', - 'setting': 'v10/subjective/hierarchical-subjective.json', - }, 'bahdanau': { - 'model': 'v10/subjective/bahdanau-subjective.pb', - 'setting': 'v10/subjective/bahdanau-subjective.json', + 'model': 'v24/subjective/bahdanau-subjective.pb', + 'setting': 'v24/subjective/subjective-dictionary.json', }, 'luong': { - 'model': 'v10/subjective/luong-subjective.pb', - 'setting': 'v10/subjective/luong-subjective.json', - }, - 'bidirectional': { - 'model': 'v10/subjective/bidirectional-subjective.pb', - 'setting': 'v10/subjective/bidirectional-subjective.json', + 'model': 'v24/subjective/luong-subjective.pb', + 'setting': 'v24/subjective/subjective-dictionary.json', }, - 'fast-text': { - 'model': 'v17/subjective/fasttext-subjective.pb', - 'setting': 'v17/subjective/fasttext-subjective.json', + 'self-attention': { + 'model': 'v24/subjective/self-attention-subjective.pb', + 'setting': 'v24/subjective/subjective-dictionary.json', }, 'multinomial': { 'model': 'v10/subjective/multinomial-subjective.pkl', @@ -630,40 +504,20 @@ } PATH_EMOTION = { - 'bert': { - 'model': home + '/emotion/bert/bert-emotion.pb', - 'setting': home + '/emotion/bert/bert-emotion.json', - 'version': 'v12', - }, - 'entity-network': { - 'model': home + '/emotion/entity-network/entity-emotion.pb', - 'setting': home + '/emotion/entity-network/entity-emotion.json', - 'version': 'v12', - }, - 'hierarchical': { - 'model': home + '/emotion/hierarchical/hierarchical-emotion.pb', - 'setting': home + '/emotion/hierarchical/hierarchical-emotion.json', - 'version': 'v12', - }, 'bahdanau': { 'model': home + '/emotion/bahdanau/bahdanau-emotion.pb', - 'setting': home + '/emotion/bahdanau/bahdanau-emotion.json', - 'version': 'v12', + 'setting': home + '/emotion/emotion-dictionary.json', + 'version': 'v24', }, 'luong': { 'model': home + '/emotion/luong/luong-emotion.pb', - 'setting': home + '/emotion/luong/luong-emotion.json', - 'version': 'v12', + 'setting': home + '/emotion/emotion-dictionary.json', + 'version': 'v24', }, - 'bidirectional': { - 'model': home + '/emotion/bidirectional/bidirectional-emotion.pb', - 'setting': home + '/emotion/bidirectional/bidirectional-emotion.json', - 'version': 'v12', - }, - 'fast-text': { - 'model': home + '/emotion/fast-text/fasttext-emotion.pb', - 'setting': home + '/emotion/fast-text/fasttext-emotion.json', - 'version': 'v17', + 'self-attention': { + 'model': home + '/emotion/luong/self-attention-emotion.pb', + 'setting': home + '/emotion/emotion-dictionary.json', + 'version': 'v24', }, 'multinomial': { 'model': home + '/emotion/multinomial/multinomial-emotion.pkl', @@ -687,33 +541,17 @@ } S3_PATH_EMOTION = { - 'bert': { - 'model': 'v12/emotion/bert-emotion.pb', - 'setting': 'v12/emotion/bert-emotion.json', - }, - 'entity-network': { - 'model': 'v12/emotion/entity-emotion.pb', - 'setting': 'v12/emotion/entity-emotion.json', - }, - 'hierarchical': { - 'model': 'v12/emotion/hierarchical-emotion.pb', - 'setting': 'v12/emotion/hierarchical-emotion.json', - }, 'bahdanau': { - 'model': 'v12/emotion/bahdanau-emotion.pb', - 'setting': 'v12/emotion/bahdanau-emotion.json', + 'model': 'v24/emotion/bahdanau-emotion.pb', + 'setting': 'v24/emotion/emotion-dictionary.json', }, 'luong': { - 'model': 'v12/emotion/luong-emotion.pb', - 'setting': 'v12/emotion/luong-emotion.json', + 'model': 'v24/emotion/luong-emotion.pb', + 'setting': 'v24/emotion/emotion-dictionary.json', }, - 'bidirectional': { - 'model': 'v12/emotion/bidirectional-emotion.pb', - 'setting': 'v12/emotion/bidirectional-emotion.json', - }, - 'fast-text': { - 'model': 'v17/emotion/fasttext-emotion.pb', - 'setting': 'v17/emotion/fasttext-emotion.json', + 'self-attention': { + 'model': 'v24/emotion/luong-emotion.pb', + 'setting': 'v24/emotion/emotion-dictionary.json', }, 'multinomial': { 'model': 'v12/emotion/multinomial-emotion.pkl', @@ -781,3 +619,26 @@ 'setting': 'v22/dependency/attention-dependency.json', }, } + +PATH_RELEVANCY = { + 'self-attention': { + 'model': home + '/relevancy/self-attention/self-attention-relevancy.pb', + 'setting': home + '/relevancy/relevancy-dictionary.json', + 'version': 'v24', + }, + 'dilated-cnn': { + 'model': home + '/relevancy/self-attention/dilated-cnn-relevancy.pb', + 'setting': home + '/relevancy/relevancy-dictionary.json', + 'version': 'v24', + }, +} +S3_PATH_RELEVANCY = { + 'self-attention': { + 'model': 'v24/relevancy/self-attention-relevancy.pb', + 'setting': 'v24/relevancy/relevancy-dictionary.json', + }, + 'dilated-cnn': { + 'model': 'v24/relevancy/dilated-cnn-relevancy.pb', + 'setting': 'v24/relevancy/relevancy-dictionary.json', + }, +} diff --git a/malaya/_utils/_server.py b/malaya/_utils/_server.py new file mode 100644 index 00000000..5b2c934d --- /dev/null +++ b/malaya/_utils/_server.py @@ -0,0 +1,113 @@ +import sys +import threading +import webbrowser +import socket +import itertools +import random +import os +import mimetypes +from http import server + + +def generate_handler(html, files = None): + if files is None: + files = {} + + class MyHandler(server.BaseHTTPRequestHandler): + def do_GET(self): + self.send_response(200) + + if self.path == '/': + self.send_header('Content-type', 'text/html') + self.end_headers() + self.wfile.write(html.encode('utf-8')) + else: + this_dir = os.path.dirname(__file__) + filepath = os.path.join(this_dir, 'web', self.path[1:]) + + mimetype, _ = mimetypes.guess_type(filepath) + self.send_header('Content-type', mimetype) + self.end_headers() + + with open(filepath, 'rb') as fh: + content = fh.read() + self.wfile.write(content) + + return MyHandler + + +def find_open_port(ip, port, n = 50): + """ + Find an open port near the specified port + """ + ports = itertools.chain( + (port + i for i in range(n)), (port + random.randint(-2 * n, 2 * n)) + ) + + for port in ports: + s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) + result = s.connect_ex((ip, port)) + s.close() + if result != 0: + return port + raise ValueError('no open ports found') + + +def serve( + html, + ip = '127.0.0.1', + port = 8888, + n_retries = 50, + files = None, + ipython_warning = False, + open_browser = True, + http_server = None, +): + """ + Start a server serving the given HTML, and (optionally) open a browser. + + Parameters + ---------- + html : string + HTML to serve + ip : string (default = '127.0.0.1') + ip address at which the HTML will be served. + port : int (default = 8888) + the port at which to serve the HTML + n_retries : int (default = 50) + the number of nearby ports to search if the specified port is in use. + files : dictionary (optional) + dictionary of extra content to serve + ipython_warning : bool (optional) + if True (default), then print a warning if this is used within IPython + open_browser : bool (optional) + if True (default), then open a web browser to the given HTML + http_server : class (optional) + optionally specify an HTTPServer class to use for showing the + figure. The default is Python's basic HTTPServer. + """ + + port = find_open_port(ip, port, n_retries) + Handler = generate_handler(html, files) + + if http_server is None: + srvr = server.HTTPServer((ip, port), Handler) + else: + srvr = http_server((ip, port), Handler) + + if ipython_warning: + print(IPYTHON_WARNING) + + print('Serving to http://{0}:{1}/ [Ctrl-C to exit]'.format(ip, port)) + sys.stdout.flush() + + if open_browser: + b = lambda: webbrowser.open('http://{0}:{1}'.format(ip, port)) + threading.Thread(target = b).start() + + try: + srvr.serve_forever() + except (KeyboardInterrupt, SystemExit): + print('\nstopping Server...') + + srvr.server_close() diff --git a/malaya/_utils/_softmax_class.py b/malaya/_utils/_softmax_class.py index 80bf4684..b2619c01 100644 --- a/malaya/_utils/_softmax_class.py +++ b/malaya/_utils/_softmax_class.py @@ -9,8 +9,17 @@ import pickle from ._utils import check_file, load_graph, check_available, generate_session from ..stem import _classification_textcleaning_stemmer -from .._models._sklearn_model import USER_XGB, USER_BAYES -from .._models._tensorflow_model import SOFTMAX, SPARSE_SOFTMAX +from .._models._sklearn_model import ( + BINARY_XGB, + BINARY_BAYES, + MULTICLASS_XGB, + MULTICLASS_BAYES, +) +from .._models._tensorflow_model import ( + BINARY_SOFTMAX, + MULTICLASS_SOFTMAX, + SPARSE_SOFTMAX, +) def sparse_deep_model( @@ -42,12 +51,12 @@ def sparse_deep_model( vector = pickle.load(fopen) return SPARSE_SOFTMAX( - os.path.dirname(path[model]['model']), - vector, - label, - output_size, - embedded_size, - len(vector.vocabulary_), + path = os.path.dirname(path[model]['model']), + vectorizer = vector, + label = label, + output_size = output_size, + embedded_size = embedded_size, + vocab_size = len(vector.vocabulary_), ) except: raise Exception( @@ -64,136 +73,40 @@ def sparse_deep_model( def deep_model( path, s3_path, class_name, label, model = 'luong', validate = True ): - if not isinstance(model, str): - raise ValueError('model must be a string') - - model = model.lower() - if model in ['bahdanau', 'luong', 'hierarchical']: - if validate: - check_file(path[model], s3_path[model]) - else: - if not check_available(path[model]): - raise Exception( - '%s/%s is not available, please `validate = True`' - % (class_name, model) - ) - try: - with open(path[model]['setting'], 'r') as fopen: - dictionary = json.load(fopen)['dictionary'] - g = load_graph(path[model]['model']) - except: - raise Exception( - "model corrupted due to some reasons, please run malaya.clear_cache('%s/%s') and try again" - % (class_name, model) - ) - return SOFTMAX( - g.get_tensor_by_name('import/Placeholder:0'), - g.get_tensor_by_name('import/logits:0'), - generate_session(graph = g), - model, - dictionary, - alphas = g.get_tensor_by_name('import/alphas:0'), - label = label, - ) - elif model in ['bidirectional', 'fast-text']: - if validate: - check_file(path[model], s3_path[model]) - else: - if not check_available(path[model]): - raise Exception( - '%s/%s is not available, please `validate = True`' - % (class_name, model) - ) - try: - with open(path[model]['setting'], 'r') as fopen: - dictionary = json.load(fopen)['dictionary'] - g = load_graph(path[model]['model']) - except: - raise Exception( - "model corrupted due to some reasons, please run malaya.clear_cache('%s/%s') and try again" - % (class_name, model) - ) - return SOFTMAX( - g.get_tensor_by_name('import/Placeholder:0'), - g.get_tensor_by_name('import/logits:0'), - generate_session(graph = g), - model, - dictionary, - label = label, - ) - elif model == 'bert': - if validate: - check_file(path['bert'], s3_path['bert']) - else: - if not check_available(path['bert']): - raise Exception( - '%s/%s is not available, please `validate = True`' - % (class_name, model) - ) - try: - with open(path['bert']['setting'], 'r') as fopen: - dictionary = json.load(fopen) - g = load_graph(path['bert']['model']) - except: - raise Exception( - "model corrupted due to some reasons, please run malaya.clear_cache('%s/%s') and try again" - % (class_name, model) - ) - return SOFTMAX( - g.get_tensor_by_name('import/Placeholder_input_ids:0'), - g.get_tensor_by_name('import/loss/logits:0'), - generate_session(graph = g), - model, - dictionary, - input_mask = g.get_tensor_by_name( - 'import/Placeholder_input_mask:0' - ), - segment_ids = g.get_tensor_by_name( - 'import/Placeholder_segment_ids:0' - ), - is_training = g.get_tensor_by_name( - 'import/Placeholder_is_training:0' - ), - label = label, - ) - elif model == 'entity-network': - if validate: - check_file(path['entity-network'], s3_path['entity-network']) - else: - if not check_available(path['entity-network']): - raise Exception( - '%s/%s is not available, please `validate = True`' - % (class_name, model) - ) - try: - with open(path['entity-network']['setting'], 'r') as fopen: - dictionary = json.load(fopen) - g = load_graph(path['entity-network']['model']) - except: + if validate: + check_file(path[model], s3_path[model]) + else: + if not check_available(path[model]): raise Exception( - "model corrupted due to some reasons, please run malaya.clear_cache('%s/%s') and try again" + '%s/%s is not available, please `validate = True`' % (class_name, model) ) - maxlen = 100 if 'sentiment' in class_name else 80 - return SOFTMAX( - g.get_tensor_by_name('import/Placeholder_question:0'), - g.get_tensor_by_name('import/logits:0'), - generate_session(graph = g), - model, - dictionary, - dropout_keep_prob = g.get_tensor_by_name( - 'import/Placeholder_dropout_keep_prob:0' - ), - story = g.get_tensor_by_name('import/Placeholder_story:0'), - label = label, - maxlen = maxlen, - ) - else: + try: + with open(path[model]['setting'], 'r') as fopen: + dictionary = json.load(fopen)['dictionary'] + g = load_graph(path[model]['model']) + except: raise Exception( - 'model subjectivity not supported, please check supported models from malaya.%s.available_deep_model()' - % (class_name) + "model corrupted due to some reasons, please run malaya.clear_cache('%s/%s') and try again" + % (class_name, model) ) + if len(label) > 2: + selected_class = MULTICLASS_SOFTMAX + else: + selected_class = BINARY_SOFTMAX + + return selected_class( + X = g.get_tensor_by_name('import/Placeholder:0'), + logits = g.get_tensor_by_name('import/logits:0'), + logits_seq = g.get_tensor_by_name('import/logits_seq:0'), + alphas = g.get_tensor_by_name('import/alphas:0'), + sess = generate_session(graph = g), + dictionary = dictionary, + class_name = class_name, + label = label, + ) + def multinomial(path, s3_path, class_name, label, validate = True): if validate: @@ -214,10 +127,15 @@ def multinomial(path, s3_path, class_name, label, validate = True): "model corrupted due to some reasons, please run malaya.clear_cache('%s/multinomial') and try again" % (class_name) ) - return USER_BAYES( - multinomial, - label, - vectorize, + + if len(label) > 2: + selected_class = MULTICLASS_BAYES + else: + selected_class = BINARY_BAYES + return selected_class( + multinomial = multinomial, + label = label, + vectorize = vectorize, cleaning = _classification_textcleaning_stemmer, ) @@ -241,6 +159,14 @@ def xgb(path, s3_path, class_name, label, validate = True): "model corrupted due to some reasons, please run malaya.clear_cache('%s/xgb') and try again" % (class_name) ) - return USER_XGB( - xgb, label, vectorize, cleaning = _classification_textcleaning_stemmer + if len(label) > 2: + selected_class = MULTICLASS_XGB + else: + selected_class = BINARY_XGB + + return selected_class( + xgb = xgb, + label = label, + vectorize = vectorize, + cleaning = _classification_textcleaning_stemmer, ) diff --git a/malaya/_utils/_utils.py b/malaya/_utils/_utils.py index a1e8d27d..00f38e18 100644 --- a/malaya/_utils/_utils.py +++ b/malaya/_utils/_utils.py @@ -1,12 +1,22 @@ from tqdm import tqdm import tensorflow as tf from tensorflow.contrib.seq2seq.python.ops import beam_search_ops +import numpy as np import requests import os from pathlib import Path from .. import _delete_folder +def add_neutral(x, alpha = 1e-2): + x = x.copy() + divide = 1 / x.shape[1] + x_minus = np.maximum(x - divide, alpha * x) + x_divide = x_minus / divide + sum_axis = x_divide.sum(axis = 1, keepdims = True) + return np.concatenate([x_divide, 1 - sum_axis], axis = 1) + + def download_file(url, filename): r = requests.get( 'http://s3-ap-southeast-1.amazonaws.com/huseinhouse-storage/' + url, diff --git a/malaya/_utils/web/index.html b/malaya/_utils/web/index.html new file mode 100644 index 00000000..07e858fe --- /dev/null +++ b/malaya/_utils/web/index.html @@ -0,0 +1,164 @@ + + + + + Malaya + + + + + + +
+
+

Malaya $label classification

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$p

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+ + + + + + + diff --git a/malaya/_utils/web/index_emotion.html b/malaya/_utils/web/index_emotion.html new file mode 100644 index 00000000..d2be2199 --- /dev/null +++ b/malaya/_utils/web/index_emotion.html @@ -0,0 +1,208 @@ + + + + + Malaya + + + + + + +
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Malaya $label classification

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+ + + + + + + diff --git a/malaya/_utils/web/index_toxic.html b/malaya/_utils/web/index_toxic.html new file mode 100644 index 00000000..d4783045 --- /dev/null +++ b/malaya/_utils/web/index_toxic.html @@ -0,0 +1,208 @@ + + + + + Malaya + + + + + + +
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Malaya $label classification

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+ + + + + + + diff --git a/malaya/_utils/web/static/admin-materialize.min.css b/malaya/_utils/web/static/admin-materialize.min.css new file mode 100644 index 00000000..cc22d1e0 --- /dev/null +++ b/malaya/_utils/web/static/admin-materialize.min.css @@ -0,0 +1,9925 @@ +body { + font-size: 14px; + font-weight: 400; + font-family: "Nunito", "Segoe UI", arial; + color: #6c757d; } + +.materialize-red { + background-color: #e51c23 !important +} + +.materialize-red-text { + color: #e51c23 !important +} + +.materialize-red.lighten-5 { + background-color: #fdeaeb !important +} + +.materialize-red-text.text-lighten-5 { + color: #fdeaeb !important +} + +.materialize-red.lighten-4 { + background-color: #f8c1c3 !important +} + +.materialize-red-text.text-lighten-4 { + color: #f8c1c3 !important +} + +.materialize-red.lighten-3 { + background-color: #f3989b !important +} + +.materialize-red-text.text-lighten-3 { + color: #f3989b !important +} + +.materialize-red.lighten-2 { + background-color: #ee6e73 !important +} + +.materialize-red-text.text-lighten-2 { + color: #ee6e73 !important +} + +.materialize-red.lighten-1 { + background-color: #ea454b !important +} + +.materialize-red-text.text-lighten-1 { + color: #ea454b !important +} + +.materialize-red.darken-1 { + background-color: #d0181e !important +} + +.materialize-red-text.text-darken-1 { + color: #d0181e !important +} + +.materialize-red.darken-2 { + background-color: #b9151b !important +} + +.materialize-red-text.text-darken-2 { + color: #b9151b !important +} + +.materialize-red.darken-3 { + background-color: #a21318 !important +} + +.materialize-red-text.text-darken-3 { + color: #a21318 !important +} + +.materialize-red.darken-4 { + background-color: #8b1014 !important +} + +.materialize-red-text.text-darken-4 { + color: #8b1014 !important +} + +.red { + background-color: #F44336 !important +} + +.red-text { + color: #F44336 !important +} + +.red.lighten-5 { + background-color: #FFEBEE !important +} + +.red-text.text-lighten-5 { + color: #FFEBEE !important +} + +.red.lighten-4 { + background-color: #FFCDD2 !important +} + +.red-text.text-lighten-4 { + color: #FFCDD2 !important +} + +.red.lighten-3 { + background-color: #EF9A9A !important +} + +.red-text.text-lighten-3 { + color: #EF9A9A !important +} + +.red.lighten-2 { + background-color: #E57373 !important +} + +.red-text.text-lighten-2 { + color: #E57373 !important +} + +.red.lighten-1 { + background-color: #EF5350 !important +} + +.red-text.text-lighten-1 { + color: #EF5350 !important +} + +.red.darken-1 { + background-color: #E53935 !important +} + +.red-text.text-darken-1 { + color: #E53935 !important +} + +.red.darken-2 { + background-color: #D32F2F !important +} + +.red-text.text-darken-2 { + color: #D32F2F !important +} + +.red.darken-3 { + background-color: #C62828 !important +} + +.red-text.text-darken-3 { + color: #C62828 !important +} + +.red.darken-4 { + background-color: #B71C1C !important +} + +.red-text.text-darken-4 { + color: #B71C1C !important +} + +.red.accent-1 { + background-color: #FF8A80 !important +} + +.red-text.text-accent-1 { + color: #FF8A80 !important +} + +.red.accent-2 { + background-color: #FF5252 !important +} + +.red-text.text-accent-2 { + color: #FF5252 !important +} + +.red.accent-3 { + background-color: #FF1744 !important +} + +.red-text.text-accent-3 { + color: #FF1744 !important +} + +.red.accent-4 { + background-color: #D50000 !important +} + +.red-text.text-accent-4 { + color: #D50000 !important +} + +.pink { + background-color: #e91e63 !important +} + +.pink-text { + color: #e91e63 !important +} + +.pink.lighten-5 { + background-color: #fce4ec !important +} + +.pink-text.text-lighten-5 { + color: #fce4ec !important +} + +.pink.lighten-4 { + background-color: #f8bbd0 !important +} + +.pink-text.text-lighten-4 { + color: #f8bbd0 !important +} + +.pink.lighten-3 { + background-color: #f48fb1 !important +} + +.pink-text.text-lighten-3 { + color: #f48fb1 !important +} + +.pink.lighten-2 { + background-color: #f06292 !important +} + +.pink-text.text-lighten-2 { + color: #f06292 !important +} + +.pink.lighten-1 { + background-color: #ec407a !important +} + +.pink-text.text-lighten-1 { + color: #ec407a !important +} + +.pink.darken-1 { + background-color: #d81b60 !important +} + +.pink-text.text-darken-1 { + color: #d81b60 !important +} + +.pink.darken-2 { + background-color: #c2185b !important +} + +.pink-text.text-darken-2 { + color: #c2185b !important +} + +.pink.darken-3 { + background-color: #ad1457 !important +} + +.pink-text.text-darken-3 { + color: #ad1457 !important +} + +.pink.darken-4 { + background-color: #880e4f !important +} + +.pink-text.text-darken-4 { + color: #880e4f !important +} + +.pink.accent-1 { + background-color: #ff80ab !important +} + +.pink-text.text-accent-1 { + color: #ff80ab !important +} + +.pink.accent-2 { + background-color: #ff4081 !important +} + +.pink-text.text-accent-2 { + color: #ff4081 !important +} + +.pink.accent-3 { + background-color: #f50057 !important +} + +.pink-text.text-accent-3 { + color: #f50057 !important +} + +.pink.accent-4 { + background-color: #c51162 !important +} + +.pink-text.text-accent-4 { + color: #c51162 !important +} + +.purple { + background-color: #9c27b0 !important +} + +.purple-text { + color: #9c27b0 !important +} + +.purple.lighten-5 { + background-color: #f3e5f5 !important +} + +.purple-text.text-lighten-5 { + color: #f3e5f5 !important +} + +.purple.lighten-4 { + background-color: #e1bee7 !important +} + +.purple-text.text-lighten-4 { + color: #e1bee7 !important +} + +.purple.lighten-3 { + background-color: #ce93d8 !important +} + +.purple-text.text-lighten-3 { + color: #ce93d8 !important +} + +.purple.lighten-2 { + background-color: #ba68c8 !important +} + +.purple-text.text-lighten-2 { + color: #ba68c8 !important +} + +.purple.lighten-1 { + background-color: #ab47bc !important +} + +.purple-text.text-lighten-1 { + color: #ab47bc 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color: #a0f !important +} + +.deep-purple { + background-color: #673ab7 !important +} + +.deep-purple-text { + color: #673ab7 !important +} + +.deep-purple.lighten-5 { + background-color: #ede7f6 !important +} + +.deep-purple-text.text-lighten-5 { + color: #ede7f6 !important +} + +.deep-purple.lighten-4 { + background-color: #d1c4e9 !important +} + +.deep-purple-text.text-lighten-4 { + color: #d1c4e9 !important +} + +.deep-purple.lighten-3 { + background-color: #b39ddb !important +} + +.deep-purple-text.text-lighten-3 { + color: #b39ddb !important +} + +.deep-purple.lighten-2 { + background-color: #9575cd !important +} + +.deep-purple-text.text-lighten-2 { + color: #9575cd !important +} + +.deep-purple.lighten-1 { + background-color: #7e57c2 !important +} + +.deep-purple-text.text-lighten-1 { + color: #7e57c2 !important +} + +.deep-purple.darken-1 { + background-color: #5e35b1 !important +} + +.deep-purple-text.text-darken-1 { + color: #5e35b1 !important +} + +.deep-purple.darken-2 { + background-color: #512da8 !important +} + +.deep-purple-text.text-darken-2 { + color: #512da8 !important +} + +.deep-purple.darken-3 { + background-color: #4527a0 !important +} + +.deep-purple-text.text-darken-3 { + color: #4527a0 !important +} + +.deep-purple.darken-4 { + background-color: #311b92 !important +} + +.deep-purple-text.text-darken-4 { + color: #311b92 !important +} + +.deep-purple.accent-1 { + background-color: #b388ff !important +} + +.deep-purple-text.text-accent-1 { + color: #b388ff !important +} + +.deep-purple.accent-2 { + background-color: #7c4dff !important +} + +.deep-purple-text.text-accent-2 { + color: #7c4dff !important +} + +.deep-purple.accent-3 { + background-color: #651fff !important +} + +.deep-purple-text.text-accent-3 { + color: #651fff !important +} + +.deep-purple.accent-4 { + background-color: #6200ea !important +} + +.deep-purple-text.text-accent-4 { + color: #6200ea !important +} + +.indigo { + background-color: #3f51b5 !important +} + +.indigo-text { + color: #3f51b5 !important +} + +.indigo.lighten-5 { + background-color: #e8eaf6 !important +} + +.indigo-text.text-lighten-5 { + color: #e8eaf6 !important +} + +.indigo.lighten-4 { + background-color: #c5cae9 !important +} + +.indigo-text.text-lighten-4 { + color: #c5cae9 !important +} + +.indigo.lighten-3 { + background-color: #9fa8da !important +} + +.indigo-text.text-lighten-3 { + color: #9fa8da !important +} + +.indigo.lighten-2 { + background-color: #7986cb !important +} + +.indigo-text.text-lighten-2 { + color: #7986cb !important +} + +.indigo.lighten-1 { + background-color: #5c6bc0 !important +} + +.indigo-text.text-lighten-1 { + color: #5c6bc0 !important +} + +.indigo.darken-1 { + background-color: #3949ab !important +} + +.indigo-text.text-darken-1 { + color: #3949ab !important +} + +.indigo.darken-2 { + background-color: #303f9f !important +} + +.indigo-text.text-darken-2 { + color: #303f9f !important +} + +.indigo.darken-3 { + background-color: #283593 !important +} + +.indigo-text.text-darken-3 { + color: #283593 !important +} + +.indigo.darken-4 { + background-color: #1a237e !important +} + +.indigo-text.text-darken-4 { + color: #1a237e !important +} + +.indigo.accent-1 { + background-color: #8c9eff !important +} + +.indigo-text.text-accent-1 { + color: #8c9eff !important +} + +.indigo.accent-2 { + background-color: #536dfe !important +} + +.indigo-text.text-accent-2 { + color: #536dfe !important +} + +.indigo.accent-3 { + background-color: #3d5afe !important +} + +.indigo-text.text-accent-3 { + color: #3d5afe !important +} + +.indigo.accent-4 { + background-color: #304ffe !important +} + +.indigo-text.text-accent-4 { + color: #304ffe !important +} + +.blue { + background-color: #2196F3 !important +} + +.blue-text { + color: #2196F3 !important +} + +.blue.lighten-5 { + background-color: #E3F2FD !important +} + +.blue-text.text-lighten-5 { + color: #E3F2FD !important +} + +.blue.lighten-4 { + background-color: #BBDEFB !important +} + +.blue-text.text-lighten-4 { + color: #BBDEFB !important +} + +.blue.lighten-3 { + background-color: #90CAF9 !important +} + +.blue-text.text-lighten-3 { + color: #90CAF9 !important +} + +.blue.lighten-2 { + background-color: #64B5F6 !important +} + +.blue-text.text-lighten-2 { + color: #64B5F6 !important +} + +.blue.lighten-1 { + background-color: #42A5F5 !important +} + +.blue-text.text-lighten-1 { + color: #42A5F5 !important +} + +.blue.darken-1 { + background-color: #1E88E5 !important +} + +.blue-text.text-darken-1 { + color: #1E88E5 !important +} + +.blue.darken-2 { + background-color: #1976D2 !important +} + +.blue-text.text-darken-2 { + color: #1976D2 !important +} + +.blue.darken-3 { + background-color: #1565C0 !important +} + +.blue-text.text-darken-3 { + color: #1565C0 !important +} + +.blue.darken-4 { + background-color: #0D47A1 !important +} + +.blue-text.text-darken-4 { + color: #0D47A1 !important +} + +.blue.accent-1 { + background-color: #82B1FF !important +} + +.blue-text.text-accent-1 { + color: #82B1FF !important +} + +.blue.accent-2 { + background-color: #448AFF !important +} + +.blue-text.text-accent-2 { + color: #448AFF !important +} + +.blue.accent-3 { + background-color: #2979FF !important +} + +.blue-text.text-accent-3 { + color: #2979FF !important +} + +.blue.accent-4 { + background-color: #2962FF !important +} + +.blue-text.text-accent-4 { + color: #2962FF !important +} + +.light-blue { + background-color: #03a9f4 !important +} + +.light-blue-text { + color: #03a9f4 !important +} + +.light-blue.lighten-5 { + background-color: #e1f5fe !important +} + +.light-blue-text.text-lighten-5 { + color: #e1f5fe !important +} + +.light-blue.lighten-4 { + background-color: #b3e5fc !important +} + +.light-blue-text.text-lighten-4 { + color: #b3e5fc !important +} + +.light-blue.lighten-3 { + background-color: #81d4fa !important +} + +.light-blue-text.text-lighten-3 { + color: #81d4fa !important +} + +.light-blue.lighten-2 { + background-color: #4fc3f7 !important +} + +.light-blue-text.text-lighten-2 { + color: #4fc3f7 !important +} + +.light-blue.lighten-1 { + background-color: #29b6f6 !important +} + +.light-blue-text.text-lighten-1 { + color: #29b6f6 !important +} + +.light-blue.darken-1 { + background-color: #039be5 !important +} + +.light-blue-text.text-darken-1 { + color: #039be5 !important +} + +.light-blue.darken-2 { + background-color: #0288d1 !important +} + +.light-blue-text.text-darken-2 { + color: #0288d1 !important +} + +.light-blue.darken-3 { + background-color: #0277bd !important +} + +.light-blue-text.text-darken-3 { + color: #0277bd !important +} + +.light-blue.darken-4 { + background-color: #01579b !important +} + +.light-blue-text.text-darken-4 { + color: #01579b !important +} + +.light-blue.accent-1 { + background-color: #80d8ff !important +} + +.light-blue-text.text-accent-1 { + color: #80d8ff !important +} + +.light-blue.accent-2 { + background-color: #40c4ff !important +} + +.light-blue-text.text-accent-2 { + color: #40c4ff !important +} + +.light-blue.accent-3 { + background-color: #00b0ff !important +} + +.light-blue-text.text-accent-3 { + color: #00b0ff !important +} + +.light-blue.accent-4 { + background-color: #0091ea !important +} + +.light-blue-text.text-accent-4 { + color: #0091ea !important +} + +.cyan { + background-color: #00bcd4 !important +} + +.cyan-text { + color: #00bcd4 !important +} + +.cyan.lighten-5 { + background-color: #e0f7fa !important +} + +.cyan-text.text-lighten-5 { + color: #e0f7fa !important +} + +.cyan.lighten-4 { + background-color: #b2ebf2 !important +} + +.cyan-text.text-lighten-4 { + color: #b2ebf2 !important +} + +.cyan.lighten-3 { + background-color: #80deea !important +} + +.cyan-text.text-lighten-3 { + color: #80deea !important +} + +.cyan.lighten-2 { + background-color: #4dd0e1 !important +} + +.cyan-text.text-lighten-2 { + color: #4dd0e1 !important +} + +.cyan.lighten-1 { + background-color: #26c6da !important +} + +.cyan-text.text-lighten-1 { + color: #26c6da !important +} + +.cyan.darken-1 { + background-color: #00acc1 !important +} + +.cyan-text.text-darken-1 { + color: #00acc1 !important +} + +.cyan.darken-2 { + background-color: #0097a7 !important +} + +.cyan-text.text-darken-2 { + color: #0097a7 !important +} + +.cyan.darken-3 { + background-color: #00838f !important +} + +.cyan-text.text-darken-3 { + color: #00838f !important +} + +.cyan.darken-4 { + background-color: #006064 !important +} + +.cyan-text.text-darken-4 { + color: #006064 !important +} + +.cyan.accent-1 { + background-color: #84ffff !important +} + +.cyan-text.text-accent-1 { + color: #84ffff !important +} + +.cyan.accent-2 { + background-color: #18ffff !important +} + +.cyan-text.text-accent-2 { + color: #18ffff !important +} + +.cyan.accent-3 { + background-color: #00e5ff !important +} + +.cyan-text.text-accent-3 { + color: #00e5ff !important +} + +.cyan.accent-4 { + background-color: #00b8d4 !important +} + +.cyan-text.text-accent-4 { + color: #00b8d4 !important +} + +.teal { + background-color: #009688 !important +} + +.teal-text { + color: #009688 !important +} + +.teal.lighten-5 { + background-color: #e0f2f1 !important +} + +.teal-text.text-lighten-5 { + color: #e0f2f1 !important +} + +.teal.lighten-4 { + background-color: #b2dfdb !important +} + +.teal-text.text-lighten-4 { + color: #b2dfdb !important +} + +.teal.lighten-3 { + background-color: #80cbc4 !important +} + +.teal-text.text-lighten-3 { + color: #80cbc4 !important +} + +.teal.lighten-2 { + background-color: #4db6ac !important +} + +.teal-text.text-lighten-2 { + color: #4db6ac !important +} + +.teal.lighten-1 { + background-color: #26a69a !important +} + +.teal-text.text-lighten-1 { + color: #26a69a !important +} + +.teal.darken-1 { + background-color: #00897b !important +} + +.teal-text.text-darken-1 { + color: #00897b !important +} + +.teal.darken-2 { + background-color: #00796b !important +} + +.teal-text.text-darken-2 { + color: #00796b !important +} + +.teal.darken-3 { + background-color: #00695c !important +} + +.teal-text.text-darken-3 { + color: #00695c !important +} + +.teal.darken-4 { + background-color: #004d40 !important +} + +.teal-text.text-darken-4 { + color: #004d40 !important +} + +.teal.accent-1 { + background-color: #a7ffeb !important +} + +.teal-text.text-accent-1 { + color: #a7ffeb !important +} + +.teal.accent-2 { + background-color: #64ffda !important +} + +.teal-text.text-accent-2 { + color: #64ffda !important +} + +.teal.accent-3 { + background-color: #1de9b6 !important +} + +.teal-text.text-accent-3 { + color: #1de9b6 !important +} + +.teal.accent-4 { + background-color: #00bfa5 !important +} + +.teal-text.text-accent-4 { + color: #00bfa5 !important +} + +.green { + background-color: #4CAF50 !important +} + +.green-text { + color: #4CAF50 !important +} + +.green.lighten-5 { + background-color: #E8F5E9 !important +} + +.green-text.text-lighten-5 { + color: #E8F5E9 !important +} + +.green.lighten-4 { + background-color: #C8E6C9 !important +} + +.green-text.text-lighten-4 { + color: #C8E6C9 !important +} + +.green.lighten-3 { + background-color: #A5D6A7 !important +} + +.green-text.text-lighten-3 { + color: #A5D6A7 !important +} + +.green.lighten-2 { + background-color: #81C784 !important +} + +.green-text.text-lighten-2 { + color: #81C784 !important +} + +.green.lighten-1 { + background-color: #66BB6A !important +} + +.green-text.text-lighten-1 { + color: #66BB6A !important +} + +.green.darken-1 { + background-color: #43A047 !important +} + +.green-text.text-darken-1 { + color: #43A047 !important +} + +.green.darken-2 { + background-color: #388E3C !important +} + +.green-text.text-darken-2 { + color: #388E3C !important +} + +.green.darken-3 { + background-color: #2E7D32 !important +} + +.green-text.text-darken-3 { + color: #2E7D32 !important +} + +.green.darken-4 { + background-color: #1B5E20 !important +} + +.green-text.text-darken-4 { + color: #1B5E20 !important +} + +.green.accent-1 { + background-color: #B9F6CA !important +} + +.green-text.text-accent-1 { + color: #B9F6CA !important +} + +.green.accent-2 { + background-color: #69F0AE !important +} + +.green-text.text-accent-2 { + color: #69F0AE !important +} + +.green.accent-3 { + background-color: #00E676 !important +} + +.green-text.text-accent-3 { + color: #00E676 !important +} + +.green.accent-4 { + background-color: #00C853 !important +} + +.green-text.text-accent-4 { + color: #00C853 !important +} + +.light-green { + background-color: #8bc34a !important +} + +.light-green-text { + color: #8bc34a !important +} + +.light-green.lighten-5 { + background-color: #f1f8e9 !important +} + +.light-green-text.text-lighten-5 { + color: #f1f8e9 !important +} + +.light-green.lighten-4 { + background-color: #dcedc8 !important +} + +.light-green-text.text-lighten-4 { + color: #dcedc8 !important +} + +.light-green.lighten-3 { + background-color: #c5e1a5 !important +} + +.light-green-text.text-lighten-3 { + color: #c5e1a5 !important +} + +.light-green.lighten-2 { + background-color: #aed581 !important +} + +.light-green-text.text-lighten-2 { + color: #aed581 !important +} + +.light-green.lighten-1 { + background-color: #9ccc65 !important +} + +.light-green-text.text-lighten-1 { + color: #9ccc65 !important +} + +.light-green.darken-1 { + background-color: #7cb342 !important +} + +.light-green-text.text-darken-1 { + color: #7cb342 !important +} + +.light-green.darken-2 { + background-color: #689f38 !important +} + +.light-green-text.text-darken-2 { + color: #689f38 !important +} + +.light-green.darken-3 { + background-color: #558b2f !important +} + +.light-green-text.text-darken-3 { + color: #558b2f !important +} + +.light-green.darken-4 { + background-color: #33691e !important +} + +.light-green-text.text-darken-4 { + color: #33691e !important +} + +.light-green.accent-1 { + background-color: #ccff90 !important +} + +.light-green-text.text-accent-1 { + color: #ccff90 !important +} + +.light-green.accent-2 { + background-color: #b2ff59 !important +} + +.light-green-text.text-accent-2 { + color: #b2ff59 !important +} + +.light-green.accent-3 { + background-color: #76ff03 !important +} + +.light-green-text.text-accent-3 { + color: #76ff03 !important +} + +.light-green.accent-4 { + background-color: #64dd17 !important +} + +.light-green-text.text-accent-4 { + color: #64dd17 !important +} + +.lime { + background-color: #cddc39 !important +} + +.lime-text { + color: #cddc39 !important +} + +.lime.lighten-5 { + background-color: #f9fbe7 !important +} + +.lime-text.text-lighten-5 { + color: #f9fbe7 !important +} + +.lime.lighten-4 { + background-color: #f0f4c3 !important +} + +.lime-text.text-lighten-4 { + color: #f0f4c3 !important +} + +.lime.lighten-3 { + background-color: #e6ee9c !important +} + +.lime-text.text-lighten-3 { + color: #e6ee9c !important +} + +.lime.lighten-2 { + background-color: #dce775 !important +} + +.lime-text.text-lighten-2 { + color: #dce775 !important +} + +.lime.lighten-1 { + background-color: #d4e157 !important +} + +.lime-text.text-lighten-1 { + color: #d4e157 !important +} + +.lime.darken-1 { + background-color: #c0ca33 !important +} + +.lime-text.text-darken-1 { + color: #c0ca33 !important +} + +.lime.darken-2 { + background-color: #afb42b !important +} + +.lime-text.text-darken-2 { + color: #afb42b !important +} + +.lime.darken-3 { + background-color: #9e9d24 !important +} + +.lime-text.text-darken-3 { + color: #9e9d24 !important +} + +.lime.darken-4 { + background-color: #827717 !important +} + +.lime-text.text-darken-4 { + color: #827717 !important +} + +.lime.accent-1 { + background-color: #f4ff81 !important +} + +.lime-text.text-accent-1 { + color: #f4ff81 !important +} + +.lime.accent-2 { + background-color: #eeff41 !important +} + +.lime-text.text-accent-2 { + color: #eeff41 !important +} + +.lime.accent-3 { + background-color: #c6ff00 !important +} + +.lime-text.text-accent-3 { + color: #c6ff00 !important +} + +.lime.accent-4 { + background-color: #aeea00 !important +} + +.lime-text.text-accent-4 { + color: #aeea00 !important +} + +.yellow { + background-color: #ffeb3b !important +} + +.yellow-text { + color: #ffeb3b !important +} + +.yellow.lighten-5 { + background-color: #fffde7 !important +} + +.yellow-text.text-lighten-5 { + color: #fffde7 !important +} + +.yellow.lighten-4 { + background-color: #fff9c4 !important +} + +.yellow-text.text-lighten-4 { + color: #fff9c4 !important +} + +.yellow.lighten-3 { + background-color: #fff59d !important +} + +.yellow-text.text-lighten-3 { + color: #fff59d !important +} + +.yellow.lighten-2 { + background-color: #fff176 !important +} + +.yellow-text.text-lighten-2 { + color: #fff176 !important +} + +.yellow.lighten-1 { + background-color: #ffee58 !important +} + +.yellow-text.text-lighten-1 { + color: #ffee58 !important +} + +.yellow.darken-1 { + background-color: #fdd835 !important +} + +.yellow-text.text-darken-1 { + color: #fdd835 !important +} + +.yellow.darken-2 { + background-color: #fbc02d !important +} + +.yellow-text.text-darken-2 { + color: #fbc02d !important +} + +.yellow.darken-3 { + background-color: #f9a825 !important +} + +.yellow-text.text-darken-3 { + color: #f9a825 !important +} + +.yellow.darken-4 { + background-color: #f57f17 !important +} + +.yellow-text.text-darken-4 { + color: #f57f17 !important +} + +.yellow.accent-1 { + background-color: #ffff8d !important +} + +.yellow-text.text-accent-1 { + color: #ffff8d !important +} + +.yellow.accent-2 { + background-color: #ff0 !important +} + +.yellow-text.text-accent-2 { + color: #ff0 !important +} + +.yellow.accent-3 { + background-color: #ffea00 !important +} + +.yellow-text.text-accent-3 { + color: #ffea00 !important +} + +.yellow.accent-4 { + background-color: #ffd600 !important +} + +.yellow-text.text-accent-4 { + color: #ffd600 !important +} + +.amber { + background-color: #ffc107 !important +} + +.amber-text { + color: #ffc107 !important +} + +.amber.lighten-5 { + background-color: #fff8e1 !important +} + +.amber-text.text-lighten-5 { + color: #fff8e1 !important +} + +.amber.lighten-4 { + background-color: #ffecb3 !important +} + +.amber-text.text-lighten-4 { + color: #ffecb3 !important +} + +.amber.lighten-3 { + background-color: #ffe082 !important +} + +.amber-text.text-lighten-3 { + color: #ffe082 !important +} + +.amber.lighten-2 { + background-color: #ffd54f !important +} + +.amber-text.text-lighten-2 { + color: #ffd54f !important +} + +.amber.lighten-1 { + background-color: #ffca28 !important +} + +.amber-text.text-lighten-1 { + color: #ffca28 !important +} + +.amber.darken-1 { + background-color: #ffb300 !important +} + +.amber-text.text-darken-1 { + color: #ffb300 !important +} + +.amber.darken-2 { + background-color: #ffa000 !important +} + +.amber-text.text-darken-2 { + color: #ffa000 !important +} + +.amber.darken-3 { + background-color: #ff8f00 !important +} + +.amber-text.text-darken-3 { + color: #ff8f00 !important +} + +.amber.darken-4 { + background-color: #ff6f00 !important +} + +.amber-text.text-darken-4 { + color: #ff6f00 !important +} + +.amber.accent-1 { + background-color: #ffe57f !important +} + +.amber-text.text-accent-1 { + color: #ffe57f !important +} + +.amber.accent-2 { + background-color: #ffd740 !important +} + +.amber-text.text-accent-2 { + color: #ffd740 !important +} + +.amber.accent-3 { + background-color: #ffc400 !important +} + +.amber-text.text-accent-3 { + color: #ffc400 !important +} + +.amber.accent-4 { + background-color: #ffab00 !important +} + +.amber-text.text-accent-4 { + color: #ffab00 !important +} + +.orange { + background-color: #ff9800 !important +} + +.orange-text { + color: #ff9800 !important +} + +.orange.lighten-5 { + background-color: #fff3e0 !important +} + +.orange-text.text-lighten-5 { + color: #fff3e0 !important +} + +.orange.lighten-4 { + background-color: #ffe0b2 !important +} + +.orange-text.text-lighten-4 { + color: #ffe0b2 !important +} + +.orange.lighten-3 { + background-color: #ffcc80 !important +} + +.orange-text.text-lighten-3 { + color: #ffcc80 !important +} + +.orange.lighten-2 { + background-color: #ffb74d !important +} + +.orange-text.text-lighten-2 { + color: #ffb74d !important +} + +.orange.lighten-1 { + background-color: #ffa726 !important +} + +.orange-text.text-lighten-1 { + color: #ffa726 !important +} + +.orange.darken-1 { + background-color: #fb8c00 !important +} + +.orange-text.text-darken-1 { + color: #fb8c00 !important +} + +.orange.darken-2 { + background-color: #f57c00 !important +} + +.orange-text.text-darken-2 { + color: #f57c00 !important +} + +.orange.darken-3 { + background-color: #ef6c00 !important +} + +.orange-text.text-darken-3 { + color: #ef6c00 !important +} + +.orange.darken-4 { + background-color: #e65100 !important +} + +.orange-text.text-darken-4 { + color: #e65100 !important +} + +.orange.accent-1 { + background-color: #ffd180 !important +} + +.orange-text.text-accent-1 { + color: #ffd180 !important +} + +.orange.accent-2 { + background-color: #ffab40 !important +} + +.orange-text.text-accent-2 { + color: #ffab40 !important +} + +.orange.accent-3 { + background-color: #ff9100 !important +} + +.orange-text.text-accent-3 { + color: #ff9100 !important +} + +.orange.accent-4 { + background-color: #ff6d00 !important +} + +.orange-text.text-accent-4 { + color: #ff6d00 !important +} + +.deep-orange { + background-color: #ff5722 !important +} + +.deep-orange-text { + color: #ff5722 !important +} + +.deep-orange.lighten-5 { + background-color: #fbe9e7 !important +} + +.deep-orange-text.text-lighten-5 { + color: #fbe9e7 !important +} + +.deep-orange.lighten-4 { + background-color: #ffccbc !important +} + +.deep-orange-text.text-lighten-4 { + color: #ffccbc !important +} + +.deep-orange.lighten-3 { + background-color: #ffab91 !important +} + +.deep-orange-text.text-lighten-3 { + color: #ffab91 !important +} + +.deep-orange.lighten-2 { + background-color: #ff8a65 !important +} + +.deep-orange-text.text-lighten-2 { + color: #ff8a65 !important +} + +.deep-orange.lighten-1 { + background-color: #ff7043 !important +} + +.deep-orange-text.text-lighten-1 { + color: #ff7043 !important +} + +.deep-orange.darken-1 { + background-color: #f4511e !important +} + +.deep-orange-text.text-darken-1 { + color: #f4511e !important +} + +.deep-orange.darken-2 { + background-color: #e64a19 !important +} + +.deep-orange-text.text-darken-2 { + color: #e64a19 !important +} + +.deep-orange.darken-3 { + background-color: #d84315 !important +} + +.deep-orange-text.text-darken-3 { + color: #d84315 !important +} + +.deep-orange.darken-4 { + background-color: #bf360c !important +} + +.deep-orange-text.text-darken-4 { + color: #bf360c !important +} + +.deep-orange.accent-1 { + background-color: #ff9e80 !important +} + +.deep-orange-text.text-accent-1 { + color: #ff9e80 !important +} + +.deep-orange.accent-2 { + background-color: #ff6e40 !important +} + +.deep-orange-text.text-accent-2 { + color: #ff6e40 !important +} + +.deep-orange.accent-3 { + background-color: #ff3d00 !important +} + +.deep-orange-text.text-accent-3 { + color: #ff3d00 !important +} + +.deep-orange.accent-4 { + background-color: #dd2c00 !important +} + +.deep-orange-text.text-accent-4 { + color: #dd2c00 !important +} + +.brown { + background-color: #795548 !important +} + +.brown-text { + color: #795548 !important +} + +.brown.lighten-5 { + background-color: #efebe9 !important +} + +.brown-text.text-lighten-5 { + color: #efebe9 !important +} + +.brown.lighten-4 { + background-color: #d7ccc8 !important +} + +.brown-text.text-lighten-4 { + color: #d7ccc8 !important +} + +.brown.lighten-3 { + background-color: #bcaaa4 !important +} + +.brown-text.text-lighten-3 { + color: #bcaaa4 !important +} + +.brown.lighten-2 { + background-color: #a1887f !important +} + +.brown-text.text-lighten-2 { + color: #a1887f !important +} + +.brown.lighten-1 { + background-color: #8d6e63 !important +} + +.brown-text.text-lighten-1 { + color: #8d6e63 !important +} + +.brown.darken-1 { + background-color: #6d4c41 !important +} + +.brown-text.text-darken-1 { + color: #6d4c41 !important +} + +.brown.darken-2 { + background-color: #5d4037 !important +} + +.brown-text.text-darken-2 { + color: #5d4037 !important +} + +.brown.darken-3 { + background-color: #4e342e !important +} + +.brown-text.text-darken-3 { + color: #4e342e !important +} + +.brown.darken-4 { + background-color: #3e2723 !important +} + +.brown-text.text-darken-4 { + color: #3e2723 !important +} + +.blue-grey { + background-color: #607d8b !important +} + +.blue-grey-text { + color: #607d8b !important +} + +.blue-grey.lighten-5 { + background-color: #eceff1 !important +} + +.blue-grey-text.text-lighten-5 { + color: #eceff1 !important +} + +.blue-grey.lighten-4 { + background-color: #cfd8dc !important +} + +.blue-grey-text.text-lighten-4 { + color: #cfd8dc !important +} + +.blue-grey.lighten-3 { + background-color: #b0bec5 !important +} + +.blue-grey-text.text-lighten-3 { + color: #b0bec5 !important +} + +.blue-grey.lighten-2 { + background-color: #90a4ae !important +} + +.blue-grey-text.text-lighten-2 { + color: #90a4ae !important +} + +.blue-grey.lighten-1 { + background-color: #78909c !important +} + +.blue-grey-text.text-lighten-1 { + color: #78909c !important +} + +.blue-grey.darken-1 { + background-color: #546e7a !important +} + +.blue-grey-text.text-darken-1 { + color: #546e7a !important +} + +.blue-grey.darken-2 { + background-color: #455a64 !important +} + +.blue-grey-text.text-darken-2 { + color: #455a64 !important +} + +.blue-grey.darken-3 { + background-color: #37474f !important +} + +.blue-grey-text.text-darken-3 { + color: #37474f !important +} + +.blue-grey.darken-4 { + background-color: #263238 !important +} + +.blue-grey-text.text-darken-4 { + color: #263238 !important +} + +.grey { + background-color: #9e9e9e !important +} + +.grey-text { + color: #9e9e9e !important +} + +.grey.lighten-5 { + background-color: #fafafa !important +} + +.grey-text.text-lighten-5 { + color: #fafafa !important +} + +.grey.lighten-4 { + background-color: #f5f5f5 !important +} + +.grey-text.text-lighten-4 { + color: #f5f5f5 !important +} + +.grey.lighten-3 { + background-color: #eee !important +} + +.grey-text.text-lighten-3 { + color: #eee !important +} + +.grey.lighten-2 { + background-color: #e0e0e0 !important +} + +.grey-text.text-lighten-2 { + color: #e0e0e0 !important +} + +.grey.lighten-1 { + background-color: #bdbdbd !important +} + +.grey-text.text-lighten-1 { + color: #bdbdbd !important +} + +.grey.darken-1 { + background-color: #757575 !important +} + +.grey-text.text-darken-1 { + color: #757575 !important +} + +.grey.darken-2 { + background-color: #616161 !important +} + +.grey-text.text-darken-2 { + color: #616161 !important +} + +.grey.darken-3 { + background-color: #424242 !important +} + +.grey-text.text-darken-3 { + color: #424242 !important +} + +.grey.darken-4 { + background-color: #212121 !important +} + +.grey-text.text-darken-4 { + color: #212121 !important +} + +.black { + background-color: #000 !important +} + +.black-text { + color: #000 !important +} + +.white { + background-color: #fff !important +} + +.white-text { + color: #fff !important +} + +.transparent { + background-color: transparent !important +} + +.transparent-text { + color: transparent !important +} + + +/*! normalize.css v7.0.0 | MIT License | github.com/necolas/normalize.css */ + +html { + line-height: 1.15; + -ms-text-size-adjust: 100%; + -webkit-text-size-adjust: 100% +} + +body { + margin: 0 +} + +article, +aside, +footer, +header, +nav, +section { + display: block +} + +h1 { + font-size: 2em; + margin: 0.67em 0 +} + +figcaption, +figure, +main { + display: block +} + +figure { + margin: 1em 40px +} + +hr { + box-sizing: content-box; + height: 0; + overflow: visible +} + +pre { + font-family: monospace, monospace; + font-size: 1em +} + +a { + background-color: transparent; + -webkit-text-decoration-skip: objects +} + +abbr[title] { + border-bottom: none; + text-decoration: underline; + text-decoration: underline dotted +} + +b, +strong { + font-weight: inherit +} + +b, +strong { + font-weight: bolder +} + +code, +kbd, +samp { + font-family: monospace, monospace; + font-size: 1em +} + +dfn { + font-style: italic +} + +mark { + background-color: #ff0; + color: #000 +} + +small { + font-size: 80% +} + +sub, +sup { + font-size: 75%; + line-height: 0; + position: relative; + vertical-align: baseline +} + +sub { + bottom: -0.25em +} + +sup { + top: -0.5em +} + +audio, +video { + display: inline-block +} + +audio:not([controls]) { + display: none; + height: 0 +} + +img { + border-style: none +} + +svg:not(:root) { + overflow: hidden +} + +button, +input, +optgroup, +select, +textarea { + font-family: sans-serif; + font-size: 100%; + line-height: 1.15; + margin: 0 +} + +button, +input { + overflow: visible +} + +button, +select { + text-transform: none +} + +button, +html [type="button"], +[type="reset"], +[type="submit"] { + -webkit-appearance: button +} + +button::-moz-focus-inner, +[type="button"]::-moz-focus-inner, +[type="reset"]::-moz-focus-inner, +[type="submit"]::-moz-focus-inner { + border-style: none; + padding: 0 +} + +button:-moz-focusring, +[type="button"]:-moz-focusring, +[type="reset"]:-moz-focusring, +[type="submit"]:-moz-focusring { + outline: 1px dotted ButtonText +} + +fieldset { + padding: 0.35em 0.75em 0.625em +} + +legend { + box-sizing: border-box; + color: inherit; + display: table; + max-width: 100%; + padding: 0; + white-space: normal +} + +progress { + display: inline-block; + vertical-align: baseline +} + +textarea { + overflow: auto +} + +[type="checkbox"], +[type="radio"] { + box-sizing: border-box; + padding: 0 +} + +[type="number"]::-webkit-inner-spin-button, +[type="number"]::-webkit-outer-spin-button { + height: auto +} + +[type="search"] { + -webkit-appearance: textfield; + outline-offset: -2px +} + +[type="search"]::-webkit-search-cancel-button, +[type="search"]::-webkit-search-decoration { + -webkit-appearance: none +} + +::-webkit-file-upload-button { + -webkit-appearance: button; + font: inherit +} + +details, +menu { + display: block +} + +summary { + display: list-item +} + +canvas { + display: inline-block +} + +template { + display: none +} + +[hidden] { + display: none +} + +html { + box-sizing: border-box +} + +*, +*:before, +*:after { + box-sizing: inherit +} + +button, +input, +optgroup, +select, +textarea { + font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Oxygen-Sans, Ubuntu, Cantarell, "Helvetica Neue", sans-serif +} + +ul:not(.browser-default) { + padding-left: 0; + list-style-type: none +} + +ul:not(.browser-default)>li { + list-style-type: none +} + +a { + color: #039be5; + text-decoration: none; + -webkit-tap-highlight-color: transparent +} + +.valign-wrapper { + display: flex; + align-items: center +} + +.clearfix { + clear: both +} + +.z-depth-0 { + box-shadow: none !important +} + +.z-depth-1, +nav, +.card-panel, +.card, +.toast, +.btn, +.btn-large, +.btn-small, +.btn-floating, +.dropdown-content, +.collapsible, +.sidenav, +.jqvmap-label, +.settings-group { + box-shadow: 0 2px 2px 0 rgba(0, 0, 0, 0.14), 0 3px 1px -2px rgba(0, 0, 0, 0.12), 0 1px 5px 0 rgba(0, 0, 0, 0.2) +} + +.z-depth-1-half, +.btn:hover, +.btn-large:hover, +.btn-small:hover, +.btn-floating:hover { + box-shadow: 0 3px 3px 0 rgba(0, 0, 0, 0.14), 0 1px 7px 0 rgba(0, 0, 0, 0.12), 0 3px 1px -1px rgba(0, 0, 0, 0.2) +} + +.z-depth-2, +#chartjs-tooltip { + box-shadow: 0 4px 5px 0 rgba(0, 0, 0, 0.14), 0 1px 10px 0 rgba(0, 0, 0, 0.12), 0 2px 4px -1px rgba(0, 0, 0, 0.3) +} + +.z-depth-3 { + box-shadow: 0 8px 17px 2px rgba(0, 0, 0, 0.14), 0 3px 14px 2px rgba(0, 0, 0, 0.12), 0 5px 5px -3px rgba(0, 0, 0, 0.2) +} + +.z-depth-4 { + box-shadow: 0 16px 24px 2px rgba(0, 0, 0, 0.14), 0 6px 30px 5px rgba(0, 0, 0, 0.12), 0 8px 10px -7px rgba(0, 0, 0, 0.2) +} + +.z-depth-5, +.modal, +.fc .fc-more-popover, +.dropdown-content.notifications { + box-shadow: 0 24px 38px 3px rgba(0, 0, 0, 0.14), 0 9px 46px 8px rgba(0, 0, 0, 0.12), 0 11px 15px -7px rgba(0, 0, 0, 0.2) +} + +.hoverable { + transition: box-shadow .25s +} + +.hoverable:hover { + box-shadow: 0 8px 17px 0 rgba(0, 0, 0, 0.2), 0 6px 20px 0 rgba(0, 0, 0, 0.19) +} + +.divider { + height: 1px; + overflow: hidden; + background-color: #e0e0e0 +} + +blockquote { + margin: 20px 0; + padding-left: 1.5rem; + border-left: 5px solid #448AFF +} + +i { + line-height: inherit +} + +i.left { + float: left; + margin-right: 15px +} + +i.right { + float: right; + margin-left: 15px +} + +i.tiny { + font-size: 1rem +} + +i.small { + font-size: 2rem +} + +i.medium { + font-size: 4rem +} + +i.large { + font-size: 6rem +} + +img.responsive-img, +video.responsive-video { + max-width: 100%; + height: auto +} + +.pagination li { + display: inline-block; + border-radius: 2px; + text-align: center; + vertical-align: top; + height: 30px +} + +.pagination li a { + color: #444; + display: inline-block; + font-size: 1.2rem; + padding: 0 10px; + line-height: 30px +} + +.pagination li.active a { + color: #fff +} + +.pagination li.active { + background-color: #448AFF +} + +.pagination li.disabled a { + cursor: default; + color: #999 +} + +.pagination li i { + font-size: 2rem +} + +.pagination li.pages ul li { + display: inline-block; + float: none +} + +@media only screen and (max-width: 992px) { + .pagination { + width: 100% + } + .pagination li.prev, + .pagination li.next { + width: 10% + } + .pagination li.pages { + width: 80%; + overflow: hidden; + white-space: nowrap + } +} + +.breadcrumb { + font-size: 18px; + color: rgba(255, 255, 255, 0.7) +} + +.breadcrumb i, +.breadcrumb [class^="mdi-"], +.breadcrumb [class*="mdi-"], +.breadcrumb i.material-icons { + display: inline-block; + float: left; + font-size: 24px +} + +.breadcrumb:before { + content: '\E5CC'; + color: rgba(255, 255, 255, 0.7); + vertical-align: top; + display: inline-block; + font-family: 'Material Icons'; + font-weight: normal; + font-style: normal; + font-size: 25px; + margin: 0 10px 0 8px; + -webkit-font-smoothing: antialiased +} + +.breadcrumb:first-child:before { + display: none +} + +.breadcrumb:last-child { + color: #fff +} + +.parallax-container { + position: relative; + overflow: hidden; + height: 500px +} + +.parallax-container .parallax { + position: absolute; + top: 0; + left: 0; + right: 0; + bottom: 0; + z-index: -1 +} + +.parallax-container .parallax img { + opacity: 0; + position: absolute; + left: 50%; + bottom: 0; + min-width: 100%; + min-height: 100%; + transform: translate3d(0, 0, 0); + transform: translateX(-50%) +} + +.pin-top, +.pin-bottom { + position: relative +} + +.pinned { + position: fixed !important +} + +ul.staggered-list li { + opacity: 0 +} + +.fade-in { + opacity: 0; + transform-origin: 0 50% +} + +@media only screen and (max-width: 600px) { + .hide-on-small-only, + .hide-on-small-and-down { + display: none !important + } +} + +@media only screen and (max-width: 992px) { + .hide-on-med-and-down { + display: none !important + } +} + +@media only screen and (min-width: 601px) { + .hide-on-med-and-up { + display: none !important + } +} + +@media only screen and (min-width: 600px) and (max-width: 992px) { + .hide-on-med-only { + display: none !important + } +} + +@media only screen and (min-width: 993px) { + .hide-on-large-only { + display: none !important + } +} + +@media only screen and (min-width: 1201px) { + .hide-on-extra-large-only { + display: none !important + } +} + +@media only screen and (min-width: 1201px) { + .show-on-extra-large { + display: block !important + } +} + +@media only screen and (min-width: 993px) { + .show-on-large { + display: block !important + } +} + +@media only screen and (min-width: 600px) and (max-width: 992px) { + .show-on-medium { + display: block !important + } +} + +@media only screen and (max-width: 600px) { + .show-on-small { + display: block !important + } +} + +@media only screen and (min-width: 601px) { + .show-on-medium-and-up { + display: block !important + } +} + +@media only screen and (max-width: 992px) { + .show-on-medium-and-down { + display: block !important + } +} + +@media only screen and (max-width: 600px) { + .center-on-small-only { + text-align: center + } +} + +.page-footer { + padding-top: 20px; + color: #fff; + background-color: #448AFF +} + +.page-footer .footer-copyright { + overflow: hidden; + min-height: 50px; + display: flex; + align-items: center; + justify-content: space-between; + padding: 10px 0px; + color: rgba(255, 255, 255, 0.8); + background-color: rgba(51, 51, 51, 0.08) +} + +table, +th, +td { + border: none +} + +table { + width: 100%; + display: table; + border-collapse: collapse; + border-spacing: 0 +} + +table.striped tr { + border-bottom: none +} + +table.striped>tbody>tr:nth-child(odd) { + background-color: rgba(242, 242, 242, 0.5) +} + +table.striped>tbody>tr>td { + border-radius: 0 +} + +table.highlight>tbody>tr { + transition: background-color .25s ease +} + +table.highlight>tbody>tr:hover { + background-color: rgba(242, 242, 242, 0.5) +} + +table.centered thead tr th, +table.centered tbody tr td { + text-align: center +} + +tr { + border-bottom: 1px solid rgba(0, 0, 0, 0.12) +} + +td, +th { + padding: 15px 5px; + display: table-cell; + text-align: left; + vertical-align: middle; + border-radius: 2px +} + +@media only screen and (max-width: 992px) { + table.responsive-table { + width: 100%; + border-collapse: collapse; + border-spacing: 0; + display: block; + position: relative + } + table.responsive-table td:empty:before { + content: '\00a0' + } + table.responsive-table th, + table.responsive-table td { + margin: 0; + vertical-align: top + } + table.responsive-table th { + text-align: left + } + table.responsive-table thead { + display: block; + float: left + } + table.responsive-table thead tr { + display: block; + padding: 0 10px 0 0 + } + table.responsive-table thead tr th::before { + content: "\00a0" + } + table.responsive-table tbody { + display: block; + width: auto; + position: relative; + overflow-x: auto; + white-space: nowrap + } + table.responsive-table tbody tr { + display: inline-block; + vertical-align: top + } + table.responsive-table th { + display: block; + text-align: right + } + table.responsive-table td { + display: block; + min-height: 1.25em; + text-align: left + } + table.responsive-table tr { + border-bottom: none; + padding: 0 10px + } + table.responsive-table thead { + border: 0; + border-right: 1px solid rgba(0, 0, 0, 0.12) + } +} + +.collection { + margin: .5rem 0 1rem 0; + border: 1px solid #e0e0e0; + border-radius: 2px; + overflow: hidden; + position: relative +} + +.collection .collection-item { + background-color: #fff; + line-height: 1.5rem; + padding: 10px 20px; + margin: 0; + border-bottom: 1px solid #e0e0e0 +} + +.collection .collection-item.avatar { + min-height: 84px; + padding-left: 72px; + position: relative +} + +.collection .collection-item.avatar:not(.circle-clipper)>.circle, +.collection .collection-item.avatar:not(.circle-clipper)>.circle { + position: absolute; + width: 42px; + height: 42px; + overflow: hidden; + left: 15px; + display: inline-block; + vertical-align: middle +} + +.collection .collection-item.avatar i.circle { + font-size: 18px; + line-height: 42px; + color: #fff; + background-color: #999; + text-align: center +} + +.collection .collection-item.avatar .title { + font-size: 16px +} + +.collection .collection-item.avatar p { + margin: 0 +} + +.collection .collection-item.avatar .secondary-content { + position: absolute; + top: 16px; + right: 16px +} + +.collection .collection-item:last-child { + border-bottom: none +} + +.collection .collection-item.active { + background-color: #26a69a; + color: #eafaf9 +} + +.collection .collection-item.active .secondary-content { + color: #fff +} + +.collection a.collection-item { + display: block; + transition: .25s; + color: #26a69a +} + +.collection a.collection-item:not(.active):hover { + background-color: #ddd +} + +.collection.with-header .collection-header { + background-color: #fff; + border-bottom: 1px solid #e0e0e0; + padding: 10px 20px +} + +.collection.with-header .collection-item { + padding-left: 30px +} + +.collection.with-header .collection-item.avatar { + padding-left: 72px +} + +.secondary-content { + float: right; + color: #26a69a +} + +.collapsible .collection { + margin: 0; + border: none +} + +.video-container { + position: relative; + padding-bottom: 56.25%; + height: 0; + overflow: hidden +} + +.video-container iframe, +.video-container object, +.video-container embed { + position: absolute; + top: 0; + left: 0; + width: 100%; + height: 100% +} + +.progress { + position: relative; + height: 4px; + display: block; + width: 80%; + background-color: rgb(0,169,231,0.5); + border-radius: 2px; + margin: .5rem 0 1rem 0; + overflow: hidden +} + +.progress .determinate { + position: absolute; + top: 0; + left: 0; + bottom: 0; + background-color: rgb(0,169,231,1.0); + transition: width .3s linear +} + +.progress .indeterminate { + background-color: #26a69a +} + +.progress .indeterminate:before { + content: ''; + position: absolute; + background-color: inherit; + top: 0; + left: 0; + bottom: 0; + will-change: left, right; + animation: indeterminate 2.1s cubic-bezier(0.65, 0.815, 0.735, 0.395) infinite +} + +.progress .indeterminate:after { + content: ''; + position: absolute; + background-color: inherit; + top: 0; + left: 0; + bottom: 0; + will-change: left, right; + animation: indeterminate-short 2.1s cubic-bezier(0.165, 0.84, 0.44, 1) infinite; + animation-delay: 1.15s +} + +@keyframes indeterminate { + 0% { + left: -35%; + right: 100% + } + 60% { + left: 100%; + right: -90% + } + 100% { + left: 100%; + right: -90% + } +} + +@keyframes indeterminate-short { + 0% { + left: -200%; + right: 100% + } + 60% { + left: 107%; + right: -8% + } + 100% { + left: 107%; + right: -8% + } +} + +.hide { + display: none !important +} + +.left-align { + text-align: left +} + +.right-align { + text-align: right +} + +.center, +.center-align { + text-align: center +} + +.left { + float: left !important +} + +.right { + float: right !important +} + +.no-select, +input[type=range], +input[type=range]+.thumb { + user-select: none +} + +.circle { + border-radius: 50% +} + +.center-block { + display: block; + margin-left: auto; + margin-right: auto +} + +.truncate { + display: block; + white-space: nowrap; + overflow: hidden; + text-overflow: ellipsis +} + +.no-padding { + padding: 0 !important +} + +span.badge { + min-width: 3rem; + padding: 0 6px; + margin-left: 14px; + text-align: center; + font-size: 1rem; + line-height: 22px; + height: 22px; + color: #757575; + float: right; + box-sizing: border-box +} + +span.badge.new { + font-weight: 300; + font-size: 0.8rem; + color: #fff; + background-color: #26a69a; + border-radius: 2px +} + +span.badge.new:after { + content: " new" +} + +span.badge[data-badge-caption]::after { + content: " " attr(data-badge-caption) +} + +nav ul a span.badge { + display: inline-block; + float: none; + margin-left: 4px; + line-height: 22px; + height: 22px; + -webkit-font-smoothing: auto +} + +.collection-item span.badge { + margin-top: calc(.75rem - 11px) +} + +.collapsible span.badge { + margin-left: auto +} + +.sidenav span.badge { + margin-top: calc(24px - 11px) +} + +table span.badge { + display: inline-block; + float: none; + margin-left: auto +} + +.material-icons { + text-rendering: optimizeLegibility; + font-feature-settings: 'liga' +} + +.container { + margin: 0 auto; + width: 90% +} + +@media only screen and (min-width: 601px) { + .container { + width: 85% + } +} + +@media only screen and (min-width: 993px) { + .container { + width: 70% + } +} + +.col .row { + margin-left: -.75rem; + margin-right: -.75rem +} + +.section { + padding-top: 1rem; + padding-bottom: 1rem +} + +.section.no-pad { + padding: 0 +} + +.section.no-pad-bot { + padding-bottom: 0 +} + +.section.no-pad-top { + padding-top: 0 +} + +.row { + margin-left: auto; + margin-right: auto; + margin-bottom: 20px +} + +.row:after { + content: ""; + display: table; + clear: both +} + +.row .col { + float: left; + box-sizing: border-box; + padding: 0 .75rem; + min-height: 1px +} + +.row .col[class*="push-"], +.row .col[class*="pull-"] { + position: relative +} + +.row .col.s1 { + width: 8.3333333333%; + margin-left: auto; + left: auto; + right: auto +} + +.row .col.s2 { + width: 16.6666666667%; + margin-left: auto; + left: auto; + right: auto +} + +.row .col.s3 { + width: 25%; + margin-left: auto; + left: auto; + right: auto +} + +.row .col.s4 { + width: 33.3333333333%; + margin-left: auto; + left: auto; + right: auto +} + +.row .col.s5 { + width: 41.6666666667%; + margin-left: auto; + left: auto; + right: auto +} + +.row .col.s6 { + width: 50%; + margin-left: auto; + left: auto; + right: auto +} + +.row .col.s7 { + width: 58.3333333333%; + margin-left: auto; + left: auto; + right: auto +} + +.row .col.s8 { + width: 66.6666666667%; + margin-left: auto; + left: auto; + right: auto +} + +.row .col.s9 { + width: 75%; + margin-left: auto; + left: auto; + right: auto +} + +.row .col.s10 { + width: 83.3333333333%; + margin-left: auto; + left: auto; + right: auto +} + +.row .col.s11 { + width: 91.6666666667%; + margin-left: auto; + left: auto; + right: auto +} + +.row .col.s12 { + width: 100%; + margin-left: auto; + left: auto; + right: auto +} + +.row .col.offset-s1 { + margin-left: 8.3333333333% +} + +.row .col.pull-s1 { + right: 8.3333333333% +} + +.row .col.push-s1 { + left: 8.3333333333% +} + +.row .col.offset-s2 { + margin-left: 16.6666666667% +} + +.row .col.pull-s2 { + right: 16.6666666667% +} + +.row .col.push-s2 { + left: 16.6666666667% +} + +.row .col.offset-s3 { + margin-left: 25% +} + +.row .col.pull-s3 { + right: 25% +} + +.row .col.push-s3 { + left: 25% +} + +.row .col.offset-s4 { + margin-left: 33.3333333333% +} + +.row .col.pull-s4 { + right: 33.3333333333% +} + +.row .col.push-s4 { + left: 33.3333333333% +} + +.row .col.offset-s5 { + margin-left: 41.6666666667% +} + +.row .col.pull-s5 { + right: 41.6666666667% +} + +.row .col.push-s5 { + left: 41.6666666667% +} + +.row .col.offset-s6 { + margin-left: 50% +} + +.row .col.pull-s6 { + right: 50% +} + +.row .col.push-s6 { + left: 50% +} + +.row .col.offset-s7 { + margin-left: 58.3333333333% +} + +.row .col.pull-s7 { + right: 58.3333333333% +} + +.row .col.push-s7 { + left: 58.3333333333% +} + +.row .col.offset-s8 { + margin-left: 66.6666666667% +} + +.row .col.pull-s8 { + right: 66.6666666667% +} + +.row .col.push-s8 { + left: 66.6666666667% +} + +.row .col.offset-s9 { + margin-left: 75% +} + +.row .col.pull-s9 { + right: 75% +} + +.row .col.push-s9 { + left: 75% +} + +.row .col.offset-s10 { + margin-left: 83.3333333333% +} + +.row .col.pull-s10 { + right: 83.3333333333% +} + +.row .col.push-s10 { + left: 83.3333333333% +} + +.row .col.offset-s11 { + margin-left: 91.6666666667% +} + +.row .col.pull-s11 { + right: 91.6666666667% +} + +.row .col.push-s11 { + left: 91.6666666667% +} + +.row .col.offset-s12 { + margin-left: 100% +} + +.row .col.pull-s12 { + right: 100% +} + +.row .col.push-s12 { + left: 100% +} + +@media only screen and (min-width: 601px) { + .row .col.m1 { + width: 8.3333333333%; + margin-left: auto; + left: auto; + right: auto + } + .row .col.m2 { + width: 16.6666666667%; + margin-left: auto; + left: auto; + right: auto + } + .row .col.m3 { + width: 25%; + margin-left: auto; + left: auto; + right: auto + } + .row .col.m4 { + width: 33.3333333333%; + margin-left: auto; + left: auto; + right: auto + } + .row .col.m5 { + width: 41.6666666667%; + margin-left: auto; + left: auto; + right: auto + } + .row .col.m6 { + width: 50%; + margin-left: auto; + left: auto; + right: auto + } + .row .col.m7 { + width: 58.3333333333%; + margin-left: auto; + left: auto; + right: auto + } + .row .col.m8 { + width: 66.6666666667%; + margin-left: auto; + left: auto; + right: auto + } + .row .col.m9 { + width: 75%; + margin-left: auto; + left: auto; + right: auto + } + .row .col.m10 { + width: 83.3333333333%; + margin-left: auto; + left: auto; + right: auto + } + .row .col.m11 { + width: 91.6666666667%; + margin-left: auto; + left: auto; + right: auto + } + .row .col.m12 { + width: 100%; + margin-left: auto; + left: auto; + right: auto + } + .row .col.offset-m1 { + margin-left: 8.3333333333% + } + .row .col.pull-m1 { + right: 8.3333333333% + } + .row .col.push-m1 { + left: 8.3333333333% + } + .row .col.offset-m2 { + margin-left: 16.6666666667% + } + .row .col.pull-m2 { + right: 16.6666666667% + } + .row .col.push-m2 { + left: 16.6666666667% + } + .row .col.offset-m3 { + margin-left: 25% + } + .row .col.pull-m3 { + right: 25% + } + .row .col.push-m3 { + left: 25% + } + .row .col.offset-m4 { + margin-left: 33.3333333333% + } + .row .col.pull-m4 { + right: 33.3333333333% + } + .row .col.push-m4 { + left: 33.3333333333% + } + .row .col.offset-m5 { + margin-left: 41.6666666667% + } + .row .col.pull-m5 { + right: 41.6666666667% + } + .row .col.push-m5 { + left: 41.6666666667% + } + .row .col.offset-m6 { + margin-left: 50% + } + .row .col.pull-m6 { + right: 50% + } + .row .col.push-m6 { + left: 50% + } + .row .col.offset-m7 { + margin-left: 58.3333333333% + } + .row .col.pull-m7 { + right: 58.3333333333% + } + .row .col.push-m7 { + left: 58.3333333333% + } + .row .col.offset-m8 { + margin-left: 66.6666666667% + } + .row .col.pull-m8 { + right: 66.6666666667% + } + .row .col.push-m8 { + left: 66.6666666667% + } + .row .col.offset-m9 { + margin-left: 75% + } + .row .col.pull-m9 { + right: 75% + } + .row .col.push-m9 { + left: 75% + } + .row .col.offset-m10 { + margin-left: 83.3333333333% + } + .row .col.pull-m10 { + right: 83.3333333333% + } + .row .col.push-m10 { + left: 83.3333333333% + } + .row .col.offset-m11 { + margin-left: 91.6666666667% + } + .row .col.pull-m11 { + right: 91.6666666667% + } + .row .col.push-m11 { + left: 91.6666666667% + } + .row .col.offset-m12 { + margin-left: 100% + } + .row .col.pull-m12 { + right: 100% + } + .row .col.push-m12 { + left: 100% + } +} + +@media only screen and (min-width: 993px) { + .row .col.l1 { + width: 8.3333333333%; + margin-left: auto; + left: auto; + right: auto + } + .row .col.l2 { + width: 16.6666666667%; + margin-left: auto; + left: auto; + right: auto + } + .row .col.l3 { + width: 25%; + margin-left: auto; + left: auto; + right: auto + } + .row .col.l4 { + width: 33.3333333333%; + margin-left: auto; + left: auto; + right: auto + } + .row .col.l5 { + width: 41.6666666667%; + margin-left: auto; + left: auto; + right: auto + } + .row .col.l6 { + width: 50%; + margin-left: auto; + left: auto; + right: auto + } + .row .col.l7 { + width: 58.3333333333%; + margin-left: auto; + left: auto; + right: auto + } + .row .col.l8 { + width: 66.6666666667%; + margin-left: auto; + left: auto; + right: auto + } + .row .col.l9 { + width: 75%; + margin-left: auto; + left: auto; + right: auto + } + .row .col.l10 { + width: 83.3333333333%; + margin-left: auto; + left: auto; + right: auto + } + .row .col.l11 { + width: 91.6666666667%; + margin-left: auto; + left: auto; + right: auto + } + .row .col.l12 { + width: 100%; + margin-left: auto; + left: auto; + right: auto + } + .row .col.offset-l1 { + margin-left: 8.3333333333% + } + .row .col.pull-l1 { + right: 8.3333333333% + } + .row .col.push-l1 { + left: 8.3333333333% + } + .row .col.offset-l2 { + margin-left: 16.6666666667% + } + .row .col.pull-l2 { + right: 16.6666666667% + } + .row .col.push-l2 { + left: 16.6666666667% + } + .row .col.offset-l3 { + margin-left: 25% + } + .row .col.pull-l3 { + right: 25% + } + .row .col.push-l3 { + left: 25% + } + .row .col.offset-l4 { + margin-left: 33.3333333333% + } + .row .col.pull-l4 { + right: 33.3333333333% + } + .row .col.push-l4 { + left: 33.3333333333% + } + .row .col.offset-l5 { + margin-left: 41.6666666667% + } + .row .col.pull-l5 { + right: 41.6666666667% + } + .row .col.push-l5 { + left: 41.6666666667% + } + .row .col.offset-l6 { + margin-left: 50% + } + .row .col.pull-l6 { + right: 50% + } + .row .col.push-l6 { + left: 50% + } + .row .col.offset-l7 { + margin-left: 58.3333333333% + } + .row .col.pull-l7 { + right: 58.3333333333% + } + .row .col.push-l7 { + left: 58.3333333333% + } + .row .col.offset-l8 { + margin-left: 66.6666666667% + } + .row .col.pull-l8 { + right: 66.6666666667% + } + .row .col.push-l8 { + left: 66.6666666667% + } + .row .col.offset-l9 { + margin-left: 75% + } + .row .col.pull-l9 { + right: 75% + } + .row .col.push-l9 { + left: 75% + } + .row .col.offset-l10 { + margin-left: 83.3333333333% + } + .row .col.pull-l10 { + right: 83.3333333333% + } + .row .col.push-l10 { + left: 83.3333333333% + } + .row .col.offset-l11 { + margin-left: 91.6666666667% + } + .row .col.pull-l11 { + right: 91.6666666667% + } + .row .col.push-l11 { + left: 91.6666666667% + } + .row .col.offset-l12 { + margin-left: 100% + } + .row .col.pull-l12 { + right: 100% + } + .row .col.push-l12 { + left: 100% + } +} + +@media only screen and (min-width: 1201px) { + .row .col.xl1 { + width: 8.3333333333%; + margin-left: auto; + left: auto; + right: auto + } + .row .col.xl2 { + width: 16.6666666667%; + margin-left: auto; + left: auto; + right: auto + } + .row .col.xl3 { + width: 25%; + margin-left: auto; + left: auto; + right: auto + } + .row .col.xl4 { + width: 33.3333333333%; + margin-left: auto; + left: auto; + right: auto + } + .row .col.xl5 { + width: 41.6666666667%; + margin-left: auto; + left: auto; + right: auto + } + .row .col.xl6 { + width: 50%; + margin-left: auto; + left: auto; + right: auto + } + .row .col.xl7 { + width: 58.3333333333%; + margin-left: auto; + left: auto; + right: auto + } + .row .col.xl8 { + width: 66.6666666667%; + margin-left: auto; + left: auto; + right: auto + } + .row .col.xl9 { + width: 75%; + margin-left: auto; + left: auto; + right: auto + } + .row .col.xl10 { + width: 83.3333333333%; + margin-left: auto; + left: auto; + right: auto + } + .row .col.xl11 { + width: 91.6666666667%; + margin-left: auto; + left: auto; + right: auto + } + .row .col.xl12 { + width: 100%; + margin-left: auto; + left: auto; + right: auto + } + .row .col.offset-xl1 { + margin-left: 8.3333333333% + } + .row .col.pull-xl1 { + right: 8.3333333333% + } + .row .col.push-xl1 { + left: 8.3333333333% + } + .row .col.offset-xl2 { + margin-left: 16.6666666667% + } + .row .col.pull-xl2 { + right: 16.6666666667% + } + .row .col.push-xl2 { + left: 16.6666666667% + } + .row .col.offset-xl3 { + margin-left: 25% + } + .row .col.pull-xl3 { + right: 25% + } + .row .col.push-xl3 { + left: 25% + } + .row .col.offset-xl4 { + margin-left: 33.3333333333% + } + .row .col.pull-xl4 { + right: 33.3333333333% + } + .row .col.push-xl4 { + left: 33.3333333333% + } + .row .col.offset-xl5 { + margin-left: 41.6666666667% + } + .row .col.pull-xl5 { + right: 41.6666666667% + } + .row .col.push-xl5 { + left: 41.6666666667% + } + .row .col.offset-xl6 { + margin-left: 50% + } + .row .col.pull-xl6 { + right: 50% + } + .row .col.push-xl6 { + left: 50% + } + .row .col.offset-xl7 { + margin-left: 58.3333333333% + } + .row .col.pull-xl7 { + right: 58.3333333333% + } + .row .col.push-xl7 { + left: 58.3333333333% + } + .row .col.offset-xl8 { + margin-left: 66.6666666667% + } + .row .col.pull-xl8 { + right: 66.6666666667% + } + .row .col.push-xl8 { + left: 66.6666666667% + } + .row .col.offset-xl9 { + margin-left: 75% + } + .row .col.pull-xl9 { + right: 75% + } + .row .col.push-xl9 { + left: 75% + } + .row .col.offset-xl10 { + margin-left: 83.3333333333% + } + .row .col.pull-xl10 { + right: 83.3333333333% + } + .row .col.push-xl10 { + left: 83.3333333333% + } + .row .col.offset-xl11 { + margin-left: 91.6666666667% + } + .row .col.pull-xl11 { + right: 91.6666666667% + } + .row .col.push-xl11 { + left: 91.6666666667% + } + .row .col.offset-xl12 { + margin-left: 100% + } + .row .col.pull-xl12 { + right: 100% + } + .row .col.push-xl12 { + left: 100% + } +} + +nav { + color: #fff; + background-color: #448AFF; + width: 100%; + height: 56px; + line-height: 56px +} + +nav.nav-extended { + height: auto +} + +nav.nav-extended .nav-wrapper { + min-height: 56px; + height: auto +} + +nav.nav-extended .nav-content { + position: relative; + line-height: normal +} + +nav a { + color: #fff +} + +nav i, +nav [class^="mdi-"], +nav [class*="mdi-"], +nav i.material-icons { + display: block; + font-size: 24px; + height: 56px; + line-height: 56px +} + +nav .nav-wrapper { + position: relative; + height: 100% +} + +@media only screen and (min-width: 993px) { + nav a.sidenav-trigger { + display: none + } +} + +nav .sidenav-trigger { + float: left; + position: relative; + z-index: 1; + height: 56px; + margin: 0 18px +} + +nav .sidenav-trigger i { + height: 56px; + line-height: 56px +} + +nav .brand-logo { + position: absolute; + color: #fff; + display: inline-block; + font-size: 2.1rem; + padding: 0 +} + +nav .brand-logo.center { + left: 50%; + transform: translateX(-50%) +} + +@media only screen and (max-width: 992px) { + nav .brand-logo { + left: 50%; + transform: translateX(-50%) + } + nav .brand-logo.left, + nav .brand-logo.right { + padding: 0; + transform: none + } + nav .brand-logo.left { + left: 0.5rem + } + nav .brand-logo.right { + right: 0.5rem; + left: auto + } +} + +nav .brand-logo.right { + right: 0.5rem; + padding: 0 +} + +nav .brand-logo i, +nav .brand-logo [class^="mdi-"], +nav .brand-logo [class*="mdi-"], +nav .brand-logo i.material-icons { + float: left; + margin-right: 15px +} + +nav .nav-title { + display: inline-block; + font-size: 32px; + padding: 28px 0 +} + +nav ul { + margin: 0 +} + +nav ul li { + transition: background-color .3s; + float: left; + padding: 0 +} + +nav ul li.active { + background-color: rgba(0, 0, 0, 0.1) +} + +nav ul a { + transition: background-color .3s; + font-size: 1rem; + color: #fff; + display: block; + padding: 0 15px; + cursor: pointer +} + +nav ul a.btn, +nav ul a.btn-large, +nav ul a.btn-small, +nav ul a.btn-large, +nav ul a.btn-flat, +nav ul a.btn-floating { + margin-top: -2px; + margin-left: 15px; + margin-right: 15px +} + +nav ul a.btn>.material-icons, +nav ul a.btn-large>.material-icons, +nav ul a.btn-small>.material-icons, +nav ul a.btn-large>.material-icons, +nav ul a.btn-flat>.material-icons, +nav ul a.btn-floating>.material-icons { + height: inherit; + line-height: inherit +} + +nav ul a:hover { + background-color: rgba(0, 0, 0, 0.1) +} + +nav ul.left { + float: left +} + +nav form { + height: 100% +} + +nav .input-field { + margin: 0; + height: 100% +} + +nav .input-field input { + height: 100%; + font-size: 1.2rem; + border: none; + padding-left: 2rem +} + +nav .input-field input:focus, +nav .input-field input[type=text]:valid, +nav .input-field input[type=password]:valid, +nav .input-field input[type=email]:valid, +nav .input-field input[type=url]:valid, +nav .input-field input[type=date]:valid { +} + +nav .input-field label { + top: 0; + left: 0 +} + +nav .input-field label i { + color: rgba(255, 255, 255, 0.7); + transition: color .3s +} + +nav .input-field label.active i { + color: #fff +} + +.navbar-fixed { + position: relative; + height: 56px; + z-index: 997 +} + +.navbar-fixed nav { + position: fixed +} + +@media only screen and (min-width: 601px) { + nav.nav-extended .nav-wrapper { + min-height: 64px + } + nav, + nav .nav-wrapper i, + nav a.sidenav-trigger, + nav a.sidenav-trigger i { + height: 64px; + line-height: 64px + } + .navbar-fixed { + height: 64px + } +} + +a { + text-decoration: none +} + +html { + line-height: 1.5; + font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Oxygen-Sans, Ubuntu, Cantarell, "Helvetica Neue", sans-serif; + font-weight: normal; + color: rgba(0, 0, 0, 0.87) +} + +@media only screen and (min-width: 0) { + html { + font-size: 14px + } +} + +@media only screen and (min-width: 992px) { + html { + font-size: 14.5px + } +} + +@media only screen and (min-width: 1200px) { + html { + font-size: 15px + } +} + +h1, +h2, +h3, +h4, +h5, +.settings-title, +h6 { + font-weight: 400; + line-height: 1.3 +} + +h1 a, +h2 a, +h3 a, +h4 a, +h5 a, +.settings-title a, +h6 a { + font-weight: inherit +} + +h1 { + font-size: 4.2rem; + line-height: 110%; + margin: 2.8rem 0 1.68rem 0 +} + +h2 { + font-size: 2.5rem; + line-height: 150%; + margin: 2.3733333333rem 0 1.0rem 0 +} + +h3 { + font-size: 2.92rem; + line-height: 110%; + margin: 1.9466666667rem 0 1.168rem 0 +} + +h4 { + font-size: 2.28rem; + line-height: 110%; + margin: 1.52rem 0 .912rem 0 +} + +h5, +.settings-title { + font-size: 1.64rem; + line-height: 110%; + margin: 1.0933333333rem 0 .656rem 0 +} + +h6 { + font-size: 1.15rem; + line-height: 110%; + margin: .7666666667rem 0 .46rem 0 +} + +em { + font-style: italic +} + +strong { + font-weight: 500 +} + +small { + font-size: 75% +} + +.light { + font-weight: 300 +} + +.thin { + font-weight: 200 +} + +@media only screen and (min-width: 360px) { + .flow-text { + font-size: 1.2rem + } +} + +@media only screen and (min-width: 390px) { + .flow-text { + font-size: 1.224rem + } +} + +@media only screen and (min-width: 420px) { + .flow-text { + font-size: 1.248rem + } +} + +@media only screen and (min-width: 450px) { + .flow-text { + font-size: 1.272rem + } +} + +@media only screen and (min-width: 480px) { + .flow-text { + font-size: 1.296rem + } +} + +@media only screen and (min-width: 510px) { + .flow-text { + font-size: 1.32rem + } +} + +@media only screen and (min-width: 540px) { + .flow-text { + font-size: 1.344rem + } +} + +@media only screen and (min-width: 570px) { + .flow-text { + font-size: 1.368rem + } +} + +@media only screen and (min-width: 600px) { + .flow-text { + font-size: 1.392rem + } +} + +@media only screen and (min-width: 630px) { + .flow-text { + font-size: 1.416rem + } +} + +@media only screen and (min-width: 660px) { + .flow-text { + font-size: 1.44rem + } +} + +@media only screen and (min-width: 690px) { + .flow-text { + font-size: 1.464rem + } +} + +@media only screen and (min-width: 720px) { + .flow-text { + font-size: 1.488rem + } +} + +@media only screen and (min-width: 750px) { + .flow-text { + font-size: 1.512rem + } +} + +@media only screen and (min-width: 780px) { + .flow-text { + font-size: 1.536rem + } +} + +@media only screen and (min-width: 810px) { + .flow-text { + font-size: 1.56rem + } +} + +@media only screen and (min-width: 840px) { + .flow-text { + font-size: 1.584rem + } +} + +@media only screen and (min-width: 870px) { + .flow-text { + font-size: 1.608rem + } +} + +@media only screen and (min-width: 900px) { + .flow-text { + font-size: 1.632rem + } +} + +@media only screen and (min-width: 930px) { + .flow-text { + font-size: 1.656rem + } +} + +@media only screen and (min-width: 960px) { + .flow-text { + font-size: 1.68rem + } +} + +@media only screen and (max-width: 360px) { + .flow-text { + font-size: 1.2rem + } +} + +.scale-transition { + transition: transform 0.3s cubic-bezier(0.53, 0.01, 0.36, 1.63) !important +} + +.scale-transition.scale-out { + transform: scale(0); + transition: transform .2s !important +} + +.scale-transition.scale-in { + transform: scale(1) +} + +.card-panel { + transition: box-shadow .25s; + padding: 24px; + margin: .5rem 0 1rem 0; + border-radius: 2px; + background-color: #fff +} + +.card { + position: relative; + margin: .5rem 0 1rem 0; + background-color: #fff; + transition: box-shadow .25s; + border-radius: 2px +} + +.card .card-title { + font-size: 24px; + font-weight: 300 +} + +.card .card-title.activator { + cursor: pointer +} + +.card.small, +.card.medium, +.card.large { + position: relative +} + +.card.small .card-image, +.card.medium .card-image, +.card.large .card-image { + max-height: 60%; + overflow: hidden +} + +.card.small .card-image+.card-content, +.card.medium .card-image+.card-content, +.card.large .card-image+.card-content { + max-height: 40% +} + +.card.small .card-content, +.card.medium .card-content, +.card.large .card-content { + max-height: 100%; + overflow: hidden +} + +.card.small .card-action, +.card.medium .card-action, +.card.large .card-action { + position: absolute; + bottom: 0; + left: 0; + right: 0 +} + +.card.small { + height: 300px +} + +.card.medium { + height: 400px +} + +.card.large { + height: 500px +} + +.card.horizontal { + display: flex +} + +.card.horizontal.small .card-image, +.card.horizontal.medium .card-image, +.card.horizontal.large .card-image { + height: 100%; + max-height: none; + overflow: visible +} + +.card.horizontal.small .card-image img, +.card.horizontal.medium .card-image img, +.card.horizontal.large .card-image img { + height: 100% +} + +.card.horizontal .card-image { + max-width: 50% +} + +.card.horizontal .card-image img { + border-radius: 2px 0 0 2px; + max-width: 100%; + width: auto +} + +.card.horizontal .card-stacked { + display: flex; + flex-direction: column; + flex: 1; + position: relative +} + +.card.horizontal .card-stacked .card-content { + flex-grow: 1 +} + +.card.sticky-action .card-action { + z-index: 2 +} + +.card.sticky-action .card-reveal { + z-index: 1; + padding-bottom: 64px +} + +.card .card-image { + position: relative +} + +.card .card-image img { + display: block; + border-radius: 2px 2px 0 0; + position: relative; + left: 0; + right: 0; + top: 0; + bottom: 0; + width: 100% +} + +.card .card-image .card-title { + color: #fff; + position: absolute; + bottom: 0; + left: 0; + max-width: 100%; + padding: 24px +} + +.card .card-content { + padding: 24px; + border-radius: 0 0 2px 2px +} + +.card .card-content p { + margin: 0 +} + +.card .card-content .card-title { + display: block; + line-height: 32px; + margin-bottom: 8px +} + +.card .card-content .card-title i { + line-height: 32px +} + +.card .card-action { + background-color: inherit; + border-top: 1px solid rgba(160, 160, 160, 0.2); + position: relative; + padding: 16px 24px +} + +.card .card-action:last-child { + border-radius: 0 0 2px 2px +} + +.card .card-action a:not(.btn):not(.btn-large):not(.btn-small):not(.btn-large):not(.btn-floating) { + color: #ffab40; + margin-right: 24px; + transition: color .3s ease; + text-transform: uppercase +} + +.card .card-action a:not(.btn):not(.btn-large):not(.btn-small):not(.btn-large):not(.btn-floating):hover { + color: #ffd8a6 +} + +.card .card-reveal { + padding: 24px; + position: absolute; + background-color: #fff; + width: 100%; + overflow-y: auto; + left: 0; + top: 100%; + height: 100%; + z-index: 3; + display: none +} + +.card .card-reveal .card-title { + cursor: pointer; + display: block +} + +#toast-container { + display: block; + position: fixed; + z-index: 10000 +} + +@media only screen and (max-width: 600px) { + #toast-container { + min-width: 100%; + bottom: 0% + } +} + +@media only screen and (min-width: 601px) and (max-width: 992px) { + #toast-container { + left: 5%; + bottom: 7%; + max-width: 90% + } +} + +@media only screen and (min-width: 993px) { + #toast-container { + top: 10%; + right: 7%; + max-width: 86% + } +} + +.toast { + border-radius: 2px; + top: 35px; + width: auto; + margin-top: 10px; + position: relative; + max-width: 100%; + height: auto; + min-height: 48px; + line-height: 1.5em; + word-break: break-all; + background-color: #323232; + padding: 10px 25px; + font-size: 1.1rem; + font-weight: 300; + color: #fff; + display: flex; + align-items: center; + justify-content: space-between; + cursor: default +} + +.toast .toast-action { + color: #eeff41; + font-weight: 500; + margin-right: -25px; + margin-left: 3rem +} + +.toast.rounded { + border-radius: 24px +} + +@media only screen and (max-width: 600px) { + .toast { + width: 100%; + border-radius: 0 + } +} + +.tabs { + position: relative; + overflow-x: auto; + overflow-y: hidden; + height: 48px; + width: 100%; + background-color: #fff; + margin: 0 auto; + white-space: nowrap +} + +.tabs.tabs-transparent { + background-color: transparent +} + +.tabs.tabs-transparent .tab a, +.tabs.tabs-transparent .tab.disabled a, +.tabs.tabs-transparent .tab.disabled a:hover { + color: rgba(255, 255, 255, 0.7) +} + +.tabs.tabs-transparent .tab a:hover, +.tabs.tabs-transparent .tab a.active { + color: #fff +} + +.tabs.tabs-transparent .indicator { + background-color: #fff +} + +.tabs.tabs-fixed-width { + display: flex +} + +.tabs.tabs-fixed-width .tab { + flex-grow: 1 +} + +.tabs .tab { + display: inline-block; + text-align: center; + line-height: 48px; + height: 48px; + padding: 0; + margin: 0; + text-transform: uppercase +} + +.tabs .tab a { + color: rgba(68, 138, 255, 0.7); + display: block; + width: 100%; + height: 100%; + padding: 0 24px; + font-size: 14px; + text-overflow: ellipsis; + overflow: hidden; + transition: color .28s ease, background-color .28s ease +} + +.tabs .tab a:focus, +.tabs .tab a:focus.active { + background-color: rgba(145, 186, 255, 0.2); + outline: none +} + +.tabs .tab a:hover, +.tabs .tab a.active { + background-color: transparent; + color: #448AFF +} + +.tabs .tab.disabled a, +.tabs .tab.disabled a:hover { + color: rgba(68, 138, 255, 0.4); + cursor: default +} + +.tabs .indicator { + position: absolute; + bottom: 0; + height: 2px; + background-color: #91baff; + will-change: left, right +} + +@media only screen and (max-width: 992px) { + .tabs { + display: flex + } + .tabs .tab { + flex-grow: 1 + } + .tabs .tab a { + padding: 0 12px + } +} + +.material-tooltip { + padding: 10px 8px; + font-size: 1rem; + z-index: 2000; + border-radius: 2px; + color: #323232 + min-height: 36px; + line-height: 120%; + opacity: 0; + position: absolute; + text-align: left; + max-width: calc(100% - 4px); + overflow: hidden; + left: 0; + top: 0; + pointer-events: none; + visibility: hidden; + background-color: #fff; + box-shadow: 0 2px 2px 0 rgba(0, 0, 0, 0.14), 0 3px 1px -2px rgba(0, 0, 0, 0.12), 0 1px 5px 0 rgba(0, 0, 0, 0.2) +} + +.backdrop { + position: absolute; + opacity: 0; + height: 7px; + width: 14px; + border-radius: 0 0 50% 50%; + background-color: #323232; + z-index: -1; + transform-origin: 50% 0%; + visibility: hidden +} + +.btn, +.btn-large, +.btn-small, +.btn-flat { + border: none; + border-radius: 2px; + display: inline-block; + height: 36px; + line-height: 36px; + padding: 0 16px; + text-transform: uppercase; + vertical-align: middle; + -webkit-tap-highlight-color: transparent +} + +.btn.disabled, +.disabled.btn-large, +.disabled.btn-small, +.btn-floating.disabled, +.btn-large.disabled, +.btn-small.disabled, +.btn-flat.disabled, +.btn:disabled, +.btn-large:disabled, +.btn-small:disabled, +.btn-floating:disabled, +.btn-large:disabled, +.btn-small:disabled, +.btn-flat:disabled, +.btn[disabled], +[disabled].btn-large, +[disabled].btn-small, +.btn-floating[disabled], +.btn-large[disabled], +.btn-small[disabled], +.btn-flat[disabled] { + pointer-events: none; + background-color: #DFDFDF !important; + box-shadow: none; + color: #9F9F9F !important; + cursor: default +} + +.btn.disabled:hover, +.disabled.btn-large:hover, +.disabled.btn-small:hover, +.btn-floating.disabled:hover, +.btn-large.disabled:hover, +.btn-small.disabled:hover, +.btn-flat.disabled:hover, +.btn:disabled:hover, +.btn-large:disabled:hover, +.btn-small:disabled:hover, +.btn-floating:disabled:hover, +.btn-large:disabled:hover, +.btn-small:disabled:hover, +.btn-flat:disabled:hover, +.btn[disabled]:hover, +[disabled].btn-large:hover, +[disabled].btn-small:hover, +.btn-floating[disabled]:hover, +.btn-large[disabled]:hover, +.btn-small[disabled]:hover, +.btn-flat[disabled]:hover { + background-color: #DFDFDF !important; + color: #9F9F9F !important +} + +.btn, +.btn-large, +.btn-small, +.btn-floating, +.btn-large, +.btn-small, +.btn-flat { + font-size: 14px; + outline: 0 +} + +.btn i, +.btn-large i, +.btn-small i, +.btn-floating i, +.btn-large i, +.btn-small i, +.btn-flat i { + font-size: 1.3rem; + line-height: inherit +} + +.btn:focus, +.btn-large:focus, +.btn-small:focus, +.btn-floating:focus { + background-color: #1d7d74 +} + +.btn, +.btn-large, +.btn-small { + text-decoration: none; + color: #fff; + background-color: #26a69a; + text-align: center; + letter-spacing: .5px; + transition: background-color .2s ease-out; + cursor: pointer +} + +.btn:hover, +.btn-large:hover, +.btn-small:hover { + background-color: #2bbbad +} + +.btn-floating { + display: inline-block; + color: #fff; + position: relative; + overflow: hidden; + z-index: 1; + width: 40px; + height: 40px; + line-height: 40px; + padding: 0; + background-color: #26a69a; + border-radius: 50%; + transition: background-color .3s; + cursor: pointer; + vertical-align: middle +} + +.btn-floating:hover { + background-color: #26a69a +} + +.btn-floating:before { + border-radius: 0 +} + +.btn-floating.btn-large { + width: 56px; + height: 56px; + padding: 0 +} + +.btn-floating.btn-large.halfway-fab { + bottom: -28px +} + +.btn-floating.btn-large i { + line-height: 56px +} + +.btn-floating.btn-small { + width: 32.4px; + height: 32.4px +} + +.btn-floating.btn-small.halfway-fab { + bottom: -16.2px +} + +.btn-floating.btn-small i { + line-height: 32.4px +} + +.btn-floating.halfway-fab { + position: absolute; + right: 24px; + bottom: -20px +} + +.btn-floating.halfway-fab.left { + right: auto; + left: 24px +} + +.btn-floating i { + width: inherit; + display: inline-block; + text-align: center; + color: #fff; + font-size: 1.6rem; + line-height: 40px +} + +button.btn-floating { + border: none +} + +.fixed-action-btn { + position: fixed; + right: 23px; + bottom: 23px; + padding-top: 15px; + margin-bottom: 0; + z-index: 997 +} + +.fixed-action-btn.active ul { + visibility: visible +} + +.fixed-action-btn.direction-left, +.fixed-action-btn.direction-right { + padding: 0 0 0 15px +} + +.fixed-action-btn.direction-left ul, +.fixed-action-btn.direction-right ul { + text-align: right; + right: 64px; + top: 50%; + transform: translateY(-50%); + height: 100%; + left: auto; + width: 500px +} + +.fixed-action-btn.direction-left ul li, +.fixed-action-btn.direction-right ul li { + display: inline-block; + margin: 7.5px 15px 0 0 +} + +.fixed-action-btn.direction-right { + padding: 0 15px 0 0 +} + +.fixed-action-btn.direction-right ul { + text-align: left; + direction: rtl; + left: 64px; + right: auto +} + +.fixed-action-btn.direction-right ul li { + margin: 7.5px 0 0 15px +} + +.fixed-action-btn.direction-bottom { + padding: 0 0 15px 0 +} + +.fixed-action-btn.direction-bottom ul { + top: 64px; + bottom: auto; + display: flex; + flex-direction: column-reverse +} + +.fixed-action-btn.direction-bottom ul li { + margin: 15px 0 0 0 +} + +.fixed-action-btn.toolbar { + padding: 0; + height: 56px +} + +.fixed-action-btn.toolbar.active>a i { + opacity: 0 +} + +.fixed-action-btn.toolbar ul { + display: flex; + top: 0; + bottom: 0; + z-index: 1 +} + +.fixed-action-btn.toolbar ul li { + flex: 1; + display: inline-block; + margin: 0; + height: 100%; + transition: none +} + +.fixed-action-btn.toolbar ul li a { + display: block; + overflow: hidden; + position: relative; + width: 100%; + height: 100%; + background-color: transparent; + box-shadow: none; + color: #fff; + line-height: 56px; + z-index: 1 +} + +.fixed-action-btn.toolbar ul li a i { + line-height: inherit +} + +.fixed-action-btn ul { + left: 0; + right: 0; + text-align: center; + position: absolute; + bottom: 64px; + margin: 0; + visibility: hidden +} + +.fixed-action-btn ul li { + margin-bottom: 15px +} + +.fixed-action-btn ul a.btn-floating { + opacity: 0 +} + +.fixed-action-btn .fab-backdrop { + position: absolute; + top: 0; + left: 0; + z-index: -1; + width: 40px; + height: 40px; + background-color: #26a69a; + border-radius: 50%; + transform: scale(0) +} + +.btn-flat { + box-shadow: none; + background-color: transparent; + color: #343434; + cursor: pointer; + transition: background-color .2s +} + +.btn-flat:focus, +.btn-flat:hover { + box-shadow: none +} + +.btn-flat:focus { + background-color: rgba(0, 0, 0, 0.1) +} + +.btn-flat.disabled { + background-color: transparent !important; + color: #b3b3b3 !important; + cursor: default +} + +.btn-large { + height: 54px; + line-height: 54px; + font-size: 15px; + padding: 0 28px +} + +.btn-large i { + font-size: 1.6rem +} + +.btn-small { + height: 32.4px; + line-height: 32.4px; + font-size: 13px +} + +.btn-small i { + font-size: 1.2rem +} + +.btn-block { + display: block +} + +.dropdown-content { + background-color: #fff; + margin: 0; + display: none; + min-width: 100px; + overflow-y: auto; + opacity: 0; + position: absolute; + left: 0; + top: 0; + z-index: 9999; + transform-origin: 0 0 +} + +.dropdown-content:focus { + outline: 0 +} + +.dropdown-content li { + clear: both; + color: rgba(0, 0, 0, 0.87); + cursor: pointer; + min-height: 50px; + line-height: 1.5rem; + width: 100%; + text-align: left +} + +.dropdown-content li:hover, +.dropdown-content li.active { + background-color: #eee +} + +.dropdown-content li:focus { + outline: none; + background-color: #dadada +} + +.dropdown-content li.divider { + min-height: 0; + height: 1px +} + +.dropdown-content li>a, +.dropdown-content li>span { + font-size: 16px; + color: #26a69a; + display: block; + line-height: 22px; + padding: 14px 16px +} + +.dropdown-content li>span>label { + top: 1px; + left: 0; + height: 18px +} + +.dropdown-content li>a>i { + height: inherit; + line-height: inherit; + float: left; + margin: 0 24px 0 0; + width: 24px +} + +.input-field.col .dropdown-content [type="checkbox"]+label { + top: 1px; + left: 0; + height: 18px; + transform: none +} + +.dropdown-trigger { + cursor: pointer +} + + +/*! + * Waves v0.6.0 + * http://fian.my.id/Waves + * + * Copyright 2014 Alfiana E. Sibuea and other contributors + * Released under the MIT license + * https://github.com/fians/Waves/blob/master/LICENSE + */ + +.waves-effect { + position: relative; + cursor: pointer; + display: inline-block; + overflow: hidden; + user-select: none; + -webkit-tap-highlight-color: transparent; + vertical-align: middle; + z-index: 1; + transition: .3s ease-out +} + +.waves-effect .waves-ripple { + position: absolute; + border-radius: 50%; + width: 20px; + height: 20px; + margin-top: -10px; + margin-left: -10px; + opacity: 0; + background: rgba(0, 0, 0, 0.2); + transition: all 0.7s ease-out; + transition-property: transform, opacity; + transform: scale(0); + pointer-events: none +} + +.waves-effect.waves-light .waves-ripple { + background-color: rgba(255, 255, 255, 0.45) +} + +.waves-effect.waves-red .waves-ripple { + background-color: rgba(244, 67, 54, 0.7) +} + +.waves-effect.waves-yellow .waves-ripple { + background-color: rgba(255, 235, 59, 0.7) +} + +.waves-effect.waves-orange .waves-ripple { + background-color: rgba(255, 152, 0, 0.7) +} + +.waves-effect.waves-purple .waves-ripple { + background-color: rgba(156, 39, 176, 0.7) +} + +.waves-effect.waves-green .waves-ripple { + background-color: rgba(76, 175, 80, 0.7) +} + +.waves-effect.waves-teal .waves-ripple { + background-color: rgba(0, 150, 136, 0.7) +} + +.waves-effect input[type="button"], +.waves-effect input[type="reset"], +.waves-effect input[type="submit"] { + border: 0; + font-style: normal; + font-size: inherit; + text-transform: inherit; + background: none +} + +.waves-effect img { + position: relative; + z-index: -1 +} + +.waves-notransition { + transition: none !important +} + +.waves-circle { + transform: translateZ(0); + -webkit-mask-image: -webkit-radial-gradient(circle, #fff 100%, #000 100%) +} + +.waves-input-wrapper { + border-radius: 0.2em; + vertical-align: bottom +} + +.waves-input-wrapper .waves-button-input { + position: relative; + top: 0; + left: 0; + z-index: 1 +} + +.waves-circle { + text-align: center; + width: 2.5em; + height: 2.5em; + line-height: 2.5em; + border-radius: 50%; + -webkit-mask-image: none +} + +.waves-block { + display: block +} + +.waves-effect .waves-ripple { + z-index: -1 +} + +.modal { + display: none; + position: fixed; + left: 0; + right: 0; + background-color: #fafafa; + padding: 0; + max-height: 70%; + width: 55%; + margin: auto; + overflow-y: auto; + border-radius: 2px; + will-change: top, opacity +} + +@media only screen and (max-width: 992px) { + .modal { + width: 80% + } +} + +.modal h1, +.modal h2, +.modal h3, +.modal h4 { + margin-top: 0 +} + +.modal .modal-content { + padding: 24px +} + +.modal .modal-close { + cursor: pointer +} + +.modal .modal-footer { + border-radius: 0 0 2px 2px; + background-color: #fafafa; + padding: 4px 6px; + height: 56px; + width: 100%; + text-align: right +} + +.modal .modal-footer .btn, +.modal .modal-footer .btn-large, +.modal .modal-footer .btn-small, +.modal .modal-footer .btn-flat { + margin: 6px 0 +} + +.modal-overlay { + position: fixed; + z-index: 999; + top: -25%; + left: 0; + bottom: 0; + right: 0; + height: 125%; + width: 100%; + background: #000; + display: none; + will-change: opacity +} + +.modal.modal-fixed-footer { + padding: 0; + height: 70% +} + +.modal.modal-fixed-footer .modal-content { + position: absolute; + height: calc(100% - 56px); + max-height: 100%; + width: 100%; + overflow-y: auto +} + +.modal.modal-fixed-footer .modal-footer { + border-top: 1px solid rgba(0, 0, 0, 0.1); + position: absolute; + bottom: 0 +} + +.modal.bottom-sheet { + top: auto; + bottom: -100%; + margin: 0; + width: 100%; + max-height: 45%; + border-radius: 0; + will-change: bottom, opacity +} + +.collapsible { + border-top: 1px solid #ddd; + border-right: 1px solid #ddd; + border-left: 1px solid #ddd; + margin: .5rem 0 1rem 0 +} + +.collapsible-header { + display: flex; + cursor: pointer; + -webkit-tap-highlight-color: transparent; + line-height: 1.5; + padding: 1rem; + background-color: #fff; + border-bottom: 1px solid #ddd +} + +.collapsible-header:focus { + outline: 0 +} + +.collapsible-header i { + width: 2rem; + font-size: 1.6rem; + display: inline-block; + text-align: center; + margin-right: 1rem +} + +.keyboard-focused .collapsible-header:focus { + background-color: #eee +} + +.collapsible-body { + display: none; + border-bottom: 1px solid #ddd; + box-sizing: border-box; + padding: 2rem +} + +.sidenav .collapsible, +.sidenav.fixed .collapsible { + border: none; + box-shadow: none +} + +.sidenav .collapsible li, +.sidenav.fixed .collapsible li { + padding: 0 +} + +.sidenav .collapsible-header, +.sidenav.fixed .collapsible-header { + background-color: transparent; + border: none; + line-height: inherit; + height: inherit; + padding: 0 16px +} + +.sidenav .collapsible-header:hover, +.sidenav.fixed .collapsible-header:hover { + background-color: rgba(0, 0, 0, 0.05) +} + +.sidenav .collapsible-header i, +.sidenav.fixed .collapsible-header i { + line-height: inherit +} + +.sidenav .collapsible-body, +.sidenav.fixed .collapsible-body { + border: 0; + background-color: #fff +} + +.sidenav .collapsible-body li a, +.sidenav.fixed .collapsible-body li a { + padding: 0 23.5px 0 31px +} + +.collapsible.popout { + border: none; + box-shadow: none +} + +.collapsible.popout>li { + box-shadow: 0 2px 5px 0 rgba(0, 0, 0, 0.16), 0 2px 10px 0 rgba(0, 0, 0, 0.12); + margin: 0 24px; + transition: margin 0.35s cubic-bezier(0.25, 0.46, 0.45, 0.94) +} + +.collapsible.popout>li.active { + box-shadow: 0 5px 11px 0 rgba(0, 0, 0, 0.18), 0 4px 15px 0 rgba(0, 0, 0, 0.15); + margin: 16px 0 +} + +.chip { + display: inline-block; + height: 32px; + font-size: 13px; + font-weight: 500; + color: rgba(0, 0, 0, 0.6); + line-height: 32px; + padding: 0 12px; + border-radius: 16px; + background-color: #e4e4e4; + margin-bottom: 5px; + margin-right: 5px +} + +.chip:focus { + outline: none; + background-color: #26a69a; + color: #fff +} + +.chip>img { + float: left; + margin: 0 8px 0 -12px; + height: 32px; + width: 32px; + border-radius: 50% +} + +.chip .close { + cursor: pointer; + float: right; + font-size: 16px; + line-height: 32px; + padding-left: 8px +} + +.chips { + border: none; + border-bottom: 1px solid #9e9e9e; + box-shadow: none; + margin: 0 0 8px 0; + min-height: 45px; + outline: none; + transition: all .3s +} + +.chips.focus { + border-bottom: 1px solid #26a69a; + box-shadow: 0 1px 0 0 #26a69a +} + +.chips:hover { + cursor: text +} + +.chips .input { + background: none; + border: 0; + color: rgba(0, 0, 0, 0.6); + display: inline-block; + font-size: 16px; + height: 3rem; + line-height: 32px; + outline: 0; + margin: 0; + padding: 0 !important; + width: 120px !important +} + +.chips .input:focus { + border: 0 !important; + box-shadow: none !important +} + +.chips .autocomplete-content { + margin-top: 0; + margin-bottom: 0 +} + +.prefix ~ .chips { + margin-left: 3rem; + width: 92%; + width: calc(100% - 3rem) +} + +.chips:empty ~ label { + font-size: 0.8rem; + transform: translateY(-140%) +} + +.materialboxed { + display: block; + cursor: zoom-in; + position: relative; + transition: opacity .4s; + -webkit-backface-visibility: hidden +} + +.materialboxed:hover:not(.active) { + opacity: .8 +} + +.materialboxed.active { + cursor: zoom-out +} + +#materialbox-overlay { + position: fixed; + top: 0; + right: 0; + bottom: 0; + left: 0; + background-color: #292929; + z-index: 1000; + will-change: opacity +} + +.materialbox-caption { + position: fixed; + display: none; + color: #fff; + line-height: 50px; + bottom: 0; + left: 0; + width: 100%; + text-align: center; + padding: 0% 15%; + height: 50px; + z-index: 1000; + -webkit-font-smoothing: antialiased +} + +select:focus { + outline: 1px solid #c9f3ef +} + +button:focus { + outline: none; + background-color: #2ab7a9 +} + +label { + font-size: .8rem; + color: #9e9e9e +} + +::placeholder { + color: #d1d1d1 +} + +input:not([type]), +input[type=text]:not(.browser-default), +input[type=password]:not(.browser-default), +input[type=email]:not(.browser-default), +input[type=url]:not(.browser-default), +input[type=time]:not(.browser-default), +input[type=date]:not(.browser-default), +input[type=datetime]:not(.browser-default), +input[type=datetime-local]:not(.browser-default), +input[type=tel]:not(.browser-default), +input[type=number]:not(.browser-default), +input[type=search]:not(.browser-default), +textarea.materialize-textarea { + background-color: transparent; + border: none; + border-bottom: 1px solid #9e9e9e; + border-radius: 0; + outline: none; + height: 3rem; + width: 100%; + font-size: 16px; + margin: 0 0 8px 0; + padding: 0; + box-shadow: none; + box-sizing: content-box; + transition: box-shadow .3s, border .3s +} + +input:not([type]):disabled, +input:not([type])[readonly="readonly"], +input[type=text]:not(.browser-default):disabled, +input[type=text]:not(.browser-default)[readonly="readonly"], +input[type=password]:not(.browser-default):disabled, +input[type=password]:not(.browser-default)[readonly="readonly"], +input[type=email]:not(.browser-default):disabled, +input[type=email]:not(.browser-default)[readonly="readonly"], +input[type=url]:not(.browser-default):disabled, +input[type=url]:not(.browser-default)[readonly="readonly"], +input[type=time]:not(.browser-default):disabled, +input[type=time]:not(.browser-default)[readonly="readonly"], +input[type=date]:not(.browser-default):disabled, +input[type=date]:not(.browser-default)[readonly="readonly"], +input[type=datetime]:not(.browser-default):disabled, +input[type=datetime]:not(.browser-default)[readonly="readonly"], +input[type=datetime-local]:not(.browser-default):disabled, +input[type=datetime-local]:not(.browser-default)[readonly="readonly"], +input[type=tel]:not(.browser-default):disabled, +input[type=tel]:not(.browser-default)[readonly="readonly"], +input[type=number]:not(.browser-default):disabled, +input[type=number]:not(.browser-default)[readonly="readonly"], +input[type=search]:not(.browser-default):disabled, +input[type=search]:not(.browser-default)[readonly="readonly"], +textarea.materialize-textarea:disabled, +textarea.materialize-textarea[readonly="readonly"] { + color: rgba(0, 0, 0, 0.42); + border-bottom: 1px dotted rgba(0, 0, 0, 0.42) +} + +input:not([type]):disabled+label, +input:not([type])[readonly="readonly"]+label, +input[type=text]:not(.browser-default):disabled+label, +input[type=text]:not(.browser-default)[readonly="readonly"]+label, +input[type=password]:not(.browser-default):disabled+label, +input[type=password]:not(.browser-default)[readonly="readonly"]+label, +input[type=email]:not(.browser-default):disabled+label, +input[type=email]:not(.browser-default)[readonly="readonly"]+label, +input[type=url]:not(.browser-default):disabled+label, +input[type=url]:not(.browser-default)[readonly="readonly"]+label, +input[type=time]:not(.browser-default):disabled+label, +input[type=time]:not(.browser-default)[readonly="readonly"]+label, +input[type=date]:not(.browser-default):disabled+label, +input[type=date]:not(.browser-default)[readonly="readonly"]+label, +input[type=datetime]:not(.browser-default):disabled+label, +input[type=datetime]:not(.browser-default)[readonly="readonly"]+label, +input[type=datetime-local]:not(.browser-default):disabled+label, +input[type=datetime-local]:not(.browser-default)[readonly="readonly"]+label, +input[type=tel]:not(.browser-default):disabled+label, +input[type=tel]:not(.browser-default)[readonly="readonly"]+label, +input[type=number]:not(.browser-default):disabled+label, +input[type=number]:not(.browser-default)[readonly="readonly"]+label, +input[type=search]:not(.browser-default):disabled+label, +input[type=search]:not(.browser-default)[readonly="readonly"]+label, +textarea.materialize-textarea:disabled+label, +textarea.materialize-textarea[readonly="readonly"]+label { + color: rgba(0, 0, 0, 0.42) +} + +input:not([type]):focus:not([readonly]), +input[type=text]:not(.browser-default):focus:not([readonly]), +input[type=password]:not(.browser-default):focus:not([readonly]), +input[type=email]:not(.browser-default):focus:not([readonly]), +input[type=url]:not(.browser-default):focus:not([readonly]), +input[type=time]:not(.browser-default):focus:not([readonly]), +input[type=date]:not(.browser-default):focus:not([readonly]), +input[type=datetime]:not(.browser-default):focus:not([readonly]), +input[type=datetime-local]:not(.browser-default):focus:not([readonly]), +input[type=tel]:not(.browser-default):focus:not([readonly]), +input[type=number]:not(.browser-default):focus:not([readonly]), +input[type=search]:not(.browser-default):focus:not([readonly]), +textarea.materialize-textarea:focus:not([readonly]) { + border-bottom: 1px solid rgb(0,169,231); + box-shadow: 0 1px 0 0 rgb(0,169,231) +} + +input:not([type]):focus:not([readonly])+label, +input[type=text]:not(.browser-default):focus:not([readonly])+label, +input[type=password]:not(.browser-default):focus:not([readonly])+label, +input[type=email]:not(.browser-default):focus:not([readonly])+label, +input[type=url]:not(.browser-default):focus:not([readonly])+label, +input[type=time]:not(.browser-default):focus:not([readonly])+label, +input[type=date]:not(.browser-default):focus:not([readonly])+label, +input[type=datetime]:not(.browser-default):focus:not([readonly])+label, +input[type=datetime-local]:not(.browser-default):focus:not([readonly])+label, +input[type=tel]:not(.browser-default):focus:not([readonly])+label, +input[type=number]:not(.browser-default):focus:not([readonly])+label, +input[type=search]:not(.browser-default):focus:not([readonly])+label, +textarea.materialize-textarea:focus:not([readonly])+label { + color: #26a69a +} + +input:not([type]):focus.valid ~ label, +input[type=text]:not(.browser-default):focus.valid ~ label, +input[type=password]:not(.browser-default):focus.valid ~ label, +input[type=email]:not(.browser-default):focus.valid ~ label, +input[type=url]:not(.browser-default):focus.valid ~ label, +input[type=time]:not(.browser-default):focus.valid ~ label, +input[type=date]:not(.browser-default):focus.valid ~ label, +input[type=datetime]:not(.browser-default):focus.valid ~ label, +input[type=datetime-local]:not(.browser-default):focus.valid ~ label, +input[type=tel]:not(.browser-default):focus.valid ~ label, +input[type=number]:not(.browser-default):focus.valid ~ label, +input[type=search]:not(.browser-default):focus.valid ~ label, +textarea.materialize-textarea:focus.valid ~ label { + color: #4CAF50 +} + +input:not([type]):focus.invalid ~ label, +input[type=text]:not(.browser-default):focus.invalid ~ label, +input[type=password]:not(.browser-default):focus.invalid ~ label, +input[type=email]:not(.browser-default):focus.invalid ~ label, +input[type=url]:not(.browser-default):focus.invalid ~ label, +input[type=time]:not(.browser-default):focus.invalid ~ label, +input[type=date]:not(.browser-default):focus.invalid ~ label, +input[type=datetime]:not(.browser-default):focus.invalid ~ label, +input[type=datetime-local]:not(.browser-default):focus.invalid ~ label, +input[type=tel]:not(.browser-default):focus.invalid ~ label, +input[type=number]:not(.browser-default):focus.invalid ~ label, +input[type=search]:not(.browser-default):focus.invalid ~ label, +textarea.materialize-textarea:focus.invalid ~ label { + color: #F44336 +} + +input:not([type]).validate+label, +input[type=text]:not(.browser-default).validate+label, +input[type=password]:not(.browser-default).validate+label, +input[type=email]:not(.browser-default).validate+label, +input[type=url]:not(.browser-default).validate+label, +input[type=time]:not(.browser-default).validate+label, +input[type=date]:not(.browser-default).validate+label, +input[type=datetime]:not(.browser-default).validate+label, +input[type=datetime-local]:not(.browser-default).validate+label, +input[type=tel]:not(.browser-default).validate+label, +input[type=number]:not(.browser-default).validate+label, +input[type=search]:not(.browser-default).validate+label, +textarea.materialize-textarea.validate+label { + width: 100% +} + +input.valid:not([type]), +input.valid:not([type]):focus, +input[type=text].valid:not(.browser-default), +input[type=text].valid:not(.browser-default):focus, +input[type=password].valid:not(.browser-default), +input[type=password].valid:not(.browser-default):focus, +input[type=email].valid:not(.browser-default), +input[type=email].valid:not(.browser-default):focus, +input[type=url].valid:not(.browser-default), +input[type=url].valid:not(.browser-default):focus, +input[type=time].valid:not(.browser-default), +input[type=time].valid:not(.browser-default):focus, +input[type=date].valid:not(.browser-default), +input[type=date].valid:not(.browser-default):focus, +input[type=datetime].valid:not(.browser-default), +input[type=datetime].valid:not(.browser-default):focus, +input[type=datetime-local].valid:not(.browser-default), +input[type=datetime-local].valid:not(.browser-default):focus, +input[type=tel].valid:not(.browser-default), +input[type=tel].valid:not(.browser-default):focus, +input[type=number].valid:not(.browser-default), +input[type=number].valid:not(.browser-default):focus, +input[type=search].valid:not(.browser-default), +input[type=search].valid:not(.browser-default):focus, +textarea.materialize-textarea.valid, +textarea.materialize-textarea.valid:focus, +.select-wrapper.valid>input.select-dropdown { + border-bottom: 1px solid #4CAF50; + box-shadow: 0 1px 0 0 #4CAF50 +} + +input.invalid:not([type]), +input.invalid:not([type]):focus, +input[type=text].invalid:not(.browser-default), +input[type=text].invalid:not(.browser-default):focus, +input[type=password].invalid:not(.browser-default), +input[type=password].invalid:not(.browser-default):focus, +input[type=email].invalid:not(.browser-default), +input[type=email].invalid:not(.browser-default):focus, +input[type=url].invalid:not(.browser-default), +input[type=url].invalid:not(.browser-default):focus, +input[type=time].invalid:not(.browser-default), +input[type=time].invalid:not(.browser-default):focus, +input[type=date].invalid:not(.browser-default), +input[type=date].invalid:not(.browser-default):focus, +input[type=datetime].invalid:not(.browser-default), +input[type=datetime].invalid:not(.browser-default):focus, +input[type=datetime-local].invalid:not(.browser-default), +input[type=datetime-local].invalid:not(.browser-default):focus, +input[type=tel].invalid:not(.browser-default), +input[type=tel].invalid:not(.browser-default):focus, +input[type=number].invalid:not(.browser-default), +input[type=number].invalid:not(.browser-default):focus, +input[type=search].invalid:not(.browser-default), +input[type=search].invalid:not(.browser-default):focus, +textarea.materialize-textarea.invalid, +textarea.materialize-textarea.invalid:focus, +.select-wrapper.invalid>input.select-dropdown, +.select-wrapper.invalid>input.select-dropdown:focus { + border-bottom: 1px solid #F44336; + box-shadow: 0 1px 0 0 #F44336 +} + +input:not([type]).valid ~ .helper-text[data-success], +input:not([type]):focus.valid ~ .helper-text[data-success], +input:not([type]).invalid ~ .helper-text[data-error], +input:not([type]):focus.invalid ~ .helper-text[data-error], +input[type=text]:not(.browser-default).valid ~ .helper-text[data-success], +input[type=text]:not(.browser-default):focus.valid ~ .helper-text[data-success], +input[type=text]:not(.browser-default).invalid ~ .helper-text[data-error], +input[type=text]:not(.browser-default):focus.invalid ~ .helper-text[data-error], +input[type=password]:not(.browser-default).valid ~ .helper-text[data-success], +input[type=password]:not(.browser-default):focus.valid ~ .helper-text[data-success], +input[type=password]:not(.browser-default).invalid ~ .helper-text[data-error], +input[type=password]:not(.browser-default):focus.invalid ~ .helper-text[data-error], +input[type=email]:not(.browser-default).valid ~ .helper-text[data-success], +input[type=email]:not(.browser-default):focus.valid ~ .helper-text[data-success], +input[type=email]:not(.browser-default).invalid ~ .helper-text[data-error], +input[type=email]:not(.browser-default):focus.invalid ~ .helper-text[data-error], +input[type=url]:not(.browser-default).valid ~ .helper-text[data-success], +input[type=url]:not(.browser-default):focus.valid ~ .helper-text[data-success], +input[type=url]:not(.browser-default).invalid ~ .helper-text[data-error], +input[type=url]:not(.browser-default):focus.invalid ~ .helper-text[data-error], +input[type=time]:not(.browser-default).valid ~ .helper-text[data-success], +input[type=time]:not(.browser-default):focus.valid ~ .helper-text[data-success], +input[type=time]:not(.browser-default).invalid ~ .helper-text[data-error], +input[type=time]:not(.browser-default):focus.invalid ~ .helper-text[data-error], +input[type=date]:not(.browser-default).valid ~ .helper-text[data-success], +input[type=date]:not(.browser-default):focus.valid ~ .helper-text[data-success], +input[type=date]:not(.browser-default).invalid ~ .helper-text[data-error], +input[type=date]:not(.browser-default):focus.invalid ~ .helper-text[data-error], +input[type=datetime]:not(.browser-default).valid ~ .helper-text[data-success], +input[type=datetime]:not(.browser-default):focus.valid ~ .helper-text[data-success], +input[type=datetime]:not(.browser-default).invalid ~ .helper-text[data-error], +input[type=datetime]:not(.browser-default):focus.invalid ~ .helper-text[data-error], +input[type=datetime-local]:not(.browser-default).valid ~ .helper-text[data-success], +input[type=datetime-local]:not(.browser-default):focus.valid ~ .helper-text[data-success], +input[type=datetime-local]:not(.browser-default).invalid ~ .helper-text[data-error], +input[type=datetime-local]:not(.browser-default):focus.invalid ~ .helper-text[data-error], +input[type=tel]:not(.browser-default).valid ~ .helper-text[data-success], +input[type=tel]:not(.browser-default):focus.valid ~ .helper-text[data-success], +input[type=tel]:not(.browser-default).invalid ~ .helper-text[data-error], +input[type=tel]:not(.browser-default):focus.invalid ~ .helper-text[data-error], +input[type=number]:not(.browser-default).valid ~ .helper-text[data-success], +input[type=number]:not(.browser-default):focus.valid ~ .helper-text[data-success], +input[type=number]:not(.browser-default).invalid ~ .helper-text[data-error], +input[type=number]:not(.browser-default):focus.invalid ~ .helper-text[data-error], +input[type=search]:not(.browser-default).valid ~ .helper-text[data-success], +input[type=search]:not(.browser-default):focus.valid ~ .helper-text[data-success], +input[type=search]:not(.browser-default).invalid ~ .helper-text[data-error], +input[type=search]:not(.browser-default):focus.invalid ~ .helper-text[data-error], +textarea.materialize-textarea.valid ~ .helper-text[data-success], +textarea.materialize-textarea:focus.valid ~ .helper-text[data-success], +textarea.materialize-textarea.invalid ~ .helper-text[data-error], +textarea.materialize-textarea:focus.invalid ~ .helper-text[data-error], +.select-wrapper.valid .helper-text[data-success], +.select-wrapper.invalid ~ .helper-text[data-error] { + color: transparent; + user-select: none; + pointer-events: none +} + +input:not([type]).valid ~ .helper-text:after, +input:not([type]):focus.valid ~ .helper-text:after, +input[type=text]:not(.browser-default).valid ~ .helper-text:after, +input[type=text]:not(.browser-default):focus.valid ~ .helper-text:after, +input[type=password]:not(.browser-default).valid ~ .helper-text:after, +input[type=password]:not(.browser-default):focus.valid ~ .helper-text:after, +input[type=email]:not(.browser-default).valid ~ .helper-text:after, +input[type=email]:not(.browser-default):focus.valid ~ .helper-text:after, +input[type=url]:not(.browser-default).valid ~ .helper-text:after, +input[type=url]:not(.browser-default):focus.valid ~ .helper-text:after, +input[type=time]:not(.browser-default).valid ~ .helper-text:after, +input[type=time]:not(.browser-default):focus.valid ~ .helper-text:after, +input[type=date]:not(.browser-default).valid ~ .helper-text:after, +input[type=date]:not(.browser-default):focus.valid ~ .helper-text:after, +input[type=datetime]:not(.browser-default).valid ~ .helper-text:after, +input[type=datetime]:not(.browser-default):focus.valid ~ .helper-text:after, +input[type=datetime-local]:not(.browser-default).valid ~ .helper-text:after, +input[type=datetime-local]:not(.browser-default):focus.valid ~ .helper-text:after, +input[type=tel]:not(.browser-default).valid ~ .helper-text:after, +input[type=tel]:not(.browser-default):focus.valid ~ .helper-text:after, +input[type=number]:not(.browser-default).valid ~ .helper-text:after, +input[type=number]:not(.browser-default):focus.valid ~ .helper-text:after, +input[type=search]:not(.browser-default).valid ~ .helper-text:after, +input[type=search]:not(.browser-default):focus.valid ~ .helper-text:after, +textarea.materialize-textarea.valid ~ .helper-text:after, +textarea.materialize-textarea:focus.valid ~ .helper-text:after, +.select-wrapper.valid ~ .helper-text:after { + content: attr(data-success); + color: #4CAF50 +} + +input:not([type]).invalid ~ .helper-text:after, +input:not([type]):focus.invalid ~ .helper-text:after, +input[type=text]:not(.browser-default).invalid ~ .helper-text:after, +input[type=text]:not(.browser-default):focus.invalid ~ .helper-text:after, +input[type=password]:not(.browser-default).invalid ~ .helper-text:after, +input[type=password]:not(.browser-default):focus.invalid ~ .helper-text:after, +input[type=email]:not(.browser-default).invalid ~ .helper-text:after, +input[type=email]:not(.browser-default):focus.invalid ~ .helper-text:after, +input[type=url]:not(.browser-default).invalid ~ .helper-text:after, +input[type=url]:not(.browser-default):focus.invalid ~ .helper-text:after, +input[type=time]:not(.browser-default).invalid ~ .helper-text:after, +input[type=time]:not(.browser-default):focus.invalid ~ .helper-text:after, +input[type=date]:not(.browser-default).invalid ~ .helper-text:after, +input[type=date]:not(.browser-default):focus.invalid ~ .helper-text:after, +input[type=datetime]:not(.browser-default).invalid ~ .helper-text:after, +input[type=datetime]:not(.browser-default):focus.invalid ~ .helper-text:after, +input[type=datetime-local]:not(.browser-default).invalid ~ .helper-text:after, +input[type=datetime-local]:not(.browser-default):focus.invalid ~ .helper-text:after, +input[type=tel]:not(.browser-default).invalid ~ .helper-text:after, +input[type=tel]:not(.browser-default):focus.invalid ~ .helper-text:after, +input[type=number]:not(.browser-default).invalid ~ .helper-text:after, +input[type=number]:not(.browser-default):focus.invalid ~ .helper-text:after, +input[type=search]:not(.browser-default).invalid ~ .helper-text:after, +input[type=search]:not(.browser-default):focus.invalid ~ .helper-text:after, +textarea.materialize-textarea.invalid ~ .helper-text:after, +textarea.materialize-textarea:focus.invalid ~ .helper-text:after, +.select-wrapper.invalid ~ .helper-text:after { + content: attr(data-error); + color: #F44336 +} + +input:not([type])+label:after, +input[type=text]:not(.browser-default)+label:after, +input[type=password]:not(.browser-default)+label:after, +input[type=email]:not(.browser-default)+label:after, +input[type=url]:not(.browser-default)+label:after, +input[type=time]:not(.browser-default)+label:after, +input[type=date]:not(.browser-default)+label:after, +input[type=datetime]:not(.browser-default)+label:after, +input[type=datetime-local]:not(.browser-default)+label:after, +input[type=tel]:not(.browser-default)+label:after, +input[type=number]:not(.browser-default)+label:after, +input[type=search]:not(.browser-default)+label:after, +textarea.materialize-textarea+label:after, +.select-wrapper+label:after { + display: block; + content: ""; + position: absolute; + top: 100%; + left: 0; + opacity: 0; + transition: .2s opacity ease-out, .2s color ease-out +} + +.input-field { + position: relative; + margin-top: 1rem; + margin-bottom: 1rem +} + +.input-field.inline { + display: inline-block; + vertical-align: middle; + margin-left: 5px +} + +.input-field.inline input, +.input-field.inline .select-dropdown { + margin-bottom: 1rem +} + +.input-field.col label { + left: .75rem +} + +.input-field.col .prefix ~ label, +.input-field.col .prefix ~ .validate ~ label { + width: calc(100% - 3rem - 1.5rem) +} + +.input-field>label { + color: #9e9e9e; + position: absolute; + top: 0; + left: 0; + font-size: 1rem; + cursor: text; + transition: transform .2s ease-out, color .2s ease-out; + transform-origin: 0% 100%; + text-align: initial; + transform: translateY(12px) +} + +.input-field>label:not(.label-icon).active { + transform: translateY(-14px) scale(0.8); + transform-origin: 0 0 +} + +.input-field>input[type]:-webkit-autofill:not(.browser-default)+label, +.input-field>input[type=date]:not(.browser-default)+label, +.input-field>input[type=time]:not(.browser-default)+label { + transform: translateY(-14px) scale(0.8); + transform-origin: 0 0 +} + +.input-field .helper-text { + position: relative; + min-height: 18px; + display: block; + font-size: 12px; + color: rgba(0, 0, 0, 0.54) +} + +.input-field .helper-text::after { + opacity: 1; + position: absolute; + top: 0; + left: 0 +} + +.input-field .prefix { + position: absolute; + width: 3rem; + font-size: 2rem; + transition: color .2s; + top: .5rem +} + +.input-field .prefix.active { + color: #26a69a +} + +.input-field .prefix ~ input, +.input-field .prefix ~ textarea, +.input-field .prefix ~ label, +.input-field .prefix ~ .validate ~ label, +.input-field .prefix ~ .helper-text, +.input-field .prefix ~ .autocomplete-content { + margin-left: 3rem; + width: 92%; + width: calc(100% - 3rem) +} + +.input-field .prefix ~ label { + margin-left: 3rem +} + +@media only screen and (max-width: 992px) { + .input-field .prefix ~ input { + width: 86%; + width: calc(100% - 3rem) + } +} + +@media only screen and (max-width: 600px) { + .input-field .prefix ~ input { + width: 80%; + width: calc(100% - 3rem) + } +} + +.input-field input[type=search] { + display: block; + line-height: inherit; + transition: .3s background-color +} + +.nav-wrapper .input-field input[type=search] { + height: inherit; + padding-left: 4rem; + width: calc(100% - 4rem); + border: 0; + box-shadow: none +} + +.input-field input[type=search]:focus:not(.browser-default) { + background-color: #fff; + border: 0; + box-shadow: none; + color: #444 +} + +.input-field input[type=search]:focus:not(.browser-default)+label i, +.input-field input[type=search]:focus:not(.browser-default) ~ .mdi-navigation-close, +.input-field input[type=search]:focus:not(.browser-default) ~ .material-icons { + color: #444 +} + +.input-field input[type=search]+.label-icon { + transform: none; + left: 1rem +} + +.input-field input[type=search] ~ .mdi-navigation-close, +.input-field input[type=search] ~ .material-icons { + position: absolute; + top: 0; + right: 1rem; + color: transparent; + cursor: pointer; + font-size: 2rem; + transition: .3s color +} + +textarea { + width: 100%; + height: 3rem; + background-color: transparent +} + +textarea.materialize-textarea { + line-height: normal; + overflow-y: hidden; + padding: .8rem 0 .8rem 0; + resize: none; + min-height: 3rem; + box-sizing: border-box +} + +.hiddendiv { + visibility: hidden; + white-space: pre-wrap; + word-wrap: break-word; + overflow-wrap: break-word; + padding-top: 1.2rem; + position: absolute; + top: 0; + z-index: -1 +} + +.autocomplete-content li .highlight { + color: #444 +} + +.autocomplete-content li img { + height: 40px; + width: 40px; + margin: 5px 15px +} + +.character-counter { + min-height: 18px +} + +[type="radio"]:not(:checked), +[type="radio"]:checked { + position: absolute; + opacity: 0; + pointer-events: none +} + +[type="radio"]:not(:checked)+span, +[type="radio"]:checked+span { + position: relative; + padding-left: 35px; + cursor: pointer; + display: inline-block; + height: 25px; + line-height: 25px; + font-size: 1rem; + transition: .28s ease; + user-select: none +} + +[type="radio"]+span:before, +[type="radio"]+span:after { + content: ''; + position: absolute; + left: 0; + top: 0; + margin: 4px; + width: 16px; + height: 16px; + z-index: 0; + transition: .28s ease +} + +[type="radio"]:not(:checked)+span:before, +[type="radio"]:not(:checked)+span:after, +[type="radio"]:checked+span:before, +[type="radio"]:checked+span:after, +[type="radio"].with-gap:checked+span:before, +[type="radio"].with-gap:checked+span:after { + border-radius: 50% +} + +[type="radio"]:not(:checked)+span:before, +[type="radio"]:not(:checked)+span:after { + border: 2px solid #5a5a5a +} + +[type="radio"]:not(:checked)+span:after { + transform: scale(0) +} + +[type="radio"]:checked+span:before { + border: 2px solid transparent +} + +[type="radio"]:checked+span:after, +[type="radio"].with-gap:checked+span:before, +[type="radio"].with-gap:checked+span:after { + border: 2px solid #26a69a +} + +[type="radio"]:checked+span:after, +[type="radio"].with-gap:checked+span:after { + background-color: #26a69a +} + +[type="radio"]:checked+span:after { + transform: scale(1.02) +} + +[type="radio"].with-gap:checked+span:after { + transform: scale(0.5) +} + +[type="radio"].tabbed:focus+span:before { + box-shadow: 0 0 0 10px rgba(0, 0, 0, 0.1) +} + +[type="radio"].with-gap:disabled:checked+span:before { + border: 2px solid rgba(0, 0, 0, 0.42) +} + +[type="radio"].with-gap:disabled:checked+span:after { + border: none; + background-color: rgba(0, 0, 0, 0.42) +} + +[type="radio"]:disabled:not(:checked)+span:before, +[type="radio"]:disabled:checked+span:before { + background-color: transparent; + border-color: rgba(0, 0, 0, 0.42) +} + +[type="radio"]:disabled+span { + color: rgba(0, 0, 0, 0.42) +} + +[type="radio"]:disabled:not(:checked)+span:before { + border-color: rgba(0, 0, 0, 0.42) +} + +[type="radio"]:disabled:checked+span:after { + background-color: rgba(0, 0, 0, 0.42); + border-color: #949494 +} + +[type="checkbox"]:not(:checked), +[type="checkbox"]:checked { + position: absolute; + opacity: 0; + pointer-events: none +} + +[type="checkbox"]+span:not(.lever) { + position: relative; + padding-left: 35px; + cursor: pointer; + display: inline-block; + height: 25px; + line-height: 25px; + font-size: 1rem; + user-select: none +} + +[type="checkbox"]+span:not(.lever):before, +[type="checkbox"]:not(.filled-in)+span:not(.lever):after { + content: ''; + position: absolute; + top: 0; + left: 0; + width: 18px; + height: 18px; + z-index: 0; + border: 2px solid #5a5a5a; + border-radius: 1px; + margin-top: 3px; + transition: .2s +} + +[type="checkbox"]:not(.filled-in)+span:not(.lever):after { + border: 0; + transform: scale(0) +} + +[type="checkbox"]:not(:checked):disabled+span:not(.lever):before { + border: none; + background-color: rgba(0, 0, 0, 0.42) +} + +[type="checkbox"].tabbed:focus+span:not(.lever):after { + transform: scale(1); + border: 0; + border-radius: 50%; + box-shadow: 0 0 0 10px rgba(0, 0, 0, 0.1); + background-color: rgba(0, 0, 0, 0.1) +} + +[type="checkbox"]:checked+span:not(.lever):before { + top: -4px; + left: -5px; + width: 12px; + height: 22px; + border-top: 2px solid transparent; + border-left: 2px solid transparent; + border-right: 2px solid #26a69a; + border-bottom: 2px solid #26a69a; + transform: rotate(40deg); + backface-visibility: hidden; + transform-origin: 100% 100% +} + +[type="checkbox"]:checked:disabled+span:before { + border-right: 2px solid rgba(0, 0, 0, 0.42); + border-bottom: 2px solid rgba(0, 0, 0, 0.42) +} + +[type="checkbox"]:indeterminate+span:not(.lever):before { + top: -11px; + left: -12px; + width: 10px; + height: 22px; + border-top: none; + border-left: none; + border-right: 2px solid #26a69a; + border-bottom: none; + transform: rotate(90deg); + backface-visibility: hidden; + transform-origin: 100% 100% +} + +[type="checkbox"]:indeterminate:disabled+span:not(.lever):before { + border-right: 2px solid rgba(0, 0, 0, 0.42); + background-color: transparent +} + +[type="checkbox"].filled-in+span:not(.lever):after { + border-radius: 2px +} + +[type="checkbox"].filled-in+span:not(.lever):before, +[type="checkbox"].filled-in+span:not(.lever):after { + content: ''; + left: 0; + position: absolute; + transition: border .25s, background-color .25s, width .20s .1s, height .20s .1s, top .20s .1s, left .20s .1s; + z-index: 1 +} + +[type="checkbox"].filled-in:not(:checked)+span:not(.lever):before { + width: 0; + height: 0; + border: 3px solid transparent; + left: 6px; + top: 10px; + transform: rotateZ(37deg); + transform-origin: 100% 100% +} + +[type="checkbox"].filled-in:not(:checked)+span:not(.lever):after { + height: 20px; + width: 20px; + background-color: transparent; + border: 2px solid #5a5a5a; + top: 0px; + z-index: 0 +} + +[type="checkbox"].filled-in:checked+span:not(.lever):before { + top: 0; + left: 1px; + width: 8px; + height: 13px; + border-top: 2px solid transparent; + border-left: 2px solid transparent; + border-right: 2px solid #fff; + border-bottom: 2px solid #fff; + transform: rotateZ(37deg); + transform-origin: 100% 100% +} + +[type="checkbox"].filled-in:checked+span:not(.lever):after { + top: 0; + width: 20px; + height: 20px; + border: 2px solid #26a69a; + background-color: #26a69a; + z-index: 0 +} + +[type="checkbox"].filled-in.tabbed:focus+span:not(.lever):after { + border-radius: 2px; + border-color: #5a5a5a; + background-color: rgba(0, 0, 0, 0.1) +} + +[type="checkbox"].filled-in.tabbed:checked:focus+span:not(.lever):after { + border-radius: 2px; + background-color: #26a69a; + border-color: #26a69a +} + +[type="checkbox"].filled-in:disabled:not(:checked)+span:not(.lever):before { + background-color: transparent; + border: 2px solid transparent +} + +[type="checkbox"].filled-in:disabled:not(:checked)+span:not(.lever):after { + border-color: transparent; + background-color: #949494 +} + +[type="checkbox"].filled-in:disabled:checked+span:not(.lever):before { + background-color: transparent +} + +[type="checkbox"].filled-in:disabled:checked+span:not(.lever):after { + background-color: #949494; + border-color: #949494 +} + +.switch, +.switch * { + -webkit-tap-highlight-color: transparent; + user-select: none +} + +.switch label { + cursor: pointer +} + +.switch label input[type=checkbox] { + opacity: 0; + width: 0; + height: 0 +} + +.switch label input[type=checkbox]:checked+.lever { + background-color: #84c7c1 +} + +.switch label input[type=checkbox]:checked+.lever:before, +.switch label input[type=checkbox]:checked+.lever:after { + left: 18px +} + +.switch label input[type=checkbox]:checked+.lever:after { + background-color: #26a69a +} + +.switch label .lever { + content: ""; + display: inline-block; + position: relative; + width: 36px; + height: 14px; + background-color: rgba(0, 0, 0, 0.38); + border-radius: 15px; + margin-right: 10px; + transition: background 0.3s ease; + vertical-align: middle; + margin: 0 16px +} + +.switch label .lever:before, +.switch label .lever:after { + content: ""; + position: absolute; + display: inline-block; + width: 20px; + height: 20px; + border-radius: 50%; + left: 0; + top: -3px; + transition: left 0.3s ease, background .3s ease, box-shadow 0.1s ease, transform .1s ease +} + +.switch label .lever:before { + background-color: rgba(0,169,231, 0.15) +} + +.switch label .lever:after { + background-color: #F1F1F1; + box-shadow: 0px 3px 1px -2px rgba(0, 0, 0, 0.2), 0px 2px 2px 0px rgba(0, 0, 0, 0.14), 0px 1px 5px 0px rgba(0, 0, 0, 0.12) +} + +input[type=checkbox]:checked:not(:disabled) ~ .lever:active::before, +input[type=checkbox]:checked:not(:disabled).tabbed:focus ~ .lever::before { + transform: scale(2.4); + background-color: rgba(0,169,231, 0.15) +} + +input[type=checkbox]:not(:disabled) ~ .lever:active:before, +input[type=checkbox]:not(:disabled).tabbed:focus ~ .lever::before { + transform: scale(2.4); + background-color: rgba(0, 0, 0, 0.08) +} + +.switch input[type=checkbox][disabled]+.lever { + cursor: default; + background-color: rgba(0, 0, 0, 0.12) +} + +.switch label input[type=checkbox][disabled]+.lever:after, +.switch label input[type=checkbox][disabled]:checked+.lever:after { + background-color: #949494 +} + +select { + display: none +} + +select.browser-default { + display: block +} + +select { + background-color: rgba(255, 255, 255, 0.9); + width: 100%; + padding: 5px; + border: 1px solid #f2f2f2; + border-radius: 2px; + height: 3rem +} + +.select-label { + position: absolute +} + +.select-wrapper { + position: relative +} + +.select-wrapper.valid+label, +.select-wrapper.invalid+label { + width: 100%; + pointer-events: none +} + +.select-wrapper input.select-dropdown { + position: relative; + cursor: pointer; + background-color: transparent; + border: none; + border-bottom: 1px solid #9e9e9e; + outline: none; + height: 3rem; + line-height: 3rem; + width: 100%; + font-size: 16px; + margin: 0 0 8px 0; + padding: 0; + display: block; + user-select: none; + z-index: 1 +} + +.select-wrapper input.select-dropdown:focus { + border-bottom: 1px solid #9e9e9e +} + +.select-wrapper .caret { + position: absolute; + right: 0; + top: 0; + bottom: 0; + margin: auto 0; + z-index: 0; + fill: rgba(0, 0, 0, 0.87) +} + +.select-wrapper+label { + position: absolute; + top: -26px; + font-size: .8rem +} + +select:disabled { + color: rgba(0, 0, 0, 0.42) +} + +.select-wrapper.disabled+label { + color: rgba(0, 0, 0, 0.42) +} + +.select-wrapper.disabled .caret { + fill: rgba(0, 0, 0, 0.42) +} + +.select-wrapper input.select-dropdown:disabled { + color: rgba(0, 0, 0, 0.42); + cursor: default; + user-select: none +} + +.select-wrapper i { + color: rgba(0, 0, 0, 0.3) +} + +.select-dropdown li.disabled, +.select-dropdown li.disabled>span, +.select-dropdown li.optgroup { + color: rgba(0, 0, 0, 0.3); + background-color: transparent +} + +.select-dropdown.dropdown-content li:hover { + background-color: rgba(0, 0, 0, 0.08) +} + +.select-dropdown.dropdown-content li.selected { + background-color: rgba(0, 0, 0, 0.03) +} + +.select-dropdown.dropdown-content li:focus { + background-color: rgba(0, 0, 0, 0.08) +} + +.prefix ~ .select-wrapper { + margin-left: 3rem; + width: 92%; + width: calc(100% - 3rem) +} + +.prefix ~ label { + margin-left: 3rem +} + +.select-dropdown li img { + height: 40px; + width: 40px; + margin: 5px 15px; + float: right +} + +.select-dropdown li.optgroup { + border-top: 1px solid #eee +} + +.select-dropdown li.optgroup.selected>span { + color: rgba(0, 0, 0, 0.7) +} + +.select-dropdown li.optgroup>span { + color: rgba(0, 0, 0, 0.4) +} + +.select-dropdown li.optgroup ~ li.optgroup-option { + padding-left: 1rem +} + +.file-field { + position: relative +} + +.file-field .file-path-wrapper { + overflow: hidden; + padding-left: 10px +} + +.file-field input.file-path { + width: 100% +} + +.file-field .btn, +.file-field .btn-large, +.file-field .btn-small { + float: left; + height: 3rem; + line-height: 3rem +} + +.file-field span { + cursor: pointer +} + +.file-field input[type=file] { + position: absolute; + top: 0; + right: 0; + left: 0; + bottom: 0; + width: 100%; + margin: 0; + padding: 0; + font-size: 20px; + cursor: pointer; + opacity: 0; + filter: alpha(opacity=0) +} + +.file-field input[type=file]::-webkit-file-upload-button { + display: none +} + +.range-field { + position: relative +} + +input[type=range], +input[type=range]+.thumb { + cursor: pointer +} + +input[type=range] { + position: relative; + background-color: transparent; + border: none; + outline: none; + width: 100%; + margin: 15px 0; + padding: 0 +} + +input[type=range]:focus { + outline: none +} + +input[type=range]+.thumb { + position: absolute; + top: 10px; + left: 0; + border: none; + height: 0; + width: 0; + border-radius: 50%; + background-color: #26a69a; + margin-left: 7px; + transform-origin: 50% 50%; + transform: rotate(-45deg) +} + +input[type=range]+.thumb .value { + display: block; + width: 30px; + text-align: center; + color: #26a69a; + font-size: 0; + transform: rotate(45deg) +} + +input[type=range]+.thumb.active { + border-radius: 50% 50% 50% 0 +} + +input[type=range]+.thumb.active .value { + color: #fff; + margin-left: -1px; + margin-top: 8px; + font-size: 10px +} + +input[type=range] { + -webkit-appearance: none +} + +input[type=range]::-webkit-slider-runnable-track { + height: 3px; + background: #c2c0c2; + border: none +} + +input[type=range]::-webkit-slider-thumb { + border: none; + height: 14px; + width: 14px; + border-radius: 50%; + background: #26a69a; + transition: box-shadow .3s; + -webkit-appearance: none; + background-color: #26a69a; + transform-origin: 50% 50%; + margin: -5px 0 0 0 +} + +.keyboard-focused input[type=range]:focus:not(.active)::-webkit-slider-thumb { + box-shadow: 0 0 0 10px rgba(0,169,231, 0.26) +} + +input[type=range] { + border: 1px solid white +} + +input[type=range]::-moz-range-track { + height: 3px; + background: #c2c0c2; + border: none +} + +input[type=range]::-moz-focus-inner { + border: 0 +} + +input[type=range]::-moz-range-thumb { + border: none; + height: 14px; + width: 14px; + border-radius: 50%; + background: #26a69a; + transition: box-shadow .3s; + margin-top: -5px +} + +input[type=range]:-moz-focusring { + outline: 1px solid #fff; + outline-offset: -1px +} + +.keyboard-focused input[type=range]:focus:not(.active)::-moz-range-thumb { + box-shadow: 0 0 0 10px rgba(0,169,231, 0.26) +} + +input[type=range]::-ms-track { + height: 3px; + background: transparent; + border-color: transparent; + border-width: 6px 0; + color: transparent +} + +input[type=range]::-ms-fill-lower { + background: #777 +} + +input[type=range]::-ms-fill-upper { + background: #ddd +} + +input[type=range]::-ms-thumb { + border: none; + height: 14px; + width: 14px; + border-radius: 50%; + background: #26a69a; + transition: box-shadow .3s +} + +.keyboard-focused input[type=range]:focus:not(.active)::-ms-thumb { + box-shadow: 0 0 0 10px rgba(0,169,231, 0.26) +} + +.table-of-contents.fixed { + position: fixed +} + +.table-of-contents li { + padding: 2px 0 +} + +.table-of-contents a { + display: inline-block; + font-weight: 300; + color: #757575; + padding-left: 16px; + height: 1.5rem; + line-height: 1.5rem; + letter-spacing: .4; + display: inline-block +} + +.table-of-contents a:hover { + color: #a8a8a8; + padding-left: 15px; + border-left: 1px solid #448AFF +} + +.table-of-contents a.active { + font-weight: 500; + padding-left: 14px; + border-left: 2px solid #448AFF +} + +.sidenav { + position: fixed; + width: 300px; + left: 0; + top: 0; + margin: 0; + transform: translateX(-100%); + height: 100%; + height: calc(100% + 60px); + height: -moz-calc(100%); + padding-bottom: 60px; + background-color: #fff; + z-index: 999; + overflow-y: auto; + will-change: transform; + backface-visibility: hidden; + transform: translateX(-105%) +} + +.sidenav.right-aligned { + right: 0; + transform: translateX(105%); + left: auto; + transform: translateX(100%) +} + +.sidenav .collapsible { + margin: 0 +} + +.sidenav li { + float: none; + line-height: 48px +} + +.sidenav li.active { + background-color: rgba(0, 0, 0, 0.05) +} + +.sidenav li>a { + color: rgba(0, 0, 0, 0.87); + display: block; + font-size: 14px; + font-weight: 400; + height: 48px; + line-height: 48px; + padding: 0 32px +} + +.sidenav li>a:hover { + background-color: rgba(0, 0, 0, 0.05) +} + +.sidenav li>a.btn, +.sidenav li>a.btn-large, +.sidenav li>a.btn-small, +.sidenav li>a.btn-large, +.sidenav li>a.btn-flat, +.sidenav li>a.btn-floating { + margin: 10px 15px +} + +.sidenav li>a.btn, +.sidenav li>a.btn-large, +.sidenav li>a.btn-small, +.sidenav li>a.btn-large, +.sidenav li>a.btn-floating { + color: #fff +} + +.sidenav li>a.btn-flat { + color: #343434 +} + +.sidenav li>a.btn:hover, +.sidenav li>a.btn-large:hover, +.sidenav li>a.btn-small:hover, +.sidenav li>a.btn-large:hover { + background-color: #2bbbad +} + +.sidenav li>a.btn-floating:hover { + background-color: #26a69a +} + +.sidenav li>a>i, +.sidenav li>a>[class^="mdi-"], +.sidenav li>a li>a>[class*="mdi-"], +.sidenav li>a>i.material-icons { + float: left; + height: 48px; + line-height: 48px; + margin: 0 32px 0 0; + width: 24px; + color: rgba(0, 0, 0, 0.54) +} + +.sidenav .divider { + margin: 8px 0 0 0 +} + +.sidenav .subheader { + cursor: initial; + pointer-events: none; + color: rgba(0, 0, 0, 0.54); + font-size: 14px; + font-weight: 500; + line-height: 48px +} + +.sidenav .subheader:hover { + background-color: transparent +} + +.sidenav .user-view { + position: relative; + padding: 32px 32px 0; + margin-bottom: 8px +} + +.sidenav .user-view>a { + height: auto; + padding: 0 +} + +.sidenav .user-view>a:hover { + background-color: transparent +} + +.sidenav .user-view .background { + overflow: hidden; + position: absolute; + top: 0; + right: 0; + bottom: 0; + left: 0; + z-index: -1 +} + +.sidenav .user-view .circle, +.sidenav .user-view .name, +.sidenav .user-view .email { + display: block +} + +.sidenav .user-view .circle { + height: 64px; + width: 64px +} + +.sidenav .user-view .name, +.sidenav .user-view .email { + font-size: 14px; + line-height: 24px +} + +.sidenav .user-view .name { + margin-top: 16px; + font-weight: 500 +} + +.sidenav .user-view .email { + padding-bottom: 16px; + font-weight: 400 +} + +.drag-target { + height: 100%; + width: 10px; + position: fixed; + top: 0; + z-index: 998 +} + +.drag-target.right-aligned { + right: 0 +} + +.sidenav.sidenav-fixed { + left: 0; + transform: translateX(0); + position: fixed +} + +.sidenav.sidenav-fixed.right-aligned { + right: 0; + left: auto +} + +@media only screen and (max-width: 992px) { + .sidenav.sidenav-fixed { + transform: translateX(-105%) + } + .sidenav.sidenav-fixed.right-aligned { + transform: translateX(105%) + } + .sidenav>a { + padding: 0 16px + } + .sidenav .user-view { + padding: 16px 16px 0 + } +} + +.sidenav .collapsible-body>ul:not(.collapsible)>li.active, +.sidenav.sidenav-fixed .collapsible-body>ul:not(.collapsible)>li.active { + background-color: #448AFF +} + +.sidenav .collapsible-body>ul:not(.collapsible)>li.active a, +.sidenav.sidenav-fixed .collapsible-body>ul:not(.collapsible)>li.active a { + color: #fff +} + +.sidenav .collapsible-body { + padding: 0 +} + +.sidenav-overlay { + position: fixed; + top: 0; + left: 0; + right: 0; + opacity: 0; + height: 120vh; + background-color: rgba(0, 0, 0, 0.5); + z-index: 997; + display: none +} + +.preloader-wrapper { + display: inline-block; + position: relative; + width: 50px; + height: 50px +} + +.preloader-wrapper.small { + width: 36px; + height: 36px +} + +.preloader-wrapper.big { + width: 64px; + height: 64px +} + +.preloader-wrapper.active { + -webkit-animation: container-rotate 1568ms linear infinite; + animation: container-rotate 1568ms linear infinite +} + +@-webkit-keyframes container-rotate { + to { + -webkit-transform: rotate(360deg) + } +} + +@keyframes container-rotate { + to { + transform: rotate(360deg) + } +} + +.spinner-layer { + position: absolute; + width: 100%; + height: 100%; + opacity: 0; + border-color: #26a69a +} + +.spinner-blue, +.spinner-blue-only { + border-color: #4285f4 +} + +.spinner-red, +.spinner-red-only { + border-color: #db4437 +} + +.spinner-yellow, +.spinner-yellow-only { + border-color: #f4b400 +} + +.spinner-green, +.spinner-green-only { + border-color: #0f9d58 +} + +.active .spinner-layer.spinner-blue { + -webkit-animation: fill-unfill-rotate 5332ms cubic-bezier(0.4, 0, 0.2, 1) infinite both, blue-fade-in-out 5332ms cubic-bezier(0.4, 0, 0.2, 1) infinite both; + animation: fill-unfill-rotate 5332ms cubic-bezier(0.4, 0, 0.2, 1) infinite both, blue-fade-in-out 5332ms cubic-bezier(0.4, 0, 0.2, 1) infinite both +} + +.active .spinner-layer.spinner-red { + -webkit-animation: fill-unfill-rotate 5332ms cubic-bezier(0.4, 0, 0.2, 1) infinite both, red-fade-in-out 5332ms cubic-bezier(0.4, 0, 0.2, 1) infinite both; + animation: fill-unfill-rotate 5332ms cubic-bezier(0.4, 0, 0.2, 1) infinite both, red-fade-in-out 5332ms cubic-bezier(0.4, 0, 0.2, 1) infinite both +} + +.active .spinner-layer.spinner-yellow { + -webkit-animation: fill-unfill-rotate 5332ms cubic-bezier(0.4, 0, 0.2, 1) infinite both, yellow-fade-in-out 5332ms cubic-bezier(0.4, 0, 0.2, 1) infinite both; + animation: fill-unfill-rotate 5332ms cubic-bezier(0.4, 0, 0.2, 1) infinite both, yellow-fade-in-out 5332ms cubic-bezier(0.4, 0, 0.2, 1) infinite both +} + +.active .spinner-layer.spinner-green { + -webkit-animation: fill-unfill-rotate 5332ms cubic-bezier(0.4, 0, 0.2, 1) infinite both, green-fade-in-out 5332ms cubic-bezier(0.4, 0, 0.2, 1) infinite both; + animation: fill-unfill-rotate 5332ms cubic-bezier(0.4, 0, 0.2, 1) infinite both, green-fade-in-out 5332ms cubic-bezier(0.4, 0, 0.2, 1) infinite both +} + +.active .spinner-layer, +.active .spinner-layer.spinner-blue-only, +.active .spinner-layer.spinner-red-only, +.active .spinner-layer.spinner-yellow-only, +.active .spinner-layer.spinner-green-only { + opacity: 1; + -webkit-animation: fill-unfill-rotate 5332ms cubic-bezier(0.4, 0, 0.2, 1) infinite both; + animation: fill-unfill-rotate 5332ms cubic-bezier(0.4, 0, 0.2, 1) infinite both +} + +@-webkit-keyframes fill-unfill-rotate { + 12.5% { + -webkit-transform: rotate(135deg) + } + 25% { + -webkit-transform: rotate(270deg) + } + 37.5% { + -webkit-transform: rotate(405deg) + } + 50% { + -webkit-transform: rotate(540deg) + } + 62.5% { + -webkit-transform: rotate(675deg) + } + 75% { + -webkit-transform: rotate(810deg) + } + 87.5% { + -webkit-transform: rotate(945deg) + } + to { + -webkit-transform: rotate(1080deg) + } +} + +@keyframes fill-unfill-rotate { + 12.5% { + transform: rotate(135deg) + } + 25% { + transform: rotate(270deg) + } + 37.5% { + transform: rotate(405deg) + } + 50% { + transform: rotate(540deg) + } + 62.5% { + transform: rotate(675deg) + } + 75% { + transform: rotate(810deg) + } + 87.5% { + transform: rotate(945deg) + } + to { + transform: rotate(1080deg) + } +} + +@-webkit-keyframes blue-fade-in-out { + from { + opacity: 1 + } + 25% { + opacity: 1 + } + 26% { + opacity: 0 + } + 89% { + opacity: 0 + } + 90% { + opacity: 1 + } + 100% { + opacity: 1 + } +} + +@keyframes blue-fade-in-out { + from { + opacity: 1 + } + 25% { + opacity: 1 + } + 26% { + opacity: 0 + } + 89% { + opacity: 0 + } + 90% { + opacity: 1 + } + 100% { + opacity: 1 + } +} + +@-webkit-keyframes red-fade-in-out { + from { + opacity: 0 + } + 15% { + opacity: 0 + } + 25% { + opacity: 1 + } + 50% { + opacity: 1 + } + 51% { + opacity: 0 + } +} + +@keyframes red-fade-in-out { + from { + opacity: 0 + } + 15% { + opacity: 0 + } + 25% { + opacity: 1 + } + 50% { + opacity: 1 + } + 51% { + opacity: 0 + } +} + +@-webkit-keyframes yellow-fade-in-out { + from { + opacity: 0 + } + 40% { + opacity: 0 + } + 50% { + opacity: 1 + } + 75% { + opacity: 1 + } + 76% { + opacity: 0 + } +} + +@keyframes yellow-fade-in-out { + from { + opacity: 0 + } + 40% { + opacity: 0 + } + 50% { + opacity: 1 + } + 75% { + opacity: 1 + } + 76% { + opacity: 0 + } +} + +@-webkit-keyframes green-fade-in-out { + from { + opacity: 0 + } + 65% { + opacity: 0 + } + 75% { + opacity: 1 + } + 90% { + opacity: 1 + } + 100% { + opacity: 0 + } +} + +@keyframes green-fade-in-out { + from { + opacity: 0 + } + 65% { + opacity: 0 + } + 75% { + opacity: 1 + } + 90% { + opacity: 1 + } + 100% { + opacity: 0 + } +} + +.gap-patch { + position: absolute; + top: 0; + left: 45%; + width: 10%; + height: 100%; + overflow: hidden; + border-color: inherit +} + +.gap-patch .circle { + width: 1000%; + left: -450% +} + +.circle-clipper { + display: inline-block; + position: relative; + width: 50%; + height: 100%; + overflow: hidden; + border-color: inherit +} + +.circle-clipper .circle { + width: 200%; + height: 100%; + border-width: 3px; + border-style: solid; + border-color: inherit; + border-bottom-color: transparent !important; + border-radius: 50%; + -webkit-animation: none; + animation: none; + position: absolute; + top: 0; + right: 0; + bottom: 0 +} + +.circle-clipper.left .circle { + left: 0; + border-right-color: transparent !important; + -webkit-transform: rotate(129deg); + transform: rotate(129deg) +} + +.circle-clipper.right .circle { + left: -100%; + border-left-color: transparent !important; + -webkit-transform: rotate(-129deg); + transform: rotate(-129deg) +} + +.active .circle-clipper.left .circle { + -webkit-animation: left-spin 1333ms cubic-bezier(0.4, 0, 0.2, 1) infinite both; + animation: left-spin 1333ms cubic-bezier(0.4, 0, 0.2, 1) infinite both +} + +.active .circle-clipper.right .circle { + -webkit-animation: right-spin 1333ms cubic-bezier(0.4, 0, 0.2, 1) infinite both; + animation: right-spin 1333ms cubic-bezier(0.4, 0, 0.2, 1) infinite both +} + +@-webkit-keyframes left-spin { + from { + -webkit-transform: rotate(130deg) + } + 50% { + -webkit-transform: rotate(-5deg) + } + to { + -webkit-transform: rotate(130deg) + } +} + +@keyframes left-spin { + from { + transform: rotate(130deg) + } + 50% { + transform: rotate(-5deg) + } + to { + transform: rotate(130deg) + } +} + +@-webkit-keyframes right-spin { + from { + -webkit-transform: rotate(-130deg) + } + 50% { + -webkit-transform: rotate(5deg) + } + to { + -webkit-transform: rotate(-130deg) + } +} + +@keyframes right-spin { + from { + transform: rotate(-130deg) + } + 50% { + transform: rotate(5deg) + } + to { + transform: rotate(-130deg) + } +} + +#spinnerContainer.cooldown { + -webkit-animation: container-rotate 1568ms linear infinite, fade-out 400ms cubic-bezier(0.4, 0, 0.2, 1); + animation: container-rotate 1568ms linear infinite, fade-out 400ms cubic-bezier(0.4, 0, 0.2, 1) +} + +@-webkit-keyframes fade-out { + from { + opacity: 1 + } + to { + opacity: 0 + } +} + +@keyframes fade-out { + from { + opacity: 1 + } + to { + opacity: 0 + } +} + +.slider { + position: relative; + height: 400px; + width: 100% +} + +.slider.fullscreen { + height: 100%; + width: 100%; + position: absolute; + top: 0; + left: 0; + right: 0; + bottom: 0 +} + +.slider.fullscreen ul.slides { + height: 100% +} + +.slider.fullscreen ul.indicators { + z-index: 2; + bottom: 30px +} + +.slider .slides { + background-color: #9e9e9e; + margin: 0; + height: 400px +} + +.slider .slides li { + opacity: 0; + position: absolute; + top: 0; + left: 0; + z-index: 1; + width: 100%; + height: inherit; + overflow: hidden +} + +.slider .slides li img { + height: 100%; + width: 100%; + background-size: cover; + background-position: center +} + +.slider .slides li .caption { + color: #fff; + position: absolute; + top: 15%; + left: 15%; + width: 70%; + opacity: 0 +} + +.slider .slides li .caption p { + color: #e0e0e0 +} + +.slider .slides li.active { + z-index: 2 +} + +.slider .indicators { + position: absolute; + text-align: center; + left: 0; + right: 0; + bottom: 0; + margin: 0 +} + +.slider .indicators .indicator-item { + display: inline-block; + position: relative; + cursor: pointer; + height: 16px; + width: 16px; + margin: 0 12px; + background-color: #e0e0e0; + transition: background-color .3s; + border-radius: 50% +} + +.slider .indicators .indicator-item.active { + background-color: #4CAF50 +} + +.carousel { + overflow: hidden; + position: relative; + width: 100%; + height: 400px; + perspective: 500px; + transform-style: preserve-3d; + transform-origin: 0% 50% +} + +.carousel.carousel-slider { + top: 0; + left: 0 +} + +.carousel.carousel-slider .carousel-fixed-item { + position: absolute; + left: 0; + right: 0; + bottom: 20px; + z-index: 1 +} + +.carousel.carousel-slider .carousel-fixed-item.with-indicators { + bottom: 68px +} + +.carousel.carousel-slider .carousel-item { + width: 100%; + height: 100%; + min-height: 400px; + position: absolute; + top: 0; + left: 0 +} + +.carousel.carousel-slider .carousel-item h2 { + font-size: 24px; + font-weight: 500; + line-height: 32px +} + +.carousel.carousel-slider .carousel-item p { + font-size: 15px +} + +.carousel .carousel-item { + visibility: hidden; + width: 200px; + height: 200px; + position: absolute; + top: 0; + left: 0 +} + +.carousel .carousel-item>img { + width: 100% +} + +.carousel .indicators { + position: absolute; + text-align: center; + left: 0; + right: 0; + bottom: 0; + margin: 0 +} + +.carousel .indicators .indicator-item { + display: inline-block; + position: relative; + cursor: pointer; + height: 8px; + width: 8px; + margin: 24px 4px; + background-color: rgba(255, 255, 255, 0.5); + transition: background-color .3s; + border-radius: 50% +} + +.carousel .indicators .indicator-item.active { + background-color: #fff +} + +.carousel.scrolling .carousel-item .materialboxed, +.carousel .carousel-item:not(.active) .materialboxed { + pointer-events: none +} + +.tap-target-wrapper { + width: 800px; + height: 800px; + position: fixed; + z-index: 1000; + visibility: hidden; + transition: visibility 0s .3s +} + +.tap-target-wrapper.open { + visibility: visible; + transition: visibility 0s +} + +.tap-target-wrapper.open .tap-target { + transform: scale(1); + opacity: .95; + transition: transform 0.3s cubic-bezier(0.42, 0, 0.58, 1), opacity 0.3s cubic-bezier(0.42, 0, 0.58, 1) +} + +.tap-target-wrapper.open .tap-target-wave::before { + transform: scale(1) +} + +.tap-target-wrapper.open .tap-target-wave::after { + visibility: visible; + animation: pulse-animation 1s cubic-bezier(0.24, 0, 0.38, 1) infinite; + transition: opacity .3s, transform .3s, visibility 0s 1s +} + +.tap-target { + position: absolute; + font-size: 1rem; + border-radius: 50%; + background-color: #448AFF; + box-shadow: 0 20px 20px 0 rgba(0, 0, 0, 0.14), 0 10px 50px 0 rgba(0, 0, 0, 0.12), 0 30px 10px -20px rgba(0, 0, 0, 0.2); + width: 100%; + height: 100%; + opacity: 0; + transform: scale(0); + transition: transform 0.3s cubic-bezier(0.42, 0, 0.58, 1), opacity 0.3s cubic-bezier(0.42, 0, 0.58, 1) +} + +.tap-target-content { + position: relative; + display: table-cell +} + +.tap-target-wave { + position: absolute; + border-radius: 50%; + z-index: 10001 +} + +.tap-target-wave::before, +.tap-target-wave::after { + content: ''; + display: block; + position: absolute; + width: 100%; + height: 100%; + border-radius: 50%; + background-color: #ffffff +} + +.tap-target-wave::before { + transform: scale(0); + transition: transform .3s +} + +.tap-target-wave::after { + visibility: hidden; + transition: opacity .3s, transform .3s, visibility 0s; + z-index: -1 +} + +.tap-target-origin { + top: 50%; + left: 50%; + transform: translate(-50%, -50%); + z-index: 10002; + position: absolute !important +} + +.tap-target-origin:not(.btn):not(.btn-large):not(.btn-small), +.tap-target-origin:not(.btn):not(.btn-large):not(.btn-small):hover { + background: none +} + +@media only screen and (max-width: 600px) { + .tap-target, + .tap-target-wrapper { + width: 600px; + height: 600px + } +} + +.pulse { + overflow: visible; + position: relative +} + +.pulse::before { + content: ''; + display: block; + position: absolute; + width: 100%; + height: 100%; + top: 0; + left: 0; + background-color: inherit; + border-radius: inherit; + transition: opacity .3s, transform .3s; + animation: pulse-animation 1s cubic-bezier(0.24, 0, 0.38, 1) infinite; + z-index: -1 +} + +@keyframes pulse-animation { + 0% { + opacity: 1; + transform: scale(1) + } + 50% { + opacity: 0; + transform: scale(1.5) + } + 100% { + opacity: 0; + transform: scale(1.5) + } +} + +.datepicker-modal { + max-width: 325px; + min-width: 300px; + max-height: none +} + +.datepicker-container.modal-content { + display: flex; + flex-direction: column; + padding: 0 +} + +.datepicker-controls { + display: flex; + justify-content: space-between; + width: 280px; + margin: 0 auto +} + +.datepicker-controls .selects-container { + display: flex +} + +.datepicker-controls .select-wrapper input { + border-bottom: none; + text-align: center; + margin: 0 +} + +.datepicker-controls .select-wrapper input:focus { + border-bottom: none +} + +.datepicker-controls .select-wrapper .caret { + display: none +} + +.datepicker-controls .select-year input { + width: 50px +} + +.datepicker-controls .select-month input { + width: 70px +} + +.month-prev, +.month-next { + margin-top: 4px; + cursor: pointer; + background-color: transparent; + border: none +} + +.datepicker-date-display { + flex: 1 auto; + background-color: rgba(0,168,231, 0.8); + color: #fff; + padding: 20px 22px; + font-weight: 500 +} + +.datepicker-date-display .year-text { + display: block; + font-size: 1.5rem; + line-height: 25px; + color: rgba(255, 255, 255, 0.7) +} + +.datepicker-date-display .date-text { + display: block; + font-size: 2.8rem; + line-height: 47px; + font-weight: 500 +} + +.datepicker-calendar-container { + flex: 2.5 auto +} + +.datepicker-table { + width: 280px; + font-size: 1rem; + margin: 0 auto +} + +.datepicker-table thead { + border-bottom: none +} + +.datepicker-table th { + padding: 10px 5px; + text-align: center +} + +.datepicker-table tr { + border: none +} + +.datepicker-table abbr { + text-decoration: none; + color: #999 +} + +.datepicker-table td { + border-radius: 50%; + padding: 0 +} + +.datepicker-table td.is-today { + color: rgba(0,168,231, 0.8) +} + +.datepicker-table td.is-selected { + background-color:rgba(0,168,231, 0.8); + color: #fff +} + +.datepicker-table td.is-outside-current-month, +.datepicker-table td.is-disabled { + color: rgba(0, 0, 0, 0.3); + pointer-events: none +} + +.datepicker-day-button { + background-color: transparent; + border: none; + line-height: 38px; + display: block; + width: 100%; + border-radius: 50%; + padding: 0 5px; + cursor: pointer; + color: inherit +} + +.datepicker-day-button:focus { + background-color: rgba(0,168,231, 0.8) +} + +.datepicker-footer { + width: 280px; + margin: 0 auto; + padding-bottom: 5px; + display: flex; + justify-content: space-between +} + +.datepicker-cancel, +.datepicker-clear, +.datepicker-today, +.datepicker-done { + color: rgba(0,168,231, 1); + padding: 0 1rem +} + +.datepicker-clear { + color: #F44336 +} + +@media only screen and (min-width: 601px) { + .datepicker-modal { + max-width: 625px + } + .datepicker-container.modal-content { + flex-direction: row + } + .datepicker-date-display { + flex: 0 1 270px + } + .datepicker-controls, + .datepicker-table, + .datepicker-footer { + width: 320px + } + .datepicker-day-button { + line-height: 44px + } +} + +.timepicker-modal { + max-width: 325px; + max-height: none +} + +.timepicker-container.modal-content { + display: flex; + flex-direction: column; + padding: 0 +} + +.text-primary { + color: #fff +} + +.timepicker-digital-display { + flex: 1 auto; + background-color: rgba(0,169,231,0.8); + padding: 10px; + font-weight: 300 +} + +.timepicker-text-container { + font-size: 4rem; + font-weight: bold; + text-align: center; + color: rgba(255, 255, 255, 0.6); + font-weight: 400; + position: relative; + user-select: none +} + +.timepicker-span-hours, +.timepicker-span-minutes, +.timepicker-span-am-pm div { + cursor: pointer +} + +.timepicker-span-hours { + margin-right: 3px +} + +.timepicker-span-minutes { + margin-left: 3px +} + +.timepicker-display-am-pm { + font-size: 1.3rem; + position: absolute; + right: 1rem; + bottom: 1rem; + font-weight: 400 +} + +.timepicker-analog-display { + flex: 2.5 auto +} + +.timepicker-plate { + background-color: #eee; + border-radius: 50%; + width: 270px; + height: 270px; + overflow: visible; + position: relative; + margin: auto; + margin-top: 25px; + margin-bottom: 5px; + user-select: none +} + +.timepicker-canvas, +.timepicker-dial { + position: absolute; + left: 0; + right: 0; + top: 0; + bottom: 0 +} + +.timepicker-minutes { + visibility: hidden +} + +.timepicker-tick { + border-radius: 50%; + color: rgba(0, 0, 0, 0.87); + line-height: 40px; + text-align: center; + width: 40px; + height: 40px; + position: absolute; + cursor: pointer; + font-size: 15px +} + +.timepicker-tick.active, +.timepicker-tick:hover { + background-color: rgba(0,169,231, 0.25) +} + +.timepicker-dial { + transition: transform 350ms, opacity 350ms +} + +.timepicker-dial-out { + opacity: 0 +} + +.timepicker-dial-out.timepicker-hours { + transform: scale(1.1, 1.1) +} + +.timepicker-dial-out.timepicker-minutes { + transform: scale(0.8, 0.8) +} + +.timepicker-canvas { + transition: opacity 175ms +} + +.timepicker-canvas line { + stroke: rgba(0,169,231,0.8); + stroke-width: 4; + stroke-linecap: round +} + +.timepicker-canvas-out { + opacity: 0.25 +} + +.timepicker-canvas-bearing { + stroke: none; + fill: rgba(0,169,231,0.8) +} + +.timepicker-canvas-bg { + stroke: none; + fill: rgba(0,169,231,0.8) +} + +.timepicker-footer { + margin: 0 auto; + padding: 5px 1rem; + display: flex; + justify-content: space-between +} + +.timepicker-clear { + color: rgba(0,168,231,0.8) +} + +.timepicker-close { + color: rgba(0,168,231,0.8) +} + +.timepicker-clear, +.timepicker-close { + padding: 0 20px +} + +@media only screen and (min-width: 601px) { + .timepicker-modal { + max-width: 600px + } + .timepicker-container.modal-content { + flex-direction: row + } + .timepicker-text-container { + top: 32% + } + .timepicker-display-am-pm { + position: relative; + right: auto; + bottom: auto; + text-align: center; + margin-top: 1.2rem + } +} + +.card .card-metrics { + display: flex +} + +.card .card-metrics.card-metrics-centered .card-metric { + flex: 1; + text-align: center +} + +.card .card-metrics.card-metrics-toggle .card-metric { + border-top: 3px solid transparent +} + +.card .card-metrics.card-metrics-toggle .card-metric.active { + border-top: 3px solid #000 +} + +.card .card-metrics.card-metrics-static .card-metric { + cursor: default +} + +.card .card-metrics.card-metrics-static .card-metric:hover { + background-color: transparent +} + +.card .card-metrics .card-metric { + cursor: pointer; + padding: 24px +} + +.card .card-metrics .card-metric:hover { + background-color: rgba(0, 0, 0, 0.05) +} + +.card .card-metrics .card-metric.active .card-metric-title, +.card .card-metrics .card-metric.active .card-metric-value { + font-weight: 500 +} + +.card .card-metrics .card-metric.colored { + border-right: 1px solid rgba(0, 0, 0, 0.08); + border-bottom: 1px solid rgba(0, 0, 0, 0.08) +} + +.card .card-metrics .card-metric.colored.active { + color: #fff; + border-color: transparent +} + +.card .card-metrics .card-metric.colored:not(.active) { + background-color: transparent !important +} + +.card .card-metrics .card-metric.colored .card-metric-title, +.card .card-metrics .card-metric.colored .card-metric-value { + font-weight: normal +} + +.card .card-metrics .card-metric .card-metric-value { + font-size: 26px; + font-weight: 300 +} + +.card .card-metrics .card-metric .card-metric-change { + font-size: 12px; + line-height: 24px +} + +.card .card-metrics .card-metric .card-metric-change.increase { + color: #4CAF50 +} + +.card .card-metrics .card-metric .card-metric-change.decrease { + color: #F44336 +} + +.card .card-metrics .card-metric .card-metric-change i.material-icons { + height: 24px; + line-height: 24px; + margin-right: 0; + vertical-align: bottom +} + +.card .card-toolbar { + display: flex; + padding: 24px; + justify-content: space-between; + align-items: center +} + +.card .card-toolbar .card-toolbar-title { + line-height: 24px +} + +.card .card-toolbar .card-toolbar-actions .dropdown-trigger .material-icons { + display: block +} + +.card .card-toolbar .card-toolbar-actions .select-dropdown { + margin: 0 +} + +.card.card-login { + display: flex +} + +.card.card-login .card-login-splash { + overflow: hidden; + position: relative; + z-index: 1; + display: flex; + align-items: center; + justify-content: center; + color: #fff; + flex: 1 +} + +.card.card-login .card-login-splash h1, +.card.card-login .card-login-splash h2, +.card.card-login .card-login-splash h3, +.card.card-login .card-login-splash h4, +.card.card-login .card-login-splash h5, +.card.card-login .card-login-splash .settings-title { + margin-top: 0 +} + +.card.card-login .card-login-splash img { + position: absolute; + top: 0; + left: 0; + min-width: 100%; + display: block; + z-index: -1 +} + +.card.card-login .card-content { + flex: 1 +} + +.fixed-chart-wrapper { + height: 568px; + padding: 40px 0; + box-sizing: content-box +} + +.fixed-chart-wrapper .fixed-chart { + height: inherit; + left: 0; + right: 0; + position: fixed +} + +.fixed-chart-wrapper .fixed-chart .card { + box-shadow: none; + background-color: transparent +} + +.fixed-chart-wrapper+.section { + overflow: initial; + border-top: 1px solid rgba(0, 0, 0, 0.1); + box-shadow: 0 -10px 20px -10px rgba(0, 0, 0, 0.1) +} + +.fixed-chart-overlap { + margin-top: -104px +} + +.has-fixed-sidenav .fixed-chart { + left: 300px +} + +@media only screen and (max-width: 992px) { + .has-fixed-sidenav .fixed-chart { + left: 0 + } +} + +.fc-today-button { + border: 0; + border-radius: 2px +} + +.fc-toolbar.fc-header-toolbar { + margin: 0; + padding: 0 16px +} + +.fc-toolbar.fc-header-toolbar h2 { + color: rgba(0, 0, 0, 0.54); + display: inline-block; + font-size: 20px; + font-weight: normal; + line-height: 64px; + margin-left: 8px +} + +.fc-toolbar.fc-header-toolbar .fc-button { + background: rgba(0, 0, 0, 0.08); + border: 0; + box-shadow: none; + height: 32px; + margin-top: 16px; + margin-bottom: 16px; + float: left; + text-shadow: none; + padding: 0 12px +} + +.fc-toolbar.fc-header-toolbar .fc-button.fc-prev-button, +.fc-toolbar.fc-header-toolbar .fc-button.fc-next-button { + margin-top: 12px +} + +.fc-toolbar.fc-header-toolbar .fc-button.fc-state-active { + background-color: #448AFF; + color: #fff; + font-weight: 500 +} + +.fc-toolbar.fc-header-toolbar .fc-left .fc-button { + margin-right: 8px +} + +.fc-toolbar.fc-header-toolbar .fc-right .fc-button { + margin-left: 8px +} + +.fc-prev-button::before { + content: 'chevron_left' +} + +.fc-next-button::before { + content: 'chevron_right' +} + +.fc-state-default.fc-corner-right { + border-top-right-radius: 2px; + border-bottom-right-radius: 2px +} + +.fc-state-default.fc-corner-left { + border-top-left-radius: 2px; + border-bottom-left-radius: 2px +} + +.fc-ltr .fc-basic-view .fc-day-top .fc-day-number { + float: left +} + +.fc tr { + border-bottom: 0 +} + +.fc-head .fc-head-container { + border-bottom: 0 +} + +.fc-head .fc-head-container .fc-day-header { + color: rgba(0, 0, 0, 0.54); + font-weight: 400; + text-align: left +} + +.fc-body>tr>.fc-widget-content { + border-top: 0 +} + +.fc .fc-view:not(.fc-agendaDay-view) .fc-widget-content.fc-today, +.fc .fc-view:not(.fc-agendaDay-view) .fc-widget-header.fc-today { + background-color: #f4f4f4 +} + +.fc-agendaDay-view .fc-widget-content.fc-today { + background-color: transparent +} + +.fc-day-top.fc-today .fc-day-number { + position: relative; + color: #fff; + padding-top: 4px; + padding-left: 4px; + width: 24px; + height: 24px; + line-height: 24px; + text-align: center +} + +.fc-day-top.fc-today .fc-day-number::before { + content: ''; + display: block; + position: absolute; + top: 4px; + left: 4px; + height: 24px; + width: 24px; + border-radius: 50%; + background-color: #448AFF; + z-index: -1 +} + +.fc-day-top .fc-day-number { + float: left +} + +.fc-day-top .fc-day-number, +.fc-week-number, +.fc-head-container .fc-day-header { + padding-top: 6px; + padding-left: 8px +} + +.fc .fc-content, +.fc .fc-divider, +.fc .fc-list-heading td, +.fc .fc-list-view, +.fc .fc-popover, +.fc .fc-row, +.fc tbody, +.fc td, +.fc th, +.fc thead { + border-color: #e0e0e0 +} + +.fc-event { + border-radius: 2px; + line-height: 22px; + margin: 2px 8px 0 0 +} + +.fc .fc-more-popover { + border: 0 +} + +.fc .fc-more-popover .fc-header { + padding: 6px 0 0 0; + background-color: #fff +} + +.fc .fc-more-popover .fc-header .fc-title { + padding-left: 10px; + line-height: 40px +} + +.fc .fc-more-popover .fc-header .fc-icon-x { + position: absolute; + right: 6px; + margin: 0 +} + +.fc .fc-more-popover .fc-header .fc-icon-x::after { + content: none +} + +.fc .fc-more-popover .fc-header .fc-icon-x::before { + content: 'close' +} + +.fc-agenda-view .fc-day-header { + font-size: 48px; + line-height: normal +} + +.fc-agenda-view .fc-day-header::before { + content: ''; + display: block; + font-size: 12px +} + +.fc-agenda-view .fc-day-header.fc-sun::before { + content: 'Sun' +} + +.fc-agenda-view .fc-day-header.fc-mon::before { + content: 'Mon' +} + +.fc-agenda-view .fc-day-header.fc-tue::before { + content: 'Tue' +} + +.fc-agenda-view .fc-day-header.fc-wed::before { + content: 'Wed' +} + +.fc-agenda-view .fc-day-header.fc-thu::before { + content: 'Thu' +} + +.fc-agenda-view .fc-day-header.fc-fri::before { + content: 'Fri' +} + +.fc-agenda-view .fc-day-header.fc-sat::before { + content: 'Sat' +} + +.fc-agenda-view .fc-day-header.fc-today { + color: #448AFF +} + +.fc-agenda-view .fc-minor .fc-widget-content { + border: 0 +} + +.fc-agenda-view .fc-divider { + padding: 0; + border-bottom: 0 +} + +.fc-list-table td { + padding: 13px 14px +} + +.fc-list-table .fc-list-heading:not(:first-child)+.fc-list-item .fc-list-item-time, +.fc-list-table .fc-list-heading:not(:first-child)+.fc-list-item .fc-list-item-marker, +.fc-list-table .fc-list-heading:not(:first-child)+.fc-list-item .fc-list-item-title { + border-top: 1px solid #e0e0e0 +} + +.fc-list-table .fc-list-heading { + display: table-caption; + height: 0; + width: 100px +} + +.fc-list-table .fc-list-heading .fc-widget-header { + padding: 8px 14px; + line-height: 16px; + background-color: transparent +} + +.fc-list-table .fc-list-heading .fc-list-heading-main, +.fc-list-table .fc-list-heading .fc-list-heading-alt { + display: block; + float: none +} + +.fc-list-table .fc-list-item { + font-size: 14px; + height: 48px +} + +.fc-list-table .fc-list-item:hover td { + background-color: transparent +} + +.fc-list-table .fc-list-item .fc-list-item-time, +.fc-list-table .fc-list-item .fc-list-item-marker, +.fc-list-table .fc-list-item .fc-list-item-title { + border-top: 0 +} + +.fc-list-table .fc-list-item .fc-list-item-time { + padding-left: 100px +} + +.card .fc-head .fc-head-container { + border-left: 0; + border-right: 0 +} + +.card .fc-body>tr>.fc-widget-content { + border: 0 +} + +@media only screen and (max-width: 600px) { + .fc-agenda-view .fc-day-header { + font-size: 24px + } +} + +#chartjs-tooltip { + position: absolute; + background-color: #fff; + border-radius: 2px; + pointer-events: none; + z-index: 1000 +} + +#chartjs-tooltip tr { + border: 0 +} + +#chartjs-tooltip th, +#chartjs-tooltip td { + padding: 0; + font-weight: 400 +} + +#chartjs-tooltip thead, +#chartjs-tooltip tfoot { + color: rgba(0, 0, 0, 0.54); + font-size: 12px; + line-height: 16px +} + +#chartjs-tooltip tbody { + font-size: 18px +} + +#chartjs-tooltip .chartjs-tooltip-key { + display: inline-block; + border-radius: 4px; + height: 4px; + width: 12px; + margin: -2px 8px 0 0; + vertical-align: middle +} + +.perc-doughnut-wrapper { + position: relative +} + +.perc-doughnut-wrapper .perc-doughnut-legend { + position: absolute; + top: 50%; + left: 50%; + transform: translate(-50%, -50%); + font-size: 32px +} + +td, +th { + border-radius: 0 +} + +.dataTables_wrapper { + border-top: 1px solid rgba(0, 0, 0, 0.12); + border-bottom: 1px solid rgba(0, 0, 0, 0.12) +} + +.dataTables_wrapper .dataTables_filter { + padding-right: 18px +} + +.dataTables_wrapper table.dataTable tr { + border-color: rgba(0, 0, 0, 0.12); + background-color: transparent +} + +.dataTables_wrapper table.dataTable tr.selected { + background-color: #7af +} + +.dataTables_wrapper .dataTables-checkbox-column { + width: 40px; + text-align: center +} + +.dataTables_wrapper .dataTables-checkbox-column label>span { + padding-left: 24px; + transform: translateY(4px) +} + +.dataTables_wrapper .dataTables-checkbox-column [type="checkbox"].filled-in:checked+span:not(.lever):before { + width: 6px +} + +.dataTables_wrapper table.dataTable thead th, +.dataTables_wrapper table.dataTable thead td { + border-color: rgba(0, 0, 0, 0.12); + padding: 8px 10px +} + +.dataTables_wrapper.no-footer .dataTables_scrollHead .dataTables_scrollHeadInner table.dataTable { + border-top: 1px solid rgba(0, 0, 0, 0.12) +} + +.dataTables_wrapper.no-footer .dataTables_scrollBody { + border-color: rgba(0, 0, 0, 0.12) +} + +.dataTables_wrapper .footer-wrapper { + display: flex; + align-items: center; + justify-content: space-between; + padding: 0 18px +} + +.dataTables_wrapper .dataTables_length .select-wrapper { + display: inline-block; + margin: 0 8px; + width: 60px +} + +.dataTables_wrapper .dataTables_info { + line-height: 48px; + padding: 0 +} + +.dataTables_wrapper .dataTables_paginate { + padding: 4px +} + +.dataTables_wrapper .dataTables_paginate .paginate_button.first::before { + content: 'first_page' +} + +.dataTables_wrapper .dataTables_paginate .paginate_button.previous::before { + content: 'chevron_left' +} + +.dataTables_wrapper .dataTables_paginate .paginate_button.next::before { + content: 'chevron_right' +} + +.dataTables_wrapper .dataTables_paginate .paginate_button.last::before { + content: 'last_page' +} + +.jqvmap-label { + border-radius: 2px; + background-color: #fff; + color: rgba(0, 0, 0, 0.87) +} + +.flag-icon { + margin-right: 5px +} + +.settings-group { + margin-bottom: 50px +} + +.settings-group .setting { + color: #181818; + font-size: 16px; + margin: 0; + background-color: #fff; + padding: 24px; + border-bottom: 1px solid #ddd +} + +.settings-group .setting .switch label .lever { + margin: 0 +} + +.settings-group .setting [type="checkbox"]+span:not(.lever) { + padding-left: 20px +} + +.collapsible.setting { + padding: 0; + border: 0; + box-shadow: none +} + +.collapsible.setting>.active .collapsible-header { + border-bottom: 0 +} + +.collapsible.setting>.active .caret { + transform: rotate(90deg) +} + +.collapsible.setting .collapsible-header, +.collapsible.setting .collapsible-body { + padding: 24px +} + +.collapsible.setting .collapsible-header { + padding: 24px; + justify-content: space-between; + transition: border 0s linear .3s +} + +.collapsible.setting .collapsible-header i.material-icons { + margin-right: 0 +} + +.collapsible.setting .collapsible-header .caret { + transition: transform .2s +} + +.collapsible.setting .collapsible-body { + background-color: inherit +} + +.dropdown-content .settings-group { + box-shadow: none; + border: 0; + margin: 0 +} + +.dropdown-content .settings-group .setting { + padding: 16px; + line-height: 24px +} + +.badged-circle::before { + display: block; + content: ''; + position: absolute; + left: 15px; + top: 42px; + height: 12px; + width: 12px; + background-color: #ddd; + border-radius: 50%; + border: 2px solid #fff; + z-index: 1 +} + +.badged-circle.online::before { + background-color: #66BB6A +} + +.collection.flush { + margin: 0; + border: 0; + border-top: 1px solid #e0e0e0; + border-bottom: 1px solid #e0e0e0 +} + +.collection .collection-item.avatar { + min-height: initial +} + +.collection .collection-item.avatar.active { + color: inherit; + background-color: #eee +} + +.collection .collection-item.avatar .title { + padding: 0 +} + +.collection .collection-item.avatar p { + font-size: 14px +} + +.chat-wrapper .chat-message { + position: relative; + float: left; + clear: both; + margin: 2px 50px 20px; + padding: 8px; + border-radius: 8px; + line-height: 22px; + background-color: #ddd +} + +.chat-wrapper .chat-message.right { + background-color: #448AFF; + color: #fff +} + +.chat-wrapper .chat-message.right .circle { + left: auto; + right: -50px +} + +.chat-wrapper .chat-message.coalesce { + margin-top: -16px +} + +.chat-wrapper .chat-message .circle { + position: absolute; + top: -2px; + left: -50px; + height: 42px; + width: 42px +} + +.chat-input { + border-top: 1px solid #e0e0e0 +} + +.chat-input form input[type="text"], +.chat-input textarea { + border: 0; + margin: 0; + padding: 24px; + line-height: 24px +} + +.chat-input form input[type="text"]:focus:not([readonly]), +.chat-input textarea:focus:not([readonly]) { + border: 0; + box-shadow: none +} + +.chat-input button[type="submit"] { + padding: 0 16px; + height: 72px +} + +.chat-input button[type="submit"] i.material-icons { + color: #448AFF +} + +.chat-input .chat-input-bar { + display: flex +} + +html { + height: 100% +} + +body { + min-height: 100%; + display: flex; + flex-direction: column +} + +main { + flex: 1 0 auto +} + +main>.container { + margin-top: 20px +} + +@media only screen and (min-width: 993px) { + body.has-fixed-sidenav { + + } + .container { + width: 90% + } +} + +button { + background: none; + border: 0 +} + +button:focus { + background: none +} + +.primary-color { + background-color: #448AFF !important +} + +.primary-color-text { + color: #448AFF !important +} + +.admin-blue { + background-color: #00b0ff !important +} + +.admin-blue-text { + color: #00b0ff !important +} + +.admin-yellow { + background-color: #ffc400 !important +} + +.admin-yellow-text { + color: #ffc400 !important +} + +.admin-pink { + background-color: #ff4081 !important +} + +.admin-pink-text { + color: #ff4081 !important +} + +.admin-green { + background-color: #70be74 !important +} + +.admin-green-text { + color: #70be74 !important +} + +.divider { + margin: 40px 0 +} + +.google-map { + width: 100%; + color: #444 +} + +.no-gutter { + padding-left: 0 !important; + padding-right: 0 !important +} + +.no-margin { + margin: 0 !important +} + +.background { + display: block; + position: absolute; + top: 0; + width: 100%; + height: 100%; + z-index: -1 +} + +.background.portrait img { + min-width: 100%; + width: auto; + min-height: none; + height: 100% +} + +.background img { + width: 100%; + min-height: 100% +} + +h1 { + margin-bottom: 50px +} + +p { + font-weight: 300; + font-size: 18px; + color: #888 +} + +p.caption { + color: inherit +} + +.feature { + margin-bottom: 50px +} + +span.date { + text-transform: uppercase; + font-weight: 300 +} + +.row.valign { + width: 100% +} + +.row.row-vertical-center { + display: flex; + flex-wrap: wrap +} + +.row.row-vertical-center .col { + display: flex; + align-items: center +} + +.row.equal-height-grid { + display: flex; + flex-wrap: wrap +} + +.row.equal-height-grid .col { + display: flex; + margin-left: 0 +} + 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#f4f4f4; + color: rgba(0, 0, 0, 0.87); + padding: 8px 16px; + width: 450px +} + +.dropdown-content.notifications .notifications-title { + height: 56px; + line-height: 56px; + text-transform: uppercase; + font-weight: 500 +} + +.dropdown-content.notifications .card { + margin: 8px 0; + line-height: normal; + box-shadow: 0 1px 3px 0 rgba(0, 0, 0, 0.2), 0 1px 1px 0 rgba(0, 0, 0, 0.14), 0 2px 1px -1px rgba(0, 0, 0, 0.12) +} + +.dropdown-content.notifications .card:focus { + outline: none +} + +.dropdown-content.notifications .card .card-content p { + font-size: 16px +} + +.dropdown-content.notifications .card .card-action { + padding: 8px; + height: auto +} + +.dropdown-content.notifications .card .card-action a { + padding: 0 8px; + margin-right: 8px !important; + height: 36px; + line-height: 36px; + display: inline-block +} + +.dropdown-content.dropdown-tabbed .tabs>li { + min-height: initial; + width: auto; + float: none +} + +.dropdown-content.dropdown-tabbed .tabs>li a { + line-height: 48px +} + +.sidenav>li { + width: 100% +} + +.sidenav .logo-container { + padding: 0 16px; + height: 64px; + line-height: 64px; + font-size: 24px; + border-bottom: 1px solid rgba(0, 0, 0, 0.2); + box-sizing: content-box +} + +.sidenav .logo-container i.material-icons { + line-height: inherit +} + +.sidenav .collapsible { + border-bottom: 1px solid rgba(0, 0, 0, 0.2) +} + +.sidenav .collapsible>li { + border-bottom: 1px solid rgba(0, 0, 0, 0.2); + transition: background-color .2s +} + +.sidenav .collapsible>li:hover { + background-color: rgba(0, 0, 0, 0.05) +} + +.sidenav .collapsible>li.waves-effect { + display: block +} + +.sidenav .collapsible>li:last-child { + border-bottom: 0 +} + +.sidenav .collapsible .collapsible-header { + font-weight: 400; + font-size: 16px; + height: 64px; + line-height: 64px +} + +.sidenav .collapsible .collapsible-header:hover { + background-color: transparent +} + +.sidenav .collapsible .collapsible-header .chevron { + float: right; + height: 24px; + width: 24px; + line-height: 24px; + margin: 20px 0 0 0; + transition: transform .2s +} + +.sidenav .collapsible .collapsible-body { + padding-bottom: 16px; + background-color: transparent +} + +.sidenav .collapsible .collapsible-body li>a { + padding: 0 16px; + transition: background-color .2s ease-out +} + +.sidenav .collapsible .collapsible-body li>a:hover, +.sidenav .collapsible .collapsible-body li>a.active { + background-color: rgba(0, 0, 0, 0.07) +} + +.sidenav .collapsible .collapsible-body li>a>i.material-icons { + margin-right: 16px +} + +.sidenav li.active>a, +.sidenav li:hover>a, +.sidenav li>a:hover, +.sidenav li>a.active { + background-color: transparent +} + +.sidenav li.active>a::before, +.sidenav li:hover>a::before, +.sidenav li>a:hover::before, +.sidenav li>a.active::before { + transform: scaleY(1) +} + +.sidenav li>a { + position: relative; + padding: 0 16px +} + +.sidenav li>a::before { + left: auto; + right: 0 +} + +.keyboard-focused .sidenav .collapsible-header:focus { + background-color: rgba(0, 0, 0, 0.1) +} + +.keyboard-focused .sidenav .collapsible-body li>a:focus { + background-color: rgba(0, 0, 0, 0.07); + outline: none +} + +.navbar-fixed nav.navbar { + width: 100%; + left: 0 +} + +@media only screen and (min-width: 993px) { + .has-fixed-sidenav .navbar-fixed nav.navbar { + width: calc(100%); + } + body:not(.has-fixed-sidenav) nav a.sidenav-trigger { + display: block + } + body:not(.has-fixed-sidenav) nav.navbar .brand-logo { + position: absolute; + left: 50%; + transform: translateX(-50%) + } +} + +@media only screen and (max-width: 992px) { + nav.navbar a.brand-logo { + position: absolute + } +} + +nav.navbar { + z-index: 10; + transition: box-shadow .3s, background-color .3s; + padding: 0 20px; + background-color: #fff; + color: rgba(0, 0, 0, 0.87) +} + +nav.navbar .brand-logo { + position: relative; + float: left; + font-size: 18px; + color: rgba(0, 0, 0, 0.87) +} + +nav.navbar .brand-logo i { + line-height: inherit; + transition: color .2s +} + +nav.navbar .nav-wrapper>ul>li>a { + display: block +} + +nav.navbar .sidenav-trigger { + margin: 0 +} + +nav.navbar ul>li.active>a::before, +nav.navbar ul>li>a.active::before, +nav.navbar ul>li>a:hover::before { + transform: scaleX(1) +} + +nav.navbar ul li.active { + background-color: transparent +} + +nav.navbar ul a { + color: rgba(0, 0, 0, 0.87); + position: relative; + transition: color .2s +} + +nav.navbar ul a:focus { + outline: none +} + +.nav-extended .nav-wrapper { + padding-left: 24px +} + +.nav-extended .nav-metrics { + border-top: 1px solid #e0e0e0; + margin-bottom: 0 +} + +.nav-extended .nav-metrics .col:last-child .card { + border-right: 0 +} + +.nav-extended .nav-metrics .card { + border-right: 1px solid #e0e0e0; + box-shadow: none; + background-color: transparent; + margin: 0 +} + +.nav-wrapper-with-search { + display: flex +} + +.nav-wrapper-with-search .search-wrapper { + flex: 1; + overflow: hidden; + margin-right: 20px; + background-color: rgba(255, 255, 255, 0.25); + border-radius: 2px; + border-right: 1px solid #ddd +} + +.nav-wrapper-with-search .search-wrapper i { + height: 56px; + line-height: 56px +} + +.nav-wrapper-with-search .search-wrapper .label-icon i { + color: #444 +} + +@media only screen and (min-width: 601px) { + .nav-wrapper-with-search .search-wrapper i { + height: 64px; + line-height: 64px + } +} + +.badge-updates { + margin: 16px 0 0 0 +} + +.badge-updates>li { + line-height: 22px; + margin-bottom: 8px +} + +.badge-updates>li::after { + content: ''; + display: block; + clear: both +} + +.badge-updates>li:last-child { + margin-bottom: 0 +} + +.badge-updates .badge { + float: left; + margin: 0 16px 0 0 +} + +.badge-updates .time { + float: right; + font-size: 12px; + color: rgba(0, 0, 0, 0.54) +} + +.section { + padding: 64px 0; + position: relative +} + +.section.chart-padding { + padding: 16px 0 +} + +.section .section-title { + margin-bottom: 30px +} + +.section .caption { + font-size: 1.4rem; + font-weight: 200 +} + +.section .col { + min-height: 1px +} + +.fc .fc-button.fc-prev-button, +.fc .fc-button.fc-next-button, +.fc-icon, +.dataTables_wrapper .dataTables_paginate .paginate_button.first, +.dataTables_wrapper .dataTables_paginate .paginate_button.previous, +.dataTables_wrapper .dataTables_paginate .paginate_button.next, +.dataTables_wrapper .dataTables_paginate .paginate_button.last { + color: rgba(0, 0, 0, 0.87) !important; + font-family: "Material Icons"; + overflow: hidden; + width: 40px; + height: 40px; + padding: 0; + margin: 0 2px; + box-shadow: none; + border: 0; + border-radius: 50%; + background: transparent +} + +.fc .fc-button.fc-prev-button::before, +.fc .fc-button.fc-next-button::before, +.fc-icon::before, +.dataTables_wrapper .dataTables_paginate .paginate_button.first::before, +.dataTables_wrapper .dataTables_paginate .paginate_button.previous::before, +.dataTables_wrapper .dataTables_paginate .paginate_button.next::before, +.dataTables_wrapper .dataTables_paginate .paginate_button.last::before { + display: block; + padding: 8px; + line-height: 24px; + font-size: 24px +} + +.fc .disabled.fc-button.fc-prev-button, +.fc .disabled.fc-button.fc-next-button, +.disabled.fc-icon, +.dataTables_wrapper .dataTables_paginate .disabled.paginate_button.first, +.dataTables_wrapper .dataTables_paginate .disabled.paginate_button.previous, +.dataTables_wrapper .dataTables_paginate .disabled.paginate_button.next, +.dataTables_wrapper .dataTables_paginate .disabled.paginate_button.last, +.fc .disabled.fc-button.fc-prev-button:hover, +.fc .disabled.fc-button.fc-next-button:hover, +.disabled.fc-icon:hover, +.dataTables_wrapper .dataTables_paginate .disabled.paginate_button.first:hover, +.dataTables_wrapper .dataTables_paginate .disabled.paginate_button.previous:hover, +.dataTables_wrapper .dataTables_paginate .disabled.paginate_button.next:hover, +.dataTables_wrapper .dataTables_paginate .disabled.paginate_button.last:hover { + color: rgba(0, 0, 0, 0.26) !important; + border: 0 +} + +.fc .fc-button.fc-prev-button:hover, +.fc .fc-button.fc-next-button:hover, +.fc-icon:hover, +.dataTables_wrapper .dataTables_paginate .paginate_button.first:hover, +.dataTables_wrapper .dataTables_paginate .paginate_button.previous:hover, +.dataTables_wrapper .dataTables_paginate .paginate_button.next:hover, +.dataTables_wrapper .dataTables_paginate .paginate_button.last:hover, +.fc .fc-button.fc-prev-button:active, +.fc .fc-button.fc-next-button:active, +.fc-icon:active, +.dataTables_wrapper .dataTables_paginate .paginate_button.first:active, +.dataTables_wrapper .dataTables_paginate .paginate_button.previous:active, +.dataTables_wrapper .dataTables_paginate .paginate_button.next:active, +.dataTables_wrapper .dataTables_paginate .paginate_button.last:active { + color: rgba(0, 0, 0, 0.87) !important; + background: rgba(0, 0, 0, 0.05); + border: 0; + box-shadow: none +} + +.card { + margin-top: 0; + margin-bottom: 20px +} + +.card .card-title { + font-weight: normal +} + +.card .background { + z-index: 0; + top: auto; + bottom: 0 +} + +.card .divider { + margin: 0 +} + +.page-footer { + background-color: #fff; + color: #444; + padding: 40px 0; + margin: 0 +} + +.page-footer .row { + margin: 0 +} + +.page-footer .materialize-logo { + margin-top: 2px +} + +.page-footer p { + margin: 5px 0 +} + +.page-footer ul li { + margin-bottom: 5px +} + +.page-footer ul li a, +.page-footer p { + color: #999; + font-size: .8rem; + font-weight: 300; + margin: 5px 0 +} + +@media only screen and (max-width: 600px), +only screen and (max-device-width: 641px) and (orientation: landscape) { + .section { + height: auto + } + .google-map { + height: 100% + } +} + + +.toast{ + background-color: #fff; + color: #323232; +} diff --git a/malaya/_utils/web/static/dashboard.min.js b/malaya/_utils/web/static/dashboard.min.js new file mode 100644 index 00000000..37546634 --- /dev/null +++ b/malaya/_utils/web/static/dashboard.min.js @@ 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T(){this.isOpen&&(this.$dropdownEl.css({width:"",height:"",left:"",top:"","transform-origin":""}),this._placeDropdown())}}],[{key:"init",value:function L(t,e){return _get(n.__proto__||Object.getPrototypeOf(n),"init",this).call(this,this,t,e)}},{key:"getInstance",value:function B(t){var e=t.jquery?t[0]:t;return e.M_Dropdown}},{key:"defaults",get:function(){return e}}]),n}(Component);i._dropdowns=[],window.M.Dropdown=i,M.jQueryLoaded&&M.initializeJqueryWrapper(i,"dropdown","M_Dropdown")}(cash,M.anime),function($,t){"use strict";var e={opacity:.5,inDuration:250,outDuration:250,onOpenStart:null,onOpenEnd:null,onCloseStart:null,onCloseEnd:null,preventScrolling:!0,dismissible:!0,startingTop:"4%",endingTop:"10%"},i=function(i){function n(t,e){_classCallCheck(this,n);var i=_possibleConstructorReturn(this,(n.__proto__||Object.getPrototypeOf(n)).call(this,n,t,e));return i.el.M_Modal=i,i.options=$.extend({},n.defaults,e),i.isOpen=!1,i.id=i.$el.attr("id"),i._openingTrigger=void 0,i.$overlay=$('
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").addClass("material-placeholder"),i.originalWidth=0,i.originalHeight=0,i.originInlineStyles=i.$el.attr("style"),i.caption=i.el.getAttribute("data-caption")||"",i.$el.before(i.placeholder),i.placeholder.append(i.$el),i._setupEventHandlers(),i}return _inherits(n,i),_createClass(n,[{key:"destroy",value:function s(){this._removeEventHandlers(),this.el.M_Materialbox=void 0}},{key:"_setupEventHandlers",value:function o(){this._handleMaterialboxClickBound=this._handleMaterialboxClick.bind(this),this.el.addEventListener("click",this._handleMaterialboxClickBound)}},{key:"_removeEventHandlers",value:function a(){this.el.removeEventListener("click",this._handleMaterialboxClickBound)}},{key:"_handleMaterialboxClick",value:function r(t){this.doneAnimating===!1||this.overlayActive&&this.doneAnimating?this.close():this.open()}},{key:"_handleWindowScroll",value:function l(){this.overlayActive&&this.close()}},{key:"_handleWindowResize",value:function h(){this.overlayActive&&this.close()}},{key:"_handleWindowEscape",value:function d(t){27===t.keyCode&&this.doneAnimating&&this.overlayActive&&this.close()}},{key:"_makeAncestorsOverflowVisible",value:function u(){this.ancestorsChanged=$();for(var t=this.placeholder[0].parentNode;null!==t&&!$(t).is(document);){var e=$(t);"visible"!==e.css("overflow")&&(e.css("overflow","visible"),void 0===this.ancestorsChanged?this.ancestorsChanged=e:this.ancestorsChanged=this.ancestorsChanged.add(e)),t=t.parentNode}}},{key:"_animateImageIn",value:function c(){var e=this,i={targets:this.el,height:[this.originalHeight,this.newHeight],width:[this.originalWidth,this.newWidth],left:M.getDocumentScrollLeft()+this.windowWidth/2-this.placeholder.offset().left-this.newWidth/2,top:M.getDocumentScrollTop()+this.windowHeight/2-this.placeholder.offset().top-this.newHeight/2,duration:this.options.inDuration,easing:"easeOutQuad",complete:function(){e.doneAnimating=!0,"function"==typeof e.options.onOpenEnd&&e.options.onOpenEnd.call(e,e.el)}};this.maxWidth=this.$el.css("max-width"),this.maxHeight=this.$el.css("max-height"),"none"!==this.maxWidth&&(i.maxWidth=this.newWidth),"none"!==this.maxHeight&&(i.maxHeight=this.newHeight),t(i)}},{key:"_animateImageOut",value:function p(){var e=this,i={targets:this.el,width:this.originalWidth,height:this.originalHeight,left:0,top:0,duration:this.options.outDuration,easing:"easeOutQuad",complete:function(){e.placeholder.css({height:"",width:"",position:"",top:"",left:""}),e.attrWidth&&e.$el.attr("width",e.attrWidth),e.attrHeight&&e.$el.attr("height",e.attrHeight),e.$el.removeAttr("style"),e.$el.attr("style",e.originInlineStyles),e.$el.removeClass("active"),e.doneAnimating=!0,e.ancestorsChanged.length&&e.ancestorsChanged.css("overflow",""),"function"==typeof e.options.onCloseEnd&&e.options.onCloseEnd.call(e,e.el)}};t(i)}},{key:"_updateVars",value:function v(){this.windowWidth=window.innerWidth,this.windowHeight=window.innerHeight,this.caption=this.el.getAttribute("data-caption")||""}},{key:"open",value:function f(){var e=this;this._updateVars(),this.originalWidth=this.el.getBoundingClientRect().width,this.originalHeight=this.el.getBoundingClientRect().height,this.doneAnimating=!1,this.$el.addClass("active"),this.overlayActive=!0,"function"==typeof this.options.onOpenStart&&this.options.onOpenStart.call(this,this.el),this.placeholder.css({width:this.placeholder[0].getBoundingClientRect().width+"px",height:this.placeholder[0].getBoundingClientRect().height+"px",position:"relative",top:0,left:0}),this._makeAncestorsOverflowVisible(),this.$el.css({position:"absolute","z-index":1e3,"will-change":"left, top, width, height"}),this.attrWidth=this.$el.attr("width"),this.attrHeight=this.$el.attr("height"),this.attrWidth&&(this.$el.css("width",this.attrWidth+"px"),this.$el.removeAttr("width")),this.attrHeight&&(this.$el.css("width",this.attrHeight+"px"),this.$el.removeAttr("height")),this.$overlay=$('
').css({opacity:0}).one("click",function(){e.doneAnimating&&e.close()}),this.$el.before(this.$overlay);var i=this.$overlay[0].getBoundingClientRect();this.$overlay.css({width:this.windowWidth+"px",height:this.windowHeight+"px",left:-1*i.left+"px",top:-1*i.top+"px"}),t.remove(this.el),t.remove(this.$overlay[0]),t({targets:this.$overlay[0],opacity:1,duration:this.options.inDuration,easing:"easeOutQuad"}),""!==this.caption&&(this.$photocaption&&t.remove(this.$photoCaption[0]),this.$photoCaption=$('
'),this.$photoCaption.text(this.caption),$("body").append(this.$photoCaption),this.$photoCaption.css({display:"inline"}),t({targets:this.$photoCaption[0],opacity:1,duration:this.options.inDuration,easing:"easeOutQuad"}));var n=0,s=this.originalWidth/this.windowWidth,o=this.originalHeight/this.windowHeight;this.newWidth=0,this.newHeight=0,s>o?(n=this.originalHeight/this.originalWidth,this.newWidth=.9*this.windowWidth,this.newHeight=.9*this.windowWidth*n):(n=this.originalWidth/this.originalHeight,this.newWidth=.9*this.windowHeight*n,this.newHeight=.9*this.windowHeight),this._animateImageIn(),this._handleWindowScrollBound=this._handleWindowScroll.bind(this),this._handleWindowResizeBound=this._handleWindowResize.bind(this),this._handleWindowEscapeBound=this._handleWindowEscape.bind(this),window.addEventListener("scroll",this._handleWindowScrollBound),window.addEventListener("resize",this._handleWindowResizeBound),window.addEventListener("keyup",this._handleWindowEscapeBound)}},{key:"close",value:function m(){var e=this;this._updateVars(),this.doneAnimating=!1,"function"==typeof this.options.onCloseStart&&this.options.onCloseStart.call(this,this.el),t.remove(this.el),t.remove(this.$overlay[0]),""!==this.caption&&t.remove(this.$photoCaption[0]),window.removeEventListener("scroll",this._handleWindowScrollBound),window.removeEventListener("resize",this._handleWindowResizeBound),window.removeEventListener("keyup",this._handleWindowEscapeBound),t({targets:this.$overlay[0],opacity:0,duration:this.options.outDuration,easing:"easeOutQuad",complete:function(){e.overlayActive=!1,e.$overlay.remove()}}),this._animateImageOut(),""!==this.caption&&t({targets:this.$photoCaption[0],opacity:0,duration:this.options.outDuration,easing:"easeOutQuad",complete:function(){e.$photoCaption.remove()}})}}],[{key:"init",value:function g(t,e){return _get(n.__proto__||Object.getPrototypeOf(n),"init",this).call(this,this,t,e)}},{key:"getInstance",value:function y(t){var e=t.jquery?t[0]:t;return e.M_Materialbox}},{key:"defaults",get:function(){return e}}]),n}(Component);M.Materialbox=i,M.jQueryLoaded&&M.initializeJqueryWrapper(i,"materialbox","M_Materialbox")}(cash,M.anime),function($){"use strict";var t={responsiveThreshold:0},e=function(e){function i(t,e){_classCallCheck(this,i);var n=_possibleConstructorReturn(this,(i.__proto__||Object.getPrototypeOf(i)).call(this,i,t,e));return n.el.M_Parallax=n,n.options=$.extend({},i.defaults,e),n._enabled=window.innerWidth>n.options.responsiveThreshold,n.$img=n.$el.find("img").first(),n.$img.each(function(){var t=this;t.complete&&$(t).trigger("load")}),n._updateParallax(),n._setupEventHandlers(),n._setupStyles(),i._parallaxes.push(n),n}return _inherits(i,e),_createClass(i,[{key:"destroy",value:function n(){i._parallaxes.splice(i._parallaxes.indexOf(this),1),this.$img[0].style.transform="",this._removeEventHandlers(),this.$el[0].M_Parallax=void 0}},{key:"_setupEventHandlers",value:function s(){this._handleImageLoadBound=this._handleImageLoad.bind(this),this.$img[0].addEventListener("load",this._handleImageLoadBound),0===i._parallaxes.length&&(i._handleScrollThrottled=M.throttle(i._handleScroll,5),window.addEventListener("scroll",i._handleScrollThrottled),i._handleWindowResizeThrottled=M.throttle(i._handleWindowResize,5),window.addEventListener("resize",i._handleWindowResizeThrottled))}},{key:"_removeEventHandlers",value:function o(){this.$img[0].removeEventListener("load",this._handleImageLoadBound),0===i._parallaxes.length&&(window.removeEventListener("scroll",i._handleScrollThrottled),window.removeEventListener("resize",i._handleWindowResizeThrottled))}},{key:"_setupStyles",value:function a(){this.$img[0].style.opacity=1}},{key:"_handleImageLoad",value:function r(){this._updateParallax()}},{key:"_updateParallax",value:function l(){var t=this.$el.height()>0?this.el.parentNode.offsetHeight:500,e=this.$img[0].offsetHeight,i=e-t,n=this.$el.offset().top+t,s=this.$el.offset().top,o=M.getDocumentScrollTop(),a=window.innerHeight,r=o+a,l=(r-s)/(t+a),h=i*l;this._enabled?n>o&&o+a>s&&(this.$img[0].style.transform="translate3D(-50%, "+h+"px, 0)"):this.$img[0].style.transform=""}}],[{key:"init",value:function h(t,e){return _get(i.__proto__||Object.getPrototypeOf(i),"init",this).call(this,this,t,e)}},{key:"getInstance",value:function d(t){var e=t.jquery?t[0]:t;return e.M_Parallax}},{key:"_handleScroll",value:function u(){for(var t=0;te.options.responsiveThreshold}}},{key:"defaults",get:function(){return t}}]),i}(Component);e._parallaxes=[],M.Parallax=e,M.jQueryLoaded&&M.initializeJqueryWrapper(e,"parallax","M_Parallax")}(cash),function($,t){"use strict";var e={duration:300,onShow:null,swipeable:!1,responsiveThreshold:1/0},i=function(i){function n(t,e){_classCallCheck(this,n);var i=_possibleConstructorReturn(this,(n.__proto__||Object.getPrototypeOf(n)).call(this,n,t,e));return i.el.M_Tabs=i,i.options=$.extend({},n.defaults,e),i.$tabLinks=i.$el.children("li.tab").children("a"),i.index=0,i._setupActiveTabLink(),i.options.swipeable?i._setupSwipeableTabs():i._setupNormalTabs(),i._setTabsAndTabWidth(),i._createIndicator(),i._setupEventHandlers(),i}return _inherits(n,i),_createClass(n,[{key:"destroy",value:function s(){this._removeEventHandlers(),this._indicator.parentNode.removeChild(this._indicator),this.options.swipeable?this._teardownSwipeableTabs():this._teardownNormalTabs(),this.$el[0].M_Tabs=void 0}},{key:"_setupEventHandlers",value:function o(){this._handleWindowResizeBound=this._handleWindowResize.bind(this),window.addEventListener("resize",this._handleWindowResizeBound),this._handleTabClickBound=this._handleTabClick.bind(this),this.el.addEventListener("click",this._handleTabClickBound)}},{key:"_removeEventHandlers",value:function a(){window.removeEventListener("resize",this._handleWindowResizeBound),this.el.removeEventListener("click",this._handleTabClickBound)}},{key:"_handleWindowResize",value:function r(){this._setTabsAndTabWidth(),0!==this.tabWidth&&0!==this.tabsWidth&&(this._indicator.style.left=this._calcLeftPos(this.$activeTabLink)+"px",this._indicator.style.right=this._calcRightPos(this.$activeTabLink)+"px")}},{key:"_handleTabClick",value:function l(t){var e=this,i=$(t.target).closest("li.tab"),n=$(t.target).closest("a");if(n.length&&n.parent().hasClass("tab")){if(i.hasClass("disabled"))return void t.preventDefault();if(!n.attr("target")){this.$activeTabLink.removeClass("active");var s=this.$content;this.$activeTabLink=n,this.$content=$(M.escapeHash(n[0].hash)),this.$tabLinks=this.$el.children("li.tab").children("a"),this.$activeTabLink.addClass("active");var o=this.index;this.index=Math.max(this.$tabLinks.index(n),0),this.options.swipeable?this._tabsCarousel&&this._tabsCarousel.set(this.index,function(){"function"==typeof e.options.onShow&&e.options.onShow.call(e,e.$content[0])}):this.$content.length&&(this.$content[0].style.display="block",this.$content.addClass("active"),"function"==typeof this.options.onShow&&this.options.onShow.call(this,this.$content[0]),s.length&&!s.is(this.$content)&&(s[0].style.display="none",s.removeClass("active"))),this._setTabsAndTabWidth(),this._animateIndicator(o),t.preventDefault()}}}},{key:"_createIndicator",value:function h(){var t=this,e=document.createElement("li");e.classList.add("indicator"),this.el.appendChild(e),this._indicator=e,setTimeout(function(){t._indicator.style.left=t._calcLeftPos(t.$activeTabLink)+"px",t._indicator.style.right=t._calcRightPos(t.$activeTabLink)+"px"},0)}},{key:"_setupActiveTabLink",value:function d(){this.$activeTabLink=$(this.$tabLinks.filter('[href="'+location.hash+'"]')),0===this.$activeTabLink.length&&(this.$activeTabLink=this.$el.children("li.tab").children("a.active").first()),0===this.$activeTabLink.length&&(this.$activeTabLink=this.$el.children("li.tab").children("a").first()),this.$tabLinks.removeClass("active"),this.$activeTabLink[0].classList.add("active"),this.index=Math.max(this.$tabLinks.index(this.$activeTabLink),0),this.$activeTabLink.length&&(this.$content=$(M.escapeHash(this.$activeTabLink[0].hash)),this.$content.addClass("active"))}},{key:"_setupSwipeableTabs",value:function u(){var t=this;window.innerWidth>this.options.responsiveThreshold&&(this.options.swipeable=!1);var e=$();this.$tabLinks.each(function(t){var i=$(M.escapeHash(t.hash));i.addClass("carousel-item"),e=e.add(i)});var i=$('
');e.first().before(i),i.append(e),e[0].style.display="";var n=this.$activeTabLink.closest(".tab").index();this._tabsCarousel=M.Carousel.init(i[0],{fullWidth:!0,noWrap:!0,onCycleTo:function(e){var i=t.index;t.index=$(e).index(),t.$activeTabLink.removeClass("active"),t.$activeTabLink=t.$tabLinks.eq(t.index),t.$activeTabLink.addClass("active"),t._animateIndicator(i),"function"==typeof t.options.onShow&&t.options.onShow.call(t,t.$content[0])}}),this._tabsCarousel.set(n)}},{key:"_teardownSwipeableTabs",value:function c(){var t=this._tabsCarousel.$el;this._tabsCarousel.destroy(),t.after(t.children()),t.remove()}},{key:"_setupNormalTabs",value:function p(){this.$tabLinks.not(this.$activeTabLink).each(function(t){if(t.hash){var e=$(M.escapeHash(t.hash));e.length&&(e[0].style.display="none")}})}},{key:"_teardownNormalTabs",value:function v(){this.$tabLinks.each(function(t){if(t.hash){var e=$(M.escapeHash(t.hash));e.length&&(e[0].style.display="")}})}},{key:"_setTabsAndTabWidth",value:function f(){this.tabsWidth=this.$el.width(),this.tabWidth=Math.max(this.tabsWidth,this.el.scrollWidth)/this.$tabLinks.length}},{key:"_calcRightPos",value:function m(t){return Math.ceil(this.tabsWidth-t.position().left-t[0].getBoundingClientRect().width)}},{key:"_calcLeftPos",value:function g(t){return Math.floor(t.position().left)}},{key:"updateTabIndicator",value:function y(){this._setTabsAndTabWidth(),this._animateIndicator(this.index)}},{key:"_animateIndicator",value:function _(e){var i=0,n=0;this.index-e>=0?i=90:n=90;var s={targets:this._indicator,left:{value:this._calcLeftPos(this.$activeTabLink),delay:i},right:{value:this._calcRightPos(this.$activeTabLink),delay:n},duration:this.options.duration,easing:"easeOutQuad"};t.remove(this._indicator),t(s)}},{key:"select",value:function b(t){var e=this.$tabLinks.filter('[href="#'+t+'"]');e.length&&e.trigger("click")}}],[{key:"init",value:function k(t,e){return _get(n.__proto__||Object.getPrototypeOf(n),"init",this).call(this,this,t,e)}},{key:"getInstance",value:function C(t){var e=t.jquery?t[0]:t;return e.M_Tabs}},{key:"defaults",get:function(){return e}}]),n}(Component);window.M.Tabs=i,M.jQueryLoaded&&M.initializeJqueryWrapper(i,"tabs","M_Tabs")}(cash,M.anime),function($,t){"use strict";var e={exitDelay:200,enterDelay:0,html:null,margin:5,inDuration:250,outDuration:200,position:"bottom",transitionMovement:10},i=function(i){function n(t,e){_classCallCheck(this,n);var i=_possibleConstructorReturn(this,(n.__proto__||Object.getPrototypeOf(n)).call(this,n,t,e));return i.el.M_Tooltip=i,i.options=$.extend({},n.defaults,e),i.isOpen=!1,i.isHovered=!1,i.isFocused=!1,i._appendTooltipEl(),i._setupEventHandlers(),i}return _inherits(n,i),_createClass(n,[{key:"destroy",value:function s(){$(this.tooltipEl).remove(),this._removeEventHandlers(),this.el.M_Tooltip=void 0}},{key:"_appendTooltipEl",value:function o(){var t=document.createElement("div");t.classList.add("material-tooltip"),this.tooltipEl=t;var e=document.createElement("div");e.classList.add("tooltip-content"),e.innerHTML=this.options.html,t.appendChild(e),document.body.appendChild(t)}},{key:"_updateTooltipContent",value:function a(){this.tooltipEl.querySelector(".tooltip-content").innerHTML=this.options.html}},{key:"_setupEventHandlers",value:function r(){this._handleMouseEnterBound=this._handleMouseEnter.bind(this),this._handleMouseLeaveBound=this._handleMouseLeave.bind(this),this._handleFocusBound=this._handleFocus.bind(this),this._handleBlurBound=this._handleBlur.bind(this),this.el.addEventListener("mouseenter",this._handleMouseEnterBound),this.el.addEventListener("mouseleave",this._handleMouseLeaveBound),this.el.addEventListener("focus",this._handleFocusBound,!0),this.el.addEventListener("blur",this._handleBlurBound,!0)}},{key:"_removeEventHandlers",value:function l(){this.el.removeEventListener("mouseenter",this._handleMouseEnterBound),this.el.removeEventListener("mouseleave",this._handleMouseLeaveBound),this.el.removeEventListener("focus",this._handleFocusBound,!0),this.el.removeEventListener("blur",this._handleBlurBound,!0)}},{key:"open",value:function h(t){this.isOpen||(t=void 0===t?!0:void 0,this.isOpen=!0,this.options=$.extend({},this.options,this._getAttributeOptions()),this._updateTooltipContent(),this._setEnterDelayTimeout(t))}},{key:"close",value:function d(){this.isOpen&&(this.isHovered=!1,this.isFocused=!1,this.isOpen=!1,this._setExitDelayTimeout())}},{key:"_setExitDelayTimeout",value:function u(){var t=this;clearTimeout(this._exitDelayTimeout),this._exitDelayTimeout=setTimeout(function(){t.isHovered||t.isFocused||t._animateOut()},this.options.exitDelay)}},{key:"_setEnterDelayTimeout",value:function c(t){var e=this;clearTimeout(this._enterDelayTimeout),this._enterDelayTimeout=setTimeout(function(){(e.isHovered||e.isFocused||t)&&e._animateIn()},this.options.enterDelay)}},{key:"_positionTooltip",value:function p(){var t=this.el,e=this.tooltipEl,i=t.offsetHeight,n=t.offsetWidth,s=e.offsetHeight,o=e.offsetWidth,a=void 0,r=this.options.margin,l=void 0,h=void 0;this.xMovement=0,this.yMovement=0,l=t.getBoundingClientRect().top+M.getDocumentScrollTop(),h=t.getBoundingClientRect().left+M.getDocumentScrollLeft(),"top"===this.options.position?(l+=-s-r,h+=n/2-o/2,this.yMovement=-this.options.transitionMovement):"right"===this.options.position?(l+=i/2-s/2,h+=n+r,this.xMovement=this.options.transitionMovement):"left"===this.options.position?(l+=i/2-s/2,h+=-o-r,this.xMovement=-this.options.transitionMovement):(l+=i+r,h+=n/2-o/2,this.yMovement=this.options.transitionMovement),a=this._repositionWithinScreen(h,l,o,s),$(e).css({top:a.y+"px",left:a.x+"px"})}},{key:"_repositionWithinScreen",value:function v(t,e,i,n){var s=M.getDocumentScrollLeft(),o=M.getDocumentScrollTop(),a=t-s,r=e-o,l={left:a,top:r,width:i,height:n},h=this.options.margin+this.options.transitionMovement,d=M.checkWithinContainer(document.body,l,h);return d.left?a=h:d.right&&(a-=a+i-window.innerWidth),d.top?r=h:d.bottom&&(r-=r+n-window.innerHeight),{x:a+s,y:r+o}}},{key:"_animateIn",value:function f(){this._positionTooltip(),this.tooltipEl.style.visibility="visible",t.remove(this.tooltipEl),t({targets:this.tooltipEl,opacity:1,translateX:this.xMovement,translateY:this.yMovement,duration:this.options.inDuration,easing:"easeOutCubic"})}},{key:"_animateOut",value:function m(){t.remove(this.tooltipEl),t({targets:this.tooltipEl,opacity:0,translateX:0,translateY:0,duration:this.options.outDuration,easing:"easeOutCubic"})}},{key:"_handleMouseEnter",value:function g(){this.isHovered=!0,this.isFocused=!1,this.open(!1)}},{key:"_handleMouseLeave",value:function y(){this.isHovered=!1,this.isFocused=!1,this.close()}},{key:"_handleFocus",value:function _(){M.tabPressed&&(this.isFocused=!0,this.open(!1))}},{key:"_handleBlur",value:function b(){this.isFocused=!1,this.close()}},{key:"_getAttributeOptions",value:function k(){var t={},e=this.el.getAttribute("data-tooltip"),i=this.el.getAttribute("data-position");return e&&(t.html=e),i&&(t.position=i),t}}],[{key:"init",value:function C(t,e){return _get(n.__proto__||Object.getPrototypeOf(n),"init",this).call(this,this,t,e)}},{key:"getInstance",value:function w(t){var e=t.jquery?t[0]:t;return e.M_Tooltip}},{key:"defaults",get:function(){return e}}]),n}(Component);M.Tooltip=i,M.jQueryLoaded&&M.initializeJqueryWrapper(i,"tooltip","M_Tooltip")}(cash,M.anime),function(t){"use strict";function e(t){return null!==t&&t===t.window}function i(t){return e(t)?t:9===t.nodeType&&t.defaultView}function n(t){var e,n,s={top:0,left:0},o=t&&t.ownerDocument;return e=o.documentElement,"undefined"!=typeof t.getBoundingClientRect&&(s=t.getBoundingClientRect()),n=i(o),{top:s.top+n.pageYOffset-e.clientTop,left:s.left+n.pageXOffset-e.clientLeft}}function s(t){var e="";for(var i in t)t.hasOwnProperty(i)&&(e+=i+":"+t[i]+";");return e}function o(t){if(d.allowEvent(t)===!1)return null;for(var e=null,i=t.target||t.srcElement;null!==i.parentNode;){if(!(i instanceof SVGElement)&&-1!==i.className.indexOf("waves-effect")){e=i;break}i=i.parentNode}return e}function a(e){var i=o(e);null!==i&&(h.show(e,i),"ontouchstart"in t&&(i.addEventListener("touchend",h.hide,!1),i.addEventListener("touchcancel",h.hide,!1)),i.addEventListener("mouseup",h.hide,!1),i.addEventListener("mouseleave",h.hide,!1),i.addEventListener("dragend",h.hide,!1))}var r=r||{},l=document.querySelectorAll.bind(document),h={duration:750,show:function(t,e){if(2===t.button)return!1;var i=e||this,o=document.createElement("div");o.className="waves-ripple",i.appendChild(o);var a=n(i),r=t.pageY-a.top,l=t.pageX-a.left,d="scale("+i.clientWidth/100*10+")";"touches"in t&&(r=t.touches[0].pageY-a.top,l=t.touches[0].pageX-a.left),o.setAttribute("data-hold",Date.now()),o.setAttribute("data-scale",d),o.setAttribute("data-x",l),o.setAttribute("data-y",r);var u={top:r+"px",left:l+"px"};o.className=o.className+" waves-notransition",o.setAttribute("style",s(u)),o.className=o.className.replace("waves-notransition",""),u["-webkit-transform"]=d,u["-moz-transform"]=d,u["-ms-transform"]=d,u["-o-transform"]=d,u.transform=d,u.opacity="1",u["-webkit-transition-duration"]=h.duration+"ms",u["-moz-transition-duration"]=h.duration+"ms",u["-o-transition-duration"]=h.duration+"ms",u["transition-duration"]=h.duration+"ms",u["-webkit-transition-timing-function"]="cubic-bezier(0.250, 0.460, 0.450, 0.940)",u["-moz-transition-timing-function"]="cubic-bezier(0.250, 0.460, 0.450, 0.940)",u["-o-transition-timing-function"]="cubic-bezier(0.250, 0.460, 0.450, 0.940)",u["transition-timing-function"]="cubic-bezier(0.250, 0.460, 0.450, 0.940)",o.setAttribute("style",s(u))},hide:function(t){d.touchup(t);var e=this,i=1.4*e.clientWidth,n=null,o=e.getElementsByClassName("waves-ripple");if(!(o.length>0))return!1;n=o[o.length-1];var a=n.getAttribute("data-x"),r=n.getAttribute("data-y"),l=n.getAttribute("data-scale"),u=Date.now()-Number(n.getAttribute("data-hold")),c=350-u;0>c&&(c=0),setTimeout(function(){var t={top:r+"px",left:a+"px",opacity:"0","-webkit-transition-duration":h.duration+"ms","-moz-transition-duration":h.duration+"ms","-o-transition-duration":h.duration+"ms","transition-duration":h.duration+"ms","-webkit-transform":l,"-moz-transform":l,"-ms-transform":l,"-o-transform":l,transform:l};n.setAttribute("style",s(t)),setTimeout(function(){try{e.removeChild(n)}catch(t){return!1}},h.duration)},c)},wrapInput:function(t){for(var e=0;e0&&(d.touches-=1)},500):"mousedown"===t.type&&d.touches>0&&(e=!1),e},touchup:function(t){d.allowEvent(t)}};r.displayEffect=function(e){e=e||{},"duration"in e&&(h.duration=e.duration),h.wrapInput(l(".waves-effect")),"ontouchstart"in t&&document.body.addEventListener("touchstart",a,!1),document.body.addEventListener("mousedown",a,!1)},r.attach=function(e){"input"===e.tagName.toLowerCase()&&(h.wrapInput([e]),e=e.parentNode),"ontouchstart"in t&&e.addEventListener("touchstart",a,!1),e.addEventListener("mousedown",a,!1)},t.Waves=r,document.addEventListener("DOMContentLoaded",function(){r.displayEffect()},!1)}(window),function($,t){"use strict";var e={html:"",displayLength:4e3,inDuration:300,outDuration:375,classes:"",completeCallback:null,activationPercent:.8},i=function(){function i(t){_classCallCheck(this,i),this.options=$.extend({},i.defaults,t),this.message=this.options.html,this.panning=!1,this.timeRemaining=this.options.displayLength,0===i._toasts.length&&i._createContainer(),i._toasts.push(this);var e=this._createToast();e.M_Toast=this,this.el=e,this.$el=$(e),this._animateIn(),this._setTimer()}return _createClass(i,[{key:"_createToast",value:function n(){var t=document.createElement("div");return t.classList.add("toast"),this.options.classes.length&&$(t).addClass(this.options.classes),("object"==typeof HTMLElement?this.message instanceof HTMLElement:this.message&&"object"==typeof this.message&&null!==this.message&&1===this.message.nodeType&&"string"==typeof this.message.nodeName)?t.appendChild(this.message):this.message.jquery?$(t).append(this.message[0]):t.innerHTML=this.message,i._container.appendChild(t),t}},{key:"_animateIn",value:function s(){t({targets:this.el,top:0,opacity:1,duration:this.options.inDuration,easing:"easeOutCubic"})}},{key:"_setTimer",value:function o(){var t=this;this.timeRemaining!==1/0&&(this.counterInterval=setInterval(function(){t.panning||(t.timeRemaining-=20),t.timeRemaining<=0&&t.dismiss()},20))}},{key:"dismiss",value:function a(){var e=this;window.clearInterval(this.counterInterval);var n=this.el.offsetWidth*this.options.activationPercent;this.wasSwiped&&(this.el.style.transition="transform .05s, opacity .05s",this.el.style.transform="translateX("+n+"px)",this.el.style.opacity=0),t({targets:this.el,opacity:0,marginTop:-40,duration:this.options.outDuration,easing:"easeOutExpo",complete:function(){"function"==typeof e.options.completeCallback&&e.options.completeCallback(),e.$el.remove(),i._toasts.splice(i._toasts.indexOf(e),1),0===i._toasts.length&&i._removeContainer()}})}}],[{key:"getInstance",value:function r(t){var e=t.jquery?t[0]:t;return e.M_Toast}},{key:"_createContainer",value:function l(){var t=document.createElement("div");t.setAttribute("id","toast-container"),t.addEventListener("touchstart",i._onDragStart),t.addEventListener("touchmove",i._onDragMove),t.addEventListener("touchend",i._onDragEnd),t.addEventListener("mousedown",i._onDragStart),document.addEventListener("mousemove",i._onDragMove),document.addEventListener("mouseup",i._onDragEnd),document.body.appendChild(t),i._container=t}},{key:"_removeContainer",value:function h(){document.removeEventListener("mousemove",i._onDragMove),document.removeEventListener("mouseup",i._onDragEnd),$(i._container).remove(),i._container=null}},{key:"_onDragStart",value:function d(t){if(t.target&&$(t.target).closest(".toast").length){var e=$(t.target).closest(".toast"),n=e[0].M_Toast;n.panning=!0,i._draggedToast=n,n.el.classList.add("panning"),n.el.style.transition="",n.startingXPos=i._xPos(t),n.time=Date.now(),n.xPos=i._xPos(t)}}},{key:"_onDragMove",value:function u(t){if(i._draggedToast){t.preventDefault();var e=i._draggedToast;e.deltaX=Math.abs(e.xPos-i._xPos(t)),e.xPos=i._xPos(t),e.velocityX=e.deltaX/(Date.now()-e.time),e.time=Date.now();var n=e.xPos-e.startingXPos,s=e.el.offsetWidth*e.options.activationPercent;e.el.style.transform="translateX("+n+"px)",e.el.style.opacity=1-Math.abs(n/s)}}},{key:"_onDragEnd",value:function c(){if(i._draggedToast){var t=i._draggedToast;t.panning=!1,t.el.classList.remove("panning");var e=t.xPos-t.startingXPos,n=t.el.offsetWidth*t.options.activationPercent,s=Math.abs(e)>n||t.velocityX>1;s?(t.wasSwiped=!0,t.dismiss()):(t.el.style.transition="transform .2s, opacity .2s",t.el.style.transform="",t.el.style.opacity=""),i._draggedToast=null}}},{key:"_xPos",value:function p(t){return t.targetTouches&&t.targetTouches.length>=1?t.targetTouches[0].clientX:t.clientX}},{key:"dismissAll",value:function v(){for(var t in i._toasts)i._toasts[t].dismiss()}},{key:"defaults",get:function(){return e}}]),i}();i._toasts=[],i._container=null,i._draggedToast=null,M.Toast=i,M.toast=function(t){return new i(t)}}(cash,M.anime),function($,t){"use strict";var e={edge:"left",draggable:!0,inDuration:250,outDuration:200,onOpenStart:null,onOpenEnd:null,onCloseStart:null,onCloseEnd:null,preventScrolling:!0},i=function(i){function n(t,e){_classCallCheck(this,n);var i=_possibleConstructorReturn(this,(n.__proto__||Object.getPrototypeOf(n)).call(this,n,t,e));return i.el.M_Sidenav=i,i.id=i.$el.attr("id"),i.options=$.extend({},n.defaults,e),i.isOpen=!1,i.isFixed=i.el.classList.contains("sidenav-fixed"),i.isDragged=!1,i.lastWindowWidth=window.innerWidth,i.lastWindowHeight=window.innerHeight,i._createOverlay(),i._createDragTarget(),i._setupEventHandlers(),i._setupClasses(),i._setupFixed(),n._sidenavs.push(i),i}return _inherits(n,i),_createClass(n,[{key:"destroy",value:function s(){this._removeEventHandlers(),this._enableBodyScrolling(),this._overlay.parentNode.removeChild(this._overlay),this.dragTarget.parentNode.removeChild(this.dragTarget),this.el.M_Sidenav=void 0,this.el.style.transform="";var t=n._sidenavs.indexOf(this);t>=0&&n._sidenavs.splice(t,1)}},{key:"_createOverlay",value:function o(){var t=document.createElement("div");this._closeBound=this.close.bind(this),t.classList.add("sidenav-overlay"),t.addEventListener("click",this._closeBound),document.body.appendChild(t),this._overlay=t}},{key:"_setupEventHandlers",value:function a(){0===n._sidenavs.length&&document.body.addEventListener("click",this._handleTriggerClick),this._handleDragTargetDragBound=this._handleDragTargetDrag.bind(this),this._handleDragTargetReleaseBound=this._handleDragTargetRelease.bind(this),this._handleCloseDragBound=this._handleCloseDrag.bind(this),this._handleCloseReleaseBound=this._handleCloseRelease.bind(this),this._handleCloseTriggerClickBound=this._handleCloseTriggerClick.bind(this),this.dragTarget.addEventListener("touchmove",this._handleDragTargetDragBound),this.dragTarget.addEventListener("touchend",this._handleDragTargetReleaseBound),this._overlay.addEventListener("touchmove",this._handleCloseDragBound),this._overlay.addEventListener("touchend",this._handleCloseReleaseBound), +this.el.addEventListener("touchmove",this._handleCloseDragBound),this.el.addEventListener("touchend",this._handleCloseReleaseBound),this.el.addEventListener("click",this._handleCloseTriggerClickBound),this.isFixed&&(this._handleWindowResizeBound=this._handleWindowResize.bind(this),window.addEventListener("resize",this._handleWindowResizeBound))}},{key:"_removeEventHandlers",value:function r(){1===n._sidenavs.length&&document.body.removeEventListener("click",this._handleTriggerClick),this.dragTarget.removeEventListener("touchmove",this._handleDragTargetDragBound),this.dragTarget.removeEventListener("touchend",this._handleDragTargetReleaseBound),this._overlay.removeEventListener("touchmove",this._handleCloseDragBound),this._overlay.removeEventListener("touchend",this._handleCloseReleaseBound),this.el.removeEventListener("touchmove",this._handleCloseDragBound),this.el.removeEventListener("touchend",this._handleCloseReleaseBound),this.el.removeEventListener("click",this._handleCloseTriggerClickBound),this.isFixed&&window.removeEventListener("resize",this._handleWindowResizeBound)}},{key:"_handleTriggerClick",value:function l(t){var e=$(t.target).closest(".sidenav-trigger");if(t.target&&e.length){var i=M.getIdFromTrigger(e[0]),n=document.getElementById(i).M_Sidenav;n&&n.open(e),t.preventDefault()}}},{key:"_startDrag",value:function h(e){var i=e.targetTouches[0].clientX;this.isDragged=!0,this._startingXpos=i,this._xPos=this._startingXpos,this._time=Date.now(),this._width=this.el.getBoundingClientRect().width,this._overlay.style.display="block",this._initialScrollTop=this.isOpen?this.el.scrollTop:M.getDocumentScrollTop(),this._verticallyScrolling=!1,t.remove(this.el),t.remove(this._overlay)}},{key:"_dragMoveUpdate",value:function d(t){var e=t.targetTouches[0].clientX,i=this.isOpen?this.el.scrollTop:M.getDocumentScrollTop();this.deltaX=Math.abs(this._xPos-e),this._xPos=e,this.velocityX=this.deltaX/(Date.now()-this._time),this._time=Date.now(),this._initialScrollTop!==i&&(this._verticallyScrolling=!0)}},{key:"_handleDragTargetDrag",value:function u(t){if(this.options.draggable&&!this._isCurrentlyFixed()&&!this._verticallyScrolling){this.isDragged||this._startDrag(t),this._dragMoveUpdate(t);var e=this._xPos-this._startingXpos,i=e>0?"right":"left";e=Math.min(this._width,Math.abs(e)),this.options.edge===i&&(e=0);var n=e,s="translateX(-100%)";"right"===this.options.edge&&(s="translateX(100%)",n=-n),this.percentOpen=Math.min(1,e/this._width),this.el.style.transform=s+" translateX("+n+"px)",this._overlay.style.opacity=this.percentOpen}}},{key:"_handleDragTargetRelease",value:function c(){this.isDragged&&(this.percentOpen>.2?this.open():this._animateOut(),this.isDragged=!1,this._verticallyScrolling=!1)}},{key:"_handleCloseDrag",value:function p(t){if(this.isOpen){if(!this.options.draggable||this._isCurrentlyFixed()||this._verticallyScrolling)return;this.isDragged||this._startDrag(t),this._dragMoveUpdate(t);var e=this._xPos-this._startingXpos,i=e>0?"right":"left";e=Math.min(this._width,Math.abs(e)),this.options.edge!==i&&(e=0);var n=-e;"right"===this.options.edge&&(n=-n),this.percentOpen=Math.min(1,1-e/this._width),this.el.style.transform="translateX("+n+"px)",this._overlay.style.opacity=this.percentOpen}}},{key:"_handleCloseRelease",value:function v(){this.isOpen&&this.isDragged&&(this.percentOpen>.8?this._animateIn():this.close(),this.isDragged=!1,this._verticallyScrolling=!1)}},{key:"_handleCloseTriggerClick",value:function f(t){var e=$(t.target).closest(".sidenav-close");e.length&&!this._isCurrentlyFixed()&&this.close()}},{key:"_handleWindowResize",value:function m(){this.lastWindowWidth!==window.innerWidth&&(window.innerWidth>992?this.open():this.close()),this.lastWindowWidth=window.innerWidth,this.lastWindowHeight=window.innerHeight}},{key:"_setupClasses",value:function g(){"right"===this.options.edge&&(this.el.classList.add("right-aligned"),this.dragTarget.classList.add("right-aligned"))}},{key:"_removeClasses",value:function y(){this.el.classList.remove("right-aligned"),this.dragTarget.classList.remove("right-aligned")}},{key:"_setupFixed",value:function _(){this._isCurrentlyFixed()&&this.open()}},{key:"_isCurrentlyFixed",value:function b(){return this.isFixed&&window.innerWidth>992}},{key:"_createDragTarget",value:function k(){var t=document.createElement("div");t.classList.add("drag-target"),document.body.appendChild(t),this.dragTarget=t}},{key:"_preventBodyScrolling",value:function C(){var t=document.body;t.style.overflow="hidden"}},{key:"_enableBodyScrolling",value:function w(){var t=document.body;t.style.overflow=""}},{key:"open",value:function E(){this.isOpen!==!0&&(this.isOpen=!0,"function"==typeof this.options.onOpenStart&&this.options.onOpenStart.call(this,this.el),this._isCurrentlyFixed()?(t.remove(this.el),t({targets:this.el,translateX:0,duration:0,easing:"easeOutQuad"}),this._enableBodyScrolling(),this._overlay.style.display="none"):(this.options.preventScrolling&&this._preventBodyScrolling(),this.isDragged&&1==this.percentOpen||this._animateIn()))}},{key:"close",value:function x(){if(this.isOpen!==!1)if(this.isOpen=!1,"function"==typeof this.options.onCloseStart&&this.options.onCloseStart.call(this,this.el),this._isCurrentlyFixed()){var t="left"===this.options.edge?"-105%":"105%";this.el.style.transform="translateX("+t+")"}else this._enableBodyScrolling(),this.isDragged&&0==this.percentOpen?this._overlay.style.display="none":this._animateOut()}},{key:"_animateIn",value:function O(){this._animateSidenavIn(),this._animateOverlayIn()}},{key:"_animateSidenavIn",value:function T(){var e=this,i="left"===this.options.edge?-1:1;this.isDragged&&(i="left"===this.options.edge?i+this.percentOpen:i-this.percentOpen),t.remove(this.el),t({targets:this.el,translateX:[100*i+"%",0],duration:this.options.inDuration,easing:"easeOutQuad",complete:function(){"function"==typeof e.options.onOpenEnd&&e.options.onOpenEnd.call(e,e.el)}})}},{key:"_animateOverlayIn",value:function L(){var e=0;this.isDragged?e=this.percentOpen:$(this._overlay).css({display:"block"}),t.remove(this._overlay),t({targets:this._overlay,opacity:[e,1],duration:this.options.inDuration,easing:"easeOutQuad"})}},{key:"_animateOut",value:function B(){this._animateSidenavOut(),this._animateOverlayOut()}},{key:"_animateSidenavOut",value:function D(){var e=this,i="left"===this.options.edge?-1:1,n=0;this.isDragged&&(n="left"===this.options.edge?i+this.percentOpen:i-this.percentOpen),t.remove(this.el),t({targets:this.el,translateX:[100*n+"%",105*i+"%"],duration:this.options.outDuration,easing:"easeOutQuad",complete:function(){"function"==typeof e.options.onCloseEnd&&e.options.onCloseEnd.call(e,e.el)}})}},{key:"_animateOverlayOut",value:function S(){var e=this;t.remove(this._overlay),t({targets:this._overlay,opacity:0,duration:this.options.outDuration,easing:"easeOutQuad",complete:function(){$(e._overlay).css("display","none")}})}}],[{key:"init",value:function I(t,e){return _get(n.__proto__||Object.getPrototypeOf(n),"init",this).call(this,this,t,e)}},{key:"getInstance",value:function A(t){var e=t.jquery?t[0]:t;return e.M_Sidenav}},{key:"defaults",get:function(){return e}}]),n}(Component);i._sidenavs=[],window.M.Sidenav=i,M.jQueryLoaded&&M.initializeJqueryWrapper(i,"sidenav","M_Sidenav")}(cash,M.anime),function($,t){"use strict";var e={throttle:100,scrollOffset:200,activeClass:"active",getActiveElement:function(t){return'a[href="#'+t+'"]'}},i=function(i){function n(t,e){_classCallCheck(this,n);var i=_possibleConstructorReturn(this,(n.__proto__||Object.getPrototypeOf(n)).call(this,n,t,e));return i.el.M_ScrollSpy=i,i.options=$.extend({},n.defaults,e),n._elements.push(i),n._count++,n._increment++,i.tickId=-1,i.id=n._increment,i._setupEventHandlers(),i._handleWindowScroll(),i}return _inherits(n,i),_createClass(n,[{key:"destroy",value:function s(){n._elements.splice(n._elements.indexOf(this),1),n._elementsInView.splice(n._elementsInView.indexOf(this),1),n._visibleElements.splice(n._visibleElements.indexOf(this.$el),1),n._count--,this._removeEventHandlers(),$(this.options.getActiveElement(this.$el.attr("id"))).removeClass(this.options.activeClass),this.el.M_ScrollSpy=void 0}},{key:"_setupEventHandlers",value:function o(){var t=M.throttle(this._handleWindowScroll,200);this._handleThrottledResizeBound=t.bind(this),this._handleWindowScrollBound=this._handleWindowScroll.bind(this),1===n._count&&(window.addEventListener("scroll",this._handleWindowScrollBound),window.addEventListener("resize",this._handleThrottledResizeBound),document.body.addEventListener("click",this._handleTriggerClick))}},{key:"_removeEventHandlers",value:function a(){0===n._count&&(window.removeEventListener("scroll",this._handleWindowScrollBound),window.removeEventListener("resize",this._handleThrottledResizeBound),document.body.removeEventListener("click",this._handleTriggerClick))}},{key:"_handleTriggerClick",value:function r(e){for(var i=$(e.target),s=n._elements.length-1;s>=0;s--){var o=n._elements[s];if(i.is('a[href="#'+o.$el.attr("id")+'"]')){e.preventDefault();var a=o.$el.offset().top+1;t({targets:[document.documentElement,document.body],scrollTop:a-o.options.scrollOffset,duration:400,easing:"easeOutCubic"});break}}}},{key:"_handleWindowScroll",value:function l(){n._ticks++;for(var t=M.getDocumentScrollTop(),e=M.getDocumentScrollLeft(),i=e+window.innerWidth,s=t+window.innerHeight,o=n._findElements(t,i,s,e),a=0;al&&r._enter(),r.tickId=n._ticks}for(var h=0;h=0&&u!==n._ticks&&(d._exit(),d.tickId=-1)}n._elementsInView=o}},{key:"_enter",value:function h(){n._visibleElements=n._visibleElements.filter(function(t){return 0!=t.height()}),n._visibleElements[0]?($(this.options.getActiveElement(n._visibleElements[0].attr("id"))).removeClass(this.options.activeClass),n._visibleElements[0][0].M_ScrollSpy&&this.id0){var h=r.$el.offset().top,d=r.$el.offset().left,u=d+r.$el.width(),c=h+r.$el.height(),p=!(d>e||s>u||h>i||l>c);p&&o.push(r)}}return o}},{key:"defaults",get:function(){return e}}]),n}(Component);i._elements=[],i._elementsInView=[],i._visibleElements=[],i._count=0,i._increment=0,i._ticks=0,M.ScrollSpy=i,M.jQueryLoaded&&M.initializeJqueryWrapper(i,"scrollSpy","M_ScrollSpy")}(cash,M.anime),function($){"use strict";var t={data:{},limit:1/0,onAutocomplete:null,minLength:1,sortFunction:function(t,e,i){return t.indexOf(i)-e.indexOf(i)}},e=function(e){function i(t,e){_classCallCheck(this,i);var n=_possibleConstructorReturn(this,(i.__proto__||Object.getPrototypeOf(i)).call(this,i,t,e));return n.el.M_Autocomplete=n,n.options=$.extend({},i.defaults,e),n.isOpen=!1,n.count=0,n.activeIndex=-1,n.oldVal,n.$inputField=n.$el.closest(".input-field"),n.$active=$(),n._mousedown=!1,n._setupDropdown(),n._setupEventHandlers(),n}return _inherits(i,e),_createClass(i,[{key:"destroy",value:function n(){this._removeEventHandlers(),this._removeDropdown(),this.el.M_Autocomplete=void 0}},{key:"_setupEventHandlers",value:function s(){this._handleInputBlurBound=this._handleInputBlur.bind(this),this._handleInputKeyupAndFocusBound=this._handleInputKeyupAndFocus.bind(this),this._handleInputKeydownBound=this._handleInputKeydown.bind(this),this._handleInputClickBound=this._handleInputClick.bind(this),this._handleContainerMousedownAndTouchstartBound=this._handleContainerMousedownAndTouchstart.bind(this),this._handleContainerMouseupAndTouchendBound=this._handleContainerMouseupAndTouchend.bind(this),this.el.addEventListener("blur",this._handleInputBlurBound),this.el.addEventListener("keyup",this._handleInputKeyupAndFocusBound),this.el.addEventListener("focus",this._handleInputKeyupAndFocusBound),this.el.addEventListener("keydown",this._handleInputKeydownBound),this.el.addEventListener("click",this._handleInputClickBound),this.container.addEventListener("mousedown",this._handleContainerMousedownAndTouchstartBound),this.container.addEventListener("mouseup",this._handleContainerMouseupAndTouchendBound),"undefined"!=typeof window.ontouchstart&&(this.container.addEventListener("touchstart",this._handleContainerMousedownAndTouchstartBound),this.container.addEventListener("touchend",this._handleContainerMouseupAndTouchendBound))}},{key:"_removeEventHandlers",value:function o(){this.el.removeEventListener("blur",this._handleInputBlurBound),this.el.removeEventListener("keyup",this._handleInputKeyupAndFocusBound),this.el.removeEventListener("focus",this._handleInputKeyupAndFocusBound),this.el.removeEventListener("keydown",this._handleInputKeydownBound),this.el.removeEventListener("click",this._handleInputClickBound),this.container.removeEventListener("mousedown",this._handleContainerMousedownAndTouchstartBound),this.container.removeEventListener("mouseup",this._handleContainerMouseupAndTouchendBound),"undefined"!=typeof window.ontouchstart&&(this.container.removeEventListener("touchstart",this._handleContainerMousedownAndTouchstartBound),this.container.removeEventListener("touchend",this._handleContainerMouseupAndTouchendBound))}},{key:"_setupDropdown",value:function a(){var t=this;this.container=document.createElement("ul"),this.container.id="autocomplete-options-"+M.guid(),$(this.container).addClass("autocomplete-content dropdown-content"),this.$inputField.append(this.container),this.el.setAttribute("data-target",this.container.id),this.dropdown=M.Dropdown.init(this.el,{autoFocus:!1,closeOnClick:!1,coverTrigger:!1,onItemClick:function(e){t.selectOption($(e))}}),this.el.removeEventListener("click",this.dropdown._handleClickBound)}},{key:"_removeDropdown",value:function r(){this.container.parentNode.removeChild(this.container)}},{key:"_handleInputBlur",value:function l(){this._mousedown||(this.close(),this._resetAutocomplete())}},{key:"_handleInputKeyupAndFocus",value:function h(t){"keyup"===t.type&&(i._keydown=!1),this.count=0;var e=this.el.value.toLowerCase();13!==t.keyCode&&38!==t.keyCode&&40!==t.keyCode&&(this.oldVal===e||!M.tabPressed&&"focus"===t.type||this.open(),this.oldVal=e)}},{key:"_handleInputKeydown",value:function d(t){i._keydown=!0;var e=t.keyCode,n=void 0,s=$(this.container).children("li").length;return e===M.keys.ENTER&&this.activeIndex>=0?(n=$(this.container).children("li").eq(this.activeIndex),void(n.length&&(this.selectOption(n),t.preventDefault()))):void((e===M.keys.ARROW_UP||e===M.keys.ARROW_DOWN)&&(t.preventDefault(),e===M.keys.ARROW_UP&&this.activeIndex>0&&this.activeIndex--,e===M.keys.ARROW_DOWN&&this.activeIndex=0&&(this.$active=$(this.container).children("li").eq(this.activeIndex),this.$active.addClass("active"))))}},{key:"_handleInputClick",value:function u(t){this.open()}},{key:"_handleContainerMousedownAndTouchstart",value:function c(t){this._mousedown=!0}},{key:"_handleContainerMouseupAndTouchend",value:function p(t){this._mousedown=!1}},{key:"_highlight",value:function v(t,e){var i=e.find("img"),n=e.text().toLowerCase().indexOf(""+t.toLowerCase()),s=n+t.length-1,o=e.text().slice(0,n),a=e.text().slice(n,s+1),r=e.text().slice(s+1);e.html(""+o+""+a+""+r+""),i.length&&e.prepend(i)}},{key:"_resetCurrentElement",value:function f(){this.activeIndex=-1,this.$active.removeClass("active")}},{key:"_resetAutocomplete",value:function m(){$(this.container).empty(),this._resetCurrentElement(),this.oldVal=null,this.isOpen=!1,this._mousedown=!1}},{key:"selectOption",value:function g(t){var e=t.text().trim();this.el.value=e,this.$el.trigger("change"),this._resetAutocomplete(),this.close(),"function"==typeof this.options.onAutocomplete&&this.options.onAutocomplete.call(this,e)}},{key:"_renderDropdown",value:function y(t,e){var i=this;this._resetAutocomplete();var n=[];for(var s in t)if(t.hasOwnProperty(s)&&-1!==s.toLowerCase().indexOf(e)){if(this.count>=this.options.limit)break;var o={data:t[s],key:s};n.push(o),this.count++}if(this.options.sortFunction){var a=function(t,n){return i.options.sortFunction(t.key.toLowerCase(),n.key.toLowerCase(),e.toLowerCase())};n.sort(a)}for(var r=0;r");l.data?h.append(''+l.key+""):h.append(""+l.key+""),$(this.container).append(h),this._highlight(e,h)}}},{key:"open",value:function _(){var t=this.el.value.toLowerCase();this._resetAutocomplete(),t.length>=this.options.minLength&&(this.isOpen=!0,this._renderDropdown(this.options.data,t)),this.dropdown.isOpen?this.dropdown.recalculateDimensions():this.dropdown.open()}},{key:"close",value:function b(){this.dropdown.close()}},{key:"updateData",value:function k(t){var e=this.el.value.toLowerCase();this.options.data=t,this.isOpen&&this._renderDropdown(t,e)}}],[{key:"init",value:function C(t,e){return _get(i.__proto__||Object.getPrototypeOf(i),"init",this).call(this,this,t,e)}},{key:"getInstance",value:function w(t){var e=t.jquery?t[0]:t;return e.M_Autocomplete}},{key:"defaults",get:function(){return t}}]),i}(Component);e._keydown=!1,M.Autocomplete=e,M.jQueryLoaded&&M.initializeJqueryWrapper(e,"autocomplete","M_Autocomplete")}(cash),function($){M.updateTextFields=function(){var t="input[type=text], input[type=password], input[type=email], input[type=url], input[type=tel], input[type=number], input[type=search], input[type=date], input[type=time], textarea";$(t).each(function(t,e){var i=$(this);t.value.length>0||$(t).is(":focus")||t.autofocus||null!==i.attr("placeholder")?i.siblings("label").addClass("active"):t.validity?i.siblings("label").toggleClass("active",t.validity.badInput===!0):i.siblings("label").removeClass("active")})},M.validate_field=function(t){var e=null!==t.attr("data-length"),i=parseInt(t.attr("data-length")),n=t[0].value.length;0!==n||t[0].validity.badInput!==!1||t.is(":required")?t.hasClass("validate")&&(t.is(":valid")&&e&&i>=n||t.is(":valid")&&!e?(t.removeClass("invalid"),t.addClass("valid")):(t.removeClass("valid"),t.addClass("invalid"))):t.hasClass("validate")&&(t.removeClass("valid"),t.removeClass("invalid"))},M.textareaAutoResize=function(t){if(t instanceof Element&&(t=$(t)),!t.length)return void console.error("No textarea element found");var e=$(".hiddendiv").first();e.length||(e=$('
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");e.html(h),t[0].offsetWidth>0&&t[0].offsetHeight>0?e.css("width",t.width()+"px"):e.css("width",window.innerWidth/2+"px"),t.data("original-height")<=e.innerHeight()?t.css("height",e.innerHeight()+"px"):t[0].value.length'),this.$slides.each(function(e,i){var n=$('
  • ');t.$indicators.append(n[0])}),this.$el.append(this.$indicators[0]),this.$indicators=this.$indicators.children("li.indicator-item"))}},{key:"_removeIndicators",value:function c(){this.$el.find("ul.indicators").remove()}},{key:"set",value:function p(e){var i=this;if(e>=this.$slides.length?e=0:0>e&&(e=this.$slides.length-1),this.activeIndex!=e){this.$active=this.$slides.eq(this.activeIndex);var n=this.$active.find(".caption");this.$active.removeClass("active"),t({targets:this.$active[0],opacity:0,duration:this.options.duration,easing:"easeOutQuad",complete:function(){i.$slides.not(".active").each(function(e){t({targets:e,opacity:0,translateX:0,translateY:0,duration:0,easing:"easeOutQuad"})})}}),this._animateCaptionIn(n[0],this.options.duration),this.options.indicators&&(this.$indicators.eq(this.activeIndex).removeClass("active"),this.$indicators.eq(e).addClass("active")),t({targets:this.$slides.eq(e)[0],opacity:1,duration:this.options.duration,easing:"easeOutQuad"}),t({targets:this.$slides.eq(e).find(".caption")[0],opacity:1,translateX:0,translateY:0,duration:this.options.duration,delay:this.options.duration,easing:"easeOutQuad"}),this.$slides.eq(e).addClass("active"),this.activeIndex=e,this.start()}}},{key:"pause",value:function v(){clearInterval(this.interval)}},{key:"start",value:function f(){clearInterval(this.interval),this.interval=setInterval(this._handleIntervalBound,this.options.duration+this.options.interval)}},{key:"next",value:function m(){var t=this.activeIndex+1;t>=this.$slides.length?t=0:0>t&&(t=this.$slides.length-1),this.set(t)}},{key:"prev",value:function g(){var t=this.activeIndex-1;t>=this.$slides.length?t=0:0>t&&(t=this.$slides.length-1),this.set(t)}}],[{key:"init",value:function y(t,e){return _get(n.__proto__||Object.getPrototypeOf(n),"init",this).call(this,this,t,e)}},{key:"getInstance",value:function _(t){var e=t.jquery?t[0]:t;return e.M_Slider}},{key:"defaults",get:function(){return e}}]),n}(Component);M.Slider=i,M.jQueryLoaded&&M.initializeJqueryWrapper(i,"slider","M_Slider")}(cash,M.anime),function($,t){$(document).on("click",".card",function(e){if($(this).children(".card-reveal").length){var i=$(e.target).closest(".card");void 0===i.data("initialOverflow")&&i.data("initialOverflow",void 0===i.css("overflow")?"":i.css("overflow"));var n=$(this).find(".card-reveal");$(e.target).is($(".card-reveal .card-title"))||$(e.target).is($(".card-reveal .card-title i"))?t({targets:n[0],translateY:0,duration:225,easing:"easeInOutQuad",complete:function(t){var e=t.animatables[0].target;$(e).css({display:"none"}),i.css("overflow",i.data("initialOverflow"))}}):($(e.target).is($(".card .activator"))||$(e.target).is($(".card .activator i")))&&(i.css("overflow","hidden"),n.css({display:"block"}),t({targets:n[0],translateY:"-100%",duration:300,easing:"easeInOutQuad"}))}})}(cash,M.anime),function($){"use strict";var t={data:[],placeholder:"",secondaryPlaceholder:"",autocompleteOptions:{},limit:1/0,onChipAdd:null,onChipSelect:null,onChipDelete:null},e=function(e){function i(t,e){_classCallCheck(this,i);var n=_possibleConstructorReturn(this,(i.__proto__||Object.getPrototypeOf(i)).call(this,i,t,e));return n.el.M_Chips=n,n.options=$.extend({},i.defaults,e),n.$el.addClass("chips input-field"),n.chipsData=[],n.$chips=$(),n._setupInput(),n.hasAutocomplete=Object.keys(n.options.autocompleteOptions).length>0,n.$input.attr("id")||n.$input.attr("id",M.guid()),n.options.data.length&&(n.chipsData=n.options.data,n._renderChips(n.chipsData)),n.hasAutocomplete&&n._setupAutocomplete(),n._setPlaceholder(),n._setupLabel(),n._setupEventHandlers(),n}return _inherits(i,e),_createClass(i,[{key:"getData",value:function n(){return this.chipsData}},{key:"destroy",value:function s(){this._removeEventHandlers(),this.$chips.remove(),this.el.M_Chips=void 0}},{key:"_setupEventHandlers",value:function o(){this._handleChipClickBound=this._handleChipClick.bind(this),this._handleInputKeydownBound=this._handleInputKeydown.bind(this),this._handleInputFocusBound=this._handleInputFocus.bind(this),this._handleInputBlurBound=this._handleInputBlur.bind(this),this.el.addEventListener("click",this._handleChipClickBound),document.addEventListener("keydown",i._handleChipsKeydown),document.addEventListener("keyup",i._handleChipsKeyup),this.el.addEventListener("blur",i._handleChipsBlur,!0),this.$input[0].addEventListener("focus",this._handleInputFocusBound),this.$input[0].addEventListener("blur",this._handleInputBlurBound),this.$input[0].addEventListener("keydown",this._handleInputKeydownBound)}},{key:"_removeEventHandlers",value:function a(){this.el.removeEventListener("click",this._handleChipClickBound),document.removeEventListener("keydown",i._handleChipsKeydown),document.removeEventListener("keyup",i._handleChipsKeyup),this.el.removeEventListener("blur",i._handleChipsBlur,!0),this.$input[0].removeEventListener("focus",this._handleInputFocusBound),this.$input[0].removeEventListener("blur",this._handleInputBlurBound),this.$input[0].removeEventListener("keydown",this._handleInputKeydownBound)}},{key:"_handleChipClick",value:function r(t){var e=$(t.target).closest(".chip"),i=$(t.target).is(".close");if(e.length){var n=e.index();i?(this.deleteChip(n),this.$input[0].focus()):this.selectChip(n)}else this.$input[0].focus()}},{key:"_handleInputFocus",value:function l(){this.$el.addClass("focus")}},{key:"_handleInputBlur",value:function h(){this.$el.removeClass("focus")}},{key:"_handleInputKeydown",value:function d(t){if(i._keydown=!0,13===t.keyCode){if(this.hasAutocomplete&&this.autocomplete&&this.autocomplete.isOpen)return;t.preventDefault(),this.addChip({tag:this.$input[0].value}),this.$input[0].value=""}else 8!==t.keyCode&&37!==t.keyCode||""!==this.$input[0].value||!this.chipsData.length||(t.preventDefault(),this.selectChip(this.chipsData.length-1))}},{key:"_renderChip",value:function u(t){if(t.tag){var e=document.createElement("div"),i=document.createElement("i");if(e.classList.add("chip"),e.textContent=t.tag,e.setAttribute("tabindex",0),$(i).addClass("material-icons close"),i.textContent="close",t.image){var n=document.createElement("img");n.setAttribute("src",t.image),e.insertBefore(n,e.firstChild)}return e.appendChild(i),e}}},{key:"_renderChips",value:function c(){this.$chips.remove();for(var t=0;t"),this.$el.append(this.$input)), +this.$input.addClass("input")}},{key:"_setupLabel",value:function f(){this.$label=this.$el.find("label"),this.$label.length&&this.$label.setAttribute("for",this.$input.attr("id"))}},{key:"_setPlaceholder",value:function m(){void 0!==this.chipsData&&!this.chipsData.length&&this.options.placeholder?$(this.$input).prop("placeholder",this.options.placeholder):(void 0===this.chipsData||this.chipsData.length)&&this.options.secondaryPlaceholder&&$(this.$input).prop("placeholder",this.options.secondaryPlaceholder)}},{key:"_isValid",value:function g(t){if(t.hasOwnProperty("tag")&&""!==t.tag){for(var e=!1,i=0;i=this.options.limit)){var e=this._renderChip(t);this.$chips.add(e),this.chipsData.push(t),$(this.$input).before(e),this._setPlaceholder(),"function"==typeof this.options.onChipAdd&&this.options.onChipAdd.call(this,this.$el,e)}}},{key:"deleteChip",value:function _(t){var e=this.$chips.eq(t);this.$chips.eq(t).remove(),this.$chips=this.$chips.filter(function(t){return $(t).index()>=0}),this.chipsData.splice(t,1),this._setPlaceholder(),"function"==typeof this.options.onChipDelete&&this.options.onChipDelete.call(this,this.$el,e[0])}},{key:"selectChip",value:function b(t){var e=this.$chips.eq(t);this._selectedChip=e,e[0].focus(),"function"==typeof this.options.onChipSelect&&this.options.onChipSelect.call(this,this.$el,e[0])}}],[{key:"init",value:function k(t,e){return _get(i.__proto__||Object.getPrototypeOf(i),"init",this).call(this,this,t,e)}},{key:"getInstance",value:function C(t){var e=t.jquery?t[0]:t;return e.M_Chips}},{key:"_handleChipsKeydown",value:function w(t){i._keydown=!0;var e=$(t.target).closest(".chips"),n=t.target&&e.length;if(!$(t.target).is("input, textarea")&&n){var s=e[0].M_Chips;if(8===t.keyCode||46===t.keyCode){t.preventDefault();var o=s.chipsData.length;if(s._selectedChip){var a=s._selectedChip.index();s.deleteChip(a),s._selectedChip=null,o=Math.max(a-1,0)}s.chipsData.length&&s.selectChip(o)}else if(37===t.keyCode){if(s._selectedChip){var r=s._selectedChip.index()-1;if(0>r)return;s.selectChip(r)}}else if(39===t.keyCode&&s._selectedChip){var l=s._selectedChip.index()+1;l>=s.chipsData.length?s.$input[0].focus():s.selectChip(l)}}}},{key:"_handleChipsKeyup",value:function E(t){i._keydown=!1}},{key:"_handleChipsBlur",value:function x(t){if(!i._keydown){var e=$(t.target).closest(".chips"),n=e[0].M_Chips;n._selectedChip=null}}},{key:"defaults",get:function(){return t}}]),i}(Component);e._keydown=!1,M.Chips=e,M.jQueryLoaded&&M.initializeJqueryWrapper(e,"chips","M_Chips"),$(document).ready(function(){$(document.body).on("click",".chip .close",function(){var t=$(this).closest(".chips");t.length&&t[0].M_Chips||$(this).closest(".chip").remove()})})}(cash),function($){"use strict";var t={top:0,bottom:1/0,offset:0,onPositionChange:null},e=function(e){function i(t,e){_classCallCheck(this,i);var n=_possibleConstructorReturn(this,(i.__proto__||Object.getPrototypeOf(i)).call(this,i,t,e));return n.el.M_Pushpin=n,n.options=$.extend({},i.defaults,e),n.originalOffset=n.el.offsetTop,i._pushpins.push(n),n._setupEventHandlers(),n._updatePosition(),n}return _inherits(i,e),_createClass(i,[{key:"destroy",value:function n(){this.el.style.top=null,this._removePinClasses(),this._removeEventHandlers();var t=i._pushpins.indexOf(this);i._pushpins.splice(t,1)}},{key:"_setupEventHandlers",value:function s(){document.addEventListener("scroll",i._updateElements)}},{key:"_removeEventHandlers",value:function o(){document.removeEventListener("scroll",i._updateElements)}},{key:"_updatePosition",value:function a(){var t=M.getDocumentScrollTop()+this.options.offset;this.options.top<=t&&this.options.bottom>=t&&!this.el.classList.contains("pinned")&&(this._removePinClasses(),this.el.style.top=this.options.offset+"px",this.el.classList.add("pinned"),"function"==typeof this.options.onPositionChange&&this.options.onPositionChange.call(this,"pinned")),tthis.options.bottom&&!this.el.classList.contains("pin-bottom")&&(this._removePinClasses(),this.el.classList.add("pin-bottom"),this.el.style.top=this.options.bottom-this.originalOffset+"px","function"==typeof this.options.onPositionChange&&this.options.onPositionChange.call(this,"pin-bottom"))}},{key:"_removePinClasses",value:function r(){this.el.classList.remove("pin-top","pinned","pin-bottom")}}],[{key:"init",value:function l(t,e){return _get(i.__proto__||Object.getPrototypeOf(i),"init",this).call(this,this,t,e)}},{key:"getInstance",value:function h(t){var e=t.jquery?t[0]:t;return e.M_Pushpin}},{key:"_updateElements",value:function d(){for(var t in i._pushpins){var e=i._pushpins[t];e._updatePosition()}}},{key:"defaults",get:function(){return t}}]),i}(Component);e._pushpins=[],M.Pushpin=e,M.jQueryLoaded&&M.initializeJqueryWrapper(e,"pushpin","M_Pushpin")}(cash),function($,t){"use strict";var e={direction:"top",hoverEnabled:!0,toolbarEnabled:!1};$.fn.reverse=[].reverse;var i=function(i){function n(t,e){_classCallCheck(this,n);var i=_possibleConstructorReturn(this,(n.__proto__||Object.getPrototypeOf(n)).call(this,n,t,e));return i.el.M_FloatingActionButton=i,i.options=$.extend({},n.defaults,e),i.isOpen=!1,i.$anchor=i.$el.children("a").first(),i.$menu=i.$el.children("ul").first(),i.$floatingBtns=i.$el.find("ul .btn-floating"),i.$floatingBtnsReverse=i.$el.find("ul .btn-floating").reverse(),i.offsetY=0,i.offsetX=0,i.$el.addClass("direction-"+i.options.direction),"top"===i.options.direction?i.offsetY=40:"right"===i.options.direction?i.offsetX=-40:"bottom"===i.options.direction?i.offsetY=-40:i.offsetX=40,i._setupEventHandlers(),i}return _inherits(n,i),_createClass(n,[{key:"destroy",value:function s(){this._removeEventHandlers(),this.el.M_FloatingActionButton=void 0}},{key:"_setupEventHandlers",value:function o(){this._handleFABClickBound=this._handleFABClick.bind(this),this._handleOpenBound=this.open.bind(this),this._handleCloseBound=this.close.bind(this),this.options.hoverEnabled&&!this.options.toolbarEnabled?(this.el.addEventListener("mouseenter",this._handleOpenBound),this.el.addEventListener("mouseleave",this._handleCloseBound)):this.el.addEventListener("click",this._handleFABClickBound)}},{key:"_removeEventHandlers",value:function a(){this.options.hoverEnabled&&!this.options.toolbarEnabled?(this.el.removeEventListener("mouseenter",this._handleOpenBound),this.el.removeEventListener("mouseleave",this._handleCloseBound)):this.el.removeEventListener("click",this._handleFABClickBound)}},{key:"_handleFABClick",value:function r(){this.isOpen?this.close():this.open()}},{key:"_handleDocumentClick",value:function l(t){$(t.target).closest(this.$menu).length||this.close()}},{key:"open",value:function h(){this.isOpen||(this.options.toolbarEnabled?this._animateInToolbar():this._animateInFAB(),this.isOpen=!0)}},{key:"close",value:function d(){this.isOpen&&(this.options.toolbarEnabled?(window.removeEventListener("scroll",this._handleCloseBound,!0),document.body.removeEventListener("click",this._handleDocumentClickBound,!0),this._animateOutToolbar()):this._animateOutFAB(),this.isOpen=!1)}},{key:"_animateInFAB",value:function u(){var e=this;this.$el.addClass("active");var i=0;this.$floatingBtnsReverse.each(function(n){t({targets:n,opacity:1,scale:[.4,1],translateY:[e.offsetY,0],translateX:[e.offsetX,0],duration:275,delay:i,easing:"easeInOutQuad"}),i+=40})}},{key:"_animateOutFAB",value:function c(){var e=this;this.$floatingBtnsReverse.each(function(i){t.remove(i),t({targets:i,opacity:0,scale:.4,translateY:e.offsetY,translateX:e.offsetX,duration:175,easing:"easeOutQuad",complete:function(){e.$el.removeClass("active")}})})}},{key:"_animateInToolbar",value:function p(){var t=this,e=void 0,i=window.innerWidth,n=window.innerHeight,s=this.el.getBoundingClientRect(),o=$('
    '),a=this.$anchor.css("background-color");this.$anchor.append(o),this.offsetX=s.left-i/2+s.width/2,this.offsetY=n-s.bottom,e=i/o[0].clientWidth,this.btnBottom=s.bottom,this.btnLeft=s.left,this.btnWidth=s.width,this.$el.addClass("active"),this.$el.css({"text-align":"center",width:"100%",bottom:0,left:0,transform:"translateX("+this.offsetX+"px)",transition:"none"}),this.$anchor.css({transform:"translateY("+-this.offsetY+"px)",transition:"none"}),o.css({"background-color":a}),setTimeout(function(){t.$el.css({transform:"",transition:"transform .2s cubic-bezier(0.550, 0.085, 0.680, 0.530), background-color 0s linear .2s"}),t.$anchor.css({overflow:"visible",transform:"",transition:"transform .2s"}),setTimeout(function(){t.$el.css({overflow:"hidden","background-color":a}),o.css({transform:"scale("+e+")",transition:"transform .2s cubic-bezier(0.550, 0.055, 0.675, 0.190)"}),t.$menu.children("li").children("a").css({opacity:1}),t._handleDocumentClickBound=t._handleDocumentClick.bind(t),window.addEventListener("scroll",t._handleCloseBound,!0),document.body.addEventListener("click",t._handleDocumentClickBound,!0)},100)},0)}},{key:"_animateOutToolbar",value:function v(){var t=this,e=window.innerWidth,i=window.innerHeight,n=this.$el.find(".fab-backdrop"),s=this.$anchor.css("background-color");this.offsetX=this.btnLeft-e/2+this.btnWidth/2,this.offsetY=i-this.btnBottom,this.$el.removeClass("active"),this.$el.css({"background-color":"transparent",transition:"none"}),this.$anchor.css({transition:"none"}),n.css({transform:"scale(0)","background-color":s}),this.$menu.children("li").children("a").css({opacity:""}),setTimeout(function(){n.remove(),t.$el.css({"text-align":"",width:"",bottom:"",left:"",overflow:"","background-color":"",transform:"translate3d("+-t.offsetX+"px,0,0)"}),t.$anchor.css({overflow:"",transform:"translate3d(0,"+t.offsetY+"px,0)"}),setTimeout(function(){t.$el.css({transform:"translate3d(0,0,0)",transition:"transform .2s"}),t.$anchor.css({transform:"translate3d(0,0,0)",transition:"transform .2s cubic-bezier(0.550, 0.055, 0.675, 0.190)"})},20)},200)}}],[{key:"init",value:function f(t,e){return _get(n.__proto__||Object.getPrototypeOf(n),"init",this).call(this,this,t,e)}},{key:"getInstance",value:function m(t){var e=t.jquery?t[0]:t;return e.M_FloatingActionButton}},{key:"defaults",get:function(){return e}}]),n}(Component);M.FloatingActionButton=i,M.jQueryLoaded&&M.initializeJqueryWrapper(i,"floatingActionButton","M_FloatingActionButton")}(cash,M.anime),function($){"use strict";var t={autoClose:!1,format:"mmm dd, yyyy",parse:null,defaultDate:null,setDefaultDate:!1,disableWeekends:!1,disableDayFn:null,firstDay:0,minDate:null,maxDate:null,yearRange:10,minYear:0,maxYear:9999,minMonth:void 0,maxMonth:void 0,startRange:null,endRange:null,isRTL:!1,showMonthAfterYear:!1,showDaysInNextAndPreviousMonths:!1,container:null,showClearBtn:!1,i18n:{cancel:"Cancel",clear:"Clear",done:"Ok",previousMonth:"‹",nextMonth:"›",months:["January","February","March","April","May","June","July","August","September","October","November","December"],monthsShort:["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"],weekdays:["Sunday","Monday","Tuesday","Wednesday","Thursday","Friday","Saturday"],weekdaysShort:["Sun","Mon","Tue","Wed","Thu","Fri","Sat"],weekdaysAbbrev:["S","M","T","W","T","F","S"]},events:[],onSelect:null,onOpen:null,onClose:null,onDraw:null},e=function(e){function i(t,e){_classCallCheck(this,i);var n=_possibleConstructorReturn(this,(i.__proto__||Object.getPrototypeOf(i)).call(this,i,t,e));n.el.M_Datepicker=n,n.options=$.extend({},i.defaults,e),e&&e.hasOwnProperty("i18n")&&"object"==typeof e.i18n&&(n.options.i18n=$.extend({},i.defaults.i18n,e.i18n)),n.options.minDate&&n.options.minDate.setHours(0,0,0,0),n.options.maxDate&&n.options.maxDate.setHours(0,0,0,0),n.id=M.guid(),n._setupVariables(),n._insertHTMLIntoDOM(),n._setupModal(),n._setupEventHandlers(),n.options.defaultDate||(n.options.defaultDate=new Date(Date.parse(n.el.value)));var s=n.options.defaultDate;return i._isDate(s)?n.options.setDefaultDate?(n.setDate(s,!0),n.setInputValue()):n.gotoDate(s):n.gotoDate(new Date),n.isOpen=!1,n}return _inherits(i,e),_createClass(i,[{key:"destroy",value:function n(){this._removeEventHandlers(),this.modal.destroy(),$(this.modalEl).remove(),this.destroySelects(),this.el.M_Datepicker=void 0}},{key:"destroySelects",value:function s(){var t=this.calendarEl.querySelector(".orig-select-year");t&&M.FormSelect.getInstance(t).destroy();var e=this.calendarEl.querySelector(".orig-select-month");e&&M.FormSelect.getInstance(e).destroy()}},{key:"_insertHTMLIntoDOM",value:function o(){this.options.showClearBtn&&($(this.clearBtn).css({visibility:""}),this.clearBtn.innerHTML=this.options.i18n.clear),this.doneBtn.innerHTML=this.options.i18n.done,this.cancelBtn.innerHTML=this.options.i18n.cancel,this.options.container?this.$modalEl.appendTo(this.options.container):this.$modalEl.insertBefore(this.el)}},{key:"_setupModal",value:function a(){var t=this;this.modalEl.id="modal-"+this.id,this.modal=M.Modal.init(this.modalEl,{onCloseEnd:function(){t.isOpen=!1}})}},{key:"toString",value:function r(t){var e=this;if(t=t||this.options.format,!i._isDate(this.date))return"";var n=t.split(/(d{1,4}|m{1,4}|y{4}|yy|!.)/g),s=n.map(function(t){return e.formats[t]?e.formats[t]():t}).join("");return s}},{key:"setDate",value:function l(t,e){if(!t)return this.date=null,this._renderDateDisplay(),this.draw();if("string"==typeof t&&(t=new Date(Date.parse(t))),i._isDate(t)){var n=this.options.minDate,s=this.options.maxDate;i._isDate(n)&&n>t?t=n:i._isDate(s)&&t>s&&(t=s),this.date=new Date(t.getTime()),this._renderDateDisplay(),i._setToStartOfDay(this.date),this.gotoDate(this.date),e||"function"!=typeof this.options.onSelect||this.options.onSelect.call(this,this.date)}}},{key:"setInputValue",value:function h(){this.el.value=this.toString(),this.$el.trigger("change",{firedBy:this})}},{key:"_renderDateDisplay",value:function d(){var t=i._isDate(this.date)?this.date:new Date,e=this.options.i18n,n=e.weekdaysShort[t.getDay()],s=e.monthsShort[t.getMonth()],o=t.getDate();this.yearTextEl.innerHTML=t.getFullYear(),this.dateTextEl.innerHTML=n+", "+s+" "+o}},{key:"gotoDate",value:function u(t){var e=!0;if(i._isDate(t)){if(this.calendars){var n=new Date(this.calendars[0].year,this.calendars[0].month,1),s=new Date(this.calendars[this.calendars.length-1].year,this.calendars[this.calendars.length-1].month,1),o=t.getTime();s.setMonth(s.getMonth()+1),s.setDate(s.getDate()-1),e=o11&&(t.year+=Math.floor(Math.abs(t.month)/12),t.month-=12),t}},{key:"nextMonth",value:function v(){this.calendars[0].month++,this.adjustCalendars()}},{key:"prevMonth",value:function f(){this.calendars[0].month--,this.adjustCalendars()}},{key:"render",value:function m(t,e,n){var s=this.options,o=new Date,a=i._getDaysInMonth(t,e),r=new Date(t,e,1).getDay(),l=[],h=[];i._setToStartOfDay(o),s.firstDay>0&&(r-=s.firstDay,0>r&&(r+=7));for(var d=0===e?11:e-1,u=11===e?0:e+1,c=0===e?t-1:t,p=11===e?t+1:t,v=i._getDaysInMonth(c,d),f=a+r,m=f;m>7;)m-=7;f+=7-m;for(var g=!1,y=0,_=0;f>y;y++){var b=new Date(t,e,1+(y-r)),k=i._isDate(this.date)?i._compareDates(b,this.date):!1,C=i._compareDates(b,o),w=-1!==s.events.indexOf(b.toDateString())?!0:!1,E=r>y||y>=a+r,M=1+(y-r),x=e,O=t,T=s.startRange&&i._compareDates(s.startRange,b),L=s.endRange&&i._compareDates(s.endRange,b),B=s.startRange&&s.endRange&&s.startRanges.maxDate||s.disableWeekends&&i._isWeekend(b)||s.disableDayFn&&s.disableDayFn(b);E&&(r>y?(M=v+M,x=d,O=c):(M-=a,x=u,O=p));var S={day:M,month:x,year:O,hasEvent:w,isSelected:k,isToday:C,isDisabled:D,isEmpty:E,isStartRange:T,isEndRange:L,isInRange:B,showDaysInNextAndPreviousMonths:s.showDaysInNextAndPreviousMonths};h.push(this.renderDay(S)),7===++_&&(l.push(this.renderRow(h,s.isRTL,g)),h=[],_=0,g=!1)}return this.renderTable(s,l,n)}},{key:"renderDay",value:function g(t){var e=[],i="false";if(t.isEmpty){if(!t.showDaysInNextAndPreviousMonths)return'';e.push("is-outside-current-month"),e.push("is-selection-disabled")}return t.isDisabled&&e.push("is-disabled"),t.isToday&&e.push("is-today"),t.isSelected&&(e.push("is-selected"),i="true"),t.hasEvent&&e.push("has-event"),t.isInRange&&e.push("is-inrange"),t.isStartRange&&e.push("is-startrange"),t.isEndRange&&e.push("is-endrange"),''+('")+""}},{key:"renderRow",value:function y(t,e,i){return''+(e?t.reverse():t).join("")+""}},{key:"renderTable",value:function _(t,e,i){return'
    '+this.renderHead(t)+this.renderBody(e)+"
    "}},{key:"renderHead",value:function b(t){var e=void 0,i=[];for(e=0;7>e;e++)i.push(''+this.renderDayName(t,e,!0)+"");return""+(t.isRTL?i.reverse():i).join("")+""}},{key:"renderBody",value:function k(t){return""+t.join("")+""}},{key:"renderTitle",value:function C(t,e,i,n,s,o){var a=void 0,r=void 0,l=void 0,h=this.options,d=i===h.minYear,u=i===h.maxYear,c='
    ',p=void 0,v=void 0,f=!0,m=!0;for(l=[],a=0;12>a;a++)l.push('");for(p='",$.isArray(h.yearRange)?(a=h.yearRange[0],r=h.yearRange[1]+1):(a=i-h.yearRange,r=1+i+h.yearRange),l=[];r>a&&a<=h.maxYear;a++)a>=h.minYear&&l.push('");v='";var g='';c+='",c+='
    ',c+=h.showMonthAfterYear?v+p:p+v,c+="
    ",d&&(0===n||h.minMonth>=n)&&(f=!1),u&&(11===n||h.maxMonth<=n)&&(m=!1);var y='';return c+='",c+="
    "}},{key:"draw",value:function w(t){if(this.isOpen||t){var e=this.options,i=e.minYear,n=e.maxYear,s=e.minMonth,o=e.maxMonth,a="",r=void 0;this._y<=i&&(this._y=i,!isNaN(s)&&this._m=n&&(this._y=n,!isNaN(o)&&this._m>o&&(this._m=o)),r="datepicker-title-"+Math.random().toString(36).replace(/[^a-z]+/g,"").substr(0,2);for(var l=0;1>l;l++)this._renderDateDisplay(),a+=this.renderTitle(this,l,this.calendars[l].year,this.calendars[l].month,this.calendars[0].year,r)+this.render(this.calendars[l].year,this.calendars[l].month,r);this.destroySelects(),this.calendarEl.innerHTML=a;var h=this.calendarEl.querySelector(".orig-select-year"),d=this.calendarEl.querySelector(".orig-select-month");M.FormSelect.init(h,{classes:"select-year",dropdownOptions:{container:document.body,constrainWidth:!1}}),M.FormSelect.init(d,{classes:"select-month",dropdownOptions:{container:document.body,constrainWidth:!1}}),h.addEventListener("change",this._handleYearChange.bind(this)),d.addEventListener("change",this._handleMonthChange.bind(this)),"function"==typeof this.options.onDraw&&this.options.onDraw(this)}}},{key:"_setupEventHandlers",value:function E(){this._handleInputKeydownBound=this._handleInputKeydown.bind(this),this._handleInputClickBound=this._handleInputClick.bind(this),this._handleInputChangeBound=this._handleInputChange.bind(this),this._handleCalendarClickBound=this._handleCalendarClick.bind(this),this._finishSelectionBound=this._finishSelection.bind(this),this._handleMonthChange=this._handleMonthChange.bind(this),this._closeBound=this.close.bind(this),this.el.addEventListener("click",this._handleInputClickBound),this.el.addEventListener("keydown",this._handleInputKeydownBound),this.el.addEventListener("change",this._handleInputChangeBound),this.calendarEl.addEventListener("click",this._handleCalendarClickBound),this.doneBtn.addEventListener("click",this._finishSelectionBound),this.cancelBtn.addEventListener("click",this._closeBound),this.options.showClearBtn&&(this._handleClearClickBound=this._handleClearClick.bind(this),this.clearBtn.addEventListener("click",this._handleClearClickBound))}},{key:"_setupVariables",value:function x(){var t=this;this.$modalEl=$(i._template),this.modalEl=this.$modalEl[0],this.calendarEl=this.modalEl.querySelector(".datepicker-calendar"),this.yearTextEl=this.modalEl.querySelector(".year-text"),this.dateTextEl=this.modalEl.querySelector(".date-text"),this.options.showClearBtn&&(this.clearBtn=this.modalEl.querySelector(".datepicker-clear")),this.doneBtn=this.modalEl.querySelector(".datepicker-done"),this.cancelBtn=this.modalEl.querySelector(".datepicker-cancel"),this.formats={d:function(){return t.date.getDate()},dd:function(){var e=t.date.getDate();return(10>e?"0":"")+e},ddd:function(){return t.options.i18n.weekdaysShort[t.date.getDay()]},dddd:function(){return t.options.i18n.weekdays[t.date.getDay()]},m:function(){return t.date.getMonth()+1},mm:function(){var e=t.date.getMonth()+1;return(10>e?"0":"")+e},mmm:function(){return t.options.i18n.monthsShort[t.date.getMonth()]},mmmm:function(){return t.options.i18n.months[t.date.getMonth()]},yy:function(){return(""+t.date.getFullYear()).slice(2)},yyyy:function(){return t.date.getFullYear()}}}},{key:"_removeEventHandlers",value:function O(){this.el.removeEventListener("click",this._handleInputClickBound),this.el.removeEventListener("keydown",this._handleInputKeydownBound),this.el.removeEventListener("change",this._handleInputChangeBound),this.calendarEl.removeEventListener("click",this._handleCalendarClickBound)}},{key:"_handleInputClick",value:function T(){this.open()}},{key:"_handleInputKeydown",value:function L(t){t.which===M.keys.ENTER&&(t.preventDefault(),this.open())}},{key:"_handleCalendarClick",value:function B(t){if(this.isOpen){var e=$(t.target);e.hasClass("is-disabled")||(!e.hasClass("datepicker-day-button")||e.hasClass("is-empty")||e.parent().hasClass("is-disabled")?e.closest(".month-prev").length?this.prevMonth():e.closest(".month-next").length&&this.nextMonth():(this.setDate(new Date(t.target.getAttribute("data-year"),t.target.getAttribute("data-month"),t.target.getAttribute("data-day"))),this.options.autoClose&&this._finishSelection()))}}},{key:"_handleClearClick",value:function D(){this.date=null,this.setInputValue(),this.close()}},{key:"_handleMonthChange",value:function S(t){this.gotoMonth(t.target.value)}},{key:"_handleYearChange",value:function I(t){this.gotoYear(t.target.value)}},{key:"gotoMonth",value:function A(t){isNaN(t)||(this.calendars[0].month=parseInt(t,10),this.adjustCalendars())}},{key:"gotoYear",value:function R(t){isNaN(t)||(this.calendars[0].year=parseInt(t,10),this.adjustCalendars())}},{key:"_handleInputChange",value:function H(t){var e=void 0;t.firedBy!==this&&(e=this.options.parse?this.options.parse(this.el.value,this.options.format):new Date(Date.parse(this.el.value)),i._isDate(e)&&this.setDate(e))}},{key:"renderDayName",value:function P(t,e,i){for(e+=t.firstDay;e>=7;)e-=7;return i?t.i18n.weekdaysAbbrev[e]:t.i18n.weekdays[e]}},{key:"_finishSelection",value:function W(){this.setInputValue(),this.close()}},{key:"open",value:function j(){return this.isOpen?void 0:(this.isOpen=!0,"function"==typeof this.options.onOpen&&this.options.onOpen.call(this),this.draw(),this.modal.open(),this)}},{key:"close",value:function F(){return this.isOpen?(this.isOpen=!1,"function"==typeof this.options.onClose&&this.options.onClose.call(this),this.modal.close(),this):void 0}}],[{key:"init",value:function N(t,e){return _get(i.__proto__||Object.getPrototypeOf(i),"init",this).call(this,this,t,e)}},{key:"_isDate",value:function q(t){return/Date/.test(Object.prototype.toString.call(t))&&!isNaN(t.getTime())}},{key:"_isWeekend",value:function z(t){var e=t.getDay();return 0===e||6===e}},{key:"_setToStartOfDay",value:function V(t){i._isDate(t)&&t.setHours(0,0,0,0)}},{key:"_getDaysInMonth",value:function Y(t,e){return[31,i._isLeapYear(t)?29:28,31,30,31,30,31,31,30,31,30,31][e]}},{key:"_isLeapYear",value:function X(t){return t%4===0&&t%100!==0||t%400===0}},{key:"_compareDates",value:function Q(t,e){return t.getTime()===e.getTime()}},{key:"_setToStartOfDay",value:function K(t){i._isDate(t)&&t.setHours(0,0,0,0)}},{key:"getInstance",value:function G(t){var e=t.jquery?t[0]:t;return e.M_Datepicker}},{key:"defaults",get:function(){return t}}]),i}(Component);e._template=['
    ','
    ','
    ','','',"
    ",'
    ','
    ','
    ','','
    ','','',"
    ","
    ","
    ","
    ","
    "].join(""),M.Datepicker=e,M.jQueryLoaded&&M.initializeJqueryWrapper(e,"datepicker","M_Datepicker")}(cash),function($){"use strict";var t={dialRadius:135,outerRadius:105,innerRadius:70,tickRadius:20,duration:350,container:null,defaultTime:"now",fromNow:0,showClearBtn:!1,i18n:{cancel:"Cancel",clear:"Clear",done:"Ok"},autoClose:!1,twelveHour:!0,vibrate:!0,onOpenStart:null,onOpenEnd:null,onCloseStart:null,onCloseEnd:null,onSelect:null},e=function(e){function i(t,e){_classCallCheck(this,i);var n=_possibleConstructorReturn(this,(i.__proto__||Object.getPrototypeOf(i)).call(this,i,t,e));return n.el.M_Timepicker=n,n.options=$.extend({},i.defaults,e),n.id=M.guid(),n._insertHTMLIntoDOM(),n._setupModal(),n._setupVariables(),n._setupEventHandlers(),n._clockSetup(),n._pickerSetup(),n}return _inherits(i,e),_createClass(i,[{key:"destroy",value:function n(){this._removeEventHandlers(),this.modal.destroy(),$(this.modalEl).remove(),this.el.M_Timepicker=void 0}},{key:"_setupEventHandlers",value:function s(){this._handleInputKeydownBound=this._handleInputKeydown.bind(this),this._handleInputClickBound=this._handleInputClick.bind(this),this._handleClockClickStartBound=this._handleClockClickStart.bind(this),this._handleDocumentClickMoveBound=this._handleDocumentClickMove.bind(this),this._handleDocumentClickEndBound=this._handleDocumentClickEnd.bind(this),this.el.addEventListener("click",this._handleInputClickBound),this.el.addEventListener("keydown",this._handleInputKeydownBound),this.plate.addEventListener("mousedown",this._handleClockClickStartBound),this.plate.addEventListener("touchstart",this._handleClockClickStartBound),$(this.spanHours).on("click",this.showView.bind(this,"hours")),$(this.spanMinutes).on("click",this.showView.bind(this,"minutes"))}},{key:"_removeEventHandlers",value:function o(){this.el.removeEventListener("click",this._handleInputClickBound),this.el.removeEventListener("keydown",this._handleInputKeydownBound)}},{key:"_handleInputClick",value:function a(){this.open()}},{key:"_handleInputKeydown",value:function r(t){t.which===M.keys.ENTER&&(t.preventDefault(),this.open())}},{key:"_handleClockClickStart",value:function l(t){t.preventDefault();var e=this.plate.getBoundingClientRect(),n={x:e.left,y:e.top};this.x0=n.x+this.options.dialRadius,this.y0=n.y+this.options.dialRadius,this.moved=!1;var s=i._Pos(t);this.dx=s.x-this.x0,this.dy=s.y-this.y0,this.setHand(this.dx,this.dy,!1),document.addEventListener("mousemove",this._handleDocumentClickMoveBound),document.addEventListener("touchmove",this._handleDocumentClickMoveBound),document.addEventListener("mouseup",this._handleDocumentClickEndBound),document.addEventListener("touchend",this._handleDocumentClickEndBound)}},{key:"_handleDocumentClickMove",value:function h(t){t.preventDefault();var e=i._Pos(t),n=e.x-this.x0,s=e.y-this.y0;this.moved=!0,this.setHand(n,s,!1,!0)}},{key:"_handleDocumentClickEnd",value:function d(t){var e=this;t.preventDefault(),document.removeEventListener("mouseup",this._handleDocumentClickEndBound),document.removeEventListener("touchend",this._handleDocumentClickEndBound);var n=i._Pos(t),s=n.x-this.x0,o=n.y-this.y0;this.moved&&s===this.dx&&o===this.dy&&this.setHand(s,o),"hours"===this.currentView?this.showView("minutes",this.options.duration/2):this.options.autoClose&&($(this.minutesView).addClass("timepicker-dial-out"),setTimeout(function(){e.done()},this.options.duration/2)),"function"==typeof this.options.onSelect&&this.options.onSelect.call(this,this.hours,this.minutes),document.removeEventListener("mousemove",this._handleDocumentClickMoveBound),document.removeEventListener("touchmove",this._handleDocumentClickMoveBound)}},{key:"_insertHTMLIntoDOM",value:function u(){this.$modalEl=$(i._template),this.modalEl=this.$modalEl[0],this.modalEl.id="modal-"+this.id;var t=document.querySelector(this.options.container);this.options.container&&t?this.$modalEl.appendTo(t):this.$modalEl.insertBefore(this.el)}},{key:"_setupModal",value:function c(){var t=this;this.modal=M.Modal.init(this.modalEl,{onOpenStart:this.options.onOpenStart,onOpenEnd:this.options.onOpenEnd,onCloseStart:this.options.onCloseStart,onCloseEnd:function(){"function"==typeof t.options.onCloseEnd&&t.options.onCloseEnd.call(t),t.isOpen=!1}})}},{key:"_setupVariables",value:function p(){this.currentView="hours",this.vibrate=navigator.vibrate?"vibrate":navigator.webkitVibrate?"webkitVibrate":null,this._canvas=this.modalEl.querySelector(".timepicker-canvas"),this.plate=this.modalEl.querySelector(".timepicker-plate"),this.hoursView=this.modalEl.querySelector(".timepicker-hours"),this.minutesView=this.modalEl.querySelector(".timepicker-minutes"),this.spanHours=this.modalEl.querySelector(".timepicker-span-hours"),this.spanMinutes=this.modalEl.querySelector(".timepicker-span-minutes"),this.spanAmPm=this.modalEl.querySelector(".timepicker-span-am-pm"),this.footer=this.modalEl.querySelector(".timepicker-footer"),this.amOrPm="PM"}},{key:"_pickerSetup",value:function v(){var t=$('").appendTo(this.footer).on("click",this.clear.bind(this));this.options.showClearBtn&&t.css({visibility:""});var e=$('
    ');$('").appendTo(e).on("click",this.close.bind(this)), +$('").appendTo(e).on("click",this.done.bind(this)),e.appendTo(this.footer)}},{key:"_clockSetup",value:function f(){this.options.twelveHour&&(this.$amBtn=$('
    AM
    '),this.$pmBtn=$('
    PM
    '),this.$amBtn.on("click",this._handleAmPmClick.bind(this)).appendTo(this.spanAmPm),this.$pmBtn.on("click",this._handleAmPmClick.bind(this)).appendTo(this.spanAmPm)),this._buildHoursView(),this._buildMinutesView(),this._buildSVGClock()}},{key:"_buildSVGClock",value:function m(){var t=this.options.dialRadius,e=this.options.tickRadius,n=2*t,s=i._createSVGEl("svg");s.setAttribute("class","timepicker-svg"),s.setAttribute("width",n),s.setAttribute("height",n);var o=i._createSVGEl("g");o.setAttribute("transform","translate("+t+","+t+")");var a=i._createSVGEl("circle");a.setAttribute("class","timepicker-canvas-bearing"),a.setAttribute("cx",0),a.setAttribute("cy",0),a.setAttribute("r",4);var r=i._createSVGEl("line");r.setAttribute("x1",0),r.setAttribute("y1",0);var l=i._createSVGEl("circle");l.setAttribute("class","timepicker-canvas-bg"),l.setAttribute("r",e),o.appendChild(r),o.appendChild(l),o.appendChild(a),s.appendChild(o),this._canvas.appendChild(s),this.hand=r,this.bg=l,this.bearing=a,this.g=o}},{key:"_buildHoursView",value:function g(){var t=$('
    ');if(this.options.twelveHour)for(var e=1;13>e;e+=1){var i=t.clone(),n=e/6*Math.PI,s=this.options.outerRadius;i.css({left:this.options.dialRadius+Math.sin(n)*s-this.options.tickRadius+"px",top:this.options.dialRadius-Math.cos(n)*s-this.options.tickRadius+"px"}),i.html(0===e?"00":e),this.hoursView.appendChild(i[0])}else for(var o=0;24>o;o+=1){var a=t.clone(),r=o/6*Math.PI,l=o>0&&13>o,h=l?this.options.innerRadius:this.options.outerRadius;a.css({left:this.options.dialRadius+Math.sin(r)*h-this.options.tickRadius+"px",top:this.options.dialRadius-Math.cos(r)*h-this.options.tickRadius+"px"}),a.html(0===o?"00":o),this.hoursView.appendChild(a[0])}}},{key:"_buildMinutesView",value:function y(){for(var t=$('
    '),e=0;60>e;e+=5){var n=t.clone(),s=e/30*Math.PI;n.css({left:this.options.dialRadius+Math.sin(s)*this.options.outerRadius-this.options.tickRadius+"px",top:this.options.dialRadius-Math.cos(s)*this.options.outerRadius-this.options.tickRadius+"px"}),n.html(i._addLeadingZero(e)),this.minutesView.appendChild(n[0])}}},{key:"_handleAmPmClick",value:function _(t){var e=$(t.target);this.amOrPm=e.hasClass("am-btn")?"AM":"PM",this._updateAmPmView()}},{key:"_updateAmPmView",value:function b(){this.options.twelveHour&&(this.$amBtn.toggleClass("text-primary","AM"===this.amOrPm),this.$pmBtn.toggleClass("text-primary","PM"===this.amOrPm))}},{key:"_updateTimeFromInput",value:function k(){var t=((this.el.value||this.options.defaultTime||"")+"").split(":");if(this.options.twelveHour&&"undefined"!=typeof t[1]&&(t[1].toUpperCase().indexOf("AM")>0?this.amOrPm="AM":this.amOrPm="PM",t[1]=t[1].replace("AM","").replace("PM","")),"now"===t[0]){var e=new Date(+new Date+this.options.fromNow);t=[e.getHours(),e.getMinutes()],this.options.twelveHour&&(this.amOrPm=t[0]>=12&&t[0]<24?"PM":"AM")}this.hours=+t[0]||0,this.minutes=+t[1]||0,this.spanHours.innerHTML=this.hours,this.spanMinutes.innerHTML=i._addLeadingZero(this.minutes),this._updateAmPmView()}},{key:"showView",value:function C(t,e){"minutes"===t&&"visible"===$(this.hoursView).css("visibility");var i="hours"===t,n=i?this.hoursView:this.minutesView,s=i?this.minutesView:this.hoursView;this.currentView=t,$(this.spanHours).toggleClass("text-primary",i),$(this.spanMinutes).toggleClass("text-primary",!i),s.classList.add("timepicker-dial-out"),$(n).css("visibility","visible").removeClass("timepicker-dial-out"),this.resetClock(e),clearTimeout(this.toggleViewTimer),this.toggleViewTimer=setTimeout(function(){$(s).css("visibility","hidden")},this.options.duration)}},{key:"resetClock",value:function w(t){var e=this.currentView,i=this[e],n="hours"===e,s=Math.PI/(n?6:30),o=i*s,a=n&&i>0&&13>i?this.options.innerRadius:this.options.outerRadius,r=Math.sin(o)*a,l=-Math.cos(o)*a,h=this;t?($(this.canvas).addClass("timepicker-canvas-out"),setTimeout(function(){$(h.canvas).removeClass("timepicker-canvas-out"),h.setHand(r,l)},t)):this.setHand(r,l)}},{key:"setHand",value:function E(t,e,n){var s=this,o=Math.atan2(t,-e),a="hours"===this.currentView,r=Math.PI/(a||n?6:30),l=Math.sqrt(t*t+e*e),h=a&&l<(this.options.outerRadius+this.options.innerRadius)/2,d=h?this.options.innerRadius:this.options.outerRadius;this.options.twelveHour&&(d=this.options.outerRadius),0>o&&(o=2*Math.PI+o);var u=Math.round(o/r);o=u*r,this.options.twelveHour?a?0===u&&(u=12):(n&&(u*=5),60===u&&(u=0)):a?(12===u&&(u=0),u=h?0===u?12:u:0===u?0:u+12):(n&&(u*=5),60===u&&(u=0)),this[this.currentView]!==u&&this.vibrate&&this.options.vibrate&&(this.vibrateTimer||(navigator[this.vibrate](10),this.vibrateTimer=setTimeout(function(){s.vibrateTimer=null},100))),this[this.currentView]=u,a?this.spanHours.innerHTML=u:this.spanMinutes.innerHTML=i._addLeadingZero(u);var c=Math.sin(o)*(d-this.options.tickRadius),p=-Math.cos(o)*(d-this.options.tickRadius),v=Math.sin(o)*d,f=-Math.cos(o)*d;this.hand.setAttribute("x2",c),this.hand.setAttribute("y2",p),this.bg.setAttribute("cx",v),this.bg.setAttribute("cy",f)}},{key:"open",value:function x(){this.isOpen||(this.isOpen=!0,this._updateTimeFromInput(),this.showView("hours"),this.modal.open())}},{key:"close",value:function O(){this.isOpen&&(this.isOpen=!1,this.modal.close())}},{key:"done",value:function T(t,e){var n=this.el.value,s=e?"":i._addLeadingZero(this.hours)+":"+i._addLeadingZero(this.minutes);this.time=s,!e&&this.options.twelveHour&&(s=s+" "+this.amOrPm),this.el.value=s,s!==n&&this.$el.trigger("change"),this.close(),this.el.focus()}},{key:"clear",value:function L(){this.done(null,!0)}}],[{key:"init",value:function B(t,e){return _get(i.__proto__||Object.getPrototypeOf(i),"init",this).call(this,this,t,e)}},{key:"_addLeadingZero",value:function D(t){return(10>t?"0":"")+t}},{key:"_createSVGEl",value:function S(t){var e="http://www.w3.org/2000/svg";return document.createElementNS(e,t)}},{key:"_Pos",value:function I(t){return t.targetTouches&&t.targetTouches.length>=1?{x:t.targetTouches[0].clientX,y:t.targetTouches[0].clientY}:{x:t.clientX,y:t.clientY}}},{key:"getInstance",value:function A(t){var e=t.jquery?t[0]:t;return e.M_Timepicker}},{key:"defaults",get:function(){return t}}]),i}(Component);e._template=['
    ','
    ','
    ','
    ','
    ','',":",'',"
    ",'
    ','
    ',"
    ","
    ","
    ",'
    ','
    ','
    ','
    ','
    ',"
    ",'
    ',"
    ","
    ","
    "].join(""),M.Timepicker=e,M.jQueryLoaded&&M.initializeJqueryWrapper(e,"timepicker","M_Timepicker")}(cash),function($){"use strict";var t={},e=function(e){function i(t,e){_classCallCheck(this,i);var n=_possibleConstructorReturn(this,(i.__proto__||Object.getPrototypeOf(i)).call(this,i,t,e));return n.el.M_CharacterCounter=n,n.options=$.extend({},i.defaults,e),n.isInvalid=!1,n.isValidLength=!1,n._setupCounter(),n._setupEventHandlers(),n}return _inherits(i,e),_createClass(i,[{key:"destroy",value:function n(){this._removeEventHandlers(),this.el.CharacterCounter=void 0,this._removeCounter()}},{key:"_setupEventHandlers",value:function s(){this._handleUpdateCounterBound=this.updateCounter.bind(this),this.el.addEventListener("focus",this._handleUpdateCounterBound,!0),this.el.addEventListener("input",this._handleUpdateCounterBound,!0)}},{key:"_removeEventHandlers",value:function o(){this.el.removeEventListener("focus",this._handleUpdateCounterBound,!0),this.el.removeEventListener("input",this._handleUpdateCounterBound,!0)}},{key:"_setupCounter",value:function a(){this.counterEl=document.createElement("span"),$(this.counterEl).addClass("character-counter").css({"float":"right","font-size":"12px",height:1}),this.$el.parent().append(this.counterEl)}},{key:"_removeCounter",value:function r(){$(this.counterEl).remove()}},{key:"updateCounter",value:function l(){var t=+this.$el.attr("data-length"),e=this.el.value.length;this.isValidLength=t>=e;var i=e;t&&(i+="/"+t,this._validateInput()),$(this.counterEl).html(i)}},{key:"_validateInput",value:function h(){this.isValidLength&&this.isInvalid?(this.isInvalid=!1,this.$el.removeClass("invalid")):this.isValidLength||this.isInvalid||(this.isInvalid=!0,this.$el.removeClass("valid"),this.$el.addClass("invalid"))}}],[{key:"init",value:function d(t,e){return _get(i.__proto__||Object.getPrototypeOf(i),"init",this).call(this,this,t,e)}},{key:"getInstance",value:function u(t){var e=t.jquery?t[0]:t;return e.M_CharacterCounter}},{key:"defaults",get:function(){return t}}]),i}(Component);M.CharacterCounter=e,M.jQueryLoaded&&M.initializeJqueryWrapper(e,"characterCounter","M_CharacterCounter")}(cash),function($){"use strict";var t={duration:200,dist:-100,shift:0,padding:0,numVisible:5,fullWidth:!1,indicators:!1,noWrap:!1,onCycleTo:null},e=function(e){function i(t,e){_classCallCheck(this,i);var n=_possibleConstructorReturn(this,(i.__proto__||Object.getPrototypeOf(i)).call(this,i,t,e));return n.el.M_Carousel=n,n.options=$.extend({},i.defaults,e),n.hasMultipleSlides=n.$el.find(".carousel-item").length>1,n.showIndicators=n.options.indicators&&n.hasMultipleSlides,n.noWrap=n.options.noWrap||!n.hasMultipleSlides,n.pressed=!1,n.dragged=!1,n.offset=n.target=0,n.images=[],n.itemWidth=n.$el.find(".carousel-item").first().innerWidth(),n.itemHeight=n.$el.find(".carousel-item").first().innerHeight(),n.dim=2*n.itemWidth+n.options.padding||1,n._autoScrollBound=n._autoScroll.bind(n),n._trackBound=n._track.bind(n),n.options.fullWidth&&(n.options.dist=0,n._setCarouselHeight(),n.showIndicators&&n.$el.find(".carousel-fixed-item").addClass("with-indicators")),n.$indicators=$('
      '),n.$el.find(".carousel-item").each(function(t,e){if(n.images.push(t),n.showIndicators){var i=$('
    • ');0===e&&i[0].classList.add("active"),n.$indicators.append(i)}}),n.showIndicators&&n.$el.append(n.$indicators),n.count=n.images.length,n.options.numVisible=Math.min(n.count,n.options.numVisible),n.xform="transform",["webkit","Moz","O","ms"].every(function(t){var e=t+"Transform";return"undefined"!=typeof document.body.style[e]?(n.xform=e,!1):!0}),n._setupEventHandlers(),n._scroll(n.offset),n}return _inherits(i,e),_createClass(i,[{key:"destroy",value:function n(){this._removeEventHandlers(),this.el.M_Carousel=void 0}},{key:"_setupEventHandlers",value:function s(){var t=this;this._handleCarouselTapBound=this._handleCarouselTap.bind(this),this._handleCarouselDragBound=this._handleCarouselDrag.bind(this),this._handleCarouselReleaseBound=this._handleCarouselRelease.bind(this),this._handleCarouselClickBound=this._handleCarouselClick.bind(this),"undefined"!=typeof window.ontouchstart&&(this.el.addEventListener("touchstart",this._handleCarouselTapBound),this.el.addEventListener("touchmove",this._handleCarouselDragBound),this.el.addEventListener("touchend",this._handleCarouselReleaseBound)),this.el.addEventListener("mousedown",this._handleCarouselTapBound),this.el.addEventListener("mousemove",this._handleCarouselDragBound),this.el.addEventListener("mouseup",this._handleCarouselReleaseBound),this.el.addEventListener("mouseleave",this._handleCarouselReleaseBound),this.el.addEventListener("click",this._handleCarouselClickBound),this.showIndicators&&this.$indicators&&(this._handleIndicatorClickBound=this._handleIndicatorClick.bind(this),this.$indicators.find(".indicator-item").each(function(e,i){e.addEventListener("click",t._handleIndicatorClickBound)}));var e=M.throttle(this._handleResize,200);this._handleThrottledResizeBound=e.bind(this),window.addEventListener("resize",this._handleThrottledResizeBound)}},{key:"_removeEventHandlers",value:function o(){var t=this;"undefined"!=typeof window.ontouchstart&&(this.el.removeEventListener("touchstart",this._handleCarouselTapBound),this.el.removeEventListener("touchmove",this._handleCarouselDragBound),this.el.removeEventListener("touchend",this._handleCarouselReleaseBound)),this.el.removeEventListener("mousedown",this._handleCarouselTapBound),this.el.removeEventListener("mousemove",this._handleCarouselDragBound),this.el.removeEventListener("mouseup",this._handleCarouselReleaseBound),this.el.removeEventListener("mouseleave",this._handleCarouselReleaseBound),this.el.removeEventListener("click",this._handleCarouselClickBound),this.showIndicators&&this.$indicators&&this.$indicators.find(".indicator-item").each(function(e,i){e.removeEventListener("click",t._handleIndicatorClickBound)}),window.removeEventListener("resize",this._handleThrottledResizeBound)}},{key:"_handleCarouselTap",value:function a(t){"mousedown"===t.type&&$(t.target).is("img")&&t.preventDefault(),this.pressed=!0,this.dragged=!1,this.verticalDragged=!1,this.reference=this._xpos(t),this.referenceY=this._ypos(t),this.velocity=this.amplitude=0,this.frame=this.offset,this.timestamp=Date.now(),clearInterval(this.ticker),this.ticker=setInterval(this._trackBound,100)}},{key:"_handleCarouselDrag",value:function r(t){var e=void 0,i=void 0,n=void 0,s=void 0;if(this.pressed)if(e=this._xpos(t),i=this._ypos(t),n=this.reference-e,s=Math.abs(this.referenceY-i),30>s&&!this.verticalDragged)(n>2||-2>n)&&(this.dragged=!0,this.reference=e,this._scroll(this.offset+n));else{if(this.dragged)return t.preventDefault(),t.stopPropagation(),!1;this.verticalDragged=!0}return this.dragged?(t.preventDefault(),t.stopPropagation(),!1):void 0}},{key:"_handleCarouselRelease",value:function l(t){return this.pressed?(this.pressed=!1,clearInterval(this.ticker),this.target=this.offset,(this.velocity>10||this.velocity<-10)&&(this.amplitude=.9*this.velocity,this.target=this.offset+this.amplitude),this.target=Math.round(this.target/this.dim)*this.dim,this.noWrap&&(this.target>=this.dim*(this.count-1)?this.target=this.dim*(this.count-1):this.target<0&&(this.target=0)),this.amplitude=this.target-this.offset,this.timestamp=Date.now(),requestAnimationFrame(this._autoScrollBound),this.dragged&&(t.preventDefault(),t.stopPropagation()),!1):void 0}},{key:"_handleCarouselClick",value:function h(t){if(this.dragged)return t.preventDefault(),t.stopPropagation(),!1;if(!this.options.fullWidth){var e=$(t.target).closest(".carousel-item").index(),i=this._wrap(this.center)-e;0!==i&&(t.preventDefault(),t.stopPropagation()),this._cycleTo(e)}}},{key:"_handleIndicatorClick",value:function d(t){t.stopPropagation();var e=$(t.target).closest(".indicator-item");e.length&&this._cycleTo(e.index())}},{key:"_handleResize",value:function u(t){this.options.fullWidth?(this.itemWidth=this.$el.find(".carousel-item").first().innerWidth(),this.imageHeight=this.$el.find(".carousel-item.active").height(),this.dim=2*this.itemWidth+this.options.padding,this.offset=2*this.center*this.itemWidth,this.target=this.offset,this._setCarouselHeight(!0)):this._scroll()}},{key:"_setCarouselHeight",value:function c(t){var e=this,i=this.$el.find(".carousel-item.active").length?this.$el.find(".carousel-item.active").first():this.$el.find(".carousel-item").first(),n=i.find("img").first();if(n.length)if(n[0].complete){var s=n.height();if(s>0)this.$el.css("height",s+"px");else{var o=n[0].naturalWidth,a=n[0].naturalHeight,r=this.$el.width()/o*a;this.$el.css("height",r+"px")}}else n.one("load",function(t,i){e.$el.css("height",t.offsetHeight+"px")});else if(!t){var l=i.height();this.$el.css("height",l+"px")}}},{key:"_xpos",value:function p(t){return t.targetTouches&&t.targetTouches.length>=1?t.targetTouches[0].clientX:t.clientX}},{key:"_ypos",value:function v(t){return t.targetTouches&&t.targetTouches.length>=1?t.targetTouches[0].clientY:t.clientY}},{key:"_wrap",value:function f(t){return t>=this.count?t%this.count:0>t?this._wrap(this.count+t%this.count):t}},{key:"_track",value:function m(){var t=void 0,e=void 0,i=void 0,n=void 0;t=Date.now(),e=t-this.timestamp,this.timestamp=t,i=this.offset-this.frame,this.frame=this.offset,n=1e3*i/(1+e),this.velocity=.8*n+.2*this.velocity}},{key:"_autoScroll",value:function g(){var t=void 0,e=void 0;this.amplitude&&(t=Date.now()-this.timestamp,e=this.amplitude*Math.exp(-t/this.options.duration),e>2||-2>e?(this._scroll(this.target-e),requestAnimationFrame(this._autoScrollBound)):this._scroll(this.target))}},{key:"_scroll",value:function y(t){var e=this;this.$el.hasClass("scrolling")||this.el.classList.add("scrolling"),null!=this.scrollingTimeout&&window.clearTimeout(this.scrollingTimeout),this.scrollingTimeout=window.setTimeout(function(){e.$el.removeClass("scrolling")},this.options.duration);var i=void 0,n=void 0,s=void 0,o=void 0,a=void 0,r=void 0,l=void 0,h=void 0,d=void 0,u=void 0,c=this.center,p=1/this.options.numVisible;if(this.offset="number"==typeof t?t:this.offset,this.center=Math.floor((this.offset+this.dim/2)/this.dim),s=this.offset-this.center*this.dim,o=0>s?1:-1,a=-o*s*2/this.dim,n=this.count>>1,this.options.fullWidth?(l="translateX(0)",u=1):(l="translateX("+(this.el.clientWidth-this.itemWidth)/2+"px) ",l+="translateY("+(this.el.clientHeight-this.itemHeight)/2+"px)",u=1-p*a),this.showIndicators){var v=this.center%this.count,f=this.$indicators.find(".indicator-item.active");f.index()!==v&&(f.removeClass("active"),this.$indicators.find(".indicator-item").eq(v)[0].classList.add("active"))}if(!this.noWrap||this.center>=0&&this.center=i;++i){if(this.options.fullWidth?(h=this.options.dist,d=i===n&&0>s?1-a:1):(h=this.options.dist*(2*i+a*o),d=1-p*(2*i+a*o)),!this.noWrap||this.center+i0?1-a:1):(h=this.options.dist*(2*i-a*o),d=1-p*(2*i-a*o)),!this.noWrap||this.center-i>=0){r=this.images[this._wrap(this.center-i)];var y=l+" translateX("+(-this.options.shift+(-this.dim*i-s)/2)+"px) translateZ("+h+"px)";this._updateItemStyle(r,d,-i,y)}}if(!this.noWrap||this.center>=0&&this.centeri?Math.abs(i+this.count)0&&Math.abs(i-this.count)i?this.target+=this.dim*Math.abs(i):i>0&&(this.target-=this.dim*i),"function"==typeof e&&(this.oneTimeCallback=e),this.offset!==this.target&&(this.amplitude=this.target-this.offset,this.timestamp=Date.now(),requestAnimationFrame(this._autoScrollBound))}},{key:"next",value:function k(t){(void 0===t||isNaN(t))&&(t=1);var e=this.center+t;if(e>this.count||0>e){if(this.noWrap)return;e=this._wrap(e)}this._cycleTo(e)}},{key:"prev",value:function C(t){(void 0===t||isNaN(t))&&(t=1);var e=this.center-t;if(e>this.count||0>e){if(this.noWrap)return;e=this._wrap(e)}this._cycleTo(e)}},{key:"set",value:function w(t,e){if((void 0===t||isNaN(t))&&(t=0),t>this.count||0>t){if(this.noWrap)return;t=this._wrap(t)}this._cycleTo(t,e)}}],[{key:"init",value:function E(t,e){return _get(i.__proto__||Object.getPrototypeOf(i),"init",this).call(this,this,t,e)}},{key:"getInstance",value:function x(t){var e=t.jquery?t[0]:t;return e.M_Carousel}},{key:"defaults",get:function(){return t}}]),i}(Component);M.Carousel=e,M.jQueryLoaded&&M.initializeJqueryWrapper(e,"carousel","M_Carousel")}(cash),function($){"use strict";var t={onOpen:void 0,onClose:void 0},e=function(e){function i(t,e){_classCallCheck(this,i);var n=_possibleConstructorReturn(this,(i.__proto__||Object.getPrototypeOf(i)).call(this,i,t,e));return n.el.M_TapTarget=n,n.options=$.extend({},i.defaults,e),n.isOpen=!1,n.$origin=$("#"+n.$el.attr("data-target")),n._setup(),n._calculatePositioning(),n._setupEventHandlers(),n}return _inherits(i,e),_createClass(i,[{key:"destroy",value:function n(){this._removeEventHandlers(),this.el.TapTarget=void 0}},{key:"_setupEventHandlers",value:function s(){this._handleDocumentClickBound=this._handleDocumentClick.bind(this),this._handleTargetClickBound=this._handleTargetClick.bind(this),this._handleOriginClickBound=this._handleOriginClick.bind(this),this.el.addEventListener("click",this._handleTargetClickBound),this.originEl.addEventListener("click",this._handleOriginClickBound);var t=M.throttle(this._handleResize,200);this._handleThrottledResizeBound=t.bind(this),window.addEventListener("resize",this._handleThrottledResizeBound)}},{key:"_removeEventHandlers",value:function o(){this.el.removeEventListener("click",this._handleTargetClickBound),this.originEl.removeEventListener("click",this._handleOriginClickBound),window.removeEventListener("resize",this._handleThrottledResizeBound)}},{key:"_handleTargetClick",value:function a(t){this.open()}},{key:"_handleOriginClick",value:function r(t){this.close()}},{key:"_handleResize",value:function l(t){this._calculatePositioning()}},{key:"_handleDocumentClick",value:function h(t){$(t.target).closest(".tap-target-wrapper").length||(this.close(),t.preventDefault(),t.stopPropagation())}},{key:"_setup",value:function d(){this.wrapper=this.$el.parent()[0],this.waveEl=$(this.wrapper).find(".tap-target-wave")[0],this.originEl=$(this.wrapper).find(".tap-target-origin")[0],this.contentEl=this.$el.find(".tap-target-content")[0],$(this.wrapper).hasClass(".tap-target-wrapper")||(this.wrapper=document.createElement("div"),this.wrapper.classList.add("tap-target-wrapper"),this.$el.before($(this.wrapper)),this.wrapper.append(this.el)),this.contentEl||(this.contentEl=document.createElement("div"),this.contentEl.classList.add("tap-target-content"),this.$el.append(this.contentEl)),this.waveEl||(this.waveEl=document.createElement("div"),this.waveEl.classList.add("tap-target-wave"),this.originEl||(this.originEl=this.$origin.clone(!0,!0),this.originEl.addClass("tap-target-origin"),this.originEl.removeAttr("id"),this.originEl.removeAttr("style"),this.originEl=this.originEl[0],this.waveEl.append(this.originEl)),this.wrapper.append(this.waveEl))}},{key:"_calculatePositioning",value:function u(){var t="fixed"===this.$origin.css("position");if(!t)for(var e=this.$origin.parents(),i=0;i=a,c=a>h,p=d>=o,v=o>d,f=a>=.25*r&&.75*r>=a,m=this.$el.outerWidth(),g=this.$el.outerHeight(),y=o+s/2-g/2,_=a+n/2-m/2,b=t?"fixed":"absolute",k=f?m:m/2+n,C=g/2,w=p?g/2:0,E=0,x=u&&!f?m/2-n:0,O=0,T=n,L=v?"bottom":"top",B=n>s?2*n:2*n,D=B,S=g/2-D/2,I=m/2-B/2,A={};A.top=p?y+"px":"",A.right=c?r-_-m+"px":"",A.bottom=v?l-y-g+"px":"",A.left=u?_+"px":"",A.position=b,$(this.wrapper).css(A),$(this.contentEl).css({width:k+"px",height:C+"px",top:w+"px",right:O+"px",bottom:E+"px",left:x+"px",padding:T+"px",verticalAlign:L}),$(this.waveEl).css({top:S+"px",left:I+"px",width:B+"px",height:D+"px"})}},{key:"open",value:function c(){this.isOpen||("function"==typeof this.options.onOpen&&this.options.onOpen.call(this,this.$origin[0]),this.isOpen=!0,this.wrapper.classList.add("open"),document.body.addEventListener("click",this._handleDocumentClickBound,!0),document.body.addEventListener("touchend",this._handleDocumentClickBound))}},{key:"close",value:function p(){this.isOpen&&("function"==typeof this.options.onClose&&this.options.onClose.call(this,this.$origin[0]),this.isOpen=!1,this.wrapper.classList.remove("open"),document.body.removeEventListener("click",this._handleDocumentClickBound,!0),document.body.removeEventListener("touchend",this._handleDocumentClickBound))}}],[{key:"init",value:function v(t,e){return _get(i.__proto__||Object.getPrototypeOf(i),"init",this).call(this,this,t,e)}},{key:"getInstance",value:function f(t){var e=t.jquery?t[0]:t;return e.M_TapTarget}},{key:"defaults",get:function(){return t}}]),i}(Component);M.TapTarget=e,M.jQueryLoaded&&M.initializeJqueryWrapper(e,"tapTarget","M_TapTarget")}(cash),function($){"use strict";var t={classes:"",dropdownOptions:{}},e=function(e){function i(t,e){_classCallCheck(this,i);var n=_possibleConstructorReturn(this,(i.__proto__||Object.getPrototypeOf(i)).call(this,i,t,e));return n.$el.hasClass("browser-default")?_possibleConstructorReturn(n):(n.el.M_FormSelect=n,n.options=$.extend({},i.defaults,e),n.isMultiple=n.$el.prop("multiple"),n.el.tabIndex=-1,n._keysSelected={},n._valueDict={},n._setupDropdown(),n._setupEventHandlers(),n)}return _inherits(i,e),_createClass(i,[{key:"destroy",value:function n(){this._removeEventHandlers(),this._removeDropdown(),this.el.M_FormSelect=void 0}},{key:"_setupEventHandlers",value:function s(){var t=this;this._handleSelectChangeBound=this._handleSelectChange.bind(this),this._handleOptionClickBound=this._handleOptionClick.bind(this),this._handleInputClickBound=this._handleInputClick.bind(this),$(this.dropdownOptions).find("li:not(.optgroup)").each(function(e){e.addEventListener("click",t._handleOptionClickBound)}),this.el.addEventListener("change",this._handleSelectChangeBound),this.input.addEventListener("click",this._handleInputClickBound)}},{key:"_removeEventHandlers",value:function o(){var t=this;$(this.dropdownOptions).find("li:not(.optgroup)").each(function(e){e.removeEventListener("click",t._handleOptionClickBound)}),this.el.removeEventListener("change",this._handleSelectChangeBound),this.input.removeEventListener("click",this._handleInputClickBound)}},{key:"_handleSelectChange",value:function a(t){this._setValueToInput()}},{key:"_handleOptionClick",value:function r(t){t.preventDefault();var e=$(t.target).closest("li")[0],i=e.id;if(!$(e).hasClass("disabled")&&!$(e).hasClass("optgroup")&&i.length){var n=!0;if(this.isMultiple){var s=$(this.dropdownOptions).find("li.disabled.selected");s.length&&(s.removeClass("selected"),s.find('input[type="checkbox"]').prop("checked",!1),this._toggleEntryFromArray(s[0].id)),n=this._toggleEntryFromArray(i)}else $(this.dropdownOptions).find("li").removeClass("selected"),$(e).toggleClass("selected",n);$(this._valueDict[i].el).prop("selected",n),this.$el.trigger("change")}t.stopPropagation()}},{key:"_handleInputClick",value:function l(){this.dropdown&&this.dropdown.isOpen&&(this._setValueToInput(),this._setSelectedStates())}},{key:"_setupDropdown",value:function h(){var t=this;this.wrapper=document.createElement("div"),$(this.wrapper).addClass("select-wrapper "+this.options.classes),this.$el.before($(this.wrapper)),this.wrapper.appendChild(this.el),this.el.disabled&&this.wrapper.classList.add("disabled"),this.$selectOptions=this.$el.children("option, optgroup"),this.dropdownOptions=document.createElement("ul"),this.dropdownOptions.id="select-options-"+M.guid(),$(this.dropdownOptions).addClass("dropdown-content select-dropdown "+(this.isMultiple?"multiple-select-dropdown":"")),this.$selectOptions.length&&this.$selectOptions.each(function(e){if($(e).is("option")){var i=void 0;i=t.isMultiple?t._appendOptionWithIcon(t.$el,e,"multiple"):t._appendOptionWithIcon(t.$el,e),t._addOptionToValueDict(e,i)}else if($(e).is("optgroup")){var n=$(e).children("option");$(t.dropdownOptions).append($('
    • '+e.getAttribute("label")+"
      • ")[0]),n.each(function(e){var i=t._appendOptionWithIcon(t.$el,e,"optgroup-option");t._addOptionToValueDict(e,i)})}}),this.$el.after(this.dropdownOptions),this.input=document.createElement("input"),$(this.input).addClass("select-dropdown dropdown-trigger"),this.input.setAttribute("type","text"),this.input.setAttribute("readonly","true"),this.input.setAttribute("data-target",this.dropdownOptions.id),this.el.disabled&&$(this.input).prop("disabled","true"),this.$el.before(this.input),this._setValueToInput();var e=$('');if(this.$el.before(e[0]),!this.el.disabled){var i=$.extend({},this.options.dropdownOptions);i.onOpenEnd=function(e){var i=$(t.dropdownOptions).find(".selected").first();if(t.dropdown.isScrollable&&i.length){var n=i[0].getBoundingClientRect().top-t.dropdownOptions.getBoundingClientRect().top;n-=t.dropdownOptions.clientHeight/2,t.dropdownOptions.scrollTop=n}},this.isMultiple&&(i.closeOnClick=!1),this.dropdown=M.Dropdown.init(this.input,i)}this._setSelectedStates()}},{key:"_addOptionToValueDict",value:function d(t,e){var i=Object.keys(this._valueDict).length,n=this.dropdownOptions.id+i,s={};e.id=n,s.el=t,s.optionEl=e,this._valueDict[n]=s}},{key:"_removeDropdown",value:function u(){$(this.wrapper).find(".caret").remove(),$(this.input).remove(),$(this.dropdownOptions).remove(),$(this.wrapper).before(this.$el),$(this.wrapper).remove()}},{key:"_appendOptionWithIcon",value:function c(t,e,i){var n=e.disabled?"disabled ":"",s="optgroup-option"===i?"optgroup-option ":"",o=this.isMultiple?'":e.innerHTML,a=$("
    • "),r=$("");r.html(o),a.addClass(n+" "+s),a.append(r);var l=e.getAttribute("data-icon");if(l){var h=$('');a.prepend(h)}return $(this.dropdownOptions).append(a[0]),a[0]}},{key:"_toggleEntryFromArray",value:function p(t){var e=!this._keysSelected.hasOwnProperty(t),i=$(this._valueDict[t].optionEl);return e?this._keysSelected[t]=!0:delete this._keysSelected[t],i.toggleClass("selected",e),i.find('input[type="checkbox"]').prop("checked",e),i.prop("selected",e),e}},{key:"_setValueToInput",value:function v(){var t=[],e=this.$el.find("option");if(e.each(function(e){if($(e).prop("selected")){var i=$(e).text();t.push(i)}}),!t.length){var i=this.$el.find("option:disabled").eq(0);i.length&&""===i[0].value&&t.push(i.text())}this.input.value=t.join(", ")}},{key:"_setSelectedStates",value:function f(){this._keysSelected={};for(var t in this._valueDict){var e=this._valueDict[t],i=$(e.el).prop("selected");$(e.optionEl).find('input[type="checkbox"]').prop("checked",i),i?(this._activateOption($(this.dropdownOptions),$(e.optionEl)),this._keysSelected[t]=!0):$(e.optionEl).removeClass("selected")}}},{key:"_activateOption",value:function m(t,e){if(e){this.isMultiple||t.find("li.selected").removeClass("selected");var i=$(e);i.addClass("selected")}}},{ +key:"getSelectedValues",value:function g(){var t=[];for(var e in this._keysSelected)t.push(this._valueDict[e].el.value);return t}}],[{key:"init",value:function y(t,e){return _get(i.__proto__||Object.getPrototypeOf(i),"init",this).call(this,this,t,e)}},{key:"getInstance",value:function _(t){var e=t.jquery?t[0]:t;return e.M_FormSelect}},{key:"defaults",get:function(){return t}}]),i}(Component);M.FormSelect=e,M.jQueryLoaded&&M.initializeJqueryWrapper(e,"formSelect","M_FormSelect")}(cash),function($,t){"use strict";var e={},i=function(i){function n(t,e){_classCallCheck(this,n);var i=_possibleConstructorReturn(this,(n.__proto__||Object.getPrototypeOf(n)).call(this,n,t,e));return i.el.M_Range=i,i.options=$.extend({},n.defaults,e),i._mousedown=!1,i._setupThumb(),i._setupEventHandlers(),i}return _inherits(n,i),_createClass(n,[{key:"destroy",value:function s(){this._removeEventHandlers(),this._removeThumb(),this.el.M_Range=void 0}},{key:"_setupEventHandlers",value:function o(){this._handleRangeChangeBound=this._handleRangeChange.bind(this),this._handleRangeMousedownTouchstartBound=this._handleRangeMousedownTouchstart.bind(this),this._handleRangeInputMousemoveTouchmoveBound=this._handleRangeInputMousemoveTouchmove.bind(this),this._handleRangeMouseupTouchendBound=this._handleRangeMouseupTouchend.bind(this),this._handleRangeBlurMouseoutTouchleaveBound=this._handleRangeBlurMouseoutTouchleave.bind(this),this.el.addEventListener("change",this._handleRangeChangeBound),this.el.addEventListener("mousedown",this._handleRangeMousedownTouchstartBound),this.el.addEventListener("touchstart",this._handleRangeMousedownTouchstartBound),this.el.addEventListener("input",this._handleRangeInputMousemoveTouchmoveBound),this.el.addEventListener("mousemove",this._handleRangeInputMousemoveTouchmoveBound),this.el.addEventListener("touchmove",this._handleRangeInputMousemoveTouchmoveBound),this.el.addEventListener("mouseup",this._handleRangeMouseupTouchendBound),this.el.addEventListener("touchend",this._handleRangeMouseupTouchendBound),this.el.addEventListener("blur",this._handleRangeBlurMouseoutTouchleaveBound),this.el.addEventListener("mouseout",this._handleRangeBlurMouseoutTouchleaveBound),this.el.addEventListener("touchleave",this._handleRangeBlurMouseoutTouchleaveBound)}},{key:"_removeEventHandlers",value:function a(){this.el.removeEventListener("change",this._handleRangeChangeBound),this.el.removeEventListener("mousedown",this._handleRangeMousedownTouchstartBound),this.el.removeEventListener("touchstart",this._handleRangeMousedownTouchstartBound),this.el.removeEventListener("input",this._handleRangeInputMousemoveTouchmoveBound),this.el.removeEventListener("mousemove",this._handleRangeInputMousemoveTouchmoveBound),this.el.removeEventListener("touchmove",this._handleRangeInputMousemoveTouchmoveBound),this.el.removeEventListener("mouseup",this._handleRangeMouseupTouchendBound),this.el.removeEventListener("touchend",this._handleRangeMouseupTouchendBound),this.el.removeEventListener("blur",this._handleRangeBlurMouseoutTouchleaveBound),this.el.removeEventListener("mouseout",this._handleRangeBlurMouseoutTouchleaveBound),this.el.removeEventListener("touchleave",this._handleRangeBlurMouseoutTouchleaveBound)}},{key:"_handleRangeChange",value:function r(){$(this.value).html(this.$el.val()),$(this.thumb).hasClass("active")||this._showRangeBubble();var t=this._calcRangeOffset();$(this.thumb).addClass("active").css("left",t+"px")}},{key:"_handleRangeMousedownTouchstart",value:function l(t){if($(this.value).html(this.$el.val()),this._mousedown=!0,this.$el.addClass("active"),$(this.thumb).hasClass("active")||this._showRangeBubble(),"input"!==t.type){var e=this._calcRangeOffset();$(this.thumb).addClass("active").css("left",e+"px")}}},{key:"_handleRangeInputMousemoveTouchmove",value:function h(){if(this._mousedown){$(this.thumb).hasClass("active")||this._showRangeBubble();var t=this._calcRangeOffset();$(this.thumb).addClass("active").css("left",t+"px"),$(this.value).html(this.$el.val())}}},{key:"_handleRangeMouseupTouchend",value:function d(){this._mousedown=!1,this.$el.removeClass("active")}},{key:"_handleRangeBlurMouseoutTouchleave",value:function u(){if(!this._mousedown){var e=parseInt(this.$el.css("padding-left")),i=7+e+"px";$(this.thumb).hasClass("active")&&(t.remove(this.thumb),t({targets:this.thumb,height:0,width:0,top:10,easing:"easeOutQuad",marginLeft:i,duration:100})),$(this.thumb).removeClass("active")}}},{key:"_setupThumb",value:function c(){this.thumb=document.createElement("span"),this.value=document.createElement("span"),$(this.thumb).addClass("thumb"),$(this.value).addClass("value"),$(this.thumb).append(this.value),this.$el.after(this.thumb)}},{key:"_removeThumb",value:function p(){$(this.thumb).remove()}},{key:"_showRangeBubble",value:function v(){var e=parseInt($(this.thumb).parent().css("padding-left")),i=-7+e+"px";t.remove(this.thumb),t({targets:this.thumb,height:30,width:30,top:-30,marginLeft:i,duration:300,easing:"easeOutQuint"})}},{key:"_calcRangeOffset",value:function f(){var t=this.$el.width()-15,e=parseFloat(this.$el.attr("max"))||100,i=parseFloat(this.$el.attr("min"))||0,n=(parseFloat(this.$el.val())-i)/(e-i);return n*t}}],[{key:"init",value:function m(t,e){return _get(n.__proto__||Object.getPrototypeOf(n),"init",this).call(this,this,t,e)}},{key:"getInstance",value:function g(t){var e=t.jquery?t[0]:t;return e.M_Range}},{key:"defaults",get:function(){return e}}]),n}(Component);M.Range=i,M.jQueryLoaded&&M.initializeJqueryWrapper(i,"range","M_Range"),i.init($("input[type=range]"))}(cash,M.anime);var debounce=function(t,e){var i;return function(){var n=Array.prototype.slice.call(arguments),s=this;clearTimeout(i),i=setTimeout(function(){t.apply(s,n)},e)}},chartExists=function(t){var e=!1;for(var i in Chart.instances)if(chart=Chart.instances[i],t.is(chart.canvas)){e=!0;break}return e?chart:!1},chartColorYellow="rgb(255,196,0)",chartColorBlue="rgb(0,176,255)",chartColorPink="rgb(255,64,129)",chartColorGreen="rgb(112,190,116)",tooltipsOpts={enabled:!1,mode:"index",intersect:!1,backgroundColor:"#fff",cornerRadius:2,caretSize:0,xPadding:12,yPadding:12,custom:function(t){function e(t){return t.lines}var i=document.getElementById("chartjs-tooltip");if(i||(i=document.createElement("div"),i.id="chartjs-tooltip",i.innerHTML="
      ",document.body.appendChild(i)),0===t.opacity)return void(i.style.opacity=0);if(i.classList.remove("above","below","no-transform"),t.yAlign?i.classList.add(t.yAlign):i.classList.add("no-transform"),t.body){var n=t.title||[],s=t.body.map(e),o=t.footer,a="";n.forEach(function(t){a+=""+t+""}),a+="",s.forEach(function(e,i){var n=t.labelColors[i],o="";if(s.length>1)var r=o='';a+=""+o+e+""}),o.length&&(a+="",o.forEach(function(t,e){a+=""+t+""}),a+=""),a+="";var r=i.querySelector("table");r.innerHTML=a}var l=this._chart.canvas.getBoundingClientRect();i.style.opacity=1,i.style.left=$(window).scrollLeft()+l.left+t.caretX+20+"px",i.style.top=$(window).scrollTop()+l.top+t.caretY+"px",i.style.fontSize=t.fontSize,i.style.fontStyle=t._fontStyle,i.style.padding=t.yPadding+"px "+t.xPadding+"px"}},areaOptions={maintainAspectRatio:!1,spanGaps:!1,elements:{line:{tension:.4}},scales:{yAxes:[{stacked:!0}]},plugins:{filler:{propagate:!1}}},flushChartOptions=Object.assign({},areaOptions);flushChartOptions.hover={hover:{mode:"index",intersect:!1}},flushChartOptions.legend={display:!1},flushChartOptions.scales={xAxes:[{display:!1}],yAxes:[{display:!1,stacked:!0}]};var percentageFooterCallback=function(t,e){var i="",n=0,s=0;t.forEach(function(t){s=e.datasets[t.datasetIndex].data[t.index],e.datasets[t.datasetIndex].data.forEach(function(t){n+=t})});var o=(s/n*100).toFixed(1);return o+"%"},percentageStackedFooterCallback=function(t,e){var i="",n=0,s=0;t.forEach(function(t){s=e.datasets[t.datasetIndex].data[t.index],e.datasets.forEach(function(e){n+=e.data[t.index]})});var o=(s/n*100).toFixed(1);return o+"%"},doughnutLegendCallback=function(t){var e=$('
      '),i=$("
        "),n=t.data.labels;if(t.data.datasets.length)for(var s=0;s'+n[s]+''+o+"");i.append(r)}return e.append(i),e},percDoughnutLegendCallback=function(t){if($legend=$('
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        "),a=n.data.reduce(function(t,e){return t+e}),r=$('
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o=t,a=t,r=a.parentNode.children[0],s=e,l=o.hierNode.modifier,u=a.hierNode.modifier,h=r.hierNode.modifier,c=s.hierNode.modifier;s=Fc(s),a=Wc(a),s&&a;){o=Fc(o),r=Wc(r),o.hierNode.ancestor=t;var d=s.hierNode.prelim+c-a.hierNode.prelim-u+n(s,a);d>0&&(Zc(Hc(s,t,i),t,d),u+=d,l+=d),c+=s.hierNode.modifier,u+=a.hierNode.modifier,l+=o.hierNode.modifier,h+=r.hierNode.modifier} +s&&!Fc(o)&&(o.hierNode.thread=s,o.hierNode.modifier+=c-l),a&&!Wc(r)&&(r.hierNode.thread=a,r.hierNode.modifier+=u-h,i=t)} +return i} +function Fc(t){var e=t.children;return e.length&&t.isExpand?e[e.length-1]:t.hierNode.thread} +function Wc(t){var e=t.children;return e.length&&t.isExpand?e[0]:t.hierNode.thread} +function Hc(t,e,i){return t.hierNode.ancestor.parentNode===e.parentNode?t.hierNode.ancestor:i} +function Zc(t,e,i){var n=i/(e.hierNode.i-t.hierNode.i);e.hierNode.change-=n,e.hierNode.shift+=i,e.hierNode.modifier+=i,e.hierNode.prelim+=i,t.hierNode.change+=n} +function Uc(t,e){return t.parentNode===e.parentNode?1:2} 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r,s=t.tree.getNodeByDataIndex(e),l=t.tree.root,a=jc(s,s.getModel(),a),u=s.parentNode===l?s:s.parentNode||s;null==(r=u.getLayout());)u=u.parentNode===l?u:u.parentNode||u;bo(i,{position:[r.x+1,r.y+1]},o,function(){n.remove(i),t.setItemGraphicEl(e,null)}),i.fadeOut(null,{keepLabel:!0});var h=i.__edge;h&&bo(h,{shape:Kc(a,r,r),style:{opacity:0}},o,function(){n.remove(h)})} +function Kc(t,e,i){var n,o,a,r,s,l,u,h,c=t.orient;if("radial"===t.layout){s=e.rawX,u=e.rawY,l=i.rawX,h=i.rawY;var d=zc(s,u),f=zc(s,u+(h-u)*t.curvature),p=zc(l,h+(u-h)*t.curvature),g=zc(l,h);return{x1:d.x,y1:d.y,x2:g.x,y2:g.y,cpx1:f.x,cpy1:f.y,cpx2:p.x,cpy2:p.y}} +return s=e.x,u=e.y,l=i.x,h=i.y,"LR"!==c&&"RL"!==c||(n=s+(l-s)*t.curvature,o=u,a=l+(s-l)*t.curvature,r=h),"TB"!==c&&"BT"!==c||(n=s,o=u+(h-u)*t.curvature,a=l,r=h+(u-h)*t.curvature),{x1:s,y1:u,x2:l,y2:h,cpx1:n,cpy1:o,cpx2:a,cpy2:r}} +function $c(t,e,i){for(var n,o=[t],a=[];n=o.pop();) +if(a.push(n),n.isExpand){var r=n.children;if(r.length) +for(var s=0;s=0;a--)n.push(o[a])}} +function Qc(t,e){var i=Bc(t,e);t.layoutInfo=i;var n=t.get("layout"),o=0,a=0,r=null;"radial"===n?(o=2*Math.PI,a=Math.min(i.height,i.width)/2,r=Rc(function(t,e){return(t.parentNode===e.parentNode?1:2)/t.depth})):(o=i.width,a=i.height,r=Rc());var s=t.getData().tree.root,l=s.children[0];if(l){Nc(s),$c(l,Oc,r),s.hierNode.modifier=-l.hierNode.prelim,Jc(l,Ec);var u=l,h=l,c=l;Jc(l,function(t){var e=t.getLayout().x;eh.getLayout().x&&(h=t),t.depth>c.depth&&(c=t)});var d=u===h?1:r(u,h)/2,f=d-u.getLayout().x,p=0,g=0,m=0,v=0;if("radial"===n)p=o/(h.getLayout().x+d+f),g=a/(c.depth-1||1),Jc(l,function(t){m=(t.getLayout().x+f)*p,v=(t.depth-1)*g;var e=zc(m,v);t.setLayout({x:e.x,y:e.y,rawX:m,rawY:v},!0)});else{var y=t.getOrient();"RL"===y||"LR"===y?(g=a/(h.getLayout().x+d+f),p=o/(c.depth-1||1),Jc(l,function(t){v=(t.getLayout().x+f)*g,m="LR"===y?(t.depth-1)*p:o-(t.depth-1)*p,t.setLayout({x:m,y:v},!0)})):"TB"!==y&&"BT"!==y||(p=o/(h.getLayout().x+d+f),g=a/(c.depth-1||1),Jc(l,function(t){m=(t.getLayout().x+f)*p,v="TB"===y?(t.depth-1)*g:a-(t.depth-1)*g,t.setLayout({x:m,y:v},!0)}))}}} +function td(t,e,i){if(t&&l(e,t.type)>=0){var n=i.getData().tree.root,o=t.targetNode;if("string"==typeof o&&(o=n.getNodeById(o)),o&&n.contains(o))return{node:o};var a=t.targetNodeId;if(null!=a&&(o=n.getNodeById(a)))return{node:o}}} +function ed(t){for(var e=[];t;)(t=t.parentNode)&&e.push(t);return e.reverse()} +function id(t,e){return l(ed(t),e)>=0} +function nd(t,e){for(var i=[];t;){var n=t.dataIndex;i.push({name:t.name,dataIndex:n,value:e.getRawValue(n)}),t=t.parentNode} +return i.reverse(),i} +function od(t){var e=0;d(t.children,function(t){od(t);var i=t.value;y(i)&&(i=i[0]),e+=i});var i=t.value;y(i)&&(i=i[0]),(null==i||isNaN(i))&&(i=e),i<0&&(i=0),y(t.value)?t.value[0]=i:t.value=i} +function ad(t,e){var i=e.get("color");if(i){var n;return d(t=t||[],function(t){var e=new Lo(t),i=e.get("color");(e.get("itemStyle.color")||i&&"none"!==i)&&(n=!0)}),n||((t[0]||(t[0]={})).color=i.slice()),t}} +function rd(t){this.group=new Zw,t.add(this.group)} +function sd(t,e,i,n,o,a){var r=[[o?t:t-DC,e],[t+i,e],[t+i,e+n],[o?t:t-DC,e+n]];return!a&&r.splice(2,0,[t+i+DC,e+n/2]),!o&&r.push([t,e+n/2]),r} +function ld(t,e,i){t.eventData={componentType:"series",componentSubType:"treemap",componentIndex:e.componentIndex,seriesIndex:e.componentIndex,seriesName:e.name,seriesType:"treemap",selfType:"breadcrumb",nodeData:{dataIndex:i&&i.dataIndex,name:i&&i.name},treePathInfo:i&&nd(i,e)}} +function ud(){var t,e=[],i={};return{add:function(t,n,o,a,r){return _(a)&&(r=a,a=0),!i[t.id]&&(i[t.id]=1,e.push({el:t,target:n,time:o,delay:a,easing:r}),!0)},done:function(e){return t=e,this},start:function(){for(var n=e.length,o=0,a=e.length;o=0;a--)null==i[a]&&(delete n[e[a]],e.pop())} +function pd(t,e){var i=t.visual,n=[];w(i)?ZC(i,function(t){n.push(t)}):null!=i&&n.push(i);var o={color:1,symbol:1};e||1!==n.length||o.hasOwnProperty(t.type)||(n[1]=n[0]),bd(t,n)} +function gd(t){return{applyVisual:function(e,i,n){e=this.mapValueToVisual(e),n("color",t(i("color"),e))},_doMap:_d([0,1])}} +function md(t){var e=this.option.visual;return e[Math.round(Eo(t,[0,1],[0,e.length-1],!0))]||{}} +function vd(t){return function(e,i,n){n(t,this.mapValueToVisual(e))}} +function yd(t){var e=this.option.visual;return e[this.option.loop&&t!==XC?t%e.length:t]} +function xd(){return this.option.visual[0]} +function _d(t){return{linear:function(e){return Eo(e,t,this.option.visual,!0)},category:yd,piecewise:function(e,i){var n=wd.call(this,i);return null==n&&(n=Eo(e,t,this.option.visual,!0)),n},fixed:xd}} +function wd(t){var e=this.option,i=e.pieceList;if(e.hasSpecialVisual){var n=i[jC.findPieceIndex(t,i)];if(n&&n.visual)return n.visual[this.type]}} +function bd(t,e){return t.visual=e,"color"===t.type&&(t.parsedVisual=f(e,function(t){return zt(t)})),e} +function Sd(t,e,i){return t?e<=i:e=o.length||t===o[t.depth])&&Md(t,kd(r,h,t,e,g,a),i,n,o,a)})}else l=Td(h),t.setVisual("color",l)}} +function Id(t,e,i,n){var o=a({},e);return d(["color","colorAlpha","colorSaturation"],function(a){var r=t.get(a,!0);null==r&&i&&(r=i[a]),null==r&&(r=e[a]),null==r&&(r=n.get(a)),null!=r&&(o[a]=r)}),o} +function Td(t){var e=Dd(t,"color");if(e){var i=Dd(t,"colorAlpha"),n=Dd(t,"colorSaturation");return n&&(e=Zt(e,null,null,n)),i&&(e=Ut(e,i)),e}} +function Ad(t,e){return null!=e?Zt(e,null,null,t):null} +function Dd(t,e){var i=t[e];if(null!=i&&"none"!==i)return i} +function Cd(t,e,i,n,o,a){if(a&&a.length){var r=Ld(e,"color")||null!=o.color&&"none"!==o.color&&(Ld(e,"colorAlpha")||Ld(e,"colorSaturation"));if(r){var 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i=this.style,n=this.path||OS,o=i.hasStroke(),a=i.hasFill(),r=i.fill,s=i.stroke,l=a&&!!r.colorStops,u=o&&!!s.colorStops,h=a&&!!r.image,c=o&&!!s.image;if(i.bind(t,this,e),this.setTransform(t),this.__dirty){var d;l&&(d=d||this.getBoundingRect(),this._fillGradient=i.getGradient(t,r,d)),u&&(d=d||this.getBoundingRect(),this._strokeGradient=i.getGradient(t,s,d))} +l?t.fillStyle=this._fillGradient:h&&(t.fillStyle=PS.call(r,t)),u?t.strokeStyle=this._strokeGradient:c&&(t.strokeStyle=PS.call(s,t));var f=i.lineDash,p=i.lineDashOffset,g=!!t.setLineDash,m=this.getGlobalScale();if(n.setScale(m[0],m[1]),this.__dirtyPath||f&&!g&&o?(n.beginPath(t),f&&!g&&(n.setLineDash(f),n.setLineDashOffset(p)),this.buildPath(n,this.shape,!1),this.path&&(this.__dirtyPath=!1)):(t.beginPath(),this.path.rebuildPath(t)),a) +if(null!=i.fillOpacity){v=t.globalAlpha;t.globalAlpha=i.fillOpacity*i.opacity,n.fill(t),t.globalAlpha=v}else n.fill(t);if(f&&g&&(t.setLineDash(f),t.lineDashOffset=p),o) +if(null!=i.strokeOpacity){var v=t.globalAlpha;t.globalAlpha=i.strokeOpacity*i.opacity,n.stroke(t),t.globalAlpha=v}else n.stroke(t);f&&g&&t.setLineDash([]),null!=i.text&&(this.restoreTransform(t),this.drawRectText(t,this.getBoundingRect()))},buildPath:function(t,e,i){},createPathProxy:function(){this.path=new MS},getBoundingRect:function(){var t=this._rect,e=this.style,i=!t;if(i){var n=this.path;n||(n=this.path=new MS),this.__dirtyPath&&(n.beginPath(),this.buildPath(n,this.shape,!1)),t=n.getBoundingRect()} +if(this._rect=t,e.hasStroke()){var o=this._rectWithStroke||(this._rectWithStroke=t.clone());if(this.__dirty||i){o.copy(t);var a=e.lineWidth,r=e.strokeNoScale?this.getLineScale():1;e.hasFill()||(a=Math.max(a,this.strokeContainThreshold||4)),r>1e-10&&(o.width+=a/r,o.height+=a/r,o.x-=a/r/2,o.y-=a/r/2)} +return o} +return t},contain:function(t,e){var i=this.transformCoordToLocal(t,e),n=this.getBoundingRect(),o=this.style;if(t=i[0],e=i[1],n.contain(t,e)){var a=this.path.data;if(o.hasStroke()){var r=o.lineWidth,s=o.strokeNoScale?this.getLineScale():1;if(s>1e-10&&(o.hasFill()||(r=Math.max(r,this.strokeContainThreshold)),Ln(a,r/s,t,e)))return!0} +if(o.hasFill())return Cn(a,t,e)} +return!1},dirty:function(t){null==t&&(t=!0),t&&(this.__dirtyPath=t,this._rect=null),this.__dirty=this.__dirtyText=!0,this.__zr&&this.__zr.refresh(),this.__clipTarget&&this.__clipTarget.dirty()},animateShape:function(t){return this.animate("shape",t)},attrKV:function(t,e){"shape"===t?(this.setShape(e),this.__dirtyPath=!0,this._rect=null):ui.prototype.attrKV.call(this,t,e)},setShape:function(t,e){var i=this.shape;if(i){if(w(t)) +for(var n in t)t.hasOwnProperty(n)&&(i[n]=t[n]);else i[t]=e;this.dirty(!0)} +return this},getLineScale:function(){var t=this.transform;return t&&NS(t[0]-1)>1e-10&&NS(t[3]-1)>1e-10?Math.sqrt(NS(t[0]*t[3]-t[2]*t[1])):1}},kn.extend=function(t){var e=function(e){kn.call(this,e),t.style&&this.style.extendFrom(t.style,!1);var i=t.shape;if(i){this.shape=this.shape||{};var 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t=this.get("borderType");return"solid"===t||null==t?null:"dashed"===t?[5,5]:[1,1]}},AM=h,DM=Bi();Lo.prototype={constructor:Lo,init:null,mergeOption:function(t){n(this.option,t,!0)},get:function(t,e){return null==t?this.option:ko(this.option,this.parsePath(t),!e&&Po(this,t))},getShallow:function(t,e){var i=this.option,n=null==i?i:i[t],o=!e&&Po(this,t);return null==n&&o&&(n=o.getShallow(t)),n},getModel:function(t,e){var i,n=null==t?this.option:ko(this.option,t=this.parsePath(t));return e=e||(i=Po(this,t))&&i.getModel(t),new Lo(n,e,this.ecModel)},isEmpty:function(){return null==this.option},restoreData:function(){},clone:function(){return new(0,this.constructor)(i(this.option))},setReadOnly:function(t){},parsePath:function(t){return"string"==typeof t&&(t=t.split(".")),t},customizeGetParent:function(t){DM(this).getParent=t},isAnimationEnabled:function(){if(!z_.node){if(null!=this.option.animation)return!!this.option.animation;if(this.parentModel)return 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i=this.layoutMode,o=i?da(t):{};n(t,e.getTheme().get(this.mainType)),n(t,this.getDefaultOption()),i&&ca(t,o,i)},mergeOption:function(t,e){n(this.option,t,!0);var i=this.layoutMode;i&&ca(this.option,t,i)},optionUpdated:function(t,e){},getDefaultOption:function(){var t=jM(this);if(!t.defaultOption){for(var e=[],i=this.constructor;i;){var o=i.prototype.defaultOption;o&&e.push(o),i=i.superClass} +for(var a={},r=e.length-1;r>=0;r--)a=n(a,e[r],!0);t.defaultOption=a} +return t.defaultOption},getReferringComponents:function(t){return this.ecModel.queryComponents({mainType:t,index:this.get(t+"Index",!0),id:this.get(t+"Id",!0)})}});Ki(YM,{registerWhenExtend:!0}),function(t){var e={};t.registerSubTypeDefaulter=function(t,i){t=Zi(t),e[t.main]=i},t.determineSubType=function(i,n){var o=n.type;if(!o){var a=Zi(i).main;t.hasSubTypes(i)&&e[a]&&(o=e[a](n))} +return o}}(YM),function(t,e){function i(t){var i={},a=[];return d(t,function(r){var s=n(i,r),u=o(s.originalDeps=e(r),t);s.entryCount=u.length,0===s.entryCount&&a.push(r),d(u,function(t){l(s.predecessor,t)<0&&s.predecessor.push(t);var e=n(i,t);l(e.successor,t)<0&&e.successor.push(r)})}),{graph:i,noEntryList:a}} +function n(t,e){return t[e]||(t[e]={predecessor:[],successor:[]}),t[e]} +function o(t,e){var i=[];return d(t,function(t){l(e,t)>=0&&i.push(t)}),i} +t.topologicalTravel=function(t,e,n,o){function a(t){s[t].entryCount--,0===s[t].entryCount&&l.push(t)} +if(t.length){var r=i(e),s=r.graph,l=r.noEntryList,u={};for(d(t,function(t){u[t]=!0});l.length;){var h=l.pop(),c=s[h],f=!!u[h];f&&(n.call(o,h,c.originalDeps.slice()),delete u[h]),d(c.successor,f?function(t){u[t]=!0,a(t)}:a)} +d(u,function(){throw new Error("Circle dependency may exists")})}}}(YM,function(t){var e=[];return d(YM.getClassesByMainType(t),function(t){e=e.concat(t.prototype.dependencies||[])}),e=f(e,function(t){return Zi(t).main}),"dataset"!==t&&l(e,"dataset")<=0&&e.unshift("dataset"),e}),h(YM,XM);var qM="";"undefined"!=typeof navigator&&(qM=navigator.platform||"");var KM={color:["#c23531","#2f4554","#61a0a8","#d48265","#91c7ae","#749f83","#ca8622","#bda29a","#6e7074","#546570","#c4ccd3"],gradientColor:["#f6efa6","#d88273","#bf444c"],textStyle:{fontFamily:qM.match(/^Win/)?"Microsoft YaHei":"sans-serif",fontSize:12,fontStyle:"normal",fontWeight:"normal"},blendMode:null,animation:"auto",animationDuration:1e3,animationDurationUpdate:300,animationEasing:"exponentialOut",animationEasingUpdate:"cubicOut",animationThreshold:2e3,progressiveThreshold:3e3,progressive:400,hoverLayerThreshold:3e3,useUTC:!1},$M=Bi(),JM={clearColorPalette:function(){$M(this).colorIdx=0,$M(this).colorNameMap={}},getColorFromPalette:function(t,e,i){var n=$M(e=e||this),o=n.colorIdx||0,a=n.colorNameMap=n.colorNameMap||{};if(a.hasOwnProperty(t))return a[t];var r=Di(this.get("color",!0)),s=this.get("colorLayer",!0),l=null!=i&&s?pa(s,i):r;if((l=l||r)&&l.length){var u=l[o];return t&&(a[t]=u),n.colorIdx=(o+1)%l.length,u}}},QM={cartesian2d:function(t,e,i,n){var o=t.getReferringComponents("xAxis")[0],a=t.getReferringComponents("yAxis")[0];e.coordSysDims=["x","y"],i.set("x",o),i.set("y",a),ma(o)&&(n.set("x",o),e.firstCategoryDimIndex=0),ma(a)&&(n.set("y",a),e.firstCategoryDimIndex=1)},singleAxis:function(t,e,i,n){var o=t.getReferringComponents("singleAxis")[0];e.coordSysDims=["single"],i.set("single",o),ma(o)&&(n.set("single",o),e.firstCategoryDimIndex=0)},polar:function(t,e,i,n){var o=t.getReferringComponents("polar")[0],a=o.findAxisModel("radiusAxis"),r=o.findAxisModel("angleAxis");e.coordSysDims=["radius","angle"],i.set("radius",a),i.set("angle",r),ma(a)&&(n.set("radius",a),e.firstCategoryDimIndex=0),ma(r)&&(n.set("angle",r),e.firstCategoryDimIndex=1)},geo:function(t,e,i,n){e.coordSysDims=["lng","lat"]},parallel:function(t,e,i,n){var o=t.ecModel,a=o.getComponent("parallel",t.get("parallelIndex")),r=e.coordSysDims=a.dimensions.slice();d(a.parallelAxisIndex,function(t,a){var s=o.getComponent("parallelAxis",t),l=r[a];i.set(l,s),ma(s)&&null==e.firstCategoryDimIndex&&(n.set(l,s),e.firstCategoryDimIndex=a)})}},tI="original",eI="arrayRows",iI="objectRows",nI="keyedColumns",oI="unknown",aI="typedArray",rI="column",sI="row";va.seriesDataToSource=function(t){return new va({data:t,sourceFormat:S(t)?aI:tI,fromDataset:!1})},ji(va);var lI=Bi(),uI="\0_ec_inner",hI=Lo.extend({init:function(t,e,i,n){i=i||{},this.option=null,this._theme=new Lo(i),this._optionManager=n},setOption:function(t,e){k(!(uI in t),"please use chart.getOption()"),this._optionManager.setOption(t,e),this.resetOption(null)},resetOption:function(t){var e=!1,i=this._optionManager;if(!t||"recreate"===t){var n=i.mountOption("recreate"===t);this.option&&"recreate"!==t?(this.restoreData(),this.mergeOption(n)):Pa.call(this,n),e=!0} 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Ra(r,t)},findComponents:function(t){var e=t.query,i=t.mainType,n=function(t){var e=i+"Index",n=i+"Id",o=i+"Name";return!t||null==t[e]&&null==t[n]&&null==t[o]?null:{mainType:i,index:t[e],id:t[n],name:t[o]}}(e);return function(e){return t.filter?g(e,t.filter):e}(Ra(n?this.queryComponents(n):this._componentsMap.get(i),t))},eachComponent:function(t,e,i){var n=this._componentsMap;"function"==typeof t?(i=e,e=t,n.each(function(t,n){d(t,function(t,o){e.call(i,n,t,o)})})):_(t)?d(n.get(t),e,i):w(t)&&d(this.findComponents(t),e,i)},getSeriesByName:function(t){return g(this._componentsMap.get("series"),function(e){return e.name===t})},getSeriesByIndex:function(t){return this._componentsMap.get("series")[t]},getSeriesByType:function(t){return g(this._componentsMap.get("series"),function(e){return e.subType===t})},getSeries:function(){return this._componentsMap.get("series").slice()},getSeriesCount:function(){return this._componentsMap.get("series").length},eachSeries:function(t,e){d(this._seriesIndices,function(i){var n=this._componentsMap.get("series")[i];t.call(e,n,i)},this)},eachRawSeries:function(t,e){d(this._componentsMap.get("series"),t,e)},eachSeriesByType:function(t,e,i){d(this._seriesIndices,function(n){var o=this._componentsMap.get("series")[n];o.subType===t&&e.call(i,o,n)},this)},eachRawSeriesByType:function(t,e,i){return d(this.getSeriesByType(t),e,i)},isSeriesFiltered:function(t){return null==this._seriesIndicesMap.get(t.componentIndex)},getCurrentSeriesIndices:function(){return(this._seriesIndices||[]).slice()},filterSeries:function(t,e){Ea(this,g(this._componentsMap.get("series"),t,e))},restoreData:function(t){var e=this._componentsMap;Ea(this,e.get("series"));var i=[];e.each(function(t,e){i.push(e)}),YM.topologicalTravel(i,YM.getAllClassMainTypes(),function(i,n){d(e.get(i),function(e){("series"!==i||!La(e,t))&&e.restoreData()})})}});h(hI,JM);var cI=["getDom","getZr","getWidth","getHeight","getDevicePixelRatio","dispatchAction","isDisposed","on","off","getDataURL","getConnectedDataURL","getModel","getOption","getViewOfComponentModel","getViewOfSeriesModel"],dI={};Ba.prototype={constructor:Ba,create:function(t,e){var i=[];d(dI,function(n,o){var a=n.create(t,e);i=i.concat(a||[])}),this._coordinateSystems=i},update:function(t,e){d(this._coordinateSystems,function(i){i.update&&i.update(t,e)})},getCoordinateSystems:function(){return this._coordinateSystems.slice()}},Ba.register=function(t,e){dI[t]=e},Ba.get=function(t){return dI[t]};var fI=d,pI=i,gI=f,mI=n,vI=/^(min|max)?(.+)$/;Va.prototype={constructor:Va,setOption:function(t,e){t&&d(Di(t.series),function(t){t&&t.data&&S(t.data)&&N(t.data)}),t=pI(t,!0);var 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this.get("progressiveThreshold")},getAxisTooltipData:null,getTooltipPosition:null,pipeTask:null,preventIncremental:null,pipelineContext:null});h(OI,LI),h(OI,JM);var EI=function(){this.group=new Zw,this.uid=No("viewComponent")};EI.prototype={constructor:EI,init:function(t,e){},render:function(t,e,i,n){},dispose:function(){},filterForExposedEvent:null};var RI=EI.prototype;RI.updateView=RI.updateLayout=RI.updateVisual=function(t,e,i,n){},Xi(EI),Ki(EI,{registerWhenExtend:!0});var zI=function(){var t=Bi();return function(e){var i=t(e),n=e.pipelineContext,o=i.large,a=i.progressiveRender,r=i.large=n.large,s=i.progressiveRender=n.progressiveRender;return!!(o^r||a^s)&&"reset"}},BI=Bi(),VI=zI();Mr.prototype={type:"chart",init:function(t,e){},render:function(t,e,i,n){},highlight:function(t,e,i,n){Tr(t.getData(),n,"emphasis")},downplay:function(t,e,i,n){Tr(t.getData(),n,"normal")},remove:function(t,e){this.group.removeAll()},dispose:function(){},incrementalPrepareRender:null,incrementalRender:null,updateTransform:null,filterForExposedEvent:null};var GI=Mr.prototype;GI.updateView=GI.updateLayout=GI.updateVisual=function(t,e,i,n){this.render(t,e,i,n)},Xi(Mr),Ki(Mr,{registerWhenExtend:!0}),Mr.markUpdateMethod=function(t,e){BI(t).updateMethod=e};var FI={incrementalPrepareRender:{progress:function(t,e){e.view.incrementalRender(t,e.model,e.ecModel,e.api,e.payload)}},render:{forceFirstProgress:!0,progress:function(t,e){e.view.render(e.model,e.ecModel,e.api,e.payload)}}},WI="\0__throttleOriginMethod",HI="\0__throttleRate",ZI="\0__throttleType",UI={createOnAllSeries:!0,performRawSeries:!0,reset:function(t,e){var i=t.getData(),n=(t.visualColorAccessPath||"itemStyle.color").split("."),o=t.get(n)||t.getColorFromPalette(t.name,null,e.getSeriesCount());if(i.setVisual("color",o),!e.isSeriesFiltered(t)){"function"!=typeof o||o instanceof cM||i.each(function(e){i.setItemVisual(e,"color",o(t.getDataParams(e)))});return{dataEach:i.hasItemOption?function(t,e){var i=t.getItemModel(e).get(n,!0);null!=i&&t.setItemVisual(e,"color",i)}:null}}}},XI={toolbox:{brush:{title:{rect:"Rectangular selection",polygon:"Circle selection",lineX:"Horizontal selection",lineY:"Vertical selection",keep:"Keep",clear:"Clear"}},dataView:{title:"Data View",lang:["Data 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d=[],p=0;pi.blockIndex?i.step:null,a=n&&n.modDataCount;return{step:o,modBy:null!=a?Math.ceil(a/o):null,modDataCount:a}}},qI.getPipeline=function(t){return this._pipelineMap.get(t)},qI.updateStreamModes=function(t,e){var i=this._pipelineMap.get(t.uid),n=t.getData().count(),o=i.progressiveEnabled&&e.incrementalPrepareRender&&n>=i.threshold,a=t.get("large")&&n>=t.get("largeThreshold"),r="mod"===t.get("progressiveChunkMode")?n:null;t.pipelineContext=i.context={progressiveRender:o,modDataCount:r,large:a}},qI.restorePipelines=function(t){var e=this,i=e._pipelineMap=R();t.eachSeries(function(t){var n=t.getProgressive(),o=t.uid;i.set(o,{id:o,head:null,tail:null,threshold:t.getProgressiveThreshold(),progressiveEnabled:n&&!(t.preventIncremental&&t.preventIncremental()),blockIndex:-1,step:Math.round(n||700),count:0}),Zr(e,t,t.dataTask)})},qI.prepareStageTasks=function(){var t=this._stageTaskMap,e=this.ecInstance.getModel(),i=this.api;d(this._allHandlers,function(n){var o=t.get(n.uid)||t.set(n.uid,[]);n.reset&&Or(this,n,o,e,i),n.overallReset&&Er(this,n,o,e,i)},this)},qI.prepareView=function(t,e,i,n){var o=t.renderTask,a=o.context;a.model=e,a.ecModel=i,a.api=n,o.__block=!t.incrementalPrepareRender,Zr(this,e,o)},qI.performDataProcessorTasks=function(t,e){Nr(this,this._dataProcessorHandlers,t,e,{block:!0})},qI.performVisualTasks=function(t,e,i){Nr(this,this._visualHandlers,t,e,i)},qI.performSeriesTasks=function(t){var e;t.eachSeries(function(t){e|=t.dataTask.perform()}),this.unfinished|=e},qI.plan=function(){this._pipelineMap.each(function(t){var e=t.tail;do{if(e.__block){t.blockIndex=e.__idxInPipeline;break} +e=e.getUpstream()}while(e)})};var KI=qI.updatePayload=function(t,e){"remain"!==e&&(t.context.payload=e)},$I=Wr(0);Pr.wrapStageHandler=function(t,e){return x(t)&&(t={overallReset:t,seriesType:Ur(t)}),t.uid=No("stageHandler"),e&&(t.visualType=e),t};var JI,QI={},tT={};Xr(QI,hI),Xr(tT,za),QI.eachSeriesByType=QI.eachRawSeriesByType=function(t){JI=t},QI.eachComponent=function(t){"series"===t.mainType&&t.subType&&(JI=t.subType)};var 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a=Math.min(i,n);o.x=t,o.y=e,o.width=a,o.height=a},circle:function(t,e,i,n,o){o.cx=t+i/2,o.cy=e+n/2,o.r=Math.min(i,n)/2},diamond:function(t,e,i,n,o){o.cx=t+i/2,o.cy=e+n/2,o.width=i,o.height=n},pin:function(t,e,i,n,o){o.x=t+i/2,o.y=e+n/2,o.width=i,o.height=n},arrow:function(t,e,i,n,o){o.x=t+i/2,o.y=e+n/2,o.width=i,o.height=n},triangle:function(t,e,i,n,o){o.cx=t+i/2,o.cy=e+n/2,o.width=i,o.height=n}},EA={};d({line:rM,rect:aM,roundRect:aM,square:aM,circle:KS,diamond:kA,pin:PA,arrow:NA,triangle:LA},function(t,e){EA[e]=new t});var RA=Fn({type:"symbol",shape:{symbolType:"",x:0,y:0,width:0,height:0},beforeBrush:function(){var t=this.style;"pin"===this.shape.symbolType&&"inside"===t.textPosition&&(t.textPosition=["50%","40%"],t.textAlign="center",t.textVerticalAlign="middle")},buildPath:function(t,e,i){var n=e.symbolType,o=EA[n];"none"!==e.symbolType&&(o||(o=EA[n="rect"]),OA[n](e.x,e.y,e.width,e.height,o.shape),o.buildPath(t,o.shape,i))}}),zA={isDimensionStacked:hl,enableDataStack:ul,getStackedDimension:cl},BA=(Object.freeze||Object)({createList:function(t){return dl(t.getSource(),t)},getLayoutRect:la,dataStack:zA,createScale:function(t,e){var i=e;Lo.isInstance(e)||h(i=new Lo(e),CA);var n=Vl(i);return n.setExtent(t[0],t[1]),Bl(n,i),n},mixinAxisModelCommonMethods:function(t){h(t,CA)},completeDimensions:rl,createDimensions:oA,createSymbol:Xl}),VA=1e-8;ql.prototype={constructor:ql,properties:null,getBoundingRect:function(){var t=this._rect;if(t)return t;for(var e=Number.MAX_VALUE,i=[e,e],n=[-e,-e],o=[],a=[],r=this.geometries,s=0;s0}),function(t){var e=t.properties,i=t.geometry,n=i.coordinates,o=[];"Polygon"===i.type&&o.push({type:"polygon",exterior:n[0],interiors:n.slice(1)}),"MultiPolygon"===i.type&&d(n,function(t){t[0]&&o.push({type:"polygon",exterior:t[0],interiors:t.slice(1)})});var a=new ql(e.name,o,e.cp);return a.properties=e,a})},FA=Bi(),WA=[0,1],HA=function(t,e,i){this.dim=t,this.scale=e,this._extent=i||[0,0],this.inverse=!1,this.onBand=!1};HA.prototype={constructor:HA,contain:function(t){var e=this._extent,i=Math.min(e[0],e[1]),n=Math.max(e[0],e[1]);return t>=i&&t<=n},containData:function(t){return this.contain(this.dataToCoord(t))},getExtent:function(){return this._extent.slice()},getPixelPrecision:function(t){return Fo(t||this.scale.getExtent(),this._extent)},setExtent:function(t,e){var i=this._extent;i[0]=t,i[1]=e},dataToCoord:function(t,e){var i=this._extent,n=this.scale;return t=n.normalize(t),this.onBand&&"ordinal"===n.type&&fu(i=i.slice(),n.count()),Eo(t,WA,i,e)},coordToData:function(t,e){var i=this._extent,n=this.scale;this.onBand&&"ordinal"===n.type&&fu(i=i.slice(),n.count());var o=Eo(t,i,WA,e);return this.scale.scale(o)},pointToData:function(t,e){},getTicksCoords:function(t){var e=(t=t||{}).tickModel||this.getTickModel(),i=Ql(this,e),n=f(i.ticks,function(t){return{coord:this.dataToCoord(t),tickValue:t}},this),o=e.get("alignWithLabel");return pu(this,n,i.tickCategoryInterval,o,t.clamp),n},getViewLabels:function(){return Jl(this).labels},getLabelModel:function(){return this.model.getModel("axisLabel")},getTickModel:function(){return this.model.getModel("axisTick")},getBandWidth:function(){var t=this._extent,e=this.scale.getExtent(),i=e[1]-e[0]+(this.onBand?1:0);0===i&&(i=1);var n=Math.abs(t[1]-t[0]);return Math.abs(n)/i},isHorizontal:null,getRotate:null,calculateCategoryInterval:function(){return lu(this)}};var ZA=GA,UA={};d(["map","each","filter","indexOf","inherits","reduce","filter","bind","curry","isArray","isString","isObject","isFunction","extend","defaults","clone","merge"],function(t){UA[t]=$_[t]});var XA={};d(["extendShape","extendPath","makePath","makeImage","mergePath","resizePath","createIcon","setHoverStyle","setLabelStyle","setTextStyle","setText","getFont","updateProps","initProps","getTransform","clipPointsByRect","clipRectByRect","Group","Image","Text","Circle","Sector","Ring","Polygon","Polyline","Rect","Line","BezierCurve","Arc","IncrementalDisplayable","CompoundPath","LinearGradient","RadialGradient","BoundingRect"],function(t){XA[t]=bM[t]}),OI.extend({type:"series.line",dependencies:["grid","polar"],getInitialData:function(t,e){return dl(this.getSource(),this)},defaultOption:{zlevel:0,z:2,coordinateSystem:"cartesian2d",legendHoverLink:!0,hoverAnimation:!0,clipOverflow:!0,label:{position:"top"},lineStyle:{width:2,type:"solid"},step:!1,smooth:!1,smoothMonotone:null,symbol:"emptyCircle",symbolSize:4,symbolRotate:null,showSymbol:!0,showAllSymbol:"auto",connectNulls:!1,sampling:"none",animationEasing:"linear",progressive:0,hoverLayerThreshold:1/0}});var jA=mu.prototype,YA=mu.getSymbolSize=function(t,e){var i=t.getItemVisual(e,"symbolSize");return i instanceof Array?i.slice():[+i,+i]};jA._createSymbol=function(t,e,i,n,o){this.removeAll();var a=Xl(t,-1,-1,2,2,e.getItemVisual(i,"color"),o);a.attr({z2:100,culling:!0,scale:vu(n)}),a.drift=yu,this._symbolType=t,this.add(a)},jA.stopSymbolAnimation=function(t){this.childAt(0).stopAnimation(t)},jA.getSymbolPath=function(){return this.childAt(0)},jA.getScale=function(){return this.childAt(0).scale},jA.highlight=function(){this.childAt(0).trigger("emphasis")},jA.downplay=function(){this.childAt(0).trigger("normal")},jA.setZ=function(t,e){var i=this.childAt(0);i.zlevel=t,i.z=e},jA.setDraggable=function(t){var e=this.childAt(0);e.draggable=t,e.cursor=t?"move":"pointer"},jA.updateData=function(t,e,i){this.silent=!1;var n=t.getItemVisual(e,"symbol")||"circle",o=t.hostModel,a=YA(t,e),r=n!==this._symbolType;if(r){var s=t.getItemVisual(e,"symbolKeepAspect");this._createSymbol(n,t,e,a,s)}else(l=this.childAt(0)).silent=!1,bo(l,{scale:vu(a)},o,e);if(this._updateCommon(t,e,a,i),r){var l=this.childAt(0),u=i&&i.fadeIn,h={scale:l.scale.slice()};u&&(h.style={opacity:l.style.opacity}),l.scale=[0,0],u&&(l.style.opacity=0),So(l,h,o,e)} +this._seriesModel=o};var qA=["itemStyle"],KA=["emphasis","itemStyle"],$A=["label"],JA=["emphasis","label"];jA._updateCommon=function(t,e,i,n){var o=this.childAt(0),r=t.hostModel,s=t.getItemVisual(e,"color");"image"!==o.type&&o.useStyle({strokeNoScale:!0});var l=n&&n.itemStyle,u=n&&n.hoverItemStyle,h=n&&n.symbolRotate,c=n&&n.symbolOffset,d=n&&n.labelModel,f=n&&n.hoverLabelModel,p=n&&n.hoverAnimation,g=n&&n.cursorStyle;if(!n||t.hasItemOption){var m=n&&n.itemModel?n.itemModel:t.getItemModel(e);l=m.getModel(qA).getItemStyle(["color"]),u=m.getModel(KA).getItemStyle(),h=m.getShallow("symbolRotate"),c=m.getShallow("symbolOffset"),d=m.getModel($A),f=m.getModel(JA),p=m.getShallow("hoverAnimation"),g=m.getShallow("cursor")}else u=a({},u);var v=o.style;o.attr("rotation",(h||0)*Math.PI/180||0),c&&o.attr("position",[Ro(c[0],i[0]),Ro(c[1],i[1])]),g&&o.attr("cursor",g),o.setColor(s,n&&n.symbolInnerColor),o.setStyle(l);var y=t.getItemVisual(e,"opacity");null!=y&&(v.opacity=y);var x=t.getItemVisual(e,"liftZ"),_=o.__z2Origin;null!=x?null==_&&(o.__z2Origin=o.z2,o.z2+=x):null!=_&&(o.z2=_,o.__z2Origin=null);var w=n&&n.useNameLabel;co(v,u,d,f,{labelFetcher:r,labelDataIndex:e,defaultText:function(e,i){return w?t.getName(e):gu(t,e)},isRectText:!0,autoColor:s}),o.off("mouseover").off("mouseout").off("emphasis").off("normal"),o.hoverStyle=u,uo(o),o.__symbolOriginalScale=vu(i),p&&r.isAnimationEnabled()&&o.on("mouseover",xu).on("mouseout",_u).on("emphasis",wu).on("normal",bu)},jA.fadeOut=function(t,e){var i=this.childAt(0);this.silent=i.silent=!0,!(e&&e.keepLabel)&&(i.style.text=null),bo(i,{style:{opacity:0},scale:[0,0]},this._seriesModel,this.dataIndex,t)},u(mu,Zw);var QA=Su.prototype;QA.updateData=function(t,e){e=Iu(e);var i=this.group,n=t.hostModel,o=this._data,a=this._symbolCtor,r=Tu(t);o||i.removeAll(),t.diff(o).add(function(n){var o=t.getItemLayout(n);if(Mu(t,o,n,e)){var s=new a(t,n,r);s.attr("position",o),t.setItemGraphicEl(n,s),i.add(s)}}).update(function(s,l){var u=o.getItemGraphicEl(l),h=t.getItemLayout(s);Mu(t,h,s,e)?(u?(u.updateData(t,s,r),bo(u,{position:h},n)):(u=new a(t,s)).attr("position",h),i.add(u),t.setItemGraphicEl(s,u)):i.remove(u)}).remove(function(t){var e=o.getItemGraphicEl(t);e&&e.fadeOut(function(){i.remove(e)})}).execute(),this._data=t},QA.isPersistent=function(){return!0},QA.updateLayout=function(){var t=this._data;t&&t.eachItemGraphicEl(function(e,i){var n=t.getItemLayout(i);e.attr("position",n)})},QA.incrementalPrepareUpdate=function(t){this._seriesScope=Tu(t),this._data=null,this.group.removeAll()},QA.incrementalUpdate=function(t,e,i){i=Iu(i);for(var n=t.start;n0&&ku(i[o-1]);o--);for(;n0&&ku(i[a-1]);a--);for(;o=0){var r=o.getItemGraphicEl(a);if(!r){var s=o.getItemLayout(a);if(!s)return;(r=new mu(o,a)).position=s,r.setZ(t.get("zlevel"),t.get("z")),r.ignore=isNaN(s[0])||isNaN(s[1]),r.__temp=!0,o.setItemGraphicEl(a,r),r.stopSymbolAnimation(!0),this.group.add(r)} +r.highlight()}else Mr.prototype.highlight.call(this,t,e,i,n)},downplay:function(t,e,i,n){var o=t.getData(),a=zi(o,n);if(null!=a&&a>=0){var r=o.getItemGraphicEl(a);r&&(r.__temp?(o.setItemGraphicEl(a,null),this.group.remove(r)):r.downplay())}else Mr.prototype.downplay.call(this,t,e,i,n)},_newPolyline:function(t){var e=this._polyline;return e&&this._lineGroup.remove(e),e=new lD({shape:{points:t},silent:!0,z2:10}),this._lineGroup.add(e),this._polyline=e,e},_newPolygon:function(t,e){var i=this._polygon;return i&&this._lineGroup.remove(i),i=new uD({shape:{points:t,stackedOnPoints:e},silent:!0}),this._lineGroup.add(i),this._polygon=i,i},_updateAnimation:function(t,e,i,n,o,a){var r=this._polyline,s=this._polygon,l=t.hostModel,u=tD(this._data,t,this._stackedOnPoints,e,this._coordSys,i,this._valueOrigin,a),h=u.current,c=u.stackedOnCurrent,d=u.next,f=u.stackedOnNext;o&&(h=Hu(u.current,i,o),c=Hu(u.stackedOnCurrent,i,o),d=Hu(u.next,i,o),f=Hu(u.stackedOnNext,i,o)),r.shape.__points=u.current,r.shape.points=h,bo(r,{shape:{points:d}},l),s&&(s.setShape({points:h,stackedOnPoints:c}),bo(s,{shape:{points:d,stackedOnPoints:f}},l));for(var p=[],g=u.status,m=0;me&&(e=t[i]);return isFinite(e)?e:NaN},min:function(t){for(var e=1/0,i=0;ie[1]&&e.reverse(),e},getOtherAxis:function(){this.grid.getOtherAxis()},pointToData:function(t,e){return this.coordToData(this.toLocalCoord(t["x"===this.dim?0:1]),e)},toLocalCoord:null,toGlobalCoord:null},u(gD,HA);var mD={show:!0,zlevel:0,z:0,inverse:!1,name:"",nameLocation:"end",nameRotate:null,nameTruncate:{maxWidth:null,ellipsis:"...",placeholder:"."},nameTextStyle:{},nameGap:15,silent:!1,triggerEvent:!1,tooltip:{show:!1},axisPointer:{},axisLine:{show:!0,onZero:!0,onZeroAxisIndex:null,lineStyle:{color:"#333",width:1,type:"solid"},symbol:["none","none"],symbolSize:[10,15]},axisTick:{show:!0,inside:!1,length:5,lineStyle:{width:1}},axisLabel:{show:!0,inside:!1,rotate:0,showMinLabel:null,showMaxLabel:null,margin:8,fontSize:12},splitLine:{show:!0,lineStyle:{color:["#ccc"],width:1,type:"solid"}},splitArea:{show:!1,areaStyle:{color:["rgba(250,250,250,0.3)","rgba(200,200,200,0.3)"]}}},vD={};vD.categoryAxis=n({boundaryGap:!0,deduplication:null,splitLine:{show:!1},axisTick:{alignWithLabel:!1,interval:"auto"},axisLabel:{interval:"auto"}},mD),vD.valueAxis=n({boundaryGap:[0,0],splitNumber:5},mD),vD.timeAxis=r({scale:!0,min:"dataMin",max:"dataMax"},vD.valueAxis),vD.logAxis=r({scale:!0,logBase:10},vD.valueAxis);var yD=["value","category","time","log"],xD=function(t,e,i,a){d(yD,function(r){e.extend({type:t+"Axis."+r,mergeDefaultAndTheme:function(e,o){var a=this.layoutMode,s=a?da(e):{};n(e,o.getTheme().get(r+"Axis")),n(e,this.getDefaultOption()),e.type=i(t,e),a&&ca(e,s,a)},optionUpdated:function(){"category"===this.option.type&&(this.__ordinalMeta=ml.createByAxisModel(this))},getCategories:function(t){var e=this.option;if("category"===e.type)return t?e.data:this.__ordinalMeta.categories},getOrdinalMeta:function(){return this.__ordinalMeta},defaultOption:o([{},vD[r+"Axis"],a],!0)})}),YM.registerSubTypeDefaulter(t+"Axis",v(i,t))},_D=YM.extend({type:"cartesian2dAxis",axis:null,init:function(){_D.superApply(this,"init",arguments),this.resetRange()},mergeOption:function(){_D.superApply(this,"mergeOption",arguments),this.resetRange()},restoreData:function(){_D.superApply(this,"restoreData",arguments),this.resetRange()},getCoordSysModel:function(){return this.ecModel.queryComponents({mainType:"grid",index:this.option.gridIndex,id:this.option.gridId})[0]}});n(_D.prototype,CA);var wD={offset:0};xD("x",_D,qu,wD),xD("y",_D,qu,wD),YM.extend({type:"grid",dependencies:["xAxis","yAxis"],layoutMode:"box",coordinateSystem:null,defaultOption:{show:!1,zlevel:0,z:0,left:"10%",top:60,right:"10%",bottom:60,containLabel:!1,backgroundColor:"rgba(0,0,0,0)",borderWidth:1,borderColor:"#ccc"}});var bD=$u.prototype;bD.type="grid",bD.axisPointerEnabled=!0,bD.getRect=function(){return this._rect},bD.update=function(t,e){var i=this._axesMap;this._updateScale(t,this.model),d(i.x,function(t){Bl(t.scale,t.model)}),d(i.y,function(t){Bl(t.scale,t.model)});var n={};d(i.x,function(t){Ju(i,"y",t,n)}),d(i.y,function(t){Ju(i,"x",t,n)}),this.resize(this.model,e)},bD.resize=function(t,e,i){function n(){d(a,function(t){var e=t.isHorizontal(),i=e?[0,o.width]:[0,o.height],n=t.inverse?1:0;t.setExtent(i[n],i[1-n]),th(t,e?o.x:o.y)})} +var o=la(t.getBoxLayoutParams(),{width:e.getWidth(),height:e.getHeight()});this._rect=o;var a=this._axesList;n(),!i&&t.get("containLabel")&&(d(a,function(t){if(!t.model.get("axisLabel.inside")){var e=Hl(t);if(e){var i=t.isHorizontal()?"height":"width",n=t.model.get("axisLabel.margin");o[i]-=e[i]+n,"top"===t.position?o.y+=e.height+n:"left"===t.position&&(o.x+=e.width+n)}}}),n())},bD.getAxis=function(t,e){var i=this._axesMap[t];if(null!=i){if(null==e) +for(var n in i) +if(i.hasOwnProperty(n))return i[n];return i[e]}},bD.getAxes=function(){return this._axesList.slice()},bD.getCartesian=function(t,e){if(null!=t&&null!=e){var i="x"+t+"y"+e;return this._coordsMap[i]} +w(t)&&(e=t.yAxisIndex,t=t.xAxisIndex);for(var n=0,o=this._coordsList;nu[1]?-1:1,c=["start"===o?u[0]-h*l:"end"===o?u[1]+h*l:(u[0]+u[1])/2,uh(o)?t.labelOffset+r*l:0],d=e.get("nameRotate");null!=d&&(d=d*MD/180);var f;uh(o)?n=AD(t.rotation,null!=d?d:t.rotation,r):(n=oh(t,o,d||0,u),null!=(f=t.axisNameAvailableWidth)&&(f=Math.abs(f/Math.sin(n.rotation)),!isFinite(f)&&(f=null)));var p=s.getFont(),g=e.get("nameTruncate",!0)||{},m=g.ellipsis,v=T(t.nameTruncateMaxWidth,g.maxWidth,f),y=null!=m&&null!=v?VM(i,v,p,m,{minChar:2,placeholder:g.placeholder}):i,x=e.get("tooltip",!0),_=e.mainType,w={componentType:_,name:i,$vars:["name"]};w[_+"Index"]=e.componentIndex;var b=new qS({anid:"name",__fullText:i,__truncatedText:y,position:c,rotation:n.rotation,silent:ah(e),z2:1,tooltip:x&&x.show?a({content:i,formatter:function(){return i},formatterParams:w},x):null});fo(b.style,s,{text:y,textFont:p,textFill:s.getTextColor()||e.get("axisLine.lineStyle.color"),textAlign:n.textAlign,textVerticalAlign:n.textVerticalAlign}),e.get("triggerEvent")&&(b.eventData=nh(e),b.eventData.targetType="axisName",b.eventData.name=i),this._dumbGroup.add(b),b.updateTransform(),this.group.add(b),b.decomposeTransform()}}},AD=ID.innerTextLayout=function(t,e,i){var n,o,a=Ho(e-t);return Zo(a)?(o=i>0?"top":"bottom",n="center"):Zo(a-MD)?(o=i>0?"bottom":"top",n="center"):(o="middle",n=a>0&&a0?"right":"left":i>0?"left":"right"),{rotation:a,textAlign:n,textVerticalAlign:o}},DD=d,CD=v,LD=Vs({type:"axis",_axisPointer:null,axisPointerClass:null,render:function(t,e,i,n){this.axisPointerClass&&yh(t),LD.superApply(this,"render",arguments),Sh(this,t,0,i,0,!0)},updateAxisPointer:function(t,e,i,n,o){Sh(this,t,0,i,0,!1)},remove:function(t,e){var i=this._axisPointer;i&&i.remove(e),LD.superApply(this,"remove",arguments)},dispose:function(t,e){Mh(this,e),LD.superApply(this,"dispose",arguments)}}),kD=[];LD.registerAxisPointerClass=function(t,e){kD[t]=e},LD.getAxisPointerClass=function(t){return t&&kD[t]};var PD=["axisLine","axisTickLabel","axisName"],ND=["splitArea","splitLine"],OD=LD.extend({type:"cartesianAxis",axisPointerClass:"CartesianAxisPointer",render:function(t,e,i,n){this.group.removeAll();var o=this._axisGroup;if(this._axisGroup=new Zw,this.group.add(this._axisGroup),t.get("show")){var a=t.getCoordSysModel(),r=Ih(a,t),s=new ID(t,r);d(PD,s.add,s),this._axisGroup.add(s.getGroup()),d(ND,function(e){t.get(e+".show")&&this["_"+e](t,a)},this),Ao(o,this._axisGroup,t),OD.superCall(this,"render",t,e,i,n)}},remove:function(){this._splitAreaColors=null},_splitLine:function(t,e){var i=t.axis;if(!i.scale.isBlank()){var n=t.getModel("splitLine"),o=n.getModel("lineStyle"),a=o.get("color");a=y(a)?a:[a];for(var s=e.coordinateSystem.getRect(),l=i.isHorizontal(),u=0,h=i.getTicksCoords({tickModel:n}),c=[],d=[],f=o.getLineStyle(),p=0;p1){var c;"string"==typeof o?c=dD[o]:"function"==typeof o&&(c=o),c&&t.setData(n.downSample(n.mapDimension(s.dim),1/h,c,fD))}}}}}("line"));var ED=OI.extend({type:"series.__base_bar__",getInitialData:function(t,e){return dl(this.getSource(),this)},getMarkerPosition:function(t){var e=this.coordinateSystem;if(e){var i=e.dataToPoint(e.clampData(t)),n=this.getData(),o=n.getLayout("offset"),a=n.getLayout("size");return i[e.getBaseAxis().isHorizontal()?0:1]+=o+a/2,i} +return[NaN,NaN]},defaultOption:{zlevel:0,z:2,coordinateSystem:"cartesian2d",legendHoverLink:!0,barMinHeight:0,barMinAngle:0,large:!1,largeThreshold:400,progressive:3e3,progressiveChunkMode:"mod",itemStyle:{},emphasis:{}}});ED.extend({type:"series.bar",dependencies:["grid","polar"],brushSelector:"rect",getProgressive:function(){return!!this.get("large")&&this.get("progressive")},getProgressiveThreshold:function(){var t=this.get("progressiveThreshold"),e=this.get("largeThreshold");return e>t&&(t=e),t}});var RD=Wb([["fill","color"],["stroke","borderColor"],["lineWidth","borderWidth"],["stroke","barBorderColor"],["lineWidth","barBorderWidth"],["opacity"],["shadowBlur"],["shadowOffsetX"],["shadowOffsetY"],["shadowColor"]]),zD={getBarItemStyle:function(t){var e=RD(this,t);if(this.getBorderLineDash){var i=this.getBorderLineDash();i&&(e.lineDash=i)} +return e}},BD=["itemStyle","barBorderWidth"];a(Lo.prototype,zD),Fs({type:"bar",render:function(t,e,i){this._updateDrawMode(t);var n=t.get("coordinateSystem");return"cartesian2d"!==n&&"polar"!==n||(this._isLargeDraw?this._renderLarge(t,e,i):this._renderNormal(t,e,i)),this.group},incrementalPrepareRender:function(t,e,i){this._clear(),this._updateDrawMode(t)},incrementalRender:function(t,e,i,n){this._incrementalRenderLarge(t,e)},_updateDrawMode:function(t){var e=t.pipelineContext.large;(null==this._isLargeDraw||e^this._isLargeDraw)&&(this._isLargeDraw=e,this._clear())},_renderNormal:function(t,e,i){var n,o=this.group,a=t.getData(),r=this._data,s=t.coordinateSystem,l=s.getBaseAxis();"cartesian2d"===s.type?n=l.isHorizontal():"polar"===s.type&&(n="angle"===l.dim);var u=t.isAnimationEnabled()?t:null;a.diff(r).add(function(e){if(a.hasValue(e)){var i=a.getItemModel(e),r=GD[s.type](a,e,i),l=VD[s.type](a,e,i,r,n,u);a.setItemGraphicEl(e,l),o.add(l),Lh(l,a,e,i,r,t,n,"polar"===s.type)}}).update(function(e,i){var l=r.getItemGraphicEl(i);if(a.hasValue(e)){var h=a.getItemModel(e),c=GD[s.type](a,e,h);l?bo(l,{shape:c},u,e):l=VD[s.type](a,e,h,c,n,u,!0),a.setItemGraphicEl(e,l),o.add(l),Lh(l,a,e,h,c,t,n,"polar"===s.type)}else o.remove(l)}).remove(function(t){var e=r.getItemGraphicEl(t);"cartesian2d"===s.type?e&&Dh(t,u,e):e&&Ch(t,u,e)}).execute(),this._data=a},_renderLarge:function(t,e,i){this._clear(),Ph(t,this.group)},_incrementalRenderLarge:function(t,e){Ph(e,this.group,!0)},dispose:B,remove:function(t){this._clear(t)},_clear:function(t){var e=this.group,i=this._data;t&&t.get("animation")&&i&&!this._isLargeDraw?i.eachItemGraphicEl(function(e){"sector"===e.type?Ch(e.dataIndex,t,e):Dh(e.dataIndex,t,e)}):e.removeAll(),this._data=null}});var VD={cartesian2d:function(t,e,i,n,o,r,s){var l=new aM({shape:a({},n)});if(r){var u=l.shape,h=o?"height":"width",c={};u[h]=0,c[h]=n[h],bM[s?"updateProps":"initProps"](l,{shape:c},r,e)} +return l},polar:function(t,e,i,n,o,a,s){var l=n.startAngle0?1:-1,r=n.height>0?1:-1;return{x:n.x+a*o/2,y:n.y+r*o/2,width:n.width-a*o,height:n.height-r*o}},polar:function(t,e,i){var n=t.getItemLayout(e);return{cx:n.cx,cy:n.cy,r0:n.r0,r:n.r,startAngle:n.startAngle,endAngle:n.endAngle}}},FD=kn.extend({type:"largeBar",shape:{points:[]},buildPath:function(t,e){for(var i=e.points,n=this.__startPoint,o=this.__valueIdx,a=0;a0&&"scale"!==u){var d=o.getItemLayout(0),f=Math.max(i.getWidth(),i.getHeight())/2,p=m(r.removeClipPath,r);r.setClipPath(this._createClipPath(d.cx,d.cy,f,d.startAngle,d.clockwise,p,t))}else r.removeClipPath();this._data=o}},dispose:function(){},_createClipPath:function(t,e,i,n,o,a,r){var s=new QS({shape:{cx:t,cy:e,r0:0,r:i,startAngle:n,endAngle:n,clockwise:o}});return So(s,{shape:{endAngle:n+(o?1:-1)*Math.PI*2}},r,a),s},containPoint:function(t,e){var i=e.getData().getItemLayout(0);if(i){var n=t[0]-i.cx,o=t[1]-i.cy,a=Math.sqrt(n*n+o*o);return a<=i.r&&a>=i.r0}}});var XD=function(t,e){d(e,function(e){e.update="updateView",Ps(e,function(i,n){var o={};return n.eachComponent({mainType:"series",subType:t,query:i},function(t){t[e.method]&&t[e.method](i.name,i.dataIndex);var n=t.getData();n.each(function(e){var i=n.getName(e);o[i]=t.isSelected(i)||!1})}),{name:i.name,selected:o}})})},jD=function(t){return{getTargetSeries:function(e){var i={},n=R();return e.eachSeriesByType(t,function(t){t.__paletteScope=i,n.set(t.uid,t)}),n},reset:function(t,e){var i=t.getRawData(),n={},o=t.getData();o.each(function(t){var e=o.getRawIndex(t);n[e]=t}),i.each(function(e){var a=n[e],r=null!=a&&o.getItemVisual(a,"color",!0);if(r)i.setItemVisual(e,"color",r);else{var s=i.getItemModel(e).get("itemStyle.color")||t.getColorFromPalette(i.getName(e)||e+"",t.__paletteScope,i.count());i.setItemVisual(e,"color",s),null!=a&&o.setItemVisual(a,"color",s)}})}}},YD=function(t,e,i,n){var o,a,r=t.getData(),s=[],l=!1;r.each(function(i){var n,u,h,c,d=r.getItemLayout(i),f=r.getItemModel(i),p=f.getModel("label"),g=p.get("position")||f.get("emphasis.label.position"),m=f.getModel("labelLine"),v=m.get("length"),y=m.get("length2"),x=(d.startAngle+d.endAngle)/2,_=Math.cos(x),w=Math.sin(x);o=d.cx,a=d.cy;var b="inside"===g||"inner"===g;if("center"===g)n=d.cx,u=d.cy,c="center";else{var S=(b?(d.r+d.r0)/2*_:d.r*_)+o,M=(b?(d.r+d.r0)/2*w:d.r*w)+a;if(n=S+3*_,u=M+3*w,!b){var I=S+_*(v+e-d.r),T=M+w*(v+e-d.r),A=I+(_<0?-1:1)*y,D=T;n=A+(_<0?-5:5),u=D,h=[[S,M],[I,T],[A,D]]} +c=b?"center":_>0?"left":"right"} +var C=p.getFont(),L=p.get("rotate")?_<0?-x+Math.PI:-x:0,k=De(t.getFormattedLabel(i,"normal")||r.getName(i),C,c,"top");l=!!L,d.label={x:n,y:u,position:g,height:k.height,len:v,len2:y,linePoints:h,textAlign:c,verticalAlign:"middle",rotation:L,inside:b},b||s.push(d.label)}),!l&&t.get("avoidLabelOverlap")&&Bh(s,o,a,e,i,n)},qD=2*Math.PI,KD=Math.PI/180,$D=function(t){return{seriesType:t,reset:function(t,e){var i=e.findComponents({mainType:"legend"});if(i&&i.length){var n=t.getData();n.filterSelf(function(t){for(var e=n.getName(t),o=0;o=0;s--){var l=2*s,u=n[l]-a/2,h=n[l+1]-r/2;if(t>=u&&e>=h&&t<=u+a&&e<=h+r)return s} +return-1}}),QD=Vh.prototype;QD.isPersistent=function(){return!this._incremental},QD.updateData=function(t){this.group.removeAll();var e=new JD({rectHover:!0,cursor:"default"});e.setShape({points:t.getLayout("symbolPoints")}),this._setCommon(e,t),this.group.add(e),this._incremental=null},QD.updateLayout=function(t){if(!this._incremental){var e=t.getLayout("symbolPoints");this.group.eachChild(function(t){if(null!=t.startIndex){var i=2*(t.endIndex-t.startIndex),n=4*t.startIndex*2;e=new Float32Array(e.buffer,n,i)} +t.setShape("points",e)})}},QD.incrementalPrepareUpdate=function(t){this.group.removeAll(),this._clearIncremental(),t.count()>2e6?(this._incremental||(this._incremental=new Gn({silent:!0})),this.group.add(this._incremental)):this._incremental=null},QD.incrementalUpdate=function(t,e){var i;this._incremental?(i=new JD,this._incremental.addDisplayable(i,!0)):((i=new JD({rectHover:!0,cursor:"default",startIndex:t.start,endIndex:t.end})).incremental=!0,this.group.add(i)),i.setShape({points:e.getLayout("symbolPoints")}),this._setCommon(i,e,!!this._incremental)},QD._setCommon=function(t,e,i){var n=e.hostModel,o=e.getVisual("symbolSize");t.setShape("size",o instanceof Array?o:[o,o]),t.symbolProxy=Xl(e.getVisual("symbol"),0,0,0,0),t.setColor=t.symbolProxy.setColor;var a=t.shape.size[0]<4;t.useStyle(n.getModel("itemStyle").getItemStyle(a?["color","shadowBlur","shadowColor"]:["color"]));var r=e.getVisual("color");r&&t.setColor(r),i||(t.seriesIndex=n.seriesIndex,t.on("mousemove",function(e){t.dataIndex=null;var i=t.findDataIndex(e.offsetX,e.offsetY);i>=0&&(t.dataIndex=i+(t.startIndex||0))}))},QD.remove=function(){this._clearIncremental(),this._incremental=null,this.group.removeAll()},QD._clearIncremental=function(){var t=this._incremental;t&&t.clearDisplaybles()},Fs({type:"scatter",render:function(t,e,i){var n=t.getData();this._updateSymbolDraw(n,t).updateData(n),this._finished=!0},incrementalPrepareRender:function(t,e,i){var n=t.getData();this._updateSymbolDraw(n,t).incrementalPrepareUpdate(n),this._finished=!1},incrementalRender:function(t,e,i){this._symbolDraw.incrementalUpdate(t,e.getData()),this._finished=t.end===e.getData().count()},updateTransform:function(t,e,i){var n=t.getData();if(this.group.dirty(),!this._finished||n.count()>1e4||!this._symbolDraw.isPersistent())return{update:!0};var o=cD().reset(t);o.progress&&o.progress({start:0,end:n.count()},n),this._symbolDraw.updateLayout(n)},_updateSymbolDraw:function(t,e){var i=this._symbolDraw,n=e.pipelineContext.large;return i&&n===this._isLargeDraw||(i&&i.remove(),i=this._symbolDraw=n?new Vh:new Su,this._isLargeDraw=n,this.group.removeAll()),this.group.add(i.group),i},remove:function(t,e){this._symbolDraw&&this._symbolDraw.remove(!0),this._symbolDraw=null},dispose:function(){}}),Es(hD("scatter","circle")),Os(cD("scatter")),u(Gh,HA),Fh.prototype.getIndicatorAxes=function(){return this._indicatorAxes},Fh.prototype.dataToPoint=function(t,e){var i=this._indicatorAxes[e];return this.coordToPoint(i.dataToCoord(t),e)},Fh.prototype.coordToPoint=function(t,e){var i=this._indicatorAxes[e].angle;return[this.cx+t*Math.cos(i),this.cy-t*Math.sin(i)]},Fh.prototype.pointToData=function(t){var e=t[0]-this.cx,i=t[1]-this.cy,n=Math.sqrt(e*e+i*i);e/=n,i/=n;for(var o,a=Math.atan2(-i,e),r=1/0,s=-1,l=0;ln[0]&&isFinite(c)&&isFinite(n[0]))}else{r.getTicks().length-1>a&&(u=i(u));var d=Math.round((n[0]+n[1])/2/u)*u,f=Math.round(a/2);r.setExtent(zo(d-f*u),zo(d+(a-f)*u)),r.setInterval(u)}})},Fh.dimensions=[],Fh.create=function(t,e){var i=[];return t.eachComponent("radar",function(n){var o=new Fh(n,t,e);i.push(o),n.coordinateSystem=o}),t.eachSeriesByType("radar",function(t){"radar"===t.get("coordinateSystem")&&(t.coordinateSystem=i[t.get("radarIndex")||0])}),i},Ba.register("radar",Fh);var tC=vD.valueAxis,eC=(Bs({type:"radar",optionUpdated:function(){var t=this.get("boundaryGap"),e=this.get("splitNumber"),o=this.get("scale"),s=this.get("axisLine"),l=this.get("axisTick"),u=this.get("axisLabel"),h=this.get("name"),c=this.get("name.show"),d=this.get("name.formatter"),p=this.get("nameGap"),g=this.get("triggerEvent"),m=f(this.get("indicator")||[],function(f){null!=f.max&&f.max>0&&!f.min?f.min=0:null!=f.min&&f.min<0&&!f.max&&(f.max=0);var m=h;if(null!=f.color&&(m=r({color:f.color},h)),f=n(i(f),{boundaryGap:t,splitNumber:e,scale:o,axisLine:s,axisTick:l,axisLabel:u,name:f.text,nameLocation:"end",nameGap:p,nameTextStyle:m,triggerEvent:g},!1),c||(f.name=""),"string"==typeof d){var v=f.name;f.name=d.replace("{value}",null!=v?v:"")}else "function"==typeof d&&(f.name=d(f.name,f));var y=a(new Lo(f,null,this.ecModel),CA);return y.mainType="radar",y.componentIndex=this.componentIndex,y},this);this.getIndicatorModels=function(){return m}},defaultOption:{zlevel:0,z:0,center:["50%","50%"],radius:"75%",startAngle:90,name:{show:!0},boundaryGap:[0,0],splitNumber:5,nameGap:15,scale:!1,shape:"polygon",axisLine:n({lineStyle:{color:"#bbb"}},tC.axisLine),axisLabel:Wh(tC.axisLabel,!1),axisTick:Wh(tC.axisTick,!1),splitLine:Wh(tC.splitLine,!0),splitArea:Wh(tC.splitArea,!0),indicator:[]}}),["axisLine","axisTickLabel","axisName"]);Vs({type:"radar",render:function(t,e,i){this.group.removeAll(),this._buildAxes(t),this._buildSplitLineAndArea(t)},_buildAxes:function(t){var e=t.coordinateSystem;d(f(e.getIndicatorAxes(),function(t){return new ID(t.model,{position:[e.cx,e.cy],rotation:t.angle,labelDirection:-1,tickDirection:-1,nameDirection:1})}),function(t){d(eC,t.add,t),this.group.add(t.getGroup())},this)},_buildSplitLineAndArea:function(t){function e(t,e,i){var n=i%e.length;return t[n]=t[n]||[],n} +var i=t.coordinateSystem,n=i.getIndicatorAxes();if(n.length){var o=t.get("shape"),a=t.getModel("splitLine"),s=t.getModel("splitArea"),l=a.getModel("lineStyle"),u=s.getModel("areaStyle"),h=a.get("show"),c=s.get("show"),p=l.get("color"),g=u.get("color");p=y(p)?p:[p],g=y(g)?g:[g];var m=[],v=[];if("circle"===o) +for(var x=n[0].getTicksCoords(),_=i.cx,w=i.cy,b=0;b"+f(i,function(i,n){var o=e.get(e.mapDimension(i.dim),t);return Qo(i.name+" : "+o)}).join("
          ")},defaultOption:{zlevel:0,z:2,coordinateSystem:"radar",legendHoverLink:!0,radarIndex:0,lineStyle:{width:2,type:"solid"},label:{position:"top"},symbol:"emptyCircle",symbolSize:4}});Fs({type:"radar",render:function(t,e,n){function o(t,e){var i=t.getItemVisual(e,"symbol")||"circle",n=t.getItemVisual(e,"color");if("none"!==i){var o=Hh(t.getItemVisual(e,"symbolSize")),a=Xl(i,-1,-1,2,2,n);return a.attr({style:{strokeNoScale:!0},z2:100,scale:[o[0]/2,o[1]/2]}),a}} +function a(e,i,n,a,r,s){n.removeAll();for(var l=0;l"+Qo(n+" : "+i)},getTooltipPosition:function(t){if(null!=t){var e=this.getData().getName(t),i=this.coordinateSystem,n=i.getRegion(e);return n&&i.dataToPoint(n.center)}},setZoom:function(t){this.option.zoom=t},setCenter:function(t){this.option.center=t},defaultOption:{zlevel:0,z:2,coordinateSystem:"geo",map:"",left:"center",top:"center",aspectScale:.75,showLegendSymbol:!0,dataRangeHoverLink:!0,boundingCoords:null,center:null,zoom:1,scaleLimit:null,label:{show:!1,color:"#000"},itemStyle:{borderWidth:.5,borderColor:"#444",areaColor:"#eee"},emphasis:{label:{show:!0,color:"rgb(100,0,0)"},itemStyle:{areaColor:"rgba(255,215,0,0.8)"}}}});h(wC,HD);var bC="\0_ec_interaction_mutex";Ps({type:"takeGlobalCursor",event:"globalCursorTaken",update:"update"},function(){}),h(ac,aw);var SC={axisPointer:1,tooltip:1,brush:1};_c.prototype={constructor:_c,draw:function(t,e,i,n,o){var a="geo"===t.mainType,r=t.getData&&t.getData();a&&e.eachComponent({mainType:"series",subType:"map"},function(e){r||e.getHostGeoModel()!==t||(r=e.getData())});var s=t.coordinateSystem;this._updateBackground(s);var l=this._regionsGroup,u=this.group,h=s.scale,c={position:s.position,scale:h};!l.childAt(0)||o?u.attr(c):bo(u,c,t),l.removeAll();var f=["itemStyle"],p=["emphasis","itemStyle"],g=["label"],m=["emphasis","label"],v=R();d(s.regions,function(e){var i=v.get(e.name)||v.set(e.name,new Zw),n=new hM({shape:{paths:[]}});i.add(n);var o,s=(C=t.getRegionModel(e.name)||t).getModel(f),u=C.getModel(p),c=vc(s),y=vc(u),x=C.getModel(g),_=C.getModel(m);if(r){o=r.indexOfName(e.name);var w=r.getItemVisual(o,"color",!0);w&&(c.fill=w)} +d(e.geometries,function(t){if("polygon"===t.type){n.shape.paths.push(new nM({shape:{points:t.exterior}}));for(var e=0;e<(t.interiors?t.interiors.length:0);e++)n.shape.paths.push(new nM({shape:{points:t.interiors[e]}}))}}),n.setStyle(c),n.style.strokeNoScale=!0,n.culling=!0;var b=x.get("show"),S=_.get("show"),M=r&&isNaN(r.get(r.mapDimension("value"),o)),I=r&&r.getItemLayout(o);if(a||M&&(b||S)||I&&I.showLabel){var T,A=a?e.name:o;(!r||o>=0)&&(T=t);var D=new qS({position:e.center.slice(),scale:[1/h[0],1/h[1]],z2:10,silent:!0});co(D.style,D.hoverStyle={},x,_,{labelFetcher:T,labelDataIndex:A,defaultText:e.name,useInsideStyle:!1},{textAlign:"center",textVerticalAlign:"middle"}),i.add(D)} +if(r)r.setItemGraphicEl(o,i);else{var C=t.getRegionModel(e.name);n.eventData={componentType:"geo",componentIndex:t.componentIndex,geoIndex:t.componentIndex,name:e.name,region:C&&C.option||{}}}(i.__regions||(i.__regions=[])).push(e),uo(i,y,{hoverSilentOnTouch:!!t.get("selectedMode")}),l.add(i)}),this._updateController(t,e,i),yc(this,t,l,i,n),xc(t,l)},remove:function(){this._regionsGroup.removeAll(),this._backgroundGroup.removeAll(),this._controller.dispose(),this._mapName&&xC.removeGraphic(this._mapName,this.uid),this._mapName=null,this._controllerHost={}},_updateBackground:function(t){var e=t.map;this._mapName!==e&&d(xC.makeGraphic(e,this.uid),function(t){this._backgroundGroup.add(t)},this),this._mapName=e},_updateController:function(t,e,i){function n(){var e={type:"geoRoam",componentType:l};return e[l+"Id"]=t.id,e} +var o=t.coordinateSystem,r=this._controller,s=this._controllerHost;s.zoomLimit=t.get("scaleLimit"),s.zoom=o.getZoom(),r.enable(t.get("roam")||!1);var l=t.mainType;r.off("pan").on("pan",function(t){this._mouseDownFlag=!1,pc(s,t.dx,t.dy),i.dispatchAction(a(n(),{dx:t.dx,dy:t.dy}))},this),r.off("zoom").on("zoom",function(t){if(this._mouseDownFlag=!1,gc(s,t.scale,t.originX,t.originY),i.dispatchAction(a(n(),{zoom:t.scale,originX:t.originX,originY:t.originY})),this._updateGroup){var e=this.group.scale;this._regionsGroup.traverse(function(t){"text"===t.type&&t.attr("scale",[1/e[0],1/e[1]])})}},this),r.setPointerChecker(function(e,n,a){return o.getViewRectAfterRoam().contain(n,a)&&!mc(e,i,t)})}},Fs({type:"map",render:function(t,e,i,n){if(!n||"mapToggleSelect"!==n.type||n.from!==this.uid){var o=this.group;if(o.removeAll(),!t.getHostGeoModel()){if(n&&"geoRoam"===n.type&&"series"===n.componentType&&n.seriesId===t.id)(a=this._mapDraw)&&o.add(a.group);else if(t.needsDrawMap){var a=this._mapDraw||new _c(i,!0);o.add(a.group),a.draw(t,e,i,this,n),this._mapDraw=a}else this._mapDraw&&this._mapDraw.remove(),this._mapDraw=null;t.get("showLegendSymbol")&&e.getComponent("legend")&&this._renderSymbols(t,e,i)}}},remove:function(){this._mapDraw&&this._mapDraw.remove(),this._mapDraw=null,this.group.removeAll()},dispose:function(){this._mapDraw&&this._mapDraw.remove(),this._mapDraw=null},_renderSymbols:function(t,e,i){var n=t.originalData,o=this.group;n.each(n.mapDimension("value"),function(e,i){if(!isNaN(e)){var a=n.getItemLayout(i);if(a&&a.point){var r=a.point,s=a.offset,l=new KS({style:{fill:t.getData().getVisual("color")},shape:{cx:r[0]+9*s,cy:r[1],r:3},silent:!0,z2:s?8:10});if(!s){var u=t.mainSeries.getData(),h=n.getName(i),c=u.indexOfName(h),d=n.getItemModel(i),f=d.getModel("label"),p=d.getModel("emphasis.label"),g=u.getItemGraphicEl(c),m=A(t.getFormattedLabel(i,"normal"),h),v=A(t.getFormattedLabel(i,"emphasis"),m),y=function(){var t=fo({},p,{text:p.get("show")?v:null},{isRectText:!0,useInsideStyle:!1},!0);l.style.extendFrom(t),l.__mapOriginalZ2=l.z2,l.z2+=1},x=function(){fo(l.style,f,{text:f.get("show")?m:null,textPosition:f.getShallow("position")||"bottom"},{isRectText:!0,useInsideStyle:!1}),null!=l.__mapOriginalZ2&&(l.z2=l.__mapOriginalZ2,l.__mapOriginalZ2=null)};g.on("mouseover",y).on("mouseout",x).on("emphasis",y).on("normal",x),x()} +o.add(l)}}})}}),Ps({type:"geoRoam",event:"geoRoam",update:"updateTransform"},function(t,e){var i=t.componentType||"series";e.eachComponent({mainType:i,query:t},function(e){var n=e.coordinateSystem;if("geo"===n.type){var o=wc(n,t,e.get("scaleLimit"));e.setCenter&&e.setCenter(o.center),e.setZoom&&e.setZoom(o.zoom),"series"===i&&d(e.seriesGroup,function(t){t.setCenter(o.center),t.setZoom(o.zoom)})}})});Os(function(t){var e={};t.eachSeriesByType("map",function(i){var n=i.getMapType();if(!i.getHostGeoModel()&&!e[n]){var o={};d(i.seriesGroup,function(e){var i=e.coordinateSystem,n=e.originalData;e.get("showLegendSymbol")&&t.getComponent("legend")&&n.each(n.mapDimension("value"),function(t,e){var a=n.getName(e),r=i.getRegion(a);if(r&&!isNaN(t)){var s=o[a]||0,l=i.dataToPoint(r.center);o[a]=s+1,n.setItemLayout(e,{point:l,offset:s})}})});var a=i.getData();a.each(function(t){var e=a.getName(t),i=a.getItemLayout(t)||{};i.showLabel=!o[e],a.setItemLayout(t,i)}),e[n]=!0}})}),Es(function(t){t.eachSeriesByType("map",function(t){var e=t.get("color"),i=t.getModel("itemStyle"),n=i.get("areaColor"),o=i.get("color")||e[t.seriesIndex%e.length];t.getData().setVisual({areaColor:n,color:o})})}),ks(IT.PROCESSOR.STATISTIC,function(t){var e={};t.eachSeriesByType("map",function(t){var i=t.getHostGeoModel(),n=i?"o"+i.id:"i"+t.getMapType();(e[n]=e[n]||[]).push(t)}),d(e,function(t,e){for(var i=bc(f(t,function(t){return t.getData()}),t[0].get("mapValueCalculation")),n=0;ne&&(e=n.height)} +this.height=e+1},getNodeById:function(t){if(this.getId()===t)return this;for(var e=0,i=this.children,n=i.length;e=0&&this.hostTree.data.setItemLayout(this.dataIndex,t,e)},getLayout:function(){return this.hostTree.data.getItemLayout(this.dataIndex)},getModel:function(t){if(!(this.dataIndex<0)){var e,i=this.hostTree,n=i.data.getItemModel(this.dataIndex),o=this.getLevelModel();return o||0!==this.children.length&&(0===this.children.length||!1!==this.isExpand)||(e=this.getLeavesModel()),n.getModel(t,(o||e||i.hostModel).getModel(t))}},getLevelModel:function(){return(this.hostTree.levelModels||[])[this.depth]},getLeavesModel:function(){return this.hostTree.leavesModel},setVisual:function(t,e){this.dataIndex>=0&&this.hostTree.data.setItemVisual(this.dataIndex,t,e)},getVisual:function(t,e){return this.hostTree.data.getItemVisual(this.dataIndex,t,e)},getRawIndex:function(){return this.hostTree.data.getRawIndex(this.dataIndex)},getId:function(){return this.hostTree.data.getId(this.dataIndex)},isAncestorOf:function(t){for(var e=t.parentNode;e;){if(e===this)return!0;e=e.parentNode} +return!1},isDescendantOf:function(t){return t!==this&&t.isAncestorOf(this)}},kc.prototype={constructor:kc,type:"tree",eachNode:function(t,e,i){this.root.eachNode(t,e,i)},getNodeByDataIndex:function(t){var e=this.data.getRawIndex(t);return this._nodes[e]},getNodeByName:function(t){return this.root.getNodeByName(t)},update:function(){for(var t=this.data,e=this._nodes,i=0,n=e.length;ia&&(a=t.depth)});var r=t.expandAndCollapse&&t.initialTreeDepth>=0?t.initialTreeDepth:a;return o.root.eachNode("preorder",function(t){var e=t.hostTree.data.getRawDataItem(t.dataIndex);t.isExpand=e&&null!=e.collapsed?!e.collapsed:t.depth<=r}),o.data},getOrient:function(){var t=this.get("orient");return"horizontal"===t?t="LR":"vertical"===t&&(t="TB"),t},setZoom:function(t){this.option.zoom=t},setCenter:function(t){this.option.center=t},formatTooltip:function(t){for(var e=this.getData().tree,i=e.root.children[0],n=e.getNodeByDataIndex(t),o=n.getValue(),a=n.name;n&&n!==i;)a=n.parentNode.name+"."+a,n=n.parentNode;return Qo(a+(isNaN(o)||null==o?"":" : "+o))},defaultOption:{zlevel:0,z:2,coordinateSystem:"view",left:"12%",top:"12%",right:"12%",bottom:"12%",layout:"orthogonal",roam:!1,nodeScaleRatio:.4,center:null,zoom:1,orient:"LR",symbol:"emptyCircle",symbolSize:7,expandAndCollapse:!0,initialTreeDepth:2,lineStyle:{color:"#ccc",width:1.5,curveness:.5},itemStyle:{color:"lightsteelblue",borderColor:"#c23531",borderWidth:1.5},label:{show:!0,color:"#555"},leaves:{label:{show:!0}},animationEasing:"linear",animationDuration:700,animationDurationUpdate:1e3}}),Fs({type:"tree",init:function(t,e){this._oldTree,this._mainGroup=new Zw,this._controller=new ac(e.getZr()),this._controllerHost={target:this.group},this.group.add(this._mainGroup)},render:function(t,e,i,n){var o=t.getData(),a=t.layoutInfo,r=this._mainGroup,s=t.get("layout");"radial"===s?r.attr("position",[a.x+a.width/2,a.y+a.height/2]):r.attr("position",[a.x,a.y]),this._updateViewCoordSys(t),this._updateController(t,e,i);var l=this._data,u={expandAndCollapse:t.get("expandAndCollapse"),layout:s,orient:t.getOrient(),curvature:t.get("lineStyle.curveness"),symbolRotate:t.get("symbolRotate"),symbolOffset:t.get("symbolOffset"),hoverAnimation:t.get("hoverAnimation"),useNameLabel:!0,fadeIn:!0};o.diff(l).add(function(e){Xc(o,e)&&Yc(o,e,null,r,t,u)}).update(function(e,i){var n=l.getItemGraphicEl(i);Xc(o,e)?Yc(o,e,n,r,t,u):n&&qc(l,i,n,r,t,u)}).remove(function(e){var i=l.getItemGraphicEl(e);i&&qc(l,e,i,r,t,u)}).execute(),this._nodeScaleRatio=t.get("nodeScaleRatio"),this._updateNodeAndLinkScale(t),!0===u.expandAndCollapse&&o.eachItemGraphicEl(function(e,n){e.off("click").on("click",function(){i.dispatchAction({type:"treeExpandAndCollapse",seriesId:t.id,dataIndex:n})})}),this._data=o},_updateViewCoordSys:function(t){var e=t.getData(),i=[];e.each(function(t){var n=e.getItemLayout(t);!n||isNaN(n.x)||isNaN(n.y)||i.push([+n.x,+n.y])});var n=[],o=[];dn(i,n,o),o[0]-n[0]==0&&(o[0]+=1,n[0]-=1),o[1]-n[1]==0&&(o[1]+=1,n[1]-=1);var a=t.coordinateSystem=new Uh;a.zoomLimit=t.get("scaleLimit"),a.setBoundingRect(n[0],n[1],o[0]-n[0],o[1]-n[1]),a.setCenter(t.get("center")),a.setZoom(t.get("zoom")),this.group.attr({position:a.position,scale:a.scale}),this._viewCoordSys=a},_updateController:function(t,e,i){var n=this._controller,o=this._controllerHost,a=this.group;n.setPointerChecker(function(e,n,o){var r=a.getBoundingRect();return r.applyTransform(a.transform),r.contain(n,o)&&!mc(e,i,t)}),n.enable(t.get("roam")),o.zoomLimit=t.get("scaleLimit"),o.zoom=t.coordinateSystem.getZoom(),n.off("pan").off("zoom").on("pan",function(e){pc(o,e.dx,e.dy),i.dispatchAction({seriesId:t.id,type:"treeRoam",dx:e.dx,dy:e.dy})},this).on("zoom",function(e){gc(o,e.scale,e.originX,e.originY),i.dispatchAction({seriesId:t.id,type:"treeRoam",zoom:e.scale,originX:e.originX,originY:e.originY}),this._updateNodeAndLinkScale(t)},this)},_updateNodeAndLinkScale:function(t){var e=t.getData(),i=this._getNodeGlobalScale(t),n=[i,i];e.eachItemGraphicEl(function(t,e){t.attr("scale",n)})},_getNodeGlobalScale:function(t){var e=t.coordinateSystem;if("view"!==e.type)return 1;var i=this._nodeScaleRatio,n=e.scale,o=n&&n[0]||1;return((e.getZoom()-1)*i+1)/o},dispose:function(){this._controller&&this._controller.dispose(),this._controllerHost={}},remove:function(){this._mainGroup.removeAll(),this._data=null}}),Ps({type:"treeExpandAndCollapse",event:"treeExpandAndCollapse",update:"update"},function(t,e){e.eachComponent({mainType:"series",subType:"tree",query:t},function(e){var i=t.dataIndex,n=e.getData().tree.getNodeByDataIndex(i);n.isExpand=!n.isExpand})}),Ps({type:"treeRoam",event:"treeRoam",update:"none"},function(t,e){e.eachComponent({mainType:"series",subType:"tree",query:t},function(e){var i=wc(e.coordinateSystem,t);e.setCenter&&e.setCenter(i.center),e.setZoom&&e.setZoom(i.zoom)})});Es(hD("tree","circle")),Os(function(t,e){t.eachSeriesByType("tree",function(t){Qc(t,e)})}),OI.extend({type:"series.treemap",layoutMode:"box",dependencies:["grid","polar"],_viewRoot:null,defaultOption:{progressive:0,hoverLayerThreshold:1/0,left:"center",top:"middle",right:null,bottom:null,width:"80%",height:"80%",sort:!0,clipWindow:"origin",squareRatio:.5*(1+Math.sqrt(5)),leafDepth:null,drillDownIcon:"▶",zoomToNodeRatio:.1024,roam:!0,nodeClick:"zoomToNode",animation:!0,animationDurationUpdate:900,animationEasing:"quinticInOut",breadcrumb:{show:!0,height:22,left:"center",top:"bottom",emptyItemWidth:25,itemStyle:{color:"rgba(0,0,0,0.7)",borderColor:"rgba(255,255,255,0.7)",borderWidth:1,shadowColor:"rgba(150,150,150,1)",shadowBlur:3,shadowOffsetX:0,shadowOffsetY:0,textStyle:{color:"#fff"}},emphasis:{textStyle:{}}},label:{show:!0,distance:0,padding:5,position:"inside",color:"#fff",ellipsis:!0},upperLabel:{show:!1,position:[0,"50%"],height:20,color:"#fff",ellipsis:!0,verticalAlign:"middle"},itemStyle:{color:null,colorAlpha:null,colorSaturation:null,borderWidth:0,gapWidth:0,borderColor:"#fff",borderColorSaturation:null},emphasis:{upperLabel:{show:!0,position:[0,"50%"],color:"#fff",ellipsis:!0,verticalAlign:"middle"}},visualDimension:0,visualMin:null,visualMax:null,color:[],colorAlpha:null,colorSaturation:null,colorMappingBy:"index",visibleMin:10,childrenVisibleMin:null,levels:[]},getInitialData:function(t,e){var i={name:t.name,children:t.data};od(i);var n=t.levels||[];n=t.levels=ad(n,e);var o={};return o.levels=n,kc.createTree(i,this,o).data},optionUpdated:function(){this.resetViewRoot()},formatTooltip:function(t){var e=this.getData(),i=this.getRawValue(t),n=$o(y(i)?i[0]:i);return Qo(e.getName(t)+": "+n)},getDataParams:function(t){var e=OI.prototype.getDataParams.apply(this,arguments),i=this.getData().tree.getNodeByDataIndex(t);return e.treePathInfo=nd(i,this),e},setLayoutInfo:function(t){this.layoutInfo=this.layoutInfo||{},a(this.layoutInfo,t)},mapIdToIndex:function(t){var e=this._idIndexMap;e||(e=this._idIndexMap=R(),this._idIndexMapCount=0);var i=e.get(t);return null==i&&e.set(t,i=this._idIndexMapCount++),i},getViewRoot:function(){return this._viewRoot},resetViewRoot:function(t){t?this._viewRoot=t:t=this._viewRoot;var e=this.getRawData().tree.root;t&&(t===e||e.contains(t))||(this._viewRoot=e)}});var DC=5;rd.prototype={constructor:rd,render:function(t,e,i,n){var o=t.getModel("breadcrumb"),a=this.group;if(a.removeAll(),o.get("show")&&i){var r=o.getModel("itemStyle"),s=r.getModel("textStyle"),l={pos:{left:o.get("left"),right:o.get("right"),top:o.get("top"),bottom:o.get("bottom")},box:{width:e.getWidth(),height:e.getHeight()},emptyItemWidth:o.get("emptyItemWidth"),totalWidth:0,renderList:[]};this._prepare(i,l,s),this._renderContent(t,l,r,s,n),ua(a,l.pos,l.box)}},_prepare:function(t,e,i){for(var n=t;n;n=n.parentNode){var o=n.getModel().get("name"),a=i.getTextRect(o),r=Math.max(a.width+16,e.emptyItemWidth);e.totalWidth+=r+8,e.renderList.push({node:n,text:o,width:r})}},_renderContent:function(t,e,i,n,o){for(var a=0,s=e.emptyItemWidth,l=t.get("breadcrumb.height"),u=sa(e.pos,e.box),h=e.totalWidth,c=e.renderList,d=c.length-1;d>=0;d--){var f=c[d],p=f.node,g=f.width,m=f.text;h>u.width&&(h-=g-s,g=s,m=null);var y=new nM({shape:{points:sd(a,0,g,l,d===c.length-1,0===d)},style:r(i.getItemStyle(),{lineJoin:"bevel",text:m,textFill:n.getTextColor(),textFont:n.getFont()}),z:10,onclick:v(o,p)});this.group.add(y),ld(y,t,p),a+=g+8}},remove:function(){this.group.removeAll()}};var CC=m,LC=Zw,kC=aM,PC=d,NC=["label"],OC=["emphasis","label"],EC=["upperLabel"],RC=["emphasis","upperLabel"],zC=10,BC=1,VC=2,GC=Wb([["fill","color"],["stroke","strokeColor"],["lineWidth","strokeWidth"],["shadowBlur"],["shadowOffsetX"],["shadowOffsetY"],["shadowColor"]]),FC=function(t){var e=GC(t);return e.stroke=e.fill=e.lineWidth=null,e};Fs({type:"treemap",init:function(t,e){this._containerGroup,this._storage={nodeGroup:[],background:[],content:[]},this._oldTree,this._breadcrumb,this._controller,this._state="ready"},render:function(t,e,i,n){if(!(l(e.findComponents({mainType:"series",subType:"treemap",query:n}),t)<0)){this.seriesModel=t,this.api=i,this.ecModel=e;var o=td(n,["treemapZoomToNode","treemapRootToNode"],t),a=n&&n.type,r=t.layoutInfo,s=!this._oldTree,u=this._storage,h="treemapRootToNode"===a&&o&&u?{rootNodeGroup:u.nodeGroup[o.node.getRawIndex()],direction:n.direction}:null,c=this._giveContainerGroup(r),d=this._doRender(c,t,h);s||a&&"treemapZoomToNode"!==a&&"treemapRootToNode"!==a?d.renderFinally():this._doAnimation(c,d,t,h),this._resetController(i),this._renderBreadcrumb(t,i,o)}},_giveContainerGroup:function(t){var e=this._containerGroup;return e||(e=this._containerGroup=new LC,this._initEvents(e),this.group.add(e)),e.attr("position",[t.x,t.y]),e},_doRender:function(t,e,i){function n(t,e,i,o,a){function r(t){return t.getId()} +function s(r,s){var l=null!=r?t[r]:null,u=null!=s?e[s]:null,c=h(l,u,i,a);c&&n(l&&l.viewChildren||[],u&&u.viewChildren||[],c,o,a+1)} +o?(e=t,PC(t,function(t,e){!t.isRemoved()&&s(e,e)})):new Hs(e,t,r,r).add(s).update(s).remove(v(s,null)).execute()} +var o=e.getData().tree,a=this._oldTree,r={nodeGroup:[],background:[],content:[]},s={nodeGroup:[],background:[],content:[]},l=this._storage,u=[],h=v(hd,e,s,l,i,r,u);n(o.root?[o.root]:[],a&&a.root?[a.root]:[],t,o===a||!a,0);var c=function(t){var e={nodeGroup:[],background:[],content:[]};return t&&PC(t,function(t,i){var n=e[i];PC(t,function(t){t&&(n.push(t),t.__tmWillDelete=1)})}),e}(l);return this._oldTree=o,this._storage=s,{lastsForAnimation:r,willDeleteEls:c,renderFinally:function(){PC(c,function(t){PC(t,function(t){t.parent&&t.parent.remove(t)})}),PC(u,function(t){t.invisible=!0,t.dirty()})}}},_doAnimation:function(t,e,i,n){if(i.get("animation")){var o=i.get("animationDurationUpdate"),r=i.get("animationEasing"),s=ud();PC(e.willDeleteEls,function(t,e){PC(t,function(t,i){if(!t.invisible){var a,l=t.parent;if(n&&"drillDown"===n.direction)a=l===n.rootNodeGroup?{shape:{x:0,y:0,width:l.__tmNodeWidth,height:l.__tmNodeHeight},style:{opacity:0}}:{style:{opacity:0}};else{var u=0,h=0;l.__tmWillDelete||(u=l.__tmNodeWidth/2,h=l.__tmNodeHeight/2),a="nodeGroup"===e?{position:[u,h],style:{opacity:0}}:{shape:{x:u,y:h,width:0,height:0},style:{opacity:0}}} +a&&s.add(t,a,o,r)}})}),PC(this._storage,function(t,i){PC(t,function(t,n){var l=e.lastsForAnimation[i][n],u={};l&&("nodeGroup"===i?l.old&&(u.position=t.position.slice(),t.attr("position",l.old)):(l.old&&(u.shape=a({},t.shape),t.setShape(l.old)),l.fadein?(t.setStyle("opacity",0),u.style={opacity:1}):1!==t.style.opacity&&(u.style={opacity:1})),s.add(t,u,o,r))})},this),this._state="animating",s.done(CC(function(){this._state="ready",e.renderFinally()},this)).start()}},_resetController:function(t){var e=this._controller;e||((e=this._controller=new ac(t.getZr())).enable(this.seriesModel.get("roam")),e.on("pan",CC(this._onPan,this)),e.on("zoom",CC(this._onZoom,this)));var i=new ue(0,0,t.getWidth(),t.getHeight());e.setPointerChecker(function(t,e,n){return i.contain(e,n)})},_clearController:function(){var t=this._controller;t&&(t.dispose(),t=null)},_onPan:function(t){if("animating"!==this._state&&(Math.abs(t.dx)>3||Math.abs(t.dy)>3)){var e=this.seriesModel.getData().tree.root;if(!e)return;var i=e.getLayout();if(!i)return;this.api.dispatchAction({type:"treemapMove",from:this.uid,seriesId:this.seriesModel.id,rootRect:{x:i.x+t.dx,y:i.y+t.dy,width:i.width,height:i.height}})}},_onZoom:function(t){var e=t.originX,i=t.originY;if("animating"!==this._state){var n=this.seriesModel.getData().tree.root;if(!n)return;var o=n.getLayout();if(!o)return;var a=new ue(o.x,o.y,o.width,o.height),r=this.seriesModel.layoutInfo;e-=r.x,i-=r.y;var s=mt();_t(s,s,[-e,-i]),bt(s,s,[t.scale,t.scale]),_t(s,s,[e,i]),a.applyTransform(s),this.api.dispatchAction({type:"treemapRender",from:this.uid,seriesId:this.seriesModel.id,rootRect:{x:a.x,y:a.y,width:a.width,height:a.height}})}},_initEvents:function(t){t.on("click",function(t){if("ready"===this._state){var e=this.seriesModel.get("nodeClick",!0);if(e){var i=this.findTarget(t.offsetX,t.offsetY);if(i){var n=i.node;if(n.getLayout().isLeafRoot)this._rootToNode(i);else if("zoomToNode"===e)this._zoomToNode(i);else if("link"===e){var o=n.hostTree.data.getItemModel(n.dataIndex),a=o.get("link",!0),r=o.get("target",!0)||"blank";a&&window.open(a,r)}}}}},this)},_renderBreadcrumb:function(t,e,i){i||(i=null!=t.get("leafDepth",!0)?{node:t.getViewRoot()}:this.findTarget(e.getWidth()/2,e.getHeight()/2))||(i={node:t.getData().tree.root}),(this._breadcrumb||(this._breadcrumb=new rd(this.group))).render(t,e,i.node,CC(function(e){"animating"!==this._state&&(id(t.getViewRoot(),e)?this._rootToNode({node:e}):this._zoomToNode({node:e}))},this))},remove:function(){this._clearController(),this._containerGroup&&this._containerGroup.removeAll(),this._storage={nodeGroup:[],background:[],content:[]},this._state="ready",this._breadcrumb&&this._breadcrumb.remove()},dispose:function(){this._clearController()},_zoomToNode:function(t){this.api.dispatchAction({type:"treemapZoomToNode",from:this.uid,seriesId:this.seriesModel.id,targetNode:t.node})},_rootToNode:function(t){this.api.dispatchAction({type:"treemapRootToNode",from:this.uid,seriesId:this.seriesModel.id,targetNode:t.node})},findTarget:function(t,e){var i;return this.seriesModel.getViewRoot().eachNode({attr:"viewChildren",order:"preorder"},function(n){var o=this._storage.background[n.getRawIndex()];if(o){var a=o.transformCoordToLocal(t,e),r=o.shape;if(!(r.x<=a[0]&&a[0]<=r.x+r.width&&r.y<=a[1]&&a[1]<=r.y+r.height))return!1;i={node:n,offsetX:a[0],offsetY:a[1]}}},this),i}});for(var WC=["treemapZoomToNode","treemapRender","treemapMove"],HC=0;HC=0&&t.call(e,i[o],o)},uL.eachEdge=function(t,e){for(var i=this.edges,n=i.length,o=0;o=0&&i[o].node1.dataIndex>=0&&i[o].node2.dataIndex>=0&&t.call(e,i[o],o)},uL.breadthFirstTraverse=function(t,e,i,n){if(Zd.isInstance(e)||(e=this._nodesMap[Hd(e)]),e){for(var o="out"===i?"outEdges":"in"===i?"inEdges":"edges",a=0;a=0&&i.node2.dataIndex>=0});for(var o=0,a=n.length;o=0&&this[t][e].setItemVisual(this.dataIndex,i,n)},getVisual:function(i,n){return this[t][e].getItemVisual(this.dataIndex,i,n)},setLayout:function(i,n){this.dataIndex>=0&&this[t][e].setItemLayout(this.dataIndex,i,n)},getLayout:function(){return this[t][e].getItemLayout(this.dataIndex)},getGraphicEl:function(){return this[t][e].getItemGraphicEl(this.dataIndex)},getRawIndex:function(){return this[t][e].getRawIndex(this.dataIndex)}}};h(Zd,hL("hostGraph","data")),h(Ud,hL("hostGraph","edgeData")),lL.Node=Zd,lL.Edge=Ud,ji(Zd),ji(Ud);var cL=function(t,e,i,n,o){for(var a=new lL(n),r=0;r "+f)),h++)} +var p,g=i.get("coordinateSystem");if("cartesian2d"===g||"polar"===g)p=dl(t,i);else{var m=Ba.get(g),v=m&&"view"!==m.type?m.dimensions||[]:[];l(v,"value")<0&&v.concat(["value"]);var y=oA(t,{coordDimensions:v});(p=new eA(y,i)).initData(t)} +var x=new eA(["value"],i);return x.initData(u,s),o&&o(p,x),Sc({mainData:p,struct:a,structAttr:"graph",datas:{node:p,edge:x},datasAttr:{node:"data",edge:"edgeData"}}),a.update(),a},dL=Gs({type:"series.graph",init:function(t){dL.superApply(this,"init",arguments),this.legendDataProvider=function(){return this._categoriesData},this.fillDataTextStyle(t.edges||t.links),this._updateCategoriesData()},mergeOption:function(t){dL.superApply(this,"mergeOption",arguments),this.fillDataTextStyle(t.edges||t.links),this._updateCategoriesData()},mergeDefaultAndTheme:function(t){dL.superApply(this,"mergeDefaultAndTheme",arguments),Ci(t,["edgeLabel"],["show"])},getInitialData:function(t,e){var i=t.edges||t.links||[],n=t.data||t.nodes||[],o=this;if(n&&i)return cL(n,i,this,!0,function(t,i){function n(t){return(t=this.parsePath(t))&&"label"===t[0]?r:t&&"emphasis"===t[0]&&"label"===t[1]?l:this.parentModel} +t.wrapMethod("getItemModel",function(t){var e=o._categoriesModels[t.getShallow("category")];return e&&(e.parentModel=t.parentModel,t.parentModel=e),t});var a=o.getModel("edgeLabel"),r=new Lo({label:a.option},a.parentModel,e),s=o.getModel("emphasis.edgeLabel"),l=new Lo({emphasis:{label:s.option}},s.parentModel,e);i.wrapMethod("getItemModel",function(t){return t.customizeGetParent(n),t})}).data},getGraph:function(){return this.getData().graph},getEdgeData:function(){return this.getGraph().edgeData},getCategoriesData:function(){return this._categoriesData},formatTooltip:function(t,e,i){if("edge"===i){var n=this.getData(),o=this.getDataParams(t,i),a=n.graph.getEdgeByIndex(t),r=n.getName(a.node1.dataIndex),s=n.getName(a.node2.dataIndex),l=[];return null!=r&&l.push(r),null!=s&&l.push(s),l=Qo(l.join(" > ")),o.value&&(l+=" : "+Qo(o.value)),l} +return dL.superApply(this,"formatTooltip",arguments)},_updateCategoriesData:function(){var t=f(this.option.categories||[],function(t){return null!=t.value?t:a({value:0},t)}),e=new eA(["value"],this);e.initData(t),this._categoriesData=e,this._categoriesModels=e.mapArray(function(t){return e.getItemModel(t,!0)})},setZoom:function(t){this.option.zoom=t},setCenter:function(t){this.option.center=t},isAnimationEnabled:function(){return dL.superCall(this,"isAnimationEnabled")&&!("force"===this.get("layout")&&this.get("force.layoutAnimation"))},defaultOption:{zlevel:0,z:2,coordinateSystem:"view",legendHoverLink:!0,hoverAnimation:!0,layout:null,focusNodeAdjacency:!1,circular:{rotateLabel:!1},force:{initLayout:null,repulsion:[0,50],gravity:.1,edgeLength:30,layoutAnimation:!0},left:"center",top:"center",symbol:"circle",symbolSize:10,edgeSymbol:["none","none"],edgeSymbolSize:10,edgeLabel:{position:"middle"},draggable:!1,roam:!1,center:null,zoom:1,nodeScaleRatio:.6,label:{show:!1,formatter:"{b}"},itemStyle:{},lineStyle:{color:"#aaa",width:1,curveness:0,opacity:.5},emphasis:{label:{show:!0}}}}),fL=rM.prototype,pL=lM.prototype,gL=Fn({type:"ec-line",style:{stroke:"#000",fill:null},shape:{x1:0,y1:0,x2:0,y2:0,percent:1,cpx1:null,cpy1:null},buildPath:function(t,e){(Xd(e)?fL:pL).buildPath(t,e)},pointAt:function(t){return Xd(this.shape)?fL.pointAt.call(this,t):pL.pointAt.call(this,t)},tangentAt:function(t){var e=this.shape,i=Xd(e)?[e.x2-e.x1,e.y2-e.y1]:pL.tangentAt.call(this,t);return q(i,i)}}),mL=["fromSymbol","toSymbol"],vL=$d.prototype;vL.beforeUpdate=function(){var t=this,e=t.childOfName("fromSymbol"),i=t.childOfName("toSymbol"),n=t.childOfName("label");if(e||i||!n.ignore){for(var o=1,a=this.parent;a;)a.scale&&(o/=a.scale[0]),a=a.parent;var r=t.childOfName("line");if(this.__dirty||r.__dirty){var s=r.shape.percent,l=r.pointAt(0),u=r.pointAt(s),h=U([],u,l);if(q(h,h),e&&(e.attr("position",l),c=r.tangentAt(0),e.attr("rotation",Math.PI/2-Math.atan2(c[1],c[0])),e.attr("scale",[o*s,o*s])),i){i.attr("position",u);var c=r.tangentAt(1);i.attr("rotation",-Math.PI/2-Math.atan2(c[1],c[0])),i.attr("scale",[o*s,o*s])} +if(!n.ignore){n.attr("position",u);var d,f,p,g=5*o;if("end"===n.__position)d=[h[0]*g+u[0],h[1]*g+u[1]],f=h[0]>.8?"left":h[0]<-.8?"right":"center",p=h[1]>.8?"top":h[1]<-.8?"bottom":"middle";else if("middle"===n.__position){var m=s/2,v=[(c=r.tangentAt(m))[1],-c[0]],y=r.pointAt(m);v[1]>0&&(v[0]=-v[0],v[1]=-v[1]),d=[y[0]+v[0]*g,y[1]+v[1]*g],f="center",p="bottom";var x=-Math.atan2(c[1],c[0]);u[0].8?"right":h[0]<-.8?"left":"center",p=h[1]>.8?"bottom":h[1]<-.8?"top":"middle";n.attr({style:{textVerticalAlign:n.__verticalAlign||p,textAlign:n.__textAlign||f},position:d,scale:[o,o]})}}}},vL._createLine=function(t,e,i){var n=t.hostModel,o=qd(t.getItemLayout(e));o.shape.percent=0,So(o,{shape:{percent:1}},n,e),this.add(o);var a=new qS({name:"label"});this.add(a),d(mL,function(i){var n=Yd(i,t,e);this.add(n),this[jd(i)]=t.getItemVisual(e,i)},this),this._updateCommonStl(t,e,i)},vL.updateData=function(t,e,i){var n=t.hostModel,o=this.childOfName("line"),a=t.getItemLayout(e),r={shape:{}};Kd(r.shape,a),bo(o,r,n,e),d(mL,function(i){var n=t.getItemVisual(e,i),o=jd(i);if(this[o]!==n){this.remove(this.childOfName(i));var a=Yd(i,t,e);this.add(a)} +this[o]=n},this),this._updateCommonStl(t,e,i)},vL._updateCommonStl=function(t,e,i){var n=t.hostModel,o=this.childOfName("line"),a=i&&i.lineStyle,s=i&&i.hoverLineStyle,l=i&&i.labelModel,u=i&&i.hoverLabelModel;if(!i||t.hasItemOption){var h=t.getItemModel(e);a=h.getModel("lineStyle").getLineStyle(),s=h.getModel("emphasis.lineStyle").getLineStyle(),l=h.getModel("label"),u=h.getModel("emphasis.label")} +var c=t.getItemVisual(e,"color"),f=D(t.getItemVisual(e,"opacity"),a.opacity,1);o.useStyle(r({strokeNoScale:!0,fill:"none",stroke:c,opacity:f},a)),o.hoverStyle=s,d(mL,function(t){var e=this.childOfName(t);e&&(e.setColor(c),e.setStyle({opacity:f}))},this);var p,g,m=l.getShallow("show"),v=u.getShallow("show"),y=this.childOfName("label");if((m||v)&&(p=c||"#000",null==(g=n.getFormattedLabel(e,"normal",t.dataType)))){var x=n.getRawValue(e);g=null==x?t.getName(e):isFinite(x)?zo(x):x} +var _=m?g:null,w=v?A(n.getFormattedLabel(e,"emphasis",t.dataType),g):null,b=y.style;null==_&&null==w||(fo(y.style,l,{text:_},{autoColor:p}),y.__textAlign=b.textAlign,y.__verticalAlign=b.textVerticalAlign,y.__position=l.get("position")||"middle"),y.hoverStyle=null!=w?{text:w,textFill:u.getTextColor(!0),fontStyle:u.getShallow("fontStyle"),fontWeight:u.getShallow("fontWeight"),fontSize:u.getShallow("fontSize"),fontFamily:u.getShallow("fontFamily")}:{text:null},y.ignore=!m&&!v,uo(this)},vL.highlight=function(){this.trigger("emphasis")},vL.downplay=function(){this.trigger("normal")},vL.updateLayout=function(t,e){this.setLinePoints(t.getItemLayout(e))},vL.setLinePoints=function(t){var e=this.childOfName("line");Kd(e.shape,t),e.dirty()},u($d,Zw);var yL=Jd.prototype;yL.isPersistent=function(){return!0},yL.updateData=function(t){var e=this,i=e.group,n=e._lineData;e._lineData=t,n||i.removeAll();var o=ef(t);t.diff(n).add(function(i){Qd(e,t,i,o)}).update(function(i,a){tf(e,n,t,a,i,o)}).remove(function(t){i.remove(n.getItemGraphicEl(t))}).execute()},yL.updateLayout=function(){var t=this._lineData;t&&t.eachItemGraphicEl(function(e,i){e.updateLayout(t,i)},this)},yL.incrementalPrepareUpdate=function(t){this._seriesScope=ef(t),this._lineData=null,this.group.removeAll()},yL.incrementalUpdate=function(t,e){for(var i=t.start;i=o/3?1:2),l=e.y-n(r)*a*(a>=o/3?1:2);r=e.angle-Math.PI/2,t.moveTo(s,l),t.lineTo(e.x+i(r)*a,e.y+n(r)*a),t.lineTo(e.x+i(e.angle)*o,e.y+n(e.angle)*o),t.lineTo(e.x-i(r)*a,e.y-n(r)*a),t.lineTo(s,l)}}),LL=2*Math.PI,kL=(Mr.extend({type:"gauge",render:function(t,e,i){this.group.removeAll();var n=t.get("axisLine.lineStyle.color"),o=gf(t,i);this._renderMain(t,e,i,n,o)},dispose:function(){},_renderMain:function(t,e,i,n,o){for(var a=this.group,r=t.getModel("axisLine").getModel("lineStyle"),s=t.get("clockwise"),l=-t.get("startAngle")/180*Math.PI,u=-t.get("endAngle")/180*Math.PI,h=(u-l)%LL,c=l,d=r.get("width"),f=0;f=t&&(0===e?0:n[e-1][0]).4?"bottom":"middle",textAlign:A<-.4?"left":A>.4?"right":"center"},{autoColor:P}),silent:!0}))} +if(g.get("show")&&T!==v){for(var N=0;N<=y;N++){var A=Math.cos(w),D=Math.sin(w),O=new rM({shape:{x1:A*c+u,y1:D*c+h,x2:A*(c-_)+u,y2:D*(c-_)+h},silent:!0,style:I});"auto"===I.stroke&&O.setStyle({stroke:n((T+N/y)/v)}),l.add(O),w+=S} +w-=S}else w+=b}},_renderPointer:function(t,e,i,n,o,a,r,s){var l=this.group,u=this._data;if(t.get("pointer.show")){var h=[+t.get("min"),+t.get("max")],c=[a,r],d=t.getData(),f=d.mapDimension("value");d.diff(u).add(function(e){var i=new CL({shape:{angle:a}});So(i,{shape:{angle:Eo(d.get(f,e),h,c,!0)}},t),l.add(i),d.setItemGraphicEl(e,i)}).update(function(e,i){var n=u.getItemGraphicEl(i);bo(n,{shape:{angle:Eo(d.get(f,e),h,c,!0)}},t),l.add(n),d.setItemGraphicEl(e,n)}).remove(function(t){var e=u.getItemGraphicEl(t);l.remove(e)}).execute(),d.eachItemGraphicEl(function(t,e){var i=d.getItemModel(e),a=i.getModel("pointer");t.setShape({x:o.cx,y:o.cy,width:Ro(a.get("width"),o.r),r:Ro(a.get("length"),o.r)}),t.useStyle(i.getModel("itemStyle").getItemStyle()),"auto"===t.style.fill&&t.setStyle("fill",n(Eo(d.get(f,e),h,[0,1],!0))),uo(t,i.getModel("emphasis.itemStyle").getItemStyle())}),this._data=d}else u&&u.eachItemGraphicEl(function(t){l.remove(t)})},_renderTitle:function(t,e,i,n,o){var a=t.getData(),r=a.mapDimension("value"),s=t.getModel("title");if(s.get("show")){var l=s.get("offsetCenter"),u=o.cx+Ro(l[0],o.r),h=o.cy+Ro(l[1],o.r),c=+t.get("min"),d=+t.get("max"),f=n(Eo(t.getData().get(r,0),[c,d],[0,1],!0));this.group.add(new qS({silent:!0,style:fo({},s,{x:u,y:h,text:a.getName(0),textAlign:"center",textVerticalAlign:"middle"},{autoColor:f,forceRich:!0})}))}},_renderDetail:function(t,e,i,n,o){var a=t.getModel("detail"),r=+t.get("min"),s=+t.get("max");if(a.get("show")){var l=a.get("offsetCenter"),u=o.cx+Ro(l[0],o.r),h=o.cy+Ro(l[1],o.r),c=Ro(a.get("width"),o.r),d=Ro(a.get("height"),o.r),f=t.getData(),p=f.get(f.mapDimension("value"),0),g=n(Eo(p,[r,s],[0,1],!0));this.group.add(new qS({silent:!0,style:fo({},a,{x:u,y:h,text:mf(p,a.get("formatter")),textWidth:isNaN(c)?null:c,textHeight:isNaN(d)?null:d,textAlign:"center",textVerticalAlign:"middle"},{autoColor:g,forceRich:!0})}))}}}),Gs({type:"series.funnel",init:function(t){kL.superApply(this,"init",arguments),this.legendDataProvider=function(){return this.getRawData()},this._defaultLabelLine(t)},getInitialData:function(t,e){return WD(this,["value"])},_defaultLabelLine:function(t){Ci(t,"labelLine",["show"]);var e=t.labelLine,i=t.emphasis.labelLine;e.show=e.show&&t.label.show,i.show=i.show&&t.emphasis.label.show},getDataParams:function(t){var e=this.getData(),i=kL.superCall(this,"getDataParams",t),n=e.mapDimension("value"),o=e.getSum(n);return i.percent=o?+(e.get(n,t)/o*100).toFixed(2):0,i.$vars.push("percent"),i},defaultOption:{zlevel:0,z:2,legendHoverLink:!0,left:80,top:60,right:80,bottom:60,minSize:"0%",maxSize:"100%",sort:"descending",gap:0,funnelAlign:"center",label:{show:!0,position:"outer"},labelLine:{show:!0,length:20,lineStyle:{width:1,type:"solid"}},itemStyle:{borderColor:"#fff",borderWidth:1},emphasis:{label:{show:!0}}}})),PL=vf.prototype,NL=["itemStyle","opacity"];PL.updateData=function(t,e,i){var n=this.childAt(0),o=t.hostModel,a=t.getItemModel(e),s=t.getItemLayout(e),l=t.getItemModel(e).get(NL);l=null==l?1:l,n.useStyle({}),i?(n.setShape({points:s.points}),n.setStyle({opacity:0}),So(n,{style:{opacity:l}},o,e)):bo(n,{style:{opacity:l},shape:{points:s.points}},o,e);var u=a.getModel("itemStyle"),h=t.getItemVisual(e,"color");n.setStyle(r({lineJoin:"round",fill:h},u.getItemStyle(["opacity"]))),n.hoverStyle=u.getModel("emphasis").getItemStyle(),this._updateLabel(t,e),uo(this)},PL._updateLabel=function(t,e){var i=this.childAt(1),n=this.childAt(2),o=t.hostModel,a=t.getItemModel(e),r=t.getItemLayout(e).label,s=t.getItemVisual(e,"color");bo(i,{shape:{points:r.linePoints||r.linePoints}},o,e),bo(n,{style:{x:r.x,y:r.y}},o,e),n.attr({rotation:r.rotation,origin:[r.x,r.y],z2:10});var l=a.getModel("label"),u=a.getModel("emphasis.label"),h=a.getModel("labelLine"),c=a.getModel("emphasis.labelLine"),s=t.getItemVisual(e,"color");co(n.style,n.hoverStyle={},l,u,{labelFetcher:t.hostModel,labelDataIndex:e,defaultText:t.getName(e),autoColor:s,useInsideStyle:!!r.inside},{textAlign:r.textAlign,textVerticalAlign:r.verticalAlign}),n.ignore=n.normalIgnore=!l.get("show"),n.hoverIgnore=!u.get("show"),i.ignore=i.normalIgnore=!h.get("show"),i.hoverIgnore=!c.get("show"),i.setStyle({stroke:s}),i.setStyle(h.getModel("lineStyle").getLineStyle()),i.hoverStyle=c.getModel("lineStyle").getLineStyle()},u(vf,Zw);Mr.extend({type:"funnel",render:function(t,e,i){var n=t.getData(),o=this._data,a=this.group;n.diff(o).add(function(t){var e=new vf(n,t);n.setItemGraphicEl(t,e),a.add(e)}).update(function(t,e){var i=o.getItemGraphicEl(e);i.updateData(n,t),a.add(i),n.setItemGraphicEl(t,i)}).remove(function(t){var e=o.getItemGraphicEl(t);a.remove(e)}).execute(),this._data=n},remove:function(){this.group.removeAll(),this._data=null},dispose:function(){}});Es(jD("funnel")),Os(function(t,e,i){t.eachSeriesByType("funnel",function(t){var i=t.getData(),n=i.mapDimension("value"),o=t.get("sort"),a=yf(t,e),r=xf(i,o),s=[Ro(t.get("minSize"),a.width),Ro(t.get("maxSize"),a.width)],l=i.getDataExtent(n),u=t.get("min"),h=t.get("max");null==u&&(u=Math.min(l[0],0)),null==h&&(h=l[1]);var c=t.get("funnelAlign"),d=t.get("gap"),f=(a.height-d*(i.count()-1))/i.count(),p=a.y,g=function(t,e){var o,r=Eo(i.get(n,t)||0,[u,h],s,!0);switch(c){case "left":o=a.x;break;case "center":o=a.x+(a.width-r)/2;break;case "right":o=a.x+a.width-r} +return[[o,e],[o+r,e]]};"ascending"===o&&(f=-f,d=-d,p+=a.height,r=r.reverse());for(var m=0;ma&&(e[1-n]=e[n]+h.sign*a),e},RL=d,zL=Math.min,BL=Math.max,VL=Math.floor,GL=Math.ceil,FL=zo,WL=Math.PI;If.prototype={type:"parallel",constructor:If,_init:function(t,e,i){var n=t.dimensions,o=t.parallelAxisIndex;RL(n,function(t,i){var n=o[i],a=e.getComponent("parallelAxis",n),r=this._axesMap.set(t,new OL(t,Vl(a),[0,0],a.get("type"),n)),s="category"===r.type;r.onBand=s&&a.get("boundaryGap"),r.inverse=a.get("inverse"),a.axis=r,r.model=a,r.coordinateSystem=a.coordinateSystem=this},this)},update:function(t,e){this._updateAxesFromSeries(this._model,t)},containPoint:function(t){var e=this._makeLayoutInfo(),i=e.axisBase,n=e.layoutBase,o=e.pixelDimIndex,a=t[1-o],r=t[o];return a>=i&&a<=i+e.axisLength&&r>=n&&r<=n+e.layoutLength},getModel:function(){return this._model},_updateAxesFromSeries:function(t,e){e.eachSeries(function(i){if(t.contains(i,e)){var n=i.getData();RL(this.dimensions,function(t){var e=this._axesMap.get(t);e.scale.unionExtentFromData(n,n.mapDimension(t)),Bl(e.scale,e.model)},this)}},this)},resize:function(t,e){this._rect=la(t.getBoxLayoutParams(),{width:e.getWidth(),height:e.getHeight()}),this._layoutAxes()},getRect:function(){return this._rect},_makeLayoutInfo:function(){var t,e=this._model,i=this._rect,n=["x","y"],o=["width","height"],a=e.get("layout"),r="horizontal"===a?0:1,s=i[o[r]],l=[0,s],u=this.dimensions.length,h=Tf(e.get("axisExpandWidth"),l),c=Tf(e.get("axisExpandCount")||0,[0,u]),d=e.get("axisExpandable")&&u>3&&u>c&&c>1&&h>0&&s>0,f=e.get("axisExpandWindow");f?(t=Tf(f[1]-f[0],l),f[1]=f[0]+t):(t=Tf(h*(c-1),l),(f=[h*(e.get("axisExpandCenter")||VL(u/2))-t/2])[1]=f[0]+t);var p=(s-t)/(u-c);p<3&&(p=0);var g=[VL(FL(f[0]/h,1))+1,GL(FL(f[1]/h,1))-1],m=p/h*f[0];return{layout:a,pixelDimIndex:r,layoutBase:i[n[r]],layoutLength:s,axisBase:i[n[1-r]],axisLength:i[o[1-r]],axisExpandable:d,axisExpandWidth:h,axisCollapseWidth:p,axisExpandWindow:f,axisCount:u,winInnerIndices:g,axisExpandWindow0Pos:m}},_layoutAxes:function(){var t=this._rect,e=this._axesMap,i=this.dimensions,n=this._makeLayoutInfo(),o=n.layout;e.each(function(t){var e=[0,n.axisLength],i=t.inverse?1:0;t.setExtent(e[i],e[1-i])}),RL(i,function(e,i){var a=(n.axisExpandable?Df:Af)(i,n),r={horizontal:{x:a.position,y:n.axisLength},vertical:{x:0,y:a.position}},s={horizontal:WL/2,vertical:0},l=[r[o].x+t.x,r[o].y+t.y],u=s[o],h=mt();wt(h,h,u),_t(h,h,l),this._axesLayout[e]={position:l,rotation:u,transform:h,axisNameAvailableWidth:a.axisNameAvailableWidth,axisLabelShow:a.axisLabelShow,nameTruncateMaxWidth:a.nameTruncateMaxWidth,tickDirection:1,labelDirection:1}},this)},getAxis:function(t){return this._axesMap.get(t)},dataToPoint:function(t,e){return this.axisCoordToPoint(this._axesMap.get(e).dataToCoord(t),e)},eachActiveState:function(t,e,i,n){null==i&&(i=0),null==n&&(n=t.count());var o=this._axesMap,a=this.dimensions,r=[],s=[];d(a,function(e){r.push(t.mapDimension(e)),s.push(o.get(e).model)});for(var l=this.hasAxisBrushed(),u=i;uo*(1-h[0])?(l="jump",r=s-o*(1-h[2])):(r=s-o*h[1])>=0&&(r=s-o*(1-h[1]))<=0&&(r=0),(r*=e.axisExpandWidth/u)?EL(r,n,a,"all"):l="none";else{o=n[1]-n[0];(n=[BL(0,a[1]*s/o-o/2)])[1]=zL(a[1],n[0]+o),n[0]=n[1]-o} +return{axisExpandWindow:n,behavior:l}}},Ba.register("parallel",{create:function(t,e){var i=[];return t.eachComponent("parallel",function(n,o){var a=new If(n,t,e);a.name="parallel_"+o,a.resize(n,e),n.coordinateSystem=a,a.model=n,i.push(a)}),t.eachSeries(function(e){if("parallel"===e.get("coordinateSystem")){var i=t.queryComponents({mainType:"parallel",index:e.get("parallelIndex"),id:e.get("parallelId")})[0];e.coordinateSystem=i.coordinateSystem}}),i}});var HL=YM.extend({type:"baseParallelAxis",axis:null,activeIntervals:[],getAreaSelectStyle:function(){return Wb([["fill","color"],["lineWidth","borderWidth"],["stroke","borderColor"],["width","width"],["opacity","opacity"]])(this.getModel("areaSelectStyle"))},setActiveIntervals:function(t){var e=this.activeIntervals=i(t);if(e) +for(var n=e.length-1;n>=0;n--)Bo(e[n])},getActiveState:function(t){var e=this.activeIntervals;if(!e.length)return"normal";if(null==t||isNaN(t))return"inactive";if(1===e.length){var i=e[0];if(i[0]<=t&&t<=i[1])return"active"}else for(var n=0,o=e.length;n5)return;var n=this._model.coordinateSystem.getSlidedAxisExpandWindow([t.offsetX,t.offsetY]);"none"!==n.behavior&&this._dispatchExpand({axisExpandWindow:n.axisExpandWindow})} +this._mouseDownPoint=null},mousemove:function(t){if(!this._mouseDownPoint&&vp(this,"mousemove")){var e=this._model,i=e.coordinateSystem.getSlidedAxisExpandWindow([t.offsetX,t.offsetY]),n=i.behavior;"jump"===n&&this._throttledDispatchExpand.debounceNextCall(e.get("axisExpandDebounce")),this._throttledDispatchExpand("none"===n?null:{axisExpandWindow:i.axisExpandWindow,animation:"jump"===n&&null})}}};Ls(function(t){wf(t),bf(t)}),OI.extend({type:"series.parallel",dependencies:["parallel"],visualColorAccessPath:"lineStyle.color",getInitialData:function(t,e){var i=this.getSource();return yp(i,this),dl(i,this)},getRawIndicesByActiveState:function(t){var e=this.coordinateSystem,i=this.getData(),n=[];return e.eachActiveState(i,function(e,o){t===e&&n.push(i.getRawIndex(o))}),n},defaultOption:{zlevel:0,z:2,coordinateSystem:"parallel",parallelIndex:0,label:{show:!1},inactiveOpacity:.05,activeOpacity:1,lineStyle:{width:1,opacity:.45,type:"solid"},emphasis:{label:{show:!1}},progressive:500,smooth:!1,animationEasing:"linear"}});var hk=.3,ck=(Mr.extend({type:"parallel",init:function(){this._dataGroup=new Zw,this.group.add(this._dataGroup),this._data,this._initialized},render:function(t,e,i,n){var o=this._dataGroup,a=t.getData(),r=this._data,s=t.coordinateSystem,l=s.dimensions,u=Sp(t);if(a.diff(r).add(function(t){Mp(bp(a,o,t,l,s),a,t,u)}).update(function(e,i){var o=r.getItemGraphicEl(i),h=wp(a,e,l,s);a.setItemGraphicEl(e,o),bo(o,{shape:{points:h}},n&&!1===n.animation?null:t,e),Mp(o,a,e,u)}).remove(function(t){var e=r.getItemGraphicEl(t);o.remove(e)}).execute(),!this._initialized){this._initialized=!0;var h=_p(s,t,function(){setTimeout(function(){o.removeClipPath()})});o.setClipPath(h)} +this._data=a},incrementalPrepareRender:function(t,e,i){this._initialized=!0,this._data=null,this._dataGroup.removeAll()},incrementalRender:function(t,e,i){for(var n=e.getData(),o=e.coordinateSystem,a=o.dimensions,r=Sp(e),s=t.start;sn&&(n=e)}),d(e,function(e){var o=new jC({type:"color",mappingMethod:"linear",dataExtent:[i,n],visual:t.get("color")}).mapValueToVisual(e.getLayout().value);e.setVisual("color",o);var a=e.getModel().get("itemStyle.color");null!=a&&e.setVisual("color",a)})}})});var mk={_baseAxisDim:null,getInitialData:function(t,e){var i,n,o=e.getComponent("xAxis",this.get("xAxisIndex")),a=e.getComponent("yAxis",this.get("yAxisIndex")),r=o.get("type"),s=a.get("type");"category"===r?(t.layout="horizontal",i=o.getOrdinalMeta(),n=!0):"category"===s?(t.layout="vertical",i=a.getOrdinalMeta(),n=!0):t.layout=t.layout||"horizontal";var l=["x","y"],u="horizontal"===t.layout?0:1,h=this._baseAxisDim=l[u],c=l[1-u],f=[o,a],p=f[u].get("type"),g=f[1-u].get("type"),m=t.data;if(m&&n){var v=[];d(m,function(t,e){var i;t.value&&y(t.value)?(i=t.value.slice(),t.value.unshift(e)):y(t)?(i=t.slice(),t.unshift(e)):i=t,v.push(i)}),t.data=v} +var x=this.defaultValueDimensions;return WD(this,{coordDimensions:[{name:h,type:Xs(p),ordinalMeta:i,otherDims:{tooltip:!1,itemName:0},dimsDef:["base"]},{name:c,type:Xs(g),dimsDef:x.slice()}],dimensionsCount:x.length+1})},getBaseAxis:function(){var t=this._baseAxisDim;return this.ecModel.getComponent(t+"Axis",this.get(t+"AxisIndex")).axis}};h(OI.extend({type:"series.boxplot",dependencies:["xAxis","yAxis","grid"],defaultValueDimensions:[{name:"min",defaultTooltip:!0},{name:"Q1",defaultTooltip:!0},{name:"median",defaultTooltip:!0},{name:"Q3",defaultTooltip:!0},{name:"max",defaultTooltip:!0}],dimensions:null,defaultOption:{zlevel:0,z:2,coordinateSystem:"cartesian2d",legendHoverLink:!0,hoverAnimation:!0,layout:null,boxWidth:[7,50],itemStyle:{color:"#fff",borderWidth:1},emphasis:{itemStyle:{borderWidth:2,shadowBlur:5,shadowOffsetX:2,shadowOffsetY:2,shadowColor:"rgba(0,0,0,0.4)"}},animationEasing:"elasticOut",animationDuration:800}}),mk,!0);var vk=["itemStyle"],yk=["emphasis","itemStyle"],xk=(Mr.extend({type:"boxplot",render:function(t,e,i){var n=t.getData(),o=this.group,a=this._data;this._data||o.removeAll();var r="horizontal"===t.get("layout")?1:0;n.diff(a).add(function(t){if(n.hasValue(t)){var e=qp(n.getItemLayout(t),n,t,r,!0);n.setItemGraphicEl(t,e),o.add(e)}}).update(function(t,e){var i=a.getItemGraphicEl(e);if(n.hasValue(t)){var s=n.getItemLayout(t);i?Kp(s,i,n,t):i=qp(s,n,t,r),o.add(i),n.setItemGraphicEl(t,i)}else o.remove(i)}).remove(function(t){var e=a.getItemGraphicEl(t);e&&o.remove(e)}).execute(),this._data=n},remove:function(t){var e=this.group,i=this._data;this._data=null,i&&i.eachItemGraphicEl(function(t){t&&e.remove(t)})},dispose:B}),kn.extend({type:"boxplotBoxPath",shape:{},buildPath:function(t,e){var i=e.points,n=0;for(t.moveTo(i[n][0],i[n][1]),n++;n<4;n++)t.lineTo(i[n][0],i[n][1]);for(t.closePath();n0?Ck:Lk)} +function n(t,e){return e.get(t>0?Ak:Dk)} +var o=t.getData(),a=t.pipelineContext.large;if(o.setVisual({legendSymbol:"roundRect",colorP:i(1,t),colorN:i(-1,t),borderColorP:n(1,t),borderColorN:n(-1,t)}),!e.isSeriesFiltered(t))return!a&&{progress:function(t,e){for(var o;null!=(o=t.next());){var a=e.getItemModel(o),r=e.getItemLayout(o).sign;e.setItemVisual(o,{color:i(r,a),borderColor:n(r,a)})}}}}},Pk="undefined"!=typeof Float32Array?Float32Array:Array,Nk={seriesType:"candlestick",plan:zI(),reset:function(t){var e=t.coordinateSystem,i=t.getData(),n=sg(t,i),o=0,a=1,r=["x","y"],s=i.mapDimension(r[o]),l=i.mapDimension(r[a],!0),u=l[0],h=l[1],c=l[2],d=l[3];if(i.setLayout({candleWidth:n,isSimpleBox:n<=1.3}),!(null==s||l.length<4))return{progress:t.pipelineContext.large?function(t,i){for(var n,r,l=new Pk(5*t.count),f=0,p=[],g=[];null!=(r=t.next());){var m=i.get(s,r),v=i.get(u,r),y=i.get(h,r),x=i.get(c,r),_=i.get(d,r);isNaN(m)||isNaN(x)||isNaN(_)?(l[f++]=NaN,f+=4):(l[f++]=rg(i,r,v,y,h),p[o]=m,p[a]=x,n=e.dataToPoint(p,null,g),l[f++]=n?n[0]:NaN,l[f++]=n?n[1]:NaN,p[a]=_,n=e.dataToPoint(p,null,g),l[f++]=n?n[1]:NaN)} +i.setLayout("largePoints",l)}:function(t,i){function r(t,i){var n=[];return n[o]=i,n[a]=t,isNaN(i)||isNaN(t)?[NaN,NaN]:e.dataToPoint(n)} +function l(t,e,i){var a=e.slice(),r=e.slice();a[o]=Yn(a[o]+n/2,1,!1),r[o]=Yn(r[o]-n/2,1,!0),i?t.push(a,r):t.push(r,a)} +function f(t){return t[o]=Yn(t[o],1),t} +for(var p;null!=(p=t.next());){var g=i.get(s,p),m=i.get(u,p),v=i.get(h,p),y=i.get(c,p),x=i.get(d,p),_=Math.min(m,v),w=Math.max(m,v),b=r(_,g),S=r(w,g),M=r(y,g),I=r(x,g),T=[];l(T,S,0),l(T,b,1),T.push(f(I),f(S),f(M),f(b)),i.setItemLayout(p,{sign:rg(i,p,m,v,h),initBaseline:m>v?S[a]:b[a],ends:T,brushRect:function(t,e,i){var s=r(t,i),l=r(e,i);return s[o]-=n/2,l[o]-=n/2,{x:s[0],y:s[1],width:a?n:l[0]-s[0],height:a?l[1]-s[1]:n}}(y,x,g)})}}}}};Ls(function(t){t&&y(t.series)&&d(t.series,function(t){w(t)&&"k"===t.type&&(t.type="candlestick")})}),Es(kk),Os(Nk),OI.extend({type:"series.effectScatter",dependencies:["grid","polar"],getInitialData:function(t,e){return dl(this.getSource(),this)},brushSelector:"point",defaultOption:{coordinateSystem:"cartesian2d",zlevel:0,z:2,legendHoverLink:!0,effectType:"ripple",progressive:0,showEffectOn:"render",rippleEffect:{period:4,scale:2.5,brushType:"fill"},symbolSize:10}});var Ok=hg.prototype;Ok.stopEffectAnimation=function(){this.childAt(1).removeAll()},Ok.startEffectAnimation=function(t){for(var e=t.symbolType,i=t.color,n=this.childAt(1),o=0;o<3;o++){var a=Xl(e,-1,-1,2,2,i);a.attr({style:{strokeNoScale:!0},z2:99,silent:!0,scale:[.5,.5]});var r=-o/3*t.period+t.effectOffset;a.animate("",!0).when(t.period,{scale:[t.rippleScale/2,t.rippleScale/2]}).delay(r).start(),a.animateStyle(!0).when(t.period,{opacity:0}).delay(r).start(),n.add(a)} +ug(n,t)},Ok.updateEffectAnimation=function(t){for(var e=this._effectCfg,i=this.childAt(1),n=["symbolType","period","rippleScale"],o=0;o "))},preventIncremental:function(){return!!this.get("effect.show")},getProgressive:function(){var t=this.option.progressive;return null==t?this.option.large?1e4:this.get("progressive"):t},getProgressiveThreshold:function(){var t=this.option.progressiveThreshold;return null==t?this.option.large?2e4:this.get("progressiveThreshold"):t},defaultOption:{coordinateSystem:"geo",zlevel:0,z:2,legendHoverLink:!0,hoverAnimation:!0,xAxisIndex:0,yAxisIndex:0,symbol:["none","none"],symbolSize:[10,10],geoIndex:0,effect:{show:!1,period:4,constantSpeed:0,symbol:"circle",symbolSize:3,loop:!0,trailLength:.2},large:!1,largeThreshold:2e3,polyline:!1,label:{show:!1,position:"end"},lineStyle:{opacity:.5}}}),Bk=dg.prototype;Bk.createLine=function(t,e,i){return new $d(t,e,i)},Bk._updateEffectSymbol=function(t,e){var i=t.getItemModel(e).getModel("effect"),n=i.get("symbolSize"),o=i.get("symbol");y(n)||(n=[n,n]);var a=i.get("color")||t.getItemVisual(e,"color"),r=this.childAt(1);this._symbolType!==o&&(this.remove(r),(r=Xl(o,-.5,-.5,1,1,a)).z2=100,r.culling=!0,this.add(r)),r&&(r.setStyle("shadowColor",a),r.setStyle(i.getItemStyle(["color"])),r.attr("scale",n),r.setColor(a),r.attr("scale",n),this._symbolType=o,this._updateEffectAnimation(t,i,e))},Bk._updateEffectAnimation=function(t,e,i){var n=this.childAt(1);if(n){var o=this,a=t.getItemLayout(i),r=1e3*e.get("period"),s=e.get("loop"),l=e.get("constantSpeed"),u=T(e.get("delay"),function(e){return e/t.count()*r/3}),h="function"==typeof u;if(n.ignore=!0,this.updateAnimationPoints(n,a),l>0&&(r=this.getLineLength(n)/l*1e3),r!==this._period||s!==this._loop){n.stopAnimation();var c=u;h&&(c=u(i)),n.__t>0&&(c=-r*n.__t),n.__t=0;var d=n.animate("",s).when(r,{__t:1}).delay(c).during(function(){o.updateSymbolPosition(n)});s||d.done(function(){o.remove(n)}),d.start()} +this._period=r,this._loop=s}},Bk.getLineLength=function(t){return ew(t.__p1,t.__cp1)+ew(t.__cp1,t.__p2)},Bk.updateAnimationPoints=function(t,e){t.__p1=e[0],t.__p2=e[1],t.__cp1=e[2]||[(e[0][0]+e[1][0])/2,(e[0][1]+e[1][1])/2]},Bk.updateData=function(t,e,i){this.childAt(0).updateData(t,e,i),this._updateEffectSymbol(t,e)},Bk.updateSymbolPosition=function(t){var e=t.__p1,i=t.__p2,n=t.__cp1,o=t.__t,a=t.position,r=rn,s=sn;a[0]=r(e[0],n[0],i[0],o),a[1]=r(e[1],n[1],i[1],o);var l=s(e[0],n[0],i[0],o),u=s(e[1],n[1],i[1],o);t.rotation=-Math.atan2(u,l)-Math.PI/2,t.ignore=!1},Bk.updateLayout=function(t,e){this.childAt(0).updateLayout(t,e);var i=t.getItemModel(e).getModel("effect");this._updateEffectAnimation(t,i,e)},u(dg,Zw);var Vk=fg.prototype;Vk._createPolyline=function(t,e,i){var n=t.getItemLayout(e),o=new oM({shape:{points:n}});this.add(o),this._updateCommonStl(t,e,i)},Vk.updateData=function(t,e,i){var n=t.hostModel;bo(this.childAt(0),{shape:{points:t.getItemLayout(e)}},n,e),this._updateCommonStl(t,e,i)},Vk._updateCommonStl=function(t,e,i){var n=this.childAt(0),o=t.getItemModel(e),a=t.getItemVisual(e,"color"),s=i&&i.lineStyle,l=i&&i.hoverLineStyle;i&&!t.hasItemOption||(s=o.getModel("lineStyle").getLineStyle(),l=o.getModel("emphasis.lineStyle").getLineStyle()),n.useStyle(r({strokeNoScale:!0,fill:"none",stroke:a},s)),n.hoverStyle=l,uo(this)},Vk.updateLayout=function(t,e){this.childAt(0).setShape("points",t.getItemLayout(e))},u(fg,Zw);var Gk=pg.prototype;Gk.createLine=function(t,e,i){return new fg(t,e,i)},Gk.updateAnimationPoints=function(t,e){this._points=e;for(var i=[0],n=0,o=1;o=0&&!(n[r]<=e);r--);r=Math.min(r,o-2)}else{for(var r=a;re);r++);r=Math.min(r-1,o-2)} +J(t.position,i[r],i[r+1],(e-n[r])/(n[r+1]-n[r]));var s=i[r+1][0]-i[r][0],l=i[r+1][1]-i[r][1];t.rotation=-Math.atan2(l,s)-Math.PI/2,this._lastFrame=r,this._lastFramePercent=e,t.ignore=!1}},u(pg,dg);var Fk=Fn({shape:{polyline:!1,curveness:0,segs:[]},buildPath:function(t,e){var i=e.segs,n=e.curveness;if(e.polyline) +for(r=0;r0){t.moveTo(i[r++],i[r++]);for(var a=1;a0){var c=(s+u)/2-(l-h)*n,d=(l+h)/2-(u-s)*n;t.quadraticCurveTo(c,d,u,h)}else t.lineTo(u,h)}},findDataIndex:function(t,e){var i=this.shape,n=i.segs,o=i.curveness;if(i.polyline) +for(var a=0,r=0;r0) +for(var l=n[r++],u=n[r++],h=1;h0){if(xn(l,u,(l+c)/2-(u-d)*o,(u+d)/2-(c-l)*o,c,d))return a}else if(vn(l,u,c,d))return a;a++} +return-1}}),Wk=gg.prototype;Wk.isPersistent=function(){return!this._incremental},Wk.updateData=function(t){this.group.removeAll();var e=new Fk({rectHover:!0,cursor:"default"});e.setShape({segs:t.getLayout("linesPoints")}),this._setCommon(e,t),this.group.add(e),this._incremental=null},Wk.incrementalPrepareUpdate=function(t){this.group.removeAll(),this._clearIncremental(),t.count()>5e5?(this._incremental||(this._incremental=new Gn({silent:!0})),this.group.add(this._incremental)):this._incremental=null},Wk.incrementalUpdate=function(t,e){var i=new Fk;i.setShape({segs:e.getLayout("linesPoints")}),this._setCommon(i,e,!!this._incremental),this._incremental?this._incremental.addDisplayable(i,!0):(i.rectHover=!0,i.cursor="default",i.__startIndex=t.start,this.group.add(i))},Wk.remove=function(){this._clearIncremental(),this._incremental=null,this.group.removeAll()},Wk._setCommon=function(t,e,i){var n=e.hostModel;t.setShape({polyline:n.get("polyline"),curveness:n.get("lineStyle.curveness")}),t.useStyle(n.getModel("lineStyle").getLineStyle()),t.style.strokeNoScale=!0;var o=e.getVisual("color");o&&t.setStyle("stroke",o),t.setStyle("fill"),i||(t.seriesIndex=n.seriesIndex,t.on("mousemove",function(e){t.dataIndex=null;var i=t.findDataIndex(e.offsetX,e.offsetY);i>0&&(t.dataIndex=i+t.__startIndex)}))},Wk._clearIncremental=function(){var t=this._incremental;t&&t.clearDisplaybles()};var Hk={seriesType:"lines",plan:zI(),reset:function(t){var e=t.coordinateSystem,i=t.get("polyline"),n=t.pipelineContext.large;return{progress:function(o,a){var r=[];if(n){var s,l=o.end-o.start;if(i){for(var u=0,h=o.start;h0){var I=a(v)?s:l;v>0&&(v=v*S+b),x[_++]=I[M],x[_++]=I[M+1],x[_++]=I[M+2],x[_++]=I[M+3]*v*256}else _+=4} +return c.putImageData(y,0,0),h},_getBrush:function(){var t=this._brushCanvas||(this._brushCanvas=Y_()),e=this.pointSize+this.blurSize,i=2*e;t.width=i,t.height=i;var n=t.getContext("2d");return n.clearRect(0,0,i,i),n.shadowOffsetX=i,n.shadowBlur=this.blurSize,n.shadowColor="#000",n.beginPath(),n.arc(-e,e,this.pointSize,0,2*Math.PI,!0),n.closePath(),n.fill(),t},_getGradient:function(t,e,i){for(var n=this._gradientPixels,o=n[i]||(n[i]=new Uint8ClampedArray(1024)),a=[0,0,0,0],r=0,s=0;s<256;s++)e[i](s/255,!0,a),o[r++]=a[0],o[r++]=a[1],o[r++]=a[2],o[r++]=a[3];return o}},Fs({type:"heatmap",render:function(t,e,i){var n;e.eachComponent("visualMap",function(e){e.eachTargetSeries(function(i){i===t&&(n=e)})}),this.group.removeAll(),this._incrementalDisplayable=null;var o=t.coordinateSystem;"cartesian2d"===o.type||"calendar"===o.type?this._renderOnCartesianAndCalendar(t,i,0,t.getData().count()):_g(o)&&this._renderOnGeo(o,t,n,i)},incrementalPrepareRender:function(t,e,i){this.group.removeAll()},incrementalRender:function(t,e,i,n){e.coordinateSystem&&this._renderOnCartesianAndCalendar(e,n,t.start,t.end,!0)},_renderOnCartesianAndCalendar:function(t,e,i,n,o){var r,s,l=t.coordinateSystem;if("cartesian2d"===l.type){var u=l.getAxis("x"),h=l.getAxis("y");r=u.getBandWidth(),s=h.getBandWidth()} +for(var c=this.group,d=t.getData(),f=t.getModel("itemStyle").getItemStyle(["color"]),p=t.getModel("emphasis.itemStyle").getItemStyle(),g=t.getModel("label"),m=t.getModel("emphasis.label"),v=l.type,y="cartesian2d"===v?[d.mapDimension("x"),d.mapDimension("y"),d.mapDimension("value")]:[d.mapDimension("time"),d.mapDimension("value")],x=i;x=e.y&&t[1]<=e.y+e.height:i.contain(i.toLocalCoord(t[1]))&&t[0]>=e.y&&t[0]<=e.y+e.height},pointToData:function(t){var e=this.getAxis();return[e.coordToData(e.toLocalCoord(t["horizontal"===e.orient?0:1]))]},dataToPoint:function(t){var e=this.getAxis(),i=this.getRect(),n=[],o="horizontal"===e.orient?0:1;return t instanceof Array&&(t=t[0]),n[o]=e.toGlobalCoord(e.dataToCoord(+t)),n[1-o]=0===o?i.y+i.height/2:i.x+i.width/2,n}},Ba.register("single",{create:function(t,e){var i=[];return t.eachComponent("singleAxis",function(n,o){var a=new Zg(n,t,e);a.name="single_"+o,a.resize(n,e),n.coordinateSystem=a,i.push(a)}),t.eachSeries(function(e){if("singleAxis"===e.get("coordinateSystem")){var i=t.queryComponents({mainType:"singleAxis",index:e.get("singleAxisIndex"),id:e.get("singleAxisId")})[0];e.coordinateSystem=i&&i.coordinateSystem}}),i},dimensions:Zg.prototype.dimensions});var $k=["axisLine","axisTickLabel","axisName"],Jk=LD.extend({type:"singleAxis",axisPointerClass:"SingleAxisPointer",render:function(t,e,i,n){var o=this.group;o.removeAll();var a=Ug(t),r=new ID(t,a);d($k,r.add,r),o.add(r.getGroup()),t.get("splitLine.show")&&this._splitLine(t),Jk.superCall(this,"render",t,e,i,n)},_splitLine:function(t){var e=t.axis;if(!e.scale.isBlank()){var i=t.getModel("splitLine"),n=i.getModel("lineStyle"),o=n.get("width"),a=n.get("color");a=a instanceof Array?a:[a];for(var r=t.coordinateSystem.getRect(),s=e.isHorizontal(),l=[],u=0,h=e.getTicksCoords({tickModel:i}),c=[],d=[],f=0;f=0)&&i({type:"updateAxisPointer",currTrigger:t,x:e&&e.offsetX,y:e&&e.offsetY})})},remove:function(t,e){lm(e.getZr(),"axisPointer"),sP.superApply(this._model,"remove",arguments)},dispose:function(t,e){lm("axisPointer",e),sP.superApply(this._model,"dispose",arguments)}}),lP=Bi(),uP=i,hP=m;(um.prototype={_group:null,_lastGraphicKey:null,_handle:null,_dragging:!1,_lastValue:null,_lastStatus:null,_payloadInfo:null,animationThreshold:15,render:function(t,e,i,n){var o=e.get("value"),a=e.get("status");if(this._axisModel=t,this._axisPointerModel=e,this._api=i,n||this._lastValue!==o||this._lastStatus!==a){this._lastValue=o,this._lastStatus=a;var r=this._group,s=this._handle;if(!a||"hide"===a)return r&&r.hide(),void(s&&s.hide());r&&r.show(),s&&s.show();var l={};this.makeElOption(l,o,t,e,i);var u=l.graphicKey;u!==this._lastGraphicKey&&this.clear(i),this._lastGraphicKey=u;var h=this._moveAnimation=this.determineAnimation(t,e);if(r){var c=v(hm,e,h);this.updatePointerEl(r,l,c,e),this.updateLabelEl(r,l,c,e)}else r=this._group=new Zw,this.createPointerEl(r,l,t,e),this.createLabelEl(r,l,t,e),i.getZr().add(r);pm(r,e,!0),this._renderHandle(o)}},remove:function(t){this.clear(t)},dispose:function(t){this.clear(t)},determineAnimation:function(t,e){var i=e.get("animation"),n=t.axis,o="category"===n.type,a=e.get("snap");if(!a&&!o)return!1;if("auto"===i||null==i){var r=this.animationThreshold;if(o&&n.getBandWidth()>r)return!0;if(a){var s=xh(t).seriesDataCount,l=n.getExtent();return Math.abs(l[0]-l[1])/s>r} +return!1} +return!0===i},makeElOption:function(t,e,i,n,o){},createPointerEl:function(t,e,i,n){var o=e.pointer;if(o){var a=lP(t).pointerEl=new bM[o.type](uP(e.pointer));t.add(a)}},createLabelEl:function(t,e,i,n){if(e.label){var o=lP(t).labelEl=new aM(uP(e.label));t.add(o),dm(o,n)}},updatePointerEl:function(t,e,i){var n=lP(t).pointerEl;n&&(n.setStyle(e.pointer.style),i(n,{shape:e.pointer.shape}))},updateLabelEl:function(t,e,i,n){var o=lP(t).labelEl;o&&(o.setStyle(e.label.style),i(o,{shape:e.label.shape,position:e.label.position}),dm(o,n))},_renderHandle:function(t){if(!this._dragging&&this.updateHandleTransform){var e=this._axisPointerModel,i=this._api.getZr(),n=this._handle,o=e.getModel("handle"),a=e.get("status");if(!o.get("show")||!a||"hide"===a)return n&&i.remove(n),void(this._handle=null);var r;this._handle||(r=!0,n=this._handle=Co(o.get("icon"),{cursor:"move",draggable:!0,onmousemove:function(t){lw(t.event)},onmousedown:hP(this._onHandleDragMove,this,0,0),drift:hP(this._onHandleDragMove,this),ondragend:hP(this._onHandleDragEnd,this)}),i.add(n)),pm(n,e,!1);var s=["color","borderColor","borderWidth","opacity","shadowColor","shadowBlur","shadowOffsetX","shadowOffsetY"];n.setStyle(o.getItemStyle(null,s));var l=o.get("size");y(l)||(l=[l,l]),n.attr("scale",[l[0]/2,l[1]/2]),Lr(this,"_doDispatchAxisPointer",o.get("throttle")||0,"fixRate"),this._moveHandleToValue(t,r)}},_moveHandleToValue:function(t,e){hm(this._axisPointerModel,!e&&this._moveAnimation,this._handle,fm(this.getHandleTransform(t,this._axisModel,this._axisPointerModel)))},_onHandleDragMove:function(t,e){var i=this._handle;if(i){this._dragging=!0;var n=this.updateHandleTransform(fm(i),[t,e],this._axisModel,this._axisPointerModel);this._payloadInfo=n,i.stopAnimation(),i.attr(fm(n)),lP(i).lastProp=null,this._doDispatchAxisPointer()}},_doDispatchAxisPointer:function(){if(this._handle){var t=this._payloadInfo,e=this._axisModel;this._api.dispatchAction({type:"updateAxisPointer",x:t.cursorPoint[0],y:t.cursorPoint[1],tooltipOption:t.tooltipOption,axesInfo:[{axisDim:e.axis.dim,axisIndex:e.componentIndex}]})}},_onHandleDragEnd:function(t){if(this._dragging=!1,this._handle){var e=this._axisPointerModel.get("value");this._moveHandleToValue(e),this._api.dispatchAction({type:"hideTip"})}},getHandleTransform:null,updateHandleTransform:null,clear:function(t){this._lastValue=null,this._lastStatus=null;var e=t.getZr(),i=this._group,n=this._handle;e&&i&&(this._lastGraphicKey=null,i&&e.remove(i),n&&e.remove(n),this._group=null,this._handle=null,this._payloadInfo=null)},doClear:function(){},buildLabel:function(t,e,i){return i=i||0,{x:t[i],y:t[1-i],width:e[i],height:e[1-i]}}}).constructor=um,Xi(um);var cP=um.extend({makeElOption:function(t,e,i,n,o){var a=i.axis,r=a.grid,s=n.get("type"),l=Mm(r,a).getOtherAxis(a).getGlobalExtent(),u=a.toGlobalCoord(a.dataToCoord(e,!0));if(s&&"none"!==s){var h=gm(n),c=dP[s](a,u,l,h);c.style=h,t.graphicKey=c.type,t.pointer=c} +_m(e,t,Ih(r.model,i),i,n,o)},getHandleTransform:function(t,e,i){var n=Ih(e.axis.grid.model,e,{labelInside:!1});return n.labelMargin=i.get("handle.margin"),{position:xm(e.axis,t,n),rotation:n.rotation+(n.labelDirection<0?Math.PI:0)}},updateHandleTransform:function(t,e,i,n){var o=i.axis,a=o.grid,r=o.getGlobalExtent(!0),s=Mm(a,o).getOtherAxis(o).getGlobalExtent(),l="x"===o.dim?0:1,u=t.position;u[l]+=e[l],u[l]=Math.min(r[1],u[l]),u[l]=Math.max(r[0],u[l]);var h=(s[1]+s[0])/2,c=[h,h];c[l]=u[l];var d=[{verticalAlign:"middle"},{align:"center"}];return{position:u,rotation:t.rotation,cursorPoint:c,tooltipOption:d[l]}}}),dP={line:function(t,e,i,n){var o=wm([e,i[0]],[e,i[1]],Im(t));return Xn({shape:o,style:n}),{type:"Line",shape:o}},shadow:function(t,e,i,n){var o=Math.max(1,t.getBandWidth()),a=i[1]-i[0];return{type:"Rect",shape:bm([e-o/2,i[0]],[o,a],Im(t))}}};LD.registerAxisPointerClass("CartesianAxisPointer",cP),Ls(function(t){if(t){(!t.axisPointer||0===t.axisPointer.length)&&(t.axisPointer={});var e=t.axisPointer.link;e&&!y(e)&&(t.axisPointer.link=[e])}}),ks(IT.PROCESSOR.STATISTIC,function(t,e){t.getComponent("axisPointer").coordSysAxesInfo=dh(t,e)}),Ps({type:"updateAxisPointer",event:"updateAxisPointer",update:":updateAxisPointer"},function(t,e,i){var n=t.currTrigger,o=[t.x,t.y],a=t,r=t.dispatchAction||m(i.dispatchAction,i),s=e.getComponent("axisPointer").coordSysAxesInfo;if(s){em(o)&&(o=eP({seriesIndex:a.seriesIndex,dataIndex:a.dataIndex},e).point);var l=em(o),u=a.axesInfo,h=s.axesInfo,c="leave"===n||em(o),d={},f={},p={list:[],map:{}},g={showPointer:nP(Yg,f),showTooltip:nP(qg,p)};iP(s.coordSysMap,function(t,e){var i=l||t.containPoint(o);iP(s.coordSysAxesInfo[e],function(t,e){var n=t.axis,a=Qg(u,t);if(!c&&i&&(!u||a)){var r=a&&a.value;null!=r||l||(r=n.pointToData(o)),null!=r&&Xg(t,r,g,!1,d)}})});var v={};return iP(h,function(t,e){var i=t.linkGroup;i&&!f[e]&&iP(i.axesInfo,function(e,n){var o=f[n];if(e!==t&&o){var a=o.value;i.mapper&&(a=t.axis.scale.parse(i.mapper(a,tm(e),tm(t)))),v[t.key]=a}})}),iP(v,function(t,e){Xg(h[e],t,g,!0,d)}),Kg(f,h,d),$g(p,o,t,r),Jg(h,0,i),d}});var fP=["x","y"],pP=["width","height"],gP=um.extend({makeElOption:function(t,e,i,n,o){var a=i.axis,r=a.coordinateSystem,s=Am(r,1-Tm(a)),l=r.dataToPoint(e)[0],u=n.get("type");if(u&&"none"!==u){var h=gm(n),c=mP[u](a,l,s,h);c.style=h,t.graphicKey=c.type,t.pointer=c} +_m(e,t,Ug(i),i,n,o)},getHandleTransform:function(t,e,i){var n=Ug(e,{labelInside:!1});return n.labelMargin=i.get("handle.margin"),{position:xm(e.axis,t,n),rotation:n.rotation+(n.labelDirection<0?Math.PI:0)}},updateHandleTransform:function(t,e,i,n){var o=i.axis,a=o.coordinateSystem,r=Tm(o),s=Am(a,r),l=t.position;l[r]+=e[r],l[r]=Math.min(s[1],l[r]),l[r]=Math.max(s[0],l[r]);var u=Am(a,1-r),h=(u[1]+u[0])/2,c=[h,h];return c[r]=l[r],{position:l,rotation:t.rotation,cursorPoint:c,tooltipOption:{verticalAlign:"middle"}}}}),mP={line:function(t,e,i,n){var o=wm([e,i[0]],[e,i[1]],Tm(t));return Xn({shape:o,style:n}),{type:"Line",shape:o}},shadow:function(t,e,i,n){var o=t.getBandWidth(),a=i[1]-i[0];return{type:"Rect",shape:bm([e-o/2,i[0]],[o,a],Tm(t))}}};LD.registerAxisPointerClass("SingleAxisPointer",gP),Vs({type:"single"});var vP=OI.extend({type:"series.themeRiver",dependencies:["singleAxis"],nameMap:null,init:function(t){vP.superApply(this,"init",arguments),this.legendDataProvider=function(){return this.getRawData()}},fixData:function(t){for(var e=t.length,i=f(Lp().key(function(t){return t[2]}).entries(t),function(t){return{name:t.key,dataList:t.values}}),n=i.length,o=-1,a=-1,r=0;ro&&(o=s,a=r)} +for(var l=0;lMath.PI/2?"right":"left"):x&&"center"!==x?"left"===x?(f=u.r0+y,p>Math.PI/2&&(x="right")):"right"===x&&(f=u.r-y,p>Math.PI/2&&(x="left")):(f=(u.r+u.r0)/2,x="center"),d.attr("style",{text:l,textAlign:x,textVerticalAlign:n("verticalAlign")||"middle",opacity:n("opacity")});var _=f*g+u.cx,w=f*m+u.cy;d.attr("position",[_,w]);var b=n("rotate"),S=0;"radial"===b?(S=-p)<-Math.PI/2&&(S+=Math.PI):"tangential"===b?(S=Math.PI/2-p)>Math.PI/2?S-=Math.PI:S<-Math.PI/2&&(S+=Math.PI):"number"==typeof b&&(S=b*Math.PI/180),d.attr("rotation",S)},wP._initEvents=function(t,e,i,n){t.off("mouseover").off("mouseout").off("emphasis").off("normal");var o=this,a=function(){o.onEmphasis(n)},r=function(){o.onNormal()};i.isAnimationEnabled()&&t.on("mouseover",a).on("mouseout",r).on("emphasis",a).on("normal",r).on("downplay",function(){o.onDownplay()}).on("highlight",function(){o.onHighlight()})},u(Pm,Zw);Mr.extend({type:"sunburst",init:function(){},render:function(t,e,i,n){function o(i,n){if(c||!i||i.getValue()||(i=null),i!==l&&n!==l) +if(n&&n.piece)i?(n.piece.updateData(!1,i,"normal",t,e),s.setItemGraphicEl(i.dataIndex,n.piece)):a(n);else if(i){var o=new Pm(i,t,e);h.add(o),s.setItemGraphicEl(i.dataIndex,o)}} +function a(t){t&&t.piece&&(h.remove(t.piece),t.piece=null)} +var r=this;this.seriesModel=t,this.api=i,this.ecModel=e;var s=t.getData(),l=s.tree.root,u=t.getViewRoot(),h=this.group,c=t.get("renderLabelForZeroData"),d=[];u.eachNode(function(t){d.push(t)});var f=this._oldChildren||[];if(function(t,e){function i(t){return t.getId()} +function n(i,n){o(null==i?null:t[i],null==n?null:e[n])} +0===t.length&&0===e.length||new Hs(e,t,i,i).add(n).update(n).remove(v(n,null)).execute()}(d,f),function(i,n){if(n.depth>0){r.virtualPiece?r.virtualPiece.updateData(!1,i,"normal",t,e):(r.virtualPiece=new Pm(i,t,e),h.add(r.virtualPiece)),n.piece._onclickEvent&&n.piece.off("click",n.piece._onclickEvent);var o=function(t){r._rootToNode(n.parentNode)};n.piece._onclickEvent=o,r.virtualPiece.on("click",o)}else r.virtualPiece&&(h.remove(r.virtualPiece),r.virtualPiece=null)}(l,u),n&&n.highlight&&n.highlight.piece){var p=t.getShallow("highlightPolicy");n.highlight.piece.onEmphasis(p)}else if(n&&n.unhighlight){var g=this.virtualPiece;!g&&l.children.length&&(g=l.children[0].piece),g&&g.onNormal()} +this._initEvents(),this._oldChildren=d},dispose:function(){},_initEvents:function(){var t=this,e=function(e){var i=!1;t.seriesModel.getViewRoot().eachNode(function(n){if(!i&&n.piece&&n.piece.childAt(0)===e.target){var o=n.getModel().get("nodeClick");if("rootToNode"===o)t._rootToNode(n);else if("link"===o){var a=n.getModel(),r=a.get("link");if(r){var s=a.get("target",!0)||"_blank";window.open(r,s)}} +i=!0}})};this.group._onclickEvent&&this.group.off("click",this.group._onclickEvent),this.group.on("click",e),this.group._onclickEvent=e},_rootToNode:function(t){t!==this.seriesModel.getViewRoot()&&this.api.dispatchAction({type:"sunburstRootToNode",from:this.uid,seriesId:this.seriesModel.id,targetNode:t})},containPoint:function(t,e){var i=e.getData().getItemLayout(0);if(i){var n=t[0]-i.cx,o=t[1]-i.cy,a=Math.sqrt(n*n+o*o);return a<=i.r&&a>=i.r0}}});var bP="sunburstRootToNode";Ps({type:bP,update:"updateView"},function(t,e){e.eachComponent({mainType:"series",subType:"sunburst",query:t},function(e,i){var n=td(t,[bP],e);if(n){var o=e.getViewRoot();o&&(t.direction=id(o,n.node)?"rollUp":"drillDown"),e.resetViewRoot(n.node)}})});var SP="sunburstHighlight";Ps({type:SP,update:"updateView"},function(t,e){e.eachComponent({mainType:"series",subType:"sunburst",query:t},function(e,i){var n=td(t,[SP],e);n&&(t.highlight=n.node)})});Ps({type:"sunburstUnhighlight",update:"updateView"},function(t,e){e.eachComponent({mainType:"series",subType:"sunburst",query:t},function(e,i){t.unhighlight=!0})});var MP=Math.PI/180;Es(v(jD,"sunburst")),Os(v(function(t,e,i,n){e.eachSeriesByType(t,function(t){var e=t.get("center"),n=t.get("radius");y(n)||(n=[0,n]),y(e)||(e=[e,e]);var o=i.getWidth(),a=i.getHeight(),r=Math.min(o,a),s=Ro(e[0],o),l=Ro(e[1],a),u=Ro(n[0],r/2),h=Ro(n[1],r/2),c=-t.get("startAngle")*MP,f=t.get("minAngle")*MP,p=t.getData().tree.root,g=t.getViewRoot(),m=g.depth,v=t.get("sort");null!=v&&Rm(g,v);var x=0;d(g.children,function(t){!isNaN(t.getValue())&&x++});var _=g.getValue(),w=Math.PI/(_||x)*2,b=g.depth>0,S=g.height-(b?-1:1),M=(h-u)/(S||1),I=t.get("clockwise"),T=t.get("stillShowZeroSum"),A=I?1:-1,D=function(t,e){if(t){var i=e;if(t!==p){var n=t.getValue(),o=0===_&&T?w:n*w;on[1]&&n.reverse(),{coordSys:{type:"polar",cx:t.cx,cy:t.cy,r:n[1],r0:n[0]},api:{coord:m(function(n){var o=e.dataToRadius(n[0]),a=i.dataToAngle(n[1]),r=t.coordToPoint([o,a]);return r.push(o,a*Math.PI/180),r}),size:m(Fm,t)}}},calendar:function(t){var e=t.getRect(),i=t.getRangeInfo();return{coordSys:{type:"calendar",x:e.x,y:e.y,width:e.width,height:e.height,cellWidth:t.getCellWidth(),cellHeight:t.getCellHeight(),rangeInfo:{start:i.start,end:i.end,weeks:i.weeks,dayCount:i.allDay}},api:{coord:function(e,i){return t.dataToPoint(e,i)}}}}};OI.extend({type:"series.custom",dependencies:["grid","polar","geo","singleAxis","calendar"],defaultOption:{coordinateSystem:"cartesian2d",zlevel:0,z:2,legendHoverLink:!0,useTransform:!0},getInitialData:function(t,e){return dl(this.getSource(),this)},getDataParams:function(t,e,i){var n=OI.prototype.getDataParams.apply(this,arguments);return i&&(n.info=i.info),n}}),Mr.extend({type:"custom",_data:null,render:function(t,e,i,n){var o=this._data,a=t.getData(),r=this.group,s=Um(t,a,e,i);a.diff(o).add(function(e){jm(null,e,s(e,n),t,r,a)}).update(function(e,i){jm(o.getItemGraphicEl(i),e,s(e,n),t,r,a)}).remove(function(t){var e=o.getItemGraphicEl(t);e&&r.remove(e)}).execute(),this._data=a},incrementalPrepareRender:function(t,e,i){this.group.removeAll(),this._data=null},incrementalRender:function(t,e,i,n,o){for(var a=e.getData(),r=Um(e,a,i,n),s=t.start;s=0;l--)null==o[l]?o.splice(l,1):delete o[l].$action},_flatten:function(t,e,i){d(t,function(t){if(t){i&&(t.parentOption=i),e.push(t);var n=t.children;"group"===t.type&&n&&this._flatten(n,e,t),delete t.children}},this)},useElOptionsToUpdate:function(){var t=this._elOptionsToUpdate;return this._elOptionsToUpdate=null,t}});Vs({type:"graphic",init:function(t,e){this._elMap=R(),this._lastGraphicModel},render:function(t,e,i){t!==this._lastGraphicModel&&this._clear(),this._lastGraphicModel=t,this._updateElements(t),this._relocate(t,i)},_updateElements:function(t){var e=t.useElOptionsToUpdate();if(e){var i=this._elMap,n=this.group;d(e,function(e){var o=e.$action,a=e.id,r=i.get(a),s=e.parentId,l=null!=s?i.get(s):n,u=e.style;"text"===e.type&&u&&(e.hv&&e.hv[1]&&(u.textVerticalAlign=u.textBaseline=null),!u.hasOwnProperty("textFill")&&u.fill&&(u.textFill=u.fill),!u.hasOwnProperty("textStroke")&&u.stroke&&(u.textStroke=u.stroke));var h=av(e);o&&"merge"!==o?"replace"===o?(ov(r,i),nv(a,l,h,i)):"remove"===o&&ov(r,i):r?r.attr(h):nv(a,l,h,i);var c=i.get(a);c&&(c.__ecGraphicWidth=e.width,c.__ecGraphicHeight=e.height,hv(c,t))})}},_relocate:function(t,e){for(var i=t.option.elements,n=this.group,o=this._elMap,a=i.length-1;a>=0;a--){var r=i[a],s=o.get(r.id);if(s){var l=s.parent;ua(s,r,l===n?{width:e.getWidth(),height:e.getHeight()}:{width:l.__ecGraphicWidth||0,height:l.__ecGraphicHeight||0},null,{hv:r.hv,boundingMode:r.bounding})}}},_clear:function(){var t=this._elMap;t.each(function(e){ov(e,t)}),this._elMap=R()},dispose:function(){this._clear()}});var PP=Bs({type:"legend.plain",dependencies:["series"],layoutMode:{type:"box",ignoreSize:!0},init:function(t,e,i){this.mergeDefaultAndTheme(t,i),t.selected=t.selected||{}},mergeOption:function(t){PP.superCall(this,"mergeOption",t)},optionUpdated:function(){this._updateData(this.ecModel);var t=this._data;if(t[0]&&"single"===this.get("selectedMode")){for(var e=!1,i=0;i=0},defaultOption:{zlevel:0,z:4,show:!0,orient:"horizontal",left:"center",top:0,align:"auto",backgroundColor:"rgba(0,0,0,0)",borderColor:"#ccc",borderRadius:0,borderWidth:0,padding:5,itemGap:10,itemWidth:25,itemHeight:14,inactiveColor:"#ccc",textStyle:{color:"#333"},selectedMode:!0,tooltip:{show:!1}}});Ps("legendToggleSelect","legendselectchanged",v(cv,"toggleSelected")),Ps("legendSelect","legendselected",v(cv,"select")),Ps("legendUnSelect","legendunselected",v(cv,"unSelect"));var NP=v,OP=d,EP=Zw,RP=Vs({type:"legend.plain",newlineDisabled:!1,init:function(){this.group.add(this._contentGroup=new EP),this._backgroundEl},getContentGroup:function(){return this._contentGroup},render:function(t,e,i){if(this.resetInner(),t.get("show",!0)){var n=t.get("align");n&&"auto"!==n||(n="right"===t.get("left")&&"vertical"===t.get("orient")?"right":"left"),this.renderInner(n,t,e,i);var o=t.getBoxLayoutParams(),a={width:i.getWidth(),height:i.getHeight()},s=t.get("padding"),l=la(o,a,s),u=this.layoutInner(t,n,l),h=la(r({width:u.width,height:u.height},o),a,s);this.group.attr("position",[h.x-u.x,h.y-u.y]),this.group.add(this._backgroundEl=fv(u,t))}},resetInner:function(){this.getContentGroup().removeAll(),this._backgroundEl&&this.group.remove(this._backgroundEl)},renderInner:function(t,e,i,n){var o=this.getContentGroup(),a=R(),r=e.get("selectedMode"),s=[];i.eachRawSeries(function(t){!t.get("legendHoverLink")&&s.push(t.id)}),OP(e.getData(),function(l,u){var h=l.get("name");if(this.newlineDisabled||""!==h&&"\n"!==h){var c=i.getSeriesByName(h)[0];if(!a.get(h)) +if(c){var d=c.getData(),f=d.getVisual("color");"function"==typeof f&&(f=f(c.getDataParams(0)));var p=d.getVisual("legendSymbol")||"roundRect",g=d.getVisual("symbol");this._createItem(h,u,l,e,p,g,t,f,r).on("click",NP(pv,h,n)).on("mouseover",NP(gv,c.name,null,n,s)).on("mouseout",NP(mv,c.name,null,n,s)),a.set(h,!0)}else i.eachRawSeries(function(i){if(!a.get(h)&&i.legendDataProvider){var o=i.legendDataProvider(),c=o.indexOfName(h);if(c<0)return;var d=o.getItemVisual(c,"color");this._createItem(h,u,l,e,"roundRect",null,t,d,r).on("click",NP(pv,h,n)).on("mouseover",NP(gv,null,h,n,s)).on("mouseout",NP(mv,null,h,n,s)),a.set(h,!0)}},this)}else o.add(new EP({newline:!0}))},this)},_createItem:function(t,e,i,n,o,r,s,l,u){var h=n.get("itemWidth"),c=n.get("itemHeight"),d=n.get("inactiveColor"),f=n.get("symbolKeepAspect"),p=n.isSelected(t),g=new EP,m=i.getModel("textStyle"),v=i.get("icon"),y=i.getModel("tooltip"),x=y.parentModel;if(o=v||o,g.add(Xl(o,0,0,h,c,p?l:d,null==f||f)),!v&&r&&(r!==o||"none"===r)){var _=.8*c;"none"===r&&(r="circle"),g.add(Xl(r,(h-_)/2,(c-_)/2,_,_,p?l:d,null==f||f))} +var w="left"===s?h+5:-5,b=s,S=n.get("formatter"),M=t;"string"==typeof S&&S?M=S.replace("{name}",null!=t?t:""):"function"==typeof S&&(M=S(t)),g.add(new qS({style:fo({},m,{text:M,x:w,y:c/2,textFill:p?m.getTextColor():d,textAlign:b,textVerticalAlign:"middle"})}));var I=new aM({shape:g.getBoundingRect(),invisible:!0,tooltip:y.get("show")?a({content:t,formatter:x.get("formatter",!0)||function(){return t},formatterParams:{componentType:"legend",legendIndex:n.componentIndex,name:t,$vars:["name"]}},y.option):null});return g.add(I),g.eachChild(function(t){t.silent=!0}),I.silent=!u,this.getContentGroup().add(g),uo(g),g.__legendDataIndex=e,g},layoutInner:function(t,e,i){var n=this.getContentGroup();UM(t.get("orient"),n,t.get("itemGap"),i.width,i.height);var o=n.getBoundingRect();return n.attr("position",[-o.x,-o.y]),this.group.getBoundingRect()}});ks(function(t){var e=t.findComponents({mainType:"legend"});e&&e.length&&t.filterSeries(function(t){for(var i=0;ii[s],f=[-h.x,-h.y];f[r]=n.position[r];var p=[0,0],g=[-c.x,-c.y],m=A(t.get("pageButtonGap",!0),t.get("itemGap",!0));d&&("end"===t.get("pageButtonPosition",!0)?g[r]+=i[s]-c[s]:p[r]+=c[s]+m),g[1-r]+=h[l]/2-c[l]/2,n.attr("position",f),o.attr("position",p),a.attr("position",g);var v=this.group.getBoundingRect();if((v={x:0,y:0})[s]=d?i[s]:h[s],v[l]=Math.max(h[l],c[l]),v[u]=Math.min(0,c[u]+g[1-r]),o.__rectSize=i[s],d){var y={x:0,y:0};y[s]=Math.max(i[s]-c[s]-m,0),y[l]=v[l],o.setClipPath(new aM({shape:y})),o.__rectSize=y[s]}else a.eachChild(function(t){t.attr({invisible:!0,silent:!0})});var x=this._getPageInfo(t);return null!=x.pageIndex&&bo(n,{position:x.contentPosition},!!d&&t),this._updatePageInfoView(t,x),v},_pageGo:function(t,e,i){var n=this._getPageInfo(e)[t];null!=n&&i.dispatchAction({type:"legendScroll",scrollDataIndex:n,legendId:e.id})},_updatePageInfoView:function(t,e){var i=this._controllerGroup;d(["pagePrev","pageNext"],function(n){var o=null!=e[n+"DataIndex"],a=i.childOfName(n);a&&(a.setStyle("fill",o?t.get("pageIconColor",!0):t.get("pageIconInactiveColor",!0)),a.cursor=o?"pointer":"default")});var n=i.childOfName("pageText"),o=t.get("pageFormatter"),a=e.pageIndex,r=null!=a?a+1:0,s=e.pageCount;n&&o&&n.setStyle("text",_(o)?o.replace("{current}",r).replace("{total}",s):o({current:r,total:s}))},_getPageInfo:function(t){function e(t){var e=t.getBoundingRect().clone();return e[f]+=t.position[h],e} +var i,n,o,a,r=t.get("scrollDataIndex",!0),s=this.getContentGroup(),l=s.getBoundingRect(),u=this._containerGroup.__rectSize,h=t.getOrient().index,c=VP[h],d=VP[1-h],f=GP[h],p=s.position.slice();this._showController?s.eachChild(function(t){t.__legendDataIndex===r&&(a=t)}):a=s.childAt(0);var g=u?Math.ceil(l[c]/u):0;if(a){var m=a.getBoundingRect(),v=a.position[h]+m[f];p[h]=-v-l[f],i=Math.floor(g*(v+m[f]+u/2)/l[c]),i=l[c]&&g?Math.max(0,Math.min(g-1,i)):-1;var y={x:0,y:0};y[c]=u,y[d]=l[d],y[f]=-p[h]-l[f];var x,_=s.children();if(s.eachChild(function(t,i){var n=e(t);n.intersect(y)&&(null==x&&(x=i),o=t.__legendDataIndex),i===_.length-1&&n[f]+n[c]<=y[f]+y[c]&&(o=null)}),null!=x){var w=e(_[x]);if(y[f]=w[f]+w[c]-y[c],x<=0&&w[f]>=y[f])n=null;else{for(;x>0&&e(_[x-1]).intersect(y);)x--;n=_[x].__legendDataIndex}}} +return{contentPosition:p,pageIndex:i,pageCount:g,pagePrevDataIndex:n,pageNextDataIndex:o}}});Ps("legendScroll","legendscroll",function(t,e){var i=t.scrollDataIndex;null!=i&&e.eachComponent({mainType:"legend",subType:"scroll",query:t},function(t){t.setScrollDataIndex(i)})}),Bs({type:"tooltip",dependencies:["axisPointer"],defaultOption:{zlevel:0,z:60,show:!0,showContent:!0,trigger:"item",triggerOn:"mousemove|click",alwaysShowContent:!1,displayMode:"single",renderMode:"auto",confine:!1,showDelay:0,hideDelay:100,transitionDuration:.4,enterable:!1,backgroundColor:"rgba(50,50,50,0.7)",borderColor:"#333",borderRadius:4,borderWidth:0,padding:5,extraCssText:"",axisPointer:{type:"line",axis:"auto",animation:"auto",animationDurationUpdate:200,animationEasingUpdate:"exponentialOut",crossStyle:{color:"#999",width:1,type:"dashed",textStyle:{}}},textStyle:{color:"#fff",fontSize:14}}});var WP=d,HP=Jo,ZP=["","-webkit-","-moz-","-o-"];wv.prototype={constructor:wv,_enterable:!0,update:function(){var t=this._container,e=t.currentStyle||document.defaultView.getComputedStyle(t),i=t.style;"absolute"!==i.position&&"absolute"!==e.position&&(i.position="relative")},show:function(t){clearTimeout(this._hideTimeout);var e=this.el;e.style.cssText="position:absolute;display:block;border-style:solid;white-space:nowrap;z-index:9999999;"+_v(t)+";left:"+this._x+"px;top:"+this._y+"px;"+(t.get("extraCssText")||""),e.style.display=e.innerHTML?"block":"none",e.style.pointerEvents=this._enterable?"auto":"none",this._show=!0},setContent:function(t){this.el.innerHTML=null==t?"":t},setEnterable:function(t){this._enterable=t},getSize:function(){var t=this.el;return[t.clientWidth,t.clientHeight]},moveTo:function(t,e){var i,n=this._zr;n&&n.painter&&(i=n.painter.getViewportRootOffset())&&(t+=i.offsetLeft,e+=i.offsetTop);var o=this.el.style;o.left=t+"px",o.top=e+"px",this._x=t,this._y=e},hide:function(){this.el.style.display="none",this._show=!1},hideLater:function(t){!this._show||this._inContent&&this._enterable||(t?(this._hideDelay=t,this._show=!1,this._hideTimeout=setTimeout(m(this.hide,this),t)):this.hide())},isShow:function(){return this._show},getOuterSize:function(){var t=this.el.clientWidth,e=this.el.clientHeight;if(document.defaultView&&document.defaultView.getComputedStyle){var i=document.defaultView.getComputedStyle(this.el);i&&(t+=parseInt(i.paddingLeft,10)+parseInt(i.paddingRight,10)+parseInt(i.borderLeftWidth,10)+parseInt(i.borderRightWidth,10),e+=parseInt(i.paddingTop,10)+parseInt(i.paddingBottom,10)+parseInt(i.borderTopWidth,10)+parseInt(i.borderBottomWidth,10))} +return{width:t,height:e}}},bv.prototype={constructor:bv,_enterable:!0,update:function(){},show:function(t){this._hideTimeout&&clearTimeout(this._hideTimeout),this.el.attr("show",!0),this._show=!0},setContent:function(t,e,i){this.el&&this._zr.remove(this.el);for(var n={},o=t,a=o.indexOf("{marker");a>=0;){var r=o.indexOf("|}"),s=o.substr(a+"{marker".length,r-a-"{marker".length);s.indexOf("sub")>-1?n["marker"+s]={textWidth:4,textHeight:4,textBorderRadius:2,textBackgroundColor:e[s],textOffset:[3,0]}:n["marker"+s]={textWidth:10,textHeight:10,textBorderRadius:5,textBackgroundColor:e[s]},a=(o=o.substr(r+1)).indexOf("{marker")} +this.el=new qS({style:{rich:n,text:t,textLineHeight:20,textBackgroundColor:i.get("backgroundColor"),textBorderRadius:i.get("borderRadius"),textFill:i.get("textStyle.color"),textPadding:i.get("padding")},z:i.get("z")}),this._zr.add(this.el);var l=this;this.el.on("mouseover",function(){l._enterable&&(clearTimeout(l._hideTimeout),l._show=!0),l._inContent=!0}),this.el.on("mouseout",function(){l._enterable&&l._show&&l.hideLater(l._hideDelay),l._inContent=!1})},setEnterable:function(t){this._enterable=t},getSize:function(){var t=this.el.getBoundingRect();return[t.width,t.height]},moveTo:function(t,e){this.el&&this.el.attr("position",[t,e])},hide:function(){this.el.hide(),this._show=!1},hideLater:function(t){!this._show||this._inContent&&this._enterable||(t?(this._hideDelay=t,this._show=!1,this._hideTimeout=setTimeout(m(this.hide,this),t)):this.hide())},isShow:function(){return this._show},getOuterSize:function(){return this.getSize()}};var UP=m,XP=d,jP=Ro,YP=new aM({shape:{x:-1,y:-1,width:2,height:2}});Vs({type:"tooltip",init:function(t,e){if(!z_.node){var i=t.getComponent("tooltip").get("renderMode");this._renderMode=Hi(i);var n;"html"===this._renderMode?(n=new wv(e.getDom(),e),this._newLine="
          "):(n=new bv(e),this._newLine="\n"),this._tooltipContent=n}},render:function(t,e,i){if(!z_.node){this.group.removeAll(),this._tooltipModel=t,this._ecModel=e,this._api=i,this._lastDataByCoordSys=null,this._alwaysShowContent=t.get("alwaysShowContent");var n=this._tooltipContent;n.update(),n.setEnterable(t.get("enterable")),this._initGlobalListener(),this._keepShow()}},_initGlobalListener:function(){var t=this._tooltipModel.get("triggerOn");im("itemTooltip",this._api,UP(function(e,i,n){"none"!==t&&(t.indexOf(e)>=0?this._tryShow(i,n):"leave"===e&&this._hide(n))},this))},_keepShow:function(){var t=this._tooltipModel,e=this._ecModel,i=this._api;if(null!=this._lastX&&null!=this._lastY&&"none"!==t.get("triggerOn")){var n=this;clearTimeout(this._refreshUpdateTimeout),this._refreshUpdateTimeout=setTimeout(function(){n.manuallyShowTip(t,e,i,{x:n._lastX,y:n._lastY})})}},manuallyShowTip:function(t,e,i,n){if(n.from!==this.uid&&!z_.node){var o=Mv(n,i);this._ticket="";var a=n.dataByCoordSys;if(n.tooltip&&null!=n.x&&null!=n.y){var r=YP;r.position=[n.x,n.y],r.update(),r.tooltip=n.tooltip,this._tryShow({offsetX:n.x,offsetY:n.y,target:r},o)}else if(a)this._tryShow({offsetX:n.x,offsetY:n.y,position:n.position,event:{},dataByCoordSys:n.dataByCoordSys,tooltipOption:n.tooltipOption},o);else if(null!=n.seriesIndex){if(this._manuallyAxisShowTip(t,e,i,n))return;var s=eP(n,e),l=s.point[0],u=s.point[1];null!=l&&null!=u&&this._tryShow({offsetX:l,offsetY:u,position:n.position,target:s.el,event:{}},o)}else null!=n.x&&null!=n.y&&(i.dispatchAction({type:"updateAxisPointer",x:n.x,y:n.y}),this._tryShow({offsetX:n.x,offsetY:n.y,position:n.position,target:i.getZr().findHover(n.x,n.y).target,event:{}},o))}},manuallyHideTip:function(t,e,i,n){var o=this._tooltipContent;!this._alwaysShowContent&&this._tooltipModel&&o.hideLater(this._tooltipModel.get("hideDelay")),this._lastX=this._lastY=null,n.from!==this.uid&&this._hide(Mv(n,i))},_manuallyAxisShowTip:function(t,e,i,n){var o=n.seriesIndex,a=n.dataIndex,r=e.getComponent("axisPointer").coordSysAxesInfo;if(null!=o&&null!=a&&null!=r){var s=e.getSeriesByIndex(o);if(s&&"axis"===(t=Sv([s.getData().getItemModel(a),s,(s.coordinateSystem||{}).model,t])).get("trigger"))return i.dispatchAction({type:"updateAxisPointer",seriesIndex:o,dataIndex:a,position:n.position}),!0}},_tryShow:function(t,e){var i=t.target;if(this._tooltipModel){this._lastX=t.offsetX,this._lastY=t.offsetY;var n=t.dataByCoordSys;n&&n.length?this._showAxisTooltip(n,t):i&&null!=i.dataIndex?(this._lastDataByCoordSys=null,this._showSeriesItemTooltip(t,i,e)):i&&i.tooltip?(this._lastDataByCoordSys=null,this._showComponentItemTooltip(t,i,e)):(this._lastDataByCoordSys=null,this._hide(e))}},_showOrMove:function(t,e){var i=t.get("showDelay");e=m(e,this),clearTimeout(this._showTimout),i>0?this._showTimout=setTimeout(e,i):e()},_showAxisTooltip:function(t,e){var i=this._ecModel,o=this._tooltipModel,a=[e.offsetX,e.offsetY],r=[],s=[],l=Sv([e.tooltipOption,o]),u=this._renderMode,h=this._newLine,c={};XP(t,function(t){XP(t.dataByAxis,function(t){var e=i.getComponent(t.axisDim+"Axis",t.axisIndex),o=t.value,a=[];if(e&&null!=o){var l=ym(o,e.axis,i,t.seriesDataIndices,t.valueLabelOpt);d(t.seriesDataIndices,function(r){var h=i.getSeriesByIndex(r.seriesIndex),d=r.dataIndexInside,f=h&&h.getDataParams(d);if(f.axisDim=t.axisDim,f.axisIndex=t.axisIndex,f.axisType=t.axisType,f.axisId=t.axisId,f.axisValue=Wl(e.axis,o),f.axisValueLabel=l,f){s.push(f);var p,g=h.formatTooltip(d,!0,null,u);if(w(g)){p=g.html;var m=g.markers;n(c,m)}else p=g;a.push(p)}});var f=l;"html"!==u?r.push(a.join(h)):r.push((f?Qo(f)+h:"")+a.join(h))}})},this),r.reverse(),r=r.join(this._newLine+this._newLine);var f=e.position;this._showOrMove(l,function(){this._updateContentNotChangedOnAxis(t)?this._updatePosition(l,f,a[0],a[1],this._tooltipContent,s):this._showTooltipContent(l,r,s,Math.random(),a[0],a[1],f,void 0,c)})},_showSeriesItemTooltip:function(t,e,i){var n=this._ecModel,o=e.seriesIndex,a=n.getSeriesByIndex(o),r=e.dataModel||a,s=e.dataIndex,l=e.dataType,u=r.getData(),h=Sv([u.getItemModel(s),r,a&&(a.coordinateSystem||{}).model,this._tooltipModel]),c=h.get("trigger");if(null==c||"item"===c){var d,f,p=r.getDataParams(s,l),g=r.formatTooltip(s,!1,l,this._renderMode);w(g)?(d=g.html,f=g.markers):(d=g,f=null);var m="item_"+r.name+"_"+s;this._showOrMove(h,function(){this._showTooltipContent(h,d,p,m,t.offsetX,t.offsetY,t.position,t.target,f)}),i({type:"showTip",dataIndexInside:s,dataIndex:u.getRawIndex(s),seriesIndex:o,from:this.uid})}},_showComponentItemTooltip:function(t,e,i){var n=e.tooltip;if("string"==typeof n){var o=n;n={content:o,formatter:o}} +var a=new Lo(n,this._tooltipModel,this._ecModel),r=a.get("content"),s=Math.random();this._showOrMove(a,function(){this._showTooltipContent(a,r,a.get("formatterParams")||{},s,t.offsetX,t.offsetY,t.position,e)}),i({type:"showTip",from:this.uid})},_showTooltipContent:function(t,e,i,n,o,a,r,s,l){if(this._ticket="",t.get("showContent")&&t.get("show")){var u=this._tooltipContent,h=t.get("formatter");r=r||t.get("position");var c=e;if(h&&"string"==typeof h)c=ta(h,i,!0);else if("function"==typeof h){var d=UP(function(e,n){e===this._ticket&&(u.setContent(n,l,t),this._updatePosition(t,r,o,a,u,i,s))},this);this._ticket=n,c=h(i,n,d)} +u.setContent(c,l,t),u.show(t),this._updatePosition(t,r,o,a,u,i,s)}},_updatePosition:function(t,e,i,n,o,a,r){var s=this._api.getWidth(),l=this._api.getHeight();e=e||t.get("position");var u=o.getSize(),h=t.get("align"),c=t.get("verticalAlign"),d=r&&r.getBoundingRect().clone();if(r&&d.applyTransform(r.transform),"function"==typeof e&&(e=e([i,n],a,o.el,d,{viewSize:[s,l],contentSize:u.slice()})),y(e))i=jP(e[0],s),n=jP(e[1],l);else if(w(e)){e.width=u[0],e.height=u[1];var f=la(e,{width:s,height:l});i=f.x,n=f.y,h=null,c=null}else "string"==typeof e&&r?(i=(p=Av(e,d,u))[0],n=p[1]):(i=(p=Iv(i,n,o,s,l,h?null:20,c?null:20))[0],n=p[1]);if(h&&(i-=Dv(h)?u[0]/2:"right"===h?u[0]:0),c&&(n-=Dv(c)?u[1]/2:"bottom"===c?u[1]:0),t.get("confine")){var p=Tv(i,n,o,s,l);i=p[0],n=p[1]} +o.moveTo(i,n)},_updateContentNotChangedOnAxis:function(t){var e=this._lastDataByCoordSys,i=!!e&&e.length===t.length;return i&&XP(e,function(e,n){var o=e.dataByAxis||{},a=(t[n]||{}).dataByAxis||[];(i&=o.length===a.length)&&XP(o,function(t,e){var n=a[e]||{},o=t.seriesDataIndices||[],r=n.seriesDataIndices||[];(i&=t.value===n.value&&t.axisType===n.axisType&&t.axisId===n.axisId&&o.length===r.length)&&XP(o,function(t,e){var n=r[e];i&=t.seriesIndex===n.seriesIndex&&t.dataIndex===n.dataIndex})})}),this._lastDataByCoordSys=t,!!i},_hide:function(t){this._lastDataByCoordSys=null,t({type:"hideTip",from:this.uid})},dispose:function(t,e){z_.node||(this._tooltipContent.hide(),lm("itemTooltip",e))}}),Ps({type:"showTip",event:"showTip",update:"tooltip:manuallyShowTip"},function(){}),Ps({type:"hideTip",event:"hideTip",update:"tooltip:manuallyHideTip"},function(){}),Pv.prototype={constructor:Pv,pointToData:function(t,e){return this.polar.pointToData(t,e)["radius"===this.dim?0:1]},dataToRadius:HA.prototype.dataToCoord,radiusToData:HA.prototype.coordToData},u(Pv,HA);var qP=Bi();Nv.prototype={constructor:Nv,pointToData:function(t,e){return this.polar.pointToData(t,e)["radius"===this.dim?0:1]},dataToAngle:HA.prototype.dataToCoord,angleToData:HA.prototype.coordToData,calculateCategoryInterval:function(){var t=this,e=t.getLabelModel(),i=t.scale,n=i.getExtent(),o=i.count();if(n[1]-n[0]<1)return 0;var a=n[0],r=t.dataToCoord(a+1)-t.dataToCoord(a),s=Math.abs(r),l=De(a,e.getFont(),"center","top"),u=Math.max(l.height,7)/s;isNaN(u)&&(u=1/0);var h=Math.max(0,Math.floor(u)),c=qP(t.model),d=c.lastAutoInterval,f=c.lastTickCount;return null!=d&&null!=f&&Math.abs(d-h)<=1&&Math.abs(f-o)<=1&&d>h?h=d:(c.lastTickCount=o,c.lastAutoInterval=h),h}},u(Nv,HA);var KP=function(t){this.name=t||"",this.cx=0,this.cy=0,this._radiusAxis=new Pv,this._angleAxis=new Nv,this._radiusAxis.polar=this._angleAxis.polar=this};KP.prototype={type:"polar",axisPointerEnabled:!0,constructor:KP,dimensions:["radius","angle"],model:null,containPoint:function(t){var e=this.pointToCoord(t);return this._radiusAxis.contain(e[0])&&this._angleAxis.contain(e[1])},containData:function(t){return this._radiusAxis.containData(t[0])&&this._angleAxis.containData(t[1])},getAxis:function(t){return this["_"+t+"Axis"]},getAxes:function(){return[this._radiusAxis,this._angleAxis]},getAxesByScale:function(t){var e=[],i=this._angleAxis,n=this._radiusAxis;return i.scale.type===t&&e.push(i),n.scale.type===t&&e.push(n),e},getAngleAxis:function(){return this._angleAxis},getRadiusAxis:function(){return this._radiusAxis},getOtherAxis:function(t){var e=this._angleAxis;return t===e?this._radiusAxis:e},getBaseAxis:function(){return this.getAxesByScale("ordinal")[0]||this.getAxesByScale("time")[0]||this.getAngleAxis()},getTooltipAxes:function(t){var e=null!=t&&"auto"!==t?this.getAxis(t):this.getBaseAxis();return{baseAxes:[e],otherAxes:[this.getOtherAxis(e)]}},dataToPoint:function(t,e){return this.coordToPoint([this._radiusAxis.dataToRadius(t[0],e),this._angleAxis.dataToAngle(t[1],e)])},pointToData:function(t,e){var i=this.pointToCoord(t);return[this._radiusAxis.radiusToData(i[0],e),this._angleAxis.angleToData(i[1],e)]},pointToCoord:function(t){var e=t[0]-this.cx,i=t[1]-this.cy,n=this.getAngleAxis(),o=n.getExtent(),a=Math.min(o[0],o[1]),r=Math.max(o[0],o[1]);n.inverse?a=r-360:r=a+360;var s=Math.sqrt(e*e+i*i);e/=s,i/=s;for(var l=Math.atan2(-i,e)/Math.PI*180,u=lr;)l+=360*u;return[s,l]},coordToPoint:function(t){var e=t[0],i=t[1]/180*Math.PI;return[Math.cos(i)*e+this.cx,-Math.sin(i)*e+this.cy]}};var $P=YM.extend({type:"polarAxis",axis:null,getCoordSysModel:function(){return this.ecModel.queryComponents({mainType:"polar",index:this.option.polarIndex,id:this.option.polarId})[0]}});n($P.prototype,CA);var JP={angle:{startAngle:90,clockwise:!0,splitNumber:12,axisLabel:{rotate:!1}},radius:{splitNumber:5}};xD("angle",$P,Ov,JP.angle),xD("radius",$P,Ov,JP.radius),Bs({type:"polar",dependencies:["polarAxis","angleAxis"],coordinateSystem:null,findAxisModel:function(t){var e;return this.ecModel.eachComponent(t,function(t){t.getCoordSysModel()===this&&(e=t)},this),e},defaultOption:{zlevel:0,z:0,center:["50%","50%"],radius:"80%"}});var QP={dimensions:KP.prototype.dimensions,create:function(t,e){var i=[];return t.eachComponent("polar",function(t,n){var o=new KP(n);o.update=Rv;var a=o.getRadiusAxis(),r=o.getAngleAxis(),s=t.findAxisModel("radiusAxis"),l=t.findAxisModel("angleAxis");zv(a,s),zv(r,l),Ev(o,t,e),i.push(o),t.coordinateSystem=o,o.model=t}),t.eachSeries(function(e){if("polar"===e.get("coordinateSystem")){var i=t.queryComponents({mainType:"polar",index:e.get("polarIndex"),id:e.get("polarId")})[0];e.coordinateSystem=i.coordinateSystem}}),i}};Ba.register("polar",QP);var tN=["axisLine","axisLabel","axisTick","splitLine","splitArea"];LD.extend({type:"angleAxis",axisPointerClass:"PolarAxisPointer",render:function(t,e){if(this.group.removeAll(),t.get("show")){var n=t.axis,o=n.polar,a=o.getRadiusAxis().getExtent(),r=n.getTicksCoords(),s=f(n.getViewLabels(),function(t){return(t=i(t)).coord=n.dataToCoord(t.tickValue),t});Gv(s),Gv(r),d(tN,function(e){!t.get(e+".show")||n.scale.isBlank()&&"axisLine"!==e||this["_"+e](t,o,r,a,s)},this)}},_axisLine:function(t,e,i,n){var o=t.getModel("axisLine.lineStyle"),a=new KS({shape:{cx:e.cx,cy:e.cy,r:n[Vv(e)]},style:o.getLineStyle(),z2:1,silent:!0});a.style.fill=null,this.group.add(a)},_axisTick:function(t,e,i,n){var o=t.getModel("axisTick"),a=(o.get("inside")?-1:1)*o.get("length"),s=n[Vv(e)],l=f(i,function(t){return new rM({shape:Bv(e,[s,s+a],t.coord)})});this.group.add(xM(l,{style:r(o.getModel("lineStyle").getLineStyle(),{stroke:t.get("axisLine.lineStyle.color")})}))},_axisLabel:function(t,e,i,n,o){var a=t.getCategories(!0),r=t.getModel("axisLabel"),s=r.get("margin");d(o,function(i,o){var l=r,u=i.tickValue,h=n[Vv(e)],c=e.coordToPoint([h+s,i.coord]),d=e.cx,f=e.cy,p=Math.abs(c[0]-d)/h<.3?"center":c[0]>d?"left":"right",g=Math.abs(c[1]-f)/h<.3?"middle":c[1]>f?"top":"bottom";a&&a[u]&&a[u].textStyle&&(l=new Lo(a[u].textStyle,r,r.ecModel));var m=new qS({silent:!0});this.group.add(m),fo(m.style,l,{x:c[0],y:c[1],textFill:l.getTextColor()||t.get("axisLine.lineStyle.color"),text:i.formattedLabel,textAlign:p,textVerticalAlign:g})},this)},_splitLine:function(t,e,i,n){var o=t.getModel("splitLine").getModel("lineStyle"),a=o.get("color"),s=0;a=a instanceof Array?a:[a];for(var l=[],u=0;u=0?"p":"n",M=y;v&&(n[r][b]||(n[r][b]={p:y,n:y}),M=n[r][b][S]);var I,T,A,D;if("radius"===h.dim){var C=h.dataToRadius(w)-y,L=a.dataToAngle(b);Math.abs(C)=0},fN.findTargetInfo=function(t,e){for(var i=this._targetInfoList,n=oy(e,t),o=0;o=0||uN(n,t.getAxis("y").model)>=0)&&a.push(t)}),e.push({panelId:"grid--"+t.id,gridModel:t,coordSysModel:t,coordSys:a[0],coordSyses:a,getPanelRect:mN.grid,xAxisDeclared:r[t.id],yAxisDeclared:s[t.id]})}))},geo:function(t,e){lN(t.geoModels,function(t){var i=t.coordinateSystem;e.push({panelId:"geo--"+t.id,geoModel:t,coordSysModel:t,coordSys:i,coordSyses:[i],getPanelRect:mN.geo})})}},gN=[function(t,e){var i=t.xAxisModel,n=t.yAxisModel,o=t.gridModel;return!o&&i&&(o=i.axis.grid.model),!o&&n&&(o=n.axis.grid.model),o&&o===e.gridModel},function(t,e){var i=t.geoModel;return i&&i===e.geoModel}],mN={grid:function(){return this.coordSys.grid.getRect().clone()},geo:function(){var t=this.coordSys,e=t.getBoundingRect().clone();return e.applyTransform(Mo(t)),e}},vN={lineX:hN(ay,0),lineY:hN(ay,1),rect:function(t,e,i){var n=e[cN[t]]([i[0][0],i[1][0]]),o=e[cN[t]]([i[0][1],i[1][1]]),a=[ny([n[0],o[0]]),ny([n[1],o[1]])];return{values:a,xyMinMax:a}},polygon:function(t,e,i){var n=[[1/0,-1/0],[1/0,-1/0]];return{values:f(i,function(i){var o=e[cN[t]](i);return n[0][0]=Math.min(n[0][0],o[0]),n[1][0]=Math.min(n[1][0],o[1]),n[0][1]=Math.max(n[0][1],o[0]),n[1][1]=Math.max(n[1][1],o[1]),o}),xyMinMax:n}}},yN={lineX:hN(ry,0),lineY:hN(ry,1),rect:function(t,e,i){return[[t[0][0]-i[0]*e[0][0],t[0][1]-i[0]*e[0][1]],[t[1][0]-i[1]*e[1][0],t[1][1]-i[1]*e[1][1]]]},polygon:function(t,e,i){return f(t,function(t,n){return[t[0]-i[0]*e[n][0],t[1]-i[1]*e[n][1]]})}},xN=["inBrush","outOfBrush"],_N="__ecBrushSelect",wN="__ecInBrushSelectEvent",bN=IT.VISUAL.BRUSH;Os(bN,function(t,e,i){t.eachComponent({mainType:"brush"},function(e){i&&"takeGlobalCursor"===i.type&&e.setBrushOption("brush"===i.key?i.brushOption:{brushType:!1}),(e.brushTargetManager=new iy(e.option,t)).setInputRanges(e.areas,t)})}),Es(bN,function(t,e,n){var o,a,s=[];t.eachComponent({mainType:"brush"},function(e,n){function l(t){return"all"===m||v[t]} +function u(t){return!!t.length} +function h(t,e){var i=t.coordinateSystem;w|=i.hasAxisBrushed(),l(e)&&i.eachActiveState(t.getData(),function(t,e){"active"===t&&(x[e]=1)})} +function c(i,n,o){var a=dy(i);if(a&&!fy(e,n)&&(d(b,function(n){a[n.brushType]&&e.brushTargetManager.controlSeries(n,i,t)&&o.push(n),w|=u(o)}),l(n)&&u(o))){var r=i.getData();r.each(function(t){cy(a,o,r,t)&&(x[t]=1)})}} +var p={brushId:e.id,brushIndex:n,brushName:e.name,areas:i(e.areas),selected:[]};s.push(p);var g=e.option,m=g.brushLink,v=[],x=[],_=[],w=0;n||(o=g.throttleType,a=g.throttleDelay);var b=f(e.areas,function(t){return py(r({boundingRect:SN[t.brushType](t)},t))}),S=Xv(e.option,xN,function(t){t.mappingMethod="fixed"});y(m)&&d(m,function(t){v[t]=1}),t.eachSeries(function(t,e){var i=_[e]=[];"parallel"===t.subType?h(t,e):c(t,e,i)}),t.eachSeries(function(t,e){var i={seriesId:t.id,seriesIndex:e,seriesName:t.name,dataIndex:[]};p.selected.push(i);var n=dy(t),o=_[e],a=t.getData(),r=l(e)?function(t){return x[t]?(i.dataIndex.push(a.getRawIndex(t)),"inBrush"):"outOfBrush"}:function(t){return cy(n,o,a,t)?(i.dataIndex.push(a.getRawIndex(t)),"inBrush"):"outOfBrush"};(l(e)?w:u(o))&&Yv(xN,S,a,r)})}),uy(e,o,a,s,n)});var SN={lineX:B,lineY:B,rect:function(t){return gy(t.range)},polygon:function(t){for(var e,i=t.range,n=0,o=i.length;ne[0][1]&&(e[0][1]=a[0]),a[1]e[1][1]&&(e[1][1]=a[1])} +return e&&gy(e)}},MN=["#ddd"];Bs({type:"brush",dependencies:["geo","grid","xAxis","yAxis","parallel","series"],defaultOption:{toolbox:null,brushLink:null,seriesIndex:"all",geoIndex:null,xAxisIndex:null,yAxisIndex:null,brushType:"rect",brushMode:"single",transformable:!0,brushStyle:{borderWidth:1,color:"rgba(120,140,180,0.3)",borderColor:"rgba(120,140,180,0.8)"},throttleType:"fixRate",throttleDelay:0,removeOnClick:!0,z:1e4},areas:[],brushType:null,brushOption:{},coordInfoList:[],optionUpdated:function(t,e){var i=this.option;!e&&jv(i,t,["inBrush","outOfBrush"]);var n=i.inBrush=i.inBrush||{};i.outOfBrush=i.outOfBrush||{color:MN},n.hasOwnProperty("liftZ")||(n.liftZ=5)},setAreas:function(t){t&&(this.areas=f(t,function(t){return my(this.option,t)},this))},setBrushOption:function(t){this.brushOption=my(this.option,t),this.brushType=this.brushOption.brushType}});Vs({type:"brush",init:function(t,e){this.ecModel=t,this.api=e,this.model,(this._brushController=new Cf(e.getZr())).on("brush",m(this._onBrush,this)).mount()},render:function(t){return this.model=t,vy.apply(this,arguments)},updateTransform:vy,updateView:vy,dispose:function(){this._brushController.dispose()},_onBrush:function(t,e){var n=this.model.id;this.model.brushTargetManager.setOutputRanges(t,this.ecModel),(!e.isEnd||e.removeOnClick)&&this.api.dispatchAction({type:"brush",brushId:n,areas:i(t),$from:n})}}),Ps({type:"brush",event:"brush"},function(t,e){e.eachComponent({mainType:"brush",query:t},function(e){e.setAreas(t.areas)})}),Ps({type:"brushSelect",event:"brushSelected",update:"none"},function(){});var IN={},TN=XI.toolbox.brush;_y.defaultOption={show:!0,type:["rect","polygon","lineX","lineY","keep","clear"],icon:{rect:"M7.3,34.7 M0.4,10V-0.2h9.8 M89.6,10V-0.2h-9.8 M0.4,60v10.2h9.8 M89.6,60v10.2h-9.8 M12.3,22.4V10.5h13.1 M33.6,10.5h7.8 M49.1,10.5h7.8 M77.5,22.4V10.5h-13 M12.3,31.1v8.2 M77.7,31.1v8.2 M12.3,47.6v11.9h13.1 M33.6,59.5h7.6 M49.1,59.5 h7.7 M77.5,47.6v11.9h-13",polygon:"M55.2,34.9c1.7,0,3.1,1.4,3.1,3.1s-1.4,3.1-3.1,3.1 s-3.1-1.4-3.1-3.1S53.5,34.9,55.2,34.9z M50.4,51c1.7,0,3.1,1.4,3.1,3.1c0,1.7-1.4,3.1-3.1,3.1c-1.7,0-3.1-1.4-3.1-3.1 C47.3,52.4,48.7,51,50.4,51z M55.6,37.1l1.5-7.8 M60.1,13.5l1.6-8.7l-7.8,4 M59,19l-1,5.3 M24,16.1l6.4,4.9l6.4-3.3 M48.5,11.6 l-5.9,3.1 M19.1,12.8L9.7,5.1l1.1,7.7 M13.4,29.8l1,7.3l6.6,1.6 M11.6,18.4l1,6.1 M32.8,41.9 M26.6,40.4 M27.3,40.2l6.1,1.6 M49.9,52.1l-5.6-7.6l-4.9-1.2",lineX:"M15.2,30 M19.7,15.6V1.9H29 M34.8,1.9H40.4 M55.3,15.6V1.9H45.9 M19.7,44.4V58.1H29 M34.8,58.1H40.4 M55.3,44.4 V58.1H45.9 M12.5,20.3l-9.4,9.6l9.6,9.8 M3.1,29.9h16.5 M62.5,20.3l9.4,9.6L62.3,39.7 M71.9,29.9H55.4",lineY:"M38.8,7.7 M52.7,12h13.2v9 M65.9,26.6V32 M52.7,46.3h13.2v-9 M24.9,12H11.8v9 M11.8,26.6V32 M24.9,46.3H11.8v-9 M48.2,5.1l-9.3-9l-9.4,9.2 M38.9-3.9V12 M48.2,53.3l-9.3,9l-9.4-9.2 M38.9,62.3V46.4",keep:"M4,10.5V1h10.3 M20.7,1h6.1 M33,1h6.1 M55.4,10.5V1H45.2 M4,17.3v6.6 M55.6,17.3v6.6 M4,30.5V40h10.3 M20.7,40 h6.1 M33,40h6.1 M55.4,30.5V40H45.2 M21,18.9h62.9v48.6H21V18.9z",clear:"M22,14.7l30.9,31 M52.9,14.7L22,45.7 M4.7,16.8V4.2h13.1 M26,4.2h7.8 M41.6,4.2h7.8 M70.3,16.8V4.2H57.2 M4.7,25.9v8.6 M70.3,25.9v8.6 M4.7,43.2v12.6h13.1 M26,55.8h7.8 M41.6,55.8h7.8 M70.3,43.2v12.6H57.2"},title:i(TN.title)};var AN=_y.prototype;AN.render=AN.updateView=function(t,e,i){var n,o,a;e.eachComponent({mainType:"brush"},function(t){n=t.brushType,o=t.brushOption.brushMode||"single",a|=t.areas.length}),this._brushType=n,this._brushMode=o,d(t.get("type",!0),function(e){t.setIconStatus(e,("keep"===e?"multiple"===o:"clear"===e?a:e===n)?"emphasis":"normal")})},AN.getIcons=function(){var t=this.model,e=t.get("icon",!0),i={};return d(t.get("type",!0),function(t){e[t]&&(i[t]=e[t])}),i},AN.onclick=function(t,e,i){var n=this._brushType,o=this._brushMode;"clear"===i?(e.dispatchAction({type:"axisAreaSelect",intervals:[]}),e.dispatchAction({type:"brush",command:"clear",areas:[]})):e.dispatchAction({type:"takeGlobalCursor",key:"brush",brushOption:{brushType:"keep"===i?n:n!==i&&i,brushMode:"keep"===i?"multiple"===o?"single":"multiple":o}})},yy("brush",_y),Ls(function(t,e){var i=t&&t.brush;if(y(i)||(i=i?[i]:[]),i.length){var n=[];d(i,function(t){var e=t.hasOwnProperty("toolbox")?t.toolbox:[];e instanceof Array&&(n=n.concat(e))});var o=t&&t.toolbox;y(o)&&(o=o[0]),o||(o={feature:{}},t.toolbox=[o]);var a=o.feature||(o.feature={}),r=a.brush||(a.brush={}),s=r.type||(r.type=[]);s.push.apply(s,n),Zv(s),e&&!s.length&&s.push.apply(s,aN)}});wy.prototype={constructor:wy,type:"calendar",dimensions:["time","value"],getDimensionsInfo:function(){return[{name:"time",type:"time"},"value"]},getRangeInfo:function(){return this._rangeInfo},getModel:function(){return this._model},getRect:function(){return this._rect},getCellWidth:function(){return this._sw},getCellHeight:function(){return this._sh},getOrient:function(){return this._orient},getFirstDayOfWeek:function(){return this._firstDayOfWeek},getDateInfo:function(t){var e=(t=Uo(t)).getFullYear(),i=t.getMonth()+1;i=i<10?"0"+i:i;var n=t.getDate();n=n<10?"0"+n:n;var o=t.getDay();return o=Math.abs((o+7-this.getFirstDayOfWeek())%7),{y:e,m:i,d:n,day:o,time:t.getTime(),formatedDate:e+"-"+i+"-"+n,date:t}},getNextNDay:function(t,e){return 0===(e=e||0)?this.getDateInfo(t):((t=new Date(this.getDateInfo(t).time)).setDate(t.getDate()+e),this.getDateInfo(t))},update:function(t,e){function i(t,e){return null!=t[e]&&"auto"!==t[e]} +this._firstDayOfWeek=+this._model.getModel("dayLabel").get("firstDay"),this._orient=this._model.get("orient"),this._lineWidth=this._model.getModel("itemStyle").getItemStyle().lineWidth||0,this._rangeInfo=this._getRangeInfo(this._initRangeOption());var n=this._rangeInfo.weeks||1,o=["width","height"],a=this._model.get("cellSize").slice(),r=this._model.getBoxLayoutParams(),s="horizontal"===this._orient?[n,7]:[7,n];d([0,1],function(t){i(a,t)&&(r[o[t]]=a[t]*s[t])});var l={width:e.getWidth(),height:e.getHeight()},u=this._rect=la(r,l);d([0,1],function(t){i(a,t)||(a[t]=u[o[t]]/s[t])}),this._sw=a[0],this._sh=a[1]},dataToPoint:function(t,e){y(t)&&(t=t[0]),null==e&&(e=!0);var i=this.getDateInfo(t),n=this._rangeInfo,o=i.formatedDate;if(e&&!(i.time>=n.start.time&&i.timea.end.time&&t.reverse(),t},_getRangeInfo:function(t){var e;(t=[this.getDateInfo(t[0]),this.getDateInfo(t[1])])[0].time>t[1].time&&(e=!0,t.reverse());var i=Math.floor(t[1].time/864e5)-Math.floor(t[0].time/864e5)+1,n=new Date(t[0].time),o=n.getDate(),a=t[1].date.getDate();if(n.setDate(o+i-1),n.getDate()!==a) +for(var r=n.getTime()-t[1].time>0?1:-1;n.getDate()!==a&&(n.getTime()-t[1].time)*r>0;)i-=r,n.setDate(o+i-1);var s=Math.floor((i+t[0].day+6)/7),l=e?1-s:s-1;return e&&t.reverse(),{range:[t[0].formatedDate,t[1].formatedDate],start:t[0],end:t[1],allDay:i,weeks:s,nthWeek:l,fweek:t[0].day,lweek:t[1].day}},_getDateByWeeksAndDay:function(t,e,i){var n=this._getRangeInfo(i);if(t>n.weeks||0===t&&en.lweek)return!1;var o=7*(t-1)-n.fweek+e,a=new Date(n.start.time);return a.setDate(n.start.d+o),this.getDateInfo(a)}},wy.dimensions=wy.prototype.dimensions,wy.getDimensionsInfo=wy.prototype.getDimensionsInfo,wy.create=function(t,e){var i=[];return t.eachComponent("calendar",function(n){var o=new wy(n,t,e);i.push(o),n.coordinateSystem=o}),t.eachSeries(function(t){"calendar"===t.get("coordinateSystem")&&(t.coordinateSystem=i[t.get("calendarIndex")||0])}),i},Ba.register("calendar",wy);var DN=YM.extend({type:"calendar",coordinateSystem:null,defaultOption:{zlevel:0,z:2,left:80,top:60,cellSize:20,orient:"horizontal",splitLine:{show:!0,lineStyle:{color:"#000",width:1,type:"solid"}},itemStyle:{color:"#fff",borderWidth:1,borderColor:"#ccc"},dayLabel:{show:!0,firstDay:0,position:"start",margin:"50%",nameMap:"en",color:"#000"},monthLabel:{show:!0,position:"start",margin:5,align:"center",nameMap:"en",formatter:null,color:"#000"},yearLabel:{show:!0,position:null,margin:30,formatter:null,color:"#ccc",fontFamily:"sans-serif",fontWeight:"bolder",fontSize:20}},init:function(t,e,i,n){var o=da(t);DN.superApply(this,"init",arguments),Sy(t,o)},mergeOption:function(t,e){DN.superApply(this,"mergeOption",arguments),Sy(this.option,t)}}),CN={EN:["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"],CN:["一月","二月","三月","四月","五月","六月","七月","八月","九月","十月","十一月","十二月"]},LN={EN:["S","M","T","W","T","F","S"],CN:["日","一","二","三","四","五","六"]};Vs({type:"calendar",_tlpoints:null,_blpoints:null,_firstDayOfMonth:null,_firstDayPoints:null,render:function(t,e,i){var n=this.group;n.removeAll();var o=t.coordinateSystem,a=o.getRangeInfo(),r=o.getOrient();this._renderDayRect(t,a,n),this._renderLines(t,a,r,n),this._renderYearText(t,a,r,n),this._renderMonthText(t,r,n),this._renderWeekText(t,a,r,n)},_renderDayRect:function(t,e,i){for(var n=t.coordinateSystem,o=t.getModel("itemStyle").getItemStyle(),a=n.getCellWidth(),r=n.getCellHeight(),s=e.start.time;s<=e.end.time;s=n.getNextNDay(s,1).time){var l=n.dataToRect([s],!1).tl,u=new aM({shape:{x:l[0],y:l[1],width:a,height:r},cursor:"default",style:o});i.add(u)}},_renderLines:function(t,e,i,n){function o(e){a._firstDayOfMonth.push(r.getDateInfo(e)),a._firstDayPoints.push(r.dataToRect([e],!1).tl);var o=a._getLinePointsOfOneWeek(t,e,i);a._tlpoints.push(o[0]),a._blpoints.push(o[o.length-1]),l&&a._drawSplitline(o,s,n)} +var a=this,r=t.coordinateSystem,s=t.getModel("splitLine.lineStyle").getLineStyle(),l=t.get("splitLine.show"),u=s.lineWidth;this._tlpoints=[],this._blpoints=[],this._firstDayOfMonth=[],this._firstDayPoints=[];for(var h=e.start,c=0;h.time<=e.end.time;c++){o(h.formatedDate),0===c&&(h=r.getDateInfo(e.start.y+"-"+e.start.m));var d=h.date;d.setMonth(d.getMonth()+1),h=r.getDateInfo(d)} +o(r.getNextNDay(e.end.time,1).formatedDate),l&&this._drawSplitline(a._getEdgesPoints(a._tlpoints,u,i),s,n),l&&this._drawSplitline(a._getEdgesPoints(a._blpoints,u,i),s,n)},_getEdgesPoints:function(t,e,i){var n=[t[0].slice(),t[t.length-1].slice()],o="horizontal"===i?0:1;return n[0][o]=n[0][o]-e/2,n[1][o]=n[1][o]+e/2,n},_drawSplitline:function(t,e,i){var n=new oM({z2:20,shape:{points:t},style:e});i.add(n)},_getLinePointsOfOneWeek:function(t,e,i){var n=t.coordinateSystem;e=n.getDateInfo(e);for(var o=[],a=0;a<7;a++){var r=n.getNextNDay(e.time,a),s=n.dataToRect([r.time],!1);o[2*r.day]=s.tl,o[2*r.day+1]=s["horizontal"===i?"bl":"tr"]} +return o},_formatterLabel:function(t,e){return"string"==typeof t&&t?ea(t,e):"function"==typeof t?t(e):e.nameMap},_yearTextPositionControl:function(t,e,i,n,o){e=e.slice();var a=["center","bottom"];"bottom"===n?(e[1]+=o,a=["center","top"]):"left"===n?e[0]-=o:"right"===n?(e[0]+=o,a=["center","top"]):e[1]-=o;var r=0;return"left"!==n&&"right"!==n||(r=Math.PI/2),{rotation:r,position:e,style:{textAlign:a[0],textVerticalAlign:a[1]}}},_renderYearText:function(t,e,i,n){var o=t.getModel("yearLabel");if(o.get("show")){var a=o.get("margin"),r=o.get("position");r||(r="horizontal"!==i?"top":"left");var s=[this._tlpoints[this._tlpoints.length-1],this._blpoints[0]],l=(s[0][0]+s[1][0])/2,u=(s[0][1]+s[1][1])/2,h="horizontal"===i?0:1,c={top:[l,s[h][1]],bottom:[l,s[1-h][1]],left:[s[1-h][0],u],right:[s[h][0],u]},d=e.start.y;+e.end.y>+e.start.y&&(d=d+"-"+e.end.y);var f=o.get("formatter"),p={start:e.start.y,end:e.end.y,nameMap:d},g=this._formatterLabel(f,p),m=new qS({z2:30});fo(m.style,o,{text:g}),m.attr(this._yearTextPositionControl(m,c[r],i,r,a)),n.add(m)}},_monthTextPositionControl:function(t,e,i,n,o){var a="left",r="top",s=t[0],l=t[1];return"horizontal"===i?(l+=o,e&&(a="center"),"start"===n&&(r="bottom")):(s+=o,e&&(r="middle"),"start"===n&&(a="right")),{x:s,y:l,textAlign:a,textVerticalAlign:r}},_renderMonthText:function(t,e,i){var n=t.getModel("monthLabel");if(n.get("show")){var o=n.get("nameMap"),r=n.get("margin"),s=n.get("position"),l=n.get("align"),u=[this._tlpoints,this._blpoints];_(o)&&(o=CN[o.toUpperCase()]||[]);var h="start"===s?0:1,c="horizontal"===e?0:1;r="start"===s?-r:r;for(var d="center"===l,f=0;f=r[0]&&t<=r[1]} +if(t===this._dataZoomModel){var n=this._dimName,o=this.getTargetSeriesModels(),a=t.get("filterMode"),r=this._valueWindow;"none"!==a&&NN(o,function(t){var e=t.getData(),o=e.mapDimension(n,!0);o.length&&("weakFilter"===a?e.filterSelf(function(t){for(var i,n,a,s=0;sr[1];if(u&&!h&&!c)return!0;u&&(a=!0),h&&(i=!0),c&&(n=!0)} +return a&&i&&n}):NN(o,function(n){if("empty"===a)t.setData(e.map(n,function(t){return i(t)?t:NaN}));else{var o={};o[n]=r,e.selectRange(o)}}),NN(o,function(t){e.setApproximateExtent(r,t)}))})}}};var RN=d,zN=PN,BN=Bs({type:"dataZoom",dependencies:["xAxis","yAxis","zAxis","radiusAxis","angleAxis","singleAxis","series"],defaultOption:{zlevel:0,z:4,orient:null,xAxisIndex:null,yAxisIndex:null,filterMode:"filter",throttle:null,start:0,end:100,startValue:null,endValue:null,minSpan:null,maxSpan:null,minValueSpan:null,maxValueSpan:null,rangeMode:null},init:function(t,e,i){this._dataIntervalByAxis={},this._dataInfo={},this._axisProxies={},this.textStyleModel,this._autoThrottle=!0,this._rangePropMode=["percent","percent"];var n=Ly(t);this.mergeDefaultAndTheme(t,i),this.doInit(n)},mergeOption:function(t){var e=Ly(t);n(this.option,t,!0),this.doInit(e)},doInit:function(t){var e=this.option;z_.canvasSupported||(e.realtime=!1),this._setDefaultThrottle(t),ky(this,t),RN([["start","startValue"],["end","endValue"]],function(t,i){"value"===this._rangePropMode[i]&&(e[t[0]]=null)},this),this.textStyleModel=this.getModel("textStyle"),this._resetTarget(),this._giveAxisProxies()},_giveAxisProxies:function(){var t=this._axisProxies;this.eachTargetAxis(function(e,i,n,o){var a=this.dependentModels[e.axis][i],r=a.__dzAxisProxy||(a.__dzAxisProxy=new EN(e.name,i,this,o));t[e.name+"_"+i]=r},this)},_resetTarget:function(){var t=this.option,e=this._judgeAutoMode();zN(function(e){var i=e.axisIndex;t[i]=Di(t[i])},this),"axisIndex"===e?this._autoSetAxisIndex():"orient"===e&&this._autoSetOrient()},_judgeAutoMode:function(){var t=this.option,e=!1;zN(function(i){null!=t[i.axisIndex]&&(e=!0)},this);var i=t.orient;return null==i&&e?"orient":e?void 0:(null==i&&(t.orient="horizontal"),"axisIndex")},_autoSetAxisIndex:function(){var t=!0,e=this.get("orient",!0),i=this.option,n=this.dependentModels;if(t){var o="vertical"===e?"y":"x";n[o+"Axis"].length?(i[o+"AxisIndex"]=[0],t=!1):RN(n.singleAxis,function(n){t&&n.get("orient",!0)===e&&(i.singleAxisIndex=[n.componentIndex],t=!1)})} +t&&zN(function(e){if(t){var n=[],o=this.dependentModels[e.axis];if(o.length&&!n.length) +for(var a=0,r=o.length;a0?100:20}},getFirstTargetAxisModel:function(){var t;return zN(function(e){if(null==t){var i=this.get(e.axisIndex);i.length&&(t=this.dependentModels[e.axis][i[0]])}},this),t},eachTargetAxis:function(t,e){var i=this.ecModel;zN(function(n){RN(this.get(n.axisIndex),function(o){t.call(e,n,o,this,i)},this)},this)},getAxisProxy:function(t,e){return this._axisProxies[t+"_"+e]},getAxisModel:function(t,e){var i=this.getAxisProxy(t,e);return i&&i.getAxisModel()},setRawRange:function(t,e){var i=this.option;RN([["start","startValue"],["end","endValue"]],function(e){null==t[e[0]]&&null==t[e[1]]||(i[e[0]]=t[e[0]],i[e[1]]=t[e[1]])},this),!e&&ky(this,t)},getPercentRange:function(){var t=this.findRepresentativeAxisProxy();if(t)return t.getDataPercentWindow()},getValueRange:function(t,e){if(null!=t||null!=e)return this.getAxisProxy(t,e).getDataValueWindow();var i=this.findRepresentativeAxisProxy();return i?i.getDataValueWindow():void 0},findRepresentativeAxisProxy:function(t){if(t)return t.__dzAxisProxy;var e=this._axisProxies;for(var i in e) +if(e.hasOwnProperty(i)&&e[i].hostedBy(this))return e[i];for(var i in e) +if(e.hasOwnProperty(i)&&!e[i].hostedBy(this))return e[i]},getRangePropMode:function(){return this._rangePropMode.slice()}}),VN=EI.extend({type:"dataZoom",render:function(t,e,i,n){this.dataZoomModel=t,this.ecModel=e,this.api=i},getTargetCoordInfo:function(){function t(t,e,i,n){for(var o,a=0;a0&&e%g)p+=f;else{var i=null==t||isNaN(t)||""===t,n=i?0:FN(t,a,u,!0);i&&!l&&e?(c.push([c[c.length-1][0],0]),d.push([d[d.length-1][0],0])):!i&&l&&(c.push([p,0]),d.push([p,0])),c.push([p,n]),d.push([p,n]),p+=f,l=i}});var m=this.dataZoomModel;this._displayables.barGroup.add(new nM({shape:{points:c},style:r({fill:m.get("dataBackgroundColor")},m.getModel("dataBackground.areaStyle").getAreaStyle()),silent:!0,z2:-20})),this._displayables.barGroup.add(new oM({shape:{points:d},style:m.getModel("dataBackground.lineStyle").getLineStyle(),silent:!0,z2:-19}))}}},_prepareDataShadowInfo:function(){var t=this.dataZoomModel,e=t.get("showDataShadow");if(!1!==e){var i,n=this.ecModel;return t.eachTargetAxis(function(o,a){d(t.getAxisProxy(o.name,a).getTargetSeriesModels(),function(t){if(!(i||!0!==e&&l(jN,t.get("type"))<0)){var r,s=n.getComponent(o.axis,a).axis,u=Py(o.name),h=t.coordinateSystem;null!=u&&h.getOtherAxis&&(r=h.getOtherAxis(s).inverse),u=t.getData().mapDimension(u),i={thisAxis:s,series:t,thisDim:o.name,otherDim:u,otherAxisInverse:r}}},this)},this),i}},_renderHandle:function(){var t=this._displayables,e=t.handles=[],i=t.handleLabels=[],n=this._displayables.barGroup,o=this._size,a=this.dataZoomModel;n.add(t.filler=new GN({draggable:!0,cursor:Ny(this._orient),drift:HN(this._onDragMove,this,"all"),onmousemove:function(t){lw(t.event)},ondragstart:HN(this._showDataInfo,this,!0),ondragend:HN(this._onDragEnd,this),onmouseover:HN(this._showDataInfo,this,!0),onmouseout:HN(this._showDataInfo,this,!1),style:{fill:a.get("fillerColor"),textPosition:"inside"}})),n.add(new GN(jn({silent:!0,shape:{x:0,y:0,width:o[0],height:o[1]},style:{stroke:a.get("dataBackgroundColor")||a.get("borderColor"),lineWidth:1,fill:"rgba(0,0,0,0)"}}))),ZN([0,1],function(t){var o=Co(a.get("handleIcon"),{cursor:Ny(this._orient),draggable:!0,drift:HN(this._onDragMove,this,t),onmousemove:function(t){lw(t.event)},ondragend:HN(this._onDragEnd,this),onmouseover:HN(this._showDataInfo,this,!0),onmouseout:HN(this._showDataInfo,this,!1)},{x:-1,y:0,width:2,height:2}),r=o.getBoundingRect();this._handleHeight=Ro(a.get("handleSize"),this._size[1]),this._handleWidth=r.width/r.height*this._handleHeight,o.setStyle(a.getModel("handleStyle").getItemStyle());var s=a.get("handleColor");null!=s&&(o.style.fill=s),n.add(e[t]=o);var l=a.textStyleModel;this.group.add(i[t]=new qS({silent:!0,invisible:!0,style:{x:0,y:0,text:"",textVerticalAlign:"middle",textAlign:"center",textFill:l.getTextColor(),textFont:l.getFont()},z2:10}))},this)},_resetInterval:function(){var t=this._range=this.dataZoomModel.getPercentRange(),e=this._getViewExtent();this._handleEnds=[FN(t[0],[0,100],e,!0),FN(t[1],[0,100],e,!0)]},_updateInterval:function(t,e){var i=this.dataZoomModel,n=this._handleEnds,o=this._getViewExtent(),a=i.findRepresentativeAxisProxy().getMinMaxSpan(),r=[0,100];EL(e,n,o,i.get("zoomLock")?"all":t,null!=a.minSpan?FN(a.minSpan,r,o,!0):null,null!=a.maxSpan?FN(a.maxSpan,r,o,!0):null);var s=this._range,l=this._range=WN([FN(n[0],o,r,!0),FN(n[1],o,r,!0)]);return!s||s[0]!==l[0]||s[1]!==l[1]},_updateView:function(t){var e=this._displayables,i=this._handleEnds,n=WN(i.slice()),o=this._size;ZN([0,1],function(t){var n=e.handles[t],a=this._handleHeight;n.attr({scale:[a/2,a/2],position:[i[t],o[1]/2-a/2]})},this),e.filler.setShape({x:n[0],y:0,width:n[1]-n[0],height:o[1]}),this._updateDataInfo(t)},_updateDataInfo:function(t){function e(t){var e=Mo(n.handles[t].parent,this.group),i=To(0===t?"right":"left",e),s=this._handleWidth/2+XN,l=Io([c[t]+(0===t?-s:s),this._size[1]/2],e);o[t].setStyle({x:l[0],y:l[1],textVerticalAlign:a===UN?"middle":i,textAlign:a===UN?i:"center",text:r[t]})} +var i=this.dataZoomModel,n=this._displayables,o=n.handleLabels,a=this._orient,r=["",""];if(i.get("showDetail")){var s=i.findRepresentativeAxisProxy();if(s){var l=s.getAxisModel().axis,u=this._range,h=t?s.calculateDataWindow({start:u[0],end:u[1]}).valueWindow:s.getDataValueWindow();r=[this._formatLabel(h[0],l),this._formatLabel(h[1],l)]}} +var c=WN(this._handleEnds.slice());e.call(this,0),e.call(this,1)},_formatLabel:function(t,e){var i=this.dataZoomModel,n=i.get("labelFormatter"),o=i.get("labelPrecision");null!=o&&"auto"!==o||(o=e.getPixelPrecision());var a=null==t||isNaN(t)?"":"category"===e.type||"time"===e.type?e.scale.getLabel(Math.round(t)):t.toFixed(Math.min(o,20));return x(n)?n(t,a):_(n)?n.replace("{value}",a):a},_showDataInfo:function(t){t=this._dragging||t;var e=this._displayables.handleLabels;e[0].attr("invisible",!t),e[1].attr("invisible",!t)},_onDragMove:function(t,e,i){this._dragging=!0;var n=Io([e,i],this._displayables.barGroup.getLocalTransform(),!0),o=this._updateInterval(t,n[0]),a=this.dataZoomModel.get("realtime");this._updateView(!a),o&&a&&this._dispatchZoomAction()},_onDragEnd:function(){this._dragging=!1,this._showDataInfo(!1),!this.dataZoomModel.get("realtime")&&this._dispatchZoomAction()},_onClickPanelClick:function(t){var e=this._size,i=this._displayables.barGroup.transformCoordToLocal(t.offsetX,t.offsetY);if(!(i[0]<0||i[0]>e[0]||i[1]<0||i[1]>e[1])){var n=this._handleEnds,o=(n[0]+n[1])/2,a=this._updateInterval("all",i[0]-o);this._updateView(),a&&this._dispatchZoomAction()}},_dispatchZoomAction:function(){var t=this._range;this.api.dispatchAction({type:"dataZoom",from:this.uid,dataZoomId:this.dataZoomModel.id,start:t[0],end:t[1]})},_findCoordRect:function(){var t;if(ZN(this.getTargetCoordInfo(),function(e){if(!t&&e.length){var i=e[0].model.coordinateSystem;t=i.getRect&&i.getRect()}}),!t){var e=this.api.getWidth(),i=this.api.getHeight();t={x:.2*e,y:.2*i,width:.6*e,height:.6*i}} +return t}});BN.extend({type:"dataZoom.inside",defaultOption:{disabled:!1,zoomLock:!1,zoomOnMouseWheel:!0,moveOnMouseMove:!0,moveOnMouseWheel:!1,preventDefaultMouseMove:!0}});var qN="\0_ec_dataZoom_roams",KN=m,$N=VN.extend({type:"dataZoom.inside",init:function(t,e){this._range},render:function(t,e,i,n){$N.superApply(this,"render",arguments),this._range=t.getPercentRange(),d(this.getTargetCoordInfo(),function(e,n){var o=f(e,function(t){return Ry(t.model)});d(e,function(e){var a=e.model,r={};d(["pan","zoom","scrollMove"],function(t){r[t]=KN(JN[t],this,e,n)},this),Oy(i,{coordId:Ry(a),allCoordIds:o,containsPoint:function(t,e,i){return a.coordinateSystem.containPoint([e,i])},dataZoomId:t.id,dataZoomModel:t,getRange:r})},this)},this)},dispose:function(){Ey(this.api,this.dataZoomModel.id),$N.superApply(this,"dispose",arguments),this._range=null}}),JN={zoom:function(t,e,i,n){var o=this._range,a=o.slice(),r=t.axisModels[0];if(r){var s=QN[e](null,[n.originX,n.originY],r,i,t),l=(s.signal>0?s.pixelStart+s.pixelLength-s.pixel:s.pixel-s.pixelStart)/s.pixelLength*(a[1]-a[0])+a[0],u=Math.max(1/n.scale,0);a[0]=(a[0]-l)*u+l,a[1]=(a[1]-l)*u+l;var h=this.dataZoomModel.findRepresentativeAxisProxy().getMinMaxSpan();return EL(0,a,[0,100],0,h.minSpan,h.maxSpan),this._range=a,o[0]!==a[0]||o[1]!==a[1]?a:void 0}},pan:Wy(function(t,e,i,n,o,a){var r=QN[n]([a.oldX,a.oldY],[a.newX,a.newY],e,o,i);return r.signal*(t[1]-t[0])*r.pixel/r.pixelLength}),scrollMove:Wy(function(t,e,i,n,o,a){return QN[n]([0,0],[a.scrollDelta,a.scrollDelta],e,o,i).signal*(t[1]-t[0])*a.scrollDelta})},QN={grid:function(t,e,i,n,o){var a=i.axis,r={},s=o.model.coordinateSystem.getRect();return t=t||[0,0],"x"===a.dim?(r.pixel=e[0]-t[0],r.pixelLength=s.width,r.pixelStart=s.x,r.signal=a.inverse?1:-1):(r.pixel=e[1]-t[1],r.pixelLength=s.height,r.pixelStart=s.y,r.signal=a.inverse?-1:1),r},polar:function(t,e,i,n,o){var a=i.axis,r={},s=o.model.coordinateSystem,l=s.getRadiusAxis().getExtent(),u=s.getAngleAxis().getExtent();return t=t?s.pointToCoord(t):[0,0],e=s.pointToCoord(e),"radiusAxis"===i.mainType?(r.pixel=e[0]-t[0],r.pixelLength=l[1]-l[0],r.pixelStart=l[0],r.signal=a.inverse?1:-1):(r.pixel=e[1]-t[1],r.pixelLength=u[1]-u[0],r.pixelStart=u[0],r.signal=a.inverse?-1:1),r},singleAxis:function(t,e,i,n,o){var a=i.axis,r=o.model.coordinateSystem.getRect(),s={};return t=t||[0,0],"horizontal"===a.orient?(s.pixel=e[0]-t[0],s.pixelLength=r.width,s.pixelStart=r.x,s.signal=a.inverse?1:-1):(s.pixel=e[1]-t[1],s.pixelLength=r.height,s.pixelStart=r.y,s.signal=a.inverse?-1:1),s}};ks({getTargetSeries:function(t){var e=R();return t.eachComponent("dataZoom",function(t){t.eachTargetAxis(function(t,i,n){d(n.getAxisProxy(t.name,i).getTargetSeriesModels(),function(t){e.set(t.uid,t)})})}),e},modifyOutputEnd:!0,overallReset:function(t,e){t.eachComponent("dataZoom",function(t){t.eachTargetAxis(function(t,i,n){n.getAxisProxy(t.name,i).reset(n,e)}),t.eachTargetAxis(function(t,i,n){n.getAxisProxy(t.name,i).filterData(n,e)})}),t.eachComponent("dataZoom",function(t){var e=t.findRepresentativeAxisProxy(),i=e.getDataPercentWindow(),n=e.getDataValueWindow();t.setRawRange({start:i[0],end:i[1],startValue:n[0],endValue:n[1]},!0)})}}),Ps("dataZoom",function(t,e){var i=Iy(m(e.eachComponent,e,"dataZoom"),PN,function(t,e){return t.get(e.axisIndex)}),n=[];e.eachComponent({mainType:"dataZoom",query:t},function(t,e){n.push.apply(n,i(t).nodes)}),d(n,function(e,i){e.setRawRange({start:t.start,end:t.end,startValue:t.startValue,endValue:t.endValue})})});var tO=d,eO=function(t){var e=t&&t.visualMap;y(e)||(e=e?[e]:[]),tO(e,function(t){if(t){Hy(t,"splitList")&&!Hy(t,"pieces")&&(t.pieces=t.splitList,delete t.splitList);var e=t.pieces;e&&y(e)&&tO(e,function(t){w(t)&&(Hy(t,"start")&&!Hy(t,"min")&&(t.min=t.start),Hy(t,"end")&&!Hy(t,"max")&&(t.max=t.end))})}})};YM.registerSubTypeDefaulter("visualMap",function(t){return t.categories||(t.pieces?t.pieces.length>0:t.splitNumber>0)&&!t.calculable?"piecewise":"continuous"});var iO=IT.VISUAL.COMPONENT;Es(iO,{createOnAllSeries:!0,reset:function(t,e){var i=[];return e.eachComponent("visualMap",function(e){var n=t.pipelineContext;!e.isTargetSeries(t)||n&&n.large||i.push(qv(e.stateList,e.targetVisuals,m(e.getValueState,e),e.getDataDimension(t.getData())))}),i}}),Es(iO,{createOnAllSeries:!0,reset:function(t,e){var i=t.getData(),n=[];e.eachComponent("visualMap",function(e){if(e.isTargetSeries(t)){var o=e.getVisualMeta(m(Zy,null,t,e))||{stops:[],outerColors:[]},a=e.getDataDimension(i),r=i.getDimensionInfo(a);null!=r&&(o.dimension=r.index,n.push(o))}}),t.getData().setVisual("visualMeta",n)}});var nO={get:function(t,e,n){var o=i((oO[t]||{})[e]);return n&&y(o)?o[o.length-1]:o}},oO={color:{active:["#006edd","#e0ffff"],inactive:["rgba(0,0,0,0)"]},colorHue:{active:[0,360],inactive:[0,0]},colorSaturation:{active:[.3,1],inactive:[0,0]},colorLightness:{active:[.9,.5],inactive:[0,0]},colorAlpha:{active:[.3,1],inactive:[0,0]},opacity:{active:[.3,1],inactive:[0,0]},symbol:{active:["circle","roundRect","diamond"],inactive:["none"]},symbolSize:{active:[10,50],inactive:[0,0]}},aO=jC.mapVisual,rO=jC.eachVisual,sO=y,lO=d,uO=Bo,hO=Eo,cO=B,dO=Bs({type:"visualMap",dependencies:["series"],stateList:["inRange","outOfRange"],replacableOptionKeys:["inRange","outOfRange","target","controller","color"],dataBound:[-1/0,1/0],layoutMode:{type:"box",ignoreSize:!0},defaultOption:{show:!0,zlevel:0,z:4,seriesIndex:"all",min:0,max:200,dimension:null,inRange:null,outOfRange:null,left:0,right:null,top:null,bottom:0,itemWidth:null,itemHeight:null,inverse:!1,orient:"vertical",backgroundColor:"rgba(0,0,0,0)",borderColor:"#ccc",contentColor:"#5793f3",inactiveColor:"#aaa",borderWidth:0,padding:5,textGap:10,precision:0,color:null,formatter:null,text:null,textStyle:{color:"#333"}},init:function(t,e,i){this._dataExtent,this.targetVisuals={},this.controllerVisuals={},this.textStyleModel,this.itemSize,this.mergeDefaultAndTheme(t,i)},optionUpdated:function(t,e){var i=this.option;z_.canvasSupported||(i.realtime=!1),!e&&jv(i,t,this.replacableOptionKeys),this.textStyleModel=this.getModel("textStyle"),this.resetItemSize(),this.completeVisualOption()},resetVisual:function(t){var e=this.stateList;t=m(t,this),this.controllerVisuals=Xv(this.option.controller,e,t),this.targetVisuals=Xv(this.option.target,e,t)},getTargetSeriesIndices:function(){var t=this.option.seriesIndex,e=[];return null==t||"all"===t?this.ecModel.eachSeries(function(t,i){e.push(i)}):e=Di(t),e},eachTargetSeries:function(t,e){d(this.getTargetSeriesIndices(),function(i){t.call(e,this.ecModel.getSeriesByIndex(i))},this)},isTargetSeries:function(t){var e=!1;return this.eachTargetSeries(function(i){i===t&&(e=!0)}),e},formatValueText:function(t,e,i){function n(t){return t===l[0]?"min":t===l[1]?"max":(+t).toFixed(Math.min(s,20))} +var o,a,r=this.option,s=r.precision,l=this.dataBound,u=r.formatter;return i=i||["<",">"],y(t)&&(t=t.slice(),o=!0),a=e?t:o?[n(t[0]),n(t[1])]:n(t),_(u)?u.replace("{value}",o?a[0]:a).replace("{value2}",o?a[1]:a):x(u)?o?u(t[0],t[1]):u(t):o?t[0]===l[0]?i[0]+" "+a[1]:t[1]===l[1]?i[1]+" "+a[0]:a[0]+" - "+a[1]:a},resetExtent:function(){var t=this.option,e=uO([t.min,t.max]);this._dataExtent=e},getDataDimension:function(t){var e=this.option.dimension,i=t.dimensions;if(null!=e||i.length){if(null!=e)return t.getDimension(e);for(var n=t.dimensions,o=n.length-1;o>=0;o--){var a=n[o];if(!t.getDimensionInfo(a).isCalculationCoord)return a}}},getExtent:function(){return this._dataExtent.slice()},completeVisualOption:function(){function t(t){sO(o.color)&&!t.inRange&&(t.inRange={color:o.color.slice().reverse()}),t.inRange=t.inRange||{color:e.get("gradientColor")},lO(this.stateList,function(e){var i=t[e];if(_(i)){var n=nO.get(i,"active",l);n?(t[e]={},t[e][i]=n):delete t[e]}},this)} +var e=this.ecModel,o=this.option,a={inRange:o.inRange,outOfRange:o.outOfRange},r=o.target||(o.target={}),s=o.controller||(o.controller={});n(r,a),n(s,a);var l=this.isCategory();t.call(this,r),t.call(this,s),function(t,e,i){var n=t[e],o=t[i];n&&!o&&(o=t[i]={},lO(n,function(t,e){if(jC.isValidType(e)){var i=nO.get(e,"inactive",l);null!=i&&(o[e]=i,"color"!==e||o.hasOwnProperty("opacity")||o.hasOwnProperty("colorAlpha")||(o.opacity=[0,0]))}}))}.call(this,r,"inRange","outOfRange"),function(t){var e=(t.inRange||{}).symbol||(t.outOfRange||{}).symbol,n=(t.inRange||{}).symbolSize||(t.outOfRange||{}).symbolSize,o=this.get("inactiveColor");lO(this.stateList,function(a){var r=this.itemSize,s=t[a];s||(s=t[a]={color:l?o:[o]}),null==s.symbol&&(s.symbol=e&&i(e)||(l?"roundRect":["roundRect"])),null==s.symbolSize&&(s.symbolSize=n&&i(n)||(l?r[0]:[r[0],r[0]])),s.symbol=aO(s.symbol,function(t){return"none"===t||"square"===t?"roundRect":t});var u=s.symbolSize;if(null!=u){var h=-1/0;rO(u,function(t){t>h&&(h=t)}),s.symbolSize=aO(u,function(t){return hO(t,[0,h],[0,r[0]],!0)})}},this)}.call(this,s)},resetItemSize:function(){this.itemSize=[parseFloat(this.get("itemWidth")),parseFloat(this.get("itemHeight"))]},isCategory:function(){return!!this.option.categories},setSelected:cO,getValueState:cO,getVisualMeta:cO}),fO=[20,140],pO=dO.extend({type:"visualMap.continuous",defaultOption:{align:"auto",calculable:!1,range:null,realtime:!0,itemHeight:null,itemWidth:null,hoverLink:!0,hoverLinkDataSize:null,hoverLinkOnHandle:null},optionUpdated:function(t,e){pO.superApply(this,"optionUpdated",arguments),this.resetExtent(),this.resetVisual(function(t){t.mappingMethod="linear",t.dataExtent=this.getExtent()}),this._resetRange()},resetItemSize:function(){pO.superApply(this,"resetItemSize",arguments);var t=this.itemSize;"horizontal"===this._orient&&t.reverse(),(null==t[0]||isNaN(t[0]))&&(t[0]=fO[0]),(null==t[1]||isNaN(t[1]))&&(t[1]=fO[1])},_resetRange:function(){var t=this.getExtent(),e=this.option.range;!e||e.auto?(t.auto=1,this.option.range=t):y(e)&&(e[0]>e[1]&&e.reverse(),e[0]=Math.max(e[0],t[0]),e[1]=Math.min(e[1],t[1]))},completeVisualOption:function(){dO.prototype.completeVisualOption.apply(this,arguments),d(this.stateList,function(t){var e=this.option.controller[t].symbolSize;e&&e[0]!==e[1]&&(e[0]=0)},this)},setSelected:function(t){this.option.range=t.slice(),this._resetRange()},getSelected:function(){var t=this.getExtent(),e=Bo((this.get("range")||[]).slice());return e[0]>t[1]&&(e[0]=t[1]),e[1]>t[1]&&(e[1]=t[1]),e[0]=i[1]||t<=e[1])?"inRange":"outOfRange"},findTargetDataIndices:function(t){var e=[];return this.eachTargetSeries(function(i){var n=[],o=i.getData();o.each(this.getDataDimension(o),function(e,i){t[0]<=e&&e<=t[1]&&n.push(i)},this),e.push({seriesId:i.id,dataIndex:n})},this),e},getVisualMeta:function(t){function e(e,i){o.push({value:e,color:t(e,i)})} +for(var i=Uy(0,0,this.getExtent()),n=Uy(0,0,this.option.range.slice()),o=[],a=0,r=0,s=n.length,l=i.length;rt[1])break;i.push({color:this.getControllerVisual(a,"color",e),offset:o/100})} +return i.push({color:this.getControllerVisual(t[1],"color",e),offset:1}),i},_createBarPoints:function(t,e){var i=this.visualMapModel.itemSize;return[[i[0]-e[0],t[0]],[i[0],t[0]],[i[0],t[1]],[i[0]-e[1],t[1]]]},_createBarGroup:function(t){var e=this._orient,i=this.visualMapModel.get("inverse");return new Zw("horizontal"!==e||i?"horizontal"===e&&i?{scale:"bottom"===t?[-1,1]:[1,1],rotation:-Math.PI/2}:"vertical"!==e||i?{scale:"left"===t?[1,1]:[-1,1]}:{scale:"left"===t?[1,-1]:[-1,-1]}:{scale:"bottom"===t?[1,1]:[-1,1],rotation:Math.PI/2})},_updateHandle:function(t,e){if(this._useHandle){var i=this._shapes,n=this.visualMapModel,o=i.handleThumbs,a=i.handleLabels;vO([0,1],function(r){var s=o[r];s.setStyle("fill",e.handlesColor[r]),s.position[1]=t[r];var l=Io(i.handleLabelPoints[r],Mo(s,this.group));a[r].setStyle({x:l[0],y:l[1],text:n.formatValueText(this._dataInterval[r]),textVerticalAlign:"middle",textAlign:this._applyTransform("horizontal"===this._orient?0===r?"bottom":"top":"left",i.barGroup)})},this)}},_showIndicator:function(t,e,i,n){var o=this.visualMapModel,a=o.getExtent(),r=o.itemSize,s=[0,r[1]],l=mO(t,a,s,!0),u=this._shapes,h=u.indicator;if(h){h.position[1]=l,h.attr("invisible",!1),h.setShape("points",Ky(!!i,n,l,r[1]));var c={convertOpacityToAlpha:!0},d=this.getControllerVisual(t,"color",c);h.setStyle("fill",d);var f=Io(u.indicatorLabelPoint,Mo(h,this.group)),p=u.indicatorLabel;p.attr("invisible",!1);var g=this._applyTransform("left",u.barGroup),m=this._orient;p.setStyle({text:(i||"")+o.formatValueText(e),textVerticalAlign:"horizontal"===m?g:"middle",textAlign:"horizontal"===m?"center":g,x:f[0],y:f[1]})}},_enableHoverLinkToSeries:function(){var t=this;this._shapes.barGroup.on("mousemove",function(e){if(t._hovering=!0,!t._dragging){var i=t.visualMapModel.itemSize,n=t._applyTransform([e.offsetX,e.offsetY],t._shapes.barGroup,!0,!0);n[1]=yO(xO(0,n[1]),i[1]),t._doHoverLinkToSeries(n[1],0<=n[0]&&n[0]<=i[0])}}).on("mouseout",function(){t._hovering=!1,!t._dragging&&t._clearHoverLinkToSeries()})},_enableHoverLinkFromSeries:function(){var t=this.api.getZr();this.visualMapModel.option.hoverLink?(t.on("mouseover",this._hoverLinkFromSeriesMouseOver,this),t.on("mouseout",this._hideIndicator,this)):this._clearHoverLinkFromSeries()},_doHoverLinkToSeries:function(t,e){var i=this.visualMapModel,n=i.itemSize;if(i.option.hoverLink){var o=[0,n[1]],a=i.getExtent();t=yO(xO(o[0],t),o[1]);var r=$y(i,a,o),s=[t-r,t+r],l=mO(t,o,a,!0),u=[mO(s[0],o,a,!0),mO(s[1],o,a,!0)];s[0]o[1]&&(u[1]=1/0),e&&(u[0]===-1/0?this._showIndicator(l,u[1],"< ",r):u[1]===1/0?this._showIndicator(l,u[0],"> ",r):this._showIndicator(l,l,"≈ ",r));var h=this._hoverLinkDataIndices,c=[];(e||Jy(i))&&(c=this._hoverLinkDataIndices=i.findTargetDataIndices(u));var d=Ri(h,c);this._dispatchHighDown("downplay",jy(d[0])),this._dispatchHighDown("highlight",jy(d[1]))}},_hoverLinkFromSeriesMouseOver:function(t){var e=t.target,i=this.visualMapModel;if(e&&null!=e.dataIndex){var n=this.ecModel.getSeriesByIndex(e.seriesIndex);if(i.isTargetSeries(n)){var o=n.getData(e.dataType),a=o.get(i.getDataDimension(o),e.dataIndex,!0);isNaN(a)||this._showIndicator(a,a)}}},_hideIndicator:function(){var t=this._shapes;t.indicator&&t.indicator.attr("invisible",!0),t.indicatorLabel&&t.indicatorLabel.attr("invisible",!0)},_clearHoverLinkToSeries:function(){this._hideIndicator();var t=this._hoverLinkDataIndices;this._dispatchHighDown("downplay",jy(t)),t.length=0},_clearHoverLinkFromSeries:function(){this._hideIndicator();var t=this.api.getZr();t.off("mouseover",this._hoverLinkFromSeriesMouseOver),t.off("mouseout",this._hideIndicator)},_applyTransform:function(t,e,i,n){var o=Mo(e,n?null:this.group);return bM[y(t)?"applyTransform":"transformDirection"](t,o,i)},_dispatchHighDown:function(t,e){e&&e.length&&this.api.dispatchAction({type:t,batch:e})},dispose:function(){this._clearHoverLinkFromSeries(),this._clearHoverLinkToSeries()},remove:function(){this._clearHoverLinkFromSeries(),this._clearHoverLinkToSeries()}});Ps({type:"selectDataRange",event:"dataRangeSelected",update:"update"},function(t,e){e.eachComponent({mainType:"visualMap",query:t},function(e){e.setSelected(t.selected)})}),Ls(eO);var SO=dO.extend({type:"visualMap.piecewise",defaultOption:{selected:null,minOpen:!1,maxOpen:!1,align:"auto",itemWidth:20,itemHeight:14,itemSymbol:"roundRect",pieceList:null,categories:null,splitNumber:5,selectedMode:"multiple",itemGap:10,hoverLink:!0,showLabel:null},optionUpdated:function(t,e){SO.superApply(this,"optionUpdated",arguments),this._pieceList=[],this.resetExtent();var n=this._mode=this._determineMode();MO[this._mode].call(this),this._resetSelected(t,e);var o=this.option.categories;this.resetVisual(function(t,e){"categories"===n?(t.mappingMethod="category",t.categories=i(o)):(t.dataExtent=this.getExtent(),t.mappingMethod="piecewise",t.pieceList=f(this._pieceList,function(t){var t=i(t);return"inRange"!==e&&(t.visual=null),t}))})},completeVisualOption:function(){function t(t,e,i){return t&&t[e]&&(w(t[e])?t[e].hasOwnProperty(i):t[e]===i)} +var e=this.option,i={},n=jC.listVisualTypes(),o=this.isCategory();d(e.pieces,function(t){d(n,function(e){t.hasOwnProperty(e)&&(i[e]=1)})}),d(i,function(i,n){var a=0;d(this.stateList,function(i){a|=t(e,i,n)||t(e.target,i,n)},this),!a&&d(this.stateList,function(t){(e[t]||(e[t]={}))[n]=nO.get(n,"inRange"===t?"active":"inactive",o)})},this),dO.prototype.completeVisualOption.apply(this,arguments)},_resetSelected:function(t,e){var i=this.option,n=this._pieceList,o=(e?i:t).selected||{};if(i.selected=o,d(n,function(t,e){var i=this.getSelectedMapKey(t);o.hasOwnProperty(i)||(o[i]=!0)},this),"single"===i.selectedMode){var a=!1;d(n,function(t,e){var i=this.getSelectedMapKey(t);o[i]&&(a?o[i]=!1:a=!0)},this)}},getSelectedMapKey:function(t){return"categories"===this._mode?t.value+"":t.index+""},getPieceList:function(){return this._pieceList},_determineMode:function(){var t=this.option;return t.pieces&&t.pieces.length>0?"pieces":this.option.categories?"categories":"splitNumber"},setSelected:function(t){this.option.selected=i(t)},getValueState:function(t){var e=jC.findPieceIndex(t,this._pieceList);return null!=e&&this.option.selected[this.getSelectedMapKey(this._pieceList[e])]?"inRange":"outOfRange"},findTargetDataIndices:function(t){var e=[];return this.eachTargetSeries(function(i){var n=[],o=i.getData();o.each(this.getDataDimension(o),function(e,i){jC.findPieceIndex(e,this._pieceList)===t&&n.push(i)},this),e.push({seriesId:i.id,dataIndex:n})},this),e},getRepresentValue:function(t){var e;if(this.isCategory())e=t.value;else if(null!=t.value)e=t.value;else{var i=t.interval||[];e=i[0]===-1/0&&i[1]===1/0?0:(i[0]+i[1])/2} +return e},getVisualMeta:function(t){function e(e,a){var r=o.getRepresentValue({interval:e});a||(a=o.getValueState(r));var s=t(r,a);e[0]===-1/0?n[0]=s:e[1]===1/0?n[1]=s:i.push({value:e[0],color:s},{value:e[1],color:s})} +if(!this.isCategory()){var i=[],n=[],o=this,a=this._pieceList.slice();if(a.length){var r=a[0].interval[0];r!==-1/0&&a.unshift({interval:[-1/0,r]}),(r=a[a.length-1].interval[1])!==1/0&&a.push({interval:[r,1/0]})}else a.push({interval:[-1/0,1/0]});var s=-1/0;return d(a,function(t){var i=t.interval;i&&(i[0]>s&&e([s,i[0]],"outOfRange"),e(i.slice()),s=i[1])},this),{stops:i,outerColors:n}}}}),MO={splitNumber:function(){var t=this.option,e=this._pieceList,i=Math.min(t.precision,20),n=this.getExtent(),o=t.splitNumber;o=Math.max(parseInt(o,10),1),t.splitNumber=o;for(var a=(n[1]-n[0])/o;+a.toFixed(i)!==a&&i<5;)i++;t.precision=i,a=+a.toFixed(i);var r=0;t.minOpen&&e.push({index:r++,interval:[-1/0,n[0]],close:[0,0]});for(var s=n[0],l=r+o;r","≥"][e[0]]];t.text=t.text||this.formatValueText(null!=t.value?t.value:t.interval,!1,i)},this)}};gO.extend({type:"visualMap.piecewise",doRender:function(){var t=this.group;t.removeAll();var e=this.visualMapModel,i=e.get("textGap"),n=e.textStyleModel,o=n.getFont(),a=n.getTextColor(),r=this._getItemAlign(),s=e.itemSize,l=this._getViewData(),u=l.endsText,h=T(e.get("showLabel",!0),!u);u&&this._renderEndsText(t,u[0],s,h,r),d(l.viewPieceList,function(n){var l=n.piece,u=new Zw;u.onclick=m(this._onItemClick,this,l),this._enableHoverLink(u,n.indexInModelPieceList);var c=e.getRepresentValue(l);if(this._createItemSymbol(u,c,[0,0,s[0],s[1]]),h){var d=this.visualMapModel.getValueState(c);u.add(new qS({style:{x:"right"===r?-i:s[0]+i,y:s[1]/2,text:l.text,textVerticalAlign:"middle",textAlign:r,textFont:o,textFill:a,opacity:"outOfRange"===d?.5:1}}))} +t.add(u)},this),u&&this._renderEndsText(t,u[1],s,h,r),UM(e.get("orient"),t,e.get("itemGap")),this.renderBackground(t),this.positionGroup(t)},_enableHoverLink:function(t,e){function i(t){var i=this.visualMapModel;i.option.hoverLink&&this.api.dispatchAction({type:t,batch:jy(i.findTargetDataIndices(e))})} +t.on("mouseover",m(i,this,"highlight")).on("mouseout",m(i,this,"downplay"))},_getItemAlign:function(){var t=this.visualMapModel,e=t.option;if("vertical"===e.orient)return Xy(t,this.api,t.itemSize);var i=e.align;return i&&"auto"!==i||(i="left"),i},_renderEndsText:function(t,e,i,n,o){if(e){var a=new Zw,r=this.visualMapModel.textStyleModel;a.add(new qS({style:{x:n?"right"===o?i[0]:0:i[0]/2,y:i[1]/2,textVerticalAlign:"middle",textAlign:n?o:"center",text:e,textFont:r.getFont(),textFill:r.getTextColor()}})),t.add(a)}},_getViewData:function(){var t=this.visualMapModel,e=f(t.getPieceList(),function(t,e){return{piece:t,indexInModelPieceList:e}}),i=t.get("text"),n=t.get("orient"),o=t.get("inverse");return("horizontal"===n?o:!o)?e.reverse():i&&(i=i.slice().reverse()),{viewPieceList:e,endsText:i}},_createItemSymbol:function(t,e,i){t.add(Xl(this.getControllerVisual(e,"symbol"),i[0],i[1],i[2],i[3],this.getControllerVisual(e,"color")))},_onItemClick:function(t){var e=this.visualMapModel,n=e.option,o=i(n.selected),a=e.getSelectedMapKey(t);"single"===n.selectedMode?(o[a]=!0,d(o,function(t,e){o[e]=e===a})):o[a]=!o[a],this.api.dispatchAction({type:"selectDataRange",from:this.uid,visualMapId:this.visualMapModel.id,selected:o})}});Ls(eO);var IO=$o,TO=Qo,AO=Bs({type:"marker",dependencies:["series","grid","polar","geo"],init:function(t,e,i,n){this.mergeDefaultAndTheme(t,i),this.mergeOption(t,i,n.createdBySelf,!0)},isAnimationEnabled:function(){if(z_.node)return!1;var t=this.__hostSeries;return this.getShallow("animation")&&t&&t.isAnimationEnabled()},mergeOption:function(t,e,i,n){var o=this.constructor,r=this.mainType+"Model";i||e.eachSeries(function(t){var i=t.get(this.mainType,!0),s=t[r];i&&i.data?(s?s.mergeOption(i,e,!0):(n&&ex(i),d(i.data,function(t){t instanceof Array?(ex(t[0]),ex(t[1])):ex(t)}),a(s=new o(i,this,e),{mainType:this.mainType,seriesIndex:t.seriesIndex,name:t.name,createdBySelf:!0}),s.__hostSeries=t),t[r]=s):t[r]=null},this)},formatTooltip:function(t){var e=this.getData(),i=this.getRawValue(t),n=y(i)?f(i,IO).join(", "):IO(i),o=e.getName(t),a=TO(this.name);return(null!=i||o)&&(a+="
          "),o&&(a+=TO(o),null!=i&&(a+=" : ")),null!=i&&(a+=TO(n)),a},getData:function(){return this._data},setData:function(t){this._data=t}});h(AO,LI),AO.extend({type:"markPoint",defaultOption:{zlevel:0,z:5,symbol:"pin",symbolSize:50,tooltip:{trigger:"item"},label:{show:!0,position:"inside"},itemStyle:{borderWidth:2},emphasis:{label:{show:!0}}}});var DO=l,CO=v,LO={min:CO(ox,"min"),max:CO(ox,"max"),average:CO(ox,"average")},kO=Vs({type:"marker",init:function(){this.markerGroupMap=R()},render:function(t,e,i){var n=this.markerGroupMap;n.each(function(t){t.__keep=!1});var o=this.type+"Model";e.eachSeries(function(t){var n=t[o];n&&this.renderSeries(t,n,e,i)},this),n.each(function(t){!t.__keep&&this.group.remove(t.group)},this)},renderSeries:function(){}});kO.extend({type:"markPoint",updateTransform:function(t,e,i){e.eachSeries(function(t){var e=t.markPointModel;e&&(cx(e.getData(),t,i),this.markerGroupMap.get(t.id).updateLayout(e))},this)},renderSeries:function(t,e,i,n){var o=t.coordinateSystem,a=t.id,r=t.getData(),s=this.markerGroupMap,l=s.get(a)||s.set(a,new Su),u=dx(o,t,e);e.setData(u),cx(e.getData(),t,n),u.each(function(t){var i=u.getItemModel(t),n=i.getShallow("symbolSize");"function"==typeof n&&(n=n(e.getRawValue(t),e.getDataParams(t))),u.setItemVisual(t,{symbolSize:n,color:i.get("itemStyle.color")||r.getVisual("color"),symbol:i.getShallow("symbol")})}),l.updateData(u),this.group.add(l.group),u.eachItemGraphicEl(function(t){t.traverse(function(t){t.dataModel=e})}),l.__keep=!0,l.group.silent=e.get("silent")||t.get("silent")}}),Ls(function(t){t.markPoint=t.markPoint||{}}),AO.extend({type:"markLine",defaultOption:{zlevel:0,z:5,symbol:["circle","arrow"],symbolSize:[8,16],precision:2,tooltip:{trigger:"item"},label:{show:!0,position:"end"},lineStyle:{type:"dashed"},emphasis:{label:{show:!0},lineStyle:{width:3}},animationEasing:"linear"}});var PO=function(t,e,o,r){var s=t.getData(),l=r.type;if(!y(r)&&("min"===l||"max"===l||"average"===l||"median"===l||null!=r.xAxis||null!=r.yAxis)){var u,h;if(null!=r.yAxis||null!=r.xAxis)u=null!=r.yAxis?"y":"x",e.getAxis(u),h=T(r.yAxis,r.xAxis);else{var c=rx(r,s,e,t);u=c.valueDataDim,c.valueAxis,h=hx(s,u,l)} +var d="x"===u?0:1,f=1-d,p=i(r),g={};p.type=null,p.coord=[],g.coord=[],p.coord[f]=-1/0,g.coord[f]=1/0;var m=o.get("precision");m>=0&&"number"==typeof h&&(h=+h.toFixed(Math.min(m,20))),p.coord[d]=g.coord[d]=h,r=[p,g,{type:l,valueIndex:r.valueIndex,value:h}]} +return r=[ax(t,r[0]),ax(t,r[1]),a({},r[2])],r[2].type=r[2].type||"",n(r[2],r[0]),n(r[2],r[1]),r};kO.extend({type:"markLine",updateTransform:function(t,e,i){e.eachSeries(function(t){var e=t.markLineModel;if(e){var n=e.getData(),o=e.__from,a=e.__to;o.each(function(e){mx(o,e,!0,t,i),mx(a,e,!1,t,i)}),n.each(function(t){n.setItemLayout(t,[o.getItemLayout(t),a.getItemLayout(t)])}),this.markerGroupMap.get(t.id).updateLayout()}},this)},renderSeries:function(t,e,i,n){function o(e,i,o){var a=e.getItemModel(i);mx(e,i,o,t,n),e.setItemVisual(i,{symbolSize:a.get("symbolSize")||g[o?0:1],symbol:a.get("symbol",!0)||p[o?0:1],color:a.get("itemStyle.color")||s.getVisual("color")})} +var a=t.coordinateSystem,r=t.id,s=t.getData(),l=this.markerGroupMap,u=l.get(r)||l.set(r,new Jd);this.group.add(u.group);var h=vx(a,t,e),c=h.from,d=h.to,f=h.line;e.__from=c,e.__to=d,e.setData(f);var p=e.get("symbol"),g=e.get("symbolSize");y(p)||(p=[p,p]),"number"==typeof g&&(g=[g,g]),h.from.each(function(t){o(c,t,!0),o(d,t,!1)}),f.each(function(t){var e=f.getItemModel(t).get("lineStyle.color");f.setItemVisual(t,{color:e||c.getItemVisual(t,"color")}),f.setItemLayout(t,[c.getItemLayout(t),d.getItemLayout(t)]),f.setItemVisual(t,{fromSymbolSize:c.getItemVisual(t,"symbolSize"),fromSymbol:c.getItemVisual(t,"symbol"),toSymbolSize:d.getItemVisual(t,"symbolSize"),toSymbol:d.getItemVisual(t,"symbol")})}),u.updateData(f),h.line.eachItemGraphicEl(function(t,i){t.traverse(function(t){t.dataModel=e})}),u.__keep=!0,u.group.silent=e.get("silent")||t.get("silent")}}),Ls(function(t){t.markLine=t.markLine||{}}),AO.extend({type:"markArea",defaultOption:{zlevel:0,z:1,tooltip:{trigger:"item"},animation:!1,label:{show:!0,position:"top"},itemStyle:{borderWidth:0},emphasis:{label:{show:!0,position:"top"}}}});var NO=function(t,e,i,n){var a=ax(t,n[0]),r=ax(t,n[1]),s=T,l=a.coord,u=r.coord;l[0]=s(l[0],-1/0),l[1]=s(l[1],-1/0),u[0]=s(u[0],1/0),u[1]=s(u[1],1/0);var h=o([{},a,r]);return h.coord=[a.coord,r.coord],h.x0=a.x,h.y0=a.y,h.x1=r.x,h.y1=r.y,h},OO=[["x0","y0"],["x1","y0"],["x1","y1"],["x0","y1"]];kO.extend({type:"markArea",updateTransform:function(t,e,i){e.eachSeries(function(t){var e=t.markAreaModel;if(e){var n=e.getData();n.each(function(e){var o=f(OO,function(o){return bx(n,e,o,t,i)});n.setItemLayout(e,o),n.getItemGraphicEl(e).setShape("points",o)})}},this)},renderSeries:function(t,e,i,n){var o=t.coordinateSystem,a=t.id,s=t.getData(),l=this.markerGroupMap,u=l.get(a)||l.set(a,{group:new Zw});this.group.add(u.group),u.__keep=!0;var h=Sx(o,t,e);e.setData(h),h.each(function(e){h.setItemLayout(e,f(OO,function(i){return bx(h,e,i,t,n)})),h.setItemVisual(e,{color:s.getVisual("color")})}),h.diff(u.__data).add(function(t){var e=new nM({shape:{points:h.getItemLayout(t)}});h.setItemGraphicEl(t,e),u.group.add(e)}).update(function(t,i){var n=u.__data.getItemGraphicEl(i);bo(n,{shape:{points:h.getItemLayout(t)}},e,t),u.group.add(n),h.setItemGraphicEl(t,n)}).remove(function(t){var e=u.__data.getItemGraphicEl(t);u.group.remove(e)}).execute(),h.eachItemGraphicEl(function(t,i){var n=h.getItemModel(i),o=n.getModel("label"),a=n.getModel("emphasis.label"),s=h.getItemVisual(i,"color");t.useStyle(r(n.getModel("itemStyle").getItemStyle(),{fill:Ut(s,.4),stroke:s})),t.hoverStyle=n.getModel("emphasis.itemStyle").getItemStyle(),co(t.style,t.hoverStyle,o,a,{labelFetcher:e,labelDataIndex:i,defaultText:h.getName(i)||"",isRectText:!0,autoColor:s}),uo(t,{}),t.dataModel=e}),u.__data=h,u.group.silent=e.get("silent")||t.get("silent")}}),Ls(function(t){t.markArea=t.markArea||{}});YM.registerSubTypeDefaulter("timeline",function(){return"slider"}),Ps({type:"timelineChange",event:"timelineChanged",update:"prepareAndUpdate"},function(t,e){var i=e.getComponent("timeline");return i&&null!=t.currentIndex&&(i.setCurrentIndex(t.currentIndex),!i.get("loop",!0)&&i.isIndexMax()&&i.setPlayState(!1)),e.resetOption("timeline"),r({currentIndex:i.option.currentIndex},t)}),Ps({type:"timelinePlayChange",event:"timelinePlayChanged",update:"update"},function(t,e){var i=e.getComponent("timeline");i&&null!=t.playState&&i.setPlayState(t.playState)});var EO=YM.extend({type:"timeline",layoutMode:"box",defaultOption:{zlevel:0,z:4,show:!0,axisType:"time",realtime:!0,left:"20%",top:null,right:"20%",bottom:0,width:null,height:40,padding:5,controlPosition:"left",autoPlay:!1,rewind:!1,loop:!0,playInterval:2e3,currentIndex:0,itemStyle:{},label:{color:"#000"},data:[]},init:function(t,e,i){this._data,this._names,this.mergeDefaultAndTheme(t,i),this._initData()},mergeOption:function(t){EO.superApply(this,"mergeOption",arguments),this._initData()},setCurrentIndex:function(t){null==t&&(t=this.option.currentIndex);var e=this._data.count();this.option.loop?t=(t%e+e)%e:(t>=e&&(t=e-1),t<0&&(t=0)),this.option.currentIndex=t},getCurrentIndex:function(){return this.option.currentIndex},isIndexMax:function(){return this.getCurrentIndex()>=this._data.count()-1},setPlayState:function(t){this.option.autoPlay=!!t},getPlayState:function(){return!!this.option.autoPlay},_initData:function(){var t=this.option,e=t.data||[],n=t.axisType,o=this._names=[];if("category"===n){var a=[];d(e,function(t,e){var n,r=Li(t);w(t)?(n=i(t)).value=e:n=e,a.push(n),_(r)||null!=r&&!isNaN(r)||(r=""),o.push(r+"")}),e=a} +var r={category:"ordinal",time:"time"}[n]||"number";(this._data=new eA([{name:"value",type:r}],this)).initData(e,o)},getData:function(){return this._data},getCategories:function(){if("category"===this.get("axisType"))return this._names.slice()}});h(EO.extend({type:"timeline.slider",defaultOption:{backgroundColor:"rgba(0,0,0,0)",borderColor:"#ccc",borderWidth:0,orient:"horizontal",inverse:!1,tooltip:{trigger:"item"},symbol:"emptyCircle",symbolSize:10,lineStyle:{show:!0,width:2,color:"#304654"},label:{position:"auto",show:!0,interval:"auto",rotate:0,color:"#304654"},itemStyle:{color:"#304654",borderWidth:1},checkpointStyle:{symbol:"circle",symbolSize:13,color:"#c23531",borderWidth:5,borderColor:"rgba(194,53,49, 0.5)",animation:!0,animationDuration:300,animationEasing:"quinticInOut"},controlStyle:{show:!0,showPlayBtn:!0,showPrevBtn:!0,showNextBtn:!0,itemSize:22,itemGap:12,position:"left",playIcon:"path://M31.6,53C17.5,53,6,41.5,6,27.4S17.5,1.8,31.6,1.8C45.7,1.8,57.2,13.3,57.2,27.4S45.7,53,31.6,53z M31.6,3.3 C18.4,3.3,7.5,14.1,7.5,27.4c0,13.3,10.8,24.1,24.1,24.1C44.9,51.5,55.7,40.7,55.7,27.4C55.7,14.1,44.9,3.3,31.6,3.3z M24.9,21.3 c0-2.2,1.6-3.1,3.5-2l10.5,6.1c1.899,1.1,1.899,2.9,0,4l-10.5,6.1c-1.9,1.1-3.5,0.2-3.5-2V21.3z",stopIcon:"path://M30.9,53.2C16.8,53.2,5.3,41.7,5.3,27.6S16.8,2,30.9,2C45,2,56.4,13.5,56.4,27.6S45,53.2,30.9,53.2z M30.9,3.5C17.6,3.5,6.8,14.4,6.8,27.6c0,13.3,10.8,24.1,24.101,24.1C44.2,51.7,55,40.9,55,27.6C54.9,14.4,44.1,3.5,30.9,3.5z M36.9,35.8c0,0.601-0.4,1-0.9,1h-1.3c-0.5,0-0.9-0.399-0.9-1V19.5c0-0.6,0.4-1,0.9-1H36c0.5,0,0.9,0.4,0.9,1V35.8z M27.8,35.8 c0,0.601-0.4,1-0.9,1h-1.3c-0.5,0-0.9-0.399-0.9-1V19.5c0-0.6,0.4-1,0.9-1H27c0.5,0,0.9,0.4,0.9,1L27.8,35.8L27.8,35.8z",nextIcon:"path://M18.6,50.8l22.5-22.5c0.2-0.2,0.3-0.4,0.3-0.7c0-0.3-0.1-0.5-0.3-0.7L18.7,4.4c-0.1-0.1-0.2-0.3-0.2-0.5 c0-0.4,0.3-0.8,0.8-0.8c0.2,0,0.5,0.1,0.6,0.3l23.5,23.5l0,0c0.2,0.2,0.3,0.4,0.3,0.7c0,0.3-0.1,0.5-0.3,0.7l-0.1,0.1L19.7,52 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BO=m,VO=d,GO=Math.PI;RO.extend({type:"timeline.slider",init:function(t,e){this.api=e,this._axis,this._viewRect,this._timer,this._currentPointer,this._mainGroup,this._labelGroup},render:function(t,e,i,n){if(this.model=t,this.api=i,this.ecModel=e,this.group.removeAll(),t.get("show",!0)){var o=this._layout(t,i),a=this._createGroup("mainGroup"),r=this._createGroup("labelGroup"),s=this._axis=this._createAxis(o,t);t.formatTooltip=function(t){return Qo(s.scale.getLabel(t))},VO(["AxisLine","AxisTick","Control","CurrentPointer"],function(e){this["_render"+e](o,a,s,t)},this),this._renderAxisLabel(o,r,s,t),this._position(o,t)} +this._doPlayStop()},remove:function(){this._clearTimer(),this.group.removeAll()},dispose:function(){this._clearTimer()},_layout:function(t,e){var i=t.get("label.position"),n=t.get("orient"),o=Ax(t,e);null==i||"auto"===i?i="horizontal"===n?o.y+o.height/2=0||"+"===i?"left":"right"},r={horizontal:i>=0||"+"===i?"top":"bottom",vertical:"middle"},s={horizontal:0,vertical:GO/2},l="vertical"===n?o.height:o.width,u=t.getModel("controlStyle"),h=u.get("show",!0),c=h?u.get("itemSize"):0,d=h?u.get("itemGap"):0,f=c+d,p=t.get("label.rotate")||0;p=p*GO/180;var g,m,v,y,x=u.get("position",!0),_=h&&u.get("showPlayBtn",!0),w=h&&u.get("showPrevBtn",!0),b=h&&u.get("showNextBtn",!0),S=0,M=l;return"left"===x||"bottom"===x?(_&&(g=[0,0],S+=f),w&&(m=[S,0],S+=f),b&&(v=[M-c,0],M-=f)):(_&&(g=[M-c,0],M-=f),w&&(m=[0,0],S+=f),b&&(v=[M-c,0],M-=f)),y=[S,M],t.get("inverse")&&y.reverse(),{viewRect:o,mainLength:l,orient:n,rotation:s[n],labelRotation:p,labelPosOpt:i,labelAlign:t.get("label.align")||a[n],labelBaseline:t.get("label.verticalAlign")||t.get("label.baseline")||r[n],playPosition:g,prevBtnPosition:m,nextBtnPosition:v,axisExtent:y,controlSize:c,controlGap:d}},_position:function(t,e){function i(t){var e=t.position;t.origin=[c[0][0]-e[0],c[1][0]-e[1]]} +function n(t){return[[t.x,t.x+t.width],[t.y,t.y+t.height]]} +function o(t,e,i,n,o){t[n]+=i[n][o]-e[n][o]} +var a=this._mainGroup,r=this._labelGroup,s=t.viewRect;if("vertical"===t.orient){var l=mt(),u=s.x,h=s.y+s.height;_t(l,l,[-u,-h]),wt(l,l,-GO/2),_t(l,l,[u,h]),(s=s.clone()).applyTransform(l)} +var c=n(s),d=n(a.getBoundingRect()),f=n(r.getBoundingRect()),p=a.position,g=r.position;g[0]=p[0]=c[0][0];var m=t.labelPosOpt;if(isNaN(m))o(p,d,c,1,v="+"===m?0:1),o(g,f,c,1,1-v);else{var v=m>=0?0:1;o(p,d,c,1,v),g[1]=p[1]+m} +a.attr("position",p),r.attr("position",g),a.rotation=r.rotation=t.rotation,i(a),i(r)},_createAxis:function(t,e){var i=e.getData(),n=e.get("axisType"),o=Vl(e,n);o.getTicks=function(){return i.mapArray(["value"],function(t){return t})};var a=i.getDataExtent("value");o.setExtent(a[0],a[1]),o.niceTicks();var r=new zO("value",o,t.axisExtent,n);return r.model=e,r},_createGroup:function(t){var e=this["_"+t]=new Zw;return this.group.add(e),e},_renderAxisLine:function(t,e,i,n){var o=i.getExtent();n.get("lineStyle.show")&&e.add(new rM({shape:{x1:o[0],y1:0,x2:o[1],y2:0},style:a({lineCap:"round"},n.getModel("lineStyle").getLineStyle()),silent:!0,z2:1}))},_renderAxisTick:function(t,e,i,n){var o=n.getData(),a=i.scale.getTicks();VO(a,function(t){var a=i.dataToCoord(t),r=o.getItemModel(t),s=r.getModel("itemStyle"),l=r.getModel("emphasis.itemStyle"),u={position:[a,0],onclick:BO(this._changeTimeline,this,t)},h=Cx(r,s,e,u);uo(h,l.getItemStyle()),r.get("tooltip")?(h.dataIndex=t,h.dataModel=n):h.dataIndex=h.dataModel=null},this)},_renderAxisLabel:function(t,e,i,n){if(i.getLabelModel().get("show")){var o=n.getData(),a=i.getViewLabels();VO(a,function(n){var a=n.tickValue,r=o.getItemModel(a),s=r.getModel("label"),l=r.getModel("emphasis.label"),u=i.dataToCoord(n.tickValue),h=new qS({position:[u,0],rotation:t.labelRotation-t.rotation,onclick:BO(this._changeTimeline,this,a),silent:!1});fo(h.style,s,{text:n.formattedLabel,textAlign:t.labelAlign,textVerticalAlign:t.labelBaseline}),e.add(h),uo(h,fo({},l))},this)}},_renderControl:function(t,e,i,n){function o(t,i,o,h){if(t){var c=Dx(n,i,u,{position:t,origin:[a/2,0],rotation:h?-r:0,rectHover:!0,style:s,onclick:o});e.add(c),uo(c,l)}} +var a=t.controlSize,r=t.rotation,s=n.getModel("controlStyle").getItemStyle(),l=n.getModel("emphasis.controlStyle").getItemStyle(),u=[0,-a/2,a,a],h=n.getPlayState(),c=n.get("inverse",!0);o(t.nextBtnPosition,"controlStyle.nextIcon",BO(this._changeTimeline,this,c?"-":"+")),o(t.prevBtnPosition,"controlStyle.prevIcon",BO(this._changeTimeline,this,c?"+":"-")),o(t.playPosition,"controlStyle."+(h?"stopIcon":"playIcon"),BO(this._handlePlayClick,this,!h),!0)},_renderCurrentPointer:function(t,e,i,n){var o=n.getData(),a=n.getCurrentIndex(),r=o.getItemModel(a).getModel("checkpointStyle"),s=this,l={onCreate:function(t){t.draggable=!0,t.drift=BO(s._handlePointerDrag,s),t.ondragend=BO(s._handlePointerDragend,s),Lx(t,a,i,n,!0)},onUpdate:function(t){Lx(t,a,i,n)}};this._currentPointer=Cx(r,r,this._mainGroup,{},this._currentPointer,l)},_handlePlayClick:function(t){this._clearTimer(),this.api.dispatchAction({type:"timelinePlayChange",playState:t,from:this.uid})},_handlePointerDrag:function(t,e,i){this._clearTimer(),this._pointerChangeTimeline([i.offsetX,i.offsetY])},_handlePointerDragend:function(t){this._pointerChangeTimeline([t.offsetX,t.offsetY],!0)},_pointerChangeTimeline:function(t,e){var i=this._toAxisCoord(t)[0],n=Bo(this._axis.getExtent().slice());i>n[1]&&(i=n[1]),ii.getHeight()&&(n.textPosition="top",l=!0);var u=l?-5-o.height:s+8;a+o.width/2>i.getWidth()?(n.textPosition=["100%",u],n.textAlign="right"):a-o.width/2<0&&(n.textPosition=[0,u],n.textAlign="left")}})}},updateView:function(t,e,i,n){d(this._features,function(t){t.updateView&&t.updateView(t.model,e,i,n)})},remove:function(t,e){d(this._features,function(i){i.remove&&i.remove(t,e)}),this.group.removeAll()},dispose:function(t,e){d(this._features,function(i){i.dispose&&i.dispose(t,e)})}});var WO=XI.toolbox.saveAsImage;Px.defaultOption={show:!0,icon:"M4.7,22.9L29.3,45.5L54.7,23.4M4.6,43.6L4.6,58L53.8,58L53.8,43.6M29.2,45.1L29.2,0",title:WO.title,type:"png",name:"",excludeComponents:["toolbox"],pixelRatio:1,lang:WO.lang.slice()},Px.prototype.unusable=!z_.canvasSupported,Px.prototype.onclick=function(t,e){var i=this.model,n=i.get("name")||t.get("title.0.text")||"echarts",o=document.createElement("a"),a=i.get("type",!0)||"png";o.download=n+"."+a,o.target="_blank";var r=e.getConnectedDataURL({type:a,backgroundColor:i.get("backgroundColor",!0)||t.get("backgroundColor")||"#fff",excludeComponents:i.get("excludeComponents"),pixelRatio:i.get("pixelRatio")});if(o.href=r,"function"!=typeof MouseEvent||z_.browser.ie||z_.browser.edge) +if(window.navigator.msSaveOrOpenBlob){for(var s=atob(r.split(",")[1]),l=s.length,u=new Uint8Array(l);l--;)u[l]=s.charCodeAt(l);var h=new Blob([u]);window.navigator.msSaveOrOpenBlob(h,n+"."+a)}else{var c=i.get("lang"),d='';window.open().document.write(d)} +else{var f=new MouseEvent("click",{view:window,bubbles:!0,cancelable:!1});o.dispatchEvent(f)}},yy("saveAsImage",Px);var HO=XI.toolbox.magicType;Nx.defaultOption={show:!0,type:[],icon:{line:"M4.1,28.9h7.1l9.3-22l7.4,38l9.7-19.7l3,12.8h14.9M4.1,58h51.4",bar:"M6.7,22.9h10V48h-10V22.9zM24.9,13h10v35h-10V13zM43.2,2h10v46h-10V2zM3.1,58h53.7",stack:"M8.2,38.4l-8.4,4.1l30.6,15.3L60,42.5l-8.1-4.1l-21.5,11L8.2,38.4z M51.9,30l-8.1,4.2l-13.4,6.9l-13.9-6.9L8.2,30l-8.4,4.2l8.4,4.2l22.2,11l21.5-11l8.1-4.2L51.9,30z M51.9,21.7l-8.1,4.2L35.7,30l-5.3,2.8L24.9,30l-8.4-4.1l-8.3-4.2l-8.4,4.2L8.2,30l8.3,4.2l13.9,6.9l13.4-6.9l8.1-4.2l8.1-4.1L51.9,21.7zM30.4,2.2L-0.2,17.5l8.4,4.1l8.3,4.2l8.4,4.2l5.5,2.7l5.3-2.7l8.1-4.2l8.1-4.2l8.1-4.1L30.4,2.2z",tiled:"M2.3,2.2h22.8V25H2.3V2.2z M35,2.2h22.8V25H35V2.2zM2.3,35h22.8v22.8H2.3V35z M35,35h22.8v22.8H35V35z"},title:i(HO.title),option:{},seriesIndex:{}};var ZO=Nx.prototype;ZO.getIcons=function(){var t=this.model,e=t.get("icon"),i={};return d(t.get("type"),function(t){e[t]&&(i[t]=e[t])}),i};var UO={line:function(t,e,i,o){if("bar"===t)return n({id:e,type:"line",data:i.get("data"),stack:i.get("stack"),markPoint:i.get("markPoint"),markLine:i.get("markLine")},o.get("option.line")||{},!0)},bar:function(t,e,i,o){if("line"===t)return n({id:e,type:"bar",data:i.get("data"),stack:i.get("stack"),markPoint:i.get("markPoint"),markLine:i.get("markLine")},o.get("option.bar")||{},!0)},stack:function(t,e,i,o){if("line"===t||"bar"===t)return n({id:e,stack:"__ec_magicType_stack__"},o.get("option.stack")||{},!0)},tiled:function(t,e,i,o){if("line"===t||"bar"===t)return n({id:e,stack:""},o.get("option.tiled")||{},!0)}},XO=[["line","bar"],["stack","tiled"]];ZO.onclick=function(t,e,i){var n=this.model,o=n.get("seriesIndex."+i);if(UO[i]){var a={series:[]};d(XO,function(t){l(t,i)>=0&&d(t,function(t){n.setIconStatus(t,"normal")})}),n.setIconStatus(i,"emphasis"),t.eachComponent({mainType:"series",query:null==o?null:{seriesIndex:o}},function(e){var o=e.subType,s=e.id,l=UO[i](o,s,e,n);l&&(r(l,e.option),a.series.push(l));var u=e.coordinateSystem;if(u&&"cartesian2d"===u.type&&("line"===i||"bar"===i)){var h=u.getAxesByScale("ordinal")[0];if(h){var c=h.dim+"Axis",d=t.queryComponents({mainType:c,index:e.get(name+"Index"),id:e.get(name+"Id")})[0].componentIndex;a[c]=a[c]||[];for(var f=0;f<=d;f++)a[c][d]=a[c][d]||{};a[c][d].boundaryGap="bar"===i}}}),e.dispatchAction({type:"changeMagicType",currentType:i,newOption:a})}},Ps({type:"changeMagicType",event:"magicTypeChanged",update:"prepareAndUpdate"},function(t,e){e.mergeOption(t.newOption)}),yy("magicType",Nx);var jO=XI.toolbox.dataView,YO=new Array(60).join("-"),qO="\t",KO=new RegExp("["+qO+"]+","g");Hx.defaultOption={show:!0,readOnly:!1,optionToContent:null,contentToOption:null,icon:"M17.5,17.3H33 M17.5,17.3H33 M45.4,29.5h-28 M11.5,2v56H51V14.8L38.4,2H11.5z M38.4,2.2v12.7H51 M45.4,41.7h-28",title:i(jO.title),lang:i(jO.lang),backgroundColor:"#fff",textColor:"#000",textareaColor:"#fff",textareaBorderColor:"#333",buttonColor:"#c23531",buttonTextColor:"#fff"},Hx.prototype.onclick=function(t,e){function i(){n.removeChild(a),x._dom=null} +var n=e.getDom(),o=this.model;this._dom&&n.removeChild(this._dom);var a=document.createElement("div");a.style.cssText="position:absolute;left:5px;top:5px;bottom:5px;right:5px;",a.style.backgroundColor=o.get("backgroundColor")||"#fff";var r=document.createElement("h4"),s=o.get("lang")||[];r.innerHTML=s[0]||o.get("title"),r.style.cssText="margin: 10px 20px;",r.style.color=o.get("textColor");var l=document.createElement("div"),u=document.createElement("textarea");l.style.cssText="display:block;width:100%;overflow:auto;";var h=o.get("optionToContent"),c=o.get("contentToOption"),d=zx(t);if("function"==typeof h){var f=h(e.getOption());"string"==typeof f?l.innerHTML=f:M(f)&&l.appendChild(f)}else l.appendChild(u),u.readOnly=o.get("readOnly"),u.style.cssText="width:100%;height:100%;font-family:monospace;font-size:14px;line-height:1.6rem;",u.style.color=o.get("textColor"),u.style.borderColor=o.get("textareaBorderColor"),u.style.backgroundColor=o.get("textareaColor"),u.value=d.value;var p=d.meta,g=document.createElement("div");g.style.cssText="position:absolute;bottom:0;left:0;right:0;";var m="float:right;margin-right:20px;border:none;cursor:pointer;padding:2px 5px;font-size:12px;border-radius:3px",v=document.createElement("div"),y=document.createElement("div");m+=";background-color:"+o.get("buttonColor"),m+=";color:"+o.get("buttonTextColor");var x=this;ut(v,"click",i),ut(y,"click",function(){var t;try{t="function"==typeof c?c(l,e.getOption()):Wx(u.value,p)}catch(t){throw i(),new Error("Data view format error "+t)} +t&&e.dispatchAction({type:"changeDataView",newOption:t}),i()}),v.innerHTML=s[1],y.innerHTML=s[2],y.style.cssText=m,v.style.cssText=m,!o.get("readOnly")&&g.appendChild(y),g.appendChild(v),ut(u,"keydown",function(t){if(9===(t.keyCode||t.which)){var 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