From 2e24587bcaee068868919b4f964e0b911cb6e9cc Mon Sep 17 00:00:00 2001 From: Maria Gorodetski Date: Wed, 14 Aug 2024 08:53:17 +0000 Subject: [PATCH 01/11] convert to int --- nbs/13_questionnaire_handler.ipynb | 93 ++++++++----- .../cgm/metadata/cgm_data_dictionary.csv | 122 +++++++++--------- .../metadata/diet_logging_data_dictionary.csv | 38 +++--- .../metadata/fundus_data_dictionary.csv | 12 +- .../sleep/metadata/sleep_data_dictionary.csv | 6 +- pheno_utils/questionnaires_handler.py | 83 +++++++----- 6 files changed, 207 insertions(+), 147 deletions(-) diff --git a/nbs/13_questionnaire_handler.ipynb b/nbs/13_questionnaire_handler.ipynb index 5e21b22..4428ccd 100644 --- a/nbs/13_questionnaire_handler.ipynb +++ b/nbs/13_questionnaire_handler.ipynb @@ -204,41 +204,54 @@ " else:\n", " code_string = convert_to_string(dict_df.loc[tab_field_name][\"data_coding\"])\n", " \n", - " #getting the data coding df from the large data coding csv\n", + " # Getting the data coding df from the large data coding csv\n", " code_df = mapping_df[mapping_df[\"code_number\"] == code_string].copy()\n", " \n", - " #Make sure no leading 0s for coding values\n", + " # Make sure no leading 0s for coding values\n", " code_df[\"coding\"] = code_df[\"coding\"].apply(convert_to_string)\n", " \n", " mapping_dict = dict(zip(code_df[code_from].astype(str), code_df[code_to]))\n", " \n", " \n", - " #adding fail safe incase older dictionaries don't have field type : TODO potentaily remove once older dictionaires are updated\n", - " if 'field_type' in dict_df.columns:\n", - " field_type = dict_df.loc[tab_field_name]['field_type']\n", - " if isinstance(field_type, pd.Series):\n", - " if field_type.nunique() == 1:\n", - " field_type = field_type.iloc[0]\n", - " else:\n", - " warnings.warn(f\"tabular field {tab_field_name} is used in 2 columns and have conflicting field types,please check and update dictionary. This field has not be converted.\")\n", - " return orig_answer\n", - " \n", - " if field_type == 'Categorical (multiple)': \n", - " normalise_answer = normalize_answers(orig_answer, field_type)\n", - " check_invalid_values(normalise_answer , code_df)\n", - " transformed_answer = normalise_answer.apply(replace_values, mapping_dict = mapping_dict)\n", + " field_type = dict_df.loc[tab_field_name]['field_type']\n", + " if isinstance(field_type, pd.Series):\n", + " if field_type.nunique() == 1:\n", + " field_type = field_type.iloc[0]\n", " else:\n", - " #if categorical single\n", - " normalized_answer = normalize_answers(orig_answer, field_type)\n", - " check_invalid_values(normalized_answer, code_df)\n", - " transformed_answer = normalized_answer.replace(mapping_dict)\n", - " transformed_answer = transformed_answer.astype(\"category\")\n", - "\n", - " return transformed_answer\n", + " warnings.warn(f\"tabular field {tab_field_name} is used in 2 columns and have conflicting field types,please check and update dictionary. This field has not be converted.\")\n", + " return orig_answer\n", + " \n", + " if field_type == 'Categorical (multiple)': \n", + " normalise_answer = normalize_answers(orig_answer, field_type)\n", + " check_invalid_values(normalise_answer , code_df)\n", + " transformed_answer = normalise_answer.apply(replace_values, mapping_dict = mapping_dict)\n", " else:\n", - " return orig_answer\n", - "\n", + " normalized_answer = normalize_answers(orig_answer, field_type)\n", + " check_invalid_values(normalized_answer, code_df)\n", + " transformed_answer = normalized_answer.replace(mapping_dict)\n", + " transformed_answer = transformed_answer.astype(\"category\")\n", "\n", + " return transformed_answer\n", + " \n", + "def convert_codings_to_int(df: pd.DataFrame, dict_df: pd.DataFrame, fields_for_translation: list, preferred_language: str) -> pd.DataFrame:\n", + " df_coding = df.copy()\n", + " if preferred_language == 'coding':\n", + " for column in fields_for_translation:\n", + " data_coding = dict_df.loc[column, 'data_coding'] # This actually should happen\n", + " if pd.notna(data_coding):\n", + " if dict_df.loc[column, 'array'] == 'Multiple':\n", + " try:\n", + " df_coding[column] = df[column].apply(lambda x : pd.Series(x).astype('Int16') if isinstance(x, np.ndarray) else x)\n", + " except: \n", + " print(column)\n", + " else: \n", + " df_coding[column] = df[column].astype('Int16')\n", + " dict_df.loc[column, 'pandas_dtype'] = 'Int16'\n", + " \n", + " return df_coding\n", + " \n", + " \n", + " \n", "def transform_dataframe(\n", " df: pd.DataFrame,\n", " transform_from: str,\n", @@ -246,12 +259,20 @@ " dict_df: pd.DataFrame,\n", " mapping_df: pd.DataFrame,\n", ") -> pd.DataFrame:\n", - " if 'data_coding' not in dict_df.columns or transform_from == transform_to:\n", - " return df\n", " \n", + " # Only fields with a code in data_coding property will be transformed\n", " fields_for_translation = dict_df[pd.notna(dict_df.data_coding)].index.intersection(df.columns)\n", - " if len(fields_for_translation) == 0:\n", + " if len(fields_for_translation) == 0: # No fields with data_coding code\n", " return df\n", + " \n", + " if transform_from == transform_to: \n", + " df = convert_codings_to_int(df=df, dict_df=dict_df, fields_for_translation=fields_for_translation, \n", + " preferred_language=transform_to)\n", + " \n", + " print('#1', df.info())\n", + " return df\n", + " \n", + " \n", " transformed_df = df.copy()\n", " for column in fields_for_translation:\n", " data_coding = dict_df.loc[column, 'data_coding']\n", @@ -268,6 +289,10 @@ " dict_df,\n", " mapping_df\n", " )\n", + " \n", + " transformed_df = convert_codings_to_int(df=transformed_df, dict_df=dict_df, fields_for_translation=fields_for_translation, \n", + " preferred_language=transform_to)\n", + " print('#2', df.info())\n", " return transformed_df" ] }, @@ -276,7 +301,13 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "# dataset = 'sleep'\n", + "# input_path = f'/home/ec2-user/workspace/pheno-utils/nbs/examples/{dataset}/metadata/{dataset}_data_dictionary.csv'\n", + "# df = pd.read_csv(input_path)\n", + "# df['data_coding'] = None\n", + "# df.to_csv(input_path, index=False)" + ] } ], "metadata": { @@ -284,6 +315,10 @@ "display_name": "python3", "language": "python", "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.11.5" } }, "nbformat": 4, diff --git a/nbs/examples/cgm/metadata/cgm_data_dictionary.csv b/nbs/examples/cgm/metadata/cgm_data_dictionary.csv index 39cfeb0..052258a 100644 --- a/nbs/examples/cgm/metadata/cgm_data_dictionary.csv +++ b/nbs/examples/cgm/metadata/cgm_data_dictionary.csv @@ -1,62 +1,62 @@ -tabular_field_name,field_string,description_string,parent_dataframe,relative_location,value_type,units,sampling_rate,field_type,array,cohorts,data_type,debut,pandas_dtype -connection_id,ConnectionID,CGM device connection id,,cgm/cgm.parquet,Text,Text,,Integer,Single,10K,tabular,2018-12-27,int -cgm_filename,CGM timeseries,Name of the file containing the participants' CGM timeseries,,cgm/cgm.parquet,Text,,,Time series file (individual),Single,10K,text,2018-12-27,string -glucose,Glucose,cgm temporal glucose values,,cgm/cgm.parquet,"Series data, continous",mg/dl,15min,Continuous,Single,10K,time series,2018-12-27,float -collection_timestamp,Collection timestamp,CGM connection first data point timestamp,,cgm/cgm.parquet,Time,Time,,Datetime,Single,10K,tabular,2018-12-27,"datetime64[us, Asia/Jerusalem]" -cgm_first_date,CGM first date,CGM connection first date,,cgm/cgm.parquet,Time,Time,,Datetime,Single,10K,tabular,2018-12-27,"datetime64[us, Asia/Jerusalem]" -cgm_last_date,CGM last date,CGM connection last date,,cgm/cgm.parquet,Time,Time,,Datetime,Single,10K,tabular,2018-12-27,"datetime64[us, Asia/Jerusalem]" -cgm_device_type,CGM Device type,CGM Device type,,cgm/cgm.parquet,Categorical (multiple),Text,,Text,Single,10K,tabular,2018-12-27,string -cgm_datapoints_lost_in_qc,CGM datapoints lost in QC,Number of CGM datapoints lost in QC,,cgm/cgm.parquet,Integer,integer,,Integer,Single,10K,tabular,2018-12-27,int -percentage_of_cgm_datapoints_lost_in_qc,,,,cgm/cgm.parquet,,,,,,,,,float -number_of_cgm_days_available,Number of CGM days available,Number of CGM days available for the connection,,cgm/cgm.parquet,Integer,integer,,Integer,Single,10K,tabular,2018-12-27,int -number_of_cgm_datapoints_available,Number of CGM datapoints available,Number of CGM datapoints available for the connection,,cgm/cgm.parquet,Integer,integer,,Integer,Single,10K,tabular,2018-12-27,int -timezone,Timezone,TImezone of measurement,,cgm/cgm.parquet,,,,,,,,,"datetime64[ns, Asia/Jerusalem]" -1st qu_,1st quantile,First quantile of all glucose values.,,cgm/cgm.parquet,Continuous,mg/dl,,Data,Single,10K,tabular,2018-12-27,float -3rd qu_,3rd quantile,Third quantile of all glucose values.,,cgm/cgm.parquet,Continuous,mg/dl,,Data,Single,10K,tabular,2018-12-27,float -above_140,% above 140,Percent of glucose measures that were larger than 140,,cgm/cgm.parquet,Continuous,%,,Data,Single,10K,tabular,2018-12-27,float -above_180,% above 180,Percent of glucose measures that were larger than 180,,cgm/cgm.parquet,Continuous,%,,Data,Single,10K,tabular,2018-12-27,float -above_250,% above 250,Percent of glucose measures that were larger than 250,,cgm/cgm.parquet,Continuous,%,,Data,Single,10K,tabular,2018-12-27,float -below_54,% below 54,Percent of glucose measures that were lower than 54,,cgm/cgm.parquet,Continuous,%,,Data,Single,10K,tabular,2018-12-27,float -below_70,% below 70,Percent of glucose measures that were lower than 70,,cgm/cgm.parquet,Continuous,%,,Data,Single,10K,tabular,2018-12-27,float -in_range_63_140,% in range 63-140,Percent of glucose measures that were in a specified range of glucose values (63-140),,cgm/cgm.parquet,Continuous,%,,Data,Single,10K,tabular,2018-12-27,float -in_range_70_180,% in range 70-180,Percent of glucose measures that were in a specified range of glucose values (70-180),,cgm/cgm.parquet,Continuous,%,,Data,Single,10K,tabular,2018-12-27,float -adrr,ADRR,Average daily risk range (ADRR) is a variability measure that was designed to be sensitive to both hyperglycemia and hypoglycemia. The ADRR is composed of the HBGI and LGBI.,,cgm/cgm.parquet,Continuous,,,Data,Single,10K,tabular,2018-12-27,float -auc,AUC,"Hourly average AUC. This measure integrates, to some extent, the severity of a high or low glucose along with the duration of the abnormality. It’s calculated for each hour in the CGM, then averaged for each day over a 24-hour period, then once again averaged between all days to receive a single AUC measure.",,cgm/cgm.parquet,Continuous,mg/dl*h,,Data,Single,10K,tabular,2018-12-27,float -cogi,COGI,"Continuous Glucose Monitoring Index (COGI). COGI uses three measures of the CGM - time in range, time below range and glucose variability (GV) and averages them using a weighted mean, usually with weights of 50%, 35% and 15% accordingly.",,cgm/cgm.parquet,Continuous,,,Data,Single,10K,tabular,2018-12-27,float -conga,Conga,"Continuous Overall Net Glycemic Action (CONGA). This measure assesses intra-day glycemic variability, by calculating the standard deviation of differences in glucose measures taken n hours apart.",,cgm/cgm.parquet,Continuous,,,Data,Single,10K,tabular,2018-12-27,float +tabular_field_name,field_string,description_string,parent_dataframe,relative_location,value_type,units,sampling_rate,field_type,array,cohorts,data_type,debut,pandas_dtype,data_coding +connection_id,ConnectionID,CGM device connection id,,cgm/cgm.parquet,Text,Text,,Integer,Single,10K,tabular,2018-12-27,int, +cgm_filename,CGM timeseries,Name of the file containing the participants' CGM timeseries,,cgm/cgm.parquet,Text,,,Time series file (individual),Single,10K,text,2018-12-27,string, +glucose,Glucose,cgm temporal glucose values,,cgm/cgm.parquet,"Series data, continous",mg/dl,15min,Continuous,Single,10K,time series,2018-12-27,float, +collection_timestamp,Collection timestamp,CGM connection first data point timestamp,,cgm/cgm.parquet,Time,Time,,Datetime,Single,10K,tabular,2018-12-27,"datetime64[us, Asia/Jerusalem]", +cgm_first_date,CGM first date,CGM connection first date,,cgm/cgm.parquet,Time,Time,,Datetime,Single,10K,tabular,2018-12-27,"datetime64[us, Asia/Jerusalem]", +cgm_last_date,CGM last date,CGM connection last date,,cgm/cgm.parquet,Time,Time,,Datetime,Single,10K,tabular,2018-12-27,"datetime64[us, Asia/Jerusalem]", +cgm_device_type,CGM Device type,CGM Device type,,cgm/cgm.parquet,Categorical (multiple),Text,,Text,Single,10K,tabular,2018-12-27,string, +cgm_datapoints_lost_in_qc,CGM datapoints lost in QC,Number of CGM datapoints lost in QC,,cgm/cgm.parquet,Integer,integer,,Integer,Single,10K,tabular,2018-12-27,int, +percentage_of_cgm_datapoints_lost_in_qc,,,,cgm/cgm.parquet,,,,,,,,,float, +number_of_cgm_days_available,Number of CGM days available,Number of CGM days available for the connection,,cgm/cgm.parquet,Integer,integer,,Integer,Single,10K,tabular,2018-12-27,int, +number_of_cgm_datapoints_available,Number of CGM datapoints available,Number of CGM datapoints available for the connection,,cgm/cgm.parquet,Integer,integer,,Integer,Single,10K,tabular,2018-12-27,int, +timezone,Timezone,TImezone of measurement,,cgm/cgm.parquet,,,,,,,,,"datetime64[ns, Asia/Jerusalem]", +1st qu_,1st quantile,First quantile of all glucose values.,,cgm/cgm.parquet,Continuous,mg/dl,,Data,Single,10K,tabular,2018-12-27,float, +3rd qu_,3rd quantile,Third quantile of all glucose values.,,cgm/cgm.parquet,Continuous,mg/dl,,Data,Single,10K,tabular,2018-12-27,float, +above_140,% above 140,Percent of glucose measures that were larger than 140,,cgm/cgm.parquet,Continuous,%,,Data,Single,10K,tabular,2018-12-27,float, +above_180,% above 180,Percent of glucose measures that were larger than 180,,cgm/cgm.parquet,Continuous,%,,Data,Single,10K,tabular,2018-12-27,float, +above_250,% above 250,Percent of glucose measures that were larger than 250,,cgm/cgm.parquet,Continuous,%,,Data,Single,10K,tabular,2018-12-27,float, +below_54,% below 54,Percent of glucose measures that were lower than 54,,cgm/cgm.parquet,Continuous,%,,Data,Single,10K,tabular,2018-12-27,float, +below_70,% below 70,Percent of glucose measures that were lower than 70,,cgm/cgm.parquet,Continuous,%,,Data,Single,10K,tabular,2018-12-27,float, +in_range_63_140,% in range 63-140,Percent of glucose measures that were in a specified range of glucose values (63-140),,cgm/cgm.parquet,Continuous,%,,Data,Single,10K,tabular,2018-12-27,float, +in_range_70_180,% in range 70-180,Percent of glucose measures that were in a specified range of glucose values (70-180),,cgm/cgm.parquet,Continuous,%,,Data,Single,10K,tabular,2018-12-27,float, +adrr,ADRR,Average daily risk range (ADRR) is a variability measure that was designed to be sensitive to both hyperglycemia and hypoglycemia. The ADRR is composed of the HBGI and LGBI.,,cgm/cgm.parquet,Continuous,,,Data,Single,10K,tabular,2018-12-27,float, +auc,AUC,"Hourly average AUC. This measure integrates, to some extent, the severity of a high or low glucose along with the duration of the abnormality. It’s calculated for each hour in the CGM, then averaged for each day over a 24-hour period, then once again averaged between all days to receive a single AUC measure.",,cgm/cgm.parquet,Continuous,mg/dl*h,,Data,Single,10K,tabular,2018-12-27,float, +cogi,COGI,"Continuous Glucose Monitoring Index (COGI). COGI uses three measures of the CGM - time in range, time below range and glucose variability (GV) and averages them using a weighted mean, usually with weights of 50%, 35% and 15% accordingly.",,cgm/cgm.parquet,Continuous,,,Data,Single,10K,tabular,2018-12-27,float, +conga,Conga,"Continuous Overall Net Glycemic Action (CONGA). This measure assesses intra-day glycemic variability, by calculating the standard deviation of differences in glucose measures taken n hours apart.",,cgm/cgm.parquet,Continuous,,,Data,Single,10K,tabular,2018-12-27,float, cv,CV,"Coefficient of variation of all glucose values. -",,cgm/cgm.parquet,Continuous,,,Data,Single,10K,tabular,2018-12-27,float -cv_measures_mean,CVmean,Mean of all daily CVs.,,cgm/cgm.parquet,Continuous,,,Data,Single,10K,tabular,2018-12-27,float -cv_measures_sd,CVsd,Standard deviations of all daily CVs.,,cgm/cgm.parquet,Continuous,,,Data,Single,10K,tabular,2018-12-27,float -ea1c,eA1C,"A linear transformation of the mean glucose value, meant to estimate the HbA1C blood test. Calculated by the following formula: (46.7 + mean(Glucose))/28.7.",,cgm/cgm.parquet,Continuous,mg/dl,,Data,Single,10K,tabular,2018-12-27,float -gmi,GMI,"A linear transformation of the mean glucose value, meant to improve the eA1C measure.",,cgm/cgm.parquet,Continuous,mg/dl,,Data,Single,10K,tabular,2018-12-27,float -grade,GRADE,Glycaemic Risk Assessment Diabetes Equation (GRADE). This clinical risk score aims to evaluate the degree of risk presented by a glucose profile.,,cgm/cgm.parquet,Continuous,,,Data,Single,10K,tabular,2018-12-27,float -grade_eugly,GRADE eugly,"Percentage of the GRADE score that is attributed to euglycemia (glucose values within the normal range, default is 70-140).",,cgm/cgm.parquet,Continuous,,,Data,Single,10K,tabular,2018-12-27,float -grade_hyper,GRADE hyper,"Percentage of the GRADE score that is attributed to hyperglycemia (glucose values above a target range, default is 140).",,cgm/cgm.parquet,Continuous,,,Data,Single,10K,tabular,2018-12-27,float -grade_hypo,GRADE hypo,"Percentage of the GRADE score that is attributed to hypoglycemia (glucose values above a target range, default is 80).",,cgm/cgm.parquet,Continuous,,,Data,Single,10K,tabular,2018-12-27,float -gvp,GVP,"Glucose Variability Percentage (GVP), which is designed to capture both the amplitude and frequency of glucose oscillations.",,cgm/cgm.parquet,Continuous,,,Data,Single,10K,tabular,2018-12-27,float -hbgi,HBGI,High Blood Glucose Index (HBGI),,cgm/cgm.parquet,Continuous,,,Data,Single,10K,tabular,2018-12-27,float -hyper_index,Hyper index,"This is a weighted average of hyperglycemic values, with larger penalties for more extreme values.",,cgm/cgm.parquet,Continuous,,,Data,Single,10K,tabular,2018-12-27,float -hypo_index,Hypo index,"This is a weighted average of hypoglycemic values, with progressively larger penalties for more extreme values",,cgm/cgm.parquet,Continuous,,,Data,Single,10K,tabular,2018-12-27,float -igc,IGC,"Index of Glycemic Control (IGC), a sum of the hypoglycemia and hyperglycemia indexes. This index was shown to be highly correlated with percentage of time in target range.",,cgm/cgm.parquet,Continuous,,,Data,Single,10K,tabular,2018-12-27,float -iqr,IQR,"Interquartile range (IQR), calculated as the distance between the 25th percentile and the 75th percentile of the glucose values.",,cgm/cgm.parquet,Continuous,mg/dl,,Data,Single,10K,tabular,2018-12-27,float -j_index,J-index,This index was designed to stress the importance of the two major glycaemia components: the mean level and the variability of glycaemia.,,cgm/cgm.parquet,Continuous,,,Data,Single,10K,tabular,2018-12-27,float -lbgi,LBGI,Low Blood Glucose Index (LBGI),,cgm/cgm.parquet,Continuous,,,Data,Single,10K,tabular,2018-12-27,float -m_value,M value,The M-value is an index aimed to describe the glycemic control of an individual.,,cgm/cgm.parquet,Continuous,,,Data,Single,10K,tabular,2018-12-27,float -mad,MAD,"Median Absolute Deviation (MAD). This is a measure of glycemic variability, which is calculated by taking the median of the differences between glucose values and their median.",,cgm/cgm.parquet,Continuous,mg/dl,,Data,Single,10K,tabular,2018-12-27,float -mag,MAG,Mean Absolute Glucose (MAG). This is a measure of glycemic variability that’s meant to be less dependent on the mean glucose value and to take into account all variability over time.,,cgm/cgm.parquet,Continuous,mg/dl,,Data,Single,10K,tabular,2018-12-27,float -mage,MAGE,"Mean Amplitude of Glycemic Excursions (MAGE), an index for glycemic variability. This index is focused on the amplitude of blood glucose changes, and as such it takes into account only changes in the blood glucose (either upwards or downwards) that are large enough to be considered as significant responses.",,cgm/cgm.parquet,Continuous,mg/dl,,Data,Single,10K,tabular,2018-12-27,float -min_,Min,Minimum of all glucose values.,,cgm/cgm.parquet,Continuous,mg/dl,,Data,Single,10K,tabular,2018-12-27,float -max_,Max,Maximum of all glucose values.,,cgm/cgm.parquet,Continuous,mg/dl,,Data,Single,10K,tabular,2018-12-27,float -mean,Mean,Mean of all glucose values.,,cgm/cgm.parquet,Continuous,mg/dl,,Data,Single,10K,tabular,2018-12-27,float -median,Median,Median of all glucose values.,,cgm/cgm.parquet,Continuous,mg/dl,,Data,Single,10K,tabular,2018-12-27,float -modd,MODD,"Mean difference between glucose values obtained at the same time of day (MODD). This is a measure of glycemic variability that is calculated by taking the mean of the absolute differences between glucose values measured at the same time, a day apart.",,cgm/cgm.parquet,Continuous,mg/dl,,Data,Single,10K,tabular,2018-12-27,float -range,Range,Difference between the maximum and minimum glucose values of the individual.,,cgm/cgm.parquet,Continuous,mg/dl,,Data,Single,10K,tabular,2018-12-27,float -sd,SD,Standard deviation of all glucose values.,,cgm/cgm.parquet,Continuous,mg/dl,,Data,Single,10K,tabular,2018-12-27,float -sd_roc,SD.Roc,Standard deviation of all the rate of change (ROC) values of an individual. ROC is calculated between every two consecutive glucose measures.,,cgm/cgm.parquet,Continuous,,,Data,Single,10K,tabular,2018-12-27,float -sdb,SDb,"SD between days, within time points. Mean value of the standard deviations calculated for values taken at each time point across all days.",,cgm/cgm.parquet,Continuous,mg/dl,,Data,Single,10K,tabular,2018-12-27,float -sdbdm,SDbdm,"SD between days, within time points, corrected for changes in daily means. Calculated by subtracting the daily mean from each glucose value, then calculating the SD of the corrected glucose values across days for each time point, and finally taking the mean of those standard deviations.",,cgm/cgm.parquet,Continuous,mg/dl,,Data,Single,10K,tabular,2018-12-27,float -sddm,SDdm,"Horizontal SD. SD of the mean glucose values, taken for each day separately.",,cgm/cgm.parquet,Continuous,mg/dl,,Data,Single,10K,tabular,2018-12-27,float -sdhhmm,SDhhmm,SD between time points. Standard deviation of the mean glucose value calculated for each time point across all days.,,cgm/cgm.parquet,Continuous,mg/dl,,Data,Single,10K,tabular,2018-12-27,float -sdw,SDw,Vertical SD within days. Average value of the SD values for each day separately.,,cgm/cgm.parquet,Continuous,mg/dl,,Data,Single,10K,tabular,2018-12-27,float -sdwsh,SDwsh,"SD within series. Taking hour-long intervals that start at each point of the glucose measures, the SD of each hour-long window is computed, and then the mean of all those SDs is taken.",,cgm/cgm.parquet,Continuous,mg/dl,,Data,Single,10K,tabular,2018-12-27,float +",,cgm/cgm.parquet,Continuous,,,Data,Single,10K,tabular,2018-12-27,float, +cv_measures_mean,CVmean,Mean of all daily CVs.,,cgm/cgm.parquet,Continuous,,,Data,Single,10K,tabular,2018-12-27,float, +cv_measures_sd,CVsd,Standard deviations of all daily CVs.,,cgm/cgm.parquet,Continuous,,,Data,Single,10K,tabular,2018-12-27,float, +ea1c,eA1C,"A linear transformation of the mean glucose value, meant to estimate the HbA1C blood test. Calculated by the following formula: (46.7 + mean(Glucose))/28.7.",,cgm/cgm.parquet,Continuous,mg/dl,,Data,Single,10K,tabular,2018-12-27,float, +gmi,GMI,"A linear transformation of the mean glucose value, meant to improve the eA1C measure.",,cgm/cgm.parquet,Continuous,mg/dl,,Data,Single,10K,tabular,2018-12-27,float, +grade,GRADE,Glycaemic Risk Assessment Diabetes Equation (GRADE). This clinical risk score aims to evaluate the degree of risk presented by a glucose profile.,,cgm/cgm.parquet,Continuous,,,Data,Single,10K,tabular,2018-12-27,float, +grade_eugly,GRADE eugly,"Percentage of the GRADE score that is attributed to euglycemia (glucose values within the normal range, default is 70-140).",,cgm/cgm.parquet,Continuous,,,Data,Single,10K,tabular,2018-12-27,float, +grade_hyper,GRADE hyper,"Percentage of the GRADE score that is attributed to hyperglycemia (glucose values above a target range, default is 140).",,cgm/cgm.parquet,Continuous,,,Data,Single,10K,tabular,2018-12-27,float, +grade_hypo,GRADE hypo,"Percentage of the GRADE score that is attributed to hypoglycemia (glucose values above a target range, default is 80).",,cgm/cgm.parquet,Continuous,,,Data,Single,10K,tabular,2018-12-27,float, +gvp,GVP,"Glucose Variability Percentage (GVP), which is designed to capture both the amplitude and frequency of glucose oscillations.",,cgm/cgm.parquet,Continuous,,,Data,Single,10K,tabular,2018-12-27,float, +hbgi,HBGI,High Blood Glucose Index (HBGI),,cgm/cgm.parquet,Continuous,,,Data,Single,10K,tabular,2018-12-27,float, +hyper_index,Hyper index,"This is a weighted average of hyperglycemic values, with larger penalties for more extreme values.",,cgm/cgm.parquet,Continuous,,,Data,Single,10K,tabular,2018-12-27,float, +hypo_index,Hypo index,"This is a weighted average of hypoglycemic values, with progressively larger penalties for more extreme values",,cgm/cgm.parquet,Continuous,,,Data,Single,10K,tabular,2018-12-27,float, +igc,IGC,"Index of Glycemic Control (IGC), a sum of the hypoglycemia and hyperglycemia indexes. This index was shown to be highly correlated with percentage of time in target range.",,cgm/cgm.parquet,Continuous,,,Data,Single,10K,tabular,2018-12-27,float, +iqr,IQR,"Interquartile range (IQR), calculated as the distance between the 25th percentile and the 75th percentile of the glucose values.",,cgm/cgm.parquet,Continuous,mg/dl,,Data,Single,10K,tabular,2018-12-27,float, +j_index,J-index,This index was designed to stress the importance of the two major glycaemia components: the mean level and the variability of glycaemia.,,cgm/cgm.parquet,Continuous,,,Data,Single,10K,tabular,2018-12-27,float, +lbgi,LBGI,Low Blood Glucose Index (LBGI),,cgm/cgm.parquet,Continuous,,,Data,Single,10K,tabular,2018-12-27,float, +m_value,M value,The M-value is an index aimed to describe the glycemic control of an individual.,,cgm/cgm.parquet,Continuous,,,Data,Single,10K,tabular,2018-12-27,float, +mad,MAD,"Median Absolute Deviation (MAD). This is a measure of glycemic variability, which is calculated by taking the median of the differences between glucose values and their median.",,cgm/cgm.parquet,Continuous,mg/dl,,Data,Single,10K,tabular,2018-12-27,float, +mag,MAG,Mean Absolute Glucose (MAG). This is a measure of glycemic variability that’s meant to be less dependent on the mean glucose value and to take into account all variability over time.,,cgm/cgm.parquet,Continuous,mg/dl,,Data,Single,10K,tabular,2018-12-27,float, +mage,MAGE,"Mean Amplitude of Glycemic Excursions (MAGE), an index for glycemic variability. This index is focused on the amplitude of blood glucose changes, and as such it takes into account only changes in the blood glucose (either upwards or downwards) that are large enough to be considered as significant responses.",,cgm/cgm.parquet,Continuous,mg/dl,,Data,Single,10K,tabular,2018-12-27,float, +min_,Min,Minimum of all glucose values.,,cgm/cgm.parquet,Continuous,mg/dl,,Data,Single,10K,tabular,2018-12-27,float, +max_,Max,Maximum of all glucose values.,,cgm/cgm.parquet,Continuous,mg/dl,,Data,Single,10K,tabular,2018-12-27,float, +mean,Mean,Mean of all glucose values.,,cgm/cgm.parquet,Continuous,mg/dl,,Data,Single,10K,tabular,2018-12-27,float, +median,Median,Median of all glucose values.,,cgm/cgm.parquet,Continuous,mg/dl,,Data,Single,10K,tabular,2018-12-27,float, +modd,MODD,"Mean difference between glucose values obtained at the same time of day (MODD). This is a measure of glycemic variability that is calculated by taking the mean of the absolute differences between glucose values measured at the same time, a day apart.",,cgm/cgm.parquet,Continuous,mg/dl,,Data,Single,10K,tabular,2018-12-27,float, +range,Range,Difference between the maximum and minimum glucose values of the individual.,,cgm/cgm.parquet,Continuous,mg/dl,,Data,Single,10K,tabular,2018-12-27,float, +sd,SD,Standard deviation of all glucose values.,,cgm/cgm.parquet,Continuous,mg/dl,,Data,Single,10K,tabular,2018-12-27,float, +sd_roc,SD.Roc,Standard deviation of all the rate of change (ROC) values of an individual. ROC is calculated between every two consecutive glucose measures.,,cgm/cgm.parquet,Continuous,,,Data,Single,10K,tabular,2018-12-27,float, +sdb,SDb,"SD between days, within time points. Mean value of the standard deviations calculated for values taken at each time point across all days.",,cgm/cgm.parquet,Continuous,mg/dl,,Data,Single,10K,tabular,2018-12-27,float, +sdbdm,SDbdm,"SD between days, within time points, corrected for changes in daily means. Calculated by subtracting the daily mean from each glucose value, then calculating the SD of the corrected glucose values across days for each time point, and finally taking the mean of those standard deviations.",,cgm/cgm.parquet,Continuous,mg/dl,,Data,Single,10K,tabular,2018-12-27,float, +sddm,SDdm,"Horizontal SD. SD of the mean glucose values, taken for each day separately.",,cgm/cgm.parquet,Continuous,mg/dl,,Data,Single,10K,tabular,2018-12-27,float, +sdhhmm,SDhhmm,SD between time points. Standard deviation of the mean glucose value calculated for each time point across all days.,,cgm/cgm.parquet,Continuous,mg/dl,,Data,Single,10K,tabular,2018-12-27,float, +sdw,SDw,Vertical SD within days. Average value of the SD values for each day separately.,,cgm/cgm.parquet,Continuous,mg/dl,,Data,Single,10K,tabular,2018-12-27,float, +sdwsh,SDwsh,"SD within series. Taking hour-long intervals that start at each point of the glucose measures, the SD of each hour-long window is computed, and then the mean of all those SDs is taken.",,cgm/cgm.parquet,Continuous,mg/dl,,Data,Single,10K,tabular,2018-12-27,float, diff --git a/nbs/examples/diet_logging/metadata/diet_logging_data_dictionary.csv b/nbs/examples/diet_logging/metadata/diet_logging_data_dictionary.csv index fd2ea07..8e961ec 100644 --- a/nbs/examples/diet_logging/metadata/diet_logging_data_dictionary.csv +++ b/nbs/examples/diet_logging/metadata/diet_logging_data_dictionary.csv @@ -1,19 +1,19 @@ -tabular_field_name,field_string,description_string,parent_dataframe,relative_location,value_type,units,field_type,array,cohorts,data_type,debut,pandas_dtype,sampling_rate -collection_timestamp,Collection timestamp,Collection timestamp,,diet_logging/diet_logging.parquet,Time,Time,Data,Single,10K,Time Series,2019-01-29,"datetime64[ns, Asia/Jerusalem]", -collection_date,Date,Datetime column relecting the time food item was logged,,diet_logging/diet_logging.parquet,Time,Time,Data,Single,10K,Time Series,2019-09-01,datetime64[ns], -food_id,Food ID,IDs in the diet logging app representing specific food ,,diet_logging/diet_logging.parquet,Categorical (single) ,None,Data,Single,10K,Time Series,2019-09-01,integer, -logging_day,Logging day per participant,Integer indicating which day of logging period ,,diet_logging/diet_logging.parquet,Integer,None ,Data,Single,10K,Time Series,2019-09-01,float, -weight,Weight,Weight of food item logged,,diet_logging/diet_logging.parquet,Continuous,g,Data,Single,10K,Time Series,2019-09-01,float, -short_food_name,Short food name,Classifcation of food item logged into a short food name category,,diet_logging/diet_logging.parquet,Categorical (single) ,None,Data,Single,10K,Time Series,2019-09-01,object, -food_category,Food category,Classifcation of food item logged into a food category,,diet_logging/diet_logging.parquet,Categorical (single) ,None,Data,Single,10K,Time Series,2019-09-01,object, -product_name,Product name ,Product name of food logged,,diet_logging/diet_logging.parquet,Categorical (single) ,None,Data,Single,10K,Time Series,2019-09-01,object, -calories,Calories,Calories of food item logged,,diet_logging/diet_logging.parquet,Continuous,kcal,Data,Single,10K,Time Series,2019-09-01,float, -carbohydrate_g,Carbohydrate intake per food logged,Carbohydrate intake per food logged,,diet_logging/diet_logging.parquet,Continuous,g,Data,Single,10K,Time Series,2019-09-01,float, -llipid_g,Fat intake per food logged,Fat intake per food logged,,diet_logging/diet_logging.parquet,Continuous,g,Data,Single,10K,Time Series,2019-09-01,float, -protein_g,Protein intake per food logged,Protein intake per food logged,,diet_logging/diet_logging.parquet,Continuous,g,Data,Single,10K,Time Series,2019-09-01,float, -sodium_mg ,Sodium intake per food logged,Sodium intake per food logged,,diet_logging/diet_logging.parquet,Continuous,mg,Data,Single,10K,Time Series,2019-09-01,float, -alcohol_g ,Alcohol intake per food logged,Alcohol intake per food logged,,diet_logging/diet_logging.parquet,Continuous,g,Data,Single,10K,Time Series,2019-09-01,float, -dietary_fiber_g,Dietary fiber intake per food logged,Dietary fiber intake per food logged,,diet_logging/diet_logging.parquet,Continuous,g,Data,Single,10K,Time Series,2019-09-01,float, -local_timestamp,Local timestamp,Local timestamp of food logging,,diet_logging/diet_logging.parquet,Time,Time,Data,Single,10K,Time Series,2019-09-01,datetime64[ns], -eaten_in_restaurant,Eaten at restaurant indication,Indication if food was eatn at home or at a restaurant,,diet_logging/diet_logging.parquet,Boolean,None,Data,Single,10K,Time Series,2019-09-01,bool, -total_logging_days,Total number of days logged,Total number of days diet was logged per research stage,,diet_logging/diet_logging.parquet,Integer,None,Data,Single,10K,Time Series,2019-09-01,integer, +tabular_field_name,field_string,description_string,parent_dataframe,relative_location,value_type,units,field_type,array,cohorts,data_type,debut,pandas_dtype,sampling_rate,data_coding +collection_timestamp,Collection timestamp,Collection timestamp,,diet_logging/diet_logging.parquet,Time,Time,Data,Single,10K,Time Series,2019-01-29,"datetime64[ns, Asia/Jerusalem]",, +collection_date,Date,Datetime column relecting the time food item was logged,,diet_logging/diet_logging.parquet,Time,Time,Data,Single,10K,Time Series,2019-09-01,datetime64[ns],, +food_id,Food ID,IDs in the diet logging app representing specific food ,,diet_logging/diet_logging.parquet,Categorical (single) ,,Data,Single,10K,Time Series,2019-09-01,integer,, +logging_day,Logging day per participant,Integer indicating which day of logging period ,,diet_logging/diet_logging.parquet,Integer,None ,Data,Single,10K,Time Series,2019-09-01,float,, +weight,Weight,Weight of food item logged,,diet_logging/diet_logging.parquet,Continuous,g,Data,Single,10K,Time Series,2019-09-01,float,, +short_food_name,Short food name,Classifcation of food item logged into a short food name category,,diet_logging/diet_logging.parquet,Categorical (single) ,,Data,Single,10K,Time Series,2019-09-01,object,, +food_category,Food category,Classifcation of food item logged into a food category,,diet_logging/diet_logging.parquet,Categorical (single) ,,Data,Single,10K,Time Series,2019-09-01,object,, +product_name,Product name ,Product name of food logged,,diet_logging/diet_logging.parquet,Categorical (single) ,,Data,Single,10K,Time Series,2019-09-01,object,, +calories,Calories,Calories of food item logged,,diet_logging/diet_logging.parquet,Continuous,kcal,Data,Single,10K,Time Series,2019-09-01,float,, +carbohydrate_g,Carbohydrate intake per food logged,Carbohydrate intake per food logged,,diet_logging/diet_logging.parquet,Continuous,g,Data,Single,10K,Time Series,2019-09-01,float,, +llipid_g,Fat intake per food logged,Fat intake per food logged,,diet_logging/diet_logging.parquet,Continuous,g,Data,Single,10K,Time Series,2019-09-01,float,, +protein_g,Protein intake per food logged,Protein intake per food logged,,diet_logging/diet_logging.parquet,Continuous,g,Data,Single,10K,Time Series,2019-09-01,float,, +sodium_mg ,Sodium intake per food logged,Sodium intake per food logged,,diet_logging/diet_logging.parquet,Continuous,mg,Data,Single,10K,Time Series,2019-09-01,float,, +alcohol_g ,Alcohol intake per food logged,Alcohol intake per food logged,,diet_logging/diet_logging.parquet,Continuous,g,Data,Single,10K,Time Series,2019-09-01,float,, +dietary_fiber_g,Dietary fiber intake per food logged,Dietary fiber intake per food logged,,diet_logging/diet_logging.parquet,Continuous,g,Data,Single,10K,Time Series,2019-09-01,float,, +local_timestamp,Local timestamp,Local timestamp of food logging,,diet_logging/diet_logging.parquet,Time,Time,Data,Single,10K,Time Series,2019-09-01,datetime64[ns],, +eaten_in_restaurant,Eaten at restaurant indication,Indication if food was eatn at home or at a restaurant,,diet_logging/diet_logging.parquet,Boolean,,Data,Single,10K,Time Series,2019-09-01,bool,, +total_logging_days,Total number of days logged,Total number of days diet was logged per research stage,,diet_logging/diet_logging.parquet,Integer,,Data,Single,10K,Time Series,2019-09-01,integer,, diff --git a/nbs/examples/fundus/metadata/fundus_data_dictionary.csv b/nbs/examples/fundus/metadata/fundus_data_dictionary.csv index 9124362..6ba2b30 100644 --- a/nbs/examples/fundus/metadata/fundus_data_dictionary.csv +++ b/nbs/examples/fundus/metadata/fundus_data_dictionary.csv @@ -1,6 +1,6 @@ -tabular_field_name,field_string,description_string,parent_dataframe,relative_location,value_type,units,sampling_rate,field_type,array,cohorts,data_type,debut,pandas_dtype -fundus_image_left,Fundus image (left),Fundus image (left),,fundus/fundus.parquet,Text ,None,,Image file (individual),Single,10K,image,2021-02-17,string -fundus_image_right,Fundus image (right),Fundus image (right),,fundus/fundus.parquet,Text ,None,,Image file (individual),Single,10K,image,2021-02-17,string -collection_date,Collection date (YYYY-MM-DD),Collection date (YYYY-MM-DD),,fundus/fundus.parquet,Date,Time,,Data,Single,10K,tabular,2021-02-17,datetime64[ns] -fractal_dimension_left,Fractal dimension (left),Fractal dimension (left),,fundus/fundus.parquet,Continuous,Time,,Data,Single,10K,tabular,2021-02-17,float -fractal_dimension_right,Fractal dimension (right),Fractal dimension (right),,fundus/fundus.parquet,Continuous,Time,,Data,Single,10K,tabular,2021-02-17,float \ No newline at end of file +tabular_field_name,field_string,description_string,parent_dataframe,relative_location,value_type,units,sampling_rate,field_type,array,cohorts,data_type,debut,pandas_dtype,data_coding +fundus_image_left,Fundus image (left),Fundus image (left),,fundus/fundus.parquet,Text ,,,Image file (individual),Single,10K,image,2021-02-17,string, +fundus_image_right,Fundus image (right),Fundus image (right),,fundus/fundus.parquet,Text ,,,Image file (individual),Single,10K,image,2021-02-17,string, +collection_date,Collection date (YYYY-MM-DD),Collection date (YYYY-MM-DD),,fundus/fundus.parquet,Date,Time,,Data,Single,10K,tabular,2021-02-17,datetime64[ns], +fractal_dimension_left,Fractal dimension (left),Fractal dimension (left),,fundus/fundus.parquet,Continuous,Time,,Data,Single,10K,tabular,2021-02-17,float, +fractal_dimension_right,Fractal dimension (right),Fractal dimension (right),,fundus/fundus.parquet,Continuous,Time,,Data,Single,10K,tabular,2021-02-17,float, diff --git a/nbs/examples/sleep/metadata/sleep_data_dictionary.csv b/nbs/examples/sleep/metadata/sleep_data_dictionary.csv index f5d6693..f0bd17e 100644 --- a/nbs/examples/sleep/metadata/sleep_data_dictionary.csv +++ b/nbs/examples/sleep/metadata/sleep_data_dictionary.csv @@ -1,3 +1,3 @@ -tabular_field_name,field_string,description_string,parent_dataframe,relative_location,value_type,units,sampling_rate,item_type,array,cohorts,field_type,debut,pandas_dtype -ahi,AHI,AHI (Apnea-Hypopnea Index),,sleep/sleep.parquet,Continuous,Events / Hour,,Data,Multiple,10K,Continuous,2020-01-15,float64 -total_sleep_time,Total sleep time,Total sleep time,,sleep/sleep.parquet,Integer,Seconds,,Data,Multiple,10K,Continuous,2020-01-15,float64 +tabular_field_name,field_string,description_string,parent_dataframe,relative_location,value_type,units,sampling_rate,item_type,array,cohorts,field_type,debut,pandas_dtype,data_coding +ahi,AHI,AHI (Apnea-Hypopnea Index),,sleep/sleep.parquet,Continuous,Events / Hour,,Data,Multiple,10K,Continuous,2020-01-15,float64, +total_sleep_time,Total sleep time,Total sleep time,,sleep/sleep.parquet,Integer,Seconds,,Data,Multiple,10K,Continuous,2020-01-15,float64, diff --git a/pheno_utils/questionnaires_handler.py b/pheno_utils/questionnaires_handler.py index 0cce268..cadfd76 100644 --- a/pheno_utils/questionnaires_handler.py +++ b/pheno_utils/questionnaires_handler.py @@ -2,7 +2,7 @@ # %% auto 0 __all__ = ['valid_codings', 'convert_to_string', 'normalize_answers', 'flatten_series', 'check_invalid_values', 'replace_values', - 'transform_answers', 'transform_dataframe'] + 'transform_answers', 'convert_codings_to_int', 'transform_dataframe'] # %% ../nbs/13_questionnaire_handler.ipynb 3 import pandas as pd @@ -147,41 +147,54 @@ def transform_answers( else: code_string = convert_to_string(dict_df.loc[tab_field_name]["data_coding"]) - #getting the data coding df from the large data coding csv + # Getting the data coding df from the large data coding csv code_df = mapping_df[mapping_df["code_number"] == code_string].copy() - #Make sure no leading 0s for coding values + # Make sure no leading 0s for coding values code_df["coding"] = code_df["coding"].apply(convert_to_string) mapping_dict = dict(zip(code_df[code_from].astype(str), code_df[code_to])) - #adding fail safe incase older dictionaries don't have field type : TODO potentaily remove once older dictionaires are updated - if 'field_type' in dict_df.columns: - field_type = dict_df.loc[tab_field_name]['field_type'] - if isinstance(field_type, pd.Series): - if field_type.nunique() == 1: - field_type = field_type.iloc[0] - else: - warnings.warn(f"tabular field {tab_field_name} is used in 2 columns and have conflicting field types,please check and update dictionary. This field has not be converted.") - return orig_answer - - if field_type == 'Categorical (multiple)': - normalise_answer = normalize_answers(orig_answer, field_type) - check_invalid_values(normalise_answer , code_df) - transformed_answer = normalise_answer.apply(replace_values, mapping_dict = mapping_dict) + field_type = dict_df.loc[tab_field_name]['field_type'] + if isinstance(field_type, pd.Series): + if field_type.nunique() == 1: + field_type = field_type.iloc[0] else: - #if categorical single - normalized_answer = normalize_answers(orig_answer, field_type) - check_invalid_values(normalized_answer, code_df) - transformed_answer = normalized_answer.replace(mapping_dict) - transformed_answer = transformed_answer.astype("category") - - return transformed_answer + warnings.warn(f"tabular field {tab_field_name} is used in 2 columns and have conflicting field types,please check and update dictionary. This field has not be converted.") + return orig_answer + + if field_type == 'Categorical (multiple)': + normalise_answer = normalize_answers(orig_answer, field_type) + check_invalid_values(normalise_answer , code_df) + transformed_answer = normalise_answer.apply(replace_values, mapping_dict = mapping_dict) else: - return orig_answer - + normalized_answer = normalize_answers(orig_answer, field_type) + check_invalid_values(normalized_answer, code_df) + transformed_answer = normalized_answer.replace(mapping_dict) + transformed_answer = transformed_answer.astype("category") + return transformed_answer + +def convert_codings_to_int(df: pd.DataFrame, dict_df: pd.DataFrame, fields_for_translation: list, preferred_language: str) -> pd.DataFrame: + df_coding = df.copy() + if preferred_language == 'coding': + for column in fields_for_translation: + data_coding = dict_df.loc[column, 'data_coding'] # This actually should happen + if pd.notna(data_coding): + if dict_df.loc[column, 'array'] == 'Multiple': + try: + df_coding[column] = df[column].apply(lambda x : pd.Series(x).astype('Int16') if isinstance(x, np.ndarray) else x) + except: + print(column) + else: + df_coding[column] = df[column].astype('Int16') + dict_df.loc[column, 'pandas_dtype'] = 'Int16' + + return df_coding + + + def transform_dataframe( df: pd.DataFrame, transform_from: str, @@ -189,12 +202,20 @@ def transform_dataframe( dict_df: pd.DataFrame, mapping_df: pd.DataFrame, ) -> pd.DataFrame: - if 'data_coding' not in dict_df.columns or transform_from == transform_to: - return df + # Only fields with a code in data_coding property will be transformed fields_for_translation = dict_df[pd.notna(dict_df.data_coding)].index.intersection(df.columns) - if len(fields_for_translation) == 0: + if len(fields_for_translation) == 0: # No fields with data_coding code + return df + + if transform_from == transform_to: + df = convert_codings_to_int(df=df, dict_df=dict_df, fields_for_translation=fields_for_translation, + preferred_language=transform_to) + + print('#1', df.info()) return df + + transformed_df = df.copy() for column in fields_for_translation: data_coding = dict_df.loc[column, 'data_coding'] @@ -211,4 +232,8 @@ def transform_dataframe( dict_df, mapping_df ) + + transformed_df = convert_codings_to_int(df=transformed_df, dict_df=dict_df, fields_for_translation=fields_for_translation, + preferred_language=transform_to) + print('#2', df.info()) return transformed_df From ded1adc52bee03ad852ddcb3595c7fcbc6c46f32 Mon Sep 17 00:00:00 2001 From: Maria Gorodetski Date: Tue, 3 Sep 2024 08:43:23 +0000 Subject: [PATCH 02/11] suport for non multiple array --- nbs/01_basic_plots.ipynb | 22 ++++++++-------- nbs/13_questionnaire_handler.ipynb | 38 ++++++++------------------- pheno_utils/basic_plots.py | 2 +- pheno_utils/questionnaires_handler.py | 19 +++++++------- 4 files changed, 33 insertions(+), 48 deletions(-) diff --git a/nbs/01_basic_plots.ipynb b/nbs/01_basic_plots.ipynb index 27ff860..45b4969 100644 --- a/nbs/01_basic_plots.ipynb +++ b/nbs/01_basic_plots.ipynb @@ -99,7 +99,7 @@ " raise ValueError(f\"Column '{gender_col}' does not exist in the DataFrame\")\n", " \n", " # Check the data type of the gender column\n", - " if df[gender_col].dtype == 'int64':\n", + " if pd.api.types.is_integer_dtype(df[gender_col]):\n", " if gender == 'male':\n", " indices = df[df[gender_col] == 1].index\n", " else:\n", @@ -206,7 +206,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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", + "image/png": 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", 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" ] @@ -300,7 +300,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -320,7 +320,7 @@ "outputs": [ { "data": { - "image/png": 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33Yeff/5Zbwnkgw8+gJubG4YNG4by8nIkJibi3//+t9mv9d///hcTJ060Kl76G1NmONiTw6bMSTi4Hda5qQSxfYlOqri4GAEBAdBoNPD399cbKysrQ3Z2NmJiYnhseD0nCALi4uIwdepU3YyDozt79iz69euHCxcuICAgwOA9/DNqptoZDsHwdm4AbHNuY6YmHG4qFR7r2pwJh5Njnw5ySCqVCp988glu375t71Bkk5OTgzVr1ogmHGSB81ukEw62ObcpUxOOtqF+TDhcBJdXyGF16dLFbofa2cKdzcVIBsbOVQF4JL0NmdLOnEfNux4mHUTkfIzVcajcmHDY2NlrGslxJhyuicsrRORcTNmpwoTDptQFWsk+HEw4XBdnOojIueSelq7j6PwEEw4bMlbHwYTDtXGmg4ici9iZKgDPVbExUwpHmXC4NiYdROQ8CrPFx7hTxaZMSTjahvopFA3VV1xeISLHV5hds6wiNcvBOg6bMWWniptKhQ7h3A7u6ph0EJFjM3ZqLFBzpgoTDptQF2ix8cQVyXt4LD3V4vIKSRozZozk+SZyKCgoQHBwMC5dumTT11HSE088gffff9/eYTg/U06NBXimio3UHk8vpWdMEzzTpxUTDgLApIPqgXfeeQePPvoooqOjAQCXLl2CSqVCcHAwbt7U33bXpUsXvPXWW1a/ZkFBASIiIqBSqVBUVKQ3tmzZMrRr1w4+Pj5o06YN1qxZU+fxRUVFmDRpEsLCwuDl5YW7774b27dv142/+eabeOedd6DRSPcqICuYcqYKwOJRG6md4aiWOEmDO1Xo77i8ImH3uTwU3Cq3awxNG3nhofYhdo3BlrRaLT777DPs2rWrztjNmzfx3nvvYc6cObK/7vjx4xEbG4urV/X/l7Z8+XKkpKRg5cqVuOeee3DkyBFMnDgRjRs3xuDBNUWIFRUVeOihhxAcHIwNGzagefPmuHz5st5psR07dkSrVq3w5ZdfYtKkSbLH7/JMWVIBWDxqI6bMcPh6uTPhoDo40yGh4FY5cjRldv0yNel54IEHMHnyZLz88sto3LgxQkJCsHLlSpSUlGDs2LHw8/ND69atsWPHDt1jqqqqMH78eMTExOj+V79kyRLJ16murkZqaqruMZ07d8aGDRt043/++SdGjhyJZs2awcfHB3fddRc+//xz0efbvn07vLy8dEfA32ny5MlYtGgRrl+/btLvgamWL1+OoqIiTJs2rc7Yf/7zHzz77LMYMWIEWrZsiSeeeALPPPMMFixYoLtn1apVKCwsxObNm9G7d29ER0ejb9++6Ny5s95zDR48GOvWmfA/cTKPqQlHSHsWj9rIjjM5kjMcbioVBnQIUzAichRMOpzIF198gaCgIBw5cgSTJ0/G888/j//7v/9Dr169cOLECfTv3x9PP/00tNqa8yiqq6sRERGBb775BufOncOsWbMwY8YMrF8v3s0xNTUVa9aswYoVK3D27FlMnToVTz31FPbv3w8AmDlzJs6dO4cdO3bg/PnzWL58OYKCxP+3c+DAAXTv3t3gWFJSElq3bo25c+eKPv65555Do0aNJL/udO7cOcydOxdr1qyBm1vdP/7l5eV1Tm/18fHBkSNHUFlZCQDYsmUL4uPjMWnSJISEhKBjx46YN28eqqqq9B7Xs2dPHDlyBOXl9p0tcyqmJByevsA/UoD2jzLhsAF1gRbaiirJe3h4G4nh8ooT6dy5M958800AQEpKCubPn4+goCBMnDgRADBr1iwsX74cp0+fxr333gsPDw+9pYuYmBikp6dj/fr1GD58eJ3nLy8vx7x58/DDDz8gPj4eANCyZUscPHgQH3/8Mfr27Qu1Wo2uXbuiR48eAKCr0xBz+fJlhIeHGxxTqVSYP38+Bg8ejKlTp6JVq1Z17pk7d67BGQtDysvLkZSUhIULF6JFixb4/fff69yTmJiITz/9FEOGDEG3bt1w/PhxfPrpp6isrER+fj7CwsLw+++/Y8+ePRg5ciS2b9+Oixcv4oUXXkBlZSVmz56te67w8HBUVFQgNzcXUVFRJsVIEkwpGuVyis3tOJMjOsbj6ckYJh1OJDb2rwp9d3d3NG3aFJ06ddJdCwmpqQ25c7li2bJlWLVqFdRqNUpLS1FRUSF6cuvFixeh1Wrx0EMP6V2vqKhA165dAQDPP/88hg0bpptZGTJkCHr16iUac2lpaZ2ZhTslJibivvvuw8yZM7F27do648HBwQgODhZ9/J1SUlLQrl07PPXUU6L3zJw5E7m5ubj33nshCAJCQkIwevRovPvuu7qZkerqagQHB+OTTz6Bu7s7unfvjqtXr2LhwoV6SYePjw8A6GaWyErnt0iPe/rWJByc3bAZY7McTDjIGCYdEpo28rJ3CGbF4OHhofe9SqXSu6ZSqQDU/NAEgHXr1mHatGl4//33ER8fDz8/PyxcuBCHDx82+Py3bt0CAGzbtg3NmzfXG/Pyqolz4MCBuHz5MrZv347du3fjwQcfxKRJk/Dee+8ZfM6goCD8+eefku9r/vz5iI+Px6uvvlpn7LnnnsOXX34p+fjauPfs2YNff/1VV4Mi/P816aCgILzxxhuYM2cOfHx8sGrVKnz88cfIy8tDWFgYPvnkE/j5+aFZs2YAgLCwMHh4eMDd3V33Gu3atUNubi4qKirg6ekJACgsrGmWVPs4soKxY+qj4oGWDygWjisy1o9jWLcIJhxkFJMOCc68awQAfvrpJ/Tq1QsvvPCC7lpWVpbo/e3bt4eXlxfUajX69u0rel+zZs0wevRojB49Gvfffz9effVV0aSja9euRpOGnj17YujQoXj99dfrjJmzvLJx40aUlpbqvj969CjGjRuHAwcO1Fm68fDwQEREBICa5Oyf//ynbqajd+/eWLt2Laqrq3XXLly4gLCwMF3CAQBnzpxBRESEZE0LmUhqloMJh80Za3Hu6+XOhINMwqTDhd11111Ys2YNdu3ahZiYGPznP//B0aNHERNjeHraz88P06ZNw9SpU1FdXY377rsPGo0GP/30E/z9/TF69GjMmjUL3bt3R4cOHVBeXo6tW7eiXbt2ojEkJiYiJSUFf/75Jxo3bix63zvvvIMOHTqgQQP9P7LmLK/8PbHIz88HUDNLUbvd9cKFCzhy5Aji4uLw559/YtGiRThz5gy++OIL3eOef/55LF26FFOmTMHkyZORmZmJefPm4aWXXtJ7/gMHDqB///4mxUYSsvaKz3J4+jLhsDFTzlThThUyFXevuLBnn30WQ4cOxYgRIxAXF4eCggK9WQ9D3n77bcycOROpqalo164dBgwYgG3btukSFU9PT6SkpCA2NhZ9+vSBu7u75LbRTp06oVu3bpI7ZgDg7rvvxrhx41BWVmb+GzVDVVUV3n//fXTu3BkPPfQQysrKcOjQIb2C2MjISOzatQtHjx5FbGwsXnrpJUyZMkVvJqasrAybN2/WFfGShYwVj7Jo1KZMOVOFsxxkDpUgSGy2dkLFxcUICAiARqOBv7+/3lhZWRmys7MRExMjWdxI8tq2bRteffVVnDlzxuA2Vke0fPlybNq0Cf/73/9kfV6X+zP60xLpWY7eLxkeI1l8vD9LsnCUu1XIXFxeIbsbNGgQMjMzcfXqVURGRto7HFl4eHjgo48+sncYjk1qWQXgLIeNHczMl0w4eIgbWYJJB9ULL7/8sr1DkNWECRPsHYJjM9YELCqeW2NtyNiyCs9UIUs5x1w2ETkPY3UcLB61KWNbY3mmClmDSQcR1S/GmoBxWcVmTDnIjTtVyBpcXjHAxWpryYE4/Z9NU5qAcVnFJozNcAA1yyqs4SBrMOm4Q233Tq1Wq2thTVSf1LZU/3v3WaeRe1p8jE3AbMaUXhxcViE5MOm4g7u7OwIDA3VnkzRs2FDXOpzIngRBgFarxfXr1xEYGKjXgt1pFGYDeecMj7GOw2ZM6cXBo+pJLkw6/iY0NBSA/qFoRPVFYGCg7s+oUzG2W4V1HLJTF2hx9poGv+XeNHove3GQXJh0/I1KpUJYWBiCg4NRWVlp73CIdP5+yJzTMOXIetZxyKq2YLTaSI0Qm3+R3Jh0iHB3d3fOf+CJ6hupOg4ACGmvTBwuZMeZHKMJB5t/kS0w6SAi+yotEh9TuQGhsYqF4gqMdRoFahKOZ/q0kryHyBJMOojIfgqzgeJrhsc8fWtqObi0IhsWjZK92b052LJlyxAdHQ1vb2/ExcXhyJEjkvcvXrwYbdq0gY+PDyIjIzF16lSbnzxKRDZQmA2cljhduPdLTDhkdvaaRnK8bagfazjIpuw60/H1118jOTkZK1asQFxcHBYvXozExERkZGQgODi4zv1r167F66+/jlWrVqFXr164cOECxowZA5VKhUWLFtnhHRCRxc5vAYRqw2P+4crG4iKkdqoM6xbBZINszq4zHYsWLcLEiRMxduxYtG/fHitWrEDDhg2xatUqg/cfOnQIvXv3xpNPPono6Gj0798fSUlJkrMj5eXlKC4u1vsiIjszdoKsT6BiobgKdYH47zc7jZJS7JZ0VFRU4Pjx40hISPgrGDc3JCQkID093eBjevXqhePHj+uSjN9//x3bt2/Hww8/LPo6qampCAgI0H05y9HpRA7L2BZZFo/ahNTSCjuNklLstrySn5+PqqoqhISE6F0PCQnBb7/9ZvAxTz75JPLz83HfffdBEATcvn0bzz33HGbMmCH6OikpKUhOTtZ9X1xczMSDyF4Ks4Ff1knfEzuctRw2ILa00jbUT+FIyJXZvZDUHPv27cO8efPw73//GydOnMC3336Lbdu24e233xZ9jJeXF/z9/fW+iMgOsvYaTzg6P8GEwwakllY6hAcoGAm5OrvNdAQFBcHd3R15eXl61/Py8kTbPM+cORNPP/00JkyYAADo1KkTSkpK8Mwzz+CNN96Am5tD5VBErsNYm3OAJ8jaiLHTY1nLQUqy209pT09PdO/eHWlpabpr1dXVSEtLQ3x8vMHHaLXaOolFbddQpz/ym8hRmZJw8EA3m6htdy6GSyukNLtumU1OTsbo0aPRo0cP9OzZE4sXL0ZJSQnGjh0LABg1ahSaN2+O1NRUAMDgwYOxaNEidO3aFXFxcbh48SJmzpyJwYMHs2U5UX1kSsKhcuOBbjZy9ppGst05l1ZIaXZNOkaMGIEbN25g1qxZyM3NRZcuXbBz505dcalardab2XjzzTehUqnw5ptv4urVq2jWrBkGDx6Md955x15vgYjEmHKQG7uO2pRUXw5fL3curZDiVIKLrUsUFxcjICAAGo2GRaVEtvTTEuleHFHxXFKxoYOZ+aItz3l6LNkLKy+JSH6F2Uw47MjYGStMOMhemHQQkfzObxEfY8Jhc1KNwNqG+jHhILth0kFE8pJqcc5dKoqQquVg8SjZE5MOIpKPseJR7lKxuYOZ+aJjPGOF7I1JBxHJR2pZxdOXu1RszFgtB89YIXtj0kFE8jBWPMpZDpvbcSZHdIyNwKg+YNJBRPIwVjzKWQ6bUhdooa2oEh1nLQfVB0w6iMh6UrMcLB5VhNSOFdZyUH3BpIOIrGPsuHouqyhCbMeKr5c7azmo3mDSQUSWK8wGTq8XH2fxqCKkjq4f0CFMwUiIpNn17BUicmDGZjgAznIoRGpphcsqVJ9wpoOIzGdshgPgLIeCxJZWuGOF6hsmHURkvvNbAKFafJzH1StGqhkYd6xQfcOkg4jMY6wfBwDEDucshwKMNQPj0grVN6zpICLz5J4WH1O5MeFQEJuBkaPhTAcRmSfvnPgYEw7FsBkYOSImHURkusJs8bHOTzDhUBCbgZEjYtJBRKaTWlphwqEoNgMjR8Skg4hMV1pk+HpIe0XDcHVsBkaOikkHEZmu+Jrh66Gxysbh4qQKSLmsQvUZkw4iMo1UPQeXVhQjVUDKHStU3zHpICLTiNVz+IcrG4eLk5rl4I4Vqu+YdBCRcYXZ4ltlfQIVDcWVSc1y+Hq5c2mF6j0mHUQkzdg5K6znUIzULAcLSMkRMOkgImnGzllhPYciOMtBzoBJBxGJM3bOCrfKKoazHOQMmHQQkThj56xwaUURnOUgZ8Gkg4jEiTUDA3jOioI4y0HOgqfMEpH5eM6KzakLtDh7TSPa7hzgLAc5HiYdRCROrAMpEw6bUhdosenkVVQLguR9nOUgR8Okg4gME+tAymZgNqUu0GLjiStG7+MsBzki1nQQkWFSRaRkE7UzHKbgLAc5IiYdRGQYO5Aq7uw1jdElFQDoGdOEsxzkkLi8QkR1SR3uxm2yNiNVNArUHOjWITyACQc5LCYdRFSX1NIKi0htQl0g3oTNTaXCY12bM9kgh8flFSKqS2xphR1IbebsNY3oGBMOchZMOohIH5dW7EJsaaVtqB8TDnIaTDqISN/5LeJjXFqxiYOZ+aJjHcIDFIyEyLaYdBDRX6QOeOPSik2oC7Q4eqlQdJyzHORMmHQQ0V+kCki5tGITUrUcbUP9FIyEyPaYdBDRX8QOePP05dKKjWhKK0XHuLRCzoZJBxEZ126wvSNwWjmaMoPX2QCMnBGTDiL6Cw94U5RUb47erYMUjIRIGUw6iKhG1l7D13nAm83sOJNj8HpYgLfCkRApg0kHEdXsWlH/bO8oXMrBzHxoK6oMjgX4eCgcDZEymHQQkXRvDh7wJjtj22RZQErOikkHkauT6s0BcKusDUhtk/X1cmcBKTktJh1Erk5qliMqnkWkNiB1muyADmEKRkKkLCYdRK5MapbD0xdo+YCi4bgCqZbn3CZLzo5JB5Erk5rlYG8O2Rmr5eA2WXJ2dk86li1bhujoaHh7eyMuLg5HjhyRvL+oqAiTJk1CWFgYvLy8cPfdd2P79u0KRUvkRIzNcnBZRXZseU6uroE9X/zrr79GcnIyVqxYgbi4OCxevBiJiYnIyMhAcHBwnfsrKirw0EMPITg4GBs2bEDz5s1x+fJlBAYGKh88kaOTOmeFsxw2wZbn5OrsmnQsWrQIEydOxNixYwEAK1aswLZt27Bq1Sq8/vrrde5ftWoVCgsLcejQIXh41Oxjj46OVjJkIueRd87wdc5y2AxbnpOrsyjpqKqqwurVq5GWlobr16+jurpab3zPnj1Gn6OiogLHjx9HSkqK7pqbmxsSEhKQnp5u8DFbtmxBfHw8Jk2ahO+++w7NmjXDk08+ienTp8Pd3d3gY8rLy1FeXq77vri42JS3SOTcCrPFxzjLYRNseU5kYdIxZcoUrF69GoMGDULHjh2hUqnMfo78/HxUVVUhJCRE73pISAh+++03g4/5/fffsWfPHowcORLbt2/HxYsX8cILL6CyshKzZ882+JjU1FTMmTPH7PiInJrU0gpnOWxCrJ6DLc/JlViUdKxbtw7r16/Hww8/LHc8kqqrqxEcHIxPPvkE7u7u6N69O65evYqFCxeKJh0pKSlITk7WfV9cXIzIyEilQiaqn8SWVkLaKxuHCxGr52DLc3IlFiUdnp6eaN26tVUvHBQUBHd3d+Tl5eldz8vLQ2hoqMHHhIWFwcPDQ28ppV27dsjNzUVFRQU8PT3rPMbLywteXl5WxUrkVKSWVth91GbE6jlYQEquxKIts6+88gqWLFkCQRAsfmFPT090794daWlpumvV1dVIS0tDfHy8wcf07t0bFy9e1KshuXDhAsLCwgwmHERkgFRvDi6t2IRUPQcLSMmVWDTTcfDgQezduxc7duxAhw4ddDtJan377bcmPU9ycjJGjx6NHj16oGfPnli8eDFKSkp0u1lGjRqF5s2bIzU1FQDw/PPPY+nSpZgyZQomT56MzMxMzJs3Dy+99JIlb4PI9Uj15uDSis2wnoOohkVJR2BgIB577DGrX3zEiBG4ceMGZs2ahdzcXHTp0gU7d+7UFZeq1Wq4uf01GRMZGYldu3Zh6tSpiI2NRfPmzTFlyhRMnz7d6liIXILULAeXVmxG7KwV1nOQq1EJ1qyROKDi4mIEBARAo9HA39/f3uEQKSdrL6D+2fCYpy/QmzOGtqAu0GLjiSsGx4Z1i+DyCrkUq5qD3bhxAxkZGQCANm3aoFmzZrIERUQyK8wWTzgA9uawoR1nckTHmHCQq7GokLSkpATjxo1DWFgY+vTpgz59+iA8PBzjx4+HViteMEVEdiK1rMIOpDahLtDi4/1Z0FZUGRznWSvkiixKOpKTk7F//358//33KCoqQlFREb777jvs378fr7zyitwxEpE1svaKF48CnOWwAXWBFptOXhVNOABulSXXZNHyysaNG7FhwwY88MADumsPP/wwfHx8MHz4cCxfvlyu+IjIGsaWVaLiOcthA2evaVAtUS7n6+XOpRVySRbNdGi12jrtywEgODiYyytE9YlUu3NPX6DlA4qF4kqkTpMFgAEdwhSKhKh+sSjpiI+Px+zZs1FW9leHvdLSUsyZM0e0sRcR2UFpkfgYl1VsQl2gFe0+CvBEWXJtFi2vLFmyBImJiYiIiEDnzp0BAL/88gu8vb2xa9cuWQMkIisUXzN8ncsqNlFbyyGGW2TJ1VmUdHTs2BGZmZn46quvdCfCJiUlYeTIkfDx8ZE1QCKykNQZK1xWsQmpWo6wAG8mHOTyLO7T0bBhQ0ycOFHOWIhITmLbZP3DlY3DhUjVcrD7KJEZSceWLVswcOBAeHh4YMsWiT3/AB555BGrAyMiK0idseITqGgorkSslsNNpeIWWSKY0Qbdzc0Nubm5CA4O1jsPpc4TqlSoqhLfm25vbINOLuGnJeJJR+cnWM9hAwcz83H0UqHBMdZyENUweabjzuPk7/w1EdUzUrMc7D5qE+oCrWjCwVoOor9YtGV2zZo1KC8vr3O9oqICa9assTooIrKCVG8ObpO1CanzVVjLQfQXi5KOsWPHQqPR1Ll+8+ZNjB071uqgiMgKYr05OMthE+oCLdudE5nIoqRDEASoVKo6169cuYKAAP4FI7Irsd4cnOWwibPX6v4HrBYbgRHpM2vLbNeuXaFSqaBSqfDggw+iQYO/Hl5VVYXs7GwMGDBA9iCJyERSvTk4y2ETv+XeNHjd18sdvVsHKRwNUf1mVtIxZMgQAMCpU6eQmJiIRo0a6cY8PT0RHR2NYcOGyRogEZmBvTkUpS4QP2uK56sQ1WVW0jF79mxUVVUhOjoa/fv3R1gY/1IR1RvszaE4qQJSLqsQ1WV2TYe7uzueffZZvcPeiKgeEJvlAIDQWOXicBFSBaRtQ/0UjobIMVhUSNqxY0f8/vvvcsdCRJZibw5FqQu02Hjiiug4d6wQGWZR0vGvf/0L06ZNw9atW5GTk4Pi4mK9LyJSmNQsB3etyMrYSbK+Xu5cWiESYdGBbw8//DCAmjNW7tw6W7uVtj63QSdyOpzlUNSOMzmiJ8kCLCAlkmJR0rF371654yAiS7EDqWKMNQLjLAeRNIuSjr59+8odBxFZKu+c4euc5ZCdVCMwN5WKsxxERliUdABAUVERPvvsM5w/fx4A0KFDB4wbN44dSYmUJNUMjLMcshNrBAYAj3VtzlkOIiMsKiQ9duwYWrVqhQ8++ACFhYUoLCzEokWL0KpVK5w4cULuGIlIjNTSCmc5ZHUwM190jEfXE5nGopmOqVOn4pFHHsHKlSt1rdBv376NCRMm4OWXX8aPP/4oa5BEJEJsaSWkvbJxODmpo+sBNgIjMpVFScexY8f0Eg4AaNCgAV577TX06NFDtuCISILU0gqbgclKqpaDjcCITGfR8oq/vz/UanWd63/88Qf8/PgXkEgRXFpRjKa0UnSMjcCITGdR0jFixAiMHz8eX3/9Nf744w/88ccfWLduHSZMmICkpCS5YyQiQ0qLDF/n0opieHQ9kXksWl557733oFKpMGrUKNy+fRsA4OHhgeeffx7z58+XNUAiMhOXVhTDo+uJzGNR0uHp6YklS5YgNTUVWVlZAIBWrVqhYUNm/ESKKb5m+DqXVmSXo6l7wGVYgLcdIiFybBb36QCAhg0bIjAwUPdrIlJIlkhXYP9wZeNwAeoCkRbzRGQ2i2o6bt++jZkzZyIgIADR0dGIjo5GQEAA3nzzTVRWihdcEZEMCrMB9c/2jsJl7DiTY+8QiJyGRTMdkydPxrfffot3330X8fHxAID09HS89dZbKCgowPLly2UNkojuIHWirE+gYmG4AqmzVgJ8PBSOhsjxWZR0rF27FuvWrcPAgQN112JjYxEZGYmkpCQmHUS2krVX/ERZgEWkMpOa5eBWWSLzWbS84uXlhejo6DrXY2Ji4OnpaW1MRGRI1l7pZZWoeBaRykhqloOnyRJZxqKk48UXX8Tbb7+N8vJy3bXy8nK88847ePHFF2ULjoj+P2MJh6cv0PIBxcJxBVJdSHmaLJFlLFpeOXnyJNLS0hAREYHOnTsDAH755RdUVFTgwQcfxNChQ3X3fvvtt/JESuSqTCkc5YmyshM7UZazHESWsyjpCAwMxLBhw/SuRUZGyhIQEf2NVLtzgMsqNiB1oixnOYgsZ1HS8fnnn8sdBxGJEWt3DtQkHFxWkRVPlCWyHauag924cQMZGRkAgDZt2qBZs2ayBEVEdxDrPMqEwyakdqzwRFki61hUSFpSUoJx48YhLCwMffr0QZ8+fRAeHo7x48dDq2X3PiLZSB1fz4RDdgcz80V3rADcJktkLYuSjuTkZOzfvx/ff/89ioqKUFRUhO+++w779+/HK6+8IneMRK5LrJ6D7c5lZ2xZhSfKElnPouWVjRs3YsOGDXjggQd01x5++GH4+Phg+PDhbA5GJBexeg52HpWd1LKKr5c7T5QlkoFFMx1arRYhISF1rgcHB3N5hUhOYvUc7DwqK6lGYAB3rBDJxaKkIz4+HrNnz0ZZ2V/HPZeWlmLOnDm6s1iIyEpS9RzcIisrqUZgXFYhko9FyyuLFy/GgAED6jQH8/b2xq5du2QNkMhliR3sxnoO2WlKDZ+OzWUVInlZlHR06tQJmZmZ+Oqrr/Dbb78BAJKSkjBy5Ej4+PjIGiCRS5I62I31HLLL0ZQZvM5lFSJ5mZ10VFZWom3btti6dSsmTpxoi5iIXJuxc1ZYzyErdYF4HRqXVYjkZXZNh4eHh14tBxHJyNg5K56+rOeQmVg9R1iAt8KREDk/iwpJJ02ahAULFuD27duyBLFs2TJER0fD29sbcXFxOHLkiEmPW7duHVQqFYYMGSJLHER2J1bHUYsHu8lO7GC3AB8PhSMhcn4W1XQcPXoUaWlp+N///odOnTrB19dXb9yck2W//vprJCcnY8WKFYiLi8PixYuRmJiIjIwMBAcHiz7u0qVLmDZtGu6//35L3gJR/VOYLV7HAfBgNxuQOtiN3UeJ5GfRTEftKbOJiYkIDw9HQECA3pc5Fi1ahIkTJ2Ls2LFo3749VqxYgYYNG2LVqlWij6mqqsLIkSMxZ84ctGzZ0pK3QFT/SJ0my3NWZMeD3YiUZ9ZMR3V1NRYuXIgLFy6goqIC/fr1w1tvvWXxjpWKigocP34cKSkpumtubm5ISEhAenq66OPmzp2L4OBgjB8/HgcOHJB8jfLycpSXl+u+Ly4utihWIpsqzAbyzhke8/RlwmEDPNiNSHlmzXS88847mDFjBho1aoTmzZvjww8/xKRJkyx+8fz8fFRVVdXpbhoSEoLc3FyDjzl48CA+++wzrFy50qTXSE1N1ZuFiYyMtDheIpsozAZOrxcfZx2H7Ix1IOXSCpFtmJV0rFmzBv/+97+xa9cubN68Gd9//z2++uorVFdX2yo+PTdv3sTTTz+NlStXIijItIY9KSkp0Gg0uq8//vjDxlESmSn3NCBI/B1iHYfs2IGUyD7MWl5Rq9V4+OGHdd8nJCRApVLh2rVriIiIMPvFg4KC4O7ujry8PL3reXl5CA0NrXN/VlYWLl26hMGD//qfX23C06BBA2RkZKBVq1Z6j/Hy8oKXl5fZsREpRuxQNwAIaa9YGK5EbMcKO5AS2ZZZMx23b9+Gt7f+3nUPDw9UVhpuIWyMp6cnunfvjrS0NN216upqpKWlGTzDpW3btvj1119x6tQp3dcjjzyCf/zjHzh16hSXTsgxiR3qpnJjIzAbkGoGxg6kRLZl1kyHIAgYM2aM3sxBWVkZnnvuOb1ts+ZsmU1OTsbo0aPRo0cP9OzZE4sXL0ZJSQnGjh0LABg1ahSaN2+O1NRUeHt7o2PHjnqPDwwMBIA614kcgtShbrHDubRiA1IFpFxWIbIts5KO0aNH17n21FNPWRXAiBEjcOPGDcyaNQu5ubno0qULdu7cqSsuVavVcHOzaGcvUf0ntk3WP5wJhw1IFZByxwqR7akEQRDsHYSSiouLERAQAI1GA39/f3uHQ65ub6rh6yHtgfaPKhuLC9jxa45oPcewbhGc6SCyMU4hENmL1NIKazlsQqqAlAkHke0x6SCyF6kOpFxakR0LSInsj0kHkb2IdSDlNlmbYAEpkf0x6SCyh6y94mNcWpEdC0iJ6gcmHURKK8wG1D+Lj3NpRXZSsxxseU6kHCYdREqTquXg0orsDmbmi85ysICUSFlMOoiUJlbLAXBpRWYHM/Mlj69nASmRsph0EClJqpYjKp5LKzJSF2glEw7OchApj0kHkVKM1XK0fECxUFyB1EmyAGc5iOyBSQeRUs5vER9jLYfsNKXiB1Hy+Hoi+2DSQaSErL1AhXhzKtZyyC9HU2bwes+YJjy+nshOmHQQ2ZqxZRXWcshOqvsoEw4i+2HSQWRrUltkPX1Zy2EDYn05wgK8FY6EiO7EpIPI1kqLxMfaDVYsDFch1ZcjwMdD4WiI6E5MOohsrfia4etcVpGdsW2y7D5KZF9MOohsSer4ei6ryEpdoMXGE1dEx9mXg8j+mHQQ2ZJYPYd/uLJxODl1gRabTl6VvId9OYjsj0kHkS2JtTz3CVQ0DGd39poG1YIgOs6+HET1A5MOIluRWlphXw5Z/ZZ7U3TM18ud22SJ6gkmHUS2IrVVlgWkspHqyeGmUnFZhageYdJBZCtiSytseS4bY8Wjj3VtzmUVonqESQeRLXBpxeaMFY+2DfVjwkFUzzDpIJJbYTbwyzrxcS6tyGLHmRzJ4lH25CCqf5h0EMmpMBs4vV58nEsrslAXaEW7jgLsyUFUXzHpIJJT7mlAqBYf59KKLMTOVgFYPEpUnzHpIJKTWPEoUHO4G5dWrGZsloPFo0T1F5MOIrlIFY+q3Hi4m0ykZjmGdYtgwkFUjzHpIJKLVF+O2OGc5ZCB1CwH6ziI6j8mHURykerLwYRDFlKzHKzjIKr/mHQQyYF9OWyOsxxEjo9JB5Ec2PLc5jjLQeT4mHQQyYEtz23qYGY+ZzmInACTDiJrcWnFptQFWhy9VCg6zlkOIsfBpIPIWlxasamz1zSiY5zlIHIsTDqIrMWlFZvSlFaKjnGWg8ixMOkgsgaXVuymZ0wTznIQORgmHUTWOL9FfIxLK7LI0ZQZvN67dZDCkRCRtZh0EFkqay9QoTU8xqUVWagLDP/+hgV4KxwJEcmBSQeRJQqzAfXP4uNcWpGFVBEpETkeJh1ElpBaVuFpsrJQF2jxW+5Ng2MBPh4KR0NEcmDSQWQuqWUVgKfJykBdoMWmk1dFxzuEBygYDRHJhUkHkTmMLatExXOWQwZnr2lQLQii49y1QuSYmHQQmUOqEZinL9DyAcVCcWZiyyoA0DbUT8FIiEhOTDqIzFFaJD7GZRVZiO1YAQA3lYpLK0QOjEkHkTmKrxm+zmUV2UjtWHmsa3MurRA5MCYdRKaS6j7KZRXZiC2ttA31Y8JB5OCYdBCZSmybrH+4snE4MamlFS6rEDk+Jh1EppDaJusTqGgozmzHmRzRMc5yEDk+Jh1ExmTtZfdRBRzMzIe2osrgGHesEDkHJh1EUoz15WD3UVkczMzH0UuFouNcWiFyDvUi6Vi2bBmio6Ph7e2NuLg4HDlyRPTelStX4v7770fjxo3RuHFjJCQkSN5PZBWpducAt8nKQF2glUw4fL3cubRC5CTsnnR8/fXXSE5OxuzZs3HixAl07twZiYmJuH79usH79+3bh6SkJOzduxfp6emIjIxE//79cfWqeMtkIosYa3fObbKyMHao24AOYQpFQkS2phIEiV7DCoiLi8M999yDpUuXAgCqq6sRGRmJyZMn4/XXXzf6+KqqKjRu3BhLly7FqFGjjN5fXFyMgIAAaDQa+Pv7Wx0/OanCbOCXdeLjUfHcJisDdYEWG09cER3vGdMEvVsHKRgREdlSA3u+eEVFBY4fP46UlBTdNTc3NyQkJCA9Pd2k59BqtaisrESTJk0MjpeXl6O8vFz3fXFxsXVBk2swdoosEw6rGavjYMJB5HzsurySn5+PqqoqhISE6F0PCQlBbm6uSc8xffp0hIeHIyEhweB4amoqAgICdF+RkZFWx01OrjCbp8jamLGEAwATDiInZPeaDmvMnz8f69atw6ZNm+Dt7W3wnpSUFGg0Gt3XH3/8oXCU5HCkZjlYx2E1Y4WjALfIEjkruy6vBAUFwd3dHXl5eXrX8/LyEBoaKvnY9957D/Pnz8cPP/yA2FjxPgleXl7w8vKSJV5ycoXZNQmH2CwHl1VkIdUADOChbkTOzK4zHZ6enujevTvS0tJ016qrq5GWlob4+HjRx7377rt4++23sXPnTvTo0UOJUMnZFWYDp9dzWcXG1AVa0QZgQM32WB7qRuS87DrTAQDJyckYPXo0evTogZ49e2Lx4sUoKSnB2LFjAQCjRo1C8+bNkZqaCgBYsGABZs2ahbVr1yI6OlpX+9GoUSM0atTIbu+DHNz5LYBQLT7OJmCykJrlYOEokfOze9IxYsQI3LhxA7NmzUJubi66dOmCnTt36opL1Wo13Nz+mpBZvnw5Kioq8Pjjj+s9z+zZs/HWW28pGTo5C2OFowBnOWQg1ebc18udCQeRC7B7nw6lsU8H1XHuOyDvnPg4e3JYRV2gxY4zOZLLKsO6RXBJhcgF2H2mg8jupBKOzk9wWcUK6gItNp28imqJ/9uwzTmR63DoLbNEVsvaKz7GhMNqO87kSCYcANucE7kSJh3kuowdWc+EwyrGdqoANcWjnOUgch1MOsg1GUs4QtorF4uTMnaQG3erELke1nSQ6ynMlk44ACBUvOEcmea33JuiYywcJXJNnOkg15N7Wnqcrc6tdjAzX3SMCQeR62LSQa6ntEh8jNtjrWbsMDcmHESui0kHuZ7ia4avM+GwmrHD3HiQG5FrY9JBrkVqiywTDqsZKx7lQW5Ero2FpOQ6pHas+IcrG4uTURdocfaaRrJ4lNtjiYhJB7kGY1tkfQIVC8XZGKvhALg9lohqcHmFnB+3yNqMsRqOWkw4iAhg0kGu4PwW6XFukbWY1FH1tVg8SkS1mHSQc8vaK31sPXesWEzqqPpabioVi0eJSIc1HeS8jC2rMOGwmCnLKm1D/dAhPIDFo0Skw6SDnJfUsoqnLxMOK0gtq/h6ueOZPq0UjIaIHAWXV8g5GVtWaTdYuVicjLHTY3lUPRGJYdJBzsfY9lgWjlpFqgEYe3EQkRQur5DzKMyuWVKRmuHgsorVxBqA+Xq5c2ssEUli0kHOoTAbOL0eEKql7+OyilWkTo/lsgoRGcPlFXIO57cYTzi4rGIVnh5LRNZi0kGOz1jRKMDtsVYylnCwARgRmYJJBzk2Y0WjABMOK5lytgobgBGRKVjTQY7LlDNVOj/BJRUrmNIEjDtWiMhUTDrIceWelh5nwmGR2mPqNaWVyNGUSd7L02OJyBxMOsgxFWYDeefEx1k0ahF1gRabTl5FtSAYvZcJBxGZizUd5Hhqt8eKYQ2HxXacyWHCQUQ2w6SDHI+x7bFMOCxirL15LTYBIyJLMekgx2Jse2xIe+VicTJSh7jVclOp2ASMiCzGmg5yHMa2x6rcgNBY5eJxEuoCLXacyZGc5QgL8EaAjwePqiciqzDpIMdgSj+O2OEsHjWTKYWjw7pFMNEgIlkw6aD6z5SEg9tjzVK7LVbs8LZavl7uTDiISDZMOqj+MuXUWIDbY81gylLKnVi/QURyYtJB9ZMpsxsAt8eayNxkA2CnUSKSH5MOqn+YcMjGkmQDYB0HEdkGkw6qX5hwyMLSZMNNpcJjXZsz4SAim2DSQfWHKQe4AUw4jDCnlfmd2ob6cUssEdkUkw6yv8LsmsPbpM5SAQBPX6DdYBaNGmFqK3OgZnfKgA5hTDSISBFMOsh+TN2dAnB2w0QHM/NNbmXOZIOIlMakg5Rl6qzGnZhwmERdoMXRS4WS9zDZICJ7YtJByjG1SPROnr5MOEx09ppGcpw7UojI3ph0kDxqZzBKiwyPF18z/zlVbjU1HGQSqe6iTDiIqD5g0kHWK8wGTq+XPm7eXCHtaw5vY9GoUbXbY8WwyRcR1RdMOshyltRnSOHuFLMdzMw3WsfRu3WQQtEQEUlj0kGWsaQ+QwxnNUxSe0ibprQSAJCjKTP6mLahfrYOi4jIZEw6yHxyJRzclWIySxt+dQgPsFFERETmY9JBpjOnrwYA+Icbvu4TyJkNM529pjE74WAtBxHVN0w6yLC/70YxZ/eJyg2IHc6kQga1SypSO1P+jr04iKi+YtJB+sydzfg71mdYzZJEo1bPmCYsHCWieotJB/3FmloN1mdI+nsRqBhTikPvFBbgDQAI8PHgYW1EVO8x6XBGxhp1GWJJ865aTDh0DCUX5iYSpmob6oeBncJs8txERLZQL5KOZcuWYeHChcjNzUXnzp3x0UcfoWfPnqL3f/PNN5g5cyYuXbqEu+66CwsWLMDDDz+sYMR3sOQHvC1ZkzyYywn6apg6A2EKWyUXhripVNyZQkQOx+5Jx9dff43k5GSsWLECcXFxWLx4MRITE5GRkYHg4OA69x86dAhJSUlITU3FP//5T6xduxZDhgzBiRMn0LFjR2WDt0UnzvqsdjeKk+w+sXQbqr21DfXjUgoROSSVINj3X9y4uDjcc889WLp0KQCguroakZGRmDx5Ml5//fU6948YMQIlJSXYunWr7tq9996LLl26YMWKFUZfr7i4GAEBAdBoNPD397cu+HPfydeNs75ygtkMMTt+zbGoWNMemGgQkTOw60xHRUUFjh8/jpSUFN01Nzc3JCQkID093eBj0tPTkZycrHctMTERmzdvNnh/eXk5ysvLdd9rNDUncRYXF1sZPYAbOUCJclPqiovsCcTcX/NrOX6/6pmcG4UoKyk3fqONhPh7SY4H+HigbZg/IpvUJhq35flzS0RkJT8/P6hUKrMfZ9ekIz8/H1VVVQgJCdG7HhISgt9++83gY3Jzcw3en5uba/D+1NRUzJkzp871yMhIC6MmIiJybZauFti9psPWUlJS9GZGqqurUVhYiKZNm1qUpSmpuLgYkZGR+OOPP6xfCqrn+F6dlyu9X75X5+RK7xUw7f36+Vl2rpNdk46goCC4u7sjLy9P73peXh5CQ0MNPiY0NNSs+728vODlpT+NHRgYaHnQduDv7+8Sf9ABvldn5krvl+/VObnSewVs837dZH02M3l6eqJ79+5IS0vTXauurkZaWhri4+MNPiY+Pl7vfgDYvXu36P1ERERUP9h9eSU5ORmjR49Gjx490LNnTyxevBglJSUYO3YsAGDUqFFo3rw5UlNTAQBTpkxB37598f7772PQoEFYt24djh07hk8++cSeb4OIiIiMsHvSMWLECNy4cQOzZs1Cbm4uunTpgp07d+qKRdVqNdzc/pqQ6dWrF9auXYs333wTM2bMwF133YXNmzcr36NDAV5eXpg9e3ad5SFnxPfqvFzp/fK9OidXeq+Abd+v3ft0EBERkWuwa00HERERuQ4mHURERKQIJh1ERESkCCYdREREpAgmHXaWmpqKe+65B35+fggODsaQIUOQkZGhd88DDzwAlUql9/Xcc8/ZKWLrvPXWW3XeS9u2bXXjZWVlmDRpEpo2bYpGjRph2LBhdZrBOYro6Og671WlUmHSpEkAHPtz/fHHHzF48GCEh4dDpVLVOftIEATMmjULYWFh8PHxQUJCAjIzM/XuKSwsxMiRI+Hv74/AwECMHz8et27dUvBdmEbqvVZWVmL69Ono1KkTfH19ER4ejlGjRuHatWt6z2Hoz8L8+fMVfiemMfbZjhkzps57GTBggN49zvDZAjD491elUmHhwoW6exzhszXl54wp//aq1WoMGjQIDRs2RHBwMF599VXcvn3brFiYdNjZ/v37MWnSJPz888/YvXs3Kisr0b9/f5SUlOjdN3HiROTk5Oi+3n33XTtFbL0OHTrovZeDBw/qxqZOnYrvv/8e33zzDfbv349r165h6NChdozWckePHtV7n7t37wYA/N///Z/uHkf9XEtKStC5c2csW7bM4Pi7776LDz/8ECtWrMDhw4fh6+uLxMRElJX9dUDiyJEjcfbsWezevRtbt27Fjz/+iGeeeUapt2Ayqfeq1Wpx4sQJzJw5EydOnMC3336LjIwMPPLII3XunTt3rt5nPXnyZCXCN5uxzxYABgwYoPde/vvf/+qNO8NnC0DvPebk5GDVqlVQqVQYNmyY3n31/bM15eeMsX97q6qqMGjQIFRUVODQoUP44osvsHr1asyaNcu8YASqV65fvy4AEPbv36+71rdvX2HKlCn2C0pGs2fPFjp37mxwrKioSPDw8BC++eYb3bXz588LAIT09HSFIrSdKVOmCK1atRKqq6sFQXCezxWAsGnTJt331dXVQmhoqLBw4ULdtaKiIsHLy0v473//KwiCIJw7d04AIBw9elR3z44dOwSVSiVcvXpVsdjN9ff3asiRI0cEAMLly5d116KiooQPPvjAtsHZgKH3O3r0aOHRRx8VfYwzf7aPPvqo0K9fP71rjvjZ/v3njCn/9m7fvl1wc3MTcnNzdfcsX75c8Pf3F8rLy01+bc501DMajQYA0KRJE73rX331FYKCgtCxY0ekpKRAq9XaIzxZZGZmIjw8HC1btsTIkSOhVqsBAMePH0dlZSUSEhJ097Zt2xYtWrRAenq6vcKVRUVFBb788kuMGzdO76BBZ/pca2VnZyM3N1fvcwwICEBcXJzuc0xPT0dgYCB69OihuychIQFubm44fPiw4jHLSaPRQKVS1Tnjaf78+WjatCm6du2KhQsXmj0tXZ/s27cPwcHBaNOmDZ5//nkUFBToxpz1s83Ly8O2bdswfvz4OmOO9tn+/eeMKf/2pqeno1OnTnqnvCcmJqK4uBhnz541+bXt3pGU/lJdXY2XX34ZvXv31uuw+uSTTyIqKgrh4eE4ffo0pk+fjoyMDHz77bd2jNYycXFxWL16Ndq0aYOcnBzMmTMH999/P86cOYPc3Fx4enrW+cc6JCQEubm59glYJps3b0ZRURHGjBmju+ZMn+udaj+rO/9xqv2+diw3NxfBwcF64w0aNECTJk0c+rMuKyvD9OnTkZSUpHdQ1ksvvYRu3bqhSZMmOHToEFJSUpCTk4NFixbZMVrLDBgwAEOHDkVMTAyysrIwY8YMDBw4EOnp6XB3d3faz/aLL76An59fneVeR/tsDf2cMeXf3tzcXIN/p2vHTMWkox6ZNGkSzpw5o1fjAEBvLbRTp04ICwvDgw8+iKysLLRq1UrpMK0ycOBA3a9jY2MRFxeHqKgorF+/Hj4+PnaMzLY+++wzDBw4EOHh4bprzvS5Uk1R6fDhwyEIApYvX643lpycrPt1bGwsPD098eyzzyI1NdXhWms/8cQTul936tQJsbGxaNWqFfbt24cHH3zQjpHZ1qpVqzBy5Eh4e3vrXXe0z1bs54xSuLxST7z44ovYunUr9u7di4iICMl74+LiAAAXL15UIjSbCgwMxN13342LFy8iNDQUFRUVKCoq0rsnLy8PoaGh9glQBpcvX8YPP/yACRMmSN7nLJ9r7Wf198r3Oz/H0NBQXL9+XW/89u3bKCwsdMjPujbhuHz5Mnbv3m30OPC4uDjcvn0bly5dUiZAG2rZsiWCgoJ0f26d7bMFgAMHDiAjI8Po32Ggfn+2Yj9nTPm3NzQ01ODf6doxUzHpsDNBEPDiiy9i06ZN2LNnD2JiYow+5tSpUwCAsLAwG0dne7du3UJWVhbCwsLQvXt3eHh4IC0tTTeekZEBtVqN+Ph4O0Zpnc8//xzBwcEYNGiQ5H3O8rnGxMQgNDRU73MsLi7G4cOHdZ9jfHw8ioqKcPz4cd09e/bsQXV1tS75chS1CUdmZiZ++OEHNG3a1OhjTp06BTc3tzrLEI7oypUrKCgo0P25dabPttZnn32G7t27o3PnzkbvrY+frbGfM6b82xsfH49ff/1VL6GsTbDbt29vVjBkR88//7wQEBAg7Nu3T8jJydF9abVaQRAE4eLFi8LcuXOFY8eOCdnZ2cJ3330ntGzZUujTp4+dI7fMK6+8Iuzbt0/Izs4WfvrpJyEhIUEICgoSrl+/LgiCIDz33HNCixYthD179gjHjh0T4uPjhfj4eDtHbbmqqiqhRYsWwvTp0/WuO/rnevPmTeHkyZPCyZMnBQDCokWLhJMnT+p2bMyfP18IDAwUvvvuO+H06dPCo48+KsTExAilpaW65xgwYIDQtWtX4fDhw8LBgweFu+66S0hKSrLXWxIl9V4rKiqERx55RIiIiBBOnTql93e4tqL/0KFDwgcffCCcOnVKyMrKEr788kuhWbNmwqhRo+z8zgyTer83b94Upk2bJqSnpwvZ2dnCDz/8IHTr1k246667hLKyMt1zOMNnW0uj0QgNGzYUli9fXufxjvLZGvs5IwjG/+29ffu20LFjR6F///7CqVOnhJ07dwrNmjUTUlJSzIqFSYedATD49fnnnwuCIAhqtVro06eP0KRJE8HLy0to3bq18Oqrrwoajca+gVtoxIgRQlhYmODp6Sk0b95cGDFihHDx4kXdeGlpqfDCCy8IjRs3Fho2bCg89thjQk5Ojh0jts6uXbsEAEJGRobedUf/XPfu3Wvwz+3o0aMFQajZNjtz5kwhJCRE8PLyEh588ME6vwcFBQVCUlKS0KhRI8Hf318YO3ascPPmTTu8G2lS7zU7O1v07/DevXsFQRCE48ePC3FxcUJAQIDg7e0ttGvXTpg3b57eD+n6ROr9arVaoX///kKzZs0EDw8PISoqSpg4caLeNkpBcI7PttbHH38s+Pj4CEVFRXUe7yifrbGfM4Jg2r+9ly5dEgYOHCj4+PgIQUFBwiuvvCJUVlaaFQuPticiIiJFsKaDiIiIFMGkg4iIiBTBpIOIiIgUwaSDiIiIFMGkg4iIiBTBpIOIiIgUwaSDiIiIFMGkg4iIiBTBpIOI6r1Lly5BpVIhOjq6zlhGRgY++ugjjBkzBp06dUKDBg2gUqnwr3/9S/lAiUgSj7YnIoe2fPlyLFmyxN5hEJEJONNBRA6tY8eOmDZtGr766iucP38eTz/9tL1DIiIRnOkgIoc2YcIEve/d3Ph/KaL6in87icgiv/32G1QqFRo3boyysjLR+3r06AGVSoXvvvsOAHDu3DnMnj0bvXv3RvPmzeHp6YmmTZsiISEB69evVyp8IrIDJh1EZJG2bdsiPj4eRUVF2Lx5s8F7fv31Vxw/fhwhISEYNGgQAGDRokWYO3cuCgsL0alTJwwdOhRt2rTB3r17MWLECCQnJyv4LohISUw6iMhi48aNAwCsXr3a4Pjnn38OAHjqqafQoEHNau7TTz+NrKwsnD9/Hjt37sS6detw6NAhnDt3DhEREfjggw9w5MgRReInImUx6SAii40YMQINGzbE7t27cfXqVb2xyspKfPnllwCAsWPH6q737dsXLVu2rPNcbdq0wcyZMwEAGzZssGHURGQvLCQlIov5+fnh8ccfx5o1a7BmzRqkpKToxrZt24YbN26gZ8+e6NChg97jbt26hR07duDkyZPIz89HRUUFACAnJwdATe8NInI+TDqIyCrjxo3DmjVrsHr1ar2ko3Zp5c5ZDgD4/vvvMXbsWBQUFIg+Z3FxsW2CJSK74vIKEVmlT58+aNWqFS5cuIBDhw4BAK5fv47t27fD29sbTzzxhO7eq1evYsSIESgoKMBrr72GX375BRqNBlVVVRAEAbt27QIACIJgl/dCRLbFpIOIrKJSqTBmzBgAf81ufPnll7h9+zaGDh2KwMBA3b3ff/89SktL8dhjj2HBggWIjY2Fv7+/rrdGZmam0uETkYKYdBCR1caMGQM3NzesX78eWq1WdGmlsLAQABAVFVXnOQRBwNq1a20fLBHZDZMOIrJaREQEHnroIRQXF2PGjBk4c+YMWrRogX79+und165dOwA1u1Nqi0YBoKqqCrNmzdItzxCRc2IhKRHJYuzYsdi1a5fu8LXa2Y87DR48GN27d8fx48dx9913o2/fvvD19cXhw4dx7do1TJ8+HQsWLDDrdU+cOIEXXnhB931WVhYA4OOPP8bWrVt11zdt2oSwsDBL3x4RyYBJBxHJYsiQIWjSpAkKCwv16jzu1KBBA+zbtw+pqanYuHEj0tLS4O/vj169emHjxo24efOm2UlHcXExDh8+XOf6lStXcOXKFd335eXlZr8nIpKXSmCZOBERESmANR1ERESkCCYdREREpAgmHURERKQIJh1ERESkCCYdREREpAgmHURERKQIJh1ERESkCCYdREREpAgmHURERKQIJh1ERESkCCYdREREpAgmHURERKSI/wdlJa2rp59rrgAAAABJRU5ErkJggg==", + "image/png": 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RkYEDBw5gx44dJp9r2bJlGDZsGLRaLb766issXboUmzZtMkpS/vvf/8LZ2dmwboZkVjtyYQ5HL2S39dQVi5KLmQPbwtWJCznVgBNTduz111/H/PnzkZycjE6dOmHYsGHYvn27yWkHS2m1WqSkpODYsWPo2rUr5syZg2XLlkneExoaih9++AE6nQ5Dhw5FdHQ0XnjhBfj4+ECr1cLLywsHDhzAiBEj0KFDB7z66qv4xz/+YaghYcqMGTOQkpJiturl4sWLZauMuXbtWrRq1QpDhw412f7dd9+hf//+6N27N7Zv344tW7bUSZA+//xzTJgwgaf5KsWSY9U1Wo5eyGzPmTyzx6sHeDhhzpAOTC5URCOI7QW0U8XFxfD29kZRURG8vLyM2ioqKpCVlYWoqCgeha1ygiAgNjYWc+bMwfjx45UOxyauXbuGjh074ujRo6JJIL9HG5ElUyM8Rl12GVdLsPWk9Km04X5ueKRXK5kiIktxioSaJI1Gg48++qjO+oim7MKFC/jnP//ZoBEmspK55ILHqCuiWqc3m1wM7NgSPVvbtmov2QYTDGqyunfvju7duysdhs307t3bZPEwakSWHrvO5EJ25VU6rN4vXdF21v3tuAVVxZhgEFHzZOnJqDxWXVZ6vYCTfxRi3znpYxJGRIcwuVA5JhhE1LxYOmoBcLeIzMqqavDl8cu4drNSsl/7IA90DPaUKSqyFhMMImo+LCmgVYu7RWSl1wsWn4Y6MjqkkaMhW2CCQUT2rbbkd3nhrROKLeHkfiu54OiFLKpq9FiVlmFR30d6tWJ1ziaCCQYR2S9L11nUYmIhO0EQLE4u4jsFIdyPNWKaCiYYRGSfCrLql1xExPFkVAWs2HPebJ+4tv6IjfLjyEUTwwSDiOzT2W8t78udIrITBAFfHDN/IipLfzdd3ONDkqZMmSJ53octXL9+HYGBgbhw4UKjvo6c7rnnHnz55ZdKh9F8FWRZtktEo2VyoZBtp3Jw+Ua5ZJ8pfSOZXDRhTDBIcW+88QYefPBBREZGArhV0VKj0SAwMBA3b9406tu9e3e89tprVr+WRqOp80hJuV3B8auvvsKQIUPQsmVLeHl5IS4uDrt27TJ6jgMHDmDUqFEIDQ2FRqPBN998U+d1Xn31Vbz88suynYNC/68gCzizxXzJb6/QW2W/WUBLMRlXSyTbX4hvD193nojalHGKRMLuM3m4XiK9H7ux+Xs4Y0jnIEVjaExlZWX4+OOP6/wnDgA3b97E22+/jUWLFtn0NT/55BMMGzbM8LWPj4/hzwcOHMCQIUOwZMkS+Pj44JNPPsGoUaNw+PBhwxHqpaWl6NatG6ZNm4YxY8aYfI3hw4djxowZ+O677zBy5Eibxk8iLFnQ6eQO9HtOnnhI1LZT0rt5Zt3fjust7ABHMCRcL6lETlGFog9LE5z77rsPs2fPxgsvvABfX18EBQVhzZo1KC0txdSpU+Hp6Yl27drhu+++M9yj0+kwffp0REVFwdXVFR07dsS7774r+Tp6vR7JycmGe7p164bNmzcb2m/cuIEJEyagZcuWcHV1Rfv27fHJJ5+IPt+OHTvg7OyMe+65p07b7NmzsXz5cly9etWivwNL+fj4IDg42PC489CwFStW4KWXXsLdd9+N9u3bY8mSJWjfvj22bt1q6DN8+HD8/e9/x0MPPST6Gg4ODhgxYoTR6Ag1Ikt3i7CuhaIEQcA7u3/D+Tzx0YtR3Vih017wU7Qj//73vxEQEIAjR45g9uzZePrpp/GXv/wFffv2xfHjxzF06FBMnDgRZWW35qb1ej1atWqFL774AmfOnMGCBQvwyiuvYNOmTaKvkZycjPXr12P16tX45ZdfMGfOHDz++OPYv38/AGD+/Pk4c+YMvvvuO5w9exYffPABAgICRJ/v+++/R69evUy2jR8/Hu3atcPixYtF7585cyY8PDwkH382a9YsBAQEoE+fPli7di2kDhTW6/W4efMm/Pz8RPuI6dOnD77//vt630f1ZOluEVblVJy5HSN3R/qhXSArdNoLTpHYkW7duuHVV18FACQlJWHp0qUICAjAE088AQBYsGABPvjgA5w6dQr33HMPHB0djaYfoqKikJ6ejk2bNmHs2LF1nr+yshJLlizBnj17EBcXBwBo06YNDh48iA8//BADBw5EdnY2evToYTi0q3ZdhZiLFy8iNDTUZJtGo8HSpUsxatQozJkzB23btq3TZ/HixZg7d675v5w7+g8aNAhubm74z3/+g2eeeQYlJSV47jnTw+Zvv/02SkpKTP59mBMaGopLly5Br9dDq2Uu32hyT5nvw6qcitt9Js9sn37t/GWIhOTCBMOOxMTEGP7s4OAAf39/REdHG64FBd1ay3HnlMOqVauwdu1aZGdno7y8HFVVVaInlGZkZKCsrAxDhgwxul5VVWVYn/D000/j4YcfNoyYjB49Gn379hWNuby83GiK4s8SEhJw7733Yv78+diwYUOd9sDAQAQGBore/2fz5883/LlHjx4oLS3FsmXLTCYYGzZswKJFi7Bly5Z6vUYtV1dX6PV6VFZWwtXVtd73k4Xyzki3B3UGgmM4eqGgc7k3cfpykWSf+zq25LoLO8MEQ4K/h7PSIdQrBkdHR6OvNRqN0bXaf7y1OxtSUlIwd+5c/OMf/0BcXBw8PT2xbNkyHD582OTzl5Tcmjfdvn07wsLCjNqcnW/FOXz4cFy8eBE7duzA7t27MXjwYMyaNQtvv/22yecMCAjAjRs3JN/X0qVLERcXh7/+9a912mbOnIlPP/1U8v7auE2JjY3F66+/jsrKSsN7AG793cyYMQNffPEF4uPjJZ9fTEFBAdzd3ZlcNKbMNPE2Fs5ShezrZdjxc45kn4d7tkJrf1botDdMMCTY8+4NAPjhhx/Qt29fPPPMM4ZrmZmZov07d+4MZ2dnZGdnY+DAgaL9WrZsicmTJ2Py5Mno378//vrXv4omGD169DCbIPTp0wdjxozByy+/XKetvlMkf3bixAn4+voaJReff/45pk2bhpSUlAbtADl9+rRhZIcagbm1F0wuFHetpBJfHpcupsVCWvaLCUYz1r59e6xfvx67du1CVFQU/vd//xc//vgjoqJMDyV7enpi7ty5mDNnDvR6Pe69914UFRXhhx9+gJeXFyZPnowFCxagV69e6NKlCyorK7Ft2zZ06tRJNIaEhAQkJSXhxo0b8PX1Fe33xhtvoEuXLmjRwvhbtj5TJFu3bkVeXh7uueceuLi4YPfu3ViyZIlRgrJhwwZMnjwZ7777LmJjY5Gbmwvg1nSHt7c3gFsjIhkZt89OyMrKwokTJ+Dn54fWrVsbrn///fcYOnSoRbFRPRVkSde6COosXywk6n/TL0q2x3cKYnJhx7jyrBl76qmnMGbMGIwbNw6xsbG4fv260WiGKa+//jrmz5+P5ORkdOrUCcOGDcP27dsNSYmTkxOSkpIQExODAQMGwMHBQXKrZnR0NHr27Cm5cwUAOnTogGnTpqGioqL+b/T/OTo6YtWqVYiLi0P37t3x4YcfYvny5Vi4cKGhz0cffYSamhrMmjULISEhhsfzzz9v6HP06FH06NHDMDqRmJiIHj16YMGCBYY+ly9fxqFDhzB16lSr4yURmWnmC2kFx0i3U6O7elP632qXUC9Et/KWKRpSgkaQ2qNnh4qLi+Ht7Y2ioiJ4eXkZtVVUVCArKwtRUVGSCw/JtrZv346//vWvOH36tN3stpg3bx5u3LiBjz76yKbP2+y/R82NXABce6ES7+z+TbStU4gnhnUNkTEaUgKnSEhxI0eOxPnz53H58mWEh4crHY5NBAYGIjExUekw7I+5A8yc3JlcqIC5HSNMLpoHJhikCi+88ILSIdjUiy++qHQI9iczTfoAM9a6UIX//JKLX64Ui7Y/c3/dejZkn5hgEJH6mSsF7uR+K7lgrQtF5RSVSyYXYb6ucG7BRZ3NBRMMIlI3c9tReYCZKuQUlSPlyCXJPg/3bCVTNKQGTDBMaGbrXqkJaZbfm+bWXXBaRFEV1TqczyvBnrPSpcDvaeMPBy0rdTYnTDDuUFv1sqysjNUXSZVqD6r7c9VWu2Vu3UVEHKdFFPTF0Uv440a52X49I3wR15bnjDQ3TDDu4ODgAB8fH8NZHW5ubqyNT6ogCALKyspw9epV+Pj4wMGhGcxjm1t3we2oiko5ko2cIvN1acL93DCwQ0sZIiK1YYLxJ8HBwQCMDwQjUgsfHx/D96hds2TdBZMLxVwtrrAouQCAh3uGme9EdokJxp9oNBqEhIQgMDAQ1dXVSodDZODo6Ng8Ri4A80ewc92FYqp1enx2ONtsP183R0zuG8lR4GaMCYYIBweH5vPDnEhNCrKkj2DnugtFrdybYbZP+yAPjIwOYXLRzDHBICL1KMgCTkmcS8N1F4oqqayRbB8ZE4JIf3c4tbCPkv/UMEwwiEg9ck8Bgl68ncmFYi5eL8VXxy+LtveM8EWHIE8ZIyK1UzzNXLVqFSIjI+Hi4oLY2FgcOXJEsv+KFSvQsWNHuLq6Ijw8HHPmzGnQCZtEpCLlheJtPIJdMRXVOsnkAgB3ilAdiiYYGzduRGJiIhYuXIjjx4+jW7duSEhIEN3BsWHDBrz88stYuHAhzp49i48//hgbN27EK6+8InPkRCQrjZZHsCtEEAR8sC9Tss+0e7kmhupSNMFYvnw5nnjiCUydOhWdO3fG6tWr4ebmhrVr15rsf+jQIfTr1w+PPfYYIiMjMXToUIwfP15y1KOyshLFxcVGDyJSqeIrpq/HjOXCToWs2HNest2phRbers2k8BvVi2IJRlVVFY4dO4b4+PjbwWi1iI+PR3p6usl7+vbti2PHjhkSit9//x07duzAiBEjRF8nOTkZ3t7ehoe9HAdOZHcKskxf9wplcqGQ737OkWx3cXTAM/fxdFQyTbFFnteuXYNOp0NQUJDR9aCgIPz6668m73nsscdw7do13HvvvRAEATU1NZg5c6bkFElSUhISExMNXxcXFzPJIFIjc2eOkKwy80vwa+5N0XZHBw1mDmzDragkSvFFnvWxb98+LFmyBP/85z9x/PhxfPXVV9i+fTtef/110XucnZ3h5eVl9CAilSnIEj9zxNVH1lDolm9PiExX/b9Z97djckGSFBvBCAgIgIODA/LyjE/gy8vLEy2FPH/+fEycOBEzZswAAERHR6O0tBRPPvkk/va3v0GrbVL5EhHVkhq94OJO2f2eXyLZ/uwgJhdknmL/Izs5OaFXr15ITU01XNPr9UhNTUVcXJzJe8rKyuokEbXVNpvlMdZE9kBq9MLJnesvZFZRrcMWidGLsXeHw9GBv8yReYoW2kpMTMTkyZPRu3dv9OnTBytWrEBpaSmmTp0KAJg0aRLCwsKQnJwMABg1ahSWL1+OHj16IDY2FhkZGZg/fz5GjRrFst5ETVFBFnAyRbydZ47I6vf8EsnkontrH4T5uMoYETVliiYY48aNQ35+PhYsWIDc3Fx0794dO3fuNCz8zM7ONhqxePXVV6HRaPDqq6/i8uXLaNmyJUaNGoU33nhDqbdARNYydxw7Ry9kVVReLZlcAMB9LKZF9aARmtncQnFxMby9vVFUVMQFn0RKMTdyAQDdHmWCIaP/Tb+AayVVou09Wvvgvo6BMkZETR0n0ohIfua2pHL0QnZSyQUAJhdUb0wwiEheUos6gVtlwbn2QlWeHNBG6RCoCeJpqkQkL6nRCyf3W8kFRy9kdexigWjbnCEdZIyE7AkTDCKST2aa9JbUfs/JGw/hUkEZDvx2TekwyA5xioSI5GFu1winRWRXrdNj87E/RNt7RvjKGA3ZGyYYRNT4uCVVlVbuzZBs798uQKZIyB4xwSCixlWQJZ1cABy9UMCuX3Il24d1DYZWy3LgZD0mGETUuMxtSY2I4+iFzARBwJkrxaLtHYI80SmEdYKoYZhgEFHjMbclNSIOaHOfbOHQLZlmDjMbEW36wEmi+mCCQUSNR2r0gsmFYraezBFt40mpZCtMMIiocZjbksrkQhHVOr1o2+geYTwplWyG30lEZHvmFnZyUadidvwsPnoR6e8mYyRk75hgEJHt5Z4Sb+OWVEX9nl8q2sapEbIlJhhEZHt5Z8TbOHqhmKoa8emRpwbyvBGyLSYYRGRbmWnibdySqqhVaeKFtdyceHIE2RYTDCKyHXNrL7iwUzHXSyqVDoGaGSYYRGQ7UmsvgjrLFwfVsfHoJdG28X1ayxgJNRdMMIjIdqTWXgTHyBcH1VFZLb7+IsjLWcZIqLlggkFEtlGQJd7GtReKOvz7ddG22SysRY2ECQYR2YbU9AjXXijm9OUiHMoUTzBasLAWNRJ+ZxGRbZQXmr7OtReKuVRQht1n8kTbh3XlmSPUeJhgEJFtFF8xfZ1rLxSz+dgfku0dgzxlioSaIyYYRNRwUusvuPZCERXVOsn2YV2DodVy7QU1HiYYRNRwYusvvELljYMMcooqRNs6hXiiU4iXjNFQc8QEg4gaTmx7qquPrGHQbd/8dNnkdWdHLYZ1DZE5GmqOmGAQUcNITY9w/YXqjIrhqBLJgwkGETXM2W/F27j+QhFlVTWiba18XWWMhJozJhhEZL2CLKCqzHQbt6cq5sP9v4u2sagWyYUJBhFZT2r0gtMjijh5qVC0La6tv3yBULPHBIOIrJOZJj564eTO6RGF7P31qmhbl1DuHCH5MMEgovozdyx7p1HyxUIW83RxVDoEakaYYBBR/UmdO8LRC8UUlVWLtk2Mi5AxEiImGERkDbFzRwCOXiho7Q+mtwx7uToiwINHspO8mGAQUf0UZImfO8Jj2VWpR2sfpUOgZogJBhFZriALOLVJvJ3HsivmUoHIglsAPcJ95AuE6P+1UDoAImpCzn4LCHrTbTx3RBGCICCnqELy5FTWviAlMMEgIstIbUsFeO6IQtYduoBCicWdvSJ8ZYyG6DZOkRCReea2pWq0LKylgNSzeZLJBQDc04bFtUgZTDCIyDypbakAEDOWiztlVqPT49QfRZJ9Ytv4wakFf8yTMvidR0TmSW1L7fYokwsFvL83Q7LdQatB37YBMkVDVBfXYBCRedyWqipVNSILbf9faz83jIgOkSkaItOYYBCRtALTxZsAcFuqQlaliY9ePNKrFcL93GSMhsg0TpEQkTSxE1O5LVURgiBItjO5ILVggkFE4qS2pnJbqiIuXBffKjy5b6R8gRCZwQSDiEwztzWV21IVcfF6qWibn7uTjJEQSWOCQUSmiU2NADwxVUE/ZReavD6mZ5i8gRCZwQSDiOoyV7WTJ6Yq4nj2DdG2IC8XGSMhMo8JBhEZMzc1wq2piqjR6bH/XL5ou4ujg4zREJnHBIOIjElV7XRy59ZUhfyae1O07e5IPxkjIbIMEwwiuq0gC8g7I97OqRFFCIKA3WfyRNv7teN5I6Q+iicYq1atQmRkJFxcXBAbG4sjR45I9i8sLMSsWbMQEhICZ2dndOjQATt27JApWiI7VpAFnNok3s6pEcV8eOB30bbBnQJ5HDupkqKVPDdu3IjExESsXr0asbGxWLFiBRISEnDu3DkEBgbW6V9VVYUhQ4YgMDAQmzdvRlhYGC5evAgfHx/5gyeyN2e/BQSJEtScGlHEb3k3UV6lE22PDvOWMRoiyymaYCxfvhxPPPEEpk6dCgBYvXo1tm/fjrVr1+Lll1+u03/t2rUoKCjAoUOH4OjoCACIjIyUM2Qi+2Ru10hQZ/liISPbT+WItoX5unL0glTLqgRDp9Nh3bp1SE1NxdWrV6HXG//Ws3fvXrPPUVVVhWPHjiEpKclwTavVIj4+Hunp6Sbv+fbbbxEXF4dZs2Zhy5YtaNmyJR577DHMmzcPDg6mV1BXVlaisrLS8HVxcbElb5Go+TC3a0SjZVEthXzz02XJ9r/0aiVTJET1Z1WC8fzzz2PdunUYOXIkunbtalUGfe3aNeh0OgQFBRldDwoKwq+//mrynt9//x179+7FhAkTsGPHDmRkZOCZZ55BdXU1Fi5caPKe5ORkLFq0qN7xETULBVnAyRTpPjFjufZCAZcKypB1Tbxq5/T+URy9IFWzKsFISUnBpk2bMGLECFvHI0mv1yMwMBAfffQRHBwc0KtXL1y+fBnLli0TTTCSkpKQmJho+Lq4uBjh4eFyhUykXuYWdQJAt0eZXChAEARsPvaHaHt0mDe8XBxljIio/qxKMJycnNCuXbsGvXBAQAAcHByQl2e89SovLw/BwcEm7wkJCYGjo6PRdEinTp2Qm5uLqqoqODnVrcPv7OwMZ2fnBsVKZJdyT0kv6uSuEcWknr0q2T64U91F8ERqY9U21RdffBHvvvuu2WODpTg5OaFXr15ITU01XNPr9UhNTUVcXJzJe/r164eMjAyjNR+//fYbQkJCTCYXRCRBqt4FC2op6ufLRaJtU/tFcmqEmgSrRjAOHjyItLQ0fPfdd+jSpYthR0etr776yqLnSUxMxOTJk9G7d2/06dMHK1asQGlpqWFXyaRJkxAWFobk5GQAwNNPP42VK1fi+eefx+zZs3H+/HksWbIEzz33nDVvg6j5KsgSb9NoWVBLQTq9+C9usW384OPGX6aoabAqwfDx8cFDDz3U4BcfN24c8vPzsWDBAuTm5qJ79+7YuXOnYeFndnY2tNrbgyzh4eHYtWsX5syZg5iYGISFheH555/HvHnzGhwLUbMiVQ6cizoVIwgC3ks9L9ret22AjNEQNYxGaMg8RxNUXFwMb29vFBUVwcvLS+lwiJSRlmz6elBnoPOD8sZCBufzbmKbSN2LNi3d8WB3HslOTUeDCm3l5+fj3LlzAICOHTuiZcuWNgmKiBpRZpp4G+tdKGr7z+JFtWJa+cgXCJENWLXIs7S0FNOmTUNISAgGDBiAAQMGIDQ0FNOnT0dZmUQ1QCJSlrmiWpwaUZTUeHK4r6t8gRDZgFUJRmJiIvbv34+tW7eisLAQhYWF2LJlC/bv348XX3zR1jESka2c/Va8jeXAFSU1W/3soHZo4aD42ZRE9WLVFMmXX36JzZs347777jNcGzFiBFxdXTF27Fh88MEHtoqPiGzF3HkjnB5RVGFZtWibI5MLaoKsSjDKysrqlPgGgMDAQE6REKlNQdatkQup5IJFtRT37/QLJq8He7vIGwiRjViVFsfFxWHhwoWoqKgwXCsvL8eiRYtEi2QRkQJqy4FLJRcsqqW4YxdviK6/6NHaR9ZYiGzFqhGMd999FwkJCWjVqhW6desGADh58iRcXFywa9cumwZIRA1w9lvpcuAAi2oprKJahwO/5Yu2R/q7yxgNke1YlWB07doV58+fx2effWY4+XT8+PGYMGECXF250plIFQqypEcuAE6NqMAXEoeaAYCLo4NkO5FaWV0Hw83NDU888YQtYyEiW5Kq1gncSi44NaK4azcrRdsmxkXIGAmRbVmcYHz77bcYPnw4HB0d8e23ElvdADzwwAMNDoyIGqAgS/owMx7Drgo//yF+qNnQLkEI8OBJ0NR0WVwqXKvVIjc3F4GBgUbng9R5Qo0GOp3OZgHaGkuFk92rXdgptvaCyYVqvLP7N9G2OUM6yBgJke1ZPIJx5xHpd/6ZiFQm95T0wk4mF6og9btdbBs/GSMhahxWbVNdv349KivrzhtWVVVh/fr1DQ6KiBpAamqE1TpVo7JGPAmMjfKXMRKixmFVgjF16lQUFdWdO7x58yamTp3a4KCIyEoFWeJtGi2rdarI+bwSk9cDvZzhoNXIHA2R7VmVYAiCAI2m7j+AP/74A97e3g0OioisJHXWSMxYTo+oyJ6zeSavt/ZzkzkSosZRr22qPXr0gEajgUajweDBg9Gixe3bdTodsrKyMGzYMJsHSUQWkKp7EdSZyYWKVFSLL4RnYS2yF/VKMEaPHg0AOHHiBBISEuDh4WFoc3JyQmRkJB5++GGbBkhEFpKqe8GpEVU5eP6aaFuYD4sVkn2oV4KxcOFC6HQ6REZGYujQoQgJCWmsuIiovsQWdzq5c/RCZX6+bLr+RdtAD2i5/oLsRL3XYDg4OOCpp54yOuiMiBSWmSbexrNGVCW3SPxnZ/92ATJGQtS4rFrk2bVrV/z++++2joWIrFGQBWT/V7ydoxeqIQgCPj+SLdru6+4kYzREjcuqBOPvf/875s6di23btiEnJwfFxcVGDyKSkdTOEda9UJWfLhWKtsVGsbgW2RerDjsbMWIEgFtnjty5XbV2+6qaS4UT2ZXMNOkTU7m4U1X2nxM/lj2uLYtrkX2xKsFIS5OY7yUieZibGuFR7E1GVIC7ydpCRE2ZVQnGwIEDbR0HEdWX1NSIkzuPYlcZvV787JHRPcJkjIRIHlYlGABQWFiIjz/+GGfPngUAdOnSBdOmTWMlTyI5SBXVArhzRIV+vFCgdAhEsrJqkefRo0fRtm1bvPPOOygoKEBBQQGWL1+Otm3b4vjx47aOkYj+TGr0glMjqiMIAg5lXjfZdlewp8zREMnDqhGMOXPm4IEHHsCaNWsM5cJramowY8YMvPDCCzhw4IBNgySiO0iNXnBqRJV+uSK+u65Ha18ZIyGSj1UJxtGjR42SCwBo0aIFXnrpJfTu3dtmwRHRnxRkASdTxNs5NaJKu8+YPtgMAIK9XWSMhEg+Vk2ReHl5ITu7brGYS5cuwdOTw31EjaIgCzi1SbydJcFVae+v4snFkM5BMkZCJC+rEoxx48Zh+vTp2LhxIy5duoRLly4hJSUFM2bMwPjx420dIxEBt9ZdCHrxdo5eqM6RrAKcvGT63BEA6BrGRfFkv6yaInn77beh0WgwadIk1NTUAAAcHR3x9NNPY+nSpTYNkIhgftcIRy9U54eMaziSJb5zJIRTI2TnNIIgiG/ONqOsrAyZmZkAgLZt28LNzc1mgTWW4uJieHt7o6ioCF5eXkqHQ2SZM1vET0vVaIGYsUwwVKSqRo9VaRmSfZ4d1A6ODlYNIhM1CVbXwQAANzc3+Pj4GP5MRI2kvFC8jcmFqgiCYDa5GB4dzOSC7J5V3+E1NTWYP38+vL29ERkZicjISHh7e+PVV19FdXW1rWMkouIrpq93e5TJhcqs2HNesr13pC/uCuboKdk/q0YwZs+eja+++gpvvfUW4uLiAADp6el47bXXcP36dXzwwQc2DZKoWcuUOPuHyYWqFFdI/4I1oENL9Ipg3QtqHqxKMDZs2ICUlBQMHz7ccC0mJgbh4eEYP348EwwiW5E60MwrVN5YyKxtJ3Mk25lcUHNi1RSJs7MzIiMj61yPioqCk5NTQ2Miolq5p8TbXH1kC4PMK6msQV5xhWj7c4PbyxgNkfKsSjCeffZZvP7666isrDRcq6ysxBtvvIFnn33WZsERNXtiO0cAIDhGvjhIkiAIWHPgd9H2Zwe1g4OWx7FT82LVFMlPP/2E1NRUtGrVCt26dQMAnDx5ElVVVRg8eDDGjBlj6PvVV1/ZJlKi5qYgS7yNB5qpyunL4meNAOCOEWqWrEowfHx88PDDDxtdCw8Pt0lARPT/pE5M5YFmqrLnrHg58M6h3DFCzZNVCcYnn3xi6ziI6E6ZaeKVO4M6yxsLSaqqkSjfDmAozxuhZqpBhbby8/Nx7tw5AEDHjh3RsmVLmwRF1KxJ7RwBuPZCZaSKaj1zf1toNFx7Qc2TVRODpaWlmDZtGkJCQjBgwAAMGDAAoaGhmD59OsrKJM5LICJp5o5j55kjqmLupAXnFg4yRUKkPlYlGImJidi/fz+2bt2KwsJCFBYWYsuWLdi/fz9efPFFW8dI1DyYO44d4ImpKrPvXL5o28M9W8kYCZH6WDVF8uWXX2Lz5s247777DNdGjBgBV1dXjB07loW2iKxh7jh27hxRlbziCpy4VCja3tqf5zNR82bVCEZZWRmCguouXAoMDOQUCZE1LDmOnTtHVGXD4WzRtklxETJGQqROViUYcXFxWLhwISoqbletKy8vx6JFiwxnkxBRPUhtSdVoOTWiMoVlVZLt/h7OMkVCpF5WTZGsWLECw4YNq1Noy8XFBbt27bJpgER2z9zoBY9jV51Pfrgg2hbfidtSiQArE4zo6GicP38en332GX799VcAwPjx4zFhwgS4urraNEAiuyc1esHj2FXn4vVS0TYXRwdEt/KWMRoi9ap3glFdXY277roL27ZtwxNPPNEYMRE1H1KjF9ySqkpfHb8s2jajPz8volr1XoPh6OhotPaCiBpA6rRUrrtQnezr0ovYeeYI0W1W/WuYNWsW3nzzTdTU1NgkiFWrViEyMhIuLi6IjY3FkSNHLLovJSUFGo0Go0ePtkkcRLITOy2Voxeq9OXxP0TbnhzQRsZIiNTPqjUYP/74I1JTU/Gf//wH0dHRcHd3N2qvzwmqGzduRGJiIlavXo3Y2FisWLECCQkJOHfuHAIDA0Xvu3DhAubOnYv+/ftb8xaIlJeZJt7G0QvVkdo50jnUC+7ODTp5gcjuWDWCUXuaakJCAkJDQ+Ht7W30qI/ly5fjiSeewNSpU9G5c2esXr0abm5uWLt2reg9Op0OEyZMwKJFi9CmDX9roCbI3HkjHL1QHamdIwldguULhKiJqFfKrdfrsWzZMvz222+oqqrCoEGD8Nprr1m9c6SqqgrHjh1DUlKS4ZpWq0V8fDzS09NF71u8eDECAwMxffp0fP/995KvUVlZicrKSsPXxcXFVsVKZFNSO0d4WqrqZFy9KdoW4OEkYyRETUe9RjDeeOMNvPLKK/Dw8EBYWBjee+89zJo1y+oXv3btGnQ6XZ2qoEFBQcjNzTV5z8GDB/Hxxx9jzZo1Fr1GcnKy0ehKeHi41fES2YS5uhc8LVVVBEHA1pM5ou1/6c2fKUSm1CvBWL9+Pf75z39i165d+Oabb7B161Z89tln0Oslzk+woZs3b2LixIlYs2YNAgICLLonKSkJRUVFhselS5caOUoiM6RGL3jeiKoIgoCPD2aJtof5usLFkSemEplSrymS7OxsjBgxwvB1fHw8NBoNrly5glat6n9yYEBAABwcHJCXl2d0PS8vD8HBdec0MzMzceHCBYwadXsBXG1y06JFC5w7dw5t27Y1usfZ2RnOzizbSyphru4FzxtRle/PX8PNCvHdcmM5ekEkql4jGDU1NXBxcTG65ujoiOrqaqte3MnJCb169UJqaqrhml6vR2pqqskzTe666y78/PPPOHHihOHxwAMP4P7778eJEyc4/UHqx7oXTUb29TIcu3hDtN3XzVHGaIiannqNYAiCgClTphiNCFRUVGDmzJlGW1Xrs001MTERkydPRu/evdGnTx+sWLECpaWlmDp1KgBg0qRJCAsLQ3JyMlxcXNC1a1ej+318fACgznUiVWLdiyahWqeXrHkBAI/F8sRUIin1SjAmT55c59rjjz/eoADGjRuH/Px8LFiwALm5uejevTt27txpWPiZnZ0NrZbV8cgOFIjP5XP0Ql3+N/2iZPu4u8Ph1II/l4ikaARBEJQOQk7FxcXw9vZGUVERvLy8lA6HmpMzW8RHMO5PMn2dFPHO7t9E20bGhKBDkKeM0RA1TUzBieQillyw7oWqnM0Rr5XTO9KXyQWRhZhgEMlBqiw4616oys7TpmvwAED/9i1ljISoaWOCQdTYMtNYFrwJcXUyXddiSOcgk9eJyDQmGESNyVxywekR1Smv0pm83jWsfucsETV3TDCIGou5A80ATo+oTEW16eQiwJPF+ojqiwkGUWORKgkOsCy4Cu3/Ld/k9SKJo9qJyDQmGESNwdyBZhFxLAuuQmeumN5B0i7QQ+ZIiJo+JhhEtlaQBZxMEW9ncqFKRy8UiLZx9whR/THBILKlgizg1Cbxdh5opkqCIOD789dE292d61X0mIhQz1LhRCTB3MgFwJLgKvWfM3mibdHcPUJkFSYYRA1VkHVrQafUmguAB5qpmNjaCwCIa+svYyRE9oMJBlFDmKtzUUuj5eiFSkkdxzSqWyinR4isxDUYRNaypM5FrZixHL1QqR8yrou2cfcIkfWYmhNZK/eU+T4aLZMLlftRZPdIgIeTzJEQ2RcmGETWKi+UbndyvzUtwuRCtc7l3hRtS+gaLGMkRPaHCQaRtYqvmL7OxKLJ2PFzjmhboKeLjJEQ2R8mGETWKMgSb+v3nHxxkNUqa0yfOwIAvSN9ZYyEyD5xkSeRNcTOGfEKlTcOsoogCPhnWqZoOyt3EjUcRzCI6iszTbzmhauPrKFQ/ZVW1uCjA7+Ltof7uckYDZH9YoJBVB/m6l7w+HXV0usFbPs5B5lXSyT7jYwOkSkiIvvGBIPIUubqXrBSp6qt+f53lFWJr7uo5erkIEM0RPaPCQaRpcTWXdRipU5V0ukFHMkqsCi5eG5wexkiImoemGAQWaIgS/qskYg4jl6o1LZTV/B7fqnZfk/f1xYOWo0MERE1D0wwiCwhNXoREccj2FXq5KVCi5KL5we3h5bJBZFNMcEgkmLupFQndyYXKnW5sBx7f70q2WdEdAjaB3owuSBqBEwwiMQUZAGnNgGCXrwP112o1uajf0i2h3i7oGOwp0zREDU/TDCIxOSekk4uuGtE1bxdW+BGWbXJtri2/oiN8pM5IqLmhQkGkRhzh5lx9ELVCstNJxedQ71wTxt/maMhan6YYBBZg7tGVEsQBKRnXocgmG4f2jlI3oCImikmGERixE5L7fYokwsVy7pWisNZBSbbwnxdodFwQSeRHHjYGZEpYqeleoUyuVC5LSdEEkMA10oqZYyEqHljgkFkSu4ppSMgK1TrJBblAhjSidMjRHJhgkH0ZwVZQN4Z0208LVXVtp0SH73oGOyJNi09ZIyGqHnjGgyiO9XWvhDD01JVq0anx4Vr4uXch3cN5voLIhlxBIPoTme/la59wfUXqvX+3gzRtjE9w5hcEMmMCQZRLXMHmgV1li8Wqpff8m5Ktkf4u8sUCRHVYoJBVEvqQDONltMjKrb9VI5o26S4CBkjIaJaTDCIACAzTXr0ImYsp0dUKjO/RLLd38NZpkiI6E5MMIgKsoDs/4q3s7CWqn0rUfdi5sC2MkZCRHdigkEkNTXCA81UrUjkvBEA6B3pC1cnBxmjIaI7cZsqNW/mFnbyQDPV+vFCAQ6evyba3r99SxmjIaI/4wgGNW9Soxc80Ey1CsuqJJMLIlIeEwxqvqQWdjq5A23ukzUcsoxOL+CTHy5I9unXLkCeYIhIFBMMap4y06QXdnJqRLV2ns412+fuSF8ZIiEiKUwwqPkxt2uECztVzVxRrQmxrVm1k0gFuMiTmh+pdRcARy9UrLSyRrSthVaDZwe1Y3JBpBJMMKh5MVdQiws7VetGaRXWHbog2j7rfiYXRGrCKRJqPsxNjUTEcWGnikklF21aukOrZXJBpCZMMKj5yD0l3sZdI6qm1wuS7fe08ZcpEiKyFBMMaj7KC8XbuO5C1W6UVUm2B3m5yBQJEVlKFQnGqlWrEBkZCRcXF8TGxuLIkSOifdesWYP+/fvD19cXvr6+iI+Pl+xPZFAscmYF112o3n/O5Im2zejPz45IjRRPMDZu3IjExEQsXLgQx48fR7du3ZCQkICrV6+a7L9v3z6MHz8eaWlpSE9PR3h4OIYOHYrLly/LHDk1KZlp4m2cGlG93KIKk9eHdA6Cp4ujzNEQkSU0giBIT242stjYWNx9991YuXIlAECv1yM8PByzZ8/Gyy+/bPZ+nU4HX19frFy5EpMmTTLbv7i4GN7e3igqKoKXl1eD46cmoCALOJlius0rFOg1Wd54qF70egHvpp432fbc4PZw4OJOIlVSdASjqqoKx44dQ3x8vOGaVqtFfHw80tPTLXqOsrIyVFdXw8/Pz2R7ZWUliouLjR7UzEjVvXD1kS0Mss6B8/mibUwuiNRL0QTj2rVr0Ol0CAoKMroeFBSE3Fzz5YABYN68eQgNDTVKUu6UnJwMb29vwyM8PLzBcVMTYu601OAY+WKhehMEAT9lF5psa+npLG8wRFQviq/BaIilS5ciJSUFX3/9NVxcTK8iT0pKQlFRkeFx6dIlmaMkxUhNjQBc3NkErE+/KNr2UI8wGSMhovpStJJnQEAAHBwckJdnvEI8Ly8PwcHBkve+/fbbWLp0Kfbs2YOYGPHfQp2dneHszN90mp2CLODUJvF21r1QvaKyahSUim9PdXdmIWIiNVN0BMPJyQm9evVCamqq4Zper0dqairi4uJE73vrrbfw+uuvY+fOnejdu7ccoVJTc/ZbQNCLt7PuharV6PRY+0OWaLufu5OM0RCRNRT/FSAxMRGTJ09G79690adPH6xYsQKlpaWYOnUqAGDSpEkICwtDcnIyAODNN9/EggULsGHDBkRGRhrWanh4eMDDw0Ox90EqYm7dBU9LVTVBEPD+3gzJPuPu5loqIrVTPMEYN24c8vPzsWDBAuTm5qJ79+7YuXOnYeFndnY2tNrbAy0ffPABqqqq8Mgjjxg9z8KFC/Haa6/JGTqplVRJcI2Woxcqd+G6RHIIYFq/KLg4OsgUDRFZS/E6GHJjHYxmIC1ZvK3boxy9ULl3dv8m2hYZ4IaHerSSMRoispbiIxhENlOQJV3zgsmF6pVW1ki2j+7OnSNETQUTDLIPmWnSR7EDTC5U7mpxBT47nC3a/kJ8e2g0LKxF1FQ06ToYRABujVyYSy6COssTC1lNKrkAwOSCqIlhgkFNn9S0SC1W7FS1n7JvSLaP79NapkiIyFY4RUJNm7ktqQArdqqYIAjIL6nEvnPi540AQLC36Uq9RKReTDCoaZMavXByv7UllcmFKlXW6PDPtEyz/WYPaidDNERka0wwqOmSGr1wcgf6PSdvPGSRK4Xl2PijZWcCcWEnUdPFBIOaLqmCWiympUqnLxdh95k88x0BDO4UyOSCqAnjIk9quvLOmL7OUuCqpNMLFicXABDTyqfxgiGiRscRDGp6zBXU4uiF6vz8RxH2nLUsufBzd8LEeyIaOSIiamxMMKhpqT2GXeqkVI5eqEpmfonFycWs+9vBqQUHVonsARMMajoKsoCTKdJ9WFBLdb49ccVsn4lxEfB3d+KaCyI7wgSDmobakQtzWFBLVSw5S3HOkA4yREJEcmOCQU3D2W+lp0UAFtRSEZ1ewNc/XcalAvEiaI4OGlboJLJjTDBI/TLTzFfr5EmpqlGj0+P9vRmSfTQa4KmBbeHowPUWRPaKCQapm7mDzDRaIGYskwsV+flykdk+D3YPY3JBZOf4L5zUTaqYFsDkQoXMnSsCAJH+bjJEQkRKYoJB6lWQJV5MC+C0iAqVVdWY7TOqWyh3ixA1A5wiIXXKTJOeGuGCTlURBAGHswqQnnldtM/Aji0R5e8OX3cnGSMjIqUwwSD1MbfuAgDa3CdLKGSeIAhYsee8ZJ+7gj3Rs7WvTBERkRpwioTUx9y6CxbTUpXtP+eY7XP/XYEyREJEasIEg9RHat2FRstiWipzPq/EbB8XRwcZIiEiNeEUCalLQZZ4m5P7rYPMuPZCcYIgQC8AecUVZvs+N7i9DBERkdowwSB1KMi6NTUiNXrR7zn54iGTBEHA9p9zLBq1GNY1GJ1CvGSIiojUiAkGKc+SE1K57kIVzC3mrDW0SxCTC6JmjgkGKcuSE1IBrrtQWGWNDv9My7S4f7tAj0aMhoiaAiYYpBxLT0h1cue6C4Xo9AI+P5KN/JuVFt/j6+YI5xZc1EnU3DHBIOVYckKqRntrYScp4r1Uy6ZEank4t8DkvpGNEwwRNSlMMEgZlpyQGtT51tQIRy9kJwgCPv3vRYv69ozwhbuTAyL83dHS07mRIyOipoIJBsmrIOvWyIVUcsETUhV3PPsGrpVUme03c2BbuDpxOoSI6mKCQY3Pki2od2JyoQhBEJCZX4qtJ69Y1P/5we2h1fLQMiIyjQkGNR5LRiv+jCekKqJap8fKvRkW9Z0Q2xqBXi6NHBERNXVMMMj2rEksAJ6QqgBBEPDz5SKknr1qUf8nBrSBhzN/bBCRefxJQbZl7ph1MRFxPCFVBpU1Oly+UY6yKh0qqnX4/vw1i+99sHsokwsishh/WpBtWDtqwfNFZHO1uAKfHc626t6eEb5o05LFs4jIckwwqOEsKfX9Z9yCKjtrkosIfzfc2z4AgZ5cc0FE9cMEg6xX390hHK1QzCc/SJxSK+KF+PbQaLhLhIiswwSDrFOftRZMLBQhCAIKSquQcbUEhWXVFt83tEsQuoR6N2JkRNQcMMEgy9V3xALg4k2FFJVVY209Ry3uCvZEQpdg1rYgIptggkGWsWadBWtaKEIQBIuTi8fviYCDVgMfV0cmFkRkU0wwSJo1oxYs9a2Y+hTMYplvImpMTDDINGu3nXJ3iCJ0egE/Zd+wuK5FbJQfkwsialRMMKgua6ZDuNZCERXVOvyWd9PiSpwA0DnUC33bBTRiVERETDCap9ppj/JC0+3Flh12BYAjFgrQ6wVcvVmJy4VlOPCb5ZU4H+oRhmBvF7g4cuSCiBofE4zmxprRCVO4zqJRlFXVoLL69mdToxdw4lIhCkor4aDVoqJah/yblfV+Xp4hQkRy40+c5ubstw1PLjhqYXM3Squw7tCFRnnuaf2imFwQkez4U8fe3TkdUp+pD1O47bRR7P8tH8cv3rD583YO9cI9bfzh7epo8+cmIjKHCYY9s/Zk0z/jdEij+fxINnKLKmz6nN3DfXD/XYE2fU4iovpigmEPTC3arO9ohVeo6euuPpwOaYDcogpkXC1BRbWuTtvPl4ts+lpRAe4Y3SPMps9JRGQtJhhNkS2nPQBOfVhApxeQU1SOayVVFvUXBAH7zuU3SiwBns4AgMpqHTqHeiEqwB3+7s5waqFtlNcjIrIGEwy1+/PohC0SijtFxDXr5EKnF3CtpBLVulsLX89cKUbWtVK43VGEqlonoKjc8sPCbGVwp9vTHFqNBmE+rvB1d5I9DiIia6giwVi1ahWWLVuG3NxcdOvWDe+//z769Okj2v+LL77A/PnzceHCBbRv3x5vvvkmRowYIWPEdzBXU6IhbJ1M1PIKbfZTH4IgIO3cVZy8ZHqaoqyq7pSGXEZ1C0W7QA/FXp+IyBYUTzA2btyIxMRErF69GrGxsVixYgUSEhJw7tw5BAbWXah26NAhjB8/HsnJyfif//kfbNiwAaNHj8bx48fRtWtXeYO3VU0JubDapsEPGddFkwslPdQjDJEB7kqHQUTUYBpBEAQlA4iNjcXdd9+NlStXAgD0ej3Cw8Mxe/ZsvPzyy3X6jxs3DqWlpdi2bZvh2j333IPu3btj9erVZl+vuLgY3t7eKCoqgpeXV8OCP7OlfoeAyenORZvNfLTiz3R6Ae+lnlfs9f+8VqKqRg9XJwc8FtsaXi7cUkpE9kHREYyqqiocO3YMSUlJhmtarRbx8fFIT083eU96ejoSExONriUkJOCbb74x2b+yshKVlbcrHxYV3fqttbi4uIHRA8jPAUptu8XQap4hgKs3ENgF8Is03ccW79kO3CitQkVpiayv6eKkxYTYCOky3VXlKK4qly8oIiIreXp6QqPRSPZRNMG4du0adDodgoKCjK4HBQXh119/NXlPbm6uyf65ubkm+ycnJ2PRokV1roeHh1sZNZF1Es13ISJqEiyZBVB8DUZjS0pKMhrx0Ov1KCgogL+/v9nsSy7FxcUIDw/HpUuXGj5t08TwvfO98703H835vQP29f49PT3N9lE0wQgICICDgwPy8vKMrufl5SE4ONjkPcHBwfXq7+zsDGdnZ6NrPj4+1gfdiLy8vJr8N521+N753psbvvfm+d6B5vP+Fa3M4+TkhF69eiE1NdVwTa/XIzU1FXFxcSbviYuLM+oPALt37xbtT0RERPJTfIokMTERkydPRu/evdGnTx+sWLECpaWlmDp1KgBg0qRJCAsLQ3JyMgDg+eefx8CBA/GPf/wDI0eOREpKCo4ePYqPPvpIybdBREREd1A8wRg3bhzy8/OxYMEC5Obmonv37ti5c6dhIWd2dja02tsDLX379sWGDRvw6quv4pVXXkH79u3xzTffyF8Dw4acnZ2xcOHCOlM5zQHfO997c8P33jzfO9D83r/idTCIiIjI/vB0JCIiIrI5JhhERERkc0wwiIiIyOaYYBAREZHNMcGQSXJyMu6++254enoiMDAQo0ePxrlz54z63HfffdBoNEaPmTNnKhSx7bz22mt13tddd91laK+oqMCsWbPg7+8PDw8PPPzww3WKqTVlkZGRdd6/RqPBrFmzANjX537gwAGMGjUKoaGh0Gg0dc4IEgQBCxYsQEhICFxdXREfH4/z540PnisoKMCECRPg5eUFHx8fTJ8+HSUl8p4dYw2p915dXY158+YhOjoa7u7uCA0NxaRJk3DlyhWj5zD1vbJ06VKZ30n9mfvcp0yZUud9DRs2zKiPPX7uAEz+29doNFi2bJmhT1P93M1hgiGT/fv3Y9asWfjvf/+L3bt3o7q6GkOHDkVpaalRvyeeeAI5OTmGx1tvvaVQxLbVpUsXo/d18OBBQ9ucOXOwdetWfPHFF9i/fz+uXLmCMWPGKBitbf34449G73337t0AgL/85S+GPvbyuZeWlqJbt25YtWqVyfa33noL7733HlavXo3Dhw/D3d0dCQkJqKi4fWjghAkT8Msvv2D37t3Ytm0bDhw4gCeffFKut2A1qfdeVlaG48ePY/78+Th+/Di++uornDt3Dg888ECdvosXLzb6Xpg9e7Yc4TeIuc8dAIYNG2b0vj7//HOjdnv83AEYveecnBysXbsWGo0GDz/8sFG/pvi5myWQIq5evSoAEPbv32+4NnDgQOH5559XLqhGsnDhQqFbt24m2woLCwVHR0fhiy++MFw7e/asAEBIT0+XKUJ5Pf/880Lbtm0FvV4vCIL9fu4AhK+//trwtV6vF4KDg4Vly5YZrhUWFgrOzs7C559/LgiCIJw5c0YAIPz444+GPt99952g0WiEy5cvyxZ7Q/35vZty5MgRAYBw8eJFw7WIiAjhnXfeadzgGpmp9z558mThwQcfFL2nOX3uDz74oDBo0CCja/bwuZvCEQyF1B4b7+fnZ3T9s88+Q0BAALp27YqkpCSUlZUpEZ7NnT9/HqGhoWjTpg0mTJiA7OxsAMCxY8dQXV2N+Ph4Q9+77roLrVu3Rnp6ulLhNpqqqip8+umnmDZtmtFhe/b6ud8pKysLubm5Rp+1t7c3YmNjDZ91eno6fHx80Lt3b0Of+Ph4aLVaHD58WPaYG1NRURE0Gk2ds5GWLl0Kf39/9OjRA8uWLUNNTY0yAdrYvn37EBgYiI4dO+Lpp5/G9evXDW3N5XPPy8vD9u3bMX369Dpt9vi5K17JsznS6/V44YUX0K9fP6MKpI899hgiIiIQGhqKU6dOYd68eTh37hy++uorBaNtuNjYWKxbtw4dO3ZETk4OFi1ahP79++P06dPIzc2Fk5NTnR+yQUFByM3NVSbgRvTNN9+gsLAQU6ZMMVyz18/9z2o/z9oqvbXu/Kxzc3MRGBho1N6iRQv4+fnZ1fdDRUUF5s2bh/HjxxsdevXcc8+hZ8+e8PPzw6FDh5CUlIScnBwsX75cwWgbbtiwYRgzZgyioqKQmZmJV155BcOHD0d6ejocHByazef+73//G56ennWmgO31c2eCoYBZs2bh9OnTRusQABjNN0ZHRyMkJASDBw9GZmYm2rZtK3eYNjN8+HDDn2NiYhAbG4uIiAhs2rQJrq6uCkYmv48//hjDhw9HaGio4Zq9fu5kWnV1NcaOHQtBEPDBBx8YtSUmJhr+HBMTAycnJzz11FNITk5u0uWlH330UcOfo6OjERMTg7Zt22Lfvn0YPHiwgpHJa+3atZgwYQJcXFyMrtvr584pEpk9++yz2LZtG9LS0tCqVSvJvrGxsQCAjIwMOUKTjY+PDzp06ICMjAwEBwejqqoKhYWFRn3y8vIQHBysTICN5OLFi9izZw9mzJgh2c9eP/faz/PPO4Tu/KyDg4Nx9epVo/aamhoUFBTYxfdDbXJx8eJF7N692+yR3bGxsaipqcGFCxfkCVAmbdq0QUBAgOF73N4/dwD4/vvvce7cObP//gH7+dyZYMhEEAQ8++yz+Prrr7F3715ERUWZvefEiRMAgJCQkEaOTl4lJSXIzMxESEgIevXqBUdHR6Smphraz507h+zsbMTFxSkYpe198sknCAwMxMiRIyX72evnHhUVheDgYKPPuri4GIcPHzZ81nFxcSgsLMSxY8cMffbu3Qu9Xm9IvJqq2uTi/Pnz2LNnD/z9/c3ec+LECWi12jrTB03dH3/8gevXrxu+x+35c6/18ccfo1evXujWrZvZvnbzuSu9yrS5ePrppwVvb29h3759Qk5OjuFRVlYmCIIgZGRkCIsXLxaOHj0qZGVlCVu2bBHatGkjDBgwQOHIG+7FF18U9u3bJ2RlZQk//PCDEB8fLwQEBAhXr14VBEEQZs6cKbRu3VrYu3evcPToUSEuLk6Ii4tTOGrb0ul0QuvWrYV58+YZXbe3z/3mzZvCTz/9JPz0008CAGH58uXCTz/9ZNgpsXTpUsHHx0fYsmWLcOrUKeHBBx8UoqKihPLycsNzDBs2TOjRo4dw+PBh4eDBg0L79u2F8ePHK/WWLCb13quqqoQHHnhAaNWqlXDixAmjnwGVlZWCIAjCoUOHhHfeeUc4ceKEkJmZKXz66adCy5YthUmTJin8zsyTeu83b94U5s6dK6SnpwtZWVnCnj17hJ49ewrt27cXKioqDM9hj597raKiIsHNzU344IMP6tzflD93c5hgyASAyccnn3wiCIIgZGdnCwMGDBD8/PwEZ2dnoV27dsJf//pXoaioSNnAbWDcuHFCSEiI4OTkJISFhQnjxo0TMjIyDO3l5eXCM888I/j6+gpubm7CQw89JOTk5CgYse3t2rVLACCcO3fO6Lq9fe5paWkmv88nT54sCMKtrarz588XgoKCBGdnZ2Hw4MF1/k6uX78ujB8/XvDw8BC8vLyEqVOnCjdv3lTg3dSP1HvPysoS/RmQlpYmCIIgHDt2TIiNjRW8vb0FFxcXoVOnTsKSJUuM/hNWK6n3XlZWJgwdOlRo2bKl4OjoKERERAhPPPGEkJuba/Qc9vi51/rwww8FV1dXobCwsM79TflzN4fHtRMREZHNcQ0GERER2RwTDCIiIrI5JhhERERkc0wwiIiIyOaYYBAREZHNMcEgIiIim2OCQURERDbHBIOIiIhsjgkGEanehQsXoNFoEBkZWaft3LlzeP/99zFlyhRER0ejRYsW0Gg0+Pvf/y5/oERkwOPaiahJ++CDD/Duu+8qHQYR/QlHMIioSevatSvmzp2Lzz77DGfPnsXEiROVDomIwBEMImriZsyYYfS1Vsvfm4jUgP8Sicgqv/76KzQaDXx9fVFRUSHar3fv3tBoNNiyZQsA4MyZM1i4cCH69euHsLAwODk5wd/fH/Hx8di0aZNc4RNRI2OCQURWueuuuxAXF4fCwkJ88803Jvv8/PPPOHbsGIKCgjBy5EgAwPLly7F48WIUFBQgOjoaY8aMQceOHZGWloZx48YhMTFRxndBRI2FCQYRWW3atGkAgHXr1pls/+STTwAAjz/+OFq0uDUjO3HiRGRmZuLs2bPYuXMnUlJScOjQIZw5cwatWrXCO++8gyNHjsgSPxE1HiYYRGS1cePGwc3NDbt378bly5eN2qqrq/Hpp58CAKZOnWq4PnDgQLRp06bOc3Xs2BHz588HAGzevLkRoyYiOXCRJxFZzdPTE4888gjWr1+P9evXIykpydC2fft25Ofno0+fPujSpYvRfSUlJfjuu+/w008/4dq1a6iqqgIA5OTkALhV24KImjYmGETUINOmTcP69euxbt06owSjdnrkztELANi6dSumTp2K69eviz5ncXFx4wRLRLLhFAkRNciAAQPQtm1b/Pbbbzh06BAA4OrVq9ixYwdcXFzw6KOPGvpevnwZ48aNw/Xr1/HSSy/h5MmTKCoqgk6ngyAI2LVrFwBAEARF3gsR2Q4TDCJqEI1GgylTpgC4PWrx6aefoqamBmPGjIGPj4+h79atW1FeXo6HHnoIb775JmJiYuDl5WWoXXH+/Hm5wyeiRsIEg4gabMqUKdBqtdi0aRPKyspEp0cKCgoAABEREXWeQxAEbNiwofGDJSJZMMEgogZr1aoVhgwZguLiYrzyyis4ffo0WrdujUGDBhn169SpE4Bbu0RqF3QCgE6nw4IFCwxTLETU9HGRJxHZxNSpU7Fr1y7DwWO1oxp3GjVqFHr16oVjx46hQ4cOGDhwINzd3XH48GFcuXIF8+bNw5tvvlmv1z1+/DieeeYZw9eZmZkAgA8//BDbtm0zXP/6668REhJi7dsjonpigkFENjF69Gj4+fmhoKDAaF3GnVq0aIF9+/YhOTkZX375JVJTU+Hl5YW+ffviyy+/xM2bN+udYBQXF+Pw4cN1rv/xxx/4448/DF9XVlbW+z0RkfU0ApdrExERkY1xDQYRERHZHBMMIiIisjkmGERERGRzTDCIiIjI5phgEBERkc0xwSAiIiKbY4JBRERENscEg4iIiGyOCQYRERHZHBMMIiIisjkmGERERGRzTDCIiIjI5v4P1zEEArrIdFUAAAAASUVORK5CYII=", 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" ] @@ -390,7 +390,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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", 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", 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", 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", 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" ] @@ -604,7 +604,7 @@ "outputs": [ { "data": { - "image/png": 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", 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SKZKSkjB27NiHPre6ulp9p2jg/mRaIiIyHGlyBX5OycO57BKUVzc+9+SPwnwcsXh0KExNtJoJQSJodlB566231N9LJBIcOnSo0bACAJ6enli+fHlLemvQ/PnzoVAoEBwcDBMTEyiVSixduhRxcXEAALlcDgCQyepPfpLJZOpjcrkcbm5u9Y6bmprCyclJXfNny5Yta3CDOyIi0q+conv4cH8GrhY0f56hrYUp3h7TFUEy3Z/5J91qdlA5ePAggPuXVh555BE899xzmDJlykN1JiYmcHJyQnBwMKRS3SfULVu24Pvvv8fGjRvRtWtXpKSkYM6cOfD09GywH11ZsGAB5s6dq36sUCjg4+PTap9HRESNq6iuw+4Lt3H2ZjEu5TV9htvcVIqe3o4I8bBDuF8HBLjaitAl6UKzg8rgwYPV3y9evBhDhgyp95xY3nzzTcyfPx+TJk0CAHTv3h03b97EsmXLMGXKFLi7uwMA8vPz4eHhoX5dfn4+wsLCAADu7u4oKCio9751dXUoKipSv/7PLCwseJNFIiIDcDLzLpbuvtJkXaiHPSb08Uagqy3sLc0glRrnTunGTqvJtIsXL9Z1H8127969h87UmJiYQKW6v1GPv78/3N3dkZCQoA4mCoUCSUlJmDFjBgAgOjoaJSUlSE5ORnh4OADgwIEDUKlUiIqKEu+HISKiZjuXXYxFOy81Wfdkby8M7uwKfxcbo72NS3ui1bWZDRs2IDw8HHl5eQ0ez8vLQ3h4eKss9R09ejSWLl2K3bt3IysrCzt27MAHH3ygngArkUgwZ84cvPPOO/j5559x4cIFTJ48GZ6envjb3/4GAAgJCcFjjz2G6dOn49SpUzh+/DhmzZqFSZMmccUPEZGBOZddjNEfH9MYUqRSCSL9nfDlcxGY2t8fAa62DClGQqst9IcOHYqqqiokJiY2WjNgwABYWFggISGhRQ3+WVlZGRYuXIgdO3agoKAAnp6eePrpp7Fo0SL1vYUEQcDixYuxfv16lJSUYMCAAVizZg06d+6sfp+ioiLMmjULu3btglQqxbhx47B69WrY2jbvuiW30Ccial1VtUq8vy8dp24Uaax7srcXxvbygqO18d5frj3TKqjIZDKMGzcOa9asabRm1qxZ2L59O27fvt2iBg0VgwoRke4JgoCkG0XYeiYXGfllGmsdrc3waVxv2FuaidQd6YNWc1RKS0vRoUMHjTX29vb17qpMRESkiSAIWLLrMpJvav5/Rxd3O8x+pBM3Z2sntAoqnp6eSElJ0ViTmpr60F4mREREDTmfW4J/7bioscbL0QofTgyDlbmJSF2RIdBqMm1MTAz27duH/fv3N3j8t99+w969exEbG9ui5oiIyLjJS6uwYm+axpAS6GqDmUMDsfbZ3gwp7ZBWc1Ru3LiBsLAw3Lt3D/Hx8Rg2bBi8vLxw69Yt/Pbbb/juu+9ga2uLs2fPtsodlA0B56gQEbXMplPZ2Hwqu9F78Xg5WuGDiT1hbd5qt6WjNkCroAIAx44dw6RJk5CXl1dvCZggCPD29saWLVta5V4/hoJBhYhIO0qVgEU7L+J8bmmjNWPCPDE5uiPMTXkPnvZO66ACADU1Ndi5cydOnTqF0tJSODo6IjIyEk888YR6qbCxYlAhIvprVCoBey/J8d9jN1Bdp2qwxs7SFGvjwuFgzZU8dF+Lgkp7xqBCRNR8lTVKTPis8b23nG3NMTnaDwODXGHGOxnTH7T4wl95eTkyMjJQUVGBgQMH6qInIiIyItV1Srz0XXKjx/t3csG8x7pwJ1lqkNZBJSsrC6+++ip+/fVXqFQqSCQS1NXVAQCOHz+O6dOnY82aNRgyZIiueiUiojbkVkklPjlwDelyBWqVD5+87+hig+f7d0QvX837clH7plVQyc7ORt++fXH37l2MGTMGcrm83nb6UVFRuHPnDjZt2sSgQkTUjqhUAo5du4OfU/OQLm98Z9lwvw5YPDqUZ1GoSVrfPbm4uBiHDx9Gv379sGTJknpBxdTUFAMHDsTx48d11igRERm2UzeKsOFEFrKL7mmsiwmR4dWYIJG6orZOq6Cyb98+jB07Fv369Wu0xs/PDwcOHNC6MSIiahtyiu5h3eHrGpcbP7BgRDCiA5xF6IqMhVZBpaioCB07dtRYIwgCqqurtXl7IiJqAwRBwKGMQnzwW4bGuuhAZ4wJ80RXTweROiNjolVQkclkuHr1qsaaCxcuwNfXV6umiIjIsJVX1+GNLam4VVLZ4HFPR0s83s0DsV3due09tYhWQWXYsGH49ttvcf78efTo0eOh40ePHsWBAwcwZ86clvZHREQGpLJGib2XbuO/x7Iarfn7o0F4NNgNUiknylLLabXhW1ZWFsLCwgAAb775JtLS0rBx40b88ssvOHHiBD744APY2NggNTUVHh4euu7ZIHDDNyJqD6pqlcgpuoeSylpcvFWKvRfluFejbLA20t8JLw8OhKudhchdkjHTemfapKQkTJo0CTdv3oREIoEgCOr/+vr6Ytu2bYiIiNB1vwaDQYWIjFl1nRJbz+Ti59Q8VDYSTB6QSoCJfXzxdKQPlxuTzrVoC/26ujrs2rULSUlJKCoqgr29PaKiojBmzBje64eIqA26V1OH/Zfz8cXRG82q7xfojIl9fBDgatvKnVF7xXv9aIlBhYiMTVLmXXz4ewYqqjWfQZFIgL4BzhjbywshHvz7j1pXi+/1Q0REbZcgCLiUp8D3STdx8Zai0TpbC1O42Vsg1MMeo3p6wsvRSsQuqT1rVlD55ptvtP6AyZMna/1aIiJqHZU1Smw8lY1TN+4ir6Sq0ToHKzO8NiwI4X5OInZH9D/NuvQjlUr/8gSpB5NrlUrNpxDbKl76IaK26G55NTadysa+S/lN1s4cGoghXdxgacZ9UEh/mnVG5auvvmrtPoiIqBWpVALe25uGM1lFDd7J+AGpBAh0tcU/R4bAxZbLjEn/OJlWSzyjQkRtRfLNIrz182WNNa52Fhjd0wOPdJHBwdpMpM6ImsbJtERERuhOeTVSsktw4vpdnM4qarTO38UGgzu74sneXtwDhQwSgwoRkREpKKvCB79l4FJe4yt4AMDa3AT/Gd8TPk7WInVGpJ1mBZWAgACt3lwikeD69etavZaIiJrvdmkllu9Jw407FVA1cUH/yd5emNrfX5zGiFqoWUFFpVJpdUqQ01+IiFpXrVKFNQev4/crTa/ikUqAlRPC0MmNu8hS29GsoJKVldXKbRAR0V+VmlOCf/90scm6UT084OdsjegAF06UpTaHc1SIiNqQsqpa7Eq9jc2ns6HppLWjtRmW/q07fJ05B4XaNp0ElYqKCigUCtjb28PGxkYXb0lERH8gCAK+PXkTP6fkobpO1Widr5M15sQEIUhmJ2J3RK1Hqu0La2pqsHTpUgQFBcHe3h7e3t6wt7dHUFAQ3n33XdTU1Oiyz3pu3bqFZ599Fs7OzrCyskL37t1x5swZ9XFBELBo0SJ4eHjAysoKMTExuHr1ar33KCoqQlxcHOzt7eHo6Ihp06ahvLy81XomItJGdZ0SB9MK8MQnx7H1TK7GkDLrkU745JleDClkVLTa8K2yshKPPvookpKSYGJigoCAAHh4eEAul+P69etQKpWIiopCQkICrKx0e+Oq4uJi9OrVC0OHDsWMGTPg6uqKq1evIjAwEIGBgQCA5cuXY9myZdiwYQP8/f2xcOFCXLhwAZcvX4alpSUA4PHHH8ft27fx2Wefoba2FlOnTkWfPn2wcePGZvXBDd+IqDVdKyjDl8duIF1epnEnWTMTCXydrLFkTDc4WHH+CRkfrYLKW2+9hbfffhsTJ07EihUr4OPjoz6Wm5uLf/zjH9i8eTMWL16MxYsX67Th+fPn4/jx4zh69GiDxwVBgKenJ15//XW88cYbAIDS0lLIZDJ8/fXXmDRpEq5cuYLQ0FCcPn0aERERAIC9e/dixIgRyM3NhaenZ5N9MKgQUWvZcjoH35682WTdygk94e9iAzMTrU+OExk8rYJKSEgIbGxs6l1u+bM+ffqgvLwcV65caVGDfxYaGorY2Fjk5ubi8OHD8PLywiuvvILp06cDADIzMxEYGIhz584hLCxM/brBgwcjLCwMH330Ef773//i9ddfR3Fxsfp4XV0dLC0tsXXrVowdO/ahz62urkZ1dbX6sUKhgI+PD4MKEelMrVKFCZ8lok7DGRRrcxOMCfPCpD4+kEq5kywZP61ieFZWFoYPH66xJiYmplWWNWdmZmLt2rUICgrCvn37MGPGDPz973/Hhg0bAAByuRwAIJPJ6r1OJpOpj8nlcri5udU7bmpqCicnJ3XNny1btgwODg7qrz+eRSIi0oXXt6Q2GlKC3GzxwkB/rH02HM9E+TKkULuh1aofa2trFBYWaqwpLCyEtbXul8WpVCpERETg3XffBQD06tULFy9exLp16zBlyhSdf94DCxYswNy5c9WPH5xRISJqiXs1dTiSUYh9l/Jx407FQ8d9nKww77Fg+DlzRSW1T1qdUenbty82b96MS5cuNXj88uXL+OGHHxAdHd2i5hri4eGB0NDQes+FhIQgOzsbAODu7g4AyM+vv0tjfn6++pi7uzsKCgrqHa+rq0NRUZG65s8sLCxgb29f74uISFsqlYAP9mcg7oskfHrwOq4VPLzq0NJMik+f6c2QQu2aVkHln//8J6qqqtCnTx/Mnj0b27Ztw9GjR7Ft2zbMmjULffr0QXV1NRYsWKDrftG/f3+kp6fXey4jIwN+fn4AAH9/f7i7uyMhIUF9XKFQICkpSR2coqOjUVJSguTkZHXNgQMHoFKpEBUVpfOeiYj+qKpWiTGfHsfBtIJGL/XYWZpiy0vRvKMxtXtaTaYFgK1bt2L69OlQKBT1/iAJggAHBwd8/vnneOqpp3TW6AOnT59Gv379sGTJEkyYMAGnTp3C9OnTsX79esTFxQG4vzz5vffeq7c8+fz58w8tT87Pz8e6devUy5MjIiK4PJmIWo1SJeCH0znYdCq70RorcxMM7OSC5/p3hJ0llxsTaR1UAKCsrAw7d+7EuXPn1DvT9urVC2PGjIGdXettOPTLL79gwYIFuHr1Kvz9/TF37lz1qh/gflhavHgx1q9fj5KSEgwYMABr1qxB586d1TVFRUWYNWsWdu3aBalUinHjxmH16tWwtW3ezboYVIiouUru1WDfJTm+O9l4QAGAucM6o38nF5ibcrkx0QMtCirtGYMKETUlX1GFOZtTUF5dp7Eu3K8DFo8O5WUeogZotepHqVSioqICtra2kEofTv4PjtvY2MDExKTFTRIRtTUH0wrwwf6MJutefTQIMaGyJuuI2iutzi8uWbIEbm5uuHv3boPHi4qKIJPJsHTp0hY1R0TU1lTVKjHz+7MaQ4p3Bys8Fe6N7TP6MaQQNUGrSz+9e/eGh4cHdu/e3WjN6NGjkZeXV29ljTHhpR8i+rMCRRWmbWh8x24A+HBiT3Ry400DiZpLq0s/mZmZGDp0qMaaLl264Pjx41o1RUTUVqhUAjLvlCPhSgF+OX+70bqZQwPxWDcPETsjMg5aBZXa2toG56b8kUQiQVVVlVZNEREZsjvl1fg5JQ/JN4txq6QSSlXjJ6Zju8rw4qBAruQh0pJWQaVTp044cOCAxpoDBw7A399fq6aIiAxRTtE9fHbkOlJzSptVv+DxYPTr5NLKXREZN60i/pNPPomUlBQsWrQISqWy3jGlUomFCxciJSUF48eP10mTRET6diSjEK98f7ZZISXY3Q7r4sMZUoh0QKvJtOXl5ejTpw8yMjIQGBiIoUOHwsvLC7du3cLBgwdx/fp1hISE4OTJk83eQK2t4WRaovahuWdRZPYWGBrshiFd3ODlaCVSd0TGT+sN3woLCzFjxgzs2LEDf3yLB7u8rlmzBs7Ozjpr1NAwqBAZt6paJb47eRM7U/IarQl2t0OQzBZje3nDxdacG7YRtYIW70ybn5+PM2fOoLS0FI6OjoiIiICbm5uu+jNYDCpExkkQBFzKU2DV7xnIV1Q3WrfiqR4I8eCffaLWxi30tcSgQmQ8quuUuHirFKezinHqRhEKyxoPKIGuNlgyphscrHjDQCIxaLXqh4ioratTqnCrpBJfHc/C+dwS1Cqb/jfbP0eEoG+AEy/xEImIQYWI2pXTWUXYeiYHGfnlGvc/+aPXhgXhkWBudU+kDwwqRGTUiitq8OO5W0i+WYRbxZVoZjaBm50FBga5YFy4N+wseZmHSF8YVIjIKGXdqcD+y/n4ObXxVTsNiY/2Q2RHJ/g5W/MSD5EBYFAhIqNSWaPEkl2XcClP0ax6JxtzdPOyx6genlzFQ2SAGFSIyCjcKqnEj8m5+O1yfpO1jtZmeCrcG48Eu/GyDpGBY1AhojZNpRKw+sBVJFwp0FhnYSrFqzFBCHC1hZudBcxMeJNAoragRUElNzcXBw8eRF5eHqqrH953QCKRYOHChS35CCKih6hUAg6kFeBQRkGTW9s72ZjjpUEBiApwhomUc06I2hqtN3x788038dFHH9W7KaEgCOrJZw++//NNC40FN3wj0o/rheWYszmlybqePg54rp8/OrkZ5/3GiNoLrc59fv7551i5ciWGDh2Kbdu2QRAETJkyBZs2bcLLL78MU1NTjB8/HgcOHNB1v0TUTpXcq8GEdYnNCikfTuyJd/7WnSGFyAhodeln/fr16NixI/bs2QOp9H7W6dixIyZOnIiJEydiwoQJGDZsGMaPH6/TZomofSquqMHk/57SWONqZ4EJEd6I7erOZcVERkSroJKWlob4+Hh1SAGAuro69feDBw/GyJEj8Z///AdPPfVUy7skonZJEASk55fhza3nG63xdLTEsid7wMnGXMTOiEgsWk+mdXR0VH9vY2ODu3fv1jvepUsX/P7771o3RkTtV1lVLQ5nFOKzw5ka61ZO6InOMjuRuiIifdAqqHh5eSE3N1f9ODAwEElJSfVqLl68CBsbm5Z1R0Ttzncnb+KH0zkaa4aHyjDrkU68xEPUDmg1mbZ///44efKk+vGYMWNw7tw5vPTSS9i9ezcWLFiAPXv2YNCgQTprlIiMW1WtEm9sTW0ypMR2lWH2o0EMKUTthFbLkw8dOoTly5dj3bp18PPzQ3l5OQYPHoxz585BIpFAEAR07NgRBw8ehJ+fX2v0rXdcnkykO79fzsfGU9koLHt4P6YHhnZxxYgeHgh25583ovZE631U/qy2thY7d+7E9evX4efnh9GjRxv1pR8GFaKWO5hWgG3JucguutdozQsD/fFoiAy2FtxIm6g90llQaW8YVIi0dyarCKt+v4rSytpGa4Jktlj6t+6wMjcRsTMiMjRazVEJCAjA6tWrNdZ8+umnCAgI0KopIjJOSpWAedvOY8muyxpDynvjumPl+J4MKUSk3aqfrKwslJSUaKwpKSnBzZs3tXl7IjIyZVW1SLhSgC+P3dBYN7GPDyb18YEpbxhIRP9fq/1tUFpaCgsLi9Z6e7X33nsPEokEc+bMUT9XVVWFmTNnwtnZGba2thg3bhzy8+vf+j07OxsjR46EtbU13Nzc8Oabb9bbtI6ItKdUCTicUYhVv2dg9qZzeObzJI0hJTrQGVteisazff0YUoionmafUTly5Ei9x1lZWQ89BwBKpRI5OTn4/vvv0blz55Z3qMHp06fx2WefoUePHvWef+2117B7925s3boVDg4OmDVrFp588kkcP35c3ePIkSPh7u6OEydO4Pbt25g8eTLMzMzw7rvvtmrPRMauskaJv28+B3lpVbPqt74cDUszXuIhooY1ezKtVCpt9r4FD+6c/PXXXyM+Pr5FDTamvLwcvXv3xpo1a/DOO+8gLCwMq1atQmlpKVxdXbFx40b19v1paWkICQlBYmIi+vbtiz179mDUqFHIy8uDTCYDAKxbtw7z5s1DYWEhzM2b3oqbk2mJHna3vBrPfXW6yToTqQTjenshLsoPUin3QyGixjX7jMqiRYvUe6S8/fbbGDx4MIYMGfJQnYmJCZycnDB06FCEhITostd6Zs6ciZEjRyImJgbvvPOO+vnk5GTU1tYiJiZG/VxwcDB8fX3VQSUxMRHdu3dXhxQAiI2NxYwZM3Dp0iX06tXroc+rrq5GdfX/9nhQKBSt9JMRtT2H0guQcKUAKTklGutCPewxNNgVfQOc4WjNe/MQUdOaHVTeeust9feHDx/G1KlTMXny5NboqUmbN2/G2bNncfr0w/9yk8vlMDc3r3cvIgCQyWSQy+Xqmj+GlAfHHxxryLJly7BkyRIddE9kPGrqVBi39oTGmqFdXBHbzR0BLrZcxUNEf5lWq34OHjyo6z6aLScnB6+++ir2798PS0tL0T53wYIFmDt3rvqxQqGAj4+PaJ9PZGgqa5R4Y1uqxpoxYZ54YSC3KSAi7bW5rR6Tk5NRUFCA3r17q59TKpU4cuQIPvnkE+zbtw81NTUoKSmpd1YlPz8f7u7uAAB3d3ecOnWq3vs+WBX0oObPLCwsRFnFRNRWPP/1aZRXN7xS7rFu7hjX2xvuDuL9Y4KIjJPW6wBzcnLw0ksvITAwEFZWVjAxMXnoy9RU9zno0UcfxYULF5CSkqL+ioiIQFxcnPp7MzMzJCQkqF+Tnp6O7OxsREdHAwCio6Nx4cIFFBQUqGv2798Pe3t7hIaG6rxnImNRUFaFbxKzMPrjYw2GFHsrU2x+sS9mDu3EkEJEOqFVksjMzERUVBSKi4vRtWtXVFdXw8/PD5aWlsjMzERtbS169uz50DwRXbCzs0O3bt3qPWdjYwNnZ2f189OmTcPcuXPh5OQEe3t7zJ49G9HR0ejbty8AYPjw4QgNDUV8fDxWrFgBuVyOf//735g5cybPmhA1oE6pwju7r+BcdjFUjawTfGVIIB7v7iFuY0Rk9LQ6o7JkyRKUlpYiISEBqan3r1FPnToVV65cQVZWFp544glUVFRg27ZtOm22uT788EOMGjUK48aNw6BBg+Du7o4ff/xRfdzExAS//PILTExMEB0djWeffRaTJ0/G22+/rZd+iQxRnVKFs9nF+OJoJsauOYHkm42HlK6e9gwpRNQqtLopoZeXF/r06YOffvoJwP09VhYvXozFixcDuL+Ut3v37hg6dCg+++wznTZsKLiPChmr8uo6bD6VjYPpBVBUat6t2drcBGN7eWFChA/3QyGiVqHVpZ87d+4gODj4f29iaop79/53m3YLCwsMGzZMHWSIqG3Yc+E2fjiTg7vlNRrrwnwcMaWfHwJcbBlQiKhVaRVUXFxcUFFRUe9xVlZW/Tc2NW3yxoVEpH8l92pwu7QKnxy8huy79zTWOlqb4alwb4wJ8xKpOyJq77QKKkFBQbh+/br6cWRkJPbt24fMzEwEBASgsLAQ27ZtQ2BgoM4aJSLdUaoEJGXexe4Lt3E+t1RjbZCbLfr4OyHcrwM6ufIMChGJS6ug8vjjj+Ott95S71UyZ84c7Nq1Cz169EBISAiuXbsGhUJRbzdbItK/mjoVfkq5hV8v3G7y8g4AfPx0L3R0sRGhMyKihmk1mVahUODKlSsIDQ2FnZ0dAGDr1q146623kJmZCT8/P8yePRszZ87UecOGgpNpqa2pqK7D1K9Oo7JW2az6n2b2hwnPnhCRnmkVVIhBhdqOkns1+O1SPr49ebPRGjMTCRytzeFsY46hwW54rKs7L/EQkUFoc1voE1HTBEFARn45VuxNQ0FZdaN1nWV2GNXDA/07ucDcVOuNqomIWk2zgkp2drbWH+Dr66v1a4nor6muU+Joxh3svnAb1wrKNdY+168jxoV7i9QZEZF2mhVUOnbsCInkr58GlkgkqKvTvGEUEenGldsKLN55qck5KEEyW7wyJBCd3OxE6oyISHvNCiqTJ09+KKhkZmbi6NGjcHR0RFhYGGQyGfLz85GSkoKSkhIMHDgQAQG8vTtRayqrqsW1gnIk3SjC7vO3m6xfOaEnOssYUIio7dBqMu2lS5fQv39/zJo1CwsWLICNzf+WL1ZUVGDp0qVYu3Ytjh8/brR3I+ZkWtKnkns1WHPoOk5m3oWmP8FudhZ4rJs7Iv2d4OfMZcZE1PZoFVRGjhyJ2tpa/Pbbb43WDBs2DJaWlti1a1eLGjRUDCqkL5U1Skxan9joDQIfeCO2CwZ2cuHqHSJq07Sa5n/8+HFERkZqrImMjMTRo0e1aoqI6hMEAUUVNdh3SY4Jn2kOKT5OVnh3bHcM7uzKkEJEbZ5Wy5NVKhWuXbumsebq1avgFi1ELVNTp8LOlFvYc1GOwiaWGQe42mBoFzeEevIMHxEZD62CyqBBg7B9+3Zs3rwZkyZNeuj4pk2b8OOPP+Lxxx9vcYNE7U1NnQq/X8lH4vW7SMkpabL+jdguGNzZtfUbIyLSA63mqFy+fBnR0dEoLy9Hjx49MGDAALi5uaGgoADHjh3D+fPnYWdnhxMnTnAyLVEzqVQC1h/NxJ6LcqiamoDy/70/vgeC3fn7R0TGS+st9C9evIhZs2bhyJEjDx0bNGgQPv30U3Tt2rXFDRoqBhXSlZJ7NTicUYgvjt5ostbMRAKvDtYIdrfDxD4+cLG1EKFDIiL9afG9fnJycpCamorS0lI4ODigZ8+e8PHx0VV/BotBhXRhZ8ot/PfYjSZX8EglwFPh3oiL8uMEWSJqV1p8rx8fH592EUyIdEUQBJzNLsYv52/jTFaxxtq/PxoEDwdL+HSwhoO1mUgdEhEZDt6UkEgkNXUqZOSXYdXvGchXNL6CBwBeGhyAkd09tLp1BRGRMWFQIWpFNXUqHEjLx6H0QqTnl6FOqfkaT3xfPwwLlaGDjblIHRIRGTYGFaJWUKtUYenuK0i+qfnSzgM9fRwwJ6YzJ8cSEf0JgwqRDtUpVUjMvIsVe9ObVT+kiysmRfrCy9GqlTsjImqbGFSIdCQ1pwSrE66iQMMOsgDg52yNHt4OeKKnF9wdLEXqjoiobWJQIWqB0spafHb4Oi7cKkXJvVqNtcNDZXiuf0fYWXL1DhFRczGoEP1FFdV12H42F0k3ipB9957GWicbc4wL98YTPT1F6o6IyLi0WlDZuXMnUlNTsWjRotb6CCLR/XI+D58dzmxW7auPBmFwF1eYmWh1k3IiIoIOdqZtzNSpU/HNN99AqVS2xtvrHXembR8EQUBBWTXO55ZidcLVZr3msW7uiI/2gz0v8RARtRgv/RD9SXWdEjvO3sK57BLcuFuBypqmw3awux2Gd3XHgE4usDI3EaFLIqL2odlBpaGbD2oil8v/cjNE+qRSCTidVYR3dl9p9mvGhHni2b5+sDRjOCEiag3NDipDhgz5S9t5C4LA7b+pTci+ew8/p95C0o2iJlfuAIBEAjwaLMPsRzrxBoFERK2s2UHFxMQEbm5ueOKJJ5pVf/DgQVy92rxr+n/FsmXL8OOPPyItLQ1WVlbo168fli9fji5duqhrqqqq8Prrr2Pz5s2orq5GbGws1qxZA5lMpq7Jzs7GjBkzcPDgQdja2mLKlClYtmwZTE15Nay9qKlTYcuZHGw/m9vk1vY2FiboIrNDmK8j+ge6wM2e+58QEYmh2f9XDg0Nxd27d7F27dpm1U+dOrVVgsrhw4cxc+ZM9OnTB3V1dfjnP/+J4cOH4/Lly7CxsQEAvPbaa9i9eze2bt0KBwcHzJo1C08++SSOHz8OAFAqlRg5ciTc3d1x4sQJ3L59G5MnT4aZmRneffddnfdMhqWwrBpHrxbiq+NZGuukEmBkDw+M7eUNF1tzniEkItKDZq/6ef7557Fhwwbcvn0bbm5uTdaLteqnsLAQbm5uOHz4MAYNGoTS0lK4urpi48aNeOqppwAAaWlpCAkJQWJiIvr27Ys9e/Zg1KhRyMvLU59lWbduHebNm4fCwkKYmzd9Qziu+ml7VCoBHx+4ht+v5Gus83GywqPBMozq6QELU849ISLSp2Zv8NCrVy8IgoDk5ORm1QcHB2PQoEFaN9ZcpaWlAAAnJycAQHJyMmpraxETE1OvF19fXyQmJgIAEhMT0b1793qXgmJjY6FQKHDp0qUGP6e6uhoKhaLeF7UdKTklGPPpcY0hZWgXV6yLD8eauHCMC/dmSCEiMgDNvvQze/ZszJ49u9lvPG/ePMybN0+rpppLpVJhzpw56N+/P7p16wbg/mojc3NzODo61quVyWTqlUhyubxeSHlw/MGxhixbtgxLlizR8U9ArelaQTmOZBRix7lbTdbOHNoJj3VzF6ErIiL6K9r0zNGZM2fi4sWLOHbsWKt/1oIFCzB37lz1Y4VCAR8fn1b/XPrrLuSW4usTWcjIL9NYZ2VmgkdD3DAu3BsuthYidUdERH9Fmw0qs2bNwi+//IIjR47A29tb/by7uztqampQUlJS76xKfn4+3N3d1TWnTp2q9375+fnqYw2xsLCAhQX/Z2aobpdW4mBaITadym6y1tbCFLMf6YR+nVxE6IyIiFqizd2ERBAEzJo1Czt27MCBAwfg7+9f73h4eDjMzMyQkJCgfi49PR3Z2dmIjo4GAERHR+PChQsoKChQ1+zfvx/29vYIDQ0V5wchndl7UY6XvzvbrJDSw9sBX0yJYEghImojmrXqRyqVarU0UyKRoK6uTqvGGvPKK69g48aN2LlzZ729UxwcHGBlZQUAmDFjBn799Vd8/fXXsLe3V8+tOXHiBID7y5PDwsLg6emJFStWQC6XIz4+Hi+88EKzlydz1Y/+ld6rxZvbUnG7tEpjnczeEuMjvBHZ0QkdbJpe0UVERIajWUHlr+5K+0cHDx7U6nWNaayPr776Cs899xyA/234tmnTpnobvv3xss7NmzcxY8YMHDp0CDY2NpgyZQree++9Zm/4xqCiP1W1SvxwOgc7zt2CUtXwr69UArjYWmDBiBB0crMVuUMiItKVVrt7srFjUNGPgrIqzNt2HnfKaxqt+dfIEET4dYCpSZu7sklERH/SZifTUvuiqKrFxwlXcTKzqNGa7t4OeGdMN95/h4jIiDCokMFbe+g6fr1wu9Hj/QKdMbyrO3r5ODKkEBEZmRYFlcTERPz+++/Iy8tDdXX1Q8clEgm+/PLLlnwEtUMl92pwvbAcR6/eQcKVgkbrrM1N8GZsF0R0dBKxOyIiEpNWc1Tq6urw9NNP48cff4QgCJBIJPjj2zx4LJFIWv1eP/rCOSq6pVQJSLpxF9vO5OJqQXmzXvP11D5w5kZtRERGTavZhitXrsT27dsxdepUnDlzBoIgYM6cOUhMTMTy5cvh6OiI8ePH4/r167rul4xQcUUNnlx7Ast+TWtWSPn7o0HYObM/QwoRUTug1aWf77//Ht26dcMXX3yhfs7R0RFRUVGIiorCiBEjEBkZiUceeQQvvfSSzpol43OtoByv/ZDSrFoXW3N8MaUPTDgPhYio3dAqqFy7dg0vvPCC+rFEIkFtba36cdeuXTF69GisXbuWQYUaVFZVi+V705CaU6qxLtyvAyxMpZg+KID34yEiaoe0Cirm5uawtrZWP7a1ta23HT0A+Pn5YdeuXS3rjoxKZY0Sl2+X4tSNYo2reKIDnTGpjw8CXLlRGxFRe6dVUPHx8UFOTo76cXBwMI4cOaKeQAsAJ0+ehJMTV2O0Z5U1SpzOKoK8tAq5JZU4ef0uKmsbn1wts7fAjCGdEO7XQcQuiYjIkGkVVAYPHoydO3eqg8nEiRPxxhtvYNSoURgxYgSOHTuGY8eO4fnnn9d1v9QGKFUCDqUXYNXvV5v9mn6Bznh9eBeYm3I3WSIi+h+tgsrzzz8PpVKJW7duwdvbG7Nnz8ahQ4fwyy+/YM+ePQCAyMhIvPfeezptlgyfvLQKi3++iLwSzTcKfCDK3wlxff3g72LTyp0REVFbpNN7/Zw5cwbXr1+Hn58fIiMjIZUa77+OuY/K/5RV1eJyngJXC8rxw+kcjbVSCdBZZocu7nYY0sWNNwwkIiKNeFNCLTGo3PfF0UzsTMlrss7KzARPRXhjSGdXuNlbitAZEREZA60u/QwcOBCTJ0/G+PHj4ejoqOOWqC1QVNVi5b50nM0u0VjXr5Mz/hbmhRCP9hvmiIhIe1qdUTEzM4NKpYK5uTlGjhyJ+Ph4jBgxAmZmZq3Ro0Fqb2dUChRVuFZQjvLqOvyUcgu5xZVo6jfniZ6emD4oQJwGiYjIKGkVVAoLC7Fx40Z8++23OHv2LCQSCTp06ICJEyciLi4O/fr1a41eDUp7CSo37lTg6+M3mjxz8sCoHh7o5GaLAFdbTpAlIqIWa/EclbS0NHz77bfYuHEjbt68CYlEAn9/f8THxyMuLg6dOnXSVa8GxViDSllVLTLyy3A5T4EtZ3Kb9RorMxOM7umBZ6L8uL09ERHplE4n0x4+fBjfffcdtm3bBoVCAYlEgrq6Ol29vUExtqCScCUfey/KkZ5f1uQlnT+yNJNi7bPh3N6eiIhahVaTaRszePBg+Pj4wMXFBR988IHRhhRjUVxRg59SbuHHs7ea/ZpObrawtTCFg5UZwv06YGiwWyt2SERE7Z1OgkpRURF++OEHfPfddzh58iQAwN7eHuPHj9fF25MOVdUqkVt8D6ezirExKbvZr3O2Nce7Y7vD09GqFbsjIiKqT+ugUlNTg59//hnfffcd9u7di5qaGpiZmWHUqFGIj4/H6NGjYWHBywGGorJGiX9sP49bxfdQq2z62k6Ih516Qmyfjk5wsjEXoUsiIqL6tAoqL7zwArZv3w6FQgFBEBAZGYn4+HhMmjQJzs7Ouu6RWihfUYUXNpxpVq2/iw3eH98DFqYmrdwVERFR07SaTCuVStGxY0c8++yziI+PR1BQUGv0ZtDawmTayhol1h6+joNpBU3WjuvthXHh3rCzbD974RARkeHT6ozKkSNHMGDAAF33Qjp0/NodvLcnTWNNpL8TfJ2sMSDIBYGuvOcOEREZHt7rR0uGeEZFpRKQkluC3edv49SNokbrnh/QEWN7eYvYGRERkXZ0ujyZ9CezsBwr92cg++49jXWfPtMbvs7WInVFRETUMgwqRuDKbQX+se28xppRPTwwtb8/zE2lInVFRETUcgwqbZAgCEjMvIvDGYVIzipGdZ2qwTorMxM8GuKGCRE+6MDlxURE1AYxqLQhVbVKHL92B3svypEmL9NYO2NIIIZ2cYOVOZcZExFR28WgYuBUKgHJ2cX49cJtnMkqbtZr1sT1ho8T56EQEVHbx6BigK4XliPhSj6uF1QgTa6AqpnrsjwcLLFyQk/uhUJEREaj3QeVTz/9FO+//z7kcjl69uyJjz/+GJGRkXrrZ/OpbGw8ld2sOxibmkgQ29UdXT3t4Wpngc5udpBKJa3fJBERkUjadVD54YcfMHfuXKxbtw5RUVFYtWoVYmNjkZ6eDjc38e8K/O3Jm9hyOqfJOqkEGN7VHeMjvOFmZylCZ0RERPrRrjd8i4qKQp8+ffDJJ58AAFQqFXx8fDB79mzMnz9f42t1veFbmlyBN7c2vsTY09ESfQOc0aejE0I97HnmhIiI2oV2e0alpqYGycnJWLBggfo5qVSKmJgYJCYmPlRfXV2N6upq9WOFQqHTfk43MlF28ehQBLja8u7FRETULrXb3b/u3LkDpVIJmUxW73mZTAa5XP5Q/bJly+Dg4KD+8vHx0Wk/tQ3shfLDS30R0dGJIYWIiNqtdhtU/qoFCxagtLRU/ZWT0/Rckr/C09ESPX0c0NPHAVZmJoiP9oO1ebs94UVERASgHV/6cXFxgYmJCfLz8+s9n5+fD3d394fqLSwsYGFh0Wr9PNbNA49182i19yciImqL2u0ZFXNzc4SHhyMhIUH9nEqlQkJCAqKjo/XYGRERET3Qbs+oAMDcuXMxZcoUREREIDIyEqtWrUJFRQWmTp2q79aIiIgI7TyoTJw4EYWFhVi0aBHkcjnCwsKwd+/ehybYEhERkX60631UWkLX+6gQERHRw9rtHBUiIiIyfAwqREREZLDa9RyVlnhwxUzXO9QSERG1F3Z2dpBINN8ShkFFS2VlZQCg8x1qiYiI2ovmzPPkZFotqVQq5OXlNSsN/hUKhQI+Pj7IycnhJN1WxrEWD8daPBxrcXG8W4ZnVFqRVCqFt7d3q72/vb09f+lFwrEWD8daPBxrcXG8Ww8n0xIREZHBYlAhIiIig8WgYmAsLCywePHiVr0BIt3HsRYPx1o8HGtxcbxbHyfTEhERkcHiGRUiIiIyWAwqREREZLAYVIiIiMhgMagQERGRwWJQISIiIoPFoEJEREQGi0HFgFRXV6O6ulrfbRAR0V9w6NAhVFZW6rsNo8Wgomf79+/HiBEj0KFDB1hbW8Pa2hodOnTAiBEj8Pvvv+u7vXbjypUrCAgI0HcbRiM1NRXvvPMO1qxZgzt37tQ7plAo8Pzzz+upM+PzxRdfYMqUKfjqq68AAD/88ANCQkIQEBCAxYsX67m79mH48OHIysrSdxtGixu+6dGGDRvwwgsv4KmnnkJsbCxkMhkAID8/H7/99hu2bduGL7/8EvHx8Xru1Pilpqaid+/eUCqV+m6lzfvtt98wevRoBAUFoaysDBUVFdi6dSuGDh0K4P7vt6enJ8daB1atWoV///vfiI2NRWJiImbOnIkPP/wQr732GpRKJVauXIn3338fL774or5bNQq9e/du8PmUlBQEBwfD0tISAHD27Fkx2zJ6vHuyHi1duhSrVq3CzJkzHzr23HPPYcCAAXj77bcZVHRg7ty5Go8XFhaK1Inxe+utt/DGG29g6dKlEAQB77//Pp544gls3boVjz32mL7bMyqfffYZ1q9fj2eeeQbnzp1DZGQk1q1bh2nTpgEAvLy8sHbtWgYVHblw4QJiYmLQt29f9XOCICA1NRVDhw6Fm5ubHrszXjyjokeWlpZITU1Fly5dGjyenp6OsLAwXvvUARMTE4SFhTV6G/by8nKcPXuW/8rXAQcHB5w9exaBgYHq5zZu3IgXX3wRmzdvRp8+fXhGRUesra2RlpYGX19fAPf/TklOTkbXrl0BANeuXUOfPn1QXFyszzaNxvHjxzFlyhTExcVh8eLFkErvz54wMzNDamoqQkND9dyhceIZFT3q2rUrvvzyS6xYsaLB4//973/5i68jnTp1wmuvvYZnn322weMpKSkIDw8XuSvjZGFhgZKSknrPPfPMM5BKpZg4cSJWrlypn8aMkLW1NSoqKtSPXV1dYWtrW6+mrq5O7LaMVv/+/ZGcnIyXX34Z/fr1w/fff18vkFPrYFDRo5UrV2LUqFHYu3cvYmJi6s1RSUhIQGZmJnbv3q3nLo1DREQEkpOTGw0qEokEPLmoG2FhYTh48OBDwW/SpEkQBAFTpkzRU2fGJzg4GOfPn0dISAgAICcnp97xtLQ0dOzYUQ+dGS8HBwds2rQJX331FQYMGIAlS5ZAIpHouy2jxqCiR0OGDMHFixexdu1anDx5EnK5HADg7u6Oxx9/HC+//DL/ktGRlStXalz63bNnT6hUKhE7Ml4zZszAkSNHGjz29NNPQxAEfP755yJ3ZZyWL18OGxubRo9nZ2fjpZdeErGj9mPq1KkYMGAA4uLieNaqlXGOChERkZZUKhXKyspgb2/PMyuthEGFiIiIDBY3fNOzNWvWICYmBhMmTEBCQkK9Y3fu3OEmZDrEsRYPx1o8HGtxcbzFx6CiR6tXr8abb76J4OBgWFhYYMSIEVi2bJn6uFKpxM2bN/XYofHgWIuHYy0ejrW4ON56IpDehIaGCt9//7368fHjxwVXV1dh4cKFgiAIglwuF6RSqb7aMyoca/FwrMXDsRYXx1s/GFT0yMrKSrhx40a95y5cuCDIZDJh/vz5/KXXIY61eDjW4uFYi4vjrR9cnqxHLi4uyMnJqbcEuVu3bjhw4AAeeeQR5OXl6a85I8OxFg/HWjwca3FxvPWDc1T0aMCAAfjxxx8fej40NBQJCQnYs2ePHroyThxr8XCsxcOxFhfHWz94RkWP5s+fj+Tk5AaPde3aFQcOHMD27dtF7so4cazFw7EWD8daXBxv/eA+KkRERGSweEbFAJw6dQqJiYn1ttCPjo5GZGSknjszPhxr8XCsxcOxFhfHW1w8o6JHBQUFePLJJ3HixAn4+vrWuylhdnY2+vfvj+3bt8PNzU3PnbZ9HGvxcKzFw7EWF8dbPziZVo9eeeUVqFQqXLlyBVlZWUhKSkJSUhKysrJw5coVqFQqzJw5U99tGgWOtXg41uLhWIuL460fPKOiR3Z2djhy5Ah69erV4PHk5GQMGTIEZWVlIndmfDjW4uFYi4djLS6Ot37wjIoeWVhYQKFQNHq8rKwMFhYWInZkvDjW4uFYi4djLS6Ot34wqOjRxIkTMWXKFOzYsaPeL79CocCOHTswdepUPP3003rs0HhwrMXDsRYPx1pcHG890ee2uO1dVVWV8PLLLwvm5uaCVCoVLC0tBUtLS0EqlQrm5ubCjBkzhKqqKn23aRQ41uLhWIuHYy0ujrd+cI6KAVAoFEhOTq631C08PBz29vZ67sz4cKzFw7EWD8daXBxvcTGoEBERkcHiHBU9q6ysxLFjx3D58uWHjlVVVeGbb77RQ1fGiWMtHo61eDjW4uJ464F+rzy1b+np6YKfn58gkUgEqVQqDBo0SLh165b6OG8Zrjsca/FwrMXDsRYXx1s/eEZFj+bNm4du3bqhoKAA6enpsLOzw4ABA5Cdna3v1owOx1o8HGvxcKzFxfHWE30npfbMzc1NOH/+vPqxSqUSXn75ZcHX11e4fv0607kOcazFw7EWD8daXBxv/eAZFT2qrKyEqen/7gspkUiwdu1ajB49GoMHD0ZGRoYeuzMuHGvxcKzFw7EWF8dbP3j3ZD0KDg7GmTNnEBISUu/5Tz75BADwxBNP6KMto8SxFg/HWjwca3FxvPWDZ1T0aOzYsdi0aVODxz755BM8/fTTELh6XCc41uLhWIuHYy0ujrd+cB8VIiIiMlg8o0JEREQGi0GFiIiIDBaDChERERksBhUiIiIyWAwqREREZLAYVIiIiMhgMagQERGRwfp/cy6GWH9pQLcAAAAASUVORK5CYII=", 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" ] @@ -702,7 +702,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -722,7 +722,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] diff --git a/nbs/13_questionnaire_handler.ipynb b/nbs/13_questionnaire_handler.ipynb index 4428ccd..211f6b3 100644 --- a/nbs/13_questionnaire_handler.ipynb +++ b/nbs/13_questionnaire_handler.ipynb @@ -18,7 +18,7 @@ "metadata": {}, "outputs": [], "source": [ - "#| default_exp questionnaires_handler\n" + "#| default_exp questionnaires_handler" ] }, { @@ -237,15 +237,18 @@ " df_coding = df.copy()\n", " if preferred_language == 'coding':\n", " for column in fields_for_translation:\n", - " data_coding = dict_df.loc[column, 'data_coding'] # This actually should happen\n", + " data_coding = dict_df.loc[column, 'data_coding']\n", + " if isinstance(data_coding, pd.Series): # In case of multiple entries, take the first one\n", + " data_coding = data_coding.iloc[0]\n", + " \n", " if pd.notna(data_coding):\n", - " if dict_df.loc[column, 'array'] == 'Multiple':\n", - " try:\n", - " df_coding[column] = df[column].apply(lambda x : pd.Series(x).astype('Int16') if isinstance(x, np.ndarray) else x)\n", - " except: \n", - " print(column)\n", + " field_array = dict_df.loc[column, 'array']\n", + " if isinstance(field_array, pd.Series):\n", + " field_array = field_array.iloc[0]\n", + " if field_array == 'Multiple':\n", + " continue\n", " else: \n", - " df_coding[column] = df[column].astype('Int16')\n", + " df_coding[column] = df[column].astype('Int16', errors='ignore')\n", " dict_df.loc[column, 'pandas_dtype'] = 'Int16'\n", " \n", " return df_coding\n", @@ -269,7 +272,6 @@ " df = convert_codings_to_int(df=df, dict_df=dict_df, fields_for_translation=fields_for_translation, \n", " preferred_language=transform_to)\n", " \n", - " print('#1', df.info())\n", " return df\n", " \n", " \n", @@ -292,22 +294,8 @@ " \n", " transformed_df = convert_codings_to_int(df=transformed_df, dict_df=dict_df, fields_for_translation=fields_for_translation, \n", " preferred_language=transform_to)\n", - " print('#2', df.info())\n", " return transformed_df" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# dataset = 'sleep'\n", - "# input_path = f'/home/ec2-user/workspace/pheno-utils/nbs/examples/{dataset}/metadata/{dataset}_data_dictionary.csv'\n", - "# df = pd.read_csv(input_path)\n", - "# df['data_coding'] = None\n", - "# df.to_csv(input_path, index=False)" - ] } ], "metadata": { @@ -315,10 +303,6 @@ "display_name": "python3", "language": "python", "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.11.5" } }, "nbformat": 4, diff --git a/pheno_utils/basic_plots.py b/pheno_utils/basic_plots.py index 5c002f2..55f4258 100644 --- a/pheno_utils/basic_plots.py +++ b/pheno_utils/basic_plots.py @@ -48,7 +48,7 @@ def get_gender_indices(df, gender='male', gender_col='sex'): raise ValueError(f"Column '{gender_col}' does not exist in the DataFrame") # Check the data type of the gender column - if df[gender_col].dtype == 'int64': + if pd.api.types.is_integer_dtype(df[gender_col]): if gender == 'male': indices = df[df[gender_col] == 1].index else: diff --git a/pheno_utils/questionnaires_handler.py b/pheno_utils/questionnaires_handler.py index cadfd76..b8f4c03 100644 --- a/pheno_utils/questionnaires_handler.py +++ b/pheno_utils/questionnaires_handler.py @@ -180,15 +180,18 @@ def convert_codings_to_int(df: pd.DataFrame, dict_df: pd.DataFrame, fields_for_t df_coding = df.copy() if preferred_language == 'coding': for column in fields_for_translation: - data_coding = dict_df.loc[column, 'data_coding'] # This actually should happen + data_coding = dict_df.loc[column, 'data_coding'] + if isinstance(data_coding, pd.Series): # In case of multiple entries, take the first one + data_coding = data_coding.iloc[0] + if pd.notna(data_coding): - if dict_df.loc[column, 'array'] == 'Multiple': - try: - df_coding[column] = df[column].apply(lambda x : pd.Series(x).astype('Int16') if isinstance(x, np.ndarray) else x) - except: - print(column) + field_array = dict_df.loc[column, 'array'] + if isinstance(field_array, pd.Series): + field_array = field_array.iloc[0] + if field_array == 'Multiple': + continue else: - df_coding[column] = df[column].astype('Int16') + df_coding[column] = df[column].astype('Int16', errors='ignore') dict_df.loc[column, 'pandas_dtype'] = 'Int16' return df_coding @@ -212,7 +215,6 @@ def transform_dataframe( df = convert_codings_to_int(df=df, dict_df=dict_df, fields_for_translation=fields_for_translation, preferred_language=transform_to) - print('#1', df.info()) return df @@ -235,5 +237,4 @@ def transform_dataframe( transformed_df = convert_codings_to_int(df=transformed_df, dict_df=dict_df, fields_for_translation=fields_for_translation, preferred_language=transform_to) - print('#2', df.info()) return transformed_df From 49905d0f0e8259b73964a50c9d4390c23472f69d Mon Sep 17 00:00:00 2001 From: alondmnt Date: Fri, 8 Nov 2024 04:59:50 +0000 Subject: [PATCH 03/11] refactored: convert_codings_to_int --- nbs/13_questionnaire_handler.ipynb | 66 ++++++++++++--------------- pheno_utils/questionnaires_handler.py | 66 ++++++++++++--------------- 2 files changed, 60 insertions(+), 72 deletions(-) diff --git a/nbs/13_questionnaire_handler.ipynb b/nbs/13_questionnaire_handler.ipynb index 211f6b3..4d08cb5 100644 --- a/nbs/13_questionnaire_handler.ipynb +++ b/nbs/13_questionnaire_handler.ipynb @@ -232,29 +232,18 @@ " transformed_answer = transformed_answer.astype(\"category\")\n", "\n", " return transformed_answer\n", - " \n", - "def convert_codings_to_int(df: pd.DataFrame, dict_df: pd.DataFrame, fields_for_translation: list, preferred_language: str) -> pd.DataFrame:\n", - " df_coding = df.copy()\n", - " if preferred_language == 'coding':\n", - " for column in fields_for_translation:\n", - " data_coding = dict_df.loc[column, 'data_coding']\n", - " if isinstance(data_coding, pd.Series): # In case of multiple entries, take the first one\n", - " data_coding = data_coding.iloc[0]\n", - " \n", - " if pd.notna(data_coding):\n", - " field_array = dict_df.loc[column, 'array']\n", - " if isinstance(field_array, pd.Series):\n", - " field_array = field_array.iloc[0]\n", - " if field_array == 'Multiple':\n", - " continue\n", - " else: \n", - " df_coding[column] = df[column].astype('Int16', errors='ignore')\n", - " dict_df.loc[column, 'pandas_dtype'] = 'Int16'\n", - " \n", - " return df_coding\n", - " \n", - " \n", - " \n", + "\n", + "def convert_codings_to_int(df: pd.Series, dict_df: pd.DataFrame) -> pd.Series:\n", + " tabular_field_name = df.name\n", + " field_array = dict_df.loc[tabular_field_name, 'array']\n", + " if isinstance(field_array, pd.Series):\n", + " field_array = field_array.iloc[0]\n", + " if field_array == 'Multiple':\n", + " return df\n", + " else: \n", + " dict_df.loc[tabular_field_name, 'pandas_dtype'] = 'Int16'\n", + " return df.astype('Int16', errors='ignore')\n", + "\n", "def transform_dataframe(\n", " df: pd.DataFrame,\n", " transform_from: str,\n", @@ -262,27 +251,28 @@ " dict_df: pd.DataFrame,\n", " mapping_df: pd.DataFrame,\n", ") -> pd.DataFrame:\n", - " \n", + " # Validate input parameters\n", + " if transform_from not in valid_codings or transform_to not in valid_codings:\n", + " raise ValueError(f\"transform_from and transform_to must be one of {valid_codings}\")\n", + "\n", " # Only fields with a code in data_coding property will be transformed\n", " fields_for_translation = dict_df[pd.notna(dict_df.data_coding)].index.intersection(df.columns)\n", " if len(fields_for_translation) == 0: # No fields with data_coding code\n", " return df\n", - " \n", - " if transform_from == transform_to: \n", - " df = convert_codings_to_int(df=df, dict_df=dict_df, fields_for_translation=fields_for_translation, \n", - " preferred_language=transform_to)\n", - " \n", - " return df\n", - " \n", - " \n", + "\n", " transformed_df = df.copy()\n", " for column in fields_for_translation:\n", " data_coding = dict_df.loc[column, 'data_coding']\n", " # Handle the case where data_coding is a Series (multiple entries)\n", " if isinstance(data_coding, pd.Series):\n", + " if data_coding.nunique() > 1:\n", + " warnings.warn(f\"Multiple different data_coding values found for column {column}. Using first value.\")\n", " data_coding = data_coding.iloc[0]\n", - " \n", - " if pd.notna(data_coding):\n", + "\n", + " if pd.isna(data_coding):\n", + " continue\n", + "\n", + " if transform_from != transform_to:\n", " transformed_df[column] = transform_answers(\n", " column,\n", " transformed_df[column],\n", @@ -292,8 +282,12 @@ " mapping_df\n", " )\n", " \n", - " transformed_df = convert_codings_to_int(df=transformed_df, dict_df=dict_df, fields_for_translation=fields_for_translation, \n", - " preferred_language=transform_to)\n", + " if transform_to == 'coding':\n", + " transformed_df[column] = convert_codings_to_int(\n", + " transformed_df[column], \n", + " dict_df=dict_df\n", + " )\n", + "\n", " return transformed_df" ] } diff --git a/pheno_utils/questionnaires_handler.py b/pheno_utils/questionnaires_handler.py index b8f4c03..7ac30bd 100644 --- a/pheno_utils/questionnaires_handler.py +++ b/pheno_utils/questionnaires_handler.py @@ -175,29 +175,18 @@ def transform_answers( transformed_answer = transformed_answer.astype("category") return transformed_answer - -def convert_codings_to_int(df: pd.DataFrame, dict_df: pd.DataFrame, fields_for_translation: list, preferred_language: str) -> pd.DataFrame: - df_coding = df.copy() - if preferred_language == 'coding': - for column in fields_for_translation: - data_coding = dict_df.loc[column, 'data_coding'] - if isinstance(data_coding, pd.Series): # In case of multiple entries, take the first one - data_coding = data_coding.iloc[0] - - if pd.notna(data_coding): - field_array = dict_df.loc[column, 'array'] - if isinstance(field_array, pd.Series): - field_array = field_array.iloc[0] - if field_array == 'Multiple': - continue - else: - df_coding[column] = df[column].astype('Int16', errors='ignore') - dict_df.loc[column, 'pandas_dtype'] = 'Int16' - - return df_coding - - - + +def convert_codings_to_int(df: pd.Series, dict_df: pd.DataFrame) -> pd.Series: + tabular_field_name = df.name + field_array = dict_df.loc[tabular_field_name, 'array'] + if isinstance(field_array, pd.Series): + field_array = field_array.iloc[0] + if field_array == 'Multiple': + return df + else: + dict_df.loc[tabular_field_name, 'pandas_dtype'] = 'Int16' + return df.astype('Int16', errors='ignore') + def transform_dataframe( df: pd.DataFrame, transform_from: str, @@ -205,27 +194,28 @@ def transform_dataframe( dict_df: pd.DataFrame, mapping_df: pd.DataFrame, ) -> pd.DataFrame: - + # Validate input parameters + if transform_from not in valid_codings or transform_to not in valid_codings: + raise ValueError(f"transform_from and transform_to must be one of {valid_codings}") + # Only fields with a code in data_coding property will be transformed fields_for_translation = dict_df[pd.notna(dict_df.data_coding)].index.intersection(df.columns) if len(fields_for_translation) == 0: # No fields with data_coding code return df - - if transform_from == transform_to: - df = convert_codings_to_int(df=df, dict_df=dict_df, fields_for_translation=fields_for_translation, - preferred_language=transform_to) - - return df - - + transformed_df = df.copy() for column in fields_for_translation: data_coding = dict_df.loc[column, 'data_coding'] # Handle the case where data_coding is a Series (multiple entries) if isinstance(data_coding, pd.Series): + if data_coding.nunique() > 1: + warnings.warn(f"Multiple different data_coding values found for column {column}. Using first value.") data_coding = data_coding.iloc[0] - - if pd.notna(data_coding): + + if pd.isna(data_coding): + continue + + if transform_from != transform_to: transformed_df[column] = transform_answers( column, transformed_df[column], @@ -235,6 +225,10 @@ def transform_dataframe( mapping_df ) - transformed_df = convert_codings_to_int(df=transformed_df, dict_df=dict_df, fields_for_translation=fields_for_translation, - preferred_language=transform_to) + if transform_to == 'coding': + transformed_df[column] = convert_codings_to_int( + transformed_df[column], + dict_df=dict_df + ) + return transformed_df From b33b8abaaded014bb7ca3f0697a33826469ae198 Mon Sep 17 00:00:00 2001 From: alondmnt Date: Fri, 8 Nov 2024 05:25:59 +0000 Subject: [PATCH 04/11] added: ordered categories sorted by codings --- nbs/13_questionnaire_handler.ipynb | 8 +++++++- pheno_utils/questionnaires_handler.py | 8 +++++++- 2 files changed, 14 insertions(+), 2 deletions(-) diff --git a/nbs/13_questionnaire_handler.ipynb b/nbs/13_questionnaire_handler.ipynb index 4d08cb5..3a3ca44 100644 --- a/nbs/13_questionnaire_handler.ipynb +++ b/nbs/13_questionnaire_handler.ipynb @@ -209,6 +209,11 @@ " \n", " # Make sure no leading 0s for coding values\n", " code_df[\"coding\"] = code_df[\"coding\"].apply(convert_to_string)\n", + " cat_ordered = code_df\\\n", + " .astype({'coding': 'int'})\\\n", + " .sort_values('coding')[code_to]\\\n", + " .astype('str')\\\n", + " .drop_duplicates()\n", " \n", " mapping_dict = dict(zip(code_df[code_from].astype(str), code_df[code_to]))\n", " \n", @@ -229,7 +234,8 @@ " normalized_answer = normalize_answers(orig_answer, field_type)\n", " check_invalid_values(normalized_answer, code_df)\n", " transformed_answer = normalized_answer.replace(mapping_dict)\n", - " transformed_answer = transformed_answer.astype(\"category\")\n", + " transformed_answer = pd.Categorical(transformed_answer)\\\n", + " .set_categories(cat_ordered, ordered=True)\n", "\n", " return transformed_answer\n", "\n", diff --git a/pheno_utils/questionnaires_handler.py b/pheno_utils/questionnaires_handler.py index 7ac30bd..ccbcd58 100644 --- a/pheno_utils/questionnaires_handler.py +++ b/pheno_utils/questionnaires_handler.py @@ -152,6 +152,11 @@ def transform_answers( # Make sure no leading 0s for coding values code_df["coding"] = code_df["coding"].apply(convert_to_string) + cat_ordered = code_df\ + .astype({'coding': 'int'})\ + .sort_values('coding')[code_to]\ + .astype('str')\ + .drop_duplicates() mapping_dict = dict(zip(code_df[code_from].astype(str), code_df[code_to])) @@ -172,7 +177,8 @@ def transform_answers( normalized_answer = normalize_answers(orig_answer, field_type) check_invalid_values(normalized_answer, code_df) transformed_answer = normalized_answer.replace(mapping_dict) - transformed_answer = transformed_answer.astype("category") + transformed_answer = pd.Categorical(transformed_answer)\ + .set_categories(cat_ordered, ordered=True) return transformed_answer From d53c5aec9a9782237cb99b5b7ef6ad4197d57a98 Mon Sep 17 00:00:00 2001 From: alondmnt Date: Mon, 11 Nov 2024 04:33:02 +0000 Subject: [PATCH 05/11] small refactor --- nbs/13_questionnaire_handler.ipynb | 4 ++-- pheno_utils/questionnaires_handler.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/nbs/13_questionnaire_handler.ipynb b/nbs/13_questionnaire_handler.ipynb index 3a3ca44..cf51f7f 100644 --- a/nbs/13_questionnaire_handler.ipynb +++ b/nbs/13_questionnaire_handler.ipynb @@ -234,8 +234,8 @@ " normalized_answer = normalize_answers(orig_answer, field_type)\n", " check_invalid_values(normalized_answer, code_df)\n", " transformed_answer = normalized_answer.replace(mapping_dict)\n", - " transformed_answer = pd.Categorical(transformed_answer)\\\n", - " .set_categories(cat_ordered, ordered=True)\n", + " transformed_answer = pd.Categorical(transformed_answer,\n", + " categories=cat_ordered, ordered=True)\n", "\n", " return transformed_answer\n", "\n", diff --git a/pheno_utils/questionnaires_handler.py b/pheno_utils/questionnaires_handler.py index ccbcd58..96c313e 100644 --- a/pheno_utils/questionnaires_handler.py +++ b/pheno_utils/questionnaires_handler.py @@ -177,8 +177,8 @@ def transform_answers( normalized_answer = normalize_answers(orig_answer, field_type) check_invalid_values(normalized_answer, code_df) transformed_answer = normalized_answer.replace(mapping_dict) - transformed_answer = pd.Categorical(transformed_answer)\ - .set_categories(cat_ordered, ordered=True) + transformed_answer = pd.Categorical(transformed_answer, + categories=cat_ordered, ordered=True) return transformed_answer From c5d244dc7bae59e60d2a5493057e6a6a13a1a72a Mon Sep 17 00:00:00 2001 From: alondmnt Date: Mon, 11 Nov 2024 07:42:19 +0000 Subject: [PATCH 06/11] fixed: ensure ordered coding categories remain ordered on get() --- nbs/13_questionnaire_handler.ipynb | 6 +++++- pheno_utils/questionnaires_handler.py | 6 +++++- 2 files changed, 10 insertions(+), 2 deletions(-) diff --git a/nbs/13_questionnaire_handler.ipynb b/nbs/13_questionnaire_handler.ipynb index cf51f7f..2d1e78f 100644 --- a/nbs/13_questionnaire_handler.ipynb +++ b/nbs/13_questionnaire_handler.ipynb @@ -235,7 +235,11 @@ " check_invalid_values(normalized_answer, code_df)\n", " transformed_answer = normalized_answer.replace(mapping_dict)\n", " transformed_answer = pd.Categorical(transformed_answer,\n", - " categories=cat_ordered, ordered=True)\n", + " categories=cat_ordered, \n", + " ordered=True\n", + " )\n", + " # We update the dictionary to ensure that the categories are not reset later\n", + " dict_df.loc[tab_field_name, 'pandas_dtype'] = 'category_ordered'\n", "\n", " return transformed_answer\n", "\n", diff --git a/pheno_utils/questionnaires_handler.py b/pheno_utils/questionnaires_handler.py index 96c313e..87d98fd 100644 --- a/pheno_utils/questionnaires_handler.py +++ b/pheno_utils/questionnaires_handler.py @@ -178,7 +178,11 @@ def transform_answers( check_invalid_values(normalized_answer, code_df) transformed_answer = normalized_answer.replace(mapping_dict) transformed_answer = pd.Categorical(transformed_answer, - categories=cat_ordered, ordered=True) + categories=cat_ordered, + ordered=True + ) + # We update the dictionary to ensure that the categories are not reset later + dict_df.loc[tab_field_name, 'pandas_dtype'] = 'category_ordered' return transformed_answer From 43311283dee7603a781102f7824e1cc41a355aec Mon Sep 17 00:00:00 2001 From: alondmnt Date: Mon, 11 Nov 2024 13:43:47 +0000 Subject: [PATCH 07/11] added: PhenoLoader data_codings property indexed by field name --- nbs/05_pheno_loader.ipynb | 14 ++++++++++++++ pheno_utils/pheno_loader.py | 14 ++++++++++++++ 2 files changed, 28 insertions(+) diff --git a/nbs/05_pheno_loader.ipynb b/nbs/05_pheno_loader.ipynb index f8452db..6a1f65c 100644 --- a/nbs/05_pheno_loader.ipynb +++ b/nbs/05_pheno_loader.ipynb @@ -740,6 +740,20 @@ " self.fields |= set(self.dfs[table_name].columns.tolist())\n", " self.fields = sorted(list(self.fields))\n", "\n", + " # Merge the data_codings dataframe with the dictionary dataframe\n", + " if 'data_coding' in self.dict.columns:\n", + " self.data_codings = self.dict\\\n", + " ['data_coding']\\\n", + " .reset_index()\\\n", + " .rename(columns={'data_coding': 'code_number'})\\\n", + " .astype({'code_number': 'str'})\\\n", + " .merge(\n", + " self.data_codings.astype({'code_number': 'str'}), \n", + " on='code_number',\n", + " how='inner'\n", + " )\\\n", + " .set_index('tabular_field_name')\n", + "\n", " def __load_one_dataframe__(self, relative_location: str) -> pd.DataFrame:\n", " \"\"\"\n", " Load one dataframe.\n", diff --git a/pheno_utils/pheno_loader.py b/pheno_utils/pheno_loader.py index c603337..15f430b 100644 --- a/pheno_utils/pheno_loader.py +++ b/pheno_utils/pheno_loader.py @@ -689,6 +689,20 @@ def __load_dataframes__(self) -> None: self.fields |= set(self.dfs[table_name].columns.tolist()) self.fields = sorted(list(self.fields)) + # Merge the data_codings dataframe with the dictionary dataframe + if 'data_coding' in self.dict.columns: + self.data_codings = self.dict\ + ['data_coding']\ + .reset_index()\ + .rename(columns={'data_coding': 'code_number'})\ + .astype({'code_number': 'str'})\ + .merge( + self.data_codings.astype({'code_number': 'str'}), + on='code_number', + how='inner' + )\ + .set_index('tabular_field_name') + def __load_one_dataframe__(self, relative_location: str) -> pd.DataFrame: """ Load one dataframe. From 70eb362454da76729626c1fe4c3765fd23726355 Mon Sep 17 00:00:00 2001 From: alondmnt Date: Mon, 11 Nov 2024 13:46:34 +0000 Subject: [PATCH 08/11] improved: PhenoLoader: removed load_func deprecation warning --- nbs/05_pheno_loader.ipynb | 4 +--- pheno_utils/pheno_loader.py | 4 +--- 2 files changed, 2 insertions(+), 6 deletions(-) diff --git a/nbs/05_pheno_loader.ipynb b/nbs/05_pheno_loader.ipynb index 6a1f65c..5c79c58 100644 --- a/nbs/05_pheno_loader.ipynb +++ b/nbs/05_pheno_loader.ipynb @@ -284,9 +284,7 @@ " return None\n", "\n", " # load data\n", - " if load_func is not None:\n", - " warnings.warn(\"The 'load_func' is deprecated and will be removed in future versions.\")\n", - " else: \n", + " if load_func is None:\n", " if 'field_type' not in self.dict:\n", " field_type = None\n", " else:\n", diff --git a/pheno_utils/pheno_loader.py b/pheno_utils/pheno_loader.py index 15f430b..ba8fbd0 100644 --- a/pheno_utils/pheno_loader.py +++ b/pheno_utils/pheno_loader.py @@ -233,9 +233,7 @@ def load_bulk_data( return None # load data - if load_func is not None: - warnings.warn("The 'load_func' is deprecated and will be removed in future versions.") - else: + if load_func is None: if 'field_type' not in self.dict: field_type = None else: From b37109a770786c11d9ab14307e6e1b6c0917283e Mon Sep 17 00:00:00 2001 From: alondmnt Date: Tue, 12 Nov 2024 02:09:37 +0000 Subject: [PATCH 09/11] improved: handle missing data_coding column --- nbs/13_questionnaire_handler.ipynb | 3 +++ pheno_utils/questionnaires_handler.py | 3 +++ 2 files changed, 6 insertions(+) diff --git a/nbs/13_questionnaire_handler.ipynb b/nbs/13_questionnaire_handler.ipynb index 2d1e78f..51f270a 100644 --- a/nbs/13_questionnaire_handler.ipynb +++ b/nbs/13_questionnaire_handler.ipynb @@ -261,6 +261,9 @@ " dict_df: pd.DataFrame,\n", " mapping_df: pd.DataFrame,\n", ") -> pd.DataFrame:\n", + " if 'data_coding' not in dict_df.columns:\n", + " warnings.warn(\"data_coding column not found in dictionary, skipping transformation\")\n", + " return df\n", " # Validate input parameters\n", " if transform_from not in valid_codings or transform_to not in valid_codings:\n", " raise ValueError(f\"transform_from and transform_to must be one of {valid_codings}\")\n", diff --git a/pheno_utils/questionnaires_handler.py b/pheno_utils/questionnaires_handler.py index 87d98fd..b213200 100644 --- a/pheno_utils/questionnaires_handler.py +++ b/pheno_utils/questionnaires_handler.py @@ -204,6 +204,9 @@ def transform_dataframe( dict_df: pd.DataFrame, mapping_df: pd.DataFrame, ) -> pd.DataFrame: + if 'data_coding' not in dict_df.columns: + warnings.warn("data_coding column not found in dictionary, skipping transformation") + return df # Validate input parameters if transform_from not in valid_codings or transform_to not in valid_codings: raise ValueError(f"transform_from and transform_to must be one of {valid_codings}") From a10ca7de73cfb49c36550a6cbf4d60b9dccfe75f Mon Sep 17 00:00:00 2001 From: alondmnt Date: Tue, 12 Nov 2024 02:13:03 +0000 Subject: [PATCH 10/11] chore: nb cleanup --- nbs/12_cohort_selector.ipynb | 9 --------- 1 file changed, 9 deletions(-) diff --git a/nbs/12_cohort_selector.ipynb b/nbs/12_cohort_selector.ipynb index da6af13..671146f 100644 --- a/nbs/12_cohort_selector.ipynb +++ b/nbs/12_cohort_selector.ipynb @@ -180,15 +180,6 @@ "For example, the following query selects participants who have moderate obstructive sleep apnea (AHI > 15) based on recordings of at least 4 hours of sleep." ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ml = MetaLoader()" - ] - }, { "cell_type": "code", "execution_count": null, From 6a12576282bf229a1d181937e209f1ee7f9bacc0 Mon Sep 17 00:00:00 2001 From: alondmnt Date: Tue, 12 Nov 2024 02:15:14 +0000 Subject: [PATCH 11/11] fixed: example data dict --- nbs/examples/sleep/metadata/sleep_data_dictionary.csv | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/nbs/examples/sleep/metadata/sleep_data_dictionary.csv b/nbs/examples/sleep/metadata/sleep_data_dictionary.csv index 55c9018..add0159 100644 --- a/nbs/examples/sleep/metadata/sleep_data_dictionary.csv +++ b/nbs/examples/sleep/metadata/sleep_data_dictionary.csv @@ -1,5 +1,5 @@ -tabular_field_name,field_string,description_string,parent_dataframe,relative_location,units,sampling_rate,array,cohorts,field_type,debut,pandas_dtype -ahi,AHI,AHI (Apnea-Hypopnea Index),,sleep/sleep.parquet,Events / Hour,,Multiple,10K,Continuous,2020-01-15,float -total_sleep_time,Total sleep time,Total sleep time,,sleep/sleep.parquet,Seconds,,Multiple,10K,Continuous,2020-01-15,int -channels_time_series,Channels time series,Sensor and derived channels time series parquet files,,sleep/sleep.parquet,,,Multiple,10K,Time series file (individual),2020-01-15,string -events_time_series,Events time series,"Events during sleep derived from the raw channels, such as sleep stages, respiratory events, pulse rate events, and others",,sleep/sleep.parquet,,Data,Multiple,10K,Time series file (group),2020-01-15,string \ No newline at end of file +tabular_field_name,field_string,description_string,parent_dataframe,relative_location,units,sampling_rate,array,cohorts,field_type,data_coding,debut,pandas_dtype +ahi,AHI,AHI (Apnea-Hypopnea Index),,sleep/sleep.parquet,Events / Hour,,Multiple,10K,Continuous,,2020-01-15,float +total_sleep_time,Total sleep time,Total sleep time,,sleep/sleep.parquet,Seconds,,Multiple,10K,Continuous,,2020-01-15,int +channels_time_series,Channels time series,Sensor and derived channels time series parquet files,,sleep/sleep.parquet,,,Multiple,10K,Time series file (individual),,2020-01-15,string +events_time_series,Events time series,"Events during sleep derived from the raw channels, such as sleep stages, respiratory events, pulse rate events, and others",,sleep/sleep.parquet,,Data,Multiple,10K,Time series file (group),,2020-01-15,string \ No newline at end of file