diff --git a/m3_learning/papers/2023_Imaging_ferroelectric_domains_by_scanning_electron_diffraction/STEM_Domains.ipynb b/m3_learning/papers/2023_Imaging_ferroelectric_domains_by_scanning_electron_diffraction/STEM_Domains.ipynb index cd4a1d56..49d06f28 100644 --- a/m3_learning/papers/2023_Imaging_ferroelectric_domains_by_scanning_electron_diffraction/STEM_Domains.ipynb +++ b/m3_learning/papers/2023_Imaging_ferroelectric_domains_by_scanning_electron_diffraction/STEM_Domains.ipynb @@ -31,10 +31,11 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "## Imports and settings" + "## Imports and settings\n" ] }, { @@ -43,23 +44,13 @@ "metadata": {}, "outputs": [], "source": [ - "%load_ext autoreload\n", - "%autoreload 2" + "# !pip install m3_learning\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [], - "source": [ - "!pip install m3_learning\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, "outputs": [ { "name": "stdout", @@ -67,25 +58,7 @@ "text": [ "printing set for seaborn\n", "Pytorch seed was set to 42\n", - "Numpy seed was set to 42\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-04-14 00:27:40.579265: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", - "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", - "2023-04-14 00:27:40.702636: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", - "2023-04-14 00:27:41.159304: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/ferroelectric/anaconda3/envs/STEM/lib/python3.9/site-packages/cv2/../../lib64:\n", - "2023-04-14 00:27:41.159359: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/ferroelectric/anaconda3/envs/STEM/lib/python3.9/site-packages/cv2/../../lib64:\n", - "2023-04-14 00:27:41.159364: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Numpy seed was set to 42\n", "tensorflow seed was set to 42\n" ] } @@ -95,31 +68,65 @@ "import numpy as np\n", "import hyperspy.api as hs\n", "import torch\n", - "\n", "from m3_learning.nn.random import random_seed\n", "from m3_learning.nn.STEM_AE.Viz import Viz\n", "from m3_learning.nn.STEM_AE.STEM_AE import ConvAutoencoder\n", "from m3_learning.viz.style import set_style\n", "from m3_learning.viz.printing import printer\n", - "from m3_learning.viz.layout import layout_fig, imagemap, labelfigs, scalebar\n", "from m3_learning.viz.Movies import make_movie\n", "from m3_learning.nn.STEM_AE.Dataset import STEM_Dataset\n", - "printing = printer(basepath='./figures/')\n", + "from m3_learning.util.file_IO import download_and_unzip\n", "\n", + "# Specify the filename and the path to save the file\n", + "save_path = './../../../Data/2023_STEM_AE'\n", + "fig_path = save_path.replace(\"Data\", \"Figures\") + '/'\n", "\n", + "# builds the printer object\n", + "printing = printer(basepath=fig_path)\n", + "\n", + "# Set the style of the plots\n", "set_style(\"printing\")\n", + "\n", + "# Set the random seed for reproducibility\n", "random_seed(seed=42)\n", "\n", - "%matplotlib inline\n", - "\n" + "%matplotlib inline\n" ] }, { - "attachments": {}, - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 3, "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using files already downloaded\n", + "Using files already downloaded\n", + "Using files already downloaded\n", + "Using files already downloaded\n", + "Using files already downloaded\n" + ] + } + ], "source": [ - "TODO: add download\n" + "# list of files to download from zenodo\n", + "files = [\n", + " \"11.1_log_epoch_0017_L1loss_0.00181_l1coef_0.0050_lr_0.00009_contrasPara_0.000050_contrasLoss_0.00020_maxiPara_0.00070_maxiLoss_0.0019_trainloss_0.2289.pkl\",\n", + " \"11.23_log_epoch_0376_coef_1e-05_trainloss_0.1362.pkl\",\n", + " \"256p_3w_step1k4_a5_cl12_50ms_nmf_4-001.hspy\",\n", + " \"4.5_log_epoch_0285_L1loss_0.0001__lr_0.00003_trainloss_0.2279.pkl\",\n", + " \"SED_256x256x12_30x30_alpha3_NBD_spot0p5nm_CL12cm_vortex.hspy\",\n", + "]\n", + "\n", + "# downloads all the files\n", + "for file in files:\n", + " # Download the data file from Zenodo\n", + " url = f\"https://zenodo.org/record/7837986/files/{file}?download=1\"\n", + "\n", + " # download the file\n", + " download_and_unzip(file, url, save_path)" ] }, { @@ -127,7 +134,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Loads the Dataset" + "## Domains \n", + "\n", + "### Loads the Dataset\n" ] }, { @@ -152,7 +161,9 @@ "# loads the dataset\n", "# this object will also compute the log of the dataset\n", "# this is available as self.log_data\n", - "domain = STEM_Dataset(data_path=\"/home/ferroelectric/data/256p_3w_step1k4_a5_cl12_50ms_nmf_4.hspy\")" + "domain = STEM_Dataset(\n", + " data_path=f\"{save_path}/256p_3w_step1k4_a5_cl12_50ms_nmf_4-001.hspy\"\n", + ")" ] }, { @@ -160,7 +171,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Instantiates the Visualizer" + "### Instantiates the Visualizer\n" ] }, { @@ -170,8 +181,8 @@ "outputs": [], "source": [ "# instantiates the visualization class\n", - "viz = Viz(printer = printing, labelfigs_=True)\n", - "viz.scalebar_ = {'width': 250, 'scale length': 100, 'units': 'nm', 'channel': -1}\n" + "viz = Viz(printer=printing, labelfigs_=True)\n", + "viz.scalebar_ = {\"width\": 250, \"scale length\": 100, \"units\": \"nm\", \"channel\": -1}" ] }, { @@ -179,7 +190,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Visualizes the Raw Data" + "### Visualizes the Raw Data\n" ] }, { @@ -191,8 +202,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "./figures/STEM_raw_and_virtual_domain.png\n", - "./figures/STEM_raw_and_virtual_domain.svg\n" + "./../../../Figures/2023_STEM_AE/STEM_raw_and_virtual_domain.png\n", + "./../../../Figures/2023_STEM_AE/STEM_raw_and_virtual_domain.svg\n" ] }, { @@ -207,11 +218,17 @@ } ], "source": [ - "bright_field_ = (117,135, 119, 135)\n", - "dark_field_ = (102,117, 114,130)\n", - "printer_ = printer(basepath=\"./figures/\")\n", + "bright_field_ = (117, 135, 119, 135)\n", + "dark_field_ = (102, 117, 114, 130)\n", "filename = \"STEM_raw_and_virtual_domain\"\n", - "viz.STEM_raw_and_virtual(domain, bright_field_=bright_field_, dark_field_=dark_field_, scalebar_=True, filename=filename, shape_ = domain.data.shape)" + "viz.STEM_raw_and_virtual(\n", + " domain,\n", + " bright_field_=bright_field_,\n", + " dark_field_=dark_field_,\n", + " scalebar_=True,\n", + " filename=filename,\n", + " shape_=domain.data.shape,\n", + ")" ] }, { @@ -219,7 +236,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Builds the Neural Network Model" + "### Builds the Neural Network Model\n" ] }, { @@ -236,18 +253,20 @@ "conv_size = 128\n", "\n", "# set device to load model\n", - "device = 'cpu'\n", + "device = \"cpu\"\n", "if torch.cuda.is_available():\n", - " device = 'cuda:1'\n", + " device = \"cuda:1\"\n", "\n", - "model = ConvAutoencoder(encoder_step_size=encoder_step_size,\n", - " pooling_list=pooling_list,\n", - " decoder_step_size=decoder_step_size,\n", - " upsampling_list=upsampling_list,\n", - " embedding_size=embedding_size,\n", - " conv_size=conv_size,\n", - " device=device,\n", - " learning_rate=3e-5)" + "model = ConvAutoencoder(\n", + " encoder_step_size=encoder_step_size,\n", + " pooling_list=pooling_list,\n", + " decoder_step_size=decoder_step_size,\n", + " upsampling_list=upsampling_list,\n", + " embedding_size=embedding_size,\n", + " conv_size=conv_size,\n", + " device=device,\n", + " learning_rate=3e-5,\n", + ")\n" ] }, { @@ -255,7 +274,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Trains or Loads Pretrained Weights" + "### Trains or Loads Pretrained Weights\n" ] }, { @@ -268,14 +287,18 @@ "train = False\n", "\n", "if train:\n", - " model.Train(domain.log_data,\n", - " coef_1=1e-5,\n", - " epochs=225,\n", - " with_scheduler=False,\n", - " folder_path=\"Domain_Weights\",\n", - " batch_size=256)\n", + " model.Train(\n", + " domain.log_data,\n", + " coef_1=1e-5,\n", + " epochs=225,\n", + " with_scheduler=False,\n", + " folder_path=\"Domain_Weights\",\n", + " batch_size=256,\n", + " )\n", "else:\n", - " model.load_weights(\"/home/ferroelectric/data/11.23_log_epoch_0376_coef_1e-05_trainloss_0.1362.pkl\")" + " model.load_weights(\n", + " f\"{save_path}/11.23_log_epoch_0376_coef_1e-05_trainloss_0.1362.pkl\"\n", + " )" ] }, { @@ -283,19 +306,23 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Computes the Embeddings" + "### Computes the Embeddings\n" ] }, { "cell_type": "code", "execution_count": 9, - "metadata": {}, + "metadata": { + "tags": [ + "hide-output" + ] + }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 2048/2048 [04:10<00:00, 8.16it/s]\n" + "100%|██████████| 2048/2048 [04:10<00:00, 8.17it/s]\n" ] } ], @@ -308,7 +335,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Visualizes the Embeddings" + "### Visualizes the Embeddings\n" ] }, { @@ -320,8 +347,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "./figures/domain wall_embedding_maps.png\n", - "./figures/domain wall_embedding_maps.svg\n" + "./../../../Figures/2023_STEM_AE/domain wall_embedding_maps.png\n", + "./../../../Figures/2023_STEM_AE/domain wall_embedding_maps.svg\n" ] }, { @@ -336,11 +363,16 @@ } ], "source": [ - "# add one scalebar\n", - "\n", - "viz.scalebar_ = {'width': 250, 'scale length': 100, 'units': 'nm'}\n", + "viz.channels = None\n", + "viz.scalebar_ = {\"width\": 250, \"scale length\": 100, \"units\": \"nm\"}\n", "viz.model = model\n", - "viz.embeddings(mod = 4, scalebar_=viz.scalebar_, name = \"domain wall\", shape_=domain.data.shape, figsize = (5, 8))" + "viz.embeddings(\n", + " mod=4,\n", + " scalebar_=viz.scalebar_,\n", + " name=\"domain wall\",\n", + " shape_=domain.data.shape,\n", + " figsize=(5, 8),\n", + ")" ] }, { @@ -348,7 +380,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Generates Data from the Latent Space" + "### Generates Data from the Latent Space\n" ] }, { @@ -371,173 +403,173 @@ "name": "stdout", "output_type": "stream", "text": [ - "./figures/generator_images_Generator_of_Figure_4_a_d/0000_maps.png\n", - "./figures/generator_images_Generator_of_Figure_4_a_d/0000_maps.svg\n" + "./../../../Figures/2023_STEM_AE/generator_images_Generator_of_Figure_4_a_d/0000_maps.png\n", + "./../../../Figures/2023_STEM_AE/generator_images_Generator_of_Figure_4_a_d/0000_maps.svg\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - " 0%| | 1/200 [00:01<03:36, 1.09s/it]" + " 0%| | 1/200 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"./../../../Figures/2023_STEM_AE/generator_images_Generator_of_Figure_4_a_d/0198_maps.svg\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "100%|█████████▉| 199/200 [03:37<00:01, 1.07s/it]" + "100%|█████████▉| 199/200 [03:35<00:01, 1.07s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "./figures/generator_images_Generator_of_Figure_4_a_d/0199_maps.png\n", - "./figures/generator_images_Generator_of_Figure_4_a_d/0199_maps.svg\n" + "./../../../Figures/2023_STEM_AE/generator_images_Generator_of_Figure_4_a_d/0199_maps.png\n", + "./../../../Figures/2023_STEM_AE/generator_images_Generator_of_Figure_4_a_d/0199_maps.svg\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 200/200 [03:38<00:00, 1.09s/it]\n" + "100%|██████████| 200/200 [03:37<00:00, 1.09s/it]\n" ] } ], "source": [ "model.embedding = embedding\n", "viz.model = model\n", - "viz.channels = [20,29]\n", - "ranges=[[0,0.018],[0,0.022]]\n", - "folder_name = 'Generator_of_Figure_4_a_d'\n", + "viz.channels = [20, 29]\n", + "ranges = [[0, 0.018], [0, 0.022]]\n", + "folder_name = \"Generator_of_Figure_4_a_d\"\n", "\n", - "viz.generator_images(ranges = ranges, folder_name = folder_name, figsize=(5, 1.25), cbar_number_format = \"%d\", shape_=domain.data.shape, divider_ = False)" + "viz.generator_images(\n", + " ranges=ranges,\n", + " folder_name=folder_name,\n", + " figsize=(5, 1.25),\n", + " cbar_number_format=\"%d\",\n", + " shape_=domain.data.shape,\n", + " divider_=False,\n", + ")" ] }, { @@ -3383,7 +3422,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Builds a Movie" + "### Builds a Movie\n" ] }, { @@ -3392,15 +3431,12 @@ "metadata": {}, "outputs": [], "source": [ - "folder = \"./figures/generator_images_Generator_of_Figure_4_a_d\"\n", + "# Builds a Movie\n", + "folder = f\"{fig_path}/generator_images_Generator_of_Figure_4_a_d\"\n", "output_folder = \"./movies/\"\n", "\n", "# function to make the movie\n", - "make_movie('Domain Wall Generator',\n", - " folder,\n", - " output_folder,\n", - " 'png',\n", - " 3)" + "make_movie(\"Domain Generator\", folder, output_folder, \"png\", 3)" ] }, { @@ -3408,7 +3444,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Vortex Domains" + "## Vortex Domains\n", + "\n", + "### Loads the Dataset\n" ] }, { @@ -3429,30 +3467,30 @@ "# loads the dataset\n", "# this object will also compute the log of the dataset\n", "# this is available as self.log_data\n", - "vortex = STEM_Dataset(data_path=\"/home/ferroelectric/data/SED_256x256x12_30x30_alpha3_NBD_spot0p5nm_CL12cm_vortex.hspy\")" + "vortex = STEM_Dataset(\n", + " data_path=f\"{save_path}/SED_256x256x12_30x30_alpha3_NBD_spot0p5nm_CL12cm_vortex.hspy\"\n", + ")" ] }, { - "cell_type": "code", - "execution_count": 14, + "attachments": {}, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "# instantiates the visualization class\n", - "viz = Viz(printer = printing, labelfigs_=True)" + "### Visualizes the Raw Data" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "./figures/STEM_raw_and_virtual_vortex_domains.png\n", - "./figures/STEM_raw_and_virtual_vortex_domains.svg\n" + "./../../../Figures/2023_STEM_AE/STEM_raw_and_virtual_vortex_domains.png\n", + "./../../../Figures/2023_STEM_AE/STEM_raw_and_virtual_vortex_domains.svg\n" ] }, { @@ -3468,23 +3506,30 @@ ], "source": [ "bright_field_ = (150, 162, 152, 165)\n", - "dark_field_ = (106,123, 110,125)\n", - "printer_ = printer(basepath=\"./figures/\")\n", + "dark_field_ = (106, 123, 110, 125)\n", "filename = \"STEM_raw_and_virtual_vortex_domains\"\n", - "viz.scalebar_ = {'width': 291, 'scale length': 100, 'units': 'nm'}\n", - "viz.STEM_raw_and_virtual(vortex, bright_field_=bright_field_, dark_field_=dark_field_, scalebar_=True, filename=filename, shape_ = vortex.data.shape)" + "viz.scalebar_ = {\"width\": 291, \"scale length\": 100, \"units\": \"nm\"}\n", + "viz.STEM_raw_and_virtual(\n", + " vortex,\n", + " bright_field_=bright_field_,\n", + " dark_field_=dark_field_,\n", + " scalebar_=True,\n", + " filename=filename,\n", + " shape_=vortex.data.shape,\n", + ")" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "## Builds the Neural Network Model" + "### Builds the Neural Network Model\n" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -3496,30 +3541,33 @@ "conv_size = 128\n", "\n", "# set device to load model\n", - "device = 'cpu'\n", + "device = \"cpu\"\n", "if torch.cuda.is_available():\n", - " device = 'cuda:1'\n", + " device = \"cuda:1\"\n", "\n", - "model = ConvAutoencoder(encoder_step_size=encoder_step_size,\n", - " pooling_list=pooling_list,\n", - " decoder_step_size=decoder_step_size,\n", - " upsampling_list=upsampling_list,\n", - " embedding_size=embedding_size,\n", - " conv_size=conv_size,\n", - " device=device,\n", - " learning_rate=3e-5)" + "model = ConvAutoencoder(\n", + " encoder_step_size=encoder_step_size,\n", + " pooling_list=pooling_list,\n", + " decoder_step_size=decoder_step_size,\n", + " upsampling_list=upsampling_list,\n", + " embedding_size=embedding_size,\n", + " conv_size=conv_size,\n", + " device=device,\n", + " learning_rate=3e-5,\n", + ")\n" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "## Trains or Loads Pretrained Weights" + "### Trains or Loads Pretrained Weights\n" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -3527,42 +3575,53 @@ "train = False\n", "\n", "if train:\n", - " model.Train(vortex.log_data,\n", - " coef_1=1e-5,\n", - " epochs=225,\n", - " with_scheduler=False,\n", - " folder_path=\"vortex_domains_Weights\",\n", - " batch_size=256)\n", - " \n", - " model.Train(vortex.log_data,\n", - " max_learning_rate = 5e-5,\n", - " coef_1=5e-4,\n", - " epochs=286,\n", - " epoch_=224,\n", - " with_scheduler=True,\n", - " folder_path=\"vortex_domains_Weights\",\n", - " batch_size=256)\n", + " model.Train(\n", + " vortex.log_data,\n", + " coef_1=1e-5,\n", + " epochs=225,\n", + " with_scheduler=False,\n", + " folder_path=\"vortex_domains_Weights\",\n", + " batch_size=256,\n", + " )\n", + "\n", + " model.Train(\n", + " vortex.log_data,\n", + " max_learning_rate=5e-5,\n", + " coef_1=5e-4,\n", + " epochs=286,\n", + " epoch_=224,\n", + " with_scheduler=True,\n", + " folder_path=\"vortex_domains_Weights\",\n", + " batch_size=256,\n", + " )\n", "else:\n", - " model.load_weights(\"/home/ferroelectric/data/4.5_log_epoch_0285_L1loss_0.0001__lr_0.00003_trainloss_0.2279.pkl\")" + " model.load_weights(\n", + " f\"{save_path}/4.5_log_epoch_0285_L1loss_0.0001__lr_0.00003_trainloss_0.2279.pkl\"\n", + " )" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "## Computes the Embeddings" + "### Computes the Embeddings\n" ] }, { "cell_type": "code", - "execution_count": 18, - "metadata": {}, + "execution_count": 17, + "metadata": { + "tags": [ + "hide-output" + ] + }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 2048/2048 [04:10<00:00, 8.18it/s]\n" + "100%|██████████| 2048/2048 [04:09<00:00, 8.19it/s]\n" ] } ], @@ -3571,23 +3630,24 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "## Visualizes the Embeddings" + "### Visualizes the Embeddings\n" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "./figures/vortex_domains_embedding_maps.png\n", - "./figures/vortex_domains_embedding_maps.svg\n" + "./../../../Figures/2023_STEM_AE/vortex_domains_embedding_maps.png\n", + "./../../../Figures/2023_STEM_AE/vortex_domains_embedding_maps.svg\n" ] }, { @@ -3603,13 +3663,28 @@ ], "source": [ "viz.model = model\n", - "viz.scalebar_ = {'width': 291, 'scale length': 100, 'units': 'nm'}\n", - "viz.embeddings(mod = 4, scalebar_=viz.scalebar_, figsize = (5, 8), name = \"vortex_domains\", shape_=vortex.data.shape)" + "viz.channels = None\n", + "viz.scalebar_ = {\"width\": 291, \"scale length\": 100, \"units\": \"nm\"}\n", + "viz.embeddings(\n", + " mod=4,\n", + " scalebar_=viz.scalebar_,\n", + " figsize=(5, 8),\n", + " name=\"vortex_domains\",\n", + " shape_=vortex.data.shape,\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Generates Data from the Latent Space" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": { "tags": [ "hide-output" @@ -3627,3029 +3702,3040 @@ "name": "stdout", "output_type": "stream", "text": [ - 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"./../../../Figures/2023_STEM_AE/generator_images_Generator_of_Figure_4_vortex_domains/0198_maps.svg\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "100%|█████████▉| 199/200 [03:25<00:01, 1.01s/it]" + "100%|█████████▉| 199/200 [03:22<00:00, 1.01it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "./figures/generator_images_Generator_of_Figure_4_vortex_domains/0199_maps.png\n", - "./figures/generator_images_Generator_of_Figure_4_vortex_domains/0199_maps.svg\n" + "./../../../Figures/2023_STEM_AE/generator_images_Generator_of_Figure_4_vortex_domains/0199_maps.png\n", + "./../../../Figures/2023_STEM_AE/generator_images_Generator_of_Figure_4_vortex_domains/0199_maps.svg\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 200/200 [03:26<00:00, 1.03s/it]\n" + "100%|██████████| 200/200 [03:23<00:00, 1.02s/it]\n" ] } ], "source": [ "model.embedding = embedding\n", "viz.model = model\n", - "viz.channels=[13,31]\n", - "ranges=[[0,0.0225],[0,0.03]]\n", - "folder_name = 'Generator_of_Figure_4_vortex_domains'\n", + "viz.channels = [13, 31]\n", + "ranges = [[0, 0.0225], [0, 0.03]]\n", + "folder_name = \"Generator_of_Figure_4_vortex_domains\"\n", "\n", - "viz.generator_images(ranges = ranges, folder_name = folder_name, figsize=(5, 1.25), cbar_number_format = \"%d\", shape_=vortex.data.shape, divider_ = False)" + "viz.generator_images(\n", + " ranges=ranges,\n", + " folder_name=folder_name,\n", + " figsize=(5, 1.25),\n", + " cbar_number_format=\"%d\",\n", + " shape_=vortex.data.shape,\n", + " divider_=False,\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Builds the Movie" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ - "## Builds a Movie\n", - "folder = \"./figures/generator_images_Generator_of_Figure_4_vortex_domains/\"\n", + "# Builds a Movie\n", + "folder = f\"{fig_path}/generator_images_Generator_of_Figure_4_vortex_domains/\"\n", "output_folder = \"./movies/\"\n", "\n", "# function to make the movie\n", - "make_movie('Vortex Domains Generator',\n", - " folder,\n", - " output_folder,\n", - " 'png',\n", - " 3)" + "make_movie(\"Vortex Domains Generator\", folder, output_folder, \"png\", 3)" ] }, { @@ -6657,52 +6743,20 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Vortex Walls" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "./figures/STEM_raw_and_virtual_vortex_walls.png\n", - "./figures/STEM_raw_and_virtual_vortex_walls.svg\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "bright_field_ = (150, 162, 152, 165)\n", - "dark_field_ = (106,123, 110,125)\n", - "printer_ = printer(basepath=\"./figures/\")\n", - "filename = \"STEM_raw_and_virtual_vortex_walls\"\n", - "viz.scalebar_ = {'width': 291, 'scale length': 100, 'units': 'nm'}\n", - "viz.STEM_raw_and_virtual(vortex, bright_field_=bright_field_, dark_field_=dark_field_, scalebar_=True, filename=filename, shape_ = vortex.data.shape)" + "## Vortex Domain Walls\n" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "## Builds the Neural Network Model" + "### Builds the Neural Network Model\n" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -6714,25 +6768,28 @@ "conv_size = 128\n", "\n", "# set device to load model\n", - "device = 'cpu'\n", + "device = \"cpu\"\n", "if torch.cuda.is_available():\n", - " device = 'cuda:1'\n", + " device = \"cuda:1\"\n", "\n", - "model = ConvAutoencoder(encoder_step_size=encoder_step_size,\n", - " pooling_list=pooling_list,\n", - " decoder_step_size=decoder_step_size,\n", - " upsampling_list=upsampling_list,\n", - " embedding_size=embedding_size,\n", - " conv_size=conv_size,\n", - " device=device,\n", - " learning_rate=3e-5)" + "model = ConvAutoencoder(\n", + " encoder_step_size=encoder_step_size,\n", + " pooling_list=pooling_list,\n", + " decoder_step_size=decoder_step_size,\n", + " upsampling_list=upsampling_list,\n", + " embedding_size=embedding_size,\n", + " conv_size=conv_size,\n", + " device=device,\n", + " learning_rate=3e-5,\n", + ")\n" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "## Trains or Loads Pretrained Weights" + "### Trains or Loads Pretrained Weights\n" ] }, { @@ -6742,24 +6799,22 @@ "source": [ "In this model we have additional custom regularization to the embedding to help with disentangling the vortices from the walls.\n", "\n", - "\n", "1. We have an l1 activity regularization:\n", - " \n", - "$$ \\sum_{i=1}^{n} |a_i| $$\n", "\n", + "$$ \\sum\\_{i=1}^{n} |a_i| $$\n", "\n", "2. We have a contrastive regularization:\n", "\n", - "$$ L = \\frac{\\lambda_1}{2N} \\sum\\limits_{i=1}^{N} \\left[\\left(\\sum\\limits_{j=1, j\\neq i}^{N} \\frac{latent_i \\cdot latent_j}{\\left\\lVert latent_i \\right\\rVert \\left\\lVert latent_j \\right\\rVert}\\right] - 1\\right)$$\n", + "$$ L = \\frac{\\lambda*1}{2N} \\sum\\limits*{i=1}^{N} \\left[\\left(\\sum\\limits_{j=1, j\\neq i}^{N} \\frac{latent_i \\cdot latent_j}{\\left\\lVert latent_i \\right\\rVert \\left\\lVert latent_j \\right\\rVert}\\right] - 1\\right)$$\n", "\n", "3. Divergence regularization:\n", "\n", - "$$L = \\frac{1}{B} \\sum\\limits_{i=1}^{B} \\left(\\frac{\\lambda_2}{2} \\sum\\limits_{j=1}^{n} \\sum\\limits_{k=j+1}^{n} \\left| latent_{i,j} - latent_{i,k} \\right|\\right)$$" + "$$L = \\frac{1}{B} \\sum\\limits_{i=1}^{B} \\left(\\frac{\\lambda_2}{2} \\sum\\limits_{j=1}^{n} \\sum\\limits_{k=j+1}^{n} \\left| latent_{i,j} - latent_{i,k} \\right|\\right)$$\n" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -6767,35 +6822,44 @@ "train = False\n", "\n", "if train:\n", - " model.Train(vortex.log_data,\n", - " coef_0=5e-3,\n", - " coef_1=5e-5,\n", - " coef_2=2e-4,\n", - " epochs=18,\n", - " with_scheduler=False,\n", - " folder_path=\"Vortex_walls_Weights\",\n", - " batch_size=256)\n", + " model.Train(\n", + " vortex.log_data,\n", + " coef_0=5e-3,\n", + " coef_1=5e-5,\n", + " coef_2=2e-4,\n", + " epochs=18,\n", + " with_scheduler=False,\n", + " folder_path=\"Vortex_walls_Weights\",\n", + " batch_size=256,\n", + " )\n", "else:\n", - " model.load_weights(\"/home/ferroelectric/data/11.1_log_epoch_0017_L1loss_0.00181_l1coef_0.0050_lr_0.00009_contrasPara_0.000050_contrasLoss_0.00020_maxiPara_0.00070_maxiLoss_0.0019_trainloss_0.2289.pkl\")" + " model.load_weights(\n", + " f\"{save_path}/11.1_log_epoch_0017_L1loss_0.00181_l1coef_0.0050_lr_0.00009_contrasPara_0.000050_contrasLoss_0.00020_maxiPara_0.00070_maxiLoss_0.0019_trainloss_0.2289.pkl\"\n", + " )" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "## Computes the Embeddings" + "### Computes the Embeddings\n" ] }, { "cell_type": "code", - "execution_count": 25, - "metadata": {}, + "execution_count": 23, + "metadata": { + "tags": [ + "hide-output" + ] + }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 2048/2048 [04:10<00:00, 8.19it/s]\n" + "100%|██████████| 2048/2048 [04:09<00:00, 8.20it/s]\n" ] } ], @@ -6804,23 +6868,24 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "## Visualizes the Embeddings" + "### Visualizes the Embeddings\n" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "./figures/Vortex_walls_embedding_maps.png\n", - "./figures/Vortex_walls_embedding_maps.svg\n" + "./../../../Figures/2023_STEM_AE/Vortex_walls_embedding_maps.png\n", + "./../../../Figures/2023_STEM_AE/Vortex_walls_embedding_maps.svg\n" ] }, { @@ -6837,13 +6902,27 @@ "source": [ "viz.channels = None\n", "viz.model = model\n", - "viz.scalebar_ = {'width': 291, 'scale length': 100, 'units': 'nm'}\n", - "viz.embeddings(mod = 4, scalebar_=viz.scalebar_, figsize = (5, 8), name = \"Vortex_walls\", shape_=vortex.data.shape)" + "viz.scalebar_ = {\"width\": 291, \"scale length\": 100, \"units\": \"nm\"}\n", + "viz.embeddings(\n", + " mod=4,\n", + " scalebar_=viz.scalebar_,\n", + " figsize=(5, 8),\n", + " name=\"Vortex_walls\",\n", + " shape_=vortex.data.shape,\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Generates Data from the Latent Space" ] }, { "cell_type": "code", - 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"100%|██████████| 200/200 [03:19<00:00, 1.00it/s]\n" + "100%|██████████| 200/200 [03:16<00:00, 1.02it/s]\n" ] } ], "source": [ "model.embedding = embedding\n", "viz.model = model\n", - "viz.channels = [0,2]\n", - "ranges=[[0,0.025],[0,0.04]]\n", - "folder_name = 'Generator_of_Figure_4_e_f_vortex_walls'\n", + "viz.channels = [0, 2]\n", + "ranges = [[0, 0.025], [0, 0.04]]\n", + "folder_name = \"Generator_of_Figure_4_e_f_vortex_walls\"\n", "\n", - "viz.generator_images(ranges = ranges, folder_name = folder_name, figsize=(5, 1.25), cbar_number_format = \"%d\", shape_=vortex.data.shape, divider_ = False)" + "viz.generator_images(\n", + " ranges=ranges,\n", + " folder_name=folder_name,\n", + " figsize=(5, 1.25),\n", + " cbar_number_format=\"%d\",\n", + " shape_=vortex.data.shape,\n", + " divider_=False,\n", + ")" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "## Builds a Movie" + "### Builds a Movie\n" ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ - "folder = \"./figures/generator_images_Generator_of_Figure_4_e_f_vortex_walls/\"\n", + "folder = f\"{fig_path}/generator_images_Generator_of_Figure_4_e_f_vortex_walls/\"\n", "output_folder = \"./movies/\"\n", "\n", "# function to make the movie\n", - "make_movie('Vortex Wall Generator',\n", - " folder,\n", - " output_folder,\n", - " 'png',\n", - " 3)" + "make_movie(\"Vortex Wall Generator\", folder, output_folder, \"png\", 3)" ] }, { diff --git a/m3_learning/papers/2023_Rapid_Fitting/1_SHO_Fitting.ipynb b/m3_learning/papers/2023_Rapid_Fitting/1_SHO_Fitting.ipynb index eade684f..acc4d725 100644 --- a/m3_learning/papers/2023_Rapid_Fitting/1_SHO_Fitting.ipynb +++ b/m3_learning/papers/2023_Rapid_Fitting/1_SHO_Fitting.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 1, "id": "f5c36aed", "metadata": {}, "outputs": [], @@ -30,16 +30,26 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 2, "id": "a6feb316", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-04-18 16:31:43.452237: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", + "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "2023-04-18 16:31:43.567021: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "2023-04-18 16:31:44.009848: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/ferroelectric/anaconda3/envs/rapidfitting/lib/python3.9/site-packages/cv2/../../lib64:\n", + "2023-04-18 16:31:44.009895: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/ferroelectric/anaconda3/envs/rapidfitting/lib/python3.9/site-packages/cv2/../../lib64:\n", + "2023-04-18 16:31:44.009900: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n", "printing set for seaborn\n", "Pytorch seed was set to 42\n", "Numpy seed was set to 42\n", @@ -64,7 +74,7 @@ "from m3_learning.be.dataset import BE_Dataset\n", "\n", "# from m3_learning.be.dataset import BE_Dataset\n", - "printing = printer(basepath = './figures/')\n", + "printing = printer(basepath = './../../../Figures/rapid_fitting/')\n", "\n", "\n", "set_style(\"printing\")\n", @@ -85,28 +95,7 @@ }, { "cell_type": "code", - "execution_count": 9, - "id": "bf67e323", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'/home/ferroelectric/m3_learning/m3_learning/papers/2023_Rapid_Fitting'" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pwd" - ] - }, - { - "cell_type": "code", - "execution_count": 16, + "execution_count": 3, "id": "e0f238cd", "metadata": { "colab": { @@ -121,27 +110,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "downloading data\n", - "...2%, 40 MB, 801 KB/s, 51 seconds passedd" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[16], line 9\u001b[0m\n\u001b[1;32m 6\u001b[0m save_path \u001b[39m=\u001b[39m \u001b[39m'\u001b[39m\u001b[39m./../../../Data/2023_rapid_fitting\u001b[39m\u001b[39m'\u001b[39m\n\u001b[1;32m 8\u001b[0m \u001b[39m# download the file\u001b[39;00m\n\u001b[0;32m----> 9\u001b[0m download_and_unzip(filename, url, save_path)\n", - "File \u001b[0;32m~/m3_learning/m3_learning/src/m3_learning/util/file_IO.py:121\u001b[0m, in \u001b[0;36mdownload_and_unzip\u001b[0;34m(filename, url, save_path, force)\u001b[0m\n\u001b[1;32m 119\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 120\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m'\u001b[39m\u001b[39mdownloading data\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m--> 121\u001b[0m download_file(url, path)\n\u001b[1;32m 123\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39m'\u001b[39m\u001b[39m.zip\u001b[39m\u001b[39m'\u001b[39m \u001b[39min\u001b[39;00m filename:\n\u001b[1;32m 124\u001b[0m \u001b[39mif\u001b[39;00m os\u001b[39m.\u001b[39mpath\u001b[39m.\u001b[39misfile(path):\n", - "File \u001b[0;32m~/m3_learning/m3_learning/src/m3_learning/util/file_IO.py:60\u001b[0m, in \u001b[0;36mdownload_file\u001b[0;34m(url, filename)\u001b[0m\n\u001b[1;32m 53\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\" A function that downloads the data file from a URL\u001b[39;00m\n\u001b[1;32m 54\u001b[0m \n\u001b[1;32m 55\u001b[0m \u001b[39mArgs:\u001b[39;00m\n\u001b[1;32m 56\u001b[0m \u001b[39m url (string): url where the file to download is located\u001b[39;00m\n\u001b[1;32m 57\u001b[0m \u001b[39m filename (string): location where to save the file\u001b[39;00m\n\u001b[1;32m 58\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 59\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m os\u001b[39m.\u001b[39mpath\u001b[39m.\u001b[39misfile(filename):\n\u001b[0;32m---> 60\u001b[0m urllib\u001b[39m.\u001b[39;49mrequest\u001b[39m.\u001b[39;49murlretrieve(url, filename, reporthook)\n", - "File \u001b[0;32m~/anaconda3/envs/rapidfitting/lib/python3.9/urllib/request.py:268\u001b[0m, in \u001b[0;36murlretrieve\u001b[0;34m(url, filename, reporthook, data)\u001b[0m\n\u001b[1;32m 265\u001b[0m reporthook(blocknum, bs, size)\n\u001b[1;32m 267\u001b[0m \u001b[39mwhile\u001b[39;00m \u001b[39mTrue\u001b[39;00m:\n\u001b[0;32m--> 268\u001b[0m block \u001b[39m=\u001b[39m fp\u001b[39m.\u001b[39;49mread(bs)\n\u001b[1;32m 269\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m block:\n\u001b[1;32m 270\u001b[0m \u001b[39mbreak\u001b[39;00m\n", - "File \u001b[0;32m~/anaconda3/envs/rapidfitting/lib/python3.9/http/client.py:458\u001b[0m, in \u001b[0;36mHTTPResponse.read\u001b[0;34m(self, amt)\u001b[0m\n\u001b[1;32m 455\u001b[0m \u001b[39mif\u001b[39;00m amt \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 456\u001b[0m \u001b[39m# Amount is given, implement using readinto\u001b[39;00m\n\u001b[1;32m 457\u001b[0m b \u001b[39m=\u001b[39m \u001b[39mbytearray\u001b[39m(amt)\n\u001b[0;32m--> 458\u001b[0m n \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mreadinto(b)\n\u001b[1;32m 459\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mmemoryview\u001b[39m(b)[:n]\u001b[39m.\u001b[39mtobytes()\n\u001b[1;32m 460\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 461\u001b[0m \u001b[39m# Amount is not given (unbounded read) so we must check self.length\u001b[39;00m\n\u001b[1;32m 462\u001b[0m \u001b[39m# and self.chunked\u001b[39;00m\n", - "File \u001b[0;32m~/anaconda3/envs/rapidfitting/lib/python3.9/http/client.py:502\u001b[0m, in \u001b[0;36mHTTPResponse.readinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m 497\u001b[0m b \u001b[39m=\u001b[39m \u001b[39mmemoryview\u001b[39m(b)[\u001b[39m0\u001b[39m:\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlength]\n\u001b[1;32m 499\u001b[0m \u001b[39m# we do not use _safe_read() here because this may be a .will_close\u001b[39;00m\n\u001b[1;32m 500\u001b[0m \u001b[39m# connection, and the user is reading more bytes than will be provided\u001b[39;00m\n\u001b[1;32m 501\u001b[0m \u001b[39m# (for example, reading in 1k chunks)\u001b[39;00m\n\u001b[0;32m--> 502\u001b[0m n \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfp\u001b[39m.\u001b[39;49mreadinto(b)\n\u001b[1;32m 503\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m n \u001b[39mand\u001b[39;00m b:\n\u001b[1;32m 504\u001b[0m \u001b[39m# Ideally, we would raise IncompleteRead if the content-length\u001b[39;00m\n\u001b[1;32m 505\u001b[0m \u001b[39m# wasn't satisfied, but it might break compatibility.\u001b[39;00m\n\u001b[1;32m 506\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_close_conn()\n", - "File \u001b[0;32m~/anaconda3/envs/rapidfitting/lib/python3.9/socket.py:704\u001b[0m, in \u001b[0;36mSocketIO.readinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m 702\u001b[0m \u001b[39mwhile\u001b[39;00m \u001b[39mTrue\u001b[39;00m:\n\u001b[1;32m 703\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 704\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_sock\u001b[39m.\u001b[39;49mrecv_into(b)\n\u001b[1;32m 705\u001b[0m \u001b[39mexcept\u001b[39;00m timeout:\n\u001b[1;32m 706\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_timeout_occurred \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n", - "File \u001b[0;32m~/anaconda3/envs/rapidfitting/lib/python3.9/ssl.py:1241\u001b[0m, in \u001b[0;36mSSLSocket.recv_into\u001b[0;34m(self, buffer, nbytes, flags)\u001b[0m\n\u001b[1;32m 1237\u001b[0m \u001b[39mif\u001b[39;00m flags \u001b[39m!=\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[1;32m 1238\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m 1239\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mnon-zero flags not allowed in calls to recv_into() on \u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m\"\u001b[39m \u001b[39m%\u001b[39m\n\u001b[1;32m 1240\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m\u001b[39m__class__\u001b[39m)\n\u001b[0;32m-> 1241\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mread(nbytes, buffer)\n\u001b[1;32m 1242\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 1243\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39msuper\u001b[39m()\u001b[39m.\u001b[39mrecv_into(buffer, nbytes, flags)\n", - "File \u001b[0;32m~/anaconda3/envs/rapidfitting/lib/python3.9/ssl.py:1099\u001b[0m, in \u001b[0;36mSSLSocket.read\u001b[0;34m(self, len, buffer)\u001b[0m\n\u001b[1;32m 1097\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m 1098\u001b[0m \u001b[39mif\u001b[39;00m buffer \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m-> 1099\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_sslobj\u001b[39m.\u001b[39;49mread(\u001b[39mlen\u001b[39;49m, buffer)\n\u001b[1;32m 1100\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 1101\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_sslobj\u001b[39m.\u001b[39mread(\u001b[39mlen\u001b[39m)\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + "Using files already downloaded\n" ] } ], @@ -159,7 +128,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "d43b4c5d", "metadata": {}, "outputs": [ @@ -204,16 +173,6 @@ " ├ UDVS\n", " ├ UDVS_Indices\n", " ├ raw_data_resampled\n", - "├ Raw_Data-SHO_Fit_000\n", - " --------------------\n", - " ├ Fit\n", - " ├ Guess\n", - " ├ SHO_LSQF\n", - " ├ SHO_LSQF_scaled\n", - " ├ Spectroscopic_Indices\n", - " ├ Spectroscopic_Values\n", - " ├ completed_fit_positions\n", - " ├ completed_guess_positions\n", "Datasets and datagroups within the file:\n", "------------------------------------\n", "/\n", @@ -249,15 +208,6 @@ "/Measurement_000/Channel_000/UDVS\n", "/Measurement_000/Channel_000/UDVS_Indices\n", "/Measurement_000/Channel_000/raw_data_resampled\n", - "/Raw_Data-SHO_Fit_000\n", - "/Raw_Data-SHO_Fit_000/Fit\n", - "/Raw_Data-SHO_Fit_000/Guess\n", - "/Raw_Data-SHO_Fit_000/SHO_LSQF\n", - "/Raw_Data-SHO_Fit_000/SHO_LSQF_scaled\n", - "/Raw_Data-SHO_Fit_000/Spectroscopic_Indices\n", - "/Raw_Data-SHO_Fit_000/Spectroscopic_Values\n", - "/Raw_Data-SHO_Fit_000/completed_fit_positions\n", - "/Raw_Data-SHO_Fit_000/completed_guess_positions\n", "\n", "The main dataset:\n", "------------------------------------\n", @@ -338,8 +288,10 @@ } ], "source": [ + "data_path = save_path + '/' + filename\n", + "\n", "# instantiate the dataset object\n", - "dataset = BE_Dataset(\"./data_raw.h5\", resample_bins = 80)\n", + "dataset = BE_Dataset(data_path, resample_bins = 80)\n", "\n", "# print the contents of the file\n", "dataset.print_be_tree()" @@ -380,13 +332,13 @@ " LSQF Phase Shift = None\n", " NN Phase Shift = None\n", " \n", - "./figures/Figure_1_raw_cantilever_response.png\n", - "./figures/Figure_1_raw_cantilever_response.svg\n" + "./../../../Figures/rapid_fitting/Figure_1_raw_cantilever_response.png\n", + "./../../../Figures/rapid_fitting/Figure_1_raw_cantilever_response.svg\n" ] }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -440,8 +392,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "./figures/Figure_2_raw_be_experiment.png\n", - "./figures/Figure_2_raw_be_experiment.svg\n" + "./../../../Figures/rapid_fitting/Figure_2_raw_be_experiment.png\n", + "./../../../Figures/rapid_fitting/Figure_2_raw_be_experiment.svg\n" ] }, { @@ -482,9 +434,74 @@ "execution_count": 8, "id": "3d9c41e7", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/ferroelectric/anaconda3/envs/rapidfitting/lib/python3.9/site-packages/sidpy/sid/translator.py:42: FutureWarning: Consider using sidpy.Reader instead of sidpy.Translator if possible and contribute your reader to ScopeReaders\n", + " warn('Consider using sidpy.Reader instead of sidpy.Translator if '\n", + "/home/ferroelectric/anaconda3/envs/rapidfitting/lib/python3.9/site-packages/BGlib/be/translators/labview_h5_patcher.py:132: UserWarning: 'channel_type' was not found as an attribute of /Measurement_000/Channel_000.\n", + "If this is BEPS or BELine data from the LabView aquisition software, please run the following piece of code. Afterwards, run this function again.\n", + "CODE: hdf.file['/Measurement_000/Channel_000'].attrs['channel_type'] = 'BE'\n", + " warn(warn_str)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Working on:\n", + "./../../../Data/2023_rapid_fitting/data_raw.h5\n", + "['Y', 'X'] [60, 60]\n", + "\n", + "\n", + "SHO Fits will be written to:\n", + "./../../../Data/2023_rapid_fitting/data_raw.h5\n", + "\n", + "\n", + "Consider calling test() to check results before calling compute() which computes on the entire dataset and writes results to the HDF5 file\n", + "\tThis class (likely) supports interruption and resuming of computations!\n", + "\tIf you are operating in a python console, press Ctrl+C or Cmd+C to abort\n", + "\tIf you are in a Jupyter notebook, click on \"Kernel\">>\"Interrupt\"\n", + "\tIf you are operating on a cluster and your job gets killed, re-run the job to resume\n", + "\n", + "Rank 0 - 100% complete. Time remaining: 0.0 msec\n", + "Finished processing the entire dataset!\n", + "\n", + "Note: SHO_Fit has already been performed with the same parameters before. These results will be returned by compute() by default. Set override to True to force fresh computation\n", + "\n", + "[]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/ferroelectric/anaconda3/envs/rapidfitting/lib/python3.9/site-packages/BGlib/be/analysis/fitter.py:140: UserWarning: status dataset not created yet\n", + " warn('status dataset not created yet')\n", + "/home/ferroelectric/anaconda3/envs/rapidfitting/lib/python3.9/site-packages/BGlib/be/analysis/fitter.py:140: UserWarning: status dataset not created yet\n", + " warn('status dataset not created yet')\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Resuming computation. 0% completed already\n", + "\tThis class (likely) supports interruption and resuming of computations!\n", + "\tIf you are operating in a python console, press Ctrl+C or Cmd+C to abort\n", + "\tIf you are in a Jupyter notebook, click on \"Kernel\">>\"Interrupt\"\n", + "\tIf you are operating on a cluster and your job gets killed, re-run the job to resume\n", + "\n", + "Rank 0 - 100% complete. Time remaining: 0.0 msec\n", + "Finished processing the entire dataset!\n", + "LSQF method took 1795.0535821914673 seconds to compute parameters\n" + ] + } + ], "source": [ - "Fit_SHO = False\n", + "Fit_SHO = True\n", "\n", "if Fit_SHO: \n", "\n", @@ -494,14 +511,14 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 10, "id": "4814df5e", "metadata": {}, "outputs": [], "source": [ "# instantiate the dataset object\n", "# it is a good idea to do this again in case the original data did not have complete SHO Fits\n", - "dataset = BE_Dataset(\"./data_raw.h5\", resample_bins = 80)\n", + "dataset = BE_Dataset(data_path, resample_bins = 80)\n", "BE_viz = Viz(dataset, printing, verbose=True)" ] }, @@ -518,7 +535,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 11, "id": "b533eda4", "metadata": {}, "outputs": [ @@ -538,8 +555,8 @@ " LSQF Phase Shift = 0\n", " NN Phase Shift = None\n", " \n", - "./figures/Figure_3_Original_LSQF_Histograms.png\n", - "./figures/Figure_3_Original_LSQF_Histograms.svg\n" + "./../../../Figures/rapid_fitting/Figure_3_Original_LSQF_Histograms.png\n", + "./../../../Figures/rapid_fitting/Figure_3_Original_LSQF_Histograms.svg\n" ] }, { @@ -571,7 +588,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 12, "id": "f5f4feec", "metadata": {}, "outputs": [ @@ -591,8 +608,8 @@ " LSQF Phase Shift = 1.5707963267948966\n", " NN Phase Shift = None\n", " \n", - "./figures/Figure_4_Phase_Shifted_LSQF_Histograms.png\n", - "./figures/Figure_4_Phase_Shifted_LSQF_Histograms.svg\n" + "./../../../Figures/rapid_fitting/Figure_4_Phase_Shifted_LSQF_Histograms.png\n", + "./../../../Figures/rapid_fitting/Figure_4_Phase_Shifted_LSQF_Histograms.svg\n" ] }, { @@ -632,7 +649,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 13, "id": "dd4ea37e", "metadata": {}, "outputs": [ @@ -652,13 +669,13 @@ " LSQF Phase Shift = 1.5707963267948966\n", " NN Phase Shift = None\n", " \n", - "./figures/Figure_5_Single_Pixel_Loops.png\n", - "./figures/Figure_5_Single_Pixel_Loops.svg\n" + "./../../../Figures/rapid_fitting/Figure_5_Single_Pixel_Loops.png\n", + "./../../../Figures/rapid_fitting/Figure_5_Single_Pixel_Loops.svg\n" ] }, { "data": { - "image/png": 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", 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" ] @@ -697,7 +714,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 14, "id": "b45251a3", "metadata": {}, "outputs": [ @@ -733,13 +750,13 @@ " LSQF Phase Shift = 1.5707963267948966\n", " NN Phase Shift = None\n", " \n", - "./figures/Figure_6_raw_cantilever_response_resampled.png\n", - "./figures/Figure_6_raw_cantilever_response_resampled.svg\n" + "./../../../Figures/rapid_fitting/Figure_6_raw_cantilever_response_resampled.png\n", + "./../../../Figures/rapid_fitting/Figure_6_raw_cantilever_response_resampled.svg\n" ] }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -758,6 +775,14 @@ "\n", "BE_viz.raw_data_comparison(raw, prediction, filename=\"Figure_6_raw_cantilever_response_resampled\")" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9937e60e", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/m3_learning/setup.py b/m3_learning/setup.py index d9d96e74..c76186de 100644 --- a/m3_learning/setup.py +++ b/m3_learning/setup.py @@ -6,7 +6,7 @@ setup( name="m3_learning", - version="0.0.15", + version="0.0.16", packages=find_packages(where="src"), url="https://github.com/jagar2/m3_learning.git", install_requires=requirements, diff --git a/m3_learning/src/m3_learning/be/__pycache__/dataset.cpython-39.pyc b/m3_learning/src/m3_learning/be/__pycache__/dataset.cpython-39.pyc index a41b7f3e..50e11cb9 100644 Binary files a/m3_learning/src/m3_learning/be/__pycache__/dataset.cpython-39.pyc and b/m3_learning/src/m3_learning/be/__pycache__/dataset.cpython-39.pyc differ diff --git a/m3_learning/src/m3_learning/be/dataset.py b/m3_learning/src/m3_learning/be/dataset.py index b78bb9cd..ec04bd81 100644 --- a/m3_learning/src/m3_learning/be/dataset.py +++ b/m3_learning/src/m3_learning/be/dataset.py @@ -21,7 +21,6 @@ from sklearn.model_selection import train_test_split - class BE_Dataset: def __init__(self, dataset, @@ -34,7 +33,7 @@ def __init__(self, dataset, resampled_bins=80, LSQF_phase_shift=None, NN_phase_shift=None, - verbose = False, + verbose=False, **kwargs): self.dataset = dataset self.resampled = resampled @@ -50,27 +49,30 @@ def __init__(self, dataset, for key, value in kwargs.items(): setattr(self, key, value) - + # make only run if SHO exist self.set_preprocessing() def set_preprocessing(self): self.set_raw_data() self.set_raw_data_resampler() - self.set_SHO_LSQF() self.raw_data_scaler = self.Raw_Data_Scaler(self.raw_data()) - + try: + self.set_SHO_LSQF() + except: + pass + def default_state(self): default_state_ = {'raw_format': "complex", - "fitter" : 'LSQF', - "output_shape" : "pixels", - "scaled" : False, - "measurement_state" : "all", - "resampled" : False, - "resampled_bins" : 80, - "LSQF_phase_shift" : None, - "NN_phase_shift" : None,} - + "fitter": 'LSQF', + "output_shape": "pixels", + "scaled": False, + "measurement_state": "all", + "resampled": False, + "resampled_bins": 80, + "LSQF_phase_shift": None, + "NN_phase_shift": None, } + self.set_state(**default_state_) def print_be_tree(self): @@ -348,7 +350,7 @@ def SHO_Scaler(self, def SHO_LSQF(self, pixel=None, voltage_step=None): with h5py.File(self.dataset, "r+") as h5_f: dataset_ = h5_f['/Raw_Data-SHO_Fit_000/SHO_LSQF'] - + if pixel is not None and voltage_step is not None: return dataset_[[pixel], :, :][:, [voltage_step], :] elif pixel is not None: @@ -481,22 +483,22 @@ def raw_spectra(self, pixel=None, voltage_step=None, fit_results=None): pixel=pixel, voltage_step=voltage_step) bins = self.frequency_bin else: - + params_shape = fit_results.shape - + # reshapes the parameters for fitting functions params = torch.tensor(fit_results.reshape(-1, 4)) - + if self.resampled: bins = self.resample_bins frequency_bins = resample(self.frequency_bin, self.resampled_bins) else: frequency_bins = self.frequency_bin - - data = eval(f"self.SHO_fit_func_{self.fitter}(params, frequency_bins)").reshape(params_shape[0], - params_shape[1],-1) - + + data = eval(f"self.SHO_fit_func_{self.fitter}(params, frequency_bins)").reshape(params_shape[0], + params_shape[1], -1) + data_shape = data.shape # does not sample if just a pixel is returned @@ -550,39 +552,37 @@ def extraction_state(self): LSQF Phase Shift = {self.LSQF_phase_shift} NN Phase Shift = {self.NN_phase_shift} ''') - - def test_train_split_(self, test_size=0.2, random_state=42, resampled = True, scaled = True, shuffle = False): - + + def test_train_split_(self, test_size=0.2, random_state=42, resampled=True, scaled=True, shuffle=False): + # makes sure you are using the resampled data self.resampled = resampled - + # makes sure you are using the scaled data self.scaled = scaled - + # gets the raw spectra real, imag = self.raw_spectra() - + # reshapes the data to be samples x timesteps real = real.reshape(-1, self.resample_bins) imag = imag.reshape(-1, self.resample_bins) - + # stacks the real and imaginary components x_data = np.stack((real, imag), axis=2) - + # gets the SHO fit results these values are scaled y_data = self.SHO_fit_results().reshape(-1, 4) - - self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(x_data, y_data, - test_size = test_size, - random_state = random_state, - shuffle = shuffle) - - if self.verbose: + + self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(x_data, y_data, + test_size=test_size, + random_state=random_state, + shuffle=shuffle) + + if self.verbose: self.extraction_state - - - return self.X_train, self.X_test, self.y_train, self.y_test + return self.X_train, self.X_test, self.y_train, self.y_test class Raw_Data_Scaler(): @@ -617,7 +617,6 @@ def inverse_transform(self, data): imag = self.imag_scaler.inverse_transform(imag) - # from m3_learning.util.h5_util import print_tree # from BGlib import be as belib # import pyUSID as usid diff --git a/m3_learning/src/m3_learning/util/__pycache__/file_IO.cpython-39.pyc b/m3_learning/src/m3_learning/util/__pycache__/file_IO.cpython-39.pyc index 86cf2859..751ae75e 100644 Binary files a/m3_learning/src/m3_learning/util/__pycache__/file_IO.cpython-39.pyc and b/m3_learning/src/m3_learning/util/__pycache__/file_IO.cpython-39.pyc differ diff --git a/m3_learning/src/m3_learning/util/file_IO.py b/m3_learning/src/m3_learning/util/file_IO.py index 63d43722..961166b8 100644 --- a/m3_learning/src/m3_learning/util/file_IO.py +++ b/m3_learning/src/m3_learning/util/file_IO.py @@ -111,6 +111,7 @@ def download_and_unzip(filename, url, save_path, force=False): save_path (str): place where the data is saved download_data (bool, optional): sets if to download the data. Defaults to True. """ + make_folder(save_path) path = save_path + '/' + filename # if np.int(get_size(save_path) / 1e9) < 1: