python_code stringlengths 0 4.04M | repo_name stringlengths 7 58 | file_path stringlengths 5 147 |
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# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
class PerlinNoiseGenerator(object):
def __init__(self, random_state=None):
self.rand = np.random if random_sta... | augmentation-corruption-fbr_main | imagenet_c_bar/utils/perlin_noise.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
#def smoothstep(low, high, x):
# return np.clip(3 * (x ** 2) - 2 * (x ** 3), 0, 1) * (high - low) + low
def smoothst... | augmentation-corruption-fbr_main | imagenet_c_bar/utils/image.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
def train_model(model, dataset, num_workers... | augmentation-corruption-fbr_main | notebook_utils/training_loop.py |
# This source code is adapted from code licensed under the MIT license
# found in third_party/wideresnet_license from the root directory of
# this source tree.
"""WideResNet implementation (https://arxiv.org/abs/1605.07146)."""
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import nump... | augmentation-corruption-fbr_main | notebook_utils/wideresnet.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from data_preprocessing import CLIMATE_VARS
from data_preprocessing.sample_quadruplets import generate_training_for_coun... | Context-Aware-Representation-Crop-Yield-Prediction-main | generate_for_deep_gaussian.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from crop_yield_prediction.models.deep_gaussian_process import *
from pathlib import Path
import torch
import argparse
... | Context-Aware-Representation-Crop-Yield-Prediction-main | crop_yield_deep_gaussian.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
import numpy as np
import pandas as pd
import argparse
import torch.optim as optim
from pathlib import Path
im... | Context-Aware-Representation-Crop-Yield-Prediction-main | crop_yield_train_semi_transformer.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
import numpy as np
import pandas as pd
import argparse
import torch.optim as optim
from pathlib import Path
im... | Context-Aware-Representation-Crop-Yield-Prediction-main | crop_yield_train_cnn_lstm.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
import numpy as np
import pandas as pd
import argparse
import torch.optim as optim
from pathlib import Path
im... | Context-Aware-Representation-Crop-Yield-Prediction-main | crop_yield_train_c3d.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
import numpy as np
import pandas as pd
import argparse
import torch.optim as optim
from pathlib import Path
im... | Context-Aware-Representation-Crop-Yield-Prediction-main | crop_yield_train_cross_location.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from crop_yield_prediction import CLIMATE_VARS
import os
import numpy as np
def generate_feature_importance_data_excl... | Context-Aware-Representation-Crop-Yield-Prediction-main | generate_feature_importance_data.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from data_preprocessing.sample_quadruplets import generate_training_for_counties
from data_preprocessing.postprocess imp... | Context-Aware-Representation-Crop-Yield-Prediction-main | generate_experiment_data.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from crop_yield_prediction.utils import Logger
from crop_yield_prediction.models import *
import os
import sys
import a... | Context-Aware-Representation-Crop-Yield-Prediction-main | crop_yield_no_spatial.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from crop_yield_prediction.dataloader import cross_location_dataloader
import os
import time
from math import sqrt
from... | Context-Aware-Representation-Crop-Yield-Prediction-main | crop_yield_prediction/train_cross_location.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
__all__ = ['CLIMATE_VARS', 'STATIC_CLIMATE_VARS', 'DYNAMIC_CLIMATE_VARS']
CLIMATE_VARS = ['ppt', 'evi', 'ndvi', 'elevat... | Context-Aware-Representation-Crop-Yield-Prediction-main | crop_yield_prediction/__init__.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from crop_yield_prediction.dataloader import c3d_dataloader
import time
from math import sqrt
from sklearn.metrics impo... | Context-Aware-Representation-Crop-Yield-Prediction-main | crop_yield_prediction/train_c3d.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from crop_yield_prediction.dataloader import semi_cropyield_dataloader
import os
import time
from math import sqrt
from... | Context-Aware-Representation-Crop-Yield-Prediction-main | crop_yield_prediction/train_semi_transformer.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from crop_yield_prediction.dataloader import cnn_lstm_dataloader
import time
from math import sqrt
from sklearn.metrics... | Context-Aware-Representation-Crop-Yield-Prediction-main | crop_yield_prediction/train_cnn_lstm.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
import matplotlib.pyplot as plt
from collections import defaultdict
def plot_loss(params):
out_dir = '..... | Context-Aware-Representation-Crop-Yield-Prediction-main | crop_yield_prediction/plot/plot_loss.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from bs4 import BeautifulSoup
from pathlib import Path
import matplotlib as mpl
import matplotlib.pyplot as plt
import p... | Context-Aware-Representation-Crop-Yield-Prediction-main | crop_yield_prediction/plot/plot_crop_yield.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from .plot_crop_yield import crop_yield_plot
from .plot_crop_yield_prediction_error import crop_yield_prediction_error_p... | Context-Aware-Representation-Crop-Yield-Prediction-main | crop_yield_prediction/plot/__init__.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from bs4 import BeautifulSoup
from pathlib import Path
import matplotlib as mpl
import matplotlib.pyplot as plt
import p... | Context-Aware-Representation-Crop-Yield-Prediction-main | crop_yield_prediction/plot/plot_crop_yield_prediction_error.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
from math import sqrt
from sklearn.metrics import r2_score, mean_squared_error
from scipy.stats.stats import p... | Context-Aware-Representation-Crop-Yield-Prediction-main | crop_yield_prediction/utils/train_utils.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from .timing import timeit, timenow
from .logger import Logger
from .train_utils import get_statistics
from .train_utils... | Context-Aware-Representation-Crop-Yield-Prediction-main | crop_yield_prediction/utils/__init__.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import sys
class Logger(object):
def __init__(self, filename="Default.log"):
self.terminal = sys.stdout
... | Context-Aware-Representation-Crop-Yield-Prediction-main | crop_yield_prediction/utils/logger.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# https://stackoverflow.com/questions/1557571/how-do-i-get-time-of-a-python-programs-execution
import atexit
from time i... | Context-Aware-Representation-Crop-Yield-Prediction-main | crop_yield_prediction/utils/timing.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from .no_spatial import predict_no_spatial
__all__ = ['predict_no_spatial']
| Context-Aware-Representation-Crop-Yield-Prediction-main | crop_yield_prediction/models/__init__.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import pandas as pd
import numpy as np
import os
import sys
from pathlib import Path
from sklearn.ensemble import Random... | Context-Aware-Representation-Crop-Yield-Prediction-main | crop_yield_prediction/models/no_spatial.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from crop_yield_prediction.models.semi_transformer.TileNet import make_tilenet
import torch
import torch.nn as nn
cla... | Context-Aware-Representation-Crop-Yield-Prediction-main | crop_yield_prediction/models/cnn_lstm/cnn_lstm.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# Based on transformer code from https://github.com/jadore801120/attention-is-all-you-need-pytorch
from crop_yield_pred... | Context-Aware-Representation-Crop-Yield-Prediction-main | crop_yield_prediction/models/cnn_lstm/__init__.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# Adapt code from https://github.com/gabrieltseng/pycrop-yield-prediction
from crop_yield_prediction import CLIMATE_VAR... | Context-Aware-Representation-Crop-Yield-Prediction-main | crop_yield_prediction/models/deep_gaussian_process/feature_engineering.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# Adapt code from https://github.com/gabrieltseng/pycrop-yield-prediction
import torch
from torch import nn
import torch... | Context-Aware-Representation-Crop-Yield-Prediction-main | crop_yield_prediction/models/deep_gaussian_process/convnet.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# Adapt code from https://github.com/gabrieltseng/pycrop-yield-prediction
import numpy as np
from scipy.spatial.distance... | Context-Aware-Representation-Crop-Yield-Prediction-main | crop_yield_prediction/models/deep_gaussian_process/gp.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from .feature_engineering import get_features_for_deep_gaussian
from .convnet import ConvModel
from .rnn import RNNModel... | Context-Aware-Representation-Crop-Yield-Prediction-main | crop_yield_prediction/models/deep_gaussian_process/__init__.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# Adapt code from https://github.com/gabrieltseng/pycrop-yield-prediction
import torch.nn.functional as F
def l1_l2_lo... | Context-Aware-Representation-Crop-Yield-Prediction-main | crop_yield_prediction/models/deep_gaussian_process/loss.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# Adapt code from https://github.com/gabrieltseng/pycrop-yield-prediction
from torch import nn
import torch
import math... | Context-Aware-Representation-Crop-Yield-Prediction-main | crop_yield_prediction/models/deep_gaussian_process/rnn.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# Adapt code from https://github.com/gabrieltseng/pycrop-yield-prediction
import torch
from torch.utils.data import Ten... | Context-Aware-Representation-Crop-Yield-Prediction-main | crop_yield_prediction/models/deep_gaussian_process/base.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# Based on transformer code from https://github.com/jadore801120/attention-is-all-you-need-pytorch
from crop_yield_pred... | Context-Aware-Representation-Crop-Yield-Prediction-main | crop_yield_prediction/models/c3d/__init__.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# Based on code from https://github.com/jfzhang95/pytorch-video-recognition/blob/master/network/C3D_model.py
# Architectu... | Context-Aware-Representation-Crop-Yield-Prediction-main | crop_yield_prediction/models/c3d/conv3d.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# Based on transformer code from https://github.com/jadore801120/attention-is-all-you-need-pytorch
from crop_yield_predi... | Context-Aware-Representation-Crop-Yield-Prediction-main | crop_yield_prediction/models/semi_transformer/AttentionModels.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.# Based on transformer code from https://github.com/jadore801120/attention-is-all-you-need-pytorch
from crop_yield_predic... | Context-Aware-Representation-Crop-Yield-Prediction-main | crop_yield_prediction/models/semi_transformer/__init__.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# Based on transformer code from https://github.com/jadore801120/attention-is-all-you-need-pytorch
''' Define the sublay... | Context-Aware-Representation-Crop-Yield-Prediction-main | crop_yield_prediction/models/semi_transformer/SubLayers.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# Based on transformer code from https://github.com/jadore801120/attention-is-all-you-need-pytorch
import torch
import t... | Context-Aware-Representation-Crop-Yield-Prediction-main | crop_yield_prediction/models/semi_transformer/Modules.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# Based on transformer code from https://github.com/jadore801120/attention-is-all-you-need-pytorch
from crop_yield_predi... | Context-Aware-Representation-Crop-Yield-Prediction-main | crop_yield_prediction/models/semi_transformer/SemiTransformer.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# Based on transformer code from https://github.com/jadore801120/attention-is-all-you-need-pytorch
from crop_yield_predi... | Context-Aware-Representation-Crop-Yield-Prediction-main | crop_yield_prediction/models/semi_transformer/Layers.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# Based on transformer code from https://github.com/jadore801120/attention-is-all-you-need-pytorch
'''A wrapper class fo... | Context-Aware-Representation-Crop-Yield-Prediction-main | crop_yield_prediction/models/semi_transformer/Optim.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# Based on tile2vec code from https://github.com/ermongroup/tile2vec
import torch
import torch.nn as nn
import torch.nn.... | Context-Aware-Representation-Crop-Yield-Prediction-main | crop_yield_prediction/models/semi_transformer/TileNet.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from torch.utils.data import Dataset, DataLoader
import torch
import numpy as np
class CnnLSTMDataset(Dataset):
""... | Context-Aware-Representation-Crop-Yield-Prediction-main | crop_yield_prediction/dataloader/cnn_lstm_dataloader.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from torch.utils.data import Dataset, DataLoader
import torch
import numpy as np
class C3DDataset(Dataset):
"""
... | Context-Aware-Representation-Crop-Yield-Prediction-main | crop_yield_prediction/dataloader/c3d_dataloader.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from crop_yield_prediction.dataloader.c3d_dataloader import c3d_dataloader
from crop_yield_prediction.dataloader.semi_cr... | Context-Aware-Representation-Crop-Yield-Prediction-main | crop_yield_prediction/dataloader/__init__.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from torch.utils.data import Dataset, DataLoader
import torch
import numpy as np
class CrossLocationDataset(Dataset):
... | Context-Aware-Representation-Crop-Yield-Prediction-main | crop_yield_prediction/dataloader/cross_location_dataloader.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from torch.utils.data import Dataset, DataLoader
import torch
import numpy as np
class SemiCropYieldDataset(Dataset):
... | Context-Aware-Representation-Crop-Yield-Prediction-main | crop_yield_prediction/dataloader/semi_cropyield_dataloader.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
__all__ = ['cdl_values_to_crops', 'crops_to_cdl_values',
'CLIMATE_VARS', 'STATIC_CLIMATE_VARS', 'DYNAMIC_CLIM... | Context-Aware-Representation-Crop-Yield-Prediction-main | data_preprocessing/__init__.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from .sample_for_counties import generate_training_for_counties
from .sample_for_pretrained import generate_training_for... | Context-Aware-Representation-Crop-Yield-Prediction-main | data_preprocessing/sample_quadruplets/__init__.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from netCDF4 import Dataset
import numpy as np
import os
import pandas as pd
import numpy.ma as ma
import matplotlib.pyp... | Context-Aware-Representation-Crop-Yield-Prediction-main | data_preprocessing/sample_quadruplets/sample_for_counties.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from netCDF4 import Dataset
import numpy as np
import os
import pandas as pd
import numpy.ma as ma
import matplotlib.pyp... | Context-Aware-Representation-Crop-Yield-Prediction-main | data_preprocessing/sample_quadruplets/sample_for_pretrained.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from .counties_plot import counties_plot, save_colorbar
__all__ = ['counties_plot', 'save_colorbar']
| Context-Aware-Representation-Crop-Yield-Prediction-main | data_preprocessing/plot/__init__.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from netCDF4 import Dataset
import matplotlib.pyplot as plt
import numpy as np
# mean of lats: 40.614586, mean of lons... | Context-Aware-Representation-Crop-Yield-Prediction-main | data_preprocessing/plot/plot_local.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from bs4 import BeautifulSoup
from pathlib import Path
import matplotlib as mpl
import matplotlib.pyplot as plt
import p... | Context-Aware-Representation-Crop-Yield-Prediction-main | data_preprocessing/plot/counties_plot.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from .merge_various_days import merge_various_days
__all__ = ['merge_various_days']
| Context-Aware-Representation-Crop-Yield-Prediction-main | data_preprocessing/merge/__init__.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from ..utils import generate_doy
import os
import numpy as np
import datetime as dt
from datetime import datetime
from ... | Context-Aware-Representation-Crop-Yield-Prediction-main | data_preprocessing/merge/merge_various_days.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import pandas as pd
from collections import defaultdict
import numpy as np
import numpy.ma as ma
from pathlib import Pat... | Context-Aware-Representation-Crop-Yield-Prediction-main | data_preprocessing/preprocess/crop_yield.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from netCDF4 import Dataset
import numpy as np
import numpy.ma as ma
def combine_landsat():
fh_out = Dataset('../... | Context-Aware-Representation-Crop-Yield-Prediction-main | data_preprocessing/preprocess/landsat.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
from netCDF4 import Dataset
# gdal_translate -of netCDF PRISM_ppt_stable_4kmM3_201806_bil.bil PRISM_ppt_stabl... | Context-Aware-Representation-Crop-Yield-Prediction-main | data_preprocessing/preprocess/prism.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
from netCDF4 import Dataset
import numpy as np
import numpy.ma as ma
from pathlib import Path
import pandas as... | Context-Aware-Representation-Crop-Yield-Prediction-main | data_preprocessing/preprocess/county_locations.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from .cdl import cdl_convert_to_nc
__all__ = ["cdl_convert_to_nc"]
| Context-Aware-Representation-Crop-Yield-Prediction-main | data_preprocessing/preprocess/__init__.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from netCDF4 import Dataset
import os
from osgeo import gdal, osr
import numpy as np
from pyproj import Proj, transform... | Context-Aware-Representation-Crop-Yield-Prediction-main | data_preprocessing/preprocess/cdl.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from netCDF4 import Dataset
import numpy.ma as ma
import datetime
import sys
sys.path.append("..")
def extract_lai(nc... | Context-Aware-Representation-Crop-Yield-Prediction-main | data_preprocessing/preprocess/lai.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from netCDF4 import Dataset
import numpy as np
import sys
sys.path.append("..")
from data_preprocessing.utils import ge... | Context-Aware-Representation-Crop-Yield-Prediction-main | data_preprocessing/preprocess/subset.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from netCDF4 import Dataset
import datetime
import calendar
from collections import defaultdict
import numpy.ma as ma
i... | Context-Aware-Representation-Crop-Yield-Prediction-main | data_preprocessing/preprocess/lst.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
from netCDF4 import Dataset
import numpy as np
import numpy.ma as ma
import sys
sys.path.append("..")
from da... | Context-Aware-Representation-Crop-Yield-Prediction-main | data_preprocessing/preprocess/landcover.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from .get_lat_lon_bins import get_lat_lon_bins
from .timing import timeit, timenow
from .generate_doy import generate_do... | Context-Aware-Representation-Crop-Yield-Prediction-main | data_preprocessing/utils/__init__.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
from datetime import date
def get_closet_date(query_date, folder):
doys = [x[:-3] for x in os.listdir(fo... | Context-Aware-Representation-Crop-Yield-Prediction-main | data_preprocessing/utils/get_closest_date.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
def get_lat_lon_bins(lats, lons):
inter_lat = np.array([(x + y) / 2.0 for x, y in zip(lats[:-1]... | Context-Aware-Representation-Crop-Yield-Prediction-main | data_preprocessing/utils/get_lat_lon_bins.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# https://stackoverflow.com/questions/1557571/how-do-i-get-time-of-a-python-programs-execution
import atexit
from time i... | Context-Aware-Representation-Crop-Yield-Prediction-main | data_preprocessing/utils/timing.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
def match_lat_lon(lats_from, lons_from, lats_to, lons_to, expand=0):
i_lat_start = i_lat_end = i_lon_start = i_lon_... | Context-Aware-Representation-Crop-Yield-Prediction-main | data_preprocessing/utils/match_lat_lon.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from datetime import date, timedelta
def generate_doy(s_doy, e_doy, delimiter):
s_doy = map(int, [s_doy[:4], s_doy... | Context-Aware-Representation-Crop-Yield-Prediction-main | data_preprocessing/utils/generate_doy.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from netCDF4 import Dataset
import numpy as np
import numpy.ma as ma
import fiona
import sys
sys.path.append("..")
fro... | Context-Aware-Representation-Crop-Yield-Prediction-main | data_preprocessing/rescaling/nws_precip.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from netCDF4 import Dataset
import numpy as np
import numpy.ma as ma
import pandas as pd
import csv
import sys
sys.pat... | Context-Aware-Representation-Crop-Yield-Prediction-main | data_preprocessing/rescaling/soil_fraction.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import numpy.ma as ma
from netCDF4 import Dataset
import sys
sys.path.append("..")
from data_prepro... | Context-Aware-Representation-Crop-Yield-Prediction-main | data_preprocessing/rescaling/us_counties.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
from netCDF4 import Dataset
import numpy as np
def get_origi_lat_lon():
in_dir = '../../processed_data/p... | Context-Aware-Representation-Crop-Yield-Prediction-main | data_preprocessing/rescaling/prism_upscale.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import numpy.ma as ma
from netCDF4 import Dataset
import sys
sys.path.append("..")
from data_prepro... | Context-Aware-Representation-Crop-Yield-Prediction-main | data_preprocessing/rescaling/elevation.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from data_preprocessing.utils import get_lat_lon_bins
from data_preprocessing import cdl_values_to_crops, crops_to_cdl_v... | Context-Aware-Representation-Crop-Yield-Prediction-main | data_preprocessing/rescaling/cdl_upscale.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from .rescale_utils import search_kdtree
from .rescale_utils import get_lat_lon_bins
__all__ = ['search_kdtree', 'get_l... | Context-Aware-Representation-Crop-Yield-Prediction-main | data_preprocessing/rescaling/__init__.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from netCDF4 import Dataset
import numpy as np
import numpy.ma as ma
import sys
sys.path.append("..")
from data_prepr... | Context-Aware-Representation-Crop-Yield-Prediction-main | data_preprocessing/rescaling/prism_downscale.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from netCDF4 import Dataset
import numpy as np
import numpy.ma as ma
import datetime
import os
import sys
sys.path.app... | Context-Aware-Representation-Crop-Yield-Prediction-main | data_preprocessing/rescaling/lst.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from scipy.spatial import cKDTree
import numpy as np
def search_kdtree(lats, lons, points):
mytree = cKDTree(np.ds... | Context-Aware-Representation-Crop-Yield-Prediction-main | data_preprocessing/rescaling/rescale_utils.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from netCDF4 import Dataset
import numpy as np
import numpy.ma as ma
import sys
sys.path.append("..")
from data_prepr... | Context-Aware-Representation-Crop-Yield-Prediction-main | data_preprocessing/rescaling/soil_moisture.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import pandas as pd
import csv
from netCDF4 import Dataset
import numpy as np
import numpy.ma as ma
from collections im... | Context-Aware-Representation-Crop-Yield-Prediction-main | data_preprocessing/postprocess/prism.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from .combine_multi_vars import mask_non_major_states
from .combine_multi_vars import generate_no_spatial_for_counties
f... | Context-Aware-Representation-Crop-Yield-Prediction-main | data_preprocessing/postprocess/__init__.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from netCDF4 import Dataset
import numpy as np
import os
import pandas as pd
import csv
from collections import defaultd... | Context-Aware-Representation-Crop-Yield-Prediction-main | data_preprocessing/postprocess/combine_multi_vars.py |
"""
"""
from __future__ import print_function
from __future__ import division
import matplotlib
matplotlib.use('agg')
import glob
import os
import torch
import argparse
import warnings
import pandas
from torch.utils.data import Dataset, DataLoader
from utils import *
from metrics import *
from dataloaders import *
fr... | ukb-cardiac-mri-master | ukb/predict.py |
import argparse
import pandas as pd
from os import makedirs
from os.path import isdir
from ensemble import *
def main(args):
pd.set_option("display.width", 100)
if args.pids_csv is not None:
pids = list(pd.read_csv(args.pids_csv)[args.pids_key])
else:
pids = None
if args.output_dir i... | ukb-cardiac-mri-master | ukb/ensemble.py |
"""
"""
from __future__ import print_function
from __future__ import division
import matplotlib
matplotlib.use('agg')
import os
import torch
import argparse
import warnings
import pandas
from torch.utils.data import Dataset, DataLoader
from utils import *
from metrics import *
from dataloaders import *
from models im... | ukb-cardiac-mri-master | ukb/train.py |
"""
Phase Contrast Cardiac MRI Segmentation
Prepare MRIs for training a CNN model. Given an input directory of numpy image tensors
containing phase contrast cardiac MRIs:
- Generate candidate value segmentations
- Rank candidates in terms of the most likely atrial value
- Write segmentation masks to numpy files
- Exp... | ukb-cardiac-mri-master | ukb/segmentMultiple.py |
"""
Phase Contrast Cardiac MRI Segmentation
Prepare MRIs for training a CNN model. Given an input directory of numpy image tensors
containing phase contrast cardiac MRIs:
- Generate candidate value segmentations
- Rank candidates in terms of the most likely atrial value
- Write segmentation masks to numpy files
- Exp... | ukb-cardiac-mri-master | ukb/segment.py |
"""
Hacky script to generate synthetic data to debug model training.
Default configuration generates sequences based on empirical attributes
"""
from __future__ import print_function
import os
import sys
import math
import argparse
import numpy as np
from skimage import draw
import matplotlib.pyplot as plt
import mat... | ukb-cardiac-mri-master | ukb/create_synthetic_data.py |
from __future__ import print_function
from __future__ import division
from sklearn.metrics import fbeta_score as sk_fbeta
from functools import wraps
__all__ = ['f05_score',
'f04_score']
def fbeta_create(beta=1.0):
def fbeta_wrapper(func):
@wraps(func)
def fbeta_score(*args, **kwargs)... | ukb-cardiac-mri-master | ukb/metrics/fbeta.py |
from .base import *
from .fbeta import *
| ukb-cardiac-mri-master | ukb/metrics/__init__.py |
from __future__ import print_function
from __future__ import division
import torch
import torch.nn as nn
from torch.autograd import Variable
import numpy as np
from sklearn.preprocessing import OneHotEncoder
from sklearn.metrics import f1_score, precision_score, recall_score, \
accuracy_score, confusion_matrix, c... | ukb-cardiac-mri-master | ukb/metrics/base.py |
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