Michael A. Alcorn airalcorn2

## minimal_numba_race_condition_example.py
import numpy as np
import os
import torch

from numba import cuda, int32, njit


@njit((int32[:, :], int32[:, :], int32[:, :], int32[:], int32[:, :], int32[:]))
def loop(keys, key_mask, key2idx, cur_idx, idx2key, idx_counts):
    for row, col in keys:

## bad_vae.py
# Adapted from: https://github.com/pytorch/examples/blob/main/vae/main.py.

import torch
import torch.utils.data

from torch import nn, optim
from torch.nn import functional as F


class VAE(nn.Module):

## sentinel2_example.py
# Heavily inspired by: https://geospatial.101workbook.org/ImportingData/ImportingImages.html.

import geopandas as gpd
import numpy as np
import pandas as pd
import requests
import stackstac

from PIL import Image
from satsearch import Search

## nn_causal_effect.py
import matplotlib.pyplot as plt
import math
import numpy as np
import pandas as pd
import sklearn.datasets
import time
import torch

from sklearn.linear_model import LinearRegression
from torch import nn, optim

## multimodal_multivariate.py
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns

N = 1000
mu = 1
sd = 0.1

# Separate dimensions example.

## create_covid19_counties_video.py
import cv2
import geopandas
import io
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import requests
import time
import zipfile

## synthetic.py
# See: https://matheusfacure.github.io/python-causality-handbook/15-Synthetic-Control.html
# and: http://ftp.iza.org/dp13670.pdf.

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns

from datetime import datetime, timedelta
from scipy.optimize import fmin_slsqp

## hook_transformer_attn.py
# Inspired by: https://towardsdatascience.com/the-one-pytorch-trick-which-you-should-know-2d5e9c1da2ca.
# Monkey patching idea suggested by @kklemon here:
# https://gist.github.com/airalcorn2/50ec06517ce96ecc143503e21fa6cb91?permalink_comment_id=4407423#gistcomment-4407423.

import torch

from torch import nn


def patch_attention(m):

## nba_covid.py
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns

csv_url = "https://raw.githubusercontent.com/gradlab/CtTrajectories/main/data/ct_dat_clean.csv"
df = pd.read_csv(csv_url)

person_ids = df["Person.ID"].unique()
(min_x, max_x) = (df["Date.Index"].min(), df["Date.Index"].max())

## cluster_covid_infection_estimates.py
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns

households = 10000
infected_households_prop = 0.01
persons_per_household = 100
sample_prop = 0.001
samples = int(sample_prop * households * persons_per_household)
	import numpy as np
	import os
	import torch

	from numba import cuda, int32, njit


	@njit((int32[:, :], int32[:, :], int32[:, :], int32[:], int32[:, :], int32[:]))
	def loop(keys, key_mask, key2idx, cur_idx, idx2key, idx_counts):
	for row, col in keys:
	# Adapted from: https://github.com/pytorch/examples/blob/main/vae/main.py.

	import torch
	import torch.utils.data

	from torch import nn, optim
	from torch.nn import functional as F


	class VAE(nn.Module):
	# Heavily inspired by: https://geospatial.101workbook.org/ImportingData/ImportingImages.html.

	import geopandas as gpd
	import numpy as np
	import pandas as pd
	import requests
	import stackstac

	from PIL import Image
	from satsearch import Search
	import matplotlib.pyplot as plt
	import math
	import numpy as np
	import pandas as pd
	import sklearn.datasets
	import time
	import torch

	from sklearn.linear_model import LinearRegression
	from torch import nn, optim
	import matplotlib.pyplot as plt
	import numpy as np
	import seaborn as sns

	N = 1000
	mu = 1
	sd = 0.1

	# Separate dimensions example.
	import cv2
	import geopandas
	import io
	import matplotlib.pyplot as plt
	import numpy as np
	import pandas as pd
	import requests
	import time
	import zipfile
	# See: https://matheusfacure.github.io/python-causality-handbook/15-Synthetic-Control.html
	# and: http://ftp.iza.org/dp13670.pdf.

	import matplotlib.pyplot as plt
	import numpy as np
	import pandas as pd
	import seaborn as sns

	from datetime import datetime, timedelta
	from scipy.optimize import fmin_slsqp
	# Inspired by: https://towardsdatascience.com/the-one-pytorch-trick-which-you-should-know-2d5e9c1da2ca.
	# Monkey patching idea suggested by @kklemon here:
	# https://gist.github.com/airalcorn2/50ec06517ce96ecc143503e21fa6cb91?permalink_comment_id=4407423#gistcomment-4407423.

	import torch

	from torch import nn


	def patch_attention(m):