airalcorn2

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())

airalcorn2

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)

airalcorn2

# This bug was fixed in PyTorch 1.9. See:
# https://github.com/pytorch/pytorch/commit/aec83ff45ebd2cb3d4890cc97bffb1f367386392.

# See: https://pytorch.org/docs/stable/notes/faq.html#my-data-loader-workers-return-identical-random-numbers
# and: https://pytorch.org/docs/stable/data.html#multi-process-data-loading
# and: https://pytorch.org/docs/stable/data.html#randomness-in-multi-process-data-loading.

import numpy as np
import torch

airalcorn2

import numpy as np

A = np.array([[1, 1, 1], [2, 2, 2], [5, 5, 5]])
B = np.array([[0, 1, 0], [1, 1, 0], [1, 1, 1]])

(i_s, j_s, k_s) = (len(A), len(A[0]), len(B[0]))
for do_sum in [True, False]:
    if do_sum:
        C = np.zeros((i_s, k_s))
        print(np.einsum("ij,jk->ik", A, B))

airalcorn2

import matplotlib.pyplot as plt
import networkx as nx
import numpy as np
import seaborn as sns


def sim_outbreak(G, exps, inf_prob, init_infect, lock):
    pop_size = len(G)
    total_infected = []
    for exp in range(exps):

airalcorn2

# Create PDF montage from a directory of images.
montage -tile 2x100 "test/*" final.png
convert final.png final.pdf

airalcorn2

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

from datetime import datetime

airalcorn2

import numpy as np
import matplotlib.pyplot as plt

from scipy.optimize import linprog

A_ub = np.eye(3)
b_ub = np.ones(3)
A_eq = np.array([[0.95, 0.9, 0.15]])
b_eq = np.ones(1)
c = -np.array([0.9, 0.02, 0.08])

airalcorn2

# Derived from: https://github.com/pydicom/pydicom/blob/master/examples/input_output/plot_read_dicom_directory.py.

import imageio
import matplotlib.pyplot as plt
import numpy as np
import pydicom

from os import mkdir
from os.path import join
from PIL import Image

airalcorn2

import os

from PIL import Image

image_fs = os.listdir()
image_fs.sort()
image_fs = image_fs[:9]
images = map(Image.open, image_fs)
(widths, heights) = zip(*(i.size for i in images))