plt.plot(np.diff(cases), linewidth=3.0)
plt.scatter(shelter_in_place,
np.diff(cases)[shelter_in_place],
color="orange",
s=64)
plt.legend(["Cases per day", "Shelter-in-place order"])
plt.title("Cases per day")
plt.xlabel("Days since first case")
plt.ylabel("Cases")
x1, x2, y1, y2 = plt.axis()
plt.axis((x1, x2, 0, my))
plt.savefig("harris_raw.svg")
plt.close()
ys = smooth.epi_smooth_dx(np.diff(cases))
plt.plot(ys, linewidth=3)
plt.scatter(shelter_in_place, ys[shelter_in_place], color="orange", s=64)
plt.legend(["Cases per day", "Shelter-in-place order"])
plt.title("Cases per day")
plt.xlabel("Days since first case")
plt.ylabel("Cases")
x1, x2, y1, y2 = plt.axis()
plt.axis((x1, x2, 0, my))
plt.savefig("harris_smoothed.svg")
plt.close()
ys = smooth.moving_average(np.diff(cases))
plt.plot(ys, linewidth=3)
plt.scatter(shelter_in_place, ys[shelter_in_place], color="orange", s=64)
import numpy as np
import pandas as pd
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import smooth
df=pd.read_csv("manually_tabulated_dallas_testing.csv",header=4)
df.set_index("date")
tests=df["tests"].values
cases=df["positives"].values
plt.plot(smooth.epi_smooth_dx(cases/tests),linewidth=4,color="orange")
plt.bar(range(0,len(cases)),cases/tests)
plt.title("Ratio of positive tests")
plt.ylabel("Positive ratio")
plt.xlabel("day")
plt.legend(["Trend","Raw data"])
plt.savefig("positive_ratio.svg")
plt.close()
plt.plot(smooth.epi_smooth_dx(cases),linewidth=4,color="orange")
plt.bar(range(0,len(cases)),cases)
plt.title("Cases per day")
plt.ylabel("cases")
plt.xlabel("day")
plt.legend(["Trend","Raw data"])
plt.savefig("cases_per_day.svg")