83 lines
3.0 KiB
Python
83 lines
3.0 KiB
Python
"""
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Plot delay of countries
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"""
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import matplotlib.pyplot as pp
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import numpy as np
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name="delay"
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def plot(data, countries, pop, **kwargs):
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figsize = (10,5)
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for loc in data:
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if loc not in countries:
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continue
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time, new_cases, new_deaths, total_cases, total_deaths, total_vaccinations = data[loc]['time'], data[loc]['new_cases'], data[loc]['new_deaths'], data[loc]['total_cases'], data[loc]['total_deaths'], data[loc]['total_vaccinations']
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pp.figure(name, figsize=figsize)
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day_of_above_hundred_cases = np.argwhere(np.array(total_cases) > 100)[0][0]
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new_time_axis = np.arange(len(time)) - day_of_above_hundred_cases
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pp.plot(new_time_axis, np.array(total_cases), label=f"{loc} - {day_of_above_hundred_cases}", marker=".")
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pp.yscale("log")
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pp.xticks(rotation=45)
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pp.legend(frameon=False)
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pp.tight_layout()
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pp.savefig(name+".png")
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# plot delay
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pp.clf()
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delay = {}
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tcases = [50, 100, 500, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 80500,81000,81500,82000,83000,835000,84000,84500] + list(np.arange(10000, np.max(data["United States"][3]), 2500))
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tcases.sort()
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time_us = data["United States"][0]
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try:
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case_date_us = np.array([time_us[np.argwhere(np.array(data["United States"][3]) > cases)[0][0]] for cases in tcases])
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except:
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print("workaround delay_us:40")
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case_date_us = []
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usdat = np.array(data["United States"][3])
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for cases in tcases:
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try:
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casepos = np.argwhere(usdat > cases)[0][0]
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casetime = time_us[casepos]
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except:
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print(f"case date for {cases} not found")
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casetime = np.nan
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#continue
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case_date_us.append(casetime)
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case_date_us = np.array(case_date_us)
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total_cases_us = data["United States"][3]
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for loc in data:
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if loc not in countries:
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continue
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if loc in ["United States", "World"]:
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continue
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this_delay = []
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for i, cases in enumerate(tcases):
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try:
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case_date = data[loc][0][np.argwhere(np.array(data[loc][3]) > cases)[0][0]]
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except:
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this_delay.append(np.nan)
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continue
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#case_date = np.array([time_us[np.argwhere(np.array(data[loc][3]) > cases)[0][0]] for cases in tcases])
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this_delay.append((case_date_us[i] - case_date).days)
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delay[loc] = this_delay
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sort = np.argsort(delay)
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for loc in delay:
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pp.plot(tcases, delay[loc], label=loc, marker=".")
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#pp.plot(np.arange(len(delay)), np.array(delay)[sort] - np.min(delay), marker="o", linestyle="")
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#pp.xticks(ticks=np.arange(len(delay)), labels=np.array(d_loc)[sort])
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pp.xticks(rotation=45)
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pp.legend(frameon=False)
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pp.xlabel("total cases")
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pp.ylabel("delay to United States [days]")
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pp.tight_layout()
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pp.savefig("img/"+name+"_usa.png")
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