172 lines
8.7 KiB
Python
172 lines
8.7 KiB
Python
"""
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Plot overview plot for each country separately
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"""
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import matplotlib.pyplot as pp
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from mpl_toolkits.axisartist.parasite_axes import HostAxes, ParasiteAxes
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import numpy as np
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import time as time_module
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import pickle
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import warnings
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warnings.filterwarnings("ignore", category=UserWarning)
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basename="all_"
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# manual y adjustments for new cases
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corr = {"Chile": 10000,
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"China": 4000,
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"Ecuador": 2500,
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"Kazakhstan": 5000,
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"Kyrgyzstan": 2200,
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"Peru": 10000,
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"Bolivia": 3000,
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"Yemen": 220,
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"Turkey": 40000,
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"Spain": 40000,
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}
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def plot(data, countries, pop, metadata={}, **kwargs):
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figsize = (10,5)
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vaccs = []
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for loc in data:
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try:
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if loc == "International":
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continue
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name = basename+loc
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time, new_cases, new_deaths, total_cases, total_deaths, total_vaccinations, stringency_index, new_vaccinations = data[loc]['time'], data[loc]['new_cases'], data[loc]['new_deaths'], data[loc]['total_cases'], data[loc]['total_deaths'], data[loc]['total_vaccinations'], data[loc]['stringency_index'], data[loc]['new_vaccinations']
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people_fully_vaccinated = data[loc]['people_fully_vaccinated']
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fig, ax1 = pp.subplots(num=name, figsize=figsize)
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ax2 = ax1.twinx()
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ax2.plot(time, np.array(total_deaths)*10, label="Total deaths (x10)", marker="", linestyle="--", color="green")
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ax2.plot(time, total_cases, label=f"Total cases", marker="", linestyle="-", color="blue")
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ax1.plot(time, np.array(new_deaths)*10, label="raw new deaths (x10)", color="grey", linestyle=":")
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ax1.plot(time[3:-3], np.convolve(new_deaths, np.ones((7,))/7, mode="valid")*10, label="new deaths 7day mean (x10)", color="black", linestyle="--", linewidth=2)
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ax1.plot(time, new_cases, label="raw new cases", color="grey", linestyle="-")
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ax1.plot(time[3:-3], np.convolve(new_cases, np.ones((7,))/7, mode="valid"), label="new cases 7day mean", color="orange", linestyle="-", linewidth=2)
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# plot vaccinations
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if not np.isnan(total_vaccinations).all():
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# notify of new vaccine programs
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if np.isnan(total_vaccinations[-2]):
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print(f"{loc} starts vaccinating, adding to plot")
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ax2.plot(time, np.array(total_vaccinations), label=f"Total vaccination doses", marker="", linestyle="-.", color="crimson")
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ax2.plot(time, np.array(people_fully_vaccinated), label="fully vaccinated", marker="", linestyle="-", color="crimson")
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# fix lower bound of plot
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for ax in (ax1, ax2):
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axis = ax.axis()
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if ax is ax1: # adjust left (new cases) axes upper boundary for given countries
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if loc in corr:
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axis = [axis[0], axis[1], axis[2], corr[loc]]
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ax.axis([axis[0], axis[1], -1, axis[3]])
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# fix population
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#try:
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# print(loc, pop[loc]['pop'] - metadata[loc]['population'])
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#except:
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# pop[loc]['pop'] = metadata[loc]['population']
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# if we know population: plot 500 new cases / 1million inhabitants as a rough measure for comparison
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# also set color for infection level indicator
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infection_level_indicator = "grey"
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try:
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#if False:
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warn_thresh = 500e-6 * pop[loc]['pop']/7
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info_thresh = 50e-6 * pop[loc]['pop']/7
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low_thresh = 5e-6 * pop[loc]['pop']/7
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actual_level = np.mean(new_cases[-7:])
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infection_level_indicator = "green"
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if actual_level > low_thresh:
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infection_level_indicator = "gold"
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if actual_level > info_thresh:
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infection_level_indicator = "peru"
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if actual_level > warn_thresh:
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infection_level_indicator = "r"
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bounds = ax1.axis()
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# for small population numbers, give floats instead of ints
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if pop[loc]['pop'] < 5e6:
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ax1.plot([bounds[0], bounds[1]], [warn_thresh]*2, color="red", linestyle=":", label=f"500 new cases / week / 1M inh.: {warn_thresh:1.2f}".replace(",", "."))
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ax1.plot([bounds[0], bounds[1]], [info_thresh]*2, color="peru", linestyle=":", label=f"50 new cases / week / 1M inh.: {info_thresh:1.2f}".replace(",", "."))
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ax1.plot([bounds[0], bounds[1]], [low_thresh]*2, color="gold", linestyle=":", label=f"5 new cases / week / 1M inh.: {low_thresh:1.2f}".replace(",", "."))
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else: # but not for big populations
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ax1.plot([bounds[0], bounds[1]], [warn_thresh]*2, color="red", linestyle=":", label=f"500 new cases / week / 1M inh.: {int(warn_thresh):,}".replace(",", "."))
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ax1.plot([bounds[0], bounds[1]], [info_thresh]*2, color="peru", linestyle=":", label=f"50 new cases / week / 1M inh.: {int(info_thresh):,}".replace(",", "."))
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ax1.plot([bounds[0], bounds[1]], [low_thresh]*2, color="gold", linestyle=":", label=f"5 new cases / week / 1M inh.: {int(low_thresh):,}".replace(",", "."))
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ax1.axis(bounds)
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except:
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print(f"=====> population unknown for {loc}, skipping plot enhancements")
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# stringency of countermeasures as background contourf
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axbounds = ax1.axis()
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ax1.contourf(time, [-1, 1e10], [stringency_index]*2, cmap="Greys", alpha=0.3, levels=99)
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ax1.axis(axbounds)
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# disabled for now: put second total-cases-per-million-inhabitants axis besides total-cases-axis
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if loc in pop and False:
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# according to https://matplotlib.org/3.2.2/gallery/axisartist/demo_parasite_axes.html
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host1 = HostAxes(ax1, [0.15, 0.1, 0.65, 0.8])
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par1 = ParasiteAxes(host1, sharex=host1)
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host1.append(par1)
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host2 = HostAxes(ax2, [0.15, 0.1, 0.65, 0.8])
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par2 = ParasiteAxes(host2, sharex=host1)
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host2.append(par2)
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ax3 = ax1.twinx()
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ax3.plot(time, total_cases*1e6/pop[loc]['pop'], linestyle="", marker="")
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ax3.set_ylabel("total cases per 1M inhabitants")
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# plot infection level indicator
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ax1.annotate('Infection status:', xy=(0.85, 1.02), xycoords="axes fraction")
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ax1.annotate('•', xy=(0.99, 1.02), xycoords="axes fraction", color=infection_level_indicator, fontsize="x-large")
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#ax1.xticks(rotation=45)
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#ax1.set_xlabel("date")
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ax1.set_ylabel("new cases")
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ax2.set_ylabel("total cases")
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fig.legend(frameon=False, loc="upper left", bbox_to_anchor=(0,1), bbox_transform=ax1.transAxes)
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title = loc
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if loc in pop:
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#pp.title(f"{loc}", population = "+f"{pop[loc]['pop']:,}".replace(",","."))
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title += ", population = "+f"{pop[loc]['pop']:,}".replace(",",".")
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if not np.isnan(people_fully_vaccinated[-1]):
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title += ", vac rate: "+f"{people_fully_vaccinated[-1]/pop[loc]['pop']*100:1.3f}%"
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vaccs.append([loc, people_fully_vaccinated[-1], people_fully_vaccinated[-1]/pop[loc]['pop']*100]) # bookkeeping for overview
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ax1.set_title(title)
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fig.tight_layout()
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pp.text(0.002,0.005, f"plot generated {time_module.strftime('%Y-%m-%d %H:%M')}, CC-by-sa-nc, origin: dukun.de/corona, datasource: ourworldindata.org/coronavirus-source-data", color="dimgrey", fontsize=8, transform=fig.transFigure)
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pp.savefig("img/ac_"+name+".png")
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pp.close(fig)
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except Exception as e:
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print(f"=====> plotting failed for {loc}, skipping plot. Error: {e}")
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## vaccination overview html
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with open("index.html", "w") as f:
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# site header
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with open("index.html.head", "r") as g:
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f.write(g.read())
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# table header
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f.write("<table><tr><th>Land</th><th>Impfungen</th><th>Impfrate</th><th>Impfstoffe</th></tr>\n")
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# data
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for loc, tvac, rvac in vaccs:
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line = f"<tr><td>{loc}</td><td>" + \
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f"{int(tvac):,d}".replace(",",".") + \
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f"</td><td>{rvac:3.3f}%</td>".replace(".", ",")
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if "vaccines" in metadata[loc]:
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line += f"<td>{metadata[loc]['vaccines']}</td></tr>\n"
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else:
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line += f"<td>-</td></tr>\n"
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f.write(line)
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# table footer
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f.write("</table>\n")
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# site footer
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with open("index.html.foot", "r") as g:
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f.write(g.read())
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