First commit.

coronaflask/corona.py

@@ -0,0 +1,181 @@
+import os
+import re
+import datetime
+import pandas
+import numpy as np
+from matplotlib.figure import Figure
+import matplotlib.dates as mdates
+import mpld3
+import requests
+import io
+
+
+class Plots:
+    @staticmethod
+    def get_date(date_s):
+        date_d = {k: int(i) + (2000 if k == 'year' else 0) for i, k in zip(date_s.split('/'), ('month', 'day', 'year'))}
+        date_o = datetime.datetime(**date_d)
+        return int(mdates.date2num(date_o) + 0.5), date_s, date_o
+
+    @staticmethod
+    def fmt_axis(ax):
+        now = int(mdates.date2num(datetime.datetime.now()) + 0.5)
+        ax.set_xlim(now - 100, now)
+        ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
+        ax.xaxis.set_major_locator(mdates.DayLocator(interval=5))
+        xy = [a.get_xydata() for a in ax.get_children() if hasattr(a, 'get_xydata')]
+        if len(xy):
+            xy = np.vstack(xy)
+            ax.set_ylim(Plots.getylim(xy[:, 1], xy[:, 0], ax.get_xlim(), log=ax.get_xscale() == 'log'))
+
+    @staticmethod
+    def getylim(y, x=None, xlim=None, margin=0.05, log=False):
+        """ get limits for plots according to data
+            copied from matplotlib.axes._base.autoscale_view
+
+            y: the y data
+            optional, for when xlim is set manually on the plot
+                x: corresponding x data
+                xlim: limits on the x-axis in the plot, example: xlim=(0, 100)
+                margin: what fraction of white-space to have at all borders
+            y and x can be lists or tuples of different data in the same plot
+
+            wp@tl20191220
+        """
+        y = np.array(y).flatten()
+        if log:
+            y = np.log(y)
+        if x is not None and xlim is not None:
+            x = np.array(x).flatten()
+            y = y[(np.nanmin(xlim) < x)*(x < np.nanmax(xlim))*(np.abs(x) > 0)]
+            if not np.any(np.isfinite(y)):
+                return 0, 1
+            if len(y) == 0:
+                return -margin, margin
+        y0t, y1t = np.nanmin(y), np.nanmax(y)
+        if np.isfinite(y1t) and np.isfinite(y0t):
+            delta = (y1t - y0t) * margin
+            if y0t == y1t:
+                delta = 0.5
+        else:  # If at least one bound isn't finite, set margin to zero
+            delta = 0
+        if log:
+            return np.exp(y0t - delta), np.exp(y1t + delta)
+        else:
+            return y0t - delta, y1t + delta
+
+    @staticmethod
+    def transform(a, b, x, xlim):
+        return np.polyval(np.polyfit(Plots.getylim(a, x, xlim), Plots.getylim(b, x, xlim), 1), a)
+
+
+class Corona:
+    def __init__(self):
+        self.home = os.path.dirname(__file__)
+        self.update()
+
+    def update(self):
+        self.last_update = datetime.datetime.now()
+        self.confirmed_global = self.get_data(
+            'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv')
+        self.deaths_global = self.get_data(
+            'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv')
+        self.regions = sorted(self.confirmed_global['Province/State'].tolist())
+
+    @staticmethod
+    def get_data(url):
+        with requests.get(url, allow_redirects=True) as r:
+            with io.BytesIO(r.content) as b:
+                p = pandas.read_csv(b)
+
+        null = p['Province/State'].isnull()
+        p.loc[null, 'Province/State'] = p.loc[null, 'Country/Region']
+
+        # make series searchable by region
+        countries = p['Country/Region'].unique()
+        states = p['Province/State'].unique()
+        for country in countries:
+            if country not in states:
+                ca = p.query('`Country/Region`=="{}"'.format(country))
+                cb = ca.sum(axis=0)
+                cb['Province/State'] = country
+                cb['Country/Region'] = country
+                cb['Lat'] /= len(ca)
+                cb['Long'] /= len(ca)
+                p.loc[p.index.max() + 1] = cb
+
+        # make World entry by summing
+        w = np.sum(p)
+        w.name = p.index.max() + 1
+        w['Province/State'] = 'World'
+        w['Country/Region'] = 'World'
+        w['Lat'] = 0
+        w['Long'] = 0
+        return p.append(w)
+
+    @staticmethod
+    def plot_series(fig, ax, dates_number, dates_obj, cum, llabel=None, rlabel=None):
+        ax.plot(dates_obj, cum, 'ro-')
+        Plots.fmt_axis(ax)
+        ax.set_ylim(Plots.getylim(cum, dates_number, ax.get_xlim()))
+
+        dcum = cum - np.interp(dates_number, dates_number + 1, cum)
+        bx = ax.twinx()
+        p, = bx.plot(dates_obj, Plots.transform(cum, dcum, dates_number, ax.get_xlim()), 'o', color='none')
+        mpld3.plugins.connect(fig, mpld3.plugins.PointLabelTooltip(p, labels=[f'{n:.2g}' for n in cum]))
+        p, = bx.plot(dates_obj, dcum, 'ko-')
+        bx.patch.set_alpha(0.0)
+        mpld3.plugins.connect(fig, mpld3.plugins.PointLabelTooltip(p, labels=[f'{n:.2g}' for n in dcum]))
+        Plots.fmt_axis(bx)
+        if llabel is not None:
+            ax.set_ylabel(llabel, color='r')
+        if rlabel is not None:
+            bx.set_ylabel(rlabel, color='k')
+
+    def plot_cases(self, place, sicktime=14):
+        if datetime.datetime.now() - self.last_update > datetime.timedelta(hours=1):
+            self.update()
+
+        n = self.confirmed_global.query(f'`Province/State`=="{place}"')
+        d = self.deaths_global.query(f'`Province/State`=="{place}"')
+        if n.empty:
+            n = self.confirmed_global.query(f'`Country/Region`=="{place}"')
+            d = self.deaths_global.query(f'`Country/Region`=="{place}"')
+        n = pandas.DataFrame(n.sum()).T
+        d = pandas.DataFrame(d.sum()).T
+
+        dates = [Plots.get_date(c) for c in n.columns if re.match('[\d/]+', c) is not None]
+
+        dates_number, dates_str, dates_obj = zip(*sorted(dates))
+        sick_cum = np.array([int(n[date]) for date in dates_str])
+        death_cum = np.array([int(d[date]) for date in dates_str])
+        dates_number = np.array(dates_number)
+        sick_now = sick_cum - np.interp(dates_number, dates_number + sicktime, sick_cum)
+
+        fig = Figure(figsize=(16, 8), dpi=100)
+        ax = fig.add_subplot(4, 1, 1)
+        self.plot_series(fig, ax, dates_number, dates_obj, sick_cum, 'sick cumulative', 'daily new cases')
+        ax.set_title('cumulative and new daily cases')
+
+        ax = fig.add_subplot(4, 1, 2)
+        self.plot_series(fig, ax, dates_number, dates_obj, sick_now, 'sick now', 'daily change')
+        ax.set_title('current sick and daily change')
+
+        ax = fig.add_subplot(4, 1, 3)
+        reproduction_rate = sick_now / np.clip(np.interp(dates_number, dates_number + 4, sick_now), 1e-15, np.inf)
+        p, = ax.plot(dates_obj, np.clip(reproduction_rate, 0, 3), 'ro-')
+        ax.plot((np.nanmin(dates_number), np.nanmax(dates_number)), (1, 1), '--k')
+        mpld3.plugins.connect(fig, mpld3.plugins.PointLabelTooltip(p, labels=[f'{n:.2f}' for n in reproduction_rate]))
+        Plots.fmt_axis(ax)
+        ax.set_ylim(0.5, 1.5)
+        ax.set_title('reproduction rate')
+        ax.set_ylabel('reproduction rate', color='r')
+
+        ax = fig.add_subplot(4, 1, 4)
+        self.plot_series(fig, ax, dates_number, dates_obj, death_cum, 'diseased cumulative', 'daily diseased')
+        ax.set_title('cumulative and new deaths')
+
+        fig.autofmt_xdate()
+        fig.tight_layout(h_pad=2)
+
+        return mpld3.fig_to_html(fig)