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plot.py
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plot.py
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import os
import itertools
import collections
import pprint
import numpy as np
import pandas as pd
from scipy import stats as sps
from scipy.interpolate import interp1d
from datetime import datetime
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
from matplotlib import ticker
import matplotlib.dates as mdates
from matplotlib.dates import date2num, num2date
from matplotlib.backends.backend_pgf import FigureCanvasPgf
from matplotlib.colors import ListedColormap
from scipy.interpolate import griddata
import matplotlib.colors as colors
from lib.rt import compute_daily_rts, R_T_RANGE
import lib.rt_nbinom
from lib.summary import *
TO_HOURS = 24.0
DPI = 200
NO_PLOT = False
TEST_LAG = 48.0 # hours
LINE_WIDTH = 7.0
COL_WIDTH = 3.333
FIG_SIZE_TRIPLE = (COL_WIDTH / 3, COL_WIDTH / 3 * 4/6)
FIG_SIZE_TRIPLE_TALL = (COL_WIDTH / 3, COL_WIDTH / 3 * 5/6)
FIG_SIZE_DOUBLE = (COL_WIDTH / 2, COL_WIDTH / 2 * 4/6)
FIG_SIZE_DOUBLE_TALL = (COL_WIDTH / 2, COL_WIDTH / 2 * 5/6)
CUSTOM_FIG_SIZE_FULL_PAGE_TRIPLE = (LINE_WIDTH / 3, COL_WIDTH / 2 * 5/6)
FIG_SIZE_FULL_PAGE_TRIPLE = (LINE_WIDTH / 3, LINE_WIDTH / 3 * 4/6)
FIG_SIZE_FULL_PAGE_TRIPLE_TALL = (LINE_WIDTH / 3, LINE_WIDTH / 3 * 5/6)
FIG_SIZE_FULL_PAGE_DOUBLE_ARXIV = (LINE_WIDTH / 2, LINE_WIDTH / 3 * 4/6) # 2
FIG_SIZE_FULL_PAGE_DOUBLE_ARXIV_TALL = (LINE_WIDTH / 2, LINE_WIDTH / 3 * 4.5/6) # 2 tall
FIG_SIZE_FULL_PAGE_TRIPLE_ARXIV = (LINE_WIDTH / 3.3, LINE_WIDTH / 3 * 3.5/6) # 4x3 full page
FIG_SIZE_FULL_PAGE_TRIPLE_ARXIV_SMALL = (LINE_WIDTH / 3.7, LINE_WIDTH / 3 * 2.5/6) # 6x4 full page
CUSTOM_FIG_SIZE_FULL_PAGE_QUAD = (LINE_WIDTH / 4, COL_WIDTH / 2 * 5/6)
SIGCONF_RCPARAMS_DOUBLE = {
# Fig params
"figure.autolayout": True, # Makes sure nothing the feature is neat & tight.
"figure.figsize": FIG_SIZE_DOUBLE, # Column width: 3.333 in, space between cols: 0.333 in.
"figure.dpi": 150, # Displays figures nicely in notebooks.
# Axes params
"axes.linewidth": 0.5, # Matplotlib's current default is 0.8.
"hatch.linewidth": 0.3,
"xtick.major.width": 0.5,
"xtick.minor.width": 0.5,
'xtick.major.pad': 1.0,
'xtick.major.size': 1.75,
'xtick.minor.pad': 1.0,
'xtick.minor.size': 1.0,
"ytick.major.width": 0.5,
"ytick.minor.width": 0.5,
'ytick.major.pad': 1.0,
'ytick.major.size': 1.75,
'ytick.minor.pad': 1.0,
'ytick.minor.size': 1.0,
"axes.labelpad": 0.5,
# Plot params
"lines.linewidth": 0.8, # Width of lines
"lines.markeredgewidth": 0.3,
# Legend params
"legend.fontsize": 8.5, # Make the legend/label fonts a little smaller
"legend.frameon": True, # Remove the black frame around the legend
"legend.handletextpad": 0.3,
"legend.borderaxespad": 0.2,
"legend.labelspacing": 0.1,
"patch.linewidth": 0.5,
# Font params
"text.usetex": True, # use LaTeX to write all text
"font.family": "serif", # use serif rather than sans-serif
"font.serif": "Linux Libertine O", # use "Linux Libertine" as the standard font
"font.size": 9,
"axes.titlesize": 8, # LaTeX default is 10pt font.
"axes.labelsize": 8, # LaTeX default is 10pt font.
"xtick.labelsize": 6,
"ytick.labelsize": 6,
# PDF settings
"pgf.texsystem": "xelatex", # Use Xelatex which is TTF font aware
"pgf.rcfonts": False, # Use pgf.preamble, ignore standard Matplotlib RC
"pgf.preamble": [
r'\usepackage{fontspec}',
r'\usepackage{unicode-math}',
r'\usepackage{libertine}',
r'\setmainfont{Linux Libertine O}',
r'\setmathfont{Linux Libertine O}',
]
}
SIGCONF_RCPARAMS_TRIPLE = {
# Fig params
"figure.autolayout": True, # Makes sure nothing the feature is neat & tight.
"figure.figsize": FIG_SIZE_TRIPLE, # Column width: 3.333 in, space between cols: 0.333 in.
"figure.dpi": 150, # Displays figures nicely in notebooks.
# Axes params
"axes.linewidth": 0.4, # Matplotlib's current default is 0.8.
"hatch.linewidth": 0.3,
"xtick.major.width": 0.4,
"xtick.minor.width": 0.4,
'xtick.major.pad': 1.0,
'xtick.major.size': 1.75,
'xtick.minor.pad': 1.0,
'xtick.minor.size': 1.0,
"ytick.major.width": 0.4,
"ytick.minor.width": 0.4,
'ytick.major.pad': 1.0,
'ytick.major.size': 1.75,
'ytick.minor.pad': 1.0,
'ytick.minor.size': 1.0,
"axes.labelpad": 0.5,
# Plot params
"lines.linewidth": 0.8, # Width of lines
"lines.markeredgewidth": 0.3,
# Legend
"legend.fontsize": 5.5, # Make the legend/label fonts a little smaller
"legend.frameon": True, # Remove the black frame around the legend
"legend.handletextpad": 0.5,
"legend.borderaxespad": 0.0,
"legend.labelspacing": 0.05,
"patch.linewidth": 0.3,
# Font params
"text.usetex": True, # use LaTeX to write all text
"font.family": "serif", # use serif rather than sans-serif
"font.serif": "Linux Libertine O", # use "Linux Libertine" as the standard font
"font.size": 6,
"axes.titlesize": 5, # LaTeX default is 10pt font.
"axes.labelsize": 5, # LaTeX default is 10pt font.
"xtick.labelsize": 5,
"ytick.labelsize": 5,
# PDF settings
"pgf.texsystem": "xelatex", # Use Xelatex which is TTF font aware
"pgf.rcfonts": False, # Use pgf.preamble, ignore standard Matplotlib RC
"pgf.preamble": [
r'\usepackage{fontspec}',
r'\usepackage{unicode-math}',
r'\usepackage{libertine}',
r'\setmainfont{Linux Libertine O}',
r'\setmathfont{Linux Libertine O}',
]
}
NEURIPS_LINE_WIDTH = 5.5 # Text width: 5.5in (double figure minus spacing 0.2in).
FIG_SIZE_NEURIPS_DOUBLE = (NEURIPS_LINE_WIDTH / 2, NEURIPS_LINE_WIDTH / 2 * 4/6)
FIG_SIZE_NEURIPS_TRIPLE = (NEURIPS_LINE_WIDTH / 3, NEURIPS_LINE_WIDTH / 3 * 4/6)
FIG_SIZE_NEURIPS_DOUBLE_TALL = (NEURIPS_LINE_WIDTH / 2, NEURIPS_LINE_WIDTH / 2 * 5/6)
FIG_SIZE_NEURIPS_TRIPLE_TALL = (NEURIPS_LINE_WIDTH / 3, NEURIPS_LINE_WIDTH / 3 * 5/6)
NEURIPS_RCPARAMS = {
"figure.autolayout": False, # Makes sure nothing the feature is neat & tight.
"figure.figsize": FIG_SIZE_NEURIPS_DOUBLE,
"figure.dpi": 150, # Displays figures nicely in notebooks.
# Axes params
"axes.linewidth": 0.5, # Matplotlib's current default is 0.8.
"xtick.major.width": 0.5,
"xtick.minor.width": 0.5,
"ytick.major.width": 0.5,
"ytick.minor.width": 0.5,
"hatch.linewidth": 0.3,
"xtick.major.width": 0.5,
"xtick.minor.width": 0.5,
'xtick.major.pad': 1.0,
'xtick.major.size': 1.75,
'xtick.minor.pad': 1.0,
'xtick.minor.size': 1.0,
'ytick.major.pad': 1.0,
'ytick.major.size': 1.75,
'ytick.minor.pad': 1.0,
'ytick.minor.size': 1.0,
"axes.labelpad": 0.5,
# Grid
"grid.linewidth": 0.3,
# Plot params
"lines.linewidth": 1.0,
"lines.markersize": 4,
'errorbar.capsize': 3.0,
# Font
"text.usetex": True, # use LaTeX to write all text
"font.family": "serif", # use serif rather than sans-serif
"font.serif": "Times New Roman", # use "Times New Roman" as the standard font
"font.size": 8.5,
"axes.titlesize": 8.5, # LaTeX default is 10pt font.
"axes.labelsize": 8.5, # LaTeX default is 10pt font.
"xtick.labelsize": 8,
"ytick.labelsize": 8,
# Legend
"legend.fontsize": 7, # Make the legend/label fonts a little smaller
"legend.frameon": True, # Remove the black frame around the legend
"legend.handletextpad": 0.3,
"legend.borderaxespad": 0.2,
"legend.labelspacing": 0.1,
"patch.linewidth": 0.5,
# PDF
"pgf.texsystem": "xelatex", # use Xelatex which is TTF font aware
"pgf.rcfonts": False, # Use pgf.preamble, ignore standard Matplotlib RC
"pgf.preamble": [
r'\usepackage{fontspec}',
r'\usepackage{unicode-math}',
r'\setmainfont{Times New Roman}',
],
}
def trans_data_to_axis(ax):
"""Compute the transform from data to axis coordinate system in axis `ax`"""
axis_to_data = ax.transAxes + ax.transData.inverted()
data_to_axis = axis_to_data.inverted()
return data_to_axis
def days_to_datetime(arr, start_date):
# timestamps
ts = arr * 24 * 60 * 60 + pd.Timestamp(start_date).timestamp()
return pd.to_datetime(ts, unit='s')
def lockdown_widget(ax, lockdown_at, start_date, lockdown_label_y, lockdown_label='Lockdown',
xshift=0.0, zorder=None, ls='--', color='black', text_off=False):
"""
Draw the lockdown widget corresponding to a vertical line at the desired location along with a
label. The data can be passed either in `float` or in `datetime` format.
Parameters
----------
ax
Axis to draw on
lockdown_at
Location of vertical lockdown line
start_date
Value of the origin of the x-axis
lockdown_label_y
Location of the text label on the y-axis
lockdown_label : str (optional, default: 'Lockdown')
Text label
xshift : float (optional, default: 0.0)
Shift in a-axis of the text label
zorder : int (optional, default: None)
z-order of the widget
ls : str (optional, default: '--')
Linestyle of the vertical line
color : str (optional, default: 'black')
color of the vertical line
text_off : bool (optional, default: False)
Indicate if the text label should be turned off
"""
if isinstance(start_date, float): # If plot with float x-axis
lckdn_x = start_date + lockdown_at
ax.axvline(lckdn_x, linestyle=ls, color=color, label='_nolegend_',
zorder=zorder)
else:
# If plot with datetime x-axis
lckdn_dt = days_to_datetime(lockdown_at, start_date=start_date) # str to datetime
lckdn_x_d = lckdn_dt.toordinal() # datetime to float in data coordinates
ax.axvline(lckdn_x_d, linestyle=ls, color=color, label='_nolegend_',
zorder=zorder)
# Display the text label
if not text_off:
if xshift == 0.0:
# Automatic shift of the text in the plot (normalized) axis coordinates
lckdn_x_a, _ = trans_data_to_axis(ax).transform([lckdn_x_d, 0.0]) # data coordinates to axis coordinates
ax.text(x=lckdn_x_a, y=lockdown_label_y, s=lockdown_label,
transform=ax.transAxes, rotation=90,
verticalalignment='bottom',
horizontalalignment='right')
else:
# NOTE: for backward-compatibility, manual shift of the text, should be removed
ax.text(x=lckdn_dt + pd.Timedelta(xshift, unit='d'),
y=lockdown_label_y, s=lockdown_label, rotation=90)
def target_widget(show_target,start_date, ax, zorder=None, ms=4.0, label='COVID-19 case data'):
txx = np.linspace(0, show_target.shape[0] - 1, num=show_target.shape[0])
txx = days_to_datetime(txx, start_date=start_date)
ax.plot(txx, show_target, ls='', marker='x', ms=ms,
color='black', label=label, zorder=zorder)
class CustomSitesProportionFixedLocator(plt.Locator):
"""
Custom locator to avoid tick font bug of matplotlib
"""
def __init__(self):
pass
def __call__(self):
return np.log(np.array([2, 5, 10, 25, 100]))
class Plotter(object):
"""
Plotting class
"""
def __init__(self):
# plot constants
# check out https://colorhunt.co/
self.color_expo = '#ffcc00'
self.color_iasy = '#00a8cc'
self.color_ipre = '#005082'
self.color_isym = '#000839'
self.color_testing = '#ffa41b'
self.color_posi = '#4daf4a'
self.color_nega = '#e41a1c'
self.color_all = '#ffa41b'
self.color_positive = '#00a8cc'
self.color_age = '#005082'
self.color_tracing = '#000839'
self.color_infected = '#000839'
self.filling_alpha = 0.2
self.color_different_scenarios = [
'#e41a1c',
'#377eb8',
'#4daf4a',
'#984ea3',
'#ff7f00',
'#ffff33',
'#a65628',
'#f781bf',
'#999999'
]
self.color_different_scenarios_alt = [
'#a1dab4',
'#41b6c4',
'#2c7fb8',
'#253494',
]
# 2D visualization
self.density_alpha = 0.7
self.marker_home = "^"
self.marker_site = "o"
self.color_home = '#000839'
self.color_site = '#000000'
self.size_home = 80
self.size_site = 300
def _set_matplotlib_params(self, format='dobule'):
matplotlib.backend_bases.register_backend('pdf', FigureCanvasPgf)
if format == 'double':
plt.rcParams.update(SIGCONF_RCPARAMS_DOUBLE)
elif format == 'triple':
plt.rcParams.update(SIGCONF_RCPARAMS_TRIPLE)
if format == 'neurips-double':
plt.rcParams.update(NEURIPS_RCPARAMS)
else:
raise ValueError('Invalid figure format.')
def _set_default_axis_settings(self, ax):
# Hide the right and top spines
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
# Only show ticks on the left and bottom spines
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
def plot_cumulative_infected(self, sim, title='Example', filename='daily_inf_0',
figsize=(6, 5), errorevery=20, acc=1000, ymax=None,
lockdown_label='Lockdown', lockdown_at=None,
lockdown_label_y=None, show_target=None,
start_date='1970-01-01',
subplot_adjust=None, legend_loc='upper right'):
''''
Plots daily infected split by group
averaged over random restarts, using error bars for std-dev
'''
if acc > sim.max_time:
acc = int(sim.max_time)
fig = plt.figure(figsize=figsize)
ax = fig.add_subplot(111)
ts, iasy_mu, iasy_sig = self.__comp_state_cumulative(sim, 'iasy', acc)
# _, ipre_mu, ipre_sig = self.__comp_state_cumulative(sim, 'ipre', acc)
_, isym_mu, isym_sig = self.__comp_state_cumulative(sim, 'isym', acc)
# _, expo_mu, iexpo_sig = self.__comp_state_cumulative(sim, 'expo', acc)
# _, posi_mu, posi_sig = self.__comp_state_cumulative(sim, 'posi', acc)
line_xaxis = np.zeros(ts.shape)
line_iasy = iasy_mu
line_isym = iasy_mu + isym_mu
error_isym = np.sqrt(iasy_sig**2 + isym_sig**2)
# Convert x-axis into posix timestamps and use pandas to plot as dates
ts = days_to_datetime(ts, start_date=start_date)
# lines
ax.plot(ts, line_iasy, c='black', linestyle='-')
ax.errorbar(ts, line_isym, yerr=error_isym, c='black', linestyle='-',
elinewidth=0.8, errorevery=errorevery, capsize=3.0)
# filling
ax.fill_between(ts, line_xaxis, line_iasy, alpha=self.filling_alpha, label='Asymptomatic',
edgecolor=self.color_iasy, facecolor=self.color_iasy, linewidth=0, zorder=0)
ax.fill_between(ts, line_iasy, line_isym, alpha=self.filling_alpha, label='Symptomatic',
edgecolor=self.color_isym, facecolor=self.color_isym, linewidth=0, zorder=0)
# limits
if ymax is None:
ymax = 1.5 * np.max(iasy_mu + isym_mu)
ax.set_ylim((0, ymax))
# ax.set_xlabel('Days')
ax.set_ylabel('People')
# extra
if lockdown_at is not None:
lockdown_widget(ax, lockdown_at, start_date,
lockdown_label_y,
lockdown_label)
if show_target is not None:
target_widget(show_target, start_date, ax)
# Hide the right and top spines
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
# Only show ticks on the left and bottom spines
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
#set ticks every week
ax.xaxis.set_major_locator(mdates.WeekdayLocator())
#set major ticks format
ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %d'))
fig.autofmt_xdate(bottom=0.2, rotation=0, ha='center')
# legend
ax.legend(loc=legend_loc, borderaxespad=0.5)
subplot_adjust = subplot_adjust or {'bottom':0.14, 'top': 0.98, 'left': 0.12, 'right': 0.96}
plt.subplots_adjust(**subplot_adjust)
plt.draw()
plt.savefig('plots/' + filename + '.png', format='png', facecolor=None,
dpi=DPI, bbox_inches='tight')
if NO_PLOT:
plt.close()
return
def plot_daily_infected(self, sim, title='Example', filename='daily_inf_0',
figsize=(6, 5), errorevery=20, acc=1000, ymax=None,
lockdown_label='Lockdown', lockdown_at=None,
lockdown_label_y=None, show_target=None,
lockdown_end=None,
start_date='1970-01-01',
subplot_adjust=None, legend_loc='upper right'):
''''
Plots daily infected split by group
averaged over random restarts, using error bars for std-dev
'''
if acc > sim.max_time:
acc = int(sim.max_time)
fig = plt.figure(figsize=figsize)
ax = fig.add_subplot(111)
ts, iasy_mu, iasy_sig = comp_state_over_time(sim, 'iasy', acc)
_, ipre_mu, ipre_sig = comp_state_over_time(sim, 'ipre', acc)
_, isym_mu, isym_sig = comp_state_over_time(sim, 'isym', acc)
# _, expo_mu, iexpo_sig = comp_state_over_time(sim, 'expo', acc)
# _, posi_mu, posi_sig = comp_state_over_time(sim, 'posi', acc)
line_xaxis = np.zeros(ts.shape)
line_iasy = iasy_mu
line_ipre = iasy_mu + ipre_mu
line_isym = iasy_mu + ipre_mu + isym_mu
error_isym = np.sqrt(iasy_sig**2 + ipre_sig**2 + isym_sig**2)
# Convert x-axis into posix timestamps and use pandas to plot as dates
ts = days_to_datetime(ts, start_date=start_date)
# lines
ax.plot(ts, line_iasy,
c='black', linestyle='-')
ax.plot(ts, line_ipre,
c='black', linestyle='-')
ax.errorbar(ts, line_isym, yerr=error_isym, c='black', linestyle='-',
elinewidth=0.8, errorevery=errorevery, capsize=3.0)
# filling
ax.fill_between(ts, line_xaxis, line_iasy, alpha=0.5, label='Asymptomatic',
edgecolor=self.color_iasy, facecolor=self.color_iasy, linewidth=0, zorder=0)
ax.fill_between(ts, line_iasy, line_ipre, alpha=0.5, label='Pre-symptomatic',
edgecolor=self.color_ipre, facecolor=self.color_ipre, linewidth=0, zorder=0)
ax.fill_between(ts, line_ipre, line_isym, alpha=0.5, label='Symptomatic',
edgecolor=self.color_isym, facecolor=self.color_isym, linewidth=0, zorder=0)
# limits
if ymax is None:
ymax = 1.5 * np.max(iasy_mu + ipre_mu + isym_mu)
ax.set_ylim((0, ymax))
# ax.set_xlabel('Days')
ax.set_ylabel('People')
# extra
if lockdown_at is not None:
lockdown_widget(ax, lockdown_at, start_date,
lockdown_label_y,
lockdown_label)
if lockdown_end is not None:
lockdown_widget(ax=ax, lockdown_at=lockdown_end, start_date=start_date,
lockdown_label_y=lockdown_label_y,
lockdown_label='End of lockdown', ls='dotted')
if show_target is not None:
target_widget(show_target, start_date, ax)
# Hide the right and top spines
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
# Only show ticks on the left and bottom spines
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
#set ticks every week
ax.xaxis.set_major_locator(mdates.WeekdayLocator())
#set major ticks format
ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %d'))
fig.autofmt_xdate(bottom=0.2, rotation=0, ha='center')
# legend
ax.legend(loc=legend_loc, borderaxespad=0.5)
subplot_adjust = subplot_adjust or {'bottom':0.14, 'top': 0.98, 'left': 0.12, 'right': 0.96}
plt.subplots_adjust(**subplot_adjust)
plt.draw()
plt.savefig('plots/' + filename + '.png', format='png', facecolor=None,
dpi=DPI, bbox_inches='tight')
if NO_PLOT:
plt.close()
return
def plot_daily_tested(self, sim, title='Example', filename='daily_tested_0', figsize=(10, 10), errorevery=20,
acc=1000, ymax=None):
''''
Plots daily tested, positive daily tested, negative daily tested
averaged over random restarts, using error bars for std-dev
'''
if acc > sim.max_time:
acc = int(sim.max_time)
fig = plt.figure(figsize=figsize)
ax = fig.add_subplot(111)
# automatically shifted by `test_lag` in the function
ts, posi_mu, posi_sig = comp_state_over_time(sim, 'posi', acc)
_, nega_mu, nega_sig = comp_state_over_time(sim, 'nega', acc)
line_xaxis = np.zeros(ts.shape)
line_posi = posi_mu
line_nega = posi_mu + nega_mu
error_posi = posi_sig
error_nega = nega_sig + posi_sig
T = posi_mu.shape[0]
# lines
ax.errorbar(ts, line_posi, yerr=posi_sig, elinewidth=0.8, errorevery=errorevery,
c='black', linestyle='dotted')
ax.errorbar(ts, line_nega, yerr=nega_sig, elinewidth=0.8, errorevery=errorevery,
c='black', linestyle='-')
# filling
ax.fill_between(ts, line_xaxis, line_posi, alpha=0.5, label=r'Positive tests',
edgecolor=self.color_posi, facecolor=self.color_posi, linewidth=0, zorder=0)
ax.fill_between(ts, line_posi, line_nega, alpha=0.5, label=r'Negative tests',
edgecolor=self.color_nega, facecolor=self.color_nega, linewidth=0, zorder=0)
# axis
ax.set_xlim((0, np.max(ts)))
if ymax is None:
ymax = 1.5 * np.max(posi_mu + nega_mu)
ax.set_ylim((0, ymax))
ax.set_xlabel(r'$t$ [days]')
ax.set_ylabel(r'Tests')
# Hide the right and top spines
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
# Only show ticks on the left and bottom spines
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
# legend
fig.legend(loc='center right', borderaxespad=0.1)
# Adjust the scaling factor to fit your legend text completely outside the plot
plt.subplots_adjust(right=0.70)
ax.set_title(title, pad=20)
plt.draw()
plt.savefig('plots/' + filename + '.png', format='png', facecolor=None,
dpi=DPI, bbox_inches='tight')
if NO_PLOT:
plt.close()
return
def plot_daily_at_home(self, sim, title='Example', filename='daily_at_home_0', figsize=(10, 10), errorevery=20, acc=1000, ymax=None):
''''
Plots daily tested, positive daily tested, negative daily tested
averaged over random restarts, using error bars for std-dev
'''
if acc > sim.max_time:
acc = int(sim.max_time)
fig = plt.figure(figsize=figsize)
ax = fig.add_subplot(111)
ts, all_mu, all_sig = comp_contained_over_time(sim, 'SocialDistancingForAllMeasure', acc)
_, positive_mu, positive_sig = comp_contained_over_time(sim, 'SocialDistancingForPositiveMeasure', acc)
_, age_mu, age_sig = comp_contained_over_time(sim, 'SocialDistancingByAgeMeasure', acc)
_, tracing_mu, tracing_sig = comp_contained_over_time(sim, 'SocialDistancingForSmartTracing', acc)
_, iasy_mu, iasy_sig = comp_state_over_time(sim, 'iasy', acc)
_, ipre_mu, ipre_sig = comp_state_over_time(sim, 'ipre', acc)
_, isym_mu, isym_sig = comp_state_over_time(sim, 'isym', acc)
line_xaxis = np.zeros(ts.shape)
line_all = all_mu
line_positive = positive_mu
line_age = age_mu
line_tracing = tracing_mu
line_infected = iasy_mu + ipre_mu + isym_mu
error_all = all_sig
error_positive = positive_sig
error_age = age_sig
error_tracing = tracing_sig
error_infected = np.sqrt(np.square(iasy_sig) + np.square(ipre_sig) + np.square(isym_sig))
# lines
ax.errorbar(ts, line_infected, label=r'Total infected', errorevery=errorevery, c=self.color_infected, linestyle='--', yerr=error_infected)
ax.errorbar(ts, line_all, yerr=error_all, elinewidth=0.8, errorevery=errorevery,
c='black', linestyle='-')
ax.errorbar(ts, line_positive, yerr=error_positive, elinewidth=0.8, errorevery=errorevery,
c='black', linestyle='-')
ax.errorbar(ts, line_age, yerr=error_age, elinewidth=0.8, errorevery=errorevery,
c='black', linestyle='-')
ax.errorbar(ts, line_tracing, yerr=error_tracing, elinewidth=0.8, errorevery=errorevery,
c='black', linestyle='-')
# filling
ax.fill_between(ts, line_xaxis, line_all, alpha=self.filling_alpha, label=r'SD for all',
edgecolor=self.color_all, facecolor=self.color_all, linewidth=0, zorder=0)
ax.fill_between(ts, line_xaxis, line_positive, alpha=self.filling_alpha, label=r'SD for positively tested',
edgecolor=self.color_positive, facecolor=self.color_positive, linewidth=0, zorder=0)
ax.fill_between(ts, line_xaxis, line_age, alpha=self.filling_alpha, label=r'SD for age group',
edgecolor=self.color_age, facecolor=self.color_age, linewidth=0, zorder=0)
ax.fill_between(ts, line_xaxis, line_tracing, alpha=self.filling_alpha, label=r'SD for traced contacts',
edgecolor=self.color_tracing, facecolor=self.color_tracing, linewidth=0, zorder=0)
# axis
ax.set_xlim((0, np.max(ts)))
if ymax is None:
ymax = 1.5 * np.max([all_mu, positive_mu, age_mu, tracing_mu])
ax.set_ylim((0, ymax))
ax.set_xlabel(r'$t$ [days]')
ax.set_ylabel(r'[people]')
# Hide the right and top spines
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
# Only show ticks on the left and bottom spines
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
# legend
fig.legend(loc='center right', borderaxespad=0.1)
# Adjust the scaling factor to fit your legend text completely outside the plot
plt.subplots_adjust(right=0.70)
ax.set_title(title, pad=20)
plt.draw()
plt.savefig('plots/' + filename + '.png', format='png', facecolor=None,
dpi=DPI, bbox_inches='tight')
if NO_PLOT:
plt.close()
return
def compare_total_infections(self, sims, titles, figtitle='Title', figformat='double',
filename='compare_inf_0', figsize=None, errorevery=20, acc=500, ymax=None, x_axis_dates=True,
lockdown_label='Lockdown', lockdown_at=None, lockdown_label_y=None, lockdown_xshift=0.0,
conditional_measures=None,
show_positives=False, show_legend=True, legend_is_left=False,
subplot_adjust=None, start_date='1970-01-01', xtick_interval=2, first_one_dashed=False,
show_single_runs=False, which_single_runs=None):
''''
Plots total infections for each simulation, named as provided by `titles`
to compare different measures/interventions taken. Colors taken as defined in __init__, and
averaged over random restarts, using error bars for std-dev
'''
assert isinstance(sims[0], str), '`sims` must be list of filepaths'
# Set double figure format
self._set_matplotlib_params(format=figformat)
# Draw figure
fig, ax = plt.subplots(1, 1, figsize=figsize)
for i, sim in enumerate(sims):
is_conditional = True if i == conditional_measures else False
try:
data = load_condensed_summary(sim, acc)
except FileNotFoundError:
acc = create_condensed_summary_from_path(sim, acc=acc)
data = load_condensed_summary(sim, acc)
ts = data['ts']
lockdown_at = data['lockdowns'] if is_conditional else lockdown_at
if x_axis_dates:
# Convert x-axis into posix timestamps and use pandas to plot as dates
ts = days_to_datetime(ts, start_date=start_date)
if not show_single_runs:
iasy_mu = data['iasy_mu']
iasy_sig = data['iasy_sig']
ipre_mu = data['ipre_mu']
ipre_sig = data['ipre_sig']
isym_mu = data['isym_mu']
isym_sig = data['isym_sig']
line_infected = iasy_mu + ipre_mu + isym_mu
error_infected = np.sqrt(np.square(iasy_sig) + np.square(ipre_sig) + np.square(isym_sig))
# lines
ax.plot(ts, line_infected, linestyle='-', label=titles[i], c=self.color_different_scenarios[i])
ax.fill_between(ts, np.maximum(line_infected - 2 * error_infected, 0), line_infected + 2 * error_infected,
color=self.color_different_scenarios[i], alpha=self.filling_alpha, linewidth=0.0)
else:
iasy = data['iasy']
ipre = data['ipre']
isym = data['isym']
lines_infected = iasy + ipre + isym
# lines
runs = [which_single_runs] if which_single_runs else range(min(show_single_runs, sim.random_repeats))
for k, r in enumerate(runs):
ax.plot(ts, lines_infected[:, r], linestyle='-', label=titles[i] if k == 0 else None,
c=self.color_different_scenarios[i])
# For conditional measures only
if lockdown_at:
for lockdown in lockdown_at[r]:
start_lockdown = lockdown[0] / TO_HOURS
end_lockdown = lockdown[1] / TO_HOURS
lockdown_widget(ax, start_lockdown, 0.0,
lockdown_label_y,
None)
lockdown_widget(ax, end_lockdown, 0.0,
lockdown_label_y,
None, ls='-')
# axis
ax.set_xlim(left=np.min(ts))
if ymax is None:
ymax = 1.5 * np.max(iasy_mu + ipre_mu + isym_mu)
ax.set_ylim((0, ymax))
# ax.set_xlabel('Days')
if x_axis_dates:
# set xticks every week
ax.xaxis.set_minor_locator(mdates.WeekdayLocator(byweekday=1, interval=1))
ax.xaxis.set_major_locator(mdates.WeekdayLocator(byweekday=1, interval=xtick_interval))
ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %d'))
fig.autofmt_xdate(bottom=0.2, rotation=0, ha='center')
else:
ax.set_xlabel(r'$t$ [days]')
ax.set_ylabel('Infected')
if not isinstance(lockdown_at, list):
if lockdown_at is not None:
lockdown_widget(ax, lockdown_at, start_date,
lockdown_label_y,
lockdown_label,
xshift=lockdown_xshift)
# Set default axes style
self._set_default_axis_settings(ax=ax)
if show_legend:
# legend
if legend_is_left:
leg = ax.legend(loc='upper left',
bbox_to_anchor=(0.001, 0.999),
bbox_transform=ax.transAxes,
# prop={'size': 5.6}
)
else:
leg = ax.legend(loc='upper right',
bbox_to_anchor=(0.999, 0.999),
bbox_transform=ax.transAxes,
# prop={'size': 5.6}
)
subplot_adjust = subplot_adjust or {'bottom':0.14, 'top': 0.98, 'left': 0.12, 'right': 0.96}
plt.subplots_adjust(**subplot_adjust)
plt.savefig('plots/' + filename + '.pdf', format='pdf', facecolor=None,
dpi=DPI, bbox_inches='tight')
if NO_PLOT:
plt.close()
return
def compare_quantity(self, sims, titles, quantity='infected', mode='total', ymax=None,
normalization_baseline_path=None,
start_date='1970-01-01', xtick_interval=3, x_axis_dates=False,
figformat='double', filename='compare_epidemics', figsize=None,
lockdown_label='Lockdown', lockdown_at=None, lockdown_label_y=None, lockdown_xshift=0.0,
show_legend=True, legend_is_left=False, subplot_adjust=None):
''''
Plots `quantity` in `mode` for each simulation, named as provided by `titles`
to compare different measures/interventions taken. Colors taken as defined in __init__, and
averaged over random restarts, using error bars for std-dev
'''
assert isinstance(sims[0], str), '`sims` must be list of filepaths'
assert mode in ['total', 'daily', 'cumulative']
assert quantity in ['infected', 'hosp', 'dead']
labeldict = {'total': {'infected': 'Infected',
'hosp': 'Hospitalized',
'dead': 'Fatalities'},
'cumulative': {'infected': 'Cumulative Infections',
'hosp': 'Cumulative Hospitalizations',
'dead': 'Cumulative Fatalities'},
'daily': {'infected': 'Daily Infections',
'hosp': 'Daily Hospitalizations',
'dead': 'Daily Fatalities'},
}
# Set double figure format
self._set_matplotlib_params(format=figformat)
# Draw figure
fig, ax = plt.subplots(1, 1, figsize=figsize)
# Load baseline data
# if normalization_baseline_path:
# baseline_data = load_condensed_summary_compat(normalization_baseline_path)
# baseline_cases, _ = get_plot_data(baseline_data, quantity=quantity, mode=mode)
for i, sim in enumerate(sims):
data = load_condensed_summary_compat(sim)
ts = data['ts'] if not x_axis_dates else days_to_datetime(data['ts'], start_date=start_date)
line_cases, error_cases = get_plot_data(data, quantity=quantity, mode=mode)
ylabel = labeldict[mode][quantity]
# if normalization_baseline_path:
# line_cases = 1 - line_cases / baseline_cases
# error_cases = error_cases / baseline_cases
# line_cases = np.nan_to_num(line_cases, nan=0.0)
# error_cases = np.nan_to_num(error_cases, nan=0.0)
# ylabel = f'Reduction in ' + ylabel
# lines
ax.plot(ts, line_cases, linestyle='-', label=titles[i], c=self.color_different_scenarios[i])
ax.fill_between(ts, np.maximum(line_cases - 2 * error_cases, 0), line_cases + 2 * error_cases,
color=self.color_different_scenarios[i], alpha=self.filling_alpha, linewidth=0.0)
# axis
ax.set_xlim(left=np.min(ts))
if ymax is None:
ymax = 1.5 * np.max(line_cases)
ax.set_ylim((0, ymax))
if x_axis_dates:
# set xticks every week
ax.xaxis.set_minor_locator(mdates.WeekdayLocator(byweekday=1, interval=1))
ax.xaxis.set_major_locator(mdates.WeekdayLocator(byweekday=1, interval=xtick_interval))
ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %d'))
fig.autofmt_xdate(bottom=0.2, rotation=0, ha='center')
else:
ax.set_xlabel(r'$t$ [days]')
ax.set_ylabel(ylabel)
if lockdown_at is not None:
lockdown_widget(ax, lockdown_at, start_date,
lockdown_label_y,
lockdown_label,
xshift=lockdown_xshift)
# Set default axes style
self._set_default_axis_settings(ax=ax)
if show_legend:
# legend
if legend_is_left:
leg = ax.legend(loc='upper left',
bbox_to_anchor=(0.001, 0.999),
bbox_transform=ax.transAxes,
# prop={'size': 5.6}
)
else:
leg = ax.legend(loc='upper right',
bbox_to_anchor=(0.999, 0.999),
bbox_transform=ax.transAxes,
# prop={'size': 5.6}
)
subplot_adjust = subplot_adjust or {'bottom':0.14, 'top': 0.98, 'left': 0.12, 'right': 0.96}
plt.subplots_adjust(**subplot_adjust)
plt.savefig('plots/' + filename + '.pdf', format='pdf', facecolor=None,
dpi=DPI, bbox_inches='tight')
if NO_PLOT:
plt.close()
return
def compare_total_fatalities_and_hospitalizations(self, sims, titles, mode='show_both',
figtitle=r'Hospitalizations and Fatalities',
lockdown_label='Lockdown', lockdown_at=None, lockdown_label_y=None,
figformat='neurips-double',
xtick_interval=2, lockdown_xshift=0.0,
filename='compare_inf_0', figsize=(10, 10), errorevery=20, acc=1000, ymax=None,
show_legend=True, legendYoffset=0.0, legend_is_left=False, legendXoffset=0.0,
subplot_adjust=None, start_date='1970-01-01', first_one_dashed=False):
''''
Plots total fatalities and hospitalizations for each simulation, named as provided by `titles`
to compare different measures/interventions taken. Colors taken as defined in __init__, and
averaged over random restarts, using error bars for std-dev
'''
# Set double figure format
self._set_matplotlib_params(format=figformat)
fig = plt.figure(figsize=figsize)
ax = fig.add_subplot(111)
# hospitalizations
for i, sim in enumerate(sims):
if isinstance(sim, str):
try:
data = load_condensed_summary(sim, acc=acc)
except FileNotFoundError:
acc = create_condensed_summary_from_path(sim, acc=acc)
data = load_condensed_summary(sim, acc=acc)
acc = data['acc']
ts = data['ts']
hosp_mu = data['hosp_mu']
hosp_sig = data['hosp_sig']
dead_mu = data['dead_mu']
dead_sig = data['dead_sig']
loaded_extracted_data = True
else:
loaded_extracted_data = False
if not loaded_extracted_data:
if acc > sim.max_time: