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plot_common.py
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plot_common.py
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'''Common plotting code.'''
import os.path
import matplotlib.collections
import matplotlib.colors
import matplotlib.pyplot
import matplotlib.ticker
import numpy
import pandas
import statsmodels.nonparametric.api
import h5
from herd.utility import arange
import run
# Science
rc = {}
# Widths: 89mm, 183mm, 120mm, 136mm.
# Sans-serif, preferably Helvetica or Arial.
rc['font.family'] = 'sans-serif'
rc['font.sans-serif'] = 'DejaVu Sans'
# Fonts between 5pt and 7pt.
# Separate panels in multi-part figures should be labelled with 8
# pt bold, upright (not italic) a, b, c...
t_name = 'time (y)'
def _build_downsampled_group(group, t, t_step, by):
# Only keep time index.
group = group.reset_index(by, drop=True)
# Shift start to 0.
group.index -= group.index.min()
# Only interpolate between start and extinction.
# Round up to the next multiple of `t_step`.
mask = (t <= (numpy.ceil(group.index.max() / t_step) * t_step))
# Interpolate from the closest point <= t.
return group.reindex(t[mask], method='ffill')
def build_downsampled(filename_in, t_min=0, t_max=10, t_step=1/365, by=None):
t = arange(t_min, t_max, t_step, endpoint=True)
base, ext = os.path.splitext(filename_in)
filename_out = base + '_downsampled' + ext
with h5.HDFStore(filename_in, mode='r') as store_in, \
h5.HDFStore(filename_out, mode='w') as store_out:
if by is None:
by = [n for n in store_in.get_index_names() if n != t_name]
for (ix, group) in store_in.groupby(by):
downsampled = _build_downsampled_group(group, t, t_step, by)
# Append `ix` to the index levels.
downsampled = pandas.concat({ix: downsampled},
names=by, copy=False)
store_out.put(downsampled.dropna(), index=False,
min_itemsize=run._min_itemsize)
store_out.create_table_index()
store_out.repack()
def get_downsampled(filename, by=None):
base, ext = os.path.splitext(filename)
filename_ds = base + '_downsampled' + ext
if not os.path.exists(filename_ds):
build_downsampled(filename, by=by)
return h5.HDFStore(filename_ds, mode='r')
def _build_infected(filename, filename_out, by=None):
store = get_downsampled(filename, by=by)
columns = ['exposed', 'infectious', 'chronic']
infected = []
for chunk in store.select(columns=columns, iterator=True):
infected.append(chunk.sum(axis='columns'))
infected = pandas.concat(infected, copy=False)
infected.name = 'infected'
h5.dump(infected, filename_out, mode='w',
min_itemsize=run._min_itemsize)
def get_infected(filename, by=None):
base, ext = os.path.splitext(filename)
filename_infected = base + '_infected' + ext
try:
infected = h5.load(filename_infected)
except OSError:
_build_infected(filename, filename_infected, by=by)
infected = h5.load(filename_infected)
return infected
def _build_extinction_time_group(infected, tmax=10):
t = infected.index.get_level_values(t_name)
time = t.max() - t.min()
observed = (infected.iloc[-1] == 0)
assert observed or (time == tmax)
return dict(time=time, observed=observed)
def _build_extinction_time(filename, filename_out, by=None):
# Only the infected columns.
columns = ['exposed', 'infectious', 'chronic']
extinction = {}
with h5.HDFStore(filename, mode='r') as store:
if by is None:
by = [n for n in store.get_index_names() if n != t_name]
for (ix, group) in store.groupby(by, columns=columns):
infected = group.sum(axis='columns')
extinction[ix] = _build_extinction_time_group(infected)
extinction = pandas.DataFrame.from_dict(extinction, orient='index')
extinction.index.names = by
extinction.sort_index(level=by, inplace=True)
h5.dump(extinction, filename_out, mode='w',
min_itemsize=run._min_itemsize)
def get_extinction_time(filename, by=None):
base, ext = os.path.splitext(filename)
filename_et = base + '_extinction_time' + ext
try:
extinction_time = h5.load(filename_et)
except OSError:
_build_extinction_time(filename, filename_et, by=by)
extinction_time = h5.load(filename_et)
return extinction_time
def set_violins_linewidth(ax, lw):
for col in ax.collections:
if isinstance(col, matplotlib.collections.PolyCollection):
col.set_linewidth(0)
def get_density(endog, times):
# Avoid errors if endog is empty.
if len(endog) > 0:
kde = statsmodels.nonparametric.api.KDEUnivariate(endog)
kde.fit(cut=0)
return kde.evaluate(times)
else:
return numpy.zeros_like(times)
def kdeplot(endog, ax=None, shade=False, cut=0, **kwds):
if ax is None:
ax = matplotlib.pyplot.gca()
endog = endog.dropna()
if len(endog) > 0:
kde = statsmodels.nonparametric.api.KDEUnivariate(endog)
kde.fit(cut=cut)
x = numpy.linspace(kde.support.min(), kde.support.max(), 301)
y = kde.evaluate(x)
line, = ax.plot(x, y, **kwds)
if shade:
shade_kws = dict(
facecolor=kwds.get('facecolor', line.get_color()),
alpha=kwds.get('alpha', 0.25),
clip_on=kwds.get('clip_on', True),
zorder=kwds.get('zorder', 1))
ax.fill_between(x, 0, y, **shade_kws)
return ax
# Erin's colors.
SAT_colors = {
1: '#2271b5',
2: '#ef3b2c',
3: '#807dba'
}
def get_cmap_SAT(SAT):
'''White to `SAT_colors[SAT]`.'''
return matplotlib.colors.LinearSegmentedColormap.from_list(
'name', ['white', SAT_colors[SAT]])