class FractionTaxaBarStack(Graph):
"""Comparing all fractions across all pools in a barstack"""
short_name = 'fraction_taxa_barstack'
def plot(self):
self.frame = OrderedDict((('%s - %s' % (p,f), getattr(p.fractions, f).rdp.phyla)
for f in ('low', 'med', 'big') for p in self.parent.pools))
self.frame = pandas.DataFrame(self.frame)
self.frame = self.frame.fillna(0)
self.frame = self.frame.transpose()
self.frame = self.frame.apply(lambda x: 100*x/x.sum(), axis=1)
# Sort the table by sum #
sums = self.frame.sum()
sums.sort(ascending=False)
self.frame = self.frame.reindex_axis(sums.keys(), axis=1)
# Plot #
fig = pyplot.figure()
axes = self.frame.plot(kind='bar', stacked=True, color=cool_colors)
fig = pyplot.gcf()
# Other #
axes.set_title('Species relative abundances per fraction per pool')
axes.set_ylabel('Relative abundances in percent')
axes.xaxis.grid(False)
axes.yaxis.grid(False)
axes.set_ylim([0,100])
# Put a legend below current axis
axes.legend(loc='upper center', bbox_to_anchor=(0.5, -0.20), fancybox=True, shadow=True, ncol=5)
# Save it #
self.save_plot(fig, axes, width=24.0, height=14.0, bottom=0.30, top=0.97, left=0.04, right=0.98)
self.frame.to_csv(self.csv_path)
pyplot.close(fig)
def perf_stats_bootstrap(returns, factor_returns=None, return_stats=True,
**kwargs):
"""Calculates various bootstrapped performance metrics of a strategy.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
factor_returns : pd.Series, optional
Daily noncumulative returns of the benchmark factor to which betas are
computed. Usually a benchmark such as market returns.
- This is in the same style as returns.
- If None, do not compute alpha, beta, and information ratio.
return_stats : boolean (optional)
If True, returns a DataFrame of mean, median, 5 and 95 percentiles
for each perf metric.
If False, returns a DataFrame with the bootstrap samples for
each perf metric.
Returns
-------
pd.DataFrame
if return_stats is True:
- Distributional statistics of bootstrapped sampling
distribution of performance metrics.
if return_stats is False:
- Bootstrap samples for each performance metric.
"""
bootstrap_values = OrderedDict()
for stat_func in SIMPLE_STAT_FUNCS:
stat_name = STAT_FUNC_NAMES[stat_func.__name__]
bootstrap_values[stat_name] = calc_bootstrap(stat_func,
returns)
if factor_returns is not None:
for stat_func in FACTOR_STAT_FUNCS:
stat_name = STAT_FUNC_NAMES[stat_func.__name__]
bootstrap_values[stat_name] = calc_bootstrap(
stat_func,
returns,
factor_returns=factor_returns)
bootstrap_values = pd.DataFrame(bootstrap_values)
if return_stats:
stats = bootstrap_values.apply(calc_distribution_stats)
return stats.T[['mean', 'median', '5%', '95%']]
else:
return bootstrap_values
def perf_stats_bootstrap(returns,
factor_returns=None,
return_stats=True,
**kwargs):
"""Calculates various bootstrapped performance metrics of a strategy.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in tears.create_full_tear_sheet.
factor_returns : pd.Series, optional
Daily noncumulative returns of the benchmark factor to which betas are
computed. Usually a benchmark such as market returns.
- This is in the same style as returns.
- If None, do not compute alpha, beta, and information ratio.
return_stats : boolean (optional)
If True, returns a DataFrame of mean, median, 5 and 95 percentiles
for each perf metric.
If False, returns a DataFrame with the bootstrap samples for
each perf metric.
Returns
-------
pd.DataFrame
if return_stats is True:
- Distributional statistics of bootstrapped sampling
distribution of performance metrics.
if return_stats is False:
- Bootstrap samples for each performance metric.
"""
bootstrap_values = OrderedDict()
for stat_func in SIMPLE_STAT_FUNCS:
stat_name = STAT_FUNC_NAMES[stat_func.__name__]
bootstrap_values[stat_name] = calc_bootstrap(stat_func, returns)
if factor_returns is not None:
for stat_func in FACTOR_STAT_FUNCS:
stat_name = STAT_FUNC_NAMES[stat_func.__name__]
bootstrap_values[stat_name] = calc_bootstrap(
stat_func, returns, factor_returns=factor_returns)
bootstrap_values = pd.DataFrame(bootstrap_values)
if return_stats:
stats = bootstrap_values.apply(calc_distribution_stats)
return stats.T[['mean', 'median', '5%', '95%']]
else:
return bootstrap_values
class FractionTaxaBarStack(Graph):
"""Comparing all fractions across all pools in a barstack"""
short_name = 'fraction_taxa_barstack'
def plot(self):
self.frame = OrderedDict(
(('%s - %s' % (p, f), getattr(p.fractions, f).rdp.phyla)
for f in ('low', 'med', 'big') for p in self.parent.pools))
self.frame = pandas.DataFrame(self.frame)
self.frame = self.frame.fillna(0)
self.frame = self.frame.transpose()
self.frame = self.frame.apply(lambda x: 100 * x / x.sum(), axis=1)
# Sort the table by sum #
sums = self.frame.sum()
sums.sort(ascending=False)
self.frame = self.frame.reindex_axis(sums.keys(), axis=1)
# Plot #
fig = pyplot.figure()
axes = self.frame.plot(kind='bar', stacked=True, color=cool_colors)
fig = pyplot.gcf()
# Other #
axes.set_title('Species relative abundances per fraction per pool')
axes.set_ylabel('Relative abundances in percent')
axes.xaxis.grid(False)
axes.yaxis.grid(False)
axes.set_ylim([0, 100])
# Put a legend below current axis
axes.legend(loc='upper center',
bbox_to_anchor=(0.5, -0.20),
fancybox=True,
shadow=True,
ncol=5)
# Save it #
self.save_plot(fig,
axes,
width=24.0,
height=14.0,
bottom=0.30,
top=0.97,
left=0.04,
right=0.98)
self.frame.to_csv(self.csv_path)
pyplot.close(fig)
class FractionTaxaBarStack(Graph):
short_name = 'fraction_taxa_barstack'
bottom = 0.4
top = 0.95
left = 0.1
right = 0.95
formats = ('pdf', 'eps')
def plot(self):
# Make Frame #
self.frame = OrderedDict(
(('%s - %s' % (p, f), getattr(p.fractions, f).rdp.phyla)
for f in ('low', 'med', 'big') for p in self.parent.pools))
self.frame = pandas.DataFrame(self.frame)
self.frame = self.frame.fillna(0)
# Rename #
new_names = {
u"run001-pool01 - low": "2-step PCR low",
u"run001-pool02 - low": "2-step PCR low",
u"run001-pool03 - low": "2-step PCR low",
u"run001-pool04 - low": "1-step PCR low",
u"run002-pool01 - low": "New chem low",
u"run001-pool01 - med": "2-step PCR med",
u"run001-pool02 - med": "2-step PCR med",
u"run001-pool03 - med": "2-step PCR med",
u"run001-pool04 - med": "1-step PCR med",
u"run002-pool01 - med": "New chem med",
u"run001-pool01 - big": "2-step PCR high",
u"run001-pool02 - big": "2-step PCR high",
u"run001-pool03 - big": "2-step PCR high",
u"run001-pool04 - big": "1-step PCR high",
u"run002-pool01 - big": "New chem high",
}
self.frame.rename(columns=new_names, inplace=True)
self.frame = self.frame.transpose()
# Group low abundant into 'others' #
low_abundance = self.frame.sum() < 30000
other_count = self.frame.loc[:, low_abundance].sum(axis=1)
self.frame = self.frame.loc[:, ~low_abundance]
self.frame['Others'] = other_count
# Normalize #
self.frame = self.frame.apply(lambda x: 100 * x / x.sum(), axis=1)
# Sort the table by sum #
sums = self.frame.sum()
sums.sort(ascending=False)
self.frame = self.frame.reindex_axis(sums.keys(), axis=1)
# Plot #
fig = pyplot.figure()
axes = self.frame.plot(kind='bar', stacked=True, color=cool_colors)
fig = pyplot.gcf()
# Other #
axes.set_ylabel('Relative abundances in percent')
axes.xaxis.grid(False)
axes.yaxis.grid(False)
axes.set_ylim([0, 100])
# Put a legend below current axis
axes.legend(loc='upper center',
bbox_to_anchor=(0.5, -0.40),
fancybox=True,
shadow=True,
ncol=5,
prop={'size': 10})
# Font size #
axes.tick_params(axis='x', which='major', labelsize=11)
# Save it #
self.save_plot(fig, axes)
self.frame.to_csv(self.csv_path)
pyplot.close(fig)
class FractionTaxaBarStack(Graph):
"""This is figure 3 of the paper"""
short_name = 'fraction_taxa_barstack'
bottom = 0.4
top = 0.95
left = 0.1
right = 0.95
formats = ('pdf', 'eps')
def plot(self):
# Make Frame #
self.frame = OrderedDict((('%s - %s' % (p,f), getattr(p.fractions, f).rdp.phyla)
for f in ('low', 'med', 'big') for p in self.parent.pools))
self.frame = pandas.DataFrame(self.frame)
self.frame = self.frame.fillna(0)
# Rename #
new_names = {
u"run001-pool01 - low": "2-step PCR low",
u"run001-pool02 - low": "2-step PCR low",
u"run001-pool03 - low": "2-step PCR low",
u"run001-pool04 - low": "1-step PCR low",
u"run002-pool01 - low": "New chem low",
u"run001-pool01 - med": "2-step PCR med",
u"run001-pool02 - med": "2-step PCR med",
u"run001-pool03 - med": "2-step PCR med",
u"run001-pool04 - med": "1-step PCR med",
u"run002-pool01 - med": "New chem med",
u"run001-pool01 - big": "2-step PCR high",
u"run001-pool02 - big": "2-step PCR high",
u"run001-pool03 - big": "2-step PCR high",
u"run001-pool04 - big": "1-step PCR high",
u"run002-pool01 - big": "New chem high",
}
self.frame.rename(columns=new_names, inplace=True)
self.frame = self.frame.transpose()
# Group low abundant into 'others' #
low_abundance = self.frame.sum() < 30000
other_count = self.frame.loc[:, low_abundance].sum(axis=1)
self.frame = self.frame.loc[:, ~low_abundance]
self.frame['Others'] = other_count
# Normalize #
self.frame = self.frame.apply(lambda x: 100*x/x.sum(), axis=1)
# Sort the table by sum #
sums = self.frame.sum()
sums.sort(ascending=False)
self.frame = self.frame.reindex_axis(sums.keys(), axis=1)
# Plot #
fig = pyplot.figure()
axes = self.frame.plot(kind='bar', stacked=True, color=cool_colors)
fig = pyplot.gcf()
# Other #
axes.set_ylabel('Relative abundances in percent')
axes.xaxis.grid(False)
axes.yaxis.grid(False)
axes.set_ylim([0,100])
# Put a legend below current axis
axes.legend(loc='upper center', bbox_to_anchor=(0.5, -0.40), fancybox=True, shadow=True, ncol=5, prop={'size':10})
# Font size #
axes.tick_params(axis='x', which='major', labelsize=11)
# Save it #
self.save_plot(fig, axes)
self.frame.to_csv(self.csv_path)
pyplot.close(fig)
