"""Utility functions"""
__all__ = ['percentile', 'plot_cdf_area', 'table_apply']
import numpy as np
import pandas as pd
import matplotlib
matplotlib.use('agg', warn=False)
import matplotlib.pyplot as plt
from scipy import stats
[docs]def percentile(p, arr=None):
"""Returns the pth percentile of the input array (the value that is at
least as great as p% of the values in the array)
If arr is not provided, percentile returns itself curried with p
>>> percentile(67, [1, 3, 5, 9])
9
>>> percentile(66, [1, 3, 5, 9])
5
>>> f = percentile(66)
>>> f([1, 3, 5, 9])
5
"""
if arr is None:
return lambda arr: percentile(p, arr)
return np.percentile(arr, p, interpolation='higher')
[docs]def plot_cdf_area(rbound, lbound=None, mean=0, sd=1):
"""Plots a normal curve with specified parameters and area below curve shaded
between ``lbound`` and ``rbound``.
Args:
``rbound`` (numeric): right boundary of shaded region
``lbound`` (numeric): left boundary of shaded region; by default is negative infinity
``mean`` (numeric): mean/expectation of normal distribution
``sd`` (numeric): standard deviation of normal distribution
"""
INF = 3.5 * sd
llabel = lbound
if lbound is None:
lbound = -INF + mean
llabel = "-$\infty$"
pdf_range = np.arange(-INF + mean, INF + mean, 0.1)
plt.plot(pdf_range, stats.norm.pdf(pdf_range, loc=mean, scale=sd), color='k', lw=1)
cdf_range = np.arange(lbound, rbound + .1, 0.1)
plt.fill_between(cdf_range, stats.norm.pdf(cdf_range, loc=mean, scale=sd), color='gold')
plt.ylim(0, stats.norm.pdf(0, loc=0, scale=sd) * 1.25)
plt.xlabel('z')
plt.ylabel('$\phi$(z)', rotation=0)
plt.title("Normal Curve ~ ($\mu$ = {0}, $\sigma$ = {1}) "
"{2} < z < {3}".format(mean, sd, llabel, rbound), fontsize=16)
plt.show()
[docs]def table_apply(table, func, subset=None):
"""Applies a function to each column and returns a Table.
Uses pandas `apply` under the hood, then converts back to a Table
Parameters
----------
table : instance of Table
The table to apply your function to
func : function
Any function that will work with DataFrame.apply
subset : list | None
A list of columns to apply the function to. If None, function
will be applied to all columns in table
Returns
-------
tab : instance of Table
A table with the given function applied. It will either be the
shape == shape(table), or shape (1, table.shape[1])
"""
from . import Table
df = table.to_df()
if subset is not None:
# Iterate through columns
subset = np.atleast_1d(subset)
if any([i not in df.columns for i in subset]):
err = np.where([i not in df.columns for i in subset])[0]
err = "Column mismatch: {0}".format(
[subset[i] for i in err])
raise ValueError(err)
for col in subset:
df[col] = df[col].apply(func)
else:
df = df.apply(func)
if isinstance(df, pd.Series):
# Reshape it so that we can easily convert back
df = pd.DataFrame(df).T
tab = Table.from_df(df)
return tab