#   scipy-yli: Helpful SciPy utilities and recipes
#   Copyright © 2022–2023  Lee Yingtong Li (RunasSudo)
#
#   This program is free software: you can redistribute it and/or modify
#   it under the terms of the GNU Affero General Public License as published by
#   the Free Software Foundation, either version 3 of the License, or
#   (at your option) any later version.
#
#   This program is distributed in the hope that it will be useful,
#   but WITHOUT ANY WARRANTY; without even the implied warranty of
#   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
#   GNU Affero General Public License for more details.
#
#   You should have received a copy of the GNU Affero General Public License
#   along with this program.  If not, see <https://www.gnu.org/licenses/>.

from scipy import stats
import statsmodels.api as sm

from .config import config
from .sig_tests import ChiSquaredResult
from .utils import check_nan

def kaplanmeier(df, time, status, by=None, ci=True, nan_policy='warn'):
	"""
	Generate a Kaplan–Meier plot
	
	Uses the Python *matplotlib* library.
	
	:param df: Data to generate plot for
	:type df: DataFrame
	:param time: Column in *df* for the time to event (numeric or timedelta)
	:type time: str
	:param status: Column in *df* for the status variable (True/False or 1/0)
	:type status: str
	:param by: Column in *df* to stratify by (categorical)
	:type by: str
	:param ci: Whether to plot confidence intervals around the survival function
	:type ci: bool
	:param nan_policy: How to handle *nan* values (see :ref:`nan-handling`)
	:type nan_policy: str
	
	:rtype: (Figure, Axes)
	"""
	
	import matplotlib.pyplot as plt
	
	# Check for/clean NaNs
	if by:
		df = check_nan(df[[time, status, by]], nan_policy)
	else:
		df = check_nan(df[[time, status]], nan_policy)
	
	if df[time].dtype == '<m8[ns]':
		df[time] = df[time].dt.total_seconds()
		
		# Auto-detect best time units
		if df[time].max() > 365.24*24*60*60:
			df[time] = df[time] / (365.24*24*60*60)
			time_units = 'years'
		elif df[time].max() > 7*24*60*60 / 12:
			df[time] = df[time] / (7*24*60*60)
			time_units = 'weeks'
		elif df[time].max() > 24*60*60:
			df[time] = df[time] / (24*60*60)
			time_units = 'days'
		elif df[time].max() > 60*60:
			df[time] = df[time] / (60*60)
			time_units = 'hours'
		elif df[time].max() > 60:
			df[time] = df[time] / 60
			time_units = 'minutes'
		else:
			time_units = 'seconds'
	else:
		time_units = None
	
	fig, ax = plt.subplots()
	
	if by is not None:
		# Group by independent variable
		groups = df.groupby(by)
		
		for group in groups.groups:
			subset = groups.get_group(group)
			handle = plot_survfunc(ax, subset[time], subset[status], ci)
			handle.set_label('{} = {}'.format(by, group))
	else:
		# No grouping
		plot_survfunc(ax, df[time], df[status], ci)
	
	if time_units:
		ax.set_xlabel('{} ({})'.format(time, time_units))
	else:
		ax.set_xlabel(time)
	ax.set_ylabel('Survival probability ({:.0%} CI)'.format(1-config.alpha) if ci else 'Survival probability')
	ax.set_ylim(0, 1)
	ax.legend()
	
	return fig, ax

def plot_survfunc(ax, time, status, ci):
	# Estimate the survival function
	sf = sm.SurvfuncRight(time, status)
	
	# Draw straight lines
	xpoints = sf.surv_times.repeat(2)[1:]
	ypoints = sf.surv_prob.repeat(2)[:-1]
	handle = ax.plot(xpoints, ypoints)[0]
	
	if ci:
		zstar = -stats.norm.ppf(config.alpha/2)
		
		# Get confidence intervals
		ci0 = sf.surv_prob - zstar * sf.surv_prob_se
		ci1 = sf.surv_prob + zstar * sf.surv_prob_se
		
		# Plot confidence intervals
		ypoints0 = ci0.repeat(2)[:-1]
		ypoints1 = ci1.repeat(2)[:-1]
		
		ax.fill_between(xpoints, ypoints0, ypoints1, alpha=0.3, label='_')
	
	return handle

def logrank(df, time, status, by, nan_policy='warn'):
	"""
	Perform the log-rank test for equality of survival functions
	
	:param df: Data to perform the test on
	:type df: DataFrame
	:param time: Column in *df* for the time to event (numeric or timedelta)
	:type time: str
	:param status: Column in *df* for the status variable (True/False or 1/0)
	:type status: str
	:param by: Column in *df* to stratify by (categorical)
	:type by: str
	:param nan_policy: How to handle *nan* values (see :ref:`nan-handling`)
	:type nan_policy: str
	
	:rtype: :class:`yli.sig_tests.ChiSquaredResult`
	"""
	
	# TODO: Example
	
	# Check for/clean NaNs
	df = check_nan(df[[time, status, by]], nan_policy)
	
	if df[time].dtype == '<m8[ns]':
		df[time] = df[time].dt.total_seconds()
	
	statistic, pvalue = sm.duration.survdiff(df[time], df[status], df[by])
	
	return ChiSquaredResult(statistic=statistic, dof=1, pvalue=pvalue)