#!/scratch/Anaconda2.4.0/bin/python
import pandas as pd
import numpy as np
import matplotlib
matplotlib.use('Agg')
from matplotlib import pyplot as plt
import os

PATH_FOR_GRAPHS = 'Z:/trex/trex-doc/images/'


def convert_dict_to_dframe(data, categories, index=''):
	data_input = {}
	for category in categories:
		data_input[category] = data[category]
	if index:
		df = pd.DataFrame(data_input, index=data[index])
	else:
		df = pd.DataFrame(data_input)
	return df


def plot_bar_by_category(data_frame, category, index='', graph_name='graph.png', show='', gtitle='', save_path=''):
	if index:
		data_frame = data_frame.sort_index(by=index)
		print data_frame[index]
	else:
		print data_frame
	data_frame = pd.DataFrame(data_frame[category], columns=category).astype(float)
	data_frame.plot(kind='bar')
	plt.xticks(rotation='horizontal')
	plt.title(gtitle)
	if save_path:
		plt.savefig(save_path + graph_name)
	if show:
		plt.show()


def generate_csv(data_frame, file_name, save_path=(os.getcwd() + "/")):
	f = open(save_path + file_name, 'w')
	data_frame.to_csv(f)
	f.close()


# category is an array of category names that will appear as metrics
def plot_bar_by_test_name(data_frame, test_name, category, graph_name='graph.png', show='', gtitle='', save_path=''):
	data_frame = data_frame[data_frame['Test_name'] == test_name]
	plot_bar_by_category(data_frame, category, 'Test_name', graph_name, show, gtitle=test_name, save_path=save_path)


def generate_dframe_for_test(test_name, test_data):
	test_results = []
	test_mins = set()
	test_maxs = set()
	for query in test_data:
		test_results.append(float(query[3]))
		test_mins.add(float(query[4]))
		test_maxs.add(float(query[5]))
	df = pd.DataFrame({test_name: test_results})
	stats = tuple([float(df.mean()), min(test_mins), max(test_maxs)])  # stats = (avg_mpps,min,max)
	return df, stats


def generate_dframe_arr_and_stats_of_tests_per_setup(date, setup_name, setup_dict):
	dframe_arr_trend = []
	stats_arr = []
	dframe_arr_latest = []
	test_names = setup_dict.keys()
	for test in test_names:
		df, stats = generate_dframe_for_test(test, setup_dict[test])
		dframe_arr_trend.append(df)
		stats_arr.append(stats)
		df_latest = float(setup_dict[test][-1][3])
		dframe_arr_latest.append(df_latest)
	dframe_arr_latest = pd.DataFrame({'Date': [date] * len(dframe_arr_latest),
									  'Setup': [setup_name],
									  'Test Name': test_names,
									  'MPPS': dframe_arr_latest},
									 index=range(1, len(dframe_arr_latest) + 1))
	stats_df = pd.DataFrame(stats_arr, index=setup_dict.keys(), columns=['Avg MPPS', 'Golden Min', 'Golden Max'])
	stats_df.index.name = 'Test Name'
	return dframe_arr_trend, stats_df, dframe_arr_latest


def create_plot_for_dframe_arr(dframe_arr, setup_name, start_date, end_date, show='no', save_path='',
							   file_name='trend_graph'):
	dframe_all = pd.concat(dframe_arr, axis=1)
	dframe_all = dframe_all.astype(float)
	dframe_all.plot()
	plt.legend(fontsize='small', loc='best')
	plt.ylabel('MPPS')
	plt.title('Setup: ' + setup_name)
	plt.tick_params(
		axis='x',
		which='both',
		bottom='off',
		top='off',
		labelbottom='off')
	plt.xlabel('Time Period: ' + start_date + ' - ' + end_date)
	if save_path:
		plt.savefig(save_path + setup_name + file_name + '.png')
	if show == 'yes':
		plt.show()


def create_bar_plot_for_latest_runs_per_setup(dframe_all_tests_latest, setup_name, show='no', save_path=''):
	plt.figure()
	dframe_all_tests_latest['MPPS'].plot(kind='bar', legend=False)
	dframe_all_tests_latest = dframe_all_tests_latest[['Test Name', 'Setup', 'Date', 'MPPS']]
	plt.xticks(rotation='horizontal')
	plt.xlabel('Index of Tests')
	plt.ylabel('MPPS')
	plt.title("Test Runs for Setup: " + setup_name)
	if save_path:
		plt.savefig(save_path + setup_name + '_latest_test_runs.png')
		dframe_all_tests_latest = dframe_all_tests_latest.round(2)
		dframe_all_tests_latest.to_csv(save_path + setup_name + '_latest_test_runs_stats.csv')
	if show == 'yes':
		plt.show()


def create_all_data_per_setup(setup_dict, setup_name, start_date, end_date, show='no', save_path='', add_stats=''):
	dframe_arr, stats_arr, dframe_latest_arr = generate_dframe_arr_and_stats_of_tests_per_setup(end_date, setup_name,
																								setup_dict)
	create_bar_plot_for_latest_runs_per_setup(dframe_latest_arr, setup_name, show=show, save_path=save_path)
	create_plot_for_dframe_arr(dframe_arr, setup_name, start_date, end_date, show, save_path)
	if add_stats:
		stats_arr = stats_arr.round(2)
		stats_arr.to_csv(save_path + setup_name + '_trend_stats.csv')
	plt.close('all')


def create_all_data(ga_data, setup_names, start_date, end_date, save_path='', add_stats=''):
	for setup_name in setup_names:
		create_all_data_per_setup(ga_data[setup_name], setup_name, start_date, end_date, show='no', save_path=save_path,
								  add_stats=add_stats)