def get_rescuetime_data(self,datetime_from,dstetime_to): s = Service.Service() p = {} k = AnalyticApiKey.AnalyticApiKey(self.key_Rescuetime, s) p['restrict_begin'] = datetime_from p['restrict_end'] = dstetime_to p['taxonomy'] = 'activity' p['rs'] = 'minute' p['by'] = 'interval' d = s.fetch_data(k,p) df = pd.DataFrame(d['rows'], columns=d['row_headers']) return df
def get_rescuetime_data_proposed_method(self,datetime_from,dstetime_to): s = Service.Service() p = {} k = AnalyticApiKey.AnalyticApiKey(self.key_Rescuetime, s) p['restrict_end'] = dstetime_to p['via'] = 'pyrt' p['restrict_begin'] = datetime_from p['taxonomy'] = 'activity' p['format'] = 'json' p['by'] = 'rank' p['request_ids'] = 'true' d = s.fetch_data(k,p) df = pd.DataFrame(d['rows'], columns=d['row_headers']) return df
def get_rescuetime_data_proposed_method_specific_program(self,datetime_from,dstetime_to,program_id): s = Service.Service() p = {} k = AnalyticApiKey.AnalyticApiKey(self.key_Rescuetime, s) p['restrict_end'] = dstetime_to p['via'] = 'pyrt' p['restrict_begin'] = datetime_from p['taxonomy'] = 'activity' p['format'] = 'json' p['taxon'] = program_id p['rs'] = 'minute' p['by'] = 'interval' p['find_by_id'] = 'true' d = s.fetch_data(k,p) df = pd.DataFrame(d['rows'], columns=d['row_headers']) return df
def get_efficiency(): try: today_date = date.today().strftime("%Y-%m-%d") tomorrow_date = (date.today() + timedelta(1)).strftime("%Y-%m-%d") s = Service.Service() k = AnalyticApiKey.AnalyticApiKey(apikey, s) p = {'restrict_begin': today_date, 'restrict_end': tomorrow_date, 'restrict_kind': 'efficiency', 'perspective': 'interval'} #YYYY-MM-DD d = s.fetch_data(k, p) df = pd.DataFrame(d['rows'], columns=d['row_headers']) efficiency = df["Efficiency (percent)"] dates = df["Date"] return int(efficiency.tail(1)), str(dates.tail(1)) except: return "F", "F"
def loadrescuetime(): s = Service.Service() k = AnalyticApiKey.AnalyticApiKey( open('/home/alex/.rescuetime/rt_key').read(), s) p = {} p['restrict_begin'] = '2014-01-01' p['restrict_end'] = (datetime.date.today() + datetime.timedelta(1)).strftime("%Y-%m-%d") p['restrict_kind'] = 'efficiency' p['perspective'] = 'interval' p['resolution_time'] = 'day' d = s.fetch_data(k, p) df = pd.DataFrame(d['rows'], columns=d['row_headers']) df['Date'] = pd.to_datetime(df.Date) df['Date'] = df['Date'] - np.timedelta64(0, 'D') df.set_index('Date', inplace=True) return df
def __init__(self, beginDay=None, endDay=None): service = Service.Service() key = AnalyticApiKey.AnalyticApiKey( open('/home/ysuzuki/MyApplication/rescueapp/rt_key').read(), service) parameters = {} parameters['restrict_kind'] = 'activity' #parameters['restrict_kind'] = 'category' parameters['perspective'] = 'interval' self.today = datetime.datetime.today().strftime("%Y%m%d") if beginDay: self.beginDay = beginDay else: self.beginDay = self.today if endDay: self.endDay = endDay else: self.endDay = self.today parameters["restrict_begin"] = self.beginDay[ 0:4] + "-" + self.beginDay[4:6] + "-" + self.beginDay[ 6:8] # %Y%m%d -> %Y-%m-%d parameters["restrict_end"] = self.endDay[0:4] + "-" + self.endDay[ 4:6] + "-" + self.endDay[6:8] # %Y%m%d -> %Y-%m-%d self.beginDatetime = datetime.datetime.strptime( self.beginDay, "%Y%m%d") self.endDatetime = datetime.datetime.strptime(self.endDay, "%Y%m%d") self.dateList = [] for plusDays in range((self.endDatetime - self.beginDatetime).days + 1): self.dateList.append( (self.beginDatetime + datetime.timedelta(days=plusDays)).strftime("%Y%m%d")) self.jsonRawData = service.fetch_data(key, parameters) self.data = RescueJSONParser(self.jsonRawData)
def get_rescuetime_data_specific_program(self,datetime_from,dstetime_to,program): s = Service.Service() p = {} k = AnalyticApiKey.AnalyticApiKey(self.key_Rescuetime, s) p['restrict_begin'] = datetime_from p['restrict_end'] = dstetime_to p['taxonomy'] = 'activity' p['taxon'] = program p['rs'] = 'minute' p['by'] = 'interval' d = s.fetch_data(k,p) df = pd.DataFrame(d['rows'], columns=d['row_headers']) print d['row_headers'] for row in d['rows']: row_str = "" for item in row: row_str += str(item) + '\t' print row_str print d['rows'] return df
# coding: utf-8 import pprint import pandas as pd import datetime import numpy as np import matplotlib.pyplot as plt from rescuetime.api.service import Service from rescuetime.api.access import AnalyticApiKey s = Service.Service() k = AnalyticApiKey.AnalyticApiKey(open('rt_key').read(), s) p = {} p['restrict_begin'] = '2014-01-01' p['restrict_end'] = '2014-12-01' p['restrict_kind'] = 'efficiency' p['perspective'] = 'interval' d = s.fetch_data(k, p) df = pd.DataFrame(d['rows'], columns=d['row_headers']) df['Date'] = pd.to_datetime(df.Date) df.set_index('Date', inplace=True) x = [] x = np.zeros([24, 7]) for i in range(24, ): dfi = df.at_time(datetime.time(i, 0))['Efficiency (percent)'] for j in range(7, ): dfij = dfi[dfi.index.dayofweek == j] x[i, j] = np.mean(dfij)