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)