def generate_span_trends(self, from_date, to_date):
"""Takes logs from self.data and changes them to activity trends for
since the previous day based on a given date tuple"""
activity_list = []
# Iterate through the log data.
for log in self.data:
# Get and format the information we need from the log.
activity = log[0]
log_start = unformat_time(tuple_time(log[1]))
log_end = unformat_time(tuple_time(log[2]))
minimum = unformat_time(
(0, 0, 0, from_date[0], from_date[1], from_date[2]))
maximum = unformat_time(
(59, 59, 23, to_date[0], to_date[1], to_date[2]))
log_time = time_in_span(log_start, log_end, minimum, maximum)
# If the log exists in the span
if log_time > 0:
in_activity_list = False
# Iterate through the activity list to check for duplicates
for entry in activity_list:
if entry[0] == activity:
# Then the entry is in the activity list
in_activity_list = True
# Set the maximum of the span to check as the end of the
# from_date
maximum = unformat_time((59, 59, 23, from_date[0],
from_date[1], from_date[2]))
# If the log did not happen on the from date
if not (time_in_span(log_start, log_end, minimum,
maximum) == 0):
# add the log's time in the span to the activity's
# start trend time.
entry[1] += time_in_span(log_start, log_end,
minimum, maximum)
else:
# Else add it the activity's end trend time
entry[2] += log_time
# If the log is not already in the activity list, add it.
if not in_activity_list:
activity_list.append([activity, log_time, 0])
# Make a list of the activities and their respective trends
trends_list = []
# Iterate through the activity list
for entry in activity_list:
# If the end trend time of the activity exists (I.E. not zero)
if not entry[2] == 0:
# Get the trend and convert it to a string with a percent symbol
percent_change = (((entry[2] - entry[1]) * 1.00) /
(entry[1] * 1.00))
percent_change = str(
(ceil(percent_change * 10000.00)) / 100.00)
if percent_change[-2] == '.':
percent_change += '0'
percent_change += '%'
trends_list.append([entry[0], str(percent_change)])
# Set data as trends
self.data = trends_list
def generate_span_trends(self, from_date, to_date): """Takes logs from self.data and changes them to activity trends for since the previous day based on a given date tuple""" activity_list = [] # Iterate through the log data. for log in self.data: # Get and format the information we need from the log. activity = log[0] log_start = unformat_time(tuple_time(log[1])) log_end = unformat_time(tuple_time(log[2])) minimum = unformat_time((0, 0, 0, from_date[0], from_date[1], from_date[2])) maximum = unformat_time((59, 59, 23, to_date[0], to_date[1], to_date[2])) log_time = time_in_span(log_start, log_end, minimum, maximum) # If the log exists in the span if log_time > 0: in_activity_list = False # Iterate through the activity list to check for duplicates for entry in activity_list: if entry[0] == activity: # Then the entry is in the activity list in_activity_list = True # Set the maximum of the span to check as the end of the # from_date maximum = unformat_time((59, 59, 23, from_date[0], from_date[1], from_date[2])) # If the log did not happen on the from date if not (time_in_span(log_start, log_end, minimum, maximum) == 0): # add the log's time in the span to the activity's # start trend time. entry[1] += time_in_span(log_start, log_end, minimum, maximum) else: # Else add it the activity's end trend time entry[2] += log_time # If the log is not already in the activity list, add it. if not in_activity_list: activity_list.append([activity, log_time, 0]) # Make a list of the activities and their respective trends trends_list = [] # Iterate through the activity list for entry in activity_list: # If the end trend time of the activity exists (I.E. not zero) if not entry[2] == 0: # Get the trend and convert it to a string with a percent symbol percent_change = (((entry[2] - entry[1])*1.00)/(entry[1]*1.00)) percent_change = str((ceil(percent_change*10000.00))/100.00) if percent_change[-2] == '.': percent_change += '0' percent_change += '%' trends_list.append([entry[0], str(percent_change)]) # Set data as trends self.data = trends_list
def create_pie_chart(self, data=None, span='all', no=None): """Creates a pie chart from the the data""" # Create the list of objects to be added to the chart chart_list = [] # If the span has been specified, then get the logs only for that time if not span == None and not span == 'all': # Iterate through the log data. for log in self.data: # Get and format the information we need from the log. activity = log[0] log_start = unformat_time(tuple_time(log[1])) log_end = unformat_time(tuple_time(log[2])) color = log[3] minimum = unformat_time(span[1]) maximum = unformat_time(span[2]) # Add the time and activity to the chart_list. log_time = time_in_span(log_start, log_end, minimum, maximum) # Check if the activity has already been added to chart_list. in_chart_list = False for entry in chart_list: # If the activity is in the chart_list, make a note and add. # its time to the existing list item. if entry[0] == activity: entry[1] += log_time in_chart_list = True # If the log is not in the chart_list and it is in the span, add # it to the chart_list. if not in_chart_list and log_time > 0: chart_list.append([activity, log_time, color]) else: # If span is not specified then the data are totals. # Set the chart_list equal to the total data. for total in data: chart_list.append((total[0], total[2], total[3])) # Add each entry is the chart_list to the chart self.sort(chart_list) # Data must be organized for day, month, etc. before using # If size has been specified if not self.size == (None, None): self.chart = Pie(style=self.style, print_values=False, fill=True, human_readable=True, include_x_axis=True, width=self.size[0], height=self.size[1]) # If size has not already been specified else: # Let the graph dynamically resize within webview self.chart = Pie(style=self.style, print_values=False, fill=True, human_readable=True, include_x_axis=True) if not chart_list == []: for entry in chart_list: self.chart.add(entry[0], entry[1])
def create_bar_chart(self, data, span):
"""Creates a bar chart from the the data"""
# Initialize the chart_list
chart_list = []
for log in data:
activity_time = 0
activity = log[0]
log_start = unformat_time(tuple_time(log[1]))
log_end = unformat_time(tuple_time(log[2]))
color = log[3]
minimum = span[1]
maximum = span[2]
minimum = unformat_time(minimum)
maximum = unformat_time(maximum)
activity_time += time_in_span(log_start, log_end, minimum, maximum)
in_chart_list = False
for entry in chart_list:
if entry[0] == activity:
entry[1] += activity_time
in_chart_list = True
if not in_chart_list and activity_time > 0:
chart_list.append([activity, activity_time, color])
self.sort(chart_list)
# Data must be organized for day, month, etc. before using
# If size has been specified
if not self.size == (None, None):
self.chart = Bar(style=self.style, y_scale=60.0,
print_values=False, include_x_axis=True,
width=self.size[0], height=self.size[1])
# If size has not already been specified
else:
# Let the graph dynamically resize within webview
self.chart = Bar(style=self.style, print_values=False,
include_x_axis=True, y_scale=60.0)
self.set_y_labels(chart_list)
## Add each entry is the chart_list to the chart
if not chart_list == []:
for entry in chart_list:
time = str(timedelta(seconds=entry[1]))
if time[1] == ':':
time = '0' + time
self.chart.add(entry[0], [{'value':entry[1], 'label':time}])
else:
self.chart = Pie(style=self.style, width=self.size[0],
height=self.size[1])
def read_total(self, current_date, span=None):
"""Extrapolates a set of totals based on information in the logs and
returns as a list of tuples"""
totals_list = []
# Iterate through the logs
for entry in self.read_log("*"):
# Get the activity from the entry
activity = entry[0]
# Get the entry's start time
start_time = tuple_time(entry[1])
# Get the entry's stop time
stop_time = tuple_time(entry[2])
# Get the entry's color
color = entry[3]
# Get the time elapsed by the entry and round to the lowest nearby
# whole integer and subtract 1
day = current_date[0]
month = current_date[1]
year = current_date[2]
stop_time = unformat_time(stop_time)
start_time = unformat_time(start_time)
minimum = unformat_time((0, 0, 0, 0, 0, 0))
maximum = unformat_time((59, 59, 23, day, month, year))
total_time = time_in_span(start_time, stop_time, minimum, maximum)
if not total_time == 0:
minimum = unformat_time((0, 0, 0, day, month, year))
time_today = time_in_span(start_time, stop_time, minimum, maximum)
is_new_activity = True
for total in totals_list:
if total[0] == entry[0]:
updated_total = (entry[0], total[1]+time_today, total[2]+total_time, color)
totals_list[totals_list.index(total)] = updated_total
is_new_activity = False
# If the entry is a new activity
if is_new_activity:
# Append it to the day_totals
totals_list.append((activity, time_today, total_time, color))
# Return totals_list
return totals_list
def generate_day_trends(self, date): """Takes logs from self.data and changes them to activity trends for since the previous day based on a given date tuple""" day_activity_list = [] previous_day_activity_list = [] trends = [] # Iterate through the log data. for log in self.data: # Get and format the information we need from the log. activity = log[0] log_start = unformat_time(tuple_time(log[1])) log_end = unformat_time(tuple_time(log[2])) minimum = unformat_time((0,0,0,date[0],date[1],date[2])) maximum = unformat_time((59,59,23,date[0],date[1],date[2])) # Add the time and activity to the chart_list. log_time = time_in_span(log_start, log_end, minimum, maximum) # Check if the activity has already been added to chart_list. in_day_activity_list = False for entry in day_activity_list: # If the activity is in the chart_list, make a note and add. # its time to the existing list item. if entry[0] == activity: entry[1] += log_time in_day_activity_list = True # If the log is not in the chart_list and it is in the span, add # it to the chart_list. if not in_day_activity_list and log_time > 0: day_activity_list.append([activity, log_time]) # Iterate through the log data. for log in self.data: # Get and format the information we need from the log. activity = log[0] log_start = unformat_time(tuple_time(log[1])) log_end = unformat_time(tuple_time(log[2])) minimum = unformat_time((0,0,0,date[0],date[1],date[2])) minimum -= unformat_time((0,0,0,1,0,0)) maximum = unformat_time((59,59,23,date[0],date[1],date[2])) maximum -= unformat_time((0,0,0,1,0,0)) # Add the time and activity to the chart_list. log_time = time_in_span(log_start, log_end, minimum, maximum) # Check if the activity has already been added to chart_list. in_previous_day_activity_list = False for entry in previous_day_activity_list: # If the activity is in the chart_list, make a note and add. # its time to the existing list item. if entry[0] == activity: entry[1] += log_time in_previous_day_activity_list = True # If the log is not in the chart_list and it is in the span, add # it to the chart_list. if not in_previous_day_activity_list and log_time > 0: previous_day_activity_list.append([activity, log_time]) # Search through the day_activity_list and previous_day_activity_list # for matching activities for activity in day_activity_list: for previous_activity in previous_day_activity_list: if activity[0] == previous_activity[0]: # Get the percent change between the two activities' times # as a string in the form of 100.00% percent_change = (((activity[1]-previous_activity[1])*1.00) /(previous_activity[1]*1.00)) percent_change = str((ceil(percent_change*10000.00))/100.00) if percent_change[-2] == '.': percent_change += '0' percent_change += '%' # Add this activity and it's percent change to trends list trends.append([activity[0], percent_change]) # Set data as trends self.data = trends
def generate_day_trends(self, date):
"""Takes logs from self.data and changes them to activity trends for
since the previous day based on a given date tuple"""
day_activity_list = []
previous_day_activity_list = []
trends = []
# Iterate through the log data.
for log in self.data:
# Get and format the information we need from the log.
activity = log[0]
log_start = unformat_time(tuple_time(log[1]))
log_end = unformat_time(tuple_time(log[2]))
minimum = unformat_time((0, 0, 0, date[0], date[1], date[2]))
maximum = unformat_time((59, 59, 23, date[0], date[1], date[2]))
# Add the time and activity to the chart_list.
log_time = time_in_span(log_start, log_end, minimum, maximum)
# Check if the activity has already been added to chart_list.
in_day_activity_list = False
for entry in day_activity_list:
# If the activity is in the chart_list, make a note and add.
# its time to the existing list item.
if entry[0] == activity:
entry[1] += log_time
in_day_activity_list = True
# If the log is not in the chart_list and it is in the span, add
# it to the chart_list.
if not in_day_activity_list and log_time > 0:
day_activity_list.append([activity, log_time])
# Iterate through the log data.
for log in self.data:
# Get and format the information we need from the log.
activity = log[0]
log_start = unformat_time(tuple_time(log[1]))
log_end = unformat_time(tuple_time(log[2]))
minimum = unformat_time((0, 0, 0, date[0], date[1], date[2]))
minimum -= unformat_time((0, 0, 0, 1, 0, 0))
maximum = unformat_time((59, 59, 23, date[0], date[1], date[2]))
maximum -= unformat_time((0, 0, 0, 1, 0, 0))
# Add the time and activity to the chart_list.
log_time = time_in_span(log_start, log_end, minimum, maximum)
# Check if the activity has already been added to chart_list.
in_previous_day_activity_list = False
for entry in previous_day_activity_list:
# If the activity is in the chart_list, make a note and add.
# its time to the existing list item.
if entry[0] == activity:
entry[1] += log_time
in_previous_day_activity_list = True
# If the log is not in the chart_list and it is in the span, add
# it to the chart_list.
if not in_previous_day_activity_list and log_time > 0:
previous_day_activity_list.append([activity, log_time])
# Search through the day_activity_list and previous_day_activity_list
# for matching activities
for activity in day_activity_list:
for previous_activity in previous_day_activity_list:
if activity[0] == previous_activity[0]:
# Get the percent change between the two activities' times
# as a string in the form of 100.00%
percent_change = ((
(activity[1] - previous_activity[1]) * 1.00) /
(previous_activity[1] * 1.00))
percent_change = str(
(ceil(percent_change * 10000.00)) / 100.00)
if percent_change[-2] == '.':
percent_change += '0'
percent_change += '%'
# Add this activity and it's percent change to trends list
trends.append([activity[0], percent_change])
# Set data as trends
self.data = trends