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<|file_name|>Class_LabExperimBased.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
"""
Class_LabExperimBased provides functionalities for data handling of data obtained in lab experiments in the field of (waste)water treatment.
Copyright (C) 2016 Chaim De Mulder
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 http://www.gnu.org/licenses/.
"""
import sys
#import os
#from os import listdir
#import pandas as pd
#import scipy as sp
#import numpy as np
#import datetime as dt
import matplotlib.pyplot as plt #plotten in python
import warnings as wn
from wwdata.Class_HydroData import HydroData
class LabExperimBased(HydroData):
"""
Superclass for a HydroData object, expanding the functionalities with
specific functions for data gathered is lab experiments.
Attributes
----------
timedata_column : str
name of the column containing the time data
data_type : str
type of the data provided
experiment_tag : str
A tag identifying the experiment; can be a date or a code used by
the producer/owner of the data.
time_unit : str
The time unit in which the time data is given
units : array
The units of the variables in the columns
"""
def __init__(self,data,timedata_column='index',data_type='NAT',
experiment_tag='No tag given',time_unit=None):
"""
initialisation of a LabExperimBased object, based on a previously defined
HydroData object.
"""
HydroData.__init__(self,data,timedata_column=timedata_column,data_type=data_type,
experiment_tag=experiment_tag,time_unit=time_unit)
def hours(self,time_column='index'):
"""
calculates the hours from the relative values
Parameters
----------
time_column : string
column containing the relative time values; default to index
"""
if time_column == 'index':
self.data['index']=self.time.values
self.data['h']= (self.data['indexes'])*24 + self.data['indexes'].shift(1)
self.data['h'].fillna(0,inplace=True)
self.data.drop('index', axis=1, inplace=True)
else:
<|fim_middle|>
def add_conc(self,column_name,x,y,new_name='default'):
"""
calculates the concentration values of the given column and adds them as
a new column to the DataFrame.
Parameters
----------
column_name : str
column with values
x : int
...
y : int
...
new_name : str
name of the new column, default to 'column_name + mg/L'
"""
if new_name == 'default':
new_name = column_name + ' ' + 'mg/L'
self.data[new_name] = self.data[column_name].values*x*y
## Instead of this function: define a dataframe/dict with conversion or
## concentration factors, so that you can have a function that automatically
## converts all parameters in the frame to concentrations
def check_ph(self,ph_column='pH',thresh=0.4):
"""
gives the maximal change in pH
Parameters
----------
ph_column : str
column with pH-values, default to 'pH'
threshold : int
threshold value for warning, default to '0.4'
"""
dph = self.data[ph_column].max()-self.data[ph_column].min()
if dph > thresh:
wn.warn('Strong change in pH during experiment!')
else:
self.delta_ph = dph
def in_out(self,columns):
"""
(start_values-end_values)
Parameters
----------
columns : array of strings
"""
inv=0
outv=0
indexes= self.time.values
for column in columns:
inv += self.data[column][indexes[0]]
for column in columns:
outv += self.data[column][indexes[-1]]
in_out = inv-outv
return in_out
def removal(self,columns):
"""
total removal of nitrogen
(1-(end_values/start_values))
Parameters
----------
columns : array of strings
"""
inv=0
outv=0
indexes= self.time.values
for column in columns:
inv += self.data[column][indexes[0]]
for column in columns:
outv += self.data[column][indexes[-1]]
removal = 1-(outv/inv)
return removal
def calc_slope(self,columns,time_column='h'):
"""
calculates the slope of the selected columns
Parameters
----------
columns : array of strings
columns to calculate the slope for
time_column : str
time used for calculation; default to 'h'
"""
for column in columns:
self.data[column + " " +'slope'] = (self.data[column].shift(1)-self.data[column])\
/(self.data[time_column]-self.data[time_column].shift(1))
def plot(self,columns,time_column='index'):
"""
calculates the slope of the selected columns
Parameters
----------
columns : array of strings
columns to plot
time_column : str
time used for calculation; default to 'h'
"""
fig = plt.figure(figsize=(10,6))
ax = fig.add_subplot(111)
if time_column=='index':
for column in columns:
ax.plot(self.time,self.data[column],marker='o')
else:
for column in columns:
ax.plot(self.data[time_column],self.data[column],marker='o')
ax.legend()
return fig,ax
#######################################
def _print_removed_output(original,new,type_):
"""
function printing the output of functions that remove datapoints.
Parameters
----------
original : int
original length of the dataset
new : int
length of the new dataset
type_ : str
'removed' or 'dropped'
"""
print('Original dataset:',original,'datapoints')
print('New dataset:',new,'datapoints')
print(original-new,'datapoints ',type_)
def _log_removed_output(log_file,original,new,type_):
"""
function writing the output of functions that remove datapoints to a log file.
Parameters
----------
log_file : str
string containing the directory to the log file to be written out
original : int
original length of the dataset
new : int
length of the new dataset
type_ : str
'removed' or 'dropped'
"""
log_file = open(log_file,'a')
log_file.write(str('\nOriginal dataset: '+str(original)+' datapoints; new dataset: '+
str(new)+' datapoints'+str(original-new)+' datapoints ',type_))
log_file.close()
<｜fim▁end｜> | self.data['h']= (self.data[time_column])*24 + self.data[time_column].shift(1)
self.data['h'].fillna(0,inplace=True) |
<|file_name|>Class_LabExperimBased.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
"""
Class_LabExperimBased provides functionalities for data handling of data obtained in lab experiments in the field of (waste)water treatment.
Copyright (C) 2016 Chaim De Mulder
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 http://www.gnu.org/licenses/.
"""
import sys
#import os
#from os import listdir
#import pandas as pd
#import scipy as sp
#import numpy as np
#import datetime as dt
import matplotlib.pyplot as plt #plotten in python
import warnings as wn
from wwdata.Class_HydroData import HydroData
class LabExperimBased(HydroData):
"""
Superclass for a HydroData object, expanding the functionalities with
specific functions for data gathered is lab experiments.
Attributes
----------
timedata_column : str
name of the column containing the time data
data_type : str
type of the data provided
experiment_tag : str
A tag identifying the experiment; can be a date or a code used by
the producer/owner of the data.
time_unit : str
The time unit in which the time data is given
units : array
The units of the variables in the columns
"""
def __init__(self,data,timedata_column='index',data_type='NAT',
experiment_tag='No tag given',time_unit=None):
"""
initialisation of a LabExperimBased object, based on a previously defined
HydroData object.
"""
HydroData.__init__(self,data,timedata_column=timedata_column,data_type=data_type,
experiment_tag=experiment_tag,time_unit=time_unit)
def hours(self,time_column='index'):
"""
calculates the hours from the relative values
Parameters
----------
time_column : string
column containing the relative time values; default to index
"""
if time_column == 'index':
self.data['index']=self.time.values
self.data['h']= (self.data['indexes'])*24 + self.data['indexes'].shift(1)
self.data['h'].fillna(0,inplace=True)
self.data.drop('index', axis=1, inplace=True)
else:
self.data['h']= (self.data[time_column])*24 + self.data[time_column].shift(1)
self.data['h'].fillna(0,inplace=True)
def add_conc(self,column_name,x,y,new_name='default'):
"""
calculates the concentration values of the given column and adds them as
a new column to the DataFrame.
Parameters
----------
column_name : str
column with values
x : int
...
y : int
...
new_name : str
name of the new column, default to 'column_name + mg/L'
"""
if new_name == 'default':
<|fim_middle|>
self.data[new_name] = self.data[column_name].values*x*y
## Instead of this function: define a dataframe/dict with conversion or
## concentration factors, so that you can have a function that automatically
## converts all parameters in the frame to concentrations
def check_ph(self,ph_column='pH',thresh=0.4):
"""
gives the maximal change in pH
Parameters
----------
ph_column : str
column with pH-values, default to 'pH'
threshold : int
threshold value for warning, default to '0.4'
"""
dph = self.data[ph_column].max()-self.data[ph_column].min()
if dph > thresh:
wn.warn('Strong change in pH during experiment!')
else:
self.delta_ph = dph
def in_out(self,columns):
"""
(start_values-end_values)
Parameters
----------
columns : array of strings
"""
inv=0
outv=0
indexes= self.time.values
for column in columns:
inv += self.data[column][indexes[0]]
for column in columns:
outv += self.data[column][indexes[-1]]
in_out = inv-outv
return in_out
def removal(self,columns):
"""
total removal of nitrogen
(1-(end_values/start_values))
Parameters
----------
columns : array of strings
"""
inv=0
outv=0
indexes= self.time.values
for column in columns:
inv += self.data[column][indexes[0]]
for column in columns:
outv += self.data[column][indexes[-1]]
removal = 1-(outv/inv)
return removal
def calc_slope(self,columns,time_column='h'):
"""
calculates the slope of the selected columns
Parameters
----------
columns : array of strings
columns to calculate the slope for
time_column : str
time used for calculation; default to 'h'
"""
for column in columns:
self.data[column + " " +'slope'] = (self.data[column].shift(1)-self.data[column])\
/(self.data[time_column]-self.data[time_column].shift(1))
def plot(self,columns,time_column='index'):
"""
calculates the slope of the selected columns
Parameters
----------
columns : array of strings
columns to plot
time_column : str
time used for calculation; default to 'h'
"""
fig = plt.figure(figsize=(10,6))
ax = fig.add_subplot(111)
if time_column=='index':
for column in columns:
ax.plot(self.time,self.data[column],marker='o')
else:
for column in columns:
ax.plot(self.data[time_column],self.data[column],marker='o')
ax.legend()
return fig,ax
#######################################
def _print_removed_output(original,new,type_):
"""
function printing the output of functions that remove datapoints.
Parameters
----------
original : int
original length of the dataset
new : int
length of the new dataset
type_ : str
'removed' or 'dropped'
"""
print('Original dataset:',original,'datapoints')
print('New dataset:',new,'datapoints')
print(original-new,'datapoints ',type_)
def _log_removed_output(log_file,original,new,type_):
"""
function writing the output of functions that remove datapoints to a log file.
Parameters
----------
log_file : str
string containing the directory to the log file to be written out
original : int
original length of the dataset
new : int
length of the new dataset
type_ : str
'removed' or 'dropped'
"""
log_file = open(log_file,'a')
log_file.write(str('\nOriginal dataset: '+str(original)+' datapoints; new dataset: '+
str(new)+' datapoints'+str(original-new)+' datapoints ',type_))
log_file.close()
<｜fim▁end｜> | new_name = column_name + ' ' + 'mg/L' |
<|file_name|>Class_LabExperimBased.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
"""
Class_LabExperimBased provides functionalities for data handling of data obtained in lab experiments in the field of (waste)water treatment.
Copyright (C) 2016 Chaim De Mulder
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 http://www.gnu.org/licenses/.
"""
import sys
#import os
#from os import listdir
#import pandas as pd
#import scipy as sp
#import numpy as np
#import datetime as dt
import matplotlib.pyplot as plt #plotten in python
import warnings as wn
from wwdata.Class_HydroData import HydroData
class LabExperimBased(HydroData):
"""
Superclass for a HydroData object, expanding the functionalities with
specific functions for data gathered is lab experiments.
Attributes
----------
timedata_column : str
name of the column containing the time data
data_type : str
type of the data provided
experiment_tag : str
A tag identifying the experiment; can be a date or a code used by
the producer/owner of the data.
time_unit : str
The time unit in which the time data is given
units : array
The units of the variables in the columns
"""
def __init__(self,data,timedata_column='index',data_type='NAT',
experiment_tag='No tag given',time_unit=None):
"""
initialisation of a LabExperimBased object, based on a previously defined
HydroData object.
"""
HydroData.__init__(self,data,timedata_column=timedata_column,data_type=data_type,
experiment_tag=experiment_tag,time_unit=time_unit)
def hours(self,time_column='index'):
"""
calculates the hours from the relative values
Parameters
----------
time_column : string
column containing the relative time values; default to index
"""
if time_column == 'index':
self.data['index']=self.time.values
self.data['h']= (self.data['indexes'])*24 + self.data['indexes'].shift(1)
self.data['h'].fillna(0,inplace=True)
self.data.drop('index', axis=1, inplace=True)
else:
self.data['h']= (self.data[time_column])*24 + self.data[time_column].shift(1)
self.data['h'].fillna(0,inplace=True)
def add_conc(self,column_name,x,y,new_name='default'):
"""
calculates the concentration values of the given column and adds them as
a new column to the DataFrame.
Parameters
----------
column_name : str
column with values
x : int
...
y : int
...
new_name : str
name of the new column, default to 'column_name + mg/L'
"""
if new_name == 'default':
new_name = column_name + ' ' + 'mg/L'
self.data[new_name] = self.data[column_name].values*x*y
## Instead of this function: define a dataframe/dict with conversion or
## concentration factors, so that you can have a function that automatically
## converts all parameters in the frame to concentrations
def check_ph(self,ph_column='pH',thresh=0.4):
"""
gives the maximal change in pH
Parameters
----------
ph_column : str
column with pH-values, default to 'pH'
threshold : int
threshold value for warning, default to '0.4'
"""
dph = self.data[ph_column].max()-self.data[ph_column].min()
if dph > thresh:
<|fim_middle|>
else:
self.delta_ph = dph
def in_out(self,columns):
"""
(start_values-end_values)
Parameters
----------
columns : array of strings
"""
inv=0
outv=0
indexes= self.time.values
for column in columns:
inv += self.data[column][indexes[0]]
for column in columns:
outv += self.data[column][indexes[-1]]
in_out = inv-outv
return in_out
def removal(self,columns):
"""
total removal of nitrogen
(1-(end_values/start_values))
Parameters
----------
columns : array of strings
"""
inv=0
outv=0
indexes= self.time.values
for column in columns:
inv += self.data[column][indexes[0]]
for column in columns:
outv += self.data[column][indexes[-1]]
removal = 1-(outv/inv)
return removal
def calc_slope(self,columns,time_column='h'):
"""
calculates the slope of the selected columns
Parameters
----------
columns : array of strings
columns to calculate the slope for
time_column : str
time used for calculation; default to 'h'
"""
for column in columns:
self.data[column + " " +'slope'] = (self.data[column].shift(1)-self.data[column])\
/(self.data[time_column]-self.data[time_column].shift(1))
def plot(self,columns,time_column='index'):
"""
calculates the slope of the selected columns
Parameters
----------
columns : array of strings
columns to plot
time_column : str
time used for calculation; default to 'h'
"""
fig = plt.figure(figsize=(10,6))
ax = fig.add_subplot(111)
if time_column=='index':
for column in columns:
ax.plot(self.time,self.data[column],marker='o')
else:
for column in columns:
ax.plot(self.data[time_column],self.data[column],marker='o')
ax.legend()
return fig,ax
#######################################
def _print_removed_output(original,new,type_):
"""
function printing the output of functions that remove datapoints.
Parameters
----------
original : int
original length of the dataset
new : int
length of the new dataset
type_ : str
'removed' or 'dropped'
"""
print('Original dataset:',original,'datapoints')
print('New dataset:',new,'datapoints')
print(original-new,'datapoints ',type_)
def _log_removed_output(log_file,original,new,type_):
"""
function writing the output of functions that remove datapoints to a log file.
Parameters
----------
log_file : str
string containing the directory to the log file to be written out
original : int
original length of the dataset
new : int
length of the new dataset
type_ : str
'removed' or 'dropped'
"""
log_file = open(log_file,'a')
log_file.write(str('\nOriginal dataset: '+str(original)+' datapoints; new dataset: '+
str(new)+' datapoints'+str(original-new)+' datapoints ',type_))
log_file.close()
<｜fim▁end｜> | wn.warn('Strong change in pH during experiment!') |
<|file_name|>Class_LabExperimBased.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
"""
Class_LabExperimBased provides functionalities for data handling of data obtained in lab experiments in the field of (waste)water treatment.
Copyright (C) 2016 Chaim De Mulder
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 http://www.gnu.org/licenses/.
"""
import sys
#import os
#from os import listdir
#import pandas as pd
#import scipy as sp
#import numpy as np
#import datetime as dt
import matplotlib.pyplot as plt #plotten in python
import warnings as wn
from wwdata.Class_HydroData import HydroData
class LabExperimBased(HydroData):
"""
Superclass for a HydroData object, expanding the functionalities with
specific functions for data gathered is lab experiments.
Attributes
----------
timedata_column : str
name of the column containing the time data
data_type : str
type of the data provided
experiment_tag : str
A tag identifying the experiment; can be a date or a code used by
the producer/owner of the data.
time_unit : str
The time unit in which the time data is given
units : array
The units of the variables in the columns
"""
def __init__(self,data,timedata_column='index',data_type='NAT',
experiment_tag='No tag given',time_unit=None):
"""
initialisation of a LabExperimBased object, based on a previously defined
HydroData object.
"""
HydroData.__init__(self,data,timedata_column=timedata_column,data_type=data_type,
experiment_tag=experiment_tag,time_unit=time_unit)
def hours(self,time_column='index'):
"""
calculates the hours from the relative values
Parameters
----------
time_column : string
column containing the relative time values; default to index
"""
if time_column == 'index':
self.data['index']=self.time.values
self.data['h']= (self.data['indexes'])*24 + self.data['indexes'].shift(1)
self.data['h'].fillna(0,inplace=True)
self.data.drop('index', axis=1, inplace=True)
else:
self.data['h']= (self.data[time_column])*24 + self.data[time_column].shift(1)
self.data['h'].fillna(0,inplace=True)
def add_conc(self,column_name,x,y,new_name='default'):
"""
calculates the concentration values of the given column and adds them as
a new column to the DataFrame.
Parameters
----------
column_name : str
column with values
x : int
...
y : int
...
new_name : str
name of the new column, default to 'column_name + mg/L'
"""
if new_name == 'default':
new_name = column_name + ' ' + 'mg/L'
self.data[new_name] = self.data[column_name].values*x*y
## Instead of this function: define a dataframe/dict with conversion or
## concentration factors, so that you can have a function that automatically
## converts all parameters in the frame to concentrations
def check_ph(self,ph_column='pH',thresh=0.4):
"""
gives the maximal change in pH
Parameters
----------
ph_column : str
column with pH-values, default to 'pH'
threshold : int
threshold value for warning, default to '0.4'
"""
dph = self.data[ph_column].max()-self.data[ph_column].min()
if dph > thresh:
wn.warn('Strong change in pH during experiment!')
else:
<|fim_middle|>
def in_out(self,columns):
"""
(start_values-end_values)
Parameters
----------
columns : array of strings
"""
inv=0
outv=0
indexes= self.time.values
for column in columns:
inv += self.data[column][indexes[0]]
for column in columns:
outv += self.data[column][indexes[-1]]
in_out = inv-outv
return in_out
def removal(self,columns):
"""
total removal of nitrogen
(1-(end_values/start_values))
Parameters
----------
columns : array of strings
"""
inv=0
outv=0
indexes= self.time.values
for column in columns:
inv += self.data[column][indexes[0]]
for column in columns:
outv += self.data[column][indexes[-1]]
removal = 1-(outv/inv)
return removal
def calc_slope(self,columns,time_column='h'):
"""
calculates the slope of the selected columns
Parameters
----------
columns : array of strings
columns to calculate the slope for
time_column : str
time used for calculation; default to 'h'
"""
for column in columns:
self.data[column + " " +'slope'] = (self.data[column].shift(1)-self.data[column])\
/(self.data[time_column]-self.data[time_column].shift(1))
def plot(self,columns,time_column='index'):
"""
calculates the slope of the selected columns
Parameters
----------
columns : array of strings
columns to plot
time_column : str
time used for calculation; default to 'h'
"""
fig = plt.figure(figsize=(10,6))
ax = fig.add_subplot(111)
if time_column=='index':
for column in columns:
ax.plot(self.time,self.data[column],marker='o')
else:
for column in columns:
ax.plot(self.data[time_column],self.data[column],marker='o')
ax.legend()
return fig,ax
#######################################
def _print_removed_output(original,new,type_):
"""
function printing the output of functions that remove datapoints.
Parameters
----------
original : int
original length of the dataset
new : int
length of the new dataset
type_ : str
'removed' or 'dropped'
"""
print('Original dataset:',original,'datapoints')
print('New dataset:',new,'datapoints')
print(original-new,'datapoints ',type_)
def _log_removed_output(log_file,original,new,type_):
"""
function writing the output of functions that remove datapoints to a log file.
Parameters
----------
log_file : str
string containing the directory to the log file to be written out
original : int
original length of the dataset
new : int
length of the new dataset
type_ : str
'removed' or 'dropped'
"""
log_file = open(log_file,'a')
log_file.write(str('\nOriginal dataset: '+str(original)+' datapoints; new dataset: '+
str(new)+' datapoints'+str(original-new)+' datapoints ',type_))
log_file.close()
<｜fim▁end｜> | self.delta_ph = dph |
<|file_name|>Class_LabExperimBased.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
"""
Class_LabExperimBased provides functionalities for data handling of data obtained in lab experiments in the field of (waste)water treatment.
Copyright (C) 2016 Chaim De Mulder
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 http://www.gnu.org/licenses/.
"""
import sys
#import os
#from os import listdir
#import pandas as pd
#import scipy as sp
#import numpy as np
#import datetime as dt
import matplotlib.pyplot as plt #plotten in python
import warnings as wn
from wwdata.Class_HydroData import HydroData
class LabExperimBased(HydroData):
"""
Superclass for a HydroData object, expanding the functionalities with
specific functions for data gathered is lab experiments.
Attributes
----------
timedata_column : str
name of the column containing the time data
data_type : str
type of the data provided
experiment_tag : str
A tag identifying the experiment; can be a date or a code used by
the producer/owner of the data.
time_unit : str
The time unit in which the time data is given
units : array
The units of the variables in the columns
"""
def __init__(self,data,timedata_column='index',data_type='NAT',
experiment_tag='No tag given',time_unit=None):
"""
initialisation of a LabExperimBased object, based on a previously defined
HydroData object.
"""
HydroData.__init__(self,data,timedata_column=timedata_column,data_type=data_type,
experiment_tag=experiment_tag,time_unit=time_unit)
def hours(self,time_column='index'):
"""
calculates the hours from the relative values
Parameters
----------
time_column : string
column containing the relative time values; default to index
"""
if time_column == 'index':
self.data['index']=self.time.values
self.data['h']= (self.data['indexes'])*24 + self.data['indexes'].shift(1)
self.data['h'].fillna(0,inplace=True)
self.data.drop('index', axis=1, inplace=True)
else:
self.data['h']= (self.data[time_column])*24 + self.data[time_column].shift(1)
self.data['h'].fillna(0,inplace=True)
def add_conc(self,column_name,x,y,new_name='default'):
"""
calculates the concentration values of the given column and adds them as
a new column to the DataFrame.
Parameters
----------
column_name : str
column with values
x : int
...
y : int
...
new_name : str
name of the new column, default to 'column_name + mg/L'
"""
if new_name == 'default':
new_name = column_name + ' ' + 'mg/L'
self.data[new_name] = self.data[column_name].values*x*y
## Instead of this function: define a dataframe/dict with conversion or
## concentration factors, so that you can have a function that automatically
## converts all parameters in the frame to concentrations
def check_ph(self,ph_column='pH',thresh=0.4):
"""
gives the maximal change in pH
Parameters
----------
ph_column : str
column with pH-values, default to 'pH'
threshold : int
threshold value for warning, default to '0.4'
"""
dph = self.data[ph_column].max()-self.data[ph_column].min()
if dph > thresh:
wn.warn('Strong change in pH during experiment!')
else:
self.delta_ph = dph
def in_out(self,columns):
"""
(start_values-end_values)
Parameters
----------
columns : array of strings
"""
inv=0
outv=0
indexes= self.time.values
for column in columns:
inv += self.data[column][indexes[0]]
for column in columns:
outv += self.data[column][indexes[-1]]
in_out = inv-outv
return in_out
def removal(self,columns):
"""
total removal of nitrogen
(1-(end_values/start_values))
Parameters
----------
columns : array of strings
"""
inv=0
outv=0
indexes= self.time.values
for column in columns:
inv += self.data[column][indexes[0]]
for column in columns:
outv += self.data[column][indexes[-1]]
removal = 1-(outv/inv)
return removal
def calc_slope(self,columns,time_column='h'):
"""
calculates the slope of the selected columns
Parameters
----------
columns : array of strings
columns to calculate the slope for
time_column : str
time used for calculation; default to 'h'
"""
for column in columns:
self.data[column + " " +'slope'] = (self.data[column].shift(1)-self.data[column])\
/(self.data[time_column]-self.data[time_column].shift(1))
def plot(self,columns,time_column='index'):
"""
calculates the slope of the selected columns
Parameters
----------
columns : array of strings
columns to plot
time_column : str
time used for calculation; default to 'h'
"""
fig = plt.figure(figsize=(10,6))
ax = fig.add_subplot(111)
if time_column=='index':
<|fim_middle|>
else:
for column in columns:
ax.plot(self.data[time_column],self.data[column],marker='o')
ax.legend()
return fig,ax
#######################################
def _print_removed_output(original,new,type_):
"""
function printing the output of functions that remove datapoints.
Parameters
----------
original : int
original length of the dataset
new : int
length of the new dataset
type_ : str
'removed' or 'dropped'
"""
print('Original dataset:',original,'datapoints')
print('New dataset:',new,'datapoints')
print(original-new,'datapoints ',type_)
def _log_removed_output(log_file,original,new,type_):
"""
function writing the output of functions that remove datapoints to a log file.
Parameters
----------
log_file : str
string containing the directory to the log file to be written out
original : int
original length of the dataset
new : int
length of the new dataset
type_ : str
'removed' or 'dropped'
"""
log_file = open(log_file,'a')
log_file.write(str('\nOriginal dataset: '+str(original)+' datapoints; new dataset: '+
str(new)+' datapoints'+str(original-new)+' datapoints ',type_))
log_file.close()
<｜fim▁end｜> | for column in columns:
ax.plot(self.time,self.data[column],marker='o') |
<|file_name|>Class_LabExperimBased.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
"""
Class_LabExperimBased provides functionalities for data handling of data obtained in lab experiments in the field of (waste)water treatment.
Copyright (C) 2016 Chaim De Mulder
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 http://www.gnu.org/licenses/.
"""
import sys
#import os
#from os import listdir
#import pandas as pd
#import scipy as sp
#import numpy as np
#import datetime as dt
import matplotlib.pyplot as plt #plotten in python
import warnings as wn
from wwdata.Class_HydroData import HydroData
class LabExperimBased(HydroData):
"""
Superclass for a HydroData object, expanding the functionalities with
specific functions for data gathered is lab experiments.
Attributes
----------
timedata_column : str
name of the column containing the time data
data_type : str
type of the data provided
experiment_tag : str
A tag identifying the experiment; can be a date or a code used by
the producer/owner of the data.
time_unit : str
The time unit in which the time data is given
units : array
The units of the variables in the columns
"""
def __init__(self,data,timedata_column='index',data_type='NAT',
experiment_tag='No tag given',time_unit=None):
"""
initialisation of a LabExperimBased object, based on a previously defined
HydroData object.
"""
HydroData.__init__(self,data,timedata_column=timedata_column,data_type=data_type,
experiment_tag=experiment_tag,time_unit=time_unit)
def hours(self,time_column='index'):
"""
calculates the hours from the relative values
Parameters
----------
time_column : string
column containing the relative time values; default to index
"""
if time_column == 'index':
self.data['index']=self.time.values
self.data['h']= (self.data['indexes'])*24 + self.data['indexes'].shift(1)
self.data['h'].fillna(0,inplace=True)
self.data.drop('index', axis=1, inplace=True)
else:
self.data['h']= (self.data[time_column])*24 + self.data[time_column].shift(1)
self.data['h'].fillna(0,inplace=True)
def add_conc(self,column_name,x,y,new_name='default'):
"""
calculates the concentration values of the given column and adds them as
a new column to the DataFrame.
Parameters
----------
column_name : str
column with values
x : int
...
y : int
...
new_name : str
name of the new column, default to 'column_name + mg/L'
"""
if new_name == 'default':
new_name = column_name + ' ' + 'mg/L'
self.data[new_name] = self.data[column_name].values*x*y
## Instead of this function: define a dataframe/dict with conversion or
## concentration factors, so that you can have a function that automatically
## converts all parameters in the frame to concentrations
def check_ph(self,ph_column='pH',thresh=0.4):
"""
gives the maximal change in pH
Parameters
----------
ph_column : str
column with pH-values, default to 'pH'
threshold : int
threshold value for warning, default to '0.4'
"""
dph = self.data[ph_column].max()-self.data[ph_column].min()
if dph > thresh:
wn.warn('Strong change in pH during experiment!')
else:
self.delta_ph = dph
def in_out(self,columns):
"""
(start_values-end_values)
Parameters
----------
columns : array of strings
"""
inv=0
outv=0
indexes= self.time.values
for column in columns:
inv += self.data[column][indexes[0]]
for column in columns:
outv += self.data[column][indexes[-1]]
in_out = inv-outv
return in_out
def removal(self,columns):
"""
total removal of nitrogen
(1-(end_values/start_values))
Parameters
----------
columns : array of strings
"""
inv=0
outv=0
indexes= self.time.values
for column in columns:
inv += self.data[column][indexes[0]]
for column in columns:
outv += self.data[column][indexes[-1]]
removal = 1-(outv/inv)
return removal
def calc_slope(self,columns,time_column='h'):
"""
calculates the slope of the selected columns
Parameters
----------
columns : array of strings
columns to calculate the slope for
time_column : str
time used for calculation; default to 'h'
"""
for column in columns:
self.data[column + " " +'slope'] = (self.data[column].shift(1)-self.data[column])\
/(self.data[time_column]-self.data[time_column].shift(1))
def plot(self,columns,time_column='index'):
"""
calculates the slope of the selected columns
Parameters
----------
columns : array of strings
columns to plot
time_column : str
time used for calculation; default to 'h'
"""
fig = plt.figure(figsize=(10,6))
ax = fig.add_subplot(111)
if time_column=='index':
for column in columns:
ax.plot(self.time,self.data[column],marker='o')
else:
<|fim_middle|>
ax.legend()
return fig,ax
#######################################
def _print_removed_output(original,new,type_):
"""
function printing the output of functions that remove datapoints.
Parameters
----------
original : int
original length of the dataset
new : int
length of the new dataset
type_ : str
'removed' or 'dropped'
"""
print('Original dataset:',original,'datapoints')
print('New dataset:',new,'datapoints')
print(original-new,'datapoints ',type_)
def _log_removed_output(log_file,original,new,type_):
"""
function writing the output of functions that remove datapoints to a log file.
Parameters
----------
log_file : str
string containing the directory to the log file to be written out
original : int
original length of the dataset
new : int
length of the new dataset
type_ : str
'removed' or 'dropped'
"""
log_file = open(log_file,'a')
log_file.write(str('\nOriginal dataset: '+str(original)+' datapoints; new dataset: '+
str(new)+' datapoints'+str(original-new)+' datapoints ',type_))
log_file.close()
<｜fim▁end｜> | for column in columns:
ax.plot(self.data[time_column],self.data[column],marker='o') |
<|file_name|>Class_LabExperimBased.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
"""
Class_LabExperimBased provides functionalities for data handling of data obtained in lab experiments in the field of (waste)water treatment.
Copyright (C) 2016 Chaim De Mulder
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 http://www.gnu.org/licenses/.
"""
import sys
#import os
#from os import listdir
#import pandas as pd
#import scipy as sp
#import numpy as np
#import datetime as dt
import matplotlib.pyplot as plt #plotten in python
import warnings as wn
from wwdata.Class_HydroData import HydroData
class LabExperimBased(HydroData):
"""
Superclass for a HydroData object, expanding the functionalities with
specific functions for data gathered is lab experiments.
Attributes
----------
timedata_column : str
name of the column containing the time data
data_type : str
type of the data provided
experiment_tag : str
A tag identifying the experiment; can be a date or a code used by
the producer/owner of the data.
time_unit : str
The time unit in which the time data is given
units : array
The units of the variables in the columns
"""
def <|fim_middle|>(self,data,timedata_column='index',data_type='NAT',
experiment_tag='No tag given',time_unit=None):
"""
initialisation of a LabExperimBased object, based on a previously defined
HydroData object.
"""
HydroData.__init__(self,data,timedata_column=timedata_column,data_type=data_type,
experiment_tag=experiment_tag,time_unit=time_unit)
def hours(self,time_column='index'):
"""
calculates the hours from the relative values
Parameters
----------
time_column : string
column containing the relative time values; default to index
"""
if time_column == 'index':
self.data['index']=self.time.values
self.data['h']= (self.data['indexes'])*24 + self.data['indexes'].shift(1)
self.data['h'].fillna(0,inplace=True)
self.data.drop('index', axis=1, inplace=True)
else:
self.data['h']= (self.data[time_column])*24 + self.data[time_column].shift(1)
self.data['h'].fillna(0,inplace=True)
def add_conc(self,column_name,x,y,new_name='default'):
"""
calculates the concentration values of the given column and adds them as
a new column to the DataFrame.
Parameters
----------
column_name : str
column with values
x : int
...
y : int
...
new_name : str
name of the new column, default to 'column_name + mg/L'
"""
if new_name == 'default':
new_name = column_name + ' ' + 'mg/L'
self.data[new_name] = self.data[column_name].values*x*y
## Instead of this function: define a dataframe/dict with conversion or
## concentration factors, so that you can have a function that automatically
## converts all parameters in the frame to concentrations
def check_ph(self,ph_column='pH',thresh=0.4):
"""
gives the maximal change in pH
Parameters
----------
ph_column : str
column with pH-values, default to 'pH'
threshold : int
threshold value for warning, default to '0.4'
"""
dph = self.data[ph_column].max()-self.data[ph_column].min()
if dph > thresh:
wn.warn('Strong change in pH during experiment!')
else:
self.delta_ph = dph
def in_out(self,columns):
"""
(start_values-end_values)
Parameters
----------
columns : array of strings
"""
inv=0
outv=0
indexes= self.time.values
for column in columns:
inv += self.data[column][indexes[0]]
for column in columns:
outv += self.data[column][indexes[-1]]
in_out = inv-outv
return in_out
def removal(self,columns):
"""
total removal of nitrogen
(1-(end_values/start_values))
Parameters
----------
columns : array of strings
"""
inv=0
outv=0
indexes= self.time.values
for column in columns:
inv += self.data[column][indexes[0]]
for column in columns:
outv += self.data[column][indexes[-1]]
removal = 1-(outv/inv)
return removal
def calc_slope(self,columns,time_column='h'):
"""
calculates the slope of the selected columns
Parameters
----------
columns : array of strings
columns to calculate the slope for
time_column : str
time used for calculation; default to 'h'
"""
for column in columns:
self.data[column + " " +'slope'] = (self.data[column].shift(1)-self.data[column])\
/(self.data[time_column]-self.data[time_column].shift(1))
def plot(self,columns,time_column='index'):
"""
calculates the slope of the selected columns
Parameters
----------
columns : array of strings
columns to plot
time_column : str
time used for calculation; default to 'h'
"""
fig = plt.figure(figsize=(10,6))
ax = fig.add_subplot(111)
if time_column=='index':
for column in columns:
ax.plot(self.time,self.data[column],marker='o')
else:
for column in columns:
ax.plot(self.data[time_column],self.data[column],marker='o')
ax.legend()
return fig,ax
#######################################
def _print_removed_output(original,new,type_):
"""
function printing the output of functions that remove datapoints.
Parameters
----------
original : int
original length of the dataset
new : int
length of the new dataset
type_ : str
'removed' or 'dropped'
"""
print('Original dataset:',original,'datapoints')
print('New dataset:',new,'datapoints')
print(original-new,'datapoints ',type_)
def _log_removed_output(log_file,original,new,type_):
"""
function writing the output of functions that remove datapoints to a log file.
Parameters
----------
log_file : str
string containing the directory to the log file to be written out
original : int
original length of the dataset
new : int
length of the new dataset
type_ : str
'removed' or 'dropped'
"""
log_file = open(log_file,'a')
log_file.write(str('\nOriginal dataset: '+str(original)+' datapoints; new dataset: '+
str(new)+' datapoints'+str(original-new)+' datapoints ',type_))
log_file.close()
<｜fim▁end｜> | __init__ |
<|file_name|>Class_LabExperimBased.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
"""
Class_LabExperimBased provides functionalities for data handling of data obtained in lab experiments in the field of (waste)water treatment.
Copyright (C) 2016 Chaim De Mulder
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 http://www.gnu.org/licenses/.
"""
import sys
#import os
#from os import listdir
#import pandas as pd
#import scipy as sp
#import numpy as np
#import datetime as dt
import matplotlib.pyplot as plt #plotten in python
import warnings as wn
from wwdata.Class_HydroData import HydroData
class LabExperimBased(HydroData):
"""
Superclass for a HydroData object, expanding the functionalities with
specific functions for data gathered is lab experiments.
Attributes
----------
timedata_column : str
name of the column containing the time data
data_type : str
type of the data provided
experiment_tag : str
A tag identifying the experiment; can be a date or a code used by
the producer/owner of the data.
time_unit : str
The time unit in which the time data is given
units : array
The units of the variables in the columns
"""
def __init__(self,data,timedata_column='index',data_type='NAT',
experiment_tag='No tag given',time_unit=None):
"""
initialisation of a LabExperimBased object, based on a previously defined
HydroData object.
"""
HydroData.__init__(self,data,timedata_column=timedata_column,data_type=data_type,
experiment_tag=experiment_tag,time_unit=time_unit)
def <|fim_middle|>(self,time_column='index'):
"""
calculates the hours from the relative values
Parameters
----------
time_column : string
column containing the relative time values; default to index
"""
if time_column == 'index':
self.data['index']=self.time.values
self.data['h']= (self.data['indexes'])*24 + self.data['indexes'].shift(1)
self.data['h'].fillna(0,inplace=True)
self.data.drop('index', axis=1, inplace=True)
else:
self.data['h']= (self.data[time_column])*24 + self.data[time_column].shift(1)
self.data['h'].fillna(0,inplace=True)
def add_conc(self,column_name,x,y,new_name='default'):
"""
calculates the concentration values of the given column and adds them as
a new column to the DataFrame.
Parameters
----------
column_name : str
column with values
x : int
...
y : int
...
new_name : str
name of the new column, default to 'column_name + mg/L'
"""
if new_name == 'default':
new_name = column_name + ' ' + 'mg/L'
self.data[new_name] = self.data[column_name].values*x*y
## Instead of this function: define a dataframe/dict with conversion or
## concentration factors, so that you can have a function that automatically
## converts all parameters in the frame to concentrations
def check_ph(self,ph_column='pH',thresh=0.4):
"""
gives the maximal change in pH
Parameters
----------
ph_column : str
column with pH-values, default to 'pH'
threshold : int
threshold value for warning, default to '0.4'
"""
dph = self.data[ph_column].max()-self.data[ph_column].min()
if dph > thresh:
wn.warn('Strong change in pH during experiment!')
else:
self.delta_ph = dph
def in_out(self,columns):
"""
(start_values-end_values)
Parameters
----------
columns : array of strings
"""
inv=0
outv=0
indexes= self.time.values
for column in columns:
inv += self.data[column][indexes[0]]
for column in columns:
outv += self.data[column][indexes[-1]]
in_out = inv-outv
return in_out
def removal(self,columns):
"""
total removal of nitrogen
(1-(end_values/start_values))
Parameters
----------
columns : array of strings
"""
inv=0
outv=0
indexes= self.time.values
for column in columns:
inv += self.data[column][indexes[0]]
for column in columns:
outv += self.data[column][indexes[-1]]
removal = 1-(outv/inv)
return removal
def calc_slope(self,columns,time_column='h'):
"""
calculates the slope of the selected columns
Parameters
----------
columns : array of strings
columns to calculate the slope for
time_column : str
time used for calculation; default to 'h'
"""
for column in columns:
self.data[column + " " +'slope'] = (self.data[column].shift(1)-self.data[column])\
/(self.data[time_column]-self.data[time_column].shift(1))
def plot(self,columns,time_column='index'):
"""
calculates the slope of the selected columns
Parameters
----------
columns : array of strings
columns to plot
time_column : str
time used for calculation; default to 'h'
"""
fig = plt.figure(figsize=(10,6))
ax = fig.add_subplot(111)
if time_column=='index':
for column in columns:
ax.plot(self.time,self.data[column],marker='o')
else:
for column in columns:
ax.plot(self.data[time_column],self.data[column],marker='o')
ax.legend()
return fig,ax
#######################################
def _print_removed_output(original,new,type_):
"""
function printing the output of functions that remove datapoints.
Parameters
----------
original : int
original length of the dataset
new : int
length of the new dataset
type_ : str
'removed' or 'dropped'
"""
print('Original dataset:',original,'datapoints')
print('New dataset:',new,'datapoints')
print(original-new,'datapoints ',type_)
def _log_removed_output(log_file,original,new,type_):
"""
function writing the output of functions that remove datapoints to a log file.
Parameters
----------
log_file : str
string containing the directory to the log file to be written out
original : int
original length of the dataset
new : int
length of the new dataset
type_ : str
'removed' or 'dropped'
"""
log_file = open(log_file,'a')
log_file.write(str('\nOriginal dataset: '+str(original)+' datapoints; new dataset: '+
str(new)+' datapoints'+str(original-new)+' datapoints ',type_))
log_file.close()
<｜fim▁end｜> | hours |
<|file_name|>Class_LabExperimBased.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
"""
Class_LabExperimBased provides functionalities for data handling of data obtained in lab experiments in the field of (waste)water treatment.
Copyright (C) 2016 Chaim De Mulder
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 http://www.gnu.org/licenses/.
"""
import sys
#import os
#from os import listdir
#import pandas as pd
#import scipy as sp
#import numpy as np
#import datetime as dt
import matplotlib.pyplot as plt #plotten in python
import warnings as wn
from wwdata.Class_HydroData import HydroData
class LabExperimBased(HydroData):
"""
Superclass for a HydroData object, expanding the functionalities with
specific functions for data gathered is lab experiments.
Attributes
----------
timedata_column : str
name of the column containing the time data
data_type : str
type of the data provided
experiment_tag : str
A tag identifying the experiment; can be a date or a code used by
the producer/owner of the data.
time_unit : str
The time unit in which the time data is given
units : array
The units of the variables in the columns
"""
def __init__(self,data,timedata_column='index',data_type='NAT',
experiment_tag='No tag given',time_unit=None):
"""
initialisation of a LabExperimBased object, based on a previously defined
HydroData object.
"""
HydroData.__init__(self,data,timedata_column=timedata_column,data_type=data_type,
experiment_tag=experiment_tag,time_unit=time_unit)
def hours(self,time_column='index'):
"""
calculates the hours from the relative values
Parameters
----------
time_column : string
column containing the relative time values; default to index
"""
if time_column == 'index':
self.data['index']=self.time.values
self.data['h']= (self.data['indexes'])*24 + self.data['indexes'].shift(1)
self.data['h'].fillna(0,inplace=True)
self.data.drop('index', axis=1, inplace=True)
else:
self.data['h']= (self.data[time_column])*24 + self.data[time_column].shift(1)
self.data['h'].fillna(0,inplace=True)
def <|fim_middle|>(self,column_name,x,y,new_name='default'):
"""
calculates the concentration values of the given column and adds them as
a new column to the DataFrame.
Parameters
----------
column_name : str
column with values
x : int
...
y : int
...
new_name : str
name of the new column, default to 'column_name + mg/L'
"""
if new_name == 'default':
new_name = column_name + ' ' + 'mg/L'
self.data[new_name] = self.data[column_name].values*x*y
## Instead of this function: define a dataframe/dict with conversion or
## concentration factors, so that you can have a function that automatically
## converts all parameters in the frame to concentrations
def check_ph(self,ph_column='pH',thresh=0.4):
"""
gives the maximal change in pH
Parameters
----------
ph_column : str
column with pH-values, default to 'pH'
threshold : int
threshold value for warning, default to '0.4'
"""
dph = self.data[ph_column].max()-self.data[ph_column].min()
if dph > thresh:
wn.warn('Strong change in pH during experiment!')
else:
self.delta_ph = dph
def in_out(self,columns):
"""
(start_values-end_values)
Parameters
----------
columns : array of strings
"""
inv=0
outv=0
indexes= self.time.values
for column in columns:
inv += self.data[column][indexes[0]]
for column in columns:
outv += self.data[column][indexes[-1]]
in_out = inv-outv
return in_out
def removal(self,columns):
"""
total removal of nitrogen
(1-(end_values/start_values))
Parameters
----------
columns : array of strings
"""
inv=0
outv=0
indexes= self.time.values
for column in columns:
inv += self.data[column][indexes[0]]
for column in columns:
outv += self.data[column][indexes[-1]]
removal = 1-(outv/inv)
return removal
def calc_slope(self,columns,time_column='h'):
"""
calculates the slope of the selected columns
Parameters
----------
columns : array of strings
columns to calculate the slope for
time_column : str
time used for calculation; default to 'h'
"""
for column in columns:
self.data[column + " " +'slope'] = (self.data[column].shift(1)-self.data[column])\
/(self.data[time_column]-self.data[time_column].shift(1))
def plot(self,columns,time_column='index'):
"""
calculates the slope of the selected columns
Parameters
----------
columns : array of strings
columns to plot
time_column : str
time used for calculation; default to 'h'
"""
fig = plt.figure(figsize=(10,6))
ax = fig.add_subplot(111)
if time_column=='index':
for column in columns:
ax.plot(self.time,self.data[column],marker='o')
else:
for column in columns:
ax.plot(self.data[time_column],self.data[column],marker='o')
ax.legend()
return fig,ax
#######################################
def _print_removed_output(original,new,type_):
"""
function printing the output of functions that remove datapoints.
Parameters
----------
original : int
original length of the dataset
new : int
length of the new dataset
type_ : str
'removed' or 'dropped'
"""
print('Original dataset:',original,'datapoints')
print('New dataset:',new,'datapoints')
print(original-new,'datapoints ',type_)
def _log_removed_output(log_file,original,new,type_):
"""
function writing the output of functions that remove datapoints to a log file.
Parameters
----------
log_file : str
string containing the directory to the log file to be written out
original : int
original length of the dataset
new : int
length of the new dataset
type_ : str
'removed' or 'dropped'
"""
log_file = open(log_file,'a')
log_file.write(str('\nOriginal dataset: '+str(original)+' datapoints; new dataset: '+
str(new)+' datapoints'+str(original-new)+' datapoints ',type_))
log_file.close()
<｜fim▁end｜> | add_conc |
<|file_name|>Class_LabExperimBased.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
"""
Class_LabExperimBased provides functionalities for data handling of data obtained in lab experiments in the field of (waste)water treatment.
Copyright (C) 2016 Chaim De Mulder
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 http://www.gnu.org/licenses/.
"""
import sys
#import os
#from os import listdir
#import pandas as pd
#import scipy as sp
#import numpy as np
#import datetime as dt
import matplotlib.pyplot as plt #plotten in python
import warnings as wn
from wwdata.Class_HydroData import HydroData
class LabExperimBased(HydroData):
"""
Superclass for a HydroData object, expanding the functionalities with
specific functions for data gathered is lab experiments.
Attributes
----------
timedata_column : str
name of the column containing the time data
data_type : str
type of the data provided
experiment_tag : str
A tag identifying the experiment; can be a date or a code used by
the producer/owner of the data.
time_unit : str
The time unit in which the time data is given
units : array
The units of the variables in the columns
"""
def __init__(self,data,timedata_column='index',data_type='NAT',
experiment_tag='No tag given',time_unit=None):
"""
initialisation of a LabExperimBased object, based on a previously defined
HydroData object.
"""
HydroData.__init__(self,data,timedata_column=timedata_column,data_type=data_type,
experiment_tag=experiment_tag,time_unit=time_unit)
def hours(self,time_column='index'):
"""
calculates the hours from the relative values
Parameters
----------
time_column : string
column containing the relative time values; default to index
"""
if time_column == 'index':
self.data['index']=self.time.values
self.data['h']= (self.data['indexes'])*24 + self.data['indexes'].shift(1)
self.data['h'].fillna(0,inplace=True)
self.data.drop('index', axis=1, inplace=True)
else:
self.data['h']= (self.data[time_column])*24 + self.data[time_column].shift(1)
self.data['h'].fillna(0,inplace=True)
def add_conc(self,column_name,x,y,new_name='default'):
"""
calculates the concentration values of the given column and adds them as
a new column to the DataFrame.
Parameters
----------
column_name : str
column with values
x : int
...
y : int
...
new_name : str
name of the new column, default to 'column_name + mg/L'
"""
if new_name == 'default':
new_name = column_name + ' ' + 'mg/L'
self.data[new_name] = self.data[column_name].values*x*y
## Instead of this function: define a dataframe/dict with conversion or
## concentration factors, so that you can have a function that automatically
## converts all parameters in the frame to concentrations
def <|fim_middle|>(self,ph_column='pH',thresh=0.4):
"""
gives the maximal change in pH
Parameters
----------
ph_column : str
column with pH-values, default to 'pH'
threshold : int
threshold value for warning, default to '0.4'
"""
dph = self.data[ph_column].max()-self.data[ph_column].min()
if dph > thresh:
wn.warn('Strong change in pH during experiment!')
else:
self.delta_ph = dph
def in_out(self,columns):
"""
(start_values-end_values)
Parameters
----------
columns : array of strings
"""
inv=0
outv=0
indexes= self.time.values
for column in columns:
inv += self.data[column][indexes[0]]
for column in columns:
outv += self.data[column][indexes[-1]]
in_out = inv-outv
return in_out
def removal(self,columns):
"""
total removal of nitrogen
(1-(end_values/start_values))
Parameters
----------
columns : array of strings
"""
inv=0
outv=0
indexes= self.time.values
for column in columns:
inv += self.data[column][indexes[0]]
for column in columns:
outv += self.data[column][indexes[-1]]
removal = 1-(outv/inv)
return removal
def calc_slope(self,columns,time_column='h'):
"""
calculates the slope of the selected columns
Parameters
----------
columns : array of strings
columns to calculate the slope for
time_column : str
time used for calculation; default to 'h'
"""
for column in columns:
self.data[column + " " +'slope'] = (self.data[column].shift(1)-self.data[column])\
/(self.data[time_column]-self.data[time_column].shift(1))
def plot(self,columns,time_column='index'):
"""
calculates the slope of the selected columns
Parameters
----------
columns : array of strings
columns to plot
time_column : str
time used for calculation; default to 'h'
"""
fig = plt.figure(figsize=(10,6))
ax = fig.add_subplot(111)
if time_column=='index':
for column in columns:
ax.plot(self.time,self.data[column],marker='o')
else:
for column in columns:
ax.plot(self.data[time_column],self.data[column],marker='o')
ax.legend()
return fig,ax
#######################################
def _print_removed_output(original,new,type_):
"""
function printing the output of functions that remove datapoints.
Parameters
----------
original : int
original length of the dataset
new : int
length of the new dataset
type_ : str
'removed' or 'dropped'
"""
print('Original dataset:',original,'datapoints')
print('New dataset:',new,'datapoints')
print(original-new,'datapoints ',type_)
def _log_removed_output(log_file,original,new,type_):
"""
function writing the output of functions that remove datapoints to a log file.
Parameters
----------
log_file : str
string containing the directory to the log file to be written out
original : int
original length of the dataset
new : int
length of the new dataset
type_ : str
'removed' or 'dropped'
"""
log_file = open(log_file,'a')
log_file.write(str('\nOriginal dataset: '+str(original)+' datapoints; new dataset: '+
str(new)+' datapoints'+str(original-new)+' datapoints ',type_))
log_file.close()
<｜fim▁end｜> | check_ph |
<|file_name|>Class_LabExperimBased.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
"""
Class_LabExperimBased provides functionalities for data handling of data obtained in lab experiments in the field of (waste)water treatment.
Copyright (C) 2016 Chaim De Mulder
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 http://www.gnu.org/licenses/.
"""
import sys
#import os
#from os import listdir
#import pandas as pd
#import scipy as sp
#import numpy as np
#import datetime as dt
import matplotlib.pyplot as plt #plotten in python
import warnings as wn
from wwdata.Class_HydroData import HydroData
class LabExperimBased(HydroData):
"""
Superclass for a HydroData object, expanding the functionalities with
specific functions for data gathered is lab experiments.
Attributes
----------
timedata_column : str
name of the column containing the time data
data_type : str
type of the data provided
experiment_tag : str
A tag identifying the experiment; can be a date or a code used by
the producer/owner of the data.
time_unit : str
The time unit in which the time data is given
units : array
The units of the variables in the columns
"""
def __init__(self,data,timedata_column='index',data_type='NAT',
experiment_tag='No tag given',time_unit=None):
"""
initialisation of a LabExperimBased object, based on a previously defined
HydroData object.
"""
HydroData.__init__(self,data,timedata_column=timedata_column,data_type=data_type,
experiment_tag=experiment_tag,time_unit=time_unit)
def hours(self,time_column='index'):
"""
calculates the hours from the relative values
Parameters
----------
time_column : string
column containing the relative time values; default to index
"""
if time_column == 'index':
self.data['index']=self.time.values
self.data['h']= (self.data['indexes'])*24 + self.data['indexes'].shift(1)
self.data['h'].fillna(0,inplace=True)
self.data.drop('index', axis=1, inplace=True)
else:
self.data['h']= (self.data[time_column])*24 + self.data[time_column].shift(1)
self.data['h'].fillna(0,inplace=True)
def add_conc(self,column_name,x,y,new_name='default'):
"""
calculates the concentration values of the given column and adds them as
a new column to the DataFrame.
Parameters
----------
column_name : str
column with values
x : int
...
y : int
...
new_name : str
name of the new column, default to 'column_name + mg/L'
"""
if new_name == 'default':
new_name = column_name + ' ' + 'mg/L'
self.data[new_name] = self.data[column_name].values*x*y
## Instead of this function: define a dataframe/dict with conversion or
## concentration factors, so that you can have a function that automatically
## converts all parameters in the frame to concentrations
def check_ph(self,ph_column='pH',thresh=0.4):
"""
gives the maximal change in pH
Parameters
----------
ph_column : str
column with pH-values, default to 'pH'
threshold : int
threshold value for warning, default to '0.4'
"""
dph = self.data[ph_column].max()-self.data[ph_column].min()
if dph > thresh:
wn.warn('Strong change in pH during experiment!')
else:
self.delta_ph = dph
def <|fim_middle|>(self,columns):
"""
(start_values-end_values)
Parameters
----------
columns : array of strings
"""
inv=0
outv=0
indexes= self.time.values
for column in columns:
inv += self.data[column][indexes[0]]
for column in columns:
outv += self.data[column][indexes[-1]]
in_out = inv-outv
return in_out
def removal(self,columns):
"""
total removal of nitrogen
(1-(end_values/start_values))
Parameters
----------
columns : array of strings
"""
inv=0
outv=0
indexes= self.time.values
for column in columns:
inv += self.data[column][indexes[0]]
for column in columns:
outv += self.data[column][indexes[-1]]
removal = 1-(outv/inv)
return removal
def calc_slope(self,columns,time_column='h'):
"""
calculates the slope of the selected columns
Parameters
----------
columns : array of strings
columns to calculate the slope for
time_column : str
time used for calculation; default to 'h'
"""
for column in columns:
self.data[column + " " +'slope'] = (self.data[column].shift(1)-self.data[column])\
/(self.data[time_column]-self.data[time_column].shift(1))
def plot(self,columns,time_column='index'):
"""
calculates the slope of the selected columns
Parameters
----------
columns : array of strings
columns to plot
time_column : str
time used for calculation; default to 'h'
"""
fig = plt.figure(figsize=(10,6))
ax = fig.add_subplot(111)
if time_column=='index':
for column in columns:
ax.plot(self.time,self.data[column],marker='o')
else:
for column in columns:
ax.plot(self.data[time_column],self.data[column],marker='o')
ax.legend()
return fig,ax
#######################################
def _print_removed_output(original,new,type_):
"""
function printing the output of functions that remove datapoints.
Parameters
----------
original : int
original length of the dataset
new : int
length of the new dataset
type_ : str
'removed' or 'dropped'
"""
print('Original dataset:',original,'datapoints')
print('New dataset:',new,'datapoints')
print(original-new,'datapoints ',type_)
def _log_removed_output(log_file,original,new,type_):
"""
function writing the output of functions that remove datapoints to a log file.
Parameters
----------
log_file : str
string containing the directory to the log file to be written out
original : int
original length of the dataset
new : int
length of the new dataset
type_ : str
'removed' or 'dropped'
"""
log_file = open(log_file,'a')
log_file.write(str('\nOriginal dataset: '+str(original)+' datapoints; new dataset: '+
str(new)+' datapoints'+str(original-new)+' datapoints ',type_))
log_file.close()
<｜fim▁end｜> | in_out |
<|file_name|>Class_LabExperimBased.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
"""
Class_LabExperimBased provides functionalities for data handling of data obtained in lab experiments in the field of (waste)water treatment.
Copyright (C) 2016 Chaim De Mulder
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 http://www.gnu.org/licenses/.
"""
import sys
#import os
#from os import listdir
#import pandas as pd
#import scipy as sp
#import numpy as np
#import datetime as dt
import matplotlib.pyplot as plt #plotten in python
import warnings as wn
from wwdata.Class_HydroData import HydroData
class LabExperimBased(HydroData):
"""
Superclass for a HydroData object, expanding the functionalities with
specific functions for data gathered is lab experiments.
Attributes
----------
timedata_column : str
name of the column containing the time data
data_type : str
type of the data provided
experiment_tag : str
A tag identifying the experiment; can be a date or a code used by
the producer/owner of the data.
time_unit : str
The time unit in which the time data is given
units : array
The units of the variables in the columns
"""
def __init__(self,data,timedata_column='index',data_type='NAT',
experiment_tag='No tag given',time_unit=None):
"""
initialisation of a LabExperimBased object, based on a previously defined
HydroData object.
"""
HydroData.__init__(self,data,timedata_column=timedata_column,data_type=data_type,
experiment_tag=experiment_tag,time_unit=time_unit)
def hours(self,time_column='index'):
"""
calculates the hours from the relative values
Parameters
----------
time_column : string
column containing the relative time values; default to index
"""
if time_column == 'index':
self.data['index']=self.time.values
self.data['h']= (self.data['indexes'])*24 + self.data['indexes'].shift(1)
self.data['h'].fillna(0,inplace=True)
self.data.drop('index', axis=1, inplace=True)
else:
self.data['h']= (self.data[time_column])*24 + self.data[time_column].shift(1)
self.data['h'].fillna(0,inplace=True)
def add_conc(self,column_name,x,y,new_name='default'):
"""
calculates the concentration values of the given column and adds them as
a new column to the DataFrame.
Parameters
----------
column_name : str
column with values
x : int
...
y : int
...
new_name : str
name of the new column, default to 'column_name + mg/L'
"""
if new_name == 'default':
new_name = column_name + ' ' + 'mg/L'
self.data[new_name] = self.data[column_name].values*x*y
## Instead of this function: define a dataframe/dict with conversion or
## concentration factors, so that you can have a function that automatically
## converts all parameters in the frame to concentrations
def check_ph(self,ph_column='pH',thresh=0.4):
"""
gives the maximal change in pH
Parameters
----------
ph_column : str
column with pH-values, default to 'pH'
threshold : int
threshold value for warning, default to '0.4'
"""
dph = self.data[ph_column].max()-self.data[ph_column].min()
if dph > thresh:
wn.warn('Strong change in pH during experiment!')
else:
self.delta_ph = dph
def in_out(self,columns):
"""
(start_values-end_values)
Parameters
----------
columns : array of strings
"""
inv=0
outv=0
indexes= self.time.values
for column in columns:
inv += self.data[column][indexes[0]]
for column in columns:
outv += self.data[column][indexes[-1]]
in_out = inv-outv
return in_out
def <|fim_middle|>(self,columns):
"""
total removal of nitrogen
(1-(end_values/start_values))
Parameters
----------
columns : array of strings
"""
inv=0
outv=0
indexes= self.time.values
for column in columns:
inv += self.data[column][indexes[0]]
for column in columns:
outv += self.data[column][indexes[-1]]
removal = 1-(outv/inv)
return removal
def calc_slope(self,columns,time_column='h'):
"""
calculates the slope of the selected columns
Parameters
----------
columns : array of strings
columns to calculate the slope for
time_column : str
time used for calculation; default to 'h'
"""
for column in columns:
self.data[column + " " +'slope'] = (self.data[column].shift(1)-self.data[column])\
/(self.data[time_column]-self.data[time_column].shift(1))
def plot(self,columns,time_column='index'):
"""
calculates the slope of the selected columns
Parameters
----------
columns : array of strings
columns to plot
time_column : str
time used for calculation; default to 'h'
"""
fig = plt.figure(figsize=(10,6))
ax = fig.add_subplot(111)
if time_column=='index':
for column in columns:
ax.plot(self.time,self.data[column],marker='o')
else:
for column in columns:
ax.plot(self.data[time_column],self.data[column],marker='o')
ax.legend()
return fig,ax
#######################################
def _print_removed_output(original,new,type_):
"""
function printing the output of functions that remove datapoints.
Parameters
----------
original : int
original length of the dataset
new : int
length of the new dataset
type_ : str
'removed' or 'dropped'
"""
print('Original dataset:',original,'datapoints')
print('New dataset:',new,'datapoints')
print(original-new,'datapoints ',type_)
def _log_removed_output(log_file,original,new,type_):
"""
function writing the output of functions that remove datapoints to a log file.
Parameters
----------
log_file : str
string containing the directory to the log file to be written out
original : int
original length of the dataset
new : int
length of the new dataset
type_ : str
'removed' or 'dropped'
"""
log_file = open(log_file,'a')
log_file.write(str('\nOriginal dataset: '+str(original)+' datapoints; new dataset: '+
str(new)+' datapoints'+str(original-new)+' datapoints ',type_))
log_file.close()
<｜fim▁end｜> | removal |
<|file_name|>Class_LabExperimBased.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
"""
Class_LabExperimBased provides functionalities for data handling of data obtained in lab experiments in the field of (waste)water treatment.
Copyright (C) 2016 Chaim De Mulder
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 http://www.gnu.org/licenses/.
"""
import sys
#import os
#from os import listdir
#import pandas as pd
#import scipy as sp
#import numpy as np
#import datetime as dt
import matplotlib.pyplot as plt #plotten in python
import warnings as wn
from wwdata.Class_HydroData import HydroData
class LabExperimBased(HydroData):
"""
Superclass for a HydroData object, expanding the functionalities with
specific functions for data gathered is lab experiments.
Attributes
----------
timedata_column : str
name of the column containing the time data
data_type : str
type of the data provided
experiment_tag : str
A tag identifying the experiment; can be a date or a code used by
the producer/owner of the data.
time_unit : str
The time unit in which the time data is given
units : array
The units of the variables in the columns
"""
def __init__(self,data,timedata_column='index',data_type='NAT',
experiment_tag='No tag given',time_unit=None):
"""
initialisation of a LabExperimBased object, based on a previously defined
HydroData object.
"""
HydroData.__init__(self,data,timedata_column=timedata_column,data_type=data_type,
experiment_tag=experiment_tag,time_unit=time_unit)
def hours(self,time_column='index'):
"""
calculates the hours from the relative values
Parameters
----------
time_column : string
column containing the relative time values; default to index
"""
if time_column == 'index':
self.data['index']=self.time.values
self.data['h']= (self.data['indexes'])*24 + self.data['indexes'].shift(1)
self.data['h'].fillna(0,inplace=True)
self.data.drop('index', axis=1, inplace=True)
else:
self.data['h']= (self.data[time_column])*24 + self.data[time_column].shift(1)
self.data['h'].fillna(0,inplace=True)
def add_conc(self,column_name,x,y,new_name='default'):
"""
calculates the concentration values of the given column and adds them as
a new column to the DataFrame.
Parameters
----------
column_name : str
column with values
x : int
...
y : int
...
new_name : str
name of the new column, default to 'column_name + mg/L'
"""
if new_name == 'default':
new_name = column_name + ' ' + 'mg/L'
self.data[new_name] = self.data[column_name].values*x*y
## Instead of this function: define a dataframe/dict with conversion or
## concentration factors, so that you can have a function that automatically
## converts all parameters in the frame to concentrations
def check_ph(self,ph_column='pH',thresh=0.4):
"""
gives the maximal change in pH
Parameters
----------
ph_column : str
column with pH-values, default to 'pH'
threshold : int
threshold value for warning, default to '0.4'
"""
dph = self.data[ph_column].max()-self.data[ph_column].min()
if dph > thresh:
wn.warn('Strong change in pH during experiment!')
else:
self.delta_ph = dph
def in_out(self,columns):
"""
(start_values-end_values)
Parameters
----------
columns : array of strings
"""
inv=0
outv=0
indexes= self.time.values
for column in columns:
inv += self.data[column][indexes[0]]
for column in columns:
outv += self.data[column][indexes[-1]]
in_out = inv-outv
return in_out
def removal(self,columns):
"""
total removal of nitrogen
(1-(end_values/start_values))
Parameters
----------
columns : array of strings
"""
inv=0
outv=0
indexes= self.time.values
for column in columns:
inv += self.data[column][indexes[0]]
for column in columns:
outv += self.data[column][indexes[-1]]
removal = 1-(outv/inv)
return removal
def <|fim_middle|>(self,columns,time_column='h'):
"""
calculates the slope of the selected columns
Parameters
----------
columns : array of strings
columns to calculate the slope for
time_column : str
time used for calculation; default to 'h'
"""
for column in columns:
self.data[column + " " +'slope'] = (self.data[column].shift(1)-self.data[column])\
/(self.data[time_column]-self.data[time_column].shift(1))
def plot(self,columns,time_column='index'):
"""
calculates the slope of the selected columns
Parameters
----------
columns : array of strings
columns to plot
time_column : str
time used for calculation; default to 'h'
"""
fig = plt.figure(figsize=(10,6))
ax = fig.add_subplot(111)
if time_column=='index':
for column in columns:
ax.plot(self.time,self.data[column],marker='o')
else:
for column in columns:
ax.plot(self.data[time_column],self.data[column],marker='o')
ax.legend()
return fig,ax
#######################################
def _print_removed_output(original,new,type_):
"""
function printing the output of functions that remove datapoints.
Parameters
----------
original : int
original length of the dataset
new : int
length of the new dataset
type_ : str
'removed' or 'dropped'
"""
print('Original dataset:',original,'datapoints')
print('New dataset:',new,'datapoints')
print(original-new,'datapoints ',type_)
def _log_removed_output(log_file,original,new,type_):
"""
function writing the output of functions that remove datapoints to a log file.
Parameters
----------
log_file : str
string containing the directory to the log file to be written out
original : int
original length of the dataset
new : int
length of the new dataset
type_ : str
'removed' or 'dropped'
"""
log_file = open(log_file,'a')
log_file.write(str('\nOriginal dataset: '+str(original)+' datapoints; new dataset: '+
str(new)+' datapoints'+str(original-new)+' datapoints ',type_))
log_file.close()
<｜fim▁end｜> | calc_slope |
<|file_name|>Class_LabExperimBased.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
"""
Class_LabExperimBased provides functionalities for data handling of data obtained in lab experiments in the field of (waste)water treatment.
Copyright (C) 2016 Chaim De Mulder
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 http://www.gnu.org/licenses/.
"""
import sys
#import os
#from os import listdir
#import pandas as pd
#import scipy as sp
#import numpy as np
#import datetime as dt
import matplotlib.pyplot as plt #plotten in python
import warnings as wn
from wwdata.Class_HydroData import HydroData
class LabExperimBased(HydroData):
"""
Superclass for a HydroData object, expanding the functionalities with
specific functions for data gathered is lab experiments.
Attributes
----------
timedata_column : str
name of the column containing the time data
data_type : str
type of the data provided
experiment_tag : str
A tag identifying the experiment; can be a date or a code used by
the producer/owner of the data.
time_unit : str
The time unit in which the time data is given
units : array
The units of the variables in the columns
"""
def __init__(self,data,timedata_column='index',data_type='NAT',
experiment_tag='No tag given',time_unit=None):
"""
initialisation of a LabExperimBased object, based on a previously defined
HydroData object.
"""
HydroData.__init__(self,data,timedata_column=timedata_column,data_type=data_type,
experiment_tag=experiment_tag,time_unit=time_unit)
def hours(self,time_column='index'):
"""
calculates the hours from the relative values
Parameters
----------
time_column : string
column containing the relative time values; default to index
"""
if time_column == 'index':
self.data['index']=self.time.values
self.data['h']= (self.data['indexes'])*24 + self.data['indexes'].shift(1)
self.data['h'].fillna(0,inplace=True)
self.data.drop('index', axis=1, inplace=True)
else:
self.data['h']= (self.data[time_column])*24 + self.data[time_column].shift(1)
self.data['h'].fillna(0,inplace=True)
def add_conc(self,column_name,x,y,new_name='default'):
"""
calculates the concentration values of the given column and adds them as
a new column to the DataFrame.
Parameters
----------
column_name : str
column with values
x : int
...
y : int
...
new_name : str
name of the new column, default to 'column_name + mg/L'
"""
if new_name == 'default':
new_name = column_name + ' ' + 'mg/L'
self.data[new_name] = self.data[column_name].values*x*y
## Instead of this function: define a dataframe/dict with conversion or
## concentration factors, so that you can have a function that automatically
## converts all parameters in the frame to concentrations
def check_ph(self,ph_column='pH',thresh=0.4):
"""
gives the maximal change in pH
Parameters
----------
ph_column : str
column with pH-values, default to 'pH'
threshold : int
threshold value for warning, default to '0.4'
"""
dph = self.data[ph_column].max()-self.data[ph_column].min()
if dph > thresh:
wn.warn('Strong change in pH during experiment!')
else:
self.delta_ph = dph
def in_out(self,columns):
"""
(start_values-end_values)
Parameters
----------
columns : array of strings
"""
inv=0
outv=0
indexes= self.time.values
for column in columns:
inv += self.data[column][indexes[0]]
for column in columns:
outv += self.data[column][indexes[-1]]
in_out = inv-outv
return in_out
def removal(self,columns):
"""
total removal of nitrogen
(1-(end_values/start_values))
Parameters
----------
columns : array of strings
"""
inv=0
outv=0
indexes= self.time.values
for column in columns:
inv += self.data[column][indexes[0]]
for column in columns:
outv += self.data[column][indexes[-1]]
removal = 1-(outv/inv)
return removal
def calc_slope(self,columns,time_column='h'):
"""
calculates the slope of the selected columns
Parameters
----------
columns : array of strings
columns to calculate the slope for
time_column : str
time used for calculation; default to 'h'
"""
for column in columns:
self.data[column + " " +'slope'] = (self.data[column].shift(1)-self.data[column])\
/(self.data[time_column]-self.data[time_column].shift(1))
def <|fim_middle|>(self,columns,time_column='index'):
"""
calculates the slope of the selected columns
Parameters
----------
columns : array of strings
columns to plot
time_column : str
time used for calculation; default to 'h'
"""
fig = plt.figure(figsize=(10,6))
ax = fig.add_subplot(111)
if time_column=='index':
for column in columns:
ax.plot(self.time,self.data[column],marker='o')
else:
for column in columns:
ax.plot(self.data[time_column],self.data[column],marker='o')
ax.legend()
return fig,ax
#######################################
def _print_removed_output(original,new,type_):
"""
function printing the output of functions that remove datapoints.
Parameters
----------
original : int
original length of the dataset
new : int
length of the new dataset
type_ : str
'removed' or 'dropped'
"""
print('Original dataset:',original,'datapoints')
print('New dataset:',new,'datapoints')
print(original-new,'datapoints ',type_)
def _log_removed_output(log_file,original,new,type_):
"""
function writing the output of functions that remove datapoints to a log file.
Parameters
----------
log_file : str
string containing the directory to the log file to be written out
original : int
original length of the dataset
new : int
length of the new dataset
type_ : str
'removed' or 'dropped'
"""
log_file = open(log_file,'a')
log_file.write(str('\nOriginal dataset: '+str(original)+' datapoints; new dataset: '+
str(new)+' datapoints'+str(original-new)+' datapoints ',type_))
log_file.close()
<｜fim▁end｜> | plot |
<|file_name|>Class_LabExperimBased.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
"""
Class_LabExperimBased provides functionalities for data handling of data obtained in lab experiments in the field of (waste)water treatment.
Copyright (C) 2016 Chaim De Mulder
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 http://www.gnu.org/licenses/.
"""
import sys
#import os
#from os import listdir
#import pandas as pd
#import scipy as sp
#import numpy as np
#import datetime as dt
import matplotlib.pyplot as plt #plotten in python
import warnings as wn
from wwdata.Class_HydroData import HydroData
class LabExperimBased(HydroData):
"""
Superclass for a HydroData object, expanding the functionalities with
specific functions for data gathered is lab experiments.
Attributes
----------
timedata_column : str
name of the column containing the time data
data_type : str
type of the data provided
experiment_tag : str
A tag identifying the experiment; can be a date or a code used by
the producer/owner of the data.
time_unit : str
The time unit in which the time data is given
units : array
The units of the variables in the columns
"""
def __init__(self,data,timedata_column='index',data_type='NAT',
experiment_tag='No tag given',time_unit=None):
"""
initialisation of a LabExperimBased object, based on a previously defined
HydroData object.
"""
HydroData.__init__(self,data,timedata_column=timedata_column,data_type=data_type,
experiment_tag=experiment_tag,time_unit=time_unit)
def hours(self,time_column='index'):
"""
calculates the hours from the relative values
Parameters
----------
time_column : string
column containing the relative time values; default to index
"""
if time_column == 'index':
self.data['index']=self.time.values
self.data['h']= (self.data['indexes'])*24 + self.data['indexes'].shift(1)
self.data['h'].fillna(0,inplace=True)
self.data.drop('index', axis=1, inplace=True)
else:
self.data['h']= (self.data[time_column])*24 + self.data[time_column].shift(1)
self.data['h'].fillna(0,inplace=True)
def add_conc(self,column_name,x,y,new_name='default'):
"""
calculates the concentration values of the given column and adds them as
a new column to the DataFrame.
Parameters
----------
column_name : str
column with values
x : int
...
y : int
...
new_name : str
name of the new column, default to 'column_name + mg/L'
"""
if new_name == 'default':
new_name = column_name + ' ' + 'mg/L'
self.data[new_name] = self.data[column_name].values*x*y
## Instead of this function: define a dataframe/dict with conversion or
## concentration factors, so that you can have a function that automatically
## converts all parameters in the frame to concentrations
def check_ph(self,ph_column='pH',thresh=0.4):
"""
gives the maximal change in pH
Parameters
----------
ph_column : str
column with pH-values, default to 'pH'
threshold : int
threshold value for warning, default to '0.4'
"""
dph = self.data[ph_column].max()-self.data[ph_column].min()
if dph > thresh:
wn.warn('Strong change in pH during experiment!')
else:
self.delta_ph = dph
def in_out(self,columns):
"""
(start_values-end_values)
Parameters
----------
columns : array of strings
"""
inv=0
outv=0
indexes= self.time.values
for column in columns:
inv += self.data[column][indexes[0]]
for column in columns:
outv += self.data[column][indexes[-1]]
in_out = inv-outv
return in_out
def removal(self,columns):
"""
total removal of nitrogen
(1-(end_values/start_values))
Parameters
----------
columns : array of strings
"""
inv=0
outv=0
indexes= self.time.values
for column in columns:
inv += self.data[column][indexes[0]]
for column in columns:
outv += self.data[column][indexes[-1]]
removal = 1-(outv/inv)
return removal
def calc_slope(self,columns,time_column='h'):
"""
calculates the slope of the selected columns
Parameters
----------
columns : array of strings
columns to calculate the slope for
time_column : str
time used for calculation; default to 'h'
"""
for column in columns:
self.data[column + " " +'slope'] = (self.data[column].shift(1)-self.data[column])\
/(self.data[time_column]-self.data[time_column].shift(1))
def plot(self,columns,time_column='index'):
"""
calculates the slope of the selected columns
Parameters
----------
columns : array of strings
columns to plot
time_column : str
time used for calculation; default to 'h'
"""
fig = plt.figure(figsize=(10,6))
ax = fig.add_subplot(111)
if time_column=='index':
for column in columns:
ax.plot(self.time,self.data[column],marker='o')
else:
for column in columns:
ax.plot(self.data[time_column],self.data[column],marker='o')
ax.legend()
return fig,ax
#######################################
def <|fim_middle|>(original,new,type_):
"""
function printing the output of functions that remove datapoints.
Parameters
----------
original : int
original length of the dataset
new : int
length of the new dataset
type_ : str
'removed' or 'dropped'
"""
print('Original dataset:',original,'datapoints')
print('New dataset:',new,'datapoints')
print(original-new,'datapoints ',type_)
def _log_removed_output(log_file,original,new,type_):
"""
function writing the output of functions that remove datapoints to a log file.
Parameters
----------
log_file : str
string containing the directory to the log file to be written out
original : int
original length of the dataset
new : int
length of the new dataset
type_ : str
'removed' or 'dropped'
"""
log_file = open(log_file,'a')
log_file.write(str('\nOriginal dataset: '+str(original)+' datapoints; new dataset: '+
str(new)+' datapoints'+str(original-new)+' datapoints ',type_))
log_file.close()
<｜fim▁end｜> | _print_removed_output |
<|file_name|>Class_LabExperimBased.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
"""
Class_LabExperimBased provides functionalities for data handling of data obtained in lab experiments in the field of (waste)water treatment.
Copyright (C) 2016 Chaim De Mulder
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 http://www.gnu.org/licenses/.
"""
import sys
#import os
#from os import listdir
#import pandas as pd
#import scipy as sp
#import numpy as np
#import datetime as dt
import matplotlib.pyplot as plt #plotten in python
import warnings as wn
from wwdata.Class_HydroData import HydroData
class LabExperimBased(HydroData):
"""
Superclass for a HydroData object, expanding the functionalities with
specific functions for data gathered is lab experiments.
Attributes
----------
timedata_column : str
name of the column containing the time data
data_type : str
type of the data provided
experiment_tag : str
A tag identifying the experiment; can be a date or a code used by
the producer/owner of the data.
time_unit : str
The time unit in which the time data is given
units : array
The units of the variables in the columns
"""
def __init__(self,data,timedata_column='index',data_type='NAT',
experiment_tag='No tag given',time_unit=None):
"""
initialisation of a LabExperimBased object, based on a previously defined
HydroData object.
"""
HydroData.__init__(self,data,timedata_column=timedata_column,data_type=data_type,
experiment_tag=experiment_tag,time_unit=time_unit)
def hours(self,time_column='index'):
"""
calculates the hours from the relative values
Parameters
----------
time_column : string
column containing the relative time values; default to index
"""
if time_column == 'index':
self.data['index']=self.time.values
self.data['h']= (self.data['indexes'])*24 + self.data['indexes'].shift(1)
self.data['h'].fillna(0,inplace=True)
self.data.drop('index', axis=1, inplace=True)
else:
self.data['h']= (self.data[time_column])*24 + self.data[time_column].shift(1)
self.data['h'].fillna(0,inplace=True)
def add_conc(self,column_name,x,y,new_name='default'):
"""
calculates the concentration values of the given column and adds them as
a new column to the DataFrame.
Parameters
----------
column_name : str
column with values
x : int
...
y : int
...
new_name : str
name of the new column, default to 'column_name + mg/L'
"""
if new_name == 'default':
new_name = column_name + ' ' + 'mg/L'
self.data[new_name] = self.data[column_name].values*x*y
## Instead of this function: define a dataframe/dict with conversion or
## concentration factors, so that you can have a function that automatically
## converts all parameters in the frame to concentrations
def check_ph(self,ph_column='pH',thresh=0.4):
"""
gives the maximal change in pH
Parameters
----------
ph_column : str
column with pH-values, default to 'pH'
threshold : int
threshold value for warning, default to '0.4'
"""
dph = self.data[ph_column].max()-self.data[ph_column].min()
if dph > thresh:
wn.warn('Strong change in pH during experiment!')
else:
self.delta_ph = dph
def in_out(self,columns):
"""
(start_values-end_values)
Parameters
----------
columns : array of strings
"""
inv=0
outv=0
indexes= self.time.values
for column in columns:
inv += self.data[column][indexes[0]]
for column in columns:
outv += self.data[column][indexes[-1]]
in_out = inv-outv
return in_out
def removal(self,columns):
"""
total removal of nitrogen
(1-(end_values/start_values))
Parameters
----------
columns : array of strings
"""
inv=0
outv=0
indexes= self.time.values
for column in columns:
inv += self.data[column][indexes[0]]
for column in columns:
outv += self.data[column][indexes[-1]]
removal = 1-(outv/inv)
return removal
def calc_slope(self,columns,time_column='h'):
"""
calculates the slope of the selected columns
Parameters
----------
columns : array of strings
columns to calculate the slope for
time_column : str
time used for calculation; default to 'h'
"""
for column in columns:
self.data[column + " " +'slope'] = (self.data[column].shift(1)-self.data[column])\
/(self.data[time_column]-self.data[time_column].shift(1))
def plot(self,columns,time_column='index'):
"""
calculates the slope of the selected columns
Parameters
----------
columns : array of strings
columns to plot
time_column : str
time used for calculation; default to 'h'
"""
fig = plt.figure(figsize=(10,6))
ax = fig.add_subplot(111)
if time_column=='index':
for column in columns:
ax.plot(self.time,self.data[column],marker='o')
else:
for column in columns:
ax.plot(self.data[time_column],self.data[column],marker='o')
ax.legend()
return fig,ax
#######################################
def _print_removed_output(original,new,type_):
"""
function printing the output of functions that remove datapoints.
Parameters
----------
original : int
original length of the dataset
new : int
length of the new dataset
type_ : str
'removed' or 'dropped'
"""
print('Original dataset:',original,'datapoints')
print('New dataset:',new,'datapoints')
print(original-new,'datapoints ',type_)
def <|fim_middle|>(log_file,original,new,type_):
"""
function writing the output of functions that remove datapoints to a log file.
Parameters
----------
log_file : str
string containing the directory to the log file to be written out
original : int
original length of the dataset
new : int
length of the new dataset
type_ : str
'removed' or 'dropped'
"""
log_file = open(log_file,'a')
log_file.write(str('\nOriginal dataset: '+str(original)+' datapoints; new dataset: '+
str(new)+' datapoints'+str(original-new)+' datapoints ',type_))
log_file.close()
<｜fim▁end｜> | _log_removed_output |
<|file_name|>models.py<|end_file_name|><｜fim▁begin｜>from django.db import models
# Create your models here.
class Profil(models.Model):
awal = ''
PILIHAN_JENJANG = (
(awal, '----'),
('Pertama', 'Perekayasa Pertama'),
('Muda', 'Perekayasa Muda'),
('Madya', 'Perekayasa Madya'),
('Utama', 'Perekayasa Utama'),
)
nip = models.CharField(max_length=50, verbose_name='NIP')
pendidikan = models.CharField(max_length=150, verbose_name='Pendidikan')
instansi = models.TextField(verbose_name='Nama Lengkap Unit')
instansi_kode = models.CharField(max_length=20, verbose_name='Singkatan Unit')
satuan = models.TextField(verbose_name='Nama Lengkap Satuan kerja', blank=True)
kantor = models.TextField(verbose_name='Nama Lengkap Kantor', blank=True)
pangkat = models.TextField(verbose_name='Pangkat/Golongan Ruang/TMT')
jabatan = models.CharField(max_length=150, verbose_name='Jabatan')
jenjang = models.CharField(max_length=10, verbose_name='Jenjang Perekayasa', choices=PILIHAN_JENJANG, default=awal)
user = models.ForeignKey('auth.User', verbose_name='Personil', on_delete=models.CASCADE)
<｜fim▁hole｜> class Meta:
verbose_name_plural = 'Profil'
def __str__(self):
return self.nip<｜fim▁end｜> | |
<|file_name|>models.py<|end_file_name|><｜fim▁begin｜>from django.db import models
# Create your models here.
class Profil(models.Model):
<|fim_middle|>
<｜fim▁end｜> | awal = ''
PILIHAN_JENJANG = (
(awal, '----'),
('Pertama', 'Perekayasa Pertama'),
('Muda', 'Perekayasa Muda'),
('Madya', 'Perekayasa Madya'),
('Utama', 'Perekayasa Utama'),
)
nip = models.CharField(max_length=50, verbose_name='NIP')
pendidikan = models.CharField(max_length=150, verbose_name='Pendidikan')
instansi = models.TextField(verbose_name='Nama Lengkap Unit')
instansi_kode = models.CharField(max_length=20, verbose_name='Singkatan Unit')
satuan = models.TextField(verbose_name='Nama Lengkap Satuan kerja', blank=True)
kantor = models.TextField(verbose_name='Nama Lengkap Kantor', blank=True)
pangkat = models.TextField(verbose_name='Pangkat/Golongan Ruang/TMT')
jabatan = models.CharField(max_length=150, verbose_name='Jabatan')
jenjang = models.CharField(max_length=10, verbose_name='Jenjang Perekayasa', choices=PILIHAN_JENJANG, default=awal)
user = models.ForeignKey('auth.User', verbose_name='Personil', on_delete=models.CASCADE)
class Meta:
verbose_name_plural = 'Profil'
def __str__(self):
return self.nip |
<|file_name|>models.py<|end_file_name|><｜fim▁begin｜>from django.db import models
# Create your models here.
class Profil(models.Model):
awal = ''
PILIHAN_JENJANG = (
(awal, '----'),
('Pertama', 'Perekayasa Pertama'),
('Muda', 'Perekayasa Muda'),
('Madya', 'Perekayasa Madya'),
('Utama', 'Perekayasa Utama'),
)
nip = models.CharField(max_length=50, verbose_name='NIP')
pendidikan = models.CharField(max_length=150, verbose_name='Pendidikan')
instansi = models.TextField(verbose_name='Nama Lengkap Unit')
instansi_kode = models.CharField(max_length=20, verbose_name='Singkatan Unit')
satuan = models.TextField(verbose_name='Nama Lengkap Satuan kerja', blank=True)
kantor = models.TextField(verbose_name='Nama Lengkap Kantor', blank=True)
pangkat = models.TextField(verbose_name='Pangkat/Golongan Ruang/TMT')
jabatan = models.CharField(max_length=150, verbose_name='Jabatan')
jenjang = models.CharField(max_length=10, verbose_name='Jenjang Perekayasa', choices=PILIHAN_JENJANG, default=awal)
user = models.ForeignKey('auth.User', verbose_name='Personil', on_delete=models.CASCADE)
class Meta:
<|fim_middle|>
def __str__(self):
return self.nip
<｜fim▁end｜> | verbose_name_plural = 'Profil' |
<|file_name|>models.py<|end_file_name|><｜fim▁begin｜>from django.db import models
# Create your models here.
class Profil(models.Model):
awal = ''
PILIHAN_JENJANG = (
(awal, '----'),
('Pertama', 'Perekayasa Pertama'),
('Muda', 'Perekayasa Muda'),
('Madya', 'Perekayasa Madya'),
('Utama', 'Perekayasa Utama'),
)
nip = models.CharField(max_length=50, verbose_name='NIP')
pendidikan = models.CharField(max_length=150, verbose_name='Pendidikan')
instansi = models.TextField(verbose_name='Nama Lengkap Unit')
instansi_kode = models.CharField(max_length=20, verbose_name='Singkatan Unit')
satuan = models.TextField(verbose_name='Nama Lengkap Satuan kerja', blank=True)
kantor = models.TextField(verbose_name='Nama Lengkap Kantor', blank=True)
pangkat = models.TextField(verbose_name='Pangkat/Golongan Ruang/TMT')
jabatan = models.CharField(max_length=150, verbose_name='Jabatan')
jenjang = models.CharField(max_length=10, verbose_name='Jenjang Perekayasa', choices=PILIHAN_JENJANG, default=awal)
user = models.ForeignKey('auth.User', verbose_name='Personil', on_delete=models.CASCADE)
class Meta:
verbose_name_plural = 'Profil'
def __str__(self):
<|fim_middle|>
<｜fim▁end｜> | return self.nip |
<|file_name|>models.py<|end_file_name|><｜fim▁begin｜>from django.db import models
# Create your models here.
class Profil(models.Model):
awal = ''
PILIHAN_JENJANG = (
(awal, '----'),
('Pertama', 'Perekayasa Pertama'),
('Muda', 'Perekayasa Muda'),
('Madya', 'Perekayasa Madya'),
('Utama', 'Perekayasa Utama'),
)
nip = models.CharField(max_length=50, verbose_name='NIP')
pendidikan = models.CharField(max_length=150, verbose_name='Pendidikan')
instansi = models.TextField(verbose_name='Nama Lengkap Unit')
instansi_kode = models.CharField(max_length=20, verbose_name='Singkatan Unit')
satuan = models.TextField(verbose_name='Nama Lengkap Satuan kerja', blank=True)
kantor = models.TextField(verbose_name='Nama Lengkap Kantor', blank=True)
pangkat = models.TextField(verbose_name='Pangkat/Golongan Ruang/TMT')
jabatan = models.CharField(max_length=150, verbose_name='Jabatan')
jenjang = models.CharField(max_length=10, verbose_name='Jenjang Perekayasa', choices=PILIHAN_JENJANG, default=awal)
user = models.ForeignKey('auth.User', verbose_name='Personil', on_delete=models.CASCADE)
class Meta:
verbose_name_plural = 'Profil'
def <|fim_middle|>(self):
return self.nip
<｜fim▁end｜> | __str__ |
<|file_name|>tight_bbox.py<|end_file_name|><｜fim▁begin｜>"""
This module is to support *bbox_inches* option in savefig command.
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
import warnings
from matplotlib.transforms import Bbox, TransformedBbox, Affine2D
def adjust_bbox(fig, bbox_inches, fixed_dpi=None):
"""
Temporarily adjust the figure so that only the specified area
(bbox_inches) is saved.
It modifies fig.bbox, fig.bbox_inches,
fig.transFigure._boxout, and fig.patch. While the figure size
changes, the scale of the original figure is conserved. A
function which restores the original values are returned.
"""
origBbox = fig.bbox
origBboxInches = fig.bbox_inches
_boxout = fig.transFigure._boxout
asp_list = []
locator_list = []
for ax in fig.axes:
pos = ax.get_position(original=False).frozen()
locator_list.append(ax.get_axes_locator())
asp_list.append(ax.get_aspect())
def _l(a, r, pos=pos):
return pos
ax.set_axes_locator(_l)
ax.set_aspect("auto")
def restore_bbox():
for ax, asp, loc in zip(fig.axes, asp_list, locator_list):
ax.set_aspect(asp)
ax.set_axes_locator(loc)
fig.bbox = origBbox
fig.bbox_inches = origBboxInches<｜fim▁hole｜> fig.patch.set_bounds(0, 0, 1, 1)
if fixed_dpi is not None:
tr = Affine2D().scale(fixed_dpi)
dpi_scale = fixed_dpi / fig.dpi
else:
tr = Affine2D().scale(fig.dpi)
dpi_scale = 1.
_bbox = TransformedBbox(bbox_inches, tr)
fig.bbox_inches = Bbox.from_bounds(0, 0,
bbox_inches.width, bbox_inches.height)
x0, y0 = _bbox.x0, _bbox.y0
w1, h1 = fig.bbox.width * dpi_scale, fig.bbox.height * dpi_scale
fig.transFigure._boxout = Bbox.from_bounds(-x0, -y0, w1, h1)
fig.transFigure.invalidate()
fig.bbox = TransformedBbox(fig.bbox_inches, tr)
fig.patch.set_bounds(x0 / w1, y0 / h1,
fig.bbox.width / w1, fig.bbox.height / h1)
return restore_bbox
def process_figure_for_rasterizing(fig, bbox_inches_restore, fixed_dpi=None):
"""
This need to be called when figure dpi changes during the drawing
(e.g., rasterizing). It recovers the bbox and re-adjust it with
the new dpi.
"""
bbox_inches, restore_bbox = bbox_inches_restore
restore_bbox()
r = adjust_bbox(figure, bbox_inches, fixed_dpi)
return bbox_inches, r<｜fim▁end｜> | fig.transFigure._boxout = _boxout
fig.transFigure.invalidate() |
<|file_name|>tight_bbox.py<|end_file_name|><｜fim▁begin｜>"""
This module is to support *bbox_inches* option in savefig command.
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
import warnings
from matplotlib.transforms import Bbox, TransformedBbox, Affine2D
def adjust_bbox(fig, bbox_inches, fixed_dpi=None):
<|fim_middle|>
def process_figure_for_rasterizing(fig, bbox_inches_restore, fixed_dpi=None):
"""
This need to be called when figure dpi changes during the drawing
(e.g., rasterizing). It recovers the bbox and re-adjust it with
the new dpi.
"""
bbox_inches, restore_bbox = bbox_inches_restore
restore_bbox()
r = adjust_bbox(figure, bbox_inches, fixed_dpi)
return bbox_inches, r
<｜fim▁end｜> | """
Temporarily adjust the figure so that only the specified area
(bbox_inches) is saved.
It modifies fig.bbox, fig.bbox_inches,
fig.transFigure._boxout, and fig.patch. While the figure size
changes, the scale of the original figure is conserved. A
function which restores the original values are returned.
"""
origBbox = fig.bbox
origBboxInches = fig.bbox_inches
_boxout = fig.transFigure._boxout
asp_list = []
locator_list = []
for ax in fig.axes:
pos = ax.get_position(original=False).frozen()
locator_list.append(ax.get_axes_locator())
asp_list.append(ax.get_aspect())
def _l(a, r, pos=pos):
return pos
ax.set_axes_locator(_l)
ax.set_aspect("auto")
def restore_bbox():
for ax, asp, loc in zip(fig.axes, asp_list, locator_list):
ax.set_aspect(asp)
ax.set_axes_locator(loc)
fig.bbox = origBbox
fig.bbox_inches = origBboxInches
fig.transFigure._boxout = _boxout
fig.transFigure.invalidate()
fig.patch.set_bounds(0, 0, 1, 1)
if fixed_dpi is not None:
tr = Affine2D().scale(fixed_dpi)
dpi_scale = fixed_dpi / fig.dpi
else:
tr = Affine2D().scale(fig.dpi)
dpi_scale = 1.
_bbox = TransformedBbox(bbox_inches, tr)
fig.bbox_inches = Bbox.from_bounds(0, 0,
bbox_inches.width, bbox_inches.height)
x0, y0 = _bbox.x0, _bbox.y0
w1, h1 = fig.bbox.width * dpi_scale, fig.bbox.height * dpi_scale
fig.transFigure._boxout = Bbox.from_bounds(-x0, -y0, w1, h1)
fig.transFigure.invalidate()
fig.bbox = TransformedBbox(fig.bbox_inches, tr)
fig.patch.set_bounds(x0 / w1, y0 / h1,
fig.bbox.width / w1, fig.bbox.height / h1)
return restore_bbox |
<|file_name|>tight_bbox.py<|end_file_name|><｜fim▁begin｜>"""
This module is to support *bbox_inches* option in savefig command.
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
import warnings
from matplotlib.transforms import Bbox, TransformedBbox, Affine2D
def adjust_bbox(fig, bbox_inches, fixed_dpi=None):
"""
Temporarily adjust the figure so that only the specified area
(bbox_inches) is saved.
It modifies fig.bbox, fig.bbox_inches,
fig.transFigure._boxout, and fig.patch. While the figure size
changes, the scale of the original figure is conserved. A
function which restores the original values are returned.
"""
origBbox = fig.bbox
origBboxInches = fig.bbox_inches
_boxout = fig.transFigure._boxout
asp_list = []
locator_list = []
for ax in fig.axes:
pos = ax.get_position(original=False).frozen()
locator_list.append(ax.get_axes_locator())
asp_list.append(ax.get_aspect())
def _l(a, r, pos=pos):
<|fim_middle|>
ax.set_axes_locator(_l)
ax.set_aspect("auto")
def restore_bbox():
for ax, asp, loc in zip(fig.axes, asp_list, locator_list):
ax.set_aspect(asp)
ax.set_axes_locator(loc)
fig.bbox = origBbox
fig.bbox_inches = origBboxInches
fig.transFigure._boxout = _boxout
fig.transFigure.invalidate()
fig.patch.set_bounds(0, 0, 1, 1)
if fixed_dpi is not None:
tr = Affine2D().scale(fixed_dpi)
dpi_scale = fixed_dpi / fig.dpi
else:
tr = Affine2D().scale(fig.dpi)
dpi_scale = 1.
_bbox = TransformedBbox(bbox_inches, tr)
fig.bbox_inches = Bbox.from_bounds(0, 0,
bbox_inches.width, bbox_inches.height)
x0, y0 = _bbox.x0, _bbox.y0
w1, h1 = fig.bbox.width * dpi_scale, fig.bbox.height * dpi_scale
fig.transFigure._boxout = Bbox.from_bounds(-x0, -y0, w1, h1)
fig.transFigure.invalidate()
fig.bbox = TransformedBbox(fig.bbox_inches, tr)
fig.patch.set_bounds(x0 / w1, y0 / h1,
fig.bbox.width / w1, fig.bbox.height / h1)
return restore_bbox
def process_figure_for_rasterizing(fig, bbox_inches_restore, fixed_dpi=None):
"""
This need to be called when figure dpi changes during the drawing
(e.g., rasterizing). It recovers the bbox and re-adjust it with
the new dpi.
"""
bbox_inches, restore_bbox = bbox_inches_restore
restore_bbox()
r = adjust_bbox(figure, bbox_inches, fixed_dpi)
return bbox_inches, r
<｜fim▁end｜> | return pos |
<|file_name|>tight_bbox.py<|end_file_name|><｜fim▁begin｜>"""
This module is to support *bbox_inches* option in savefig command.
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
import warnings
from matplotlib.transforms import Bbox, TransformedBbox, Affine2D
def adjust_bbox(fig, bbox_inches, fixed_dpi=None):
"""
Temporarily adjust the figure so that only the specified area
(bbox_inches) is saved.
It modifies fig.bbox, fig.bbox_inches,
fig.transFigure._boxout, and fig.patch. While the figure size
changes, the scale of the original figure is conserved. A
function which restores the original values are returned.
"""
origBbox = fig.bbox
origBboxInches = fig.bbox_inches
_boxout = fig.transFigure._boxout
asp_list = []
locator_list = []
for ax in fig.axes:
pos = ax.get_position(original=False).frozen()
locator_list.append(ax.get_axes_locator())
asp_list.append(ax.get_aspect())
def _l(a, r, pos=pos):
return pos
ax.set_axes_locator(_l)
ax.set_aspect("auto")
def restore_bbox():
<|fim_middle|>
if fixed_dpi is not None:
tr = Affine2D().scale(fixed_dpi)
dpi_scale = fixed_dpi / fig.dpi
else:
tr = Affine2D().scale(fig.dpi)
dpi_scale = 1.
_bbox = TransformedBbox(bbox_inches, tr)
fig.bbox_inches = Bbox.from_bounds(0, 0,
bbox_inches.width, bbox_inches.height)
x0, y0 = _bbox.x0, _bbox.y0
w1, h1 = fig.bbox.width * dpi_scale, fig.bbox.height * dpi_scale
fig.transFigure._boxout = Bbox.from_bounds(-x0, -y0, w1, h1)
fig.transFigure.invalidate()
fig.bbox = TransformedBbox(fig.bbox_inches, tr)
fig.patch.set_bounds(x0 / w1, y0 / h1,
fig.bbox.width / w1, fig.bbox.height / h1)
return restore_bbox
def process_figure_for_rasterizing(fig, bbox_inches_restore, fixed_dpi=None):
"""
This need to be called when figure dpi changes during the drawing
(e.g., rasterizing). It recovers the bbox and re-adjust it with
the new dpi.
"""
bbox_inches, restore_bbox = bbox_inches_restore
restore_bbox()
r = adjust_bbox(figure, bbox_inches, fixed_dpi)
return bbox_inches, r
<｜fim▁end｜> | for ax, asp, loc in zip(fig.axes, asp_list, locator_list):
ax.set_aspect(asp)
ax.set_axes_locator(loc)
fig.bbox = origBbox
fig.bbox_inches = origBboxInches
fig.transFigure._boxout = _boxout
fig.transFigure.invalidate()
fig.patch.set_bounds(0, 0, 1, 1) |
<|file_name|>tight_bbox.py<|end_file_name|><｜fim▁begin｜>"""
This module is to support *bbox_inches* option in savefig command.
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
import warnings
from matplotlib.transforms import Bbox, TransformedBbox, Affine2D
def adjust_bbox(fig, bbox_inches, fixed_dpi=None):
"""
Temporarily adjust the figure so that only the specified area
(bbox_inches) is saved.
It modifies fig.bbox, fig.bbox_inches,
fig.transFigure._boxout, and fig.patch. While the figure size
changes, the scale of the original figure is conserved. A
function which restores the original values are returned.
"""
origBbox = fig.bbox
origBboxInches = fig.bbox_inches
_boxout = fig.transFigure._boxout
asp_list = []
locator_list = []
for ax in fig.axes:
pos = ax.get_position(original=False).frozen()
locator_list.append(ax.get_axes_locator())
asp_list.append(ax.get_aspect())
def _l(a, r, pos=pos):
return pos
ax.set_axes_locator(_l)
ax.set_aspect("auto")
def restore_bbox():
for ax, asp, loc in zip(fig.axes, asp_list, locator_list):
ax.set_aspect(asp)
ax.set_axes_locator(loc)
fig.bbox = origBbox
fig.bbox_inches = origBboxInches
fig.transFigure._boxout = _boxout
fig.transFigure.invalidate()
fig.patch.set_bounds(0, 0, 1, 1)
if fixed_dpi is not None:
tr = Affine2D().scale(fixed_dpi)
dpi_scale = fixed_dpi / fig.dpi
else:
tr = Affine2D().scale(fig.dpi)
dpi_scale = 1.
_bbox = TransformedBbox(bbox_inches, tr)
fig.bbox_inches = Bbox.from_bounds(0, 0,
bbox_inches.width, bbox_inches.height)
x0, y0 = _bbox.x0, _bbox.y0
w1, h1 = fig.bbox.width * dpi_scale, fig.bbox.height * dpi_scale
fig.transFigure._boxout = Bbox.from_bounds(-x0, -y0, w1, h1)
fig.transFigure.invalidate()
fig.bbox = TransformedBbox(fig.bbox_inches, tr)
fig.patch.set_bounds(x0 / w1, y0 / h1,
fig.bbox.width / w1, fig.bbox.height / h1)
return restore_bbox
def process_figure_for_rasterizing(fig, bbox_inches_restore, fixed_dpi=None):
<|fim_middle|>
<｜fim▁end｜> | """
This need to be called when figure dpi changes during the drawing
(e.g., rasterizing). It recovers the bbox and re-adjust it with
the new dpi.
"""
bbox_inches, restore_bbox = bbox_inches_restore
restore_bbox()
r = adjust_bbox(figure, bbox_inches, fixed_dpi)
return bbox_inches, r |
<|file_name|>tight_bbox.py<|end_file_name|><｜fim▁begin｜>"""
This module is to support *bbox_inches* option in savefig command.
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
import warnings
from matplotlib.transforms import Bbox, TransformedBbox, Affine2D
def adjust_bbox(fig, bbox_inches, fixed_dpi=None):
"""
Temporarily adjust the figure so that only the specified area
(bbox_inches) is saved.
It modifies fig.bbox, fig.bbox_inches,
fig.transFigure._boxout, and fig.patch. While the figure size
changes, the scale of the original figure is conserved. A
function which restores the original values are returned.
"""
origBbox = fig.bbox
origBboxInches = fig.bbox_inches
_boxout = fig.transFigure._boxout
asp_list = []
locator_list = []
for ax in fig.axes:
pos = ax.get_position(original=False).frozen()
locator_list.append(ax.get_axes_locator())
asp_list.append(ax.get_aspect())
def _l(a, r, pos=pos):
return pos
ax.set_axes_locator(_l)
ax.set_aspect("auto")
def restore_bbox():
for ax, asp, loc in zip(fig.axes, asp_list, locator_list):
ax.set_aspect(asp)
ax.set_axes_locator(loc)
fig.bbox = origBbox
fig.bbox_inches = origBboxInches
fig.transFigure._boxout = _boxout
fig.transFigure.invalidate()
fig.patch.set_bounds(0, 0, 1, 1)
if fixed_dpi is not None:
<|fim_middle|>
else:
tr = Affine2D().scale(fig.dpi)
dpi_scale = 1.
_bbox = TransformedBbox(bbox_inches, tr)
fig.bbox_inches = Bbox.from_bounds(0, 0,
bbox_inches.width, bbox_inches.height)
x0, y0 = _bbox.x0, _bbox.y0
w1, h1 = fig.bbox.width * dpi_scale, fig.bbox.height * dpi_scale
fig.transFigure._boxout = Bbox.from_bounds(-x0, -y0, w1, h1)
fig.transFigure.invalidate()
fig.bbox = TransformedBbox(fig.bbox_inches, tr)
fig.patch.set_bounds(x0 / w1, y0 / h1,
fig.bbox.width / w1, fig.bbox.height / h1)
return restore_bbox
def process_figure_for_rasterizing(fig, bbox_inches_restore, fixed_dpi=None):
"""
This need to be called when figure dpi changes during the drawing
(e.g., rasterizing). It recovers the bbox and re-adjust it with
the new dpi.
"""
bbox_inches, restore_bbox = bbox_inches_restore
restore_bbox()
r = adjust_bbox(figure, bbox_inches, fixed_dpi)
return bbox_inches, r
<｜fim▁end｜> | tr = Affine2D().scale(fixed_dpi)
dpi_scale = fixed_dpi / fig.dpi |
<|file_name|>tight_bbox.py<|end_file_name|><｜fim▁begin｜>"""
This module is to support *bbox_inches* option in savefig command.
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
import warnings
from matplotlib.transforms import Bbox, TransformedBbox, Affine2D
def adjust_bbox(fig, bbox_inches, fixed_dpi=None):
"""
Temporarily adjust the figure so that only the specified area
(bbox_inches) is saved.
It modifies fig.bbox, fig.bbox_inches,
fig.transFigure._boxout, and fig.patch. While the figure size
changes, the scale of the original figure is conserved. A
function which restores the original values are returned.
"""
origBbox = fig.bbox
origBboxInches = fig.bbox_inches
_boxout = fig.transFigure._boxout
asp_list = []
locator_list = []
for ax in fig.axes:
pos = ax.get_position(original=False).frozen()
locator_list.append(ax.get_axes_locator())
asp_list.append(ax.get_aspect())
def _l(a, r, pos=pos):
return pos
ax.set_axes_locator(_l)
ax.set_aspect("auto")
def restore_bbox():
for ax, asp, loc in zip(fig.axes, asp_list, locator_list):
ax.set_aspect(asp)
ax.set_axes_locator(loc)
fig.bbox = origBbox
fig.bbox_inches = origBboxInches
fig.transFigure._boxout = _boxout
fig.transFigure.invalidate()
fig.patch.set_bounds(0, 0, 1, 1)
if fixed_dpi is not None:
tr = Affine2D().scale(fixed_dpi)
dpi_scale = fixed_dpi / fig.dpi
else:
<|fim_middle|>
_bbox = TransformedBbox(bbox_inches, tr)
fig.bbox_inches = Bbox.from_bounds(0, 0,
bbox_inches.width, bbox_inches.height)
x0, y0 = _bbox.x0, _bbox.y0
w1, h1 = fig.bbox.width * dpi_scale, fig.bbox.height * dpi_scale
fig.transFigure._boxout = Bbox.from_bounds(-x0, -y0, w1, h1)
fig.transFigure.invalidate()
fig.bbox = TransformedBbox(fig.bbox_inches, tr)
fig.patch.set_bounds(x0 / w1, y0 / h1,
fig.bbox.width / w1, fig.bbox.height / h1)
return restore_bbox
def process_figure_for_rasterizing(fig, bbox_inches_restore, fixed_dpi=None):
"""
This need to be called when figure dpi changes during the drawing
(e.g., rasterizing). It recovers the bbox and re-adjust it with
the new dpi.
"""
bbox_inches, restore_bbox = bbox_inches_restore
restore_bbox()
r = adjust_bbox(figure, bbox_inches, fixed_dpi)
return bbox_inches, r
<｜fim▁end｜> | tr = Affine2D().scale(fig.dpi)
dpi_scale = 1. |
<|file_name|>tight_bbox.py<|end_file_name|><｜fim▁begin｜>"""
This module is to support *bbox_inches* option in savefig command.
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
import warnings
from matplotlib.transforms import Bbox, TransformedBbox, Affine2D
def <|fim_middle|>(fig, bbox_inches, fixed_dpi=None):
"""
Temporarily adjust the figure so that only the specified area
(bbox_inches) is saved.
It modifies fig.bbox, fig.bbox_inches,
fig.transFigure._boxout, and fig.patch. While the figure size
changes, the scale of the original figure is conserved. A
function which restores the original values are returned.
"""
origBbox = fig.bbox
origBboxInches = fig.bbox_inches
_boxout = fig.transFigure._boxout
asp_list = []
locator_list = []
for ax in fig.axes:
pos = ax.get_position(original=False).frozen()
locator_list.append(ax.get_axes_locator())
asp_list.append(ax.get_aspect())
def _l(a, r, pos=pos):
return pos
ax.set_axes_locator(_l)
ax.set_aspect("auto")
def restore_bbox():
for ax, asp, loc in zip(fig.axes, asp_list, locator_list):
ax.set_aspect(asp)
ax.set_axes_locator(loc)
fig.bbox = origBbox
fig.bbox_inches = origBboxInches
fig.transFigure._boxout = _boxout
fig.transFigure.invalidate()
fig.patch.set_bounds(0, 0, 1, 1)
if fixed_dpi is not None:
tr = Affine2D().scale(fixed_dpi)
dpi_scale = fixed_dpi / fig.dpi
else:
tr = Affine2D().scale(fig.dpi)
dpi_scale = 1.
_bbox = TransformedBbox(bbox_inches, tr)
fig.bbox_inches = Bbox.from_bounds(0, 0,
bbox_inches.width, bbox_inches.height)
x0, y0 = _bbox.x0, _bbox.y0
w1, h1 = fig.bbox.width * dpi_scale, fig.bbox.height * dpi_scale
fig.transFigure._boxout = Bbox.from_bounds(-x0, -y0, w1, h1)
fig.transFigure.invalidate()
fig.bbox = TransformedBbox(fig.bbox_inches, tr)
fig.patch.set_bounds(x0 / w1, y0 / h1,
fig.bbox.width / w1, fig.bbox.height / h1)
return restore_bbox
def process_figure_for_rasterizing(fig, bbox_inches_restore, fixed_dpi=None):
"""
This need to be called when figure dpi changes during the drawing
(e.g., rasterizing). It recovers the bbox and re-adjust it with
the new dpi.
"""
bbox_inches, restore_bbox = bbox_inches_restore
restore_bbox()
r = adjust_bbox(figure, bbox_inches, fixed_dpi)
return bbox_inches, r
<｜fim▁end｜> | adjust_bbox |
<|file_name|>tight_bbox.py<|end_file_name|><｜fim▁begin｜>"""
This module is to support *bbox_inches* option in savefig command.
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
import warnings
from matplotlib.transforms import Bbox, TransformedBbox, Affine2D
def adjust_bbox(fig, bbox_inches, fixed_dpi=None):
"""
Temporarily adjust the figure so that only the specified area
(bbox_inches) is saved.
It modifies fig.bbox, fig.bbox_inches,
fig.transFigure._boxout, and fig.patch. While the figure size
changes, the scale of the original figure is conserved. A
function which restores the original values are returned.
"""
origBbox = fig.bbox
origBboxInches = fig.bbox_inches
_boxout = fig.transFigure._boxout
asp_list = []
locator_list = []
for ax in fig.axes:
pos = ax.get_position(original=False).frozen()
locator_list.append(ax.get_axes_locator())
asp_list.append(ax.get_aspect())
def <|fim_middle|>(a, r, pos=pos):
return pos
ax.set_axes_locator(_l)
ax.set_aspect("auto")
def restore_bbox():
for ax, asp, loc in zip(fig.axes, asp_list, locator_list):
ax.set_aspect(asp)
ax.set_axes_locator(loc)
fig.bbox = origBbox
fig.bbox_inches = origBboxInches
fig.transFigure._boxout = _boxout
fig.transFigure.invalidate()
fig.patch.set_bounds(0, 0, 1, 1)
if fixed_dpi is not None:
tr = Affine2D().scale(fixed_dpi)
dpi_scale = fixed_dpi / fig.dpi
else:
tr = Affine2D().scale(fig.dpi)
dpi_scale = 1.
_bbox = TransformedBbox(bbox_inches, tr)
fig.bbox_inches = Bbox.from_bounds(0, 0,
bbox_inches.width, bbox_inches.height)
x0, y0 = _bbox.x0, _bbox.y0
w1, h1 = fig.bbox.width * dpi_scale, fig.bbox.height * dpi_scale
fig.transFigure._boxout = Bbox.from_bounds(-x0, -y0, w1, h1)
fig.transFigure.invalidate()
fig.bbox = TransformedBbox(fig.bbox_inches, tr)
fig.patch.set_bounds(x0 / w1, y0 / h1,
fig.bbox.width / w1, fig.bbox.height / h1)
return restore_bbox
def process_figure_for_rasterizing(fig, bbox_inches_restore, fixed_dpi=None):
"""
This need to be called when figure dpi changes during the drawing
(e.g., rasterizing). It recovers the bbox and re-adjust it with
the new dpi.
"""
bbox_inches, restore_bbox = bbox_inches_restore
restore_bbox()
r = adjust_bbox(figure, bbox_inches, fixed_dpi)
return bbox_inches, r
<｜fim▁end｜> | _l |
<|file_name|>tight_bbox.py<|end_file_name|><｜fim▁begin｜>"""
This module is to support *bbox_inches* option in savefig command.
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
import warnings
from matplotlib.transforms import Bbox, TransformedBbox, Affine2D
def adjust_bbox(fig, bbox_inches, fixed_dpi=None):
"""
Temporarily adjust the figure so that only the specified area
(bbox_inches) is saved.
It modifies fig.bbox, fig.bbox_inches,
fig.transFigure._boxout, and fig.patch. While the figure size
changes, the scale of the original figure is conserved. A
function which restores the original values are returned.
"""
origBbox = fig.bbox
origBboxInches = fig.bbox_inches
_boxout = fig.transFigure._boxout
asp_list = []
locator_list = []
for ax in fig.axes:
pos = ax.get_position(original=False).frozen()
locator_list.append(ax.get_axes_locator())
asp_list.append(ax.get_aspect())
def _l(a, r, pos=pos):
return pos
ax.set_axes_locator(_l)
ax.set_aspect("auto")
def <|fim_middle|>():
for ax, asp, loc in zip(fig.axes, asp_list, locator_list):
ax.set_aspect(asp)
ax.set_axes_locator(loc)
fig.bbox = origBbox
fig.bbox_inches = origBboxInches
fig.transFigure._boxout = _boxout
fig.transFigure.invalidate()
fig.patch.set_bounds(0, 0, 1, 1)
if fixed_dpi is not None:
tr = Affine2D().scale(fixed_dpi)
dpi_scale = fixed_dpi / fig.dpi
else:
tr = Affine2D().scale(fig.dpi)
dpi_scale = 1.
_bbox = TransformedBbox(bbox_inches, tr)
fig.bbox_inches = Bbox.from_bounds(0, 0,
bbox_inches.width, bbox_inches.height)
x0, y0 = _bbox.x0, _bbox.y0
w1, h1 = fig.bbox.width * dpi_scale, fig.bbox.height * dpi_scale
fig.transFigure._boxout = Bbox.from_bounds(-x0, -y0, w1, h1)
fig.transFigure.invalidate()
fig.bbox = TransformedBbox(fig.bbox_inches, tr)
fig.patch.set_bounds(x0 / w1, y0 / h1,
fig.bbox.width / w1, fig.bbox.height / h1)
return restore_bbox
def process_figure_for_rasterizing(fig, bbox_inches_restore, fixed_dpi=None):
"""
This need to be called when figure dpi changes during the drawing
(e.g., rasterizing). It recovers the bbox and re-adjust it with
the new dpi.
"""
bbox_inches, restore_bbox = bbox_inches_restore
restore_bbox()
r = adjust_bbox(figure, bbox_inches, fixed_dpi)
return bbox_inches, r
<｜fim▁end｜> | restore_bbox |
<|file_name|>tight_bbox.py<|end_file_name|><｜fim▁begin｜>"""
This module is to support *bbox_inches* option in savefig command.
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
import warnings
from matplotlib.transforms import Bbox, TransformedBbox, Affine2D
def adjust_bbox(fig, bbox_inches, fixed_dpi=None):
"""
Temporarily adjust the figure so that only the specified area
(bbox_inches) is saved.
It modifies fig.bbox, fig.bbox_inches,
fig.transFigure._boxout, and fig.patch. While the figure size
changes, the scale of the original figure is conserved. A
function which restores the original values are returned.
"""
origBbox = fig.bbox
origBboxInches = fig.bbox_inches
_boxout = fig.transFigure._boxout
asp_list = []
locator_list = []
for ax in fig.axes:
pos = ax.get_position(original=False).frozen()
locator_list.append(ax.get_axes_locator())
asp_list.append(ax.get_aspect())
def _l(a, r, pos=pos):
return pos
ax.set_axes_locator(_l)
ax.set_aspect("auto")
def restore_bbox():
for ax, asp, loc in zip(fig.axes, asp_list, locator_list):
ax.set_aspect(asp)
ax.set_axes_locator(loc)
fig.bbox = origBbox
fig.bbox_inches = origBboxInches
fig.transFigure._boxout = _boxout
fig.transFigure.invalidate()
fig.patch.set_bounds(0, 0, 1, 1)
if fixed_dpi is not None:
tr = Affine2D().scale(fixed_dpi)
dpi_scale = fixed_dpi / fig.dpi
else:
tr = Affine2D().scale(fig.dpi)
dpi_scale = 1.
_bbox = TransformedBbox(bbox_inches, tr)
fig.bbox_inches = Bbox.from_bounds(0, 0,
bbox_inches.width, bbox_inches.height)
x0, y0 = _bbox.x0, _bbox.y0
w1, h1 = fig.bbox.width * dpi_scale, fig.bbox.height * dpi_scale
fig.transFigure._boxout = Bbox.from_bounds(-x0, -y0, w1, h1)
fig.transFigure.invalidate()
fig.bbox = TransformedBbox(fig.bbox_inches, tr)
fig.patch.set_bounds(x0 / w1, y0 / h1,
fig.bbox.width / w1, fig.bbox.height / h1)
return restore_bbox
def <|fim_middle|>(fig, bbox_inches_restore, fixed_dpi=None):
"""
This need to be called when figure dpi changes during the drawing
(e.g., rasterizing). It recovers the bbox and re-adjust it with
the new dpi.
"""
bbox_inches, restore_bbox = bbox_inches_restore
restore_bbox()
r = adjust_bbox(figure, bbox_inches, fixed_dpi)
return bbox_inches, r
<｜fim▁end｜> | process_figure_for_rasterizing |
<|file_name|>implicitgrant.py<|end_file_name|><｜fim▁begin｜>#-*- coding: utf-8 -*-
from .grant import Grant
from ..endpoint import AuthorizationEndpoint
class ImplicitGrant(Grant):
"""
The implicit grant type is used to obtain access tokens (it does not
support the issuance of refresh tokens) and is optimized for public
clients known to operate a particular redirection URI. These clients
are typically implemented in a browser using a scripting language
such as JavaScript.
+----------+
| Resource |
| Owner |
| |
+----------+
^
|
(B)
+----|-----+ Client Identifier +---------------+
| -+----(A)-- & Redirection URI --->| |
| User- | | Authorization |
| Agent -|----(B)-- User authenticates -->| Server |
| | | |
| |<---(C)--- Redirection URI ----<| |
| | with Access Token +---------------+
| | in Fragment
| | +---------------+
| |----(D)--- Redirection URI ---->| Web-Hosted |
| | without Fragment | Client |
| | | Resource |
| (F) |<---(E)------- Script ---------<| |
| | +---------------+
+-|--------+
| |
(A) (G) Access Token
| |
^ v
+---------+
| |
| Client |
| |
+---------+<｜fim▁hole｜> Figure 4: Implicit Grant Flow
"""
def get_redirection_uri(self, expires_in):
self._authorization_endpoint = AuthorizationEndpoint(self._server, self._request, self._client)
return self._authorization_endpoint.implicit(expires_in)<｜fim▁end｜> |
Note: The lines illustrating steps (A) and (B) are broken into two
parts as they pass through the user-agent.
|
<|file_name|>implicitgrant.py<|end_file_name|><｜fim▁begin｜>#-*- coding: utf-8 -*-
from .grant import Grant
from ..endpoint import AuthorizationEndpoint
class ImplicitGrant(Grant):
<|fim_middle|>
<｜fim▁end｜> | """
The implicit grant type is used to obtain access tokens (it does not
support the issuance of refresh tokens) and is optimized for public
clients known to operate a particular redirection URI. These clients
are typically implemented in a browser using a scripting language
such as JavaScript.
+----------+
| Resource |
| Owner |
| |
+----------+
^
|
(B)
+----|-----+ Client Identifier +---------------+
| -+----(A)-- & Redirection URI --->| |
| User- | | Authorization |
| Agent -|----(B)-- User authenticates -->| Server |
| | | |
| |<---(C)--- Redirection URI ----<| |
| | with Access Token +---------------+
| | in Fragment
| | +---------------+
| |----(D)--- Redirection URI ---->| Web-Hosted |
| | without Fragment | Client |
| | | Resource |
| (F) |<---(E)------- Script ---------<| |
| | +---------------+
+-|--------+
| |
(A) (G) Access Token
| |
^ v
+---------+
| |
| Client |
| |
+---------+
Note: The lines illustrating steps (A) and (B) are broken into two
parts as they pass through the user-agent.
Figure 4: Implicit Grant Flow
"""
def get_redirection_uri(self, expires_in):
self._authorization_endpoint = AuthorizationEndpoint(self._server, self._request, self._client)
return self._authorization_endpoint.implicit(expires_in) |
<|file_name|>implicitgrant.py<|end_file_name|><｜fim▁begin｜>#-*- coding: utf-8 -*-
from .grant import Grant
from ..endpoint import AuthorizationEndpoint
class ImplicitGrant(Grant):
"""
The implicit grant type is used to obtain access tokens (it does not
support the issuance of refresh tokens) and is optimized for public
clients known to operate a particular redirection URI. These clients
are typically implemented in a browser using a scripting language
such as JavaScript.
+----------+
| Resource |
| Owner |
| |
+----------+
^
|
(B)
+----|-----+ Client Identifier +---------------+
| -+----(A)-- & Redirection URI --->| |
| User- | | Authorization |
| Agent -|----(B)-- User authenticates -->| Server |
| | | |
| |<---(C)--- Redirection URI ----<| |
| | with Access Token +---------------+
| | in Fragment
| | +---------------+
| |----(D)--- Redirection URI ---->| Web-Hosted |
| | without Fragment | Client |
| | | Resource |
| (F) |<---(E)------- Script ---------<| |
| | +---------------+
+-|--------+
| |
(A) (G) Access Token
| |
^ v
+---------+
| |
| Client |
| |
+---------+
Note: The lines illustrating steps (A) and (B) are broken into two
parts as they pass through the user-agent.
Figure 4: Implicit Grant Flow
"""
def get_redirection_uri(self, expires_in):
<|fim_middle|>
<｜fim▁end｜> | self._authorization_endpoint = AuthorizationEndpoint(self._server, self._request, self._client)
return self._authorization_endpoint.implicit(expires_in) |
<|file_name|>implicitgrant.py<|end_file_name|><｜fim▁begin｜>#-*- coding: utf-8 -*-
from .grant import Grant
from ..endpoint import AuthorizationEndpoint
class ImplicitGrant(Grant):
"""
The implicit grant type is used to obtain access tokens (it does not
support the issuance of refresh tokens) and is optimized for public
clients known to operate a particular redirection URI. These clients
are typically implemented in a browser using a scripting language
such as JavaScript.
+----------+
| Resource |
| Owner |
| |
+----------+
^
|
(B)
+----|-----+ Client Identifier +---------------+
| -+----(A)-- & Redirection URI --->| |
| User- | | Authorization |
| Agent -|----(B)-- User authenticates -->| Server |
| | | |
| |<---(C)--- Redirection URI ----<| |
| | with Access Token +---------------+
| | in Fragment
| | +---------------+
| |----(D)--- Redirection URI ---->| Web-Hosted |
| | without Fragment | Client |
| | | Resource |
| (F) |<---(E)------- Script ---------<| |
| | +---------------+
+-|--------+
| |
(A) (G) Access Token
| |
^ v
+---------+
| |
| Client |
| |
+---------+
Note: The lines illustrating steps (A) and (B) are broken into two
parts as they pass through the user-agent.
Figure 4: Implicit Grant Flow
"""
def <|fim_middle|>(self, expires_in):
self._authorization_endpoint = AuthorizationEndpoint(self._server, self._request, self._client)
return self._authorization_endpoint.implicit(expires_in)
<｜fim▁end｜> | get_redirection_uri |
<|file_name|>__init__.py<|end_file_name|><｜fim▁begin｜>VERSION = (1, 0, 0,)
__version__ = '.'.join(map(str, VERSION))
default_app_config = 'admin_sso.apps.AdminSSOConfig'
# Do not use Django settings at module level as recommended
try:
from django.utils.functional import LazyObject
except ImportError:<｜fim▁hole｜>else:
class LazySettings(LazyObject):
def _setup(self):
from admin_sso import default_settings
self._wrapped = Settings(default_settings)
class Settings(object):
def __init__(self, settings_module):
for setting in dir(settings_module):
if setting == setting.upper():
setattr(self, setting, getattr(settings_module, setting))
settings = LazySettings()<｜fim▁end｜> | pass |
<|file_name|>__init__.py<|end_file_name|><｜fim▁begin｜>VERSION = (1, 0, 0,)
__version__ = '.'.join(map(str, VERSION))
default_app_config = 'admin_sso.apps.AdminSSOConfig'
# Do not use Django settings at module level as recommended
try:
from django.utils.functional import LazyObject
except ImportError:
pass
else:
class LazySettings(LazyObject):
<|fim_middle|>
class Settings(object):
def __init__(self, settings_module):
for setting in dir(settings_module):
if setting == setting.upper():
setattr(self, setting, getattr(settings_module, setting))
settings = LazySettings()
<｜fim▁end｜> | def _setup(self):
from admin_sso import default_settings
self._wrapped = Settings(default_settings) |
<|file_name|>__init__.py<|end_file_name|><｜fim▁begin｜>VERSION = (1, 0, 0,)
__version__ = '.'.join(map(str, VERSION))
default_app_config = 'admin_sso.apps.AdminSSOConfig'
# Do not use Django settings at module level as recommended
try:
from django.utils.functional import LazyObject
except ImportError:
pass
else:
class LazySettings(LazyObject):
def _setup(self):
<|fim_middle|>
class Settings(object):
def __init__(self, settings_module):
for setting in dir(settings_module):
if setting == setting.upper():
setattr(self, setting, getattr(settings_module, setting))
settings = LazySettings()
<｜fim▁end｜> | from admin_sso import default_settings
self._wrapped = Settings(default_settings) |
<|file_name|>__init__.py<|end_file_name|><｜fim▁begin｜>VERSION = (1, 0, 0,)
__version__ = '.'.join(map(str, VERSION))
default_app_config = 'admin_sso.apps.AdminSSOConfig'
# Do not use Django settings at module level as recommended
try:
from django.utils.functional import LazyObject
except ImportError:
pass
else:
class LazySettings(LazyObject):
def _setup(self):
from admin_sso import default_settings
self._wrapped = Settings(default_settings)
class Settings(object):
<|fim_middle|>
settings = LazySettings()
<｜fim▁end｜> | def __init__(self, settings_module):
for setting in dir(settings_module):
if setting == setting.upper():
setattr(self, setting, getattr(settings_module, setting)) |
<|file_name|>__init__.py<|end_file_name|><｜fim▁begin｜>VERSION = (1, 0, 0,)
__version__ = '.'.join(map(str, VERSION))
default_app_config = 'admin_sso.apps.AdminSSOConfig'
# Do not use Django settings at module level as recommended
try:
from django.utils.functional import LazyObject
except ImportError:
pass
else:
class LazySettings(LazyObject):
def _setup(self):
from admin_sso import default_settings
self._wrapped = Settings(default_settings)
class Settings(object):
def __init__(self, settings_module):
<|fim_middle|>
settings = LazySettings()
<｜fim▁end｜> | for setting in dir(settings_module):
if setting == setting.upper():
setattr(self, setting, getattr(settings_module, setting)) |
<|file_name|>__init__.py<|end_file_name|><｜fim▁begin｜>VERSION = (1, 0, 0,)
__version__ = '.'.join(map(str, VERSION))
default_app_config = 'admin_sso.apps.AdminSSOConfig'
# Do not use Django settings at module level as recommended
try:
from django.utils.functional import LazyObject
except ImportError:
pass
else:
<|fim_middle|>
<｜fim▁end｜> | class LazySettings(LazyObject):
def _setup(self):
from admin_sso import default_settings
self._wrapped = Settings(default_settings)
class Settings(object):
def __init__(self, settings_module):
for setting in dir(settings_module):
if setting == setting.upper():
setattr(self, setting, getattr(settings_module, setting))
settings = LazySettings() |
<|file_name|>__init__.py<|end_file_name|><｜fim▁begin｜>VERSION = (1, 0, 0,)
__version__ = '.'.join(map(str, VERSION))
default_app_config = 'admin_sso.apps.AdminSSOConfig'
# Do not use Django settings at module level as recommended
try:
from django.utils.functional import LazyObject
except ImportError:
pass
else:
class LazySettings(LazyObject):
def _setup(self):
from admin_sso import default_settings
self._wrapped = Settings(default_settings)
class Settings(object):
def __init__(self, settings_module):
for setting in dir(settings_module):
if setting == setting.upper():
<|fim_middle|>
settings = LazySettings()
<｜fim▁end｜> | setattr(self, setting, getattr(settings_module, setting)) |
<|file_name|>__init__.py<|end_file_name|><｜fim▁begin｜>VERSION = (1, 0, 0,)
__version__ = '.'.join(map(str, VERSION))
default_app_config = 'admin_sso.apps.AdminSSOConfig'
# Do not use Django settings at module level as recommended
try:
from django.utils.functional import LazyObject
except ImportError:
pass
else:
class LazySettings(LazyObject):
def <|fim_middle|>(self):
from admin_sso import default_settings
self._wrapped = Settings(default_settings)
class Settings(object):
def __init__(self, settings_module):
for setting in dir(settings_module):
if setting == setting.upper():
setattr(self, setting, getattr(settings_module, setting))
settings = LazySettings()
<｜fim▁end｜> | _setup |
<|file_name|>__init__.py<|end_file_name|><｜fim▁begin｜>VERSION = (1, 0, 0,)
__version__ = '.'.join(map(str, VERSION))
default_app_config = 'admin_sso.apps.AdminSSOConfig'
# Do not use Django settings at module level as recommended
try:
from django.utils.functional import LazyObject
except ImportError:
pass
else:
class LazySettings(LazyObject):
def _setup(self):
from admin_sso import default_settings
self._wrapped = Settings(default_settings)
class Settings(object):
def <|fim_middle|>(self, settings_module):
for setting in dir(settings_module):
if setting == setting.upper():
setattr(self, setting, getattr(settings_module, setting))
settings = LazySettings()
<｜fim▁end｜> | __init__ |
<|file_name|>test_write_worksheet.py<|end_file_name|><｜fim▁begin｜>###############################################################################
#
# Tests for XlsxWriter.
#
# Copyright (c), 2013-2015, John McNamara, [email protected]<｜fim▁hole｜>from ...worksheet import Worksheet
class TestWriteWorksheet(unittest.TestCase):
"""
Test the Worksheet _write_worksheet() method.
"""
def setUp(self):
self.fh = StringIO()
self.worksheet = Worksheet()
self.worksheet._set_filehandle(self.fh)
def test_write_worksheet(self):
"""Test the _write_worksheet() method"""
self.worksheet._write_worksheet()
exp = """<worksheet xmlns="http://schemas.openxmlformats.org/spreadsheetml/2006/main" xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships">"""
got = self.fh.getvalue()
self.assertEqual(got, exp)<｜fim▁end｜> | #
import unittest
from ...compatibility import StringIO |
<|file_name|>test_write_worksheet.py<|end_file_name|><｜fim▁begin｜>###############################################################################
#
# Tests for XlsxWriter.
#
# Copyright (c), 2013-2015, John McNamara, [email protected]
#
import unittest
from ...compatibility import StringIO
from ...worksheet import Worksheet
class TestWriteWorksheet(unittest.TestCase):
<|fim_middle|>
<｜fim▁end｜> | """
Test the Worksheet _write_worksheet() method.
"""
def setUp(self):
self.fh = StringIO()
self.worksheet = Worksheet()
self.worksheet._set_filehandle(self.fh)
def test_write_worksheet(self):
"""Test the _write_worksheet() method"""
self.worksheet._write_worksheet()
exp = """<worksheet xmlns="http://schemas.openxmlformats.org/spreadsheetml/2006/main" xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships">"""
got = self.fh.getvalue()
self.assertEqual(got, exp) |
<|file_name|>test_write_worksheet.py<|end_file_name|><｜fim▁begin｜>###############################################################################
#
# Tests for XlsxWriter.
#
# Copyright (c), 2013-2015, John McNamara, [email protected]
#
import unittest
from ...compatibility import StringIO
from ...worksheet import Worksheet
class TestWriteWorksheet(unittest.TestCase):
"""
Test the Worksheet _write_worksheet() method.
"""
def setUp(self):
<|fim_middle|>
def test_write_worksheet(self):
"""Test the _write_worksheet() method"""
self.worksheet._write_worksheet()
exp = """<worksheet xmlns="http://schemas.openxmlformats.org/spreadsheetml/2006/main" xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships">"""
got = self.fh.getvalue()
self.assertEqual(got, exp)
<｜fim▁end｜> | self.fh = StringIO()
self.worksheet = Worksheet()
self.worksheet._set_filehandle(self.fh) |
<|file_name|>test_write_worksheet.py<|end_file_name|><｜fim▁begin｜>###############################################################################
#
# Tests for XlsxWriter.
#
# Copyright (c), 2013-2015, John McNamara, [email protected]
#
import unittest
from ...compatibility import StringIO
from ...worksheet import Worksheet
class TestWriteWorksheet(unittest.TestCase):
"""
Test the Worksheet _write_worksheet() method.
"""
def setUp(self):
self.fh = StringIO()
self.worksheet = Worksheet()
self.worksheet._set_filehandle(self.fh)
def test_write_worksheet(self):
<|fim_middle|>
<｜fim▁end｜> | """Test the _write_worksheet() method"""
self.worksheet._write_worksheet()
exp = """<worksheet xmlns="http://schemas.openxmlformats.org/spreadsheetml/2006/main" xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships">"""
got = self.fh.getvalue()
self.assertEqual(got, exp) |
<|file_name|>test_write_worksheet.py<|end_file_name|><｜fim▁begin｜>###############################################################################
#
# Tests for XlsxWriter.
#
# Copyright (c), 2013-2015, John McNamara, [email protected]
#
import unittest
from ...compatibility import StringIO
from ...worksheet import Worksheet
class TestWriteWorksheet(unittest.TestCase):
"""
Test the Worksheet _write_worksheet() method.
"""
def <|fim_middle|>(self):
self.fh = StringIO()
self.worksheet = Worksheet()
self.worksheet._set_filehandle(self.fh)
def test_write_worksheet(self):
"""Test the _write_worksheet() method"""
self.worksheet._write_worksheet()
exp = """<worksheet xmlns="http://schemas.openxmlformats.org/spreadsheetml/2006/main" xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships">"""
got = self.fh.getvalue()
self.assertEqual(got, exp)
<｜fim▁end｜> | setUp |
<|file_name|>test_write_worksheet.py<|end_file_name|><｜fim▁begin｜>###############################################################################
#
# Tests for XlsxWriter.
#
# Copyright (c), 2013-2015, John McNamara, [email protected]
#
import unittest
from ...compatibility import StringIO
from ...worksheet import Worksheet
class TestWriteWorksheet(unittest.TestCase):
"""
Test the Worksheet _write_worksheet() method.
"""
def setUp(self):
self.fh = StringIO()
self.worksheet = Worksheet()
self.worksheet._set_filehandle(self.fh)
def <|fim_middle|>(self):
"""Test the _write_worksheet() method"""
self.worksheet._write_worksheet()
exp = """<worksheet xmlns="http://schemas.openxmlformats.org/spreadsheetml/2006/main" xmlns:r="http://schemas.openxmlformats.org/officeDocument/2006/relationships">"""
got = self.fh.getvalue()
self.assertEqual(got, exp)
<｜fim▁end｜> | test_write_worksheet |
<|file_name|>0001_initial.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models, migrations
from django.conf import settings
import django_pgjson.fields
import django.utils.timezone
import django.db.models.deletion
import djorm_pgarray.fields
import taiga.projects.history.models
class Migration(migrations.Migration):
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
('users', '0002_auto_20140903_0916'),
]
operations = [
migrations.CreateModel(
name='Membership',
fields=[
('id', models.AutoField(serialize=False, primary_key=True, auto_created=True, verbose_name='ID')),
('is_owner', models.BooleanField(default=False)),
('email', models.EmailField(max_length=255, null=True, default=None, verbose_name='email', blank=True)),
('created_at', models.DateTimeField(default=django.utils.timezone.now, verbose_name='creado el')),
('token', models.CharField(max_length=60, null=True, default=None, verbose_name='token', blank=True)),
('invited_by_id', models.IntegerField(null=True, blank=True)),
],
options={
'ordering': ['project', 'user__full_name', 'user__username', 'user__email', 'email'],
'verbose_name_plural': 'membershipss',
'permissions': (('view_membership', 'Can view membership'),),
'verbose_name': 'membership',
},
bases=(models.Model,),
),
migrations.CreateModel(
name='Project',
fields=[
('id', models.AutoField(serialize=False, primary_key=True, auto_created=True, verbose_name='ID')),
('tags', djorm_pgarray.fields.TextArrayField(dbtype='text', verbose_name='tags')),
('name', models.CharField(max_length=250, unique=True, verbose_name='name')),
('slug', models.SlugField(max_length=250, unique=True, verbose_name='slug', blank=True)),
('description', models.TextField(verbose_name='description')),
('created_date', models.DateTimeField(default=django.utils.timezone.now, verbose_name='created date')),
('modified_date', models.DateTimeField(verbose_name='modified date')),
('total_milestones', models.IntegerField(null=True, default=0, verbose_name='total of milestones', blank=True)),
('total_story_points', models.FloatField(default=0, verbose_name='total story points')),
('is_backlog_activated', models.BooleanField(default=True, verbose_name='active backlog panel')),
('is_kanban_activated', models.BooleanField(default=False, verbose_name='active kanban panel')),
('is_wiki_activated', models.BooleanField(default=True, verbose_name='active wiki panel')),
('is_issues_activated', models.BooleanField(default=True, verbose_name='active issues panel')),
('videoconferences', models.CharField(max_length=250, null=True, choices=[('appear-in', 'AppearIn'), ('talky', 'Talky'), ('jitsi', 'Jitsi')], verbose_name='videoconference system', blank=True)),
('videoconferences_salt', models.CharField(max_length=250, null=True, verbose_name='videoconference room salt', blank=True)),
('anon_permissions', djorm_pgarray.fields.TextArrayField(choices=[('view_project', 'View project'), ('view_milestones', 'View milestones'), ('view_us', 'View user stories'), ('view_tasks', 'View tasks'), ('view_issues', 'View issues'), ('view_wiki_pages', 'View wiki pages'), ('view_wiki_links', 'View wiki links')], dbtype='text', default=[], verbose_name='anonymous permissions')),
('public_permissions', djorm_pgarray.fields.TextArrayField(choices=[('view_project', 'View project'), ('view_milestones', 'View milestones'), ('view_us', 'View user stories'), ('view_issues', 'View issues'), ('vote_issues', 'Vote issues'), ('view_tasks', 'View tasks'), ('view_wiki_pages', 'View wiki pages'), ('view_wiki_links', 'View wiki links'), ('request_membership', 'Request membership'), ('add_us_to_project', 'Add user story to project'), ('add_comments_to_us', 'Add comments to user stories'), ('add_comments_to_task', 'Add comments to tasks'), ('add_issue', 'Add issues'), ('add_comments_issue', 'Add comments to issues'), ('add_wiki_page', 'Add wiki page'), ('modify_wiki_page', 'Modify wiki page'), ('add_wiki_link', 'Add wiki link'), ('modify_wiki_link', 'Modify wiki link')], dbtype='text', default=[], verbose_name='user permissions')),
('is_private', models.BooleanField(default=False, verbose_name='is private')),
('tags_colors', djorm_pgarray.fields.TextArrayField(dbtype='text', dimension=2, default=[], null=False, verbose_name='tags colors')),<｜fim▁hole｜> ],
options={
'ordering': ['name'],
'verbose_name_plural': 'projects',
'permissions': (('view_project', 'Can view project'),),
'verbose_name': 'project',
},
bases=(models.Model,),
),
migrations.AddField(
model_name='project',
name='members',
field=models.ManyToManyField(to=settings.AUTH_USER_MODEL, related_name='projects', verbose_name='members', through='projects.Membership'),
preserve_default=True,
),
migrations.AddField(
model_name='project',
name='owner',
field=models.ForeignKey(to=settings.AUTH_USER_MODEL, related_name='owned_projects', verbose_name='owner'),
preserve_default=True,
),
migrations.AddField(
model_name='membership',
name='user',
field=models.ForeignKey(blank=True, default=None, to=settings.AUTH_USER_MODEL, null=True, related_name='memberships'),
preserve_default=True,
),
migrations.AddField(
model_name='membership',
name='project',
field=models.ForeignKey(default=1, to='projects.Project', related_name='memberships'),
preserve_default=False,
),
migrations.AlterUniqueTogether(
name='membership',
unique_together=set([('user', 'project')]),
),
migrations.AddField(
model_name='membership',
name='role',
field=models.ForeignKey(related_name='memberships', to='users.Role', default=1),
preserve_default=False,
),
]<｜fim▁end｜> | |
<|file_name|>0001_initial.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models, migrations
from django.conf import settings
import django_pgjson.fields
import django.utils.timezone
import django.db.models.deletion
import djorm_pgarray.fields
import taiga.projects.history.models
class Migration(migrations.Migration):
<|fim_middle|>
<｜fim▁end｜> | dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
('users', '0002_auto_20140903_0916'),
]
operations = [
migrations.CreateModel(
name='Membership',
fields=[
('id', models.AutoField(serialize=False, primary_key=True, auto_created=True, verbose_name='ID')),
('is_owner', models.BooleanField(default=False)),
('email', models.EmailField(max_length=255, null=True, default=None, verbose_name='email', blank=True)),
('created_at', models.DateTimeField(default=django.utils.timezone.now, verbose_name='creado el')),
('token', models.CharField(max_length=60, null=True, default=None, verbose_name='token', blank=True)),
('invited_by_id', models.IntegerField(null=True, blank=True)),
],
options={
'ordering': ['project', 'user__full_name', 'user__username', 'user__email', 'email'],
'verbose_name_plural': 'membershipss',
'permissions': (('view_membership', 'Can view membership'),),
'verbose_name': 'membership',
},
bases=(models.Model,),
),
migrations.CreateModel(
name='Project',
fields=[
('id', models.AutoField(serialize=False, primary_key=True, auto_created=True, verbose_name='ID')),
('tags', djorm_pgarray.fields.TextArrayField(dbtype='text', verbose_name='tags')),
('name', models.CharField(max_length=250, unique=True, verbose_name='name')),
('slug', models.SlugField(max_length=250, unique=True, verbose_name='slug', blank=True)),
('description', models.TextField(verbose_name='description')),
('created_date', models.DateTimeField(default=django.utils.timezone.now, verbose_name='created date')),
('modified_date', models.DateTimeField(verbose_name='modified date')),
('total_milestones', models.IntegerField(null=True, default=0, verbose_name='total of milestones', blank=True)),
('total_story_points', models.FloatField(default=0, verbose_name='total story points')),
('is_backlog_activated', models.BooleanField(default=True, verbose_name='active backlog panel')),
('is_kanban_activated', models.BooleanField(default=False, verbose_name='active kanban panel')),
('is_wiki_activated', models.BooleanField(default=True, verbose_name='active wiki panel')),
('is_issues_activated', models.BooleanField(default=True, verbose_name='active issues panel')),
('videoconferences', models.CharField(max_length=250, null=True, choices=[('appear-in', 'AppearIn'), ('talky', 'Talky'), ('jitsi', 'Jitsi')], verbose_name='videoconference system', blank=True)),
('videoconferences_salt', models.CharField(max_length=250, null=True, verbose_name='videoconference room salt', blank=True)),
('anon_permissions', djorm_pgarray.fields.TextArrayField(choices=[('view_project', 'View project'), ('view_milestones', 'View milestones'), ('view_us', 'View user stories'), ('view_tasks', 'View tasks'), ('view_issues', 'View issues'), ('view_wiki_pages', 'View wiki pages'), ('view_wiki_links', 'View wiki links')], dbtype='text', default=[], verbose_name='anonymous permissions')),
('public_permissions', djorm_pgarray.fields.TextArrayField(choices=[('view_project', 'View project'), ('view_milestones', 'View milestones'), ('view_us', 'View user stories'), ('view_issues', 'View issues'), ('vote_issues', 'Vote issues'), ('view_tasks', 'View tasks'), ('view_wiki_pages', 'View wiki pages'), ('view_wiki_links', 'View wiki links'), ('request_membership', 'Request membership'), ('add_us_to_project', 'Add user story to project'), ('add_comments_to_us', 'Add comments to user stories'), ('add_comments_to_task', 'Add comments to tasks'), ('add_issue', 'Add issues'), ('add_comments_issue', 'Add comments to issues'), ('add_wiki_page', 'Add wiki page'), ('modify_wiki_page', 'Modify wiki page'), ('add_wiki_link', 'Add wiki link'), ('modify_wiki_link', 'Modify wiki link')], dbtype='text', default=[], verbose_name='user permissions')),
('is_private', models.BooleanField(default=False, verbose_name='is private')),
('tags_colors', djorm_pgarray.fields.TextArrayField(dbtype='text', dimension=2, default=[], null=False, verbose_name='tags colors')),
],
options={
'ordering': ['name'],
'verbose_name_plural': 'projects',
'permissions': (('view_project', 'Can view project'),),
'verbose_name': 'project',
},
bases=(models.Model,),
),
migrations.AddField(
model_name='project',
name='members',
field=models.ManyToManyField(to=settings.AUTH_USER_MODEL, related_name='projects', verbose_name='members', through='projects.Membership'),
preserve_default=True,
),
migrations.AddField(
model_name='project',
name='owner',
field=models.ForeignKey(to=settings.AUTH_USER_MODEL, related_name='owned_projects', verbose_name='owner'),
preserve_default=True,
),
migrations.AddField(
model_name='membership',
name='user',
field=models.ForeignKey(blank=True, default=None, to=settings.AUTH_USER_MODEL, null=True, related_name='memberships'),
preserve_default=True,
),
migrations.AddField(
model_name='membership',
name='project',
field=models.ForeignKey(default=1, to='projects.Project', related_name='memberships'),
preserve_default=False,
),
migrations.AlterUniqueTogether(
name='membership',
unique_together=set([('user', 'project')]),
),
migrations.AddField(
model_name='membership',
name='role',
field=models.ForeignKey(related_name='memberships', to='users.Role', default=1),
preserve_default=False,
),
] |
<|file_name|>msp.py<|end_file_name|><｜fim▁begin｜>import sys
import cv2
import helper as hp
class MSP():
name = "MSP"
def __init__(self):
self.__patterns_num = []<｜fim▁hole｜> self.__labels_num = []
self.__labels_sym = []
msp_num, msp_sym = "msp/num", "msp/sym"
self.__load_num_patterns(msp_num)
self.__load_sym_patterns(msp_sym)
print 'loading MSP...'
def __load_num_patterns(self, input_dir):
paths = hp.get_paths(input_dir)
self.__patterns_num = [hp.get_gray_image(input_dir, path) for path in paths]
self.__labels_num = [hp.get_test(path, "num")[0] for path in paths]
def __load_sym_patterns(self, input_dir):
paths = hp.get_paths(input_dir)
self.__patterns_sym = [hp.get_gray_image(input_dir, path) for path in paths]
self.__labels_sym = [hp.get_test(path, "sym")[0] for path in paths]
def __get_mode(self, mode):
if mode == "num":
return self.__labels_num, self.__patterns_num
elif mode == "sym":
return self.__labels_sym, self.__patterns_sym
def rec(self, img, mode):
tmp_max, tmp, rec = sys.maxint, 0, 0
labels, patterns = self.__get_mode(mode)
for pattern, label in zip(patterns, labels):
tmp = cv2.countNonZero(pattern - img)
if tmp < tmp_max: tmp_max, rec = tmp, label
return rec<｜fim▁end｜> | self.__patterns_sym = [] |
<|file_name|>msp.py<|end_file_name|><｜fim▁begin｜>import sys
import cv2
import helper as hp
class MSP():
<|fim_middle|>
<｜fim▁end｜> | name = "MSP"
def __init__(self):
self.__patterns_num = []
self.__patterns_sym = []
self.__labels_num = []
self.__labels_sym = []
msp_num, msp_sym = "msp/num", "msp/sym"
self.__load_num_patterns(msp_num)
self.__load_sym_patterns(msp_sym)
print 'loading MSP...'
def __load_num_patterns(self, input_dir):
paths = hp.get_paths(input_dir)
self.__patterns_num = [hp.get_gray_image(input_dir, path) for path in paths]
self.__labels_num = [hp.get_test(path, "num")[0] for path in paths]
def __load_sym_patterns(self, input_dir):
paths = hp.get_paths(input_dir)
self.__patterns_sym = [hp.get_gray_image(input_dir, path) for path in paths]
self.__labels_sym = [hp.get_test(path, "sym")[0] for path in paths]
def __get_mode(self, mode):
if mode == "num":
return self.__labels_num, self.__patterns_num
elif mode == "sym":
return self.__labels_sym, self.__patterns_sym
def rec(self, img, mode):
tmp_max, tmp, rec = sys.maxint, 0, 0
labels, patterns = self.__get_mode(mode)
for pattern, label in zip(patterns, labels):
tmp = cv2.countNonZero(pattern - img)
if tmp < tmp_max: tmp_max, rec = tmp, label
return rec |
<|file_name|>msp.py<|end_file_name|><｜fim▁begin｜>import sys
import cv2
import helper as hp
class MSP():
name = "MSP"
def __init__(self):
<|fim_middle|>
def __load_num_patterns(self, input_dir):
paths = hp.get_paths(input_dir)
self.__patterns_num = [hp.get_gray_image(input_dir, path) for path in paths]
self.__labels_num = [hp.get_test(path, "num")[0] for path in paths]
def __load_sym_patterns(self, input_dir):
paths = hp.get_paths(input_dir)
self.__patterns_sym = [hp.get_gray_image(input_dir, path) for path in paths]
self.__labels_sym = [hp.get_test(path, "sym")[0] for path in paths]
def __get_mode(self, mode):
if mode == "num":
return self.__labels_num, self.__patterns_num
elif mode == "sym":
return self.__labels_sym, self.__patterns_sym
def rec(self, img, mode):
tmp_max, tmp, rec = sys.maxint, 0, 0
labels, patterns = self.__get_mode(mode)
for pattern, label in zip(patterns, labels):
tmp = cv2.countNonZero(pattern - img)
if tmp < tmp_max: tmp_max, rec = tmp, label
return rec
<｜fim▁end｜> | self.__patterns_num = []
self.__patterns_sym = []
self.__labels_num = []
self.__labels_sym = []
msp_num, msp_sym = "msp/num", "msp/sym"
self.__load_num_patterns(msp_num)
self.__load_sym_patterns(msp_sym)
print 'loading MSP...' |
<|file_name|>msp.py<|end_file_name|><｜fim▁begin｜>import sys
import cv2
import helper as hp
class MSP():
name = "MSP"
def __init__(self):
self.__patterns_num = []
self.__patterns_sym = []
self.__labels_num = []
self.__labels_sym = []
msp_num, msp_sym = "msp/num", "msp/sym"
self.__load_num_patterns(msp_num)
self.__load_sym_patterns(msp_sym)
print 'loading MSP...'
def __load_num_patterns(self, input_dir):
<|fim_middle|>
def __load_sym_patterns(self, input_dir):
paths = hp.get_paths(input_dir)
self.__patterns_sym = [hp.get_gray_image(input_dir, path) for path in paths]
self.__labels_sym = [hp.get_test(path, "sym")[0] for path in paths]
def __get_mode(self, mode):
if mode == "num":
return self.__labels_num, self.__patterns_num
elif mode == "sym":
return self.__labels_sym, self.__patterns_sym
def rec(self, img, mode):
tmp_max, tmp, rec = sys.maxint, 0, 0
labels, patterns = self.__get_mode(mode)
for pattern, label in zip(patterns, labels):
tmp = cv2.countNonZero(pattern - img)
if tmp < tmp_max: tmp_max, rec = tmp, label
return rec
<｜fim▁end｜> | paths = hp.get_paths(input_dir)
self.__patterns_num = [hp.get_gray_image(input_dir, path) for path in paths]
self.__labels_num = [hp.get_test(path, "num")[0] for path in paths] |
<|file_name|>msp.py<|end_file_name|><｜fim▁begin｜>import sys
import cv2
import helper as hp
class MSP():
name = "MSP"
def __init__(self):
self.__patterns_num = []
self.__patterns_sym = []
self.__labels_num = []
self.__labels_sym = []
msp_num, msp_sym = "msp/num", "msp/sym"
self.__load_num_patterns(msp_num)
self.__load_sym_patterns(msp_sym)
print 'loading MSP...'
def __load_num_patterns(self, input_dir):
paths = hp.get_paths(input_dir)
self.__patterns_num = [hp.get_gray_image(input_dir, path) for path in paths]
self.__labels_num = [hp.get_test(path, "num")[0] for path in paths]
def __load_sym_patterns(self, input_dir):
<|fim_middle|>
def __get_mode(self, mode):
if mode == "num":
return self.__labels_num, self.__patterns_num
elif mode == "sym":
return self.__labels_sym, self.__patterns_sym
def rec(self, img, mode):
tmp_max, tmp, rec = sys.maxint, 0, 0
labels, patterns = self.__get_mode(mode)
for pattern, label in zip(patterns, labels):
tmp = cv2.countNonZero(pattern - img)
if tmp < tmp_max: tmp_max, rec = tmp, label
return rec
<｜fim▁end｜> | paths = hp.get_paths(input_dir)
self.__patterns_sym = [hp.get_gray_image(input_dir, path) for path in paths]
self.__labels_sym = [hp.get_test(path, "sym")[0] for path in paths] |
<|file_name|>msp.py<|end_file_name|><｜fim▁begin｜>import sys
import cv2
import helper as hp
class MSP():
name = "MSP"
def __init__(self):
self.__patterns_num = []
self.__patterns_sym = []
self.__labels_num = []
self.__labels_sym = []
msp_num, msp_sym = "msp/num", "msp/sym"
self.__load_num_patterns(msp_num)
self.__load_sym_patterns(msp_sym)
print 'loading MSP...'
def __load_num_patterns(self, input_dir):
paths = hp.get_paths(input_dir)
self.__patterns_num = [hp.get_gray_image(input_dir, path) for path in paths]
self.__labels_num = [hp.get_test(path, "num")[0] for path in paths]
def __load_sym_patterns(self, input_dir):
paths = hp.get_paths(input_dir)
self.__patterns_sym = [hp.get_gray_image(input_dir, path) for path in paths]
self.__labels_sym = [hp.get_test(path, "sym")[0] for path in paths]
def __get_mode(self, mode):
<|fim_middle|>
def rec(self, img, mode):
tmp_max, tmp, rec = sys.maxint, 0, 0
labels, patterns = self.__get_mode(mode)
for pattern, label in zip(patterns, labels):
tmp = cv2.countNonZero(pattern - img)
if tmp < tmp_max: tmp_max, rec = tmp, label
return rec
<｜fim▁end｜> | if mode == "num":
return self.__labels_num, self.__patterns_num
elif mode == "sym":
return self.__labels_sym, self.__patterns_sym |
<|file_name|>msp.py<|end_file_name|><｜fim▁begin｜>import sys
import cv2
import helper as hp
class MSP():
name = "MSP"
def __init__(self):
self.__patterns_num = []
self.__patterns_sym = []
self.__labels_num = []
self.__labels_sym = []
msp_num, msp_sym = "msp/num", "msp/sym"
self.__load_num_patterns(msp_num)
self.__load_sym_patterns(msp_sym)
print 'loading MSP...'
def __load_num_patterns(self, input_dir):
paths = hp.get_paths(input_dir)
self.__patterns_num = [hp.get_gray_image(input_dir, path) for path in paths]
self.__labels_num = [hp.get_test(path, "num")[0] for path in paths]
def __load_sym_patterns(self, input_dir):
paths = hp.get_paths(input_dir)
self.__patterns_sym = [hp.get_gray_image(input_dir, path) for path in paths]
self.__labels_sym = [hp.get_test(path, "sym")[0] for path in paths]
def __get_mode(self, mode):
if mode == "num":
return self.__labels_num, self.__patterns_num
elif mode == "sym":
return self.__labels_sym, self.__patterns_sym
def rec(self, img, mode):
<|fim_middle|>
<｜fim▁end｜> | tmp_max, tmp, rec = sys.maxint, 0, 0
labels, patterns = self.__get_mode(mode)
for pattern, label in zip(patterns, labels):
tmp = cv2.countNonZero(pattern - img)
if tmp < tmp_max: tmp_max, rec = tmp, label
return rec |
<|file_name|>msp.py<|end_file_name|><｜fim▁begin｜>import sys
import cv2
import helper as hp
class MSP():
name = "MSP"
def __init__(self):
self.__patterns_num = []
self.__patterns_sym = []
self.__labels_num = []
self.__labels_sym = []
msp_num, msp_sym = "msp/num", "msp/sym"
self.__load_num_patterns(msp_num)
self.__load_sym_patterns(msp_sym)
print 'loading MSP...'
def __load_num_patterns(self, input_dir):
paths = hp.get_paths(input_dir)
self.__patterns_num = [hp.get_gray_image(input_dir, path) for path in paths]
self.__labels_num = [hp.get_test(path, "num")[0] for path in paths]
def __load_sym_patterns(self, input_dir):
paths = hp.get_paths(input_dir)
self.__patterns_sym = [hp.get_gray_image(input_dir, path) for path in paths]
self.__labels_sym = [hp.get_test(path, "sym")[0] for path in paths]
def __get_mode(self, mode):
if mode == "num":
<|fim_middle|>
elif mode == "sym":
return self.__labels_sym, self.__patterns_sym
def rec(self, img, mode):
tmp_max, tmp, rec = sys.maxint, 0, 0
labels, patterns = self.__get_mode(mode)
for pattern, label in zip(patterns, labels):
tmp = cv2.countNonZero(pattern - img)
if tmp < tmp_max: tmp_max, rec = tmp, label
return rec
<｜fim▁end｜> | return self.__labels_num, self.__patterns_num |
<|file_name|>msp.py<|end_file_name|><｜fim▁begin｜>import sys
import cv2
import helper as hp
class MSP():
name = "MSP"
def __init__(self):
self.__patterns_num = []
self.__patterns_sym = []
self.__labels_num = []
self.__labels_sym = []
msp_num, msp_sym = "msp/num", "msp/sym"
self.__load_num_patterns(msp_num)
self.__load_sym_patterns(msp_sym)
print 'loading MSP...'
def __load_num_patterns(self, input_dir):
paths = hp.get_paths(input_dir)
self.__patterns_num = [hp.get_gray_image(input_dir, path) for path in paths]
self.__labels_num = [hp.get_test(path, "num")[0] for path in paths]
def __load_sym_patterns(self, input_dir):
paths = hp.get_paths(input_dir)
self.__patterns_sym = [hp.get_gray_image(input_dir, path) for path in paths]
self.__labels_sym = [hp.get_test(path, "sym")[0] for path in paths]
def __get_mode(self, mode):
if mode == "num":
return self.__labels_num, self.__patterns_num
elif mode == "sym":
<|fim_middle|>
def rec(self, img, mode):
tmp_max, tmp, rec = sys.maxint, 0, 0
labels, patterns = self.__get_mode(mode)
for pattern, label in zip(patterns, labels):
tmp = cv2.countNonZero(pattern - img)
if tmp < tmp_max: tmp_max, rec = tmp, label
return rec
<｜fim▁end｜> | return self.__labels_sym, self.__patterns_sym |
<|file_name|>msp.py<|end_file_name|><｜fim▁begin｜>import sys
import cv2
import helper as hp
class MSP():
name = "MSP"
def __init__(self):
self.__patterns_num = []
self.__patterns_sym = []
self.__labels_num = []
self.__labels_sym = []
msp_num, msp_sym = "msp/num", "msp/sym"
self.__load_num_patterns(msp_num)
self.__load_sym_patterns(msp_sym)
print 'loading MSP...'
def __load_num_patterns(self, input_dir):
paths = hp.get_paths(input_dir)
self.__patterns_num = [hp.get_gray_image(input_dir, path) for path in paths]
self.__labels_num = [hp.get_test(path, "num")[0] for path in paths]
def __load_sym_patterns(self, input_dir):
paths = hp.get_paths(input_dir)
self.__patterns_sym = [hp.get_gray_image(input_dir, path) for path in paths]
self.__labels_sym = [hp.get_test(path, "sym")[0] for path in paths]
def __get_mode(self, mode):
if mode == "num":
return self.__labels_num, self.__patterns_num
elif mode == "sym":
return self.__labels_sym, self.__patterns_sym
def rec(self, img, mode):
tmp_max, tmp, rec = sys.maxint, 0, 0
labels, patterns = self.__get_mode(mode)
for pattern, label in zip(patterns, labels):
tmp = cv2.countNonZero(pattern - img)
if tmp < tmp_max: <|fim_middle|>
return rec
<｜fim▁end｜> | tmp_max, rec = tmp, label |
<|file_name|>msp.py<|end_file_name|><｜fim▁begin｜>import sys
import cv2
import helper as hp
class MSP():
name = "MSP"
def <|fim_middle|>(self):
self.__patterns_num = []
self.__patterns_sym = []
self.__labels_num = []
self.__labels_sym = []
msp_num, msp_sym = "msp/num", "msp/sym"
self.__load_num_patterns(msp_num)
self.__load_sym_patterns(msp_sym)
print 'loading MSP...'
def __load_num_patterns(self, input_dir):
paths = hp.get_paths(input_dir)
self.__patterns_num = [hp.get_gray_image(input_dir, path) for path in paths]
self.__labels_num = [hp.get_test(path, "num")[0] for path in paths]
def __load_sym_patterns(self, input_dir):
paths = hp.get_paths(input_dir)
self.__patterns_sym = [hp.get_gray_image(input_dir, path) for path in paths]
self.__labels_sym = [hp.get_test(path, "sym")[0] for path in paths]
def __get_mode(self, mode):
if mode == "num":
return self.__labels_num, self.__patterns_num
elif mode == "sym":
return self.__labels_sym, self.__patterns_sym
def rec(self, img, mode):
tmp_max, tmp, rec = sys.maxint, 0, 0
labels, patterns = self.__get_mode(mode)
for pattern, label in zip(patterns, labels):
tmp = cv2.countNonZero(pattern - img)
if tmp < tmp_max: tmp_max, rec = tmp, label
return rec
<｜fim▁end｜> | __init__ |
<|file_name|>msp.py<|end_file_name|><｜fim▁begin｜>import sys
import cv2
import helper as hp
class MSP():
name = "MSP"
def __init__(self):
self.__patterns_num = []
self.__patterns_sym = []
self.__labels_num = []
self.__labels_sym = []
msp_num, msp_sym = "msp/num", "msp/sym"
self.__load_num_patterns(msp_num)
self.__load_sym_patterns(msp_sym)
print 'loading MSP...'
def <|fim_middle|>(self, input_dir):
paths = hp.get_paths(input_dir)
self.__patterns_num = [hp.get_gray_image(input_dir, path) for path in paths]
self.__labels_num = [hp.get_test(path, "num")[0] for path in paths]
def __load_sym_patterns(self, input_dir):
paths = hp.get_paths(input_dir)
self.__patterns_sym = [hp.get_gray_image(input_dir, path) for path in paths]
self.__labels_sym = [hp.get_test(path, "sym")[0] for path in paths]
def __get_mode(self, mode):
if mode == "num":
return self.__labels_num, self.__patterns_num
elif mode == "sym":
return self.__labels_sym, self.__patterns_sym
def rec(self, img, mode):
tmp_max, tmp, rec = sys.maxint, 0, 0
labels, patterns = self.__get_mode(mode)
for pattern, label in zip(patterns, labels):
tmp = cv2.countNonZero(pattern - img)
if tmp < tmp_max: tmp_max, rec = tmp, label
return rec
<｜fim▁end｜> | __load_num_patterns |
<|file_name|>msp.py<|end_file_name|><｜fim▁begin｜>import sys
import cv2
import helper as hp
class MSP():
name = "MSP"
def __init__(self):
self.__patterns_num = []
self.__patterns_sym = []
self.__labels_num = []
self.__labels_sym = []
msp_num, msp_sym = "msp/num", "msp/sym"
self.__load_num_patterns(msp_num)
self.__load_sym_patterns(msp_sym)
print 'loading MSP...'
def __load_num_patterns(self, input_dir):
paths = hp.get_paths(input_dir)
self.__patterns_num = [hp.get_gray_image(input_dir, path) for path in paths]
self.__labels_num = [hp.get_test(path, "num")[0] for path in paths]
def <|fim_middle|>(self, input_dir):
paths = hp.get_paths(input_dir)
self.__patterns_sym = [hp.get_gray_image(input_dir, path) for path in paths]
self.__labels_sym = [hp.get_test(path, "sym")[0] for path in paths]
def __get_mode(self, mode):
if mode == "num":
return self.__labels_num, self.__patterns_num
elif mode == "sym":
return self.__labels_sym, self.__patterns_sym
def rec(self, img, mode):
tmp_max, tmp, rec = sys.maxint, 0, 0
labels, patterns = self.__get_mode(mode)
for pattern, label in zip(patterns, labels):
tmp = cv2.countNonZero(pattern - img)
if tmp < tmp_max: tmp_max, rec = tmp, label
return rec
<｜fim▁end｜> | __load_sym_patterns |
<|file_name|>msp.py<|end_file_name|><｜fim▁begin｜>import sys
import cv2
import helper as hp
class MSP():
name = "MSP"
def __init__(self):
self.__patterns_num = []
self.__patterns_sym = []
self.__labels_num = []
self.__labels_sym = []
msp_num, msp_sym = "msp/num", "msp/sym"
self.__load_num_patterns(msp_num)
self.__load_sym_patterns(msp_sym)
print 'loading MSP...'
def __load_num_patterns(self, input_dir):
paths = hp.get_paths(input_dir)
self.__patterns_num = [hp.get_gray_image(input_dir, path) for path in paths]
self.__labels_num = [hp.get_test(path, "num")[0] for path in paths]
def __load_sym_patterns(self, input_dir):
paths = hp.get_paths(input_dir)
self.__patterns_sym = [hp.get_gray_image(input_dir, path) for path in paths]
self.__labels_sym = [hp.get_test(path, "sym")[0] for path in paths]
def <|fim_middle|>(self, mode):
if mode == "num":
return self.__labels_num, self.__patterns_num
elif mode == "sym":
return self.__labels_sym, self.__patterns_sym
def rec(self, img, mode):
tmp_max, tmp, rec = sys.maxint, 0, 0
labels, patterns = self.__get_mode(mode)
for pattern, label in zip(patterns, labels):
tmp = cv2.countNonZero(pattern - img)
if tmp < tmp_max: tmp_max, rec = tmp, label
return rec
<｜fim▁end｜> | __get_mode |
<|file_name|>msp.py<|end_file_name|><｜fim▁begin｜>import sys
import cv2
import helper as hp
class MSP():
name = "MSP"
def __init__(self):
self.__patterns_num = []
self.__patterns_sym = []
self.__labels_num = []
self.__labels_sym = []
msp_num, msp_sym = "msp/num", "msp/sym"
self.__load_num_patterns(msp_num)
self.__load_sym_patterns(msp_sym)
print 'loading MSP...'
def __load_num_patterns(self, input_dir):
paths = hp.get_paths(input_dir)
self.__patterns_num = [hp.get_gray_image(input_dir, path) for path in paths]
self.__labels_num = [hp.get_test(path, "num")[0] for path in paths]
def __load_sym_patterns(self, input_dir):
paths = hp.get_paths(input_dir)
self.__patterns_sym = [hp.get_gray_image(input_dir, path) for path in paths]
self.__labels_sym = [hp.get_test(path, "sym")[0] for path in paths]
def __get_mode(self, mode):
if mode == "num":
return self.__labels_num, self.__patterns_num
elif mode == "sym":
return self.__labels_sym, self.__patterns_sym
def <|fim_middle|>(self, img, mode):
tmp_max, tmp, rec = sys.maxint, 0, 0
labels, patterns = self.__get_mode(mode)
for pattern, label in zip(patterns, labels):
tmp = cv2.countNonZero(pattern - img)
if tmp < tmp_max: tmp_max, rec = tmp, label
return rec
<｜fim▁end｜> | rec |
<|file_name|>0012_auto_20150422_2019.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models, migrations
class Migration(migrations.Migration):
dependencies = [
('managers', '0011_auto_20150422_2018'),
]
<｜fim▁hole｜> field=models.ImageField(default=b'/static/assets/admin/layout/img/avatar.jpg', upload_to=b'profiles'),
preserve_default=True,
),
]<｜fim▁end｜> | operations = [
migrations.AlterField(
model_name='managerprofile',
name='picture', |
<|file_name|>0012_auto_20150422_2019.py<|end_file_name|><｜fim▁begin｜># -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models, migrations
class Migration(migrations.Migration):
<|fim_middle|>
<｜fim▁end｜> | dependencies = [
('managers', '0011_auto_20150422_2018'),
]
operations = [
migrations.AlterField(
model_name='managerprofile',
name='picture',
field=models.ImageField(default=b'/static/assets/admin/layout/img/avatar.jpg', upload_to=b'profiles'),
preserve_default=True,
),
] |
<|file_name|>test_expand_path.py<|end_file_name|><｜fim▁begin｜># coding=utf-8
# Copyright 2014 Pants project contributors (see CONTRIBUTORS.md).
# Licensed under the Apache License, Version 2.0 (see LICENSE).
from __future__ import (nested_scopes, generators, division, absolute_import, with_statement,
print_function, unicode_literals)
from contextlib import contextmanager
import os
import unittest2 as unittest
from pants.fs.fs import expand_path
from pants.util.contextutil import environment_as, pushd, temporary_dir
class ExpandPathTest(unittest.TestCase):
def test_pure_relative(self):
with self.root() as root:
self.assertEquals(os.path.join(root, 'a'), expand_path('a'))
<｜fim▁hole｜> def test_dot_relative(self):
with self.root() as root:
self.assertEquals(os.path.join(root, 'a'), expand_path('./a'))
def test_absolute(self):
self.assertEquals('/tmp/jake/bob', expand_path('/tmp/jake/bob'))
def test_user_expansion(self):
with environment_as(HOME='/tmp/jake'):
self.assertEquals('/tmp/jake/bob', expand_path('~/bob'))
def test_env_var_expansion(self):
with self.root() as root:
with environment_as(A='B', C='D'):
self.assertEquals(os.path.join(root, 'B/D/E'), expand_path('$A/${C}/E'))
@contextmanager
def root(self):
with temporary_dir() as root:
# Avoid OSX issues where tmp dirs are reported as symlinks.
real_root = os.path.realpath(root)
with pushd(real_root):
yield real_root<｜fim▁end｜> | |
<|file_name|>test_expand_path.py<|end_file_name|><｜fim▁begin｜># coding=utf-8
# Copyright 2014 Pants project contributors (see CONTRIBUTORS.md).
# Licensed under the Apache License, Version 2.0 (see LICENSE).
from __future__ import (nested_scopes, generators, division, absolute_import, with_statement,
print_function, unicode_literals)
from contextlib import contextmanager
import os
import unittest2 as unittest
from pants.fs.fs import expand_path
from pants.util.contextutil import environment_as, pushd, temporary_dir
class ExpandPathTest(unittest.TestCase):
<|fim_middle|>
<｜fim▁end｜> | def test_pure_relative(self):
with self.root() as root:
self.assertEquals(os.path.join(root, 'a'), expand_path('a'))
def test_dot_relative(self):
with self.root() as root:
self.assertEquals(os.path.join(root, 'a'), expand_path('./a'))
def test_absolute(self):
self.assertEquals('/tmp/jake/bob', expand_path('/tmp/jake/bob'))
def test_user_expansion(self):
with environment_as(HOME='/tmp/jake'):
self.assertEquals('/tmp/jake/bob', expand_path('~/bob'))
def test_env_var_expansion(self):
with self.root() as root:
with environment_as(A='B', C='D'):
self.assertEquals(os.path.join(root, 'B/D/E'), expand_path('$A/${C}/E'))
@contextmanager
def root(self):
with temporary_dir() as root:
# Avoid OSX issues where tmp dirs are reported as symlinks.
real_root = os.path.realpath(root)
with pushd(real_root):
yield real_root |
<|file_name|>test_expand_path.py<|end_file_name|><｜fim▁begin｜># coding=utf-8
# Copyright 2014 Pants project contributors (see CONTRIBUTORS.md).
# Licensed under the Apache License, Version 2.0 (see LICENSE).
from __future__ import (nested_scopes, generators, division, absolute_import, with_statement,
print_function, unicode_literals)
from contextlib import contextmanager
import os
import unittest2 as unittest
from pants.fs.fs import expand_path
from pants.util.contextutil import environment_as, pushd, temporary_dir
class ExpandPathTest(unittest.TestCase):
def test_pure_relative(self):
<|fim_middle|>
def test_dot_relative(self):
with self.root() as root:
self.assertEquals(os.path.join(root, 'a'), expand_path('./a'))
def test_absolute(self):
self.assertEquals('/tmp/jake/bob', expand_path('/tmp/jake/bob'))
def test_user_expansion(self):
with environment_as(HOME='/tmp/jake'):
self.assertEquals('/tmp/jake/bob', expand_path('~/bob'))
def test_env_var_expansion(self):
with self.root() as root:
with environment_as(A='B', C='D'):
self.assertEquals(os.path.join(root, 'B/D/E'), expand_path('$A/${C}/E'))
@contextmanager
def root(self):
with temporary_dir() as root:
# Avoid OSX issues where tmp dirs are reported as symlinks.
real_root = os.path.realpath(root)
with pushd(real_root):
yield real_root
<｜fim▁end｜> | with self.root() as root:
self.assertEquals(os.path.join(root, 'a'), expand_path('a')) |
<|file_name|>test_expand_path.py<|end_file_name|><｜fim▁begin｜># coding=utf-8
# Copyright 2014 Pants project contributors (see CONTRIBUTORS.md).
# Licensed under the Apache License, Version 2.0 (see LICENSE).
from __future__ import (nested_scopes, generators, division, absolute_import, with_statement,
print_function, unicode_literals)
from contextlib import contextmanager
import os
import unittest2 as unittest
from pants.fs.fs import expand_path
from pants.util.contextutil import environment_as, pushd, temporary_dir
class ExpandPathTest(unittest.TestCase):
def test_pure_relative(self):
with self.root() as root:
self.assertEquals(os.path.join(root, 'a'), expand_path('a'))
def test_dot_relative(self):
<|fim_middle|>
def test_absolute(self):
self.assertEquals('/tmp/jake/bob', expand_path('/tmp/jake/bob'))
def test_user_expansion(self):
with environment_as(HOME='/tmp/jake'):
self.assertEquals('/tmp/jake/bob', expand_path('~/bob'))
def test_env_var_expansion(self):
with self.root() as root:
with environment_as(A='B', C='D'):
self.assertEquals(os.path.join(root, 'B/D/E'), expand_path('$A/${C}/E'))
@contextmanager
def root(self):
with temporary_dir() as root:
# Avoid OSX issues where tmp dirs are reported as symlinks.
real_root = os.path.realpath(root)
with pushd(real_root):
yield real_root
<｜fim▁end｜> | with self.root() as root:
self.assertEquals(os.path.join(root, 'a'), expand_path('./a')) |
<|file_name|>test_expand_path.py<|end_file_name|><｜fim▁begin｜># coding=utf-8
# Copyright 2014 Pants project contributors (see CONTRIBUTORS.md).
# Licensed under the Apache License, Version 2.0 (see LICENSE).
from __future__ import (nested_scopes, generators, division, absolute_import, with_statement,
print_function, unicode_literals)
from contextlib import contextmanager
import os
import unittest2 as unittest
from pants.fs.fs import expand_path
from pants.util.contextutil import environment_as, pushd, temporary_dir
class ExpandPathTest(unittest.TestCase):
def test_pure_relative(self):
with self.root() as root:
self.assertEquals(os.path.join(root, 'a'), expand_path('a'))
def test_dot_relative(self):
with self.root() as root:
self.assertEquals(os.path.join(root, 'a'), expand_path('./a'))
def test_absolute(self):
<|fim_middle|>
def test_user_expansion(self):
with environment_as(HOME='/tmp/jake'):
self.assertEquals('/tmp/jake/bob', expand_path('~/bob'))
def test_env_var_expansion(self):
with self.root() as root:
with environment_as(A='B', C='D'):
self.assertEquals(os.path.join(root, 'B/D/E'), expand_path('$A/${C}/E'))
@contextmanager
def root(self):
with temporary_dir() as root:
# Avoid OSX issues where tmp dirs are reported as symlinks.
real_root = os.path.realpath(root)
with pushd(real_root):
yield real_root
<｜fim▁end｜> | self.assertEquals('/tmp/jake/bob', expand_path('/tmp/jake/bob')) |
<|file_name|>test_expand_path.py<|end_file_name|><｜fim▁begin｜># coding=utf-8
# Copyright 2014 Pants project contributors (see CONTRIBUTORS.md).
# Licensed under the Apache License, Version 2.0 (see LICENSE).
from __future__ import (nested_scopes, generators, division, absolute_import, with_statement,
print_function, unicode_literals)
from contextlib import contextmanager
import os
import unittest2 as unittest
from pants.fs.fs import expand_path
from pants.util.contextutil import environment_as, pushd, temporary_dir
class ExpandPathTest(unittest.TestCase):
def test_pure_relative(self):
with self.root() as root:
self.assertEquals(os.path.join(root, 'a'), expand_path('a'))
def test_dot_relative(self):
with self.root() as root:
self.assertEquals(os.path.join(root, 'a'), expand_path('./a'))
def test_absolute(self):
self.assertEquals('/tmp/jake/bob', expand_path('/tmp/jake/bob'))
def test_user_expansion(self):
<|fim_middle|>
def test_env_var_expansion(self):
with self.root() as root:
with environment_as(A='B', C='D'):
self.assertEquals(os.path.join(root, 'B/D/E'), expand_path('$A/${C}/E'))
@contextmanager
def root(self):
with temporary_dir() as root:
# Avoid OSX issues where tmp dirs are reported as symlinks.
real_root = os.path.realpath(root)
with pushd(real_root):
yield real_root
<｜fim▁end｜> | with environment_as(HOME='/tmp/jake'):
self.assertEquals('/tmp/jake/bob', expand_path('~/bob')) |
<|file_name|>test_expand_path.py<|end_file_name|><｜fim▁begin｜># coding=utf-8
# Copyright 2014 Pants project contributors (see CONTRIBUTORS.md).
# Licensed under the Apache License, Version 2.0 (see LICENSE).
from __future__ import (nested_scopes, generators, division, absolute_import, with_statement,
print_function, unicode_literals)
from contextlib import contextmanager
import os
import unittest2 as unittest
from pants.fs.fs import expand_path
from pants.util.contextutil import environment_as, pushd, temporary_dir
class ExpandPathTest(unittest.TestCase):
def test_pure_relative(self):
with self.root() as root:
self.assertEquals(os.path.join(root, 'a'), expand_path('a'))
def test_dot_relative(self):
with self.root() as root:
self.assertEquals(os.path.join(root, 'a'), expand_path('./a'))
def test_absolute(self):
self.assertEquals('/tmp/jake/bob', expand_path('/tmp/jake/bob'))
def test_user_expansion(self):
with environment_as(HOME='/tmp/jake'):
self.assertEquals('/tmp/jake/bob', expand_path('~/bob'))
def test_env_var_expansion(self):
<|fim_middle|>
@contextmanager
def root(self):
with temporary_dir() as root:
# Avoid OSX issues where tmp dirs are reported as symlinks.
real_root = os.path.realpath(root)
with pushd(real_root):
yield real_root
<｜fim▁end｜> | with self.root() as root:
with environment_as(A='B', C='D'):
self.assertEquals(os.path.join(root, 'B/D/E'), expand_path('$A/${C}/E')) |
<|file_name|>test_expand_path.py<|end_file_name|><｜fim▁begin｜># coding=utf-8
# Copyright 2014 Pants project contributors (see CONTRIBUTORS.md).
# Licensed under the Apache License, Version 2.0 (see LICENSE).
from __future__ import (nested_scopes, generators, division, absolute_import, with_statement,
print_function, unicode_literals)
from contextlib import contextmanager
import os
import unittest2 as unittest
from pants.fs.fs import expand_path
from pants.util.contextutil import environment_as, pushd, temporary_dir
class ExpandPathTest(unittest.TestCase):
def test_pure_relative(self):
with self.root() as root:
self.assertEquals(os.path.join(root, 'a'), expand_path('a'))
def test_dot_relative(self):
with self.root() as root:
self.assertEquals(os.path.join(root, 'a'), expand_path('./a'))
def test_absolute(self):
self.assertEquals('/tmp/jake/bob', expand_path('/tmp/jake/bob'))
def test_user_expansion(self):
with environment_as(HOME='/tmp/jake'):
self.assertEquals('/tmp/jake/bob', expand_path('~/bob'))
def test_env_var_expansion(self):
with self.root() as root:
with environment_as(A='B', C='D'):
self.assertEquals(os.path.join(root, 'B/D/E'), expand_path('$A/${C}/E'))
@contextmanager
def root(self):
<|fim_middle|>
<｜fim▁end｜> | with temporary_dir() as root:
# Avoid OSX issues where tmp dirs are reported as symlinks.
real_root = os.path.realpath(root)
with pushd(real_root):
yield real_root |
<|file_name|>test_expand_path.py<|end_file_name|><｜fim▁begin｜># coding=utf-8
# Copyright 2014 Pants project contributors (see CONTRIBUTORS.md).
# Licensed under the Apache License, Version 2.0 (see LICENSE).
from __future__ import (nested_scopes, generators, division, absolute_import, with_statement,
print_function, unicode_literals)
from contextlib import contextmanager
import os
import unittest2 as unittest
from pants.fs.fs import expand_path
from pants.util.contextutil import environment_as, pushd, temporary_dir
class ExpandPathTest(unittest.TestCase):
def <|fim_middle|>(self):
with self.root() as root:
self.assertEquals(os.path.join(root, 'a'), expand_path('a'))
def test_dot_relative(self):
with self.root() as root:
self.assertEquals(os.path.join(root, 'a'), expand_path('./a'))
def test_absolute(self):
self.assertEquals('/tmp/jake/bob', expand_path('/tmp/jake/bob'))
def test_user_expansion(self):
with environment_as(HOME='/tmp/jake'):
self.assertEquals('/tmp/jake/bob', expand_path('~/bob'))
def test_env_var_expansion(self):
with self.root() as root:
with environment_as(A='B', C='D'):
self.assertEquals(os.path.join(root, 'B/D/E'), expand_path('$A/${C}/E'))
@contextmanager
def root(self):
with temporary_dir() as root:
# Avoid OSX issues where tmp dirs are reported as symlinks.
real_root = os.path.realpath(root)
with pushd(real_root):
yield real_root
<｜fim▁end｜> | test_pure_relative |
<|file_name|>test_expand_path.py<|end_file_name|><｜fim▁begin｜># coding=utf-8
# Copyright 2014 Pants project contributors (see CONTRIBUTORS.md).
# Licensed under the Apache License, Version 2.0 (see LICENSE).
from __future__ import (nested_scopes, generators, division, absolute_import, with_statement,
print_function, unicode_literals)
from contextlib import contextmanager
import os
import unittest2 as unittest
from pants.fs.fs import expand_path
from pants.util.contextutil import environment_as, pushd, temporary_dir
class ExpandPathTest(unittest.TestCase):
def test_pure_relative(self):
with self.root() as root:
self.assertEquals(os.path.join(root, 'a'), expand_path('a'))
def <|fim_middle|>(self):
with self.root() as root:
self.assertEquals(os.path.join(root, 'a'), expand_path('./a'))
def test_absolute(self):
self.assertEquals('/tmp/jake/bob', expand_path('/tmp/jake/bob'))
def test_user_expansion(self):
with environment_as(HOME='/tmp/jake'):
self.assertEquals('/tmp/jake/bob', expand_path('~/bob'))
def test_env_var_expansion(self):
with self.root() as root:
with environment_as(A='B', C='D'):
self.assertEquals(os.path.join(root, 'B/D/E'), expand_path('$A/${C}/E'))
@contextmanager
def root(self):
with temporary_dir() as root:
# Avoid OSX issues where tmp dirs are reported as symlinks.
real_root = os.path.realpath(root)
with pushd(real_root):
yield real_root
<｜fim▁end｜> | test_dot_relative |
<|file_name|>test_expand_path.py<|end_file_name|><｜fim▁begin｜># coding=utf-8
# Copyright 2014 Pants project contributors (see CONTRIBUTORS.md).
# Licensed under the Apache License, Version 2.0 (see LICENSE).
from __future__ import (nested_scopes, generators, division, absolute_import, with_statement,
print_function, unicode_literals)
from contextlib import contextmanager
import os
import unittest2 as unittest
from pants.fs.fs import expand_path
from pants.util.contextutil import environment_as, pushd, temporary_dir
class ExpandPathTest(unittest.TestCase):
def test_pure_relative(self):
with self.root() as root:
self.assertEquals(os.path.join(root, 'a'), expand_path('a'))
def test_dot_relative(self):
with self.root() as root:
self.assertEquals(os.path.join(root, 'a'), expand_path('./a'))
def <|fim_middle|>(self):
self.assertEquals('/tmp/jake/bob', expand_path('/tmp/jake/bob'))
def test_user_expansion(self):
with environment_as(HOME='/tmp/jake'):
self.assertEquals('/tmp/jake/bob', expand_path('~/bob'))
def test_env_var_expansion(self):
with self.root() as root:
with environment_as(A='B', C='D'):
self.assertEquals(os.path.join(root, 'B/D/E'), expand_path('$A/${C}/E'))
@contextmanager
def root(self):
with temporary_dir() as root:
# Avoid OSX issues where tmp dirs are reported as symlinks.
real_root = os.path.realpath(root)
with pushd(real_root):
yield real_root
<｜fim▁end｜> | test_absolute |
<|file_name|>test_expand_path.py<|end_file_name|><｜fim▁begin｜># coding=utf-8
# Copyright 2014 Pants project contributors (see CONTRIBUTORS.md).
# Licensed under the Apache License, Version 2.0 (see LICENSE).
from __future__ import (nested_scopes, generators, division, absolute_import, with_statement,
print_function, unicode_literals)
from contextlib import contextmanager
import os
import unittest2 as unittest
from pants.fs.fs import expand_path
from pants.util.contextutil import environment_as, pushd, temporary_dir
class ExpandPathTest(unittest.TestCase):
def test_pure_relative(self):
with self.root() as root:
self.assertEquals(os.path.join(root, 'a'), expand_path('a'))
def test_dot_relative(self):
with self.root() as root:
self.assertEquals(os.path.join(root, 'a'), expand_path('./a'))
def test_absolute(self):
self.assertEquals('/tmp/jake/bob', expand_path('/tmp/jake/bob'))
def <|fim_middle|>(self):
with environment_as(HOME='/tmp/jake'):
self.assertEquals('/tmp/jake/bob', expand_path('~/bob'))
def test_env_var_expansion(self):
with self.root() as root:
with environment_as(A='B', C='D'):
self.assertEquals(os.path.join(root, 'B/D/E'), expand_path('$A/${C}/E'))
@contextmanager
def root(self):
with temporary_dir() as root:
# Avoid OSX issues where tmp dirs are reported as symlinks.
real_root = os.path.realpath(root)
with pushd(real_root):
yield real_root
<｜fim▁end｜> | test_user_expansion |
<|file_name|>test_expand_path.py<|end_file_name|><｜fim▁begin｜># coding=utf-8
# Copyright 2014 Pants project contributors (see CONTRIBUTORS.md).
# Licensed under the Apache License, Version 2.0 (see LICENSE).
from __future__ import (nested_scopes, generators, division, absolute_import, with_statement,
print_function, unicode_literals)
from contextlib import contextmanager
import os
import unittest2 as unittest
from pants.fs.fs import expand_path
from pants.util.contextutil import environment_as, pushd, temporary_dir
class ExpandPathTest(unittest.TestCase):
def test_pure_relative(self):
with self.root() as root:
self.assertEquals(os.path.join(root, 'a'), expand_path('a'))
def test_dot_relative(self):
with self.root() as root:
self.assertEquals(os.path.join(root, 'a'), expand_path('./a'))
def test_absolute(self):
self.assertEquals('/tmp/jake/bob', expand_path('/tmp/jake/bob'))
def test_user_expansion(self):
with environment_as(HOME='/tmp/jake'):
self.assertEquals('/tmp/jake/bob', expand_path('~/bob'))
def <|fim_middle|>(self):
with self.root() as root:
with environment_as(A='B', C='D'):
self.assertEquals(os.path.join(root, 'B/D/E'), expand_path('$A/${C}/E'))
@contextmanager
def root(self):
with temporary_dir() as root:
# Avoid OSX issues where tmp dirs are reported as symlinks.
real_root = os.path.realpath(root)
with pushd(real_root):
yield real_root
<｜fim▁end｜> | test_env_var_expansion |
<|file_name|>test_expand_path.py<|end_file_name|><｜fim▁begin｜># coding=utf-8
# Copyright 2014 Pants project contributors (see CONTRIBUTORS.md).
# Licensed under the Apache License, Version 2.0 (see LICENSE).
from __future__ import (nested_scopes, generators, division, absolute_import, with_statement,
print_function, unicode_literals)
from contextlib import contextmanager
import os
import unittest2 as unittest
from pants.fs.fs import expand_path
from pants.util.contextutil import environment_as, pushd, temporary_dir
class ExpandPathTest(unittest.TestCase):
def test_pure_relative(self):
with self.root() as root:
self.assertEquals(os.path.join(root, 'a'), expand_path('a'))
def test_dot_relative(self):
with self.root() as root:
self.assertEquals(os.path.join(root, 'a'), expand_path('./a'))
def test_absolute(self):
self.assertEquals('/tmp/jake/bob', expand_path('/tmp/jake/bob'))
def test_user_expansion(self):
with environment_as(HOME='/tmp/jake'):
self.assertEquals('/tmp/jake/bob', expand_path('~/bob'))
def test_env_var_expansion(self):
with self.root() as root:
with environment_as(A='B', C='D'):
self.assertEquals(os.path.join(root, 'B/D/E'), expand_path('$A/${C}/E'))
@contextmanager
def <|fim_middle|>(self):
with temporary_dir() as root:
# Avoid OSX issues where tmp dirs are reported as symlinks.
real_root = os.path.realpath(root)
with pushd(real_root):
yield real_root
<｜fim▁end｜> | root |
<|file_name|>connectors_local.py<|end_file_name|><｜fim▁begin｜>import time
from threading import Thread
import threading
from wtfj_ids import *
from wtfj_utils import *
class Printer:
''' Opens a new output window and prints messages sent to it '''
def __init__(self,header=''):
self._header = header
<｜fim▁hole｜> print(self._header+string)
class Console:
''' Allows user to enter commands '''
def __init__(self,prompt='[$] '):
self._prompt = prompt
self._at = ''
def poll(self,wait_s=None,uid=None):
try:
prompt = str(self._at)+str(self._prompt)
msg = raw_input(prompt)
if msg == '':
self._at = ''
return []
if msg[0] == '@':
self._at = msg.split()[0]+' '
else:
msg = self._at+msg
return [msg]
except Exception as e:
print(repr(e))
return []
def subscribe(self,*uids):
pass
class Script:
''' Runs a script passed as a list, default frequency = 1000Hz '''
def __init__(self,msgs):
self._msgs = msgs
self._index = 0
self._period = 0.001
self._pid = 'SCRIPT'
def poll(self,wait_s=None,uid=None):
period = self._period if wait_s is None else wait_s
time.sleep(period)
try:
msg = self._msgs[self._index]
print(self._pid+' SEND > '+msg)
self._index += 1
return [msg]
except IndexError:
return []
def subscribe(self,*uids):
for uid in uids:
if uid is not None:
if uid[0] is '@': assert uid[1:] in get_attr(Uid)
else: assert uid in get_attr(Uid)
return self
def load(self,msg_array):
self._msgs += msg_array
return self
def set_period(self,period):
self._period = period
return self
def run(self):
t = threading.current_thread()
self._pid = str(t.ident)+' '+str(t.name)
while len(self.poll()) > 0: pass
def run_async(self):
Thread(target=self.run).start()
if __name__ == '__main__':
Printer('A simple printer: ').send('Just printing a msg to current_thread')
script = [
'@other_uid topic data',
'@other_uid topic',
'uid topic data',
'uid topic'
]
async = ['async topic '+str(n) for n in [1,2,3,4,5,6,7,8,9,0]]
async2 = ['async2 topic '+str(n) for n in [1,2,3,4,5,6,7,8,9,0]]
Script(script).set_period(1).run()
Script(async).set_period(0.15).run_async()
Script(async2).set_period(0.2).run_async()<｜fim▁end｜> | def send(self,string): |
<|file_name|>connectors_local.py<|end_file_name|><｜fim▁begin｜>import time
from threading import Thread
import threading
from wtfj_ids import *
from wtfj_utils import *
class Printer:
<|fim_middle|>
class Console:
''' Allows user to enter commands '''
def __init__(self,prompt='[$] '):
self._prompt = prompt
self._at = ''
def poll(self,wait_s=None,uid=None):
try:
prompt = str(self._at)+str(self._prompt)
msg = raw_input(prompt)
if msg == '':
self._at = ''
return []
if msg[0] == '@':
self._at = msg.split()[0]+' '
else:
msg = self._at+msg
return [msg]
except Exception as e:
print(repr(e))
return []
def subscribe(self,*uids):
pass
class Script:
''' Runs a script passed as a list, default frequency = 1000Hz '''
def __init__(self,msgs):
self._msgs = msgs
self._index = 0
self._period = 0.001
self._pid = 'SCRIPT'
def poll(self,wait_s=None,uid=None):
period = self._period if wait_s is None else wait_s
time.sleep(period)
try:
msg = self._msgs[self._index]
print(self._pid+' SEND > '+msg)
self._index += 1
return [msg]
except IndexError:
return []
def subscribe(self,*uids):
for uid in uids:
if uid is not None:
if uid[0] is '@': assert uid[1:] in get_attr(Uid)
else: assert uid in get_attr(Uid)
return self
def load(self,msg_array):
self._msgs += msg_array
return self
def set_period(self,period):
self._period = period
return self
def run(self):
t = threading.current_thread()
self._pid = str(t.ident)+' '+str(t.name)
while len(self.poll()) > 0: pass
def run_async(self):
Thread(target=self.run).start()
if __name__ == '__main__':
Printer('A simple printer: ').send('Just printing a msg to current_thread')
script = [
'@other_uid topic data',
'@other_uid topic',
'uid topic data',
'uid topic'
]
async = ['async topic '+str(n) for n in [1,2,3,4,5,6,7,8,9,0]]
async2 = ['async2 topic '+str(n) for n in [1,2,3,4,5,6,7,8,9,0]]
Script(script).set_period(1).run()
Script(async).set_period(0.15).run_async()
Script(async2).set_period(0.2).run_async()<｜fim▁end｜> | ''' Opens a new output window and prints messages sent to it '''
def __init__(self,header=''):
self._header = header
def send(self,string):
print(self._header+string) |
<|file_name|>connectors_local.py<|end_file_name|><｜fim▁begin｜>import time
from threading import Thread
import threading
from wtfj_ids import *
from wtfj_utils import *
class Printer:
''' Opens a new output window and prints messages sent to it '''
def __init__(self,header=''):
<|fim_middle|>
def send(self,string):
print(self._header+string)
class Console:
''' Allows user to enter commands '''
def __init__(self,prompt='[$] '):
self._prompt = prompt
self._at = ''
def poll(self,wait_s=None,uid=None):
try:
prompt = str(self._at)+str(self._prompt)
msg = raw_input(prompt)
if msg == '':
self._at = ''
return []
if msg[0] == '@':
self._at = msg.split()[0]+' '
else:
msg = self._at+msg
return [msg]
except Exception as e:
print(repr(e))
return []
def subscribe(self,*uids):
pass
class Script:
''' Runs a script passed as a list, default frequency = 1000Hz '''
def __init__(self,msgs):
self._msgs = msgs
self._index = 0
self._period = 0.001
self._pid = 'SCRIPT'
def poll(self,wait_s=None,uid=None):
period = self._period if wait_s is None else wait_s
time.sleep(period)
try:
msg = self._msgs[self._index]
print(self._pid+' SEND > '+msg)
self._index += 1
return [msg]
except IndexError:
return []
def subscribe(self,*uids):
for uid in uids:
if uid is not None:
if uid[0] is '@': assert uid[1:] in get_attr(Uid)
else: assert uid in get_attr(Uid)
return self
def load(self,msg_array):
self._msgs += msg_array
return self
def set_period(self,period):
self._period = period
return self
def run(self):
t = threading.current_thread()
self._pid = str(t.ident)+' '+str(t.name)
while len(self.poll()) > 0: pass
def run_async(self):
Thread(target=self.run).start()
if __name__ == '__main__':
Printer('A simple printer: ').send('Just printing a msg to current_thread')
script = [
'@other_uid topic data',
'@other_uid topic',
'uid topic data',
'uid topic'
]
async = ['async topic '+str(n) for n in [1,2,3,4,5,6,7,8,9,0]]
async2 = ['async2 topic '+str(n) for n in [1,2,3,4,5,6,7,8,9,0]]
Script(script).set_period(1).run()
Script(async).set_period(0.15).run_async()
Script(async2).set_period(0.2).run_async()<｜fim▁end｜> | self._header = header |
<|file_name|>connectors_local.py<|end_file_name|><｜fim▁begin｜>import time
from threading import Thread
import threading
from wtfj_ids import *
from wtfj_utils import *
class Printer:
''' Opens a new output window and prints messages sent to it '''
def __init__(self,header=''):
self._header = header
def send(self,string):
<|fim_middle|>
class Console:
''' Allows user to enter commands '''
def __init__(self,prompt='[$] '):
self._prompt = prompt
self._at = ''
def poll(self,wait_s=None,uid=None):
try:
prompt = str(self._at)+str(self._prompt)
msg = raw_input(prompt)
if msg == '':
self._at = ''
return []
if msg[0] == '@':
self._at = msg.split()[0]+' '
else:
msg = self._at+msg
return [msg]
except Exception as e:
print(repr(e))
return []
def subscribe(self,*uids):
pass
class Script:
''' Runs a script passed as a list, default frequency = 1000Hz '''
def __init__(self,msgs):
self._msgs = msgs
self._index = 0
self._period = 0.001
self._pid = 'SCRIPT'
def poll(self,wait_s=None,uid=None):
period = self._period if wait_s is None else wait_s
time.sleep(period)
try:
msg = self._msgs[self._index]
print(self._pid+' SEND > '+msg)
self._index += 1
return [msg]
except IndexError:
return []
def subscribe(self,*uids):
for uid in uids:
if uid is not None:
if uid[0] is '@': assert uid[1:] in get_attr(Uid)
else: assert uid in get_attr(Uid)
return self
def load(self,msg_array):
self._msgs += msg_array
return self
def set_period(self,period):
self._period = period
return self
def run(self):
t = threading.current_thread()
self._pid = str(t.ident)+' '+str(t.name)
while len(self.poll()) > 0: pass
def run_async(self):
Thread(target=self.run).start()
if __name__ == '__main__':
Printer('A simple printer: ').send('Just printing a msg to current_thread')
script = [
'@other_uid topic data',
'@other_uid topic',
'uid topic data',
'uid topic'
]
async = ['async topic '+str(n) for n in [1,2,3,4,5,6,7,8,9,0]]
async2 = ['async2 topic '+str(n) for n in [1,2,3,4,5,6,7,8,9,0]]
Script(script).set_period(1).run()
Script(async).set_period(0.15).run_async()
Script(async2).set_period(0.2).run_async()<｜fim▁end｜> | print(self._header+string) |
<|file_name|>connectors_local.py<|end_file_name|><｜fim▁begin｜>import time
from threading import Thread
import threading
from wtfj_ids import *
from wtfj_utils import *
class Printer:
''' Opens a new output window and prints messages sent to it '''
def __init__(self,header=''):
self._header = header
def send(self,string):
print(self._header+string)
class Console:
<|fim_middle|>
class Script:
''' Runs a script passed as a list, default frequency = 1000Hz '''
def __init__(self,msgs):
self._msgs = msgs
self._index = 0
self._period = 0.001
self._pid = 'SCRIPT'
def poll(self,wait_s=None,uid=None):
period = self._period if wait_s is None else wait_s
time.sleep(period)
try:
msg = self._msgs[self._index]
print(self._pid+' SEND > '+msg)
self._index += 1
return [msg]
except IndexError:
return []
def subscribe(self,*uids):
for uid in uids:
if uid is not None:
if uid[0] is '@': assert uid[1:] in get_attr(Uid)
else: assert uid in get_attr(Uid)
return self
def load(self,msg_array):
self._msgs += msg_array
return self
def set_period(self,period):
self._period = period
return self
def run(self):
t = threading.current_thread()
self._pid = str(t.ident)+' '+str(t.name)
while len(self.poll()) > 0: pass
def run_async(self):
Thread(target=self.run).start()
if __name__ == '__main__':
Printer('A simple printer: ').send('Just printing a msg to current_thread')
script = [
'@other_uid topic data',
'@other_uid topic',
'uid topic data',
'uid topic'
]
async = ['async topic '+str(n) for n in [1,2,3,4,5,6,7,8,9,0]]
async2 = ['async2 topic '+str(n) for n in [1,2,3,4,5,6,7,8,9,0]]
Script(script).set_period(1).run()
Script(async).set_period(0.15).run_async()
Script(async2).set_period(0.2).run_async()<｜fim▁end｜> | ''' Allows user to enter commands '''
def __init__(self,prompt='[$] '):
self._prompt = prompt
self._at = ''
def poll(self,wait_s=None,uid=None):
try:
prompt = str(self._at)+str(self._prompt)
msg = raw_input(prompt)
if msg == '':
self._at = ''
return []
if msg[0] == '@':
self._at = msg.split()[0]+' '
else:
msg = self._at+msg
return [msg]
except Exception as e:
print(repr(e))
return []
def subscribe(self,*uids):
pass |
<|file_name|>connectors_local.py<|end_file_name|><｜fim▁begin｜>import time
from threading import Thread
import threading
from wtfj_ids import *
from wtfj_utils import *
class Printer:
''' Opens a new output window and prints messages sent to it '''
def __init__(self,header=''):
self._header = header
def send(self,string):
print(self._header+string)
class Console:
''' Allows user to enter commands '''
def __init__(self,prompt='[$] '):
<|fim_middle|>
def poll(self,wait_s=None,uid=None):
try:
prompt = str(self._at)+str(self._prompt)
msg = raw_input(prompt)
if msg == '':
self._at = ''
return []
if msg[0] == '@':
self._at = msg.split()[0]+' '
else:
msg = self._at+msg
return [msg]
except Exception as e:
print(repr(e))
return []
def subscribe(self,*uids):
pass
class Script:
''' Runs a script passed as a list, default frequency = 1000Hz '''
def __init__(self,msgs):
self._msgs = msgs
self._index = 0
self._period = 0.001
self._pid = 'SCRIPT'
def poll(self,wait_s=None,uid=None):
period = self._period if wait_s is None else wait_s
time.sleep(period)
try:
msg = self._msgs[self._index]
print(self._pid+' SEND > '+msg)
self._index += 1
return [msg]
except IndexError:
return []
def subscribe(self,*uids):
for uid in uids:
if uid is not None:
if uid[0] is '@': assert uid[1:] in get_attr(Uid)
else: assert uid in get_attr(Uid)
return self
def load(self,msg_array):
self._msgs += msg_array
return self
def set_period(self,period):
self._period = period
return self
def run(self):
t = threading.current_thread()
self._pid = str(t.ident)+' '+str(t.name)
while len(self.poll()) > 0: pass
def run_async(self):
Thread(target=self.run).start()
if __name__ == '__main__':
Printer('A simple printer: ').send('Just printing a msg to current_thread')
script = [
'@other_uid topic data',
'@other_uid topic',
'uid topic data',
'uid topic'
]
async = ['async topic '+str(n) for n in [1,2,3,4,5,6,7,8,9,0]]
async2 = ['async2 topic '+str(n) for n in [1,2,3,4,5,6,7,8,9,0]]
Script(script).set_period(1).run()
Script(async).set_period(0.15).run_async()
Script(async2).set_period(0.2).run_async()<｜fim▁end｜> | self._prompt = prompt
self._at = '' |
<|file_name|>connectors_local.py<|end_file_name|><｜fim▁begin｜>import time
from threading import Thread
import threading
from wtfj_ids import *
from wtfj_utils import *
class Printer:
''' Opens a new output window and prints messages sent to it '''
def __init__(self,header=''):
self._header = header
def send(self,string):
print(self._header+string)
class Console:
''' Allows user to enter commands '''
def __init__(self,prompt='[$] '):
self._prompt = prompt
self._at = ''
def poll(self,wait_s=None,uid=None):
<|fim_middle|>
def subscribe(self,*uids):
pass
class Script:
''' Runs a script passed as a list, default frequency = 1000Hz '''
def __init__(self,msgs):
self._msgs = msgs
self._index = 0
self._period = 0.001
self._pid = 'SCRIPT'
def poll(self,wait_s=None,uid=None):
period = self._period if wait_s is None else wait_s
time.sleep(period)
try:
msg = self._msgs[self._index]
print(self._pid+' SEND > '+msg)
self._index += 1
return [msg]
except IndexError:
return []
def subscribe(self,*uids):
for uid in uids:
if uid is not None:
if uid[0] is '@': assert uid[1:] in get_attr(Uid)
else: assert uid in get_attr(Uid)
return self
def load(self,msg_array):
self._msgs += msg_array
return self
def set_period(self,period):
self._period = period
return self
def run(self):
t = threading.current_thread()
self._pid = str(t.ident)+' '+str(t.name)
while len(self.poll()) > 0: pass
def run_async(self):
Thread(target=self.run).start()
if __name__ == '__main__':
Printer('A simple printer: ').send('Just printing a msg to current_thread')
script = [
'@other_uid topic data',
'@other_uid topic',
'uid topic data',
'uid topic'
]
async = ['async topic '+str(n) for n in [1,2,3,4,5,6,7,8,9,0]]
async2 = ['async2 topic '+str(n) for n in [1,2,3,4,5,6,7,8,9,0]]
Script(script).set_period(1).run()
Script(async).set_period(0.15).run_async()
Script(async2).set_period(0.2).run_async()<｜fim▁end｜> | try:
prompt = str(self._at)+str(self._prompt)
msg = raw_input(prompt)
if msg == '':
self._at = ''
return []
if msg[0] == '@':
self._at = msg.split()[0]+' '
else:
msg = self._at+msg
return [msg]
except Exception as e:
print(repr(e))
return [] |
<|file_name|>connectors_local.py<|end_file_name|><｜fim▁begin｜>import time
from threading import Thread
import threading
from wtfj_ids import *
from wtfj_utils import *
class Printer:
''' Opens a new output window and prints messages sent to it '''
def __init__(self,header=''):
self._header = header
def send(self,string):
print(self._header+string)
class Console:
''' Allows user to enter commands '''
def __init__(self,prompt='[$] '):
self._prompt = prompt
self._at = ''
def poll(self,wait_s=None,uid=None):
try:
prompt = str(self._at)+str(self._prompt)
msg = raw_input(prompt)
if msg == '':
self._at = ''
return []
if msg[0] == '@':
self._at = msg.split()[0]+' '
else:
msg = self._at+msg
return [msg]
except Exception as e:
print(repr(e))
return []
def subscribe(self,*uids):
<|fim_middle|>
class Script:
''' Runs a script passed as a list, default frequency = 1000Hz '''
def __init__(self,msgs):
self._msgs = msgs
self._index = 0
self._period = 0.001
self._pid = 'SCRIPT'
def poll(self,wait_s=None,uid=None):
period = self._period if wait_s is None else wait_s
time.sleep(period)
try:
msg = self._msgs[self._index]
print(self._pid+' SEND > '+msg)
self._index += 1
return [msg]
except IndexError:
return []
def subscribe(self,*uids):
for uid in uids:
if uid is not None:
if uid[0] is '@': assert uid[1:] in get_attr(Uid)
else: assert uid in get_attr(Uid)
return self
def load(self,msg_array):
self._msgs += msg_array
return self
def set_period(self,period):
self._period = period
return self
def run(self):
t = threading.current_thread()
self._pid = str(t.ident)+' '+str(t.name)
while len(self.poll()) > 0: pass
def run_async(self):
Thread(target=self.run).start()
if __name__ == '__main__':
Printer('A simple printer: ').send('Just printing a msg to current_thread')
script = [
'@other_uid topic data',
'@other_uid topic',
'uid topic data',
'uid topic'
]
async = ['async topic '+str(n) for n in [1,2,3,4,5,6,7,8,9,0]]
async2 = ['async2 topic '+str(n) for n in [1,2,3,4,5,6,7,8,9,0]]
Script(script).set_period(1).run()
Script(async).set_period(0.15).run_async()
Script(async2).set_period(0.2).run_async()<｜fim▁end｜> | pass |
<|file_name|>connectors_local.py<|end_file_name|><｜fim▁begin｜>import time
from threading import Thread
import threading
from wtfj_ids import *
from wtfj_utils import *
class Printer:
''' Opens a new output window and prints messages sent to it '''
def __init__(self,header=''):
self._header = header
def send(self,string):
print(self._header+string)
class Console:
''' Allows user to enter commands '''
def __init__(self,prompt='[$] '):
self._prompt = prompt
self._at = ''
def poll(self,wait_s=None,uid=None):
try:
prompt = str(self._at)+str(self._prompt)
msg = raw_input(prompt)
if msg == '':
self._at = ''
return []
if msg[0] == '@':
self._at = msg.split()[0]+' '
else:
msg = self._at+msg
return [msg]
except Exception as e:
print(repr(e))
return []
def subscribe(self,*uids):
pass
class Script:
<|fim_middle|>
if __name__ == '__main__':
Printer('A simple printer: ').send('Just printing a msg to current_thread')
script = [
'@other_uid topic data',
'@other_uid topic',
'uid topic data',
'uid topic'
]
async = ['async topic '+str(n) for n in [1,2,3,4,5,6,7,8,9,0]]
async2 = ['async2 topic '+str(n) for n in [1,2,3,4,5,6,7,8,9,0]]
Script(script).set_period(1).run()
Script(async).set_period(0.15).run_async()
Script(async2).set_period(0.2).run_async()<｜fim▁end｜> | ''' Runs a script passed as a list, default frequency = 1000Hz '''
def __init__(self,msgs):
self._msgs = msgs
self._index = 0
self._period = 0.001
self._pid = 'SCRIPT'
def poll(self,wait_s=None,uid=None):
period = self._period if wait_s is None else wait_s
time.sleep(period)
try:
msg = self._msgs[self._index]
print(self._pid+' SEND > '+msg)
self._index += 1
return [msg]
except IndexError:
return []
def subscribe(self,*uids):
for uid in uids:
if uid is not None:
if uid[0] is '@': assert uid[1:] in get_attr(Uid)
else: assert uid in get_attr(Uid)
return self
def load(self,msg_array):
self._msgs += msg_array
return self
def set_period(self,period):
self._period = period
return self
def run(self):
t = threading.current_thread()
self._pid = str(t.ident)+' '+str(t.name)
while len(self.poll()) > 0: pass
def run_async(self):
Thread(target=self.run).start() |
<|file_name|>connectors_local.py<|end_file_name|><｜fim▁begin｜>import time
from threading import Thread
import threading
from wtfj_ids import *
from wtfj_utils import *
class Printer:
''' Opens a new output window and prints messages sent to it '''
def __init__(self,header=''):
self._header = header
def send(self,string):
print(self._header+string)
class Console:
''' Allows user to enter commands '''
def __init__(self,prompt='[$] '):
self._prompt = prompt
self._at = ''
def poll(self,wait_s=None,uid=None):
try:
prompt = str(self._at)+str(self._prompt)
msg = raw_input(prompt)
if msg == '':
self._at = ''
return []
if msg[0] == '@':
self._at = msg.split()[0]+' '
else:
msg = self._at+msg
return [msg]
except Exception as e:
print(repr(e))
return []
def subscribe(self,*uids):
pass
class Script:
''' Runs a script passed as a list, default frequency = 1000Hz '''
def __init__(self,msgs):
<|fim_middle|>
def poll(self,wait_s=None,uid=None):
period = self._period if wait_s is None else wait_s
time.sleep(period)
try:
msg = self._msgs[self._index]
print(self._pid+' SEND > '+msg)
self._index += 1
return [msg]
except IndexError:
return []
def subscribe(self,*uids):
for uid in uids:
if uid is not None:
if uid[0] is '@': assert uid[1:] in get_attr(Uid)
else: assert uid in get_attr(Uid)
return self
def load(self,msg_array):
self._msgs += msg_array
return self
def set_period(self,period):
self._period = period
return self
def run(self):
t = threading.current_thread()
self._pid = str(t.ident)+' '+str(t.name)
while len(self.poll()) > 0: pass
def run_async(self):
Thread(target=self.run).start()
if __name__ == '__main__':
Printer('A simple printer: ').send('Just printing a msg to current_thread')
script = [
'@other_uid topic data',
'@other_uid topic',
'uid topic data',
'uid topic'
]
async = ['async topic '+str(n) for n in [1,2,3,4,5,6,7,8,9,0]]
async2 = ['async2 topic '+str(n) for n in [1,2,3,4,5,6,7,8,9,0]]
Script(script).set_period(1).run()
Script(async).set_period(0.15).run_async()
Script(async2).set_period(0.2).run_async()<｜fim▁end｜> | self._msgs = msgs
self._index = 0
self._period = 0.001
self._pid = 'SCRIPT' |
<|file_name|>connectors_local.py<|end_file_name|><｜fim▁begin｜>import time
from threading import Thread
import threading
from wtfj_ids import *
from wtfj_utils import *
class Printer:
''' Opens a new output window and prints messages sent to it '''
def __init__(self,header=''):
self._header = header
def send(self,string):
print(self._header+string)
class Console:
''' Allows user to enter commands '''
def __init__(self,prompt='[$] '):
self._prompt = prompt
self._at = ''
def poll(self,wait_s=None,uid=None):
try:
prompt = str(self._at)+str(self._prompt)
msg = raw_input(prompt)
if msg == '':
self._at = ''
return []
if msg[0] == '@':
self._at = msg.split()[0]+' '
else:
msg = self._at+msg
return [msg]
except Exception as e:
print(repr(e))
return []
def subscribe(self,*uids):
pass
class Script:
''' Runs a script passed as a list, default frequency = 1000Hz '''
def __init__(self,msgs):
self._msgs = msgs
self._index = 0
self._period = 0.001
self._pid = 'SCRIPT'
def poll(self,wait_s=None,uid=None):
<|fim_middle|>
def subscribe(self,*uids):
for uid in uids:
if uid is not None:
if uid[0] is '@': assert uid[1:] in get_attr(Uid)
else: assert uid in get_attr(Uid)
return self
def load(self,msg_array):
self._msgs += msg_array
return self
def set_period(self,period):
self._period = period
return self
def run(self):
t = threading.current_thread()
self._pid = str(t.ident)+' '+str(t.name)
while len(self.poll()) > 0: pass
def run_async(self):
Thread(target=self.run).start()
if __name__ == '__main__':
Printer('A simple printer: ').send('Just printing a msg to current_thread')
script = [
'@other_uid topic data',
'@other_uid topic',
'uid topic data',
'uid topic'
]
async = ['async topic '+str(n) for n in [1,2,3,4,5,6,7,8,9,0]]
async2 = ['async2 topic '+str(n) for n in [1,2,3,4,5,6,7,8,9,0]]
Script(script).set_period(1).run()
Script(async).set_period(0.15).run_async()
Script(async2).set_period(0.2).run_async()<｜fim▁end｜> | period = self._period if wait_s is None else wait_s
time.sleep(period)
try:
msg = self._msgs[self._index]
print(self._pid+' SEND > '+msg)
self._index += 1
return [msg]
except IndexError:
return [] |
<|file_name|>connectors_local.py<|end_file_name|><｜fim▁begin｜>import time
from threading import Thread
import threading
from wtfj_ids import *
from wtfj_utils import *
class Printer:
''' Opens a new output window and prints messages sent to it '''
def __init__(self,header=''):
self._header = header
def send(self,string):
print(self._header+string)
class Console:
''' Allows user to enter commands '''
def __init__(self,prompt='[$] '):
self._prompt = prompt
self._at = ''
def poll(self,wait_s=None,uid=None):
try:
prompt = str(self._at)+str(self._prompt)
msg = raw_input(prompt)
if msg == '':
self._at = ''
return []
if msg[0] == '@':
self._at = msg.split()[0]+' '
else:
msg = self._at+msg
return [msg]
except Exception as e:
print(repr(e))
return []
def subscribe(self,*uids):
pass
class Script:
''' Runs a script passed as a list, default frequency = 1000Hz '''
def __init__(self,msgs):
self._msgs = msgs
self._index = 0
self._period = 0.001
self._pid = 'SCRIPT'
def poll(self,wait_s=None,uid=None):
period = self._period if wait_s is None else wait_s
time.sleep(period)
try:
msg = self._msgs[self._index]
print(self._pid+' SEND > '+msg)
self._index += 1
return [msg]
except IndexError:
return []
def subscribe(self,*uids):
<|fim_middle|>
def load(self,msg_array):
self._msgs += msg_array
return self
def set_period(self,period):
self._period = period
return self
def run(self):
t = threading.current_thread()
self._pid = str(t.ident)+' '+str(t.name)
while len(self.poll()) > 0: pass
def run_async(self):
Thread(target=self.run).start()
if __name__ == '__main__':
Printer('A simple printer: ').send('Just printing a msg to current_thread')
script = [
'@other_uid topic data',
'@other_uid topic',
'uid topic data',
'uid topic'
]
async = ['async topic '+str(n) for n in [1,2,3,4,5,6,7,8,9,0]]
async2 = ['async2 topic '+str(n) for n in [1,2,3,4,5,6,7,8,9,0]]
Script(script).set_period(1).run()
Script(async).set_period(0.15).run_async()
Script(async2).set_period(0.2).run_async()<｜fim▁end｜> | for uid in uids:
if uid is not None:
if uid[0] is '@': assert uid[1:] in get_attr(Uid)
else: assert uid in get_attr(Uid)
return self |
<|file_name|>connectors_local.py<|end_file_name|><｜fim▁begin｜>import time
from threading import Thread
import threading
from wtfj_ids import *
from wtfj_utils import *
class Printer:
''' Opens a new output window and prints messages sent to it '''
def __init__(self,header=''):
self._header = header
def send(self,string):
print(self._header+string)
class Console:
''' Allows user to enter commands '''
def __init__(self,prompt='[$] '):
self._prompt = prompt
self._at = ''
def poll(self,wait_s=None,uid=None):
try:
prompt = str(self._at)+str(self._prompt)
msg = raw_input(prompt)
if msg == '':
self._at = ''
return []
if msg[0] == '@':
self._at = msg.split()[0]+' '
else:
msg = self._at+msg
return [msg]
except Exception as e:
print(repr(e))
return []
def subscribe(self,*uids):
pass
class Script:
''' Runs a script passed as a list, default frequency = 1000Hz '''
def __init__(self,msgs):
self._msgs = msgs
self._index = 0
self._period = 0.001
self._pid = 'SCRIPT'
def poll(self,wait_s=None,uid=None):
period = self._period if wait_s is None else wait_s
time.sleep(period)
try:
msg = self._msgs[self._index]
print(self._pid+' SEND > '+msg)
self._index += 1
return [msg]
except IndexError:
return []
def subscribe(self,*uids):
for uid in uids:
if uid is not None:
if uid[0] is '@': assert uid[1:] in get_attr(Uid)
else: assert uid in get_attr(Uid)
return self
def load(self,msg_array):
<|fim_middle|>
def set_period(self,period):
self._period = period
return self
def run(self):
t = threading.current_thread()
self._pid = str(t.ident)+' '+str(t.name)
while len(self.poll()) > 0: pass
def run_async(self):
Thread(target=self.run).start()
if __name__ == '__main__':
Printer('A simple printer: ').send('Just printing a msg to current_thread')
script = [
'@other_uid topic data',
'@other_uid topic',
'uid topic data',
'uid topic'
]
async = ['async topic '+str(n) for n in [1,2,3,4,5,6,7,8,9,0]]
async2 = ['async2 topic '+str(n) for n in [1,2,3,4,5,6,7,8,9,0]]
Script(script).set_period(1).run()
Script(async).set_period(0.15).run_async()
Script(async2).set_period(0.2).run_async()<｜fim▁end｜> | self._msgs += msg_array
return self |
<|file_name|>connectors_local.py<|end_file_name|><｜fim▁begin｜>import time
from threading import Thread
import threading
from wtfj_ids import *
from wtfj_utils import *
class Printer:
''' Opens a new output window and prints messages sent to it '''
def __init__(self,header=''):
self._header = header
def send(self,string):
print(self._header+string)
class Console:
''' Allows user to enter commands '''
def __init__(self,prompt='[$] '):
self._prompt = prompt
self._at = ''
def poll(self,wait_s=None,uid=None):
try:
prompt = str(self._at)+str(self._prompt)
msg = raw_input(prompt)
if msg == '':
self._at = ''
return []
if msg[0] == '@':
self._at = msg.split()[0]+' '
else:
msg = self._at+msg
return [msg]
except Exception as e:
print(repr(e))
return []
def subscribe(self,*uids):
pass
class Script:
''' Runs a script passed as a list, default frequency = 1000Hz '''
def __init__(self,msgs):
self._msgs = msgs
self._index = 0
self._period = 0.001
self._pid = 'SCRIPT'
def poll(self,wait_s=None,uid=None):
period = self._period if wait_s is None else wait_s
time.sleep(period)
try:
msg = self._msgs[self._index]
print(self._pid+' SEND > '+msg)
self._index += 1
return [msg]
except IndexError:
return []
def subscribe(self,*uids):
for uid in uids:
if uid is not None:
if uid[0] is '@': assert uid[1:] in get_attr(Uid)
else: assert uid in get_attr(Uid)
return self
def load(self,msg_array):
self._msgs += msg_array
return self
def set_period(self,period):
<|fim_middle|>
def run(self):
t = threading.current_thread()
self._pid = str(t.ident)+' '+str(t.name)
while len(self.poll()) > 0: pass
def run_async(self):
Thread(target=self.run).start()
if __name__ == '__main__':
Printer('A simple printer: ').send('Just printing a msg to current_thread')
script = [
'@other_uid topic data',
'@other_uid topic',
'uid topic data',
'uid topic'
]
async = ['async topic '+str(n) for n in [1,2,3,4,5,6,7,8,9,0]]
async2 = ['async2 topic '+str(n) for n in [1,2,3,4,5,6,7,8,9,0]]
Script(script).set_period(1).run()
Script(async).set_period(0.15).run_async()
Script(async2).set_period(0.2).run_async()<｜fim▁end｜> | self._period = period
return self |
<|file_name|>connectors_local.py<|end_file_name|><｜fim▁begin｜>import time
from threading import Thread
import threading
from wtfj_ids import *
from wtfj_utils import *
class Printer:
''' Opens a new output window and prints messages sent to it '''
def __init__(self,header=''):
self._header = header
def send(self,string):
print(self._header+string)
class Console:
''' Allows user to enter commands '''
def __init__(self,prompt='[$] '):
self._prompt = prompt
self._at = ''
def poll(self,wait_s=None,uid=None):
try:
prompt = str(self._at)+str(self._prompt)
msg = raw_input(prompt)
if msg == '':
self._at = ''
return []
if msg[0] == '@':
self._at = msg.split()[0]+' '
else:
msg = self._at+msg
return [msg]
except Exception as e:
print(repr(e))
return []
def subscribe(self,*uids):
pass
class Script:
''' Runs a script passed as a list, default frequency = 1000Hz '''
def __init__(self,msgs):
self._msgs = msgs
self._index = 0
self._period = 0.001
self._pid = 'SCRIPT'
def poll(self,wait_s=None,uid=None):
period = self._period if wait_s is None else wait_s
time.sleep(period)
try:
msg = self._msgs[self._index]
print(self._pid+' SEND > '+msg)
self._index += 1
return [msg]
except IndexError:
return []
def subscribe(self,*uids):
for uid in uids:
if uid is not None:
if uid[0] is '@': assert uid[1:] in get_attr(Uid)
else: assert uid in get_attr(Uid)
return self
def load(self,msg_array):
self._msgs += msg_array
return self
def set_period(self,period):
self._period = period
return self
def run(self):
<|fim_middle|>
def run_async(self):
Thread(target=self.run).start()
if __name__ == '__main__':
Printer('A simple printer: ').send('Just printing a msg to current_thread')
script = [
'@other_uid topic data',
'@other_uid topic',
'uid topic data',
'uid topic'
]
async = ['async topic '+str(n) for n in [1,2,3,4,5,6,7,8,9,0]]
async2 = ['async2 topic '+str(n) for n in [1,2,3,4,5,6,7,8,9,0]]
Script(script).set_period(1).run()
Script(async).set_period(0.15).run_async()
Script(async2).set_period(0.2).run_async()<｜fim▁end｜> | t = threading.current_thread()
self._pid = str(t.ident)+' '+str(t.name)
while len(self.poll()) > 0: pass |
<|file_name|>connectors_local.py<|end_file_name|><｜fim▁begin｜>import time
from threading import Thread
import threading
from wtfj_ids import *
from wtfj_utils import *
class Printer:
''' Opens a new output window and prints messages sent to it '''
def __init__(self,header=''):
self._header = header
def send(self,string):
print(self._header+string)
class Console:
''' Allows user to enter commands '''
def __init__(self,prompt='[$] '):
self._prompt = prompt
self._at = ''
def poll(self,wait_s=None,uid=None):
try:
prompt = str(self._at)+str(self._prompt)
msg = raw_input(prompt)
if msg == '':
self._at = ''
return []
if msg[0] == '@':
self._at = msg.split()[0]+' '
else:
msg = self._at+msg
return [msg]
except Exception as e:
print(repr(e))
return []
def subscribe(self,*uids):
pass
class Script:
''' Runs a script passed as a list, default frequency = 1000Hz '''
def __init__(self,msgs):
self._msgs = msgs
self._index = 0
self._period = 0.001
self._pid = 'SCRIPT'
def poll(self,wait_s=None,uid=None):
period = self._period if wait_s is None else wait_s
time.sleep(period)
try:
msg = self._msgs[self._index]
print(self._pid+' SEND > '+msg)
self._index += 1
return [msg]
except IndexError:
return []
def subscribe(self,*uids):
for uid in uids:
if uid is not None:
if uid[0] is '@': assert uid[1:] in get_attr(Uid)
else: assert uid in get_attr(Uid)
return self
def load(self,msg_array):
self._msgs += msg_array
return self
def set_period(self,period):
self._period = period
return self
def run(self):
t = threading.current_thread()
self._pid = str(t.ident)+' '+str(t.name)
while len(self.poll()) > 0: pass
def run_async(self):
<|fim_middle|>
if __name__ == '__main__':
Printer('A simple printer: ').send('Just printing a msg to current_thread')
script = [
'@other_uid topic data',
'@other_uid topic',
'uid topic data',
'uid topic'
]
async = ['async topic '+str(n) for n in [1,2,3,4,5,6,7,8,9,0]]
async2 = ['async2 topic '+str(n) for n in [1,2,3,4,5,6,7,8,9,0]]
Script(script).set_period(1).run()
Script(async).set_period(0.15).run_async()
Script(async2).set_period(0.2).run_async()<｜fim▁end｜> | Thread(target=self.run).start() |
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