Commit cb8c438b authored by Bichen Li's avatar Bichen Li

- Update the dockerfile, add the pull of py4mod package

parent 836bc55b
# ignore results
*.csv
*.mat
# ignore symbolic links
*.egg-info
*.eggs
# ignore compiled python files
*.pyc
# ignore logging files
*.log
# ignore generated dymola files
buildlog.txt
dsfinal.txt
dsin.txt
dslog.txt
dsmodel*
dymosim*
# ignore matlab dumping file
*.mdmp
# ignore spyder project
.spyderproject
.spyproject
# ignore pycharm files
.idea
__pycache__
# ignore jupyter notebook files
.ipynb_checkpoints
# ignore results
*.csv
*.mat
# ignore symbolic links
*.egg-info
*.eggs
# ignore compiled python files
*.pyc
# ignore logging files
*.log
# ignore generated dymola files
buildlog.txt
dsfinal.txt
dsin.txt
dslog.txt
dsmodel*
dymosim*
# ignore matlab dumping file
*.mdmp
# ignore spyder project
.spyderproject
.spyproject
# ignore pycharm files
.idea
__pycache__
# ignore jupyter notebook files
.ipynb_checkpoints
Test.py
\ No newline at end of file
# Dataprocessing toolkit for RWTH ACS simulators
## Copyright
2017, Institute for Automation of Complex Power Systems, EONERC, RWTH Aachen University
## License
This project is released under the terms of the [GPL version 3](COPYING.md).
```
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
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 General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
```
For other licensing options please consult [Prof. Antonello Monti](mailto:amonti@eonerc.rwth-aachen.de).
## Contact
[![EONERC ACS Logo](doc/eonerc_logo.png)](http://www.acs.eonerc.rwth-aachen.de)
- Markus Mirz <mmirz@eonerc.rwth-aachen.de>
- Jan Dinkelbach <JDinkelbach@eonerc.rwth-aachen.de>
- Steffen Vogel <stvogel@eonerc.rwth-aachen.de>
[Institute for Automation of Complex Power Systems (ACS)](http://www.acs.eonerc.rwth-aachen.de)
[EON Energy Research Center (EONERC)](http://www.eonerc.rwth-aachen.de)
[RWTH University Aachen, Germany](http://www.rwth-aachen.de)
# Dataprocessing toolkit for RWTH ACS simulators
## Copyright
2017, Institute for Automation of Complex Power Systems, EONERC, RWTH Aachen University
## License
This project is released under the terms of the [GPL version 3](COPYING.md).
```
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
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 General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
```
For other licensing options please consult [Prof. Antonello Monti](mailto:amonti@eonerc.rwth-aachen.de).
## Contact
[![EONERC ACS Logo](doc/eonerc_logo.png)](http://www.acs.eonerc.rwth-aachen.de)
- Markus Mirz <mmirz@eonerc.rwth-aachen.de>
- Jan Dinkelbach <JDinkelbach@eonerc.rwth-aachen.de>
- Steffen Vogel <stvogel@eonerc.rwth-aachen.de>
[Institute for Automation of Complex Power Systems (ACS)](http://www.acs.eonerc.rwth-aachen.de)
[EON Energy Research Center (EONERC)](http://www.eonerc.rwth-aachen.de)
[RWTH University Aachen, Germany](http://www.rwth-aachen.de)
from dataprocessing.readtools import *
from dataprocessing.timeseries import *
def get_node_voltage_phasors(dpsim_timeseries_list):
"""Calculate voltage phasors of all nodes
:param dpsim_timeseries_list: timeseries list retrieved from dpsim results
:return:
"""
voltage_phasor_list = {}
for ts in dpsim_timeseries_list:
ts_abs = ts.abs(ts.name + '_abs')
ts_phase = ts.phase(ts.name + '_phase')
ts_phasor = {}
ts_phasor['abs'] = ts_abs
ts_phasor['phase'] = ts_phase
voltage_phasor_list[ts.name] = ts_phasor
return voltage_phasor_list
def get_node_emt_voltages(timeseries_list, freq):
"""Calculate voltage phasors of all nodes
:param timeseries_list: timeseries list retrieved from dpsim results
:return:
"""
voltages_list = {}
for ts in timeseries_list:
ts_emt = ts.dynphasor_shift_to_emt(ts.name, freq)
voltages_list[ts.name] = ts_emt
return voltages_list
from dataprocessing.readtools import *
from dataprocessing.timeseries import *
def get_node_voltage_phasors(dpsim_timeseries_list):
"""Calculate voltage phasors of all nodes
:param dpsim_timeseries_list: timeseries list retrieved from dpsim results
:return:
"""
voltage_phasor_list = {}
for ts in dpsim_timeseries_list:
ts_abs = ts.abs(ts.name + '_abs')
ts_phase = ts.phase(ts.name + '_phase')
ts_phasor = {}
ts_phasor['abs'] = ts_abs
ts_phasor['phase'] = ts_phase
voltage_phasor_list[ts.name] = ts_phasor
return voltage_phasor_list
def get_node_emt_voltages(timeseries_list, freq):
"""Calculate voltage phasors of all nodes
:param timeseries_list: timeseries list retrieved from dpsim results
:return:
"""
voltages_list = {}
for ts in timeseries_list:
ts_emt = ts.dynphasor_shift_to_emt(ts.name, freq)
voltages_list[ts.name] = ts_emt
return voltages_list
import matplotlib.pyplot as plt
import numpy as np
from .timeseries import *
def plot_timeseries(figure_id, timeseries, plt_linestyle='-', plt_linewidth=2, plt_color=None, plt_legend_loc='lower right'):
"""
This function plots either a single timeseries or several timeseries in the figure defined by figure_id.
Several timeseries (handed over in a list) are plotted in several subplots.
In order to plot several timeseries in one plot, the function is to be called several times (hold is activated).
"""
plt.figure(figure_id)
if not isinstance(timeseries, list):
if plt_color:
plt.plot(timeseries.time, timeseries.values, linestyle=plt_linestyle, label=timeseries.label, linewidth=plt_linewidth, color=plt_color)
else:
plt.plot(timeseries.time, timeseries.values, linestyle=plt_linestyle, label=timeseries.label, linewidth=plt_linewidth)
plt.gca().autoscale(axis='x', tight=True)
plt.legend(loc=plt_legend_loc)
else:
for ts in timeseries:
plt.subplot(len(timeseries), 1, timeseries.index(ts) + 1)
if plt_color:
plt.plot(ts.time, ts.values, linestyle=plt_linestyle, label=ts.label, linewidth=plt_linewidth, color=plt_color)
else:
plt.plot(ts.time, ts.values, linestyle=plt_linestyle, label=ts.label, linewidth=plt_linewidth)
plt.gca().autoscale(axis='x', tight=True)
plt.legend()
def set_timeseries_labels(timeseries, timeseries_labels):
"""
Sets label attribute of timeseries, later used in plotting functions.
Suitable for single timeseries as well as for several timeseries (handed over in a list).
"""
if not isinstance(timeseries, list):
timeseries.label = timeseries_labels
else:
for ts in timeseries:
ts.label = timeseries_labels[timeseries.index(ts)]
import matplotlib.pyplot as plt
import numpy as np
from .timeseries import *
def plot_timeseries(figure_id, timeseries, plt_linestyle='-', plt_linewidth=2, plt_color=None, plt_legend_loc='lower right'):
"""
This function plots either a single timeseries or several timeseries in the figure defined by figure_id.
Several timeseries (handed over in a list) are plotted in several subplots.
In order to plot several timeseries in one plot, the function is to be called several times (hold is activated).
"""
plt.figure(figure_id)
if not isinstance(timeseries, list):
if plt_color:
plt.plot(timeseries.time, timeseries.values, linestyle=plt_linestyle, label=timeseries.label, linewidth=plt_linewidth, color=plt_color)
else:
plt.plot(timeseries.time, timeseries.values, linestyle=plt_linestyle, label=timeseries.label, linewidth=plt_linewidth)
plt.gca().autoscale(axis='x', tight=True)
plt.legend(loc=plt_legend_loc)
else:
for ts in timeseries:
plt.subplot(len(timeseries), 1, timeseries.index(ts) + 1)
if plt_color:
plt.plot(ts.time, ts.values, linestyle=plt_linestyle, label=ts.label, linewidth=plt_linewidth, color=plt_color)
else:
plt.plot(ts.time, ts.values, linestyle=plt_linestyle, label=ts.label, linewidth=plt_linewidth)
plt.gca().autoscale(axis='x', tight=True)
plt.legend()
def set_timeseries_labels(timeseries, timeseries_labels):
"""
Sets label attribute of timeseries, later used in plotting functions.
Suitable for single timeseries as well as for several timeseries (handed over in a list).
"""
if not isinstance(timeseries, list):
timeseries.label = timeseries_labels
else:
for ts in timeseries:
ts.label = timeseries_labels[timeseries.index(ts)]
#!/usr/bin/python
# coding: utf8
import numpy as np
import pandas as pd
from timeseries import *
import re
def read_timeseries_Modelica(filename, timeseries_names=None, is_regex=False):
from modelicares import SimRes
sim = SimRes(filename)
if timeseries_names is None and is_regex is False:
# No trajectory names or regex specified, thus read in all
timeseries = []
for name in sim.names():
timeseries.append(TimeSeries(name, sim(name).times(), sim(name).values()))
timeseries_names = sim.names()
elif is_regex is True:
# Read in variables which match with regex
timeseries = []
p = re.compile(timeseries_names)
timeseries_names = [name for name in sim.names() if p.search(name)]
timeseries_names.sort()
for name in timeseries_names:
timeseries.append(TimeSeries(name, sim(name).times(), sim(name).values()))
else:
# Read in specified time series
if not isinstance(timeseries_names, list):
timeseries = TimeSeries(timeseries_names, sim(timeseries_names).times(), sim(timeseries_names).values())
else:
timeseries = []
for name in timeseries_names:
timeseries.append(TimeSeries(name, sim(name).times(), sim(name).values()))
return timeseries
def read_timeseries_PLECS(filename, timeseries_names=None):
pd_df = pd.read_csv(filename)
timeseries_list = []
if timeseries_names is None:
# No trajectory names specified, thus read in all
timeseries_names = list(pd_df.columns.values)
timeseries_names.remove('Time')
for name in timeseries_names:
timeseries_list.append(TimeSeries(name, pd_df['Time'].values, pd_df[name].values))
else:
# Read in specified time series
for name in timeseries_names:
timeseries_list.append(TimeSeries(name, pd_df['Time'].values, pd_df[name].values))
print('PLECS results column names: ' + str(timeseries_names))
print('PLECS results number: ' + str(len(timeseries_list)))
return timeseries_list
def read_timeseries_simulink(filename, timeseries_names=None):
pd_df = pd.read_csv(filename)
timeseries_list = []
if timeseries_names is None:
# No trajectory names specified, thus read in all
timeseries_names = list(pd_df.columns.values)
timeseries_names.remove('time')
for name in timeseries_names:
timeseries_list.append(TimeSeries(name, pd_df['time'].values, pd_df[name].values))
else:
# Read in specified time series
for name in timeseries_names:
timeseries_list.append(TimeSeries(name, pd_df['time'].values, pd_df[name].values))
print('Simulink results column names: ' + str(timeseries_names))
print('Simulink results number: ' + str(len(timeseries_list)))
return timeseries_list
def read_timeseries_dpsim_real(filename, timeseries_names=None):
"""Reads real time series data from DPsim log file which may have a header.
Timeseries names are assigned according to the header names if available.
:param filename: name of the csv file that has the data
:param timeseries_names: column names which should be read
:return: list of Timeseries objects
"""
timeseries_list = []
pd_df = pd.read_csv(filename)
if timeseries_names is None:
# No column names specified, thus read in all and strip spaces
pd_df.rename(columns=lambda x: x.strip(), inplace=True)
column_names = list(pd_df.columns.values)
# Remove timestamps column name and store separately
column_names.remove('time')
timestamps = pd_df.iloc[:,0]
for name in column_names:
timeseries_list.append(TimeSeries(name, timestamps, pd_df[name].values))
else:
# Read in specified time series
print('no column names specified yet')
print('DPsim results column names: ' + str(column_names))
print('DPsim results number: ' + str(len(timeseries_list)))
return timeseries_list
def read_timeseries_dpsim_cmpl(filename, timeseries_names=None):
"""Reads complex time series data from DPsim log file. Real and
imaginary part are stored in one complex variable.
:param filename: name of the csv file that has the data
:param timeseries_names: column name which should be read
:return: list of Timeseries objects
"""
pd_df = pd.read_csv(filename)
timeseries_list = []
if timeseries_names is None:
# No column names specified, thus read in all and strip off spaces
pd_df.rename(columns=lambda x: x.strip(), inplace=True)
column_names = list(pd_df.columns.values)
# Remove timestamps column name and store separately
column_names.remove('time')
timestamps = pd_df.iloc[:,0]
# Calculate number of network nodes since array is [real, imag]
node_number = int(len(column_names) / 2)
node_index = 1
for column in column_names:
if node_index <= node_number:
ts_name = 'n'+ str(node_index)
timeseries_list.append(
TimeSeries(ts_name, timestamps, np.vectorize(complex)(pd_df.iloc[:,node_index],pd_df.iloc[:,node_index + node_number])))
else:
break
node_index = node_index + 1
else:
# Read in specified time series
print('cannot read specified columns yet')
print('DPsim results column names: ' + str(column_names))
print('DPsim results number: ' + str(len(timeseries_list)))
return timeseries_list
def read_timeseries_dpsim_cmpl_separate(filename, timeseries_names=None):
"""Deprecated - Reads complex time series data from DPsim log file. Real and
imaginary part are stored separately.
:param filename: name of the csv file that has the data
:param timeseries_names: column name which should be read
:return: list of Timeseries objects
"""
pd_df = pd.read_csv(filename, header=None)
timeseries_list = []
if timeseries_names is None:
# No trajectory names specified, thus read in all
column_names = list(pd_df.columns.values)
# Remove timestamps column name and store separately
column_names.remove(0)
timestamps = pd_df.iloc[:, 0]
# Calculate number of network nodes since array is [real, imag]
node_number = int(len(column_names) / 2)
node_index = 1
for column in column_names:
if node_index <= node_number:
node_name = 'node '+ str(node_index) +' Re'
timeseries_list.append(TimeSeries(node_name, timestamps, pd_df.iloc[:,column]))
else:
node_name = 'node '+ str(node_index - node_number) +' Im'
timeseries_list.append(TimeSeries(node_name, timestamps, pd_df.iloc[:,column]))
node_index = node_index + 1
else:
# Read in specified time series
print('no column names specified yet')
print('DPsim results file length:')
print(len(timeseries_list))
for result in timeseries_list:
print(result.name)
return timeseries_list
def read_timeseries_NEPLAN_loadflow(file_name, timeseries_names=None, is_regex=False):
"""
Read in NEPLAN loadflow result from result file, the result is in angle notation, amplitude and angle are stored
separately
To keep consistent with the names of voltage in most cases, the name of voltage variables are changed into '.V*'
instead of '.U*' as in the result file
:param file_name: name of the mat file for the loadflow result from neplan
:param timeseries_names: column name to be read
:param is_regex: flag for using regular expression
:return: list of Timeseries objects
"""
str_tmp = open(file_name, "r") # Read in files
low = 0 # flag for the start of a new data in str_cmp
high = 0 # flag for the end of this new data in str_cmp
flag = True # To judge if this is the first line of the file, which will be the names for the data type
# Read in data from result file of neplan
seq = [] # list for data type names
value = [] # list for data
namelist = ['U', 'ANGLEU', 'P', 'Q', 'I', 'ANGLEI'] # Suffix of the data name
timeseries = []
line_del = [] # a list for the value to be deleted
isfloat = re.compile(r'^[-+]?[0-9]+\.[0-9]+$') # regular expression to find float values
# Transfer ',' in the floats in result file to '.'
for line in str_tmp.readlines(): # Check the data to find out floats with ','
line = line.replace(",", ".")
high -= high
low -= low
del value[:]
# read in different data and start processing
for letter in line:
if letter == " " or letter == "\n": # different data(separated by ' ') or end(/n)
if low is not high: # if low is equal to high, no data read in
if flag: # first line of the file, list for data-type name
seq.append(line[low:high])
else: # not first line of the file,list for data
if isfloat.match(line[low:high]):
value.append(float(line[low:high]))
else:
value.append(line[low:high])
else: # no data for this datatype
value.append(r'#') # No value, set as #
low = high + 1 # refresh low flag
high += 1
"""
A typical line current in neplan has two parts from both end, but we doesn't have to calculate them
with the assumption that the topology of the gird should be correct with which we can validate the
current by comparing the voltage of the nodes connected to the ends of the line
"""
if flag is not True: # flag is true when it's the first line
if value[3] is not '#':
for m in range(6):
timeseries.append(TimeSeries(value[3] + '.' + namelist[m],
np.array([0., 1.]), np.array([value[m + 6], value[m + 6]])))
else:
for m in range(2):
timeseries.append(TimeSeries(value[1] + '.' + namelist[m],
np.array([0., 1.]), np.array([value[m + 6], value[m + 6]])))
flag = False
str_tmp.close()
# Read in variables which match with regex
if is_regex is True:
p = re.compile(timeseries_names)
length = len(timeseries)
for rule_check in range(length):
if p.search(timeseries[rule_check].name):
pass
else:
line_del.append(rule_check)
# Read in specified time series
elif timeseries_names is not None:
length = len(timeseries)
for rule_check in range(length):
if timeseries_names == timeseries[rule_check].name:
pass
else:
line_del.append(rule_check)
# delete those values that are not needed.
line_del = set(line_del)
line_del = sorted(line_del)
for num_to_del in range(len(line_del)):
del timeseries[line_del[len(line_del) - num_to_del - 1]]
return timeseries
def read_timeseries_simulink_loadflow(file_name, timeseries_names=None, is_regex=False):
"""
Read in simulink load-flow result from result file(.rep), the result is in angle notation, amplitude and angle are stored
separately.
A suffix is used to tag different data for a component:
.Arms/.IDegree for current/current angle,
.Vrms/.VDegree for voltage/voltage angle.
:param file_name:path of the .rep file for the loadflow result from simulink
:param timeseries_names: specific values to be read
:param is_regex: flag for using regular expression
:return: list of Timeseries objects
"""
str_tmp = open(file_name, 'r', encoding='latin-1') # Read in files, using latin-1 to decode /xb0
# Read in data from result file of neplan
name = [] # list for data type names
value = [] # list for data
timeseries = []
line_del = [] # a list for the value to be deleted
for line in str_tmp.readlines():
line = line.replace("°", "")
del value[:]
del name[:]
# read in different data and start processing
if len(line) > 37:
if line[31:35] == '--->':
if line[13:17] == 'Arms':
name = [line[37:len(line)].rstrip() + '.Arms', line[37:len(line)].rstrip() + '.IDegree']
elif line[13:17] == 'Vrms':
name = [line[37:len(line)].rstrip() + '.Vrms', line[37:len(line)].rstrip() + '.VDegree']
value = [float(line[0:13]), float(line[18:31])]
timeseries.append(TimeSeries(name[0],
np.array([0., 1.]), np.array([value[0], value[0]])))
timeseries.append(TimeSeries(name[1],
np.array([0., 1.]), np.array([value[1], value[1]])))
# Read in variables which match with regex
if is_regex is True:
p = re.compile(timeseries_names)
length = len(timeseries)
for rule_check in range(length):
if p.search(timeseries[rule_check].name):
pass
else:
line_del.append(rule_check)
# Read in specified time series
elif timeseries_names is not None:
length = len(timeseries)
for rule_check in range(length):
if timeseries_names == timeseries[rule_check].name:
pass
else:
line_del.append(rule_check)
# delete those values that are not needed.
line_del = set(line_del)
line_del = sorted(line_del)
for num_to_del in range(len(line_del)):
del timeseries[line_del[len(line_del) - num_to_del - 1]]
return timeseries
#!/usr/bin/python
# coding: utf8
import numpy as np
import pandas as pd
from timeseries import *
import re
def read_timeseries_Modelica(filename, timeseries_names=None, is_regex=False):
from modelicares import SimRes
sim = SimRes(filename)
if timeseries_names is None and is_regex is False:
# No trajectory names or regex specified, thus read in all
timeseries = []
for name in sim.names():
timeseries.append(TimeSeries(name, sim(name).times(), sim(name).values()))
timeseries_names = sim.names()
elif is_regex is True:
# Read in variables which match with regex
timeseries = []
p = re.compile(timeseries_names)
timeseries_names = [name for name in sim.names() if p.search(name)]
timeseries_names.sort()
for name in timeseries_names:
timeseries.append(TimeSeries(name, sim(name).times(), sim(name).values()))
else:
# Read in specified time series
if not isinstance(timeseries_names, list):
timeseries = TimeSeries(timeseries_names, sim(timeseries_names).times(), sim(timeseries_names).values())
else:
timeseries = []
for name in timeseries_names:
timeseries.append(TimeSeries(name, sim(name).times(), sim(name).values()))
return timeseries
def read_timeseries_PLECS(filename, timeseries_names=None):
pd_df = pd.read_csv(filename)
timeseries_list = []
if timeseries_names is None:
# No trajectory names specified, thus read in all
timeseries_names = list(pd_df.columns.values)
timeseries_names.remove('Time')
for name in timeseries_names:
timeseries_list.append(TimeSeries(name, pd_df['Time'].values, pd_df[name].values))
else:
# Read in specified time series
for name in timeseries_names:
timeseries_list.append(TimeSeries(name, pd_df['Time'].values, pd_df[name].values))
print('PLECS results column names: ' + str(timeseries_names))
print('PLECS results number: ' + str(len(timeseries_list)))
return timeseries_list
def read_timeseries_simulink(filename, timeseries_names=None):
pd_df = pd.read_csv(filename)
timeseries_list = []
if timeseries_names is None:
# No trajectory names specified, thus read in all
timeseries_names = list(pd_df.columns.values)
timeseries_names.remove('time')
for name in timeseries_names:
timeseries_list.append(TimeSeries(name, pd_df['time'].values, pd_df[