def __init__(self):
n_motor = 13
n_sensor = 4
output_scale = diva_output_scale
min_motor_values = np.array(
[-3, -3, -3, -3, -3, -3, -3, -3, -3, -3, 0., 0., 0.])
max_motor_values = np.array(
[3.3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1., 1., 1.])
name = 'DivaWrapper'
self.name = name
self.n_motor = n_motor
self.n_sensor = n_sensor
self.min_motor_values = min_motor_values
self.max_motor_values = max_motor_values
self.motor_command = [0.0] * n_motor
self.sensor_out = [0.0] * n_sensor
self.matlabSession = ml.session_factory()
abs_path = os.path.dirname(os.path.abspath(__file__))
command_ = 'cd ' + abs_path + '/DivaMatlab/'
self.matlabSession.run(command_) # Path to DivaMatlab functions
self.matlabSession.putvalue('outputScale', output_scale)
def __init__(self):
logger.info('instantiating matlab')
import pymatlab
# self.session = pymatlab.session_factory('-nodisplay -nosplash')
# self.session = pymatlab.session_factory('-nosplash -nodesktop -nodisplay')
self.session = pymatlab.session_factory()
def __init__(self):
logger.info('instantiating matlab')
import pymatlab
# self.session = pymatlab.session_factory('-nodisplay -nosplash')
# self.session = pymatlab.session_factory('-nosplash -nodesktop -nodisplay')
self.session = pymatlab.session_factory()
def script_sda_detector(resolution):
import pymatlab
session = pymatlab.session_factory()
nruns = 20
partiDetMethod = 'log_detector'
for nrun in range(18, nruns + 1):
basefilename = '../final_results/baseline_resized/{0:05d}/models/res_baseline_resized_{0:05d}_111111/{1:05d}_{2:03d}_'.format(
resolution, nrun, resolution)
trainfilename = basefilename + 'train_ids.pkl.gz'
valfilename = basefilename + 'val_ids.pkl.gz'
trainfinalfilename = basefilename + 'trainfinal_ids.pkl.gz'
valfinalfilename = basefilename + 'valfinal_ids.pkl.gz'
testfilename = basefilename + 'test_ids.pkl.gz'
# [0,max_ids] -> [1,max_ids+1]
train_ids = load_savedgzdata(trainfilename) + 1
val_ids = load_savedgzdata(valfilename) + 1
trainfinal_ids = load_savedgzdata(trainfinalfilename) + 1
valfinal_ids = load_savedgzdata(valfinalfilename) + 1
test_ids = load_savedgzdata(testfilename) + 1
print >> sys.stderr, train_ids
print >> sys.stderr, val_ids
print >> sys.stderr, trainfinal_ids
print >> sys.stderr, valfinal_ids
print >> sys.stderr, test_ids
session.putvalue('partiDetMethod', partiDetMethod)
session.putvalue('resolution', str(resolution) + '_' + str(nrun))
session.putvalue('train_ids', train_ids)
session.putvalue('val_ids', val_ids)
session.putvalue('trainfinal_ids', trainfinal_ids)
session.putvalue('valfinal_ids', valfinal_ids)
session.putvalue('test_ids', test_ids)
mscript = """
data = struct();
data.partiDetMethod = partiDetMethod;
data.resolution = resolution;
data.train_ids = train_ids;
data.val_ids = val_ids;
data.trainfinal_ids = trainfinal_ids;
data.valfinal_ids = valfinal_ids;
data.test_ids = test_ids;
res = script_sda_detector( data )
"""
session.putvalue('MSCRIPT', mscript)
session.run('eval(MSCRIPT)')
res = session.getvalue('res')
print res
def __init__(self,limitCoresTo = None):
if limitCoresTo is None:
os.system('comsol server &')
else:
os.system('comsol server -np '+str(int(limitCoresTo))+' &')
#time.sleep(1)
self.session = pymatlab.session_factory()
self.session.run('mphstart(2036)')
self.session.run('import com.comsol.model.*')
self.session.run('import com.comsol.model.util.*')
def script_sda_detector(resolution):
import pymatlab
session = pymatlab.session_factory()
nruns = 20
partiDetMethod = 'log_detector'
for nrun in range(18,nruns+1):
basefilename = '../final_results/baseline_resized/{0:05d}/models/res_baseline_resized_{0:05d}_111111/{1:05d}_{2:03d}_'.format(resolution,nrun,resolution)
trainfilename = basefilename + 'train_ids.pkl.gz'
valfilename = basefilename + 'val_ids.pkl.gz'
trainfinalfilename = basefilename + 'trainfinal_ids.pkl.gz'
valfinalfilename = basefilename + 'valfinal_ids.pkl.gz'
testfilename = basefilename + 'test_ids.pkl.gz'
# [0,max_ids] -> [1,max_ids+1]
train_ids = load_savedgzdata(trainfilename)+1
val_ids = load_savedgzdata(valfilename)+1
trainfinal_ids = load_savedgzdata(trainfinalfilename)+1
valfinal_ids = load_savedgzdata(valfinalfilename)+1
test_ids = load_savedgzdata(testfilename)+1
print >> sys.stderr, train_ids
print >> sys.stderr, val_ids
print >> sys.stderr, trainfinal_ids
print >> sys.stderr, valfinal_ids
print >> sys.stderr, test_ids
session.putvalue('partiDetMethod',partiDetMethod)
session.putvalue('resolution',str(resolution) + '_' + str(nrun))
session.putvalue('train_ids',train_ids)
session.putvalue('val_ids',val_ids)
session.putvalue('trainfinal_ids',trainfinal_ids)
session.putvalue('valfinal_ids',valfinal_ids)
session.putvalue('test_ids',test_ids)
mscript = """
data = struct();
data.partiDetMethod = partiDetMethod;
data.resolution = resolution;
data.train_ids = train_ids;
data.val_ids = val_ids;
data.trainfinal_ids = trainfinal_ids;
data.valfinal_ids = valfinal_ids;
data.test_ids = test_ids;
res = script_sda_detector( data )
"""
session.putvalue('MSCRIPT', mscript)
session.run('eval(MSCRIPT)')
res = session.getvalue('res')
print res
def decompose_tensor(filters):
""" filters is of type input feature maps, output feature maps, wxh of filter
Output is a structure P which contains lambda, U{1}, U{2}, U{3}
"""
# Set logging to DEBUG to see CP-ALS information
logging.basicConfig(level=logging.DEBUG)
print filters.shape
filters = np.array(filters)
print filters.shape
print filters.dtype
nbr_filters = filters.shape[0]
fwidth = filters.shape[2]
fheight = filters.shape[3]
Pstruct = []
for chanel in range(filters.shape[1]):
filter_for_channel = filters[:,chanel,:,:]
filter_for_channel.reshape(nbr_filters, fwidth, fheight)
filter_for_channel = np.swapaxes(filter_for_channel, 0,2);
print 'Number of filters ', nbr_filters
print 'filter_for channel shape ', filter_for_channel.shape
fig, axes = plt.subplots(nrows=5, ncols=4)
fig.tight_layout()
for f in xrange(nbr_filters):
img = filter_for_channel[:,:,f]
plt.subplot(5,4,f)
plt.imshow(img)
plt.show(block=False)
T = dtensor(filter_for_channel);
rank = np.floor(nbr_filters*0.6);
print 'rank is ', rank
session = pymatlab.session_factory()
session.putvalue('A',rank)
del session
## P.U, P.lmbda
print 'P U0,U1,U2, lambda sizes: ', P.U[0].size, P.U[1].size, P.U[2].size, P.lmbda
print 'fit was ', fit
Pstruct.append(P)
#dtensor(ktensor(U).toarray())
print np.allclose(T, P.totensor())
U = [np.random.rand(i,3) for i in (20, 10, 14)]
Tn = dtensor(ktensor(U).toarray())
P, fit, itr, _ = cp_als(Tn, 10)
print 'P U0,U1,U2, lambda sizes: ', P.U[0].size, P.U[1].size, P.U[2].size, P.lmbda
print 'fit was ', fit
print np.allclose(Tn, P.totensor())
return Pstruct
def __init__(self):
self.session = mlb.session_factory()
self.session.run('cd ./voc-dpm/')
self.model = None
def trainDPMmodel(self, model, pos, neg, warp, randneg, nbiter, nbnegiter,
maxnumexamples, overlap, numfp, cont, tag, C):
""" Trains a model opptimizing a WL-SSVM or LSVM.
(thin wrapper around matlab code by Girshick, R. B.)
Returns:
model The new model
Arguments
warp 1 => use warped positives
0 => use latent positives
randneg 1 => use random negaties
0 => use hard negatives
iter The number of training iterations
negiter The number of data-mining steps within each training iteration
max_num_examples
The maximum number of negative examples that the feature vector
cache may hold
overlap The minimum overlap in latent positive search
cont True => restart training from a previous run
C Regularization/surrogate loss tradeoff parameter
"""
self.session.run('cd ./train/')
self.session.putvalue('model', model)
self.session.putvalue('pos', pos)
self.session.putvalue('neg', neg)
self.session.putvalue('warp', warp)
self.session.putvalue('randneg', randneg)
self.session.putvalue('iter', nbiter)
self.session.putvalue('negiter', nbnegiter)
self.session.putvalue('maximum_examples', maximumexamples)
self.session.putvalue('overlap', overlap)
self.session.putvalue('num_fp', numfp)
self.session.putvalue('cont', cont)
self.session.putvalue('tag', tag)
self.session.putvalue('C', C)
self.session.run('nmodel = train(model,pos,neg.warp,randneg,iter,negiter,max_num_exampples,overlap,num_fp,cont,tag,C)')
self.model = self.session.getvalue('nmodel')
self.session.run('cd ../')
def detectObject(image, thresh, max_num):
if self.model == None:
self.session.run('cd ./features/')
self.session.putvalue('im', image)
self.session.putvalue('model', self.model)
def matlab_launcher(job):
# Run it as a Matlab function.
try:
import pymatlab
except:
raise Exception(
"Cannot import pymatlab. pymatlab is required for Matlab jobs. It is installable with pip."
)
sys.stderr.write("Booting up Matlab...\n")
session = pymatlab.session_factory()
# Add directory to the Matlab path.
session.run("cd('%s')" % os.path.realpath(job['expt_dir']))
session.run('params = struct()')
for name, param in job['params'].iteritems():
vals = param['values']
# sys.stderr.write('%s = %s\n' % (param['name'], str(vals)))
# should have dtype=float explicitly, otherwise
# if they are ints it will automatically do int64, which
# matlab will receive, and will tend to break matlab scripts
# because in matlab things tend to always be double type
session.putvalue('params_%s' % name, np.array(vals, dtype=float))
session.run("params.%s = params_%s" % (name, name))
# pymatlab sucks, so I cannot put the value directly into a struct
# instead i do this silly workaround to put it in a variable and then
# copy that over into the struct
# session.run('params_%s'%param['name'])
sys.stderr.write('Running function %s\n' % job['function-name'])
# Execute the function
session.run('result = %s(params)' % job['function-name'])
# Get the result
result = session.getvalue('result')
# TODO: this only works for single-task right now
result = float(result)
sys.stderr.write("Got result %s\n" % (result))
del session
return result
def interpac(t, px, py):
newpx = []
newpy = []
session = pymatlab.session_factory()
session.putvalue('t', t)
session.putvalue('x', np.darray(px))
session.putvalue('y', np.darray(py))
session.run('out=interpac(t,x,y)')
outval = session.getvalue('out')
newpx = outval[:,0]
newpy = outval[:,1]
return newpx, newpy
def matlab_launcher(job):
# Run it as a Matlab function.
try:
import pymatlab
except:
raise Exception("Cannot import pymatlab. pymatlab is required for Matlab jobs. It is installable with pip.")
sys.stderr.write("Booting up Matlab...\n")
session = pymatlab.session_factory()
# Add directory to the Matlab path.
session.run("cd('%s')" % os.path.realpath(job['expt_dir']))
session.run('params = struct()')
for name, param in job['params'].iteritems():
vals = param['values']
# sys.stderr.write('%s = %s\n' % (param['name'], str(vals)))
# should have dtype=float explicitly, otherwise
# if they are ints it will automatically do int64, which
# matlab will receive, and will tend to break matlab scripts
# because in matlab things tend to always be double type
session.putvalue('params_%s' % name, np.array(vals, dtype=float))
session.run("params.%s = params_%s" % (name, name))
# pymatlab sucks, so I cannot put the value directly into a struct
# instead i do this silly workaround to put it in a variable and then
# copy that over into the struct
# session.run('params_%s'%param['name'])
sys.stderr.write('Running function %s\n' % job['function-name'])
# Execute the function
session.run('result = %s(params)' % job['function-name'])
# Get the result
result = session.getvalue('result')
# TODO: this only works for single-task right now
result = float(result)
sys.stderr.write("Got result %s\n" % (result))
del session
return result
def setUpClass(cls):
cls.session = pymatlab.session_factory()
def __init__(self, diva_path, sample_rate=11025):
self.session = pymatlab.session_factory()
self.session.run('path(\'' + diva_path + '\', path)')
self.session.putvalue('sr', np.array([sample_rate]))
diva_path = os.path.abspath(diva_path)
self.session.run(['path(\'', diva_path, '\', path)'])
import sys
__author__ = 'Sancar'
from gurobipy import *
import os
import copy
import pymatlab
import numpy as np
import ggplot as gg
from scipy import stats as st
from pandas import DataFrame as pd_df
pr_dir = os.environ.get('PROJECT_DIR')
session = pymatlab.session_factory()
#pr_dir = '~/projects/'
print pr_dir
root_dir = os.path.join(pr_dir, 'SeededGraphMatch')
n = 45
m = 10
mplusn = m+n
p = 0.2
corr = 0.6
def __init__(self, sample_rate=11025):
self.session = pymatlab.session_factory()
# sample rate setting not working yet
self.session.putvalue('sr', array([sample_rate]))
import pymatlab
if __name__ == '__main__':
print 'Start Example'
numTraj = 0
numPrediction = 0
baseDir = "./"
obsTrajCsvFile = baseDir + "csvFiles/obsTraj.csv"
predTrajCsvFile = baseDir + "csvFiles/predTraj.csv"
testTrajCsvFile = baseDir + "csvFiles/testTraj.csv"
modelDir = baseDir + "TRO_model.mat"
print 'baseDir:%s' % baseDir
# Initialize Matlab
print 'Loading MATLAB.'
session = pymatlab.session_factory()
print 'Finish loading MATLAB.'
session.run('cd ' + baseDir)
# Initialize Model
print 'Start Initializing the model.'
session.run('UOLA_init(\'' + modelDir + '\')')
print 'Finish Initializing the model'
# Model Update
print 'Start Updating the model using one trajectory'
session.run('UOLA_learn(\'' + modelDir + '\',\'' + obsTrajCsvFile + '\')')
print 'Finish Updating the model using one trajectory'
# Prediction
print 'Start Predicting the given trajectory.'
session.run('UOLA_predict(\'' + modelDir + '\',\'' + testTrajCsvFile +
'\',\'' + predTrajCsvFile + '\')')
print 'Finish Predicting the given trajectory.'
print 'End Example'
def __init__(self, sample_rate=11025):
import pymatlab
self.session = pymatlab.session_factory()
# sample rate setting not working yet
self.session.putvalue('sr', array([sample_rate]))
def __init__(self):
"""Initializes the model parameters"""
super(Shapiro, self).__init__()
self._session = pymatlab.session_factory('-nodisplay -nojvm')
gevent.sleep(1)
print 'Matlab over'
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='EEG signal recorder.')
parser.add_argument('var', metavar='var', type=str, nargs='?')
args = parser.parse_args(sys.argv[1:])
# Creating headset Object. No console output. No research headset
headset = Emotiv(False, SERIAL, False)
connect = MatlabConnect(headset)
# Matlab needs to be started the current workspace to be used
raw_input("Press Enter when Matlab is started")
connect.session = pymatlab.session_factory()
# Create a Greenlet for the setup...
setupGLet = gevent.spawn(headset.setup)
gevent.sleep(1) # ... and pass the control
if not setupGLet.started:
print 'Error: Is the device connected?'
headset.close()
quit()
connect.isRunning = True
# Create the sensor data generator
g1 = gevent.spawn(connect.get_sensors_info)
# Create plot routine
g2 = gevent.spawn(connect.matlabBridge, args.var or 'data')
gevent.sleep(1)
print 'Matlab over'
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='EEG signal recorder.')
parser.add_argument('var', metavar='var', type=str, nargs='?')
args = parser.parse_args(sys.argv[1:])
# Creating headset Object. No console output. No research headset
headset = Emotiv(False, SERIAL, False)
connect = MatlabConnect(headset)
# Matlab needs to be started the current workspace to be used
raw_input("Press Enter when Matlab is started")
connect.session = pymatlab.session_factory()
# Create a Greenlet for the setup...
setupGLet = gevent.spawn(headset.setup)
gevent.sleep(1) # ... and pass the control
if not setupGLet.started:
print 'Error: Is the device connected?'
headset.close()
quit()
connect.isRunning = True
# Create the sensor data generator
g1 = gevent.spawn(connect.get_sensors_info)
# Create plot routine
g2 = gevent.spawn(connect.matlabBridge, args.var or 'data')
def __init__(self, diva_path, sample_rate = 11025):
self.session = pymatlab.session_factory()
self.session.run('path(\'' + diva_path + '\', path)')
self.session.putvalue('sr', np.array([sample_rate]))
diva_path = os.path.abspath(diva_path)
self.session.run(['path(\'', diva_path, '\', path)'])
def setUpClass(cls):
cls.session = pymatlab.session_factory()
#!/usr/bin/env python
from tf.transformations import euler_from_quaternion, quaternion_from_euler
from jcontroller import jcontroller
import pymatlab
import time
import numpy as np
from math import *
from random import *
#def main():
# initiate robot
j = jcontroller()
# Start matlab session
matSess = pymatlab.session_factory()
matSess.run("addpath(genpath('~/catkin_ws/src'))")
def joinLists(a, b, steps=30):
# generate intermediary stepts
c = []
loa = [] # list of actuators
ini = a[-1]
end = b[0]
for i in range(4): # for each leg
for j in range(3): # for each motor
step = (end[i][j] - ini[i][j]) / steps
if step == 0:
loa.append([ini[i][j]] * steps)
else:
loa.append(np.arange(ini[i][j], end[i][j], step).tolist())
for i in range(steps):
import time
import copy
import numpy as np
import ggplot as gg
from scipy import stats as st
from pandas import DataFrame as pd_df
os.environ["LD_LIBRARY_PATH"] = os.environ["LD_LIBRARY_PATH"] + ":/usr/lib/x86_64-linux-gnu/libstdc++.so.6"
import pymatlab
pr_dir = os.environ.get('PROJECT_DIR')
session = pymatlab.session_factory(options=" -logfile sgm_ip_run_log"+time.strftime("%H:%M:%S")+".txt")
#pr_dir = 'G:\Sancar\Documents\projects'
pr_dir = '~/projects/'
print pr_dir
pr_dir = '/home/sadali1/projects/'
root_dir = os.path.join(pr_dir, 'SeededGraphMatch')
models_dir = os.path.join(root_dir, 'optim_models')
chdir_script = """cd """ + root_dir
#chdir_script = """cd G:\\Sancar\\Documents\\projects\\SeededGraphMatch"""
session.putvalue('chdir_script', chdir_script)
