class MatlabCaller:
"""
bridging class with matlab
"""
def __init__(self, port=14001, id=None):
if id is None:
id = numpy.random.RandomState(1234)
# Initialise MATLAB
self.mlab = Matlab(port=port, id=id)
def start(self):
# Start the server
self.mlab.start()
def stop(self):
self.mlab.stop()
os.system('pkill MATLAB')
def call_fn(self, path=None, params=None):
"""
path: the path to your .m function
params: dictionary contains all parameters
"""
assert path is not None
assert isinstance(params, dict)
return self.mlab.run(path, params)['result']
def start_mlab():
dir_path = os.path.dirname(os.path.realpath(__file__))
dir_list = ['/HHT_MATLAB_package', '/HHT_MATLAB_package/EEMD',
'/HHT_MATLAB_package/checkIMFs', '/HHT_MATLAB_package/HSA']
mlab = Matlab()
mlab.start()
for d in dir_list:
res = mlab.run_func('addpath', dir_path + d)
return mlab
class MatlabProcessor(PwebProcessor):
"""Runs Matlab code usinng python-matlab-brigde"""
def __init__(self, parsed, source, mode, formatdict):
from pymatbridge import Matlab
self.matlab = Matlab()
self.matlab.start()
PwebProcessor.__init__(self, parsed, source, mode, formatdict)
def getresults(self):
self.matlab.stop()
return copy.copy(self.executed)
def loadstring(self, code_string, chunk=None, scope=PwebProcessorGlobals.globals):
result = self.matlab.run_code(code_string)
if chunk is not None and len(result["content"]["figures"]) > 0:
chunk["matlab_figures"] = result["content"]["figures"]
return result["content"]["stdout"]
def loadterm(self, code_string, chunk=None):
result = self.loadstring(code_string)
return result
def init_matplotlib(self):
pass
def savefigs(self, chunk):
if not "matlab_figures" in chunk:
return []
if chunk['name'] is None:
prefix = self.basename + '_figure' + str(chunk['number'])
else:
prefix = self.basename + '_' + chunk['name']
figdir = os.path.join(self.cwd, rcParams["figdir"])
if not os.path.isdir(figdir):
os.mkdir(figdir)
fignames = []
i = 1
for fig in reversed(chunk["matlab_figures"]): #python-matlab-bridge returns figures in reverse order
#TODO See if its possible to get diffent figure formats. Either fork python-matlab-bridge or use imagemagick
name = figdir + "/" + prefix + "_" + str(i) + ".png" #self.formatdict['figfmt']
shutil.copyfile(fig, name)
fignames.append(name)
i += 1
return fignames
def add_echo(self, code_str):
"""Format inline chunk code to show results"""
return "disp(%s)" % code_str
def start_matlab():
prog = find_executable('matlab')
if not prog:
sys.exit("Could not find MATLAB on the PATH. Exiting...")
else:
print("Found MATLAB in "+prog)
mlab = Matlab(executable=prog)
mlab.start()
return mlab
def __init__(self, *args, **kwargs):
super(MatlabKernel, self).__init__(*args, **kwargs)
executable = os.environ.get('MATLAB_EXECUTABLE', 'matlab')
subprocess.check_call([executable, '-e'], stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
self._matlab = Matlab(executable)
self._matlab.start()
def __init__(self):
super().__init__()
self.matlab = Matlab()
# As of July 2016, there seems to be a bug which wrecks the data
# dimensionality when feeding it to MATLAB, causing a matrix dimension
# mismatch to happen.
raise ValueError("MATLAB interop via pymatbridge doesn't work.")
def random_sampling_matlab(self):
# Perform test via MATLAB
from pymatbridge import Matlab
mlab = Matlab()
mlab.start()
mlab.run_code('cvx_solver mosek;')
mlab.run_code("addpath '~/mosek/7/toolbox/r2012a';")
self.mlab = mlab
if self.block_sizes is not None and len(self.block_sizes) == \
self.A.shape[1]:
self.output['error'] = "Trivial example: nblocks == nroutes"
logging.error(self.output['error'])
self.mlab.stop()
self.mlab = None
return
duration_time = time.time()
p = self.mlab.run_func('%s/scenario_to_output.m' % self.CS_PATH,
{ 'filename' : self.fname, 'type': self.test,
'algorithm' : self.method,
'outpath' : self.OUT_PATH })
duration_time = time.time() - duration_time
self.mlab.stop()
return array(p['result'])
def __init__(self):
if 'OCTAVE_EXECUTABLE' in os.environ:
self._engine = Octave(os.environ['OCTAVE_EXECUTABLE'])
self._engine.start()
self.name = 'octave'
elif matlab_native:
self._engine = matlab.engine.start_matlab()
self.name = 'matlab'
else:
executable = os.environ.get('MATLAB_EXECUTABLE', 'matlab')
self._engine = Matlab(executable)
self._engine.start()
self.name = 'pymatbridge'
# add MATLAB-side helper functions to MATLAB's path
if self.name != 'octave':
kernel_path = os.path.dirname(os.path.realpath(__file__))
toolbox_path = os.path.join(kernel_path, 'toolbox')
self.run_code("addpath('%s');" % toolbox_path)
class MATLAB:
def __init__(self):
print "Initializing MATLAB."
self.mlab = Matlab()
print "Done initializing MATLAB."
def connect(self):
self.mlab.start()
def disconnect(self):
if(self.mlab.started):
self.mlab.stop()
else:
print "Tried to disconnect from MATLAB without being connected."
def run_code(self, code):
try:
r = self.mlab.run_code(code)
except Exception as exc:
raise RuntimeError("Problem executing matlab code: %s" % exc)
else:
if(not r['success']):
raise RuntimeError(
"Problem executing matlab code: %s: %s" % (code, r['content']))
def getvalue(self, var):
return self.mlab.get_variable(var)
def __init__(self, worker_id, mlab_path, socket_prefix, id_prefix):
threading.Thread.__init__(self)
self.redis_obj = redis.Redis()
self.worker_id = worker_id
#Create a matlab instance; ie one matlab instance is created per worker
cv_socket = socket_prefix + worker_id
mlab_id = id_prefix + worker_id
self.mlab_inst = Matlab(matlab=mlab_path, socket_addr=cv_socket, id=mlab_id)
self.mlab_inst.start()
time.sleep(2)
self.createHash()
def test_init_end_to_end_distance(run_dir):
"""
Plot the end-to-end distance distribution for a set of runs' r0 versus the
theoretical distribution.
"""
if not os.path.isdir(os.path.join(run_dir, 'data', 'end_to_end_r0')):
wdata.aggregate_r0_group_NP_L0(run_dir)
m = Matlab()
m.start()
m.run_code("help pcalc")
m.stop()
def network_hill(panel, prior_graph=[], lambdas=[], max_indegree=3, reg_mode='full', stdise=1, silent=0, maxtime=120):
'''
run_hill(panel)
input: dataframe
should be a T x N dataframe with T time points and N samples.
output: dict containing key 'e' and key 'i' from Hill's code
'''
from scipy.io import savemat
from scipy.io import loadmat
# start matlab
mlab = Matlab(maxtime=maxtime)
mlab.start()
# .mat shuttle files
# add path check
inPath = os.path.join('..', 'cache', 'dbn_wrapper_in.mat')
outPath = os.path.join('..', 'cache', 'dbn_wrapper_out.mat')
D = np.transpose(panel.values)
num_rows = np.shape(D)[0]
num_cols = np.shape(D)[1]
D = np.reshape(D, (num_rows, num_cols, 1))
#D = np.transpose(panel, (2,1,0))
# save the matlab object that the DBN wrapper will load
# contains all the required parameters for the DBN code
savemat(inPath, {"D" : D,
"max_indegree" : max_indegree,
"prior_graph" : prior_graph,
"lambdas" : lambdas,
"reg_mode" : reg_mode,
"stdise" : stdise,
"silent" : silent})
# DBN wrapper just needs an input and output path
args = {"inPath" : inPath, "outPath" : outPath}
# call DBN code
res = mlab.run_func('dbn_wrapper.m', args, maxtime=maxtime)
mlab.stop()
out = loadmat(outPath)
edge_prob = pd.DataFrame(out['e'], index=panel.columns, columns=panel.columns)
edge_sign = pd.DataFrame(out['i'], index=panel.columns, columns=panel.columns)
#edge_prob = out['e']
#edge_sign = out['i']
return (edge_prob, edge_sign)
def fix_model(self, W, intMat, drugMat, targetMat, seed=None):
R = W*intMat
drugMat = (drugMat+drugMat.T)/2
targetMat = (targetMat+targetMat.T)/2
mlab = Matlab()
mlab.start()
# print os.getcwd()
# self.predictR = mlab.run_func(os.sep.join([os.getcwd(), "kbmf2k", "kbmf.m"]), {'Kx': drugMat, 'Kz': targetMat, 'Y': R, 'R': self.num_factors})['result']
self.predictR = mlab.run_func(os.path.realpath(os.sep.join(['../kbmf2k', "kbmf.m"])), {'Kx': drugMat, 'Kz': targetMat, 'Y': R, 'R': self.num_factors})['result']
# print os.path.realpath(os.sep.join(['../kbmf2k', "kbmf.m"]))
mlab.stop()
def plot_deltahist(fileName1, fileName2):
prog = find_executable('matlab')
if not prog:
sys.exit("Could not find MATLAB on the PATH. Exiting...")
else:
print("Found MATLAB in "+prog)
mlab = Matlab(executable=prog)
mlab.start()
results = mlab.run_func('./plot_deltahist.m', {'arg1': fileName1, 'arg2': fileName2})
mlab.stop()
def objective_function(self, x):
'''
matlabpath: we need the path to matlab since we need to run it.
IMPORTANT: walker_simulation.m must be included in the
WGCCM_three_link_walker_example file in order to work.
So simply modify the path below.
'''
mlab = Matlab(executable= matlabpath)
mlab.start()
output = mlab.run_func(os.path.join(self.matlab_code_directory, 'walker_simulation.m'),
{'arg1': x[:, 0],'arg2': x[:, 1],'arg3': x[:, 2],
'arg4': x[:, 3],'arg5':x[:, 4],'arg6': x[:, 5],
'arg7': x[:, 6],'arg8': x[:, 7],'arg9': self.num_steps})
answer = output['result']
simul_output = answer['speed']
mlab.stop()
def main():
#start matlab connection
mlab = Matlab()
mlab.start()
vehicle = None
data=None
vehicle1=None
vehicle2=None
vehicle3=None
vehicle4=None
vehicle5=None
actor_list = []
sensors = []
argparser = argparse.ArgumentParser(
description=__doc__)
argparser.add_argument(
'--host',
metavar='H',
default='127.0.0.1',
help='IP of the host server (default: 127.0.0.1)')
argparser.add_argument(
'-p', '--port',
metavar='P',
default=2000,
type=int,
help='TCP port to listen to (default: 2000)')
args = argparser.parse_args()
try:
client = carla.Client(args.host, args.port)
client.set_timeout(2.0)
world = client.get_world()
ourMap = world.get_map()
debug = world.debug;
#********* setting fixed time stamp for simulation **************
settings = world.get_settings();
settings.fixed_delta_seconds = 0.05;
world.apply_settings(settings);
spectator = world.get_spectator();
#*************** deffinition of sensors ****************************
camera_blueprint = world.get_blueprint_library().find('sensor.camera.rgb')
#*********** definition of blueprints for vehicles *******************
blueprint = world.get_blueprint_library().find('vehicle.audi.tt') #for main(ego) vehicle
blueprint.set_attribute('role_name', 'hero')
#*********** for non-player vehicles *********************************
non_playerBlueprint1 = random.choice(world.get_blueprint_library().filter('vehicle.bmw.grandtourer'))
non_playerBlueprint2 = random.choice(world.get_blueprint_library().filter('vehicle.tesla.*'))
non_playerBlueprint4 = random.choice(world.get_blueprint_library().filter('vehicle.mercedes-benz.coupe'))
non_playerBlueprint3 = random.choice(world.get_blueprint_library().filter('vehicle.toyota.*'))
#******************************* set weather **********************************
weather = carla.WeatherParameters(
cloudyness=0.0,
precipitation=0.0,
sun_altitude_angle=70.0,
sun_azimuth_angle=50.0)
world.set_weather(weather)
#************************ spawn non-player vehicles ******************************
class spawn_vehicle:
def __init__(self, blueprint, x, y):
self.vehicle=None;
spawn_points = ourMap.get_spawn_points()
spawn_point = spawn_points[30];
spawn_point.location.x += x;
spawn_point.location.y += y;
self.vehicle = world.spawn_actor(blueprint, spawn_point)
#first non-player vehicle
data=spawn_vehicle(non_playerBlueprint1, -20, 1.0);
vehicle1=data.vehicle;
actor_list.append(vehicle1)
#second non-player vehicle
data=spawn_vehicle(non_playerBlueprint2, -12, 1.0);
vehicle2=data.vehicle;
actor_list.append(vehicle2)
# 3rd non-player vehicle
data=spawn_vehicle(non_playerBlueprint3, 2.0, 1.0);
vehicle3=data.vehicle;
actor_list.append(vehicle3)
#4th nonplayer vehicle
data=spawn_vehicle(non_playerBlueprint4, 10.0, 1.0);
vehicle4=data.vehicle;
actor_list.append(vehicle4)
#********************** spawn main vehicle *****************************
data=spawn_vehicle(blueprint, -23.0, -1.5);
vehicle=data.vehicle;
actor_list.append(vehicle)
#set the first movement of ego car
control = carla.VehicleControl(throttle=0.1)
vehicle.apply_control(control)
#************************ camera-sensor settings ***********************
camera_location = carla.Transform(carla.Location(x=1.3, z=2.0))
camera_blueprint.set_attribute('sensor_tick', '0.4');
camera_sensor = world.spawn_actor(camera_blueprint, camera_location, attach_to=vehicle);
#=======================================obstacle sensors for maneuver==============================================================
class ObstacleSensor(object):
def __init__(self, parent_actor,x,y,z,angle):
self.sensor = None
self._parent = parent_actor
self.actor = 0.0
self.distance = 0.0
self.x=x
self.y=y
self.z=z
self.angle=angle;
world = self._parent.get_world()
bp = world.get_blueprint_library().find('sensor.other.obstacle')
bp.set_attribute('debug_linetrace', 'false');
self.sensor = world.spawn_actor(bp, carla.Transform(carla.Location(x=self.x, y=self.y, z=self.z), carla.Rotation(yaw=self.angle)), attach_to=self._parent);
sensors.append(self.sensor);
weak_self = weakref.ref(self)
self.sensor.listen(lambda event: ObstacleSensor._on_event(weak_self, event))
@staticmethod
def _on_event(weak_self, event):
self = weak_self()
if not self:
return
self.actorId = event.other_actor.id
self.actorName = event.other_actor.type_id
self.distance = event.distance
forward_sensor = ObstacleSensor(vehicle, x=1.3, y=0.9, z=0.6, angle=90);
rearSide_sensor = ObstacleSensor(vehicle, x=-1.3, y=0.9, z=0.6, angle=90);
center_sensor = ObstacleSensor(vehicle, x=0.0, y=0.9, z=0.6, angle=90);
back_sensor = ObstacleSensor(vehicle, x=-1.3, y=0.0, z=0.6, angle=180);
#====================================step1:Parking Decision==========================================================
global image;
time.sleep(5.0);
textLocation = vehicle.get_location();
textLocation.x +=5.0;
textLocation.y -=0.8;
textLocation.z +=1.8;
startText = "Search for parking place"
debug.draw_string(textLocation, startText, True, carla.Color(255,255,0), 3, True);
textLocation.y +=1.0;
debug.draw_box(carla.BoundingBox(textLocation,carla.Vector3D(0.2,1.5,0.5)),carla.Rotation(yaw=180), 0.1, carla.Color(255,0,0),3, True);
camera_sensor.listen(lambda image: parking_decision(image));
def parking_decision(data):
camera_sensor.stop();
cameraControl = carla.VehicleControl(throttle=0);
vehicle.apply_control(cameraControl);
output=interface.decision(data,mlab);
overlapRatio = output['result'];
print('rate of Overlap:', overlapRatio);
if overlapRatio < 0.1:
print('parking place is between the next 2 vehicles');
location = vehicle.get_location();
location.x +=15;
location.y +=1.0;
location.z +=1.8;
text = 'Parking vacancy detected';
debug.draw_string(location, text, True, carla.Color(255,255,0), 4.0, True);
location.y +=1.5;
debug.draw_box(carla.BoundingBox(location,carla.Vector3D(0.2,2.0,0.8)),carla.Rotation(yaw=180), 0.1, carla.Color(255,0,0),4.0, True);
time.sleep(3.0);
mlab.end();
if hasattr(center_sensor, 'actorId'):
print('center_sensor info in detection time:', center_sensor.actorName, center_sensor.actorId);
if center_sensor.actorId != 0:
number=2;
else:
number=1;
print('matlab disconnected');
parking_preparation(number);
else:
print('no parking place has been detected right now');
cameraControl = carla.VehicleControl(throttle=0.2);
vehicle.apply_control(cameraControl);
print('car is moving');
camera_sensor.listen(lambda image: parking_decision(image));
#==================================step2:Parking Preparation(position phase)=============================================
def parking_preparation(limit):
print('limit',limit);
x1=x2=0;
vehicle.apply_control(carla.VehicleControl(throttle=0.3));
while config.count < limit:
current_vhcl = center_sensor.actorId;
if config.nonplayer != current_vhcl and current_vhcl != 0:
config.nonplayer = current_vhcl;
config.count += 1;
continue;
else:
first_parked_car = forward_sensor.actorId;
print('first_parked_car', first_parked_car);
while config.count == limit:
if forward_sensor.actorId == 0:
print('first_parked_car passed');
config.parkingWidth = forward_sensor.distance; #lateral distance of the parking bay
x1 = vehicle.get_location().x; #vehicle first place in parking bay
x1 -= 1.3; #because this location is from center of the car
print('parkingWidth:', config.parkingWidth, 'x1:', x1);
break;
while forward_sensor.actorId == 0:
continue;
else:
if forward_sensor.actorId != 0 and forward_sensor.actorId != first_parked_car:
print('we reached second static car:', forward_sensor.actorId);
print('second_parked_car', forward_sensor.actorId);
x2 = vehicle.get_location().x; #vehicle second place in parking bay
x2 += 1.3;
config.parkingLength = abs(x2 - x1); #length of the parking
print('parkingLength:', config.parkingLength, 'x2:', x2);
while rearSide_sensor.distance > 1.0:
vehicle.apply_control(carla.VehicleControl(steer=0.0, throttle=0.2));
time.sleep(1.0);
if rearSide_sensor.actorId != 0: #when the static vehicle detected
vehicle.apply_control(carla.VehicleControl(steer=0.0, throttle=0.0, brake=1.0));
parking_maneuver();
break;
else:
vehicle.apply_control(carla.VehicleControl(steer=0.0, throttle=0.0, brake=1.0));
time.sleep(3.0);
parking_maneuver();
#====================================step3:Parking Maneuver=================================================================
def parking_maneuver():
time.sleep(3.0);
maneuver.calculate_maneuverTime(vehicle);
maneuver.calculate_max_steeringAng(vehicle);
time.sleep(3.0);
for t in numpy.arange(0,config.T,config.sampling_period):
time.sleep(0.08);
if (hasattr(back_sensor, 'actorId') and (back_sensor.actorId != 0) and (back_sensor.distance < 4)): #or (hasattr(rearSide_sensor, 'actorId') and (rearSide_sensor.actorId == 0) and (rearSide_sensor.distance < 5.0)):
print('1****vehicle should be stopped!', back_sensor.distance);
vehicle.apply_control(carla.VehicleControl(throttle=0, steer=0.0, brake=1.0));
control=vehicle.get_control();
print('throttle', control.throttle, 'brake', control.brake, 'steer', control.steer)
else:
print('2****move');
maneuver.parking(t,vehicle); #call parking function from maneuver.py
control=vehicle.get_control();
print('throttle', control.throttle, 'brake', control.brake, 'steer', control.steer)
#set simulator view to the location of the vehicle
while True:
time.sleep(1.0)
viewLocation = vehicle.get_location();
viewLocation.z += 1.0;
spectator.set_transform(get_transform(viewLocation, -180))
finally:
print('\ndestroying %d actors' % len(actor_list))
for actor in actor_list:
if actor is not None:
actor.destroy()
for sensor in sensors:
if sensor is not None:
sensor.destroy()
class DataPreprocess:
def __init__(self):
file = xlrd.open_workbook('excel/0.xls')
table = file.sheets()[sheet] #通过索引顺序获取工作表
nrows = table.nrows #行数
self.mlab = Matlab()
self.mlab.start()
# 该文件无用,只是为了避免一个路径引起的bug
res = self.mlab.run_func(
'C:/Users/ovewa/Desktop/git-storage/OS-ELM-matlab/test.m', 1)
self.ditu = []
for i in range(1, nrows):
self.ditu.append(table.row_values(i)[1:])
# 生成中间数据
def get_median(self):
for j in range(0, 20):
file = xlrd.open_workbook('excel/' + str(j) + '.xls')
table = file.sheets()[sheet] #通过索引顺序获取工作表
nrows = table.nrows #行数
data = []
for i in range(1, nrows):
data.append(table.row_values(i)[1:])
if os.path.isdir('middata' + str(sheet) + '/'):
pass
else:
os.mkdir('middata' + str(sheet) + '/')
with open('middata' + str(sheet) + '/' + str(j) + '.txt',
'wt') as f:
for y in range(len(data)):
for x in range(len(data[0])):
if int(data[y][x]) != 0 and int(self.ditu[y][x]) != 0:
f.write(
str(data[y][x] / self.ditu[y][x] - 1) + " " +
str(x) + " " + str(y) + "\n")
def get_none(self):
with open('middata' + str(sheet) + '/none.txt', 'wt') as f:
for y in range(13):
for x in range(10):
f.write(str(0) + " " + str(x) + " " + str(y) + "\n")
# 训练过程
# 可优化
# 1是训练条件不同结果不同
# 2是随机参数不同结果不同
# 多次训练取最优解
def training(self, model_num):
# 初始训练
res = self.mlab.run_func('OSELM_initial_training.m',
'middata' + str(sheet) + '/0.txt',
10,
'sin',
nargout=5)
IW = res['result'][0]
Bias = res['result'][1]
M = res['result'][2]
beta = res['result'][3]
# 增量学习
# ####!!!添加将 每一次增量学习结果的误差输出出来并可视化
for x in range(1, model_num + 1):
res = self.mlab.run_func('OSELM_increase_study.m',
'middata' + str(sheet) + '/' + str(x) +
'.txt',
IW,
Bias,
M,
beta,
'sin',
1,
nargout=4)
IW = res['result'][0]
Bias = res['result'][1]
M = res['result'][2]
beta = res['result'][3]
# 获取完整指纹库
res = self.mlab.run_func('OSELM_test_value.m',
'middata' + str(sheet) + '/none.txt', IW,
Bias, beta, 'sin')
result = res['result']
y = 0
data = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
for x in range(len(result)):
###重点,现在直接将初次训练结果保存,没有进行比较分析,待完善
data[x % 10][y] = int(self.ditu[y][x % 10] * (result[x] + 1))
if (x + 1) % 10 == 0:
y = y + 1
return data
def mlab_stop(self):
self.mlab.stop()
""" This script was used to generate dwt_matlabR2012a_result.npz by storing
the outputs from Matlab R2012a. """
from __future__ import division, print_function, absolute_import
import numpy as np
import pywt
try:
from pymatbridge import Matlab
mlab = Matlab()
_matlab_missing = False
except ImportError:
print("To run Matlab compatibility tests you need to have MathWorks "
"MATLAB, MathWorks Wavelet Toolbox and the pymatbridge Python "
"package installed.")
_matlab_missing = True
if _matlab_missing:
raise EnvironmentError("Can't generate matlab data files without MATLAB")
size_set = 'reduced'
# list of mode names in pywt and matlab
modes = [('zero', 'zpd'),
('constant', 'sp0'),
('symmetric', 'sym'),
('periodic', 'ppd'),
('smooth', 'sp1'),
('periodization', 'per')]
:Created on: 26/01/2015 9:55
"""
__author__ = 'bejar'
from pymatbridge import Matlab
from config.experiments import experiments, lexperiments
import time
# lexperiments = ['e130716', 'e130827', 'e130903', 'e141113', 'e141029', 'e141016', 'e140911', 'e140311', 'e140225', 'e140220']
lexperiments = ['130827']#'e130827','e140225', 'e140220', 'e141016', 'e140911']
mlab = Matlab(executable='/home/bejar/bin/MATLAB/R2014b/bin/matlab')
mlab.start()
a = mlab.run_code('cd(\'/home/bejar/PycharmProjects/PeakDataAnalysis/Matlab/\')')
print a
datasufix = ''#'-RawResampled'
wtime = '120e-3' # Window length in miliseconds
for expname in lexperiments:
datainfo = experiments[expname]
sampling = datainfo.sampling #/ 6.0
for file in [datainfo.datafiles[0]]:
print time.ctime()
same_label = []
row = 0
for i in range(len(samples)):
for j in range(i + 1, len(samples)):
pdist[row, :] = distance_vector(i, j)
same_label.append(1 if samples[i].label == samples[j].label else -1)
row += 1
theta = np.ones((31, )) / np.sqrt(31)
class_size = Counter(same_label)
sl = np.array(same_label).astype(float)
sl[sl > 0] = 1.0 / class_size[1]
sl[sl < 0] = -1.0 / class_size[1]
A = sl.reshape(len(same_label), 1) * pdist
np.linalg.norm(np.dot(A, theta))
from pymatbridge import Matlab
mlab = Matlab(matlab='/m/fs/software/matlab/r2014a/bin/glnxa64/MATLAB',
maxtime=10)
mlab.start()
N = len(samples)
involved = [[] for _ in range(N)]
row = 0
for i in range(N):
for j in range(i + 1, N):
involved[i].append(row)
involved[j].append(row)
row += 1
rows = set(range(row))
notinvolved = [list(rows.difference(_)) for _ in involved]
dsts = np.zeros((len(samples), 2))
thetas = np.zeros((len(samples), 31))
i = 0
for train_row, test_row in zip(notinvolved, involved):
'confidenceLine.m', 'dist_value.m', 'extrema.m']
for files in checkIMFs_mfile_list:
shutil.move('./Matlab_runcode/' + files, checkIMFs_dir)
HSA_mfile_list = os.listdir('./Matlab_runcode/')
for files in HSA_mfile_list:
shutil.move('./Matlab_runcode/' + files, HSA_dir)
# Delete unnecessary directories/files
os.remove('EEMD.zip')
os.remove('Matlab_runcode.zip')
os.rmdir('Matlab_runcode')
print('...Done.')
# Check the MATLAB version & replace the deprecated function with the new one.
mlab = Matlab()
print('* Checking your MATLAB version...')
mlab.start()
mlab.run_code('v = version;')
version = mlab.get_variable('v')
mlab.stop()
print('Your MATLAB version is: ' + version)
version = version.split('.')
if int(version[0]) >= 8:
print('The function "getDefaultStream" in eemd.m is no longer be used ' +
'in your MATLAB version.')
print('* Replacing it with the function "getGlobalStream"...')
with open(EEMD_dir + 'eemd.m', 'r', encoding='iso-8859-1') as infile:
data = infile.read().replace('getDefaultStream', 'getGlobalStream')
infile.close()
with open(EEMD_dir + 'eemd2.m', 'w',encoding='iso-8859-1') as outfile:
def train_save_sp_tensor(self, pmi=True):
gatherer = self.get_pmi_gatherer(3)
if pmi:
print('creating PPMI tensor...')
else:
print('creating sparse count tensor...')
indices, values = gatherer.create_pmi_tensor(positive=True,
debug=True,
symmetric=False,
pmi=pmi,
shift=-np.log2(15.))
matfile_name = 'sp_tensor_{}_{}_log15.mat'.format(
self.num_sents, self.min_count)
scipy.io.savemat(matfile_name, {'indices': indices, 'values': values})
print('saved {}. exiting.'.format(matfile_name))
sys.exit()
from pymatbridge import Matlab
session = Matlab('/usr/local/bin/matlab')
print('starting matlab session...')
session.start()
#session.set_variable('indices', indices+1)
#session.set_variable('vals', values)
print('setting up variables...')
session.run_code("d = load('/home/eric/code/gensim/sp_tensor.mat');")
session.run_code("indices = d.indices + 1;")
session.run_code("vals = d.values';")
#session.run_code('size_ = [{0} {0} {0}];'.format(len(self.model.vocab)))
session.run_code('size_ = [{0} {0} {0}];'.format(8))
session.run_code('R = {};'.format(self.embedding_dim))
import pdb
pdb.set_trace()
res = session.run_code("T = sptensor(indices, vals, size_);")
print('running ALS...')
t = time.time()
res = session.run_code('[P, U0, out] = cp_als(T, R)')
print('ALS took {} secs'.format(time.time() - t))
session.run_code('lambda = P.lambda;')
session.run_code('U = P{1,1};')
session.run_code('V = P{2,1};')
session.run_code('W = P{3,1};')
lambda_ = session.get_variable('lambda')
U = session.get_variable('U')
import pdb
pdb.set_trace()
'''
def process(self):
mlab = Matlab()
mlab.start()
return mlab
n_test = np.repeat(ref_n[..., None], ref_llr.shape[-1], axis=1)
# Apply conjugate operator to features
if enc_cfg['conj_inputs']:
y_test = np.matmul(np.conj(np.swapaxes(h_test, -2, -1)), y_test[..., None])
h_test = np.matmul(np.conj(np.swapaxes(h_test, -2, -1)), h_test)
y_test = np.reshape(y_test, (-1, num_rx))
h_test = np.reshape(h_test, h_test.shape[:-2] + (-1, ))
h_test = np.reshape(h_test, (-1, num_rx * num_tx))
n_test = np.reshape(n_test, (-1, 1))
# Convert complex to reals
y_test = y_test.view(np.float64)
h_test = h_test.view(np.float64)
# Start Matlab engine
eng = Matlab()
eng.start()
# Move to right path
eng.run_code(
'cd /home/yanni/marius/deep-llr-quantization-master/deep-llr-quantization-master/'
)
# How many runs
num_runs = 1
# Global metrics
local_seed_collect = np.zeros((num_runs, ))
bler_ae, ber_ae = np.zeros((num_runs, num_snr)), np.zeros((num_runs, num_snr))
bler_aeq, ber_aeq = np.zeros((num_runs, len(bits_per_dim), num_snr)), np.zeros(
(num_runs, len(bits_per_dim), num_snr))
bler_enc, ber_enc = np.zeros((num_runs, num_snr)), np.zeros(
(num_runs, num_snr))
ground_truth_folder=args.ground_truth_path),
batch_size=batch_size,
shuffle=True,
drop_last=True)
test_loader = data.DataLoader(dataset=CleansingDataset(
root=os.path.join(args.dataset, 'test_dataset'),
transform=test_transform,
dataset=args.dataset,
special_list=[],
filter_type='not_in',
feature_folder=feature_floder,
ground_truth_folder='annotation_folder'),
batch_size=batch_size,
shuffle=True,
drop_last=True)
train(model, train_loader, test_loader, epochs, args.logdir, savepath)
get_resnet_result(model, args.dataset, savepath)
endtime = datetime.datetime.now()
print('code ends at ', endtime - starttime)
if args.use_pymatbridge:
from pymatbridge import Matlab
mlab = Matlab()
mlab.start()
results = mlab.run_code('run {}/eva/Main.m'.format(args.dataset))
meanJacc, stdJacc, meanAcc, stdAcc = mlab.get_variable(
'meanJacc'), mlab.get_variable('stdJacc'), mlab.get_variable(
'meanAcc'), mlab.get_variable('stdAcc')
def __init__(self, port=14001, id=None):
if id is None:
id = numpy.random.RandomState(1234)
# Initialise MATLAB
self.mlab = Matlab(port=port, id=id)
def backtest(filename, data, schedule_data):
f = open(filename)
f.readline()
player_data = {}
time_data = []
for i in xrange(50):
line = f.readline()
if line is None or len(line) == 0:
break
date = int(line[:3])
print date
jsonvalue = "{"+f.readline()+"}"
value = json.loads(jsonvalue)
time_data.insert(0,(date,value))
for p in value:
if not p in player_data:
player_data[p] = [0]
player_data[p].insert(0,value[p])
time_data2 = convertToPlayersTimeData(time_data, data)
teams = set([i.team for i in data])
for i in xrange(len(time_data2)):
stamp_data = time_data2[i][1]
Tracer()()
portfolio = ["rohit sharma", "ajinkya rahane", "david warner", "glenn maxwell", "robin uthappa", "shane watson", "sandeep sharma", "sunil narine", "pravin tambe", "yuzvendra chahal", "bhuvneshwar kumar"]
# portfolio = ["yuzvendra chahal", "shakib al hasan", "shane watson", "rohit sharma", "sandeep sharma", "sunil narine", "ajinkya rahane", "jacques kallis", "robin uthappa", "jayant yadav","bhuvneshwar kumar"]
# portfolio = ["manish pandey", "rohit sharma","jacques kallis","robin uthappa", "aditya tare", "ambati rayudu", "morne morkel","piyush chawla","sunil narine","lasith malinga","pragyan ojha"]
power_player = "glenn maxwell"
# power_player = "bhuvneshwar kumar"
portfolio_p = set([getPlayer(data, p)[0] for p in portfolio])
power_player_p = getPlayer(data, power_player)[0]
points = 0
subs = 75
mlab = Matlab(matlab='/Applications/MATLAB_R2013a.app/bin/matlab')
mlab.start()
for i in xrange(4,len(time_data2)):
# START = str(time_data2[i][0])
# CURRENT_TEAM = set(portfolio)
# SUBSTITUTIONS = subs
# PAST_STATS = time_data[i][1]
print "\n\n\n\n\n\n"
print (subs, str(time_data2[i][0]))
print set(portfolio_p)
print points
print "\n\n\n\n\n\n"
# print time_data[i-1][1]
# Tracer()()
inp = (subs, str(time_data2[i][0]), set(portfolio), time_data[i-1][1])
backtest_pickteam.pickTeam(data, schedule_data, inp)
res = mlab.run_func('/Users/deedy/Dev/FantasyIPL-Moneyball/python2matlab.m', {}, maxtime = 500)
changes = backtest_results.getResults(data, schedule_data, res['result'])
subs -= changes[2]
portfolio_p = changes[0]
power_player_p = changes[1]
# Tracer()()
# update portfolio
# update subs
# update power player
# Tracer()()
teams = [(p,time_data2[i][1][p] - time_data2[i-1][1][p] ) for p in time_data2[i][1] if p in portfolio_p]
print teams
pthis = 0
for i in teams:
if power_player_p == i[0]:
pthis += 2*i[1]
else:
pthis += i[1]
points+= pthis
print "{0}\t{1}\t{2}\n\n".format(points, pthis, subs)
# print "{0}\t{1}".format(time_data2[i][0] , teams)
mlab.stop()
Tracer()()
f.close()
scaled_for_color = math.ceil(32 * pixel_normalized_val + 32)
if scaled_for_color == 0:
scaled_for_color = 1
group["intelligent_icon"][row][column] = scaled_for_color
if add_struct_comma_to_expr:
matlab_intelligent_icon_visualization_data_expr = matlab_intelligent_icon_visualization_data_expr + ","
else:
add_struct_comma_to_expr = True
matlab_intelligent_icon_visualization_data_expr = matlab_intelligent_icon_visualization_data_expr + "struct('title', '" + group_name + "', 'patches', ["
add_pixel_comma_to_expr = False
#flatten the bitmap into a vector of pixels (see matlab/show_icons.m)
for row in range(row_pixel_count):
for column in range(row_pixel_count):
if add_pixel_comma_to_expr:
matlab_intelligent_icon_visualization_data_expr = matlab_intelligent_icon_visualization_data_expr + ","
else:
add_pixel_comma_to_expr = True
matlab_intelligent_icon_visualization_data_expr = matlab_intelligent_icon_visualization_data_expr + str(group["intelligent_icon"][row][column])
matlab_intelligent_icon_visualization_data_expr = matlab_intelligent_icon_visualization_data_expr + "])"
matlab_intelligent_icon_visualization_data_expr = matlab_intelligent_icon_visualization_data_expr + "]"
print("rendering intelligent icon grid")
#initialise matlab bridge
mlab = Matlab(port=4000, matlab=matlab_path)
mlab.start()
#i cant imagine more ghetto way to do this, but it seems to be the only way to pass this data through some bug in the data parsing part of the bridge (Which i didnt write, for the record!!!)
icon_data_expr_encoded = base64.b64encode(matlab_intelligent_icon_visualization_data_expr)
print(icon_data_expr_encoded)
mlab.run(base_dir + "/matlab/show_icons.m", {"encoded": icon_data_expr_encoded})
print("done without errors")
def readSUN3D_singleData(list_dir, threshold_PCE, threshold_camDist,
threshold_overlap, threshold_iter, choose_secondFrame,
output):
mlab = Matlab(executable='/usr/bin/matlab')
mlab.start()
dataDir = list_dir[random.randint(0, len(list_dir) - 1)]
print(dataDir)
minFrame = 1
maxFrame = len(glob(dataDir + 'image/*'))
frameLength = maxFrame
#print (minFrame,maxFrame)
frameID = random.randint(minFrame, maxFrame)
frameIDAnother = -1
count = 1
while (True):
if (frameID == frameIDAnother):
frameID = random.randint(1, frameLength)
else:
while (True):
frameIDAnother = random.randint(
max(minFrame, frameID - choose_secondFrame),
min(maxFrame, frameID + choose_secondFrame))
if (frameID != frameIDAnother):
break
output_tmp = dataLoad_SUN3D(mlab, dataDir, frameID, frameIDAnother,
'', './SUN3Dflow_py.m', False)
#print(output_tmp['camDist'], output_tmp['pce'] ,output_tmp['overlapRatio'])
if (output_tmp['camDist'] > threshold_camDist
and output_tmp['pce'] < threshold_PCE
and output_tmp['overlapRatio'] > threshold_overlap
and output_tmp['overlapRatio'] < 1):
"""
egomotion_extended = np.zeros(7)
egomotion_tmp = output_tmp['egomotion']
scale_tmp = np.linalg.norm(egomotion_tmp[0:3])
egomotion_tmp[0:3] = egomotion_tmp[0:3]/scale_tmp
egomotion_extended[0:6] = egomotion_tmp
egomotion_extended[6] = scale_tmp
output_tmp['egomotion'] = egomotion_extended
"""
print('finished')
output.put(output_tmp)
break
"""
scale_tmp = np.linalg.norm(egomotion_tmp[0:3])
target_egomotion[iterBatch,0:3] = egomotion_tmp[0:3]/scale_tmp
target_egomotion[iterBatch,3:3] = egomotion_tmp[3:3]
target_egomotion[iterBatch,6] = scale_tmp
"""
else:
# print(count)
count = count + 1
if (output_tmp['overlapRatio'] <= threshold_overlap):
minFrame = min(frameID, frameIDAnother)
maxFrame = max(frameID, frameIDAnother)
if (count == threshold_iter):
count = 1
frameIDAnother = frameID
minFrame = 1
maxFrame = frameLength
""" This script was used to generate dwt_matlabR2012a_result.npz by storing
the outputs from Matlab R2012a. """
from __future__ import division, print_function, absolute_import
import numpy as np
import pywt
try:
from pymatbridge import Matlab
mlab = Matlab()
_matlab_missing = False
except ImportError:
print("To run Matlab compatibility tests you need to have MathWorks "
"MATLAB, MathWorks Wavelet Toolbox and the pymatbridge Python "
"package installed.")
_matlab_missing = True
if _matlab_missing:
raise EnvironmentError("Can't generate matlab data files without MATLAB")
size_set = 'reduced'
# list of mode names in pywt and matlab
modes = [('zero', 'zpd'),
('constant', 'sp0'),
('symmetric', 'sym'),
('periodic', 'ppd'),
('smooth', 'sp1'),
('periodization', 'per')]
class MatlabKernel(MetaKernel):
implementation = 'Matlab Kernel'
implementation_version = __version__,
language = 'matlab'
language_version = '0.1',
banner = "Matlab Kernel"
language_info = {
'mimetype': 'text/x-matlab',
'name': 'octave',
'file_extension': '.m',
'help_links': MetaKernel.help_links,
}
_first = True
def __init__(self, *args, **kwargs):
super(MatlabKernel, self).__init__(*args, **kwargs)
executable = os.environ.get('MATLAB_EXECUTABLE', 'matlab')
subprocess.check_call([executable, '-e'],
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
self._matlab = Matlab(executable)
self._matlab.start()
def get_usage(self):
return "This is the Matlab kernel."
def do_execute_direct(self, code):
if self._first:
self._first = False
fig_code = "set(0, 'defaultfigurepaperunits', 'inches');"
self._matlab.run_code(fig_code)
self._matlab.run_code("set(0, 'defaultfigureunits', 'inches');")
self.handle_plot_settings()
self.log.debug('execute: %s' % code)
resp = self._matlab.run_code(code.strip())
self.log.debug('execute done')
if 'stdout' not in resp['content']:
raise ValueError(resp)
if 'figures' in resp['content']:
for fname in resp['content']['figures']:
try:
im = Image(filename=fname)
self.Display(im)
except Exception as e:
self.Error(e)
if not resp['success']:
self.Error(resp['content']['stdout'].strip())
else:
return resp['content']['stdout'].strip()
def get_kernel_help_on(self, info, level=0, none_on_fail=False):
obj = info.get('help_obj', '')
if not obj or len(obj.split()) > 1:
if none_on_fail:
return None
else:
return ""
return self.do_execute_direct('help %s' % obj)
def handle_plot_settings(self):
"""Handle the current plot settings"""
settings = self.plot_settings
settings.setdefault('size', '560,420')
width, height = 560, 420
if isinstance(settings['size'], tuple):
width, height = settings['size']
elif settings['size']:
try:
width, height = settings['size'].split(',')
width, height = int(width), int(height)
except Exception as e:
self.Error(e)
size = "set(0, 'defaultfigurepaperposition', [0 0 %s %s])\n;"
self.do_execute_direct(size % (width / 150., height / 150.))
def repr(self, obj):
return obj
def restart_kernel(self):
"""Restart the kernel"""
self._matlab.stop()
def do_shutdown(self, restart):
with open('test.txt', 'w') as fid:
fid.write('hey hey\n')
self._matlab.stop()
y(n, s, :) = tempt.*(tempx*2 - 1);
rt(n, s, :) = tempt;
acc(n, s, :) = tempx;
end
end
'''.format(nsims, nsubs, ntrials)
# Write out code
modelfile = 'simultrialparams.m'
f = open(modelfile, 'w')
f.write(matlabcode)
f.close()
# Simulate some data from simuldiff in MATLAB (find a way to do this in Python)
mlab = Matlab(maxtime=240) # Increase max start time
mlab.start()
results2 = mlab.run_code('simultrialparams;')
genparam = dict()
genparam['tersub'] = mlab.get_variable('tersub')
genparam['alphasub'] = mlab.get_variable('alphasub')
genparam['deltasub'] = mlab.get_variable('deltasub')
genparam['tertrialsd'] = mlab.get_variable('tertrialsd')
genparam['deltatrialsd'] = mlab.get_variable('deltatrialsd')
genparam['prob_mindwander'] = mlab.get_variable('prob_mindwander')
genparam['rt'] = mlab.get_variable('rt')
genparam['acc'] = mlab.get_variable('acc')
genparam['y'] = mlab.get_variable('y')
mlab.stop()
sio.savemat('genparam_test.mat', genparam)
#_*_ coding: utf-8 _*_
from block import render_notext, path
from experiment_global import features
#from experiment_global import data as ed
from dataset import DataSet
import json
from pymatbridge import Matlab
mlab = Matlab(executable='matlab')
mlab.start()
#extract features from the website.png
def get_feature(url):
if url:
sitename = render_notext(url)
print sitename
data = DataSet()
data.load_sample(path, sitename)
f2 = features.extract(data.data[0])
dstr = '[%s]' % (','.join(map(str, list(f2))), )
#dstr is a matrix of array
return dstr
#send features to matlab and return score
def matlab(d):
if not d:
return 'err'
def print_matlab_script(path):
with open(path, 'r') as f:
code = f.read()
print_matlab_code(code)
def print_python_function(fun):
code = inspect.getsource(fun)
print_python_code(code)
# ## Start Matlab
matlab = Matlab()
matlab.start()
# ## Add `NoiseTools` to the Matlab's path
matlab.run_code('addpath(\'{}\')'.format(noise_tools_dir))
# # Simulate data
# Let's look at the example 1 code:
print_matlab_script(example_1)
# Let's create synthetic data in Matlab and transfer it here.
example_1_code = open(example_1, 'r').readlines()
class FingerprintUpdate:
def __init__(self):
self.mlab = Matlab()
self.mlab.start()
# 该文件无用,只是为了避免一个路径引起的bug
res = self.mlab.run_func(
'C:/Users/ovewa/Desktop/git-storage/OS-ELM-matlab/test.m', 1)
# 生成中间数据
def get_median(self, ditu, data, model_num, ap_num):
if os.path.isdir('middata' + str(ap_num) + '/'):
pass
else:
os.mkdir('middata' + str(ap_num) + '/')
with open('middata' + str(ap_num) + '/' + str(model_num) + '.txt',
'wt') as f:
for x in range(len(data)):
for y in range(len(data[0])):
if int(data[x][y]) != 0 and int(ditu[x][y]) != 0:
f.write(
str(data[x][y] / ditu[x][y] - 1) + " " + str(x) +
" " + str(y) + "\n")
def get_none(self, ditu, ap_num):
if os.path.isdir('middata' + str(ap_num) + '/'):
pass
else:
os.mkdir('middata' + str(ap_num) + '/')
with open('middata' + str(ap_num) + '/none.txt', 'wt') as f:
for x in range(len(ditu)):
for y in range(len(ditu[0])):
f.write(str(0) + " " + str(x) + " " + str(y) + "\n")
# 训练过程
# 可优化
# 1是训练条件不同结果不同
# 2是随机参数不同结果不同
# 多次训练取最优解
def training(self, model_num, ditu, ap_num):
# 初始训练
res = self.mlab.run_func('OSELM_initial_training.m',
'middata' + str(ap_num) + '/1.txt',
10,
'sin',
nargout=5)
IW = res['result'][0]
Bias = res['result'][1]
M = res['result'][2]
beta = res['result'][3]
# 增量学习
# ####!!!添加将每一次增量学习结果的误差输出出来并可视化
for x in range(2, (model_num + 1)):
res = self.mlab.run_func('OSELM_increase_study.m',
'middata' + str(ap_num) + '/' + str(x) +
'.txt',
IW,
Bias,
M,
beta,
'sin',
1,
nargout=4)
IW = res['result'][0]
Bias = res['result'][1]
M = res['result'][2]
beta = res['result'][3]
# 获取完整指纹库
res = self.mlab.run_func('OSELM_test_value.m',
'middata' + str(ap_num) + '/none.txt', IW,
Bias, beta, 'sin')
result = res['result']
y = 0
data = []
for i in range(len(ditu)):
data.append([])
for j in range(len(ditu[0])):
data[i].append(-1)
for x in range(len(result)):
###重点,现在直接将初次训练结果保存,没有进行比较分析,待完善
data[x % 10][y] = int(ditu[x % 10][y] * (result[x] + 1))
if (x + 1) % 10 == 0:
y = y + 1
return data
def mlab_stop(self, ap_mac):
self.mlab.stop()
# 删除中间文件
for x in range(len(ap_mac)):
if os.path.isdir('middata' + str(x) + '/'):
shutil.rmtree('middata' + str(x) + '/')
import pymatbridge
from pymatbridge import Matlab
# Initialise MATLAB
mlab = Matlab()
# Start the server
mlab.start()
# Run a test function: just adds 1 to the argument a
res = []
for i in range(5):
res.append(mlab.run_func('demo_func.m', {'a': i})['result'])
print res[-1]
# test the JSON parsing.
# quotes, and \n
res.append(mlab.run_code('fprintf(1,char([34,104,105,34,10]));'))
print res[-1]
# \b, \n
res.append(mlab.run_code('fprintf(1,char([8,10]));'))
print res[-1]
# \f, \n
res.append(mlab.run_code('fprintf(1,char([12,10]));'))
print res[-1]
# \r, \n
res.append(mlab.run_code('fprintf(1,char([13,10]));'))
print res[-1]
# \t, \n
res.append(mlab.run_code('fprintf(1,char([9,10]));'))
print res[-1]
# \\, \n
def __init__(self):
self.mlab = Matlab()
self.mlab.start()
# 该文件无用,只是为了避免一个路径引起的bug
res = self.mlab.run_func(
'C:/Users/ovewa/Desktop/git-storage/OS-ELM-matlab/test.m', 1)
def Run(C, R, mic, Plot):
CHUNK = 44100 # number of data points to read at a time 4096
CHUNK = C
# 4096 byte
# the number of frames
RATE = 44100 # 176400 # time resolution for reading device (Hz) 44100 samples/second
RATE = R
# sampling rate i.e the number of frames per second
serSignal = 'S'
KnockSignal = 'K'
Input_Device_Index = 2
Input_Device_Index = mic
plot = Plot
# Define the serial port
ser_port = "COM8" # for window computer, int must be used COM1 = 0,COM2=1 ...
baud_rate = 9600
count = 0
flag = False
signal = False
mlab = Matlab(executable=r"D:\MATLAB\bin\matlab.exe")
mlab.start()
p = pyaudio.PyAudio()
# while True:
# ser.write(serSignal.encode('utf-8'))
# if ser.readline().decode('utf-8') != "Spray":
# break
stream = p.open(format=pyaudio.paInt16,
channels=1,
rate=RATE,
input=True,
input_device_index=None,
frames_per_buffer=CHUNK)
ser = serial.Serial(ser_port, baud_rate)
print(ser.readline().decode("utf-8"))
print("Input delay is %f" % stream.get_input_latency())
while (True):
for i in range(int(3)): #only loop forA int(??) times
#if(count>1):
# sleep(1)
if (count == 1):
ser.write(KnockSignal.encode(
"utf-8")) # encode is used for string.encode()
sleep(.32) # **change here (0.1s per 5000samples)
flag = True
print("Must Knock Here")
# The input device id "2" => built-in microphone
# info = p.get_host_api_info_by_index(0)
# numdevices = info.get('deviceCount')
# for i in range(0, numdevices):
# if (p.get_device_info_by_host_api_device_index(0, i).get('maxInputChannels')) > 0:
# pass
#print('Input Device id', i, '-', p.get_device_info_by_host_api_device_index(0, i).get('name'))
# get the default device info
#print(p.get_default_input_device_info())
# create a numpy array holding a single read of audio data
#now = datetime.now()
if flag == True:
# if count ==1:
# sleep(.5)
np.set_printoptions(threshold=sys.maxsize)
data = np.fromstring(stream.read(CHUNK), dtype=np.short)
#print(stream)
time = np.arange(0, CHUNK)
#peak=np.average(np.abs(data))*21
#bars="#"*int(50*peak/2**16)
#print("%04d %s"%(i,data))
#print("%s %s" % (data/32768,now ))
#print("Input data is ", type(data))
# Test Matlab data 1
#res = mlab.run_func('jk.m', {'arg1': data})
#print("Output data is ", type(res['result']))
#data1 = res['result'] # The data in matlab is float64 (e.g for 64bit window) https://stackoverflow.com/questions/8855574/convert-ndarray-from-float64-to-integer
#M_data1 = data1[0] / 32768
#print("jk.m is",res)
# data1 = np.array(res['result'], dtype=np.float64).astype(np.int64)
# print(type(data1))
#Write data to text file before matlab
# with open("SignalTest1.txt", "wt") as file:
# file.write("%s" % (str(M_data1).lstrip('[').rstrip(']')))
# file.flush()
# file.close()
# # file.writelines("%s %04d %s\n"%(now,i,data))
# # close the stream gracefully
# max_val =np.amax(data)
# print(max_val)
# if max_val >30000:
#data/32768
#print(M_data1)
if count == 1:
print("Write")
with open("SignalTest.txt", "wt") as out_file:
out_file.writelines(
str(data)) #it can only write string
if plot == True and count == 2:
past = stream.get_time()
np.set_printoptions(threshold=sys.maxsize)
data = np.fromstring(stream.read(CHUNK), dtype=np.short)
present = stream.get_time()
delay = present - past
print("The delay is %f" % delay)
plt.title('AudioSample')
plt.plot(time, data)
plt.ylim(-40000, 40000)
plt.ylabel('Amplitude')
plt.xlabel('Sample Size')
#plt.pause(.0000000000000000000000000000000000000000000000000000000001)
#plt.clf()
#print(stream.get_time())
dataprocess = mlab.run_func(
'final_judge.m', {"arg1": data}) # ,{'arg1':data}
# print("The input data is ",M_data1)
print(np.amax(data))
print(dataprocess['result'])
d1 = dataprocess['result']
if d1 == 1:
ser.write(serSignal.encode(
"utf-8")) # encode is used for string.encode()
# print(ser.write(serSignal.encode("utf-8")))
#print(ser.readline().decode("utf-8"))
#d1 = 2
plt.show()
flag = False
count = 0
count += 1
#ser.reset_output_buffer()
mlab.stop()
out_file.close()
stream.stop_stream()
stream.close()
p.terminate()
sys.exit(0)
from IPython.display import clear_output
import matplotlib.pyplot as plt
import numpy as np
from numpy import sin, cos
from pymatbridge import Matlab
get_ipython().magic(u'matplotlib inline')
execfile('../../matplotlibrc.py')
# Run Matlab code and fetch relevant data
mlab = Matlab()
mlab.start()
results = mlab.run_code(open('fem1d.m').read())
K = mlab.get_variable('K')
U = mlab.get_variable('U')
nodeLocs = mlab.get_variable('nodeLocs')
mlab.stop()
clear_output()
print('K')
print(K)
print('U')
print(U)
x = nodeLocs[:,0]
xex = np.linspace(0, 1);
w = np.sqrt(2);
uex = (cos(w) - 1)*sin(w*xex)/(2.0*sin(w)) - 0.5*cos(w*xex) + 0.5
fig, ax = plt.subplots(figsize=(14, 10))
ax.plot(xex, uex, 'k-', label='Exact')
ax.plot(x, U, 'b-o', label='FEM')
def __init__(self, parsed, source, mode, formatdict):
from pymatbridge import Matlab
self.matlab = Matlab()
self.matlab.start()
PwebProcessor.__init__(self, parsed, source, mode, formatdict)
for (dirpath, dirnames, filenames) in os.walk("Database"):
first_level_in_database.extend(dirnames)
break
first_level_in_database = sorted(first_level_in_database)
# Name of the file in each directory
for i in range(0, len(first_level_in_database)):
for root, dirs, files in os.walk(
os.path.abspath("Database/" + first_level_in_database[i])):
for file in sorted(files):
second_level.append(os.path.join(root, file))
if not os.path.exists("Database_5pt"):
os.makedirs("Database_5pt")
size = 144 * 144
mlab = Matlab(matlab='/Applications/MATLAB_R2015a.app/bin/matlab')
mlab.start()
for i in range(0, len(second_level)):
name = second_level[i].split("Database/", 1)[1].rsplit(".", 1)[0]
address, name2 = os.path.split(name)
if name2 != ".DS_Store" and name2 != '':
if not os.path.exists('Database_5pt/' + address):
os.makedirs('Database_5pt/' + address)
pt = open("Database_5pt/" + address + '/' + name2 + ".5pt", "w+")
img = io.imread(second_level[i])
dets = detector(img, 1)
for k, d in enumerate(dets):
shape = predictor(img, d)
left_eye = shape.part(45)
right_eye = shape.part(36)
""" This script was used to generate dwt_matlabR2012a_result.npz by storing
the outputs from Matlab R2012a. """
from __future__ import division, print_function, absolute_import
import numpy as np
import pywt
try:
from pymatbridge import Matlab
mlab = Matlab()
_matlab_missing = False
except ImportError:
print("To run Matlab compatibility tests you need to have MathWorks "
"MATLAB, MathWorks Wavelet Toolbox and the pymatbridge Python "
"package installed.")
_matlab_missing = True
if _matlab_missing:
raise EnvironmentError("Can't generate matlab data files without MATLAB")
size_set = 'reduced'
# list of mode names in pywt and matlab
modes = [('zero', 'zpd'), ('constant', 'sp0'), ('symmetric', 'sym'),
('periodic', 'ppd'), ('smooth', 'sp1'), ('periodization', 'per')]
families = ('gaus', 'mexh', 'morl', 'cgau', 'shan', 'fbsp', 'cmor')
wavelets = sum([pywt.wavelist(name) for name in families], [])
rstate = np.random.RandomState(1234)
numImf = 10
runCEEMD = 1
maxSift = 10
typeSpline = 2
toModifyBC = 1
randType = 2
checksignal = 1
##
data = np.loadtxt(filename)
time = data[:, 0]
amp = data[:, 1]
dt = time[1] - time[0]
##
mlab = Matlab()
mlab.start()
mlab.run_code('addpaths')
# Fast EEMD
res = mlab.run_func('feemd_post_pro', amp, Nstd, NE, numImf, runCEEMD, maxSift,
typeSpline, toModifyBC, randType, seedNo, checksignal)
imfs = res['result']
# Orthogonality Checking
oi = mlab.run_func('ratio1', imfs)
oi_pair = mlab.run_func('ratioa', imfs)
print('Non-orthogonal leakage of components:')
print(oi['result'])
print('Non-orthogonal leakage for pair of adjoining components:')
def startMatlab():
logging.info("startMatlab: was called")
mlab = Matlab(executable=Constants.MATLAB_EXECUTABLE)
mlab.start()
return mlab
def __init__(self, *args, **kwargs):
excecutable = kwargs.pop('excecutable', 'matlab')
super(MatlabKernel, self).__init__(*args, **kwargs)
self._matlab = Matlab(excecutable)
self._matlab.start()
# Run a more comprehensive set of problem sizes. This could take more than
# an hour to complete.
size_set = 'full'
use_precomputed = False
else:
size_set = 'reduced'
use_precomputed = True
if use_precomputed:
data_dir = os.path.join(os.path.dirname(__file__), 'data')
matlab_data_file = os.path.join(data_dir, 'dwt_matlabR2012a_result.npz')
matlab_result_dict = np.load(matlab_data_file)
else:
try:
from pymatbridge import Matlab
mlab = Matlab()
_matlab_missing = False
except ImportError:
print("To run Matlab compatibility tests you need to have MathWorks "
"MATLAB, MathWorks Wavelet Toolbox and the pymatbridge Python "
"package installed.")
_matlab_missing = True
# list of mode names in pywt and matlab
modes = [('zero', 'zpd'), ('constant', 'sp0'), ('symmetric', 'sym'),
('reflect', 'symw'), ('periodic', 'ppd'), ('smooth', 'sp1'),
('periodization', 'per')]
families = ('db', 'sym', 'coif', 'bior', 'rbio')
wavelets = sum([pywt.wavelist(name) for name in families], [])
from IPython.display import clear_output
import matplotlib.pyplot as plt
import numpy as np
from numpy import sin, cos
from pymatbridge import Matlab
get_ipython().magic(u'matplotlib inline')
execfile('../../matplotlibrc.py')
# Run Matlab code and fetch relevant data
mlab = Matlab()
mlab.start()
results = mlab.run_code(open('fem1d.m').read())
K = mlab.get_variable('K')
U = mlab.get_variable('U')
nodeLocs = mlab.get_variable('nodeLocs')
mlab.stop()
clear_output()
print('K')
print(K)
print('U')
print(U)
x = nodeLocs[:, 0]
xex = np.linspace(0, 1)
w = np.sqrt(2)
uex = (cos(w) - 1) * sin(w * xex) / (2.0 * sin(w)) - 0.5 * cos(w * xex) + 0.5
fig, ax = plt.subplots(figsize=(14, 10))
ax.plot(xex, uex, 'k-', label='Exact')
ax.plot(x, U, 'b-o', label='FEM')
im_gt = []
files_gt = glob.glob(IMAGE_GT_FILE)
for f in files_gt:
#print 'loading', f
im = np.array(Image.open(f))
im = utils.modcrop(im, UP_SCALE).astype(np.float32)
im_gt += [im]
im_l = []
if len(IMAGE_FILE) > 0:
assert (len(im_gt) == 1)
im_l = [np.array(Image.open(IMAGE_FILE)).astype(np.float32)]
else: #down scale from ground truth using Matlab
try:
from pymatbridge import Matlab
mlab = Matlab()
mlab.start()
for im in im_gt:
mlab.set_variable('a', im)
mlab.set_variable('s', 1.0 / UP_SCALE)
mlab.run_code('b=imresize(a, s);')
im_l += [mlab.get_variable('b')]
mlab.stop()
except:
print 'failed to load Matlab!'
assert (0)
#im_l = utils.imresize(im_gt, 1.0/UP_SCALE)
#upscaling
#sr = Bicubic()
sr = SCN(MODEL_FILE)
def __init__(self, *args, **kwargs):
super(MatlabKernel, self).__init__(*args, **kwargs)
executable = os.environ.get('MATLAB_EXECUTABLE', 'matlab')
self._matlab = Matlab(executable)
self._matlab.start()
from pymatbridge import Matlab
mlab = Matlab()
mlab.start()
res = mlab.run_code('a=10, b=a+3')
mlab.stop()
im_gt = []
files_gt = glob.glob(IMAGE_GT_FILE)
for f in files_gt:
#print 'loading', f
im = np.array(Image.open(f))
im = utils.modcrop(im, UP_SCALE).astype(np.float32)
im_gt += [im]
im_l = []
if len(IMAGE_FILE)>0:
assert(len(im_gt)==1)
im_l = [np.array(Image.open(IMAGE_FILE)).astype(np.float32)]
else: #down scale from ground truth using Matlab
try:
from pymatbridge import Matlab
mlab = Matlab()
mlab.start()
for im in im_gt:
mlab.set_variable('a', im)
mlab.set_variable('s', 1.0/UP_SCALE)
mlab.run_code('b=imresize(a, s);')
im_l += [mlab.get_variable('b')]
mlab.stop()
except:
print 'failed to load Matlab!'
assert(0)
#im_l = utils.imresize(im_gt, 1.0/UP_SCALE)
#upscaling
#sr = Bicubic()
sr = SCN(MODEL_FILE)
def __init__(self, *args, **kwargs):
super(MatlabKernel, self).__init__(*args, **kwargs)
executable = os.environ.get('MATLAB_EXECUTABLE', 'matlab')
self._matlab = Matlab(executable)
self._matlab.start()
import os
import sys
from pymatbridge import Matlab
sys.path.append('/Users/stephane/Documents/git/takumi/fapm/cine_local')
matlab_path = '/Applications/MATLAB_R2019a.app/bin/matlab'
faqm_dir = os.path.split(os.path.realpath(__file__))[0]
matlabdir = os.path.join(faqm_dir, 'matlab_codes')
# DATA ARCHITECTURE
scriptdir, scriptname = os.path.split(os.path.realpath(__file__))
mlab = Matlab(executable=matlab_path)
mlabstart = mlab.start()
mlab.run_code('addpath %s; savepath;' % matlabdir)
print('DONE')
print('... MATLAB may crash by this process, but the setup was successful.')
mlab.quit()
from pymatbridge import Matlab
mlab = Matlab(matlab='/Applications/MATLAB_R2014a.app/bin/matlab')
mlab.start()
res = mlab.run('/Users/yanguango/Lab/SpamFilter/Project/src/PCA.m', {'arg1': 3, 'arg2': 5})
print res['result']
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Wed Jan 9 15:53:25 2019
@author: rdamseh
"""
import matlab.engine as eng
from pymatbridge import Matlab
mlab = Matlab()
mlab.start()
mlab.run_code('x=[1,2,3,4,5,6].^2')
x=mlab.get_variable('x')
class MatlabKernel(MetaKernel):
implementation = 'Matlab Kernel'
implementation_version = __version__,
language = 'matlab'
language_version = '0.1',
banner = "Matlab Kernel"
language_info = {
'mimetype': 'text/x-matlab',
'name': 'octave',
'file_extension': '.m',
'help_links': MetaKernel.help_links,
}
_first = True
def __init__(self, *args, **kwargs):
super(MatlabKernel, self).__init__(*args, **kwargs)
executable = os.environ.get('MATLAB_EXECUTABLE', 'matlab')
subprocess.check_call([executable, '-e'], stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
self._matlab = Matlab(executable)
self._matlab.start()
def get_usage(self):
return "This is the Matlab kernel."
def do_execute_direct(self, code):
if self._first:
self._first = False
fig_code = "set(0, 'defaultfigurepaperunits', 'inches');"
self._matlab.run_code(fig_code)
self._matlab.run_code("set(0, 'defaultfigureunits', 'inches');")
self.handle_plot_settings()
self.log.debug('execute: %s' % code)
resp = self._matlab.run_code(code.strip())
self.log.debug('execute done')
if 'stdout' not in resp['content']:
raise ValueError(resp)
if 'figures' in resp['content']:
for fname in resp['content']['figures']:
try:
im = Image(filename=fname)
self.Display(im)
except Exception as e:
self.Error(e)
if not resp['success']:
self.Error(resp['content']['stdout'].strip())
else:
return resp['content']['stdout'].strip()
def get_kernel_help_on(self, info, level=0, none_on_fail=False):
obj = info.get('help_obj', '')
if not obj or len(obj.split()) > 1:
if none_on_fail:
return None
else:
return ""
return self.do_execute_direct('help %s' % obj)
def handle_plot_settings(self):
"""Handle the current plot settings"""
settings = self.plot_settings
settings.setdefault('size', '560,420')
width, height = 560, 420
if isinstance(settings['size'], tuple):
width, height = settings['size']
elif settings['size']:
try:
width, height = settings['size'].split(',')
width, height = int(width), int(height)
except Exception as e:
self.Error(e)
size = "set(0, 'defaultfigurepaperposition', [0 0 %s %s])\n;"
self.do_execute_direct(size % (width / 150., height / 150.))
def repr(self, obj):
return obj
def restart_kernel(self):
"""Restart the kernel"""
self._matlab.stop()
def do_shutdown(self, restart):
with open('test.txt', 'w') as fid:
fid.write('hey hey\n')
self._matlab.stop()
Created on Wed May 16 19:52:46 2018
Dynamic Flux Analysis & Constrained Flux Regulation
@author: scott
"""
from __future__ import print_function
import sys, os
import cobra, cobra.test, json
from escher import Builder
from gurobipy import *
from os.path import join
from cobra.io.mat import model_to_pymatbridge
from pymatbridge import Matlab
# if you're going to be working with both Matlab and Python:
mlab = Matlab()
mlab.start()
results = mlab.run_code('a=1;')
model.solver = 'gurobi'
# redirect towards the datafiles
data_dir = r'C:\\Users\\scott\\Google Drive\\Work\\UM\\Research\\Sriram\\Projects\\Lyssiotis_Proteomics'
# we need to use a recon1 model with an objective function:
recon1_model = cobra.io.load_matlab_model(join(data_dir, "model_human_duarte.mat"))
model_to_pymatbridge(recon1_model, variable_name="metabolicmodel")
recon1_model.metabolicmodel.S
#Palsson_core = cobra.io.load_matlab_model(join(data_dir, "Core_Model_Palsson.mat"), variable_name="core_genecomb")
def flux_activity_coeff(model, timecourse_metabolomics, sheetname='Sheet1', kappa=None, kappa2=None, genedelflag=None, rxndelflag=None, normquantile=None):
def __init__(self, *args, **kwargs):
super(MatlabProcessor, self).__init__(*args, **kwargs)
from pymatbridge import Matlab
self.matlab = Matlab()
self.matlab.start()
# Parameters Setting
filename = "./Input_data/example_lena.png"
npixs = 512
Nstd = 0.4
NE = 20
seedNo = 1
numImf = 6
runCEEMD = 1
maxSift = 10
typeSpline = 2
toModifyBC = 1
randType = 2
checksignal = 1
##
mlab = Matlab()
mlab.start()
mlab.run_code("addpaths")
# Fast 2dEEMD
res = mlab.run_func(
"meemd", filename, npixs, Nstd, NE, numImf, runCEEMD, maxSift, typeSpline, toModifyBC, randType, seedNo, checksignal
)
imfs = res["result"]
# Plot Results
HHTplots.example_lena(filename, imfs)
mlab.stop()