コード例 #1
0
ファイル: bridge_util.py プロジェクト: xiaoyili/xylearn
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']
コード例 #2
0
ファイル: matlab.py プロジェクト: HHTpy/HHTpywrapper
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
コード例 #3
0
ファイル: processors.py プロジェクト: abukaj/Pweave
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
コード例 #4
0
ファイル: testbench.py プロジェクト: asarhegyi/adcpp
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
コード例 #5
0
ファイル: matlab_kernel.py プロジェクト: zharl/matlab_kernel
 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()
コード例 #6
0
ファイル: bridge.py プロジェクト: AndreiBarsan/crowd
    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.")
コード例 #7
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    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'])
コード例 #8
0
 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)
コード例 #9
0
ファイル: MATLAB.py プロジェクト: wonkykludge/fsspmnl
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)
コード例 #10
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    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()
コード例 #11
0
ファイル: tests.py プロジェクト: SpakowitzLab/BasicWLC
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()
コード例 #12
0
ファイル: models.py プロジェクト: szairis/DREAM8
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)
コード例 #13
0
ファイル: kbmf.py プロジェクト: hansaimlim/PyDTI
 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()
コード例 #14
0
ファイル: testbench.py プロジェクト: asarhegyi/adcpp
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()
コード例 #15
0
ファイル: walker.py プロジェクト: aaronkl/RoBO
    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()
コード例 #16
0
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()
コード例 #17
0
ファイル: dataPreprocess.py プロジェクト: ove-wak/Git-storage
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()
コード例 #18
0
""" 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')]
コード例 #19
0
: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()
コード例 #20
0
ファイル: learn_weights.py プロジェクト: wsgan001/illalla
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):
コード例 #21
0
                        '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:
コード例 #22
0
    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()
        '''
コード例 #23
0
 def process(self):
     mlab = Matlab()
     mlab.start()
     return mlab
コード例 #24
0
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))
コード例 #25
0
ファイル: resnet_baseline.py プロジェクト: VIE-PKU/OHFM
            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')
コード例 #26
0
ファイル: bridge_util.py プロジェクト: xiaoyili/xylearn
    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)
コード例 #27
0
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()
コード例 #28
0
ファイル: analyze.py プロジェクト: pvnick/tardis
            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")
コード例 #29
0
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
コード例 #30
0
ファイル: generate_matlab_data.py プロジェクト: aaren/pywt
""" 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')]
コード例 #31
0
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()
コード例 #32
0
		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)
コード例 #33
0
#_*_ 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'
コード例 #34
0

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()
コード例 #35
0
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) + '/')
コード例 #36
0
ファイル: test.py プロジェクト: pawskow/python-matlab-bridge
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
コード例 #37
0
 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)
コード例 #38
0
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)
コード例 #39
0
ファイル: problem2.py プロジェクト: selimb/schoolstuff
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')
コード例 #40
0
ファイル: processors.py プロジェクト: abukaj/Pweave
 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)
コード例 #42
0
""" 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)
コード例 #43
0
ファイル: main1d.py プロジェクト: YihaoSu/HHTpywrapper
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:')
コード例 #44
0
def startMatlab():
    logging.info("startMatlab: was called")
    mlab = Matlab(executable=Constants.MATLAB_EXECUTABLE)
    mlab.start()
    return mlab
コード例 #45
0
 def __init__(self, *args, **kwargs):
     excecutable = kwargs.pop('excecutable', 'matlab')
     super(MatlabKernel, self).__init__(*args, **kwargs)
     self._matlab = Matlab(excecutable)
     self._matlab.start()
コード例 #46
0
    # 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], [])
コード例 #47
0
ファイル: problem2.py プロジェクト: selimb/schoolstuff
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')
コード例 #48
0
ファイル: main_test.py プロジェクト: pherrusa7/foodCAT
    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)
コード例 #49
0
 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()
コード例 #50
0
ファイル: pymatlabTest.py プロジェクト: TmanTman/SkripsieKode
from pymatbridge import Matlab
mlab = Matlab()
mlab.start()

res = mlab.run_code('a=10, b=a+3')

mlab.stop()
コード例 #51
0
ファイル: main_test.py プロジェクト: CV-IP/SCN_Matlab
    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)
コード例 #52
0
ファイル: matlab_kernel.py プロジェクト: febret/matlab_kernel
 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()
コード例 #53
0
ファイル: setup.py プロジェクト: tmatsuzawa/tflow
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()







コード例 #54
0
ファイル: test.py プロジェクト: yanguango/spam_filter
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']
コード例 #55
0
#!/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')
コード例 #56
0
ファイル: matlab_kernel.py プロジェクト: zharl/matlab_kernel
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()
コード例 #57
0
ファイル: imat.py プロジェクト: ScottCampit/kinomic-analysis
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):
コード例 #58
0
ファイル: processors.py プロジェクト: trnkatomas/Pweave
 def __init__(self, *args, **kwargs):
     super(MatlabProcessor, self).__init__(*args, **kwargs)
     from pymatbridge import Matlab
     self.matlab = Matlab()
     self.matlab.start()
コード例 #59
0
ファイル: main2d.py プロジェクト: YihaoSu/HHTpywrapper
#  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()