Exemplo n.º 1
0
def plot_timeVfreq(date_operator, collection):
    if collection == "drop":
        field = "pickup_time"
    elif collection == "pickup":
        field = "drop_time"
    else:
        raise Exception(
            "Error: the collection arguments to timeVfreq are 'drop', or 'pickup'. Not",
            collection)
    graph_size = {"hour": (0, 23), "dayOfYear": (1, 366), "dayOfWeek": (1, 8)}
    if date_operator not in graph_size.keys():
        raise Exception(
            "Error: the date_operator argument to timeVfreq must be 'hour', 'dayOfWeek', or 'dayOfYear'. Not",
            date_operator)

    tvf = get_timeVfreq(date_operator, collection, field)

    min_freq = ndarray.min(tvf[1])
    max_freq = ndarray.max(tvf[1])

    fig = pyplot.figure(1, figsize=(7, 6))
    myplot = fig.add_subplot(111)
    myplot.set_xlim(graph_size[date_operator])

    norm = colors.Normalize(min_freq, max_freq)

    for i in range(len(tvf[0])):
        color = colors.rgb2hex(cm.cool(norm(tvf[1][i])))
        pyplot.bar(tvf[0][i], tvf[1][i], color=color, ec=color, align='edge')

    pyplot.xlabel(date_operator)
    pyplot.ylabel("# of rides")
    pyplot.savefig(date_operator + "_" + collection, dpi=180)
Exemplo n.º 2
0
def plot_timeVfreq(date_operator, collection):
    if collection == "drop":
         field = "pickup_time"
    elif collection == "pickup":
         field = "drop_time"
    else:
         raise Exception("Error: the collection arguments to timeVfreq are 'drop', or 'pickup'. Not", collection)
    graph_size = {"hour":(0, 23), "dayOfYear":(1,366), "dayOfWeek":(1,8)}
    if date_operator not in graph_size.keys():
        raise Exception("Error: the date_operator argument to timeVfreq must be 'hour', 'dayOfWeek', or 'dayOfYear'. Not", date_operator)

    tvf = get_timeVfreq(date_operator, collection, field)

    min_freq = ndarray.min(tvf[1])
    max_freq = ndarray.max(tvf[1])

    fig = pyplot.figure(1, figsize=(7, 6))
    myplot = fig.add_subplot(111)
    myplot.set_xlim(graph_size[date_operator])

    norm = colors.Normalize(min_freq, max_freq)

    for i in range(len(tvf[0])):
        color = colors.rgb2hex(cm.cool(norm(tvf[1][i])))
        pyplot.bar(tvf[0][i], tvf[1][i], color=color, ec=color, align='edge')

    pyplot.xlabel(date_operator)
    pyplot.ylabel("# of rides")
    pyplot.savefig(date_operator + "_" + collection, dpi=180)
Exemplo n.º 3
0
    def max(self, *args, **kwargs):
        """
    Returns the maximum Date.

    For a description of the input parameters, please refer to numpy.max.
        """
        obj = ndarray.max(self, *args, **kwargs)
        if not obj.shape:
            return Date(self.freq, obj)
        return obj
Exemplo n.º 4
0
    def max(self, *args, **kwargs):
        """
    Returns the maximum Date.

    For a description of the input parameters, please refer to numpy.max.
        """
        obj = ndarray.max(self, *args, **kwargs)
        if not obj.shape:
            return Date(self.freq, obj)
        return obj
Exemplo n.º 5
0
def BackgroundImage(request):
    chad = User.objects.get(id=1)
    image = ThreeDimensional.objects.filter(user=chad)[0]
        
    image_handle = nibabel.load(image.brain_image.file.name)
    image_data = image_handle.get_data() 
    image_list_data = image_data.tolist()
    
    #for some reason ndarray.max returns an ndarray with 1 member
    #which is a numpy.flaot32. this converts all that to a regular python float    
    max = ndarray.max(image_data).tolist()
    min = ndarray.min(image_data).tolist()
   
    
    json_object = {
                   'data' : image_list_data,
                   'max' : max,
                   'min' : min,
    }

    
    json_data = json.dumps(json_object)
    
    return HttpResponse(json_data, mimetype='application/json')
Exemplo n.º 6
0
def make_validation_plots(opt, tlm, db):
    """
    Make validation output plots.

    :param outdir: output directory
    :param tlm: telemetry
    :param db: database handle
    :returns: list of plot info including plot file names
    """
    outdir = opt.outdir
    start = tlm['date'][0]
    stop = tlm['date'][-1]
    states = get_states(start, stop, db)

    # Create array of times at which to calculate PSMC temperatures, then do it
    logger.info('Calculating PSMC thermal model for validation')

    model = calc_model(opt.model_spec, states, start, stop)

    # Interpolate states onto the tlm.date grid
    # state_vals = cmd_states.interpolate_states(states, model.times)
    pred = {'1pdeaat': model.comp['1pdeaat'].mvals,
            'pitch': model.comp['pitch'].mvals,
            'tscpos': model.comp['sim_z'].mvals
            }

    idxs = Ska.Numpy.interpolate(np.arange(len(tlm)), tlm['date'], model.times,
                                 method='nearest')
    tlm = tlm[idxs]

    labels = {'1pdeaat': 'Degrees (C)',
              'pitch': 'Pitch (degrees)',
              'tscpos': 'SIM-Z (steps/1000)',
              }

    scales = {'tscpos': 1000.}

    fmts = {'1pdeaat': '%.2f',
            'pitch': '%.3f',
            'tscpos': '%d'}

    good_mask = np.ones(len(tlm),dtype='bool')
    for interval in model.bad_times:
        bad = ((tlm['date'] >= DateTime(interval[0]).secs)
            & (tlm['date'] < DateTime(interval[1]).secs))
        good_mask[bad] = False

    plots = []
    logger.info('Making PSMC model validation plots and quantile table')
    quantiles = (1, 5, 16, 50, 84, 95, 99)
    # store lines of quantile table in a string and write out later
    quant_table = ''
    quant_head = ",".join(['MSID'] + ["quant%d" % x for x in quantiles])
    quant_table += quant_head + "\n"
    for fig_id, msid in enumerate(sorted(pred)):
        plot = dict(msid=msid.upper())
        fig = plt.figure(10 + fig_id, figsize=(7, 3.5))
        fig.clf()
        scale = scales.get(msid, 1.0)
        ticklocs, fig, ax = plot_cxctime(model.times, tlm[msid] / scale,
                                         fig=fig, fmt='-r')
        ticklocs, fig, ax = plot_cxctime(model.times, pred[msid] / scale,
                                         fig=fig, fmt='-b')
        if  np.any(~good_mask) :
            ticklocs, fig, ax = plot_cxctime(model.times[~good_mask], tlm[msid][~good_mask] / scale,
                                         fig=fig, fmt='.c')

        ax.set_title(msid.upper() + ' validation')
        ax.set_ylabel(labels[msid])
        ax.grid()
        filename = msid + '_valid.png'
        outfile = os.path.join(outdir, filename)
        logger.info('Writing plot file %s' % outfile)
        fig.savefig(outfile)
        plot['lines'] = filename

        # Make quantiles
        if msid == '1pdeaat':
            ok = (( tlm[msid] > 30.0 ) & good_mask )
            ok2 =(( tlm[msid] > 40.0 ) & good_mask )
        else:
            ok = np.ones(len(tlm[msid]), dtype=bool)
        diff = np.sort(tlm[msid][ok] - pred[msid][ok])
        quant_line = "%s" % msid
        for quant in quantiles:
            quant_val = diff[(len(diff) * quant) // 100]
            plot['quant%02d' % quant] = fmts[msid] % quant_val
            quant_line += (',' + fmts[msid] % quant_val)
        quant_table += quant_line + "\n"

        for histscale in ('log', 'lin'):
            fig = plt.figure(20 + fig_id, figsize=(4, 3))
            fig.clf()
            ax = fig.gca()
            ax.hist(diff / scale, bins=50, log=(histscale == 'log'))
            if msid == '1pdeaat':
                diff2=np.sort(tlm[msid][ok2] - pred[msid][ok2])
                ax.hist(diff2 / scale, bins=50, log=(histscale == 'log'),
                        color = 'red')
            ax.set_title(msid.upper() + ' residuals: data - model')
            ax.set_xlabel(labels[msid])
            fig.subplots_adjust(bottom=0.18)
            filename = '%s_valid_hist_%s.png' % (msid, histscale)
            outfile = os.path.join(outdir, filename)
            logger.info('Writing plot file %s' % outfile)
            fig.savefig(outfile)
            plot['hist' + histscale] = filename

        plots.append(plot)
                    
    filename = os.path.join(outdir, 'validation_quant.csv')
    logger.info('Writing quantile table %s' % filename)
    f = open(filename, 'w')
    f.write(quant_table)
    f.close()

    # If run_start is specified this is likely for regression testing
    # or other debugging.  In this case write out the full predicted and
    # telemetered dataset as a pickle.
    if opt.run_start:
        filename = os.path.join(outdir, 'validation_data.pkl')
        logger.info('Writing validation data %s' % filename)
        f = open(filename, 'w')
        pickle.dump({'pred': pred, 'tlm': tlm}, f, protocol=-1)
        f.close()

    # adding stuff for resid plots--rje 6/24/14
    fig = plt.figure(36)
    fig.clf()

    # this is the python equivalent of the IDL where() function
    # note parens are required for the & cases.
    msid='1pdeaat'
    hot_hrcs = ((tlm['tscpos'] < -85000.0 ) & ( pred[msid] > 40.0 ) & good_mask )
    hot_hrci = ( ( -85000.0 < tlm['tscpos'] ) & ( tlm['tscpos'] < 0.0 ) & ( pred[msid] > 40.0 ) & good_mask )
    hot_aciss = ( ( 0.0 < tlm['tscpos'] ) & ( tlm['tscpos'] < 80000.0 ) & ( pred[msid] > 40.0 ) & good_mask )
    hot_acisi = ((tlm['tscpos'] > 80000.0 ) & ( pred[msid] > 40.0 ) & good_mask )
    warm_hrcs = ((tlm['tscpos'] < -85000.0 ) & ( pred[msid] > 30.0 ) & ( pred[msid] < 40.0 ) & good_mask )
    warm_hrci = ( ( -85000.0 < tlm['tscpos'] ) & ( tlm['tscpos'] < 0.0 )& ( pred[msid] > 30.0 ) & ( pred[msid] < 40.0 ) & good_mask )
    warm_aciss = ( ( 0.0 < tlm['tscpos'] ) & ( tlm['tscpos'] < 80000.0 )& ( pred[msid] > 30.0 ) & ( pred[msid] < 40.0 ) & good_mask )
    warm_acisi = ((tlm['tscpos'] > 80000.0 ) & ( pred[msid] > 30.0 ) & ( pred[msid] < 40.0 ) & good_mask )
    cold_hrcs = ( (tlm['tscpos'] < -85000.0 ) & ( pred[msid] < 30.0 ) & good_mask )
    cold_hrci = ( ( -85000.0 < tlm['tscpos'] ) & ( tlm['tscpos'] < 0.0 ) & ( pred[msid] < 30.0 ) & good_mask )
    cold_aciss = ( ( 0.0 < tlm['tscpos'] ) & ( tlm['tscpos'] < 80000.0 ) & ( pred[msid] < 30.0 ) & good_mask )
    cold_acisi = ( (tlm['tscpos'] > 80000.0 ) & ( pred[msid] < 30.0 ) & good_mask )

    plt.plot(tlm['pitch'][hot_hrci],  tlm[msid][hot_hrci] - pred[msid][hot_hrci],  "ob", markersize=5)
    plt.plot(tlm['pitch'][hot_hrcs], tlm[msid][hot_hrcs] - pred[msid][hot_hrcs],  "ok", markersize=5)
    plt.plot(tlm['pitch'][hot_aciss], tlm[msid][hot_aciss] - pred[msid][hot_aciss], "or", markersize=5)
    plt.plot(tlm['pitch'][hot_acisi], tlm[msid][hot_acisi] - pred[msid][hot_acisi], "og", markersize=5)

    plt.plot(tlm['pitch'][warm_hrci], tlm[msid][warm_hrci] - pred[msid][warm_hrci],  "sb", markersize=3)
    plt.plot(tlm['pitch'][warm_hrcs], tlm[msid][warm_hrcs] - pred[msid][warm_hrcs],  "sk", markersize=3)
    plt.plot(tlm['pitch'][warm_aciss], tlm[msid][warm_aciss] - pred[msid][warm_aciss], "sr", markersize=3)
    plt.plot(tlm['pitch'][warm_acisi], tlm[msid][warm_acisi] - pred[msid][warm_acisi], "sg", markersize=3)

    plt.plot(tlm['pitch'][cold_hrci], tlm[msid][cold_hrci] - pred[msid][cold_hrci],  ".b", markersize=2)
    plt.plot(tlm['pitch'][cold_hrcs], tlm[msid][cold_hrcs] - pred[msid][cold_hrcs],  ".k", markersize=2)
    plt.plot(tlm['pitch'][cold_aciss], tlm[msid][cold_aciss] - pred[msid][cold_aciss], ".r", markersize=2)
    plt.plot(tlm['pitch'][cold_acisi], tlm[msid][cold_acisi] - pred[msid][cold_acisi], ".g", markersize=2)
    # plt.plot(tlm['pitch'][htr_on], tlm[msid][htr_on] - pred[msid][htr_on], "*m", markersize=10)

    plt.ylabel('1PDEAAT Data - Model')
    plt.xlabel('pitch angle')
    plt.title('b,k,r,g=hrci,hrcs,aciss,acisi, mod.temp: 0<.<30<s<40<o')
    plt.grid()

    outfile=os.path.join(outdir,'1pdeaat_resid_pitch.png')
    fig.savefig(outfile)

    # adding stuff for resid plots--rje 6/24/14


    fig = plt.figure(35)
    fig.clf()

    # this is the python equivalent of the IDL where() function
    # note parens are required for the & cases.
    fwd_hrcs = ((tlm['tscpos'] < -85000.0 ) & ( tlm['pitch'] < 65.0 ) & good_mask )
    fwd_hrci = ( ( -85000.0 < tlm['tscpos'] ) & ( tlm['tscpos'] < 0.0 ) & ( tlm['pitch'] < 65.0 ) & good_mask )
    fwd_aciss = ( ( 0.0 < tlm['tscpos'] ) & ( tlm['tscpos'] < 80000.0 ) & ( tlm['pitch'] < 65.0 ) & good_mask )
    fwd_acisi = ((tlm['tscpos'] > 80000.0 ) & ( tlm['pitch'] < 65.0 ) & good_mask )

    m80_hrcs = ((tlm['tscpos'] < -85000.0 ) & ( tlm['pitch'] > 65.0 ) & ( tlm['pitch'] < 80.0 ) & good_mask )
    m80_hrci = ( ( -85000.0 < tlm['tscpos'] ) & ( tlm['tscpos'] < 0.0 )& ( tlm['pitch'] > 65.0 ) & ( tlm['pitch'] < 80.0 ) & good_mask )
    m80_aciss = ( ( 0.0 < tlm['tscpos'] ) & ( tlm['tscpos'] < 80000.0 )& ( tlm['pitch'] > 65.0 ) & ( tlm['pitch'] < 80.0 ) & good_mask )
    m80_acisi = ((tlm['tscpos'] > 80000.0 ) & ( tlm['pitch'] > 65.0 ) & ( tlm['pitch'] < 80.0 ) & good_mask )

    mid_hrcs = ((tlm['tscpos'] < -85000.0 ) & ( tlm['pitch'] > 80.0 ) & ( tlm['pitch'] < 90.0 ) & good_mask )
    mid_hrci = ( ( -85000.0 < tlm['tscpos'] ) & ( tlm['tscpos'] < 0.0 )& ( tlm['pitch'] > 80.0 ) & ( tlm['pitch'] < 90.0 ) & good_mask )
    mid_aciss = ( ( 0.0 < tlm['tscpos'] ) & ( tlm['tscpos'] < 80000.0 )& ( tlm['pitch'] > 80.0 ) & ( tlm['pitch'] < 90.0 ) & good_mask )
    mid_acisi = ((tlm['tscpos'] > 80000.0 ) & ( tlm['pitch'] > 80.0 ) & ( tlm['pitch'] < 90.0 ) & good_mask )

    aft_hrcs = ( (tlm['tscpos'] < -85000.0 ) & ( tlm['pitch'] > 90.0 ) & good_mask )
    aft_hrci = ( ( -85000.0 < tlm['tscpos'] ) & ( tlm['tscpos'] < 0.0 ) & ( tlm['pitch'] > 90.0 ) & good_mask )
    aft_aciss = ( ( 0.0 < tlm['tscpos'] ) & ( tlm['tscpos'] < 80000.0 ) & ( tlm['pitch'] > 90.0 ) & good_mask )
    aft_acisi = ( (tlm['tscpos'] > 80000.0 ) & ( tlm['pitch'] > 90.0 ) & good_mask )

    msid='1pdeaat'
    plt.plot(pred[msid][fwd_hrci], tlm[msid][fwd_hrci] - pred[msid][fwd_hrci],  "ob", markersize=5)
    plt.plot(pred[msid][fwd_hrcs], tlm[msid][fwd_hrcs] - pred[msid][fwd_hrcs],  "ok", markersize=5)
    plt.plot(pred[msid][fwd_aciss], tlm[msid][fwd_aciss] - pred[msid][fwd_aciss], "or", markersize=5)
    plt.plot(pred[msid][fwd_acisi], tlm[msid][fwd_acisi] - pred[msid][fwd_acisi], "og", markersize=5)

    plt.plot(pred[msid][m80_hrci], tlm[msid][m80_hrci] - pred[msid][m80_hrci],  "vb", markersize=5)
    plt.plot(pred[msid][m80_hrcs], tlm[msid][m80_hrcs] - pred[msid][m80_hrcs],  "vk", markersize=5)
    plt.plot(pred[msid][m80_aciss], tlm[msid][m80_aciss] - pred[msid][m80_aciss], "vr", markersize=5)
    plt.plot(pred[msid][m80_acisi], tlm[msid][m80_acisi] - pred[msid][m80_acisi], "vg", markersize=5)

    plt.plot(pred[msid][mid_hrci], tlm[msid][mid_hrci] - pred[msid][mid_hrci],  "^b", markersize=5)
    plt.plot(pred[msid][mid_hrcs], tlm[msid][mid_hrcs] - pred[msid][mid_hrcs],  "^k", markersize=5)
    plt.plot(pred[msid][mid_aciss], tlm[msid][mid_aciss] - pred[msid][mid_aciss], "^r", markersize=5)
    plt.plot(pred[msid][mid_acisi], tlm[msid][mid_acisi] - pred[msid][mid_acisi], "^g", markersize=5)

    plt.plot(pred[msid][aft_hrci], tlm[msid][aft_hrci] - pred[msid][aft_hrci],  ".b", markersize=2)
    plt.plot(pred[msid][aft_hrcs], tlm[msid][aft_hrcs] - pred[msid][aft_hrcs],  ".k", markersize=2)
    plt.plot(pred[msid][aft_aciss], tlm[msid][aft_aciss] - pred[msid][aft_aciss], ".r", markersize=2)
    plt.plot(pred[msid][aft_acisi], tlm[msid][aft_acisi] - pred[msid][aft_acisi], ".g", markersize=2)

    maxmodeltemp=ndarray.max(pred[msid][good_mask])
    maxresid=ndarray.max(tlm[msid][good_mask]-pred[msid][good_mask])
    x = np.array(np.linspace(52.5-maxresid,maxmodeltemp,num=5))
    my_y = 52.5 - x
    plt.plot( x, my_y )

    plt.ylabel('Data - Model')
    plt.xlabel('1pdeaat Model')
    plt.title('blue,black,red,green=hrci,hrcs,aciss,acisi, 45<o<65<v<80<^<90<.')
    plt.grid()

    # raise ValueError
    outfile=os.path.join(outdir,'1pdeaat_resid.png')
    fig.savefig(outfile)

    return plots
Exemplo n.º 7
0
def main_test():
    # As a main function, this function tests other functions written in domain_functions.py:
    # - compute triangulation of domain
    # -
    nodes = np.array([[0., 0.], [0., 10.], [15., 10.], [15., 0.], [6., 10.],
                      [9., 10.], [11., 0.], [2., 8.], [8., 7.], [11., 2.],
                      [5., 5.]])
    tri = spatial.Delaunay(nodes)
    print ''
    print 'Entered points, triangles by vertex, and neighbors:'
    print tri.points  # Numbering of the points is in order of input
    print ''
    print tri.simplices  # From point numbers, give the triangles, starting with # 0
    print ''
    print tri.neighbors  # Give the triangle number of neighbors, and -1 for boundary
    # Test which triangle:
    # print tri.find_simplex(np.array([2., 2.]))

    # Choose a simplex and facet control idx:
    num = 0
    facet_id = 0

    # Extract points:
    points = tri.points
    vertices = tri.simplices
    vertices_here = vertices[num, :]
    print ''
    print 'vertices_here:'
    print vertices_here

    # Extract normals:
    nhat_array = nhat_tri_facet(tri, num)
    print ''
    print 'Local normal vectors:'
    print nhat_array

    u_optimal, fg_array = u_for_F_v1(tri, num, 0.01, np.eye(2), facet_id)
    u_stay, fg_array_stay = u_for_stay_v1(tri, num, 0.01, np.eye(2))
    print ''
    print 'Optimal u at each vertex:'
    print u_optimal
    print ''
    print 'fg_array for select triangle and facet:'
    print fg_array
    print ''
    print 'Optimal u to stay:'
    print u_stay

    fg_master_array = fg_for_domain(tri, 0.01, np.eye(2), 2)
    print ''
    print 'fg_master_array:'
    print fg_master_array

    x_array = points[vertices_here, 0]
    y_array = points[vertices_here, 1]
    x_lim = np.array([ndarray.min(x_array), ndarray.max(x_array)])
    y_lim = np.array([ndarray.min(y_array), ndarray.max(y_array)])

    domain_plot = 1
    control_plot = 1
    # Plot partitioning of domain:
    if domain_plot == 1:
        plt.rc('text', usetex=True)
        plt.rc('font', family='serif')
        plt.triplot(nodes[:, 0], nodes[:, 1], tri.simplices.copy())
        plt.plot(nodes[:, 0], nodes[:, 1], 'o')
        plt.title('Partitioned Domain')
        plt.grid(linestyle="--", linewidth=0.1, color='.25', zorder=-10)

    # Quiver velocity field to leave selected triangle:
    if control_plot == 1:
        X, Y = np.meshgrid(np.arange(x_lim[0] - 0.5, x_lim[1] + 0.5, 0.5),
                           np.arange(y_lim[0] - 0.5, y_lim[1] + 0.5, 0.5))
        U = fg_array[0, 0] * X + fg_array[0, 1] * Y + fg_array[0, 2]
        V = fg_array[1, 0] * X + fg_array[1, 1] * Y + fg_array[1, 2]
        fig = plt.figure()
        ax = fig.add_subplot(1, 1, 1)
        plt.rc('text', usetex=True)
        plt.rc('font', family='serif')
        plt.title(
            'Control Vector Field for Chosen Simplex - Exit Chosen Facet')
        ax.triplot(nodes[:, 0], nodes[:, 1], tri.simplices.copy())
        ax.plot(nodes[:, 0], nodes[:, 1], 'o')
        ax.quiver(X, Y, U, V)
        ax.grid(linestyle="--", linewidth=0.1, color='.25', zorder=-10)
        # ax.set_xlim(x_lim)
        # ax.set_ylim(y_lim)

        X, Y = np.meshgrid(np.arange(x_lim[0] - 0.5, x_lim[1] + 0.5, 0.5),
                           np.arange(y_lim[0] - 0.5, y_lim[1] + 0.5, 0.5))
        U = fg_array_stay[0, 0] * X + fg_array_stay[0, 1] * Y + fg_array_stay[
            0, 2]
        V = fg_array_stay[1, 0] * X + fg_array_stay[1, 1] * Y + fg_array_stay[
            1, 2]
        fig = plt.figure()
        ax = fig.add_subplot(1, 1, 1)
        plt.rc('text', usetex=True)
        plt.rc('font', family='serif')
        plt.title('Control Vector Field for Chosen Simplex - Remain')
        ax.triplot(nodes[:, 0], nodes[:, 1], tri.simplices.copy())
        ax.plot(nodes[:, 0], nodes[:, 1], 'o')
        ax.quiver(X, Y, U, V)
        ax.grid(linestyle="--", linewidth=0.1, color='.25', zorder=-10)
        # ax.set_xlim(x_lim)
        # ax.set_ylim(y_lim)
    if (domain_plot == 1) or (control_plot == 1):
        plt.show()
    return


# Execute test:
# main_test()
Exemplo n.º 8
0
def main_func():

    image = cv2.imread('asl_alphabet.jpg')

    resized_image = cv2.resize(image, (570, 720))

    # loading the pre-trained model
    model = load_model('SeqModel.h5')

    camera = cv2.VideoCapture(0)  # 0 -> index of camera

    camera.set(3, 1080)
    camera.set(4, 720)

    predict_letter_list = []
    word_str = ''
    word_list = []

    while True:

        check, frame1 = camera.read()

        cv2.rectangle(frame1, (20, 70), (275, 325), (0, 255, 0), thickness=1)

        gray_frame = cv2.cvtColor(frame1, cv2.COLOR_BGR2GRAY)

        median_blurred = cv2.medianBlur(gray_frame,
                                        3)  # blurring (median) the gray frame

        gaus_blurred = cv2.GaussianBlur(
            median_blurred, (3, 3),
            0)  # blurring (gaussian) the already blurred gray frame

        canny_frame = cv2.Canny(gaus_blurred, 30, 30)

        cropped_frame = canny_frame[71:325, 21:275]

        cropFrame_array = feed_preprocessing(cropped_frame)

        # predict
        predictions_prob = model.predict(cropFrame_array)

        # print(predictions_prob)

        # take the value with the highest probability
        most_prob = ndarray.max(predictions_prob)

        # print(most_prob)

        # [0] -> A , [1] -> B , [2]-> C
        # print(type(predictions_label)) --> numpy.ndarray
        predictions_label = model.predict_classes(cropFrame_array)

        # convert [0] to 0 for future operations
        string_label = functools.reduce(lambda x, y: x + str(y),
                                        predictions_label, '')
        integer_label = int(string_label)

        # 0 -> A, 1 -> B, 2-> C
        # print(integer_label)

        # matching the labels to the actual values
        # most_prob > number (where number determined based on measures) to avoid random results
        if most_prob > 0.65:

            # print(f'accuracy: {most_prob}')
            # print(f'letter {write_letter(integer_label)}')

            letter = write_letter(integer_label)
            # append the predicted letter into the list
            predict_letter_list.append(letter)
            print(predict_letter_list)
            # every x letters
            if len(predict_letter_list) % 15 == 0:
                # check that ALL (x) the letters in the list are the same
                if all(x == predict_letter_list[0]
                       for x in predict_letter_list):
                    if integer_label == 20:
                        word_list += ' '
                    elif integer_label == 4:  # trycatch should be added for empty list(deleting letter from empty word)
                        try:
                            word_list.pop()
                        except:
                            pass

                    else:
                        # add to the word that will be printed one of the identical items of the list
                        word_list.append(predict_letter_list[0])
                    time.sleep(.5)

                    print(word_list)

                # every x letter clear the list so the process repeats again from the start
                predict_letter_list.clear()
                # convert list into string for putText function
                word_str = "".join(word_list)

        frame1 = cv2.putText(frame1, 'Predicted Word: ' + word_str, (20, 60),
                             cv2.FONT_HERSHEY_SIMPLEX, 2, (128, 0, 0), 3)
        # frame1 = cv2.putText(frame1, 'Predicted Letter: ' + write_letter(integer_label), (20, 500),
        #                     cv2.FONT_HERSHEY_SIMPLEX, 2, (128, 0, 0), 3)

        if not check:
            break

        cv2.imshow('WINDOW', frame1)
        cv2.imshow('CROPPED', cropped_frame)
        cv2.imshow('ALPHABET', resized_image)

        if frame1 is None:
            break

        keyboard = cv2.waitKey(1) & 0xff
        if keyboard == 27:  # esc to terminate
            break

    camera.release()
    cv2.destroyAllWindows()