#Enter the specific 
    # jsdata = request.form['v1data']

    # specific_stimuli = jsdata
    # specific_stimuli = open('xhr.js', 'selected_value')
    specific_stimuli = '02_Berlin_S1.jpg'

    plaats = r"static\MetroMapsEyeTracking\data_csv\all_fixation_data_cleaned_up.csv"
    trdata = pd.read_csv(plaats, encoding='latin1', delimiter='\t') 
    trdata.head()

    location_prefix = r"static\MetroMapsEyeTracking\stimuli\\"
    concatenate = location_prefix + specific_stimuli

    with Image.open(concatenate) as img:
        width, height = img.size
    
    testset = trdata[trdata['StimuliName'] == specific_stimuli].reset_index().copy()
    outofbounds = (testset['MappedFixationPointX']>width) | (testset['MappedFixationPointY']>height) | (testset['MappedFixationPointX']<0) | (testset['MappedFixationPointY']<0)
    maxduration = trdata[['FixationDuration']].max()
    meanduration = trdata[['FixationDuration']].mean()
    testset = testset[np.logical_not(outofbounds)].reset_index().copy()    

    x=np.array([])
    y=np.array([])
    weight=np.array([])

    for index, row in testset.iterrows():
        x = np.append(x, np.array([row['MappedFixationPointX']]))
        y = np.append(y, np.array([row['MappedFixationPointY']]))
        weight = np.append(weight, np.array([row['FixationDuration']]))

    H, xe, ye = np.histogram2d(y, x, bins=500, weights=weight)  
    H = filters.gaussian_filter(H, sigma=4)
    H = np.flipud(H)

    img_orig = plt.imread(concatenate)
    img = img_orig.copy().astype(np.uint8) 

    if img.ndim == 2:  # gray input
        img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGBA)
    elif img.ndim == 3:  # rgb input
        img = cv2.cvtColor(img, cv2.COLOR_RGB2RGBA) 

    img = np.flipud(img)    

    p = figure(
        x_range=(0, width),
        y_range=(height, 0),
        plot_width=int(width/2), 
        plot_height=int(height/2)
    )

    p.xaxis.visible = False
    p.yaxis.visible = False

    source = bokeh.plotting.ColumnDataSource(data=dict(
        img=[img], x=[0], y=[img.shape[0]],
        dw=[img.shape[1]], dh=[img.shape[0]],
        R=[img[::-1, :, 0]], G=[img[::-1, :, 1]], B=[img[::-1, :, 2]]))
    p.image_rgba(source=source, image='img', x='x', y='y', dw='dw', dh='dh')
    p.image(image=[H], x=0, y=height, dw=width, dh=height, palette="Viridis11", global_alpha=0.6)