Visualize emBODY data with matplotlib

embody_plot.py

+#!/usr/bin/env python
+
+"""
+Visualize emBODY data
+
+This python script is based on matlab code found from:
+https://version.aalto.fi/gitlab/eglerean/embody/tree/master/matlab
+
+Data is loaded from hardcoded experiment (exp_id)
+-> TODO: create argument parser where user determines which 
+         experiment is used or if data is loaded from all answers
+
+Requirements:
+    - python 3+
+    - matplotlib
+    - numpy
+    - scipy
+
+Run:
+python embody_plot.py
+"""
+
+import matplotlib.pyplot as plt
+import matplotlib.image as mpimg
+import numpy as np
+import scipy.ndimage as ndimage
+import sys
+import time
+import json
+import resource
+import mysql.connector as mariadb
+
+mariadb_connection = mariadb.connect(
+    user='rating', 
+    password='rating_passwd', 
+    database='rating_db'
+    )
+cursor = mariadb_connection.cursor()
+
+start = time.time()
+
+def matlab_style_gauss2D(shape=(1,1),sigma=5):
+    """2D gaussian mask - should give the same result as MATLAB's
+    fspecial('gaussian',[shape],[sigma])"""
+
+    m,n = [(ss-1.)/2. for ss in shape]
+    y,x = np.ogrid[-m:m+1,-n:n+1]
+    h = np.exp( -(x*x + y*y) / (2.*sigma*sigma) )
+    h[ h < np.finfo(h.dtype).eps*h.max() ] = 0
+    sumh = h.sum()
+    if sumh != 0:
+        h /= sumh
+    return h
+
+# Hard coded image size
+WIDTH = 207
+HEIGHT = 600
+
+# import image
+image_path = './app/static/img/dummy_600.png'
+image_path_mask = './app/static/img/dummy_600_mask.png'
+
+# Load image to a plot
+image = mpimg.imread(image_path)
+image_mask = mpimg.imread(image_path_mask)
+
+# Interpolation methods
+methods = ['none','bilinear', 'bicubic', 'gaussian']
+
+SELECT_ALL = ("SELECT coordinates from embody_answer")
+SELECT_BY_EXP_ID = 'select coordinates from embody_answer as em JOIN (SELECT idanswer_set FROM answer_set as a JOIN experiment as e ON a.experiment_idexperiment=e.idexperiment AND e.idexperiment=%s) as ida ON em.answer_set_idanswer_set=ida.idanswer_set'
+
+exp_id = 2
+cursor.execute(SELECT_BY_EXP_ID, (exp_id,))
+
+# Init coordinate arrays
+x=[]
+y=[]
+
+# Loop through all of the saved coordinates and push them to coordinates arrays
+for coordinate in cursor:
+    coordinates = json.loads(coordinate[0])
+    x.extend(coordinates['x'])
+    y.extend(coordinates['y'])
+
+
+def map_coordinates(a,b):
+    return [a,b]
+
+coordinates = list(map(map_coordinates, x,y))
+
+# Plot coordinates as points
+plt.subplot2grid((2, 2), (0, 0))
+plt.title("raw points")
+plt.plot(x,y, 'ro', alpha=0.2)
+plt.imshow(image)
+plt.grid(True)
+
+# Draw circles from coordinates (imshow don't need interpolation)
+plt.subplot2grid((2, 2), (0, 1))
+plt.title("gaussian disk around points")
+frame = np.zeros((HEIGHT,WIDTH))
+for point in coordinates:
+    frame[point[1], point[0]] = 1
+    point = ndimage.gaussian_filter(frame, sigma=5)
+    plt.imshow(point, cmap='hot', interpolation='none')
+
+plt.imshow(image_mask)
+plt.grid(True)
+
+# Draw a gaussian heatmap on the whole image
+# NOT USABLE
+x_min = min(x)
+x_max = max(x)
+y_min = min(y)
+y_max = max(y)
+extent=[x_min, x_max, y_min, y_max]
+extent_all = [0,WIDTH,0,HEIGHT]
+plt.subplot2grid((2, 2), (1, 1))
+plt.title('gaussian heatmap')
+plt.imshow(image)
+plt.imshow(coordinates, extent=extent, cmap='hot', interpolation='gaussian')
+plt.imshow(image_mask)
+
+end = time.time()
+print("Drawing image took:", end - start)
+
+mng = plt.get_current_fig_manager()
+mng.resize(*mng.window.maxsize())
+plt.show()
+