data2d_tmp = ref2d.as_numpy_array() data2d[:, :] += numpy.float64(data2d_tmp) #n = write_2d(flex.double(data2d)) from matplotlib import pyplot as plt print "Plotting data2d" plt.imshow(data2d, interpolation = "nearest") plt.show() n_times = 1 data2dsmoth_tmp = smooth_2d(flex.int(data2d), n_times).as_numpy_array() data2dsmoth = numpy.float64(data2dsmoth_tmp) n_times = 5 data2dsmoth_2t_tmp = smooth_2d(flex.int(data2d), n_times).as_numpy_array() data2dsmoth_2t = numpy.float64(data2dsmoth_2t_tmp) #n = write_2d(flex.double(data2dsmoth)) print "Plotting data2dsmoth" plt.imshow(data2dsmoth, interpolation = "nearest", cmap = pylab.gray()) plt.show()
data2d = flex.int(flex.grid(550, 950)) for x_loc in range(950): for y_loc in range(550): data2d[y_loc, x_loc] = int(np_data2d[y_loc, x_loc]) #''' # code that will become production code: # from data2d flex array that contains an image # it should return a flex array with the mask from dials.algorithms.peak_finding import smooth_2d from dials.algorithms.peak_finding import find_mask_2d n_times = 3 data2dsmoth = smooth_2d(data2d, n_times) mask2d = find_mask_2d(data2d, data2dsmoth, n_times) # end code that will become production code from matplotlib import pyplot as plt col_from = 0 col_to = 950 row_from = 0 row_to = 550 print "Plotting data2d" data2d = data2d.as_numpy_array() data2d = data2d[row_from:row_to,col_from:col_to] plt.imshow(data2d, interpolation = "nearest")
data2d = flex.int(flex.grid(550, 950)) for x_loc in range(950): for y_loc in range(550): data2d[y_loc, x_loc] = int(np_data2d[y_loc, x_loc]) #''' # code that will become production code: # from data2d flex array that contains an image # it should return a flex array with the mask from dials.algorithms.peak_finding import smooth_2d from dials.algorithms.peak_finding import find_mask_2d n_times = 3 data2dsmoth = smooth_2d(data2d, n_times) mask2d = find_mask_2d(data2d, data2dsmoth, n_times) # end code that will become production code from matplotlib import pyplot as plt col_from = 0 col_to = 950 row_from = 0 row_to = 550 print("Plotting data2d") data2d = data2d.as_numpy_array() data2d = data2d[row_from:row_to, col_from:col_to] plt.imshow(data2d, interpolation="nearest")