def plot_intersection_matrix(elements: List[CMapMatrixElement], index, save): # fig = graph.plot_matrix_cmap_plain( # elements, nvalves, nrows, "Valve Sequence", "sample x" + str(rowskip), "valves", xlabels, ylabels) fig = graph.plot_matrix_cmap_plain(elements, nvalves, nrows, "", "", "valves", xlabels, ylabels, None) if save: graph.save_figure(fig, "./figs/valve_sequence_" + str(index))
nrowskip = 0 nrows += 1 for e in elements_buffer: elements.append(e) except: pass # print(elements) # quit() xlabels = [("v" + str(i + 1)) for i in range(nvalves)] ylabels = [(str(int(i * rowskip / 100))) for i in range(nrows)] # xlabels = [] # ylabels = [] # intersection_matrix = np.random.randint(0, 10, size=(max_val, max_val)) intersection_matrix = np.zeros((nvalves, nrows)) # print(intersection_matrix) for e in elements: intersection_matrix[e.i][e.j] = e.val print(intersection_matrix) fig = graph.plot_matrix_cmap_plain(elements, nvalves, nrows, "Valve Sequence", "sample x" + str(rowskip), "valves", xlabels, ylabels, None, None) graph.save_figure(fig, "./figs/valve_sequence")
def plot_intersection_matrix(elements, index, nrows, ncols, save, scale): fig = graph.plot_matrix_cmap_plain( elements, nrows, ncols, "Sequence evaluation", "sample bins", "", xlabels, ylabels, scale, (16,7)) if save: graph.save_figure(fig, "./figs/valve_sequence_random_prediction")
def plot_intersection_matrix2(elements, index, nrows, ncols, save, scale): fig = graph.plot_matrix_cmap_plain(elements, nrows, ncols, "", "", "", xlabels, ylabels, scale, None) if save: graph.save_figure(fig, "./figs/valve_sequence_" + str(index))