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))