diff --git a/analysis_pmf_weights.py b/analysis_pmf_weights.py
index cbcb0bdd52535fc80d81c1946b153c44ce783873..807e94f05084eb6da22b723f56f21e02366982a1 100644
--- a/analysis_pmf_weights.py
+++ b/analysis_pmf_weights.py
@@ -7,15 +7,47 @@ from mpl_toolkits import mplot3d
from matplotlib.patches import ConnectionPatch
+show_weight_augmentations = True
+
+all_weight_trajectories = pickle.load(open("multi_chain_saves/all_weight_trajectories.p", 'rb'))
+first_and_last_pmf = np.array(pickle.load(open("multi_chain_saves/first_and_last_pmf.p", 'rb')))
+all_sudden_changes = pickle.load(open("multi_chain_saves/all_sudden_changes.p", 'rb'))
+all_sudden_transition_changes = pickle.load(open("multi_chain_saves/all_sudden_transition_changes.p", 'rb'))
+
+if show_weight_augmentations:
+ aug_all_sudden_changes = pickle.load(open("multi_chain_saves/aug_all_sudden_changes.p", 'rb'))
+ aug_all_sudden_transition_changes = pickle.load(open("multi_chain_saves/aug_all_sudden_transition_changes.p", 'rb'))
+
performance_points = np.array([-1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0])
reduced_points = np.array([1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1], dtype=bool)
weight_colours = ['blue', 'red', 'green', 'goldenrod', 'darkorange']
-weight_colours_aug = ['blue', 'red', 'purple', 'green', 'goldenrod', 'darkorange']
+weight_colours_aug = ['blue', 'red', 'green', 'goldenrod', 'darkorange', 'purple']
ylabels = ["Cont left", "Cont right", "Persevere", "Bias left", "Bias right"]
-ylabels_aug = ["Cont left", "Cont right", "Cont diff", "Persevere", "Bias left", "Bias right"]
+ylabels_aug = ["Cont left", "Cont right", "Persevere", "Bias left", "Bias right", "PMF span"]
folder = "./reward_analysis/"
+local_ylabels = ylabels_aug if show_weight_augmentations else ylabels
+local_weight_colours = weight_colours_aug if show_weight_augmentations else weight_colours
+
+n_weights = all_weight_trajectories[0][0].shape[0]
+n_types = 3
+
+
+def create_nested_list(list_of_ns):
+ """
+ Create a complex nested list, according to the specifications of list_of_ns.
+ E.g. [3, 2, 4] will return a list with 3 sublists, each containing 2 sublists again, each containing 4 sublists yet again.
+ """
+ ret = []
+ if len(list_of_ns) == 0:
+ return ret
+
+ for _ in range(list_of_ns[0]):
+ ret.append(create_nested_list(list_of_ns=list_of_ns[1:]))
+
+ return ret
+
def pmf_to_perf(pmf):
# determine performance of a pmf, but only on the omnipresent strongest contrasts
@@ -41,331 +73,216 @@ def pmf_type_rew(weights):
return 2
-if True:
- all_weight_trajectories = pickle.load(open("multi_chain_saves/all_weight_trajectories.p", 'rb'))
- first_and_last_pmf = np.array(pickle.load(open("multi_chain_saves/first_and_last_pmf.p", 'rb')))
- all_sudden_changes = pickle.load(open("multi_chain_saves/all_sudden_changes.p", 'rb'))
-
- n_weights = all_weight_trajectories[0][0].shape[0]
-
- all_sudden_transition_changes = pickle.load(open("multi_chain_saves/all_sudden_transition_changes.p", 'rb'))
+def plot_traces_and_collate_data(data, augmented_data=None, title=""):
+ """Plot all the individual weight change traces, ann collect the data we need for the other plots"""
+ show_weight_augmentations = augmented_data is not None
+ sudden_changes = len(data) == n_types - 1
+ average = np.zeros((n_weights + 1 + show_weight_augmentations, n_types - sudden_changes, 2))
+ counter = np.zeros((n_weights + 1 + show_weight_augmentations, n_types - sudden_changes))
+ all_datapoints = create_nested_list([n_weights + 1 + show_weight_augmentations, n_types - sudden_changes, 2])
- average = np.zeros((n_weights + 1, 3, 2))
- counter = np.zeros((n_weights + 1, 3))
- all_datapoints = [[[[], []], [[], []], [[], []]], [[[], []], [[], []], [[], []]], [[[], []], [[], []], [[], []]], [[[], []], [[], []], [[], []]], [[[], []], [[], []], [[], []]]]
- f, axs = plt.subplots(n_weights + 1, 3, figsize=(12, 9)) # We add another weight slot to split the bias
- for state_type, weight_gaps in enumerate(all_sudden_transition_changes):
+ f, axs = plt.subplots(n_weights + 1 + show_weight_augmentations, n_types - sudden_changes, figsize=(4 * (3 - sudden_changes), 9)) # We add another weight slot to split the bias
+ for state_type, weight_gaps in enumerate(data):
for gap in weight_gaps:
for i in range(n_weights):
if i == n_weights - 1:
- axs[i + (gap[1][i] > 0), state_type + 1].plot([0, 1], [gap[0][i], gap[-1][i]], marker="o") # plot how the weight evolves from first to last appearance
- average[i + (gap[1][i] > 0), state_type + 1] += np.array([gap[0][i], gap[-1][i]])
- counter[i + (gap[1][i] > 0), state_type + 1] += 1
- all_datapoints[i + (gap[1][i] > 0)][state_type + 1][0].append(gap[0][i])
- all_datapoints[i + (gap[1][i] > 0)][state_type + 1][1].append(gap[-1][i])
- else:
- axs[i, state_type + 1].plot([0, 1], [gap[0][i], gap[-1][i]], marker="o") # plot how the weight evolves from first to last appearance
- average[i, state_type + 1] += np.array([gap[0][i], gap[-1][i]])
- counter[i, state_type + 1] += 1
- all_datapoints[i][state_type + 1][0].append(gap[0][i])
- all_datapoints[i][state_type + 1][1].append(gap[-1][i])
-
- plt.tight_layout()
- plt.savefig("./summary_figures/weight_changes/sudden_weight_change at transitions")
- plt.show()
-
-
- f, axs = plt.subplots(1, 3, figsize=(12, 6))
- for i in range(1, 3):
- for j in range(n_weights + 1):
- axs[i].plot([0, 1], average[j, i] / counter[j, i], marker="o", color=weight_colours[j], label=ylabels[j])
- axs[i].set_ylim(-3.5, 3.5)
- axs[i].spines['top'].set_visible(False)
- axs[i].spines['right'].set_visible(False)
- axs[i].set_xticks([])
- if i == 0:
- pass
- # axs[i].set_ylabel("Weights", size=24)
- # if j < n_weights - 1:
- # axs[i].plot([0.1], [np.mean(first_and_last_pmf[:, 0, j])], marker='*', color=weight_colours[j]) # also plot weights of very first state average
- # if j == n_weights - 1:
- # mask = first_and_last_pmf[:, 0, -1] < 0
- # axs[i].plot([0.1], [np.mean(first_and_last_pmf[mask, 0, -1])], marker='*', color=weight_colours[j]) # separete biases again
- # if j == n_weights:
- # mask = first_and_last_pmf[:, 0, -1] > 0
- # axs[i].plot([0.1], [np.mean(first_and_last_pmf[mask, 0, -1])], marker='*', color=weight_colours[j])
- else:
- axs[i].yaxis.set_ticklabels([])
- if i == 2:
- pass
- # if j < n_weights - 1:
- # axs[i].plot([0.9], [np.mean(first_and_last_pmf[:, 1, j])], marker='*', color=weight_colours[j]) # also plot weights of very last state average
- # if j == n_weights - 1:
- # mask = first_and_last_pmf[:, 1, -1] < 0
- # axs[i].plot([0.9], [np.mean(first_and_last_pmf[mask, 1, -1])], marker='*', color=weight_colours[j]) # separete biases again
- # if j == n_weights:
- # mask = first_and_last_pmf[:, 1, -1] > 0
- # axs[i].plot([0.9], [np.mean(first_and_last_pmf[mask, 1, -1])], marker='*', color=weight_colours[j])
- if j == 0:
- axs[i].set_title("Type {}".format(i + 1), size=26)
- if j == n_weights and i == 1:
- axs[i].set_xlabel("Lifetime weight change", size=24)
- axs[0].legend(frameon=False, fontsize=14)
- plt.tight_layout()
- plt.savefig("./summary_figures/weight_changes/compact sudden_weight_changes at transitions")
- plt.close()
-
- average = np.zeros((n_weights + 1, 3, 2))
- counter = np.zeros((n_weights + 1, 3))
- all_datapoints = [[[[], []], [[], []], [[], []]], [[[], []], [[], []], [[], []]], [[[], []], [[], []], [[], []]], [[[], []], [[], []], [[], []]], [[[], []], [[], []], [[], []]]]
- f, axs = plt.subplots(n_weights + 1, 3, figsize=(12, 9)) # I added a third pseudo weight for distance between the two sensitivities, and do another extra to split the bias
- for state_type, weight_gaps in enumerate(all_sudden_changes):
-
- for gap in weight_gaps:
- for i in range(n_weights):
- if i == n_weights - 1:
- axs[i + (gap[1][i] > 0), state_type].plot([0, 1], [gap[0][i], gap[-1][i]], marker="o") # plot how the weight evolves from first to last appearance
+ axs[i + (gap[1][i] > 0), state_type].plot([0, 1], [gap[0][i], gap[-1][i]], marker="o") # plot how the weight of the new state differs from the previous closest weight
average[i + (gap[1][i] > 0), state_type] += np.array([gap[0][i], gap[-1][i]])
counter[i + (gap[1][i] > 0), state_type] += 1
all_datapoints[i + (gap[1][i] > 0)][state_type][0].append(gap[0][i])
all_datapoints[i + (gap[1][i] > 0)][state_type][1].append(gap[-1][i])
else:
- axs[i, state_type].plot([0, 1], [gap[0][i], gap[-1][i]], marker="o") # plot how the weight evolves from first to last appearance
+ axs[i, state_type].plot([0, 1], [gap[0][i], gap[-1][i]], marker="o") # plot how the weight of the new state differs from the previous closest weight
average[i, state_type] += np.array([gap[0][i], gap[-1][i]])
counter[i, state_type] += 1
all_datapoints[i][state_type][0].append(gap[0][i])
all_datapoints[i][state_type][1].append(gap[-1][i])
+ if show_weight_augmentations:
+ for state_type, weight_gaps in enumerate(augmented_data):
+ for gap in weight_gaps:
+ axs[n_weights + 1, state_type].plot([0, 1], [gap[0], gap[-1]], marker="o") # plot how the weight of the new state differs from the previous closest weight
+ average[n_weights + 1, state_type] += np.array([gap[0], gap[-1]])
+ counter[n_weights + 1, state_type] += 1
+ all_datapoints[n_weights + 1][state_type][0].append(gap[0])
+ all_datapoints[n_weights + 1][state_type][1].append(gap[-1])
+
plt.tight_layout()
- plt.savefig("./summary_figures/weight_changes/sudden_weight_change")
+ plt.savefig("./summary_figures/weight_changes/" + title + " augmented" * show_weight_augmentations)
plt.close()
- f, axs = plt.subplots(1, 3, figsize=(12, 6))
- for i in range(3):
- for j in range(n_weights + 1):
- axs[i].plot([0, 1], average[j, i] / counter[j, i], marker="o", color=weight_colours[j], label=ylabels[j])
+ return average, counter, all_datapoints
+
+
+def plot_compact(average, counter, title, show_first_and_last=False, show_weight_augmentations=False):
+ """Plot the means of the weight change traces, split by the different weight types, possibly with augmentations"""
+ sudden_changes = average.shape[1] == n_types - 1
+ f, axs = plt.subplots(1, n_types - sudden_changes, figsize=(4 * (3 - sudden_changes), 6))
+ for i in range(n_types - sudden_changes):
+ for j in range(n_weights + 1 + show_weight_augmentations):
+ axs[i].plot([0, 1], average[j, i] / counter[j, i], marker="o", color=local_weight_colours[j], label=local_ylabels[j])
axs[i].set_ylim(-3.5, 3.5)
axs[i].spines['top'].set_visible(False)
axs[i].spines['right'].set_visible(False)
axs[i].set_xticks([])
- if i == 0:
- pass
- # axs[i].set_ylabel("Weights", size=24)
- # if j < n_weights - 1:
- # axs[i].plot([0.1], [np.mean(first_and_last_pmf[:, 0, j])], marker='*', color=weight_colours[j]) # also plot weights of very first state average
- # if j == n_weights - 1:
- # mask = first_and_last_pmf[:, 0, -1] < 0
- # axs[i].plot([0.1], [np.mean(first_and_last_pmf[mask, 0, -1])], marker='*', color=weight_colours[j]) # separete biases again
- # if j == n_weights:
- # mask = first_and_last_pmf[:, 0, -1] > 0
- # axs[i].plot([0.1], [np.mean(first_and_last_pmf[mask, 0, -1])], marker='*', color=weight_colours[j])
+ if i == 0 and show_first_and_last:
+ axs[i].set_ylabel("Weights", size=24)
+ if j < n_weights - 1:
+ axs[i].plot([0.1], [np.mean(first_and_last_pmf[:, 0, j])], marker='*', color=local_weight_colours[j]) # also plot weights of very first state average
+ if j == n_weights - 1:
+ mask = first_and_last_pmf[:, 0, -1] < 0
+ axs[i].plot([0.1], [np.mean(first_and_last_pmf[mask, 0, -1])], marker='*', color=local_weight_colours[j]) # separete biases again
+ if j == n_weights:
+ mask = first_and_last_pmf[:, 0, -1] > 0
+ axs[i].plot([0.1], [np.mean(first_and_last_pmf[mask, 0, -1])], marker='*', color=local_weight_colours[j])
else:
axs[i].yaxis.set_ticklabels([])
- if i == 2:
- pass
- # if j < n_weights - 1:
- # axs[i].plot([0.9], [np.mean(first_and_last_pmf[:, 1, j])], marker='*', color=weight_colours[j]) # also plot weights of very last state average
- # if j == n_weights - 1:
- # mask = first_and_last_pmf[:, 1, -1] < 0
- # axs[i].plot([0.9], [np.mean(first_and_last_pmf[mask, 1, -1])], marker='*', color=weight_colours[j]) # separete biases again
- # if j == n_weights:
- # mask = first_and_last_pmf[:, 1, -1] > 0
- # axs[i].plot([0.9], [np.mean(first_and_last_pmf[mask, 1, -1])], marker='*', color=weight_colours[j])
+ if i == 2 and show_first_and_last:
+ if j < n_weights - 1:
+ axs[i].plot([0.9], [np.mean(first_and_last_pmf[:, 1, j])], marker='*', color=local_weight_colours[j]) # also plot weights of very last state average
+ if j == n_weights - 1:
+ mask = first_and_last_pmf[:, 1, -1] < 0
+ axs[i].plot([0.9], [np.mean(first_and_last_pmf[mask, 1, -1])], marker='*', color=local_weight_colours[j]) # separete biases again
+ if j == n_weights:
+ mask = first_and_last_pmf[:, 1, -1] > 0
+ axs[i].plot([0.9], [np.mean(first_and_last_pmf[mask, 1, -1])], marker='*', color=local_weight_colours[j])
if j == 0:
- axs[i].set_title("Type {}".format(i + 1), size=26)
+ axs[i].set_title("Type {}".format(i + 1 + sudden_changes), size=26)
if j == n_weights and i == 1:
axs[i].set_xlabel("Lifetime weight change", size=24)
axs[0].legend(frameon=False, fontsize=14)
plt.tight_layout()
- plt.savefig("./summary_figures/weight_changes/compact sudden_weight_changes")
+ plt.savefig("./summary_figures/weight_changes/" + title + " augmented" * show_weight_augmentations)
plt.close()
- bin_sets = [np.linspace(-6.5, 6.5, 30), np.linspace(-1, 2, 30), np.linspace(-3.5, 3.5, 30)]
- if True:
- x_lim_used_full, x_lim_used_half = 56, 28
- f, axs = plt.subplots(n_weights + 1, 3 * 2, figsize=(12, 9))
- for i in range(3):
- for j in range(n_weights + 1):
- # axs[j, i].plot([0, 1], average[j, i] / counter[j, i], marker="o")
- if j < 2:
- bins = bin_sets[0]
- elif j == 2:
- bins = bin_sets[1]
- else:
- bins = bin_sets[2]
- axs[j, i * 2].hist(all_datapoints[j][i][0], orientation='horizontal', bins=bins, color='grey', alpha=0.5)
+
+def plot_histogram_diffs(all_datapoints, x_lim_used_normal, x_lim_used_bias, bin_sets, title, x_lim_used_augment=0, show_deltas=True, show_first_and_last=False, show_weight_augmentations=False):
+ """Plot histograms over the weights, and the mean changes connecting them.
+ Might have to mess quite a bit with the y-axis"""
+ sudden_changes = len(all_datapoints[0]) == 2
+ f, axs = plt.subplots(n_weights + 1 + show_weight_augmentations, (n_types - sudden_changes) * 2, figsize=(4 * (3 - sudden_changes), 9))
+ for i in range(n_types - sudden_changes):
+ for j in range(n_weights + 1 + show_weight_augmentations):
+ if j < 2:
+ bins = bin_sets[0]
+ elif j == 2:
+ bins = bin_sets[1]
+ elif j in [3, 4]:
+ bins = bin_sets[2]
+ else:
+ bins = bin_sets[3]
+ axs[j, i * 2].hist(all_datapoints[j][i][0], orientation='horizontal', bins=bins, color='grey', alpha=0.5)
+ if show_deltas:
+ axs[j, i * 2 + 1].hist(np.array(all_datapoints[j][i][1]) - np.array(all_datapoints[j][i][0]), orientation='horizontal', bins=bins, color='red', alpha=0.5)
+ else:
axs[j, i * 2 + 1].hist(all_datapoints[j][i][1], orientation='horizontal', bins=bins, color='grey', alpha=0.5)
- if j < 2:
- axs[j, i * 2].set_ylim(-6.5, 6.5)
- axs[j, i * 2 + 1].set_ylim(-6.5, 6.5)
- elif j == 2:
- axs[j, i * 2].set_ylim(-1, 2)
- axs[j, i * 2 + 1].set_ylim(-1, 2)
- else:
- axs[j, i * 2].set_ylim(-3.5, 3.5)
- axs[j, i * 2 + 1].set_ylim(-3.5, 3.5)
- axs[j, i * 2].spines['top'].set_visible(False)
- axs[j, i * 2].spines['right'].set_visible(False)
- axs[j, i * 2].set_xticks([])
- axs[j, i * 2 + 1].spines['top'].set_visible(False)
- axs[j, i * 2 + 1].spines['right'].set_visible(False)
- axs[j, i * 2 + 1].set_xticks([])
-
- axs[j, i * 2].annotate("Var {:.2f}".format(np.var(all_datapoints[j][i][0])), xy=(0.65, 0.8), xycoords='axes fraction')
+ axs[j, i * 2].set_ylim(bins[0], bins[-1])
+ axs[j, i * 2 + 1].set_ylim(bins[0], bins[-1])
+
+ axs[j, i * 2].spines['top'].set_visible(False)
+ axs[j, i * 2].spines['right'].set_visible(False)
+ axs[j, i * 2].set_xticks([])
+ axs[j, i * 2 + 1].spines['top'].set_visible(False)
+ axs[j, i * 2 + 1].spines['right'].set_visible(False)
+ axs[j, i * 2 + 1].set_xticks([])
+
+ axs[j, i * 2].annotate("Var {:.2f}".format(np.var(all_datapoints[j][i][0])), xy=(0.65, 0.8), xycoords='axes fraction')
+ if show_deltas:
+ axs[j, i * 2 + 1].annotate("Var {:.2f}".format(np.var(np.array(all_datapoints[j][i][1]) - np.array(all_datapoints[j][i][0]))), xy=(0.65, 0.8), xycoords='axes fraction')
+ else:
axs[j, i * 2 + 1].annotate("Var {:.2f}".format(np.var(all_datapoints[j][i][1])), xy=(0.65, 0.8), xycoords='axes fraction')
+ if j < n_weights - 1:
+ assert x_lim_used_normal > max(axs[j, i * 2].set_xlim()[0], axs[j, i * 2 + 1].set_xlim()[1]), "Hists are cut off ({} vs {})".format(x_lim_used_normal, max(axs[j, i * 2].set_xlim()[0], axs[j, i * 2 + 1].set_xlim()[1]))
+ axs[j, i * 2].set_xlim(0, x_lim_used_normal)
+ axs[j, i * 2 + 1].set_xlim(0, x_lim_used_normal)
+ means = average[j, i] / counter[j, i]
+ con = ConnectionPatch(xyA=(x_lim_used_normal / 12, means[0]), xyB=(0, means[1]), coordsA="data", coordsB="data",
+ axesA=axs[j, i * 2], axesB=axs[j, i * 2 + 1], color="blue")
+ axs[j, i * 2 + 1].add_artist(con)
+ elif j < n_weights + 1:
+ assert x_lim_used_bias > max(axs[j, i * 2].set_xlim()[0], axs[j, i * 2 + 1].set_xlim()[1]), "Hists are cut off ({} vs {})".format(x_lim_used_bias, max(axs[j, i * 2].set_xlim()[0], axs[j, i * 2 + 1].set_xlim()[1]))
+ axs[j, i * 2].set_xlim(0, x_lim_used_bias)
+ axs[j, i * 2 + 1].set_xlim(0, x_lim_used_bias)
+ means = average[j, i] / counter[j, i]
+ con = ConnectionPatch(xyA=(x_lim_used_bias / 12, means[0]), xyB=(0, means[1]), coordsA="data", coordsB="data",
+ axesA=axs[j, i * 2], axesB=axs[j, i * 2 + 1], color="blue")
+ axs[j, i * 2 + 1].add_artist(con)
+ else:
+ assert x_lim_used_augment > max(axs[j, i * 2].set_xlim()[0], axs[j, i * 2 + 1].set_xlim()[1]), "Hists are cut off ({} vs {})".format(x_lim_used_augment, max(axs[j, i * 2].set_xlim()[0], axs[j, i * 2 + 1].set_xlim()[1]))
+ axs[j, i * 2].set_xlim(0, x_lim_used_augment)
+ axs[j, i * 2 + 1].set_xlim(0, x_lim_used_augment)
+ means = average[j, i] / counter[j, i]
+ con = ConnectionPatch(xyA=(x_lim_used_augment / 12, means[0]), xyB=(0, means[1]), coordsA="data", coordsB="data",
+ axesA=axs[j, i * 2], axesB=axs[j, i * 2 + 1], color="blue")
+ axs[j, i * 2 + 1].add_artist(con)
+
+ if i == 0:
+ axs[j, i].set_ylabel(local_ylabels[j])
+ axs[j, i * 2 + 1].yaxis.set_ticklabels([])
+ if show_first_and_last:
+ if j < n_weights - 1:
+ axs[j, i * 2].plot([x_lim_used_normal / 8], [np.mean(first_and_last_pmf[:, 0, j])], marker='*', c='red') # also plot weights of very first state average
+ if j == n_weights - 1:
+ mask = first_and_last_pmf[:, 0, -1] < 0
+ axs[j, i * 2].plot([x_lim_used_bias / 8], [np.mean(first_and_last_pmf[mask, 0, -1])], marker='*', c='red') # separete biases again
+ if j == n_weights:
+ mask = first_and_last_pmf[:, 0, -1] > 0
+ axs[j, i * 2].plot([x_lim_used_augment / 8], [np.mean(first_and_last_pmf[mask, 0, -1])], marker='*', c='red')
+ else:
+ axs[j, i * 2].yaxis.set_ticklabels([])
+ axs[j, i * 2 + 1].yaxis.set_ticklabels([])
+ if i == n_types - 1 and show_first_and_last:
if j < n_weights - 1:
- assert x_lim_used_full > max(axs[j, i * 2].set_xlim()[0], axs[j, i * 2 + 1].set_xlim()[1]), "Hists are cut off ({} vs {})".format(x_lim_used_full, max(axs[j, i * 2].set_xlim()[0], axs[j, i * 2 + 1].set_xlim()[1]))
- axs[j, i * 2].set_xlim(0, x_lim_used_full)
- axs[j, i * 2 + 1].set_xlim(0, x_lim_used_full)
- means = average[j, i] / counter[j, i]
- con = ConnectionPatch(xyA=(x_lim_used_full / 8, means[0]), xyB=(x_lim_used_full / 8, means[1]), coordsA="data", coordsB="data",
- axesA=axs[j, i * 2], axesB=axs[j, i * 2 + 1], color="blue")
- axs[j, i * 2 + 1].add_artist(con)
+ axs[j, i * 2 + 1].plot([x_lim_used_normal / 8], [np.mean(first_and_last_pmf[:, 1, j])], marker='*', c='red') # also plot weights of very last state average
+ if j == n_weights - 1:
+ mask = first_and_last_pmf[:, 1, -1] < 0
+ axs[j, i * 2 + 1].plot([x_lim_used_bias / 8], [np.mean(first_and_last_pmf[mask, 1, -1])], marker='*', c='red') # separete biases again
+ if j == n_weights:
+ mask = first_and_last_pmf[:, 1, -1] > 0
+ axs[j, i * 2 + 1].plot([x_lim_used_augment / 8], [np.mean(first_and_last_pmf[mask, 1, -1])], marker='*', c='red')
+ if j == 0:
+ axs[j, i * 2].set_title("Type {}".format(i + 1), loc='right')
+ if j == n_weights + show_weight_augmentations and i == 0:
+ axs[j, 0].set_xlabel("Initial distribution")
+ if show_deltas:
+ axs[j, 1].set_xlabel("Weight change")
else:
- assert x_lim_used_half > max(axs[j, i * 2].set_xlim()[0], axs[j, i * 2 + 1].set_xlim()[1]), "Hists are cut off ({} vs {})".format(x_lim_used_half, max(axs[j, i * 2].set_xlim()[0], axs[j, i * 2 + 1].set_xlim()[1]))
- axs[j, i * 2].set_xlim(0, x_lim_used_half)
- axs[j, i * 2 + 1].set_xlim(0, x_lim_used_half)
- means = average[j, i] / counter[j, i]
- con = ConnectionPatch(xyA=(x_lim_used_half / 8, means[0]), xyB=(x_lim_used_half / 8, means[1]), coordsA="data", coordsB="data",
- axesA=axs[j, i * 2], axesB=axs[j, i * 2 + 1], color="blue")
- axs[j, i * 2 + 1].add_artist(con)
+ axs[j, 1].set_xlabel("Changed distribution")
- if i == 0:
- axs[j, i].set_ylabel(ylabels[j])
- axs[j, i * 2 + 1].yaxis.set_ticklabels([])
- # if j < n_weights - 1:
- # axs[j, i * 2].plot([x_lim_used_full / 8], [np.mean(first_and_last_pmf[:, 0, j])], marker='*', c='red') # also plot weights of very first state average
- # if j == n_weights - 1:
- # mask = first_and_last_pmf[:, 0, -1] < 0
- # axs[j, i * 2].plot([x_lim_used_half / 8], [np.mean(first_and_last_pmf[mask, 0, -1])], marker='*', c='red') # separete biases again
- # if j == n_weights:
- # mask = first_and_last_pmf[:, 0, -1] > 0
- # axs[j, i * 2].plot([x_lim_used_half / 8], [np.mean(first_and_last_pmf[mask, 0, -1])], marker='*', c='red')
- else:
- axs[j, i * 2].yaxis.set_ticklabels([])
- axs[j, i * 2 + 1].yaxis.set_ticklabels([])
- if i == 2:
- pass
- # if j < n_weights - 1:
- # axs[j, i * 2 + 1].plot([x_lim_used_full / 8], [np.mean(first_and_last_pmf[:, 1, j])], marker='*', c='red') # also plot weights of very last state average
- # if j == n_weights - 1:
- # mask = first_and_last_pmf[:, 1, -1] < 0
- # axs[j, i * 2 + 1].plot([x_lim_used_half / 8], [np.mean(first_and_last_pmf[mask, 1, -1])], marker='*', c='red') # separete biases again
- # if j == n_weights:
- # mask = first_and_last_pmf[:, 1, -1] > 0
- # axs[j, i * 2 + 1].plot([x_lim_used_half / 8], [np.mean(first_and_last_pmf[mask, 1, -1])], marker='*', c='red')
- if j == 0:
- axs[j, i * 2].set_title("Type {}".format(i + 1), loc='right')
- if j == n_weights:
- axs[j, i * 2].set_xlabel("Lifetime weight change", loc='right')
+ plt.savefig("./summary_figures/weight_changes/" + title)
+ plt.close()
- plt.savefig("./summary_figures/weight_changes/weight changes sudden hists")
- plt.close()
- x_lim_used_full, x_lim_used_half = 120, 60
- bin_sets = [np.linspace(-6.5, 6.5, 30), np.linspace(-1, 2, 30), np.linspace(-3.5, 3.5, 30)]
- f, axs = plt.subplots(n_weights + 1, 3 * 2, figsize=(12, 9))
- for i in range(3):
- for j in range(n_weights + 1):
- # axs[j, i].plot([0, 1], average[j, i] / counter[j, i], marker="o")
- if j < 2:
- bins = bin_sets[0]
- elif j == 2:
- bins = bin_sets[1]
- else:
- bins = bin_sets[2]
- axs[j, i * 2].hist(all_datapoints[j][i][0], orientation='horizontal', bins=bins, color='grey', alpha=0.5)
- axs[j, i * 2 + 1].hist(np.array(all_datapoints[j][i][1]) - np.array(all_datapoints[j][i][0]), orientation='horizontal', bins=bins, color='grey', alpha=0.5)
+if True:
- if j < 2:
- axs[j, i * 2].set_ylim(-6.5, 6.5)
- axs[j, i * 2 + 1].set_ylim(-6.5, 6.5)
- elif j == 2:
- axs[j, i * 2].set_ylim(-1, 2)
- axs[j, i * 2 + 1].set_ylim(-1, 2)
- else:
- axs[j, i * 2].set_ylim(-3.5, 3.5)
- axs[j, i * 2 + 1].set_ylim(-3.5, 3.5)
- axs[j, i * 2].spines['top'].set_visible(False)
- axs[j, i * 2].spines['right'].set_visible(False)
- axs[j, i * 2].set_xticks([])
- axs[j, i * 2 + 1].spines['top'].set_visible(False)
- axs[j, i * 2 + 1].spines['right'].set_visible(False)
- axs[j, i * 2 + 1].set_xticks([])
-
- axs[j, i * 2].annotate("Var {:.2f}".format(np.var(all_datapoints[j][i][0])), xy=(0.65, 0.8), xycoords='axes fraction')
- axs[j, i * 2 + 1].annotate("Var {:.2f}".format(np.var(np.array(all_datapoints[j][i][1]) - np.array(all_datapoints[j][i][0]))), xy=(0.65, 0.8), xycoords='axes fraction')
+ bin_sets = [np.linspace(-6.5, 6.5, 30), np.linspace(-1, 2, 30), np.linspace(-3.5, 3.5, 30), np.linspace(-0.2, 1, 30)] # TODO: make show_augmentation robuse
- if j < n_weights - 1:
- assert x_lim_used_full > max(axs[j, i * 2].set_xlim()[0], axs[j, i * 2 + 1].set_xlim()[1]), "Hists are cut off ({} vs {})".format(x_lim_used_full, max(axs[j, i * 2].set_xlim()[0], axs[j, i * 2 + 1].set_xlim()[1]))
- axs[j, i * 2].set_xlim(0, x_lim_used_full)
- axs[j, i * 2 + 1].set_xlim(0, x_lim_used_full)
- means = average[j, i] / counter[j, i]
- con = ConnectionPatch(xyA=(x_lim_used_full / 8, means[0]), xyB=(x_lim_used_full / 8, means[1]), coordsA="data", coordsB="data",
- axesA=axs[j, i * 2], axesB=axs[j, i * 2 + 1], color="blue")
- axs[j, i * 2 + 1].add_artist(con)
- else:
- assert x_lim_used_half > max(axs[j, i * 2].set_xlim()[0], axs[j, i * 2 + 1].set_xlim()[1]), "Hists are cut off ({} vs {})".format(x_lim_used_half, max(axs[j, i * 2].set_xlim()[0], axs[j, i * 2 + 1].set_xlim()[1]))
- axs[j, i * 2].set_xlim(0, x_lim_used_half)
- axs[j, i * 2 + 1].set_xlim(0, x_lim_used_half)
- means = average[j, i] / counter[j, i]
- con = ConnectionPatch(xyA=(x_lim_used_half / 8, means[0]), xyB=(x_lim_used_half / 8, means[1]), coordsA="data", coordsB="data",
- axesA=axs[j, i * 2], axesB=axs[j, i * 2 + 1], color="blue")
- axs[j, i * 2 + 1].add_artist(con)
+ average, counter, all_datapoints = plot_traces_and_collate_data(data=all_sudden_transition_changes, augmented_data=aug_all_sudden_transition_changes, title="sudden_weight_change at transitions")
- if i == 0:
- axs[j, i].set_ylabel(ylabels[j])
- axs[j, i * 2 + 1].yaxis.set_ticklabels([])
- # if j < n_weights - 1:
- # axs[j, i * 2].plot([x_lim_used_full / 8], [np.mean(first_and_last_pmf[:, 0, j])], marker='*', c='red') # also plot weights of very first state average
- # if j == n_weights - 1:
- # mask = first_and_last_pmf[:, 0, -1] < 0
- # axs[j, i * 2].plot([x_lim_used_half / 8], [np.mean(first_and_last_pmf[mask, 0, -1])], marker='*', c='red') # separete biases again
- # if j == n_weights:
- # mask = first_and_last_pmf[:, 0, -1] > 0
- # axs[j, i * 2].plot([x_lim_used_half / 8], [np.mean(first_and_last_pmf[mask, 0, -1])], marker='*', c='red')
- else:
- axs[j, i * 2].yaxis.set_ticklabels([])
- axs[j, i * 2 + 1].yaxis.set_ticklabels([])
- if i == 2:
- pass
- # if j < n_weights - 1:
- # axs[j, i * 2 + 1].plot([x_lim_used_full / 8], [np.mean(first_and_last_pmf[:, 1, j])], marker='*', c='red') # also plot weights of very last state average
- # if j == n_weights - 1:
- # mask = first_and_last_pmf[:, 1, -1] < 0
- # axs[j, i * 2 + 1].plot([x_lim_used_half / 8], [np.mean(first_and_last_pmf[mask, 1, -1])], marker='*', c='red') # separete biases again
- # if j == n_weights:
- # mask = first_and_last_pmf[:, 1, -1] > 0
- # axs[j, i * 2 + 1].plot([x_lim_used_half / 8], [np.mean(first_and_last_pmf[mask, 1, -1])], marker='*', c='red')
- if j == 0:
- axs[j, i * 2].set_title("Type {}".format(i + 1), loc='right')
- if j == n_weights:
- axs[j, i * 2].set_xlabel("Lifetime weight change", loc='right')
+ plot_compact(average, counter, title="compact sudden_weight_change at transitions", show_weight_augmentations=show_weight_augmentations)
- plt.savefig("./summary_figures/weight_changes/weight changes sudden delta hists")
- plt.close()
+ plot_histogram_diffs(all_datapoints, x_lim_used_normal=22, x_lim_used_bias=11, x_lim_used_augment=40, bin_sets=bin_sets, title="weight changes sudden at transitions hists", show_deltas=False, show_weight_augmentations=show_weight_augmentations)
+ plot_histogram_diffs(all_datapoints, x_lim_used_normal=22, x_lim_used_bias=11, x_lim_used_augment=40, bin_sets=bin_sets, title="weight changes sudden at transitions delta hists", show_deltas=True, show_weight_augmentations=show_weight_augmentations)
- # for weight_traj in all_weight_trajectories:
- # if len(weight_traj) == 1:
- # continue
- # state_type = pmf_type(weights_to_pmf(weight_traj[0]))
+ average, counter, all_datapoints = plot_traces_and_collate_data(data=all_sudden_changes, augmented_data=aug_all_sudden_changes, title="sudden_weight_change")
- # if state_type == 2 and weight_traj[0][0] > 0.8:
- # plt.plot(weights_to_pmf(weight_traj[0]))
- # plt.ylim(0, 1)
- # plt.show()
+ plot_compact(average, counter, title="compact sudden_weight_change", show_weight_augmentations=show_weight_augmentations)
- dur_lims = [(52, 25), (46, 23), (38, 19), (35, 17), (30, 15), (25, 12), (20, 10), (18, 9)]
+ plot_histogram_diffs(all_datapoints, x_lim_used_normal=56, x_lim_used_bias=28, x_lim_used_augment=140, bin_sets=bin_sets, title="weight changes sudden hists", show_deltas=False, show_weight_augmentations=show_weight_augmentations)
+ plot_histogram_diffs(all_datapoints, x_lim_used_normal=120, x_lim_used_bias=60, x_lim_used_augment=180, bin_sets=bin_sets, title="weight changes sudden delta hists", show_deltas=True, show_weight_augmentations=show_weight_augmentations)
+
+ dur_lims = [(52, 25), (46, 23), (38, 19), (35, 17), (30, 15), (25, 12), (20, 10), (18, 9)]
for min_dur_counter, min_dur in enumerate([2, 3, 4, 5, 7, 9, 11, 15]):
- f, axs = plt.subplots(n_weights + 1, 3, figsize=(12, 9)) # add another space to split bias into stating left versus starting right
- average = np.zeros((n_weights + 1, 3, 2))
- counter = np.zeros((n_weights + 1, 3))
- all_datapoints = [[[[], []], [[], []], [[], []]], [[[], []], [[], []], [[], []]], [[[], []], [[], []], [[], []]], [[[], []], [[], []], [[], []]], [[[], []], [[], []], [[], []]]]
+ average = np.zeros((n_weights + 1, n_types, 2))
+ counter = np.zeros((n_weights + 1, n_types))
+ all_datapoints = create_nested_list([n_weights + 1, n_types, 2])
+
+ f, axs = plt.subplots(n_weights + 1, n_types, figsize=(12, 9)) # add another space to split bias into stating left versus starting right
for weight_traj in all_weight_trajectories:
if len(weight_traj) < min_dur or len(weight_traj) > 15: # take out too short trajectories, and too long ones
@@ -388,61 +305,26 @@ if True:
all_datapoints[i][state_type][1].append(weight_traj[-1][i])
for i in range(3):
for j in range(n_weights + 1):
- axs[j, i].set_ylim(-9, 9)
- axs[j, i].spines['top'].set_visible(False)
- axs[j, i].spines['right'].set_visible(False)
- axs[j, i].set_xticks([])
- if i == 0:
- axs[j, i].set_ylabel(ylabels[j])
- else:
- axs[j, i].yaxis.set_ticklabels([])
- if j == 0:
- axs[j, i].set_title("Type {}".format(i + 1))
- if j == n_weights:
- axs[j, i].set_xlabel("Lifetime weight change")
-
- plt.tight_layout()
- plt.savefig("./summary_figures/weight_changes/all weight changes min dur {}".format(min_dur))
- plt.close()
-
- f, axs = plt.subplots(1, 3, figsize=(12, 6))
- for i in range(3):
- for j in range(n_weights + 1):
- axs[i].plot([0, 1], average[j, i] / counter[j, i], marker="o", color=weight_colours[j], label=ylabels[j])
- axs[i].set_ylim(-3.5, 3.5)
- axs[i].spines['top'].set_visible(False)
- axs[i].spines['right'].set_visible(False)
- axs[i].set_xticks([])
+ axs[j, i].set_ylim(-9, 9)
+ axs[j, i].spines['top'].set_visible(False)
+ axs[j, i].spines['right'].set_visible(False)
+ axs[j, i].set_xticks([])
if i == 0:
- axs[i].set_ylabel("Weights", size=24)
- if j < n_weights - 1:
- axs[i].plot([0.1], [np.mean(first_and_last_pmf[:, 0, j])], marker='*', color=weight_colours[j]) # also plot weights of very first state average
- if j == n_weights - 1:
- mask = first_and_last_pmf[:, 0, -1] < 0
- axs[i].plot([0.1], [np.mean(first_and_last_pmf[mask, 0, -1])], marker='*', color=weight_colours[j]) # separete biases again
- if j == n_weights:
- mask = first_and_last_pmf[:, 0, -1] > 0
- axs[i].plot([0.1], [np.mean(first_and_last_pmf[mask, 0, -1])], marker='*', color=weight_colours[j])
+ axs[j, i].set_ylabel(ylabels[j])
else:
- axs[i].yaxis.set_ticklabels([])
- if i == 2:
- if j < n_weights - 1:
- axs[i].plot([0.9], [np.mean(first_and_last_pmf[:, 1, j])], marker='*', color=weight_colours[j]) # also plot weights of very last state average
- if j == n_weights - 1:
- mask = first_and_last_pmf[:, 1, -1] < 0
- axs[i].plot([0.9], [np.mean(first_and_last_pmf[mask, 1, -1])], marker='*', color=weight_colours[j]) # separete biases again
- if j == n_weights:
- mask = first_and_last_pmf[:, 1, -1] > 0
- axs[i].plot([0.9], [np.mean(first_and_last_pmf[mask, 1, -1])], marker='*', color=weight_colours[j])
+ axs[j, i].yaxis.set_ticklabels([])
if j == 0:
- axs[i].set_title("Type {}".format(i + 1), size=26)
- if j == n_weights and i == 1:
- axs[i].set_xlabel("Lifetime weight change", size=24)
- axs[0].legend(frameon=False, fontsize=14)
+ axs[j, i].set_title("Type {}".format(i + 1))
+ if j == n_weights:
+ axs[j, i].set_xlabel("Lifetime weight change")
+
plt.tight_layout()
- plt.savefig("./summary_figures/weight_changes/compact changes min dur {}".format(min_dur))
+ plt.savefig("./summary_figures/weight_changes/all weight changes min dur {}".format(min_dur))
plt.close()
+ plot_compact(average, counter, title="compact changes min dur {}".format(min_dur), show_first_and_last=True)
+
+ # means all split up
f, axs = plt.subplots(n_weights + 1, 3, figsize=(12, 9))
for i in range(3):
for j in range(n_weights + 1):
@@ -482,168 +364,14 @@ if True:
plt.savefig("./summary_figures/weight_changes/weight changes min dur {}".format(min_dur))
plt.close()
- x_lim_used_full, x_lim_used_half = dur_lims[min_dur_counter]
-
- # also histograms
- f, axs = plt.subplots(n_weights + 1, 3 * 2, figsize=(12, 9))
- for i in range(3):
- for j in range(n_weights + 1):
- if j < 2:
- bins = bin_sets[0]
- elif j == 2:
- bins = bin_sets[1]
- else:
- bins = bin_sets[2]
- axs[j, i * 2].hist(all_datapoints[j][i][0], orientation='horizontal', bins=bins, color='grey', alpha=0.5)
- axs[j, i * 2 + 1].hist(all_datapoints[j][i][1], orientation='horizontal', bins=bins, color='grey', alpha=0.5)
-
- if j < 2:
- axs[j, i * 2].set_ylim(-6.5, 6.5)
- axs[j, i * 2 + 1].set_ylim(-6.5, 6.5)
- elif j == 2:
- axs[j, i * 2].set_ylim(-1, 2)
- axs[j, i * 2 + 1].set_ylim(-1, 2)
- else:
- axs[j, i * 2].set_ylim(-3.5, 3.5)
- axs[j, i * 2 + 1].set_ylim(-3.5, 3.5)
- axs[j, i * 2].spines['top'].set_visible(False)
- axs[j, i * 2].spines['right'].set_visible(False)
- axs[j, i * 2].set_xticks([])
- axs[j, i * 2 + 1].spines['top'].set_visible(False)
- axs[j, i * 2 + 1].spines['right'].set_visible(False)
- axs[j, i * 2 + 1].set_xticks([])
-
- axs[j, i * 2].annotate("Var {:.2f}".format(np.var(all_datapoints[j][i][0])), xy=(0.65, 0.8), xycoords='axes fraction')
- axs[j, i * 2 + 1].annotate("Var {:.2f}".format(np.var(all_datapoints[j][i][1])), xy=(0.65, 0.8), xycoords='axes fraction')
-
- if j < n_weights - 1:
- assert x_lim_used_full > max(axs[j, i * 2].set_xlim()[0], axs[j, i * 2 + 1].set_xlim()[1]), "Hists are cut off ({} vs {})".format(x_lim_used_full, max(axs[j, i * 2].set_xlim()[0], axs[j, i * 2 + 1].set_xlim()[1]))
- axs[j, i * 2].set_xlim(0, x_lim_used_full)
- axs[j, i * 2 + 1].set_xlim(0, x_lim_used_full)
- means = average[j, i] / counter[j, i]
- con = ConnectionPatch(xyA=(x_lim_used_full / 8, means[0]), xyB=(x_lim_used_full / 8, means[1]), coordsA="data", coordsB="data",
- axesA=axs[j, i * 2], axesB=axs[j, i * 2 + 1], color="blue")
- axs[j, i * 2 + 1].add_artist(con)
- else:
- assert x_lim_used_half > max(axs[j, i * 2].set_xlim()[0], axs[j, i * 2 + 1].set_xlim()[1]), "Hists are cut off ({} vs {})".format(x_lim_used_half, max(axs[j, i * 2].set_xlim()[0], axs[j, i * 2 + 1].set_xlim()[1]))
- axs[j, i * 2].set_xlim(0, x_lim_used_half)
- axs[j, i * 2 + 1].set_xlim(0, x_lim_used_half)
- means = average[j, i] / counter[j, i]
- con = ConnectionPatch(xyA=(x_lim_used_half / 8, means[0]), xyB=(x_lim_used_half / 8, means[1]), coordsA="data", coordsB="data",
- axesA=axs[j, i * 2], axesB=axs[j, i * 2 + 1], color="blue")
- axs[j, i * 2 + 1].add_artist(con)
-
- if i == 0:
- axs[j, i].set_ylabel(ylabels[j])
- axs[j, i * 2 + 1].yaxis.set_ticklabels([])
- if j < n_weights - 1:
- axs[j, i * 2].plot([x_lim_used_full / 8], [np.mean(first_and_last_pmf[:, 0, j])], marker='*', c='red') # also plot weights of very first state average
- if j == n_weights - 1:
- mask = first_and_last_pmf[:, 0, -1] < 0
- axs[j, i * 2].plot([x_lim_used_half / 8], [np.mean(first_and_last_pmf[mask, 0, -1])], marker='*', c='red') # separete biases again
- if j == n_weights:
- mask = first_and_last_pmf[:, 0, -1] > 0
- axs[j, i * 2].plot([x_lim_used_half / 8], [np.mean(first_and_last_pmf[mask, 0, -1])], marker='*', c='red')
- else:
- axs[j, i * 2].yaxis.set_ticklabels([])
- axs[j, i * 2 + 1].yaxis.set_ticklabels([])
- if i == 2:
- if j < n_weights - 1:
- axs[j, i * 2 + 1].plot([x_lim_used_full / 8], [np.mean(first_and_last_pmf[:, 1, j])], marker='*', c='red') # also plot weights of very last state average
- if j == n_weights - 1:
- mask = first_and_last_pmf[:, 1, -1] < 0
- axs[j, i * 2 + 1].plot([x_lim_used_half / 8], [np.mean(first_and_last_pmf[mask, 1, -1])], marker='*', c='red') # separete biases again
- if j == n_weights:
- mask = first_and_last_pmf[:, 1, -1] > 0
- axs[j, i * 2 + 1].plot([x_lim_used_half / 8], [np.mean(first_and_last_pmf[mask, 1, -1])], marker='*', c='red')
- if j == 0:
- axs[j, i * 2].set_title("Type {}".format(i + 1), loc='right')
- if j == n_weights:
- axs[j, i * 2].set_xlabel("Lifetime weight change", loc='right')
-
- plt.savefig("./summary_figures/weight_changes/weight changes min dur {} hists".format(min_dur))
- plt.close()
-
- # also histograms of deltas
- x_lim_used_full, x_lim_used_half = x_lim_used_full * 2.5, x_lim_used_half * 2.5
- f, axs = plt.subplots(n_weights + 1, 3 * 2, figsize=(12, 9))
- for i in range(3):
- for j in range(n_weights + 1):
- if j < 2:
- bins = bin_sets[0]
- elif j == 2:
- bins = bin_sets[1]
- else:
- bins = bin_sets[2]
- axs[j, i * 2].hist(all_datapoints[j][i][0], orientation='horizontal', bins=bins, color='grey', alpha=0.5)
- axs[j, i * 2 + 1].hist(np.array(all_datapoints[j][i][1]) - np.array(all_datapoints[j][i][0]), orientation='horizontal', bins=bins, color='grey', alpha=0.5)
-
- if j < 2:
- axs[j, i * 2].set_ylim(-6.5, 6.5)
- axs[j, i * 2 + 1].set_ylim(-6.5, 6.5)
- elif j == 2:
- axs[j, i * 2].set_ylim(-1, 2)
- axs[j, i * 2 + 1].set_ylim(-1, 2)
- else:
- axs[j, i * 2].set_ylim(-3.5, 3.5)
- axs[j, i * 2 + 1].set_ylim(-3.5, 3.5)
- axs[j, i * 2].spines['top'].set_visible(False)
- axs[j, i * 2].spines['right'].set_visible(False)
- axs[j, i * 2].set_xticks([])
- axs[j, i * 2 + 1].spines['top'].set_visible(False)
- axs[j, i * 2 + 1].spines['right'].set_visible(False)
- axs[j, i * 2 + 1].set_xticks([])
-
- axs[j, i * 2].annotate("Var {:.2f}".format(np.var(all_datapoints[j][i][0])), xy=(0.65, 0.8), xycoords='axes fraction')
- axs[j, i * 2 + 1].annotate("Var {:.2f}".format(np.var(np.array(all_datapoints[j][i][1]) - np.array(all_datapoints[j][i][0]))), xy=(0.65, 0.8), xycoords='axes fraction')
+ x_lim_used_normal, x_lim_used_bias = dur_lims[min_dur_counter]
+ plot_histogram_diffs(all_datapoints=all_datapoints, x_lim_used_normal=x_lim_used_normal, x_lim_used_bias=x_lim_used_bias, bin_sets=bin_sets,
+ title="weight changes min dur {} hists".format(min_dur), show_deltas=False, show_first_and_last=True)
- if j < n_weights - 1:
- assert x_lim_used_full > max(axs[j, i * 2].set_xlim()[0], axs[j, i * 2 + 1].set_xlim()[1]), "Hists are cut off ({} vs {})".format(x_lim_used_full, max(axs[j, i * 2].set_xlim()[0], axs[j, i * 2 + 1].set_xlim()[1]))
- axs[j, i * 2].set_xlim(0, x_lim_used_full)
- axs[j, i * 2 + 1].set_xlim(0, x_lim_used_full)
- means = average[j, i] / counter[j, i]
- con = ConnectionPatch(xyA=(x_lim_used_full / 8, means[0]), xyB=(x_lim_used_full / 8, means[1]), coordsA="data", coordsB="data",
- axesA=axs[j, i * 2], axesB=axs[j, i * 2 + 1], color="blue")
- axs[j, i * 2 + 1].add_artist(con)
- else:
- assert x_lim_used_half > max(axs[j, i * 2].set_xlim()[0], axs[j, i * 2 + 1].set_xlim()[1]), "Hists are cut off ({} vs {})".format(x_lim_used_half, max(axs[j, i * 2].set_xlim()[0], axs[j, i * 2 + 1].set_xlim()[1]))
- axs[j, i * 2].set_xlim(0, x_lim_used_half)
- axs[j, i * 2 + 1].set_xlim(0, x_lim_used_half)
- means = average[j, i] / counter[j, i]
- con = ConnectionPatch(xyA=(x_lim_used_half / 8, means[0]), xyB=(x_lim_used_half / 8, means[1]), coordsA="data", coordsB="data",
- axesA=axs[j, i * 2], axesB=axs[j, i * 2 + 1], color="blue")
- axs[j, i * 2 + 1].add_artist(con)
+ x_lim_used_normal, x_lim_used_bias = x_lim_used_normal * 2.5, x_lim_used_bias * 2.5
+ plot_histogram_diffs(all_datapoints=all_datapoints, x_lim_used_normal=x_lim_used_normal, x_lim_used_bias=x_lim_used_bias, bin_sets=bin_sets,
+ title="weight changes min dur {} delta hists".format(min_dur), show_deltas=True, show_first_and_last=True)
- if i == 0:
- axs[j, i].set_ylabel(ylabels[j])
- axs[j, i * 2 + 1].yaxis.set_ticklabels([])
- if j < n_weights - 1:
- axs[j, i * 2].plot([x_lim_used_full / 8], [np.mean(first_and_last_pmf[:, 0, j])], marker='*', c='red') # also plot weights of very first state average
- if j == n_weights - 1:
- mask = first_and_last_pmf[:, 0, -1] < 0
- axs[j, i * 2].plot([x_lim_used_half / 8], [np.mean(first_and_last_pmf[mask, 0, -1])], marker='*', c='red') # separete biases again
- if j == n_weights:
- mask = first_and_last_pmf[:, 0, -1] > 0
- axs[j, i * 2].plot([x_lim_used_half / 8], [np.mean(first_and_last_pmf[mask, 0, -1])], marker='*', c='red')
- else:
- axs[j, i * 2].yaxis.set_ticklabels([])
- axs[j, i * 2 + 1].yaxis.set_ticklabels([])
- if i == 2:
- if j < n_weights - 1:
- axs[j, i * 2 + 1].plot([x_lim_used_full / 8], [np.mean(first_and_last_pmf[:, 1, j])], marker='*', c='red') # also plot weights of very last state average
- if j == n_weights - 1:
- mask = first_and_last_pmf[:, 1, -1] < 0
- axs[j, i * 2 + 1].plot([x_lim_used_half / 8], [np.mean(first_and_last_pmf[mask, 1, -1])], marker='*', c='red') # separete biases again
- if j == n_weights:
- mask = first_and_last_pmf[:, 1, -1] > 0
- axs[j, i * 2 + 1].plot([x_lim_used_half / 8], [np.mean(first_and_last_pmf[mask, 1, -1])], marker='*', c='red')
- if j == 0:
- axs[j, i * 2].set_title("Type {}".format(i + 1), loc='right')
- if j == n_weights:
- axs[j, i * 2].set_xlabel("Lifetime weight change", loc='right')
-
- plt.savefig("./summary_figures/weight_changes/weight changes min dur {} delta hists".format(min_dur))
- plt.close()
quit()
# all pmf weights