diff --git a/.gitignore b/.gitignore
index e0d406ba7e6131d35c436488d857b2167bbaaacf..cf46344d8c1547072eaeb155bf6c9a94fbbb5514 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,5 @@
summary_figures/
+multi_chain_saves/
old_ana_code/
figures/
dynamic_figures/
diff --git a/__pycache__/analysis_pmf.cpython-37.pyc b/__pycache__/analysis_pmf.cpython-37.pyc
index f1b5b0e7abdf15bba7ca9ffc05dffe4b341f0893..2bd712fdb5b1e22d6a5abe907020f531291133dc 100644
Binary files a/__pycache__/analysis_pmf.cpython-37.pyc and b/__pycache__/analysis_pmf.cpython-37.pyc differ
diff --git a/__pycache__/dyn_glm_chain_analysis.cpython-37.pyc b/__pycache__/dyn_glm_chain_analysis.cpython-37.pyc
index 306e8d1ace118fb56561c0e4c743f7ff9528fdc1..5f0086ead07f7514bdf27bf4a72c0cb419ac2b61 100644
Binary files a/__pycache__/dyn_glm_chain_analysis.cpython-37.pyc and b/__pycache__/dyn_glm_chain_analysis.cpython-37.pyc differ
diff --git a/__pycache__/simplex_plot.cpython-37.pyc b/__pycache__/simplex_plot.cpython-37.pyc
index d1a178aa1d78d9cd467d1b86bd96b0a477756845..bd2589fbd826e684ab1e735fba426a1302e20cf8 100644
Binary files a/__pycache__/simplex_plot.cpython-37.pyc and b/__pycache__/simplex_plot.cpython-37.pyc differ
diff --git a/analysis_pmf.py b/analysis_pmf.py
index ca0bb8d1ba07ab4d5bbbb5ef2c421f6ae5e8f95a..0eae6f603a7a5c4f73eab2c87d70e01ccc3f508b 100644
--- a/analysis_pmf.py
+++ b/analysis_pmf.py
@@ -582,14 +582,14 @@ if __name__ == "__main__":
def_points, pmf_values = pmf
type_array[j][def_points] = pmf_values[def_points]
- percentiles = np.nanpercentile(type_array, [2.5, 97.5], axis=0)
if i == 0:
x = [0, 1, 9, 10]
elif i == 4:
x = np.arange(11)
else:
x = [0, 1, 2, 8, 9, 10]
- axs[i].plot(x, np.nanmean(type_array, axis=0))
+ percentiles = np.percentile(type_array, [2.5, 97.5], axis=0)
+ axs[i].plot(x, np.mean(type_array[x], axis=0))
axs[i].fill_between(x, percentiles[1], percentiles[0], alpha=0.2)
axs[i].annotate("N=".format(len(type_save)), (0.75, 0.1))
diff --git a/analysis_pmf_weights.py b/analysis_pmf_weights.py
index 8d2f703b1f4ea7d7c02227d2aece8e898670e4df..7a4345166b44c920a22f11c51256da384964a026 100644
--- a/analysis_pmf_weights.py
+++ b/analysis_pmf_weights.py
@@ -4,11 +4,18 @@ import pickle
from scipy.stats import gaussian_kde
from analysis_pmf import pmf_type, type2color
from mpl_toolkits import mplot3d
+from matplotlib.patches import ConnectionPatch
+
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']
+ylabels = ["Cont left", "Cont right", "Persevere", "Bias left", "Bias right"]
+ylabels_aug = ["Cont left", "Cont right", "Cont diff", "Persevere", "Bias left", "Bias right"]
+
+folder = "./reward_analysis/"
-folder = "./temp_reward_analysis/"
def pmf_to_perf(pmf):
# determine performance of a pmf, but only on the omnipresent strongest contrasts
@@ -23,6 +30,7 @@ def weights_to_pmf(weights, with_bias=1):
psi = weights[0] * contrasts_R + weights[1] * contrasts_L + with_bias * weights[-1]
return 1 / (1 + np.exp(psi))
+
def pmf_type_rew(weights):
rew = pmf_to_perf(weights_to_pmf(weights))
if rew < 0.6:
@@ -32,140 +40,631 @@ def pmf_type_rew(weights):
else:
return 2
-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')))
-# for weight_traj in all_weight_trajectories:
-# if len(weight_traj) == 1:
-# continue
-# state_type = pmf_type(weights_to_pmf(weight_traj[0]))
+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'))
-# 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()
+ n_weights = all_weight_trajectories[0][0].shape[0]
-n_weights = all_weight_trajectories[0][0].shape[0]
-ylabels = ["Cont left", "Cont right", "Persevere", "Bias left", "Bias right"]
+ all_sudden_transition_changes = pickle.load(open("multi_chain_saves/all_sudden_transition_changes.p", 'rb'))
-for min_dur in [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 = [[[[], []], [[], []], [[], []]], [[[], []], [[], []], [[], []]], [[[], []], [[], []], [[], []]], [[[], []], [[], []], [[], []]], [[[], []], [[], []], [[], []]]]
- for weight_traj in all_weight_trajectories:
+ 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):
+
+ 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])
- if len(weight_traj) < min_dur:
- continue
+ plt.tight_layout()
+ plt.savefig("./summary_figures/weight_changes/sudden_weight_change at transitions")
+ plt.show()
- state_type = pmf_type(weights_to_pmf(weight_traj[0]))
- for i in range(n_weights):
- if i == n_weights - 1:
- axs[i + (weight_traj[0][i] > 0), state_type].plot([0, 1], [weight_traj[0][i], weight_traj[-1][i]], marker="o") # plot how the weight evolves from first to last appearance
- average[i + (weight_traj[0][i] > 0), state_type] += np.array([weight_traj[0][i], weight_traj[-1][i]])
- counter[i + (weight_traj[0][i] > 0), state_type] += 1
- all_datapoints[i + (weight_traj[0][i] > 0)][state_type][0].append(weight_traj[0][i])
- all_datapoints[i + (weight_traj[0][i] > 0)][state_type][1].append(weight_traj[-1][i])
+ 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, state_type].plot([0, 1], [weight_traj[0][i], weight_traj[-1][i]], marker="o") # plot how the weight evolves from first to last appearance
- average[i, state_type] += np.array([weight_traj[0][i], weight_traj[-1][i]])
- counter[i, state_type] += 1
- all_datapoints[i][state_type][0].append(weight_traj[0][i])
- all_datapoints[i][state_type][1].append(weight_traj[-1][i])
+ 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.show()
+
+ 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
+ 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
+ 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])
+
+ plt.tight_layout()
+ plt.savefig("./summary_figures/weight_changes/sudden_weight_change")
+ plt.show()
+
+ f, axs = plt.subplots(1, 3, figsize=(12, 6))
for i in range(3):
for j in range(n_weights + 1):
- axs[j, i].set_ylim(-9, 9)
+ 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")
+ plt.show()
+
+ 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)
+ 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:
+ 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/weight changes sudden hists")
+ plt.show()
+
+ 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 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')
+
+ 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:
+ 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/weight changes sudden delta hists")
+ plt.show()
+
+
+ # for weight_traj in all_weight_trajectories:
+ # if len(weight_traj) == 1:
+ # continue
+ # state_type = pmf_type(weights_to_pmf(weight_traj[0]))
+
+ # 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()
+
+ 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 = [[[[], []], [[], []], [[], []]], [[[], []], [[], []], [[], []]], [[[], []], [[], []], [[], []]], [[[], []], [[], []], [[], []]], [[[], []], [[], []], [[], []]]]
+ 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
+ continue
+
+ state_type = pmf_type(weights_to_pmf(weight_traj[0]))
+
+ for i in range(n_weights):
+ if i == n_weights - 1:
+ axs[i + (weight_traj[0][i] > 0), state_type].plot([0, 1], [weight_traj[0][i], weight_traj[-1][i]], marker="o") # plot how the weight evolves from first to last appearance
+ average[i + (weight_traj[0][i] > 0), state_type] += np.array([weight_traj[0][i], weight_traj[-1][i]])
+ counter[i + (weight_traj[0][i] > 0), state_type] += 1
+ all_datapoints[i + (weight_traj[0][i] > 0)][state_type][0].append(weight_traj[0][i])
+ all_datapoints[i + (weight_traj[0][i] > 0)][state_type][1].append(weight_traj[-1][i])
+ else:
+ axs[i, state_type].plot([0, 1], [weight_traj[0][i], weight_traj[-1][i]], marker="o") # plot how the weight evolves from first to last appearance
+ average[i, state_type] += np.array([weight_traj[0][i], weight_traj[-1][i]])
+ counter[i, state_type] += 1
+ all_datapoints[i][state_type][0].append(weight_traj[0][i])
+ 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([])
+ 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])
+ 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])
+ 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 changes min dur {}".format(min_dur))
+ plt.close()
+
+ f, axs = plt.subplots(n_weights + 1, 3, 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:
+ axs[j, i].set_ylim(-4.5, 4.5)
+ else:
+ axs[j, i].set_ylim(-2, 2)
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])
+ if j < n_weights - 1:
+ axs[j, i].plot([0], [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].plot([0], [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].plot([0], [np.mean(first_and_last_pmf[mask, 0, -1])], marker='*', c='red')
else:
axs[j, i].yaxis.set_ticklabels([])
+ if i == 2:
+ if j < n_weights - 1:
+ axs[j, i].plot([1], [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].plot([1], [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].plot([1], [np.mean(first_and_last_pmf[mask, 1, -1])], marker='*', c='red')
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.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')
- plt.tight_layout()
- plt.savefig("./summary_figures/weight_changes/all weight changes min dur {}".format(min_dur))
- plt.close()
-
- f, axs = plt.subplots(n_weights + 1, 3, 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:
- axs[j, i].set_ylim(-4.5, 4.5)
- else:
- axs[j, i].set_ylim(-2, 2)
- 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])
if j < n_weights - 1:
- axs[j, i].plot([0], [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].plot([0], [np.mean(first_and_last_pmf[mask, 0, -1])], marker='*', c='red') # separete biases again
+ 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:
- mask = first_and_last_pmf[:, 0, -1] > 0
- axs[j, i].plot([0], [np.mean(first_and_last_pmf[mask, 0, -1])], marker='*', c='red')
- else:
- axs[j, i].yaxis.set_ticklabels([])
- if i == 2:
+ 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')
+
if j < n_weights - 1:
- axs[j, i].plot([1], [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].plot([1], [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].plot([1], [np.mean(first_and_last_pmf[mask, 1, -1])], marker='*', c='red')
- 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.savefig("./summary_figures/weight_changes/weight changes min dur {}".format(min_dur))
- plt.show()
+ 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)
- f, axs = plt.subplots(n_weights + 1, 3, 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])
- axs[j, i].boxplot(all_datapoints[j][i], positions=[0, 1])
- if j < 2:
- axs[j, i].set_ylim(-8, 8)
- else:
- axs[j, i].set_ylim(-4, 4)
- 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.savefig("./summary_figures/weight_changes/weight change boxplot min dur {}".format(min_dur))
- plt.close()
+ 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')
-quit()
+ plt.savefig("./summary_figures/weight_changes/weight changes min dur {} delta hists".format(min_dur))
+ plt.close()
+ quit()
+# all pmf weights
+only_first_states = True
-# all pmf weights
-apw = np.array(pickle.load(open("all_pmf_weights.p", 'rb')))
+if only_first_states:
+ all_first_pmfs_typeless = pickle.load(open("all_first_pmfs_typeless.p", 'rb'))
+ first_pmfs_list = []
+ for subject in all_first_pmfs_typeless.keys():
+ for pmf in all_first_pmfs_typeless[subject]:
+ first_pmfs_list.append(pmf[1])
+ first_pmfs = np.array(first_pmfs_list)
+else:
+ apw = np.array(pickle.load(open("all_pmf_weights.p", 'rb')))
-colors = [type2color[pmf_type(weights_to_pmf(x))] for x in apw]
-colors_rew = [type2color[pmf_type_rew(x)] for x in apw]
+ colors = [type2color[pmf_type(weights_to_pmf(x))] for x in apw]
+ colors_rew = [type2color[pmf_type_rew(x)] for x in apw]
-if True:
+if False:
for i, weights in enumerate(apw):
if pmf_type(weights_to_pmf(weights)) != pmf_type_rew(weights):
plt.plot(weights_to_pmf(weights))
@@ -181,18 +680,33 @@ type1_rews = []
type2_rews = []
type3_rews = []
all_rews = []
-for weights in apw:
- type = pmf_type(weights_to_pmf(weights))
- all_rews.append(pmf_to_perf(weights_to_pmf(weights)))
- if type == 0:
- if pmf_to_perf(weights_to_pmf(weights)) > 0.8:
- plt.plot(weights_to_pmf(weights))
- plt.show()
- type1_rews.append(pmf_to_perf(weights_to_pmf(weights)))
- elif type == 1:
- type2_rews.append(pmf_to_perf(weights_to_pmf(weights)))
- elif type == 2:
- type3_rews.append(pmf_to_perf(weights_to_pmf(weights)))
+
+if only_first_states:
+ for pmfs in first_pmfs:
+ type = pmf_type(pmfs)
+ all_rews.append(pmf_to_perf(pmfs))
+ if type == 0:
+ if pmf_to_perf(pmfs) > 0.8:
+ plt.plot(pmfs)
+ plt.show()
+ type1_rews.append(pmf_to_perf(pmfs))
+ elif type == 1:
+ type2_rews.append(pmf_to_perf(pmfs))
+ elif type == 2:
+ type3_rews.append(pmf_to_perf(pmfs))
+else:
+ for weights in apw:
+ type = pmf_type(weights_to_pmf(weights))
+ all_rews.append(pmf_to_perf(weights_to_pmf(weights)))
+ if type == 0:
+ if pmf_to_perf(weights_to_pmf(weights)) > 0.8:
+ plt.plot(weights_to_pmf(weights))
+ plt.show()
+ type1_rews.append(pmf_to_perf(weights_to_pmf(weights)))
+ elif type == 1:
+ type2_rews.append(pmf_to_perf(weights_to_pmf(weights)))
+ elif type == 2:
+ type3_rews.append(pmf_to_perf(weights_to_pmf(weights)))
type1_rews, type2_rews, type3_rews = np.array(type1_rews), np.array(type2_rews), np.array(type3_rews)
@@ -225,14 +739,14 @@ plt.hist([type1_rews, type2_rews, type3_rews], bins=bins, label=["Type 1", "Type
plt.axvline(0.6, c='k')
plt.axvline(0.7827, c='k')
plt.legend()
-plt.savefig(folder + "hist 1")
+plt.savefig(folder + "hist 1" + " only first states" * only_first_states)
plt.show()
plt.hist([type1_rews, type2_rews, type3_rews], bins=bins, label=["Type 1", "Type 2", "Type 3"], stacked=True)
plt.axvline(0.6, c='k')
plt.axvline(0.7827, c='k')
plt.legend()
-plt.savefig(folder + "hist 2")
+plt.savefig(folder + "hist 2" + " only first states" * only_first_states)
plt.show()
plt.subplot(1, 3, 1)
@@ -253,7 +767,7 @@ plt.hist(all_rews, bins=55)
plt.axvline(0.6, c='k')
plt.axvline(0.7827, c='k')
-plt.savefig(folder + "hists compare")
+plt.savefig(folder + "hists compare" + " only first states" * only_first_states)
plt.show()
@@ -267,9 +781,11 @@ plt.xlabel("Reward rate", size=28)
plt.gca().spines[['right', 'top']].set_visible(False)
plt.tight_layout()
-plt.savefig(folder + "single hist")
+plt.savefig(folder + "single hist" + " only first states" * only_first_states)
plt.show()
+quit()
+
xy = np.vstack([apw[:, i] for i in range(4)])
z = gaussian_kde(xy)(xy)
diff --git a/dyn_glm_chain_analysis.py b/dyn_glm_chain_analysis.py
index fa7eb589fcabaf29d7c8f13bbd0b5d17c2307854..f2ad67379c35bf8d866234ca3f866988c9224c24 100644
--- a/dyn_glm_chain_analysis.py
+++ b/dyn_glm_chain_analysis.py
@@ -500,6 +500,98 @@ def return_ascending_shuffled():
np.random.shuffle(temp)
return temp
+def augment_weights(w):
+ # we want to keep track of how much the the span of the pmf changes
+ augmented_weights = np.zeros(5)
+ augmented_weights[:4] = w
+ pmf = weights_to_pmf(w)
+ augmented_weights[-1] = max(pmf[-2:]) - min(pmf[:2])
+ return augmented_weights
+
+def sudden_state_changes(test, state_sets, consistencies, pmf_weights):
+ n = test.results[0].n_sessions
+ trial_counter = 0
+ state_pmfs = {}
+ state_counter = {}
+ changes = [[], [], []]
+ aug_changes = [[], [], []] # also save any augmentations, such as the span of the PMF
+ changes_across_types = [[], []] # also specifically capture changes when type changes
+ aug_changes_across_types = [[], []] # also specifically capture changes when type changes
+ type_1_occured, type_2_occured, type_3_occured = False, False, False # flags for keeping track
+
+ for seq_num in range(n):
+ state_occurences = np.zeros((len(state_sets), len(test.results[0].models[0].stateseqs[seq_num])))
+
+ for state, trials in enumerate(state_sets):
+ relevant_trials = trials[np.logical_and(trial_counter <= trials, trials < trial_counter + len(test.results[0].models[0].stateseqs[seq_num]))]
+ active_trials = np.zeros(len(test.results[0].models[0].stateseqs[seq_num]))
+
+ # this has something to do with checking for every trial in the current session, how connected it is to all other trials within this state
+ active_trials[relevant_trials - trial_counter] = np.sum(consistencies[tuple(np.meshgrid(relevant_trials, trials))], axis=0)
+ active_trials[relevant_trials - trial_counter] -= 1
+ active_trials[relevant_trials - trial_counter] = active_trials[relevant_trials - trial_counter] / (trials.shape[0] - 1)
+
+ state_occurences[state] = active_trials
+
+ trial_counter += len(test.results[0].models[0].stateseqs[seq_num])
+
+ occuring_states, first_trials = [], []
+ for state in range(len(state_sets)):
+ occurence_trials = np.where(state_occurences[state] > 0.05)[0]
+ if occurence_trials.shape[0] == 0:
+ continue
+
+ occuring_states.append(state)
+ first_trials.append(occurence_trials[0])
+
+ occuring_states = [x for _, x in sorted(zip(first_trials, occuring_states))]
+
+ for state in occuring_states:
+ if state in state_pmfs:
+ state_counter[state] += 1
+ state_pmfs[state] = pmf_weights[state][state_counter[state]]
+ if pmf_type(weights_to_pmf(pmf_weights[state][state_counter[state]])) == 1:
+ type_2_occured = True
+ elif pmf_type(weights_to_pmf(pmf_weights[state][state_counter[state]])) == 2:
+ type_3_occured = True
+ else:
+ if len(state_pmfs) > 0:
+ # find out which individual weights of previous states are closest to new state
+ diffs = np.ones(5) * 50 # start at a high diff to accept first value in comparison later
+ contender = np.zeros(5)
+
+ for existing_state in state_pmfs:
+ temp_diffs = np.abs(augment_weights(pmf_weights[state][0]) - augment_weights(state_pmfs[existing_state]))
+ mask = temp_diffs < diffs
+ contender[mask] = augment_weights(state_pmfs[existing_state])[mask]
+ diffs[mask] = np.abs(augment_weights(pmf_weights[state][0]) - augment_weights(state_pmfs[existing_state]))[mask]
+
+ changes[pmf_type(weights_to_pmf(pmf_weights[state][0]))].append((contender[:-1], augment_weights(pmf_weights[state][0])[:-1]))
+ aug_changes[pmf_type(weights_to_pmf(pmf_weights[state][0]))].append((contender[-1], augment_weights(pmf_weights[state][0])[-1]))
+ if pmf_type(weights_to_pmf(pmf_weights[state][0])) == 0 and not type_2_occured and not type_3_occured:
+ type_1_occured = True
+ elif pmf_type(weights_to_pmf(pmf_weights[state][0])) == 1 and type_1_occured and not type_2_occured and not type_3_occured:
+ changes_across_types[0].append((contender[:-1], augment_weights(pmf_weights[state][0])[:-1]))
+ aug_changes_across_types[0].append((contender[-1], augment_weights(pmf_weights[state][0])[-1]))
+ type_2_occured = True
+ elif pmf_type(weights_to_pmf(pmf_weights[state][0])) == 2 and not type_3_occured and (type_2_occured or type_1_occured):
+ changes_across_types[1].append((contender[:-1], augment_weights(pmf_weights[state][0])[:-1]))
+ aug_changes_across_types[1].append((contender[-1], augment_weights(pmf_weights[state][0])[-1]))
+ type_3_occured = True
+
+ state_counter[state] = 0
+ state_pmfs[state] = pmf_weights[state][0]
+ if pmf_type(weights_to_pmf(pmf_weights[state][0])) == 0:
+ type_1_occured = True
+ elif pmf_type(weights_to_pmf(pmf_weights[state][0])) == 1:
+ type_2_occured = True
+ elif pmf_type(weights_to_pmf(pmf_weights[state][0])) == 2:
+ type_3_occured
+
+ assert len(changes_across_types[0]) <= 1 and len(changes_across_types[1]) <= 1
+
+ return changes, changes_across_types, aug_changes, aug_changes_across_types
+
def contrasts_plot(test, state_sets, subject, save=False, show=False, dpi='figure', save_append='', consistencies=None, CMF=False):
n = test.results[0].n_sessions
@@ -1203,11 +1295,11 @@ def state_development(test, state_sets, indices, save=True, save_append='', show
for state, trials in enumerate(state_sets):
dur_params = dur_hists(test, trials, indices)
- plt.subplot(4, 4, 1 + 2 * test.state_mapping[state])
+ plt.subplot(4, 5, 1 + 2 * test.state_mapping[state])
plt.hist(dur_params[:, 0])
plt.axvline(truth['durs'][state][0], color='red')
- plt.subplot(4, 4, 2 + 2 * test.state_mapping[state])
+ plt.subplot(4, 5, 2 + 2 * test.state_mapping[state])
plt.hist(dur_params[:, 1])
plt.axvline(truth['durs'][state][1], color='red')
@@ -1591,6 +1683,11 @@ if __name__ == "__main__":
all_weight_trajectories = []
bias_sessions = []
first_and_last_pmf = []
+ all_pmfs_named = {}
+ all_sudden_changes = [[], [], []]
+ all_sudden_transition_changes = [[], []]
+ aug_all_sudden_changes = [[], [], []]
+ aug_all_sudden_transition_changes = [[], []]
new_counter, transform_counter = 0, 0
state_types_interpolation = np.zeros((3, 150))
@@ -1602,10 +1699,11 @@ if __name__ == "__main__":
ultimate_counter = 0
for subject in subjects:
- if subject.startswith('GLM_Sim_') or subject in ['SWC_065', 'ZFM-05245', 'ZFM-04019', 'ibl_witten_18']:
+ if subject.startswith('GLM_Sim_') or subject in ['SWC_065', 'ZFM-05245', 'ZFM-04019', 'ibl_witten_18', 'UCLA006']:
# ibl_witten_18 is a weird one, super good session in the middle, ending phase 1, never to re-appear, bad at the end
# ZFM-05245 is neuromodulator mouse, never reaches ephys it seems... same for ZFM-04019
# SWC_065 never reaches type 3
+ # UCLA006 is too large to load both the canonical result and its consistencies
continue
print()
@@ -1615,13 +1713,13 @@ if __name__ == "__main__":
test = pickle.load(open("multi_chain_saves/canonical_result_{}_{}.p".format(subject, fit_type), 'rb'))
- # all_state_starts = test.state_appearance_posterior(subject)
- # pop_state_starts += all_state_starts
+ all_state_starts = test.state_appearance_posterior(subject)
+ pop_state_starts += all_state_starts
- # a, b, c = test.state_start_and_dur()
- # state_appear += a
- # state_dur += b
- # state_appear_dist += c
+ a, b, c = test.state_start_and_dur()
+ state_appear += a
+ state_dur += b
+ state_appear_dist += c
mode_specifier = 'first'
try:
@@ -1647,11 +1745,62 @@ if __name__ == "__main__":
# _ = state_development(test, [s for s in state_sets if len(s) > 40], mode_indices, save_append='step 3', show=1, separate_pmf=1, type_coloring=False, dont_plot=list(range(4)), plot_until=13)
states, pmfs, pmf_weights, durs, state_types, contrast_intro_type, intros_by_type, undiv_intros, states_per_type = state_development(test, [s for s in state_sets if len(s) > 40], mode_indices, save=False, show=0, separate_pmf=1, type_coloring=True)
- first_and_last_pmf.append((pmf_weights[np.argmax(states[:, 0])][0], pmf_weights[np.argmax(states[:, -1])][-1]))
+ new = type_2_appearance(states, pmfs)
+
+ if new == 2:
+ print('____________________________')
+ print(subject)
+ print('____________________________')
+ if new == 1:
+ new_counter += 1
+ if new == 0:
+ transform_counter += 1
+ print(new_counter, transform_counter)
+ ultimate_counter += 1
+ # first_and_last_pmf.append((pmf_weights[np.argmax(states[:, 0])][0], pmf_weights[np.argmax(states[:, -1])][-1]))
+
+ try:
+ consistencies = pickle.load(open("multi_chain_saves/first_mode_consistencies_{}_{}.p".format(subject, fit_type), 'rb'))
+ except FileNotFoundError:
+ consistencies = pickle.load(open("multi_chain_saves/consistencies_{}_{}.p".format(subject, fit_type), 'rb'))
+
+ consistencies /= consistencies[0, 0]
+ sudden_changes, sudden_transition_changes, aug_sudden_changes, aug_sudden_transition_changes = sudden_state_changes(test, [s for s in state_sets if len(s) > 40], consistencies=consistencies, pmf_weights=pmf_weights)
+ all_sudden_changes[0] += sudden_changes[0]
+ all_sudden_changes[1] += sudden_changes[1]
+ all_sudden_changes[2] += sudden_changes[2]
+ all_sudden_transition_changes[0] += sudden_transition_changes[0]
+ all_sudden_transition_changes[1] += sudden_transition_changes[1]
+ aug_all_sudden_changes[0] += aug_sudden_changes[0]
+ aug_all_sudden_changes[1] += aug_sudden_changes[1]
+ aug_all_sudden_changes[2] += aug_sudden_changes[2]
+ aug_all_sudden_transition_changes[0] += aug_sudden_transition_changes[0]
+ aug_all_sudden_transition_changes[1] += aug_sudden_transition_changes[1]
+ pickle.dump(all_sudden_changes, open("multi_chain_saves/all_sudden_changes.p", 'wb'))
+ pickle.dump(all_sudden_transition_changes, open("multi_chain_saves/all_sudden_transition_changes.p", 'wb'))
+ pickle.dump(aug_all_sudden_changes, open("multi_chain_saves/aug_all_sudden_changes.p", 'wb'))
+ pickle.dump(aug_all_sudden_transition_changes, open("multi_chain_saves/aug_all_sudden_transition_changes.p", 'wb'))
+
+ continue
+
+ if new != len(sudden_transition_changes[0]):
+ print("look into{}".format(subject))
+
+ continue
+
+ all_first_pmfs_typeless[subject] = []
+ for pmf in pmfs:
+ all_first_pmfs_typeless[subject].append((pmf[0], pmf[1][0]))
+ all_pmfs.append(pmf)
+
+ pickle.dump(all_first_pmfs_typeless, open("all_first_pmfs_typeless.p", 'wb'))
+
+ continue
# all_weight_trajectories += pmf_weights
abs_state_durs.append(durs)
- ultimate_counter += 1
+
+ all_pmfs_named[subject] = pmfs
info_dict = pickle.load(open("./{}/{}_info_dict.p".format('session_data', subject), "rb"))
if 'ephys_start' in info_dict:
@@ -1660,6 +1809,8 @@ if __name__ == "__main__":
bias_sessions.append(info_dict['n_sessions'] - info_dict['bias_start'])
print(subject, info_dict['n_sessions'], info_dict['bias_start'])
+ continue
+
state_types_interpolation[0] += np.interp(np.linspace(1, state_types.shape[1], 150), np.arange(1, 1 + state_types.shape[1]), state_types[0])
state_types_interpolation[1] += np.interp(np.linspace(1, state_types.shape[1], 150), np.arange(1, 1 + state_types.shape[1]), state_types[1])
@@ -1674,18 +1825,6 @@ if __name__ == "__main__":
# temp = contrasts_plot(test, [s for s in state_sets if len(s) > 40], dpi=300, subject=subject, save=True, show=True, consistencies=consistencies, CMF=False)
# quit()
- # new = type_2_appearance(states, pmfs)
-
- # if new == 2:
- # print('____________________________')
- # print(subject)
- # print('____________________________')
- # if new == 1:
- # new_counter += 1
- # if new == 0:
- # transform_counter += 1
- # print(new_counter, transform_counter)
-
# state_dict = write_results(test, [s for s in state_sets if len(s) > 40], mode_indices)
# pickle.dump(state_dict, open("state_dict_{}".format(subject), 'wb'))
@@ -1789,11 +1928,10 @@ if __name__ == "__main__":
# pickle.dump(all_first_pmfs, open("all_first_pmfs.p", 'wb'))
# pickle.dump(all_changing_pmfs, open("changing_pmfs.p", 'wb'))
# pickle.dump(all_changing_pmf_names, open("changing_pmf_names.p", 'wb'))
- pickle.dump(all_first_pmfs_typeless, open("all_first_pmfs_typeless.p", 'wb'))
- # pickle.dump(all_pmfs, open("all_pmfs.p", 'wb'))
+ # pickle.dump(all_first_pmfs_typeless, open("all_first_pmfs_typeless.p", 'wb'))
# pickle.dump(all_intros, open("all_intros.p", 'wb'))
# pickle.dump(all_intros_div, open("all_intros_div.p", 'wb'))
- # pickle.dump(all_states_per_type, open("all_states_per_type.p", 'wb'))
+ # pickle.dump(all_stateckle.dump(all_pmfs, open("all_pmfs.p", 'wb'))s_per_type, open("all_states_per_type.p", 'wb'))
# pickle.dump(regressions, open("regressions.p", 'wb'))
# pickle.dump(regression_diffs, open("regression_diffs.p", 'wb'))
# pickle.dump(all_bias_flips, open("all_bias_flips.p", 'wb'))
@@ -1809,16 +1947,15 @@ if __name__ == "__main__":
# pickle.dump(state_appear_dist, open("multi_chain_saves/state_appear_dist.p", 'wb'))
# pickle.dump(all_weight_trajectories, open("multi_chain_saves/all_weight_trajectories.p", 'wb'))
# pickle.dump(bias_sessions, open("multi_chain_saves/bias_sessions.p", 'wb'))
+ # pickle.dump(all_pmfs_named, open("multi_chain_saves/all_pmfs_named.p", 'wb'))
# pickle.dump(first_and_last_pmf, open("multi_chain_saves/first_and_last_pmf.p", 'wb'))
-
- # abs_state_durs = pickle.load(open("multi_chain_saves/abs_state_durs.p", 'rb'))
- # bias_sessions = pickle.load(open("multi_chain_saves/bias_sessions.p", 'rb'))
-
- quit()
+ # pickle.dump(all_sudden_changes, open("multi_chain_saves/all_sudden_changes.p", 'wb'))
+ # pickle.dump(all_sudden_transition_changes, open("multi_chain_saves/all_sudden_transition_changes.p", 'wb'))
print("Ultimate count is {}".format(ultimate_counter))
if False:
abs_state_durs = pickle.load(open("multi_chain_saves/abs_state_durs.p", 'rb'))
+ bias_sessions = pickle.load(open("multi_chain_saves/bias_sessions.p", 'rb'))
print("Correlations")
from scipy.stats import pearsonr
@@ -1847,7 +1984,7 @@ if __name__ == "__main__":
from simplex_plot import plotSimplex
- plotSimplex(np.array(abs_state_durs), c='k', show=True, vertexcolors=[type2color[i] for i in range(3)], vertexlabels=['', '', ''])
+ plotSimplex(np.array(abs_state_durs), facecolors='none', edgecolors='k', linewidths=1.5, show=True, vertexcolors=[type2color[i] for i in range(3)], vertexlabels=['', '', ''])
plt.hist(abs_state_durs.sum(1), color='grey', bins=12)
sns.despine()
@@ -1910,6 +2047,7 @@ if __name__ == "__main__":
if False:
pop_state_starts = pickle.load(open("multi_chain_saves/pop_state_starts.p", 'rb'))
+ state_appear_dist = pickle.dump(open("multi_chain_saves/state_appear_dist.p", 'rb'))
f, (ax, ax2) = plt.subplots(2, 1, sharex=True, figsize=(9, 9))
# plot the same data on both axes
@@ -1954,38 +2092,83 @@ if __name__ == "__main__":
plt.savefig('./summary_figures/states within session', dpi=300)
plt.close()
- if False:
- f, (a1, a2) = plt.subplots(1, 2, figsize=(18, 9), gridspec_kw={'width_ratios': [1.4, 1]})
+ if True:
+ pop_state_starts = pickle.load(open("multi_chain_saves/pop_state_starts.p", 'rb'))
+ state_appear_dist = pickle.load(open("multi_chain_saves/state_appear_dist.p", 'rb'))
+ f, axs = plt.subplots(2, 2, figsize=(16, 9), sharex=True)
+ gs = axs[0, 0].get_subplotspec().get_gridspec()
+
+ # clear the left column for the subfigure:
+ for a in axs[:, 0]:
+ a.remove()
+
+ ax, ax2 = axs[0, 1], axs[1, 1]
+ # plot the same data on both axes
+ # ax.plot(np.linspace(0, 1, 20), pop_state_starts, color='grey')
+ # ax2.plot(np.linspace(0, 1, 20), pop_state_starts, color='grey')
+ ax.bar(np.linspace(0, 1, 20), pop_state_starts, align='edge', width=1/19, color='grey')
+ ax2.bar(np.linspace(0, 1, 20), pop_state_starts, align='edge', width=1/19, color='grey')
+
+ # zoom-in / limit the view to different portions of the data
+ ax.set_ylim(245, 290)
+ ax2.set_ylim(0, 25)
+
+ ax.set_yticks([250, 275])
+ ax.set_yticklabels([250, 275])
+ ax2.set_yticks([0, 8, 16, 24])
+ ax2.set_xticks([0, .25, .5, .75, 1])
+ ax2.set_yticklabels([0, 8, 16, 24])
+ ax2.set_xticklabels([0, .25, .5, .75, 1])
+
+ # hide the spines between ax and ax2
+ ax.spines['bottom'].set_visible(False)
+ ax.spines['top'].set_visible(False)
+ ax.spines['right'].set_visible(False)
+ ax2.spines['top'].set_visible(False)
+ ax2.spines['right'].set_visible(False)
+ ax.tick_params(bottom=False)
+
+ ax2.set_xlim(left=0, right=1)
+ ax2.set_xlabel('Session time at appearance', fontsize=35)
+ ax2.tick_params(axis='both', labelsize=20)
+ ax.tick_params(axis='both', labelsize=20)
+
+ d = .015 # how big to make the diagonal lines in axes coordinates
+ kwargs = dict(transform=ax.transAxes, color='k', clip_on=False)
+ ax.plot((-d, +d), (-d, +d), **kwargs) # top-left diagonal
+
+ kwargs.update(transform=ax2.transAxes) # switch to the bottom axes
+ ax2.plot((-d, +d), (1 - d, 1 + d), **kwargs) # bottom-left diagonal
+
+ subfig = f.add_subfigure(gs[:, 0])
+ a1 = subfig.subplots(1, 1)
+
a1.bar(np.linspace(0, 1, 11), state_appear_dist, align='edge', width=1/10, color='grey')
# a1.hist(state_appear_mode, color='grey')
- a1.set_xlim(left=0)
+ a1.set_xlim(left=0, right=1)
# plt.title('First appearence of ', fontsize=22)
a1.set_ylabel('# of states', fontsize=35)
a1.set_xlabel('Training time at appearance', fontsize=35)
a1.tick_params(axis='both', labelsize=20)
- # a1.set_xticks([0, .25, .5, .75, 1])
- # a1.set_xticklabels([0, .25, .5, .75, 1])
- sns.despine()
+ a1.set_xticks([0, .25, .5, .75, 1])
+ a1.set_xticklabels([0, .25, .5, .75, 1])
+ a1.spines['top'].set_visible(False)
+ a1.spines['right'].set_visible(False)
+
+ # a2.scatter(np.clip(state_appear, 0.005, None), state_dur, color='black', alpha=0.08)
+ # state_appear = np.array(state_appear)
+ # state_dur = np.array(state_dur)
+ # medians = np.zeros(10)
+ # for i in range(10):
+ # temp = state_dur[np.logical_and(state_appear >= i * 0.1, state_appear < (i+1) * 0.1)]
+ # medians[i] = np.median(temp)
+ # plt.plot(np.linspace(0, 1, 10), medians, 'k', label='Median')
+ # plt.legend(frameon=False, fontsize=28)
+ # plt.xlim(left=0, right=1)
+ # plt.ylim(bottom=0, top=1)
+ # a2.set_xticks([0, .25, .5, .75, 1])
+ # a2.set_xticklabels([0, .25, .5, .75, 1])
- a2.scatter(np.clip(state_appear, 0.005, None), state_dur, color='black', alpha=0.08)
- state_appear = np.array(state_appear)
- state_dur = np.array(state_dur)
- medians = np.zeros(10)
- for i in range(10):
- temp = state_dur[np.logical_and(state_appear >= i * 0.1, state_appear < (i+1) * 0.1)]
- medians[i] = np.median(temp)
- plt.plot(np.linspace(0, 1, 10), medians, 'k', label='Median')
- plt.legend(frameon=False, fontsize=28)
- plt.xlim(left=0, right=1)
- plt.ylim(bottom=0, top=1)
- a2.set_xticks([0, .25, .5, .75, 1])
- a2.set_xticklabels([0, .25, .5, .75, 1])
-
- a2.set_xlabel('Training time at appearance', fontsize=35)
- # # a0.set_xticks([4, 7, 10])
- a2.set_ylabel('Lasts for training time', fontsize=35)
- a2.tick_params(axis='both', labelsize=20)
- sns.despine()
plt.tight_layout()
plt.savefig('./summary_figures/states in sessions', dpi=300)
- plt.close()
+ plt.show()
diff --git a/dynamic_GLMiHMM_fit.py b/dynamic_GLMiHMM_fit.py
index a16e7376dd52cb9a02256f1782c0ea1ffbd923fb..5e0767763926c964627b094f4dc4d115689a6926 100644
--- a/dynamic_GLMiHMM_fit.py
+++ b/dynamic_GLMiHMM_fit.py
@@ -312,7 +312,6 @@ for loop_count_i, (s, cv_num) in enumerate(product(subjects, cv_nums)):
# if not os.path.isfile('./{}/data_save_{}.p'.format(data_folder, params['subject'])):
pickle.dump(data_save, open('./{}/data_save_{}.p'.format(data_folder, params['subject']), 'wb'))
- quit()
# states_solution = pickle.load(open("states_{}_{}_condition_{}_{}.p".format('DY_013', 'all', 'nothing', '0_01'), 'rb')) # todo: remove!
time_save = time.time()
likes = np.zeros(params['n_samples'])
@@ -324,7 +323,6 @@ for loop_count_i, (s, cv_num) in enumerate(product(subjects, cv_nums)):
print(j)
posteriormodel.resample_model()
- quit()
likes[j] = posteriormodel.log_likelihood()
model_save = copy.deepcopy(posteriormodel)
diff --git a/raw_fit_processing_part2.py b/raw_fit_processing_part2.py
index 4a0835c4357c6dd189b4ae53cf06119cba4e9fc3..7d59bc54c98ebc5e592fb856b00d425dd3779b0e 100644
--- a/raw_fit_processing_part2.py
+++ b/raw_fit_processing_part2.py
@@ -223,7 +223,7 @@ elif fit_type == 'prebias':
subjects = list(loading_info.keys())
# error: KS043, KS045, 'NYU-12', ibl_witten_15, NYU-21, CSHL052, KS003
# done: NYU-46, NYU-39, ibl_witten_19, NYU-48
-subjects = ['DY_013', 'ZFM-01592', 'NYU-39', 'NYU-27', 'NYU-46', 'ZFM-01936', 'ZFM-02372', 'ZFM-01935', 'ibl_witten_26', 'ZM_2241', 'KS084', 'ZFM-01576']
+subjects = ['ZM_2245', 'GLM_Sim_18', 'SWC_039', 'ZFM-01937']
for subject in subjects:
test = pickle.load(open("multi_chain_saves/canonical_result_{}_{}.p".format(subject, fit_type), 'rb'))
diff --git a/simple plots/daft_model.py b/simple plots/daft_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..866bc83d106600d288adbfe5d45da47d6aadae80
--- /dev/null
+++ b/simple plots/daft_model.py
@@ -0,0 +1,102 @@
+"""Plot graphical model of iHMM behavioural state model."""
+import daft
+from matplotlib import rc
+import matplotlib.pyplot as plt
+import numpy as np
+
+np.random.seed(3)
+
+rc("font", family="serif", size=12)
+rc("text", usetex=True)
+
+
+# Instantiate the PGM.
+pgm = daft.PGM()
+
+local_offset = -0.75
+arrow_width = 0.15
+arrow_length = 0.2
+
+pgm.add_node('gamma', r"$\gamma$", 0, 0 * local_offset, plot_params={'edgecolor': 'w'}, offset=(5, 0))
+pgm.add_node('alpha', r"$\alpha$", 0, 1 * local_offset, plot_params={'edgecolor': 'w'}, offset=(5, 0))
+pgm.add_node('eta_vartheta', r"$\eta_{\vartheta}$", 0, 2 * local_offset, plot_params={'edgecolor': 'w'}, offset=(5, 0))
+pgm.add_node('eta_theta', r"$\eta_\theta$", 0, 4 * local_offset, plot_params={'edgecolor': 'w'}, offset=(5, 0))
+
+pgm.add_node('beta', r"$\beta$", 1, 0 * local_offset)
+pgm.add_node('pi^i', r"$\pi_i$", 1, 1 * local_offset)
+pgm.add_node('vartheta', r"$\vartheta_i$", 1, 2 * local_offset)
+pgm.add_node('theta', r"$\theta_i$", 1, 4 * local_offset)
+# And a plate.
+pgm.add_plate([0.6, 4 * local_offset - 1/3, 0.8, -4 * local_offset], label=r"$L$", label_offset=(35, 0))
+
+pgm.add_edge("gamma", "beta", plot_params={'head_width': arrow_width, 'head_length': arrow_length})
+pgm.add_edge("alpha", "pi^i", plot_params={'head_width': arrow_width, 'head_length': arrow_length})
+pgm.add_edge("eta_vartheta", "vartheta", plot_params={'head_width': arrow_width, 'head_length': arrow_length})
+pgm.add_edge("eta_theta", "theta", plot_params={'head_width': arrow_width, 'head_length': arrow_length})
+pgm.add_edge("beta", "pi^i", plot_params={'head_width': arrow_width, 'head_length': arrow_length})
+
+
+internal_dist = 0.65
+for i, offset in zip(['1', '2', 'S'], [3, 5, 7.5]):
+ pgm.add_node('z_{}'.format(i), r"$z_{}$".format(i), offset, 1.8 * local_offset)
+ pgm.add_node('d_{}'.format(i), r"$d_{}$".format(i), offset - internal_dist, 2.3 * local_offset, scale=.75)
+ if i == '1':
+ pgm.add_node('x_1', r"$x_1$", offset - internal_dist, 3.3 * local_offset)
+ elif i == 'S':
+ pgm.add_node('x_S', r"$x_S$", offset - internal_dist, 3.3 * local_offset)
+ else:
+ pgm.add_node('x_{}'.format(i), r"$x_{{t({})}}$".format(i), offset - internal_dist, 3.3 * local_offset)
+ pgm.add_node('...{}'.format(i), r"$...$", offset, 3.3 * local_offset, plot_params={'edgecolor': 'w'})
+ pgm.add_node('x{}'.format(i), '', offset + internal_dist, 3.3 * local_offset)
+
+ pgm.add_edge('z_{}'.format(i), 'x_{}'.format(i), plot_params={'head_width': arrow_width, 'head_length': arrow_length})
+ pgm.add_edge('z_{}'.format(i), '...{}'.format(i), plot_params={'head_width': arrow_width, 'head_length': arrow_length})
+ pgm.add_edge('z_{}'.format(i), 'x{}'.format(i), plot_params={'head_width': arrow_width, 'head_length': arrow_length})
+ pgm.add_edge('z_{}'.format(i), 'd_{}'.format(i), plot_params={'head_width': arrow_width, 'head_length': arrow_length})
+
+ if i == '1':
+ pgm.add_node('y_1', r"$y_1$", offset - internal_dist, 4.8 * local_offset)
+ elif i == 'S':
+ pgm.add_node('y_S', r"$y_S$", offset - internal_dist, 4.8 * local_offset)
+ else:
+ pgm.add_node('y_{}'.format(i), r"$y_{{t({})}}$".format(i), offset - internal_dist, 4.8 * local_offset)
+ pgm.add_node('y{}'.format(i), '', offset + internal_dist, 4.8 * local_offset)
+
+ pgm.add_edge('x_{}'.format(i), 'y_{}'.format(i), plot_params={'head_width': arrow_width, 'head_length': arrow_length})
+ pgm.add_edge('x{}'.format(i), 'y{}'.format(i), plot_params={'head_width': arrow_width, 'head_length': arrow_length})
+
+ pgm.add_edge('pi^i', 'z_{}'.format(i), plot_params={'head_width': arrow_width, 'head_length': arrow_length})
+ pgm.add_edge('vartheta', 'd_{}'.format(i), plot_params={'head_width': arrow_width, 'head_length': arrow_length})
+ pgm.add_edge('theta', 'y_{}'.format(i), plot_params={'head_width': arrow_width, 'head_length': arrow_length})
+ pgm.add_edge('theta', 'y{}'.format(i), plot_params={'head_width': arrow_width, 'head_length': arrow_length})
+
+
+# Render and save.
+pgm.render()
+plt.show()
+#pgm.savefig("classic.pdf")
+pgm.savefig("graphical model.png", dpi=200)
+quit()
+height = 1.5
+
+# Hierarchical parameters.
+gap = 0.8
+
+
+
+for i in range(10):
+ if i+1 in blank:
+ pgm.add_node(str(i+1), "...", i*gap, 0, plot_params={'alpha': 0.})
+ else:
+ pgm.add_node(str(i+1), r"${}$".format(num_to_lab[i]), i*gap, 0, observed=True)
+ c = 'white' if np.random.rand() > 0.5 else 'black'
+ pgm.add_node("color square {}".format(i+1), "", i*gap, -0.5, shape='rectangle', plot_params={'facecolor': c, 'edgecolor': 'k'}, aspect=1.5, scale=0.6)
+ pgm.add_node("color square 2 {}".format(i+1), "", i*gap, -1, shape='rectangle', plot_params={'facecolor': str(np.random.rand()), 'edgecolor': 'k'}, aspect=1.5, scale=0.6)
+
+
+
+# Comment out this to remove contrast dependence
+pgm.add_node("contrast", r"$C_n$", -0.5, 1)
+for i in range(10):
+ if i+1 not in blank:
+ pgm.add_edge("contrast", str(i+1))
diff --git a/simple plots/datf_test.py b/simple plots/datf_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..05801d4e1d946a68845a1a8c82baaf051aef0929
--- /dev/null
+++ b/simple plots/datf_test.py
@@ -0,0 +1,71 @@
+"""Plot graphical model of iHMM behavioural state model."""
+import daft
+from matplotlib import rc
+import matplotlib.pyplot as plt
+import numpy as np
+
+np.random.seed(3)
+
+rc("font", family="serif", size=12)
+rc("text", usetex=True)
+
+
+# Instantiate the PGM.
+pgm = daft.PGM()
+
+height = 1.5
+
+# Hierarchical parameters.
+gap = 0.8
+
+blank = [3, 6, 9]
+num_to_lab = dict(zip(range(10), [1, 2, None, 'i', 'i+1', None, 'i\'', 'i\'+1', None, 'N']))
+pgm.add_node('_0', "Trial", -gap, 0., plot_params={'edgecolor': 'w'})
+pgm.add_node('_1', "Response", -gap, -0.5, plot_params={'edgecolor': 'w'})
+pgm.add_node('_2', "RT", -gap, -1, plot_params={'edgecolor': 'w'})
+for i in range(10):
+ if i+1 in blank:
+ pgm.add_node(str(i+1), "...", i*gap, 0, plot_params={'alpha': 0.})
+ else:
+ pgm.add_node(str(i+1), r"${}$".format(num_to_lab[i]), i*gap, 0, observed=True)
+ c = 'white' if np.random.rand() > 0.5 else 'black'
+ pgm.add_node("color square {}".format(i+1), "", i*gap, -0.5, shape='rectangle', plot_params={'facecolor': c, 'edgecolor': 'k'}, aspect=1.5, scale=0.6)
+ pgm.add_node("color square 2 {}".format(i+1), "", i*gap, -1, shape='rectangle', plot_params={'facecolor': str(np.random.rand()), 'edgecolor': 'k'}, aspect=1.5, scale=0.6)
+
+
+
+pgm.add_node("state1", r"", 1.5*gap, height)
+pgm.add_node("state2", r"", 5*gap, height)
+pgm.add_node("state3", r"", 8*gap, height)
+
+# Add in the edges.
+pgm.add_edge("state1", "1")
+pgm.add_edge("state1", "2")
+pgm.add_edge("state1", "3")
+pgm.add_edge("state1", "4")
+
+pgm.add_edge("state2", "5")
+pgm.add_edge("state2", "6")
+pgm.add_edge("state2", "7")
+
+pgm.add_edge("state3", "8")
+pgm.add_edge("state3", "9")
+pgm.add_edge("state3", "10")
+
+pgm.add_edge("state1", "state2")
+pgm.add_edge("state2", "state3")
+
+
+# Comment out this to remove contrast dependence
+pgm.add_node("contrast", r"$C_n$", -0.5, 1)
+for i in range(10):
+ if i+1 not in blank:
+ pgm.add_edge("contrast", str(i+1))
+
+# And a plate.
+pgm.add_plate([-1, 0.65, 1.35, 1], label=r"$n = 1, \cdots, N$", label_offset=(5, 40))
+# Render and save.
+pgm.render()
+plt.show()
+#pgm.savefig("classic.pdf")
+pgm.savefig("classic.png", dpi=200)
diff --git a/simple plots/drawing (Kopie).svg b/simple plots/drawing (Kopie).svg
new file mode 100644
index 0000000000000000000000000000000000000000..2d5bf378370928b25c228ba3cc5f6d0cfdd80f9e
--- /dev/null
+++ b/simple plots/drawing (Kopie).svg
@@ -0,0 +1,5908 @@
+<?xml version="1.0" encoding="UTF-8" standalone="no"?>
+<!-- Created with Inkscape (http://www.inkscape.org/) -->
+
+<svg
+ xmlns:dc="http://purl.org/dc/elements/1.1/"
+ xmlns:cc="http://creativecommons.org/ns#"
+ xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
+ xmlns:svg="http://www.w3.org/2000/svg"
+ xmlns="http://www.w3.org/2000/svg"
+ xmlns:xlink="http://www.w3.org/1999/xlink"
+ xmlns:sodipodi="http://sodipodi.sourceforge.net/DTD/sodipodi-0.dtd"
+ xmlns:inkscape="http://www.inkscape.org/namespaces/inkscape"
+ width="210mm"
+ height="297mm"
+ viewBox="0 0 210 297"
+ version="1.1"
+ id="svg8"
+ inkscape:version="0.92.3 (2405546, 2018-03-11)"
+ sodipodi:docname="drawing (Kopie).svg"
+ inkscape:export-filename="/home/sebastian/Nextcloud/PhD/model_figures/log_reg_version.png"
+ inkscape:export-xdpi="300"
+ inkscape:export-ydpi="300">
+ <defs
+ id="defs2">
+ <marker
+ inkscape:stockid="TriangleOutL"
+ orient="auto"
+ refY="0.0"
+ refX="0.0"
+ id="TriangleOutL"
+ style="overflow:visible"
+ inkscape:isstock="true">
+ <path
+ id="path1062"
+ d="M 5.77,0.0 L -2.88,5.0 L -2.88,-5.0 L 5.77,0.0 z "
+ style="fill-rule:evenodd;stroke:#000000;stroke-width:1pt;stroke-opacity:1;fill:#000000;fill-opacity:1"
+ transform="scale(0.8)" />
+ </marker>
+ <marker
+ inkscape:stockid="Arrow2Lend"
+ orient="auto"
+ refY="0.0"
+ refX="0.0"
+ id="Arrow2Lend"
+ style="overflow:visible;"
+ inkscape:isstock="true">
+ <path
+ id="path941"
+ style="fill-rule:evenodd;stroke-width:0.625;stroke-linejoin:round;stroke:#000000;stroke-opacity:1;fill:#000000;fill-opacity:1"
+ d="M 8.7185878,4.0337352 L -2.2072895,0.016013256 L 8.7185884,-4.0017078 C 6.9730900,-1.6296469 6.9831476,1.6157441 8.7185878,4.0337352 z "
+ transform="scale(1.1) rotate(180) translate(1,0)" />
+ </marker>
+ <marker
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+</svg>
diff --git a/simple plots/dynamic_pmf_plot.py b/simple plots/dynamic_pmf_plot.py
new file mode 100644
index 0000000000000000000000000000000000000000..ffce4dec377f79fbed4eb8d462ebc36c9633ce54
--- /dev/null
+++ b/simple plots/dynamic_pmf_plot.py
@@ -0,0 +1,57 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+import matplotlib.cm as cm
+import pickle
+
+
+bias_cont_ticks = ([0, 2, 5, 9, 11], [-1, -.25, 0, .25, 1])
+cmap = cm.get_cmap('magma')
+
+# try
+all_pmfs_named = pickle.load(open("../multi_chain_saves/all_pmfs_named.p", 'rb'))
+
+state_nums = [0, 0, 0, 1, 0, 0, 3, 0, 0, 0, 0]
+subjects = ['SWC_042', 'ZFM-02368', 'ZM_1898', 'ZM_3003', 'NYU-37', 'ibl_witten_17', 'DY_014', 'DY_013', 'DY_008', 'CSH_ZAD_029', 'CSH_ZAD_025']
+for state, subj in zip(state_nums, subjects):
+ plt.figure(figsize=(11, 9))
+ for i, pmf in enumerate(all_pmfs_named[subj][state][1]):
+ plt.plot(pmf, c=cmap(i / (len(all_pmfs_named[subj][state][1]) - 1)))
+ sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=1, vmax=len(all_pmfs_named[subj][state][1])))
+ clb = plt.colorbar(sm)
+ clb.ax.set_title('Session', size=38, pad=14)
+ clb.ax.tick_params(labelsize=22)
+
+ plt.xticks(*bias_cont_ticks, size=22)
+ plt.xlim(left=0)
+ plt.ylim(0, 1)
+ plt.yticks(size=22)
+ plt.xlabel('Contrast', size=38)
+ plt.ylabel('P(answer rightward)', size=38)
+
+ sns.despine()
+ plt.tight_layout()
+ plt.show()
+
+start = np.array([0.04, 0.06, 0.1, 0.21, 0.5, 0.79, 0.9, 0.94, 0.96])
+end = np.array([0.27, 0.33, 0.47, 0.65, 0.82, 0.88, 0.95, 0.97, 0.99])
+
+plt.figure(figsize=(11, 9))
+for w in range(10):
+ plt.plot((1 - (w/10)) * start + (w/10) * end + np.random.normal(0, 0.008, size=9), c=cmap(w / 10))
+
+sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=1, vmax=10))
+clb = plt.colorbar(sm)
+clb.ax.set_title('Session', size=38, pad=14)
+clb.ax.tick_params(labelsize=22)
+
+plt.xticks(*bias_cont_ticks, size=22)
+plt.xlim(left=0)
+plt.yticks(size=22)
+plt.xlabel('Contrast', size=38)
+plt.ylabel('P(answer rightward)', size=38)
+
+sns.despine()
+plt.tight_layout()
+plt.savefig("dynamic_pmf_plot", dpi=300)
+plt.show()
diff --git a/simple plots/neg_bin.py b/simple plots/neg_bin.py
new file mode 100644
index 0000000000000000000000000000000000000000..a9d56eb1fc948ce2a1f122a8cda5b9e705ecc7b7
--- /dev/null
+++ b/simple plots/neg_bin.py
@@ -0,0 +1,28 @@
+import matplotlib.pyplot as plt
+import numpy as np
+from scipy.stats import nbinom
+import seaborn as sns
+
+
+ns = [20, 600]
+ps = [0.05, 0.6]
+
+fs = 38
+ts = 22
+
+x = np.arange(600)
+
+for n, p in zip(ns, ps):
+ plt.figure(figsize=(11, 9))
+ plt.plot(x, nbinom.pmf(x, n, p), 'ko', ms=8)
+ plt.xlabel("State Duration", size=fs)
+ plt.ylabel("Probability", size=fs)
+
+ plt.ylim(bottom=0, top=0.02)
+ plt.xlim(left=0, right=600)
+ # plt.xticks([0, 10, 20, 30], [0, 10, 20, 30], size=ts)
+ # plt.yticks([0.04, 0.08, 0.12], [0.04, 0.08, 0.12], size=ts)
+ sns.despine()
+ plt.tight_layout()
+ plt.savefig(str(n) + '.png', dpi=300)
+ plt.show()
diff --git a/simple plots/plot_random_walk.py b/simple plots/plot_random_walk.py
new file mode 100644
index 0000000000000000000000000000000000000000..4f3e9f3e9fe8ca5b7810f398ec91defcc8e0db52
--- /dev/null
+++ b/simple plots/plot_random_walk.py
@@ -0,0 +1,22 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+n = 200
+seed = np.random.randint(10000)
+print(seed)
+np.random.seed(1441)
+
+noise = np.random.normal(size=(n, 2))
+walk = np.cumsum(noise, axis=0)
+plt.plot(walk[:, 0], walk[:, 1])
+
+plt.ylabel("weight dimension 2", fontsize=24)
+plt.xlabel("weight dimension 1", fontsize=24)
+
+plt.xticks([])
+plt.yticks([])
+sns.despine()
+plt.tight_layout()
+plt.savefig("rw", dpi=300)
+plt.show()
diff --git a/simple plots/pmf_plot.py b/simple plots/pmf_plot.py
new file mode 100644
index 0000000000000000000000000000000000000000..fb4f4bd040868459778f8b04335c9023d614414c
--- /dev/null
+++ b/simple plots/pmf_plot.py
@@ -0,0 +1,36 @@
+import matplotlib.pyplot as plt
+import seaborn as sns
+import numpy as np
+import pickle
+
+first_and_last_pmf = np.array(pickle.load(open("../multi_chain_saves/first_and_last_pmf.p", 'rb')))
+
+contrasts_L = np.array([1., 0.987, 0.848, 0.555, 0.302, 0, 0, 0, 0, 0, 0])
+contrasts_R = np.array([1., 0.987, 0.848, 0.555, 0.302, 0, 0, 0, 0, 0, 0])[::-1]
+
+
+def weights_to_pmf(weights, prev_resps=0, with_bias=1):
+ psi = weights[0] * contrasts_R + weights[1] * contrasts_L + with_bias * weights[-1] + weights[-2] * prev_resps
+ return 1 / (1 + np.exp(psi)) # we somehow got the answers twisted, so we drop the minus here to get the opposite response probability for plotting
+
+
+for pmf in first_and_last_pmf:
+ pmf = pmf[1]
+
+ plt.figure(figsize=(11, 9))
+ plt.plot(range(11), weights_to_pmf(pmf, -1), 'k', lw=6)
+ plt.plot(range(11), weights_to_pmf(pmf, 1), 'b', lw=6)
+ plt.xticks(range(11), [-1, -0.5, -0.25, -0.12, -0.06, 0, 0.06, 0.12, 0.25, 0.5, 1], size=22)
+ plt.annotate("Previous\nresponses:", (5.6, 0.3), color='b', size=38)
+ plt.arrow(5.8, 0.23, 1, 0, color='b', head_width=0.05, head_length=0.25)
+ plt.annotate("Previous\nresponses:", (2, 0.7), color='k', size=38)
+ plt.arrow(3.5, 0.63, -1, 0, color='k', head_width=0.05, head_length=0.25)
+ plt.yticks(size=22)
+ plt.xlim(left=0, right=10)
+ plt.ylim(bottom=0, top=1)
+ sns.despine()
+ plt.xlabel('Contrast', size=38)
+ plt.ylabel('P(answer rightward)', size=38)
+ plt.tight_layout()
+ plt.savefig('simple_pmf', dpi=300)
+ plt.show()
diff --git a/simple plots/stick_break.py b/simple plots/stick_break.py
new file mode 100644
index 0000000000000000000000000000000000000000..87b52875d7ce8141ba407825935801395186d4c3
--- /dev/null
+++ b/simple plots/stick_break.py
@@ -0,0 +1,216 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.stats import beta
+from matplotlib.transforms import Bbox
+
+def next_higher(a, r):
+ i = 0
+ s = a[0]
+ while s < r:
+ i += 1
+ s += a[i]
+ return i
+
+class stick_breaking_DP:
+
+ def __init__(self, dist, alpha):
+
+ self.dist = dist
+ self.alpha = alpha
+
+ self.counts = np.array([]) # atom counts
+ self.pis = np.array([]) # atom probs
+ self.pi_dashs_temp = []
+ self.pi_dash_product = 1 # summarized memory of pi dashs for normalizing (no array needed)
+ self.atoms = np.array([])
+
+ def draw(self):
+
+ r = np.random.rand()
+
+ if r > 1 - self.pi_dash_product: # this works instead of np.sum(self.pis)
+ atom = self.dist()
+ pi_dash = np.random.beta(1, self.alpha)
+ self.pi_dashs_temp.append(pi_dash)
+ pi = pi_dash * self.pi_dash_product
+
+ self.atoms = np.append(self.atoms, atom)
+ self.pis = np.append(self.pis, pi)
+ self.pi_dash_product *= (1 - pi_dash)
+ self.counts = np.append(self.counts, 1)
+ return atom
+
+ else:
+ num = next_higher(self.pis, r)
+ self.counts[num] += 1
+ return self.atoms[num]
+
+
+ def draw_n(self, n):
+ for _ in range(n):
+ self.draw()
+
+ def plot(self):
+ plt.bar(x=self.atoms, height=self.counts, width=0.01, facecolor='k', edgecolor='k')
+
+ plt.ylabel('Count', fontsize=22)
+ plt.xlabel('Atom', fontsize=22)
+ plt.title(r'$\alpha = {}$'.format(alpha), fontsize=22)
+ plt.gca().spines['right'].set_visible(False)
+ plt.gca().spines['top'].set_visible(False)
+
+ plt.tight_layout()
+ plt.savefig('dp.png')
+ plt.show()
+
+ def plot_sticks(self, n, title=''):
+
+ plt.figure(figsize=(8, 2))
+ plt.plot([0, 0], [-1.25, 1.25], 'k')
+ plt.plot([1, 1], [-1.25, 1.25], 'k')
+
+ so_far = 0
+ for i in range(min(n, len(self.pis))):
+ plt.plot([so_far, so_far + self.pis[i]], [1, 1], 'k')
+ plt.plot([so_far, so_far + self.pis[i]], [-1, -1], 'k')
+ plt.plot([so_far + self.pis[i], so_far + self.pis[i]], [-1, 1], 'k')
+
+ if self.pis[i] > 0.05 and (i + 1 < 10):
+ plt.annotate(r'$S_{}$'.format(i+1), (so_far + self.pis[i] / 2 - 0.029, -0.29), fontsize=25)#, rotation='vertical')
+ #plt.annotate('S{0:.0f}'.format(self.atoms[i]), (so_far + self.pis[i] / 2 - 0.029, -0.29), fontsize=25)
+ #plt.annotate('{0:.0f}'.format(self.atoms[i]), (so_far + self.pis[i] / 2 - 0.04, -0.29 + 1.6 * (i % 2 == 0) - 1.6 * (i % 2 != 0)), fontsize=25)
+ #plt.plot([so_far + self.pis[i] / 2, so_far + self.pis[i] / 2], [0, 1.18 * 2 * (i % 2 == 0) - 1.18], 'k')
+ so_far += self.pis[i]
+ #plt.annotate(r'$\eta_3$', (0.46, 1.2), fontsize=25, clip_on=False)
+ #plt.annotate(r'$\eta_{2, a_1}$', (0.44, 1.213), fontsize=25, clip_on=False)
+ plt.plot([so_far, 1], [0, 0], 'k')
+
+ plt.xticks([0, 1], [0, 1], fontsize=25)#, rotation='vertical')
+ plt.xlim(left=-0.05, right=1)
+ plt.ylim(bottom=-1.2, top=1.2)
+ plt.gca().tick_params(axis=u'both', which=u'both',length=0)
+ plt.yticks([])
+ plt.gca().spines['right'].set_visible(False)
+ plt.gca().spines['top'].set_visible(False)
+ plt.gca().spines['left'].set_visible(False)
+ plt.gca().spines['bottom'].set_visible(False)
+
+ plt.tight_layout()
+ plt.savefig(title + '_{}.png'.format(n), transparent=True, dpi=300)
+ plt.show()
+
+def discrete_dist():
+ a = np.random.rand()
+ if a < 0.2:
+ return 0
+ elif a < 0.5:
+ return 1
+ else:
+ return 2
+
+def discrete_dist2():
+ a = np.random.rand()
+ if a < 0.6:
+ return 0
+ elif a < 0.8:
+ return 1
+ else:
+ return 2
+
+def discrete_dist3():
+ a = np.random.rand()
+ if a < 0.2:
+ return 0
+ elif a < 0.9:
+ return 1
+ else:
+ return 2
+
+if __name__ == "__main__":
+ seed = int(10000 * np.random.rand())
+ print(seed)
+ np.random.seed(2551)
+ plt.rcParams.update({'font.size': 20})
+ alpha = 3
+
+ dp = stick_breaking_DP(lambda: np.random.normal(0, 1), alpha)
+
+ """x = np.linspace(0, 1) # beta dist and growing sticks
+ plt.plot(x, beta.pdf(x, 1, alpha), 'k', label=r'Beta(1, $\alpha$={})'.format(alpha))
+ plt.legend(frameon=0, fontsize=22)
+ plt.gca().spines['right'].set_visible(False)
+ plt.gca().spines['top'].set_visible(False)
+ plt.xlim(left=0, right=1)
+ plt.ylim(bottom=0, top=3.25)
+ plt.yticks([0, 1, 2, 3], [0, 1, 2, 3])
+ plt.xticks([0, 0.25, 0.5, 0.75, 1], [0, 0.25, 0.5, 0.75, 1])
+ plt.tight_layout()
+ plt.savefig('beta_dist.png')
+ plt.close()
+
+ dp.draw_n(100)
+ print(dp.pis)
+ print(dp.pi_dashs_temp)
+ dp.plot_sticks(1)
+ dp.plot_sticks(2)
+ dp.plot_sticks(3)
+ dp.plot_sticks(24)
+
+ quit()"""
+
+ # dp = stick_breaking_DP(discrete_dist, alpha)
+ # dp.pis = [0.55, 0.35, 0.1]
+ # dp.atoms = [1, 2, 3]
+ # dp.plot_sticks(31, title='eta_3')
+ #
+ # quit()
+
+ for i in range(10):
+ dp = stick_breaking_DP(lambda: np.random.normal(0, 1), 3)
+ dp.draw_n(100)
+ dp.plot_sticks(50 + i, title='alpha = 3')
+
+ quit()
+
+ np.random.seed(4)
+ dp_t = stick_breaking_DP(discrete_dist, alpha)
+ dp_t.draw_n(100)
+ dp_t.plot_sticks(30)
+
+ dp = stick_breaking_DP(discrete_dist, alpha)
+ dp.pis = [0.2, 0.3, 0.5]
+ dp.atoms = [0, 1, 2]
+ dp.plot_sticks(31)
+
+ dp = stick_breaking_DP(discrete_dist, alpha)
+ dp.pis = [np.sum(dp_t.pis[dp_t.atoms == 0]), np.sum(dp_t.pis[dp_t.atoms == 1]), np.sum(dp_t.pis[dp_t.atoms == 2])]
+ dp.atoms = [0, 1, 2]
+ dp.plot_sticks(32, extra=1)
+
+ dp_t = stick_breaking_DP(discrete_dist2, alpha)
+ dp_t.draw_n(100)
+ dp_t.plot_sticks(33)
+
+ dp = stick_breaking_DP(discrete_dist2, alpha)
+ dp.pis = [0.2, 0.3, 0.5]
+ dp.atoms = [0, 1, 2]
+ dp.plot_sticks(34)
+
+ dp = stick_breaking_DP(discrete_dist2, alpha)
+ dp.pis = [np.sum(dp_t.pis[dp_t.atoms == 0]), np.sum(dp_t.pis[dp_t.atoms == 1]), np.sum(dp_t.pis[dp_t.atoms == 2])]
+ dp.atoms = [0, 1, 2]
+ dp.plot_sticks(35, extra=0)
+
+ dp_t = stick_breaking_DP(discrete_dist3, alpha)
+ dp_t.draw_n(100)
+ dp_t.plot_sticks(36)
+
+ dp = stick_breaking_DP(discrete_dist3, alpha)
+ dp.pis = [0.2, 0.3, 0.5]
+ dp.atoms = [0, 1, 2]
+ dp.plot_sticks(37)
+
+ dp = stick_breaking_DP(discrete_dist3, alpha)
+ dp.pis = [np.sum(dp_t.pis[dp_t.atoms == 0]), np.sum(dp_t.pis[dp_t.atoms == 1]), np.sum(dp_t.pis[dp_t.atoms == 2])]
+ dp.atoms = [0, 1, 2]
+ dp.plot_sticks(38, extra=2)
diff --git a/temp_test.py b/temp_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..f049ebd4e87b1f6dbc253cae8989cb6803009d12
--- /dev/null
+++ b/temp_test.py
@@ -0,0 +1,35 @@
+import numpy as np
+
+def find_exceeding_indices(array):
+ # Create a boolean mask where elements are greater than 0.5
+ mask = array > 0.5
+
+ # Find the column indices where each row first exceeds 0.5
+ col_indices = np.argmax(mask, axis=1)
+
+ # Find the row indices where the condition is True
+ row_indices = np.flatnonzero(mask.any(axis=1))
+ print(mask)
+ print(mask.any(axis=1))
+ print(np.flatnonzero(mask.any(axis=1)))
+ print(np.nonzero(mask.any(axis=1))[0])
+
+ # Sort both lists based on the second (col_indices) list
+ sorted_indices = np.argsort(col_indices[row_indices])
+ row_indices = row_indices[sorted_indices]
+ col_indices = col_indices[row_indices]
+
+ return row_indices.tolist(), col_indices.tolist()
+
+# Test case
+if __name__ == "__main__":
+ test_array = np.array([[0.1, 0.51, 0.6, 0.7],
+ [0.4, 0.3, 0.8, 0.2],
+ [0.1, 0.5, 0.2, 0.1]])
+
+ row_indices, col_indices = find_exceeding_indices(test_array)
+ print("Row Indices that exceed 0.5:", row_indices) # Output: [0, 2, 1]
+ print("Column Indices where rows first exceed 0.5:", col_indices) # Output: [2, 2, 2]
+
+
+
diff --git a/test_codes/__pycache__/communal_funcs.cpython-37.pyc b/test_codes/__pycache__/communal_funcs.cpython-37.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..11129ddb22838763d21a6a5233b61368536162a5
Binary files /dev/null and b/test_codes/__pycache__/communal_funcs.cpython-37.pyc differ
diff --git a/test_codes/dynamic_glm_dist_test.py b/test_codes/dynamic_glm_dist_test.py
index da5214a15ae5b94df22f09b7483a2b29cf84366b..b296d928263864b38e6bc6b83ea36593c0b2a585 100644
--- a/test_codes/dynamic_glm_dist_test.py
+++ b/test_codes/dynamic_glm_dist_test.py
@@ -18,7 +18,7 @@ T = 30
n_inputs = 2
n_samples = 150
Q = np.tile(np.eye(n_inputs), (T, 1, 1))
-test = distributions.Dynamic_GLM(n_inputs=n_inputs, T=T, P_0=np.eye(n_inputs), Q=Q * 0.01)
+test = distributions.Dynamic_GLM(n_regressors=n_inputs, T=T, P_0=np.eye(n_inputs), Q=Q * 0.01, prior_mean=np.zeros(n_inputs))
# predictors = []
# for _ in range(T):
@@ -112,12 +112,11 @@ test = distributions.Dynamic_GLM(n_inputs=n_inputs, T=T, P_0=np.eye(n_inputs), Q
#
# quit()
-
""" Time point test """
T = 42
n_inputs = 5
Q = np.tile(np.eye(n_inputs), (T, 1, 1))
-test = distributions.Dynamic_GLM(n_inputs=n_inputs, T=T, P_0=np.eye(n_inputs), Q=Q * 0.01)
+test = distributions.Dynamic_GLM(n_regressors=n_inputs, T=T, P_0=np.eye(n_inputs), Q=Q * 0.01, prior_mean=np.zeros(n_inputs), jumplimit=3)
test_points = [5, 6, 7, 9, 10, 13, 14, 18, 23, 30]
predictors = []
@@ -135,14 +134,12 @@ for _ in range(T):
predictors.append(pred)
sample = test.rvs(predictors, list(range(T)))
-learn = distributions.Dynamic_GLM(n_inputs=n_inputs, T=T, P_0=np.eye(n_inputs), Q=Q * 0.01)
+learn = distributions.Dynamic_GLM(n_regressors=n_inputs, T=T, P_0=np.eye(n_inputs), Q=Q * 0.01, prior_mean=np.zeros(n_inputs), jumplimit=3)
# print(test.log_likelihood(sample[3], 3))
-print(sample)
-print()
solution = {5: 1, 6: 3, 7: 5, 8: 6, 9: 8, 10: 10, 11: 11, 12: 12, 13: 14, 14: 16, 15: 17, 16: 18, 17: 19, 18: 21, 19: 22, 20: 23, 21: 24, 22: 24, 23: 26, 24: 27, 25: 28, 26: 29, 27: 29, 28: 29, 29: 29, 30: 31}
-timedict = test.resample(sample)
+timedict = test.resample(sample, testing_flag=True)
# print(test.weights)
assert timedict == solution
print('all good')
diff --git a/test_codes/dynglm_timing_test.py b/test_codes/dynglm_timing_test.py
index 8c83dc96f1e853033c879946803bbeae4c4e6b3c..7f178785f331780ab77351a5767a254d53631986 100644
--- a/test_codes/dynglm_timing_test.py
+++ b/test_codes/dynglm_timing_test.py
@@ -20,7 +20,7 @@ n_inputs = 2
n_samples = 200
step_size = 0.5
Q = np.tile(np.eye(n_inputs), (T, 1, 1))
-test = distributions.Dynamic_GLM(n_inputs=n_inputs, T=T, P_0=10 * np.eye(n_inputs), Q=Q * step_size)
+test = distributions.Dynamic_GLM(n_regressors=n_inputs, T=T, P_0=10 * np.eye(n_inputs), Q=Q * step_size, prior_mean=np.zeros(n_inputs))
test_points = [5, 6, 7, 8, 10, 18]
predictors = []
@@ -39,7 +39,7 @@ for _ in range(T):
predictors.append(pred)
sample = test.rvs(predictors, list(range(T)))
-learn = distributions.Dynamic_GLM(n_inputs=n_inputs, T=T, P_0=np.eye(n_inputs), Q=Q * step_size)
+learn = distributions.Dynamic_GLM(n_regressors=n_inputs, T=T, P_0=np.eye(n_inputs), Q=Q * step_size, prior_mean=np.zeros(n_inputs))
samples = []
pseudo_samples = []