From cb6433077d18f74cb065e890fabe05e95f14670d Mon Sep 17 00:00:00 2001
From: SebastianBruijns <>
Date: Thu, 13 Jul 2023 11:56:11 +0200
Subject: [PATCH] code update
---
__pycache__/analysis_pmf.cpython-37.pyc | Bin 11418 -> 11748 bytes
.../dyn_glm_chain_analysis.cpython-37.pyc | Bin 53918 -> 53764 bytes
analysis_pmf.py | 94 +++++++++++++-----
analysis_pmf_weights.py | 27 ++++-
dyn_glm_chain_analysis.py | 35 ++++---
5 files changed, 107 insertions(+), 49 deletions(-)
diff --git a/__pycache__/analysis_pmf.cpython-37.pyc b/__pycache__/analysis_pmf.cpython-37.pyc
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diff --git a/__pycache__/dyn_glm_chain_analysis.cpython-37.pyc b/__pycache__/dyn_glm_chain_analysis.cpython-37.pyc
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diff --git a/analysis_pmf.py b/analysis_pmf.py
index 8be3eecf..ca0bb8d1 100644
--- a/analysis_pmf.py
+++ b/analysis_pmf.py
@@ -523,46 +523,88 @@ if __name__ == "__main__":
plt.show()
# All first PMFs
+
+ # I had an issue where 0 contrasts were undefined for all PMFs, check that that's not the case
+ at_least_once = False
+ for key in all_first_pmfs_typeless:
+ for pmf in all_first_pmfs_typeless[key]:
+ def_points, _ = pmf
+ if def_points[5]:
+ at_least_once = True
+ assert at_least_once
+
+ type_saves = [[], [], [], [], []]
+ for key in all_first_pmfs_typeless:
+ for pmf in all_first_pmfs_typeless[key]:
+
+ defined_points, pmf = pmf
+ temp_type = pmf_type(pmf)
+
+ if temp_type == 0:
+ defined_points = np.array([ True, True, False, False, False, False, False, False, False, True, True])
+ elif temp_type == 1:
+ defined_points = np.array([ True, True, True, False, False, False, False, False, True, True, True])
+ else:
+ defined_points[:] = True
+
+ if temp_type == 0:
+ type_saves[temp_type].append((defined_points, pmf))
+ elif temp_type == 1:
+ if np.abs(pmf[0] + pmf[-1] - 1) <= 0.1:
+ type_saves[3].append((defined_points, pmf))
+ else:
+ type_saves[1 + int(pmf[0] > 1 - pmf[-1])].append((defined_points, pmf))
+ else:
+ type_saves[4].append((defined_points, pmf))
+
tick_size = 14
label_size = 26
all_first_pmfs = pickle.load(open("all_first_pmfs.p", 'rb'))
- n_rows, n_cols = 1, 3
+ n_rows, n_cols = 1, 5
_, axs = plt.subplots(n_rows, n_cols, figsize=(16, 9))
save_title = "all types" if True else "KS014 types"
+
if save_title == "KS014 types":
all_first_pmfs_typeless = {'KS014': all_first_pmfs_typeless['KS014']}
- for key in all_first_pmfs_typeless:
- x = all_first_pmfs_typeless[key]
- for pmf in x:
-
- defined_points, pmf = pmf
- pmf_min = min(pmf[0], pmf[1])
- pmf_max = max(pmf[-2], pmf[-1])
- defined_points = np.logical_and(np.logical_and(defined_points, ~ (pmf > pmf_max)), ~ (pmf < pmf_min))
- axs[pmf_type(pmf)].plot(np.where(defined_points)[0], pmf[defined_points], c=type2color[pmf_type(pmf)])
- # axs[pmf_type(pmf[1])].plot(np.where(pmf[0])[0], pmf[1][pmf[0]], c=type2color[pmf_type(pmf[1])])
- axs[0].set_ylim(0, 1)
- axs[1].set_ylim(0, 1)
- axs[2].set_ylim(0, 1)
- axs[0].set_xticks(np.arange(11), all_conts, size=tick_size, rotation=45)
- axs[1].set_xticks(np.arange(11), all_conts, size=tick_size, rotation=45)
- axs[2].set_xticks(np.arange(11), all_conts, size=tick_size, rotation=45)
- axs[0].set_yticks([0, 0.25, 0.5, 0.75, 1], [0, 0.25, 0.5, 0.75, 1], size=tick_size)
- axs[1].set_yticks([0, 0.25, 0.5, 0.75, 1], [0, 0.25, 0.5, 0.75, 1], size=tick_size)
- axs[2].set_yticks([0, 0.25, 0.5, 0.75, 1], [0, 0.25, 0.5, 0.75, 1], size=tick_size)
- axs[0].spines[['right', 'top']].set_visible(False)
- axs[1].spines[['right', 'top']].set_visible(False)
- axs[2].spines[['right', 'top']].set_visible(False)
- axs[0].set_xlim(0, 10)
- axs[1].set_xlim(0, 10)
- axs[2].set_xlim(0, 10)
+ # for key in all_first_pmfs_typeless:
+ # x = all_first_pmfs_typeless[key]
+ # for pmf in x:
+ # defined_points, pmf = pmf
+ # pmf_min = min(pmf[0], pmf[1])
+ # pmf_max = max(pmf[-2], pmf[-1])
+ # defined_points = np.logical_and(np.logical_and(defined_points, ~ (pmf > pmf_max)), ~ (pmf < pmf_min))
+ # axs[pmf_type(pmf)].plot(np.where(defined_points)[0], pmf[defined_points], c=type2color[pmf_type(pmf)])
+ for i, type_save in enumerate(type_saves):
+ type_array = np.empty((len(type_save), 11))
+ type_array[:] = np.nan
+ for j, pmf in enumerate(type_save):
+ 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))
+ axs[i].fill_between(x, percentiles[1], percentiles[0], alpha=0.2)
+ axs[i].annotate("N=".format(len(type_save)), (0.75, 0.1))
+
+ axs[i].set_ylim(0, 1)
+ axs[i].set_xticks(np.arange(11), all_conts, size=tick_size, rotation=45)
+ axs[i].set_yticks([0, 0.25, 0.5, 0.75, 1], [0, 0.25, 0.5, 0.75, 1], size=tick_size)
+ axs[i].spines[['right', 'top']].set_visible(False)
+ axs[i].set_xlim(0, 10)
axs[0].set_ylabel("P(rightwards)", size=label_size)
axs[0].set_xlabel("Contrasts", size=label_size)
plt.tight_layout()
plt.savefig("./summary_figures/" + save_title)
plt.show()
+ quit()
if save_title == "KS014 types":
quit()
diff --git a/analysis_pmf_weights.py b/analysis_pmf_weights.py
index a2b06196..8d2f703b 100644
--- a/analysis_pmf_weights.py
+++ b/analysis_pmf_weights.py
@@ -33,7 +33,7 @@ def pmf_type_rew(weights):
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:
@@ -82,7 +82,7 @@ for min_dur in [2, 3, 4, 5, 7, 9, 11, 15]:
if i == 0:
axs[j, i].set_ylabel(ylabels[j])
else:
- axs[j, i].set_yticks([])
+ axs[j, i].yaxis.set_ticklabels([])
if j == 0:
axs[j, i].set_title("Type {}".format(i + 1))
if j == n_weights:
@@ -105,14 +105,31 @@ for min_dur in [2, 3, 4, 5, 7, 9, 11, 15]:
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].set_yticks([])
+ 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()
+ plt.show()
f, axs = plt.subplots(n_weights + 1, 3, figsize=(12, 9))
for i in range(3):
@@ -129,7 +146,7 @@ for min_dur in [2, 3, 4, 5, 7, 9, 11, 15]:
if i == 0:
axs[j, i].set_ylabel(ylabels[j])
else:
- axs[j, i].set_yticks([])
+ axs[j, i].yaxis.set_ticklabels([])
if j == 0:
axs[j, i].set_title("Type {}".format(i + 1))
if j == n_weights:
diff --git a/dyn_glm_chain_analysis.py b/dyn_glm_chain_analysis.py
index 37bb5dad..fa7eb589 100644
--- a/dyn_glm_chain_analysis.py
+++ b/dyn_glm_chain_analysis.py
@@ -1610,7 +1610,8 @@ if __name__ == "__main__":
print()
print(subject)
- continue
+ print(ultimate_counter)
+
test = pickle.load(open("multi_chain_saves/canonical_result_{}_{}.p".format(subject, fit_type), 'rb'))
@@ -1645,10 +1646,9 @@ if __name__ == "__main__":
# _ = state_development(test, [s for s in state_sets if len(s) > 40], mode_indices, save_append='step 2', show=1, separate_pmf=1, type_coloring=False, dont_plot=list(range(6)), plot_until=7)
# _ = 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]))
- continue
# all_weight_trajectories += pmf_weights
abs_state_durs.append(durs)
ultimate_counter += 1
@@ -1660,7 +1660,6 @@ 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])
@@ -1675,17 +1674,17 @@ 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)
+ # 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)
+ # 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)
@@ -1708,7 +1707,7 @@ if __name__ == "__main__":
all_first_pmfs[subject] = first_pmfs
- quit()
+ continue
# b_flips = bias_flips(states, pmfs, durs)
# all_bias_flips.append(b_flips)
@@ -1790,7 +1789,7 @@ 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_first_pmfs_typeless, open("all_first_pmfs_typeless.p", 'wb'))
# pickle.dump(all_pmfs, open("all_pmfs.p", 'wb'))
# pickle.dump(all_intros, open("all_intros.p", 'wb'))
# pickle.dump(all_intros_div, open("all_intros_div.p", 'wb'))
@@ -1812,8 +1811,8 @@ if __name__ == "__main__":
# pickle.dump(bias_sessions, open("multi_chain_saves/bias_sessions.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'))
+ # 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()
print("Ultimate count is {}".format(ultimate_counter))
--
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