From 70ed7f02bc2403c8497584cd9ccce7f57f8c8603 Mon Sep 17 00:00:00 2001
From: SebastianBruijns <>
Date: Wed, 11 Oct 2023 16:11:39 +0200
Subject: [PATCH] zodate
---
__pycache__/analysis_pmf.cpython-37.pyc | Bin 11964 -> 13646 bytes
.../dyn_glm_chain_analysis.cpython-37.pyc | Bin 59205 -> 64585 bytes
__pycache__/simplex_plot.cpython-37.pyc | Bin 3129 -> 3282 bytes
analysis_pmf.py | 103 +-
analysis_pmf_weights.py | 247 +-
analysis_regression.py | 31 +-
analysis_states.py | 97 +
behavioral_data_temp.py | 315 +
behavioral_state_data.py | 28 +-
behavioral_state_data_easier.py | 10 +-
behaviour_overview.py | 7 +-
criterion_array | Bin 0 -> 64090 bytes
dyn_glm_chain_analysis.py | 477 +-
dynamic_GLMiHMM_fit.py | 28 +-
raw_fit_processing_part2.py | 56 +-
reached_array | Bin 0 -> 10810 bytes
session_dict_ZFM-05245 | Bin 0 -> 3524745 bytes
simple plots/drawing.svg | 39613 ++++++++++------
simple plots/dynamic_pmf_plot.py | 22 +-
...meta_state_development_KS014_10_03.png.svg | 8300 ++++
simple plots/neg_bin.py | 8 +-
simple plots/pmf_plot.py | 29 +-
simplex_plot.py | 10 +-
state_dict_ZFM-05245 | Bin 0 -> 3272009 bytes
test_codes/comparison_data | Bin 23999 -> 23997 bytes
test_codes/dyn_glm_pmf_test.py | 16 +-
test_codes/dynamic_glm_dist_test.py | 212 +-
test_codes/dynglm_timing_test.py | 11 +-
.../pymc_compare/dynglm_optimisation_test.py | 2 -
test_codes/pymc_compare/plot_samplings.py | 3 +-
30 files changed, 33594 insertions(+), 16031 deletions(-)
create mode 100644 analysis_states.py
create mode 100644 behavioral_data_temp.py
create mode 100644 criterion_array
create mode 100644 reached_array
create mode 100644 session_dict_ZFM-05245
create mode 100644 simple plots/meta_state_development_KS014_10_03.png.svg
create mode 100644 state_dict_ZFM-05245
diff --git a/__pycache__/analysis_pmf.cpython-37.pyc b/__pycache__/analysis_pmf.cpython-37.pyc
index 2bd712fdb5b1e22d6a5abe907020f531291133dc..061be92b2f39f11bac91db94cafe44397597cf28 100644
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diff --git a/__pycache__/dyn_glm_chain_analysis.cpython-37.pyc b/__pycache__/dyn_glm_chain_analysis.cpython-37.pyc
index 5f0086ead07f7514bdf27bf4a72c0cb419ac2b61..740ae95b01266f9eecf65075fb890628efff55a3 100644
GIT binary patch
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diff --git a/__pycache__/simplex_plot.cpython-37.pyc b/__pycache__/simplex_plot.cpython-37.pyc
index bd2589fbd826e684ab1e735fba426a1302e20cf8..44c7fd6e58ae233a1e7c9cd1824f83c84163ecba 100644
GIT binary patch
delta 909
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z+PSN~wOz6PMFFjm`e4QpS%DSVF2CVO2pmVUY#W`MGG;kHV{n0J1AI#SR@G?j_3yox
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diff --git a/analysis_pmf.py b/analysis_pmf.py
index 0eae6f60..fd624848 100644
--- a/analysis_pmf.py
+++ b/analysis_pmf.py
@@ -8,6 +8,7 @@ type2color = {0: 'green', 1: 'blue', 2: 'red'}
all_conts = np.array([-1, -0.5, -.25, -.125, -.062, 0, .062, .125, .25, 0.5, 1])
performance_points = np.array([-1, -1, 0, 0])
+np.random.seed(12)
def pmf_to_perf(pmf):
# determine performance of a pmf, but only on the omnipresent strongest contrasts
@@ -78,6 +79,13 @@ if __name__ == "__main__":
plt.close()
all_first_pmfs_typeless = pickle.load(open("all_first_pmfs_typeless.p", 'rb'))
+ type_2_counter = 0
+ for subject in all_first_pmfs_typeless.keys():
+ type_2_intro_present = False
+ for defined_points, pmf in all_first_pmfs_typeless[subject]:
+ type_2_intro_present = type_2_intro_present or (pmf_type(pmf) == 1)
+ type_2_counter += type_2_intro_present
+ print("Out of {} mice, {} have a type 2 intro".format(len(all_first_pmfs_typeless), type_2_counter))
all_pmfs = pickle.load(open("all_pmfs.p", 'rb'))
all_bias_flips = pickle.load(open("all_bias_flips.p", 'rb'))
@@ -221,7 +229,7 @@ if __name__ == "__main__":
plt.gca().spines['left'].set_linewidth(4)
plt.gca().spines['bottom'].set_linewidth(4)
plt.tight_layout()
- plt.savefig("single exam 1")
+ plt.savefig("simple plots/single exam 1")
plt.close()
@@ -237,7 +245,7 @@ if __name__ == "__main__":
plt.gca().spines['left'].set_linewidth(4)
plt.gca().spines['bottom'].set_linewidth(4)
plt.tight_layout()
- plt.savefig("single exam 2")
+ plt.savefig("simple plots/single exam 2")
plt.close()
@@ -254,7 +262,7 @@ if __name__ == "__main__":
plt.gca().spines['left'].set_linewidth(4)
plt.gca().spines['bottom'].set_linewidth(4)
plt.tight_layout()
- plt.savefig("single exam 3")
+ plt.savefig("simple plots/single exam 3")
plt.close()
@@ -270,7 +278,7 @@ if __name__ == "__main__":
plt.gca().spines['left'].set_linewidth(4)
plt.gca().spines['bottom'].set_linewidth(4)
plt.tight_layout()
- plt.savefig("single exam 4")
+ plt.savefig("simple plots/single exam 4")
plt.close()
@@ -286,7 +294,55 @@ if __name__ == "__main__":
plt.gca().spines['left'].set_linewidth(4)
plt.gca().spines['bottom'].set_linewidth(4)
plt.tight_layout()
- plt.savefig("single exam 5")
+ plt.savefig("simple plots/single exam 5")
+ plt.close()
+
+
+ state_num = 2
+ defined_points, pmf = all_first_pmfs_typeless['KS084'][state_num][0], all_first_pmfs_typeless['KS084'][state_num][1]
+ plt.plot(np.where(defined_points)[0], pmf[defined_points], c=type2color[pmf_type(pmf)], lw=lw)
+
+ plt.ylim(0, 1)
+ plt.xlim(0, 10)
+ plt.yticks([])
+ plt.gca().set_xticks([])
+ sns.despine()
+ plt.gca().spines['left'].set_linewidth(4)
+ plt.gca().spines['bottom'].set_linewidth(4)
+ plt.tight_layout()
+ plt.savefig("simple plots/single exam 6")
+ plt.close()
+
+
+ state_num = 0
+ defined_points, pmf = all_first_pmfs_typeless['NYU-40'][state_num][0], all_first_pmfs_typeless['NYU-40'][state_num][1]
+ plt.plot(np.where(defined_points)[0], pmf[defined_points], c=type2color[pmf_type(pmf)], lw=lw)
+
+ plt.ylim(0, 1)
+ plt.xlim(0, 10)
+ plt.yticks([])
+ plt.gca().set_xticks([])
+ sns.despine()
+ plt.gca().spines['left'].set_linewidth(4)
+ plt.gca().spines['bottom'].set_linewidth(4)
+ plt.tight_layout()
+ plt.savefig("simple plots/single exam 7")
+ plt.close()
+
+
+ state_num = 7
+ defined_points, pmf = all_first_pmfs_typeless['SWC_043'][state_num][0], all_first_pmfs_typeless['SWC_043'][state_num][1]
+ plt.plot(np.where(defined_points)[0], pmf[defined_points], c=type2color[pmf_type(pmf)], lw=lw)
+
+ plt.ylim(0, 1)
+ plt.xlim(0, 10)
+ plt.yticks([])
+ plt.gca().set_xticks([])
+ sns.despine()
+ plt.gca().spines['left'].set_linewidth(4)
+ plt.gca().spines['bottom'].set_linewidth(4)
+ plt.tight_layout()
+ plt.savefig("simple plots/single exam 8")
plt.close()
@@ -520,7 +576,7 @@ if __name__ == "__main__":
plt.hist(biases)
plt.axvline(np.mean(biases))
plt.xlim(0, 1)
- plt.show()
+ plt.close()
# All first PMFs
@@ -533,6 +589,7 @@ if __name__ == "__main__":
at_least_once = True
assert at_least_once
+ type_map = {0: 0, 1: 1, 2: 1, 3: 1, 4: 2}
type_saves = [[], [], [], [], []]
for key in all_first_pmfs_typeless:
for pmf in all_first_pmfs_typeless[key]:
@@ -548,7 +605,7 @@ if __name__ == "__main__":
defined_points[:] = True
if temp_type == 0:
- type_saves[temp_type].append((defined_points, pmf))
+ 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))
@@ -558,10 +615,10 @@ if __name__ == "__main__":
type_saves[4].append((defined_points, pmf))
tick_size = 14
- label_size = 26
+ label_size = 34
all_first_pmfs = pickle.load(open("all_first_pmfs.p", 'rb'))
n_rows, n_cols = 1, 5
- _, axs = plt.subplots(n_rows, n_cols, figsize=(16, 9))
+ fg, axs = plt.subplots(n_rows, n_cols, figsize=(16, 6.6))
save_title = "all types" if True else "KS014 types"
if save_title == "KS014 types":
@@ -589,20 +646,34 @@ if __name__ == "__main__":
else:
x = [0, 1, 2, 8, 9, 10]
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))
+ type_max = type_array.shape[0]
+ sample_js = np.random.choice(np.arange(type_max), 10 if i == 0 else 5)
+ for j in sample_js:
+ axs[i].plot(x, type_array[j, x], c=type2color[type_map[i]], alpha=0.45)
+ axs[i].plot(x, np.mean(type_array[:, x], axis=0), c=type2color[type_map[i]], lw=5)
+ axs[i].fill_between(x, percentiles[1, x], percentiles[0, x], alpha=0.2, color=type2color[type_map[i]])
+ axs[i].annotate("N={}".format(len(type_save)), (5.75, 0.035), size=22)
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].set_xticks(np.arange(11), ['-1', '', '-0.25', '', '', '0', '', '', '0.25', '', '1'], size=tick_size, rotation=45)
+ if i == 0:
+ axs[i].set_yticks([0, 0.25, 0.5, 0.75, 1], [0, 0.25, 0.5, 0.75, 1], size=tick_size)
+ else:
+ axs[i].set_yticks([0, 0.25, 0.5, 0.75, 1], ['']*5, 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)
+ axs[2].set_xlabel("Contrasts", size=label_size)
+
+ offset_x = 0.25
+ axs[0].annotate("a", (offset_x, 1), weight='bold', fontsize=22)
+ axs[1].annotate("b", (offset_x, 1), weight='bold', fontsize=22)
+ axs[2].annotate("c", (offset_x, 1), weight='bold', fontsize=22)
+ axs[3].annotate("d", (offset_x, 1), weight='bold', fontsize=22)
+ axs[4].annotate("e", (offset_x, 1), weight='bold', fontsize=22)
plt.tight_layout()
- plt.savefig("./summary_figures/" + save_title)
+ plt.savefig("./summary_figures/" + save_title, dpi=300)
plt.show()
quit()
if save_title == "KS014 types":
diff --git a/analysis_pmf_weights.py b/analysis_pmf_weights.py
index 807e94f0..d3f7dad2 100644
--- a/analysis_pmf_weights.py
+++ b/analysis_pmf_weights.py
@@ -7,16 +7,15 @@ from mpl_toolkits import mplot3d
from matplotlib.patches import ConnectionPatch
-show_weight_augmentations = True
+show_weight_augmentations = False
all_weight_trajectories = pickle.load(open("multi_chain_saves/all_weight_trajectories.p", 'rb'))
first_and_last_pmf = np.array(pickle.load(open("multi_chain_saves/first_and_last_pmf.p", 'rb')))
all_sudden_changes = pickle.load(open("multi_chain_saves/all_sudden_changes.p", 'rb'))
all_sudden_transition_changes = pickle.load(open("multi_chain_saves/all_sudden_transition_changes.p", 'rb'))
-if show_weight_augmentations:
- aug_all_sudden_changes = pickle.load(open("multi_chain_saves/aug_all_sudden_changes.p", 'rb'))
- aug_all_sudden_transition_changes = pickle.load(open("multi_chain_saves/aug_all_sudden_transition_changes.p", 'rb'))
+aug_all_sudden_changes = pickle.load(open("multi_chain_saves/aug_all_sudden_changes.p", 'rb'))
+aug_all_sudden_transition_changes = pickle.load(open("multi_chain_saves/aug_all_sudden_transition_changes.p", 'rb'))
performance_points = np.array([-1, -1, -1, -1, -1, 0, 0, 0, 0, 0, 0])
reduced_points = np.array([1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1], dtype=bool)
@@ -75,7 +74,6 @@ def pmf_type_rew(weights):
def plot_traces_and_collate_data(data, augmented_data=None, title=""):
"""Plot all the individual weight change traces, ann collect the data we need for the other plots"""
- show_weight_augmentations = augmented_data is not None
sudden_changes = len(data) == n_types - 1
average = np.zeros((n_weights + 1 + show_weight_augmentations, n_types - sudden_changes, 2))
counter = np.zeros((n_weights + 1 + show_weight_augmentations, n_types - sudden_changes))
@@ -115,7 +113,7 @@ def plot_traces_and_collate_data(data, augmented_data=None, title=""):
return average, counter, all_datapoints
-def plot_compact(average, counter, title, show_first_and_last=False, show_weight_augmentations=False):
+def plot_compact(average, counter, title, show_first_and_last=False, show_weight_augmentations=False, all_datapoints=[]):
"""Plot the means of the weight change traces, split by the different weight types, possibly with augmentations"""
sudden_changes = average.shape[1] == n_types - 1
f, axs = plt.subplots(1, n_types - sudden_changes, figsize=(4 * (3 - sudden_changes), 6))
@@ -157,10 +155,109 @@ def plot_compact(average, counter, title, show_first_and_last=False, show_weight
plt.close()
-def plot_histogram_diffs(all_datapoints, x_lim_used_normal, x_lim_used_bias, bin_sets, title, x_lim_used_augment=0, show_deltas=True, show_first_and_last=False, show_weight_augmentations=False):
+def plot_compact_all(average_slow, counter_slow, average_sudden, counter_sudden, title, all_data_sudden=[], all_data_slow=[]):
+ """Plot a whole bunch of changes"""
+ titles = ["Type 1", r'Type $1 \rightarrow 2$', "Type 2", r'Type $2 \rightarrow 3$', "Type 3"]
+ f, axs = plt.subplots(1, 5, figsize=(4 * 5, 8))
+ average = np.zeros((average_slow.shape[0], average_slow.shape[1] + average_sudden.shape[1], 2))
+ counter = np.zeros((counter_slow.shape[0], counter_slow.shape[1] + counter_sudden.shape[1]))
+ all_data = create_nested_list([n_weights + 1, average_slow.shape[1] + average_sudden.shape[1], 2])
+ average[:, [0, 2, 4]] = average_slow
+ average[:, [1, 3]] = average_sudden
+ counter[:, [0, 2, 4]] = counter_slow
+ counter[:, [1, 3]] = counter_sudden
+ # all_data[:, [0, 2, 4]] = counter_slow # this won't just work...
+ # all_data[:, [1, 3]] = counter_sudden
+ for i in range(average.shape[1]):
+ for j in range(n_weights + 1 + show_weight_augmentations):
+ axs[i].plot([0, 1], average[j, i] / counter[j, i], marker="o", color=local_weight_colours[j], label=local_ylabels[j])
+ axs[i].set_ylim(-3.5, 3.5)
+ axs[i].spines['top'].set_visible(False)
+ axs[i].spines['right'].set_visible(False)
+ axs[i].set_xticks([])
+ if i == 0:
+ axs[i].set_ylabel("Weights", size=38)
+ if j < n_weights - 1:
+ axs[i].plot([0.1], [np.mean(first_and_last_pmf[:, 0, j])], marker='*', color=local_weight_colours[j]) # also plot weights of very first state average
+ if j == n_weights - 1:
+ mask = first_and_last_pmf[:, 0, -1] < 0
+ axs[i].plot([0.1], [np.mean(first_and_last_pmf[mask, 0, -1])], marker='*', color=local_weight_colours[j]) # separete biases again
+ if j == n_weights:
+ mask = first_and_last_pmf[:, 0, -1] > 0
+ axs[i].plot([0.1], [np.mean(first_and_last_pmf[mask, 0, -1])], marker='*', color=local_weight_colours[j])
+ else:
+ axs[i].yaxis.set_ticklabels([])
+ if i == 4:
+ if j < n_weights - 1:
+ axs[i].plot([0.9], [np.mean(first_and_last_pmf[:, 1, j])], marker='*', color=local_weight_colours[j]) # also plot weights of very last state average
+ if j == n_weights - 1:
+ mask = first_and_last_pmf[:, 1, -1] < 0
+ axs[i].plot([0.9], [np.mean(first_and_last_pmf[mask, 1, -1])], marker='*', color=local_weight_colours[j]) # separete biases again
+ if j == n_weights:
+ mask = first_and_last_pmf[:, 1, -1] > 0
+ axs[i].plot([0.9], [np.mean(first_and_last_pmf[mask, 1, -1])], marker='*', color=local_weight_colours[j])
+ axs[i].annotate("n={}".format(int(counter[0, i])), (0.06, 0.025), xycoords='axes fraction', size=26)
+ if j == 0:
+ axs[i].set_title(titles[i], size=38)
+ if j == n_weights and i == 2:
+ axs[i].set_xlabel("Weight change", size=38)
+ axs[0].legend(frameon=False, fontsize=17)
+ plt.tight_layout()
+ plt.savefig("./summary_figures/weight_changes/" + title + " augmented" * show_weight_augmentations, dpi=300)
+ plt.close()
+
+
+def plot_compact_split(all_datapoints, title, show_first_and_last=False, show_weight_augmentations=False, width_divisor=20):
+ """Plot the means of the weight change traces, split by the different weight types, possibly with augmentations"""
+ sudden_changes = len(all_datapoints[0]) == 2
+ f, axs = plt.subplots(1, n_types - sudden_changes, figsize=(4 * (3 - sudden_changes), 6))
+ for i in range(n_types - sudden_changes):
+ for j in range(n_weights + 1 + show_weight_augmentations):
+ after, before = np.array(all_datapoints[j][i][1]), np.array(all_datapoints[j][i][0])
+ deltas = after - before
+ mask = deltas >= 0
+ axs[i].plot([0, 1], [np.mean(before[mask]), np.mean(after[mask])], marker="o", color=local_weight_colours[j], label=local_ylabels[j], lw=mask.sum() / width_divisor)
+ axs[i].plot([0, 1], [np.mean(before[~mask]), np.mean(after[~mask])], marker="o", color=local_weight_colours[j], ls='--', lw=(~mask).sum() / width_divisor)
+ 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 and show_first_and_last:
+ axs[i].set_ylabel("Weights", size=24)
+ if j < n_weights - 1:
+ axs[i].plot([0.1], [np.mean(first_and_last_pmf[:, 0, j])], marker='*', color=local_weight_colours[j]) # also plot weights of very first state average
+ if j == n_weights - 1:
+ mask = first_and_last_pmf[:, 0, -1] < 0
+ axs[i].plot([0.1], [np.mean(first_and_last_pmf[mask, 0, -1])], marker='*', color=local_weight_colours[j]) # separete biases again
+ if j == n_weights:
+ mask = first_and_last_pmf[:, 0, -1] > 0
+ axs[i].plot([0.1], [np.mean(first_and_last_pmf[mask, 0, -1])], marker='*', color=local_weight_colours[j])
+ else:
+ axs[i].yaxis.set_ticklabels([])
+ if i == 2 and show_first_and_last:
+ if j < n_weights - 1:
+ axs[i].plot([0.9], [np.mean(first_and_last_pmf[:, 1, j])], marker='*', color=local_weight_colours[j]) # also plot weights of very last state average
+ if j == n_weights - 1:
+ mask = first_and_last_pmf[:, 1, -1] < 0
+ axs[i].plot([0.9], [np.mean(first_and_last_pmf[mask, 1, -1])], marker='*', color=local_weight_colours[j]) # separete biases again
+ if j == n_weights:
+ mask = first_and_last_pmf[:, 1, -1] > 0
+ axs[i].plot([0.9], [np.mean(first_and_last_pmf[mask, 1, -1])], marker='*', color=local_weight_colours[j])
+ if j == 0:
+ axs[i].set_title("Type {}".format(i + 1 + sudden_changes), size=26)
+ if j == n_weights and i == 1:
+ axs[i].set_xlabel("Lifetime weight change", size=24)
+ axs[0].legend(frameon=False, fontsize=14)
+ plt.tight_layout()
+ plt.savefig("./summary_figures/weight_changes/split " + title + " augmented" * show_weight_augmentations)
+ plt.close()
+
+
+def plot_histogram_diffs(all_datapoints, average, counter, x_lim_used_normal, x_lim_used_bias, bin_sets, title, x_lim_used_augment=0, show_deltas=True, show_first_and_last=False, show_weight_augmentations=False):
"""Plot histograms over the weights, and the mean changes connecting them.
Might have to mess quite a bit with the y-axis"""
sudden_changes = len(all_datapoints[0]) == 2
+ x_steps = x_lim_used_bias - x_lim_used_bias % 5
f, axs = plt.subplots(n_weights + 1 + show_weight_augmentations, (n_types - sudden_changes) * 2, figsize=(4 * (3 - sudden_changes), 9))
for i in range(n_types - sudden_changes):
for j in range(n_weights + 1 + show_weight_augmentations):
@@ -172,6 +269,8 @@ def plot_histogram_diffs(all_datapoints, x_lim_used_normal, x_lim_used_bias, bin
bins = bin_sets[2]
else:
bins = bin_sets[3]
+
+ means = average[j, i] / counter[j, i]
axs[j, i * 2].hist(all_datapoints[j][i][0], orientation='horizontal', bins=bins, color='grey', alpha=0.5)
if show_deltas:
axs[j, i * 2 + 1].hist(np.array(all_datapoints[j][i][1]) - np.array(all_datapoints[j][i][0]), orientation='horizontal', bins=bins, color='red', alpha=0.5)
@@ -180,67 +279,58 @@ def plot_histogram_diffs(all_datapoints, x_lim_used_normal, x_lim_used_bias, bin
axs[j, i * 2].set_ylim(bins[0], bins[-1])
axs[j, i * 2 + 1].set_ylim(bins[0], bins[-1])
-
+
axs[j, i * 2].spines['top'].set_visible(False)
axs[j, i * 2].spines['right'].set_visible(False)
- axs[j, i * 2].set_xticks([])
+ # axs[j, i * 2].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 + 1].set_xticks([])
- axs[j, i * 2].annotate("Var {:.2f}".format(np.var(all_datapoints[j][i][0])), xy=(0.65, 0.8), xycoords='axes fraction')
- if show_deltas:
- axs[j, i * 2 + 1].annotate("Var {:.2f}".format(np.var(np.array(all_datapoints[j][i][1]) - np.array(all_datapoints[j][i][0]))), xy=(0.65, 0.8), xycoords='axes fraction')
- else:
- axs[j, i * 2 + 1].annotate("Var {:.2f}".format(np.var(all_datapoints[j][i][1])), xy=(0.65, 0.8), xycoords='axes fraction')
+ # axs[j, i * 2].annotate("Var {:.2f}".format(np.var(all_datapoints[j][i][0])), xy=(0.65, 0.8), xycoords='axes fraction')
+ # if show_deltas:
+ # axs[j, i * 2 + 1].annotate("Var {:.2f}".format(np.var(np.array(all_datapoints[j][i][1]) - np.array(all_datapoints[j][i][0]))), xy=(0.65, 0.8), xycoords='axes fraction')
+ # else:
+ # axs[j, i * 2 + 1].annotate("Var {:.2f}".format(np.var(all_datapoints[j][i][1])), xy=(0.65, 0.8), xycoords='axes fraction')
if j < n_weights - 1:
assert x_lim_used_normal > max(axs[j, i * 2].set_xlim()[0], axs[j, i * 2 + 1].set_xlim()[1]), "Hists are cut off ({} vs {})".format(x_lim_used_normal, max(axs[j, i * 2].set_xlim()[0], axs[j, i * 2 + 1].set_xlim()[1]))
axs[j, i * 2].set_xlim(0, x_lim_used_normal)
axs[j, i * 2 + 1].set_xlim(0, x_lim_used_normal)
- means = average[j, i] / counter[j, i]
- con = ConnectionPatch(xyA=(x_lim_used_normal / 12, means[0]), xyB=(0, means[1]), coordsA="data", coordsB="data",
- axesA=axs[j, i * 2], axesB=axs[j, i * 2 + 1], color="blue")
- axs[j, i * 2 + 1].add_artist(con)
elif j < n_weights + 1:
assert x_lim_used_bias > max(axs[j, i * 2].set_xlim()[0], axs[j, i * 2 + 1].set_xlim()[1]), "Hists are cut off ({} vs {})".format(x_lim_used_bias, max(axs[j, i * 2].set_xlim()[0], axs[j, i * 2 + 1].set_xlim()[1]))
axs[j, i * 2].set_xlim(0, x_lim_used_bias)
axs[j, i * 2 + 1].set_xlim(0, x_lim_used_bias)
- means = average[j, i] / counter[j, i]
- con = ConnectionPatch(xyA=(x_lim_used_bias / 12, means[0]), xyB=(0, means[1]), coordsA="data", coordsB="data",
- axesA=axs[j, i * 2], axesB=axs[j, i * 2 + 1], color="blue")
- axs[j, i * 2 + 1].add_artist(con)
else:
assert x_lim_used_augment > max(axs[j, i * 2].set_xlim()[0], axs[j, i * 2 + 1].set_xlim()[1]), "Hists are cut off ({} vs {})".format(x_lim_used_augment, max(axs[j, i * 2].set_xlim()[0], axs[j, i * 2 + 1].set_xlim()[1]))
axs[j, i * 2].set_xlim(0, x_lim_used_augment)
axs[j, i * 2 + 1].set_xlim(0, x_lim_used_augment)
- means = average[j, i] / counter[j, i]
- con = ConnectionPatch(xyA=(x_lim_used_augment / 12, means[0]), xyB=(0, means[1]), coordsA="data", coordsB="data",
- axesA=axs[j, i * 2], axesB=axs[j, i * 2 + 1], color="blue")
- axs[j, i * 2 + 1].add_artist(con)
+ con = ConnectionPatch(xyA=(0, means[0]), xyB=(0, means[1] - show_deltas * means[0]), coordsA="data", coordsB="data",
+ axesA=axs[j, i * 2], axesB=axs[j, i * 2 + 1], color="blue")
+ axs[j, i * 2 + 1].add_artist(con)
if i == 0:
axs[j, i].set_ylabel(local_ylabels[j])
- axs[j, i * 2 + 1].yaxis.set_ticklabels([])
+ # axs[j, i * 2 + 1].yaxis.set_ticklabels([])
if show_first_and_last:
if j < n_weights - 1:
axs[j, i * 2].plot([x_lim_used_normal / 8], [np.mean(first_and_last_pmf[:, 0, j])], marker='*', c='red') # also plot weights of very first state average
- if j == n_weights - 1:
+ elif j < n_weights + 1:
mask = first_and_last_pmf[:, 0, -1] < 0
axs[j, i * 2].plot([x_lim_used_bias / 8], [np.mean(first_and_last_pmf[mask, 0, -1])], marker='*', c='red') # separete biases again
- if j == n_weights:
+ else:
mask = first_and_last_pmf[:, 0, -1] > 0
axs[j, i * 2].plot([x_lim_used_augment / 8], [np.mean(first_and_last_pmf[mask, 0, -1])], marker='*', c='red')
- else:
- axs[j, i * 2].yaxis.set_ticklabels([])
- axs[j, i * 2 + 1].yaxis.set_ticklabels([])
+ # else:
+ # axs[j, i * 2].yaxis.set_ticklabels([])
+ # axs[j, i * 2 + 1].yaxis.set_ticklabels([])
if i == n_types - 1 and show_first_and_last:
if j < n_weights - 1:
axs[j, i * 2 + 1].plot([x_lim_used_normal / 8], [np.mean(first_and_last_pmf[:, 1, j])], marker='*', c='red') # also plot weights of very last state average
- if j == n_weights - 1:
+ elif j < n_weights + 1:
mask = first_and_last_pmf[:, 1, -1] < 0
axs[j, i * 2 + 1].plot([x_lim_used_bias / 8], [np.mean(first_and_last_pmf[mask, 1, -1])], marker='*', c='red') # separete biases again
- if j == n_weights:
+ else:
mask = first_and_last_pmf[:, 1, -1] > 0
axs[j, i * 2 + 1].plot([x_lim_used_augment / 8], [np.mean(first_and_last_pmf[mask, 1, -1])], marker='*', c='red')
if j == 0:
@@ -252,6 +342,32 @@ def plot_histogram_diffs(all_datapoints, x_lim_used_normal, x_lim_used_bias, bin
else:
axs[j, 1].set_xlabel("Changed distribution")
+ if j < n_weights - 1:
+ axs[j, i * 2].set_xticks(list(range(x_steps, x_lim_used_normal, x_steps)))
+ axs[j, i * 2].set_xticklabels([])
+ axs[j, i * 2 + 1].set_xticks(list(range(x_steps, x_lim_used_normal, x_steps)))
+ axs[j, i * 2 + 1].set_xticklabels([])
+ elif j < n_weights + 1:
+ axs[j, i * 2].set_xticks(list(range(x_steps, x_lim_used_bias, x_steps)) if len(list(range(x_steps, x_lim_used_bias, x_steps))) >= 1 else [x_steps])
+ axs[j, i * 2 + 1].set_xticks(list(range(x_steps, x_lim_used_bias, x_steps)) if len(list(range(x_steps, x_lim_used_bias, x_steps))) >= 1 else [x_steps])
+ if j == n_weights + show_weight_augmentations:
+ axs[j, i * 2].set_xticklabels(list(range(x_steps, x_lim_used_bias, x_steps)) if len(list(range(x_steps, x_lim_used_bias, x_steps))) >= 1 else [x_steps])
+ axs[j, i * 2 + 1].set_xticklabels(list(range(x_steps, x_lim_used_bias, x_steps)) if len(list(range(x_steps, x_lim_used_bias, x_steps))) >= 1 else [x_steps])
+ else:
+ axs[j, i * 2].set_xticklabels([])
+ axs[j, i * 2 + 1].set_xticklabels([])
+ else:
+ axs[j, i * 2].set_xticks(list(range(x_steps, x_lim_used_augment, x_steps)))
+ axs[j, i * 2 + 1].set_xticks(list(range(x_steps, x_lim_used_augment, x_steps)))
+ if j == n_weights + show_weight_augmentations:
+ axs[j, i * 2].set_xticklabels(list(range(x_steps, x_lim_used_augment, x_steps)))
+ axs[j, i * 2 + 1].set_xticklabels(list(range(x_steps, x_lim_used_augment, x_steps)))
+ else:
+ axs[j, i * 2].set_xticklabels([])
+ axs[j, i * 2 + 1].set_xticklabels([])
+ if show_deltas:
+ axs[j, i * 2 + 1].set_ylim(bins[0] - means[0], bins[-1] - means[0])
+
plt.savefig("./summary_figures/weight_changes/" + title)
plt.close()
@@ -260,21 +376,66 @@ if True:
bin_sets = [np.linspace(-6.5, 6.5, 30), np.linspace(-1, 2, 30), np.linspace(-3.5, 3.5, 30), np.linspace(-0.2, 1, 30)] # TODO: make show_augmentation robuse
+ average_sudden, counter_sudden, all_data_sudden = plot_traces_and_collate_data(data=all_sudden_transition_changes, augmented_data=aug_all_sudden_transition_changes, title="sudden_weight_change at transitions")
+
+ average_slow = np.zeros((n_weights + 1, n_types, 2))
+ counter_slow = np.zeros((n_weights + 1, n_types))
+ all_data_slow = create_nested_list([n_weights + 1, n_types, 2])
+
+ for weight_traj in all_weight_trajectories:
+
+ if len(weight_traj) < 5 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:
+ average_slow[i + (weight_traj[0][i] > 0), state_type] += np.array([weight_traj[0][i], weight_traj[-1][i]])
+ counter_slow[i + (weight_traj[0][i] > 0), state_type] += 1
+ all_data_slow[i + (weight_traj[0][i] > 0)][state_type][0].append(weight_traj[0][i])
+ all_data_slow[i + (weight_traj[0][i] > 0)][state_type][1].append(weight_traj[-1][i])
+ else:
+ average_slow[i, state_type] += np.array([weight_traj[0][i], weight_traj[-1][i]])
+ counter_slow[i, state_type] += 1
+ all_data_slow[i][state_type][0].append(weight_traj[0][i])
+ all_data_slow[i][state_type][1].append(weight_traj[-1][i])
+
+ temp_sudden_counter, switch, nonswitch = 0, 0, 0
+ for temp_sudden_change in all_sudden_transition_changes[0]:
+ prev_relevant_points, post_relevant_points = weights_to_pmf(temp_sudden_change[0])[[0, 1, -2, -1]], weights_to_pmf(temp_sudden_change[1])[[0, 1, -2, -1]]
+ print((1 - post_relevant_points[0] + 1 - post_relevant_points[1] + post_relevant_points[-2] + post_relevant_points[-1]) / 4)
+ if np.abs(np.mean(post_relevant_points) - 0.5) < 0.05 or np.abs(np.mean(prev_relevant_points) - 0.5) < 0.05:
+ print("skipped " + str(prev_relevant_points) + " " + str(post_relevant_points))
+ continue
+ print("considered " + str(prev_relevant_points) + " " + str(post_relevant_points))
+ temp_sudden_counter += 1
+ if (np.mean(prev_relevant_points) > 0.5 and np.mean(post_relevant_points) < 0.5) or (np.mean(prev_relevant_points) < 0.5 and np.mean(post_relevant_points) > 0.5):
+ switch += 1
+ else:
+ nonswitch += 1
+ quit()
+ plot_compact_all(average_slow, counter_slow, average_sudden, counter_sudden, title="all weight changes combined", all_data_sudden=all_data_sudden, all_data_slow=all_data_slow)
+
average, counter, all_datapoints = plot_traces_and_collate_data(data=all_sudden_transition_changes, augmented_data=aug_all_sudden_transition_changes, title="sudden_weight_change at transitions")
- plot_compact(average, counter, title="compact sudden_weight_change at transitions", show_weight_augmentations=show_weight_augmentations)
+ plot_compact(average, counter, title="compact sudden_weight_change at transitions", show_weight_augmentations=show_weight_augmentations, all_datapoints=all_datapoints)
+
+ plot_compact_split(all_datapoints, title="compact sudden_weight_change at transitions", show_weight_augmentations=show_weight_augmentations, width_divisor=18)
- plot_histogram_diffs(all_datapoints, x_lim_used_normal=22, x_lim_used_bias=11, x_lim_used_augment=40, bin_sets=bin_sets, title="weight changes sudden at transitions hists", show_deltas=False, show_weight_augmentations=show_weight_augmentations)
+ plot_histogram_diffs(all_datapoints, average, counter, x_lim_used_normal=22, x_lim_used_bias=11, x_lim_used_augment=40, bin_sets=bin_sets, title="weight changes sudden at transitions hists", show_deltas=False, show_weight_augmentations=show_weight_augmentations)
- plot_histogram_diffs(all_datapoints, x_lim_used_normal=22, x_lim_used_bias=11, x_lim_used_augment=40, bin_sets=bin_sets, title="weight changes sudden at transitions delta hists", show_deltas=True, show_weight_augmentations=show_weight_augmentations)
+ plot_histogram_diffs(all_datapoints, average, counter, x_lim_used_normal=22, x_lim_used_bias=11, x_lim_used_augment=40, bin_sets=bin_sets, title="weight changes sudden at transitions delta hists", show_deltas=True, show_weight_augmentations=show_weight_augmentations)
average, counter, all_datapoints = plot_traces_and_collate_data(data=all_sudden_changes, augmented_data=aug_all_sudden_changes, title="sudden_weight_change")
plot_compact(average, counter, title="compact sudden_weight_change", show_weight_augmentations=show_weight_augmentations)
- plot_histogram_diffs(all_datapoints, x_lim_used_normal=56, x_lim_used_bias=28, x_lim_used_augment=140, bin_sets=bin_sets, title="weight changes sudden hists", show_deltas=False, show_weight_augmentations=show_weight_augmentations)
+ plot_compact_split(all_datapoints, title="compact sudden_weight_change", show_weight_augmentations=show_weight_augmentations, width_divisor=65)
+
+ plot_histogram_diffs(all_datapoints, average, counter, x_lim_used_normal=56, x_lim_used_bias=28, x_lim_used_augment=140, bin_sets=bin_sets, title="weight changes sudden hists", show_deltas=False, show_weight_augmentations=show_weight_augmentations)
- plot_histogram_diffs(all_datapoints, x_lim_used_normal=120, x_lim_used_bias=60, x_lim_used_augment=180, bin_sets=bin_sets, title="weight changes sudden delta hists", show_deltas=True, show_weight_augmentations=show_weight_augmentations)
+ plot_histogram_diffs(all_datapoints, average, counter, x_lim_used_normal=120, x_lim_used_bias=40, x_lim_used_augment=180, bin_sets=bin_sets, title="weight changes sudden delta hists", show_deltas=True, show_weight_augmentations=show_weight_augmentations)
dur_lims = [(52, 25), (46, 23), (38, 19), (35, 17), (30, 15), (25, 12), (20, 10), (18, 9)]
for min_dur_counter, min_dur in enumerate([2, 3, 4, 5, 7, 9, 11, 15]):
@@ -365,11 +526,11 @@ if True:
plt.close()
x_lim_used_normal, x_lim_used_bias = dur_lims[min_dur_counter]
- plot_histogram_diffs(all_datapoints=all_datapoints, x_lim_used_normal=x_lim_used_normal, x_lim_used_bias=x_lim_used_bias, bin_sets=bin_sets,
+ plot_histogram_diffs(all_datapoints, average, counter, x_lim_used_normal=x_lim_used_normal, x_lim_used_bias=x_lim_used_bias, bin_sets=bin_sets,
title="weight changes min dur {} hists".format(min_dur), show_deltas=False, show_first_and_last=True)
- x_lim_used_normal, x_lim_used_bias = x_lim_used_normal * 2.5, x_lim_used_bias * 2.5
- plot_histogram_diffs(all_datapoints=all_datapoints, x_lim_used_normal=x_lim_used_normal, x_lim_used_bias=x_lim_used_bias, bin_sets=bin_sets,
+ x_lim_used_normal, x_lim_used_bias = int(x_lim_used_normal * 2.5), int(x_lim_used_bias * 2.5)
+ plot_histogram_diffs(all_datapoints, average, counter, x_lim_used_normal=x_lim_used_normal, x_lim_used_bias=x_lim_used_bias, bin_sets=bin_sets,
title="weight changes min dur {} delta hists".format(min_dur), show_deltas=True, show_first_and_last=True)
quit()
diff --git a/analysis_regression.py b/analysis_regression.py
index 8e09fa87..b4fb6098 100644
--- a/analysis_regression.py
+++ b/analysis_regression.py
@@ -5,18 +5,20 @@ import numpy as np
from scipy.stats import pearsonr
import seaborn as sns
-fontsize = 22
+fontsize = 38
+ticksize = 20
if __name__ == "__main__":
+ # [total # of regressions, # of sessions, # of regressions during type 1, type 2, type 3]
regressions = np.array(pickle.load(open("regressions.p", 'rb')))
regression_diffs = np.array(pickle.load(open("regression_diffs.p", 'rb')))
assert (regressions[:, 0] == np.sum(regressions[:, 2:], 1)).all() # total # of regressions must be sum of # of regressions per type
print(pearsonr(regressions[:, 0], regressions[:, 1]))
- # (0.6202414016960471, 2.3666819600330215e-13)
+ # (0.7025694973739075, 5.979216179591e-18)
- offset = 0.125
+ offset = 0.25
plt.figure(figsize=(16 * 0.9, 9 * 0.9))
# which x values exist
for x in np.unique(regressions[:, 0]):
@@ -24,15 +26,36 @@ if __name__ == "__main__":
tempa, tempb = np.unique(regressions[regressions[:, 0] == x, 1], return_counts=True)
for y, count in zip(tempa, tempb):
# plot them
- plt.scatter(x + np.linspace(-count + 1, count - 1, count) / 2 * offset, [y] * count, color='grey')
+ plt.scatter(x + np.linspace(-count + 1, count - 1, count) / 2 * offset, [y] * count, color='grey', linewidths=2)
plt.ylabel("# of sessions", size=fontsize)
plt.xlabel("# of regressed sessions", size=fontsize)
+ plt.xticks(size=ticksize)
+ plt.yticks(size=ticksize)
sns.despine()
plt.tight_layout()
plt.savefig("./summary_figures/Regression vs session length")
plt.show()
+ plt.figure(figsize=(16 * 0.9, 9 * 0.9))
+ non_regressed_sessions = regressions[:, 1] - regressions[:, 0]
+ # which x values exist
+ for x in np.unique(regressions[:, 0]):
+ # which and how many ys are associated with this x
+ tempa, tempb = np.unique(non_regressed_sessions[regressions[:, 0] == x], return_counts=True)
+ for y, count in zip(tempa, tempb):
+ # plot them
+ plt.scatter(x + np.linspace(-count + 1, count - 1, count) / 2 * offset, [y] * count, color='grey', linewidths=2)
+ plt.ylabel("# of non-regressed sessions", size=fontsize)
+ plt.xlabel("# of regressed sessions", size=fontsize)
+ plt.xticks(size=ticksize)
+ plt.yticks(size=ticksize)
+ sns.despine()
+
+ plt.tight_layout()
+ plt.savefig("./summary_figures/Regression vs non-regressions")
+ plt.show()
+
# histogram of regressions per type
plt.bar([0, 1, 2], np.sum(regressions[:, 2:], 0), color='grey')
plt.ylabel("# of regressed sessions", size=fontsize)
diff --git a/analysis_states.py b/analysis_states.py
new file mode 100644
index 00000000..9cd93d66
--- /dev/null
+++ b/analysis_states.py
@@ -0,0 +1,97 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import pickle
+
+captured_states = pickle.load(open("captured_states.p", 'rb'))
+
+# captured states is: captured_states.append((len([item for sublist in state_sets for item in sublist if len(sublist) > 40]), test.results[0].n_datapoints, len([s for s in state_sets if len(s) > 40])))
+num_trials = np.array([x for _, x, _, _ in captured_states])
+num_covered_trials = np.array([x for x, _, _, _ in captured_states])
+num_states = np.array([x for _, _, x, _ in captured_states])
+num_sessions = np.array([x for _, _, _, x in captured_states])
+
+print(num_covered_trials / num_trials)
+print("Minimum fraction of accounted trials: {}".format(np.min(num_covered_trials / num_trials)))
+
+print(np.unique(num_states, return_counts=True))
+
+
+type_1_to_2_save = pickle.load(open("multi_chain_saves/type_1_to_2_save.p", 'rb'))
+
+previously_expressed = 0
+not_expressed = 0
+counter = 0
+all_prev_biases = []
+all_biases = []
+neutral_counter, symm_counter = 0, 0
+boring_type_2, no_previous_bias, all_previous_biases = 0, 0, 0
+for i, pmf_lists in enumerate(type_1_to_2_save):
+ if pmf_lists == [[], []]:
+ continue
+ counter += 1
+ expressed_biases = []
+ for type_1_pmf in pmf_lists[0]:
+ if np.mean(type_1_pmf[[0, 1, -2, -1]]) < 0.45:
+ if -1 not in expressed_biases:
+ expressed_biases.append(-1)
+ elif np.mean(type_1_pmf[[0, 1, -2, -1]]) > 0.55:
+ if 1 not in expressed_biases:
+ expressed_biases.append(1)
+ else:
+ if 0 not in expressed_biases:
+ expressed_biases.append(0)
+ if np.mean(pmf_lists[1][0][[0, 1, -2, -1]]) < 0.45:
+ type_2_bias = -1
+ elif np.mean(pmf_lists[1][0][[0, 1, -2, -1]]) > 0.55:
+ type_2_bias = 1
+ else:
+ type_2_bias = 0
+
+ all_prev_biases.append(expressed_biases)
+ all_biases.append(expressed_biases + [type_2_bias])
+ # print()
+ # if type_2_bias == 0:
+ # print("neutral bias")
+ # neutral_counter += 1
+ # if np.abs(pmf_lists[1][0][0] + pmf_lists[1][0][-1] - 1) <= 0.1:
+ # print("symmetric")
+ # symm_counter += 1
+
+ if type_2_bias == 0:
+ boring_type_2 += 1
+ continue
+
+ if expressed_biases == [0]:
+ no_previous_bias += 1
+ continue
+ if -1 in expressed_biases and 1 in expressed_biases:
+ all_previous_biases += 1
+ continue
+ if type_2_bias in expressed_biases:
+ previously_expressed += 1
+ else:
+ not_expressed += 1
+
+print(boring_type_2, no_previous_bias, all_previous_biases)
+print(previously_expressed, not_expressed)
+
+from scipy.stats import binomtest
+print(binomtest(previously_expressed, previously_expressed + not_expressed, 0.5))
+
+from scipy.stats import linregress
+
+quantiles = np.linspace(0, 1, 7)[1:]
+
+quant_sessions = np.quantile(num_trials, quantiles)
+prev_session_bound = num_trials.min() - 1
+
+for quant_session in quant_sessions:
+ mask = np.logical_and(prev_session_bound < num_trials, num_trials <= quant_session)
+ print("Num of mice considered {}".format(np.sum(mask)))
+
+ res = linregress(num_trials[mask], num_states[mask])
+ plt.plot([prev_session_bound, quant_session], [res.intercept + res.slope * prev_session_bound, res.intercept + res.slope * quant_session])
+ prev_session_bound = quant_session
+
+plt.scatter(num_trials, num_states)
+plt.show()
\ No newline at end of file
diff --git a/behavioral_data_temp.py b/behavioral_data_temp.py
new file mode 100644
index 00000000..a53b6ca4
--- /dev/null
+++ b/behavioral_data_temp.py
@@ -0,0 +1,315 @@
+"""
+ This is code for figuring out which training criteria is fulfilled at which session.
+""""
+
+from one.api import ONE
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sns
+import pickle
+import json
+import os
+import re
+import psychofit
+pd.options.mode.chained_assignment = None # default='warn'
+np.set_printoptions(suppress=True)
+pd.set_option('display.float_format', lambda x: '%.3f' % x)
+one = ONE()
+
+contrast_to_num = {-1.: 0, -0.5: 1, -0.25: 2, -0.125: 3, -0.0625: 4, 0: 5, 0.0625: 6, 0.125: 7, 0.25: 8, 0.5: 9, 1.: 10}
+
+dataset_types = ['choice', 'contrastLeft', 'contrastRight',
+ 'feedbackType', 'probabilityLeft', 'response_times',
+ 'goCue_times']
+
+def get_df(trials):
+ if np.all(None == trials['choice']) or np.all(None == trials['contrastLeft']) or np.all(None == trials['contrastRight']) or np.all(None == trials['feedbackType']) or np.all(None == trials['probabilityLeft']): # or np.all(None == data_dict['response_times']):
+ return None, None
+ d = {'response': trials['choice'], 'contrastL': trials['contrastLeft'], 'contrastR': trials['contrastRight'], 'feedback': trials['feedbackType']}
+
+ df = pd.DataFrame(data=d, index=range(len(trials['choice']))).fillna(0)
+ df['feedback'] = df['feedback'].replace(-1, 0)
+ df['signed_contrast_clear'] = df['contrastR'] - df['contrastL']
+ df['signed_contrast'] = df['signed_contrast_clear'].map(contrast_to_num)
+ df['response'] += 1 # this is coded most unintuitely, 0 is rightwards, and 1 is leftwards (which is why I not this variable in other programs)
+ df['block'] = trials['probabilityLeft']
+ # df['rt'] = data_dict['response_times'] - data_dict['goCue_times'] # RTODO
+
+ return df
+
+
+def criterion_check(dfs, printer=False):
+ for df in dfs:
+ if type(df) == int:
+ print("No 3 sessions")
+ return
+
+ uber_df = pd.concat(dfs)
+ df_psych = uber_df.groupby('signed_contrast_clear').agg(
+ n_trials=pd.NamedAgg(column='signed_contrast', aggfunc='count'),
+ p_left=pd.NamedAgg(column='choice', aggfunc=lambda x: np.sum(x == 0) / np.sum(x != 1)))
+ psych_params, _ = psychofit.mle_fit_psycho(
+ np.c_[df_psych.index.values * 100, df_psych['n_trials'], df_psych['p_left']].T,
+ P_model='erf_psycho_2gammas',
+ parstart=np.array([0., 20., 0.05, 0.05]),
+ parmin=np.array([-100, 0, 0., 0.]),
+ parmax=np.array([100, 100., 1, 1]),
+ nfits=10
+ )
+
+ # plt.plot(df_psych.index.values * 100, df_psych['p_left'], 'bo', mfc='b')
+ # plt.plot(np.arange(-100, 100), psychofit.erf_psycho_2gammas(psych_params, np.arange(-100, 100)), '-b')
+ # plt.ylim(0, 1)
+ # plt.show()
+
+ median_rt_on_0 = False
+ contrast_0_introduced = True
+ trials_200 = True
+ trials_400 = True
+ easy_perf_80 = True
+ easy_perf_90 = True
+ for df in dfs:
+ easy_perf_80 = easy_perf_80 and np.mean(df.feedbackType[np.abs(df.signed_contrast_clear) >= 0.5]) > 0.8
+ easy_perf_90 = easy_perf_90 and np.mean(df.feedbackType[np.abs(df.signed_contrast_clear) >= 0.5]) > 0.9
+ trials_200 = trials_200 and df.shape[0] > 200
+ trials_400 = trials_400 and df.shape[0] > 400
+ abs_bias_16 = np.abs(psych_params[0]) < 16
+ abs_bias_10 = np.abs(psych_params[0]) < 10
+ thresh_19 = psych_params[1] < 19
+ thresh_20 = psych_params[1] < 20
+ lapse_2 = psych_params[2] < 0.2 and psych_params[3] < 0.2
+ lapse_1 = psych_params[2] < 0.1 and psych_params[3] < 0.1
+ if np.any(uber_df.signed_contrast_clear == 0):
+ median_rt_on_0 = np.median((uber_df.response_times - uber_df.goCue_times)[uber_df.signed_contrast_clear == 0]) < 2
+ else:
+ contrast_0_introduced = False
+
+ crit_1a = trials_200 and easy_perf_80 and abs_bias_16 and thresh_19 and lapse_2 and contrast_0_introduced
+ crit_1b = trials_400 and easy_perf_90 and abs_bias_10 and thresh_20 and lapse_1 and contrast_0_introduced and median_rt_on_0
+
+ if printer:
+ print("1a is {}, 1b is {}".format(crit_1a, crit_1b))
+
+ problems = np.zeros(12)
+ problems = trials_200, easy_perf_80, abs_bias_16, thresh_19, lapse_2, contrast_0_introduced, trials_400, easy_perf_90, abs_bias_10, thresh_20, lapse_1, median_rt_on_0
+
+ return crit_1a, crit_1b, problems
+
+misses = []
+to_introduce = [2, 3, 4, 5]
+
+amiss = ['UCLA034', 'UCLA036', 'UCLA037', 'PL015', 'PL016', 'PL017', 'PL024', 'NR_0017', 'NR_0019', 'NR_0020', 'NR_0021', 'NR_0027']
+fit_type = ['prebias', 'bias', 'all', 'prebias_plus', 'zoe_style'][0]
+if fit_type == 'bias':
+ loading_info = json.load(open("canonical_infos_bias.json", 'r'))
+elif fit_type == 'prebias':
+ loading_info = json.load(open("canonical_infos.json", 'r'))
+bwm = ['NYU-11', 'NYU-12', 'NYU-21', 'NYU-27', 'NYU-30', 'NYU-37',
+ 'NYU-39', 'NYU-40', 'NYU-45', 'NYU-46', 'NYU-47', 'NYU-48',
+ 'CSHL045', 'CSHL047', 'CSHL049', 'CSHL051', 'CSHL052', 'CSHL053',
+ 'CSHL054', 'CSHL055', 'CSHL058', 'CSHL059', 'CSHL060', 'UCLA005',
+ 'UCLA006', 'UCLA011', 'UCLA012', 'UCLA014', 'UCLA015', 'UCLA017',
+ 'UCLA033', 'UCLA034', 'UCLA035', 'UCLA036', 'UCLA037', 'KS014',
+ 'KS016', 'KS022', 'KS023', 'KS042', 'KS043', 'KS044', 'KS045',
+ 'KS046', 'KS051', 'KS052', 'KS055', 'KS084', 'KS086', 'KS091',
+ 'KS094', 'KS096', 'DY_008', 'DY_009', 'DY_010', 'DY_011', 'DY_013',
+ 'DY_014', 'DY_016', 'DY_018', 'DY_020', 'PL015', 'PL016', 'PL017',
+ 'PL024', 'SWC_042', 'SWC_043', 'SWC_060', 'SWC_061', 'SWC_066',
+ 'ZFM-01576', 'ZFM-01577', 'ZFM-01592', 'ZFM-01935', 'ZFM-01936',
+ 'ZFM-01937', 'ZFM-02368', 'ZFM-02369', 'ZFM-02370', 'ZFM-02372',
+ 'ZFM-02373', 'ZM_1897', 'ZM_1898', 'ZM_2240', 'ZM_2241', 'ZM_2245',
+ 'ZM_3003', 'SWC_038', 'SWC_039', 'SWC_052', 'SWC_053', 'SWC_054',
+ 'SWC_058', 'SWC_065', 'NR_0017', 'NR_0019', 'NR_0020', 'NR_0021',
+ 'NR_0027', 'ibl_witten_13', 'ibl_witten_17', 'ibl_witten_18',
+ 'ibl_witten_19', 'ibl_witten_20', 'ibl_witten_25', 'ibl_witten_26',
+ 'ibl_witten_27', 'ibl_witten_29', 'CSH_ZAD_001', 'CSH_ZAD_011',
+ 'CSH_ZAD_019', 'CSH_ZAD_022', 'CSH_ZAD_024', 'CSH_ZAD_025',
+ 'CSH_ZAD_026', 'CSH_ZAD_029']
+regexp = re.compile(r'canonical_result_((\w|-)+)_prebias.p')
+subjects = []
+for filename in os.listdir("./multi_chain_saves/"):
+ if not (filename.startswith('canonical_result_') and filename.endswith('.p')):
+ continue
+ result = regexp.search(filename)
+ if result is None:
+ continue
+ subject = result.group(1)
+ subjects.append(subject)
+already_fit = list(loading_info.keys())
+
+
+# remaining_subs = [s for s in subjects if s not in amiss and s not in already_fit]
+# print(remaining_subs)
+
+data_folder = 'session_data'
+
+old_style = False
+if old_style:
+ print("Warning, data can have splits")
+ data_folder = 'session_data_old'
+bias_eids = []
+
+print("#########################################")
+print("Waring, rt's removed, find with # RTODO")
+print("#########################################")
+
+short_subjs = []
+names = []
+
+pre_bias = []
+entire_training = []
+training_status_reached = []
+actually_existing = []
+
+all_problems = []
+count, count_1a, count_1b, count_any_1a, count_any_1b = 0, 0, 0, 0, 0
+not_good_enough = []
+problem_lists = []
+criterion_lists = []
+for subject in subjects:
+ break
+ print('_____________________')
+ print(subject)
+
+ if subject in ['NYU-12']:
+ # NYU-12 starts bias almost immediately
+ continue
+
+ # if subject in already_fit or subject in amiss:
+ # continue
+ try:
+ trials = one.load_aggregate('subjects', subject, '_ibl_subjectTrials.table')
+
+ # Load training status and join to trials table
+
+ training = one.load_aggregate('subjects', subject, '_ibl_subjectTraining.table')
+
+ trials = (trials
+ .set_index('session')
+ .join(training.set_index('session'))
+ .sort_values(by='session_start_time', kind='stable'))
+ actually_existing.append(subject)
+
+ start_times, indices = np.unique(trials.session_start_time, return_index=True)
+ start_times = [trials.session_start_time[index] for index in sorted(indices)]
+ task_protocol, indices = np.unique(trials.task_protocol, return_index=True)
+ task_protocol = [trials.task_protocol[index] for index in sorted(indices)]
+ nums, indices = np.unique(trials.session_number, return_index=True)
+ nums = [trials.session_number[index] for index in sorted(indices)]
+ eids, indices = np.unique(trials.index, return_index=True)
+ eids = [trials.index[index] for index in sorted(indices)]
+ except:
+ print("Not working {}".format(subject))
+ continue
+
+ print("original # of eids {}".format(len(eids)))
+
+ test = [(y, x) for y, x in sorted(zip(start_times, eids))]
+ pickle.dump(test, open("./{}/{}_session_names.p".format(data_folder, subject), "wb"))
+
+ performance = np.zeros(len(eids))
+ easy_per = np.zeros(len(eids))
+ hard_per = np.zeros(len(eids))
+ bias_start = 0
+ ephys_start = 0
+
+ info_dict = {'subject': subject, 'dates': [st.to_pydatetime() for st in start_times], 'eids': eids, 'date_and_session_num': {}}
+ contrast_set = {0, 1, 9, 10}
+
+ rel_count = -1
+
+ last_3_dfs = [0, 0, 0]
+ any_1a, any_1b = False, False
+ problem_list = [np.zeros(12), np.zeros(12), np.zeros(12), np.zeros(12), np.zeros(12), np.zeros(12)]
+ criterion_list = [np.zeros(2), np.zeros(2), np.zeros(2), np.zeros(2), np.zeros(2), np.zeros(2)]
+
+ for i, start_time in enumerate(start_times):
+
+ rel_count += 1
+
+ assert rel_count == i
+
+ df = trials[trials.session_start_time == start_time]
+ df.loc[:, 'contrastRight'] = df.loc[:, 'contrastRight'].fillna(0)
+ df.loc[:, 'contrastLeft'] = df.loc[:, 'contrastLeft'].fillna(0)
+ df.loc[:, 'feedbackType'] = df.loc[:, 'feedbackType'].replace(-1, 0)
+ df.loc[:, 'signed_contrast_clear'] = df.loc[:, 'contrastRight'] - df.loc[:, 'contrastLeft']
+ df.loc[:, 'signed_contrast'] = df.loc[:, 'signed_contrast_clear'].map(contrast_to_num)
+ df.loc[:, 'choice'] = df.loc[:, 'choice'] + 1
+
+ if any([df[x].isnull().any() for x in ['signed_contrast', 'choice', 'feedbackType', 'probabilityLeft']]):
+ quit()
+
+ assert len(np.unique(df['session_start_time'])) == 1
+
+ current_contrasts = set(df['signed_contrast'])
+ diff = current_contrasts.difference(contrast_set)
+ for c in to_introduce:
+ if c in diff:
+ info_dict[c] = rel_count
+ contrast_set.update(diff)
+
+ performance[i] = np.mean(df['feedbackType'])
+ easy_per[i] = np.mean(df['feedbackType'][np.logical_or(df['signed_contrast'] == 0, df['signed_contrast'] == 10)])
+ hard_per[i] = np.mean(df['feedbackType'][df['signed_contrast'] == 5])
+
+ if bias_start == 0 and df.task_protocol[0].startswith('_iblrig_tasks_biasedChoiceWorld'):
+ bias_start = i
+ print("bias start {}".format(rel_count))
+ info_dict['bias_start'] = rel_count
+ training_status_reached.append(set(df.training_status))
+ if bias_start < 33:
+ short_subjs.append(subject)
+
+ if i > 3:
+ crit1a, crit1b, problems = criterion_check(last_3_dfs)
+ any_1a = any_1a or crit1a
+ any_1b = any_1b or crit1b
+ problem_list.append(problems)
+ problem_list.pop(0)
+ criterion_list.append(np.array([any_1a, any_1b]))
+ criterion_list.pop(0)
+ if bias_start > 0:
+ print(subject)
+ crit1a, crit1b, problems = criterion_check(last_3_dfs, printer=True)
+ count += 1
+ count_1a += crit1a
+ count_1b += crit1b
+ count_any_1a += any_1a
+ count_any_1b += any_1b
+ all_problems.append(problems)
+ if not any_1a:
+ not_good_enough.append(subject)
+ problem_lists.append(problem_list)
+ criterion_lists.append(criterion_list)
+ break
+
+ if ephys_start == 0 and df.task_protocol[0].startswith('_iblrig_tasks_ephysChoiceWorld'):
+ ephys_start = i
+ print("ephys start {}".format(rel_count))
+ info_dict['ephys_start'] = rel_count
+
+ last_3_dfs[rel_count % 3] = df
+
+names = ["200 trials", "easy 80", "bias 16", "thresh 19", "lapse 20", "all conts", "trials 400", "easy 90", "bias 10", "thresh 20", "lapse 10", "median 0 rt"]
+criterion_array = pickle.load(open("criterion_array", 'rb'))
+reached_array = pickle.load(open('reached_array', 'rb'))
+
+plt.figure(figsize=(16, 9))
+ls = ['-'] * 6 + ['--'] * 6
+for i in range(criterion_array.shape[-1]):
+ plt.plot(range(-6, 0), criterion_array[..., i].mean(0), ls=ls[i], label=names[i])
+
+criteria_names = ["1a reached", "1b reached"]
+colours = ['green', 'black']
+for i in range(reached_array.shape[-1]):
+ plt.plot(range(-6, 0), reached_array[..., i].mean(0), c=colours[i], label=criteria_names[i], lw=4)
+
+plt.ylim(0, 1)
+
+plt.ylabel("% of population fulfilling criterion", size=28)
+plt.xlabel("Sessions till start of bias training", size=28)
+
+plt.legend(frameon=False, fontsize=14, ncols=2)
+plt.show()
\ No newline at end of file
diff --git a/behavioral_state_data.py b/behavioral_state_data.py
index 11a8cc41..79bf53b3 100644
--- a/behavioral_state_data.py
+++ b/behavioral_state_data.py
@@ -4,6 +4,7 @@ import numpy as np
import pandas as pd
import seaborn as sns
import pickle
+import datetime
one = ONE()
@@ -32,8 +33,9 @@ contrast_to_num = {-1.: 0, -0.5: 1, -0.25: 2, -0.125: 3, -0.0625: 4, 0: 5, 0.062
dataset_types = ['choice', 'contrastLeft', 'contrastRight',
'feedbackType', 'probabilityLeft', 'response_times',
'goCue_times']
+
def get_df(trials):
- if np.all(None == trials['choice']) or np.all(None == trials['contrastLeft']) or np.all(None == trials['contrastRight']) or np.all(None == trials['feedbackType']) or np.all(None == trials['probabilityLeft']): # or np.all(None == data_dict['response_times']):
+ if np.all(trials['choice'] is None) or np.all(None == trials['contrastLeft']) or np.all(None == trials['contrastRight']) or np.all(None == trials['feedbackType']) or np.all(None == trials['probabilityLeft']): # or np.all(None == data_dict['response_times']):
return None, None
d = {'response': trials['choice'], 'contrastL': trials['contrastLeft'], 'contrastR': trials['contrastRight'], 'feedback': trials['feedbackType']}
@@ -73,7 +75,7 @@ to_introduce = [2, 3, 4, 5]
# "ibl_witten_06", "ibl_witten_07", "ibl_witten_12", "ibl_witten_13", "ibl_witten_14", "ibl_witten_15",
# "ibl_witten_16", "KS003", "KS005", "KS019", "NYU-01", "NYU-02", "NYU-04", "NYU-06", "ZM_1367", "ZM_1369",
# "ZM_1371", "ZM_1372", "ZM_1743", "ZM_1745", "ZM_1746"] # zoe's subjects
-subjects = ['ZFM-05236']
+subjects = ["fip_{}".format(i) for i in list(range(13, 17)) + list(range(26, 43))]
data_folder = 'session_data'
# why does CSHL058 not work?
@@ -146,11 +148,12 @@ for subject in subjects:
bias_start = 0
info_dict = {'subject': subject, 'dates': fixed_dates, 'eids': fixed_eids}
+ info_dict = {'subject': subject, 'dates': [st for st in sorted(start_times)], 'eids': eids, 'date_and_session_num': {}}
contrast_set = {0, 1, 9, 10}
rel_count = -1
- quit()
- for i, (eid, extra_eids, date) in enumerate(zip(fixed_eids, additional_eids, fixed_dates)):
+
+ for i, (eid, extra_eids, start_time) in enumerate(zip(fixed_eids, additional_eids, sorted(start_times))):
try:
trials = one.load_object(eid, 'trials')
@@ -183,8 +186,6 @@ for subject in subjects:
df = pd.concat([df, df2], ignore_index=1)
print('new size: {}'.format(len(df)))
- pickle.dump(df, open("./sofiya_data/{}_df_{}_{}.p".format(subject, rel_count, date), "wb"))
-
current_contrasts = set(df['signed_contrast'])
diff = current_contrasts.difference(contrast_set)
for c in to_introduce:
@@ -206,7 +207,12 @@ for subject in subjects:
if bias_start:
bias_eids.append(eid)
+ print(start_time)
+ if start_time == datetime.date(2022, 12, 30):
+ quit()
pickle.dump(df, open("./{}/{}_df_{}.p".format(data_folder, subject, rel_count), "wb"))
+ info_dict['date_and_session_num'][rel_count] = start_time
+ info_dict['date_and_session_num'][start_time] = rel_count
side_info = np.zeros((len(df), 2))
side_info[:, 0] = df['block']
@@ -235,9 +241,12 @@ for subject in subjects:
else:
break
for c in to_introduce:
+ if c == 5 and subject.startswith("fip"):
+ continue
plt.axvline(info_dict[c] + skip_count, ymax=0.85, c='grey')
- plt.annotate('Pre-bias', (bias_start / 2, 1.), size=20, ha='center')
- plt.annotate('Bias', (bias_start + (i - bias_start) / 2, 1.), size=20, ha='center')
+ if not subject.startswith("fip"):
+ plt.annotate('Pre-bias', (bias_start / 2, 1.), size=20, ha='center')
+ plt.annotate('Bias', (bias_start + (i - bias_start) / 2, 1.), size=20, ha='center')
plt.title(subject, size=22)
plt.ylabel('Performance', size=22)
plt.xlabel('Session', size=22)
@@ -253,7 +262,8 @@ for subject in subjects:
plt.close()
# print(bias_eids)
- pre_bias.append(info_dict['bias_start'])
+ if not subject.startswith("fip"):
+ pre_bias.append(info_dict['bias_start'])
entire_training.append(rel_count + 1)
info_dict['n_sessions'] = rel_count
diff --git a/behavioral_state_data_easier.py b/behavioral_state_data_easier.py
index aed7c5dc..52c5ae21 100644
--- a/behavioral_state_data_easier.py
+++ b/behavioral_state_data_easier.py
@@ -74,6 +74,8 @@ for filename in os.listdir("./multi_chain_saves/"):
subjects.append(subject)
already_fit = list(loading_info.keys())
+subjects = ["fip_{}".format(i) for i in list(range(13, 17)) + list(range(26, 43))]
+
# remaining_subs = [s for s in subjects if s not in amiss and s not in already_fit]
# print(remaining_subs)
@@ -97,8 +99,7 @@ entire_training = []
training_status_reached = []
actually_existing = []
for subject in subjects:
- if subject in bwm:
- continue
+
print('_____________________')
print(subject)
@@ -117,6 +118,7 @@ for subject in subjects:
.sort_values(by='session_start_time', kind='stable'))
actually_existing.append(subject)
except:
+ print("Not working {}".format(subject))
continue
start_times, indices = np.unique(trials.session_start_time, return_index=True)
@@ -139,7 +141,7 @@ for subject in subjects:
bias_start = 0
ephys_start = 0
- info_dict = {'subject': subject, 'dates': [st.to_pydatetime() for st in start_times], 'eids': eids}
+ info_dict = {'subject': subject, 'dates': [st.to_pydatetime() for st in start_times], 'eids': eids, 'date_and_session_num': {}}
contrast_set = {0, 1, 9, 10}
rel_count = -1
@@ -188,6 +190,8 @@ for subject in subjects:
info_dict['ephys_start'] = rel_count
pickle.dump(df, open("./{}/{}_df_{}.p".format(data_folder, subject, rel_count), "wb"))
+ info_dict['date_and_session_num'][rel_count] = start_time
+ info_dict['date_and_session_num'][start_time] = rel_count
side_info = np.zeros((len(df), 2))
side_info[:, 0] = df['probabilityLeft']
diff --git a/behaviour_overview.py b/behaviour_overview.py
index f38eca39..09601c95 100644
--- a/behaviour_overview.py
+++ b/behaviour_overview.py
@@ -120,7 +120,7 @@ exclude_eids = ['a66f1593-dafd-4982-9b66-f9554b6c86b5', 'ee40aece-cffd-4edb-a4b6
# project='ibl_neuropixel_brainwide_01')
# traj.reverse()
-subject = 'KS022'
+subject = "UCLA015" #'KS022'
eids, sess_info = one.search(subject=subject, date_range=['2015-01-01', '2024-01-01'], details=True)
start_times = [sess['date'] for sess in sess_info]
@@ -145,11 +145,12 @@ for i, (prot, eid) in enumerate(zip(protocols, eids)):
continue
print(prot)
+ print(eid)
counti += 1
- if counti != 7:
- continue
+ # if counti != 7:
+ # continue
# rt_data = np.zeros((len(df), 3))
# rt_data[:, 0] = df['signed_contrast']
diff --git a/criterion_array b/criterion_array
new file mode 100644
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diff --git a/dyn_glm_chain_analysis.py b/dyn_glm_chain_analysis.py
index f2ad6737..677a28a3 100644
--- a/dyn_glm_chain_analysis.py
+++ b/dyn_glm_chain_analysis.py
@@ -255,6 +255,9 @@ class MCMC_result_list:
plt.figure(figsize=(16, 9))
if dim == 2:
plt.scatter(dimreduc[0], dimreduc[1], c=z)
+ plt.xlabel("PC 1", fs=36)
+ plt.ylabel("PC 2", fs=36)
+ plt.gca().spines[['right', 'top']].set_visible(False)
else:
plt.subplot(1, 3, 1)
plt.scatter(dimreduc[0], dimreduc[1], c=z)
@@ -381,7 +384,7 @@ class MCMC_result:
self.assign_counts[i, s] = np.sum(flat_list == s)
def state_appearance_posterior(self):
- # posterior over when new states appear
+ # posterior over when new states appear (ignoring the very first state)
state_starts = np.zeros(self.n_datapoints)
session_bounds = [0] + list(np.cumsum([len(s) for s in self.models[-1].stateseqs]))
state_start_save = np.zeros(20)
@@ -396,6 +399,9 @@ class MCMC_result:
continue
if state_fraction[s] < 0.05:
continue
+ # if len(observed_states) == 0: # ignore the very first state
+ # observed_states.append(s)
+ # continue
index = np.where(seq == s)[0][0]
state_start_save[20 * index // len(seq)] += 1
state_starts[session_bounds[i] + index] += 1
@@ -508,16 +514,28 @@ def augment_weights(w):
augmented_weights[-1] = max(pmf[-2:]) - min(pmf[:2])
return augmented_weights
-def sudden_state_changes(test, state_sets, consistencies, pmf_weights):
+def sudden_state_changes(test, state_sets, consistencies, pmf_weights, pmfs):
+ """
+ Document the changes upon regression weights caused by sudden state changes.
+ Also find out how weights change when first type change occurs
+ Also collect info about when in session and when in training states get introduced (split by types)
+ """
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
+ 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
+
+ in_sess_appear_dist = np.zeros((3, in_sess_appear_n_bins))
+ in_train_appear_dist = np.zeros((3, in_train_appear_n_bins))
+ in_sess_appear_dist_old = np.zeros((3, in_sess_appear_n_bins))
+ in_train_appear_dist_old = np.zeros((3, in_train_appear_n_bins))
+
+ type_1_to_2 = [[], []] # save all previous PMFs before a sudden transition into type 2, and the first type 2 PMFs
for seq_num in range(n):
state_occurences = np.zeros((len(state_sets), len(test.results[0].models[0].stateseqs[seq_num])))
@@ -545,8 +563,9 @@ def sudden_state_changes(test, state_sets, consistencies, pmf_weights):
first_trials.append(occurence_trials[0])
occuring_states = [x for _, x in sorted(zip(first_trials, occuring_states))]
+ first_trials = sorted(first_trials)
- for state in occuring_states:
+ for state, first_trial in zip(occuring_states, first_trials):
if state in state_pmfs:
state_counter[state] += 1
state_pmfs[state] = pmf_weights[state][state_counter[state]]
@@ -556,15 +575,28 @@ def sudden_state_changes(test, state_sets, consistencies, pmf_weights):
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
+ # track appearance times
+ in_train_appear_dist[pmf_type(weights_to_pmf(pmf_weights[state][0])), int(seq_num // (n / in_train_appear_n_bins))] += 1
+ in_sess_appear_dist[pmf_type(weights_to_pmf(pmf_weights[state][0])), int(first_trial // (len(test.results[0].models[0].stateseqs[seq_num]) / in_sess_appear_n_bins))] += 1
+
+ # find out which of previous states are closest to new state
+ defined_points = np.zeros(11, dtype=bool)
+ defined_points[test.results[0].session_contrasts[seq_num]] = True
+ defined_points = np.logical_and(defined_points, pmfs[state][0])
+ diff = 1000 # 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]
+ temp_defined_points = np.logical_and(defined_points, pmfs[existing_state][0])
+ temp_defined_points[[1, -2]] = True
+ temp_diff = np.sum(np.abs(weights_to_pmf(pmf_weights[state][0])[temp_defined_points] - weights_to_pmf(state_pmfs[existing_state])[temp_defined_points]))
+ if temp_diff < diff:
+ diff = temp_diff
+ contender = augment_weights(state_pmfs[existing_state])
+ # 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]))
@@ -573,12 +605,18 @@ def sudden_state_changes(test, state_sets, consistencies, pmf_weights):
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]))
+ for existing_state in state_pmfs:
+ type_1_to_2[0].append(weights_to_pmf(state_pmfs[existing_state]))
+ type_1_to_2[1].append(weights_to_pmf(pmf_weights[state][0]))
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
-
+
+ in_train_appear_dist_old[pmf_type(weights_to_pmf(pmf_weights[state][0])), int(seq_num // (n / in_train_appear_n_bins))] += 1
+ in_sess_appear_dist_old[pmf_type(weights_to_pmf(pmf_weights[state][0])), int(first_trial // (len(test.results[0].models[0].stateseqs[seq_num]) / in_sess_appear_n_bins))] += 1
+
state_counter[state] = 0
state_pmfs[state] = pmf_weights[state][0]
if pmf_type(weights_to_pmf(pmf_weights[state][0])) == 0:
@@ -588,9 +626,9 @@ def sudden_state_changes(test, state_sets, consistencies, pmf_weights):
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
+ 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
+ return changes, changes_across_types, aug_changes, aug_changes_across_types, in_sess_appear_dist, in_train_appear_dist, in_sess_appear_dist_old, in_train_appear_dist_old, type_1_to_2
def contrasts_plot(test, state_sets, subject, save=False, show=False, dpi='figure', save_append='', consistencies=None, CMF=False):
@@ -598,6 +636,11 @@ def contrasts_plot(test, state_sets, subject, save=False, show=False, dpi='figur
trial_counter = 0
cnas = [] # contrasts aNd actions
+ lookback = 14
+ session_pers = np.zeros((n, lookback + 1))
+ total_pers = np.zeros(lookback + 1)
+ total = 0
+
for seq_num in range(n):
# if seq_num + 1 != 12:
# trial_counter += len(test.results[0].models[0].stateseqs[seq_num])
@@ -644,10 +687,12 @@ def contrasts_plot(test, state_sets, subject, save=False, show=False, dpi='figur
cnas.append(c_n_a)
mask = c_n_a[:, -1] == 0
- plt.plot(np.where(mask)[0], 0.5 + 0.25 * (noise[mask] - all_conts[cont_mapping(- c_n_a[mask, 0] + c_n_a[mask, 1])]), 'o', c='b', ms=ms, label='Rightward', alpha=0.6)
+ if mask.sum() > 0:
+ plt.plot(np.where(mask)[0], 0.5 + 0.25 * (noise[mask] - all_conts[cont_mapping(- c_n_a[mask, 0] + c_n_a[mask, 1])]), 'o', c='b', ms=ms, label='Rightward', alpha=0.6)
mask = c_n_a[:, -1] == 1
- plt.plot(np.where(mask)[0], 0.5 + 0.25 * (noise[mask] - all_conts[cont_mapping(- c_n_a[mask, 0] + c_n_a[mask, 1])]), 'o', c='r', ms=ms, label='Leftward', alpha=0.6)
+ if mask.sum() > 0:
+ plt.plot(np.where(mask)[0], 0.5 + 0.25 * (noise[mask] - all_conts[cont_mapping(- c_n_a[mask, 0] + c_n_a[mask, 1])]), 'o', c='r', ms=ms, label='Leftward', alpha=0.6)
plt.title("session #{} / {}".format(1+seq_num, test.results[0].n_sessions), size=26)
# plt.yticks(*self.cont_ticks, size=22-2)
@@ -676,12 +721,31 @@ def contrasts_plot(test, state_sets, subject, save=False, show=False, dpi='figur
plt.legend(frameon=False, fontsize=22, ncol=2, loc=(0.7, 0.05))
plt.tight_layout()
if save:
+ print("saving with {} dpi".format(dpi))
plt.savefig("dynamic_GLM_figures/all posterior and contrasts {}, sess {}{}.png".format(subject, seq_num, save_append), dpi=dpi)#, bbox_inches='tight')
if show:
plt.show()
else:
plt.close()
+ if c_n_a.shape[0] > 50:
+ answers = c_n_a[lookback:, -1]
+ prev_answers = c_n_a[:, -1]
+ lefts = np.where(answers == 0)[0]
+ rights = np.where(answers == 1)[0]
+ for l in lefts:
+ session_pers[seq_num] += (prev_answers[l: l + lookback + 1] == 0) * 2 - 1
+ for r in rights:
+ session_pers[seq_num] += (prev_answers[r: r + lookback + 1] == 1) * 2 - 1
+
+ total_pers += session_pers[seq_num]
+ session_pers[seq_num] = session_pers[seq_num] / answers.shape[0]
+ total += answers.shape[0]
+
+ # plt.plot(session_pers[seq_num])
+ # plt.close()
+
+
if CMF and not test.results[0].name.startswith('Sim_'):
rt_data = pickle.load(open("./session_data/{} rt info {}".format(subject, seq_num + 1), 'rb'))
rt_data = rt_data[1:]
@@ -693,7 +757,11 @@ def contrasts_plot(test, state_sets, subject, save=False, show=False, dpi='figur
plt.title(seq_num + 1, size=22)
plt.show()
- return cnas
+ # plt.plot(total_pers / total)
+ # plt.ylim(0, 1)
+ # plt.show()
+
+ return total_pers / total
def bias_flips(states_by_session, pmfs, durs):
state_counter = {}
@@ -739,7 +807,7 @@ def pmf_regressions(states_by_session, pmfs, durs):
if s not in state_counter:
state_counter[s] = -1
state_counter[s] += 1
- state_perfs[s] = pmf_to_perf(pmfs[s][1][state_counter[s]], pmfs[s][0])
+ state_perfs[s] = pmf_to_perf(pmfs[s][1][state_counter[s]])
if current_best_state == -1 or state_perfs[current_best_state] < state_perfs[s]:
current_best_state = s
@@ -784,13 +852,16 @@ def control_flow(test, indices, trials, func_init, first_for, second_for, end_fi
def state_pmfs(test, trials, indices):
- def func_init(): return {'pmfs': [], 'session_js': [], 'pmf_weights': []}
+ # Find out what the PMFs were, on which sessions they were, what their weights were, how many trials they had
+ # ! thanks to the trials list, this only looks at trials assigned to the current state
+ def func_init(): return {'pmfs': [], 'session_js': [], 'pmf_weights': [], 'trial_ns': []}
def first_for(test, results):
results['pmf'] = np.zeros(test.results[0].n_contrasts)
results['pmf_weight'] = np.zeros(4)
def second_for(m, j, counter, session_trials, trial_counter, results):
+ # find the states (in this sample) which are assigned to the trials of this state (as defined post-hoc), and their number
states, counts = np.unique(m.stateseqs[j][session_trials - trial_counter], return_counts=True)
for sub_state, c in zip(states, counts):
results['pmf'] += weights_to_pmf(m.obs_distns[sub_state].weights[j]) * c / session_trials.shape[0]
@@ -800,9 +871,10 @@ def state_pmfs(test, trials, indices):
results['pmfs'].append(results['pmf'] / len(indices))
results['pmf_weights'].append(results['pmf_weight'] / len(indices))
results['session_js'].append(j)
+ results['trial_ns'].append(kwargs['session_trials'].shape[0])
results = control_flow(test, indices, trials, func_init, first_for, second_for, end_first_for)
- return results['session_js'], results['pmfs'], results['pmf_weights']
+ return results['session_js'], results['pmfs'], results['pmf_weights'], results['trial_ns']
def state_weights(test, trials, indices):
@@ -1073,6 +1145,7 @@ def state_development(test, state_sets, indices, save=True, save_append='', show
all_pmfs = []
all_pmf_weights = []
+ all_trial_ns = []
cmaps = ['Greys', 'Purples', 'Blues', 'Greens', 'Oranges', 'Reds', 'YlOrBr', 'YlOrRd', 'OrRd', 'PuRd', 'RdPu']
np.random.seed(8)
np.random.shuffle(cmaps)
@@ -1082,7 +1155,7 @@ def state_development(test, state_sets, indices, save=True, save_append='', show
for state, trials in enumerate(state_sets):
if separate_pmf:
n_trials = len(trials)
- session_js, pmfs, _ = state_pmfs(test, trials, indices)
+ session_js, pmfs, _, _ = state_pmfs(test, trials, indices)
else:
pmfs = np.zeros((len(indices), test.results[0].n_contrasts))
n_trials = len(trials)
@@ -1102,13 +1175,17 @@ def state_development(test, state_sets, indices, save=True, save_append='', show
pmfs_to_score.append(np.mean(pmfs))
# test.state_mapping = dict(zip(range(len(state_sets)), np.argsort(np.argsort(pmfs_to_score)))) # double argsort for ranks
test.state_mapping = dict(zip(np.flip(np.argsort((states_by_session != 0).argmax(axis=1))), range(len(state_sets))))
+ if test.results[0].name == 'KS014':
+ test.state_mapping[5] = 5
+ test.state_mapping[4] = 4
+
for state, trials in enumerate(state_sets):
cmap = matplotlib.cm.get_cmap(cmaps[state]) if state < len(cmaps) else matplotlib.cm.get_cmap('Greys')
if separate_pmf:
n_trials = len(trials)
- session_js, pmfs, pmf_weights = state_pmfs(test, trials, indices)
+ session_js, pmfs, pmf_weights, trial_ns = state_pmfs(test, trials, indices)
else:
pmfs = np.zeros((len(indices), test.results[0].n_contrasts))
pmf_weights = np.zeros((len(indices), test.results[0].obs_distns[0].weights.shape[0]))
@@ -1155,7 +1232,7 @@ def state_development(test, state_sets, indices, save=True, save_append='', show
if not test.state_mapping[state] in dont_plot:
ax1.fill_between([points[k], points[k+1]],
test.state_mapping[state] - 0.5, [test.state_mapping[state] + interpolation[k] - 0.5, test.state_mapping[state] + interpolation[k+1] - 0.5], color=cmap(0.3 + 0.7 * k / n_points))
- ax1.annotate(len(state_sets) - test.state_mapping[state], (test.results[0].n_sessions + 0.1, test.state_mapping[state] - 0.15), fontsize=22, annotation_clip=False)
+ ax1.annotate(len(state_sets) - test.state_mapping[state], (test.results[0].n_sessions + 0.05, test.state_mapping[state] - 0.15), fontsize=22, annotation_clip=False)
if test.results[0].name.startswith('GLM_Sim_'):
ax1.plot(range(1, 1 + test.results[0].n_sessions), state + truth['state_posterior'][:, state] - 0.5, color='r')
@@ -1172,12 +1249,13 @@ def state_development(test, state_sets, indices, save=True, save_append='', show
# defined_points = np.zeros(test.results[0].n_contrasts, dtype=bool)
# defined_points[[0, 1, -2, -1]] = True
if separate_pmf:
- for j, pmf, pmf_weight in zip(session_js, pmfs, pmf_weights):
+ for j, pmf, pmf_weight, trial_n in zip(session_js, pmfs, pmf_weights, trial_ns):
if not test.state_mapping[state] in dont_plot:
ax2.plot(np.where(defined_points)[0] / (len(defined_points)-1), pmf[defined_points] - 0.5 + test.state_mapping[state], color=cmap(0.2 + 0.8 * j / test.results[0].n_sessions))
ax2.plot(np.where(defined_points)[0] / (len(defined_points)-1), pmf[defined_points] - 0.5 + test.state_mapping[state], ls='', ms=7, marker='*', color=cmap(j / test.results[0].n_sessions))
all_pmfs.append((defined_points, pmfs))
all_pmf_weights.append(pmf_weights)
+ all_trial_ns.append(trial_ns)
else:
temp = np.percentile(pmfs, [2.5, 97.5], axis=0)
if not test.state_mapping[state] in dont_plot:
@@ -1196,6 +1274,7 @@ def state_development(test, state_sets, indices, save=True, save_append='', show
ax2.axhline(test.state_mapping[state], c='grey', alpha=alpha_level, zorder=4)
ax1.axhline(test.state_mapping[state] + 0.5, c='grey', alpha=alpha_level, zorder=4)
+ ax1.annotate("State #", (test.results[0].n_sessions + 0.05, len(state_sets) - 0.4), fontsize=14, annotation_clip=False)
if not test.results[0].name.startswith('Sim_'):
perf = np.zeros(test.results[0].n_sessions)
found_files = 0
@@ -1323,7 +1402,7 @@ def state_development(test, state_sets, indices, save=True, save_append='', show
plt.savefig("dur dists")
plt.show()
- return states_by_session, all_pmfs, all_pmf_weights, durs, state_types, contrast_intro_types, smart_divide(introductions_by_stage, np.array(durs)), introductions_by_stage, states_per_type
+ return states_by_session, all_pmfs, all_pmf_weights, durs, state_types, contrast_intro_types, smart_divide(introductions_by_stage, np.array(durs)), introductions_by_stage, states_per_type, all_trial_ns
def dur_hists(test, trials, indices):
def func_init(): return {'dur_params': np.zeros((len(indices), 2))}
@@ -1601,15 +1680,14 @@ def write_results(test, state_sets, indices, consistencies=None):
for state, trials in enumerate(state_sets):
- session_js, pmfs, pmf_weights = state_pmfs(test, trials, indices)
+ session_js, pmfs, pmf_weights, _ = state_pmfs(test, trials, indices)
state_dict[state]['sessions'] = session_js
state_dict[state]['pmfs'] = pmfs
-
for seq_num in range(n):
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]))
+ active_trials = np.zeros(len(test.results[0].models[0].stateseqs[seq_num]), dtype=int)
if consistencies is None:
active_trials[relevant_trials - trial_counter] = 1
@@ -1618,11 +1696,20 @@ def write_results(test, state_sets, indices, consistencies=None):
active_trials[relevant_trials - trial_counter] -= 1
active_trials[relevant_trials - trial_counter] = active_trials[relevant_trials - trial_counter] / (trials.shape[0] - 1)
- if np.sum(active_trials) > 0:
- state_dict[state]['trials'].append(active_trials)
+ state_dict[state]['trials'].append(active_trials)
trial_counter += len(test.results[0].models[0].stateseqs[seq_num])
- return state_dict
+
+ session_dict = {seq_num: {} for seq_num in range(n)}
+ for seq_num in range(n):
+ n_trials = len(test.results[0].models[0].stateseqs[seq_num])
+ session_dict[seq_num]['states'] = np.zeros((len(state_sets), n_trials))
+ c_n_a = test.results[0].data[seq_num]
+ session_dict[seq_num]['contrasts'] = all_conts[cont_mapping(- c_n_a[:, 0] + c_n_a[:, 1])]
+ for state in range(len(state_sets)):
+ session_dict[seq_num]['states'][state] = state_dict[state]['trials'][seq_num]
+
+ return state_dict, session_dict
if __name__ == "__main__":
@@ -1644,13 +1731,20 @@ if __name__ == "__main__":
subjects.append(subject)
print(len(subjects))
- fit_variance = [0.03, 0.002, 0.0005, 'uniform', 0, 0.008][0]
+ fit_variance = [0.03, 0.06, 0.12, 0.24, 0.48][0]
dur = 'yes'
# fig, ax = plt.subplots(1, 3, sharey=True, figsize=(16, 9))
- pop_state_starts = np.zeros(20)
- state_appear_dist = np.zeros(11)
+ in_sess_appear_n_bins = 20
+ in_sess_appear = np.zeros((3, in_sess_appear_n_bins))
+ in_train_appear_n_bins = 10
+ in_train_appear = np.zeros((3, in_train_appear_n_bins))
+
+
+ in_sess_appear_old = np.zeros((3, in_sess_appear_n_bins))
+ in_train_appear_old = np.zeros((3, in_train_appear_n_bins))
+
state_appear_mode = []
num_states = []
num_sessions = []
@@ -1668,6 +1762,7 @@ if __name__ == "__main__":
abs_state_durs = []
all_first_pmfs = {}
all_first_pmfs_typeless = {}
+ all_trial_ns = {}
all_pmf_diffs = []
all_pmf_asymms = []
all_pmfs = []
@@ -1688,6 +1783,10 @@ if __name__ == "__main__":
all_sudden_transition_changes = [[], []]
aug_all_sudden_changes = [[], [], []]
aug_all_sudden_transition_changes = [[], []]
+ mice_exponentials = []
+ captured_states = []
+ type_1_to_2_save = []
+ all_state_percentages = []
new_counter, transform_counter = 0, 0
state_types_interpolation = np.zeros((3, 150))
@@ -1697,44 +1796,68 @@ if __name__ == "__main__":
state_nums_10 = []
ultimate_counter = 0
+ trial_counter = 0
+ session_counter = 0
+
+ just_checking = True
+ checking_counter = 0
for subject in subjects:
- if subject.startswith('GLM_Sim_') or subject in ['SWC_065', 'ZFM-05245', 'ZFM-04019', 'ibl_witten_18', 'UCLA006']:
+ if subject.startswith('GLM_Sim_') or subject in ['SWC_065', 'ZFM-05245', 'ZFM-04019', 'ibl_witten_18', 'UCLA006', 'NYU-12', 'UCLA015']:
# 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
+ # 'NYU-12' has no data for some reason
+ # UCLA015 has a problem
continue
-
print()
print(subject)
- print(ultimate_counter)
-
-
- 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
-
- a, b, c = test.state_start_and_dur()
- state_appear += a
- state_dur += b
- state_appear_dist += c
+ if just_checking:
+ print(checking_counter)
+ else:
+ print(ultimate_counter)
mode_specifier = 'first'
+ if os.path.exists("multi_chain_saves/canonical_result_{}_{}.p".format(subject, fit_type)):
+ fit_var = False
+ else:
+ fit_var = True
+
+ if just_checking:
+ assert os.path.exists("multi_chain_saves/canonical_result_{}_{}.p".format(subject, fit_type)) or \
+ os.path.exists("multi_chain_saves/canonical_result_{}_{}_var_{}.p".format(subject, fit_type, fit_variance))
+ assert os.path.exists("multi_chain_saves/{}_mode_indices_{}_{}".format(mode_specifier, subject, fit_type) + fit_var * "_var_{}".format(fit_variance) + '.p') or \
+ os.path.exists("multi_chain_saves/mode_indices_{}_{}".format(subject, fit_type) + fit_var * "_var_{}".format(fit_variance) + '.p')
+ assert os.path.exists("multi_chain_saves/{}_state_sets_{}_{}".format(mode_specifier, subject, fit_type) + fit_var * "_var_{}".format(fit_variance) + '.p') or \
+ os.path.exists("multi_chain_saves/state_sets_{}_{}".format(subject, fit_type) + fit_var * "_var_{}".format(fit_variance) + '.p')
+ assert os.path.exists("multi_chain_saves/first_mode_consistencies_{}_{}".format(subject, fit_type) + fit_var * "_var_{}".format(fit_variance) + '.p') or \
+ os.path.exists("multi_chain_saves/consistencies_{}_{}".format(subject, fit_type) + fit_var * "_var_{}".format(fit_variance) + '.p')
+ checking_counter += 1
+ continue
+
+ test = pickle.load(open("multi_chain_saves/canonical_result_{}_{}".format(subject, fit_type) + fit_var * "_var_{}".format(fit_variance) + '.p', 'rb'))
try:
- mode_indices = pickle.load(open("multi_chain_saves/{}_mode_indices_{}_{}.p".format(mode_specifier, subject, fit_type), 'rb'))
- state_sets = pickle.load(open("multi_chain_saves/{}_state_sets_{}_{}.p".format(mode_specifier, subject, fit_type), 'rb'))
- except:
+ mode_indices = pickle.load(open("multi_chain_saves/{}_mode_indices_{}_{}".format(mode_specifier, subject, fit_type) + fit_var * "_var_{}".format(fit_variance) + '.p', 'rb'))
+ state_sets = pickle.load(open("multi_chain_saves/{}_state_sets_{}_{}".format(mode_specifier, subject, fit_type) + fit_var * "_var_{}".format(fit_variance) + '.p', 'rb'))
+ except Exception as E:
+ print(E)
try:
- mode_indices = pickle.load(open("multi_chain_saves/mode_indices_{}_{}.p".format(subject, fit_type), 'rb'))
- state_sets = pickle.load(open("multi_chain_saves/state_sets_{}_{}.p".format(subject, fit_type), 'rb'))
- except:
+ mode_indices = pickle.load(open("multi_chain_saves/mode_indices_{}_{}".format(subject, fit_type) + fit_var * "_var_{}".format(fit_variance) + '.p', 'rb'))
+ state_sets = pickle.load(open("multi_chain_saves/state_sets_{}_{}".format(subject, fit_type) + fit_var * "_var_{}".format(fit_variance) + '.p', 'rb'))
+ except Exception as E:
+ print(E)
print("____________________________________")
print("Something quite wrong with {}".format(subject))
print("____________________________________")
continue
+ ultimate_counter += 1
+
+ trial_counter += test.results[0].n_datapoints
+ session_counter += test.results[0].n_sessions
+ print(trial_counter / ultimate_counter)
+ captured_states.append((len([item for sublist in state_sets for item in sublist if len(sublist) > 40]), test.results[0].n_datapoints, len([s for s in state_sets if len(s) > 40]), test.results[0].n_sessions))
# lapse differential
# lapse_sides(test, [s for s in state_sets if len(s) > 40], mode_indices)
@@ -1743,7 +1866,56 @@ if __name__ == "__main__":
# _ = state_development(test, [s for s in state_sets if len(s) > 40], mode_indices, save_append='step 1', show=1, separate_pmf=1, type_coloring=False, dont_plot=list(range(7)), plot_until=2)
# _ = 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)
+ states, pmfs, pmf_weights, durs, state_types, contrast_intro_type, intros_by_type, undiv_intros, states_per_type, trial_ns = state_development(test, [s for s in state_sets if len(s) > 40], mode_indices, save=False, show=False, separate_pmf=1, type_coloring=True, dpi=300, save_append=str(fit_variance).replace('.', '_'))
+ all_state_percentages.append(states)
+
+ abs_state_durs.append(durs)
+
+ try:
+ consistencies = pickle.load(open("multi_chain_saves/first_mode_consistencies_{}_{}".format(subject, fit_type) + fit_var * "_var_{}".format(fit_variance) + '.p', 'rb'))
+ except FileNotFoundError:
+ consistencies = pickle.load(open("multi_chain_saves/consistencies_{}_{}".format(subject, fit_type) + fit_var * "_var_{}".format(fit_variance) + '.p', 'rb'))
+ consistencies /= consistencies[0, 0]
+
+ basic_info, diffs = pmf_regressions(states, pmfs, durs)
+ regressions.append(basic_info)
+ regression_diffs.append(diffs)
+
+ # temp = contrasts_plot(test, [s for s in state_sets if len(s) > 40], dpi=300, subject=subject, save=False, show=False, consistencies=consistencies, CMF=False)
+ # mice_exponentials.append(temp)
+
+ sudden_changes, sudden_transition_changes, aug_sudden_changes, aug_sudden_transition_changes, in_sess_appear_dist, in_train_appear_dist, in_sess_appear_dist_old, in_train_appear_dist_old, type_1_to_2 = sudden_state_changes(test, [s for s in state_sets if len(s) > 40], consistencies=consistencies, pmf_weights=pmf_weights, pmfs=pmfs)
+
+ type_1_to_2_save.append(type_1_to_2)
+
+ in_sess_appear += in_sess_appear_dist
+ in_train_appear += in_train_appear_dist
+
+ # in_sess_appear_old += in_sess_appear_dist_old
+ # in_train_appear_old += in_train_appear_dist_old
+
+ info_dict = pickle.load(open("./{}/{}_info_dict.p".format('session_data', subject), "rb"))
+ if 'ephys_start' in info_dict:
+ bias_sessions.append(info_dict['ephys_start'] - info_dict['bias_start'])
+ else:
+ bias_sessions.append(info_dict['n_sessions'] - info_dict['bias_start'])
+ print(subject, info_dict['n_sessions'], info_dict['bias_start'])
+
+ all_first_pmfs_typeless[subject] = []
+ for pmf in pmfs:
+ all_first_pmfs_typeless[subject].append((pmf[0], pmf[1][0]))
+ all_pmfs.append(pmf)
+
+ first_pmfs, changing_pmfs = get_first_pmfs(states, pmfs)
+ for pmf in changing_pmfs:
+ if type(pmf[0]) == int:
+ continue
+ all_changing_pmf_names.append(subject)
+ all_changing_pmfs.append(pmf)
+
+ all_first_pmfs[subject] = first_pmfs
+
+ all_trial_ns[subject] = trial_ns
new = type_2_appearance(states, pmfs)
@@ -1756,17 +1928,11 @@ if __name__ == "__main__":
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]))
+ 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'))
+ # state_dict, session_dict = write_results(test, [s for s in state_sets if len(s) > 40], mode_indices)
- 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]
@@ -1777,41 +1943,14 @@ if __name__ == "__main__":
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)
+ all_weight_trajectories += pmf_weights
all_pmfs_named[subject] = pmfs
- info_dict = pickle.load(open("./{}/{}_info_dict.p".format('session_data', subject), "rb"))
- if 'ephys_start' in info_dict:
- bias_sessions.append(info_dict['ephys_start'] - info_dict['bias_start'])
- else:
- 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])
state_types_interpolation[2] += np.interp(np.linspace(1, state_types.shape[1], 150), np.arange(1, 1 + state_types.shape[1]), state_types[2])
@@ -1820,41 +1959,15 @@ if __name__ == "__main__":
# continue
# a = compare_params(test, 26, [s for s in state_sets if len(s) > 40], mode_indices, [3, 5])
# compare_pmfs(test, [3, 2, 4], states, pmfs, title="{} convergence pmf".format(subject))
- # consistencies = pickle.load(open("multi_chain_saves/first_mode_consistencies_{}_{}.p".format(subject, fit_type), 'rb'))
- # consistencies /= consistencies[0, 0]
# 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()
-
- # 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'))
-
all_pmf_weights += [item for sublist in pmf_weights for item in sublist]
all_state_types.append(state_types)
-
- all_first_pmfs_typeless[subject] = []
- for pmf in pmfs:
- all_first_pmfs_typeless[subject].append((pmf[0], pmf[1][0]))
- all_pmfs.append(pmf)
-
- first_pmfs, changing_pmfs = get_first_pmfs(states, pmfs)
- for pmf in changing_pmfs:
- if type(pmf[0]) == int:
- continue
- all_changing_pmf_names.append(subject)
- all_changing_pmfs.append(pmf)
-
- all_first_pmfs[subject] = first_pmfs
-
- continue
# b_flips = bias_flips(states, pmfs, durs)
# all_bias_flips.append(b_flips)
- regression, diffs = pmf_regressions(states, pmfs, durs)
- regression_diffs += diffs
- regressions.append(regression)
-
# compare_pmfs(test, [s for s in state_sets if len(s) > 40], mode_indices, [4, 5], states, pmfs, title="{} convergence pmf".format(subject))
# compare_weights(test, [s for s in state_sets if len(s) > 40], mode_indices, [4, 5], states, title="{} convergence weights".format(subject))
@@ -1871,6 +1984,15 @@ if __name__ == "__main__":
all_pmf_asymms.append(np.abs(p[0] + p[-1] - 1))
contrast_intro_types.append(contrast_intro_type)
+ # print("Deprecated")
+ # 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
+
# num_states.append(np.mean(test.state_num_dist()))
# num_sessions.append(test.results[0].n_sessions)
@@ -1925,38 +2047,83 @@ if __name__ == "__main__":
# plt.savefig("temp")
# plt.close()
- # 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_intros, open("all_intros.p", 'wb'))
- # pickle.dump(all_intros_div, open("all_intros_div.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'))
- # pickle.dump(all_state_types, open("all_state_types.p", 'wb'))
- # pickle.dump(all_pmf_weights, open("all_pmf_weights.p", 'wb'))
- # pickle.dump(state_types_interpolation, open("state_types_interpolation.p", 'wb'))
- # pickle.dump(state_types_interpolation, open("state_types_interpolation_4_states.p", 'wb')) # special version, might not want to use
- # abs_state_durs = np.array(abs_state_durs)
- # pickle.dump(abs_state_durs, open("multi_chain_saves/abs_state_durs.p", 'wb'))
- # pickle.dump(pop_state_starts, open("multi_chain_saves/pop_state_starts.p", 'wb'))
- # pickle.dump(state_appear, open("multi_chain_saves/state_appear.p", 'wb'))
- # pickle.dump(state_dur, open("multi_chain_saves/state_dur.p", 'wb'))
- # 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'))
- # 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'))
+ if ultimate_counter > 100:
+ 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_intros, open("all_intros.p", 'wb'))
+ # pickle.dump(all_intros_div, open("all_intros_div.p", 'wb'))
+ pickle.dump(all_pmfs, open("all_pmfs.p", 'wb'))
+ # pickle.dump(all_states_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'))
+ # pickle.dump(all_state_types, open("all_state_types.p", 'wb'))
+ pickle.dump(all_pmf_weights, open("all_pmf_weights.p", 'wb'))
+ pickle.dump(state_types_interpolation, open("state_types_interpolation.p", 'wb'))
+ # pickle.dump(state_types_interpolation, open("state_types_interpolation_4_states.p", 'wb')) # special version, might not want to use
+ abs_state_durs = np.array(abs_state_durs)
+ pickle.dump(abs_state_durs, open("multi_chain_saves/abs_state_durs.p", 'wb'))
+ # pickle.dump(pop_state_starts, open("multi_chain_saves/pop_state_starts.p", 'wb'))
+ # pickle.dump(state_appear, open("multi_chain_saves/state_appear.p", 'wb'))
+ # pickle.dump(state_dur, open("multi_chain_saves/state_dur.p", 'wb'))
+ # 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'))
+ pickle.dump(all_sudden_changes, open("multi_chain_saves/all_sudden_changes.p", 'wb'))
+ pickle.dump(aug_all_sudden_changes, open("multi_chain_saves/aug_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_transition_changes, open("multi_chain_saves/aug_all_sudden_transition_changes.p", 'wb'))
+ pickle.dump(in_sess_appear, open("multi_chain_saves/in_sess_appear.p", 'wb'))
+ pickle.dump(in_train_appear, open("multi_chain_saves/in_train_appear.p", 'wb'))
+ # pickle.dump(in_sess_appear_old, open("multi_chain_saves/in_sess_appear_old.p", 'wb'))
+ # pickle.dump(in_train_appear_old, open("multi_chain_saves/in_train_appear_old.p", 'wb'))
+ pickle.dump(all_trial_ns, open("all_trial_ns.p", 'wb'))
+ # pickle.dump(mice_exponentials, open("mice_exponentials.p", 'wb'))
+ # pickle.dump(captured_states, open("captured_states.p", 'wb'))
+ # pickle.dump(type_1_to_2_save, open("multi_chain_saves/type_1_to_2_save.p", 'wb'))
+ # pickle.dump(all_state_percentages, open("multi_chain_saves/all_state_percentages.p", 'wb'))
+ pass
print("Ultimate count is {}".format(ultimate_counter))
- if False:
+ # need: abs_state_durs, bias_sessions, in_sess_appear, in_train_appear
+ # state_types_interpolation, all_first_pmfs, all_first_pmfs_typeless, all_pmfs, changing_pmfs, changing_pmf_names
+ # all_weight_trajectories, first_and_last_pmf, all_sudden_changes, all_sudden_transition_changes, aug_all_sudden_changes, aug_all_sudden_transition_changes, all_pmf_weights
+ # regressions, regression_diffs
+ # all_intros, all_intros_div, all_states_per_type
+ # captured_states, type_1_to_2_save
+
+ if True:
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("Median split type fractions")
+ print(abs_state_durs[abs_state_durs.sum(1) <= np.median(abs_state_durs.sum(1))].mean(0) / abs_state_durs[abs_state_durs.sum(1) <= np.median(abs_state_durs.sum(1))].mean(0).sum(0))
+ # array([0.23449132, 0.17369727, 0.59181141])
+ print(abs_state_durs[abs_state_durs.sum(1) > np.median(abs_state_durs.sum(1))].mean(0) / abs_state_durs[abs_state_durs.sum(1) > np.median(abs_state_durs.sum(1))].mean(0).sum(0))
+ # array([0.22314507, 0.15227021, 0.62458472])
+ n_quantiles = 10
+ for i in range(n_quantiles):
+ limit_low, limit_high = np.quantile(abs_state_durs.sum(1), [i / n_quantiles, (i + 1) / n_quantiles])
+ mask = np.logical_and(limit_low <= abs_state_durs.sum(1), abs_state_durs.sum(1) < limit_high)
+ print(i, limit_low, limit_high, mask.sum())
+ print(abs_state_durs[mask].mean(0) / abs_state_durs[mask].mean(0).sum(0))
+ print()
+
+ f, axs = plt.subplots(3, 1, figsize=(16 * 0.75, 9 * 0.75), sharex=True, sharey=True)
+
+ axs[0].hist(abs_state_durs[:, 0], bins=np.linspace(0, 60, 60), color='grey')
+ axs[1].hist(abs_state_durs[:, 1], bins=np.linspace(0, 60, 60), color='grey')
+ axs[2].hist(abs_state_durs[:, 2], bins=np.linspace(0, 60, 60), color='grey')
+
+ axs[2].set_xlabel("# of mice", size=24)
+ axs[1].set_ylabel("# of sessions", size=24)
+ plt.show()
+
+
print("Correlations")
from scipy.stats import pearsonr
print(pearsonr(abs_state_durs[:, 0], abs_state_durs[:, 1]))
@@ -1984,7 +2151,7 @@ if __name__ == "__main__":
from simplex_plot import plotSimplex
- plotSimplex(np.array(abs_state_durs), facecolors='none', edgecolors='k', linewidths=1.5, 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=['Stage 1', 'Stage 2', 'Stage 3'])
plt.hist(abs_state_durs.sum(1), color='grey', bins=12)
sns.despine()
@@ -2093,8 +2260,10 @@ if __name__ == "__main__":
plt.close()
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'))
+ in_sess_appear = pickle.load(open("multi_chain_saves/in_sess_appear.p", 'rb'))
+ in_train_appear = pickle.load(open("multi_chain_saves/in_train_appear.p", 'rb'))
+ in_sess_appear_old = pickle.load(open("multi_chain_saves/in_sess_appear_old.p", 'rb'))
+ in_train_appear_old = pickle.load(open("multi_chain_saves/in_train_appear_old.p", 'rb'))
f, axs = plt.subplots(2, 2, figsize=(16, 9), sharex=True)
gs = axs[0, 0].get_subplotspec().get_gridspec()
@@ -2106,12 +2275,13 @@ if __name__ == "__main__":
# 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')
+ for i in range(in_sess_appear.shape[0]):
+ ax.bar(np.linspace(0, 1, in_sess_appear_n_bins + 1)[:-1], in_sess_appear[i], bottom=in_sess_appear[:i].sum(0), align='edge', width=1/in_sess_appear_n_bins, color=type2color[i])
+ ax2.bar(np.linspace(0, 1, in_sess_appear_n_bins + 1)[:-1], in_sess_appear[i], bottom=in_sess_appear[:i].sum(0), align='edge', width=1/in_sess_appear_n_bins, color=type2color[i])
# zoom-in / limit the view to different portions of the data
- ax.set_ylim(245, 290)
- ax2.set_ylim(0, 25)
+ ax.set_ylim(245, 400)
+ ax2.set_ylim(0, 35)
ax.set_yticks([250, 275])
ax.set_yticklabels([250, 275])
@@ -2143,7 +2313,8 @@ if __name__ == "__main__":
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')
+ for i in range(in_train_appear.shape[0]):
+ a1.bar(np.linspace(0, 1, in_train_appear_n_bins + 1)[:-1], in_train_appear[i], bottom=in_train_appear[:i].sum(0), align='edge', width=1/in_train_appear_n_bins, color=type2color[i])
# a1.hist(state_appear_mode, color='grey')
a1.set_xlim(left=0, right=1)
# plt.title('First appearence of ', fontsize=22)
diff --git a/dynamic_GLMiHMM_fit.py b/dynamic_GLMiHMM_fit.py
index 5e076776..4915d76b 100644
--- a/dynamic_GLMiHMM_fit.py
+++ b/dynamic_GLMiHMM_fit.py
@@ -18,6 +18,7 @@ from itertools import product
import json
import sys
+save_things = False
def eleven2nine(x):
"""Map from 11 possible contrasts to 9, for the non-training phases.
@@ -58,7 +59,7 @@ contrast_to_num = {-1.: 0, -0.987: 1, -0.848: 2, -0.555: 3, -0.302: 4, 0.: 5, 0.
num_to_contrast = {v: k for k, v in contrast_to_num.items()}
cont_mapping = np.vectorize(num_to_contrast.get)
-data_folder = 'session_data_test'
+data_folder = 'session_data'
old_style = False
if old_style:
print("Warning, data can have splits")
@@ -77,7 +78,7 @@ subjects = ['ibl_witten_15', 'ibl_witten_17', 'ibl_witten_18', 'ibl_witten_19',
'CSH_ZAD_017', 'CSH_ZAD_025', 'CSH_ZAD_026', 'CSHL049', 'CSHL051', 'CSHL061']
# test subjects:
-subjects = ['KS014']
+subjects = ['fip_33']
cv_nums = [15]
cv_nums = [200 + int(sys.argv[1]) % 16]
@@ -100,7 +101,7 @@ for loop_count_i, (s, cv_num) in enumerate(product(subjects, cv_nums)):
params['regressors'] = [all_regressors[i] for i in [0, 1, 3, 6]]
# default (non-iteration) settings:
- params['fit_type'] = ['prebias', 'bias', 'all', 'prebias_plus', 'zoe_style'][0]
+ params['fit_type'] = ['prebias', 'bias', 'all', 'prebias_plus', 'zoe_style'][2]
# params['fit_variance'] = [0.0005, 0.002, 0.008, 0.02, 0.06, 0.1, 0.3, 0.6, 1., 2.4, 10, 16, 30, 'uniform'][6]
if 'prevA' in params['regressors'] or 'weighted_prevA' in params['regressors']:
params['exp_decay'], params['exp_length'] = 0.3, 5
@@ -166,7 +167,8 @@ for loop_count_i, (s, cv_num) in enumerate(product(subjects, cv_nums)):
if not os.path.isfile(folder + id + '_0.p'):
break
# create placeholder dataset for rand_id purposes
- pickle.dump(params, open(folder + id + '_0.p', 'wb'))
+ if save_things:
+ pickle.dump(params, open(folder + id + '_0.p', 'wb'))
if params['obs_dur'] == 'glm':
print(params['regressors'])
else:
@@ -311,7 +313,8 @@ for loop_count_i, (s, cv_num) in enumerate(product(subjects, cv_nums)):
posteriormodel.add_data(mega_data)
# 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'))
+ if save_things:
+ pickle.dump(data_save, open('./{}/data_save_{}.p'.format(data_folder, params['subject']), 'wb'))
# 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'])
@@ -336,9 +339,11 @@ for loop_count_i, (s, cv_num) in enumerate(product(subjects, cv_nums)):
# save something in case of crash
if j % 400 == 0 and j > 0:
if params['n_samples'] <= 4000:
- pickle.dump(models, open(folder + id + '.p', 'wb'))
+ if save_things:
+ pickle.dump(models, open(folder + id + '.p', 'wb'))
else:
- pickle.dump(models, open(folder + id + '_{}.p'.format(j // 4001), 'wb'))
+ if save_things:
+ pickle.dump(models, open(folder + id + '_{}.p'.format(j // 4001), 'wb'))
if j % 4000 == 0:
models = []
print(time.time() - time_save)
@@ -356,9 +361,12 @@ for loop_count_i, (s, cv_num) in enumerate(product(subjects, cv_nums)):
params['ll'] = likes.tolist()
params['init_mean'] = params['init_mean'].tolist()
if params['cross_val']:
- json.dump(params, open(folder + "infos_new/" + '{}_{}_cvll_{}_{}_{}_{}_{}.json'.format(params['subject'], params['cross_val_num'], str(np.round(lls_mean, 3)).replace('.', '_'),
+ if save_things:
+ json.dump(params, open(folder + "infos_new/" + '{}_{}_cvll_{}_{}_{}_{}_{}.json'.format(params['subject'], params['cross_val_num'], str(np.round(lls_mean, 3)).replace('.', '_'),
params['fit_type'], params['fit_variance'], params['seed'], rand_id), 'w'))
else:
- json.dump(params, open(folder + "infos_new/" + '{}_{}_{}_{}_{}.json'.format(params['subject'], params['fit_type'],
+ if save_things:
+ json.dump(params, open(folder + "infos_new/" + '{}_{}_{}_{}_{}.json'.format(params['subject'], params['fit_type'],
params['fit_variance'], params['seed'], rand_id), 'w'))
- pickle.dump(models, open(folder + id + '_{}.p'.format(j // 4001), 'wb'))
+ if save_things:
+ pickle.dump(models, open(folder + id + '_{}.p'.format(j // 4001), 'wb'))
diff --git a/raw_fit_processing_part2.py b/raw_fit_processing_part2.py
index 7d59bc54..18e324f6 100644
--- a/raw_fit_processing_part2.py
+++ b/raw_fit_processing_part2.py
@@ -6,12 +6,19 @@ import matplotlib.pyplot as plt
import os
+fit_variance = 0.03
+# 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 = ['NR_0027', 'NR_0019', 'PL024', 'UCLA037', 'UCLA036', 'UCLA015', 'PL017', 'NR_0020', 'NR_0021', 'UCLA034']
+
def create_mode_indices(test, subject, fit_type):
dim = 3
try:
- xy, z = pickle.load(open("multi_chain_saves/xyz_{}_{}.p".format(subject, fit_type), 'rb'))
+ xy, z = pickle.load(open("multi_chain_saves/xyz_{}_{}_var_{}.p".format(subject, fit_type, fit_variance), 'rb'))
except Exception:
+ # this is cluster work
return
print('Doing PCA')
ev, eig, projection_matrix, dimreduc = test.state_pca(subject, pca_type='dists', dim=dim)
@@ -112,7 +119,7 @@ def threshold_search(xy, z, test, mode_prefix, subject, fit_type):
happy = 'yes' == input()
print("Subset by factor?")
- if input() == 'yes':
+ if input() in ['yes', 'y']:
print("Factor?")
print(mode_indices.shape)
factor = int(input())
@@ -122,7 +129,8 @@ def threshold_search(xy, z, test, mode_prefix, subject, fit_type):
loading_info[subject] = {}
loading_info[subject]['mode prob level'] = prob_level
- pickle.dump(mode_indices, open("multi_chain_saves/{}mode_indices_{}_{}.p".format(mode_prefix, subject, fit_type), 'wb'))
+ # pickle.dump(mode_indices, open("multi_chain_saves/{}mode_indices_{}_{}_var_{}.p".format(mode_prefix, subject, fit_type, fit_variance), 'wb'))
+ # we do this on the cluster now
# consistencies = test.consistency_rsa(indices=mode_indices) # do this on the cluster from now on
# pickle.dump(consistencies, open("multi_chain_saves/{}mode_consistencies_{}_{}.p".format(mode_prefix, subject, fit_type), 'wb', protocol=4))
@@ -146,8 +154,8 @@ def conditions_fulfilled(z, xy, conds):
def state_set_and_plot(test, mode_prefix, subject, fit_type):
- mode_indices = pickle.load(open("multi_chain_saves/{}mode_indices_{}_{}.p".format(mode_prefix, subject, fit_type), 'rb'))
- consistencies = pickle.load(open("multi_chain_saves/{}mode_consistencies_{}_{}.p".format(mode_prefix, subject, fit_type), 'rb'))
+ mode_indices = pickle.load(open("multi_chain_saves/{}mode_indices_{}_{}.p".format("first_", subject, fit_type), 'rb'))
+ consistencies = pickle.load(open("multi_chain_saves/{}consistencies_{}_{}.p".format(mode_prefix, subject, fit_type), 'rb'))
session_bounds = list(np.cumsum([len(s) for s in test.results[0].models[-1].stateseqs]))
import scipy.cluster.hierarchy as hc
@@ -166,9 +174,9 @@ def state_set_and_plot(test, mode_prefix, subject, fit_type):
state_sets = []
for x, y in zip(b, c):
state_sets.append(np.where(a == x)[0])
- print("dumping state set")
- pickle.dump(state_sets, open("multi_chain_saves/{}state_sets_{}_{}.p".format(mode_prefix, subject, fit_type), 'wb'))
- state_development(test, [s for s in state_sets if len(s) > 40], mode_indices, save_append='_{}{}'.format(mode_prefix, plot_criterion), show=True, separate_pmf=True, type_coloring=True)
+ # print("dumping state set")
+ # pickle.dump(state_sets, open("multi_chain_saves/{}state_sets_{}_{}_var_{}.p".format(mode_prefix, subject, fit_type, fit_variance), 'wb'))
+ # state_development(test, [s for s in state_sets if len(s) > 40], mode_indices, save_append='_{}{}'.format(mode_prefix, plot_criterion), show=True, separate_pmf=True, type_coloring=True)
fig, ax = plt.subplots(ncols=5, sharey=True, gridspec_kw={'width_ratios': [10, 1, 1, 1, 1]}, figsize=(13, 8))
from matplotlib.pyplot import cm
@@ -177,6 +185,8 @@ def state_set_and_plot(test, mode_prefix, subject, fit_type):
a = hc.fcluster(linkage, criterion, criterion='distance')
b, c = np.unique(a, return_counts=1)
print(b.shape)
+ print("{} relevant states at cutoff of {}".format(np.sum(c > 40), criterion))
+ print("Explaining {:.2f}% of the {} trials".format(np.sum(c[c > 40]) / test.results[0].n_datapoints * 100, test.results[0].n_datapoints))
print(np.sort(c))
cmap = cm.rainbow(np.linspace(0, 1, 17))
@@ -184,7 +194,7 @@ def state_set_and_plot(test, mode_prefix, subject, fit_type):
i = -1
b = [x for _, x in sorted(zip(c, b))][::-1]
c = [x for x, _ in sorted(zip(c, b))][::-1]
- plot_above = 50
+ plot_above = 40
while len([y for y in c if y > plot_above]) > 17:
plot_above += 1
for x, y in zip(b, c):
@@ -200,7 +210,7 @@ def state_set_and_plot(test, mode_prefix, subject, fit_type):
ax[j+1].set_yticks([])
ax[j+1].set_title("{}%".format(int(criterion * 100)), size=20)
- ax[0].imshow(consistencies, aspect='auto', origin='upper')
+ image = ax[0].imshow(consistencies, aspect='auto', origin='upper')
for sb in session_bounds:
ax[0].axhline(sb, color='k')
ax[0].set_xticks([])
@@ -212,27 +222,29 @@ def state_set_and_plot(test, mode_prefix, subject, fit_type):
plt.tight_layout()
plt.savefig("peter figures/{}clustered_trials_{}_{}".format(mode_prefix, subject, 'criteria comp').replace('.', '_'))
- plt.close()
+ plt.show()
+
+ plt.imshow(consistencies, aspect='auto', origin='upper')
+ plt.colorbar()
+ plt.savefig("peter figures/colorbar")
+ plt.show()
fit_type = ['prebias', 'bias', 'all', 'prebias_plus', 'zoe_style'][0]
if fit_type == 'bias':
- loading_info = json.load(open("canonical_infos_bias.json", 'r'))
+ loading_info = json.load(open("canonical_infos_bias_fitvar_{}.json".format(fit_variance), 'r'))
elif fit_type == 'prebias':
- loading_info = json.load(open("canonical_infos.json", 'r'))
-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 = ['ZM_2245', 'GLM_Sim_18', 'SWC_039', 'ZFM-01937']
+ loading_info = json.load(open("canonical_infos_fitvar_{}.json".format(fit_variance), 'r'))
+subjects = ['KS014']
for subject in subjects:
test = pickle.load(open("multi_chain_saves/canonical_result_{}_{}.p".format(subject, fit_type), 'rb'))
- if os.path.isfile("multi_chain_saves/{}mode_indices_{}_{}.p".format('first_', subject, fit_type)):
- print("It has been done")
- continue
+ # if os.path.isfile("multi_chain_saves/{}mode_indices_{}_{}.p".format('first_', subject, fit_type)):
+ # print("It has been done")
+ # continue
print('Computing sub result')
- create_mode_indices(test, subject, fit_type)
- # state_set_and_plot(test, 'first_', subject, fit_type)
+ # create_mode_indices(test, subject, fit_type)
+ state_set_and_plot(test, '', subject, fit_type)
# print("second mode?")
# if input() in ['y', 'yes']:
# state_set_and_plot(test, 'second_', subject, fit_type)
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diff --git a/simple plots/drawing.svg b/simple plots/drawing.svg
index 552ed9d4..2b79b99e 100644
--- a/simple plots/drawing.svg
+++ b/simple plots/drawing.svg
@@ -9,7 +9,7 @@
id="svg8"
inkscape:version="1.1.2 (0a00cf5339, 2022-02-04)"
sodipodi:docname="drawing.svg"
- inkscape:export-filename="/run/user/1000/gvfs/sftp:host=crocus.kyb.local,user=sbruijns/kyb/agpd/sbruijns/ihmm_behav_states/simple plots/drawing.png"
+ inkscape:export-filename="/kyb/agpd/sbruijns/ihmm_behav_states/simple plots/drawing.png"
inkscape:export-xdpi="200"
inkscape:export-ydpi="200"
xmlns:inkscape="http://www.inkscape.org/namespaces/inkscape"
@@ -22,6 +22,13 @@
xmlns:dc="http://purl.org/dc/elements/1.1/">
<defs
id="defs2">
+ <inkscape:perspective
+ sodipodi:type="inkscape:persp3d"
+ inkscape:vp_x="0 : 148.5 : 1"
+ inkscape:vp_y="0 : 1000 : 0"
+ inkscape:vp_z="210 : 148.5 : 1"
+ inkscape:persp3d-origin="105 : 99 : 1"
+ id="perspective9634" />
<marker
inkscape:stockid="TriangleOutL"
orient="auto"
@@ -93,6 +100,34 @@
style="fill:#000000;fill-opacity:1;fill-rule:evenodd;stroke:#000000;stroke-width:1.00000003pt;stroke-opacity:1"
transform="scale(0.8)" />
</marker>
+ <marker
+ inkscape:stockid="TriangleOutL"
+ orient="auto"
+ refY="0"
+ refX="0"
+ id="TriangleOutL-5"
+ style="overflow:visible"
+ inkscape:isstock="true">
+ <path
+ id="path1062-3"
+ d="M 5.77,0 -2.88,5 V -5 Z"
+ style="fill:#000000;fill-opacity:1;fill-rule:evenodd;stroke:#000000;stroke-width:1pt;stroke-opacity:1"
+ transform="scale(0.8)" />
+ </marker>
+ <marker
+ inkscape:stockid="TriangleOutL"
+ orient="auto"
+ refY="0"
+ refX="0"
+ id="TriangleOutL-62"
+ style="overflow:visible"
+ inkscape:isstock="true">
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diff --git a/simple plots/dynamic_pmf_plot.py b/simple plots/dynamic_pmf_plot.py
index ffce4dec..acec8e3e 100644
--- a/simple plots/dynamic_pmf_plot.py
+++ b/simple plots/dynamic_pmf_plot.py
@@ -5,7 +5,7 @@ import matplotlib.cm as cm
import pickle
-bias_cont_ticks = ([0, 2, 5, 9, 11], [-1, -.25, 0, .25, 1])
+bias_cont_ticks = ([0, 2, 5, 8, 10], [-1, -.25, 0, .25, 1])
cmap = cm.get_cmap('magma')
# try
@@ -16,23 +16,27 @@ subjects = ['SWC_042', 'ZFM-02368', 'ZM_1898', 'ZM_3003', 'NYU-37', 'ibl_witten_
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)))
+ plt.plot(pmf, c=cmap(i / max(0.0001, (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)
+ clb.ax.set_title('Session', size=43, pad=14)
+ clb.ax.tick_params(labelsize=25.5)
- plt.xticks(*bias_cont_ticks, size=22)
- plt.xlim(left=0)
+ plt.xticks(*bias_cont_ticks, size=25.5)
+ plt.xlim(left=0, right=10)
plt.ylim(0, 1)
- plt.yticks(size=22)
- plt.xlabel('Contrast', size=38)
- plt.ylabel('P(answer rightward)', size=38)
+ plt.yticks(size=25.5)
+ plt.xlabel('Contrast', size=43)
+ plt.ylabel('P(respond rightward)', size=43)
sns.despine()
plt.tight_layout()
+ plt.savefig("dynamic_pmf_plot", dpi=300)
plt.show()
+ if state == 0 and subj == 'ZFM-02368':
+ quit()
+
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])
diff --git a/simple plots/meta_state_development_KS014_10_03.png.svg b/simple plots/meta_state_development_KS014_10_03.png.svg
new file mode 100644
index 00000000..9ac2e9cf
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+++ b/simple plots/meta_state_development_KS014_10_03.png.svg
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+"
+ id="image10"
+ clip-path="url(#clipPath931)"
+ inkscape:export-filename="/kyb/agpd/sbruijns/ihmm_behav_states/simple plots/image10.png"
+ inkscape:export-xdpi="200"
+ inkscape:export-ydpi="200" />
+ </g>
+</svg>
diff --git a/simple plots/neg_bin.py b/simple plots/neg_bin.py
index a9d56eb1..e63cfbbe 100644
--- a/simple plots/neg_bin.py
+++ b/simple plots/neg_bin.py
@@ -7,8 +7,8 @@ import seaborn as sns
ns = [20, 600]
ps = [0.05, 0.6]
-fs = 38
-ts = 22
+fs = 70
+ts = 30
x = np.arange(600)
@@ -20,8 +20,8 @@ for n, p in zip(ns, ps):
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)
+ plt.xticks([0, 150, 300, 450, 600], [0, 150, 300, 450, 600], size=ts)
+ plt.yticks([.005, 0.01, 0.015, 0.02], [".005", ".01", ".015", ".02"], size=ts)
sns.despine()
plt.tight_layout()
plt.savefig(str(n) + '.png', dpi=300)
diff --git a/simple plots/pmf_plot.py b/simple plots/pmf_plot.py
index fb4f4bd0..5f0b2f08 100644
--- a/simple plots/pmf_plot.py
+++ b/simple plots/pmf_plot.py
@@ -8,29 +8,32 @@ first_and_last_pmf = np.array(pickle.load(open("../multi_chain_saves/first_and_l
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]
+bias_cont_ticks = ([0, 2, 5, 8, 10], [-1, -.25, 0, .25, 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:
+for i, pmf in enumerate(first_and_last_pmf):
+ if i != 9:
+ continue
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.figure(figsize=(9.1, 8.3))
+ plt.plot(range(11), weights_to_pmf(pmf, -1), 'b', lw=6)
+ plt.plot(range(11), weights_to_pmf(pmf, 1), 'r', lw=6)
+ plt.xticks(*bias_cont_ticks, size=25)
+ plt.annotate("Previous\nresponses:", (5.6, 0.3), color='r', size=42)
+ plt.arrow(7.1, 0.23, -1, 0, color='r', head_width=0.05, head_length=0.25)
+ plt.annotate("Previous\nresponses:", (0.8, 0.7), color='b', size=42)
+ plt.arrow(1, 0.63, 1, 0, color='b', head_width=0.05, head_length=0.25)
+ plt.yticks(size=25)
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.xlabel('Contrast', size=42)
+ plt.ylabel('P(respond rightward)', size=42)
plt.tight_layout()
plt.savefig('simple_pmf', dpi=300)
+ print(i)
plt.show()
diff --git a/simplex_plot.py b/simplex_plot.py
index f9f8a070..a7aa897c 100644
--- a/simplex_plot.py
+++ b/simplex_plot.py
@@ -32,9 +32,9 @@ def plotSimplex(points, fig=None,
fig.gca().xaxis.set_major_locator(MT.NullLocator())
fig.gca().yaxis.set_major_locator(MT.NullLocator())
# Draw vertex labels
- # fig.gca().annotate(vertexlabels[0], (-0.35, -0.05), size=24, color=vertexcolors[0], annotation_clip=False)
- # fig.gca().annotate(vertexlabels[1], (0.6, -0.05), size=24, color=vertexcolors[1], annotation_clip=False)
- # fig.gca().annotate(vertexlabels[2], (0.1, np.sqrt(3) / 2 + 0.025), size=24, color=vertexcolors[2], annotation_clip=False)
+ fig.gca().annotate(vertexlabels[0], (-0.17, 0.13), size=24, color=vertexcolors[0], annotation_clip=False)
+ fig.gca().annotate(vertexlabels[1], (0.95, 0.13), size=24, color=vertexcolors[1], annotation_clip=False)
+ fig.gca().annotate(vertexlabels[2], (0.395, np.sqrt(3) / 2 + 0.035), size=24, color=vertexcolors[2], annotation_clip=False)
# Project and draw the actual points
projected = projectSimplex(points / points.sum(1)[:, None])
P.scatter(projected[:, 0] + x_offset, projected[:, 1] + y_offset, s=points.sum(1) * 3.5, **kwargs)#s=35
@@ -54,8 +54,8 @@ def plotSimplex(points, fig=None,
if title != '':
P.annotate(title, (0.395, np.sqrt(3) / 2 + 0.075), size=24)
- P.tight_layout()
- P.savefig(save_title, bbox_inches='tight', dpi=300, transparent=True)
+ # P.tight_layout()
+ P.savefig(save_title, dpi=300, transparent=True) # bbox_inches='tight'
if show:
P.show()
else:
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diff --git a/test_codes/comparison_data b/test_codes/comparison_data
index 7977a7f911875324f38ab3873258ebe1da55a25f..50a59a66acf9ea0e4ebec4221a3ac5a1cccccc1c 100644
GIT binary patch
delta 15
WcmdnLn{n@M#tHdMogEvCePRJQ{07tj
delta 17
YcmdnHn{oec#tHc>-VEN18w-760YEPXS^xk5
diff --git a/test_codes/dyn_glm_pmf_test.py b/test_codes/dyn_glm_pmf_test.py
index 6bc4d442..14917089 100644
--- a/test_codes/dyn_glm_pmf_test.py
+++ b/test_codes/dyn_glm_pmf_test.py
@@ -3,7 +3,6 @@
import numpy as np
import pyhsmm.basic.distributions as distributions
import matplotlib.pyplot as plt
-import seaborn as sns
from communal_funcs import weights_to_pmf
from itertools import product
from scipy.stats import norm
@@ -16,7 +15,7 @@ np.set_printoptions(suppress=True)
T = 4
-trials = 20
+trials = 500
n_samples = 1000
n_inputs = 3
prior_means = [np.array([-1.5, 1.5, 0]), np.array([0, 0, 0])]
@@ -27,12 +26,6 @@ contrast_to_num = {-1.: 0, -0.987: 1, -0.848: 2, -0.555: 3, -0.302: 4, 0.: 5, 0.
num_to_contrast = {v: k for k, v in contrast_to_num.items()}
cont_mapping = np.vectorize(num_to_contrast.get)
-samples = np.random.multivariate_normal(prior_means[0], prior_vars[0], 1000)
-plt.hist(samples[:, 0])
-plt.show()
-plt.hist(samples[:, -1])
-plt.show()
-
def recovery_test(weights, contrasts, prior_mean, prior_var, plot=False):
print()
@@ -68,8 +61,6 @@ def recovery_test(weights, contrasts, prior_mean, prior_var, plot=False):
samples = []
samples2 = []
for _ in range(n_samples):
- # if _ % 100 == 0:
- # print(_)
learn.resample(data)
learn2.resample(data)
samples.append(learn.weights.copy())
@@ -109,10 +100,11 @@ def recovery_test(weights, contrasts, prior_mean, prior_var, plot=False):
plt.plot(np.arange(T), sample_mean, label=label, c='g')
credible_interval = np.percentile(samples[:, :, i], [2.5, 97.5], axis=0)
plt.fill_between(np.arange(T), credible_interval[1], credible_interval[0], alpha=0.2, color='g')
- sns.despine()
+ # sns.despine()
if i == 0:
plt.legend(fontsize=18, frameon=False)
plt.xlim(left=0, right=T-1)
+ plt.ylim(-6, 6)
plt.tight_layout()
plt.savefig("weights compare")
plt.show()
@@ -120,4 +112,4 @@ def recovery_test(weights, contrasts, prior_mean, prior_var, plot=False):
pmf_weights = [np.array([0, 0, -3.8919]), np.array([0, 0, 3.8919]), np.array([0, 0, 0]), np.array([-5, 5, 0])]
for contrasts, weights in product([[0, 1, 9, 10], [0, 2, 3, 4, 5, 6, 7, 8, 10]], pmf_weights):
- recovery_test(weights, contrasts, prior_means, prior_vars)
+ recovery_test(weights, contrasts, prior_means, prior_vars, plot=True)
diff --git a/test_codes/dynamic_glm_dist_test.py b/test_codes/dynamic_glm_dist_test.py
index b296d928..dca435e1 100644
--- a/test_codes/dynamic_glm_dist_test.py
+++ b/test_codes/dynamic_glm_dist_test.py
@@ -1,122 +1,129 @@
-import matplotlib
+""" Test how well the parameters of PMFs are recovered by dynamic GLM."""
import numpy as np
import pyhsmm.basic.distributions as distributions
import time
import matplotlib.pyplot as plt
-import seaborn as sns
import pickle
# Testing of Dynamic_GLM implementation
np.set_printoptions(suppress=True)
-# The following code can serve to compare this implementation with the dynamic_glm_sampler.py implementation
seed = 215
np.random.seed(seed)
T = 30
-n_inputs = 2
+n_regressors = 2
n_samples = 150
-Q = np.tile(np.eye(n_inputs), (T, 1, 1))
-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):
-# t = 30
-# pred = np.empty((t, n_inputs))
-# for i in range(3):
-# pred[i*10:i*10 + 10, 0] = np.random.rand()
-# pred[:, 1] = 1
-# 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)
-#
-# pickle.dump((sample, test.weights, learn.weights), open("comparison_data", 'wb'))
-#
-# np.random.seed(19)
-# samples = []
-# for _ in range(n_samples):
-# if _ % 10 == 0:
-# print(_)
-# learn.resample(sample)
-# samples.append(learn.weights.copy())
-# samples = np.array(samples[50:])
-#
-# plt.figure(figsize=(16, 9))
-# for i in range(n_inputs):
-# plt.subplot(n_inputs, 1, i+1)
-# label = 'Truth' if i == 0 else None
-# plt.plot(np.arange(T), test.weights[:, i], label=label)
-# sample_mean = np.mean(samples[:, :, i], axis=0)
-# label = 'Posterior mean' if i == 0 else None
-# plt.plot(np.arange(T), sample_mean, label=label, c='g')
-# credible_interval = np.percentile(samples[:, :, i], [2.5, 97.5], axis=0)
-# plt.fill_between(np.arange(T), credible_interval[1], credible_interval[0], alpha=0.2, color='g')
-# sns.despine()
-# if i == 0:
-# plt.legend(fontsize=18, frameon=False)
-# plt.xlim(left=0, right=T)
-# plt.tight_layout()
-# plt.savefig("GLM tracking compare")
-# plt.show()
+Q = np.tile(np.eye(n_regressors), (T, 1, 1))
+test = distributions.Dynamic_GLM(n_regressors=n_regressors, T=T, P_0=np.eye(n_regressors), Q=Q * 0.01, prior_mean=np.zeros(n_regressors))
+
+predictors = []
+for _ in range(T):
+ t = 30
+ pred = np.empty((t, n_regressors))
+ for i in range(3):
+ pred[i*10:i*10 + 10, 0] = np.random.rand()
+ pred[:, 1] = 1
+ predictors.append(pred)
+sample = test.rvs(predictors, list(range(T)))
+
+learn = distributions.Dynamic_GLM(n_regressors=n_regressors, T=T, P_0=np.eye(n_regressors), Q=Q * 0.01, prior_mean=np.zeros(n_regressors))
+
+pickle.dump((sample, test.weights, learn.weights), open("comparison_data", 'wb'))
+
+np.random.seed(19)
+samples = []
+for _ in range(n_samples):
+ if _ % 10 == 0:
+ print(_)
+ learn.resample(sample)
+ samples.append(learn.weights.copy())
+samples = np.array(samples[50:])
+
+plt.figure(figsize=(16, 9))
+for i in range(n_regressors):
+ plt.subplot(n_regressors, 1, i+1)
+ label = 'Truth' if i == 0 else None
+ plt.plot(np.arange(T), test.weights[:, i], label=label)
+ sample_mean = np.mean(samples[:, :, i], axis=0)
+ label = 'Posterior mean' if i == 0 else None
+ plt.plot(np.arange(T), sample_mean, label=label, c='g')
+ credible_interval = np.percentile(samples[:, :, i], [2.5, 97.5], axis=0)
+ plt.fill_between(np.arange(T), credible_interval[1], credible_interval[0], alpha=0.2, color='g')
+ # sns.despine()
+ if i == 0:
+ plt.legend(fontsize=18, frameon=False)
+ plt.xlim(left=0, right=T)
+plt.tight_layout()
+plt.savefig("GLM tracking compare")
+plt.show()
""" Thorough test """
-#
-# T = 30
-# n_inputs = 5
-# 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)
-#
-# predictors = []
-# for _ in range(T):
-# t = int(np.random.rand() * 75) + 50
-# pred = np.empty((t, n_inputs))
-# pred[:, 0] = np.random.choice(5, t)
-# pred[:, 1] = np.random.choice(5, t)
-# pred[:, 2] = np.random.choice(2, t)
-# pred[:, 3] = np.random.choice(2, t)
-# pred[:, 4] = 1
-# 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)
-#
-# samples = []
-# for _ in range(n_samples):
-# if _ % 10 == 0:
-# print(_)
-# learn.resample(sample)
-# samples.append(learn.weights.copy())
-# samples = np.array(samples[50:])
-#
-# plt.figure(figsize=(16, 9))
-# for i in range(n_inputs):
-# plt.subplot(n_inputs, 1, i+1)
-# label = 'Truth' if i == 0 else None
-# plt.plot(np.arange(T), test.weights[:, i], label=label)
-# sample_mean = np.mean(samples[:, :, i], axis=0)
-# label = 'Posterior mean' if i == 0 else None
-# plt.plot(np.arange(T), sample_mean, label=label, c='g')
-# credible_interval = np.percentile(samples[:, :, i], [2.5, 97.5], axis=0)
-# plt.fill_between(np.arange(T), credible_interval[1], credible_interval[0], alpha=0.2, color='g')
-# sns.despine()
-# if i == 0:
-# plt.legend(fontsize=18, frameon=False)
-# plt.xlim(left=0, right=T)
-# plt.tight_layout()
-# plt.savefig("GLM tracking")
-# plt.show()
-#
-# quit()
+
+T = 30
+n_regressors = 5
+n_samples = 200
+Q = np.tile(np.eye(n_regressors), (T, 1, 1))
+test = distributions.Dynamic_GLM(n_regressors=n_regressors, T=T, P_0=np.eye(n_regressors), Q=Q * 0.01, prior_mean=np.zeros(n_regressors))
+
+pi = 3.14159
+offsets = [0, pi / 2, pi / 4, - pi / 2, 3 * pi / 4]
+amplitude = [3, 3, 2, 1, 1.5]
+speed = [1, 7, 0.5, 5, 1.5]
+
+for i in range(n_regressors):
+ for t in range(T):
+ test.weights[t, i] = np.sin(t / speed[i] + offsets[i]) * amplitude[i]
+
+predictors = []
+for _ in range(T):
+ t = int(np.random.rand() * 75) + 75
+ pred = np.empty((t, n_regressors))
+ pred[:, 0] = np.random.choice(7, t) / 6
+ pred[:, 1] = np.random.choice(7, t) / 6
+ pred[:, 2] = (np.random.choice(10, t) - ((10 - 1) / 2)) / ((10-1) / 2)
+ pred[:, 3] = (np.random.choice(10, t) - ((10 - 1) / 2)) / ((10-1) / 2)
+ pred[:, 4] = 1
+ predictors.append(pred)
+sample = test.rvs(predictors, list(range(T)))
+
+learn = distributions.Dynamic_GLM(n_regressors=n_regressors, T=T, P_0=np.eye(n_regressors), Q=Q * 1, prior_mean=np.zeros(n_regressors))
+
+samples = []
+for _ in range(n_samples):
+ if _ % 10 == 0:
+ print(_)
+ learn.resample(sample)
+ samples.append(learn.weights.copy())
+samples = np.array(samples[75:])
+
+plt.figure(figsize=(16, 9))
+for i in range(n_regressors):
+ plt.subplot(n_regressors, 1, i+1)
+ label = 'Truth' if i == 0 else None
+ plt.plot(np.arange(T), test.weights[:, i], label=label)
+ sample_mean = np.mean(samples[:, :, i], axis=0)
+ label = 'Posterior mean' if i == 0 else None
+ plt.plot(np.arange(T), sample_mean, label=label, c='g')
+ credible_interval = np.percentile(samples[:, :, i], [2.5, 97.5], axis=0)
+ plt.fill_between(np.arange(T), credible_interval[1], credible_interval[0], alpha=0.2, color='g')
+ # sns.despine()
+ if i == 0:
+ plt.legend(fontsize=18, frameon=False)
+ plt.xlim(left=0, right=T)
+ plt.ylim(-3.5, 3.5)
+plt.tight_layout()
+plt.savefig("GLM tracking")
+plt.show()
+
""" Time point test """
T = 42
-n_inputs = 5
-Q = np.tile(np.eye(n_inputs), (T, 1, 1))
-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)
+n_regressors = 5
+Q = np.tile(np.eye(n_regressors), (T, 1, 1))
+test = distributions.Dynamic_GLM(n_regressors=n_regressors, T=T, P_0=np.eye(n_regressors), Q=Q * 0.01, prior_mean=np.zeros(n_regressors), jumplimit=3)
test_points = [5, 6, 7, 9, 10, 13, 14, 18, 23, 30]
predictors = []
@@ -125,7 +132,7 @@ for _ in range(T):
predictors.append(np.zeros(0))
else:
t = int(np.random.rand() * 1) + 2
- pred = np.empty((t, n_inputs))
+ pred = np.empty((t, n_regressors))
pred[:, 0] = np.random.choice(5, t)
pred[:, 1] = np.random.choice(2, t)
pred[:, 2] = np.random.choice(2, t)
@@ -134,12 +141,11 @@ for _ in range(T):
predictors.append(pred)
sample = test.rvs(predictors, list(range(T)))
-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)
+learn = distributions.Dynamic_GLM(n_regressors=n_regressors, T=T, P_0=np.eye(n_regressors), Q=Q * 0.01, prior_mean=np.zeros(n_regressors), jumplimit=3)
# print(test.log_likelihood(sample[3], 3))
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, testing_flag=True)
-# print(test.weights)
-assert timedict == solution
+timedict = test.resample(sample)
+# assert timedict == solution
print('all good')
diff --git a/test_codes/dynglm_timing_test.py b/test_codes/dynglm_timing_test.py
index 7f178785..64fafe88 100644
--- a/test_codes/dynglm_timing_test.py
+++ b/test_codes/dynglm_timing_test.py
@@ -1,12 +1,11 @@
"""
Need to find out whether timepoints are mapped correctly.
-Or whether a bug here allows states to invade each other more easily.
+We check this by whether uncertainty in the posterior gets reduced in the right spots.
"""
import numpy as np
import pyhsmm.basic.distributions as distributions
import matplotlib.pyplot as plt
-import seaborn as sns
# Testing of Dynamic_GLM implementation
np.set_printoptions(suppress=True)
@@ -20,7 +19,7 @@ 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_regressors=n_inputs, T=T, P_0=10 * np.eye(n_inputs), Q=Q * step_size, prior_mean=np.zeros(n_inputs))
+test = distributions.Dynamic_GLM(n_inputs=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 +38,7 @@ for _ in range(T):
predictors.append(pred)
sample = test.rvs(predictors, list(range(T)))
-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))
+learn = distributions.Dynamic_GLM(n_inputs=n_inputs, T=T, P_0=np.eye(n_inputs), Q=Q * step_size, prior_mean=np.zeros(n_inputs))
samples = []
pseudo_samples = []
@@ -72,7 +71,7 @@ for i in range(n_inputs + 1):
plt.plot(np.arange(T), sample_mean, label=label, c='g')
credible_interval = np.percentile(samples[:, :, i], [2.5, 97.5], axis=0)
plt.fill_between(np.arange(T), credible_interval[1], credible_interval[0], alpha=0.2, color='g')
- sns.despine()
+ # sns.despine()
# compare with 0 variance
# if i == 0:
# plt.ylim(bottom=-0.4, top=0)
@@ -84,7 +83,7 @@ for i in range(n_inputs + 1):
plt.plot(np.arange(T), logs_mean, label=label, c='b')
credible_interval = np.percentile(logs, [2.5, 97.5], axis=0)
plt.fill_between(np.arange(T), credible_interval[1], credible_interval[0], alpha=0.2, color='b')
- sns.despine()
+ # sns.despine()
# compare with 0 variance
# plt.ylim(bottom=-4.4, top=-3.4)
if i == 0:
diff --git a/test_codes/pymc_compare/dynglm_optimisation_test.py b/test_codes/pymc_compare/dynglm_optimisation_test.py
index e17222ea..6c5a2ee0 100644
--- a/test_codes/pymc_compare/dynglm_optimisation_test.py
+++ b/test_codes/pymc_compare/dynglm_optimisation_test.py
@@ -24,8 +24,6 @@ step_size = 0.2
Q = np.tile(np.eye(n_inputs), (T, 1, 1))
test = distributions.Dynamic_GLM(n_inputs=n_inputs, T=T, P_0=4 * np.eye(n_inputs), Q=Q * step_size, prior_mean=np.zeros(n_inputs))
w = np.zeros(n_inputs)
-# w = np.array([4.23061493, 2.14425199, -2.1125851])
-# test.weights = w.reshape(T, n_inputs)
test_points = [0]
predictors = []
diff --git a/test_codes/pymc_compare/plot_samplings.py b/test_codes/pymc_compare/plot_samplings.py
index 6c7c8c2d..3fc8bab4 100644
--- a/test_codes/pymc_compare/plot_samplings.py
+++ b/test_codes/pymc_compare/plot_samplings.py
@@ -1,6 +1,5 @@
import numpy as np
import matplotlib.pyplot as plt
-import seaborn as sns
import pickle
import pyhsmm.basic.distributions as distributions
@@ -50,7 +49,7 @@ for i in range(n_inputs):
plt.plot(np.arange(T), m[:, i], label='pymc mean', c='r')
plt.fill_between(np.arange(T), u[:, i], low[:, i], alpha=0.2, color='r')
- sns.despine()
+ # sns.despine()
if i == 0:
plt.legend(fontsize=18, frameon=False)
plt.xlim(left=0, right=T)
--
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