diff --git a/dyn_glm_chain_analysis.py b/dyn_glm_chain_analysis.py
index 9b977369a2029101d52d48de0845217e14b6bdf6..fc7be25aa6fab9564c2d1753081bc62797bfad4c 100644
--- a/dyn_glm_chain_analysis.py
+++ b/dyn_glm_chain_analysis.py
@@ -719,7 +719,7 @@ def lapse_sides(test, state_sets, indices):
"""Compute and plot a lapse differential across sessions.
Takes a single mouse and plots (1 - lapse_left) - lapse_right across sessions, with sessions boundaries shown."""
-
+
def func_init(): return {'lapse_side': np.zeros(test.results[0].n_datapoints) + 10, 'session_bounds': []}
def first_for(test, results):
@@ -1384,24 +1384,46 @@ len_to_bools = {
}
+def state_type_durs(states, pmfs):
+ # Takes states and pmfs, first creates an array of when which type is how active, then computes the number of sessions each type lasts.
+ # A type lasts until a more advanced type takes up more than 50% of a session (and cannot return)
+ # Returns the durations for all the different state types, and an array which holds the state percentages
+ state_types = np.zeros((3, states.shape[1]))
+ for s, pmf in zip(states, pmfs):
+ pmf_counter = -1
+ for i in range(states.shape[1]):
+ if s[i]:
+ pmf_counter += 1
+ state_types[pmf_type(pmf[1][pmf_counter][pmf[0]]), i] += s[i] # indexing horror
+
+ durs = (np.where(state_types[1] > 0.5)[0][0],
+ np.where(state_types[2] > 0.5)[0][0] - np.where(state_types[1] > 0.5)[0][0],
+ states.shape[1] - np.where(state_types[2] > 0.5)[0][0])
+ if np.where(state_types[2] > 0.5)[0][0] < np.where(state_types[1] > 0.5)[0][0]:
+ durs = (np.where(state_types[2] > 0.5)[0][0],
+ 0,
+ states.shape[1] - np.where(state_types[2] > 0.5)[0][0])
+ return durs, state_types
+
+
def state_cluster_interpolation(states, pmfs):
+ #
+ # Used to contain a first_type_count variable, which seemed to just count the number of states?
pmf_examples = [[], [], []]
state_trans = np.zeros((3, 3))
state_types = np.zeros((3, states.shape[1]))
- first_type_count = 0
for state, pmf in zip(states, pmfs):
sessions = np.where(state)[0]
for i, sess_pmf in enumerate(pmf[1]):
if i == 0:
state_type = pmf_type(sess_pmf[pmf[0]])
- first_type_count += state_type == 0
if i > 0 and state_type != pmf_type(sess_pmf[pmf[0]]):
state_trans[state_type, pmf_type(sess_pmf[pmf[0]])] += 1
state_type = pmf_type(sess_pmf[pmf[0]])
pmf_examples[pmf_type(sess_pmf[pmf[0]])].append(sess_pmf[pmf[0]])
state_types[pmf_type(sess_pmf[pmf[0]]), sessions[i]] += state[sessions[i]]
- return state_types, state_trans, first_type_count, pmf_examples
+ return state_types, state_trans, pmf_examples
def pmf_type(pmf):
@@ -1477,7 +1499,6 @@ if __name__ == "__main__":
n_points = 150
state_trajs = np.zeros((3, n_points))
state_trans = np.zeros((3, 3))
- first_type_count = 0
not_yet = True
@@ -1496,69 +1517,48 @@ if __name__ == "__main__":
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'))
- lapse_sides(test, [s for s in state_sets if len(s) > 40], mode_indices)
- continue
- #
- state_sets = pickle.load(open("multi_chain_saves/state_sets_{}_{}.p".format(subject, fit_type), 'rb'))
- states, pmfs = state_development(test, [s for s in state_sets if len(s) > 40], mode_indices, show=True, separate_pmf=True)
+
+ # lapse differential
+ # lapse_sides(test, [s for s in state_sets if len(s) > 40], mode_indices)
+
+ # training overview
+ # states, pmfs = state_development(test, [s for s in state_sets if len(s) > 40], mode_indices, show=True, separate_pmf=True)
# state_development_single_sample(test, [mode_indices[0]], show=True, separate_pmf=True, save=False)
- consistencies = pickle.load(open("multi_chain_saves/consistencies_{}_{}.p".format(subject, fit_type), 'rb'))
- consistencies /= consistencies[0, 0]
- contrasts_plot(test, [s for s in state_sets if len(s) > 40], subject=subject, save=True, show=True, consistencies=consistencies)
- quit()
+ # session overview
+ # consistencies = pickle.load(open("multi_chain_saves/consistencies_{}_{}.p".format(subject, fit_type), 'rb'))
+ # consistencies /= consistencies[0, 0]
+ # contrasts_plot(test, [s for s in state_sets if len(s) > 40], dpi=300, subject=subject, save=True, show=True, consistencies=consistencies)
- # state_types = np.zeros((3, states.shape[1]))
- # for s, pmf in zip(states, pmfs):
- # pmf_counter = -1
- # for i in range(states.shape[1]):
- # if s[i]:
- # pmf_counter += 1
- # state_types[pmf_type(pmf[1][pmf_counter][pmf[0]]), i] += s[i] # indexing horror
- #
- # durs = (np.where(state_types[1] > 0.5)[0][0],
- # np.where(state_types[2] > 0.5)[0][0] - np.where(state_types[1] > 0.5)[0][0],
- # states.shape[1] - np.where(state_types[2] > 0.5)[0][0])
- # if np.where(state_types[2] > 0.5)[0][0] < np.where(state_types[1] > 0.5)[0][0]:
- # durs = (np.where(state_types[2] > 0.5)[0][0],
- # 0,
- # states.shape[1] - np.where(state_types[2] > 0.5)[0][0])
- # if durs[0] < 0 or durs[1] < 0 or durs[2] < 0:
- # quit()
+ # duration of different state types (and also percentage of type activities)
+ # durs, _ = state_type_durs(states, pmfs)
# abs_state_durs.append(durs)
- # if durs[1] < 0:
- # state_development(test, [s for s in state_sets if len(s) > 40], mode_indices, show=False, separate_pmf=True)
# rel_state_durs.append((durs[0] / states.shape[1], durs[1] / states.shape[1], durs[2] / states.shape[1]))
- # continue
- # contrasts_plot(test, [s for s in state_sets if len(s) > 40], dpi=300, subject=subject, save=True, show=True)
- # continue
- # ret, trans, count, pmf_types = state_cluster_interpolation(states, pmfs)
- # state_trans += trans
- # first_type_count += count
- # points = np.linspace(1, test.results[0].n_sessions, n_points)
- #
- # # plt.plot(np.arange(1, 1 + test.results[0].n_sessions), ret.T)
- # for i, r in enumerate(ret):
- # state_trajs[i] += np.interp(points, np.arange(1, 1 + test.results[0].n_sessions), r)
- # # plt.plot(points, np.interp(points, np.arange(1, 1 + test.results[0].n_sessions), r))
- # # plt.show()
- # continue
+ ret, trans, count, pmf_types = state_cluster_interpolation(states, pmfs)
+ state_trans += trans
+ points = np.linspace(1, test.results[0].n_sessions, n_points)
+ # plt.plot(np.arange(1, 1 + test.results[0].n_sessions), ret.T)
+ for i, r in enumerate(ret):
+ state_trajs[i] += np.interp(points, np.arange(1, 1 + test.results[0].n_sessions), r)
+ plt.plot(points, np.interp(points, np.arange(1, 1 + test.results[0].n_sessions), r))
+ plt.show()
+ continue
- # plt.plot(ret.T, label=[0, 1, 2])
- # plt.legend()
- # plt.show()
- # continue
+ plt.plot(ret.T, label=[0, 1, 2])
+ plt.legend()
+ plt.show()
+ continue
- # for i, pmfs in enumerate(pmf_types):
- # plt.subplot(1, 3, i + 1)
- # plt.plot([0, 11], [0.5, 0.5], 'grey', alpha=1/3)
- # for pmf in pmfs:
- # plt.plot(len_to_bools[len(pmf)], pmf)
- # plt.ylim(0, 1)
- # plt.show()
- # continue
+ for i, pmfs in enumerate(pmf_types):
+ plt.subplot(1, 3, i + 1)
+ plt.plot([0, 11], [0.5, 0.5], 'grey', alpha=1/3)
+ for pmf in pmfs:
+ plt.plot(len_to_bools[len(pmf)], pmf)
+ plt.ylim(0, 1)
+ plt.show()
+ continue
# quit()
# for pmf in pmfs: