mouseBehaviorData/analysis.py at master · corbennett/mouseBehaviorData · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
import numpy as np
import glob, os
from matplotlib import pyplot as plt
import matplotlib.patches as mpatches

def checkforMouseBehaviorObject(directory, mouseID):

	print('checking {} for mouse object pkl'.format(directory))
	pkllist = [f for f in os.listdir(directory) if '.pkl' in f]
	mouseID = str(mouseID)
	pklpath = None
	for pkl in pkllist:
		if pkl[:6]==mouseID:
			pklpath = os.path.join(directory, pkl)
			print('Found existing mouseBehaviorObject'
			' for mouse {}'.format(mouseID))
	if pklpath is None:
		print('Did not find existing'
			'mouseBehaviorObject for mouse {}'.format(mouseID))
	return pklpath


def filter_sessions_by_stage(beh_df, filter_string='HAB'):

	if isinstance(filter_string, list):
		filtered_df = beh_df.copy(deep=True)
		for fs in filter_string:
			filtered_df = filter_sessions_by_stage(filtered_df, fs)
	else:
		filtered = beh_df['stage'].str.contains(filter_string)
		filtered_df = beh_df.loc[filtered]

	return filtered_df


def filter_out_pretest(beh_df):

	filtered = beh_df['stage'].str.contains('pretest')

	return beh_df.loc[~filtered]


def filter_sessions_by_rig(beh_df, rig='NP'):

	filtered = beh_df['rig'].str.contains(rig)

	return beh_df.loc[filtered]


def plot_weight_over_time(beh_df):

	fig, ax = plt.subplots()
	ax.plot(beh_df['session_datetime_local'], beh_df['Wt_g'], 'k-o')
	ax.set_title('Wt_g')
	ax.tick_params(axis='x', labelrotation=45)


def plot_water_allotment(beh_df):

	fig, ax = plt.subplots()
	ax.plot(beh_df['session_datetime_local'], beh_df['WE_ml'], 'b-o')
	ax.plot(beh_df['session_datetime_local'], beh_df['WS_ml'], 'r-o')
	ax.plot(beh_df['session_datetime_local'], beh_df['WE_ml']+beh_df['WS_ml'], 'k-o')
	ax.legend(['WE_ml', 'WS_ml', 'Total'])
	ax.tick_params(axis='x', labelrotation=45)


def plot_inferred_presession_weight(beh_df, water_loss_during_session=0.3):

	post_wt = beh_df['Wt_g'].astype(float)
	earned_wt = beh_df['WE_ml'].astype(float)
	inferred_wt = post_wt - earned_wt + water_loss_during_session

	fig, ax = plt.subplots()
	ax.plot(beh_df['session_datetime_local'], inferred_wt, 'g-o')
	ax.set_title('Wt_g - WE_ml + {}: inferred pre-session weight'.format(water_loss_during_session))
	ax.tick_params(axis='x', labelrotation=45)


def plotSessionHistory(beh_df):

        def getColorAlphaFill(row):
            a = 1.0
            f = 'full'
            if 'NP' not in row['rig']:
                c = 'k'
            elif 'HAB' in row['stage']:
                c = 'm'
            elif 'EPHYS' in row['stage']:
                c = 'g'

            if '3uL' in row['stage']:
                a = 0.3

            return c,a,f

        #mouseID = str(mouseID)
        fig, ax = plt.subplots()
        fig.set_size_inches([12, 6])
        artists_for_legend = []
        labels_for_legend = []
        colors_used = []
        for ir, row in beh_df.iterrows():
            num_rewards = row['trials']['cumulative_reward_number'].max()
            c,a,f = getColorAlphaFill(row)
            ax.plot(row['session_datetime_local'], num_rewards, c+'o', alpha=a, fillstyle=f, mew=3)

        ax.set_xlabel('Sessions')
        ax.set_ylabel('num rewards')
        ax.set_xticks([row['session_datetime_local'] for _,row in beh_df.iterrows()][::2])
        ax.set_xticklabels([row['session_datetime_local'].date() for _,row in beh_df.iterrows()], rotation=90)
        #title = mouseID + 'Rewards per Session'
        plt.tight_layout()

        k_patch = mpatches.Patch(color='k', label='NSB')
        m_patch = mpatches.Patch(color='m', label='HAB')
        g_patch = mpatches.Patch(color='g', label='EPHYS')

        ax.legend(handles=[k_patch, m_patch, g_patch])


def findSaturationTime(trialdf):
    startTrial = np.where(trialdf['cumulative_volume']>0)[0]
    saturationTrial = np.where(trialdf['cumulative_volume'] > 0.90*trialdf['cumulative_volume'].max())[0]
    if len(saturationTrial)==0:
        saturationTrial = 0
        startTrial = 0
    else:
        saturationTrial = saturationTrial[0]
        startTrial = startTrial[0]
    return np.array(trialdf['endtime'])[saturationTrial] - np.array(trialdf['endtime'])[startTrial]

def findAbortFraction(trialdf):
	trial_types = trialdf['trial_type']
	fraction_aborted = (np.sum(trial_types=='aborted'))/float(len(trial_types))

	return fraction_aborted