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boot implementations using cupy

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This commit is contained in:
Till Raab 2023-05-26 10:06:02 +02:00
parent d8d28df2ea
commit 6d7e58ef80
1 changed files with 67 additions and 16 deletions
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75
event_time_analysis.py
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@ -2,11 +2,16 @@ import os
import sys
import argparse
import numpy as np
try:
import cupy as cp
except ImportError:
import numpy as cp
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import pandas as pd
from IPython import embed
from tqdm import tqdm
def load_and_converete_boris_events(trial_path, recording, sr):
@ -86,30 +91,76 @@ def kde(event_dt, max_dt = 60):
plt.plot(conv_t, conv_array)
def permulation_kde(event_dt, repetitions = 100, max_dt = 60):
def permulation_kde(select_event_dt, repetitions = 2000, max_dt = 60, max_mem_use_GB = 1):
def chunk_permutation(select_event_dt, conv_tt, n_chuck, max_jitter, kernal_w, kernal_h):
# array.shape = (120, 100, 15486) = (len(conv_t), repetitions, len(event_dt))
# event_dt_perm = cp.tile(event_dt, (len(conv_t), repetitions, 1))
event_dt_perm = cp.tile(select_event_dt, (len(conv_tt), n_chuck, 1))
jitter = np.random.uniform(-max_jitter, max_jitter, size=(event_dt_perm.shape[1], event_dt_perm.shape[2]))
jitter = np.expand_dims(jitter, axis=0)
event_dt_perm += jitter
# conv_t_perm = cp.tile(conv_tt, (1, repetitions, len(event_dt)))
gauss_3d = cp.exp(-((conv_tt - event_dt_perm) / kernal_w) ** 2 / 2) * kernal_h
kde_3d = cp.sum(gauss_3d, axis = 2).transpose()
try:
kde_3d_numpy = cp.asnumpy(kde_3d)
del event_dt_perm, gauss_3d, kde_3d
return kde_3d_numpy
except AttributeError:
del event_dt_perm, gauss_3d
return kde_3d
embed()
quit()
kernal_w = 1
kernal_h = 0.2
max_jitter = 30
select_event_dt = event_dt[np.abs(event_dt) <= max_dt + max_jitter*2]
conv_t = cp.arange(-max_dt, max_dt, 1)
conv_tt = cp.reshape(conv_t, (len(conv_t), 1, 1))
# array.shape = (120, 100, 15486) = (len(conv_t), repetitions, len(event_dt))
event_dt_perm = cp.tile(event_dt, (len(conv_t), repetitions, 1))
# conv_t_perm = cp.tile(conv_tt, (1, repetitions, len(event_dt)))
chunk_size = int(np.floor(max_mem_use_GB / (select_event_dt.nbytes * conv_t.size / 1e9)))
chunk_collector =[]
# for _ in range(repetitions // chunk_size):
for _ in range(3):
chunk_boot_KDE = chunk_permutation(select_event_dt, conv_tt, chunk_size, max_jitter, kernal_w, kernal_h)
chunk_collector.extend(chunk_boot_KDE)
# # array.shape = (120, 100, 15486) = (len(conv_t), repetitions, len(event_dt))
# # event_dt_perm = cp.tile(event_dt, (len(conv_t), repetitions, 1))
# event_dt_perm = cp.tile(event_dt, (len(conv_t), chunk_size, 1))
# jitter = np.random.uniform(-max_jitter, max_jitter, size=(event_dt_perm.shape[1], event_dt_perm.shape[2]))
# jitter = np.expand_dims(jitter, axis=0)
#
# event_dt_perm += jitter
# # conv_t_perm = cp.tile(conv_tt, (1, repetitions, len(event_dt)))
#
# gauss_3d = cp.exp(-((conv_tt - event_dt_perm) / kernal_w) ** 2 / 2) * kernal_h
# kde_3d = cp.sum(gauss_3d, axis = 2).transpose()
# try:
# kde_3d_numpy = cp.asnumpy(kde_3d)
# chunk_collector.extend(kde_3d_numpy)
# except AttributeError:
# chunk_collector.extend(kde_3d)
# del event_dt_perm, gauss_3d, kde_3d
chunk_boot_KDE = chunk_permutation(select_event_dt, conv_tt, repetitions % chunk_size, max_jitter, kernal_w, kernal_h)
chunk_collector.extend(chunk_boot_KDE)
gauss_3d = cp.exp(-((conv_tt - event_dt_perm) / kernal_w) ** 2 / 2) * kernal_h
kde_3d = cp.sum(gauss_3d, axis = 2).transpose()
kde_3d_numpy = cp.asnumpy(kde_3d)
def main(base_path):
trial_summary = pd.read_csv('trial_summary.csv', index_col=0)
lose_chrips_centered_on_ag_off_t = []
for index, trial in trial_summary.iterrows():
for index, trial in tqdm(trial_summary.iterrows()):
trial_path = os.path.join(base_path, trial['recording'])
if trial['group'] < 5:
@ -128,12 +179,12 @@ def main(base_path):
load_and_converete_boris_events(trial_path, trial['recording'], sr=20_000)
### communication
got_chirps = False
if os.path.exists(os.path.join(trial_path, 'chirp_times_cnn.npy')):
if not os.path.exists(os.path.join(trial_path, 'chirp_times_cnn.npy')):
continue
chirp_t = np.load(os.path.join(trial_path, 'chirp_times_cnn.npy'))
chirp_ids = np.load(os.path.join(trial_path, 'chirp_ids_cnn.npy'))
chirp_times = [chirp_t[chirp_ids == trial['win_ID']], chirp_t[chirp_ids == trial['lose_ID']]]
got_chirps = True
rise_idx = np.load(os.path.join(trial_path, 'analysis', 'rise_idx.npy'))[::sorter]
rise_idx_int = [np.array(rise_idx[i][~np.isnan(rise_idx[i])], dtype=int) for i in range(len(rise_idx))]