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FigureS4.py
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# Copyright 2018 Martin Haesemeyer. All rights reserved.
#
# Licensed under the MIT license
"""
Script for panels of Figure S4 (Zebrafish phototaxis network)
"""
import os
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as pl
import seaborn as sns
from mo_types import MoTypes
import core as c
import analysis as a
import h5py
from global_defs import GlobalDefs
from Figure3 import mpath
from scipy.signal import convolve
from sklearn.decomposition import PCA
from Figure4 import test_loss, plot_pc
# file definitions
base_path_zf = "./model_data/Adam_1e-4/sepInput_mixTrain/"
paths_512_zf = [f + '/' for f in os.listdir(base_path_zf) if "_3m512_" in f]
base_path_pt = "./model_data/Phototaxis/"
paths_512_pt = [f + '/' for f in os.listdir(base_path_pt) if "_3m512_" in f]
# The following functions are necessary since phototaxis simulations are currently not part of the data store scheme
def do_simulation(path):
"""
Uses a model identified by path to run a naive and a trained simulation
:param path: The model path
:return:
[0]: The facing angle bin centers
[1]: The occupancy of the naive model
[2]: The occupancy of the trained model
"""
global std_pt
bins = np.linspace(-np.pi, np.pi, 100)
# bin-centers in degress
bcenters = bins[:-1]+np.diff(bins)/2
# naive simulation
mdata = c.ModelData(path)
model_naive = c.ZfGpNetworkModel()
model_naive.load(mdata.ModelDefinition, mdata.FirstCheckpoint)
model_trained = c.ZfGpNetworkModel()
model_trained.load(mdata.ModelDefinition, mdata.LastCheckpoint)
sim = MoTypes(False).pt_sim(model_naive, std_pt, 100)
pos_naive = sim.run_simulation(GlobalDefs.n_steps)
h_naive = a.bin_simulation_pt(pos_naive, bins)
sim = MoTypes(False).pt_sim(model_trained, std_pt, 100)
pos_trained = sim.run_simulation(GlobalDefs.n_steps)
h_trained = a.bin_simulation_pt(pos_trained, bins)
return bcenters, h_naive, h_trained
def get_cell_responses(path, temp):
"""
Loads a model and computes the temperature response of all neurons returning response matrix
:param path: Model path
:param temp: Temperature stimulus
:return: n-timepoints x m-neurons matrix of responses
"""
global std_pt
mdata = c.ModelData(path)
# create our model and load from last checkpoint
gpn = c.ZfGpNetworkModel()
gpn.load(mdata.ModelDefinition, mdata.LastCheckpoint)
# prepend lead-in to stimulus
lead_in = np.full(gpn.input_dims[2] - 1, np.mean(temp[:10]))
temp = np.r_[lead_in, temp]
activities = gpn.unit_stimulus_responses(temp, None, None, std_pt)
return np.hstack(activities['t']) if 't' in activities else np.hstack(activities['m'])
if __name__ == "__main__":
save_folder = "./DataFigures/FigureS4/"
if not os.path.exists(save_folder):
os.makedirs(save_folder)
sns.reset_orig()
mpl.rcParams['pdf.fonttype'] = 42
# Panel 1 - test error during phototaxis training
test_time = test_loss(base_path_pt, paths_512_pt[0])[0]
test_512 = np.vstack([test_loss(base_path_pt, lp)[1] for lp in paths_512_pt])
fig, ax = pl.subplots()
sns.tsplot(np.log10(test_512), test_time, ax=ax, color="C1", n_boot=1000, condition="512 HU")
epoch_times = np.linspace(0, test_time.max(), 10, endpoint=False)
for e in epoch_times:
ax.plot([e, e], [-.5, .1], 'k--', lw=0.25)
ax.set_ylabel("log(Squared test error)")
ax.set_xlabel("Training step")
ax.set_xlim(-10000)
ax.set_xticks([0, 100000, 200000, 300000, 400000])
ax.legend()
sns.despine(fig, ax)
fig.savefig(save_folder+"pt_test_errors.pdf", type="pdf")
std_zf = c.GradientData.load_standards("gd_training_data.hdf5")
ana_zf = a.Analyzer(MoTypes(False), std_zf, "sim_store.hdf5", "activity_store.hdf5")
std_pt = c.GradientData.load_standards("photo_training_data.hdf5")
# load and interpolate temperature stimulus
dfile = h5py.File("stimFile.hdf5", 'r')
tsin = np.array(dfile['sine_L_H_temp'])
x = np.arange(tsin.size) # stored at 20 Hz !
xinterp = np.linspace(0, tsin.size, tsin.size * GlobalDefs.frame_rate // 20)
temperature = np.interp(xinterp, x, tsin)
dfile.close()
# get cell responses
all_cells_zf = []
for i, p in enumerate(paths_512_zf):
cell_res, ids = ana_zf.temperature_activity(mpath(base_path_zf, p), temperature, i)
all_cells_zf.append(cell_res)
all_cells_zf = np.hstack(all_cells_zf)
all_cells_pt = []
for p in paths_512_pt:
all_cells_pt.append(get_cell_responses(mpath(base_path_pt, p), temperature))
all_cells_pt = np.hstack(all_cells_pt)
# convolve activity with nuclear gcamp calcium kernel
tau_on = 1.4 # seconds
tau_on *= GlobalDefs.frame_rate # in frames
tau_off = 2 # seconds
tau_off *= GlobalDefs.frame_rate # in frames
kframes = np.arange(10 * GlobalDefs.frame_rate) # 10 s long kernel
kernel = 2 ** (-kframes / tau_off) * (1 - 2 ** (-kframes / tau_on))
kernel = kernel / kernel.sum()
# convolve with our kernel
for i in range(all_cells_zf.shape[1]):
all_cells_zf[:, i] = convolve(all_cells_zf[:, i], kernel, mode='full')[:all_cells_zf.shape[0]]
for i in range(all_cells_pt.shape[1]):
all_cells_pt[:, i] = convolve(all_cells_pt[:, i], kernel, mode='full')[:all_cells_pt.shape[0]]
# Panel 2 - naive and trained phototaxis performance
all_n = []
t_512 = []
bins = None
for p512 in paths_512_pt:
bins, naive, trained = do_simulation(mpath(base_path_pt, p512))[:3]
all_n.append(naive)
t_512.append(trained)
t_512 = np.vstack(t_512)
all_n = np.vstack(all_n)
fig, ax = pl.subplots()
sns.tsplot(t_512, np.rad2deg(bins), n_boot=1000, ax=ax, color="C1", condition="Trained")
sns.tsplot(all_n, np.rad2deg(bins), n_boot=1000, ax=ax, color="k", condition="Naive")
ax.plot([0, 0], ax.get_ylim(), 'k--')
ax.set_ylim(0)
ax.legend()
ax.set_xticks([-180, -90, 0, 90, 180])
ax.set_ylabel("Proportion")
ax.set_xlabel("Angle to light source")
sns.despine(fig, ax)
fig.savefig(save_folder + "Phototaxis_navigation.pdf", type="pdf")
# Panel 4 - PCA space comparison of zfish gradient and phototaxis responses
all_cells = np.hstack((a.trial_average(all_cells_zf, 3), a.trial_average(all_cells_pt, 3))).T
max_vals = np.max(all_cells, 1, keepdims=True)
max_vals[max_vals == 0] = 1 # these cells do not show any response
all_cells /= max_vals
species_id = np.zeros(all_cells.shape[0])
species_id[all_cells_zf.shape[1]:] = 1
pca = PCA(4)
pca.fit(all_cells)
coords = pca.transform(all_cells)
for i in range(pca.n_components):
plot_pc(i, coords, species_id, pca.explained_variance_, "zf_pt")