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fig3.py
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fig3.py
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"""
Copright © 2023 Howard Hughes Medical Institute, Authored by Carsen Stringer and Marius Pachitariu.
"""
import matplotlib.pyplot as plt
from matplotlib import patches
import os
import numpy as np
from fig_utils import *
def panel_neuron_pos(fig, grid1, il, yratio, xpos0, ypos0, isort, brain_img):
xpos, ypos = xpos0.copy(), -1*ypos0.copy()
ylim = np.array([ypos.min(), ypos.max()])
xlim = np.array([xpos.min(), xpos.max()])
ylr = np.diff(ylim)[0] / np.diff(xlim)[0]
ax = fig.add_subplot(grid1[:1])
poss = ax.get_position().bounds
ax.set_position([poss[0]-0.01, poss[1]-.11, 1.4*poss[2], 1.4*poss[2]/ylr * yratio])
poss = ax.get_position().bounds
transl = mtransforms.ScaledTranslation(-10 / 72, -12 / 72, fig.dpi_scale_trans)
il = plot_label(ltr, il, ax, transl, fs_title)
memb = np.zeros_like(isort)
memb[isort] = np.arange(0, len(isort))
subsample = 5
ax.scatter(ypos[::subsample], xpos[::subsample], cmap=cmap_emb,
s=0.5, alpha=0.5, c=memb[::subsample], rasterized=True)
ax.axis("off")
add_apml(ax, xpos, ypos)
axin = fig.add_axes([poss[0]+poss[2]*0.5, poss[1] +poss[3]*.6, poss[2]*0.5, poss[3]*0.5])
axin.imshow(brain_img)
axin.axis("off")
return il
def panels_beh_traces(grid1, il, face_img, beh, beh_names, tcam, tneural, itest, xmin, xmax):
ax = plt.subplot(grid1[1])
poss = ax.get_position().bounds
ax.set_position([poss[0]-0.01, poss[1]-0.07, 1.4*poss[2], 1.4*poss[3]])
ax.imshow(face_img, vmin=100, vmax=150)
ax.set_title(" behaviors")
ax.axis("off")
transl = mtransforms.ScaledTranslation(-10 / 72, 7 / 72, grid1.figure.dpi_scale_trans)
il = plot_label(ltr, il, ax, transl, fs_title)
t0 = np.abs(tneural[itest.flatten()][xmin] - tcam).argmin()
t1 = np.abs(tneural[itest.flatten()][xmax] - tcam).argmin()
ax = plt.subplot(grid1[2])
poss = ax.get_position().bounds
ax.set_position([poss[0]-0.01, poss[1]-0.03, 1.4*poss[2], 1.6*poss[3]])
for k in range(beh.shape[1]):
by = beh[t0:t1, k].copy()
if k==0:
by = np.maximum(0, by)
by -= by.min()
by /= by.max()
ax.plot(by - k*1.5, color=kp_colors[k], lw=0.5);
ax.text(t1-t0, -k*1.5, beh_names[k], va="top", ha="right", color=kp_colors[k])
ax.plot([0, 50*10], -1.5*k*np.ones(2), color="k")
ax.text(0,0, "10 sec.", transform=ax.transAxes, va="top")
ax.set_ylim([-1.5*k-0.1, 1])
ax.set_xlim([0, t1-t0])
ax.axis("off")
return il
def panels_rfs(grid, il, yh, padding, ipl,rfs, beh_names):
nn = rfs.shape[0]
xw = 1
ax = plt.subplot(grid[4])
poss = ax.get_position().bounds
ax.set_position([poss[0]+0.02, poss[1], poss[2], poss[3]*yh])
transl = mtransforms.ScaledTranslation(-15 / 72, 38 / 72, grid.figure.dpi_scale_trans)
il = plot_label(ltr, il, ax, transl, fs_title)
dy = 300
dx = 1.
l = np.array([0,1,2])
npl = len(ipl)
h = 4
for i in range(npl):
ir = ipl[i]
rf = rfs[ir, 201-100:201+100].copy() / dx
for k in range(rfs.shape[-1]):
ax.plot(np.arange(0,rf.shape[-2]) + k*dy, rf[:,k]*-h + ir, color=kp_colors[k], lw=1)
ax.set_ylim([0, nn*(1+padding)])
ax.invert_yaxis()
ax.axis("off")
ax.text(0.01,1.09, "behavioral receptive fields", transform=ax.transAxes, fontsize="large")
for k in range(len(kp_colors)):
ax.text(k/len(kp_colors),1.005,beh_names[k], color=kp_colors[k],
rotation=45, transform=ax.transAxes, size="small")
ax = plt.subplot(grid[5])
poss = ax.get_position().bounds
ax.set_position([poss[0]+0.0, poss[1], poss[2], poss[3]*yh])
cmap_rb = plt.get_cmap("RdBu_r")
cmap_rb.set_bad("white")
rf_mat = rfs[:,201-80:201+80].transpose(0,2,1).reshape(rfs.shape[0], -1).copy()
rf_mat = np.minimum(3.9, rf_mat)
rf_mat[:,:10] = np.nan
for k in range(1,rfs.shape[-1]):
rf_mat[:,k*160-10:k*160+10] = np.nan
vmax = 5
im = ax.imshow(rf_mat, aspect="auto", vmin=-vmax, vmax=vmax, cmap=cmap_rb)#"RdBu_r")
nn = rfs.shape[0]
for k in range(len(kp_colors)):
ax.text(k/len(kp_colors),1.005,beh_names[k], color=kp_colors[k],
rotation=45, transform=ax.transAxes, size="small")
ax.set_ylim([0, nn*(1+padding)])
ax.invert_yaxis()
ax.axis("off")
poss = ax.get_position().bounds
xw = 0.05
cax = grid.figure.add_axes([poss[0]+poss[2]-xw, poss[1]-poss[3]*0.005, xw, poss[3]*0.01])
plt.colorbar(im, cax, orientation="horizontal")
cax.set_xticks([-5,0,5])
cax.set_xlabel("norm. units")
return il
def panels_rasters(fig, grid, il, yh, padding, ipl, sn_test, sn_pred_test,
run, itest, xmin, xmax):
npl = len(ipl)
xr = xmax - xmin
nn = sn_test.shape[0]
titles = ["spontaneous neural activity (test data)", "behavioral prediction of activity"]
for k in range(2):
ax = plt.subplot(grid[5*k + 1 : 5*k + 4])
pos = ax.get_position().bounds
ax.axis("off")
ax.remove()
# run raster
padding_x = 0.01
ax = fig.add_axes([pos[0]+0.02*(k==0), pos[1]+pos[3]*(yh+0.01), pos[2], pos[3]*(1-yh-0.01)])
ax.fill_between(np.arange(0, xr), run[itest.flatten()][xmin:xmax],
color=kp_colors[0])
ax.set_xlim([0*xr, xr*(1+padding_x*2)])
#ax.set_xlim([0, 1.008*xr])
ax.set_ylim([0,1.2])
ax.axis("off")
transl = mtransforms.ScaledTranslation(-15 / 72, 12 / 72, fig.dpi_scale_trans)
il = plot_label(ltr, il, ax, transl, fs_title)
if k==0:
il+=2
if k==0:
ax.text(0.75,0.8,"running speed", transform=ax.transAxes, color=kp_colors[0])
ax.text(0,1.5,titles[k], transform=ax.transAxes, fontsize="large")
# spk raster
ax = fig.add_axes([pos[0]+0.02*(k==0), pos[1], pos[2], pos[3]*yh])
if k==0:
ax0 = ax
xw = pos[2]*0.1
if k==0:
cax = fig.add_axes([pos[0]+0.02, pos[1]-pos[3]*0.005, xw, pos[3]*0.01])
else:
cax = None
plot_raster(ax, sn_test if k==0 else sn_pred_test,
xmin=xmin, xmax=xmax, vmax=1.5, fs=3.38,
nper=50, n_neurons=5000, label=k==1,
padding=padding, padding_x=padding_x,
cax=cax, label_pos="right")
if k==0:
cax = fig.add_axes([pos[0]+0.02-pos[2]*0.02, pos[1], pos[2]*0.01, pos[3]*yh])
nn = sn_test.shape[0]
cols = cmap_emb(np.linspace(0, 1, nn))
cax.imshow(cols[:,np.newaxis], aspect="auto")
cax.set_ylim([0, (1+padding)*nn])
cax.invert_yaxis()
cax.axis("off")
else:
for i in range(npl):
ir = ipl[i]
xy0 = (0,ir)
xy1 = (xr,ir)
con = patches.ConnectionPatch(xyA=xy0, xyB=xy1, coordsA="data", coordsB="data",
axesA=ax0, axesB=ax, color=.5*np.ones(3), lw=0.5)
ax.add_artist(con)
return il
def _fig3(brain_img, face_img, xpos, ypos, isort,
isort2, sn, cc_nodes, sn_rand,
beh, beh_names, tcam, tneural,
itest, rfs, run, sn_test, sn_pred_test):
fig = plt.figure(figsize=(14,7))
yratio = 14 / 7
grid = plt.GridSpec(1,9, figure=fig, left=0.02, right=0.98, top=0.94, bottom=0.07,
wspace = 0.35, hspace = 0.3)
#xmin = 185
xmin = 688*4
xmax = xmin+500
padding = 0.015
yh = 0.94 # fraction raster vs run
npl = 18
nn = rfs.shape[0]
ipl = np.linspace(8, nn-8, npl).astype("int")
print(ipl)
ax = fig.add_subplot(grid[0])
ax.axis("off")
grid1 = matplotlib.gridspec.GridSpecFromSubplotSpec(3,1, subplot_spec=ax,
wspace=0.2, hspace=0.5)
ax.remove()
il = 0
il+=2
il = panels_beh_traces(grid1, il, face_img, beh, beh_names, tcam, tneural, itest, xmin, xmax)
il-=3
il = panel_neuron_pos(fig, grid1, il, yratio, xpos, ypos, isort, brain_img)
il+=2
il = panels_rfs(grid, il, yh, padding, ipl, rfs, beh_names)
il-=3
il = panels_rasters(fig, grid, il, yh, padding, ipl, sn_test, sn_pred_test,
run, itest, xmin, xmax)
return fig
def fig3(root, save_figure=True):
d = np.load(os.path.join(root, "results", "spont_proc.npz"), allow_pickle=True)
try:
brain_img = plt.imread(os.path.join(root, "figures", "brain_windows.png"))
face_img = plt.imread(os.path.join(root, "figures", "mouse_face_labeled.png"))
except:
brain_img = np.zeros((50,50))
face_img = np.zeros((50,50))
fig = _fig3(brain_img, face_img, **d);
if save_figure:
fig.savefig(os.path.join(root, "figures", "fig3.pdf"), dpi=200)
def suppfig_random(root, save_figure=True):
d = np.load(os.path.join(root, "results", "spont_proc.npz"), allow_pickle=True)
sn = d["sn"]
sn_rand = d["sn_rand"]
run = d["run"]
itest = d["itest"]
fig = plt.figure(figsize=(14,8))
grid = plt.GridSpec(1,2, figure=fig, left=0.02, right=0.99, top=0.9, bottom=0.13,
wspace = 0.15, hspace = 0.3)
il = 0
titles = ["random sorting",
"Rastermap sorting"]
xmin = 688*4
xmax = xmin+500
padding = 0.015
yh = 0.94 # fraction raster vs run
xr = xmax - xmin
for k in range(2):
ax = plt.subplot(grid[k])
pos = ax.get_position().bounds
ax.axis("off")
ax.remove()
# run raster
ax = fig.add_axes([pos[0]+0.02*(k==0), pos[1]+pos[3]*(yh+0.01), pos[2], pos[3]*(1-yh-0.01)])
ax.fill_between(np.arange(0, xr), run[itest.flatten()][xmin:xmax],
color=kp_colors[0])
ax.set_xlim([0, 1.008*xr])
ax.set_ylim([0,1.2])
ax.spines["left"].set_visible(False)
ax.set_yticks([])
ax.set_xticks([])
transl = mtransforms.ScaledTranslation(-15 / 72, 12 / 72, fig.dpi_scale_trans)
il = plot_label(ltr, il, ax, transl, fs_title)
if k==0:
ax.text(0.75,0.8,"running speed", transform=ax.transAxes, color=kp_colors[0])
ax.text(0,1.5,titles[k], transform=ax.transAxes, fontsize="large")
# spk raster
ax = fig.add_axes([pos[0]+0.02*(k==0), pos[1], pos[2], pos[3]*yh])
if k==0:
ax0 = ax
xw = pos[2]*0.1
if k==0:
cax = fig.add_axes([pos[0]+0.02, pos[1]-pos[3]*0.005, xw, pos[3]*0.01])
else:
cax = None
plot_raster(ax, sn_rand[:,itest.flatten()] if k==0 else sn[:, itest.flatten()],
xmin=xmin, xmax=xmax, vmax=1.5, fs=3.38, label=k==0,
nper=50, n_neurons=5000,
padding=padding, padding_x=0.01, cax=cax, label_pos="right")
if save_figure:
fig.savefig(os.path.join(root, "figures", "suppfig_random.pdf"))
def suppfig_locality(root, save_figure=True):
d = np.load(os.path.join(root, "results", "asym_vr_spont.npz"), allow_pickle=True)
ccs_vr = d["ccs_vr"]
ccs_spont = d["ccs_spont"]
localities = d["localities"]
scores_vr = d["scores_vr"]
scores_spont = d["scores_spont"]
fig = plt.figure(figsize=(14,6))
grid = plt.GridSpec(2,5, figure=fig, left=0.01, right=0.99, top=0.86, bottom=0.01,
wspace = 0.2, hspace = 0.45)
transl = mtransforms.ScaledTranslation(-15 / 72, 38 / 72, fig.dpi_scale_trans)
il = 0
xmin = 185
xmax = xmin+500
xr = xmax - xmin
yh = 0.9
padding_x = 0.008
nbin = 200
vmax = 0.75
tstrs = ["neural activity in virtual reality", "spontaneous neural activity"]
for j in range(2):
ccs = ccs_vr.copy() if j==0 else ccs_spont.copy()
scores = scores_vr.copy() if j==0 else scores_spont.copy()
for k in range(5):
ax = plt.subplot(grid[j, k])
cc = ccs[k].copy()
cc -= np.diag(np.diag(cc))
im = ax.imshow(cc, vmin=-vmax, vmax=vmax, cmap="RdBu_r")
ax.axis("off")
if k==0:
ax.text(0., 1.05, f"locality = {localities[k]}\nscores (global / local): {scores[k][0]:.2f} / {scores[k][1]:.2f}", transform=ax.transAxes,
fontsize="medium")#, ha="center")
il = plot_label(ltr, il, ax, transl, fs_title)
ax.set_title(f"{tstrs[j]} - asymmetric similarity matrix",
y=1.22)
cax = ax.inset_axes([1.05, 0.55, 0.05, 0.3])
plt.colorbar(im, cax=cax)
else:
ax.text(0., 1.05, f"locality = {localities[k]}\nscores: {scores[k][0]:.2f} / {scores[k][1]:.2f}", transform=ax.transAxes,
fontsize="medium")#, ha="center")
if save_figure:
fig.savefig(os.path.join(root, "figures", "suppfig_asym.pdf"))
def suppfig_beh(root, save_figure=True):
d = np.load(os.path.join(root, "results", "spont_proc.npz"), allow_pickle=True)
sn = d["sn"]
itest = d["itest"]
dbeh = np.load(os.path.join(root, "results", "spont_corrs_beh.npz"), allow_pickle=True)
sn_beh = dbeh["sn_beh"]
vars = dbeh["vars"]
fig = plt.figure(figsize=(14,3.7))
grid = plt.GridSpec(1,5, figure=fig, left=0.02, right=0.99, top=0.82, bottom=0.04,
wspace = 0.2, hspace = 0.5)
transl = mtransforms.ScaledTranslation(-15 / 72, 20 / 72, fig.dpi_scale_trans)
il = 0
xmin = 688*4
xmax = xmin+500
xr = xmax - xmin
yh = 0.9
padding_x = 0.008
nbin = 50
padding = 0.015
ax = plt.subplot(grid[0, 0])
pos = ax.get_position().bounds
ax.axis("off")
ax.remove()
ax = fig.add_axes([pos[0], pos[1]+pos[3]*(yh+0.03), pos[2], pos[3]*(1-yh-0.01)])
ax.text(0, 1.4, "Spontaneous neural activity\nRastermap sorting",
fontsize="large", transform=ax.transAxes, fontstyle="italic")
il = plot_label(ltr, il, ax, transl, fs_title)
ax.axis("off")
ax = fig.add_axes([pos[0], pos[1], pos[2], pos[3]*yh])
plot_raster(ax, sn[:,itest.flatten()],
xmin=xmin, xmax=xmax, vmax=1.5, fs=3.38,
nper=nbin, n_neurons=5000, label=True,
padding=padding, padding_x=padding_x)
titles = ["running speed", "whisking speed", "nose speed", "eye area"]
vmax = [0.2, 0.1, 0.1, 0.2]
ylim = [[0, 1.2], [0, 3], [0, 3], [-1.5, 3]]
for k in range(4):
ax = plt.subplot(grid[0, k+1])
pos = ax.get_position().bounds
ax.axis("off")
ax.remove()
# beh plot
ax = fig.add_axes([pos[0], pos[1]+pos[3]*(yh+0.03), pos[2], pos[3]*(1-yh-0.01)])
if k<3:
ax.fill_between(np.arange(0, xr), vars[itest.flatten(),k][xmin:xmax],
color=kp_colors[[0,2,4]][k])
ax.set_ylim(ylim[k])
else:
ax.plot(np.arange(0, xr), vars[itest.flatten(),k][xmin:xmax], color=kp_colors[1])
ax.set_xlim([-2*padding_x*xr, xr])
ax.axis("off")
ax.set_title(titles[k], color=kp_colors[[0,2,4,1]][k], fontsize="medium", y=0.8)
if k==0:
il = plot_label(ltr, il, ax, transl, fs_title)
elif k==1:
ax.text(1, 1.8, "sorting by correlation with behavioral variables", ha="center",
fontsize="large", transform=ax.transAxes, fontstyle="italic")
# spk raster
sn_test = sn_beh[k][::-1]
#isort = isorts_beh[k][::-1].copy()
#sn_test = zscore(utils.bin1d(spks[isort][:,itest.flatten()], nbin, axis=0), axis=1)
ax = fig.add_axes([pos[0], pos[1], pos[2], pos[3]*yh])
plot_raster(ax, sn_test,
xmin=xmin, xmax=xmax, vmax=1.5, fs=3.38,
nper=nbin, n_neurons=5000, label=False,
padding=padding, padding_x=padding_x)
if save_figure:
fig.savefig(os.path.join(root, "figures/suppfig_beh.pdf"), dpi=200)