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plot.py
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plot.py
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import numbers, os, copy, pprint, sys
from collections import defaultdict
from scipy import interpolate, signal
import numpy as np
from mpi4py import MPI
from neuroh5.io import NeuroH5ProjectionGen, bcast_cell_attributes, read_cell_attributes, read_population_names, \
read_population_ranges, read_projection_names, read_tree_selection
import h5py
import matplotlib as mpl
import matplotlib.cm as cm
import matplotlib.lines as mlines
import matplotlib.pyplot as plt
from matplotlib import gridspec
from matplotlib.animation import FuncAnimation, writers
from matplotlib.colors import BoundaryNorm
from matplotlib.offsetbox import AnchoredText
from matplotlib.ticker import FormatStrFormatter, MaxNLocator
from mpl_toolkits.axes_grid1 import make_axes_locatable
import ca1
from ca1.env import Env
from ca1.utils import get_module_logger, Struct, viewitems, make_geometric_graph, zip_longest, apply_filter, butter_bandpass_filter, signal_psd, signal_power_spectrogram
from ca1.io_utils import get_h5py_attr, set_h5py_attr
from ca1.neuron_utils import interplocs, h
from ca1 import spikedata, cells, synapses
# This logger will inherit its settings from the root logger, created in ca1.env
logger = get_module_logger(__name__)
# Default figure configuration
default_fig_options = Struct(figFormat='png', lw=2, figSize=(10,8), fontSize=14,
saveFig=None, showFig=True,
colormap='jet', saveFigDir=None)
dflt_colors = ["#009BFF", "#E85EBE", "#00FF00", "#0000FF", "#FF0000", "#01FFFE", "#FFA6FE",
"#FFDB66", "#006401", "#010067", "#95003A", "#007DB5", "#FF00F6", "#FFEEE8", "#774D00",
"#90FB92", "#0076FF", "#D5FF00", "#FF937E", "#6A826C", "#FF029D", "#FE8900", "#7A4782",
"#7E2DD2", "#85A900", "#FF0056", "#A42400", "#00AE7E", "#683D3B", "#BDC6FF", "#263400",
"#BDD393", "#00B917", "#9E008E", "#001544", "#C28C9F", "#FF74A3", "#01D0FF", "#004754",
"#E56FFE", "#788231", "#0E4CA1", "#91D0CB", "#BE9970", "#968AE8", "#BB8800", "#43002C",
"#DEFF74", "#00FFC6", "#FFE502", "#620E00", "#008F9C", "#98FF52", "#7544B1", "#B500FF",
"#00FF78", "#FF6E41", "#005F39", "#6B6882", "#5FAD4E", "#A75740", "#A5FFD2", "#FFB167"]
rainbow_colors = ["#9400D3", "#4B0082", "#00FF00", "#FFFF00", "#FF7F00", "#FF0000"]
raster_colors = ['#8dd3c7', '#ffed6f', '#bebada', '#fb8072', '#80b1d3', '#fdb462',
'#b3de69', '#fccde5', '#d9d9d9', '#bc80bd', '#ccebc5']
def hex2rgb(hexcode):
if hasattr(hexcode, 'decode'):
return tuple([ float(b)/255.0 for b in map(ord,hexcode[1:].decode('hex')) ])
else:
import codecs
bhexcode = bytes(hexcode[1:], 'utf-8')
return tuple([ float(b)/255.0 for b in codecs.decode(bhexcode, 'hex') ])
mpl.rcParams['svg.fonttype'] = 'none'
mpl.rcParams['font.size'] = 14.
mpl.rcParams['font.sans-serif'] = 'Arial'
mpl.rcParams['text.usetex'] = False
def show_figure():
plt.show()
def close_figure(fig):
plt.close(fig)
def save_figure(file_name_prefix, fig=None, **kwargs):
"""
:param file_name_prefix:
:param fig: :class:'plt.Figure'
:param kwargs: dict
"""
fig_options = copy.copy(default_fig_options)
fig_options.update(kwargs)
fig_file_path = f'{file_name_prefix}.{fig_options.figFormat}'
if fig_options.saveFigDir is not None:
fig_file_path = f'{fig_options.saveFigDir}/{fig_file_path}'
if fig is not None:
fig.savefig(fig_file_path)
else:
plt.savefig(fig_file_path)
def plot_graph(x, y, z, start_idx, end_idx, edge_scalars=None, edge_color=None, **kwargs):
"""
Shows graph edges using Mayavi
Parameters
-----------
x: ndarray
x coordinates of the points
y: ndarray
y coordinates of the points
z: ndarray
z coordinates of the points
edge_scalars: ndarray, optional
optional data to give the color of the edges.
kwargs:
extra keyword arguments are passed to quiver3d.
"""
from mayavi import mlab
if edge_color is not None:
kwargs['color'] = edge_color
vec = mlab.quiver3d(x[start_idx],
y[start_idx],
z[start_idx],
x[end_idx] - x[start_idx],
y[end_idx] - y[start_idx],
z[end_idx] - z[start_idx],
scalars=edge_scalars,
scale_factor=1,
mode='2ddash',
**kwargs)
b = mlab.points3d(x[0],y[0],z[0],
mode='cone',
scale_factor=3,
**kwargs)
if edge_scalars is not None:
vec.glyph.color_mode = 'color_by_scalar'
cb = mlab.colorbar(vec, label_fmt='%.1f')
cb.label_text_property.font_size=14
return vec
def plot_spatial_bin_graph(graph_dict, **kwargs):
import hiveplot as hv
import networkx as nx
edge_dflt_colors = ['red','crimson','coral','purple']
fig_options = copy.copy(default_fig_options)
fig_options.update(kwargs)
label = graph_dict['label']
GU = graph_dict['U graph']
destination = graph_dict['destination']
sources = graph_dict['sources']
nodes = {}
nodes[destination] = [(s,d) for s, d in GU.nodes() if s == destination]
for source in sources:
nodes[source] = [(s,d) for s, d in GU.nodes() if s == source]
snodes = {}
for group, nodelist in viewitems(nodes):
snodes[group] = sorted(nodelist)
edges = {}
for source in sources:
edges[source] = [(u,v,d) for u,v,d in GU.edges(data=True) if v[0] == source]
nodes_cmap = dict()
nodes_cmap[destination] = 'blue'
for i, source in enumerate(sources):
nodes_cmap[source] = raster_colors[i]
edges_cmap = dict()
for i, source in enumerate(sources):
edges_cmap[source] = dflt_colors[i]
hvpl = hv.HivePlot(snodes, edges, nodes_cmap, edges_cmap)
hvpl.draw()
filename = f'{label}.{fig_options.figFormat}'
plt.savefig(filename)
def plot_coordinates(coords_path, population, namespace, index = 0, graph_type = 'scatter', bin_size = 0.01, xyz = False, **kwargs):
"""
Plot coordinates
:param coords_path:
:param namespace:
:param population:
"""
fig_options = copy.copy(default_fig_options)
fig_options.update(kwargs)
soma_coords = read_cell_attributes(coords_path, population, namespace=namespace)
fig = plt.figure(1, figsize=plt.figaspect(1.) * 2.)
ax = plt.gca()
coord_U = {}
coord_V = {}
if xyz:
for k,v in soma_coords:
coord_U[k] = v['X Coordinate'][index]
coord_V[k] = v['Y Coordinate'][index]
else:
for k,v in soma_coords:
coord_U[k] = v['U Coordinate'][index]
coord_V[k] = v['V Coordinate'][index]
coord_U_array = np.asarray([coord_U[k] for k in sorted(coord_U.keys())])
coord_V_array = np.asarray([coord_V[k] for k in sorted(coord_V.keys())])
x_min = np.min(coord_U_array)
x_max = np.max(coord_U_array)
y_min = np.min(coord_V_array)
y_max = np.max(coord_V_array)
dx = int((x_max - x_min) / bin_size)
dy = int((y_max - y_min) / bin_size)
if graph_type == 'scatter':
ax.scatter(coord_U_array, coord_V_array, alpha=0.1, linewidth=0)
ax.axis([x_min, x_max, y_min, y_max])
elif graph_type == 'histogram2d':
(H, xedges, yedges) = np.histogram2d(coord_U_array, coord_V_array, bins=[dx, dy])
X, Y = np.meshgrid(xedges, yedges)
Hint = H[:-1, :-1]
levels = MaxNLocator(nbins=25).tick_values(Hint.min(), Hint.max())
cmap = plt.get_cmap('jet')
norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
p = ax.contourf(X[:-1,:-1] + (bin_size / 2), Y[:-1,:-1]+(bin_size / 2), H.T, levels=levels, cmap=cmap)
fig.colorbar(p, ax=ax, shrink=0.5, aspect=20)
else:
raise ValueError(f'Unknown graph type {graph_type}')
if xyz:
ax.set_xlabel('X coordinate (um)', fontsize=fig_options.fontSize)
ax.set_ylabel('Y coordinate (um)', fontsize=fig_options.fontSize)
else:
ax.set_xlabel('U coordinate (septal - temporal)', fontsize=fig_options.fontSize)
ax.set_ylabel('V coordinate (supra - infrapyramidal)', fontsize=fig_options.fontSize)
ax.set_title(f'Coordinate distribution for population: {population}',
fontsize=fig_options.fontSize)
if fig_options.saveFig:
if isinstance(fig_options.saveFig, str):
filename = fig_options.saveFig
else:
filename = f'{population} Coordinates.{fig_options.figFormat}'
plt.savefig(filename)
if fig_options.showFig:
show_figure()
return ax
def plot_coords_in_volume(populations, coords_path, coords_namespace, config, scale=25., subpopulation=-1, subvol=False, verbose=False, mayavi=False):
from neural_geometry.geometry import get_total_extents
env = Env(config_file=config)
rotate = env.geometry['Parametric Surface']['Rotation']
layer_extents = env.geometry['Parametric Surface']['Layer Extents']
rotate = env.geometry['Parametric Surface']['Rotation']
(extent_u, extent_v, extent_l) = get_total_extents(layer_extents)
logger.info('Reading coordinates...')
pop_min_extent = None
pop_max_extent = None
xcoords = []
ycoords = []
zcoords = []
cmap = cm.get_cmap('Dark2')
cmap_range = np.linspace(0,1,num=len(populations))
colors = []
for (pop_id, population) in enumerate(populations):
coords = read_cell_attributes(coords_path, population, namespace=coords_namespace)
count = 0
cxcoords = []
cycoords = []
czcoords = []
for (k,v) in coords:
count += 1
cxcoords.append(v['X Coordinate'][0])
cycoords.append(v['Y Coordinate'][0])
czcoords.append(v['Z Coordinate'][0])
if subpopulation > -1 and count > subpopulation:
ridxs = np.random.choice(np.arange(len(cxcoords)), replace=False, size=subpopulation)
cxcoords = list(np.asarray(cxcoords)[ridxs])
cycoords = list(np.asarray(cycoords)[ridxs])
czcoords = list(np.asarray(czcoords)[ridxs])
colors += [cmap(cmap_range[pop_id]) for _ in range(len(cxcoords))]
xcoords += cxcoords
ycoords += cycoords
zcoords += czcoords
logger.info(f'Read {count} coordinates...')
pop_distribution = env.geometry['Cell Distribution'][population]
pop_layers = []
for layer in pop_distribution:
num_layer = pop_distribution[layer]
if num_layer > 0:
pop_layers.append(layer)
if pop_min_extent is None:
pop_min_extent = np.asarray(layer_extents[layer][0])
else:
pop_min_extent = np.minimum(pop_min_extent, np.asarray(layer_extents[layer][0]))
if pop_max_extent is None:
pop_max_extent = np.asarray(layer_extents[layer][1])
else:
pop_max_extent = np.maximum(pop_min_extent, np.asarray(layer_extents[layer][1]))
pts = np.concatenate((np.asarray(xcoords).reshape(-1,1), \
np.asarray(ycoords).reshape(-1,1), \
np.asarray(zcoords).reshape(-1,1)),axis=1)
if mayavi:
from mayavi import mlab
else:
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
logger.info('Plotting coordinates...')
if mayavi:
mlab.points3d(*pts.T, color=(1, 1, 0), scale_factor=scale)
else:
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(*pts.T, c=colors, s=int(scale))
logger.info('Constructing volume...')
from ca1.CA1_volume import make_CA1_volume
if subvol:
subvol = make_CA1_volume ((pop_min_extent[0], pop_max_extent[0]), \
(pop_min_extent[1], pop_max_extent[1]), \
(pop_min_extent[2], pop_max_extent[2]), \
resolution=[3, 3, 3], \
rotate=rotate)
else:
vol = make_CA1_volume ((extent_u[0], extent_u[1]),
(extent_v[0], extent_v[1]),
(extent_l[0], extent_l[1]),
resolution=[3, 3, 3],
rotate=rotate)
logger.info('Plotting volume...')
if subvol:
if mayavi:
subvol.mplot_surface(color=(0, 0.4, 0), opacity=0.33)
else:
subvol.mplot_surface(color='k', alpha=0.33, figax=[fig, ax])
else:
if mayavi:
vol.mplot_surface(color=(0, 1, 0), opacity=0.33)
else:
vol.mplot_surface(color='k', alpha=0.33, figax=[fig, ax])
if mayavi:
mlab.show()
else:
ax.view_init(-90,0)
plt.show()
def plot_cell_tree(population, gid, tree_dict, line_width=1., sample=0.05, color_edge_scalars=True, mst=False, conn_loc=True, mayavi=False, **kwargs):
import networkx as nx
fig_options = copy.copy(default_fig_options)
fig_options.update(kwargs)
xcoords = tree_dict['x']
ycoords = tree_dict['y']
zcoords = tree_dict['z']
swc_type = tree_dict['swc_type']
layer = tree_dict['layer']
secnodes = tree_dict['section_topology']['nodes']
src = tree_dict['section_topology']['src']
dst = tree_dict['section_topology']['dst']
loc = tree_dict['section_topology']['loc']
x = xcoords.reshape(-1,)
y = ycoords.reshape(-1,)
z = zcoords.reshape(-1,)
edges = []
for sec, nodes in viewitems(secnodes):
for i in range(1, len(nodes)):
srcnode = nodes[i-1]
dstnode = nodes[i]
edges.append((srcnode, dstnode))
loc_x = []
loc_y = []
loc_z = []
for (s,d,l) in zip(src,dst,loc):
srcnode = secnodes[s][l]
dstnode = secnodes[d][0]
edges.append((srcnode, dstnode))
loc_x.append(x[srcnode])
loc_y.append(y[srcnode])
loc_z.append(z[srcnode])
conn_loc_x = np.asarray(loc_x, dtype=np.float64)
conn_loc_y = np.asarray(loc_y, dtype=np.float64)
conn_loc_z = np.asarray(loc_z, dtype=np.float64)
# Make a NetworkX graph out of our point and edge data
g = make_geometric_graph(x, y, z, edges)
edges = g.edges
# Compute minimum spanning tree using networkx
# nx.mst returns an edge generator
if mst:
edges = nx.minimum_spanning_tree(g).edges(data=True)
edge_array = np.array(list(edges)).T
start_idx = edge_array[0, :]
end_idx = edge_array[1, :]
start_idx = start_idx.astype(np.int)
end_idx = end_idx.astype(np.int)
if color_edge_scalars:
edge_scalars = z[start_idx]
edge_color = None
else:
edge_scalars = None
edge_color = hex2rgb(rainbow_colors[gid%len(rainbow_colors)])
if mayavi:
from mayavi import mlab
mlab.figure(bgcolor=(0,0,0))
fig = mlab.gcf()
# Plot this with Mayavi
g = plot_graph(x, y, z, start_idx, end_idx, edge_scalars=edge_scalars, edge_color=edge_color, \
opacity=0.8, colormap='summer', line_width=line_width, figure=fig)
if conn_loc:
conn_pts = mlab.points3d(conn_loc_x, conn_loc_y, conn_loc_z, figure=fig,
mode='2dcross', colormap='copper', scale_factor=10)
fig.scene.x_plus_view()
if fig_options.saveFig:
mlab.savefig(f'{population}_{gid}_cell_tree.x3d', figure=fig, magnification=10)
if fig_options.showFig:
mlab.show()
else:
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure(figsize=fig_options.figSize)
ax = Axes3D(fig)
layer_set = set(layer)
sct = ax.scatter(x, y, z, c=layer, alpha=0.7, )
# produce a legend with the unique colors from the scatter
legend_elements = sct.legend_elements()
layer_legend = ax.legend(*legend_elements, loc="upper right", title="Layer")
ax.add_artist(layer_legend)
for i,j in g.edges:
e_x = (x[i], x[j])
e_y = (y[i], y[j])
e_z = (z[i], z[j])
ax.plot(e_x, e_y, e_z, c='black', alpha=0.5)
ax.view_init(30)
ax.set_axis_off
if fig_options.saveFig:
if isinstance(fig_options.saveFig, str):
filename = fig_options.saveFig
else:
filename = f'{population}_{gid}_cell_tree.{fig_options.figFormat}'
plt.savefig(filename)
if fig_options.showFig:
show_figure()
return fig
## Plot spike raster
def plot_spike_raster (input_path, namespace_id, include = ['eachPop'], time_range = None, time_variable='t', max_spikes = int(1e6), labels = 'legend', pop_rates = True, spike_hist = None, spike_hist_bin = 5, include_artificial=True, marker='.', **kwargs):
'''
Raster plot of network spike times. Returns the figure handle.
input_path: file with spike data
namespace_id: attribute namespace for spike events
time_range ([start:stop]): Time range of spikes shown; if None shows all (default: None)
time_variable: Name of variable containing spike times (default: 't')
max_spikes (int): maximum number of spikes that will be plotted (default: 1e6)
labels = ('legend', 'overlay'): Show population labels in a legend or overlayed on one side of raster (default: 'legend')
pop_rates = (True|False): Include population rates (default: False)
spike_hist (None|'overlay'|'subplot'): overlay line over raster showing spike histogram (spikes/bin) (default: False)
spike_hist_bin (int): Size of bin in ms to use for histogram (default: 5)
marker (char): Marker for each spike (default: '|')
'''
fig_options = copy.copy(default_fig_options)
fig_options.update(kwargs)
(population_ranges, N) = read_population_ranges(input_path)
population_names = read_population_names(input_path)
total_num_cells = 0
pop_num_cells = {}
pop_start_inds = {}
for k in population_names:
pop_start_inds[k] = population_ranges[k][0]
pop_num_cells[k] = population_ranges[k][1]
total_num_cells += population_ranges[k][1]
include = list(include)
# Replace 'eachPop' with list of populations
if 'eachPop' in include:
include.remove('eachPop')
for pop in population_names:
include.append(pop)
# sort according to start index
include.sort(key=lambda x: pop_start_inds[x])
spkdata = spikedata.read_spike_events (input_path, include, namespace_id,
include_artificial=include_artificial,
spike_train_attr_name=time_variable,
time_range=time_range)
spkpoplst = spkdata['spkpoplst']
spkindlst = spkdata['spkindlst']
spktlst = spkdata['spktlst']
num_cell_spks = spkdata['num_cell_spks']
pop_active_cells = spkdata['pop_active_cells']
tmin = spkdata['tmin']
tmax = spkdata['tmax']
fraction_active = { pop_name: float(len(pop_active_cells[pop_name])) / float(pop_num_cells[pop_name]) for pop_name in include }
time_range = [tmin, tmax]
# Calculate spike histogram if requested
if spike_hist:
all_spkts = np.concatenate([np.concatenate(lst, axis=0) for lst in spktlst])
sphist_y, bin_edges = np.histogram(all_spkts, bins = np.arange(time_range[0], time_range[1], spike_hist_bin))
sphist_x = bin_edges[:-1]+(spike_hist_bin / 2)
maxN = 0
minN = N
avg_rates = {}
tsecs = ((time_range[1]-time_range[0]) / 1e3)
for i,pop_name in enumerate(spkpoplst):
pop_num = len(pop_active_cells[pop_name])
maxN = max(maxN, max(pop_active_cells[pop_name]))
minN = min(minN, min(pop_active_cells[pop_name]))
if pop_num > 0:
if num_cell_spks[pop_name] == 0:
avg_rates[pop_name] = 0
else:
avg_rates[pop_name] = ((num_cell_spks[pop_name] / pop_num) / tsecs)
pop_colors = { pop_name: dflt_colors[ipop%len(raster_colors)] for ipop, pop_name in enumerate(spkpoplst) }
pop_spk_dict = { pop_name: (pop_spkinds, pop_spkts) for (pop_name, pop_spkinds, pop_spkts) in zip(spkpoplst, spkindlst, spktlst) }
if spike_hist is None:
fig, axes = plt.subplots(nrows=len(spkpoplst), sharex=True, figsize=fig_options.figSize)
elif spike_hist == 'subplot':
fig, axes = plt.subplots(nrows=len(spkpoplst)+1, sharex=True, figsize=fig_options.figSize,
gridspec_kw={'height_ratios': [1]*len(spkpoplst) + [2]})
fig.suptitle ('CA1 Spike Raster', fontsize=fig_options.fontSize)
sctplots = []
for i, pop_name in enumerate(spkpoplst):
if pop_name not in pop_spk_dict:
continue
pop_spkinds, pop_spkts = pop_spk_dict[pop_name]
if max_spikes is not None:
if int(max_spikes) < len(pop_spkinds):
logger.info(' Displaying only randomly sampled {max_spikes} out of {len(pop_spkts)} spikes for population {pop_name}')
sample_inds = np.random.randint(0, len(pop_spkinds)-1, size=int(max_spikes))
pop_spkts = pop_spkts[sample_inds]
pop_spkinds = pop_spkinds[sample_inds]
sct = None
if len(pop_spkinds) > 0:
sct = axes[i].scatter(pop_spkts, pop_spkinds, s=10, linewidths=fig_options.lw, marker=marker, c=pop_colors[pop_name], alpha=0.5, label=pop_name)
axes[i].spines["top"].set_visible(False)
axes[i].spines["bottom"].set_visible(False)
axes[i].spines["left"].set_visible(False)
axes[i].spines["right"].set_visible(False)
sctplots.append(sct)
N = pop_num_cells[pop_name]
S = pop_start_inds[pop_name]
axes[i].set_ylim(S, S+N-1)
lgd_info = [(100. * fraction_active.get(pop_name, 0.), avg_rates.get(pop_name, 0.))
for pop_name in spkpoplst ]
# set raster plot y tick labels to the middle of the index range for each population
for pop_name, a in zip_longest(spkpoplst, fig.axes[:-1]):
if pop_name not in pop_active_cells:
continue
if len(pop_active_cells[pop_name]) > 0:
maxN = max(pop_active_cells[pop_name])
minN = min(pop_active_cells[pop_name])
loc = pop_start_inds[pop_name] + 0.5 * (maxN - minN)
yaxis = a.get_yaxis()
yaxis.set_ticks([loc])
yaxis.set_ticklabels([pop_name])
yaxis.set_tick_params(length=0)
a.get_xaxis().set_tick_params(length=0)
# Plot spike histogram
pch = interpolate.pchip(sphist_x, sphist_y)
res_npts = int((sphist_x.max() - sphist_x.min()))
sphist_x_res = np.linspace(sphist_x.min(), sphist_x.max(), res_npts, endpoint=True)
sphist_y_res = pch(sphist_x_res)
if spike_hist == 'overlay':
ax2 = axes[-1].twinx()
ax2.plot (sphist_x_res, sphist_y_res, linewidth=0.5)
ax2.set_ylabel('Spike count', fontsize=fig_options.fontSize) # add yaxis label in opposite side
ax2.set_xlim(time_range)
elif spike_hist == 'subplot':
ax2=axes[-1]
ax2.plot (sphist_x_res, sphist_y_res, linewidth=1.0)
ax2.set_xlabel('Time (ms)', fontsize=fig_options.fontSize)
ax2.set_ylabel('Spikes', fontsize=fig_options.fontSize)
ax2.set_xlim(time_range)
# locator=MaxNLocator(prune='both', nbins=10)
# ax2.xaxis.set_major_locator(locator)
if labels == 'legend':
# Shrink axes by 15%
for ax in axes:
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.85, box.height])
if pop_rates:
lgd_labels = [ '%s (%.02f%% active; %.3g Hz)' % (pop_name, info[0], info[1]) for pop_name, info in zip_longest(spkpoplst, lgd_info) ]
else:
lgd_labels = [ '%s (%.02f%% active)' % (pop_name, info[0]) for pop_name, info in zip_longest(spkpoplst, lgd_info) ]
# Add legend
lgd = fig.legend(sctplots, lgd_labels, loc = 'center right',
fontsize='small', scatterpoints=1, markerscale=5.,
bbox_to_anchor=(1.002, 0.5), bbox_transform=plt.gcf().transFigure)
fig.artists.append(lgd)
elif labels == 'overlay':
if pop_rates:
lgd_labels = [ '%s (%.02f%% active; %.3g Hz)' % (pop_name, info[0], info[1]) for pop_name, info in zip_longest(spkpoplst, lgd_info) ]
else:
lgd_labels = [ '%s (%.02f%% active)' % (pop_name, info[0]) for pop_name, info in zip_longest(spkpoplst, lgd_info) ]
for i, lgd_label in enumerate(lgd_labels):
at = AnchoredText(pop_name + ' ' + lgd_label,
loc='upper right', borderpad=0.01, prop=dict(size=fig_options.fontSize))
axes[i].add_artist(at)
max_label_len = max([len(l) for l in lgd_labels])
elif labels == 'yticks':
for pop_name, info, a in zip_longest(spkpoplst, lgd_info, fig.axes[:-1]):
if pop_rates:
label = '%.02f%%\n%.2g Hz' % (info[0], info[1])
else:
label = '%.02f%%\n' % (info[0])
maxN = max(pop_active_cells[pop_name])
minN = min(pop_active_cells[pop_name])
loc = pop_start_inds[pop_name] + 0.5 * (maxN - minN)
a.set_yticks([loc, loc])
a.set_yticklabels([pop_name, label])
yticklabels = a.get_yticklabels()
# Create offset transform in x direction
dx = -66/72.; dy = 0/72.
offset = mpl.transforms.ScaledTranslation(dx, dy, fig.dpi_scale_trans)
# apply offset transform to labels.
yticklabels[0].set_transform(yticklabels[0].get_transform() + offset)
dx = -55/72.; dy = 0/72.
offset = mpl.transforms.ScaledTranslation(dx, dy, fig.dpi_scale_trans)
yticklabels[1].set_ha('left')
yticklabels[1].set_transform(yticklabels[1].get_transform() + offset)
plt.subplots_adjust(wspace=0.2, hspace=0.2)
# save figure
if fig_options.saveFig:
if isinstance(fig_options.saveFig, str):
filename = fig_options.saveFig
else:
filename = f'{namespace_id} raster.{fig_options.figFormat}'
plt.savefig(filename)
# show fig
if fig_options.showFig:
show_figure()
return fig
def plot_spike_histogram (input_path, namespace_id, config_path=None, include = ['eachPop'], time_variable='t', time_range = None,
pop_rates = False, bin_size = 5., smooth = 0, quantity = 'rate', include_artificial=True, progress = False,
overlay=True, graph_type='bar', **kwargs):
'''
Plots spike histogram. Returns figure handle.
- input_path: file with spike data
- namespace_id: attribute namespace for spike events
- include (['eachPop'|<population name>]): List of data series to include.
(default: ['eachPop'] - expands to the name of each population)
- time_variable: Name of variable containing spike times (default: 't')
- time_range ([start:stop]): Time range of spikes shown; if None shows all (default: None)
- bin_size (int): Size in ms of each bin (default: 5)
- overlay (True|False): Whether to overlay the data lines or plot in separate subplots (default: True)
- graph_type ('line'|'bar'): Type of graph to use (line graph or bar plot) (default: 'line')
- quantity ('rate'|'count'): Quantity of y axis (firing rate in Hz, or spike count) (default: 'rate')
'''
fig_options = copy.copy(default_fig_options)
fig_options.update(kwargs)
baks_config = copy.copy(kwargs)
env = None
if config_path is not None:
env = Env(config_file=config_path)
if env.analysis_config is not None:
baks_config.update(env.analysis_config['Firing Rate Inference'])
(population_ranges, N) = read_population_ranges(input_path)
population_names = read_population_names(input_path)
pop_num_cells = {}
for k in population_names:
pop_num_cells[k] = population_ranges[k][1]
# Replace 'eachPop' with list of populations
if 'eachPop' in include:
include.remove('eachPop')
for pop in population_names:
include.append(pop)
include.reverse()
spkdata = spikedata.read_spike_events (input_path, include, namespace_id, spike_train_attr_name=time_variable,
time_range=time_range, include_artificial=include_artificial)
spkpoplst = spkdata['spkpoplst']
spkindlst = spkdata['spkindlst']
spktlst = spkdata['spktlst']
num_cell_spks = spkdata['num_cell_spks']
pop_active_cells = spkdata['pop_active_cells']
tmin = spkdata['tmin']
tmax = spkdata['tmax']
time_range = [tmin, tmax]
avg_rates = {}
maxN = 0
minN = N
if pop_rates:
tsecs = (time_range[1]-time_range[0]) / 1e3
for i,pop_name in enumerate(spkpoplst):
pop_num = len(pop_active_cells[pop_name])
maxN = max(maxN, max(pop_active_cells[pop_name]))
minN = min(minN, min(pop_active_cells[pop_name]))
if pop_num > 0:
if num_cell_spks[pop_name] == 0:
avg_rates[pop_name] = 0
else:
avg_rates[pop_name] = ((num_cell_spks[pop_name] / pop_num) / tsecs)
# Y-axis label
if quantity == 'rate':
yaxisLabel = 'Mean cell firing rate (Hz)'
elif quantity == 'count':
yaxisLabel = 'Spike count'
elif quantity == 'active':
yaxisLabel = 'Active cell count'
else:
logger.error(f'Invalid quantity value {quantity}')
return
# create fig
fig, axes = plt.subplots(len(spkpoplst), 1, figsize=fig_options.figSize, sharex=True)
time_bins = np.arange(time_range[0], time_range[1], bin_size)
hist_dict = {}
if quantity == 'rate':
for subset, spkinds, spkts in zip(spkpoplst, spkindlst, spktlst):
spkdict = spikedata.make_spike_dict(spkinds, spkts)
sdf_dict = spikedata.spike_density_estimate(subset, spkdict, time_bins, progress=progress, **baks_config)
bin_dict = defaultdict(lambda: {'rates':0.0, 'active': 0})
for (ind, dct) in viewitems(sdf_dict):
rate = dct['rate']
for ibin in range(0, len(time_bins)):
d = bin_dict[ibin]
bin_rate = rate[ibin]
d['rates'] += bin_rate
d['active'] += 1
hist_dict[subset] = bin_dict
logger.info(('Calculated spike rates for %i cells in population %s' % (len(sdf_dict), subset)))
else:
for subset, spkinds, spkts in zip(spkpoplst, spkindlst, spktlst):
spkdict = spikedata.make_spike_dict(spkinds, spkts)
count_bin_dict = spikedata.spike_bin_counts(spkdict, time_bins)
bin_dict = defaultdict(lambda: {'counts':0, 'active': 0})
for (ind, counts) in viewitems(count_bin_dict):
for ibin in range(0, len(time_bins)-1):
d = bin_dict[ibin]
d['counts'] += counts[ibin]
d['active'] += 1
hist_dict[subset] = bin_dict
logger.info(('Calculated spike counts for %i cells in population %s' % (len(count_bin_dict), subset)))
del spkindlst, spktlst
# Plot separate line for each entry in include
for iplot, subset in enumerate(spkpoplst):
hist_x = time_bins+(bin_size / 2)
bin_dict = hist_dict[subset]
if quantity=='rate':
hist_y = np.asarray([(bin_dict[ibin]['rates'] / bin_dict[ibin]['active']) if bin_dict[ibin]['active'] > 0 else 0.
for ibin in range(0, len(time_bins))])
elif quantity=='active':
hist_y = np.asarray([bin_dict[ibin]['active'] for ibin in range(0, len(time_bins))])
else:
hist_y = np.asarray([bin_dict[ibin]['counts'] for ibin in range(0, len(time_bins))])
del bin_dict
del hist_dict[subset]
color = dflt_colors[iplot%len(dflt_colors)]
if pop_rates:
label = str(subset) + ' (%i active; %.3g Hz)' % (len(pop_active_cells[subset]), avg_rates[subset])
else:
label = str(subset) + ' (%i active)' % (len(pop_active_cells[subset]))
ax = plt.subplot(len(spkpoplst),1,(iplot+1))
plt.title (label, fontsize=fig_options.fontSize)
ax.tick_params(labelsize=fig_options.fontSize)
if iplot < len(spkpoplst)-1:
ax.xaxis.set_visible(False)
if smooth:
hsignal = signal.savgol_filter(hist_y, window_length=2*((len(hist_y) / 16)) + 1, polyorder=smooth)
else:
hsignal = hist_y
if graph_type == 'line':
ax.plot (hist_x, hsignal, linewidth=fig_options.lw, color = color)
elif graph_type == 'bar':
ax.bar(hist_x, hsignal, width = bin_size, color = color, edgecolor='black', alpha=0.85)
if iplot == 0:
ax.set_ylabel(yaxisLabel, fontsize=fig_options.fontSize)
if iplot == len(spkpoplst)-1:
ax.set_xlabel('Time (ms)', fontsize=fig_options.fontSize)
else:
ax.tick_params(labelbottom='off')
ax.set_xlim(time_range)
plt.tight_layout()
# Add legend
if overlay:
for i,subset in enumerate(spkpoplst):
plt.plot(0,0,color=dflt_colors[i%len(dflt_colors)],label=str(subset))
plt.legend(fontsize=fig_options.fontSize, bbox_to_anchor=(1.04, 1), loc=2, borderaxespad=0.)
maxLabelLen = min(10,max([len(str(l)) for l in include]))
plt.subplots_adjust(right=(0.9-0.012*maxLabelLen))
if fig_options.saveFig:
if isinstance(fig_options.saveFig, str):
filename = fig_options.saveFig
else:
filename = namespace_id+' '+'histogram.%s' % fig_options.figFormat
plt.savefig(filename)
if fig_options.showFig:
show_figure()
return fig
def plot_lfp(input_path, config_path=None, time_range = None, compute_psd=False, window_size=4096, frequency_range=(0, 400.), overlap=0.9, bandpass_filter=False, dt=None, **kwargs):
'''
Line plot of LFP state variable (default: v). Returns figure handle.
config: path to model configuration file
input_path: file with LFP trace data
time_range ([start:stop]): Time range of spikes shown; if None shows all (default: None)
'''
fig_options = copy.copy(default_fig_options)
fig_options.update(kwargs)
env = None
if config_path is not None:
env = Env(config_file=config_path)
nrows = 1
if env is not None:
nrows = len(env.LFP_config)
ncols = 1
psd_col = 1
if compute_psd:
ncols += 1
gs = gridspec.GridSpec(nrows, ncols, width_ratios=[3,1] if ncols > 1 else [1])
fig = plt.figure(figsize=fig_options.figSize)
if env is None:
lfp_array = np.loadtxt(input_path, dtype=np.dtype([("t", np.float32),
("v", np.float32)]))
if time_range is None:
t = lfp_array['t']
v = lfp_array['v']
else:
tlst = []
vlst = []
for (t,v) in zip(lfp_array['t'], lfp_array['v']):
if time_range[0] <= t <= time_range[1]:
tlst.append(t)
vlst.append(v)
t = np.asarray(tlst)
v = np.asarray(vlst)
if dt is None:
raise RuntimeError("plot_lfp: dt must be provided when config_path is None")
Fs = 1000. / dt
if compute_psd:
psd, freqs, peak_index = signal_psd(v, frequency_range=frequency_range, Fs=Fs, window_size=window_size, overlap=overlap)
filtered_v = None
if bandpass_filter:
filtered_v = apply_filter(v, butter_bandpass_filter(max(bandpass_filter[0], 1.0), bandpass_filter[1], Fs, order=2))
iplot=0
ax = plt.subplot(gs[iplot,0])
ax.set_title('LFP', fontsize=fig_options.fontSize)
ax.plot(t, v, linewidth=fig_options.lw)
ax.set_xlabel('Time (ms)', fontsize=fig_options.fontSize)
ax.set_ylabel('Field Potential (mV)', fontsize=fig_options.fontSize)
if bandpass_filter:
if filtered_v is not None:
ax.plot(t, filtered_v, label='Filtered LFP',
color='red', linewidth=fig_options.lw)
if compute_psd:
ax = plt.subplot(gs[iplot,psd_col])
ax.plot(freqs, psd, linewidth=fig_options.lw)
ax.set_xlabel('Frequency (Hz)', fontsize=fig_options.fontSize)
ax.set_ylabel('Power Spectral Density (dB/Hz)', fontsize=fig_options.fontSize)
ax.set_title('PSD (peak: %.3g Hz)' % (freqs[peak_index]), fontsize=fig_options.fontSize)
if fig_options.saveFig:
if isinstance(fig_options.saveFig, str):
filename = fig_options.saveFig
else:
filename = f'CA1 LFP.{fig_options.figFormat}'
plt.savefig(filename)
# show fig