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plot.py
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import numbers, os, copy, pprint, sys, time
from collections import defaultdict
from scipy import interpolate, signal, ndimage, spatial
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 dentate
from dentate.graph import vertex_distribution, vertex_metrics
from dentate.statedata import read_state, query_state
from dentate.cells import default_ordered_sec_types, get_distance_to_node, make_biophys_morph_graph
from dentate.env import Env
from dentate.synapses import get_syn_filter_dict, get_syn_mech_param
from dentate.utils import get_module_logger, Promise, Struct, add_bins, update_bins, finalize_bins
from dentate.utils import get_low_pass_filtered_trace, power_spectrogram, butter_bandpass_filter, apply_filter, kde_scipy, make_geometric_graph, viewitems, \
zip_longest
from dentate.neuron_utils import interplocs
from dentate.io_utils import get_h5py_attr, set_h5py_attr
try:
import dentate.spikedata as spikedata
except ImportError as e:
print(('dentate.plot: problem importing module required by dentate.spikedata:', e))
try:
import dentate.stimulus as stimulus
except ImportError as e:
print(('dentate.plot: problem importing module required by dentate.stimulus:', e))
try:
from dentate.geometry import DG_volume, measure_distance_extents, get_total_extents
except ImportError as e:
print(('dentate.plot: problem importing module required by dentate.geometry:', e))
# This logger will inherit its settings from the root logger, created in dentate.env
logger = get_module_logger(__name__)
# Default figure configuration
default_fig_options = Struct(figFormat='png', lw=2, figSize=(15,10), 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():
try:
plt.show(block=False)
except:
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 = '%s.%s' % (file_name_prefix, fig_options.figFormat)
if fig_options.saveFigDir is not None:
fig_file_path = '%s/%s' % (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=10,
**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 make_morph_graph(tree_dict, section_attrs={}, mst=True):
"""
Creates a morphological graph using NetworkX
"""
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 secnodes.items():
this_section_attrs = section_attrs.get(sec, {})
for i in range(1, len(nodes)):
srcnode = nodes[i - 1]
dstnode = nodes[i]
edges.append((srcnode, dstnode, this_section_attrs))
for s, d, l in zip(src, dst, loc):
srcnode = secnodes[s][l]
dstnode = secnodes[d][0]
edges.append((srcnode, dstnode, {}))
# Make a NetworkX graph out of our point and edge data
g = make_geometric_graph(x, y, z, edges)
return g
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 = '%s.%s' % (label, fig_options.figFormat)
plt.savefig(filename)
def plot_vertex_metrics(env, connectivity_path, coords_path, vertex_metrics_namespace, distances_namespace, destination, sources, bin_size = 50., metric='Indegree', normed = False, graph_type = 'histogram2d', **kwargs):
"""
Plot vertex metric with respect to septo-temporal position (longitudinal and transverse arc distances to reference points).
:param connectivity_path:
:param coords_path:
:param distances_namespace:
:param destination_pop:
"""
fig_options = copy.copy(default_fig_options)
fig_options.update(kwargs)
(distance_U, distance_V, degrees_dict) = \
vertex_metrics(connectivity_path, coords_path, vertex_metrics_namespace, distances_namespace, destination,
sources, bin_size, metric)
distance_x_min = np.min(distance_U)
distance_x_max = np.max(distance_U)
distance_y_min = np.min(distance_V)
distance_y_max = np.max(distance_V)
((x_min, x_max), (y_min, y_max)) = measure_distance_extents(env)
dx = int((distance_x_max - distance_x_min) / bin_size)
dy = int((distance_y_max - distance_y_min) / bin_size)
for source, degrees in viewitems(degrees_dict):
fig = plt.figure(figsize=fig_options.figSize)
ax = plt.gca()
ax.axis([x_min, x_max, y_min, y_max])
if graph_type == 'histogram1d':
bins_U = np.linspace(x_min, x_max, dx)
bins_V = np.linspace(y_min, y_max, dy)
hist_vals_U, bin_edges_U = np.histogram(distance_U, bins = bins_U, weights=degrees)
hist_vals_V, bin_edges_V = np.histogram(distance_V, bins = bins_V, weights=degrees)
gs = gridspec.GridSpec(3, 1, height_ratios=[2,1,2])
ax1 = plt.subplot(gs[0])
ax1.plot (bin_edges_U[:-1], hist_vals_U)
ax1.set_title('%s distribution for destination: %s source: %s' % (metric, destination, source), fontsize=fig_options.fontSize)
ax2 = plt.subplot(gs[2])
ax2.plot (bin_edges_V[:-1], hist_vals_V)
ax1.set_xlabel('Arc distance (septal - temporal) (um)', fontsize=fig_options.fontSize)
ax2.set_xlabel('Arc distance (supra - infrapyramidal) (um)', fontsize=fig_options.fontSize)
ax1.set_ylabel('Number of edges', fontsize=fig_options.fontSize)
ax2.set_ylabel('Number of edges', fontsize=fig_options.fontSize)
ax1.tick_params(labelsize=fig_options.fontSize)
ax2.tick_params(labelsize=fig_options.fontSize)
plt.subplot(gs[1]).remove()
elif graph_type == 'histogram2d':
if normed:
(H1, xedges, yedges) = np.histogram2d(distance_U, distance_V, bins=[dx, dy], weights=degrees, normed=normed)
(H2, xedges, yedges) = np.histogram2d(distance_U, distance_V, bins=[dx, dy])
H = np.zeros(H1.shape)
nz = np.where(H2 > 0.0)
H[nz] = np.divide(H1[nz], H2[nz])
H[nz] = np.divide(H[nz], np.max(H[nz]))
else:
(H, xedges, yedges) = np.histogram2d(distance_U, distance_V, bins=[dx, dy], weights=degrees)
X, Y = np.meshgrid(xedges, yedges)
pcm = ax.pcolormesh(X, Y, H.T)
cb = fig.colorbar(pcm, ax=ax, shrink=0.5, aspect=20)
cb.ax.tick_params(labelsize=fig_options.fontSize)
else:
raise ValueError('Unknown graph type %s' % graph_type)
ax.tick_params(labelsize=fig_options.fontSize)
ax.set_xlabel('Arc distance (septal - temporal) (um)', fontsize=fig_options.fontSize)
ax.set_ylabel('Arc distance (supra - infrapyramidal) (um)', fontsize=fig_options.fontSize)
ax.set_title('%s distribution for destination: %s source: %s' % (metric, destination, source), fontsize=fig_options.fontSize)
ax.set_aspect('equal')
if fig_options.saveFig:
if isinstance(fig_options.saveFig, str):
filename = fig_options.saveFig
else:
filename = '%s to %s %s %s.%s' % (source, destination, metric, graph_type, fig_options.figFormat)
plt.savefig(filename)
if fig_options.showFig:
show_figure()
def plot_vertex_distribution(connectivity_path, coords_path, distances_namespace, destination, sources,
bin_size=20.0, cache_size=100, comm=None, **kwargs):
"""
Plot vertex distribution with respect to septo-temporal distance
:param connectivity_path:
:param coords_path:
:param distances_namespace:
:param destination:
:param source:
"""
fig_options = copy.copy(default_fig_options)
fig_options.update(kwargs)
if comm is None:
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
has_vertex_dist_metrics = False
if rank == 0:
f = h5py.File(connectivity_path, 'r')
if 'Vertex Distribution' in f:
has_vertex_dist_metrics = True
vertex_distribution_dict = {dist_type: { destination: {} }
for dist_type in ['Total distance', 'U distance', 'V distance']}
for source in sources:
dist_hist_array = f['Vertex Distribution']['Total distance'][destination][source]
dist_u_hist_array = f['Vertex Distribution']['U distance'][destination][source]
dist_v_hist_array = f['Vertex Distribution']['V distance'][destination][source]
vertex_distribution_dict['Total distance'][destination][source] = dist_hist_array[()]
vertex_distribution_dict['U distance'][destination][source] = dist_u_hist_array[()]
vertex_distribution_dict['V distance'][destination][source] = dist_v_hist_array[()]
f.close()
has_vertex_dist_metrics = comm.bcast(has_vertex_dist_metrics, root=0)
if not has_vertex_dist_metrics:
vertex_distribution_dict = vertex_distribution(connectivity_path, coords_path,
distances_namespace,
destination, sources,
bin_size, cache_size, comm=comm)
if rank == 0:
for source in sources:
if has_vertex_dist_metrics:
dist_hist_array = vertex_distribution_dict['Total distance'][destination][source]
dist_u_hist_array = vertex_distribution_dict['U distance'][destination][source]
dist_v_hist_array =vertex_distribution_dict['V distance'][destination][source]
dist_hist_vals = dist_hist_array[0,:]
dist_bin_edges = dist_hist_array[1,:]
dist_u_hist_vals = dist_u_hist_array[0,:]
dist_u_bin_edges = dist_u_hist_array[1,:]
dist_v_hist_vals = dist_v_hist_array[0,:]
dist_v_bin_edges = dist_v_hist_array[1,:]
else:
dist_hist_vals, dist_bin_edges = vertex_distribution_dict['Total distance'][destination][source]
dist_u_hist_vals, dist_u_bin_edges = vertex_distribution_dict['U distance'][destination][source]
dist_v_hist_vals, dist_v_bin_edges = vertex_distribution_dict['V distance'][destination][source]
fig, (ax2, ax3) = plt.subplots(1, 2, figsize=fig_options.figSize)
fig.suptitle('Distribution of connection distances for projection %s -> %s' % (source, destination), fontsize=fig_options.fontSize)
#ax1.fill_between(dist_bin_edges, dist_hist_vals, alpha=0.5)
#ax1.set_xlabel('Total distance (um)', fontsize=fig_options.fontSize)
#ax1.set_ylabel('Number of connections', fontsize=fig_options.fontSize)
#ax1.tick_params(labelsize=fig_options.fontSize)
ax2.fill_between(dist_u_bin_edges, dist_u_hist_vals, alpha=0.5)
ax2.set_xlabel('Septal - temporal (um)', fontsize=fig_options.fontSize)
ax3.fill_between(dist_v_bin_edges, dist_v_hist_vals, alpha=0.5)
ax3.set_xlabel('Supra - infrapyramidal (um)', fontsize=fig_options.fontSize)
ax2.tick_params(labelsize=fig_options.fontSize)
ax3.tick_params(labelsize=fig_options.fontSize)
if fig_options.saveFig:
if isinstance(fig_options.saveFig, str):
filename = fig_options.saveFig
else:
filename = 'Connection distance %s to %s.%s' % (source, destination, fig_options.figFormat)
plt.savefig(filename)
if fig_options.showFig:
show_figure()
comm.barrier()
def plot_single_vertex_dist(env, connectivity_path, coords_path, distances_namespace, target_gid,
destination, source, extent_type='local', direction='in',
bin_size=20.0, normed=False, comm=None, **kwargs):
"""
Plot vertex distribution with respect to septo-temporal distance for a single postsynaptic cell
:param connectivity_path:
:param coords_path:
:param distances_namespace:
:param destination_gid:
:param destination:
:param source:
"""
fig_options = copy.copy(default_fig_options)
fig_options.update(kwargs)
(population_ranges, _) = read_population_ranges(coords_path)
destination_start = population_ranges[destination][0]
destination_count = population_ranges[destination][1]
if comm is None:
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
source_soma_distances = bcast_cell_attributes(coords_path, source, namespace=distances_namespace, comm=comm, root=0)
destination_soma_distances = \
bcast_cell_attributes(coords_path, destination, namespace=distances_namespace, comm=comm, root=0)
((total_x_min,total_x_max),(total_y_min,total_y_max)) = measure_distance_extents(env)
source_soma_distance_U = {}
source_soma_distance_V = {}
destination_soma_distance_U = {}
destination_soma_distance_V = {}
for k,v in source_soma_distances:
source_soma_distance_U[k] = v['U Distance'][0]
source_soma_distance_V[k] = v['V Distance'][0]
for k,v in destination_soma_distances:
destination_soma_distance_U[k] = v['U Distance'][0]
destination_soma_distance_V[k] = v['V Distance'][0]
del(source_soma_distances)
del(destination_soma_distances)
g = NeuroH5ProjectionGen (connectivity_path, source, destination, comm=comm, cache_size=20)
dist_bins = {}
if direction == 'in':
for (destination_gid,rest) in g:
if destination_gid == target_gid:
(source_indexes, attr_dict) = rest
for source_gid in source_indexes:
dist_u = source_soma_distance_U[source_gid]
dist_v = source_soma_distance_V[source_gid]
update_bins(dist_bins, bin_size, dist_u, dist_v)
break
elif direction == 'out':
for (destination_gid,rest) in g:
if rest is not None:
(source_indexes, attr_dict) = rest
for source_gid in source_indexes:
if source_gid == target_gid:
dist_u = destination_soma_distance_U[destination_gid]
dist_v = destination_soma_distance_V[destination_gid]
update_bins(dist_bins, bin_size, dist_u, dist_v)
else:
raise RuntimeError('Unknown direction type %s' % str(direction))
add_bins_op = MPI.Op.Create(add_bins, commute=True)
dist_bins = comm.reduce(dist_bins, op=add_bins_op)
if rank == 0:
dist_hist_vals, dist_u_bin_edges, dist_v_bin_edges = finalize_bins(dist_bins, bin_size)
dist_x_min = dist_u_bin_edges[0]
dist_x_max = dist_u_bin_edges[-1]
dist_y_min = dist_v_bin_edges[0]
dist_y_max = dist_v_bin_edges[-1]
if extent_type == 'local':
x_min = dist_x_min
x_max = dist_x_max
y_min = dist_y_min
y_max = dist_y_max
elif extent_type == 'global':
x_min = total_x_min
x_max = total_x_max
y_min = total_y_min
y_max = total_y_max
else:
raise RuntimeError('Unknown extent type %s' % str(extent_type))
X, Y = np.meshgrid(dist_u_bin_edges, dist_v_bin_edges)
fig = plt.figure(figsize=fig_options.figSize)
ax = plt.gca()
ax.axis([x_min, x_max, y_min, y_max])
if direction == 'in':
ax.plot(destination_soma_distance_U[target_gid], \
destination_soma_distance_V[target_gid], \
'r+', markersize=12, mew=3)
elif direction == 'out':
ax.plot(source_soma_distance_U[target_gid], \
source_soma_distance_V[target_gid], \
'r+', markersize=12, mew=3)
else:
raise RuntimeError('Unknown direction type %s' % str(direction))
H = np.array(dist_hist_vals.todense())
if normed:
H = np.divide(H.astype(float), float(np.max(H)))
pcm_boundaries = np.arange(0, np.max(H), .1)
cmap_pls = plt.cm.get_cmap(fig_options.colormap, len(pcm_boundaries))
pcm_colors = list(cmap_pls(np.arange(len(pcm_boundaries))))
pcm_cmap = mpl.colors.ListedColormap(pcm_colors[:-1], "")
pcm_cmap.set_under(pcm_colors[0], alpha=0.0)
pcm = ax.pcolormesh(X, Y, H.T, cmap=pcm_cmap)
# pcm = ax.pcolormesh(X, Y, H.T, cmap=pcm_cmap,
# norm = mpl.colors.BoundaryNorm(pcm_boundaries, ncolors=len(pcm_boundaries)-1,
# clip=False))
clb_label = 'Normalized number of connections' if normed else 'Number of connections'
clb = fig.colorbar(pcm, ax=ax, shrink=0.5, label=clb_label)
clb.ax.tick_params(labelsize=fig_options.fontSize)
ax.set_aspect('equal')
ax.set_facecolor(pcm_colors[0])
ax.tick_params(labelsize=fig_options.fontSize)
ax.set_xlabel('Arc distance (septal - temporal) (um)', fontsize=fig_options.fontSize)
ax.set_ylabel('Arc distance (supra - infrapyramidal) (um)', fontsize=fig_options.fontSize)
ax.set_title('Connectivity distribution (%s) of %s to %s for gid: %i' % (direction, source, destination, target_gid), \
fontsize=fig_options.fontSize)
if fig_options.showFig:
show_figure()
if fig_options.saveFig:
if isinstance(fig_options.saveFig, str):
filename = fig_options.saveFig
else:
filename = 'Connection distance %s %s to %s gid %i.%s' % (direction, source, destination, target_gid, fig_options.figFormat)
plt.savefig(filename)
def plot_tree_metrics(env, forest_path, coords_path, population, metric_namespace='Tree Measurements', distances_namespace='Arc Distances', metric='dendrite_length', metric_index=0, percentile=None, **kwargs):
"""
Plot tree length or area with respect to septo-temporal position (longitudinal and transverse arc distances).
:param forest_path:
:param coords_path:
:param distances_namespace:
:param measures_namespace:
:param population:
"""
dx = 50
dy = 50
fig_options = copy.copy(default_fig_options)
fig_options.update(kwargs)
soma_distances = read_cell_attributes(coords_path, population, namespace=distances_namespace)
tree_metrics = { k: v[metric][metric_index] for (k,v) in read_cell_attributes(forest_path, population, namespace=metric_namespace) }
fig = plt.figure(1, figsize=plt.figaspect(1.) * 2.)
ax = plt.gca()
distance_U = {}
distance_V = {}
for k,v in soma_distances:
distance_U[k] = v['U Distance'][0]
distance_V[k] = v['V Distance'][0]
sorted_keys = sorted(tree_metrics.keys())
tree_metrics_array = np.array([tree_metrics[k] for k in sorted_keys])
tree_metric_stats = (np.min(tree_metrics_array), np.max(tree_metrics_array), np.mean(tree_metrics_array))
print(('min: %f max: %f mean: %f' % (tree_metric_stats)))
if percentile is not None:
percentile_value = np.percentile(tree_metrics_array, percentile)
sample = np.where(tree_metrics_array >= percentile_value)
tree_metrics_array = tree_metrics_array[sample]
sorted_keys = np.asarray(sorted_keys)[sample]
distance_U_array = np.array([distance_U[k] for k in sorted_keys])
distance_V_array = np.array([distance_V[k] for k in sorted_keys])
((x_min, x_max), (y_min, y_max)) = measure_distance_extents(env)
(H, xedges, yedges) = np.histogram2d(distance_U_array, distance_V_array, bins=[dx, dy], weights=tree_metrics_array)
ax.axis([x_min, x_max, y_min, y_max])
X, Y = np.meshgrid(xedges, yedges)
pcm = ax.pcolormesh(X, Y, H.T)
ax.set_xlabel('Arc distance (septal - temporal) (um)', fontsize=fig_options.fontSize)
ax.set_ylabel('Arc distance (supra - infrapyramidal) (um)', fontsize=fig_options.fontSize)
ax.set_title('%s distribution for population: %s' % (metric, population), fontsize=fig_options.fontSize)
ax.set_aspect('equal')
fig.colorbar(pcm, ax=ax, shrink=0.5, aspect=20)
if fig_options.saveFig:
if isinstance(fig_options.saveFig, str):
filename = fig_options.saveFig
else:
filename = population+' %s.%s' % (metric, fig_options.figFormat)
plt.savefig(filename)
if fig_options.showFig:
show_figure()
return ax
def plot_tree_metric_histogram(env, forest_path, population, metric_namespace='Tree Measurements', metric='dendrite_mean_diam_hist', **kwargs):
"""
Plot tree length or area with respect to septo-temporal position (longitudinal and transverse arc distances).
:param forest_path:
:param measures_namespace:
:param population:
"""
fig_options = copy.copy(default_fig_options)
fig_options.update(kwargs)
tree_dendrite_distance_counts = { k: v['dendrite_distance_counts']
for (k,v) in read_cell_attributes(forest_path, population, namespace=metric_namespace) }
tree_dendrite_hist_edges = { k: v['dendrite_hist_edges']
for (k,v) in read_cell_attributes(forest_path, population, namespace=metric_namespace) }
tree_metric_hist = { k: v[metric] for (k,v) in read_cell_attributes(forest_path, population, namespace=metric_namespace) }
all_hist_edges = []
for k in tree_dendrite_hist_edges:
edges = tree_dendrite_hist_edges[k]
for e in edges:
if not np.any(np.isclose(e, all_hist_edges, atol=1e-4, rtol=1e-4)):
all_hist_edges.append(e)
new_hist_edges = np.sort(all_hist_edges)
new_tree_metric_bins = [ [0., 0] for i in range(len(new_hist_edges)) ]
for k in tree_dendrite_distance_counts:
metric_hist = tree_metric_hist[k]
edges = tree_dendrite_hist_edges[k]
for i, dcount in enumerate(tree_dendrite_distance_counts[k]):
e = edges[i]
bin_idx = np.searchsorted(new_hist_edges, e, side="left")
new_tree_metric_bins[bin_idx][1] = new_tree_metric_bins[bin_idx][1] + dcount
new_tree_metric_bins[bin_idx][0] = new_tree_metric_bins[bin_idx][0] + metric_hist[i]
new_tree_metric_hist = [ x[0] / float(x[1]) for x in new_tree_metric_bins ]
fig = plt.figure(1, figsize=plt.figaspect(1.) * 2.)
ax = plt.gca()
ax.bar(new_hist_edges + 0.5, new_tree_metric_hist)
ax.set_xlabel('Distance from the soma (um)', fontsize=fig_options.fontSize)
ax.set_ylabel('Mean dendritic diameter (um)', fontsize=fig_options.fontSize)
ax.set_title('%s for population: %s' % (metric, population), fontsize=fig_options.fontSize)
if fig_options.saveFig:
if isinstance(fig_options.saveFig, str):
filename = fig_options.saveFig
else:
filename = population+' %s.%s' % (metric, fig_options.figFormat)
plt.savefig(filename)
if fig_options.showFig:
show_figure()
return ax
def plot_positions(env, label, distances, bin_size=50., graph_type ='kde', **kwargs):
"""
Plot septo-temporal position (longitudinal and transverse arc distances).
:param label:
:param distances:
"""
fig_options = copy.copy(default_fig_options)
fig_options.update(kwargs)
fig = plt.figure(1, figsize=plt.figaspect(1.) * 2.)
ax = plt.gca()
distance_U = {}
distance_V = {}
for k,v in distances:
distance_U[k] = v['U Distance'][0]
distance_V[k] = v['V Distance'][0]
distance_U_array = np.asarray([distance_U[k] for k in sorted(distance_U.keys())])
distance_V_array = np.asarray([distance_V[k] for k in sorted(distance_V.keys())])
distance_x_min = np.min(distance_U_array)
distance_x_max = np.max(distance_U_array)
distance_y_min = np.min(distance_V_array)
distance_y_max = np.max(distance_V_array)
((x_min, x_max), (y_min, y_max)) = measure_distance_extents(env)
ax.axis([x_min, x_max, y_min, y_max])
dx = int((distance_x_max - distance_x_min) / bin_size)
dy = int((distance_y_max - distance_y_min) / bin_size)
if graph_type == 'histogram1d':
bins_U = np.linspace(x_min, x_max, dx)
bins_V = np.linspace(y_min, y_max, dy)
hist_vals_U, bin_edges_U = np.histogram(distance_U_array, bins = bins_U)
hist_vals_V, bin_edges_V = np.histogram(distance_V_array, bins = bins_V)
gs = gridspec.GridSpec(2, 1, height_ratios=[2,1])
ax1 = plt.subplot(gs[0])
ax1.bar (bin_edges_U[:-1], hist_vals_U, width=dx)
ax1.set_title('Position distribution for %s' % (label), fontsize=fig_options.fontSize)
ax2 = plt.subplot(gs[1])
ax2.bar (bin_edges_V[:-1], hist_vals_V, width=dy)
ax1.set_xlabel('Arc distance (septal - temporal) (um)', fontsize=fig_options.fontSize)
ax2.set_xlabel('Arc distance (supra - infrapyramidal) (um)', fontsize=fig_options.fontSize)
ax1.set_ylabel('Number of cells', fontsize=fig_options.fontSize)
ax2.set_ylabel('Number of cells', fontsize=fig_options.fontSize)
elif graph_type == 'histogram2d':
(H, xedges, yedges) = np.histogram2d(distance_U_array, distance_V_array, bins=[dx, dy])
X, Y = np.meshgrid(xedges, yedges)
Hint = H[:-1, :-1]
levels = MaxNLocator(nbins=150).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)
elif graph_type == 'kde':
X, Y, Z = kde_scipy(distance_U_array, distance_V_array, bin_size)
p = ax.imshow(Z, origin='lower', aspect='auto', extent=[x_min, x_max, y_min, y_max])
fig.colorbar(p, ax=ax, shrink=0.5, aspect=20)
else:
raise ValueError('Unknown graph type %s' % graph_type)
ax.set_xlabel('Arc distance (septal - temporal) (um)', fontsize=fig_options.fontSize)
ax.set_ylabel('Arc distance (supra - infrapyramidal) (um)', fontsize=fig_options.fontSize)
ax.set_title('Position distribution for %s' % (label), fontsize=fig_options.fontSize)
ax.set_aspect('equal')
if fig_options.saveFig:
if isinstance(fig_options.saveFig, str):
filename = fig_options.saveFig
else:
filename = '%s Positions.%s' % (label, fig_options.figFormat)
plt.savefig(filename)
if fig_options.showFig:
show_figure()
return ax
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('Unknown graph type %s' % 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('Coordinate distribution for population: %s' % (population), fontsize=fig_options.fontSize)
if fig_options.saveFig:
if isinstance(fig_options.saveFig, str):
filename = fig_options.saveFig
else:
filename = population+' Coordinates.%s' % fig_options.figFormat
plt.savefig(filename)
if fig_options.showFig:
show_figure()
return ax
def plot_projected_coordinates(coords_path, population, namespace, index = 0, graph_type = 'scatter', bin_size = 10.0, project = 3.1, rotate = None, **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_X = {}
coord_Y = {}
for k,v in soma_coords:
ucoord = v['U Coordinate'][index]
vcoord = v['V Coordinate'][index]
xyz = DG_volume (ucoord, vcoord, project, rotate=rotate)
coord_X[k] = xyz[0,0]
coord_Y[k] = xyz[0,1]
coord_X_array = np.asarray([coord_X[k] for k in sorted(coord_X.keys())])
coord_Y_array = np.asarray([coord_Y[k] for k in sorted(coord_Y.keys())])
x_min = np.min(coord_X_array)
x_max = np.max(coord_X_array)
y_min = np.min(coord_Y_array)
y_max = np.max(coord_Y_array)
dx = int((x_max - x_min) / bin_size)
dy = int((y_max - y_min) / bin_size)
if graph_type == 'scatter':
ax.scatter(coord_X_array, coord_Y_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_X_array, coord_Y_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('Unknown graph type %s' % graph_type)
ax.set_xlabel('X coordinate (um)', fontsize=fig_options.fontSize)
ax.set_ylabel('Y coordinate (um)', fontsize=fig_options.fontSize)
ax.set_title('Coordinate distribution for population: %s' % (population), fontsize=fig_options.fontSize)
if fig_options.saveFig:
if isinstance(fig_options.saveFig, str):
filename = fig_options.saveFig
else:
filename = population+' Coordinates.%s' % fig_options.figFormat
plt.savefig(filename)
if fig_options.showFig:
show_figure()
return ax
def plot_reindex_positions(env, coords_path, population, distances_namespace='Arc Distances',
reindex_namespace='Tree Reindex', reindex_attribute='New Cell Index',
**kwargs):
"""
Plot septo-temporal position (longitudinal and transverse arc distances).
:param coords_path:
:param distances_namespace:
:param population:
"""
dx = 50
dy = 50
fig_options = copy.copy(default_fig_options)
fig_options.update(kwargs)
soma_distances = read_cell_attributes(coords_path, population, namespace=distances_namespace)
cell_reindex = read_cell_attributes(coords_path, population, namespace=reindex_namespace)
cell_reindex_dict = {}
for k,v in cell_reindex:
cell_reindex_dict[k] = v[reindex_attribute][0]
fig = plt.figure(1, figsize=plt.figaspect(1.) * 2.)
ax = plt.gca()
distance_U = {}
distance_V = {}
for k,v in soma_distances:
if k in cell_reindex_dict:
distance_U[k] = v['U Distance'][0]
distance_V[k] = v['V Distance'][0]
distance_U_array = np.asarray([distance_U[k] for k in sorted(distance_U.keys())])
distance_V_array = np.asarray([distance_V[k] for k in sorted(distance_V.keys())])
((x_min, x_max), (y_min, y_max)) = measure_distance_extents(env)
(H, xedges, yedges) = np.histogram2d(distance_U_array, distance_V_array, bins=[dx, dy])
ax.axis([x_min, x_max, y_min, y_max])
X, Y = np.meshgrid(xedges, yedges)
pcm = ax.pcolormesh(X, Y, H.T)
ax.set_xlabel('Arc distance (septal - temporal) (um)', fontsize=fig_options.fontSize)
ax.set_ylabel('Arc distance (supra - infrapyramidal) (um)', fontsize=fig_options.fontSize)
ax.set_title('Position distribution for population: %s' % (population), fontsize=fig_options.fontSize)
ax.set_aspect('equal')
fig.colorbar(pcm, ax=ax, shrink=0.5, aspect=20)
if fig_options.saveFig:
if isinstance(fig_options.saveFig, str):
filename = fig_options.saveFig
else:
filename = population+' Reindex Positions.%s' % fig_options.figFormat
plt.savefig(filename)
if fig_options.showFig:
show_figure()