def main(dir):
container = NDataContainer(load_on_fly=True)
container.add_axona_files_from_dir(dir, recursive=True)
container.setup()
print(container.string_repr(True))
nca.place_cell_summary(
container, dpi=200, out_dirname="nc_spat_plots", output_format="pdf",
burst_thresh=6, isi_bin_length=1, filter_place_cells=False)
def main(dir):
container = NDataContainer(load_on_fly=True)
container.add_axona_files_from_dir(dir, recursive=True)
container.setup()
print(container.string_repr(True))
nca.place_cell_summary(container, dpi=200, out_dirname="nc_spat_plots")
def main(args, config):
# Unpack out the cfg file into easier names
in_dir = config.get("Setup", "in_dir")
regex_filter = config.get("Setup", "regex_filter")
regex_filter = None if regex_filter == "None" else regex_filter
analysis_flags = json.loads(config.get("Setup", "analysis_flags"))
tetrode_list = json.loads(config.get("Setup", "tetrode_list"))
should_filter = config.getboolean("Setup", "should_filter")
seaborn_style = config.getboolean("Plot", "seaborn_style")
plot_order = json.loads(config.get("Plot", "plot_order"))
fixed_color = config.get("Plot", "path_color")
fixed_color = None if fixed_color == "None" else fixed_color
if len(fixed_color) > 1:
fixed_color = json.loads(fixed_color)
s_color = config.getboolean("Plot", "should_color")
plot_outname = config.get("Plot", "output_dirname")
dot_size = config.get("Plot", "dot_size")
dot_size = None if dot_size == "None" else int(dot_size)
summary_dpi = int(config.get("Plot", "summary_dpi"))
hd_predict = config.getboolean("Plot", "hd_predict")
output_format = config.get("Output", "output_format")
save_bin_data = config.getboolean("Output", "save_bin_data")
output_spaces = config.getboolean("Output", "output_spaces")
opt_end = config.get("Output", "optional_end")
max_units = int(config.get("Setup", "max_units"))
isi_bound = int(config.get("Params", "isi_bound"))
isi_bin_length = int(config.get("Params", "isi_bin_length"))
setup_logging(in_dir)
if output_format == "pdf":
matplotlib.use("pdf")
if seaborn_style:
sns.set(palette="colorblind")
else:
sns.set_style("ticks", {
'axes.spines.right': False,
'axes.spines.top': False
})
# Automatic extraction of files from starting dir onwards
container = NDataContainer(load_on_fly=True)
out_name = container.add_axona_files_from_dir(in_dir,
tetrode_list=tetrode_list,
recursive=True,
re_filter=regex_filter,
verbose=False,
unit_cutoff=(0, max_units))
container.setup()
if len(container) is 0:
print(
"Unable to find any files matching regex {}".format(regex_filter))
exit(-1)
# Show summary of place
if analysis_flags[0]:
place_cell_summary(container,
dpi=summary_dpi,
out_dirname=plot_outname,
filter_place_cells=should_filter,
filter_low_freq=should_filter,
opt_end=opt_end,
base_dir=in_dir,
output_format=output_format,
isi_bound=isi_bound,
isi_bin_length=isi_bin_length,
output=plot_order,
save_data=save_bin_data,
fixed_color=fixed_color,
point_size=dot_size,
color_isi=s_color,
burst_thresh=6,
hd_predict=hd_predict)
plt.close("all")
# Do numerical analysis of bursting
should_plot = analysis_flags[2]
if analysis_flags[1]:
import re
out_name = remove_extension(out_name) + "csv"
out_name = re.sub(r"file_list_", r"cell_stats_", out_name)
print("Computing cell stats to save to {}".format(out_name))
cell_classification_stats(in_dir,
container,
out_name,
should_plot=should_plot,
opt_end=opt_end,
output_spaces=output_spaces)
# Do PCA based analysis
if analysis_flags[3]:
print("Computing pca clustering")
pca_clustering(container, in_dir, opt_end=opt_end, s_color=s_color)