325 lines
13 KiB
Python
325 lines
13 KiB
Python
import os
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import glob
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import nixio as nix
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import numpy as np
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import scipy.signal as sig
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import matplotlib.pyplot as plt
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from sklearn.metrics import roc_curve, roc_auc_score
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from IPython import embed
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from util import firing_rate, despine, extract_am, within_group_distance, across_group_distance
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figure_folder = "figures"
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data_folder = "data"
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def read_baseline(block):
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spikes = []
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if "baseline" not in block.name:
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print("Block %s does not appear to be a baseline block!" % block.name )
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return spikes
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spikes = block.data_arrays[0][:]
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return spikes
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def sort_blocks(nix_file):
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block_map = {}
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contrasts = []
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deltafs = []
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conditions = []
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for b in nix_file.blocks:
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if "baseline" not in b.name.lower():
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name_parts = b.name.split("_")
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cntrst = float(name_parts[1])
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if cntrst not in contrasts:
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contrasts.append(cntrst)
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cndtn = name_parts[3]
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if cndtn not in conditions:
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conditions.append(cndtn)
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dltf = float(name_parts[5])
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if dltf not in deltafs:
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deltafs.append(dltf)
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block_map[(cntrst, dltf, cndtn)] = b
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else:
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block_map["baseline"] = b
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return block_map, contrasts, deltafs, conditions
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def get_spikes(block):
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"""Get the spike trains.
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Args:
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block ([type]): [description]
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Returns:
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list of np.ndarray: the spike trains.
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"""
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response_map = {}
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spikes = []
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for da in block.data_arrays:
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if "spike_times" in da.type and "response" in da.name:
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resp_id = int(da.name.split("_")[-1])
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response_map[resp_id] = da
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for k in sorted(response_map.keys()):
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spikes.append(response_map[k][:])
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return spikes
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def get_rates(spike_trains, duration, dt, kernel_width):
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"""Convert the spike trains (list of spike_times) to rates using a Gaussian kernel of the given size.
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Args:
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spike_trains ([type]): [description]
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duration ([type]): [description]
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dt ([type]): [description]
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kernel_width ([type]): [description]
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Returns:
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np.ndarray: Matrix of firing rates, 1. dimension is the number of trials
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np.ndarray: the time vector
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"""
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time = np.arange(0.0, duration, dt)
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rates = np.zeros((len(spike_trains), len(time)))
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for i, sp in enumerate(spike_trains):
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rates[i, :] = firing_rate(sp, duration, kernel_width, dt)
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return rates, time
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def get_firing_rate(block_map, df, contrast, condition, kernel_width=0.0005):
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"""Retruns the firing rates and the spikes
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Args:
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block_map ([type]): [description]
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df ([type]): [description]
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contrast ([type]): [description]
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condition ([type]): [description]
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kernel_width (float, optional): [description]. Defaults to 0.0005.
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Returns:
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np.ndarray: the time vector.
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np.ndarray: the rates with the first dimension representing the trials.
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np.adarray: the spike trains.
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"""
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block = block_map[(contrast, df, condition)]
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spikes = get_spikes(block)
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duration = float(block.metadata["stimulus parameter"]["duration"])
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dt = float(block.metadata["stimulus parameter"]["dt"])
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rates, time = get_rates(spikes, duration, dt, kernel_width)
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return time, rates, spikes
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def get_signals(block):
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"""Read the fish signals from block.
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Args:
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block ([type]): the block containing the data for a given df, contrast and condition
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Raises:
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ValueError: when the complete stimulus data is not found
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ValueError: when the no-other animal data is not found
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Returns:
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np.ndarray: the complete signal
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np.ndarray: the frequency profile of the recorded fish
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np.ndarray: the frequency profile of the other fish
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np.ndarray: the time axis
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"""
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self_freq = None
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other_freq = None
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signal = None
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time = None
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if "complete stimulus" not in block.data_arrays or "self frequency" not in block.data_arrays:
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raise ValueError("Signals not stored in block!")
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if "no-other" not in block.name and "other frequency" not in block.data_arrays:
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raise ValueError("Signals not stored in block!")
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signal = block.data_arrays["complete stimulus"][:]
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time = np.asarray(block.data_arrays["complete stimulus"].dimensions[0].axis(len(signal)))
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self_freq = block.data_arrays["self frequency"][:]
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if "no-other" not in block.name:
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other_freq = block.data_arrays["other frequency"][:]
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return signal, self_freq, other_freq, time
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def create_response_plot(block_map, all_dfs, all_contrasts, all_conditions, current_df, figure_name=None):
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conditions = ["no-other", "self", "other"]
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condition_labels = ["soliloquy", "self chirping", "other chirping"]
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min_time = 0.5
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max_time = min_time + 0.5
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fig = plt.figure(figsize=(6.5, 5.5))
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fig_grid = (len(all_contrasts)*2 + 6, len(all_conditions)*3+2)
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all_contrasts = sorted(all_contrasts, reverse=True)
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for i, condition in enumerate(conditions):
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# plot the signals
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block = block_map[(all_contrasts[0], current_df, condition)]
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signal, self_freq, other_freq, time = get_signals(block)
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am = extract_am(signal)
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self_eodf = block.metadata["stimulus parameter"]["eodfs"]["self"]
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other_eodf = block.metadata["stimulus parameter"]["eodfs"]["other"]
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# plot frequency traces
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ax = plt.subplot2grid(fig_grid, (0, i * 3 + i), rowspan=2, colspan=3, fig=fig)
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ax.plot(time[(time > min_time) & (time < max_time)], self_freq[(time > min_time) & (time < max_time)],
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color="#ff7f0e", label="%iHz" % self_eodf)
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ax.text(min_time-0.05, self_eodf, "%iHz" % self_eodf, color="#ff7f0e", va="center", ha="right", fontsize=9)
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if other_freq is not None:
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ax.plot(time[(time > min_time) & (time < max_time)], other_freq[(time > min_time) & (time < max_time)],
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color="#1f77b4", label="%iHz" % other_eodf)
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ax.text(min_time-0.05, other_eodf, "%iHz" % other_eodf, color="#1f77b4", va="center", ha="right", fontsize=9)
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ax.set_title(condition_labels[i])
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despine(ax, ["top", "bottom", "left", "right"], True)
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# plot the am
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ax = plt.subplot2grid(fig_grid, (3, i * 3 + i), rowspan=2, colspan=3, fig=fig)
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ax.plot(time[(time > min_time) & (time < max_time)], signal[(time > min_time) & (time < max_time)],
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color="#2ca02c", label="signal")
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ax.plot(time[(time > min_time) & (time < max_time)], am[(time > min_time) & (time < max_time)],
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color="#d62728", label="am")
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despine(ax, ["top", "bottom", "left", "right"], True)
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ax.set_ylim([-1.25, 1.25])
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ax.legend(ncol=2, loc=(0.01, -0.5), fontsize=7, markerscale=0.5, frameon=False)
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# for each contrast plot the firing rate
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for j, contrast in enumerate(all_contrasts):
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t, rates, _ = get_firing_rate(block_map, current_df, contrast, condition)
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avg_resp = np.mean(rates, axis=0)
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error = np.std(rates, axis=0)
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ax = plt.subplot2grid(fig_grid, (j*2 + 6, i * 3 + i), rowspan=2, colspan=3)
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ax.plot(t[(t > min_time) & (t < max_time)], avg_resp[(t > min_time) & (t < max_time)], color="k", lw=0.5)
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ax.fill_between(t[(t > min_time) & (t < max_time)], (avg_resp - error)[(t > min_time) & (t < max_time)],
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(avg_resp + error)[(t > min_time) & (t < max_time)], color="k", lw=0.0, alpha=0.25)
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ax.set_ylim([0, 750])
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ax.set_xlabel("")
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ax.set_ylabel("")
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ax.set_xticks(np.arange(min_time, max_time+.01, 0.250))
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ax.set_xticklabels(map(int, (np.arange(min_time, max_time + .01, 0.250) - min_time) * 1000))
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ax.set_xticks(np.arange(min_time, max_time+.01, 0.125), minor=True)
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if j < len(all_contrasts) -1:
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ax.set_xticklabels([])
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ax.set_yticks(np.arange(0.0, 751., 500))
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ax.set_yticks(np.arange(0.0, 751., 125), minor=True)
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if i > 0:
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ax.set_yticklabels([])
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despine(ax, ["top", "right"], False)
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if i == 2:
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ax.text(max_time + 0.025*max_time, 350, "c=%.3f" % all_contrasts[j],
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color="#d62728", ha="left", fontsize=7)
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if i == 1:
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ax.set_xlabel("time [ms]")
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if i == 0:
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ax.set_ylabel("frequency [Hz]", va="center")
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ax.yaxis.set_label_coords(-0.45, 3.5)
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name = figure_name if figure_name is not None else "chirp_responses.pdf"
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name = (name + ".pdf") if ".pdf" not in name else name
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plt.savefig(os.path.join(figure_folder, name))
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plt.close()
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def foreign_fish_detection_beat(block_map, df, all_contrasts, all_conditions, kernel_width=0.0005):
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detection_performance = {}
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for contrast in all_contrasts:
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print(" " * 50, end="\r")
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print("Contrast: %.3f" % contrast, end="\r")
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no_other_block = block_map[(contrast, df, "no-other")]
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self_block = block_map[(contrast, df, "self")]
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# get some metadata assuming they are all the same for each conditionm, which they should
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duration = float(self_block.metadata["stimulus parameter"]["duration"])
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dt = float(self_block.metadata["stimulus parameter"]["dt"])
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chirp_duration = self_block.metadata["stimulus parameter"]["chirp_duration"]
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chirp_times = self_block.metadata["stimulus parameter"]["chirp_times"]
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interchirp_starts = np.add(chirp_times, 1.5 * chirp_duration)[:-1]
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interchirp_ends = np.subtract(chirp_times, 1.5 * chirp_duration)[1:]
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ici = np.floor(np.mean(np.subtract(interchirp_ends, interchirp_starts))*1000) / 1000
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# get the spiking responses
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no_other_spikes = get_spikes(no_other_block)
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self_spikes = get_spikes(self_block)
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# get firing rates
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no_other_rates, _ = get_rates(no_other_spikes, duration, dt, kernel_width)
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self_rates, _ = get_rates(self_spikes, duration, dt, kernel_width)
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# get the response snippets between chrips
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no_other_snippets = np.zeros((len(interchirp_starts) * no_other_rates.shape[0], int(ici / dt)))
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self_snippets = np.zeros_like(no_other_snippets)
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for i in range(no_other_rates.shape[0]):
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for j, start in enumerate(interchirp_starts):
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start_index = int(start/dt)
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end_index = start_index + no_other_snippets.shape[1]
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index = i * len(interchirp_starts) + j
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no_other_snippets[index, :] = no_other_rates[i, start_index:end_index]
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self_snippets[index, :] = self_rates[i, start_index:end_index]
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# get the distances
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baseline_dist = within_group_distance(no_other_snippets)
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comp_dist = across_group_distance(no_other_snippets, self_snippets)
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# sort and perfom roc
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triangle_indices = np.tril_indices_from(baseline_dist, -1)
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valid_distances_baseline = baseline_dist[triangle_indices]
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temp1 = np.zeros_like(valid_distances_baseline)
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valid_distances_comparison = comp_dist.ravel()
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temp2 = np.ones_like(valid_distances_comparison)
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group = np.hstack((temp1, temp2))
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score = np.hstack((valid_distances_baseline, valid_distances_comparison))
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fpr, tpr, _ = roc_curve(group, score, pos_label=1)
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auc = roc_auc_score(group, score)
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detection_performance[(contrast, kernel_width)] = {"auc": auc, "true positives": tpr, "false positives": fpr}
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print("\n")
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return detection_performance
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def foreign_fish_detection_chirp(block_map, df, all_contrasts, all_conditions, kernel_width=0.0005):
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#
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return None
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def foreign_fish_detection(block_map, all_dfs, all_contrasts, all_conditions, current_df=None, kernel_width=0.0005):
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dfs = [current_df] if current_df is not None else all_dfs
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detection_performance_beat = {}
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detection_performance_chirp = {}
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for df in dfs:
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detection_performance_beat[df] = foreign_fish_detection_beat(block_map, df, all_contrasts, all_conditions, kernel_width)
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detection_performance_chirp[df] = foreign_fish_detection_chirp(block_map, df, all_contrasts, all_conditions, kernel_width)
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return detection_performance_beat, detection_performance_chirp
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def process_cell(filename, dfs=[], contrasts=[], conditions=[]):
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nf = nix.File.open(filename, nix.FileMode.ReadOnly)
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block_map, all_contrasts, all_dfs, all_conditions = sort_blocks(nf)
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if "baseline" in block_map.keys():
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baseline_spikes = read_baseline(block_map["baseline"])
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else:
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print("ERROR: no baseline data for file %s!" % filename)
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# fig_name = filename.split(os.path.sep)[-1].split(".nix")[0] + "_df_20Hz.pdf"
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# create_response_plot(block_map, all_dfs, all_contrasts, all_conditions, 20, figure_name=fig_name)
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# fig_name = filename.split(os.path.sep)[-1].split(".nix")[0] + "_df_-100Hz.pdf"
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# create_response_plot(block_map, all_dfs, all_contrasts, all_conditions, -100, figure_name=fig_name)
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foreign_fish_detection(block_map, all_dfs, all_contrasts, all_conditions, current_df=20)
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nf.close()
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def main():
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nix_files = sorted(glob.glob(os.path.join(data_folder, "cell*.nix")))
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for nix_file in nix_files:
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process_cell(nix_file, dfs=[20], contrasts=[20], conditions=["self"])
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if __name__ == "__main__":
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main() |