place fields

.gitignore

@@ -129,3 +129,6 @@ dmypy.json
 
 # nwb
 **.nwb
+nwbwidgets/controllers/group_and_sort_controllers_multi_select.py
+nwbwidgets/placefield_group_and_sort.py
+nwbwidgets/towers_task_placefield.py

nwbwidgets/analysis/placefields.py

@@ -0,0 +1,300 @@
+from functools import lru_cache
+
+import numpy as np
+from scipy.ndimage.filters import gaussian_filter
+
+
+def find_nearest(arr, tt):
+    """Used for picking out elements of a TimeSeries based on spike times
+
+    Parameters
+    ----------
+    arr: array-like
+    tt: array-like
+
+    Returns
+    -------
+    indices: array-like
+
+    """
+    arr = arr[arr > tt[0]]
+    arr = arr[arr < tt[-1]]
+    return np.searchsorted(tt, arr)
+
+
+def smooth(y, box_pts):
+    """Moving average
+
+    Parameters
+    ----------
+    y: array-like
+    box_pts: int
+
+    Returns
+    -------
+    output: np.array(dtype=float)
+
+    """
+    box = np.ones(box_pts) / box_pts
+    return np.convolve(y, box, mode='same')
+
+
+def compute_speed(pos, pos_tt, smooth_param=40):
+    """Compute boolean of whether the speed of the animal was above a threshold
+    for each time point
+
+    Parameters
+    ----------
+    pos: np.ndarray(dtype=float)
+        in meters
+    pos_tt: np.ndarray(dtype=float)
+        in seconds
+    smooth_param: float, optional
+
+    Returns
+    -------
+    running: np.ndarray(dtype=bool)
+
+    """
+    if len(pos.shape) > 1:
+        speed = np.hstack((0, np.sqrt(np.sum(np.diff(pos.T) ** 2, axis=0)) / np.diff(pos_tt)))
+    else:
+        speed = np.hstack((0, np.sqrt(np.diff(pos.T) ** 2) / np.diff(pos_tt)))
+    return smooth(speed, smooth_param)
+
+
+def compute_2d_occupancy(pos, pos_tt, edges_x, edges_y, pixel_width, speed_thresh=0.03, velocity=None):
+    """Computes occupancy per bin in seconds
+
+    Parameters
+    ----------
+    pos: np.ndarray(dtype=float)
+        in meters
+    pos_tt: np.ndarray(dtype=float)
+        in seconds
+    edges_x: array-like
+        edges of histogram in meters
+    edges_y: array-like
+        edges of histogram in meters
+    pixel_width: array-like
+    speed_thresh: float, optional
+        in meters. Default = 3.0 cm/s
+    velocity: np.ndarray(dtype=float)
+        pre-computed velocity
+
+    Returns
+    -------
+    occupancy: np.ndarray(dtype=float)
+        in seconds
+    running: np.ndarray(dtype=bool)
+
+    """
+
+    sampling_period = (np.max(pos_tt) - np.min(pos_tt)) / len(pos_tt)
+    np.seterr(invalid='ignore')
+    if velocity is None:
+        if pixel_width[1] is not int(1):
+            is_running = compute_speed(pos, pos_tt) > speed_thresh
+        else:
+            is_running = compute_speed(pos[:, 0], pos_tt) > speed_thresh
+    else:
+        is_running = np.linalg.norm(velocity) > speed_thresh
+
+    run_pos = pos[is_running, :]
+    occupancy = np.histogram2d(run_pos[:, 1],
+                               run_pos[:, 0],
+                               [edges_y, edges_x])[0] * sampling_period  # in seconds
+
+    return occupancy, is_running
+
+
+def compute_2d_n_spikes(pos, pos_tt, spikes, edges_x, edges_y, pixel_width, speed_thresh=0.03, velocity=None):
+    """Returns speed-gated position during spikes
+
+    Parameters
+    ----------
+    pos: np.ndarray(dtype=float)
+        (time x 2) in meters
+    pos_tt: np.ndarray(dtype=float)
+        (time,) in seconds
+    spikes: np.ndarray(dtype=float)
+        in seconds
+    edges_x: np.ndarray(dtype=float)
+        edges of histogram in meters
+    edges_y: np.ndarray(dtype=float)
+        edges of histogram in meters
+    pixel_width: array
+    speed_thresh: float
+        in meters. Default = 3.0 cm/s
+    velocity: np.ndarray(dtype=float)
+        pre-computed velocity
+
+    Returns
+    -------
+    """
+    np.seterr(invalid='ignore')
+    if velocity is None:
+        if pixel_width[1] is not int(1):
+            is_running = compute_speed(pos, pos_tt) > speed_thresh
+        else:
+            is_running = compute_speed(pos[:, 0], pos_tt) > speed_thresh
+    else:
+        is_running = np.linalg.norm(velocity) > speed_thresh
+
+    spike_pos_inds = find_nearest(spikes, pos_tt)
+    spike_pos_inds = spike_pos_inds[is_running[spike_pos_inds]]
+    pos_on_spikes = pos[spike_pos_inds, :]
+
+    n_spikes = np.histogram2d(pos_on_spikes[:, 1],
+                              pos_on_spikes[:, 0],
+                              [edges_y, edges_x])[0]
+
+    return n_spikes
+
+
+def compute_2d_firing_rate(pos, pos_tt, spikes,
+                           pixel_width,
+                           speed_thresh=0.03,
+                           gaussian_sd_x=0.0184,
+                           gaussian_sd_y=0.0184,
+                           x_start=None, x_stop=None,
+                           y_start=None, y_stop=None,
+                           velocity=None):
+    """Returns speed-gated occupancy and speed-gated and
+    Gaussian-filtered firing rate
+
+    Parameters
+    ----------
+    pos: np.ndarray(dtype=float)
+        (time x 2), in meters
+    pos_tt: np.ndarray(dtype=float)
+        (time,) in seconds
+    spikes: np.ndarray(dtype=float)
+        in seconds
+    pixel_width: array-like
+    speed_thresh: float, optional
+        in meters. Default = 3.0 cm/s
+    gaussian_sd_x: float, optional
+        width of gaussian kernel in x-dim, in meters. Default = 1.84 cm
+    gaussian_sd_y: float, optional
+        width of gaussian kernel in y-dim, in meters. Default = 1.84 cm
+    x_start: float, optional
+    x_stop: float, optional
+    y_start: float, optional
+    y_stop: float, optional
+    velocity: np.ndarray(dtype=float)
+        pre-computed velocity
+
+    Returns
+    -------
+
+    occupancy: np.ndarray
+        in seconds
+    filtered_firing_rate: np.ndarray(shape=(cell, x, y), dtype=float)
+        in Hz
+
+    """
+
+    x_start = np.nanmin(pos[:, 0]) if x_start is None else x_start
+    x_stop = np.nanmax(pos[:, 0]) if x_stop is None else x_stop
+
+    y_start = np.nanmin(pos[:, 1]) if y_start is None else y_start
+    y_stop = np.nanmax(pos[:, 1]) if y_stop is None else y_stop
+
+    edges_x = np.arange(x_start, x_stop, pixel_width[0])
+    edges_y = np.arange(y_start, y_stop, pixel_width[1])
+
+    occupancy, running = compute_2d_occupancy(pos, pos_tt, edges_x, edges_y, pixel_width, speed_thresh, velocity)
+
+    n_spikes = compute_2d_n_spikes(pos, pos_tt, spikes, edges_x, edges_y, pixel_width, speed_thresh, velocity)
+
+    np.seterr(divide='ignore')
+    firing_rate = n_spikes / occupancy  # in Hz
+    firing_rate[np.isnan(firing_rate)] = 0  # get rid of NaNs so convolution works
+    sigmas = [gaussian_sd_y / pixel_width[1], gaussian_sd_x / pixel_width[0]]
+    filtered_firing_rate = gaussian_filter(firing_rate, sigmas)
+
+    # filter occupancy to create a mask so non-explored regions are nan'ed
+    sigmas_occ = [gaussian_sd_y / pixel_width[1] / 8, gaussian_sd_x / pixel_width[0] / 8]
+    filtered_occupancy = gaussian_filter(occupancy, sigmas_occ)
+    filtered_firing_rate[filtered_occupancy.astype('bool') < .00001] = np.nan
+
+    return occupancy, filtered_firing_rate, [edges_x, edges_y]
+
+
+def compute_1d_occupancy(pos, pos_tt, spatial_bins, sampling_rate, speed_thresh=0.03, velocity=None):
+
+    np.seterr(invalid='ignore')
+    if velocity is None:
+        is_running = compute_speed(pos, pos_tt) > speed_thresh
+    else:
+        is_running = np.linalg.norm(velocity) > speed_thresh
+
+    run_pos = pos[is_running, :]
+    finite_lin_pos = run_pos[np.isfinite(run_pos)]
+
+    occupancy = np.histogram(
+        finite_lin_pos, bins=spatial_bins)[0][:-2] / sampling_rate
+
+    return occupancy
+
+
+def compute_linear_firing_rate(pos, pos_tt, spikes, gaussian_sd=0.0557,
+                               spatial_bin_len=0.0168, speed_thresh=0.03, velocity=None):
+    """The occupancy and number of spikes, speed-gated, binned, and smoothed
+    over position
+
+    Parameters
+    ----------
+    pos: np.ndarray
+        linearized position
+    pos_tt: np.ndarray
+        sample times in seconds
+    spikes: np.ndarray
+        for a single cell in seconds
+    gaussian_sd: float (optional)
+        in meters. Default = 5.57 cm
+    spatial_bin_len: float (optional)
+        in meters. Default = 1.68 cm
+    speed_thresh: float (optional)
+        in m/s. Default = 0.03
+    velocity: np.ndarray(dtype=float)
+        pre-computed velocity
+
+    Returns
+    -------
+    xx: np.ndarray
+        center of position bins in meters
+    occupancy: np.ndarray
+        time in each spatial bin in seconds, during appropriate trials and
+        while running
+    filtered_n_spikes: np.ndarray
+        number of spikes in each spatial bin,  during appropriate trials, while
+        running, and processed with a Gaussian filter
+
+    """
+    spatial_bins = np.arange(np.nanmin(pos), np.nanmax(pos) + spatial_bin_len, spatial_bin_len)
+
+    sampling_rate = len(pos_tt) / (np.nanmax(pos_tt) - np.nanmin(pos_tt))
+
+    occupancy = compute_1d_occupancy(pos, pos_tt, spatial_bins, sampling_rate, speed_thresh, velocity)
+
+    np.seterr(invalid='ignore')
+    is_running = compute_speed(pos, pos_tt) > speed_thresh
+
+    # find pos_tt bin associated with each spike
+    spike_pos_inds = find_nearest(spikes, pos_tt)
+    spike_pos_inds = spike_pos_inds[is_running[spike_pos_inds]]
+    pos_on_spikes = pos[spike_pos_inds]
+    finite_pos_on_spikes = pos_on_spikes[np.isfinite(pos_on_spikes)]
+
+    n_spikes = np.histogram(finite_pos_on_spikes, bins=spatial_bins)[0][:-2]
+
+    np.seterr(divide='ignore')
+    firing_rate = np.nan_to_num(n_spikes / occupancy)
+
+    filtered_firing_rate = gaussian_filter(
+        firing_rate, gaussian_sd / spatial_bin_len)
+    xx = spatial_bins[:-3] + (spatial_bins[1] - spatial_bins[0]) / 2
+
+    return xx, occupancy, filtered_firing_rate

nwbwidgets/ecephys.py

@@ -1,11 +1,12 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import plotly.graph_objects as go
-from plotly.colors import DEFAULT_PLOTLY_COLORS
+import pynwb
 from ipywidgets import widgets, ValueWidget
+
 from pynwb.ecephys import SpikeEventSeries, ElectricalSeries
+
 from scipy.signal import stft
-import pynwb
 
 from .base import fig2widget, lazy_tabs, render_dataframe
 from .timeseries import BaseGroupedTraceWidget