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mayofaulknermayofaulkner
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Merge pull request #446 from int-brain-lab/more_docs
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2 parents 8dbbe98 + d644ea8 commit d7fb868

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‎brainbox/ephys_plots.py‎

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@@ -496,3 +496,30 @@ def histc(x, bins):
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return data.convert2dict(), fig, ax
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return data
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def image_raw_data(raw, fs, chn_coords=None, cmap='bone', title=None, display=False, gain=-90, **kwargs):
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def gain2level(gain):
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return 10 ** (gain / 20) * 4 * np.array([-1, 1])
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ylabel = 'Channel index' if chn_coords is None else 'Distance from probe tip (um)'
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title = title or 'Raw data'
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y = np.arange(raw.shape[1]) if chn_coords is None else chn_coords[:, 1]
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x = np.array([0, raw.shape[0] - 1]) / fs * 1e3
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data = ImagePlot(raw, y=y, cmap=cmap)
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data.set_labels(title=title, xlabel='Time (ms)',
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ylabel=ylabel, clabel='Power (uV)')
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clim = gain2level(gain)
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data.set_clim(clim=clim)
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data.set_xlim(xlim=x)
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data.set_ylim()
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if display:
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ax, fig = plot_image(data.convert2dict(), **kwargs)
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return data.convert2dict(), fig, ax
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return data

‎brainbox/io/one.py‎

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@@ -8,11 +8,11 @@
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import pandas as pd
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from scipy.interpolate import interp1d
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from one.api import ONE
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from one.api import ONE, One
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import one.alf.io as alfio
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from one.alf import cache
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from iblutil.util import Bunch
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from ibllib.io import spikeglx
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from ibllib.io.extractors.training_wheel import extract_wheel_moves, extract_first_movement_times
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from ibllib.ephys.neuropixel import SITES_COORDINATES, TIP_SIZE_UM, trace_header
@@ -872,10 +872,15 @@ def load_channels_from_insertion(ins, depths=None, one=None, ba=None):
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class SpikeSortingLoader:
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"""
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Object that will load spike sorting data for a given probe insertion.
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This class can be instantiated in several manners
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- With Alyx database probe id:
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SpikeSortingLoader(pid=pid, one=one)
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- With Alyx database eic and probe name:
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SpikeSortingLoader(eid=eid, pname='probe00', one=one)
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- From a local session and probe name:
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SpikeSortingLoader(session_path=session_path, pname='probe00')
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"""
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one: ONE
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one: ONE = None
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atlas: None = None
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pid: str = None
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eid: str = ''
@@ -891,14 +896,26 @@ class SpikeSortingLoader:
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spike_sorting_path: Path = None
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def __post_init__(self):
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# pid gets precedence
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if self.pid is not None:
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self.eid, self.pname = self.one.pid2eid(self.pid)
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if self.atlas is None:
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self.atlas = AllenAtlas()
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self.session_path = self.one.eid2path(self.eid)
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self.session_path = self.one.eid2path(self.eid)
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# then eid / pname combination
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elif self.session_path is None or self.session_path == '':
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self.session_path = self.one.eid2path(self.eid)
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# fully local providing a session path
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else:
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self.one = One(cache_dir=self.session_path.parents[2], mode='local')
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df_sessions = cache._make_sessions_df(self.session_path)
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self.one._cache['sessions'] = df_sessions.set_index('id')
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self.one._cache['datasets'] = cache._make_datasets_df(self.session_path, hash_files=False)
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self.eid = str(self.session_path.relative_to(self.session_path.parents[2]))
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# populates default properties
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self.collections = self.one.list_collections(
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self.eid, filename='spikes*', collection=f"alf/{self.pname}*")
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self.datasets = self.one.list_datasets(self.eid)
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if self.atlas is None:
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self.atlas = AllenAtlas()
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self.files = {}
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@staticmethod

‎brainbox/plot_base.py‎

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@@ -185,7 +185,11 @@ def _get_scale(self, axis):
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:param axis: 'x' or 'y'
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:return:
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"""
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lim = self._set_lim(axis)
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if axis == 'x':
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lim = self.xlim
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else:
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lim = self.ylim
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lim = self._set_lim(axis, lim=lim)
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scale = (lim[1] - lim[0]) / self.data['c'].shape[axis_dict[axis]]
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return scale
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‎examples/atlas/Working with ibllib atlas.ipynb‎

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‎examples/atlas/atlas_circular_pyramidal_flatmap.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "f199bec2",
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"metadata": {},
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"source": [
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"# Plotting brain region values on circular flatmap"
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]
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},
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{
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"cell_type": "markdown",
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"id": "94c08e66",
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"metadata": {},
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"source": [
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"This example walks through various ways to overlay brain region values on a circular flatmap"
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]
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},
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{
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"cell_type": "markdown",
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"id": "17fd07ec",
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"metadata": {},
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"source": [
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"## The circular flatmap"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3ca88864",
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"metadata": {},
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"source": [
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"The circular flatmap is obtained by sampling the volume using concentric circles through the brain."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1178246b",
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"metadata": {},
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"outputs": [],
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"source": [
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"from ibllib.atlas import FlatMap\n",
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"flmap_cr = FlatMap(flatmap='circles')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "490614c3",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Display the concentric circles used in flatmap\n",
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"ax = flmap_cr.plot_top(volume='image')\n",
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"ax.plot(flmap_cr.ml_scale * 1e6, flmap_cr.ap_scale * 1e6)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "135dd187",
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"metadata": {},
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"source": [
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"This results in a flatmap that can be displayed in the following way"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8b8c4223",
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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"fig, ax = plt.subplots(figsize=(18,4))\n",
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"flmap_cr.plot_flatmap(ax)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ec15f88c",
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"metadata": {},
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"source": [
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"It is also possible to display this flatmap such that each circle is stacked on top of eachother. For this, the **pyramid** flatmap should be used"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7461e3f8",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Instantiate flatmap with circles arranged vetically on top of eachother\n",
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"flmap_py = FlatMap(flatmap='pyramid')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1f78b2ab",
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"metadata": {},
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"outputs": [],
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"source": [
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"fig, ax = plt.subplots(figsize=(8, 8))\n",
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"flmap_py.plot_flatmap(ax=ax)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e7af738a",
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"metadata": {},
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"source": [
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"## Data preparation"
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]
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},
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{
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"cell_type": "markdown",
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"id": "40fa09d0",
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"metadata": {},
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"source": [
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"In order to plot brain regions values on the flatmap an array of acronyms and an array of values corresponding to each acronym must be provided. A detailed overview of how to prepare your data can be found [here](https://int-brain-lab.github.io/iblenv/notebooks_external/atlas_plotting_scalar_on_slice.html#Data-preparation)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "20a1db83",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"# prepare array of acronyms\n",
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"acronyms = np.array(['VPM', 'PO', 'LP', 'CA1', 'DG-mo', 'VISa5', 'SSs5'])\n",
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"# assign data to each acronym\n",
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"values = np.arange(acronyms.size)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e6ae51d0",
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"metadata": {},
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"outputs": [],
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"source": [
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"from ibllib.atlas.regions import BrainRegions\n",
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"br = BrainRegions()\n",
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"# prepare array of acronyms with beryl mapping\n",
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"acronyms_beryl = np.unique(br.acronym2acronym(acronyms, mapping='Beryl'))\n",
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"values_beryl = np.arange(acronyms_beryl.size)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3724b968",
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"metadata": {},
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"outputs": [],
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"source": [
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"# prepare different values for left and right hemipshere for Beryl acronyms\n",
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"values_beryl_lh = np.random.randint(0, 10, acronyms_beryl.size)\n",
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"values_beryl_rh = np.random.randint(0, 10, acronyms_beryl.size)\n",
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"values_beryl_lr = np.c_[values_beryl_lh, values_beryl_rh]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "74fe528a",
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"metadata": {},
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"source": [
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"## Examples"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "dfa2d623",
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"metadata": {},
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"outputs": [],
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"source": [
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"from ibllib.atlas.plots import plot_scalar_on_flatmap\n",
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"# Plot region values on the left hemisphere of circle flatmap overlaid on brain region boundaries using Allen mapping\n",
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"fig, ax = plt.subplots(figsize=(18,4))\n",
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"fig, ax = plot_scalar_on_flatmap(acronyms, values, hemisphere='left', mapping='Allen', flmap_atlas=flmap_cr, ax=ax)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "cc78a1c7",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Plot region values on the both hemispheres of circle flatmap overlaid on the dwi Allen image using Beryl mapping\n",
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"fig, ax = plt.subplots(figsize=(18,4))\n",
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"fig, ax = plot_scalar_on_flatmap(acronyms_beryl, values_beryl, hemisphere='both', mapping='Beryl', background='image', \n",
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" cmap='Reds', flmap_atlas=flmap_cr, ax=ax)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "37bf7bd8",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Plot region values on the right hemisphere of pyramidal flatmap overlaid on the dwi Allen image using Allen mapping\n",
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"fig, ax = plt.subplots(figsize=(8,8))\n",
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"fig, ax = plot_scalar_on_flatmap(acronyms, values, hemisphere='right', mapping='Allen', background='image', \n",
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" cmap='Reds', flmap_atlas=flmap_py, ax=ax)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "28f7f30c",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Plot two column region values on the both hemispheres of pyramidal flatmap overlaid on brain region boundaries \n",
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"# using Beryl mapping\n",
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"fig, ax = plt.subplots(figsize=(8,8))\n",
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"fig, ax = plot_scalar_on_flatmap(acronyms_beryl, values_beryl_lr, hemisphere='both', mapping='Beryl', \n",
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" background='boundary', cmap='Blues', flmap_atlas=flmap_py, ax=ax)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python [conda env:iblenv] *",
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"language": "python",
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"name": "conda-env-iblenv-py"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

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