-
Notifications
You must be signed in to change notification settings - Fork 4
Logic for design matrix creation #6
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Open
pqz317
wants to merge
9
commits into
learning-2-learn:main
Choose a base branch
from
pqz317:main
base: main
Could not load branches
Branch not found: {{ refName }}
Loading
Could not load tags
Nothing to show
Loading
Are you sure you want to change the base?
Some commits from the old base branch may be removed from the timeline,
and old review comments may become outdated.
Open
Changes from 6 commits
Commits
Show all changes
9 commits
Select commit
Hold shift + click to select a range
5faba0a
cleanup
pqz317 6451011
Merge branch 'main' of github.com:learning-2-learn/wcst_encode
pqz317 740755f
beh utils, work on generating design mat
pqz317 4b24017
Merge branch 'main' of github.com:learning-2-learn/wcst_encode
pqz317 b0449f7
finished design matrix logic, refactored into data utils
pqz317 c7cc127
refactor into another notebook
pqz317 19f9d3d
refactor, remove num_neuron constant
pqz317 d0c1984
address comments
pqz317 9ff1e12
valueerror, math.floor/ceil
pqz317 File filter
Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
There are no files selected for viewing
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,6 +1,12 @@ | ||
| # useful constants during analysis | ||
|
|
||
| FEATURES = [ | ||
| 'CIRCLE', 'SQUARE', 'STAR', 'TRIANGLE', | ||
| 'CYAN', 'GREEN', 'MAGENTA', 'YELLOW', | ||
| 'ESCHER', 'POLKADOT', 'RIPPLE', 'SWIRL' | ||
| ] | ||
| ] | ||
|
|
||
| NUM_UNITS = 59 | ||
|
|
||
| COLUMN_NAMES_W_UNITS = FEATURES + ["CORRECT", "INCORRECT"] + [f"unit_{i}" for i in range(0, NUM_UNITS)] | ||
| COLUMN_NAMES = FEATURES + ["CORRECT", "INCORRECT"] | ||
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,99 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "### Notebook to create and store a design matrix of behavior and spikes " | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 6, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "The autoreload extension is already loaded. To reload it, use:\n", | ||
| " %reload_ext autoreload\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "%load_ext autoreload\n", | ||
| "%autoreload 2\n", | ||
| "\n", | ||
| "import numpy as np\n", | ||
| "import pandas as pd\n", | ||
| "from spike_tools import (\n", | ||
| " general as spike_general,\n", | ||
| " analysis as spike_analysis,\n", | ||
| ")\n", | ||
| "import data_utils\n", | ||
| "from constants import FEATURES, COLUMN_NAMES_W_UNITS\n", | ||
| "\n", | ||
| "species = 'nhp'\n", | ||
| "subject = 'SA'\n", | ||
| "exp = 'WCST'\n", | ||
| "session = 20180802 # this is the session for which there are spikes at the moment. \n", | ||
| "\n", | ||
| "tau_pre = 20\n", | ||
| "tau_post = 0" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 2, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "spikes_by_bins = pd.read_pickle('/data/processed/sub-SA_sess-20180802_spike_counts_binsize_50.pickle')\n", | ||
| "beh_by_bins = pd.read_pickle('/data/processed/sub-SA_sess-20180802_behavior_binsize_50.pickle')\n", | ||
| "intervals = pd.read_pickle(\"/data/processed/sub-SA_sess-20180802_interval_1500_fb_1500_binsize_50.pickle\")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "design_mat = data_utils.get_design_matrix(spikes_by_bins, beh_by_bins, COLUMN_NAMES_W_UNITS, tau_pre, tau_post)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "design_mat.to_pickle(\"/data/processed/sub-SA_sess-20180802_design_mat_taupre_20_taupost_0_binsize_50.pickle\")" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python 3", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.10.6" | ||
| }, | ||
| "orig_nbformat": 4 | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,128 @@ | ||
| from constants import FEATURES | ||
pqz317 marked this conversation as resolved.
Show resolved
Hide resolved
|
||
| import numpy as np | ||
| import pandas as pd | ||
|
|
||
| def get_behavior_by_bins(bin_size, beh): | ||
| """ | ||
| bin_size: in miliseconds, bin size | ||
| data: dataframe for behavioral data from object features csv | ||
| Returns: new dataframe with one-hot encoding of features, feedback | ||
| """ | ||
| max_time = np.max(beh["TrialEnd"].values) | ||
| max_bin_idx = int(max_time / bin_size) + 1 | ||
| columns = FEATURES + ["CORRECT", "INCORRECT"] | ||
| types = ["f4" for _ in columns] | ||
| zipped = list(zip(columns, types)) | ||
| dtype = np.dtype(zipped) | ||
| arr = np.zeros((max_bin_idx), dtype=dtype) | ||
|
|
||
| for _, row in beh.iterrows(): | ||
| # grab features of item chosen | ||
| item_chosen = int(row["ItemChosen"]) | ||
| color = row[f"Item{item_chosen}Color"] | ||
| shape = row[f"Item{item_chosen}Shape"] | ||
| pattern = row[f"Item{item_chosen}Pattern"] | ||
|
|
||
| chosen_time = row["FeedbackOnset"] - 800 | ||
| chosen_bin = int(chosen_time / bin_size) | ||
| arr[chosen_bin][color] = 1 | ||
| arr[chosen_bin][shape] = 1 | ||
| arr[chosen_bin][pattern] = 1 | ||
|
|
||
| feedback_bin = int(row["FeedbackOnset"] / bin_size) | ||
| # print(feedback_bin) | ||
| if row["Response"] == "Correct": | ||
| arr[feedback_bin]["CORRECT"] = 1 | ||
| else: | ||
| arr[feedback_bin]["INCORRECT"] = 1 | ||
| df = pd.DataFrame(arr) | ||
| df["bin_idx"] = np.arange(len(df)) | ||
| return df | ||
|
|
||
|
|
||
| def get_spikes_by_bins(bin_size, spike_times): | ||
| """Given a bin_size and a series of spike times, return spike counts by bin. | ||
| Args: | ||
| bin_size: size of bins in miliseconds | ||
| spike_times: dataframe with unit_id, spike times. | ||
| Returns: | ||
| df with bin_idx, unit_* as columns, filled with spike counts | ||
| """ | ||
|
|
||
| units = np.unique(spike_times.UnitID.values) | ||
| time_stamp_max = int(spike_times.SpikeTime.max()) + 1 | ||
|
|
||
| num_time_bins = int(time_stamp_max/bin_size) + 1 | ||
| bins = np.arange(num_time_bins) * bin_size | ||
|
|
||
| df = pd.DataFrame(data={'bin_idx': np.arange(num_time_bins)[:-1]}) | ||
| for unit in units: | ||
| unit_spike_times = spike_times[spike_times.UnitID==unit].SpikeTime.values | ||
| unit_spike_counts, bin_edges = np.histogram(unit_spike_times, bins=bins) | ||
| df[f'unit_{unit}'] = unit_spike_counts | ||
| return df | ||
|
|
||
| def get_trial_intervals(behavioral_data, event="FeedbackOnset", pre_interval=0, post_interval=0, bin_size=50): | ||
| """Per trial, finds time interval surrounding some event in the behavioral data | ||
|
|
||
| Args: | ||
| behavioral_data: Dataframe describing each trial, must contain | ||
| columns: TrialNumber, whatever 'event' param describes | ||
| event: name of event to align around, must be present as a | ||
| column name in behavioral_data Dataframe | ||
| pre_interval: number of miliseconds before event | ||
| post_interval: number of miliseconds after event | ||
|
|
||
| Returns: | ||
| DataFrame with num_trials length, columns: TrialNumber, | ||
| IntervalStartTime, IntervalEndTime | ||
| """ | ||
| trial_event_times = behavioral_data[["TrialNumber", event]] | ||
|
|
||
| intervals = np.empty((len(trial_event_times), 3)) | ||
| intervals[:, 0] = trial_event_times["TrialNumber"] | ||
| intervals[:, 1] = trial_event_times[event] - pre_interval | ||
| intervals[:, 2] = trial_event_times[event] + post_interval | ||
| intervals_df = pd.DataFrame(columns=["TrialNumber", "IntervalStartTime", "IntervalEndTime"]) | ||
| intervals_df["TrialNumber"] = trial_event_times["TrialNumber"].astype(int) | ||
| intervals_df["IntervalStartTime"] = trial_event_times[event] - pre_interval | ||
| intervals_df["IntervalEndTime"] = trial_event_times[event] + post_interval | ||
| intervals_df["IntervalStartBin"] = (intervals_df["IntervalStartTime"] / bin_size).astype(int) | ||
| intervals_df["IntervalEndBin"] = (intervals_df["IntervalEndTime"] / bin_size).astype(int) | ||
| return intervals_df | ||
|
|
||
|
|
||
| def get_design_matrix(spikes_by_bins, beh_by_bins, columns, tau_pre, tau_post): | ||
| """ | ||
| Reformats data as a design matrix dataframe, where for each of the specified columns, | ||
| additional columns are added for each of the time points between tau_pre and tau_post | ||
| Args: | ||
| spike_by_bins: df with bin_idx, unit_* as columns | ||
| beh_by_bins: df with bin_idx, behavioral vars of interest as columns | ||
| columns: columns to include, must be present in either spike_by_bins or beh_by_bins | ||
| tau_pre: number of bins to look in the past | ||
| tau_post: number of bins to look in the future | ||
| Returns: | ||
| df with bin_idx, columns for each time points between tau_pre and tau_post | ||
| """ | ||
| joint = pd.merge(spikes_by_bins, beh_by_bins, on="bin_idx", how="inner") | ||
| res = pd.DataFrame() | ||
| taus = np.arange(-tau_pre, tau_post) | ||
| for tau in taus: | ||
| shift_idx = -1 * tau | ||
| column_names = [f"{x}_{tau}" for x in columns] | ||
| res[column_names] = joint.shift(shift_idx)[columns] | ||
| res["bin_idx"] = joint["bin_idx"] | ||
| return res | ||
|
|
||
|
|
||
| def get_interval_bins(intervals): | ||
| """ | ||
| Gets all the bins belonging to all the intervals | ||
| Args: | ||
| intervals: df with trialnumber, IntervalStartBin, IntervalEndBin | ||
| Returns: | ||
| np array of all bins for all trials falling between startbin and endbin | ||
| """ | ||
| interval_bins = intervals.apply(lambda x: np.arange(x.IntervalStartBin, x.IntervalEndBin).astype(int), axis=1) | ||
| return np.concatenate(interval_bins.to_numpy()) | ||
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Uh oh!
There was an error while loading. Please reload this page.