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Logic for design matrix creation #6
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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
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,6 +1,12 @@ | ||
| # useful constants during analysis | ||
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| FEATURES = [ | ||
| 'CIRCLE', 'SQUARE', 'STAR', 'TRIANGLE', | ||
| 'CYAN', 'GREEN', 'MAGENTA', 'YELLOW', | ||
| 'ESCHER', 'POLKADOT', 'RIPPLE', 'SWIRL' | ||
| ] | ||
| ] | ||
|
|
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| NUM_UNITS = 59 | ||
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| COLUMN_NAMES_W_UNITS = FEATURES + ["CORRECT", "INCORRECT"] + [f"unit_{i}" for i in range(0, NUM_UNITS)] | ||
| COLUMN_NAMES = FEATURES + ["CORRECT", "INCORRECT"] | ||
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| 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 | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,128 @@ | ||
| from constants import FEATURES | ||
|
pqz317 marked this conversation as resolved.
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|
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| import numpy as np | ||
| import pandas as pd | ||
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| 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) | ||
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| 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"] | ||
|
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| 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 | ||
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| 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 | ||
|
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| 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 | ||
| """ | ||
|
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| units = np.unique(spike_times.UnitID.values) | ||
| time_stamp_max = int(spike_times.SpikeTime.max()) + 1 | ||
|
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| num_time_bins = int(time_stamp_max/bin_size) + 1 | ||
| bins = np.arange(num_time_bins) * bin_size | ||
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| 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 | ||
|
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| 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 | ||
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| 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 | ||
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| Returns: | ||
| DataFrame with num_trials length, columns: TrialNumber, | ||
| IntervalStartTime, IntervalEndTime | ||
| """ | ||
| trial_event_times = behavioral_data[["TrialNumber", event]] | ||
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| 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 | ||
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| 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 | ||
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| 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()) | ||
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