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added colab link
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‎content/assignments/Assignment_3:Context_Maintenance_and_Retrieval_Model/cmr_assignment_template.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": "1a163c99",
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"metadata": {},
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"source": [
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"# Submission Template\n",
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"\n",
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"This notebook provides a suggested starter template for completing the [CMR model assignment](https://contextlab.github.io/memory-models-course/assignments/Assignment_3%3AContext_Maintenance_and_Retrieval_Model/README.html).\n",
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"\n",
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"You should submit your assignment by uploading your completed notebook to [Canvas](https://canvas.dartmouth.edu/courses/71051/assignments/517355). Please ensure that your notebook runs without errors in [Google Colaboratory](https://colab.research.google.com/)."
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]
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "view-in-github",
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"colab_type": "text"
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},
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"source": [
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"<a href=\"https://colab.research.google.com/github/ContextLab/memory-models-course/blob/main/content/assignments/Assignment_3%3AContext_Maintenance_and_Retrieval_Model/cmr_assignment_template.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1a163c99",
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"metadata": {
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"id": "1a163c99"
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},
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"source": [
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"# Submission Template\n",
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"\n",
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"This notebook provides a suggested starter template for completing the [CMR model assignment](https://contextlab.github.io/memory-models-course/assignments/Assignment_3%3AContext_Maintenance_and_Retrieval_Model/README.html).\n",
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"\n",
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"You should submit your assignment by uploading your completed notebook to [Canvas](https://canvas.dartmouth.edu/courses/71051/assignments/517355). Please ensure that your notebook runs without errors in [Google Colaboratory](https://colab.research.google.com/)."
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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": 1,
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"id": "137eb31f",
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"metadata": {
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"id": "137eb31f"
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},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"\n",
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"import requests\n",
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"import os\n",
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"\n",
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"from scipy.io import loadmat\n",
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"from tempfile import NamedTemporaryFile"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1539bcff",
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"metadata": {
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"id": "1539bcff"
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},
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"source": [
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"Download the stimuli and behavioral data, returned as a dictionary with the following fields:\n",
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" - 'words': a list of 1297 strings (one per word in the stimulus pool)\n",
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" - 'sem_mat': a 1297 x 1297 NumPy array of semantic similarities (range: -1 to 1) between every pair of words in the stimulus pool\n",
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" - 'presented_items': a number-of-trials by list-length array of items for each presented list (represented using 0-indexed indices in the word pool)\n",
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" - 'recalled_items': a number-of-trials by max-number-of-recalled-items array of recalled items from each list (represented using 0-indexed indices in the word pool). -1s correspond to extra-list intrusions. Trials are right-padded with nans as needed.\n",
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" - 'task': a number-of-trials by list-length array of task labels for each presented item (0: size task; 1: animacy task)\n",
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" - 'session': session labels for each trial (a NumPy array of length number-of-trials)\n",
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" - 'subject': subject labels for each trial (a NumPy array of length number-of-trials)\n",
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" - 'list_type': list type labels for each trial (a NumPy array of length number-of-trials; 0: all items studied using the size task; 1: all items studied using the animacy task; 2: task-shift list)\n",
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" - 'list_length': a scalar value containing the list length (an integer)"
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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": 2,
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"id": "b32a7915",
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"metadata": {
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"id": "b32a7915"
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},
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"outputs": [],
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"source": [
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"def load_data():\n",
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" # Download the files\n",
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" base_url = \"https://raw.githubusercontent.com/ContextLab/memory-models-course/refs/heads/main/content/assignments/Assignment_3%3AContext_Maintenance_and_Retrieval_Model/PolyEtal09%20data/\"\n",
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"\n",
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" # download the stimuli\n",
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" response = requests.get(base_url + \"stimuli.mat\")\n",
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" response.raise_for_status()\n",
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" with NamedTemporaryFile(delete=False) as temp_file:\n",
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" temp_file.write(response.content)\n",
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" stimuli_data = loadmat(temp_file.name)\n",
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" words = [str(x[0][0]) for x in stimuli_data['tfr_wp']]\n",
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" sem_mat = stimuli_data['sem_mat']\n",
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" os.remove(temp_file.name)\n",
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"\n",
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" # download the behavioral data\n",
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" response = requests.get(base_url + \"behavior.mat\")\n",
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" response.raise_for_status()\n",
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" with NamedTemporaryFile(delete=False) as temp_file:\n",
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" temp_file.write(response.content)\n",
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" behavioral_data = loadmat(temp_file.name)\n",
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" presented_items = behavioral_data['data'][0][0][0][0][0]['pres_itemnos']\n",
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" recalled_items = behavioral_data['data'][0][0][0][0][0]['rec_itemnos']\n",
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" task = behavioral_data['data'][0][0][0][0][0]['pres_task']\n",
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" session = behavioral_data['data'][0][0][0][0][0]['session'].flatten()\n",
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" subject = behavioral_data['data'][0][0][0][0][0]['subject'].flatten()\n",
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" list_type = behavioral_data['data'][0][0][0][0][0]['listType'].flatten()\n",
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" os.remove(temp_file.name)\n",
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"\n",
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" return {'words': words,\n",
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" 'sem_mat': sem_mat,\n",
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" 'presented_items': presented_items - 1,\n",
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" 'recalled_items': recalled_items - 1,\n",
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" 'task': task,\n",
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" 'session': session,\n",
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" 'subject': subject,\n",
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" 'list_type': list_type,\n",
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" 'list_length': int(behavioral_data['data'][0][0][0][0][0]['listLength'].flatten()[0])}\n",
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"\n",
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"data = load_data()"
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]
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},
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{
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"cell_type": "code",
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"source": [],
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"metadata": {
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"id": "IjVqOOsZEM4q"
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},
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"id": "IjVqOOsZEM4q",
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"execution_count": null,
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"outputs": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "memory-course",
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"language": "python",
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"name": "memory-course"
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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": "3.11.0"
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},
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"colab": {
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"provenance": [],
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"include_colab_link": true
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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": 3,
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"id": "137eb31f",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"\n",
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"import requests\n",
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"import os\n",
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"\n",
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"from scipy.io import loadmat\n",
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"from tempfile import NamedTemporaryFile"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1539bcff",
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"metadata": {},
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"source": [
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"Download the stimuli and behavioral data, returned as a dictionary with the following fields:\n",
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" - 'words': a list of 1297 strings (one per word in the stimulus pool)\n",
41-
" - 'sem_mat': a 1297 x 1297 NumPy array of semantic similarities (range: -1 to 1) between every pair of words in the stimulus pool\n",
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" - 'presented_items': a number-of-trials by list-length array of items for each presented list (represented using 0-indexed indices in the word pool)\n",
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" - 'recalled_items': a number-of-trials by max-number-of-recalled-items array of recalled items from each list (represented using 0-indexed indices in the word pool). -1s correspond to extra-list intrusions. Trials are right-padded with nans as needed.\n",
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" - 'task': a number-of-trials by list-length array of task labels for each presented item (0: size task; 1: animacy task)\n",
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" - 'session': session labels for each trial (a NumPy array of length number-of-trials)\n",
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" - 'subject': subject labels for each trial (a NumPy array of length number-of-trials)\n",
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" - 'list_type': list type labels for each trial (a NumPy array of length number-of-trials; 0: all items studied using the size task; 1: all items studied using the animacy task; 2: task-shift list)\n",
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" - 'list_length': a scalar value containing the list length (an integer)"
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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": 108,
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"id": "b32a7915",
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"metadata": {},
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"outputs": [],
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"source": [
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"def load_data():\n",
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" # Download the files\n",
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" base_url = \"https://raw.githubusercontent.com/ContextLab/memory-models-course/refs/heads/main/content/assignments/Assignment_3%3AContext_Maintenance_and_Retrieval_Model/PolyEtal09%20data/\"\n",
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"\n",
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" # download the stimuli\n",
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" response = requests.get(base_url + \"stimuli.mat\")\n",
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" response.raise_for_status()\n",
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" with NamedTemporaryFile(delete=False) as temp_file:\n",
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" temp_file.write(response.content)\n",
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" stimuli_data = loadmat(temp_file.name)\n",
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" words = [str(x[0][0]) for x in stimuli_data['tfr_wp']]\n",
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" sem_mat = stimuli_data['sem_mat']\n",
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" os.remove(temp_file.name)\n",
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"\n",
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" # download the behavioral data\n",
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" response = requests.get(base_url + \"behavior.mat\")\n",
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" response.raise_for_status()\n",
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" with NamedTemporaryFile(delete=False) as temp_file:\n",
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" temp_file.write(response.content)\n",
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" behavioral_data = loadmat(temp_file.name)\n",
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" presented_items = behavioral_data['data'][0][0][0][0][0]['pres_itemnos']\n",
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" recalled_items = behavioral_data['data'][0][0][0][0][0]['rec_itemnos']\n",
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" task = behavioral_data['data'][0][0][0][0][0]['pres_task']\n",
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" session = behavioral_data['data'][0][0][0][0][0]['session'].flatten()\n",
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" subject = behavioral_data['data'][0][0][0][0][0]['subject'].flatten()\n",
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" list_type = behavioral_data['data'][0][0][0][0][0]['listType'].flatten() \n",
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" os.remove(temp_file.name)\n",
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"\n",
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" return {'words': words,\n",
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" 'sem_mat': sem_mat,\n",
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" 'presented_items': presented_items - 1,\n",
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" 'recalled_items': recalled_items - 1,\n",
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" 'task': task,\n",
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" 'session': session,\n",
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" 'subject': subject,\n",
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" 'list_type': list_type,\n",
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" 'list_length': int(behavioral_data['data'][0][0][0][0][0]['listLength'].flatten()[0])}\n",
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"\n",
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"data = load_data()"
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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": "memory-course",
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"language": "python",
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"name": "memory-course"
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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": "3.11.0"
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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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"nbformat": 4,
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"nbformat_minor": 5
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}

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