Adding statistical tests, code to make the tiled images for the brain diffuser failed cases, and updating data filtering criterion to match prolifics guidance (#9)

- Adding statistical tests, code to make the tiled images for the brain diffuser failed cases, and updating data filtering criterion to match prolifics guidance (b6d55b428d616ecbac59a4ff51805225c5bacddc)


Co-authored-by: Reese Kneeland <[email protected]>

human_trials_mindeye2.ipynb

@@ -0,0 +1,412 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os, sys, shutil\n",
+    "from tqdm import tqdm\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib as plt\n",
+    "from PIL import Image\n",
+    "from matplotlib.lines import Line2D\n",
+    "import matplotlib as mpl\n",
+    "import math\n",
+    "import matplotlib.image as mpimg\n",
+    "import random\n",
+    "from datetime import datetime\n",
+    "from torchvision import transforms\n",
+    "import torch\n",
+    "from scipy.stats import binom_test\n",
+    "# os.chdir(\"..\")\n",
+    "experiment_version = 4\n",
+    "os.makedirs(f\"stimuli_v{experiment_version}\", exist_ok=True)\n",
+    "os.makedirs(f\"responses_v{experiment_version}\", exist_ok=True)\n",
+    "os.makedirs(f\"dataframes_v{experiment_version}\", exist_ok=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# CREATE EXPERIMENT DATAFRAME AND TRIAL FILES FOR MEADOWS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Experiment column key:\n",
+    "# 1: Experiment 1, mindeye vs second sight\n",
+    "# 2: Experiment 2, second sight two way identification\n",
+    "# 3: Experiment 3, mental imagery two way identification\n",
+    "df_exp = pd.DataFrame(columns=[\"experiment\", \"stim1\", \"stim2\", \"stim3\", \"sample\", \"subject\", \"target_on_left\", \"catch_trial\", \"rep\"])\n",
+    "i=0\n",
+    "random_count = 0\n",
+    "gt_tensor_block = torch.load(\"raw_stimuli/all_images_425.pt\")\n",
+    "for subj in [1,2,5,7]: #1,2,5,7\n",
+    "    subject_enhanced_recons_40 = torch.load(f\"raw_stimuli/final_subj0{subj}_pretrained_40sess_24bs_all_enhancedrecons.pt\")\n",
+    "    subject_unclip_recons_40 = torch.load(f\"raw_stimuli/final_subj0{subj}_pretrained_40sess_24bs_all_recons.pt\")\n",
+    "    subject_enhanced_recons_1 = torch.load(f\"raw_stimuli/final_subj0{subj}_pretrained_1sess_24bs_all_enhancedrecons.pt\")\n",
+    "    subject_braindiffuser_recons_1 = torch.load(f\"raw_stimuli/subj0{subj}_brain_diffuser_750_all_recons.pt\")\n",
+    "    #Experiment 1, mindeye two way identification\n",
+    "    random_indices = random.sample(range(1000), 300)\n",
+    "    for sample in tqdm(random_indices):\n",
+    "        \n",
+    "        # Get random sample to compare against\n",
+    "        random_number = random.choice([x for x in range(1000) if x != sample])\n",
+    "        # Extract the stimulus images from tensor blocks and save as pngs to stimuli folder\n",
+    "        gt_sample = transforms.ToPILImage()(gt_tensor_block[sample])\n",
+    "        sample_enhanced_recons_40 = transforms.ToPILImage()(subject_enhanced_recons_40[sample]).resize((425,425))\n",
+    "        random_enhanced_recons_40 = transforms.ToPILImage()(subject_enhanced_recons_40[random_number]).resize((425,425))\n",
+    "        sample_enhanced_recons_40.save(f\"stimuli_v{experiment_version}/{sample}_subject{subj}_mindeye_enhanced_40.png\")\n",
+    "        random_enhanced_recons_40.save(f\"stimuli_v{experiment_version}/{random_number}_subject{subj}_mindeye_enhanced_40.png\")\n",
+    "        gt_sample.save(f\"stimuli_v{experiment_version}/{sample}_ground_truth.png\")\n",
+    "        \n",
+    "        # Configure stimuli names and order in experiment dataframe\n",
+    "        sample_names = [f\"{random_number}_subject{subj}_mindeye_enhanced_40\", f\"{sample}_subject{subj}_mindeye_enhanced_40\"]\n",
+    "        order = random.randrange(2)\n",
+    "        left_sample = sample_names.pop(order)\n",
+    "        right_sample = sample_names.pop()\n",
+    "        gt_sample = f\"{sample}_ground_truth\"\n",
+    "        df_exp.loc[i] = {\"experiment\" : 1, \"stim1\" : gt_sample, \"stim2\" : left_sample, \"stim3\" : right_sample, \"sample\" : sample, \"subject\" : subj, \n",
+    "                         \"target_on_left\" : order == 1, \"catch_trial\" : None, \"rep\" : 0}\n",
+    "        i+=1\n",
+    "        \n",
+    "    #Experiment 2, refined vs unrefined\n",
+    "    random_indices = random.sample(range(1000), 300)\n",
+    "    for sample in tqdm(random_indices):\n",
+    "        \n",
+    "        # Extract the stimulus images from tensor blocks and save as pngs to stimuli folder\n",
+    "        gt_sample = transforms.ToPILImage()(gt_tensor_block[sample])\n",
+    "        sample_enhanced_recons_40 = transforms.ToPILImage()(subject_enhanced_recons_40[sample]).resize((425,425))\n",
+    "        sample_unclip_recons_40 = transforms.ToPILImage()(subject_unclip_recons_40[sample]).resize((425,425))\n",
+    "        sample_enhanced_recons_40.save(f\"stimuli_v{experiment_version}/{sample}_subject{subj}_mindeye_enhanced_40.png\")\n",
+    "        sample_unclip_recons_40.save(f\"stimuli_v{experiment_version}/{sample}_subject{subj}_mindeye_unclip_40.png\")\n",
+    "        gt_sample.save(f\"stimuli_v{experiment_version}/{sample}_ground_truth.png\")\n",
+    "        \n",
+    "        # Configure stimuli names and order in experiment dataframe\n",
+    "        sample_names = [f\"{sample}_subject{subj}_mindeye_unclip_40\", f\"{sample}_subject{subj}_mindeye_enhanced_40\"]\n",
+    "        order = random.randrange(2)\n",
+    "        left_sample = sample_names.pop(order)\n",
+    "        right_sample = sample_names.pop()\n",
+    "        gt_sample = f\"{sample}_ground_truth\"\n",
+    "        df_exp.loc[i] = {\"experiment\" : 2, \"stim1\" : gt_sample, \"stim2\" : left_sample, \"stim3\" : right_sample, \"sample\" : sample, \"subject\" : subj, \n",
+    "                         \"target_on_left\" : order == 1, \"catch_trial\" : None, \"rep\" : 0}\n",
+    "        i+=1\n",
+    "        \n",
+    "    #Experiment 3, refined 1 session vs brain diffuser 1 session\n",
+    "    random_indices = random.sample(range(1000), 300)\n",
+    "    for sample in tqdm(random_indices):\n",
+    "        \n",
+    "        # Extract the stimulus images from tensor blocks and save as pngs to stimuli folder\n",
+    "        gt_sample = transforms.ToPILImage()(gt_tensor_block[sample])\n",
+    "        sample_enhanced_recons_1 = transforms.ToPILImage()(subject_enhanced_recons_1[sample]).resize((425,425))\n",
+    "        sample_braindiffuser_1 = transforms.ToPILImage()(subject_braindiffuser_recons_1[sample]).resize((425,425))\n",
+    "        sample_enhanced_recons_1.save(f\"stimuli_v{experiment_version}/{sample}_subject{subj}_mindeye_enhanced_1.png\")\n",
+    "        sample_braindiffuser_1.save(f\"stimuli_v{experiment_version}/{sample}_subject{subj}_braindiffuser_1.png\")\n",
+    "        gt_sample.save(f\"stimuli_v{experiment_version}/{sample}_ground_truth.png\")\n",
+    "        \n",
+    "        # Configure stimuli names and order in experiment dataframe\n",
+    "        sample_names = [f\"{sample}_subject{subj}_braindiffuser_1\", f\"{sample}_subject{subj}_mindeye_enhanced_1\"]\n",
+    "        order = random.randrange(2)\n",
+    "        left_sample = sample_names.pop(order)\n",
+    "        right_sample = sample_names.pop()\n",
+    "        gt_sample = f\"{sample}_ground_truth\"\n",
+    "        df_exp.loc[i] = {\"experiment\" : 3, \"stim1\" : gt_sample, \"stim2\" : left_sample, \"stim3\" : right_sample, \"sample\" : sample, \"subject\" : subj, \n",
+    "                         \"target_on_left\" : order == 1, \"catch_trial\" : None, \"rep\" : 0}\n",
+    "        i+=1\n",
+    "df_exp = df_exp.sample(frac=1)\n",
+    "print(len(df_exp))\n",
+    "print(df_exp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Check if all images are present in final stimuli folder\n",
+    "count_not_found = 0\n",
+    "stim_path = f\"stimuli_v{experiment_version}/\"\n",
+    "for index, row in df_exp.iterrows():\n",
+    "    if not (os.path.exists(f\"{stim_path}{row['stim1']}.png\")):\n",
+    "        print(f\"{row['stim1']}.png\")\n",
+    "        count_not_found += 1\n",
+    "    if not (os.path.exists(f\"{stim_path}{row['stim2']}.png\")):\n",
+    "        print(f\"{row['stim2']}.png\")\n",
+    "        count_not_found += 1\n",
+    "    if not (os.path.exists(f\"{stim_path}{row['stim3']}.png\")):\n",
+    "        print(f\"{row['stim3']}.png\")\n",
+    "        count_not_found += 1\n",
+    "print(count_not_found)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Add participant ID column\n",
+    "pIDs = []\n",
+    "for i in range(len(df_exp)):\n",
+    "    pIDs.append(i // 60)\n",
+    "df_exp.insert(0, \"pID\", pIDs)\n",
+    "print(len(df_exp[(df_exp['pID'] == 0)]))\n",
+    "#Add catch trials within each pID section\n",
+    "for pID in range(max(pIDs)):\n",
+    "    df_pid = df_exp[(df_exp['experiment'] == 1) & (df_exp['pID'] == pID)]\n",
+    "    \n",
+    "    # Ground truth catch trials\n",
+    "    gt_catch_trials = df_pid.sample(n=9)\n",
+    "    gt_catch_trials['catch_trial'] = \"ground_truth\"\n",
+    "    for index, row in gt_catch_trials.iterrows():\n",
+    "        \n",
+    "        order = random.randrange(2)\n",
+    "        ground_truth = row['stim1']\n",
+    "        stims = [row['stim2'], ground_truth]\n",
+    "        \n",
+    "        gt_catch_trials.at[index, 'stim2'] = stims.pop(order)\n",
+    "        gt_catch_trials.at[index, 'stim3'] = stims.pop()\n",
+    "        # Target on left here means the ground truth repeat is on the left\n",
+    "        gt_catch_trials.at[index, 'target_on_left'] = (order == 1)\n",
+    "        \n",
+    "    # repeated trial catch trials, first sample indices\n",
+    "    sampled_indices = df_pid.sample(n=9).index\n",
+    "    #mark the trials at these indices as catch trials\n",
+    "    df_exp.loc[sampled_indices]['catch_trial'] = \"repeat\"\n",
+    "    #create duplicate trials for these samples to repeat\n",
+    "    repeat_catch_trials_rep1 = df_exp.loc[sampled_indices].copy()\n",
+    "    repeat_catch_trials_rep2 = df_exp.loc[sampled_indices].copy()\n",
+    "    repeat_catch_trials_rep1['rep'] = 1\n",
+    "    repeat_catch_trials_rep2['rep'] = 2\n",
+    "    \n",
+    "    \n",
+    "    df_exp = pd.concat([df_exp, gt_catch_trials, repeat_catch_trials_rep1, repeat_catch_trials_rep2])\n",
+    "    \n",
+    "df_exp = df_exp.sample(frac=1).sort_values(by='pID', kind='mergesort')\n",
+    "print(len(df_exp))\n",
+    "print(len(df_exp[(df_exp['pID'] == 0)]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "df_exp.to_csv(f'dataframes_v{experiment_version}/experiment_v{experiment_version}.csv', index=False)\n",
+    "\n",
+    "df_exp_tsv = df_exp[['pID', 'stim1', 'stim2', 'stim3']].copy()\n",
+    "df_exp_tsv.to_csv(f\"dataframes_v{experiment_version}/meadow_trials_v{experiment_version}.tsv\", sep=\"\\t\", index=False, header=False) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# THE FOLLOWING CELLS ARE FOR PROCESSING RESPONSES"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "response_path = f\"responses_v{experiment_version}/\"\n",
+    "dataframe_path = f\"dataframes_v{experiment_version}/\"\n",
+    "df_experiment = pd.read_csv(dataframe_path + f\"experiment_v{experiment_version}.csv\")\n",
+    "response_version = \"2\"\n",
+    "df_responses = pd.read_csv(f\"{response_path}deployment_v{response_version}.csv\")\n",
+    "print(df_responses)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_responses.head()\n",
+    "df_trial = pd.DataFrame(columns=[\"experiment\", \"stim1\", \"stim2\", \"stim3\", \"sample\", \"subject\", \"target_on_left\", \"method\", \"catch_trial\", \"rep\", \"picked_left\", \"participant\"])\n",
+    "df_experiment['picked_left'] = None\n",
+    "for index, row in tqdm(df_responses.iterrows()):\n",
+    "    if row['label'] == row['stim2_id']:\n",
+    "        picked_left = True\n",
+    "    elif row['label'] == row['stim3_id']:\n",
+    "        picked_left = False\n",
+    "    else:\n",
+    "        print(\"Error\")\n",
+    "        break\n",
+    "    start_timestamp = row['time_trial_start']\n",
+    "    end_timestamp = row['time_trial_response']\n",
+    "    start = datetime.fromisoformat(start_timestamp.replace(\"Z\", \"+00:00\"))\n",
+    "    end = datetime.fromisoformat(end_timestamp.replace(\"Z\", \"+00:00\"))\n",
+    "    # Calculate the difference in seconds\n",
+    "    time_difference_seconds = (end - start).total_seconds()\n",
+    "    \n",
+    "    df_trial.loc[index] = df_experiment[(df_experiment['stim1'] == row['stim1_name']) & (df_experiment['stim2'] == row['stim2_name']) & (df_experiment['stim3'] == row['stim3_name'])].iloc[0]\n",
+    "    df_trial.loc[index, 'picked_left'] = picked_left\n",
+    "    df_trial.loc[index, 'participant'] = row['participation']\n",
+    "    df_trial.loc[index, 'response_time'] = time_difference_seconds\n",
+    "    \n",
+    "df_trial[\"picked_target\"] = df_trial[\"picked_left\"] == df_trial[\"target_on_left\"]\n",
+    "print(df_trial)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# number of participants\n",
+    "print(\"Total participants:\", len(df_trial[\"participant\"].unique()))\n",
+    "\n",
+    "gt_failures = df_trial[(df_trial['catch_trial'] == 'ground_truth') & (df_trial['picked_target'] == False)].groupby('participant').size()\n",
+    "# Identify participants who failed more than 1 ground truth catch trial\n",
+    "participants_to_remove_rule1 = gt_failures[gt_failures > 1].index.tolist()\n",
+    "print(\"Participants to remove 1:\", participants_to_remove_rule1)\n",
+    "\n",
+    "# Remove participants who failed the repeat catch trial, and gave different responses for identical trials\n",
+    "repeat_trials = df_trial[df_trial['rep'] > 0]\n",
+    "grouped_repeat_trials = repeat_trials.groupby(['stim1', 'stim2', 'stim3'])\n",
+    "participant_failures = {}\n",
+    "# Iterate through groups to check consistency in \"picked_target\" across repetitions\n",
+    "for _, group in grouped_repeat_trials:\n",
+    "    if group['picked_target'].nunique() != 1:  # Inconsistent \"picked_target\" within the group\n",
+    "        for participant in group['participant'].unique(): \n",
+    "            participant_failures[participant] = participant_failures.get(participant, 0) + 1\n",
+    "\n",
+    "# Identify participants who failed at least one set of trial repetitions\n",
+    "participants_to_remove_rule2 = [participant for participant, failures in participant_failures.items() if failures > 1]\n",
+    "print(\"Participants to remove 2:\", participants_to_remove_rule2)\n",
+    "\n",
+    "participants_to_remove = set(participants_to_remove_rule1).union(set(participants_to_remove_rule2))\n",
+    "filtered_df = df_trial[~df_trial['participant'].isin(participants_to_remove)]\n",
+    "print(\"Clean participants:\", len(filtered_df[\"participant\"].unique()))\n",
+    "print(len(df_trial), len(filtered_df))\n",
+    "print(participants_to_remove)\n",
+    "filtered_df.to_csv(f'{dataframe_path}filtered_responses_v{response_version}.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load filtered responses\n",
+    "filtered_df = pd.read_csv(f'{dataframe_path}filtered_responses_v{response_version}.csv')\n",
+    "# Filter out catch trials\n",
+    "df_trial_exp = filtered_df[(filtered_df['catch_trial'].isnull() & (filtered_df['rep'] == 0))]\n",
+    "\n",
+    "# Grab results from an individual experiment and print them out\n",
+    "df_trial_exp1 = df_trial_exp[df_trial_exp['experiment'] == 3]\n",
+    "\n",
+    "# Perform a binomial test\n",
+    "# The null hypothesis is that the probability of success is 0.5 (chance level)\n",
+    "p_value = binom_test(df_trial_exp1['picked_target'].sum(), n=len(df_trial_exp1['picked_target']), p=0.5, alternative='two-sided')\n",
+    "\n",
+    "print(\"Number of experiment trials:\", len(df_trial_exp1))\n",
+    "print(\"Success rate: \", len(df_trial_exp1[df_trial_exp1[\"picked_target\"]]) / len(df_trial_exp1))\n",
+    "print(f'P-value: {p_value}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import shutil\n",
+    "from PIL import Image, ImageDraw, ImageFont\n",
+    "\n",
+    "# Filter for experiment 3 rows where picked_target is false\n",
+    "df_exp3_failures = df_trial_exp[df_trial_exp['experiment'] == 3]\n",
+    "df_exp3_failures = df_exp3_failures[df_exp3_failures['picked_target'] == False]\n",
+    "\n",
+    "# Create the \"brain_diffuser_failures\" folder if it doesn't exist\n",
+    "os.makedirs(\"brain_diffuser_failures_tiled\", exist_ok=True)\n",
+    "\n",
+    "# Copy the stimuli from stimuli_v4 to the \"brain_diffuser_failures\" folder\n",
+    "# Set the dimensions for the concatenated image\n",
+    "width = 3 * 425\n",
+    "height = 450\n",
+    "\n",
+    "# Create a blank canvas for the concatenated image\n",
+    "concatenated_image = Image.new('RGB', (width, height), (255, 255, 255))\n",
+    "draw = ImageDraw.Draw(concatenated_image)\n",
+    "\n",
+    "# Set the font properties for the title captions\n",
+    "font = ImageFont.truetype(\"arial.ttf\", 16)\n",
+    "\n",
+    "# Iterate over the rows in df_exp3_failures\n",
+    "for index, row in df_exp3_failures.iterrows():\n",
+    "    # Get the paths for the stimuli images\n",
+    "    stim1_path = f\"stimuli_v4/{row['stim1']}.png\"\n",
+    "    stim2_path = f\"stimuli_v4/{row['stim2']}.png\"\n",
+    "    stim3_path = f\"stimuli_v4/{row['stim3']}.png\"\n",
+    "    \n",
+    "    # Open the stimuli images\n",
+    "    stim1_image = Image.open(stim1_path)\n",
+    "    stim2_image = Image.open(stim2_path)\n",
+    "    stim3_image = Image.open(stim3_path)\n",
+    "    \n",
+    "    # Resize the stimuli images to match the desired dimensions\n",
+    "    stim1_image = stim1_image.resize((425, 425))\n",
+    "    stim2_image = stim2_image.resize((425, 425))\n",
+    "    stim3_image = stim3_image.resize((425, 425))\n",
+    "    \n",
+    "    # Calculate the positions for the stimuli images\n",
+    "    x1 = 0\n",
+    "    x2 = 425\n",
+    "    x3 = 2 * 425\n",
+    "    y = 0\n",
+    "    \n",
+    "    # Paste the stimuli images onto the concatenated image\n",
+    "    concatenated_image.paste(stim1_image, (x1, y))\n",
+    "    concatenated_image.paste(stim2_image, (x2, y))\n",
+    "    concatenated_image.paste(stim3_image, (x3, y))\n",
+    "    \n",
+    "    # Add the title captions for each image\n",
+    "    draw.text((x1, y + 425), f\"Stim1 (GT): {row['stim1']}\", font=font, fill=(0, 0, 0))\n",
+    "    draw.text((x2, y + 425), f\"Stim2: {row['stim2']}\", font=font, fill=(0, 0, 0))\n",
+    "    draw.text((x3, y + 425), f\"Stim3: {row['stim3']}\", font=font, fill=(0, 0, 0))\n",
+    "\n",
+    "    # Save the concatenated image\n",
+    "    concatenated_image.save(f\"brain_diffuser_failures_tiled/{index}.png\")\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "SS",
+   "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": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}