From 3eed0b81f827f7b457ca2b5fc6c9447e53561910 Mon Sep 17 00:00:00 2001
From: clarkenj <natashajayne@gmail.com>
Date: Thu, 29 Feb 2024 14:20:17 -0500
Subject: [PATCH 01/25] first draft of pheno/qc matching

---
 code/filter_pheno_qc.py | 89 +++++++++++++++++++++++++++++++++++++++++
 1 file changed, 89 insertions(+)
 create mode 100644 code/filter_pheno_qc.py

diff --git a/code/filter_pheno_qc.py b/code/filter_pheno_qc.py
new file mode 100644
index 0000000..8ae8d25
--- /dev/null
+++ b/code/filter_pheno_qc.py
@@ -0,0 +1,89 @@
+"""Load phenotypic data for N datasets, filter according to scans that passed QC on specific column, and output a merged tsv with pheno and QC results.
+
+To run, needs:
+- rest_df.tsv, a TSV file of concatenated QC results, generated using qc_output/qc.ipynb
+- TSV of pheno data for each specificed dataset, generated using the corresponding script in this repo
+
+Author: Natasha Clarke; last edit 2024-02-29
+
+"""
+
+import argparse
+import pandas as pd
+from pathlib import Path
+
+
+def filter_pheno_qc(qc_df, pheno_df, which_qc_col, dataset):
+    pheno_df["participant_id"] = pheno_df["participant_id"].astype(str)
+    qc_df["participant_id"] = qc_df["participant_id"].astype(str)
+    qc_df_filtered = qc_df[qc_df["dataset"] == dataset]
+
+    merged_df = qc_df_filtered.merge(pheno_df, on="participant_id", how="left")
+
+    # Handle site columns
+    merged_df.drop(columns=["site_x"], inplace=True)
+    merged_df.rename(columns={"site_y": "site"}, inplace=True)
+
+    # Filter for passed QC
+    filtered_df = merged_df[merged_df[which_qc_col] == True]
+
+    # Select relevant columns
+    filtered_df = filtered_df[
+        [
+            "identifier",
+            "participant_id",
+            "ses",
+            "run",
+            "age",
+            "sex",
+            "site",
+            "proportion_kept",
+            "mean_fd_raw",
+            "diagnosis",
+            "dataset",
+        ]
+    ]
+
+    return filtered_df
+
+
+def create_master(rest_qc_p, datasets, which_qc_col):
+    # Load QC df once
+    qc_df = pd.read_csv(rest_qc_p, sep="\t")
+
+    # Process each dataset and create a master of scans which passed QC
+    master_df = pd.DataFrame()
+    for dataset in datasets:
+        pheno_p = Path(root_p / pheno_p_template.format(dataset=dataset))
+        pheno_df = pd.read_csv(pheno_p, sep="\t", dtype={"participant_id": str})
+        df = filter_pheno_qc(qc_df, pheno_df, which_qc_col, dataset)
+        master_df = pd.concat([master_df, df], ignore_index=True)
+
+    # Save master DataFrame to a file
+    master_df.to_csv(output_p / "passed_qc_master.tsv", sep="\t", index=False)
+
+    print(f"Data have been processed and output to {output_p}")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Filter phenotype data according to QC result"
+    )
+    parser.add_argument("--root", type=str, help="Root path for data")
+    parser.add_argument(
+        "--datasets", nargs="+", type=str, help="List of datasets to process"
+    )
+    parser.add_argument(
+        "--which_qc_col", type=str, help="Column for filtering QC. Default=pass_func_qc"
+    )
+
+    args = parser.parse_args()
+    datasets = args.datasets
+    which_qc_col = args.which_qc_col if args.which_qc_col else "pass_func_qc"
+
+    root_p = Path(args.root)
+    pheno_p_template = "wrangling-phenotype/outputs/{dataset}_pheno.tsv"
+    rest_qc_p = Path(root_p / "qc_output/rest_df.tsv")
+    output_p = Path(root_p / "wrangling-phenotype/outputs")
+
+    create_master(rest_qc_p, datasets, which_qc_col)

From a33c013367ea5aa396406ba7d2a6e068bd89638a Mon Sep 17 00:00:00 2001
From: clarkenj <natashajayne@gmail.com>
Date: Thu, 29 Feb 2024 14:38:46 -0500
Subject: [PATCH 02/25] added function to filter by diagnosis

---
 code/filter_pheno_qc.py | 23 ++++++++++++++++++-----
 1 file changed, 18 insertions(+), 5 deletions(-)

diff --git a/code/filter_pheno_qc.py b/code/filter_pheno_qc.py
index 8ae8d25..f48a8d8 100644
--- a/code/filter_pheno_qc.py
+++ b/code/filter_pheno_qc.py
@@ -59,10 +59,11 @@ def create_master(rest_qc_p, datasets, which_qc_col):
         df = filter_pheno_qc(qc_df, pheno_df, which_qc_col, dataset)
         master_df = pd.concat([master_df, df], ignore_index=True)
 
-    # Save master DataFrame to a file
-    master_df.to_csv(output_p / "passed_qc_master.tsv", sep="\t", index=False)
+    return master_df
 
-    print(f"Data have been processed and output to {output_p}")
+
+def filter_diagnoses(df, diagnoses):
+    return df[df["diagnosis"].isin(diagnoses)]
 
 
 if __name__ == "__main__":
@@ -76,14 +77,26 @@ def create_master(rest_qc_p, datasets, which_qc_col):
     parser.add_argument(
         "--which_qc_col", type=str, help="Column for filtering QC. Default=pass_func_qc"
     )
+    parser.add_argument(
+        "--diagnoses", nargs="+", type=str, help="Diagnoses of interest"
+    )
 
     args = parser.parse_args()
     datasets = args.datasets
-    which_qc_col = args.which_qc_col if args.which_qc_col else "pass_func_qc"
+    which_qc_col = args.which_qc_col or "pass_func_qc"
+    diagnoses = args.diagnoses if args.diagnoses else None
 
     root_p = Path(args.root)
     pheno_p_template = "wrangling-phenotype/outputs/{dataset}_pheno.tsv"
     rest_qc_p = Path(root_p / "qc_output/rest_df.tsv")
     output_p = Path(root_p / "wrangling-phenotype/outputs")
 
-    create_master(rest_qc_p, datasets, which_qc_col)
+    master_df = create_master(rest_qc_p, datasets, which_qc_col)
+    sub_df = filter_diagnoses(master_df, diagnoses)
+
+    if diagnoses:
+        sub_df.to_csv(output_p / "passed_qc_master.tsv", sep="\t", index=False)
+    else:
+        master_df.to_csv(output_p / "passed_qc_master.tsv", sep="\t", index=False)
+
+    print(f"Data have been processed and output to {output_p}")

From c178b00bc35c28112ed93d5592a726963a5692c3 Mon Sep 17 00:00:00 2001
From: clarkenj <natashajayne@gmail.com>
Date: Thu, 29 Feb 2024 16:19:06 -0500
Subject: [PATCH 03/25] fixed error

---
 code/filter_pheno_qc.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/code/filter_pheno_qc.py b/code/filter_pheno_qc.py
index f48a8d8..2688aeb 100644
--- a/code/filter_pheno_qc.py
+++ b/code/filter_pheno_qc.py
@@ -92,10 +92,11 @@ def filter_diagnoses(df, diagnoses):
     output_p = Path(root_p / "wrangling-phenotype/outputs")
 
     master_df = create_master(rest_qc_p, datasets, which_qc_col)
-    sub_df = filter_diagnoses(master_df, diagnoses)
 
     if diagnoses:
+        sub_df = filter_diagnoses(master_df, diagnoses)
         sub_df.to_csv(output_p / "passed_qc_master.tsv", sep="\t", index=False)
+
     else:
         master_df.to_csv(output_p / "passed_qc_master.tsv", sep="\t", index=False)
 

From 1f52dabcf2c8a877b5659586d836227b1f89ffde Mon Sep 17 00:00:00 2001
From: clarkenj <natashajayne@gmail.com>
Date: Thu, 29 Feb 2024 16:47:41 -0500
Subject: [PATCH 04/25] added first draft qc report

---
 code/filtered_qc_report.ipynb | 602 ++++++++++++++++++++++++++++++++++
 1 file changed, 602 insertions(+)
 create mode 100644 code/filtered_qc_report.ipynb

diff --git a/code/filtered_qc_report.ipynb b/code/filtered_qc_report.ipynb
new file mode 100644
index 0000000..fc4853e
--- /dev/null
+++ b/code/filtered_qc_report.ipynb
@@ -0,0 +1,602 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0a7e29ba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import pandas as pd\n",
+    "\n",
+    "from pathlib import Path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2ddd34a9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def boxplot_diagnosis_dataset(df, y_column):\n",
+    "    plt.figure(figsize=(12, 8))\n",
+    "    sns.boxplot(x='diagnosis', y=y_column, hue='dataset', data=df, palette=\"colorblind\")\n",
+    "    plt.title(f'{y_column} by diagnosis across datasets')\n",
+    "    plt.xlabel('Diagnosis')\n",
+    "    plt.ylabel(y_column)\n",
+    "    plt.xticks(rotation=45)\n",
+    "    plt.legend(title='dataset')\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "26a1e863",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def histogram_diagnosis_dataset(df, y_column):\n",
+    "    g = sns.FacetGrid(df, col=\"dataset\", row=\"diagnosis\", hue=\"diagnosis\", \n",
+    "                  height=4, sharex=True, sharey=False, palette=\"colorblind\")\n",
+    "\n",
+    "    # Map the plt.hist function to each subplot\n",
+    "    g.map(plt.hist, y_column, bins=10, edgecolor=\"k\").add_legend()\n",
+    "\n",
+    "    g.fig.suptitle(f'{y_column} by diagnosis across datasets', fontsize=16, y=1.05)\n",
+    "    g.set_axis_labels(y_column, 'Count')\n",
+    "    g.set_titles(\"{col_name}\", size=14)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "00946003",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def boxplot_diagnosis_sex(df, y_column, group):\n",
+    "    plt.figure(figsize=(6, 4))\n",
+    "    sns.boxplot(data=df[df['diagnosis'] == group], x='sex', y=y_column, palette=\"colorblind\")\n",
+    "    plt.title(f'{y_column} by diagnosis and sex ({group} only)')\n",
+    "    plt.xlabel('Sex')\n",
+    "    plt.ylabel(y_column)\n",
+    "    plt.xticks(rotation=45)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ce11f4d",
+   "metadata": {},
+   "source": [
+    "# Load TSV of scans passed QC, generated using script filter_pheno_qc.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "981dd01c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "passed_qc_p = Path(\"/home/neuromod/wrangling-phenotype/outputs/passed_qc_master.tsv\")\n",
+    "df = pd.read_csv(passed_qc_p, sep=\"\\t\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "3ea537a0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dataset\n",
+       "cobre        122\n",
+       "ds000030     241\n",
+       "hcpep        697\n",
+       "srpbs       1067\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# How many scans passed QC?\n",
+    "df.groupby('dataset').size()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "211de586",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dataset\n",
+       "cobre        122\n",
+       "ds000030     241\n",
+       "hcpep        179\n",
+       "srpbs       1067\n",
+       "Name: participant_id, dtype: int64"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# How many individual participants passed QC?\n",
+    "df.groupby('dataset').nunique()['participant_id']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "28ab4a0a",
+   "metadata": {},
+   "source": [
+    "# Take a look at diagnoses"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "695cfe74",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Optionally for HCP-EP, replace PSYC diagnosis with SCHZ for ease\n",
+    "df.loc[df['diagnosis'] == 'PSYC', 'diagnosis'] = 'SCHZ'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "e0e99a72",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>dataset</th>\n",
+       "      <th>cobre</th>\n",
+       "      <th>ds000030</th>\n",
+       "      <th>hcpep</th>\n",
+       "      <th>srpbs</th>\n",
+       "      <th>TOTAL</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>diagnosis</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>ADHD</th>\n",
+       "      <td>0</td>\n",
+       "      <td>37</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ASD</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>117</td>\n",
+       "      <td>117</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>BIPOLAR</th>\n",
+       "      <td>0</td>\n",
+       "      <td>49</td>\n",
+       "      <td>0</td>\n",
+       "      <td>33</td>\n",
+       "      <td>82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>CON</th>\n",
+       "      <td>67</td>\n",
+       "      <td>111</td>\n",
+       "      <td>57</td>\n",
+       "      <td>605</td>\n",
+       "      <td>823</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>DYSTHYMIA</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MDD</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>167</td>\n",
+       "      <td>167</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>OTHER</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>14</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PAIN</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>22</td>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SCHZ</th>\n",
+       "      <td>53</td>\n",
+       "      <td>44</td>\n",
+       "      <td>120</td>\n",
+       "      <td>106</td>\n",
+       "      <td>323</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>TOTAL</th>\n",
+       "      <td>120</td>\n",
+       "      <td>241</td>\n",
+       "      <td>177</td>\n",
+       "      <td>1067</td>\n",
+       "      <td>1540</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "dataset    cobre  ds000030  hcpep  srpbs  TOTAL\n",
+       "diagnosis                                      \n",
+       "ADHD           0        37      0      0     37\n",
+       "ASD            0         0      0    117    117\n",
+       "BIPOLAR        0        49      0     33     82\n",
+       "CON           67       111     57    605    823\n",
+       "DYSTHYMIA      0         0      0      3      3\n",
+       "MDD            0         0      0    167    167\n",
+       "OTHER          0         0      0     14     14\n",
+       "PAIN           0         0      0     22     22\n",
+       "SCHZ          53        44    120    106    323\n",
+       "TOTAL        120       241    177   1067   1540"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create pivot table of diagnoses per dataset, and totals\n",
+    "def count_unique(series):\n",
+    "    return series.nunique()\n",
+    "\n",
+    "pivot_table = df.pivot_table(index='diagnosis', columns='dataset', values='participant_id', aggfunc=count_unique, fill_value=0, margins=True, margins_name='TOTAL')\n",
+    "pivot_table"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "0c21e97b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set_theme(style=\"white\")\n",
+    "\n",
+    "# Create a heatmap to show diagnoses numbers per dataset\n",
+    "plt.figure(figsize=(10, 8))\n",
+    "sns.heatmap(pivot_table, annot=True, fmt=\"d\", cmap=\"YlGnBu\")\n",
+    "plt.title('Diagnoses by dataset (unique participants)')\n",
+    "plt.ylabel('Diagnosis')\n",
+    "plt.xlabel('Dataset')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "4bc0ad91",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Mean FD across diagnoses\n",
+    "sns.boxplot(data=df, x='diagnosis', y='mean_fd_raw', palette=\"colorblind\")\n",
+    "\n",
+    "plt.xticks(rotation=45) \n",
+    "plt.title('Mean FD across diagnoses')\n",
+    "plt.xlabel('Diagnosis')\n",
+    "plt.ylabel('mean_fd_raw')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "5e68fdbb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Proportion kept across diagnoses\n",
+    "sns.boxplot(data=df, x='diagnosis', y='proportion_kept', palette=\"colorblind\")\n",
+    "\n",
+    "plt.xticks(rotation=45) \n",
+    "plt.title('Proportion of scans kept across diagnoses')\n",
+    "plt.xlabel('Diagnosis')\n",
+    "plt.ylabel('proportion_kept')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0bc74390",
+   "metadata": {},
+   "source": [
+    "# Generate more detailed plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "985136db",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# If needed, select subset of diagnoses\n",
+    "diagnoses = ['CON', 'SCHZ']\n",
+    "df = df.loc[df['diagnosis'].isin(diagnoses)].copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "d027f13d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "boxplot_diagnosis_dataset(df, 'mean_fd_raw')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "0c27a13f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "boxplot_diagnosis_dataset(df, 'age')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "65dde205",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "boxplot_diagnosis_dataset(df, 'proportion_kept')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "d84a4e78",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1696.38x800 with 8 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "histogram_diagnosis_dataset(df, 'mean_fd_raw')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "fa698608",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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VR6r5jTfe0HfffWexjldycrKOHTumdevWSbq7rldaWpqkv69zo0aNMqznZWdn98CpC++d5i+dp6enpLtrPaXbu3evJOm1117L9Divv/66JOn333+XJJUrV04FCxbUwYMH1apVK82fP1+nT5+WdPd70rJlyweGaO7u7nr++ed14cIFtWjRQjNmzFBERIQkqVSpUmrTpo3FVHz3ExQUpOnTp1uc6/bt2zp06JA5lLz3PZtef8OGDTNdG+3HH3/UwoULLYJUSRbrH0l313U6cOCAbGxs1LRp0wzHsbe3N78f9uzZI0ny8/OTJM2cOVO9evXSunXrzKFY48aN9dZbb6lQoUKSZJ4ec+PGjXrvvfe0bNkyxcbGSpLq1Kmjt99+2zx93v38m/fr4743H2To0KGaMmWKRSAYHx+vsLAwHThwwHzsdOnX57///a9Gjhyp0NBQ83u0Xbt2atq0qTkoTA8A69Spk+m504PO9Gv/IH5+fkpMTFRAQIAmT56sQ4cO6c6dO3J3d1dQUJBq1Kjx0GMAAAAATwPWrAIAAECOce7cOS1cuFB//PGHTp8+rfj4eEkyf7BvMpnMfS9cuCDpbjCVmeeee05//PFHhv4RERHm9WLu58KFC/Lw8HhovfcbXZI+OiN9hIok1atXT15eXtq9e7cuXryoIkWKaM2aNUpLS9Pbb7/90HOlHyuzoE2SihUr9tBjpLtx44aWLl2qHTt26NSpU7p48aLu3LljEaCkX+v063a/cOJ+11+SChQokKEtPTxIH6Ui/f3a7nc9019beoiYN29eTZw4Uf369dOhQ4d06NAhSVLx4sXVuHFjtW7dOtM1y+41fvx49erVSydPntTYsWM1duxYeXp6qlGjRmrdurXKly//wP3TXblyRYsWLdKePXt0+vRpXblyRSaTKdP3bHr9Dwt6/uneAFeSrl69qpSUFBUqVMhiVN+9/nnNqlSposGDB+ubb77Rpk2btGnTJtnY2KhixYpq2rSpWrVqZT5P0aJF9dVXX+mzzz7Trl27tGvXLkl3Q8ImTZqoTZs25tDxfv7t+/Vx3psPExERoUWLFik8PFxnz55VQkKCpMx/ngQHB+vkyZNavXq15s+fr/nz58vBwUG1a9fWG2+8oebNm5vfu+n/JkaPHq3Ro0ff9/zpAd+DjBw5Uh9++KHCw8M1adIkTZo0SQULFlSDBg30zjvvqGbNmo/0WgEAAABrI6wCAABAjrB+/XoNHDhQqampKlmypGrVqqWyZcuqYsWKunPnjnr27GnRPyUlRZJl6HGvf36gnd7vueeeU/Xq1R9YS758+R6p5vSp2e537nunhrO1tVVAQIC+++47rV+/Xp07d9aaNWtkZ2ent95666Hnymwkzr3+Oe3h/URFRaljx46Ki4uTu7u7KlasqNdee00vvviiatasmWH6u/TrfL+A4FGDgwdJP8b9XmP6dgcHB3NbrVq1tHXrVoWGhmrbtm0KCwvTuXPnNGfOHC1YsEATJkx44NSIPj4+2rBhg8LCwrRt2zbt2rVLp0+f1pIlS7R06VJ9+umnateu3QPr3rNnj7p3765bt26paNGiqly5ssqUKaPy5cvL29tb77zzjkX/1NTUR7oe/5Q+oifdw67XvX3uvWYdO3ZU8+bNtWXLFoWGhmrPnj0KDw9XeHi45s2bp0WLFql48eKS7o5mq1+/vrZt26Zff/1Vu3fvVlRUlKKiojRv3jzNnTs3w+jFe/2b9+vjvjcfZObMmfr6668l3R2ZVr9+fZUrV06VKlXSmTNnNHz4cIv+efLk0ZdffqkePXro559/1o4dO/THH38oNDRUoaGhWrZsmebMmSMHBwfzz5JatWo9MLT75wi5zHh5eWn58uXav3+/tmzZot9++00RERFas2aN1qxZo65du6pfv36P/LoBAAAAayGsAgAAwDPv5s2b+uyzzyRJU6dOzRAy/HOdJ+nuh7ynTp3ShQsXVK5cuQzb/zmqIX2klJeXl8aOHftE6r535NS9oqOjzee6V2BgoKZNm6ZNmzbJ399fUVFRatSo0SON4ko/VvqxH7WWf/r8888VFxenrl27qk+fPhZByLVr1+573vTRJP/0KKNHHsbT01OnTp3S+fPnVbZs2Qzb09cW+ud0cg4ODmrcuLEaN24sSTp58qSmTZumtWvX6uuvv37oOl52dnaqW7eu6tatK0mKiYnRDz/8oDlz5mjs2LF69913LdZsupfJZNKQIUN069YtDR06VG3btrXYfvTo0UxfpyRdvHgx02OGhYXpypUrql279gPfEwULFlSePHl09epV3bhxI9PRVefOnZOU8Zq5ubnp3Xff1bvvvqs7d+7owIEDGj16tA4fPqwZM2aY126TpPz58+vNN9/Um2++KenuVJrjxo3Tzp07NWHChAeu7/Zv3q+P+968n3Pnzumbb76Ri4uLZsyYoapVq1psj4qKuu++pUqV0vvvv6/3339ft2/f1vbt2zV8+HDt27dPmzdv1htvvGH+3jRv3lwtW7Z85LoepHr16uYQPS4uTitWrNC3336rWbNmqX379vcdoQYAAAA8LVizCgAAAM+8qKgo3bx5U+XKlcs0YEhfs+reUTzp69ps3749Q/8rV64oPDzcoq1SpUpycnLS8ePHM/2g/OLFi3rttdcUHBysmzdvPlLdf/zxh27cuJGhfdOmTRY1pitevLhq1qypQ4cO6ccff5SkR5oCUJIqVKigAgUK6OjRo4qJicmwPbPrkJmDBw9Kkrp165ZhxE76dG+SLEaPSNKvv/6aYRSVyWR65PM+SPp6QenX7Z82bNggSeYp0X766Sc1adJE3333nUW/MmXKmEPPB4Vov//+u1577TVz33TPPfecPv74Y7m6uurWrVu6fv36fY9x5coVnTt3Tq6urhmCKunv9+y9I//Sw4gdO3Zkesxx48apX79+5ukv7ydPnjyqWrWq7ty5k2mQm5qaam5P//59+eWXqlu3rnl9MOnuiK0aNWqoR48ekv6+ZnPmzFGjRo20evVqi+NWqFBB/fv3t+h7P//m/fq4700p8xFc6es+1apVK0NQJWX8eXLnzh116NBBdevWtVhLLW/evHrttdfMYV16YJv+fv31118zvnDdXXvsrbfe0tSpUx9Y519//aXmzZurS5cuFu3u7u56//335ePjozt37tw33AQAAACeJoRVAAAAeOalT5d16tQp/fXXX+Z2k8mkRYsWaenSpZKkpKQk87bAwEA5Ozub17dJd/v2bQ0aNMg8fV36h8TOzs569913devWLfXv319xcXHmfW7evKlBgwbpr7/+krOz8yNPA3jjxg19+umn5nNJ0uzZsxUWFiZvb2/ziJ97BQYGymQy6ccff1ShQoXUsGHDRzpXnjx51LZtW6WlpWnAgAEWIdnmzZszBAv3k36tt27datG+d+9ejRgxwvx1cnKypLsBS4UKFRQZGanJkyebP+A3mUyaPHmyjh8/Lunh0749yLvvvitnZ2etWrUqw+tYsWKF1qxZI2dnZwUEBEi6G0qdPXtW8+bNs3i/SNLatWsl3Q0n76dcuXI6e/asVq9erf3791ts2759uxISElSsWLEHTuPm4uKiPHnyKCEhwSIAkqSff/7ZHFSkX0dJql27tsqVK6ejR49q2rRpFvv8+OOPOnTokF544QUZDIb7njddx44dJUlfffWVxSiulJQUDR8+XGfPntWLL75oDsi8vLx0+fJljRs3zuK9k5qaqo0bN0qSeVq/4sWLKyYmRt999515zSvp7vc8/fo+aApA6d+9Xx/3vSn9Pc3hvcFi+nH+/PNPi3/nKSkpGj9+vPnnRfrPE1tbW7m4uJivT1pamnmfq1evKjQ01OI1v/766/L09NTmzZs1Z84cixD30KFDGj9+vI4fP27xfUyv897rULJkSV25ckU7d+7U//73P4vXfPjwYZ08eVL58uXT888/n+FaAQAAAE8bpgEEAADAM69EiRLy9/fXtm3b1KJFC9WsWVOOjo7mURlly5bViRMndOXKFfM+np6eGjp0qD7++GN17dpV1atXl7u7u/bv369bt27J3d1dcXFxFutG9e3bV8eOHdPu3bvVpEkTVapUSXnz5tUff/yhq1evqlSpUhnWsnmQ8uXLa8uWLXrllVfMa+EcP35cLi4uGj9+vMV6QemaNm2qESNG6Pr162revHmmfe6nZ8+eOnDggPbs2aPGjRvLz89PV65c0YEDB1S1alX98ccfDz1GcHCwRo8erYEDB2rJkiXy8PDQ2bNndezYMRUsWFAeHh66fPmyLl++bJ5ebvTo0QoKCtLkyZO1ceNGlStXTidOnNCJEydUvHhxnTt3zuI6P64iRYroyy+/VJ8+fTRw4EDNmTNHpUuX1qlTp3T8+HHlzZtXX3/9tby9vSVJL7zwgjp06KAffvhBzZs3V7Vq1VSoUCHz9Xd2dtagQYPue75ChQqpf//+Gj16tNq1a6cqVarI09NTFy9e1MGDB2Vvb59h1NU/OTk5qXXr1po/f746duwoPz8/ubq6KioqSqdOnZK3t7fi4+N1/fp1JSYmysnJSTY2Nvrmm2/UqVMnffvtt1qzZo3KlStnrjt//vz69ttvH+maNW7cWO+9955mz56td955R9WrV1ehQoX0559/KjY2Vt7e3vr222/NI5TatGmjn376SQcOHJC/v78qV64sBwcHi39j6QHYyy+/rCZNmmjz5s1q0qSJqlWrpnz58ikyMlKnT5+Wh4eHQkJCHlrj475f/817s1SpUpLuTh/6xx9/6K233lKjRo1Uvnx5HT16VE2bNlWNGjUk3Q2S4uLiVK5cOUVFRVn8PBk4cKD27dunefPmacuWLXrxxReVnJysAwcO6MaNG2revLl5ZF/evHk1YcIEvf/++xozZowWLFggHx8fXb16VQcOHJDJZFKHDh0swur0OpctW6bY2Fg1bNhQLVu21Oeff66QkBD997//VYUKFVSsWDHFx8dr//79SktL06effpr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+      "text/plain": [
+       "<Figure size 1696.38x800 with 8 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "histogram_diagnosis_dataset(df, 'age')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "fe6db4bb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "boxplot_diagnosis_sex(df, 'mean_fd_raw', 'SCHZ')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "7e4d40e5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "boxplot_diagnosis_sex(df, 'mean_fd_raw', 'CON')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "638d7e5a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From eb007ccbd595ec9dc74ae14eedb86728d4c87c7d Mon Sep 17 00:00:00 2001
From: clarkenj <natashajayne@gmail.com>
Date: Tue, 12 Mar 2024 15:03:38 -0400
Subject: [PATCH 05/25] added summary table

---
 code/filter_pheno_qc.py | 219 ++++++++++++++++++++++++++++++++++++----
 1 file changed, 202 insertions(+), 17 deletions(-)

diff --git a/code/filter_pheno_qc.py b/code/filter_pheno_qc.py
index 2688aeb..a06d618 100644
--- a/code/filter_pheno_qc.py
+++ b/code/filter_pheno_qc.py
@@ -1,10 +1,13 @@
-"""Load phenotypic data for N datasets, filter according to scans that passed QC on specific column, and output a merged tsv with pheno and QC results.
+"""Load phenotypic data for N datasets, summarise QC pass rate, and output a .tsv with passed QC scans in order to obtain connectomes. Optionally, pass specific diagnoses but note that the summary is currently calculated on the whole dataset.
+
+
+filter according to scans that passed QC on specific column, and output a merged tsv with pheno and QC results.
 
 To run, needs:
 - rest_df.tsv, a TSV file of concatenated QC results, generated using qc_output/qc.ipynb
 - TSV of pheno data for each specificed dataset, generated using the corresponding script in this repo
 
-Author: Natasha Clarke; last edit 2024-02-29
+Author: Natasha Clarke; last edit 2024-03-12
 
 """
 
@@ -13,7 +16,20 @@
 from pathlib import Path
 
 
-def filter_pheno_qc(qc_df, pheno_df, which_qc_col, dataset):
+def merge_pheno_qc(qc_df, pheno_df, which_qc_col, dataset):
+    """
+    Merges QC data with phenotype data for a specific dataset.
+
+    Parameters:
+    - qc_df (pd.DataFrame): DataFrame containing QC data, must include 'participant_id' and 'dataset' columns.
+    - pheno_df (pd.DataFrame): DataFrame containing phenotype data, must include 'participant_id'.
+    - which_qc_col (str): Name of the QC column to indicate QC status.
+    - dataset (str): Name of the dataset to filter the merged DataFrame on.
+
+    Returns:
+    - pd.DataFrame: A DataFrame merged on 'participant_id', filtered by 'dataset',
+      with relevant columns selected, including the specified QC status column.
+    """
     pheno_df["participant_id"] = pheno_df["participant_id"].astype(str)
     qc_df["participant_id"] = qc_df["participant_id"].astype(str)
     qc_df_filtered = qc_df[qc_df["dataset"] == dataset]
@@ -24,11 +40,8 @@ def filter_pheno_qc(qc_df, pheno_df, which_qc_col, dataset):
     merged_df.drop(columns=["site_x"], inplace=True)
     merged_df.rename(columns={"site_y": "site"}, inplace=True)
 
-    # Filter for passed QC
-    filtered_df = merged_df[merged_df[which_qc_col] == True]
-
     # Select relevant columns
-    filtered_df = filtered_df[
+    merged_df = merged_df[
         [
             "identifier",
             "participant_id",
@@ -42,30 +55,182 @@ def filter_pheno_qc(qc_df, pheno_df, which_qc_col, dataset):
             "diagnosis",
             "dataset",
         ]
+        + [which_qc_col]
     ]
 
-    return filtered_df
+    return merged_df
 
 
-def create_master(rest_qc_p, datasets, which_qc_col):
-    # Load QC df once
-    qc_df = pd.read_csv(rest_qc_p, sep="\t")
+def create_master_df(qc_df, datasets, which_qc_col):
+    """
+    Creates a master DataFrame by merging QC data with phenotype data across multiple datasets.
 
-    # Process each dataset and create a master of scans which passed QC
+    Parameters:
+    - qc_df (pd.DataFrame): DataFrame containing QC data, including a column for QC status and 'participant_id'.
+    - datasets (list of str): List containing the names of the datasets to process.
+    - which_qc_col (str): Name of the QC column to indicate QC status.
+
+    Returns:
+    - pd.DataFrame: A master DataFrame containing merged QC and phenotype data for all specified datasets.
+                     The DataFrame includes an additional column specified by which_qc_col to indicate the
+                     QC status of each scan.
+
+    Note:
+    - Assumes that there is a phenotypic df available for each dataset.
+    """
     master_df = pd.DataFrame()
     for dataset in datasets:
         pheno_p = Path(root_p / pheno_p_template.format(dataset=dataset))
         pheno_df = pd.read_csv(pheno_p, sep="\t", dtype={"participant_id": str})
-        df = filter_pheno_qc(qc_df, pheno_df, which_qc_col, dataset)
+        df = merge_pheno_qc(qc_df, pheno_df, which_qc_col, dataset)
         master_df = pd.concat([master_df, df], ignore_index=True)
 
     return master_df
 
 
 def filter_diagnoses(df, diagnoses):
+    """
+    Filters a DataFrame to retain only rows that match specified diagnoses.
+
+    Parameters:
+    - df (pd.DataFrame): DataFrame to be filtered, expected to include a 'diagnosis' column.
+    - diagnoses (list of str): List of diagnoses to filter by. Rows with a 'diagnosis' value
+                               matching any item in this list will be retained.
+
+    Returns:
+    - pd.DataFrame: A filtered DataFrame containing only the rows that match one of the specified diagnoses.
+    """
     return df[df["diagnosis"].isin(diagnoses)]
 
 
+def summarise_sessions(dataset_df, which_qc_col):
+    """
+    Summarises session pass rates based on the specified QC column. For a given session, it is counted as one pass if any run passed QC.
+
+    Parameters:
+    - dataset_df (pd.DataFrame): DataFrame containing session data. Must include
+                                 columns for 'participant_id', 'ses' (session), and the specified QC column.
+    - which_qc_col (str): Name of the QC column to indicate QC status.
+
+    Returns:
+    - tuple:
+        - pd.DataFrame: DataFrame of unique sessions with an additional column 'session_passed' indicating
+                        if any run in the session passed QC.
+        - int: The total number of unique sessions.
+        - int: The number of unique sessions that passed QC.
+        - float: The percentage of unique sessions that passed QC.
+    """
+
+    # Fill in blank session labels with a default value
+    dataset_df["ses"] = dataset_df["ses"].fillna("ses1").replace("", "ses1")
+
+    # Calculate if any run in a session passed QC
+    session_passed = dataset_df.groupby(["participant_id", "ses"])[
+        which_qc_col
+    ].transform(lambda x: any(x))
+    dataset_df.loc[:, "session_passed"] = session_passed
+
+    # Drop duplicates to ensure each session is counted once
+    unique_sessions_df = dataset_df.drop_duplicates(subset=["participant_id", "ses"])
+
+    # Count total and passed sessions
+    total_sessions = len(unique_sessions_df)
+    passed_sessions = len(
+        unique_sessions_df[unique_sessions_df["session_passed"] == True]
+    )
+
+    # Calculate percentage of sessions that passed
+    if total_sessions > 0:
+        percentage_passed = (passed_sessions / total_sessions) * 100
+    else:
+        percentage_passed = 0
+
+    return unique_sessions_df, total_sessions, passed_sessions, percentage_passed
+
+
+def summarise_subjects(dataset_df, unique_sessions_df):
+    """
+    Summarises the QC pass rates for unique subjects based on session data. For a given subject, they are counted as one pass if any session passed QC.
+
+    Parameters:
+    - dataset_df (pd.DataFrame): DataFrame containing subject data. Must include a
+                                 'participant_id' column.
+    - unique_sessions_df (pd.DataFrame): DataFrame of session data, including a 'session_passed'
+                                         column indicating if any run in the session passed QC.
+
+    Returns:
+    - tuple:
+        - int: The total number of unique subjects in dataset_df.
+        - int: The number of unique subjects with at least one session that passed QC.
+        - float: The percentage of unique subjects that passed QC based on session data.
+    """
+    # Total unique subjects
+    total_unique_subjects = dataset_df["participant_id"].nunique()
+
+    # Identify unique subjects that passed any session
+    passed_subjects_df = unique_sessions_df[
+        unique_sessions_df["session_passed"] == True
+    ]
+    unique_passed_subjects = passed_subjects_df["participant_id"].nunique()
+
+    # Calculate percentage of unique subjects that passed
+    if total_unique_subjects > 0:
+        percentage_unique_subjects_passed = (
+            unique_passed_subjects / total_unique_subjects
+        ) * 100
+    else:
+        percentage_unique_subjects_passed = 0
+
+    return (
+        total_unique_subjects,
+        unique_passed_subjects,
+        percentage_unique_subjects_passed,
+    )
+
+
+def summarise_passed_qc(merged_df, dataset, which_qc_col):
+    """
+    Generates a summary DataFrame indicating QC pass rates for both sessions and subjects within a specific dataset.
+
+    Parameters:
+    - merged_df (pd.DataFrame): Merged DataFrame containing both QC and phenotype data.
+    - dataset (str): Name of the dataset to filter for.
+    - which_qc_col (str): Name of the QC column to indicate QC status.
+
+    Returns:
+    - pd.DataFrame: A DataFrame containing a summary of QC pass rates, including the total number of sessions, the number of sessions that passed QC, the percentage of sessions that passed, the total number of subjects, the number of subjects that passed QC, and the percentage of subjects that passed QC for the specified dataset.
+    """
+    # Filter for the specified dataset
+    dataset_df = merged_df[merged_df["dataset"] == dataset].copy()
+
+    # Summarise unique session passes
+    unique_sessions_df, total_sessions, passed_sessions, percentage_passed = (
+        summarise_sessions(dataset_df, which_qc_col)
+    )
+    # Summarise unique subject passes
+    total_unique_subjects, unique_passed_subjects, percentage_unique_subjects_passed = (
+        summarise_subjects(dataset_df, unique_sessions_df)
+    )
+
+    # Create a summary df
+    qc_summary_df = pd.DataFrame(
+        [
+            {
+                "dataset": dataset,
+                "qc_column": which_qc_col,
+                "total_sessions": total_sessions,
+                "sessions_passed_qc (any 1 run)": passed_sessions,
+                "percentage_sessiond_passed_qc": percentage_passed,
+                "total_subjects": total_unique_subjects,
+                "subjects_passed_qc (any 1 session)": unique_passed_subjects,
+                "percentage_subjects_passed_qc": percentage_unique_subjects_passed,
+            }
+        ]
+    )
+
+    return qc_summary_df
+
+
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(
         description="Filter phenotype data according to QC result"
@@ -75,7 +240,9 @@ def filter_diagnoses(df, diagnoses):
         "--datasets", nargs="+", type=str, help="List of datasets to process"
     )
     parser.add_argument(
-        "--which_qc_col", type=str, help="Column for filtering QC. Default=pass_func_qc"
+        "--which_qc_col",
+        type=str,
+        help="Column for evaluating QC. Default=pass_func_qc",
     )
     parser.add_argument(
         "--diagnoses", nargs="+", type=str, help="Diagnoses of interest"
@@ -91,13 +258,31 @@ def filter_diagnoses(df, diagnoses):
     rest_qc_p = Path(root_p / "qc_output/rest_df.tsv")
     output_p = Path(root_p / "wrangling-phenotype/outputs")
 
-    master_df = create_master(rest_qc_p, datasets, which_qc_col)
+    # Load QC df once
+    qc_df = pd.read_csv(rest_qc_p, sep="\t")
+
+    # Create df of pheno and qc results
+    master_df = create_master_df(qc_df, datasets, which_qc_col)
+
+    # Summarise QC results
+    qc_summary_df_list = []
+    for dataset in datasets:
+        qc_summary_df = summarise_passed_qc(master_df, dataset, which_qc_col)
+        qc_summary_df_list.append(qc_summary_df)
+
+    qc_summary_df = pd.concat(qc_summary_df_list, ignore_index=True)
 
+    # Filter df for scans that passed QC only, optionally for specific diagnoses
     if diagnoses:
         sub_df = filter_diagnoses(master_df, diagnoses)
-        sub_df.to_csv(output_p / "passed_qc_master.tsv", sep="\t", index=False)
+        filtered_df = sub_df[sub_df[which_qc_col] == True]
+        filtered_df.to_csv(output_p / "passed_qc_master.tsv", sep="\t", index=False)
 
     else:
-        master_df.to_csv(output_p / "passed_qc_master.tsv", sep="\t", index=False)
+        filtered_df = master_df[master_df[which_qc_col] == True]
+
+    # Save output
+    qc_summary_df.to_csv(output_p / "qc_summary.tsv", sep="\t", index=False)
+    filtered_df.to_csv(output_p / "passed_qc_master.tsv", sep="\t", index=False)
 
     print(f"Data have been processed and output to {output_p}")

From c0671ebcd5c9fec7156bb852aef1f0aa513cba84 Mon Sep 17 00:00:00 2001
From: clarkenj <natashajayne@gmail.com>
Date: Tue, 12 Mar 2024 15:37:58 -0400
Subject: [PATCH 06/25] rounded percentages

---
 code/filter_pheno_qc.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/code/filter_pheno_qc.py b/code/filter_pheno_qc.py
index a06d618..a35ed74 100644
--- a/code/filter_pheno_qc.py
+++ b/code/filter_pheno_qc.py
@@ -220,10 +220,12 @@ def summarise_passed_qc(merged_df, dataset, which_qc_col):
                 "qc_column": which_qc_col,
                 "total_sessions": total_sessions,
                 "sessions_passed_qc (any 1 run)": passed_sessions,
-                "percentage_sessiond_passed_qc": percentage_passed,
+                "percentage_sessiond_passed_qc": round(percentage_passed),
                 "total_subjects": total_unique_subjects,
                 "subjects_passed_qc (any 1 session)": unique_passed_subjects,
-                "percentage_subjects_passed_qc": percentage_unique_subjects_passed,
+                "percentage_subjects_passed_qc": round(
+                    percentage_unique_subjects_passed
+                ),
             }
         ]
     )

From 612298eb55f06ee8155a4cff7d97935a4ae223b5 Mon Sep 17 00:00:00 2001
From: clarkenj <natashajayne@gmail.com>
Date: Thu, 14 Mar 2024 10:09:47 -0400
Subject: [PATCH 07/25] added first draft

---
 code/count_frames.py | 69 ++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 69 insertions(+)
 create mode 100644 code/count_frames.py

diff --git a/code/count_frames.py b/code/count_frames.py
new file mode 100644
index 0000000..bf77cc3
--- /dev/null
+++ b/code/count_frames.py
@@ -0,0 +1,69 @@
+import pandas as pd
+import argparse
+import h5py
+
+from pathlib import Path
+
+
+def count_frames(hdf5_object, target_suffix="atlas-MIST_desc-64_timeseries", path=""):
+    data = []
+    for name in hdf5_object:
+        item_path = f"{path}/{name}" if path else name
+        if isinstance(hdf5_object[name], h5py.Dataset) and name.endswith(target_suffix):
+            # Collect dataset name and the first dimension of its shape.
+            data.append({"file": name, "n_frames": hdf5_object[name].shape[0]})
+        elif isinstance(hdf5_object[name], h5py.Group):
+            # Recursive call to navigate through groups and collect data
+            data.extend(count_frames(hdf5_object[name], target_suffix, item_path))
+    return data
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Create a .tsv listing number of frames remaining for speicific atlas/strategy"
+    )
+    parser.add_argument(
+        "input_p",
+        type=Path,
+        help="Path to either a directory containing subject directories [participant] or .h5 file containing connectomes [group]",
+    )
+    parser.add_argument("output_p", type=Path, help="Output path")
+
+    parser.add_argument(
+        "level",
+        choices=["group", "participant"],
+        help="Analysis level: 'group' or 'participant'",
+    )
+
+    args = parser.parse_args()
+
+    data = []
+
+    if args.level == "participant":
+        for sub_directory in args.input_p.iterdir():
+            if sub_directory.is_dir() and sub_directory.name != "working_directory":
+                hdf5_file_p = (
+                    sub_directory
+                    / f"sub-{sub_directory.name}_atlas-MIST_desc-scrubbing.5+gsr.h5"
+                )
+                if hdf5_file_p.exists():
+                    with h5py.File(hdf5_file_p, "r") as file:
+                        data.extend(count_frames(file))
+                        print(data)
+                else:
+                    print(f"File {hdf5_file_p} does not exist.")
+    elif args.level == "group":
+        with h5py.File(args.input_p, "r") as file:
+            data = count_frames(file)
+
+    # Convert the list of dictionaries into a DataFrame
+    df = pd.DataFrame(data)
+
+    # Extract file details. Session and run are optional
+    pattern = (
+        r"sub-([^\s/_]+)(?:_ses-([a-zA-Z0-9]+))_task-([a-zA-Z0-9]+)(?:_run-(\d+))?"
+    )
+    df[["participant_id", "session", "task", "run"]] = df["file"].str.extract(pattern)
+
+    # Save output
+    df.to_csv(args.output_p / "hcpep_frames.tsv", sep="\t", index=False)

From 7b51a6a3a6208eff1bf1aa0f2dae76726197dd02 Mon Sep 17 00:00:00 2001
From: clarkenj <natashajayne@gmail.com>
Date: Thu, 14 Mar 2024 10:19:11 -0400
Subject: [PATCH 08/25] updated path handling

---
 code/count_frames.py | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

diff --git a/code/count_frames.py b/code/count_frames.py
index bf77cc3..f99c086 100644
--- a/code/count_frames.py
+++ b/code/count_frames.py
@@ -10,7 +10,7 @@ def count_frames(hdf5_object, target_suffix="atlas-MIST_desc-64_timeseries", pat
     for name in hdf5_object:
         item_path = f"{path}/{name}" if path else name
         if isinstance(hdf5_object[name], h5py.Dataset) and name.endswith(target_suffix):
-            # Collect dataset name and the first dimension of its shape.
+            # Collect dataset name and the first dimension of its shape
             data.append({"file": name, "n_frames": hdf5_object[name].shape[0]})
         elif isinstance(hdf5_object[name], h5py.Group):
             # Recursive call to navigate through groups and collect data
@@ -25,7 +25,7 @@ def count_frames(hdf5_object, target_suffix="atlas-MIST_desc-64_timeseries", pat
     parser.add_argument(
         "input_p",
         type=Path,
-        help="Path to either a directory containing subject directories [participant] or .h5 file containing connectomes [group]",
+        help="Path to directory containing subject directories [participant] or .h5 file of connectomes [group]",
     )
     parser.add_argument("output_p", type=Path, help="Output path")
 
@@ -38,7 +38,6 @@ def count_frames(hdf5_object, target_suffix="atlas-MIST_desc-64_timeseries", pat
     args = parser.parse_args()
 
     data = []
-
     if args.level == "participant":
         for sub_directory in args.input_p.iterdir():
             if sub_directory.is_dir() and sub_directory.name != "working_directory":
@@ -49,11 +48,11 @@ def count_frames(hdf5_object, target_suffix="atlas-MIST_desc-64_timeseries", pat
                 if hdf5_file_p.exists():
                     with h5py.File(hdf5_file_p, "r") as file:
                         data.extend(count_frames(file))
-                        print(data)
                 else:
                     print(f"File {hdf5_file_p} does not exist.")
     elif args.level == "group":
-        with h5py.File(args.input_p, "r") as file:
+        hdf5_file_p = args.input_p / "atlas-MIST_desc-scrubbing.5+gsr.h5"
+        with h5py.File(hdf5_file_p, "r") as file:
             data = count_frames(file)
 
     # Convert the list of dictionaries into a DataFrame
@@ -66,4 +65,4 @@ def count_frames(hdf5_object, target_suffix="atlas-MIST_desc-64_timeseries", pat
     df[["participant_id", "session", "task", "run"]] = df["file"].str.extract(pattern)
 
     # Save output
-    df.to_csv(args.output_p / "hcpep_frames.tsv", sep="\t", index=False)
+    df.to_csv(args.output_p / "cobre_frames.tsv", sep="\t", index=False)

From 96c926a650fce24b74ad7db609b58b795e02a3fa Mon Sep 17 00:00:00 2001
From: clarkenj <natashajayne@gmail.com>
Date: Thu, 14 Mar 2024 10:36:15 -0400
Subject: [PATCH 09/25] edited df header

---
 code/count_frames.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/code/count_frames.py b/code/count_frames.py
index f99c086..a95de62 100644
--- a/code/count_frames.py
+++ b/code/count_frames.py
@@ -62,7 +62,7 @@ def count_frames(hdf5_object, target_suffix="atlas-MIST_desc-64_timeseries", pat
     pattern = (
         r"sub-([^\s/_]+)(?:_ses-([a-zA-Z0-9]+))_task-([a-zA-Z0-9]+)(?:_run-(\d+))?"
     )
-    df[["participant_id", "session", "task", "run"]] = df["file"].str.extract(pattern)
+    df[["participant_id", "ses", "task", "run"]] = df["file"].str.extract(pattern)
 
     # Save output
     df.to_csv(args.output_p / "cobre_frames.tsv", sep="\t", index=False)

From b2dc9293107dec0f834a62dc740afd4820bee798 Mon Sep 17 00:00:00 2001
From: clarkenj <natashajayne@gmail.com>
Date: Thu, 14 Mar 2024 10:48:37 -0400
Subject: [PATCH 10/25] updated path handling

---
 code/filter_pheno_qc.py | 15 ++++++++++-----
 1 file changed, 10 insertions(+), 5 deletions(-)

diff --git a/code/filter_pheno_qc.py b/code/filter_pheno_qc.py
index a35ed74..5dc0a08 100644
--- a/code/filter_pheno_qc.py
+++ b/code/filter_pheno_qc.py
@@ -145,6 +145,9 @@ def summarise_sessions(dataset_df, which_qc_col):
     else:
         percentage_passed = 0
 
+    # Replace the dummy session variable
+    unique_sessions_df["ses"] = unique_sessions_df["ses"].replace("ses1", "")
+
     return unique_sessions_df, total_sessions, passed_sessions, percentage_passed
 
 
@@ -237,7 +240,7 @@ def summarise_passed_qc(merged_df, dataset, which_qc_col):
     parser = argparse.ArgumentParser(
         description="Filter phenotype data according to QC result"
     )
-    parser.add_argument("--root", type=str, help="Root path for data")
+    parser.add_argument("--root", type=Path, help="Root path for data")
     parser.add_argument(
         "--datasets", nargs="+", type=str, help="List of datasets to process"
     )
@@ -255,13 +258,13 @@ def summarise_passed_qc(merged_df, dataset, which_qc_col):
     which_qc_col = args.which_qc_col or "pass_func_qc"
     diagnoses = args.diagnoses if args.diagnoses else None
 
-    root_p = Path(args.root)
     pheno_p_template = "wrangling-phenotype/outputs/{dataset}_pheno.tsv"
-    rest_qc_p = Path(root_p / "qc_output/rest_df.tsv")
-    output_p = Path(root_p / "wrangling-phenotype/outputs")
+    rest_qc_p = args.root / "qc_output/rest_df.tsv"
+    output_p = args.root / "wrangling-phenotype/outputs"
 
-    # Load QC df once
+    # Load QC and frames data
     qc_df = pd.read_csv(rest_qc_p, sep="\t")
+    frames_df = pd.read_csv(rest_qc_p, sep="\t")
 
     # Create df of pheno and qc results
     master_df = create_master_df(qc_df, datasets, which_qc_col)
@@ -283,6 +286,8 @@ def summarise_passed_qc(merged_df, dataset, which_qc_col):
     else:
         filtered_df = master_df[master_df[which_qc_col] == True]
 
+    # Match QC df with number of frames remaining (separate .tsv)
+
     # Save output
     qc_summary_df.to_csv(output_p / "qc_summary.tsv", sep="\t", index=False)
     filtered_df.to_csv(output_p / "passed_qc_master.tsv", sep="\t", index=False)

From bc5c6f66163d81550aa9cbed761999a1b57be60b Mon Sep 17 00:00:00 2001
From: clarkenj <natashajayne@gmail.com>
Date: Thu, 14 Mar 2024 11:08:50 -0400
Subject: [PATCH 11/25] added dataset column

---
 code/count_frames.py | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/code/count_frames.py b/code/count_frames.py
index a95de62..fd2de00 100644
--- a/code/count_frames.py
+++ b/code/count_frames.py
@@ -35,6 +35,8 @@ def count_frames(hdf5_object, target_suffix="atlas-MIST_desc-64_timeseries", pat
         help="Analysis level: 'group' or 'participant'",
     )
 
+    parser.add_argument("dataset", type=str, help="Name of the dataset")
+
     args = parser.parse_args()
 
     data = []
@@ -64,5 +66,9 @@ def count_frames(hdf5_object, target_suffix="atlas-MIST_desc-64_timeseries", pat
     )
     df[["participant_id", "ses", "task", "run"]] = df["file"].str.extract(pattern)
 
+    # Drop the file column and add dataset as a column
+    df.drop(columns=["file"], inplace=True)
+    df["dataset"] = args.dataset
+
     # Save output
-    df.to_csv(args.output_p / "cobre_frames.tsv", sep="\t", index=False)
+    df.to_csv(args.output_p / f"{args.dataset}_frames.tsv", sep="\t", index=False)

From ec03756075e4666586b0be3d755daf4438a9c448 Mon Sep 17 00:00:00 2001
From: clarkenj <natashajayne@gmail.com>
Date: Thu, 14 Mar 2024 11:54:40 -0400
Subject: [PATCH 12/25] added merging with frames

---
 code/filter_pheno_qc.py | 24 +++++++++++++++---------
 1 file changed, 15 insertions(+), 9 deletions(-)

diff --git a/code/filter_pheno_qc.py b/code/filter_pheno_qc.py
index 5dc0a08..f60b39c 100644
--- a/code/filter_pheno_qc.py
+++ b/code/filter_pheno_qc.py
@@ -61,7 +61,7 @@ def merge_pheno_qc(qc_df, pheno_df, which_qc_col, dataset):
     return merged_df
 
 
-def create_master_df(qc_df, datasets, which_qc_col):
+def create_master_df(root_p, qc_df, datasets, which_qc_col):
     """
     Creates a master DataFrame by merging QC data with phenotype data across multiple datasets.
 
@@ -80,7 +80,9 @@ def create_master_df(qc_df, datasets, which_qc_col):
     """
     master_df = pd.DataFrame()
     for dataset in datasets:
-        pheno_p = Path(root_p / pheno_p_template.format(dataset=dataset))
+        # Ensure the placeholder for `dataset` is correctly placed within the string
+        pheno_p_template = "wrangling-phenotype/outputs/{dataset}_pheno.tsv"
+        pheno_p = args.root_p / pheno_p_template.format(dataset=dataset)
         pheno_df = pd.read_csv(pheno_p, sep="\t", dtype={"participant_id": str})
         df = merge_pheno_qc(qc_df, pheno_df, which_qc_col, dataset)
         master_df = pd.concat([master_df, df], ignore_index=True)
@@ -240,7 +242,7 @@ def summarise_passed_qc(merged_df, dataset, which_qc_col):
     parser = argparse.ArgumentParser(
         description="Filter phenotype data according to QC result"
     )
-    parser.add_argument("--root", type=Path, help="Root path for data")
+    parser.add_argument("--root_p", type=Path, help="Root path for data")
     parser.add_argument(
         "--datasets", nargs="+", type=str, help="List of datasets to process"
     )
@@ -258,16 +260,16 @@ def summarise_passed_qc(merged_df, dataset, which_qc_col):
     which_qc_col = args.which_qc_col or "pass_func_qc"
     diagnoses = args.diagnoses if args.diagnoses else None
 
-    pheno_p_template = "wrangling-phenotype/outputs/{dataset}_pheno.tsv"
-    rest_qc_p = args.root / "qc_output/rest_df.tsv"
-    output_p = args.root / "wrangling-phenotype/outputs"
+    rest_qc_p = args.root_p / "qc_output/rest_df.tsv"
+    frames_p = args.root_p / "wrangling-phenotype/outputs/cobre_frames.tsv"
+    output_p = args.root_p / "wrangling-phenotype/outputs"
 
     # Load QC and frames data
     qc_df = pd.read_csv(rest_qc_p, sep="\t")
-    frames_df = pd.read_csv(rest_qc_p, sep="\t")
+    frames_df = pd.read_csv(frames_p, sep="\t", dtype={"participant_id": str})
 
     # Create df of pheno and qc results
-    master_df = create_master_df(qc_df, datasets, which_qc_col)
+    master_df = create_master_df(args.root_p, qc_df, datasets, which_qc_col)
 
     # Summarise QC results
     qc_summary_df_list = []
@@ -287,9 +289,13 @@ def summarise_passed_qc(merged_df, dataset, which_qc_col):
         filtered_df = master_df[master_df[which_qc_col] == True]
 
     # Match QC df with number of frames remaining (separate .tsv)
+    frames_df = frames_df.loc[frames_df["task"] == "rest"]
+    matched_df = filtered_df.merge(
+        frames_df, on=["participant_id", "ses", "run", "dataset"], how="inner"
+    )
 
     # Save output
     qc_summary_df.to_csv(output_p / "qc_summary.tsv", sep="\t", index=False)
-    filtered_df.to_csv(output_p / "passed_qc_master.tsv", sep="\t", index=False)
+    matched_df.to_csv(output_p / "passed_qc_master.tsv", sep="\t", index=False)
 
     print(f"Data have been processed and output to {output_p}")

From f3a802db5e9697c0eef3989ad7907a69e239a51e Mon Sep 17 00:00:00 2001
From: clarkenj <natashajayne@gmail.com>
Date: Fri, 15 Mar 2024 17:08:13 -0400
Subject: [PATCH 13/25] updated regex expression

---
 code/count_frames.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/code/count_frames.py b/code/count_frames.py
index fd2de00..97c609c 100644
--- a/code/count_frames.py
+++ b/code/count_frames.py
@@ -62,7 +62,7 @@ def count_frames(hdf5_object, target_suffix="atlas-MIST_desc-64_timeseries", pat
 
     # Extract file details. Session and run are optional
     pattern = (
-        r"sub-([^\s/_]+)(?:_ses-([a-zA-Z0-9]+))_task-([a-zA-Z0-9]+)(?:_run-(\d+))?"
+        r"sub-([^\s/_]+)_task-([a-zA-Z0-9]+)(?:_ses-([a-zA-Z0-9]+))?(?:_run-(\d+))?"
     )
     df[["participant_id", "ses", "task", "run"]] = df["file"].str.extract(pattern)
 

From 61a889156dcf329bcd006727385aed57bf7b6844 Mon Sep 17 00:00:00 2001
From: clarkenj <natashajayne@gmail.com>
Date: Mon, 18 Mar 2024 15:48:38 -0400
Subject: [PATCH 14/25] updated regex

---
 code/count_frames.py | 16 +++++++++++-----
 1 file changed, 11 insertions(+), 5 deletions(-)

diff --git a/code/count_frames.py b/code/count_frames.py
index 97c609c..6ecca8a 100644
--- a/code/count_frames.py
+++ b/code/count_frames.py
@@ -11,7 +11,9 @@ def count_frames(hdf5_object, target_suffix="atlas-MIST_desc-64_timeseries", pat
         item_path = f"{path}/{name}" if path else name
         if isinstance(hdf5_object[name], h5py.Dataset) and name.endswith(target_suffix):
             # Collect dataset name and the first dimension of its shape
-            data.append({"file": name, "n_frames": hdf5_object[name].shape[0]})
+            data.append(
+                {"file": name, "total_frames": hdf5_object[name].shape[0]}
+            )  # n_frames
         elif isinstance(hdf5_object[name], h5py.Group):
             # Recursive call to navigate through groups and collect data
             data.extend(count_frames(hdf5_object[name], target_suffix, item_path))
@@ -45,7 +47,7 @@ def count_frames(hdf5_object, target_suffix="atlas-MIST_desc-64_timeseries", pat
             if sub_directory.is_dir() and sub_directory.name != "working_directory":
                 hdf5_file_p = (
                     sub_directory
-                    / f"sub-{sub_directory.name}_atlas-MIST_desc-scrubbing.5+gsr.h5"
+                    / f"sub-{sub_directory.name}_atlas-MIST_desc-simple.h5"  # atlas-MIST_desc-scrubbing.5+gsr.h5"
                 )
                 if hdf5_file_p.exists():
                     with h5py.File(hdf5_file_p, "r") as file:
@@ -53,7 +55,9 @@ def count_frames(hdf5_object, target_suffix="atlas-MIST_desc-64_timeseries", pat
                 else:
                     print(f"File {hdf5_file_p} does not exist.")
     elif args.level == "group":
-        hdf5_file_p = args.input_p / "atlas-MIST_desc-scrubbing.5+gsr.h5"
+        hdf5_file_p = (
+            args.input_p / "atlas-MIST_desc-simple.h5"
+        )  # "atlas-MIST_desc-scrubbing.5+gsr.h5"
         with h5py.File(hdf5_file_p, "r") as file:
             data = count_frames(file)
 
@@ -62,13 +66,15 @@ def count_frames(hdf5_object, target_suffix="atlas-MIST_desc-64_timeseries", pat
 
     # Extract file details. Session and run are optional
     pattern = (
-        r"sub-([^\s/_]+)_task-([a-zA-Z0-9]+)(?:_ses-([a-zA-Z0-9]+))?(?:_run-(\d+))?"
+        r"sub-([^\s/_]+)(?:_ses-([a-zA-Z0-9]+))?_task-([a-zA-Z0-9]+)(?:_run-(\d+))?"
     )
     df[["participant_id", "ses", "task", "run"]] = df["file"].str.extract(pattern)
+    df["participant_id"] = df["participant_id"].astype(str)
 
     # Drop the file column and add dataset as a column
     df.drop(columns=["file"], inplace=True)
     df["dataset"] = args.dataset
 
     # Save output
-    df.to_csv(args.output_p / f"{args.dataset}_frames.tsv", sep="\t", index=False)
+    # df.to_csv(args.output_p / f"{args.dataset}_frames.tsv", sep="\t", index=False)
+    df.to_csv(args.output_p / f"{args.dataset}_frames_total.tsv", sep="\t", index=False)

From cb3cbdc17b99d9137e2523f5fd0c37aee4115cac Mon Sep 17 00:00:00 2001
From: clarkenj <natashajayne@gmail.com>
Date: Tue, 19 Mar 2024 13:25:52 -0400
Subject: [PATCH 15/25] fixed merging on strings

---
 code/filter_pheno_qc.py | 16 +++++++---------
 1 file changed, 7 insertions(+), 9 deletions(-)

diff --git a/code/filter_pheno_qc.py b/code/filter_pheno_qc.py
index f60b39c..78dada1 100644
--- a/code/filter_pheno_qc.py
+++ b/code/filter_pheno_qc.py
@@ -80,7 +80,6 @@ def create_master_df(root_p, qc_df, datasets, which_qc_col):
     """
     master_df = pd.DataFrame()
     for dataset in datasets:
-        # Ensure the placeholder for `dataset` is correctly placed within the string
         pheno_p_template = "wrangling-phenotype/outputs/{dataset}_pheno.tsv"
         pheno_p = args.root_p / pheno_p_template.format(dataset=dataset)
         pheno_df = pd.read_csv(pheno_p, sep="\t", dtype={"participant_id": str})
@@ -148,6 +147,7 @@ def summarise_sessions(dataset_df, which_qc_col):
         percentage_passed = 0
 
     # Replace the dummy session variable
+    unique_sessions_df = unique_sessions_df.copy()
     unique_sessions_df["ses"] = unique_sessions_df["ses"].replace("ses1", "")
 
     return unique_sessions_df, total_sessions, passed_sessions, percentage_passed
@@ -225,7 +225,7 @@ def summarise_passed_qc(merged_df, dataset, which_qc_col):
                 "qc_column": which_qc_col,
                 "total_sessions": total_sessions,
                 "sessions_passed_qc (any 1 run)": passed_sessions,
-                "percentage_sessiond_passed_qc": round(percentage_passed),
+                "percentage_sessions_passed_qc": round(percentage_passed),
                 "total_subjects": total_unique_subjects,
                 "subjects_passed_qc (any 1 session)": unique_passed_subjects,
                 "percentage_subjects_passed_qc": round(
@@ -261,7 +261,7 @@ def summarise_passed_qc(merged_df, dataset, which_qc_col):
     diagnoses = args.diagnoses if args.diagnoses else None
 
     rest_qc_p = args.root_p / "qc_output/rest_df.tsv"
-    frames_p = args.root_p / "wrangling-phenotype/outputs/cobre_frames.tsv"
+    frames_p = args.root_p / "wrangling-phenotype/data/frames/total_frames_master.tsv"
     output_p = args.root_p / "wrangling-phenotype/outputs"
 
     # Load QC and frames data
@@ -282,14 +282,12 @@ def summarise_passed_qc(merged_df, dataset, which_qc_col):
     # Filter df for scans that passed QC only, optionally for specific diagnoses
     if diagnoses:
         sub_df = filter_diagnoses(master_df, diagnoses)
-        filtered_df = sub_df[sub_df[which_qc_col] == True]
-        filtered_df.to_csv(output_p / "passed_qc_master.tsv", sep="\t", index=False)
-
+        filtered_df = sub_df[sub_df[which_qc_col] == True].copy()
     else:
-        filtered_df = master_df[master_df[which_qc_col] == True]
+        filtered_df = master_df[master_df[which_qc_col] == True].copy()
 
-    # Match QC df with number of frames remaining (separate .tsv)
-    frames_df = frames_df.loc[frames_df["task"] == "rest"]
+    # Match QC df with frames df (total frames or number of frames remaining)
+    frames_df = frames_df.loc[frames_df["task"] == "rest"].copy()
     matched_df = filtered_df.merge(
         frames_df, on=["participant_id", "ses", "run", "dataset"], how="inner"
     )

From 7586a25fdd13511112efde7d2ff864205ef1af2f Mon Sep 17 00:00:00 2001
From: clarkenj <natashajayne@gmail.com>
Date: Fri, 22 Mar 2024 14:02:14 -0400
Subject: [PATCH 16/25] fixed some bugs

---
 code/filter_pheno_qc.py       |   5 +-
 code/filtered_qc_report.ipynb | 194 +++++++++++++---------------------
 2 files changed, 78 insertions(+), 121 deletions(-)

diff --git a/code/filter_pheno_qc.py b/code/filter_pheno_qc.py
index 78dada1..bad8254 100644
--- a/code/filter_pheno_qc.py
+++ b/code/filter_pheno_qc.py
@@ -265,7 +265,9 @@ def summarise_passed_qc(merged_df, dataset, which_qc_col):
     output_p = args.root_p / "wrangling-phenotype/outputs"
 
     # Load QC and frames data
-    qc_df = pd.read_csv(rest_qc_p, sep="\t")
+    qc_df = pd.read_csv(
+        rest_qc_p, sep="\t", low_memory=False
+    )  # dtype={"participant_id": str, "ses": str}
     frames_df = pd.read_csv(frames_p, sep="\t", dtype={"participant_id": str})
 
     # Create df of pheno and qc results
@@ -295,5 +297,6 @@ def summarise_passed_qc(merged_df, dataset, which_qc_col):
     # Save output
     qc_summary_df.to_csv(output_p / "qc_summary.tsv", sep="\t", index=False)
     matched_df.to_csv(output_p / "passed_qc_master.tsv", sep="\t", index=False)
+    # filtered_df.to_csv(output_p / "passed_qc_master.tsv", sep="\t", index=False)
 
     print(f"Data have been processed and output to {output_p}")
diff --git a/code/filtered_qc_report.ipynb b/code/filtered_qc_report.ipynb
index fc4853e..2faf0ea 100644
--- a/code/filtered_qc_report.ipynb
+++ b/code/filtered_qc_report.ipynb
@@ -98,10 +98,13 @@
      "data": {
       "text/plain": [
        "dataset\n",
-       "cobre        122\n",
-       "ds000030     241\n",
-       "hcpep        697\n",
-       "srpbs       1067\n",
+       "adni        3294\n",
+       "cimaq        268\n",
+       "cobre        120\n",
+       "ds000030     154\n",
+       "hcpep        689\n",
+       "oasis3      6913\n",
+       "srpbs        711\n",
        "dtype: int64"
       ]
      },
@@ -125,10 +128,13 @@
      "data": {
       "text/plain": [
        "dataset\n",
-       "cobre        122\n",
-       "ds000030     241\n",
-       "hcpep        179\n",
-       "srpbs       1067\n",
+       "adni        501\n",
+       "cimaq       107\n",
+       "cobre       120\n",
+       "ds000030    154\n",
+       "hcpep       177\n",
+       "oasis3      783\n",
+       "srpbs       711\n",
        "Name: participant_id, dtype: int64"
       ]
      },
@@ -157,8 +163,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Optionally for HCP-EP, replace PSYC diagnosis with SCHZ for ease\n",
-    "df.loc[df['diagnosis'] == 'PSYC', 'diagnosis'] = 'SCHZ'"
+    "# Optionally re-classify some diagnoses, for ease\n",
+    "df[\"diagnosis\"] = df[\"diagnosis\"].replace({\"EMCI\": \"MCI\", \"LMCI\": \"MCI\", \"PSYC\":\"SCHZ\", \"ADD(M)\": \"ADD\"})"
    ]
   },
   {
@@ -188,9 +194,12 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>dataset</th>\n",
+       "      <th>adni</th>\n",
+       "      <th>cimaq</th>\n",
        "      <th>cobre</th>\n",
        "      <th>ds000030</th>\n",
        "      <th>hcpep</th>\n",
+       "      <th>oasis3</th>\n",
        "      <th>srpbs</th>\n",
        "      <th>TOTAL</th>\n",
        "    </tr>\n",
@@ -201,106 +210,79 @@
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>ADHD</th>\n",
-       "      <td>0</td>\n",
-       "      <td>37</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>37</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ASD</th>\n",
-       "      <td>0</td>\n",
+       "      <th>ADD</th>\n",
+       "      <td>68</td>\n",
+       "      <td>3</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>117</td>\n",
-       "      <td>117</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>BIPOLAR</th>\n",
        "      <td>0</td>\n",
-       "      <td>49</td>\n",
+       "      <td>156</td>\n",
        "      <td>0</td>\n",
-       "      <td>33</td>\n",
-       "      <td>82</td>\n",
+       "      <td>227</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CON</th>\n",
+       "      <td>309</td>\n",
+       "      <td>59</td>\n",
        "      <td>67</td>\n",
-       "      <td>111</td>\n",
+       "      <td>110</td>\n",
        "      <td>57</td>\n",
+       "      <td>647</td>\n",
        "      <td>605</td>\n",
-       "      <td>823</td>\n",
+       "      <td>1837</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>DYSTHYMIA</th>\n",
-       "      <td>0</td>\n",
+       "      <th>MCI</th>\n",
+       "      <td>163</td>\n",
+       "      <td>51</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>MDD</th>\n",
        "      <td>0</td>\n",
+       "      <td>4</td>\n",
        "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>167</td>\n",
-       "      <td>167</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>OTHER</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>14</td>\n",
-       "      <td>14</td>\n",
+       "      <td>218</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>PAIN</th>\n",
-       "      <td>0</td>\n",
+       "      <th>SCHZ</th>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>22</td>\n",
-       "      <td>22</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>SCHZ</th>\n",
        "      <td>53</td>\n",
        "      <td>44</td>\n",
        "      <td>120</td>\n",
+       "      <td>0</td>\n",
        "      <td>106</td>\n",
        "      <td>323</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TOTAL</th>\n",
+       "      <td>501</td>\n",
+       "      <td>107</td>\n",
        "      <td>120</td>\n",
-       "      <td>241</td>\n",
+       "      <td>154</td>\n",
        "      <td>177</td>\n",
-       "      <td>1067</td>\n",
-       "      <td>1540</td>\n",
+       "      <td>783</td>\n",
+       "      <td>711</td>\n",
+       "      <td>2498</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "dataset    cobre  ds000030  hcpep  srpbs  TOTAL\n",
-       "diagnosis                                      \n",
-       "ADHD           0        37      0      0     37\n",
-       "ASD            0         0      0    117    117\n",
-       "BIPOLAR        0        49      0     33     82\n",
-       "CON           67       111     57    605    823\n",
-       "DYSTHYMIA      0         0      0      3      3\n",
-       "MDD            0         0      0    167    167\n",
-       "OTHER          0         0      0     14     14\n",
-       "PAIN           0         0      0     22     22\n",
-       "SCHZ          53        44    120    106    323\n",
-       "TOTAL        120       241    177   1067   1540"
+       "dataset    adni  cimaq  cobre  ds000030  hcpep  oasis3  srpbs  TOTAL\n",
+       "diagnosis                                                           \n",
+       "ADD          68      3      0         0      0     156      0    227\n",
+       "CON         309     59     67       110     57     647    605   1837\n",
+       "MCI         163     51      0         0      0       4      0    218\n",
+       "SCHZ          0      0     53        44    120       0    106    323\n",
+       "TOTAL       501    107    120       154    177     783    711   2498"
       ]
      },
      "execution_count": 9,
@@ -325,7 +307,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x800 with 2 Axes>"
       ]
@@ -354,7 +336,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -374,34 +356,6 @@
     "plt.show()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "5e68fdbb",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Proportion kept across diagnoses\n",
-    "sns.boxplot(data=df, x='diagnosis', y='proportion_kept', palette=\"colorblind\")\n",
-    "\n",
-    "plt.xticks(rotation=45) \n",
-    "plt.title('Proportion of scans kept across diagnoses')\n",
-    "plt.xlabel('Diagnosis')\n",
-    "plt.ylabel('proportion_kept')\n",
-    "plt.show()"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "0bc74390",
@@ -412,25 +366,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "id": "985136db",
    "metadata": {},
    "outputs": [],
    "source": [
     "# If needed, select subset of diagnoses\n",
-    "diagnoses = ['CON', 'SCHZ']\n",
-    "df = df.loc[df['diagnosis'].isin(diagnoses)].copy()"
+    "#diagnoses = ['CON', 'ADD', 'MCI']\n",
+    "#df = df.loc[df['diagnosis'].isin(diagnoses)].copy()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "id": "d027f13d",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 1200x800 with 1 Axes>"
       ]
@@ -445,13 +399,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "id": "0c27a13f",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1200x800 with 1 Axes>"
       ]
@@ -466,13 +420,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "id": "65dde205",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1200x800 with 1 Axes>"
       ]
@@ -487,15 +441,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "id": "d84a4e78",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
-       "<Figure size 1696.38x800 with 8 Axes>"
+       "<Figure size 2896.38x1600 with 28 Axes>"
       ]
      },
      "metadata": {},
@@ -508,15 +462,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "id": "fa698608",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
-       "<Figure size 1696.38x800 with 8 Axes>"
+       "<Figure size 2896.38x1600 with 28 Axes>"
       ]
      },
      "metadata": {},
@@ -529,13 +483,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "id": "fe6db4bb",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 600x400 with 1 Axes>"
       ]
@@ -545,18 +499,18 @@
     }
    ],
    "source": [
-    "boxplot_diagnosis_sex(df, 'mean_fd_raw', 'SCHZ')"
+    "boxplot_diagnosis_sex(df, 'mean_fd_raw', 'ADD')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "id": "7e4d40e5",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 600x400 with 1 Axes>"
       ]

From 2ccfc3fe8c182ad60dad3c9af9559c0b58f3ca48 Mon Sep 17 00:00:00 2001
From: clarkenj <natashajayne@gmail.com>
Date: Sun, 24 Mar 2024 11:19:52 -0400
Subject: [PATCH 17/25] draft fixing longitudinal datasets

---
 code/filter_pheno_qc.py | 20 +++++++++++++++++++-
 1 file changed, 19 insertions(+), 1 deletion(-)

diff --git a/code/filter_pheno_qc.py b/code/filter_pheno_qc.py
index bad8254..c4e4bd1 100644
--- a/code/filter_pheno_qc.py
+++ b/code/filter_pheno_qc.py
@@ -271,7 +271,15 @@ def summarise_passed_qc(merged_df, dataset, which_qc_col):
     frames_df = pd.read_csv(frames_p, sep="\t", dtype={"participant_id": str})
 
     # Create df of pheno and qc results
-    master_df = create_master_df(args.root_p, qc_df, datasets, which_qc_col)
+    # First for cross sectional datasets
+    for dataset in datasets:
+        if dataset in ["abide1", "abide2", "cobre", "hcpep", "srpbs", "ds000030"]:
+            cross_sectional_df = create_master_df(args.root_p, qc_df, datasets, which_qc_col)
+
+        elif:
+            dataset == "adni": # etc, work out how to match these
+
+    master_df = pd.concat(cross_sectional_df, ...)
 
     # Summarise QC results
     qc_summary_df_list = []
@@ -294,6 +302,16 @@ def summarise_passed_qc(merged_df, dataset, which_qc_col):
         frames_df, on=["participant_id", "ses", "run", "dataset"], how="inner"
     )
 
+    # Rework adni subject id's so they match other adni spreadsheets
+    matched_df["participant_id"] = matched_df.apply(
+        lambda row: (
+            row["participant_id"].split("S")[-1]
+            if row["dataset"] == "adni"
+            else row["participant_id"]
+        ),
+        axis=1,
+    )
+
     # Save output
     qc_summary_df.to_csv(output_p / "qc_summary.tsv", sep="\t", index=False)
     matched_df.to_csv(output_p / "passed_qc_master.tsv", sep="\t", index=False)

From 0ab3d84eb4606469d5e116dbec7d529c1ff62a0b Mon Sep 17 00:00:00 2001
From: clarkenj <natashajayne@gmail.com>
Date: Mon, 25 Mar 2024 10:12:39 -0400
Subject: [PATCH 18/25] new draft

---
 code/filter_pheno_qc.py |   2 +-
 code/test_filter.py     | 303 ++++++++++++++++++++++++++++++++++++++++
 2 files changed, 304 insertions(+), 1 deletion(-)
 create mode 100644 code/test_filter.py

diff --git a/code/filter_pheno_qc.py b/code/filter_pheno_qc.py
index c4e4bd1..0405c9e 100644
--- a/code/filter_pheno_qc.py
+++ b/code/filter_pheno_qc.py
@@ -277,7 +277,7 @@ def summarise_passed_qc(merged_df, dataset, which_qc_col):
             cross_sectional_df = create_master_df(args.root_p, qc_df, datasets, which_qc_col)
 
         elif:
-            dataset == "adni": # etc, work out how to match these
+            dataset == "adni": # etc, merge on session also. may have to use nearest
 
     master_df = pd.concat(cross_sectional_df, ...)
 
diff --git a/code/test_filter.py b/code/test_filter.py
new file mode 100644
index 0000000..2299b65
--- /dev/null
+++ b/code/test_filter.py
@@ -0,0 +1,303 @@
+"""Load phenotypic data for N datasets, summarise QC pass rate, and output a .tsv with passed QC scans in order to obtain connectomes. Optionally, pass specific diagnoses but note that the summary is currently calculated on the whole dataset.
+
+
+filter according to scans that passed QC on specific column, and output a merged tsv with pheno and QC results.
+
+To run, needs:
+- rest_df.tsv, a TSV file of concatenated QC results, generated using qc_output/qc.ipynb
+- TSV of pheno data for each specificed dataset, generated using the corresponding script in this repo
+
+Author: Natasha Clarke; last edit 2024-03-12
+
+"""
+
+import argparse
+import pandas as pd
+from pathlib import Path
+
+
+def merge_pheno_qc(qc_df, pheno_df, which_qc_col, dataset):
+    """
+    Merges QC data with phenotype data for a specific dataset.
+
+    Parameters:
+    - qc_df (pd.DataFrame): DataFrame containing QC data, must include 'participant_id' and 'dataset' columns.
+    - pheno_df (pd.DataFrame): DataFrame containing phenotype data, must include 'participant_id'.
+    - which_qc_col (str): Name of the QC column to indicate QC status.
+    - dataset (str): Name of the dataset to filter the merged DataFrame on.
+
+    Returns:
+    - pd.DataFrame: A DataFrame merged on 'participant_id', filtered by 'dataset',
+      with relevant columns selected, including the specified QC status column.
+    """
+    pheno_df["participant_id"] = pheno_df["participant_id"].astype(str)
+    qc_df["participant_id"] = qc_df["participant_id"].astype(str)
+    qc_df_filtered = qc_df[qc_df["dataset"] == dataset]
+
+    merged_df = qc_df_filtered.merge(pheno_df, on="participant_id", how="left")
+
+    # Handle site columns
+    merged_df.drop(columns=["site_x"], inplace=True)
+    merged_df.rename(columns={"site_y": "site"}, inplace=True)
+
+    # Select relevant columns
+    merged_df = merged_df[
+        [
+            "identifier",
+            "participant_id",
+            "ses",
+            "run",
+            "age",
+            "sex",
+            "site",
+            "proportion_kept",
+            "mean_fd_raw",
+            "diagnosis",
+            "dataset",
+        ]
+        + [which_qc_col]
+    ]
+
+    return merged_df
+
+
+def create_master_df(root_p, qc_df, datasets, which_qc_col):
+    """
+    Creates a master DataFrame by merging QC data with phenotype data across multiple datasets.
+
+    Parameters:
+    - qc_df (pd.DataFrame): DataFrame containing QC data, including a column for QC status and 'participant_id'.
+    - datasets (list of str): List containing the names of the datasets to process.
+    - which_qc_col (str): Name of the QC column to indicate QC status.
+
+    Returns:
+    - pd.DataFrame: A master DataFrame containing merged QC and phenotype data for all specified datasets.
+                     The DataFrame includes an additional column specified by which_qc_col to indicate the
+                     QC status of each scan.
+
+    Note:
+    - Assumes that there is a phenotypic df available for each dataset.
+    """
+    # master_df = pd.DataFrame()
+    for dataset in datasets:
+        pheno_p_template = "wrangling-phenotype/outputs/{dataset}_pheno.tsv"
+        pheno_p = args.root_p / pheno_p_template.format(dataset=dataset)
+        pheno_df = pd.read_csv(pheno_p, sep="\t", dtype={"participant_id": str})
+        df = merge_pheno_qc(qc_df, pheno_df, which_qc_col, dataset)
+        master_df = pd.concat([master_df, df], ignore_index=True)
+
+    return master_df
+
+
+def filter_diagnoses(df, diagnoses):
+    """
+    Filters a DataFrame to retain only rows that match specified diagnoses.
+
+    Parameters:
+    - df (pd.DataFrame): DataFrame to be filtered, expected to include a 'diagnosis' column.
+    - diagnoses (list of str): List of diagnoses to filter by. Rows with a 'diagnosis' value
+                               matching any item in this list will be retained.
+
+    Returns:
+    - pd.DataFrame: A filtered DataFrame containing only the rows that match one of the specified diagnoses.
+    """
+    return df[df["diagnosis"].isin(diagnoses)]
+
+
+def summarise_sessions(dataset_df, which_qc_col):
+    """
+    Summarises session pass rates based on the specified QC column. For a given session, it is counted as one pass if any run passed QC.
+
+    Parameters:
+    - dataset_df (pd.DataFrame): DataFrame containing session data. Must include
+                                 columns for 'participant_id', 'ses' (session), and the specified QC column.
+    - which_qc_col (str): Name of the QC column to indicate QC status.
+
+    Returns:
+    - tuple:
+        - pd.DataFrame: DataFrame of unique sessions with an additional column 'session_passed' indicating
+                        if any run in the session passed QC.
+        - int: The total number of unique sessions.
+        - int: The number of unique sessions that passed QC.
+        - float: The percentage of unique sessions that passed QC.
+    """
+
+    # Fill in blank session labels with a default value
+    dataset_df["ses"] = dataset_df["ses"].fillna("ses1").replace("", "ses1")
+
+    # Calculate if any run in a session passed QC
+    session_passed = dataset_df.groupby(["participant_id", "ses"])[
+        which_qc_col
+    ].transform(lambda x: any(x))
+    dataset_df.loc[:, "session_passed"] = session_passed
+
+    # Drop duplicates to ensure each session is counted once
+    unique_sessions_df = dataset_df.drop_duplicates(subset=["participant_id", "ses"])
+
+    # Count total and passed sessions
+    total_sessions = len(unique_sessions_df)
+    passed_sessions = len(
+        unique_sessions_df[unique_sessions_df["session_passed"] == True]
+    )
+
+    # Calculate percentage of sessions that passed
+    if total_sessions > 0:
+        percentage_passed = (passed_sessions / total_sessions) * 100
+    else:
+        percentage_passed = 0
+
+    # Replace the dummy session variable
+    unique_sessions_df = unique_sessions_df.copy()
+    unique_sessions_df["ses"] = unique_sessions_df["ses"].replace("ses1", "")
+
+    return unique_sessions_df, total_sessions, passed_sessions, percentage_passed
+
+
+def summarise_subjects(dataset_df, unique_sessions_df):
+    """
+    Summarises the QC pass rates for unique subjects based on session data. For a given subject, they are counted as one pass if any session passed QC.
+
+    Parameters:
+    - dataset_df (pd.DataFrame): DataFrame containing subject data. Must include a
+                                 'participant_id' column.
+    - unique_sessions_df (pd.DataFrame): DataFrame of session data, including a 'session_passed'
+                                         column indicating if any run in the session passed QC.
+
+    Returns:
+    - tuple:
+        - int: The total number of unique subjects in dataset_df.
+        - int: The number of unique subjects with at least one session that passed QC.
+        - float: The percentage of unique subjects that passed QC based on session data.
+    """
+    # Total unique subjects
+    total_unique_subjects = dataset_df["participant_id"].nunique()
+
+    # Identify unique subjects that passed any session
+    passed_subjects_df = unique_sessions_df[
+        unique_sessions_df["session_passed"] == True
+    ]
+    unique_passed_subjects = passed_subjects_df["participant_id"].nunique()
+
+    # Calculate percentage of unique subjects that passed
+    if total_unique_subjects > 0:
+        percentage_unique_subjects_passed = (
+            unique_passed_subjects / total_unique_subjects
+        ) * 100
+    else:
+        percentage_unique_subjects_passed = 0
+
+    return (
+        total_unique_subjects,
+        unique_passed_subjects,
+        percentage_unique_subjects_passed,
+    )
+
+
+def summarise_passed_qc(merged_df, dataset, which_qc_col):
+    """
+    Generates a summary DataFrame indicating QC pass rates for both sessions and subjects within a specific dataset.
+
+    Parameters:
+    - merged_df (pd.DataFrame): Merged DataFrame containing both QC and phenotype data.
+    - dataset (str): Name of the dataset to filter for.
+    - which_qc_col (str): Name of the QC column to indicate QC status.
+
+    Returns:
+    - pd.DataFrame: A DataFrame containing a summary of QC pass rates, including the total number of sessions, the number of sessions that passed QC, the percentage of sessions that passed, the total number of subjects, the number of subjects that passed QC, and the percentage of subjects that passed QC for the specified dataset.
+    """
+    # Filter for the specified dataset
+    dataset_df = merged_df[merged_df["dataset"] == dataset].copy()
+
+    # Summarise unique session passes
+    unique_sessions_df, total_sessions, passed_sessions, percentage_passed = (
+        summarise_sessions(dataset_df, which_qc_col)
+    )
+    # Summarise unique subject passes
+    total_unique_subjects, unique_passed_subjects, percentage_unique_subjects_passed = (
+        summarise_subjects(dataset_df, unique_sessions_df)
+    )
+
+    # Create a summary df
+    qc_summary_df = pd.DataFrame(
+        [
+            {
+                "dataset": dataset,
+                "qc_column": which_qc_col,
+                "total_sessions": total_sessions,
+                "sessions_passed_qc (any 1 run)": passed_sessions,
+                "percentage_sessions_passed_qc": round(percentage_passed),
+                "total_subjects": total_unique_subjects,
+                "subjects_passed_qc (any 1 session)": unique_passed_subjects,
+                "percentage_subjects_passed_qc": round(
+                    percentage_unique_subjects_passed
+                ),
+            }
+        ]
+    )
+
+    return qc_summary_df
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Filter phenotype data according to QC result"
+    )
+    parser.add_argument("--root_p", type=Path, help="Root path for data")
+    parser.add_argument(
+        "--datasets", nargs="+", type=str, help="List of datasets to process"
+    )
+    parser.add_argument(
+        "--which_qc_col",
+        type=str,
+        help="Column for evaluating QC. Default=pass_func_qc",
+    )
+    parser.add_argument(
+        "--diagnoses", nargs="+", type=str, help="Diagnoses of interest"
+    )
+
+    args = parser.parse_args()
+    datasets = args.datasets
+    which_qc_col = args.which_qc_col or "pass_func_qc"
+    diagnoses = args.diagnoses if args.diagnoses else None
+
+    rest_qc_p = args.root_p / "qc_output/rest_df.tsv"
+    frames_p = args.root_p / "wrangling-phenotype/data/frames/total_frames_master.tsv"
+    output_p = args.root_p / "wrangling-phenotype/outputs"
+
+    # Load QC and frames data
+    qc_df = pd.read_csv(
+        rest_qc_p, sep="\t", low_memory=False
+    )  # dtype={"participant_id": str, "ses": str}
+    frames_df = pd.read_csv(frames_p, sep="\t", dtype={"participant_id": str})
+
+    # Create df of pheno and qc results
+    master_df = pd.DataFrame()
+    for dataset in datasets:
+        pheno_p_template = "wrangling-phenotype/outputs/{dataset}_pheno.tsv"
+        pheno_p = args.root_p / pheno_p_template.format(dataset=dataset)
+        pheno_df = pd.read_csv(pheno_p, sep="\t", dtype={"participant_id": str})
+
+        # if dataset == "adni":
+        if dataset == "oasis":
+            qc_df_filtered = qc_df[qc_df["dataset"] == dataset].copy()
+            qc_df_filtered["ses"] = pd.to_datetime(qc_df_filtered["ses"])
+            qc_df_filtered = qc_df_filtered.sort_values(by="ses")
+
+            pheno_df["ses"] = pd.to_datetime(pheno_df["ses"])
+            pheno_df = pheno_df.sort_values(by="ses")
+
+            merged_df = pd.merge_asof(
+                qc_df_filtered,
+                pheno_df,
+                by="participant_id",  # Match participants
+                on="ses",  # Find the nearest match based on session date
+                direction="nearest",
+                tolerance=pd.Timedelta(days=183),
+            )
+
+    # Save output
+    # qc_summary_df.to_csv(output_p / "qc_summary.tsv", sep="\t", index=False)
+    # matched_df.to_csv(output_p / "passed_qc_master.tsv", sep="\t", index=False)
+    merged_df.to_csv(output_p / "test.tsv", sep="\t", index=False)
+
+    print(f"Data have been processed and output to {output_p}")

From 051698d8bb7c5cb6a06cd61a4d3f13f19452ea10 Mon Sep 17 00:00:00 2001
From: clarkenj <natashajayne@gmail.com>
Date: Mon, 25 Mar 2024 14:31:11 -0400
Subject: [PATCH 19/25] working on new update

---
 code/test_filter.py | 257 ++++----------------------------------------
 1 file changed, 20 insertions(+), 237 deletions(-)

diff --git a/code/test_filter.py b/code/test_filter.py
index 2299b65..02baf9b 100644
--- a/code/test_filter.py
+++ b/code/test_filter.py
@@ -16,226 +16,23 @@
 from pathlib import Path
 
 
-def merge_pheno_qc(qc_df, pheno_df, which_qc_col, dataset):
-    """
-    Merges QC data with phenotype data for a specific dataset.
-
-    Parameters:
-    - qc_df (pd.DataFrame): DataFrame containing QC data, must include 'participant_id' and 'dataset' columns.
-    - pheno_df (pd.DataFrame): DataFrame containing phenotype data, must include 'participant_id'.
-    - which_qc_col (str): Name of the QC column to indicate QC status.
-    - dataset (str): Name of the dataset to filter the merged DataFrame on.
-
-    Returns:
-    - pd.DataFrame: A DataFrame merged on 'participant_id', filtered by 'dataset',
-      with relevant columns selected, including the specified QC status column.
-    """
-    pheno_df["participant_id"] = pheno_df["participant_id"].astype(str)
-    qc_df["participant_id"] = qc_df["participant_id"].astype(str)
-    qc_df_filtered = qc_df[qc_df["dataset"] == dataset]
-
-    merged_df = qc_df_filtered.merge(pheno_df, on="participant_id", how="left")
-
-    # Handle site columns
-    merged_df.drop(columns=["site_x"], inplace=True)
-    merged_df.rename(columns={"site_y": "site"}, inplace=True)
-
-    # Select relevant columns
-    merged_df = merged_df[
-        [
-            "identifier",
-            "participant_id",
-            "ses",
-            "run",
-            "age",
-            "sex",
-            "site",
-            "proportion_kept",
-            "mean_fd_raw",
-            "diagnosis",
-            "dataset",
-        ]
-        + [which_qc_col]
-    ]
-
-    return merged_df
-
-
-def create_master_df(root_p, qc_df, datasets, which_qc_col):
-    """
-    Creates a master DataFrame by merging QC data with phenotype data across multiple datasets.
-
-    Parameters:
-    - qc_df (pd.DataFrame): DataFrame containing QC data, including a column for QC status and 'participant_id'.
-    - datasets (list of str): List containing the names of the datasets to process.
-    - which_qc_col (str): Name of the QC column to indicate QC status.
-
-    Returns:
-    - pd.DataFrame: A master DataFrame containing merged QC and phenotype data for all specified datasets.
-                     The DataFrame includes an additional column specified by which_qc_col to indicate the
-                     QC status of each scan.
-
-    Note:
-    - Assumes that there is a phenotypic df available for each dataset.
-    """
-    # master_df = pd.DataFrame()
-    for dataset in datasets:
-        pheno_p_template = "wrangling-phenotype/outputs/{dataset}_pheno.tsv"
-        pheno_p = args.root_p / pheno_p_template.format(dataset=dataset)
-        pheno_df = pd.read_csv(pheno_p, sep="\t", dtype={"participant_id": str})
-        df = merge_pheno_qc(qc_df, pheno_df, which_qc_col, dataset)
-        master_df = pd.concat([master_df, df], ignore_index=True)
-
-    return master_df
-
-
-def filter_diagnoses(df, diagnoses):
-    """
-    Filters a DataFrame to retain only rows that match specified diagnoses.
-
-    Parameters:
-    - df (pd.DataFrame): DataFrame to be filtered, expected to include a 'diagnosis' column.
-    - diagnoses (list of str): List of diagnoses to filter by. Rows with a 'diagnosis' value
-                               matching any item in this list will be retained.
-
-    Returns:
-    - pd.DataFrame: A filtered DataFrame containing only the rows that match one of the specified diagnoses.
-    """
-    return df[df["diagnosis"].isin(diagnoses)]
-
-
-def summarise_sessions(dataset_df, which_qc_col):
-    """
-    Summarises session pass rates based on the specified QC column. For a given session, it is counted as one pass if any run passed QC.
-
-    Parameters:
-    - dataset_df (pd.DataFrame): DataFrame containing session data. Must include
-                                 columns for 'participant_id', 'ses' (session), and the specified QC column.
-    - which_qc_col (str): Name of the QC column to indicate QC status.
-
-    Returns:
-    - tuple:
-        - pd.DataFrame: DataFrame of unique sessions with an additional column 'session_passed' indicating
-                        if any run in the session passed QC.
-        - int: The total number of unique sessions.
-        - int: The number of unique sessions that passed QC.
-        - float: The percentage of unique sessions that passed QC.
-    """
-
-    # Fill in blank session labels with a default value
-    dataset_df["ses"] = dataset_df["ses"].fillna("ses1").replace("", "ses1")
-
-    # Calculate if any run in a session passed QC
-    session_passed = dataset_df.groupby(["participant_id", "ses"])[
-        which_qc_col
-    ].transform(lambda x: any(x))
-    dataset_df.loc[:, "session_passed"] = session_passed
-
-    # Drop duplicates to ensure each session is counted once
-    unique_sessions_df = dataset_df.drop_duplicates(subset=["participant_id", "ses"])
-
-    # Count total and passed sessions
-    total_sessions = len(unique_sessions_df)
-    passed_sessions = len(
-        unique_sessions_df[unique_sessions_df["session_passed"] == True]
-    )
-
-    # Calculate percentage of sessions that passed
-    if total_sessions > 0:
-        percentage_passed = (passed_sessions / total_sessions) * 100
-    else:
-        percentage_passed = 0
-
-    # Replace the dummy session variable
-    unique_sessions_df = unique_sessions_df.copy()
-    unique_sessions_df["ses"] = unique_sessions_df["ses"].replace("ses1", "")
-
-    return unique_sessions_df, total_sessions, passed_sessions, percentage_passed
-
-
-def summarise_subjects(dataset_df, unique_sessions_df):
-    """
-    Summarises the QC pass rates for unique subjects based on session data. For a given subject, they are counted as one pass if any session passed QC.
-
-    Parameters:
-    - dataset_df (pd.DataFrame): DataFrame containing subject data. Must include a
-                                 'participant_id' column.
-    - unique_sessions_df (pd.DataFrame): DataFrame of session data, including a 'session_passed'
-                                         column indicating if any run in the session passed QC.
-
-    Returns:
-    - tuple:
-        - int: The total number of unique subjects in dataset_df.
-        - int: The number of unique subjects with at least one session that passed QC.
-        - float: The percentage of unique subjects that passed QC based on session data.
-    """
-    # Total unique subjects
-    total_unique_subjects = dataset_df["participant_id"].nunique()
-
-    # Identify unique subjects that passed any session
-    passed_subjects_df = unique_sessions_df[
-        unique_sessions_df["session_passed"] == True
-    ]
-    unique_passed_subjects = passed_subjects_df["participant_id"].nunique()
-
-    # Calculate percentage of unique subjects that passed
-    if total_unique_subjects > 0:
-        percentage_unique_subjects_passed = (
-            unique_passed_subjects / total_unique_subjects
-        ) * 100
-    else:
-        percentage_unique_subjects_passed = 0
-
-    return (
-        total_unique_subjects,
-        unique_passed_subjects,
-        percentage_unique_subjects_passed,
+def merge_adni(qc_df_filtered, pheno_df):
+    qc_df_filtered["ses"] = pd.to_datetime(qc_df_filtered["ses"])
+    qc_df_filtered = qc_df_filtered.sort_values(by="ses")
+
+    pheno_df["ses"] = pd.to_datetime(pheno_df["ses"])
+    pheno_df = pheno_df.sort_values(by="ses")
+
+    merged_df = pd.merge_asof(
+        qc_df_filtered,
+        pheno_df,
+        by="participant_id",  # Match participants
+        on="ses",  # Find the nearest match based on session date
+        direction="nearest",
+        tolerance=pd.Timedelta(days=183),
     )
 
-
-def summarise_passed_qc(merged_df, dataset, which_qc_col):
-    """
-    Generates a summary DataFrame indicating QC pass rates for both sessions and subjects within a specific dataset.
-
-    Parameters:
-    - merged_df (pd.DataFrame): Merged DataFrame containing both QC and phenotype data.
-    - dataset (str): Name of the dataset to filter for.
-    - which_qc_col (str): Name of the QC column to indicate QC status.
-
-    Returns:
-    - pd.DataFrame: A DataFrame containing a summary of QC pass rates, including the total number of sessions, the number of sessions that passed QC, the percentage of sessions that passed, the total number of subjects, the number of subjects that passed QC, and the percentage of subjects that passed QC for the specified dataset.
-    """
-    # Filter for the specified dataset
-    dataset_df = merged_df[merged_df["dataset"] == dataset].copy()
-
-    # Summarise unique session passes
-    unique_sessions_df, total_sessions, passed_sessions, percentage_passed = (
-        summarise_sessions(dataset_df, which_qc_col)
-    )
-    # Summarise unique subject passes
-    total_unique_subjects, unique_passed_subjects, percentage_unique_subjects_passed = (
-        summarise_subjects(dataset_df, unique_sessions_df)
-    )
-
-    # Create a summary df
-    qc_summary_df = pd.DataFrame(
-        [
-            {
-                "dataset": dataset,
-                "qc_column": which_qc_col,
-                "total_sessions": total_sessions,
-                "sessions_passed_qc (any 1 run)": passed_sessions,
-                "percentage_sessions_passed_qc": round(percentage_passed),
-                "total_subjects": total_unique_subjects,
-                "subjects_passed_qc (any 1 session)": unique_passed_subjects,
-                "percentage_subjects_passed_qc": round(
-                    percentage_unique_subjects_passed
-                ),
-            }
-        ]
-    )
-
-    return qc_summary_df
+    return merged_df
 
 
 if __name__ == "__main__":
@@ -270,34 +67,20 @@ def summarise_passed_qc(merged_df, dataset, which_qc_col):
     )  # dtype={"participant_id": str, "ses": str}
     frames_df = pd.read_csv(frames_p, sep="\t", dtype={"participant_id": str})
 
-    # Create df of pheno and qc results
+    # Merge pheno and qc data for each dataset
     master_df = pd.DataFrame()
     for dataset in datasets:
         pheno_p_template = "wrangling-phenotype/outputs/{dataset}_pheno.tsv"
         pheno_p = args.root_p / pheno_p_template.format(dataset=dataset)
         pheno_df = pd.read_csv(pheno_p, sep="\t", dtype={"participant_id": str})
 
-        # if dataset == "adni":
-        if dataset == "oasis":
-            qc_df_filtered = qc_df[qc_df["dataset"] == dataset].copy()
-            qc_df_filtered["ses"] = pd.to_datetime(qc_df_filtered["ses"])
-            qc_df_filtered = qc_df_filtered.sort_values(by="ses")
-
-            pheno_df["ses"] = pd.to_datetime(pheno_df["ses"])
-            pheno_df = pheno_df.sort_values(by="ses")
-
-            merged_df = pd.merge_asof(
-                qc_df_filtered,
-                pheno_df,
-                by="participant_id",  # Match participants
-                on="ses",  # Find the nearest match based on session date
-                direction="nearest",
-                tolerance=pd.Timedelta(days=183),
-            )
+        qc_df_filtered = qc_df[qc_df["dataset"] == dataset].copy()
+        if dataset == "adni":
+            df = merge_adni(qc_df_filtered, pheno_df)
 
     # Save output
     # qc_summary_df.to_csv(output_p / "qc_summary.tsv", sep="\t", index=False)
     # matched_df.to_csv(output_p / "passed_qc_master.tsv", sep="\t", index=False)
-    merged_df.to_csv(output_p / "test.tsv", sep="\t", index=False)
+    df.to_csv(output_p / "test.tsv", sep="\t", index=False)
 
     print(f"Data have been processed and output to {output_p}")

From d63c4367cf4b9ce3572a2218cd1ae19af8c1f2b9 Mon Sep 17 00:00:00 2001
From: clarkenj <natashajayne@gmail.com>
Date: Tue, 26 Mar 2024 09:09:22 -0400
Subject: [PATCH 20/25] re-drafting

---
 code/test_filter.py | 44 +++++++++++++++++++++++++++++++++++++++-----
 1 file changed, 39 insertions(+), 5 deletions(-)

diff --git a/code/test_filter.py b/code/test_filter.py
index 02baf9b..e786563 100644
--- a/code/test_filter.py
+++ b/code/test_filter.py
@@ -7,7 +7,10 @@
 - rest_df.tsv, a TSV file of concatenated QC results, generated using qc_output/qc.ipynb
 - TSV of pheno data for each specificed dataset, generated using the corresponding script in this repo
 
-Author: Natasha Clarke; last edit 2024-03-12
+Note:
+- Currently diagnoses are matched to scans according to the nearest diagnosis, with no limit. The tolerance can be added to set a limit, e.g. within 1 year.
+
+Author: Natasha Clarke; last edit 2024-03-25
 
 """
 
@@ -17,11 +20,11 @@
 
 
 def merge_adni(qc_df_filtered, pheno_df):
+    pheno_df["ses"] = pd.to_datetime(pheno_df["ses"])
     qc_df_filtered["ses"] = pd.to_datetime(qc_df_filtered["ses"])
-    qc_df_filtered = qc_df_filtered.sort_values(by="ses")
 
-    pheno_df["ses"] = pd.to_datetime(pheno_df["ses"])
     pheno_df = pheno_df.sort_values(by="ses")
+    qc_df_filtered = qc_df_filtered.sort_values(by="ses")
 
     merged_df = pd.merge_asof(
         qc_df_filtered,
@@ -29,9 +32,30 @@ def merge_adni(qc_df_filtered, pheno_df):
         by="participant_id",  # Match participants
         on="ses",  # Find the nearest match based on session date
         direction="nearest",
-        tolerance=pd.Timedelta(days=183),
+    )  # tolerance=pd.Timedelta(days=365),
+
+    return merged_df
+
+
+def merge_oasis3(qc_df_filtered, pheno_df):
+    pheno_df["ses_numeric"] = pheno_df["ses"].str.replace("d", "").astype(int)
+    qc_df_filtered["ses_numeric"] = (
+        qc_df_filtered["ses"].str.replace("d", "").astype(int)
     )
 
+    pheno_df = pheno_df.sort_values(by="ses_numeric")
+    qc_df_filtered = qc_df_filtered.sort_values(by="ses_numeric")
+
+    merged_df = pd.merge_asof(
+        qc_df_filtered,
+        pheno_df,
+        by="participant_id",  # Match participants
+        on="ses_numeric",  # Find the nearest match based on session date
+        direction="nearest",
+    )  # tolerance=365
+
+    merged_df["ses_numeric"] = pheno_df["ses"]
+
     return merged_df
 
 
@@ -68,7 +92,9 @@ def merge_adni(qc_df_filtered, pheno_df):
     frames_df = pd.read_csv(frames_p, sep="\t", dtype={"participant_id": str})
 
     # Merge pheno and qc data for each dataset
+    df_list = []
     master_df = pd.DataFrame()
+
     for dataset in datasets:
         pheno_p_template = "wrangling-phenotype/outputs/{dataset}_pheno.tsv"
         pheno_p = args.root_p / pheno_p_template.format(dataset=dataset)
@@ -77,10 +103,18 @@ def merge_adni(qc_df_filtered, pheno_df):
         qc_df_filtered = qc_df[qc_df["dataset"] == dataset].copy()
         if dataset == "adni":
             df = merge_adni(qc_df_filtered, pheno_df)
+            df_list.append(df)
+
+        elif dataset == "oasis3":
+            df = merge_oasis3(qc_df_filtered, pheno_df)
+            df_list.append(df)
+
+        master_df = pd.concat([master_df, df], ignore_index=True)
+    master_df = master_df.drop(columns=["site_x"])
 
     # Save output
     # qc_summary_df.to_csv(output_p / "qc_summary.tsv", sep="\t", index=False)
     # matched_df.to_csv(output_p / "passed_qc_master.tsv", sep="\t", index=False)
-    df.to_csv(output_p / "test.tsv", sep="\t", index=False)
+    master_df.to_csv(output_p / "test.tsv", sep="\t", index=False)
 
     print(f"Data have been processed and output to {output_p}")

From cc8c2cd1dab21cfa5cc06957a951953bf8dbcb20 Mon Sep 17 00:00:00 2001
From: clarkenj <natashajayne@gmail.com>
Date: Tue, 26 Mar 2024 17:38:11 -0400
Subject: [PATCH 21/25] reworked script

---
 code/filter_pheno_qc.py | 320 ----------------------------------------
 code/filter_qc.py       | 161 ++++++++++++++++++++
 code/test_filter.py     | 120 ---------------
 3 files changed, 161 insertions(+), 440 deletions(-)
 delete mode 100644 code/filter_pheno_qc.py
 create mode 100644 code/filter_qc.py
 delete mode 100644 code/test_filter.py

diff --git a/code/filter_pheno_qc.py b/code/filter_pheno_qc.py
deleted file mode 100644
index 0405c9e..0000000
--- a/code/filter_pheno_qc.py
+++ /dev/null
@@ -1,320 +0,0 @@
-"""Load phenotypic data for N datasets, summarise QC pass rate, and output a .tsv with passed QC scans in order to obtain connectomes. Optionally, pass specific diagnoses but note that the summary is currently calculated on the whole dataset.
-
-
-filter according to scans that passed QC on specific column, and output a merged tsv with pheno and QC results.
-
-To run, needs:
-- rest_df.tsv, a TSV file of concatenated QC results, generated using qc_output/qc.ipynb
-- TSV of pheno data for each specificed dataset, generated using the corresponding script in this repo
-
-Author: Natasha Clarke; last edit 2024-03-12
-
-"""
-
-import argparse
-import pandas as pd
-from pathlib import Path
-
-
-def merge_pheno_qc(qc_df, pheno_df, which_qc_col, dataset):
-    """
-    Merges QC data with phenotype data for a specific dataset.
-
-    Parameters:
-    - qc_df (pd.DataFrame): DataFrame containing QC data, must include 'participant_id' and 'dataset' columns.
-    - pheno_df (pd.DataFrame): DataFrame containing phenotype data, must include 'participant_id'.
-    - which_qc_col (str): Name of the QC column to indicate QC status.
-    - dataset (str): Name of the dataset to filter the merged DataFrame on.
-
-    Returns:
-    - pd.DataFrame: A DataFrame merged on 'participant_id', filtered by 'dataset',
-      with relevant columns selected, including the specified QC status column.
-    """
-    pheno_df["participant_id"] = pheno_df["participant_id"].astype(str)
-    qc_df["participant_id"] = qc_df["participant_id"].astype(str)
-    qc_df_filtered = qc_df[qc_df["dataset"] == dataset]
-
-    merged_df = qc_df_filtered.merge(pheno_df, on="participant_id", how="left")
-
-    # Handle site columns
-    merged_df.drop(columns=["site_x"], inplace=True)
-    merged_df.rename(columns={"site_y": "site"}, inplace=True)
-
-    # Select relevant columns
-    merged_df = merged_df[
-        [
-            "identifier",
-            "participant_id",
-            "ses",
-            "run",
-            "age",
-            "sex",
-            "site",
-            "proportion_kept",
-            "mean_fd_raw",
-            "diagnosis",
-            "dataset",
-        ]
-        + [which_qc_col]
-    ]
-
-    return merged_df
-
-
-def create_master_df(root_p, qc_df, datasets, which_qc_col):
-    """
-    Creates a master DataFrame by merging QC data with phenotype data across multiple datasets.
-
-    Parameters:
-    - qc_df (pd.DataFrame): DataFrame containing QC data, including a column for QC status and 'participant_id'.
-    - datasets (list of str): List containing the names of the datasets to process.
-    - which_qc_col (str): Name of the QC column to indicate QC status.
-
-    Returns:
-    - pd.DataFrame: A master DataFrame containing merged QC and phenotype data for all specified datasets.
-                     The DataFrame includes an additional column specified by which_qc_col to indicate the
-                     QC status of each scan.
-
-    Note:
-    - Assumes that there is a phenotypic df available for each dataset.
-    """
-    master_df = pd.DataFrame()
-    for dataset in datasets:
-        pheno_p_template = "wrangling-phenotype/outputs/{dataset}_pheno.tsv"
-        pheno_p = args.root_p / pheno_p_template.format(dataset=dataset)
-        pheno_df = pd.read_csv(pheno_p, sep="\t", dtype={"participant_id": str})
-        df = merge_pheno_qc(qc_df, pheno_df, which_qc_col, dataset)
-        master_df = pd.concat([master_df, df], ignore_index=True)
-
-    return master_df
-
-
-def filter_diagnoses(df, diagnoses):
-    """
-    Filters a DataFrame to retain only rows that match specified diagnoses.
-
-    Parameters:
-    - df (pd.DataFrame): DataFrame to be filtered, expected to include a 'diagnosis' column.
-    - diagnoses (list of str): List of diagnoses to filter by. Rows with a 'diagnosis' value
-                               matching any item in this list will be retained.
-
-    Returns:
-    - pd.DataFrame: A filtered DataFrame containing only the rows that match one of the specified diagnoses.
-    """
-    return df[df["diagnosis"].isin(diagnoses)]
-
-
-def summarise_sessions(dataset_df, which_qc_col):
-    """
-    Summarises session pass rates based on the specified QC column. For a given session, it is counted as one pass if any run passed QC.
-
-    Parameters:
-    - dataset_df (pd.DataFrame): DataFrame containing session data. Must include
-                                 columns for 'participant_id', 'ses' (session), and the specified QC column.
-    - which_qc_col (str): Name of the QC column to indicate QC status.
-
-    Returns:
-    - tuple:
-        - pd.DataFrame: DataFrame of unique sessions with an additional column 'session_passed' indicating
-                        if any run in the session passed QC.
-        - int: The total number of unique sessions.
-        - int: The number of unique sessions that passed QC.
-        - float: The percentage of unique sessions that passed QC.
-    """
-
-    # Fill in blank session labels with a default value
-    dataset_df["ses"] = dataset_df["ses"].fillna("ses1").replace("", "ses1")
-
-    # Calculate if any run in a session passed QC
-    session_passed = dataset_df.groupby(["participant_id", "ses"])[
-        which_qc_col
-    ].transform(lambda x: any(x))
-    dataset_df.loc[:, "session_passed"] = session_passed
-
-    # Drop duplicates to ensure each session is counted once
-    unique_sessions_df = dataset_df.drop_duplicates(subset=["participant_id", "ses"])
-
-    # Count total and passed sessions
-    total_sessions = len(unique_sessions_df)
-    passed_sessions = len(
-        unique_sessions_df[unique_sessions_df["session_passed"] == True]
-    )
-
-    # Calculate percentage of sessions that passed
-    if total_sessions > 0:
-        percentage_passed = (passed_sessions / total_sessions) * 100
-    else:
-        percentage_passed = 0
-
-    # Replace the dummy session variable
-    unique_sessions_df = unique_sessions_df.copy()
-    unique_sessions_df["ses"] = unique_sessions_df["ses"].replace("ses1", "")
-
-    return unique_sessions_df, total_sessions, passed_sessions, percentage_passed
-
-
-def summarise_subjects(dataset_df, unique_sessions_df):
-    """
-    Summarises the QC pass rates for unique subjects based on session data. For a given subject, they are counted as one pass if any session passed QC.
-
-    Parameters:
-    - dataset_df (pd.DataFrame): DataFrame containing subject data. Must include a
-                                 'participant_id' column.
-    - unique_sessions_df (pd.DataFrame): DataFrame of session data, including a 'session_passed'
-                                         column indicating if any run in the session passed QC.
-
-    Returns:
-    - tuple:
-        - int: The total number of unique subjects in dataset_df.
-        - int: The number of unique subjects with at least one session that passed QC.
-        - float: The percentage of unique subjects that passed QC based on session data.
-    """
-    # Total unique subjects
-    total_unique_subjects = dataset_df["participant_id"].nunique()
-
-    # Identify unique subjects that passed any session
-    passed_subjects_df = unique_sessions_df[
-        unique_sessions_df["session_passed"] == True
-    ]
-    unique_passed_subjects = passed_subjects_df["participant_id"].nunique()
-
-    # Calculate percentage of unique subjects that passed
-    if total_unique_subjects > 0:
-        percentage_unique_subjects_passed = (
-            unique_passed_subjects / total_unique_subjects
-        ) * 100
-    else:
-        percentage_unique_subjects_passed = 0
-
-    return (
-        total_unique_subjects,
-        unique_passed_subjects,
-        percentage_unique_subjects_passed,
-    )
-
-
-def summarise_passed_qc(merged_df, dataset, which_qc_col):
-    """
-    Generates a summary DataFrame indicating QC pass rates for both sessions and subjects within a specific dataset.
-
-    Parameters:
-    - merged_df (pd.DataFrame): Merged DataFrame containing both QC and phenotype data.
-    - dataset (str): Name of the dataset to filter for.
-    - which_qc_col (str): Name of the QC column to indicate QC status.
-
-    Returns:
-    - pd.DataFrame: A DataFrame containing a summary of QC pass rates, including the total number of sessions, the number of sessions that passed QC, the percentage of sessions that passed, the total number of subjects, the number of subjects that passed QC, and the percentage of subjects that passed QC for the specified dataset.
-    """
-    # Filter for the specified dataset
-    dataset_df = merged_df[merged_df["dataset"] == dataset].copy()
-
-    # Summarise unique session passes
-    unique_sessions_df, total_sessions, passed_sessions, percentage_passed = (
-        summarise_sessions(dataset_df, which_qc_col)
-    )
-    # Summarise unique subject passes
-    total_unique_subjects, unique_passed_subjects, percentage_unique_subjects_passed = (
-        summarise_subjects(dataset_df, unique_sessions_df)
-    )
-
-    # Create a summary df
-    qc_summary_df = pd.DataFrame(
-        [
-            {
-                "dataset": dataset,
-                "qc_column": which_qc_col,
-                "total_sessions": total_sessions,
-                "sessions_passed_qc (any 1 run)": passed_sessions,
-                "percentage_sessions_passed_qc": round(percentage_passed),
-                "total_subjects": total_unique_subjects,
-                "subjects_passed_qc (any 1 session)": unique_passed_subjects,
-                "percentage_subjects_passed_qc": round(
-                    percentage_unique_subjects_passed
-                ),
-            }
-        ]
-    )
-
-    return qc_summary_df
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(
-        description="Filter phenotype data according to QC result"
-    )
-    parser.add_argument("--root_p", type=Path, help="Root path for data")
-    parser.add_argument(
-        "--datasets", nargs="+", type=str, help="List of datasets to process"
-    )
-    parser.add_argument(
-        "--which_qc_col",
-        type=str,
-        help="Column for evaluating QC. Default=pass_func_qc",
-    )
-    parser.add_argument(
-        "--diagnoses", nargs="+", type=str, help="Diagnoses of interest"
-    )
-
-    args = parser.parse_args()
-    datasets = args.datasets
-    which_qc_col = args.which_qc_col or "pass_func_qc"
-    diagnoses = args.diagnoses if args.diagnoses else None
-
-    rest_qc_p = args.root_p / "qc_output/rest_df.tsv"
-    frames_p = args.root_p / "wrangling-phenotype/data/frames/total_frames_master.tsv"
-    output_p = args.root_p / "wrangling-phenotype/outputs"
-
-    # Load QC and frames data
-    qc_df = pd.read_csv(
-        rest_qc_p, sep="\t", low_memory=False
-    )  # dtype={"participant_id": str, "ses": str}
-    frames_df = pd.read_csv(frames_p, sep="\t", dtype={"participant_id": str})
-
-    # Create df of pheno and qc results
-    # First for cross sectional datasets
-    for dataset in datasets:
-        if dataset in ["abide1", "abide2", "cobre", "hcpep", "srpbs", "ds000030"]:
-            cross_sectional_df = create_master_df(args.root_p, qc_df, datasets, which_qc_col)
-
-        elif:
-            dataset == "adni": # etc, merge on session also. may have to use nearest
-
-    master_df = pd.concat(cross_sectional_df, ...)
-
-    # Summarise QC results
-    qc_summary_df_list = []
-    for dataset in datasets:
-        qc_summary_df = summarise_passed_qc(master_df, dataset, which_qc_col)
-        qc_summary_df_list.append(qc_summary_df)
-
-    qc_summary_df = pd.concat(qc_summary_df_list, ignore_index=True)
-
-    # Filter df for scans that passed QC only, optionally for specific diagnoses
-    if diagnoses:
-        sub_df = filter_diagnoses(master_df, diagnoses)
-        filtered_df = sub_df[sub_df[which_qc_col] == True].copy()
-    else:
-        filtered_df = master_df[master_df[which_qc_col] == True].copy()
-
-    # Match QC df with frames df (total frames or number of frames remaining)
-    frames_df = frames_df.loc[frames_df["task"] == "rest"].copy()
-    matched_df = filtered_df.merge(
-        frames_df, on=["participant_id", "ses", "run", "dataset"], how="inner"
-    )
-
-    # Rework adni subject id's so they match other adni spreadsheets
-    matched_df["participant_id"] = matched_df.apply(
-        lambda row: (
-            row["participant_id"].split("S")[-1]
-            if row["dataset"] == "adni"
-            else row["participant_id"]
-        ),
-        axis=1,
-    )
-
-    # Save output
-    qc_summary_df.to_csv(output_p / "qc_summary.tsv", sep="\t", index=False)
-    matched_df.to_csv(output_p / "passed_qc_master.tsv", sep="\t", index=False)
-    # filtered_df.to_csv(output_p / "passed_qc_master.tsv", sep="\t", index=False)
-
-    print(f"Data have been processed and output to {output_p}")
diff --git a/code/filter_qc.py b/code/filter_qc.py
new file mode 100644
index 0000000..583ebe6
--- /dev/null
+++ b/code/filter_qc.py
@@ -0,0 +1,161 @@
+"""Load phenotypic and QC data for datasets, summarise QC pass rate, and output a .tsv with passed QC scans in order to obtain connectomes.
+Optionally, pass specific diagnoses.
+
+To run, needs:
+- TSV of pheno and qc data for each specificed dataset, generated using the corresponding script in this repo
+
+Author: Natasha Clarke; last edit 2024-03-26
+
+"""
+
+import argparse
+import pandas as pd
+from pathlib import Path
+
+
+def create_master_df(root_p, datasets):
+    # Create one df of QC results for specified datasets
+    cols = [
+        "identifier",
+        "participant_id",
+        "ses",
+        "run",
+        "age",
+        "sex",
+        "site",
+        "diagnosis",
+        "dataset",
+        "mean_fd_raw",
+        "mean_fd_scrubbed",
+        "proportion_kept",
+        "functional_dice",
+        "pass_func_qc",
+        "anatomical_dice",
+        "pass_anat_qc",
+        "pass_all_qc",
+        "different_func_affine",
+    ]
+
+    master_df = pd.DataFrame()
+    for dataset in datasets:
+        path_template = "wrangling-phenotype/outputs/{dataset}_qc_pheno.tsv"
+        df_p = root_p / path_template.format(dataset=dataset)
+        df = pd.read_csv(df_p, sep="\t", dtype={"participant_id": str})
+        df = df[cols]
+        master_df = pd.concat([master_df, df], ignore_index=True)
+
+    return master_df
+
+
+def filter_diagnoses(df, diagnoses):
+    return df[df["diagnosis"].isin(diagnoses)]
+
+
+def summarise_qc(df):
+    # Across all scans i.e. rows
+    scan_summary = (
+        master_df.groupby("dataset")
+        .agg(
+            total_scans=("participant_id", "count"),  # Count all scans per dataset
+            total_scan_passes=(which_qc_col, "sum"),  # Sum passes per dataset
+        )
+        .reset_index()
+    )
+
+    # Across sessions i.e. only count one run per session
+    session_passes = (
+        master_df.groupby(["dataset", "participant_id", "ses"])[which_qc_col]
+        .any()
+        .reset_index()
+    )
+    session_summary = session_passes.groupby("dataset").agg(
+        total_sessions=("ses", "count"),  # Count unique sessions per dataset
+        total_session_passes=(which_qc_col, "sum"),  # Count sessions that passed
+    )
+
+    # Across participants i.e. only count one session per participant
+    subject_summary = (
+        session_passes.groupby(["dataset", "participant_id"])[which_qc_col]
+        .any()
+        .reset_index()
+    )
+    subject_summary = subject_summary.groupby("dataset").agg(
+        total_subjects=("participant_id", "count"),  # Count unique subjects per dataset
+        total_subject_passes=(which_qc_col, "sum"),  # Count subjects that passed
+    )
+
+    return (scan_summary, session_summary, subject_summary)
+
+
+def combine_summaries(scan_summary, session_summary, subject_summary):
+    # Ensure 'dataset' is a column for joining
+    session_summary = session_summary.reset_index()
+    subject_summary = subject_summary.reset_index()
+
+    # Merge summaries
+    combined_summary = pd.merge(
+        scan_summary, session_summary, on="dataset", how="outer"
+    )
+    combined_summary = pd.merge(
+        combined_summary, subject_summary, on="dataset", how="outer"
+    )
+
+    combined_summary.columns = [
+        "dataset",
+        "Total Scans",
+        "Total Scan Passes",
+        "Total Sessions",
+        "Total Session Passes",
+        "Total Subjects",
+        "Total Subject Passes",
+    ]
+
+    return combined_summary
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Generate QC summary metrics and .tsv of scans passed QC"
+    )
+    parser.add_argument("--root_p", type=Path, help="Root path for data")
+    parser.add_argument(
+        "--datasets", nargs="+", type=str, help="List of datasets to process"
+    )
+    parser.add_argument(
+        "--which_qc_col",
+        type=str,
+        help="Column for evaluating QC. Default=pass_func_qc",
+    )
+    parser.add_argument(
+        "--diagnoses", nargs="+", type=str, help="Diagnoses of interest"
+    )
+
+    args = parser.parse_args()
+    datasets = args.datasets
+    which_qc_col = args.which_qc_col or "pass_func_qc"
+    diagnoses = args.diagnoses if args.diagnoses else None
+    output_p = args.root_p / "wrangling-phenotype/outputs"
+
+    # Create df of QC results and pheno data for specified datasets
+    master_df = create_master_df(args.root_p, datasets)
+
+    # Optionally filter df for specific diagnoses
+    if diagnoses:
+        master_df = filter_diagnoses(master_df, diagnoses)
+
+    # Summarise QC results
+    # Fill in blank session labels with a dummy variable to enable aggregation
+    master_df["ses"] = master_df["ses"].fillna("dummy").replace("", "dummy")
+    scan_summary, session_summary, subject_summary = summarise_qc(master_df)
+    qc_summary = combine_summaries(scan_summary, session_summary, subject_summary)
+    # Remove dummy variable
+    master_df["ses"].replace("dummy", "", inplace=True)
+
+    # Filter for scans that passed QC based on specified column (default = pass_func_qc)
+    filtered_df = master_df[master_df[which_qc_col] == True].copy()
+
+    # Save output
+    qc_summary.to_csv(output_p / "qc_summary.tsv", sep="\t", index=False)
+    filtered_df.to_csv(output_p / "passed_qc_master.tsv", sep="\t", index=False)
+
+    print(f"Data have been processed and output to {output_p}")
diff --git a/code/test_filter.py b/code/test_filter.py
deleted file mode 100644
index e786563..0000000
--- a/code/test_filter.py
+++ /dev/null
@@ -1,120 +0,0 @@
-"""Load phenotypic data for N datasets, summarise QC pass rate, and output a .tsv with passed QC scans in order to obtain connectomes. Optionally, pass specific diagnoses but note that the summary is currently calculated on the whole dataset.
-
-
-filter according to scans that passed QC on specific column, and output a merged tsv with pheno and QC results.
-
-To run, needs:
-- rest_df.tsv, a TSV file of concatenated QC results, generated using qc_output/qc.ipynb
-- TSV of pheno data for each specificed dataset, generated using the corresponding script in this repo
-
-Note:
-- Currently diagnoses are matched to scans according to the nearest diagnosis, with no limit. The tolerance can be added to set a limit, e.g. within 1 year.
-
-Author: Natasha Clarke; last edit 2024-03-25
-
-"""
-
-import argparse
-import pandas as pd
-from pathlib import Path
-
-
-def merge_adni(qc_df_filtered, pheno_df):
-    pheno_df["ses"] = pd.to_datetime(pheno_df["ses"])
-    qc_df_filtered["ses"] = pd.to_datetime(qc_df_filtered["ses"])
-
-    pheno_df = pheno_df.sort_values(by="ses")
-    qc_df_filtered = qc_df_filtered.sort_values(by="ses")
-
-    merged_df = pd.merge_asof(
-        qc_df_filtered,
-        pheno_df,
-        by="participant_id",  # Match participants
-        on="ses",  # Find the nearest match based on session date
-        direction="nearest",
-    )  # tolerance=pd.Timedelta(days=365),
-
-    return merged_df
-
-
-def merge_oasis3(qc_df_filtered, pheno_df):
-    pheno_df["ses_numeric"] = pheno_df["ses"].str.replace("d", "").astype(int)
-    qc_df_filtered["ses_numeric"] = (
-        qc_df_filtered["ses"].str.replace("d", "").astype(int)
-    )
-
-    pheno_df = pheno_df.sort_values(by="ses_numeric")
-    qc_df_filtered = qc_df_filtered.sort_values(by="ses_numeric")
-
-    merged_df = pd.merge_asof(
-        qc_df_filtered,
-        pheno_df,
-        by="participant_id",  # Match participants
-        on="ses_numeric",  # Find the nearest match based on session date
-        direction="nearest",
-    )  # tolerance=365
-
-    merged_df["ses_numeric"] = pheno_df["ses"]
-
-    return merged_df
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(
-        description="Filter phenotype data according to QC result"
-    )
-    parser.add_argument("--root_p", type=Path, help="Root path for data")
-    parser.add_argument(
-        "--datasets", nargs="+", type=str, help="List of datasets to process"
-    )
-    parser.add_argument(
-        "--which_qc_col",
-        type=str,
-        help="Column for evaluating QC. Default=pass_func_qc",
-    )
-    parser.add_argument(
-        "--diagnoses", nargs="+", type=str, help="Diagnoses of interest"
-    )
-
-    args = parser.parse_args()
-    datasets = args.datasets
-    which_qc_col = args.which_qc_col or "pass_func_qc"
-    diagnoses = args.diagnoses if args.diagnoses else None
-
-    rest_qc_p = args.root_p / "qc_output/rest_df.tsv"
-    frames_p = args.root_p / "wrangling-phenotype/data/frames/total_frames_master.tsv"
-    output_p = args.root_p / "wrangling-phenotype/outputs"
-
-    # Load QC and frames data
-    qc_df = pd.read_csv(
-        rest_qc_p, sep="\t", low_memory=False
-    )  # dtype={"participant_id": str, "ses": str}
-    frames_df = pd.read_csv(frames_p, sep="\t", dtype={"participant_id": str})
-
-    # Merge pheno and qc data for each dataset
-    df_list = []
-    master_df = pd.DataFrame()
-
-    for dataset in datasets:
-        pheno_p_template = "wrangling-phenotype/outputs/{dataset}_pheno.tsv"
-        pheno_p = args.root_p / pheno_p_template.format(dataset=dataset)
-        pheno_df = pd.read_csv(pheno_p, sep="\t", dtype={"participant_id": str})
-
-        qc_df_filtered = qc_df[qc_df["dataset"] == dataset].copy()
-        if dataset == "adni":
-            df = merge_adni(qc_df_filtered, pheno_df)
-            df_list.append(df)
-
-        elif dataset == "oasis3":
-            df = merge_oasis3(qc_df_filtered, pheno_df)
-            df_list.append(df)
-
-        master_df = pd.concat([master_df, df], ignore_index=True)
-    master_df = master_df.drop(columns=["site_x"])
-
-    # Save output
-    # qc_summary_df.to_csv(output_p / "qc_summary.tsv", sep="\t", index=False)
-    # matched_df.to_csv(output_p / "passed_qc_master.tsv", sep="\t", index=False)
-    master_df.to_csv(output_p / "test.tsv", sep="\t", index=False)
-
-    print(f"Data have been processed and output to {output_p}")

From e4633a1935e30f71fa5ef7e0de2bb85f17f9feb8 Mon Sep 17 00:00:00 2001
From: clarkenj <natashajayne@gmail.com>
Date: Tue, 23 Apr 2024 13:44:01 -0400
Subject: [PATCH 22/25] add scanner variables to output

---
 code/filter_qc.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/code/filter_qc.py b/code/filter_qc.py
index 583ebe6..f21f20d 100644
--- a/code/filter_qc.py
+++ b/code/filter_qc.py
@@ -25,6 +25,8 @@ def create_master_df(root_p, datasets):
         "site",
         "diagnosis",
         "dataset",
+        "scanner",
+        "site_scanner",
         "mean_fd_raw",
         "mean_fd_scrubbed",
         "proportion_kept",

From dd6e7ce01556c18ada3c3aac9c0784a02022a662 Mon Sep 17 00:00:00 2001
From: clarkenj <natashajayne@gmail.com>
Date: Tue, 30 Apr 2024 09:23:03 -0400
Subject: [PATCH 23/25] removed site_scanner variable

---
 code/filter_qc.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/code/filter_qc.py b/code/filter_qc.py
index f21f20d..f974c35 100644
--- a/code/filter_qc.py
+++ b/code/filter_qc.py
@@ -26,7 +26,6 @@ def create_master_df(root_p, datasets):
         "diagnosis",
         "dataset",
         "scanner",
-        "site_scanner",
         "mean_fd_raw",
         "mean_fd_scrubbed",
         "proportion_kept",

From ee25495f5d83737bb9e67f1f1ccaddc47b5a6fc5 Mon Sep 17 00:00:00 2001
From: clarkenj <natashajayne@gmail.com>
Date: Tue, 16 Jul 2024 17:34:14 -0400
Subject: [PATCH 24/25] removed scanner column

---
 code/filter_qc.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/code/filter_qc.py b/code/filter_qc.py
index f974c35..1b1ce8b 100644
--- a/code/filter_qc.py
+++ b/code/filter_qc.py
@@ -4,7 +4,7 @@
 To run, needs:
 - TSV of pheno and qc data for each specificed dataset, generated using the corresponding script in this repo
 
-Author: Natasha Clarke; last edit 2024-03-26
+Author: Natasha Clarke; last edit 2024-07-16
 
 """
 
@@ -25,7 +25,6 @@ def create_master_df(root_p, datasets):
         "site",
         "diagnosis",
         "dataset",
-        "scanner",
         "mean_fd_raw",
         "mean_fd_scrubbed",
         "proportion_kept",
@@ -35,12 +34,13 @@ def create_master_df(root_p, datasets):
         "pass_anat_qc",
         "pass_all_qc",
         "different_func_affine",
-    ]
+    ]  # scanner
 
     master_df = pd.DataFrame()
     for dataset in datasets:
         path_template = "wrangling-phenotype/outputs/{dataset}_qc_pheno.tsv"
         df_p = root_p / path_template.format(dataset=dataset)
+        print(df_p)
         df = pd.read_csv(df_p, sep="\t", dtype={"participant_id": str})
         df = df[cols]
         master_df = pd.concat([master_df, df], ignore_index=True)

From c824239d817aee1962a45df79527c0f5c6b232a0 Mon Sep 17 00:00:00 2001
From: clarkenj <natashajayne@gmail.com>
Date: Tue, 16 Jul 2024 17:34:55 -0400
Subject: [PATCH 25/25] removed scanner column

---
 code/filter_qc.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/code/filter_qc.py b/code/filter_qc.py
index 1b1ce8b..b82cf1a 100644
--- a/code/filter_qc.py
+++ b/code/filter_qc.py
@@ -40,7 +40,6 @@ def create_master_df(root_p, datasets):
     for dataset in datasets:
         path_template = "wrangling-phenotype/outputs/{dataset}_qc_pheno.tsv"
         df_p = root_p / path_template.format(dataset=dataset)
-        print(df_p)
         df = pd.read_csv(df_p, sep="\t", dtype={"participant_id": str})
         df = df[cols]
         master_df = pd.concat([master_df, df], ignore_index=True)