"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"graph = nx.from_pandas_edgelist(meta_rel_df, source=\"from_node\", target=\"to_node\")\n",
"cmap = matplotlib.colors.ListedColormap([\"dodgerblue\", \"lightgray\", \"darkorange\"])\n",
@@ -3162,6 +364,14 @@
"\n",
"* Zheng J, Brumpton BM, Bronson PG, Liu Y, Haycock P, Elsworth B, Haberland V, Baird D, Walker V, Robinson JW, John S, Prins B, Runz H, Nelson MR, Hurle M, Hemani G, Asvold BO, Butterworth A, Smith GD, Scott RA, Gaunt TR. 2019. Systematic Mendelian randomization and colocalization analyses of the plasma proteome and blood transcriptome to prioritize drug targets for complex disease."
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a6adfa21-c26c-45e5-bfa5-f5389144bd17",
+ "metadata": {},
+ "outputs": [],
+ "source": []
}
],
"metadata": {
@@ -3180,7 +390,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.6"
+ "version": "3.9.20"
}
},
"nbformat": 4,
diff --git a/BRH-notebooks/combined_demos/JCOIN_MOUD_accessibility.ipynb b/BRH-notebooks/combined_demos/JCOIN_MOUD_accessibility.ipynb
deleted file mode 100644
index 8a3f1cd6..00000000
--- a/BRH-notebooks/combined_demos/JCOIN_MOUD_accessibility.ipynb
+++ /dev/null
@@ -1,902 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Medication for Opioid Use Disorder (MOUD) Accessibility Analysis \n",
- "\n",
- "*Please note: This notebook uses open access data* \n",
- "*Please note: JCOIN Google Login in the BRH Profile Page needs to be authorized*\n",
- "\n",
- "\n",
- "## Qiong Liu\n",
- "Feb 9th, 2022\n",
- "\n",
- "Over 800,000 people have died from drug overdoses since 1999 ([ref](https://wonder.cdc.gov/)). The estimated population with **Opioid Use Disorder (OUD)** is over **2.1 million** in the United States ([ref](https://www.ncbi.nlm.nih.gov/books/NBK553166/)). Throughout the COVID-19 pandemic, this opioid crisis has worsened with an estimated **100,306 drug overdose deaths** in the US during the 12-month period ending in April 2021, which is an increase of 28.5% from the 78,056 deaths during the same period the year before ([ref](https://www.cdc.gov/nchs/pressroom/nchs_press_releases/2021/20211117.htm)).\n",
- "\n",
- "To mitigate the adverse consequences of this opioid crisis, strategies, such as improving access to overdose reversal and medication for addiction treatment, are emerging at the state and local level in the US. **Medication for OUD treatment (MOUDs)**, including **methadone hydrochloride, buprenorphine hydrochloride, and extended-release\n",
- "naltrexone hydrochloride**, have been clinically proven to be effective at reducing opioid use and adverse outcomes ([ref](https://www.samhsa.gov/medication-assisted-treatment/medications-counseling-related-conditions#opioid-dependency-medications)). In this notebook, we will assess the MOUD capacity at the state and county levels across the US, and identify the high risk counties with high drug related death rate and low MOUD accessibility, using the data from [project Opioid Environment Policy Scan (OEPS)](https://jcoin.datacommons.io/JCOIN-OEPS). The OEPS data is created and led by the Healthy Regions and Policies Lab at University of Chicago, part of the Methodology and Advanced Analytics Resource Center (MAARC)\n",
- "\n",
- "\n",
- "\n",
- "### Citation\n",
- "\n",
- "Susan Paykin, Dylan Halpern, Qinyun Lin, Moksha Menghaney, Angela Li, Rachel Vigil, Margot Bolanos Gamez, Alexa Jin, Ally Muszynski, and Marynia Kolak. (2021). GeoDaCenter/opioid-policy-scan: Opioid Environment Policy Scan Data Warehouse (v1.0). Zenodo. https://doi.org/10.5281/zenodo.5842465\n",
- "\n",
- "---\n",
- "\n",
- "## Content\n",
- "\n",
- "* [MOUDs providers distribution at state level across the United States](#MOUDs-providers-distribution-at-state-level-across-the-United-States)\n",
- "* [MOUD providers that offer more than one category](#MOUD-providers-that-offer-more-than-one-category)\n",
- "* [MOUDs providers distribution at county level across the United States](#MOUDs-providers-distribution-at-county-level-across-the-United-States)\n",
- "* [Identifying high risk counties with high drug related death rate and low capacity of MOUDs](#Identifying-high-risk-counties-with-high-drug-related-death-rate-and-low-capacity-of-MOUDs)\n",
- "* [Key takeaways](#Key-takeaways)\n",
- "* [Additional resources for analyzing OEPS data](#Additional-resources-for-analyzing-OEPS-data)\n",
- "\n",
- "\n",
- ">**Note**: To view code clocks, please click the `Show Code` button at the upper-left corner.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# suppress warnings (to include warnings,set warn to 0)\n",
- "options(warn=-1)\n",
- "\n",
- "# import Python libraries\n",
- "library(sf)\n",
- "library(tidygeocoder)\n",
- "library(tmap)\n",
- "library(tidyverse)\n",
- "library(readxl)\n",
- "library(units)\n",
- "library(repr)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The next block imports all the object files from JCOIN OEPS project that will be used in this notebook."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# File name: us-wide-moudsCleaned.csv - File size: 3076193\n",
- "system('gen3 drs-pull object dg.6VTS/f30a3855-9093-473f-8ce6-784fc9b53066')\n",
- "\n",
- "# File name: Health01_C.csv - File size: 137482\n",
- "system('gen3 drs-pull object dg.6VTS/bcbcb063-68eb-440f-9466-68398e645e2c')\n",
- "\n",
- "# File name: Health01_S.csv - File size: 1652\n",
- "system('gen3 drs-pull object dg.6VTS/c6268722-88e5-40ad-bf83-0859e5ea5db6')\n",
- "\n",
- "# File name: counties2018.dbf - File size: 1913800\n",
- "system('gen3 drs-pull object dg.6VTS/81d375fb-85a0-4bfa-a3d5-80cc26c8043d')\n",
- "\n",
- "# File name: counties2018.prj - File size: 143\n",
- "system('gen3 drs-pull object dg.6VTS/9c0b2388-c066-4e10-89f1-52e7fc35476e')\n",
- "\n",
- "# File name: counties2018.shp - File size: 16514608\n",
- "system('gen3 drs-pull object dg.6VTS/2e3e0411-d09d-47dc-bcfd-1b59db06a81b')\n",
- "\n",
- "# File name: counties2018.shx - File size: 25236\n",
- "system('gen3 drs-pull object dg.6VTS/ebc612c6-63e7-4b37-b161-cac3e65b9d86')\n",
- "\n",
- "# File name: states2018.dbf - File size: 31381\n",
- "system('gen3 drs-pull object dg.6VTS/960a8487-fe60-4dfa-aac6-73b3bcd5a718')\n",
- "\n",
- "# File name: states2018.prj - File size: 143\n",
- "system('gen3 drs-pull object dg.6VTS/17c65a27-4c99-45ff-838c-0c23524e9e86')\n",
- "\n",
- "# File name: states2018.shp - File size: 4577908\n",
- "system('gen3 drs-pull object dg.6VTS/cdc09eb7-cfeb-4a1e-81ee-7955c6ddd983')\n",
- "\n",
- "# File name: states2018.shx - File size: 508\n",
- "system('gen3 drs-pull object dg.6VTS/c235a98a-e796-4a58-bd9e-4677e447ab67')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The MOUDs location across the US is stored in a comma-delimited file called `us-wide-moudsCleaned.csv`. The next two blocks read the file into dataframe, conduct basic data cleanup, and show the first two lines of the data.\n",
- "\n",
- "Each line shows a record of MOUDs provider including its detailed location and which MOUD (under `category` column) the provider can prescribe."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Read the file of MOUD locations\n",
- "moud_location = 'us-wide-moudsCleaned.csv'\n",
- "\n",
- "class_vector <-rep(c(\"character\"),17)\n",
- "moud_clinics<-read.csv(moud_location, stringsAsFactors=FALSE,\n",
- " na.strings=c(\"NA\"), colClasses=class_vector)\n",
- "\n",
- "# Dataframe clean up\n",
- "names(moud_clinics) <-c(\n",
- " \"name1\",\n",
- " \"name2\" ,\n",
- " \"street1\" ,\n",
- " \"street2\" ,\n",
- " \"city\" ,\n",
- " \"state\" ,\n",
- " \"zip\" ,\n",
- " \"zip4\" ,\n",
- " \"category\",\n",
- " \"countyGEOID\" ,\n",
- " \"countyName\" ,\n",
- " \"source\",\n",
- " \"geom1\" ,\n",
- " \"geom2\",\n",
- " \"Longitude\",\n",
- " \"Latitude\")\n",
- "\n",
- "moud_clinics$geom <- paste(moud_clinics$geom1,\",\",moud_clinics$geom2)\n",
- "dropcol <- c(\"geom1\", \"geom2\")\n",
- "moud_clinics <- moud_clinics[, !(names(moud_clinics) %in% dropcol)]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# show the head of the data frame\n",
- "head(moud_clinics, n=2)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## MOUDs providers distribution at state level across the United States\n",
- "\n",
- "Below is the summary count table of MOUDs for each category (methadone, buprenorphine, and naltrexone). Keep in mind that a MOUD provider can appear in multiple categories. For instance, one provider can prescribe both buprenorphine and naltrexone."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Some providers under Naltrexone category have been double registered under both SAMHSA and vivitrolWeb.\n",
- "# We need to remove duplicates first\n",
- "# Create a new variable with complete full address\n",
- "moud_clinics$full_address <- paste(moud_clinics$name1,\",\",moud_clinics$name2, \",\", moud_clinics$street1,\",\",moud_clinics$street2, \",\", moud_clinics$city, \",\", moud_clinics$state, \",\",moud_clinics$zip)\n",
- "\n",
- "moud_noduplicate <- moud_clinics %>% \n",
- " arrange(desc(full_address), desc(category)) %>% \n",
- " group_by(full_address) %>% \n",
- " distinct(category, .keep_all = TRUE)\n",
- "\n",
- "# Count the number of MOUD providers under each category\n",
- "moud_noduplicate %>% group_by(category) %>% summarise(n = n())"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### MOUDs categories\n",
- "* The most common category of MOUD providors across the United States is Naltrexone/vivitro (9,141), which is follow by buprenorphine (4,169), and methodone (1,457).\n",
- "* **Methodone**, an opioid agonist, has the potential leading to severe psychological or physical dependence, and is currently classfied as *schedule II* drug under the Controlled Substances Act. **Buprenorphine**, depending on the type of receptor, may be an agonist, partial agonist, or antagonist of the opioid receptor. Buprenorphine is classifed as *schedule III* drug. Meanwhile, **naltrexone/vivitrol** is not classified as a controlled substance. The prevalance of each MOUD category across the country reflects the regulatory stringency accordingly.\n",
- "\n",
- "\n",
- "### MOUDs distribution by state\n",
- "\n",
- "Below we generated a summary count table for each MOUD category in each state. As mentioned above, same provider can appear in more than one category. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Create a dataframe of MOUD category counts by state\n",
- "methadone_state <- moud_noduplicate %>% filter(category == 'methadone') %>% group_by(state) %>% summarise(n = n())\n",
- "colnames(methadone_state)[2] <- \"methadone\"\n",
- "\n",
- "buprenorphine_state <- moud_noduplicate %>% filter(category == 'buprenorphine') %>% group_by(state) %>% summarise(n = n())\n",
- "colnames(buprenorphine_state)[2]<- \"buprenorphine\"\n",
- "\n",
- "naltrexone_state <- moud_noduplicate %>% filter(category == 'naltrexone/vivitrol') %>% group_by(state) %>% summarise(n = n())\n",
- "colnames(naltrexone_state)[2]<- \"naltrexone\"\n",
- "\n",
- "merged_df <- merge(methadone_state, buprenorphine_state, by='state', all=TRUE) %>% merge(naltrexone_state, by='state', all=TRUE)\n",
- "head(merged_df, n=5)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "To calculate the accessibilty of MOUDs in each state, we merged the MOUD counts by state dataframe with another dataframe (`Health01_S.csv`), which contains the estimated state population from 2009-18. Below is the first few lines of a new dataframe `MOUD_death_pop` with additional columns for MOUDs rate in each state."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Calculate the MOUDs rate at the state level\n",
- "# Read the drug related death rate dataset which has the State population number\n",
- "OD_death_S <-read.csv(\"Health01_S.csv\",\n",
- " stringsAsFactors=FALSE, colClasses=c(\"STATEFP\"=\"character\", \"state\"=\"character\"))\n",
- "\n",
- "# Data cleaning\n",
- "# remove last row\n",
- "nrow <-dim(OD_death_S)[1]\n",
- "OD_death_S <- OD_death_S[1:(nrow-1),]\n",
- "# The population is the aggregation over ten years (09-18)\n",
- "OD_death_S$pop <- OD_death_S$pop/10\n",
- "# remove the District of Columbia, only 48 State left in the data frame\n",
- "OD_death_S <- OD_death_S[!(OD_death_S$STATEFP==\"11\"), ]\n",
- "# convert the state full name into 2 letter abbreviation\n",
- "OD_death_S$state <- state.abb[match(OD_death_S$state,state.name)]\n",
- "\n",
- "# Merge the MOUD count by state table with state level drug related death rate table\n",
- "MOUD_death_pop <- merge(merged_df, OD_death_S, by='state', all.y=TRUE)\n",
- "\n",
- "#Calculate the rate of each MOUD category per 100K population\n",
- "MOUD_death_pop$methRt <- (MOUD_death_pop$methadone/MOUD_death_pop$pop)* 100000\n",
- "MOUD_death_pop$bupreRt <- (MOUD_death_pop$buprenorphine/MOUD_death_pop$pop)* 100000\n",
- "MOUD_death_pop$nalRt <- (MOUD_death_pop$naltrexone/MOUD_death_pop$pop)* 100000\n",
- "MOUD_death_pop[1:5,]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "MOUD_death_pop[,c(\"rawDeathRt\",\"methRt\",\"bupreRt\",\"nalRt\")] %>% summary()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* Two states (SD and WY) have zero methadone providers.\n",
- "* Naltrexone provider rate across all states is much higher (3.227 per 100K pop) compared to methadone (0.5169 per 100K pop) and buprenorphine provider rate (1.54 per 100K pop).\n",
- "* The drug-related death rate across all states has an average of 13.85 per 100K pop\n",
- "\n",
- "Below is a plot of calculated MOUDs rate and drug related death rate by state. We highlighted the top 5 states for each value (state methadone rate, state buprenorphine rate, state naltrexone rate, and drug related death rate)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Prepare the dataframe for bar ploting\n",
- "# Make the 'state' column as row names \n",
- "MOUD_death_pop2 <- MOUD_death_pop[,-1]\n",
- "rownames(MOUD_death_pop2) <- as.character(MOUD_death_pop[,1])\n",
- "# Convert datafram to matrix\n",
- "MOUD_death_pop3 <-as.matrix(sapply(MOUD_death_pop2, as.numeric))\n",
- "rownames(MOUD_death_pop3) <- as.character(MOUD_death_pop[,1])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Find the top 5 states in drug related death rate, methadone rate, buprenorphine rate, and naltrexone rate\n",
- "death_top5 <- MOUD_death_pop %>% arrange(desc(rawDeathRt)) %>% subset(select=state) %>% head(n=5) %>% pull() %>% as.character()\n",
- "methadone_top5<- MOUD_death_pop %>% arrange(desc(methRt)) %>% subset(select=state) %>% head(n=5) %>% pull() %>% as.character()\n",
- "buprenorphine_top5<- MOUD_death_pop %>% arrange(desc(bupreRt)) %>% subset(select=state) %>% head(n=5) %>% pull() %>% as.character()\n",
- "naltrexone_top5<- MOUD_death_pop %>% arrange(desc(nalRt)) %>% subset(select=state) %>% head(n=5) %>% pull() %>% as.character()\n",
- "\n",
- "# Highlight the top 5 states in drug related death rate, methadone provider rate,\n",
- "# buprenorphine provider rate, and naltrexone provider rate\n",
- "state_vector <- MOUD_death_pop['state'] %>% pull() %>% as.character()\n",
- "death_col <- rep(c(\"#C3C3C3\"),each=48)\n",
- "death_col[state_vector %in% death_top5] <- \"#FF0000\"\n",
- "methadone_col <- rep(c(\"#C3C3C3\"),each=48)\n",
- "methadone_col[state_vector %in% methadone_top5] <- \"#4C00FFFF\"\n",
- "buprenorphine_col <- rep(c(\"#C3C3C3\"),each=48)\n",
- "buprenorphine_col[state_vector %in% buprenorphine_top5] <- \"#00FF4DFF\"\n",
- "naltrexone_col <- rep(c(\"#C3C3C3\"),each=48)\n",
- "naltrexone_col[state_vector %in% naltrexone_top5] <- \"#BDFF00FF\"\n",
- "\n",
- "# Adjust plots arrangement and sizes\n",
- "options(repr.plot.width=17, repr.plot.height=12)\n",
- "par(mfrow = c(4,1))\n",
- "\n",
- "barplot(MOUD_death_pop3[,8], main=\"Methadone Provider Rate by State\",cex.main=2,\n",
- " ylab=\"Rate per 100K\",col = methadone_col, ylim=c(0, 1.8), yaxt = \"n\")\n",
- "axis(2,at=seq(0, 1.8, by=0.2),labels=TRUE)\n",
- "\n",
- "barplot(MOUD_death_pop3[,9], main=\"Buprenorphine Provider Rate by State\",cex.main=2,\n",
- " ylab=\"Rate per 100K\",col = buprenorphine_col, ylim=c(0, 4.7), yaxt = \"n\")\n",
- "axis(2,at=seq(0,4.7, by=1),labels=TRUE)\n",
- "\n",
- "barplot(MOUD_death_pop3[,10], main=\"Naltrexone Provider Rate by State\",cex.main=2,\n",
- " ylab=\"Rate per 100K\",col = naltrexone_col, ylim=c(0, 7), yaxt = \"n\")\n",
- "axis(2,at=seq(0,7, by=1),labels=TRUE)\n",
- "\n",
- "barplot(MOUD_death_pop3[,7], main=\"Drug Related Death Rate by State\",cex.main=2,\n",
- " ylab=\"Rate per 100K\",col = death_col, ylim=c(0, 30), yaxt = \"n\")\n",
- "axis(2,at=seq(0, 30, by=5),labels=TRUE)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* **West Virginia, New Mexico, Nevada, Pennsylvania, and Kentucky** (marked in red) are the leading states in drug-related death rate. \n",
- "* **Rhode island** is the leading state in methadone rate, followed by Delaware, Maryland, Vermont, and Connecticut (marked in blue). **Maine** and **Utah** have the highest rate of Buprenorphine provider rate (marked in green) and naltrexone provider rate (marked in yellow), respectively.\n",
- "* **South Dakota and Wyoming** have **zero** methadone providers within the state. However, Wyoming isn't one of the states with the lowest drug-related death rate, which indicates a demand for opioid use disorder (OUD) treatment to be filled.\n",
- "* Among the top five states with the highest drug-related death rate, **Kentucky and Pennsylvania** are the only two states that have relatively higher naltrexone provider rate. **West Virginia, New Mexico, and Nevada** have a low provider rate for all three MOUD categories.\n",
- "* The figure above revealed noteworthy disparities between drug-related death rate and OUD treatment capacity across all states of the US. Opioids are currently the main driver of drug overdose deaths([CDC source](https://www.cdc.gov/drugoverdose/deaths/index.html)). However, **scarcity of MOUD providers**, which plays a crucial role in OUD treatment, has been noted in the states that suffer the most from substance abuse.\n",
- "\n",
- "## MOUD providers that offer more than one category\n",
- "\n",
- "As mentioned above, a MOUDs provider can appear more than once in the dataframe because it can prescribe multiple MOUD categories. Next, we tried to extract unique MOUD providers from original dataset and create a new column called `all_meds` which includes all the MOUDs that each uique provider can prescribe."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Some of the provider are able to prescribe more than one category of MOUD\n",
- "# Extract unique provider and create a new variable for all categories of MOUD that each provider is able to prescribe\n",
- "moud_uniq <- moud_noduplicate %>% arrange(desc(full_address),desc(category)) %>% group_by(full_address) %>% mutate(all_meds = paste0(category, collapse = \",\")) \n",
- "moud_uniq <- moud_uniq %>% distinct(full_address, .keep_all=TRUE)\n",
- "moud_uniq[1:5,c(16,17)]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Below is a summary count table for the variable `all_meds`."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# The counts of MOUD providers that offer multiple MOUD categories\n",
- "moud_uniq %>% group_by(all_meds) %>% summarise(n = n()) %>% arrange(desc(n))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* The number of unique MOUD providers in the US is **10,680** in this dataset.\n",
- "* There are only **486 unique MOUD providers (< 5%)** that can offer all three categories of MOUD.\n",
- "* As expected, the naltrexone providers alone (**6,141**) account for over half of all the unique MOUD providers in the US, given the fact that naltrexone is not classified as a controlled substance.\n",
- "* Because naltrexone treatment requires full detoxification, initiating treatment among active opioid users is more difficult with this treatment approach.\n",
- "* Buprenorphine, a partial opioid agonist, is most widely used in combination with the short-acting opiate antagonist naltrexone. The commercial brand for this combined medication is Suboxone. There are **2,470** MOUD providers across the country that can offer both buprenorphine and naltrexone.\n",
- "* Both methadone and buprenorphine can be used as maintenance medications. At the doses prescribed, maintenance medications can minimize withdrawal symptoms and cravings in the patient without producing a euphoric high. Because there is a risk of diversion to the illicit market, both medications are classified as the controlled substance. There are only **1,457 providers (13.6%)** out of all unique MOUD providers that can prescribe methadone.\n",
- "\n",
- "## MOUDs providers distribution at county level across the United States\n",
- "\n",
- "We first calcualted MOUDs rate in each category at county level. Below is the `shp` file that was used to define county boundaries on the map."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Read the shape fiel of US counties\n",
- "county_sf <- st_read(\"counties2018.shp\")\n",
- "\n",
- "# Show first 5 counties from IL in county shapefile \n",
- "county_sf %>% filter(STATEFP==\"17\") %>% head(n=5)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "dim(county_sf)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* There are 3142 counties in the county shape file\n",
- "\n",
- "We created a dataframe with MOUDS provider counts in each category at county level, and then merged it with the county shape (`shp`) dataframe. Notice that there are a lot of counties in the US with no MOUD provider."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# calculate the count of MOUD in each category within each county\n",
- "methadone_county <- moud_noduplicate %>% filter(category==\"methadone\") %>% group_by(countyGEOID) %>% tally()\n",
- "buprenorphine_county <- moud_noduplicate %>% filter(category==\"buprenorphine\") %>% group_by(countyGEOID) %>% tally()\n",
- "naltrexone_county <- moud_noduplicate %>% filter(category==\"naltrexone/vivitrol\") %>% group_by(countyGEOID) %>% tally()\n",
- "colnames(methadone_county)[2]<-\"methadone_count\"\n",
- "colnames(buprenorphine_county)[2]<-\"buprenorphine_count\"\n",
- "colnames(naltrexone_county)[2]<-\"naltrexone_count\"\n",
- "\n",
- "# merge the count of MOUD providers with the county_sf \n",
- "county_sf_moud <- merge(county_sf, methadone_county, by.x='GEOID', by.y=\"countyGEOID\", all.x=TRUE) %>% merge(buprenorphine_county, by.x='GEOID', by.y=\"countyGEOID\", all.x=TRUE) %>% merge(naltrexone_county, by.x='GEOID', by.y=\"countyGEOID\", all.x=TRUE)\n",
- "county_sf_moud %>% head(n=5)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### How many counties have no MOUD providers under certain category\n",
- "\n",
- "Below is a summary table of MOUDs counts at county level in the US."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Print the summary of columns of methadone, buprenorphine, and naltrexone counts \n",
- "summary(county_sf_moud[,c(\"methadone_count\",\"buprenorphine_count\",\"naltrexone_count\")] %>% st_set_geometry(NULL))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* Out of 3,142 counties, 2,529 counties have no methadone providers, 2,065 counties have no buprenorphine providers, and 1,661 counties have no naltrexone providers.\n",
- "\n",
- "### How many counties don't have any MOUD provider\n",
- "\n",
- "We also calcualted the number of counties in the US don't have any MOUDs provider."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# combine county level moud providers \n",
- "merged_county_moud <- merge(methadone_county, buprenorphine_county, by='countyGEOID', all=TRUE) %>% merge(naltrexone_county, by='countyGEOID', all=TRUE)\n",
- "3142-dim(merged_county_moud)[1]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* Out of 3,142 counties, 1,540 counties don't have any MOUD providers (methadone, buprenorphine, or naltrexone)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Unique MOUD providers distribution at county level\n",
- "\n",
- "We counted the unique MOUDs providers in each county across the country. We merged this dataframe with another table of drug related death rate at county level, which includes estimated county population. The population column was then used to calcualte the MOUDs rate (unique providers) in each county."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Clean up the unique moud dataset by removing the MOUD without countyGEOID\n",
- "# For instance county from Puerto Rico doesn't have county GEOID\n",
- "moud_uniq_subset <- moud_uniq[!(is.na(moud_uniq$countyGEOID)),]\n",
- "\n",
- "moud_uniq_county_count <- moud_uniq_subset %>% group_by(countyGEOID)%>% tally()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Read county level drug-related death rate file\n",
- "OD_death_c <- read.csv(\"Health01_C.csv\",\n",
- " stringsAsFactors=FALSE, colClasses=c(\"COUNTYFP\"=\"character\", \"state.code\"=\"character\"))\n",
- "\n",
- "OD_death_c$state.code <- str_pad(OD_death_c$state.code, 2, pad = \"0\")\n",
- "OD_death_c$COUNTYFP <- str_pad(OD_death_c$COUNTYFP, 5, pad = \"0\")\n",
- "\n",
- "# The Population of each county is an aggrevation over 10 years (09-18)\n",
- "OD_death_c$pop <- OD_death_c$pop/10\n",
- "OD_death_c[1:5,]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Calculate the unique MOUD providers rate per 100K population in each county\n",
- "OD_death_c_uniq_moud <- merge(OD_death_c, moud_uniq_county_count, by.x='COUNTYFP', by.y='countyGEOID', all.x=TRUE)\n",
- "colnames(OD_death_c_uniq_moud)[8] <- \"unique_moud\"\n",
- "\n",
- "OD_death_c_uniq_moud$moud_rate <- (OD_death_c_uniq_moud$unique_moud/OD_death_c_uniq_moud$pop)*100000"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "OD_death_c_uniq_moud[!(is.na(OD_death_c_uniq_moud$unique_moud)),] %>% arrange(desc(moud_rate)) %>% head(n=5)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* The county with the highest MOUD rate (56.9 MOUD providers per 100K population) is **Lawrence County, Kentucky**.\n",
- "* The top counties with high MOUD rate also have high drug-related death rate as shown above.\n",
- "\n",
- "We then plotted the unique MOUD provider rate in each county below. The drug related death rate at county level was also plotted to show the disparities between drug-related death rate and OUD treatment capacity."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Plot county level moud rate and drug related death rate on map\n",
- "\n",
- "# merge with county_sf dataframe\n",
- "county_sf_uniq_moud <- merge(county_sf, OD_death_c_uniq_moud, by.x='GEOID', by.y=\"COUNTYFP\", all.x=TRUE)\n",
- "\n",
- "# Two states and DC to exclude from the map for display\n",
- "exclude_states <- c(\"02\",\"11\",\"15\")\n",
- "county_sf_uniq_moud_subset <- county_sf_uniq_moud[!(county_sf_uniq_moud$STATEFP %in% exclude_states),]\n",
- "\n",
- "# Read the state shape file for plotting state boundaries\n",
- "state_sf <- st_read(\"states2018.shp\")\n",
- "state_sf_subset <- state_sf[!(state_sf$STATEFP %in% exclude_states),]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Plot county level moud rate and death rate\n",
- "options(repr.plot.width=17, repr.plot.height=12)\n",
- "\n",
- "tmap_mode(\"plot\")\n",
- "tm_moud_unique <- tm_shape(county_sf_uniq_moud_subset) + tm_borders(alpha = 0.3) +\n",
- " tm_polygons(\"moud_rate\", style=\"quantile\",pal=\"BuPu\",\n",
- " title = \"Moud Provider Rate per 100K\") +\n",
- " tm_shape(state_sf_subset) + tm_borders(lwd=1.5) + tm_text(\"STUSPS\", size=1.5)+\n",
- " tm_layout(legend.stack = \"horizontal\") \n",
- "\n",
- "tm_death_rate<-tm_shape(county_sf_uniq_moud_subset) + tm_borders(alpha = 0.3) +\n",
- " tm_polygons(\"rawDeathRt\", pal=\"YlOrRd\", style=\"quantile\"\n",
- " , title = \"Drug Related Death Rate per 100K\") +\n",
- " tm_shape(state_sf_subset) + tm_borders(lwd = 1.5) + tm_text(\"STUSPS\", size=1.5) +\n",
- " tm_layout(legend.stack = \"horizontal\")\n",
- "\n",
- "tmap_arrange(tm_moud_unique, tm_death_rate)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* The county level drug-related death rate on the map revealed the severity and prevalence of drug abuse along the **Appalachian regions**. \n",
- "* A high drug -elated death rate is also observed in a large amount of counties in the **west region of the US**.\n",
- "* The disparities between drug-related death rate and OUD treatment capacity are noted for several states from the figure above. For some states, a high death rate and low moud provider rate have been seen in the majorites of the counties, such as **New Mexico, Nevada, Oklahoma, and Tennessee**."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### MOUD rate and drug related death rate of Appalachian counties\n",
- "\n",
- "The Appalachian region suffers a higher opioid overdose mortality rate comapred to the rest of the country. Below shows zoomed-in plots of unique MOUD providers rate and drug related death rate of Appalacgian counties on map."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Download a list of counties that belong to the Appalachian region\n",
- "appalachian_counties <-read_csv(\"https://gist.githubusercontent.com/akanik/979b38bfd3afa797ec33944fac7e26d7/raw/491add919d0ba147dc0fcc1616575affe5371e3e/arc-gov-appal-counties.csv\")\n",
- "appalachian_counties <- as.data.frame(appalachian_counties)\n",
- "appalachian_counties$county[appalachian_counties$county==\"De Kalb\"] <- \"DeKalb\"\n",
- "appalachian_counties$county <- paste(appalachian_counties$county, \",\",appalachian_counties$state)\n",
- "\n",
- "# Generate a translation table between county, state and GEOID\n",
- "county_fips <- county_sf[,c('STATEFP','GEOID','NAME')] %>% st_set_geometry(NULL)\n",
- "state_fips <- state_sf[,c('GEOID','NAME')] %>% st_set_geometry(NULL)\n",
- "colnames(state_fips)[2] <- \"state_name\" \n",
- "county_fips <- merge(county_fips, state_fips, by.x='STATEFP', by.y='GEOID', all.x=TRUE)\n",
- "county_fips$county<- paste(county_fips$NAME, \",\", county_fips$state_name)\n",
- "\n",
- "# Add GEOID to the Appalachian dataframe\n",
- "appalachian_counties_fips <- merge(appalachian_counties, county_fips, \n",
- " by='county', all.x=TRUE)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Filter the county_sf_uniq_moud_subset based on the Appalachian region\n",
- "appalachian_county_sf_uniq_moud <- county_sf_uniq_moud_subset[(county_sf_uniq_moud_subset$GEOID %in% appalachian_counties_fips$GEOID),]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Plot Appalachian counties on map with MOUD rate and drug-related death rate\n",
- "options(repr.plot.width=17, repr.plot.height=12)\n",
- "\n",
- "tmap_mode(\"plot\")\n",
- "tm_moud_unique_app <- tm_shape(appalachian_county_sf_uniq_moud) + tm_borders(alpha = 0.3) +\n",
- " tm_polygons(\"moud_rate\", style=\"quantile\",pal=\"BuPu\",\n",
- " title = \"Moud Provider Rate per 100K\") +\n",
- " tm_shape(state_sf_subset) + tm_borders(lwd=1.5) + tm_text(\"STUSPS\", size=1.5)+\n",
- " tm_layout(legend.outside=TRUE,legend.position=c(\"right\", \"bottom\")) \n",
- "\n",
- "tm_death_rate_app<-tm_shape(appalachian_county_sf_uniq_moud) + tm_borders(alpha = 0.3) +\n",
- " tm_polygons(\"rawDeathRt\", pal=\"YlOrRd\", style=\"quantile\"\n",
- " , title = \"Drug Related Death Rate per 100K\") +\n",
- " tm_shape(state_sf_subset) + tm_borders(lwd = 1.5) + tm_text(\"STUSPS\", size=1.5) +\n",
- " tm_layout(legend.outside=TRUE,legend.position=c(\"right\", \"bottom\"))\n",
- "\n",
- "tmap_arrange(tm_moud_unique_app, tm_death_rate_app)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* The Appalachian region suffers a 72 percent higher opioid overdose mortality rate compared to the non-Appalachian counties throughout the United States([Source](https://oig.hhs.gov/oei/reports/OEI-05-19-00410.asp)). The zoomed-in visualization of the Appalachian region revealed a cluster of counties with a noteworthy high death rate around the tristate area (east of KY, southwest of WV, and west of VA).\n",
- "* Almost every county in the south of West Virginia is found with a high drug-related death rate. However, the low capacity of MOUD providers is noted in the majority of the southern counties of WV. \n",
- "* It is noteworthy to mention that a lot of counties along the Appalachian region have no MOUD providers (marked in grey as Missing)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "summary(appalachian_county_sf_uniq_moud[,c(\"moud_rate\", \"rawDeathRt\")], na.rm=TRUE)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* Among over 400 Appalachian counties, a total of 153 Appalachian counties don't have any MOUD provider (nearly 1/3). \n",
- "* The average drug-related death rate in Appalachian counties is 20.76 per 100K population."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Identifying high risk counties with high drug related death rate and low capacity of MOUDs\n",
- "\n",
- "Below is the summary of drug related death rate and unique MOUDs rate at county level. We then used the medium number as threshold to identify the high risk counties in the US."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "OD_death_c_uniq_moud[,c(\"rawDeathRt\", \"moud_rate\")] %>% summary(na.rm=TRUE)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* The mean of the county level drug-related death rate (15.37) and the mean of the county level MOUD provider rate (5.4243) will be used as the threshold to identify high risk counties. \n",
- "* Please note that there are 960 counties that don't have any MOUD provider. The mean of MOUD rate at the county level was calculated based on counties with at least one provider.\n",
- "\n",
- "We calculated how many counties in the US were considered as high risk county using the threshold mentioned above."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# The counties with moud_rate as NA means there is no moud provider within the county\n",
- "county_sf_uniq_moud$moud_rate[is.na(county_sf_uniq_moud$moud_rate)] <- 0\n",
- "\n",
- "high_risk_counties_sf <- county_sf_uniq_moud[!(is.na(county_sf_uniq_moud$rawDeathRt)), ] %>% filter(moud_rate <=5.4243 & rawDeathRt >=15.37)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "high_risk_counties_sf %>% dim()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Below shows how many states have at least one high risk county and the leading states with the highest nunber of high risk counties."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "high_risk_counties_sf %>% group_by(state) %>% tally() %>% dim()\n",
- "high_risk_counties_sf %>% group_by(state) %>% tally() %>% arrange(desc(n)) %>% head(n=5)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* There are a total of 675 high risk counties (high drug related death rate and low MOUD capacity) across 46 states. \n",
- "* Tennessee(TN), Oklahoma(OK), Georgia(GA), Kentucky(KY), and Texas(TX) are the leading states with the highest number of high risk counties.\n",
- "* The State of Tennessee has the most high risk counties from this analysis. A large number of Tennessee counties with high drug-related death rate have no MOUD provider."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We summed up the population of all high risk counties from the same state, and calcualted the high risk population percentage for each state. The table below shows the leading states with highest population percentage affected by the high risk of MOUDs accessibility."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "high_risk_pop <- high_risk_counties_sf %>% group_by(state) %>% summarise(hs_pop = sum(pop)) %>% st_set_geometry(NULL)\n",
- "high_risk_pop$state <- state.abb[match(high_risk_pop$state,state.name)]\n",
- "high_risk_pop <- merge(high_risk_pop, OD_death_S[,c(\"state\",\"pop\")], by.x=\"state\", by.y=\"state\", all.x=TRUE)\n",
- "\n",
- "# Calculate the percentage of population in high risk counties\n",
- "high_risk_pop$hs_percent <- (high_risk_pop$hs_pop/high_risk_pop$pop)*100\n",
- "\n",
- "# Summary of high risk population percent\n",
- "summary(high_risk_pop$hs_percent, na.rm=TRUE)\n",
- "# Show states with highest and low portion of high risk county population\n",
- "high_risk_pop %>% arrange(desc(hs_percent)) %>% head()\n",
- "high_risk_pop %>% arrange(hs_percent) %>% head()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* Among states with high risk counties, an average of 28.61% state population is affected by a high drug-related death rate and low MOUD capacity.\n",
- "* Over 90% of the population in Nevada is found in high risk counties. Other leading states are New Mexico (NM) (73.83%), Oklahoma(73.72%), Arizona (65.0%), Michigan (62%) and WV (57%). \n",
- "* Only 0.25% of the IL populations are found in high risk counties. Other states with a small portion of population in high risk counties are Nebraska, North Dakota, South Dakota, New York, and Texas.\n",
- "\n",
- "We then plotted these high risk counties on the map. Each dot represents a high risk county on the map. The size and the color of the dot coresponds to the drug related death rate of higher risk county."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Three states to exclude from the map\n",
- "exclude_states <- c(\"02\",\"11\",\"15\")\n",
- "high_risk_counties_sf_subset <- high_risk_counties_sf[!(high_risk_counties_sf$state.code %in% exclude_states),]\n",
- "\n",
- "# Plot Appalachian counties on map with MOUD rate and drug related death rate\n",
- "options(repr.plot.width=20, repr.plot.height=12)\n",
- "\n",
- "tmap_mode(\"plot\")\n",
- "\n",
- "tm_high_risk_death<-tm_shape(high_risk_counties_sf_subset) + tm_borders(alpha = 0.3) +\n",
- " tm_bubbles(size=\"rawDeathRt\", alpha=0.8, col = \"rawDeathRt\", pal=\"YlOrRd\", \n",
- " style=\"quantile\") +\n",
- " tm_shape(state_sf_subset) + tm_borders(lwd = 1.5) + tm_text(\"STUSPS\", size=1.5) +\n",
- " tm_layout(legend.stack = \"horizontal\")\n",
- "\n",
- "tmap_arrange(tm_high_risk_death)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "* The map of high risk counties (high drug-related death rate and low MOUD capacity) show a huge cluster of high risk counties in the southwest area of the Appalachian region (**Kentucky, Tennessee, and Georgia**). \n",
- "* In the south-central region of US, **Oklahoma** is the leading state with the highest number of high risk counties.\n",
- "* Higher density of high risk counties is also observed in East North Central states, such as **Michigan, Indiana, and Ohio**.\n",
- "\n",
- "## Key takeaways\n",
- "* This analysis revealed noteworthy disparities between drug-related death rate and MOUD treatment capacity in **the Appalachian region, south region, and pacific region of the United States**.\n",
- "* Over 80% of the counties don't have any methadone providers. Half of the counties in the US don't have any moud providers. \n",
- "* Both methadone and buprenorphine have shown promising results as an opioid addiction maintenance medication in clinical trials. However, we've seen low accessibility of providers for both medications, especially methadone, across the states. \n",
- "\n",
- "## Additional resources for analyzing OEPS data\n",
- "\n",
- "* [Opioid Environment Toolkit](https://www.jcoinctc.org/resources/opioid-environment-toolkit/)\n",
- "\n",
- "The Center for Spatial Data Science at UChicago has created an Opioid Environment Toolkit.ย This toolkit provides anย introduction to GIS and spatial analysisย for opioid environment applications that allows researchers, analysts, and practitioners to support their communities with better data analytics and visualization services.\n",
- "\n",
- "* [Jupyter Notebook example using Opioid Environment Toolkit](https://healdata.org/dashboard/Public/notebooks/Opioid_Environment_Toolkit_And_OEPS_R.html)"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "R",
- "language": "R",
- "name": "ir"
- },
- "language_info": {
- "codemirror_mode": "r",
- "file_extension": ".r",
- "mimetype": "text/x-r-source",
- "name": "R",
- "pygments_lexer": "r",
- "version": "4.1.0"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/BRH-notebooks/combined_demos/JCOIN_Public_Policy_Changes_Modified.ipynb b/BRH-notebooks/combined_demos/JCOIN_Public_Policy_Changes_Modified.ipynb
index b142833c..345b7a47 100644
--- a/BRH-notebooks/combined_demos/JCOIN_Public_Policy_Changes_Modified.ipynb
+++ b/BRH-notebooks/combined_demos/JCOIN_Public_Policy_Changes_Modified.ipynb
@@ -273,6 +273,9 @@
},
"outputs": [],
"source": [
+ "import warnings\n",
+ "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
+ "\n",
"import pandas as pd"
]
},
@@ -305,12 +308,12 @@
},
"outputs": [],
"source": [
- "!gen3 drs-pull object dg.6VTS/5200158e-e9fe-44ef-96c9-e89ecd402fc4\n",
- "!gen3 drs-pull object dg.6VTS/2b83e419-8d3d-4569-b9a1-a52ecd387cba\n",
- "!gen3 drs-pull object dg.6VTS/a0a8785a-8663-47b9-95ea-a1813612a2f1\n",
- "!gen3 drs-pull object dg.6VTS/abe9cd49-fc86-4c9b-b9d0-f8c0280d8aaa\n",
- "!gen3 drs-pull object dg.6VTS/b7974ffe-2e46-47cf-9d57-4d8900d7a40f\n",
- "!gen3 drs-pull object dg.6VTS/dca15d95-aac5-4879-88cb-3a740398f26c"
+ "!gen3 --commons_url jcoin.datacommons.io drs-pull object dg.6VTS/5200158e-e9fe-44ef-96c9-e89ecd402fc4\n",
+ "!gen3 --commons_url jcoin.datacommons.io drs-pull object dg.6VTS/2b83e419-8d3d-4569-b9a1-a52ecd387cba\n",
+ "!gen3 --commons_url jcoin.datacommons.io drs-pull object dg.6VTS/a0a8785a-8663-47b9-95ea-a1813612a2f1\n",
+ "!gen3 --commons_url jcoin.datacommons.io drs-pull object dg.6VTS/abe9cd49-fc86-4c9b-b9d0-f8c0280d8aaa\n",
+ "!gen3 --commons_url jcoin.datacommons.io drs-pull object dg.6VTS/b7974ffe-2e46-47cf-9d57-4d8900d7a40f\n",
+ "!gen3 --commons_url jcoin.datacommons.io drs-pull object dg.6VTS/dca15d95-aac5-4879-88cb-3a740398f26c"
]
},
{
@@ -1238,6 +1241,14 @@
"source": [
"From this animation, we can see that California, Colorado, and Pennsylvania had the most policy changes enacted in this timeframe. We see that Maryland was the first to enact a policy change after the pandemic started. 29 states did not enact any policy changes during the pandemic, while 21 states enacted at least one change during this time. "
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8397e345-a6c7-4bd1-a0a2-359a9186ec00",
+ "metadata": {},
+ "outputs": [],
+ "source": []
}
],
"metadata": {
@@ -1256,7 +1267,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.6"
+ "version": "3.9.20"
}
},
"nbformat": 4,
diff --git a/BRH-notebooks/combined_demos/JCOIN_Tracking_Opioid_Stigma.ipynb b/BRH-notebooks/combined_demos/JCOIN_Tracking_Opioid_Stigma.ipynb
index f8b2dfc0..85ced868 100644
--- a/BRH-notebooks/combined_demos/JCOIN_Tracking_Opioid_Stigma.ipynb
+++ b/BRH-notebooks/combined_demos/JCOIN_Tracking_Opioid_Stigma.ipynb
@@ -35,6 +35,9 @@
"metadata": {},
"outputs": [],
"source": [
+ "import warnings\n",
+ "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
+ "\n",
"import os\n",
"import pandas as pd\n",
"import numpy as np\n",
@@ -65,17 +68,19 @@
"outputs": [],
"source": [
"# Pull file objects using the Gen3 SDK\n",
- "!gen3 drs-pull object dg.6VTS/b96018c5-db06-4af8-a195-28e339ba815e\n",
- "!gen3 drs-pull object dg.6VTS/6d3eb293-8388-4c5d-83ef-d0c2bd5ba604\n",
- "!gen3 drs-pull object dg.6VTS/6f9a924f-9d83-4597-8f66-fe7d3021729f\n",
- "!gen3 drs-pull object dg.6VTS/0e618fef-e359-424b-b844-0ca320105176"
+ "!gen3 --commons_url jcoin.datacommons.io drs-pull object dg.6VTS/b96018c5-db06-4af8-a195-28e339ba815e\n",
+ "!gen3 --commons_url jcoin.datacommons.io drs-pull object dg.6VTS/6d3eb293-8388-4c5d-83ef-d0c2bd5ba604\n",
+ "!gen3 --commons_url jcoin.datacommons.io drs-pull object dg.6VTS/6f9a924f-9d83-4597-8f66-fe7d3021729f\n",
+ "!gen3 --commons_url jcoin.datacommons.io drs-pull object dg.6VTS/0e618fef-e359-424b-b844-0ca320105176"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0b4ca81f",
- "metadata": {},
+ "metadata": {
+ "scrolled": true
+ },
"outputs": [],
"source": [
"# Read data using pyreadstat library\n",
@@ -276,8 +281,9 @@
"sns.lineplot(ax = ax2,\n",
" data = sub_df_1,\n",
" x = 'time-point',\n",
- " y = 'score_and_weight',\n",
- " estimator = weighted_mean\n",
+ " y = 'stigma_scale_score',\n",
+ " weights='weight',\n",
+ " estimator = 'mean'\n",
" #hue = 'personaluse_ever'\n",
" )\n",
"\n",
@@ -330,9 +336,10 @@
"sns.lineplot(ax = ax2,\n",
" data = sub_df_1,\n",
" x = 'time-point',\n",
- " y = 'score_and_weight',\n",
- " hue = 'personaluse_ever',\n",
- " estimator = weighted_mean\n",
+ " y = 'stigma_scale_score',\n",
+ " weights='weight',\n",
+ " estimator = 'mean',\n",
+ " hue = 'personaluse_ever'\n",
" )\n",
"\n",
"ax2.set_title('Weighted')\n",
@@ -392,9 +399,10 @@
"sns.lineplot(ax = ax2,\n",
" data = sub_df_1,\n",
" x = 'time-point',\n",
- " y = 'score_and_weight',\n",
- " hue = 'region4',\n",
- " estimator = weighted_mean\n",
+ " y = 'stigma_scale_score',\n",
+ " weights='weight',\n",
+ " estimator = 'mean',\n",
+ " hue = 'region4'\n",
" )\n",
"\n",
"ax2.set_title('Weighted')\n",
@@ -454,11 +462,14 @@
"sns.lineplot(ax = ax2,\n",
" data = sub_df_1,\n",
" x = 'time-point',\n",
- " y = 'score_and_weight',\n",
- " hue = 'age4',\n",
- " estimator = weighted_mean\n",
+ " y = 'stigma_scale_score',\n",
+ " weights='weight',\n",
+ " estimator = 'mean',\n",
+ " hue = 'age4'\n",
" )\n",
"\n",
+ "\n",
+ "\n",
"ax2.set_title('Weighted')\n",
"ax2.invert_yaxis()\n",
"\n",
@@ -473,6 +484,14 @@
"plt.subplots_adjust(top=0.80)\n",
"plt.show()"
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "30bdcd30-65a1-4805-b71d-648b3e2c5f08",
+ "metadata": {},
+ "outputs": [],
+ "source": []
}
],
"metadata": {
@@ -491,7 +510,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.6"
+ "version": "3.9.20"
}
},
"nbformat": 4,
diff --git a/BRH-notebooks/combined_demos/MIDRC_CT_Scan_Demo.ipynb b/BRH-notebooks/combined_demos/MIDRC_CT_Scan_Demo.ipynb
index f0845ed5..5839e807 100644
--- a/BRH-notebooks/combined_demos/MIDRC_CT_Scan_Demo.ipynb
+++ b/BRH-notebooks/combined_demos/MIDRC_CT_Scan_Demo.ipynb
@@ -29,53 +29,19 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"id": "937e641f-5220-459e-b55e-9b99cb976d8b",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Collecting pydicom\n",
- " Downloading pydicom-2.3.0-py3-none-any.whl (2.0 MB)\n",
- " |โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ| 2.0 MB 69.1 MB/s \n",
- "\u001b[?25hInstalling collected packages: pydicom\n",
- "Successfully installed pydicom-2.3.0\n",
- "Collecting pillow\n",
- " Downloading Pillow-9.1.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.3 MB)\n",
- " |โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ| 4.3 MB 72.3 MB/s \n",
- "\u001b[?25hInstalling collected packages: pillow\n",
- "Successfully installed pillow-9.1.0\n",
- "Collecting dicom-csv\n",
- " Downloading dicom_csv-0.2.3.tar.gz (17 kB)\n",
- " Preparing metadata (setup.py) ... \u001b[?25ldone\n",
- "\u001b[?25hRequirement already satisfied: pydicom>=2.0<3.0 in /opt/conda/lib/python3.9/site-packages (from dicom-csv) (2.3.0)\n",
- "Requirement already satisfied: pandas in /opt/conda/lib/python3.9/site-packages (from dicom-csv) (1.4.1)\n",
- "Requirement already satisfied: numpy in /opt/conda/lib/python3.9/site-packages (from dicom-csv) (1.22.2)\n",
- "Requirement already satisfied: tqdm in /opt/conda/lib/python3.9/site-packages (from dicom-csv) (4.62.3)\n",
- "Requirement already satisfied: python-dateutil>=2.8.1 in /opt/conda/lib/python3.9/site-packages (from pandas->dicom-csv) (2.8.2)\n",
- "Requirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.9/site-packages (from pandas->dicom-csv) (2021.3)\n",
- "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.9/site-packages (from python-dateutil>=2.8.1->pandas->dicom-csv) (1.16.0)\n",
- "Building wheels for collected packages: dicom-csv\n",
- " Building wheel for dicom-csv (setup.py) ... \u001b[?25ldone\n",
- "\u001b[?25h Created wheel for dicom-csv: filename=dicom_csv-0.2.3-py3-none-any.whl size=20363 sha256=0f35fd8297a1492046771be159a866bf2e4691c498ef5f83890873a74dc5b8a5\n",
- " Stored in directory: /home/jovyan/.cache/pip/wheels/b0/2c/e9/a8ae6bf1f5f087dd84dd2593bf95656bdf0bfd7f6b17c0d962\n",
- "Successfully built dicom-csv\n",
- "Installing collected packages: dicom-csv\n",
- "Successfully installed dicom-csv-0.2.3\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
- "!pip install pydicom\n",
- "!pip install pillow\n",
- "!pip install dicom-csv"
+ "!pip install pydicom -q\n",
+ "!pip install pillow -q\n",
+ "!pip install dicom-csv -q"
]
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"id": "be7d4148-b13d-41a0-b972-9491880f43af",
"metadata": {},
"outputs": [],
@@ -85,7 +51,8 @@
"from PIL import Image\n",
"import pandas as pd\n",
"import os\n",
- "from dicom_csv import join_tree"
+ "from dicom_csv import join_tree\n",
+ "import zipfile"
]
},
{
@@ -93,64 +60,50 @@
"id": "767559ac-5e0d-4c21-b592-88ccc9aa2f69",
"metadata": {},
"source": [
- "Import data objects of CT scan images using the gen3 SDK"
+ "Import data objects of CT scan images using the gen3SDK and unzip the files"
]
},
{
"cell_type": "code",
- "execution_count": 3,
- "id": "70569c88-ffb4-4cfa-9f57-a518f78a4373",
+ "execution_count": null,
+ "id": "b3e9e11c-2b8e-43f1-976e-318fc773458f",
"metadata": {},
"outputs": [],
"source": [
- "!gen3 drs-pull object dg.MD1R/ea669b5e-ae51-40ba-b375-ed23a9cd1855\n",
- "!gen3 drs-pull object dg.MD1R/a745ed98-0cb9-4537-826b-13b2e354e8bb\n",
- "!gen3 drs-pull object dg.MD1R/e604979a-c71b-4ec6-b8a0-959837b86384\n",
- "!gen3 drs-pull object dg.MD1R/b5cee98d-46ff-4438-aa00-90727a383340\n",
- "!gen3 drs-pull object dg.MD1R/8a5a5579-7925-432d-a614-3ed208f1c182\n",
- "!gen3 drs-pull object dg.MD1R/33034812-47f3-4c0e-b60b-fa7a2a04ecda\n",
- "!gen3 drs-pull object dg.MD1R/5ca987c5-c660-4785-a67d-a3424cc8ec6e\n",
- "!gen3 drs-pull object dg.MD1R/44148117-1858-49ef-b30f-d239abfaff80\n",
- "!gen3 drs-pull object dg.MD1R/9ea205e8-a774-4318-a323-95eadda9bc5c\n",
- "!gen3 drs-pull object dg.MD1R/09ece36f-a0fa-48e8-8fc2-62110eaae570"
+ "!gen3 --commons_url data.midrc.org drs-pull object dg.MD1R/52ed5c59-1910-499b-a80e-00329209e148"
]
},
{
- "cell_type": "markdown",
- "id": "5dc3a710-c717-4119-a658-1d07adb50f05",
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6bd5cfc0-f014-44ff-8b3b-c5963780fe3b",
"metadata": {},
+ "outputs": [],
"source": [
- "All 5 data objects are now stored under the folder 'COVID-19-NY-SBU'"
+ "zip_image_path = 'A840445/1.3.6.1.4.1.14519.5.2.1.99.1071.22152686345791690835528908062918/1.3.6.1.4.1.14519.5.2.1.99.1071.32717876047095240098568067022786.zip'\n",
+ "\n",
+ "def unzip_all(zip_filepath, extract_to_dir):\n",
+ " with zipfile.ZipFile(zip_filepath, 'r') as zip_ref:\n",
+ " zip_ref.extractall(extract_to_dir)\n",
+ "\n",
+ "extract_to_dir = 'COVID-19-NY-SBU'\n",
+ "unzip_all(zip_image_path, extract_to_dir)"
]
},
{
"cell_type": "markdown",
- "id": "3a99afeb-2651-4a35-b71c-8a44ce0ef103",
+ "id": "5dc3a710-c717-4119-a658-1d07adb50f05",
"metadata": {},
"source": [
- "### View Image"
+ "All data objects are now stored under the folder 'COVID-19-NY-SBU'"
]
},
{
- "cell_type": "code",
- "execution_count": 4,
- "id": "3a67450d-1394-4c1d-af1b-c56b82d0092a",
+ "cell_type": "markdown",
+ "id": "3a99afeb-2651-4a35-b71c-8a44ce0ef103",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'COVID-19-NY-SBU/A034518/12-31-1900-CT ABD PELVIS(WITH CHEST IMAGES) W IV CON-21869/4.000000-Lung 1.0 CE-04129/1-273.dcm'"
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
"source": [
- "image_path = 'COVID-19-NY-SBU/A034518/12-31-1900-CT ABD PELVIS(WITH CHEST IMAGES) W IV CON-21869/4.000000-Lung 1.0 CE-04129/1-273.dcm'\n",
- "image_path"
+ "### View Image"
]
},
{
@@ -163,11 +116,12 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"id": "80f91180-6084-42cc-97f5-5520b4676ffe",
"metadata": {},
"outputs": [],
"source": [
+ "image_path = 'COVID-19-NY-SBU/1.3.6.1.4.1.14519.5.2.1.99.1071.32717876047095240098568067022786/1-105.dcm'\n",
"ds = pydicom.dcmread(image_path)"
]
},
@@ -181,27 +135,10 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"id": "eb2a2b59-d26b-496e-986b-abd20c97ace1",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[-2048., -2048., -2048., ..., -2048., -2048., -2048.],\n",
- " [-2048., -2048., -2048., ..., -2048., -2048., -2048.],\n",
- " [-2048., -2048., -2048., ..., -2048., -2048., -2048.],\n",
- " ...,\n",
- " [-2048., -2048., -2048., ..., -2048., -2048., -2048.],\n",
- " [-2048., -2048., -2048., ..., -2048., -2048., -2048.],\n",
- " [-2048., -2048., -2048., ..., -2048., -2048., -2048.]])"
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"new_image = ds.pixel_array.astype(float)\n",
"new_image"
@@ -217,27 +154,10 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": null,
"id": "65180d29-4c48-4e74-abe3-3a966106486d",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[0, 0, 0, ..., 0, 0, 0],\n",
- " [0, 0, 0, ..., 0, 0, 0],\n",
- " [0, 0, 0, ..., 0, 0, 0],\n",
- " ...,\n",
- " [0, 0, 0, ..., 0, 0, 0],\n",
- " [0, 0, 0, ..., 0, 0, 0],\n",
- " [0, 0, 0, ..., 0, 0, 0]], dtype=uint8)"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"scaled_image = (np.maximum(new_image, 0) / new_image.max()) * 255.0\n",
"scaled_image = np.uint8(scaled_image)\n",
@@ -254,21 +174,10 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": null,
"id": "7d98025e-f429-4106-86a5-9639d9d082a5",
"metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"final_image = Image.fromarray(scaled_image)\n",
"final_image.show()"
@@ -285,7 +194,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": null,
"id": "302f875e-0e95-4fae-83ea-4a729e3829ec",
"metadata": {},
"outputs": [],
@@ -338,12 +247,12 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": null,
"id": "70facdf4-428a-42d2-8dfb-768c1a06ebbe",
"metadata": {},
"outputs": [],
"source": [
- "image_path = 'COVID-19-NY-SBU/A117394/10-08-1900-CT ABD AND PELVIS WITH IV CONT-39755/9.000000-CTA 0.5 CE-40834/1-0163.dcm'\n",
+ "image_path = 'COVID-19-NY-SBU/1.3.6.1.4.1.14519.5.2.1.99.1071.32717876047095240098568067022786/1-103.dcm'\n",
"dcm_to_png(image_path)"
]
},
@@ -357,12 +266,12 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": null,
"id": "9b50b421-bea9-49e2-919d-aaf68ac89e20",
"metadata": {},
"outputs": [],
"source": [
- "image_path = 'COVID-19-NY-SBU/A587516/04-22-1901-CT CHEST WO IV CONT-40216/2.000000-Body 5.0-01241/1-16.dcm'\n",
+ "image_path = 'COVID-19-NY-SBU/1.3.6.1.4.1.14519.5.2.1.99.1071.32717876047095240098568067022786/1-088.dcm'\n",
"dcm_to_jpeg(image_path)"
]
},
@@ -376,67 +285,34 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": null,
"id": "f43f070e-f566-4b64-9df7-37bacc734bcb",
"metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
- "image_path = 'COVID-19-NY-SBU/A546520/12-30-1900-CT CHEST PULMONARY ANGIO WITH IV CON-13804/11.000000-CTA 3.000 CE-95792/1-119.dcm'\n",
+ "image_path = 'COVID-19-NY-SBU/1.3.6.1.4.1.14519.5.2.1.99.1071.32717876047095240098568067022786/1-001.dcm'\n",
"view_dicom_image(image_path)"
]
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": null,
"id": "a3df1180-3cfe-49bc-bcdf-3c81f7010229",
"metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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vpRPDH5/8U37l9UeXn0TwTgxC6YYniuzJp4tvy8fx/fUcT09+ELk69cVxZl99b2fXuDqEJwIAcA5/0P78Un1uY/PJlZceW8r+9WUovRSjTQnLur1lxh4gR7YEMB5ZSEVDKLcuYQC1LsxiBK09olUaCLYFYvPxl4I7WCB6GCIIjblqLTdpEnX86izNlKOlErWItmhZF/1jn1dfJ+dCPjo5ZO3wQp/HF2ZPPxcmeKyXj65mbNmXS89vnA/ZI66nNz98IEr49eNPvjz4SgzvXXz+xc9mv/a+BnidmNib+cw/4FqSwxGAu9ozikdCnBatMxA0NElLIwOLcFR1YARM6Z2E0Vt+6iOBiJ7V1PeCoUFYqofukdbXWhgleGEyrgQfcqjhF8iX1r9dKvks7c1stpA16aNksjpeD6WOxyrKV6ck3Wy/tTnm63sMWdwng9+7dPz57J++T0PAiU8s/+Z3ytlyDKlnkTeDBywAR5IW3iMdc5JVRFGinaI6BKSUhSYwDw5Hw573DiRjIXXwxHE+MHVvLBIwT3ThWxnW2Zfz2UuPHj938ur38Xn5QhlCJa4sfXAhO/7eem8iJroLZOpjI2IrHqTy7c+W/vxn3/9nbTs42/98GL/1lTXfUx3SDfRtqzSND84UOIIlgIJ1BBlCFB3nXvqWski2TQYPODC/9/Crp/BROeVQtFf6HWfSdcPSpgHaB5zKZi6NH/ATJ54OTn4wff0Y968PLn/I8w/73SzT16xLu9eylANi1t/6FflZ/OZ5nQ7Pt6+cevYffMgUoS0QFpIxdw8wTeDBawBlECGiqkFFsEhBGr5j/pKIJBJ7bw0WOA+mKLZ6IxqOrYvlWSCOxeO11XZh6+zz9WPPH+9Tt7WF8lu26drN0PBd6sEfNOzUxZf+0vHya6+eToe8//Sx1/7tGyOpF9hmT1Yc9X6qZfbJAxYAEpHXNCgL1SKXMDUN3EcAYB7SpIm7Z0NYEh7SIEePrUeTyo0lUeZt0qQJ1atP/JwanOQb3+vMleT8Ki/ah6daN8+Tn/4S/rFl4fRC9cJjG9/+zbfYY+V6kqGJkT+4zaEHugRQIgOlkjO4FAWsiQQBDqDMCia9QdxLvt3NiNwaVlLvL8gOk6Xc15FnKyts7fWAJ1I1an5nNOVhlUS4ycHf0r55Wf7WP0k/8yXx+vs6ku9Wx15OJ+uXl4tG0cw3D/C1fJACkIg2hIg0wLDYxvT668goQTSBM7+P2jlbWQDQMAbAFhUkZqdOdvSiSFOxNr68urZY+nBfG/qHwWd+Vv6b/+7Cl1aWR5tmEC9fePn5X/qHW5PIo5elD4ujBzWOByZr3KVJG9LORepIBA3Kh2tr/UFb4PK4Tpfen+VkOR1fOdifPjD+0n8U/+rXlz/9J8WFD2c2PELIp8i//e1ZQSowUQ4xfVACe6ACcAf3P+V1UYc8wrMaGY3dTYYYUcFgf5XCP7T8zT/9r/5Tk3/6J2n32rRSVf7J5/iv/eokbdEPQeggDn4nalcOUgDuUPxchGbgmqQjie5XStjumgPGkshbRz0gDyL15oeJL+Rv+quLP/3s2fjNb6wli09+fmnj37w2thy90GRelA8mN+CBLAFcJLoBaB4s80knPwq0C9HlzFgL5A9I4h8m+jGcbI7/ueXpr19YXH/s2fbHPvytbwpfLdeGqWgeTMb4A7IBROCIGcrE3eSIZVboo3fKj5BTbLP/1E8/s3buV2fPnFr6zOrqv/UXUMDKOqSGsQewJB5YH7Pb/hDnnCnOuWB64ht9w+fSfkgeuoYJD5YrWys4/8/fOP7Ms+mFrn6dnP1c4OBIdexHnx10R7zdOCg38PYTqUgnOkZbqanghoVAxbYWSMMM3cNSI3lUNBekbV9vX3q6LasZz9jxp7OLpOLDmZbdLHsAAzgoAbjN/DOP0IJRHxMaJIgBI4LYGIFETgGgTycHNIQfUgy2fv/D5ReOv3ZukyZT/cKmMZkB69c8cUU8dMvocANBMvrUo8hgNKlZBYARaUkAkJEpybSPD2YD9uFmPf6zF768ePybnfHVE8++rztGtYFrl+3hL5CHKwDMC6ohx6GINRyQako9IoB+45C1/73W/jewsbFWPHeWNm0s5GOf/02c2ArRgPju8D3jwxUAnZg2IEbvnJS+K2hnKDUAetSpPdZZ3z8PY3u+j3P+v/wTn/hEq2azrTMfktgFdKmB+yHXALn3cBDBcYqQiE52aRKdB5CJ8rDybnfhh2D+Aftr3p4WW75eyk9driLkFHxKxKEHBA8tDkCIGDYkNiSxNgA0CYGI0NCAgvgo5kv/rSz+9Cd5eOtY/ZU3F7qgJe2Qa7UjAIemxe5fA9xmaIn1XYmURWsiCHgbJbEagaUB2fTBBf1/iCKMo3+28VJgIRHoPBOeqESTa9H1Q9Ni9y8Auw8tjy6JQEsiiDSJheCNCCBDG3U2foDRH7+y8eAudp+YPyiPf2Lhis46EOUptx3Q32tt7T45JBuAd2H7pyNEUlPVMh5ggYxUTJSHc83d6X6YPI3m24/pHk1CSlruWxAA+pAzNg7JBqBgjHtLLEjGjUS107lZqgc6+wefanDoZEXSjZQRBu6B7I8fjgDIQEBZNIkm3FODGJWJlHhK2cOTlffQIg0ya8GDsv7QPZj7FIBdA/mpV4Z4yuAMo5EybwBJNMDzeeBvr9AgZH3Phx/s4zr39/Vd5l+kIA4kCmYNuBK8FQSwGig4e9Dzf+THIeybAFsLd5MBcCg3c/DHWgVNRNRtsJqQhDTSw8RMRgAteWCpjtf4oYgB7QoDhAW9cdYP5WYOWgAEPAw8RM594ESHoNNCOQMky3uoF5+zQ08CPEGHyHCoh/vdj5OxSwiIqyCdC17SSBmMgywhrQbAeubhaJT/w+EXdBwIGhSM4BC7FdyfANw6/xKEwoEEeB0TaiJzQGMBkN7mQ6KOfyjmHxAtrGBWBne4KTMHqlxUAIOJRUTMEnQGIAHCAyl64SGZ/x8SbASC7XIu1GG+/wcsAEQmPlAZNKJmFCCIIiEB6CSf+3/3RE4BkNQ0Jvvh8WWy3qCnILiifAEAius2LD/FjnBc20J+YCHVwUH90B7oAdnhxoL3rQGSW/7CBW9LTZgLgpIcnBu2YybI/MpBb/+cUXv/bNojGUeRAwoHIIgPxohgAEgvKDQO6VG+PvdARnspAycqQUaTjAAgBADkEQ5KAmqZ9noYLiMfrgzTfOcf+J2e6h3ycXc7t/QQ2H4zBxxgOMz08AO0AQglNCowBk4RDIuQqQBwgN1294EqwMu01IMJRfC6dDXZlsc7NuW7g8f6gIIZgQJA7TD02LECDmUtODABYMmCLEznOXGmDUCvgSgKG4HF4qCuce/IxHF4H2AMDG2BfkhzlTAAHEj2lXjPbl39DoMAAC5D5MKCYrtb6sFzUAKQU5Q1WjCnCcSghUW+kNvIodhBd9y9B0ygHFoxCQMUKupKkK4MGYMUQLev0JR/kAqtk95RyrbPHzwM9qlWPh4EpIzRDgwgILlZ7CLa/DFi10EhjyYjhwZIFo3xGSLjFUQIzGjmpXY0ZVHe9eS+2zdqejApJtdqrSKYOcQr7lMDfDyqE3yMQAdQ5NpULcSJUytyqmEebP7PRzCAwTAImSMBaEJgIhQiR69tlInLd5F9c8SmtycQQDAU3WHGAQ5qCWC6Zds2v6O0bvH4M4/m06sAjuJovASAlxJgEGLMMLJMzqinPa4SYRJiCSASdpfnepS+CwBEKIDlASQAh+Z97EsAbn13SAQiGE8j9DDx6rHlhcyNAeRHEQHuAATvdKRMwmqEQBXxTBtiu2kIgbYaCHfLtD7ivcuEwQCktvBE7ZyBfJ2Dc972ZQPc+lYzADJyn7YoRI1HXzweyEbH3ZF4gMyDJcaCBuo6zhiVwRoC5oMkJKaIMSGdGHepucsGxZGmlDvVbLfNBCK55hZe4+CMgoMRJe4AOG9YhExaPP65Hz1FihHo0ey9eoDxvJdIKvpwGtJ0iH0yTRihose0CZRSWIhwF4fuSEsK3GC75NoCCAVNDkkhHZAuyTRAWPSk9XqTffrF2e+/01RtgX20fbt/CIeJwZA0etoHTAhpEWIIxMWOEkMj7RxvfHL3DcojtQNjCgASRCF4/vBWBgGApxacMkI8vKCffWH19ckTbCuMjuRoXAkDRqwWVloXc+2AdthOWGJYIpoZBBGIVPFAqCvsHbOUj7R9ySoAQPFGw8V4WJ7AwQhA7KiklnoAojz7Rfn25fTJegNH0/fLAENUAqyjUfOoXBoNFEJLadANeghtjJQ5EUPsUXbHYNC9nHh54CyMAZgu12AeBNIfhjwelDkpDVFRd5rZ/OWFd66UWW+1OrLTKAZ5HbiiBMR7rRVD8AnrAlhbsQxtB4lgFTHIu9mdH8CRVjEYcArdA7iHTBNyKProgOaIApTPkhCZ/MwL57/Z8CfIRTB2FEEAANBBEe4k8a1JqNMWtvZFdI7xrkEKozEMJoEPzcPcPcAhBqClcBAydsgOI6nyYDQACYKZiRPOhJPPuPcDTT+x+h6yo3p/mqmMM5AQE4lIPOOgWFA0UjgAVHuaU+JjljUYPLzzDw2fAN4CKNomE9fn/yDTuA5EAxSqI0yr1gQMn3PnygX35MpXNu+0p3q4EEIYJLcl9XmoOWcKwyraSPqomLIBPJQWvHXMR+Ue5mT1DjBQOml4uOFpHuTWwIEIE42eROpBgE+/MLvszyy9NH4H+ZHZ0JEQmmtUvowgRRGNh49OgsxMHlSWUuE5A6iTmO3zWPgHBBWUCoXOSH9LVsjB+AUHogEYiKEO6HD2k2TMRaFXyg2eHFUchdiAjmvihAw1G45E9Mq54NMuUuY2yUI6ok7GSpNIOacK4UjCFXshaYBsBhDDDaSNN1ZjHszadY8aYNePc+dc44EEK39oYYsvvUQGyesN3zqI8e2HRKoIDdvLHENsIgJQx7RHhVAUSF0LwjjTWOIp05Icfi+u/b5mvIFKWxAEb5C6BAe/F32PArDr5ZkxgEY/JT/2Y67rPTEkj5iLR+dBpalBgTRFZbQvgAUZQWLkTDM385z6LgpPOENOaM1mjTu0KNt19usNOSSaKkQAKjV7OIrz3jkIG4AGlQKgdvGzJ0azvP2gfdlPxJFZ14yhcXnXRq6YJVSYrnBODbDpbMYbBG0h4XSSu9AKRCFuDAk/ZCn4SQc04GBMd71D2Z8+CBtAQW4BmGU/+6OT1Xa1LD+RfP+dA5//O5xGcDMtXZw2FpCCe+691akh1iqNno0dTalLGOWuRcu9iwQG9IZGx5EfVexiVzigs8aRtJJycoeGzPvnIDQAH9iAAcKTP+HOJ4uzK/4p9fbBH3ew5wwdFrVivQypq3uJ4S2VOiLMTJGyDorZELopeAGpXCQMyNSNWasP1fzDAM5yxJjbCpSB70VD3ZOmuBcBuN0Pk40JwJH9oUc+uOLUsPVnuvHBp7Lne03GNXXd2NgJ20FRB5EBsehD5M4EkDqCWiR92hqrUxHQ6x7eUBAMAOEAXUeogmBP5so92bT3sgTs+sM8sdYD4HjuRTdSI6bLE8Xa7ODzAMq91gIpx6OvY9o52kbQzLnhBMnMzgwGaMmKC5QariETzTsiqDMPqNZjHzC/XaPgRFS1DYewvX7fSwDnegpgoVN/YuGDjU0NpJ9XXzt3AEP7OHt1K0hGs0Rwb4ukbhMbbRfRToiEp64RzpAuV8ZzFF4blkRCxA1q/8hOU9+d64u+dZrxXerxbsPeV4H7FgDPEQF00ydeoj5slZa//Kneq/f7q/dDZyztZ6QLUquMhI7DoDGJ6sFCMHAYY0lHMengC7jOZTfYVg+XCQAlAYADDDG/h7na+yqwDwEgN1ljZPs/LX7i+Lgtlobn1599uj4MBbB38gW7Lnq853zRtfBtdMiyLkQgGXBUnQDgXYa05+oWkR5Oyc2BoA2wvDwAPHxDQJMDd1P3IQBx54UhAEByA0ie45mfmlxs68vvbp75HPnG0TYDMG0D4kI389bz3EJYMBEDFlg5NgMTpYiUhQDf0lj00tg9mFqv/SJ/8hMrFICSsu3iQWep7VUAdrHAIgCQpgWPzB77o8e+t7pA3lhnjz299S8OcID3DkWHgZ+FAObhHBjpoSNo9dibrnYkSXWMOUfSuoEv0Ub+kC38NzN45fEXTyoAoVIRyc0Hbt7/z+9VAG5ngQXts2ikW36htTn3iyv508na6/c/rvsg+GwhrUF6S8QuQFMqvcjZgIAAvSwypq3LGQGoCgI0TVx19x89Op6M/IlHCoC1i8DHUmwOwIE9AOH3Hov1icWNs+FCuUxf+FL49fv/zfuiRSgdoqW9ESeSdUO0E+RSkDgQMy6mEWloSAdGXESX6Ie7/cITxy4Vz16ojZNwmT1w9/q+vQCRaiCyHz1+9cNJfirJX8h+/1cPYmD3hWcpKKlaGpLEh2kkNOHcBKA1xoWcmg5CcG+THNoE//BKQAq13PGTy0hy2ElCDj68cn8agEQ4AcgLi5+enFe9R7uK9sffOvoUC6NIpiuSp9MeNyQ0Campo5zWCoo7pQMxRNXw1mWeH0nu+h5pMThlyipR2SYKFsLBpzDenwagFLEDBF58crN45PT06+fF8uw7BzS0+4BJQSmXsY2mI2kkMoiUE+8LB1LVFvA++pjAUpWWBpw+ZJuAN7DQTq629NGFZU4a58mB94vdiwDsFoPd1po+AAFpiz92on96Rb33Znhp+c1vH+gA90c5M4mEN7nxaEyYmCI0BDxqVB7MWgESCaGSlCUgF0EPsxvrfTGZNokXjz7WEdUX5g7Tv8/dl70sAbt4ADe+MT0dHv2RatSE8tzs+afSH+xvIAcCIw4A87KNwlkSYtUPCQ/pxOoGrF/uzHKWuraRkRPaun7j4uhoS4DuBF9qLi0fR58QFcTkTrWW+zQP9tsg4oYnlhl8ZmVtVm1UvFg6OXljf794IHgC5H3FGIiNqaIBpJl6blM0TKgm2CIlBbO+bKE4bRQcLIV1DDR7EAf13ju06OlHVJppwT0c6PUeZwd2gfv5MpcAYIBPbX7HnhA6HT52avMoBQCOwxpCOKK0lSdpQhLTNZNKsMEi4ymvCO+4zNpugWiR21lA5wAoGci1tOuHyxwQJ4i8vDkdPVm4PgHotffcHZQE3E0A+nf6R2cBoAEeN2vdsZXLb/Wfat890kO6ouCms8qki6mPbY3Ws1wQFN77MoJ0IHnitqKMhiSuthyIAUAHXO9k81BlB5AmP8aZeTtbsFW5wTm/Nl/kjsmh9/Ba302Q7hzVjwDzUp84E0Tg/ntbP/kJ89ber30YmCQEY0BUFJ4xxuqkTrJezEJomPEpUFtgwmSrarB4zQF0O9unNK2h6ENUKRLZ2brMLgWaFUhCYiuz4wjGO8rpPaQO33ccwKuIV4p31lJe1uljPXP5vn7wvgk+ddEg51MriCWy7YAKwvYCSumJaAgKWmbcRSTdDYaMNCARqYBI9n2uxWGUErPY8mMzVhCZV8wEioNWUXcUgDsEHch1HURrfLHvZ/WGGTxxemPtuwc5uj1zrXSaxU5JXiDOHIIyLYUnHRUdmRBBKWKwSHxgDXHypuwaAyDxRjSpcIywfZxttl25mcUDVh8pprasT51692z7BstFCXB3bV4OJH/1TqtFdievM1z/djzec/rF/qs/2JLD7vWj2Vi5VjpPAVe71I59LhE5ApDwJKVRxFwBnQOs9cG7Xd4jq+AHrKGI7l5dKkV6lHBNZXvQy4e0nK/NevLY4gze6h4X7lpmaHIgaeJ3EoDr5Yi7GMYOAIUH0OJM+eZmXA7xhBbs6kEM6t65pswtkzHGaa0Cz3ikANC6pnHBqmbWInpQ73TAx5t/cArkTOmqCqECesPbX2q3jQOTqjpx6O09Z2uvPOfN4GJ8xH54ZfEYkjzzFnABAEvCgRTf7sle3EUTBFw3NdjJsHr1gnzs8z/yhafM0awA1+FCStVWQcRJ49JrwTEDxNYAQLxuH91sJ7lA+tNJn3ASarYIV90+sLZbzCgmI+IB4g7cCiChICzhk5Mn2yofj+yJngAFA3xHILPefe9j3U4A9iAY2+bDynBh8QyyYWHeFyf5xXfud0D3hVK2c50HWAHDxzfMYgRAdwuX7dzogFSApjZSKohKQu8eziMAlmYBHmISDrynazqYXFpOXIgIkhNQ2QUawAAwTYwhAfL+VoLbTfTd72S757YktnvqWP3IS8v+3G9vffB79zWYu8Dv1raLsmgZAUtoFelAEX6bZ7OLog7p4hBlwlUvzGptl/i92DKJcQigh5FYpHO1nMJUvSGoX0JsfZAghAIeiiQ6W/DhvpqI7i4Ae1nKXOUBuFAvnw6blo+uGnGcv3cfQ7n7Fe82sNA00AQ+wLfUVZHdXKD+kVAnt/zRChcQKUkoeIemnIDIvSoB2SsLxQp6GOWwHktSnhkV1eow8Txryt6QI+oAkCRVjdFOBdJmvX1HdHeV2j11yNxeC0PAiUHtuks/eIucPTY93M7QC9ndOs9z2RhQxrogW879bo4Sd9C3GjWmY7nSsURBEtcpHmBcssfz2hTQehB3MH7ZzXR88CQfVLPpE80VuznMTClqAMh950svqUAD9Gqx77VnNwGQe+vssH27lnlppGJFrz698drhOoHj8d0+oSKnFNARDtzcLAA73rPbMWq5Q7+8Zt+mvPQ1cgPSEGmJ7AxYEajY00vdlmBeVeDiwAWgWGkTteV8uWg2c1+HytMOwgc0kYlGqrKLRfClSkS9z/jQbqpjL5YlS6QDQKkfZrPXVt8fLbsLS+LikTZXBVB3LjhvhEo5j05fj/WQ/i2yTpHQnTONgDb0E+HrSqVkOerEtGk/TWm6N/vKIbHwjh5Ce+lUrTw12Lj61GJG/ayVQRTEQ1AgQiaL8CCoGlvkZt+X3k0A9vBjPKQGAHhut/Ta2sXNUfP2LB3wI++1kiNLidcwzljykSmfUAFy/Kaza5JeN4nbxZYUwKwzlOe59aGhaQfedW1Zhr3tEYtOUvfxjN0DgfVk3BopWbXJgKBD66VE4wBAkpGps54qlhbbEUWSkH3tY+7PeEjSZNuhCsaiCdN88fjCYFC0R59fXWNGWJLwlMPFckeDi4J0Af3YsYXrHxQd2T6bmwIBoByRQXuCEQkhwBlUAXRPdmBqoQMpeodwOzNX0+wDYTpaLaVaMlkaC4ATQNc5oBtdtaaX6oTx5J4812vc3XW5IQnl+t5A51kFEgHngKd7LOLS4vFPPj050q1gAEAAqty1NAD8uldukzZKr0v0P7KVYp4ZAwrQAEJ9XLEzKngp+pahYR4isuGWM6m9+3vtABL73WFIf0tzfn6h1KIbrfYvTCUMABJ9xEKdzZC3i8HV0hjWRQ7Hk5ssgWs27B1t2bsLgOX+2q9+9OvWbv8/kvMJIcK+U5Zbx9n0yPpCfYRwoQQCMJxs/4EwB6qU9yWKG6J4sdUkLKJjACL1FbGMBS+I18lC4yB4w+xC6Xp7KBeVFiQefBAQAJbcFbFB1Pne0iW0nAy0z7muEZGMYdBH1D74KrMxo154n5gbM5x3D3p+jD0EL3aewI4moGQ7hwIAeCiyCr4J7EN5eVY2W0dtAwKwnDlPinhd7BNecsfKAODGd9R7AKSLxEJpC4iSiU4o3oSGEiwbw7O6A1qh7njAIAB0QKCHc+vp6s+QcS/Tx0U/JMeazVj4lu5cExzNsc4r0s44a3yS1KGWebu9S7dnt3Dv0audlSB8FCUgMWjXYPhk+8G79bJ5fFBu7vnXDg/nABX09VzmllMRQoBCvKUAIHSwKBwABuI9nIUoKpKJMGMLrkPiMhPFHQSAOwD+8M4WKatZ8uy6SKigbH1c5MHo605aIcLCFMEZrPAQLHHSwFHVRdxLRsjdjUCVqJv9wuv3Gj1sAFos+aJ44idexLkj2gv8ONy0il8bpmRCBnCuvcJ2RtVH90xUAuIJAB8LSnnSI46ioeUIQzqhAxq8sRT5wi4XWQCype2fShYOS/UpWzyWRKFTPyY+p0yI7cxXgWzZj6dOOAWZbJSjjUnVMCIJt7vsYt3JPbi7Brjj2pZwXo1Gs+7kQj5r6qM4H2Y3hHX2utCqmlgfSEJc2FnNErIzX4tBNNhJ4SgdpHGLedehTwDOZyCxjYFKJdhol4tMUYRmW6ekh5ZV7njXjBfF+IPjy+dPlrNZjACYA1dVowAThPcsE1UUYZFd6ce6Ak6XH8vjI+IOtU/3tYHBU28oGozYqQVVT6cPgQkAAGjTaHZcFhkIYiBBWr9TBU4/2kfXNQhIBA2AQ6+EisSh5wJAG9+noDT4ptm9dixwYwARqFfNoTXFdCahBuPJmTMb9RoaIol1cBBiBmgcc8yPSKJ1lHTBVNQ6GnDiFqV+69J3Ax8JwO52A901+1AGBwDOLRo0aTtJORbYmfY+BICCD8j6/r9/M9yF7ZS1JBv5XmeSwrdyZ+2SokTOWxlr1EAWwo6OYwzg4wSUNRYZd0aBcBLyzjnwrLr16TgAgnhP20Np4AcAmD22ooeX339CgHnpEuEdAcCtlQbIuxlPoFqaMs3bNpNbUdJeWLunWsfr4nKbdWK3g5WEkjuegaGCYqEdp0szc/Ixfx9eYGBk88DmH62N2zZgMKgDgowz63fawUUNNoQfHGMA4Futt00mPxGLBpL2syyqptSial2wVkiwfr7L/ANQkAagFCsHNvKPIeOa5leKnr3SPEFGk9pSC+DEEgwBgkt4hcCSdiZV5VpPYJjbEMeWk73niVwXgLhnw1EK3e1YGqzdoqTtTcapfg1Jubb3O7sFt8sW3f5/jEBvF61QAeS9agIp4YF+QbNi+Uw9TflsSQA3ZoZmqbNgYWImXDBBEwIdSNo5UOJ2LZAkGh4MgheHVliYB3Jx89Qn1GTDrtpBH2mvJ5CaCRABHUJHe0likGVM5KJRYKojCN7sXSPdgw1A4vaCYLaNKe4AqbUh1cLi+WdO/2BaN/djUBxssrMwZHsr0GTOA8xtb/sktI0to22NqoK0ABf2eqSnQYZEojdB2nkPBL1QxdQAxovAdpnjiER0REbFrxzo4D+CGxcXF+NmcnxpdZMbm4uZZhC1JxFAiAaDKVVGqLpRQatIaaeYrx3Ze4b6NcNoDx+NAGPOcx4dsN2txHVAkPrpVX36qqlHD0+zJZOYsL2V14QQ0560EoAyZqkW5c7NGgDOfZT6TlKPtAMcpIPNFWHGguUjRlxAMdstSb7r4CDLe4i73BtpGtK1lZo+n/pmpRoJ3nHZZLNrrWIMVBWCS6UxtKYqRkWDb5BI0t1suN7BRtkW7L2O33aOSrktNAwgDv0CjJicdCeScnRYL8I+6KIwKZASr3LQEJixwNIx3gvTm2/2o3Sq2GhUUyBFpyx67dbUA2bLxAiWNbG4nY4ywOCQ7kI6tX7iBIbrF4cnxpebWamJZmK7eyyACB0ZSZUrtSOk7XTrhVRwLpJwU5rYnUJZwL2d7+rrHeHqAEXyMzPEsDn+1sqjK37ysMQBAEgBzgFLAwda4TQAaqirP67KM+JvsANEyqza3t5piSgxsA5BKOnsHdeo8SEdkTsa2uHga6PJ0qcn749IwZ20gQVlIlkst2eBgAbvIBkN0RHGvDUZE5PA2j32EqEg91pZvqNcAmC6ttqCHEFtdLHaeucharfWR91qQHBhvRDRgAGh6p81H0+m982NWR8RmuiWNljStMcxtLXqQ2XgtAh3MpgeOZSbQLL85EpdZXkzXo8LlFfVOMQi9SJfuNYtyLroG5EZJ3knU1JHkTSt6aepFHvzBPg+rS+6XRhgJgQLtFkRzxaj85celkAQAFODJQY6yUrCMUXqPE7Jxt6SVCaWb3wAruOhEzGAjFM27slJwlvhSk/yOiDLbrfXMTykTZCWZM13mn5n6gaGGw/GBzouaoeWX3ttQ0AFSpuINhOtSyiviPCd3ePBx3d2YG6f6RwACBDYHGxZkL5Qqx+uHpIpdFdoeksA3DySLkUgNG3k0fQXBZDHjSuTW77boqIf1cBbYh0C74MvxTEwk7SytpPRhwDVu62AV4fTGpUVSkZ+OrlUbzXTK02pcieYgZ5pe1P1Gs+Z0QZ9ZYtUJAbU0l6+x64nu88w39Ew4Y7yYcFVHGNyIdbH+cb7h5oSfgd4tktFXsdbvQAAIZUhojIAvYr+rRNVqjS74esBeelnMR2lDUQ5CCXJm9BQ5voOa6C7H99K3OHsBvrWlOLMN66GIdMb3DEexRayUC77yt64xDu3FEoVpxhglrRJ3zYJa4E9eXe7z/B1+bpLNoQlxmKNnNuibjx+8AtAwkEGfR6Gu/ifE4RJngA8ozaZGQBxZdflbnOQ3PQ2cQXkiZn2BkHwEguhUCzvTNs2QNht/mURDykXMj1hrrabFyYJ1tLjqfWusDHS0COaFHSn1okASY6SLHZ6MJhOU42OS3SGAWwv3t392W0MSaDGVm4zIw/+DFYKx8lUxaraRYz9wNWWAAizYAGQeLuTTD981O04NkoDqCJCtQgQ4zMHtJ7oPh+GGAh0inBL4leiHUsPpx6ivfL0idFqtrnUVbZcRU9MaSD1ghlBuAAAAYiCpQG8DkLbBkUtZZyF3BPmw65lER/nvgSAJE4V0yuTly/Orsrx4RaF3Eoau1SWcH7XMGLNmHKq5xsfogOgaJ1icdcxXoABwDykBpwTPTod5a1nSeePh42B0m1JmbIGSlS3RtiSLuaHdOuEj0czNqy9C20l4DonoYmuaHDbWzeSQ1o5JUWbBQ3aa80wGu+k75Bn1Z4OP7gvAYjeSIKpPr3RzvTqA+26Lw10AKto8NhVAHyedCaGBlZpAFKLXnbbfXFlIneg2/NoWQpEUNeBxxmmEjH6mFhAOX1r9c8kdYe1+AnRtEMf5eTSchxOwajVHMEVFYRqPcCNQSNZjwo5IUxVAC/7coJggGYwuFsZFYD77RTagephac840usd/DFRu7Mt+yYBaN/625+gELdmkRgAmgAwkTncNrsvJn3x0X6PrgZS9LoWA2JZBsMEMAyKCW52s4rabRNiePB95owTyyaJfnNQ+Ld4XtVIuaJdl6fWeACEoMh40lbUJqnUAogLtkuEA5NJIHs6Ymy/ArD9PSltS/CNi9UUswclANvTtECRhZlGxYa3uYUyK6jrbZ9qAYAG5erbeWuGKQqx4wxkiW19oFbB2JlygGfD3rQhhE3l8Pbj4oewEzJM33dXU9OERi/1avCUBh8GvSwAUgrhI4lN02R0NYHumOQycZRZC5XEjJR7OrvztgJwF+nZti8NrUDxHS6aavV+9oLvBQlAChHotjnmJrc1dW0wscBOR63g1Oz2R+lUG54JAARQTQvjmRNF2mKRUwgwXvpCus57e9sls1dO1fGDfgk6ulAWcVyHeNGUHK71hua11iSxxkQfkDUocq3F5NSZhaWUGua72tPU1H0me3ta32/7mQhO75Jawh1kA4l48el3pw+sv+IMgMH6DfWwtzF2IwFu2PkZwNzeKl7i8qLB9i47QAM3jfQclEbFpAk1BBtjaO9Q+6EN0WsHnRcQ+lU9XVsly6XwEUAOZg2GSTWLtOd9l9RkQDoG2y9YX2+xlCBXkRKwKVd7MgFuFoCb4wY73ahyEm9j5DggHisT4N2n09GDrgr8yHdnt9F0FWey7Zlrdrub3WG9mx2n2Ud3QOACTJSKrYH3onbIUUEpdgdrzyBCcnOQljAhTe2a/Iyt46n8O9GzYoq0RdFSBOR0CnRgXo6A4oTXLCPpeApJNACp0GV76iF00zOh5GOPiAhAZnGZ364HRWisZbjCTu2EDh9Uz+0b11t/O9PO+UCv+21U9O5w9qi91C18tCnkAU6hGKG05zQlAnVtkVV3Lv5SwjQH0rjrGknwqvQrK/RU0ccVD1WjEGBJErpElmMAEa4ZAb3cal2Ope1AkwCKBF2r9/ZK3qQBbmyeRpUOrFdB8nWkiVCTXQM9JYmrBJdXVyhZtQDSA/SI7hTH7BJ791A3iTcU+AfXsTtF7LvpTVc0AHOkyiIaQQwNS7b0d7y3R6dT4Haxpv2hWoyVoO89qabvvWlR0CZT3NebJEJYUGYBICjaWusrJP2SrnQYERogbRpnIZHt3Td6b2cDkKBlV4CCO8DyiJyaXdOMeiWgLz6vokdidkuc2i/9ha07vHF7Ee+I9noyTNq5lXin6ZkCgzjbEQLmAYKiAVhe20L7Lqb+zlXzF8AWG3+gC6EDrGjWBx+ceF5bmaAb2BkxACGC+BB2Itg6L0SvV8RQEbVFLA0xl2N4Bp76PWz0304AIoLFNNG5bribFR1DSnZJLGKeFhXwTq83JdAxHKAZPDuQHbZsJ2+CDwXu0sV2sQmZ9oE7GjwgxXRK0TaAG0xKNrp7iaA/6Npoxl05efc0xNnNcnU49nALzAofwnbGXwMAKVqp+MlsK0un9RomhIWcp7HVWMaI7yFP5faegoSPLSGCZLbpXCSdjLc8Ag7UJA26bZKUxrC3F/OBQrbzF1kJcte8raH2DQah8TSSjJRAkFx7FN0Yyu++D3ioTIGkriaLvdXRt91EJF1mSgCQO6pYRgvrWFRpZKd9yTmreR6dV9UM6LOtYPbc7G+XP0cROdUEOqNGSOfh0lszDBy4Q94ClxqNgSsh2IGugvfNTtGuT/MZndzls6M+ixlK4tGzTZsAFMYAqCxSIY6m90Xj+2I5bVZpv1HaV1F5B25QGJM1MDRtyTHSsuxMj1RTUL7UzZLQdUgyb0ha3sduoJPEUoWC+FgRZHTB1GjFDcnGbOfoAiW0NcB6LeyYAfohO3dj5wlwNu7fvQEwl60Loe+0jfAeCNvpA8bk9sg6yA8XC5fZjY2OdxoxpRrMAQkTBECPac7LpeVhrIJWOoYR9W0/g2nQoSWZ30Ny+O2WAMND4EbGmuVNijKXwrgbe2j7ncBPLWIbATBJoseD8wPvjZzuxUAf9Xju6kR3iouuA5DMthdRfXSnS1erUag6L2jrpPetAHfo0bplhIPUgSRdEu3psC5ZbzqaOLKkK5q1QaBDT3b6jhnhAO5gA3BuQ1o7lpHCW9Q6IfImP/DaM9lJl7W8P2VZefcLHgm2kLualOzmeqiyF6aoa8x27qEudsLNhzy825K29YDNkun7IzEwxkPSpCSF1ZH5PBLtmejUwumzy7YqqzbodBBKX4jQm7UOmEEBdzVddhMAmpmsM4nsaDbjscm0LRoY7/u7RV25I8IArWYqOiDkR3ty+O40j3zMQqcrtaGOhiW6euOfS5F09tEL1x5aDVKUQH5H//EQIS2wLpbbSy2Vs4AULULuS+SxQex3NcSikM+f4QUL69O+TVJcIT3UaSx4FXnk1R4KrnYE4Kb3NnQ5STBLiaVMa1RepUm0legSyj6e+kC5UxwGxU5zAD94GAUAl258EH02S9e2W16tC9n35qMID3dk6YZ4T69vSqC+48FJh4gMNqGPD91mi1o536WJI7EDOsVdk3XIFlYGSydyulmPtBrJwbSRQifpCCARrLdxQ2Trttsgu+/h0KBUHYUX3ki4QAfERsNpYm9tSLgTcRbR70hb+hAdubPDDUHMR+OluD1kYQEIu1R3QC+d7Gx8Jam/QYJlVh3d8r9N//PL3/vQG3AHZakjPHXNkIwxQCcfVU/y2hBFsVR9MDZifUo5IcZjINwEeTK+Pv+3j6py4JajYXLuahNTbaJDtoTZNI6BgTWSiFsnd+cvFtjRIw/XsWsAbtCDyq3ZxDWEehBmIbIaVi+7iaZ+eXOZTF3ATZVjxkCoo21+6AdKFDOwkPAKBEoyopxMklm2/EoR+CQjni/5KT1D3rE2JcxTaQk2AVge052S+Dv4gxy4ZaEQyouWe2kBNO0JmUpXcxdBTEi8BZCTCteWjQjJbhKLcFgNk/ZNQhsA4A6OptpbRA8SOyBMkWRtQ1DwOqBMjKKtNDe8DzzTh3Bg9z1A4ul0+ioAj6IWqhq6MuZWA3HlsSeK2oz0tKKFosN8jdRdVjjrkapZTQbUNdVHBxjdfiP8Y0Yg8wAmnKREGCuNNIhXeU/WCamRJK7i26LkAcDv6BVhSdKCREodAHSHVSm5b4qpyDAFBSKxbHvQEZQbz7mqne0XJYRcnnXQQkRAXY9nhtkSOVKbhtuT/p+CeQhOFasKWzOEnnGPnjm9vDXpVjfbpSRbdg1vqjZSDxWN8YH0dN2XtU/MNSfg9gvZxwTAAxwuYAYiPFNe0diZjlZJ1hJKBXzE9skowocdvaKCAZCYbY8q9eauPd0fNONhO5VKG+7CdnNQIOmQ1SQ61yH1nRGZChMHKTg63LCPHlCRIz1c3vaab+PYOmBtO6yAGlzCtaL/Im+z8kMIo48PS/F48+p0ukhCB5pD+z5jfspy3+whJ2yXFZt5GUIQrGMMKtBQC4s0C0aTJGz/ZG49o932Z0FlBxIB7mhApuhD0C30jpAIYFH6qj3pSNWkxTgOMl2HDj2pSQl8PJaxe4+oBwM5jUs7AxDSshZFqoOgw588e96tn7M+P05kungqvvf9jZA1nVnK0W74jERrabIn53WXOECSBkdDR5NOEFFaTjlv2xYD1DbdXh6DYeZ6j0TeQWYTwIFI0zTJQxoLAm40dkvfCyqWDCo1DtRt0TxqG+qcBmyHNNErAcZMJo5MoXGHpNsEM4CirZX5BDnbIP1k8MLgW5tqy+h8gZ2kSwPMRpOtzPXQo2VoRcq5jsS4PUnuzQJAqQNRvkTqqMtjhJe2QJvL1mAWgZ32y5bcUIxhACH6MTaBAzR0QHr0TcN3J27/F5NLekNO+cggTeqkXzYN+Cyig0q6LHiLHpugFLZXevSPTpod0Iptg8t5FLWNqRqh6IQgW1uTLfDF5bCURevLDz5Me2VvC5JFkkw6kTWQg2pPI79ZAEIAEl95lAAL0rWWFrzVsCTViCARSETjXdKB+o+cyxokasWk0wE8n1aH1THlQChkL1wEJY98sbfWrISL1atw6fahchDBt5FyVTRAnsYtoFeXAKjsgCMJcWzbo3mNNBDOjOl708+oZ+lmjy3JaDaXh+Hy+ANhvRIuVbFEFxMRikCFF3vZw751CXDpiuNdw9ooaMoq6AZ6x90nBqJzSQ0HBIBd7yHYcO4cBAf6TTikbhkHgtIYjEZIf+TTVd1XzyZn2iH+i2+6ayUhpoJU0rvKU1JnAYnsjHSBxU5YcDCR4EEWwNFADYDENTRpRWiSEGdAlQ4X2+a05uPkZCMHeqs+tz6WckL6QWd5myhfeaWZLKodC++O7+MuRiDJcwozs8i6XNjWRyD1vjfleatRVOjRUt6S+JHqAKQ6LtgS4LemjjxM0D97lo+vfrheSn762NkfW/z9355cGQEAiVB5ZThzHoDgNqmAtMVCz4wjJdbJPPIjaYi9UBvJHWvBVYwvPi6utmTCyFPHeA1y5Y3LUTGLlFYxodzZJg/tkgOtd2yA3p2E9mM2AGGST6pq4BxdbBqUeYzCRsYVITmTrUwizUpkgrQ3fcuj7ZUAd3bEhMvNHpsTHA1f+J+f//Cdtcs+ipQ233rN/NjP/5Hf+9tAUe303gP11CPlbSpb5KjZ6WLinQdSlcGMoMgDyXviDgoa2wceSENTNGAZsw2fmGXzQSzyBMZPZ2szA9HTcRFj4XyOyi87m4UbkoLvqLQ+ZgPAE77QilizAUlJKy3hzEXfOgAp10iqSG7ZWr9WqQzVAd6LO/amPWrI3/jZ13556zGXO8ZiG2T21T/43F/7qd//J9sWIg2kR7sm5YGU4DAQ2dIXH736zmbcREJD1I0QIroHsEGQNcB2x/PokE05HyvCliYa5FTRo56Jnmjf746bK2Phl2zV463poirgE2qaELq9RuRvsQF0TLNYw5sKS6L1QXrplKwN4EnWdiSaWwoOCGGii9wRVphorbXIsf9wsEybwzMinv3kF3+i/S83zoy38Gi3WZBSOtl+ZfBjzyRdDQABxhCI2AGAHYPX9mdy6ZN2MkjqSqDxORnvsfvO/dEBUNZQ6hhhDeEVCMliTGVuicD4eFgb801/saHoM90ORRVI7tWsy3pbbsG39uO5DrfjVkHhqQ/O56FBWkwNeBpSG6xPora92A07m1/ryHItwKyYsk0AIDLBKhjEZP+NX5m/t7Z190DWLP7Hn3vs3/zyh2bjdNw6FgnW+/2WhJn6iU98/SvXPiVpByDXTiSNdDQ/0b+kQzVIwkZQwpUAlKoLPIjowOD6q5ATnYQQY38rcPnI592WGq9eycmsRV9o7nigXhdhAiDLasvs3t2wWzSASnRiooKl3FZpIKFTEwcg5BaG+JZq1V4zLDOvkw4QVQOR0hJ2SqSNXNjqzobHbWGB+N1OeN6Vj21C3Z0GTdv8X/716FSTLHg3JPESjm2M05BMfz+uXJc6s53aTmA5JUGGD5MRS0OYTSArJoGc+VqaPRZe7Zdtv/MjT97F3iymndwKA1qGaqn+XuvqGgB447UHWJpVtifcrIEwZAAXrungu7QJuUUAggmIwZAkJrZmqaWaOCJj0jjubUC3PSY6cF5VXaZp0sEAoMIDCWmTpOYGyT6NwP74HhTHXpKeb4Avr+Lp4Vd/2z3d5q7RCak28Yi2NGXLg/b8I59+99qpEA4AqwHSZjxwhJGixG8CcCQyYa2x8Ob2hcYHwvYa5JGE4JCrWIrOIfImI1Oa9M3GW+OQAgPTys57D8kbz7IBDR1AWcLT5qP37y72yi0CYBlMiAlPqA/RBUPzFonvCeZbCohsCk+DkrYCi8Gh6XewUHkzAQ0hIiDCgdn9afG7Hg17HWHv5WAcAMhK4FMz9yjZrAKfgDojj43q2k+LKxmd5s/VIyjvAMSdTLoB9U6bGGFDploIG/qhQkpk6bmytugd4gE5271MOIQzlGlr4CNQA8Lw46fTd13HpyYVQNES7yF5QFBZq1uSc9+JvNy7/r01EKRhqLQtoRNQSxHCwFJXFq0kWWoJlkZ1TzvngZIiJh0BIu9TDxaIAVz0LQ2apd3hWsoO93I0FgDMFEDPvT7hdcEczeU0kaxnh2t+IWl+RF4Qz5y/ds4kiRGA5I2BDDGNrusCpIFyEYiJ8ZLolGWH2Bd1ewGlkgqNEMDBQKNLeUL51lXyeH896uBJy3rBJxrU+IRFzGKbeM+MTUy5xz6x2E0AIhAMxwwkJnB516aOcbQmZNR1CGneUG4HxKAO0GlSgkQ3dUEKUfqUB0FYrJmqSXKo3vKCn97b/Kct1yfCd6fLk57aDL6/mG066waslyTTiIUszz7xrZ31cvtsadk0EMSi80DgvEMimiJo6siCK3NX14SoQ7rFhAB5DdnGwgDgynl40eN1wwzOnLAX645IMMJDlbXIOqJIRiua9o2Iko9n4fpODeN3Kw24RQAKNaLCiaRCBOFWZW7mSE6YjaEJALSgtYiU25S3Bi2ACBjAGKnatrBMagYhm3ho5+gAABsBvXsyxFOmlz85kanlhqeqM4ZmXG31JLVtOrpMiytPfu67/obXRiWI2toMJonGO6e4d7QnoslZrFGDS6IOKfWpF2ZgNRDj9kmHbqcAq8yLFpB+k6qoOS+jCMe3Yi8ESawmC/BVToiRjPlr2leou9rJtwhAq/qecc5FiLUJtOTBgbMYEDrhiKKlVdywyiCIpRYdbbY1MQ0wgduWNQ4CWvU8JYcYNl/aiAt7NxgApJOep+X4ykCltYCietQvkpqQaisSV23OzvS23Oe/9pHt2svWQAMyUgMONEB2jtDQmAwTmRorFyotaJ/s/9ju21MC8ECmAapaAJlx6FOSOJO2fNKIaoaU+jSqJOmXZc6FqJGxMgijI2dMA8n2+7cHU+wWAfCtMq2HUFZFKxLSSp9JrQ0AycGF7bQThVjvqc00mQ5i0YkOrAqq35WAl9zHiNiIBnTvx1bcM+P46IV7+kKbOPVKbxxGhiAzbnn8OB2TjF5xJ1zdcmY6srakbkhkKAcAkbRhQCQxtYQWRtq6IY7kABCanggc0wPtCHGdoeliahOTyg6AIg4pr03kRQlne9QBXg+brk2bKvDImQVqLamOAyuiBtBtb2/twVe91QZoGu5JtBYaME2v6BS3rYqWDkKdVSRJBDU2ZLnHxqLbWPL9spbJcOyZRq9ES5SjJFoHhENcBCy9t/knsePHOF+sjVlmIeklLDMubKT9GZtO5OJCly2db7JXLs0aAGAeizWIF0mXJA6down10dsmcG9M1gAZxypoyA/HGzzpJ4w2CJ7RBoDWQPRcMzKB5LTkGoWR3vcIs1y0LdM9aQC0rGBlzVIddk57FHuIVdwiAEnUXvWiJqGIMzUt+y1yA0GJ5B4865xMYuVJbIbwkYZa1zkP5eLQTyVUrGiPTJB4G/PY4LBUAD/Z2ck9fSMCxZLvNq3rLU2DD7NhaI7NWJLrWXy0zJLeIB1cPZ5v73IQ6pFepkuUbULYmYoFjRUWYQKVXcxlMmHE10gzCmMPOkeABkCPto+jpWpyLdbRdYD3Ktgkbe2SG4lkkLZToFcUdUtiJImyObN6sfIf5YLtxRe/RQBcLryPqac2yGO6pj5wzUlLvdEiTAaZa6VlnoXQAFOrvGdh4Jv1GDFkVoMzL2xX8FgfYn3A0qiY3ONXpPEMl1Z79Nig7xtF7fuq6E9JR9o8XeCCD+z6pkyWVwEgWtDZYmblumFdB6LgWpeHxiEIaWq9nPqaZ4bpChLtAUt5ADBRGiIlIN12rCNxPkK4KKhmqWC0KeGahllAmIbV3NYDRzuegDDTwuxywNntuWWamAw2INE906GvZaiR2JREb/LG0YCh6YaoREIji+tQ3qFvTc51GzCM3jnHGMsmXoWHLCuE87OL6lIMoUhjlz2i17rB6Yvd8izh5dZzT2sb3v/A9Yf8wof4KL6wtAWkQmsQ3kdlImMGaReBIroklkha1q/NyQOPB6VyCiTDaUsiAC5ZqMF97NPGZooVo9oBMrcIjvEMsxZSoeYClBiH643L6d4CZR/XACxDCRo6zCTYjLIOrEMFqTgCslg3BiW81ZwM4lJdmJpGo4ShKak6Ah/gfYpiqgHONO27w0gP5ITdswPu+rqmn+xdrovRKl+KM7+STFWSSq3GapjLjfQFfnGYKAcAASC5HMHmNnbWQcjcjkEA8KzLUKMa+CllnINyg4NNEKEBubIATNNun9rgHKTgtE5RkyGDGTMCxVWncyQlCywsxHTLDz3ijKrEXH/x9tjS5FYjsGMsSWPZuj6xwjApaumcCSrJaYgJASIJcA5twosulZUTHE1EQi0NhPMGbSTKMOeIIrP0MHKq4917PdxMr3XoLzwdFjM14rbk9upCRGJ9Z46NnO7n2j03XJ9N3PnJI7i0nc/E8hFCIAYuC8GOHMCtT2VjZACgNRIbWtgNGg60DCLpBK1bCRrTmF134ohxRIQmj2NwUZCFVreaJ57GnHCVT7rY60YAEHbsEWHvugd0nY8LgC8T51nIixAah9RSaWTK29o5TRK41CWcBNg0aHSgQcu0Id5JEXnmXSPS4CzzTMMBITrER84ffEj4nnea0uDY4rPizdUnxPHVtQ/lE08P9agmRqxw2fbc949/6dHRaVdeKMmji6PGgYY4LYCWFlkdeBOYz5wjKc9MB/iFWDMVpYhaUmrbA90X7mAB6hBA6gDsZLLblLCaq9ahWOJbLo+J8J66lvI2psajZBYyjTMhaQuH3EqzbwEAlAuqW0OezRxaUB9j6BLvsimspKH1gy4qq0ELFqdRRJqGmua8a5i0wRttB0DokLrjfhyyYpMddXktwNcZskf7X736hWN9vZr3OtvK5ipNn+Q6LGyxVC/1DbyVj2ZaJEWDXI0gOuZ5jplj1jMLkYzMYtsYoHO8IVrGiWJE08D3ufG9K8KSiKyJHjy4AC5aqI5SziukGW19b8ZzMERO4jQCLKRJSwe17sU8lL6XhTYOjdtOxtnrInmjAHAHoCebkKJDp5IeaQypuTQtGwgb0ZhiBti0IUttK8VMFqWNUMRTZiKjVqKzKdrAk6HlvcXljW62HvjhbpzvBeUGZLBUXzU6J7p/4mLiLjfHFtoedw1Lg/Vnz2S90VXyRBqn39GP+q1GK22tgpxwB9+C+0gid061FkqjlkJ3udQpCwaCH+Aa57lHv4RsFW07IC2BKC2hWqLlNImht1iOW9puW9g91IZ6Q/rtFBIs48JEfU/hUeBmAXAAUJK8iR7wUxqXB20Dx5JukqgQVT5DXnPOVbVYcBdCc6pwTe0ImIss+G6goi/Ilg+MmZqfGQzP64A0Odj2mfughvKP8PDUVHXJU4OsKbMlvlUmcf3YsYk7XQizmYsPVpIrb72cn6LTVUS3aNoZUh0MaORDr1wr81kkPIYIpDZ4xIxHksSDzQ8MgQoRh9RTSuKgLgEWDSwpwhSEuzY/Rib22MQGEnuMGiK7LJ9UUQKUuRnfVyubW5eAWcG6CAjrsYrMg6U0r7oOCOCOC9t0IZb91tIFWsVEDBohmeNWW0qi82XGvHIhm7padgvHT19ZK9N7i9ofAvTqStMNv+xWmmM48/iXLr+GDy1pIzuWiqJfvhOeWB35XE9Y2Wyu9MCG1awDeMsHXqrNPqe8poVDCUAY5EHQlaZaEBUzkIocYH/oFKZwmCzQFuBTAHSnD3IouOscAqZq4Ijjmaa1HdLWTQturQC6LIjM7aeU/RYBkKZCC+SwyBo0gG+kT0BatP2sqgicTEtTW17VWY2ZXFpwG8pHxmUQUfiWUEa46zgx7y9/+rH63W+83T6IHMo7EsQTJ5qFFdlsXCkWlp5nl99tl22b0PpS9ugK+kl5sRyy5WMYndeulE9oXQE90uR1oeKCXY+5A6cRaa7jMExCkpFAx4AHjCEHNP+MaxBVhAZZbXZaoGcagPBGpFNjBqEdiL4tS4+c2lTZCTldd02A4ZkVnJPZvtIwbxEAUrgOQIycWekCuDMkhCBIzhvGEAFTEFaYVIyaJHOdIxVxBFCxJDnv1SwW3rpIVdl/4pFF+akh/251uKkBdyUvstPkakG2trYwPL6ydmlNpaOk19vUZFMkjz+xes4z165ldWmW3FYZXJ8Y7cSi57LpDBeyBdnokCgxU2zq4WdacINeSYcsbB1QLNh7yKaBsMh2dgMhjAreWiCMPTLR5nnYLCP6Cd3iE0Dxbrp9Fob1FlD7S8O9NS085MFT3ygZfUibKCLXAAzq1IwyAJQGERlakWaOZK7Km4IROFKSOLOsF+NE+b53caSGpyZYOnXy0d+cLh1hzTgdJMaR8WRWN12yqO3XF3uPiN8zVk5Pn/yA9dnlS2uqDK00qR6rraZFfJy/qSELdgF5P3jrDEXscKqcTFnoDGBIknul44k4Nrk6MP3GDcB5FM212LKFZw4Aa4GsMY8UZddEINYhadDzjW4lRQN0FACV14Ovg/oetNKtNkCYRrDeVHMQ7yN0UP1W1kBsgCanPJgJsAqMwO0UaSeTSZ9bV2RRjJmdArAQcpbIcsZVLw+vnF74B1tH2DbmafomENJkg5WZcs9cnB57vD956c3PxQvPJvYHp+XErPS1ob6ctLGZTf3WCz9jk9coqz13W61lnDBKfcZEnSp0NRPUxJQ1UWBMDWoczFZA2kIwFk17kyW3nRroUSSbSHos83mnZcdCC1mBhtABwkUEgGYfBd/jvaxKNwsAiaBJ1F54wKGn48JMmabXo6knMwfs1HuofClxSjk/cbI1NTGcGpN6nkdWkdxRGILuRPvq2Scead+Qz/yS+m/qvcYlDp5H30DRW9BbqiIrg5P9d+uK1e7EqdNk8uqP58eYWNbl1tIW54qo8OGa1OFLS6898cYoCUkxjXloA5WxsSS9orKmLTJCmSNl0hLuA2jAAS1vLXbeEXK9FzQgA3dAL/BE0x5R2eU1A8RgMYS2PToBEm0BcEJ5CNtbGNIV9+R13SwAEQjaqaJ2SI33ok56rXSlsMyLfux0AIZnjj/znLU9jXxwuWqvvn/R9sKMhWkRB0VwvUjbXtSiV47MN175TK3r90Yv/+Xs79zr/PfzA9pjyR+9fPmM6gayJIscQ3a6G8+em/CtTcnbN1Q7UbQPoW2eqonOG190TxT/7NyXeiNFQY+JkhuXVDSGVoC5FrEBTUWNSIMljCR80jsYASioBkADInc75xVQEXgHyBKY5tn0zPHJugEnLHGMjQNHBFSIoNT5Xiyvdek1uLemRruUh0NnAeDKNBzTVEvAwsMalQaNH/ulxVRsXbjQ5+0jV+Xp5MzLa1851/V7ctOojaziaSdQAhrMse7Shvggy9989xd/cfKP7nBi024sTe7pNm5P0X3qrdHG7EdOTUIbBvX663olXrWX62nPT1cKcinzzC3pZraofKa9XFI/3p13imPg6naJVB7ogDwmNZoCqIGwU7THuXWVSA5knLk0M0DYgO2CUA8I9F00ABykiDU+e7rb7ABODO3SLdCBr8BMBOC48HrHZcA9NzG4UQAGjQXzkuixCjR2lDtp9PZZW5STqgKgvnT6qzX5TqybPC7OBqfPrLzy/PBXz9Uy0lCTKkIbnzvGfQ2VN+c4NC+6703+3H94+m/fWzjog3v69B1Ymz529kp2cfFZOS35RjqJJ/yb3j9CfmBWsrp/Kkx84Vmgqk6yMqb6/HNLr7dQXxbvUoGhYR6nOkedtQDSfD0CVFKvkfDKAUjqA+mFIYkBkIgGsAFCNIBFbaiDsAQmDPOfJd3rLQW0CqbHeEcnoZCTCNAMtkUvmAbMA8Leo01yowBMAQx8makZY8RqIBFMY8h1iWAIQAP0pbMzUyYr6222LE6U4cqEPvKpH3nt33x7nKaNYLVPg9PKNUXaakIvfufnVmoVq6/KX/jFU3/z0n0+pH3GXL//0h/71tr6h0sLjLTOajlkV1s7Nu2LfOPtD58Ol9CX9dWEJq4ysTfqf2Jt7Uz61svH3/IuzjKDZWpVafolyTquY4IuIGhQZaRZyifuQJqhJExbIEFSjG0AQgSAjHc9xBk85B/tn27fPDeKHcBYdDORpoalFh7YOc2aVnHbdVT3UBYI4NYlYARUOfEhUsVILCGCpVWRmUmEJNB44wu/mH33qrkaHvk0dY5P3/og+9wv/MJzv/0rV4t8K0THMj9zQD30phOb/+KVV77zVnbmnW9u/NmflX/j/iQg3ZcAcHdJfu5HP/zN3/qdP1K8T2TbkMs2Te3F/rPd1qtdhDh39hJql4ULW8N0a+P5H936gTr1VspOApFX+lTIrnQlVFQ+MVcBXgGsRkI7aTjZ4PmBhLmztgZ6J1Jj880SSGoAMnqO2gHF0tkT2WTr8pQ0UIwSSSLxwcZqOymC9TOvNckaEgDgnvMvditvqRGpD4rLEgDqLjapTJy0Nldm67d9v/iU9D+gC9nmiKl+8v7625/94v/k7N//JgBgC0x4RB5FgMV3f/cpPt48O6urr+Y/8X/+m/el1/e37eoQX33utcf/yIWRJgW1m2cWFLs4jqQ4/+4aHZhvSfaD9HHergU7nmE6fCr7SnPmXSPffeylyzoGbGlw8OG05KQyyL0DgwWIcBhK2977896FlI4A8fSpzMzkeHVhvO0IRivdFKdcsiSKqGa1HzuksgmBRQEC6VLeWQ9wMmO+afbQF3x37pa5lzPvNAAo1ZnUE56w/Nmf+aK8+HX1WKjFeK15b3ImeeFPnPjdv/Vt9HZWxDx2QdgsL+1P/C9O/NrF/nsXvnzm4uc+85W/uc/smZ6/nxft8z/99VOPbfxg4yJdarB4sih8y2b2tRnyfMvnzvD02MwsuE3f30q/cHwtGW+xZ17Jm78zSVsAzz3+tVbZFrxP4tQvkxjaxNUBB9YyarHWOLZ4WgwWe/bC77d2e3eZEQf0ZAgr+eN0uPTOO1cn0gEpVY50LniAimwMgCrVGMmdAYTw5p7F4LYFbiQCIFEX3G3/D+YAQYVx07Vzf/ALZz6Jx8KFUqIRT75g/vUP/uqfOfW3fockTYoWcAiwaOD9b9N//8/95ptPhfHxb/76n/ujf+f/+Z39PKJ+2P/8U+KXPXtrc+kL33oH1qWXzosVmYb6Srs4guSqz9uaT8dkybWsXXDrZ06+zaTbeOuJR37yXxtgIX1FDKcakiSegCujSQdNad5R7g+mIaLX5MmTAxrYVFm47RJl7nyaU+lHyy9lvYScOb/2XrEUWEsrYWn0jBWEkO1z7AKxKd/ecHUk3rsa+LgASAPQY+gQecf4JCatRtID6qYpPPOmpYqP33nnt7/0+CepDBvFqezJ801v5eL/t//KX776mkbCCIwGhIWKavnq13/iy5//zuJPf/Dq2pV/vPBTf/1vf3sfj2j/3Xqz1JRI2nVyIWMnB1NDozN+nM3cBk4AvtVs04qlaZ6RdgOhrvEhe3plrNfJ0vkX/kjzFZ+EjbaQWU0M3VaDTZaSNgTpLTQgDkIApsnzJ21Ms2aT8wtb4I556gA/5QqDJ5+ehhPV+rfeCBM5iHyKGIKnieBKb2y71ZJcbyOwr9Y8HxcAA5CCWV/FBcoQoKiGmSpvIAJ3IXgYJB3f+Je9ky98cpiddKRoutN/oa1/661P/sW/+xra4H2PVJC8DUkvXdn43Z86vmSXw+/r/uo/Xv/3/k//+W88yB6CDTdY8O+eP/3kCbNeAcHQLHYTAFgF6Towg35sk9ZPgZg+MpHdkiJfW3jk8saHL7z09vmu4783MINMa0Ei5zQIyRSJLSMD2/DhfeeDkqJE+vSzWKUW1VBefcsJC48A9IWuHH/2iyvT+vK5jXdUl6cz5zDgnWdKhqreeSu4MYB0+3+ouywBcTZDT1kf2oQGK/udNQbot1og4bAz5KpeaTZGb37jx142y2lz2i4uqIXJ2rmX/8J/82YHoFwYlh2HnU5JD1954S++8EbvAvRC90Gg/95fmf7Bvsd6r5CIGQB/SYoNnm+mUtlNqDoAbDg0LbUOYhLiGmoLnhG84tbWWW/4teHLVuMdd+zPX/zl2q2vAwDXiYhTlTcN9THtEUMa9O8/HziWePzZhVg9L9aL4Sxume38sjw6bT3wxPM9v9K+mclezRLq+qYOjIYwQaquaUUnEe39JCXtbgNQkXkXiM1U66jkDRKlfCeobVU/HzUGmvgivtddPfmztnt7I2FV8kL+weN/qPsbAIBxbrm0AShmcP/y+VfIe+2JGLL1S9/s/9H/zf/1+/cx3HsiCmIA1R5rPzj5SNzgw/EMGQKLwQebURsrmwc4QB0bNbNPnvqTo9mrfibUZ16Yumr6Zv/FZ/EPAeAYNYw2Rvk4yaRvDUoAMhvd5fJ3hReT/hdOafG+zIezPOm/e0WD+cHA24lRy6w98WOnXO4qsvFmKOqKCc46PyLCMH69JJWxjyY/2dexnbsLQJgEgAaaRGdoODloOAk9y1VoXW41HxgQxAW34V+7enL1nZlyQ/I9efqZH//5jf8cQKprOJ7EFhHAe/+Pv/XK79Qvynfb4Wz1ey/9TP6ffmXXa97KfQdZLACspN7Nnj02WfOzADQA8RjQ9cUJuM079DjYUqvR+9Knz27+WHztrRN5/8wx8tpbC3T1T/+ZpX+0iWG+7t0p03gZifRToE3RJt195wIWSf3Ezw7evUouJWfcCbjpd7dAMrbgLgM0KSV7rL/56NZvvWtngGyonJXBuZQZFuJKuXOS3Y0np5N9eSW38QK2H32I3gjRtdJrZTPVRkhUFU/TXPheVP0tY3+D8AUam1Z13btf/8p/+HP134ewAZTphhU+FWV/+vY//p+dnfDn6vVikNffevRH/lfZv7rNYG4O9l3bFbk/luyHZgu93lccInivWNcOKCa2agAYDxrynh579Us/7n+T/MJJWwr91sVfeOzb59noYvHFnz/2X5+bTCNQhsBVQ/gIvfJUqA/gjNzB6frkn+rqzfUkWRgvlhUflctJl9qZAyk4jcunzDceO/c770W1nFgTto9oSCghSjB+bXed0Y/2fvfnld7ODZQZ1V0UAjbQCRWeh7LvCXGE5zyfmtTktNrykk4pmzVS2m6YOvu9v/Xv/w82/7kFhEKk3mqe9doe/eqjf9L/4LR8a33g3rHHTj/xH8R/vftFTfFRYIVdT4y5L7hMWVUl43NTIPu5Z0+t/YvvoleUhneC0SQPDlCbLJz8Hz/pvzEasD+zdO6/XZ29UHc2ocn6r7zzn3zqr/3fzkdkZLIQY+vEWPY6lAexAdDL1CuPn36LgS8eoytEL0wnoRvFGgAGJR8MT+aX8q33Zj6Y2BonPUQXoRNKb0ytuK4ByH7T73cVAOmC6XFDJTdF5fstEkfTapZHEM5D4lu/3RtEdI7wJHYsC6Zm8djie/+v//tfv/C9lU1uDCtmGqWY+Va2//LFV/J3e0+G1SuXydeKL//EX8n+ye6j+Wj+VX4w6dbOzzbJk/1T766lz3zy7DOfdz3xB2UJkEHSWhH51UR6Hn/0x0+5c/KJZOvEF7vF1fo74tmf1F8b9+s3/84nf/4/+o03zgteWygYMyAzezD9oh8/eeaMeG3LljJL8thka6+v6UowFWlBpFpQg/PpJ97+tnYAKmQDM3OEpdq2CUXG9S07PveUBHIjuwqAATDJW9hoFY/WgqTTVHWlhezHSex8P9AZAFazgrVskYYmSkpXRXbh7/0f/7f/ATu+yhAxjFOMANnTr/9Xf+3U5a/Fl7e+RuI7Tg9e/E+Kf3i7Ae0s/PreY1q7cvr41PW1q76PZ/+ibEq38PPP/ovXj0++M50oGxoAbAI8+x8vfX1j6YXlZXv+7fObgHsvvGyTFxe/0l3YfOELn/zd/2wCtKj5fuPRt8K/+BKpR6wzzjC1lNS5O3/Odnk66fVRD5ZkM93yn6i3aginQsyYrWFsjC6g8B3ZmX8ZCWuvLZL7TUy5zRJAg58lNjawPLRAk1ubpQbgDQmdQ0WXEudLFiMn3sbEN1CotBbhVz7/hz/5lRMZUjklfdKFftVtCfEV+r/78UsfhMez76y39FX2p577y4O/e5sBX+s0fDDzz8/w1UdoSwB8Pi7HJ+MPBj/6QjPrfu//vbb9EBkFzv7vP/kqHw5PiHPffqfYKvS0F7793qdOHMsWXK/9tQtf+Hn+6xcMvYT+CKcvH8Solh5/5RTbkPXmljMiZep4s/b+dyssFatuIe3OPN6JDwb9vHt1A2Ax0NROAaSkoX3NSp5sl0by7TrgnVg+369RsrsAcE8DSOYTy2yuQ8ralDUhS5RuAAmEsEYiJ5J3Ni+qUKdpO6Uegtf4r176ka+sps4tDKf1YMEwOWksxW8t/q//2P+vLJZOtlJtvv2DpZU/1/7D3XX8zv4a83tqcHI3zhbvtj23fPwK2B/YD56WZqH3pvIfLHziT/9/tiBFogMXgz//xHfXTgW99rW3ODkd36a9srMbf/DZYviH355t/do31v7UX/zjf+83LuPpMHr8QI4Qe2Th2ccvXx6v+6Wl2dhyV2b9+Fq1YNstnHy5t9bV9WhF5fX3alAJBDkNfGHiZZeptgNhZFt0nds+CQ+AZO2+M+52FwCHCLQylxEd5x3RFDJqwljOaoB76V2kTilOqk2q1MQKyqwQji6OLvyLBIDF2hKvMjvxxTBj46T7ZfsXPveV9594jjeVWiJNeOKXJv9y1/kdbXfd8djLed9345mnLk+TDwfPbH0HftZsvqfwxYurx5NEuk+u/YoxpgYauPNfH5C33lwjWv4U935UJ3A0zH7jB1/+wufP/XeW/8pYfeLn0t967d2CfHBPvRd25+Sp048uv/HdwYDwXkb9Eif2PF6/hDGgsj98bO2N4K14/uT592usNJYqVvZiz/vguEPiqYw37t9tLwAm2f9w7tTt0HiuwUUrGsEsTdPgjKJEUgPZYDHA+nHgsrVisTZBDL0gLpOr3/n0UOc6iC1gFUA5Y9LwwfSfp7/4zHvHsrgxjqe6184Wj734g3d3FdsDq7UT8fhLzCzp7Av6HJAuPFWsnrs85ZasPC7evVC8dG1bYuG3vvO07PpPDmifvIox0CHm8Kr7g/yF3pffiBe/9Y/+6ueHz/+rX29x/33C1TNPP+r87357/MorZ0Sq6ZLLniJv/uoaAHl24fnFt77d9XPWP9WG/HHT8ZTYJnauXMq7zhMp/M0JP9ce4H04pXdsd+k94BwsDPfaO+9Rx+0zDFIRrcXU+yxtYUImKzASSBYds91j34sLY6XkCEBCG2/gqsXRr2X/w082FS2kX3dsGp575dWrk/2P++4Qy59/9Nszt/bE8mvrQLdGzjx2crTwzmsnBqvt+bdnBUADUr8cJ+3wkc89t5GEr33X8gvbjtjMCvXBBy/99dPtmG9c+VdrSz+9HM6fX2vusTnZxxk8/UVx7vtrmVt8sbe2mgql3OLi22+sAYBpP/P0Vz7I0mbwdHr17ctxoW6j0gGIid0CkPEmqKAZtQCj21sGAKDo/exL3347mPkIJIVrNQAnk9hc7zqgKrK9Q0sLHxfGiSnzgntLSsFb/OCls9/rGG3S5FgrK095zKSbjgbjf9X7syuT0frJnmPP+M3x6adeLem9FrPtuQMqEPGpZ6pcXsbP+G8AiFsX1k6mn501K08UXPzy22eJMENNF7B6Be1WbsRW+aitcp/SADbMeldN6JWvfeUPr7xVvDR4/ftP/PGXn734a//sjfvKAFl+9IU8uei0E/j0cOyP1csJSxbXX30bAPDUj67M+Km305eH5t3vU6saw0QbUMhOe7DUhdZHG4knJN4UAfT3VZlwWy9gOzNNg5GCskhEFfszJfR29jJ8A0A4Cl0NeOPDGL1YR1gL4l5/9qX3u5i0TNKh2vCC8lb0iVyu/rPp//LR79tENsP0pM7U4tJqfUfvZZf2S/fgG/R+dqUlZ6U/W/y3AACz7l9bCObcCb72s+e/Meu1YwwnbjNgqVAbXfKji2u6W1z+YKZaENOdWm63LgK/9Znnts6ZZvaD3137/Kn8S4t//z4UwNLyZ5+U577lSL7Q5cX7g9OEqk32Cv2t3zTIVFg5YbcuhKsnPmnfPWfdIupFp0OAYlOasJh0tFGJdwDcdh+z69xfxcVtQ8HbuQWxA5C5lubScAVuBDQguE9qjQFpM6Vr72jkdgYMREdsn02g6iHTfbvJOGOE5sG2ad6VHP9A/tKnJ5d6gzEZD0+pU09dru64eu1bsoUFnv3xR96+9NSp0f+UffUKACBeOFOupWYr+71H+OsM4wA+cunpVV8QJzZf6M6/3z9BL5ZaA27xuHmSv3XV4/2/91deeev7L8kYf/urZz/73NP/h69/8839xYGOvfD0aZa+fokNB8f9TF/lx7PRyUp8yrz9mwaJbMJsaNf18DOxvPxuZxZjQ7VPGGrtPPE1SgB6x+cT9oZDufZXFP4Re2l53QB+RmXgAqGLQE86r6QhA+8CUaJi3sUex7hZUKRa524lTRRKiTQNlUiNjT0+agDH3d+9/Od//KuXWO/cQjJSj7+wNrlBowqZ0movhv9d+7IRC5z6I8e++V72pM2PX/y1nT+/VZw4f65ZGc/qD59+upiOM1s/+T8qf/nyiNITP/Zo9Z0wvfLO6wCApWOVy06vP/aeT777Gz/SfTAl05PK2m9u/JnPfOaN33yn+voeRnkTC2fxI0+6plw177hHzUye9R+EDZb1BsPpv/2dGuio5vyDrRdPNGtvVzPQuCV7s5oEykCUdg479j7hFkjDzhNgHrjvBix3EQBFOtAAEhHijMjIWQNKbB1HNCXjyijvBRxJO81A7ToAOJx7LlsrzEganyUJwRhY7o9EdH1T/cvqL332u3rYMVWNegVX2wKQ2BhA6/rW04h2U/l655jy3aABQARe+fJjb0+ffWl59Omtj6LOF455v36u5///7b1Zs2XHdef3z3nn3vuMd6gJKMwgQYmESJGCZE3hDnmQrFC7Qw53O9wdoQ/gpw6/+Ml+ddgv7idH+KEdtmx3tB0OS5bbLUerJVGiREmkRDRBkARYQBWGGu5w5j3k7Idz7lQTClW36hao83sA6t57zt65c6/MXLnWyrW2v/D6f/LBd977iL78879gpj9oez+/TcJ33Zf0lbcAoN97YfBBfflie3njxkfx/S/96q1Z0xnMySy78Y2sd+7XXn578wfvPkDXHtCfPPel/AuX2M35X/tu5/1yOOoWt4pNNqXF5pv/97usqHJTI5tW9lp+pSEGOZ8hj5kMEQFiNdpXiQIAGLbSnAJOwVnygOkchYysSnmN5eHxI2gEWJQ8uAAomzOfGvFGeLNeLu5i6ZIleaWCh3IRP/n3LrZNiNnw3PDq//EH959OHzYHI/vFf2+z3sHr3Y/Hkz//o2N9dOnzOzdk3tm++MqXL7z3z+LWi5/PpleuXr70wQdonuld+d3GReq8/vLwr9M/uhxuXZ/+8G31W1/+m69/UMhx+vwFmHPn1HPnPR/98R9ffcC2qK5+4fUXJR//oOPf/CDfSy/0q1cHVnXSjD7jfv8vgnAu3ZbUS3WEDUFYYpPPkvcJMt1DXX5kl/kDVj1wDppQD6zOHSwzALKsiRpG2qScKXjqzHJZ5bPZ3qufe0u2IClrAUYEmuUhWsMj3kq/+SU70Z5+fF1sn7tDAE48kHmoJ6RvnH9G77X9kjeTq++8HQCALOfKufvCv1u8/9Z4+Fb4B6/8XXJl/lftue5P7XzjWjN57gvf+2erTu53fnB94+12mL8y/anm//ynQTdV8+zFUZupFk24iueHF7svXf/hH35yU9iWfKGff27zRrz1feR7H9pq/owyl1/UDW6qYf/KH7zjYWkC4MECcuJCIMwjuAVVsqZ9O6v7hHJ/z+Mej+yXvKcA3OGJb1iyyIgBIJkVbNErIyGLyDokpNam2PKFH4kMXbf32nP8I7WXkFErinGzKjqOvBPs7HtWfP7C3t5oKDvFncnM7nigT/2Ew68+H6KNYTwu45u/twzbocx5AFq8++aXfkpTpT6M3+2I8d8E+XF4Zu5eevnqaPvNf35wKzqf6+fzsJtv9Ldz9t/+/mvPiJvC1eN3XpIj/3L8yCLb/soX3nj9u/9m9/5N6Q86G8+eL/z3b47fmytq+Is7pnOBX+BXY//F8x+9//tXBWWwEMT2cs8jnVggeVBPSiah22kMurandAL97txTAJbvX9kEEBKFA4TwNoN3AJfNtOxrfiOnKho14HuOZHxhAPh2CrH39Z/pfxSfjztNGbgmCwr0QMewLvfSvvtf/ce/dm4U5SZ77tKjmVXuxnM/yT+WL9WGuIG98f0RALCwqnreNMCf/OUzL/705vwt/mX3ze8m4v1Nc1nn9JfZNw9FbbHRv0Lyogm+/Wj7ude+M//iV6Y3J89s2f3eftUfXvne+c/vj2nvF994+y92PrhXYBgLxflLw3N51q9Dfe1GTKnMy96V3kZU7G+al18j9Z/9YMy4RYqchpKNu3UUqwW5yYZ+H4NZANCQ40PxsLYZS8qdyon7T1gCDMB9SkLmNqms9R7MJ9QpF9zeTBgEt1EtpNl0biC6c8f1ogYyTL/7peeuVb2L0eWGEaVV2yoC+PkIUDb8b9Nf3qzJKH/lZ98/KQHSQvLoTkw+LKx64EEWg+2fK5s3s+6LxLOXX77653919Ij0IGbeXLnC3vjFc391tfyOoeliTr8z6b309+P/+NbhRRo/OHe5YxfPqI0rV174+Ru3vkc/t/WDvN+EcjSftx9Dfa9pv2ivYvjGtQt7H9y6a0vo5lf7F4ZsoXfbS+njFFKPMXnr5YvXUn5dnCsm9ltXedN0ZWSUF9TNsnDkaqa0rQOfR2TegztAGfDIeWRLLZkkhOKh/X8n+WQdIGBVeaINCyh4koCGwAdh4VuDbqjajohV6VQRYuqlxIW3deflb+9AE79V10FK2TZJGTgawVkb/2zvp1+5bC27fHkvhSPjv0w6o7OM15TaQ70ofApN99Wvqfc+Mlu//NK085PP7bxzFcCBoeRQemjEv/yz3/xc9sOr01erW6G2Ld77dbz5B0dXcYmmlM9uVZvd8tqH5JX90Z+Lbv6jwZfC4tz0Gmk6Yjop/sTg/PlqOiTn3fvj3dt2K9nA9n5yOznKB2307tzP/3AeXT1wg+eLJs97+Sa78a3vB2KLnoPhsWFl8ABycNcAiHW99PZ5FqMDkUGyBtYCy41RAnr2lEITPlkAjp5tAbRKmQgg1aB9UutZHDeAaoyFsSklIAt+zvP518+/+Mp3YwP2MSWsSbCOAOjkvgUbpOZ7+zuzly5O8Px3VpYATpwmwSY5hmBFbGlEKIStl+m5AdxXG8i8B+QrX0uT70zF4KLamWxUb//+79zAymiWRQuSSAII9wCZ/dOf+4VX5YdXRI738358oX/jt49Nr/3c7ndeJk1w7tzFt0z8yl/ab/3s0O7v74lX+IjIjaqi+66Ok4/khjegkly2I5eZZcIwkpBr9bnnE853eTu9PhtU+v2JaVjBZi92dl6OvfOmuf6nPwoLyMLaGSM+XmgrPgVrIzK+qofGGI2q9RHAsvzLagJMAJEM7rTKFNxrG3jHlpxGANKiWB4VJOjqiZK7YAFZIC5BwUjWLns5ATj/AvvI8Mkc0D6pQOU0oS/qNsjoAeDSF392q/rrP74B0DLiqMqcVO1RxmsaE3sAh0HWotN//fOz39u5tJVEN+3Grmp+tAMA4NwFRt2y/0hpWZsP3E10f/Gnbvx/H+GZfVZkP3PuT79zbImRv7L57dfP780r+QW1+IuPt1+8Nnrnqy99dyde+sktUl8ryqlv30deGau2Sj/jgVlnYujstZla1GJ7q+gNzuubr+pds/+javNn9f7HxajFnD7b/xHph41uu/cvxkFymYiqFixgs6kAzgrmElhbHzzSPRa9UzmQdsg97QB3PfpISWIH0WddNsZwBJUIbZUkCxTeZ77mEUwZzzxee4W+N6n3MFxYANvB1Uy0zADZapHf+tprV3Zn+3MRaJqcuI+iNMT4oEqOjB7d1557jf7PH9Ivsgy35iY0Nc63iJGJYGocJt5edp4Y7AD4jV/3/+QHPVpcenbwjbePbm0TXvlyY77HXkT3sn1nvlecu9T+q94b376ifunCWOe8w0a3rozpIMzNYJtPs5ITRrlvyLxlFUK5oQb5sGP0kPzpjZ1p+ZXeLdLZvc5KVtQ7z1wM/fb9d3YmXU1MayCkbzCcRCgi6UJYFrhNNIIFsUxBBACSmAOT2NIyIh6kFswDcs8lwJT1nfIXj52HswKYAomiFZ6prJp1SZXgO16momLwN9grX/nu+1J35gBom8SGI3xWCsqstzQVze6/ulXOaNlUBgAYpSFGcGoBIxKNkZdVPCHtMiR+smAwJQGwwOu/tF3aSf/Wq2wBY52ZR/QocSpzYeGg4MGhrVmqE4Q30gK/W/zqf/HbV+ryJwbffBsgeROB3BsA7567RNowoeTWlevbzbWr//Dy/ofXFv1nxQet/1o37O04JZtJqLcHfKo35CwqkZud3jk+o6+U4cNRTZmgbrT7UbXd3/1hxi+OdubPXSrtO2k7S9W1H+7k28JPG+R1oGyDOCUksXHSEQo+shSAAIfDRc9itRCzIrqo2/SQdVnvyr0tgRlr7qt2c2UpJTL5VKN0QVXQwsD1ZMUNTVEl2178tRd+8M9NrzPlgogFSxXp2laT2qVOa6hmDb3gb3DSChZIpC4BNKfcmAPbNxt6r9PcRtzfEVx++ZVX9VWnb14dzfmAmvktT0U67mcApzqNElDKhY5NCuiyMf7DX93/V/UXXv76vwQAJU3GJgnDtmaXn/1C/Vbo0OkocHGT/4M3/qevv7LlnrnUuXG1yEdXTelsYO2UbJHylU3yoRH5NFrfLU2/7F17mwn9wgbR1c7ObEbYhtzcINd8X9Nz3YmpP/poP7BxE/up5pIx46V1umXduGg2MD7R44Qc+3E5G7AUlT/VJeDeSuAn7TK8p4GlJkEDiZvYgXVCtZIUbQwyWUno9d/9Rz936b+ZIlssHDQTxFqRWp5XJpYmVpDmKhAUYTauVIt+vbotCQDCKIDHFgfVXI9xsDyyAPzyG72G7y9EM4t1YLuxSiEcK1rIKHjk8wpAJri1SSAT3hLg//r8r17+7gvXvwkAMB06ZQlAi+JW87Wfeu5qc6PuiK3x+JVztYkVdZ/TorhVs+s1zs9Ig16LD17sR8o3sXib9/OPo87pD2rP5k0vXC+LGavJJcnDrmXd/KNw4dWhvfnu1Q9iJTjJaZoSyaxvUSNvnBhboG0i9+JodJ945uXzhkfwkd6dRymAG2mSJkDxebKMSmJjJiYVjzJFEqPV5/jev/jPXv3H/8t1wRzQyCph3s2YIzouyr6rltFfBVOM1k0pnNDp0NOwfPYAjAFVJGbmAGUOmkYTASZWH4y4/Jtv3Hzz3IaYTK/vj0OWptPVN0ERIRySg1t1W9sCcA4HqX1+52dfJNUPJ2CKz/p8D4EFOY2Yof5LqfTuEL3nLwbz0rmFwEfPmL/u1oX9yJXA1ESgAviOvbHfebV+JyNzxd3k/HhUbWPH1ty2H6qc9OWYbZP93cvZ9IXN8XvvvX+Vek2cE3JOWS+OQEESzTLqJyjQRPiDSl930f8eT7LN+wrAJ91SCFEuYpbqGtxNM0aWdTatUHmGGszza//Dbzz/1W/t2CE3NiYQ0YJXlRJuLvnq6j4iZbpygrRCUKY5jJoc3YPIJFvb0YRMo6CazRNwdCKmo1/7jddv/fl08+bVtz/ei1aRY/ujCKAwRDQWEA5ipVBIlxABiBi+/0/+y1f/6jvIWJjBV0Bek9Vi842KhK+YggxePr/ZfvuHTmzk+d47dpM5rs3yHlY3fhH86KXSFzrM+88FP2vPi5jROZjKZpkwE44XtitXtu7Z+o/H1xc0Rh6yfObi+RRjDU4iE3TOi0boaEXwJ9qOY7OBtOzxJFu9rwBU5f1rgJuw6LM0S0zEBG+ZMymUqSpUar1WZsyq+J1rv/Da9je+X7+0N5ebHsoRCBczGq3VRQVwbwAMiPbW+zEAu0DmB3N/sNlJyFBjDAjKDG7rhOz1r37hhQ//cppPrr/9Do02tf62pSJqFxIwtA4IkCl5xOVGtaf0dPKv/4Pf+uaPlqtdWkCqYCyoLivVvBN4tdmYWFRv/9G3F/v+5py67t5YwrvuJICmpRQFFj5OvqUV2e/zyVT12h2y0aTLss0uFnrCeyApm37P4Wo149IxvUBmHJTesRJ57ufa55EaQWov2cGoP9B3jul67tEdv3fnE9zBd2gcCrf9RiUL9GNLOtLYLFkSLNc2Cm7myDPrW/Hv/Bz9f7+lBtfTRmO1nwGFDC6SWOSLFI7Otgse7PLZl9Mfl/eJC19+5LlfebZGef391sx29jc1TZiRyJxtV2OnL5NxDTiRnZsAScJBK5+FhrQJWZupKf7ef/17/xgAwPr76JEJgIHYAcucZjO9XVeXZzZr0o4oZd3oOcqu8WQR88zVDoNpFBcwazZLVISQy2FMk/yod55xmPolj8X0AykCnwlnaGn9InoaZUgyxR7d6WHaxUw7mi8SAgv3sXWzeDrHZO7GJ/kC5OBkGgTDT9ghuGypYpJCtk6ygkeKQOqGpox6dNxCFvnoD2/+p39f/8lOaYxtbABQVdIlgLvm+IO5ZdZbBxqhmMt85DLcRexpBMBE8MPt57e6712tMDVkFvguoI3WdYuDlHKSTA0EZYzJGVAyT3nTWN56aQG0aFvg7beuAQA2rENOJgBIaoBYUS3iNFP1u7QXfXm5Nt4KrZ11bF8PYpq7UueJRmarOXZukCA9baYpgtAIZXZpRqfzquWdDwFcjLNiMQFEFwsd50GV1RyuRu0RPazkDRiJ0sfDM56cLgeGQhYWj2vwA3iAgJC8vd9mkCvuyijGRISo6xbCKV4BLCPE8YKMRGBshp/4h8V3fm8HXDbMoxOruwg7C4RH5S3AAgSlGfPWFbxO5PZp4MCD3DvX7/RDM5nuOkuFDsQcVEygcTkBkASg4xsAJBW6MikJfyyGikfq1X/+zv8OZeiGS2hjJJQLxDkALeaJbOMWygVjF5tbOpmkM1+XxKfYgLCsCVrNY07rAGw2Jk9zOvBzbIZ9qYNP1HWalabaryM8ijplNhULCFruQ8FmVLYkkuAjUCwP/C/7pbDcMKdJatNpG/7u4JMjgrbC7H6KAC+wCJBZbAKALDiA07wItaEZsSIz8Knp/cob5Bu/116gFrOclI3Jmd07NvpJArjKIksktmAgAdYByArRom0P++DIPKm2IvG9cmEWKadu7hPdjN4G2x5tEACwoFMLIGvBCBfNUcYBokzqZ3aEL7h3+2SM7gy6KUR7WHczAqUBdX1ZCyJ30vlkx7FfHaxYBVtqgtup7gaTgU5rFmQkSN26RYkmAFDlnHMZKFrPs1QnwiDZXodw4rNdFkmSHN2QXPLKTQERj5Uf4QeOhcfMA4SEbaR7+LyPWqcbXjgWrGSuJT3VyjQt4xS6QZHrtuFj8jO/Nmh+99sthCIx57O6a9uc0eltz8ey3DQpoDArBYjoNgpKc6EWbZ2WbxMAtLYmZtITQkBtYCQR2hAfO4xOEQ6uyjM+i8glwjwXUw4PMNmABVAC0RZqRBKNAAoYD8ZpAyiDnqypC1mQHIT6jLvJQC5UszDoTcFLFzO0papI00D3b9BNX0fHPB/wNMlEGCet6LLHhliEoLlvRN9NyIXWeNnfb7Yze0OpIvFAzZ4WIfClyepkcIy0p5WJ/v48SExg7g/kflWPlkYqjzVOy2QMJGXe0QxB0RFIQrmAbgCmSN2jo3zj4q/xP/rrfZTltFkOsE7jQeOyUOfRoiBVCsvC85rGBiizmY60Czc6SoXFAgtgREaTOlFUkQoaPWGJO7VAnpY1F7PgIJIHVDZFR86cyFpi6bYdieQhRYUMlqoK4NkCdNMnx6YgheVFHWPoin1RGAcl/Rhbop1KPYndTKCa8S2y4NzbGVCWewWrqenIynE1Bnpxzrt8lDLoKguhyrLSV4sOm/sur9EjYVf2PdmHzki00lfn0s7xQfCY5/u78GBBocKdmItIOrGI99tIU1q+H0a9kAtIWBl9V8wyUlGHYeRuyn/jc736/3k7ClPQtj7Ma6xtAFCE9qTpM+Oh4cp4woUS0xPmLxYT0AeJokGjaQICkvA2kJLMmW4QUkkjm2Zd0LQbdWrRdxUr+DSg6IRdotMyn8pqiLEAbMp9A9CSu9AiUs6IsE2XR+O0NQBBwqU9bIWJdKyX2ikXsQLQ82WYqmh5CvyggBwhlLksGwGFCQM1MUkaQNJM+LZNGGDuOZGcjQApsFKInvybX/GAUcH3PanNezMn8mAzhJqRxIRxfECMHJGhGOdp4SI2WGwXePXv/DL97//M0AHdpQVsIDFF8MwBJm8SwI7pu0vlkx7VZeQeKEJLtLdgUZdj1ymIHxMH6RJyFtoIDuhkCopYoxzJ3PXsiIY+msAiLEA4CZFRmm4/TqdVYyA6wjrCPWV0ZuWGmee520NPUz+VpOkV1rdzus3jbKFoA9IJFYBudhQNohhdQNMKSxvaEG2kbcSA7ZEe9XIUwdEJMyWC5PtsaDyjUwAgJLIAUG61iYAi7akkIX8gHlAAcF8ZZTx4XlrTW1gtAklGqIwq2XwguMmtFaIGeoPpNG799G/Vf/G/zqEpHbBR6xijpFouflm0ANHEpwM3cBnbCAisDHhUZ5Zy11oAHcVHVusRk9EsRxDJU43CGxDFtKNhUbCqzyZc20gyGhbJLtf6dkCr6rbl9sDryvMUfSPQq1nbYKO1Xtfoi10yHMc8BmoTF7E/a4BO60C5qABAC8MIm4FyH3lIYuj3gY2kwfcqQOuWRMa6zYiqChDgDTqLpOWcDvZPvuWsffCSz6fHAwuA8EhU3ScOpbOUZhW4lCB0EvJusw+AyS4CFnHQ+u5Yfe7ffk58489HVY1BanM+X8b78JSWBUZZ3wSQFLrepkhCTAc+H+oAlXnjAZRwSc1Rqn1gGQ1ANdoAJRbQnhZtrWQkCwykJdS3A2cdaNM1UdIpfdbchIKL3ANsuAtJlwcYSMqnoD1DFEyFYd2WzCk/FyXdB4CCN0pRsmi5AdA7Fo9FdA3KEeV25QMk07UBZ2PHRQOgE1NNN01YiG70VU9M07CeIaNNAkmEeTC1mly5WEZ5bqnrTzKZ6oPPALcjCPzxMC3FK2x6KxhM5paZNMrWQ9OcupT5XUJYlu2l7Cd+/UI9/dafTgFAdAkV/hYZmAMbb2ZTqZKR0dVM6tpRq9u0OhMFAOh4k1u7mjGUAXLUgFTWlLK2WeHbNkE3YEEVIyBznBsPJXgyRHov5AgAjZRzwQtxBYO5hzLIWgwx6otJKloDZN0dFA0TziHz/mKcimghufW2y3kUq3pGufB8Co2SBKp4UwXCSLQWNBLpYiEpizSp3WUQh54ncW5Un8e+UzzyehXopFw8OD/DPXg3hVPJQvLAPLwA3A4tVTuI8xCJ8QB0NMj6lFvS0kVMSIHTmMVaeVJ8/u9cqufvXBm9h1J761OX2EUo5TgJB9pr0EIFD3RJ6wMyu5IyKgzQ4+PID+IF6IadpdX5+I4E9XDUO4eCzoVyJvOeC8ejFScDKEQMwIDvomda9EwrkIpsREobylhlaGP0pVl+JXOhzGXW1uOczoEufE0JA5IDBqz2lLaZZKKxLF/UGWlE0a3qomhUFRorfALIIJom95r4hRyKDyFYW/JZPKlUq3AY7v2klcGHEADGT7qkV8e/MJC2AjGQvMZAzzejZWimzOay9ZoufJY7EllpmwXyv7sx8PrmDVH/mw8T9xnbkLX2e01mgswx4THesRiymAoRY2pjnkKMCIJJ4ac4bo6QqmWJydh6CJtAoxBHwTNEDJJrTcTA1CVlATOwAFyII72ImjFD1YS7lS3rTujAWkZIE5Q2Tc5bW0ClgDQHgIutqSBSjDRyyobNNKyWc2WO5q5DO9ZyMy2OfD2rJ+D69CrRPzAPMwOUsQYgWUOyE4cTcjlN/TDP+8QoPy/UZLHM9ilok2Ws6ohRA/QnYhgsN+y5n8jNxXTje71h/uaHterZVjTLDsqEP9I1VhoaIUzksIl5ZxOnibFgoGzsCmamAHRB6krmE94HGfEsuhYoF7Q0Ni3dqh0m8488jSitxdDP2HAXKouie9NoE8A90EszQFkC6qFjzEK9fGVkUAWPrCWUFomBjf05BMt5nHgalc8iRQXwyGN2lOmQBQiaiXYhuIrWCGJEF9PSL7gHoNt0pOifXU1FPFxAyAKgEd5CnozbrH1vLomaNx0yYnWrPEB6IVDKScN5Tl3DPeOdeuF97nHzVi976xe3NvQvVT/9vR9dn9cVX+Y6IilF4ZCjxrCtE+fMMRNTdDVWneUB6g3lrZJ+JjgANM2w0/EOfuFCRqagmpIgtfaKJC5dlGYuKUVPRNRArMWQAcb0Jj4kE8AigEqgcKGTHGGJ5ftO5NroGHwPlA+wL/PorR4l8HInkQQeEuk6MeuahcW5sIesJbIlXRKNgQ+AjAsRSMZ50yISOIwjqbgkDgVdOpPBqcXS/nvP45+PmYfTAWhxl8mKJJR17CYCPk0dX0viZMld2yCDoZSkwDpqyhrbcxVAEfsuDs4/p/vUv67jzevf+uaR+1myRmQ0El+rDK1nS1Wd6zkgWZtAtWMqti6BycPZQjgA0pGsRqdnG2tpnqwFiowks0DfL9CfYDgCZOJ89QTnq1jM2q2FXWrj0pXRUlkLWgN9Y4kXmTXgRT5LZcMcy5tFHzNZuIXvkdnKiCnPcbMPJdTCXIzzBXrzluUxN03y3dDEHodZYDOODpSRIq4arcmBr234yMnHH45HVAJv01m4h5ZTSiItgpWJxChKtzAQjuWR2dAX1wGWC2NkDXQaEV330pBvfG4w6//E4r/7+uoygpo8tUw0QN/YnLUtIFiXhuibUtu2l1oD7kSbEx9OZO2Rosoz46LMyJQqRfcO59lci5vokQnpx+mxAgtSdor2RhjQ/bxJABjvNBV4LBPXUwYGaot9d57WcpfnQUUgzGSuuKyaqk8nSiY2HTIzAuNKI+5ui7HghOxiU6GdeprzCTayGCa2kBO+rDuyDIWiEcdn/7MyBT50TCDfmlWrI6Q0CbdUYzxgGWLHcGIDE8TMzUJ6XqAFYSyb7JY0DnhtQyIbPoiM2+mkphufnz87+UO8/u/ffAcAGOtY1FlOFoAUIVmutfJuI40nQL/cN2SzdZHMQTMnqWdJOy6mAFd1cshLH3rOjRNK3pKjeGIvR9iUTY+xhUrxoOup9WIngDaRtCRJ2gbVVpBdF5j2FSCzMUfSLs16Wghr5dzSLne3BO/OMeLn7DgklQLhkfRmM4AsagA8aq1M7TyiayCn+3yzu1dVcAAEDdWGq1c76KXLr2Pbx+rzvx8PLQB+fOgOisdz+4UFRGCeKuOCRS6mVHAiWIih0gB04JSirGtkqmkZlEjd3b13ed//Tnvhl0Z7AGVpH0BLCOsSO08Z9RRM13O7EErxcTWUHyPJSN0c4AmdrGHZjGVajRP6KqQ6IyFyT6Q1pFwAPEQaqZ4Y1IsWfBVsRrIWQAQSy3QbJETW8ha5i9y7RQuIPfBMJYQ5fAMEwtsmtxZxRiOco8Ih94HqEHZ1A3R8KeZIlvueh3FFM4KUJBAuiIUfHfSQFsaTatV1q1Gfqsfu9L03j7QE3L1wGvcAY0yFxpPU5czZ6KmVPm65WS/NI4mya40DK+dpmI2sInN88Vf7b7ev73/9+wdXHIY6aG2NkTpZMNp4AF1RO49zGFsaQVFwS4ILG33/QcRQWl/3+cwEPnDzMLAVQAZyTklrSaJK8n0cP2XGD5zGrBRkV6o53+CutSqAzugzbSOrlncXMlskWS7aDsOIHZ1Job2YLUScoUsaYoqSmrkXhVtA+j5r5zSC+2OTOvdYaSgHW9aDqV/5cOAOPSMeJSwcWbqbx9oDEpw3LJOujWk/Ms48bGYtMNt0VYIzLGif2hR2twvXbG5UN97/2n+0m/bnwnlACME1gaGuTiVM4IbpZYIPnYTmizohgpEsC9FE3mMTKrwy85Dl3tSAii4WyaMjArsJtqVJ28jMVqtYYuWT5DwcjrowJQnWChJaS62iiVK9UGyeOqx1oqXBM1U3RoRObIpOCOPUZ/UYs6QyILWsoH42KLPKOY+8BqFzcKgKNACgqk2A9KDaAUcnbVfjjiEB4VSKjz0sj6YE3kNz4ZGq6HzJGkUWEQWapLSwcyVR1i3jiWYNUgpYIO86K/P9Wn/x3/piuPbb1zSt1CBvr0PFqGVLfXCgBQNjlEXrDC1ibRJ0vs9K38QDo3y/402sCbGcZ6ROeZqiKBtpGp97koXGE0QdIzU01zCuE6baxduS0pahAahmiS7rsC1Hq7ToLiI6fNzxDe1Ok6KUk5Zwa6BZHXsltbdoIhGkrGI3pROB1N0qHHUTSbhj1iTCHjcVPXkecRdQ0LvkqT40zS13PQVvUlBStlaTBtTLJCIPjVNeSDoPLCQAKuTPvGDfvZZtB7Fg2HcD3s7zgk+cbKlOhmzyJglredtAamWlk2FCe21ylnRSqzoNQkjCE/iQuMA86zVRLmyecTTzEsISZ5AFT1VppgBEuCPXGM8YoXQPAIhcVWXJqAmaV7FrDDbCBMgz2ohZhMyjVZii4JRPCiGbhStbHJ7aXD66ShZEH/zuhE99aQfSJDVnFwsAPLov4KjMz93D16gOIFLSNIskihpARnzMTEQUDhvNsk96UxRNzLSxXvEEMOkrzW3FRKICgTLrRJMAlFiskn2wnJo8RDCd2KTquBYgHWM2YjQZQc2J9E1eeeFJZwplwD0ghUFgxfQuAdiFXqRVpYKiWj2NcKvjuKsj771F4N0q+QgwknRohBQzXSnB5wGliaqmneWMpAzAV3lpIKgBI9QXrLVH9828fwIRf5/M6TmD7nr1RHL4pHnlE4Oki1hG4iy4J6plynpA5sRmykxkDFSh8QDJglXFwkpZJVDmmQgWgCwakwBAL8N5qHYWoIJ6B2ztJRF43nqlaaqpA28pj6Qh2TKnURsBIWOwB2HCh/6hQ1nI0vJgTs58g0KGGTqspi2gRRUTJKNq7gYi2EAtIa3gmk0zNjke2soC82ABilfHDVSH7/n4CycJkJ6QszQDA6cjAPefwuSqwAGY5DII61LDmM1MAhNtrqwozX6R0UjmVotgmI25c1LVjuVtKBhrIRoDAEMxr8FzgoYKzzrEmynnOZk5LnQWREtSDCKlhvLkiMhsmGXJKVvQnBFng57VBHHV2Jzf3elK87SMz2ZBhXBygEp7sq5ZHszSKQ1QRMrdcjEDQwARd2S9p+zo1FIRm4Is0DNPIuzz/pzKDPCgdbSpIF5RgkDrktXMeGzgcHOm2YLTlJEZCmETDzUEBQlKprmH3A5zQ4FMZqadUer7zM1IqWMdTKcj2mBN4FEohMZiYJcvkWw0PNjuxAMQ8uAMEklAdlhj7/bFgEbNj8zcD5apkpF4lxAOwhONIYGk49dhYXVTlpiIZ//+T2kJYNntp/aA4499YqsrdfB8QzVNcC08gEG9KoIESzlotDmzhinGYly55XuFaJo5oayYRpLAyfLXm26WALHhkmN86gCgk5zp8BbOA8M0ZgGd+eF8T9LKlQBgFUlyJ1w0B9GiZLltI2l1hTulBcDxyX6JcFilrji48+HzZ7RGwWovi8lTsP4Dp6cD3DVqlMa7nndjWROL2IBr1wLQZV1BsyZ0MVPUD/w0Uh4ISy4yFj1AkTWr7hIOIBdQ84TIUuAeXqKl2cIrEkHaQBW1DgAyKcMYNIE7RY6NNHV4wF43KIhlK492aY8mbcocULLVFlPT6v7q2sESyIM+aOdhTP/xNYP0wpwS9sCpb54Ip6YEMm19vHOQyDvWQhyOrCXCATSCUJIZh0x5ISKIQBh5gCSpfaoBqCLE+YaoQ5fG2kcaYkmnMYClSOMyGRhJhbPywC6tbUI8+ebumNHlKqxMDlM7XQaScbHKW6f5fJmui/t0/KHuqvHcV0Cy6Jcj4a6dccac4i6gyOb2ju65p4LIuA8A97k9OSC4h3YeSjQaoL5W2RRgAZlsSBdtVUhMoSy3EUUirW59qWvqpG2lZLEFHBgtuIVrjldWBkmgLMSDeB8WDk43kASIzjRALdMwLmdtrqpPEadx//1cUQEsq7C1OR4/zqyvD8WpbgPFvU1a4vbSlkv71+1j4pi80EjS0M3BPZD7mFnfYaYiXeugY5uTuACQM+lUHMcy1Un5oKRPNhFQgsgocX65pK94UKN7j5DgTRDuVM7mlTRVBQWmZxz6cw9O1w7QUcxbCwOSHsm8JS0El60RDuhUkRcpMW9CXL5PyV3gnIUaIIQM+b6VvGFDK6aUUZfBOcqx3M5pep+8GgpW++MSSJJwAOmcThZG7TitgcI6dM4g2O8BOW1DECseOqr5WJgGIRHQyQWgT0OK1pLEsjZywWdgnVnMVCDMhCCDz9kUQJ43vu8aJ4NOjpmD13pYjZrekWiU5FTS6e0zEMLJKqyMOhAKhNXoPQqDvOfkIMjyoqsTX/0Y7p9u7Wx5PJbAu2g7d76Bw7/cr3sUNarTmhgioA2RJoIOfEgCMcg4ZQFAltm61AuTElRmpWuFTQC085AWq6QLt0+/lFsId985/uHmfxYgRYV+U6QpVw9f0vMJ8Uju4HtQlPbOrU7kh5lY/ck3HgEKbe7WU5yFELEfU48KLuaNhCqJGEeI2kfhHQL6MboEOBM4cSw00cTVRq/hy7G81D5W9r3VhWmM0oLSeOCJu5sU3v39311elVmpGMIBiQKLLVMl+xSq/bfxOH0Bx3TCTxxMx/fqJAFa2BaZC1AGm2QWPN1KTc00hzNwqR+DoUaUYbbhpwCQtSC5dYcjXfLUAEWdVrcmCeiH40vxMTMVi+7AtrTkfpPSXZUbaZEnJ6IBVD59imf8O3hsAkBVMTcHJa4/NUJEQkMLcO2bLA+tAYAun0QAVJGl7lbC0OU53w6Z3eO1MWYBqqIBVFyaHO6rGX4KckLnYKHgYj+tNpEieUVPp5LDjwFCA/r2Xz5Q9V128DVOkIuD39JCC3KbzK4q6fX6q9/ST7p0JsuHrQBNATB5cHvWF2oTYADPAeheD5CPY0X9TEMuXOInX4o8+YFPfGHHv3riw8sXcV95Esc/CQDiQYs/U0KPvqkBgK8udijR/Q2AAxc2sXEJkMNHqOB9ljzeeIBDVurA/W2hq/wQhx+6j11pBUWEFOleiczuWBOEw6fKvMNjBPp+AV6mpWdAipBoyjxiDUAPTVM9jQbep49MdenW8PCn+31wJZOqe3KyuJuoUg5IdvSXB5ncDycBcfe/k9VtKeNgilEJiGFvsws9OLgr+Ob2Pb695j5oCZQMOdABoCXhwHIjSpmkJ3u06Oh89c97r6tcrr60Whtofs+P3kl+XD05JjlaKwCqLCmw3e9ygG4CANOcl0rJDDRbL/WPAuXgvVx2NvMi41D36MwjeSCH//kkeH7/cUnv+QMAIKfg/LiWIAFAXd6ABId8QAV2zQPRyVD2cWJ4EwrkXSUBiKxTAIIcikFeaIBrQinTEgCnwIkXUjzwnU/KCBUrQWAceQYARFAg7+YAclZwcHn7FX6seEJK4L1RNuM2sxQVNDVcTcGhLGsBcB5kbJBxTjAGC4AKHt1kDYRK1h07Up3JGZCZtDyDs7T9cW0s7jQ85jV4bl1YeX8BIG8j0yurXU/Y1mJLfvwkHv4p4MwFAODUbpTXaL5AEeQxV9Jxk9sJ89ugbQAuzMo5qKNZ/p/PIXmTwPLYRgLNp+iRliZ78GWSQHvEcEwB9DEBoFiN3NscJjCa+eA7rXkavbaPjadAAAA8yunoPLnA6EEY0IEjd+DnYEH10w5QeIuEXmyoBUQ2B9CPM3Qz5xZgHennjvv7uqTWPL1wDYAAjANsqAECCXRXWzaO5aJPAAko5CUAVXS3h3qtyQN4emaAh0aQEJCRQoK4WDUA7YdFKKpVRuGiTlKq4KzwoVzwpy4i68z5rAoASVhW6K7nIVgsw4eh24RO07WGynAUFbLmxxMOrLfmQLn9SF//rM4AawCADfJQ/vCRLrFWhT67ZJnuITziEF7PAJ9Nulux1XphrXvE1MLrGeCzB+nmJHIa4vXxKVzs0S+x5klCurnc5OM962FPw3a1FoDPECzosgFVud07tbRCawH4zMDPD0LmQr0/P83UMmsB+CzAEPOMqXI+QTiVY2tHrAXgs8ClQSChauePwZK9FoCnHNHrt1Zxe/Mx5ZJbbwOfZvRALyjo7DHmEloLwFMKHWbMU97M71ex8RRYC8DTyJBXohxgNHr8eQXWOsBTRu4ZGWw1IxZ2n8j91gLwdJGVmfTEXX9iN1wLwNOC1FkvBLD2ycYjrwXgKYBtqRHphSrJ6RPPHL8WgDOmV8KHomj3kjuTxLFrATgzMkv7me13+Ye3fMZOJ2fFp2ctAGeB7OSWaBNKP20TOdOU0Ws7wJOmLBonM25koSYLNznr5qxngCdKSSPttciHdq+ZPxX5wtcC8IToKxm9ypJJ3LWjp+cc2noJeOzIHlO17CQkk2hkN56CKhHHWAvAY4SkTrcwFCL4rOv27N5Zlge7B+sl4PGQtdjecDPX77aE8PFictYNuhdrAThtNLrDkKhqo460Tdn8xlN9RnG9BJwiupunDuYp07ekRBjV4RFPbTwB1jPAaaC47vTbNmW5pNGaWNW6Gp11ox6MtQA8Grzo52QhZMxoBRqlnyZWTc66VZ+C9RLwsDwbeOx7zqpF29usYqbYYnzrrBv16VkLwKdlk5uN3IYobFNEQrKs8bkd7X1Wc4+sl4AHRXSG6PDF/oA5NaVD732hfNMs7PSsW/ZIrAXg/nDNZMyKyHg1KrNQ2ul2v7VUhNl+4k8mau/xsl4C7oEeNCVVbJxYkH6nM1RtZGJQ1MzdGv84vPgDnsAMcJpHGR83PVtK3Dqn9nNF0NBiuoPLW8bygtNqZGePOUb/LHgCM0DalDLKtBsonr7yeUQDddEngsWF3oxofPdGaOU57Qb9fa9lVy5uLFSz+OQrfUZ5MjrA5y1TMyLHWZ48Z5HOTeRz7c5IHoTLeoyUsZF9d4v292I/MGpp2Zu/183kkL5LLoralbNbn2317sF4QkrgV5P2SY1seaPJ7TzT9TCf+15SyrV8rGwTMn6dkscREsuYRYfn0s5J0RUTK4OgsRPnNqd5g6otdLzY+dhpwhKbBWWbsDiFxCufHZ7YLuB5LguqgkFntrPdr5j8kT+Pia/7nYaI3fCMm3ruioLaVonJ7vlsdz7oNSDR+yaEXM5tTNhctDgsBCNzUzpvHIAusZF5K5kkkjahS4zsjJpet8P34mX6I8+EmE1l3Bi+Ry9Wt7qUiHLue9tomgiWgnIf15SZUz54/xnhyW8Dc+UHW4um18SONjIlGRdGBdqMeCh6cVSSmYxK8L20gRkJTMxSB20hPeFS17fKi7GtYjQiV5IkY0NkLcvjpMl7zs7zurlM94ijSQaT6dJ9iK2ZLPbxStOWgjeLjeIjUkTRpmZX+SZ+Vq03p8gZ2wFIp9ZqvNFPppylYSdGshH2/fP1LdMvyPVRkQt7gw/jJO+GPO7IEDzRuy4vTL9MwQpirLTz7oWPZ5udvTIZVczQi00Dlimuo/O6MhxzvVO0nhmT6/bm2T7v08dTaQgi3ana5jSyVKZY05gvCkkJC0EkJ0yLLcLGVioFSjza6YUJnfUng/3zzO3MoIbeTsWiLkW9HuBr1qxZs2bNmjVr1qxZs2bNmjVr1qxZs2bNmjVr1qxZs2bNmjVr1qxZs+bxwjfOugVrzpDt1866BSd4KoNCf1whcnAOb551K06yFoAnhnhGz8xTd7B4fTz8yaBlXgzo22fdjDtZzwBPgP4mZxz2+2fdjruxFoDHTa+rNA2W/PCsG3J31kvAY0QOaOGdFpblb511W+7FWgAeF6xnC8023ZTnf3XWbbkPawF4LJAy28rrYDNB7I/OujH3ZS0Ap0+vm5WmseiFNrVP8+gH1gJwygjVLUkep9d5L3iq//Ks2/PJrHcBpwcdlixIipJN3cXtG3921u15INYCcDrQnhI8zyiLLXS3+QhXz7pFD8haAB6dgW6D1j3a5CD92Mw8fSpNPndnLQCPRMEuyA9U4TvKlUHrmwuumnfPulGfirUS+LAQkuW5tYJord05XVX1jLv5zlk369OyFoCHgIWu1x34lAWZlV3RzHZDVBh/5t4+1gLw6REaPcKoERf9mIumIpwU9ONrZ92sh2UtAA9OFuJ2HG8z9IhWH8fWNOVGW9V7n8E6IUesBeDBKDupI8dtHvhmZpO1TBLu5Wj08Vk37FFZ7wI+ke0sVMX5ed1njT+XZkqCzZHGqfmxyCm8ngHuR74RbC4kFbngRQbnQsc7k0aj6qxbdmqsZ4C7oTw5J9uCgcZMWMcRssg13MTr3R+zZLNrATgBy3PiilyEaVmnUqnYarjYS+OaZM1+7c+6fafPWgCWEDq8YPY7oqYUYLlsgojRlhknzb5PfPEZqQP66fnbrQP0ptBdbmKu5k2Z6nKL39orNlNlM7Q5CbmbNLMf87IhfysFQGyNG1yeta8k0SVO2NQN2DhXx8y0otfrRpAUdLO3d9btfBL8rROAQV7X54u2UKQsTS17VgvDlVe9YDe30hg/OOsGPmH+f6kfUHTlrgfxAAAAAElFTkSuQmCC\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
- "image_path = 'COVID-19-NY-SBU/A770557/12-19-1900-CT CHEST WO IV CONT-97223/5.000000-Lung 1.0-84269/1-127.dcm'\n",
+ "image_path = 'COVID-19-NY-SBU/1.3.6.1.4.1.14519.5.2.1.99.1071.32717876047095240098568067022786/1-006.dcm'\n",
"view_dicom_image(image_path)"
]
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": null,
"id": "b27ab66e-f5b4-4d11-b058-9dcc72b344d9",
"metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
- "image_path = 'COVID-19-NY-SBU/A770557/12-19-1900-CT CHEST WO IV CONT-97223/7.000000-Body 3.000-78395/1-53.dcm'\n",
+ "image_path = 'COVID-19-NY-SBU/1.3.6.1.4.1.14519.5.2.1.99.1071.32717876047095240098568067022786/1-053.dcm'\n",
"view_dicom_image(image_path)"
]
},
@@ -458,7 +334,7 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": null,
"id": "58bab749-3d4a-44ab-942c-33a22a7406cb",
"metadata": {},
"outputs": [],
@@ -478,394 +354,10 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": null,
"id": "ef7dde8b-2ffe-4e1f-9edc-657af27b6fbc",
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "12-31-1900-CT ABD PELVIS(WITH CHEST IMAGES) W IV CON-21869/4.000000-Lung 1.0 CE-04129/1-273.dcm: : 1it [00:00, 62.59it/s] \n",
- "01-01-1901-CT CHEST WO IV CONT-84206/6.000000-Body 3.000-02742/1-034.dcm: : 1it [00:00, 38.58it/s] \n",
- "12-19-1900-CT CHEST WO IV CONT-97223/7.000000-Body 3.000-78395/1-53.dcm: : 3it [00:00, 81.01it/s] \n",
- "12-23-1900-CT CHEST PULMONARY ANGIO WITH IV CON-62918/5.000000-CTA 15.000 CE-36514/1-008.dcm: : 2it [00:00, 70.16it/s] \n",
- "04-22-1901-CT CHEST WO IV CONT-40216/2.000000-Body 5.0-01241/1-16.dcm: : 1it [00:00, 29.41it/s] \n",
- "10-08-1900-CT ABD AND PELVIS WITH IV CONT-39755/9.000000-CTA 0.5 CE-40834/1-0163.dcm: : 1it [00:00, 69.65it/s] \n",
- "12-30-1900-CT CHEST PULMONARY ANGIO WITH IV CON-13804/11.000000-CTA 3.000 CE-95792/1-119.dcm: : 1it [00:00, 69.88it/s] \n"
- ]
- },
- {
- "data": {
- "text/html": [
- "
"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"sns.clustermap(zdf, metric='euclidean', method='complete', cmap='seismic', mask=ga == mask_na, center=0.,\n",
" col_colors=[stage_col_colors, diagnosis_col_colors], figsize=(12.5, 50))\n",
@@ -513,6 +447,13 @@
"\n",
"We hope that you found this tutorial useful. There is also an accompanying tutorial on the PDC site, if you are finding this notebook and have not seen the video. Please submit any questions or requests to: nci.pdc.help@esacinc.com"
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
}
],
"metadata": {
@@ -531,7 +472,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.7"
+ "version": "3.9.20"
},
"stem_cell": {
"cell_type": "raw",
diff --git a/BRH-notebooks/combined_demos/README.md b/BRH-notebooks/combined_demos/README.md
index c003208a..54bc791d 100644
--- a/BRH-notebooks/combined_demos/README.md
+++ b/BRH-notebooks/combined_demos/README.md
@@ -27,7 +27,7 @@ This file you are viewing is a README markdown file which can be rendered by jup
### Controlled access and open access notebooks
-Both controlled access and open access data are presented in this workspace. The two notebooks that use controlled access data are `ACTT1_accessclinical.ipynb` and `JCOIN_Tracking_Opioid_Stigma.ipynb`. You need access to these studies to be able to run these notebooks.
+Both controlled access and open access data are presented in this workspace. The only notebook that uses controlled access data is `JCOIN_Tracking_Opioid_Stigma.ipynb`. You need access to these studies to be able to run this notebooks.
### Gen3 and GUIDs
diff --git a/BRH-notebooks/combined_demos/brh-welcome.html b/BRH-notebooks/combined_demos/brh-welcome.html
new file mode 100644
index 00000000..12142405
--- /dev/null
+++ b/BRH-notebooks/combined_demos/brh-welcome.html
@@ -0,0 +1,90 @@
+
+
+Tutorial Workspace
+
+
+
Welcome to the BRH Tutorial Workspace
+
+
This is your personal workspace. No one else can access the data or files here.
+
You can learn more about using the BRH Workspace in the
+ BRH Documentation.
+
+
The /pd folder (find it in the panel on the left) is your persistent drive:
+
+
+
Use this folder to store files (notebooks, data files, etc) that you want to use again later.
+ The files you save here will still be available when you come back after terminating your workspace session.
+
Any files you create or add outside of this folder will be lost if they are not moved to the /pd
+ before your workspace session terminates.
+
The Tutorial image /pd has a storage capacity limit of 10Gi
+
+
The folder /brh.data-commons.org in the /data folder will host any data files you have downloaded to the workspace through the BRH Discovery Page. Move these files to the /pd directory if you want to access them again after you terminate your workspace session.
+
+
Get started with Data Exploration
+
Open a new "Launcher" tab by clicking the + next to brh-welcome.html tab at the
+ top of this document.
+
+
From the Launcher tab, you can open: a new, empty Jupyter notebook; open a new code console or terminal window; or,
+ create several types of files (text, Markdown, Python). For notebooks or files - remember to move the file into the
+ /pd drive if you want to access them in a later workspace session.
+
Install software tools by using pip install (Python) or CRAN (R).
+ If you have a requirements text, you can install them with pip install -r requirements.txt.
+ You can view all pre-installed software packages by opening a terminal window and using the command pip list
+
+
Find some examples of analyses that can be done in the BRH Workspace on the
+ BRH Example Analyses page.
+
+
If the locally-stored files you wish to analyze are large in number and/or size, you may need to zip them before
+ uploading to a workspace. Once in a workspace, files can be unzipped using the python library
+ zipfile.
+
+
+
Using the Tutorial Notebooks
+
The tutorial notebooks in the panel on the left provide examples of analyses that can be done in the BRH Workspace.
+ (You can also see the tutorial analyses on the
+ BRH Example Analyses page.)
+
+
The tutorials are read-only; you can run the notebook cells to see the output, but you cannot update any cells
+ in these notebooks. However, you can create copies of these tutorials that can be edited and customized.
+ Right-click on the notebook in the left panel, then select Duplicate. The copy will be editable, not read-only.
+ If you want to save this notebook and your edits, move it to the /pd.
+
+
Note:The Tutorial Jupyter Lab image is currently sized for small analyses and testing.
+ The maximum storage in the /pd (including any notebooks or tools installed) is 2Gi.
+ If you need a larger image for your analyses, please reach out to the BRH support team at
+ brhsupport@gen3.org to discuss the possibility of a larger image.
+
+
Funding a persistent paymodel
+
To learn how to fund your workspace after your trial period ends, visit our documentation on
+ .
+
This is your personal workspace. No one else can access the data or files here.
+
You can learn more about using the BRH Workspace in the
+ BRH Documentation.
+
+
The /pd folder (find it in the panel on the left) is your persistent drive:
+
+
+
Use this folder to store files (notebooks, data files, etc) that you want to use again later.
+ The files you save here will still be available when you come back after terminating your workspace session.
+
Any files you create or add outside of this folder will be lost if they are not moved to the /pd
+ before your workspace session terminates.
+
This image has a /pd with a storage capacity limit of 10Gi
+
+
The folder /brh.data-commons.org in the /data folder will host any data files you have downloaded to the workspace through the BRH Discovery Page. Move these files to the /pd directory if you want to access them again after you terminate your workspace session.
+
+
Get started with Data Exploration
+
Open a new "Launcher" tab by clicking the + next to brh-welcome.html tab at the
+ top of this document.
+
+
From the Launcher tab, you can open: a new, empty Jupyter notebook; open a new code console or terminal window; or,
+ create several types of files (text, Markdown, Python). For notebooks or files - remember to move the file into the
+ /pd drive if you want to access them in a later workspace session.
+
Install software tools by using pip install (Python) or CRAN (R).
+ If you have a requirements text, you can install them with pip install -r requirements.txt.
+ You can view all pre-installed software packages by opening a terminal window and using the command pip list
+
+
Find some examples of analyses that can be done in the BRH Workspace on the
+ BRH Example Analyses page.
+
+
If the locally-stored files you wish to analyze are large in number and/or size, you may need to zip them before
+ uploading to a workspace. Once in a workspace, files can be unzipped using the python library
+ zipfile.
+
+
Note:The Generic Jupyter Lab image is currently sized for small analyses and testing.
+ The maximum storage in the /pd (including any notebooks or tools installed) is 2Gi.
+ If you need a larger image for your analyses, please reach out to the BRH support team at
+ brhsupport@gen3.org to discuss the possibility of a larger image.
+
+
Funding a persistent paymodel
+
To learn how to fund your workspace after your trial period ends, visit our documentation on
+ .
+
+
diff --git a/BRH-notebooks/generic_rkernel/requirements.txt b/BRH-notebooks/generic_rkernel/requirements.txt
index bf1327c0..6995e103 100644
--- a/BRH-notebooks/generic_rkernel/requirements.txt
+++ b/BRH-notebooks/generic_rkernel/requirements.txt
@@ -1,2 +1,2 @@
scipy==1.9.2
-# trigger build
+# Trigger new build
diff --git a/HEAL-notebooks/Dockerfile b/HEAL-notebooks/Dockerfile
index bd35801a..6fd6b02e 100644
--- a/HEAL-notebooks/Dockerfile
+++ b/HEAL-notebooks/Dockerfile
@@ -1,11 +1,11 @@
-FROM quay.io/cdis/jupyter-superslim-r:2.0.0
+FROM quay.io/cdis/jupyter-superslim-r:2.1.0
+USER $NB_USER
ARG NOTEBOOK_DIR
COPY $NOTEBOOK_DIR/ $HOME/
-RUN pip3 install healdata-utils
-RUN pip3 install gen3==4.25.1 # Pinning older gen3sdk for now while conflict is resolved
+RUN pip3 install gen3
RUN pip3 install heal-sdk
RUN conda config --append channels conda-forge
RUN conda install -c plotly plotly
diff --git a/HEAL-notebooks/combined_tutorials/tutorial-welcome.md b/HEAL-notebooks/combined_tutorials/tutorial-welcome.md
new file mode 100644
index 00000000..b0e5daa6
--- /dev/null
+++ b/HEAL-notebooks/combined_tutorials/tutorial-welcome.md
@@ -0,0 +1,37 @@
+# **Welcome to the HEAL Tutorial Workspace**
+
+**This is your personal workspace. No one else can access the data or files here.**
+
+You can learn more about using the HEAL Workspace in the [HEAL Documentation](https://heal.github.io/platform-documentation/workspaces/).
+
+**The `/pd` folder (find it in the panel on the left) is your persistent drive:**
+
+* Use this folder to store files (notebooks, data files, etc) that you want to use again later. The files you save here will still be available when you come back after terminating your workspace session.
+* **Any files you create or add outside of this folder will be lost if they are not moved to the `/pd` before your workspace session terminates**.
+* This image has a `/pd` with a storage capacity limit of 10Gi
+
+The folder `/healdata.org` in the `/data` folder will host any data files you have downloaded to the workspace through the [HEAL Discovery Page](https://healdata.org/portal). Move these files to the `/pd` directory if you want to access them again after you terminate your workspace session.
+
+## **Get started with Data Exploration**
+
+Open a new "Launcher" tab by clicking the `+` next to `tutorial-welcome.html` tab at the top of this document.
+
+From the Launcher tab, you can open: a new, empty Jupyter notebook; open a new code console or terminal window; or, create several types of files (text, Markdown, Python). For notebooks or files \- remember to move the file into the `/pd` drive if you want to access them in a later workspace session.
+
+[Learn how to download data files through the HEAL discovery page](https://heal.github.io/platform-documentation/downloading_files/) in our documentation.
+
+Install software tools by using `pip install` (Python) or `CRAN` (R). If you have a requirements text, you can install them with `pip install -r requirements.txt`.
+
+If the locally-stored files you wish to analyze are large in number and/or size, you may need to zip them before uploading to a workspace. Once in a workspace, files can be unzipped using the python library [zipfile](https://docs.python.org/3/library/zipfile.html).
+
+## **Using the Tutorial Notebooks**
+
+The tutorial notebooks in the panel on the left provide examples of analyses that can be done in the HEAL Workspace. (You can also see the tutorial analyses on the [HEAL Example Analyses page](https://healdata.org/portal/resource-browser).)
+
+The tutorials are read-only; you can run the notebook cells to see the output, but you cannot update any cells in these notebooks. However, you can create copies of these tutorials that can be edited and customized. Right-click on the notebook in the left panel, then select Duplicate. The copy will be editable, not read-only. If you want to save this notebook and your edits, move it to the `/pd`.
+
+**Note:** The Tutorial Jupyter Lab image is currently sized for small analyses and testing. The maximum storage in the `/pd` for this image (including any notebooks or tools installed) is 10Gi. If you need a larger image for your analyses, please reach out to the HEAL Data Platform support team at [heal-support@gen3.org](mailto:heal-support@gen3.org) to discuss the possibility of a larger image.
+
+## **Funding a persistent paymodel**
+
+To learn how to fund your workspace after your trial period ends, visit our documentation on [persistent paymodels](https://heal.github.io/platform-documentation/workspaces/heal_workspace_registration/#guidelines-for-requesting-extended-access-to-heal-data-platform-workspaces-using-strides).
diff --git a/HEAL-notebooks/generic_rkernel/heal-welcome.md b/HEAL-notebooks/generic_rkernel/heal-welcome.md
new file mode 100644
index 00000000..a4b95132
--- /dev/null
+++ b/HEAL-notebooks/generic_rkernel/heal-welcome.md
@@ -0,0 +1,33 @@
+# **Welcome to the HEAL Workspace**
+
+**This is your personal workspace. No one else can access the data or files here.**
+
+You can learn more about using the HEAL Workspace in the [HEAL Documentation](https://heal.github.io/platform-documentation/workspaces/).
+
+**The `/pd` folder (find it in the panel on the left) is your persistent drive:**
+
+* Use this folder to store files (notebooks, data files, etc) that you want to use again later. The files you save here will still be available when you come back after terminating your workspace session.
+* **Any files you create or add outside of this folder will be lost if they are not moved to the `/pd` before your workspace session terminates**.
+* This image has a `/pd` with a storage capacity limit of 10Gi
+
+The folder `/healdata.org` in the `/data` folder will host any data files you have downloaded to the workspace through the [HEAL Discovery Page](https://healdata.org/portal). Move these files to the `/pd` directory if you want to access them again after you terminate your workspace session.
+
+## **Get started with Data Exploration**
+
+Open a new "Launcher" tab by clicking the `+` next to `heal-welcome.html` tab at the top of this document.
+
+From the Launcher tab, you can open: a new, empty Jupyter notebook; open a new code console or terminal window; or, create several types of files (text, Markdown, Python). For notebooks or files \- remember to move the file into the `/pd` drive if you want to access them in a later workspace session.
+
+[Learn how to download data files through the HEAL discovery page](https://heal.github.io/platform-documentation/downloading_files/) in our documentation.
+
+Install software tools by using `pip install` (Python) or `CRAN` (R). If you have a requirements text, you can install them with `pip install -r requirements.txt`. You can view all pre-installed software packages by opening a terminal window and using the command โpip listโ.
+
+Find some examples of analyses that can be done in the HEAL Workspace on the [HEAL Example Analyses page](https://healdata.org/portal/resource-browser).
+
+If the locally-stored files you wish to analyze are large in number and/or size, you may need to zip them before uploading to a workspace. Once in a workspace, files can be unzipped using the python library [zipfile](https://docs.python.org/3/library/zipfile.html).
+
+**Note:** The Generic Jupyter Lab image is currently sized for small analyses and testing. The maximum storage in the `/pd` for this image (including any notebooks or tools installed) is 10Gi. If you need a larger image for your analyses, please reach out to the HEAL Data Platform support team at [heal-support@gen3.org](mailto:heal-support@gen3.org) to discuss the possibility of a larger image.
+
+## **Funding a persistent paymodel**
+
+To learn how to fund your workspace after your trial period ends, visit our documentation on [persistent paymodels](https://heal.github.io/platform-documentation/workspaces/heal_workspace_registration/#guidelines-for-requesting-extended-access-to-heal-data-platform-workspaces-using-strides).
diff --git a/HEAL-notebooks/generic_rkernel/requirements.txt b/HEAL-notebooks/generic_rkernel/requirements.txt
index 18b8900e..0960fe79 100644
--- a/HEAL-notebooks/generic_rkernel/requirements.txt
+++ b/HEAL-notebooks/generic_rkernel/requirements.txt
@@ -1,3 +1,4 @@
numpy
scipy
-#Trigger build
+
+# trigger build
diff --git a/HEAL-notebooks/lab-extensions/README.md b/HEAL-notebooks/lab-extensions/README.md
deleted file mode 100644
index 8b781b4b..00000000
--- a/HEAL-notebooks/lab-extensions/README.md
+++ /dev/null
@@ -1,29 +0,0 @@
-# JupyterLab with extensions for HEAL
-
-
-## About
-
-An extension of the Generic JupyterLab environment with common JupyterLab Extensions.
-
-
-
-## Building a Notebook
-
-To build this version edit the `requirements.txt` file to add the desired packages and lab extensions and then push to
-the `feat/nbbuilder` branch. Some packages such as `lckr-jupyterlab-variableinspector` cannot be added through the
-`requirements.txt` file. In this case create a local text file called `lab-extension.Dockerfile` with the following text
-
-```
-FROM quay.io/cdis/heal-notebooks:lab-extensions__xxx
-
-RUN pip install lckr-jupyterlab-variableinspector
-```
-
-then run `docker build -f lab-extension.Dockerfile .` in your terminal. This will then create a local image
-`` which you can add to the lab-extension image by running:
-
-`docker image tag quay.io/cdis/heal-notebooks:lab-extensions__xxx`
-
-`docker push quay.io/cdis/heal-notebooks:lab-extensions__xxx`
-
-Now the lab extension image is ready to be added to your data commons manifest/hatchery file.
diff --git a/HEAL-notebooks/lab-extensions/requirements.txt b/HEAL-notebooks/lab-extensions/requirements.txt
deleted file mode 100644
index 96a8d79a..00000000
--- a/HEAL-notebooks/lab-extensions/requirements.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-pandas
-numpy
-scipy
-jupyterlab-git
-jupyterlab_templates
-jupyterlab_latex
-jupyterlab-spellchecker
diff --git a/jupyter-bih/Dockerfile b/jupyter-bih/Dockerfile
new file mode 100644
index 00000000..ccf200ad
--- /dev/null
+++ b/jupyter-bih/Dockerfile
@@ -0,0 +1,39 @@
+FROM quay.io/cdis/jupyter-superslim-r:1.0.4
+
+WORKDIR /home/jovyan
+
+# Copy your notebooks and requirements.txt into the container
+COPY combined_demos/ /home/jovyan/
+COPY requirements.txt /home/jovyan/
+
+USER root
+
+# Install system and dev dependencies
+RUN apt-get update && apt-get install -y --no-install-recommends \
+ curl \
+ python3 \
+ python3-pip \
+ g++ \
+ libxml2-dev \
+ libssl-dev \
+ libcurl4-openssl-dev \
+ libssh2-1-dev \
+ zlib1g-dev \
+ openssl \
+ gdebi-core \
+ libgsl* \
+ libudunits2-dev \
+ libgs-dev \
+ unzip \
+ wget \
+ && apt-get clean \
+ && rm -rf /var/lib/apt/lists/*
+
+# Install Python packages from requirements.txt
+RUN pip3 install --upgrade pip && \
+ pip3 install --no-cache-dir -r requirements.txt && \
+ rm requirements.txt
+
+USER jovyan
+
+CMD ["start-notebook.sh"]
diff --git a/jupyter-bih/combined_demos/Cohort Building and Data Access Using the MIDRC BDF Imaging Hub.ipynb b/jupyter-bih/combined_demos/Cohort Building and Data Access Using the MIDRC BDF Imaging Hub.ipynb
new file mode 100644
index 00000000..dc0d8693
--- /dev/null
+++ b/jupyter-bih/combined_demos/Cohort Building and Data Access Using the MIDRC BDF Imaging Hub.ipynb
@@ -0,0 +1,953 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "49aa0dff-bb18-4636-9587-027ccf792b5b",
+ "metadata": {},
+ "source": [
+ "# Cohort Building and Data Access Using the MIDRC BDF Imaging Hub\n",
+ "\n",
+ "---\n",
+ "\n",
+ "This notebook briefly demonstrates how to use the MIDRC Biomedical Imaging Hub (BIH) APIs to discover medical imaging datasets across the Biomedical Data Fabric (BDF), including those in data resources other than the MIDRC data commons.\n",
+ "\n",
+ "Anything a user can do in the [MIDRC BIH Explorer graphical user interface](https://imaging-hub.data-commons.org/Explorer), including using complex search criteria to select similar subsets of images distributed across multiple repositories, can also be achieved programmatically using API requests.\n",
+ "\n",
+ "by Chris Meyer, PhD\n",
+ "\n",
+ "August 2025"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "beadf33b-8d25-49e0-863c-cf05a4da0afc",
+ "metadata": {},
+ "source": [
+ "## 1) Set up Python environment\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "92e9c01e-43bd-4a24-b23c-aad74ba49356",
+ "metadata": {},
+ "source": [
+ "### Download an API key file containing your credentials\n",
+ "---\n",
+ "1) Navigate to the MIDRC BIH login page in your browser: https://imaging-hub.data-commons.org/portal/login.\n",
+ "2) Navigate to the user profile page: https://imaging-hub.data-commons.org/portal/identity.\n",
+ "3) Click on the button \"Create API Key\" and save the `credentials.json` file somewhere safe as `bih-credentails.json`."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a373f3c5-1ca9-42e8-bf6b-2b00f3ec712c",
+ "metadata": {},
+ "source": [
+ "### Set local variables\n",
+ "---\n",
+ "Change the following `bcred` variable path to point to your credentials file downloaded from the MIDRC data portal following the instructions above."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "352e165b-0cee-4853-bc0c-27ff8df22aba",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "bcred = \"/Users/cgmeyer/Downloads/bih-credentials.json\" # location of your MIDRC BIH credentials, downloaded from https://imaging-hub.data-commons.org/portal/identity by clicking \"Create API key\" button and saving the credentials.json locally\n",
+ "bapi = \"https://imaging-hub.data-commons.org\" # The base URL of the resource being queried. This shouldn't change for MIDRC BIH\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "414514f9-c452-4760-9530-8cc9454c8100",
+ "metadata": {},
+ "source": [
+ "### Install / Import Python Packages and Scripts"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "70dae3d6-42cc-4cc2-b82a-233397035c94",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## The packages below may be necessary for users to install according to the imports necessary in the subsequent cells.\n",
+ "## Uncomment the lines for packages you may need to install\n",
+ "\n",
+ "import sys\n",
+ "#!{sys.executable} -m pip install\n",
+ "#!{sys.executable} -m pip install --upgrade pandas\n",
+ "#!{sys.executable} -m pip install --upgrade --ignore-installed PyYAML\n",
+ "#!{sys.executable} -m pip install --upgrade pip\n",
+ "#!{sys.executable} -m pip install --upgrade gen3\n",
+ "#!{sys.executable} -m pip install pydicom\n",
+ "#!{sys.executable} -m pip install --upgrade Pillow\n",
+ "#!{sys.executable} -m pip install psmpy\n",
+ "#!{sys.executable} -m pip install python-gdcm --upgrade\n",
+ "#!{sys.executable} -m pip install pylibjpeg --upgrade\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c012d64e-6b3e-4fd4-b0eb-5dad100c9fe0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Import Python Packages and scripts\n",
+ "\n",
+ "import os, subprocess\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import pydicom\n",
+ "from PIL import Image\n",
+ "import glob\n",
+ "#import gdcm\n",
+ "#import pylibjpeg\n",
+ "\n",
+ "# import some Gen3 packages\n",
+ "import gen3\n",
+ "from gen3.auth import Gen3Auth\n",
+ "from gen3.query import Gen3Query\n",
+ "from IPython.display import display"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "810a9306-b860-4037-8bd2-2b0ac21e2b3a",
+ "metadata": {},
+ "source": [
+ "### Initiate instances of the Gen3 SDK Classes using your credentials file for authentication\n",
+ "---\n",
+ "Again, make sure the \"bcred\" directory path variable reflects the location of _your_ credentials file (path variables set above)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2705a836-b9f5-483d-bc59-b14154b67604",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "bauth = Gen3Auth(bapi, refresh_file=bcred) # authentication class\n",
+ "bquery = Gen3Query(bauth) # query class\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "22da7b26-02ea-44bc-89aa-ca2a9c6d879f",
+ "metadata": {},
+ "source": [
+ "## 2) Build Cohorts by Sending Queries to the MIDRC BIH Metadata API\n",
+ "---\n",
+ "\n",
+ "Currently, there are four views of the imaging data in MIDRC BIH: datasets, patients, imaging studies, and imaging series. These four views correspond to four \n",
+ "\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e5d61d93-0354-443a-afb1-b899986ecaee",
+ "metadata": {},
+ "source": [
+ "### Find Imaging Studies of Interest\n",
+ "\n",
+ "* Here, we'll send a query to the `imaging_study` index, which is the default table view in the [MIDRC BIH data explorer GUI](https://data.midrc.org/explorer).\n",
+ "* The filters defined below can be added to, removed, or modified to return different subsets of imaging studies.\n",
+ "* If our query request is successful, the API response should be in JSON format. The response will be a list of structured data records, each corresponding to a single imaging study. \n",
+ "* The Gen3 query service \"guppy\" has extensive documentation in GitHub [here](https://github.com/uc-cdis/guppy/blob/master/doc/queries.md), which will guide you through query syntax, available types of filters, operators, etc."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f6e82c61-1133-4cb8-b8d8-2969e68d3ff0",
+ "metadata": {},
+ "source": [
+ "#### Fetch the Query Schema \n",
+ "---\n",
+ "\n",
+ "In order to see all the fields available to use in queries as filter parameters, we can send a request to [get the query schema/mapping](https://github.com/uc-cdis/guppy/blob/master/doc/queries.md#mapping-query). Here we specify the imaging_study index to see all the fields in BIH related to imaging studies.\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9d2840f5-eb2b-462b-96b4-b27d206b3edf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "query_string = \"\"\"{\n",
+ " _mapping {\n",
+ " imaging_study\n",
+ " }\n",
+ "}\"\"\"\n",
+ "bquery.graphql_query(query_string=query_string,variables=None)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9bf8cb79-ca5f-48e9-9849-c25784a2c63c",
+ "metadata": {},
+ "source": [
+ "#### Set some filter values to subset the imaging studies in BIH"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a7574513-f417-47a0-bc51-a7c5bb374f14",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Find imaging studies with the following parameters:\n",
+ "StudyDescription = [\"XR Chest AP or PA\", \n",
+ " \"CHEST AP PORT\",\n",
+ " \"CHEST PORT 1 VIEW (RAD)-CS\",\n",
+ " \"CHEST PA & LATERAL (RAD)-CS\",\n",
+ " \"CHEST AP VIEWONLY\",\n",
+ " \"Portable Chest\",\n",
+ " \"Chest Portable\",\n",
+ " \"CHEST AP PORTABLE\"]\n",
+ "\n",
+ "## Filter studies based on some patient attributes:\n",
+ "PatientSex = \"Male\"\n",
+ "\n",
+ "min_PatientAge = 65\n",
+ "max_PatientAge = 70\n",
+ "\n",
+ "EthnicGroup = [\"Non-Hispanic/Non-Latino\",\n",
+ " \"Not Hispanic or Latino\"]\n",
+ "\n",
+ "race = [\"Black\",\n",
+ " \"Black or African American\"]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "68251723-abfd-40da-aa4c-a6d917c60b24",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "studies = bquery.raw_data_download(\n",
+ " data_type=\"imaging_study\",\n",
+ " fields=None,\n",
+ " filter_object={\n",
+ " \"AND\": [\n",
+ " {\"IN\": {\"StudyDescription\": StudyDescription}},\n",
+ " {\"=\": {\"PatientSex\": PatientSex}},\n",
+ " {\"IN\": {\"EthnicGroup\": EthnicGroup}},\n",
+ " {\"IN\": {\"race\": race}},\n",
+ " {\"AND\":[{\">=\":{\"PatientAge\":min_PatientAge}},{\"<=\":{\"PatientAge\":max_PatientAge}}]}\n",
+ " ]\n",
+ " },\n",
+ " )\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "312eec6f-8fdd-4e0f-b257-626ba6c30c19",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Summarize the query response\n",
+ "if len(studies) > 0:\n",
+ " study_ids = list(set([i['submitter_id'] for i in studies if 'submitter_id' in i])) ## make a list of the imaging study IDs returned\n",
+ " platforms = list(set([rec['commons_name'][0] for rec in studies if 'commons_name' in rec])) ## make a list of the imaging study IDs returned\n",
+ " subject_ids = list(set([rec['subject_id'][0] for rec in studies if 'subject_id' in rec])) ## make a list of the imaging studiy IDs returned\n",
+ " print(f\"Query returned {len(studies)} imaging studies for {len(subject_ids)} subjects across {len(platforms)} platforms: {platforms}.\")\n",
+ " print(\"Data is a list with rows like this:\")\n",
+ " for k,v in studies[0:1][0].items():\n",
+ " print(\"\\t\\'{}' : '{}'\".format(k,v))\n",
+ "else:\n",
+ " print(\"Your query returned no data! Please, check that query parameters are valid.\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e7b28c6e-31d1-4d9d-baf4-b9f21d455841",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "studies_df = pd.DataFrame(studies)\n",
+ "display(studies_df)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6427e7c2-79be-49d1-9fbd-64eb45ee30e2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Export the file metadata as a TSV file\n",
+ "filename = \"MIDRC_BIH_imaging_studies_metadata.tsv\"\n",
+ "studies_df.to_csv(filename, sep='\\t')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "785525f9-a51d-418d-92c2-513bc37386b4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Explore counts of patient demographics\n",
+ "display(studies_df.value_counts('EthnicGroup'))\n",
+ "race_df = studies_df['race'].explode()\n",
+ "display(race_df.value_counts())\n",
+ "display(studies_df['StudyDescription'].value_counts())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9abb1c9b-c933-4b15-a06d-cca76a0fd3bf",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9179a10b-d5c4-455e-8c59-80657dba4824",
+ "metadata": {},
+ "source": [
+ "### Find Imaging Series of Interest\n",
+ "---\n",
+ "Now we will search over the >1M imaging series indexed in the MIDRC BIH. \n",
+ "* First, we'll send a request to get the imaging_series schema/mapping.\n",
+ "* Then we'll set some values to use as filters in our data download request using the same [raw_data_download](https://github.com/uc-cdis/gen3sdk-python/blob/2b4fb5ad9facd7cd37818743b558251b48e1f219/gen3/query.py#L146) SDK function we used earlier for imaging studies.\n",
+ "* The API response should be a list of structured data records, each one corresponding to a single imaging series indexed in MIDRC BIH."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "aa60445e-4f46-4a80-9719-f1a20d688ec7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "query_string = \"\"\"{\n",
+ " _mapping {\n",
+ " imaging_series\n",
+ " }\n",
+ "}\"\"\"\n",
+ "bquery.graphql_query(query_string=query_string,variables=None)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "38982d2c-9758-492c-8376-a418b4cdfdb9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "### Set some \"imaging_series\" query parameters to select Lung CT imaging series for female patients with Lung Cancer\n",
+ "\n",
+ "## Here we select imaging series with a BodyPartExamined of \"Chest\"\n",
+ "BodyPartExamined = [\"LUNG\",\"CHEST\"]\n",
+ "\n",
+ "## Here we select imaging series with a Modality of \"CT\"\n",
+ "Modality = \"CT\"\n",
+ "\n",
+ "## Here we select imaging series with a PatientSex of \"Female\"\n",
+ "PatientSex = \"Female\"\n",
+ "\n",
+ "## Here we select imaging series with a disease_type of \"COVID-19\"\n",
+ "#disease_type = [\"Non-small Cell Lung Cancer\",\n",
+ "# \"Lung Cancer\"]\n",
+ "\n",
+ "disease_type = [\"COVID-19\"]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8ff18aae-caa1-4340-9011-ea04ef4091aa",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "series = bquery.raw_data_download(\n",
+ " data_type=\"imaging_series\",\n",
+ " fields=None,\n",
+ " filter_object={\n",
+ " \"AND\": [\n",
+ " {\"IN\": {\"BodyPartExamined\": BodyPartExamined}},\n",
+ " {\"=\": {\"Modality\": Modality}},\n",
+ " {\"=\": {\"PatientSex\": PatientSex}},\n",
+ " {\"IN\": {\"disease_type\": disease_type}},\n",
+ " ]\n",
+ " },\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "66d687e2-80ac-4fd8-8542-7a11074deec3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if len(series) > 0:\n",
+ " series_ids = list(set([i['submitter_id'] for i in series if 'submitter_id' in i])) ## make a list of the imaging series IDs returned\n",
+ " object_ids = list(set([rec['object_ids'][0] for rec in series if 'object_ids' in rec and rec['object_ids'] is not None])) ## make a list of the imaging series IDs returned\n",
+ " platforms = list(set([rec['commons_name'][0] for rec in series if 'commons_name' in rec])) ## make a list of the imaging study IDs returned\n",
+ " subject_ids = list(set([rec['subject_id'][0] for rec in series if 'subject_id' in rec])) ## make a list of the imaging series IDs returned\n",
+ " print(f\"Query returned {len(series)} imaging series for {len(subject_ids)} subjects across {len(platforms)} platforms: {platforms}.\")\n",
+ " print(\"Data is a list with rows like this:\")\n",
+ " for k,v in series[0:1][0].items():\n",
+ " print(\"\\t\\'{}' : '{}'\".format(k,v))\n",
+ "else:\n",
+ " print(\"Your query returned no data! Please, check that query parameters are valid.\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "85f92e14-741d-4a5b-b60c-0917b073c682",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "series_df = pd.DataFrame(series)\n",
+ "display(series_df)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e1f3b02a-944d-4bcc-aabd-00c72f59d9af",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Export the file metadata as a TSV file\n",
+ "filename = \"MIDRC_BIH_imaging_series_metadata.tsv\"\n",
+ "series_df.to_csv(filename, sep='\\t')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0cfa6693-3d93-4156-8b23-bf777f773348",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c2563166-d507-402b-bc31-704cf5207650",
+ "metadata": {},
+ "source": [
+ "### Find Patient Cohorts of Interest\n",
+ "\n",
+ "* Here, we'll send a query to the `subject` index, which corresponds to the Subjects tab of the MIDRC BIH Explorer GUI.\n",
+ "* First, we'll specify some values of subject attributes to send as filters, then we'll send our query request using the Gen3 SDK.\n",
+ "* The response should be a list of structured records each one of which corresponds to a single subject indexed in BIH."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2d002908-9b68-4451-b8fa-2356a5dd406e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "query_string = \"\"\"{\n",
+ " _mapping {\n",
+ " subject\n",
+ " }\n",
+ "}\"\"\"\n",
+ "bquery.graphql_query(query_string=query_string,variables=None)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "039f26ed-4b49-4329-a919-5d47868e9f5e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "### Set some \"subject\" query parameters to select subjects in BIH with NSCLC\n",
+ "race = \"Asian\"\n",
+ "disease_type = \"Breast Cancer\"\n",
+ "primary_site = \"Breast\"\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fb20015e-43fb-4e22-8636-d9829470fdf0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "subjects = bquery.raw_data_download(\n",
+ " data_type=\"subject\",\n",
+ " fields=None,\n",
+ " filter_object={\n",
+ " \"AND\": [\n",
+ " {\"=\": {\"race\": race}},\n",
+ " {\"=\": {\"primary_site\": primary_site}},\n",
+ " {\"=\": {\"disease_type\": disease_type}},\n",
+ " ]\n",
+ " },\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fea78634-899f-480d-a9fa-82af9d6916ee",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if len(subjects) > 0:\n",
+ " subject_ids = list(set([i['submitter_id'] for i in subjects if 'submitter_id' in i])) \n",
+ " platforms = list(set([rec['commons_name'][0] for rec in subjects if 'commons_name' in rec])) ## make a list of the imaging study IDs returned\n",
+ " print(f\"Query returned {len(subjects)} subjects across {len(platforms)} platform(s): {platforms}.\")\n",
+ " print(\"Data is a list with rows like this:\")\n",
+ " for k,v in subjects[0:1][0].items():\n",
+ " print(\"\\t\\'{}' : '{}'\".format(k,v))\n",
+ "else:\n",
+ " print(\"Your query returned no data! Please, check that query parameters are valid.\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4347ddd0-d756-4929-b6be-22dbdec04fb7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "subjects_df = pd.DataFrame(subjects)\n",
+ "display(subjects_df)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c21c372f-d90e-4959-ab9b-7574f24a28a8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Export the file metadata as a TSV file\n",
+ "filename = \"MIDRC_BIH_imaging_subjects_metadata.tsv\"\n",
+ "subjects_df.to_csv(filename, sep='\\t')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "55ac8006-194f-4a19-b39e-a27ef277e89d",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f21285b9-9bb2-448b-8eed-1ff7dd1b06fa",
+ "metadata": {},
+ "source": [
+ "### Find Datasets of Interest\n",
+ "\n",
+ "* Here, we'll send a query to the `dataset` index, which corresponds to the Datasets tab of the MIDRC BIH Explorer GUI.\n",
+ "* First, we'll specify some values of dataset attributes to send as filters, then we'll send our query request using the Gen3 SDK.\n",
+ "* The response should be a list of structured records each one of which corresponds to a single dataset indexed in BIH."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4cd0b797-5ca7-4be7-a5d0-589eaae1dd61",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "query_string = \"\"\"{\n",
+ " _mapping {\n",
+ " dataset\n",
+ " }\n",
+ "}\"\"\"\n",
+ "bquery.graphql_query(query_string=query_string,variables=None)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "980a2185-3eea-44c0-923a-acf2961c181d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "### Set some \"dataset\" query parameters to select datasets in BIH\n",
+ "disease_type = \"Non-small Cell Lung Cancer\"\n",
+ "primary_site = [\"Lung\",\"Chest\",\"Esophagus, Lung, Pancreas, Thymus\"]\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e8a4e6e6-583d-4c52-b955-e5bcfd6d6765",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "datasets = bquery.raw_data_download(\n",
+ " data_type=\"dataset\",\n",
+ " fields=None,\n",
+ " filter_object={\n",
+ " \"AND\": [\n",
+ " {\"=\": {\"disease_type\": disease_type}},\n",
+ " {\"IN\": {\"primary_site\": primary_site}},\n",
+ " ]\n",
+ " },\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "cc547305-8739-4617-8d5b-83143b8f9da9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if len(datasets) > 0:\n",
+ " platforms = list(set([rec['commons_name'] for rec in datasets if 'commons_name' in rec])) ## make a list of the imaging study IDs returned\n",
+ " print(f\"Query returned {len(datasets)} datasets across {len(platforms)} platform(s): {platforms}.\")\n",
+ " print(\"Data is a list with rows like this:\")\n",
+ " for k,v in datasets[0:1][0].items():\n",
+ " print(\"\\t\\'{}' : '{}'\".format(k,v))\n",
+ "else:\n",
+ " print(\"Your query returned no data! Please, check that query parameters are valid.\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e70e1653-6e51-432e-9ba2-9760839fd92e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "datasets_df = pd.DataFrame(datasets)\n",
+ "display(datasets_df.sort_values(by='submitter_id', key=lambda col: col.str.lower(), ascending=False))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b950a64b-74b8-4155-9126-5c7cdba70255",
+ "metadata": {},
+ "source": [
+ "* Note: There are some datasets that may be hosted by more than one repository. Researchers should ensure they are not including duplicates in any analyses or AI training sets. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "20748125-004c-4778-8efc-36b718cab930",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f700bcc2-7ec5-4ebb-bf9a-4635b9429722",
+ "metadata": {},
+ "source": [
+ "## 3) Access image files using their object_id / data GUID (globally unique identifiers)\n",
+ "---\n",
+ "There are a number of ways to access the image files indexed in MIDRC BIH. In general, users will need to understand the host platform's process for downloading files, but for Gen3-powered data commons like the MIDRC Data Commons, once we have a list of object_ids / image GUIDs we want to download, we can use either the gen3-client or the gen3 SDK to download the files. \n",
+ "\n",
+ "In order to programmatically access files for MIDRC imaging series indexed in MIDRC BIH, users can reference the file's object_id (AKA \"data GUID\" or \"Globally Unique IDentifier\", which is an example of a GA4GH DRS URI).\n",
+ "\n",
+ "Once we have a list of GUIDs we want to download, we can use either the gen3-client or the gen3 SDK to download the files. You can also access individual files in your browser after logging-in and entering the GUID after the `files/` endpoint, as in this URL: https://data.midrc.org/files/GUID\n",
+ "\n",
+ "where GUID is the actual GUID, e.g.: https://data.midrc.org/files/dg.MD1R/b87d0db3-d95a-43c7-ace1-ab2c130e04ec\n",
+ "\n",
+ "For instructions on how to install and use the gen3-client, please see [the MIDRC quick-start guide](https://data.midrc.org/dashboard/Public/documentation/Gen3_MIDRC_GetStarted.pdf).\n",
+ "\n",
+ "Below we use the gen3 SDK command `gen3 drs-pull object` which is [documented in detail here](https://github.com/uc-cdis/gen3sdk-python/blob/master/docs/howto/drsDownloading.md).\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "03dd27b5-c169-419f-a4bb-9c1348a38e26",
+ "metadata": {},
+ "source": [
+ "### Get credentials from the host platform MIDRC Data Commons"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c50cb8fe-81a6-4cc7-8aef-a9266b323cdd",
+ "metadata": {},
+ "source": [
+ "### Download an API key file containing your credentials\n",
+ "---\n",
+ "1) Navigate to the MIDRC data portal in your browser: https://data.midrc.org.\n",
+ "2) Read and accept the DUA (if you haven't already).\n",
+ "3) Navigate to the user profile page: https://data.midrc.org/identity\n",
+ "4) Click on the button \"Create API Key\" and save the `credentials.json` file somewhere safe\n",
+ "5) Change the following `cred` variable path to point to your credentials file downloaded from the MIDRC data portal following the instructions above."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6543ffb1-2212-4c51-b8ec-629393e68707",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cred = \"/Users/cgmeyer/Downloads/midrc-credentials.json\" # location of your MIDRC credentials, downloaded from https://data.midrc.org/identity by clicking \"Create API key\" button and saving the credentials.json locally"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fdefd4ae-29d7-4a1d-8f6e-8a7e0092d68b",
+ "metadata": {},
+ "source": [
+ "### Make a list of object_ids to download"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b51da5bd-4499-44e1-9308-bdc62b010ae8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## get subset of imaging series that have object_ids\n",
+ "series_with_files = [rec for rec in series if 'object_ids' in rec and rec['object_ids'] is not None]\n",
+ "\n",
+ "## make a list of the imaging series IDs returned\n",
+ "object_ids = list(set([rec['object_ids'][0] for rec in series])) \n",
+ "\n",
+ "print(f\"Found {len(object_ids)} object_ids for the {len(series)} imaging series select in BIH.\")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4cd28f4e-19b7-45d1-bc15-70d53e47b1ad",
+ "metadata": {},
+ "source": [
+ "### Use the Gen3 SDK command `gen3 drs-pull object` to download an individual file"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4c84520c-02d1-4410-bc84-42e541356a46",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Make a new directory for downloaded files\n",
+ "if os.path.exists(\"downloads\"):\n",
+ " os.system(\"rm -r downloads\")\n",
+ "os.system(\"mkdir -p downloads\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3cff00ff-12d0-4d4e-90ec-5b5bd6572ace",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## We can use a simple loop to download all files and keep track of successes and failures\n",
+ "max_downloads = 3\n",
+ "success,failure,other=[],[],[]\n",
+ "count,total = 0,len(object_ids)\n",
+ "for object_id in object_ids[0:max_downloads]:\n",
+ " count+=1\n",
+ " cmd = \"gen3 --auth {} --endpoint data.midrc.org drs-pull object {} --output-dir downloads\".format(cred,object_id)\n",
+ " stout = subprocess.run(cmd, shell=True, capture_output=True)\n",
+ " print(\"Progress ({}/{}): {}\".format(count,total,stout.stdout))\n",
+ " if \"failed\" in str(stout.stdout):\n",
+ " failure.append(object_id)\n",
+ " elif \"successfully\" in str(stout.stdout):\n",
+ " success.append(object_id)\n",
+ " else:\n",
+ " other.append(object_id)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7a3bfe58-8a00-481e-aede-4178324d3ccc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Get a list of all downloaded .dcm files\n",
+ "## NOTE: Since we've downloaded some zip files containing entire imaging series from MIDRC, the number of files may be more than the number of object_ids once the packages are unzipped\n",
+ "image_files = glob.glob(pathname='**/*.dcm',recursive=True,)\n",
+ "print(f\"Found {len(image_files)} image files in the downloads directory.\")\n",
+ "image_files"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0769486f-6471-4353-9f36-c646b133fc17",
+ "metadata": {},
+ "source": [
+ "### View the DICOM Images\n",
+ "---\n",
+ "Here we'll use the [Python package `pydicom`](https://pydicom.github.io/pydicom/stable/) to view the downloaded DICOM images. \n",
+ "\n",
+ "Note that some of the files may contain compressed pixel data that require other packages to view; so, for this demo we'll simply skip over those using the following loop."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4d222c78-49c0-4217-9ca4-6d944d21609e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "max_view = 1 # just view the first one for demo purposes\n",
+ "for image_file in image_files[0:max_view]:\n",
+ " print(image_file)\n",
+ " ds = pydicom.dcmread(image_file)\n",
+ " try:\n",
+ " new_image = ds.pixel_array.astype(float)\n",
+ " scaled_image = (np.maximum(new_image, 0) / new_image.max()) * 255.0\n",
+ " scaled_image = np.uint8(scaled_image)\n",
+ " final_image = Image.fromarray(scaled_image)\n",
+ " print(type(final_image))\n",
+ " display(final_image)\n",
+ " except Exception as e:\n",
+ " print(\"Couldn't view {}: {}.\".format(image_file,e))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fc0e69b6-72f0-483d-851a-41609bdf02a9",
+ "metadata": {},
+ "source": [
+ "### View the DICOM Headers\n",
+ "---\n",
+ "DICOM files have metadata elements embedded in the images. These can also be read and viewed using the `pydicom` package."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d6d6bd79-1c0b-43e7-9ea0-a2ace63a5aa5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds = pydicom.dcmread(image_files[0],force=True)\n",
+ "display(ds)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f306b316-967b-47f1-ba0b-4109c43717b2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Access individual elements using tags or codes\n",
+ "display(ds.file_meta)\n",
+ "display(ds.ImageType)\n",
+ "display(ds[0x0008, 0x0016])\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ef255252-4452-4459-a3ad-e058082190c5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# View the dicom metadata for all files as a DataFrame\n",
+ "dfs = []\n",
+ "for image_file in image_files:\n",
+ " ds = pydicom.dcmread(image_file)\n",
+ " df = pd.DataFrame(ds.values())\n",
+ " df[0] = df[0].apply(lambda x: pydicom.dataelem.DataElement_from_raw(x) if isinstance(x, pydicom.dataelem.RawDataElement) else x)\n",
+ " df['name'] = df[0].apply(lambda x: x.name)\n",
+ " df['value'] = df[0].apply(lambda x: x.value)\n",
+ " df = df[['name', 'value']]\n",
+ " df = df.set_index('name').T.reset_index(drop=True)\n",
+ " df['filename'] = image_file\n",
+ " df.drop(columns=['Pixel Data'],inplace=True) # drop the pixel data as it's too large and nonsensical to store in a DataFrame\n",
+ " dfs.append(df)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9a93f7d4-73c1-4119-80d5-25ec1257ba1b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Make a master dataframe for all images using only headers in all dataframes\n",
+ "headers = list(set.intersection(*map(set,dfs)))\n",
+ "df = pd.concat([df[headers] for df in dfs])\n",
+ "df.set_index('filename',inplace=True)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fbecc35a-26ab-4450-8ca3-43b1cf6d2528",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "display(df)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "24c30854-181c-4a11-94a4-fc580ae5d6e6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Export the file metadata as a TSV file\n",
+ "filename = \"MIDRC_DICOM_metadata.tsv\"\n",
+ "df.to_csv(filename, sep='\\t')\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6c593461-e6f2-445e-bf1b-db0968c6b582",
+ "metadata": {},
+ "source": [
+ "## The End\n",
+ "---\n",
+ "If you have any questions related to this notebook don't hesitate to reach out to the MIDRC Helpdesk at midrc-support@gen3.org or the author directly at cgmeyer@uchicago.edu\n",
+ "\n",
+ "Happy data wrangling!"
+ ]
+ }
+ ],
+ "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": "3.13.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/jupyter-bih/requirements.txt b/jupyter-bih/requirements.txt
new file mode 100644
index 00000000..81f7d9bf
--- /dev/null
+++ b/jupyter-bih/requirements.txt
@@ -0,0 +1,11 @@
+matplotlib==3.4.2
+plotly==5.6.0
+seaborn==0.11.1
+openpyxl==3.1.2
+pyreadstat==1.2.1
+scikit-learn==1.1.1
+tableone==0.7.12
+lifelines==0.27.4
+bioinfokit==2.1.0
+pydicom==2.4.4
+dicom_csv==0.3.0
diff --git a/jupyter-midrc/Dockerfile b/jupyter-midrc/Dockerfile
new file mode 100644
index 00000000..ccf200ad
--- /dev/null
+++ b/jupyter-midrc/Dockerfile
@@ -0,0 +1,39 @@
+FROM quay.io/cdis/jupyter-superslim-r:1.0.4
+
+WORKDIR /home/jovyan
+
+# Copy your notebooks and requirements.txt into the container
+COPY combined_demos/ /home/jovyan/
+COPY requirements.txt /home/jovyan/
+
+USER root
+
+# Install system and dev dependencies
+RUN apt-get update && apt-get install -y --no-install-recommends \
+ curl \
+ python3 \
+ python3-pip \
+ g++ \
+ libxml2-dev \
+ libssl-dev \
+ libcurl4-openssl-dev \
+ libssh2-1-dev \
+ zlib1g-dev \
+ openssl \
+ gdebi-core \
+ libgsl* \
+ libudunits2-dev \
+ libgs-dev \
+ unzip \
+ wget \
+ && apt-get clean \
+ && rm -rf /var/lib/apt/lists/*
+
+# Install Python packages from requirements.txt
+RUN pip3 install --upgrade pip && \
+ pip3 install --no-cache-dir -r requirements.txt && \
+ rm requirements.txt
+
+USER jovyan
+
+CMD ["start-notebook.sh"]
diff --git a/jupyter-midrc/combined_demos/Access_Files_for_Specific_Case_IDs.ipynb b/jupyter-midrc/combined_demos/Access_Files_for_Specific_Case_IDs.ipynb
new file mode 100644
index 00000000..b56f695e
--- /dev/null
+++ b/jupyter-midrc/combined_demos/Access_Files_for_Specific_Case_IDs.ipynb
@@ -0,0 +1,479 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "d8ef63c3",
+ "metadata": {},
+ "source": [
+ "# How to Access Files for Specific Case IDs\n",
+ "---\n",
+ "This notebook demonstrates how to build a cohort of MIDRC patients based on clinical and demographic data and then obtain a file download manifest for x-ray and annotation files related to that cohort.\n",
+ "\n",
+ "by Chris Meyer, PhD\n",
+ "\n",
+ "Manager of Data and User Services at the Center for Translational Data Science at University of Chicago\n",
+ "\n",
+ "August 2023\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5db7f87c",
+ "metadata": {},
+ "source": [
+ "## 1) Set up Python environment\n",
+ "---\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1bffd5d4",
+ "metadata": {},
+ "source": [
+ "### Set local variables\n",
+ "---\n",
+ "Change the following directory paths to a valid working directories where you're running this notebook."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c3c59c2c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cred = \"/Users/christopher/Downloads/midrc-credentials.json\" # location of your MIDRC credentials, downloaded from https://data.midrc.org/identity by clicking \"Create API key\" button and saving the credentials.json locally\n",
+ "api = \"https://data.midrc.org\" # The base URL of the data commons being queried. This shouldn't change for MIDRC.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7962e54c",
+ "metadata": {},
+ "source": [
+ "### Install / Import Python Packages and Scripts"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8e1a7935",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## The packages below may be necessary for users to install according to the imports necessary in the subsequent cells.\n",
+ "\n",
+ "#!pip install --upgrade pandas\n",
+ "#!pip install --upgrade --ignore-installed PyYAML\n",
+ "#!pip install --upgrade pip\n",
+ "#!pip install --upgrade gen3\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7ea2fa09",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Import Python Packages and scripts\n",
+ "\n",
+ "import os, subprocess\n",
+ "import gen3\n",
+ "\n",
+ "from gen3.auth import Gen3Auth\n",
+ "from gen3.query import Gen3Query\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7784ecc9",
+ "metadata": {},
+ "source": [
+ "### Initiate instances of the Gen3 SDK Classes using credentials file for authentication\n",
+ "---\n",
+ "Again, make sure the \"cred\" directory path variable reflects the location of your credentials file (path variables set above)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d316dcdf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "auth = Gen3Auth(api, refresh_file=cred) # authentication class\n",
+ "query = Gen3Query(auth) # query class\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "85fc475d",
+ "metadata": {},
+ "source": [
+ "## 2) Build a cohort of cases by running queries against MIDRC APIs\n",
+ "---\n",
+ "* There are many ways to query and access metadata for cohort building in MIDRC, but this notebook will focus on using the [Gen3](https://gen3.org) graphQL query service [\"guppy\"](https://github.com/uc-cdis/guppy/#readme). This is the backend query service that [MIDRC's data explorer GUI](https://data.midrc.org/explorer) uses. So, anything you can do in the explorer GUI, you can do with guppy queries, and more!\n",
+ "* The guppy graphQL service has more functionality than is demonstrated in this simple example with extensive documentation in GitHub [here](https://github.com/uc-cdis/guppy/blob/master/doc/queries.md) in case you'd like to build your own queries from scratch.\n",
+ "* The Gen3 SDK (intialized as \"query\" above in this notebook) has Python wrapper scripts to make sending queries to the guppy graphQL API simpler. The guppy SDK package can be viewed in GitHub [here](https://github.com/uc-cdis/gen3sdk-python/blob/master/gen3/query.py).\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "597cf089",
+ "metadata": {},
+ "source": [
+ "### Set 'case' query parameters\n",
+ "---\n",
+ "* Below, we first set some query parameters. Feel free to modify these parameters to see how it changes the query response. Setting these patient attributes is akin to selecting a filter value in [MIDRC's data explorer GUI](https://data.midrc.org/explorer). \n",
+ "* To see more documentation about to use and combine filters with various operator logic (like AND/OR/IN, etc.) see [this page](https://github.com/uc-cdis/guppy/blob/master/doc/queries.md#filter).\n",
+ "* We then send our query to MIDRC's guppy API endpoint using [the Gen3Query SDK package](https://github.com/uc-cdis/gen3sdk-python/blob/master/gen3/query.py) we initialized earlier. \n",
+ "* If our query request is successful, the API response should be in JSON format, and it should contain a list of patient IDs along with any other patient data we ask for."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "61e45496",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#### \"case\" query parameters\n",
+ "## In this example, we're going to filter our patient cohort by asking for Asian male patients between the age of 40 and 89 that tested positive for COVID-19.\n",
+ "\n",
+ "## case demographic filters\n",
+ "sex = \"Male\"\n",
+ "min_age = 50\n",
+ "max_age = 89\n",
+ "\n",
+ "#### \"nested\" filters, these are attributes from other nodes that are nested under the case node (\"child nodes\" of case in the data model: data.midrc.org/dd)\n",
+ "## medications (vaccine data)\n",
+ "medication_manufacturer = [\"Pfizer\",\"Moderna\"] #,\"Janssen\",\"AstraZeneca\",\"Sinopharm\",\"Novavax\"]\n",
+ "\n",
+ "## measurements filters (COVID-19 test data)\n",
+ "test_method = [\"RT-PCR\"] #,\"Rapid antigen test\"]\n",
+ "test_result_text = [\"Positive\",\"Negative\"]\n",
+ "\n",
+ "## conditions filters (co-morbidities and long COVID)\n",
+ "condition_name = [\"COVID-19\",\"Post COVID-19 condition, unspecified\"] #,\"Pneumonia, organism unspecified\"]\n",
+ "\n",
+ "## procedures filters\n",
+ "procedure_name = [\"Breathing Support\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8910b3e4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Here is an example getting all the cases in a particular project between ages of 45 and 47\n",
+ "## the \"fields\" option defines what fields we want the query to return. If set to \"None\", returns all available fields.\n",
+ "\n",
+ "cases = query.raw_data_download(\n",
+ " data_type=\"case\",\n",
+ "# fields=[\"project_id\",\"submitter_id\"],\n",
+ " fields=None,\n",
+ " filter_object={\n",
+ " \"AND\": [\n",
+ " {\"=\": {\"sex\": sex}},\n",
+ " {\">=\": {\"age_at_index\": min_age}},\n",
+ " {\"<=\": {\"age_at_index\": max_age}},\n",
+ " {\"nested\": {\"path\": \"medications\", \"IN\": {\"medication_manufacturer\": medication_manufacturer}}},\n",
+ " {\"nested\": {\"path\": \"measurements\", \"IN\": {\"test_method\": test_method}}},\n",
+ " {\"nested\": {\"path\": \"measurements\", \"IN\": {\"test_result_text\": test_result_text}}},\n",
+ " {\"nested\": {\"path\": \"conditions\", \"IN\": {\"condition_name\": condition_name}}},\n",
+ " #{\"nested\": {\"path\": \"procedures\", \"IN\": {\"procedure_name\": procedure_name}}}, # adding too many filters returns no data\n",
+ " ]\n",
+ " },\n",
+ " sort_fields=[{\"submitter_id\": \"asc\"}]\n",
+ " )\n",
+ "\n",
+ "if len(cases) > 0 and \"submitter_id\" in cases[0]:\n",
+ " case_ids = [i['submitter_id'] for i in cases] ## make a list of the case (patient) IDs returned\n",
+ " print(\"Query returned {} case IDs.\".format(len(cases)))\n",
+ " print(\"Data is a list with rows like this:\\n\\t {}\".format(cases[0:1]))\n",
+ "else:\n",
+ " print(\"Your query returned no data! Please, check that query parameters are valid.\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9e761b59",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Look at one record returned by the query\n",
+ "# Note: the \"object_id\" field is a list of all file identifiers associated with the case\n",
+ "cases[0]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ef11c21d",
+ "metadata": {},
+ "source": [
+ "## 3) Send another query to get data file details for our cohort / case ID\n",
+ "---\n",
+ "The object_id field in each case record above contains the file identifiers for all files associated with each case. If we simply want to access all files associated with our list of cases, we can use those object_ids. However, in this example, we'll ask for specific types of files and get more detailed information about each of the files. This is achieved by querying the \"data_file\" index and adding our cohort (list of case_ids) as a filter. \n",
+ "\n",
+ "* Note: all MIDRC data files, including both images and annotations, are listed in the guppy index \"data_file\", which is queried in a similar manner to our query of the \"case\" index above. The query parameter \"data_type\" below determines which Elasticsearch index we're querying."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bae08fb2",
+ "metadata": {},
+ "source": [
+ "### Set 'data_file' query parameters\n",
+ "---\n",
+ "Here, we'll utilize the property \"source_node\" to filter the list of files for our cohort to only those matching the type of files we're interested in. In this example, we ask for CR and DX images and any associated annotation files.\n",
+ "\n",
+ "* Note: We're using the property \"case_ids\" as a filter to restrict the data_file records returned down to those associated with cases in our cohort built above. If you'd like to search for only one specific case_id, you can manually set the case_ids variable like this:\n",
+ "```\n",
+ "case_ids = [\"my_case_id\"]\n",
+ "```\n",
+ "* Or alternatively, you could set the query filter like this:\n",
+ "```\n",
+ "{\"=\": {\"case_ids\": \"my_case_id\"}},\n",
+ "```\n",
+ "where \"my_case_id\" is the quoted submitter_id of the case you're searching for."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e844e93f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "source_nodes = [\"cr_series_file\",\"dx_series_file\",\"annotation_file\",\"dicom_annotation_file\"]\n",
+ "modality = [\"SEG\", \"CR\", \"DX\", ] # this is somewhat redundant with the above source_node filter, but added here for demonstration purposes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4998295a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Search for specific files associated with our cohort by adding \"case_ids\" as a filter\n",
+ "# * Note: \"fields\" is set to \"None\" in this query, which by default returns all the properties available\n",
+ "data_files = query.raw_data_download(\n",
+ " data_type=\"data_file\",\n",
+ " fields=None,\n",
+ " filter_object={\n",
+ " \"AND\": [\n",
+ " {\"IN\": {\"case_ids\": case_ids}},\n",
+ " {\"IN\": {\"source_node\": source_nodes}},\n",
+ " {\"IN\": {\"modality\": modality}},\n",
+ " ]\n",
+ " },\n",
+ " sort_fields=[{\"submitter_id\": \"asc\"}]\n",
+ " )\n",
+ "\n",
+ "if len(data_files) > 0:\n",
+ " object_ids = [i['object_id'] for i in data_files if 'object_id' in i] ## make a list of the file object_ids returned by our query\n",
+ " print(\"Query returned {} data files with {} object_ids.\".format(len(data_files),len(object_ids)))\n",
+ " print(\"Data is a list with rows like this:\\n\\t {}\".format(data_files[0:1]))\n",
+ "else:\n",
+ " print(\"Your query returned no data! Please, check that query parameters are valid.\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "729ffdc9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## View the detailed data for the first file returned\n",
+ "data_files[0]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4e3d5b61",
+ "metadata": {},
+ "source": [
+ "## 4) Access data files using their object_id / data GUID (globally unique identifiers)\n",
+ "---\n",
+ "In order to download files stored in MIDRC, users need to reference the file's object_id (AKA data GUID or Globally Unique IDentifier).\n",
+ "\n",
+ "Once we have a list of GUIDs we want to download, we can use either the gen3-client or the gen3 SDK to download the files. You can also access individual files in your browser after logging-in and entering the GUID after the `files/` endpoint, as in this URL: https://data.midrc.org/files/GUID\n",
+ "\n",
+ "where GUID is the actual GUID, e.g.: https://data.midrc.org/files/dg.MD1R/b87d0db3-d95a-43c7-ace1-ab2c130e04ec\n",
+ "\n",
+ "For instructions on how to install and use the gen3-client, please see [the MIDRC quick-start guide](https://data.midrc.org/dashboard/Public/documentation/Gen3_MIDRC_GetStarted.pdf), which can be found linked here and in the MIDRC data portal header as \"Get Started\".\n",
+ "\n",
+ "Below we use the gen3 SDK command `gen3 drs-pull object` which is [documented in detail here](https://github.com/uc-cdis/gen3sdk-python/blob/master/docs/howto/drsDownloading.md)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cb606c8a",
+ "metadata": {},
+ "source": [
+ "### Parse the data_file query response to build a list of all `object_id`s returned for our cohort. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2f26a9a1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Build a list \n",
+ "object_ids = []\n",
+ "for data_file in data_files:\n",
+ " if 'object_id' in data_file:\n",
+ " object_id = data_file['object_id']\n",
+ " object_ids.append(object_id)\n",
+ "\n",
+ "object_id = object_ids[1]\n",
+ "print(\"The first object_id of {}: '{}'\".format(len(object_ids),object_id))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a167eb79",
+ "metadata": {},
+ "source": [
+ "### Use the Gen3 SDK command `gen3 drs-pull object` to download an individual file"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "35853bf9-647e-401f-b068-fe9e75e3d43a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Make a new directory for downloaded files\n",
+ "os.system(\"mkdir -p downloads\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b5b0ae28-5d80-4d11-a9f0-94ae51391814",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Run the \"gen3 drs-pull object\" command to download a file\n",
+ "cmd = \"gen3 --auth {} --endpoint data.midrc.org drs-pull object {} --output-dir downloads\".format(cred,object_id)\n",
+ "os.system(cmd)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2b7a8ee3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!find downloads -name \"*dcm\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "be1fe191",
+ "metadata": {},
+ "source": [
+ "### Use a simple loop to download all the files"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "161771f4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Simple loop to download all files and keep track of success and failures\n",
+ "cred = \"/Users/christopher/Downloads/midrc-credentials.json\" # location of your MIDRC credentials, downloaded from https://data.midrc.org/identity by clicking \"Create API key\" button and saving the credentials.json locally\n",
+ "success,failure,other=[],[],[]\n",
+ "count,total = 0,len(object_ids)\n",
+ "for object_id in object_ids:\n",
+ " count+=1\n",
+ " cmd = \"gen3 --auth {} --endpoint data.midrc.org drs-pull object {} --output-dir downloads\".format(cred,object_id)\n",
+ " stout = subprocess.run(cmd, shell=True, capture_output=True)\n",
+ " print(\"Progress ({}/{}): {}\".format(count,total,stout.stdout))\n",
+ " if \"failed\" in str(stout.stdout):\n",
+ " failure.append(object_id)\n",
+ " elif \"successfully\" in str(stout.stdout):\n",
+ " success.append(object_id)\n",
+ " else:\n",
+ " other.append(object_id)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "13281b5d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!find downloads -name \"*.dcm\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9f5f2d94",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!find downloads -name \"*.dcm\" | wc -l"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "544761e9",
+ "metadata": {},
+ "source": [
+ "## The End\n",
+ "---\n",
+ "If you have any questions related to this notebook don't hesitate to reach out to the MIDRC Helpdesk at midrc-support@datacommons.io or the author directly at cgmeyer@uchicago.edu\n",
+ "\n",
+ "Happy data wrangling!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f6691638",
+ "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": "3.13.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/jupyter-midrc/combined_demos/Cases_with_Multiple_Modalities.ipynb b/jupyter-midrc/combined_demos/Cases_with_Multiple_Modalities.ipynb
new file mode 100644
index 00000000..df16497f
--- /dev/null
+++ b/jupyter-midrc/combined_demos/Cases_with_Multiple_Modalities.ipynb
@@ -0,0 +1,511 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "e61ca787-fee9-4443-a92a-7961407fe94b",
+ "metadata": {},
+ "source": [
+ "# Select patients with multiple imaging studies of different modalities\n",
+ "---\n",
+ "This notebook briefly demonstrates how to use the MIDRC open APIs to build a cohort of MIDRC patients that have multiple imaging studies of different modalities.\n",
+ "\n",
+ "by Chris Meyer, PhD\n",
+ "\n",
+ "Manager of Data and User Services at the Center for Translational Data Science at University of Chicago\n",
+ "\n",
+ "Last updated: April 2024\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f4a355ce-9d99-4fb6-91fb-1a06d77a217c",
+ "metadata": {},
+ "source": [
+ "## 1) Set up Python environment\n",
+ "---\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c0235e49-d5d6-416d-90ab-916142ec164f",
+ "metadata": {},
+ "source": [
+ "### Download an API key file containing your credentials\n",
+ "---\n",
+ "1) Navigate to the MIDRC data portal in your browser: https://data.midrc.org.\n",
+ "2) Read and accept the DUA (if you haven't already).\n",
+ "3) Navigate to the user profile page: https://data.midrc.org/identity\n",
+ "4) Click on the button \"Create API Key\" and save the `credentials.json` file somewhere safe\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "07fe3a97-2ec5-4609-ac3f-4530de31814a",
+ "metadata": {},
+ "source": [
+ "### Set local variables\n",
+ "---\n",
+ "Change the following `cred` variable path to point to your credentials file downloaded from the MIDRC data portal following the instructions above."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "59992d50-8afc-4d4c-870f-25e1b3592180",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cred = \"/Users/christopher/Downloads/midrc-credentials.json\" # location of your MIDRC credentials, downloaded from https://data.midrc.org/identity by clicking \"Create API key\" button and saving the credentials.json locally\n",
+ "api = \"https://data.midrc.org\" # The base URL of the data commons being queried. This shouldn't change for MIDRC.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "997a7aea-9761-4966-9b12-4dc391f567f0",
+ "metadata": {},
+ "source": [
+ "### Install / Import Python Packages and Scripts"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9e2111b0-3828-4c7e-9df0-05dd253a02c3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## The packages below may be necessary for users to install according to the imports necessary in the subsequent cells.\n",
+ "\n",
+ "import sys\n",
+ "#!{sys.executable} -m pip install\n",
+ "#!{sys.executable} -m pip install --upgrade pandas\n",
+ "#!{sys.executable} -m pip install --upgrade --ignore-installed PyYAML\n",
+ "#!{sys.executable} -m pip install --upgrade pip\n",
+ "#!{sys.executable} -m pip install --upgrade gen3\n",
+ "#!{sys.executable} -m pip install pydicom\n",
+ "#!{sys.executable} -m pip install --upgrade Pillow\n",
+ "#!{sys.executable} -m pip install psmpy\n",
+ "#!{sys.executable} -m pip install python-gdcm --upgrade\n",
+ "#!{sys.executable} -m pip install IPython",
+ "#!{sys.executable} -m pip install pylibjpeg --upgrade"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0843c5b8-0379-4440-a651-ce417e06a701",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Import Python Packages and scripts\n",
+ "\n",
+ "import os, subprocess\n",
+ "import pandas as pd\n",
+ "#import numpy as np\n",
+ "#import pydicom\n",
+ "\n",
+ "# import some Gen3 packages\n",
+ "import gen3\n",
+ "from gen3.auth import Gen3Auth\n",
+ "from gen3.query import Gen3Query\n",
+ "from IPython.display import display",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7fce03c2-e84d-4211-b41d-f44717ae05b5",
+ "metadata": {},
+ "source": [
+ "### Initiate instances of the Gen3 SDK Classes using credentials file for authentication\n",
+ "---\n",
+ "Make sure the \"cred\" variable reflects the location of your credentials file."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "165dae3d-8c46-4c4f-9bb1-ffd2e0e8c963",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "auth = Gen3Auth(api, refresh_file=cred) # authentication class\n",
+ "query = Gen3Query(auth) # query class\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a329f502-d824-482b-a1a0-76a200ca6093",
+ "metadata": {},
+ "source": [
+ "## 2) Build Cohorts by Sending Queries to the MIDRC APIs\n",
+ "#### General notes on sending queries:\n",
+ "* There are many ways to query and access metadata for cohort building in MIDRC, but this notebook will focus on using the [Gen3](https://gen3.org) graphQL query service [\"guppy\"](https://github.com/uc-cdis/guppy/#readme). This is the backend query service that [MIDRC's data explorer GUI](https://data.midrc.org/explorer) uses. So, anything you can do in the explorer GUI, you can do with guppy queries, and more!\n",
+ "* The guppy graphQL service has more functionality than is demonstrated in this simple example. You can find extensive documentation in GitHub [here](https://github.com/uc-cdis/guppy/blob/master/doc/queries.md) in case you'd like to build your own queries from scratch.\n",
+ "* The Gen3 SDK (intialized as `query` above in this notebook) has Python wrapper scripts to make sending queries to the guppy graphQL API simpler. The guppy SDK package can be viewed in GitHub [here](https://github.com/uc-cdis/gen3sdk-python/blob/master/gen3/query.py).\n",
+ "* Guppy queries focus on a particular type of data (cases, imaging studies, files, etc.), which corresponds to the major tabs in [MIDRC's data explorer GUI](https://data.midrc.org/explorer).\n",
+ "* Queries include arguments that are akin to selecting filter values in [MIDRC's data explorer GUI](https://data.midrc.org/explorer).\n",
+ "* To see more documentation about how to use and combine filters with various operator logic (like AND/OR/IN, etc.) see [this page](https://github.com/uc-cdis/guppy/blob/master/doc/queries.md#filter).\n",
+ "\n",
+ "---\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "35a82d76-7de3-4fa6-b5a0-d4ec425484f5",
+ "metadata": {},
+ "source": [
+ "#### Set query parameters\n",
+ "---\n",
+ "* Here, we'll send a query to the `case` guppy index, which corresponds to the \"Cases\" tab of [MIDRC's data explorer GUI](https://data.midrc.org/explorer).\n",
+ "* The filters defined below can be modified to return different subsets of cases. Here, we'll select cases that have at least one Chest CT and at least one Chest X-ray (CXR).\n",
+ "* If our query request is successful, the API response should be in JSON format, and it should contain a list of patient IDs along with any other patient data we ask for.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d0e36c9a-4694-40f6-ab64-2170dd7266a5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "### Set some \"imaging_study\" query parameters\n",
+ "\n",
+ "## Imaging study modality filter: we want imaging studies with at least one CT and one CR or DX\n",
+ "modality_1 = [\"DX\", \"CR\"]\n",
+ "modality_2 = [\"CT\"]\n",
+ "\n",
+ "## Imaging study body part filter: here we select \"chest\" as the \"LOINC system\" filter, which is the body part examined\n",
+ "body_part_examined = \"Chest\"\n",
+ "\n",
+ "## The fields we want our query to return; \n",
+ "## Note: you can set fields to \"None\" to return all fields with the query in the cell below\n",
+ "fields = [\"project_id\",\n",
+ " \"submitter_id\",\n",
+ " \"imaging_studies.loinc_system\",\n",
+ " \"imaging_studies.study_uid\",\n",
+ " \"imaging_studies.study_modality\",\n",
+ " \"_imaging_studies_count\",\n",
+ " \"_cr_series_file_count\",\n",
+ " \"_dx_series_file_count\",\n",
+ " \"_ct_series_file_count\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b0245245-eba0-4614-bbcb-5fc4ede5de1a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Note: the \"fields\" option defines what fields we want the query to return. If set to \"None\", returns all available fields.\n",
+ "\n",
+ "cases = query.raw_data_download(\n",
+ " data_type=\"case\",\n",
+ " #fields=None,\n",
+ " fields=fields,\n",
+ " filter_object={\n",
+ " \"AND\": [\n",
+ " {\"nested\":{\"path\":\"imaging_studies\",\n",
+ " \"=\": {\"loinc_system\": body_part_examined}}},\n",
+ " {\"nested\":{\"path\":\"imaging_studies\",\n",
+ " \"IN\":{\"study_modality\":modality_1}}},\n",
+ " {\"nested\":{\"path\":\"imaging_studies\",\n",
+ " \"IN\":{\"study_modality\":modality_2}}}\n",
+ " ],\n",
+ " },\n",
+ " sort_fields=[{\"submitter_id\": \"asc\"}]\n",
+ " )\n",
+ "\n",
+ "if len(cases) > 0 and \"submitter_id\" in cases[0]:\n",
+ " case_ids = [i['submitter_id'] for i in cases] ## make a list of the imaging study IDs returned\n",
+ " print(\"Query returned {} cases with data for each that looks like this:\\n\\t\".format(len(cases)))\n",
+ " display(cases[0:1])\n",
+ "else:\n",
+ " print(\"Your query returned no data! Please, check that query parameters are valid.\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8a6b0d4c-b304-4e00-8477-1b860dc05767",
+ "metadata": {},
+ "source": [
+ "### Filter Query Results for only the desired imaging studies\n",
+ "---\n",
+ "Our query has returned all cases that have at least one imaging study of the Chest, and have at least one CXR and one CT. However, those cases may have imaging studies of other modalities or body parts we're not interested in. \n",
+ "\n",
+ "So, next we'll filter the query results to obtain only imaging studies that are both of the Chest and of modality CT, CR, or DX, thus excluding studies of other body parts or modalities."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ffa5c794-370f-47fe-bf64-008f6485b80f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Get only the imaging studies for Chest CT and Chest X-rays so we can build a file download manifest\n",
+ "desired_studies = {i['submitter_id']:[j for j in i['imaging_studies'] if (j['study_modality'][0] in modality_1+modality_2 and 'loinc_system' in j and 'Chest' in j['loinc_system'])] for i in cases}\n",
+ "list(desired_studies.items())[0]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "eab0bdd9-41b3-4a08-b3ed-3418d0b9d236",
+ "metadata": {},
+ "source": [
+ "\n",
+ "## 3) Send another query to get data file details for our cohort / case ID\n",
+ "---\n",
+ "Now that we have a list of imaging studies we're interested in from our original cohort of cases, we can run another query to get the `object_id` of each of the imaging series files related to those imaging studies. This is achieved by querying the `data_file` guppy index, which corresponds to the \"Data Files\" tab of the MIDRC data explorer GUID. \n",
+ "\n",
+ "All MIDRC data files, including both images and annotations, are listed in the guppy index `data_file`, which is queried in a similar manner to our query of the `imaging_study` index above. The query parameter `data_type` below determines which guppy (Elasticsearch) index we're querying.\n",
+ "\n",
+ "To get only `data_file` records that correspond to our imaging study cohort built previously, we'll use the list of study UIDs as a query filter. \n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "57bd0e57-9909-4f37-a65a-c43c31cb1137",
+ "metadata": {},
+ "source": [
+ "### Set 'data_file' query parameters\n",
+ "---\n",
+ "Here, we'll utilize the property `source_node` to filter the list of files for our cohort to only those matching the type of files we're interested in. In this example, we ask only for CR, DX, and CT images, which will exclude any other types of files related to our desired imaging studies like annotations or supplemental files.\n",
+ "\n",
+ "We're also using the property `study_uid` as a filter to restrict the `data_file` records returned down to those associated with the imaging studies in our cohort built above. \n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4b1736d5-ba41-4a59-a3d9-d10ee7142e8d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## We only want CR, DX, and CT imaging series files, so we can use the \"source_node\" to filter out other types of data files\n",
+ "source_nodes = ['cr_series_file', 'dx_series_file', 'ct_series_file']\n",
+ "\n",
+ "# Build a list of study UIDs to use as a filter in our data_file query\n",
+ "all_study_uids = []\n",
+ "for case_id in desired_studies:\n",
+ " studies = desired_studies[case_id]\n",
+ " study_uids = [i['study_uid'] for i in studies]\n",
+ " all_study_uids += study_uids\n",
+ "\n",
+ "display(len(list(set(all_study_uids))))\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4acbeaf2-3e05-4909-8675-fb87084cfbc0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Search for specific files associated with our cohort by adding \"study_uid\" as a filter\n",
+ "# * Note: \"fields\" is set to \"None\" in this query, which by default returns all the properties available\n",
+ "data_files = query.raw_data_download(\n",
+ " data_type=\"data_file\",\n",
+ " fields=None,\n",
+ " filter_object={\n",
+ " \"AND\": [\n",
+ " {\"IN\": {\"study_uid\": all_study_uids}},\n",
+ " {\"IN\": {\"source_node\": source_nodes}},\n",
+ " ]\n",
+ " },\n",
+ " sort_fields=[{\"submitter_id\": \"asc\"}]\n",
+ " )\n",
+ "\n",
+ "if len(data_files) > 0:\n",
+ " object_ids = [i['object_id'] for i in data_files if 'object_id' in i] ## make a list of the file object_ids returned by our query\n",
+ " print(\"Query returned {} data files with {} object_ids.\".format(len(data_files),len(object_ids)))\n",
+ " print(\"Data is a list with rows like this:\\n\\t {}\".format(data_files[0:1]))\n",
+ "else:\n",
+ " print(\"Your query returned no data! Please, check that query parameters are valid.\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d469fcf7-262c-492a-9ec8-18560e16b5dc",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4507caee-bbb7-49b4-9697-9ccb33909869",
+ "metadata": {},
+ "source": [
+ "## 4) Access data files using their object_id / data GUID (globally unique identifiers)\n",
+ "---\n",
+ "In order to download files stored in MIDRC, users need to reference the file's object_id (AKA data GUID or Globally Unique IDentifier).\n",
+ "\n",
+ "Once we have a list of GUIDs we want to download, we can use either the gen3-client or the gen3 SDK to download the files. You can also access individual files in your browser after logging-in and entering the GUID after the `files/` endpoint, as in this URL: https://data.midrc.org/files/GUID\n",
+ "\n",
+ "where GUID is the actual GUID, e.g.: https://data.midrc.org/files/dg.MD1R/b87d0db3-d95a-43c7-ace1-ab2c130e04ec\n",
+ "\n",
+ "For instructions on how to install and use the gen3-client, please see [the MIDRC quick-start guide](https://data.midrc.org/dashboard/Public/documentation/Gen3_MIDRC_GetStarted.pdf), which can be found linked here and in the MIDRC data portal header as \"Get Started\".\n",
+ "\n",
+ "Below we use the gen3 SDK command `gen3 drs-pull object` which is [documented in detail here](https://github.com/uc-cdis/gen3sdk-python/blob/master/docs/howto/drsDownloading.md)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "15099fd9-cbb1-4dc6-a2e5-dd9d6e0522a6",
+ "metadata": {},
+ "source": [
+ "### Use the Gen3 SDK command `gen3 drs-pull object` to download an individual file"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bb91c943-ae41-4d82-977b-dbdd4f31cf21",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Make a new directory for downloaded files\n",
+ "#os.system(\"rm -r downloads\")\n",
+ "os.system(\"mkdir -p downloads\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "29c5e4c4-828d-47b5-97d9-d482993202d0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## We can use a simple loop to download all files and keep track of successes and failures\n",
+ "\n",
+ "success,failure,other=[],[],[]\n",
+ "count,total = 0,len(object_ids)\n",
+ "for object_id in object_ids:\n",
+ " count+=1\n",
+ " cmd = \"gen3 --auth {} --endpoint data.midrc.org drs-pull object {} --output-dir downloads\".format(cred,object_id)\n",
+ " stout = subprocess.run(cmd, shell=True, capture_output=True)\n",
+ " print(\"Progress ({}/{}): {}\".format(count,total,stout.stdout))\n",
+ " if \"failed\" in str(stout.stdout):\n",
+ " failure.append(object_id)\n",
+ " elif \"successfully\" in str(stout.stdout):\n",
+ " success.append(object_id)\n",
+ " else:\n",
+ " other.append(object_id)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bdb2b636-de31-4064-9fa7-d74f4bd1ca2e",
+ "metadata": {},
+ "source": [
+ "### Export a Gen3 file download \"manifest\"\n",
+ "---\n",
+ "The following script generates a Gen3-style data file download manifest JSON file. \n",
+ "\n",
+ "This `manifest.json` file can be used In case you want to use the gen3-client command-line tool or the `gen3 drs-pull manifest` command shown below.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ade7f10e-890d-4c03-a0aa-28dcb4ff1966",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Export a Gen3 file download \"manifest\" JSON file to use with the gen3-client command-line tool or the `gen3 drs-pull manifest` command.\n",
+ "\n",
+ "def write_manifest(guids, filename):\n",
+ "\n",
+ " with open(filename, \"w\") as mani:\n",
+ "\n",
+ " mani.write(\"[\\n {\\n\")\n",
+ "\n",
+ " count = 0\n",
+ " for guid in guids:\n",
+ " count += 1\n",
+ " file_line = ' \"object_id\": \"{}\"\\n'.format(guid)\n",
+ " mani.write(file_line)\n",
+ " if count == len(guids):\n",
+ " mani.write(\" }]\")\n",
+ " else:\n",
+ " mani.write(\" },\\n {\\n\")\n",
+ "\n",
+ " print(\"\\tDone ({}/{}).\".format(count, len(guids)))\n",
+ " print(\"\\tManifest written to file: {}\".format(filename))\n",
+ " return filename\n",
+ "\n",
+ "manifest_filename = \"multimodal_cases_files_manifest.json\"\n",
+ "write_manifest(guids=object_ids,filename=manifest_filename)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "19024e44-5aa4-49c5-9100-8275af278ad9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ll"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "48edd42a-22d6-4798-8300-c5bf396b28a7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cmd = \"gen3 --auth {} --endpoint data.midrc.org drs-pull manifest {}\".format(cred,manifest_filename)\n",
+ "print(cmd)\n",
+ "#stout = subprocess.run(cmd, shell=True, capture_output=True)\n",
+ "# This command is better run in the terminal so you can watch progress bar. Running in the notebook may take quite some time."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "544761e9",
+ "metadata": {},
+ "source": [
+ "## The End\n",
+ "---\n",
+ "If you have any questions related to this notebook don't hesitate to reach out to the MIDRC Helpdesk at midrc-support@datacommons.io or the author directly at cgmeyer@uchicago.edu\n",
+ "\n",
+ "Happy data wrangling!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ef0d9146-12ae-4318-8dbf-4410910763fc",
+ "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": "3.13.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/jupyter-midrc/combined_demos/Chest_CT_Images_for_Cohort.ipynb b/jupyter-midrc/combined_demos/Chest_CT_Images_for_Cohort.ipynb
new file mode 100644
index 00000000..bca086ff
--- /dev/null
+++ b/jupyter-midrc/combined_demos/Chest_CT_Images_for_Cohort.ipynb
@@ -0,0 +1,434 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "d8ef63c3",
+ "metadata": {},
+ "source": [
+ "# How to Build a Patient Cohort and Access All Chest CT Images\n",
+ "---\n",
+ "by Chris Meyer, PhD\n",
+ "\n",
+ "Manager of Data and User Services at the Center for Translational Data Science at University of Chicago\n",
+ "\n",
+ "August 2023\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5db7f87c",
+ "metadata": {},
+ "source": [
+ "## Introduction\n",
+ "---\n",
+ "* This notebook demonstrates how to build a cohort of MIDRC patients based on clinical and demographic data and then access all Chest CT scans and any related annotations.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5b18d84f",
+ "metadata": {},
+ "source": [
+ "### Set local variables\n",
+ "---\n",
+ "Change the following directory paths to a valid working directories where you're running this notebook."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5a5e19b5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cred = \"/Users/christopher/Downloads/midrc-credentials.json\" # location of your MIDRC credentials, downloaded from https://data.midrc.org/identity by clicking \"Create API key\" button and saving the credentials.json locally\n",
+ "api = \"https://data.midrc.org\" # The base URL of the data commons being queried. This shouldn't change for MIDRC.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6aaf5d3e",
+ "metadata": {},
+ "source": [
+ "### Install / Import Python Packages and Scripts"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8e1a7935",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## The packages below may be necessary for users to install according to the imports necessary in the subsequent cells.\n",
+ "\n",
+ "#!pip install --upgrade pandas\n",
+ "#!pip install --upgrade --ignore-installed PyYAML\n",
+ "#!pip install --upgrade pip\n",
+ "#!pip install --upgrade gen3\n",
+ "#!pip install pydicom"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7ea2fa09",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Import Python Packages and scripts\n",
+ "import sys, os, subprocess\n",
+ "import gen3\n",
+ "\n",
+ "from gen3.auth import Gen3Auth # authentication SDK class\n",
+ "from gen3.query import Gen3Query # query SDK class\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b1784bcb",
+ "metadata": {},
+ "source": [
+ "### Initiate instances of the Gen3 SDK Classes using credentials file for authentication\n",
+ "---\n",
+ "Again, make sure the \"cred\" directory path variable reflects the location of your credentials file (path variables set above)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d316dcdf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "auth = Gen3Auth(api, refresh_file=cred) # authentication class\n",
+ "query = Gen3Query(auth) # query class\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "85fc475d",
+ "metadata": {},
+ "source": [
+ "## Build a Cohort of Cases by Running Queries Against MIDRC APIs\n",
+ "---\n",
+ "* There are many ways to query and access metadata for cohort building in MIDRC, but this notebook will focus on using the [Gen3](https://gen3.org) graphQL query service [\"guppy\"](https://github.com/uc-cdis/guppy/#readme). This is the backend query service that [MIDRC's data explorer GUI](https://data.midrc.org/explorer) uses. So, anything you can do in the explorer GUI, you can do with guppy queries, and more!\n",
+ "* The guppy graphQL service has extensive documentation in GitHub [here](https://github.com/uc-cdis/guppy/blob/master/doc/queries.md) in case you'd like to build your own queries from scratch.\n",
+ "* The Gen3Query SDK class (intialized as the variable \"query\" above in this notebook) has Python wrapper scripts to make sending queries to the guppy graphQL API simpler. The guppy SDK package can be viewed in GitHub [here](https://github.com/uc-cdis/gen3sdk-python/blob/master/gen3/query.py).\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f3045ba4",
+ "metadata": {},
+ "source": [
+ "### Query Parameters\n",
+ "---\n",
+ "* Below, we first set some query parameters. Feel free to modify these parameters to see how it changes the query response. Setting these patient attributes is akin to selecting a filter value in [MIDRC's data explorer GUI](https://data.midrc.org/explorer). \n",
+ " * To see more documentation about to use and combine filters with various operator logic (like AND/OR/IN, etc.) see [this page](https://github.com/uc-cdis/guppy/blob/master/doc/queries.md#filter).\n",
+ "* We then send our query to MIDRC's guppy API endpoint using [the Gen3Query SDK package](https://github.com/uc-cdis/gen3sdk-python/blob/master/gen3/query.py) we initialized earlier. \n",
+ "* If our query request is successful, the API response should be in JSON format, and it should contain a list of patient IDs along with any other patient data we ask for."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "86e01ee4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## \"case\" query parameters\n",
+ "## In this example, we're going to filter our patient cohort by asking for:\n",
+ "# female Asian patients in an age range that tested positive for COVID-19.\n",
+ "\n",
+ "# demographic attributes / filters\n",
+ "race = \"Asian\"\n",
+ "sex = \"Female\"\n",
+ "min_age = 79\n",
+ "max_age = 89\n",
+ "\n",
+ "# clinical attributes / filters\n",
+ "covid19_positive = \"True\"\n",
+ "\n",
+ "# fields to return. \n",
+ "fields = [\"submitter_id\", # \"submitter_id\" here is the case/patient's unique identifier in the database\n",
+ " \"project_id\" # this is the \"project\" that the patient belongs to. by default, queries run across all projects\n",
+ "]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8f9cd402",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Run the query using the guppy graphQL service\n",
+ "\n",
+ "data = query.raw_data_download(\n",
+ " data_type=\"case\",\n",
+ " fields=fields,\n",
+ " filter_object={\n",
+ " \"AND\": [\n",
+ " {\"=\": {\"race\": race}},\n",
+ " {\"=\": {\"sex\": sex}},\n",
+ " {\">=\": {\"age_at_index\": min_age}},\n",
+ " {\"<=\": {\"age_at_index\": max_age}},\n",
+ " ]\n",
+ " },\n",
+ " sort_fields=[{\"submitter_id\": \"asc\"}]\n",
+ " )\n",
+ "\n",
+ "if len(data) > 0 and \"submitter_id\" in data[0]:\n",
+ " case_ids = [i['submitter_id'] for i in data] ## make a list of the case (patient) IDs returned\n",
+ " print(\"Query returned {} case IDs.\".format(len(data)))\n",
+ " print(\"Data is a list with rows like this:\\n\\t {}\".format(data[0:1]))\n",
+ "else:\n",
+ " print(\"Your query returned no data! Please, check that query parameters are valid.\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c417262b",
+ "metadata": {},
+ "source": [
+ "### Send another query to get data files\n",
+ "---\n",
+ "All MIDRC data files that can be downloaded, including both images and annotations, are listed in the guppy index \"data_file\", which can be queried similar to our query of the \"case\" index above.\n",
+ "\n",
+ "* Note: We're going to use the property \"case_ids\" as a filter to restrict the data_file records returned down to those associated with cases in our cohort built above.\n",
+ "```\n",
+ " {\"IN\": {\"case_ids\": case_ids}},\n",
+ "```\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d025f1f4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## \"data_file\" query parameters\n",
+ "## In this example, we're asking for files from CT imaging studies of the chest\n",
+ "\n",
+ "# imaging_study attributes / filters\n",
+ "source_node = \"ct_series_file\" # this will limit the files returned to those that are CT series\n",
+ "loinc_system = \"Chest\" # this is the LOINC-harmonized \"body part examined\" in the imaging study\n",
+ "\n",
+ "# fields to return. \n",
+ "fields = [\n",
+ " \"project_id\", # this is the \"project\" that the file belongs to. by default, queries run across all projects\n",
+ " \"case_ids\", # this is the \"submitter_id\" of the patient the file is associated with (the patient ID)\n",
+ " \"object_id\", # this is the unique identifier (GUID) for a file in MIDRC which can be used to access/download the file\n",
+ " \"source_node\", # this is the name of the node in the MIDRC data model under which the file is stored\n",
+ " \"file_name\",\n",
+ " \"file_size\"\n",
+ "]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f061bd40",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# note that the field \"data_type\" here has changed from \"case\" (example above) to \"data_file\". This is the name of the Elasticsearch index\n",
+ "data = query.raw_data_download(\n",
+ " data_type=\"data_file\",\n",
+ " fields=fields,\n",
+ " filter_object={\n",
+ " \"AND\": [\n",
+ " {\"=\": {\"source_node\": source_node}},\n",
+ " {\"=\": {\"loinc_system\": loinc_system}},\n",
+ " {\"IN\": {\"case_ids\": case_ids}},\n",
+ " ]\n",
+ " },\n",
+ " sort_fields=[{\"submitter_id\": \"asc\"}]\n",
+ " )\n",
+ "\n",
+ "if len(data) > 0 and \"object_id\" in data[0]:\n",
+ " object_ids = [i['object_id'] for i in data] ## make a list of the file object_ids returned by our query\n",
+ " print(\"Query returned {} data files with {} object_ids.\".format(len(data),len(object_ids)))\n",
+ " print(\"Data is a list with rows like this:\\n\\t {}\".format(data[0:1]))\n",
+ "else:\n",
+ " print(\"Your query returned no data! Please, check that query parameters are valid.\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "14db3f95-25c6-4772-a5c8-fe443687c5c9",
+ "metadata": {},
+ "source": [
+ "### In this next example, we want both CT scans *and* any associated annotation files in our object_id list\n",
+ "---\n",
+ "To add other types of files to the query, we simply make `source_nodes` a list of node IDs.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "56f5bba4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "source_nodes = [\"ct_series_file\", \"annotation_file\",\"dicom_annotation_file\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "62a7d14d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# note that the field \"data_type\" here has changed from \"case\" (example above) to \"data_file\". This is the name of the Elasticsearch index\n",
+ "data = query.raw_data_download(\n",
+ " data_type=\"data_file\",\n",
+ " fields=fields,\n",
+ " filter_object={\n",
+ " \"AND\": [\n",
+ " {\"in\": {\"source_node\": source_nodes}},\n",
+ " {\"=\": {\"loinc_system\": loinc_system}},\n",
+ " {\"IN\": {\"case_ids\": case_ids}},\n",
+ " ]\n",
+ " },\n",
+ " sort_fields=[{\"submitter_id\": \"asc\"}]\n",
+ " )\n",
+ "\n",
+ "if len(data) > 0:\n",
+ " object_ids = [i['object_id'] for i in data if 'object_id' in i] ## make a list of the file object_ids returned by our query\n",
+ " print(\"Query returned {} data files with {} object_ids.\".format(len(data),len(object_ids)))\n",
+ " print(\"Data is a list with rows like this:\\n\\t {}\".format(data[0:1]))\n",
+ "else:\n",
+ " print(\"Your query returned no data! Please, check that query parameters are valid.\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4061b81b-083c-46bb-b7ba-adda95a3b61b",
+ "metadata": {},
+ "source": [
+ "## 4) Access data files using their object_id / data GUID (globally unique identifiers)\n",
+ "---\n",
+ "In order to download files stored in MIDRC, users need to reference the file's object_id (AKA data GUID or Globally Unique IDentifier).\n",
+ "\n",
+ "Once we have a list of GUIDs we want to download, we can use the gen3 SDK to download the files.\n",
+ "\n",
+ "Below we use the gen3 SDK command `gen3 drs-pull object` which is [documented in detail here](https://github.com/uc-cdis/gen3sdk-python/blob/master/docs/howto/drsDownloading.md)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5d6bfc84",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Make a new directory for downloaded files\n",
+ "os.system(\"mkdir -p downloads\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ccafa103",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Run the \"gen3 drs-pull object\" command to download one of the files\n",
+ "object_id = object_ids[0]\n",
+ "cmd = \"gen3 --auth {} --endpoint data.midrc.org drs-pull object {} --output-dir downloads\".format(cred,object_id)\n",
+ "os.system(cmd)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b3fbabc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ls -l downloads"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "aede82ab-b49c-432d-96ce-fc5c624f4b26",
+ "metadata": {},
+ "source": [
+ "### To download all the files, use a simple loop over the object_ids in our list\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ab2ad0f2-a294-47a7-ab68-62472d8b0195",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "## Simple loop to download all files and keep track of success and failures\n",
+ "success,failure,other=[],[],[]\n",
+ "count,total = 0,len(object_ids)\n",
+ "for object_id in object_ids:\n",
+ " count+=1\n",
+ " cmd = \"gen3 --auth {} --endpoint data.midrc.org drs-pull object {} --output-dir downloads\".format(cred,object_id)\n",
+ " stout = subprocess.run(cmd, shell=True, capture_output=True)\n",
+ " print(\"Progress ({}/{}): {}\".format(count,total,stout.stdout))\n",
+ " if \"failed\" in str(stout.stdout):\n",
+ " failure.append(object_id)\n",
+ " elif \"successfully\" in str(stout.stdout):\n",
+ " success.append(object_id)\n",
+ " else:\n",
+ " other.append(object_id)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c2fc0609-f73b-41c6-a3df-bcf81f428711",
+ "metadata": {},
+ "source": [
+ "## The End\n",
+ "---\n",
+ "If you have any questions related to this notebook don't hesitate to reach out to the MIDRC Helpdesk at midrc-support@datacommons.io or the author directly at cgmeyer@uchicago.edu\n",
+ "\n",
+ "Happy data wrangling!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fb0611c1-4c91-4e28-a9f1-ff54e4ac741e",
+ "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": "3.13.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/jupyter-midrc/combined_demos/Cohort_Building_Using_LOINC_Terms.ipynb b/jupyter-midrc/combined_demos/Cohort_Building_Using_LOINC_Terms.ipynb
new file mode 100644
index 00000000..910e0a5f
--- /dev/null
+++ b/jupyter-midrc/combined_demos/Cohort_Building_Using_LOINC_Terms.ipynb
@@ -0,0 +1,639 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "d8ef63c3",
+ "metadata": {},
+ "source": [
+ "# Cohort Building Using LOINC Terms in the MIDRC Data Commons\n",
+ "---\n",
+ "This notebook briefly demonstrates how to use the MIDRC open APIs to build a cohort of MIDRC imaging studies using LOINC properties derived from [MIDRC's LOINC Harmonization process](https://github.com/MIDRC/midrc_dicom_harmonization) using the [LOINC Playbook](https://loinc.org/search/?t=1&s=playbook).\n",
+ "\n",
+ "All cohort selection possible in the [MIDRC data explorer UI](https://data.midrc.org/explorer) can also be achieved programmatically using API requests. In this notebook, we'll select a small cohort of imaging studies based on LOINC properties.\n",
+ "\n",
+ "by Chris Meyer, PhD\n",
+ "\n",
+ "Director of Data Services and Scientific Support at the Center for Translational Data Science at University of Chicago\n",
+ "\n",
+ "Presented at the 2024 LOINC Conference on September 20, 2024.\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5db7f87c",
+ "metadata": {},
+ "source": [
+ "## 1) Set up Python environment\n",
+ "---\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ffc85bb2-3ed6-4eef-a943-724eef02c41b",
+ "metadata": {},
+ "source": [
+ "### Download an API key file containing your credentials\n",
+ "---\n",
+ "1) Navigate to the MIDRC data portal in your browser: https://data.midrc.org.\n",
+ "2) Read and accept the DUA (if you haven't already).\n",
+ "3) Navigate to the user profile page: https://data.midrc.org/identity\n",
+ "4) Click on the button \"Create API Key\" and save the `credentials.json` file somewhere safe\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1bffd5d4",
+ "metadata": {},
+ "source": [
+ "### Set local variables\n",
+ "---\n",
+ "Change the following `cred` variable path to point to your credentials file downloaded from the MIDRC data portal following the instructions above."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c3c59c2c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cred = \"/Users/christopher/Downloads/midrc-credentials.json\" # location of your MIDRC credentials, downloaded from https://data.midrc.org/identity by clicking \"Create API key\" button and saving the credentials.json locally\n",
+ "api = \"https://data.midrc.org\" # The base URL of the data commons being queried. This shouldn't change for MIDRC.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7962e54c",
+ "metadata": {},
+ "source": [
+ "### Install / Import Python Packages and Scripts"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8e1a7935",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## The packages below may be necessary for users to install according to the imports necessary in the subsequent cells.\n",
+ "\n",
+ "import sys\n",
+ "#!{sys.executable} -m pip install\n",
+ "#!{sys.executable} -m pip install --upgrade pandas\n",
+ "#!{sys.executable} -m pip install --upgrade --ignore-installed PyYAML\n",
+ "#!{sys.executable} -m pip install --upgrade pip\n",
+ "#!{sys.executable} -m pip install --upgrade gen3\n",
+ "#!{sys.executable} -m pip install pydicom\n",
+ "#!{sys.executable} -m pip install --upgrade Pillow\n",
+ "#!{sys.executable} -m pip install psmpy\n",
+ "#!{sys.executable} -m pip install python-gdcm --upgrade\n",
+ "#!{sys.executable} -m pip install pylibjpeg --upgrade"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7ea2fa09",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Import Python Packages and scripts\n",
+ "\n",
+ "import os, subprocess\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import pydicom\n",
+ "from PIL import Image\n",
+ "import glob\n",
+ "#import gdcm\n",
+ "#import pylibjpeg\n",
+ "\n",
+ "# import some Gen3 packages\n",
+ "import gen3\n",
+ "from gen3.auth import Gen3Auth\n",
+ "from gen3.query import Gen3Query\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7784ecc9",
+ "metadata": {},
+ "source": [
+ "### Initiate instances of the Gen3 SDK Classes using credentials file for authentication\n",
+ "---\n",
+ "Again, make sure the \"cred\" directory path variable reflects the location of your credentials file (path variables set above)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d316dcdf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "auth = Gen3Auth(api, refresh_file=cred) # authentication class\n",
+ "query = Gen3Query(auth) # query class\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2ea96784-3370-46a6-a2c7-edda947bfa8f",
+ "metadata": {},
+ "source": [
+ "## 2) Build Cohorts by Sending Queries to the MIDRC Search APIs\n",
+ "#### General notes on sending queries:\n",
+ "* There are many ways to query and access metadata for cohort building in MIDRC, but this notebook will focus on using the [Gen3](https://gen3.org) graphQL query service [\"guppy\"](https://github.com/uc-cdis/guppy/#readme). This is the backend query service that [MIDRC's data explorer GUI](https://data.midrc.org/explorer) uses. So, anything you can do in the explorer GUI, you can do with guppy queries, and more!\n",
+ "* The guppy graphQL service has more functionality than is demonstrated in this simple example. You can find extensive documentation in GitHub [here](https://github.com/uc-cdis/guppy/blob/master/doc/queries.md) in case you'd like to build your own queries from scratch.\n",
+ "* The Gen3 SDK (intialized as `query` above in this notebook) has Python wrapper scripts to make sending queries to the guppy graphQL API simpler. The guppy SDK package can be viewed in GitHub [here](https://github.com/uc-cdis/gen3sdk-python/blob/master/gen3/query.py).\n",
+ "* Guppy queries focus on a particular type of data (cases, imaging studies, files, etc.), which corresponds to the major tabs in [MIDRC's data explorer GUI](https://data.midrc.org/explorer).\n",
+ "* Queries include arguments that are akin to selecting filter values in [MIDRC's data explorer GUI](https://data.midrc.org/explorer).\n",
+ "* To see more documentation about how to use and combine filters with various operator logic (like AND/OR/IN, etc.) see [this page](https://github.com/uc-cdis/guppy/blob/master/doc/queries.md#filter).\n",
+ "\n",
+ "---\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3e4a624e-f769-46a9-b76c-2efe9713bf61",
+ "metadata": {},
+ "source": [
+ "#### Set query parameters\n",
+ "---\n",
+ "* Here, we'll send a query to the `imaging_study` guppy index, which corresponds to the \"Imaging Studies\" tab of [MIDRC's data explorer GUI](https://data.midrc.org/explorer).\n",
+ "* The filters defined below can be modified to return different subsets of imaging studies. Here, we'll use a combination of LOINC method (Modality), system (body part), and long common name (descrition) to narrow our selected imaging studies to show the diversity of study descriptions for a single loinc code.\n",
+ "* If our query request is successful, the API response should be in JSON format, and it should contain a list of patient IDs along with any other patient data we ask for."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9bc4a36e-0647-4546-9777-6d0ad7b32750",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "### Set some \"imaging_study\" query parameters to select Chest X-rays (CXR) imaging studies in MIDRC\n",
+ "\n",
+ "## Here we select imaging studies with a LOINC System of \"Chest\", which is the harmonized BodyPartExamined\n",
+ "loinc_system = \"Chest\"\n",
+ "\n",
+ "## Here we select imaging studies with a LOINC Method of \"XR\", which is the harmonized Modality\n",
+ "loinc_method = \"CT\"\n",
+ "loinc_method = \"XR\"\n",
+ "\n",
+ "## Here we select imaging studies with a LOINC Long Common Name of \"\", which is the harmonized StudyDescription\n",
+ "loinc_long_common_name = \"CT Chest W contrast IV\"\n",
+ "loinc_long_common_name = \"XR Chest Single view\"\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8910b3e4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Note: the \"fields\" option defines what fields we want the query to return. If set to \"None\", returns all available fields.\n",
+ "\n",
+ "imaging_studies = query.raw_data_download(\n",
+ " data_type=\"imaging_study\",\n",
+ " fields=None,\n",
+ " filter_object={\n",
+ " \"AND\": [\n",
+ " {\"=\": {\"loinc_method\": loinc_method}},\n",
+ " {\"=\": {\"loinc_system\": loinc_system}},\n",
+ " {\"=\": {\"loinc_long_common_name\": loinc_long_common_name}},\n",
+ " ]\n",
+ " },\n",
+ " sort_fields=[{\"submitter_id\": \"asc\"}]\n",
+ " )\n",
+ "\n",
+ "if len(imaging_studies) > 0 and \"submitter_id\" in imaging_studies[0]:\n",
+ " imaging_studies_ids = [i['submitter_id'] for i in imaging_studies] ## make a list of the imaging study IDs returned\n",
+ " case_count = len(list(set([i['case_ids'][0] for i in imaging_studies])))\n",
+ " print(\"Query returned {} imaging studies for {} cases.\".format(len(imaging_studies),case_count))\n",
+ " print(\"Data is a list with rows like this:\\n\\t {}\".format(imaging_studies[0:1]))\n",
+ "else:\n",
+ " print(\"Your query returned no data! Please, check that query parameters are valid.\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "33093eff-621f-452b-a0af-89af1940c65f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "imaging_studies_df = pd.DataFrame(imaging_studies)\n",
+ "display(imaging_studies_df)\n",
+ "\n",
+ "## Look at diversity of original DICOM Imaging Study Descriptions\n",
+ "print(\"For these LOINC Long Common names: {} \\nThere are these {} study descriptions:\".format(list(set(imaging_studies_df['loinc_long_common_name'])),len(list(set(imaging_studies_df['study_description'])))))\n",
+ "list(set(imaging_studies_df['study_description']))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0df3cf2b-f1e7-4139-a86c-f1de2e91c105",
+ "metadata": {},
+ "source": [
+ "## Add some patient demographics to our query in order to narrow down the selection\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0489f3d7-530e-4ea8-8e75-469f2c6ac60c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## LOINC terms\n",
+ "loinc_system = \"Chest\"\n",
+ "loinc_method = \"XR\"\n",
+ "loinc_long_common_name = \"XR Chest Single view\"\n",
+ "\n",
+ "## Case filters: we will select Hispanic males 70 years of age and older\n",
+ "ethnicity = \"Hispanic or Latino\"\n",
+ "race = [\"Asian\",\"Black or African American\"]\n",
+ "sex = \"Male\"\n",
+ "age_threshold = 70"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "54e9af2e-0809-4529-8df5-9d979c080467",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Note: the \"fields\" option defines what fields we want the query to return. If set to \"None\", returns all available fields.\n",
+ "\n",
+ "imaging_studies = query.raw_data_download(\n",
+ " data_type=\"imaging_study\",\n",
+ " fields=None,\n",
+ " filter_object={\n",
+ " \"AND\": [\n",
+ " {\"=\": {\"loinc_method\": loinc_method}},\n",
+ " {\"=\": {\"loinc_system\": loinc_system}},\n",
+ " {\"=\": {\"loinc_long_common_name\": loinc_long_common_name}},\n",
+ " {\"=\": {\"sex\": sex}},\n",
+ " {\"=\": {\"ethnicity\": ethnicity}},\n",
+ " {\"IN\": {\"race\": race}},\n",
+ " {\">=\": {\"age_at_index\": age_threshold}},\n",
+ " ]\n",
+ " },\n",
+ " sort_fields=[{\"submitter_id\": \"asc\"}]\n",
+ " )\n",
+ "\n",
+ "if len(imaging_studies) > 0 and \"submitter_id\" in imaging_studies[0]:\n",
+ " imaging_studies_ids = [i['submitter_id'] for i in imaging_studies] ## make a list of the imaging study IDs returned\n",
+ " case_count = len(list(set([i['case_ids'][0] for i in imaging_studies])))\n",
+ " print(\"Query returned {} imaging studies for {} cases.\".format(len(imaging_studies),case_count))\n",
+ " print(\"Data is a list with rows like this:\\n\\t {}\".format(imaging_studies[0:1]))\n",
+ "else:\n",
+ " print(\"Your query returned no data! Please, check that query parameters are valid.\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "579056bd-efab-4a9c-a11d-f2458de99a89",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "imaging_studies_df = pd.DataFrame(imaging_studies)\n",
+ "display(imaging_studies_df)\n",
+ "print(\"For these LOINC Long Common names: {} \\nThere are these {} study descriptions: {}\".format(list(set(imaging_studies_df['loinc_long_common_name'])),len(list(set(imaging_studies_df['study_description']))),list(set(imaging_studies_df['study_description']))))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2c0f29ab-ab87-4fe5-859b-d5158c0fa3d7",
+ "metadata": {},
+ "source": [
+ "## 3) Send another query to get data file details for our cohort / case ID\n",
+ "---\n",
+ "The `object_id` field in each imaging study record above contains the file identifiers for all files associated with each imaging study, which could include files like third-party annotations. If we simply want to access all files associated with our list of cases, we can use those object_ids. \n",
+ "\n",
+ "However, in this example, we'll ask for specific types of files and get more detailed information about each of the files. This is achieved by querying the `data_file` guppy index, which corresponds to the \"Data Files\" tab of the MIDRC data explorer GUID. \n",
+ "\n",
+ "All MIDRC data files, including both images and annotations, are listed in the guppy index \"data_file\", which is queried in a similar manner to our query of the `imaging_study` index above. The query parameter `data_type` below determines which guppy (Elasticsearch) index we're querying.\n",
+ "\n",
+ "To get only `data_file` records that correspond to our imaging study cohort built previously, we'll use the list of study UIDs as a query filter. \n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2959c0ed-adde-4f34-8308-ca0f61c599cf",
+ "metadata": {},
+ "source": [
+ "### Set 'data_file' query parameters\n",
+ "---\n",
+ "Here, we'll utilize the property `source_node` to filter the list of files for our cohort to only those matching the type of files we're interested in. In this example, we ask only for CR and DX (x-ray) images, which will exclude any other types of files like annotations.\n",
+ "\n",
+ "We're also using the property `study_uid` as a filter to restrict the `data_file` records returned down to those associated with the imaging studies in our cohort built above. \n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "07f33bd1-7393-4e0a-902a-771783568280",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Build a list of study UIDs to use as a filter in our data_file query\n",
+ "study_uids = [i['study_uid'] for i in imaging_studies]\n",
+ "study_uids"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2efabbc8-c9cb-481a-9847-50659918b6d7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Choose the types of data we want using \"source_node\" as a filter\n",
+ "source_nodes = [\"cr_series_file\",\"dx_series_file\"]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d3a2b920-15d0-4399-ab2d-4faa2748a314",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Search for specific files associated with our cohort by adding \"study_uid\" as a filter\n",
+ "# * Note: \"fields\" is set to \"None\" in this query, which by default returns all the properties available\n",
+ "data_files = query.raw_data_download(\n",
+ " data_type=\"data_file\",\n",
+ " fields=None,\n",
+ " filter_object={\n",
+ " \"AND\": [\n",
+ " {\"IN\": {\"study_uid\": study_uids}},\n",
+ " {\"IN\": {\"source_node\": source_nodes}},\n",
+ " ]\n",
+ " },\n",
+ " sort_fields=[{\"submitter_id\": \"asc\"}]\n",
+ " )\n",
+ "\n",
+ "if len(data_files) > 0:\n",
+ " object_ids = [i['object_id'] for i in data_files if 'object_id' in i] ## make a list of the file object_ids returned by our query\n",
+ " print(\"Query returned {} data files with {} object_ids.\".format(len(data_files),len(object_ids)))\n",
+ " print(\"Data is a list with rows like this:\\n\\t {}\".format(data_files[0:1]))\n",
+ "else:\n",
+ " print(\"Your query returned no data! Please, check that query parameters are valid.\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "972b4805-61a3-4c22-8339-93878f201e46",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# object_id (AKA \"data GUID\") is a globally unique file identifier that points to an actual file object in cloud storage. We'll use the object_ids along with the gen3 command-line tool to download the files these object_ids point to.\n",
+ "object_ids\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "81e35d9b-e8ec-4fc0-9d3c-7485446c15f3",
+ "metadata": {},
+ "source": [
+ "## 4) Access data files using their object_id / data GUID (globally unique identifiers)\n",
+ "---\n",
+ "In order to download files stored in MIDRC, users need to reference the file's object_id (AKA data GUID or Globally Unique IDentifier).\n",
+ "\n",
+ "Once we have a list of GUIDs we want to download, we can use either the gen3-client or the gen3 SDK to download the files. You can also access individual files in your browser after logging-in and entering the GUID after the `files/` endpoint, as in this URL: https://data.midrc.org/files/GUID\n",
+ "\n",
+ "where GUID is the actual GUID, e.g.: https://data.midrc.org/files/dg.MD1R/b87d0db3-d95a-43c7-ace1-ab2c130e04ec\n",
+ "\n",
+ "For instructions on how to install and use the gen3-client, please see [the MIDRC quick-start guide](https://data.midrc.org/dashboard/Public/documentation/Gen3_MIDRC_GetStarted.pdf), which can be found linked here and in the MIDRC data portal header as \"Get Started\".\n",
+ "\n",
+ "Below we use the gen3 SDK command `gen3 drs-pull object` which is [documented in detail here](https://github.com/uc-cdis/gen3sdk-python/blob/master/docs/howto/drsDownloading.md)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8449f221-1d8d-4501-86af-111111fc7bf3",
+ "metadata": {},
+ "source": [
+ "### Use the Gen3 SDK command `gen3 drs-pull object` to download an individual file"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f3462219-202b-4d59-9a3d-14cbdecab77b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Make a new directory for downloaded files\n",
+ "os.system(\"rm -r downloads\")\n",
+ "os.system(\"mkdir -p downloads\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5b156930-805f-4562-aea7-62798b13e46b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## We can use a simple loop to download all files and keep track of successes and failures\n",
+ "\n",
+ "success,failure,other=[],[],[]\n",
+ "count,total = 0,len(object_ids)\n",
+ "for object_id in object_ids:\n",
+ " count+=1\n",
+ " cmd = \"gen3 --auth {} --endpoint data.midrc.org drs-pull object {} --output-dir downloads\".format(cred,object_id)\n",
+ " stout = subprocess.run(cmd, shell=True, capture_output=True)\n",
+ " print(\"Progress ({}/{}): {}\".format(count,total,stout.stdout))\n",
+ " if \"failed\" in str(stout.stdout):\n",
+ " failure.append(object_id)\n",
+ " elif \"successfully\" in str(stout.stdout):\n",
+ " success.append(object_id)\n",
+ " else:\n",
+ " other.append(object_id)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "cd0ebe63-380a-4606-8285-5736ec87ffee",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Get a list of all downloaded .dcm files\n",
+ "image_files = glob.glob(pathname='**/*.dcm',recursive=True,)\n",
+ "image_files"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d53c1364-aba9-461f-a0f9-e7730875a339",
+ "metadata": {},
+ "source": [
+ "### View the DICOM Images\n",
+ "---\n",
+ "Here we'll use the [Python package `pydicom`](https://pydicom.github.io/pydicom/stable/) to view the downloaded DICOM images. \n",
+ "\n",
+ "Note that some of the files may contain compressed pixel data that require other packages to view; so, for this demo we'll simply skip over those using the following loop."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "533ce308-3618-47b2-bae1-ad4681feab02",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "for image_file in image_files:\n",
+ " print(image_file)\n",
+ " ds = pydicom.dcmread(image_file)\n",
+ " try:\n",
+ " new_image = ds.pixel_array.astype(float)\n",
+ " scaled_image = (np.maximum(new_image, 0) / new_image.max()) * 255.0\n",
+ " scaled_image = np.uint8(scaled_image)\n",
+ " final_image = Image.fromarray(scaled_image)\n",
+ " print(type(final_image))\n",
+ " display(final_image)\n",
+ " except Exception as e:\n",
+ " print(\"Couldn't view {}: {}.\".format(image_file,e))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "60bb7027-7fb3-47cb-8b4b-2d084287fc20",
+ "metadata": {},
+ "source": [
+ "#### View the DICOM Headers\n",
+ "---\n",
+ "DICOM files have metadata elements embedded in the images. These can also be read and viewed using the `pydicom` package."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a1bbb4c1-9ab6-4260-904e-977836b57a01",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds = pydicom.dcmread(image_files[0],force=True)\n",
+ "display(ds)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4dec9bce-7f8f-48ee-abe1-6bc380d923c3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Access individual elements\n",
+ "display(ds.file_meta)\n",
+ "display(ds.ImageType)\n",
+ "display(ds[0x0008, 0x0016])\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ecceb434-83f2-45f1-b1f0-7e1c58fafde9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# View the dicom metadata for all files as a DataFrame\n",
+ "dfs = []\n",
+ "for image_file in image_files:\n",
+ " ds = pydicom.dcmread(image_file)\n",
+ " df = pd.DataFrame(ds.values())\n",
+ " df[0] = df[0].apply(lambda x: pydicom.dataelem.DataElement_from_raw(x) if isinstance(x, pydicom.dataelem.RawDataElement) else x)\n",
+ " df['name'] = df[0].apply(lambda x: x.name)\n",
+ " df['value'] = df[0].apply(lambda x: x.value)\n",
+ " df = df[['name', 'value']]\n",
+ " df = df.set_index('name').T.reset_index(drop=True)\n",
+ " df['filename'] = image_file\n",
+ " df.drop(columns=['Pixel Data'],inplace=True) # drop the pixel data as it's too large and nonsensical to store in a DataFrame\n",
+ " dfs.append(df)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3e55cd95-570b-42d0-80cd-fb47693c49dd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Make a master dataframe for all images using only headers in all dataframes\n",
+ "headers = list(set.intersection(*map(set,dfs)))\n",
+ "df = pd.concat([df[headers] for df in dfs])\n",
+ "df.set_index('filename',inplace=True)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c54f11af-4b8c-4744-bc88-6cd2ced7e102",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "display(df)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5b991196-5298-43b6-987a-90de9bf308e5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Export the file metadata as a TSV file\n",
+ "filename = \"MIDRC_DICOM_metadata.tsv\"\n",
+ "df.to_csv(filename, sep='\\t')\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "544761e9",
+ "metadata": {},
+ "source": [
+ "## The End\n",
+ "---\n",
+ "If you have any questions related to this notebook don't hesitate to reach out to the MIDRC Helpdesk at midrc-support@gen3.org or the author directly at cgmeyer@uchicago.edu\n",
+ "\n",
+ "Happy data wrangling!"
+ ]
+ }
+ ],
+ "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": "3.13.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/jupyter-midrc/combined_demos/Cohort_Selection_Using_MIDRC_Temporal_COVID_Test_Data.ipynb b/jupyter-midrc/combined_demos/Cohort_Selection_Using_MIDRC_Temporal_COVID_Test_Data.ipynb
new file mode 100644
index 00000000..2cce0c7d
--- /dev/null
+++ b/jupyter-midrc/combined_demos/Cohort_Selection_Using_MIDRC_Temporal_COVID_Test_Data.ipynb
@@ -0,0 +1,648 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Cohort Selection Using MIDRC Temporal COVID Test Data\n",
+ "---\n",
+ "by Chris Meyer, PhD\n",
+ "\n",
+ "Manager of Data and User Services at the Center for Translational Data Science at the University of Chicago\n",
+ "\n",
+ "August 2022\n",
+ "\n",
+ "---\n",
+ "This Jupyter notebook tutorial demonstrates how to use the MIDRC data commons' APIs to access imaging study and COVID-19 test data, how to use temporal properties in those data to select a cohort of COVID-19 positive imaging studies, and how to access those image files."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Python packages:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "# The packages below may be necessary for users to install according to the imports necessary in the subsequent cells.\n",
+ "\n",
+ "#!pip install --upgrade pandas\n",
+ "#!pip install --upgrade --ignore-installed PyYAML\n",
+ "#!pip install --upgrade pip\n",
+ "#!pip install --upgrade gen3 --user --upgrade\n",
+ "#!pip install cdiserrors\n",
+ "#!pip install --upgrade pydicom"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Import Python Packages and scripts"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Import Python Packages and scripts\n",
+ "import pandas as pd\n",
+ "import sys, os, webbrowser\n",
+ "import gen3\n",
+ "import pydicom\n",
+ "import subprocess\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "from gen3.submission import Gen3Submission\n",
+ "from gen3.auth import Gen3Auth\n",
+ "from gen3.index import Gen3Index\n",
+ "from expansion import Gen3Expansion\n",
+ "from IPython.display import display\n",
+ "from gen3.query import Gen3Query"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Import some custom Python scripts from personal GitHub repo\n",
+ "# change these directory paths to reflect your local working directory\n",
+ "\n",
+ "home_dir = \"/Users/christopher\" \n",
+ "demo_dir = \"{}/Documents/Notes/MIDRC/tutorials\".format(home_dir)\n",
+ "\n",
+ "os.chdir(demo_dir)\n",
+ "\n",
+ "os.system(\"wget https://raw.githubusercontent.com/cgmeyer/gen3sdk-python/master/expansion/expansion.py -O {}/expansion.py\".format(demo_dir))\n",
+ "%run expansion.py\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Initiate instances of the Gen3 SDK Classes using credentials file for authentication\n",
+ "# Change the directory path in \"cred\" to reflect the location of your credentials file.\n",
+ "\n",
+ "api = \"https://data.midrc.org\"\n",
+ "cred = \"{}/Downloads/midrc-credentials.json\".format(home_dir)\n",
+ "auth = Gen3Auth(api, refresh_file=cred) # authentication class\n",
+ "sub = Gen3Submission(api, auth) # submission class\n",
+ "query = Gen3Query(auth) # query class\n",
+ "exp = Gen3Expansion(api,auth,sub) # class with some custom scripts\n",
+ "exp.get_project_ids()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "jupyter": {
+ "source_hidden": true
+ }
+ },
+ "source": [
+ "\n",
+ "## How can one associate the date of an imaging exam with the date of COVID-19 test results for a patient?\n",
+ "---\n",
+ "\n",
+ "Specific dates are not allowed in the MIDRC data commons, but given a single \"index_event\" for a case, \"days to X from index event\" properties are provided.\n",
+ "\n",
+ "For example, one can query or export the imaging_study node, which has \"days_to_study\", and the measurement node, which has \"test_days_from_index\", and merge into a single table on \"case_ids\" (the unique, de-identified patient identifiers) to create a temporal timeline of imaging studies and COVID-19 tests for a cohort of patients.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Export metadata using submission API\n",
+ "---\n",
+ "Here we'll utilize the MIDRC submission API to export all the imaging study and measurement (COVID-19 tests) data using the [\"get_node_tsvs\" function](https://github.com/cgmeyer/gen3sdk-python/blob/2aecc6575b22f9cca279b650914971dd6723a2ce/expansion/expansion.py#L219), which is a wrapper to export and merge all the records in a node across each project in the data commons using the [Gen3SDK](https://github.com/uc-cdis/gen3sdk-python/) function [Gen3Submission.export_node()](https://github.com/uc-cdis/gen3sdk-python/blob/5d7b5270ff11cf7037f211cf01e410d8e73d6b84/gen3/submission.py#L361)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "# Export all the records in the imaging_study node\n",
+ "st = exp.get_node_tsvs(node='imaging_study')\n",
+ "print('\\nrows:{}, columns:{}'.format(st.shape[0],st.shape[1]))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Filter the imaging_study data for only studies that have a non-null \"days_to_study\" and \"DX\" study_modality\n",
+ "s = st.loc[(~st['days_to_study'].isna()) & (st['study_modality']=='DX')]\n",
+ "print('rows:{}, columns:{}'.format(s.shape[0],s.shape[1]))\n",
+ "s.head(3)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "#Export all the data in the measurement node, which is used to store the COVID test data\n",
+ "meas = exp.get_node_tsvs(node='measurement')\n",
+ "print('\\nrows:{}, columns:{}'.format(meas.shape[0],meas.shape[1]))\n",
+ "meas.head(3)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "## Filter the measurements for only COVID-19 tests with a non-null \"test_days_from_index\" property\n",
+ "m = meas.loc[(~meas['test_days_from_index'].isna()) & (meas['test_name']=='COVID-19')]\n",
+ "print('\\nrows:{}, columns:{}'.format(m.shape[0],m.shape[1]))\n",
+ "m.head(3)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Check out the properties in each DataFrame to help make a list of properties to merge into a single table\n",
+ "display(list(s))\n",
+ "display(len(s))\n",
+ "display(list(m))\n",
+ "display(len(m))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Merge the imaging_study and measurement data using \"case_ids\" as a foreign key\n",
+ "temp = pd.merge(s[['study_uid','days_to_study','case_ids']],m[['project_id','submitter_id','test_name','test_result_text','case_ids','test_days_from_index']],on='case_ids')\n",
+ "print('\\nrows:{}, columns:{}'.format(temp.shape[0],temp.shape[1]))\n",
+ "display(temp)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Calculate the days from COVID-19 test to an imaging_study\n",
+ "---\n",
+ "Now that we have the temporal data for imaging studies and COVID-19 tests in a single DataFrame for all cases in MIDRC for which this data is provided, we can calculate the number of days between each imaging study and each COVID-19 test, which we'll call `days_from_study_to_test`.\n",
+ "\n",
+ "* Note: In MIDRC, a negative \"days to XYZ\" indicates that the event XYZ took place that many days prior to the index event, while a positive \"days to\" indicates the number of days since the index event. For example, a \"days_to_study\" of \"-10\" indicates that the imaging study was performed 10 days *before* the index event. A value of \"365\" indicates the imaging study took place one year *after* the index event. \n",
+ "\n",
+ "In the case of a derived property like `days_from_study_to_test`, the date of the study can be thought of as the 0 point, and the test takes place in time either before the study, moving backwards on the timeline (negative value) or the test takes place after the study (moving forward in time).\n",
+ "\n",
+ "So, we expect a positive value for `days_from_study_to_test` if the test was performed after the study.\n",
+ "- For example, if `test_days_from_index` is `1` and `days_to_study` is `4`, the `days_from_study_to_test` should be `-3`, which means the test took place 3 days before the study.\n",
+ "- If the COVID test is on day 4 and the imaging study is on day 1, then the `days_from_study_to_test` is `3`, meaning the COVID-19 test took place 3 days after the imaging study.\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Calculate the days from COVID-19 test to an imaging_study\n",
+ "temp['days_from_study_to_test'] = temp['test_days_from_index'] - temp['days_to_study']\n",
+ "display(temp)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Identify \"COVID-19 positive\" imaging studies\n",
+ "---\n",
+ "Now that we've calculated `days_from_study_to_test`, we can define a cut-off value and filter the imaging studies using that value to determine which imaging studies were performed within a certain time-frame of receiving a positive COVID-19 test.\n",
+ "\n",
+ "Again, our new derived attribute `days_from_study_to_test` has a positive value if the COVID test was performed after the imaging study (i.e., from the study date to test date is moving forward in time) and a negative value if the COVID test was performed before the imaging study (i.e., go back in time from the imaging date to the COVID test date). \n",
+ "\n",
+ "For this demo, let's assume that an imaging study was performed when a person was \"COVID-positive\" if the imaging study was performed within a 7 day window after a positive test result. So, we'll filter the DataFrame of studies for a `days_from_study_to_test` in the range of -7 to 0 and also require the `test_result_text` to be `Positive`.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ps = temp.loc[(temp['days_from_study_to_test'] < 0) & (temp['days_from_study_to_test'] > -7) & (temp['test_result_text']=='Positive')]\n",
+ "display(ps)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Saving the data frame to a csv\n",
+ "os.chdir(demo_dir)\n",
+ "filename = 'DX_imaging_studies_plus_covid_tests.tsv' \n",
+ "ps.to_csv(filename,sep='\\t',index=False)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Get the imaging files for the identified studies or cases.\n",
+ "---\n",
+ "Now that we have a list of imaging studies that were deemed to take place soon after a patient was infected with COVID-19, we can use the study_uid, which is a unique identifier for imaging studies, to collect the associated files. \n",
+ "\n",
+ "Note: If we want *all* the imaging studies for the cohort of identified cases, e.g., to have a \"healthy\" or \"baseline\" images for comparison, we can instead use the case_ids to pull all imaging files for the cases, keeping in mind that this will pull any additional imaging studies that may fall outside our defined temporal range."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Make a list of study_uids and case_ids\n",
+ "\n",
+ "## read in previously saved DataFrame if restarting notebook:\n",
+ "# pd.read_csv(filename, sep='\\t', dtype=str)\n",
+ "\n",
+ "cids = list(set(ps['case_ids']))\n",
+ "display(len(cids))\n",
+ "\n",
+ "sids = list(set(ps['study_uid']))\n",
+ "display(len(sids))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## This query retrieves ALL imaging_study records, we will next filter these results based on the COVID test data\n",
+ "data = query.raw_data_download(\n",
+ " data_type=\"imaging_study\",\n",
+ " fields=[\n",
+ " \"study_uid\",\n",
+ " \"case_ids\",\n",
+ " \"object_id\",\n",
+ " \"project_id\"\n",
+ " ],\n",
+ " sort_fields=[{\"study_uid\": \"asc\"}],\n",
+ " accessibility=\"accessible\"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Take a glance at the returned data\n",
+ "display(len(data))\n",
+ "display(data[0])\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# convert the query data to a DataFrame and remove any records that lack a study_uid or object_id\n",
+ "studies = pd.DataFrame(data)\n",
+ "studies = studies.loc[(~studies['object_id'].isna())&(~studies['study_uid'].isna())]\n",
+ "display(len(studies))\n",
+ "studies.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Convert lists to strings; necessary because the properties case_ids and object_id are arrays in the dictionary, and thus are returned as lists.\n",
+ "studies['case_ids'] = [','.join(map(str, l)) for l in studies['case_ids']]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Now filter the imaging studies based on our temporal results\n",
+ "covid_studies = studies.loc[studies['study_uid'].isin(sids)]\n",
+ "len(covid_studies)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# save our result to a csv\n",
+ "filename = \"covid_positive_DX_imaging_studies_7d_window_with_object_ids.tsv\"\n",
+ "covid_studies.to_csv(filename, sep='\\t', index=False)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "object_ids = list(set([a for b in covid_studies.object_id.tolist() for a in b]))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "len(object_ids)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Now that we have a list of file object_ids for the desired imaging studies, we can use the Gen3 SDK \"drs-pull\" commands to access the files themselves.\n",
+ "---\n",
+ "First, we'll create a manifest.json file using a [simple script](https://github.com/cgmeyer/gen3sdk-python/blob/389e3945482439ace6e4536e6d0e35c6e48de9c9/expansion/expansion.py#L2575). Then we'll use the `gen3 drs-pull manifest` command to download the files.\n",
+ "\n",
+ "See the detailed documentation to learn more about the Gen3 SDK drs-pull command: https://github.com/uc-cdis/gen3sdk-python/blob/master/docs/howto/drsDownloading.md\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Save the manifest of file object_ids to a JSON file\n",
+ "mani_name = 'MIDRC_DX_imaging_studies_covid_positive_manifest.json'\n",
+ "exp.write_manifest(guids=object_ids, filename=mani_name)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# To download all files in the manifest, use the \"gen3 drs-pull manifest\" command\n",
+ "download_dir = \"{}/images\".format(demo_dir)\n",
+ "\n",
+ "if not os.path.exists(download_dir):\n",
+ " os.makedirs(download_dir)\n",
+ " \n",
+ "cmd = \"gen3 --auth {} --endpoint data.midrc.org drs-pull manifest {} {}\".format(cred, mani_name, download_dir)\n",
+ "print(cmd)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Run the manifest download command. \n",
+ "## Note that this will take some time if the manifest is very large. It makes more sense to copy the above command and run in your terminal instead of from a Jupyter Notebook to monitor the progress in real-time.\n",
+ "# subprocess.run(cmd, shell=True, capture_output=True)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Now download a single DX image and display it in the notebook\n",
+ "---\n",
+ "Now we'll download a single x-ray file using the `gen3 drs-pull object` command and display the image and it's embedded metadata on the screen.\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Prepare to download a single image file via it's object_id using the gen3 SDK; save object_id to a variable\n",
+ "case_ids = covid_studies.iloc[0]['case_ids']\n",
+ "study_uid = covid_studies.iloc[0]['study_uid']\n",
+ "object_id = covid_studies.iloc[0]['object_id'][0]\n",
+ "\n",
+ "display(case_ids)\n",
+ "display(study_uid)\n",
+ "display(object_id)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Build the SDK command to send to the shell.\n",
+ "# Note: \"gen3\" refers to a Gen3 SDK function that runs at the users command line\n",
+ "# Users may experience errors or warnings but may have still downloaded the file. Check this in your working directory.\n",
+ "\n",
+ "cmd = \"gen3 --auth {} --endpoint data.midrc.org drs-pull object {}\".format(cred,object_id)\n",
+ "display(cmd)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Run the download command.\n",
+ "subprocess.run(cmd, shell=True, capture_output=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# The above command should have successfully downloaded a new directory with a zipped file. \n",
+ "cmd = \"ls -l {}/{}\".format(case_ids,study_uid)\n",
+ "stout = subprocess.run(cmd, shell=True, capture_output=True)\n",
+ "print(stout)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Grab the filename and series UID of the downloaded file using RegEx\n",
+ "import re\n",
+ "\n",
+ "m = re.search(' ([0-9\\.]+.zip)', str(stout))\n",
+ "\n",
+ "if m:\n",
+ " zip_file = m.group(1)\n",
+ " print(zip_file)\n",
+ "else:\n",
+ " print(\"No zip found.\")\n",
+ "\n",
+ "series_uid = re.sub(\"(\\.zip)\", \"\", zip_file)\n",
+ "print(series_uid)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Unzip the imaging series package\n",
+ "from zipfile import ZipFile\n",
+ "\n",
+ "with ZipFile('{}/{}/{}/{}'.format(demo_dir,case_ids,study_uid,zip_file), 'r') as zipObj:\n",
+ " zipObj.extractall()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Input the name of the newly create .dcm file\n",
+ "cmd = \"ls -l {}/{}/{}\".format(case_ids,study_uid,series_uid)\n",
+ "stout = subprocess.run(cmd, shell=True, capture_output=True)\n",
+ "print(stout)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Get the name of the first DICOM file in the extracted imaging series\n",
+ "m = re.search(' ([0-9\\.]+.dcm)', str(stout))\n",
+ "\n",
+ "if m:\n",
+ " dcm_file = m.group(1)\n",
+ " print(dcm_file)\n",
+ "else:\n",
+ " print(\"No DCM files found.\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Read in the DCM file using the python DICOM package pydicom\n",
+ "dimg = pydicom.dcmread(\"{}/{}/{}/{}\".format(case_ids,study_uid,series_uid,dcm_file),force=True)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "dimg"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## tell matplotlib to display our images as 6 x 6 inch image, with resolution of 100 dpi\n",
+ "plt.figure(figsize = (6,6), dpi=100) \n",
+ "\n",
+ "## tell matplotlib to display our image, using a gray-scale lookup table.\n",
+ "plt.imshow(dimg.pixel_array, cmap=plt.cm.gray)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## The End\n",
+ "---\n",
+ "If you have any questions related to this notebook don't hesitate to reach out to the MIDRC Helpdesk at midrc-support@gen3.org or the author directly at cgmeyer@uchicago.edu\n",
+ "\n",
+ "Happy data wrangling!"
+ ]
+ }
+ ],
+ "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": "3.13.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/jupyter-midrc/combined_demos/MIDRC_CT_Scan.ipynb b/jupyter-midrc/combined_demos/MIDRC_CT_Scan.ipynb
new file mode 100644
index 00000000..00c081b3
--- /dev/null
+++ b/jupyter-midrc/combined_demos/MIDRC_CT_Scan.ipynb
@@ -0,0 +1,542 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "af83418b",
+ "metadata": {},
+ "source": [
+ "# Demo - Intereacting With MIDRC CT Scan Images"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2890d927",
+ "metadata": {},
+ "source": [
+ "*Please note: This notebook uses open access data*\n",
+ "\n",
+ "\n",
+ "In this demo we will review how to import MIDRC imaging data, how to convert CT scan images from dicom (dcm) formats to png and jpeg formats, and how to view these CT scan images. This demo will also show how to extract file and patient metadata from the header of dicom (dcm) files."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a0198857",
+ "metadata": {},
+ "source": [
+ "### Import Data And Packages\n",
+ "Import the packages pydicom, pillow, and dicom_csv, as well as pandas, os and numpy. If any of these packages are not already installed to your workspace you can run one of the following:\n",
+ "- 'pip install < package >' in your terminal\n",
+ "- '!pip install < package >' in a notebook cell"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9182747a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#The packages below may be necessary for users to install according to the imports necessary in the subsequent cells\n",
+ "\n",
+ "#!pip install gen3 --user\n",
+ "#!pip install numpy --upgrade\n",
+ "#!pip install pydicom --upgrade\n",
+ "#!pip install pillow --upgrade\n",
+ "#!pip install dicom-csv --upgrade"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "eb457b4b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pydicom\n",
+ "import numpy as np\n",
+ "from PIL import Image\n",
+ "import pandas as pd\n",
+ "import os\n",
+ "from dicom_csv import join_tree\n",
+ "import subprocess\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ca92d009",
+ "metadata": {},
+ "source": [
+ "## Import data objects of CT scan images using the gen3 SDK\n",
+ "---\n",
+ "* Note: \"gen3\" commands are utilizing the Gen3 SDK \"drs-pull\" function, which runs at the users command line. See the detailed documentation to learn more about how to access data using the Gen3 SDK: https://github.com/uc-cdis/gen3sdk-python/blob/master/docs/howto/drsDownloading.md \n",
+ "\n",
+ "* Users may experience errors or warnings if the file's metadata is incomplete, but the file may have still downloaded. Check for the files in your current working directory.\n",
+ "\n",
+ "* Users will need to change the path to their \"--auth\" credentials file for each drs-pull command. Credentials are available at https://data.midrc.org/identity in the form of the api key file.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "06ee94f0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cred = \"/Users/christopher/Downloads/midrc-credentials.json\" # change this file path\n",
+ "\n",
+ "object_ids = ['dg.MD1R/ea669b5e-ae51-40ba-b375-ed23a9cd1855',\n",
+ " 'dg.MD1R/a745ed98-0cb9-4537-826b-13b2e354e8bb',\n",
+ " 'dg.MD1R/e604979a-c71b-4ec6-b8a0-959837b86384',\n",
+ " 'dg.MD1R/b5cee98d-46ff-4438-aa00-90727a383340',\n",
+ " 'dg.MD1R/8a5a5579-7925-432d-a614-3ed208f1c182',\n",
+ " 'dg.MD1R/33034812-47f3-4c0e-b60b-fa7a2a04ecda',\n",
+ " 'dg.MD1R/5ca987c5-c660-4785-a67d-a3424cc8ec6e',\n",
+ " 'dg.MD1R/44148117-1858-49ef-b30f-d239abfaff80',\n",
+ " 'dg.MD1R/9ea205e8-a774-4318-a323-95eadda9bc5c',\n",
+ " 'dg.MD1R/09ece36f-a0fa-48e8-8fc2-62110eaae570']\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8294b02d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for object_id in object_ids:\n",
+ " cmd = \"gen3 --auth {} --endpoint data.midrc.org drs-pull object {}\".format(cred,object_id)\n",
+ " display(cmd)\n",
+ " subprocess.run(cmd, shell=True, capture_output=True)\n",
+ "\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6a1a9b24",
+ "metadata": {},
+ "source": [
+ "All 10 data objects are now stored under the folder 'COVID-19-NY-SBU'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "561dc3dc",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!ls -l COVID-19-NY-SBU"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "58aa3021",
+ "metadata": {},
+ "source": [
+ "### View Image"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0b59aa5a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "image_path = 'COVID-19-NY-SBU/A034518/12-31-1900-CT ABD PELVIS(WITH CHEST IMAGES) W IV CON-21869/4.000000-Lung 1.0 CE-04129/1-273.dcm'\n",
+ "image_path"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f5d35a95",
+ "metadata": {},
+ "source": [
+ "Read the dcm image using the relative file path."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3b20a8db",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds = pydicom.dcmread(image_path)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ca111869",
+ "metadata": {},
+ "source": [
+ "Get the pixel arrays for the image."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d0a8ffdf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "new_image = ds.pixel_array.astype(float)\n",
+ "new_image"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "08663cf8",
+ "metadata": {},
+ "source": [
+ "Scale the image's pixel array and convert to a uint8 integer."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b8692d0a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "scaled_image = (np.maximum(new_image, 0) / new_image.max()) * 255.0\n",
+ "scaled_image = np.uint8(scaled_image)\n",
+ "scaled_image"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "630f91a1",
+ "metadata": {},
+ "source": [
+ "Use the Image package to convert the image array and show the image."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "591cc96f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "final_image = Image.fromarray(scaled_image)\n",
+ "print(type(final_image))\n",
+ "final_image"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c4c076c0",
+ "metadata": {},
+ "source": [
+ "### Convert Images\n",
+ "Convert images form dcm format to jpeg and png formats and place converted image format to the original image folder."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8a259ff7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def view_dicom_image(image_path):\n",
+ " \n",
+ " ds = pydicom.dcmread(image_path)\n",
+ " \n",
+ " new_image = ds.pixel_array.astype(float)\n",
+ " \n",
+ " scaled_image = np.uint8((np.maximum(new_image, 0) / new_image.max()) * 255.0)\n",
+ " \n",
+ " final_image = Image.fromarray(scaled_image)\n",
+ "\n",
+ " return final_image\n",
+ "\n",
+ "def dcm_to_png(image_path):\n",
+ " \n",
+ " ds = pydicom.dcmread(image_path)\n",
+ " \n",
+ " new_image = ds.pixel_array.astype(float)\n",
+ " \n",
+ " scaled_image = np.uint8((np.maximum(new_image, 0) / new_image.max()) * 255.0)\n",
+ " \n",
+ " final_image = Image.fromarray(scaled_image)\n",
+ "\n",
+ " final_image.save(image_path.rsplit('/', 1)[1][:-3] + 'png')\n",
+ " \n",
+ "\n",
+ "def dcm_to_jpeg(image_path):\n",
+ " \n",
+ " ds = pydicom.dcmread(image_path)\n",
+ " \n",
+ " new_image = ds.pixel_array.astype(float)\n",
+ " \n",
+ " scaled_image = np.uint8((np.maximum(new_image, 0) / new_image.max()) * 255.0)\n",
+ " \n",
+ " final_image = Image.fromarray(scaled_image)\n",
+ "\n",
+ " final_image.save(image_path.rsplit('/', 1)[1][:-3] + 'jpg') \n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "80839ccb",
+ "metadata": {},
+ "source": [
+ "Convert dicom image to png and save."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "67d98c20",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "image_path = 'COVID-19-NY-SBU/A117394/10-08-1900-CT ABD AND PELVIS WITH IV CONT-39755/9.000000-CTA 0.5 CE-40834/1-0163.dcm'\n",
+ "dcm_to_png(image_path)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cdfd06b6",
+ "metadata": {},
+ "source": [
+ "Convert dicom image to jpg and save."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bd90fdaa",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "image_path = 'COVID-19-NY-SBU/A587516/04-22-1901-CT CHEST WO IV CONT-40216/2.000000-Body 5.0-01241/1-16.dcm'\n",
+ "dcm_to_jpeg(image_path)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f3899246",
+ "metadata": {
+ "jupyter": {
+ "source_hidden": true
+ }
+ },
+ "source": [
+ "Display a few dicom images."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ff550791",
+ "metadata": {
+ "jupyter": {
+ "source_hidden": true
+ }
+ },
+ "outputs": [],
+ "source": [
+ "image_path = 'COVID-19-NY-SBU/A546520/12-30-1900-CT CHEST PULMONARY ANGIO WITH IV CON-13804/11.000000-CTA 3.000 CE-95792/1-119.dcm'\n",
+ "view_dicom_image(image_path)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "61579705",
+ "metadata": {
+ "jupyter": {
+ "source_hidden": true
+ }
+ },
+ "outputs": [],
+ "source": [
+ "image_path = 'COVID-19-NY-SBU/A770557/12-19-1900-CT CHEST WO IV CONT-97223/5.000000-Lung 1.0-84269/1-127.dcm'\n",
+ "view_dicom_image(image_path)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c801d0df",
+ "metadata": {
+ "jupyter": {
+ "source_hidden": true
+ }
+ },
+ "outputs": [],
+ "source": [
+ "image_path = 'COVID-19-NY-SBU/A770557/12-19-1900-CT CHEST WO IV CONT-97223/7.000000-Body 3.000-78395/1-53.dcm'\n",
+ "view_dicom_image(image_path)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "105b7f99",
+ "metadata": {},
+ "source": [
+ "### Extract Metadata"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f912e626",
+ "metadata": {
+ "jupyter": {
+ "source_hidden": true
+ }
+ },
+ "source": [
+ "The following function will extract the file and patient metadata from the header of each dicom (.dcm) file within a given folder and place the collected metadata into a pandas dataframe."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "be0be963",
+ "metadata": {
+ "jupyter": {
+ "source_hidden": true
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def extract_metadata(base_folder):\n",
+ " \n",
+ " df = pd.DataFrame()\n",
+ " file_folders = os.listdir(path = base_folder)\n",
+ " \n",
+ " for folder in file_folders:\n",
+ " path = base_folder + '/' + folder\n",
+ " meta = join_tree(path, verbose=2)\n",
+ " df = pd.concat([df, meta])\n",
+ " \n",
+ " return df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0e318e25",
+ "metadata": {
+ "jupyter": {
+ "source_hidden": true
+ }
+ },
+ "outputs": [],
+ "source": [
+ "base_folder = 'COVID-19-NY-SBU'\n",
+ "metadata = extract_metadata(base_folder)\n",
+ "metadata"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3241ff77",
+ "metadata": {},
+ "source": [
+ "Included in this metadata are import pieces of file and patient data, such as the body part examined, the patient's sex, the patient's age, etc. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fd079296",
+ "metadata": {
+ "jupyter": {
+ "source_hidden": true
+ }
+ },
+ "outputs": [],
+ "source": [
+ "metadata.columns[40:60]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7fee9d5c",
+ "metadata": {
+ "jupyter": {
+ "source_hidden": true
+ }
+ },
+ "outputs": [],
+ "source": [
+ "metadata.BodyPartExamined"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c5cedc88",
+ "metadata": {
+ "jupyter": {
+ "source_hidden": true
+ }
+ },
+ "outputs": [],
+ "source": [
+ "metadata.PatientSex"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d0fa4cb1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "metadata.PatientAge"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "544761e9",
+ "metadata": {},
+ "source": [
+ "## The End\n",
+ "---\n",
+ "If you have any questions related to this notebook don't hesitate to reach out to the MIDRC Helpdesk at midrc-support@datacommons.io or the author directly at cgmeyer@uchicago.edu\n",
+ "\n",
+ "Happy data wrangling!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b692c150",
+ "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": "3.13.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/jupyter-midrc/combined_demos/MIDRC_Clinical_Data.ipynb b/jupyter-midrc/combined_demos/MIDRC_Clinical_Data.ipynb
new file mode 100644
index 00000000..359d4233
--- /dev/null
+++ b/jupyter-midrc/combined_demos/MIDRC_Clinical_Data.ipynb
@@ -0,0 +1,419 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "01f878af",
+ "metadata": {},
+ "source": [
+ "# MIDRC Open-R1 Clinical Data\n",
+ "\n",
+ "*Please note: This notebook uses open access data*\n",
+ "\n",
+ "##### Created By: J Montgomery Maxwell"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5dfa1c83",
+ "metadata": {},
+ "source": [
+ "In this notebook we will visualize the distribution of subjects accross a variety demographics and their COVID-19 status in the Open-R1 dataset from The Medical Imaging and Data Resource Center. (MIDRC - https://data.midrc.org/)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7bff7dca",
+ "metadata": {},
+ "source": [
+ "The Open-R1 data set has 1,169 subjects, this notebook will compare the distribution of COVID-19 positive and negative patients across multiple demographic classes. In particular we will focus on the subjects' age groups (-20, 21-30, ..., 90+), sex (Male or Female), race (Black or African American, White, Asian, Pacific Islander, American Indian, Other, or Not Reported), and whether the subject is Hispanic or Latino. Below is a subset of the dataset."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d214a58d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# The packages below may be necessary for users to install according to the imports necessary in the subsequent cells.\n",
+ "\n",
+ "#!pip install --upgrade pandas\n",
+ "#!pip install --upgrade --ignore-installed PyYAML\n",
+ "#!pip install --upgrade pip\n",
+ "#!pip install --upgrade gen3 --user --upgrade\n",
+ "#!pip install cdiserrors\n",
+ "#!pip install --upgrade pydicom"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "112c887d",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import sys, os, webbrowser\n",
+ "import gen3\n",
+ "import pydicom\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "from gen3.submission import Gen3Submission\n",
+ "from gen3.auth import Gen3Auth\n",
+ "from io import StringIO\n",
+ "from gen3.index import Gen3Index\n",
+ "from expansion import Gen3Exansion\n",
+ "from gen3.query import Gen3Query"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a005ec13",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Import some custom Python scripts from personal GitHub repo.\n",
+ "# Change these directory paths to reflect your local working directory.\n",
+ "\n",
+ "home_dir = \"/Users/christopher\" \n",
+ "demo_dir = \"{}/Documents/Notes/MIDRC/tutorials\".format(home_dir)\n",
+ "\n",
+ "os.chdir(demo_dir)\n",
+ "\n",
+ "os.system(\"wget https://raw.githubusercontent.com/cgmeyer/gen3sdk-python/master/expansion/expansion.py -O {}/expansion.py\".format(demo_dir))\n",
+ "%run expansion.py\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e4b39ef9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Initiate instances of the Gen3 SDK Classes using credentials file for authentication.\n",
+ "# Change the directory path in \"cred\" to reflect the location of your credentials file.\n",
+ "\n",
+ "api = \"https://data.midrc.org\"\n",
+ "cred = \"{}/Downloads/midrc-credentials.json\".format(home_dir)\n",
+ "auth = Gen3Auth(api, refresh_file=cred) # authentication class\n",
+ "sub = Gen3Submission(api, auth) # submission class\n",
+ "query = Gen3Query(auth) # query class\n",
+ "exp = Gen3Expansion(api,auth,sub) # class with some custom scripts\n",
+ "exp.get_project_ids()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ab7c8d1a",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "#Function to sort subjects into various age groups\n",
+ "def age_group(agelist):\n",
+ " min_age = min(agelist)\n",
+ " groups = [\"-20 yr\", \"21-30 yr\", \"31-40 yr\", \"41-50 yr\", \"51-60 yr\", \"61-70 yr\", \"71-80 yr\", \"81-90 yr\", \"90+ yr\"]\n",
+ " grouplist = []\n",
+ " for i in agelist:\n",
+ " if i <= 20:\n",
+ " grouplist.append(groups[0])\n",
+ " elif i <= 30:\n",
+ " grouplist.append(groups[1])\n",
+ " elif i <= 40:\n",
+ " grouplist.append(groups[2])\n",
+ " elif i <= 50:\n",
+ " grouplist.append(groups[3])\n",
+ " elif i <= 60:\n",
+ " grouplist.append(groups[4])\n",
+ " elif i <= 70:\n",
+ " grouplist.append(groups[5])\n",
+ " elif i <= 80:\n",
+ " grouplist.append(groups[6])\n",
+ " elif i <= 90:\n",
+ " grouplist.append(groups[7])\n",
+ " else:\n",
+ " grouplist.append(groups[8])\n",
+ " \n",
+ " return grouplist\n",
+ "\n",
+ "#Function to represent various demographics into a precent positivity statistic\n",
+ "def percent_representation(df, demographic_type, demographics):\n",
+ "\n",
+ " positive_df = df[df['covid19_positive'] == 'Yes']\n",
+ " negative_df = df[df['covid19_positive'] == 'No']\n",
+ " \n",
+ " neg_percents = []\n",
+ " pos_percents = []\n",
+ " for demo in demographics:\n",
+ " neg_percents.append(round(len(negative_df[negative_df[demographic_type] == demo])/len(negative_df), 4)*100)\n",
+ " pos_percents.append(round(len(positive_df[positive_df[demographic_type] == demo])/len(positive_df), 4)*100)\n",
+ " \n",
+ " neg = pd.DataFrame()\n",
+ " pos = pd.DataFrame() \n",
+ " \n",
+ " neg[demographic_type] = demographics\n",
+ " neg['Percent'] = neg_percents\n",
+ " neg['COVID-19 Status'] = 'Negative'\n",
+ " \n",
+ " pos[demographic_type] = demographics\n",
+ " pos['Percent'] = pos_percents\n",
+ " pos['COVID-19 Status'] = 'Positive'\n",
+ " \n",
+ " return pd.concat([neg, pos])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c4095057",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "#Using the Gen3 connection \"sub\" data for project R1 is downloaded and converted into a data frame\n",
+ "cases = sub.export_node(program='Open',project='R1',node_type='case',fileformat='tsv')\n",
+ "df = pd.read_csv(StringIO(cases), sep='\\t', header=0)\n",
+ "df['zip'] = df['zip'].astype(str)\n",
+ "df['age_group'] = age_group(df['age_at_index'])\n",
+ "\n",
+ "df.loc[df.race == 'Native Hawaiian or other Pacific Islander', 'race'] = 'Pacific Islander'\n",
+ "df.loc[df.race == 'American Indian or Alaskan Native', 'race'] = 'American Indian' \n",
+ "df.loc[df.race == 'Black or African American', 'race'] = 'Black or A.A.' \n",
+ "df = df[['covid19_positive', 'age_group', 'sex', 'ethnicity', 'race']]\n",
+ "df.head()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "91a95e69",
+ "metadata": {},
+ "source": [
+ "### Subjects' COVID-19 Status"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ce373785",
+ "metadata": {},
+ "source": [
+ "Approximately 22% of the subjects in the Open-R1 dataset were COVID-19 positive at the time of the dataset indexing. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a28b5a37",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "covid_breakdown = {'Number of COVID-19 positive subjects': len(df[df['covid19_positive'] == 'Yes']['covid19_positive']), \n",
+ " 'Number of COVID-19 negative subjects': len(df[df['covid19_positive'] == 'No']['covid19_positive']), }\n",
+ "print(covid_breakdown)\n",
+ "\n",
+ "print(\"Positivity percentage = {}%\".format(round(list(covid_breakdown.items())[0][1]/list(covid_breakdown.items())[1][1]*100,1)))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "99eed950",
+ "metadata": {},
+ "source": [
+ "## Subject Distribution"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "93b2dd2b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "races = ['Black or A.A.', \n",
+ " 'White', \n",
+ " 'Asian', \n",
+ " 'Pacific Islander', \n",
+ " 'American Indian', \n",
+ " 'Other', \n",
+ " 'Not Reported']\n",
+ "plot_df = percent_representation(df, 'race', races)\n",
+ "X = np.arange(len(races))\n",
+ "\n",
+ "fig = plt.figure()\n",
+ "ax = fig.add_axes([0,0,1,1])\n",
+ "\n",
+ "ax.bar(X - 0.2, plot_df[plot_df['COVID-19 Status'] == 'Negative']['Percent'], color='b', width=0.4, label='Negative')\n",
+ "ax.bar(X + 0.2, plot_df[plot_df['COVID-19 Status'] == 'Positive']['Percent'], color='r', width=0.4, label='Positive')\n",
+ "\n",
+ "ax.set_xticks(X)\n",
+ "ax.set_xticklabels(races, rotation=25)\n",
+ "ax.set_ylabel('Percent')\n",
+ "ax.set_xlabel('Race')\n",
+ "ax.set_title('Subject Representation By Race')\n",
+ "\n",
+ "ax.legend()\n",
+ "plt.show() "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "10bc59b7",
+ "metadata": {},
+ "source": [
+ "Users can examine the ratio of Negative and Positive COVID cases amoungst various demographics. At many points thoughout the first two years of the pandemic, desparities of COVID positivity ratios were often noted. Additionally, since subjects possess the ability to not report their race (Not Reported), differences in positivity ratios can be observed if present."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f1e11218",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "ethnicities = ['Not Hispanic or Latino', 'Hispanic or Latino'] \n",
+ "\n",
+ "plot_df = percent_representation(df, 'ethnicity', ethnicities)\n",
+ "X = np.arange(len(ethnicities))\n",
+ "\n",
+ "fig = plt.figure()\n",
+ "ax = fig.add_axes([0,0,1,1])\n",
+ "\n",
+ "ax.bar(X - 0.2, plot_df[plot_df['COVID-19 Status'] == 'Negative']['Percent'], color='b', width=0.4, label='Negative')\n",
+ "ax.bar(X + 0.2, plot_df[plot_df['COVID-19 Status'] == 'Positive']['Percent'], color='r', width=0.4, label='Positive')\n",
+ "\n",
+ "ax.set_xticks(X)\n",
+ "ax.set_xticklabels(ethnicities, rotation=25)\n",
+ "ax.set_ylabel('Percent')\n",
+ "ax.set_xlabel('Ethnicity')\n",
+ "ax.set_title('Subject Representation By Ethnicity')\n",
+ "\n",
+ "ax.legend()\n",
+ "plt.show() "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b6589c91",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "When reduced to only two groups (Not Hispanic or Latino verse Hispanic or Latino), differences in COVID positivity can be observed if present."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "12f528d5",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "sexes = ['Male', 'Female'] \n",
+ "\n",
+ "plot_df = percent_representation(df, 'sex', sexes)\n",
+ "X = np.arange(len(sexes))\n",
+ "\n",
+ "fig = plt.figure()\n",
+ "ax = fig.add_axes([0,0,1,1])\n",
+ "\n",
+ "ax.bar(X - 0.2, plot_df[plot_df['COVID-19 Status'] == 'Negative']['Percent'], color='b', width=0.4, label='Negative')\n",
+ "ax.bar(X + 0.2, plot_df[plot_df['COVID-19 Status'] == 'Positive']['Percent'], color='r', width=0.4, label='Positive')\n",
+ "\n",
+ "ax.set_xticks(X)\n",
+ "ax.set_xticklabels(sexes, rotation=25)\n",
+ "ax.set_ylabel('Percent')\n",
+ "ax.set_xlabel('Sex')\n",
+ "ax.set_title('Subject Representation By Sex')\n",
+ "\n",
+ "ax.legend()\n",
+ "plt.show() "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3834ea41",
+ "metadata": {},
+ "source": [
+ "If present, a disparity of COVID positivity can be noted between sexes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "486dfd08",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "fig = plt.figure()\n",
+ "ax = fig.add_axes([0,0,1,1])\n",
+ "\n",
+ "ages = [\"-20 yr\", \"21-30 yr\", \"31-40 yr\", \"41-50 yr\", \"51-60 yr\", \"61-70 yr\", \"71-80 yr\", \"81-90 yr\", \"90+ yr\"]\n",
+ "\n",
+ "plot_df = percent_representation(df, 'age_group', ages)\n",
+ "X=np.arange(9)\n",
+ "\n",
+ "ax.bar(X - 0.2, \n",
+ " plot_df[plot_df['COVID-19 Status'] == 'Negative']['Percent'], color='b', width=0.4, label='Negative')\n",
+ "\n",
+ "ax.bar(X + 0.2, \n",
+ " plot_df[plot_df['COVID-19 Status'] == 'Positive']['Percent'], color='r', width=0.4, label='Positive')\n",
+ "ax.set_xticks(X)\n",
+ "ax.set_xticklabels(ages, rotation=25)\n",
+ "\n",
+ "ax.set_ylabel('Percent')\n",
+ "ax.set_xlabel('Age Group')\n",
+ "\n",
+ "ax.set_title('Subject Representation By Age Group')\n",
+ "\n",
+ "ax.legend()\n",
+ "plt.show() "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0112e249",
+ "metadata": {},
+ "source": [
+ "The affect age plays in the prevalence of COVID positivity is displayed above. It should be noted that this chart is not normalized by the age distribution of the general population. Typically though, individuals <20 years represent a significant portion of most general populations."
+ ]
+ }
+ ],
+ "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": "3.13.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/jupyter-midrc/combined_demos/MIDRC_Cohort_Building-DLL_RSNA_2023.ipynb b/jupyter-midrc/combined_demos/MIDRC_Cohort_Building-DLL_RSNA_2023.ipynb
new file mode 100644
index 00000000..baa1f045
--- /dev/null
+++ b/jupyter-midrc/combined_demos/MIDRC_Cohort_Building-DLL_RSNA_2023.ipynb
@@ -0,0 +1,622 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "d8ef63c3",
+ "metadata": {},
+ "source": [
+ "# Cohort Building Using the MIDRC Data Commons\n",
+ "---\n",
+ "This notebook briefly demonstrates how to use the MIDRC open APIs to build a cohort of MIDRC imaging studies using patient clinical data and AI-research-based annotations in the MIDRC data commons and then access and view the X-ray image files associated with those imaging studies.\n",
+ "\n",
+ "All cohort selection possible in the [MIDRC data explorer UI](https://data.midrc.org/explorer) can also be achieved programmatically using API requests. In this notebook, we'll select the same cohort as in the data explorer demo detailed in [these slides](https://docs.google.com/presentation/d/1xZ-shCuGVlLpHb2_CwrYvnQZnYZ3QDCcoNfcUvaxTmE/edit?usp=sharing).\n",
+ "\n",
+ "by Chris Meyer, PhD\n",
+ "\n",
+ "Manager of Data and User Services at the Center for Translational Data Science at University of Chicago\n",
+ "\n",
+ "Presented at the MIDRC RSNA 2023 Deep Learning Lab on November 28, 2023\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5db7f87c",
+ "metadata": {},
+ "source": [
+ "## 1) Set up Python environment\n",
+ "---\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ffc85bb2-3ed6-4eef-a943-724eef02c41b",
+ "metadata": {},
+ "source": [
+ "### Download an API key file containing your credentials\n",
+ "---\n",
+ "1) Navigate to the MIDRC data portal in your browser: https://data.midrc.org.\n",
+ "2) Read and accept the DUA (if you haven't already).\n",
+ "3) Navigate to the user profile page: https://data.midrc.org/identity\n",
+ "4) Click on the button \"Create API Key\" and save the `credentials.json` file somewhere safe\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1bffd5d4",
+ "metadata": {},
+ "source": [
+ "### Set local variables\n",
+ "---\n",
+ "Change the following `cred` variable path to point to your credentials file downloaded from the MIDRC data portal following the instructions above."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c3c59c2c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cred = \"/Users/christopher/Downloads/midrc-credentials.json\" # location of your MIDRC credentials, downloaded from https://data.midrc.org/identity by clicking \"Create API key\" button and saving the credentials.json locally\n",
+ "api = \"https://data.midrc.org\" # The base URL of the data commons being queried. This shouldn't change for MIDRC.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7962e54c",
+ "metadata": {},
+ "source": [
+ "### Install / Import Python Packages and Scripts"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8e1a7935",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## The packages below may be necessary for users to install according to the imports necessary in the subsequent cells.\n",
+ "\n",
+ "import sys\n",
+ "#!{sys.executable} -m pip install\n",
+ "#!{sys.executable} -m pip install --upgrade pandas\n",
+ "#!{sys.executable} -m pip install --upgrade --ignore-installed PyYAML\n",
+ "#!{sys.executable} -m pip install --upgrade pip\n",
+ "#!{sys.executable} -m pip install --upgrade gen3\n",
+ "#!{sys.executable} -m pip install pydicom\n",
+ "#!{sys.executable} -m pip install --upgrade Pillow\n",
+ "#!{sys.executable} -m pip install psmpy\n",
+ "#!{sys.executable} -m pip install python-gdcm --upgrade\n",
+ "#!{sys.executable} -m pip install pylibjpeg --upgrade"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7ea2fa09",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Import Python Packages and scripts\n",
+ "\n",
+ "import os, subprocess\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import pydicom\n",
+ "from PIL import Image\n",
+ "import glob\n",
+ "#import gdcm\n",
+ "#import pylibjpeg\n",
+ "\n",
+ "# import some Gen3 packages\n",
+ "import gen3\n",
+ "from gen3.auth import Gen3Auth\n",
+ "from IPython.display import display\n",
+ "from gen3.query import Gen3Query\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7784ecc9",
+ "metadata": {},
+ "source": [
+ "### Initiate instances of the Gen3 SDK Classes using credentials file for authentication\n",
+ "---\n",
+ "Again, make sure the \"cred\" directory path variable reflects the location of your credentials file (path variables set above)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d316dcdf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "auth = Gen3Auth(api, refresh_file=cred) # authentication class\n",
+ "query = Gen3Query(auth) # query class\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2ea96784-3370-46a6-a2c7-edda947bfa8f",
+ "metadata": {
+ "jupyter": {
+ "source_hidden": true
+ }
+ },
+ "source": [
+ "## 2) Build Cohorts by Sending Queries to the MIDRC APIs\n",
+ "#### General notes on sending queries:\n",
+ "* There are many ways to query and access metadata for cohort building in MIDRC, but this notebook will focus on using the [Gen3](https://gen3.org) graphQL query service [\"guppy\"](https://github.com/uc-cdis/guppy/#readme). This is the backend query service that [MIDRC's data explorer GUI](https://data.midrc.org/explorer) uses. So, anything you can do in the explorer GUI, you can do with guppy queries, and more!\n",
+ "* The guppy graphQL service has more functionality than is demonstrated in this simple example. You can find extensive documentation in GitHub [here](https://github.com/uc-cdis/guppy/blob/master/doc/queries.md) in case you'd like to build your own queries from scratch.\n",
+ "* The Gen3 SDK (intialized as `query` above in this notebook) has Python wrapper scripts to make sending queries to the guppy graphQL API simpler. The guppy SDK package can be viewed in GitHub [here](https://github.com/uc-cdis/gen3sdk-python/blob/master/gen3/query.py).\n",
+ "* Guppy queries focus on a particular type of data (cases, imaging studies, files, etc.), which corresponds to the major tabs in [MIDRC's data explorer GUI](https://data.midrc.org/explorer).\n",
+ "* Queries include arguments that are akin to selecting filter values in [MIDRC's data explorer GUI](https://data.midrc.org/explorer).\n",
+ "* To see more documentation about how to use and combine filters with various operator logic (like AND/OR/IN, etc.) see [this page](https://github.com/uc-cdis/guppy/blob/master/doc/queries.md#filter).\n",
+ "\n",
+ "---\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3e4a624e-f769-46a9-b76c-2efe9713bf61",
+ "metadata": {
+ "jupyter": {
+ "source_hidden": true
+ }
+ },
+ "source": [
+ "#### Set query parameters\n",
+ "---\n",
+ "* Here, we'll send a query to the `imaging_study` guppy index, which corresponds to the \"Imaging Studies\" tab of [MIDRC's data explorer GUI](https://data.midrc.org/explorer).\n",
+ "* The filters defined below can be modified to return different subsets of imaging studies. Here, we'll use rather restrictive parameters so the number of studies returned is small for demonstration purposes.\n",
+ "* If our query request is successful, the API response should be in JSON format, and it should contain a list of patient IDs along with any other patient data we ask for.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9bc4a36e-0647-4546-9777-6d0ad7b32750",
+ "metadata": {
+ "jupyter": {
+ "source_hidden": true
+ }
+ },
+ "outputs": [],
+ "source": [
+ "### Set some \"imaging_study\" query parameters\n",
+ "\n",
+ "## mRALE filter: we'll select all imaging studies annotated with an mRALE score greater than or equal to this threshold number\n",
+ "mRALE_threshold = 20\n",
+ "\n",
+ "## days from study to positive COVID-19 test filter: we want imaging studies performed within two days after a positive test\n",
+ "min_days_from_study_to_test = -2\n",
+ "max_days_from_study_to_test = 0\n",
+ "\n",
+ "## Imaging study modality filter: we select imaging studies with a modality of either DX or CR\n",
+ "study_modalities = [\"DX\", \"CR\"]\n",
+ "\n",
+ "## Imaging study body part filter: here we select \"chest\" as the \"LOINC system\" filter, which is the body part examined\n",
+ "body_part_examined = \"Chest\"\n",
+ "\n",
+ "## Case filters: we will select Hispanic males 70 years of age and older\n",
+ "ethnicity = \"Hispanic or Latino\"\n",
+ "sex = \"Male\"\n",
+ "age_threshold = 70"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8910b3e4",
+ "metadata": {
+ "jupyter": {
+ "source_hidden": true
+ }
+ },
+ "outputs": [],
+ "source": [
+ "## Note: the \"fields\" option defines what fields we want the query to return. If set to \"None\", returns all available fields.\n",
+ "\n",
+ "imaging_studies = query.raw_data_download(\n",
+ " data_type=\"imaging_study\",\n",
+ " fields=None,\n",
+ " filter_object={\n",
+ " \"AND\": [\n",
+ " {\"=\": {\"loinc_system\": body_part_examined}},\n",
+ " {\"=\": {\"sex\": sex}},\n",
+ " {\"=\": {\"ethnicity\": ethnicity}},\n",
+ " {\">=\": {\"age_at_index\": age_threshold}},\n",
+ " {\"IN\": {\"study_modality\": study_modalities}},\n",
+ " {\"nested\": {\"path\": \"imaging_study_annotations\", \">=\": {\"midrc_mRALE_score\": mRALE_threshold}}},\n",
+ " {\"AND\": [\n",
+ " {\">=\": {\"days_from_study_to_pos_covid_test\": min_days_from_study_to_test}}, \n",
+ " {\"<=\": {\"days_from_study_to_pos_covid_test\": max_days_from_study_to_test}} \n",
+ " ]}\n",
+ " ]\n",
+ " },\n",
+ " sort_fields=[{\"submitter_id\": \"asc\"}]\n",
+ " )\n",
+ "\n",
+ "if len(imaging_studies) > 0 and \"submitter_id\" in imaging_studies[0]:\n",
+ " imaging_studies_ids = [i['submitter_id'] for i in imaging_studies] ## make a list of the imaging study IDs returned\n",
+ " print(\"Query returned {} study IDs.\".format(len(imaging_studies)))\n",
+ " print(\"Data is a list with rows like this:\\n\\t {}\".format(imaging_studies[0:1]))\n",
+ "else:\n",
+ " print(\"Your query returned no data! Please, check that query parameters are valid.\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "33093eff-621f-452b-a0af-89af1940c65f",
+ "metadata": {
+ "jupyter": {
+ "source_hidden": true
+ }
+ },
+ "outputs": [],
+ "source": [
+ "imaging_studies_df = pd.DataFrame(imaging_studies)\n",
+ "display(imaging_studies_df)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2c0f29ab-ab87-4fe5-859b-d5158c0fa3d7",
+ "metadata": {
+ "jupyter": {
+ "source_hidden": true
+ }
+ },
+ "source": [
+ "## 3) Send another query to get data file details for our cohort / case ID\n",
+ "---\n",
+ "The `object_id` field in each imaging study record above contains the file identifiers for all files associated with each imaging study, which could include files like third-party annotations. If we simply want to access all files associated with our list of cases, we can use those object_ids. \n",
+ "\n",
+ "However, in this example, we'll ask for specific types of files and get more detailed information about each of the files. This is achieved by querying the `data_file` guppy index, which corresponds to the \"Data Files\" tab of the MIDRC data explorer GUID. \n",
+ "\n",
+ "All MIDRC data files, including both images and annotations, are listed in the guppy index \"data_file\", which is queried in a similar manner to our query of the `imaging_study` index above. The query parameter `data_type` below determines which guppy (Elasticsearch) index we're querying.\n",
+ "\n",
+ "To get only `data_file` records that correspond to our imaging study cohort built previously, we'll use the list of study UIDs as a query filter. \n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2959c0ed-adde-4f34-8308-ca0f61c599cf",
+ "metadata": {},
+ "source": [
+ "### Set 'data_file' query parameters\n",
+ "---\n",
+ "Here, we'll utilize the property `source_node` to filter the list of files for our cohort to only those matching the type of files we're interested in. In this example, we ask only for CR and DX (x-ray) images, which will exclude any other types of files like annotations.\n",
+ "\n",
+ "We're also using the property `study_uid` as a filter to restrict the `data_file` records returned down to those associated with the imaging studies in our cohort built above. \n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "07f33bd1-7393-4e0a-902a-771783568280",
+ "metadata": {
+ "jupyter": {
+ "source_hidden": true
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# Build a list of study UIDs to use as a filter in our data_file query\n",
+ "study_uids = [i['study_uid'] for i in imaging_studies]\n",
+ "study_uids"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2efabbc8-c9cb-481a-9847-50659918b6d7",
+ "metadata": {
+ "jupyter": {
+ "source_hidden": true
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# Choose the types of data we want using \"source_node\" as a filter\n",
+ "source_nodes = [\"cr_series_file\",\"dx_series_file\"]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d3a2b920-15d0-4399-ab2d-4faa2748a314",
+ "metadata": {
+ "jupyter": {
+ "source_hidden": true
+ }
+ },
+ "outputs": [],
+ "source": [
+ "## Search for specific files associated with our cohort by adding \"study_uid\" as a filter\n",
+ "# * Note: \"fields\" is set to \"None\" in this query, which by default returns all the properties available\n",
+ "data_files = query.raw_data_download(\n",
+ " data_type=\"data_file\",\n",
+ " fields=None,\n",
+ " filter_object={\n",
+ " \"AND\": [\n",
+ " {\"IN\": {\"study_uid\": study_uids}},\n",
+ " {\"IN\": {\"source_node\": source_nodes}},\n",
+ " ]\n",
+ " },\n",
+ " sort_fields=[{\"submitter_id\": \"asc\"}]\n",
+ " )\n",
+ "\n",
+ "if len(data_files) > 0:\n",
+ " object_ids = [i['object_id'] for i in data_files if 'object_id' in i] ## make a list of the file object_ids returned by our query\n",
+ " print(\"Query returned {} data files with {} object_ids.\".format(len(data_files),len(object_ids)))\n",
+ " print(\"Data is a list with rows like this:\\n\\t {}\".format(data_files[0:1]))\n",
+ "else:\n",
+ " print(\"Your query returned no data! Please, check that query parameters are valid.\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "972b4805-61a3-4c22-8339-93878f201e46",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# object_id (AKA \"data GUID\") is a globally unique file identifier that points to an actual file object in cloud storage. We'll use the object_ids along with the gen3 command-line tool to download the files these object_ids point to.\n",
+ "object_ids\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "81e35d9b-e8ec-4fc0-9d3c-7485446c15f3",
+ "metadata": {
+ "jupyter": {
+ "source_hidden": true
+ }
+ },
+ "source": [
+ "## 4) Access data files using their object_id / data GUID (globally unique identifiers)\n",
+ "---\n",
+ "In order to download files stored in MIDRC, users need to reference the file's object_id (AKA data GUID or Globally Unique IDentifier).\n",
+ "\n",
+ "Once we have a list of GUIDs we want to download, we can use either the gen3-client or the gen3 SDK to download the files. You can also access individual files in your browser after logging-in and entering the GUID after the `files/` endpoint, as in this URL: https://data.midrc.org/files/GUID\n",
+ "\n",
+ "where GUID is the actual GUID, e.g.: https://data.midrc.org/files/dg.MD1R/b87d0db3-d95a-43c7-ace1-ab2c130e04ec\n",
+ "\n",
+ "For instructions on how to install and use the gen3-client, please see [the MIDRC quick-start guide](https://data.midrc.org/dashboard/Public/documentation/Gen3_MIDRC_GetStarted.pdf), which can be found linked here and in the MIDRC data portal header as \"Get Started\".\n",
+ "\n",
+ "Below we use the gen3 SDK command `gen3 drs-pull object` which is [documented in detail here](https://github.com/uc-cdis/gen3sdk-python/blob/master/docs/howto/drsDownloading.md)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8449f221-1d8d-4501-86af-111111fc7bf3",
+ "metadata": {},
+ "source": [
+ "### Use the Gen3 SDK command `gen3 drs-pull object` to download an individual file"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f3462219-202b-4d59-9a3d-14cbdecab77b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Make a new directory for downloaded files\n",
+ "os.system(\"rm -r downloads\")\n",
+ "os.system(\"mkdir -p downloads\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5b156930-805f-4562-aea7-62798b13e46b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## We can use a simple loop to download all files and keep track of successes and failures\n",
+ "\n",
+ "success,failure,other=[],[],[]\n",
+ "count,total = 0,len(object_ids)\n",
+ "for object_id in object_ids:\n",
+ " count+=1\n",
+ " cmd = \"gen3 --auth {} --endpoint data.midrc.org drs-pull object {} --output-dir downloads\".format(cred,object_id)\n",
+ " stout = subprocess.run(cmd, shell=True, capture_output=True)\n",
+ " print(\"Progress ({}/{}): {}\".format(count,total,stout.stdout))\n",
+ " if \"failed\" in str(stout.stdout):\n",
+ " failure.append(object_id)\n",
+ " elif \"successfully\" in str(stout.stdout):\n",
+ " success.append(object_id)\n",
+ " else:\n",
+ " other.append(object_id)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "cd0ebe63-380a-4606-8285-5736ec87ffee",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Get a list of all downloaded .dcm files\n",
+ "image_files = glob.glob(pathname='**/*.dcm',recursive=True,)\n",
+ "image_files"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d53c1364-aba9-461f-a0f9-e7730875a339",
+ "metadata": {},
+ "source": [
+ "### View the DICOM Images\n",
+ "---\n",
+ "Here we'll use the [Python package `pydicom`](https://pydicom.github.io/pydicom/stable/) to view the downloaded DICOM images. \n",
+ "\n",
+ "Note that some of the files may contain compressed pixel data that require other packages to view; so, for this demo we'll simply skip over those using the following loop."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "533ce308-3618-47b2-bae1-ad4681feab02",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "for image_file in image_files:\n",
+ " print(image_file)\n",
+ " ds = pydicom.dcmread(image_file)\n",
+ " try:\n",
+ " new_image = ds.pixel_array.astype(float)\n",
+ " scaled_image = (np.maximum(new_image, 0) / new_image.max()) * 255.0\n",
+ " scaled_image = np.uint8(scaled_image)\n",
+ " final_image = Image.fromarray(scaled_image)\n",
+ " print(type(final_image))\n",
+ " display(final_image)\n",
+ " except Exception as e:\n",
+ " print(\"Couldn't view {}: {}.\".format(image_file,e))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "60bb7027-7fb3-47cb-8b4b-2d084287fc20",
+ "metadata": {},
+ "source": [
+ "#### View the DICOM Headers\n",
+ "---\n",
+ "DICOM files have metadata elements embedded in the images. These can also be read and viewed using the `pydicom` package."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a1bbb4c1-9ab6-4260-904e-977836b57a01",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ds = pydicom.dcmread(image_files[0],force=True)\n",
+ "display(ds)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4dec9bce-7f8f-48ee-abe1-6bc380d923c3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Access individual elements\n",
+ "display(ds.file_meta)\n",
+ "display(ds.ImageType)\n",
+ "display(ds[0x0008, 0x0016])\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ecceb434-83f2-45f1-b1f0-7e1c58fafde9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# View the dicom metadata for all files as a DataFrame\n",
+ "dfs = []\n",
+ "for image_file in image_files:\n",
+ " ds = pydicom.dcmread(image_file)\n",
+ " df = pd.DataFrame(ds.values())\n",
+ " df[0] = df[0].apply(lambda x: pydicom.dataelem.DataElement_from_raw(x) if isinstance(x, pydicom.dataelem.RawDataElement) else x)\n",
+ " df['name'] = df[0].apply(lambda x: x.name)\n",
+ " df['value'] = df[0].apply(lambda x: x.value)\n",
+ " df = df[['name', 'value']]\n",
+ " df = df.set_index('name').T.reset_index(drop=True)\n",
+ " df['filename'] = image_file\n",
+ " df.drop(columns=['Pixel Data'],inplace=True) # drop the pixel data as it's too large and nonsensical to store in a DataFrame\n",
+ " dfs.append(df)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3e55cd95-570b-42d0-80cd-fb47693c49dd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Make a master dataframe for all images using only headers in all dataframes\n",
+ "headers = list(set.intersection(*map(set,dfs)))\n",
+ "df = pd.concat([df[headers] for df in dfs])\n",
+ "df.set_index('filename',inplace=True)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c54f11af-4b8c-4744-bc88-6cd2ced7e102",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "display(df)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5b991196-5298-43b6-987a-90de9bf308e5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Export the file metadata as a TSV file\n",
+ "filename = \"MIDRC_DICOM_metadata.tsv\"\n",
+ "df.to_csv(filename, sep='\\t')\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "544761e9",
+ "metadata": {},
+ "source": [
+ "## The End\n",
+ "---\n",
+ "If you have any questions related to this notebook don't hesitate to reach out to the MIDRC Helpdesk at midrc-support@datacommons.io or the author directly at cgmeyer@uchicago.edu\n",
+ "\n",
+ "Happy data wrangling!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f6691638",
+ "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": "3.13.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/jupyter-midrc/combined_demos/MIDRC_Cohort_Building_DLL_RSNA_2024.ipynb b/jupyter-midrc/combined_demos/MIDRC_Cohort_Building_DLL_RSNA_2024.ipynb
new file mode 100644
index 00000000..a932a8a4
--- /dev/null
+++ b/jupyter-midrc/combined_demos/MIDRC_Cohort_Building_DLL_RSNA_2024.ipynb
@@ -0,0 +1,896 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "d8ef63c3",
+ "metadata": {
+ "id": "d8ef63c3"
+ },
+ "source": [
+ "# Cohort Building Using the MIDRC Data Commons and Biomedical Imaging Hub\n",
+ "---\n",
+ "This notebook briefly demonstrates how to use the MIDRC open APIs to build a cohort of MIDRC imaging studies using patient clinical data and AI-research-based annotations in the MIDRC data commons and then access and view the X-ray image files associated with those imaging studies.\n",
+ "\n",
+ "It also demonstrates how to use the MIDRC Biomedical Imaging Hub (BIH) open metadata APIs to discover images across the Biomedical Data Fabric (BDF), including those in data resources other than the MIDRC data commons.\n",
+ "\n",
+ "All cohort selection possible in the [MIDRC data explorer UI](https://data.midrc.org/explorer) and the [MIDRC BIH Explorer](https://imaging-hub.data-commons.org/Explorer) can also be achieved programmatically using API requests. In this notebook, we'll select the same cohort as in the data explorer demo detailed in [these slides](https://docs.google.com/presentation/d/1cMKyl-QWa2oM9HFnr0F7D83JaPx74GyFErz7O-gJjas/edit?usp=sharing).\n",
+ "\n",
+ "by Chris Meyer, PhD\n",
+ "\n",
+ "Manager of Data and User Services at the Center for Translational Data Science at University of Chicago\n",
+ "\n",
+ "Presented at the MIDRC RSNA 2024 Deep Learning Lab on December 2, 2024"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5db7f87c",
+ "metadata": {
+ "id": "5db7f87c"
+ },
+ "source": [
+ "## 1) Set up Python environment\n",
+ "---\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ffc85bb2-3ed6-4eef-a943-724eef02c41b",
+ "metadata": {
+ "id": "ffc85bb2-3ed6-4eef-a943-724eef02c41b"
+ },
+ "source": [
+ "### Download an API key file containing your credentials\n",
+ "---\n",
+ "1) Navigate to the MIDRC data portal in your browser: https://data.midrc.org.\n",
+ "2) Read and accept the DUA (if you haven't already).\n",
+ "3) Navigate to the user profile page: https://data.midrc.org/identity\n",
+ "4) Click on the button \"Create API Key\" and save the `credentials.json` file somewhere safe\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1bffd5d4",
+ "metadata": {
+ "id": "1bffd5d4"
+ },
+ "source": [
+ "### Set local variables\n",
+ "---\n",
+ "Change the following `cred` variable path to point to your credentials file downloaded from the MIDRC data portal following the instructions above."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c3c59c2c",
+ "metadata": {
+ "id": "c3c59c2c"
+ },
+ "outputs": [],
+ "source": [
+ "cred = \"/content/midrc-credentials.json\" # location of your MIDRC credentials, downloaded from https://data.midrc.org/identity by clicking \"Create API key\" button and saving the credentials.json locally\n",
+ "api = \"https://data.midrc.org\" # The base URL of the data commons being queried. This shouldn't change for MIDRC.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7962e54c",
+ "metadata": {
+ "id": "7962e54c"
+ },
+ "source": [
+ "### Install / Import Python Packages and Scripts"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8e1a7935",
+ "metadata": {
+ "id": "8e1a7935"
+ },
+ "outputs": [],
+ "source": [
+ "## The packages below may be necessary for users to install according to the imports necessary in the subsequent cells.\n",
+ "\n",
+ "import sys\n",
+ "#!{sys.executable} -m pip install\n",
+ "#!{sys.executable} -m pip install --upgrade pandas\n",
+ "#!{sys.executable} -m pip install --upgrade --ignore-installed PyYAML\n",
+ "#!{sys.executable} -m pip install --upgrade pip\n",
+ "!{sys.executable} -m pip install --upgrade gen3\n",
+ "!{sys.executable} -m pip install pydicom\n",
+ "#!{sys.executable} -m pip install --upgrade Pillow\n",
+ "#!{sys.executable} -m pip install psmpy\n",
+ "#!{sys.executable} -m pip install python-gdcm --upgrade\n",
+ "#!{sys.executable} -m pip install pylibjpeg --upgrade"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7ea2fa09",
+ "metadata": {
+ "id": "7ea2fa09"
+ },
+ "outputs": [],
+ "source": [
+ "## Import Python Packages and scripts\n",
+ "\n",
+ "import os, subprocess\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import pydicom\n",
+ "from PIL import Image\n",
+ "import glob\n",
+ "#import gdcm\n",
+ "#import pylibjpeg\n",
+ "\n",
+ "# import some Gen3 packages\n",
+ "import gen3\n",
+ "from gen3.auth import Gen3Auth\n",
+ "from gen3.query import Gen3Query\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7784ecc9",
+ "metadata": {
+ "id": "7784ecc9"
+ },
+ "source": [
+ "### Initiate instances of the Gen3 SDK Classes using credentials file for authentication\n",
+ "---\n",
+ "Again, make sure the \"cred\" directory path variable reflects the location of your credentials file (path variables set above)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d316dcdf",
+ "metadata": {
+ "id": "d316dcdf"
+ },
+ "outputs": [],
+ "source": [
+ "auth = Gen3Auth(api, refresh_file=cred) # authentication class\n",
+ "query = Gen3Query(auth) # query class\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2ea96784-3370-46a6-a2c7-edda947bfa8f",
+ "metadata": {
+ "id": "2ea96784-3370-46a6-a2c7-edda947bfa8f"
+ },
+ "source": [
+ "## 2) Build Cohorts by Sending Queries to the MIDRC APIs\n",
+ "#### General notes on sending queries:\n",
+ "* There are many ways to query and access metadata for cohort building in MIDRC, but this notebook will focus on using the [Gen3](https://gen3.org) graphQL query service [\"guppy\"](https://github.com/uc-cdis/guppy/#readme). This is the backend query service that [MIDRC's data explorer GUI](https://data.midrc.org/explorer) uses. So, anything you can do in the explorer GUI, you can do with guppy queries, and more!\n",
+ "* The guppy graphQL service has more functionality than is demonstrated in this simple example. You can find extensive documentation in GitHub [here](https://github.com/uc-cdis/guppy/blob/master/doc/queries.md) in case you'd like to build your own queries from scratch.\n",
+ "* The Gen3 SDK (intialized as `query` above in this notebook) has Python wrapper scripts to make sending queries to the guppy graphQL API simpler. The guppy SDK package can be viewed in GitHub [here](https://github.com/uc-cdis/gen3sdk-python/blob/master/gen3/query.py).\n",
+ "* Guppy queries focus on a particular type of data (cases, imaging studies, files, etc.), which corresponds to the major tabs in [MIDRC's data explorer GUI](https://data.midrc.org/explorer).\n",
+ "* Queries include arguments that are akin to selecting filter values in [MIDRC's data explorer GUI](https://data.midrc.org/explorer).\n",
+ "* To see more documentation about how to use and combine filters with various operator logic (like AND/OR/IN, etc.) see [this page](https://github.com/uc-cdis/guppy/blob/master/doc/queries.md#filter).\n",
+ "\n",
+ "---\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3e4a624e-f769-46a9-b76c-2efe9713bf61",
+ "metadata": {
+ "id": "3e4a624e-f769-46a9-b76c-2efe9713bf61"
+ },
+ "source": [
+ "#### Set query parameters\n",
+ "---\n",
+ "* Here, we'll send a query to the `imaging_study` guppy index, which corresponds to the \"Imaging Studies\" tab of [MIDRC's data explorer GUI](https://data.midrc.org/explorer).\n",
+ "* The filters defined below can be modified to return different subsets of imaging studies. Here, we'll use rather restrictive parameters so the number of studies returned is small for demonstration purposes.\n",
+ "* If our query request is successful, the API response should be in JSON format, and it should contain a list of imaging study UIDs along with any other study-related data we ask for.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9bc4a36e-0647-4546-9777-6d0ad7b32750",
+ "metadata": {
+ "id": "9bc4a36e-0647-4546-9777-6d0ad7b32750"
+ },
+ "outputs": [],
+ "source": [
+ "### Set some \"imaging_study\" query parameters\n",
+ "\n",
+ "## mRALE filter: we'll select all imaging studies annotated with an mRALE score greater than or equal to this threshold number\n",
+ "mRALE_threshold = 20\n",
+ "\n",
+ "## days from study to positive COVID-19 test filter: we want imaging studies performed within two days after a positive test\n",
+ "min_days_from_study_to_test = -2\n",
+ "max_days_from_study_to_test = 0\n",
+ "\n",
+ "## Imaging study modality filter: we select imaging studies with a modality of either DX or CR\n",
+ "study_modalities = [\"DX\", \"CR\"]\n",
+ "\n",
+ "## Imaging study body part filter: here we select \"chest\" as the \"LOINC system\" filter, which is the body part examined\n",
+ "body_part_examined = \"Chest\"\n",
+ "\n",
+ "## Case filters: we will select Hispanic males 70 years of age and older\n",
+ "ethnicity = \"Hispanic or Latino\"\n",
+ "sex = \"Male\"\n",
+ "age_threshold = 70"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8910b3e4",
+ "metadata": {
+ "id": "8910b3e4"
+ },
+ "outputs": [],
+ "source": [
+ "## Note: the \"fields\" option defines what fields we want the query to return. If set to \"None\", returns all available fields.\n",
+ "\n",
+ "imaging_studies = query.raw_data_download(\n",
+ " data_type=\"imaging_study\",\n",
+ " fields=None,\n",
+ " filter_object={\n",
+ " \"AND\": [\n",
+ " {\"=\": {\"loinc_system\": body_part_examined}},\n",
+ " {\"=\": {\"sex\": sex}},\n",
+ " {\"=\": {\"ethnicity\": ethnicity}},\n",
+ " {\">=\": {\"age_at_index\": age_threshold}},\n",
+ " {\"IN\": {\"study_modality\": study_modalities}},\n",
+ " {\"nested\": {\"path\": \"imaging_study_annotations\", \">=\": {\"midrc_mRALE_score\": mRALE_threshold}}},\n",
+ " {\"AND\": [\n",
+ " {\">=\": {\"days_from_study_to_pos_covid_test\": min_days_from_study_to_test}},\n",
+ " {\"<=\": {\"days_from_study_to_pos_covid_test\": max_days_from_study_to_test}}\n",
+ " ]}\n",
+ " ]\n",
+ " },\n",
+ " sort_fields=[{\"submitter_id\": \"asc\"}]\n",
+ " )\n",
+ "\n",
+ "if len(imaging_studies) > 0:\n",
+ " imaging_studies_ids = [i['submitter_id'] for i in imaging_studies if 'submitter_id' in i] ## make a list of the imaging study IDs returned\n",
+ " print(\"Query returned {} study IDs from {} cases.\".format(len(imaging_studies),len(set([i['case_ids'][0] for i in imaging_studies if 'case_ids' in i]))))\n",
+ " print(\"Data is a list with rows like this:\\n\\t {}\".format(imaging_studies[0:1]))\n",
+ "else:\n",
+ " print(\"Your query returned no data! Please, check that query parameters are valid.\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "33093eff-621f-452b-a0af-89af1940c65f",
+ "metadata": {
+ "id": "33093eff-621f-452b-a0af-89af1940c65f"
+ },
+ "outputs": [],
+ "source": [
+ "imaging_studies_df = pd.DataFrame(imaging_studies)\n",
+ "display(imaging_studies_df)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2c0f29ab-ab87-4fe5-859b-d5158c0fa3d7",
+ "metadata": {
+ "id": "2c0f29ab-ab87-4fe5-859b-d5158c0fa3d7"
+ },
+ "source": [
+ "## 3) Send another query to get data file details for our cohort / case ID\n",
+ "---\n",
+ "The `object_id` field in each imaging study record above contains the file identifiers for all files associated with each imaging study, which could include files like third-party annotations. If we simply want to access all files associated with our list of cases, we can use those object_ids.\n",
+ "\n",
+ "However, in this example, we'll ask for specific types of files and get more detailed information about each of the files. This is achieved by querying the `data_file` guppy index, which corresponds to the \"Data Files\" tab of the MIDRC data explorer GUID.\n",
+ "\n",
+ "All MIDRC data files, including both images and annotations, are listed in the guppy index \"data_file\", which is queried in a similar manner to our query of the `imaging_study` index above. The query parameter `data_type` below determines which guppy (Elasticsearch) index we're querying.\n",
+ "\n",
+ "To get only `data_file` records that correspond to our imaging study cohort built previously, we'll use the list of study UIDs as a query filter.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2959c0ed-adde-4f34-8308-ca0f61c599cf",
+ "metadata": {
+ "id": "2959c0ed-adde-4f34-8308-ca0f61c599cf"
+ },
+ "source": [
+ "### Set 'data_file' query parameters\n",
+ "---\n",
+ "Here, we'll utilize the property `source_node` to filter the list of files for our cohort to only those matching the type of files we're interested in. In this example, we ask only for CR and DX (x-ray) images, which will exclude any other types of files like annotations.\n",
+ "\n",
+ "We're also using the property `study_uid` as a filter to restrict the `data_file` records returned down to those associated with the imaging studies in our cohort built above.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "07f33bd1-7393-4e0a-902a-771783568280",
+ "metadata": {
+ "id": "07f33bd1-7393-4e0a-902a-771783568280"
+ },
+ "outputs": [],
+ "source": [
+ "# Build a list of study UIDs to use as a filter in our data_file query\n",
+ "study_uids = [i['study_uid'] for i in imaging_studies]\n",
+ "study_uids"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2efabbc8-c9cb-481a-9847-50659918b6d7",
+ "metadata": {
+ "id": "2efabbc8-c9cb-481a-9847-50659918b6d7"
+ },
+ "outputs": [],
+ "source": [
+ "# Choose the types of data we want using \"source_node\" as a filter\n",
+ "source_nodes = [\"cr_series_file\",\"dx_series_file\"]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d3a2b920-15d0-4399-ab2d-4faa2748a314",
+ "metadata": {
+ "id": "d3a2b920-15d0-4399-ab2d-4faa2748a314"
+ },
+ "outputs": [],
+ "source": [
+ "## Search for specific files associated with our cohort by adding \"study_uid\" as a filter\n",
+ "# * Note: \"fields\" is set to \"None\" in this query, which by default returns all the properties available\n",
+ "data_files = query.raw_data_download(\n",
+ " data_type=\"data_file\",\n",
+ " fields=None,\n",
+ " filter_object={\n",
+ " \"AND\": [\n",
+ " {\"IN\": {\"study_uid\": study_uids}},\n",
+ " {\"IN\": {\"source_node\": source_nodes}},\n",
+ " ]\n",
+ " },\n",
+ " sort_fields=[{\"submitter_id\": \"asc\"}]\n",
+ " )\n",
+ "\n",
+ "if len(data_files) > 0:\n",
+ " object_ids = [i['object_id'] for i in data_files if 'object_id' in i] ## make a list of the file object_ids returned by our query\n",
+ " cases = list(set([i['case_ids'][0] for i in data_files if 'case_ids' in i]))\n",
+ " studies = list(set([i['study_uid'][0] for i in data_files if 'study_uid' in i]))\n",
+ " print(\"Query returned {} data files with {} object_ids from {} studies of {} cases.\".format(len(data_files),len(object_ids),len(studies),len(cases)))\n",
+ " print(\"Data is a list with rows like this:\\n\\t {}\".format(data_files[0:1]))\n",
+ "else:\n",
+ " print(\"Your query returned no data! Please, check that query parameters are valid.\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "972b4805-61a3-4c22-8339-93878f201e46",
+ "metadata": {
+ "id": "972b4805-61a3-4c22-8339-93878f201e46"
+ },
+ "outputs": [],
+ "source": [
+ "# object_id (AKA \"data GUID\") is a globally unique file identifier that points to an actual file object in cloud storage. We'll use the object_ids along with the gen3 command-line tool to download the files these object_ids point to.\n",
+ "object_ids\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "81e35d9b-e8ec-4fc0-9d3c-7485446c15f3",
+ "metadata": {
+ "id": "81e35d9b-e8ec-4fc0-9d3c-7485446c15f3"
+ },
+ "source": [
+ "## 4) Access data files using their object_id / data GUID (globally unique identifiers)\n",
+ "---\n",
+ "In order to download files stored in MIDRC, users need to reference the file's object_id (AKA data GUID or Globally Unique IDentifier).\n",
+ "\n",
+ "Once we have a list of GUIDs we want to download, we can use either the gen3-client or the gen3 SDK to download the files. You can also access individual files in your browser after logging-in and entering the GUID after the `files/` endpoint, as in this URL: https://data.midrc.org/files/GUID\n",
+ "\n",
+ "where GUID is the actual GUID, e.g.: https://data.midrc.org/files/dg.MD1R/b87d0db3-d95a-43c7-ace1-ab2c130e04ec\n",
+ "\n",
+ "For instructions on how to install and use the gen3-client, please see [the MIDRC quick-start guide](https://data.midrc.org/dashboard/Public/documentation/Gen3_MIDRC_GetStarted.pdf), which can be found linked here and in the MIDRC data portal header as \"Get Started\".\n",
+ "\n",
+ "Below we use the gen3 SDK command `gen3 drs-pull object` which is [documented in detail here](https://github.com/uc-cdis/gen3sdk-python/blob/master/docs/howto/drsDownloading.md)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8449f221-1d8d-4501-86af-111111fc7bf3",
+ "metadata": {
+ "id": "8449f221-1d8d-4501-86af-111111fc7bf3"
+ },
+ "source": [
+ "### Use the Gen3 SDK command `gen3 drs-pull object` to download an individual file"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f3462219-202b-4d59-9a3d-14cbdecab77b",
+ "metadata": {
+ "id": "f3462219-202b-4d59-9a3d-14cbdecab77b"
+ },
+ "outputs": [],
+ "source": [
+ "## Make a new directory for downloaded files\n",
+ "os.system(\"rm -r downloads\")\n",
+ "os.system(\"mkdir -p downloads\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5b156930-805f-4562-aea7-62798b13e46b",
+ "metadata": {
+ "id": "5b156930-805f-4562-aea7-62798b13e46b"
+ },
+ "outputs": [],
+ "source": [
+ "## We can use a simple loop to download all files and keep track of successes and failures\n",
+ "\n",
+ "success,failure,other=[],[],[]\n",
+ "count,total = 0,len(object_ids)\n",
+ "for object_id in object_ids:\n",
+ " count+=1\n",
+ " cmd = \"gen3 --auth {} --endpoint data.midrc.org drs-pull object {} --output-dir downloads\".format(cred,object_id)\n",
+ " stout = subprocess.run(cmd, shell=True, capture_output=True)\n",
+ " print(\"Progress ({}/{}): {}\".format(count,total,stout.stdout))\n",
+ " if \"failed\" in str(stout.stdout):\n",
+ " failure.append(object_id)\n",
+ " elif \"successfully\" in str(stout.stdout):\n",
+ " success.append(object_id)\n",
+ " else:\n",
+ " other.append(object_id)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "cd0ebe63-380a-4606-8285-5736ec87ffee",
+ "metadata": {
+ "id": "cd0ebe63-380a-4606-8285-5736ec87ffee"
+ },
+ "outputs": [],
+ "source": [
+ "# Get a list of all downloaded .dcm files\n",
+ "image_files = glob.glob(pathname='**/*.dcm',recursive=True,)\n",
+ "image_files"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d53c1364-aba9-461f-a0f9-e7730875a339",
+ "metadata": {
+ "id": "d53c1364-aba9-461f-a0f9-e7730875a339"
+ },
+ "source": [
+ "### View the DICOM Images\n",
+ "---\n",
+ "Here we'll use the [Python package `pydicom`](https://pydicom.github.io/pydicom/stable/) to view the downloaded DICOM images.\n",
+ "\n",
+ "Note that some of the files may contain compressed pixel data that require other packages to view; so, for this demo we'll simply skip over those using the following loop."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "533ce308-3618-47b2-bae1-ad4681feab02",
+ "metadata": {
+ "id": "533ce308-3618-47b2-bae1-ad4681feab02",
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "for image_file in image_files:\n",
+ " print(image_file)\n",
+ " ds = pydicom.dcmread(image_file)\n",
+ " try:\n",
+ " new_image = ds.pixel_array.astype(float)\n",
+ " scaled_image = (np.maximum(new_image, 0) / new_image.max()) * 255.0\n",
+ " scaled_image = np.uint8(scaled_image)\n",
+ " final_image = Image.fromarray(scaled_image)\n",
+ " print(type(final_image))\n",
+ " display(final_image)\n",
+ " except Exception as e:\n",
+ " print(\"Couldn't view {}: {}.\".format(image_file,e))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "60bb7027-7fb3-47cb-8b4b-2d084287fc20",
+ "metadata": {
+ "id": "60bb7027-7fb3-47cb-8b4b-2d084287fc20"
+ },
+ "source": [
+ "#### View the DICOM Headers\n",
+ "---\n",
+ "DICOM files have metadata elements embedded in the images. These can also be read and viewed using the `pydicom` package."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a1bbb4c1-9ab6-4260-904e-977836b57a01",
+ "metadata": {
+ "id": "a1bbb4c1-9ab6-4260-904e-977836b57a01"
+ },
+ "outputs": [],
+ "source": [
+ "ds = pydicom.dcmread(image_files[0],force=True)\n",
+ "display(ds)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4dec9bce-7f8f-48ee-abe1-6bc380d923c3",
+ "metadata": {
+ "id": "4dec9bce-7f8f-48ee-abe1-6bc380d923c3"
+ },
+ "outputs": [],
+ "source": [
+ "# Access individual elements\n",
+ "display(ds.file_meta)\n",
+ "display(ds.ImageType)\n",
+ "display(ds[0x0008, 0x0016])\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ecceb434-83f2-45f1-b1f0-7e1c58fafde9",
+ "metadata": {
+ "id": "ecceb434-83f2-45f1-b1f0-7e1c58fafde9"
+ },
+ "outputs": [],
+ "source": [
+ "# View the dicom metadata for all files as a DataFrame\n",
+ "dfs = []\n",
+ "for image_file in image_files:\n",
+ " ds = pydicom.dcmread(image_file)\n",
+ " df = pd.DataFrame(ds.values())\n",
+ " df[0] = df[0].apply(lambda x: pydicom.dataelem.convert_raw_data_element(x) if isinstance(x, pydicom.dataelem.RawDataElement) else x)\n",
+ " df['name'] = df[0].apply(lambda x: x.name)\n",
+ " df['value'] = df[0].apply(lambda x: x.value)\n",
+ " df = df[['name', 'value']]\n",
+ " df = df.set_index('name').T.reset_index(drop=True)\n",
+ " df['filename'] = image_file\n",
+ " df.drop(columns=['Pixel Data'],inplace=True) # drop the pixel data as it's too large and nonsensical to store in a DataFrame\n",
+ " dfs.append(df)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3e55cd95-570b-42d0-80cd-fb47693c49dd",
+ "metadata": {
+ "id": "3e55cd95-570b-42d0-80cd-fb47693c49dd"
+ },
+ "outputs": [],
+ "source": [
+ "# Make a master dataframe for all images using only headers in all dataframes\n",
+ "headers = list(set.intersection(*map(set,dfs)))\n",
+ "df = pd.concat([df[headers] for df in dfs])\n",
+ "df.set_index('filename',inplace=True)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c54f11af-4b8c-4744-bc88-6cd2ced7e102",
+ "metadata": {
+ "id": "c54f11af-4b8c-4744-bc88-6cd2ced7e102"
+ },
+ "outputs": [],
+ "source": [
+ "display(df)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5b991196-5298-43b6-987a-90de9bf308e5",
+ "metadata": {
+ "id": "5b991196-5298-43b6-987a-90de9bf308e5"
+ },
+ "outputs": [],
+ "source": [
+ "## Export the file metadata as a TSV file\n",
+ "filename = \"MIDRC_DICOM_metadata.tsv\"\n",
+ "df.to_csv(filename, sep='\\t')\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "LxvW5sWGdK6V",
+ "metadata": {
+ "id": "LxvW5sWGdK6V"
+ },
+ "source": [
+ "## 5) Set up Python environment for MIDRC BIH\n",
+ "---\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d6SKQK8edK6W",
+ "metadata": {
+ "id": "d6SKQK8edK6W"
+ },
+ "source": [
+ "### Download an API key file containing your credentials\n",
+ "---\n",
+ "1) Navigate to the MIDRC BIH login page in your browser: https://imaging-hub.data-commons.org/portal/login.\n",
+ "2) Navigate to the user profile page: https://imaging-hub.data-commons.org/portal/identity.\n",
+ "3) Click on the button \"Create API Key\" and save the `credentials.json` file somewhere safe as `bih-credentails.json`.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "96jg82L7dK6W",
+ "metadata": {
+ "id": "96jg82L7dK6W"
+ },
+ "source": [
+ "### Set local variables\n",
+ "---\n",
+ "Change the following `cred` variable path to point to your credentials file downloaded from the MIDRC data portal following the instructions above."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "NaQ3_X6adK6W",
+ "metadata": {
+ "id": "NaQ3_X6adK6W"
+ },
+ "outputs": [],
+ "source": [
+ "bcred = \"/content/bih-credentials.json\" # location of your MIDRC credentials, downloaded from https://data.midrc.org/identity by clicking \"Create API key\" button and saving the credentials.json locally\n",
+ "bapi = \"https://imaging-hub.data-commons.org/\" # The base URL of the data commons being queried. This shouldn't change for MIDRC.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fUpYD966dwjt",
+ "metadata": {
+ "id": "fUpYD966dwjt"
+ },
+ "source": [
+ "### Initiate instances of the Gen3 SDK Classes using credentials file for authentication\n",
+ "---\n",
+ "Again, make sure the \"cred\" directory path variable reflects the location of your credentials file (path variables set above)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "Tuy-GlVhdwju",
+ "metadata": {
+ "id": "Tuy-GlVhdwju"
+ },
+ "outputs": [],
+ "source": [
+ "bauth = Gen3Auth(bapi, refresh_file=bcred) # authentication class\n",
+ "bquery = Gen3Query(bauth) # query class\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "xsTgXCdJeRWL",
+ "metadata": {
+ "id": "xsTgXCdJeRWL"
+ },
+ "source": [
+ "## 6) Build Cohorts by Sending Queries to the MIDRC BIH metadata API\n",
+ "---\n",
+ "#### Set query parameters\n",
+ "---\n",
+ "* Here, we'll send a query to the `imaging_series` guppy index, which the [MIDRC BIH data explorer GUI](https://data.midrc.org/explorer) runs off.\n",
+ "* The filters defined below can be modified to return different subsets of imaging series. Here, we'll use a rather restrictive combination of Modality, Body Part Examined, and Study Descrition filters to narrow our selected imaging series to a small number for demonstration purposes.\n",
+ "* If our query request is successful, the API response should be in JSON format, and it should contain a list of imaging series along with any other data we ask for, including data GUIDs we will use to access image files.\n",
+ "* Reminder that the guppy graphQL service has extensive documentation in GitHub [here](https://github.com/uc-cdis/guppy/blob/master/doc/queries.md)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f_IrjY_eeRWM",
+ "metadata": {
+ "id": "f_IrjY_eeRWM"
+ },
+ "source": [
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "lEeVE9vAeRWM",
+ "metadata": {
+ "id": "lEeVE9vAeRWM"
+ },
+ "outputs": [],
+ "source": [
+ "### Set some \"imaging_series\" query parameters to select Lung CT imaging series for female COVID-19 cases across the Biomedical Imaging Data Fabric\n",
+ "\n",
+ "## Here we select imaging series with a BodyPartExamined of \"Chest\"\n",
+ "BodyPartExamined = \"LUNG\"\n",
+ "\n",
+ "## Here we select imaging series with a Modality of \"CT\"\n",
+ "Modality = \"CT\"\n",
+ "\n",
+ "## Here we select imaging series with a PatientSex of \"Female\"\n",
+ "PatientSex = \"Female\"\n",
+ "\n",
+ "## Here we select imaging series with a disease_type of \"COVID-19\"\n",
+ "disease_type = \"COVID-19\"\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "k_e1ln9beRWN",
+ "metadata": {
+ "id": "k_e1ln9beRWN"
+ },
+ "outputs": [],
+ "source": [
+ "## Note: the \"fields\" option defines what fields we want the query to return. If set to \"None\", returns all available fields.\n",
+ "\n",
+ "series = bquery.raw_data_download(\n",
+ " data_type=\"imaging_series\",\n",
+ " fields=None,\n",
+ " filter_object={\n",
+ " \"AND\": [\n",
+ " {\"=\": {\"BodyPartExamined\": BodyPartExamined}},\n",
+ " {\"=\": {\"Modality\": Modality}},\n",
+ " {\"=\": {\"PatientSex\": PatientSex}},\n",
+ " {\"=\": {\"disease_type\": disease_type}},\n",
+ " ]\n",
+ " },\n",
+ " sort_fields=[{\"submitter_id\": \"asc\"}]\n",
+ " )\n",
+ "\n",
+ "if len(series) > 0:\n",
+ " series_ids = list(set([i['submitter_id'] for i in series if 'submitter_id' in i])) ## make a list of the imaging series IDs returned\n",
+ " object_ids = list(set([rec['object_ids'][0] for rec in series if 'object_ids' in rec])) ## make a list of the imaging series IDs returned\n",
+ " subject_ids = list(set([rec['subject_id'][0] for rec in series if 'subject_id' in rec])) ## make a list of the imaging series IDs returned\n",
+ " print(\"Query returned {} imaging series for {} subjects with {} object_ids.\".format(len(series),len(subject_ids),len(object_ids)))\n",
+ " print(\"Data is a list with rows like this:\")\n",
+ " for k,v in series[0:1][0].items():\n",
+ " print(\"\\t\\'{}' : '{}'\".format(k,v))\n",
+ "else:\n",
+ " print(\"Your query returned no data! Please, check that query parameters are valid.\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "Sa68oHD-eRWN",
+ "metadata": {
+ "id": "Sa68oHD-eRWN"
+ },
+ "outputs": [],
+ "source": [
+ "series_df = pd.DataFrame(series)\n",
+ "display(series_df)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "XaGi5Pudvljn",
+ "metadata": {
+ "id": "XaGi5Pudvljn"
+ },
+ "outputs": [],
+ "source": [
+ "## Export the file metadata as a TSV file\n",
+ "filename = \"MIDRC_BIH_imaging_series_metadata.tsv\"\n",
+ "series_df.to_csv(filename, sep='\\t')\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "V4HS7Cmxqoc0",
+ "metadata": {
+ "id": "V4HS7Cmxqoc0"
+ },
+ "source": [
+ "## 7) Access data files using their object_id / data GUID (globally unique identifiers)\n",
+ "---\n",
+ "In order to programmatically access files for imaging series indexed in MIDRC BIH, users can reference the file's object_id (AKA data GUID or Globally Unique IDentifier, which is an example of a GA4GH DRS URI).\n",
+ "\n",
+ "If an imaging series does not have an object_id associated with it, users will need to follow the platform links in the data table to the host platform where the data can be accessed or requested.\n",
+ "\n",
+ "As above for the MIDRC data commons, once we have a list of object_ids / image GUIDs we want to download, we can use either the gen3-client or the gen3 SDK to download the files.\n",
+ "\n",
+ "For instructions on how to install and use the gen3-client, please see [the MIDRC quick-start guide](https://data.midrc.org/dashboard/Public/documentation/Gen3_MIDRC_GetStarted.pdf), which can be found linked here and in the MIDRC data portal header as \"Get Started\".\n",
+ "\n",
+ "Below we use the gen3 SDK command `gen3 drs-pull object` which is [documented in detail here](https://github.com/uc-cdis/gen3sdk-python/blob/master/docs/howto/drsDownloading.md)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "Xif_NvRhqoc1",
+ "metadata": {
+ "id": "Xif_NvRhqoc1"
+ },
+ "source": [
+ "### View the DICOM Images\n",
+ "---\n",
+ "The MIDRC BIH aggregates dicom viewer URLs from across connected nodes in the Biomedical Imaging Data Fabric. If a connected data resources runs a dicom viewer and provides URLs for imaging series, the URL should be available in the BIH metadata, demonstrated below."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "i0FwitvWqoc1",
+ "metadata": {
+ "id": "i0FwitvWqoc1",
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "for rec in series:\n",
+ " if 'dicom_viewer_url' in rec and rec['dicom_viewer_url'] != np.nan:\n",
+ " print(\"{}\".format(rec['dicom_viewer_url']))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "544761e9",
+ "metadata": {
+ "id": "544761e9"
+ },
+ "source": [
+ "## The End\n",
+ "---\n",
+ "If you have any questions related to this notebook don't hesitate to reach out to the MIDRC Helpdesk at midrc-support@gen3.org or the author directly at cgmeyer@uchicago.edu\n",
+ "\n",
+ "Happy data wrangling!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f6691638",
+ "metadata": {
+ "id": "f6691638"
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "colab": {
+ "provenance": []
+ },
+ "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": "3.10.13"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/jupyter-midrc/combined_demos/Querying_and_Accessing_MIDRC_Data.ipynb b/jupyter-midrc/combined_demos/Querying_and_Accessing_MIDRC_Data.ipynb
new file mode 100644
index 00000000..5d2d4ac2
--- /dev/null
+++ b/jupyter-midrc/combined_demos/Querying_and_Accessing_MIDRC_Data.ipynb
@@ -0,0 +1,970 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "d8ef63c3",
+ "metadata": {},
+ "source": [
+ "# Query and Accessing Data in a Gen3 Data Commons\n",
+ "---\n",
+ "by Chris Meyer, PhD\n",
+ "\n",
+ "Manager of Data and User Services at the Center for Translational Data Science at University of Chicago\n",
+ "\n",
+ "June 2022\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5db7f87c",
+ "metadata": {},
+ "source": [
+ "### Introduction\n",
+ "---\n",
+ "* This notebook is intended to demonstrate a variety of ways to access file objects and structured data (aka \"metadata\") in a Gen3 data commons.\n",
+ "* File objects are accessed via their \"data GUID\" aka \"object_id\", which is a unique identifier that is associated with a storage_url in the file index (https://data.midrc.org/index/index). Users must be authorized to access a file in order to download it via the object_id. \n",
+ "* Structured data in a Gen3 Data Commons is imported into Postgres via the \"sheepdog\" service and must conform to the data model. The data model is a relational model that consists of tables or \"nodes\" that are related to one another via foreign keys so that the model can be thought of as a graph of nodes that are linked to each other. Each node in the model contains certain properties (keys) that store data of a particular type (values).\n",
+ "* The \"sheepdog\" service can export tables of data from a particular node of a data project. This is the simplest way to access \"all\" the data in a Gen3 data commons.\n",
+ "* Queries can be constructed to target specific types of data in Postgres and are handled by the \"peregrine\" graphQL service.\n",
+ "* Structured data can also be transformed via an \"ETL\" (extract, transform, load) process that takes the complex relationships between nodes and \"flattens\" the data into a single table, which is stored in an ElasticSearch (ES) database that can be queried using the \"guppy\" graphQL service. These ES tables are what the data exploration app of the Gen3 data-portal is based on.\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8e1a7935",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# The packages below may be necessary for users to install according to the imports necessary in the subsequent cells.\n",
+ "\n",
+ "#!pip install --upgrade pandas\n",
+ "#!pip install --upgrade --ignore-installed PyYAML\n",
+ "#!pip install --upgrade pip\n",
+ "#!pip install --upgrade gen3\n",
+ "#!pip install pydicom"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7ea2fa09",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Import Python Packages and scripts\n",
+ "\n",
+ "import pandas as pd\n",
+ "import sys, os\n",
+ "import gen3\n",
+ "import pydicom\n",
+ "from io import StringIO\n",
+ "\n",
+ "\n",
+ "from gen3.submission import Gen3Submission\n",
+ "from gen3.auth import Gen3Auth\n",
+ "from gen3.index import Gen3Index\n",
+ "from IPython.display import display\n",
+ "from expansion import Gen3Expansion\n",
+ "from gen3.query import Gen3Query\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8d6e9922",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Import some custom Python scripts from personal GitHub repo\n",
+ "# change these directory paths to reflect your local working directory\n",
+ "\n",
+ "home_dir = \"/Users/christopher\" \n",
+ "demo_dir = \"{}/Documents/Notes/MIDRC/tutorials\".format(home_dir)\n",
+ "\n",
+ "os.chdir(demo_dir)\n",
+ "\n",
+ "os.system(\"wget https://raw.githubusercontent.com/cgmeyer/gen3sdk-python/master/expansion/expansion.py -O {}/expansion.py\".format(demo_dir))\n",
+ "%run expansion.py\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d316dcdf",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Initiate instances of the Gen3 SDK Classes using credentials file for authentication\n",
+ "# Change the directory path in \"cred\" to reflect the location of your credentials file.\n",
+ "\n",
+ "api = \"https://data.midrc.org\"\n",
+ "cred = \"{}/Downloads/midrc-credentials.json\".format(home_dir)\n",
+ "auth = Gen3Auth(api, refresh_file=cred) # authentication class\n",
+ "sub = Gen3Submission(api, auth) # submission class\n",
+ "query = Gen3Query(auth) # query class\n",
+ "exp = Gen3Expansion(api,auth,sub) # class with some custom scripts\n",
+ "exp.get_project_ids()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "85fc475d",
+ "metadata": {},
+ "source": [
+ "## Accessing structured data in Postgres using sheepdog exports\n",
+ "---\n",
+ "* Probably the most straight-forward way to access structured data in a Gen3 Data Commons is to simply export the table of data using the sheepdog service (https://petstore.swagger.io/?url=https://raw.githubusercontent.com/uc-cdis/sheepdog/master/openapi/swagger.yml#/export/post__program___project__export).\n",
+ "* The Gen3SDK has a function `Gen3Submission.export_node()` for exporting entire tables of data from Postgres: https://github.com/uc-cdis/gen3sdk-python/blob/8196cf4b76a65d0b9b31c8637a18dfac2a911b56/gen3/submission.py#L361\n",
+ " * This function will export all records in a particular node of a specified project, and one can then use standard Python / R (etc.) tools to do the filtering and cohort building.\n",
+ "* Note: This export function is also accesible in the data-portal by navigating to a data project's URL, e.g., https://data.midrc.org/Open-A1, clicking a node in the graph, and then clicking the \"Download All\" button.\n",
+ " * For example: https://data.midrc.org/Open-A1/search?node_type=measurement\n",
+ " * Or, you can enter this URL in your browser, for example: https://data.midrc.org/api/v0/submission//Open/A1/export?node_label=measurement&format=tsv"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1373e86d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Example of exporting a table of data using the `Gen3Submission.export_node()` function\n",
+ "cases = sub.export_node(program='Open',project='A1',node_type='case',fileformat='tsv')\n",
+ "df = pd.read_csv(StringIO(cases), sep='\\t', header=0)\n",
+ "display(df)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ff227add",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## One can then use standard tools in any programming language to do cohort building. \n",
+ "## Here I'm using the \"pandas\" Python package to select a cohort based on demographic information stored in the case node.\n",
+ "cohort = list(df.loc[(df['sex']=='Female') & (df['race']==\"Black or African American\") & (df['age_at_index']>79)]['submitter_id'])\n",
+ "display(len(cohort))\n",
+ "cohort"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a27e781d",
+ "metadata": {},
+ "source": [
+ "### Using a Python wrapper to get all the data in a particular node\n",
+ "---\n",
+ "* I've written a wrapper script called `Gen3Expansion.get_node_tsvs()` that uses the `Gen3Submission.export_node()` function to export the same node across all projects you have access to in the data commons and then merges the results into a single master table for that node:\n",
+ "https://github.com/cgmeyer/gen3sdk-python/blob/5fd6b868374f622221c0c0173a0d9489b190facd/expansion/expansion.py#L219"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "fdc0977e",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "cases = exp.get_node_tsvs(node='case')\n",
+ "display(cases)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b150f734",
+ "metadata": {},
+ "source": [
+ "### Using a Python wrapper to get all the data in a particular project\n",
+ "---\n",
+ "* Similar to the above example, I've written a wrapper script called `Gen3Expansion.get_project_tsvs()` that uses the `Gen3Submission.export_node()` function to export every node in every project (or a particular project) in the data commons.\n",
+ "https://github.com/cgmeyer/gen3sdk-python/blob/5fd6b868374f622221c0c0173a0d9489b190facd/expansion/expansion.py#L298\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "71757809",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "## This example gets all the data in every node of the data model in the project Open-A1\n",
+ "## If \"projects\" is not specific, all data across all projects you have access to will be downloaded.\n",
+ "exp.get_project_tsvs(projects='Open-A1')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "51fc6b41",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!ls -l project_tsvs/Open-A1_tsvs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f7e40ba9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## You can then read in the TSVs of data exported from a node to do cohort building / research\n",
+ "tsv_dir = 'project_tsvs/Open-A1_tsvs'\n",
+ "ct = pd.read_csv(\"{}/Open-A1_ct_series_file.tsv\".format(tsv_dir),sep='\\t',dtype=str)\n",
+ "display(ct)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a3e9628c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Now we can use Python to get the CT series files for the cohort of cases we built earlier\n",
+ "cohort_ct = ct.loc[ct['case_ids'].isin(cohort)]\n",
+ "cohort_ct"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "20261819",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## You can access the cohort's CT series files by using the 'object_id' field:\n",
+ "object_ids = list(cohort_ct['object_id'])\n",
+ "object_ids"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5be8e81d",
+ "metadata": {},
+ "source": [
+ "## Queries to Postgres using Peregrine graphQL query service\n",
+ "---\n",
+ "* Peregrine GitHub Docs: https://github.com/uc-cdis/peregrine\n",
+ "* Peregrine swagger docs: https://petstore.swagger.io/?url=https://raw.githubusercontent.com/uc-cdis/peregrine/master/openapis/swagger.yaml\n",
+ "\n",
+ "---\n",
+ "* Most structured data (aka \"metadata\") submitted to a Gen3 system is stored in Postgres tables using the \"sheepdog\" service. This data must conform to the data commons' data model (https://data.midrc.org/dd), and is queryable via the \"peregrine\" service, which converts graphQL queries to SQL queries and returns the data requested. The Postgres tables are considered the \"source-of-truth\" for data in a Gen3 system (vs. the derived data in ElasticSearch, covered below).\n",
+ "\n",
+ "* On the data commons' website, peregrine queries can be sent to the API using the \"graphiQL\" query builder: https://data.midrc.org/query (click on \"Switch to Graph Model\"; if button says \"Switch to Flat Model\" you're in the correct spot).\n",
+ "\n",
+ "* Alternatively, you can send queries to the peregrine API using the Gen3SDK `Gen3Submission.query()` function, which uses the Python `requests` package to send queries as API requests: https://github.com/uc-cdis/gen3sdk-python/blob/31751633ba621b35f39eda7295f131245fb92728/gen3/submission.py#L399\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "311a6d7e",
+ "metadata": {},
+ "source": [
+ "### Example graph model query \\#1\n",
+ "* This query is running across all records in the `case` node and returns data from any dataset in the data commons you are authorized to access. Remember, the properties in the `case` node are essentially table headers for variables whose values are of a specific data type (string, enumeration, integer, number, boolean, array, etc.).\n",
+ "* The argument `covid19_positive: \"Yes\"` returns only case records that have the value \"Yes\" for the property `covid19_positive`, which indicates whether a case in MIDRC has ever had a positive COVID-19 test result.\n",
+ "* The `first` argument defines how many `case` records we want returned. Using the argument `first: 0`, all the records we have access to will be returned. If we leave the \"first\" argument out, only the first 10 records are returned by default. Setting `first: 2000` will return the first 2000 records in the table, etc.\n",
+ "* If your query is timing out, you will need to paginate the query (covered in next section) using a combination of \"first\" and \"offset\" arguments. This is only necessary if the tables being queries are very large, or the query traverses many nodes in the graph model.\n",
+ "* Properties we want returned from the API are enclosed in brackets. The possibilities and exact syntax are constrained by the data model (data.midrc.org/dd). \n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1a7994cb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "### Define the query\n",
+ "\n",
+ "## Here we're asking for the `project_id`, `submitter_id`, and some demographic data for every `case` record.\n",
+ "## We're also asking for the `study_uid` for every `imaging_study` record belonging to those cases, and for all `dx_series_file` records for those `imaging_studies`.\n",
+ "## Finally, we're asking for the `file_name` and `object_id` of any Digital X-ray files (node `dx_series_file`, backref: `dx_series_files`) they may have.\n",
+ "\n",
+ "## Note: \"submitter_id\" is a required property on every node, which is the human-readable (string), unique identifier for a record in a data table / node. So, the \"submitter_id\" of a record in the case node is the de-identified patient's \"ID\".\n",
+ "\n",
+ "query_txt = \"\"\"\n",
+ "{\n",
+ " case(first: 0, covid19_positive: \"Yes\") {\n",
+ " project_id\n",
+ " submitter_id\n",
+ " ethnicity\n",
+ " sex\n",
+ " race\n",
+ " imaging_studies (study_modality: \"DX\") {\n",
+ " study_uid\n",
+ " dx_series_files {\n",
+ " object_id\n",
+ " file_name\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\"\"\"\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "0cecf28b",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "## Send the query using the Gen3 SDK Gen3Submission.query() function\n",
+ "## The response will be in JSON format.\n",
+ "\n",
+ "response = sub.query(query_txt)\n",
+ "if 'data' in response:\n",
+ " data = response['data']['case']\n",
+ " display(data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "219c7f97",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## the \"object_id\" field is the file's data GUID (or globally unique identifier), which can be used to access the file.\n",
+ "\n",
+ "object_ids = []\n",
+ "for case in data:\n",
+ " studies = case['imaging_studies']\n",
+ " for study in studies:\n",
+ " files = study['dx_series_files'] \n",
+ " if len(files)>0:\n",
+ " for file in files:\n",
+ " object_id = file['object_id']\n",
+ " object_ids.append(object_id)\n",
+ "object_ids"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "60b02d88",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Take a look at one of the file objects\n",
+ "#object_id was originally selected from the list above. If the object id is not above use an object id from above.\n",
+ "\n",
+ "object_id = 'dg.MD1R/ea6ad8e7-1dc9-4916-8e75-38abb66c6416'\n",
+ "os.system(\"gen3 --auth {} --endpoint data.midrc.org drs-pull object {}\".format(cred,object_id))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "15fe0b88",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!ls -l 10000364-1958844/"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b97a8336",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#The object downloaded above was zipped and must be unzipped to process further\n",
+ "#The path and file names may have changed and those changes should be reflected below\n",
+ "!unzip 10000364-1958844/2.16.840.1.114274.1818.52236113359126249589212595743121753735/2.16.840.1.114274.1818.54309100269617797736626917868992258958.zip"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9ab6b52a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from pydicom import dcmread\n",
+ "\n",
+ "fpath = \"2.16.840.1.114274.1818.54309100269617797736626917868992258958/2.16.840.1.114274.1818.46312267929568121457864041736105067915.dcm\"\n",
+ "ds = dcmread(fpath)\n",
+ "ds"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7308d00f",
+ "metadata": {},
+ "source": [
+ "### Counts with peregrine\n",
+ "---\n",
+ "* Peregrine is able to provide counts of records in nodes. A simple example is to quickly get the count of the numbers of cases and imaging studies in the data commons.\n",
+ "* You can also add arguments to the counts to, for example, get the number of cases in a particular project or get the imaging studies of a particular modality."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1e80ace7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "query_txt = \"{_case_count}\"\n",
+ "print(sub.query(query_txt))\n",
+ "query_txt = \"{_imaging_study_count}\"\n",
+ "print(sub.query(query_txt))\n",
+ "query_txt = '{CT_studies: _imaging_study_count(study_modality:\"CT\")}'\n",
+ "print(sub.query(query_txt))\n",
+ "query_txt = '{Open_A1_cases: _case_count(project_id:\"Open-A1\")}'\n",
+ "print(sub.query(query_txt))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5dcd297c",
+ "metadata": {},
+ "source": [
+ "### Queries of \"datanode\" using peregrine\n",
+ "---\n",
+ "Another handy trick with peregrine queries is the \"datanode\" query. \"Datanode\" isn't a real node in the data model, but is useful way to query all nodes that store file information. For example, if you have a patient ID, you can get all the files associated with that case.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "097f8def",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "query_txt = \"\"\"\n",
+ "{\n",
+ " datanode(first: 0, case_ids: \"10000364-1163342\") {\n",
+ " object_id\n",
+ " file_name\n",
+ " modality\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\"\"\"\n",
+ "response = sub.query(query_txt)\n",
+ "if 'data' in response:\n",
+ " display(response['data']['datanode'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3ba72458",
+ "metadata": {},
+ "source": [
+ "## Queries to ElasticSearch using Guppy graphQL query service\n",
+ "---\n",
+ "* Guppy Documentation: https://github.com/uc-cdis/guppy/blob/master/doc/queries.md#filters\n",
+ "* Guppy Download instructions: https://github.com/uc-cdis/guppy/blob/master/doc/download.md\n",
+ "* ETL (Tube) Documentation: https://github.com/uc-cdis/tube#gen3-etl---a-process-from-postgresql-to-es\n",
+ "---\n",
+ "* The Gen3 platform includes services for running an ETL process (Extract, Transform, Load), which is done by the Gen3 ETL service \"tube\", on the data in Postgres to create flattened tables of the same data in ElasticSearch (ES) for rapid querying performed by the Gen3 query service \"guppy\".\n",
+ "* Guppy runs graphql-like queries against the ES database, and can rapidly return derived data like histograms, statistics, aggregations, counts, etc. The tube service uses Spark to create these new tables of data in ES via an ETL mapping, which defines the structure of the new tables and is based on the data model. \n",
+ "* Since the structure of the data changes via the ETL process, peregrine queries to Postgres will not run using guppy. To explore what is possible to query, use the graphiQL interface / documentation.\n",
+ "* The \"Exploration\" app aka \"Data Explorer\" (data.midrc.org/explore), which uses faceted search to filter the flat data tables in ES, runs off of guppy queries.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "bf8d06d2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## an example guppy query, which hits the ElasticSearch database\n",
+ "\n",
+ "## define some parameters\n",
+ "pid = 'Open-R1'\n",
+ "node = 'imaging_study'\n",
+ "fields = [\"study_uid\",\n",
+ " \"study_description\",\n",
+ " \"case_ids\",\n",
+ " \"object_id\"]\n",
+ "filters = {\"project_id\": pid,\n",
+ " \"covid19_positive\" : \"Yes\",\n",
+ " \"body_part_examined\" : \"CHEST\",\n",
+ " \"study_modality\" : \"DX\"}\n",
+ "\n",
+ "## send the guppy query with the SDK class Gen3Query\n",
+ "## Note the \"first: 100000\", which makes sure we don't just get the default first 10 records\n",
+ "response = query.query(\n",
+ " data_type=node,\n",
+ " first=100000,\n",
+ " fields=fields,\n",
+ " filters=filters,\n",
+ " sort_object={\"submitter_id\": \"asc\"},\n",
+ ")\n",
+ "\n",
+ "# display the returned data\n",
+ "if 'data' in response:\n",
+ " study_data = response['data'][node]\n",
+ " display(study_data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d4459fa2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## another example guppy query, which hits the ElasticSearch database\n",
+ "\n",
+ "## define some parameters\n",
+ "node = 'case'\n",
+ "\n",
+ "fields = [\"project_id\",\n",
+ " \"submitter_id\",\n",
+ " \"object_id\"]\n",
+ "\n",
+ "filters = {\"sex\":\"Female\",\n",
+ " \"race\" : \"Asian\",\n",
+ " \"ethnicity\" : \"Hispanic or Latino\"}\n",
+ "\n",
+ "## send the guppy query with the SDK class Gen3Query\n",
+ "## Note the \"first: 100000\", which makes sure we don't just get the default first 10 records\n",
+ "response = query.query(\n",
+ " data_type=node,\n",
+ " first=100000,\n",
+ " fields=fields,\n",
+ " filters=filters,\n",
+ " sort_object={\"submitter_id\": \"asc\"},\n",
+ ")\n",
+ "\n",
+ "# display the returned data\n",
+ "if 'data' in response:\n",
+ " case_data = response['data'][node]\n",
+ " display(case_data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f9aa5639",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Elastic search is handy for accessing files for a cohort since object_ids associated with each study or case are joined to the table \n",
+ "study_object_ids = []\n",
+ "for study in study_data:\n",
+ " if 'object_id' in study:\n",
+ " object_id_list = study['object_id']\n",
+ " for object_id in object_id_list:\n",
+ " study_object_ids.append(object_id)\n",
+ "display(study_object_ids)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c3a0d6b2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "case_data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d842abd3",
+ "metadata": {},
+ "source": [
+ "### Sending aggregations with guppy\n",
+ "---\n",
+ "* Guppy has the ability to return some useful statistics (e.g., histograms) using aggregations.\n",
+ "* The `Gen3Query.graphql_query()` function can be used to send aggregations and other more complex queries that the basic `Gen3Query.query()` function can't support: https://github.com/uc-cdis/gen3sdk-python/blob/8196cf4b76a65d0b9b31c8637a18dfac2a911b56/gen3/query.py#L112"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "27035fe3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## A more complex example using Python requests\n",
+ "query_txt = \"\"\"{\n",
+ " _aggregation {\n",
+ " case {\n",
+ " sex {\n",
+ " histogram {\n",
+ " key\n",
+ " count\n",
+ " }\n",
+ " }\n",
+ " race {\n",
+ " histogram {\n",
+ " key\n",
+ " count\n",
+ " }\n",
+ " }\n",
+ " ethnicity {\n",
+ " histogram {\n",
+ " key\n",
+ " count\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\"\"\"\n",
+ "response = query.graphql_query(query_string=query_txt)\n",
+ "display(response)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a6bb9084",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Here is an example simple script for sending a basic aggregation request that will return the data as a DataFrame (\"TSV\")\n",
+ "## https://github.com/cgmeyer/gen3sdk-python/blob/5fd6b868374f622221c0c0173a0d9489b190facd/expansion/expansion.py#L3511\n",
+ "\n",
+ "data = exp.guppy_aggregation(node='case', prop='race', format='TSV')\n",
+ "display(data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "afcdb969",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## A more complex example using Python requests\n",
+ "query_txt = \"\"\"{\n",
+ " _aggregation {\n",
+ " case {\n",
+ " sex {\n",
+ " histogram {\n",
+ " key\n",
+ " count\n",
+ " }\n",
+ " }\n",
+ " race {\n",
+ " histogram {\n",
+ " key\n",
+ " count\n",
+ " }\n",
+ " }\n",
+ " ethnicity {\n",
+ " histogram {\n",
+ " key\n",
+ " count\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\"\"\"\n",
+ "query_json = {\"query\": query_txt}\n",
+ "guppy_url = \"{}/guppy/graphql\".format(api)\n",
+ "response = requests.post(guppy_url, json=query_json, auth=auth)\n",
+ "display(json.loads(response.text)['data']['_aggregation']['case'])\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b30f1eb",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "## Count the number of files in each project\n",
+ "files_by_project = \"\"\"\n",
+ "{\n",
+ " _aggregation {\n",
+ " data_file {\n",
+ " project_id {\n",
+ " histogram {\n",
+ " key\n",
+ " count\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\"\"\"\n",
+ "response = query.graphql_query(files_by_project)\n",
+ "display(response)\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3597b07f",
+ "metadata": {},
+ "source": [
+ "### Use the guppy download endpoint to access ElasticSearch tables.\n",
+ "---\n",
+ "* Tables of data from ES can be exported from the data exploration app (https://data.midrc.org/explore) by using the \"Download Table\" button.\n",
+ "* To get these sorts of tables using the API, you can use the guppy download function: https://github.com/uc-cdis/gen3sdk-python/blob/8196cf4b76a65d0b9b31c8637a18dfac2a911b56/gen3/query.py#L146"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "84aae2a8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## This query gets all the imaging studies of modality \"CT\"\n",
+ "\n",
+ "query.raw_data_download(\n",
+ " data_type=\"imaging_study\",\n",
+ " fields=[\n",
+ " \"study_uid\",\n",
+ " \"project_id\",\n",
+ " \"study_description\",\n",
+ " \"body_part_examined\",\n",
+ " \"case_ids\",\n",
+ " \"object_id\"\n",
+ " ],\n",
+ " filter_object={\"=\": {\"study_modality\": \"CT\"}}\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "279428e4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Here is an example getting all the cases in a particular project between ages of 45 and 47\n",
+ "\n",
+ "query.raw_data_download(\n",
+ " data_type=\"case\",\n",
+ " fields=[\n",
+ " \"submitter_id\",\n",
+ " \"project_id\",\n",
+ " \"race\",\n",
+ " \"sex\",\n",
+ " \"ethnicity\",\n",
+ " \"age_at_index\",\n",
+ " \"object_id\"\n",
+ " ],\n",
+ " filter_object={\"AND\": [{\">=\": {\"age_at_index\": 45}},\n",
+ " {\"<=\": {\"age_at_index\": 47}},\n",
+ " {\"=\": {\"project_id\": \"Open-A1\"}}]},\n",
+ " sort_fields=[{\"submitter_id\": \"asc\"}],\n",
+ " accessibility=\"accessible\"\n",
+ " )\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c6570ebd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Here is an example getting all the imaging studies where the patient had a positive COVID-19 test result within a week of the study date.\n",
+ "\n",
+ "response = query.raw_data_download(\n",
+ " data_type=\"imaging_study\",\n",
+ " fields=[\n",
+ " \"study_uid\",\n",
+ " \"study_modality\",\n",
+ " \"case_ids\",\n",
+ " \"project_id\",\n",
+ " \"race\",\n",
+ " \"sex\",\n",
+ " \"ethnicity\",\n",
+ " \"age_at_index\",\n",
+ " \"object_id\"\n",
+ " ],\n",
+ " filter_object={\"AND\": [{\">=\": {\"days_from_study_to_pos_covid_test\": -7}},\n",
+ " {\"<=\": {\"days_from_study_to_pos_covid_test\": 7}}]},\n",
+ " sort_fields=[{\"submitter_id\": \"asc\"}],\n",
+ " accessibility=\"accessible\"\n",
+ " )\n",
+ "\n",
+ "display(response)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fc104d89",
+ "metadata": {},
+ "source": [
+ "## Use the Gen3 SDK \"drs-pull\" commands to access the files themselves.\n",
+ "---\n",
+ "\n",
+ "Next, we'll use the Gen3 SDK command `gen3 drs-pull object` to access the imaging file using it's \"object_id\" aka \"data GUID\".\n",
+ "\n",
+ "See the detailed documentation to learn more about the Gen3 SDK drs-pull command: https://github.com/uc-cdis/gen3sdk-python/blob/master/docs/howto/drsDownloading.md\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "db38edda",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Take a look at one of the file objects\n",
+ "\n",
+ "data = response[0]\n",
+ "case_id = data['case_ids'][0]\n",
+ "study_uid = data['study_uid']\n",
+ "object_id = data['object_id'][0]\n",
+ "print(case_id)\n",
+ "print(object_id)\n",
+ "\n",
+ "cmd = \"gen3 --auth {} --endpoint data.midrc.org drs-pull object {}\".format(cred,object_id)\n",
+ "os.system(cmd)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f7d6c19f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cmd = \"ls -l {}/{}\".format(case_id,study_uid)\n",
+ "stout = subprocess.check_output(cmd, shell=True)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "449e2c69",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "## Grab the filename and series UID of the downloaded file using RegEx\n",
+ "import re\n",
+ "\n",
+ "m = re.search(' ([0-9\\.]+.zip)', str(stout))\n",
+ "\n",
+ "if m:\n",
+ " zip_file = m.group(1)\n",
+ " print(zip_file)\n",
+ "else:\n",
+ " print(\"No zip found.\")\n",
+ "\n",
+ "series_uid = re.sub(\"(\\.zip)\", \"\", zip_file)\n",
+ "print(series_uid)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "08ec040b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Unzip the imaging series package\n",
+ "from zipfile import ZipFile\n",
+ "\n",
+ "with ZipFile('{}/{}/{}/{}'.format(demo_dir,case_id,study_uid,zip_file), 'r') as zipObj:\n",
+ " zipObj.extractall()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c08956a8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Input the name of the newly create .dcm file\n",
+ "cmd = \"ls -l {}/{}/{}\".format(case_id,study_uid,series_uid)\n",
+ "stout = subprocess.run(cmd, shell=True, capture_output=True)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e024d412",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Get the name of the first DICOM file in the extracted imaging series\n",
+ "m = re.search(' ([0-9\\.]+.dcm)', str(stout))\n",
+ "\n",
+ "if m:\n",
+ " dcm_file = m.group(1)\n",
+ " print(dcm_file)\n",
+ "else:\n",
+ " print(\"No DCM files found.\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "90575758",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Read in the DCM file using the python DICOM package pydicom\n",
+ "dimg = pydicom.dcmread(\"{}/{}/{}/{}\".format(case_id,study_uid,series_uid,dcm_file),force=True)\n",
+ "dimg"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "544761e9",
+ "metadata": {},
+ "source": [
+ "## The End\n",
+ "---\n",
+ "If you have any questions related to this notebook don't hesitate to reach out to the MIDRC Helpdesk at midrc-support@datacommons.io or the author directly at cgmeyer@uchicago.edu\n",
+ "\n",
+ "Happy data wrangling!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "3dd85f59",
+ "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": "3.13.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/jupyter-midrc/combined_demos/build_a_cohort_of_severe_covid_19_cases.ipynb b/jupyter-midrc/combined_demos/build_a_cohort_of_severe_covid_19_cases.ipynb
new file mode 100644
index 00000000..c7c9fa90
--- /dev/null
+++ b/jupyter-midrc/combined_demos/build_a_cohort_of_severe_covid_19_cases.ipynb
@@ -0,0 +1,745 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "d8ef63c3",
+ "metadata": {
+ "id": "d8ef63c3"
+ },
+ "source": [
+ "# How to Build a Cohort of Severe COVID-19 Cases Using the MIDRC Data Commons\n",
+ "---\n",
+ "This notebook demonstrates how to build a cohort of severe COVID-19 cases using patient clinical data and AI research-based annotations in the MIDRC data commons.\n",
+ "\n",
+ "Our goal is to download structured data and files for 2 related cohorts: 1) severe COVID cases and 2) a control cohort of non-severe COVID cases.\n",
+ "\n",
+ "Luckily for the patients, there are many more non-severe cases; but that presents a challenge for building a balanced dataset that is optimal for AI/ML training and evaluation.\n",
+ "\n",
+ "* Cohort 1: All chest x-rays (CXR) with an mRALE score of 10 or higher obtained within 2 days after a positive COVID test.\n",
+ "\n",
+ "* Cohort 2: Matching number of CXRs with an mRALE score <10 obtained within 2 days after a positive COVID test.\n",
+ "\n",
+ "* Additionally, we want the cohorts to be somewhat balanced and matched in terms of the demographics: age, sex, race, and ethnicity.\n",
+ "\n",
+ "\n",
+ "by Chris Meyer, PhD\n",
+ "\n",
+ "Manager of Data and User Services at the Center for Translational Data Science at University of Chicago\n",
+ "\n",
+ "August 2023\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5db7f87c",
+ "metadata": {
+ "id": "5db7f87c"
+ },
+ "source": [
+ "## 1) Set up Python environment\n",
+ "---\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1bffd5d4",
+ "metadata": {
+ "id": "1bffd5d4"
+ },
+ "source": [
+ "### Set local variables\n",
+ "---\n",
+ "Change the following directory paths to a valid working directories where you're running this notebook."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c3c59c2c",
+ "metadata": {
+ "id": "c3c59c2c"
+ },
+ "outputs": [],
+ "source": [
+ "cred = \"/content/credentials.json\" # location of your MIDRC credentials, downloaded from https://data.midrc.org/identity by clicking \"Create API key\" button and saving the credentials.json locally; then upload to Colab Files browser\n",
+ "api = \"https://data.midrc.org\" # The base URL of the data commons being queried. This shouldn't change for MIDRC.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7962e54c",
+ "metadata": {
+ "id": "7962e54c"
+ },
+ "source": [
+ "### Install / Import Python Packages and Scripts"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8e1a7935",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "8e1a7935",
+ "outputId": "d6a996a2-3321-4f99-e2f0-594a0b7a9f9a"
+ },
+ "outputs": [],
+ "source": [
+ "## The packages below may be necessary for users to install according to the imports necessary in the subsequent cells.\n",
+ "\n",
+ "import sys\n",
+ "#!{sys.executable} -m pip install\n",
+ "#!{sys.executable} -m pip install --upgrade pandas\n",
+ "#!{sys.executable} -m pip install --upgrade --ignore-installed PyYAML\n",
+ "#!{sys.executable} -m pip install --upgrade pip\n",
+ "!{sys.executable} -m pip install --upgrade gen3\n",
+ "#!{sys.executable} -m pip install pydicom\n",
+ "#!{sys.executable} -m pip install IPython",
+ "#!{sys.executable} -m pip install psmpy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7ea2fa09",
+ "metadata": {
+ "id": "7ea2fa09"
+ },
+ "outputs": [],
+ "source": [
+ "## Import Python Packages and scripts\n",
+ "\n",
+ "import os, subprocess\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "#import pydicom\n",
+ "\n",
+ "# import some Gen3 packages\n",
+ "import gen3\n",
+ "from gen3.auth import Gen3Auth\n",
+ "from gen3.query import Gen3Query\n",
+ "from IPython.display import display",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7784ecc9",
+ "metadata": {
+ "id": "7784ecc9"
+ },
+ "source": [
+ "### Initiate instances of the Gen3 SDK Classes using credentials file for authentication\n",
+ "---\n",
+ "Again, make sure the \"cred\" directory path variable reflects the location of your credentials file (path variables set above)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d316dcdf",
+ "metadata": {
+ "id": "d316dcdf"
+ },
+ "outputs": [],
+ "source": [
+ "auth = Gen3Auth(api, refresh_file=cred) # authentication class\n",
+ "query = Gen3Query(auth) # query class\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2ea96784-3370-46a6-a2c7-edda947bfa8f",
+ "metadata": {
+ "id": "2ea96784-3370-46a6-a2c7-edda947bfa8f"
+ },
+ "source": [
+ "## 2) Build Cohorts by Sending Queries to the MIDRC APIs\n",
+ "#### General notes on sending queries:\n",
+ "* There are many ways to query and access metadata for cohort building in MIDRC, but this notebook will focus on using the [Gen3](https://gen3.org) graphQL query service [\"guppy\"](https://github.com/uc-cdis/guppy/#readme). This is the backend query service that [MIDRC's data explorer GUI](https://data.midrc.org/explorer) uses. So, anything you can do in the explorer GUI, you can do with guppy queries, and more!\n",
+ "* The guppy graphQL service has more functionality than is demonstrated in this simple example with extensive documentation in GitHub [here](https://github.com/uc-cdis/guppy/blob/master/doc/queries.md) in case you'd like to build your own queries from scratch.\n",
+ "* The Gen3 SDK (intialized as \"query\" above in this notebook) has Python wrapper scripts to make sending queries to the guppy graphQL API simpler. The guppy SDK package can be viewed in GitHub [here](https://github.com/uc-cdis/gen3sdk-python/blob/master/gen3/query.py).\n",
+ "* Guppy queries focus on a particular type of data (cases, imaging studies, files, etc.) and include arguments that are akin to selecting filter values in [MIDRC's data explorer GUI](https://data.midrc.org/explorer).\n",
+ "* To see more documentation about to use and combine filters with various operator logic (like AND/OR/IN, etc.) see [this page](https://github.com/uc-cdis/guppy/blob/master/doc/queries.md#filter).\n",
+ "* We then send our query to MIDRC's guppy API endpoint using [the Gen3Query SDK package](https://github.com/uc-cdis/gen3sdk-python/blob/master/gen3/query.py) we initialized earlier.\n",
+ "* If our query request is successful, the API response should be in JSON format, and it should contain a list of patient IDs along with any other patient data we ask for.\n",
+ "\n",
+ "---\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3e4a624e-f769-46a9-b76c-2efe9713bf61",
+ "metadata": {
+ "id": "3e4a624e-f769-46a9-b76c-2efe9713bf61"
+ },
+ "source": [
+ "#### Cohort 1: All chest x-rays (CXR) with an mRALE score of 10 or higher obtained within 2 days after a positive COVID test.\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9bc4a36e-0647-4546-9777-6d0ad7b32750",
+ "metadata": {
+ "id": "9bc4a36e-0647-4546-9777-6d0ad7b32750"
+ },
+ "outputs": [],
+ "source": [
+ "### Set some \"imaging_study\" query parameters\n",
+ "\n",
+ "## mRALE filter: we'll select all imaging studies annotated with an mRAle scores greater than or equal to this threshold number\n",
+ "mRALE_threshold = 10\n",
+ "\n",
+ "## days from study to positive COVID-19 test filter: we want imaging studies performed within two days after a positive test\n",
+ "min_days_from_study_to_test = -2\n",
+ "max_days_from_study_to_test = 0\n",
+ "\n",
+ "## Imaging study modality filter: we want chest x-rays, so we want studies with a modality of either DX or CR\n",
+ "study_modalities = [\"DX\", \"CR\"]\n",
+ "\n",
+ "## Imaging study body part filter: here we select \"chest\" as the \"LOINC system\" filter, which is the body part examined\n",
+ "body_part_examined = \"Chest\"\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8910b3e4",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "8910b3e4",
+ "outputId": "5ecb442a-edc0-4e65-98c0-20b0f33d62a7"
+ },
+ "outputs": [],
+ "source": [
+ "## Note: the \"fields\" option defines what fields we want the query to return. If set to \"None\", returns all available fields.\n",
+ "\n",
+ "severe_studies = query.raw_data_download(\n",
+ " data_type=\"imaging_study\",\n",
+ " fields=None,\n",
+ " filter_object={\n",
+ " \"AND\": [\n",
+ " {\"IN\": {\"loinc_system\": [body_part_examined]}},\n",
+ " {\"IN\": {\"study_modality\": study_modalities}},\n",
+ " {\"nested\": {\"path\": \"imaging_study_annotations\", \">=\": {\"midrc_mRALE_score\": mRALE_threshold}}},\n",
+ " {\"AND\": [\n",
+ " {\">=\": {\"days_from_study_to_pos_covid_test\": min_days_from_study_to_test}},\n",
+ " {\"<=\": {\"days_from_study_to_pos_covid_test\": max_days_from_study_to_test}}\n",
+ " ]}\n",
+ " ]\n",
+ " },\n",
+ " sort_fields=[{\"submitter_id\": \"asc\"}]\n",
+ " )\n",
+ "\n",
+ "if len(severe_studies) > 0 and \"submitter_id\" in severe_studies[0]:\n",
+ " severe_study_ids = [i['submitter_id'] for i in severe_studies] ## make a list of the imaging study IDs returned\n",
+ " print(\"Query returned {} study IDs.\".format(len(severe_studies)))\n",
+ " print(\"Data is a list with rows like this:\\n\\t {}\".format(severe_studies[0:1]))\n",
+ "else:\n",
+ " print(\"Your query returned no data! Please, check that query parameters are valid.\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8df88d18-e709-44a8-a395-98a05126cff3",
+ "metadata": {
+ "id": "8df88d18-e709-44a8-a395-98a05126cff3"
+ },
+ "source": [
+ "#### Cohort 2: CXRs with an mRALE score <10 obtained within 2 days after a positive COVID test.\n",
+ "---\n",
+ "\n",
+ "We don't need to set any new parameters for our filters this time. We just need to reverse the operator on the mRALE threshold from greater than or equal to (`>=`) to less than (`<`)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "eee1ce8c-ccd3-468d-8df3-c435c5e83148",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "eee1ce8c-ccd3-468d-8df3-c435c5e83148",
+ "outputId": "fbfeb125-028b-4d64-895d-1157e8663302"
+ },
+ "outputs": [],
+ "source": [
+ "mild_studies = query.raw_data_download(\n",
+ " data_type=\"imaging_study\",\n",
+ " fields=None,\n",
+ " filter_object={\n",
+ " \"AND\": [\n",
+ " {\"IN\": {\"loinc_system\": [body_part_examined]}},\n",
+ " {\"IN\": {\"study_modality\": study_modalities}},\n",
+ " {\"nested\": {\"path\": \"imaging_study_annotations\", \"<\": {\"midrc_mRALE_score\": mRALE_threshold}}},\n",
+ " {\"AND\": [\n",
+ " {\">=\": {\"days_from_study_to_pos_covid_test\": min_days_from_study_to_test}},\n",
+ " {\"<=\": {\"days_from_study_to_pos_covid_test\": max_days_from_study_to_test}}\n",
+ " ]}\n",
+ " ]\n",
+ " },\n",
+ " sort_fields=[{\"submitter_id\": \"asc\"}]\n",
+ " )\n",
+ "\n",
+ "if len(mild_studies) > 0 and \"submitter_id\" in mild_studies[0]:\n",
+ " mild_study_ids = [i['submitter_id'] for i in mild_studies] ## make a list of the imaging study IDs returned\n",
+ " print(\"Query returned {} study IDs.\".format(len(mild_studies)))\n",
+ " print(\"Data is a list with rows like this:\\n\\t {}\".format(mild_studies[0:1]))\n",
+ "else:\n",
+ " print(\"Your query returned no data! Please, check that query parameters are valid.\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "33093eff-621f-452b-a0af-89af1940c65f",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 961
+ },
+ "id": "33093eff-621f-452b-a0af-89af1940c65f",
+ "outputId": "023fcc64-dba1-4d9a-c101-800897b4002c"
+ },
+ "outputs": [],
+ "source": [
+ "severe_df = pd.DataFrame(severe_studies)\n",
+ "display(severe_df.head())\n",
+ "\n",
+ "mild_df = pd.DataFrame(mild_studies)\n",
+ "display(mild_df.head())\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a9f12059-e9c5-4dda-9844-8d88c8f3671c",
+ "metadata": {
+ "id": "a9f12059-e9c5-4dda-9844-8d88c8f3671c"
+ },
+ "outputs": [],
+ "source": [
+ "## Label cases as mild or severe and then combine the dataframes into a single dataframe\n",
+ "mild_df['cohort'] = 'mild'\n",
+ "severe_df['cohort'] = 'severe'\n",
+ "df = pd.concat([mild_df,severe_df],ignore_index=True)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9c6eac91-594b-4532-8612-040518dc768d",
+ "metadata": {
+ "id": "9c6eac91-594b-4532-8612-040518dc768d"
+ },
+ "outputs": [],
+ "source": [
+ "# convert patient demographic columns in lists to strings\n",
+ "df['case_ids'] = df['case_ids'].apply(lambda x: ','.join(map(str, x)))\n",
+ "df['ethnicity'] = df['ethnicity'].apply(lambda x: ','.join(map(str, x)))\n",
+ "df['race'] = df['race'].apply(lambda x: ','.join(map(str, x)))\n",
+ "df['age_at_index'] = df['age_at_index'].apply(lambda x: ','.join(map(str, x)))\n",
+ "df['age_at_index'] = df['age_at_index'].astype(int)\n",
+ "df['sex'] = df['sex'].apply(lambda x: ','.join(map(str, x)))\n",
+ "\n",
+ "# add binned ages for calculating age distributions later\n",
+ "age_bins = np.arange(10,100,10)\n",
+ "df['age_bin'] = pd.cut(df['age_at_index'], bins=age_bins)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f7a61074-2105-4b10-93de-f1baec4f31ee",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "f7a61074-2105-4b10-93de-f1baec4f31ee",
+ "outputId": "e5eff9b4-d415-4a9d-f7cf-d1c310734f96"
+ },
+ "outputs": [],
+ "source": [
+ "# The dataset is inbalanced with more mild COVID patients than severe\n",
+ "df['cohort'].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6a150187-a6d7-4e94-8f0e-e952ba73de09",
+ "metadata": {
+ "id": "6a150187-a6d7-4e94-8f0e-e952ba73de09"
+ },
+ "source": [
+ "## 3) Now we use re-sampling techniques to balance the dataset\n",
+ "---\n",
+ "In order to create a mild COVID cohort of the same size as the smaller severe COVID cohort that roughly matches the demographics of the smaller cohort, we need to sample cases from the larger mild COVID cohort through a process called \"undersampling\" until the size of the two cohorts is equal.\n",
+ "\n",
+ "\"Undersampling\" refers to a group of techniques designed to balance the class distribution for a classification dataset that has a skewed class distribution and is a common technique used in machine learning to balance imbalanced datasets. In this case, we want to undersample the larger patient cohort while ensuring that the resulting two cohorts have a similar distribution of four demographic variables: sex, race, ethnicity, and age.\n",
+ "\n",
+ "The following column headers in the Pandas DataFrame we created above will be used for our sampling script: `sex`, `ethnicity`, `race`, and `age_at_index`."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0ee79442-e1f3-4575-ad3e-10de028da958",
+ "metadata": {
+ "id": "0ee79442-e1f3-4575-ad3e-10de028da958"
+ },
+ "source": [
+ "### Calculate the Size of the Smaller Cohort:\n",
+ "\n",
+ "Determine the size you want for the smaller cohort. If both cohorts need to be of the same size, you can calculate the size as the minimum size of the two cohorts. You can do this by using the `min` function.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f7040419-ed23-4cee-ac72-b03abb18184f",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ },
+ "id": "f7040419-ed23-4cee-ac72-b03abb18184f",
+ "outputId": "254ce3ba-f5c8-4ca1-c958-65b247aa1962"
+ },
+ "outputs": [],
+ "source": [
+ "cohorts = ['mild', 'severe']\n",
+ "cohort_sizes = {}\n",
+ "for cohort in cohorts:\n",
+ " cohort_sizes[cohort] = len(df[df['cohort']==cohort])\n",
+ "display(cohort_sizes)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "55b7cd74-6583-4812-a0ad-e18c4b3e3559",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "55b7cd74-6583-4812-a0ad-e18c4b3e3559",
+ "outputId": "f45b24fa-481b-422d-a316-eebdaf78c783"
+ },
+ "outputs": [],
+ "source": [
+ "smaller_cohort_size = min(cohort_sizes.values())\n",
+ "smaller_cohort_name = list(cohort_sizes.keys())[list(cohort_sizes.values()).index(smaller_cohort_size)]\n",
+ "sdf = df.loc[df['cohort']==smaller_cohort_name] # smaller cohort DataFrame\n",
+ "\n",
+ "larger_cohort_size = max(cohort_sizes.values())\n",
+ "larger_cohort_name = list(cohort_sizes.keys())[list(cohort_sizes.values()).index(larger_cohort_size)]\n",
+ "ldf = df.loc[df['cohort']==larger_cohort_name] # larger cohort DataFrame\n",
+ "\n",
+ "print(\"The smaller cohort is '{}' with a size of '{}'.\".format(smaller_cohort_name,smaller_cohort_size))\n",
+ "print(\"The larger cohort is '{}' with a size of '{}'.\".format(larger_cohort_name,larger_cohort_size))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "93ee95c5-66ca-4897-89f9-c833a252d511",
+ "metadata": {
+ "id": "93ee95c5-66ca-4897-89f9-c833a252d511"
+ },
+ "source": [
+ "### Undersampling:\n",
+ "\n",
+ "Now, we undersample the larger cohort to match the smaller cohort's size while maintaining the desired distribution of demographic variables. For this, we'll use the `sample` function in Pandas.\n",
+ "\n",
+ "To use the Pandas `sample` function to undersample the larger cohort (mild COVID cases) while considering the four demographic variables and their distribution in the smaller cohort (severe COVID cases), you can create a custom sampling probability based on the distribution of the smaller cohort.\n",
+ "\n",
+ "#### Strategy\n",
+ "---\n",
+ "1) Determine the frequency of all combinations of demographic properties in the smaller cohort,\n",
+ "2) Add this frequency to each row in the larger cohort by matching the demographics combinations, and\n",
+ "3) Undersample the larger cohort using the inverse of these frequencies as weights\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1b77fb61-addd-4a99-8209-e9e6178158c9",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "id": "1b77fb61-addd-4a99-8209-e9e6178158c9",
+ "outputId": "69e400ba-c119-440f-d126-39e61e962714"
+ },
+ "outputs": [],
+ "source": [
+ "# Make a list of all combinations of demographic variables found in the master DataFrame using pd.value_counts()\n",
+ "\n",
+ "# list of properties to consider (dprops: \"demographic properties\")\n",
+ "#dprops = ['sex','ethnicity']\n",
+ "dprops = ['sex','ethnicity','race','age_bin']\n",
+ "\n",
+ "print(\"Counts of Demographic Property Combinations in Master DataFrame:\")\n",
+ "mvc = df[dprops].value_counts() # mvc: \"master value counts\"\n",
+ "#mvc[('Male', 'Not Hispanic or Latino')] # this is how you access individual values\n",
+ "display(mvc)\n",
+ "\n",
+ "print(\"\\nAll Combinations of Demographic Properties in Master DataFrame:\")\n",
+ "combos = mvc.index.tolist()\n",
+ "display(combos)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "1ec6aa1e-2945-459c-aaa3-daeb455d086f",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "1ec6aa1e-2945-459c-aaa3-daeb455d086f",
+ "outputId": "06b5b12d-8b14-41d7-e4ea-7a23ac86e732"
+ },
+ "outputs": [],
+ "source": [
+ "# Now look at the frequencies of each demographic combo in the smaller cohort\n",
+ "svc = sdf[dprops].value_counts(normalize=False).reindex(combos) # svc: \"smaller cohort value counts\"\n",
+ "print(\"Smaller Cohort Demographics Value Counts\\n{}\\n\\n\".format(svc))\n",
+ "\n",
+ "# use normalize=True to get the relative frequencies (count of a demographic / sum of all demographics)\n",
+ "svf = sdf[dprops].value_counts(normalize=True).reindex(combos) # svf: \"smaller cohort value frequencies\"\n",
+ "print(\"Smaller Cohort Demographics Frequencies\\n{}\\n\\n\".format(svf))\n",
+ "\n",
+ "# sampling weights should be the inverse of their frequencies; less frequent demographics get a higher probability of sampling\n",
+ "svw = 1/svf # svw: \"smaller cohort value weights\"\n",
+ "print(\"Smaller Cohort Demographics Weights for Undersampling\\n{}\\n\\n\".format(svw))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "133b2126-e0c2-44c6-9712-c74b5643f0d5",
+ "metadata": {
+ "id": "133b2126-e0c2-44c6-9712-c74b5643f0d5"
+ },
+ "source": [
+ "Demographics in the larger cohort not represented in the smaller cohort get NaN for counts. Here we'll convert the `NaN`s to `0` to use as weights:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2b737a4c-e4af-4c72-beba-63dddc650a83",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 243
+ },
+ "id": "2b737a4c-e4af-4c72-beba-63dddc650a83",
+ "outputId": "9355c255-ae6e-47a4-e166-86ff20d54faf",
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "# Replace NaNs with 0:\n",
+ "for key in list(svw.keys()):\n",
+ " if np.isnan(svw[key]):\n",
+ " svw[key] = 0\n",
+ "display(svw)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a7277895-c364-4512-917e-51490b1bb2d7",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "a7277895-c364-4512-917e-51490b1bb2d7",
+ "outputId": "fc91006a-7f54-4938-ac3c-afe4107fb55f"
+ },
+ "outputs": [],
+ "source": [
+ "# Now apply the weights to each row of the larger cohort (mild COVID cases) to use in undersampling\n",
+ "for combo in combos:\n",
+ " print(\"{}: {}\".format(combo,svw[combo]))\n",
+ " ldf.loc[(ldf[dprops[0]] == combo[0]) & (ldf[dprops[1]] == combo[1]) & (ldf[dprops[2]] == combo[2]) & (ldf[dprops[3]] == combo[3]),'weight'] = svw[combo]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "24965406-a2ee-41e4-8e0c-33e231ff8e16",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 99
+ },
+ "id": "24965406-a2ee-41e4-8e0c-33e231ff8e16",
+ "outputId": "6f423178-034d-4536-9467-6a5917dd49c0"
+ },
+ "outputs": [],
+ "source": [
+ "# Double check that all rows in the larger cohort were assigned a weight. If this is an empty DataFrame, then each row has a non-NaN weight.\n",
+ "ldf.loc[ldf['weight'].isna()]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "17a9dad0-d020-471f-8e7f-4eec7aba59f7",
+ "metadata": {
+ "id": "17a9dad0-d020-471f-8e7f-4eec7aba59f7"
+ },
+ "source": [
+ "### Undersample the Larger Cohort:\n",
+ "\n",
+ "Use the `sample` function with the calculated weights to undersample the larger cohort."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "cfbf5f4c-304a-4b20-b297-9183de58483d",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 965
+ },
+ "id": "cfbf5f4c-304a-4b20-b297-9183de58483d",
+ "outputId": "ed52b40e-172d-406e-a708-dbc025a5e9e3"
+ },
+ "outputs": [],
+ "source": [
+ "# Undersample the larger cohort (mild COVID cases) using weights\n",
+ "\n",
+ "udf = ldf.sample(n=smaller_cohort_size, weights=ldf['weight'], random_state=np.random.RandomState(41)) # undersampled larger cohort DataFrame, can set random_state in order to have reproducible sampling\n",
+ "#udf = ldf.sample(n=smaller_cohort_size, weights=ldf['weight']) # undersampled larger cohort DataFrame, leave random_state out to get a non-reproducible, random sample\n",
+ "udf.reset_index(drop=True,inplace=True)\n",
+ "udf"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9dc6eaa2-0f66-4a14-9284-63581785e084",
+ "metadata": {
+ "id": "9dc6eaa2-0f66-4a14-9284-63581785e084"
+ },
+ "source": [
+ "### Combine the Two Cohorts:\n",
+ "\n",
+ "After undersampling, you'll have two cohorts of the same size, and the larger cohort should be balanced with respect to the four demographic variables.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7093ca7c-79e2-478a-8c33-169c034b909a",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 965
+ },
+ "id": "7093ca7c-79e2-478a-8c33-169c034b909a",
+ "outputId": "c7a26214-b4b1-46d2-956e-a491ab209f56"
+ },
+ "outputs": [],
+ "source": [
+ "# Combine the undersampled larger cohort with the smaller cohort\n",
+ "bdf = pd.concat([udf.drop(columns=[\"weight\"]), sdf]).reset_index(drop=True) # balanced DataFrame\n",
+ "bdf"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "005b208d-eea3-4ea5-8658-fb18319934c9",
+ "metadata": {
+ "id": "005b208d-eea3-4ea5-8658-fb18319934c9"
+ },
+ "source": [
+ "## 4) Verification:\n",
+ "\n",
+ "Ensure that the demographic distributions of the two cohorts are now balanced for all four variables. You can use the `pandas` `groupby` and `value_counts` methods or other appropriate methods to check the distributions.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a41T5PIOgPPR",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "id": "a41T5PIOgPPR",
+ "outputId": "345dd571-d327-4eef-df00-34e8261a0351"
+ },
+ "outputs": [],
+ "source": [
+ "# Use pandas groupby and plot functions to view relative counts of different demographics in the balanced cohort\n",
+ "for prop in dprops:\n",
+ " dfu = bdf.groupby([prop],observed=True).cohort.value_counts().unstack()\n",
+ " ax = dfu.plot(kind='bar', figsize=(7, 5), xlabel=prop, ylabel='Count', rot=90) # change rot=0 or rot=45 to change x-axis label display angle\n",
+ " ax.legend(title='cohort', bbox_to_anchor=(1, 1), loc='upper left')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "544761e9",
+ "metadata": {
+ "id": "544761e9"
+ },
+ "source": [
+ "## The End\n",
+ "---\n",
+ "If you have any questions related to this notebook don't hesitate to reach out to the MIDRC Helpdesk at midrc-support@datacommons.io or the author directly at cgmeyer@uchicago.edu\n",
+ "\n",
+ "Happy data wrangling!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f6691638",
+ "metadata": {
+ "id": "f6691638"
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "colab": {
+ "provenance": []
+ },
+ "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": "3.13.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/jupyter-midrc/requirements.txt b/jupyter-midrc/requirements.txt
new file mode 100644
index 00000000..81f7d9bf
--- /dev/null
+++ b/jupyter-midrc/requirements.txt
@@ -0,0 +1,11 @@
+matplotlib==3.4.2
+plotly==5.6.0
+seaborn==0.11.1
+openpyxl==3.1.2
+pyreadstat==1.2.1
+scikit-learn==1.1.1
+tableone==0.7.12
+lifelines==0.27.4
+bioinfokit==2.1.0
+pydicom==2.4.4
+dicom_csv==0.3.0
diff --git a/jupyter-nextflow/nextflow-welcome.html b/jupyter-nextflow/nextflow-welcome.html
index 05a9afd8..9f634175 100644
--- a/jupyter-nextflow/nextflow-welcome.html
+++ b/jupyter-nextflow/nextflow-welcome.html
@@ -28,37 +28,82 @@
padding: 10px
}
-
Welcome to the Nextflow Workspace
+
Welcome to the BRH Nextflow Workspace
-
This is your personal workspace. The "pd" folder represents your
- persistent drive:
+
This is your personal workspace. No one else can access the data or files here.
+
You can learn more about using the BRH Workspace in the
+ BRH Documentation.
+
+
The /pd folder (find it in the panel on the left) is your persistent drive:
+
-
The files you save here will still be available when you come back
- after terminating your workspace session.
-
Any personal files outside of this folder will be lost.
+
Use this folder to store files (notebooks, data files, etc) that you want to use again later.
+ The files you save here will still be available when you come back after terminating your workspace session.
+
Any files you create or add outside of this folder will be lost if they are not moved to the /pd
+ before your workspace session terminates.
+
The Nextflow image /pd has a storage capacity limit of 10Gi
-
Get started with Nextflow
-
If you are new to Nextflow, visit nextflow.io for detailed information.
+
The folder /brh.data-commons.org in the /data folder will host any data files you have
+ downloaded to the workspace through the BRH Discovery Page.
+ Move these files to the /pd directory if you want to access them again after you terminate your workspace session.
+
+
Get started with Data Exploration
+
Open a new "Launcher" tab by clicking the + next to nextflow-welcome.html tab at the
+ top of this document.
+
+
From the Launcher tab, you can open: a new, empty Jupyter notebook; open a new code console or terminal window; or,
+ create several types of files (text, Markdown, Python). For notebooks or files - remember to move the file into the
+ /pd drive if you want to access them in a later workspace session.
+
Install software tools by using pip install (Python) or CRAN (R).
+ If you have a requirements text, you can install them with pip install -r requirements.txt.
+ You can view all pre-installed software packages by opening a terminal window and using the command pip list
+
+
There are some Nextflow examples that can be run without external containers in the
+ BRH documentation.
+ Find other examples of analyses that can be done in the BRH Workspace on the
+ BRH Example Analyses page.
+
+
If the locally-stored files you wish to analyze are large in number and/or size, you may need to zip them before
+ uploading to a workspace. Once in a workspace, files can be unzipped using the python library
+ zipfile.
+
+
+
Get started with Nextflow
+
Note: The Nextflow CPU image is currently sized for smaller analyses and testing.
+ The maximum storage in the /pd (including any notebooks or tools installed) is 10Gi.
+ If you need a larger image for your analyses, please reach out to the BRH support team at
+ brhsupport@gen3.org to discuss the possibility of a larger image.
+
+
If you are new to Nextflow, visit nextflow.io for detailed information.
You can also learn more about using Nextflow in BRH Workspace in the BRH Documentation.
+ href=https://uc-cdis.github.io/BRH-documentation/01-home>BRH Documentation.
+
+
This workspace is set up to run Nextflow workflows in AWS Batch.
+ Your Nextflow configuration must include:
+
+
+
the Batch queue (in your config file, process.queue),
+
IAM role ARN (aws.batch.jobRole), and
+
work directory (workDir)
+
+
These were created for you automatically, and the sample config file is already configured with these settings.
+ It will allow you to run simple workflows and can be adapted to your needs.
-
This workspace is set up to run Nextflow workflows in AWS Batch. Your
- Nextflow configuration must include the Batch queue (in your config file,
- process.queue), IAM role ARN (aws.batch.jobRole), and work
- directory (workDir) that were created for you automatically. The sample config
- file is already configured with these settings. It will allow you to run simple workflows and can be adapted to your
- needs. To get started:
+
To get started:
-
If you want your nextflow.config file to persist, first double-click on the "pd" folder.
-
Open a new "Launcher" tab by clicking the + next to nextflow-welcome.html
-
Scroll down until you see the option for terminal. Click to open a terminal.
-
You can find your sample configuration file in the "data" directory.
- Copy it to a new "nextflow.config" file by running this command in the
- terminal: cp
- ../data/sample-nextflow-config.txt ./nextflow.config
-
Launch a Hello World workflow by running this command in the terminal:
- nextflow run hello
+
If you want your nextflow.config file to persist, first double-click on the /pd folder.
+
Open a new "Launcher" tab by clicking the + next to nextflow-welcome.html
+
Scroll down until you see the option for terminal. Click to open a terminal.
+
You can find your sample configuration file in the /data directory.
+ Copy it to a new nextflow.config file by running this command in the terminal:
+ cp ../data/sample-nextflow-config.txt ./nextflow.config.
+ Because you opened the /pd folder before you ran the command, it will save your config in the /pd.
+
Launch a Hello World workflow by running this command in the terminal:
+ nextflow run hello
diff --git a/jupyter-prometheus/Dockerfile b/jupyter-prometheus/Dockerfile
index 46ce3e18..31ed791f 100644
--- a/jupyter-prometheus/Dockerfile
+++ b/jupyter-prometheus/Dockerfile
@@ -1,44 +1,88 @@
-FROM quay.io/cdis/jupyter-superslim-r:1.0.4
+FROM quay.io/cdis/jupyter-superslim-r:2.1.0
ARG NOTEBOOK_DIR
-COPY $NOTEBOOK_DIR/ $HOME/
USER root
-RUN apt-get update && apt-get install -y --no-install-recommends \
- curl \
+
+# Install system dependencies
+RUN dnf update -y && dnf install -y \
+ # curl \
python3 \
python3-pip \
- g++ \
- libxml2-dev \
- libssl-dev \
- libcurl4-openssl-dev \
- libssh2-1-dev \
- zlib1g-dev \
+ gcc-c++ \
+ libxml2-devel \
+ openssl-devel \
+ libcurl-devel \
+ libssh2-devel \
+ zlib-devel \
openssl \
- gdebi-core \
- libgsl* \
- libudunits2-dev \
- libgs-dev \
+ gsl-devel \
+ ghostscript-devel \
unzip \
- wget && \
- apt-get clean && \
- rm -rf /var/lib/apt/lists/*
-
-RUN pip uninstall -y gen3
-RUN pip3 install gen3
-RUN pip3 install pandas
-RUN pip3 install seaborn
-RUN pip3 install matplotlib
-RUN pip3 install scipy
-
-RUN printf '\n\
-if [ -f $HOME/requirements.txt ]; then \n\
- echo "Will install requirements.txt" \n\
- conda install --file $HOME/requirements.txt \n\
- rm $HOME/requirements.txt \n\
-else \n\
- echo "No dependencies to install." \n\
-fi \n\
-' | bash
-
-RUN echo 'function ve(){ mkdir virtualenv; cd virtualenv; VENV="$1"; python3 -m venv $VENV --system-site-packages; source $VENV/bin/activate; python -m ipykernel install --user --name=$VENV; deactivate;}' >> ~/.bashrc
\ No newline at end of file
+ wget \
+ tar \
+ gzip \
+ shadow-utils \
+ && dnf clean all
+
+# Install udunits2 from source since it's not in AL2023 repos
+# RUN wget -q https://artifacts.unidata.ucar.edu/repository/downloads-udunits/2.2.28/udunits-2.2.28.tar.gz && \
+# tar -xzf udunits-2.2.28.tar.gz && \
+# cd udunits-2.2.28 && \
+# ./configure --prefix=/usr/local && \
+# make && make install && \
+# cd .. && rm -rf udunits-2.2.28* && \
+# echo "/usr/local/lib" >> /etc/ld.so.conf.d/udunits2.conf && \
+# ldconfig
+
+# Create user and home directory if they don't exist
+RUN if ! id -u jovyan >/dev/null 2>&1; then \
+ useradd -m -s /bin/bash jovyan; \
+ fi
+
+# Set HOME variable
+ENV HOME=/home/jovyan
+
+# Copy notebook directory
+COPY $NOTEBOOK_DIR/ $HOME/
+
+# Switch to root for installations
+USER root
+
+# Uninstall gen3 if preinstalled
+RUN pip3 uninstall -y gen3 || true
+
+# Install everything from requirements.txt
+COPY requirements.txt .
+# RUN pip3 install --no-cache-dir -r requirements.txt
+
+
+# Install conda (miniconda) for AL2023
+# RUN wget -q https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O /tmp/miniconda.sh && \
+# bash /tmp/miniconda.sh -b -p /opt/conda && \
+# rm /tmp/miniconda.sh && \
+# /opt/conda/bin/conda clean -afy
+
+# Add conda to PATH
+ENV PATH="/opt/conda/bin:${PATH}"
+
+# Handle requirements.txt if present
+RUN if [ -f $HOME/requirements.txt ]; then \
+ echo "Will install requirements.txt"; \
+ /opt/conda/bin/conda install --file $HOME/requirements.txt || \
+ pip3 install -r $HOME/requirements.txt; \
+ rm $HOME/requirements.txt; \
+ else \
+ echo "No dependencies to install."; \
+ fi
+
+# Add virtual environment function to bashrc
+RUN echo 'function ve(){ mkdir -p virtualenv; cd virtualenv; VENV="$1"; python3 -m venv $VENV --system-site-packages; source $VENV/bin/activate; python -m ipykernel install --user --name=$VENV; deactivate;}' >> /home/jovyan/.bashrc
+
+# Fix ownership
+RUN chown -R jovyan $HOME
+
+# Switch to non-root user
+USER jovyan
+
+WORKDIR $HOME
diff --git a/jupyter-prometheus/combined_demos/TCIA_notebook.ipynb b/jupyter-prometheus/combined_demos/TCIA_notebook.ipynb
new file mode 100644
index 00000000..36b4ef1c
--- /dev/null
+++ b/jupyter-prometheus/combined_demos/TCIA_notebook.ipynb
@@ -0,0 +1,953 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "194d48a6-3f40-41f9-832a-66b771969140",
+ "metadata": {},
+ "source": [
+ "# ๐งฌ๐ **Cross-Commons Radiogenomic Integration Notebook** \n",
+ "\n",
+ "### *Cohort A โ 32 Veterans Affairs RePOP patients present in both* \n",
+ "**VPODC** *(VA Precision Oncology Data Commons โ clinical + omics)* \n",
+ "& \n",
+ "**TCIA** *(The Cancer Imaging Archive โ multi-modal DICOM)* \n",
+ "\n",
+ "---\n",
+ "\n",
+ "**Why this notebook?** \n",
+ "Modern precision-oncology studies rarely live in a single repository. Genomic\n",
+ "VCFs may sit in an institutional commons, while matched CT / MR scans land in\n",
+ "TCIAโand clinical annotations are scattered elsewhere. Researchers therefore\n",
+ "need a **transparent, code-first recipe** that:\n",
+ "\n",
+ "1. **Discovers** overlapping patient identities across repositories \n",
+ "2. **Consolidates** imaging, genomics, and phenotypic metadata into one tidy\n",
+ " dataframe \n",
+ "3. **Validates** the join with quick visual sanity-checks \n",
+ "4. **Lays the groundwork** for downstream radiogenomic analytics or\n",
+ " ML pipelines.\n",
+ "\n",
+ "*This notebook delivers that recipe for RePOP Cohort A.*\n",
+ "\n",
+ "---\n",
+ "\n",
+ "### ๐ At a glance\n",
+ "\n",
+ "| Aspect | Details |\n",
+ "|--------|---------|\n",
+ "| **Patients** | 32 individuals labelled `AP-xxxx` present in *both* VPODC metadata and TCIA collection **VAREPOP-APOLLO** |\n",
+ "| **Modalities** | โข DICOM imaging: CT, MR, PET, MG โข Omics files: VCF, BAM, RNA-Seq quant, proteomics |\n",
+ "| **Outputs** | `shared_cohort_summary.csv` โ counts of imaging series (`n_series`) and omics files (`n_files`) per patient |\n",
+ "| **Tech stack** | *tcia_utils / requests / pandas / pydicom / matplotlib* โ **no credentials** required (public endpoints only) |\n",
+ "\n",
+ "> ๐ก **Goal:** demonstrate a full *discover โ merge โ preview* loop that you can\n",
+ "> fork for any multi-source cohort, then extend with deep-learning\n",
+ "> segmentation, MC-2DP dashboards, or radiogenomic hypothesis testing.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4d87b798-f486-42f0-92ff-9038a32ed745",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "### Notebook goals \n",
+ "1. **Identify** the 32 patients that appear in **both** \n",
+ " * VPODC (omics + clinical) \n",
+ " * TCIA (imaging) \n",
+ "2. **Blend metadata** into an analysis-ready table \n",
+ "3. **Describe** cohort demographics & disease attributes \n",
+ "4. **Preview**: \n",
+ " * raw DICOM slices (CT/MR) \n",
+ " * a toy segmentation mask overlay \n",
+ "\n",
+ "---\n",
+ "\n",
+ "๐ท **Data Sources**\n",
+ "\n",
+ "| Commons | Modalities | Access |\n",
+ "|---------|------------|--------|\n",
+ "| VPODC | VCF files, WXS/WGS BAM, H&E slides, clinical JSON | Open / Controlled |\n",
+ "| TCIA | CT ยท MR ยท PET ยท MG (DICOM) | Open |\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "48eaf2d8-e029-49d6-aa2c-3509acbfca09",
+ "metadata": {},
+ "source": [
+ "### โ๏ธ Environment setup & imports "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "8183107d-b9f6-47f4-bf4d-25a90eb60a1c",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "!pip install tcia_utils gen3 pandas requests tqdm pydicom matplotlib > /dev/null 2>&1\n",
+ "from tqdm import tqdm\n",
+ "import json, random, re, os, zipfile, io, requests, pydicom, pandas as pd, matplotlib.pyplot as plt\n",
+ "plt.rcParams['figure.dpi'] = 150 \n",
+ "from pathlib import Path\n",
+ "import numpy as np\n",
+ "from scipy.ndimage import gaussian_filter"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0a4bfe7d-b5ff-443c-b856-60747ece9a17",
+ "metadata": {},
+ "source": [
+ "### ๐ Cohort-construction logic & data harvest \n",
+ "\n",
+ "This code block does the heavy lifting that underpins the whole notebook:\n",
+ "\n",
+ "1. **Query TCIA** \n",
+ " * Pull the full patient roster (`getPatient`) and all imaging-series\n",
+ " metadata (`getSeries`) for the *VAREPOP-APOLLO* collection. \n",
+ " * Result: **32 TCIA patient IDs** (`AP-xxxx` format).\n",
+ "\n",
+ "2. **Crawl VPODC metadata (MDS)** \n",
+ " * Iterate through every metadata record (max 5 000). \n",
+ " * Extract **any** field that can hold a TCIA-style ID: \n",
+ " `apollo_id`, `data_type[].file_data_source_id`, \n",
+ " `data_source_ids[].id_value`. \n",
+ " * Simultaneously capture each matching fileโs unique identifier\n",
+ " and basic attributes (data type, source).\n",
+ "\n",
+ "3. **Intersect the ID sets** \n",
+ " * `TCIA โฉ VPODC โ 32 shared patients` (assertion guard). \n",
+ " * Build two in-memory DataFrames: \n",
+ " * `imaging_df` โ one row per DICOM series (TCIA) \n",
+ " * `omics_df` โ one row per omics file (VPODC)\n",
+ "\n",
+ "4. **Aggregate & merge** \n",
+ " * `groupby` each DataFrame to simple counts: \n",
+ " `n_series`, `n_files`. \n",
+ " * Inner-join on `patient_id` to produce **`summary`**.\n",
+ "\n",
+ "5. **Persist** \n",
+ " * Write `shared_cohort_summary.csv` โ a compact, analysis-ready table\n",
+ " used by subsequent visualisation cells.\n",
+ "\n",
+ "\n",
+ "Feel free to adjust the `limit` parameter if the VPODC index grows in the\n",
+ "future.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "fdda8645-dcb2-4359-94bf-509bf8b3ef6a",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "โถ TCIA โฆ\n",
+ "โ TCIA patients: 32\n",
+ "โถ VPODC โฆ\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "VPODC metadata: 100%|โโโโโโโโโโ| 2000/2000 [01:24<00:00, 23.71it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "โ VPODC AP IDs: 202\n",
+ "โ Shared cohort size: 32\n",
+ "โ Imaging series rows: 1131\n",
+ "โ Omics file rows : 1173\n",
+ "โ Saved โ shared_cohort_summary.csv\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "\"\"\"\n",
+ "Build a 32-patient cohort shared between:\n",
+ " โข TCIA collection VAREPOP-APOLLO\n",
+ " โข VPODC (metadata-only Gen3 instance)\n",
+ "\n",
+ "Outputs: shared_cohort_summary.csv (n_series, n_files per patient)\n",
+ "\"\"\"\n",
+ "\n",
+ "# ------------------------------------------------------------\n",
+ "TCIA_BASE = \"https://services.cancerimagingarchive.net/nbia-api/services/v1\"\n",
+ "COLLECTION = \"VAREPOP-APOLLO\"\n",
+ "VPODC_BASE = \"https://externalgen3.prometheus.data-commons.org\"\n",
+ "\n",
+ "AP_RX = re.compile(r\"AP-\\w{3,6}\", re.IGNORECASE) # e.g. AP-7DC5 AP-TBFQ\n",
+ "\n",
+ "# ------------------------------------------------------------\n",
+ "def tcia(endpoint: str):\n",
+ " r = requests.get(f\"{TCIA_BASE}/{endpoint}?Collection={COLLECTION}\", timeout=30)\n",
+ " r.raise_for_status()\n",
+ " return r.json()\n",
+ "\n",
+ "print(\"โถ TCIA โฆ\")\n",
+ "tcia_patients = tcia(\"getPatient\")\n",
+ "tcia_series = tcia(\"getSeries\")\n",
+ "\n",
+ "tcia_ids = sorted({p[\"PatientId\"].strip().upper() for p in tcia_patients})\n",
+ "print(f\"โ TCIA patients: {len(tcia_ids)}\")\n",
+ "\n",
+ "# ------------------------------------------------------------\n",
+ "def extract_ap_ids(meta: dict) -> list[str]:\n",
+ " \"\"\"\n",
+ " Return every AP-xxxx style ID found inside a single VPODC metadata JSON.\n",
+ " Looks at gen3_discovery.* and top-level.*\n",
+ " \"\"\"\n",
+ " ids = set()\n",
+ "\n",
+ " buckets = [meta]\n",
+ " if \"gen3_discovery\" in meta:\n",
+ " buckets.append(meta[\"gen3_discovery\"])\n",
+ "\n",
+ " for b in buckets:\n",
+ " # 1) direct apollo_id (string)\n",
+ " ap = b.get(\"apollo_id\")\n",
+ " if isinstance(ap, str) and AP_RX.match(ap):\n",
+ " ids.add(ap.upper())\n",
+ "\n",
+ " # 2) data_type list\n",
+ " for dt in b.get(\"data_type\", []):\n",
+ " fid = dt.get(\"file_data_source_id\", \"\")\n",
+ " if isinstance(fid, str) and AP_RX.match(fid):\n",
+ " ids.add(fid.upper())\n",
+ "\n",
+ " # 3) data_source_ids list\n",
+ " for d in b.get(\"data_source_ids\", []):\n",
+ " val = d.get(\"id_value\", \"\")\n",
+ " if isinstance(val, str) and AP_RX.match(val):\n",
+ " ids.add(val.upper())\n",
+ "\n",
+ " return list(ids)\n",
+ "\n",
+ "def scan_vpodc(limit=5000):\n",
+ " \"\"\"\n",
+ " Walk metadata index, pull AP IDs and file rows concurrently.\n",
+ " Returns: (patient_id list, omics_row list)\n",
+ " \"\"\"\n",
+ " idx = requests.get(f\"{VPODC_BASE}/mds/metadata?limit={limit}\", timeout=60).json()\n",
+ " patient_ids, omics_rows = set(), []\n",
+ "\n",
+ " for mid in tqdm(idx, desc=\"VPODC metadata\"):\n",
+ " js = requests.get(f\"{VPODC_BASE}/mds/metadata/{mid}\", timeout=30)\n",
+ " if not js.ok:\n",
+ " continue\n",
+ " meta = js.json()\n",
+ "\n",
+ " # ---- collect IDs ----\n",
+ " ap_ids = extract_ap_ids(meta)\n",
+ " patient_ids.update(ap_ids)\n",
+ "\n",
+ " # ---- file rows for any AP IDs we just saw ----\n",
+ " for pid in ap_ids:\n",
+ " for dt in meta.get(\"gen3_discovery\", {}).get(\"data_type\", []):\n",
+ " if dt.get(\"file_data_source_id\", \"\").upper() == pid:\n",
+ " omics_rows.append(\n",
+ " {\n",
+ " \"patient_id\": pid,\n",
+ " \"file_uid\": dt.get(\"file_unique_identifier\"),\n",
+ " \"data_type\": dt.get(\"file_data_type\"),\n",
+ " \"source\": dt.get(\"file_data_source\"),\n",
+ " }\n",
+ " )\n",
+ "\n",
+ " return sorted(patient_ids), omics_rows\n",
+ "\n",
+ "print(\"โถ VPODC โฆ\")\n",
+ "vpodc_ids, omics_rows = scan_vpodc()\n",
+ "print(f\"โ VPODC AP IDs: {len(vpodc_ids)}\")\n",
+ "\n",
+ "# ------------------------------------------------------------\n",
+ "shared_ids = sorted(set(tcia_ids) & set(vpodc_ids))\n",
+ "print(f\"โ Shared cohort size: {len(shared_ids)}\")\n",
+ "assert len(shared_ids) == 32, \"Did not find exactly 32 shared patients\"\n",
+ "\n",
+ "# ---- imaging rows ----\n",
+ "def imaging_rows(series_json, cohort):\n",
+ " rows = []\n",
+ " for s in series_json:\n",
+ " pid = (s.get(\"PatientID\") or s.get(\"PatientId\", \"\")).strip().upper()\n",
+ " if pid in cohort:\n",
+ " rows.append(\n",
+ " {\n",
+ " \"patient_id\": pid,\n",
+ " \"series_uid\": s[\"SeriesInstanceUID\"],\n",
+ " \"study_uid\": s[\"StudyInstanceUID\"],\n",
+ " \"modality\": s.get(\"Modality\"),\n",
+ " }\n",
+ " )\n",
+ " return pd.DataFrame(rows)\n",
+ "\n",
+ "imaging_df = imaging_rows(tcia_series, set(shared_ids))\n",
+ "omics_df = pd.DataFrame([r for r in omics_rows if r[\"patient_id\"] in shared_ids])\n",
+ "\n",
+ "print(f\"โ Imaging series rows: {len(imaging_df)}\")\n",
+ "print(f\"โ Omics file rows : {len(omics_df)}\")\n",
+ "\n",
+ "# ------------------------------------------------------------\n",
+ "summary = (\n",
+ " imaging_df.groupby(\"patient_id\").size().to_frame(\"n_series\")\n",
+ " .join(omics_df.groupby(\"patient_id\").size().to_frame(\"n_files\"))\n",
+ " .fillna(0).astype(int)\n",
+ " .sort_values([\"n_series\", \"n_files\"], ascending=False)\n",
+ ")\n",
+ "\n",
+ "summary.to_csv(\"shared_cohort_summary.csv\")\n",
+ "print(\"โ Saved โ shared_cohort_summary.csv\")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7af6281b-938e-459a-bd61-74b826cd5e52",
+ "metadata": {},
+ "source": [
+ "### ๐ Build merged metadata table \n",
+ "\n",
+ "Here we assemble a **master dataframe**โ`summary`โthat unifies key counts\n",
+ "from the two repositories for every cross-linked patient:\n",
+ "\n",
+ "| Column | Meaning | Source |\n",
+ "|-------------|-------------------------------------------------------|--------|\n",
+ "| `patient_id`| Shared AP-code that uniquely identifies each subject | TCIA & VPODC |\n",
+ "| `n_series` | Total number of DICOM *imaging series* available | TCIA |\n",
+ "| `n_files` | Total number of *omics files* (VCF / BAM / etc.) | VPODC |\n",
+ "\n",
+ "**Steps**\n",
+ "\n",
+ "1. **TCIA series aggregation** โ count all DICOM series per patient \n",
+ "2. **VPODC file aggregation** โ count all omics files per patient \n",
+ "3. **Inner-join** on `patient_id` so only the 32 shared subjects remain \n",
+ "4. **Sort** by `n_series` (and `n_files`) to surface the busiest cases\n",
+ "\n",
+ "The resulting `summary` dataframe underpins the comparative plots and\n",
+ "down-stream analyses that follow.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "cebe7e12-b5e3-466a-9e1c-b80c69c08e63",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "ax = summary[['n_series', 'n_files']].plot(\n",
+ " kind='bar',\n",
+ " figsize=(14, 7),\n",
+ " width=0.85,\n",
+ " alpha=0.8,\n",
+ ")\n",
+ "ax.set_ylabel(\"Count\")\n",
+ "ax.set_title(\"Imaging series vs omics files per patient\", size=15)\n",
+ "plt.xticks(rotation=70, ha='right')\n",
+ "plt.tight_layout()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "dca934ef-b7c0-47bf-bbc5-7f141c464d7d",
+ "metadata": {},
+ "source": [
+ "### ๐ฆ๐ง Proportional view โ imaging vs omics share per patient \n",
+ "\n",
+ "The stacked bar chart below normalises each patientโs record counts to **100 %**, so you\n",
+ "see the relative split rather than absolute numbers:\n",
+ "\n",
+ "* **Blue segment** โ proportion of DICOM imaging series \n",
+ "* **Orange segment** โ proportion of omics files \n",
+ "\n",
+ "Because most bars are evenly divided (50 % / 50 %), the cohort is\n",
+ "well-balanced โ every imaging series has a matching omics file. \n",
+ "Patients where the orange slice dominates indicate additional molecular\n",
+ "assays without a corresponding imaging study, and vice-versa.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "159ba8dc-6dea-424f-9633-6f08a9ac2dac",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
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+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "stack = summary[['n_series','n_files']].div(\n",
+ " summary[['n_series','n_files']].sum(axis=1), axis=0)\n",
+ "\n",
+ "stack.plot(kind='bar', stacked=True, figsize=(14,7), colormap='tab20c')\n",
+ "plt.ylabel(\"Fraction of total\")\n",
+ "plt.title(\"Relative imaging / omics contribution per patient\", size=15)\n",
+ "plt.xticks(rotation=70, ha='right')\n",
+ "plt.legend(['Series','Files'])\n",
+ "plt.tight_layout()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "121208d3-53b4-48a4-bfd2-44e808ebacc6",
+ "metadata": {},
+ "source": [
+ "### ๐ผ Interactive imaging preview โ randomly-sampled patient \n",
+ "\n",
+ "To give a quick visual sense of the radiology data, we:\n",
+ "\n",
+ "1. **Randomly choose** one of the 32 cross-linked patients \n",
+ "2. **Query TCIA** for all imaging series tied to that patient \n",
+ "3. **Download a single series** (DICOM zip) and extract the first slice \n",
+ "4. **Render** the slice inline, showing modality and SeriesInstanceUID\n",
+ "\n",
+ "Feel free to re-run this cellโeach execution selects a new patient and\n",
+ "series, offering a different anatomical plane or imaging modality every time.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "3577c265-b57d-4b0d-8984-b1119cf73d70",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Collecting pydicom\n",
+ " Downloading pydicom-2.4.4-py3-none-any.whl (1.8 MB)\n",
+ "\u001b[2K \u001b[90mโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ\u001b[0m \u001b[32m1.8/1.8 MB\u001b[0m \u001b[31m46.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m\n",
+ "\u001b[?25hRequirement already satisfied: matplotlib in /opt/conda/lib/python3.9/site-packages (3.9.4)\n",
+ "Requirement already satisfied: pillow>=8 in /opt/conda/lib/python3.9/site-packages (from matplotlib) (11.2.1)\n",
+ "Requirement already satisfied: numpy>=1.23 in /opt/conda/lib/python3.9/site-packages (from matplotlib) (1.24.2)\n",
+ "Requirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.9/site-packages (from matplotlib) (4.58.4)\n",
+ "Requirement already satisfied: pyparsing>=2.3.1 in /opt/conda/lib/python3.9/site-packages (from matplotlib) (3.2.3)\n",
+ "Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.9/site-packages (from matplotlib) (23.1)\n",
+ "Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.9/site-packages (from matplotlib) (0.12.1)\n",
+ "Requirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.9/site-packages (from matplotlib) (2.8.2)\n",
+ "Requirement already satisfied: contourpy>=1.0.1 in /opt/conda/lib/python3.9/site-packages (from matplotlib) (1.3.0)\n",
+ "Requirement already satisfied: kiwisolver>=1.3.1 in /opt/conda/lib/python3.9/site-packages (from matplotlib) (1.4.7)\n",
+ "Requirement already satisfied: importlib-resources>=3.2.0 in /opt/conda/lib/python3.9/site-packages (from matplotlib) (5.12.0)\n",
+ "Requirement already satisfied: zipp>=3.1.0 in /opt/conda/lib/python3.9/site-packages (from importlib-resources>=3.2.0->matplotlib) (3.15.0)\n",
+ "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.9/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
+ "Installing collected packages: pydicom\n",
+ "Successfully installed pydicom-2.4.4\n",
+ "Patient has 94 series\n"
+ ]
+ }
+ ],
+ "source": [
+ "PATIENT = \"AP-AMT4\" # <-- change to any ID in shared_ids\n",
+ "OUT_DIR = Path(\"downloads\").joinpath(PATIENT).resolve()\n",
+ "OUT_DIR.mkdir(parents=True, exist_ok=True)\n",
+ "\n",
+ "# Series + omics dataframes from the previous run\n",
+ "# (re-read them or keep them in memory)\n",
+ "imaging_df = pd.read_csv(\"shared_cohort_summary.csv\", index_col=\"patient_id\")\n",
+ "print(\"Patient has\", imaging_df.loc[PATIENT, \"n_series\"], \"series\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "8371bacf-6230-4edc-bfd4-2fcae29e7ea4",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Downloading series: 1.3.6.1.4.1.14519.5.2.1.259728963592145893154576060489913213800\n",
+ "โ extracted to /home/jovyan/pd/downloads/AP-AMT4\n"
+ ]
+ }
+ ],
+ "source": [
+ "TCIA_BASE = \"https://services.cancerimagingarchive.net/nbia-api/services/v1\"\n",
+ "\n",
+ "# 2a. Get all series metadata for this patient\n",
+ "series_url = f\"{TCIA_BASE}/getSeries?Collection=VAREPOP-APOLLO&PatientID={PATIENT}\"\n",
+ "series_meta = requests.get(series_url).json()\n",
+ "\n",
+ "# 2b. Pick the first (or random) series\n",
+ "chosen = random.choice(series_meta)\n",
+ "series_uid = chosen[\"SeriesInstanceUID\"]\n",
+ "print(\"Downloading series:\", series_uid)\n",
+ "\n",
+ "# 2c. Request the zip\n",
+ "image_url = f\"{TCIA_BASE}/getImage?SeriesInstanceUID={series_uid}\"\n",
+ "zip_bytes = requests.get(image_url).content\n",
+ "\n",
+ "# 2d. Extract to folder\n",
+ "with zipfile.ZipFile(io.BytesIO(zip_bytes)) as zf:\n",
+ " zf.extractall(OUT_DIR)\n",
+ "print(\"โ extracted to\", OUT_DIR)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "887d1eec-52b0-400c-8096-177b2e0b78b5",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "dicom_files = sorted(OUT_DIR.glob(\"*.dcm\"))\n",
+ "ds = pydicom.dcmread(dicom_files[0])\n",
+ "\n",
+ "plt.imshow(ds.pixel_array, cmap=\"gray\")\n",
+ "plt.title(f\"{PATIENT} | {series_uid} | {ds.Modality}\")\n",
+ "plt.axis(\"off\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cac9108c-f555-44c6-8923-72dc9a816b2a",
+ "metadata": {},
+ "source": [
+ "### โ๏ธ Illustrative segmentation example โ intensity-threshold mask \n",
+ "\n",
+ "Below we demonstrate a **minimal, deterministic pipeline** that generates a\n",
+ "binary segmentation mask directly from the raw DICOM slice:\n",
+ "\n",
+ "1. **Pre-processing** โ apply a light Gaussian blur to suppress noise \n",
+ "2. **Adaptive threshold** โ compute ยต + ฯ of the slice and flag voxels\n",
+ " whose intensity exceeds that value \n",
+ "3. **Overlay** โ render the original image with the mask contour in red\n",
+ "\n",
+ "This threshold-based approach is *not* intended for clinical inference; it\n",
+ "simply shows how anatomical or lesion-like regions can be isolated and\n",
+ "visualised in-line. The same scaffold can be replaced by a fully trained\n",
+ "deep-learning model (e.g. nnU-Net) without changing the display code."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "f9cf1415-030f-41b9-9bfc-79a09b85c7d9",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# convert to float for maths\n",
+ "img = ds.pixel_array.astype(float)\n",
+ "\n",
+ "# denoise a bit, then threshold at ฮผ + ฯ\n",
+ "blurred = gaussian_filter(img, sigma=1)\n",
+ "thr = blurred.mean() + blurred.std()\n",
+ "mask = blurred > thr\n",
+ "\n",
+ "# visualise\n",
+ "plt.figure(figsize=(6, 6))\n",
+ "plt.imshow(img, cmap=\"gray\")\n",
+ "plt.contour(mask, colors=\"red\", linewidths=0.6)\n",
+ "plt.title(\"Red = simple ฮผ+ฯ threshold mask\")\n",
+ "plt.axis(\"off\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b1da05cd-8154-4a1a-9bac-79a6598502b5",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "\n",
+ "## ๐ฏ Key take-aways \n",
+ "\n",
+ "| Milestone | Outcome |\n",
+ "|-----------|---------|\n",
+ "| **Patient reconciliation** | Identified **32** subjects present in both VPODC and TCIA (collection *VAREPOP-APOLLO*). |\n",
+ "| **Multi-omics binding** | Produced a harmonised table linking *1 131* imaging series with *1 173* molecular files. |\n",
+ "| **Visual sanity-check** | Verified counts with bar charts and inspected raw DICOM slices alongside an illustrative intensity-mask overlay. |\n",
+ "\n",
+ "### ๐ Deliverables \n",
+ "* `summary` dataframe โโโ **`shared_cohort_summary.csv`** \n",
+ " โข Columns: `patient_id`, `n_series`, `n_files` \n",
+ "* Notebook code cells that fetch, merge, and preview the dataโfully reproducible with public endpoints only.\n",
+ "\n",
+ "---\n",
+ "\n",
+ "## ๐ Where to go next\n",
+ "\n",
+ "1. **Enterprise analytics (MC-2DP)** \n",
+ " Upload `shared_cohort_summary.csv` plus the detailed per-file tables and build interactive dashboards: survival curves, mutation heat-maps, imaging volume metrics.\n",
+ "\n",
+ "2. **Production-grade segmentation** \n",
+ " Swap the illustrative threshold mask for a deep-learning pipeline (e.g. **MONAI**, **nnU-Net**). \n",
+ " * Auto-segment tumoursโ* Quantify radiomic featuresโ* Correlate with mutational load.\n",
+ "\n",
+ "3. **Radiogenomic integrative plots** \n",
+ " * Volcano / Manhattan plots of variant burden vs. CT-derived lesion volume \n",
+ " * Heat-map linking copy-number alterations to PET SUVmax \n",
+ " * Longitudinal spider plots if follow-up imaging exists.\n",
+ "\n",
+ "4. **Quality control & governance** \n",
+ " * Programmatic checks for missing modalities or incomplete metadata \n",
+ " * Provenance trackingโhash every DICOM / VCF to guarantee downstream auditability.\n",
+ "\n",
+ "With these extensions the notebook evolves from a showcase into a\n",
+ "production-ready radiogenomic analysis scaffold.\n"
+ ]
+ }
+ ],
+ "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": "3.9.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/jupyter-prometheus/combined_demos/VPODC_data_download_guide.ipynb b/jupyter-prometheus/combined_demos/VPODC_data_download_guide.ipynb
new file mode 100644
index 00000000..6d8fe1f9
--- /dev/null
+++ b/jupyter-prometheus/combined_demos/VPODC_data_download_guide.ipynb
@@ -0,0 +1,2146 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# ๐งฌ VPODC Cohort File Selection & Download Guide\n",
+ "### Using Gen3 Client for Automated File Retrieval\n",
+ "---\n",
+ "\n",
+ "**Last Updated**: May 2025 \n",
+ "**Tools Used**: Python, Gen3 Client, Jupyter Notebook \n",
+ "\n",
+ "This notebook provides a step-by-step walkthrough for:\n",
+ "1. Selecting a VPODC cohort from `Gen3`.\n",
+ "2. Listing associated VCF object IDs.\n",
+ "3. Downloading files using the `gen3-client`.\n",
+ "\n",
+ "\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## โ๏ธ Install the Gen3\n",
+ "\n",
+ "Before proceeding, we need to install the [Gen3](https://gen3.org). \n",
+ "This SDK enables programmatic access to Gen3 commons, including authentication and data submission APIs.\n",
+ "\n",
+ "The command below installs the SDK silently (hides verbose output) and prints a short confirmation when done.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Installing Gen3...\n",
+ "โ Gen3 installation complete.\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Install the Gen3 Python SDK via pip\n",
+ "print(\"Installing Gen3...\")\n",
+ "!pip install --user --force --upgrade gen3 --ignore-installed certifi > /dev/null 2>&1\n",
+ "print(\"โ Gen3 installation complete.\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Import some Python packages\n",
+ "import requests, json, fnmatch, os, os.path, sys, subprocess, glob, ntpath, copy, re\n",
+ "import pandas as pd\n",
+ "import gen3\n",
+ "from gen3.auth import Gen3Auth\n",
+ "from gen3.submission import Gen3Submission"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## ๐ฆ Make sure you have a valid profile and credentials configured in your Gen3 client."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# Set `creds` to the location of your credentials.json downloaded from the \"Profile\" page.\n",
+ "# This should be the only line you may need to edit for this notebook to work.\n",
+ "profile = 'vpodc'\n",
+ "api = 'https://vpodc.data-commons.org'\n",
+ "creds = '/home/jovyan/pd/vpodc-credentials.json'\n",
+ "client = '/home/jovyan/pd/.gen3/gen3-client'\n",
+ "\n",
+ "auth = Gen3Auth(api, refresh_file=creds)\n",
+ "sub = Gen3Submission(api, auth)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " % Total % Received % Xferd Average Speed Time Time Time Current\n",
+ " Dload Upload Total Spent Left Speed\n",
+ "100 276 100 276 0 0 9399 0 --:--:-- --:--:-- --:--:-- 9517\n",
+ "unzip: cannot find or open dataclient_linux.zip, dataclient_linux.zip.zip or dataclient_linux.zip.ZIP.\n",
+ "mv: cannot stat 'gen3-client': No such file or directory\n",
+ "rm: cannot remove 'dataclient_linux.zip': No such file or directory\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Download the gen3-client (for downloading files) and configure a profile \n",
+ "!curl https://api.github.com/repos/uc-cdis/cdis-data-client/releases/latest | grep browser_download_url.*linux | cut -d '\"' -f 4 | wget -qi -\n",
+ "!unzip dataclient_linux.zip\n",
+ "!mkdir -p /home/jovyan/pd/.gen3\n",
+ "!mv gen3-client /home/jovyan/pd/.gen3\n",
+ "!rm dataclient_linux.zip\n",
+ "\n",
+ "# Configure a profile\n",
+ "cmd = client +' configure --profile='+profile+' --apiendpoint='+api+' --cred='+creds\n",
+ "try:\n",
+ " output = subprocess.check_output(cmd, stderr=subprocess.STDOUT, shell=True).decode('UTF-8')\n",
+ "except Exception as e:\n",
+ " output = e.output.decode('UTF-8')\n",
+ " print(\"ERROR:\" + output)\n",
+ "\n",
+ "# Check authorization privileges\n",
+ "cmd = client +' auth --profile='+profile\n",
+ "try:\n",
+ " output = subprocess.check_output(cmd, stderr=subprocess.STDOUT, shell=True).decode('UTF-8')\n",
+ " #print(\"\\n\"+output)\n",
+ "except Exception as e:\n",
+ " output = e.output.decode('UTF-8')\n",
+ " #print(\"ERROR:\" + output)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "--2025-05-20 18:51:05-- https://raw.githubusercontent.com/cgmeyer/gen3sdk-python/master/expansion/expansion.py\n",
+ "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.110.133, 185.199.109.133, ...\n",
+ "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
+ "HTTP request sent, awaiting response... 200 OK\n",
+ "Length: 235484 (230K) [text/plain]\n",
+ "Saving to: โexpansion.pyโ\n",
+ "\n",
+ "expansion.py 100%[===================>] 229.96K --.-KB/s in 0.002s \n",
+ "\n",
+ "2025-05-20 18:51:05 (132 MB/s) - โexpansion.pyโ saved [235484/235484]\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Download some extra functions for interacting with APIs\n",
+ "!rm -f -- expansion.py\n",
+ "!wget https://raw.githubusercontent.com/cgmeyer/gen3sdk-python/master/expansion/expansion.py\n",
+ "%run ./expansion.py\n",
+ "exp = Gen3Expansion(api, auth, sub)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## ๐ Query Primary Site Values from Gen3\n",
+ "\n",
+ "We query all available values of `\"PrimarysiteX\"` from the `oncology_primary` node \n",
+ "within the specified project (`VA-PODR-COHORT-A`) using the `paginate_query` function.\n",
+ "\n",
+ "This function helps avoid timeouts by breaking the request into chunks. \n",
+ "We specifically request the column: \n",
+ "- `PrimarysiteX` \n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "\tFound 202 records in 'oncology_primary' node of project 'VA-PODR-COHORT-A'. \n",
+ "\tRecords retrieved: 202 of 202 (100%), offset: 10000, chunk_size: 10000. "
+ ]
+ }
+ ],
+ "source": [
+ "props = ['PrimarysiteX']\n",
+ "data = exp.paginate_query(node='oncology_primary',project_id='VA-PODR-COHORT-A',props=props,format='tsv', chunk_size=10000)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### ๐ซ Filter for Lung-Related Sites\n",
+ "\n",
+ "From the full list of `PrimarysiteX` values, we filter those containing the string `\"LUNG\"`. \n",
+ "This gives us a focused list of lung-related primary tumor sites to use in downstream filtering.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['LUNG, LOWER LOBE',\n",
+ " 'LUNG, UPPER LOBE',\n",
+ " 'LUNG, MIDDLE LOBE',\n",
+ " 'LUNG, MAIN BRONCHUS',\n",
+ " 'LUNG NOS']"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "sites = [x for x in list(set(data.PrimarysiteX)) if x is not None]\n",
+ "lung_sites = [i for i in sites if 'LUNG' in i] \n",
+ "lung_sites"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### ๐งฌ Retrieve Case Submitter IDs for Lung Cases\n",
+ "\n",
+ "Using the filtered `lung_sites`, we call `paginate_query` again to retrieve `submitter_id`s \n",
+ "associated with lung cases from the `oncology_primary` node.\n",
+ "\n",
+ "Each `lung_site` value is used in a query loop to match corresponding entries in Gen3.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "\tFound 1880 records in 'case' node of project 'None'. \n",
+ "\tRecords retrieved: 1743 of 1880 (92%), offset: 10000, chunk_size: 10000. \n",
+ "\tFound 4115 records in 'case' node of project 'None'. \n",
+ "\tRecords retrieved: 3748 of 4115 (91%), offset: 10000, chunk_size: 10000. \n",
+ "\tFound 318 records in 'case' node of project 'None'. \n",
+ "\tRecords retrieved: 300 of 318 (94%), offset: 10000, chunk_size: 10000. \n",
+ "\tFound 231 records in 'case' node of project 'None'. \n",
+ "\tRecords retrieved: 225 of 231 (97%), offset: 10000, chunk_size: 10000. \n",
+ "\tFound 878 records in 'case' node of project 'None'. \n",
+ "\tRecords retrieved: 857 of 878 (97%), offset: 10000, chunk_size: 10000. "
+ ]
+ }
+ ],
+ "source": [
+ "props = ['submitter_id']\n",
+ "lung_site = lung_sites[0]\n",
+ "ids = []\n",
+ "for lung_site in lung_sites:\n",
+ " args = 'with_path_to:{type:\"oncology_primary\", PrimarysiteX: \"%s\"}' % lung_site\n",
+ " data = exp.paginate_query(node='case', project_id=None, props=props, args=args, format='tsv', chunk_size=10000)\n",
+ " ids1 = list(set(data.submitter_id))\n",
+ " ids += ids1\n",
+ "case_submitter_ids = list(set(ids))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "6587"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(case_submitter_ids)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## ๐ Extracting Object IDs from Structured Data\n",
+ "\n",
+ "We can download all the **structured data** from the data file nodes and use **Pandas** to filter the relevant `object_id`s for download.\n",
+ "\n",
+ "#### ๐ Steps Overview:\n",
+ "- ๐ ๏ธ Use the function `get_node_tsvs` to fetch structured data from a node.\n",
+ "- ๐ This function returns a **DataFrame** containing fields like:\n",
+ " - `case_submitter_id`\n",
+ " - `cases.submitter_id#1` (usually the same as `case_submitter_id`)\n",
+ "- ๐ฏ Filter the DataFrame to include only rows where `case_submitter_id` matches one in our predefined list.\n",
+ "- ๐ฆ Extract the corresponding `object_id`s from those rows.\n",
+ "- โฌ๏ธ Use the extracted `object_id`s to download the data files with `gen3-client`.\n",
+ "\n",
+ "> โ This approach ensures you're only downloading the exact files tied to your case list.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Output written to file: node_tsvs/variant_call_file_tsvs/VA-PODR-COHORT-A_variant_call_file.tsv\n",
+ "node_tsvs/variant_call_file_tsvs/VA-PODR-COHORT-A_variant_call_file.tsv has 152 records.\n",
+ "length of all dfs: 152\n",
+ "Master node TSV with 152 total records written to master_variant_call_file.tsv.\n"
+ ]
+ },
+ {
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+ " \n",
+ "
\n",
+ "
5 rows ร 23 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " type id project_id \\\n",
+ "0 variant_call_file 0034ce46-f41d-42ef-a03c-0333b8081a76 VA-PODR-COHORT-A \n",
+ "1 variant_call_file 0367675c-96e2-4626-83c9-da46f0eb9ad7 VA-PODR-COHORT-A \n",
+ "2 variant_call_file 03cbabb7-4680-4a25-80a8-fcf08b4d1898 VA-PODR-COHORT-A \n",
+ "3 variant_call_file 066d3640-ed34-4d42-96cd-6370397fa588 VA-PODR-COHORT-A \n",
+ "4 variant_call_file 0d9f6a8f-0e97-466c-8ea4-33332464b81e VA-PODR-COHORT-A \n",
+ "\n",
+ " submitter_id data_category \\\n",
+ "0 VA-PODR-COHORT-A_C179813604_2_ef1d Simple Nucleotide Variation \n",
+ "1 VA-PODR-COHORT-A_C2040737988_1_a1b4 Simple Nucleotide Variation \n",
+ "2 VA-PODR-COHORT-A_C1361659370_1_8682 Simple Nucleotide Variation \n",
+ "3 VA-PODR-COHORT-A_C240403488_1_2240 Simple Nucleotide Variation \n",
+ "4 VA-PODR-COHORT-A_C179813604_3_b334 Simple Nucleotide Variation \n",
+ "\n",
+ " data_format data_type file_name file_size \\\n",
+ "0 VCF Annotated Somatic Mutation C179813604_2.vcf 15737 \n",
+ "1 VCF Annotated Somatic Mutation C2040737988_1.vcf 1895 \n",
+ "2 VCF Annotated Somatic Mutation C1361659370_1.vcf 12818 \n",
+ "3 VCF Annotated Somatic Mutation C240403488_1.vcf 13778 \n",
+ "4 VCF Annotated Somatic Mutation C179813604_3.vcf 16190 \n",
+ "\n",
+ " md5sum ... state_comment \\\n",
+ "0 10931a53640eb6b4d10e5b3262678e61 ... NaN \n",
+ "1 8ead48f026250bd47c1e2d4f984695f1 ... NaN \n",
+ "2 dfd44a7ac69522f5e98949c07c83ce5c ... NaN \n",
+ "3 811bd9abfbff9d69e0afa249f551ba88 ... NaN \n",
+ "4 9d008680794aec1857340d0c441f7a30 ... NaN \n",
+ "\n",
+ " variant_calling_workflow aligned_reads_files.id \\\n",
+ "0 NaN NaN \n",
+ "1 NaN NaN \n",
+ "2 NaN NaN \n",
+ "3 NaN NaN \n",
+ "4 NaN NaN \n",
+ "\n",
+ " aligned_reads_files.submitter_id cases.id \\\n",
+ "0 NaN 6c845a82-c75d-4070-8368-d1d9f78f4624 \n",
+ "1 NaN 9355ddf9-be48-48dd-8b6a-9a9af64a2767 \n",
+ "2 NaN b44eca18-0f91-4eef-a60d-0055fcd3b0ae \n",
+ "3 NaN 9a439d4f-a852-44ed-afda-dcec5446b016 \n",
+ "4 NaN 6c845a82-c75d-4070-8368-d1d9f78f4624 \n",
+ "\n",
+ " cases.submitter_id core_metadata_collections.id \\\n",
+ "0 C179813604 4254caef-09ea-4399-b4ee-bdc271f99037 \n",
+ "1 C2040737988 4254caef-09ea-4399-b4ee-bdc271f99037 \n",
+ "2 C1361659370 4254caef-09ea-4399-b4ee-bdc271f99037 \n",
+ "3 C240403488 4254caef-09ea-4399-b4ee-bdc271f99037 \n",
+ "4 C179813604 4254caef-09ea-4399-b4ee-bdc271f99037 \n",
+ "\n",
+ " core_metadata_collections.submitter_id unaligned_reads_files.id \\\n",
+ "0 VCF_collection-01 NaN \n",
+ "1 VCF_collection-01 NaN \n",
+ "2 VCF_collection-01 NaN \n",
+ "3 VCF_collection-01 NaN \n",
+ "4 VCF_collection-01 NaN \n",
+ "\n",
+ " unaligned_reads_files.submitter_id \n",
+ "0 NaN \n",
+ "1 NaN \n",
+ "2 NaN \n",
+ "3 NaN \n",
+ "4 NaN \n",
+ "\n",
+ "[5 rows x 23 columns]"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "node = 'variant_call_file'\n",
+ "projects = ['VA-PODR-COHORT-A']\n",
+ "df = exp.get_node_tsvs(node,projects,overwrite=True)\n",
+ "\n",
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### ๐งพ Extract Object IDs for Lung Cases\n",
+ "\n",
+ "From the main structured DataFrame `df`, we filter for rows where the `case_submitter_id` is found in our list of lung cases. \n",
+ "Then, we extract the corresponding `object_id` values, which will be used to download files tied to these records.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "65"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "vcf_object_ids = list(df.loc[df['case_submitter_id'].isin(case_submitter_ids)]['object_id'])\n",
+ "len(vcf_object_ids)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### โฌ๏ธ Download Files via Gen3 Client\n",
+ "\n",
+ "We loop through the list of `object_id`s and use the `gen3-client` command-line tool to download each file.\n",
+ "\n",
+ "- The download path is set to a specific directory.\n",
+ "- The `--no-prompt` flag is used to skip overwrite confirmation.\n",
+ "- This process downloads one file per iteration based on its GUID.\n",
+ "\n",
+ "> โ ๏ธ If files already exist in the destination folder, they will be silently overwritten unless `--rename` is set.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2025/05/20 18:51:12 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 18m15s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
+ "2025/05/20 18:51:12 Total number of objects in manifest: 1\n",
+ "2025/05/20 18:51:12 Preparing file info for each file, please wait...\n",
+ " 1 / 1 [============================================================] 100.00% 0s\n",
+ "2025/05/20 18:51:12 File info prepared successfully\n",
+ "C240403488_1.vcf 13.46 KiB / 13.46 KiB [==============================] 100.00%\n",
+ "C240403488_1.vcf 13.46 KiB / 13.46 KiB [==============================] 100.00%\n",
+ "2025/05/20 18:51:12 1 files downloaded.\n",
+ "2025/05/20 18:51:13 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 18m14s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
+ "2025/05/20 18:51:13 Total number of objects in manifest: 1\n",
+ "2025/05/20 18:51:13 Preparing file info for each file, please wait...\n",
+ " 1 / 1 [============================================================] 100.00% 0s\n",
+ "2025/05/20 18:51:13 File info prepared successfully\n",
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+ "C214284022_1.vcf 1.68 KiB / 1.68 KiB [================================] 100.00%\n",
+ "2025/05/20 18:51:13 1 files downloaded.\n",
+ "2025/05/20 18:51:14 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 18m13s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
+ "2025/05/20 18:51:14 Total number of objects in manifest: 1\n",
+ "2025/05/20 18:51:14 Preparing file info for each file, please wait...\n",
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+ "2025/05/20 18:51:14 File info prepared successfully\n",
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+ "2025/05/20 18:51:14 1 files downloaded.\n",
+ "2025/05/20 18:51:14 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 15m12s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
+ "2025/05/20 18:51:14 Total number of objects in manifest: 1\n",
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+ "2025/05/20 18:51:15 1 files downloaded.\n",
+ "2025/05/20 18:51:15 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 18m11s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
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+ "2025/05/20 18:51:16 1 files downloaded.\n",
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+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
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+ "2025/05/20 18:51:16 1 files downloaded.\n",
+ "2025/05/20 18:51:17 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 18m09s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
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+ "2025/05/20 18:51:17 1 files downloaded.\n",
+ "2025/05/20 18:51:18 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 15m09s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
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+ "2025/05/20 18:51:18 1 files downloaded.\n",
+ "2025/05/20 18:51:19 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 15m08s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
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+ "2025/05/20 18:51:19 1 files downloaded.\n",
+ "2025/05/20 18:51:20 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 15m07s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
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+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
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+ "2025/05/20 18:51:21 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 15m05s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
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+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
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+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
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+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
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+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
+ "2025/05/20 18:51:27 Total number of objects in manifest: 1\n",
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+ "2025/05/20 18:51:28 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 17m59s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
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+ "2025/05/20 18:51:29 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 17m58s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
+ "2025/05/20 18:51:29 Total number of objects in manifest: 1\n",
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+ "2025/05/20 18:51:29 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 17m57s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
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+ "2025/05/20 18:51:30 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 17m56s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
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+ "2025/05/20 18:51:31 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 17m55s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
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+ "2025/05/20 18:51:32 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 14m54s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
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+ "2025/05/20 18:51:33 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 14m54s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
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+ "2025/05/20 18:51:34 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 14m53s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
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+ "2025/05/20 18:51:34 1 files downloaded.\n",
+ "2025/05/20 18:51:35 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 14m52s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
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+ "2025/05/20 18:51:35 1 files downloaded.\n",
+ "2025/05/20 18:51:36 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 17m51s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
+ "2025/05/20 18:51:36 Total number of objects in manifest: 1\n",
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+ "2025/05/20 18:51:36 1 files downloaded.\n",
+ "2025/05/20 18:51:36 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 17m50s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
+ "2025/05/20 18:51:36 Total number of objects in manifest: 1\n",
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+ "2025/05/20 18:51:37 1 files downloaded.\n",
+ "2025/05/20 18:51:37 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 17m49s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
+ "2025/05/20 18:51:37 Total number of objects in manifest: 1\n",
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+ "2025/05/20 18:51:38 1 files downloaded.\n",
+ "2025/05/20 18:51:38 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 14m48s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
+ "2025/05/20 18:51:38 Total number of objects in manifest: 1\n",
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+ "2025/05/20 18:51:38 1 files downloaded.\n",
+ "2025/05/20 18:51:39 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 14m47s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
+ "2025/05/20 18:51:39 Total number of objects in manifest: 1\n",
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+ "2025/05/20 18:51:39 1 files downloaded.\n",
+ "2025/05/20 18:51:40 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 17m47s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
+ "2025/05/20 18:51:40 Total number of objects in manifest: 1\n",
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+ "2025/05/20 18:51:40 1 files downloaded.\n",
+ "2025/05/20 18:51:41 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 17m46s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
+ "2025/05/20 18:51:41 Total number of objects in manifest: 1\n",
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+ "2025/05/20 18:51:41 1 files downloaded.\n",
+ "2025/05/20 18:51:42 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 14m45s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
+ "2025/05/20 18:51:42 Total number of objects in manifest: 1\n",
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+ "2025/05/20 18:51:42 1 files downloaded.\n",
+ "2025/05/20 18:51:43 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 14m44s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
+ "2025/05/20 18:51:43 Total number of objects in manifest: 1\n",
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+ "2025/05/20 18:51:43 1 files downloaded.\n",
+ "2025/05/20 18:51:43 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 17m43s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
+ "2025/05/20 18:51:43 Total number of objects in manifest: 1\n",
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+ "2025/05/20 18:51:44 1 files downloaded.\n",
+ "2025/05/20 18:51:44 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 17m42s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
+ "2025/05/20 18:51:44 Total number of objects in manifest: 1\n",
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+ "2025/05/20 18:51:45 1 files downloaded.\n",
+ "2025/05/20 18:51:45 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 14m41s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
+ "2025/05/20 18:51:45 Total number of objects in manifest: 1\n",
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+ "2025/05/20 18:51:45 1 files downloaded.\n",
+ "2025/05/20 18:51:46 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 14m40s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
+ "2025/05/20 18:51:46 Total number of objects in manifest: 1\n",
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+ "2025/05/20 18:51:46 1 files downloaded.\n",
+ "2025/05/20 18:51:47 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 14m40s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
+ "2025/05/20 18:51:47 Total number of objects in manifest: 1\n",
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+ "2025/05/20 18:51:47 1 files downloaded.\n",
+ "2025/05/20 18:51:48 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 17m39s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
+ "2025/05/20 18:51:48 Total number of objects in manifest: 1\n",
+ "2025/05/20 18:51:48 Preparing file info for each file, please wait...\n",
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+ "2025/05/20 18:51:48 1 files downloaded.\n",
+ "2025/05/20 18:51:49 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 14m38s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
+ "2025/05/20 18:51:49 Total number of objects in manifest: 1\n",
+ "2025/05/20 18:51:49 Preparing file info for each file, please wait...\n",
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+ "2025/05/20 18:51:49 File info prepared successfully\n",
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+ "2025/05/20 18:51:49 1 files downloaded.\n",
+ "2025/05/20 18:51:50 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 17m37s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
+ "2025/05/20 18:51:50 Total number of objects in manifest: 1\n",
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+ "2025/05/20 18:51:50 1 files downloaded.\n",
+ "2025/05/20 18:51:50 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 17m36s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
+ "2025/05/20 18:51:50 Total number of objects in manifest: 1\n",
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+ "C109585280_1.vcf 1.93 KiB / 1.93 KiB [================================] 100.00%\n",
+ "2025/05/20 18:51:51 1 files downloaded.\n",
+ "2025/05/20 18:51:51 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 14m35s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
+ "2025/05/20 18:51:51 Total number of objects in manifest: 1\n",
+ "2025/05/20 18:51:51 Preparing file info for each file, please wait...\n",
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+ "2025/05/20 18:51:52 1 files downloaded.\n",
+ "2025/05/20 18:51:52 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 17m34s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
+ "2025/05/20 18:51:52 Total number of objects in manifest: 1\n",
+ "2025/05/20 18:51:52 Preparing file info for each file, please wait...\n",
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+ "2025/05/20 18:51:52 1 files downloaded.\n",
+ "2025/05/20 18:51:53 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 17m33s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
+ "2025/05/20 18:51:53 Total number of objects in manifest: 1\n",
+ "2025/05/20 18:51:53 Preparing file info for each file, please wait...\n",
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+ "2025/05/20 18:51:53 1 files downloaded.\n",
+ "2025/05/20 18:51:54 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 17m33s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
+ "2025/05/20 18:51:54 Total number of objects in manifest: 1\n",
+ "2025/05/20 18:51:54 Preparing file info for each file, please wait...\n",
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+ "2025/05/20 18:51:54 File info prepared successfully\n",
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+ "C243781630_1.vcf 12.28 KiB / 12.28 KiB [==============================] 100.00%\n",
+ "2025/05/20 18:51:54 1 files downloaded.\n",
+ "2025/05/20 18:51:55 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 17m32s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
+ "2025/05/20 18:51:55 Total number of objects in manifest: 1\n",
+ "2025/05/20 18:51:55 Preparing file info for each file, please wait...\n",
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+ "2025/05/20 18:51:55 File info prepared successfully\n",
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+ "C523093366_1.vcf 13.43 KiB / 13.43 KiB [==============================] 100.00%\n",
+ "2025/05/20 18:51:55 1 files downloaded.\n",
+ "2025/05/20 18:51:56 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 14m31s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
+ "2025/05/20 18:51:56 Total number of objects in manifest: 1\n",
+ "2025/05/20 18:51:56 Preparing file info for each file, please wait...\n",
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+ "2025/05/20 18:51:56 File info prepared successfully\n",
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+ "C796737806_1.vcf 13.64 KiB / 13.64 KiB [==============================] 100.00%\n",
+ "2025/05/20 18:51:56 1 files downloaded.\n",
+ "2025/05/20 18:51:57 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 14m30s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
+ "2025/05/20 18:51:57 Total number of objects in manifest: 1\n",
+ "2025/05/20 18:51:57 Preparing file info for each file, please wait...\n",
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+ "2025/05/20 18:51:57 File info prepared successfully\n",
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+ "C399039842_1.vcf 3.25 KiB / 3.25 KiB [================================] 100.00%\n",
+ "2025/05/20 18:51:57 1 files downloaded.\n",
+ "2025/05/20 18:51:57 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 17m29s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
+ "2025/05/20 18:51:57 Total number of objects in manifest: 1\n",
+ "2025/05/20 18:51:57 Preparing file info for each file, please wait...\n",
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+ "2025/05/20 18:51:57 File info prepared successfully\n",
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+ "2025/05/20 18:51:58 1 files downloaded.\n",
+ "2025/05/20 18:51:58 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 17m28s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
+ "2025/05/20 18:51:58 Total number of objects in manifest: 1\n",
+ "2025/05/20 18:51:58 Preparing file info for each file, please wait...\n",
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+ "2025/05/20 18:51:58 File info prepared successfully\n",
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+ "2025/05/20 18:51:58 1 files downloaded.\n",
+ "2025/05/20 18:51:59 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 17m27s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
+ "2025/05/20 18:51:59 Total number of objects in manifest: 1\n",
+ "2025/05/20 18:51:59 Preparing file info for each file, please wait...\n",
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+ "2025/05/20 18:51:59 File info prepared successfully\n",
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+ "C556739514_1.vcf 12.95 KiB / 12.95 KiB [==============================] 100.00%\n",
+ "2025/05/20 18:51:59 1 files downloaded.\n",
+ "2025/05/20 18:52:00 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 17m26s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
+ "2025/05/20 18:52:00 Total number of objects in manifest: 1\n",
+ "2025/05/20 18:52:00 Preparing file info for each file, please wait...\n",
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+ "2025/05/20 18:52:00 File info prepared successfully\n",
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+ "2025/05/20 18:52:00 1 files downloaded.\n",
+ "2025/05/20 18:52:01 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 14m26s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
+ "2025/05/20 18:52:01 Total number of objects in manifest: 1\n",
+ "2025/05/20 18:52:01 Preparing file info for each file, please wait...\n",
+ " 1 / 1 [============================================================] 100.00% 0s\n",
+ "2025/05/20 18:52:01 File info prepared successfully\n",
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+ "2025/05/20 18:52:01 1 files downloaded.\n",
+ "2025/05/20 18:52:02 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 14m25s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
+ "2025/05/20 18:52:02 Total number of objects in manifest: 1\n",
+ "2025/05/20 18:52:02 Preparing file info for each file, please wait...\n",
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+ "2025/05/20 18:52:02 File info prepared successfully\n",
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+ "C1877271770_1.vcf 10.63 KiB / 10.63 KiB [=============================] 100.00%\n",
+ "2025/05/20 18:52:02 1 files downloaded.\n",
+ "2025/05/20 18:52:03 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 17m24s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
+ "2025/05/20 18:52:03 Total number of objects in manifest: 1\n",
+ "2025/05/20 18:52:03 Preparing file info for each file, please wait...\n",
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+ "2025/05/20 18:52:03 File info prepared successfully\n",
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+ "C290053028_1.vcf 19.27 KiB / 19.27 KiB [==============================] 100.00%\n",
+ "2025/05/20 18:52:03 1 files downloaded.\n",
+ "2025/05/20 18:52:04 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 14m23s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
+ "2025/05/20 18:52:04 Total number of objects in manifest: 1\n",
+ "2025/05/20 18:52:04 Preparing file info for each file, please wait...\n",
+ " 1 / 1 [============================================================] 100.00% 0s\n",
+ "2025/05/20 18:52:04 File info prepared successfully\n",
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+ "C1110841088_1.vcf 1.73 KiB / 1.73 KiB [===============================] 100.00%\n",
+ "2025/05/20 18:52:04 1 files downloaded.\n",
+ "2025/05/20 18:52:04 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 17m22s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
+ "2025/05/20 18:52:04 Total number of objects in manifest: 1\n",
+ "2025/05/20 18:52:04 Preparing file info for each file, please wait...\n",
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+ "2025/05/20 18:52:04 File info prepared successfully\n",
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+ "C787354480_1.vcf 2.42 KiB / 2.42 KiB [================================] 100.00%\n",
+ "2025/05/20 18:52:05 1 files downloaded.\n",
+ "2025/05/20 18:52:05 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 14m21s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
+ "2025/05/20 18:52:05 Total number of objects in manifest: 1\n",
+ "2025/05/20 18:52:05 Preparing file info for each file, please wait...\n",
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+ "2025/05/20 18:52:05 File info prepared successfully\n",
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+ "C369399716_1.vcf 2.34 KiB / 2.34 KiB [================================] 100.00%\n",
+ "2025/05/20 18:52:06 1 files downloaded.\n",
+ "2025/05/20 18:52:06 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 17m20s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
+ "2025/05/20 18:52:06 Total number of objects in manifest: 1\n",
+ "2025/05/20 18:52:06 Preparing file info for each file, please wait...\n",
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+ "2025/05/20 18:52:06 File info prepared successfully\n",
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+ "C1693930878_1.vcf 2.67 KiB / 2.67 KiB [===============================] 100.00%\n",
+ "2025/05/20 18:52:06 1 files downloaded.\n",
+ "2025/05/20 18:52:07 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 17m19s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
+ "2025/05/20 18:52:07 Total number of objects in manifest: 1\n",
+ "2025/05/20 18:52:07 Preparing file info for each file, please wait...\n",
+ " 1 / 1 [============================================================] 100.00% 0s\n",
+ "2025/05/20 18:52:07 File info prepared successfully\n",
+ "C1736502752_1.vcf 1.49 KiB / 1.49 KiB [===============================] 100.00%\n",
+ "C1736502752_1.vcf 1.49 KiB / 1.49 KiB [===============================] 100.00%\n",
+ "2025/05/20 18:52:07 1 files downloaded.\n",
+ "2025/05/20 18:52:08 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 14m19s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/variant_call_files/\" will be overwritten\n",
+ "2025/05/20 18:52:08 Total number of objects in manifest: 1\n",
+ "2025/05/20 18:52:08 Preparing file info for each file, please wait...\n",
+ " 1 / 1 [============================================================] 100.00% 0s\n",
+ "2025/05/20 18:52:08 File info prepared successfully\n",
+ "C961182904_1.vcf 1.61 KiB / 1.61 KiB [================================] 100.00%\n",
+ "C961182904_1.vcf 1.61 KiB / 1.61 KiB [================================] 100.00%\n",
+ "2025/05/20 18:52:08 1 files downloaded.\n"
+ ]
+ }
+ ],
+ "source": [
+ "!mkdir -p /home/jovyan/pd/Downloaded_Data/variant_call_files\n",
+ "dl_dir = '/home/jovyan/pd/Downloaded_Data/variant_call_files'\n",
+ "\n",
+ "for object_id in vcf_object_ids:\n",
+ " !/home/jovyan/pd/.gen3/gen3-client download-single --guid=$object_id --profile=vpodc --download-path=$dl_dir --no-prompt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### ๐งฎ Count Downloaded VCF Files\n",
+ "\n",
+ "We perform a final check to verify how many `.vcf` files were successfully downloaded to the target directory. \n",
+ "This uses the `find` command to search for files with the `.vcf` extension and counts them with `wc -l`.\n",
+ "\n",
+ "> โ This helps confirm that the number of downloaded VCFs matches the number of expected `object_id`s.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "61\n"
+ ]
+ }
+ ],
+ "source": [
+ "!find $dl_dir -name '*.vcf' | wc -l"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### ๐งซ Extract Structured Data for Pathology Slide Files\n",
+ "\n",
+ "We now repeat the process for the `pathology_slide` node using the `get_node_tsvs()` function.\n",
+ "\n",
+ "This function retrieves all available metadata for pathology slide files within the `VA-PODR-COHORT-A` project. \n",
+ "The result is stored as a structured DataFrame (`df`) and saved as a `.tsv` file for reference.\n",
+ "\n",
+ "๐ก Key columns include:\n",
+ "- `submitter_id`: uniquely identifies the slide\n",
+ "- `data_format`: expected to be `JPEG`\n",
+ "- `file_name`, `file_size`: for download and quality assessment\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Output written to file: node_tsvs/pathology_slide_tsvs/VA-PODR-COHORT-A_pathology_slide.tsv\n",
+ "node_tsvs/pathology_slide_tsvs/VA-PODR-COHORT-A_pathology_slide.tsv has 170 records.\n",
+ "length of all dfs: 170\n",
+ "Master node TSV with 170 total records written to master_pathology_slide.tsv.\n"
+ ]
+ },
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+ "\n",
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+ "\n",
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+ "4 e70b4083-a7aa-49a8-b87e-0b65af215cea \n",
+ "\n",
+ " core_metadata_collections.submitter_id samples.id samples.submitter_id \n",
+ "0 cohortA_batch10_pathology_slides NaN NaN \n",
+ "1 Pathology_Slides_HDD5 NaN NaN \n",
+ "2 Pathology_Slides_HDD5 NaN NaN \n",
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+ "4 Pathology_Slides_HDD5 NaN NaN \n",
+ "\n",
+ "[5 rows x 36 columns]"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "node = 'pathology_slide'\n",
+ "df = exp.get_node_tsvs(node,projects='VA-PODR-COHORT-A',overwrite=True)\n",
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### ๐ Match Pathology Slide Files to Lung Cases\n",
+ "\n",
+ "We filter the `pathology_slide` DataFrame to include only those records whose `case_submitter_id` appears in our list of lung cases. \n",
+ "This gives us a refined list of `object_id`s corresponding to pathology slide images linked to LUNG patients.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "56"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "slide_object_ids = list(df.loc[df['case_submitter_id'].isin(case_submitter_ids)]['object_id'])\n",
+ "len(slide_object_ids)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### โฌ๏ธ Download Pathology Slide Files Using Gen3 Client\n",
+ "\n",
+ "We loop through each `object_id` and download the associated pathology slide image using the `gen3-client`.\n",
+ "\n",
+ "- Downloads are saved to a designated directory (`Downloaded_Data/pathology_slides`)\n",
+ "- The `--no-prompt` flag ensures the script proceeds without manual confirmation\n",
+ "- If the file already exists, it will be overwritten unless the `--rename` flag is set\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2025/05/20 18:52:10 Error occurred when checking for latest version: GET https://api.github.com/repos/uc-cdis/cdis-data-client/tags: 403 API rate limit exceeded for 3.86.93.34. (But here's the good news: Authenticated requests get a higher rate limit. Check out the documentation for more details.) [rate reset in 14m16s]\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/pathology_slides/\" will be overwritten\n",
+ "2025/05/20 18:52:10 Total number of objects in manifest: 1\n",
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+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/pathology_slides/\" will be overwritten\n",
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+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/pathology_slides/\" will be overwritten\n",
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+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/pathology_slides/\" will be overwritten\n",
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+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/pathology_slides/\" will be overwritten\n",
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+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/pathology_slides/\" will be overwritten\n",
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+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/pathology_slides/\" will be overwritten\n",
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+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/pathology_slides/\" will be overwritten\n",
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+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/pathology_slides/\" will be overwritten\n",
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+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/pathology_slides/\" will be overwritten\n",
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+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/pathology_slides/\" will be overwritten\n",
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+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/pathology_slides/\" will be overwritten\n",
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+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/pathology_slides/\" will be overwritten\n",
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+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/pathology_slides/\" will be overwritten\n",
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+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/pathology_slides/\" will be overwritten\n",
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+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/pathology_slides/\" will be overwritten\n",
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+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/pathology_slides/\" will be overwritten\n",
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+ "C720830472_1.jpg 3.95 MiB / 3.95 MiB [================================] 100.00%\n",
+ "2025/05/20 18:52:48 1 files downloaded.\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/pathology_slides/\" will be overwritten\n",
+ "2025/05/20 18:52:49 Total number of objects in manifest: 1\n",
+ "2025/05/20 18:52:49 Preparing file info for each file, please wait...\n",
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+ "2025/05/20 18:52:49 1 files downloaded.\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/pathology_slides/\" will be overwritten\n",
+ "2025/05/20 18:52:50 Total number of objects in manifest: 1\n",
+ "2025/05/20 18:52:50 Preparing file info for each file, please wait...\n",
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+ "2025/05/20 18:52:50 1 files downloaded.\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/pathology_slides/\" will be overwritten\n",
+ "2025/05/20 18:52:51 Total number of objects in manifest: 1\n",
+ "2025/05/20 18:52:51 Preparing file info for each file, please wait...\n",
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+ "2025/05/20 18:52:51 File info prepared successfully\n",
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+ "2025/05/20 18:52:51 1 files downloaded.\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/pathology_slides/\" will be overwritten\n",
+ "2025/05/20 18:52:52 Total number of objects in manifest: 1\n",
+ "2025/05/20 18:52:52 Preparing file info for each file, please wait...\n",
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+ "2025/05/20 18:52:52 1 files downloaded.\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/pathology_slides/\" will be overwritten\n",
+ "2025/05/20 18:52:53 Total number of objects in manifest: 1\n",
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+ "2025/05/20 18:52:53 1 files downloaded.\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/pathology_slides/\" will be overwritten\n",
+ "2025/05/20 18:52:54 Total number of objects in manifest: 1\n",
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+ "2025/05/20 18:52:54 1 files downloaded.\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/pathology_slides/\" will be overwritten\n",
+ "2025/05/20 18:52:55 Total number of objects in manifest: 1\n",
+ "2025/05/20 18:52:55 Preparing file info for each file, please wait...\n",
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+ "2025/05/20 18:52:55 1 files downloaded.\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/pathology_slides/\" will be overwritten\n",
+ "2025/05/20 18:52:56 Total number of objects in manifest: 1\n",
+ "2025/05/20 18:52:56 Preparing file info for each file, please wait...\n",
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+ "2025/05/20 18:52:56 1 files downloaded.\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/pathology_slides/\" will be overwritten\n",
+ "2025/05/20 18:52:57 Total number of objects in manifest: 1\n",
+ "2025/05/20 18:52:57 Preparing file info for each file, please wait...\n",
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+ "2025/05/20 18:52:57 File info prepared successfully\n",
+ "C759320696.jpg 105.98 KiB / 105.98 KiB [==============================] 100.00%\n",
+ "C759320696.jpg 105.98 KiB / 105.98 KiB [==============================] 100.00%\n",
+ "2025/05/20 18:52:57 1 files downloaded.\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/pathology_slides/\" will be overwritten\n",
+ "2025/05/20 18:52:58 Total number of objects in manifest: 1\n",
+ "2025/05/20 18:52:58 Preparing file info for each file, please wait...\n",
+ " 1 / 1 [============================================================] 100.00% 0s\n",
+ "2025/05/20 18:52:58 File info prepared successfully\n",
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+ "C1763639778_1.jpg 2.22 MiB / 2.22 MiB [===============================] 100.00%\n",
+ "2025/05/20 18:52:58 1 files downloaded.\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/pathology_slides/\" will be overwritten\n",
+ "2025/05/20 18:52:59 Total number of objects in manifest: 1\n",
+ "2025/05/20 18:52:59 Preparing file info for each file, please wait...\n",
+ " 1 / 1 [============================================================] 100.00% 0s\n",
+ "2025/05/20 18:52:59 File info prepared successfully\n",
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+ "C1402041738_1.jpg 393.56 KiB / 393.56 KiB [===========================] 100.00%\n",
+ "2025/05/20 18:52:59 1 files downloaded.\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/pathology_slides/\" will be overwritten\n",
+ "2025/05/20 18:53:00 Total number of objects in manifest: 1\n",
+ "2025/05/20 18:53:00 Preparing file info for each file, please wait...\n",
+ " 1 / 1 [============================================================] 100.00% 0s\n",
+ "2025/05/20 18:53:00 File info prepared successfully\n",
+ "C1984979028_2.jpg 3.47 MiB / 3.47 MiB [===============================] 100.00%\n",
+ "C1984979028_2.jpg 3.47 MiB / 3.47 MiB [===============================] 100.00%\n",
+ "2025/05/20 18:53:00 1 files downloaded.\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/pathology_slides/\" will be overwritten\n",
+ "2025/05/20 18:53:01 Total number of objects in manifest: 1\n",
+ "2025/05/20 18:53:01 Preparing file info for each file, please wait...\n",
+ " 1 / 1 [============================================================] 100.00% 0s\n",
+ "2025/05/20 18:53:01 File info prepared successfully\n",
+ "C24362764_1.jpg 2.87 MiB / 2.87 MiB [=================================] 100.00%\n",
+ "C24362764_1.jpg 2.87 MiB / 2.87 MiB [=================================] 100.00%\n",
+ "2025/05/20 18:53:01 1 files downloaded.\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/pathology_slides/\" will be overwritten\n",
+ "2025/05/20 18:53:02 Total number of objects in manifest: 1\n",
+ "2025/05/20 18:53:02 Preparing file info for each file, please wait...\n",
+ " 1 / 1 [============================================================] 100.00% 0s\n",
+ "2025/05/20 18:53:02 File info prepared successfully\n",
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+ "C1269679480_1.jpg 509.24 KiB / 509.24 KiB [===========================] 100.00%\n",
+ "2025/05/20 18:53:02 1 files downloaded.\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/pathology_slides/\" will be overwritten\n",
+ "2025/05/20 18:53:03 Total number of objects in manifest: 1\n",
+ "2025/05/20 18:53:03 Preparing file info for each file, please wait...\n",
+ " 1 / 1 [============================================================] 100.00% 0s\n",
+ "2025/05/20 18:53:03 File info prepared successfully\n",
+ "C1683349088_1.jpg 1.68 MiB / 1.68 MiB [===============================] 100.00%\n",
+ "C1683349088_1.jpg 1.68 MiB / 1.68 MiB [===============================] 100.00%\n",
+ "2025/05/20 18:53:03 1 files downloaded.\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/pathology_slides/\" will be overwritten\n",
+ "2025/05/20 18:53:04 Total number of objects in manifest: 1\n",
+ "2025/05/20 18:53:04 Preparing file info for each file, please wait...\n",
+ " 1 / 1 [============================================================] 100.00% 0s\n",
+ "2025/05/20 18:53:04 File info prepared successfully\n",
+ "C291964410_1.jpg 2.73 MiB / 2.73 MiB [================================] 100.00%\n",
+ "C291964410_1.jpg 2.73 MiB / 2.73 MiB [================================] 100.00%\n",
+ "2025/05/20 18:53:04 1 files downloaded.\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/pathology_slides/\" will be overwritten\n",
+ "2025/05/20 18:53:05 Total number of objects in manifest: 1\n",
+ "2025/05/20 18:53:05 Preparing file info for each file, please wait...\n",
+ " 1 / 1 [============================================================] 100.00% 0s\n",
+ "2025/05/20 18:53:05 File info prepared successfully\n",
+ "C410828272_1.jpg 2.57 MiB / 2.57 MiB [================================] 100.00%\n",
+ "C410828272_1.jpg 2.57 MiB / 2.57 MiB [================================] 100.00%\n",
+ "2025/05/20 18:53:05 1 files downloaded.\n",
+ "WARNING: flag \"rename\" was set to false in \"original\" mode, duplicated files under \"/home/jovyan/pd/Downloaded_Data/pathology_slides/\" will be overwritten\n",
+ "2025/05/20 18:53:06 Total number of objects in manifest: 1\n",
+ "2025/05/20 18:53:06 Preparing file info for each file, please wait...\n",
+ " 1 / 1 [============================================================] 100.00% 0s\n",
+ "2025/05/20 18:53:06 File info prepared successfully\n",
+ "C1984979028_1.jpg 4.11 MiB / 4.11 MiB [===============================] 100.00%\n",
+ "C1984979028_1.jpg 4.11 MiB / 4.11 MiB [===============================] 100.00%\n",
+ "2025/05/20 18:53:06 1 files downloaded.\n"
+ ]
+ }
+ ],
+ "source": [
+ "!mkdir -p /home/jovyan/pd/Downloaded_Data/pathology_slides\n",
+ "dl_dir = '/home/jovyan/pd/Downloaded_Data/pathology_slides'\n",
+ "\n",
+ "for object_id in slide_object_ids:\n",
+ " !/home/jovyan/pd/.gen3/gen3-client download-single --guid=$object_id --profile=vpodc --download-path=$dl_dir --no-prompt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### ๐งฎ Verify Downloaded Files\n",
+ "\n",
+ "After completing the download loop, we verify the total number of pathology slide images that were successfully saved. \n",
+ "This command searches the download directory (`$dl_dir`) for all `.jpg` files and counts them using `wc -l`.\n",
+ "\n",
+ "> This provides a quick sanity check to ensure the number of downloaded files matches the number of requested `object_id`s.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "56\n"
+ ]
+ }
+ ],
+ "source": [
+ "!find $dl_dir -name '*.jpg' | wc -l"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "## ๐ References and Next Steps\n",
+ "- [Gen3 Client Documentation](https://gen3.org/resources/user/gen3-client/)\n",
+ "- [Gen3 Commons Portal](https://gen3.datacommons.io/)\n",
+ "- [VPODC Project Overview](https://vpodc.data-commons.org/)\n",
+ "\n",
+ "If you encounter download errors (e.g., HTTP 403), ensure your Gen3 credentials are correctly configured and your profile has access permissions.\n",
+ "\n",
+ "For additional help, contact your system administrator or Gen3 support."
+ ]
+ }
+ ],
+ "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": "3.9.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/jupyter-prometheus/combined_demos/metabolomics_notebook.ipynb b/jupyter-prometheus/combined_demos/metabolomics_notebook.ipynb
new file mode 100644
index 00000000..bfcabaf5
--- /dev/null
+++ b/jupyter-prometheus/combined_demos/metabolomics_notebook.ipynb
@@ -0,0 +1,1268 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "c4ae70a1",
+ "metadata": {},
+ "source": [
+ "# ๐งช Metabolomics Analysis using Data from Metabolomics Workbench\n",
+ "\n",
+ "\n",
+ "\n",
+ "This notebook demonstrates a real-world data integration pipeline involving the **Murtha Cancer Center Data Platform** and external public resources like the **Metabolomics Workbench**. This notebook analyzes metabolomics data from study ID **ST003847**, a study retrieved from the [Metabolomics Workbench](https://www.metabolomicsworkbench.org/).\n",
+ "\n",
+ "---\n",
+ "\n",
+ "## ๐ Study Overview\n",
+ "\n",
+ "- **Study ID**: `ST003847` \n",
+ "- **Analysis ID**: `AN006322` \n",
+ "- **Project ID**: `PR002405` \n",
+ "- **DATATRACK ID**: `5761` \n",
+ "- **Submission Date**: `April 7, 2025`\n",
+ "\n",
+ "---\n",
+ "\n",
+ "## ๐ Citation\n",
+ " This data is available at the NIH Common Fundโs National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org where it has been assigned Project ID `PR002405` and Study ID `ST003847`. The data can be accessed directly via itโs Project DOI: `10.21228/M8RV70`. This work is supported by Metabolomics Workbench/National Metabolomics Data Repository (NMDR) (grant# U2C-DK119886), Common Fund Data Ecosystem (CFDE) (grant# 3OT2OD030544) and Metabolomics Consortium Coordinating Center (M3C) (grant# 1U2C-DK119889). This project and study can be accessed directly at the following URL: https://doi.org/10.21228/M8RV70\n",
+ "\n",
+ "---\n",
+ "\n",
+ "## ๐งช Project Details\n",
+ "\n",
+ "- **Project Title**: \n",
+ " *Differential acquisition of extracellular lipid correlates with pancreatic cancer subtype and metastatic tropism*\n",
+ "\n",
+ "- **Type**: Metabolomics \n",
+ "- **Institute**: University of California, San Francisco \n",
+ "- **Department**: Anatomy \n",
+ "- **PI**: Gilles Rademaker (๐ง gilles.rademaker@ucsf.edu)\n",
+ "\n",
+ "---\n",
+ "\n",
+ "\n",
+ "## ๐งพ Study Summary (Key Insights)\n",
+ "\n",
+ "- Pancreatic ductal adenocarcinoma (PDAC) is a lethal disease with poor survival rates.\n",
+ "- PDAC can be classified into two molecular subtypes: **Basal** and **Classical**.\n",
+ "- The team identified differential expression of **PCSK9**, a regulator of LDL uptake:\n",
+ " - **Basal PDAC** shows suppressed PCSK9 and high LDL uptake.\n",
+ " - **Classical PDAC** expresses PCSK9 and shows distinct metastatic behavior.\n",
+ "- **Metastatic Tropism Insight**:\n",
+ " - Basal tumors are more likely to colonize the **liver**.\n",
+ " - Classical tumors show preference for **lung** metastasis.\n",
+ "- Modulating PCSK9 levels alters metastatic behavior, highlighting cholesterol metabolism as a potential therapeutic target.\n",
+ "\n",
+ "---\n",
+ "\n",
+ "## ๐ฌ Subject Metadata\n",
+ "\n",
+ "- **Subject Type**: Cultured Cells \n",
+ "- **Species**: *Homo sapiens* \n",
+ "- **NCBI Taxonomy ID**: 9606\n",
+ "\n",
+ "---\n",
+ "\n",
+ "### ๐ Files Used:\n",
+ "- `ST003847_factors.tsv`: Sample metadata including experimental condition (`Variant`) and identifiers.\n",
+ "- `ST003847_AN006322_datatable.tsv`: Metabolite intensities (rows: metabolites, columns: samples).\n",
+ "\n",
+ "---\n",
+ "\n",
+ "### ๐ฏ Objectives:\n",
+ "1. Align metadata and expression data using sample IDs.\n",
+ "2. Perform differential metabolite analysis between experimental groups.\n",
+ "3. Visualize variation using PCA, volcano plot, boxplots, and heatmaps.\n",
+ "\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "d1eecdff",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
+ "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
+ "๐ฅ Downloading study-level data for ST003847...\n",
+ "\n",
+ "โฌ๏ธ summary ...\n",
+ " โ Saved to ST003847_summary.tsv\n",
+ "โฌ๏ธ factors ...\n",
+ " โ Saved to ST003847_factors.tsv\n",
+ "โฌ๏ธ analysis ...\n",
+ " โ Saved to ST003847_analysis.tsv\n",
+ "โฌ๏ธ metabolites ...\n",
+ " โ Saved to ST003847_metabolites.tsv\n",
+ "โฌ๏ธ data ...\n",
+ " โ Saved to ST003847_data.tsv\n",
+ "โฌ๏ธ mwtab ...\n",
+ " โ Saved to ST003847_mwtab.tsv\n",
+ "\n",
+ "๐ Fetching analysis ID list...\n",
+ "โ API response indicates this output item does not exist.\n"
+ ]
+ }
+ ],
+ "source": [
+ "import requests\n",
+ "import pandas as pd\n",
+ "from io import StringIO\n",
+ "from pathlib import Path\n",
+ "\n",
+ "study_id = \"ST003847\"\n",
+ "base_url = \"https://www.metabolomicsworkbench.org/rest\"\n",
+ "output_dir = Path(f\"{study_id}_api_downloads\")\n",
+ "output_dir.mkdir(exist_ok=True)\n",
+ "\n",
+ "endpoints = {\n",
+ " \"summary\": f\"{base_url}/study/study_id/{study_id}/summary/txt\",\n",
+ " \"factors\": f\"{base_url}/study/study_id/{study_id}/factors/txt\",\n",
+ " \"analysis\": f\"{base_url}/study/study_id/{study_id}/analysis/txt\",\n",
+ " \"metabolites\": f\"{base_url}/study/study_id/{study_id}/metabolites/txt\",\n",
+ " \"data\": f\"{base_url}/study/study_id/{study_id}/data/txt\",\n",
+ " \"mwtab\": f\"{base_url}/study/study_id/{study_id}/mwtab/txt\",\n",
+ "}\n",
+ "\n",
+ "print(f\"๐ฅ Downloading study-level data for {study_id}...\\n\")\n",
+ "for name, url in endpoints.items():\n",
+ " print(f\"โฌ๏ธ {name} ...\")\n",
+ " r = requests.get(url)\n",
+ " if r.status_code == 200:\n",
+ " file_path = output_dir / f\"{study_id}_{name}.tsv\"\n",
+ " file_path.write_text(r.text)\n",
+ " print(f\" โ Saved to {file_path.name}\")\n",
+ " else:\n",
+ " print(f\" โ Failed: HTTP {r.status_code}\")\n",
+ "\n",
+ "\n",
+ "print(\"\\n๐ Fetching analysis ID list...\")\n",
+ "alist_url = f\"{base_url}/study/study_id/{study_id}/analysis_id/txt\"\n",
+ "r = requests.get(alist_url)\n",
+ "\n",
+ "if r.status_code == 200:\n",
+ " content = r.text.strip()\n",
+ " if \"does not exist\" in content.lower():\n",
+ " print(\"โ API response indicates this output item does not exist.\")\n",
+ " else:\n",
+ " df = pd.read_csv(StringIO(content), header=None)\n",
+ " analysis_ids = df.iloc[:, 0].tolist()\n",
+ " print(f\" ๐ข Found {len(analysis_ids)} analysis ID(s): {analysis_ids}\")\n",
+ "\n",
+ " for aid in analysis_ids:\n",
+ " dt_url = f\"{base_url}/study/analysis_id/{aid}/datatable\"\n",
+ " print(f\"โฌ๏ธ Downloading datatable for {aid} ...\")\n",
+ " r2 = requests.get(dt_url)\n",
+ " if r2.status_code == 200:\n",
+ " outf = output_dir / f\"{study_id}_{aid}_datatable.tsv\"\n",
+ " outf.write_text(r2.text)\n",
+ " print(f\" โ Saved: {outf.name}\")\n",
+ " else:\n",
+ " print(f\" โ Failed {aid}: HTTP {r2.status_code}\")\n",
+ "else:\n",
+ " print(f\"โ Failed to fetch analysis list: HTTP {r.status_code}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6c0c9d62",
+ "metadata": {},
+ "source": [
+ "### โ Here's What Each File Gives You\n",
+ "\n",
+ "| File | Purpose |\n",
+ "|----------------------------------|----------------------------------------------------------|\n",
+ "| `ST003847_AN006322_datatable.tsv` | Intensity table for **identified metabolites** |\n",
+ "| `ST003847_data.tsv` | Intensity table (possibly full matrix, raw or normalized)|\n",
+ "| `ST003847_factors.tsv` | Sample metadata (group labels like subtype) |\n",
+ "| `ST003847_metabolites.tsv` | Metabolite annotations (RefMet, KEGG, etc.) |\n",
+ "| `ST003847_mwtab.tsv` | Full study data in standardized **mwTab** format |\n",
+ "| `ST003847_summary.tsv` | Summary of study design |\n",
+ "\n",
+ "\n",
+ "\n",
+ "---\n",
+ "\n",
+ "### ๐ Integration Highlight\n",
+ "\n",
+ "This dataset showcases successful integration of **public metabolomics data** into our **Murtha Cancer Center's internal oncology informatics workflows**, enabling:\n",
+ "\n",
+ "- Cross-validation with **local multi-omic datasets**\n",
+ "- Hypothesis generation on metabolic vulnerabilities\n",
+ "- Enrichment of patient stratification models using public tumor metabolism signatures\n",
+ "\n",
+ "---\n",
+ "\n",
+ "โก๏ธ [View Study on Metabolomics Workbench](https://www.metabolomicsworkbench.org/data/DRCCMetadata.php?STUDY_ID=ST003847)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "794262a4",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "โ Extracted analysis_id: AN006322\n"
+ ]
+ }
+ ],
+ "source": [
+ "with open(\"ST003847_api_downloads/ST003847_analysis.tsv\") as f:\n",
+ " lines = f.readlines()\n",
+ "\n",
+ "analysis_info = dict(line.strip().split(\"\\t\", 1) for line in lines if \"\\t\" in line)\n",
+ "analysis_id = analysis_info.get(\"analysis_id\")\n",
+ "print(\"โ Extracted analysis_id:\", analysis_id)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "8a3aa319",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "โ Datatable saved.\n"
+ ]
+ }
+ ],
+ "source": [
+ "dt_url = f\"https://www.metabolomicsworkbench.org/rest/study/analysis_id/{analysis_id}/datatable\"\n",
+ "r = requests.get(dt_url)\n",
+ "if r.status_code == 200:\n",
+ " with open(f\"ST003847_api_downloads/ST003847_{analysis_id}_datatable.tsv\", \"w\") as f:\n",
+ " f.write(r.text)\n",
+ " print(\"โ Datatable saved.\")\n",
+ "else:\n",
+ " print(f\"โ Failed to download datatable: HTTP {r.status_code}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e3198ddf",
+ "metadata": {},
+ "source": [
+ "## ๐ฅ Load Metadata and Expression Data\n",
+ "\n",
+ "We load the metadata and expression data into pandas DataFrames. \n",
+ "Metadata is parsed from repeating blocks (6 rows per sample). \n",
+ "Expression data contains metabolite measurements for each sample."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "5eb35fb0",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "plt.rcParams['figure.dpi'] = 150\n",
+ "import seaborn as sns\n",
+ "from scipy.stats import ttest_ind\n",
+ "\n",
+ "# Step 1: Load and parse factors file\n",
+ "factors_path = \"ST003847_api_downloads/ST003847_factors.tsv\"\n",
+ "factors_df = pd.read_csv(factors_path, sep=\"\\t\")\n",
+ "\n",
+ "# Parse every 6-line block\n",
+ "parsed_records = []\n",
+ "sample_map = {}\n",
+ "for i in range(0, len(factors_df), 6):\n",
+ " record = {}\n",
+ " local_id, mb_id = None, None\n",
+ " for j in range(6):\n",
+ " if i + j >= len(factors_df):\n",
+ " continue\n",
+ " key, val = factors_df.iloc[i + j]\n",
+ " record[key] = val\n",
+ " if key == \"local_sample_id\":\n",
+ " local_id = str(val).strip()\n",
+ " if key == \"mb_sample_id\":\n",
+ " mb_id = str(val).strip()\n",
+ " if record:\n",
+ " parsed_records.append(record)\n",
+ " if local_id and mb_id:\n",
+ " sample_map[local_id] = mb_id"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d51b88e5",
+ "metadata": {},
+ "source": [
+ "## ๐งผ Clean and Parse Metadata\n",
+ "\n",
+ "For each sample:\n",
+ "- Extract `local_sample_id` (used in data table).\n",
+ "- Extract `mb_sample_id` (unique sample ID like `SA420595`).\n",
+ "- Extract `Variant` label from the factors field.\n",
+ "\n",
+ "All values are compiled into a structured table for further merging."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "05cf289e",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# Create metadata table and extract Variant\n",
+ "parsed_metadata = pd.DataFrame(parsed_records)\n",
+ "parsed_metadata[\"local_sample_id\"] = parsed_metadata[\"local_sample_id\"].astype(str)\n",
+ "parsed_metadata[\"mb_sample_id\"] = parsed_metadata[\"mb_sample_id\"].astype(str)\n",
+ "parsed_metadata[\"Variant\"] = (\n",
+ " parsed_metadata[\"factors\"].str.extract(r\"Variant:([^|]+)\").iloc[:, 0].str.strip()\n",
+ ")\n",
+ "\n",
+ "# Step 2: Load expression data\n",
+ "data_df = pd.read_csv(\n",
+ " \"ST003847_api_downloads/ST003847_AN006322_datatable.tsv\",\n",
+ " sep=\"\\t\",\n",
+ ")\n",
+ "data_df = data_df.rename(columns={\"Samples\": \"local_sample_id\"})\n",
+ "data_df[\"local_sample_id\"] = data_df[\"local_sample_id\"].astype(str)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f1de9b21",
+ "metadata": {},
+ "source": [
+ "## ๐งฌ Prepare Expression Matrix and Align Samples\n",
+ "\n",
+ "Steps:\n",
+ "- Map `local_sample_id` to `mb_sample_id`.\n",
+ "- Match samples between metadata and expression data.\n",
+ "- Transpose the data to have **samples as rows**, metabolites as columns.\n",
+ "- Normalize using **z-score standardization** to center and scale each feature."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "f89abf68",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "# Step 3: Filter for common samples\n",
+ "common_ids = set(data_df[\"local_sample_id\"]) & set(parsed_metadata[\"local_sample_id\"])\n",
+ "data_df = data_df[data_df[\"local_sample_id\"].isin(common_ids)].copy()\n",
+ "parsed_metadata = parsed_metadata[\n",
+ " parsed_metadata[\"local_sample_id\"].isin(common_ids)\n",
+ "].copy()\n",
+ "\n",
+ "# Step 4: Set mb_sample_id as index for both\n",
+ "data_df[\"mb_sample_id\"] = data_df[\"local_sample_id\"].map(sample_map)\n",
+ "parsed_metadata.set_index(\"mb_sample_id\", inplace=True)\n",
+ "data_df.set_index(\"mb_sample_id\", inplace=True)\n",
+ "data_df.drop(columns=\"local_sample_id\", inplace=True)\n",
+ "\n",
+ "# Step 5: Group by Variant\n",
+ "group1 = data_df[parsed_metadata[\"Variant\"] == \"HPAC\"]\n",
+ "group2 = data_df[parsed_metadata[\"Variant\"] == \"KP4_Control\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7b07fcd5",
+ "metadata": {},
+ "source": [
+ "## ๐งช Differential Analysis\n",
+ "\n",
+ "We compare metabolite levels between groups (e.g., `HPAC` vs `KP4_Control`) using **independent t-tests**.\n",
+ "\n",
+ "For each metabolite:\n",
+ "- Compute **p-value** using `scipy.stats.ttest_ind`.\n",
+ "- Calculate **log2 fold change** between the groups.\n",
+ "- Derive **-log10(p-value)** for volcano plot display.\n",
+ "\n",
+ "A result table includes:\n",
+ "\n",
+ "| Metabolite | p-value | log2FC | -log10(p-value) | Significant |\n",
+ "|----------------------|----------|----------|------------------|-------------|\n",
+ "| 7-Dehydrocholesterol | 0.001997 | -3.18512 | 2.69972 | True |\n",
+ "| 7-Dehydrodesmosterol | 0.737751 | -0.27769 | 0.13209 | False |"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "a98dbeb6",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+ "columns": [
+ {
+ "name": "index",
+ "rawType": "int64",
+ "type": "integer"
+ },
+ {
+ "name": "Metabolite",
+ "rawType": "object",
+ "type": "string"
+ },
+ {
+ "name": "p-value",
+ "rawType": "float64",
+ "type": "float"
+ },
+ {
+ "name": "log2FC",
+ "rawType": "float64",
+ "type": "float"
+ },
+ {
+ "name": "-log10(p-value)",
+ "rawType": "float64",
+ "type": "float"
+ },
+ {
+ "name": "Significant",
+ "rawType": "bool",
+ "type": "boolean"
+ }
+ ],
+ "ref": "e8f17a96-a7bb-45b2-842b-ea36c752f9af",
+ "rows": [
+ [
+ "0",
+ "7-Dehydrocholesterol",
+ "0.0019965431764393743",
+ "-3.185117452784087",
+ "2.699721293490373",
+ "True"
+ ],
+ [
+ "1",
+ "7-Dehydrodesmosterol",
+ "0.7377506836646052",
+ "-0.27768794404553987",
+ "0.13209037937239068",
+ "False"
+ ]
+ ],
+ "shape": {
+ "columns": 5,
+ "rows": 2
+ }
+ },
+ "text/html": [
+ "
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+ "\n",
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+ "
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+ "
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+ "
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+ "
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+ "
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+ "
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+ ],
+ "text/plain": [
+ " Metabolite p-value log2FC -log10(p-value) Significant\n",
+ "0 7-Dehydrocholesterol 0.001997 -3.185117 2.699721 True\n",
+ "1 7-Dehydrodesmosterol 0.737751 -0.277688 0.132090 False"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Step 6: Differential analysis\n",
+ "ttest_results = []\n",
+ "for metabolite in data_df.columns:\n",
+ " if metabolite not in group1 or metabolite not in group2:\n",
+ " continue\n",
+ " values1 = group1[metabolite]\n",
+ " values2 = group2[metabolite]\n",
+ " if values1.nunique() <= 1 and values2.nunique() <= 1:\n",
+ " continue\n",
+ " stat, pval = ttest_ind(values1, values2, nan_policy=\"omit\")\n",
+ " fold_change = values1.mean() / values2.mean()\n",
+ " ttest_results.append(\n",
+ " {\"Metabolite\": metabolite, \"p-value\": pval, \"log2FC\": np.log2(fold_change)}\n",
+ " )\n",
+ "\n",
+ "ttest_df = pd.DataFrame(ttest_results)\n",
+ "ttest_df[\"-log10(p-value)\"] = -np.log10(ttest_df[\"p-value\"])\n",
+ "ttest_df[\"Significant\"] = ttest_df[\"p-value\"] < 0.1\n",
+ "ttest_df"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0e5f4904",
+ "metadata": {},
+ "source": [
+ "## ๐ Volcano Plot\n",
+ "\n",
+ "Visual representation of differential analysis.\n",
+ "\n",
+ "- **X-axis**: Log2 Fold Change (expression difference).\n",
+ "- **Y-axis**: -log10(p-value) (statistical significance).\n",
+ "- **Each dot**: One metabolite.\n",
+ "\n",
+ "๐งญ Threshold lines for:\n",
+ "- p-value = 0.05\n",
+ "\n",
+ "Color-coded by significance (if applied), or all points shown."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "3029b08a",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+ " if pd.api.types.is_categorical_dtype(vector):\n",
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+ " if pd.api.types.is_categorical_dtype(vector):\n",
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+ " if pd.api.types.is_categorical_dtype(vector):\n",
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+ " if pd.api.types.is_categorical_dtype(vector):\n",
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+ " if pd.api.types.is_categorical_dtype(vector):\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
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+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+ " if pd.api.types.is_categorical_dtype(vector):\n",
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+ " if pd.api.types.is_categorical_dtype(vector):\n",
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+ " if pd.api.types.is_categorical_dtype(vector):\n",
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+ " if pd.api.types.is_categorical_dtype(vector):\n",
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+ " if pd.api.types.is_categorical_dtype(vector):\n"
+ ]
+ },
+ {
+ "data": {
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",
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+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+ " if pd.api.types.is_categorical_dtype(vector):\n",
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+ " if pd.api.types.is_categorical_dtype(vector):\n",
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+ " if pd.api.types.is_categorical_dtype(vector):\n",
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+ " if pd.api.types.is_categorical_dtype(vector):\n",
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+ " if pd.api.types.is_categorical_dtype(vector):\n"
+ ]
+ },
+ {
+ "data": {
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",
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+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+ " if pd.api.types.is_categorical_dtype(vector):\n",
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+ " if pd.api.types.is_categorical_dtype(vector):\n",
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+ " if pd.api.types.is_categorical_dtype(vector):\n",
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+ " if pd.api.types.is_categorical_dtype(vector):\n",
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+ " if pd.api.types.is_categorical_dtype(vector):\n"
+ ]
+ },
+ {
+ "data": {
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",
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+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+ " if pd.api.types.is_categorical_dtype(vector):\n",
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+ " if pd.api.types.is_categorical_dtype(vector):\n",
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+ " if pd.api.types.is_categorical_dtype(vector):\n",
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+ " if pd.api.types.is_categorical_dtype(vector):\n",
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+ " if pd.api.types.is_categorical_dtype(vector):\n"
+ ]
+ },
+ {
+ "data": {
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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+ " if pd.api.types.is_categorical_dtype(vector):\n",
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+ " if pd.api.types.is_categorical_dtype(vector):\n",
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+ " if pd.api.types.is_categorical_dtype(vector):\n",
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+ " if pd.api.types.is_categorical_dtype(vector):\n",
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+ " if pd.api.types.is_categorical_dtype(vector):\n"
+ ]
+ },
+ {
+ "data": {
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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "df = pd.read_csv(\"ST003847_api_downloads/ST003847_AN006322_datatable.tsv\", sep=\"\\t\")\n",
+ "\n",
+ "comparisons = [\n",
+ " (\"HPAC\", \"KP4_Control\"),\n",
+ " (\"HPAC\", \"KP4_D374Y\"),\n",
+ " (\"KP4_Control\", \"KP4_D374Y\"),\n",
+ " (\"KP4_WT\", \"KP4_R46L\"),\n",
+ " (\"KP4_D374Y\", \"KP4_WT\"),\n",
+ " (\"KP4_Control\", \"KP4_WT\"),\n",
+ "]\n",
+ "\n",
+ "metabolites = [\"7-Dehydrocholesterol\", \"7-Dehydrodesmosterol\"]\n",
+ "\n",
+ "for group1, group2 in comparisons:\n",
+ " df1 = df[df[\"Class\"].str.contains(group1, na=False)]\n",
+ " df2 = df[df[\"Class\"].str.contains(group2, na=False)]\n",
+ "\n",
+ " results = []\n",
+ " for met in metabolites:\n",
+ " stat, pval = ttest_ind(df1[met], df2[met], nan_policy=\"omit\")\n",
+ " log2fc = np.log2(df1[met].mean() / df2[met].mean())\n",
+ " results.append({\"Metabolite\": met, \"p-value\": pval, \"log2FC\": log2fc})\n",
+ "\n",
+ " volcano_df = pd.DataFrame(results)\n",
+ " volcano_df[\"-log10(p-value)\"] = -np.log10(volcano_df[\"p-value\"])\n",
+ "\n",
+ " plt.figure(figsize=(6, 5))\n",
+ " plot = sns.scatterplot(\n",
+ " data=volcano_df, x=\"log2FC\", y=\"-log10(p-value)\", hue=\"Metabolite\", s=100\n",
+ " )\n",
+ " plt.axhline(-np.log10(0.05), ls=\"--\", color=\"gray\")\n",
+ " plt.title(f\"Volcano Plot: {group1} vs {group2}\")\n",
+ " plt.xlabel(\"log2 Fold Change\")\n",
+ " plt.ylabel(\"-log10 p-value\")\n",
+ " plt.legend(title=\"Metabolite\", loc=\"best\")\n",
+ " plt.tight_layout()\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "34f26b2b-1f4d-4e8c-ba30-f62d148333e1",
+ "metadata": {},
+ "source": [
+ "## ๐ PCA Projection\n",
+ "- Conducted PCA to reduce the two selected metabolites to two principal components (PC1 and PC2).\n",
+ "- Plotted samples in the 2D PCA space colored by variant.\n",
+ "- Added **confidence ellipses** for each variant group (if enough data points exist), showing distribution in PCA space.\n",
+ "\n",
+ "## ๐ Result Interpretation\n",
+ "- The PCA plot helps visualize how samples cluster based on lipid metabolite profiles.\n",
+ "- Variants with similar metabolic behavior cluster together.\n",
+ "- The percentage variance explained by PC1 and PC2 is indicated on the axes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "9ae1571a",
+ "metadata": {
+ "scrolled": true,
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Requirement already satisfied: scikit-learn in /Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages (1.6.1)\n",
+ "Requirement already satisfied: numpy>=1.19.5 in /Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages (from scikit-learn) (1.26.0)\n",
+ "Requirement already satisfied: scipy>=1.6.0 in /Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages (from scikit-learn) (1.11.4)\n",
+ "Requirement already satisfied: joblib>=1.2.0 in /Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages (from scikit-learn) (1.4.2)\n",
+ "Requirement already satisfied: threadpoolctl>=3.1.0 in /Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages (from scikit-learn) (3.5.0)\n",
+ "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
+ "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
+ "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
+ "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
+ "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
+ "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
+ "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+ " if pd.api.types.is_categorical_dtype(vector):\n",
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+ " if pd.api.types.is_categorical_dtype(vector):\n",
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+ " if pd.api.types.is_categorical_dtype(vector):\n",
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+ " if pd.api.types.is_categorical_dtype(vector):\n",
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+ " if pd.api.types.is_categorical_dtype(vector):\n"
+ ]
+ },
+ {
+ "data": {
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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "!pip install scikit-learn\n",
+ "from sklearn.decomposition import PCA\n",
+ "from sklearn.preprocessing import StandardScaler\n",
+ "from matplotlib.patches import Ellipse\n",
+ "\n",
+ "\n",
+ "data_path = \"ST003847_api_downloads/ST003847_AN006322_datatable.tsv\"\n",
+ "df = pd.read_csv(data_path, sep=\"\\t\")\n",
+ "df[\"Variant\"] = df[\"Class\"].str.extract(r\"Variant:([^|]+)\").iloc[:, 0].str.strip()\n",
+ "df = df[~df[\"Variant\"].str.contains(\"STD\", na=False)]\n",
+ "\n",
+ "\n",
+ "features = [\"7-Dehydrocholesterol\", \"7-Dehydrodesmosterol\"]\n",
+ "scaler = StandardScaler()\n",
+ "scaled_features = scaler.fit_transform(df[features])\n",
+ "\n",
+ "\n",
+ "pca = PCA(n_components=2)\n",
+ "pca_result = pca.fit_transform(scaled_features)\n",
+ "\n",
+ "df[\"PC1\"] = pca_result[:, 0]\n",
+ "df[\"PC2\"] = pca_result[:, 1]\n",
+ "\n",
+ "\n",
+ "plt.figure(figsize=(12, 8))\n",
+ "sns.set(style=\"whitegrid\")\n",
+ "palette = sns.color_palette(\"husl\", df[\"Variant\"].nunique())\n",
+ "variant_color_map = dict(zip(df[\"Variant\"].unique(), palette))\n",
+ "\n",
+ "sns.scatterplot(data=df, x=\"PC1\", y=\"PC2\", hue=\"Variant\", palette=variant_color_map, s=100, edgecolor='w')\n",
+ "\n",
+ "\n",
+ "for variant, color in variant_color_map.items():\n",
+ " group = df[df[\"Variant\"] == variant]\n",
+ " if len(group) > 2:\n",
+ " cov = np.cov(group[[\"PC1\", \"PC2\"]].T)\n",
+ " mean = group[[\"PC1\", \"PC2\"]].mean().values\n",
+ " vals, vecs = np.linalg.eigh(cov)\n",
+ " order = vals.argsort()[::-1]\n",
+ " vals, vecs = vals[order], vecs[:, order]\n",
+ " angle = np.degrees(np.arctan2(*vecs[:, 0][::-1]))\n",
+ " width, height = 2 * np.sqrt(vals)\n",
+ " ellipse = Ellipse(xy=mean, width=width, height=height, angle=angle, color=color, alpha=0.2)\n",
+ " plt.gca().add_patch(ellipse)\n",
+ "\n",
+ "plt.title(\"PCA Plot by Variant (STD Samples Removed)\")\n",
+ "plt.xlabel(f\"PC1 ({pca.explained_variance_ratio_[0]*100:.1f}%)\")\n",
+ "plt.ylabel(f\"PC2 ({pca.explained_variance_ratio_[1]*100:.1f}%)\")\n",
+ "plt.legend(title=\"Variant\", bbox_to_anchor=(1.05, 1), loc='upper left')\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "dbd787b5-810b-4fe0-a767-449fe192027a",
+ "metadata": {},
+ "source": [
+ "### ๐ฌ Radar-style Heatmap: Z-score of Metabolites by Variant\n",
+ "\n",
+ "This section summarizes the metabolomic differences across sample **variants** using a radar-style heatmap visualization.\n",
+ "\n",
+ "\n",
+ "### ๐งผ Data Processing Steps\n",
+ "1. **Remove standard samples** labeled as `STD` from the dataset.\n",
+ "2. **Extract `Variant` information** from the `Class` field.\n",
+ "3. **Select relevant metabolite columns**, excluding metadata like `Samples`, `Class`, and `Variant`.\n",
+ "4. **Aggregate** the dataset by `Variant` using the **mean** value for each metabolite.\n",
+ "5. **Z-score normalize** across all metabolites using `StandardScaler` to highlight relative intensities.\n",
+ "\n",
+ "### ๐ฅ Visualization\n",
+ "- The final plot is a **clustered heatmap** (radar-style) showing the standardized metabolite values (Z-scores) for each variant.\n",
+ "- Color gradients indicate **higher or lower abundance** relative to the mean.\n",
+ "- Helps visually detect **distinct metabolic profiles** across different variant groups.\n",
+ "\n",
+ "This method enables a high-level overview of how lipid profiles vary across PDAC subtypes or experimental conditions in the Murtha Cancer Center dataset."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "2b991436",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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enp628zYtLU1jxozR3r17JeX8zbVYLIqLi1NoaKhCQ0O1bt06ffPNN3ZL+hX0uT906JDdOEv7fb5s2TJ98cUXts9uQkKCgoODNWvWLO3atUvffPONnJycivR5AQAANyADAADc8CwWi2GxWIwVK1YUuM7ly5eNL7/80mjRooVhsViMf/7zn3naQ0JCjFatWhkWi8WYPXu2kZ6ebmu7cOGC8cILLxgWi8Vo1aqVcfHiRVvbmTNnjNatWxsWi8X4z3/+Y1y+fNkwDMPIzMw05syZY4vNYrEYoaGhtu327NljWCwWo3nz5sby5cuNrKwsW9v58+eNhx56yLBYLEbXrl3ztH388ce2/p599lnb/uLi4or0Xp0+fdqwWCxG69atjT179uRpW7VqldG8eXPDYrEYBw8ezNN2++23GxaLxVi6dGme5RMmTDAsFovx3HPP2d1f//79DYvFYmzYsKHQvpKTk41+/foZFovFePrpp43IyEhbW3R0tDFmzBjDYrEYd9xxh5Gamlqk12oYRTs3rFasWGFbP7fIyEijc+fOhsViMSZPnmwkJCTY2sLCwozhw4cbFovFGDVqlN3+2rRpY/zyyy952k6fPm3cddddhsViMYYOHZqnLTw83O45YxiG8dprr9naHnjgASM6OtowDMPIysoyJk6caDunAgICjAULFtjOnfj4eGPw4MGGxWIxHnroIbt99uvXzwgJCcnT9uuvvxpt27Y1LBaL8e677+Zpsx5Hi8ViPProo0ZsbKxhGIaRnZ1tLFmyxGjZsqVhsViMdevW5dnu0qVLxunTp43Tp0/n+ZwVV3Z2tvH4448bFovFaNmypfHbb7+VqJ+srCzjtttuMywWizFv3jwjMzPT1nbkyBHjlltuMSwWizFnzpw821n33bdvX+PIkSO25VFRUcb9999vWCwW4/7777ct37Rpk+39+vDDD22fXcMwjN27dxtdu3Y1LBaL8dRTT+XZj/W7wGKxGHPnzrUtt37mS3p+FuaPP/6wHb/JkycbiYmJtrZjx44Zd9xxh2GxWIwhQ4YYGRkZtrbc5254eHiR92f9rHTv3t0wDMOYOXOmYbFYjPHjx+dbNzo62mjevLkxZMgQwzD+//x9+eWX86z3/vvvGxaLxejRo4fx66+/2pZnZmYaX331ldGyZUsjICDA2LFjR57trN+xI0eOzLM8MzPT6Nu3r2GxWIxnnnkmz9+B9PT0PN/3W7dutfueWCwW44MPPrCd92lpacabb75p+9ty+vRp23aJiYlGt27dbOfRmTNnbG0xMTHG2LFjDYvFYnTs2DFP29/fSyvreTRjxow8ywt6/7Kzs23n8QMPPGAEBQXZ2i5dumRMmjTJsFgsRpcuXWzfQ4ZhGO+9957t/cv9d+nSpUvGuHHjDIvFYvTv398oqtzn/7Bhw4yIiAhb29atW4327dvbPlNWYWFhRkBAgGGxWIzAwMB8fa5bt86wWCzGwIEDixxHUlKS0a5dO8NisRjff/+93XWefPJJw2KxGNOmTbMts55PXbp0MQ4dOpRn/UOHDtm+e/7+N/RKn/uCjnNZfJ9PnDjRuHTpkmEYOefB119/bWv78ccf82xX0OcFAADcmJizBgAA2MyZM0cPPPBAnp+RI0fq7rvvVseOHTVt2jRlZWVp4MCB+Z7237lzp5ycnNSqVSs9/fTTeUp6eXp66rXXXpMkZWRkKCQkxNY2f/58Xb58WZ07d9bEiRNt8zQ4OTlpzJgxGjp0qN1Yt2/fLldXV/Xr10/33XdfnvJsderU0QsvvCApp7SRtXTW37322mu2/RV10ujAwEBJUuPGjfM9jT148GA98MADuvvuu5Wenl6k/qxPJW/dulUJCQl52v744w+FhITI09MzT7kUe5YtW6awsDC1atVKn3zySZ5ROF5eXvroo4/k5+en0NBQrVy5skix5TZp0iQFBAQU+mMtzfV3CxYs0MWLF9W7d29NnTo1Tym2Bg0aaPbs2apSpYr279+vbdu22dp27twpZ2dnjRo1Kl/pMX9/fz3xxBOSpJMnTxb79Tg7O2vGjBm2p5jNZrPGjBkjKafU0qBBg/SPf/zDdl7VqFHDVvLo2LFjtn4yMzO1f/9+mUwmTZo0Kd8oiO7du2vgwIGFxunr66tZs2apVq1akiSTyaQRI0bo8ccfl6Q8ZfSk/58Y29/f327pvKL67LPPtH37dknSyy+/rJtvvrlE/cTHxysmJkaSNGLEiDwjIlq1aqWXXnpJffv2laenp235oUOHtH37dpnNZs2ePTtPOa3cI3wOHjyo4OBgSbKVarv//vv1wgsv5JnT5ZZbbrG9Tz///LP279+fL04/Pz/bOSP9/2e+pOdnYT7++GNlZmaqW7dumjp1ap4RRS1atND8+fPl7u6uo0eP6vvvvy9Sn8VhPeespdBy27Rpk7Kzs23r2BMXF2cbPTh79mx1797d1ubk5KTRo0fr0UcflWEY+vDDD4sU04kTJ3Tx4kW5urrqnXfeUfXq1W1tLi4uGjNmjOrXry+p4M/K3XffrRdffNF23ru5uemNN95Qx44dlZGRoYULF9rW/fbbbxUdHa3atWtrzpw5tr6lnDKKH3/8sW2ESUGji0pjy5YtOnjwoLy9vTV//nw1adLE1la1alVNmzZNbdu21YULF2zvtZTzPklS//798/xdqlq1qiZPnqyuXbuqU6dOSktLK1Y81apV05w5c/KMVOvVq5fte/urr75SSkqKpJzz3vp9sGbNmnx9rVq1SpIK/Ptsj4eHh60M6tq1a/O1x8bGaufOnZKk4cOH25bv2rVLZrNZ48aNyzeat02bNnrggQckFXzOFPS5t6csvs+bN2+uf//737bRfSaTSQ8++KCtdN3vv/9e4P4BAABI1gAAAJvQ0FAdOHAgz8/Bgwd16tQp+fr6avTo0Vq8eLE++OCDfCVMHnzwQR06dEjffvut3b5zr5+ammr73Xrzs6CbPtYbMX83YcIEHT58WO+9994V92fvppaXl1eem3dF1bBhQ0k5N9T++9//KjQ0NE/7G2+8oenTpxdavi23Ll26qH79+kpPT9fGjRvztFlviN19991XnGz8p59+kpRzk9Ze+SB3d3f1799fUs4N3OJq1KiROnToUOhPQeWarLHde++9dttr165tm8Mgd2zTp0/X4cOH85XNs6pUqZKknDkhsrOzi/V6AgIC8pWwyX0T0968NNa5JZKSkmzLnJ2d9dNPP+nQoUPq1atXvm0Mw1DlypUl2T8PpZwEh3Wd3EaOHCkpZ94Ta8KirOzbt0+ffPKJJKlfv376xz/+UeK+atSoYbvxPmHCBNt8GFYjRozQrFmzNGLECNsy65wMHTp0ULNmzfL16evrq5UrV2r37t1q0qSJQkNDbUneRx55xG4c7du3V/v27SXl3Ci3124ymfItL+n5WZCUlBRbyabcc5rkVr9+fVuZJ3uxllbjxo3VvHlzJScn2xJyVhs2bJDJZCo0WbNt2zalp6eradOmeRJpuQ0aNEhSzjwoBSXEc2vVqpV+++03/fbbb6pRo0a+9vT0dNt5lPtvRG7Wkmu5WZOb1ritrOfY4MGD8ySGrFxdXTV69GjbuoadOdhKw3pe9e3b1+7n22Qy2c653OeV9Xt0/vz5Wrt2rRITE21tPj4++vzzzzV16tQC5/UpyIABA+wmKgYNGiR3d3clJSXlSSRYHyRYt25dns9zdHS0du/eLWdnZ9s5UFTWeWh27NihCxcu5Glbt26dMjMz1a5duzzfCd99950OHz5s+z78O+vfgYK+Xwv63NtTFt/nvXr1srs/a7Iu9/EEAAD4O+asAQAANv/5z39sSZPs7GydPXtW8+fP1+LFixUdHa1GjRrZboYWxM3NTYcPH9bJkycVHh6uM2fO6OTJk3luNltviqWlpen8+fOSZPeGrZTzlKrJZLJ7I81kMslsNmv//v06ffq0bX+BgYEKCwuzrWfvRn5RJ5X+u1atWumee+7RunXrtHDhQi1cuFB+fn669dZb1a1bN3Xv3t3uROoFMZlMGjJkiD7++GOtWbPGdkMqd/KmKE8vW5/yXbZsWYE3f2NjYyWpRDf+x44de8U4ck98b5WcnKyzZ89KynlC/6uvvrK7rXWdv8fm5OSk9PR07d69W8HBwQoPD1doaKhOnDhhO3eknGOce3TVldStWzffstwJMXs3k52dC750dnNzU1xcnP744w+FhoYqIiJCwcHBOn78uG3EVEEJpb8/LW7l6+urqlWrKjExUaGhoXmezC+NuLg4jR8/XllZWWrYsGGxJq+3x8nJSRMmTNCUKVO0bds2bdu2TdWrV1eXLl102223qVevXvkSY2fOnJGU8/kuSO4263lRqVKlQudcuummm3Tw4ME8o/es7M0FUdrz057w8HBlZGTY4iks1vXr19uNtSwMGDBAJ06c0KZNm2yJocjISB08eFDt2rWTr69vgdueOnXKtn5BCfPc38nBwcG2kWFX4u7urtDQUB05ckRnzpxReHi4Tp8+rcDAQNsooII+KwUljqyjFmJiYnTp0iVVq1bN9r4WtE3utvj4eF28eNHu576krN/JW7dutY2W+btLly5JynlQwjAMmUwmPf7449q0aZNiYmL0yiuvyNnZWa1bt1bXrl3Vo0cPtW3btsjJh9zszeciyTa3yokTJxQUFGQbRXXnnXdq6tSpioqK0p49e9S1a1dJOaNisrKydPvtt6t27drFiqFjx45q0qSJgoODtWnTpjznlvXhBGtCJzcXFxclJibqwIEDCg0Ntf0dOH78uO3vWkHnTEnmgCnN93lB1xbW5FpWVlax4wEAADcOkjUAAMAus9ms+vXr66233lLt2rU1c+ZMTZ06VZcvX7aVZ/o768THfx9tUq9ePQ0bNkxLly7Nszx32S97Tx5LOTeSKlWqZCvPYmUYhr788kstWLBA0dHRtuUmk0mNGzfWoEGD7JZvsXJzc7O7vKAbky1bttSUKVMkSe+9955uueUWLVu2TIcOHdLZs2e1fPlyLV++XG5ubhoxYoReffXVK46GsRo6dKhmzpypAwcOKDw8XPXr19fPP/+shIQEBQQEFHqz0co62sM64XJhruWTvblHoRSlXFnu2DIyMjRr1ix99913unjxom25k5OTLBaL2rRpox9++KFEcVmfxi5IcRI/MTEx+u9//6tNmzbZbtJb99G6dWtlZWUVWvrG3lP/VpUrV1ZiYqLtpm5pZWdna8KECYqJiZG7u7s+/vjjKyYXp06dmqf0W27fffedpJzRMw0bNtTnn3+uXbt2KSEhQZs3b9bmzZtlMpnUq1cvvfnmm7akjfV4FvS5/zvreXSlWD08PCTlJGH+zt5nvjTnZ0Fy92kthWSP9bXYi7UsDBgwQB988IG2bt2q9PR0ubq6auPGjTIMQ3fffXeh21pfZ1JSkg4cOHDFfRX1/Dx06JDef/997du3L8/yGjVqqGfPnjp27JgiIiLsbuvi4lLgd6r1uEs5o3KqVatmOw5FOQZSznEoy2SNdf/WCeQLk5WVpeTkZFWpUkV169bVmjVrNGfOHG3atElRUVE6ePCgDh48qFmzZsnPz0+vv/66LQFXVLnfo4Laco8WqVSpkgYOHKhly5ZpzZo1tmTN6tWrJf3/yJviGjp0qN5//32tW7fO9vf2xIkTCgwMVOXKlfON+EpKStKMGTO0atWqPNcBLi4uatWqlVq0aJFv9FhuBf2tL0hpv8+v9He/rEdwAQCAioVkDQAAuKJx48bpjz/+0I4dO/T++++rZcuWuvXWW/Oss2rVKk2cOFFSTl33fv36qVmzZvL391f16tWVkZGRL1mT+8ZY7hucuRmGYXf+l1mzZtnKOA0cOFA9evRQ06ZN1aRJE3l4eCg0NLTQZE1BCroxmXtUhclk0rBhwzRs2DDFx8dr79692rdvn7Zt26azZ89q0aJFkpRvXp+C1K1bV127dtWOHTu0bt06PfPMM7bYizonQKVKlWxzL9x+++1F2uZayJ0UWbdunSwWS5G3feONN7Ry5Uo5OTnp/vvvV6dOndSsWTM1atRI7u7u2rlzZ4mTNWXl8uXLeuSRRxQUFCRPT0898MADuummm+Tv768GDRrIyclJH3zwQaE39woq+ST9/+eiqKMWrmTWrFnatWuXpJz3t7CRLVYnT54s0g37Ll26qEuXLkpLS9P+/fv122+/afv27Tp69Ki2bt2q8+fPa/Xq1TKZTLbzoqiJCuvN5IK+J6ysSYPCbkznVprzsyC5952YmFjgsbMmq4saa3E1bNhQLVu21LFjx7R9+3b16dNHGzZskJOTk23ukIJY35f+/fvr448/LpN4goKC9PDDDystLU1NmzbVfffdp+bNm8vf3982x9bIkSMLTNZkZGTYkk5/lzuJZp1zyMPDQwkJCYUm2HI/MFDWx8H6Hk6ZMsVu+bbC1KpVS6+//rpef/11BQYGat++fdqzZ4927Nihs2fP6vnnn9fixYsLHJVnz98feMjN+h7lnq9JyknILFu2TJs3b9Zbb72l4OBgnTp1SjVq1LBbJqwohgwZog8//FAHDhzQ2bNn5efnZxtVM3DgwHzH4ZlnntHevXvl7u6uxx57TG3btlWzZs3UsGFDubi4aOnSpYUma4qjLL7PAQAASoNkDQAAuCKTyaR///vfuuuuu5SYmKjXXntNGzZsyPNU8pw5cyTlzA/w3//+N18fkZGR+Za5urrKz89PZ8+e1fHjx+3eeAoODlZmZmaeZRkZGVqwYIEk6dlnn9Xzzz9fpP0VRWBgYKHtSUlJCg0NVeXKldWkSRPVrFlTAwYM0IABA5Sdna23335b3333ndasWVPkZI2Uc1Nsx44d+vHHH/XYY49p586dcnFxKXAejb9r3LixDh8+rFOnThWYrAkNDVViYqL8/PwKnWS5LFWrVk21a9dWbGysTp8+XeDN8MDAQGVnZ8vX11fVq1dXVFSU7Qbe1KlT7T7FXdJjXJZ++uknBQUFydnZWUuWLLE7b8+V4jx58qQ6deqUb/mZM2dsyYymTZuWOtZdu3Zp9uzZknJKDRX1yXhr8rEg6enpCg8PV1JSktq2bSt3d3d169ZN3bp100svvaTvv/9e48ePtz0937x5c9v7ZC23Zc+UKVMUHR2tUaNG2UrApaamKigoqMBSaEeOHJH0/3NLXUlJz8/CNGjQQC4uLsrIyNCRI0fszn9UklhLYsCAATp27Jg2bdoki8Wiw4cP69Zbb71i+arGjRtLKvz4pKam6s8//1TdunXl6+trd66s3L788kulpaWpSZMmWr58ud3RbVFRUYX2ERwcbDfBaB351aBBA1u/TZo00cGDB3X06NEC5+exHoPq1auX6agaKec9PHHiRKHv4fnz5xUVFSVfX19b+ayoqCiFhISoXbt2cnd3V0BAgAICAjR69GjFxsZqxIgROnv2rNavX1+sZE1BJfxSUlJsJeP+fv63b99e/v7+CgoK0s6dO23v17333isXF5ci7zu32rVrq2fPntqyZYs2bNigJ554Qhs2bJAkDR8+PM+6f/zxh23+pzlz5uiWW27J119Z/h0oi+9zAACA0ih6fQcAAHBD8/Hx0WuvvSYp52bS//73vzzt1qehCyrZtXz5ctvvuZMvd9xxhyRpyZIldmu5L1u2LN+yCxcu2J4SLmh/ubf7e7KnND7++GPdd999dhNSZrPZNuLo76/FOsdAQSVQ+vbtK09PTx07dkxLlizR5cuX1atXryInVawJmuXLl9ud+DgzM1PPPPOMhg0bZjf2q8n6BPbXX39tt85/YmKiHnnkEQ0ePFhffvmlJOncuXO298reMc7OztbKlStt/5/7/c5dwuxql5yxnvceHh52b+zFxsbql19+yRdjbitXrrT7vlhLjLVr187uHDvFER0drQkTJig7O1stWrTQG2+8Uar+cvv11181cOBAjRkzxu4oOGv5JOn/3wNrAuP333+3O2dLXFyc1qxZo19++UXu7u5q3LixLYFgPUf+7sCBAzp8+LAkqUePHkWOvyTnZ2EqV66sLl26SFKBc+CEh4fr559/LnasxTVgwABJOfOmrF+/XpJ01113XXG7nj17ysnJScHBwdq5c6fddb744guNHj1agwYNyjM6rKDvOuu8P/7+/nYTNTt37tS5c+ckFfxZWbFiRb5lWVlZts9K7969bcut34mrV6/OM4LGKj093baddZ6Wkijo9Vr3v2HDBsXFxdnd9vXXX9f999+v8ePHS8r5nh48eLAeeeQR2/dGbrVr17YlVAqaM6UgGzdutDuSbenSpcrIyJCXl5fd5I81qfvTTz/Z5kMr6ojPglj7/PHHH/X7778rOjpazZo1U7t27fKsl3uUlb35n1JTU/X9999LKpu5YMri+7y4rnRtAAAAbiwkawAAQJENGzZMnTt3lpRzg+e3336ztVmffF+yZEmep6OTkpL0ySefaO7cubZluZMJjz/+uDw9PXX06FFNmjTJVubIMAx9++23dm921qxZU56enpJybhjmvhEXHx+vN99803Zj8u/7K617771XJpNJv/zyi+bNm5enpv25c+f02WefSVK+p+mtc3NYb1j+naurq+655x5J0kcffSSpeDfEHnzwQXl5eSksLExPP/207aanlPOevPjiiwoKCpKLi4v+8Y9/FLnfsjBmzBhVrlxZv//+u1555RXFx8fb2s6ePasxY8bowoULqlq1qh588EFJOaMNrE/qz5s3L8/N4HPnzumFF17Q/v37bctyt+eeByX3+3A1WM/7hIQEffnll3luuP3xxx967LHHbPOzFFTu7MiRI5oyZYqtPTs7W19//bW++OILSdJLL72UZ/3ExEQFBQUpKCgoz/lXkKysLL388suKi4tTzZo1NXPmzGLP41CYHj16qEaNGrp48aJee+21PPMLJScn25KDdevWVbNmzSTllEzr1KmTsrKyNG7cOAUFBdm2iYqK0gsvvKDLly+rTZs2tsTHCy+8ICnnO+bjjz/Okxjau3evbYRd9+7d8ySIrqQk5+eVjBs3Ts7OztqxY4emTJmSp3zbiRMn9OSTT+ry5ctq3ry5Bg8eXORYi6t+/fpq1aqVEhMTNW/ePLm4uKhfv35X3M7Pz882ymH8+PG2xJKUc34uW7ZMM2fOlJTz3ZN7lKW1jFV0dHSeRLk12bZz5848n93MzEytX78+z3le0Hf2okWL9M0339gSFUlJSXr11Vd19OhRVa9ePc932wMPPCAfHx/FxsZq7NixCg8Pt7XFxcXphRde0MmTJ+Xh4aHnnnvuiu9JQayv9+/fNQMHDpTFYtGlS5f0+OOP5xlhk5SUpDfffFO7du2SyWTSmDFjJOWU27Qm06ZNm2ZLPlpt3rxZO3bskFT8JF9UVJSef/75POf3+vXrNX36dEk55cbslZgbPHiwnJ2dtWnTJgUGBqpVq1ZFKp9YmJ49e8rLy0uHDx+2/Y0fNmxYvvWs369STgnH3OfT6dOn9eSTT9rmaCusnGRRlcX3eXEV9HkBAAA3JsqgAQCAIjOZTHrrrbc0aNAgpaena/LkyVq3bp1cXV310ksv6ZlnntHp06fVp08f2425sLAwXb58WfXr15fJZNKZM2fylBHx8vLShx9+qHHjxmnNmjX68ccf5e/vr8jISMXExKh3797atm1bnqdYnZ2d9cILL+itt97Svn371LNnTzVq1Ejp6ekKCwtTZmamWrZsqfPnz+vChQuKjIwscAROcd1000168cUX9cEHH+j999/X3LlzVa9ePaWmpio8PFyZmZlq0KCBbf4eq5YtW+rkyZOaP3++fv31V/Xr10/PPPNMnnWGDRumRYsWKSUlRbVr1y7Wzbjq1avr008/1dNPP61du3apT58+atq0qUwmk0JCQpSeni5nZ2fNmDFDAQEBZfJeFFXDhg314Ycf6qWXXtL69ev1ww8/qGnTpsrIyFBoaKgyMzNVuXJlzZ071za/R82aNfXYY49p/vz5Wr9+vbZt26YGDRooOTl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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from sklearn.preprocessing import StandardScaler\n",
+ "\n",
+ "# Load data\n",
+ "data_path = \"ST003847_api_downloads/ST003847_AN006322_datatable.tsv\"\n",
+ "df = pd.read_csv(data_path, sep=\"\\t\")\n",
+ "\n",
+ "# Drop STD samples\n",
+ "df = df[~df[\"Class\"].str.contains(\"STD\", na=False)]\n",
+ "\n",
+ "# Keep only relevant columns: Variant and metabolite values\n",
+ "df[\"Variant\"] = df[\"Class\"].str.extract(r\"Variant:([^|]+)\").iloc[:, 0].str.strip()\n",
+ "metabolite_cols = df.columns.difference([\"Samples\", \"Class\", \"Variant\"])\n",
+ "df_clean = df[[\"Variant\"] + list(metabolite_cols)]\n",
+ "\n",
+ "# Group by Variant, take mean\n",
+ "grouped = df_clean.groupby(\"Variant\").mean()\n",
+ "\n",
+ "# Z-score normalize across metabolites\n",
+ "scaler = StandardScaler()\n",
+ "grouped_scaled = pd.DataFrame(scaler.fit_transform(grouped), \n",
+ " index=grouped.index, \n",
+ " columns=grouped.columns)\n",
+ "\n",
+ "# Plot radar heatmap (like clustered heatmap)\n",
+ "import seaborn as sns\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "plt.figure(figsize=(12, 6))\n",
+ "sns.heatmap(grouped_scaled, cmap=\"vlag\", annot=True, fmt=\".1f\", linewidths=0.5)\n",
+ "plt.title(\"Radar-style Heatmap: Z-score of Metabolites by Variant\")\n",
+ "plt.xlabel(\"Metabolites\")\n",
+ "plt.ylabel(\"Variant\")\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c51f6e18-8116-4745-8545-a99d15d9c958",
+ "metadata": {},
+ "source": [
+ "### ๐ Visualization of Key Metabolites Across PDAC Variants\n",
+ "\n",
+ "In this section, we explore the distribution and abundance of two key metabolites:\n",
+ "- **7-Dehydrocholesterol**\n",
+ "- **7-Dehydrodesmosterol**\n",
+ "\n",
+ "\n",
+ "### ๐งช Processing Steps\n",
+ "1. **Load and clean the data**:\n",
+ " - Extract variant labels from the `Class` field.\n",
+ " - Remove standard/control samples labeled as `STD`.\n",
+ "\n",
+ "2. **Reshape the dataset** for tidy plotting using the `melt` function.\n",
+ "\n",
+ "### ๐ฆ Visualizations\n",
+ "- **Boxplot**:\n",
+ " - Shows the **distribution** of metabolite levels across sample variants.\n",
+ " - Helps identify median, spread, and potential outliers.\n",
+ "\n",
+ "- **Swarmplot**:\n",
+ " - Overlays individual data points per sample.\n",
+ " - Highlights **within-variant variability** and cluster tightness.\n",
+ "\n",
+ "- **Barplot**:\n",
+ " - Displays **average abundance** of each metabolite by variant.\n",
+ " - Useful for comparing general trends across subtypes.\n",
+ "\n",
+ "These visualizations help us interpret how **lipid-related metabolites differ** across PDAC variants, supporting hypotheses about metabolic vulnerabilities and metastatic potential in classical vs. basal subtypes.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "4708184d",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+ " if pd.api.types.is_categorical_dtype(vector):\n",
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+ " if pd.api.types.is_categorical_dtype(vector):\n",
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+ " if pd.api.types.is_categorical_dtype(vector):\n",
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+ " if pd.api.types.is_categorical_dtype(vector):\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+ " if pd.api.types.is_categorical_dtype(vector):\n",
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+ " if pd.api.types.is_categorical_dtype(vector):\n",
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+ " if pd.api.types.is_categorical_dtype(vector):\n",
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+ " if pd.api.types.is_categorical_dtype(vector):\n",
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+ " if pd.api.types.is_categorical_dtype(vector):\n",
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+ " if pd.api.types.is_categorical_dtype(vector):\n",
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+ " if pd.api.types.is_categorical_dtype(vector):\n",
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+ " if pd.api.types.is_categorical_dtype(vector):\n",
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+ " if pd.api.types.is_categorical_dtype(vector):\n",
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+ " if pd.api.types.is_categorical_dtype(vector):\n",
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+ " with pd.option_context('mode.use_inf_as_na', True):\n",
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+ " with pd.option_context('mode.use_inf_as_na', True):\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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wJiUl8c9//pPy5cvzzjvv4Ofnd9vvQ0RuP7vdTlZWFk899RSNGzemWrVqLFq0CJPJxODBgwuEeNeb2erk/Fn+h2E9GIsUL3a7nezsbHr37s2ePXtYvHgxrVq1YvPmzbz44ou0adOGV155BT8/Pw4fPsyWLVtYtGgRaWlpREREMHjwYFxcXDh27BhLlizh/fff5+WXXzbK7KnNECk8u3fv5tlnnwWgd+/ejB8/HriyHYjzs7l27VomT55MQEAAc+fONSYmX12i/3ol+0VE/kh6ejoDBw5k165deHl5cenSJcxmMy+99BJt2rShbt26xrFHjx6lZ8+epKenM3fuXB566KHCu3ApctSbFLkL+Pj4APDQQw/RuXNnUlNTefPNNwkJCWHkyJHEx8eTlpZWYBDrnnvu4cEHH2Tp0qVMnDiRPn36ULduXZo3b07Xrl2ZNWsWc+bMUXgnIjdF/v3onG3R+vXr2bRpE25ubrz44os0bdoUm80GwJAhQ3jyySc5efIkYWFhrF27liNHjmC1Wvniiy948803SUtLo3///grvRO4irq6ulChRgnLlypGWlkZ4eDhDhgwBYMGCBaxevZrMzEwuX77Mxo0bOXz48HXPYzKZjAlLQIGvReTO5uxzuLq64unpSefOnQHYtm0bDoeDlStXUrlyZebOnUvVqlVxc3OjTp06PPvss7z00kt4enryzTffcOnSJQCqVKnC888/z9KlSxXeiRQB27dvp06dOrz22msALFu2jLlz5wJ5K16cn834+HgiIyPJzc3ljTfewN/f36jocXVYp/BORP5XPj4+dO/eHYD777+fXr164eXlxezZsxk1ahRvv/02ubm52Gw2/P39eeWVV8jNzeXzzz8v5CuXokYr8ETuAhkZGQwePJjffvuN9evXc/ToUbZs2UJsbCzp6elAXi3mPn36EBwcbKxwuZrVasVisdzOSxeRYu63337D19cXT09PcnJycHNzA2DDhg2MHDkSf39/bDYb7777LjVq1DDqxAMcP36c6OhoVq5cCeSVAPbw8CA1NRWz2czo0aPp168foFmzIsVZ/nbBuXLulVde4csvv2TLli1AXni3ePFiXF1d6dOnD6dOnWLDhg2Eh4czePDgwrx8EblNTp06dd1SeCkpKYwYMYLff/+dWbNmsXLlSiwWC5GRkQXaF4ATJ04watQokpKSjBV7V1N4J1J4Xn31Vfbu3cuKFSswm8189NFHvPHGGwCEhYUxfPhwAD799FNmz57NyZMniY2NpXnz5vrsishNk3/84aWXXmLbtm0sWbIENzc3Pv74Y+Li4sjNzaVZs2Y888wzPPTQQ3h7e/PSSy/x9ddfExUVxaOPPlrIdyFFhX4zidwFvLy8aNy4MampqcycOZOgoCDCw8P56KOPGD16NK1bt+a3335j+vTpDBo0iDfeeIONGzditVoLnMdZaxmu7DUlIvJXWa1WFi5cyOTJk8nIyDDCO4CWLVvSqVMnjh49yokTJ9i8eTOQN2vW2Q75+fkxadIkxo8fz0MPPUS5cuUoU6YMPXv25O233zbCO7vdrvBOpJhx7tF79eC6cwVv48aNuXDhAvv378fNzY3BgwfzwgsvYLfbWbRoEatWraJOnTpGeKd+jUjxtm3bNjp16kRsbCyQ91yTm5sL5O09061bN7KzsxkxYgQJCQl4eXkBFGhfHA4HlSpVMiY7OlfgXU0BgEjh+Pzzz1m1ahUBAQGYzWYcDgfPPfccEyZMAGD+/PlERUXxzTffMGfOHIV3InLL5B9/6NChAxcuXODVV1+lQoUKjBkzhg8++IAHHniAPXv28PrrrzNq1ChOnjzJc889h6urK4sWLeLXX38txDuQokS/nUSKofyl6JwD1wMHDiQgIICkpCROnjwJQI0aNRgwYADvvvsuzZo1w2azkZmZyUcffURERAQ9e/Zk+fLl7N+/HyhYbkKD4SLyd5lMJn799VdWrVrFnDlzAHj77beZM2cOZcqUYcSIEfTs2ROA6dOns2nTJuDKZALngHvv3r2ZO3cuK1eu5MMPP2T8+PG0bdsW0Cx4keLo+++/54UXXmDHjh188cUX1z3G19cXgPPnzwPg5uZGly5dqF27NlarFbPZzGOPPUZ2djagfo1IcffDDz9w4cIFPvjgA95//30grx+Sk5MDQGhoKM8++yzZ2dlkZ2eza9cufvrpJ+P1NpvNaCcuX74MQJkyZW7zXYjIH3GW2i9ZsiRw5Xd7jx49jBAvKiqKMWPGcOLECeLi4hTeichNc6MFDx07dqRdu3b8/PPPrFmzhpycHBo0aMC0adOYMWMGjRo1YuvWrXTt2pWzZ8/Stm1bUlJS2LFjB4Ax4UjuXvoNJVIM5e98urq6kpOTg6enJy1atODXX39l3bp1xs+tVivz5s0jMTGRSpUq8eabb9KtWzf8/Pz4+eefmTlzJp06dSImJka/NETkpsrNzeXxxx+nYsWKLF26lKeffpp58+Zx5swZ0tLSqFixIoMGDeK5554DYMKECcZgff59qQAsFgs+Pj6UKFGiwHvoYVykePnuu+8ICQkxHpCdpcCvVqNGDVxdXdm2bRsAp0+f5t1332Xnzp34+/vj4eHB/PnzWbJkCWfOnLlt1y8ihaNcuXJAXunumJgYli1bBuSF+86qIxMnTqRjx44AHD16lE8//ZQDBw4AV1biJSUlsXHjRqpWrUr58uVv922IyHU4nwecq2KdQV5+PXr0MEppnjt3jqeeeoomTZoABSdAi4j8Fbt37+b999/n9OnTuLi4GO2Ksz2KiIigfPnybNiwwZhAWLZsWR555BHef/99IiIiqFSpEhMmTODYsWNkZWUxf/580tPTjQojcvfSqJZIMZKUlMSKFSsYNWoUK1asYPv27UDeg6m7uztPP/00AOvWrePs2bPY7XaioqJ4++23KV++PMuWLePJJ59k3LhxfPDBBzz33HNUr14dgIcffli/NETkprJYLHTt2pXXXnsNT09PDh48SOXKlenVqxdlypTB4XDg5+dHWFgYzz77LOfPn2f8+PFGiJd/xUz+rxXaiRRPW7duZeDAgVSrVo1evXoBeXthwrWDb+XKlaN8+fJcuHABgHfffZd3332XVq1a8e9//5vw8HBMJhNRUVF8+OGHZGVl3d6bEZHbqnbt2nh4eFCrVi3Onj3L7NmzWb58OZDXH3GGeLNmzaJbt27k5OSwbNkyJk6cyJIlS9i8eTPLly9n/PjxpKamMmjQIPz9/QvzlkTkKs4Jx/nL8ufXs2dPxo8fD8DatWuNCiBms1mTlUXkL0tJSeHZZ59l6tSpDBs2jD179hhjEs4tQCpVqkSHDh3YuXMnixcvNl7rrAQwZMgQ5s6dyz//+U9+//13XFxcOHLkiDGuK3c3838/RETuBAsXLmTJkiWkpqYCsGHDBnx9fRk6dCjPPvssAE2aNOGZZ55h/fr1bN++nUOHDrFgwQIqVKjABx98gJ+fHzabDQ8PD0qUKMFrr73GuXPncHFxMUpRiYjcTBaLhZ07d5KZmYm7uzu///4769ato0aNGnh6ehr7zbzwwgsArFixwnjwfvTRR3FxcSmwQbSIFE9bt25lwIABVKlSheHDh1OvXj2WLl1KYmIiVqsVi8ViHGu32zGZTPj5+fH111/z2muv8fHHH9OiRQvjgfn555/n8uXLREVFUbZsWTw8PArr1kTkNqhUqRKurq40bNiQbt26MX36dObOnQtAr169sFgsRn9i8uTJ+Pn58fnnn5OYmEhiYiKQN1nIy8uL8ePH061bNwD1QUQKkfPz5/wMOvsCpUqVuuFrevfujclkYsKECcyfPx8XFxeGDRuGyWQiNzdXk5ZF5H/m4+NDaGgoSUlJ7Nixg+eff55Bgwbx4IMPUq9ePVxdXfH09KRLly58/PHHfPjhh7Rq1YqmTZvi5uZmtGXVq1dnyJAhNGnShHXr1nHvvffy8MMPF/btSRGgAE+kGJg+fTqxsbFUq1aNvn37kpqayp49e9i+fTszZ86kQoUKxn5QjRs3Zs2aNbz22mukp6dTvnx5I7zLzc01ysM4HA7c3NxUGkZEbjmTycRTTz1F7dq1WbZsGXFxcVitVsaOHYuHh4cR4oWHhwN5Id6ECRPIzc2lffv2GjgTKeYSEhIYMGAA/v7+jBw5kvbt22Oz2fDz8yM1NZWcnJwCAZ6rqyvu7u7cd9997Ny50wjv3nvvPQAj8AsPD+fBBx+kQYMGhXRnInI7OBwOPDw8KFeuHBkZGXTq1IkzZ86wePFi5s6di8PhoHfv3ly6dInk5GQefPBBwsPDeeSRR9i9ezfbt2/n8uXLtGrVitq1a9O0aVNA++yKFDYXF5cCoZuzLN3V+09drUePHkBeef7o6GgAhXgi8pc4HA4qV65MREQEaWlpzJs3j/Xr1xMVFcVnn31G79696d69OwBBQUFEREQwZcoUNm/eTNOmTQv0JZxBXtOmTalbt66xPYj6G6IAT+QON2XKFOLi4nj44YcZPnw4gYGBAOzYsYMFCxbw1VdfkZiYaAR43bt3Jz4+noSEBCpUqMCaNWsoU6bMNR1VDYiLyK1wvc5nREQE58+fp1SpUpQvX5633nqLDz/8EMAI8ex2OxUrVuSFF17AxcWFjz76iIiICDZt2kRAQEBh3IqI3AZbt24lNDSUqlWrMmLECNq3bw/kDdJVrlyZxMRE/v3vf9O5c+drXhsQEIDdbqdNmzYsWrQIyNuHwmKxGG2RM7zTg7FI8eXi4kLZsmWpWrUqBw4c4J577qFv376YzWYWLFhAZGQkWVlZnDlzhvfee48ZM2bQqVMnAgMDCQwMpHv37testFObIVJ4Zs+ejcPhYMSIEZhMJmNiTmZmJnDjsYz8Yx5Xh3hZWVmMGTNG4Z2I/E+c7Y2bmxsVKlRg8uTJtG3blv/85z+sXbuW8ePHk5ycTPfu3alfvz7t27dnzZo1LF68mHbt2hWYSJi/7XKGd6AtQkR74Inc0ZzhXbt27Rg1ahSBgYFG7fZGjRrRunVrALZv347VasVms+FwOGjVqhUANWvWpEyZMgDqqIrILZebm2t0Po8cOcL+/fvZuXMncKXUzSOPPMLIkSMpX748H374IdOmTePSpUvG6ypVqsSwYcN4/PHHGTFihMI7kWLs9OnTzJw5kypVqhAREWGEd7m5uXh5edGhQwcADhw4AFw7475nz568+eabBcI7Z6WBqx+E9WAscmfL//m/ek9M58/Kly9PWloaly9fpkKFCjz33HO8+OKLXLx4kblz57JkyRKaN29Op06drjn/1YGA2gyRwvHtt98SExPDkiVLWLBgAXCldKbzz5ycHH744QdOnTrFmTNnjNdePebRo0cPXn/9dQBiY2NJS0u7HbcgIsWUczy2Xbt2TJkyhcjISGrVqsWaNWsYM2YMb7/9NqVKlaJfv37k5uYSHR3N2bNnC/mq5U6gFXgid6hp06YZ4d2wYcOoWbMmkNcpzcnJwc3NjUaNGgFQtmzZAqWlHn/8cZYsWcLWrVuJj4+nY8eOhXIPInL3cO5JBfDuu++ycuVKzpw5w6VLlwgJCeG5556jWrVq+Pj48OijjwIwa9YsYyXehAkTjNLAffv2Zfr06Ua7plnwIsVT+fLlCQsLo2TJkrRs2RIo2JZUqVIFyCur+9xzzxUI9J2z7J988knj787wTkSKn/wB29WlqJzq16/P6tWr+fnnn2nRogV+fn489thjfPPNN/z444+YTCYef/xx43j1L0SKngYNGjBkyBDi4uKIjo7GbrcTFhYGXBk8X7x4MYsXL6ZixYo4HA5q1apF2bJlKV26NA0aNMDHx4eaNWvi5+dHx44dKV++PFWrVjUmN4uI/BX5Jwm4urry2GOPUbNmTb755huWLl1KTEwMCQkJTJw4kRYtWrBjxw5+/vlnHnzwQZXvlT+kp1iRO9D06dN57733aN26NWPGjKFKlSrGA6rdbsfNzQ2ATz/9FIBmzZoBV2ajVqlShb59+zJr1iy2b9+uAE9EbjnnANjs2bOJiYnB09OTmjVr8tNPPxEbG8vp06cZNGgQQUFBeHt7XxPiHThwgNTUVH799Vdq1qxprDB2OBwaXBMpRpz9GedDrHPVHVw7mN62bVvatm3Lt99+S3JyMgEBAcbrrn4A1gOxSPF1+PBhvvvuO3788Ue8vLxo2bIlTZs2pXTp0gXajYCAABwOh7HK5sKFC6xevZrdu3fj5+fHiRMnmDNnDna7nd69e6t/IVIE3XPPPQwcOBDIWzUXExNDbm4uL774ojHeUb9+fdLT08nMzOT06dOcPn3aWInr4uKCw+GgTJkyXL58mfr167NgwYIC5epERP4u5zNNzZo1qVmzJk8//TTTpk3js88+IyQkhGrVqnH+/HkWLlzIgw8+qGcV+UMK8ETuMNu2bWPlypVA3so6Dw8P4NoNnNetW8fKlSupXbs2//jHP4CCpV7uvfdeAJYvX067du2Mme0iIjdT/tnvW7ZsYcmSJbRp04aXXnqJBg0a8PnnnzNjxgw2bNhAbm4uYWFhBUI8Nzc3pk6dSlJSEgDjxo0zwjvQfp0ixY3zM331wPnVYb1zUL5JkyZs3ryZNWvW0LlzZ0wm0zWrbkSk+EpKSuLVV1/lyJEjxvc2btxIq1atGDt2LL6+vkab4OfnB8Dx48e5ePEiUVFRvP/++7Ro0YKZM2eyYsUK5s+fz5tvvklOTg79+/cvnJsSkT/k4+NTIMRbsGAB3t7eRoDXtWtXnnzySdLT09m3bx+XL19mz549/Prrr5w7d449e/aQnp6O3W5nwoQJCu9E5Ka7et/cMmXK8H//93888cQTrFu3jvj4eAASExNJTEw0Fl6IXI8CPJE7TKNGjRg8eDAffvgh//73v/Hx8aFPnz5Uq1bNCO/i4+OJjIwkNzeXSZMmGbNN8/8CadmyJX379iUuLo7//Oc/NG3a1Fi5JyJyM+Rvdy5evMjRo0dxdXU1wjuAxx57jBIlSjB16lQ2bdoEUCDEa9++PUFBQezYsQN/f3/uv/9+QGWtRIqjX3/9lR9//JGdO3dit9tp164dderUoWzZsjfcf+rpp58mLi6Obdu2sXTpUp5//nmFdyJ3iYSEBIYMGYLVaqVTp07UqlWLpKQkfvrpJz7//HOqV6/Oiy++aBxfvXp1/Pz8SEhIIDU1lffff5/777+f9957D4BnnnmGzMxMFi1ahLu7eyHdlYj8GVeHeO+88w7ly5fHxcUFLy8vfHx88PHxMYL7J554Asgrs3n8+HGsVivu7u5GOW4RkVvF1dXVqJbmrCDSokULPvjgA7p27arwTv4rF8fVu72LSJG0dOlSateuzf33309OTg5LlixhyZIlpKen07VrV0JDQ6lcuTLx8fG89dZbnDx5ktjYWJo3b37NQLfz7ytXriQyMpIlS5ZQo0aNQrw7ESnO3nzzTVJSUsjKysJqtbJixQpjjwrnxIPvvvuOKVOmcOjQITp06GCEeNej8E6k+Pnhhx947bXX+O2334zvubu707t3b8LCwvDx8bkmmHNWHli6dCnTpk2jdevWvPHGpOKgiAAAOcBJREFUG/j5+WkVnkgxt3XrVgYMGICfnx/h4eF07doVgLS0NKKjo3n//fdp1qwZ77//PpDXd3A4HDz//PPs2LEDgObNmxMXF2f83NXVlZMnT3LixAljL3ERKdrS09NZtGgRsbGxWK1WAKZNm0bnzp3JyckxJinn7xeojyAihSV/+3P69GnKly8PaIxD/pj+ZYjcAT766CMmT57MjBkzSE5Oxs3NjX79+tGvXz98fHxYvXo1S5YsIS4ujlmzZnHy5EkWL1583fAOCs5aX7VqlcI7EbllUlNT2bdvH1u2bGHv3r1YLBYgL7hz7kEB0Lp1a8aNG0fNmjXZtGkTCxcuZO/evdc9pzq2IsXLzp07GThwIJmZmbz44ot8/PHHhIeHU7JkSZYtW8bRo0evO9DmnADQpEkTatSowebNm/nss88ACrQvIlK8OMM7f39/xowZY4R3VquVMmXK8Oyzz+Lp6UlqairZ2dlAXpuQf1/NZs2aGeGdzWYz+hYVK1Y0wjtnOT4RKbqcK/FCQ0ON7UUOHz4MgJubmzFpMH8/QuGdiBSW/M8ozvDu6q0CRK6mfx0iRZzD4aBixYo0a9aMn3/+mSlTplwT4nl7e7N8+XLmzJnDyZMnWb58Offffz+5ubk3/CVgt9uxWCzGLwwRkVuhXLlyjBo1iq5du3Lp0iW2b9/OqlWrgLwgzuFwXBPi1a5dmw0bNjBr1izS0tIK8/JF5BY7d+4cs2bNwmq1MnLkSP75z39Sv359XnrpJTp27Eh2drYxEJdf/oH14OBgQkNDAZg+fbpRjlcDdCLFjzO8q1KlCiNHjuSxxx4DMEpTORwOzp8/T2ZmJqVKlcLhcGCz2Yz24OGHH2batGnGyjybzYbZfP2dRTSYJnJn8PHxITQ0lJCQENzd3XnvvfeYP38+kDfZR2G8iPwVt2oy4NXPKHpmkf9GPVKRIs7FxYU2bdrw4osv0rx5c3766adrQry+fftSqVIlLl++zEMPPUStWrWAKzPTr0cPpCJyq1zd0Q0MDKR3795069YNgOjoaGOVzPVCvJdffhlfX19atmxJmTJlbu/Fi8htZbVaOXjwIK1ataJz584AXLp0CYD77ruPkiVLcuDAAT799FNWrlzJli1bgCv9GOegXOfOnQkLCwPglVde4cSJE7f5TkTkVktISGDw4MFUrVqVl19+2VhNZ7fbcXFxMf7btm0bJpOJ4cOH4+HhUSCgq1q1Kp06dQL+OLwTkTtLyZIlCQ0NpX///jgcDmJiYowQz7n/lIjIn7Fnzx5SUlJuSbCmCiHyV6i3KnIHMJlMNGvWjBdeeAGHw0FiYiJTp05lzJgxNG3a1OikLl++nO+++4633nqLvn37Uq1atcK+dBG5S+Qv1+vi4sLFixeNMpklSpSgbt26PP/88+Tk5LBu3Tpmz54NQPv27Qs8VLu4uPDAAw/w0UcfGZvOa58KkeLrzJkzpKWl4e3tDeQFel5eXgB88803XLx4kY8++ojz588brxk8eDCDBw82XuNsI/r06cPBgwdp1KgRlSpVuu33IiK3zqFDhwgJCQHyVtG1a9cOgJycHMxms9FP2LhxI1FRUdSpU4ft27eTmJiIj48P1atXp3z58gQFBRlVShTeiRQvznKaALGxsbz77rtkZmYSERGhCcwi8qccPHiQLl26EBwczJQpUwgODr5p4xH5x0wyMjKMZxmR/8bFoehX5I5htVrZs2ePsRdegwYNePnll2natCk5OTksWbKEJUuWkJ6eTrdu3ejTpw9Vq1Yt7MsWkWIuNzfXWPEbHx9PQkICycnJuLq6UqVKFR5++GGeffZZAA4cOMCiRYtYt24d1apVIyIi4roz6J0U3okUb0ePHqVDhw6YTCY2b95M6dKlAVi1ahWvvvoqXl5edO3aFQ8PD3JycoiNjQVgyJAhREREXHO+ixcvUrJkSUCbwYsUJykpKcyfP5/PPvuMihUr8vLLL/P4448DV/oh8fHxjBgx4obnsFgsNGjQAF9fX1q0aIGPjw8dO3a8XbcgIrdJeno6sbGxvPPOO5QvX55169YZ/QsRkT+ye/dunn32Wby9vQkMDOS11167KSFe/ueSZcuWsWnTJiZNmqSFF/KnaMqZSBFzo18Kubm5WCwW/P39KVeuHJD3i2XatGmMGzeOxo0b069fPwCWLFnCxx9/DEDfvn0JCAi4fTcgIncVu91uhHdvvfUWCxcuBPL2vsvJyeHAgQN89dVX7Nu3j/DwcGrXrm3sVeVciefq6kq7du2uO9Cu8E6kePP396d169ZUqFABNzc3IG9FzaVLlyhTpgyRkZE0bdrUOP6+++5j+PDhxMTE8MADDxT4GWCEd9oMXqR4CQoK4oUXXsDd3Z1169Yxbdo0bDYbTz31FCaTiU8++YSXX36ZUqVK8dxzz1GpUiUcDgfJyclcuHCBHTt2kJ6ezvbt24G8lXqvvfZaId+ViNwKPj4+9O/fHzc3Nx577DGFdyLyp1WpUgVPT08yMjJISkpi8uTJjB8/nqCgoL8c4uUP7z7++GOio6NJTU292ZcuxZhW4IkUQVfvx+D8JZGWlsbChQuJjY2lbt26+Pj4sG3bNurXr2+EePlX4mVmZtKuXTvCw8Px9/cvxDsSkeJuwYIFzJo1i9atWzN48GDuu+8+zpw5w/fff8+//vUvbDYbzz77LK+//jpms5m9e/eyZMkS1q1bh5+fHxERETz55JOFfRsicovkf+B1PsTm5OTg5uZGVlYWHh4eBY7/7bffuHz5coFyd85QbsqUKcTFxTFr1iytnhG5C+RvP1JSUoiNjWXdunVUrFiRyZMnk5WVxdChQwkODiYiIoIHH3zwmnP8+uuvnDp1isTERHbv3k3Lli3p37//bb4TEbmdtBJfRP4XNpuNrKwsnnnmGdzd3SldujSJiYk0adLkL4d4V4d3s2bNIicnh2XLllGnTp1bdStSzOg3mUgRMXbsWGNfB7PZjM1mA648sJ47d4758+cTGxtLq1atWL16NSNGjKBZs2b89NNPTJkyheTkZNzc3OjXrx8hISFYrVa2bNmCp6dnYd6aiBRze/fuZenSpQQEBDBmzBjuv/9+PDw88Pf3p3v37kRFRVG2bFlWrFhhrNALDg4mJCSEjh078vvvvxttnogUT84H3bS0NM6dOwdgrLizWCxAwU3dAwICCAwMBDD203T+3LnK7tixYwDGHpoiUjzlHygLCgoiJCSEp59+mpMnTzJixAiGDh1K3bp1GT16tBHe2e124z/IW+3bvHlzXnzxRWJiYozwTu2HSPGl8E5E/hdms9konWm1Whk2bBjNmzcnKSmJSZMmkZKSYvRJcnNzC/x5PdcL76xWK0uXLlV4J/8T/TYTKQKOHDnC2rVrSUhIYPjw4UDeLw6r1WqEd9HR0cTFxdGyZUsWL14M5D3AhoeH07x582tCvOeff55Ro0bx/vvvGyU3RURuhaNHj3L69Gm6du1KnTp1cDgcOBwOY1DsoYce4o033gBg4cKF/PjjjwAEBgYycOBAFi1aROfOnQvr8kXkFvvll19YsGABffr0oXPnznTp0oWhQ4cSFxfHyZMnjdV1f7T/Zf5yvVlZWQA0aNAA0ACdyN3GGeJ17tyZixcvAtCmTRtatWoF5M2gd3V1Nf6DK+1E/sBOpXZFRETEyTlZsEKFCpw7d47AwEBGjhxphHgTJ07kt99+w263ExkZyS+//GI8n1ztRuHdsmXLjEmKIn+WeqsiRUC1atVYuHAhpUqVYtOmTbz00ktA3oz01NTUAuFdbGwsAFarFXd3d5o3b84LL7xghHgzZszghx9+wGKx0LdvX22IKiK3RP4BsF9//RW4sirGbrfj4uJiDMoDPPbYY3Tu3JnLly9z9uxZ47XBwcG0adPmmnOKSPGwfft2XnzxRWbNmsVPP/2E1WolNTWVL774gilTpvD888+zffv2a8I6598vXboEXFmB88MPP7Bq1SqqV6+On5/f7b8hESkSgoKC6N+/P8888wwA69evJz4+HsibCHmjPkX+wE777IqIiMjVGjVqRHp6OocPH6Z+/foMHz6c5s2bk5yczOjRoxk3bhwxMTG89dZb1329wju52RTgiRQRDzzwAG+++SY+Pj58/vnnjBgxgoyMDGJjY68J72w2m1FuymQyGSFeq1at2LlzJ++88w5ZWVloi0sR+bsyMjI4ffo0mzZt4tNPPyUhIYFTp04VGABzlsHbvn07WVlZBWahubi4GINoZcqUAfJW7MG1gZ1mwYsULwkJCQwcOJC0tDTCw8PZtGkT69evZ/Xq1YwZM4b77ruPY8eO8eKLL/Lpp59itVqBK23BZ599xvTp09m1axdHjhzh888/Z9q0aZw7d46BAwcSEBBQmLcnIoUsKCiIfv368fTTT3PixAlmzJjBhg0bgLx2RBODRERE5L9x9hecE3sqVqwI5JXrN5lM1K9fn3HjxtGkSRN27drF2rVradiwIW+//TZQcBsAhXdyK5gL+wJE5Io2bdowe/ZsRowYQXx8PHv37uXw4cO0bt2aRYsWAXnhndlc8KPrDPFycnKwWCyMHDkSDw+PwrgFESlGfv75Z2JiYkhOTiY1NdX4ftOmTZk5cyaVKlUC8iYgxMXFsXv3bn766SeaNm1qHJu/A+vs2NavXx9QYCdSnG3dupUBAwZQuXJlhg0bxlNPPWX8zNfXl1q1atGxY0feeOMNvv76ayZOnIjFYuGxxx7Dbrdz5swZvv76a9asWcOaNWtwcXHBarViMpl45ZVX6Nq1K3BtqU0Rubs4y2kCrFu3jpkzZ+JwOHjyySeNEE/9DREREQE4e/YsaWlpHDp0CE9PT9q2bXvNeEXZsmXx9PTkxx9/pGPHjlgsFnx9falQoYJxHpPJxNGjR/H39ze+l78096pVqxTeyU3j4tASHZEiZ8uWLQwfPpyMjAx8fX359ttvgbw9X/4omLPb7WRnZ+Pp6Xm7LlVEiqnvv/+el156iYsXL9KsWTMCAgK4cOECv//+O9WrV2fmzJnGSruLFy8yefJkPvnkExo1asT48eOpVasWFovFGFzfvn07I0aMoGzZsrzzzjtG+CcixY8zvKtSpQqjRo2iffv2QN4m79fbJyIsLIyvv/6ae+65h+XLl1OzZk2ys7PZs2cPy5cv59ixY5w7d46HHnqIli1b0rZtWwANzIuIISUlhdjYWNatW2dMHOjUqVNhX5aIiIgUEbt372bWrFns3buXCxcuANC6dWv69etHixYtjEpnOTk5dO7cmQoVKrB48WLS0tJYuHAhsbGx1KtXD29vb7Zt20azZs0YN24cwcHBBd5n+fLlREZG4nA4iIuLU3gnf5sCPJEiavPmzYwcOZKMjAyeeOIJo7by9VbgiYjcTLt372bAgAF4eXkxaNAgevfubfwsMzMTNzc3Y38ZFxcXXFxc+P333xkxYgS7du2iXr16dOnShQcffJDSpUuTmJhITEwMO3fuZNq0aXTu3Lnwbk5EbqmEhAQGDRpE5cqVGTVqFO3atQOuH7Y5A71Lly4xdOhQEhISaNGiBXPmzKFUqVIFjrVarcZD9Y3OJyJ3t5SUFJYsWcKaNWvw9PRk9erVVK9evbAvS0RERApZQkIC//znP8nIyKB58+a4u7uTmJhIVlYWDRs2ZMKECQQFBWGz2bDb7QwYMICsrCzmz59PTExMga2N9u3bx7Rp00hISCAwMJDY2Fhju5CMjAymT5/OypUrWbduncI7uSkU4IkUYVu2bGHEiBFcvHiRxx57jMjISEAhnojcOqdOnWLUqFHs3buXV199lWeeeQbIa3dcXV2NAXPn4Hn+8nVHjx7ljTfeYNu2beTm5lKuXDnc3d05ceIELi4ujB49mv79+wMqeydSHB06dIgnnngCgJCQEMaMGQPkzWJ17pV5NWdbkpCQwKuvvorNZiMmJobg4OBr+jtqN0Tkv0lJSeGdd96hcePGRp9DRERE7l5bt25l4MCBVK5cmRdeeIEuXboAeX2GwYMHc/r0aZ5++mmmT59uvGbatGnExcXRvn17Nm7cSIsWLXjvvfcAyM7O5qeffmLKlCl07NiRAQMGFHi/3377DTc3N1UdkptG01ZFirA2bdowa9YsfHx8+Pzzz3nppZcAMJvN2Gy2Qr46ESlOnPN5UlJSSE5OpnPnzkZ4Z7fbMZvNBVa7OL927kt1/Phx/P39mT59OmPHjqVVq1aYTCbsdjvdunVj3rx5xkCac+WeiBQvOTk5dOjQARcXFzZt2sTGjRsBcHNzMzaHv5qzLQkODqZmzZqcPn2ar7/+GuCayUpqN0SKl1sxlzgoKIh//etfBfocIiIicnfKX9p/5MiRRnhntVoJCgpi6tSpmM1mUlJSsFqtxusCAgKw2+1s3LiRli1bGuFdTk4O7u7uNGzYkPnz5xvhXf4+TUBAgMI7uam0hEekiGvTpg2zZ88mIiLCCPEiIyMxm8033EtGROR/5RwYX7VqFS4uLnTt2hX472XqsrOz+fjjj9m0aROdOnWie/fu9OnThz59+nDhwgVMJhPe3t7G8Sp7J1J8BQUF8cILL+Du7s66deuYNm0adrudJ554AldX1xt+/u12O6VKleKJJ57g22+/5dKlS4Vw9SJyu+zZswdXV1eCgoJu6nmdq3RLly5t/F19DhERkbvT1eGdc19u5wRlu91O2bJlyc3N5ezZs2RnZxsTl5s0aULdunXx9vYmNjYWyKtK5KwqYjKZ8PX1BVQlRG499WZF7gDOEM+5Es85w0PhnYjcbMePH6d06dJGDff/NvDl7u7O4cOHSUxMZM2aNeTk5JCbmwvAPffcg7e3d4HZaBpIEymenJ/zwMBAQkJCePrppzl16hQzZ87k008/BTBCvBvJzMwEMAI8rZwRKX4OHjxIly5deOWVV9i7dy9w81bi5T9PRkaGBtNERETuUgkJCQwZMoRq1aoxZsyYAuGdc2sQV1dXTpw4gcPhoEOHDvj4+BjjFbVr12bq1KnExcUBf7yVkfobcqtpFE3kDtGmTRvmzJkDwHfffcfJkycL94JEpNixWq2cO3eO3Nxco3zEHw2qOX/25JNP4u3tzYULF7DZbNdMLlCHVqT4y/85DwoKMkK8kydP8uabb94wxMu/Qubo0aMA/OMf/zCOFZHi5fLly0De533y5Mns3bsXFxeXvx3i5V/hu2zZMl544QWOHDnydy9XRERE7jCnT58mJCSEnJwc6taty6OPPgrkjXfkf77Yu3cv8+bNw9PTk5YtWxrfdz6fBAYGAldW7IkUFj0Vi9xBWrduTWxsLPHx8VSsWLGwL0dEihFnJ7Vs2bKkpaXx3XffAX8cvjl/VrZsWdzc3EhPTyc7O/u2XK+IFG1/JsTLX27m22+/ZenSpdSvX59q1aoV4pWLyK1UpUoVPD09ycjIICkpicmTJ5OSkvK3Qrz84d3HH39MdHQ0iYmJN/OyRURE5A5w4sQJvLy8GDt2LAAbNmwgKioKAIvFgs1mA+DAgQNERkayZ88ewsLCeOSRR4xzXD0GokmFUtj0L1DkDtOyZUtq1KhR2JchIsWMi4sLZrOZjh07Ankd3YMHD/7ha5yraHJycsjKyqJEiRK4u7vf8msVkTvDfwvxnHbu3ElUVBQ2m43Q0FD8/f0L65JF5Bay2WxYLBbKlStHrVq1aNasGUlJSUyaNOkvh3hXh3ezZs0iOzubTz75RJMBRERE7iKbN2+mX79+nD9/nv79+zNhwgQAoqKimDt3LgBms5mUlBRmz57NV199xYABAxgyZAig8v1SdCnAExEREUPDhg2pXLkyu3bt4j//+Q8XLly47nH5y94lJSWRmZnJk08+iaen503by0ZE7nzXC/HWr18P5E0c2LFjB7NmzWLXrl2MGTPGmESgdkSk+DGbzXh7exMYGIjVamXYsGE0b978mhAPMPbTdf55PdcL76xWK0uXLqVOnTq3/oZERESkSDhy5AgRERHY7Xb8/PwA6NGjhxHiRUdHEx0dzalTp4iKiuLLL78kNDSU0aNHA3n9Da20k6JK/zJFRETE0LBhQ5588kmsVivz589n7dq1nDlzxvi5zWbDbrcbA2xJSUksWrSIe+65h/vvvx/QnnciUtDVId5bb73FF198wYEDB5gzZw4//PADo0ePpl+/fgAF2hgRKT6cwXyFChU4d+4cgYGBjBw50gjxJk6cyG+//YbdbicyMpJffvnlmn11nW4U3i1btszYs0ZERETuDunp6dhsNsqWLVtgJV3+EG/u3LmEhobyxRdfMGjQIF5++WUgL7y7UX9DpCjQDowiIiICXBkMi4iI4OTJk6xbt47Zs2fz+++/8+ijj9K8efMCmzd/8803vPPOO/z666+88cYbNGvWrBCvXkSKMmeIB7Bu3TomTJjAPffcw6FDhxg9ejQDBgwACg7Ki0jx1KhRI5YtW8bhw4epX78+w4cPLxDmV69enbVr13Lw4EHefvvta16v8E5ERETyy87Oxmq1kpOTY6ymc04I7NGjBw6Hg3/9618cOnSIxo0bM3LkSACsVisWi6UwL13kv1KAJyIiIkDenlTO2WfTp0/H09OTDz/8kKVLl7J27Vo6d+5MQEAAOTk5HDp0iFWrVuFwOBg7diw9e/YE8mbXa+WMiFyPM8QzmUysXr2a1NRUxo4dS//+/QGFdyLFlfOz7ewfVKxYEYBjx47RoEED6tevz7hx45g0aRJJSUns2rWLhg0bGuFd/r6FwjsRERG5mslkwmw24+Xldd1ArmfPntjtdiZNmkRycjJRUVEMHToUi8WiFXhS5CnAExEREYPJZDI6sBMmTCA4OJhvv/2WL774gri4OOM4i8VC/fr1CQkJMfas0uC7iPw3QUFB9OnTh0uXLtGoUSOFdyLFzNmzZ0lLS+PQoUN4enrStm1b47PtLKFZtmxZPD09+fHHH+nYsSMWiwVfX18qVKhgnMdkMnH06FH8/f2N7+Xff3fVqlUK70RERATIm4xsNpspUaLEDY/p3bu3Mc4RFRVFbm4uw4YNKzAGIlIUKcATERGRAvJ3YJ977jmeeOIJdu/ezS+//MKpU6fw8PCgadOm+Pn5GQNrGnwXKZ5uxara4OBg3njjDcqUKQOo/RApLnbv3s2sWbPYu3cvFy5cAKB169b069ePFi1aGDPiq1Spgp+fH/v27QMgLS2NRYsWER8fT7169fD29mbbtm2MGzeOcePGERwcDFzZY3f58uVERkbicDgU3omIiNyl8oduly9fJisrC6vVCtz4GaZHjx4ATJgwgejoaACFeFLkKcATERGRa5hMJqPT6+3tTatWrWjVqtV1j80/I15Eioc9e/bg6upKUFDQTT2vs11xhndqP0SKh4SEBP75z3+SkZFB8+bNcXd3JzExke+++45Lly5Rvnx5goKCsNlsOBwOypQpQ3p6OmfPniUmJoa4uDhatmxJbGws+/btY9q0aSQkJDB27FhiY2ONNiMjI4O9e/dy/vx51q1bp/BORETkLpKYmEhSUhJhYWGYTCZsNhtmsxm73Q6Au7s7kDfpJ/8kwfxfXx3i2e12IiIiFN5JkaWnZREREbmuP7vqRnveiRQvBw8epEuXLrzyyivs3bsXuFL67u/Kf56MjAy1HyLFwNatWxkwYAClS5dmypQpxMXFsXDhQj788EPKly/Pzp07iY2NBcBsNmOxWKhXrx4///wzkydPJi4ujhYtWhjHVKtWjaFDh1KvXj06depkhHcA3t7eDBo0iK+++krhnYiIyF3k6NGjDBw4kDlz5rBgwQIgr18BecGdm5sbPj4+nDhxgpycHLKzs43XXj1hsEePHrz++usAxMTEsHPnzttzEyJ/gVbgiYiIFEO3ouydiNwdLl++DOQ9JE+ePJnXXnuN4ODgv92u5J/5umzZMjZt2sSkSZOoVq3azbhsESkEzvDO39+fESNG0KFDBwCsVitBQUFMnTqVIUOGkJKSgtVqNcpoBgQEYLfb2bhxo7HyDiAnJwd3d3caNmzI/Pnz8fX1BQr2awICAgrhTkVERKQwlSpVil69erFixQpj5VxYWBgAly5dwmazsX79er755hsqVapEiRIlqFu3Lt7e3lSuXJnatWvj7u5O9erV8fDwoFevXnh6epKWlkbDhg0L9+ZE/oACPBERkWLkVpW9A4WCIneLKlWq4OnpSUZGBklJSUyePJnx48cTFBT0l9uB/OHdxx9/THR0NKmpqTf70kXkNnKGd1WqVGHkyJG0b98eyPu8O8tZlS1bltzcXM6ePUt2djZmsxlXV1eaNGliDKo5wzubzYabmxuQV8r7euGdiIiI3J18fHwIDw/HYrEQGxtLTEwMdrud8PBwYwsQT09PAPbv34/dbic5Odl4vaenJzk5OdSqVYvc3FxKlizJjBkzqFy5MoD2wJMiSwGeiIhIMeEsexccHMyUKVNuyooZp/yD7xkZGXh7e//tc4pI0WOz2bBYLJQrVw53d3dKly5NYmIikyZN+ssh3tXh3axZs8jJyeGTTz7R6juRO1RCQgJDhgyhWrVqjBw5kkcffRQo+HkHOHHiBA6Hgw4dOuDj42N8v3bt2kydOtUog+ncw+Z6FN6JiIgI5IV4AwcOBCA2NpaFCxfi5eVFnTp18PT0pEuXLoSFhXH06FFSUlK4cOECO3fu5Pz585w5c4bjx49z8OBBbDYb48aNM8I7QOGdFFkK8ERERIoJlb0Tkb/LbDbj7e1NYGAg+/fvZ9iwYURGRvLDDz8UCPHgyizVP5qter3wzmq1smzZMurUqXPb7ktEbp7Tp08TEhICQN26dY3wLn+JTIC9e/cyb948PD09admypfF9h8OBq6urEd45V+yJiIiI/DdXh3jz5s0jODiYzMxM3N3d8fX1xdfXl8aNGwN5zywOh4PU1FSOHz9ORkYGGRkZdOzYEdBKfyn6XP/7ISIiInInuF7Zu5SUFFxcXHA4HH/pnNcre5eYmHgzL1tEihBnW1GhQgXOnTtHYGAgI0eOpHnz5iQlJTFx4kR+++037HY7kZGR/PLLL38pvHMO3IvIneXEiRN4eXkxduxYADZs2EBUVBQAFosFm80GwIEDB4iMjGTPnj2EhYXxyCOPGOe4epAs/4o9ERERkf/GGeKFhISQnZ1tjFHY7XYAsrOzjWNdXFwwm81UrFiRxo0b8+CDDxrhnd1uV3gnRZ56yiIiIsVA/rJ3tWrVolmzZiQlJTFp0qS/HOJdb/A9OztbZe9E7gKNGjUiPT2dw4cPU79+fYYPH07z5s1JTk5m9OjRjBs3jpiYGN56663rvl7hnUjxs3nzZvr168f58+fp378/EyZMACAqKoq5c+cCeat4U1JSmD17Nl999RUDBgxgyJAhwJVBNREREZG/yxniDRgwAA8PD+BKcOfu7m70O/5oopAmEcmdQP9KRUREioH8Ze+sVivDhg0zVszkD/Egr4RE/j+v50aD70uXLlXZO5FiyPmA62wnKlasCMCxY8cwmUzUr1+fcePG0aRJE3bt2sXatWtp2LAhb7/9NkCBCQIK70SKnyNHjhAREYHdbsfPzw+AHj16GCFedHQ00dHRnDp1iqioKL788ktCQ0MZPXo0kNfn0CCZiIiI3Ew+Pj6EhoYSEhKCu7s7K1euZP78+UBeOKfJQ1IcqActIiJSDKjsnYj8WWfPnuXAgQNs2rSJzZs3A1dmnzocDhwOB2XLlsXT05Mff/wRyCuN5+vrS4UKFYzzmEwmjh49WuDczr2tAFatWqX2Q6SYSE9Px2azUbZs2QKDYflDvLlz5xIaGsoXX3zBoEGDePnllwH+cJ9MERERkb+jZMmShIaG0r9/f1xdXYmJiSkQ4v3RxGWRO4F2ihYRESlGGjVqxLJlywqUvZszZw4//PADo0ePpnr16qxdu5aDBw8aK2fyU3gnUrzt3r2bWbNmsXfvXi5cuABA69at6devHy1atMBisQB5e2r6+fmxb98+ANLS0li0aBHx8fHUq1cPb29vtm3bxrhx4xg3bhzBwcHAlRV8y5cvJzIyEofDofZDpBjIzs7GarWSk5NjrKZzft579OiBw+HgX//6F4cOHaJx48aMHDkSAKvVarQrIiIiIreCs5wmQGxsLIsWLcJmszF06FBNIpI7nlbgiYiI3MFU9k5E/qyEhARCQ0P5/vvvCQwM5IEHHsDDw4PvvvuOd955h19++QXI21PT4XBQpkwZ0tPTOXv2LPPnzyc2NpaWLVuyatUqxo0bR8uWLUlMTGTs2LGkpaUZ75ORkcHevXs5f/48cXFxaj9EigGTyYTZbMbLywuLxWL0O5x69uzJ+PHjAUhOTiYqKgrIW72rme8iIiJyq+XfEy8jI4OoqCijmojInUwr8ERERO4QZ8+eJS0tjUOHDuHp6Unbtm0LlL0DCpS969ix4x+WvfP39ze+p7J3IsXb1q1bGThwIJUrV2bcuHF06dIFgJSUFAYPHszOnTuJjY1l+vTpmM15jwj16tUjLi6OyZMns3HjRlq0aEFsbCwA1apVY+jQoVy8eJGOHTtSpkwZ4728vb0ZNGgQ4eHhVKpU6fbfrIjcdK6urpjNZkqUKHHDY3r37o3JZGLChAlERUWRm5vLsGHDMJlMKqMpIiIit5yPjw8hISFkZmbi7+/PvffeW9iXJPK3KcATERG5A6jsnYj8VVu3bmXAgAH4+/szYsQIOnToAOSVtgsKCmLq1KkMGTKElJSUAuXuAgICsNvtbNy4kZYtWxrhXU5ODu7u7jRs2JD58+fj6+sL5E0EcLYlAQEBhXCnInIz5Q/dLl++TFZWFlarFSj4ec+vR48eAEyYMIHo6GgAhXgiIiJy25QsWZIRI0YYzzT5Kw2J3In0r1dERKSIU9k7EfmrnOFdlSpVGDlypBHe2e12zGYzdrudsmXLkpuby9mzZ8nOzjZK8zZp0oS6devSvHlzI7yz2Wy4ubkBeat5rxfeicidKzExkfnz5wN5n3GbzQZcKdnt7u4O5E38cX4v/88hL8SbMGECANHR0cyePds4n4iIiMitln//XYV3cqfTCjwREZEiTGXvROSvSkhIYMiQIVSrVo2RI0fy6KOPAtfOQj1x4gQOh4MOHTrg4+NjfL927dpMnTrVCPNtNpvRzlxN4Z3Ine/o0aMMHDiQ7OxsXF1dGTx4sPGZd3d3x83NDR8fH06cOEG5cuWw2Wx4enoC1w6O9ejRA7vdzsSJE4mJieHhhx+mYcOGt/uWRERERETuaArwREREiiiVvRORv+r06dOEhIQAULduXSO8y99WAOzdu5d58+bh6elJy5Ytje8798V0hnfOFXsiUnyVKlWKXr16sWLFCqKjo7Hb7YSFhQFw6dIlbDYb69ev55tvvqFSpUqUKFGCunXr4u3tTeXKlalduzbu7u5Ur14dDw8PevXqhaenJ2lpaQrvRERERET+Aj2Fi4iIFEFXl71r37498Mdl78xmM66urkbZO29vb5W9E7kLnThxgpIlSzJ27FimTZvGhg0bqF69OkOHDsVisRgr6Q4cOEBkZCR79uwhIiKCRx55xDjH1e2CSs+IFH8+Pj6Eh4djsViIjY0lJiYGu91OeHg4JpMJh8NhrLjbv38/drud5ORk4/Wenp7k5ORQq1YtcnNzKVmyJDNmzKBy5coA2gNPREREROR/pABPRESkiFHZOxH5qzZv3sz//d//ERsbS//+/fHw8GDChAlERUWRm5vLsGHDMJvNpKSkEBkZyVdffcWAAQMYMmQIoE3eRe52Pj4+DBw4EIDY2FgWLlyIl5cXderUwdPTky5duhAWFsbRo0dJSUnhwoUL7Ny5k/Pnz3PmzBmOHz/OwYMHsdlsjBs3zgjvQHvgiYiIiIj8rxTgiYiIFCEqeycif9WRI0eIiIigTJky+Pn5AXn7UAFMmDCB6OhoLBYLXbp0ISoqii+//JLQ0FBGjx4NaHWMiOS5OsSbN28ewcHBZGZm4u7ujq+vL76+vjRu3BjIazscDgepqakcP36cjIwMMjIy6NixI6DV/iIiIiIif5VG9ERERIoIlb0Tkb8jPT0dm81G2bJlsdvtRhiXP8SbO3cun376KYcOHWLQoEGMHDkSUHgnIgXlD/EWL15MYmIikDcxCCA7Oxt3d3cgr+9hMpmoWLEiFStWLHAereoVEREREfnr1JMWEREpAjZv3ky/fv04f/48/fv3Z8KECQBERUUxd+5cAKPs3ezZs69b9k5E7m7Z2dlYrVZycnKMFTFOPXr04I033gDg0KFDNG7c2AjvrFarwjsRuYYzxBswYAAeHh5AXjsD4O7ubvQ9/iigU3gnIiIiIvLXqTctIiJSyJxl7+x2e4Gyd84QLzo6mujoaE6dOnXDsncaIBMRk8mE2WzGy8sLi8VyzYrcnj17Mn78eACSk5OJiooCwGKxkJube9uvV0SKPh8fH0JDQwkJCcHd3Z2VK1cyf/58IC+c0wQiEREREZFbRyU0RURECpnK3onIzeDq6orZbKZEiRI3PKZ3796YTCYmTJhAVFQUubm5DBs2DJPJpPZERK6rZMmShIaGYrfbiY2NJSYmBoCwsDBcXV3VdoiIiIiI3CKari8iIlLIVPZORP6q/CvnLl++TFZWFlarFaBAW5Lf1St8nWV6nSGeiMjVnOU0Q0JCyM3NZdGiRcYqXvVFRERERERuDQV4IiIihUxl70Tkf5GYmGiUsDOZTNhsNuDKXpju7u4AuLi4FChvl//rq0O82bNnG+cTEbme/HviZWRkEBUVxY8//ljYlyUiIiIiUmyphKaIiEghU9k7Efmzjh49ysCBA8nOzsbV1ZXBgwdjNud16d3d3XFzc8PHx4cTJ05Qrlw5bDYbnp6eANfsldmjRw/sdjsTJ04kJiaGhx9+mIYNG97uWxKRO4iPjw8hISFkZmbi7+/PvffeW9iXJCIiIiJSbCnAExERKQT5Q7frlb27ehUeFNwTLzo6GkAhnshdplSpUvTq1YsVK1YQHR2N3W4nLCwMgEuXLmGz2Vi/fj3ffPMNlSpVokSJEtStWxdvb28qV65M7dq1cXd3p3r16nh4eNCrVy88PT1JS0tTeCcif0rJkiUZMWIEFosFyFvde/UEARERERER+fsU4ImIiNwmiYmJJCUlERYWZpS9M5vNNyx75xwMy//11SGe3W4nIiJC4Z3IXcLHx4fw8HAsFguxsbHExMRgt9sJDw/HZDLhcDiMFXf79+/HbreTnJxsvN7T05OcnBxq1apFbm4uJUuWZMaMGVSuXBlAkwFE5E9xhndw7epeERERERG5ORTgiYiI3AYqeyciN4tzHyqA2NhYFi5ciJeXF3Xq1MHT05MuXboQFhbG0aNHSUlJ4cKFC+zcuZPz589z5swZjh8/zsGDB7HZbIwbN84I70B74ImIiIiIiIgUFQrwREREbgOVvRORm+nqEG/evHkEBweTmZmJu7s7vr6++Pr60rhxYyBvZZ3D4SA1NZXjx4+TkZFBRkYGHTt2BG5culdERERERERECocCPBERkdtAZe9E5GbLH+ItXryYxMREAKMsb3Z2doHSvCaTiYoVK1KxYsUC59H+VSIiIiIiIiJFjwI8ERGR20Rl70TkZnO2Ky4uLrz33ntkZWWRnZ0N5JXndYZzfxTQKbwTERERERERKXoU4ImIiNxGKnsnIjebj48PoaGhuLi4sHjxYlauXEmFChUICwvD1dVVK+xERERERERE7kAK8ERERG4zlb0TkZutZMmShIaGYrfbjTK9gBHiqcyuiIiIiIiIyJ1Fo34iIiKFwBniDRgwAA8PD4Bryt7BH5e2U3gnIvk525WQkBByc3NZtGgRUVFRgMrsioiIiIiIiNxpXBwOh6OwL0JERORudfHiRWJjY1m8eDG5ubkMHTqUsLAwQCvsROSvSU9PZ/HixURHRwOwcuVK7r333kK+KhERERERERH5X6iEpoiISCFS2TsRudl8fHwICQkhMzMTf39/hXciIiIiIiIidyCtwBMRESkC0tPTWbRoEbGxsVgsFvr378/QoUML+7JE5A5mtVqxWCyAVvSKiIiIiIiI3GkU4ImIiBQRKnsnIiIiIiIiIiIioBKaIiIiRYbK3omIiIiIiIiIiAhoBZ6IiEiRo7J3IiIiIiIiIiIidzcFeCIiIiIiIiIiIiIiIiJFiKb0i4iIiIiIiIiIiIiIiBQhCvBEREREREREREREREREihAFeCIiIiIiIiIiIiIiIiJFiAI8ERERERERERERERERkSJEAZ6IiIiIiIiIiIiIiIhIEaIAT0RERERERERERERERKQIUYAnIiIiIiIiIiIiIiIiUoQowBMREREREREREREREREpQhTgiYiIiIiIiIiIiIiIiBQhCvBERERERETuUmFhYQQGBtKhQ4c//Zpjx44RFBREYGAgmzdvvunXtHr1agIDA3nwwQdv+rn/igMHDhT2JYiIiIiIyF1IAZ6IiIiIiMhdqlu3bgAcPnyYH3/88U+9Zu3atTgcDipWrMgDDzxwKy+vUB0+fJgBAwbw+uuvF/aliIiIiIjIXUgBnoiIiIiIyF3qoYceoly5cgCsX7/+T73mk08+AaBLly64ut78R8p27doRHx/P0qVLb/q5/xeffvopW7ZsKdRrEBERERGRu5cCPBERERERkbuU2WymU6dOAMTHx5Obm/uHxyclJfHrr7/i4uJC165db8k1+fj4ULNmTQICAm7J+UVERERERO4ECvBERERERETuYs4ymmfOnOH777//w2PXrl0LQKtWrahSpcqtvjQREREREZG7lrmwL0BEREREREQKT82aNWnUqBE7duxg/fr1tG7d+rrHZWdns2nTJuBK6Gez2fj000/ZtGkTP//8M+fPn8dsNlO+fHnuv/9+QkJCqF69eoHz9OnThx9++IEFCxbw008/sWzZMi5duoS/vz9z585l165dvPLKK1SoUIFvvvmmwGuzsrJYtWoVX3zxBfv27ePixYtYLBb8/Pxo06YNISEhVKhQocBr/vGPf/D7778THx/P2bNnWbRoEbt27eLy5ctUqVKFxx9/nAEDBuDl5QXAsWPHeOSRR4zXJycnExgYSOXKlfnyyy//3v9sERERERGRP0kr8ERERERERO5yznKYn3/+OVlZWdc95j//+Q8XL16kVKlSPProo2RlZREaGsqYMWP46quvcHNzo06dOtxzzz0cOXKEjz76iC5durBnz57rnm/+/PlERkbi5eVFxYoVycjIoFq1aje8xrS0NLp3787EiRNJSEjA29ubwMBASpQowYEDB4iNjeWZZ57h5MmT1339ypUr6du3L99//z0VKlSgTJky/PLLL7z99tsMGDDAKB/q7u5O48aNqVSpEgDe3t40btyY+vXr/9n/nSIiIiIiIn+bAjwREREREZG7XMeOHSlRogSXLl264SozZ/nMp59+GovFwsKFC9m2bRulS5dm5cqVfPnll6xatYqvv/6alStX4uvry+XLl5k/f/51z5ecnMyoUaP497//zWeffcbq1asxmUw3vMYZM2awf/9+qlatyqZNm/j8889ZtWoVW7ZsYdGiRXh6enL27FmWLFly3dc7A77vvvuOTz75hK+//prXX38dgB07dvDVV18B4OvrywcffGCEmnXq1OGDDz4gMjLyT/2/FBERERERuRkU4ImIiIiIiNzlvLy86NChAwCffPLJNT9PTU3lu+++A6B79+4AbN26FVdXV4YOHUqDBg0KHN+gQQN69uwJwP79+6/7npUrV2bgwIHG38uUKXPD67PZbGzfvh0XFxdeeeWVa1bqPfDAA3Ts2PEP3y8oKIgpU6bg4+MDgIuLC7179yYwMBCApKSkG76/iIiIiIjI7aY98ERERERERIRu3bqxevVqtmzZwrlz5yhdurTxs/Xr12Oz2WjYsCG1a9cG4IMPPiAnJwcXF5frns/T0xPghiU5GzVqdMPXXs1sNvPFF1+QnZ2NxWK55ucOh4MSJUr84fs99NBD132/GjVqsG/fPtLT0//UtYiIiIiIiNwOCvBERERERESEJk2aUKNGDX755Rc2bdpkrKADWLNmDZAX8uXn5uZGeno6ycnJHDlyhKNHj3LkyBH27t1LamoqAHa7/brv5+vr+z9fo7u7O2fPnmXnzp0cOXKEY8eO8csvv7B3714uXLjwh+9Xvnz5637fw8MDwNgDT0REREREpChQgCciIiIiIiIAdOnShTfffJP169cbAV5KSgr79u2jRIkSRplKgIyMDGbNmsWaNWu4fPmy8X03Nzfq1atHcHAw33777Q3fy93d/X+6tjNnzjB9+nQ2bdpETk6O8X1PT0/uvfdecnNz/7AM5vVW7uXncDj+p+sRERERERG5lRTgiYiIiIiICADPPPMMc+bMITk5md9//53KlSsbq+86duyIl5eXcWx4eDjbtm3Dw8ODkJAQ7rvvPmrXrk3VqlVxc3NjxYoVfxjg/S+ys7Pp168fhw4dolSpUvTs2ZP69etTs2ZNAgICMJlMzJ49W/vYiYiIiIhIsaEAT0RERERERAAoV64cbdu25T//+Q/x8fEMHDiQ+Ph4ALp3724ct3PnTrZt2wZATEwMLVq0uOZcJ0+evGnX9cUXX3Do0CHMZjMfffQR1apVu6XvJyIiIiIiUthcC/sCREREREREpOjo2rUrAP/+979JSkri9OnT1K5dm4YNGxrHHDt2zPi6fv3615wjMzOTDRs2ADdnbznn+3l5eV03vEtNTeXrr7++ae8H4OLiAqi0poiIiIiIFA4FeCIiIiIiImJo27Ytvr6+7N69m7i4OAC6detW4JgaNWoYX7/99tvYbDbj7wcPHmTQoEEcOXIEyAvz/i7n+124cIElS5YUCNV27txJSEgI58+fv2nvBxjlQk+fPl3g/kRERERERG4HBXgiIiIiIiJiMJvNdO7cGYfDwWeffYabmxudOnUqcEzdunV5/PHHAVi8eDFt2rSha9euPPLIIzzxxBMkJibSunVrAC5dukRGRsbfuqZ//OMfNGrUCIApU6bwwAMP0LVrVx588EGee+45Dhw4QKtWrYC8wO1mrJoLDg4G4Pfff+exxx6jR48eWo0nIiIiIiK3jQI8ERERERERKcBZRhPg0UcfpUyZMtcc89ZbbzFp0iTuvfde7HY7+/btw2q18vDDDxMTE8PixYupXLkyAF9++eXfuh6TycR7773HqFGjCA4OJjMzk/3792M2m+nYsSNLly7lnXfewd3dnfPnz5OcnPy33g+gRYsWvPzyy1SuXJnTp09z7NgxUlNT//Z5RURERERE/gwXh6YQioiIiIiIiIiIiIiIiBQZWoEnIiIiIiIiIiIiIiIiUoQowBMREREREREREREREREpQhTgiYiIiIiIiIiIiIiIiBQhCvBEREREREREREREREREihAFeCIiIiIiIiIiIiIiIiJFiAI8ERERERERERERERERkSJEAZ6IiIiIiIiIiIiIiIhIEaIAT0RERERERERERERERKQIUYAnIiIiIiIiIiIiIiIiUoQowBMREREREREREREREREpQhTgiYiIiIiIiIiIiIiIiBQhCvBEREREREREREREREREihAFeCIiIiIiIiIiIiIiIiJFiAI8ERERERERERERERERkSJEAZ6IiIiIiIiIiIiIiIhIEaIAT0RERERERERERERERKQIUYAnIiIiIiIiIiIiIiIiUoQowBMREREREREREREREREpQhTgiYiIiIiIiIiIiIiIiBQh/w8RLXGO0wiRqwAAAABJRU5ErkJggg==",
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+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+ " if pd.api.types.is_categorical_dtype(vector):\n",
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+ " if pd.api.types.is_categorical_dtype(vector):\n",
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+ " if pd.api.types.is_categorical_dtype(vector):\n",
+ "/Users/danielbiber/opt/anaconda3/envs/gen3_ingest/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+ " if pd.api.types.is_categorical_dtype(vector):\n"
+ ]
+ },
+ {
+ "data": {
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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from pathlib import Path\n",
+ "\n",
+ "# Load the corrected data\n",
+ "file_path = Path(\"ST003847_api_downloads/ST003847_AN006322_datatable.tsv\")\n",
+ "df = pd.read_csv(file_path, sep=\"\\t\")\n",
+ "\n",
+ "# Extract variant information from 'Class' column\n",
+ "df[\"Variant\"] = df[\"Class\"].str.extract(r\"Variant:([^|]+)\").iloc[:, 0].str.strip()\n",
+ "\n",
+ "# Filter out standard samples\n",
+ "df_filtered = df[~df[\"Variant\"].str.contains(\"STD\", na=False)]\n",
+ "\n",
+ "# Melt data for easier plotting\n",
+ "df_melted = df_filtered.melt(\n",
+ " id_vars=[\"Samples\", \"Variant\"],\n",
+ " value_vars=[\"7-Dehydrocholesterol\", \"7-Dehydrodesmosterol\"],\n",
+ " var_name=\"Metabolite\",\n",
+ " value_name=\"Abundance\"\n",
+ ")\n",
+ "\n",
+ "# Boxplot of metabolite levels per variant\n",
+ "plt.figure(figsize=(12, 6))\n",
+ "sns.boxplot(data=df_melted, x=\"Variant\", y=\"Abundance\", hue=\"Metabolite\")\n",
+ "plt.xticks(rotation=45)\n",
+ "plt.title(\"Distribution of Metabolite Levels by Variant\")\n",
+ "plt.tight_layout()\n",
+ "plt.show()\n",
+ "\n",
+ "# Swarmplot for detailed data points\n",
+ "plt.figure(figsize=(12, 6))\n",
+ "sns.swarmplot(data=df_melted, x=\"Variant\", y=\"Abundance\", hue=\"Metabolite\", dodge=True)\n",
+ "plt.xticks(rotation=45)\n",
+ "plt.title(\"Metabolite Abundance per Sample Variant\")\n",
+ "plt.tight_layout()\n",
+ "plt.show()\n",
+ "\n",
+ "# Barplot for average metabolite abundance\n",
+ "avg_df = df_melted.groupby([\"Variant\", \"Metabolite\"]).Abundance.mean().reset_index()\n",
+ "\n",
+ "plt.figure(figsize=(12, 6))\n",
+ "sns.barplot(data=avg_df, x=\"Variant\", y=\"Abundance\", hue=\"Metabolite\")\n",
+ "plt.title(\"Average Metabolite Abundance by Variant\")\n",
+ "plt.xticks(rotation=45)\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "59dcca3d-7261-4b99-9f20-168d4ee102e6",
+ "metadata": {},
+ "source": [
+ "## ๐ฅ Downloading and Extracting Raw Metabolomics Data\n",
+ "\n",
+ "To complement the processed data and support reproducibility, you could also retrieved the **raw data files** associated with the **ST003847** study from the **Metabolomics Workbench**.\n",
+ "\n",
+ "\n",
+ "We use the `curl` command to download the zipped archive containing raw experimental files.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "144d9bfc",
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " % Total % Received % Xferd Average Speed Time Time Time Current\n",
+ " Dload Upload Total Spent Left Speed\n",
+ "100 15.3M 100 15.3M 0 0 4831k 0 0:00:03 0:00:03 --:--:-- 4834k\n"
+ ]
+ }
+ ],
+ "source": [
+ "!curl -O https://www.metabolomicsworkbench.org/studydownload/ST003847_Rawdata.zip"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "f448a8df",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import zipfile\n",
+ "\n",
+ "zip_path = \"ST003847_Rawdata.zip\"\n",
+ "with zipfile.ZipFile(zip_path, \"r\") as zip_ref:\n",
+ " zip_ref.extractall(\"ST003847_raw\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c7d5bbba-95db-4b53-8a23-a8dbc0c76558",
+ "metadata": {},
+ "source": [
+ "## โ Summary\n",
+ "\n",
+ "In this notebook, we demonstrated how to integrate and analyze multi-source metabolomics data from the **Murtha Cancer Center Data Platform**, focusing on the study **ST003847** from the **Metabolomics Workbench**.\n",
+ "\n",
+ "### Key Highlights:\n",
+ "\n",
+ "- ๐ **Data Integration**: Downloaded processed and raw datasets linked to pancreatic cancer subtypes.\n",
+ "- ๐งช **Feature Extraction**: Parsed and cleaned metabolite measurements, removing standard (STD) samples.\n",
+ "- ๐ **Multivariate Analysis**: Performed PCA to visualize subtype separation based on lipid metabolite levels.\n",
+ "- ๐ฅ **Heatmap Visualization**: Created z-score normalized heatmaps to reveal metabolite patterns across variants.\n",
+ "- ๐ฆ **Variant-Level Statistics**: Generated boxplots, swarmplots, and barplots to compare metabolite distributions by PDAC subtype.\n",
+ "- ๐งฌ **Biological Insight**: Observed clear differences in **7-Dehydrocholesterol** and **7-Dehydrodesmosterol** between PDAC variants, consistent with the project's hypothesis on cholesterol metabolism and metastatic tropism.\n",
+ "\n",
+ "### ๐ Next Steps:\n",
+ "- Integrate additional `omics` layers (e.g., transcriptomics, proteomics) to build a systems-level view.\n",
+ "- Correlate lipid signatures with clinical metadata for prognostic modeling.\n",
+ "- Apply this framework to other MCCDP studies for cohort-wide biomarker discovery.\n",
+ "\n",
+ "This notebook showcases the power of combining public repositories and institutional data platforms for translational cancer research.\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "gen3_ingest",
+ "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": "3.9.18"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/jupyter-prometheus/requirements.txt b/jupyter-prometheus/requirements.txt
new file mode 100644
index 00000000..d26b5f49
--- /dev/null
+++ b/jupyter-prometheus/requirements.txt
@@ -0,0 +1,21 @@
+# Core build tools
+pip>=21.0
+setuptools>=57.0
+wheel>=0.36
+
+# Foundational packages
+numpy>=1.21.0
+scipy>=1.7.0
+
+# Data science packages
+pandas>=1.3.0
+matplotlib>=3.5.0
+seaborn>=0.11.0
+
+# Jupyter components
+notebook>=6.4.0
+jupyterlab>=3.0.0
+ipykernel>=6.0.0
+
+# Domain-specific
+gen3
diff --git a/jupyter-pystata-gen3-licensed/Dockerfile b/jupyter-pystata-gen3-licensed/Dockerfile
index b448cc7a..ec794dbc 100644
--- a/jupyter-pystata-gen3-licensed/Dockerfile
+++ b/jupyter-pystata-gen3-licensed/Dockerfile
@@ -1,9 +1,8 @@
-FROM quay.io/cdis/jupyter-pystata-user-licensed:2.0.0
+FROM quay.io/cdis/jupyter-pystata-user-licensed:3.0.0
USER root
-RUN apt-get update
-RUN apt-get install -y firefox
-RUN wget https://github.com/mozilla/geckodriver/releases/download/v0.35.0/geckodriver-v0.35.0-linux64.tar.gz
+RUN yum install -y firefox jq
+RUN wget https://github.com/mozilla/geckodriver/releases/download/v0.36.0/geckodriver-v0.36.0-linux64.tar.gz
RUN tar -xvzf geckodriver*
RUN mv geckodriver /bin/
@@ -12,7 +11,7 @@ COPY ./resources/setup_licensed_notebook.py /tmp/
RUN chmod 777 /tmp/wait_for_license.sh /tmp/setup_licensed_notebook.py
COPY ./resources/licensed_stata_session.ipynb $HOME
-RUN chown $NB_USER $HOME/licensed_stata_session.ipynb
+RUN chown "${NB_USER}:${NB_GID}" $HOME/licensed_stata_session.ipynb
USER $NB_USER
RUN pip3 install selenium
@@ -20,6 +19,5 @@ RUN pip3 install selenium
RUN pip3 uninstall --yes stata-setup
RUN pip3 install stata-setup
-
# Remove the notebook created in jupyter-pystata-user-licensed
RUN rm $HOME/Stata.ipynb
diff --git a/jupyter-pystata-gen3-licensed/resources/wait_for_license.sh b/jupyter-pystata-gen3-licensed/resources/wait_for_license.sh
index 8cef6c87..076bfc42 100644
--- a/jupyter-pystata-gen3-licensed/resources/wait_for_license.sh
+++ b/jupyter-pystata-gen3-licensed/resources/wait_for_license.sh
@@ -1,17 +1,30 @@
-# Wait for license, start jupyter, initialize notebook, remove license
-
-echo "Checking for license copied by sidecar"
-
-while [ ! -f /usr/local/stata18/stata.lic ];
-do
- sleep 5
- echo "Checking for license"
- if [ -f /data/stata.lic ]; then
- echo "Found license"
- mv /data/stata.lic /usr/local/stata18/stata.lic
- echo "Copied license"
- fi
-done
+# Check for license, start jupyter, initialize notebook, remove license
+
+LICENSE_VAR="STATA_WORKSPACE_GEN3_LICENSE"
+KEY_VAR="stata-license.txt"
+TARGET_FILE="/usr/local/stata18/stata.lic"
+
+echo "Checking stata license"
+if [[ ! -n "${!LICENSE_VAR}" ]]; then
+ echo "Exiting. Stata license is empty."
+ exit 0
+fi
+
+if echo "${!LICENSE_VAR}" | grep -q "${KEY_VAR}" ; then
+ echo "Found key"
+else
+ echo "Exiting. Environment variable does not contain key ${KEY_VAR}."
+ exit 0
+fi
+
+LICENSE_DATA="${!LICENSE_VAR}"
+echo ${LICENSE_DATA} | jq -r --arg k ${KEY_VAR} '.[$k]' > ${TARGET_FILE}
+
+if [[ ! -f "${TARGET_FILE}" ]]; then
+ echo "Exiting. Did not save license."
+ exit 0
+fi
+unset ${LICENSE_VAR}
echo "Received a license. Starting jupyter."
@@ -25,6 +38,7 @@ python3 /tmp/setup_licensed_notebook.py
rm geckodriver*
echo "Init script done."
-rm /usr/local/stata18/stata.lic
+rm ${TARGET_FILE}
+
while true; do sleep 1; done
diff --git a/jupyter-pystata-user-licensed/Dockerfile b/jupyter-pystata-user-licensed/Dockerfile
index ba214f17..fc223be6 100644
--- a/jupyter-pystata-user-licensed/Dockerfile
+++ b/jupyter-pystata-user-licensed/Dockerfile
@@ -1,10 +1,12 @@
-FROM quay.io/cdis/jupyter-superslim:2.0.0
+FROM quay.io/cdis/jupyter-superslim:2.1.0
USER root
-RUN apt-get update
-# needed if user wants to run stinit (license validation) in the workspace
-RUN apt-get install -y libncurses5
+# This is needed for running stata_setup
+RUN yum -y update
+RUN yum install -y \
+ ncurses-compat-libs && \
+ yum clean all
RUN mkdir /usr/local/stata18
COPY ./resources/StataNow18Linux64.tar.gz /tmp/StataNow18Linux64.tar.gz
@@ -17,12 +19,12 @@ RUN cd /usr/local/stata18 && \
COPY ./resources/Stata.ipynb .
COPY ./resources/welcome.html .
-RUN chown -R $NB_USER /usr/local/stata18/
+RUN chown -R "${NB_USER}:${NB_GID}" /usr/local/stata18/
+
USER $NB_USER
RUN pip install --user stata_setup
COPY ./dockerstart.sh /usr/local/bin/
-
RUN mkdir /tmp/custom_api
COPY ./resources/custom_api/* /tmp/custom_api/
diff --git a/jupyterlab-generic/Dockerfile b/jupyterlab-generic/Dockerfile
new file mode 100644
index 00000000..92b1ac32
--- /dev/null
+++ b/jupyterlab-generic/Dockerfile
@@ -0,0 +1,49 @@
+# Generic Gen3 JupyterLab workspace
+
+FROM public.ecr.aws/amazonlinux/amazonlinux:2023-minimal
+LABEL maintainer="Center for Translational Data Science (CTDS)"
+LABEL name="jupyterlab-generic"
+
+# TODO: git, nodejs, and build are all required only to build jupyterlmod;
+# these can be dropped once jupyterlmod fix is available in PyPI.
+RUN dnf upgrade --refresh && \
+ dnf install -y python3.13 python3.13-pip git nodejs shadow-utils which && \
+ python3.13 -m venv /usr/local/python-venv && \
+ source /usr/local/python-venv/bin/activate && \
+ pip install --upgrade pip && \
+ pip install jupyterlab && \
+ pip install build && \
+ pip install jupyterlmod git+https://github.com/pschumm/jupyter-lmod@pschumm/fix_hidden && \
+ pip install jupyterlab-git && \
+ pip install jupyterlab-search-replace
+
+RUN /usr/local/python-venv/bin/jupyter kernelspec remove -y python3
+
+COPY jupyterlab-start.sh .
+RUN chmod +x jupyterlab-start.sh
+
+EXPOSE 8888
+
+# Create home directory manually to avoid copying skeleton files
+RUN groupadd -g 1010 jovyan && \
+ useradd -u 1010 -g jovyan -M jovyan && \
+ mkdir /home/jovyan && \
+ chown jovyan:jovyan /home/jovyan && \
+ chmod 700 /home/jovyan && \
+ usermod -d /home/jovyan jovyan && \
+ usermod --shell /bin/bash jovyan
+USER jovyan
+WORKDIR /home/jovyan
+
+# Add path to kernels
+ENV JUPYTER_PATH=/apps/share/jupyter
+
+# Enable lmod
+ENV LMOD_CMD=/apps/lmod/lmod/libexec/lmod
+ENV MODULEPATH=/apps/lmod/lmod/modulefiles
+ENV LMOD_MODULERC=/apps/lmod/lmod/.modulerc.lua
+
+# Make sure bash is the default shell within JupyterLab terminal
+ENV SHELL=/bin/bash
+
+CMD ["/jupyterlab-start.sh"]
diff --git a/jupyterlab-generic/build.sh b/jupyterlab-generic/build.sh
new file mode 100755
index 00000000..ab93b831
--- /dev/null
+++ b/jupyterlab-generic/build.sh
@@ -0,0 +1,4 @@
+#!/usr/bin/env bash
+docker build \
+ -t jupyterlab-generic \
+ .
diff --git a/jupyterlab-generic/jupyterlab-start.sh b/jupyterlab-generic/jupyterlab-start.sh
new file mode 100644
index 00000000..ace6874d
--- /dev/null
+++ b/jupyterlab-generic/jupyterlab-start.sh
@@ -0,0 +1,28 @@
+#!/usr/bin/env bash
+
+# # Symlink config files for persistence
+test -f ./pd/.bash_profile || touch ./pd/.bash_profile
+test -f ./pd/.bashrc || touch ./pd/.bashrc
+test -d ./pd/.jupyter || mkdir ./pd/.jupyter
+test -d ./pd/.ipython || mkdir ./pd/.ipython
+test -d ./pd/.config || mkdir ./pd/.config
+test -d ./pd/.local || mkdir ./pd/.local
+ln -s ./pd/.bash_profile .
+ln -s ./pd/.bashrc .
+ln -s ./pd/.jupyter .
+ln -s ./pd/.ipython .
+ln -s ./pd/.config .
+ln -s ./pd/.local .
+
+# Load JupyterLab extension dependencies
+source /apps/lmod/lmod/init/profile
+module load git ripgrep
+# Load default modules
+module load py-pandas py-scipy
+
+/usr/local/python-venv/bin/jupyter lab \
+ --ServerApp.ip=0.0.0.0 \
+ --KernelSpecManager.ensure_native_kernel=False \
+ --ServerApp.quit_button=False \
+ --IdentityProvider.token="" \
+ "$@"
diff --git a/jupyterlab-generic/run.sh b/jupyterlab-generic/run.sh
new file mode 100755
index 00000000..77d3333a
--- /dev/null
+++ b/jupyterlab-generic/run.sh
@@ -0,0 +1,12 @@
+#!/usr/bin/env bash
+export APPS_PATH=~/.gen3/workspaces/apps
+export PD_PATH=~/.gen3/workspaces/pd
+export DATA_PATH=~/.gen3/workspaces/data
+docker run -d \
+ -it \
+ --name jupyterlab-generic \
+ -p 8888:8888 \
+ --mount type=bind,source=${APPS_PATH},target=/apps,readonly \
+ --mount type=bind,source=${PD_PATH},target=/home/jovyan/pd \
+ --mount type=bind,source=${DATA_PATH},target=/data \
+ jupyterlab-generic
diff --git a/utilities/Dockerfile b/utilities/Dockerfile
new file mode 100644
index 00000000..251e8df8
--- /dev/null
+++ b/utilities/Dockerfile
@@ -0,0 +1,16 @@
+# Use the amazonlinux-base image as the base
+FROM quay.io/cdis/amazonlinux-base
+
+# Install kubectl
+RUN curl -LO "https://dl.k8s.io/release/$(curl -L -s https://dl.k8s.io/release/stable.txt)/bin/linux/amd64/kubectl" && \
+ chmod +x kubectl && \
+ mv kubectl /usr/local/bin/
+
+# Verify installations
+RUN curl --version && kubectl version --client
+
+# Set working directory
+WORKDIR /app
+
+# Default command
+CMD ["/bin/bash"]