From 0c2a162324b1679beb7748b3e17980a814129e1a Mon Sep 17 00:00:00 2001
From: Ted Lawless <tlawless@brown.edu>
Date: Mon, 18 Jun 2018 15:18:48 -0400
Subject: [PATCH] Initial commit.

---
 .gitignore                 | 121 ++++++++++
 LICENSE.txt                |  20 ++
 README.md                  | 144 ++++++++++++
 layouts/credit_scores.yaml |  14 ++
 layouts/tax.yaml           |  22 ++
 requirements.txt           |   3 +
 scatterplot.png            | Bin 0 -> 52119 bytes
 scatterplot.py             |  24 ++
 simulate.py                | 120 ++++++++++
 sirad-id-validation.ipynb  | 436 +++++++++++++++++++++++++++++++++++++
 sirad_config.py            |  12 +
 11 files changed, 916 insertions(+)
 create mode 100644 .gitignore
 create mode 100644 LICENSE.txt
 create mode 100644 README.md
 create mode 100644 layouts/credit_scores.yaml
 create mode 100644 layouts/tax.yaml
 create mode 100644 requirements.txt
 create mode 100644 scatterplot.png
 create mode 100644 scatterplot.py
 create mode 100644 simulate.py
 create mode 100644 sirad-id-validation.ipynb
 create mode 100644 sirad_config.py

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..36061f1
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,121 @@
+*dblite
+raw/*
+build/*
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+© 2018 GitHub, Inc.
+Terms
+Privacy
+Security
+Status
+Help
+Contact GitHub
+API
+Training
+Shop
+Blog
+About
+
diff --git a/LICENSE.txt b/LICENSE.txt
new file mode 100644
index 0000000..fb11842
--- /dev/null
+++ b/LICENSE.txt
@@ -0,0 +1,20 @@
+Copyright 2018, Brown University, Providence, RI.
+ 
+                        All Rights Reserved
+ 
+Permission to use, copy, modify, and distribute this software and its
+documentation for any purpose other than its incorporation into a
+commercial product is hereby granted without fee, provided that the
+above copyright notice appear in all copies and that both that
+copyright notice and this permission notice appear in supporting
+documentation, and that the name of Brown University not be used in
+advertising or publicity pertaining to distribution of the software
+without specific, written prior permission.
+ 
+BROWN UNIVERSITY DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE,
+INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR ANY
+PARTICULAR PURPOSE.  IN NO EVENT SHALL BROWN UNIVERSITY BE LIABLE FOR
+ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES
+WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN
+ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF
+OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..a78e00d
--- /dev/null
+++ b/README.md
@@ -0,0 +1,144 @@
+# Worked example of Secure Infrastructure for Research with Administrative Data (SIRAD)
+
+`sirad` is an integration framework for data from administrative systems. It
+deidentifies administrative data by removing and replacing personally
+identifiable information (PII) with a global anonymized identifier, allowing
+researchers to securely join data on an individual from multiple tables without
+knowing the individual's identity.
+
+This is a simplified demonstration of how `sirad` works on simulated data; for
+more details on how it is used in practice with real administrative data,
+please see our manuscript preprint:
+
+> J.S. Hastings, M. Howison, T. Lawless, J. Ucles, P. White. 2018.
+> Integrating Administrative Data for Policy Insights.
+> (link to arXiv preprint)
+
+In this worked example, we simulate two administrative data sets:
+
+**1. IRS 1040 tax returns**, identified by social security number (SSN), first/last
+   name, and date of birth (DOB)  
+**2. Credit history**, identified by first/last name and date of birth (DOB)
+
+`sirad` uses a deterministic matching algorithm to match records across the two
+data sets corresponding to the same individual. It then assigns an anonymized
+identifier (the `sirad_id`) to each matched individual, and creates a
+deidentified table for each data set where the SSNs, names, and DOBs have been
+replaced with the `sirad_id`. Finally, we demonstrate an analysis that uses the
+`sirad_id` to join adjusted gross income from the tax returns table to credit
+scores in the credit history table.
+
+**Note**: the data are simulated by the `simulate.py` script using
+[Faker](https://github.com/joke2k/faker), which creates realistic PII that does
+not represent actual individuals. Any data in this example that looks
+personally identifiable is not!
+
+## Installing dependencies
+
+Requires Python 3.6 or later.  There are several options for installing the
+dependencies (list in `requirements.txt`).
+
+You can use **pip** to install them globally with  
+`pip install -r requirements.txt`.
+
+If you do not have write access to install globally, you can install into your
+home directory with  
+`pip install --user -r requirements.txt`.
+
+If you have Anaconda Python, you can use **conda** to install them in your
+root environment with  
+`conda install -c riipl-org --file requirements.txt`.
+
+Or if you would prefer to create a named conda environment, use  
+`conda install -c riipl-org -n sirad-example --file requirements.txt`  
+and activate it with  
+`source activate sirad-example`.
+
+## Running the example
+
+### Step 1: Simulate data
+
+Command: `python simulate.py`
+
+### Step 2: Process the raw data into separate PII, data, and link files
+
+Command: `sirad process`
+
+`sirad` processes a set of **raw** data files specified by a set of **layout
+files**. In this example, there are two simulated raw data files generated in
+Step 1: tax records (`raw/tax.txt`) and credit history
+(`raw/credit_scores.txt`). Their layouts are `layouts/tax.yaml` and
+`layouts/credit_scores.yaml`. The layouts are YAML files that describe the
+column layout and field types in the raw data files.
+
+The processing step uses the `pii` properties in the layout to split the PII
+fields from the data fields in each row of the raw files. It randomly shuffles
+the order of the PII rows when writing to the PII file. The data file has the
+same row order as the raw data file.  The link file provides a lookup table
+that re-links the shuffled PII rows to the data rows.
+
+### Step 3: Stage the processed files in a database
+
+Command: `sirad stage`
+
+This step stages the PII, data, and link files in a relational database.
+
+### Step 4: Create a versioned research database
+
+Command: `sirad research --version 1`
+
+This step uses the PII database to construct a global anonymized identifier
+(the `sirad_id`), then uses the link files to attach it to each data table in
+the database.  The result is a **research** database which contains no PII, but
+in which individual-level data in different tables can be joined by the
+anonymized identifier. Research databases are versioned to support reproducible
+analysis.
+
+## Resulting database
+
+After the build finishes, an sqlite database called `research_v1.db` will be
+created in the `build` directory.  This database has two tables created from
+the simulated data:
+
+### tax
+
+sirad_id | record_id | job | file_date | adjusted_gross_income | import_dt
+-|-|-|-|-|-
+
+### credit_scores
+
+sirad_id | record_id | credit_score | import_dt
+-|-|-|-
+
+Notes:
+* `sirad_id` is an anonymized identifier created from the PII.
+* `record_id` is a primary key for the research/data records, and `pii_id` is a
+  shuffled primary key for the PII records.
+* `import_dt` is a timestamp for when the raw data were processed.
+* All PII fields (SSN, first/last, DOB) have been removed from the research database.
+
+The results are organized in the following directory structure:
+* `raw/`: the simulated raw data files
+* `build/processed`: processed data files (organized by `data`, `pii`, and `link`)
+* `build/db`: the staging databases for the processed files
+* `build/research_v1.db`: the final research database
+
+In a real-world application, only the `research_v1.db` database would be
+accessible to researchers.  The `raw`, `processed`, and `db` directories should
+be stored in a restricted location that is inaccessible to any individual
+researcher, for example by using encryption with a multi-party key or
+passphrase, auditing, real-time alerting, and/or other appropriate security
+controls that ensure an individual researcher cannot access build files that
+contain PII.
+
+## Example analysis
+
+`scatterplot.py` demonstrates an analysis that uses the `sirad_id` to
+anonymously join records about individuals. It selects adjusted gross income
+from the `tax` table joined to the corresponding credit score from the
+`credit_scores` table, then generates this scatter plot:
+
+![scatterplot](scatterplot.png)
+
+**Note:** these variables are correlated by construction, and were drawn from a
+joint distribution (with added noise) in the simulation.
diff --git a/layouts/credit_scores.yaml b/layouts/credit_scores.yaml
new file mode 100644
index 0000000..46f6b17
--- /dev/null
+++ b/layouts/credit_scores.yaml
@@ -0,0 +1,14 @@
+source: credit_scores.txt
+type: csv
+delimiter: "|"
+fields:
+- first:
+    pii: first_name
+- last_name:
+    pii: last_name
+- birth_date:
+    pii: dob
+    type: date
+    format: "%m-%d-%Y"
+- credit_score:
+    type: int
diff --git a/layouts/tax.yaml b/layouts/tax.yaml
new file mode 100644
index 0000000..0bb7b08
--- /dev/null
+++ b/layouts/tax.yaml
@@ -0,0 +1,22 @@
+source: tax.txt
+type: csv
+delimiter: "|"
+fields:
+- first_name:
+    pii: first_name
+- last_name:
+    pii: last_name
+- ssn:
+    hash: true
+    pii: ssn
+    ssn: true
+- job
+- birth_date:
+    pii: dob
+    type: date
+    format: "%m-%d-%Y"
+- file_date:
+    type: date
+    format: "%m/%d/%Y"
+- adjusted_gross_income:
+    type: int
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..270c29c
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,3 @@
+faker
+numpy
+sirad
diff --git a/scatterplot.png b/scatterplot.png
new file mode 100644
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diff --git a/scatterplot.py b/scatterplot.py
new file mode 100644
index 0000000..8d0a27a
--- /dev/null
+++ b/scatterplot.py
@@ -0,0 +1,24 @@
+import matplotlib.pyplot as plt
+import pandas as pd
+import seaborn as sns
+import sqlite3
+
+sql = """
+      SELECT t.sirad_id,
+             adjusted_gross_income,
+             credit_score
+        FROM tax t
+  INNER JOIN credit_scores cs
+          ON t.sirad_id=cs.sirad_id
+      """
+
+with sqlite3.connect("build/research_v1.db") as cxn:
+    df = pd.read_sql(sql, cxn, index_col="sirad_id")
+
+print(df.head())
+
+sns.set_style("whitegrid")
+sns.regplot("adjusted_gross_income", "credit_score", data=df, truncate=True)
+plt.tight_layout()
+plt.savefig("scatterplot.png")
+
diff --git a/simulate.py b/simulate.py
new file mode 100644
index 0000000..9d844d1
--- /dev/null
+++ b/simulate.py
@@ -0,0 +1,120 @@
+"""
+Simulate data for IRS 1040 tax returns and credit history.
+"""
+
+from faker import Faker
+import csv
+import math
+import numpy as np
+from pathlib import Path
+import random
+import os
+from datetime import date
+
+from sirad import config
+
+N = 1000
+SEED = 1010
+
+fake = Faker()
+fake.seed(SEED)
+random.seed(SEED)
+np.random.seed(SEED)
+
+out = []
+
+def get_birth_date():
+    """
+    Faker on Windows is not generating dates prior to 1970.
+    Work around this for now.
+    See: https://stackoverflow.com/a/35709408/758157
+    Start year: 1950
+    End year: 1999
+    """
+    return date(
+        random.randint(1949, 1999),
+        random.randint(1, 12),
+        random.randint(1, 28)
+    )
+
+def simulate_tax(number):
+    perc_bad_ssn = 0.05
+    for n in range(0, number):
+        d = {}
+        if n % 2 == 0:
+            d['first'] = fake.first_name_female()
+        else:
+            d['first'] = fake.first_name_male()
+        d['last'] = fake.last_name()
+        d['ssn'] = fake.ssn()
+        if (random.random() <= perc_bad_ssn):
+            d['ssn'] = '000-' + d['ssn'][4:]
+        d['job'] = fake.job()
+        bd = get_birth_date()
+        d['birth_date'] = bd.strftime("%m-%d-%Y")
+        d['file_date'] = fake.date_this_year().strftime("%m/%d/%Y")
+        d['agi'] = int(20000 * np.random.lognormal())
+        out.append(d)
+
+    return out
+
+def simulate_credit_scores(path):
+    """
+    Create fake credit scores for subset of main person list.
+
+    For some perc of people replace full first name with first initial
+    """
+    perc_included = .60
+    perc_first_intial = .10
+    perc_drop_last = .005
+    perc_drop_birth = .01
+    out = []
+    with open(path) as f:
+        reader = csv.DictReader(f, delimiter="|")
+        for row in reader:
+            d = {}
+            if random.random() <= perc_included:
+                for fld in ['first', 'last', 'birth_date']:
+                    if (fld == 'first') and (random.random() <= perc_first_intial):
+                        d[fld] = row[fld][0]
+                    elif (fld == 'last') and (random.random() <= perc_drop_last):
+                        d[fld] = ""
+                    elif (fld == 'birth_date') and (random.random() <= perc_drop_birth):
+                        d[fld] = ""
+                    else:
+                        d[fld] = row[fld]
+                # Simulate credit score as joint distribution with AGI with a normal error term
+                cscore = 300 + 225 * (math.log(float(row['agi']) / 20000.0) + 0.5 * np.random.normal())
+                # Clamp score
+                cscore = np.clip(cscore, 300, 850)
+                d['credit_score'] = int(cscore)
+                out.append(d)
+    return out
+
+def write_file(records, path, separator="|"):
+    fieldnames = records[0].keys()
+    with open(path, "w") as outf:
+        writer = csv.DictWriter(
+            outf,
+            lineterminator='\n',
+            delimiter=separator,
+            fieldnames=fieldnames
+        )
+        writer.writeheader()
+        writer.writerows(records)
+    return path
+
+
+def main(tax_path, cscore_path):
+    tax = simulate_tax(N)
+    people = write_file(tax, tax_path)
+    cscores = simulate_credit_scores(people)
+    write_file(cscores, cscore_path)
+
+
+if __name__ == '__main__':
+    outdir = config.get_option("RAW")
+    Path(outdir).mkdir(parents=True, exist_ok=True)
+    tax_path = os.path.join(outdir, 'tax.txt')
+    cscore_path = os.path.join(outdir, 'credit_scores.txt')
+    main(tax_path, cscore_path)
diff --git a/sirad-id-validation.ipynb b/sirad-id-validation.ipynb
new file mode 100644
index 0000000..320ff30
--- /dev/null
+++ b/sirad-id-validation.ipynb
@@ -0,0 +1,436 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<script>\n",
+       "code_show=true; \n",
+       "function code_toggle() {\n",
+       " if (code_show){\n",
+       " $('div.input').hide();\n",
+       " } else {\n",
+       " $('div.input').show();\n",
+       " }\n",
+       " code_show = !code_show\n",
+       "} \n",
+       "$( document ).ready(code_toggle);\n",
+       "</script>\n",
+       "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Show/hide code blocks.\"></form>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 140,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#From: https://stackoverflow.com/a/28073228/758157\n",
+    "from IPython.display import HTML\n",
+    "\n",
+    "HTML('''<script>\n",
+    "code_show=true; \n",
+    "function code_toggle() {\n",
+    " if (code_show){\n",
+    " $('div.input').hide();\n",
+    " } else {\n",
+    " $('div.input').show();\n",
+    " }\n",
+    " code_show = !code_show\n",
+    "} \n",
+    "$( document ).ready(code_toggle);\n",
+    "</script>\n",
+    "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Show/hide code blocks.\"></form>''')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "matplotlib.style.use('ggplot')\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {
+    "collapsed": true,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "from src.db import pii_eng"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Summary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "total_pii_records = pii_eng.execute('SELECT COUNT(*) records FROM sirad_id').fetchone()[0]\n",
+    "records_with_valid_sirad_id = pii_eng.execute('SELECT COUNT(*) FROM sirad_id WHERE sirad_id <> 0').fetchone()[0]\n",
+    "unique_sirad_ids = pii_eng.execute('SELECT COUNT(DISTINCT sirad_id) FROM sirad_id WHERE sirad_id <> 0').fetchone()[0]\n",
+    "unique_sirad_ids_with_valid_ssn = pii_eng.execute('SELECT COUNT(*) FROM (SELECT MIN(valid_ssn) valid_ssn FROM sirad_id GROUP BY sirad_id) WHERE valid_ssn = 0').fetchone()[0]\n",
+    "q = \"\"\"\n",
+    "SELECT SUM(records)\n",
+    "FROM (\n",
+    "    SELECT sirad_id, \n",
+    "           COUNT(*) AS records,\n",
+    "           CASE WHEN MIN(valid_ssn) = 0 THEN 1 ELSE 0 END valid_ssn\n",
+    "    FROM sirad_id\n",
+    "    GROUP BY sirad_id\n",
+    ")\n",
+    "WHERE VALID_SSN = 1\n",
+    "GROUP BY VALID_SSN\n",
+    "\"\"\"\n",
+    "recs_with_valid_ssn = pii_eng.execute(q).fetchone()[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {
+    "collapsed": false,
+    "deletable": true,
+    "editable": true,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "PII records: 1580\n",
+      "Records with valid SIRAD_ID: 1574\n",
+      "Unique SIRAD_IDs: 1383\n",
+      "Unique SIRAD_IDs with a valid SSN associated: 642\n",
+      "Total records with valid SSN attached: 642\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "msg = \"\"\"\n",
+    "PII records: {total_pii}\n",
+    "Records with valid SIRAD_ID: {valid_sirad}\n",
+    "Unique SIRAD_IDs: {uniq_sirad}\n",
+    "Unique SIRAD_IDs with a valid SSN associated: {valid_ssn}\n",
+    "Total records with valid SSN attached: {total_valid_ssn}\n",
+    "\"\"\".format(\n",
+    "    total_pii=total_pii_records, \n",
+    "    valid_sirad=records_with_valid_sirad_id,\n",
+    "    uniq_sirad=unique_sirad_ids,\n",
+    "    valid_ssn=unique_sirad_ids_with_valid_ssn,\n",
+    "    total_valid_ssn=recs_with_valid_ssn\n",
+    ")\n",
+    "print(msg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dataset breakdown"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "q = \"\"\"\n",
+    "SELECT dsn AS dataset, \n",
+    "       count(*) AS records,\n",
+    "       SUM(CASE WHEN SIRAD_ID <> 0 THEN 1 ELSE 0 END) sirad_ids,\n",
+    "       SUM(CASE WHEN valid_ssn = 0 THEN 1 ELSE 0 END) valid_ssn,\n",
+    "       SUM(CASE WHEN valid_ssn = 1 AND ssn IS NOT NULL THEN 1 ELSE 0 END) invalid_ssn,\n",
+    "       SUM(CASE WHEN valid_ssn = 2 THEN 1 ELSE 0 END) questionable_ssn,\n",
+    "       SUM(CASE WHEN ssn IS null THEN 1 ELSE 0 END) missing_ssn,\n",
+    "       SUM(CASE WHEN first_name IS NOT null THEN 1 ELSE 0 END) first_name,\n",
+    "       SUM(CASE WHEN last_name IS NOT null THEN 1 ELSE 0 END) last_name,\n",
+    "       SUM(CASE WHEN dob is NOT null THEN 1 ELSE 0 END) dob\n",
+    "from sirad_id\n",
+    "group by dsn\n",
+    "\"\"\"\n",
+    "dataset_breakdown = pd.read_sql(q, con=pii_eng, index_col='dataset')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Vil9//ZXo6GguXLhAUFAQffr0YdWqVWRkZDg6sohIiWIxDMNwdIiS4mTnJo6OICJyT9X4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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5405276f60>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dataset_breakdown['records'].plot(kind=\"barh\", title=\"SIRAD IDs by dataset\");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f54054c6828>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dataset_breakdown_pct = dataset_breakdown.apply(lambda x: x / x['records'], axis=1)\n",
+    "dataset_breakdown_pct.plot(\n",
+    "    kind=\"barh\",\n",
+    "    subplots=True,\n",
+    "    layout=(1,9),\n",
+    "    sharey=True,\n",
+    "    legend=False,\n",
+    "    figsize=(20, 10),\n",
+    "    title=\"Proportion of SIRAD ID by dataset\"\n",
+    ");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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IchhuREQkOQw3IiKSHIYbERFJDsONiIgkh+FGRESSw3AjIiLJYbgREZHkaBVu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\nDsONiIgk5/8ABXrxtDQ0rw8AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f540542dc88>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "q = \"\"\"\n",
+    "WITH t AS\n",
+    "(\n",
+    "SELECt dsn, COUNT(*) as records\n",
+    "FROM sirad_id\n",
+    "GROUP BY dsn\n",
+    "), SID as (\n",
+    "SELECT dsn, COUNT(*) AS records_with_sid\n",
+    "FROM sirad_id\n",
+    "WHERE sirad_id <> 0\n",
+    "GROUP BY dsn\n",
+    ")\n",
+    "SELECT dsn as dataset, (CAST(records_with_sid AS FLOAT) / records) * 100 as perc\n",
+    "FROM t\n",
+    "JOIN sid USING(dsn) \n",
+    "\"\"\"\n",
+    "dsn_perc = pd.read_sql(q, con=pii_eng, index_col='dataset')\n",
+    "dsn_perc.plot(kind=\"barh\", title=\"Percentage of records with valid SIRAD IDs by dataset\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Birth year histogram"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {
+    "collapsed": false,
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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UqpjIJ2Uv9B8NhPuthESGgMDC1h7Tbj2sJXxKC1JNSKRsjWTtD+Q/11qD2rsy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\nwjopKxU+JwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f54054b3fd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "q = \"\"\"\n",
+    "SELECT dsn AS dataset, CAST(STRFTIME('%Y', dob) AS INT) dob, COUNT(*) AS records\n",
+    "FROM sirad_id\n",
+    "GROUP BY DSN, STRFTIME('%Y', dob) ;\n",
+    "\"\"\"\n",
+    "dob_df = pd.read_sql(q, con=pii_eng)\n",
+    "pivoted_dob = dob_df.pivot(index='dob', columns='dataset', values='records')\n",
+    "pivoted_dob = pivoted_dob[pd.Series(index=pivoted_dob.index, data=np.isfinite(pivoted_dob.index))]\n",
+    "pivoted_dob.fillna(0, inplace=True)\n",
+    "pivoted_dob[1:].plot(kind='area', xlim=[1940, 2040]);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Missing data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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cfH75+fkl3r/i069Zsya2bt2KLVu2oHnz5ti2bRvefvvtUrugbG1t0ahRI8TFxSEmJgYu\nLi5o06YNLly4AKVSidOnT0uh9Dy0Xc7inl5GbTVu3BjvvfceVq1ahejoaNSqVUvqdinN0wcOfXx8\nYGZmhh49emiMt2PHDqSnp8PFxQXOzs5wdnbG1atXERoaCqVSWWFdFy9eRKNGjZ5pmYpLSEiASqV6\npmkJFdLdu3dHYWEhdu3ahYSEBPTu3Vvj+eItWbVajTt37qB27dqoW7cuDAwMcOXKlTLHT09Ph52d\nHd555x2sW7cOEyZMKPNsg3r16pXoI7t+/Trq16//nEtYsZycHOTn56N9+/aYPXs29u7di7S0NJw4\ncQJKpRKZmZlwcnLCBx98gJ07d6J58+Yl+vK1YWNjo/H+pKSk4N69e2WOb29vj7t370qP79+/r9Ga\nOnfuHIYMGQIHBwfIZLIK+97OnTuHPn36oHHjxpDL5bhx40alj6I/D0dHR1y+fFljWHp6unQwr169\neiX6x7dv345r167BwcEBKSkp0gE04Mn2IZPJSrSWi7z22msa7x8AjW0sPz8f2dnZaNmyJaZOnYo9\ne/bAzMwMBw8eLHV6RV0ep0+fRrt27WBhYQGFQoHw8HAYGBjgjTfe0P7NKEO9evVw48YNjZ3J9evX\nUb16ddjY2MDExAR5eXkaO+vKfNu8cuUKFi5cWKIf2cLCAm3atEF6errW0/L19UVycrLGge8LFy7g\n8uXL2LBhA8LCwqR/ISEhyMjIqLAhkZKSgpCQEPTr10/rOsoSFBSE5s2bl/iGrg2hQtrY2Bh9+vTB\n8uXL4enpCQsLC43nf//9d8TGxiI/Px9bt25FRkYGunXrhurVq6N///74+uuvkZiYCKVSiZ07d8Lb\n2xtpaWk4e/YsunXrhpiYGKhUKmRlZeHPP/8s88DEoEGD8MsvvyAhIQEFBQXYs2cPzp8/X+ZBwhdp\nypQp+PTTT5GRkQG1Wo3Lly+joKAAjo6O2LBhA0aOHCl9EO7cuYO0tLRn2nl07NgRISEhuHPnDrKy\nsvDll19qfH1/WpcuXbBz504kJycjOzsbK1eulLoGgCdfmc+dO4eCggKcO3cOISEhkMvl0hkPxsbG\nuH//PtLT05Gbm4u6desiPj4eeXl5uH79Or7++mvY2NhI41e1oUOH4s8//8TGjRuRm5uLlJQUTJw4\nEV999RWAJ9tAfHw8IiIiUFBQgF9//RVffPEFTExM4OHhgRo1amDlypXIzc1FamoqgoKC0LVrV1hb\nW5c6Pw8PDyQmJiI8PBz5+fk4fPgw4uLipOf9/f0xZcoU6XS1mzdvIjMzs8x16+rqisOHDyMrK0v6\n4Lu4uCA4OBidOnUqtaVb9E3l1q1bWvX59unTBxkZGfj++++Rn5+PxMREBAcHY8CAAZDL5WjQoAEK\nCwsRGRkp/V98x1c0v8TERI0dWhE7Ozvs27cPvr6++Ouvv6BSqZCXl4eoqCjs3bsXffr0qbDGIpaW\nlvDz80NgYKB0bGL79u1wdnZGp06d4OjoKP1r2rQpunfvjh07dpQ6rYKCAhw/fhzvv/8+2rRpg+HD\nh2tdx9OSkpIwb948nDlzRusDrk8Tpk+6yKBBg7Bt27ZSA3HQoEHYsGEDTp48CQsLC6xcuVLq+50/\nfz6WLFmCoUOHIj8/H40bN8Z3330He3t72NvbY+bMmfDz80NqairMzMzQoUMHzJ8/v9QaxowZg8zM\nTEybNg33799HgwYN8N1332n0gVUVf39/LFy4EN26dUNhYSHq1KkDf39/NGnSBA0aNEBaWhqGDRuG\nzMxM2NjYoE+fPhg2bFil5zNnzhzMmjULvXr1gkKhwJw5c3D+/PkyuxCmT5+Ohw8fwsfHB6amppg0\naZJGN8tnn32GBQsWoF27dmjVqhWWLl0KU1NT+Pn5wdTUFN27d8f27dvRpUsXBAQEYObMmZgzZw7a\nt2+P119/Hf7+/oiMjMTatWthbm6OUaNGPfN7qA1HR0esXr0aq1atQkBAAKysrNCjRw/pDI0mTZog\nKCgIy5Ytw7x58+Do6IhVq1ZJLeX169dj6dKlcHd3h4mJCbp06VLmgWgAaNGiBRYuXIivvvoKn332\nGd566y2MHj1aOgNi5syZWLRoEby9vZGbm4vXXnsNEyZMQNeuXUudXrt27XD79m14eHhIgdy2bVv8\n+OOPUhff09544w24uLhg9OjRGDVqFDw8PMp9j2rXro1169bh66+/xrfffgsrKyv06dNHuoLxjTfe\nwPvvv4/PP/8cn376Kfr27YuhQ4dKXUa2trbo1asX5s6dCy8vL3z99dca07exscH27duxdu1ajB49\nGg8ePIBcLkejRo0wa9YsDB48uNz6nvb2229jz549mDNnDjZu3Ih9+/aVeerfkCFDMHbsWKk7qej0\nUOBJV46joyMGDBiAMWPGoFq1ysXksGHDIJPJoFarYWlpic6dOyM0NLTMb1kVkYn2yyyHDx/GkiVL\ncOjQIY3AKH5lD70YeXl5Umu4sLAQrVu3xvLly6UDZESkf0J1dyQlJWHZsmWYOHHiCzmiSmX79ttv\n0bt3byQnJ6OgoADr1q2DoaEhOnTooO/SiKgYYbo7PvvsM+zfvx+DBw8u9dp7erH+85//IC0tDUOH\nDkVOTg4aNWqEb775ptzT8IhI94Tr7iAiov9hnwIRkcAY0kREAmNIExEJrEoOHKakpFTFZIWgUChe\n6eV7lXHdvdxe9fWnUChKHc6WNBGRwBjSREQCY0gTEQmMIU1EJDCGNBGRwCo8uyMhIQEBAQHSr3jU\nq1evzLtsERHRi6XVKXjNmjXDjBkzqroWIiJ6Crs7iIgEVuENlhISErB+/XrUqlUL2dnZGDJkCFq2\nbKmr+ohIUEl9XPRdQpVyiIjVdwkAtOjuqF27NoYMGYJOnTohNTUVCxcuRGBgYLm/VvCqXxX0Ki/f\nq4zrjipD19vKM19xaG1tDVdXV8hkMtSqVQuWlpaV+oFIIiJ6dhWG9LFjx7Bnzx4AwMOHD5GRkVHm\nj20SEdGLVWF3h4uLC1atWoXY2FgolUq8//77lf5hRiIiejYVpq2pqSl8fX11UQsRET2Fp+AREQmM\nIU1EJDCGNBGRwBjSREQCY0gTEQmMIU1EJDCGNBGRwBjSREQCY0gTEQmMIU1EJDCGNBGRwBjSREQC\nY0gTEQmMIU1EJDCGNBGRwBjSREQCY0gTEQmMIU1EJDCGNBGRwBjSREQCY0gTEQmMIU1EJDCGNBGR\nwBjSREQCY0gTEQmMIU1EJDCGNBGRwBjSREQCY0gTEQmMIU1EJDCGNBGRwBjSREQC0yqk8/PzMWXK\nFBw9erSKyyEiouK0CumQkBBUr169qmshIqKnVBjSd+7cQXJyMpydnXVRDxERFVOtohE2b96McePG\nVaqrQ6FQPE9NwnvVl+9VxnX34iTpu4AqJsq2Um5IR0dHo3HjxrC3t6/URFNSUp6rKJEpFIpXevle\nZVx3VBm63lbK2imUG9JxcXFIS0tDXFwc7t+/D0NDQ1hbW6Nly5ZVUiQREWkqN6Q/+eQT6e+ff/4Z\n9vb2DGgiIh3iedJERAKr8MBhkXfeeacq6yAiolKwJU1EJDCGNBGRwBjSREQCY0gTEQmMIU1EJDCG\nNBGRwBjSREQCY0gTEQmMIU1EJDCGNBGRwBjSREQCY0gTEQmMIU1EJDCGNBGRwBjSREQCY0gTEQmM\nIU1EJDCGNBGRwBjSREQCY0gTEQmMIU1EJDCGNBGRwBjSREQCY0gTEQmMIU1EJDCGNBGRwBjSREQC\nY0gTEQmMIU1EJDCGNBGRwBjSREQCY0gTEQmsWkUj5OXlYc2aNcjIyEBBQQEGDRqEtm3b6qI2IqJ/\nvQpD+syZM2jUqBH69++Pe/fuwd/fnyFNRKQjFYa0q6ur9Pf9+/dhbW1dpQUREdH/VBjSRfz8/HD/\n/n34+vpWZT1ERFSMTK1Wq7UdOTExEUFBQVixYgVkMllV1kVEgkvq46LvEqqUQ0SsvksAoEVL+ubN\nm6hRowZsbW1Rv359FBYWIjMzEzVr1izzNSkpKS+0SJEoFIpXevleZVx3VBm63lYUCkWpwys8Be/S\npUvYu3cvAODhw4fIzc2FhYXFi62OiIhKVWFLukePHli7di0+++wz5OfnY9y4cZDLeXo1EZEuVBjS\nRkZG+Pjjj3VRCxERPYVNYiIigTGkiYgExpAmIhIYQ5qISGAMaSIigTGkiYgExpAmIhIYQ5qISGAM\naSIigTGkiYgExpAmIhIYQ5qISGAMaSIigTGkiYgExpAmIhIYQ5qISGAMaSIigTGkiYgExpAmIhIY\nQ5qISGAMaSIigTGkiYgExpAmIhIYQ5qISGAMaSIigTGkiYgExpAmIhIYQ5qISGAMaSIigTGkiYgE\nxpAmIhIYQ5qISGAMaSIigVXTZqQff/wRly9fhkqlgo+PDzp06FDVdREREbQI6YsXLyIpKQlLlixB\nVlYWZs+ezZAmItKRCkO6WbNmcHJyAgCYm5sjLy8PKpUKcjl7SoiIqlqFIS2Xy2FiYgIAOHLkCJyd\nnSsMaIVC8WKqE9SrvnyvMq67FydJ3wVUMVG2Fa36pAHg9OnTOHLkCPz8/CocNyUl5bmKEplCoXil\nl+9VxnVHlaHrbaWsnYJWfRbnzp1DaGgo5s2bBzMzsxdaGBERla3CkM7JycGPP/4IX19fVK9eXRc1\nERHR/6uwu+PEiRPIysrCV199JQ2bPHkybG1tq7QwIiLSIqS9vLzg5eWli1qIiOgpPI+OiEhgDGki\nIoExpImIBMaQJiISGEOaiEhgDGkiIoExpImIBMaQJiISGEOaiEhgDGkiIoExpImIBMaQJiISGEOa\niEhgDGkiIoExpImIBMaQJiISGEOaiEhgDGkiIoExpImIBMaQJiISGEOaiEhgDGkiIoExpImIBMaQ\nJiISGEOaiEhgDGkiIoExpImIBMaQJiISGEOaiEhgDGkiIoExpImIBMaQJiISmFYhffv2bUyZMgWR\nkZFVXQ8RERVTYUjn5uZi48aNePPNN3VRDxERFVNhSBsaGmLu3LmwsrLSRT1ERFRMtYpGMDAwgIGB\ngS5qISKip1QY0s9CoVBUxWSF8aovn64k9XHR7fx0OjfAISJWx3PULV2/n7omyue8SkI6JSWlKiYr\nBIVC8UovH7043E5ebrpef2XtFHgKHhGRwCpsSd+8eRObN2/GvXv3YGBggD/++AMzZ85E9erVdVEf\nEdG/WoUh3bBhQ3z++ec6KIWIiJ7G7g4iIoExpImIBMaQJiISGEOaiEhgDGkiIoExpImIBMaQJiIS\nGEOaiEhgDGkiIoExpImIBMaQJiISGEOaiEhgDGkiIoExpImIBMaQJiISGEOaiEhgDGkiIoExpImI\nBMaQJiISGEOaiEhgDGkiIoExpImIBMaQJiISGEOaiEhgDGkiIoExpImIBMaQJiISGEOaiEhgDGki\nIoExpImIBMaQJiISGEOaiEhgDGkiIoFV02akTZs24fr165DJZBgzZgycnJyqui4iIoIWLelLly7h\n77//xpIlSzBx4kRs3LhRF3URERG0COn4+Hi0a9cOAFC3bl08evQIOTk5VV4YERFp0d3x8OFDNGzY\nUHpco0YNPHz4EGZmZmW+RqFQvJjqBPWqL5/ORMTquwJ6Hlx/OlHpA4dqtboq6iAiolJUGNJWVlZ4\n+PCh9PjBgwewsrKq0qKIiOiJCkO6VatW+OOPPwAAN2/ehJWVFUxNTau8MCIiAmRqLfovfvrpJ1y+\nfBkymQzjxo1D/fr1dVAaERFpFdJERKQfvOKQiEhgDGkiIoExpImIBMaQ1lJBQQHS0tL0XQbRv052\ndjays7P1XYbeaHWDpX+748ePIzQ0FACwcuVKBAcHo1GjRvDw8NBzZaSNwsJCnDx5Eunp6ejXrx9u\n374NhUKBatW4+YssKioKP//8M8zMzKBWq5GXl4dhw4ahc+fO+i5Np9iS1sKBAwewfPly1KhRAwAw\ncuRIHDx4UM9Vkba+/fZbJCYmSuf7X7p0CUFBQXquiiqyb98+rFixAitXrkRAQACWLl2K3bt367ss\nnWNIa0Eul2u0ugwNDfVYDVXW/fv3MXLkSBgbGwMAevXqhQcPHui5KqqItbW1xj2CLCws8Nprr+mx\nIv3g9z10MauRAAAH30lEQVQtNG3aFIGBgUhPT0dYWBjOnDmDFi1a6Lss0pJSqcSjR4+kx8nJyVAq\nlXqsiMqzZcsWyGQyGBkZYc6cOWjSpAlkMhmuXbuGOnXq6Ls8nePFLFq6cuUKrl69CkNDQzg5OaFx\n48b6Lom0dPnyZWzatAl3796FtbU1ZDIZJkyYgKZNm+q7NCrF0aNHy32+S5cuOqlDFAxpLaSlpSE2\nNrbEfbQHDx6sp4roWWRkZMDQ0LDc2+ySOJRKJX7//XfcunULcrkcjRo1gqurK+Tyf1cvLbs7tLB0\n6VJ07NgRNWvW1Hcp9AwOHjyIw4cPIycnR+NWuzx4KLa1a9fC3NwczZs3h1KpxKVLl3Dx4kVMnDhR\n36XpFENaC3Z2dhg6dKi+y6BndODAAcyePZs72ZdMeno6pkyZIj12c3PDwoUL9ViRfjCktdC1a1cs\nW7YMDRo0gIGBgTSc3R0vh0aNGsHY2BgmJib6LoUqQalUIj09HdbW1gCenKVTWFio56p0jyGthR07\ndrC74yXm6OiISZMmoWbNmjAwMIBarYZMJmN3h+CGDRuGxYsXQyaTSetswoQJ+i5L5xjSWrC3t8e7\n776r7zLoGf36668ICAiApaWlvkshLXz00UeQyWQAnvxcX3Z2NmQyGczNzREUFITAwEA9V6hbDGkt\n1KpVC6tXr4aTk5NGd0fPnj31WBVpq3HjxrCwsGB3x0ti5cqVAIDQ0FDUr18fzZs3h1qtxsWLF3H3\n7l09V6d7DGktWFhYwMLCQuOCiKI9PYkvNTUVkyZNQq1atTRO31q6dKkeq6KyFO1Mr169iuHDh0vD\nO3fujMWLF+urLL1hSGthyJAhyM3Nle7EVVBQgA0bNui5KtLW5MmTSwx7/PixHiqhyjA0NMTmzZvR\nuHFjyOVy3LhxAyqVSt9l6RwvZtHCzp07cfToUWRlZcHW1hb//PMPunfvjpEjR+q7NNLCo0ePcOzY\nMWknq1QqER0djbVr1+q5MirP48ePcezYMSQnJ0OtVkOhUMDDw+NfdzESW9JaOHv2LIKCgrBw4UIs\nWLAAN2/elO6oRuILCAhAkyZNcPz4cXh5eeHSpUsYO3asvsuiCpiamqJHjx76LkPv/l3XVz6jolOA\nCgsLkZ+fj4YNG+LKlSv6Lou0pFar8c4778DKygre3t6YO3cuoqKi9F0WkVbYktZCx44dERERgc6d\nO2PWrFmoWbOmdNtLEp9SqURiYiKMjY1x4cIF2NvbIzU1Vd9lEWmFfdKV9M8//yAzMxMNGjTgGR4v\nib/++gsZGRmwtLTExo0bkZ2djZ49e8LLy0vfpRFViCGthXPnzuHQoUMlbtCzYMECPVZFlZGTkyPd\nxbDo6jVbW1s9V0VUMXZ3aOGHH37A6NGjYWNjo+9S6BmsW7cOZ8+ele4BUYTnSdPLgCGtBXt7e7Ru\n3VrfZdAzSkxMxLp169g9RS8lhrQWFAoFAgIC0LRpU14W/hJydHREVlaW9EPCRC8ThrQWzMzMYGZm\npnFZOL08UlNTMWXKFF4WTi8lHjh8TitWrMCsWbP0XQaV4969e6UOt7Ozw/Xr1/H666/ruCIi7fFi\nluf09O8eknjs7OxK/QcAW7du1XN1ROVjSBMRCYwhTUQkMIY0EZHAGNJaKO3HL4tue2lubq7rcojo\nX4QhXY7CwkLk5uZi0aJFyM/PR15eHvLy8pCTk4PPP/8cADBz5kz9FkkVOnPmTIlhv//+OwDAzc1N\n1+UQVQrPky7H2bNnERERgRs3bmD69OnSfTvkcjmaNWum5+qoIjdu3MCNGzewf/9+/PPPP9LwwsJC\n7NmzB507d+ZNlkh4DOlyuLi4wMXFBb/99hvc3d31XQ5VkqWlJUxMTKBUKpGZmSkNl8lkmDRpkh4r\nI9IeL2bRQnR0NAoLC+Hu7o7ly5cjOzsbXbt25a9GvCQyMzOhVqtRs2ZNpKSkIDk5Ga1bt4aRkZG+\nSyOqEPuktXDw4EF4eHjg5MmTqFevHpYuXcqfz3qJBAcH4+rVq0hLS0NAQACSk5OxZs0afZdFpBWG\ntBbkcjkMDAzwxx9/4K233gLw5BfD6eWQkZGB9u3b48SJE+jVqxcGDhwonZ1DJDqGtBYaNmyIKVOm\nQKlUon79+ti/fz/vLf0SycvLw5UrV3Ds2DG0b98ejx494s2y6KXBPmkt3Lp1C3Z2dqhevTqAJzfs\nsbKyQrVqPO76Mjh//jwOHDiAjh07wt3dHSEhIbCzs+PBYHopMKS1sHDhQvj5+WncS5peXkqlEuvX\nr8fEiRP1XQpRhdgU1IKxsTGmTp0KR0dHjdbz9OnT9VgVaevIkSPYsWMHsrKyYGhoCJVKhTZt2ui7\nLCKtMKS10K9fP32XQM/h119/RWBgIJYuXYoFCxYgNjYWaWlp+i6LSCs8cFiO06dPAwBu376NpKSk\nEv/o5WBoaAgjIyMolUqoVCq4uLhI65ZIdGxJl2PTpk1o164dbt68CXt7e32XQ8/IyckJkZGRaNmy\nJRYtWgQbGxvk5+fruywirTCky1GzZk3MmTMHf//9NxQKRYnnBw8erIeqSFtbtmyBTCaDTCaTfkJL\nJpPh6tWraN++vZ6rI9IOz+4oR2FhIR48eIAffvgBo0aNKvF80U8wkZiOHj1a7vNdunTRSR1Ez4Mh\nTUQkMB44JCISGEOaiEhgDGkiIoExpImIBMaQJiIS2P8BeGVsRk6KJk0AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5405417a20>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "q = \"\"\"\n",
+    "select \n",
+    "  sum(case when first_name is null then 1 else 0 end) first_name,\n",
+    "  sum(case when last_name is null then 1 else 0 end) last_name,\n",
+    "  sum(case when dob is null then 1 else 0 end) dob\n",
+    "from sirad_id\n",
+    "where sirad_id=0\n",
+    "\"\"\"\n",
+    "missing_df = pd.read_sql(q, con=pii_eng)\n",
+    "missing_series = missing_df.loc[0]\n",
+    "missing_series.plot(kind=\"bar\", title=\"Types of missing data in records without SIRAD ID\");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f54054ef518>"
+      ]
+     },
+     "execution_count": 169,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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6iEg1pKam0qlTJx566CG2b99OSkpKmdvnrmG7a9cukpKSOHDgABMnTqRPnz4sWLDAeu3Y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\nRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5405c7dbe0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "q = \"\"\"\n",
+    "SELECT \n",
+    "  CASE WHEN valid_ssn IS null THEN \"missing\"\n",
+    "  WHEN valid_ssn IS 1 THEN \"invalid\"\n",
+    "  WHEN valid_ssn IS 2 THEN \"questionable\"\n",
+    "  WHEN valid_ssn IS 0 THEN \"valid\"\n",
+    "  END as ssn_flag, count(*) as ssns\n",
+    "FROM sirad_id\n",
+    "GROUP BY valid_ssn\n",
+    "\"\"\"\n",
+    "ssn_v = pd.read_sql(q, con=pii_eng, index_col=\"ssn_flag\")\n",
+    "ssn_v.plot(kind=\"bar\", title=\"All records by SSN validity\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/sirad_config.py b/sirad_config.py
new file mode 100644
index 0000000..6637867
--- /dev/null
+++ b/sirad_config.py
@@ -0,0 +1,12 @@
+"""
+Sample configuration file for the sirad command line tool and API. 
+
+See https://github.com/riipl-org/sirad#Configuration
+"""
+LAYOUTS   = "layouts"
+RAW       = "raw"
+PROCESSED = "build/processed"
+STAGED    = "build/staged"
+RESEARCH  = "build/research_v{}.db"
+PII_SALT  = "pii-secret"
+DATA_SALT = "data-secret"