1.0 attempt 2

.devcontainer/devcontainer.json

@@ -0,0 +1,23 @@
+{
+	"name": "Python 3",
+	"image": "mcr.microsoft.com/devcontainers/python:3.7",
+	"features": {
+		"ghcr.io/devcontainers/features/node:1": {
+			"version": "none"
+		},
+		"ghcr.io/devcontainers/features/docker-in-docker:1": {},
+		"ghcr.io/dhoeric/features/act:1": {}
+	}
+
+	// Use 'forwardPorts' to make a list of ports inside the container available locally.
+	// "forwardPorts": [],
+
+	// Use 'postCreateCommand' to run commands after the container is created.
+	// "postCreateCommand": "pip3 install --user -r requirements.txt",
+
+	// Configure tool-specific properties.
+	// "customizations": {},
+
+	// Uncomment to connect as root instead. More info: https://aka.ms/dev-containers-non-root.
+	// "remoteUser": "root"
+}

.gitattributes

@@ -1,2 +1,4 @@
 # Auto detect text files and perform LF normalization
 * text=auto
+*.xml linguist-vendored
+*.ipynb linguist-prose

.github/workflows/pytest.yml

@@ -0,0 +1,22 @@
+name: run tests
+
+on: [push, pull_request]
+
+jobs:
+  pytest:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - uses: actions/setup-python@v4
+        with:
+          python-version: "3.7.1"
+      - name: Install poetry
+        uses: abatilo/actions-poetry@v2
+        with:
+          poetry-version: 1.1.15
+      - name: Install requirements
+        run: poetry install
+      - name: Install requirements
+        run: poetry run pytest --cov=./ --cov-report=xml
+      - name: Upload coverage reports to Codecov with GitHub Action
+        uses: codecov/codecov-action@v3

.gitignore

@@ -1,5 +1,5 @@
 # Byte-compiled / optimized / DLL files
-__pycache__/
+**/__pycache__/
 *.py[cod]
 
 # C extensions

README.md

@@ -20,12 +20,20 @@ xml = MortXML(3282)
 
 This `MortXML` class is a wrapper around the [underlying XML](https://mort.soa.org/About.aspx). The autocompletions you get on attributes improve the developer experience over using the underlying XML directly.
 
-![](./assets/auto.png)
+![autocompletions](./assets/auto.png)
 
-### Examples
+Also, mortality rate tables are Pandas DataFrames.
+
+![rate table as a dataframe](./assets/rate-table.png)
+
+
+## Accessing mortality rates
+
+For a select and ultimate table we can retrieve rates as follows.
 
 ```py
 from pymort import MortXML
+# Table 3265 is 2015 VBT Smoker Distinct Male Non-Smoker ANB, see https://mort.soa.org/ 
 xml = MortXML(3265)
 # This is the select table as a MultiIndex (age/duration) DataFrame.
 xml.Tables[0].Values
@@ -37,7 +45,7 @@ xml.Tables[1].Values
 
 ## Usage with tensor libraries
 
-Once we get the data from Pandas to NumPy it should be easy to get into JAX or any other tensor library.
+We can get the data from Pandas to NumPy.
 
 ```py
 select = MortXML(3265).Tables[0].Values.unstack().values
@@ -49,65 +57,3 @@ ultimate.shape # (103,) is age 18 to 120
 # Be careful when indexing into these, ultimate[0] is the rate at age 18!
 ```
 
-## Relational tables
-
-Pymort provides **3 normalized tables** related by [primary/foreign keys](https://www.ibm.com/docs/en/ida/9.1.1?topic=entities-primary-foreign-keys), a design taken from relational databases.
-
-Currently tables from 2015 VBT and 2017 CSO are supported. If you want support for tables from other studies, open an issue on GitHub.
-
-XML files with multiple "select" (2-dimensional) or multiple "ultimate" (1-dimensional) tables like table [2636](https://mort.soa.org/ViewTable.aspx?&TableIdentity=2636) are not supported so that we can avoid adding another column.
-
-### Metadata
-
-Each table has a unique identifier called an `id`. There is information associated with this table like
-
-- What is the name of the mortality study producing the table? (i.e. 2017 CSO vs. 2015 VBT)
-- Is there a grouping that the table belongs to within the study? (i.e. unloaded preferred_structure gender_distinct ANB vs. loaded smoker_distinct gender_blended ALB)
-- Gender (male vs. female)
-- Risk (smoker vs. nonsmoker)
-
-We call this information about a table the `metadata` and store it as an attribute of our `Relational` object.
-
-```py
-from pymort import Relational
-
-db = Relational()
-
-print(db.metadata)
-```
-
-![](./assets/meta2.png)
-
-### Select
-
-Select tables depend on the issue age, and the years since issuing the contract.
-
-```py
-print(db.select)
-```
-
-![](./assets/sel2.png)
-
-### Ultimate
-
-Ultimate tables depend on the attained age only.
-
-```py
-print(db.ultimate)
-```
-
-![](./assets/ult2.png)
-
-### Groupings
-
-If you want to get the IDs for all tables having the same study and group there is a function for this, although you could do it yourself in Pandas.
-
-```py
-from pymort import getIdGroup
-
-getIdGroup(3265)
-```
-
-This returns a dataclass representing the grouping.
-
-![](./assets/grouping.png)

low-level-api-proposal.ipynb

@@ -6,6 +6,8 @@
    "source": [
     "# Background\n",
     "\n",
+    "**This notebook was authored when I was trying to figure out the structure of the XML so I could write the API**\n",
+    "\n",
     "## XTbML\n",
     "\n",
     "An XML standard for representing mortality tables called XTbML was developed in a joint effort between the Technology Section of the SOA and the standards-setting body ACORD. For more information, read [this](https://www.soa.org/news-and-publications/newsletters/compact/2013/april/com-2013-iss47/tables-database-goes-xtbml/).\n",