Merge branch 'main' of github.com:worldbank/GOSTurban into std-doc-st…

…rings

.gitignore

@@ -137,3 +137,6 @@ docs/notebooks/
 # ignore the generated sphinx conf.py file
 # we generate it during the doc build using jupyter-book from _conf.py
 docs/conf.py
+
+# ignore implementations output
+notebooks/Implementations/output/

.pre-commit-config.yaml

@@ -39,3 +39,7 @@ repos:
         args: [--fix]
       - id: ruff-format
         types_or: [python, pyi, jupyter]
+  - repo: https://github.com/kynan/nbstripout
+    rev: 0.7.1
+    hooks:
+      - id: nbstripout

CITATION.cff

@@ -0,0 +1,14 @@
+cff-version: 1.2.0
+title: "GOSTurban"
+abstract: "Multiple functions, tools, and tutorials for calculating urbanization based on gridded population data"
+authors:
+  - affiliation: World Bank
+    family-names: Stewart
+    given-names: Benjamin P.
+  - affiliation: World Bank
+    family-names: Chamorro
+    given-names: Andres
+keywords:
+  - Urban Analysis
+repository-code: https://github.com/worldbank/GOSTurban
+type: software

README.md

@@ -18,23 +18,23 @@ pip install GOSTurban
 
 1. Clone or download this repository to your local machine. Then, navigate to the root directory of the repository:
 
-    ```shell
-    git clone https://github.com/worldbank/GOSTurban.git
-    cd GOSTurban
-    ```
+   ```shell
+   git clone https://github.com/worldbank/GOSTurban.git
+   cd GOSTurban
+   ```
 
 2. Create a virtual environment (optional but recommended):
 
-    ```shell
-    python3 -m venv venv
-    source venv/bin/activate  # On Windows, use `venv\Scripts\activate`
-    ```
+   ```shell
+   python3 -m venv venv
+   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`
+   ```
 
 3. Install the package with dependencies:
 
-    ```shell
-    pip install .
-    ```
+   ```shell
+   pip install .
+   ```
 
 ### Developer Installation
 

notebooks/Implementations/URB_LKA_CCDR_Support/LKA_Urban_Comp.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "9fd5e3dd",
+   "id": "0",
    "metadata": {},
    "source": [
     "# Urbanization comparison\n",
@@ -20,7 +20,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "9c6da02f",
+   "id": "1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -30,13 +30,14 @@
     "import pandas as pd\n",
     "import geopandas as gpd\n",
     "\n",
-    "import GOSTurban.UrbanRaster as urban"
+    "import GOSTurban.UrbanRaster as urban\n",
+    "import GOSTurban"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "df1bf2ed",
+   "id": "2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,7 +52,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b135cd57",
+   "id": "3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -84,7 +85,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "97add9be",
+   "id": "4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -115,7 +116,17 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "50115a0d",
+   "id": "5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir(GOSTurban)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6",
    "metadata": {},
    "outputs": [],
    "source": []

notebooks/Implementations/URB_SCAUR_UKR_B_I_urbanizationReview/URB_SCAUR_UKR_B_I_urbanizationReview.ipynb

@@ -444,8 +444,14 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "worldbank",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "name": "python",
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,

notebooks/Implementations/URB_SEAU1_NovelUrbanization/Create_Mosaick_Datasets.ipynb

@@ -3,7 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "3d5639a9",
+   "id": "0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "79b8cf0a",
+   "id": "1",
    "metadata": {},
    "source": [
     "# Mosaic the DoU layers"
@@ -44,7 +44,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "b7e89905",
+   "id": "2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -84,7 +84,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e1c94276",
+   "id": "3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -103,7 +103,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "5ad5ea55",
+   "id": "4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -129,7 +129,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "25e95213",
+   "id": "5",
    "metadata": {},
    "source": [
     "# Mosaick data from PP"
@@ -138,7 +138,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "cd0122d2",
+   "id": "6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -177,7 +177,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "135aeb41",
+   "id": "7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -196,7 +196,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4b7350ea",
+   "id": "8",
    "metadata": {},
    "outputs": [],
    "source": []