readme add

README.md

@@ -1,17 +1,25 @@
-# Introduction
+# Rivercatch
+![Continuous Integration build in GitHub Actions](https://github.com/<st-te>/python-intermediate-catchment/workflows/CI/badge.svg?branch=main)
 
-This is a template software project repository used by the Earth Science focused [Intermediate Research Software Development Skills In Python](https://carpentries-incubator.github.io/python-intermediate-development-earth-sciences) course.
+RiverCatch is a data management system written in Python that manages measurement data collected in river catchment surveys and campaigns.
 
-## Purpose
+## Main Features
+Here are some key features of Inflam:
 
-This repository is intended to be used as a code template which is copied by learners at [Intermediate Research Software Development Skills In Python](https://carpentries-incubator.github.io/python-intermediate-development-earth-sciences) workshops.
-This can be done using the `Use this template` button towards the top right of this repo's GitHub page.
+- Provide basic statistical analyses of data
+- Ability to work on measurement data in Comma-Separated Value (CSV) format
+- Generate plots of measurement data
+- Analytical functions and views can be easily extended based on its Model-View-Controller architecture
 
-This software project is not finished, is currently failing to run and contains some code style issues. It is used as a starting point for the course - issues will be fixed and code will be added in a number of places during the course by learners in their own copies of the repository, as course topics are introduced.
+## Prerequisites
+RiverCatch requires the following Python packages:
 
-## Tests
+- [NumPy](https://www.numpy.org/) - makes use of NumPy's statistical functions
+- [Pandas](https://pandas.pydata.org/) - makes use of Panda's dataframes
+- [GeoPandas](https://geopandas.org/) - makes use of GeoPanda's spatial operations
+- [Matplotlib](https://matplotlib.org/stable/index.html) - uses Matplotlib to generate statistical plots
 
-Several tests have been implemented already, some of which are currently failing.
-These failing tests set out the requirements for the additional code to be implemented during the workshop.
+The following optional packages are required to run RiverCatch's unit tests:
 
-The tests should be run using `pytest`, which will be introduced during the workshop.
+- [pytest](https://docs.pytest.org/en/stable/) - RiverCatch's unit tests are written using pytest
+- [pytest-cov](https://pypi.org/project/pytest-cov/) - Adds test coverage stats to unit testing

examples.ipynb

@@ -0,0 +1,82 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import pandas.testing as pdt\n",
+    "from catchment.models import daily_mean\n",
+    "import datetime\n",
+    "import pytest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_input = pd.DataFrame(\n",
+    "                data=[[1.0, 2.0],\n",
+    "                      [3.0, 4.0],\n",
+    "                      [5.0, 6.0]],\n",
+    "                index=[pd.to_datetime('2000-01-01 01:00'),\n",
+    "                       pd.to_datetime('2000-01-01 02:00'),\n",
+    "                       pd.to_datetime('2000-01-01 03:00')],\n",
+    "                columns=['A', 'B']\n",
+    ")\n",
+    "test_result = pd.DataFrame(\n",
+    "                 data=[[3.0, 4.0]],\n",
+    "                 index=[datetime.date(2000, 1, 1)],\n",
+    "                 columns=['A', 'B']\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_output = pd.DataFrame(\n",
+    "    data=[[3.0, 3.0]],\n",
+    "    index=[datetime.date(2000, 1, 1)],\n",
+    "    columns=['A', 'B']      \n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pdt.assert_frame_equal(daily_mean(test_input), test_result)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "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": 2
+}

mypy_tutorial.py

@@ -0,0 +1,41 @@
+from typing import Union
+
+flag : bool = True
+
+def greet(name : str) -> None:
+    """Say hello to everyone"""
+    print("Hi " + name)
+
+greet("Manchester")
+greet("Steven")
+
+def myAbs(x : float) -> float:
+    """Take the absolute of the floating-point input"""
+    if x < 0:
+        return (-x)
+    else:
+        return (x)
+
+
+myAbs(12)
+
+def greetAll(names : list[str]) -> None:
+    for name in names: 
+        greet(name)
+
+greetAll(["Alice", "John", "Joe"])
+
+#greetAll([12, 12])
+
+some_data : tuple[int, bool, str] = (41, True, "Manchester")
+#some_other_data : tuple[int, bool, str] = (41, 232132, "Manchester")
+
+def myDiv(x : float, y : float) -> Union[float, None]:
+    if y != 0: return x/y
+    else: return None
+
+
+myDict : dict[str, Union[float, str]] = {"temp": 273.0, "units": "Kelvin"}
+
+#reveal_type(len)
+