Feature/plot labels

.gitignore

@@ -18,4 +18,5 @@
 .pytest_cache
 
 # Virtual environments
-
+venv/
+.venv/

LICENSE.md

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) [year] [fullname]
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1,15 +1,23 @@
-# Introduction
+# LcAnalyzer
+![Continuous Integration build in GitHub Actions](https://github.com/<your_github_username>/light-curve-analysis/workflows/CI/badge.svg?branch=main)
+LcAnalyzer is a package written in Python that allows you to inspect light curves.
 
-This is a template software project repository used by the [Intermediate Python for Astronomical Software Development](https://shrra.github.io/python-intermediate-development/index.html).
+## Main features
+Here are some key features of LcAnalyzer:
 
-## Purpose
+- Reading CSV and Pickle files;
+- Giving the list of unique objects present in the data;
+- Selecting observations of a given star in a given bands;
+...
 
-This repository is intended to be used as a code template which is copied by learners at [Intermediate Python for Astronomical Software Development](https://shrra.github.io/python-intermediate-development/index.html) course.
-This can be done using the `Fork` button towards the top right of this repo's GitHub page.
+## Prerequisites
+LcAnalyzer requires the following Python packages:
 
-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.
+- [Pandas](https://pandas.pydata.org/) - makes use of Pandas's data types and statistical functions
+- [Matplotlib](https://matplotlib.org/stable/index.html) - uses Matplotlib to generate statistical plots
 
-## Tests
+The following optional packages are required to run LcAnalyzer's unit tests:
+
+- [pytest](https://docs.pytest.org/en/stable/) - LcAnalyzer's unit tests are written using pytest
+- [pytest-cov](https://pypi.org/project/pytest-cov/) - Adds test coverage stats to unit testing
 
-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.

lcanalyzer/models.py

@@ -14,23 +14,34 @@
 def load_dataset(filename):
     """Load a table from CSV file.
 
-    :param filename: The name of the .csv file to load
+    :param filename: The name of the .csv file to load.
+    :raises Eror: when the file is not CSV/
+                  no file was given as an inut/
+                  the file or directory was not found
     :returns: pd.DataFrame with the data from the file.
     """
     return pd.read_csv(filename)
 
 
-def mean_mag(data,mag_col):
-    """Calculate the mean magnitude of a lightcurve"""
+def mean_mag(data, mag_col):
+    """Calculate the mean magnitude of a lightcurve
+    :param data: pd.DataFrame with observed magnitudes for a single source
+    :param mag_col: a string with the name of the column for calculating the min value.
+    :returns: the mean for the mag_col coming from the data table
+    """
     return data[mag_col].mean()
 
 
-def max_mag(data,mag_col):
-    """Calculate the max magnitude of a lightcurve"""
+def max_mag(data, mag_col):
+    """Calculate the max magnitude of a lightcurve
+    :param data: pd.DataFrame with observed magnitudes for a single source
+    :param: a string with the name of the column for calculating the max value.
+    :returns: the max for the mag_col coming from the data table
+    """
     return data[mag_col].max()
 
 
-def min_mag(data,mag_col):
+def min_mag(data, mag_col):
     """Calculate the min magnitude of a lightcurve
     :param data: pd.DataFrame with observed magnitudes for a single source.
     :param mag_col: a string with the name of the column for calculating the min value.

lcanalyzer/views.py

@@ -20,7 +20,8 @@ def plotUnfolded(data,mag_col,time_col,color,marker):
     )
     ax.minorticks_on()
     ax.set_xlabel("MJD (days)")
-    ax.set_ylabel('Mag')
+    ax.set_ylabel('Mag' + mag_col)
+    ax.set_title('Light curve')
     fig.tight_layout()
     plt.show()