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# # The first runpandas release is alive! Welcome to the release 0.1.0!
# > What runpandas packages comes with the first 0.1.0 alpha release!
#
# - toc: false 
# - badges: true
# - comments: true
# - author: <NAME>
# - categories: [general, jupyter, releases]
# - image: images/releasev01.png

# > This  current state of the project is `early beta`, which means that features can be added, removed or changed in backwards incompatible ways.

# Today we are happy to announce the initial release of runpandas, which makes it easier to perform data analysis from your running sessions stored at tracking files from cellphones and GPS smartwatches or social sports applications such as Strava, MapMyRUn, NikeRunClub, etc. It is designed to make the pandas data analysis package support reading, transforming and enable running metrics analytics.

# ## How to use runpandas 

# Require the runpandas module in you Python Code and use our tracking file reader in case of reading local files in tcx, gpx or fit format.

#hide
from IPython.display import set_matplotlib_formats
set_matplotlib_formats('png')

# # !pip install runpandas
import runpandas as rpd
activity = rpd.read_file('./data/sample.tcx')

#
# The data file is loaded into a ``runpandas.Activity`` , which is a subclass of the ``pandas.DataFrame`` and provides some additional features. The library
# also provides for certain columns specific methods that extends the ``pandas.Series`` subclasses. In this example below,
# we get time, altitude, distance, heart rate and geo position (lat/long).

activity.head(5)

# For instance, if you want to get the base unit for the altitude ``alt`` data or the distance ``dist`` data:
#

activity.alt.base_unit

activity.alt.sum()

activity.dist.base_unit

activity.dist[-1]

# Since the Activity is a DataFrame, we can play with data and create some visualizations. In this example we use the built in
# matplotlib based plot function to present the distance x time.

activity[['dist']].plot()

# Finally, let's watch a  glimpse of the map route by plotting a 2d map using logintude vs latitude.

activity.plot(x='lon', y='lat')

# That's some of features available! Please check out the runpandas docummentation [link](https://www.runpandas.run/en/latest/) for the package's full API.
#

# ## How to install runpandas 

# The release version of runpandas (0.1.0) is available on PyPI, the Python Package Index repository as runpandas [link](https://pypi.org/project/runpandas/).

# If you haven't already installed, start by downloading and installing with pip:
#
# ```console
# $ pip install runpandas --upgrade
# ```
#
# Please note that we currently only support Python 3.6+. You are now ready to start using the runpandas.

# ## Roadmap

#
# We have big plans for runpandas, including not limited to:
#
# -  Adding suport reading running tracking sessions from social application Strava;
# -  Adding support reading track files giving a directory as parameter;
# -  Better docummentation with user guide and install guidelines;
# -  Support to session of activities 
# -  Support to special plots such as routes, average heart zones, personal bests for specific distances.
#
# You can see the live roadmap of features at our [issues repository](https://github.com/corriporai/runpandas/issues).

# ## Thanks

# It is our first release after two months of planning and coding. We hope that you find runpandas helpful - it is still very young, and result of some of my ideas and needs to perform personal running analysis. 
# So don't be afraid to get in touch and let us know what features you would like to add next,  Feel free to raise an issue if you spot any bugs!
#

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