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A Python project to visualize snowpit

Project description

Simon Filhol, November 2016, copyright under the MIT license terms, see the License.txt file

LAST MODIFIED: September 2017 (or see date on github file history)

Feel free to contribute to the project!!!! Many new features can be added…

To do:

High Priority

  • write function to save and load pit to and from pickle format (currently not working)
  • reorganize package example, standard excel and csv snowpit file
  • make ground appear to comfirm the user that the pit reached ground. add note about ground type.

Low priority

  • specify the figure size and adjust font size in respect
  • render the medatadata text better, convert date to a readable date
  • put option to adjust figure size to desired size and dpi. Return axis variable from plotting function for more advanced plotting if needed (i.e. multiple samples)
  • add option to save pits in Pickle format or CSV
  • add option to save figure in matplotlib format
  • add option to plot when multiple sample columns are given.

Objective

The objective of this library is to provide visualization tool for snowpit data. Started for the need of the Svalbard Snow Research group, this package should evolve to include more snowpit type and visualization scheme.

The snow grain classification follows the guidelines provided by the UNESCO International Classification for Seasonal Snow on the Ground (Fierz et al., 2009)

Fierz, C., Amstrong, R.L., Durand, Y., Etchevers, P., Greene, E., McClung, D.M., Nishimura, K., Satyawali, P.K. and Sokratov, S.A. 2009.The International Classification for Seasonal Snow on the Ground. IHP-VII Technical Documents in Hydrology N°83, IACS Contribution N°1, UNESCO-IHP, Paris.

Installation

Last stable version from the Pypi repository

Simply run the following in your terminal:

pip install snowpyt

Last development version for contributing to the project:

Clone the github repository to a local directory using the following command in your terminal

git clone https://github.com/ArcticSnow/snowpyt.git

or by downloading the package

The branch ‘master’ consists of the latest stable version. Other develepment versions are included in other git branches.

The package contains all the functions to plot the snowpit if library requirements are met. It also contains data samples to test the library.

requirements

Python 2.7.9 with the following libraries: - numpy - matplotlib - pandas - xlrd

Use

  1. Snowpit must be formated following the template file “Standard_pit.xlsx”
  • requirements:
    • To see the supported list of snowflakes input, see the python dictionnary snowflake_dict in the pit_class.py file.
    • Only ASCII characters in fields. Currently not supporting special characters
  1. Save the excel or libreoffice file in .xslx format (default Excel format).
  2. Load the snowpit into snowpyt
from snowpyt import pit_class as pc

path_to_file = 'snowpyt/data_example/20170209_Finse_snowpit.xlsx'

mypit = pc.Snowpit()
mypit.filename = path_to_file
mypit.import_xlsx()

mypit.plot(metadata=True)
mypit.plot(plots_order=['density', 'temperature', 'stratigraphy','crystal size'], metadata=True)

mypit.metadata.__dict__
  1. It is also possible to load data from an .xml file formatted according to CAAML format
from snowpyt import pit_class as pc

path_to_file = 'path to xml file in CAAML format'

mypit = pc.Snowpit()
mypit.filename = path_to_file
mypit.import_xml()

mypit.plot(metadata=True)
mypit.plot(plots_order=['density', 'temperature', 'stratigraphy','crystal size'], metadata=True)
  1. All the data table are loaded as a Pandas dataframe within the snowpyt class object

Type the following in your Python console to see the loaded datatable:

mypit.table

This allows for custom plotting using the library of your choice on top of the existing plotting function

  1. Extra Sample Values

Extra column of sample values can be added to the excel file. Column name must be unique

The current plotting functions will not plot these extra columns, only the first one. However the values are loaded via pandas in the table as a dataframe (see 5.)

Want to contribute?

Once you have cloned the project to your home directory, create a git branch and here you go. When your edits are stable, merge with the master branch. See this neat tutorial about git branching and merging, here

List of Contributor

  • Simon Filhol
  • Guillaume Sutter
  • [add your name]

Example

Example snowpit

Example snowpit

Project details


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