Snowpyt: an open-source library to visualize snowpits in Python

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

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

Documentation available here: https://snowpyt.readthedocs.io/en/latest/index.html

To do:

High Priority

• write class to import and plot CROCUS results
• add same colormap for snowgrain type as the snowtool_git for CROCUS
• write function to save and load pit to and from pickle format (currently not working)
• 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 Snowpyt if library requirements are met. It also contains data samples to test the library. Message us to be added as a contributor, then if you can also modify the code to your own convenience with the following steps:

To work on a development version and keep using the latest change install it with the following

pip install -e [path2folder/snowpyt]

and to upload latest change to Pypi.org, simply:

1. change the version number in the file snowpyt/__version__.py
2. run from a terminal from the snowpyt folder, given your $HOME/.pyc is correctly set:

python setup.py upload

requirements

Python >= 3.6 with the following libraries:

• numpy
• matplotlib
• pandas
• xlrd
• xlm
• skimage
• opencv

Usage

Currently Snowpyt can be used for two purposes: 1) read and work with CAAML files, and 2) processing NIR images of snowpit to extract refelctance, SSA and optical diameter.

Work with snowpit data: CAAML file

There are three ways to import data into Snowpyt:

1. digitalize your pit with https://niviz.org/ and export your pit as a CAAMLv6 (This format follows an international standard for snowpit). Them use the import_caamlv6() function. More information about the CAAML format
2. input directly data into the snowpit class object

from snowpyt import pit_class as pc

############################################################
# Example 1 - using a caamlv6 file:
p = pc.Snowpit()
p.caaml_file= '[PATH TO YOUR FILE].caaml'
p.import_caamlv6()
p.plot(plot_order=['density', 'temperature', 'stratigraphy', 'hardness'])

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

# import isotope values (dD, dO18, d-ex)
p.sample_file = '[PATH TO YOUR FILE].csv'
p.import_sample_csv()

p.plot(plot_order=['dD', 'd18O','d-ex', 'hardness'])

The isotope .csv file should be following this format:

number,height_top,height_bot,dD,d18O,dxs,ice_type
0,94,93.0,-57.55,-8.16,7.73,S
1,93,89.8,-61.56,-8.76,8.54,S
2,89.8,86.6,-75.45,-10.64,9.68,S

Many more columns can be added. Create a plotting function to plot any newly named column

3. All the data table are loaded as a Pandas dataframe or Numpy arrays 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

4. 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.)

5. Compute SWE

p = pc.Snowpit()
p.caaml_file= '[PATH TO YOUR FILE].caaml'
p.calc_SWE(method='avg')

NIR photography

The nirpy.py file contains method to process

from snowpyt import nirpy
import numpy as np
import matplotlib.pyplot as plt

fnir = '/home/simonfi/Desktop/202202_finse_livox/NIR_cam/20220224_NIR/DSC01493.JPG'
fcalib = '/home/simonfi/Downloads/Foc0200Diaph028-FlatField.tif'
mo = nirpy.nir(fname_nir=fnir, fname_calib=None, kernel_size=500)

mo.pick_targets()
mo.convert_all()
mo.scale_spatially()
mo.extract_profile(['SSA', 'd_optical', 'reflectance'], param={'method': 'skimage',
                                                               'linewidth': 5,
                                                               'reduce_func': np.median,
                                                               'spline_order': 2})

fig, ax = plt.subplots(1, 3, sharey=True)
ax[0].plot(mo.profile.reflectance, mo.profile.dist)
ax[0].grid(':')
ax[0].set_xlabel('Reflectance [%]')

ax[1].plot(mo.profile.SSA, mo.profile.dist)
ax[1].grid(':')
ax[1].set_xlabel('SSA [mm$^{-1}$]')

ax[2].plot(mo.profile.d_optical, mo.profile.dist)
ax[2].grid(':')
ax[2].set_xlabel('d$_{optical}$ [mm]')
plt.show()

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
• Mika Lanzky

Example

CAAML File Interpretation

Near-IR Photography