jddingemanse/pycamtET

Meteorology analysis for Ethiopia # pycamtET In the package pycamtET, relevant data processing tools for Ethiopian meteorological data are bundled. The package pycamtETinterface holds code for a Jupyter-Notebook widget interface that can operate the functions of pycamtET. Both packages are fully under development, so you can expect regular changes. ## Installation First, make sure git is installed. ``` conda install git ``` If git is installed, you can install pycamtET with: ``` pip install git+github.com ``` ## Package structure Functions for dataprocessing and plotting are stored in three different modules: .dataFunctions, .plotFunctions and .mapFunctions. Functions from .datafunctions can be used to process data. The processed data can be plotted as timeseries data with different functions under .plotFunctions, and on a map with different functions under .mapFunctions. Processing and plotting of data requires as input a datafile with meteorological station data from the Ethiopian Meteorology Institute. The package assumes this data is a .csv file with 40 columns: 9 columns with identifying data (location, element, year, month, etcetera) and 31 columns of 31 days per month. ## Dependencies In pycamtET, all .mapfunctions are depending on the package Geopandas. One of the .mapFunctions is kriMap(); that function is depending on the package pykrige. In the .plotFunctions, there is the function windRose(); that function is depending on the package windrose. ## Use examples ### Plot of one year versus other years To create a plot for the precipitation in the year 2015 versus all other years in your datafile, for the station Assela, with a dekadal-timestep, you must: - load and preprocess data with dataFunctions.dataLoad() - select data for the station Assela with the function dataFunctions.locSelect() - process the precipitation data of Assela to dekadal-averages with dataFunctions.timeData() - plot this processed data with plotFunctions.recentHistoric() ``` fro …