| #!/usr/bin/env bash | |
| # Test VIC output for nans | |
| branch="upstream/develop" | |
| testdir="...path with global file..." | |
| vic_repo="...path to /src/ directory in VIC repo..." | |
| cd $vic_repo | |
| # set number of commits to loop through |
| import numpy as np | |
| import matplotlib as mpl | |
| from matplotlib import cm | |
| def cmap_discretize(cmap, N=10): | |
| cmap = cm.get_cmap(eval(cmap)) | |
| colors_i = np.concatenate((np.linspace(0, 1., N), (0.,0.,0.,0.))) | |
| colors_rgba = cmap(colors_i) | |
| indices = np.linspace(0, 1., N+1) |
| import datetime | |
| import xray | |
| import pandas as pd | |
| import numpy as np | |
| from collections import OrderedDict | |
| dpm = {'noleap': [0, 31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31], | |
| '365_day': [0, 31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31], | |
| 'standard': [0, 31, 28.25, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31], | |
| 'gregorian': [0, 31, 28.25, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31], |
| Fixing the land-atmosphere coupling in RACM 33 | |
| ======== | |
| To "fix" the surface layer in RACM. We need to add an upper stability limit to the aerodynamic resistance calculation in both `Fixed calc_surf_energy_bal.c` and `SnowPackEnergyBalance`. | |
| These changes, along with the addition of the constant limit in `vicNl_def.h` are shown here. |
| Breaking the land-atmosphere coupling in RASM.1.0 | |
| ======== | |
| To "break" the surface layer in RASM, we need to remove the upper stability limit to the aerodynamic resistance calculation in both `Fixed calc_surf_energy_bal.c` and `SnowPackEnergyBalance`. | |
| This limit was established c.RACM33. | |
| These changes are shown here. |
| typedef float float_t; | |
| typedef double double_t; | |
| extern int __math_errhandling(void); | |
| extern int __fpclassifyf(float); | |
| extern int __fpclassifyd(double); | |
| extern int __fpclassifyl(long double); | |
| extern __inline __attribute__((__gnu_inline__)) __attribute__ ((__always_inline__)) int __inline_isfinitef(float); | |
| extern __inline __attribute__((__gnu_inline__)) __attribute__ ((__always_inline__)) int __inline_isfinited(double); | |
| extern __inline __attribute__((__gnu_inline__)) __attribute__ ((__always_inline__)) int __inline_isfinitel(long double); |
| #!/usr/local/bin/python | |
| import numpy as np | |
| ################################################################################## | |
| ## Find Indicies | |
| ## Given an input lat or lon, the function returns the nearest index location | |
| ################################################################################## | |
| def find_nearest(array,value): | |
| """ Find the index location in (array) with value nearest to (value)""" | |
| idx = (np.abs(array-value)).argmin() |
| #!/usr/local/bin/python | |
| import numpy as np | |
| from netCDF4 import Dataset | |
| ################################################################################## | |
| ## Read netCDF Inputs | |
| ## Read data from input netCDF. | |
| ################################################################################## | |
| def read_netcdf(ncFile,vars = [],coords = False, verbose = False): |
| { | |
| "metadata": { | |
| "name": "rasm_cpl_plots" | |
| }, | |
| "nbformat": 3, | |
| "nbformat_minor": 0, | |
| "worksheets": [ | |
| { | |
| "cells": [ | |
| { |
This guide documents how we set up an easy workflow for using the IPython Notebook on our compute cluster managed with Sun Grid Engine (SGE).
Summary: We provide a script to the cluster users that runs qrsh to schedule an ipython notebook job using SSL and password protection.
