jgomezdans

# Find Cholesky decomposition
cvalue = numpy.linalg.cholesky( covar_matrix )
# Locally store size of matrix
dims = covar_matrix.shape
# Add normal deviate to value, preserving lower triangular
covar_proposed = numpy.multiply( cvalue + random_update, numpy.tri(dims[0]))
# Square and replace
covar_proposed = covar_proposed*transpose(covar_proposed)

jgomezdans

# -*- coding: utf-8 -*-
import numpy, pylab
pylab.plot (numpy.arange(10), numpy.arange(10,0,-1), 'o')
ax = pylab.gca()
ax.set_xticks( (2, 4, 6, 8) )
labels = ax.set_xticklabels(('Martin', 'deKauwe', 'Jose', 'Gomez'))
for l in labels:
	l.set_rotation(45)
	l.set_fontsize(12)
	

jgomezdans

import ctypes

"""
The Coord struct in C-land
typedef struct
{
  double lon,lat;
  double area;   
} Coord;
The Config struct in C-land

jgomezdans

.read init_spatialite-2.3.sql utf-8
.loadshp <path> <table> <utf-8> <srid> <column_name>
SELECT AddGeometryColumn('<my_table>', 'geom', 4326, 'POLYGON', 2);
UPDATE my_table SET geom=( SELECT g.geom FROM my_table d, my_shape g WHERE d.grid_id=g.id) ;

jgomezdans

import matplotlib,numpy
import pylab
# A random colormap for matplotlib
cmap = matplotlib.colors.ListedColormap ( numpy.random.rand ( 256,3))
pylab.imshow ( Z, cmap = cmap)
pylab.show()

jgomezdans

# -*- coding: utf-8 -*-
"""
Created on Tue May 18 12:18:12 2010
This script takes 2 or three input parameters:

* shapefile name
* FieldName1 to aggregate on
* FieldName2 to aggregate (Optional)

It opens the shapefile, and iterates through features

jgomezdans

def process_LST_files(self, modis_sd, lon, lat, view_zenith_angle):
        UNDEF = 0
        sds_name = {'lst':'LST_Day_1km', 'qc': 'QC_Day', 
                    'view':'Day_view_time', 'angle':'Day_view_angl',
                    'lstnight': 'LST_Night_1km', 'qcnight': 'QC_Night', \
                    'viewnight':'Night_view_time',
                    'anglenight':'Night_view_angl' }

        data = dict(zip ( [var for var in sds_name.iterkeys()], \
                     [ modis_sd.select(layer).get() \

jgomezdans

from __future__ import division
from scikits.audiolab import flacread
from numpy.fft import rfft, irfft
from numpy import argmax, sqrt, mean, diff, log
from matplotlib.mlab import find
from scipy.signal import blackmanharris, fftconvolve
from time import time
import sys

# Faster version from http://projects.scipy.org/scipy/browser/trunk/scipy/signal/signaltools.py

jgomezdans

# Version from SciPy.signal

def correlate(in1, in2, mode='full'):
    """Cross-correlate two N-dimensional arrays.

  Description:

     Cross-correlate in1 and in2 with the output size determined by mode.

  Inputs:

jgomezdans

#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
A script to create daily averae values from NCEP reanalysis netcdf data.

Saves the data as GeoTIFFs with daily

:version: 1
:author: Jose Gomez-Dans < j.gomez.dans@geog.ucl.ac.uk>
"""