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""" Calculate mean std of clipped array"""
import numpy as np
from scipy import interpolate

def calc_func_clipped(X, Y, func=np.mean, npoints=None, verbose=False):
    """ Calculates func of Y values, where Y is clipped to the longest continuous
    values of X to prevent aliasing
        
    """
    # Size of each data vector

daguiam

import numpy as np 
def expand_time(time, shape, verbose=True):
    if verbose:
        print 'Old time shape:',time.shape,'new shape:',shape
    
    output = np.tile(time, shape[1]).reshape(np.fliplr([shape])[0]).T
    if verbose:
        print 'output time shape:',output.shape
    return output

daguiam

rant = data['r'][0][0]
zant = data['z'][0][0]
sep_z = data['sep_z']
sep_r = data['sep_r']
sep_ra = np.linalg.norm(np.array([sep_r-rant,sep_z-zant]),axis=0)

daguiam

%%%%%%%%%%%%%%%%%
% This is altacv.cls (v1.1.3, 30 April 2017) written by
% LianTze Lim ([email protected]).
% 
%% It may be distributed and/or modified under the
%% conditions of the LaTeX Project Public License, either version 1.3
%% of this license or (at your option) any later version.
%% The latest version of this license is in
%%    http://www.latex-project.org/lppl.txt
%% and version 1.3 or later is part of all distributions of LaTeX

daguiam

import numpy as np
from mpl_toolkits.mplot3d import axes3d

import matplotlib
matplotlib.use("Agg")

import matplotlib.pyplot as plt
import matplotlib.animation as animation
import matplotlib.colors as colors

daguiam

#include <stdio.h>



typedef enum {
    NORTH = 0,
    EAST = 1,
    SOUTH = 2,
    WEST = 3
} directionValues;

daguiam

# Reducing sample names of QN61 by 1 value to match QN62, QN63 and the DieMap

root_dir = os.path.abspath(path_string)
files = os.listdir(root_dir)
prefix =  ".csv"
files = [file for file in files if prefix in file]
for file in files:
    number = int(file[1:4])-1
#     print("%03d"%number)
    newfile = "S%03d.csv"%number

daguiam

import numpy as np
from PIL import Image
def save_array_as_image(array, filename):
    """Saves a 2D numpy array to an image"""
    image = Image.fromarray(array)
    image.save(filename)

    
array = np.random.random((512,512))*255
array = np.uint8(array)

daguiam

def convert_array_to_levels(array, levels, limits=[-np.pi,np.pi], output_limits=None, verbose=False):
    """Dgitizes the given array to within the number of levels provided 
    
    Parameters
    ----------
    array : input array of values
    levels : number of levels to consider
    limits : the input data limits to consider for the interval calculation
    output_limits : if given, multiplies the digitized input to the output range
    

daguiam

def remove_extension(filename, separator="."):
    return separator.join(filename.split(separator)[:-1])