I'm working on setting some boundary conditions for a water table model, and I am able to set the entire first row to a constant value, but not the entire first column. I am using np.zeros((11,1001)) to make an empty matrix. Does anyone know why I am successful at defining...

I've been trying to scipy.mstats.zscore a dataset that is intentionally organized into a nested list, and it gives: TypeError: unsupported operand type(s) for /: 'list' and 'long' which probably suggests that scipy.stats doesn't work for nested lists. What can I do about it? Does a for loop affect the nature...

I have a list which contains 1000 integers. The 1000 integers represent 20X50 elements of dimensional array which I read from a file into the list. I need to walk through the list with an indicator in order to find close elements to each other. I want that my indicator...

I'm new to python and I have a question about numpy.reshape. I currently have 2 lists of values like this: x = [0,1,2,3] y = [4,5,6,7] And I want them to be in separate 2D arrays, where each item is repeated for the length of the original lists, like this:...

I'm trying to send live video frame that I catch with my camera to a server and process them. I'm usig opencv for image processing and python for the language. Here is my code client_cv.py import cv2 import numpy as np import socket import sys import pickle cap=cv2.VideoCapture(0) clientsocket=socket.socket(socket.AF_INET,socket.SOCK_STREAM) clientsocket.connect(('localhost',8089))...

I am using pandas to factorize an array consisting of two types of strings. I want to make sure that one of the strings "XYZ" is always coded as a 0 and the other string "ABC" is always coded as 1. Is it possible to do this? I looked up...

I would like to take an NxM matrix, for simplicity, we'll use x=np.arange(25).reshape((5,5)) And I would like to create a new matrix, A, in which I can store a node for each element in the first row, its N-direction index in the second row, its M-direction index in the third...

I am trying to figure out how to speed up the following Python code. Basically, the code builds the matrix of outter products of a matrix C and stores it as block diagonal sparse matrix. I use numpy.repeat() to build indices into the block diagonal. Profiling the code revealed that...

I have an numpy array with dimensions (200, 200, 3). It is an RGB image. I also have the (xmin,ymin,xmax,ymax) coordinates of a region of this image that I would like to set to zero. This region should be zero in all three channels. I can of course solve this...

I'm implementing a RANSAC algorithm for circle detection in images. I profiled the execution and I get: 13699392 function calls in 799.981 seconds Random listing order was used ncalls tottime percall cumtime percall filename:lineno(function) 1 0.000 0.000 0.000 0.000 {time.time} 579810 0.564 0.000 0.564 0.000 {getattr} 289905 2.343 0.000 8.661...

With numpy, I am able to select an arbitrary set of items from an array with a list of integers: >>> import numpy as np >>> a = np.array([1,2,3]) >>> a[[0,2]] array([1, 3]) The same does not seem to work with sympy matrices, as the code: >>> import sympy as...

I have a raster with a set of unique ID patches/regions which I've converted into a two-dimensional Python numpy array. I would like to calculate pairwise Euclidean distances between all regions to obtain the minimum distance separating the nearest edges of each raster patch. As the array was originally a...

I have one data frame and pairwise correlation were calculated >>> df1 = pd.read_csv("/home/zebrafish/Desktop/stack.csv") >>> df1.corr() GA PN PC MBP GR AP GA 1.000000 0.070541 0.259937 -0.452661 0.115722 0.268014 PN 0.070541 1.000000 0.512536 0.447831 -0.042238 0.263601 PC 0.259937 0.512536 1.000000 0.331354 -0.254312 0.958877 MBP -0.452661 0.447831 0.331354 1.000000 -0.467683 0.229870...

I have a python array such as: [[1], [2], [3], [4] ] I want to make it to: [ [1 0 0 0], [2 0 0 0 ], [3 0 0 0], [4 0 0 0] ] What is the python way to do this? Suppose I use numpy....

I have the following code x = -10 for i in range(2,10): print i, " | ",np.exp(-x**i) with the following output: 2 | 3.72007597602e-44 3 | inf 4 | 0.0 5 | inf 6 | 0.0 7 | inf 8 | 0.0 9 | inf Why is the results ~0...

>>> A = np.matrix(np.zeros(2, 3))) >>> A.shape (2, 3) >>> A matrix([[ 0., 0., 0.], [ 0., 0., 0.]]) Does the matrix A have two rows with three zeros or two columns with three zeros?...

I have a simple python code as follows: import numpy as np import matplotlib.pyplot as plt """ Here are the solutions and the plot. """ # Create the axis and plot. plt.axis([0, 10, 0, 10]) axis_x = range(1, 11) grd = [1.1, 2.1, 3.1, 4.1, 5.1, 6.1, 7.1, 8.1, 9.1,...

I am currently using Scipy 0.7.2 with Numpy 1.4.1. My Python version is 2.6.6. I have written a simple code to read a coo sparse matrix from a .mtx file as follows: data = scipy.io.mmread('matrix.mtx') On running the code, I got the following error: Traceback (most recent call last): File...

I'm trying to port a "C" code to Python, this is what I coded: scale = 1.0 / (rows * cols) RemoveConstantBiasFrom(rarray, scale) zarray = rarray[:] zarray = DCT(zarray, rows, cols) zarray = zarray.flatten() beta = np.dot(rarray, zarray) if iloop == 0: parray = zarray[:] else: btemp = beta /...

I have a large data set I want to be able to "zoom" in on. What I really want is for the data to be rebinned based on the selection and then update the data in the graph. So the graph will show different limits but maintain the same resolution....

I want to get dot product of N vector pairs (a_vec[i, :], b_vec[i, :]). a_vec has shape [N, 3], bvec has the same shape (N 3D vectors). I know that it can be easily done in cycle via numpy.dot function. But cannot it be done somehow simpler and faster?...

I have two arrays. One is the raw signal of length (1000, ) and the other one is the smooth signal of length (100,). I want to visually represent how the smooth signal represents the raw signal. Since these arrays are of different length, I am not able to plot...

I have a list of float numbers and I would like to convert it to numpy array so I can use numpy.where() to get indices of elements that are bigger than 0.0 (not zero) I tried this, but with no luck: import numpy as np arr = np.asarray(enumerate(grade_list)) g_indices =...

I have a code in python to represent the energy decay in a damped oscilator, it reads like this: def E(wt, Q): return (np.e**(-x/Q))*(1-(1/2*Q)*np.sin(2*x)) x = np.linspace(0,20,1000) y0 = E(x,2) y1 = E(x,4) y2 = E(x,8) y3 = E(x,16) plt.plot(x, y0, 'p', label=r'$Q=2$') plt.plot(x, y1, 'r', label=r'$Q=4$') plt.plot(x, y2, 'g',...

I have a list as follows: l = [['A', 'C', 'D'], ['B', 'E'], ['A', 'C', 'D'], ['A', 'C', 'D'], ['B', 'E'], ['F']] The result should be: [['A', 'C', 'D'], ['B', 'E'], ['F']] The order of elements is also not important. I tried as: print list(set(l)) Does numpy has better way...

I'm trying to extract data from the text output of a cheminformatics program called NWChem, I've already extraced the part of the output that I'm interested in(the vibrational modes), here is the string that I have extracted: s = ''' 1 2 3 4 5 6 P.Frequency -0.00 0.00 0.00...

I try to load CSV file to numpy-array and use the array in LogisticRegression etc. Now, I am struggling with error is shown below: import numpy as np import pandas as pd from sklearn import preprocessing from sklearn.linear_model import LogisticRegression dataset = pd.read_csv('../Bookie_test.csv').values X = dataset[1:, 32:34] y = dataset[1:,...

I've started to use NumPy instead of MATLAB for a lot of things and for most things it appears to be much faster. I've just tried to replicate a code in Python and it is much slower though. I was wondering if someone who knows both could have a look...

When doing: import numpy A = numpy.array([1,2,3,4,5,6,7,8,9,10]) B = numpy.array([1,2,3,4,5,6]) A[7:7+len(B)] = B # A[7:7+len(B)] has in fact length 3 ! we get this typical error: ValueError: could not broadcast input array from shape (6) into shape (3) This is 100% normal because A[7:7+len(B)] has length 3, and not a...

I am using a NumPy array to feature class in Esri ArcGIS. I have a tuple of items that I am appending to a NumPy array. One field/column should be able to use multiple values depnding on the proxy input, i.e. if the category is a bulky item then the...

Seems to be a rather simple question but actually not that easy with no experience in image handling/processing. I do not just want to stack 2D-arrays on top of each other but create a 3D volume by merging multiple binary 2D slices which are separated by empty space. Example: Define...

So I have a DB with a couple of years worth of site data. I am now attempting to use that data for analytics - plotting and sorting of advertising costs by keyword, etc. One of the data grabs from the DB takes minutes to complete. While I could spend...

I'm trying to fill missing datavalues in a pandas dataframe based on date column. df.head() col1 col2 col3 date 2014-06-20 3 752 4028 2014-06-21 4 752 4028 2014-06-22 32 752 4028 2014-06-25 44 882 4548 2014-06-26 32 882 4548 I tried the following idx = pd.date_range(df.index[0], df.index[-1]) df = df.reindex(idx).reset_index()...

I already achieved the goal described in the title but I was wondering if there was a more efficient (or generally better) way to do it. First of all let me introduce the problem. I have a set of images of different sizes but with a width/height ratio less than...

I need to do a random choice with a given probability for selecting a tuple from a list. EDIT: The probabiliy for each tuple is in probabilit list I do not know forget the parameter replacement, by default is none The same problem using an array instead a list The...

I am trying to create an empty numpy array and then insert newly created arrays into than one. It is important for me not to shape the first numpy array and it has to be empty and then I can be able to add new numpy arrays with different sizes...

I have a csv file with 3 columns emotion, pixels, Usage consisting of 35000 rows e.g. 0,70 23 45 178 455,Training. I used pandas.read_csv to read the csv file as pd.read_csv(filename, dtype={'emotion':np.int32, 'pixels':np.int32, 'Usage':str}). When I try the above, it says ValueError: invalid literal for long() with base 10: '70...

I have two big (432*136*136*46) 'numpy.ndarray' H1 and H2 which encompass altitude values corresponding to two simulations. I want to generate an array with 1 when H1 and H2 have the same altitude and 0 when they don't. Then, I want to know how many elements I selected, so I...

I have a function f(x,t) = cos(t)*t + x and i want to display the change of the result over the width x and time t at discretised time steps t_i and discretised width steps x_j. Now I am a while here on SX and feel really embarrassed to only...

This question already has an answer here: Get intersecting rows across two 2D numpy arrays 3 answers I have two large lists of points in 2D and I want to find their common sublists, if they have some. Both of the lists are quite large and efficiency is an...

I'd like to know how to calculate the factorial of a matrix elementwise. For example, import numpy as np mat = np.array([[1,2,3],[2,3,4]]) np.the_function_i_want(mat) would give a matrix mat2 such that mat2[i,j] = mat[i,j]!. I've tried something like np.fromfunction(lambda i,j: np.math.factorial(mat[i,j])) but it passes the entire matrix as argument for np.math.factorial....

Attempting to read a binary file produced in Fortran into Python, which has some integers, some reals and logicals. At the moment I read the first few numbers correctly with: x = np.fromfile(filein, dtype=np.int32, count=-1) firstint= x[1] ... (np is numpy). But the next item is a logical. And later...

I've just check the simple linear programming problem with scipy.optimize.linprog: 1*x[1] + 2x[2] -> max 1*x[1] + 0*x[2] <= 5 0*x[1] + 1*x[2] <= 5 1*x[1] + 0*x[2] >= 1 0*x[1] + 1*x[2] >= 1 1*x[1] + 1*x[2] <= 6 And got the very strange result, I expected that x[1]...

I'm using f2py to offload performance critical sections of my python code to fortran. I have two fortran compilers, gfortran and ifort. Since the code I run will be on intel processors, ifort is usually faster. But for the most critical part of my code, I observe the opposite, with...

I want to compare two variables input_items and temp for equality. To give you an idea of their datatype - print input_items prints - [array([ 50., 1., 0., ..., 0., 0., 0.], dtype=float32), array([ 50., -2., 0., ..., 0., 0., 0.], dtype=float32)] What's the best way to do that in...

The maxwell-boltzmann distribution is given by . The scipy.stats.maxwell distribution uses loc and scale parameters to define this distribution. How are the parameters in the two definitions connected? I also would appreciate if someone could tell in general how to determine the relation between parameters in scipy.stats and their usual...

I am trying to calculate a set of FDR-corrected p-values using Benjamini & Hochberg's method. However, the vector I am trying to run this on contains over 10 billion values. Given the amount of data the normal method from statsmodel's multicomp module quickly runs out of memory. Looking at the...

I want to select in an numpy array all odd off diagonals. Basically the following: a = np.arange(16).reshape(4,4) array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11], [12, 13, 14, 15]]) b = a[::2,1::2] and at the same time a[1::2,::2] array([[ 1, 3], [...

I'm doing quite a bit of scientific numerical integration in Python, using Numpy and ode. I use several arrays, and I wanted to turn a 1d array into a list for exporting and easier manipulation. Since then I've found easier and more pythonic methods without resorting to lists, but before...

I'd like to convert a numpy matrix to R an matrix. I'm aware that you to do this: from rpy2 import robjects as ro ro.conversion.py2ri = ro.numpy2ri ro.numpy2ri.activate() And then build an R matrix: mat_r = ro.r.matrix(mat_py) But the problem is, whenever I refer to the new matrix in python...

I have a collection of 2D narrays, depending on two integer indexes, say p1 and p2, with each matrix of the same shape. Then I need to find, for each pair (p1,p2), the maximum value of the matrix and the indexes of these maxima. A trivial, albeit slow, way to...

I have one list of data as follows: from shapely.geometry import box data = [box(1,2,3,4), box(4,5,6,7), box(1,2,3,4)] sublists = [A,B,C] The list 'data' has following sub-lists: A = box(1,2,3,4) B = box(4,5,6,7) C = box(1,2,3,4) I have to check if sub-lists intersect. If intersect they should put in one tuple;...

I have a 2 dimension array and based if the value is greater than 0 I want to do a operation (example with x+1). In plain python something like this: a = [[2,5], [4,0], [0,2]] for x in range(3): for y in range(2): if a[x][y] > 0: a[x][y] = a[x][y]...

Let's say I have a time series represented in a numpy array, where every 3 seconds, I get a data point. It looks something like this (but with many more data points): z = np.array([1, 2, 1, 2.2, 3, 4.4, 1, 1.2, 2, 3, 2.1, 1.2, 5, 0.5]) I want...

I have a 3D color image im (shape 512 512 3), and a 2D array mask(512 512). I want to annotate this color image by the mask: im = im[mask>threshold] + im[mask<threshold] * 0.2 + (255,0,0) * [mask<threshold]. How do I write this in Python efficiently?...

I have a list, my_list, with mixed data types that I want to convert into a numpy array. However, I get the error TypeError: expected a readable buffer object. See code below. I've tried to base my code on the NumPy documentation. my_list = [['User_0', '2012-2', 1, 6, 0, 1.0],...

Say I want to fit two arrays x_data_one and y_data_one with an exponential function. In order to do that I might use the following code (in which x_data_one and y_data_one are given dummy definitions): import numpy as np from scipy.optimize import curve_fit def power_law(x, a, b, c): return a *...

I have a 3D numpy array that looks like this: X = [[[10 1] [ 2 10] [-5 3]] [[-1 10] [ 0 2] [ 3 10]] [[ 0 3] [10 3] [ 1 2]] [[ 0 2] [ 0 0] [10 0]]] At first I want the maximum along...

I'm trying to create an LMDB data base file in Python to be used with Caffe according to this tutorial. The commands import numpy as np and import caffe run perfectly fine. However, when I try to run import lmdb and import deepdish as dd, I'm getting the following errors:...

I think the title says it all, but just to be specific, say I have some list of numbers named "coeffs". Assuming the polynomial with said coefficients has exactly k unique roots, will the following code ever set number_of_unique_roots to be a number greater than k? import numpy as np...

I am trying to concatenate two arrays: a and b, where a.shape (1460,10) b.shape (1460,) I tried using hstack and concatenate as: np.hstack((a,b)) c=np.concatenate(a,b,0) I am stuck with the error ValueError: all the input arrays must have same number of dimensions Please guide me for concatenation and generating array c...

I have a large number (M) of time series, each with N time points, stored in an MxN matrix. Then I also have a separate time series with N time points that I would like to correlate with all the time series in the matrix. An easy solution is to...

I have an array like this a= np.arange(4).reshape(2,2) array([[0, 1],[2, 3]]) I want to add a value to each element in the array. I want my result return 4 array like array([[1, 1],[2, 3]]) array([[0, 2],[2, 3]]) array([[0, 1],[3, 3]]) array([[0, 1],[2, 4]]) ...

How can I split an array's columns into three arrays x, y, z without manually writing each of the [:,0],[:,1],[:,2] separately? import numpy as np data = np.array([[1,2,3],[4,5,6],[7,8,9]]) print data [[1 2 3] [4 5 6] [7 8 9]] x, y, z = data[:,0], data[:,1], data[:,2] ## Help me here!...

How can I transform this numpy array: [[[10 10]] [[300 300]] [[10 300]]] Into this one: [[[ 10 10] [300 300] [ 10 300]]] ...

I have obtained the coefficients for the Legendre polynomial that best fits my data. Now I am needing to determine the value of that polynomial at each time-step of my data. I need to do this so that I can subtract the fit from my data. I have looked at...

Can one explain why after estimation of kernel density d = gaussian_kde(g[:,1]) And calculation of integral sum of it: x = np.linspace(0, g[:,1].max(), 1500) integral = np.trapz(d(x), x) I got resulting integral sum completely different to 1: print integral Out: 0.55618 ...

In Matlab, I can do the following: X = randn(25,25,25); size(X(:,:)) ans = 25 625 I often find myself wanting to quickly collapse the trailing dimensions of an array, and do not know how to do this in numpy. I know I can do this: In [22]: x = np.random.randn(25,25,25)...

I'm writing a code that part of it is reading an image source and displaying it on the screen for the user to interact with. I also need the sharpened image data. I use the following to read the data and display it in pyGame def image_and_sharpen_array(file_name): #read the image...

I have looked into documentations and also other question in here, but seems I have not got the hang of subsetting in numpy arrays yet. I have a numpy array, and for the sake of argument, let it be defined as follows: import numpy as np a = np.arange(100) a.shape...

I have wrapped a C function calculate_something in Python. The function takes 3 floats, does some calculations, and returns 2 floats. I want to use this function on 3 arrays of floats. That is, for each index in the arrays, call calculate_something on the corresponding floats and store the outputs...

I have a vector orig which is a p dimensional vector Now, I sampled c elements from this vector (with replacement), lets call it sampled_vec. So basically,sampled_vec has elements from orig Now, I want to find out the indices of these elements (in sampled_vec) from orig. Probably, an example would...

I want to calculate this fomula. I think the result is A. So I write a python code using numpy. But depending on the computation sequence, result is not A. What brought this on? import numpy as np from numpy import * from numpy.random import * import decimal #generate matrix...

My code: #!/usr/bin/python import numpy as np names = np.array(['Bob', 'Joe', 'Will', 'Bob', 'Will', 'Joe', 'Joe']) data = np.random.randn(7, 4) + 0.8 print (data) mask2= ((names != 'Joe') == 7.0) d2 = data[mask2] print (d2) d3 = data[names != 'Joe'] = 7.0 print (d3) Actually,my intention was to get the...

I want to reshape the following data frame: index id numbers 1111 5 58.99 2222 5 75.65 1000 4 66.54 11 4 60.33 143 4 62.31 145 51 30.2 1 7 61.28 The reshaped data frame should be like the following: id 1 2 3 5 58.99 75.65 nan 4...

I have a question similar to this and that, but the solutions to those is that all lists are "united" with lack of "differentiation". My Python code is like following: y = np.empty([1,len(test)]) count = -1 for feature in test : N = engine.neighbours(np.asarray(feature)) if len(N) != 4096: print "error"...

For a large set of randomly distributed points in a 2D lattice, I want to efficiently extract a subarray, which contains only the elements that, approximated as indices, are assigned to non-zero values in a separate 2D binary matrix. Currently, my script is the following: lat_len = 100 # lattice...

I have a function f in theano which takes two parameters, one of them optional. When I call the function with the optional parameter being None the check inside f fails. This script reproduces the error: import theano import theano.tensor as T import numpy as np # function setup def...

I created a module in c++ and need to use the results in python. Already wrote a wrapper and it is working with this code a = np.empty([r, hn]) for i in xrange(r): for j in xrange(hn): a[i,j]=self.thisptr.H[i*hn+j] return a The code is working, but I think there should be...

I have the following problem in Python I need to solve: Given two coordinate matrices (NumPy ndarrays) A and B, find for all coordinate vectors a in A the corresponding coordinate vectors b in B, such that the Euclidean distance ||a-b|| is minimized. The coordinate matrices A and B can...

I created this toy problem that reflects my much bigger problem: import numpy as np ind = np.ones((3,2,4)) # shape=(3L, 2L, 4L) dist = np.array([[0.1,0.3],[1,2],[0,1]]) # shape=(3L, 2L) ans = np.array([np.dot(dist[i],ind[i]) for i in xrange(dist.shape[0])]) # shape=(3L, 4L) print ans """ prints: [[ 0.4 0.4 0.4 0.4] [ 3. 3....

How do I get x to become a 1D array? I found it convenient to create x like this, x=np.array([[0,-1,0]*12,[-1,0,0]*4]) print x print len(x) returns array([ [0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0, 0, -1, 0,...

I have two big csv files with different number of rows which I am importing as follows: tdata = pd.read_csv(tfilepath, sep=',', parse_dates=['date_1']) print(tdata.iloc[:, [0,3]]) TBA date_1 0 0 2010-01-04 1 9 2010-01-05 2 0 2010-01-06 3 8 2010-01-07 4 0 2010-01-08 5 0 2010-01-09 pdata = pd.read_csv(pfilepath, sep=',', parse_dates=['date_2']) print(pdata.iloc[:,...

I have a little piece of code which is running well, however I am struggling to determine a way of outputting it into a .txt file. Here is the code: with open("Coord") as f: line=f.readline() for line in f: coords=map(float,line.split(" ")) if poly.contains(Point(coords[0],coords[1])): print line The print command works and...

I try get mean from csv line. I get data from csv in string list, further i convert it to array with numpy. Its work perfect when i try plot some graphics. But when i calculate mean i get some errors with my data. If i use NumPy i get:...

I'm new to python so I need some help with this: I have 2D array of numbers that represent density of a circle of material in a space and I want to locate the centre. So I want to get the index of the numbers representing the diameter and then...

I'd like to superimpose a binary mask over a color image, such that where the mask is "on", the pixel value changes by an amount that I can set. The result should look like this: I am using OpenCV 2.4 and Python 2.7.6. I have a way that works well,...

I have this numpy array with points, something like [(x1,y1), (x2,y2), (x3,y3), (x4,y4), (x5,y5)] What I would like to do, is to get an array of all minimum distances. So for point 1 (x1, y1), I want the distance of the point closest to it, same for point 2 (x2,y2),...

A = np.array([[1,2,3],[3,4,5],[5,6,7]]) X = np.array([[0, 1, 0]]) for i in xrange(np.shape(X)[0]): for j in xrange(np.shape(X)[1]): if X[i,j] == 0.0: A = np.delete(A, (j), axis=0) I am trying to delete j from A if in X there is 0 at index j. I get IndexError: index 2 is out of...

I have a correlation matrix named corrdata that I calculated using numpy.corrcoef. Then what I do is extract one or a few rows of this matrix, and now just want to plot them instead of the whole matrix. Because the matrix is no longer square, it is not possible to...

The question is more focused on performance of calculation. I have 2 matrix with the same number of columns and different number of rows. One matrix is the 'pattern' whose rows have to be compared separately with the other matrix rows (all rows), then to be able to extract statistical...

I have three arrays lat=[15,15.25,15.75,16,....30] long=[91,91.25,91.75,92....102] data= array([[ 0. , 0. , 0. , ..., 0. , 0. , 0. ], [ 0. , 0. , 0. , ..., 0. , 0. , 0. ], [ 0. , 0. , 0. , ..., 0. , 0. , 0. ], ...,...

I've one dimensional position list of 10x10 grid: [(0, 0), (0, 1), (0, 2), ..., (9, 9)] I would like numpy array like this (list of 10 length list): array([[ (0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (0, 6), (0, 7), (0, 8), (0, 9)...

I am trying to read one input file of below format. Where Col[1] is x axis and Col[2] is y axis and col[3] is some name. I need to plot multiple line graphs for separate names of col[3]. Eg: Name sd with x,y values will have one line graph and...

I wrote a differentiable Heaviside function and vectorised it. However, the output seems to be odd and binary. The code is as follows: import numpy as np import matplotlib.pyplot as plt def heaviside(x, epis): if (x>= epis): y=1 elif (x< -epis): y=0; else: y = 0.5 + x/(2*epis) + np.sin(np.pi*x/epis)/(2*np.pi);...

I'm very new to Python but I'm trying to produce a 2D Gaussian fit for some data. Specifically, stellar fluxes linked to certain positions in a coordinate system/grid. However not all of the positions in my grid have corresponding flux values. I don't really want to set these values to...

If we convert a large array containing 0 and 1 as boolean to another array containing 0 and 1 as float, the size of array would be almost 10 times larger. What is the best way (if any) to handle this issue in python (Numpy) if we need this conversion?

I'm trying to implement some machine learning algorithms, but I'm having some difficulties putting the data together. In the example below, I load a example data-set from UCI, remove lines with missing data (thanks to the help from a previous question), and now I would like to try to normalize...

I have a file with 44,586 lines of data. It is read in using pylab: data = pl.loadtxt("20100101.txt") density = data[:,0] I need to run something like... densities = np.random.normal(density, 30, 1) np.savetxt('1.txt', np.vstack((densities.ravel())).T) ...and create a new file named 1.txt which has all 44,586 lines of my data randomised...

I was trying to get this ConvexHull function running, and I needed numpy (I think) to get it to work. I'm going to try to get numpy uninstalled and reinstalled, but I'm not sure why/how this happened so that I can prevent it from happening again. While I was running...