numpy

问题 I have some data with multiple observations for a given Collector, Date, Sample, and Type where the observation values vary by ID. import StringIO import pandas as pd data = """Collector,Date,Sample,Type,ID,Value Emily,2014-06-20,201,HV,A,34 Emily,2014-06-20,201,HV,B,22 Emily,2014-06-20,201,HV,C,10 Emily,2014-06-20,201,HV,D,5 John,2014-06-22,221,HV,A,40 John,2014-06-22,221,HV,B,39 John,2014-06-22,221,HV,C,11 John,2014-06-22,221,HV,D,2 Emily,2014-06-23,203,HV,A,33 Emily,2014-06-23,203,HV,B,35

问题 I am trying to use H5T_ARRAY inside the H5T_COMPOUND structure using Python. Basically, I am writing hdf5 file and if you open it using H5Dump, the structure looks like this. HDF5 "SO_64449277np.h5" { GROUP "/" { DATASET "Table3" { DATATYPE H5T_COMPOUND { H5T_COMPOUND { H5T_STD_I16LE "id"; H5T_STD_I16LE "timestamp"; } "header"; H5T_COMPOUND { H5T_IEEE_F32LE "latency"; H5T_STD_I16LE "segments_k"; H5T_COMPOUND { H5T_STD_I16LE "segment_id"; H5T_IEEE_F32LE "segment_quality"; H5T_IEEE_F32LE

问题 I will need to store N lists, where N is large (1 million). For example, [2,3] [4,5,6] ... [4,5,6,7] Each item is a list of about 0-10000 elements. I wanted to use a numpy array of lists, like np.array([[2,3],[4,5,6]) Then I got efficiency issues when trying to append to the lists in the numpy array. Also I was told here: Efficiently append an element to each of the lists in a large numpy array, to not use numpy array of lists. What would be a good data structure for storing such data, in

问题 I have two virtually identical scripts running in the same PyCharm IDE. They both call into a third script that uses matplotlib to output a Numpy array to a PNG. One of the scripts works fine and outputs the PNG. The other script raises the following error: qt.qpa.plugin: Could not load the Qt platform plugin "windows" in "" even though it was found. The differences between the scripts are minimal - they only vary in that they each import a different pytorch model (both models created by me).

问题 I have a bunch of lines created by connecting some points. I have the number number of lines as a numpy array: line_no_A= np.arange (17, 31) sep_A=23 These lines are arranged in two perpendicular direction, shown as blue and red lines. From the sep , direction of lines changes. I have also the number of points createing these lines: point_rep_A=np.array([4, 3, 3, 1]) Now, I want to extract some specific lines. I uploaded a fig and shown the specifics line with blue circles. To find the red

问题 Got a big data-set that I want to shuffle. The entire set won't fit into RAM so it would be good if I could open several files (e.g. hdf5, numpy) simultaneously, loop through my data chronologically and randomly assign each data-point to one of the piles (then afterwards shuffle each pile). I'm really inexperienced with working with data in python so I'm not sure if it's possible to write to files without holding the rest of its contents in RAM (been using np.save and savez with little

问题 Got a big data-set that I want to shuffle. The entire set won't fit into RAM so it would be good if I could open several files (e.g. hdf5, numpy) simultaneously, loop through my data chronologically and randomly assign each data-point to one of the piles (then afterwards shuffle each pile). I'm really inexperienced with working with data in python so I'm not sure if it's possible to write to files without holding the rest of its contents in RAM (been using np.save and savez with little

问题 I was trying to normalize a simple numpy array a as follows: a = np.ones((3,3)) cv2.normalize(a) On running this, OpenCV throws an error saying TypeError: Required argument 'dst' (pos 2) not found . So I put the dst argument as also mentioned in the documentation. Here is how I did: b = np.asarray([]) cv2.normalize(a, b) This call returns the normalized array but the value of b is still empty. Why is it so? On the other hand, if I try the following: b = np.copy(a) cv2.normalize(a,b) The

问题 I've been experimenting with vectorizing a function of mine and have hit a strange bug in my code that I haven't been able to figure out. Here's the numpy array in question (https://filebin.net/c14dcklwakrv1hw8) import numpy as np example = np.load("example_array.npy") #Shape (2, 5, 5) The problem I was trying to solve was to normalize the values in each row such that they summed to 1, except of course rows that are entirely 0's. Since numpy divide has an option to skip 0's when dividing the

问题 I am running a model on Google Colab.As I reproduce the code,I want to run one of the pieces of code to get the experimental results.And I get some error.Here is the code: cluster_args = { 'cluster_layers' : {1:400, 3:240}, 'conv_feature_size' : 1, 'reshape_exists' : False, 'features' : 'both', 'channel_reduction' : 'fro', 'use_bias' : False, 'linkage_method' : 'ward', 'distance_metric' : 'euclidean', 'cluster_criterion' : 'hierarchical_trunc', 'distance_threshold' : 1.60, 'merge_criterion' :