pymatreader | python package to read all kinds and all versions of Matlab

by larsoner HTML Version: Current License: Non-SPDX

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kandi X-RAY | pymatreader Summary

pymatreader is a HTML library. pymatreader has no bugs, it has no vulnerabilities and it has low support. However pymatreader has a Non-SPDX License. You can download it from GitLab.

A Python module to read Matlab files. This module works with both the old (< 7.3) and the new (>= 7.3) HDF5 based format. The output should be the same for both kinds of files.

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pymatreader has a low active ecosystem.

It has 0 star(s) with 0 fork(s). There are no watchers for this library.

It had no major release in the last 6 months.

pymatreader has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of pymatreader is current.

Quality

pymatreader has no bugs reported.

Security

pymatreader has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

pymatreader has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

Reuse

pymatreader releases are not available. You will need to build from source code and install.

Installation instructions are not available. Examples and code snippets are available.

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Community Discussions

Trending Discussions on pymatreader

How can I improve the performance of my script?

How to fix: ValueError: Error when checking input: expected conv2d_130_input to have shape (1, 512, 512) but got array with shape (79, 512, 512)

QUESTION

How can I improve the performance of my script?

Asked 2020-Feb-27 at 18:33

I have a "seed" GeoDataFrame (GDF)(RED) which contains a 0.5 arc minutes global grid ((180*2)*(360*2) = 259200). Each cell contains an absolute population estimate. In addition, I have a "leech" GDF (GREEN) with roughly 8250 adjoining non-regular shapes of various sizes (watersheds).

I wrote a script to allocate the population estimates to the geometries in the leech GDF based on the overlapping area between grid cells (seed GDF) and the geometries in the leech GDF. The script works perfectly fine for my sample data (see below). However, once I run it on my actual data, it is very slow. I ran it overnight and the next morning only 27% of the calculations had been performed. I will have to run this script many times and waiting for two days each time, is simply not an option.

After doing a bit of literature research, I already replaced (?) for loops with for index i in df.iterrows() (or is this the same as "conventional" python for loops) but it didn't bring about the performance imporvement I had hoped for.

Any suggestion son how I can speed up my code? In twelve hours, my script only processed only ~30000 rows out of ~200000.

My expected output is the column leech_df['leeched_values'].

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ANSWER

Answered 2020-Feb-27 at 18:33

Introduction

It might be worthy to profile your code in details to get precise insights of what is your bottleneck.

Bellow some advises to already improve your script performance:

Avoid list.append(1) to count occurrences, use collection.Counter instead;
Avoid pandas.DataFrame.iterrows, use pandas.DataFrame.itertuples instead;
Avoid extra assignation that are not needed, use pandas.DataFrame.fillna instead:

Eg. this line:

Source https://stackoverflow.com/questions/60338228

QUESTION

How to fix: ValueError: Error when checking input: expected conv2d_130_input to have shape (1, 512, 512) but got array with shape (79, 512, 512)

Asked 2020-Feb-09 at 06:48

I am a beginner at working with CNNs.

So, I am building a 2D convolutional neural network that predicts brain tumor type and have a question about NumPy arrays. The input-shape of my model is (1, 512, 512) as (channels, img_height, img_width). The 4th dimension is num_images which seems to be automatically defined by TensorFlow. This is just a quick background. I have 3064 ".mat" extension files with MRI scans of brain tumors. Everything is setup. I converted ".mat" files into numpy matrices and appended the entire list of matrices in a single numpy array to pass as input for the CNN. I also have the corresponding labels (index-linked to the images when passing input into the model) as a numpy array. All the numbers are of float type in both images and labels.

Again, my input shape is (1, 512, 512). However, when fitting my model I get the following error:

ValueError: Error when checking input: expected conv2d_130_input to have shape (1, 512, 512) but got array with shape (79, 512, 512)

So, I am slicing my NumPy arrays to create train_images, train_labels, test_images, test_labels. I have verified the length of each both train and test sets with there labels match. They are also arrays, I checked multiple times. And this is a value error. So, how do I fix this?

I don't even know where the input shape became (79,512,512). I have a loop to convert f"{n}.mat" images to a matrix. I am using 100 images to test and have 80 train and 20 test. I think the mistake is here, the input shape is (channels, img-hght, img-wdth), but the number of images left to train is being placed in the channel's value instead. So, the input is being placed as (num_images, img-hght, img-wdth). This is wrong and should be changed, but I don't know how to do it. Or, I could be wrong and what I said might not make sense. I am providing all the code, running it on Colab. Make sure to change the image paths if you download the code and want to run it in order to help me out. Thanks a lot!

Dataset: https://figshare.com/articles/brain_tumor_dataset/1512427/5

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ANSWER

Answered 2020-Feb-09 at 06:48

Add the line:

Source https://stackoverflow.com/questions/60133220

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