# scipy | SciPy library main repository

## kandi X-RAY | scipy Summary

## kandi X-RAY | scipy Summary

SciPy library main repository

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### Top functions reviewed by kandi - BETA

- Calculate least squares .
- Linear Grammar algorithm .
- r Solve an IVP .
- r Solve a problem .
- Compute the lsqr of A and b .
- Integrate quadratic integrand .
- Test a permutation test .
- r Solve the linear operator .
- r Solve a binary quadratic problem .
- Solve linear problem .

## scipy Key Features

## scipy Examples and Code Snippets

```
if __name__ == "__main__":
from numpy.distutils.core import setup
#setup(**configuration(top_path='').todict())
setup(configuration=configuration)
```

```
def _convert_scipy_sparse_tensor(value, expected_input):
"""Handle scipy sparse tensor conversions.
This method takes a value 'value' and returns the proper conversion. If
value is a scipy sparse tensor and the expected input is a dense tensor
```

```
def name_scope(name):
"""A context manager for use when defining a Python op.
This context manager pushes a name scope, which will make the name of all
operations added within it have a prefix.
For example, to define a new Python op called
```

```
def _scipy_sparse_to_sparse_tensor(t):
"""Converts a SciPy sparse matrix to a SparseTensor."""
sparse_coo = t.tocoo()
row, col = sparse_coo.row, sparse_coo.col
data, shape = sparse_coo.data, sparse_coo.shape
if issubclass(data.dtype.type, n
```

```
import altair as alt
from vega_datasets import data
df = data.cars()
# 1. Create violin plot
violin = alt.Chart(df).transform_density(
"Horsepower",
as_=["Horsepower", "density"],
).mark_area().encode(
x="Horsepower:Q",
y
```

```
In [166]: from scipy import sparse
In [167]: M = sparse.random(5,5,.2,'csc')
In [168]: M
Out[168]:
<5x5 sparse matrix of type ''
with 5 stored elements in Compressed Sparse Column format>
In [169]: M.A
Out[169]:
array([[0.24975
```

```
np.random.seed(1)
x = np.random.rand(30, 2).astype(np.float128)
np.random.seed(2)
x_test = np.random.rand(5,2).astype(np.float128)
```

```
print(gx[:,0] - gx0)
```

```
[0. 0. 0. 0. 0.]
```

```
chunk_val = 2000000 # it is arbitrary and must be choosed based on the system rams size
chunk = xi.shape[0] // chunk_val
chunk_res = xi.shape[0] % chunk_val
# by array
di = np.array([])
start = 0
for i in range(chunk + 1
```

```
P(X) = probability of X being True = (# of True elements in X) / (# of elements in X)
P(Y) = probability of Y being True = (# of True elements in Y) / (# of elements in Y)
P(X and Y) = probability of both X and Y being True = P(X) * P(Y)
```

## Community Discussions

Trending Discussions on scipy

QUESTION

I have source (`src`

) image(s) I wish to align to a destination (`dst`

) image using an Affine Transformation whilst retaining the full extent of both images during alignment (even the non-overlapping areas).

I am already able to calculate the Affine Transformation rotation and offset matrix, which I feed to `scipy.ndimage.interpolate.affine_transform`

to recover the `dst`

-aligned `src`

image.

The problem is that, when the images are not fuly overlapping, the resultant image is cropped to only the common footprint of the two images. What I need is the full extent of both images, placed on the same pixel coordinate system. This question is almost a duplicate of this one - and the excellent answer and repository there provides this functionality for OpenCV transformations. I unfortunately need this for `scipy`

's implementation.

Much too late, after repeatedly hitting a brick wall trying to translate the above question's answer to `scipy`

, I came across this issue and subsequently followed to this question. The latter question did give some insight into the wonderful world of `scipy`

's affine transformation, but I have as yet been unable to crack my particular needs.

The transformations from `src`

to `dst`

can have translations and rotation. I can get *translations only* working (an example is shown below) and I can get *rotations only* working (largely hacking around the below and taking inspiration from the use of the `reshape`

argument in `scipy.ndimage.interpolation.rotate`

). However, I am getting thoroughly lost combining the two. I have tried to calculate what should be the correct `offset`

(see this question's answers again), but I can't get it working in all scenarios.

Translation-only working example of padded affine transformation, which follows largely this repo, explained in this answer:

...ANSWER

Answered 2022-Mar-22 at 16:44If you have two images that are similar (or the same) and you want to align them, you can do it using both functions rotate and shift :

QUESTION

Unable to import functions from scipy module.

Gives error :

...ANSWER

Answered 2022-Feb-17 at 08:30I encountered the same problem while using `statsmodels~=0.12.x`

. Increasing the statsmodels package to version `0.13.2`

, this import issue is resolved.

UPDATE with more notes:

- before:
- installation of fixed version of
`statsmodels==0.12.2`

which is dependent on`scipy`

- there was newly released
`scipy==1.8.0`

- 2022-02-05- when installing it, got this problem:

- installation of fixed version of

QUESTION

The installation on the m1 chip for the following packages: Numpy 1.21.1, pandas 1.3.0, torch 1.9.0 and a few other ones works fine for me. They also seem to work properly while testing them. However when I try to install scipy or scikit-learn via pip this error appears:

**ERROR: Failed building wheel for numpy**

**Failed to build numpy**

**ERROR: Could not build wheels for numpy which use PEP 517 and cannot be installed directly**

Why should Numpy be build again when I have the latest version from pip already installed?

Every previous installation was done using `python3.9 -m pip install ...`

on Mac OS 11.3.1 with the apple m1 chip.

Maybe somebody knows how to deal with this error or if its just a matter of time.

...ANSWER

Answered 2021-Aug-02 at 14:33Please see this note of `scikit-learn`

about

**Installing on Apple Silicon M1 hardware**

The recently introduced

`macos/arm64`

platform (sometimes also known as`macos/aarch64`

) requires the open source community to upgrade the build configuation and automation to properly support it.At the time of writing (January 2021),

the only way to get a working installation of scikit-learn on this hardware is to install scikit-learn and its dependencies from the conda-forge distribution, for instance using the miniforge installers:https://github.com/conda-forge/miniforge

The following issue tracks progress on making it possible to install scikit-learn from PyPI with pip:

QUESTION

I was simulating the solar system (Sun, Earth and Moon). When I first started working on the project, I used the base units: meters for distance, seconds for time, and metres per second for velocity. Because I was dealing with the solar system, the numbers were pretty big, for example the distance between the Earth and Sun is 150·10⁹ m.

When I numerically integrated the system with `scipy.solve_ivp`

, the results were completely wrong. Here is an example of Earth and Moon trajectories.

But then I got a suggestion from a friend that I should use standardised units: astronomical unit (AU) for distance and years for time. And the simulation started working flawlessly!

My question is: Why is this a generally valid advice for problems such as mine? (Mind that this is not about my specific problem which was already solved, but rather why the solution worked.)

...ANSWER

Answered 2021-Jul-25 at 07:42Most, if not all integration modules work best out of the box if:

- your dynamical variables have the same order of magnitude;
- that order of magnitude is 1;
- the smallest time scale of your dynamics also has the order of magnitude 1.

This typically fails for astronomical simulations where the orders of magnitude vary and values as well as time scales are often large in typical units.

The reason for the above behaviour of integrators is that they use step-size adaption, i.e., the integration step is adjusted to keep the estimated error at a defined level. The step-size adaption in turn is governed by a lot of parameters like absolute tolerance, relative tolerance, minimum time step, etc. You can usually tweak these parameters, but if you don’t, there need to be some default values and these default values are chosen with the above setup in mind.

DigressionYou might ask yourself: Can these parameters not be chosen more dynamically? As a developer and maintainer of an integration module, I would roughly expect that introducing such automatisms has the following consequences:

- About twenty in a thousand users will not run into problems like yours.
- About fifty a thousand users (including the above) miss an opportunity to learn rudimentary knowledge about how integrators work and reading documentations.
- About one in thousand users will run into a horrible problem with the automatisms that is much more difficult to solve than the above.
- I need to introduce new parameters governing the automatisms that are even harder to grasp for the average user.
- I spend a lot of time in devising and implementing the automatisms.

QUESTION

I am working on a spatial search case for spheres in which I want to find connected spheres. For this aim, I searched around each sphere for spheres that centers are in a (* maximum sphere diameter*) distance from the searching sphere’s center. At first, I tried to use scipy related methods to do so, but scipy method takes longer times comparing to equivalent numpy method. For scipy, I have determined the number of K-nearest spheres firstly and then find them by

`cKDTree.query`

, which lead to more time consumption. However, it is slower than numpy method even by omitting the first step with a constant value (it is not good to omit the first step in this case). *So, I tried to use some list-loops instead some numpy lines for speeding up using numba*

**It is contrary to my expectations about scipy spatial searching speed.**`prange`

. Numba run the code a little faster, but I believe that this code can be optimized for better performances, perhaps by vectorization, using other alternative numpy modules or using numba in another way. I have used iteration on all spheres due to prevent probable memory leaks and …, where number of spheres are high.ANSWER

Answered 2022-Feb-14 at 10:23Have you tried FLANN?

This code doesn't solve your problem completely. It simply finds the nearest 50 neighbors to each point in your 500000 point dataset:

QUESTION

I have pretrained model for object detection (Google Colab + TensorFlow) inside Google Colab and I run it two-three times per week for new images I have and everything was fine for the last year till this week. Now when I try to run model I have this message:

...ANSWER

Answered 2022-Feb-07 at 09:19It happened the same to me last friday. I think it has something to do with Cuda instalation in Google Colab but I don't know exactly the reason

QUESTION

I am trying to install conda on EMR and below is my bootstrap script, it looks like conda is getting installed but it is not getting added to environment variable. When I manually update the `$PATH`

variable on EMR master node, it can identify `conda`

. I want to use conda on Zeppelin.

I also tried adding condig into configuration like below while launching my EMR instance however I still get the below mentioned error.

...ANSWER

Answered 2022-Feb-05 at 00:17I got the conda working by modifying the script as below, emr python versions were colliding with the conda version.:

QUESTION

How do I assign to each edge a weight equals to the number of times node i and j interacted from an edge list?

...ANSWER

Answered 2022-Feb-02 at 14:20You can first aggregate the `pandas`

tables to have a weight column, and then load it to `networkx`

with that edge column:

QUESTION

Error while installing manimce, I have been trying to install manimce library on windows subsystem for linux and after running

...ANSWER

Answered 2022-Jan-28 at 02:24```
apt-get install sox ffmpeg libcairo2 libcairo2-dev
apt-get install texlive-full
pip3 install manimlib # or pip install manimlib
```

QUESTION

I have created a working CNN model in Keras/Tensorflow, and have successfully used the CIFAR-10 & MNIST datasets to test this model. The functioning code as seen below:

...ANSWER

Answered 2021-Dec-16 at 10:18If the hyperspectral dataset is given to you as a large image with many channels, I suppose that the classification of each pixel should depend on the pixels around it (otherwise I would not format the data as an image, i.e. without grid structure). Given this assumption, breaking up the input picture into 1x1 parts is not a good idea as you are loosing the grid structure.

I further suppose that the order of the channels is arbitrary, which implies that convolution over the channels is probably not meaningful (which you however did not plan to do anyways).

Instead of reformatting the data the way you did, you may want to create a model that takes an image as input and also outputs an "image" containing the classifications for each pixel. I.e. if you have 10 classes and take a (145, 145, 200) image as input, your model would output a (145, 145, 10) image. In that architecture you would not have any fully-connected layers. Your output layer would also be a convolutional layer.

That however means that you will not be able to keep your current architecture. That is because the tasks for MNIST/CIFAR10 and your hyperspectral dataset are not the same. For MNIST/CIFAR10 you want to classify an image in it's entirety, while for the other dataset you want to assign a class to each pixel (while most likely also using the pixels around each pixel).

Some further ideas:

- If you want to turn the pixel classification task on the hyperspectral dataset into a classification task for an entire image, maybe you can reformulate that task as "classifying a hyperspectral image as the class of it's center (or top-left, or bottom-right, or (21th, 104th), or whatever) pixel". To obtain the data from your single hyperspectral image, for each pixel, I would shift the image such that the target pixel is at the desired location (e.g. the center). All pixels that "fall off" the border could be inserted at the other side of the image.
- If you want to stick with a pixel classification task but need more data, maybe split up the single hyperspectral image you have into many smaller images (e.g. 10x10x200). You may even want to use images of many different sizes. If you model only has convolution and pooling layers and you make sure to maintain the sizes of the image, that should work out.

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

## Vulnerabilities

No vulnerabilities reported

## Install scipy

You can use scipy like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

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