fast_interp | numba accelerated interpolation on regular grids | Data Manipulation library

by dbstein Python Version: Current License: Apache-2.0

X-Ray Key Features Code Snippets Community Discussions(10)Vulnerabilities Install Support

kandi X-RAY | fast_interp Summary

fast_interp is a Python library typically used in Utilities, Data Manipulation, Numpy applications. fast_interp has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

This code provides functionality similar to the scipy.interpolation functions for smooth functions defined on regular arrays in 1, 2, and 3 dimensions. Like the scipy.interpolate functions (and unlike map_coordinates or some other fast interpolation packages), this function is asmptotically accurate up to the boundary, meaning that the interpolation accuracy is second-, fourth-, and sixth-order accurate for k=1, 3, and 5, respectively, even when interpolating to points that are close to the edges of the domains on which the data is defined. It is even asymptotically accurate when extrapolating, although this in general is not recommended as it is numerically unstable. Unlike the scipy.interpolate functions, this is not based on spline interpolation, but rather the evaluation of local Taylor expansions to the required order, with derivatives estimated using finite differences. Some rearrangement of terms and the order in which things are evaluated makes the code surprisingly stable. The provided data is padded (by local extrapolation, or periodic wrapping when the user specifies) in order to maintain accuracy at the boundary. If near boundary interpolation is not needed, the user can specify this, and the padding step is skipped. For dimensions that the user specifies are periodic, the interpolater does the correct thing for any input value. For non-periodic dimensions, constant extrapolation is done outside of the specified interpolation region. The user can request that extrapolation is done along a dimension to some distance (specified in units of gridspacing). Although I have attempted to make the computation of this reasonably stable, extrapolation is dangerous, use at your own risk.

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Support

fast_interp has a low active ecosystem.

It has 39 star(s) with 12 fork(s). There are 4 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 3 have been closed. On average issues are closed in 91 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of fast_interp is current.

Quality

fast_interp has no bugs reported.

Security

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

License

fast_interp is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

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

Build file is available. You can build the component from source.

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

Top functions reviewed by kandi - BETA

kandi has reviewed fast_interp and discovered the below as its top functions. This is intended to give you an instant insight into fast_interp implemented functionality, and help decide if they suit your requirements.

Extrapolate 3d array
Extrapolate 1d x into f
Extrapolate 1D y
Extrapolate 1d z
Fill in fb
Fill fill in fb
Extrapolate 2d array
Fill f in fb
Computes the bounding box of the curve
Compute bounding box
Extrapulates a 1D array with the given k
Wrapper around njit njit
Wrapper function for njit

Get all kandi verified functions for this library.

fast_interp Key Features

No Key Features are available at this moment for fast_interp.

fast_interp Examples and Code Snippets

No Code Snippets are available at this moment for fast_interp.

Community Discussions

Trending Discussions on Data Manipulation

R: Is there a "Un-Character" Command in R?

Creating new columns based on data in row separated by specific character in R

Multiplying and Adding Values across Rows

How to make a rank column in R

How to return the column title wherein the row contains the greatest value in Pandas Dataframe

Split large csv file into multiple files based on column(s)

Get the first non-null value from selected cells in a row

pivot_longer with column pairs

Simulating Random Draws From a "Hat"

Break Apart a String into Separate Columns R

QUESTION

R: Is there a "Un-Character" Command in R?

Asked 2022-Apr-10 at 17:37

I am working with the R programming language.

I have the following dataset:

...

ANSWER

Answered 2022-Apr-10 at 05:36

Up front, "1,3,4" != 1. It seems you should look to split the strings using strsplit(., ",").

Source https://stackoverflow.com/questions/71813866

QUESTION

Creating new columns based on data in row separated by specific character in R

Asked 2022-Mar-15 at 08:48

I've the following table

Owner Pet Housing_Type A Cats;Dog;Rabbit 3 B Dog;Rabbit 2 C Cats 2 D Cats;Rabbit 3 E Cats;Fish 1

The code is as follows:

...

ANSWER

Answered 2022-Mar-15 at 08:48

One approach is to define a helper function that matches for a specific animal, then bind the columns to the original frame.

Note that some wrangling is done to get rid of whitespace to identify the unique animals to query.

Source https://stackoverflow.com/questions/71478316

QUESTION

Multiplying and Adding Values across Rows

Asked 2022-Mar-10 at 08:24

I have this data frame:

...

ANSWER

Answered 2022-Mar-10 at 04:12

We can use stri_replace_all_regex to replace your color_1 into integers together with the arithmetic operator.

Here I've stored your values into a vector color_1_convert. We can use this as the input in stri_replace_all_regex for better management of the values.

Source https://stackoverflow.com/questions/71418533

QUESTION

How to make a rank column in R

Asked 2022-Mar-07 at 16:19

I have a database with columns M1, M2 and M3. These M values correspond to the values obtained by each method. My idea is now to make a rank column for each of them. For M1 and M2, the rank will be from the highest value to the lowest value and M3 in reverse. I made the output table for you to see.

...

ANSWER

Answered 2022-Mar-07 at 14:15

Using rank and relocate:

Source https://stackoverflow.com/questions/71381995

QUESTION

How to return the column title wherein the row contains the greatest value in Pandas Dataframe

Asked 2022-Feb-24 at 20:56

I working on a Python project that has a DataFrame like this:

...

ANSWER

Answered 2022-Feb-24 at 20:48

You could use the idxmax method on axis:

Source https://stackoverflow.com/questions/71258033

QUESTION

Split large csv file into multiple files based on column(s)

Asked 2022-Feb-07 at 12:49

I would like to know of a fast/efficient way in any program (awk/perl/python) to split a csv file (say 10k columns) into multiple small files each containing 2 columns. I would be doing this on a unix machine.

...

ANSWER

Answered 2021-Dec-12 at 05:22

With your show samples, attempts; please try following awk code. Since you are opening files all together it may fail with infamous "too many files opened error" So to avoid that have all values into an array and in END block of this awk code print them one by one and I am closing them ASAP all contents are getting printed to output file.

Source https://stackoverflow.com/questions/70320648

QUESTION

Get the first non-null value from selected cells in a row

Asked 2022-Feb-04 at 09:55

Good afternoon, friends!

I'm currently performing some calculations in R (df is displayed below). My goal is to display in a new column the first non-null value from selected cells for each row.

My df is:

...

ANSWER

Answered 2022-Feb-03 at 11:16

One option with dplyr could be:

Source https://stackoverflow.com/questions/70970158

QUESTION

pivot_longer with column pairs

Asked 2022-Feb-03 at 14:02

I am again struggling with transforming a wide df into a long one using pivot_longer The data frame is a result of power analysis for different effect sizes and sample sizes, this is how the original df looks like:

...

ANSWER

Answered 2022-Feb-03 at 10:59

library(tidyverse)

example %>% 
  pivot_longer(cols = starts_with("es"), names_to = "type", names_prefix = "es_", values_to = "es") %>%
  pivot_longer(cols = starts_with("pwr"), names_to = "pwr", names_prefix = "pwr_") %>% 
  filter(substr(type, 1, 3) == substr(pwr, 1, 3)) %>% 
  mutate(pwr = parse_number(pwr)) %>% 
  arrange(pwr, es, type)

Source https://stackoverflow.com/questions/70969176

QUESTION

Simulating Random Draws From a "Hat"

Asked 2021-Dec-28 at 21:50

Suppose I have the following 10 variables (num_var_1, num_var_2, num_var_3, num_var_4, num_var_5, factor_var_1, factor_var_2, factor_var_3, factor_var_4, factor_var_5):

...

ANSWER

Answered 2021-Dec-26 at 10:11

You may define a function FUN(n) that creates a data set as shown in OP.

Source https://stackoverflow.com/questions/70483731

QUESTION

Break Apart a String into Separate Columns R

Asked 2021-Dec-17 at 20:39

I am trying to tidy up some data that is all contained in 1 column called "game_info" as a string. This data contains college basketball upcoming game data, with the Date, Time, Team IDs, Team Names, etc. Ideally each one of those would be their own column. I have tried separating with a space delimiter, but that has not worked well since there are teams such as "Duke" with 1 part to their name, and teams with 2 to 3 parts to their name (Michigan State, South Dakota State, etc). There also teams with "-" dashes in their name.

Here is my data:

...

ANSWER

Answered 2021-Dec-16 at 15:25

Here's one with regex. See regex101 link for the regex explanations

Source https://stackoverflow.com/questions/70381064

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

Vulnerabilities

No vulnerabilities reported

Install fast_interp

You can download it from GitHub.
You can use fast_interp 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.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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