| Name | testgrumps JSON |
| Version |
1.0.0
JSON |
| download |
| home_page | None |
| Summary | Genomic distance based Rapid Uncovering of Microbial Population Structures |
| upload_time | 2024-09-03 23:44:26 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.7 |
| license | Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License. "Source" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. "Object" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. "Work" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). "Derivative Works" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. "Contribution" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, "submitted" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as "Not a Contribution." "Contributor" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work. 2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions: (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and (b) You must cause any modified files to carry prominent notices stating that You changed the files; and (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and (d) If the Work includes a "NOTICE" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. The contents of the NOTICE file are for informational purposes only and do not modify the License. You may add Your own attribution notices within Derivative Works that You distribute, alongside or as an addendum to the NOTICE text from the Work, provided that such additional attribution notices cannot be construed as modifying the License. You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License. 5. Submission of Contributions. Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions. 6. Trademarks. This License does not grant permission to use the trade names, trademarks, service marks, or product names of the Licensor, except as required for reasonable and customary use in describing the origin of the Work and reproducing the content of the NOTICE file. 7. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License. 8. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages. 9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. END OF TERMS AND CONDITIONS APPENDIX: How to apply the Apache License to your work. To apply the Apache License to your work, attach the following boilerplate notice, with the fields enclosed by brackets "[]" replaced with your own identifying information. (Don't include the brackets!) The text should be enclosed in the appropriate comment syntax for the file format. We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier identification within third-party archives. Copyright 2022 Kaleb Abram Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. |
| keywords |
ani
clustering
data cleaning
genome
machine learning
mash
population structure
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# GRUMPS
***G***enomic distance based ***R***apid ***U***ncovering of ***M***icrobial ***P***opulation ***S***tructures.
`grumps` is designed to assist and speed up the construction of species level population structures using Mash distances as an input. ANI values can be used as well if the ANI values are converted to a decimal difference value {i.e. (100 - ANIvalue) /100}. Additional helper scripts are provided if you do not have a correctly formatted distance matrix to input.
`grumps` reads a normalized distance matrix containing NxN pairwise genome comparisons and returns a filtered result.
It can be run in `summary`, `regular`, `strict`, `sigma`, `target`,
`clique`, and `small` modes.
For more information about these modes please see the white paper:
https://doi.org/10.1101/2022.12.19.521123
## Installation
`grumps` is installable from conda, pip or from source.
The best installation method for `grumps` is conda within a new environment.
### Installing from conda
#### Creating a new environment
```sh
conda env create -n grumps_env kabram::grumps
```
**Note:** using [mamba](https://mamba.readthedocs.io/en/latest/) as a replacement for conda can spead up the environment creation process.
#### Installing into existing environment
```sh
conda install kabram::grumps
```
`grumps` has two components in the conda package: `py-grumps` and `r-grumps`. Each component is a conda package which contains all necessary dependencies for that component.
To install the python component:
```sh
conda install kabram::py-grumps
```
To install the R component:
```sh
conda install kabram::r-grumps
```
### Installing from other sources
#### pip
```sh
pip install grumps
```
**Note:** the pip version does not have the R dependecies needed for `r-grumps`.
#### r-devtools
```sh
R -e 'devtools::install_github("kalebabram/r_grumps")'
```
**Note:** R library `devtools` is required: `install.packages('devtools')` or `conda install r-devtools` for this approach. To get the CLI entrypoint, download `cligrumps.R` from the repository. You can rename the Rscript to `r-grumps`, relocate it to a directory in your $PATH, and make it executable for equivalent behavior to the conda install of `r-grumps`.
#### Source
All neccessary files needed to build the python package of `grumps` are found in `src/grumps` within this repository.
All neccessary files needed to build the R package of `grumps` are found in the `r-grumps` repository: https://github.com/kalebabram/r-grumps.git
In order to get the CLI entrypoint for the R package, simply download the Rscript `cligrumps.R` to your computer. You can rename the Rscript to `r-grumps`, relocate it to a directory in your `$PATH`, and make it executable for equivalent behavior to the conda install of `r-grumps`.
## Dependencies
`grumps` utilizes the following python libraries:
* python
* pandas
* networkx
* seaborn
* scipy
* scikit-learn
`r-grumps` utilizes the following R libraries:
* MASS
* optparse
* ape
* grDevices
* RColorBrewer
* sparcl
* stats
* utils
## `grumps` library support
### python
`grumps` also is available as a python library to allow easy integration into existing python based workflows.
While `grumps` has many components, the following overview summarizes functions which users are intended to interact with:
```sh
grumps
├── .api
│ └── .pipeLine()
├── .core
│ ├── .grumpsObj()
│ └── .distmatConverter()
└── .modes
├── .regularMode()
├── .removerMode()
├── .targetMode()
├── .smallMode()
├── .sigmaMode()
├── .summaryMode()
└── .cliqueMode()
```
**Note:** `import grumps.api as grumps` will automatically load all the above functions which can be accessed via `grumps.<function_name>` (i.e. `grumps.grumpsObj()`)
The intended use of the python `grumps` library is as follows:
```py
import grumps.api as grumps
# if you need to convert your input file to NxN distance matrix. The location of the converted file is printed.
grumps.distmatConverter('/path/to/input/file.tab')
# load in the NxN distance matrix
data = grumps.grumpsObj('/path/to/distmat/file.csv')
# change these grumpsObj defaults or the cleaning modes/pipeline will run with defaults
data.mode = 'regular'
data.cutOff = 0.05
data.clusterMethod = 'ward'
data.makeHeatmap = 'yes'
data.figFormat = 'png'
data.targetFilePath = ''
data.removeFilePath = ''
data.sigma = 'yes'
data.medoid = 'yes'
# if you want to run grumps automatically
grumps.pipeLine(data)
# if you want to use a specific cleaning mode
data = grumps.regularMode(data)
```
### R
`r-grumps` also has an R library available.
An example RScript using `r-grumps` is given below:
```R
library(rgrumps)
# change filepath to path of the distance matrix
# change mode to one of the following: 'heatmap', 'dendrogram', or 'general'
grumps <- grumpsFunc(filepath='', mode = '', cutoff = 1.25E-02, clusteringmethod = 'ward.D2', tree = 'yes')
grumps = dataframeFunc(grumps)
grumps = clusterFunc(grumps)
if (grumps$mode == 'heatmap'){
grumps= mclFunc(grumps)
heatmapFunc(grumps)
labeloutFunc(grumps)
dendrogramFunc(grumps)
if (grumps$tree == 'yes'){
treeFunc(grumps)
}
}
if (grumps$mode == 'dendrogram'){
grumps = mclFunc(grumps)
labeloutFunc(grumps)
dendrogramFunc(grumps)
if (grumps$tree == 'yes'){
treeFunc(grumps)
}
}
if (grumps$mode == 'general'){
heatmapFunc(grumps)
grumps = heightCutter(grumps)
dendrogramFunc(grumps)
if (grumps$tree == 'yes'){
treeFunc(grumps)
}
}
```
## Usage Summary
### `grumps`
The following section provides a set of minimal command line commands to use `grumps`. Please use the help page, `grumps -h`, to see all command line options and what modes these options can be used with.
* **Produce help page.** Quickly check the software usage and available command line options.
```sh
grumps -h
```
* **Produce summary of input dataset.** Quickly obtain multiple statistical summaries as well as a histogram for the input dataset
```sh
grumps -m summary [filepath_to_dataset]
```
* **Clean input dataset using `regular` cleaning mode.** Clean the input dataset using K-means clustering.
```sh
grumps -m regular [filepath_to_dataset]
```
* **Clean input dataset using `strict` cleaning mode.** Clean the input dataset using K-means clustering followed by a three-sigma rule based cleaning step using the means of each genome.
```sh
grumps -m strict [filepath_to_dataset]
```
* **Clean input dataset using `clique` cleaning mode.** Clean the input dataset with a graph-based clustering approach. Useful for dividing datasets containing multiple species into a collection of uncleaned species level datasets.
```sh
grumps -m clique [filepath_to_dataset]
```
* **Clean input dataset using `sigma` cleaning mode.** Clean the input dataset using a three-sigma rule based cleaning step applied to the extreme left and right tails of value distribution for each genome. **Note:** this step is automatically performed in `regular` and `strict` cleaning modes if `-s no` not specified.
```sh
grumps -m sigma [filepath_to_dataset]
```
* **Clean input dataset using `target` cleaning mode.** Clean the input dataset using a set of target genomes. Any genome that has a value greater than the cutoff (default 0.05) to any of the provided target genomes are removed.
```sh
grumps -m target -t [filepath_to_file_with_target_ids] [filepath_to_dataset]
```
* **Clean input dataset using `remover` cleaning mode.** Remove a set of genomes from the input dataset by ID.
```sh
grumps -m remover -r [filepath_to_file_with_ids_to_remove] [filepath_to_dataset]
```
### `r-grumps`
The following section provides an overview of the command line component of `r-grumps`. Please use the help page, `r-grumps -h`, to see all command line options and what modes these options can be used with.
**Note:** all modes print the height used to cut the clustered dendrogram and produce clusters (this information is also found in the filenames output by `r-grumps`).
* **Produce help page.** Quickly check the software usage and available command line options.
```sh
r-grumps -h
```
* **Produce a clustered heatmap of input dataset.** Produces publication quality clustered heatmap of the supplied dataset and outputs the clustering results in the following 3 files: a colored dendrogram of the clustering results as a .png, a csv file (genomeID and clusterID as columns), and a .nwk file contaning a newick tree of the dendrogram used to create the clustered heatmap.
```sh
r-grumps -m heatmap -f [filepath_to_dataset]
```
* **Produce dendrogram of clustering results.** Performs clustering without creating a clustered heatmap. Outputs the clustering results in the following 3 files: a colored dendrogram of the clustering results as a .png, a csv file (genomeID and clusterID as columns), and a .nwk file contaning a newick tree of the dendrogram used to create the clustered heatmap.
```sh
r-grumps -m dendrogram -f [filepath_to_dataset]
```
* **Create clusters at a different cutoff.** `r-grumps` by default uses the max height of the dendrogram multiplied by 1.25E-02 to cut the clustered dendrogram and produce clusters (for E. coli, this height roughly corresponds to subgroups at the phylogroup/phylotype level). The value supplied to `-c`/`--cutoff` will be what the max height of the clustered dendrogram will be multiplied to obtain clusters (i.e. `-c 1` would cut the tree at the root creating a single cluster)
```sh
r-grumps -m heatmap -c 1.25E-01 -f [filepath_to_dataset]
```
* **Create clusters at a set cutoff.** `r-grumps` by default uses the max height of the dendrogram multiplied by 1.25E-02 to produce clusters (for E. coli, this height roughly corresponds to subgroups at the phylogroup/phylotype level). The value supplied to `-c`/`--cutoff` will be what the height of the clustered dendrogram will be cut to obtain clusters. Note: this height is dataset dependent and should not be applied in a "one size fits all" fashion.
```sh
r-grumps -m general -f [filepath_to_dataset]
```
## Helper Script
`distmat_converter` reads a regularly delimited file and returns a .csv distance matrix result. By default, the output of `mash dist` can be used by `distmat_converter` to obtain a Mash distance matrix for `grumps`
```sh
distmat_converter [filepath_to_mash_output.tab]
```
If an ANI delimited file is input, please specify how `distmat_converter` should handle the ANI values with the options `-c yes` or `-i yes`. **Note:** `-c` or `-i` are conflicting options with `-c` having a higher priority. `-c yes` converts the ANI values to Mash values via (100-ANI)/1. `-i yes` simply inverts ANI values via 100-ANI.
```sh
distmat_converter -c yes [filepath_to_fastANI_output.tab]
```
## Example
In the data folder of this repository is a Mash distance matrix containing 776 ***Staphylococcus epidermidis*** genomes which will be used in the following example `grumps` analysis.
### Step 1: Run `grumps` in `summary` mode to obtain an overview of the dataset
```sh
grumps -m summary ./data/Staphylococcus_epidermidis.tab_distmat.csv
```
In addition to a set of three files summarizing the distribution of values for each genome, the overall dataset, and the means of the dataset, a histogram of all the values in the dataset is also produced by this mode.
Looking at the above histogram, there is a noticeable set of comparisons present above 0.2 (which is well above the Mash distance species boundary of 0.05) and is a clear indicator that this uncleaned dataset contains several outlier genomes.
To address this issue, we will run `grumps` in `regular` mode with a cutoff of 0.05, the optional `sigma` filtering step applied, and we will allow `grumps` to create a clustered heatmap to visualize our cleaned dataset.
### Step 2: Run `grumps` in `regular` mode using a cutoff of 0.05 with the optional `sigma` filtering step and output the clustered heatmap as a .png file
```sh
grumps -m regular -c 0.05 -s yes -p yes -f png -o ward ./data/Staphylococcus_epidermidis.tab_distmat.csv
```
**Note:** The above step is the equivalent of running `grumps -m regular ./data/Staphylococcus_epidermidis.tab_distmat.csv` as the command line options used in **Step 2** are the same as the default values for these options.
The population structure of ***Staphylococcus epidermidis*** can then be observed in the clustered heatmap output by the command in **Step 2**.
As the maximum value contained in the clustered heatmap is below 0.05 and the population structure is clearly visible in the clustered heatmap, we can consider this dataset cleaned. We will now run **GRUMPS** in 'summary' mode again to obtain an updated summary of the now cleaned ***Staphylococcus epidermidis*** dataset.
### Step 3: Run `grumps` in `summary` mode to obtain an overview of the cleaned dataset
```sh
grumps -m summary ./data/Staphylococcus_epidermidis.tab_distmat_cleaned_regular_sigma_0.05_ward_distmat.csv
```
In addition to a set of three files summarizing the distribution of values for each genome, the overall dataset, and the means of the dataset, a histogram of all the values in the dataset is also produced by this mode.
Viewing the histogram for the cleaned dataset, we can see that there are no more comparisons above the species boundary of 0.05 as well as a similar topology to the histogram produced in **Step 1**.
Now that we have our final cleaned dataset and the summary statistics, we can use the Rscript `r-grumps` to produce the final heatmap for publication.
### Step 4: Run `r-grumps` to obtain the final clustered heatmap and grouping information
```sh
r-grumps -f ./data/Staphylococcus_epidermidis.tab_distmat_cleaned_regular_sigma_0.05_ward_distmat.csv -m heatmap -c 0.0125 -g ward.D2
```
**Note:** The above step is the equivalent of running `r-grumps -f ./data/Staphylococcus_epidermidis.tab_distmat_cleaned_regular_sigma_0.05_ward_distmat.csv` as the command line options used in **Step 4** are the same as the default values for these options.
We can now take the clustered heatmap from **Step 2** and **Step 4** and open them with GIMP to create the final figure.
### Step 5: Post-processing using GIMP
To assist users in quickly creating publication ready figures from the output of **GRUMPS**, we recommend the following steps to create the final images which can be used either as a standalone figure or as panels within a figure.
* Open GIMP
* Open the Create a New Image dialogue box (File > New)
* Set width and height to 36 cm
* Click Advanced Options
* Set X and Y resolution to 600 pixels/in
* Change Fill with: to White and click OK
* Open the R heatmap as a new layer (File > Open as Layers)
* Crop this layer to content (Layer > Crop to Content)
* Move the R heatmap layer so that the bottom right corner is in the bottom right corner with no visible white pixels
* Open the Python heatmap as a new layer (File > Open as Layers)
* Use the Rectangle Selection Tool (default shortcut key: R) to draw a rectangular selection around the color scale of the Python heatmap
* Crop the Python heatmap layer to the selection (Layer > Crop to Selection)
* Crop the Python heatmap layer to content (Layer > Crop to Content)
* Scale the Python heatmap layer to either 756 pixels wide or 1564 pixels high without Interpolation (Layer > Scale Layer > Interpolation: None)
* If using the image as a panel, create a textbox (default shortcut key: T) containing the panel letter with bold Sans-Serif font size 300 and move the textbox so the upper left corner of the textbox is in the upper left corner of the image
* Move the color scale bar found in the Python heatmap layer to the upper left corner of the image (positioning is personal preference. If you are making a multi-panel figure, we recommend moving the color scale layer so the upper right hand corner of the color scale is touching the bottom left corner of the textbox)
* Save the xcf file before further modification so you have the original multilayer unscaled file if you need to make additional modifications without having to recreate the file
The resultant 36cm by 36cm 600 pixels/in figure can then be scaled to the appropriate size depending on the use.
* If the figure is standalone, scale the image to 18cm by 18cm without interpolation (Image > Scale Image > Interpolation: None)
* If the figure is going to be a quarter panel, scale the image to 9cm by 9cm without interpolation (Image > Scale Image > Interpolation: None)
* If the figure is going to be a quarter panel with other **GRUMPS** generated heatmaps (as done in the **GRUMPS** whitepaper), we recommend scaling the image to 8.75cm by 8.75cm to ensure panels don't look cramped
Raw data
{
"_id": null,
"home_page": null,
"name": "testgrumps",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.7",
"maintainer_email": "Kaleb Abram <abram.kaleb@gmail.com>",
"keywords": "ANI, clustering, data cleaning, genome, machine learning, mash, population structure",
"author": null,
"author_email": "Kaleb Abram <abram.kaleb@gmail.com>",
"download_url": "https://files.pythonhosted.org/packages/71/a7/731a6638f76b1d49fca1471d9fd5ef4630481aa9b3a83567b7a2c697b177/testgrumps-1.0.0.tar.gz",
"platform": null,
"description": "# GRUMPS\n***G***enomic distance based ***R***apid ***U***ncovering of ***M***icrobial ***P***opulation ***S***tructures.\n\n`grumps` is designed to assist and speed up the construction of species level population structures using Mash distances as an input. ANI values can be used as well if the ANI values are converted to a decimal difference value {i.e. (100 - ANIvalue) /100}. Additional helper scripts are provided if you do not have a correctly formatted distance matrix to input.\n\n`grumps` reads a normalized distance matrix containing NxN pairwise genome comparisons and returns a filtered result. \n\nIt can be run in `summary`, `regular`, `strict`, `sigma`, `target`,\n`clique`, and `small` modes. \n\nFor more information about these modes please see the white paper:\nhttps://doi.org/10.1101/2022.12.19.521123\n\n## Installation\n\n`grumps` is installable from conda, pip or from source. \n\nThe best installation method for `grumps` is conda within a new environment. \n\n### Installing from conda\n#### Creating a new environment\n```sh\nconda env create -n grumps_env kabram::grumps\n```\n**Note:** using [mamba](https://mamba.readthedocs.io/en/latest/) as a replacement for conda can spead up the environment creation process.\n\n#### Installing into existing environment\n```sh\nconda install kabram::grumps\n```\n`grumps` has two components in the conda package: `py-grumps` and `r-grumps`. Each component is a conda package which contains all necessary dependencies for that component. \n\nTo install the python component:\n```sh\nconda install kabram::py-grumps\n```\nTo install the R component:\n```sh\nconda install kabram::r-grumps\n```\n### Installing from other sources\n#### pip\n```sh\npip install grumps\n```\n**Note:** the pip version does not have the R dependecies needed for `r-grumps`. \n\n#### r-devtools\n```sh\nR -e 'devtools::install_github(\"kalebabram/r_grumps\")'\n```\n**Note:** R library `devtools` is required: `install.packages('devtools')` or `conda install r-devtools` for this approach. To get the CLI entrypoint, download `cligrumps.R` from the repository. You can rename the Rscript to `r-grumps`, relocate it to a directory in your $PATH, and make it executable for equivalent behavior to the conda install of `r-grumps`. \n\n#### Source\nAll neccessary files needed to build the python package of `grumps` are found in `src/grumps` within this repository.\n\nAll neccessary files needed to build the R package of `grumps` are found in the `r-grumps` repository: https://github.com/kalebabram/r-grumps.git\n\nIn order to get the CLI entrypoint for the R package, simply download the Rscript `cligrumps.R` to your computer. You can rename the Rscript to `r-grumps`, relocate it to a directory in your `$PATH`, and make it executable for equivalent behavior to the conda install of `r-grumps`. \n\n## Dependencies\n`grumps` utilizes the following python libraries:\n* python \n* pandas\n* networkx\n* seaborn\n* scipy\n* scikit-learn\n\n`r-grumps` utilizes the following R libraries:\n* MASS\n* optparse\n* ape\n* grDevices\n* RColorBrewer\n* sparcl\n* stats\n* utils\n\n## `grumps` library support\n### python\n`grumps` also is available as a python library to allow easy integration into existing python based workflows.\n\nWhile `grumps` has many components, the following overview summarizes functions which users are intended to interact with:\n\n```sh\ngrumps\n\u251c\u2500\u2500 .api\n\u2502 \u2514\u2500\u2500 .pipeLine()\n\u251c\u2500\u2500 .core\n\u2502 \u251c\u2500\u2500 .grumpsObj()\n\u2502 \u2514\u2500\u2500 .distmatConverter()\n\u2514\u2500\u2500 .modes\n \u251c\u2500\u2500 .regularMode()\n \u251c\u2500\u2500 .removerMode()\n \u251c\u2500\u2500 .targetMode()\n \u251c\u2500\u2500 .smallMode()\n \u251c\u2500\u2500 .sigmaMode()\n \u251c\u2500\u2500 .summaryMode()\n \u2514\u2500\u2500 .cliqueMode()\n```\n**Note:** `import grumps.api as grumps` will automatically load all the above functions which can be accessed via `grumps.<function_name>` (i.e. `grumps.grumpsObj()`)\n\nThe intended use of the python `grumps` library is as follows:\n```py\nimport grumps.api as grumps\n# if you need to convert your input file to NxN distance matrix. The location of the converted file is printed.\ngrumps.distmatConverter('/path/to/input/file.tab')\n# load in the NxN distance matrix\ndata = grumps.grumpsObj('/path/to/distmat/file.csv')\n# change these grumpsObj defaults or the cleaning modes/pipeline will run with defaults\ndata.mode = 'regular'\ndata.cutOff = 0.05\ndata.clusterMethod = 'ward'\ndata.makeHeatmap = 'yes'\ndata.figFormat = 'png'\ndata.targetFilePath = ''\ndata.removeFilePath = ''\ndata.sigma = 'yes'\ndata.medoid = 'yes'\n# if you want to run grumps automatically\ngrumps.pipeLine(data)\n# if you want to use a specific cleaning mode\ndata = grumps.regularMode(data)\n```\n\n### R\n`r-grumps` also has an R library available. \n\nAn example RScript using `r-grumps` is given below:\n```R\nlibrary(rgrumps)\n# change filepath to path of the distance matrix\n# change mode to one of the following: 'heatmap', 'dendrogram', or 'general'\ngrumps <- grumpsFunc(filepath='', mode = '', cutoff = 1.25E-02, clusteringmethod = 'ward.D2', tree = 'yes')\ngrumps = dataframeFunc(grumps)\ngrumps = clusterFunc(grumps)\nif (grumps$mode == 'heatmap'){\n grumps= mclFunc(grumps)\n heatmapFunc(grumps)\n labeloutFunc(grumps)\n dendrogramFunc(grumps)\n if (grumps$tree == 'yes'){\n treeFunc(grumps)\n }\n}\n\nif (grumps$mode == 'dendrogram'){\n grumps = mclFunc(grumps)\n labeloutFunc(grumps)\n dendrogramFunc(grumps)\n if (grumps$tree == 'yes'){\n treeFunc(grumps)\n }\n}\n\nif (grumps$mode == 'general'){\n heatmapFunc(grumps)\n grumps = heightCutter(grumps)\n dendrogramFunc(grumps)\n if (grumps$tree == 'yes'){\n treeFunc(grumps)\n } \n}\n```\n\n## Usage Summary\n### `grumps`\nThe following section provides a set of minimal command line commands to use `grumps`. Please use the help page, `grumps -h`, to see all command line options and what modes these options can be used with. \n\n* **Produce help page.** Quickly check the software usage and available command line options.\n```sh\ngrumps -h\n```\n\n* **Produce summary of input dataset.** Quickly obtain multiple statistical summaries as well as a histogram for the input dataset\n```sh\ngrumps -m summary [filepath_to_dataset]\n```\n\n* **Clean input dataset using `regular` cleaning mode.** Clean the input dataset using K-means clustering. \n```sh\ngrumps -m regular [filepath_to_dataset] \n```\n\n* **Clean input dataset using `strict` cleaning mode.** Clean the input dataset using K-means clustering followed by a three-sigma rule based cleaning step using the means of each genome.\n```sh\ngrumps -m strict [filepath_to_dataset]\n```\n\n* **Clean input dataset using `clique` cleaning mode.** Clean the input dataset with a graph-based clustering approach. Useful for dividing datasets containing multiple species into a collection of uncleaned species level datasets.\n```sh\ngrumps -m clique [filepath_to_dataset]\n```\n\n* **Clean input dataset using `sigma` cleaning mode.** Clean the input dataset using a three-sigma rule based cleaning step applied to the extreme left and right tails of value distribution for each genome. **Note:** this step is automatically performed in `regular` and `strict` cleaning modes if `-s no` not specified.\n```sh\ngrumps -m sigma [filepath_to_dataset]\n```\n\n* **Clean input dataset using `target` cleaning mode.** Clean the input dataset using a set of target genomes. Any genome that has a value greater than the cutoff (default 0.05) to any of the provided target genomes are removed.\n```sh\ngrumps -m target -t [filepath_to_file_with_target_ids] [filepath_to_dataset]\n```\n\n* **Clean input dataset using `remover` cleaning mode.** Remove a set of genomes from the input dataset by ID. \n```sh\ngrumps -m remover -r [filepath_to_file_with_ids_to_remove] [filepath_to_dataset]\n```\n### `r-grumps`\nThe following section provides an overview of the command line component of `r-grumps`. Please use the help page, `r-grumps -h`, to see all command line options and what modes these options can be used with. \n\n**Note:** all modes print the height used to cut the clustered dendrogram and produce clusters (this information is also found in the filenames output by `r-grumps`). \n\n* **Produce help page.** Quickly check the software usage and available command line options.\n```sh\nr-grumps -h\n```\n\n* **Produce a clustered heatmap of input dataset.** Produces publication quality clustered heatmap of the supplied dataset and outputs the clustering results in the following 3 files: a colored dendrogram of the clustering results as a .png, a csv file (genomeID and clusterID as columns), and a .nwk file contaning a newick tree of the dendrogram used to create the clustered heatmap.\n```sh\nr-grumps -m heatmap -f [filepath_to_dataset]\n```\n\n* **Produce dendrogram of clustering results.** Performs clustering without creating a clustered heatmap. Outputs the clustering results in the following 3 files: a colored dendrogram of the clustering results as a .png, a csv file (genomeID and clusterID as columns), and a .nwk file contaning a newick tree of the dendrogram used to create the clustered heatmap.\n```sh\nr-grumps -m dendrogram -f [filepath_to_dataset] \n```\n* **Create clusters at a different cutoff.** `r-grumps` by default uses the max height of the dendrogram multiplied by 1.25E-02 to cut the clustered dendrogram and produce clusters (for E. coli, this height roughly corresponds to subgroups at the phylogroup/phylotype level). The value supplied to `-c`/`--cutoff` will be what the max height of the clustered dendrogram will be multiplied to obtain clusters (i.e. `-c 1` would cut the tree at the root creating a single cluster)\n```sh\nr-grumps -m heatmap -c 1.25E-01 -f [filepath_to_dataset]\n```\n\n* **Create clusters at a set cutoff.** `r-grumps` by default uses the max height of the dendrogram multiplied by 1.25E-02 to produce clusters (for E. coli, this height roughly corresponds to subgroups at the phylogroup/phylotype level). The value supplied to `-c`/`--cutoff` will be what the height of the clustered dendrogram will be cut to obtain clusters. Note: this height is dataset dependent and should not be applied in a \"one size fits all\" fashion.\n```sh\nr-grumps -m general -f [filepath_to_dataset]\n```\n## Helper Script\n`distmat_converter` reads a regularly delimited file and returns a .csv distance matrix result. By default, the output of `mash dist` can be used by `distmat_converter` to obtain a Mash distance matrix for `grumps`\n```sh\ndistmat_converter [filepath_to_mash_output.tab]\n```\nIf an ANI delimited file is input, please specify how `distmat_converter` should handle the ANI values with the options `-c yes` or `-i yes`. **Note:** `-c` or `-i` are conflicting options with `-c` having a higher priority. `-c yes` converts the ANI values to Mash values via (100-ANI)/1. `-i yes` simply inverts ANI values via 100-ANI. \n```sh\ndistmat_converter -c yes [filepath_to_fastANI_output.tab]\n```\n\n## Example \nIn the data folder of this repository is a Mash distance matrix containing 776 ***Staphylococcus epidermidis*** genomes which will be used in the following example `grumps` analysis. \n\n### Step 1: Run `grumps` in `summary` mode to obtain an overview of the dataset\n```sh\ngrumps -m summary ./data/Staphylococcus_epidermidis.tab_distmat.csv\n```\n\nIn addition to a set of three files summarizing the distribution of values for each genome, the overall dataset, and the means of the dataset, a histogram of all the values in the dataset is also produced by this mode. \n\nLooking at the above histogram, there is a noticeable set of comparisons present above 0.2 (which is well above the Mash distance species boundary of 0.05) and is a clear indicator that this uncleaned dataset contains several outlier genomes. \n\nTo address this issue, we will run `grumps` in `regular` mode with a cutoff of 0.05, the optional `sigma` filtering step applied, and we will allow `grumps` to create a clustered heatmap to visualize our cleaned dataset. \n\n### Step 2: Run `grumps` in `regular` mode using a cutoff of 0.05 with the optional `sigma` filtering step and output the clustered heatmap as a .png file\n```sh\ngrumps -m regular -c 0.05 -s yes -p yes -f png -o ward ./data/Staphylococcus_epidermidis.tab_distmat.csv\n```\n**Note:** The above step is the equivalent of running `grumps -m regular ./data/Staphylococcus_epidermidis.tab_distmat.csv` as the command line options used in **Step 2** are the same as the default values for these options. \n\nThe population structure of ***Staphylococcus epidermidis*** can then be observed in the clustered heatmap output by the command in **Step 2**.\n\nAs the maximum value contained in the clustered heatmap is below 0.05 and the population structure is clearly visible in the clustered heatmap, we can consider this dataset cleaned. We will now run **GRUMPS** in 'summary' mode again to obtain an updated summary of the now cleaned ***Staphylococcus epidermidis*** dataset. \n\n### Step 3: Run `grumps` in `summary` mode to obtain an overview of the cleaned dataset\n```sh\ngrumps -m summary ./data/Staphylococcus_epidermidis.tab_distmat_cleaned_regular_sigma_0.05_ward_distmat.csv\n```\nIn addition to a set of three files summarizing the distribution of values for each genome, the overall dataset, and the means of the dataset, a histogram of all the values in the dataset is also produced by this mode. \n\nViewing the histogram for the cleaned dataset, we can see that there are no more comparisons above the species boundary of 0.05 as well as a similar topology to the histogram produced in **Step 1**.\n\nNow that we have our final cleaned dataset and the summary statistics, we can use the Rscript `r-grumps` to produce the final heatmap for publication.\n\n### Step 4: Run `r-grumps` to obtain the final clustered heatmap and grouping information\n```sh\nr-grumps -f ./data/Staphylococcus_epidermidis.tab_distmat_cleaned_regular_sigma_0.05_ward_distmat.csv -m heatmap -c 0.0125 -g ward.D2 \n```\n**Note:** The above step is the equivalent of running `r-grumps -f ./data/Staphylococcus_epidermidis.tab_distmat_cleaned_regular_sigma_0.05_ward_distmat.csv` as the command line options used in **Step 4** are the same as the default values for these options.\n\n\nWe can now take the clustered heatmap from **Step 2** and **Step 4** and open them with GIMP to create the final figure.\n\n### Step 5: Post-processing using GIMP\nTo assist users in quickly creating publication ready figures from the output of **GRUMPS**, we recommend the following steps to create the final images which can be used either as a standalone figure or as panels within a figure.\n* Open GIMP\n* Open the Create a New Image dialogue box (File > New)\n* Set width and height to 36 cm\n* Click Advanced Options\n* Set X and Y resolution to 600 pixels/in\n* Change Fill with: to White and click OK\n* Open the R heatmap as a new layer (File > Open as Layers)\n* Crop this layer to content (Layer > Crop to Content)\n* Move the R heatmap layer so that the bottom right corner is in the bottom right corner with no visible white pixels\n* Open the Python heatmap as a new layer (File > Open as Layers)\n* Use the Rectangle Selection Tool (default shortcut key: R) to draw a rectangular selection around the color scale of the Python heatmap\n* Crop the Python heatmap layer to the selection (Layer > Crop to Selection)\n* Crop the Python heatmap layer to content (Layer > Crop to Content)\n* Scale the Python heatmap layer to either 756 pixels wide or 1564 pixels high without Interpolation (Layer > Scale Layer > Interpolation: None)\n* If using the image as a panel, create a textbox (default shortcut key: T) containing the panel letter with bold Sans-Serif font size 300 and move the textbox so the upper left corner of the textbox is in the upper left corner of the image\n* Move the color scale bar found in the Python heatmap layer to the upper left corner of the image (positioning is personal preference. If you are making a multi-panel figure, we recommend moving the color scale layer so the upper right hand corner of the color scale is touching the bottom left corner of the textbox)\n* Save the xcf file before further modification so you have the original multilayer unscaled file if you need to make additional modifications without having to recreate the file\n\nThe resultant 36cm by 36cm 600 pixels/in figure can then be scaled to the appropriate size depending on the use.\n* If the figure is standalone, scale the image to 18cm by 18cm without interpolation (Image > Scale Image > Interpolation: None)\n* If the figure is going to be a quarter panel, scale the image to 9cm by 9cm without interpolation (Image > Scale Image > Interpolation: None) \n* If the figure is going to be a quarter panel with other **GRUMPS** generated heatmaps (as done in the **GRUMPS** whitepaper), we recommend scaling the image to 8.75cm by 8.75cm to ensure panels don't look cramped\n",
"bugtrack_url": null,
"license": "Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. \"License\" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. \"Licensor\" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. \"Legal Entity\" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, \"control\" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. \"You\" (or \"Your\") shall mean an individual or Legal Entity exercising permissions granted by this License. \"Source\" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. \"Object\" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. \"Work\" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). \"Derivative Works\" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. \"Contribution\" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, \"submitted\" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as \"Not a Contribution.\" \"Contributor\" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work. 2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions: (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and (b) You must cause any modified files to carry prominent notices stating that You changed the files; and (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and (d) If the Work includes a \"NOTICE\" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. The contents of the NOTICE file are for informational purposes only and do not modify the License. You may add Your own attribution notices within Derivative Works that You distribute, alongside or as an addendum to the NOTICE text from the Work, provided that such additional attribution notices cannot be construed as modifying the License. You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License. 5. Submission of Contributions. Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions. 6. Trademarks. This License does not grant permission to use the trade names, trademarks, service marks, or product names of the Licensor, except as required for reasonable and customary use in describing the origin of the Work and reproducing the content of the NOTICE file. 7. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License. 8. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages. 9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. END OF TERMS AND CONDITIONS APPENDIX: How to apply the Apache License to your work. To apply the Apache License to your work, attach the following boilerplate notice, with the fields enclosed by brackets \"[]\" replaced with your own identifying information. (Don't include the brackets!) The text should be enclosed in the appropriate comment syntax for the file format. We also recommend that a file or class name and description of purpose be included on the same \"printed page\" as the copyright notice for easier identification within third-party archives. Copyright 2022 Kaleb Abram Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.",
"summary": "Genomic distance based Rapid Uncovering of Microbial Population Structures",
"version": "1.0.0",
"project_urls": {
"Homepage": "https://github.com/kalebabram/grumps",
"Issues": "https://github.com/kalebabram/GRUMPS/issues",
"News": "https://github.com/kalebabram/grumps/blob/master/CHANGELOG.md",
"Readme": "https://github.com/kalebabram/grumps/blob/master/README.md"
},
"split_keywords": [
"ani",
" clustering",
" data cleaning",
" genome",
" machine learning",
" mash",
" population structure"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "e9700b8cf7c0a1d9f445a1116a3b7f3366fdb58d1a271650540026b02aa51d4c",
"md5": "250f638ef99050ddc79e28efa90d6fd1",
"sha256": "2b5caa34f94ce0a8fff44acc688d8bf8c90ac6b6223e97fb762e6e2e93d03469"
},
"downloads": -1,
"filename": "testgrumps-1.0.0-py3-none-any.whl",
"has_sig": false,
"md5_digest": "250f638ef99050ddc79e28efa90d6fd1",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.7",
"size": 38647,
"upload_time": "2024-09-03T23:44:24",
"upload_time_iso_8601": "2024-09-03T23:44:24.590678Z",
"url": "https://files.pythonhosted.org/packages/e9/70/0b8cf7c0a1d9f445a1116a3b7f3366fdb58d1a271650540026b02aa51d4c/testgrumps-1.0.0-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "71a7731a6638f76b1d49fca1471d9fd5ef4630481aa9b3a83567b7a2c697b177",
"md5": "afa5960437caa63bc6188333c44164a3",
"sha256": "5622ce672bfa647d449ff9c3bdef9a068319f915f845e9214a78a83547456e2e"
},
"downloads": -1,
"filename": "testgrumps-1.0.0.tar.gz",
"has_sig": false,
"md5_digest": "afa5960437caa63bc6188333c44164a3",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.7",
"size": 35805,
"upload_time": "2024-09-03T23:44:26",
"upload_time_iso_8601": "2024-09-03T23:44:26.355085Z",
"url": "https://files.pythonhosted.org/packages/71/a7/731a6638f76b1d49fca1471d9fd5ef4630481aa9b3a83567b7a2c697b177/testgrumps-1.0.0.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-09-03 23:44:26",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "kalebabram",
"github_project": "grumps",
"travis_ci": false,
"coveralls": false,
"github_actions": false,
"lcname": "testgrumps"
}
