semvec/pypi-packages huggingface dataset:
rapidpro-flow-tools 1.0.5
Toolkit for using spreadsheets to create and modify RapidPro flows
Requires Python: >=3.7

# RapidPro Flow Toolkit
Toolkit for using spreadsheets to create and modify RapidPro flows 

This is a clean up of https://github.com/IDEMSInternational/conversation-parser

In the future this should also include a rewrite of https://github.com/geoo89/rapidpro_abtesting

## Setup
1. Install python `>=3.6`
2. Run `pip install -r requirements.txt`

## Console tool
```
main.py {create_flows,flow_to_sheet} input output --format {csv,xlsx,google_sheets} [--datamodels DATAMODELS]
```

Example:
```
main.py create_flows tests/input/example1/content_index.csv out.json --format=csv --datamodels=tests.input.example1.nestedmodel
```

`main.py -h` for more details.

## Processing Google sheets

Follow the steps _Enable the API_ and _Authorize credentials for a desktop application_ from 
https://developers.google.com/sheets/api/quickstart/python

Note: Google sheets need to be in native Google sheets format,
not `XLSX`, `XLS`, `ODS`, etc

## Running Tests
1. Run `python -m unittest`

# Components

## Generic parsers from spreadsheets to data models

### Cell parser

See `./parsers/common/cellparser.py`. Parser to convert a spreadsheet cell
into a nested list. (Currently no nesting as only `;` is supported as an
element separator.)

### Row parser

See `./parsers/common/rowparser.py`. Parser to turn rows of a sheet
into a specified data model. Column headers determine which field of the
model the column contains data for, and different ways to address fields
in the data models are supported. See `./parsers/common/tests/test_full_rows.py`
and `./parsers/common/tests/test_differentways.py` for examples.

The reverse operation is also supported, but only to a limited extent:
All models are spread out into a flat dict of fields, each becoming the
header of a column.

### Sheet parser

See `./parsers/common/sheetparser.py`.

## RapidPro tools

### RapidPro models

See `./rapidpro/models`. Models for flows, nodes, etc, with convenience
functions to assemble RapidPro flows. Each model has a `render` method
to render the model into a dictionary, that can be exported to a json
file whose fields are consistent with the format used by RapidPro.

### Standard format flow parser

See `./parsers/creation/flowparser.py`. Parser to turn sheets in
the standard format (Documentation TBD) into RapidPro flows.
See `./tests/input` and `./tests/output` for some examples.

Examples: 
- `./tests/test_flowparser.py`
- `./parsers/creation/tests/test_flowparser.py`

### Parsing collections of flows (with templating)

See `./parsers/creation/contentindexparser.py`, `parse_all_flows`.
Examples: 
- `./tests/test_contentindexparser.py`
- `./parsers/creation/tests/test_contentindexparser.py`

Documentation (request access): https://docs.google.com/document/d/1Onx2RhNoWKW9BQvFrgTc5R5hcwDy1OMsLKnNB7YxQH0/edit?pli=1#
tempcf 0.6.7
A toolbox for flagging and cleaning ground temperature data

# TEMPCF: Cleaning and Filtering for Ground Temperature Data

This package provides a graphical user interface for viewing, flagging, and cleaning ground temperature data for permafrost applications. It is designed to work with a variety of different input data types including data loggers, and databases. It uses read / write functionality provided by the [teaspoon](https://gitlab.com/permafrostnet/teaspoon) package ([Brown, 2022](https://joss.theoj.org/papers/10.21105/joss.04704)).

For a full description of the package, including some examples, please see the [documentation pages](https://permafrostnet.gitlab.io/permafrost-tempcf/).


# How to get tempcf
There are a few ways to get tempcf running on your computer.

## Install from pip

You can install tempcf with pip:
```cmd
pip install tempcf
```

Then you can run it from the command line with:
```cmd
tempcf
```

## Running pre-compiled executables

As an alternative to command-line installation, its possible to download the latest files from the [Releases](https://gitlab.com/permafrostnet/permafrost-tempcf/-/releases) page of the GitLab repository. This requires no coding experience whatsoever, but only works on Windows and Linux at the moment. These also won't be the most up-to-date versions.

If you are familiar with python and would rather run tempf from the source code, follow the instructions below. This is the preferred option if you intend to contribute, or if you want to make any modifications.

## Install from source

### Step 1: Download the files

```
git clone https://gitlab.com/permafrostnet/permafrost-tempcf
```

### Step 2: Create a virtual environment

This makes sure that the python packages that get installed don't conflict with other python packages you might have installed.
On Mac or Linux, make sure to replace '\' with '/' in the last line.

```cmd
cd permafrost-tempcf
python -m venv env
.\env\Scripts\activate 
```

### Step 3: Install tempcf

These commands will install the required dependencies, and launch tempcf

```cmd
python setup.py install
cd tempcf
python main.py
```
sha256bit-matic 0.0.1
SHA256 with bit granularity for message input length
Requires Python: >=3.7

# sha256bit
SHA256 with bit granularity for message input length.


## Build the package
Use the ``package`` script and give it the python interpreter to use as argument:
````
./package python3
````

## Test
Tests can run without creating/installing the package:
````
python3 -m test.test
````