semvec/pypi-packages huggingface dataset:
plot-model 0.20
plot model

## plot_model

GitHub:[https://github.com/Qinbf/plot_model.git](https://github.com/Qinbf/plot_model.git)

plot_model is a API for model visualization reference to [tensorflow.keras.utils.plot_model](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/utils/vis_utils.py).

 <p align="center"><img  src="https://raw.githubusercontent.com/Qinbf/plot_model/master/img/1.png" width="50%" height="50%"></p>

 ------------------

 ## Installation

Before installing plot_model, please install one of its engines: **TensorFlow**, **Keras**. 


You may also consider installing the following :

```sh
pip install pydot
pip install pydot_ng
pip install graphviz
```
- Finally,download and install [graphviz](https://graphviz.gitlab.io/download/) to plot model graphs.

Then, you can install plot_model itself. There are two ways to install plot_model:

- **Install plot_model from PyPI (recommended):**

Note: These installation steps assume that you are on a Linux or Mac environment.
If you are on Windows, you will need to remove `sudo` to run the commands below.

```sh
sudo pip install plot_model
```

If you are using a virtualenv, you may want to avoid using sudo:

```sh
pip install plot_model
```

- **Alternatively: install plot_model from the GitHub source:**

First, clone plot_model using `git`:

```sh
git clone https://github.com/Qinbf/plot_model.git
```

 Then, `cd` to the plot_model folder and run the install command:
```sh
cd plot_model
sudo python setup.py install
```

------------------

## Getting started

API is similar to tensorflow.keras.utils.plot_model

```python
from plot_model import plot_model
plot_model(model)
```

default parameters:
```python
plot_model(model, to_file='model.png', show_shapes=True, show_layer_names=False, rankdir='TB', expand_nested=False, style=0, color=True, dpi=96)
```

**color**: whether to display color. Default True.

**style**: 0 new style. 1 original style. Default 0. 

------------------

## Examples
<h5 align="center">ResNet</h5>
<p align="center"><img  src="https://raw.githubusercontent.com/Qinbf/plot_model/master/img/2.png" width="25%" height="25%"></p>


<h5 align="center">Inception</h5>
<p align="center"><img  src="https://raw.githubusercontent.com/Qinbf/plot_model/master/img/3.png" width="50%" height="50%"></p>


<h5 align="center">ResNeXt</h5>
<p align="center"><img  src="https://raw.githubusercontent.com/Qinbf/plot_model/master/img/4.png" width="65%" height="65%"></p>
harvestcli 0.1.1
harvestcli - Harvest cli utility for running reports

# harvest-reporting

Harvest Time tracking and reporting API implementation

Docs: [Harvest Reporting Docs](https://taosdevops.github.io/harvest-reporting/)

## Configration of Notifications

### YAML Settings

There is an [example yaml](./examples/config.yaml) that shows how to
set up every kind of integration.

### Global Hooks

There are two types of global hooks. Emails and WebHooks.

Emails are in the block labeled `globalEmails`. All emails in this block receive *all*
notifications. This includes client names and usage information. This should only be
used for internal notification for project owners or business management.

```.yaml
globalEmails:
  - firstEmailAddress@example.com
  - secnodEmailAddress@example.com
```

Hooks are listed in the `globalHooks`. All hooks in this block will receive *all*
notifications. This includes client names and usage information. This should only be
used for internal notification for project owners or business management.

```.yaml
globalHooks:
  - https://hooks.slack.com/services/GlobalHooksForAllClients
  - https://hooks.slack.com/services/GlobalHookForProjects
```

### Client Specific Hooks

Each client listed in the configuration has its own section.
This section has two types of hooks that can be enabled.

All webhooks are to be listed under `hooks:` for the client. This
list is provider agnostic and will auto-switch the formatting based on
if MS Teams or Slack is detected. This hook will only send specific client data.

```.yaml
clients:
  - name: LotsOfHours
    hours: 160
    hooks:
      - https://hooks.slack.com/services/dude/what!
      - https://outlook.office.com/webhook/lotsOfhashes
```

All destination email addresses are to be listed under `emails:`

```.yaml
clients:
  - name: Email Only
    hours: 160
    emails:
      - primary@emailonly.com
      - secondary@emailonly.com
```

## CLI

### Installing as CLI

you can install this package off of pypi

`pip install harvestcli`

or you can clone the project install the project from that directory.

`pip install .`

### Environment Variables

When using the cli Environment Variables can be used as an alternative to
providing the input via command line flags.

| Variable | Option |
| -------- | ------ |
| BEARER_TOKEN | -b, --bearer-token |
| HARVEST_ACCOUNT_ID | --account-id |
| HARVEST_CONFIG | --config-path |
| SENDGRID_API_KEY| -s, --send-grid |

### Local Development

Create virtual env

`pipenv shell`

Install Dev dependencies

`pipenv install --dev --skip-lock`

#### Generate Docs

Install the devrequirements to install sphinx and its dependencies then run

`sphinx-build .docs docs`

#### Hosting the docs

`cd docs && python -m http.server`

This will host the docs [locally on 8000](http://localhost:8000)
map-dictionary-keys 1.0.0
A function to map the keys in a dictionary

# Map Dictionary Keys

## Installation

```pip install map-dictionary-keys```

## Usage 

```python
from map_dictionary_keys import map_dictionary_keys

d = {'some_key_to_map': 'value'}

mapped_dictionary = map_dictionary_keys(d, lambda key: key.upper())
```

In the above example, `mapped_dictionary` will be mapped according to the function `some_mapping_function` as follows:

```python
mapped_dictionary = {'SOME_KEY_TO_MAP': 'value'}
``` 

This function _will_ work for all levels of nested dictionaries. In the following example, both `'sub_dictionary'` and `'key_name'` will be converted as per the mapping function passed to `map_dictionary_keys`. 

```python
dictionary = {
    'sub_dictionary': {
        'key_name': 'value'
    }
}
```

It _will_ also work for nested lists of dictionaries. For example, in the following example, `'key_name'` will be mapped according to the mapping function provided to `map_dictionary_keys`.  

```python
dictionary = {
    'list_of_dictionaries': [
        {
            'key_name': 'value'
        }
    ]
}
```

This function *will not* currently work for lists nested beyond the first level. For example, in the following example, `'key_name'` will _not_ be mapped according to the mapping function provided to `map_dictionary_keys`. 

```python
dictionary = {
    'my_list': [
        [
            {
                'key_name': 'value'
            }
        ]
    ]
}
```