valleydata 0.0.9

# Valleydata

# Getting Started

First you need to install a few necessary dependencies.

* VirtualBox (https://www.virtualbox.org/).
* Vagrant (https://www.vagrantup.com/).
* Install Guest Additions plugin for Vagrant (`vagrant plugin install vagrant-vbguest`)

If you already use Vagrant, please make sure you update your box before starting it, do it with `vagrant box update`.

Then you can clone this project and run `vagrant up` at the root folder. After this finishes, `vagrant ssh` to get inside the vm. Note: if you already use vagrant, it wont hurt running `vagrant box update` so we use the latest version available.

Packages included in the vm:

* Python 3.5.2 with virtualenv.
* Latest Nodejs installed with nvm.

# Usage

## MySql

You can connect using cli or your favorite client using the following config:

* Host: localhost
* Port: 33061
* User: root
* Password: root

## Python

Run `vagrant ssh` to get inside the VM, then you will need to make a new virtualenv and install the dependencies.

After that you should be able to run `python test.py`


# Testing
Run `python test.py`


# Usage

## valleydata class

```
valleydata = valleydata(date_list=<list_of_dates>)
```

Note that `date_list` is a required parameter

### Methods

- **get_top_hours**: Returns a list of tuples ((str)hour, (int)occurrences) representing most active hours. For example:
```
[('03 PM', 5), ('09 PM', 5), ('01 PM', 4)]
```

- **get_top_weekdays**: Returns a list of tuples ((str)weekday, (int)ocurrences) representing most active weekdays. For example:
```
[('Saturday', 12), ('Sunday', 9), ('Tuesday', 7), ('Friday', 7), ('Monday', 6)]
```

- **get_ip_info**: Returns a dict with info about a valid ip address (uses `ipinfo.io` external api). For example:
```
{
'loc': '45.5186,-73.5545',
'city': 'Montr\xe9al',
'country': 'CA',
'region': 'Quebec',
'hostname': '198-48-208-217.cpe.pppoe.ca',
'ip': u'198.48.208.217',
'org': u'AS5645 TekSavvy Solutions, Inc.',
'postal': u'H2L'
}
```


## TextAnalyzer class

```
text_analyzer = TextAnalyzer(texts=<list_of_dicts>)
```

Note that `texts` is a required parameter and should have the following structure:

```
{
'text': "string",
'status': "string" (texts will be grouped by this string)
}
```

### Methods

- **most_used_words (group=string)**: Returns a list of tuples ((str)word, (int)occurrences). For example:
```
[(u'magni', 3), (u'praesentium', 3), (u'amet', 3), (u'quae', 2), (u'non', 2), (u'molestias', 2)]
```

- **get_group**: Returns a list of strings that belongs to the given group.

- **get_ranking**: Returns a dict AND a int. The dict contains points for each group and the int indicates the total score. Currently the score goes up if the group is called `approved`, otherwise it will go down.