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Make PowerPoint presentations from data

Project description

# PPTGen

PPTGen lets you modify the contents of a PowerPoint presentation based on data.
For example, you can:

- Update charts, images and text from data
- Create a series of slides using a template from spreadsheets or database

It is a command line utility and a Python function packaged with Gramex.
It forms the basis of PPTXHandler.

## Command line usage

PPTGen uses a configuration to modify files. The configuration defines the
`source`, `target`, `data` and any number of rules.

On the command line, it accepts a YAML file as input. For example, this
`text.yaml` copies input.pptx to output.pptx and changes the title to "New Title":

```yaml
source: input.pptx # optional path to source. Default to blank PPT with 1 slide
target: output.pptx # required path to save output as
change:
Title 1: # Take the shape named "Title 1"
text: New Title # Replace its text with "New Title"
```

This can be run as:

pptgen text.yaml

You can override parameters from the command line like this:

pptgen config.yaml --target new.pptx "--change.Title 1.text" "Updated title"

## API usage

As an API, it accepts keyword arguments as the configuration. For example:

```python
from pptgen import pptgen
pptgen(
source='input.pptx', # optional path to source. Defaults to blank PPT with 1 slide
target='output.pptx', # optional target. Otherwise, returns the pptx.Presentation()
change={ # Configurations are same as when loading from the YAML file
'Title 1': { # Take the shape named "Title 1"
'text' 'New Title' # Replace its text with "New Title"
}
}
)
```

Here is another example that changes a bar chart based on data:

```python
import pandas as pd
from pptgen import pptgen
data = {'chart_data': pd.read_csv('data.csv')}
pptgen(
source='input.pptx',
target='output.pptx',
edit_chart={
'Bar Chart Shape Name': {
'chart': {
'data': data['chart_data'],
'x': 'Category',
'size': 'PreY',
'color': {
'function': "{'CurY': '#D73027', 'PreY': '#1A9850', 'PPresY': '#FFFFBF', 'Size': '#cccccc'}"
}
}
}
}
)
```

# Configuration

The configuration accepts the following top-level keys:

- `source`: optional. Path to input Presentation to be used as the source.
Defaults to a blank presentation with 1 slide.
- `target`: required for the command line, and is where the output PPTX is saved.
It is optional for the API. If None, `pptgen` returns the Presentation object.
- `data`: optional dataset or a dictionary. This is described below.
- `register`: Optional to register any new custom commands to pptgen. It accepts a function which accepts three parameter `shape`, `spec`, and `data`. Available `immutable` commands in pptgen are `css`, `text`, `image`, `chart`, `table`, `sankey`, `bullet`, `replace`, `treemap`, `heatgrid` and `calendarmap`.
- All other keys are treated as rules that are described below.

## Data

PPTGen can change presentations with data from various sources. It uses
`gramex.data.filter`. It supports these keys:

- `url:` Pandas DataFrame, sqlalchemy URL or file name
- `ext:` file extension (if url is a file). Defaults to url extension
- `args`: optional filters to apply to dataset. Passed as a dict of lists
- `table:`: table name (if url is an SQLAlchemy URL)
- `query:` optional SQL query to execute (if url is a database)
- `transform:`: optional in-memory transform. Takes a DataFrame and returns a DataFrame
- Any additional keys are passed to `gramex.cache.open` or `sqlalchemy.create_engine`

```yaml
data:
cities: {url: cities.csv} # Load cities.csv into "cities" key
sales: {url: sales.xlsx, sheet: Sheet1} # Load Sheet1 from sales.xslx into "sales" key
tweets: {url: tweets.json} # Load JSON data into "tweets" key
sample: {url: mysql://server/db, table: sample} # Load sample data from MySQL
filter:
url: cities.csv # Load cities.csv
args: # Filter results
Country: [Egypt, Sudan] # WHERE column Country is Egypt or Sudan
Population>: 100000 # AND column Population is 100,000+
```

## Rules

The configuration can define any number of rules. Rules have:

- one or more [shape names](#shapes), and the list of [commands](#commands) to
apply to those shapes.
- an optional [slide selector](#slides) that restricts which slide they apply to.
By default, rules apply to all slides.

In the example below, there is 1 rule called `change`. It does no slide selector,
so it applies to all slides. It has 1 shape: `Title 1` with 1 command: `text`:

```yaml
source: input.pptx # optional path to source. Default to blank PPT with 1 slide
target: output.pptx # required path to save output as
change:
Title 1: # Take the shape named "Title 1"
text: New Title # Replace its text with "New Title"
```

### Slides

By default, changes are applied to all slides. To restrict changes to a specific
slide, use:

1. `slide-number` slide number or list (with the first slide as slide 1).
1. `slide-title` is a regular expression that matches the slide title.

```yaml
source: input.pptx
target: output.pptx
rule-1: # rule-1 applies to all slides
...
rule-2:
slide-number: 1 # rule-2 applies only to the first slide of the source
...
rule-3:
slide-title: Hello # rule-3 applies to slides with the title "Hello" (regex)
...
...
```

To create multiple slides from data, add `data:` to the change. For example:

```yaml
source: input.pptx
target: output.pptx
data:
sales: {url: sales.xlsx}
change-title:
data: data['sales'].ix[0].to_dict(orient='records') # For each row in the sales dataset (defined above)
slide-number: 1 # ... copy slide 1 and apply this change
Title 1:
text: "Region {{ region }} has sales of ${{ sales }}"
```

This `data:` is an [expression](#expressions) using the root `data:` variables.
It can be used with

- `slide-number` to repeat 1 or more slides. For example `slide-number: [1,2]`
will copy slides 1 & 2 as many times as there are rows of data
- `slide-title` to repeat individual slides or multiple single slides

Slide numbers always refers to the source slide number, not the target slide
number. Even if a slide is duplicated in the target, source slide numbers do not
change.

### Shapes

In PowerPoint, all shapes have names. To see shape names, select Home tab >
Drawing group > Arrange drop-down > Selection pane. Or press ALT + F10.

![Selection pane](help/selection-pane.png)

To change the shape names, double-click on the name in the selection pane.

You can specify changes to one or more shapes in a [rule](#rules). For example:

```yaml
rule-1:
Title 1:
text: New title
background-color: red
Text 1:
text: New text
color: green
```

... changes 2 shapes named `Title 1` and `Text 1`.

Shape names may refer to native elements or [groups](#groups).

### Groups

Shape names may refer to groups. To change groups' contents, use a nested
configuration. For example, if "Group 1" has "Caption" and "Picture" inside it,
this `config-group.yaml` replaces those:

```yaml
source: input.pptx
target: output.pptx
change-image:
Group 1: # Take the shape named "Group 1"
Caption: # Find the shape named "Caption" inside it
text: New caption # Change its text to "New caption"
Picture: # Find the shape named "Picture" inside it
image: sample.png # Replace the image with sample.png
```

### Register: Register a new command to PPTGen

Register let you create a custom command. It accepts a function which will accepts three parameters, `shape`, `spec`(configuration for a shape, config under the shape name), `data` in same order. It will not accept any other parameter except these 3. Any existing command can not be overwrite. Return an immutable command list.

```yaml
source: input.pptx
target: output.pptx
data:
load_data: {url: data.csv}
register:
custom_command1: view.custom_command1 # Registering a new command as `custom_command1.`
custom_command2: view.custom_command2 # Registering a new command as `custom_command2.`
custom_command3:
function: view.custom_command3 # Registering a new command as `custom_command3.`

custom-config: # Rule
Shape Name 1: # Shape Name
custom_command:
.... Configuration
Shape Name 2:
custom_command2:
.... Configuration
Shape Name 3:
custom_command3:
.... Configuration

```

## Commands

Shapes can be changed using 1 or more commands. These commands can change the
shape's style and content, or add new content (like charts).

### CSS

The following CSS-like commands change the shape's display attributes:

- `data`: Loads data
- `style`: Accepts css like properties

- `opacity`: sets the shape's opacity level as a decimal from 0 - 1
- `color`: sets the text / foreground color as CSS colors
- `fill`: sets the shape's background color as CSS colors
- `stroke`: sets the shape outline color as CSS colors
- `width`: sets the shape width in points
- `height`: sets the shape height in points
- `left`: sets the shape X position in points
- `top`: sets the shape Y position in points
- `font-size`: sets the font size in points
- `font-family`: sets the font family as a font name

Example:
```yaml
Rectangle 1: # Take the shape named "Rectangle 1"
css:
data: data['sales']
style:
opacity: 0.5
color: '#ff0000'
fill: '#ffff00'
stroke: '#ffff00'
width: 100
height: 150
left: 30
top: 50
font-size: 14
font-family: Georgia
```
CSS colors can be specified in the same way they can in CSS.
1 point is 1/72 inches. All `style` elements and `data` will accept python expression or python function.

Values support [expressions](#expressions).

### Text

To change the title on the input slide to "New title", use this configuration:

```yaml
Title 1: # Take the shape named "Title 1"
text: New Title # Replace its text with "New Title"
```

`text:` values support [templates](#templates).

### Replace

To *substitute* text instead of [replacing the full text](#text), use:

```yaml
Title 1: # Take the shape named "Title 1"
replace: # Replace these keywords
"Old": "New" # Old -> New
"Title": "Heading" # Title -> Heading
```

Replacement only works for words that have the same formatting. For example, in
some_where_, "where" is underlined. You cannot replace "somewhere". But you can
replace "some" and "where" independently.

`replace:` values support [templates](#templates).

### Image

To change the picture on an image, use:

```yaml
Picture 1: # Take the shape named "Picture 1"
image: sample.png # Replace the image with sample.png
```

`image:` values support [template](#templates), and can be a URL or file path.

### Table

Modifies existing tables. It accepts these keys:

- `data:` optional data [expression](#expressions) to render as the table. The
table expands on shrinks to accommodate the rows and columns in the data.
- `style:` optional common css for all columns. E.g.- color, fill, font-size etc. These properties can be ovewrite inside a column. If not then property will be common for all columns.
- `bold`: True or False, if True text will be show in bold.
- `fill`: Color of the cells.
- `color`: Text color.
- `italic`: To set text's itallic style.
- `underline`: To set text's underline style.
- `font-size`: Font size of text.
- `font-family`: Text's font family.
- `gradient:` optional gradient name (binary, Blues, BuGn, BuPu, gist_yarg, GnBu, Greens, Greys, Oranges, OrRd, PuBu, PuBuGn, PuRd, Purples, RdPu, Reds, YlGn, YlGnBu, YlOrBr, YlOrRd, BrBG, bwr, coolwarm, PiYG, PRGn, PuOr, RdBu, RdGy, RdYlBu, RdYlGn, seismic).
- `min:` optional minimum. Defaults to the column's min value
- `max:` optional maximum. Defaults to the column's max value
- `columns:` A dictionary config for the columns. Inside this style properties can be defined and can overwrite common styles from `style` section. Only defined columns inside `columns` section will get populated inside table.

```yaml
columns:
Category: {} # Not overwriting common css styles but Category column will be shown in table
Sales: # Defining style for Sales column
gradient: Greens
font-size: 14
Profit: # Defining style for Profit column
font-family: Georgia
```

```yaml
source: table-input.pptx
target: table-output.pptx
data:
table_data: {ext: csv, url: table-data.csv}

new-edit-table:
Table:
table:
data: data['table_data']
style: # Common CSS for all the cells
font-size: 18
text-align: center
italic: True
underline: True
columns:
Sales: # Common CSS will get over-write for Sales column
gradient: Reds
GrossProfit: # Common CSS will get over-write for GrossProfit column
font-size: 30
bold: False
underline: False
italic: False
color: '#ff00ff'
```

### Replicate

To create multiple shapes using data, use `replicate:` and `data:`. For example:

```yaml
data:
sales: {xlsx: sales.xlsx}
multiple-objects: # Rule
# Slide 1, 2 will get replicated for all Categories (that is unique groups from groupby below), if slide-number is defined else all slides will get replicated.
slide-number: [1, 2] # This rule will get apply only on these slides.
data: data['sales'].groupby('Category')
replicate: True # Entire rule will replicate for defined slides, if slide-number is defined else all slides will get replicated.
Picture 1: # Take the Picture 1 shape
margin: 10 # With a padding of 10 units
image: "{{ region }}.png" # Change the picture using this template
```

### Stack

Replicate a shape multiple times based on data vertically or horizontally. For example:

```yaml
data:
sales: {xlsx: sales.xlsx}
multiple-objects: # Rule
Text 1: # Take the Picture 1 shape
data: data['sales'].to_dict(orient='records')
stack: horizontal # Lay the images out horizontally to the right
margin: 10 # With a padding of 10 units
text: "{{ Category }}" # Change the text using this template
```


This `data:` is an [expression](#expressions) using the root `data:` variables.
For each row in `data`, the shape is duplicated and laid out based on `replicate:`.

`stack:` supports these layouts:

- `horizontal` copies the element right with an optional `margin` (default: 0)
- `vertical` copies the element below with an optional `margin` (default: 0)

### Templates

For commands that support templates, values inside `{{ ... }}` are evaluated as
Python expressions in the context of `data`.

For example:

```yaml
data:
tweets: tweets.json
change:
Title 1:
text: `Tweet from @{{ tweets[0]['user']['screen_name'] }}`
```

... will replace the contents inside `{{ ... }}` with the value of
`tweets[0]['user']['screen_name']` evaluated in Python. The variable `tweets` is
the result of loading `tweets.json`.

### Expressions

For commands that support expressions, values are evaluated as Python expressions
in the context of data. For example:

```yaml
data:
tweets: tweets.json
change:
slide: 1
data: sales.groupby('city') # replicates slide 1 for every item in sales.groupby('city')
```

### Deprecated commands

- `rectangle`: use [CSS](#css) commands instead
- `oval`: use [CSS](#css) commands instead


## Native charts

To modify the data and attributes for an existing native chart, use `chart:`.
This supports the following chart types:

- Bar charts: Clustered Bar, Stacked Bar, 100% Stacked Bar
- Column charts: Clustered Column, Stacked Column, 100% Stacked Column
- Line charts: Line, Stacked Line, 100% Stacked Line, Line with Markers, Stacked Line with Markers, 100% Stacked Line with Markers
- Area charts: Area, Stacked Area, 100% Stacked Area (3D area not supported)
- Scatter charts: Scatter, Scatter with Straight Lines, Scatter with Smooth Lines, Scatter with Straight Lines and Markers, Scatter with Smooth Lines and Markers
- Bubble charts: Bubble, 3-D Bubble
- Radar charts: Radar, Radar with Markers, Filled Radar
- Donut charts: Doughnut, Doughnut Exploded
- Pie charts: Pie, Pie Exploded, Bar of Pie (3D pie not supported)

Here are examples that assume the following configuration:

```yaml
source: input.pptx # This must already have the relevant chart
target: output.pptx
data: # This dictionary is available to all charts as "data"
sales: {url: sales.csv} # The examples assume a dataset called "sales"
```

Here are examples for various charts:

```yaml
edit-charts: # Rule name
Bar Chart Name:
chart:
data: data['sales'][['Category', 'Sales', 'Profit', 'Growth']]
x: Category
color: # Define colors
Sales: #D73027 # Specify color of sales line in 6-digit hex
Profit: #1A9850 # Specify color of profit line
Growth: #cccccc # Specify color of profit line

Column Chart Name:
chart:
data: data['sales'][['Category', 'Sales', 'Profit', 'Growth']]
x: Category
color: # Define colors
Sales: #D73027 # Specify color of sales line in 6-digit hex
Profit: #1A9850 # Specify color of profit line
Growth: #cccccc # Specify color of profit line

Line Chart Name:
chart:
data: data['sales'][['Category', 'Sales', 'Profit', 'Growth']]
x: Category
color: # Define colors
Sales: #D73027 # Specify color of sales line in 6-digit hex
Profit: #1A9850 # Specify color of profit line
Growth: #cccccc # Specify color of profit line

Area Chart Name: # Name of the chart shape. Case sensitive
chart:
data: data['sales'][['Category', 'Sales', 'Profit', 'Growth']] # Use sales data
x: Category # The x-axis is the Category column. Other columns are Y-axis values
color: # Define colors
Sales: #D73027 # Specify color of sales line in 6-digit hex
Profit: #1A9850 # Specify color of profit line
Growth: #cccccc # Specify color of profit line
opacity: 0.50 # Constant opacity for all lines

Scatter Chart Name:
chart:
data: data['sales'][['Category', 'Sales', 'Profit', 'Growth']]
x: Category
color: # Define colors
Sales: #D73027 # Specify color of sales line in 6-digit hex
Profit: #1A9850 # Specify color of profit line
Growth: #cccccc # Specify color of profit line

Bubble Chart Name:
chart:
data: data['sales'][['Category', 'Sales', 'Profit', 'Growth']]
x: Category
size: Growth # Optional: Column name from data for the size of the bubbles, if not defined default size will be 1
color: # Define colors
Sales: #D73027 # Specify color of sales line in 6-digit hex
Profit: #1A9850 # Specify color of profit line
Growth: #cccccc # Specify color of profit line

Radar Chart Name:
chart:
data: data['sales'][['Category', 'Sales', 'Profit', 'Growth']]
x: Category
color: # Define colors
Sales: #D73027 # Specify color of sales line in 6-digit hex
Profit: #1A9850 # Specify color of profit line
Growth: #cccccc # Specify color of profit line

Donut Chart Name:
chart:
data: data['sales'][['Category', 'Sales', 'Profit', 'Growth']]
x: Category
color: # Define colors
Sales: #D73027 # Specify color of sales line in 6-digit hex
Profit: #1A9850 # Specify color of profit line
Growth: #cccccc # Specify color of profit line

Pie Chart Name:
chart:
data: data['sales'][['Category', 'Sales', 'Profit', 'Growth']]
x: Category
color: # Define colors
Sales: #D73027 # Specify color of sales line in 6-digit hex
Profit: #1A9850 # Specify color of profit line
Growth: #cccccc # Specify color of profit line
```

The following keys can also be specified as an [expression](#expressions) and python functions:
`data:`, `x:`, `color:`, `opacity:`, `size:`.

For example, this example sets the opacity of columns named "dummy" as 0.2, and
other columns as 1.0.

```yaml
opacity:
function: '{col: 0.2 if "dummy" in col else 1.0 for col in data.columns}'
```

## Custom charts

pptgen lets you create these custom charts:

- Bullet chart
- Calendarmap
- Heatgrid
- Sankey
- Treemap

To create these, add a rectangle shape (no other shape is allowed) in your slide.
When a custom chart is applied on that rectangle, it replaces the rectangle with
the chart.

### Bullet

- `data`: Actual value.
- `poor`: Poor value.
- `good`: Good value.
- `target`: Target value.
- `gradient`: Optional. Default `RdYlGn`.
- `text`: Default `True`. Optional, if present text will be shown as per format. Text can be overwrite inside `style.data.text` or `style.target.text` section if defined there.
- `style`: Optional `dict`, accepts css properties `e.g:- font-color, fill, opacity etc.`

Example:

```yaml
draw-bullet:
Bullet Rectangle:
bullet:
data: data['bullet_data']['data'].ix[0]
poor: data['bullet_data']['poor'].ix[0]
good: data['bullet_data']['good'].ix[0]
target: data['bullet_data']['target'].ix[0]
average: data['bullet_data']['average'].ix[0]
orient: horizontal
gradient: 'Oranges'
text:
function: "lambda v: '%.1f' % v"
style:
font-size: 10 # Common css for all items(data, target, poor, good and average)
color: '#ff0000'
data: # Overwriting CSS for data
font-size: 12
fill: #ff00ff
target: # Overwriting CSS for target
text: False # Overwriting text for text. Text will not be shown
font-size: 12
color: '#cccccc'
```

The following keys can also be specified as an [expression](#expressions) and python functions:
`data:`, `target:`, `poor:`, `good:`, `average:`, `gradient`, `text` along will all style properties such as under style section `font-size`, `opacity`, `fill`, `color` etc.

### Calendarmap

- `width`: Width and height of each cell(in pixel) in calendarmap.
- `weekstart`: Weekstart value `date`.
- `gradient`: Optional, default `RdYlGn`.
- `format`: Number format to be shown in top/left bar if `label_top/lebel_left` defined.
- `lo`: Min value for color scale `default min from data`.
- `hi`: High value for color scale `default max from data`.
- `label_top`: Top margin for the calendermap `default 0`.
- `label_left`: Left margin for the calendermap `default 0`.
- `style`: A `dict`, accepts `CSS` properties e.g-`color, fill, opacity, font-size, etc.`

Example:

```yaml
draw-calendar:
Calendar Rectangle:
calendarmap:
data:
function: data['calendar_data'].sort_values(by=['date_time']).set_index('date_time')['random_column']
width: 40
weekstart: 6
label_top: 80
label_left: 80
startdate: data.index[0]
style:
color: '#000000'
```

The following keys can also be specified as an [expression](#expressions) and python functions:
`data:`, `startdate:`, `lo:`, `hi:`, `weekstart:` and subelements of `style` section.

### Heatgrid

- `data`: A DataFrame.
- `row`: Columns name from data which will be get treated as `row` in heatgrid.
- `column`: Columns name from data which will be get treated as `column` in heatgrid.
- `value`: Columns name from data to show for each cell heatgrid.
- `text`: Default `False` if defined text inside cell will be formated.
- `left-margin`: In percentage(0-1) of total width of shape. Left margin from the shape from where heatgrid will start populating.
- `cell-width`: Width of each cell. Default based on columns width of shape will defined.
- `cell-height`: Height of each cell. Default based on number of rows height of shape will defined.
- `na-text`: Treat `NA` values text representation.
- `na-color`: Cell color for `NA` values.
- `style`: optional `dict`, to apply css properties.

Example:

```yaml
draw-heatgrid:
Heatgrid Rectangle:
heatgrid:
data: data['heatgrid_data']
row: name
column: hour
value: value
text: True
left-margin: 0.20
cell-width: 30
cell-height: 30
na-text: NA
na-color: '#cccccc'
style:
gradient: RdYlGn
color: '#ff0000'
font-size: 14
margin: 10
text-align: center
```

The following keys can also be specified as an [expression](#expressions) and python functions:
`data:`, `row:`, `column:`, `value:` and subelements of `style` section.


### Sankey

- `data`: A DataFrame.
- `sort`: `True` or `False` if true data will get sorted while drawing sankey.
- `text`: Function to show text in sankey.
- `order`: Groups order.
- `color`: Color function based on which
- `groups`: Group column names from data.

Example:

```yaml
draw-sankey:
Sankey Rectangle:
sankey:
data: data['sankey_data']
sort: True
text:
function: "lambda g: g.sum().apply(lambda v: 'Node %s' % (v.name,), axis=1)"
order:
function: "lambda g: -g['D'].sum() / g['E'].sum()"
color:
function: "lambda g: _color.gradient(g['D'].sum() / g['E'].sum() * 2 - 1, _color.RdYlGn)"
groups: ['A', 'B', 'C']
stroke: '#ffffff'
```

The following keys can also be specified as an [expression](#expressions) and python functions:
`data:`, `size:`, `order:`, `text:`, `color`.

### Treemap

- `data`: A DataFrame.
- `keys`: Group column names as `list`.
- `values`: Aggregate function to apply on each group.
- `size`: Treemap's box size function.
- `sort`: Function to sort treemap's rectangles based on `sort` function.
- `color`: A DataFrame.
- `text`: Function to show text in treemap.

Example:

```yaml
draw-treemap:
Treemap Rectangle:
treemap:
data: data['treemap_data']
keys: ['A', 'B']
values: "{'C': 'sum', 'D': 'sum'}"
size:
function: "lambda v: v['C']"
sort:
function: "lambda v: v.sort_values(by=['C'], ascending=False)"
color:
function: "lambda v: _color.gradient(v['C'] / v['D'] - 1, _color.RdYlGn)"
text:
function: "lambda v: 'Num %d' % v['index']"
```

The following keys can also be specified as an [expression](#expressions) and python functions:
`data:`, `size:`, `keys:`, `values:`, `sort`, `color`, `text`.

# Development

To set up the development environment, clone this repo. Then run:

pip uninstall pptgen
pip install -e .

Create a branch for local development using `git checkout -b <branch>`.
Test your changes by running `make clean tests`.
Commit your branch and send a merge request.

## Release

When releasing a new version of pptgen:

1. Check [build errors](http://code.gramener.com/sanjay.yadav/pptgen/pipelines).
2. Run `make clean tests` on Python 2.7 and on 3.x
3. Update version number in `pptgen/release.json`
4. Push `dev` branch to the server. Ensure that there are no build errors.
5. Merge with master, create an annotated tag and push the code:

git checkout master
git merge dev
git tag -a v1.x.x # Annotate with a one-line summary of features
git push --follow-tags
git checkout dev # Switch back to dev

6. Release to PyPi

python setup.py sdist bdist_wheel --universal
twine upload dist/*


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pptgen-0.2.1-py2.py3-none-any.whl (52.0 kB) Copy SHA256 hash SHA256 Wheel py2.py3 Nov 1, 2017
pptgen-0.2.1.tar.gz (49.9 kB) Copy SHA256 hash SHA256 Source None Nov 1, 2017

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