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create_readme.py
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__author__ = ['[Nasrudin Salim](http://nasrudinsalim.com)']
__date__ = '2019.12.26'
"""
Generates the README for
[nasdin/Good-Tech-Companies-Singapore](https://github.com/Nasdin/Good-Tech-Companies-Singapore)
Parses the config.yaml to recreate the README each time.
The README consists of a dynamically generated table that consists of data we can get from GlassDoor or PayScale.
Which is input into config.yaml, then this create_readme.py is run to generate a new README.md
"""
import yaml
import datetime
from generate import make_table_headers, parse_row_data, row_data_to_row_markdown
today = datetime.datetime.today().strftime('%d %b %Y')
README_PATH = "README.md"
CONFIG_PATH = 'config.yaml'
intro = f'''
# Good-tier Tech companies in Singapore
## [](https://github.com/Nasdin/Good-Tech-Companies-Singapore/issues) \
### Updated: {today}
This project uses a [config.yaml](config.yaml) and a python [script](generate.py) to \
automatically [regenerate](create_readme.py) this [README](README.md) file.
### To contribute:
1. Fork repository
2. Edit [config.yaml](config.yaml)
3. Run [create_readme.py](create_readme.py), which will generate new README.md
4. Open a pull request!
#### Definitions
1. __Flexible hours__
>Choose own work hours, flexible entry and exit times or the ability to take time whenever during a workday
for personal appointments without consuming leaves. Or to work at home on any moment's notice
2. __Mid-level__
>Someone who is in their mid to late 20s. Past the junior phase and has 2-4 years experience.
But 5 years and above can command a much higher salary; they would have "senior" or "lead" in their titles.
3. __Striked through__
>This company has fallen below certain standards and is in the grey area of a good tier tech company
4. __Very good pantry__
> Generally means a never ending flowing pantry, topped up DAILY with premium ice cream of any kind, drinks, bars,
magnum ice-cream, free flow alcohol. Milo, coconut water etc. Non-junk snacks, Good would be similar but slightly lower.
Use your imagination to figure out the other ranges.
'''
with open(CONFIG_PATH) as f:
data = yaml.load(f)
columns_mapping = data.pop('COLUMNS')
table_headers = make_table_headers(columns_mapping.values())
company_rows = [row_data_to_row_markdown(*parse_row_data(
key=company, data=company_data, columns_mapping=columns_mapping)
) for company, company_data in data.items()]
# Structure of readme is more readable when its created via a list
readme_content = '\n'.join([intro,
f"## List of companies(Total: {len(company_rows)})",
f"Data updated at {today}",
'\n',
table_headers,
*company_rows,
f'\nCoded by: {"&".join(__author__)}'])
with open(README_PATH, 'w') as f:
f.write(readme_content)