hdx-python-utilities 3.0.3

HDX Python Utilities

The HDX Python Utilities Library provides a range of helpful utilities:

1. Easy downloading of files with support for authentication, streaming and hashing
2. Retrieval of data from url with saving to file or from data previously saved
3. Loading and saving JSON and YAML (inc. with OrderedDict)
4. Dictionary and list utilities
5. HTML utilities (inc. BeautifulSoup helper)
6. Compare files (eg. for testing)
7. Simple emailing
8. Easy logging setup
9. Path utilities
10. Date parsing utilities
11. Text processing
12. Encoding utilities
13. Py3-like raise from for Py2
14. Check valid UUID
15. Easy building and packaging

This library is part of the Humanitarian Data Exchange (HDX) project. If you have humanitarian related data, please upload your datasets to HDX.

Usage

The library has detailed API documentation which can be found here: http://ocha-dap.github.io/hdx-python-utilities/. The code for the library is here: https://github.com/ocha-dap/hdx-python-utilities.

Breaking Changes

From 3.0.1, removed raisefrom function

From 3.0.0, only supports Python >= 3.6

From 2.6.9, the Download class and get_session have optional allowed_methods instead of optional method_whitelist

From 2.5.5, the Database class and all the libraries on which it depended have been moved to the new HDX Python Database library.

From 2.1.2, get_tabular_rows in the Download class returns headers, iterator and a new method get_tabular_rows_as_list returns only the iterator.

From 2.1.4, read_list_from_csv and write_list_to_csv change the order of their parameters to be more logical. Arguments about choosing between dict and list are all made consistent - dict_form.

Overview of the Utilities

Downloading files

Various utilities to help with downloading files. Includes retrying by default.

For example, given YAML file extraparams.yml:

mykey:
    basic_auth: "XXXXXXXX"
    locale: "en"

We can create a downloader as shown below that will use the authentication defined in basic_auth and add the parameter locale=en to each request (eg. for get request http://myurl/lala?param1=p1&locale=en):

with Download(user_agent='test', extra_params_yaml='extraparams.yml', extra_params_lookup='mykey') as downloader:
    response = downloader.download(url)  # get requests library response
    json = response.json()

    # Download file to folder/filename
    f = downloader.download_file('http://myurl', post=False,
                                 parameters=OrderedDict([('b', '4'), ('d', '3')]),
                                 folder=tmpdir, filename=filename)
    filepath = abspath(f)

    # Read row by row from tabular file
    for row in downloader.get_tabular_rows('http://myurl/my.csv', dict_rows=True, headers=1)
        a = row['col']

If we want to limit the rate of get and post requests to say 1 per 0.1 seconds, then the rate_limit parameter can be passed:

with Download(rate_limit={'calls': 1, 'period': 0.1}) as downloader:
    response = downloader.download(url)  # get requests library response

If we want a user agent that will be used in all relevant HDX Python Utilities methods (and all HDX Python API ones too if that library is included), then it can be configured once and used automatically:

UserAgent.set_global('test')
with Download() as downloader:
    response = downloader.download(url)  # get requests library response

The response is of the form produced by teh requests library. It may not be needed as there are functions directly on the Download object eg.

assert downloader.get_status() == 200
assert len(downloader.get_headers()) == 24
assert bool(re.match(r'7\d\d', downloader.get_header('Content-Length'))) is True
assert downloader.get_text() == 'XXX'
assert downloader.get_json() == {...}
assert downloader.get_yaml() == {...}

The get_tabular_rows method enables iteration through tabular data. It returns the header of tabular file pointed to by the url and an iterator where each row is returned as a list or dictionary depending on the dict_rows argument.

The headers argument is either a row number or list of row numbers (in case of multi-line headers) to be considered as headers (rows start counting at 1), or the actual headers defined a list of strings. It defaults to 1 and cannot be None. The dict_form arguments specifies if each row should be returned as a dictionary or a list, defaulting to a list.

Optionally, headers can be inserted at specific positions. This is achieved using the header_insertions argument. If supplied, it is a list of tuples of the form (position, header) to be inserted. Optionally a function can be called on each row. If supplied, it takes as arguments: headers (prior to any insertions) and row (which will be in dict or list form depending upon the dict_rows argument) and outputs a modified row. Example:

def testfn(headers, row):
    row['la'] = 'lala'
    return row

insertions = {'headers': [(2, 'la')], 'function': testfn}
headers, generator = downloader.get_tabular_rows(url, headers=3, 
                                                 header_insertions=[(2, 'la')], row_function=testfn)

Other useful functions:

# Iterate through tabular file returning lists for each row
for row in downloader.get_tabular_rows_as_list(url):
    ...
# Get hxl row
assert Download.hxl_row(['a', 'b', 'c'], {'b': '#b', 'c': '#c'}, dict_form=True)
# == {'a': '', 'b': '#b', 'c': '#c'}        
# Build get url from url and dictionary of parameters
Download.get_url_for_get('http://www.lala.com/hdfa?a=3&b=4',
                         OrderedDict([('c', 'e'), ('d', 'f')]))
# == 'http://www.lala.com/hdfa?a=3&b=4&c=e&d=f'

# Extract url and dictionary of parameters from get url
Download.get_url_params_for_post('http://www.lala.com/hdfa?a=3&b=4',
                                 OrderedDict([('c', 'e'), ('d', 'f')]))
# == ('http://www.lala.com/hdfa',
          OrderedDict([('a', '3'), ('b', '4'), ('c', 'e'), ('d', 'f')]))
# Get mapping of columns positions of headers          
Download.get_column_positions(['a', 'b', 'c'])
# == {'a': 0, 'b': 1, 'c': 2}

For more detail and additional functions, check the API docs mentioned earlier in the usage section.

Retrieving files

When you download a file, you can opt to download from the web as usual or download from the web and and save for future reuse or use the previously downloaded file. The advantage is this is all handled in the class so you don't need to do lots of if-else conditions for the different cases for each download in your code. This is helpful for example when trying to generate test data.

All the downloads in your code can be switched between the different modes by setting the save and use_saved flags when constructing the Retrieve object.

retriever = Retrieve(downloader, fallback_dir, saved_dir, temp_dir, save, use_saved)

• save=False, use_saved=False - download from web as normal (files will go in temp_folder and be discarded)
• save=True, use_saved=False - download from web as normal (files will go in saved_dir and will be kept)
• save=False, use_saved=True - use files from saved_dir (don't download at all)

fallback_dir is a folder containing static fallback files which can optionally be used if the download fails.

Methods in the Retrieve class are:

• retrieve_file returns a path to a file
• retrieve_text returns the text in a file
• retrieve_json returns the JSON in a Python dict
• retrieve_yaml returns the YAML in a Python dict.

Examples:

with Download() as downloader:
    # Downloads file returning the path to the downloaded file and using a fallback file if the download 
    # fails. Since saved is False, the file will be saved with name filename in temp_dir
    retriever = Retrieve(downloader, fallback_dir, saved_dir, temp_dir, save=False, use_saved=False) 
    path = retriever.retrieve_file(url, filename, logstr='my file', fallback=True)

    # Downloads text file saving it for future usage and returning the text data (with no fallback) 
    # Since saved is True, the file will be saved with name filename in saved_dir
    retriever = Retrieve(downloader, fallback_dir, saved_dir, temp_dir, save=True, use_saved=False)
    text = retriever.retrieve_text(url, filename, logstr='test text', fallback=False)
    # Downloads YAML file saving it for future usage and returning the YAML data with fallback taken
    # from fallback_dir if needed.
    data = retriever.retrieve_yaml(url, filename, logstr='test yaml', fallback=True)

    # Uses previously downloaded JSON file in saved_dir returning the JSON data (with no fallback) 
    retriever = Retrieve(downloader, fallback_dir, saved_dir, temp_dir, save=False, use_saved=True)
    data = retriever.retrieve_json(url, filename, logstr='test json', fallback=False)

Loading and Saving JSON and YAML

Examples:

# Load YAML
mydict = load_yaml('my_yaml.yml')

# Load 2 YAMLs and merge into dictionary
mydict = load_and_merge_yaml('my_yaml1.yml', 'my_yaml2.yml')

# Load YAML into existing dictionary
mydict = load_yaml_into_existing_dict(existing_dict, 'my_yaml.yml')

# Load JSON
mydict = load_json('my_json.yml')

# Load 2 JSONs and merge into dictionary
mydict = load_and_merge_json('my_json1.json', 'my_json2.json')

# Load JSON into existing dictionary
mydict = load_json_into_existing_dict(existing_dict, 'my_json.json')

# Save dictionary to YAML file in pretty format
# preserving order if it is an OrderedDict
save_yaml(mydict, 'mypath.yml', pretty=True, sortkeys=False)

# Save dictionary to JSON file in compact form
# sorting the keys
save_json(mydict, 'mypath.json', pretty=False, sortkeys=False)

Dictionary and list utilities

Examples:

# Merge dictionaries
d1 = {1: 1, 2: 2, 3: 3, 4: ['a', 'b', 'c']}
d2 = {2: 6, 5: 8, 6: 9, 4: ['d', 'e']}
result = merge_dictionaries([d1, d2])
assert result == {1: 1, 2: 6, 3: 3, 4: ['d', 'e'], 5: 8, 6: 9}

# Diff dictionaries
d1 = {1: 1, 2: 2, 3: 3, 4: {'a': 1, 'b': 'c'}}
d2 = {4: {'a': 1, 'b': 'c'}, 2: 2, 3: 3, 1: 1}
diff = dict_diff(d1, d2)
assert diff == {}
d2[3] = 4
diff = dict_diff(d1, d2)
assert diff == {3: (3, 4)}

# Add element to list in dict
d = dict()
dict_of_lists_add(d, 'a', 1)
assert d == {'a': [1]}
dict_of_lists_add(d, 2, 'b')
assert d == {'a': [1], 2: ['b']}
dict_of_lists_add(d, 'a', 2)
assert d == {'a': [1, 2], 2: ['b']}

# Add element to set in dict
d = dict()
dict_of_sets_add(d, 'a', 1)
assert d == {'a': {1}}
dict_of_sets_add(d, 2, 'b')
assert d == {'a': {1}, 2: {'b'}}

# Add element to dict in dict
d = dict()
dict_of_dicts_add(d, 'a', 1, 3.0)
assert d == {'a': {1: 3.0}}
dict_of_dicts_add(d, 2, 'b', 5.0)
assert d == {'a': {1: 3.0}, 2: {'b': 5.0}}

# Spread items in list so similar items are further apart
input_list = [3, 1, 1, 1, 2, 2]
result = list_distribute_contents(input_list)
assert result == [1, 2, 1, 2, 1, 3]

# Get values for the same key in all dicts in list
input_list = [{'key': 'd', 1: 5}, {'key': 'd', 1: 1}, {'key': 'g', 1: 2},
              {'key': 'a', 1: 2}, {'key': 'a', 1: 3}, {'key': 'b', 1: 5}]
result = extract_list_from_list_of_dict(input_list, 'key')
assert result == ['d', 'd', 'g', 'a', 'a', 'b']

# Cast either keys or values or both in dictionary to type
d1 = {1: 2, 2: 2.0, 3: 5, 'la': 4}
assert key_value_convert(d1, keyfn=int) == {1: 2, 2: 2.0, 3: 5, 'la': 4}
assert key_value_convert(d1, keyfn=int, dropfailedkeys=True) == {1: 2, 2: 2.0, 3: 5}
d1 = {1: 2, 2: 2.0, 3: 5, 4: 'la'}
assert key_value_convert(d1, valuefn=int) == {1: 2, 2: 2.0, 3: 5, 4: 'la'}
assert key_value_convert(d1, valuefn=int, dropfailedvalues=True) == {1: 2, 2: 2.0, 3: 5}

# Cast keys in dictionary to integer
d1 = {1: 1, 2: 1.5, 3.5: 3, '4': 4}
assert integer_key_convert(d1) == {1: 1, 2: 1.5, 3: 3, 4: 4}

# Cast values in dictionary to integer
d1 = {1: 1, 2: 1.5, 3: '3', 4: 4}
assert integer_value_convert(d1) == {1: 1, 2: 1, 3: 3, 4: 4}

# Cast values in dictionary to float
d1 = {1: 1, 2: 1.5, 3: '3', 4: 4}
assert float_value_convert(d1) == {1: 1.0, 2: 1.5, 3: 3.0, 4: 4.0}

# Average values by key in two dictionaries
d1 = {1: 1, 2: 1.0, 3: 3, 4: 4}
d2 = {1: 2, 2: 2.0, 3: 5, 4: 4, 7: 3}
assert avg_dicts(d1, d2) == {1: 1.5, 2: 1.5, 3: 4, 4: 4}

# Read and write lists to csv
l = [[1, 2, 3, 'a'],
     [4, 5, 6, 'b'],
     [7, 8, 9, 'c']]
write_list_to_csv(filepath, l, headers=['h1', 'h2', 'h3', 'h4'])
newll = read_list_from_csv(filepath)
newld = read_list_from_csv(filepath, headers=1, dict_form=True)
assert newll == [['h1', 'h2', 'h3', 'h4'], ['1', '2', '3', 'a'], ['4', '5', '6', 'b'], ['7', '8', '9', 'c']]
assert newld == [{'h1': '1', 'h2': '2', 'h4': 'a', 'h3': '3'},
                {'h1': '4', 'h2': '5', 'h4': 'b', 'h3': '6'},
                {'h1': '7', 'h2': '8', 'h4': 'c', 'h3': '9'}]

## Convert command line arguments to dictionary
args = 'a=1,big=hello,1=3'
assert args_to_dict(args) == {'a': '1', 'big': 'hello', '1': '3'}

HTML utilities

These are built on top of BeautifulSoup and simplify its setup.

Examples:

# Get soup for url with optional kwarg downloader=Download() object
soup = get_soup('http://myurl', user_agent='test')
# user agent can be set globally using:
# UserAgent.set_global('test')
tag = soup.find(id='mytag')

# Get text of tag stripped of leading and trailing whitespace
# and newlines and with &nbsp replaced with space
result = get_text('mytag')

# Extract HTML table as list of dictionaries
result = extract_table(tabletag)

Compare files

Compare two files:

result = compare_files(testfile1, testfile2)
# Result is of form eg.:
# ["- coal   ,3      ,7.4    ,'needed'\n",
#  '?         ^\n',
#  "+ coal   ,1      ,7.4    ,'notneeded'\n",
#  '?         ^                +++\n']

Emailing

Example of setup and sending email:

smtp_initargs = {
    'host': 'localhost',
    'port': 123,
    'local_hostname': 'mycomputer.fqdn.com',
    'timeout': 3,
    'source_address': ('machine', 456),
}
username = '[e-mail]'
password = 'pass'
email_config_dict = {
    'connection_type': 'ssl',
    'username': username,
    'password': password
}
email_config_dict.update(smtp_initargs)

recipients = ['[e-mail]', '[e-mail]', '[e-mail]']
subject = 'hello'
text_body = 'hello there'
html_body = """\
<html>
  <head></head>
  <body>
    <p>Hi!<br>
       How are you?<br>
       Here is the <a href="https://www.python.org">link</a> you wanted.
    </p>
  </body>
</html>
"""
sender = '[e-mail]'

with Email(email_config_dict=email_config_dict) as email:
    email.send(recipients, subject, text_body, sender=sender)

Configuring Logging

The library provides coloured logs with a simple default setup which should be adequate for most cases. If you wish to change the logging configuration from the defaults, you will need to call setup_logging with arguments.

from hdx.utilities.easy_logging import setup_logging
...
logger = logging.getLogger(__name__)
setup_logging(KEYWORD ARGUMENTS)

KEYWORD ARGUMENTS can be:

Choose	Argument	Type	Value	Default
One of:	logging_config_dict	dict	Logging configuration dictionary
or	logging_config_json	str	Path to JSON Logging configuration
or	logging_config_yaml	str	Path to YAML Logging configuration	Library's internal logging_configuration.yml
One of:	smtp_config_dict	dict	Email Logging configuration dictionary
or	smtp_config_json	str	Path to JSON Email Logging configuration
or	smtp_config_yaml	str	Path to YAML Email Logging configuration

Do not supply smtp_config_dict, smtp_config_json or smtp_config_yaml unless you are using the default logging configuration!

If you are using the default logging configuration, you have the option to have a default SMTP handler that sends an email in the event of a CRITICAL error by supplying either smtp_config_dict, smtp_config_json or smtp_config_yaml. Here is a template of a YAML file that can be passed as the smtp_config_yaml parameter:

handlers:
    error_mail_handler:
        toaddrs: EMAIL_ADDRESSES
        subject: "RUN FAILED: MY_PROJECT_NAME"

Unless you override it, the mail server mailhost for the default SMTP handler is localhost and the from address fromaddr is <noreply@localhost>.

To use logging in your files, simply add the line below to the top of each Python file:

logger = logging.getLogger(__name__)

Then use the logger like this:

logger.debug('DEBUG message')
logger.info('INFORMATION message')
logger.warning('WARNING message')
logger.error('ERROR message')
logger.critical('CRITICAL error message')

Path utilities

Examples:

# Gets temporary directory from environment variable
# TEMP_DIR and falls back to os function
temp_folder = get_temp_dir()

# Gets temporary directory from environment variable
# TEMP_DIR and falls back to os function,
# optionally appends the given folder, creates the
# folder and deletes the folder if exiting 
# successfully else keeps the folder if tehre was
# an exception
with temp_dir('papa', delete_on_success=True, delete_on_failure=False) as tempdir:
    ...
# Sometimes it is necessary to be able to resume runs if they fail. The following
# example creates a temporary folder and iterates through a list of items.
# On each iteration, the current state of progress is stored in the temporary
# folder. If the iteration were to fail, the temporary folder is not deleted and
# on the next run, it will resume where it failed. Once the whole list is iterated
# through, the temporary folder is deleted.
# What is returned each iteration is a tuple with 2 dictionaries. The first contains 
# key folder which is the temporary directory optionally with folder appended (and 
# created if it doesn't exist). In key progress is held the current position in the 
# iterator. It also contains the key batch containing a batch code to be passed as
# the batch parameter in create_in_hdx or update_in_hdx calls. The second dictionary 
# is the next dictionary in the iterator.
# The environment variable WHERETOSTART can be set to the starting value for example
# iso3=SDN in the example below. If it is
# set to RESET, then the temporary folder is deleted before the run starts to ensure
# it starts from the beginning.    
iterator = [{'iso3': 'AFG', 'name': 'Afghanistan'}, {'iso3': 'SDN', 'name': 'Sudan'},
            {'iso3': 'YEM', 'name': 'Yemen'}, {'iso3': 'ZAM', 'name': 'Zambia'}]
result = list()
for info, nextdict in progress_storing_tempdir(tempfolder, iterator, 'iso3'):
    ...

# Get current directory of script
dir = script_dir(ANY_PYTHON_OBJECT_IN_SCRIPT)

# Get current directory of script with filename appended
path = script_dir_plus_file('myfile.txt', ANY_PYTHON_OBJECT_IN_SCRIPT)

# Get filename or (filename, extension) from url
url = 'https://raw.githubusercontent.com/OCHA-DAP/hdx-python-utilities/master/tests/fixtures/test_data.csv'
filename = get_filename_from_url(fixtureurl)
assert filename == 'test_data.csv'
filename, extension = get_filename_extension_from_url(fixtureurl)
assert filename == 'test_data'
assert extension == '.csv'

Date parsing utilities

Ambiguous dates are parsed as day first D/M/Y where there are values in front of the year and day last Y/M/D where there are values after the year.

Examples:

# Parse dates
assert parse_date('20/02/2013') == datetime(2013, 2, 20, 0, 0)
assert parse_date('20/02/2013', '%d/%m/%Y') == datetime(2013, 2, 20, 0, 0)

# Parse date ranges
parse_date_range('20/02/2013')
# == datetime(2013, 2, 20, 0, 0), datetime(2013, 2, 20, 0, 0)
parse_date_range('20/02/2013 10:00:00')
# == datetime(2013, 2, 20, 10, 0), datetime(2013, 2, 20, 10, 0)
parse_date_range('20/02/2013 10:00:00', zero_time=True)
# == datetime(2013, 2, 20, 0, 0), datetime(2013, 2, 20, 0, 0)
parse_date_range('20/02/2013', '%d/%m/%Y')
# == datetime(2013, 2, 20, 0, 0), datetime(2013, 2, 20, 0, 0)
parse_date_range('02/2013')
# == datetime(2013, 2, 1, 0, 0), datetime(2013, 2, 28, 0, 0)
parse_date_range('2013')
# == datetime(2013, 1, 1, 0, 0), datetime(2013, 12, 31, 0, 0)

# Pass dict in fuzzy activates fuzzy matching that allows for looking for dates within a sentence
fuzzy = dict()
parse_date_range('date is 20/02/2013 for this test', fuzzy=fuzzy)
# == datetime(2013, 2, 20, 0, 0), datetime(2013, 2, 20, 0, 0)    
assert fuzzy == {'startdate': datetime(2013, 2, 20, 0, 0), 'enddate': datetime(2013, 2, 20, 0, 0), 
                 'nondate': ('date is ', ' for this test'), 'date': ('20/02/2013',)}
fuzzy = dict()
parse_date_range('date is 02/2013 for this test', fuzzy=fuzzy)
# == datetime(2013, 2, 1, 0, 0), datetime(2013, 2, 28, 0, 0)
assert fuzzy == {'startdate': datetime(2013, 2, 1, 0, 0), 'enddate': datetime(2013, 2, 28, 0, 0), 
                 'nondate': ('date is ', ' for this test'), 'date': ('02/2013',)}

Text processing

Examples:

a = 'The quick brown fox jumped over the lazy dog. It was so fast!'

# Remove whitespace and punctuation from end of string
assert remove_end_characters('lalala,.,"') == 'lalala'
assert remove_end_characters('lalala, .\t/,"', '%s%s' % (punctuation, whitespace)) == 'lalala'

# Remove list of items from end of string, stripping any whitespace
result = remove_from_end(a, ['fast!', 'so'], 'Transforming %s -> %s')
assert result == 'The quick brown fox jumped over the lazy dog. It was'

# Remove string from another string and delete any preceding end characters - by default 
# punctuation (eg. comma) and any whitespace following the punctuation
assert remove_string('lala, 01/02/2020 ', '01/02/2020') == 'lala '
assert remove_string('lala,(01/02/2020) ', '01/02/2020') == 'lala) '
assert remove_string('lala, 01/02/2020 ', '01/02/2020', PUNCTUATION_MINUS_BRACKETS) == 'lala '
assert remove_string('lala,(01/02/2020) ', '01/02/2020', PUNCTUATION_MINUS_BRACKETS) == 'lala,() '

# Replace multiple strings in a string simultaneously
result = multiple_replace(a, {'quick': 'slow', 'fast': 'slow', 'lazy': 'busy'})
assert result == 'The slow brown fox jumped over the busy dog. It was so slow!'

# Extract words from a string sentence into a list
result = get_words_in_sentence("Korea (Democratic People's Republic of)")
assert result == ['Korea', 'Democratic', "People's", 'Republic', 'of']

# Find matching text in strings
a = 'The quick brown fox jumped over the lazy dog. It was so fast!'
b = 'The quicker brown fox leapt over the slower fox. It was so fast!'
c = 'The quick brown fox climbed over the lazy dog. It was so fast!'
result = get_matching_text([a, b, c], match_min_size=10)
assert result == ' brown fox  over the  It was so fast!'

Encoding utilities

Examples:

# Base 64 encode and decode string
a = 'The quick brown fox jumped over the lazy dog. It was so fast!'
b = str_to_base64(a)
c = base64_to_str(b)

Raise from

Examples:

# Raise an exception from another exception on Py2 or Py3
except IOError as e:
    raisefrom(IOError, 'My Error Message', e)

Valid UUID

Examples:

assert is_valid_uuid('jpsmith') is False
assert is_valid_uuid('c9bf9e57-1685-4c89-bafb-ff5af830be8a') is True

Easy building and packaging

The clean command of setup.py has been extended to use the --all flag by default and to clean the dist folder. Two new commands folder have been created. package calls the new clean command and also sdist and also bdist_wheel. In other words, it cleans thoroughly and builds source and wheel distributions. publish publishes to pypi and creates a git tag. It requires that the command line git tool be installed as well as the Python package twine (which is not in the requirements of HDX Python Utilities and must be separately installed eg. with pip).

python setup.py clean
python setup.py package
python setup.py publish

To use these commands, create a setup.py like this:

requirements = ['ckanapi>=4.2']

classifiers = [
    "Development Status :: 5 - Production/Stable",
    "Intended Audience :: Developers",
    "Natural Language :: English",
    "License :: OSI Approved :: MIT License",
    "Operating System :: OS Independent",
    "Programming Language :: Python",
    "Programming Language :: Python :: 2.7",
    "Programming Language :: Python :: 3",
    "Topic :: Software Development :: Libraries :: Python Modules",
]

# Version of project in plain text file in src/hdx/version.txt
PublishCommand.version = load_file_to_str(join('src', 'hdx', 'version.txt'), strip=True)

setup(
    name='hdx-python-api',
    description='HDX Python Library',
    license='MIT',
    url='https://github.com/OCHA-DAP/hdx-python-api',
    version=PublishCommand.version,
    author='Michael Rans',
    author_email='[e-mail]',
    keywords=['HDX', 'API', 'library'],
    long_description=load_file_to_str('README.md'),
    long_description_content_type='text/markdown',
    packages=find_packages(where='src'),
    package_dir={'': 'src'},
    include_package_data=True,
    setup_requires=['pytest-runner'],
    tests_require=['pytest'],
    zip_safe=True,
    classifiers=classifiers,
    install_requires=requirements,
    cmdclass={'clean': CleanCommand, 'package': PackageCommand, 'publish': PublishCommand},
)