dhis2.py ########

A Python library for DHIS2 <https://dhis2.org> wrapping requests <http://docs.python-requests.org/en/master/user/quickstart/> for general-purpose API interaction with DHIS2. It attempts to be useful for any data/metadata import and export tasks including various utilities like file loading, UID generation and logging. A strong focus is on JSON.

Supported and tested on Linux/macOS, Windows and DHIS2 versions >= 2.25. Python 3.6+ is required.

.. contents:: .. section-numbering::

Installation

Python 3.6+ is required.

.. code:: bash

pip install dhis2.py

For instructions on installing Python / pip for your operating system see realpython.com/installing-python <https://realpython.com/installing-python>_.

Note: this project is not related with the module dhis2 <https://pypi.org/project/dhis2/>__ which is installed with pip install dhis2. However, the import statement is for example from dhis2 import Api which is similar to the other dhis2 module.

Quickstart

Create an Api object:

.. code:: python

from dhis2 import Api

api = Api('play.dhis2.org/demo', 'admin', 'district')

Then run requests on it:

.. code:: python

r = api.get('organisationUnits/Rp268JB6Ne4', params={'fields': 'id,name'})

print(r.json())
# { "name": "Adonkia CHP", "id": "Rp268JB6Ne4" }

r = api.post('metadata', json={'dataElements': [ ... ] })
print(r.status_code) # 200

api.get()
api.post()
api.put()
api.patch()
api.delete()

see below for more methods.

They all return a Response object from requests <http://docs.python-requests.org/en/master/user/quickstart/>_ except noted otherwise. This means methods and attributes are equally available (e.g. Response.url, Response.text, Response.status_code etc.).

Usage

Api instance creation

Authentication in code ^^^^^^^^^^^^^^^^^^^^^^

Create an API object

.. code:: python

from dhis2 import Api

api = Api('play.dhis2.org/demo', 'admin', 'district')

optional arguments:

api_version: DHIS2 API version
user_agent: submit your own User-Agent header. This is useful if you need to parse e.g. Nginx logs later.

Authentication from file ^^^^^^^^^^^^^^^^^^^^^^^^^

Load from a auth JSON file in order to not store credentials in scripts. Must have the following structure:

.. code:: json

{
  "dhis": {
    "baseurl": "http://localhost:8080",
    "username": "admin",
    "password": "district"
  }
}

.. code:: python

from dhis2 import Api

api = Api.from_auth_file('path/to/auth.json', api_version=29, user_agent='myApp/1.0')

If no file path is specified, it tries to find a file called dish.json in:

the DHIS_HOME environment variable
your Home folder

Get info about the DHIS2 instance ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

API version as a string:

.. code:: python

print(api.version)
# '2.30'

API version as an integer:

.. code:: python

print(api.version_int)
# 30

API revision / build:

.. code:: python

print(api.revision)
# '17f7f0b'

API URL:

.. code:: python

print(api.api_url)
# 'https://play.dhis2.org/demo/api/30'

Base URL:

.. code:: python

print(api.base_url)
# 'https://play.dhis2.org/demo'

system info (this is persisted across the session):

.. code:: python

print(api.info)
# {
#   "lastAnalyticsTableRuntime": "11 m, 51 s",
#   "systemId": "eed3d451-4ff5-4193-b951-ffcc68954299",
#   "contextPath": "https://play.dhis2.org/2.30",
#   ...

Getting things

Normal method: api.get(), e.g.

.. code:: python

r = api.get('organisationUnits/Rp268JB6Ne4', params={'fields': 'id,name'})
data = r.json()

Parameters:

timeout: to override the timeout value (default: 5 seconds) in order to prevent the client to wait indefinitely on a server response.

Paging ^^^^^^

Paging for larger GET requests via api.get_paged()

Two possible ways:

a) Process every page as they come in:

.. code:: python

for page in api.get_paged('organisationUnits', page_size=100):
    print(page)
    # { "organisationUnits": [ {...}, {...} ] } (100 organisationUnits)

b) Load all pages before proceeding (this may take a long time) - to do this, do not use for and add merge=True:

.. code:: python

all_pages = api.get_paged('organisationUnits', page_size=100, merge=True):
print(all_pages)
# { "organisationUnits": [ {...}, {...} ] } (all organisationUnits)

Note: Returns directly a JSON object, not a requests.Response object unlike normal GETs.

SQL Views ^^^^^^^^^^

Get SQL View data as if you'd open a CSV file, optimized for larger payloads, via api.get_sqlview()

.. code:: python

# poll a sqlView of type VIEW or MATERIALIZED_VIEW:
for row in api.get_sqlview('YOaOY605rzh', execute=True, criteria={'name': '0-11m'}):
    print(row)
    # {'code': 'COC_358963', 'name': '0-11m'}

# similarly, poll a sqlView of type QUERY:
for row in api.get_sqlview('qMYMT0iUGkG', var={'valueType': 'INTEGER'}):
    print(row)

# if you want a list directly, cast it to a ``list`` or add ``merge=True``:
data = list(api.get_sqlview('qMYMT0iUGkG', var={'valueType': 'INTEGER'}))
# OR
# data = api.get_sqlview('qMYMT0iUGkG', var={'valueType': 'INTEGER'}, merge=True)

Note: Returns directly a JSON object, not a requests.response object unlike normal GETs.

Beginning of 2.26 you can also use normal filtering on sqlViews. In that case, it's recommended to use the stream=True parameter of the Dhis.get() method.

GET other content types ^^^^^^^^^^^^^^^^^^^^^^^

Usually defaults to JSON but you can get other file types:

.. code:: python

r = api.get('organisationUnits/Rp268JB6Ne4', file_type='xml')
print(r.text)
# <?xml version='1.0' encoding='UTF-8'?><organisationUnit ...

r = api.get('organisationUnits/Rp268JB6Ne4', file_type='pdf')
with open('/path/to/file.pdf', 'wb') as f:
    f.write(r.content)

Updating / deleting things

Normal methods:

api.post()
api.put()
api.patch()
api.delete()

Post partitioned payloads ^^^^^^^^^^^^^^^^^^^^^^^^^^

If you have such a large payload (e.g. metadata imports) that you frequently get a HTTP Error: 413 Request Entity Too Large response e.g. from Nginx you might benefit from using the following method that splits your payload in partitions / chunks and posts them one-by-one. You define the amount of elements in each POST by specifying a number in thresh (default: 1000).

Note that it is only possible to submit one key per payload (e.g. dataElements only, not additionally organisationUnits in the same payload).

api.post_partitioned()

.. code:: python

import json

data = {
    "organisationUnits": [
        {...},
        {...} # very large number of org units
    ]
{
for response in api.post_partitioned('metadata', json=data, thresh=5000):
    text = json.loads(response.text)
    print('[{}] - {}'.format(text['status'], json.dumps(text['stats'])))

Multiple params with same key

If you need to pass multiple parameters to your request with the same key, you may submit as a list of tuples instead when e.g.:

.. code:: python

r = api.get('dataValueSets', params=[
        ('dataSet', 'pBOMPrpg1QX'), ('dataSet', 'BfMAe6Itzgt'),
        ('orgUnit', 'YuQRtpLP10I'), ('orgUnit', 'vWbkYPRmKyS'),
        ('startDate', '2013-01-01'), ('endDate', '2013-01-31')
    ]
)

alternatively:

.. code:: python

r = api.get('dataValueSets', params={
    'dataSet': ['pBOMPrpg1QX', 'BfMAe6Itzgt'],
    'orgUnit': ['YuQRtpLP10I', 'vWbkYPRmKyS'],
    'startDate': '2013-01-01',
    'endDate': '2013-01-31'
})

Utilities

Load JSON file ^^^^^^^^^^^^^^^^^

.. code:: python

from dhis2 import load_json

json_data = load_json('/path/to/file.json')
print(json_data)
# { "id": ... }

Load CSV file ^^^^^^^^^^^^^^^^

Via a Python generator:

.. code:: python

from dhis2 import load_csv

for row in load_csv('/path/to/file.csv'):
    print(row)
    # { "id": ... }

Via a normal list, loaded fully into memory:

.. code:: python

data = list(load_csv('/path/to/file.csv'))

Generate UID ^^^^^^^^^^^^

Create a DHIS2 UID:

.. code:: python

uid = generate_uid()
print(uid)
# 'Rp268JB6Ne4'

To create a list of 1000 UIDs:

.. code:: python

uids = [generate_uid() for _ in range(1000)]

Validate UID ^^^^^^^^^^^^

Check if something is a valid DHIS2 UID:

.. code:: python

uid = 'MmwcGkxy876'
print(is_valid_uid(uid))
# True

uid = 25329
print(is_valid_uid(uid))
# False

uid = 'MmwcGkxy876 '
print(is_valid_uid(uid))
# False

Clean an object ^^^^^^^^^^^^^^^^

Useful for deep-removing certain keys in an object, e.g. remove all sharing by recursively removing all user and userGroupAccesses fields.

.. code:: python

from dhis2 import clean_obj

metadata = {
    "dataElements": [
        {
            "name": "ANC 1st visit",
            "id": "fbfJHSPpUQD",
            "publicAccess": "rw------",
            "userGroupAccesses": [
                {
                    "access": "r-r-----",
                    "userGroupUid": "Rg8wusV7QYi",
                    "displayName": "HIV Program Coordinators",
                    "id": "Rg8wusV7QYi"
                },
                {
                    "access": "rwr-----",
                    "userGroupUid": "qMjBflJMOfB",
                    "displayName": "Family Planning Program",
                    "id": "qMjBflJMOfB"
                }
            ]
        }
    ],
    "dataSets": [
        {
            "name": "ART monthly summary",
            "id": "lyLU2wR22tC",
            "publicAccess": "rwr-----",
            "userGroupAccesses": [
                {
                    "access": "r-rw----",
                    "userGroupUid": "GogLpGmkL0g",
                    "displayName": "_DATASET_Child Health Program Manager",
                    "id": "GogLpGmkL0g"
                }
            ]
        }
    ]
}

cleaned = clean_obj(metadata, ['userGroupAccesses', 'publicAccess'])
pretty_json(cleaned)

Which would eventually recursively remove all keys matching to userGroupAccesses or publicAccess:

.. code:: json

{
  "dataElements": [
    {
      "name": "ANC 1st visit",
      "id": "fbfJHSPpUQD"
    }
  ],
  "dataSets": [
    {
      "name": "ART monthly summary",
      "id": "lyLU2wR22tC"
    }
  ]
}

Print pretty JSON ^^^^^^^^^^^^^^^^^

Print easy-readable JSON objects with colors, utilizes Pygments <http://pygments.org/>_.

.. code:: python

from dhis2 import pretty_json

obj = {"dataElements": [{"name": "Accute Flaccid Paralysis (Deaths < 5 yrs)", "id": "FTRrcoaog83", "aggregationType": "SUM"}]}
pretty_json(obj)

... prints (in a terminal it will have colors):

.. code:: json

{
  "dataElements": [
    {
      "aggregationType": "SUM",
      "id": "FTRrcoaog83",
      "name": "Accute Flaccid Paralysis (Deaths < 5 yrs)"
    }
  ]
}

Check import response ^^^^^^^^^^^^^^^^^^^^^^

Check the importSummary response from e.g. /api/dataValues or /api/metadata import. Returns true if import went well, false if there are ignored values or the status reports not a OK or SUCCESS. This can be useful if the response returns a 200 OK but the summary reports ignored data.

.. code:: python

from dhis2 import import_response_ok

# response as e.g. from response = api.post('metadata', data=payload).json()
response = {
    "description": "The import process failed: java.lang.String cannot be cast to java.lang.Boolean",
    "importCount": {
        "deleted": 0,
        "ignored": 1,
        "imported": 0,
        "updated": 0
    },
    "responseType": "ImportSummary",
    "status": "WARNING"
}

import_successful = import_response_ok(response)
print(import_successful)
# False

Logging

Logging utilizes logzero <https://github.com/metachris/logzero>_.

Color output depending on log level
DHIS2 log format including the line of the caller
optional logfile= specifies a rotating log file path (20 x 10MB files)

.. code:: python

from dhis2 import setup_logger, logger

setup_logger(logfile='/var/log/app.log')

logger.info('my log message')
logger.warning('missing something')
logger.error('something went wrong')
logger.exception('with stacktrace')

* INFO  2018-06-01 18:19:40,001  my log message [script:86]
* ERROR  2018-06-01 18:19:40,007  something went wrong [script:87]

Use setup_logger(include_caller=False) if you want to remove [script:86] from logs.

Exceptions

There are two exceptions:

RequestException: DHIS2 didn't like what you requested. See the exception's code, url and description.
ClientException: Something didn't work with the client not involving DHIS2.

They both inherit from Dhis2PyException.

Examples

Real-world script examples can be found in the examples folder.
dhis2.py is used in dhis2-pk <https://github.com/davidhuser/dhis2-pk>_ (dhis2-pocket-knife)

Changelog

Versions changelog <https://github.com/davidhuser/dhis2.py/blob/master/CHANGELOG.rst>_

Contribute

Feedback welcome!

Add issue <https://github.com/davidhuser/dhis2.py/issues/new>_
Install the dev environment (see below)
Fork, add changes to master branch, ensure tests pass with full coverage and add a Pull Request

.. code:: bash

pip install pipenv
git clone https://github.com/davidhuser/dhis2.py
cd dhis2.py
pipenv install --dev
pipenv run tests

# install pre-commit hooks
pipenv run pre-commit install

# run type annotation check
pipenv run mypy dhis2

# run flake8 style guide enforcement
pipenv run flake8