dhis2.py 2.0.1

Python wrapper for DHIS2

A Python library for DHIS2 wrapping requests for general-purpose API interaction with DHIS2. It attempts to be useful for any data/metadata import tasks including various utilities like file loading, UID generation and logging. A strong focus is on JSON.

Supported and tested on Python 2 and 3 on Linux/macOS, Windows and DHIS2 versions >= 2.25.

Contents

• 1 Installation

• 2 Quickstart

• 3 Usage

  • 3.1 Getting things

    • 3.1.1 Paging

    • 3.1.2 SQL Views

    • 3.1.3 GET other content types

  • 3.2 Updating / deleting things

    • 3.2.1 Post partitioned payloads

  • 3.3 Multiple params with same key

  • 3.4 Utilities

    • 3.4.1 Load JSON file

    • 3.4.2 Load CSV file

    • 3.4.3 Generate UID

    • 3.4.4 Validate UID

    • 3.4.5 Clean an object

    • 3.4.6 Print pretty JSON

  • 3.5 Logging

  • 3.6 Exceptions

• 4 Examples

• 5 Changelog

• 6 Contribute

• 7 License

1 Installation

pip install dhis2.py

For instructions on installing Python / pip for your operating system see The Hitchhiker’s Guide to Python.

2 Quickstart

Create an Api object:

from dhis2 import Api

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

Then run requests on it:

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 except noted otherwise. This means methods and attributes are equally available (e.g. Response.url, Response.text, Response.status_code etc.).

3 Usage

3.1 Getting things

Normal method: api.get()

3.1.1 Paging

Paging for larger GET requests via api.get_paged()

Two possible ways:

1. Process every page as they come in:

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

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

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.

3.1.2 SQL Views

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

# 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.

3.1.3 GET other content types

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

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)

3.2 Updating / deleting things

Normal methods:

• api.post()

• api.put()

• api.patch()

• api.delete()

3.2.1 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()

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'])))

3.3 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.:

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

alternatively:

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

3.4 Utilities

3.4.1 Load JSON file

from dhis2 import load_json

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

3.4.2 Load CSV file

Via a Python generator:

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:

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

3.4.3 Generate UID

Create a DHIS2 UID:

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

To create a list of 1000 UIDs:

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

3.4.4 Validate UID

Check if something is a valid DHIS2 UID:

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

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

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

3.4.5 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.

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:

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

3.4.6 Print pretty JSON

Print easy-readable JSON objects with colors, utilizes Pygments.

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):

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

3.5 Logging

Logging utilizes 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)

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.

3.6 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.

4 Examples

• Real-world script examples can be found in the examples folder.

• dhis2.py is used in dhis2-pk (dhis2-pocket-knife)

5 Changelog

Versions changelog

6 Contribute

Feedback welcome!

• Add issue

• Install the dev environment (see below)

• Fork, add changes to master branch, ensure tests pass with full coverage and add a Pull Request

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

7 License

dhis2.py’s source is provided under MIT license. See LICENCE for details.

• Copyright (c), 2019, David Huser