edelweiss-data 0.2.19

Python client for EdelweissData

This python client library allows easy access to Edelweiss Data servers.

Table of Contents

• Overview
• Getting started
  • Requirements
  • Installation
• Common use cases
  • Initialization
  • Authentication
  • Create a new dataset
  • Search for datasets
  • Filter and retrieve data
  • Delete a new dataset
• API reference

Overview

The core concept of Edelweiss Data is that of a Dataset. A Dataset is a single table of data (usually originating from a csv file) and carries the following additional pieces of information:

• a schema describing the structure of the tabular data (data types, explanatory text for each column etc)
• a human readable description text (markdown formatted - like the readme of a repository on github)
• a metadata json structure (of arbitrary complexity - this can be used to store things like author information, instrument settings used to generate the data, ...).

Datasets are versioned through a processes called publishing. Once a version of a dataset is published, it is "frozen" and becomes immutable. Any change to it has to be done by creating a new version. Users of Edelweiss Data will always see the version history of a dataset and be able to ask for the latest version or specific earlier version.

Datasets can be public or access restricted. Public datasets can be accessed without any access restrictions. To access restricted datasets or to upload/edit your own dataset OpenIDConnect/OAuth is used - in the python client this process is done by calling the authenticate method on the Api instance that triggers a web based login at the end of which a token is confirmed.

When retrieving the tabular data of a dataset, the data can be filtered and ordered and only specific columns requested - this makes request for subsets of data much faster than if all filtering happened only on the client. Conditions for filtering and ordering are created by constructing QueryExpression instances using classmethods on the QueryExpression class to create specific Expressions. You can access the data either in it's raw form (as json data) or, more conveniently, as a Pandas Dataframe.

Just like the tabular data of one particular dataset can be retrieved as a Pandas DataFrame, you can also query for datasets using the same filtering and ordering capabilities - i.e. you can retrieve a DataFrame where each row represents a Dataset with it's name, description and optionally metadata and schema (not including the data though).

When you are searching for Datasets, a lot of the interesting information that you may want to filter by is hidden in the metadata (e.g. maybe most of your datasets have a metadata field "Species" at the top level of the metadata json that indicates from what kind of animal cells the data in this dataset originate from). To make such filtering easy, our Datasets query function take an optional list of "column mappings" that allow you to specify a JsonPath expression to extract a field from the metadata and include it with a given name in the resulting DataFrame. In the Species example, you could pass the list of column mappings [("Species from Metadata", "$.Species")] and the resulting DataFrame would contain an additional column "Species from Metadata" and for every row the result of evaluating the JsonPath $.Species would be included in this column and you could filter on it using conditions to e.g. only retrieve Datasets where the Species is set to "Mouse".

Edelweiss Data servers provide a rich User Interface as well that let's you visually browse and filter datasets and the data (and associated information) of each dataset. This UI is built to integrate nicely with the python client. The DataExplorer that is used to explore a dataset has a button in the upper right corner to generate the python code to get the exact same filtering and ordering you see in the UI into a Pandas DataFrame using the Edelweiss Data library for your convenience.

More information and examples

The official EdelweissData documentation has more in-depth information about the various parts of EdelweissData.

In the examples directory of this libraries Github page you can find several Jupyter notebooks which repeat the walkthroughs from the official EdelweissData documentation but using the edelweiss_data library instead of direct HTTP calls shown with JavaScript.

Getting started

Requirements

Python 3.6+

Installation

pip install edelweiss_data

Common use cases

In addition to the brief overview below, do check out the examples directory of this libraries Github page that contains several Jupyter notebooks demonstrating common operations and showing e.g. all possible filter operators in use.

Initialization

You interact with the Edelweiss Data API mainly via the API class of the edelweiss_data python library. Import it, point it at the Edelweiss Data instance you want to interact with and instantiate it like this:

from edelweiss_data import API, QueryExpression as Q

# Set this to the url of the Edelweiss Data server you want to interact with
edelweiss_api_url = 'https://api.develop.edelweiss.douglasconnect.com'

api = API(edelweiss_api_url)

Authentication

Some operations in Edelweiss Data are accessible without authentication (e.g. retrieving public datasets). For others (e.g. to create datasets), you need to be authenticated. Authentication is done with the authenticate call. Be aware that this call is currently built for interactive use like in a Jupyter environment - it will block execution for a up to a few minutes while it waits for you to log in to your account and confirm the access to the API on your behalf. Once accepted the python client will store the authentication token so that you will not have to enter it again for a few days (the token is stored in your home directory in the .edelweiss directory).

api.authenticate()

Create a new dataset

Creating and publishing a new dataset form a csv file can be done in one quick operation like so:

metadata = {"metadata-dummy-string": "string value", "metadata-dummy-number": 42.0}
with open ('FILENAME') as f:
    dataset = api.create_published_dataset_from_csv_file("DATASETNAME", f, metadata)

This creates a new dataset form the file FILENAME with the name DATASETNAME. A trivial example metadata is used here as well.

When creating and publishing datasets like this you don't have a lot of control over details of the schema or to set a more elaborate dataset description. If you need more control, you can create a dataset like so:

datafile = '../../tests/Serialization/data/small1.csv'
name = 'My dataset'
schemafile = None # if none, schema will be inferred below
metadata = None # dict object that will be serialized to json or None
metadatafile = None # path to the metadata file or None
description = "This is a *markdown* description that can use [hyperlinks](https://edelweissconnect.com)"

dataset1 = api.create_in_progress_dataset(name)
print('DATASET:', dataset1)
try:
    with open(datafile) as f:
        dataset1.upload_data(f)
    if schemafile is not None:
        print('uploading schema from file ...')
        with open(schemafile) as f:
            dataset1.upload_schema_file(f)
    else:
        print('inferring schema from file ...')
        dataset1.infer_schema()
    if metadata is not None:
        print('uploading metadata ...')
        dataset1.upload_metadata(metadata)
    elif metadatafile is not None:
        print('uploading metadata from file ...')
        with open(metadatafile) as f:
            dataset1.upload_metadata_file(f)

    dataset1.set_description(description)

    published_dataset = dataset1.publish('My first commit')
    print('DATASET published:',published_dataset)
except requests.HTTPError as err:
    print('not published: ', err.response.text)

Filter and retrieve data

The tabular data of an individual dataset can be retrieved into a pandas dataframe easily like this:

dataframe = dataset.get_data()

You can also filter and order data with QueryExpressions, often aliased to Q in the import statement. In the following example we assume the data to have a column "Species" which we want to filter to the value "Mouse" with fuzzy text matching and "Chemical name" which we want to order by ascending:

dataframe = dataset.get_data(condition=Q.fuzzy_search(Q.column("Species"), "Mouse"), order_by=[Q.column("Chemical name")])

In this example you can see how to do a chemical substructure search so that only molecules with the fragment "CC=O" are returned and the results are sorted descending by similarity to the molecule "C(C(CO)(CO)N)O". Chemical similarity for ordering is calculated using the rdkit library using tanimoto distance between rdkit fingerprints (other fingerprints or distance metrics could be supported in the future)

dataframe = dataset.get_data(condition=Q.substructure_search("CC=O", Q.column("SMILES")), order_by=[Q.tanimoto_similarity("C(C(CO)(CO)N)O", Q.column("SMILES"))], ascending=False)

Search for datasets

To just retrieve a pandas dataframe with all published datasets that you are allowed to see use get_published_datasets(). This will return a pandas dataframe with a multiindex of the dataset id and version and then a single object column for the dataset class instance. This class instance can be used to retrieve e.g. the name property of the dataset or it can be used to retrieve the data for this dataset by calling the .get_data() method or similar operations.

datasets = api.get_published_datasets()
dataset = datasets.iloc[0].dataset
print("Found {} datasets. The name of the first is: ".format(len(datasets), dataset.name))

To retrieve a single dataset by index value rather than based on numerical position use the pandas multiindex functionality passing in tuples:

datasetid = '49fd99ee-ec6f-44df-a8cd-73f0f8bbcd76' # example dataset ID
version = 1
dataset = datasets.loc[(datasetid, version)]

Just like above with data you can use QueryExpressions to filter to only find datasets matching certain predicates. Below we filter on datasets that have the string "LTKB" somewhere in them (name etc)

datasets_filter = Q.search_anywhere("LTKB")
datasets = api.get_published_datasets(condition=datasets_filter)

Since very often the most interesting filter and sort critieria will be in the metadata (which is a Json of arbitrary structure), the Api gives you a way to add additional columns by extracting pieces from the metadata json with JsonPath expressions. Below we attempt to treat the metadata json of each dataset as an object with a key "Species" and if it is present we extract it and map it into the "Species from metadata json" column:

columns = [("Species from metadata json", "$.Species")]
datasets = api.get_published_datasets(columns=columns)

The result of such a query will always be a column containing lists of results as the jsonpath query could return not just a single primitive value or null or an object but also json arrays. Working with columns that contain lists in Pandas can be a bit awkward as some operations like filtering default to operating on series instead of individual cells. For cases like these, working with the Pandas apply method usually makes things easier - e.g. if you want to extract the first element if any from the above column this an easy way to do it that also extends well to more complex operations:

datasets['first_species_or_none'] = datasets.apply(lambda row: row['Species from metadata json'][0] if len(row['Species from metadata json']) > 0 else None, axis=1)

Delete a dataset

To delete a dataset and all versions call delete_all_versions:

dataset.delete_all_versions()

API reference

API

The central interaction point with EdelweissData. You instantiate this class with the base url of the

EdelweissData server you want to interact with. The init method will verify that the server is reachable and speaks a compatible API version, then you can use instance methods to interact with the API server.

add_dataset_group_permission

Add a group to a dataset

dataset_id : the id of the dataset

group : the Group to add

add_dataset_user_permission

Add a user to a dataset

dataset_id : the id of the dataset

user : the User to add

authenticate

change_dataset_visibility

Set if the dataset should be public or access protected when published

dataset_id : the id of the dataset

is_public : boolean to indicate if the dataset should be public

create_in_progress_dataset

Creates a new in-progress dataset on the server and returns it.

Returns: The InProgressDataset that was created.

name : the name of the dataset to create

create_in_progress_dataset_from_csv_file

Creates a new in-progress dataset from a CSV file on the server.

Returns: the updated dataset

name : the name of the dataset

file : opened text file to read the csv data from

metadata : dict of the metadata to store as json together with the dataset

description : description text for the dataset (markdown formatted)

is_public : flag to indicate if the dataset should be public or access restricted after publishing

create_published_dataset_from_csv_file

Creates a new published dataset from a CSV file on the server.

Returns: the published dataset

name : the name of the dataset

file : opened text file to read the csv data from

metadata : dict of the metadata to store as json together with the dataset

changelog : Publishing message to store for the first version

description : description text for the dataset (markdown formatted)

is_public : flag to indicate if the dataset should be public or access restricted after publishing

delete

Sends a DELETE request to a server.

Returns: dict with the JSON response.

route : route to which the request will be sent

get

Sends a GET request to a server.

Returns: dict with the JSON response.

route : route to which the request will be sent

json : dict with the JSON body to send

get_dataset_permissions

Get the permissions for the given dataset id

Returns: the DatasetPermissions instance for this dataset

dataset_id : the id of the dataset

get_in_progress_dataset

Returns an in-progress datasets with a given id.

Returns: an InProgressDataset

id : the id of the dataset to retrieve

get_in_progress_datasets

Returns a list of all in-progress datasets you are allowed to access (needs authentication).

Returns: a list of InProgressDatasets

limit : Number of datasets to retrieve - if None (default) it will retrieve all.

offset : Starting offset from which to retrieve the datasets

get_published_dataset

Returns a published dataset with a given id and version.

Returns: the PublishedDataset

id : id of the dataset to retrieve

version : version of the dataset to retrieve. Defaults to LATEST if none specified.

get_published_dataset_aggregations

Returns aggregation buckets and their sizes for each column.

Returns: aggregations as a Series with an index of buckets and terms, for example bucket term organ liver 10 kidney 20 species mouse 5 elephant 30

columns : same as in self.get_published_datasets

condition : same as in self.get_published_datasets

aggregation_filters : same as in self.get_published_datasets

get_published_dataset_versions

Returns all published versions of dataset with a given id.

Returns: a list of PublishedDatasets

id : id of the dataset

get_published_datasets

Returns a dataframe of all published datasets that match query.

Returns: a dataframe indexed by the id and version, which in addition to user-specified columns, contains a column with a PublishedDataset object for each dataset. Unless included explicitly, description, schema, and metadata are omitted from the datasets and the corresponding attributes are set to None. On the first access to any of the missing attributes of a given dataset, all three them are fetched from the server and set to the actual values, resulting in a single request for each dataset. If there are many datasets for which the attributes are required, it makes sense to include the content in the bulk request.

columns : a list of pairs (column_name, json_path) describing the name of the new column to generate and which jsonpath to use to extract the values from the metadata to fill this column.

condition : a QueryExpression object limiting the fetched datasets.

include_description : a boolean specifying if the datasets in the response should include the description

include_schema : a boolean specifying if the datasets in the response should include the schema

include_metadata : a boolean specifying if the datasets in the response should include the metadata

aggregation_filters : a dict limiting the fetched datasets to ones where column values fall into one of the selected aggregation buckets. For example, using the dict {'organ': ['liver', 'kidney'], 'species': ['mouse', 'elephant']} would return the datasets where both organ is either liver or kidney, AND species is either mouse or elephant.

limit : the number of rows to return. Returns all rows if set to None (default).

offset : the initial offset (default 0).

order_by : a list of QueryExpression objects by which to order the resulting datasets.

ascending : a boolean or list of booleans to select the ordering. If the single boolean is True (the default), the list is ascending according to order_by, if False, it is descending. If given as a list, it must be of the same length as the order_by list, and the order is the ascending/descending for each particular component.

dataset_column_name : the name of the dataframe column in which the corresponding PublishedDataset objects are available.

latest_only : a boolean specifying whether to return only the latest version of each dataset

get_raw_datasets

Get the published datasets. Unlike the more high-level get_published_datasets this method

does not create a dataframe but returns the raw list of dicts representing the json response. Unless explicity included the fields schema, metadata and description will not be included in the response.

Returns: The published datasets as a list of dicts (raw json response)

columns : a list of pairs (column_name, json_path) describing columns in the dataframe.

condition : a QueryExpression object limiting the fetched datasets.

include_description : a boolean specifying if the datasets in the response should include the description

include_schema : a boolean specifying if the datasets in the response should include the schema

include_metadata : a boolean specifying if the datasets in the response should include the metadata

aggregation_filters : a dict limiting the fetched datasets to ones where column values fall into one of the selected aggregation buckets. For example, using the dict {'organ': ['liver', 'kidney'], 'species': ['mouse', 'elephant']} would return the datasets where both organ is either liver or kidney, AND species is either mouse or elephant.

limit : the number of rows to return (default 100). Returns all rows if set to None.

offset : the initial offset (default 0).

order_by : a list of QueryExpression objects by which to order the resulting datasets.

ascending : a boolean or list of booleans to select the ordering. If the single boolean is True (the default), the list is ascending according to order_by, if False, it is descending. If given as a list, it must be of the same length as the order_by list, and the order is the ascending/descending for each particular component.

latest_only : a boolean specifying whether to return only the latest version of each dataset

oidc_config

Returns the OpenID Connect configuration.

openapi

Returns the OpenAPI definition of the entire EdelweissData REST API.

Returns: The OpenAPI definition as a dict

openapi_documents

Returns a list of all dataset specific openapi descriptions (i.e. one openapi document for each dataset with the

precise Json Schema of the particular datasets data endpoint).

Returns: A list of url strings at which to retrieve the openapi.json documents for the documents

post

Sends a POST request to a server.

Returns: dict with the JSON response.

route : route to which the request will be sent

json : dict with the JSON body to send

post_raw

Sends a POST request with a given body to a server.

Returns: dict with the JSON response.

route : route to which the request will be sent

body : raw body to send (a bytes object or a string that will be encoded as UTF-8)

remove_dataset_group_permission

Remove a group from a dataset

dataset_id : the id of the dataset

name : the name of the group to remove

remove_dataset_user_permission

Remove a user from a dataset

dataset_id : the id of the dataset

user : the email of the user to remove

upload

Uploads a POST request that uploads files to a server.

Returns: dict with the JSON response.

route : route to which the request will be sent

files : a dictionary of files in which the keys are filenames and corresponding values are file objects

DatasetPermissions

The permission information for a dataset. A list of users (email + flag if they can write), groups (name + flag if they can write) and

an is_public field that indicates whether unauthenticated users can see this dataset when published.

Group

decode

encode

User

decode

encode

decode

encode

InProgressDataset

InProgressDataset - datasets that are not yet published and for which data can be uploaded, the schema modified, metadata changed etc.

copy_from

Copies all content from a PublishedDataset to this InProgressDataset. Useful to create new versions.

This is a lightweight operation, which works by re-using the same underlying data source.

decode

delete

Deletes the InProgressDataset

encode

get_permissions

infer_schema

Triggers schema inference from uploaded data (this creates a schema on the server and sets it on the InProgressDataset)

publish

Attempts to publish the dataset. This means that a new version of a PublishedDataset will be created (and returned by this call)

and this InProgressDataset is no longer useable.

sample

Retrieve a list of lists representing a sample of the tabular data of this dataset. This

includes only a sample (e.g. the first N rows) of the data so that they can be displayed to a user as an example or similar.

set_data_source

Set the data source for an in-progress dataset. This allows you to efficiently re-use the data of a PublishedDataset

to create a new dataset without re-uploading the data. It is also useful if you want to create a new version of a PublishedDataset to fix a mistake in the metadata or description.

dataset : the PublishedDataset to copy data from when publishing

set_description

Set the description of the dataset. The description is assumed to be markdown formatted text, similar to a Github README.md

set_name

Set the name of the dataset.

update

Update various attributes of a in-progress dataset. All parameters are options; those that are

None will not have their values changed.

name : A new name for the dataset

description : A new description for the dataset

data_source : A new data_source for the dataset. See set_data_source for a description of a data source.

schema : A new schema for the dataset.

metadata : A new metadata object for the dataset.

upload_data

Upload tabular data (a CSV file)

data : An open text file containing the csv data to upload

upload_dataframe_data

Upload a pandas dataframe as the data content into an InProgress dataset

dataframe : A Pandas dataframe containing the data to upload

upload_metadata

Upload metadata (as a dict, not a file).

schema : The metadata to upload

upload_metadata_file

Upload a metadata file (an open text file containing the metadata in Json form).

file : The open text file to upload the metadata from

upload_schema

Upload a Schema (an instance of the class, not a file).

schema : The schema to upload

upload_schema_file

Upload a schema file (an open text file containing the schema in Json form).

file : The open text file to upload the schema from

PublishedDataset

Represents a published dataset

decode

delete_all_versions

Deletes all versions of a published dataset

encode

get_data

Gets the (tabular) data of a PublishedDataset as a pandas Dataframe. The data can be filtered so that only required columns or rows

are retrieved.

Returns: A pandas DataFrame with the tabular data

columns : a list of column names that should appear in the result. If None, all columns are included.

condition : a QueryExpression object limiting the fetched datasets.

aggregation_filters : a dict limiting the fetched datasets to ones where column values fall into one of the selected aggregation buckets. For example, using the dict {'organ': ['liver', 'kidney'], 'species': ['mouse', 'elephant']} would return the datasets where both organ is either liver or kidney, AND species is either mouse or elephant.

limit : the number of rows to return. Returns all rows if set to None (default).

offset : the initial offset (default 0).

order_by : a list of QueryExpression objects by which to order the resulting datasets.

ascending : a boolean or list of booleans to select the ordering. If the single boolean is True (the default), the list is ascending according to order_by, if False, it is descending. If given as a list, it must be of the same length as the order_by list, and the order is the ascending/descending for each particular component.

get_data_aggregations

Returns aggregation buckets and their sizes for each column.

Returns: aggregations as a Series with an index of buckets and terms, for example bucket term organ liver 10 kidney 20 species mouse 5 elephant 30

columns : a list of column names that should appear in the result. If None, all columns are included.

condition : a QueryExpression object limiting the fetched datasets.

aggregation_filters : a dict limiting the fetched datasets to ones where column values fall into one of the selected aggregation buckets. For example, using the dict {'organ': ['liver', 'kidney'], 'species': ['mouse', 'elephant']} would return the datasets where both organ is either liver or kidney, AND species is either mouse or elephant.

get_permissions

Returns the Permissions object of this PublishedDataset

get_raw_data

Gets the raw tabular data JSON response for a PublishedDataset. The data can be filtered so that only required columns or rows

are retrieved.

Returns: A dict representing the JSON response

columns : a list of column names that should appear in the result. If None, all columns are included.

condition : a QueryExpression object limiting the fetched datasets.

aggregation_filters : a dict limiting the fetched datasets to ones where column values fall into one of the selected aggregation buckets. For example, using the dict {'organ': ['liver', 'kidney'], 'species': ['mouse', 'elephant']} would return the datasets where both organ is either liver or kidney, AND species is either mouse or elephant.

limit : the number of rows to return. Returns all rows if set to None (default).

offset : the initial offset (default 0).

order_by : a list of QueryExpression objects by which to order the resulting datasets.

ascending : a boolean or list of booleans to select the ordering. If the single boolean is True (the default), the list is ascending according to order_by, if False, it is descending. If given as a list, it must be of the same length as the order_by list, and the order is the ascending/descending for each particular component.

new_version

Create a new version of this PublishedDataset. This will create and return a new InProgressDataset

that can be filled with content by uploading new files or copying data from a PublishedDataset

Returns: The InProgressDataset

openapi

Returns a OpenAPI descriptions for the data endpoint of this PublishedDataset, taking the schema

and thus the precise JSON structure of the response into account.

Returns: A dict respresenting the JSON decoded OpenAPI document

QueryExpression

Used to create filters or expressions to order records by. Use the classmethods on this

class to create instances, e.g. QueryExpression.fuzzySearch(QueryExpression.column("species"), "Monkey")

cast

Creates a Cast expression. This attempts to convert one datatype into another.

expr : The expression to cast

data_type : The datatype to cast to

column

Constructs a Column expression.

column_name : the name of the column

contained_in

Creates a ContainedIn expression. Tests if an expression is contained in an element. Often used

to check if columns of an Array datatype are contained in a value.

expr : The expression to search for

element : The element to search in

contains

Creates a Contains expression. Tests if an expression contains an element. Often used

to check if columns of an Array datatype contain a value.

expr : The expression to search in

element : The element to search for

decode

encode

exact_search

Constructs an ExactSearch expression. Only rows where the expr expression exactly matches the term will be returned. This can be used

to match exact substrings or exact numerical values

expr : the search expression to evaluate (often a column QueryExpression)

term : the search term

fuzzy_search

Constructs a FuzzySearch expression. Only rows where the expr expression fuzzy-matches the term will be returned. Fuzzy-matching

uses trigram indexing to match slightly different spellings.

expr : the search expression to evaluate (often a column QueryExpression)

term : the search term

search_anywhere

Constructs a SearchAnywhere expression. Only rows will be returned that contain the search term in one of their text-like columns.

term : The string to search for in all text-like columns.

substructure_search

Constructs a SubstructureSearch expression that uses chemical substructure testing. Only rows where the chemical substructure is contained in

the chemical superstructure are returned.

substructure : the substructure to search (often a SMILES string constant value)

superstructure : the search term (often a Column of datatype SMILES)

system_column

Constructs a SystemColumn expression. SystemColumns are special columns maintained by EdelweissData.

The following SystemColumns are available: name (text): the name of a dataset created (text/datetime): the timestamp the dataset was created at version: (int): the version number of the dataset

column_name : the name of the column

tanimoto_similarity

Calculates the tanimoto distance between two molecular fingerprints.

left : the left argument. Often a SMILES string constant value or Column of datatype SMILES.

right : the right argument. Often a SMILES string constant value or Column of datatype SMILES.

Schema

The schema of the dataset describing the columns (name, description, datatype, rdf predicate, ...)

Column

The schema data of one column. This tells EdelweissData the name of the column, the datatype to use, how to handle missing values, ...

decode

encode

decode

encode

cast

Cast a value to a type.

This returns the value unchanged. To the type checker this signals that the return value has the designated type, but at runtime we intentionally don't check anything (we want this to be as fast as possible).