Package: northgravity
NORTH GRAVITY PYTHON SDKThis document describes the North Gravity Python SDK which enables external users to use the North Gravity platform tools within their Python scripts.The Python SDK can be used within:a single Python script that is ran thanks to the Python Runner task within a pipeline in the North Gravity applicationa single Jupyter Notebook that is ran thanks to the Jupyter Runner task within a pipeline in the North Gravity applicationan ensemble of Python scripts that are part of a container, for a Task created by the user, used in a pipeline in the North Gravity applicationExamples of usage can be found at the bottom of the documentation.Note that the SDK does not cover everything from API documentation but rather commonly used features.The scope of the SDK:Datalake Handler- downloading / uploading / reading files from the data lakeStatus Handler- sending statuses about the task runTask Handler- enables communication between tasks within a pipeline and reading/writing parametersTime Series Handler- retrieves data directly from the time series databaseHow to install and set the package:Installpip3 install northgravity==0.1.14As the library is available from pip, it can be installed as a specific version within a Python Task from within requirements.txt just by adding:northgravity==0.1.14The package relies on the requests library so, in the project, the user must install this library in the requirements.txt file.pip3 install requestsEnvironment VariablesThe package uses information from the environment variables. They are automatically provided when running a script within a pipeline (as a Task or within the Python/Jupyter Runners).
If running locally the script, users must set them in the project to be able to run the project locally.Mandatory environment variables to set:LOGIN → login received from North GravityPASSWORD → password to log in. Credentials are used to generate the token so that each request is authenticated.NG_API_ENDPOINT → the URL to the North Gravity platform API (by default, the url is set tohttps://api.northgravity.com)This allows to pass the authentication process and directs users' requests to the North Gravity environment API.
Alternatively, instead of the LOGIN and PASSWORD, you may set NG_API_KEY environment variable with an API key generated within the North Gravity platform to enable the authentication process.Other variables may be useful when creating the tasks within the platform:NG_STATUS_GROUP_NAME → the group on the data lake where the pipeline is located, and is used to display the statusesJOBID → any value; when the pipeline is executed, this value is set by the North Gravity platformPIPELINE_ID → any value; when the pipeline is created, this value is set by the North Gravity platformLoggerPlease note that the default logging level for this package isWARNING. For more verbosity this can be changed to debug or info in following way:importloggingimportnorthgravityasnglogger=logging.getLogger('NG_SDK')logger.setLevel(logging.DEBUG)Datalake HandlerHow to download or read a file from data lake by its name ?The DatalakeHandler class can be used as follow within a script to download or upload a file:importnorthgravityasngimportpandasaspd# Instantiate the Datalake Handlerdh=ng.DatalakeHandler()# download file from data lake with name and group name# it will be saved locally with name local_name.csvdh.download_by_name(file_name='my_file.csv',group_name='My Group',file_type='SOURCE',dest_file_name='folder/local_name.csv',save=True,unzip=False)# OR read file from data lake with name and group name# it returns a BytesIO object (kept in the RAM, not saved in the disk)fileIO=dh.download_by_name(file_name='my_file.csv',group_name='My Group',file_type='SOURCE',dest_file_name=None,save=False,unzip=False)# read the object as pandas DataFramedf=pd.read_csv(fileIO)The download methods allows to either:download and save locally the wanted file, ifsave=Trueread the file directly from the datalake and get a BytesIO object (kept in memory only, that can for example be read by pandas as a dataframe directly)Note that by default:the file is NOT saved locally, but returned as a BytesIO object (streamed from the datalake).the argumentdest_file_name=None, which will save the downloaded file to the root folder with its original name.How to download or read a file from data lake by its ID ?In the case that the file ID is known, it can be directly downloaded/read as follow:importnorthgravityasngimportpandasaspd# Instantiate the Datalake Handlerdh=ng.DatalakeHandler()# download file from data lake by its ID# it will be saved locally with name local_name.csvdh.download_by_id(file_id='XXXX-XXXX',dest_file_name='folder/local_name.csv',save=True,unzip=False)# read file from data lake by its ID# it returns a BytesIO objectfileIO=dh.download_by_id(file_id='XXXX-XXXX',dest_file_name=None,save=False,unzip=False)# read the object as pandas DataFramedf=pd.read_csv(fileIO)The download methods allows to either:download and save locally the wanted file, ifsave=Trueread the file directly from the datalake and get a BytesIO object (kept in memory only, that can for example be read by pandas as a dataframe directly)Note that by default:the file is NOT saved locally, but returned as a BytesIO object (streamed from the datalake).the argumentdest_file_name=None, which will save the downloaded file to the root folder with its original name.How to upload a file to the data lake?The uploading method will upload to the given group the file at the specified path, and returns its ID on the lake:importnorthgravityasng# Instantiate the Datalake Handlerdh=ng.DatalakeHandler()# upload file to data lakefile_id=dh.upload_file(file='path/local_name.csv',group_name='My Group',file_upload_name='name_in_the_datalake.csv')It is possible as well to stream a python object's content directly to the datalake from memory, without having to save the file on the disk.The prerequisite is to pass to the uploading method a BytesIO object (not other objects such as pandas Dataframe).importnorthgravityasngimportio# Instantiate the Datalake Handlerdh=ng.DatalakeHandler()# Turn the pandas DataFrame (df) to BytesIO for streamingfileIO=io.BytesIO(df.to_csv().encode())# upload file to data lakefile_id=dh.upload_file(file=fileIO,group_name='My Group',file_upload_name='name_in_the_datalake.csv')Timeseries QueriesHow to get the list of existing symbols for a given group ?Data saved in the time series database is structured by group, keys and timestamp.
Each set of keys has unique dates entries in the database, with corresponding columns values.To explore what are the available symbols for a given group, the following method can be used:importnorthgravityasng# Instantiate Timeseries classts=ng.Timeseries()# Get the list of symbols for given groupgroup='My Group'group_symbols=ts.get_symbols(group_name=group)The default size of the returned list is 1 000 items.Note that the return object from the get_symbols() method is a JSON (python dict) where the keys and columns are accessible in theitemskey of the JSON.How to query by metadata or descriptions?In order to find the symbols querying by the metadata, column or symbol names, search_for parameter may be used.
It will look for passed string in the whole time series database and return the JSON with keys and columns where searched string appears.importnorthgravityasng# Instantiate Timeseries classts=ng.Timeseries()# Get the list of symbols for given groupsearch='Data description'searched_symbols=ts.get_symbols(search_for=search)Passing both group_name and search_for parameters of the get_symbols() method allows to narrow down the results from selected group.
The user must provide either group_name or search_for to the method in order to obtain the symbols.If trying to get the list of symbols from a group that contains more than 1 000 items, the results will be paginated (by default into chunks of 1 000 items).
To navigate in the large results theget_symbols()method takes as extra arguments the size of the returned list and the from page:importnorthgravityasng# Instantiate Timeseries classts=ng.Timeseries()# Get the list of symbols for given groupgroup='My Group'# Get the results into chunks of 200 items for page 5# meaning the results from the 1000th till the 1200thgroup_symbols=ts.get_symbols(group_name=group,_size=200,_from=5)By default, these parameters are _size=2000 (the maximum limit for the items lengths) and _from=0.How to read data from Timeseries database?It is possible to use the SDK to directly query the TimeSeries database for data, given the symbol's keys, columns and the datalake group it is stored on.On the application, it is similar of creating a Dataprep instance, that selects a set of symbols from groups into a basket.The retrieved data can be:streamed directly to memory, retrieved as a BytesIO object, by settingfile_nameas None (default value),saved as a csv file locally with the provided path and name asfile_name.The symbols are the keys the data was saved with in the database. For a given symbol, all the keys must be passed, as a dictionary object with the key name and value.
It is possible to use a wildcard for the symbols values, to have all the values for that key, using *.The wanted columns are then passed as a list that can contain one or more items.
If an empty list [ ] is passed to the function, it returns all the available columns.To read all available data for specific symbols and columns with no time frame, no start or end date are passed to the method.Extra settings are as well available to query data:Metadata: to either return it in the query of notFormat: either get a Normalized CSV (NCSV) or a dataframe formatTimezone: get the timestamps in the timezone of the user's account or get the data in a specific timezoneCorrections: how to handle corrections to the TimeSeries database (corrections set to 'yes', 'no', 'history' or 'only')Delta: whether to show by datatimestamp file (delta=False) or insert time (delta=True)The following code shows an example of how to query the TimseSeries database: :importnorthgravityasngimportpandasaspd# Instantiate Timeseries classts=ng.Timeseries()# Symbols to query from databasesymbols={'Key1':"Val1","Key2":"Val2"}columns=['Open','Close']# retrieve all available data from group acccording to keys & columns# and save as query.csv in test/ts.retrieve_data_as_csv(file_name='test/query.csv',symbols=symbols,columns=columns,group_name='My Group')# The retrieved data can be read as a pandas dataframedf=pd.read_csv("test/query.csv")# retrieve all available data from group acccording to keys & columns# and stream to memory as BytesIO objectfileIO=ts.retrieve_data_as_csv(file_name=None,symbols=symbols,columns=columns,group_name='My Group')# read as pandas dataframedf=pd.read_csv(fileIO)How to read data from Timeseries for specific dates?To retrieve data the data within specific time frame, user can specify the start and end date.There are two options how the start and end date may look like:only date (e.g., 2021-01-04)date and time (e.g., 2021-02-01T12:00:00; ISO format must be followed)For example, if user specified start_date=2021-02-01 and end_date=2021-02-06, then data will be retrieved like this: from 2021-02-01 00:00:00 till 2021-02-06 23:59:59.If date and time is specified then data will be retrieved exactly for the specified time frame.Note that ISO format must be followed: YYYY-MM-DDTHH:mm:ss. Pay attention to the "T" letter between date and time.importnorthgravityasngimportpandasaspd# Instantiate Timeseries classts=ng.Timeseries()# Symbols to query from databasesymbols={'Key1':"Val1","Key2":"Val2"}columns='Open'# retrieve data between start_date and end_date# data will be retrieved between 2021-01-04 00:00:00 and 2021-02-05 23:59:59# saved as a csv file named test.csvts.retrieve_data_as_csv(file_name='test/test.csv',symbols=symbols,columns=columns,group_name='My Group',start_date='2021-01-04',end_date='2021-02-05')# retrieve data for specific time frame# from 2021-01-04 12:30:00# to 2021-02-05 09:15:00ts.retrieve_data_as_csv(file_name='test/test.csv',symbols=symbols,columns=columns,group_name='My Group',start_date='2021-01-04T12:30:00',end_date='2021-02-05T09:15:00')# For given keys, columns, group and dates range# Streaming instead of savingfileIO=ts.retrieve_data_as_csv(file_name=None,symbols=symbols,columns=columns,group_name='My Group',start_date='2021-01-04',end_date='2021-02-05')# read as pandas dataframedf=pd.read_csv(fileIO)How to use a wildcard for a key's values ?To get all the value for one a several keys for the query, the character * can be used as a wildcard.
The argumentallow_wildcardshould be set to True in the retrieval function to enable the use of wildcard.Please note that by default, the use of wildcards isDISABLED.importnorthgravityasngimportpandasaspd# Instantiate Timeseries classts=ng.Timeseries()# Symbols to query from databasesymbols={'Key1':"Val1","Key2":"Val2","Key3":"*"}columns=['Open','Close']# retrieve all history for the symbols with keys and columns# all values for Key3 will be returned# the data will be streamed to memoryfileIO=ts.retrieve_data_as_csv(file_name=None,symbols=symbols,columns=columns,group_name='My Group',allow_wildcard=True)# read as pandas dataframedf=pd.read_csv(fileIO)How to get all the columns for a given set of keys ?To get all the columns values for a given set of keys in the database, the query can take an empty list as the queried columns, as follow:importnorthgravityasngimportpandasaspd# Instantiate Timeseries classts=ng.Timeseries()# Symbols to query from databasesymbols={'Key1':"Val1","Key2":"Val2","Key3":"Val3"}columns=[]# retrieve all history for the symbols with keys and columns# all columns for the set of keys will be returned# the data will be streamed to memoryfileIO=ts.retrieve_data_as_csv(file_name=None,symbols=symbols,columns=columns,group_name='My Group')# read as pandas dataframedf=pd.read_csv(fileIO)Note that this configuration can be used with keys wildcards (withallow_wildacrd=True) and any other setting.How to modify the Time Zone of the data ?The timestamps in the queried time series are set by default in the timezone of the user's account, who created the script or the pipeline.
It is described in the Date column header between brackets (for exampleDate(UTC))To modify the time zone in the retrieved dataset, the timezone can be passed directly to the retrieval function as follow.
It must respect the Continent/City format.importnorthgravityasngimportpandasaspd# Instantiate Timeseries classts=ng.Timeseries()# Symbols to query from databasesymbols={'Key1':"Val1","Key2":"Val2"}columns=['Open','Close']# retrieve all available data from group according to keys & columns# and stream to memory as BytesIO objectfileIO=ts.retrieve_data_as_csv(file_name=None,symbols=symbols,columns=columns,group_name='My Group',timezone='Europe/London')# read as pandas dataframedf=pd.read_csv(fileIO)How to get the metadata along with the data ?It is possible to get extra columns in the retrieved data, along with the keys & columns values, containing the metadata of the symbols.
It is done by setting the argumentmetadata=Truein the retrieval function.By default, no metadata is included in the queried data.importnorthgravityasngimportpandasaspd# Instantiate Timeseries classts=ng.Timeseries()# Symbols to query from databasesymbols={'Key1':"Val1","Key2":"Val2"}columns=['Open','Close']# retrieve all available data from group according to keys & columns# and stream to memory as BytesIO objectfileIO=ts.retrieve_data_as_csv(file_name=None,symbols=symbols,columns=columns,group_name='My Group',metadata=True)# read as pandas dataframedf=pd.read_csv(fileIO)How to modify the format of the received data ?The queried data comes by default as Normalized CSV format (NCSV), with in this order:the keys columns,the date column, with timestamps in either the default timezone or the specified one (timezoneargument in the function),the values columns,the metadata columns, if wanted (metadata=True)By settingNCSV=Falsein the retrieval method, the data will be returned as Dataframe format (PANDAS in API docs), as a JSON.
The JSON (python dict) has timestamps as keys and a dictionary containing pairs of symbols_columns and their value.importnorthgravityasngimportpandasaspd# Instantiate Timeseries classts=ng.Timeseries()# Symbols to query from databasesymbols={'Key1':"Val1","Key2":"Val2"}columns=['Open','Close']# retrieve all available data from group according to keys & columns# and stream to memory as BytesIO objectfile_json=ts.retrieve_data_as_csv(file_name=None,symbols=symbols,columns=columns,group_name='My Group',metadata=True,NCSV=False)# read as pandas dataframe# Transpose to have datetime as rows indexdf=pd.DataFrame(file_json).TNote that the dataframe, created from the JSON containing the data, is then transposed to have timestamps as DatetimeIndex (along rows axis).Task HandlerUsers can extend the existing set of tasks on North Gravity platform by executing scripts or notebooks respectively from the Python Runner Task or the Jupyter Runner Task.This task then can be used in a pipeline and be able to communicate with other tasks by:reading outputs from other tasks, as inputswriting outputs, that can be used by others tasks as inputsA task can as well receive inputs directly as a file picked from the datalake, either for a specific file either for the newest available version of a file on the lake, given its name and group.Whithin a python script, run into either one of thePython Runner Task,  this is implemented as follow:Read a Task InputThe input file passed to the Task can be:either downloaded to the disk,either to be read on the fly (useful when limited space on the disk but not in memory).importnorthgravityasngimportpandasaspd# Instantiate TaskHandler classth=ng.TaskHandler()# the current task reads the file passed by a previous task, that is connected to it.# The passed file is downloaded and saved on the disk as data.csvth.download_from_input_parameter(arg_name='Input #1',dest_file_name='data.csv',save=True)# The passed file is downloaded and kept in the memory (streamed, not saved on the disk)file_content=th.download_from_input_parameter(arg_name='Input #2',dest_file_name=None,save=False)# If a csv file was streamed, it can be read as pandas Dataframe for exampledf=pd.read_csv(file_content)If dest_file_name=None then the file is saved on the disk with its original name from the datalake.How to obtain additional file informationThere is a possibility to retrieve information related to the input parameters using the DatalakeHandler (dh.get_info_from_id()) and TaskHandler (th.read_task_parameter_value()) together.importnorthgravityasngimportpandasaspd# Instantiate TaskHandler classth=ng.TaskHandler()# Instantiate the Datalake Handlerdh=ng.DatalakeHandler()dh.get_info_from_id(th.read_task_parameter_value('Input #1'))This can be used to retrieve any metadata associated with the file itself like file name or arrival time (time it was uploaded to the system)Set a Task OutputThe output of the Task can be set :either by uploading a file saved on the disk to the datalake,either by streaming the python object content to the datalake as the destination file.Once uploaded, the Output is set to point to the file on the datalake (by its ID, name ,group name)importnorthgravityasngimportio# Instantiate TaskHandler classth=ng.TaskHandler()# the first task uploads the file dataset.csv to My Final Group and pass the info about this file# so that the next task can read this file by connecting its input to the output Prepared Datasetth.upload_to_output_parameter(output_name='Output #1',file='path/dataset.csv',group_name='My Final Group',file_upload_name=None,file_type='SOURCE')# Convert the python object to upload as BytesIO objectdf_io=io.BytesIO(df.to_csv().encode())# Stream to the datalake as the destination fileth.upload_to_output_parameter(output_name='Output #1',file=df_io,group_name='My Final Group',file_upload_name='dataset.csv',file_type='SOURCE')If file_upload_name=None then the saved file will be uploaded with its original name.
If the file is streamed directly to the datalake, the file_upload_name argument must be set.StatusesSending status can be used to show in the application what is the progress of the task execution. It allows to use 3 different levels:FINISHED (green),WARNING (orange),ERROR (red).Sending statuses remains optional as the North Gravity platform sends general statuses.
Only if user needs to pass some specific information in the status, this is worth using.importnorthgravityasng# Instantiate the Status Handlersh=ng.StatusHandler()# Generic status sendersh.send_status(status='INFO',message='Crucial Information')# there are pre-defined statusessh.info(message='Pipeline Finished Successfully')sh.warn(message='Something suspicious is happening ...')sh.error(message='Oops, the task failed ...')Note that the info status is informing the status service that the task executed successfully and is finished.There is also a possibility to send a warning status with a custom warning message under some circumstances and immediately stop the execution of the pipeline.fromnorthgravity.ExceptionHandlerimportPythonStepWarnExceptioni=1ifi>1:raisePythonStepWarnException(message='The value of i is bigger than 1! Stopping pipeline execution.')Example 1 - OOPTo simplify the use of the SDK methods in a script, Python SDK methods can be inherited by the user’s main class.Below is an example of a class that has 3 methods:Download raw data (or take from the previous task)Process the dataUpload the data to datalake and pass it to the next taskimportioimportnorthgravityasngimportpandasaspdclassRunner:def__init__(self):# Inherit the methods from the SDK Task Handler classself.handler=ng.TaskHandler()self.df=Nonedefdownload_data(self):# the method from TaskHandler can be used directly# it downloads the file passed as input Dataset and save it as data.csvself.handler.download_from_input_parameter(arg_name='Dataset',dest_file_name='data.csv',save=True)# Read as pandas dataframereturnpd.read_csv("data.csv")defprocess_data(self,df):# any logic here that processed the downloaded dataset and saves it as processed_data.csvreturndf_processeddefupload_data(self,df_processed):# Encode as bytesIOfileIO=io.BytesIO(df_processed.to_csv().encode())# pass the processed data csv file as the output of the task called Processed Dataself.handler.upload_to_output_parameter(output_name='Processed Dataset',file=fileIO,group_name='Final Group')defrun(self):df=self.download_data()df_processed=self.process_data(df)self.upload_data(df_processed)if__name__=='__main__':status=ng.StatusHandler()Runner().run()status.info('Test Pipeline Finished')Example 2 - functional programmingThe SDK methods may be used in functional programming simple scripts.
Below is an example of a script that:Downloads the data from Input #1Processes the dataUploads the data to datalake and passes it to the next task#####This part consists of local running setup for development and testing purposes.# It is not needed when running on the northgravity platform, since those variables are available in the platform's environmentimportos# login and password to the northgravity application for authenticationos.environ['LOGIN']=''os.environ['PASSWORD']=''# filename and name of the DataLake group to download the data fromos.environ['Input #1']="{'name':'', 'groupName':''}"# the DataLake group to save the file to# on the platform defaults to the group where the pipeline running the script is savedos.environ['NG_STATUS_GROUP_NAME']=''# specify the api endpoint to northgravity applicationos.environ['NG_API_ENDPOINT']=''####importos,sysimportioimportnorthgravityasngimportpandasaspd# loggingimportlogging;logging.basicConfig(stream=sys.stdout,level=logging.DEBUG)log=logging.getLogger()# initialize taskhandler from the Python SDK to communicate between pipeline tasksth=ng.TaskHandler()# download data from Input #1 of the task (Python Runner)df_io=th.download_from_input_parameter('Input #1')df=pd.read_csv(df_io)log.info('Data from Input #1 downloaded.')# your code heredf_processed=your_processing_function(df)# stream the dataframe as the Bytes IO file (does not save the csv to disc)file_io=io.BytesIO(df_processed.to_csv(index=False).encode())# upload the file to Output #1 of the taskth.upload_to_output_parameter(output_name='Output #1',file=file_io,# add your file name herefile_upload_name='',# the group name defaults to the pipeline group# you can also use it explicitly: group_name='Name of The Group on Datalake'group_name=os.environ.get('NG_STATUS_GROUP_NAME'),# the dataframe is saved as a flat file# use file_type='NCSV' to save NCSV-type datasets into Timeseries databasefile_type='SOURCE')SSL Verification BypassThis package includes a feature designed to bypass SSL certificate verification for HTTP requests, intended for development or testing purposes in trusted environments. Activating this feature allows you to send requests without verifying the SSL certificate of the server you're connecting to, which can be useful when working with self-signed certificates or servers with certificate issues. To use this feature, simply pass the argumentverify=Falseto init function of the component you are using:importnorthgravityasngimportosos.environ['LOGIN']=''os.environ['PASSWORD']=''dh=ng.DatalakeHandler(verify_ssl=False)th=ng.TaskHandler(verify_ssl=False)ts=ng.Timeseries(verify_ssl=False)Warning:Disabling SSL certificate verification poses a security risk by exposing your application to man-in-the-middle attacks. Therefore, it is strongly recommended to use this feature only in controlled and secure environments, and always ensure SSL verification is enabled in production.Further documentation on bypassing SSL verification can be foundhere.Who do I talk to?Admin: NorthGravityinfo@northgravity.com