Download this example as a Jupyter notebook or a Python script.

---

Create an Excel import job

You use an Excel import job to import data from a properly formatted Excel spreadsheet.

This example shows how to create an Excel import job request, submit it to the job queue, and interact with the resulting Excel import job object returned by the server.

Information on how to create a properly formatted Excel import template is outside the scope of this example. For information on using Excel to import data into Granta MI, see the Granta MI documentation or consult your ACE representative.

Connect to Granta MI

Import the Connection class and create the connection. For more information, see the Connect and access the job queue example.

from ansys.grantami.jobqueue import Connection

server_url = "http://my_grantami_server/mi_servicelayer"
client = Connection(server_url).with_credentials("user_name", "password").connect()

Create an ExcelImportJobRequest object

The first step in importing an Excel file with the job queue is to create an ExcelImportJobRequest object. When creating this object, specify the name of the job and the files to import. You can also specify an optional description and the scheduled execution date, if the import should be deferred until that date and time.

Different job types require different input files. For example, an Excel import can use a template, one or more data files, or combined files, which include both the template and data files. You should specicy any additional files to imported as file or picture attributes as attachment files. You can provide these additional files as relative or absolute paths or as pathlib.Path objects.

from ansys.grantami.jobqueue import ExcelImportJobRequest

separate_excel_import_request = ExcelImportJobRequest(
    name="Excel Import (separate template and data files)",
    description="An example excel import job",
    template_file="assets/import_template.xlsx",
    data_files=["assets/data_file_1.xlsx", "assets/data_file_2.xlsx"],
)
separate_excel_import_request

<ExcelImportJobRequest: name: "Excel Import (separate template and data files)">

Submit the job to the server

Next, submit the jobs to the server. There are two methods for submitting job requests:

• create_job(): Submit the job request to the server and immediately return an AsyncJob object in the pending state.

• create_job_and_wait(): Submit the job request to the server and block until the job either completes or fails. Return an AsyncJob object in the succeeded or failed state.

This example uses the create_job_and_wait() method. For an example that shows how to create and submit a job that runs asynchronously, see Schedule and modify jobs.

completed_job = client.create_job_and_wait(separate_excel_import_request)

Access output files

Finally, access the results of the job. Import jobs typically create log files, but the exact type of files generated varies based on the type of import template. In this case, the files are all plain text.

Access the list of files generated by the job with the output_file_names property. This returns a list of file names.

completed_job.output_file_names

['Excel Import (separate template and data files).log', 'summary.json']

In general, an Excel import job returns two files:

• <job name>.log: Log file of the import operation on the server

• summary.json: Data file that summarizes the number of records impacted by the import job and provides details of any errors that occurred during processing.

This cell shows how to access the content of the log file as bytes using the AsyncJob.get_file_content() method:

log_file_name = next(name for name in completed_job.output_file_names if "log" in name)
log_file_content = completed_job.get_file_content(log_file_name)
log_file_string = log_file_content.decode("utf-8")
print(f"{log_file_name} (first 200 characters):")
print(f"{log_file_string[:500]}...")

Excel Import (separate template and data files).log (first 200 characters):
2024-06-10 10:28:35,971 [35] INFO  Task started: Template:'import_template', Step:'Pre-Import Validation: Problematic Values & Functions', Datafile:'data_file_1', Culture:'en-US'
2024-06-10 10:28:36,018 [35] INFO  Task finished.
2024-06-10 10:28:36,034 [35] INFO  Task started: Template:'import_template', Step:'Pre-Import Validation: Problematic Values & Functions', Datafile:'data_file_2', Culture:'en-US'
2024-06-10 10:28:36,034 [35] INFO  Task finished.
2024-06-10 10:28:36,034 [35] INFO  Ta...

This next cell shows how to download the import summary file to disk using the AsyncJob.download_file() method:

summary_file_name = next(name for name in completed_job.output_file_names if name == "summary.json")
output_path = f"./{summary_file_name}"
completed_job.download_file(summary_file_name, output_path)
f"{summary_file_name} saved to disk"

'summary.json saved to disk'