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Before you can use this library you'll need:
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- An environment based on python 3.6 or later
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- A `ServiceX` end-point. For example, `http://localhost:5000/servicex`, if `ServiceX` is running on a local `k8` cluster and the proper ports are open, or the public servicex instance (contact IRIS-HEP at xxx if you are part of the LHC to request an account, or with help setting up an instance).
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- A `ServiceX` end-point. This is usually gotten by logging into and getting approved at the servicex endpoint. Once you do that, you'll have an API token, which this library needs to access the `ServiceX` endpoint, and the web address where you got that token (the `endpoint` address).
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### How to access your endpoint
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The `servicex` library searches for configuration information in several locations to determine what end-point it should connect to, in the following order:
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The API access information is normally placed in a `.servicex` file (to keep this confidential information form accidentally getting checked into a public repository). The `servicex` library searches for configuration information in several locations to determine what end-point it should connect to, in the following order:
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1. A `.servicex` file in the current working directory
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1. A `.servicex` file in the user's home directory (`$HOME` on Linux and Mac, and your profile
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The following lines will return a `pandas.DataFrame` containing all the jet pT's from an ATLAS xAOD file containing Z->ee Monte Carlo:
When doing backend development, often ports 9000 and 5000 are forwarded to the local machine exposing the `minio` and `ServiceX_App` instances. In that case, you'll need to create a `.servicex` file that has `http://localhost:5000` as the end point. No API token is necessary if the development `ServiceX` instance doesn't have authorization turned on.
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## API
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Everything is based around the `ServiceXDataset` object. Below is the documentation for the most common parameters.
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