Getting started #143
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Getting started #143
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achoum
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Jun 1, 2023
README.md
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| @@ -36,19 +34,26 @@ sma = tp.simple_moving_average(node, window_length=tp.days(7)) | |||
| result = sma.evaluate(evset) | |||
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What about adding a print and plot?
tp.read_event_set("path/to/temporal_data.csv", timestamp_column="time")
What about showing / describing what the csv file contains. Alternatively, what about creating an example by hand.
achoum
reviewed
Jun 1, 2023
README.md
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| @@ -36,19 +34,26 @@ sma = tp.simple_moving_average(node, window_length=tp.days(7)) | |||
| result = sma.evaluate(evset) | |||
| ``` | |||
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| Check the [Getting Started tutorial](examples/tutorials/getting_started.ipynb) to try it! | |||
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Should this point to the website or github?
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Sorry, this one should point to the docs website, definitely
achoum
approved these changes
Jun 1, 2023
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Should this folder be ignored? 
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ianspektor
approved these changes
Jun 5, 2023
| ## Key features | ||
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| These are what set Temporian apart. | ||
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| - **Simple and powerful API**: Temporian exports high level operations making processing complex programs short and ready to read. | ||
| - **Flexible data model**: Temporian models temporal data as a sequence of events, supporting non-uniform sampling timestamps seamlessly. |
README.md
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| - **Prevents modeling errors**: Temporian programs are guaranteed not to have future leakage unless the user calls the `leak` function, ensuring that models are not trained on future data. | ||
| - **Iterative development**: Temporian can be used to develop preprocessing pipelines in Colab or local notebooks, allowing users to visualize results each step of the way to identify and correct errors early on. | ||
| - **Efficient and well-tested implementations**: Temporian contains efficient and well-tested implementations of a variety of temporal data processing functions. For instance, our implementation of window operators is **x2000** faster than the same function implemented with NumPy. | ||
| - **Wide range of preprocessing functions**: Temporian contains a wide range of preprocessing functions, including moving window operations, lagging, calendar features, arithmetic operations, index manipulation and propagation, resampling, and more. For a full list of the available operators, see the [operators documentation](https://temporian.readthedocs.io/en/latest/reference/temporian/core/operators/). | ||
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| ## Documentation | ||
| ## Why Temporian? |
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I'd kill this section, especially since the M5 notebook isn't really readable atm
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Agree. Removed 😬
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It's already ignored via |
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README fixed and comments addressed. Make sure to force refresh on the docs preview to see the changes. |
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This PR promotes the
toy_example.ipynb->getting_started.ipynbafter some improvements to it:I'm also proposing to link it from the
README, with a couple more changes there. Further discussion about those is expected and welcome 🙂Note: This should be merged after #141 , since it introduces some changes to the toy_example notebook.