update: add tutorial notebook and include new detection criteria in s…

…idebar

notebooks/_quarto.yml

@@ -21,6 +21,8 @@ website:
         href: index.qmd
       - text: "About"
         href: about.qmd
+      - text: Tutorial
+        href: notebooks/tutorial.qmd
     tools:
       - icon: github
         href: "https://github.com/Beforerr/discontinuitypy"

notebooks/sidebar.yml

@@ -12,6 +12,7 @@ website:
       - section: detection
         contents:
           - detection/01_variance.ipynb
+          - detection/02_TS_criterion.ipynb
       - section: missions
         contents:
           - missions/solo.ipynb
@@ -27,6 +28,9 @@ website:
         contents:
           - properties/00_duration.ipynb
           - properties/00_mva.ipynb
+      - section: tutorials
+        contents:
+          - tutorials/tutorial.qmd
       - section: utils
         contents:
           - utils/00_basic.ipynb

notebooks/tutorials/tutorial.qmd

@@ -0,0 +1,73 @@
+---
+jupyter: python3
+---
+
+A step-by-step guide to using the package
+
+```{python}
+%load_ext autoreload
+%autoreload 2
+```
+
+```{python}
+from datetime import timedelta
+from space_analysis.utils.speasy import get_polars_ldf
+from discontinuitypy.mission.wind import WindConfigBase, wi_mfi_h2_bgse
+from discontinuitypy.config import SpeasyIDsConfig
+from discontinuitypy.core.pipeline import ids_finder
+from discontinuitypy.detection import detect_variance, detect_gradient
+from rich import print
+```
+
+```{python}
+timerange = ["2021-05-03", "2021-05-04"]
+tau = timedelta(seconds=30)
+```
+
+```{python}
+data = get_polars_ldf(wi_mfi_h2_bgse, 'cda', timerange)
+data
+```
+
+```{python}
+bcols = data.collect_schema().names()
+bcols.remove("time")
+detect_gradient(data, bcols)
+```
+
+```{python}
+ids_finder(data, detect_kwargs={"tau": tau})
+```
+
+```{python}
+detect_variance(data, tau, bcols)
+```
+
+We could use the print from rich to print the output in a more readable way.
+
+```{python}
+class WindConfig(WindConfigBase, SpeasyIDsConfig):
+    pass
+    
+config = WindConfig(
+    timerange = timerange,
+    tau = 30
+)
+```
+
+```{python}
+print(config.model_dump(exclude_none=True))
+```
+
+```{python}
+config.produce_or_load()
+```
+
+```{python}
+config.get_data()
+```
+
+```{python}
+config.mag_meta.data[0].plot()
+```
+