doc improvements

d-chambers · d-chambers · commit 84ec6cb5e318 · 2025-09-20T14:43:06.000+01:00
diff --git a/docs/index.qmd b/docs/index.qmd
@@ -13,26 +13,14 @@ Version-specific documentation builds are attached to the [release pages](https:
 
 DASCore is a Python library for distributed acoustic sensing (DAS). It provides:
 
-1. IO support for various DAS data formats
+1. IO support for [many DAS data formats](supported_formats.qmd)
 2. Common processing routines
 3. Basic visualizations
 
 :::{.callout-note}
-DASCore is a foundational package of the [DAS Data Analysis Ecosystem (DASDAE)](https://dasdae.org). It facilitates rapid development of other DASDAE packages that do more specialized analysis/visualization.
+DASCore is part of the [DAS Data Analysis Ecosystem (DASDAE)](https://dasdae.org). 
 :::
 
-# Supported file formats
-```{python}
-#| echo: false
-#| output: asis
-import pandas as pd
-from dascore.io.core import FiberIO
-out_str = FiberIO.get_supported_io_table().replace(True, value='✅').replace(False, value='❌').to_markdown(index=False, stralign="center")
-
-out_str += '\n: {.striped}'
-print(out_str)
-```
-
 # Introductory usage
 
 ## Read a file
diff --git a/docs/notes/dft_notes.qmd b/docs/notes/dft_notes.qmd
@@ -265,7 +265,7 @@ Patch methods [dft](`dascore.transform.fourier.dft`) and [idft](`dascore.transfo
 
 ## Useful Resources
 
-[Sclaing of DFT](https://appliedacousticschalmers.github.io/scaling-of-the-dft/AES2020_eBrief/): Concise overview of the DFT and its properties compared to the CFT.
+[Scaling of DFT](https://appliedacousticschalmers.github.io/scaling-of-the-dft/AES2020_eBrief/): Concise overview of the DFT and its properties compared to the CFT.
 
 [xrft](https://xrft.readthedocs.io/en/latest/index.html): A very useful resource for DASCore's implementation.
 
diff --git a/docs/recipes/contributing_to_documentation.qmd b/docs/recipes/contributing_to_documentation.qmd
@@ -37,7 +37,7 @@ quarto preview docs
 ```
 
 :::{.note}
-This will take a few minutes the first time you run it. After that, the results are cached on only the changed files are re-rendered.
+This will take a few minutes the first time you run it. After that, the results are cached and only the changed files are re-rendered.
 :::
 
 Now, you can make new documentation or make changes to the "index.qmd" file on the /dascore/docs/ directory and so on.
diff --git a/docs/supported_formats.qmd b/docs/supported_formats.qmd
@@ -0,0 +1,16 @@
+---
+title: Supported File Formats
+execute:
+    warning: false
+---
+
+```{python}
+#| echo: false
+#| output: asis
+import pandas as pd
+from dascore.io.core import FiberIO
+out_str = FiberIO.get_supported_io_table().replace(True, value='✅').replace(False, value='❌').to_markdown(index=False, stralign="center")
+
+out_str += '\n: {.striped}'
+print(out_str)
+```
diff --git a/docs/tutorial/patch.qmd b/docs/tutorial/patch.qmd
@@ -132,7 +132,7 @@ array[:10] = 12  # then this works
 
 ## Coords
 
-DASCore implements a class called [CoordManager](`dascore.core.coordmanager.CoordManager`) which managers dimension names, coordinate labels, selecting, sorting, etc. `CoordManager` has several convenience methods for accessing contained information:
+DASCore implements a class called [CoordManager](`dascore.core.coordmanager.CoordManager`) which manages dimension names, coordinate labels, selecting, sorting, etc. `CoordManager` has several convenience methods for accessing contained information:
 
 ```{python}
 import dascore as dc
diff --git a/docs/tutorial/processing.qmd b/docs/tutorial/processing.qmd
@@ -168,7 +168,7 @@ smoothed = patch.rolling(time=50*dt).mean()
 smoothed.viz.waterfall(scale=.1);
 ```
 
-Notice the nan values at the start of the time axis. These can be trimmed with [`Patch`.dropna](`dascore.Patch.dropna`).
+Notice the nan values at the start of the time axis. These can be trimmed with [`Patch.dropna`](`dascore.Patch.dropna`).
 
 
 # Whiten
@@ -271,7 +271,7 @@ white_patch = patch.whiten(smooth_size=2, time=(10, 20, 80, 90))
 _ = plot_time_and_frequency(white_patch)
 ```
 
-`Whiten` also accepts patches in the frequency domain, in which case a frequency patches are returned. This might be useful if whiten is only a part of a frequency domain workflow.
+`Whiten` also accepts patches in the frequency domain, in which case frequency patches are returned. This might be useful if whiten is only a part of a frequency domain workflow.
 
 ```{python}
 fft_patch = patch.dft("time", real=True)
diff --git a/docs/tutorial/visualization.qmd b/docs/tutorial/visualization.qmd
@@ -19,7 +19,7 @@ patch = dc.get_example_patch('example_event_2')
 patch.viz.waterfall(show=True, scale=(-50,50))
 ```
 
-## wiggle
+## Wiggle
 The [`wiggle patch function`](`dascore.viz.wiggle`) creates a wiggle plot of the patch data. We'll use the same patch as above to model this function.
 
 ```{python}
