update docs

docs/usage/config.md

@@ -4,21 +4,21 @@ title: Site Configuration
 
 !!!abstract
 
-    TLDR: Use `site_config` to set default configurations for your `StateBuilder` objects.
-    After your imports, the recommended default code is: `site_config.set_default_config(target_site='spelunker')`
+    TLDR: Use `site_utils` to set default configurations for your `StateBuilder` objects.
+    After your imports, the recommended default code is: `site_utils.set_default_config(target_site='spelunker')`
 
-## Using `site_config` to configure your StateBuilder
+## Using `site_utils` to configure your StateBuilder
 
-The `site_config` module is intended to access and change the baseline configuration of StateBuilder sites.
+The `site_utils` submodule of `statebuilder` is intended to access and change the baseline configuration of StateBuilder sites.
 This will let you configure a target site, url, and caveclient to use as defaults throughout your analysis without further specification.
 
 The first thing to note is the options within the default configuration.
 You can see this via
 
 ```python
-from nglui import site_config, statebuilder
+from nglui import statebuilder
 
-site_config.get_default_config()
+statebuilder.site_config.get_default_config()
 ```
 
 This will return a dictionary with a number of variables:
@@ -58,7 +58,7 @@ the result will be a `url` that is based on the value in `target_url` and is con
 You can change any of these values through the `set_default_config` method. For example, to change the target site to `spelunker`, you can use
 
 ```python
-site_config.set_default_config(target_site='spelunker')
+statebuilder.site_utils.set_default_config(target_site='spelunker')
 ```
 
 If you run this and then check the default config again, you will see that the `target_site` has changed to `spelunker` and the `target_url` has changed to `https://spelunker.cave-explorer.org/` the value that was shown in `mainline_fallback_url`.
@@ -69,7 +69,7 @@ You can also set a `caveclient` object here directly or via a `datastack_name`.
 For example, to set the default datastack to `minnie65_public` (the public MICrONs dataset), you can use
 
 ```python
-site_utils.set_default_config(datastack=CAVEclient('minnie65_public'))
+statebuilder.site_utils.set_default_config(datastack_name='minnie65_public')
 ```
 
 This is particularly useful when working with `spelunker` datasets in particular, as they require a resolution to be specified to work properly and the CAVEclient object can provide this value.
@@ -79,7 +79,7 @@ You may see a warning if you do not include a caveclient object, but the code wi
 If the resulting state does not work, you may need to specify the version of Neuroglancer you are using or add a caveclient.
 
 
-## Why these different types of Neuroglancer?
+## Why are there different types of Neuroglancer?
 
 Neuroglancer is a webapp for viewing large scale 3d image datasets that has been developed over many years.
 At one point, there was a major fork by the lab of Sebastian Seung to allow proofreading of connectomics data, which led to the development of the `seunglab` version of Neuroglancer.
@@ -101,5 +101,5 @@ If you are not sure, you should use the `spelunker` version. This will give you
 The simplest way to do this is to set the default configuration to `spelunker` as shown above:
 
 ```python
-site_config.set_default_config(target_site='spelunker')
+statebuilder.site_utils.set_default_config(target_site='spelunker')
 ```

docs/usage/segmentprops.md

@@ -94,6 +94,28 @@ Any tag without a description will be passed through directly.
 If you look at the JSON output above, you see that the `soma_depth` property has a field "data_type" that specifies the type of the numerical property, in this case `int32`.
 Data type values are inferred from the column dtype and safe conversions are validated.
 
+### Random number columns
+
+Often, segment properties are used for very large lists of root ids.
+If you want to sample just a few percent of these in Neuroglancer, it can be hard to get a small number of random ids without a lot of clicking.
+Adding a numerical property that is just a random number between 0 and 1 will give you a way to load small subsets of the data using the filter tools.
+You can add random columns when building a SegmentProperty object from a dataframe by setting the `random_columns` argument with the number of extra columns you want to add.
+For example, to add a single random column:
+
+```python
+seg_prop = SegmentProperties.from_dataframe(
+    df,
+    id_col='segment_id',
+    number_cols='soma_depth',
+    random_columns=1,
+)
+```
+
+Will add one random column as a numerical property with the default name `random_sample`.
+Using numbers higher than 1 will add multiple random columns with names `random_sample_0`, `random_sample_1`, etc.
+The name of the random column can be changed with the `random_column_prefix` argument.
+Note that a value error will be raised if you try to add a random column with a name that already exists in the dataframe.
+
 ### Building segment properties manually
 
 It is also possible to build segment properties manually with the `SegmentProperties` class.

src/nglui/segmentprops/base.py

@@ -473,6 +473,8 @@ def from_dataframe(
             else:
                 random_column_names = [random_column_prefix]
             for col in random_column_names:
+                if col in df.columns:
+                    raise ValueError(f"Column {col} already exists in dataframe")
                 df[col] = np.random.rand(len(df))
             if number_cols is None:
                 number_cols = []