### Install FlowUtils from GitHub source

Source: https://flowutils.readthedocs.io/en/latest

Clone the repository and install from source. This is required when a pre-built binary is not available for your environment.

```bash
git clone https://github.com/whitews/flowutils
cd flowutils
pip install .

```

--------------------------------

### Install FlowUtils from GitHub source

Source: https://flowutils.readthedocs.io/en/latest/_sources/index.rst.txt

Clone the FlowUtils repository and install it from source. This is required when pre-built binaries are not available for your environment.

```python
git clone https://github.com/whitews/flowutils
cd flowutils
pip install .
```

--------------------------------

### Install FlowUtils from PyPI

Source: https://flowutils.readthedocs.io/en/latest

Install the pre-built binary from PyPI using pip. If no pre-built binary is available, the C extensions must be compiled from source.

```bash
pip install flowutils

```

--------------------------------

### Install FlowUtils from PyPI

Source: https://flowutils.readthedocs.io/en/latest/_sources/index.rst.txt

Install the FlowUtils package using pip. Pre-built binaries are available for most common platforms and Python versions.

```python
pip install flowutils
```

--------------------------------

### hyperlog()

Source: https://flowutils.readthedocs.io/en/latest/api.html

Applies the Hyperlog transformation to FCS event data.

```APIDOC
## hyperlog()

### Description
Applies the Hyperlog transformation to FCS event data.

### Method
Function

### Endpoint
`hyperlog()`
```

--------------------------------

### logicle()

Source: https://flowutils.readthedocs.io/en/latest/api.html

Applies the Logicle transformation to FCS event data.

```APIDOC
## logicle()

### Description
Applies the Logicle transformation to FCS event data.

### Method
Function

### Endpoint
`logicle()`
```

--------------------------------

### logicle() (in module flowutils.transforms)

Source: https://flowutils.readthedocs.io/en/latest/genindex.html

Function in the flowutils.transforms module for applying the logicle transform.

```APIDOC
## logicle()

### Description
Applies the logicle transform to data.

### Module
flowutils.transforms

### Method
logicle()
```

--------------------------------

### parse_compensation_matrix(compensation, channel_labels, null_channels=None)

Source: https://flowutils.readthedocs.io/en/latest/compensate.html

Parses a compensation matrix from various input formats and returns a NumPy array with the matrix and channel indices.

```APIDOC
## parse_compensation_matrix(compensation, channel_labels, null_channels=None)

### Description
Returns a NumPy array with the compensation matrix, where the first row contains the indices of the fluorescent channels.

### Parameters
- **compensation** (various) - Required - Compensation matrix: may be a NumPy array, a CSV file path, a pathlib Path object to a CSV or TSV file, or a string of CSV text. If a string, both multi-line, traditional CSV, and the single line FCS spill formats are supported. If a NumPy array, we assume the columns are in the same order as the channel labels.
- **channel_labels** (list) - Required - Channel labels from the FCS file's PnN fields, must be in the same order as they appear in the FCS file.
- **null_channels** (list, optional) - Optional - Specify any empty channels that were collected and present in the channel_labels argument. These will be ignored when validating and creating the compensation matrix.

### Returns
Compensation matrix as NumPy array where header contains the channel numbers (not indices!).
```

--------------------------------

### log()

Source: https://flowutils.readthedocs.io/en/latest/api.html

Applies the logarithmic transformation to FCS event data.

```APIDOC
## log()

### Description
Applies the logarithmic transformation to FCS event data.

### Method
Function

### Endpoint
`log()`
```

--------------------------------

### hyperlog() (in module flowutils.transforms)

Source: https://flowutils.readthedocs.io/en/latest/genindex.html

Function in the flowutils.transforms module for applying the hyperlog transform.

```APIDOC
## hyperlog()

### Description
Applies the hyperlog transform to data.

### Module
flowutils.transforms

### Method
hyperlog()
```

--------------------------------

### log() (in module flowutils.transforms)

Source: https://flowutils.readthedocs.io/en/latest/genindex.html

Function in the flowutils.transforms module for applying a logarithmic transform.

```APIDOC
## log()

### Description
Applies a logarithmic transform to data.

### Module
flowutils.transforms

### Method
log()
```

--------------------------------

### compensate() (in module flowutils.compensate)

Source: https://flowutils.readthedocs.io/en/latest/genindex.html

Function in the flowutils.compensate module for performing fluorescence compensation.

```APIDOC
## compensate()

### Description
Performs fluorescence compensation on flow cytometry data.

### Module
flowutils.compensate

### Method
compensate()
```

--------------------------------

### logicle_inverse() (in module flowutils.transforms)

Source: https://flowutils.readthedocs.io/en/latest/genindex.html

Function in the flowutils.transforms module for applying the inverse logicle transform.

```APIDOC
## logicle_inverse()

### Description
Applies the inverse of the logicle transform to data.

### Module
flowutils.transforms

### Method
logicle_inverse()
```

--------------------------------

### compensate()

Source: https://flowutils.readthedocs.io/en/latest/api.html

Applies a compensation matrix to FCS event data (as a NumPy array).

```APIDOC
## compensate()

### Description
Applies a compensation matrix to FCS event data (as a NumPy array).

### Method
Function

### Endpoint
`compensate()`
```

--------------------------------

### flowutils.transforms.logicle

Source: https://flowutils.readthedocs.io/en/latest/transforms.html

Logicle transformation implemented as defined in the GatingML 2.0 specification. Transforms FCS event data using the Logicle scale.

```APIDOC
## flowutils.transforms.logicle(data, channel_indices, t=262144, m=4.5, w=0.5, a=0)

### Description
Logicle transformation, implemented as defined in the GatingML 2.0 specification. The transformation is defined as the root of a modified bi-exponential function.

### Method
Function call

### Endpoint
flowutils.transforms.logicle

### Parameters
- **data** (NumPy array) - Required - NumPy array of FCS event data. If a 1-D array, channel_indices option is ignored.
- **channel_indices** (list or None) - Required - Channel indices to transform. Other channels returned in place, untransformed. If None, all events will be transformed.
- **t** (int) - Optional - Parameter for the top of the linear scale (e.g. 262144). Default: 262144.
- **m** (float) - Optional - Parameter for the number of decades the true logarithmic scale approaches at the high end of the scale. Default: 4.5.
- **w** (float) - Optional - Parameter for the approximate number of decades in the linear region. Default: 0.5.
- **a** (int) - Optional - Parameter for the additional number of negative decades. Default: 0.

### Returns
NumPy array of transformed events.
```

--------------------------------

### logicle_inverse()

Source: https://flowutils.readthedocs.io/en/latest/api.html

Applies the inverse of the Logicle transformation to FCS event data.

```APIDOC
## logicle_inverse()

### Description
Applies the inverse of the Logicle transformation to FCS event data.

### Method
Function

### Endpoint
`logicle_inverse()`
```

--------------------------------

### log_inverse() (in module flowutils.transforms)

Source: https://flowutils.readthedocs.io/en/latest/genindex.html

Function in the flowutils.transforms module for applying the inverse logarithmic transform.

```APIDOC
## log_inverse()

### Description
Applies the inverse of the logarithmic transform to data.

### Module
flowutils.transforms

### Method
log_inverse()
```

--------------------------------

### compensate_spectral_ols(event_data, spill_matrix, fluoro_indices=None)

Source: https://flowutils.readthedocs.io/en/latest/compensate.html

Compensates spectral event data using ordinary least squares (OLS) method with a spectral spillover matrix.

```APIDOC
## compensate_spectral_ols(event_data, spill_matrix, fluoro_indices=None)

### Description
Compensate spectral event data given a spectral spillover matrix using the ordinary least squares method (OLS). Unlike conventional flow cytometry where one detector is used per fluorochrome, spectral flow cytometry utilizes a larger number of detectors than fluorochromes, resulting in a spectral matrix that is not square, with more columns than rows. The rows (M) in the matrix correspond to the "true" fluorochromes (i.e. those detectors with dedicated fluorochromes). This function assumes the order of the columns in the matrix is such that the first M columns match the row order, and the remaining columns are the extra detectors. Also note that because of the overdetermined system, there is no inverse function for this method of compensation.

### Parameters
- **event_data** (numpy.ndarray) - Required - NumPy array of the event data.
- **spill_matrix** (numpy.ndarray) - Required - Compensation matrix as a NumPy array (without headers).
- **fluoro_indices** (list, optional) - Optional - Optional list of indices of the fluorescent channels (only these will be extracted & compensated). If None (default), all columns will be compensated.

### Returns
NumPy array of compensated event data. If fluoro_indices were given, the data is returned with the column order given, with the non-fluorescent columns unmodified.
```

--------------------------------

### asinh() (in module flowutils.transforms)

Source: https://flowutils.readthedocs.io/en/latest/genindex.html

Function in the flowutils.transforms module for applying the inverse hyperbolic sine transform.

```APIDOC
## asinh()

### Description
Applies the inverse hyperbolic sine (asinh) transform to data.

### Module
flowutils.transforms

### Method
asinh()
```

--------------------------------

### hyperlog_inverse() (in module flowutils.transforms)

Source: https://flowutils.readthedocs.io/en/latest/genindex.html

Function in the flowutils.transforms module for applying the inverse hyperlog transform.

```APIDOC
## hyperlog_inverse()

### Description
Applies the inverse of the hyperlog transform to data.

### Module
flowutils.transforms

### Method
hyperlog_inverse()
```

--------------------------------

### flowutils.transforms.hyperlog

Source: https://flowutils.readthedocs.io/en/latest/transforms.html

Hyperlog transformation implemented as defined in the GatingML 2.0 specification. Transforms FCS data using the Hyperlog scale.

```APIDOC
## flowutils.transforms.hyperlog(data, channel_indices, t=262144, m=4.5, w=0.5, a=0)

### Description
Hyperlog transformation, implemented as defined in the GatingML 2.0 specification. The transformation is defined as the root of a modified bi-exponential function.

### Method
Function call

### Endpoint
flowutils.transforms.hyperlog

### Parameters
- **data** (NumPy array) - Required - NumPy array of FCS event data. If a 1-D array, channel_indices option is ignored.
- **channel_indices** (list or None) - Required - Channel indices to transform. Other channels returned in place, untransformed. If None, all events will be transformed.
- **t** (int) - Optional - Parameter for the top of the linear scale (e.g. 262144). Default: 262144.
- **m** (float) - Optional - Parameter for desired number of decades. Default: 4.5.
- **w** (float) - Optional - Parameter for the approximate number of decades in the linear region. Default: 0.5.
- **a** (int) - Optional - Parameter for the additional number of negative decades. Default: 0.

### Returns
NumPy array of transformed events.
```

--------------------------------

### parse_compensation_matrix() (in module flowutils.compensate)

Source: https://flowutils.readthedocs.io/en/latest/genindex.html

Function in the flowutils.compensate module for parsing a compensation matrix.

```APIDOC
## parse_compensation_matrix()

### Description
Parses a compensation matrix from input data.

### Module
flowutils.compensate

### Method
parse_compensation_matrix()
```

--------------------------------

### hyperlog_inverse()

Source: https://flowutils.readthedocs.io/en/latest/api.html

Applies the inverse of the Hyperlog transformation to FCS event data.

```APIDOC
## hyperlog_inverse()

### Description
Applies the inverse of the Hyperlog transformation to FCS event data.

### Method
Function

### Endpoint
`hyperlog_inverse()`
```

--------------------------------

### points_in_polygon() (in module flowutils.gating)

Source: https://flowutils.readthedocs.io/en/latest/genindex.html

Function in the flowutils.gating module for determining points inside a polygon.

```APIDOC
## points_in_polygon()

### Description
Determines which points are inside a specified polygon.

### Module
flowutils.gating

### Method
points_in_polygon()
```

--------------------------------

### parse_compensation_matrix()

Source: https://flowutils.readthedocs.io/en/latest/api.html

Creates a compensation matrix from CSV or NumPy input types.

```APIDOC
## parse_compensation_matrix()

### Description
Creates a compensation matrix from CSV or NumPy input types.

### Method
Function

### Endpoint
`parse_compensation_matrix()`
```

--------------------------------

### log_inverse()

Source: https://flowutils.readthedocs.io/en/latest/api.html

Applies the inverse of the logarithmic transformation to FCS event data.

```APIDOC
## log_inverse()

### Description
Applies the inverse of the logarithmic transformation to FCS event data.

### Method
Function

### Endpoint
`log_inverse()`
```

--------------------------------

### asinh()

Source: https://flowutils.readthedocs.io/en/latest/api.html

Applies the inverse hyperbolic sine (asinh) transformation to FCS event data.

```APIDOC
## asinh()

### Description
Applies the inverse hyperbolic sine (asinh) transformation to FCS event data.

### Method
Function

### Endpoint
`asinh()`
```

--------------------------------

### flowutils.transforms.log

Source: https://flowutils.readthedocs.io/en/latest/transforms.html

Applies the parametrized logarithmic transformation to FCS event data.

```APIDOC
## flowutils.transforms.log(data, channel_indices, t, m)

### Description
Parametrized logarithmic transformation, implemented as defined in the GatingML 2.0 specification: flog(x, T, M) = (1 / M) * log_10(x / T) + 1.

### Parameters
- **data** (NumPy array) - Required - NumPy array of FCS event data. If a 1-D array, channel_indices option is ignored.
- **channel_indices** (list) - Required - Channel indices to transform (other channels returned in place, untransformed). If None, then all events will be transformed.
- **t** (float) - Required - Parameter for the top of the linear scale (e.g. 262144).
- **m** (float) - Required - Parameter for desired number of decades.

### Returns
NumPy array of transformed events.
```

--------------------------------

### compensate(event_data, spill_matrix, fluoro_indices=None)

Source: https://flowutils.readthedocs.io/en/latest/compensate.html

Applies compensation to NumPy event data using a given spillover matrix.

```APIDOC
## compensate(event_data, spill_matrix, fluoro_indices=None)

### Description
Compensate NumPy event data given spillover matrix.

### Parameters
- **event_data** (numpy.ndarray) - Required - NumPy array of the event data.
- **spill_matrix** (numpy.ndarray) - Required - Compensation matrix as a NumPy array (without headers).
- **fluoro_indices** (list, optional) - Optional - List of fluorescent channel indices in given event_data in the order found in the columns of the provided spill_matrix. Required if the number of columns in event_data does not match the spill_matrix or if the order of the columns does not match. If None (default), the event_data columns are assumed to match the spill_matrix columns.

### Returns
NumPy array of compensated event data. If fluoro_indices were given, the data is returned with the column order found in event_data, with the non-fluorescent columns unmodified.
```

--------------------------------

### inverse_compensate() (in module flowutils.compensate)

Source: https://flowutils.readthedocs.io/en/latest/genindex.html

Function in the flowutils.compensate module for performing inverse compensation.

```APIDOC
## inverse_compensate()

### Description
Performs inverse compensation on flow cytometry data.

### Module
flowutils.compensate

### Method
inverse_compensate()
```

--------------------------------

### asinh_inverse() (in module flowutils.transforms)

Source: https://flowutils.readthedocs.io/en/latest/genindex.html

Function in the flowutils.transforms module for applying the inverse of the asinh transform.

```APIDOC
## asinh_inverse()

### Description
Applies the inverse of the asinh transform to data.

### Module
flowutils.transforms

### Method
asinh_inverse()
```

--------------------------------

### flowutils.transforms.asinh

Source: https://flowutils.readthedocs.io/en/latest/transforms.html

Applies the parametrized inverse hyperbolic sine (asinh) transformation to FCS event data.

```APIDOC
## flowutils.transforms.asinh(data, channel_indices, t, m, a)

### Description
Applies the parametrized inverse hyperbolic sine (asinh) transformation to FCS event data.

### Parameters
- **data** (NumPy array) - Required - NumPy array of FCS event data. If a 1-D array, channel_indices option is ignored.
- **channel_indices** (list) - Required - Channel indices to transform (other channels returned in place, untransformed). If None, then all events will be transformed.
- **t** (float) - Required - Parameter specifying the top of the scale (e.g. 262144).
- **m** (float) - Required - Parameter for the number of decades.
- **a** (float) - Required - Parameter for the number of additional negative decades.

### Returns
NumPy array of transformed events.
```

--------------------------------

### asinh_inverse()

Source: https://flowutils.readthedocs.io/en/latest/api.html

Applies the inverse of the asinh transformation to FCS event data.

```APIDOC
## asinh_inverse()

### Description
Applies the inverse of the asinh transformation to FCS event data.

### Method
Function

### Endpoint
`asinh_inverse()`
```

--------------------------------

### get_spill() (in module flowutils.compensate)

Source: https://flowutils.readthedocs.io/en/latest/genindex.html

Function in the flowutils.compensate module for retrieving the spillover matrix.

```APIDOC
## get_spill()

### Description
Retrieves the spillover matrix used for compensation.

### Module
flowutils.compensate

### Method
get_spill()
```

--------------------------------

### points_in_ellipsoid() (in module flowutils.gating)

Source: https://flowutils.readthedocs.io/en/latest/genindex.html

Function in the flowutils.gating module for determining points inside an ellipsoid.

```APIDOC
## points_in_ellipsoid()

### Description
Determines which points are inside a specified ellipsoid.

### Module
flowutils.gating

### Method
points_in_ellipsoid()
```

--------------------------------

### inverse_compensate()

Source: https://flowutils.readthedocs.io/en/latest/api.html

Applies the inverse of a compensation matrix to FCS event data.

```APIDOC
## inverse_compensate()

### Description
Applies the inverse of a compensation matrix to FCS event data.

### Method
Function

### Endpoint
`inverse_compensate()`
```

--------------------------------

### inverse_compensate(event_data, spill_matrix, fluoro_indices=None)

Source: https://flowutils.readthedocs.io/en/latest/compensate.html

Inverts the compensation on NumPy event data using a given spillover matrix.

```APIDOC
## inverse_compensate(event_data, spill_matrix, fluoro_indices=None)

### Description
Inverse the compensation on NumPy event data given spillover matrix.

### Parameters
- **event_data** (numpy.ndarray) - Required - NumPy array of the event data.
- **spill_matrix** (numpy.ndarray) - Required - Compensation matrix as a NumPy array (without headers).
- **fluoro_indices** (list, optional) - Optional - Optional list of indices of the fluorescent channels (only these will be extracted & un-compensated). If None (default), all columns will be un-compensated.

### Returns
NumPy array of un-compensated event data. If fluoro_indices were given, the data is returned with the column order given, with the non-fluorescent columns unmodified.
```

--------------------------------

### compensate_spectral_ols()

Source: https://flowutils.readthedocs.io/en/latest/api.html

Performs spectral compensation using ordinary least squares.

```APIDOC
## compensate_spectral_ols()

### Description
Performs spectral compensation using ordinary least squares.

### Method
Function

### Endpoint
`compensate_spectral_ols()`
```

--------------------------------

### flowutils.transforms.asinh_inverse

Source: https://flowutils.readthedocs.io/en/latest/transforms.html

Applies the inverse of the parametrized inverse hyperbolic sine (asinh) transformation to FCS event data.

```APIDOC
## flowutils.transforms.asinh_inverse(data, channel_indices, t, m, a)

### Description
Inverse of the parametrized inverse hyperbolic sine function as defined in the GatingML 2.0 specification.

### Parameters
- **data** (NumPy array) - Required - NumPy array of FCS event data. If a 1-D array, channel_indices option is ignored.
- **channel_indices** (list) - Required - Channel indices to transform (other channels returned in place, untransformed). If None, then all events will be transformed.
- **t** (float) - Required - Parameter specifying the top of the scale (e.g. 262144).
- **m** (float) - Required - Parameter for the number of decades.
- **a** (float) - Required - Parameter for the number of additional negative decades.

### Returns
NumPy array of transformed events.
```

--------------------------------

### flowutils.transforms.logicle_inverse

Source: https://flowutils.readthedocs.io/en/latest/transforms.html

Inverse of the Logicle transformation. Reverts the Logicle transformation applied to FCS data.

```APIDOC
## flowutils.transforms.logicle_inverse(data, channel_indices, t=262144, m=4.5, w=0.5, a=0)

### Description
Inverse of the Logicle transformation (see logicle() documentation for more details).

### Method
Function call

### Endpoint
flowutils.transforms.logicle_inverse

### Parameters
- **data** (NumPy array) - Required - NumPy array of FCS event data. If a 1-D array, channel_indices option is ignored.
- **channel_indices** (list or None) - Required - Channel indices to transform. Other channels returned in place, untransformed. If None, all events will be transformed.
- **t** (int) - Optional - Parameter for the top of the linear scale (e.g. 262144). Default: 262144.
- **m** (float) - Optional - Parameter for the number of decades the true logarithmic scale approaches at the high end of the scale. Default: 4.5.
- **w** (float) - Optional - Parameter for the approximate number of decades in the linear region. Default: 0.5.
- **a** (int) - Optional - Parameter for the additional number of negative decades. Default: 0.

### Returns
NumPy array of transformed events.
```

--------------------------------

### flowutils.transforms.hyperlog_inverse

Source: https://flowutils.readthedocs.io/en/latest/transforms.html

Inverse of the Hyperlog transformation. Reverts the Hyperlog transformation applied to FCS data.

```APIDOC
## flowutils.transforms.hyperlog_inverse(data, channel_indices, t=262144, m=4.5, w=0.5, a=0)

### Description
Inverse of the Hyperlog transformation, implemented as defined in the GatingML 2.0 specification (see hyperlog() documentation for more details).

### Method
Function call

### Endpoint
flowutils.transforms.hyperlog_inverse

### Parameters
- **data** (NumPy array) - Required - NumPy array of FCS event data. If a 1-D array, channel_indices option is ignored.
- **channel_indices** (list or None) - Required - Channel indices to transform. Other channels returned in place, untransformed. If None, all events will be transformed.
- **t** (int) - Optional - Parameter for the top of the linear scale (e.g. 262144). Default: 262144.
- **m** (float) - Optional - Parameter for desired number of decades. Default: 4.5.
- **w** (float) - Optional - Parameter for the approximate number of decades in the linear region. Default: 0.5.
- **a** (int) - Optional - Parameter for the additional number of negative decades. Default: 0.

### Returns
NumPy array of transformed events.
```

--------------------------------

### points_in_ellipsoid()

Source: https://flowutils.readthedocs.io/en/latest/api.html

Evaluates whether points are inside an ellipsoid gate.

```APIDOC
## points_in_ellipsoid()

### Description
Evaluates whether points are inside an ellipsoid gate.

### Method
Function

### Endpoint
`points_in_ellipsoid()`
```

--------------------------------

### points_in_polygon()

Source: https://flowutils.readthedocs.io/en/latest/api.html

Evaluates whether points are inside a polygon gate.

```APIDOC
## points_in_polygon()

### Description
Evaluates whether points are inside a polygon gate.

### Method
Function

### Endpoint
`points_in_polygon()`
```

--------------------------------

### get_spill()

Source: https://flowutils.readthedocs.io/en/latest/api.html

Parses the spillover keyword value from the metadata within an FCS file.

```APIDOC
## get_spill()

### Description
Parses the spillover keyword value from the metadata within an FCS file.

### Method
Function

### Endpoint
`get_spill()`
```

--------------------------------

### get_spill(text)

Source: https://flowutils.readthedocs.io/en/latest/compensate.html

Extracts the spillover matrix from an FCS text entry, typically from the $SPILL or $SPILLOVER metadata keyword.

```APIDOC
## get_spill(text)

### Description
Extracts spillover matrix from FCS text entry.

### Parameters
- **text** (string) - Required - Text value from the $SPILL or $SPILLOVER metadata keyword in an FCS file.

### Returns
A tuple containing: (spillover matrix new_spill, column headers).
```

--------------------------------

### flowutils.gating.points_in_polygon

Source: https://flowutils.readthedocs.io/en/latest/gating.html

Determines whether points in an array are inside a polygon. Points on the edge of the polygon are considered inclusive. This function uses the winding number method and is robust to complex polygons with crossing boundaries, including the presence of 'holes' created by boundary crosses.

```APIDOC
## flowutils.gating.points_in_polygon

### Description
Determines whether points in an array are inside a polygon. Points on the edge of the polygon are considered inclusive. This function uses the winding number method and is robust to complex polygons with crossing boundaries, including the presence of 'holes' created by boundary crosses.

### Method
Function call

### Endpoint
flowutils.gating.points_in_polygon(poly_vertices, points)

### Parameters
- **poly_vertices** (NumPy array of 2-D points) - Required - Polygon vertices
- **points** (NumPy array) - Required - Array of data points to test for polygon inclusion

### Returns
NumPy 1-D array of boolean values for each point. True is inside polygon.
```

--------------------------------

### flowutils.gating.points_in_ellipsoid

Source: https://flowutils.readthedocs.io/en/latest/gating.html

Determines whether points in an array are inside an ellipsoid. Points on the edge are considered inclusive. True ellipsoids in n-dimensions are supported.

```APIDOC
## flowutils.gating.points_in_ellipsoid

### Description
Determines whether points in an array are inside an ellipsoid. Points on the edge are considered inclusive. True ellipsoids in n-dimensions are supported.

### Method
Function call

### Endpoint
flowutils.gating.points_in_ellipsoid(ellipsoid_covariance_matrix, ellipsoid_means, ellipsoid_distance_square, points)

### Parameters
- **ellipsoid_covariance_matrix** (NxN array) - Required - Covariance matrix for the ellipsoid shape
- **ellipsoid_means** (array) - Required - Center point of the ellipsoid for n-dimensions
- **ellipsoid_distance_square** (float) - Required - Square of the Mahalanobis distance, controlling the size of the ellipsoid. The distance square parameter is conceptually similar to the number of standard deviations representing the boundary for an n-dimensional distribution of points.
- **points** (NumPy array) - Required - Array of data points to test for ellipsoid inclusion

### Returns
NumPy 1-D array of boolean values for each point. True is inside ellipsoid.
```

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