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# NumPy | logspace method

schedule Mar 5, 2023
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Numpy's `logspace(~)` method creates a Numpy array with values that are evenly spaced in a log-scale.

# Parameters

1. `start` | `number`

The starting value of the Numpy array.

2. `stop` | `number`

The ending value of the Numpy array. This is inclusive.

3. `num` | `int` | `optional`

The number of samples you want to generate. By default, `num=50`.

4. `endpoint` | `boolean` | `optional`

If set to `True`, then stop will be the last value of the Numpy array. By default, `endpoint=False`.

5. `base` | `float` | `optional`

The base of the logarithm to use. By default, base=10.

6. `dtype` | `string` or `type` | `optional`

The desired data type for the Numpy array. This overrides the default behaviour of using the same data-type as the source array.

# Return value

A Numpy array with values that are evenly spaced in a log-scale.

# Examples

## Basic Usage

``` np.logspace(2, 5, 4) array([ 100., 1000., 10000., 100000.]) ```

Here, the numbers in the array are generated like so:

```  10^2 = 100 10^3 = 1000 10^4 = 10000 10^5 = 100000 ```

## Excluding the endpoint

We set `endpoint=False`, like follows:

``` np.logspace(2,5,4, endpoint=False) array([ 100. , 562.34132519, 3162.27766017, 17782.79410039]) ```

## Explicit typing

We set `dtype=float` to obtain a Numpy array of type float.

``` np.logspace(2,5,4, dtype=float) array([ 100., 1000., 10000., 100000.]) ```
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