Contents

## What is numpy logspace?

**The Numpy logspace() function returns or creates the array by using the numbers that are evenly separated on a log scale.** In the linear space, the sequence basically starts at base ** start and ends at base ** stop.

## Syntax

**np.logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None, axis=0)**

## Parameters

**Start:**It is used to tell the starting value of the interval in the base.**Stop:**It is used to tell the ending value of the interval in the base.**num:**It is an integer value that is optional in the input. It is used to tell how many samples to generate. By default, the value is set to 50.**endpoint:**It is a boolean value that is optional in the input. If we set the Boolean value to True, then the stop is the last sample. Otherwise, we don’t include it. By default, it is always True.**base:**It is also optional in the input. It tells about the base of logspace. By default, it is equal to 10.0.**dtype:**It is the type of output array. If it is not given, the dtype is inferred from the start and stop.**axis:**It is the integer value that is optional in the input. It is the result to store the samples.

## Return value of numpy logspace()

An ndarray with the specified range equally spaced on a log scale.

## Examples of numpy logspace()

Let us understand the concept of numpy logspace() function in detail with the help of examples:

### 1. Using start, Stop and num parameter

In this example, we will import the numpy library to use the logspace() function and use the start value, stop value, and the num parameter to return the spaced log scale evenly. Let see the output with these parameters:

```
#import numpy library
import numpy as np
Output = np.logspace(2.0, 3.0, num=5)
print("output array : ",Output)
```

**Output:**

```
output array : [ 100. 177.827941 316.22776602 562.34132519 1000. ]
```

**Explanation:**

Here firstly, we have imported the numpy module as np. Secondly, we have applied the logspace() function with start, stop, and num parameters. At last, we have printed the output of the program. Hence, we can see the result as an array that is generated.

### 2. Using base parameter

In this example, we will import the numpy library to use the logspace() function and use the start value, stop value, base, and num parameter to return the spaced log scale evenly. Let see the output with these parameters:

```
#import numpy library
import numpy as np
Output = np.logspace(2.0, 3.0, num=5, base = 30)
print("output array : ",Output)
```

**Output:**

```
output array : [ 900. 2106.31258739 4929.50301755 11536.7491727
27000. ]
```

**Explanation:**

Here firstly, we have imported the numpy module as np. Secondly, we have applied the logspace() function with start, stop, base, and num parameters. At last, we have printed the output of the program. Hence, we can see the result as an array that is generated.

### 3. Using dtype parameter

In this example, we will import the numpy library to use the logspace() function and use the start value, stop value, type, and num parameter to return the spaced log scale evenly. Let see the output with these parameters:

```
#import numpy library
import numpy as np
Output = np.logspace(2.0, 3.0, num=5, dtype=int)
print("output array : ",Output)
```

**Output:**

`output array : [ 100 177 316 562 1000]`

**Explanation:**

Here firstly, we have imported the numpy module as np. Secondly, we have applied the logspace() function with start, stop, type, and num parameters. In this dtype parameter, will print the array with the integer value. At last, we have printed the output of the program. Hence, we can see the result as an array that is generated.

### 4. Using endpoint parameter

In this example, we will import the numpy library to use the logspace() function and use the start value, stop value, endpoint, type, and the num parameter to return the spaced log scale evenly. Let see the output with these parameters:

```
#import numpy library
import numpy as np
Output = np.logspace(2.0, 3.0, num=5, endpoint = False,dtype=int)
print("output array : ",Output)
print("\n")
Output = np.logspace(2.0, 3.0, num=5, endpoint = True,dtype=int)
print("output array : ",Output)
```

**Output:**

```
output array : [100 158 251 398 630]
output array : [ 100 177 316 562 1000]
```

**Explanation:**

Here firstly, we have imported the numpy module as np. Secondly, we have applied the logspace() function with start, stop, type, and num parameters. Thirdly, we have taken the endpoint parameter, which is set to False and True, both the boolean values, so that you can see the change in the output and understand the concept easily. At last, we have printed the output of the program. Hence, we can see the result as an array that is generated.

### 5. Using all the parameters

In this example, we will import the numpy library to use the logspace() function and use the start value, stop value, endpoint, dtype, base, axis, and the num parameter to return the spaced log scale evenly. Let see the output with these parameters:

```
#import numpy library
import numpy as np
Output = np.logspace(2.0,3.0,num=5,base=30,endpoint=False,dtype=int,axis=0)
print("output array : ",Output)
```

**Output:**

`output array : [ 900 1776 3508 6926 13675]`

**Explanation:**

Here firstly, we have imported the numpy module as np. Secondly, we have applied the logspace() function with all the parameters such as start, stop, dtype, endpoint, axis, base, and num parameter. The operation which the parameters are performing can be seen in the parameter section. At last, we have printed the output of the program. Hence, we can see the result as an array that is generated.

NOTE: All the input is kept the same so that you understand the concept more precisely.

## Graphical Representation of numpy logspace

In this example, we will be using the matplotlib module, which will help you understand the numpy logspace() function.

```
#import numpy and matplotlib library
import numpy as np
import matplotlib.pyplot as plt
n=20
x1 = np.logspace(0.1, 1, n, endpoint=True)
x2 = np.logspace(0.1, 1, n, endpoint=False)
y = np.zeros(n)
plt.plot(x1, y, 'o')
plt.plot(x2, y + 0.5, 'o')
plt.ylim([-0.6, 1])
plt.show()
```

**Output:**

**Explanation:**

Here firstly, we have imported the numpy library as np. Secondly, we have imported the matplotlib.pyplot library as plt. Thirdly, we have taken a variable n which is set to 20. Fourthly, we have taken the x1 and x2 variables in which we have applied the logspace() function with some parameters. Fifthly, we have a variable y which np.zeros() function. Then, we have applied the plt library in which x1 and x2 are the x-axis and y is the y-axis for both the color graph. plt.ylim() function sets the y-limits of the current axis. At last, we have called plt.show () which will generate the graph, and you can see the output. Hence, we can see the result as a graph generated.

NOTE:This program will not be able to run on online compiler so try to run it in spyder or any other compiler.

## Conclusion

In this tutorial, we have learned about how we can use the numpy logspace() function. We have explained the concept in detail by explaining all the examples in it. We have also shown the working and diagrammatic representation of the numpy logspace() method in the matplotlib library. So you can use the program according to your need.