Gwydion allows users to generate pseudo-random scientific data easily.

Project Description
## Gwydion

## Examples

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Gwydion allows users to generate pseudo-random scientific data easily.

In the spirit of Faker, Gwydion allows you to generate pseudo-random data using a simple, clean, and customisable API.

Gwydion is named after a trickster from Welsh mythology.

Some basic examples of Gwydion objects are given below.

In the first example, we create a simple `Linear` object, given by the mathematical relationship `y = mx + c`.
When parameters are not set by the user, Gwydion objects will default to suitable random values. Objects will also, by default, add some random noise to the y-data.
In the example below we allow the `Linear` object to generate all of the parameters, but set the number of data points `N` manually.

from gwydion import Linear lin = Linear(N=6) x, y = lin.data print(x, y, sep='\n') # [ 0. 2. 4. 6. 8. 10.] # [ -0.17387604 5.59216341 11.77162695 17.70041889 23.55609025 28.67617757]

In this second example, an `Exponential` function is created with various manually selected parameters.
Exponential functions are given by `y = I * base**(k*x)`.
In the example below we have set:

- The number of data points
`N = 3`, - The intensity
`I = 10`, - The exponent multiplier
`k = -1`, - The x-limits
`xlim = (0, 10)`, - And chosen to not add any random noise to the data
`add_rand = False`.

For the `Exponential` object the default `base` is not random, but is instead to Euler’s number `e = 2.71828...`.
This fact, combined with `k = -1`, means that our object below is effectively giving us exponential decay.

from gwydion import Exponential exp = Exponential(N=3, I=10, k=-1, xlim=(0,10), add_rand=False) x, y = exp.data print(x, y, sep='\n') # [ 0. 5. 10.] # [ 1.00000000e+01 6.73794700e-02 4.53999298e-04]

Finally, let’s look at how `Gwydion` objects work with `matplotlib`. In the example below, we generate
5 `Sine` objects using a list comprehension. We can then use the `plot` function to plot each data set easily.

from gwydion import Sine import matplotlib.pyplot as plt sines = [Sine(xlim=(0,5)) for _ in range(5)] fig, ax = plt.subplots() for sine in sines: sine.plot(ax=ax) ax.set_xlabel('Time') ax.set_ylabel('Intensity') plt.show()

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File Name & Checksum SHA256 Checksum Help | Version | File Type | Upload Date |
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gwydion-0.1.win-amd64.exe (233.9 kB) Copy SHA256 Checksum SHA256 | any | Windows Installer | Mar 29, 2015 |

gwydion-0.1.zip (14.0 kB) Copy SHA256 Checksum SHA256 | – | Source | Mar 29, 2015 |