### Get Quantiles from Probabilities using metalog.q()

Source: https://github.com/kimsergeo/metalog/blob/master/README.rst

This snippet demonstrates how to use the `metalog.q()` function to calculate quantiles (x-values) given a metalog model (`m`), a list of probabilities (`y`), and the number of terms (`term`). This is the inverse operation of `metalog.p()`. For the input `y=[0.00275336, 0.82349578, 0.98686581]`, the function returns an array of quantiles such as `[50.02583336, 109.99861143, 149.99737059]`.

```Python
metalog.q(m=metalog_salmon, y=[0.00275336, 0.82349578, 0.98686581], term=10)
```

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

### Draw Samples from Metalog Distribution in Python

Source: https://github.com/kimsergeo/metalog/blob/master/README.rst

This code demonstrates how to generate random samples from a fitted metalog distribution using `metalog.r()`. It allows specifying the number of samples (`n`) and the number of terms (`term`) to use for the sampling process, useful for simulations or further analysis.

```Python
metalog.r(m=metalog_salmon, n=5, term=10)
```

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

### Summarize Fitted Metalog Distribution in Python

Source: https://github.com/kimsergeo/metalog/blob/master/README.rst

This snippet shows how to obtain a summary of a previously fitted metalog distribution object using `metalog.summary()`. The output provides details such as term limits, boundedness, bounds, step length, fit method, number of data points, and validation status for each term.

```Python
metalog.summary(m=metalog_salmon)
```

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

### Fit Data to Bounded Metalog Distribution in Python

Source: https://github.com/kimsergeo/metalog/blob/master/README.rst

This code demonstrates how to use `metalog.fit()` to fit the prepared salmon data to a metalog distribution. It specifies a 'bounded' distribution type with bounds from 0 to 200 and sets a term limit of 10 for the fitting process.

```Python
metalog_salmon = metalog.fit(x=salmon, boundedness='b', bounds=[0, 200], term_limit=10)
```

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

### Import Metalog Distribution Package in Python

Source: https://github.com/kimsergeo/metalog/blob/master/README.rst

This simple snippet shows the standard way to import the 'metalog' package into a Python script, making its functions available for use in statistical analysis.

```Python
from metalog import metalog
```

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

### Prepare Salmon Length Data for Metalog Analysis in Python

Source: https://github.com/kimsergeo/metalog/blob/master/README.rst

This snippet demonstrates how to import necessary libraries (numpy, pandas) and load/filter salmon body length data from a CSV file. The data is then converted to a NumPy array, preparing it for use with the metalog distribution fitting functions.

```Python
import numpy as np
import pandas as pd

salmon = pd.read_csv("Chinook and forage fish lengths.csv")

# Filtered data for eelgrass vegetation and chinook salmon
salmon = salmon[(salmon['Vegetation'] == 'Eelgrass') & (salmon['Species'] == 'Chinook_salmon')]
salmon = np.array(salmon['Length'])
```

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

### APIDOC: metalog.fit() Function Parameters

Source: https://github.com/kimsergeo/metalog/blob/master/README.rst

This section details the parameters for the `metalog.fit()` function, used to fit data to a metalog distribution. It describes inputs such as the data array, boundedness type and values, term limits, step length, probabilities, fit method, and data saving options.

```APIDOC
metalog.fit(x, bounds, boundedness, term_limit, term_lower_bound, step_len, probs, fit_method, save_data)
  x: data.
  bounds: bounds of metalog distribution. Depending on 'boundedness' argument can take zero, one or two values.
  boundedness: boundedness of metalog distribution. Can take values 'u' for unbounded, 'sl' for semi-bounded lower, 'su' for semi-bounded upper and 'b' for bounded on both sides.
  term_limit: maximum number of terms to specify the metalog distribution. Can take values from 3 to 30.
  term_lower_bound: the lowest number of terms to specify the metalog distribution. Must be greater or equal to 2 and less than 'term_limit'. The argument is optional. Default value is 2.
  step_len: size of steps to summarize the distribution. The argument is optional. Default value is 0.01.
  probs: probabilities corresponding to data. The argument is optional. Default value is numpy.nan.
  fit_method: fit method 'OLS', 'LP' or 'any'. The argument is optional. Default value is 'any'.
  save_data: if True then data will be saved for future update. The argument is optional. Default values is False.
```

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

### Plot PDF and CDF of Metalog Distribution in Python

Source: https://github.com/kimsergeo/metalog/blob/master/README.rst

This snippet illustrates how to visualize the Probability Density Function (PDF) and Cumulative Distribution Function (CDF) of a fitted metalog distribution. The `metalog.plot()` function generates a graphical representation, aiding in the understanding of the distribution's shape.

```Python
metalog.plot(m=metalog_salmon)
```

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

### Calculate Densities from Quantiles of Metalog Distribution in Python

Source: https://github.com/kimsergeo/metalog/blob/master/README.rst

This snippet shows how to compute the probability densities for specified quantiles of a fitted metalog distribution using `metalog.d()`. It takes a vector of quantiles (`q`) and the number of terms (`term`) to return the corresponding density values.

```Python
metalog.d(m=metalog_salmon, q=[50, 110, 150], term=10)
```

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

### Calculate Probabilities from Quantiles using metalog.p()

Source: https://github.com/kimsergeo/metalog/blob/master/README.rst

This snippet demonstrates how to use the `metalog.p()` function to calculate probabilities (y-values) given a metalog model (`m`), a list of quantiles (`q`), and the number of terms (`term`). For the input `q=[50, 110, 150]`, the function returns an array of probabilities such as `[0.00275336, 0.82349578, 0.98686581]`.

```Python
metalog.p(m=metalog_salmon, q=[50, 110, 150], term=10)
```

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