Macroeco

http://macroeco.org/
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Macroeco is a Python package for analyzing empirical patterns in ecological data, predicting...
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### Import macroeco.compare

Source: http://macroeco.org/generated/macroeco.compare.r_squared.html

Initial setup for using the macroeco comparison module.

```python
>>> import numpy as np
>>> import macroeco.compare as comp
```

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

### MacroecoDesktop Parameter File Example

Source: http://macroeco.org/tutorial_macroeco_desktop.html

This configuration file demonstrates how to set up analyses in MacroecoDesktop. It includes examples for calculating the probability mass function (pmf) of a geometric distribution, determining the shape parameter of a truncated geometric distribution, fitting a lognormal distribution to data, and generating random variables from a conditioned negative binomial distribution.

```ini
[Geom-pmf]
analysis = geom.pmf

x = 0,1,2,3,4,5
p = 0.5

[GeomUptrunc-p]
analysis = geom_uptrunc.translate_args

mu = 5
b = 20

[Lognorm-fit]
analysis = lognorm.fit_mle

data = 2,2,5,8,4,3

[Cnbinom-random]
analysis = cnbinom.rvs

mu = 10
k_agg = 2
b = 15
size = 10


```

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

### geom_uptrunc Examples

Source: http://macroeco.org/generated/macroeco.models.geom_uptrunc.html

Illustrative examples demonstrating how to use the geom_uptrunc distribution for various tasks, including parameter translation, PMF calculation, rank abundance generation, and fitting.

```APIDOC
## geom_uptrunc Examples

### Setup
```python
import macroeco.models as md
import numpy as np
```

### Get geom parameters from a mean and upper limit
```python
mu = 20
b = 200
p, b = md.geom_uptrunc.translate_args(mu, b)
print((p, b))
# Expected output: (array(0.047592556687674925), 200)
```

### Get the PMF
```python
print(md.geom_uptrunc.pmf(np.arange(0, 5), p, b))
# Expected output: array([ 0.04759519,  0.04533002,  0.04317264,  0.04111795,  0.03916104])
```

### Generate a rank abundance distribution
```python
rad = md.geom_uptrunc.rank(20, p, b)
print(rad)
# Expected output: array([  0.,   1.,   2.,   3.,   5.,   6.,   8.,   9.,  11.,  13.,  15.,
#     17.,  20.,  23.,  26.,  30.,  35.,  42.,  53.,  75.])
```

### Fit the geom to data
```python
print(md.geom_uptrunc.fit_mle(rad))
# Expected output: (0.048309175638750035, 394.0)
```
```

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

### Importing macroeco modules

Source: http://macroeco.org/generated/macroeco.compare.AIC.html

Initial setup required to access model and comparison functions.

```python
>>> import macroeco.models as md
>>> import macroeco.compare as comp
```

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

### Importing macroeco.compare and numpy

Source: http://macroeco.org/generated/macroeco.compare.preston_bin.html

Initial setup required to access the preston_bin function and numpy utilities.

```python
>>> import macroeco.compare as comp
>>> import numpy as np
```

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

### Create MacroecoDesktop Parameter File

Source: http://macroeco.org/tutorial_getting_started.html

Example content for a parameter file to analyze species abundance distribution using ANBO data. Save this content in a file named 'new_parameters.txt'.

```ini
[SAD-ANBO]

analysis = sad

metadata = ANBO.txt

models = logser_uptrunc; lognorm
log_y = True


```

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

### expon_uptrunc Examples

Source: http://macroeco.org/generated/macroeco.models.expon_uptrunc.html

Illustrates how to use the expon_uptrunc distribution for various statistical calculations, including parameter translation, PDF, CDF, PPF, random sampling, and model fitting.

```APIDOC
### Examples

```python
>>> import macroeco.models as md
>>> import numpy as np
```

#### Get the rate parameter of the exponential distribution from a mean
```python
>>> md.expon_uptrunc.translate_args(20, 100)
(array(0.04801007549722518), 100)
```

#### Get the pdf
```python
>>> md.expon_uptrunc.pdf(np.linspace(0.1, 10, num=10), 0.05, 100)
array([ 0.05008812,  0.04740766,  0.04487064,  0.0424694 ,  0.04019665,
    0.03804554,  0.03600953,  0.03408249,  0.03225857,  0.03053226])
```

#### Get the cdf
```python
>>> md.expon_uptrunc.cdf(np.linspace(0.1, 10, num=10), 0.05, 100)
array([ 0.00502135,  0.05863052,  0.10937079,  0.15739571,  0.20285058,
    0.24587294,  0.28659296,  0.32513386,  0.36161225,  0.3961385 ])
```

#### Get the ppf
```python
>>> md.expon_uptrunc.ppf(0.8, 0.05, 100)
31.656858541834165
```

#### Draw a random sample
```python
>>> samp = md.expon_uptrunc.rvs(0.05, 100, size=100)
```

#### Fit the model to data
```python
>>> md.expon_uptrunc.fit_mle(samp)
(0.06080396315704938, 1644.6296393823973)
```
```

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

### Install macroeco using pip

Source: http://macroeco.org/tutorial_getting_started.html

Install the macroeco Python package using pip. Ensure you have a compatible scientific Python environment with numpy, scipy, pandas, matplotlib, and wxpython.

```bash
pip install macroeco
```

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

### Install core scientific packages with conda

Source: http://macroeco.org/tutorial_getting_started.html

Install essential scientific Python packages using conda. This is a prerequisite for installing macroeco if you are setting up a new environment.

```bash
conda install numpy scipy pandas matplotlib wxpython pip
```

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

### macroeco.misc.log_start_end

Source: http://macroeco.org/generated/macroeco.misc.log_start_end.html

A decorator that logs the start and end of a function call.

```APIDOC
## log_start_end(f)

### Description
Decorator to log start and end of function. Uses the decorator module to ensure that argspec will inspect the wrapped function rather than the decorator itself.

### Parameters
- **f** (function) - Required - The function to be decorated.
```

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

### Macroeco Metadata File Structure

Source: http://macroeco.org/tutorial_own_data.html

This is an example of a metadata file for Macroeco. It includes a [Description] section with general information and datapath, followed by sections for each column in the data table, specifying their properties.

```ini
[Description]
name = Anzo Borrego
author = Mary Ellen Harte and John Harte
description = Vegetation census conducted at Anza-Borrego Desert State Park. Site in Indian Valley at N 32' 52.091", W 116' 14.447". Elevation 1195 feet. Census was conducted on a 4 m x 4 m grid, with 16 grid cells each 1 m2 in area.
citation = Unpublished

datapath = ANBO.csv
cols = spp_col:spp; count_col: count; x_col: row; y_col: column

[year]
description = Year of census

[cell]
description = Unique cell identifier, from 0 to 15 (total of 16 cells)

[row]
description = Row of cell in gridded plot
min = 0
max = 3
step = 1

[column]
description = Column of cell in gridded plot
min = 0
max = 3
step = 1

[spp]
description = Name of species

[count]
description = Number of individuals of a species in a cell

```

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

### Define a Species Area Analysis Run

Source: http://macroeco.org/tutorial_macroeco_desktop.html

Example configuration for a species area relationship (SAR) analysis within a parameter file.

```ini
[SAR]
analysis = ssad

metadata = ANBO.txt
subset = column >= 2
cols = spp_col:spp; count_col:count; x_col:row; y_col:column
splits = row:2
divs = 1,1; 1,2; 2,1; 2,2
ear = False
```

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

### Binning data with preston_bin

Source: http://macroeco.org/generated/macroeco.compare.preston_bin.html

Example of using preston_bin to process an array of data and return the binned results and bin edges.

```python
>>> # Load some data and get Preston bins
>>> data = np.array([1, 1, 1, 1, 4, 5, 6, 7, 12, 34, 56])
>>> comp.preston_bin(data, np.max(data))
(array([4, 0, 1, 3, 1, 0, 2]),
array([  1.,   2.,   3.,   5.,   9.,  17.,  33.,  65.]))
```

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

### Macroeco Analysis Log Output

Source: http://macroeco.org/tutorial_getting_started.html

Example of the log file content generated during a Macroeco analysis run, showing the progression of the analysis and file paths.

```text
[2015/09/02 15:21:47 PM] {meco} INFO Running macroeco
[2015/09/02 15:21:47 PM] {meco} INFO Parameters file at /Users/mqwilber/Desktop/new_parameters.txt
[2015/09/02 15:21:47 PM] {meco} INFO Starting analysis
[2015/09/02 15:21:47 PM] {meco} INFO Starting run SAD-ANBO
[2015/09/02 15:21:47 PM] {meco} INFO Starting sad
[2015/09/02 15:21:47 PM] {meco} INFO Finished sad
[2015/09/02 15:21:47 PM] {meco} INFO Fitting models
[2015/09/02 15:21:47 PM] {meco} INFO Saving all results
[2015/09/02 15:21:48 PM] {meco} INFO Finished run SAD-ANBO
[2015/09/02 15:21:48 PM] {meco} INFO Finished analysis successfully
[2015/09/02 15:21:48 PM] {meco} INFO Results available at /Users/mqwilber/Desktop
```

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

### Initialize macroeco models

Source: http://macroeco.org/generated/macroeco.models.expon_uptrunc.html

Import the necessary modules for working with the distribution.

```python
>>> import macroeco.models as md
>>> import numpy as np
```

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

### main(param_path)

Source: http://macroeco.org/main.html

The entry point function for executing analysis based on provided parameter files.

```APIDOC
## main(param_path)

### Description
Entry point function for analysis based on parameter files.

### Parameters
#### Path Parameters
- **param_path** (string) - Optional - The file path to the parameter configuration file used for the analysis.
```

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

### Import macroeco models

Source: http://macroeco.org/generated/macroeco.models.logser.html

Initialize the macroeco models module.

```python
>>> import macroeco.models as md
```

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

### Import METE models

Source: http://macroeco.org/generated/macroeco.models.mete_ear.html

Initializes the macroeco models module for subsequent calculations.

```python
>>> # For a base area of A = 10, downscale species richness using the
>>> # METE SAR
>>> import macroeco.models as md
```

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

### Get Rate Parameter from Mean

Source: http://macroeco.org/generated/macroeco.models.expon.html

Calculate the rate parameter (lambda) of the exponential distribution given its mean.

```python
# Get the rate parameter of the exponential distribution from a mean
md.expon.translate_args(20)
0.05
```

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

### Initialize macroeco models

Source: http://macroeco.org/generated/macroeco.models.sampling_sar_iterative.html

Import the models module to access SAR and EAR functions.

```python
>>> # Use a standard SAR with a Broken Stick SAD and an truncated geometric
>>> # binomial SSAD. sad_k = 1, ssad_k = 1
>>> import macroeco.models as md
```

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

### Run MacroecoDesktop Analysis from Command Line

Source: http://macroeco.org/tutorial_getting_started.html

Execute a MacroecoDesktop analysis using a parameter file from the command line. Replace '\path\to\parameterfile' with the actual path to your parameter file.

```python
python -c "import macroeco; macroeco.desktop('\path\to\parameterfile')"
```

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

### Iterative SAR with Non-Doubled Areas

Source: http://macroeco.org/generated/macroeco.models.sampling_ear.html

This example shows the `sampling_sar_iterative.vals` function handling areas that are not exact doublings, illustrating how it rounds up.

```python
>>> # Iterative SAR with not quite doubled areas
>>> areas_up = [1, 2.1, 4.1, 8.1]
>>> md.sampling_sar_iterative.vals(areas_up, S0, N0, sad_k=0, ssad_k=1, approx=True)
array([ 50.        ,  64.72987319,  72.58968402,  80.75754743])
```

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

### Iterative SAR with Doubled Areas

Source: http://macroeco.org/generated/macroeco.models.sampling_ear.html

Employ `sampling_sar_iterative.vals` for iterative SAR calculations. This example demonstrates its use with areas that are exact doublings.

```python
>>> # Iterative SAR with doubled areas
>>> areas_up = [1, 2, 4, 8]
>>> md.sampling_sar_iterative.vals(areas_up, S0, N0, sad_k=0, ssad_k=1, approx=True)
array([ 50.        ,  57.19380999,  64.72987319,  72.58968402])
```

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

### Import macroeco Library

Source: http://macroeco.org/generated/macroeco.empirical.Patch.html

Import the macroeco library for use in your project. This is a common first step for utilizing its functionalities.

```python
import macroeco as meco
```

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

### macroeco.main.main

Source: http://macroeco.org/generated/macroeco.main.main.html

The main entry point function for executing analysis based on a provided parameter file.

```APIDOC
## macroeco.main.main

### Description
Entry point function for analysis based on parameter files.

### Parameters
#### Path Parameters
- **param_path** (str) - Optional - Path to user-generated parameter file (default: 'parameters.txt')
```

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

### Open MacroecoDesktop GUI

Source: http://macroeco.org/tutorial_getting_started.html

Run this command to open the MacroecoDesktop graphical interface for creating parameter files. Mac users might need to use 'pythonw' instead of 'python'.

```python
python -c "import macroeco; macroeco.desktop()"
```

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

### Initialize spatial aggregation results

Source: http://macroeco.org/tutorial_macroeco.html

Prepares a dictionary to store aggregation parameters for species spatial distributions.

```python
# Store the results
agg_res = {}
```

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

### Upscale Species Richness with Sampling SAR

Source: http://macroeco.org/generated/macroeco.models.sampling_ear.html

Use `sampling_sar.vals` to calculate species richness for a list of areas, starting from initial values S0 and N0. Set `approx=True` for approximation.

```python
>>> # Upscaling species richness
>>> areas = [5, 10, 11, 13]
>>> md.sampling_sar.vals(areas, S0, N0, sad_k=0, ssad_k=1, approx=True)
array([ 50.        ,  57.19380999,  58.10403751,  59.66376591])
```

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

### Initialize Patch Object

Source: http://macroeco.org/tutorial_macroeco.html

Create a Patch object to hold ecological data. Requires a path to a metadata file.

```python
>>> pat = meco.empirical.Patch('~/Desktop/ANBO.txt')
```

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

### Calculate CDF for plnorm_ztrunc

Source: http://macroeco.org/generated/macroeco.models.plnorm_ztrunc.html

Get the cumulative density function (CDF) for the zero-truncated poisson lognormal distribution. Provide an array of quantiles and the distribution's mu and sigma parameters.

```python
>>> # Get the CDF
>>> md.plnorm_ztrunc.cdf([1, 15, 10000], mu=.1, sigma=2)
array([ 0.27575055,  0.84270355,  0.99999442])
```

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

### Calculate PMF for plnorm_ztrunc

Source: http://macroeco.org/generated/macroeco.models.plnorm_ztrunc.html

Get the probability mass function (PMF) for the zero-truncated poisson lognormal distribution with specified mu and sigma parameters. The input x should be an array-like of quantiles.

```python
>>> # Get the pmf for the zero-truncated poisson lognormal with mu = -1
>>> # and sigma = 3
>>> md.plnorm_ztrunc.pmf(np.arange(1, 11), -1, 3)
array([ 0.27334111,  0.12990549,  0.08013071,  0.05598538,  0.04206434,
    0.03314746,  0.02701614,  0.02257919,  0.019242  ,  0.01665499])
```

```python
>>> md.plnorm_ztrunc.pmf([1, 50, 1000], 2.34, 5)
array([  9.27474648e-02,   2.12916164e-03,   7.36783061e-05])
```

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

### Generate random data for model fitting

Source: http://macroeco.org/generated/macroeco.compare.AIC_compare.html

Initializes a sample dataset using the zero-truncated negative binomial distribution.

```python
>>> # Generate random data
>>> rand_samp = md.nbinom_ztrunc.rvs(20, 0.5, size=100)
```

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

### Load Data with Patch Class

Source: http://macroeco.org/tutorial_getting_started.html

Initialize a Patch object to hold data and metadata. Requires a path to a metadata file.

```python
import macroeco as meco
pat = meco.empirical.Patch('~/Desktop/demo/ANBO.txt')
```

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

### Get Mean and Variance of Discrete Gamma Distribution

Source: http://macroeco.org/generated/macroeco.models.dgamma.html

Calculates and returns the approximate mean and variance of the discrete gamma distribution. This provides a summary of the distribution's central tendency and spread.

```python
# Get the approximate mean and variance
dgamma_dist.stats()
```

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

### Import Libraries

Source: http://macroeco.org/generated/macroeco.models.expon.html

Import necessary libraries for using the macroeco models.

```python
import macroeco.models as md
import numpy as np
```

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

### Split Patch and Get Empirical SADs

Source: http://macroeco.org/tutorial_macroeco.html

Splits a given patch based on specified row and column divisions to obtain empirical species-abundance distributions (SADs) for each resulting cell. Requires the 'meco' library.

```python
# Split row by 2, split column by 4
split_sads = meco.empirical.sad(pat, "spp_col:spp; count_col:count", splits="row:2; column:4")
```

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

### Calculate SAD for subplots with absent species

Source: http://macroeco.org/generated/macroeco.empirical.sad.html

Calculate the SAD for multiple subplots within the full plot. This example demonstrates splitting the data by 'row' and 'column' into two divisions each. Setting `clean=False` ensures that species with zero abundance in a subplot are retained in the results.

```python
# Get SAD for 4 subplots within the full plot and keep absent species
# using clean = False
sad_subplots = meco.empirical.sad(pat, 'spp_col:spp; count_col:count', splits = "row:2; column:2", clean=False)
print(len(sad_subplots))
```

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

### Generate sample data

Source: http://macroeco.org/generated/macroeco.compare.r_squared.html

Create synthetic data for testing regression metrics.

```python
>>> # Generate some data
>>> x_vals = np.linspace(1, 20, num=100)
>>> y_vals = np.random.normal(4 + x_vals*2, 1)
```

```python
>>> # Generate some other data
>>> y_vals = np.random.normal(x_vals, 1)
```

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

### Generate and Fit Random Sample

Source: http://macroeco.org/generated/macroeco.models.cnbinom.html

Generate random variates from the distribution and then fit the distribution parameters to this sample using maximum likelihood estimation.

```python
# Get and fit random sample
>>> samp = md.cnbinom.rvs(mu=10, k_agg=1, b=300, size=100)
>>> md.cnbinom.fit_mle(samp)
(11.640000000000001, 1.2000000000000002, 1164)
```

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

### Import macroeco.models

Source: http://macroeco.org/generated/macroeco.models.cnbinom.html

Import the macroeco.models module to access distributions.

```python
import macroeco.models as md
```

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

### Execute MacroecoDesktop analysis from R

Source: http://macroeco.org/tutorial_with_r.html

This script demonstrates writing a parameter file, running the analysis via a system call, and plotting the resulting data.

```R
param_dir <- "/Users/jkitzes/Desktop/"
param_file <- "new_parameters.txt"

cat("
[DistanceDecay]

analysis = comm_grid

metadata = ANBO.txt
cols = spp_col: spp; count_col: count; y_col: row; x_col: column
divs = 4,4
models = power_law
",file=paste(param_dir,param_file,sep=""), sep="\n")

system(paste("/Users/jkitzes/anaconda/bin/python -c  \"import macroeco; macroeco.desktop('", param_dir, param_file, "')\"", sep=""))


data_models <- read.csv(paste(param_dir, "results/DistanceDecay/1_data_models.csv", sep=""))
test_statistics <- read.csv(paste(param_dir, "results/DistanceDecay/1_test_statistics.csv", sep=""))

plot(data_models$x, data_models$empirical)
lines(data_models$x, data_models$power_law)

test_statistics$R2
```

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

### Import core macroeco libraries

Source: http://macroeco.org/tutorial_macroeco.html

Import the main macroeco package, matplotlib for plotting, and numpy for numerical operations. Sets the plotting style to 'ggplot' for enhanced visualization.

```python
import macroeco as meco
import matplotlib.pyplot as plt
import numpy as np
plt.style.use('ggplot') # For pretty plotting
```

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

### expon_uptrunc Methods

Source: http://macroeco.org/generated/macroeco.models.expon_uptrunc.html

Lists and describes the available methods for the expon_uptrunc distribution, including statistical functions and parameter estimation.

```APIDOC
## expon_uptrunc Methods

### Random Variates
- **rvs(lam, b, loc=0, scale=1, size=1)**: Generates random variates from the distribution.

### Probability Density and Cumulative Functions
- **pdf(x, lam, b, loc=0, scale=1)**: Probability density function.
- **logpdf(x, lam, b, loc=0, scale=1)**: Log of the probability density function.
- **cdf(x, lam, b, loc=0, scale=1)**: Cumulative density function.
- **logcdf(x, lam, b, loc=0, scale=1)**: Log of the cumulative density function.
- **sf(x, lam, b, loc=0, scale=1)**: Survival function (1-cdf — sometimes more accurate).
- **logsf(x, lam, b, loc=0, scale=1)**: Log of the survival function.

### Inverse Distribution Functions
- **ppf(q, lam, b, loc=0, scale=1)**: Percent point function (inverse of cdf — percentiles).
- **isf(q, lam, b, loc=0, scale=1)**: Inverse survival function (inverse of sf).

### Moments and Statistics
- **moment(n, lam, b, loc=0, scale=1)**: Non-central moment of order n.
- **stats(lam, b, loc=0, scale=1, moments=’mv’)**: Mean(‘m’), variance(‘v’), skew(‘s’), and/or kurtosis(‘k’).
- **entropy(lam, b, loc=0, scale=1)**: (Differential) entropy of the RV.
- **median(lam, b, loc=0, scale=1)**: Median of the distribution.
- **mean(lam, b, loc=0, scale=1)**: Mean of the distribution.
- **var(lam, b, loc=0, scale=1)**: Variance of the distribution.
- **std(lam, b, loc=0, scale=1)**: Standard deviation of the distribution.
- **interval(alpha, lam, b, loc=0, scale=1)**: Endpoints of the range that contains alpha percent of the distribution.

### Parameter Estimation
- **fit(data, lam, b, loc=0, scale=1)**: Parameter estimates for generic data.
- **fit_mle(samp)**: Maximum Likelihood Estimation for fitting the model to data.

### Expectation
- **expect(func, lam, b, loc=0, scale=1, lb=None, ub=None, conditional=False, **kwds)**: Expected value of a function (of one argument) with respect to the distribution.
```

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

### Initialize Patch Object with Metadata Path

Source: http://macroeco.org/generated/macroeco.empirical.Patch.html

Create a Patch object by providing the path to the metadata file. This loads the census data described by the metadata.

```python
pat = meco.empirical.Patch('~/Desktop/ANBO.txt')
```

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

### Import macroeco modules

Source: http://macroeco.org/generated/macroeco.compare.nll.html

Import necessary modules from macroeco for models and comparison functions.

```python
import macroeco.models as md
import macroeco.compare as comp
```

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

### Initialize Patch Object with Subset

Source: http://macroeco.org/generated/macroeco.empirical.Patch.html

Initialize a Patch object with a metadata path and a subset string. The subset string filters the data upon loading, allowing for analysis of specific data segments. Conditions should be separated by semicolons.

```python
pat = meco.empirical.Patch('~/Desktop/ANBO.txt',
                                subset="year==2010; row>2")
```

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

### Instantiate Discrete Gamma Distribution

Source: http://macroeco.org/generated/macroeco.models.dgamma.html

Creates an instance of the discrete gamma distribution with specified alpha and theta parameters. This is useful for further analysis or simulation.

```python
# dgamma takes two parameters
dgamma_dist = md.dgamma(alpha=1, theta=2)
```

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

### Direct method calls

Source: http://macroeco.org/generated/macroeco.models.logser_uptrunc.html

Calculate PMF and CDF directly using the model class by passing parameters.

```python
>>> # You can also use the following notation
>>> md.logser_uptrunc.pmf(1, 0.9, 1000)
0.39086503371292664
>>> md.logser_uptrunc.cdf(10, 0.9, 1000)
0.9201603889810761
```

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

### Calculate Probability Density Function (PDF)

Source: http://macroeco.org/generated/macroeco.models.expon.html

Compute the probability density function for a given set of quantiles and the rate parameter.

```python
# Get the pdf
md.expon.pdf(np.linspace(0.1, 10, num=10), 0.05)
array([ 0.04975062,  0.04708823,  0.04456831,  0.04218324,  0.03992581,
    0.03778919,  0.0357669 ,  0.03385284,  0.03204121,  0.03032653])
```

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

### Create Patch object

Source: http://macroeco.org/generated/macroeco.empirical.sad.html

Instantiate a Patch object by providing the path to the metadata file. This object is required for subsequent analyses.

```python
# Pass in path to metadata in order to make patch object
pat = meco.empirical.Patch('~/Desktop/ANBO.txt')
```

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

### Generating a rank abundance distribution

Source: http://macroeco.org/generated/macroeco.models.geom_uptrunc.html

Create a rank abundance distribution using the distribution parameters.

```python
>>> # Generate a rank abundance distribution
>>> rad = md.geom_uptrunc.rank(20, p, b)
>>> rad
array([  0.,   1.,   2.,   3.,   5.,   6.,   8.,   9.,  11.,  13.,  15.,
    17.,  20.,  23.,  26.,  30.,  35.,  42.,  53.,  75.])
```

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

### Fitting parameters

Source: http://macroeco.org/generated/macroeco.models.logser_uptrunc.html

Estimate distribution parameters from data using maximum likelihood estimation.

```python
>>> # Fit the logser_uptrunc to data and estimate the parameters
>>> md.logser_uptrunc.fit_mle(rad)
(0.8957385644316679, 114.0)
```

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

### Create a Patch object

Source: http://macroeco.org/generated/macroeco.empirical.ssad.html

Loads data from a file path to create a Patch object required for empirical analysis.

```python
>>> # Pass in path to metadata in order to make patch object
>>> pat = meco.empirical.Patch('~/Desktop/ANBO.txt')
```

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

### Direct distribution method calls

Source: http://macroeco.org/generated/macroeco.models.logser.html

Calculate PMF and CDF directly using the distribution class methods.

```python
>>> # You can also use the following notation
>>> md.logser.pmf(1, 0.9)
0.39086503371292664
>>> md.logser.cdf(10, 0.9)
0.9201603889810761
```

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

### Support Functions

Source: http://macroeco.org/misc.html

Utility functions and decorators for enhancing other macroeco functions.

```APIDOC
## Decorator: log_start_end

### Description
Decorator to log the start and end of a function's execution.

### Method
Decorator

### Endpoint
N/A

### Parameters
None

### Request Example
```python
@log_start_end
def my_function():
    pass
```

### Response
N/A
```

```APIDOC
## Decorator: inherit_docstring_from

### Description
Modifies the decorated function's docstring by replacing occurrences of '%(super)s' with the docstring of the method of the same name from the specified superclass.

### Method
Decorator

### Endpoint
N/A

### Parameters
- **cls** (class) - Required - The superclass from which to inherit the docstring.

### Request Example
```python
class Parent:
    def my_method(self):
        """This is the parent docstring."""
        pass

class Child(Parent):
    @inherit_docstring_from(Parent)
    def my_method(self):
        """My method docstring: %(super)s"""
        pass
```

### Response
N/A
```

```APIDOC
## Decorator: doc_sub

### Description
Decorator for performing substitutions in docstrings.

### Method
Decorator

### Endpoint
N/A

### Parameters
- **sub** (tuple of strings) - Required - Substitutions to perform in the docstring.

### Request Example
```python
@doc_sub('old', 'new')
def my_function():
    """This docstring has an old value."""
    pass
```

### Response
N/A
```

```APIDOC
## Function: check_parameter_file

### Description
Performs a rudimentary check whether the 'cols', 'splits', and 'divs' columns in parameter files are formatted properly.

### Method
Function

### Endpoint
N/A

### Parameters
- **filename** (str) - Required - The path to the parameter file to check.

### Request Example
```python
check_parameter_file('my_params.csv')
```

### Response
N/A
```

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

### Compare iterative and non-iterative SAR

Source: http://macroeco.org/tutorial_macroeco.html

Demonstrate the difference between iterative and non-iterative approaches and visualize the results.

```python
>>> areas = [1, 0.5, 0.25, 0.125, 0.0625]
>>> noiter_sar = meco.models.sampling_sar.vals(areas, S0, N0, 1, 1)
>>> iter_sar = meco.models.sampling_sar_iterative.vals(areas, S0, N0, 1, 1)
```

```python
>>> # Plot the results
>>> plt.plot(areas, noiter_sar, label="Non-iterative SAR")
>>> plt.plot(areas, iter_sar, label="Iterative SAR")
>>> plt.legend(loc="center right")
>>> plt.xlabel("Area")
>>> plt.ylabel("# of Species")
```

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

### Import advanced macroeco model components

Source: http://macroeco.org/tutorial_macroeco.html

Import specific internal modules for distributions and curves from macroeco.models. Use this if you need access to the underlying functions for building models.

```python
import macroeco.models._distributions as dist
import macroeco.models._curves as curves
```

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

### Generate random samples

Source: http://macroeco.org/generated/macroeco.models.expon_uptrunc.html

Draw random variates from the distribution.

```python
>>> # Draw a random sample
>>> samp = md.expon_uptrunc.rvs(0.05, 100, size=100)
```

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

### Define model parameters

Source: http://macroeco.org/generated/macroeco.models.mete_ear.html

Sets the areas and base-scale metrics required for METE calculations.

```python
>>> # Set the areas at which to downscale species richness
>>> areas = [10, 8, 5, 3, 0.5]
```

```python
>>> # Set the species richness and abundance at the base scale
>>> S0 = 50
>>> N0 = 4356
```

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

### macroeco.misc.check_parameter_file

Source: http://macroeco.org/generated/macroeco.misc.check_parameter_file.html

Performs a rudimentary check on parameter files to ensure columns like 'cols', 'splits', and 'divs' are formatted properly. It catches basic mistakes.

```APIDOC
## macroeco.misc.check_parameter_file

### Description
Function does a rudimentary check whether the cols, splits and divs columns in the parameter files are formatted properly. Just provides a preliminary check. Will only catch basic mistakes.

### Method
N/A (Python function)

### Endpoint
N/A (Python function)

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Request Example
None

### Response
#### Success Response (200)
- **return value** (list) - Contains the number of possible bad strings detected

#### Response Example
```json
[
  1,
  3,
  0
]
```
```

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

### Load Patch and Define Splits for SAD Analysis

Source: http://macroeco.org/tutorial_macroeco.html

Loads a 'Patch' object and defines various spatial splits (scales) for analyzing empirical species-abundance distributions (SADs). Requires 'meco'.

```python
# Redefining the patch
pat = meco.empirical.Patch("~/Desktop/ANBO.txt")

# Get the empirical SAD in each 1m x 1m cell
splits1 = "row:4; column:4"

# Get the empirical SAD in each 2m x 1m cell
splits2 = "row:2; column:4"

# Get the empirical SAD in 4 2m x 2m cells (upper left , upper right, lower left, lower right)
splits3 = "row:2; column:2"

# Get the empirical SAD in left half and right half 4m x 2m cells
splits4 = "row:1; column:2"

# Get the SAD for the full plot
splits5 = "row:1; column:1"

all_splits = [splits1, splits2, splits3, splits4, splits5]

# Store all the empirical SAD results
results = []

for split in all_splits:
    results.append(meco.empirical.sad(pat, 'spp_col:spp; count_col:count', splits=split))
```

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

### Calculating AIC with custom parameters and correction settings

Source: http://macroeco.org/generated/macroeco.compare.AIC.html

Demonstrates providing explicit parameter counts and toggling the small-sample size correction.

```python
>>> # Call AIC with params given as 2 (should be the same as above)
>>> comp.AIC(rand_samp, md.nbinom_ztrunc(*mle_nbd), params=2)
765.51518598676421

>>> # Call AIC without sample size correction
>>> comp.AIC(rand_samp, md.nbinom_ztrunc(*mle_nbd), params=2, corrected=False)
765.39147464655798
```

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

### Fit Finite Negative Binomial Distribution and Store Results

Source: http://macroeco.org/tutorial_macroeco.html

Fits a finite negative binomial distribution to species abundance data and stores the k parameter and total abundance for each species. Requires the 'meco' and 'numpy' libraries.

```python
for spp_name, data in all_spp_ssads:

    # Fit finite negative binomial distribution
    k_param = meco.models.cnbinom.fit_mle(data['y'], k_array=np.linspace(0.01, 5, num=1000))[1]

    # Get total abundance for a given species
    total_abund = data['y'].sum()

    # Store k parameter and total abundance for each species
    agg_res[spp_name] = (k_param, total_abund)
```

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

### Visualize SAR model comparison

Source: http://macroeco.org/tutorial_macroeco.html

Plots the empirical SAR against fitted power law and METE models on a log-log scale.

```python
>>> plt.loglog(combined_sar['x'][::-1], pred_power_law, '-o', label="Power Law SAR")
>>> plt.loglog(combined_sar['x'][::-1], pred_mete, '-o', label="METE SAR")
>>> plt.legend()
>>> plt.show()
```

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

### Exponential Distribution Methods

Source: http://macroeco.org/generated/macroeco.models.expon.html

Overview of the methods available for the exponential distribution model, including PDF, CDF, and parameter estimation.

```APIDOC
## Exponential Distribution Model

### Description
Provides methods for working with an exponential continuous random variable defined by f(x)=λe−λx for x >= 0.

### Parameters
- **x** (array_like) - Quantiles
- **q** (array_like) - Lower or upper tail probability
- **lam** (array_like) - Shape parameter (rate)
- **loc** (array_like) - Location parameter (default=0)
- **scale** (array_like) - Scale parameter (default=1)

### Methods
- **rvs(lam, loc, scale, size)**: Generate random variates.
- **pdf(x, lam, loc, scale)**: Probability density function.
- **cdf(x, lam, loc, scale)**: Cumulative density function.
- **ppf(q, lam, loc, scale)**: Percent point function (inverse of cdf).
- **fit_mle(data)**: Fit the model to data using maximum likelihood estimation.
- **translate_args(mean)**: Get the rate parameter from a mean value.

### Request Example
```python
import macroeco.models as md
# Get the rate parameter from a mean of 20
rate = md.expon.translate_args(20)
# Get the pdf
pdf_vals = md.expon.pdf(np.linspace(0.1, 10, num=10), 0.05)
```
```
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