PyDaddy

https://pydaddy.readthedocs.io/en/latest/
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PyDaddy is a Python package for discovering data-derived stochastic differential equations from time...
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### Check Git Installation

Source: https://pydaddy.readthedocs.io/en/latest/installation.html

Verify if Git is installed on your system. This is a prerequisite for installing the development version.

```bash
git --version

```

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

### Install PyDaddy on Colab

Source: https://pydaddy.readthedocs.io/en/latest/tutorials.html

Use this command to install PyDaddy in your Google Colab environment before running the tutorial notebooks.

```python
%pip install git+https://github.com/tee-lab/PyDaddy.git

```

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

### Start Jupyter Notebook Server

Source: https://pydaddy.readthedocs.io/en/latest/usage.html

Command to start a Jupyter notebook server, necessary for using PyDaddy through its Python interface in a notebook environment.

```bash
jupyter notebook

```

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

### Verify PyDaddy Installation

Source: https://pydaddy.readthedocs.io/en/latest/installation.html

Open an IPython terminal and import the pydaddy library to verify a successful installation. This should not throw any errors.

```python
import pydaddy

```

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

### Install PyDaddy using conda

Source: https://pydaddy.readthedocs.io/en/latest/installation.html

Install the release version of PyDaddy using conda. This command installs from the 'tee-lab' channel.

```bash
conda install -c tee-lab pydaddy

```

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

### Get PyDaddy Help Information

Source: https://pydaddy.readthedocs.io/en/latest/usage.html

Display help information for PyDaddy, including available options and flags, to understand its command-line usage.

```bash
pydaddy --help

```

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

### Install PyDaddy using pip

Source: https://pydaddy.readthedocs.io/en/latest/installation.html

Install the latest release version of PyDaddy using pip. Ensure Python 3 is set up before running this command.

```bash
pip install pydaddy

```

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

### Install Development Version using pip

Source: https://pydaddy.readthedocs.io/en/latest/installation.html

Install the latest development version of PyDaddy directly from the GitHub repository using pip.

```bash
pip install git+https://github.com/tee-lab/PyDaddy.git

```

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

### pydaddy.load_sample_dataset

Source: https://pydaddy.readthedocs.io/en/latest/api.html

Loads one of the sample datasets provided by PyDaddy. This function is useful for testing and examples.

```APIDOC
## pydaddy.load_sample_dataset

### Description
Loads one of the sample datasets. For more details on the datasets, see Sample Datasets.
Available data sets:
```
'fish-data-etroplus'
'cell-data-cellhopping'
'model-data-scalar-pairwise'
'model-data-scalar-ternary'
'model-data-vector-pairwise'
'model-data-vector-ternary'
```

### Parameters
#### Path Parameters
- **name** (str) - Required - name of the data set 

### Returns
- **data** (list) - timeseries data
- **t** (float, array) - timescale
```

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

### Check PyDaddy Version

Source: https://pydaddy.readthedocs.io/en/latest/installation.html

After importing pydaddy, check the installed version by accessing the __version__ attribute. This confirms the installation and version number.

```python
pydaddy.__version__

```

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

### parameters

Source: https://pydaddy.readthedocs.io/en/latest/api.html

Get all given and assumed parameters used for the analysis.

```APIDOC
## parameters

### Description
Get all given and assumed parameters used for the analysis.

### Returns
- **params** (dict or json) - all parameters given and assumed used for analysis.
```

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

### Load Sample Dataset

Source: https://pydaddy.readthedocs.io/en/latest/datasets.html

Use this function to load any of the available sample datasets. Replace `<dataset-name>` with the name of the desired dataset.

```python
pydaddy.load_sample_dataset(<dataset-name>)
```

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

### pydaddy.Daddy.summary

Source: https://pydaddy.readthedocs.io/en/latest/api.html

Provides a summary of the analysis.

```APIDOC
## pydaddy.Daddy.summary

### Description
Provides a summary of the analysis.

### Method
`summary()`
```

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

### pydaddy.Characterize

Source: https://pydaddy.readthedocs.io/en/latest/api.html

Initializes a PyDaddy object for further analysis. This class is used to process time series data for drift and diffusion analysis.

```APIDOC
## pydaddy.Characterize

### Description
Initializes a PyDaddy object for further analysis. This class is used to process time series data for drift and diffusion analysis.

### Parameters
#### Path Parameters
- **data** (list) - Required - Time series data to be analysed. data = [x] for scalar data and data = [x1, x2] for vector where x, x1 and x2 are of numpy.array object type
- **t** (float, array, optional) - Default: 1.0 - t can be either a float representing the time-interval between observations, or a numpy array containing the time-stamps of the individual observations (Note: PyDaddy only supports uniformly spaced time-series, even when time-stamps are provided).
- **bins** (int, optional) - Default: 20 - Number of bins for computing bin-wise averages of drift and diffusion (Binwise averages are used only for visualization.)
- **show_summary** (bool, optional) - Default: True - If true, a summary text and summary figure will be shown.
- **Dt** (int, optional) - Default: 1 - Subsampling factor for drift computation. When provided, the time-series will be sub-sampled by this factor while computing drift.
- **dt** (int, optional) - Default: 1 - Subsampling factor for diffusion computation. When provided, the time-series will be sub-sampled by this factor while computing diffusion.
- **inc** (float, optional) - Default: 0.01 - For scalar data, instead of specifying bins, the widths (increments) of the bins can also be provided.
- **inc_x** (float, optional) - Default: 0.1 - For vector data, instead of specifying bins, the widths (increments) of the bins can also be provided. inc_x is the increment in the x-dimension.
- **inc_y** (float, optional) - Default: 0.1 - For vector data, instead of specifying bins, the widths (increments) of the bins can also be provided. inc_y is the increment in the y-dimension.
- **n_trials** (int, optional) - Default: 1 - Number of trials, concatenated timeseries of multiple trials is used.

### Returns
- **output** (pydaddy.daddy.Daddy) - Daddy object which can be used for further analysis and visualization. See `pyaddy.daddy.Daddy` for details.
```

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

### pydaddy.Daddy.parameters

Source: https://pydaddy.readthedocs.io/en/latest/api.html

Returns the model parameters.

```APIDOC
## pydaddy.Daddy.parameters

### Description
Returns the model parameters.

### Method
`parameters()`
```

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

### pydaddy.Daddy.simulate

Source: https://pydaddy.readthedocs.io/en/latest/api.html

Simulates data based on the model.

```APIDOC
## pydaddy.Daddy.simulate

### Description
Simulates data based on the model.

### Method
`simulate()`
```

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

### simulate

Source: https://pydaddy.readthedocs.io/en/latest/api.html

Generate simulated time-series with the fitted SDE model.

```APIDOC
## simulate

### Description
Generate simulated time-series with the fitted SDE model. Generates a simulated timeseries, with specified sampling time and duration, based on the SDE model discovered by PyDaddy. The drift and diffusion functions should be fit using fit() function before using simulate().

### Parameters
#### Path Parameters
- **t_int** (float) - Required - Sampling time for the simulated time-series
- **timepoints** (int) - Required - Number of time-points to simulate
#### Query Parameters
- **x0** (float or list of two floats) - Optional - Initial condition. If no value is passed, 0 ([0, 0] for vector) is taken as the initial condition.

### Returns
- **x** (Simulated timeseries) - Simulated timeseries with `timepoints` timepoints.
```

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

### Run PyDaddy One-line Operation

Source: https://pydaddy.readthedocs.io/en/latest/usage.html

Invoke PyDaddy from the command-line for a single-command analysis of a specified data file, generating an HTML report. Ensure your conda environment is activated if applicable.

```bash
pydaddy <file-name> --column_format xyt

```

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

### pydaddy.Daddy.model_diagnostics

Source: https://pydaddy.readthedocs.io/en/latest/api.html

Performs model diagnostics.

```APIDOC
## pydaddy.Daddy.model_diagnostics

### Description
Performs model diagnostics.

### Method
`model_diagnostics()`
```

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

### summary

Source: https://pydaddy.readthedocs.io/en/latest/api.html

Print summary of data and show summary plots chart.

```APIDOC
## summary

### Description
Print summary of data and show summary plots chart. (This is the same summary plot produced by Characterize().)

### Parameters
#### Query Parameters
- **start** (int) - Optional - starting index, begin plotting timeseries from this point (default: 0).
- **end** (int) - Optional - end point, plots timeseries till this index (default: 1000).
- **kde** (bool) - Optional - if True, plot kde for histograms (default: False).
- **title_size** (int) - Optional - title font size (default: 15).
- **tick_size** (int) - Optional - axis tick size (default: 12).
- **label_size** (int) - Optional - label font size (default: 15).
- **label_pad** (int) - Optional - label padding.
- **n_ticks** (int) - Optional - number of ticks.
- **ret_fig** (bool) - Optional - return figure.
- **plot_text** - Optional - 
```

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

### pydaddy.Daddy.noise_diagnostics

Source: https://pydaddy.readthedocs.io/en/latest/api.html

Performs noise diagnostics.

```APIDOC
## pydaddy.Daddy.noise_diagnostics

### Description
Performs noise diagnostics.

### Method
`noise_diagnostics()`
```

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

### pydaddy.Daddy.fit

Source: https://pydaddy.readthedocs.io/en/latest/api.html

Fits the model to the data.

```APIDOC
## pydaddy.Daddy.fit

### Description
Fits the model to the data.

### Method
`fit()`
```

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

### model_diagnostics

Source: https://pydaddy.readthedocs.io/en/latest/api.html

Perform model self-consistency diagnostics by simulating a time series and re-estimating drift and diffusion.

```APIDOC
## model_diagnostics

### Description
Perform model self-consistency diagnostics. Generates a simulated time series with the same length and sampling interval as the original time series, and re-estimates the drift and diffusion from the simulated time series. The re-estimated drift and diffusion should match the original estimates.

### Parameters
#### Query Parameters
- **oversample** (int) - Optional - Factor by which to oversample while simulating the SDE. If provided, the SDE will be simulated at t_int / oversample and then subsampled to t_int. This is useful when t_int is large enough to cause large errors in the SDE simulation (default: 1).
- **plotted** (The following are) - Optional - Histogram of the original time series overlaid with that of the simulated time series. Drift and diffusion of the original time series overlaid with that of the simulated time series.
```

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

### fit

Source: https://pydaddy.readthedocs.io/en/latest/api.html

Fit analytical expressions to drift/diffusion functions using sparse regression. Can fit polynomials or custom function libraries.

```APIDOC
## fit

### Description
Fit analytical expressions to drift/diffusion functions using sparse regression. By default, a polynomial with a specified maximum degree will be fitted. Alternatively, you can also provide a library of custom functions for fitting.

### Parameters
#### Path Parameters
- **function_name** (str) - Required - Name of the function to fit. Can be ‘F’ or ‘G’ for scalar; ‘F1’, ‘F2’, ‘G11’, ‘G22’, ‘G12’, ‘G21’ for vector
#### Query Parameters
- **order** (int) - Optional - Order (maximum degree) of the polynomial to fit.
- **threshold** (float) - Optional - Sparsification threshold (default: 0.05).
- **tune** (bool) - Optional - If True, the sparsification threshold will be automatically set using cross-validation (default: False).
- **alpha** (float) - Optional - Optional regularization term for ridge regression. Useful when data is too noisy, but has a side effect of shrinking the estimated coefficients when set to high values (default: 0.0).
- **thresholds** (list) - Optional - With `tune=True`, a list of thresholds over which to search for can optionally be provided. If not present, this will be chosen automatically as the range between the minimum and maximum coefficients in the initial fit.
- **library** (list) - Optional - A custom library of non-polynomial functions can optionally be provided. If provided, the functions will be fitted as a sparse linear combination of the terms in the library.

### Returns
- **res** (fitters.Poly1D or fitters.Poly2D) - Represents the fitted polynomial.
```

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

### noise_diagnostics

Source: https://pydaddy.readthedocs.io/en/latest/api.html

Perform diagnostics on the noise-residual to ensure all assumptions for SDE estimation are met.

```APIDOC
## noise_diagnostics

### Description
Perform diagnostics on the noise-residual, to ensure that all assumptions for SDE estimation are met. Generates a plot with:
- Distribution (1D or 2D histogram) of the residuals, and their QQ-plots against a theoretically expected Gaussian. The residual distribution is expected to be a Gaussian.
- Autocorrelation of the residuals. The autocorrelation time should be close to 0.
- Plot of the 2nd versus 4th jump moments. This plot should be a straight line. (Only for scalar data.)

### Parameters
#### Query Parameters
- **loc** (None) - Optional - 
```

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

### pydaddy.Daddy.drift

Source: https://pydaddy.readthedocs.io/en/latest/api.html

Computes the drift of the data.

```APIDOC
## pydaddy.Daddy.drift

### Description
Computes the drift of the data.

### Method
`drift()`
```

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

### pydaddy.Daddy

Source: https://pydaddy.readthedocs.io/en/latest/api.html

The Daddy function allows for customizable plotting with options for axis label padding, number of ticks, figure return, and extensive plot text configuration for both scalar and vector analysis summaries.

```APIDOC
## pydaddy.Daddy

### Description

This function generates plots with customizable text elements for titles and labels. It supports configurations for scalar and vector analysis, allowing fine-grained control over plot aesthetics.

### Parameters

- **label_pad** (_int_, default=8) – Padding for axis labels.
- **n_ticks** (_int_, default=3) – Number of ticks to display on the axes.
- **ret_fig** (_bool_, default=True) – If True, the figure object is returned.
- **plot_text** – A dictionary or object containing various text elements for plot customization. This includes titles, x-axis labels, y-axis labels, and z-axis labels for different plot types (timeseries, drift, diffusion, histograms) in both scalar and vector analysis.

#### Scalar Analysis Plot Text Options:
- `timeseries_title`
- `timeseries_xlabel`
- `timeseries_ylabel`
- `drift_title`
- `drift_xlabel`
- `drift_ylabel`
- `diffusion_title`
- `diffusion_xlabel`
- `diffusion_ylabel`

#### Vector Analysis Plot Text Options:
- `timeseries1_title`
- `timeseries1_ylabel`
- `timeseries1_xlabel`
- `timeseries1_legend1`
- `timeseries1_legend2`
- `timeseries2_title`
- `timeseries2_xlabel`
- `timeseries2_ylabel`
- `2dhist1_title`
- `2dhist1_xlabel`
- `2dhist1_ylabel`
- `2dhist2_title`
- `2dhist2_xlabel`
- `2dhist2_ylabel`
- `2dhist3_title`
- `2dhist3_xlabel`
- `2dhist3_ylabel`
- `3dhist_title`
- `3dhist_xlabel`
- `3dhist_ylabel`
- `3dhist_zlabel`
- `driftx_title`
- `driftx_xlabel`
- `driftx_ylabel`
- `driftx_zlabel`
- `drifty_title`
- `drifty_xlabel`
- `drifty_ylabel`
- `drifty_zlabel`
- `diffusionx_title`
- `diffusionx_xlabel`
- `diffusionx_ylabel`
- `diffusionx_zlabel`
- `diffusiony_title`
- `diffusiony_xlabel`
- `diffusiony_ylabel`
- `diffusiony_zlabel`

### Return Type

None, or a matplotlib figure object if `ret_fig` is True.

### Raises

- **ValueError**: If a start parameter is greater than an end parameter (specific conditions not detailed in source).
```

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

### pydaddy.Daddy.diffusion

Source: https://pydaddy.readthedocs.io/en/latest/api.html

Computes the diffusion of the data.

```APIDOC
## pydaddy.Daddy.diffusion

### Description
Computes the diffusion of the data.

### Method
`diffusion()`
```

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

### pydaddy.Daddy.export_data

Source: https://pydaddy.readthedocs.io/en/latest/api.html

Exports the processed data.

```APIDOC
## pydaddy.Daddy.export_data

### Description
Exports the processed data.

### Method
`export_data()`
```

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

### pydaddy.Daddy.cross_diffusion

Source: https://pydaddy.readthedocs.io/en/latest/api.html

Computes the cross-diffusion of the data.

```APIDOC
## pydaddy.Daddy.cross_diffusion

### Description
Computes the cross-diffusion of the data.

### Method
`cross_diffusion()`
```

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

### Daddy.cross_diffusion

Source: https://pydaddy.readthedocs.io/en/latest/api.html

Shows an interactive figure of the cross-diffusion function, suitable for vector data. Allows customization of plot limits, polar projection, and plot text.

```APIDOC
## cross_diffusion

### Description
Show an interactive figure of the cross-diffusion function (only for vector data). The bin-wise averaged estimates of the drift will be shown. If a polynomial has already been fitted with fit() function, a line (scalar)/surface (vector) plot of the fitted function will also be shown.

### Parameters
* **limits** (tuple, optional): Sets the y-axis limits for the cross diffusion function. Defaults to None.
* **polar** (bool, optional): If True, plot the cross diffusion function only within a unit circle. Defaults to False.
* **plot_text** (dict, optional): Dictionary to customize captions, labels, and layout of the plot. Available options depend on whether the analysis is scalar or vector.

### Method
cross_diffusion(slider_timescales=None, limits=None, polar=False, **plot_text)
```

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

### pydaddy.Daddy.autocorrelation

Source: https://pydaddy.readthedocs.io/en/latest/api.html

Computes the autocorrelation of the data.

```APIDOC
## pydaddy.Daddy.autocorrelation

### Description
Computes the autocorrelation of the data.

### Method
`autocorrelation()`
```

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

### Daddy.drift

Source: https://pydaddy.readthedocs.io/en/latest/api.html

Shows an interactive figure of the drift function. Allows customization of plot limits, polar projection, and plot text for scalar and vector data.

```APIDOC
## drift

### Description
Show an interactive figure of the drift function. The bin-wise averaged estimates of the drift will be shown. If a polynomial has already been fitted with fit() function, a line (scalar)/surface (vector) plot of the fitted function will also be shown.

### Parameters
* **limits** (tuple, optional): Sets the y-axis limits for the drift function. Defaults to None.
* **polar** (bool, optional): If True, plot the drift function only within a unit circle. Defaults to False.
* **plot_text** (dict, optional): Dictionary to customize captions, labels, and layout of the plot. Available options depend on whether the analysis is scalar or vector.

### Method
drift(limits=None, polar=False, slider_timescales=None, **plot_text)
```

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

### Daddy.export_data

Source: https://pydaddy.readthedocs.io/en/latest/api.html

Returns a pandas DataFrame containing drift and diffusion values. Optionally saves the data as a CSV file. Can return raw unbinned data if specified.

```APIDOC
## export_data

### Description
Returns a pandas dataframe containing the drift and diffusion values. Optionally, the data is also saved as a CSV file.

### Parameters
* **filename** (str, optional): If provided, the data will be saved as a CSV at the given path. Else, a dataframe will be returned. Defaults to None.
* **raw** (bool, optional): If True, the raw, the drift and diffusion will be returned as raw unbinned data. Otherwise (default), drift and diffusion as binwise-average Kramers-Moyal coefficients are returned. Defaults to False.

### Returns
* **DataFrame**: A pandas DataFrame containing the drift and diffusion values.

### Method
export_data(filename=None, raw=False)
```

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

### Daddy.diffusion

Source: https://pydaddy.readthedocs.io/en/latest/api.html

Displays an interactive figure of the diffusion function. Supports customization of plot limits, polar projection, and plot text for scalar and vector data.

```APIDOC
## diffusion

### Description
Show an interactive figure of the diffusion function. The bin-wise averaged estimates of the drift will be shown. If a polynomial has already been fitted with fit() function, a line (scalar)/surface (vector) plot of the fitted function will also be shown.

### Parameters
* **limits** (tuple, optional): Sets the y-axis limits for the diffusion function. Defaults to None.
* **polar** (bool, optional): If True, plot the diffusion function only within a unit circle. Defaults to False.
* **plot_text** (dict, optional): Dictionary to customize captions, labels, and layout of the plot. Available options depend on whether the analysis is scalar or vector.

### Method
diffusion(slider_timescales=None, limits=None, polar=False, **plot_text)
```

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

### Daddy.autocorrelation

Source: https://pydaddy.readthedocs.io/en/latest/api.html

Displays the autocorrelation plot of the data. Accepts an optional 'lags' parameter to specify the number of lags.

```APIDOC
## autocorrelation

### Description
Show the autocorrelation plot of the data.

### Parameters
* **lags** (int, optional): The number of lags to consider. Defaults to 1000.

### Method
autocorrelation(lags=1000)
```
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