seqlogo 5.29.11
Python port of the R Bioconductor `seqlogo` package
License: BSD-3-Clause
Requires Python: >=3.4
Keywords: sequence, logo, seqlogo, bioinformatics, genomics, weblogo


[![PyPI version](https://badge.fury.io/py/seqlogo.svg)](https://pypi.org/project/seqlogo/)
[![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat-square)](http://bioconda.github.io/recipes/seqlogo/README.html)
[![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://github.com/betteridiot/seqlogo/blob/master/LICENSE)
</br>

# seqlogo
Python port of Bioconductor's [seqLogo](http://bioconductor.org/packages/release/bioc/html/seqLogo.html) served by [WebLogo](http://weblogo.threeplusone.com/)

## Overview

In the field of bioinformatics, a common task is to look for sequence motifs at 
different sites along the genome or within a protein sequence. One aspect of this
analysis involves creating a variant of a Position Matrix (PM): Position Frequency Matrix (PFM),
Position Probability Matrix (PPM), and Position Weight Matrix (PWM). The formal format for
a PWM file can be found [here](http://bioinformatics.intec.ugent.be/MotifSuite/pwmformat.php).

---
#### Specification
A PM file can be just a plain text, whitespace delimited matrix, such that the number of columns
matches the number of letters in your desired alphabet and the number of rows is the number of positions
in your sequence. Any comment lines that start with `#` will be skipped.

*Note*: [TRANSFAC matrix](http://meme-suite.org/doc/transfac-format.html) and [MEME Motif](http://meme-suite.org/doc/meme-format.html) formats are not directly supported.

<a href="https://www.codecogs.com/eqnedit.php?latex=\large&space;\mathit{PWM}_{m,n}&space;=&space;\begin{pmatrix}&space;a_{1,1}&space;&&space;a_{1,2}&space;&&space;\cdots&space;&&space;a_{1,n}&space;\\&space;a_{2,1}&space;&&space;a_{2,2}&space;&&space;\cdots&space;&&space;a_{2,n}&space;\\&space;\vdots&space;&&space;\vdots&space;&&space;\ddots&space;&&space;\vdots&space;\\&space;a_{m,1}&space;&&space;a_{m,2}&space;&&space;\cdots&space;&&space;a_{m,n}&space;\end{pmatrix}" target="_blank"><img src="https://latex.codecogs.com/svg.latex?\large&space;\mathit{PWM}_{m,n}&space;=&space;\begin{pmatrix}&space;a_{1,1}&space;&&space;a_{1,2}&space;&&space;\cdots&space;&&space;a_{1,n}&space;\\&space;a_{2,1}&space;&&space;a_{2,2}&space;&&space;\cdots&space;&&space;a_{2,n}&space;\\&space;\vdots&space;&&space;\vdots&space;&&space;\ddots&space;&&space;\vdots&space;\\&space;a_{m,1}&space;&&space;a_{m,2}&space;&&space;\cdots&space;&&space;a_{m,n}&space;\end{pmatrix}" title="\large \mathit{PWM}_{m,n} = \begin{pmatrix} a_{1,1} & a_{1,2} & \cdots & a_{1,n} \\ a_{2,1} & a_{2,2} & \cdots & a_{2,n} \\ \vdots & \vdots & \ddots & \vdots \\ a_{m,1} & a_{m,2} & \cdots & a_{m,n} \end{pmatrix}" /></a>

Where <a href="https://www.codecogs.com/eqnedit.php?latex=\large&space;a_{m,n}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\large&space;a_{m,n}" title="\large a_{m,n}" /></a> is the probability that at <a href="https://www.codecogs.com/eqnedit.php?latex=\large&space;m" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\large&space;m" title="\large m" /></a> position, <a href="https://www.codecogs.com/eqnedit.php?latex=\large&space;n" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\large&space;n" title="\large n" /></a> letter is seen.

This is often generated in a frequentist fashion. If a pipeline
tallies all observed letters at each position, this is called a Position Frequency Matrix (PFM).

The PFM can be converted to a PPM in a straight-forward manner, creating a matrix
that for any given position and letter, the probability of that letter at that position
is reported.

A PWM is the PPM converted into log-likelihood. Pseudocounts can be applied to prevent 
probabilities of 0 from turing into -inf in the conversion process. Lastly, each position's
log-likelihood is corrected for some background probability for every given letter in the
selected alphabet.

---
#### Features
* `seqlogo` can use any PM as entry points for analysis (from a file or in array formats)
and, subsequently, plot the sequence logos.

* `seqlogo` was written to support BIOINF 529 :Bioinformatics Concepts and Algorithms
at the University of Michigan in the Department of Computational Medicine & Bioinformatics.

* `seqlogo` attempts to blend the user-friendly api of Bioconductor's [seqLogo](http://bioconductor.org/packages/release/bioc/html/seqLogo.html) 
and the rendering power of the [WebLogo](http://weblogo.threeplusone.com/)Python API.

* `seqlogo` supports the following alphabets:

    | Alphabet name |  Alphabet Letters |
    | :--- |  :--- |
    | **`"DNA"`** | `"ACGT"` |
    | `"reduced DNA"` | `"ACGTN-"` |
    | `"ambig DNA"` | `"ACGTRYSWKMBDHVN-"` |
    | **`"RNA"`** | `"ACGU"` |
    | `"reduced RNA"` | `"ACGUN-"` |
    | `"ambig RNA"` | `"ACGURYSWKMBDHVN-"` |
    | **`"AA"`** | `"ACDEFGHIKLMNPQRSTVWY"` |
    | `"reduced AA"` | `"ACDEFGHIKLMNPQRSTVWYX*-"` |
    | `"ambig AA"` | `"ACDEFGHIKLMNOPQRSTUVWYBJZX*-"` |
    (**Bolded** alphabet names are the most commonly used)
* `seqlogo` can also render sequence logos in a number of formats:
    * `"svg"`
    * `"eps"`
    * `"pdf"`
    * `"jpeg"`
    * `"png"` (default)

* All plots can be rendered in 4 different sizes:
    * `"small"`: 3.54" wide
    * `"medium"`: 5" wide
    * `"large"`: 7.25" wide
    * `"xlarge"`: 10.25" wide

*Note*: all sizes taken from [this](http://www.sciencemag.org/sites/default/files/Figure_prep_guide.pdf) publication
guide from Science Magazine.

---
#### Recommended settings:
* For best results, implement `seqlogo` within a IPython/Jupyter environment (for inline plotting purposes).
* Initially written for Python 3.7, but has shown to work in versions 3.5+ (**Python 2.7 is not supported**)

***
## Setup

### Minimal Requirements:
1. `numpy`
2. `pandas`
3. `weblogo`

**Note**: it is strongly encouraged that `jupyter` is installed as well.

---
#### `conda` environment:

To produce the ideal virtual environment that will run `seqlogo` on a `conda`-based
build, clone the repo or download the environment.yml within the repo. Then run the following
command:

```bash

$ conda env create -f environment.yml

```

---
#### Installation

To install using `pip`: (recommended)

```bash

$ pip install seqlogo

```

To install using `conda`

```bash

$ conda install -c bioconda seqlogo

```


Or install from GitHub directly

```bash

$ pip install git+https://github.com/betteridiot/seqlogo.git#egg=seqlogo

```

***
## Quickstart

### Importing

```python

import numpy as np
import pandas as pd
import seqlogo

```

### Generate some PM data (without frequency data)

For many demonstrations that speak to PWMs, they are often started with PPM data.
Many packages preclude sequence logo generation from this entry point. However,
`seqlogo` can handle it just fine. One point to make though is that if no count 
data is provided, `seqlogo` just generates the PFM data by multiplying the
probabilities by 100. This is **only** for `weblogolib` compatability.

```python

# Setting seed for demonstration purposes
>>> np.random.seed(42)

# Making a fake PPM
>>> random_ppm = np.random.dirichlet(np.ones(4), size=6)
>>> ppm = seqlogo.Ppm(random_ppm)
>>> ppm
          A         C         G         T
0  0.082197  0.527252  0.230641  0.159911
1  0.070375  0.070363  0.024826  0.834435
2  0.161962  0.216972  0.003665  0.617401
3  0.735638  0.098290  0.082638  0.083434
4  0.179898  0.368931  0.280463  0.170708
5  0.498510  0.079138  0.182004  0.240349

```

### Generate some frequency data and convert to PWM
Sometimes the user has frequency data instead of PWM. To construct a `Pwm` instance
that automatically computes Information Content and PWM values, the user can use
the `seqlogo.pfm2pwm()` function.

```python

# Setting seed for demonstration purposes
>>> np.random.seed(42)

# Making some fake Position Frequency Data (PFM)
>>> pfm = pd.DataFrame(np.random.randint(0, 36, size=(8, 4)))

# Convert to Position Weight Matrix (PWM)
>>> pwm = seqlogo.pfm2pwm(pfm)
>>> pwm
          A         C         G         T
0  0.698830 -0.301170 -1.301170  0.213404
1  0.263034  0.552541 -0.584962 -0.584962
2  0.148523  0.754244  0.148523 -3.375039
3  0.182864 -4.209453  0.314109  0.648528
4 -4.000000  0.321928  1.000000 -0.540568
5 -0.222392 -0.029747  0.085730  0.140178
6  0.697437  0.597902 -2.209453 -0.624491
7  0.736966 -0.584962  0.502500 -2.000000

```

### `seqlogo.CompletePm` demo

Here is a quickstart guide on how to leverage the power of `seqlogo.CompletePm`

```python

# Setting seed for demonstration purposes
>>> np.random.seed(42)

# Making a fake PWM
>>> random_ppm = np.random.dirichlet(np.ones(4), size=6)
>>> cpm = seqlogo.CompletePM(ppm = random_ppm)

# Pfm was imputed
>>> print(cpm.pfm)
    A   C   G   T
0   8  52  23  15
1   7   7   2  83
2  16  21   0  61
3  73   9   8   8
4  17  36  28  17
5  49   7  18  24

# Shows the how the PPM data was formatted
>>> print(cpm.ppm)
          A         C         G         T
0  0.082197  0.527252  0.230641  0.159911
1  0.070375  0.070363  0.024826  0.834435
2  0.161962  0.216972  0.003665  0.617401
3  0.735638  0.098290  0.082638  0.083434
4  0.179898  0.368931  0.280463  0.170708
5  0.498510  0.079138  0.182004  0.240349

# Computing the PWM using default background and pseudocounts
>>> print(cpm.pwm)
          A         C         G         T
0 -1.604773  1.076564 -0.116281 -0.644662
1 -1.828788 -1.829031 -3.331983  1.738871
2 -0.626276 -0.204418 -6.091862  1.304279
3  1.557068 -1.346815 -1.597049 -1.583223
4 -0.474749  0.561423  0.165882 -0.550396
5  0.995695 -1.659494 -0.457960 -0.056800

# See the consensus sequence
>>> print(cpm.consensus)
CTTACA

# See the Information Content
>>> print(cpm.ic)
0    0.305806
1    1.110856
2    0.637149
3    0.748989
4    0.074286
5    0.268034
dtype: float64

```

### Plot the sequence logo with information content scaling

```python

# Setting seed for demonstration purposes
>>> np.random.seed(42)

# Making a fake PWM
>>> random_ppm = np.random.dirichlet(np.ones(4), size=6)
>>> ppm = seqlogo.Ppm(random_ppm)
>>> seqlogo.seqlogo(ppm, ic_scale = False, format = 'png', size = 'medium')

```

The above code will produce:

![](https://github.com/betteridiot/seqlogo/blob/master/docs/figures/ic_scale.svg)


### Plot the sequence logo with no information content scaling

```python

# Setting seed for demonstration purposes
>>> np.random.seed(42)

# Making a fake PWM
>>> random_ppm = np.random.dirichlet(np.ones(4), size=6)
>>> ppm = seqlogo.Ppm(random_ppm)
>>> seqlogo.seqlogo(ppm, ic_scale = False, format = 'png', size = 'medium')

```

The above code will produce:

![](https://github.com/betteridiot/seqlogo/blob/master/docs/figures/no_ic_scale.svg)

***
## Documentation

`seqlogo` exposes 5 classes to the user for handling PM data:
1. `seqlogo.Pm`: the base class for all other specialized PM subclasses
2. `seqlogo.Pfm`: The class used for handling PFM data
3. `seqlogo.Ppm`: The class used for handling PPM data
4. `seqlogo.Pwm`: The class used for handling PWM data
5. `seqlogo.CompletePm`: This final class will take any/all of the other PM subclass data
    and compute any of the other missing data. That is, if the user only provides a `seqlogo.Pfm`
    and passes it to `seqlogo.CompletePm`, it will solve for the PPM, PWM, consensus sequence, and
    information content.

Additionally, `seqlogo` also provides 6 methods for converting PM structures:
1. `seqlogo.pfm2ppm`: converts a PFM to a PPM
2. `seqlogo.pfm2pwm`: converts a PFM to a PWM
3. `seqlogo.ppm2pfm`: converts a PPM to a PFM
4. `seqlogo.ppm2pwm`: converts a PPM to a PWM
5. `seqlogo.pwm2pfm`: converts a PWM to a PFM
6. `seqlogo.pwm2ppm`: converts a PWM to a PPM

The signatures for each item above are as follows:

### Classes
```python

seqlogo.CompletePm(pfm = None, ppm = None, pwm = None, background = None, pseudocount = None,
                 alphabet_type = 'DNA', alphabet = None, default_pm = 'ppm'):
    """
    Creates the CompletePm instance. If the user does not define any `pm_filename_or_array`,
    it will be initialized to empty. Will generate all other attributes as soon
    as a `pm_filename_or_array` is supplied.

    Args:
        pfm (str or `numpy.ndarray` or `pandas.DataFrame` or Pm): The user supplied
            PFM. If it is a filename, the file will be opened
            and parsed. If it is an `numpy.ndarray` or `pandas.DataFrame`,
            it will just be assigned. (default: None, skips '#' comment lines)
        ppm (str or `numpy.ndarray` or `pandas.DataFrame` or Pm): The user supplied
            PPM. If it is a filename, the file will be opened
            and parsed. If it is an `numpy.ndarray` or `pandas.DataFrame`,
            it will just be assigned. (default: None, skips '#' comment lines)
        pwm (str or `numpy.ndarray` or `pandas.DataFrame` or Pm): The user supplied
            PWM. If it is a filename, the file will be opened
            and parsed. If it is an `numpy.ndarray` or `pandas.DataFrame`,
            it will just be assigned. (default: None, skips '#' comment lines)
        background (constant or Collection): Offsets used to calculate background letter probabilities (defaults: If 
            using an Nucleic Acid alphabet: 0.25; if using an Aminio Acid alphabet: Robinson-Robinson Frequencies)
        pseudocount (constant): Some constant to offset PPM conversion to PWM to prevent -/+ inf. (defaults to 1e-10)
        alphabet_type (str): Desired alphabet to use. Order matters (default: 'DNA')
            "DNA" := "ACGT"
            "reduced DNA" := "ACGTN-"
            "ambig DNA" := "ACGTRYSWKMBDHVN-"
            "RNA" := "ACGU"
            "reduced RNA" := "ACGUN-"
            "ambig RNA" := "ACGURYSWKMBDHVN-"
            "AA" : = "ACDEFGHIKLMNPQRSTVWY"
            "reduced AA" := "ACDEFGHIKLMNPQRSTVWYX*-"
            "ambig AA" := "ACDEFGHIKLMNOPQRSTUVWYBJZX*-"
            "custom" := None
            (default: 'DNA')
        alphabet (str): if 'custom' is selected or a specialize alphabet is desired, this accepts a string (default: None)
        default_pm (str): which of the 3 pm's do you want to call '*home*'? (default: 'ppm')
    """

seqlogo.Pm(pm_filename_or_array = None, pm_type = 'ppm', alphabet_type = 'DNA', alphabet = None, 
    background = None, pseudocount = None):
    """Initializes the Pm

    Creates the Pm instance. If the user does not define `pm_filename_or_array`,
    it will be initialized to empty. Will generate all other attributes as soon
    as a `pm_filename_or_array` is supplied.

    Args:
        pm_filename_or_array (str or `numpy.ndarray` or `pandas.DataFrame` or Pm): The user supplied
            PM. If it is a filename, the file will be opened
            and parsed. If it is an `numpy.ndarray` or `pandas.DataFrame`,
            it will just be assigned. (default: None, skips '#' comment lines)
        alphabet_type (str): Desired alphabet to use. Order matters (default: 'DNA')
            "DNA" := "ACGT"
            "reduced DNA" := "ACGTN-"
            "ambig DNA" := "ACGTRYSWKMBDHVN-"
            "RNA" := "ACGU"
            "reduced RNA" := "ACGUN-"
            "ambig RNA" := "ACGURYSWKMBDHVN-"
            "AA" : = "ACDEFGHIKLMNPQRSTVWY"
            "reduced AA" := "ACDEFGHIKLMNPQRSTVWYX*-"
            "ambig AA" := "ACDEFGHIKLMNOPQRSTUVWYBJZX*-"
            "custom" := None
            (default: 'DNA')
        alphabet (str): if 'custom' is selected or a specialize alphabet is desired, this accepts a string (default: None)
        background (constant or Collection): Offsets used to calculate background letter probabilities (defaults: If 
            using an Nucleic Acid alphabet: 0.25; if using an Aminio Acid alphabet: Robinson-Robinson Frequencies)
        pseudocount (constant): Some constant to offset PPM conversion to PWM to prevent -/+ inf. (default: 1e-10)
    """

seqlogo.Pfm(pfm_filename_or_array = None, pm_type = 'pfm', alphabet_type = 'DNA', alphabet = None, 
    background = None, pseudocount = None):
    """Initializes the Pfm

    Creates the Pfm instance. If the user does not define `pfm_filename_or_array`,
    it will be initialized to empty. Will generate all other attributes as soon
    as a `pfm_filename_or_array` is supplied.

    Args:
        pfm_filename_or_array (str or `numpy.ndarray` or `pandas.DataFrame` or Pm): The user supplied
            PFM. If it is a filename, the file will be opened
            and parsed. If it is an `numpy.ndarray` or `pandas.DataFrame`,
            it will just be assigned. (default: None, skips '#' comment lines)
        alphabet_type (str): Desired alphabet to use. Order matters (default: 'DNA')
            "DNA" := "ACGT"
            "reduced DNA" := "ACGTN-"
            "ambig DNA" := "ACGTRYSWKMBDHVN-"
            "RNA" := "ACGU"
            "reduced RNA" := "ACGUN-"
            "ambig RNA" := "ACGURYSWKMBDHVN-"
            "AA" : = "ACDEFGHIKLMNPQRSTVWY"
            "reduced AA" := "ACDEFGHIKLMNPQRSTVWYX*-"
            "ambig AA" := "ACDEFGHIKLMNOPQRSTUVWYBJZX*-"
            "custom" := None
            (default: 'DNA')
        alphabet (str): if 'custom' is selected or a specialize alphabet is desired, this accepts a string (default: None)
        background (constant or Collection): Offsets used to calculate background letter probabilities (defaults: If 
            using an Nucleic Acid alphabet: 0.25; if using an Aminio Acid alphabet: Robinson-Robinson Frequencies)
        pseudocount (constant): Some constant to offset PPM conversion to PWM to prevent -/+ inf. (default: 1e-10)
    """

seqlogo.Ppm(ppm_filename_or_array = None, pm_type = 'ppm', alphabet_type = 'DNA', alphabet = None, 
    background = None, pseudocount = None):
    """Initializes the Ppm

    Creates the Ppm instance. If the user does not define `ppm_filename_or_array`,
    it will be initialized to empty. Will generate all other attributes as soon
    as a `ppm_filename_or_array` is supplied.

    Args:
        ppm_filename_or_array (str or `numpy.ndarray` or `pandas.DataFrame` or Pm): The user supplied
            PPM. If it is a filename, the file will be opened
            and parsed. If it is an `numpy.ndarray` or `pandas.DataFrame`,
            it will just be assigned. (default: None, skips '#' comment lines)
        alphabet_type (str): Desired alphabet to use. Order matters (default: 'DNA')
            "DNA" := "ACGT"
            "reduced DNA" := "ACGTN-"
            "ambig DNA" := "ACGTRYSWKMBDHVN-"
            "RNA" := "ACGU"
            "reduced RNA" := "ACGUN-"
            "ambig RNA" := "ACGURYSWKMBDHVN-"
            "AA" : = "ACDEFGHIKLMNPQRSTVWY"
            "reduced AA" := "ACDEFGHIKLMNPQRSTVWYX*-"
            "ambig AA" := "ACDEFGHIKLMNOPQRSTUVWYBJZX*-"
            "custom" := None
            (default: 'DNA')
        alphabet (str): if 'custom' is selected or a specialize alphabet is desired, this accepts a string (default: None)
        background (constant or Collection): Offsets used to calculate background letter probabilities (defaults: If 
            using an Nucleic Acid alphabet: 0.25; if using an Aminio Acid alphabet: Robinson-Robinson Frequencies)
        pseudocount (constant): Some constant to offset PPM conversion to PWM to prevent -/+ inf. (default: 1e-10)
   """
   
seqlogo.Pwm(pwm_filename_or_array = None, pm_type = 'pwm', alphabet_type = 'DNA', alphabet = None, 
    background = None, pseudocount = None):
    """Initializes the Pwm

    Creates the Pwm instance. If the user does not define `pwm_filename_or_array`,
    it will be initialized to empty. Will generate all other attributes as soon
    as a `pwm_filename_or_array` is supplied.

    Args:
        pwm_filename_or_array (str or `numpy.ndarray` or `pandas.DataFrame` or Pm): The user supplied
            PWM. If it is a filename, the file will be opened
            and parsed. If it is an `numpy.ndarray` or `pandas.DataFrame`,
            it will just be assigned. (default: None, skips '#' comment lines)
        alphabet_type (str): Desired alphabet to use. Order matters (default: 'DNA')
            "DNA" := "ACGT"
            "reduced DNA" := "ACGTN-"
            "ambig DNA" := "ACGTRYSWKMBDHVN-"
            "RNA" := "ACGU"
            "reduced RNA" := "ACGUN-"
            "ambig RNA" := "ACGURYSWKMBDHVN-"
            "AA" : = "ACDEFGHIKLMNPQRSTVWY"
            "reduced AA" := "ACDEFGHIKLMNPQRSTVWYX*-"
            "ambig AA" := "ACDEFGHIKLMNOPQRSTUVWYBJZX*-"
            "custom" := None
            (default: 'DNA')
        alphabet (str): if 'custom' is selected or a specialize alphabet is desired, this accepts a string (default: None)
        background (constant or Collection): Offsets used to calculate background letter probabilities (defaults: If 
            using an Nucleic Acid alphabet: 0.25; if using an Aminio Acid alphabet: Robinson-Robinson Frequencies)
        pseudocount (constant): Some constant to offset PPM conversion to PWM to prevent -/+ inf. (default: 1e-10)
   """
   
```

### Conversion Methods

```python

seqlogo.pfm2ppm(pfm):
    """Converts a Pfm to a ppm array

    Args:
        pfm (Pfm): a fully initialized Pfm

    Returns:
        (np.array): converted values
    """
    
seqlogo.pfm2pwm(pfm, background = None, pseudocount = None):
    """Converts a Pfm to a pwm array

    Args:
        pfm (Pfm): a fully initialized Pfm
        background: accounts for relative weights from background. Must be a constant or same number of columns as Pwm (default: None)
        pseudocount (const): The number used to offset log-likelihood conversion from probabilites (default: None -> 1e-10)

    Returns:
        (np.array): converted values
    """

seqlogo.ppm2pfm(ppm):
    """Converts a Ppm to a pfm array

    Args:
        ppm (Ppm): a fully initialized Ppm

    Returns:
        (np.array): converted values
    """

seqlogo.ppm2pwm(ppm, background= None, pseudocount = None):
    """Converts a Ppm to a pwm array

    Args:
        ppm (Ppm): a fully initialized Ppm
        background: accounts for relative weights from background. Must be a constant or same number of columns as Pwm (default: None)
        pseudocount (const): The number used to offset log-likelihood conversion from probabilites (default: None -> 1e-10)

    Returns:
        (np.array): converted values

    Raises:
        ValueError: if the pseudocount isn't a constant or the same length as sequence
    """

seqlogo.pwm2pfm(pwm, background = None, pseudocount = None):
    """Converts a Pwm to a pfm array

    Args:
        pwm (Pwm): a fully initialized Pwm
        background: accounts for relative weights from background. Must be a constant or same number of columns as Pwm (default: None)
        pseudocount (const): The number used to offset log-likelihood conversion from probabilites (default: None -> 1e-10)

    Returns:
        (np.array): converted values
    """

seqlogo.pwm2ppm(pwm, background = None, pseudocount = None):
    """Converts a Pwm to a ppm array

    Args:
        pwm (Pwm): a fully initialized Pwm
        background: accounts for relative weights from background. Must be a constant or same number of columns as Pwm (default: None)
        pseudocount (const): The number used to offset log-likelihood conversion from probabilites (default: None -> 1e-10)

    Returns:
        (np.array): converted values

    Raises:
        ValueError: if the pseudocount isn't a constant or the same length as sequence
    """
    
```

***
## Contributing

Please see our contribution guidelines [here](https://github.com/betteridiot/seqlogo/blob/master/CONTRIBUTING.md)

***
## Acknowledgments

1. Bembom O (2018). seqlogo: Sequence logos for DNA sequence alignments. R package version 1.48.0.
2. Crooks GE, Hon G, Chandonia JM, Brenner SE WebLogo: A sequence logo generator,
Genome Research, 14:1188-1190, (2004).