### Install PyMSAviz

Source: https://github.com/moshi4/pymsaviz/blob/main/docs/getting_started.ipynb

Installs the pymsaviz library using pip. This is typically the first step before using the library.

```Python
# %pip install pymsaviz 

```

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

### Create MSA Visualization with Markers and Text Annotations

Source: https://github.com/moshi4/pymsaviz/blob/main/example/api_example_run.ipynb

This example demonstrates advanced customization by specifying a start and end range for the visualization, adding custom markers at specific positions (including calculated ones based on consensus identity), and adding text annotations to highlight regions.

```Python
from pymsaviz import MsaViz, get_msa_testdata

msa_file = get_msa_testdata("MRGPRG.fa")
mv = MsaViz(msa_file, start=1, end=180, wrap_length=60, show_consensus=True)

# Extract MSA positions less than 50% consensus identity
pos_ident_less_than_50 = []
ident_list = mv._get_consensus_identity_list()
for pos, ident in enumerate(ident_list, 1):
    if ident <= 50:
        pos_ident_less_than_50.append(pos)

# Add markers
mv.add_markers([1])
mv.add_markers([10, 20], color="orange", marker="o")
mv.add_markers([30, (40, 50), 55], color="green", marker="+ ")
mv.add_markers(pos_ident_less_than_50, marker="x", color="blue")
# Add text annotations
mv.add_text_annotation((76, 102), "Gap Region", text_color="red", range_color="red")
mv.add_text_annotation((112, 123), "Gap Region", text_color="green", range_color="green")

mv.savefig("api_example03.png")
```

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

### Create MSA Visualization with Default Settings

Source: https://github.com/moshi4/pymsaviz/blob/main/example/api_example_run.ipynb

This example demonstrates the basic usage of PyMSAvis to create an MSA visualization. It loads a test MSA file and saves the visualization to a PNG file with default settings.

```Python
from pymsaviz import MsaViz, get_msa_testdata

msa_file = get_msa_testdata("HIGD2A.fa")
mv = MsaViz(msa_file, wrap_length=60, show_count=True)
mv.savefig("api_example01.png")
```

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

### pymsaviz Example 1: Identity Color Scheme

Source: https://github.com/moshi4/pymsaviz/blob/main/docs/cli-docs/pymsaviz.md

An example command for pymsaviz that visualizes an MSA file using the 'Identity' color scheme and saves the output as a PNG image.

```bash
pymsaviz -i ./example/HIGD2A.fa -o cli_example01.png --color_scheme Identity
```

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

### Install pyMSAviz via PyPI

Source: https://github.com/moshi4/pymsaviz/blob/main/docs/index.md

Installs the pyMSAviz package using pip, the Python package installer. This is the standard method for installing Python libraries from the Python Package Index (PyPI).

```bash
pip install pymsaviz
```

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

### pymsaviz Example 2: Wrapped MSA with Consensus

Source: https://github.com/moshi4/pymsaviz/blob/main/docs/cli-docs/pymsaviz.md

This example demonstrates using pymsaviz with specific options: wrapping the MSA at 80 characters, applying the 'Taylor' color scheme, and showing the consensus sequence and character counts.

```bash
pymsaviz -i ./example/MRGPRG.fa -o cli_example02.png --wrap_length 80 \
             --color_scheme Taylor --show_consensus --show_count
```

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

### PyMSAviz CLI Example 1: Basic Visualization

Source: https://github.com/moshi4/pymsaviz/blob/main/README.md

Generates a basic MSA visualization using the 'Identity' color scheme. This example demonstrates the core functionality of converting an MSA file to a visual representation.

```bash
pymsaviz -i ./example/HIGD2A.fa -o cli_example01.png --color_scheme Identity
```

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

### pymsaviz Example 3: Specific Range and Grid

Source: https://github.com/moshi4/pymsaviz/blob/main/docs/cli-docs/pymsaviz.md

An example showcasing pymsaviz for visualizing a specific range (100-160) of an MSA, using the 'Flower' color scheme, displaying a grid, the consensus sequence, and a custom consensus color.

```bash
pymsaviz -i ./example/MRGPRG.fa -o cli_example03.png --start 100 --end 160 \
             --color_scheme Flower --show_grid --show_consensus --consensus_color tomato
```

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

### Install pyMSAviz via Conda

Source: https://github.com/moshi4/pymsaviz/blob/main/docs/index.md

Installs the pyMSAviz package using Conda, a popular package and environment management system. This command installs from both the conda-forge and bioconda channels.

```bash
conda install -c conda-forge -c bioconda pymsaviz
```

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

### Create MSA Visualization with Custom Color Scheme and Grid

Source: https://github.com/moshi4/pymsaviz/blob/main/example/api_example_run.ipynb

This example shows how to customize the MSA visualization by applying a specific color scheme ('Taylor'), setting the wrap length, and enabling the display of the consensus sequence and grid lines.

```Python
from pymsaviz import MsaViz, get_msa_testdata

msa_file = get_msa_testdata("MRGPRG.fa")
mv = MsaViz(msa_file, color_scheme="Taylor", wrap_length=80, show_grid=True, show_consensus=True)
mv.savefig("api_example02.png")
```

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

### PyMSAviz CLI Example 2: Wrapped and Consensus Visualization

Source: https://github.com/moshi4/pymsaviz/blob/main/README.md

Creates an MSA visualization with wrapped sequences and displays the consensus sequence and character counts. This example utilizes the 'Taylor' color scheme and specific display options for enhanced readability.

```bash
pymsaviz -i ./example/MRGPRG.fa -o cli_example02.png --wrap_length 80 \
             --color_scheme Taylor --show_consensus --show_count
```

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

### Simple MSA Visualization

Source: https://github.com/moshi4/pymsaviz/blob/main/docs/getting_started.ipynb

Creates an MsaViz instance from an MSA file and plots the figure using the plotfig method. This is a basic example of visualizing an MSA.

```Python
from pymsaviz import MsaViz, get_msa_testdata

msa_file = get_msa_testdata("HIGD2A.fa")
mv = MsaViz(msa_file)
fig = mv.plotfig()  # If save figure, use savefig method [e.g. mv.savefig("result.png")]

```

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

### PyMSAviz CLI Example 3: Specific Range and Grid Visualization

Source: https://github.com/moshi4/pymsaviz/blob/main/README.md

Generates an MSA visualization for a specific range of sequences, including a grid and custom consensus bar color. This example showcases fine-grained control over the visualization output.

```bash
pymsaviz -i ./example/MRGPRG.fa -o cli_example03.png --start 100 --end 160 \
             --color_scheme Flower --show_grid --show_consensus --consensus_color tomato
```

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

### MSA Visualization with Grid (Marker List Reference)

Source: https://github.com/moshi4/pymsaviz/blob/main/docs/getting_started.ipynb

Initializes an MsaViz object with a limited end range and displays a grid. This example also includes a comment referencing the matplotlib marker API.

```Python
from pymsaviz import MsaViz, get_msa_testdata

msa_file = get_msa_testdata("MRGPRG.fa")
mv = MsaViz(msa_file, end=60, show_grid=True)


```

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

### Visualize MSA with Default Settings

Source: https://github.com/moshi4/pymsaviz/blob/main/README.md

This example demonstrates the basic usage of the MsaViz class to visualize a Multiple Sequence Alignment (MSA) file. It loads a test MSA file, creates an MsaViz object with a specified wrap length and option to show sequence counts, and saves the visualization to a PNG file.

```Python
from pymsaviz import MsaViz, get_msa_testdata

msa_file = get_msa_testdata("HIGD2A.fa")
mv = MsaViz(msa_file, wrap_length=60, show_count=True)
mv.savefig("api_example01.png")
```

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

### Visualize MSA with Custom Color Scheme and Consensus

Source: https://github.com/moshi4/pymsaviz/blob/main/README.md

This example shows how to customize the MSA visualization by applying a specific color scheme ('Taylor'), setting the wrap length, and displaying the consensus sequence. It loads a different test MSA file and saves the customized visualization.

```Python
from pymsaviz import MsaViz, get_msa_testdata

msa_file = get_msa_testdata("MRGPRG.fa")
mv = MsaViz(msa_file, color_scheme="Taylor", wrap_length=80, show_grid=True, show_consensus=True)
mv.savefig("api_example02.png")
```

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

### Annotate MSA with Markers and Text

Source: https://github.com/moshi4/pymsaviz/blob/main/README.md

This example demonstrates advanced customization of MSA visualization by adding markers and text annotations. It calculates positions with less than 50% consensus identity and marks them, adds custom markers with specific colors and styles, and annotates specific regions with text.

```Python
from pymsaviz import MsaViz, get_msa_testdata

msa_file = get_msa_testdata("MRGPRG.fa")
mv = MsaViz(msa_file, end=180, wrap_length=60, show_consensus=True)

# Extract MSA positions less than 50% consensus identity
pos_ident_less_than_50 = []
ident_list = mv._get_consensus_identity_list()
for pos, ident in enumerate(ident_list, 1):
    if ident <= 50:
        pos_ident_less_than_50.append(pos)

# Add markers
mv.add_markers([1])
mv.add_markers([10, 20], color="orange", marker="o")
mv.add_markers([30, (40, 50), 55], color="green", marker="+")
mv.add_markers(pos_ident_less_than_50, marker="x", color="blue")
# Add text annotations
mv.add_text_annotation((76, 102), "Gap Region", text_color="red", range_color="red")
mv.add_text_annotation((112, 123), "Gap Region", text_color="green", range_color="green")

mv.savefig("api_example03.png")
```

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

### Apply Color Schemes to MSA Visualization in pyMSAviz

Source: https://github.com/moshi4/pymsaviz/blob/main/docs/color_schemes.ipynb

This example demonstrates how to apply different color schemes to a Multiple Sequence Alignment (MSA) visualization using pyMSAviz. It iterates through available color schemes, creates an MsaViz object for a specified MSA region, and plots the result with the scheme name as the title.

```python
from pymsaviz import MsaViz, get_msa_testdata

msa_file = get_msa_testdata("MRGPRG.fa")
for color_scheme in MsaViz.available_color_schemes():
    mv = MsaViz(msa_file, start=100, end=160, color_scheme=color_scheme, show_label=False)
    fig = mv.plotfig()
    fig.suptitle(f"Color Scheme = {color_scheme}", x=0.5, y=1.15, va="bottom", size=15)
```

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

### MSA Visualization with Range Limit and Character Hiding

Source: https://github.com/moshi4/pymsaviz/blob/main/docs/getting_started.ipynb

Limits the MSA visualization to a specific start and end range and hides the display of individual sequence characters. It also shows the consensus sequence with a specified color.

```Python
from pymsaviz import MsaViz, get_msa_testdata

msa_file = get_msa_testdata("MRGPRG.fa")
mv = MsaViz(msa_file, start=100, end=160, show_seq_char=False, show_consensus=True, consensus_color="tomato")
fig = mv.plotfig()

```

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

### Change Plot Parameters

Source: https://github.com/moshi4/pymsaviz/blob/main/docs/getting_started.ipynb

This example shows how to modify various plot parameters for an MSA visualization using the `set_plot_params` method. It customizes tick intervals, unit sizes, grid color, and identity color.

```Python
from pymsaviz import MsaViz, get_msa_testdata

msa_file = get_msa_testdata("MRGPRG.fa")
mv = MsaViz(msa_file, start=100 ,end=140, show_grid=True, show_count=True, color_scheme="Identity")
mv.set_plot_params(ticks_interval=5, x_unit_size=0.20, grid_color="black", identity_color="tomato")
fig = mv.plotfig()
```

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

### pymsaviz Help and Options

Source: https://github.com/moshi4/pymsaviz/blob/main/docs/cli-docs/pymsaviz.md

Demonstrates how to access the help message for pymsaviz, which lists all available command-line options for customizing MSA visualizations, including input/output files, color schemes, and display settings.

```bash
pymsaviz --help
```

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

### PyMSAviz Help and Basic Usage

Source: https://github.com/moshi4/pymsaviz/blob/main/README.md

Displays the help message for the pymsaviz CLI tool, outlining its purpose and available options. The basic usage involves specifying an input MSA file and an output visualization file.

```bash
pymsaviz --help
usage: pymsaviz [options] -i msa.fa -o msa_viz.png

MSA(Multiple Sequence Alignment) visualization CLI tool

optional arguments:
  -i I, --infile I    Input MSA file
  -o O, --outfile O   Output MSA visualization file (*.png|*.jpg|*.svg|*.pdf)
  --format            MSA file format (Default: 'fasta')
  --color_scheme      Color scheme (Default: 'Zappo')
  --start             Start position of MSA visualization (Default: 1)
  --end               End position of MSA visualization (Default: 'MSA Length')
  --wrap_length       Wrap length (Default: None)
  --wrap_space_size   Space size between wrap MSA plot area (Default: 3.0)
  --label_type        Label type ('id'[default]|'description')
  --show_grid         Show grid (Default: OFF)
  --show_count        Show seq char count without gap on right side (Default: OFF)
  --show_consensus    Show consensus sequence (Default: OFF)
  --consensus_color   Consensus identity bar color (Default: '#1f77b4')
  --consensus_size    Consensus identity bar height size (Default: 2.0)
  --sort              Sort MSA order by NJ tree constructed from MSA distance matrix (Default: OFF)
  --dpi               Figure DPI (Default: 300)
  -v, --version       Print version information
  -h, --help          Show this help message and exit
```

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

### Basic pymsaviz Usage

Source: https://github.com/moshi4/pymsaviz/blob/main/docs/cli-docs/pymsaviz.md

This snippet shows the fundamental command structure for using the pymsaviz CLI. It requires an input MSA file and specifies an output file for the visualization.

```bash
pymsaviz -i [MSA file] -o [MSA visualization file]
```

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

### Styled MSA Visualization with Color Scheme

Source: https://github.com/moshi4/pymsaviz/blob/main/docs/getting_started.ipynb

Visualizes an MSA with a specified wrap length and color scheme ('Flower'). It also shows the count of sequences.

```Python
from pymsaviz import MsaViz, get_msa_testdata

msa_file = get_msa_testdata("HIGD2A.fa")
mv = MsaViz(msa_file, wrap_length=60, color_scheme="Flower", show_count=True)
fig = mv.plotfig()

```

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

### Add Markers with Different Colors

Source: https://github.com/moshi4/pymsaviz/blob/main/docs/getting_started.ipynb

This snippet demonstrates how to add various marker types to an MSA visualization, cycling through a predefined list of colors. It utilizes the `add_markers` method of the MsaViz object.

```Python
markers = [".", ",", "o", "v", "^", "<", ">", "1", "2", "3", "4", "8", "s", "p", "*", "h", "H", "+", "x", "D", "d", "|", "_"]
colors = ["red", "blue", "green", "chocolate", "magenta"]
for idx, marker in enumerate(markers, 1):
    color = colors[idx % len(colors)]
    mv.add_markers([idx * 2], marker=marker, color=color, size=8)
fig = mv.plotfig()
```

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

### MSA Visualization with Grid and Consensus

Source: https://github.com/moshi4/pymsaviz/blob/main/docs/getting_started.ipynb

Displays an MSA with a grid and shows the consensus sequence along with an identity bar. This helps in analyzing sequence conservation.

```Python
from pymsaviz import MsaViz, get_msa_testdata

msa_file = get_msa_testdata("MRGPRG.fa")
mv = MsaViz(msa_file, wrap_length=60, show_grid=True, show_consensus=True)
fig = mv.plotfig()

```

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

### List Available Color Schemes in pyMSAviz

Source: https://github.com/moshi4/pymsaviz/blob/main/docs/color_schemes.ipynb

This snippet shows how to retrieve a list of all color schemes supported by the pyMSAviz library. It imports the MsaViz class and calls the static method `available_color_schemes()`.

```python
from pymsaviz import MsaViz

print(MsaViz.available_color_schemes())
```

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

### Color Block MSA Display

Source: https://github.com/moshi4/pymsaviz/blob/main/docs/getting_started.ipynb

This snippet illustrates how to display a color-blocked MSA with specific configurations. It sets the color scheme to 'Clustal', hides sequence characters, shows counts, sorts the sequences, and displays consensus with a grey color.

```Python
from pymsaviz import MsaViz, get_msa_testdata

msa_file = get_msa_testdata("MRGPRG.fa")
mv = MsaViz(msa_file, color_scheme="Clustal", show_seq_char=False, show_count=True, sort=True, show_consensus=True, consensus_color="grey")
mv.set_plot_params(ticks_interval=50, x_unit_size=0.04, show_consensus_char=False)
fig = mv.plotfig()
```

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

### Add Markers and Text Annotations to MSA

Source: https://github.com/moshi4/pymsaviz/blob/main/docs/getting_started.ipynb

Adds markers and text annotations to the MSA visualization. This includes adding markers at specific positions, with custom colors and styles, and annotating regions like 'Gap Region'.

```Python
from pymsaviz import MsaViz, get_msa_testdata

msa_file = get_msa_testdata("MRGPRG.fa")
mv = MsaViz(msa_file, start=1, end=180, wrap_length=60, show_consensus=True)

# Extract MSA positions less than 50% consensus identity
pos_ident_less_than_50 = []
ident_list = mv._get_consensus_identity_list()
for pos, ident in enumerate(ident_list, 1):
    if ident <= 50:
        pos_ident_less_than_50.append(pos)

# Add markers
mv.add_markers([1])
mv.add_markers([10, 20], color="orange", marker="o")
mv.add_markers([30, (40, 50), 55], color="green", marker="+ ")
mv.add_markers(pos_ident_less_than_50, marker="x", color="blue")
# Add text annotations
mv.add_text_annotation((76, 102), "Gap Region", text_color="red", range_color="red")
mv.add_text_annotation((112, 123), "Gap Region", text_color="green", range_color="green")

fig = mv.plotfig()

```

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

### Custom MSA Color Scheme

Source: https://github.com/moshi4/pymsaviz/blob/main/docs/getting_started.ipynb

Applies a user-defined custom color scheme to the MSA visualization using the set_custom_color_scheme method. This allows for specific coloring of amino acids.

```Python
from pymsaviz import MsaViz, get_msa_testdata

msa_file = get_msa_testdata("MRGPRG.fa")
mv = MsaViz(msa_file, start=100, end=160)
mv.set_custom_color_scheme({"A": "red", "P": "skyblue", "C": "lime", "H": "orange", "L": "pink"})
fig = mv.plotfig()

```

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

### Highlight MSA by Identity Threshold

Source: https://github.com/moshi4/pymsaviz/blob/main/docs/getting_started.ipynb

Highlights positions in the MSA based on a consensus identity threshold. This method helps identify regions with varying degrees of conservation.

```Python
from pymsaviz import MsaViz, get_msa_testdata

msa_file = get_msa_testdata("MRGPRG.fa")
mv = MsaViz(msa_file, start=100, end=160, show_consensus=True)
# Only highlight 0 - 80 [%] consensus identity positions
mv.set_highlight_pos_by_ident_thr(min_thr=0, max_thr=80)
fig = mv.plotfig()

```

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

### Highlight MSA Positions

Source: https://github.com/moshi4/pymsaviz/blob/main/docs/getting_started.ipynb

Highlights specific positions or ranges within the MSA using the set_highlight_pos method. This is useful for drawing attention to particular regions of interest.

```Python
from pymsaviz import MsaViz, get_msa_testdata

msa_file = get_msa_testdata("MRGPRG.fa")
mv = MsaViz(msa_file, wrap_length=60)
mv.set_highlight_pos([10, 20, 30, (40, 120), (150, 200), 220, 270])
fig = mv.plotfig()

```

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

### Custom MSA Color Function

Source: https://github.com/moshi4/pymsaviz/blob/main/docs/getting_started.ipynb

Sets a user-defined function to determine the color of each sequence character based on its position and the MSA content. This provides fine-grained control over coloring.

```Python
from pymsaviz import MsaViz, get_msa_testdata
from Bio.Align import MultipleSeqAlignment

msa_file = get_msa_testdata("MRGPRG.fa")
mv = MsaViz(msa_file, start=100, end=160, show_grid=True)

def custom_color_func(
    row_pos: int, col_pos: int, seq_char: str, msa: MultipleSeqAlignment
):
    # Note: Both `row_pos` and `col_pos` are 0-index starts
    if col_pos < 115 and seq_char != "-":
        return "salmon"
    if 115 <= col_pos < 130:
        if seq_char in ("A", "V", "P"):
            return "skyblue"
        else:
            return "lightyellow"
    if 130 <= col_pos < 145:
        if 1 <= row_pos <= 4:
            return "lime"
        else:
            return "white"
    # Use default color setting in other column (145 <= col_pos < 160)
    return None

mv.set_custom_color_func(custom_color_func)
fig = mv.plotfig()

```

=== COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.