### Configure Notebook Plotting

Source: https://github.com/grahamgower/demesdraw/blob/main/docs/quickstart.md

Sets the matplotlib backend to SVG format for high-quality vector rendering within Jupyter notebooks.

```python
from matplotlib_inline.backend_inline import set_matplotlib_formats
set_matplotlib_formats("svg")
```

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

### Install DemesDraw

Source: https://github.com/grahamgower/demesdraw/blob/main/README.md

Instructions for installing the DemesDraw package using standard Python package managers.

```bash
python3 -m pip install demesdraw
```

```bash
conda install -c conda-forge demesdraw
```

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

### Plot Demes as Tubes

Source: https://github.com/grahamgower/demesdraw/blob/main/docs/quickstart.md

Generates a schematic overview of demographic models representing demes as tubes. This visualization highlights ancestor-descendant relationships and migration events.

```python
import demes
import demesdraw

gutenkunst_ooa = demes.load("../examples/stdpopsim/HomSap__OutOfAfrica_3G09.yaml")
demesdraw.tubes(gutenkunst_ooa, num_lines_per_migration=6, log_time=True, seed=1234);
```

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

### Plot Population Size History

Source: https://github.com/grahamgower/demesdraw/blob/main/docs/quickstart.md

Visualizes population size over time for demographic models. Supports log-scaling for time and size axes, and handles both single and multi-deme models.

```python
import demes
import demesdraw

zigzag = demes.load("../examples/stdpopsim/HomSap__Zigzag_1S14.yaml")
demesdraw.size_history(zigzag, invert_x=True, log_time=True);

gutenkunst_ooa = demes.load("../examples/stdpopsim/HomSap__OutOfAfrica_3G09.yaml")
demesdraw.size_history(gutenkunst_ooa, invert_x=True, log_size=True);
```

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

### Define and visualize a deme model with demesdraw

Source: https://github.com/grahamgower/demesdraw/blob/main/docs/layout.md

Demonstrates how to load a demes model, calculate a separation heuristic for horizontal spacing, and visualize the model using the tubes function.

```python
import demes
import demesdraw

model2 = demes.loads(
    """
time_units: generations
defaults:
  epoch:
    start_size: 500
demes:
  - name: A
  - name: B
    start_time: 100
    ancestors: [A]
  - name: C
    start_time: 50
    ancestors: [A]
    """
)
w = demesdraw.utils.separation_heuristic(model2)
positions = {deme.name: j*w for j, deme in enumerate(model2.demes)}
demesdraw.tubes(model2, positions=positions);
```

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

### Visualize Demes Models with Tubes

Source: https://github.com/grahamgower/demesdraw/blob/main/docs/layout.md

Demonstrates the use of the demesdraw.tubes function to visualize demographic models, including the effect of the inf_ratio parameter on time axis scaling.

```python
import matplotlib.pyplot as plt
import demesdraw

fig, axs = plt.subplots(ncols=2, constrained_layout=True)
demesdraw.tubes(model1, inf_ratio=0.2, ax=axs[0])
demesdraw.tubes(model1, inf_ratio=0.5, ax=axs[1])
```

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

### Visualize Demes Models via CLI

Source: https://github.com/grahamgower/demesdraw/blob/main/README.md

Use the command-line interface to generate demographic model plots from YAML files or piped input. Supports various output formats and interactive windows.

```bash
demesdraw tubes --log-time examples/stdpopsim/HomSap__AmericanAdmixture_4B11.yaml AmericanAdmixture_4B11_tubes.svg
```

```bash
N0=`python -c 'theta=11.2; mu=1e-8; length=7000; print(theta/(4*mu*length))'`
demes ms -N0 $N0 -I 2 3 12 -g 1 44.36 -n 2 0.125 -eg 0.03125 1 0.0 -en 0.0625 2 0.05 -ej 0.09375 2 1 | demesdraw tubes --scale-bar -
```

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

### Optimize deme positions using quadratic programming

Source: https://github.com/grahamgower/demesdraw/blob/main/docs/layout.md

Refines deme positions by minimizing squared distances between related demes while maintaining order constraints, utilizing the optimise_positions function.

```python
positions1 = demesdraw.utils.minimal_crossing_positions(
    model3, sep=w, unique_interactions=False
)
positions2 = demesdraw.utils.optimise_positions(
    model3, positions=positions1, sep=w, unique_interactions=False,
)
demesdraw.tubes(model3, positions=positions2, log_time=True);
```

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

### Visualize Demes Models via Python API

Source: https://github.com/grahamgower/demesdraw/blob/main/README.md

Programmatically generate demographic model plots using the Python API for advanced control over layout, positions, and labeling.

```python
import demes
import demesdraw

graph = demes.load("examples/stdpopsim/HomSap__AmericanAdmixture_4B11.yaml")
w = demesdraw.utils.separation_heuristic(graph)
positions = dict(ancestral=0, AMH=0, AFR=0, OOA=1.5 * w, EAS=1 * w, EUR=2 * w, ADMIX=-w)
ax = demesdraw.tubes(graph, log_time=True, positions=positions, labels="xticks-legend")
ax.figure.savefig("AmericanAdmixture_4B11_tubes_custom.svg")
```

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

### BibTeX Citation for DemesDraw Software

Source: https://github.com/grahamgower/demesdraw/blob/main/docs/CITATION.md

This BibTeX entry is for citing the DemesDraw software specifically. It includes the software title, author, version, and publication year, along with a URL for the repository.

```bibtex
@misc{gower_demesdraw_2022,
  title = {DemesDraw: Drawing functions for {Demes} demographic models},
  howpublished = {https://github.com/grahamgower/demesdraw},
  author = {Gower, Graham},
  version = {0.4.1},
  year = 2022,
}
```

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

### Visualize Demographic Models from YAML

Source: https://github.com/grahamgower/demesdraw/blob/main/docs/examples.ipynb

This script loads demographic models from YAML files and generates two types of visualizations: a size history plot and a tube plot. It uses heuristics to determine logarithmic scaling for time and population size and outputs the results as SVG figures.

```python
import math
import pathlib
import warnings

import demes
import demesdraw
import demesdraw.utils
import matplotlib.pyplot as plt

from matplotlib_inline.backend_inline import set_matplotlib_formats
set_matplotlib_formats("svg")

warnings.filterwarnings("ignore", "Multiple pulses", UserWarning, "demes")

def plot_from_yaml(yaml_filename):
    graph = demes.load(yaml_filename)
    log_time = demesdraw.utils.log_time_heuristic(graph)
    log_size = demesdraw.utils.log_size_heuristic(graph)

    ax1 = demesdraw.size_history(
        graph,
        invert_x=True,
        log_time=log_time,
        log_size=log_size,
        title=example.name,
    )

    ax2 = demesdraw.tubes(
        graph,
        log_time=log_time,
        title=example.name,
    )

    plt.show(ax1.figure)
    plt.show(ax2.figure)
    
    plt.close(ax1.figure)
    plt.close(ax2.figure)

cwd = pathlib.Path(".").parent.resolve()
examples = list((cwd / ".." / "examples").glob("**/*.yaml"))
for example in sorted(examples):
    plot_from_yaml(example)
```

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

### Automated Separation Heuristic

Source: https://github.com/grahamgower/demesdraw/blob/main/docs/layout.md

Uses the demesdraw.utils.separation_heuristic function to calculate optimal spacing between demes based on model complexity and population sizes.

```python
w = demesdraw.utils.separation_heuristic(im)
positions = {deme.name: j*w for j, deme in enumerate(im.demes)}
demesdraw.tubes(im, positions=positions)
```

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

### Minimize line crossings in deme visualizations

Source: https://github.com/grahamgower/demesdraw/blob/main/docs/layout.md

Uses the minimal_crossing_positions utility to find an optimal left-to-right ordering of demes, which helps reduce visual clutter caused by crossing lines.

```python
model3 = demes.load("../examples/reticulate.yaml")
w = demesdraw.utils.separation_heuristic(model3)
positions = demesdraw.utils.minimal_crossing_positions(
    model3, sep=w, unique_interactions=False
)
demesdraw.tubes(model3, positions=positions, log_time=True);
```

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

### Customize Deme Horizontal Positioning

Source: https://github.com/grahamgower/demesdraw/blob/main/docs/layout.md

Manually defines the horizontal midpoint of demes using a dictionary to control the layout of the tubes plot.

```python
positions = dict(A=0, B=1000, C=2000)
demesdraw.tubes(model1, positions=positions, scale_bar=True)
```

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

### BibTeX Citation for Demes Paper

Source: https://github.com/grahamgower/demesdraw/blob/main/docs/CITATION.md

This BibTeX entry can be used to cite the original Demes paper. It includes details such as title, authors, journal, and publication year.

```bibtex
@article{gower_demes_2022,
  title        = {Demes: a standard format for demographic models},
  url          = {https://doi.org/10.1093/genetics/iyac131},
  doi          = {10.1093/genetics/iyac131},
  journal      = {Genetics},
  author       = {Gower, Graham and Ragsdale, Aaron P and Bisschop,
    Gertjan and Gutenkunst, Ryan N and Hartfield, Matthew and Noskova,
    Ekaterina and Schiffels, Stephan and Struck, Travis J and
    Kelleher, Jerome and Thornton, Kevin R},
  month        = sep,
  year         = 2022,
  pages        = {iyac131},
}
```

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