### Install msprime using Pip (User Install)

Source: https://tskit.dev/msprime/docs/latest/installation.html

Installs msprime into the user's local Python installation, which is useful when root access is not available. Ensure '~/.local/bin' is in your PATH.

```bash
$ python3 -m pip install msprime --user

```

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

### Install Dependencies and Build C Module

Source: https://tskit.dev/msprime/docs/latest/development.html

Installs all necessary Python development dependencies and builds the low-level C extension module. This command is essential for a functional development setup.

```bash
$ uv sync

```

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

### Install msprime using Pip (User Install)

Source: https://tskit.dev/msprime/docs/latest/_sources/installation.md

Installs msprime into the user's local Python installation, which is useful when root access is not available. Ensure that '~/.local/bin' is in your PATH to use executables like 'mspms'.

```bash
$ python3 -m pip install msprime --user
```

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

### Install Meson Build System

Source: https://tskit.dev/msprime/docs/latest/_sources/development.md

Install the Meson build system using the uv tool. Meson is used in conjunction with ninja-build for compiling C code.

```bash
$ uv tool install meson
```

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

### Install Meson build system

Source: https://tskit.dev/msprime/docs/latest/development.html

Install the Meson build system, which is used in conjunction with ninja-build for compiling C code and unit tests.

```bash
$ uv tool install meson

```

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

### INFO log output example

Source: https://tskit.dev/msprime/docs/latest/logging.html

Example of INFO level log messages during msprime simulation.

```log
2026-04-16 12:13:36,067 [3347] INFO     msprime.ancestry: Sampling 10 individuals with ploidy 2 in population 0 (name='pop_0') at time 0

```

```log
2026-04-16 12:13:36,070 [3347] INFO     msprime.ancestry: Starting replicate 0

```

```log
2026-04-16 12:13:36,070 [3347] INFO     msprime.ancestry: model[0] {'name': 'dtwf'} started at time=0 nodes=20 edges=0

```

```log
2026-04-16 12:13:36,071 [3347] INFO     msprime.ancestry: Running model {'name': 'dtwf'} until max time: 100.000000

```

```log
2026-04-16 12:13:36,073 [3347] INFO     msprime.ancestry: model[1] {'name': 'hudson'} started at time=100 nodes=25 edges=10

```

```log
2026-04-16 12:13:36,073 [3347] INFO     msprime.ancestry: Running model {'name': 'hudson'} until max time: inf

```

```log
2026-04-16 12:13:36,073 [3347] INFO     msprime.ancestry: Completed at time=5876.22 nodes=39 edges=38

```

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

### Install GSL on FreeBSD

Source: https://tskit.dev/msprime/docs/latest/_sources/installation.md

Installs the GNU Scientific Library package on FreeBSD.

```bash
pkg install gsl
```

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

### Install msprime from source

Source: https://tskit.dev/msprime/docs/latest/_sources/installation.md

Installs the msprime package from source using pip, ensuring no pre-built binaries are used. This command should be run after installing necessary system dependencies like GSL.

```bash
python3 -m pip install msprime --no-binary msprime
```

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

### Build Documentation Locally

Source: https://tskit.dev/msprime/docs/latest/development.html

Navigate to the 'docs' directory and run 'make' to build the HTML documentation. Ensure all requirements are installed before building.

```bash
make

```

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

### Install C Development Libraries

Source: https://tskit.dev/msprime/docs/latest/_sources/development.md

Install necessary libraries for compiling C code and running unit tests on Debian/Ubuntu systems. Libconfig and CUnit are required for development CLI and unit tests respectively.

```bash
$ sudo apt-get install libcunit1-dev libconfig-dev ninja-build
```

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

### Install Dependencies and Build C Extension

Source: https://tskit.dev/msprime/docs/latest/_sources/development.md

Installs all necessary Python development packages and builds the low-level C extension module required for msprime development.

```bash
uv sync
```

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

### Create Conda Environment and Install msprime

Source: https://tskit.dev/msprime/docs/latest/_sources/installation.md

Creates a new conda environment named 'msprime-env' and installs msprime from the conda-forge channel. Always use a dedicated environment for package installations to avoid conflicts.

```bash
$ conda create --name msprime-env
...

  environment location: /home/jean/miniconda3/envs/msprime-env

Proceed ([y]/n)? y

Preparing transaction: ...working... done
Verifying transaction: ...working... done
Executing transaction: ...working... done
#
# To activate this environment, use
#
#     $ conda activate msprime-env
#
# To deactivate an active environment, use
#
#     $ conda deactivate

$ conda activate msprime-env
(msprime-env) $ conda install -c conda-forge msprime
...

  added / updated specs:
    - msprime

The following NEW packages will be INSTALLED:
...

Proceed ([y]/n)? y

Downloading and Extracting Packages

Preparing transaction: ...working... done
Verifying transaction: ...working... done
Executing transaction: ...working... done
```

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

### Install C development libraries on Debian/Ubuntu

Source: https://tskit.dev/msprime/docs/latest/development.html

Install necessary development libraries for compiling the C code, including CUnit for unit tests and libconfig for the development CLI.

```bash
$ sudo apt-get install libcunit1-dev libconfig-dev ninja-build

```

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

### Population Metadata Example

Source: https://tskit.dev/msprime/docs/latest/demography.html

Example output of population metadata, showing the 'name' and 'description' for each population defined in the simulation.

```python
{
'description': '', 'name': 'human'
}
{
'description': '', 'name': 'chimpanzee'
}
{
'description': '', 'name': 'gorilla'
}
{
'description': '', 'name': 'orangutan'
}
{
'description': '', 'name': 'pop_4'
}
{
'description': '', 'name': 'pop_5'
}
{
'description': '', 'name': 'pop_6'
}
```

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

### Install msprime using Pip

Source: https://tskit.dev/msprime/docs/latest/_sources/installation.md

Installs the msprime package using pip. It is generally recommended to use 'python3 -m pip' to ensure the correct Python installation is targeted. This command installs the package for the current user.

```bash
$ python3 -m pip install msprime
```

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

### Setup INFO logging with daiquiri

Source: https://tskit.dev/msprime/docs/latest/logging.html

Use this to set the logging level to INFO for high-level simulation status. Requires importing daiquiri and msprime.

```python
import daiquiri
import msprime

daiquiri.setup(level="INFO")
ts = msprime.sim_ancestry(
    10,
    population_size=1000,
    model=[
        msprime.DiscreteTimeWrightFisher(duration=100),
        msprime.StandardCoalescent(),
    ],
    random_seed=1234
)

```

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

### Check Pip Installation

Source: https://tskit.dev/msprime/docs/latest/_sources/installation.md

Verifies that pip is installed and accessible. If Python {{ min_python_version }} or greater is not detected, it is recommended to use the conda installation method instead.

```bash
$ python3 -m pip --version
```

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

### Verify msprime Installation with Python

Source: https://tskit.dev/msprime/docs/latest/_sources/installation.md

After activating the conda environment, this snippet shows how to import msprime and simulate a simple ancestry, then draw it as text. This confirms the installation was successful and the library is functional.

```python
(msprime-env) $ python
Python 3.8.5
[GCC 7.3.0] :: Anaconda, Inc. on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import msprime
>>> ts = msprime.sim_ancestry(3)
>>> print(ts.draw_text())
3.18┊      10     ┊
    ┊    ┏━━┻━━┓  ┊
2.08┊    9     ┃  ┊
    ┊  ┏━┻━┓   ┃  ┊
0.80┊  ┃   ┃   8  ┊
    ┊  ┃   ┃  ┏┻┓ ┊
0.42┊  ┃   7  ┃ ┃ ┊
    ┊  ┃  ┏┻┓ ┃ ┃ ┊
0.34┊  6  ┃ ┃ ┃ ┃ ┊
    ┊ ┏┻┓ ┃ ┃ ┃ ┃ ┊
0.00┊ 0 4 1 3 2 5 ┊
  0.00          1.00
```

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

### Install GSL on Debian/Ubuntu

Source: https://tskit.dev/msprime/docs/latest/_sources/installation.md

Installs the GNU Scientific Library and Python development headers on Debian-based systems.

```bash
apt-get install python-dev libgsl0-dev
```

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

### Install GSL on Redhat/Fedora

Source: https://tskit.dev/msprime/docs/latest/_sources/installation.md

Installs the GNU Scientific Library development package on Red Hat or Fedora systems.

```bash
yum install gsl-devel
```

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

### Install Pre-commit Hook

Source: https://tskit.dev/msprime/docs/latest/_sources/development.md

Installs the pre-commit hook for the project. This helps maintain code quality by running checks before each commit.

```bash
uv run prek install
```

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

### Install msprime using Pip

Source: https://tskit.dev/msprime/docs/latest/installation.html

Installs the msprime package using pip. It is recommended to use 'python3 -m pip' to ensure the package is installed for the correct Python interpreter.

```bash
$ python3 -m pip install msprime

```

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

### Example Output of Segregating Sites Calculation

Source: https://tskit.dev/msprime/docs/latest/replication.html

This is the expected output format for the mean and variance of segregating sites (S) from the generator-based simulation example.

```python
(np.float64(1.52), np.float64(1.5495999999999999))
```

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

### Verify msprime Installation with Python

Source: https://tskit.dev/msprime/docs/latest/installation.html

After activating the msprime conda environment, this snippet shows how to import msprime, simulate ancestry, and draw the resulting tree. This confirms a successful installation.

```python
(msprime-env) $ python
Python 3.8.5
[GCC 7.3.0] :: Anaconda, Inc. on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import msprime
>>> ts = msprime.sim_ancestry(3)
>>> print(ts.draw_text())
3.18┊      10     ┊
    ┊    ┏━━┻━━┓  ┊
2.08┊    9     ┃  ┊
    ┊  ┏━┻━┓   ┃  ┊
0.80┊  ┃   ┃   8  ┊
    ┊  ┃   ┃  ┏┻┓ ┊
0.42┊  ┃   7  ┃ ┃ ┊
    ┊  ┃  ┏┻┓ ┃ ┃ ┊
0.34┊  6  ┃ ┃ ┃ ┃ ┊
    ┊ ┏┻┓ ┃ ┃ ┃ ┃ ┊
0.00┊ 0 4 1 3 2 5 ┊
  0.00          1.00

```

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

### Setup DEBUG logging with daiquiri

Source: https://tskit.dev/msprime/docs/latest/logging.html

Use this to set the logging level to DEBUG for detailed simulation progress. Useful for large simulations.

```python
daiquiri.setup(level="DEBUG")
ts = msprime.sim_ancestry(
    10 ** 5,
    population_size=10000,
    recombination_rate=2e-8,
    sequence_length=1e6,
    random_seed=32
)

```

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

### Install GSL on OS X using Homebrew

Source: https://tskit.dev/msprime/docs/latest/_sources/installation.md

Installs the GNU Scientific Library on OS X using the Homebrew package manager after updating Homebrew.

```bash
brew update
brew install gsl
```

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

### Get Sample Configuration

Source: https://tskit.dev/msprime/docs/latest/_modules/msprime/ancestry.html

Returns a list of the number of samples in each population. It iterates through nodes to count samples flagged as such.

```python
@property
def sample_configuration(self):
        """
        Returns a list of the number of samples in each of the populations.
        """
        tables = self.copy_tables()
        num_samples = [0 for _ in tables.populations]
        for node in tables.nodes:
            if (node.flags & tskit.NODE_IS_SAMPLE) != 0:
                num_samples[node.population] += 1
        return num_samples
```

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

### Simulate Ancestry with Custom Start Time

Source: https://tskit.dev/msprime/docs/latest/_sources/ancestry.md

Simulates ancestry for 2 samples, but advances the simulation to a start time of 10 before generating random events. This is an advanced feature for specific simulation needs.

```python
ts = msprime.sim_ancestry(2, start_time=10, random_seed=42)
ts.tables.nodes
```

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

### Run Tests

Source: https://tskit.dev/msprime/docs/latest/development.html

Executes the test suite to verify that the project is working correctly after setup or changes. This is a standard step to ensure code integrity.

```bash
$ uv run pytest

```

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

### Get Epoch Start Times

Source: https://tskit.dev/msprime/docs/latest/_modules/msprime/demography.html

Returns a numpy array containing the start times for each epoch defined in the demographic model.

```python
@property
def epoch_start_time(self):
        """
        The array of epoch start_times defined by the demographic model.
        """
        return np.array([x.start_time for x in self.epochs])
```

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

### Set up Meson build directory

Source: https://tskit.dev/msprime/docs/latest/development.html

Initialize the build directory for the C library using Meson. This command should be run from the 'lib' directory.

```bash
cd lib
meson build

```

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

### Initialize Meson Build Directory

Source: https://tskit.dev/msprime/docs/latest/_sources/development.md

Set up the build directory for the C library using Meson. This command should be run from the 'lib' directory.

```bash
cd lib
meson build
```

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

### Get Population Size History

Source: https://tskit.dev/msprime/docs/latest/_modules/msprime/demography.html

Returns a numpy array representing the starting population size for each population across all epochs. The shape is (number of populations, number of epochs).

```python
@property
def population_size_history(self):
        """
        Returns a (num_pops, num_epochs) numpy array giving the starting population size
        for each population in each epoch.
        """
        pop_size = np.zeros((self.num_populations, self.num_epochs))
        for j, epoch in enumerate(self.epochs):
            for k, pop in enumerate(epoch.populations):
                pop_size[k, j] = pop.start_size
        return pop_size
```

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

### Format All Files

Source: https://tskit.dev/msprime/docs/latest/development.html

Manually format all project files using the 'prek --all-files' command.

```bash
$ uv run prek --all-files

```

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

### Simulate using development CLI

Source: https://tskit.dev/msprime/docs/latest/development.html

Run a simulation using the development CLI, specifying a configuration file and an output file path. The configuration file uses libconfig syntax.

```bash
./build/dev-cli simulate dev-tools/example.cfg -o out.trees

```

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

### Publish and Preview Benchmark Reports

Source: https://tskit.dev/msprime/docs/latest/_sources/development.md

Generate a browsable report of benchmark results. Use 'asv publish' to create the report and 'asv preview' to view it.

```bash
asv publish
```

```bash
asv preview
```

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

### Set up INFO level logging for msprime

Source: https://tskit.dev/msprime/docs/latest/_sources/logging.md

Initializes daiquiri logging to INFO level and runs a basic msprime simulation. Use this for high-level simulation status.

```python
import daiquiri
import msprime

daiquiri.setup(level="INFO")
ts = msprime.sim_ancestry(
    10,
    population_size=1000,
    model=[
        msprime.DiscreteTimeWrightFisher(duration=100),
        msprime.StandardCoalescent(),
    ],
    random_seed=1234
)
```

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

### DEBUG log output example

Source: https://tskit.dev/msprime/docs/latest/logging.html

Example of DEBUG level log messages during a larger msprime simulation.

```log
2026-04-16 12:13:36,080 [3347] INFO     msprime.ancestry: Sampling 100000 individuals with ploidy 2 in population 0 (name='pop_0') at time 0

```

```log
2026-04-16 12:13:36,173 [3347] INFO     msprime.ancestry: Starting replicate 0

```

```log
2026-04-16 12:13:36,183 [3347] INFO     msprime.ancestry: model[0] {'name': 'hudson'} started at time=0 nodes=200000 edges=0

```

```log
2026-04-16 12:13:36,184 [3347] INFO     msprime.ancestry: Running model {'name': 'hudson'} until max time: inf

```

```log
2026-04-16 12:13:36,223 [3347] DEBUG    msprime.ancestry: time=0.0104295 ancestors=190080 ret=1

```

```log
2026-04-16 12:13:36,256 [3347] DEBUG    msprime.ancestry: time=0.0219435 ancestors=180166 ret=1

```

```log
2026-04-16 12:13:36,288 [3347] DEBUG    msprime.ancestry: time=0.0346803 ancestors=170256 ret=1

```

```log
2026-04-16 12:13:36,321 [3347] DEBUG    msprime.ancestry: time=0.0491285 ancestors=160336 ret=1

```

```log
2026-04-16 12:13:36,356 [3347] DEBUG    msprime.ancestry: time=0.0657941 ancestors=150418 ret=1

```

```log
2026-04-16 12:13:36,391 [3347] DEBUG    msprime.ancestry: time=0.0847891 ancestors=140540 ret=1

```

```log
2026-04-16 12:13:36,428 [3347] DEBUG    msprime.ancestry: time=0.106283 ancestors=130644 ret=1

```

```log
2026-04-16 12:13:36,467 [3347] DEBUG    msprime.ancestry: time=0.131827 ancestors=120740 ret=1

```

```log
2026-04-16 12:13:36,509 [3347] DEBUG    msprime.ancestry: time=0.161742 ancestors=110874 ret=1

```

```log
2026-04-16 12:13:36,552 [3347] DEBUG    msprime.ancestry: time=0.197291 ancestors=101020 ret=1

```

```log
2026-04-16 12:13:36,599 [3347] DEBUG    msprime.ancestry: time=0.240122 ancestors=91192 ret=1

```

```log
2026-04-16 12:13:36,649 [3347] DEBUG    msprime.ancestry: time=0.293227 ancestors=81390 ret=1

```

```log
2026-04-16 12:13:36,702 [3347] DEBUG    msprime.ancestry: time=0.361519 ancestors=71580 ret=1

```

```log
2026-04-16 12:13:36,760 [3347] DEBUG    msprime.ancestry: time=0.45156 ancestors=61818 ret=1

```

```log
2026-04-16 12:13:36,822 [3347] DEBUG    msprime.ancestry: time=0.573763 ancestors=52074 ret=1

```

```log
2026-04-16 12:13:36,890 [3347] DEBUG    msprime.ancestry: time=0.755864 ancestors=42402 ret=1

```

```log
2026-04-16 12:13:36,963 [3347] DEBUG    msprime.ancestry: time=1.03995 ancestors=32814 ret=1

```

```log
2026-04-16 12:13:37,038 [3347] DEBUG    msprime.ancestry: time=1.54834 ancestors=23356 ret=1

```

```log
2026-04-16 12:13:37,126 [3347] DEBUG    msprime.ancestry: time=2.72085 ancestors=14190 ret=1

```

```log
2026-04-16 12:13:37,237 [3347] DEBUG    msprime.ancestry: time=7.38436 ancestors=5736 ret=1

```

```log
2026-04-16 12:13:37,336 [3347] DEBUG    msprime.ancestry: time=150.174 ancestors=558 ret=1

```

```log
2026-04-16 12:13:37,354 [3347] DEBUG    msprime.ancestry: time=4639.98 ancestors=154 ret=1

```

```log
2026-04-16 12:13:37,365 [3347] DEBUG    msprime.ancestry: time=17020.3 ancestors=100 ret=1

```

```log
2026-04-16 12:13:37,374 [3347] DEBUG    msprime.ancestry: time=35240.4 ancestors=96 ret=1

```

```log
2026-04-16 12:13:37,381 [3347] DEBUG    msprime.ancestry: time=66125.6 ancestors=62 ret=1

```

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

### Initialize Submodules in Existing Repository

Source: https://tskit.dev/msprime/docs/latest/development.html

If you have already cloned the repository, use this command to initialize and update all submodules. This is crucial for ensuring all necessary components are present.

```bash
$ git submodule update --init --recursive

```

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

### Example Output of Ancestry Simulation

Source: https://tskit.dev/msprime/docs/latest/replication.html

This is the expected output format for the mean and variance of TMRCA from the ancestry simulation example.

```python
(np.float64(3.799291711240622), np.float64(3.920289255957004))
```

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

### Example Output for Beta-Coalescent TMRCA

Source: https://tskit.dev/msprime/docs/latest/ancestry.html

Example numerical output for TMRCA calculations from Beta-coalescent simulations with different population sizes.

```text
0.14959691919068155
14.286394871874865
```

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

### Simulate Ancestry with Start Time and Duration

Source: https://tskit.dev/msprime/docs/latest/_sources/ancestry.md

Illustrates how start_time affects the total simulation duration when a model duration is specified. The simulation continues for the specified duration from the start_time.

```python
ts = msprime.sim_ancestry(
    3,
    population_size=10,
    start_time=5,
    model=msprime.DiscreteTimeWrightFisher(duration=10),
    random_seed=1234)
SVG(ts.draw_svg(y_axis=True))
```

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

### Example Output for Beta-Coalescent TMRCA (Alpha=1.1)

Source: https://tskit.dev/msprime/docs/latest/ancestry.html

Example numerical output for TMRCA calculations from Beta-coalescent simulations with alpha=1.1 and different population sizes.

```text
16.311807036386615
25.85247192870844
```

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

### Create initial state tree sequence

Source: https://tskit.dev/msprime/docs/latest/_sources/ancestry.md

Create an initial tree sequence by simulating ancestry for a specified duration. This sets up the base state for further simulations.

```python
initial_ts = msprime.sim_ancestry(1, end_time=0)
initial_ts
```

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

### Create and Visualize Island Model with Demes

Source: https://tskit.dev/msprime/docs/latest/_sources/demography.md

Creates an msprime island model, converts it to a Demes Graph, and visualizes it using demesdraw. Requires matplotlib for plotting.

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

demography = msprime.Demography.island_model([300] * 3, 1e-5)
demography.add_population(name="ancestral", initial_size=1000)
demography.add_population_split(time=500, derived=[0, 1, 2], ancestral="ancestral")
graph = msprime.Demography.to_demes(demography)
fig, ax = plt.subplots()  # use plt.rcParams["figure.figsize"]
demesdraw.tubes(graph, ax=ax, seed=1)
plt.show()
```

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

### Initialize and Configure Demography Object

Source: https://tskit.dev/msprime/docs/latest/_sources/demography.md

Create a Demography object and add populations with their initial sizes, growth rates, and descriptions. The 'initially_active' flag is crucial for populations that are ancestral and contemporary.

```python
import msprime

demography = msprime.Demography()
demography.add_population(
    name="YRI",
    description="Africa",
    initial_size=12300,
    initially_active=True,
);
demography.add_population(
    name="CEU",
    description="European",
    initial_size=N_CEU,
    growth_rate=r_CEU,
)
demography.add_population(
    name="CHB",
    description="East Asian",
    initial_size=N_CHB,
    growth_rate=r_CHB,
)
demography.add_population(
    name="OOA",
    description="Bottleneck out-of-Africa population",
    initial_size=2100,
)
```

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

### Check Pip Version

Source: https://tskit.dev/msprime/docs/latest/installation.html

Verifies that pip is installed and reports its version. This is a prerequisite check before installing msprime using pip, ensuring Python 3.11 or greater is available.

```bash
$ python3 -m pip --version

```

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

### Initialize Submodules in Existing Repository

Source: https://tskit.dev/msprime/docs/latest/_sources/development.md

If you have already cloned the repository without submodules, use this command to initialize and update them recursively.

```bash
git submodule update --init --recursive
```

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

### Run msprime program with User Install

Source: https://tskit.dev/msprime/docs/latest/installation.html

Executes the 'mspms' program installed via pip with the '--user' flag. This requires the '~/.local/bin' directory to be in your system's PATH.

```bash
$ ~/.local/bin/mspms

```

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

### Get the mean rate of a RateMap

Source: https://tskit.dev/msprime/docs/latest/_sources/rate_maps.md

Access the mean_rate attribute to get the weighted average rate across the entire RateMap. This provides a single value representing the overall rate.

```python
rate_map.mean_rate
```

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

### Simulate ancestry with default parameters

Source: https://tskit.dev/msprime/docs/latest/_sources/ancestry.md

Runs a simple ancestry simulation with 2 samples and displays a summary of the resulting tree sequence.

```python
ts = msprime.sim_ancestry(2)
ts
```

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

### Initialize msprime Simulator with Demography

Source: https://tskit.dev/msprime/docs/latest/ancestry.html

Initializes the msprime Simulator with various parameters, including demographic models, recombination and gene conversion maps, and simulation time bounds. Ensure that the demography object is correctly configured before simulation.

```python
return Simulator(
    tables=initial_state,
    recombination_map=recombination_map,
    gene_conversion_map=gene_conversion_map,
    gene_conversion_tract_length=gene_conversion_tract_length,
    discrete_genome=discrete_genome,
    ploidy=ploidy,
    demography=demography,
    models=models,
    store_migrations=record_migrations,
    additional_nodes=additional_nodes,
    coalescing_segments_only=coalescing_segments_only,
    start_time=start_time,
    end_time=end_time,
    num_labels=num_labels,
    random_generator=random_generator,
    stop_at_local_mrca=stop_at_local_mrca,
)
```

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

### Publish Benchmark Report

Source: https://tskit.dev/msprime/docs/latest/development.html

Generate a browsable report of benchmark results.

```bash
asv publish

```

```bash
asv preview

```

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

### Simulate Ancestry with Demography and Start Time

Source: https://tskit.dev/msprime/docs/latest/_sources/ancestry.md

Simulates ancestry with a defined population demography and samples taken at specific times before the simulation's start time. This demonstrates how fixed-time events are handled relative to the start_time.

```python
demography = msprime.Demography()
demography.add_population(name="A", initial_size=100)
demography.add_population(name="B", initial_size=100)
demography.add_population(name="C", initial_size=100)
demography.add_population_split(time=5, derived=["A", "B"], ancestral="C")

ts = msprime.sim_ancestry(
    samples=[
        msprime.SampleSet(1, population="A", time=1),
        msprime.SampleSet(1, population="B", time=2),
        msprime.SampleSet(1, population="C", time=6),
    ],
    demography=demography,
    ploidy=1,
    start_time=10,
    random_seed=1)

ts.tables.nodes
```

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

### Getting the First Tree

Source: https://tskit.dev/msprime/docs/latest/_sources/CHANGELOG.md

The `TreeSequence.first()` method returns the first tree in the sequence.

```python
ts.first()
```

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

### Initialize Out of Africa Demography Model

Source: https://tskit.dev/msprime/docs/latest/_sources/demography.md

Sets up the 'Out of Africa' demographic model using msprime's built-in function. This is a prerequisite for comparing inverse coalescence rates to actual population sizes.

```python
demography = msprime.Demography._ooa_model()
```

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

### Get Number of Epochs

Source: https://tskit.dev/msprime/docs/latest/_modules/msprime/demography.html

Returns the total number of epochs defined within the demographic model.

```python
@property
def num_epochs(self):
        """
        Returns the number of epochs defined by the demographic model.
        """
        return len(self.epochs)
```

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

### Get Sequence Length

Source: https://tskit.dev/msprime/docs/latest/_modules/msprime/intervals.html

Returns the total physical length of the chromosome represented by the recombination map.

```python
    def get_sequence_length(self):
        return self.map.sequence_length
```