### Example unit test output

Source: https://github.com/bdiemer/colossus/blob/master/README.rst

This is an example of the expected output when running the Colossus unit tests. 'ok' indicates successful test execution.

```shell
test_home_dir (colossus.tests.test_utils.TCGen) ... ok
test_Ez (colossus.tests.test_cosmology.TCComp) ... ok
...
test_DK14ConstructorOuter (colossus.tests.test_halo_profile.TCDK14) ... ok
test_DK14ConstructorWrapper (colossus.tests.test_halo_profile.TCDK14) ... ok

----------------------------------------------------------------------
Ran 86 tests in 7.026s

OK
```

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

### Install Colossus using pip

Source: https://github.com/bdiemer/colossus/blob/master/README.rst

Use this command to install the Colossus package. You may need to use 'sudo' depending on your system's permissions.

```shell
pip install colossus
```

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

### Example Unit Test Output

Source: https://github.com/bdiemer/colossus/blob/master/docs/source/installation.md

This is an example of the expected output when running the Colossus unit tests. Successful execution will show 'ok' for each test case.

```text
test_get_version (colossus.tests.test_utils.TCVersions) ... ok
test_versions (colossus.tests.test_utils.TCVersions) ... ok
... 
test_DK14ConstructorOuter (colossus.tests.test_halo_profile.TCDK14) ... ok
test_DK14ConstructorWrapper (colossus.tests.test_halo_profile.TCDK14) ... ok

----------------------------------------------------------------------
Ran 97 tests in 16.697s

OK
```

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

### Check Colossus Version

Source: https://github.com/bdiemer/colossus/blob/master/docs/source/faq.md

Execute this code to print the installed Colossus version. If this fails, an update may be needed.

```python
from colossus import version
print(version.__version__)
```

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

### Create and Plot NFW Profile

Source: https://github.com/bdiemer/colossus/blob/master/docs/source/_static/tutorial_halo_profile.html

Demonstrates creating an NFW profile with given mass, concentration, and redshift, then plotting its density profile against the mean density. Ensure the colossus library is installed and the desired cosmology is set.

```python
from colossus.halo import profile_nfw

Mvir = 1E15
cvir = 5.0
z = 0.0
p_nfw = profile_nfw.NFWProfile(M = Mvir, c = cvir, z = z, mdef = 'vir')

r = 10**np.arange(0,4,0.02)
rho_m = cosmo.rho_m(z)
rho_nfw = p_nfw.density(r)

plt.figure()
plt.loglog()
plt.xlabel('r(kpc/h)')
plt.ylabel('density / mean')
plt.plot(r, rho_nfw / rho_m, '-', label = 'NFW');
plt.ylim(1E0, 1E7)
plt.legend()
```

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

### Run Colossus unit tests

Source: https://github.com/bdiemer/colossus/blob/master/README.rst

After installation, import and run the unit tests to ensure Colossus is working correctly. The output should indicate that all tests passed.

```python
from colossus.tests import run_tests
```

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

### Import Libraries and Set Cosmology

Source: https://github.com/bdiemer/colossus/blob/master/docs/source/_static/tutorial_lss_bias.html

Imports necessary libraries and sets the cosmology for calculations. Ensure Colossus is installed.

```python
from __future__ import print_function
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
```

```python
from colossus.cosmology import cosmology
cosmology.setCosmology('planck18');
```

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

### Import Colossus Modules and Set Cosmology

Source: https://github.com/bdiemer/colossus/blob/master/docs/source/_static/tutorial_halo_splashback.html

Imports necessary Colossus modules and sets the cosmological parameters. Ensure Colossus is installed and a cosmology is selected before proceeding.

```python
from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
```

```python
from colossus.cosmology import cosmology
cosmology.setCosmology('planck18');
```

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

### Import Colossus Modules

Source: https://github.com/bdiemer/colossus/blob/master/docs/source/_static/tutorial_halo_so.html

Import necessary Colossus modules for cosmology and halo mass calculations. This is a standard setup for using the library.

```python
from __future__ import print_function 
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
```

```python
from colossus.cosmology import cosmology
cosmology.setCosmology('planck18');
```

```python
from colossus.halo import mass_so
```

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

### Set Cosmology and Prepare Mass Array for Comparison

Source: https://github.com/bdiemer/colossus/blob/master/docs/source/_static/tutorial_halo_concentration.html

Sets the cosmology to 'bolshoi' and creates a wide range of halo masses for comparing different concentration models. This is a setup step for a comparative analysis.

```python
cosmology.setCosmology('bolshoi')
M = 10**np.arange(8.0, 15.4, 0.1)
```

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

### User-Defined Power Spectrum from File

Source: https://github.com/bdiemer/colossus/blob/master/docs/source/_static/tutorial_cosmology_powerspec.html

This example shows how to load and use a user-defined matter power spectrum from a text file. The file should contain two columns: log10(k) and log10(P). Ensure the requested wavenumbers are within the range of the provided file.

```python
from colossus.utils import utilities

file_path = utilities.getCodeDir()[:-9] + '/docs/source/_data/power_spectrum.txt'
ps_args_table = dict(model='mytable', path=file_path)

k_table = 10**np.linspace(-4.0, 2.2, 200)
P_table = cosmo.matterPowerSpectrum(k_table, **ps_args_table)

plt.figure()
plt.loglog()
plt.xlabel('k(h / Mpc)')
plt.ylabel('P(k)')
plt.plot(k_table, P_table, '-')
```

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

### Generate and Compare Power Spectra with CAMB

Source: https://github.com/bdiemer/colossus/blob/master/docs/source/_static/tutorial_cosmology_powerspec.html

This snippet generates the Eisenstein & Hu (1998) and CAMB matter power spectra and plots their relative difference. Ensure CAMB is installed (`pip install camb`).

```python
import numpy as np
import matplotlib.pyplot as plt
from pycolossus import cosmology

cosmo = cosmology.Cosmology()

k = 10**np.linspace(-4.0, 2.2, 200)
P_eh98 = cosmo.matterPowerSpectrum(k, model = 'eisenstein98')
P_camb = cosmo.matterPowerSpectrum(k, model = 'camb')

plt.figure()
plt.xscale('log')
plt.xlabel('k(h / Mpc)')
plt.ylabel('Difference in P(k)')
plt.axhline(0.0, ls = '--', color = 'gray')
plt.plot(k, P_eh98 / P_camb - 1.0, '-');
```

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

### Compute and Compare Power Spectra with CAMB and Reionization

Source: https://github.com/bdiemer/colossus/blob/master/docs/source/_static/tutorial_cosmology_powerspec.html

This snippet computes the matter power spectrum using CAMB with default parameters and then with reionization enabled. It plots the ratio of the reionized spectrum to the default spectrum, highlighting the differences on various scales. Ensure 'camb' is installed and imported.

```python
import camb

k = 10**np.linspace(-4.0, 1.5, 200)

P_def = cosmo.matterPowerSpectrum(k, model = 'camb')
cosmo2 = cosmology.setCosmology('planck18', persistence = 'r')
Reion = camb.reionization.TanhReionization(Reionization = True)
P_reion = cosmo2.matterPowerSpectrum(k, model = 'camb', Reion = Reion)

plt.figure()
plt.xscale('log')
plt.xlabel('k(h / Mpc)')
plt.ylabel('CDM / Total - 1')
plt.axhline(0.0, ls = '--', color = 'gray')
plt.plot(k, P_reion / P_def - 1.0, '-');
```

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

### Load and Plot Cosmological Power Spectrum

Source: https://github.com/bdiemer/colossus/blob/master/docs/source/_static/tutorial_cosmology_powerspec.html

Loads cosmological data and plots the power spectrum. Ensure necessary libraries are installed.

```python
import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import InterpolatedUnivariateSpline
from classy import Class

# Set parameters for CLASS
params = {
    'output': 'tCl,lCl,dCl', 
    'l_max': 2500,
    'lensing': 'yes',
    'non linear': 'halofit'
}

# Initialize CLASS
m = Class()
m.set(params)
m.compute()

# Get power spectra
spectra = m.get_results()['cls']

# Extract relevant spectra (e.g., TT, EE, TE)
ls = np.arange(2, params['l_max'] + 1)
TT = spectra['tt'][2:]
EE = spectra['ee'][2:]
TE = spectra['te'][2:]

# Plotting
plt.figure(figsize=(10, 6))

# Plot TT spectrum
plt.loglog(ls, TT, label='TT')

# Plot EE spectrum
plt.loglog(ls, EE, label='EE')

# Plot TE spectrum
plt.loglog(ls, TE, label='TE')

plt.xlabel(r'$l$')
plt.ylabel(r'$l(l+1)C_l / 2\pi$ [$\/\mu K^2$]')
plt.title('Cosmological Power Spectra')
plt.legend()
plt.grid(True, which='both', linestyle='--', linewidth=0.5)
plt.show()

```

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

### Compare Eisenstein-1998 and CAMB Power Spectra

Source: https://github.com/bdiemer/colossus/blob/master/tutorials/tutorial_cosmology_powerspec.ipynb

Compares the matter power spectrum calculated using the Eisenstein-1998 model with the one computed by CAMB. Requires CAMB to be installed. The first execution may take a few seconds due to CAMB's more involved calculation.

```python
import camb
import matplotlib.pyplot as plt
import numpy as np
from pycolossus import cosmology

cosmo = cosmology.setCosmology('planck18')
k = 10**np.linspace(-4.0, 2.2, 200)
P_eh98 = cosmo.matterPowerSpectrum(k, model = 'eisenstein98')
P_camb = cosmo.matterPowerSpectrum(k, model = 'camb')

plt.figure()
plt.xscale('log')
plt.xlabel('k(h / Mpc)')
plt.ylabel('Difference in P(k)')
plt.axhline(0.0, ls = '--', color = 'gray')
plt.plot(k, P_eh98 / P_camb - 1.0, '-');
```

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

### Add Colossus to PYTHONPATH

Source: https://github.com/bdiemer/colossus/blob/master/docs/source/installation.md

Manually add the Colossus installation directory to your PYTHONPATH environment variable. Replace the example path with the actual location of Colossus on your system. This is typically added to your shell's initialization script (e.g., bashrc).

```shell
export PYTHONPATH=$PYTHONPATH:/users/me/code/colossus
```

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

### Initialize Other Density Profiles

Source: https://github.com/bdiemer/colossus/blob/master/tutorials/tutorial_halo_profile.ipynb

Initializes Einasto and Diemer-23 density profiles for comparison with the NFW profile.

```python
from colossus.halo import profile_einasto
from colossus.halo import profile_diemer23

p_einasto = profile_einasto.EinastoProfile(M = Mvir, c = cvir, z = z, mdef = 'vir')
p_d23 = profile_diemer23.ModelAProfile(M = Mvir, c = cvir, z = z, mdef = 'vir')

rho_einasto = p_einasto.density(r)
rho_d23 = p_d23.density(r)
```

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

### Initialize and Plot NFW Profile

Source: https://github.com/bdiemer/colossus/blob/master/tutorials/tutorial_halo_profile.ipynb

Creates an NFW density profile initialized by mass and concentration, then plots its density profile relative to the mean density.

```python
from colossus.halo import profile_nfw

Mvir = 1E15
cvir = 5.0
z = 0.0
p_nfw = profile_nfw.NFWProfile(M = Mvir, c = cvir, z = z, mdef = 'vir')

r = 10**np.arange(0,4,0.02)
rho_m = cosmo.rho_m(z)
rho_nfw = p_nfw.density(r)

plt.figure()
plt.loglog()
plt.xlabel('r(kpc/h)')
plt.ylabel('density / mean')
plt.plot(r, rho_nfw / rho_m, '-', label = 'NFW');
plt.ylim(1E0, 1E7)
plt.legend();
```

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

### Initialize MCMC Parameters

Source: https://github.com/bdiemer/colossus/blob/master/tutorials/tutorial_utils_mcmc.ipynb

Sets up initial guess for parameters and the number of walkers for the MCMC simulation.

```python
param_names = ['x1', 'x2']
x_initial = np.array([1.0, 1.0])
n_params = len(param_names)
```

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

### Initialize Cosmology and Compute Power Spectrum

Source: https://github.com/bdiemer/colossus/blob/master/docs/source/_static/tutorial_cosmology_powerspec.html

This snippet demonstrates initializing a cosmology and computing the matter power spectrum for a given set of wavenumbers. It highlights the potential for initial computation time due to interpolation table generation.

```python
from colossus.cosmology import cosmology

cosmo = cosmology.setCosmology('planck18')

k = np.logspace(-4, 1, 100)
P = cosmo.matterPowerSpectrum(k)
```

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

### Get Current Cosmology

Source: https://github.com/bdiemer/colossus/blob/master/docs/source/_static/tutorial_cosmology_basics.html

Retrieve the currently active cosmology object and print its name.

```python
cosmo = cosmology.getCurrent()
print(cosmo.name)
```

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

### Call plotChain function

Source: https://github.com/bdiemer/colossus/blob/master/docs/source/_static/tutorial_halo_profile.html

This is an example call to the plotChain function, passing the thinned MCMC results and parameter labels.

```python
plotChain(fit_results['chain_thin'], ['rhos', 'rs'])
```

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

### Import Colossus Cosmology Module

Source: https://github.com/bdiemer/colossus/blob/master/tutorials/tutorial_cosmology_basics.ipynb

Import the necessary cosmology module from the Colossus library. Ensure Colossus is installed to avoid errors.

```python
from colossus.cosmology import cosmology
```

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

### Get Current Cosmology

Source: https://github.com/bdiemer/colossus/blob/master/tutorials/tutorial_cosmology_basics.ipynb

Retrieve the currently active cosmology object from anywhere in the code using getCurrent(). Access its name attribute.

```python
cosmo = cosmology.getCurrent()
print(cosmo.name)
```

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

### Define Likelihood Function

Source: https://github.com/bdiemer/colossus/blob/master/tutorials/tutorial_utils_mcmc.ipynb

Defines a double Gaussian likelihood function with correlated parameters. This function is used to guide the MCMC sampling.

```python
def likelihood(x):

    sig1 = 1.0
    sig2 = 2.0
    r = 0.95
    r2 = r * r
    res = np.exp(-0.5 * ((x[:, 0] / sig1)**2 + (x[:, 1] / sig2)**2 - 2.0 * r * x[:, 0] * x[:, 1] 
            / (sig1 * sig2)) / (1.0 - r2)) / (2 * np.pi * sig1 * sig2) / np.sqrt(1.0 - r2)

    return res
```

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

### Instantiate and Compare Density Profiles

Source: https://github.com/bdiemer/colossus/blob/master/docs/source/_static/tutorial_halo_profile.html

Instantiate the custom HernquistProfile using both direct parameter setting and conversion from mass and concentration. Compare its surface density with the NFW profile.

```python
Mvir = 1E12
cvir = 7.0
p_nfw = profile_nfw.NFWProfile(M = Mvir, c = cvir, z = z, mdef = 'vir')

p_hernquist1 = HernquistProfile(rhos = p_nfw.par['rhos'], rs = p_nfw.par['rs'])
p_hernquist2 = HernquistProfile(M = Mvir, c = cvir, z = z, mdef = 'vir')
```

```python
r = 10**np.arange(0,3,0.02)
Sigma_nfw = p_nfw.surfaceDensity(r)
Sigma_hernquist = p_hernquist2.surfaceDensity(r)

plt.figure()
plt.loglog()
plt.xlabel('r(kpc/h)')
plt.ylabel('Surface density')
plt.plot(r, Sigma_nfw, '-', label = 'NFW');
plt.plot(r, Sigma_hernquist, '-', label = 'Hernquist');
plt.ylim(1E5, 1E9)
plt.legend();
```

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

### Switch Between Cosmologies

Source: https://github.com/bdiemer/colossus/blob/master/tutorials/tutorial_cosmology_basics.ipynb

Demonstrates how to set a new cosmology, make it current, and then switch back to a previously stored cosmology object.

```python
cosmo2 = cosmology.setCosmology('WMAP9')
cosmology.setCurrent(cosmo2)
print(cosmology.getCurrent().name)
cosmology.setCurrent(cosmo)
print(cosmology.getCurrent().name)
```

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

### Get Age of the Universe

Source: https://github.com/bdiemer/colossus/blob/master/docs/source/_static/tutorial_cosmology_basics.html

After setting a cosmology, you can evaluate standard cosmological quantities. The `age()` function returns the age of the universe in Gyr, taking redshift as an argument.

```python
cosmo = cosmology.setCosmology('planck18')
print(cosmo.age(0.0))
```

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

### Handle Unsupported Instantaneous MAR Calculation

Source: https://github.com/bdiemer/colossus/blob/master/tutorials/tutorial_halo_accretion_history.ipynb

Demonstrates error handling when requesting an instantaneous MAR ('inst') for a model that does not support it. The 'bera26' model, for example, only provides time-averaged rates.

```python
try:
    macc.massAccretionRate(1E12, 2.0, model = 'bera26', delta_t = 'inst')
except Exception as e:
    print('Got expected error:', e)
```

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

### Load and Use Custom Power Spectrum from File

Source: https://github.com/bdiemer/colossus/blob/master/tutorials/tutorial_cosmology_powerspec.ipynb

Loads a custom matter power spectrum from a text file and computes it over a specified range of wavenumbers. Ensure the file contains two columns: log10(k) and log10(P).

```python
from colossus.utils import utilities

file_path = utilities.getCodeDir()[:-9] + '/docs/source/_data/power_spectrum.txt'
ps_args_table = dict(model = 'mytable', path = file_path)

k_table = 10**np.linspace(-4.0, 2.2, 200)
P_table = cosmo.matterPowerSpectrum(k_table, **ps_args_table)

plt.figure()
plt.loglog()
plt.xlabel('k(h / Mpc)')
plt.ylabel('P(k)')
plt.plot(k_table, P_table, '-');
```

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

### Set Cosmology Interpolation Range

Source: https://github.com/bdiemer/colossus/blob/master/docs/source/faq.md

When using tabulated power spectra, adjust the interpolation range to avoid 'x must be strictly increasing' errors. This example limits interpolation at smaller radii.

```python
R_min_sigma = 1E-3
```

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

### Import Libraries for Colossus Tutorial

Source: https://github.com/bdiemer/colossus/blob/master/tutorials/tutorial_lss_bias.ipynb

Imports necessary libraries for plotting and numerical operations, including matplotlib and numpy. Sets up the environment for inline plotting.

```python
from __future__ import print_function 
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
```