### Calculate Lorentz Profile Absorption

Source: https://context7.com/hitranonline/hapi/llms.txt

Calculates absorption coefficients for pressure-dominated regimes. The second example demonstrates saving the output directly to a file.

```python
nu, coef = absorptionCoefficient_Lorentz(
    SourceTables='CH4',
    Components=[(6, 1)],  # CH4, main isotopologue
    Environment={'T': 296., 'p': 10.},  # 10 atm pressure
    WavenumberRange=[3000, 3100],
    WavenumberStep=0.01,
    HITRAN_units=True
)

# Save to file
nu, coef = absorptionCoefficient_Lorentz(
    SourceTables='CH4',
    Components=[(6, 1)],
    Environment={'T': 296., 'p': 1.},
    WavenumberRange=[3050, 3060],
    WavenumberStep=0.001,
    File='ch4_lorentz.txt',
    Format='%12.6f %12.6e'
)
```

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

### Get Available Tables with tableList

Source: https://context7.com/hitranonline/hapi/llms.txt

Returns a list of all tables currently loaded in the database cache. This function is useful for managing multiple datasets within the HAPI environment.

```python
from hapi import *

db_begin('data')
fetch('H2O', 1, 1, 1000, 1100)
fetch('CO2', 2, 1, 2000, 2100)

# Get list of all available tables
tables = tableList()
print("Tables:", tables)
# Output: Tables: ['H2O', 'CO2']
```

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

### Calculate Speed-Dependent Voigt Profile

Source: https://context7.com/hitranonline/hapi/llms.txt

Accounts for velocity dependence of collisional parameters. The example compares the result against a standard Voigt profile.

```python
from hapi import *

db_begin('data')
fetch('CO2', 2, 1, 2380, 2400)

# Speed-dependent Voigt profile
nu, coef = absorptionCoefficient_SDVoigt(
    SourceTables='CO2',
    Components=[(2, 1)],
    Environment={'T': 296., 'p': 1.},
    WavenumberRange=[2380, 2400],
    WavenumberStep=0.001,
    Diluent={'air': 1.0},
    HITRAN_units=True
)

# Compare with standard Voigt
nu_v, coef_v = absorptionCoefficient_Voigt(
    SourceTables='CO2',
    Components=[(2, 1)],
    Environment={'T': 296., 'p': 1.},
    WavenumberRange=[2380, 2400],
    WavenumberStep=0.001
)

import matplotlib.pyplot as plt
plt.plot(nu, coef, 'b-', label='SD-Voigt')
plt.plot(nu_v, coef_v, 'r--', label='Voigt')
plt.legend()
plt.xlabel('Wavenumber (cm-1)')
plt.ylabel('Absorption Coefficient')
plt.show()
```

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

### Calculate Hartmann-Tran Profile

Source: https://context7.com/hitranonline/hapi/llms.txt

Utilizes the most comprehensive line shape model. Includes examples for standard usage and custom environment configurations.

```python
from hapi import *

db_begin('data')
fetch('CO2', 2, 1, 2380, 2400)

# Hartmann-Tran (HT) profile - most accurate
nu, coef = absorptionCoefficient_HT(
    SourceTables='CO2',
    Components=[(2, 1)],
    Environment={'T': 296., 'p': 1.},
    WavenumberRange=[2380, 2400],
    WavenumberStep=0.001,
    Diluent={'air': 1.0},
    HITRAN_units=True,
    LineMixingRosen=True  # Include first-order line mixing
)

# HT profile with custom environment
nu, coef = absorptionCoefficient_HT(
    SourceTables='CO2',
    Components=[(2, 1)],
    Environment={'T': 250., 'p': 0.5},
    WavenumberRange=[2385, 2395],
    WavenumberStep=0.0005,
    Diluent={'air': 0.8, 'self': 0.2},
    HITRAN_units=False  # returns cm^-1
)
```

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

### Get Molecule Name with moleculeName

Source: https://context7.com/hitranonline/hapi/llms.txt

Retrieves the name of a HITRAN molecule using its molecule number.

```python
from hapi import *

# Get molecule names
print(moleculeName(1))   # 'H2O'
print(moleculeName(2))   # 'CO2'
print(moleculeName(3))   # 'O3'
print(moleculeName(4))   # 'N2O'
print(moleculeName(5))   # 'CO'
print(moleculeName(6))   # 'CH4'
```

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

### Get Natural Abundance with abundance

Source: https://context7.com/hitranonline/hapi/llms.txt

Returns the natural abundance of a HITRAN isotopologue. Requires molecule and isotopologue numbers as input.

```python
from hapi import *

# Get abundance of main H2O isotopologue (H2-16O)
ab_h2o = abundance(1, 1)
print(f"H2O abundance: {ab_h2o}")  # ~0.997

# Get abundance of HDO
ab_hdo = abundance(1, 4)
print(f"HDO abundance: {ab_hdo}")  # ~3.1e-4

# Get abundance of 13CO2
ab_co2_13 = abundance(2, 2)
print(f"13CO2 abundance: {ab_co2_13}")  # ~0.011
```

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

### Get Isotopologue Name with isotopologueName

Source: https://context7.com/hitranonline/hapi/llms.txt

Returns the name of a specific isotopologue given its molecule and isotopologue numbers.

```python
from hapi import *

# Get isotopologue names for water
print(isotopologueName(1, 1))  # 'H2(16O)'
print(isotopologueName(1, 2))  # 'H2(18O)'
print(isotopologueName(1, 3))  # 'H2(17O)'
print(isotopologueName(1, 4))  # 'HD(16O)'

# Get isotopologue names for CO2
print(isotopologueName(2, 1))  # '(12C)(16O)2'
print(isotopologueName(2, 2))  # '(13C)(16O)2'
```

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

### Get Molecular Mass with molecularMass

Source: https://context7.com/hitranonline/hapi/llms.txt

Returns the molecular mass in atomic mass units for a given isotopologue. Requires molecule and isotopologue numbers.

```python
from hapi import *

# Get mass of main H2O isotopologue
mass_h2o = molecularMass(1, 1)
print(f"H2O mass: {mass_h2o} amu")  # ~18.01

# Get mass of CO2
mass_co2 = molecularMass(2, 1)
print(f"CO2 mass: {mass_co2} amu")  # ~43.99

# Get mass of heavy water D2O
mass_d2o = molecularMass(1, 7)
print(f"D2O mass: {mass_d2o} amu")  # ~20.02
```

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

### Initialize Database Connection with db_begin

Source: https://context7.com/hitranonline/hapi/llms.txt

Opens a database connection to store and retrieve spectroscopic data. Data is stored in a folder specified by the `db` parameter, with the default being the current directory. Use `tableList()` to see available tables.

```python
from hapi import *

# Initialize database in default directory (current folder)
db_begin()

# Initialize database in a custom directory
db_begin('my_spectroscopy_data')

# List all tables in the current database
tables = tableList()
print("Available tables:", tables)
```

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

### Complete Atmospheric Data Workflow

Source: https://context7.com/hitranonline/hapi/llms.txt

Demonstrates a full analysis pipeline including database initialization, property retrieval, spectral calculation, convolution, and multi-panel plotting.

```python
from hapi import *
import matplotlib.pyplot as plt

# Initialize database
db_begin('atmospheric_data')

# Download CO2 data in the 15 micron band
fetch('CO2', 2, 1, 600, 750)

# Display table information
print("Available tables:", tableList())
describe('CO2')

# Get molecular properties
print(f"Molecule: {moleculeName(2)}")
print(f"Isotopologue: {isotopologueName(2, 1)}")
print(f"Mass: {molecularMass(2, 1)} amu")
print(f"Abundance: {abundance(2, 1)}")

# Calculate partition sum
Q_296 = partitionSum(2, 1, 296)
print(f"Partition sum at 296K: {Q_296}")

# Calculate high-resolution absorption coefficient
nu, coef = absorptionCoefficient_Voigt(
    SourceTables='CO2',
    Components=[(2, 1)],
    Environment={'T': 296., 'p': 1.},
    WavenumberRange=[660, 680],
    WavenumberStep=0.01,
    HITRAN_units=False
)

# Calculate transmittance for 10 m path
nu, trans = transmittanceSpectrum(nu, coef, Environment={'l': 1000.})

# Simulate low-resolution measurement
nu_lr, trans_lr, _, _, _ = convolveSpectrum(
    nu, trans,
    Resolution=1.0,
    AF_wing=5.0,
    SlitFunction=SLIT_GAUSSIAN
)

# Plot results
fig, axes = plt.subplots(2, 2, figsize=(14, 10))

# Stick spectrum
x, y = getStickXY('CO2')
axes[0, 0].plot(x, y)
axes[0, 0].set_xlim([660, 680])
axes[0, 0].set_xlabel('Wavenumber (cm-1)')
axes[0, 0].set_ylabel('Line Intensity')
axes[0, 0].set_title('Stick Spectrum')

# Absorption coefficient
axes[0, 1].semilogy(nu, coef)
axes[0, 1].set_xlabel('Wavenumber (cm-1)')
axes[0, 1].set_ylabel('Absorption Coefficient (cm-1)')
axes[0, 1].set_title('Voigt Absorption Coefficient')

# High-res transmittance
axes[1, 0].plot(nu, trans)
axes[1, 0].set_xlabel('Wavenumber (cm-1)')
axes[1, 0].set_ylabel('Transmittance')
axes[1, 0].set_ylim([0, 1])
axes[1, 0].set_title('High-Resolution Transmittance (10m path)')

# Low-res transmittance
axes[1, 1].plot(nu_lr, trans_lr)
axes[1, 1].set_xlabel('Wavenumber (cm-1)')
axes[1, 1].set_ylabel('Transmittance')
axes[1, 1].set_ylim([0, 1])
axes[1, 1].set_title('Convolved Spectrum (1 cm-1 resolution)')

plt.tight_layout()
plt.savefig('co2_analysis.png', dpi=150)
plt.show()

# Save database
db_commit()
```

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

### Calculate Partition Sums with partitionSum

Source: https://context7.com/hitranonline/hapi/llms.txt

Calculates total internal partition sums at specified temperatures using TIPS data. Supports various TIPS versions and temperature ranges.

```python
from hapi import *

# Calculate partition sum at single temperature
Q_296 = partitionSum(1, 1, 296)  # H2O at 296 K
print(f"Q(296K) = {Q_296}")

# Calculate partition sums at multiple temperatures
temps = [200, 296, 500, 1000]
Q_list = partitionSum(1, 1, temps)
for T, Q in zip(temps, Q_list):
    print(f"Q({T}K) = {Q}")

# Calculate over temperature range with step
T_grid, Q_grid = partitionSum(1, 1, [200, 1000], step=10)

# Use specific TIPS version
Q_2021 = partitionSum(1, 1, 296, version=2021)
Q_2017 = partitionSum(1, 1, 296, version=2017)
Q_2011 = partitionSum(1, 1, 296, version=2011)

# Plot partition sum vs temperature
import matplotlib.pyplot as plt
T, Q = partitionSum(2, 1, [100, 3000], step=10)  # CO2
plt.plot(T, Q)
plt.xlabel('Temperature (K)')
plt.ylabel('Partition Sum Q(T)')
plt.title('CO2 Partition Sum')
plt.show()
```

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

### Display Table Information with describe

Source: https://context7.com/hitranonline/hapi/llms.txt

Prints detailed information about a table including parameter names, formats, number of rows, and wavenumber range. This is useful for understanding the structure of downloaded spectroscopic data.

```python
from hapi import *

db_begin('data')
fetch('H2O', 1, 1, 1000, 1100)

# Display table structure and metadata
describe('H2O')
# Output:
#   Name: H2O
#   Rows: 1234
#   Wavenumber range: 1000.000 - 1100.000 cm-1
#   Parameters: molec_id, local_iso_id, nu, sw, a, gamma_air, ...
```

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

### partitionSum

Source: https://context7.com/hitranonline/hapi/llms.txt

Calculates total internal partition sums at specified temperatures using TIPS data.

```APIDOC
## partitionSum

### Description
Calculates total internal partition sums at specified temperatures using TIPS (Total Internal Partition Sums) data. Supports TIPS-2011, TIPS-2017, TIPS-2021, and TIPS-2025 versions.

### Parameters
#### Request Body
- **molecule_number** (int) - Required - HITRAN molecule number
- **isotopologue_number** (int) - Required - HITRAN isotopologue number
- **temperature** (float/list) - Required - Temperature(s) in Kelvin
- **version** (int) - Optional - TIPS version year

### Request Example
partitionSum(1, 1, 296, version=2021)
```

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

### Download Single Isotopologue Data with fetch

Source: https://context7.com/hitranonline/hapi/llms.txt

Downloads line-by-line spectroscopic data from HITRANonline for a single molecule/isotopologue combination and saves it to a local table. You can specify molecule name, isotopologue ID, wavenumber range, and optional parameters like broadening.

```python
from hapi import *

db_begin('data')

# Fetch water vapor (M=1) main isotopologue (I=1) between 1000-1100 cm-1
fetch('H2O', 1, 1, 1000, 1100)

# Fetch CO2 (M=2) main isotopologue (I=1) between 2000-2100 cm-1
fetch('CO2', 2, 1, 2000, 2100)

# Fetch with additional parameters (e.g., broadening parameters)
fetch('H2O_extended', 1, 1, 1000, 1100,
      ParameterGroups=['Standard', 'Broadening'],
      Parameters=[])

# Verify download by describing the table
describe('H2O')
# Output includes: parameter names, formats, wavenumber range
```

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

### Calculate Transmittance Spectrum

Source: https://context7.com/hitranonline/hapi/llms.txt

Initializes the calculation of transmittance using the Beer-Lambert law.

```python
from hapi import *

db_begin('data')
fetch('CO2', 2, 1, 2300, 2400)
```

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

### Calculate Gas Mixture Absorption

Source: https://context7.com/hitranonline/hapi/llms.txt

Demonstrates calculating absorption for mixtures by specifying mixing ratios or summing individual species contributions.

```python
from hapi import *

db_begin('data')

# Fetch multiple species
fetch('H2O', 1, 1, 1500, 1600)
fetch('CO2', 2, 1, 1500, 1600)
fetch('CH4', 6, 1, 1500, 1600)

# Calculate mixture absorption (all in one table)
fetch_by_ids('atmosphere', [1, 7, 26], 1500, 1600)

nu, coef = absorptionCoefficient_Voigt(
    SourceTables='atmosphere',
    Components=[
        (1, 1, 0.01),    # H2O, 1% mixing ratio
        (2, 1, 0.0004),  # CO2, 400 ppm
        (6, 1, 0.00002)  # CH4, 20 ppm
    ],
    Environment={'T': 296., 'p': 1.},
    WavenumberRange=[1500, 1600],
    WavenumberStep=0.01,
    HITRAN_units=False
)

# Calculate individual species and sum
nu_h2o, coef_h2o = absorptionCoefficient_Voigt(
    SourceTables='H2O', Components=[(1, 1, 0.01)],
    Environment={'T': 296., 'p': 1.},
    WavenumberRange=[1500, 1600], WavenumberStep=0.01
)

nu_co2, coef_co2 = absorptionCoefficient_Voigt(
    SourceTables='CO2', Components=[(2, 1, 0.0004)],
    Environment={'T': 296., 'p': 1.},
    WavenumberRange=[1500, 1600], WavenumberStep=0.01
)
```

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

### Simulate FTIR Spectrum with Michelson Function

Source: https://context7.com/hitranonline/hapi/llms.txt

Calculates the absorption coefficient and transmittance, then applies a Michelson interferometer convolution.

```python
db_begin('data')
fetch('CO2', 2, 1, 2380, 2400)

nu, coef = absorptionCoefficient_Voigt(
    SourceTables='CO2',
    Components=[(2, 1)],
    Environment={'T': 296., 'p': 1.},
    WavenumberRange=[2380, 2400],
    WavenumberStep=0.001
)

nu, trans = transmittanceSpectrum(nu, coef, Environment={'l': 10.})

# Michelson interferometer simulation
nu_ftir, trans_ftir, _, _, _ = convolveSpectrum(
    nu, trans,
    Resolution=0.1,  # 0.1 cm-1 resolution
    AF_wing=2.0,
    SlitFunction=SLIT_MICHELSON
)
```

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

### Plot Stick Spectrum Data

Source: https://context7.com/hitranonline/hapi/llms.txt

Retrieves stick spectrum data using getStickXY and visualizes it alongside calculated Voigt absorption coefficients.

```python
from hapi import *

db_begin('data')
fetch('CO2', 2, 1, 2380, 2400)

# Get stick spectrum data
x, y = getStickXY('CO2')

import matplotlib.pyplot as plt

# Plot stick spectrum
plt.figure(figsize=(12, 4))
plt.plot(x, y, 'b-')
plt.xlabel('Wavenumber (cm-1)')
plt.ylabel('Line Intensity')
plt.title('CO2 Stick Spectrum')
plt.xlim([2380, 2400])
plt.show()

# Combine with calculated absorption coefficient
nu, coef = absorptionCoefficient_Voigt(
    SourceTables='CO2',
    Components=[(2, 1)],
    Environment={'T': 296., 'p': 1.},
    WavenumberRange=[2380, 2400],
    WavenumberStep=0.01
)

fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 8), sharex=True)
ax1.plot(x, y, 'b-')
ax1.set_ylabel('Line Intensity')
ax1.set_title('Stick Spectrum')

ax2.plot(nu, coef, 'r-')
ax2.set_xlabel('Wavenumber (cm-1)')
ax2.set_ylabel('Absorption Coefficient')
ax2.set_title('Voigt Absorption')
plt.tight_layout()
plt.show()
```

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

### Database Management

Source: https://context7.com/hitranonline/hapi/llms.txt

Functions for initializing and committing changes to the HAPI database.

```APIDOC
## Database Management

### db_begin - Initialize Database Connection

Opens a database connection to store and retrieve spectroscopic data. Data is stored in a folder specified by the `db` parameter, with default being the current directory.

```python
from hapi import *

# Initialize database in default directory (current folder)
db_begin()

# Initialize database in a custom directory
db_begin('my_spectroscopy_data')

# List all tables in the current database
tables = tableList()
print("Available tables:", tables)
```

### db_commit - Save Database Changes

Commits all changes made to the opened database, saving all tables to their corresponding files on disk.

```python
from hapi import *

db_begin('spectral_data')

# After making modifications to tables...
# Save all changes to disk
db_commit()
```
```

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

### Data Fetching

Source: https://context7.com/hitranonline/hapi/llms.txt

Functions for downloading spectroscopic line-by-line data from HITRANonline.

```APIDOC
## Data Fetching

### fetch - Download Single Isotopologue Data

Downloads line-by-line spectroscopic data from HITRANonline for a single molecule/isotopologue combination and saves it to a local table.

```python
from hapi import *

db_begin('data')

# Fetch water vapor (M=1) main isotopologue (I=1) between 1000-1100 cm-1
fetch('H2O', 1, 1, 1000, 1100)

# Fetch CO2 (M=2) main isotopologue (I=1) between 2000-2100 cm-1
fetch('CO2', 2, 1, 2000, 2100)

# Fetch with additional parameters (e.g., broadening parameters)
fetch('H2O_extended', 1, 1, 1000, 1100,
      ParameterGroups=['Standard', 'Broadening'],
      Parameters=[])

# Verify download by describing the table
describe('H2O')
# Output includes: parameter names, formats, wavenumber range
```

### fetch_by_ids - Download Multiple Isotopologues

Downloads line-by-line data for multiple isotopologues using global isotopologue IDs, allowing multiple species in a single table.

```python
from hapi import *

db_begin('data')

# Fetch multiple water isotopologues (IDs 1,2,3,4) in 4000-4100 cm-1 range
fetch_by_ids('water_isotopes', [1, 2, 3, 4], 4000, 4100)

# Fetch all main isotopologues of H2O, CO2, and O3
# (global IDs: H2O=1, CO2=7, O3=16)
fetch_by_ids('atmospheric_mix', [1, 7, 16], 2000, 2500)

describe('water_isotopes')
```
```

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

### Download Multiple Isotopologues with fetch_by_ids

Source: https://context7.com/hitranonline/hapi/llms.txt

Downloads line-by-line data for multiple isotopologues using global isotopologue IDs, allowing multiple species in a single table. This is useful for fetching data for a mixture of gases or different isotopes of the same gas.

```python
from hapi import *

db_begin('data')

# Fetch multiple water isotopologues (IDs 1,2,3,4) in 4000-4100 cm-1 range
fetch_by_ids('water_isotopes', [1, 2, 3, 4], 4000, 4100)

# Fetch all main isotopologues of H2O, CO2, and O3
# (global IDs: H2O=1, CO2=7, O3=16)
fetch_by_ids('atmospheric_mix', [1, 7, 16], 2000, 2500)

describe('water_isotopes')
```

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

### Calculate Radiance Spectrum

Source: https://context7.com/hitranonline/hapi/llms.txt

Computes thermal emission radiance using the Planck function for different temperatures.

```python
from hapi import *

db_begin('data')
fetch('CO2', 2, 1, 600, 700)

# Calculate absorption coefficient
nu, coef = absorptionCoefficient_Voigt(
    SourceTables='CO2',
    Components=[(2, 1)],
    Environment={'T': 296., 'p': 1.},
    WavenumberRange=[600, 700],
    WavenumberStep=0.1,
    HITRAN_units=False
)

# Calculate radiance spectrum (thermal emission)
nu, radi = radianceSpectrum(
    nu, coef,
    Environment={'l': 100., 'T': 296.}  # path length and temperature
)

# Radiance at different temperatures
nu, radi_hot = radianceSpectrum(
    nu, coef,
    Environment={'l': 100., 'T': 500.}
)

import matplotlib.pyplot as plt
plt.plot(nu, radi, label='296 K')
plt.plot(nu, radi_hot, label='500 K')
plt.xlabel('Wavenumber (cm-1)')
plt.ylabel('Radiance (W/sr/cm²/cm⁻¹)')
plt.legend()
plt.title('CO2 Radiance Spectrum')
plt.show()
```

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

### Calculate Absorption Spectrum

Source: https://context7.com/hitranonline/hapi/llms.txt

Computes the absorption spectrum as 1 minus transmittance.

```python
from hapi import *

db_begin('data')
fetch('H2O', 1, 1, 1500, 1600)

# Calculate absorption coefficient
nu, coef = absorptionCoefficient_Voigt(
    SourceTables='H2O',
    Components=[(1, 1)],
    Environment={'T': 296., 'p': 1.},
    WavenumberRange=[1500, 1600],
    WavenumberStep=0.01,
    HITRAN_units=False
)

# Calculate absorption spectrum
nu, absorp = absorptionSpectrum(
    nu, coef,
    Environment={'l': 100.}  # 1 meter path
)

import matplotlib.pyplot as plt
plt.plot(nu, absorp)
plt.xlabel('Wavenumber (cm-1)')
plt.ylabel('Absorption (1 - Transmittance)')
plt.title('H2O Absorption Spectrum')
plt.show()
```

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

### Calculate Lorentz Absorption Coefficient

Source: https://context7.com/hitranonline/hapi/llms.txt

Calculates absorption coefficient using the Lorentzian profile, suitable for high-pressure conditions. Requires database initialization and data fetching.

```python
from hapi import *

db_begin('data')
fetch('CH4', 6, 1, 3000, 3100)

```

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

### Calculate Transmittance Spectrum

Source: https://context7.com/hitranonline/hapi/llms.txt

Calculates transmittance for various path lengths and saves results to a file.

```python
# Calculate absorption coefficient first
nu, coef = absorptionCoefficient_Voigt(
    SourceTables='CO2',
    Components=[(2, 1)],
    Environment={'T': 296., 'p': 1.},
    WavenumberRange=[2300, 2400],
    WavenumberStep=0.01,
    HITRAN_units=False  # cm^-1 for transmittance calculation
)

# Calculate transmittance for 10 cm path length
nu, trans = transmittanceSpectrum(
    nu, coef,
    Environment={'l': 10.}  # path length in cm
)

# Calculate for 1 meter path
nu, trans_1m = transmittanceSpectrum(
    nu, coef,
    Environment={'l': 100.}  # 100 cm = 1 m
)

# Save to file
nu, trans = transmittanceSpectrum(
    nu, coef,
    Environment={'l': 10.},
    File='transmittance.txt'
)

import matplotlib.pyplot as plt
plt.plot(nu, trans)
plt.xlabel('Wavenumber (cm-1)')
plt.ylabel('Transmittance')
plt.ylim([0, 1])
plt.title('CO2 Transmittance (10 cm path)')
plt.show()
```

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

### Apply Instrumental Slit Functions

Source: https://context7.com/hitranonline/hapi/llms.txt

Convolves high-resolution spectra with various instrumental slit functions to simulate experimental data.

```python
from hapi import *

db_begin('data')
fetch('CO2', 2, 1, 2380, 2400)

# Calculate high-resolution spectrum
nu, coef = absorptionCoefficient_Voigt(
    SourceTables='CO2',
    Components=[(2, 1)],
    Environment={'T': 296., 'p': 1.},
    WavenumberRange=[2380, 2400],
    WavenumberStep=0.001,  # High resolution
    HITRAN_units=False
)

nu, trans = transmittanceSpectrum(nu, coef, Environment={'l': 10.})

# Convolve with rectangular slit function
nu_conv, trans_conv, i1, i2, slit = convolveSpectrum(
    nu, trans,
    Resolution=0.5,        # Instrumental resolution in cm-1
    AF_wing=5.0,           # Wing of apparatus function
    SlitFunction=SLIT_RECTANGULAR
)

# Convolve with Gaussian slit function
nu_gauss, trans_gauss, _, _, _ = convolveSpectrum(
    nu, trans,
    Resolution=0.5,
    AF_wing=5.0,
    SlitFunction=SLIT_GAUSSIAN
)

# Convolve with triangular slit function
nu_tri, trans_tri, _, _, _ = convolveSpectrum(
    nu, trans,
    Resolution=0.5,
    AF_wing=5.0,
    SlitFunction=SLIT_TRIANGULAR
)

import matplotlib.pyplot as plt
plt.plot(nu, trans, 'b-', alpha=0.5, label='High-res')
plt.plot(nu_conv, trans_conv, 'r-', label='Rectangular')
plt.plot(nu_gauss, trans_gauss, 'g-', label='Gaussian')
plt.xlabel('Wavenumber (cm-1)')
plt.ylabel('Transmittance')
plt.legend()
plt.title('Effect of Slit Functions')
plt.show()
```

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

### Save Database Changes with db_commit

Source: https://context7.com/hitranonline/hapi/llms.txt

Commits all changes made to the opened database, saving all tables to their corresponding files on disk. Ensure `db_begin()` has been called prior to using this function.

```python
from hapi import *

db_begin('spectral_data')

# After making modifications to tables...
# Save all changes to disk
db_commit()
```

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

### Extract Column Data with getColumns

Source: https://context7.com/hitranonline/hapi/llms.txt

Retrieves specified columns from HITRAN data tables as Python lists or numpy arrays. Requires database initialization and data fetching.

```python
from hapi import *

db_begin('data')
fetch('H2O', 1, 1, 1000, 1100)

# Get single column
wavenumbers = getColumns('H2O', ['nu'])

# Get multiple columns
nu, sw, gamma_air = getColumns('H2O', ['nu', 'sw', 'gamma_air'])

# Use for plotting
import matplotlib.pyplot as plt
plt.semilogy(nu, sw, 'b.')
plt.xlabel('Wavenumber (cm-1)')
plt.ylabel('Line Intensity')
plt.show()
```

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

### List Available Slit Functions

Source: https://context7.com/hitranonline/hapi/llms.txt

Enumerates the available slit function constants in HAPI.

```python
from hapi import *

# Available slit functions:
# SLIT_RECTANGULAR - Boxcar function
# SLIT_TRIANGULAR  - Triangular function
# SLIT_GAUSSIAN    - Gaussian function
# SLIT_DISPERSION  - Lorentzian (dispersion) function
# SLIT_COSINUS     - Cosine function
# SLIT_DIFFRACTION - Diffraction pattern
# SLIT_MICHELSON   - Michelson interferometer sinc function
```

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

### Calculate Voigt Absorption Coefficient

Source: https://context7.com/hitranonline/hapi/llms.txt

Calculates absorption coefficient using the Voigt profile, combining Doppler and Lorentzian broadening. Supports custom diluent mixtures and units.

```python
from hapi import *

db_begin('data')
fetch('CO2', 2, 1, 2000, 2100)

# Basic Voigt absorption coefficient calculation
nu, coef = absorptionCoefficient_Voigt(
    SourceTables='CO2',
    Components=[(2, 1)],  # CO2, main isotopologue
    Environment={'T': 296., 'p': 1.},
    WavenumberRange=[2000, 2100],
    WavenumberStep=0.01,
    HITRAN_units=True  # cm^2/molecule
)

# With custom diluent mixture (70% N2, 30% self)
nu, coef = absorptionCoefficient_Voigt(
    SourceTables='CO2',
    Components=[(2, 1)],
    Environment={'T': 300., 'p': 0.5},
    WavenumberRange=[2050, 2070],
    WavenumberStep=0.001,
    Diluent={'air': 0.7, 'self': 0.3},
    HITRAN_units=False  # cm^-1
)

# Plot result
import matplotlib.pyplot as plt
plt.plot(nu, coef)
plt.xlabel('Wavenumber (cm-1)')
plt.ylabel('Absorption Coefficient')
plt.title('CO2 Voigt Absorption')
plt.show()
```

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

### Filter and Query Data with select

Source: https://context7.com/hitranonline/hapi/llms.txt

Filters table data using SQL-like conditions and optionally outputs to a new table or file. This function allows for precise selection of spectral lines based on various parameters and criteria.

```python
from hapi import *

db_begin('data')
fetch('H2O', 1, 1, 1000, 1100)

# Select lines with intensity greater than 1e-25
select('H2O', DestinationTableName='H2O_strong',
       Conditions=('>', 'sw', 1e-25))

# Select specific parameters with multiple conditions
# Lines with nu between 1050-1060 cm-1 AND sw > 1e-26
select('H2O', DestinationTableName='H2O_filtered',
       ParameterNames=['nu', 'sw', 'gamma_air'],
       Conditions=('and',
                   ('>', 'nu', 1050),
                   ('<', 'nu', 1060),
                   ('>', 'sw', 1e-26)))

# Output to file
select('H2O', File='h2o_lines.txt')
```

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

### getColumns

Source: https://context7.com/hitranonline/hapi/llms.txt

Retrieves one or more columns from a table as Python lists or numpy arrays.

```APIDOC
## getColumns

### Description
Retrieves one or more columns from a table as Python lists or numpy arrays.

### Parameters
#### Request Body
- **molecule** (string) - Required - Name of the molecule
- **columns** (list) - Required - List of column names to retrieve

### Request Example
getColumns('H2O', ['nu', 'sw', 'gamma_air'])
```

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

### Calculate Doppler Profile Absorption

Source: https://context7.com/hitranonline/hapi/llms.txt

Calculates absorption using pure Gaussian broadening, suitable for low-pressure or high-temperature conditions.

```python
from hapi import *

db_begin('data')
fetch('CO', 5, 1, 2100, 2200)

# Doppler profile (low pressure regime)
nu, coef = absorptionCoefficient_Doppler(
    SourceTables='CO',
    Components=[(5, 1)],  # CO, main isotopologue
    Environment={'T': 1000., 'p': 0.001},  # High T, low p
    WavenumberRange=[2100, 2200],
    WavenumberStep=0.001,  # Fine step for narrow Doppler lines
    HITRAN_units=True
)

import matplotlib.pyplot as plt
plt.plot(nu, coef)
plt.xlabel('Wavenumber (cm-1)')
plt.ylabel('Absorption Coefficient (cm²/molecule)')
plt.title('CO Doppler Profile at 1000K')
plt.show()
```

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

### Table Operations

Source: https://context7.com/hitranonline/hapi/llms.txt

Functions for inspecting and manipulating data tables within the HAPI database.

```APIDOC
## Table Operations

### describe - Display Table Information

Prints detailed information about a table including parameter names, formats, number of rows, and wavenumber range.

```python
from hapi import *

db_begin('data')
fetch('H2O', 1, 1, 1000, 1100)

# Display table structure and metadata
describe('H2O')
# Output:
#   Name: H2O
#   Rows: 1234
#   Wavenumber range: 1000.000 - 1100.000 cm-1
#   Parameters: molec_id, local_iso_id, nu, sw, a, gamma_air, ...
```

### tableList - Get Available Tables

Returns a list of all tables currently loaded in the database cache.

```python
from hapi import *

db_begin('data')
fetch('H2O', 1, 1, 1000, 1100)
fetch('CO2', 2, 1, 2000, 2100)

# Get list of all available tables
tables = tableList()
print("Tables:", tables)
# Output: Tables: ['H2O', 'CO2']
```

### select - Filter and Query Data

Filters table data using SQL-like conditions and optionally outputs to a new table or file.

```python
from hapi import *

db_begin('data')
fetch('H2O', 1, 1, 1000, 1100)

# Select lines with intensity greater than 1e-25
select('H2O', DestinationTableName='H2O_strong',
       Conditions=(">", 'sw', 1e-25))

# Select specific parameters with multiple conditions
# Lines with nu between 1050-1060 cm-1 AND sw > 1e-26
select('H2O', DestinationTableName='H2O_filtered',
       ParameterNames=['nu', 'sw', 'gamma_air'],
       Conditions=('and',
                   ('>', 'nu', 1050),
                   ('<', 'nu', 1060),
                   ('>', 'sw', 1e-26)))

# Output to file
select('H2O', File='h2o_lines.txt')
```
```

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

### abundance

Source: https://context7.com/hitranonline/hapi/llms.txt

Returns the natural (Earth) abundance of a specified HITRAN isotopologue.

```APIDOC
## abundance

### Description
Returns the natural (Earth) abundance of a specified HITRAN isotopologue.

### Parameters
#### Request Body
- **molecule_number** (int) - Required - HITRAN molecule number
- **isotopologue_number** (int) - Required - HITRAN isotopologue number

### Request Example
abundance(1, 1)
```

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

### absorptionCoefficient_Voigt

Source: https://context7.com/hitranonline/hapi/llms.txt

Calculates absorption coefficient using the Voigt line profile, which combines Doppler and Lorentzian broadening.

```APIDOC
## absorptionCoefficient_Voigt

### Description
Calculates absorption coefficient using the Voigt line profile, which combines Doppler and Lorentzian broadening.

### Parameters
#### Request Body
- **SourceTables** (string) - Required - Name of the source table
- **Components** (list) - Required - List of (molecule, isotopologue) tuples
- **Environment** (dict) - Required - Dictionary containing 'T' (temperature) and 'p' (pressure)
- **WavenumberRange** (list) - Required - [min, max] range
- **WavenumberStep** (float) - Required - Step size
- **HITRAN_units** (bool) - Optional - Whether to use cm^2/molecule units

### Request Example
absorptionCoefficient_Voigt(SourceTables='CO2', Components=[(2, 1)], Environment={'T': 296., 'p': 1.}, WavenumberRange=[2000, 2100], WavenumberStep=0.01)
```

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