# Sensitivity of DIC to pH and pCO2¶

Based on CO2SYSExample1.m for MATLAB by Steven van Heuven.

## Introduction¶

This is an example of the use of PyCO2SYS that uses its ability to process arrays of data.

We will generate a figure that shows the sensitivity of pH and pCO2 to changes in DIC, while keeping everything else constant.

You can find further information about this way of using PyCO2SYS in its documentation.

## Define input conditions¶

The first step is to define the input conditions that we want to use with PyCO2SYS. In this case, every input has a single constant value except for DIC (par2), which is a NumPy array of values increasing from 2100 to 2300 μmol·kg-1 in increments of 5 μmol·kg-1:

In [ ]:
# Import NumPy to make the DIC array
import numpy as np

# Define input conditions
kwargs = dict(
par1 = 2400,  # Value of the first parameter
par2 = np.arange(2100, 2305, 5),  # Value of the second parameter, which is a long vector of different DIC's!
par1_type = 1,  # The first parameter supplied is of type "1", which is "alkalinity"
par2_type = 2,  # The second parameter supplied is of type "2", which is "DIC"
salinity = 35,  # Salinity of the sample
temperature = 10,  # Temperature at input conditions
total_silicate = 50,  # Concentration of silicate  in the sample (in umol/kg)
total_phosphate = 2,  # Concentration of phosphate in the sample (in umol/kg)
opt_k_carbonic = 4,  # Choice of H2CO3 and HCO3- dissociation constants K1 and K2 ("4" means "Mehrbach refit")
opt_k_bisulfate = 1,  # Choice of HSO4- dissociation constants KSO4 ("1" means "Dickson")
)
print("Input conditions have been set!")


## Run PyCO2SYS¶

Once we have defined the input conditions above, solving the marine carbonate system is as simple as importing and running the pyco2.sys function:

In [ ]:
# Import PyCO2SYS
import PyCO2SYS as pyco2

# Run CO2SYS!
results = pyco2.sys(**kwargs)
print('PyCO2SYS ran successfully!')


## Visualise the results¶

Finally, we can easily visualise the results using a plotting package such as Matplotlib:

In [ ]:
# Import plotting package
from matplotlib import pyplot as plt
%matplotlib notebook

# Prepare an empty figure
fig, ax = plt.subplots(2, 1, figsize=(6, 7))

# The calculated pCO2's are in the field 'pCO2' of the results CO2dict
# Show these in the first subplot
ax[0].plot('par2', 'pCO2', data=results, c='r', marker='o')
ax[0].set_xlabel("DIC [umol/kg]")
ax[0].set_ylabel("pCO2 [uatm]")

# The calculated pH's are in the field 'pH' of the results CO2dict
# Show these in the second subplot
ax[1].plot('par2', 'pH', data=results, c='r', marker='o')
ax[1].set_xlabel("DIC [umol/kg]")
ax[1].set_ylabel("pH");