plt.tight_layout()
plt.savefig("y_vs_x.pdf", format='pdf')
plt.show()
# # Compare fits for ethane
# In[15]:
ch3ch3_interpolated = pyiast.InterpolatorIsotherm(df_ch3ch3, loading_key="Loading(mmol/g)",
pressure_key="Pressure(bar)",
fill_value=df_ch3ch3["Loading(mmol/g)"].max())
ch3ch3_langmuir = pyiast.ModelIsotherm(df_ch3ch3, loading_key="Loading(mmol/g)",
pressure_key="Pressure(bar)",
model="Langmuir")
p_plot = np.logspace(np.log(df_ch3ch3['Pressure(bar)'].min()), np.log(80), num=200)
fig = plt.figure()
plt.xlabel("Pressure (bar)")
plt.ylabel("Ethane uptake (mmol/g)")
plt.scatter(df_ch3ch3['Pressure(bar)'], df_ch3ch3['Loading(mmol/g)'],
marker='o', color='r', s=40, label='Data', clip_on=False)
plt.plot(p_plot, ch3ch3_interpolated.loading(p_plot), color='k', linewidth=1,
label='Linear interpolation', linestyle='--')
df_isotherm = pd.read_csv(filename)
# set defaults for optimization algorithm guesses
min = None
good_guess = {'K':1, 'M':1}
# run through set of starting guesses for the optimization algorithm
for K in [300, 350, 400, 450, 500]:
for M in ['-inf', 'inf', 1, 2, 3, 4, 5, 6]:
# this is in try block because pyiast throws an exception if its optimization takes too long
# (also a large reason why many satrting guesses are tried
try:
# print guesses
print(f"{K}, {M}")
# create model
isotherm = pyiast.ModelIsotherm(df_isotherm, loading_key="Q" ,pressure_key = "pressure" , model = "Langmuir", param_guess={'K': K, 'M':M})
# if it's the best model, use those parameter guesses
if isotherm.rmse < min or min == None:
good_guess['K'], good_guess['M'] = K, M
except:
pass
# in try block for same reason
# (if no good guesses are found and it still fails)
try:
print("\n\nattempt\n\n")
isotherm = pyiast.ModelIsotherm(df_isotherm, loading_key="Q" ,pressure_key = "pressure" , model = "Langmuir", param_guess={'K':good_guess['K'], 'M':good_guess['M']})
# if no exception is thrown, prints "success"
print("\n\nsuccess\n\n")
isotherm.print_params()
def run(test=False):
# Set to False if you **do not** want to recalculate pure gas adsorption isotherms
is_simulate_loadings = False
loadings_file = 'loadings.dat'
# Option to draw the isotherms: it is either **'ToFile'** or **'ToScreen'** (case insensitive).
# Any other value will be interpreted as no graphics
graphing = 'none'
# Gas names as they will be referred through simulations
gas_names = ['ch4', 'co2']
# Corresponding mole fractions of gases that will allow us to calculate their partial pressures through the Dalton's law
mol_frac = [0.5, 0.5]
# Calibrated previously functional forms of chemical potentials of gases for GCMC simulations as a functions of pressure
chem_pots = [
lambda x: 2.4153 * numpy.log(x) - 36.722,
lambda x: 2.40 * numpy.log(x) - 40.701
]
# Root directory for some data (For PySIMM examples it is )
data_dir = osp.join('..', '09_cassandra_simulations', 'gcmc')
# Setup of adsorbate model
gases = []
for gn in gas_names:
gases.append(system.read_lammps(osp.join(data_dir, gn + '.lmps')))
gases[-1].forcefield = 'trappe/amber'
# Setup of adsorbent model
frame = system.read_lammps('pim.lmps')
frame.forcefield = 'trappe/amber'
# Constant for loadings calculations
molec2mmols_g = 1e+3 / frame.mass
# Setup of the GCMC simulations
css = cassandra.Cassandra(frame)
sim_settings = css.read_input('run_props.inp')
# This function in given context will calculate the loading from short GCMC simulations
def calculate_isotherm_point(gas_name, press):
run_fldr = osp.join(gas_name, str(press))
idx = gas_names.index(gas_name)
# sim_settings.update({'Run_Name': 'gcmc'})
css.add_gcmc(species=gases[idx],
is_new=True,
chem_pot=chem_pots[idx](press),
out_folder=run_fldr,
props_file='gcmc.inp',
**sim_settings)
css.run()
full_prp = css.run_queue[0].get_prp()
return molec2mmols_g * numpy.average(
full_prp[3][int(len(2 * full_prp[3]) / 3):])
# This function in given context will load the pre-calculated loading value from previously done GCMC simulations
def load_isotherm_point(gas_name, press):
with open(loadings_file, 'r') as pntr:
stream = pntr.read()
tmp = stream.split('\n' + gas_name)[1]
idx = re.search('[a-zA-Z]|\Z', tmp)
value = re.findall('\n{:}\s+\d+\.\d+'.format(press),
tmp[:idx.start()])[0]
return float(re.split('\s+', value)[-1])
# Calculation of adsorption isotherms for pure CH4 and CO2 gases for further usage in IAST simulations.
# This is the **MOST TIME CONSUMING STEP** in this example, if you want to skip it switch the key is_simulated to False
# The IAST will be done using PyIAST package, thus isotherms are wrapped into the corresponding object
gas_press = [0.1, 1, 5, 10, 25, 50]
lk = 'Loading(mmol/g)'
pk = 'Pressure(bar)'
isotherms = []
loadings = dict.fromkeys(gas_names)
for gn in gas_names:
loadings[gn] = []
for p in gas_press:
if is_simulate_loadings:
data = calculate_isotherm_point(gn, p)
else:
data = load_isotherm_point(gn, p)
loadings[gn].append(data)
isotherms.append(
pyiast.ModelIsotherm(pandas.DataFrame(zip(gas_press, loadings[gn]),
columns=[pk, lk]),
loading_key=lk,
pressure_key=pk,
model='BET',
optimization_method='Powell'))
# The PyIAST run for calculating of mixed adsorption isotherm
# Initial guesses of adsorbed mole fractions do span broad range of values, because PyIAST might not find
# solution at certain values of mole fractions and through an exception
guesses = [[a, 1 - a] for a in numpy.linspace(0.01, 0.99, 50)]
for in_g in guesses:
mix_loadings = []
try:
for p in gas_press:
mix_loadings.append(
list(
pyiast.iast(p * numpy.array(mol_frac),
isotherms,
verboseflag=False,
adsorbed_mole_fraction_guess=in_g)))
mix_loadings = numpy.array(mix_loadings)
break
except:
print('Initial guess {:} had failed to converge'.format(in_g))
continue
mix_loadings = numpy.sum(mix_loadings, axis=1)
mix_isotherm = pyiast.ModelIsotherm(pandas.DataFrame(zip(
gas_press, mix_loadings),
columns=[pk, lk]),
loading_key=lk,
pressure_key=pk,
model='BET',
optimization_method='L-BFGS-B')
# Output: Graphing of constructed isotherms
def _plot_isotherms(ax, loc_gp, loc_isoth, loc_mix_load, loc_mix_isoth):
rng = numpy.linspace(min(loc_gp), max(loc_gp), 100)
ax.plot(loc_gp,
loadings[gas_names[0]],
'og',
lw=2.5,
label='{:} loadings'.format(gas_names[0].upper()))
ax.plot(rng, [loc_isoth[0].loading(t) for t in rng],
'--g',
lw=2,
label='BET fit of {:} loadings'.format(gas_names[0].upper()))
ax.plot(loc_gp,
loadings[gas_names[1]],
'or',
lw=2.5,
label='{:} loadings'.format(gas_names[1].upper()))
ax.plot(rng, [loc_isoth[1].loading(t) for t in rng],
'--r',
lw=2,
label='BET fit of {:} loadings'.format(gas_names[1].upper()))
ax.plot(loc_gp,
loc_mix_load,
'ob',
lw=2.5,
label='1-to-1 mixture loadings')
ax.plot(rng, [loc_mix_isoth.loading(t) for t in rng],
'--b',
lw=2,
label='BET fit of 1-to-1 mixture loadings')
ax.set_xlabel('Gas pressure [bar]', fontsize=20)
ax.set_ylabel('Loading [mmol / g]', fontsize=20)
ax.tick_params(axis='both', labelsize=16)
ax.grid(True)
ax.legend(fontsize=16)
mplp.tight_layout()
if graphing.lower() == 'tofile':
fig, axs = mplp.subplots(1, 1, figsize=(10, 5))
_plot_isotherms(axs, gas_press, isotherms, mix_loadings, mix_isotherm)
mplp.savefig('pim1_mix_adsorption.png', dpi=192)
elif graphing.lower() == 'toscreen':
mplp.figure()
axs = mplp.gca()
_plot_isotherms(axs, gas_press, isotherms, mix_loadings, mix_isotherm)
mplp.show()
with open('iast_loadings.dat', 'w') as pntr:
pntr.write('{:}\t\t{:}\n'.format(pk, lk))
pntr.write('{:}-{:} 1-to-1\n'.format(gas_names[0], gas_names[1]))
for p, ml in zip(gas_press, mix_loadings):
pntr.write('{:}\t\t{:}\n'.format(p, ml))
# ## Test model fits
# Loop through all models, generate synthetic data using parameters in `model_params` and the `loading` function here, then fit model using pyIAST. Plot data and fits, check that pyIAST identified parameters match the model.
# In[5]:
for model in models:
print("Testing model:", model)
# Generate synthetic data
df = pd.DataFrame()
df['P'] = np.linspace(0, 1, 40)
df['L'] = loading(df['P'], model)
# use pyIAST to fit model to data
isotherm = pyiast.ModelIsotherm(
df, pressure_key='P', loading_key='L', model=model)
isotherm.print_params()
# plot fit
P_plot = np.linspace(0, 1, 100)
fig = plt.figure()
plt.scatter(df['P'], df['L'], label='Synthetic data', clip_on=False)
plt.plot(P_plot, isotherm.loading(P_plot), label='pyIAST fit')
plt.xlim([0, 1])
plt.ylim(ymin=0)
plt.xlabel('Pressure')
plt.ylabel('Uptake')
plt.title(model)
plt.legend(loc='lower right')
plt.show()
dfs = [
pd.DataFrame({
'P':
pressure,
'L':
M * langmuirKs[i] * pressure / (1.0 + langmuirKs[i] * pressure)
}) for i in range(3)
]
# Use pyIAST to fit Lanmguir models to the data, then plot fits
# In[4]:
isotherms = [
pyiast.ModelIsotherm(dfs[i],
pressure_key='P',
loading_key='L',
model='Langmuir') for i in range(3)
]
for i in range(len(isotherms)):
isotherms[i].print_params()
pyiast.plot_isotherm(isotherms[i])
# Plot synthetic data all in one plot for paper
# In[5]:
p_plot = np.logspace(-3, np.log10(11)) # for plotting
fig = plt.figure(facecolor='w')
for i in range(len(isotherms)):
import pyiast as pt
import pandas as pd
my_data = pd.read_excel("path_to_excel_file") # load dataframe from excel
my_data.to_csv("path_to_save_file.csv") # save it as .csv
isotherm = pt.ModelIsotherm(
my_data,
loading_key="Loading(mmol/g)",
pressure_key="Pressure(bar)",
model="Langmuir"
) # "Langmuir", "M", "K", "Quadratic", "BET", "DSLangmuir", "TemkinApprox", "Henry"
isotherm.print_params()
small = None
big = None
while True:
inp = input("please input a number:")
if inp == "done":
break
try:
num = int(inp)
except ValueError:
print("Error: not a number")
continue
if small is None:
small = big = num
import pyiast
import pandas as pd
#####################################################
### plots a model of the isotherm ###
### see pyIAST documentation for available models ###
#####################################################
# replace path with csv containing isotherm data
df_isotherm = pd.read_csv("/users/noahwhelpley/Downloads/Book2.csv")
isotherm = pyiast.ModelIsotherm(df_isotherm,
loading_key="Q",
pressure_key="pressure",
model="Langmuir")
isotherm.print_params()
pyiast.plot_isotherm(isotherm)
lk = 'Loading(mmol/g)'
pk = 'Pressure(bar)'
isotherms = []
loadings = dict.fromkeys(gas_names)
for gn in gas_names:
loadings[gn] = []
for p in gas_press:
if is_simulate_loadings:
data = calculate_isotherm_point(gn, p)
else:
data = load_isotherm_point(gn, p)
loadings[gn].append(data)
isotherms.append(
pyiast.ModelIsotherm(pandas.DataFrame(zip(gas_press, loadings[gn]),
columns=[pk, lk]),
loading_key=lk,
pressure_key=pk,
model='BET',
optimization_method='Powell'))
# The PyIAST run for calculating of mixed adsorption isotherm
# Initial guesses of adsorbed mole fractions do span broad range of values, because PyIAST might not find
# solution at certain values of mole fractions and through an exception
guesses = [[a, 1 - a] for a in numpy.linspace(0.01, 0.99, 50)]
for in_g in guesses:
mix_loadings = []
try:
for p in gas_press:
mix_loadings.append(
list(
pyiast.iast(p * numpy.array(mol_frac),
isotherms,
