def graph_experimental_data(DATA_DIR, OUTPUT_DIR):
# make output directory
DIR = os.path.join(OUTPUT_DIR, 'experimental')
if not os.path.exists(DIR):
os.makedirs(DIR)
# concentration range
low = 0.0
high = 1.0
# get the csv data in a list
Csvs = get_data(DATA_DIR)
# filnames
# filnames
fnames = [f.split('.csv')[0] for f in os.listdir(DATA_DIR)]
fig = plt.figure()
# export a graph for the fitting of the integral reaction rate
Plot = os.path.join(DIR, 'experimental_conversion.' + graph_format)
for indx, Csv in enumerate(Csvs):
df = pd.read_csv(Csv)
# read data file
conversion, time, temperature = read_filtrated_datafile(df, low, high)
# read variable units
timeUnits, tempUnits = read_units(df)
plt.scatter(time, conversion, s=10, label=str(temperature) + tempUnits)
# export experimental data
data = {
'time': time,
'conversion': conversion,
'temperature': temperature,
'temperature_units': tempUnits
}
df = pd.DataFrame(data)
csv_name = fnames[indx] + '_experimental_conversion.csv'
df.to_csv(os.path.join(DIR, csv_name), index=False)
plt.xlim(0, )
plt.ylim(0, 1.0)
plt.legend()
plt.ylabel('conversion')
plt.xlabel('time [' + timeUnits + ']')
plt.tight_layout()
plt.savefig(Plot, format=graph_format, dpi=graph_dpi)
plt.close() # to avoid memory warnings
def ratedata2Fit(DATA_DIR,OUTPUT_DIR,modelNames,low,high,pdeg,npoints,fitExp):
# low : lower limit for conversion fraction
# high : upper limit for conversion fraction
# DATA_DIR : directory containing data
# OUTPUT_DIR : output directory
# make output directory
DIR = os.path.join(OUTPUT_DIR,'conversion_regression')
if not os.path.exists(DIR):
os.makedirs(DIR)
# get csvs
Csvs = get_data(DATA_DIR)
# filnames
fnames = os.listdir(DATA_DIR)
for indx, Csv in enumerate(Csvs):
# get dataframe
df = pd.read_csv(Csv)
# get experimental conversion
conversion, time, temperature = read_filtrated_datafile(df,low,high)
# read variable units
timeUnits, tempUnits = read_units(df)
# experimental reaction rate from polynomial conversion
dadt_polynomial = data2Polrate(Csv,low,high,pdeg,npoints)
# experimental reaction rate from actual conversion
dadt_numerical = data2Exprate(Csv,low,high)
# accuracy criteria
ss_res = [] # sum of square residuals (ideal = 0)
mse = [] # mean square error (ideal = 0)
res_AEr = [] # residuals absolute error (ideal = 0)
res_REr = [] # residuals relative error (ideal = 0)
k_arrhenius = [] # Arrhenius rate constant
# loop over all models
for modelName in modelNames:
# pick up a model
model = Model(modelName)
if modelName not in ['D2','D4']:
# experimental integral reaction rate
y = conversion
# perform regression
k, yfit = conversionRegression(time,conversion, modelName)
# calculate the modeled differential reaction rate
dadt_model = np.array( [k*model.f(a) for a in yfit] )
yfit = dadt_model
else:
# experimental integral reaction rate
y = np.array([model.g(c) for c in conversion])
# perform regression
k, yfit = integralRateRegression(time,conversion, modelName)
# calculate the modeled differential reaction rate
dadt_model = np.array( [k*model.f(a) for a in conversion] )
yfit = dadt_model
if fitExp:
y = dadt_numerical
else:
y = dadt_polynomial
# calculate validation errors
ss_res.append(ssRes(y,yfit))
mse.append(MSE(y,yfit))
res_AEr.append(resAEr(y,yfit))
res_REr.append(resREr(y,yfit))
k_arrhenius.append(k)
# export regression accuracy data
error_data = {
'model' : modelNames,
'ss_res' : ss_res,
'mse' : mse,
'resAEr' : res_AEr,
'resREr' : res_REr,
'temperature' : temperature,
'temperature_units': tempUnits
}
df = pd.DataFrame(error_data)
prefix = fnames[indx].split('.csv')[0]
if fitExp:
df.to_csv(os.path.join(DIR,prefix + '_experimental_rate_fit_accuracy.csv'),index=False)
else:
df.to_csv(os.path.join(DIR,prefix + '_polynomial_rate_fit_accuracy.csv'),index=False)
def rateFitGraphs(DATA_DIR, OUTPUT_DIR, low, high, pdeg, npoints, fitExp):
# get names (without format suffix) of the data csv files
# bnames : base names
bnames = [f.split('.csv')[0] for f in os.listdir(DATA_DIR)]
# paths of the experimental data csv files
data_Csvs = get_data(DATA_DIR)
# metrics directory
METRICS_DIR = os.path.join(OUTPUT_DIR, 'conversion_regression')
# paths of the metrics from the conversion regression
metrics_Csvs = get_data(METRICS_DIR)
# filter proper csvs
metrics_Csvs = [
f for f in metrics_Csvs if 'conversion_regression_accuracy' in f
]
# zip data files and metrics
data = sortOnData(bnames, data_Csvs)
metrics = sortOnData(bnames, metrics_Csvs)
data_and_metrics = list(zip(data, metrics))
# loop over all data
for i_csv, csv in enumerate(data_and_metrics):
# make directory for the graphs
DIR = os.path.join(METRICS_DIR, 'png')
DIR = os.path.join(DIR, 'rate_fit')
GRAPH_DIR = os.path.join(DIR, bnames[i_csv])
if not os.path.exists(GRAPH_DIR):
os.makedirs(GRAPH_DIR)
# data dataframe
data_df = pd.read_csv(csv[0])
# metrics dataframe
metrics_df = pd.read_csv(csv[1])
# data
conversion, time, temperature = read_filtrated_datafile(
data_df, low, high)
# read variable units
timeUnits, tempUnits = read_units(data_df)
# experimental reaction rate from polynomial conversion
dadt_polynomial = data2Polrate(csv[0], low, high, pdeg, npoints)
# experimental reaction rate from actual conversion
dadt_numerical = data2Exprate(csv[0], low, high)
modelNames = metrics_df['model'].tolist()
ks = metrics_df['k_arrhenius'].to_numpy()
# calculate experimental reaction rate
if fitExp:
y = dadt_numerical
else:
y = dadt_polynomial
x = time
# loop over models
for i_model, modelName in enumerate(modelNames):
# pick up a model
model = Model(modelName)
# choose the corresponding arrhenius rate constant
k = ks[i_model]
if modelName not in ['D2', 'D4']:
# calculate the modeled differential reaction rate
tfit = np.linspace(time[0], time[-1], num=npoints)
yfit = np.array([model.alpha(t, k) for t in tfit])
dadt_model = np.array([k * model.f(a) for a in yfit])
yfit = dadt_model
xfit = tfit
# export a graph for the fitting of the integral reaction rate
fig = plt.figure()
if fitExp:
ext = '_experimental_rate_fit.'
else:
ext = '_polynomial_rate_fit.'
fname = modelName + '_' + bnames[i_csv] + ext + graph_format
Plot = os.path.join(GRAPH_DIR, fname)
plt.scatter(x, y, s=10, label='experimental')
plt.plot(xfit, yfit, lw=lwidth, label=modelName)
plt.legend()
plt.ylabel(r'reaction rate')
plt.xlabel('time [' + timeUnits + ']')
plt.tight_layout()
plt.savefig(Plot, format=graph_format, dpi=graph_dpi)
plt.close() # to avoid memory warnings
def integralRegressionGraphs(DATA_DIR, OUTPUT_DIR, low, high, npoints):
# get names (without format suffix) of the data csv files
# bnames : base names
bnames = [f.split('.csv')[0] for f in os.listdir(DATA_DIR)]
# paths of the experimental data csv files
data_Csvs = get_data(DATA_DIR)
# metrics directory
METRICS_DIR = os.path.join(OUTPUT_DIR, 'integral_regression')
# paths of the metrics from the integral regression
metrics_Csvs = get_data(METRICS_DIR)
# zip data files and metrics
data = sortOnData(bnames, data_Csvs)
metrics = sortOnData(bnames, metrics_Csvs)
data_and_metrics = list(zip(data, metrics))
# loop over all data
for i_csv, csv in enumerate(data_and_metrics):
# make directory for the graphs
DIR = os.path.join(METRICS_DIR, 'png')
GRAPH_DIR = os.path.join(DIR, bnames[i_csv])
if not os.path.exists(GRAPH_DIR):
os.makedirs(GRAPH_DIR)
# data dataframe
data_df = pd.read_csv(csv[0])
# metrics dataframe
metrics_df = pd.read_csv(csv[1])
# data
conversion, time, temperature = read_filtrated_datafile(
data_df, low, high)
# read variable units
timeUnits, tempUnits = read_units(data_df)
modelNames = metrics_df['model'].tolist()
ks = metrics_df['k_arrhenius'].to_numpy()
# loop over models
for i_model, modelName in enumerate(modelNames):
# pick up a model
model = Model(modelName)
# choose the corresponding arrhenius rate constant
k = ks[i_model]
# calculate experimental integral reaction rate
y = np.array([model.g(a) for a in conversion])
x = time
# fit
tfit = np.linspace(time[0], time[-1], num=npoints)
yfit = k * tfit
xfit = tfit
# export a graph for the fitting of the integral reaction rate
fig = plt.figure()
fname = modelName + '_' + bnames[
i_csv] + '_integral_regression.' + graph_format
Plot = os.path.join(GRAPH_DIR, fname)
plt.scatter(x, y, s=10, label='experimental')
plt.plot(xfit, yfit, lw=lwidth, label=r'kt')
plt.legend()
plt.ylabel(r'g (a)')
plt.xlabel('time [' + timeUnits + ']')
plt.tight_layout()
plt.savefig(Plot, format=graph_format, dpi=graph_dpi)
plt.close() # to avoid memory warnings