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Added r-squared printout to parity plot. Fixed default optimizer to detect number of dimensions and update bounds to match.

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pull/1/head
titusquah 5 years ago
parent 07d6a0ac3c
commit a0e8bdb9fd
6 changed files with 107 additions and 67 deletions
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2
.idea/dictionaries/Titus.xml
Unescape View File

@ -3,6 +3,7 @@
<words> <words>
<w>coeffs</w> <w>coeffs</w>
<w>conc</w> <w>conc</w>
<w>csvs</w>
<w>diluant</w> <w>diluant</w>
<w>disp</w> <w>disp</w>
<w>dodecane</w> <w>dodecane</w>
@ -18,6 +19,7 @@
<w>scipy</w> <w>scipy</w>
<w>slsqp</w> <w>slsqp</w>
<w>thermo</w> <w>thermo</w>
<w>xmls</w>
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89
.idea/workspace.xml
Unescape View File

@ -1,13 +1,13 @@
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README.md
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@ -1,5 +1,5 @@
# REEPS # REEPS
REEPS is a toolkit for estimating standard thermodynamic parameters for Gibbs minimization. REEPS (Rare Earth Element Parameter Searcher) is a toolkit for estimating standard thermodynamic parameters for Gibbs minimization.
Extend a methodology for estimating standard thermodynamic parameters for Gibbs minimization in multiphase, multicomponent separations systems Extend a methodology for estimating standard thermodynamic parameters for Gibbs minimization in multiphase, multicomponent separations systems
@ -23,4 +23,12 @@ $ pip install -e.
REEPS uses packages: cantera (https://cantera.org/), pandas, numpy, scipy, xml, seaborn, and matplotlib REEPS uses packages: cantera (https://cantera.org/), pandas, numpy, scipy, xml, seaborn, and matplotlib
## Usage ## Usage
Do random stuff and pray. Check out examples in docs/examples
```python
from reeps import REEPS
searcher = REEPS(**REEPS_parameters_dictionary)
optimized_parameter_dictionary = searcher.fit()
searcher.update_xml(optimized_parameter_dictionary)
searcher.parity_plot()
print(seacher.r_squared())
```

2
data/xmls/twophase.xml
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@ -50,7 +50,7 @@
<const_cp Tmax="300.0" Tmin="298.0"> <const_cp Tmax="300.0" Tmin="298.0">
<t0 units="K">298.14999999999998</t0> <t0 units="K">298.14999999999998</t0>
<h0 units="J/mol" updated="Updated at 14:15 6-5-2020">-4704703.645715787</h0> <h0 units="J/mol" updated="Updated at 14:33 6-5-2020">-4704699.156668724</h0>
<s0 units="J/mol/K"> 1117.965 </s0> <s0 units="J/mol/K"> 1117.965 </s0>
<cp0 units="J/mol/K">0.0</cp0> <cp0 units="J/mol/K">0.0</cp0>
</const_cp> </const_cp>

28
docs/Examples/1_getting_started.ipynb
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41
reeps.py
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@ -11,8 +11,9 @@ import shutil
import copy import copy
from inspect import signature from inspect import signature
import os import os
sns.set()
sns.set()
sns.set(font_scale=1.6)
class REEPS: class REEPS:
"""REEPS (Rare earth extraction parameter searcher) """REEPS (Rare earth extraction parameter searcher)
@ -113,7 +114,7 @@ class REEPS:
@staticmethod @staticmethod
def log_mean_squared_error(predicted_dict, meas_df): def log_mean_squared_error(predicted_dict, meas_df):
meas = meas_df['D(m)'].values meas = meas_df.values[:, 2]
pred = predicted_dict['re_org'] / predicted_dict['re_aq'] pred = predicted_dict['re_org'] / predicted_dict['re_aq']
log_pred = np.log10(pred) log_pred = np.log10(pred)
log_meas = np.log10(meas) log_meas = np.log10(meas)
@ -123,7 +124,7 @@ class REEPS:
@staticmethod @staticmethod
def slsqp_optimizer(objective, x_guess): def slsqp_optimizer(objective, x_guess):
optimizer_kwargs = {"method": 'SLSQP', optimizer_kwargs = {"method": 'SLSQP',
"bounds": [(1e-1, 1e1) * len(x_guess)], "bounds": [(1e-1, 1e1)] * len(x_guess),
"constraints": (), "constraints": (),
"options": {'disp': True, 'maxiter': 1000, 'ftol': 1e-6}} "options": {'disp': True, 'maxiter': 1000, 'ftol': 1e-6}}
res = minimize(objective, x_guess, **optimizer_kwargs) res = minimize(objective, x_guess, **optimizer_kwargs)
@ -400,7 +401,8 @@ class REEPS:
for _ in opt_dict[species_name].keys(): for _ in opt_dict[species_name].keys():
i += 1 i += 1
x = np.array(x) x = np.array(x)
if i == len(x.shape):
if len(x.shape) == 1:
xs = np.array([x]) xs = np.array([x])
vectorized_x = False vectorized_x = False
else: else:
@ -449,11 +451,9 @@ class REEPS:
optimizer = self._optimizer optimizer = self._optimizer
opt_dict = copy.deepcopy(self._opt_dict) opt_dict = copy.deepcopy(self._opt_dict)
# x_guess = []
i = 0 i = 0
for species_name in opt_dict.keys(): for species_name in opt_dict.keys():
for _ in opt_dict[species_name].keys(): for _ in opt_dict[species_name].keys():
# x_guess.append(opt_dict[species_name][thermo_prop])
i += 1 i += 1
x_guess = np.ones(i) x_guess = np.ones(i)
@ -504,7 +504,8 @@ class REEPS:
self.set_phases(self._phases_xml_filename, self._phase_names) self.set_phases(self._phases_xml_filename, self._phase_names)
return None return None
def parity_plot(self, species='re_aq'): # noinspection PyUnusedLocal
def parity_plot(self, species='re_aq', save_path=None, print_r_squared=False):
"""Parity plot between measured and predicted rare earth composition""" """Parity plot between measured and predicted rare earth composition"""
phases_copy = self._phases.copy() phases_copy = self._phases.copy()
mix = ct.Mixture(phases_copy) mix = ct.Mixture(phases_copy)
@ -520,16 +521,32 @@ class REEPS:
aq_ind, rare_earth_ion_name)] aq_ind, rare_earth_ion_name)]
pred.append(re_aq) pred.append(re_aq)
pred = np.array(pred) pred = np.array(pred)
meas = exp_df['REeq(m)'].values meas = exp_df.values[:, 1]
min_data = np.min([pred, meas]) min_data = np.min([pred, meas])
max_data = np.max([pred, meas]) max_data = np.max([pred, meas])
min_max_data = np.array([min_data, max_data]) min_max_data = np.array([min_data, max_data])
fig, ax = plt.subplots() fig, ax = plt.subplots()
sns.scatterplot(meas, pred, color="r", re_element = ''
label="Rare earth equilibrium concentration (mol/L)") n_plus = 0
sns.lineplot(min_max_data, min_max_data, color="b", label="") for char in self._rare_earth_ion_name:
if char.isalpha():
re_element = '{0}{1}'.format(re_element, char)
else:
n_plus += 1
re_ion_name = '$%s^{%d+}$' % (re_element, n_plus)
p1 = sns.scatterplot(meas, pred, color="r",
label="{0} eq. conc. (mol/L)".format(re_ion_name),
legend=False)
p2 = sns.lineplot(min_max_data, min_max_data, color="b", label="")
if print_r_squared:
p1.text(min_max_data[0], min_max_data[1]*0.9, '$R^2$={0:.2f}'.format(self.r_squared()))
plt.legend(loc='lower right')
else:
plt.legend()
ax.set(xlabel='Measured', ylabel='Predicted') ax.set(xlabel='Measured', ylabel='Predicted')
plt.show() plt.show()
if save_path is not None:
plt.savefig(save_path, bbox_inches='tight')
return None return None
def r_squared(self): def r_squared(self):
@ -548,7 +565,7 @@ class REEPS:
aq_ind, rare_earth_ion_name)] aq_ind, rare_earth_ion_name)]
pred.append(re_aq) pred.append(re_aq)
predicted_y = np.array(pred) predicted_y = np.array(pred)
actual_y = exp_df['REeq(m)'].values actual_y = exp_df.values[:, 1]
num = sum((actual_y - predicted_y) ** 2) num = sum((actual_y - predicted_y) ** 2)
den = sum((actual_y - np.mean(actual_y)) ** 2) den = sum((actual_y - np.mean(actual_y)) ** 2)
r_2 = (1 - num / den) r_2 = (1 - num / den)

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