mirror of
https://github.com/ANL-CEEESA/LLEPE.git
synced 2025-12-06 01:48:53 -06:00
Edited parity plot to allow color to represent 3rd dimension. Still need to improve colorbar axis name.
This commit is contained in:
titusquah
2
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<state width="1899" height="280" key="GridCell.Tab.0.right/0.0.1920.1040@0.0.1920.1040" timestamp="1592250647211" />
|
||||
<state x="-1460" y="164" key="SettingsEditor" timestamp="1591824476757">
|
||||
<screen x="-1920" y="2" width="1920" height="1040" />
|
||||
</state>
|
||||
@@ -335,12 +339,12 @@
|
||||
<screen x="-1920" y="2" width="1920" height="1040" />
|
||||
</state>
|
||||
<state x="-1368" y="256" key="Vcs.Push.Dialog.v2/0.0.1536.824/-1920.2.1920.1040@-1920.2.1920.1040" timestamp="1591047662716" />
|
||||
<state x="579" y="298" key="com.intellij.ide.util.TipDialog" timestamp="1591971684146">
|
||||
<state x="263" y="182" width="1283" height="717" key="com.intellij.ide.util.TipDialog" timestamp="1592167059593">
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||||
<screen x="0" y="0" width="1920" height="1040" />
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||||
</state>
|
||||
<state x="-1341" y="300" key="com.intellij.ide.util.TipDialog/0.0.1536.824/-1920.2.1920.1040@-1920.2.1920.1040" timestamp="1591885589059" />
|
||||
<state x="463" y="236" key="com.intellij.ide.util.TipDialog/0.0.1536.824/-1920.2.1920.1040@0.0.1536.824" timestamp="1590784072879" />
|
||||
<state x="579" y="298" key="com.intellij.ide.util.TipDialog/0.0.1920.1040@0.0.1920.1040" timestamp="1591971684146" />
|
||||
<state x="263" y="182" width="1283" height="717" key="com.intellij.ide.util.TipDialog/0.0.1920.1040@0.0.1920.1040" timestamp="1592167059593" />
|
||||
<state x="-1151" y="440" key="com.intellij.openapi.vcs.update.UpdateOrStatusOptionsDialogupdate-v2" timestamp="1591821870917">
|
||||
<screen x="-1920" y="2" width="1920" height="1040" />
|
||||
</state>
|
||||
@@ -351,10 +355,10 @@
|
||||
</state>
|
||||
<state x="-1219" y="166" key="refactoring.ChangeSignatureDialog/0.0.1536.824/-1920.2.1920.1040@-1920.2.1920.1040" timestamp="1591059193684" />
|
||||
<state x="701" y="164" key="refactoring.ChangeSignatureDialog/0.0.1920.1040@0.0.1920.1040" timestamp="1591641371174" />
|
||||
<state x="-1297" y="227" width="672" height="678" key="search.everywhere.popup" timestamp="1591739392623">
|
||||
<state x="-1297" y="227" width="672" height="678" key="search.everywhere.popup" timestamp="1591993050079">
|
||||
<screen x="-1920" y="2" width="1920" height="1040" />
|
||||
</state>
|
||||
<state x="-1297" y="227" width="672" height="678" key="search.everywhere.popup/0.0.1536.824/-1920.2.1920.1040@-1920.2.1920.1040" timestamp="1591739392623" />
|
||||
<state x="-1297" y="227" width="672" height="678" key="search.everywhere.popup/0.0.1536.824/-1920.2.1920.1040@-1920.2.1920.1040" timestamp="1591993050079" />
|
||||
<state x="623" y="225" width="672" height="678" key="search.everywhere.popup/0.0.1920.1040@0.0.1920.1040" timestamp="1591649653766" />
|
||||
</component>
|
||||
<component name="XDebuggerManager">
|
||||
|
||||
106
reeps/reeps.py
106
reeps/reeps.py
@@ -64,7 +64,8 @@ class REEPS:
|
||||
|
||||
The ordering of the columns needs to be:
|
||||
|
||||
[h_i, h_eq, z_i, z_eq, {RE_1}_aq_i, {RE_1}_aq_eq, {RE_1}_d_eq,
|
||||
[h_i, h_eq, z_i, z_eq,
|
||||
{RE_1}_aq_i, {RE_1}_aq_eq, {RE_1}_d_eq,
|
||||
{RE_2}_aq_i, {RE_2}_aq_eq, {RE_2}_d_eq,...
|
||||
{RE_N}_aq_i, {RE_N}_aq_eq, {RE_N}_d_eq]
|
||||
|
||||
@@ -347,15 +348,17 @@ class REEPS:
|
||||
self.update_predicted_dict()
|
||||
|
||||
@staticmethod
|
||||
def slsqp_optimizer(objective, x_guess):
|
||||
def scipy_minimize(objective, x_guess, optimizer_kwargs=None):
|
||||
""" The default optimizer for REEPS
|
||||
|
||||
Uses scipy.minimize with options
|
||||
Uses scipy.minimize
|
||||
|
||||
By default, options are
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
default_kwargs= {"method": 'SLSQP',
|
||||
"bounds": [(1e-1, 1e1)*len(x_guess)],
|
||||
"bounds": [(1e-1, 1e1)]*len(x_guess),
|
||||
"constraints": (),
|
||||
"options": {'disp': True,
|
||||
'maxiter': 1000,
|
||||
@@ -363,14 +366,16 @@ class REEPS:
|
||||
|
||||
:param objective: (func) the objective function
|
||||
:param x_guess: (np.ndarray) the initial guess (always 1)
|
||||
:param optimizer_kwargs: (dict) dictionary of options for minimize
|
||||
:returns: (np.ndarray) Optimized parameters
|
||||
"""
|
||||
optimizer_kwargs = {"method": 'SLSQP',
|
||||
"bounds": [(1e-1, 1e1)] * len(x_guess),
|
||||
"constraints": (),
|
||||
"options": {'disp': True,
|
||||
'maxiter': 1000,
|
||||
'ftol': 1e-6}}
|
||||
if optimizer_kwargs is None:
|
||||
optimizer_kwargs = {"method": 'SLSQP',
|
||||
"bounds": [(1e-1, 1e1)] * len(x_guess),
|
||||
"constraints": (),
|
||||
"options": {'disp': True,
|
||||
'maxiter': 1000,
|
||||
'ftol': 1e-6}}
|
||||
res = minimize(objective, x_guess, **optimizer_kwargs)
|
||||
est_parameters = res.x
|
||||
return est_parameters
|
||||
@@ -450,7 +455,7 @@ class REEPS:
|
||||
"""Change list of Cantera solutions by inputting
|
||||
new xml file name and phase names
|
||||
|
||||
Also runs set_in_moles to set initial molality to 1 g/L
|
||||
Also runs set_in_moles to set feed volume to 1 L
|
||||
|
||||
:param phases_xml_filename: (str) xml file with parameters
|
||||
for equilibrium calc
|
||||
@@ -656,7 +661,7 @@ class REEPS:
|
||||
|
||||
This function also calls update_predicted_dict
|
||||
|
||||
:param feed_vol: (float) feed volume of mixture (g/L)
|
||||
:param feed_vol: (float) feed volume of mixture (L)
|
||||
"""
|
||||
phases_copy = self._phases.copy()
|
||||
exp_df = self._exp_df.copy()
|
||||
@@ -809,7 +814,7 @@ class REEPS:
|
||||
"with at least 2 arguments: "
|
||||
"f(objective_func,x_guess, kwargs)")
|
||||
if optimizer == 'SLSQP':
|
||||
optimizer = self.slsqp_optimizer
|
||||
optimizer = self.scipy_minimize
|
||||
self._optimizer = optimizer
|
||||
return None
|
||||
|
||||
@@ -939,7 +944,9 @@ class REEPS:
|
||||
i = 0
|
||||
for species_name in opt_dict.keys():
|
||||
for thermo_prop in opt_dict[species_name].keys():
|
||||
opt_dict[species_name][thermo_prop] *= x[i]
|
||||
if not np.isnan(
|
||||
x[i]): # if nan, do not update xml with nan
|
||||
opt_dict[species_name][thermo_prop] *= x[i]
|
||||
i += 1
|
||||
self.update_xml(opt_dict, temp_xml_file_path)
|
||||
|
||||
@@ -969,9 +976,12 @@ class REEPS:
|
||||
with optimizer. Returns dictionary with opt_dict structure
|
||||
|
||||
:param objective_function: (function) function to compute objective
|
||||
If 'None', last set objective or default function is used
|
||||
:param optimizer: (function) function to perform optimization
|
||||
:param optimizer_kwargs: (dict) arguments for optimizer
|
||||
:param objective_kwargs: (dict) arguments for objective function
|
||||
If 'None', last set optimizer or default is used
|
||||
:param optimizer_kwargs: (dict) optional arguments for optimizer
|
||||
:param objective_kwargs: (dict) optional arguments
|
||||
for objective function
|
||||
:returns opt_dict: (dict) optimized opt_dict. Has identical structure
|
||||
as opt_dict
|
||||
"""
|
||||
@@ -1043,6 +1053,8 @@ class REEPS:
|
||||
|
||||
def parity_plot(self,
|
||||
compared_value=None,
|
||||
color_axis=None,
|
||||
plot_title=None,
|
||||
save_path=None,
|
||||
print_r_squared=False):
|
||||
"""
|
||||
@@ -1052,6 +1064,16 @@ class REEPS:
|
||||
:param compared_value: (str) Quantity to compare predicted and
|
||||
experimental data. Can be any column containing "eq" in exp_df i.e.
|
||||
h_eq, z_eq, {RE}_d_eq, etc.
|
||||
:param plot_title: (str or boolean)
|
||||
|
||||
If None (default): Plot title will be generated from compared_value
|
||||
Recommend to just explore. If h_eq, plot_title is
|
||||
"H^+ eq conc".
|
||||
|
||||
If str: Plot title will be plot_title string
|
||||
|
||||
If "False": No plot title
|
||||
:param color_axis: (dict)
|
||||
:param save_path: (str) save path for parity plot
|
||||
:param print_r_squared: (boolean) To plot or not to plot r-squared
|
||||
value. Prints 2 places past decimal
|
||||
@@ -1071,33 +1093,57 @@ class REEPS:
|
||||
name_breakdown = re.findall('[^_\W]+', compared_value)
|
||||
compared_species = name_breakdown[0]
|
||||
if compared_species == 'h':
|
||||
species_name = '$H^+$'
|
||||
default_title = '$H^+$ eq. conc. (mol/L)'
|
||||
elif compared_species == 'z':
|
||||
species_name = extractant_name
|
||||
default_title = '{0} eq. conc. (mol/L)'.format(extractant_name)
|
||||
else:
|
||||
phase = name_breakdown[1]
|
||||
if phase == 'aq':
|
||||
re_charge = re_charges[re_species_list.index(compared_species)]
|
||||
species_name = '$%s^{%d+}$' % (compared_species, re_charge)
|
||||
default_title = '$%s^{%d+}$ eq. conc. (mol/L)' \
|
||||
% (compared_species, re_charge)
|
||||
elif phase == 'd':
|
||||
species_name = '{0} distribution ratio'.format(
|
||||
default_title = '{0} distribution ratio'.format(
|
||||
compared_species)
|
||||
else:
|
||||
species_name = '{0} complex'.format(compared_species)
|
||||
default_title = '{0} complex eq. conc. (mol/L)'.format(
|
||||
compared_species)
|
||||
fig, ax = plt.subplots()
|
||||
p1 = sns.scatterplot(meas, pred, color="r",
|
||||
label="{0} eq. conc. (mol/L)".format(
|
||||
species_name),
|
||||
legend=False)
|
||||
if color_axis is None:
|
||||
sns.scatterplot(meas, pred, color="r",
|
||||
legend=False)
|
||||
else:
|
||||
key = list(color_axis.keys())[0]
|
||||
value = list(color_axis.values())[0]
|
||||
if key == 'predicted':
|
||||
y = self.get_predicted_dict()[value]
|
||||
elif key == 'measured':
|
||||
y = self.get_exp_df()[value].values
|
||||
else:
|
||||
raise Exception('color_axis must be a dictionary with key'
|
||||
'"predicted" or "measured"')
|
||||
y = np.array(y)
|
||||
meas = np.array(meas)
|
||||
pred = np.array(pred)
|
||||
|
||||
p1 = ax.scatter(meas, pred, c=y, alpha=1, cmap='viridis')
|
||||
c_bar = fig.colorbar(p1, format='%.2f')
|
||||
# Fix next line. value is just the dictionary value.
|
||||
c_bar.set_label(value, rotation=270, labelpad=20)
|
||||
sns.lineplot(min_max_data, min_max_data, color="b", label="")
|
||||
|
||||
if print_r_squared:
|
||||
p1.text(min_max_data[0],
|
||||
ax.text(min_max_data[0],
|
||||
min_max_data[1] * 0.9,
|
||||
'$R^2$={0:.2f}'.format(self.r_squared(compared_value)))
|
||||
plt.legend(loc='lower right')
|
||||
else:
|
||||
plt.legend()
|
||||
# plt.legend(loc='lower right')
|
||||
# else:
|
||||
# plt.legend()
|
||||
ax.set(xlabel='Measured', ylabel='Predicted')
|
||||
if plot_title is None:
|
||||
ax.set_title(default_title)
|
||||
elif isinstance(plot_title, str):
|
||||
ax.set_title(plot_title)
|
||||
plt.show()
|
||||
if save_path is not None:
|
||||
plt.savefig(save_path, bbox_inches='tight')
|
||||
@@ -1110,7 +1156,7 @@ class REEPS:
|
||||
|
||||
:param compared_value: (str) Quantity to compare predicted and
|
||||
experimental data. Can be any column containing "eq" in exp_df i.e.
|
||||
h_eq, z_eq, {RE}_d_eq, etc.
|
||||
h_eq, z_eq, {RE}_d_eq, etc. default is {RE}_aq_eq
|
||||
"""
|
||||
exp_df = self.get_exp_df()
|
||||
predicted_dict = self.get_predicted_dict()
|
||||
|
||||
@@ -15,8 +15,10 @@ def optimizer(func, x_guess):
|
||||
ub = np.array([1e1])
|
||||
bounds = (lb, ub)
|
||||
options = {'c1': 1e-3, 'c2': 1e-3, 'w': 0.9}
|
||||
mini_optimizer = ps.single.global_best.GlobalBestPSO(n_particles=100, dimensions=1,
|
||||
options=options, bounds=bounds)
|
||||
mini_optimizer = ps.single.global_best.GlobalBestPSO(n_particles=100,
|
||||
dimensions=1,
|
||||
options=options,
|
||||
bounds=bounds)
|
||||
f_opt, x_opt = mini_optimizer.optimize(func, iters=100)
|
||||
|
||||
return x_opt
|
||||
|
||||
Reference in New Issue
Block a user