def test_get_data_quantities_AL_NI_VA_interaction():
"""Test that an interaction with a VA produces the correct data quantities
We just have a template database that has the phase defined. We then hot
patch the Model object to have the GM from the fixed model we printed out
and the data we printed out. The hot patch is needed because this is
formation enthalpy data and the model needs to have the lower order terms
in composition.
One possible issue is that the new GM in the fixed model does not have any
individual contributions, so it cannot be used to test excluded model
contributions. The only excluded model contributions in this data are
idmix, but the property we are testing is HM_FORM, so the feature transform
of the idmix property should be zero.
"""
# Hack the namespace to make the copy-pasted Gibbs energy function work
from sympy import log, Piecewise
T = v.T
data = [{'components': ['AL', 'NI', 'VA'], 'phases': ['BCC_B2'], 'solver': {'mode': 'manual', 'sublattice_occupancies': [[1.0, [0.5, 0.5], 1.0], [1.0, [0.75, 0.25], 1.0]], 'sublattice_site_ratios': [0.5, 0.5, 1.0], 'sublattice_configurations': (('AL', ('NI', 'VA'), 'VA'), ('AL', ('NI', 'VA'), 'VA')), 'comment': 'BCC_B2 sublattice configuration (2SL)'}, 'conditions': {'P': 101325.0, 'T': np.array([300.])}, 'reference_state': 'SGTE91', 'output': 'HM_FORM', 'values': np.array([[[-40316.61077, -56361.58554]]]), 'reference': 'C. Jiang 2009 (constrained SQS)', 'excluded_model_contributions': ['idmix']}, {'components': ['AL', 'NI', 'VA'], 'phases': ['BCC_B2'], 'solver': {'mode': 'manual', 'sublattice_occupancies': [[1.0, [0.5, 0.5], 1.0], [1.0, [0.75, 0.25], 1.0]], 'sublattice_site_ratios': [0.5, 0.5, 1.0], 'sublattice_configurations': (('AL', ('NI', 'VA'), 'VA'), ('AL', ('NI', 'VA'), 'VA')), 'comment': 'BCC_B2 sublattice configuration (2SL)'}, 'conditions': {'P': 101325.0, 'T': np.array([300.])}, 'reference_state': 'SGTE91', 'output': 'HM_FORM', 'values': np.array([[[-41921.43363, -57769.49473]]]), 'reference': 'C. Jiang 2009 (relaxed SQS)', 'excluded_model_contributions': ['idmix']}]
NEW_GM = 8.3145*T*(0.5*Piecewise((v.SiteFraction("BCC_B2", 0, "AL")*log(v.SiteFraction("BCC_B2", 0, "AL")), v.SiteFraction("BCC_B2", 0, "AL") > 1.0e-16), (0, True))/(0.5*v.SiteFraction("BCC_B2", 0, "AL") + 0.5*v.SiteFraction("BCC_B2", 0, "NI") + 0.5*v.SiteFraction("BCC_B2", 1, "AL") + 0.5*v.SiteFraction("BCC_B2", 1, "NI")) + 0.5*Piecewise((v.SiteFraction("BCC_B2", 0, "NI")*log(v.SiteFraction("BCC_B2", 0, "NI")), v.SiteFraction("BCC_B2", 0, "NI") > 1.0e-16), (0, True))/(0.5*v.SiteFraction("BCC_B2", 0, "AL") + 0.5*v.SiteFraction("BCC_B2", 0, "NI") + 0.5*v.SiteFraction("BCC_B2", 1, "AL") + 0.5*v.SiteFraction("BCC_B2", 1, "NI")) + 0.5*Piecewise((v.SiteFraction("BCC_B2", 0, "VA")*log(v.SiteFraction("BCC_B2", 0, "VA")), v.SiteFraction("BCC_B2", 0, "VA") > 1.0e-16), (0, True))/(0.5*v.SiteFraction("BCC_B2", 0, "AL") + 0.5*v.SiteFraction("BCC_B2", 0, "NI") + 0.5*v.SiteFraction("BCC_B2", 1, "AL") + 0.5*v.SiteFraction("BCC_B2", 1, "NI")) + 0.5*Piecewise((v.SiteFraction("BCC_B2", 1, "AL")*log(v.SiteFraction("BCC_B2", 1, "AL")), v.SiteFraction("BCC_B2", 1, "AL") > 1.0e-16), (0, True))/(0.5*v.SiteFraction("BCC_B2", 0, "AL") + 0.5*v.SiteFraction("BCC_B2", 0, "NI") + 0.5*v.SiteFraction("BCC_B2", 1, "AL") + 0.5*v.SiteFraction("BCC_B2", 1, "NI")) + 0.5*Piecewise((v.SiteFraction("BCC_B2", 1, "NI")*log(v.SiteFraction("BCC_B2", 1, "NI")), v.SiteFraction("BCC_B2", 1, "NI") > 1.0e-16), (0, True))/(0.5*v.SiteFraction("BCC_B2", 0, "AL") + 0.5*v.SiteFraction("BCC_B2", 0, "NI") + 0.5*v.SiteFraction("BCC_B2", 1, "AL") + 0.5*v.SiteFraction("BCC_B2", 1, "NI")) + 0.5*Piecewise((v.SiteFraction("BCC_B2", 1, "VA")*log(v.SiteFraction("BCC_B2", 1, "VA")), v.SiteFraction("BCC_B2", 1, "VA") > 1.0e-16), (0, True))/(0.5*v.SiteFraction("BCC_B2", 0, "AL") + 0.5*v.SiteFraction("BCC_B2", 0, "NI") + 0.5*v.SiteFraction("BCC_B2", 1, "AL") + 0.5*v.SiteFraction("BCC_B2", 1, "NI")) + Piecewise((v.SiteFraction("BCC_B2", 2, "VA")*log(v.SiteFraction("BCC_B2", 2, "VA")), v.SiteFraction("BCC_B2", 2, "VA") > 1.0e-16), (0, True))/(0.5*v.SiteFraction("BCC_B2", 0, "AL") + 0.5*v.SiteFraction("BCC_B2", 0, "NI") + 0.5*v.SiteFraction("BCC_B2", 1, "AL") + 0.5*v.SiteFraction("BCC_B2", 1, "NI"))) + (45262.9*v.SiteFraction("BCC_B2", 0, "AL")*v.SiteFraction("BCC_B2", 0, "NI")*v.SiteFraction("BCC_B2", 1, "AL")*v.SiteFraction("BCC_B2", 2, "VA") + 45262.9*v.SiteFraction("BCC_B2", 0, "AL")*v.SiteFraction("BCC_B2", 1, "AL")*v.SiteFraction("BCC_B2", 1, "NI")*v.SiteFraction("BCC_B2", 2, "VA"))/(0.5*v.SiteFraction("BCC_B2", 0, "AL") + 0.5*v.SiteFraction("BCC_B2", 0, "NI") + 0.5*v.SiteFraction("BCC_B2", 1, "AL") + 0.5*v.SiteFraction("BCC_B2", 1, "NI")) + (1.0*v.SiteFraction("BCC_B2", 0, "AL")*v.SiteFraction("BCC_B2", 1, "AL")*v.SiteFraction("BCC_B2", 2, "VA")*Piecewise((10083 - 4.813*T, (T >= 298.15) & (T < 2900.0)), (0, True)) + v.SiteFraction("BCC_B2", 0, "AL")*v.SiteFraction("BCC_B2", 1, "NI")*v.SiteFraction("BCC_B2", 2, "VA")*(9.52839e-8*T**3 + 0.00123463*T**2 + 0.000871898*T*log(T) + 1.31471*T - 64435.3 + 23095.2/T) + v.SiteFraction("BCC_B2", 0, "AL")*v.SiteFraction("BCC_B2", 1, "VA")*v.SiteFraction("BCC_B2", 2, "VA")*(10.0*T + 16432.5) + v.SiteFraction("BCC_B2", 0, "NI")*v.SiteFraction("BCC_B2", 1, "AL")*v.SiteFraction("BCC_B2", 2, "VA")*(9.52839e-8*T**3 + 0.00123463*T**2 + 0.000871898*T*log(T) + 1.31471*T - 64435.3 + 23095.2/T) + 1.0*v.SiteFraction("BCC_B2", 0, "NI")*v.SiteFraction("BCC_B2", 1, "NI")*v.SiteFraction("BCC_B2", 2, "VA")*Piecewise((8715.084 - 3.556*T, (T >= 298.15) & (T < 3000.0)), (0, True)) + 32790.6*v.SiteFraction("BCC_B2", 0, "NI")*v.SiteFraction("BCC_B2", 1, "VA")*v.SiteFraction("BCC_B2", 2, "VA") + v.SiteFraction("BCC_B2", 0, "VA")*v.SiteFraction("BCC_B2", 1, "AL")*v.SiteFraction("BCC_B2", 2, "VA")*(10.0*T + 16432.5) + 32790.6*v.SiteFraction("BCC_B2", 0, "VA")*v.SiteFraction("BCC_B2", 1, "NI")*v.SiteFraction("BCC_B2", 2, "VA"))/(0.5*v.SiteFraction("BCC_B2", 0, "AL") + 0.5*v.SiteFraction("BCC_B2", 0, "NI") + 0.5*v.SiteFraction("BCC_B2", 1, "AL") + 0.5*v.SiteFraction("BCC_B2", 1, "NI"))
dbf = Database("""$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$
$ Date: 2019-12-08 18:05
$ Components: AL, NI, VA
$ Phases: BCC_B2
$ Generated by brandon (pycalphad 0.8.1.post1)
$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$
ELEMENT AL FCC_A1 26.982 4577.3 28.322 !
ELEMENT NI FCC_A1 58.69 4787.0 29.796 !
ELEMENT VA VACUUM 0.0 0.0 0.0 !
TYPE_DEFINITION % SEQ * !
DEFINE_SYSTEM_DEFAULT ELEMENT 2 !
DEFAULT_COMMAND DEFINE_SYSTEM_ELEMENT VA !
PHASE BCC_B2 % 3 0.5 0.5 1 !
CONSTITUENT BCC_B2 :AL,NI,VA:AL,NI,VA:VA: !
""")
mod = Model(dbf, ['AL', 'NI', 'VA'], 'BCC_B2')
dd = {ky: 0.0 for ky in mod.models.keys()}
dd['GM'] = NEW_GM
mod.models = dd
print(mod.HM)
config_tup = (('AL',), ('NI', 'VA'), ('VA',))
calculate_dict = get_prop_samples(data, config_tup)
sample_condition_dicts = _get_sample_condition_dicts(calculate_dict, list(map(len, config_tup)))
qty = get_data_quantities('HM_FORM', mod, [0], data, sample_condition_dicts)
print(qty)
assert np.all(np.isclose([-6254.7802775, -5126.1206475, -7458.3974225, -6358.04118875], qty))
def _compare_data_to_parameters(dbf,
comps,
phase_name,
desired_data,
mod,
configuration,
x,
y,
ax=None):
"""
Return one set of plotted Axes with data compared to calculated parameters
Parameters
----------
dbf : Database
pycalphad thermodynamic database containing the relevant parameters.
comps : list
Names of components to consider in the calculation.
phase_name : str
Name of the considered phase phase
desired_data :
mod : Model
A pycalphad Model. The Model may or may not have the reference state zeroed out for formation properties.
configuration :
x : str
Model property to plot on the x-axis e.g. 'T', 'HM_MIX', 'SM_FORM'
y : str
Model property to plot on the y-axis e.g. 'T', 'HM_MIX', 'SM_FORM'
ax : matplotlib.Axes
Default axes used if not specified.
Returns
-------
matplotlib.Axes
"""
all_samples = np.array(get_samples(desired_data), dtype=np.object)
endpoints = endmembers_from_interaction(configuration)
interacting_subls = [
c for c in list_to_tuple(configuration) if isinstance(c, tuple)
]
disordered_config = False
if (len(set(interacting_subls)) == 1) and (len(interacting_subls[0]) == 2):
# This configuration describes all sublattices with the same two elements interacting
# In general this is a high-dimensional space; just plot the diagonal to see the disordered mixing
endpoints = [endpoints[0], endpoints[-1]]
disordered_config = True
if not ax:
fig = plt.figure(figsize=plt.figaspect(1))
ax = fig.gca()
bar_chart = False
bar_labels = []
bar_data = []
if y.endswith('_FORM'):
# We were passed a Model object with zeroed out reference states
yattr = y[:-5]
else:
yattr = y
if len(endpoints) == 1:
# This is an endmember so we can just compute T-dependent stuff
temperatures = np.array([i[0] for i in all_samples], dtype=np.float)
if temperatures.min() != temperatures.max():
temperatures = np.linspace(temperatures.min(),
temperatures.max(),
num=100)
else:
# We only have one temperature: let's do a bar chart instead
bar_chart = True
temperatures = temperatures.min()
endmember = _translate_endmember_to_array(
endpoints[0], mod.ast.atoms(v.SiteFraction))[None, None]
predicted_quantities = calculate(dbf,
comps, [phase_name],
output=yattr,
T=temperatures,
P=101325,
points=endmember,
model=mod,
mode='numpy')
if y == 'HM' and x == 'T':
# Shift enthalpy data so that value at minimum T is zero
predicted_quantities[yattr] -= predicted_quantities[yattr].sel(
T=temperatures[0]).values.flatten()
response_data = predicted_quantities[yattr].values.flatten()
if not bar_chart:
extra_kwargs = {}
if len(response_data) < 10:
extra_kwargs['markersize'] = 20
extra_kwargs['marker'] = '.'
extra_kwargs['linestyle'] = 'none'
extra_kwargs['clip_on'] = False
ax.plot(temperatures,
response_data,
label='This work',
color='k',
**extra_kwargs)
ax.set_xlabel(plot_mapping.get(x, x))
ax.set_ylabel(plot_mapping.get(y, y))
else:
bar_labels.append('This work')
bar_data.append(response_data[0])
elif len(endpoints) == 2:
# Binary interaction parameter
first_endpoint = _translate_endmember_to_array(
endpoints[0], mod.ast.atoms(v.SiteFraction))
second_endpoint = _translate_endmember_to_array(
endpoints[1], mod.ast.atoms(v.SiteFraction))
point_matrix = np.linspace(0, 1, num=100)[None].T * second_endpoint + \
(1 - np.linspace(0, 1, num=100))[None].T * first_endpoint
# TODO: Real temperature support
point_matrix = point_matrix[None, None]
predicted_quantities = calculate(dbf,
comps, [phase_name],
output=yattr,
T=300,
P=101325,
points=point_matrix,
model=mod,
mode='numpy')
response_data = predicted_quantities[yattr].values.flatten()
if not bar_chart:
extra_kwargs = {}
if len(response_data) < 10:
extra_kwargs['markersize'] = 20
extra_kwargs['marker'] = '.'
extra_kwargs['linestyle'] = 'none'
extra_kwargs['clip_on'] = False
ax.plot(np.linspace(0, 1, num=100),
response_data,
label='This work',
color='k',
**extra_kwargs)
ax.set_xlim((0, 1))
ax.set_xlabel(
str(':'.join(endpoints[0])) + ' to ' +
str(':'.join(endpoints[1])))
ax.set_ylabel(plot_mapping.get(y, y))
else:
bar_labels.append('This work')
bar_data.append(response_data[0])
else:
raise NotImplementedError(
'No support for plotting configuration {}'.format(configuration))
bib_reference_keys = sorted(
list({entry['reference']
for entry in desired_data}))
symbol_map = bib_marker_map(bib_reference_keys)
for data in desired_data:
indep_var_data = None
response_data = np.zeros_like(data['values'], dtype=np.float)
if x == 'T' or x == 'P':
indep_var_data = np.array(data['conditions'][x],
dtype=np.float).flatten()
elif x == 'Z':
if disordered_config:
# Take the second element of the first interacting sublattice as the coordinate
# Because it's disordered all sublattices should be equivalent
# TODO: Fix this to filter because we need to guarantee the plot points are disordered
occ = data['solver']['sublattice_occupancies']
subl_idx = np.nonzero(
[isinstance(c, (list, tuple)) for c in occ[0]])[0]
if len(subl_idx) > 1:
subl_idx = int(subl_idx[0])
else:
subl_idx = int(subl_idx)
indep_var_data = [c[subl_idx][1] for c in occ]
else:
interactions = np.array([i[1][1] for i in get_samples([data])],
dtype=np.float)
indep_var_data = 1 - (interactions + 1) / 2
if y.endswith('_MIX') and data['output'].endswith('_FORM'):
# All the _FORM data we have still has the lattice stability contribution
# Need to zero it out to shift formation data to mixing
mod_latticeonly = Model(
dbf,
comps,
phase_name,
parameters={'GHSER' + c.upper(): 0
for c in comps})
mod_latticeonly.models = {
key: value
for key, value in mod_latticeonly.models.items()
if key == 'ref'
}
temps = data['conditions'].get('T', 300)
pressures = data['conditions'].get('P', 101325)
points = build_sitefractions(
phase_name, data['solver']['sublattice_configurations'],
data['solver']['sublattice_occupancies'])
for point_idx in range(len(points)):
missing_variables = mod_latticeonly.ast.atoms(
v.SiteFraction) - set(points[point_idx].keys())
# Set unoccupied values to zero
points[point_idx].update(
{key: 0
for key in missing_variables})
# Change entry to a sorted array of site fractions
points[point_idx] = list(
OrderedDict(sorted(points[point_idx].items(),
key=str)).values())
points = np.array(points, dtype=np.float)
# TODO: Real temperature support
points = points[None, None]
stability = calculate(dbf,
comps, [phase_name],
output=data['output'][:-5],
T=temps,
P=pressures,
points=points,
model=mod_latticeonly,
mode='numpy')
response_data -= stability[data['output'][:-5]].values
response_data += np.array(data['values'], dtype=np.float)
response_data = response_data.flatten()
if not bar_chart:
extra_kwargs = {}
if len(response_data) < 10:
extra_kwargs['markersize'] = 8
extra_kwargs['linestyle'] = 'none'
extra_kwargs['clip_on'] = False
ref = data.get('reference', '')
mark = symbol_map[ref]['markers']
ax.plot(indep_var_data,
response_data,
label=symbol_map[ref]['formatted'],
marker=mark['marker'],
fillstyle=mark['fillstyle'],
**extra_kwargs)
else:
bar_labels.append(data.get('reference', None))
bar_data.append(response_data[0])
if bar_chart:
ax.barh(0.02 * np.arange(len(bar_data)),
bar_data,
color='k',
height=0.01)
endmember_title = ' to '.join([':'.join(i) for i in endpoints])
ax.get_figure().suptitle('{} (T = {} K)'.format(
endmember_title, temperatures),
fontsize=20)
ax.set_yticks(0.02 * np.arange(len(bar_data)))
ax.set_yticklabels(bar_labels, fontsize=20)
# This bar chart is rotated 90 degrees, so "y" is now x
ax.set_xlabel(plot_mapping.get(y, y))
else:
ax.set_frame_on(False)
leg = ax.legend(loc='best')
leg.get_frame().set_edgecolor('black')
return ax
def plot_interaction(dbf, comps, phase_name, configuration, output, datasets=None, symmetry=None, ax=None, plot_kwargs=None, dataplot_kwargs=None) -> plt.Axes:
"""
Return one set of plotted Axes with data compared to calculated parameters
Parameters
----------
dbf : Database
pycalphad thermodynamic database containing the relevant parameters.
comps : Sequence[str]
Names of components to consider in the calculation.
phase_name : str
Name of the considered phase phase
configuration : Tuple[Tuple[str]]
ESPEI-style configuration
output : str
Model property to plot on the y-axis e.g. ``'HM_MIX'``, or ``'SM_MIX'``.
Must be a ``'_MIX'`` property.
datasets : tinydb.TinyDB
symmetry : list
List of lists containing indices of symmetric sublattices e.g. [[0, 1], [2, 3]]
ax : plt.Axes
Default axes used if not specified.
plot_kwargs : Optional[Dict[str, Any]]
Keyword arguments to ``ax.plot`` for the predicted data.
dataplot_kwargs : Optional[Dict[str, Any]]
Keyword arguments to ``ax.plot`` the observed data.
Returns
-------
plt.Axes
"""
if not output.endswith('_MIX'):
raise ValueError("`plot_interaction` only supports HM_MIX, SM_MIX, or CPM_MIX outputs.")
if not plot_kwargs:
plot_kwargs = {}
if not dataplot_kwargs:
dataplot_kwargs = {}
if not ax:
ax = plt.subplot()
# Plot predicted values from the database
grid, predicted_values = _get_interaction_predicted_values(dbf, comps, phase_name, configuration, output)
plot_kwargs.setdefault('label', 'This work')
plot_kwargs.setdefault('color', 'k')
ax.plot(grid, predicted_values, **plot_kwargs)
# Plot the observed values from the datasets
# TODO: model exclusions handling
# TODO: better reference state handling
mod_srf = Model(dbf, comps, phase_name, parameters={'GHSER'+c.upper(): 0 for c in comps})
mod_srf.models = {'ref': mod_srf.models['ref']}
# _MIX assumption
prop = output.split('_MIX')[0]
desired_props = (f"{prop}_MIX", f"{prop}_FORM")
if datasets is not None:
solver_qry = (tinydb.where('solver').test(symmetry_filter, configuration, recursive_tuplify(symmetry) if symmetry else symmetry))
desired_data = get_prop_data(comps, phase_name, desired_props, datasets, additional_query=solver_qry)
desired_data = filter_configurations(desired_data, configuration, symmetry)
desired_data = filter_temperatures(desired_data)
else:
desired_data = []
species = unpack_components(dbf, comps)
# phase constituents are Species objects, so we need to be doing intersections with those
phase_constituents = dbf.phases[phase_name].constituents
# phase constituents must be filtered to only active
constituents = [[sp.name for sp in sorted(subl_constituents.intersection(species))] for subl_constituents in phase_constituents]
subl_dof = list(map(len, constituents))
calculate_dict = get_prop_samples(desired_data, constituents)
sample_condition_dicts = _get_sample_condition_dicts(calculate_dict, subl_dof)
interacting_subls = [c for c in recursive_tuplify(configuration) if isinstance(c, tuple)]
if (len(set(interacting_subls)) == 1) and (len(interacting_subls[0]) == 2):
# This configuration describes all sublattices with the same two elements interacting
# In general this is a high-dimensional space; just plot the diagonal to see the disordered mixing
endpoints = endmembers_from_interaction(configuration)
endpoints = [endpoints[0], endpoints[-1]]
disordered_config = True
else:
disordered_config = False
bib_reference_keys = sorted({entry.get('reference', '') for entry in desired_data})
symbol_map = bib_marker_map(bib_reference_keys)
for data in desired_data:
indep_var_data = None
response_data = np.zeros_like(data['values'], dtype=np.float_)
if disordered_config:
# Take the second element of the first interacting sublattice as the coordinate
# Because it's disordered all sublattices should be equivalent
# TODO: Fix this to filter because we need to guarantee the plot points are disordered
occ = data['solver']['sublattice_occupancies']
subl_idx = np.nonzero([isinstance(c, (list, tuple)) for c in occ[0]])[0]
if len(subl_idx) > 1:
subl_idx = int(subl_idx[0])
else:
subl_idx = int(subl_idx)
indep_var_data = [c[subl_idx][1] for c in occ]
else:
interactions = np.array([cond_dict[Symbol('YS')] for cond_dict in sample_condition_dicts])
indep_var_data = 1 - (interactions+1)/2
if data['output'].endswith('_FORM'):
# All the _FORM data we have still has the lattice stability contribution
# Need to zero it out to shift formation data to mixing
temps = data['conditions'].get('T', 298.15)
pressures = data['conditions'].get('P', 101325)
points = build_sitefractions(phase_name, data['solver']['sublattice_configurations'],
data['solver']['sublattice_occupancies'])
for point_idx in range(len(points)):
missing_variables = mod_srf.ast.atoms(v.SiteFraction) - set(points[point_idx].keys())
# Set unoccupied values to zero
points[point_idx].update({key: 0 for key in missing_variables})
# Change entry to a sorted array of site fractions
points[point_idx] = list(OrderedDict(sorted(points[point_idx].items(), key=str)).values())
points = np.array(points, dtype=np.float_)
# TODO: Real temperature support
points = points[None, None]
stability = calculate(dbf, comps, [phase_name], output=data['output'][:-5],
T=temps, P=pressures, points=points,
model=mod_srf)
response_data -= stability[data['output'][:-5]].values.squeeze()
response_data += np.array(data['values'], dtype=np.float_)
response_data = response_data.flatten()
ref = data.get('reference', '')
dataplot_kwargs.setdefault('markersize', 8)
dataplot_kwargs.setdefault('linestyle', 'none')
dataplot_kwargs.setdefault('clip_on', False)
# Cannot use setdefault because it won't overwrite previous iterations
dataplot_kwargs['label'] = symbol_map[ref]['formatted']
dataplot_kwargs['marker'] = symbol_map[ref]['markers']['marker']
dataplot_kwargs['fillstyle'] = symbol_map[ref]['markers']['fillstyle']
ax.plot(indep_var_data, response_data, **dataplot_kwargs)
ax.set_xlim((0, 1))
ax.set_xlabel(str(':'.join(endpoints[0])) + ' to ' + str(':'.join(endpoints[1])))
ax.set_ylabel(plot_mapping.get(output, output))
leg = ax.legend(loc='center left', bbox_to_anchor=(1, 0.5)) # legend outside
leg.get_frame().set_edgecolor('black')
return ax