def get_rdf_figure(self, isite, normalized=True, figsize=None, step_function=None):
"""
Get the Radial Distribution Figure for a given site.
Args:
isite: Index of the site.
normalized: Whether to normalize distances.
figsize: Size of the figure.
step_function: Type of step function to be used for the RDF.
Returns:
Matplotlib figure.
"""
def dp_func(dp):
return 1.0 - 1.0 / np.power(dp, 3.0)
import matplotlib.pyplot as plt
if step_function is None:
step_function = {"type": "normal_cdf", "scale": 0.0001}
# Initializes the figure
if figsize is None:
fig = plt.figure()
else:
fig = plt.figure(figsize=figsize)
subplot = fig.add_subplot(111)
if normalized:
dists = self.neighbors_normalized_distances[isite]
else:
dists = self.neighbors_distances[isite]
if step_function["type"] == "step_function":
isorted = np.argsort([dd["min"] for dd in dists])
sorted_dists = [dists[ii]["min"] for ii in isorted]
dnb_dists = [len(dists[ii]["dnb_indices"]) for ii in isorted]
xx = [0.0]
yy = [0.0]
for idist, dist in enumerate(sorted_dists):
xx.append(dist)
xx.append(dist)
yy.append(yy[-1])
yy.append(yy[-1] + dnb_dists[idist])
xx.append(1.1 * xx[-1])
yy.append(yy[-1])
elif step_function["type"] == "normal_cdf":
scale = step_function["scale"]
mydists = [dp_func(dd["min"]) for dd in dists]
mydcns = [len(dd["dnb_indices"]) for dd in dists]
xx = np.linspace(0.0, 1.1 * max(mydists), num=500)
yy = np.zeros_like(xx)
for idist, dist in enumerate(mydists):
yy += mydcns[idist] * normal_cdf_step(xx, mean=dist, scale=scale)
else:
raise ValueError(f"Step function of type \"{step_function['type']}\" is not allowed")
subplot.plot(xx, yy)
return fig
def get_sadf_figure(self, isite, normalized=True, figsize=None, step_function=None):
"""
Get the Solid Angle Distribution Figure for a given site.
Args:
isite: Index of the site.
normalized: Whether to normalize angles.
figsize: Size of the figure.
step_function: Type of step function to be used for the SADF.
Returns:
Matplotlib figure.
"""
def ap_func(ap):
return np.power(ap, -0.1)
import matplotlib.pyplot as plt
if step_function is None:
step_function = {"type": "step_function", "scale": 0.0001}
# Initializes the figure
if figsize is None:
fig = plt.figure()
else:
fig = plt.figure(figsize=figsize)
subplot = fig.add_subplot(111)
if normalized:
angs = self.neighbors_normalized_angles[isite]
else:
angs = self.neighbors_angles[isite]
if step_function["type"] == "step_function":
isorted = np.argsort([ap_func(aa["min"]) for aa in angs])
sorted_angs = [ap_func(angs[ii]["min"]) for ii in isorted]
dnb_angs = [len(angs[ii]["dnb_indices"]) for ii in isorted]
xx = [0.0]
yy = [0.0]
for iang, ang in enumerate(sorted_angs):
xx.append(ang)
xx.append(ang)
yy.append(yy[-1])
yy.append(yy[-1] + dnb_angs[iang])
xx.append(1.1 * xx[-1])
yy.append(yy[-1])
elif step_function["type"] == "normal_cdf":
scale = step_function["scale"]
myangs = [ap_func(aa["min"]) for aa in angs]
mydcns = [len(dd["dnb_indices"]) for dd in angs]
xx = np.linspace(0.0, 1.1 * max(myangs), num=500)
yy = np.zeros_like(xx)
for iang, ang in enumerate(myangs):
yy += mydcns[iang] * normal_cdf_step(xx, mean=ang, scale=scale)
else:
raise ValueError(f"Step function of type \"{step_function['type']}\" is not allowed")
subplot.plot(xx, yy)
return fig
def get_rdf_figure(self,
isite,
normalized=True,
figsize=None,
step_function=None):
def dp_func(dp):
return 1.0 - 1.0 / np.power(dp, 3.0)
import matplotlib.pyplot as plt
if step_function is None:
step_function = {'type': 'normal_cdf', 'scale': 0.0001}
# Initializes the figure
if figsize is None:
fig = plt.figure()
else:
fig = plt.figure(figsize=figsize)
subplot = fig.add_subplot(111)
if normalized:
dists = self.neighbors_normalized_distances[isite]
else:
dists = self.neighbors_distances[isite]
if step_function['type'] == 'step_function':
isorted = np.argsort([dd['min'] for dd in dists])
sorted_dists = [dists[ii]['min'] for ii in isorted]
dnb_dists = [len(dists[ii]['dnb_indices']) for ii in isorted]
xx = [0.0]
yy = [0.0]
for idist, dist in enumerate(sorted_dists):
xx.append(dist)
xx.append(dist)
yy.append(yy[-1])
yy.append(yy[-1] + dnb_dists[idist])
xx.append(1.1 * xx[-1])
yy.append(yy[-1])
elif step_function['type'] == 'normal_cdf':
scale = step_function['scale']
mydists = [dp_func(dd['min']) for dd in dists]
mydcns = [len(dd['dnb_indices']) for dd in dists]
xx = np.linspace(0.0, 1.1 * max(mydists), num=500)
yy = np.zeros_like(xx)
for idist, dist in enumerate(mydists):
yy += mydcns[idist] * normal_cdf_step(
xx, mean=dist, scale=scale)
else:
raise ValueError(
'Step function of type "{}" is not allowed'.format(
step_function['type']))
subplot.plot(xx, yy)
return fig
def get_sadf_figure(self,
isite,
normalized=True,
figsize=None,
step_function=None):
def ap_func(ap):
return np.power(ap, -0.1)
import matplotlib.pyplot as plt
if step_function is None:
step_function = {'type': 'step_function', 'scale': 0.0001}
# Initializes the figure
if figsize is None:
fig = plt.figure()
else:
fig = plt.figure(figsize=figsize)
subplot = fig.add_subplot(111)
if normalized:
angs = self.neighbors_normalized_angles[isite]
else:
angs = self.neighbors_angles[isite]
if step_function['type'] == 'step_function':
isorted = np.argsort([ap_func(aa['min']) for aa in angs])
sorted_angs = [ap_func(angs[ii]['min']) for ii in isorted]
dnb_angs = [len(angs[ii]['dnb_indices']) for ii in isorted]
xx = [0.0]
yy = [0.0]
for iang, ang in enumerate(sorted_angs):
xx.append(ang)
xx.append(ang)
yy.append(yy[-1])
yy.append(yy[-1] + dnb_angs[iang])
xx.append(1.1 * xx[-1])
yy.append(yy[-1])
elif step_function['type'] == 'normal_cdf':
scale = step_function['scale']
myangs = [ap_func(aa['min']) for aa in angs]
mydcns = [len(dd['dnb_indices']) for dd in angs]
xx = np.linspace(0.0, 1.1 * max(myangs), num=500)
yy = np.zeros_like(xx)
for iang, ang in enumerate(myangs):
yy += mydcns[iang] * normal_cdf_step(xx, mean=ang, scale=scale)
else:
raise ValueError(
'Step function of type "{}" is not allowed'.format(
step_function['type']))
subplot.plot(xx, yy)
return fig
def get_sadf_figure(self, isite, normalized=True, figsize=None,
step_function=None):
def ap_func(ap):
return np.power(ap, -0.1)
import matplotlib.pyplot as plt
if step_function is None:
step_function = {'type': 'step_function', 'scale': 0.0001}
# Initializes the figure
if figsize is None:
fig = plt.figure()
else:
fig = plt.figure(figsize=figsize)
subplot = fig.add_subplot(111)
if normalized:
angs = self.neighbors_normalized_angles[isite]
else:
angs = self.neighbors_angles[isite]
if step_function['type'] == 'step_function':
isorted = np.argsort([ap_func(aa['min']) for aa in angs])
sorted_angs = [ap_func(angs[ii]['min']) for ii in isorted]
dnb_angs = [len(angs[ii]['dnb_indices']) for ii in isorted]
xx = [0.0]
yy = [0.0]
for iang, ang in enumerate(sorted_angs):
xx.append(ang)
xx.append(ang)
yy.append(yy[-1])
yy.append(yy[-1]+dnb_angs[iang])
xx.append(1.1*xx[-1])
yy.append(yy[-1])
elif step_function['type'] == 'normal_cdf':
scale = step_function['scale']
myangs = [ap_func(aa['min']) for aa in angs]
mydcns = [len(dd['dnb_indices']) for dd in angs]
xx = np.linspace(0.0, 1.1*max(myangs), num=500)
yy = np.zeros_like(xx)
for iang, ang in enumerate(myangs):
yy += mydcns[iang] * normal_cdf_step(xx, mean=ang, scale=scale)
else:
raise ValueError('Step function of type "{}" is not allowed'.format(step_function['type']))
subplot.plot(xx, yy)
return fig
def get_rdf_figure(self, isite, normalized=True, figsize=None,
step_function=None):
def dp_func(dp):
return 1.0 - 1.0 / np.power(dp, 3.0)
import matplotlib.pyplot as plt
if step_function is None:
step_function = {'type': 'normal_cdf', 'scale': 0.0001}
# Initializes the figure
if figsize is None:
fig = plt.figure()
else:
fig = plt.figure(figsize=figsize)
subplot = fig.add_subplot(111)
if normalized:
dists = self.neighbors_normalized_distances[isite]
else:
dists = self.neighbors_distances[isite]
if step_function['type'] == 'step_function':
isorted = np.argsort([dd['min'] for dd in dists])
sorted_dists = [dists[ii]['min'] for ii in isorted]
dnb_dists = [len(dists[ii]['dnb_indices']) for ii in isorted]
xx = [0.0]
yy = [0.0]
for idist, dist in enumerate(sorted_dists):
xx.append(dist)
xx.append(dist)
yy.append(yy[-1])
yy.append(yy[-1]+dnb_dists[idist])
xx.append(1.1*xx[-1])
yy.append(yy[-1])
elif step_function['type'] == 'normal_cdf':
scale = step_function['scale']
mydists = [dp_func(dd['min']) for dd in dists]
mydcns = [len(dd['dnb_indices']) for dd in dists]
xx = np.linspace(0.0, 1.1*max(mydists), num=500)
yy = np.zeros_like(xx)
for idist, dist in enumerate(mydists):
yy += mydcns[idist] * normal_cdf_step(xx, mean=dist, scale=scale)
else:
raise ValueError('Step function of type "{}" is not allowed'.format(step_function['type']))
subplot.plot(xx, yy)
return fig
