def __init__(self):
BaseFeaturizer.__init__(self)
self._types = ["cn", "lin"]
self._labels = ["cn", "lin"]
self._paras = [[], []]
for i in range(5, 180, 5):
self._types.append("bent")
self._labels.append("bent{}".format(i))
self._paras.append([float(i), 0.0667])
for t in ["tet", "oct", "bcc", "q2", "q4", "q6", "reg_tri", "sq", \
"sq_pyr", "tri_bipyr"]:
self._types.append(t)
self._labels.append(t)
self._paras.append([])
def generate_tables():
"""
Generate nicely formatted tables of all features in RST format.
Args:
None
Returns:
Prints a formatted string, where each main entry is a separate table
representing one module of featurizers.
"""
mmfeat = "====================\nTable of Featurizers\n====================\n"
mmdes = "Below, you will find a description of each featurizer, listed in " \
"tables grouped by module.\n"
subclasses = []
scnames = BaseFeaturizer.__subclasses__() + [BaseFeaturizer]
scnames += conversions.ConversionFeaturizer.__subclasses__()
for sc in scnames:
scdict = {"name": sc.__name__}
scdict["doc"] = sc.__doc__.splitlines()[1].lstrip()
scdict["module"] = sc.__module__
scdict["type"] = sc.__module__.split(".")[-1]
subclasses.append(scdict)
df = pd.DataFrame(subclasses)
print(mmfeat)
print(mmdes)
for ftype in np.unique(df['type']):
dftable = df[df['type'] == ftype]
dftable['codename'] = [":code:`" + n + "`" for n in dftable['name']]
ftype_border = "-" * len(ftype)
mod = "(" + dftable['module'].iloc[0] + ")"
des_border = "-" * len(mod_summs[ftype])
print(ftype_border)
print(ftype)
print(ftype_border)
print(mod_summs[ftype])
print(des_border + "\n")
print(mod)
print("\n.. list-table::")
print(" :align: left")
print(" :widths: 30 70")
# print(" :width: 70%")
print(" :header-rows: 1\n")
print(" * - Name")
print(" - Description")
for i, n in enumerate(dftable['codename']):
# url = url_base + dftable["module"].iloc[0] + "." + \
# dftable["name"].iloc[i] + ">`_"
url = ""
print(f" * - {n}")
description = dftable["doc"].iloc[i]
print(f" - {description} {url} ")
print("\n\n")
def generate_tables():
"""
Generate nicely formatted tables of all features in RST format.
Args:
None
Returns:
tables ([str]): A list of formatted strings, where each entry is a
separate table representing one module.
"""
mmfeat = "====================\nTable of Featurizers\n====================\n"
mmdes = "Below, you will find a description of each featurizer, listed in " \
"tables grouped by module.\n"
tables = [mmfeat, mmdes]
subclasses = []
scnames = BaseFeaturizer.__subclasses__() + [BaseFeaturizer]
scnames += conversions.ConversionFeaturizer.__subclasses__()
for sc in scnames:
scdict = {"name": sc.__name__}
scdict["doc"] = sc.__doc__.splitlines()[1].lstrip()
scdict["module"] = sc.__module__
scdict["type"] = sc.__module__.split(".")[-1]
subclasses.append(scdict)
df = pd.DataFrame(subclasses)
for ftype in np.unique(df['type']):
dftable = df[df['type'] == ftype]
dftable['codename'] = [":code:`" + n + "`" for n in dftable['name']]
mod = "\n(" + dftable['module'].iloc[0] + ")\n\n"
namelen = max([len(n) for n in dftable['codename']])
# doclen = max([len(d) for d in dftable['doc']])
doclen = 400
borderstr = "=" * namelen + " " + "=" * doclen + "\n"
headerstr = "Name" + " " * (namelen - 1) + "Description\n"
tablestr = ""
for i, n in enumerate(dftable['codename']):
url = url_base + dftable["module"].iloc[0] + "." + \
dftable["name"].iloc[i] + ">`_"
tablestr += n + " " * (namelen - len(n) + 3) + \
dftable['doc'].iloc[i] + url + "\n"
ftype_border = "\n" + "-" * len(ftype) + "\n"
des_border = "-" * len(mod_summs[ftype]) + "\n"
tables.append(ftype_border + ftype + ftype_border + mod_summs[ftype] +
des_border + mod + borderstr + headerstr + borderstr +
tablestr + borderstr + "\n\n")
return tables
def generate_tables():
"""
Generate nicely formatted tables of all features in RST format.
Args:
None
Returns:
tables ([str]): A list of formatted strings, where each entry is a
separate table representing one module.
"""
mmfeat = "====================\nTable of Featurizers\n====================\n"
mmdes = "Below, you will find a description of each featurizer, listed in " \
"tables grouped by module.\n"
tables = [mmfeat, mmdes]
subclasses = []
scnames = BaseFeaturizer.__subclasses__() + [BaseFeaturizer]
scnames += conversions.ConversionFeaturizer.__subclasses__()
for sc in scnames:
scdict = {"name": sc.__name__}
scdict["doc"] = sc.__doc__.splitlines()[1].lstrip()
scdict["module"] = sc.__module__
scdict["type"] = sc.__module__.split(".")[-1]
subclasses.append(scdict)
df = pd.DataFrame(subclasses)
for ftype in np.unique(df['type']):
dftable = df[df['type'] == ftype]
dftable['codename'] = [":code:`" + n + "`" for n in dftable['name']]
mod = "\n(" + dftable['module'].iloc[0] + ")\n\n"
namelen = max([len(n) for n in dftable['codename']])
# doclen = max([len(d) for d in dftable['doc']])
doclen = 400
borderstr = "=" * namelen + " " + "=" * doclen + "\n"
headerstr = "Name" + " " * (namelen - 1) + "Description\n"
tablestr = ""
for i, n in enumerate(dftable['codename']):
url = url_base + dftable["module"].iloc[0] + "." + \
dftable["name"].iloc[i] + ">`_"
tablestr += n + " " * (namelen - len(n) + 3) + \
dftable['doc'].iloc[i] + url + "\n"
ftype_border = "\n" + "-" * len(ftype) + "\n"
des_border = "-" * len(mod_summs[ftype]) + "\n"
tables.append(ftype_border + ftype + ftype_border + mod_summs[ftype] +
des_border + mod + borderstr + headerstr + borderstr +
tablestr + borderstr + "\n\n")
return tables
def __init__(self):
BaseFeaturizer.__init__(self)
def __init__(self, featurizer: BaseFeaturizer, composition: Composition):
self._featurizer = featurizer
self._composition = composition
self._values = featurizer.featurize(self._composition)