def build_group_table(ssa, group, columns, mdl_name=[]):
"""
Build the table for devices in a group in an ADNES System.
Parameters
----------
ssa : andes.system.System
The ADNES system to build the table
group : string
The ADNES group
columns : list of string
The common columns of a group that to be included in the table.
mdl_name : list of string
The list of models that to be included in the table. Default as all models.
Returns
-------
DataFrame
The output Dataframe contains the columns from the device
"""
group_df = pd.DataFrame(columns=columns)
group = getattr(ssa, group)
if not mdl_name:
mdl_dict = getattr(group, 'models')
for key in mdl_dict:
mdl = getattr(ssa, key)
group_df = pd.concat([group_df, mdl.as_df()[columns]], axis=0)
else:
for key in mdl_name:
mdl = getattr(ssa, key)
group_df = pd.concat([group_df, mdl.as_df()[columns]], axis=0)
return group_df
def as_df(self, vin=False):
"""
Export all parameters as a `pandas.DataFrame` object.
This function utilizes `as_dict` for preparing data.
Returns
-------
DataFrame
A dataframe containing all model data. An `uid` column is added.
vin : bool
If True, export all parameters from original input (``vin``).
"""
if vin is False:
out = pd.DataFrame(self.as_dict()).set_index('uid')
else:
out = pd.DataFrame(self.as_dict(vin=True)).set_index('uid')
return out
def _sumPF_ppn(ppn):
"""Summarize PF results of a pandapower net"""
rg = pd.DataFrame()
rg['gen'] = ppn.res_gen['p_mw']
rg['bus'] = ppn.gen['bus']
rg = rg.groupby('bus').sum()
rg.reset_index(inplace=True)
rd = pd.DataFrame()
rd['demand'] = ppn.res_load['p_mw']
rd['bus'] = ppn.load['bus']
rd = rd.groupby('bus').sum()
rd.reset_index(inplace=True)
rp = pd.DataFrame()
rp['bus'] = ppn.bus.index
rp = pd.merge(rp, rg, how='left', on='bus')
rp = pd.merge(rp, rd, how='left', on='bus')
rp.fillna(0, inplace=True)
rp['ngen'] = rp['gen'] - rp['demand']
return rp
def _sumPF_ppn(ppn):
"""Summarize PF results of a pandapower net"""
rg = pd.concat([ppn.res_gen[['p_mw']], ppn.gen[['bus']]],
axis=1).rename(columns={'p_mw': 'gen'})
rd = pd.concat([ppn.res_load[['p_mw']], ppn.load[['bus']]],
axis=1).rename(columns={'p_mw': 'demand'})
rp = pd.DataFrame()
rp['bus'] = ppn.bus.index
rp = rp.merge(rg, on='bus', how='left')
rp = rp.merge(rd, on='bus', how='left')
rp.fillna(0, inplace=True)
rp['ngen'] = rp['gen'] - rp['demand']
rp = rp.groupby('bus').sum().reset_index(drop=True)
rp['bus'] = rp.index
return rp
def as_df_local(self):
"""
Export local variable values and services to a DataFrame.
"""
out = dict()
out['uid'] = np.arange(self.n)
out['idx'] = self.idx.v
for name, instance in self.cache.all_vars.items():
out[name] = instance.v
for name, instance in self.services.items():
out[name] = instance.v
return pd.DataFrame(out).set_index('uid')
def _read_dyr_dict(file):
"""
Parse dyr file into a dict where keys are model names and values are dataframes.
"""
input_list = andes.io.read_file_like(file)
# concatenate multi-line device data
input_concat_dict = defaultdict(list)
multi_line = list()
for line in input_list:
if line == '':
continue
if '/' not in line:
multi_line.append(line)
else:
multi_line.append(line.split('/')[0])
single_line = ' '.join(multi_line)
if single_line.strip() == '':
continue
single_list = single_line.split("'")
psse_model = single_list[1].strip()
input_concat_dict[psse_model].append(single_list[0] +
single_list[2])
multi_line = list()
# construct pandas dataframe for all models
dyr_dict = dict() # input data from dyr file
for psse_model, all_rows in input_concat_dict.items():
dev_params_num = [([to_number(cell) for cell in row.split()])
for row in all_rows]
dyr_dict[psse_model] = pd.DataFrame(dev_params_num)
return dyr_dict
def read_add(system, file):
"""
Read addition PSS/E dyr file.
Parameters
----------
system
file
Returns
-------
"""
warn_experimental("PSS/E dyr support")
with open(file, 'r') as f:
input_list = [line.strip() for line in f]
# concatenate multi-line device data
input_concat_dict = defaultdict(list)
multi_line = list()
for i, line in enumerate(input_list):
if line == '':
continue
if '/' not in line:
multi_line.append(line)
else:
multi_line.append(line.split('/')[0])
single_line = ' '.join(multi_line)
single_list = single_line.split("'")
psse_model = single_list[1].strip()
input_concat_dict[psse_model].append(single_list[0] +
single_list[2])
multi_line = list()
# construct pandas dataframe for all models
dyr_dict = dict()
for psse_model, all_rows in input_concat_dict.items():
dev_params_num = [([to_number(cell) for cell in row.split()])
for row in all_rows]
dyr_dict[psse_model] = pd.DataFrame(dev_params_num)
# read yaml and set header for each pss/e model
dirname = os.path.dirname(__file__)
with open(f'{dirname}/psse-dyr.yaml', 'r') as f:
dyr_yaml = yaml.full_load(f)
for psse_model in dyr_dict:
if psse_model in dyr_yaml:
if 'inputs' in dyr_yaml[psse_model]:
dyr_dict[psse_model].columns = dyr_yaml[psse_model]['inputs']
# load data into models
for psse_model in dyr_dict:
if psse_model not in dyr_yaml:
logger.error(f"PSS/E Model <{psse_model}> is not supported.")
continue
dest = dyr_yaml[psse_model]['destination']
find = {}
if 'find' in dyr_yaml[psse_model]:
for name, source in dyr_yaml[psse_model]['find'].items():
for model, conditions in source.items():
cond_names = conditions.keys()
cond_values = [
dyr_dict[psse_model][col]
for col in conditions.values()
]
find[name] = system.__dict__[model].find_idx(
cond_names, cond_values)
if 'get' in dyr_yaml[psse_model]:
for name, source in dyr_yaml[psse_model]['get'].items():
for model, conditions in source.items():
idx_name = conditions['idx']
if idx_name in dyr_dict[psse_model]:
conditions['idx'] = dyr_dict[psse_model][idx_name]
else:
conditions['idx'] = find[idx_name]
find[name] = system.__dict__[model].get(**conditions)
if 'outputs' in dyr_yaml[psse_model]:
output_keys = list(dyr_yaml[psse_model]['outputs'].keys())
output_exprs = list(dyr_yaml[psse_model]['outputs'].values())
out_dict = {}
for idx in range(len(output_exprs)):
out_key = output_keys[idx]
expr = output_exprs[idx]
if expr in find:
out_dict[out_key] = find[expr]
elif ';' in expr:
args, func = expr.split(';')
func = eval(func)
args = args.split(',')
argv = [pairs.split('.') for pairs in args]
argv = [dyr_dict[model][param] for model, param in argv]
out_dict[output_keys[idx]] = func(*argv)
else:
out_dict[output_keys[idx]] = dyr_dict[psse_model][expr]
df = pd.DataFrame.from_dict(out_dict)
for row in df.to_dict(orient='records'):
system.add(dest, row)
system.link_ext_param(system.__dict__[dest])
return True
def read_add(system, file):
"""
Read an addition PSS/E dyr file.
Parameters
----------
system : System
System instance to which data will be loaded
file : str
Path to the additional `dyr` file
Returns
-------
bool
data parsing status
"""
with open(file, 'r') as f:
input_list = [line.strip() for line in f]
# concatenate multi-line device data
input_concat_dict = defaultdict(list)
multi_line = list()
for i, line in enumerate(input_list):
if line == '':
continue
if '/' not in line:
multi_line.append(line)
else:
multi_line.append(line.split('/')[0])
single_line = ' '.join(multi_line)
if single_line.strip() == '':
continue
single_list = single_line.split("'")
psse_model = single_list[1].strip()
input_concat_dict[psse_model].append(single_list[0] +
single_list[2])
multi_line = list()
# construct pandas dataframe for all models
dyr_dict = dict() # input data from dyr file
for psse_model, all_rows in input_concat_dict.items():
dev_params_num = [([to_number(cell) for cell in row.split()])
for row in all_rows]
dyr_dict[psse_model] = pd.DataFrame(dev_params_num)
# read yaml and set header for each pss/e model
dirname = os.path.dirname(__file__)
with open(f'{dirname}/psse-dyr.yaml', 'r') as f:
dyr_yaml = yaml.full_load(f)
sorted_models = sort_psse_models(dyr_yaml)
for psse_model in dyr_dict:
if psse_model in dyr_yaml:
if 'inputs' in dyr_yaml[psse_model]:
dyr_dict[psse_model].columns = dyr_yaml[psse_model]['inputs']
# collect not supported models
not_supported = []
for model in dyr_dict:
if model not in sorted_models:
not_supported.append(model)
# print out debug messages
if len(dyr_dict):
logger.debug(f'dyr contains models {", ".join(dyr_dict.keys())}')
if len(not_supported):
logger.warning(f'Models not yet supported: {", ".join(not_supported)}')
else:
logger.debug('All dyr models are supported.')
# load data into models
for psse_model in sorted_models:
if psse_model not in dyr_dict:
# device not exist
continue
if psse_model not in dyr_yaml:
logger.error(f"PSS/E Model <{psse_model}> is not supported.")
continue
logger.debug(f'Parsing PSS/E model {psse_model}')
dest = dyr_yaml[psse_model]['destination']
find = {}
if 'find' in dyr_yaml[psse_model]:
for name, source in dyr_yaml[psse_model]['find'].items():
for model, conditions in source.items():
allow_none = conditions.pop('allow_none', 0)
cond_names = conditions.keys()
cond_values = []
for col in conditions.values():
if col in find:
cond_values.append(find[col])
else:
cond_values.append(dyr_dict[psse_model][col])
try:
find[name] = system.__dict__[model].find_idx(
cond_names, cond_values, allow_none=allow_none)
except IndexError as e:
logger.error(
"Data file likely contains references to unsupported models."
)
logger.error(e)
return False
if 'get' in dyr_yaml[psse_model]:
for name, source in dyr_yaml[psse_model]['get'].items():
for model, conditions in source.items():
idx_name = conditions['idx']
if idx_name in dyr_dict[psse_model]:
conditions['idx'] = dyr_dict[psse_model][idx_name]
else:
conditions['idx'] = find[idx_name]
find[name] = system.__dict__[model].get(**conditions)
if 'outputs' in dyr_yaml[psse_model]:
output_keys = list(dyr_yaml[psse_model]['outputs'].keys())
output_exprs = list(dyr_yaml[psse_model]['outputs'].values())
out_dict = {}
for idx in range(len(output_exprs)):
out_key = output_keys[idx]
expr = output_exprs[idx]
if expr in find:
out_dict[out_key] = find[expr]
elif ';' in expr:
args, func = expr.split(';')
func = eval(func)
args = args.split(',')
argv = [pairs.split('.') for pairs in args]
argv = [dyr_dict[model][param] for model, param in argv]
out_dict[output_keys[idx]] = func(*argv)
else:
out_dict[output_keys[idx]] = dyr_dict[psse_model][expr]
df = pd.DataFrame.from_dict(out_dict)
for row in df.to_dict(orient='records'):
system.add(dest, row)
system.link_ext_param(system.__dict__[dest])
return True
def read_add(system, file):
"""
Read addition PSS/E dyr file.
TODO: implement this function
Parameters
----------
system
file
Returns
-------
"""
warn_experimental("PSS/E dyr support is incomplete.")
with open(file, 'r') as f:
input_list = [line.strip() for line in f]
# concatenate multi-line device data
all_dict = defaultdict(list)
data_list = list()
for i, line in enumerate(input_list):
if line == '':
continue
if '/' not in line:
data_list.append(line)
else:
data_list.append(line.split('/')[0])
data_str = ' '.join(data_list)
data_split = data_str.split("'")
model_name = data_split[1].strip()
all_dict[model_name].append(data_split[0] + data_split[2])
data_list = list()
# construct pandas dataframe for all models
df_dict = dict()
for key, val in all_dict.items():
df_1row = pd.DataFrame(val)
df_str = df_1row[0].str.split(expand=True)
df_dict[key] = df_str.astype(float)
# set header for each
dirname = os.path.dirname(__file__)
with open('{}/psse-dyr.yaml'.format(dirname), 'r') as f:
dyr_format = yaml.full_load(f)
dyr_dict = dict()
for item in dyr_format:
dyr_dict.update(item)
for key in df_dict:
if key in dyr_dict:
if 'inputs' in dyr_dict[key]:
df_dict[key].columns = dyr_dict[key]['inputs']
system.df_dict = df_dict
# TODO: Load data into models
return True
