def select_prediction_data(data, *new_data):
"""
Selects the data required to predict the CO2 emissions with CO2MPAS model.
:param data:
Output data.
:type data: dict
:param new_data:
New data.
:type new_data: dict
:return:
Data required to predict the CO2 emissions with CO2MPAS model.
:rtype: dict
"""
ids = _prediction_data
from .physical.defaults import dfl
if not dfl.functions.select_prediction_data.theoretical:
ids = ids + _prediction_data_ts
data = sh.selector(ids, data, allow_miss=True)
if new_data:
new_data = sh.combine_dicts(*new_data)
data = sh.combine_dicts(data, new_data)
if 'gears' in data and 'gears' not in new_data:
if data.get('gear_box_type', 0) == 'automatic' or \
len(data.get('velocities', ())) != len(data['gears']):
data.pop('gears')
return data
def _add2summary(total_summary, summary, base_keys=None):
base_keys = base_keys or {}
for k, v in sh.stack_nested_keys(summary, depth=3):
d = sh.get_nested_dicts(total_summary, *k, default=list)
if isinstance(v, list):
for j in v:
d.append(sh.combine_dicts(j, base_keys))
else:
d.append(sh.combine_dicts(v, base_keys))
def _summary2df(data):
res = []
summary = data.get('summary', {})
if 'results' in summary:
r = {}
index = ['cycle', 'stage', 'usage']
for k, v in sh.stack_nested_keys(summary['results'], depth=4):
l = sh.get_nested_dicts(r, k[0], default=list)
l.append(sh.combine_dicts(sh.map_list(index, *k[1:]), v))
if r:
df = _dd2df(r,
index=index,
depth=2,
col_key=functools.partial(_sort_key,
p_keys=('param', ) * 2),
row_key=functools.partial(_sort_key, p_keys=index))
import pandas as pd
df.columns = pd.MultiIndex.from_tuples(_add_units(df.columns))
setattr(df, 'name', 'results')
res.append(df)
if 'selection' in summary:
df = _dd2df(summary['selection'], ['model_id'],
depth=2,
col_key=functools.partial(_sort_key,
p_keys=('stage', 'cycle')),
row_key=functools.partial(_sort_key, p_keys=()))
setattr(df, 'name', 'selection')
res.append(df)
if 'comparison' in summary:
r = {}
for k, v in sh.stack_nested_keys(summary['comparison'], depth=3):
v = sh.combine_dicts(v, base={'param_id': k[-1]})
sh.get_nested_dicts(r, *k[:-1], default=list).append(v)
if r:
df = _dd2df(r, ['param_id'],
depth=2,
col_key=functools.partial(_sort_key,
p_keys=('stage', 'cycle')),
row_key=functools.partial(_sort_key, p_keys=()))
setattr(df, 'name', 'comparison')
res.append(df)
if res:
return {'summary': res}
return {}
def _define_inputs(sol, inputs):
kw = dict(
sources=inputs, check_inputs=False, graph=sol.dsp.dmap,
_update_links=False
)
keys = set(sol) - set(sol.dsp.get_sub_dsp_from_workflow(**kw).data_nodes)
return sh.combine_dicts({k: sol[k] for k in keys}, inputs)
def append(self, token):
if isinstance(token, (Operator, Function)):
try:
tokens = [self.pop() for _ in range(token.get_n_args)][::-1]
except IndexError:
raise FormulaError()
token.update_input_tokens(*tokens)
inputs = [self.get_node_id(i) for i in tokens]
token.set_expr(*tokens)
out, dmap, get_id = token.node_id, self.dsp.dmap, get_unused_node_id
if out not in self.dsp.nodes:
func = token.compile()
kw = dict(
function_id=get_id(dmap, token.name),
function=func,
inputs=inputs or None,
outputs=[out],
)
if isinstance(func, dict):
_inputs = func.get('extra_inputs', {})
for k, v in _inputs.items():
if v is not sh.NONE:
self.dsp.add_data(k, v)
kw = sh.combine_dicts(
{'inputs': (list(_inputs) + inputs) or None}, func,
base=kw
)
self.dsp.add_function(**kw)
else:
self.nodes[token] = n_id = get_id(dmap, out, 'c%d>{}')
self.dsp.add_function(None, sh.bypass, [out], [n_id])
elif isinstance(token, Operand):
self.missing_operands.add(token)
self._deque.append(token)
def add_sheet(self, worksheet, context):
get_in = sh.get_nested_dicts
if isinstance(worksheet, str):
book = get_in(self.books, context['excel'], BOOK)
worksheet = book[_get_name(worksheet, book.sheetnames)]
context = sh.combine_dicts(context,
base={'sheet': worksheet.title.upper()})
d = get_in(self.books, context['excel'], SHEETS, context['sheet'])
if 'formula_references' not in d:
d['formula_references'] = formula_references = {
k: v['ref']
for k, v in worksheet.formula_attributes.items()
if v.get('t') == 'array' and 'ref' in v
}
else:
formula_references = d['formula_references']
if 'formula_ranges' not in d:
d['formula_ranges'] = {
Ranges().push(ref, context=context)
for ref in formula_references.values()
}
return worksheet, context
class DispatcherSphinxDirective(Graphviz):
required_arguments = 1
img_opt = {
'height': directives.length_or_unitless,
'width': directives.length_or_percentage_or_unitless,
}
option_spec = {
'graphviz_dot': directives.unchanged, # sphinx==1.3.5
'index': bool_option
}
sh.combine_dicts(img_opt, Graphviz.option_spec, base=option_spec)
def run(self):
node = super(DispatcherSphinxDirective, self).run()[0]
# noinspection PyUnresolvedReferences
node = dsp(node.rawsource, *node.children, **node.attributes)
node['img_opt'] = sh.selector(self.img_opt,
self.options,
allow_miss=True)
node['index'] = self.options.get('index', False)
env = self.state.document.settings.env
argument = search_image_for_language(self.arguments[0], env)
dirpath = osp.dirname(env.relfn2path(argument)[1])
node['dirpath'] = dirpath if osp.isdir(dirpath) else None
return [node]
def _calibrate_gsm(
velocity_speed_ratios, on_engine, anomalies, gear, velocities,
stop_velocity, idle_engine_speed):
# noinspection PyProtectedMember
from .at_gear.cmv import CMV, _filter_gear_shifting_velocity as filter_gs
idle = idle_engine_speed[0] - idle_engine_speed[1]
_vsr = sh.combine_dicts(velocity_speed_ratios, base={0: 0})
limits = {
0: {False: [0]},
1: {True: [stop_velocity]},
max(_vsr): {True: [dfl.INF]}
}
shifts = np.unique(sum(map(_shift, (on_engine, anomalies)), []))
for i, j in sh.pairwise(shifts):
if on_engine[i:j].all() and not anomalies[i:j].any():
for v in np.array(list(sh.pairwise(_shift(gear[i:j])))) + i:
if j != v[1]:
v, (g, ng) = velocities[slice(*v)], gear[[v[1] - 1, v[1]]]
up = g < ng
sh.get_nested_dicts(limits, g, up, default=list).append(
v.max() if up else v.min()
)
for k, v in list(limits.items()):
limits[k] = v.get(False, [_vsr[k] * idle] * 2), v.get(True, [])
d = {j: i for i, j in enumerate(sorted(limits))}
gsm = CMV(filter_gs(sh.map_dict(d, limits), stop_velocity))
gsm.velocity_speed_ratios = sh.selector(gsm, sh.map_dict(d, _vsr))
gsm.convert(_vsr)
return gsm
def parse_cmd_flags(cmd_flags=None):
"""
Parses the command line options.
:param cmd_flags:
Command line options.
:type cmd_flags: dict
:return:
Default parameters of process model.
:rtype: tuple
"""
flags = sh.combine_dicts(cmd_flags or {},
base={
'only_summary': False,
'hard_validation': False,
'declaration_mode': False,
'enable_selector': False,
'type_approval_mode': False,
'encryption_keys': None,
'sign_key': None,
'output_template': sh.NONE,
'encryption_keys_passwords': None,
'output_folder': './outputs',
'augmented_summary': False
})
flags['declaration_mode'] |= flags['type_approval_mode']
flags['hard_validation'] |= flags['declaration_mode']
if flags['declaration_mode'] and not flags['type_approval_mode'] and \
flags['enable_selector']:
log.info('Since CO2MPAS is launched in declaration mode the option '
'--enable-selector is not used.\n'
'If you want to use it remove -DM from the cmd.')
flags['enable_selector'] = False
return sh.selector(_cmd_flags, flags, output_type='list')
def parse_solution(solution):
"""
Parse the CO2MPAS model solution.
:param solution:
CO2MPAS model solution.
:type solution: schedula.Solution
:return:
CO2MPAS outputs.
:rtype: dict[dict]
"""
res = {}
for k, v in solution.items():
k = k.split('.')
sh.get_nested_dicts(res, *k[:-1])[k[-1]] = v
for k, v in list(sh.stack_nested_keys(res, depth=3)):
n, k = k[:-1], k[-1]
if n == ('output', 'calibration') and k in ('wltp_l', 'wltp_h'):
v = sh.selector(('co2_emission_value', ), v, allow_miss=True)
if v:
d = sh.get_nested_dicts(res, 'target', 'prediction')
d[k] = sh.combine_dicts(v, d.get(k, {}))
res['pipe'] = solution.pipe
return res
def _eval(s, error=None, usersyms=None, **kwargs):
error = error or 'cannot be eval!'
from asteval import Interpreter
usersyms = sh.combine_dicts(_usersyms, usersyms or {})
return Or(And(str, Use(Interpreter(usersyms=usersyms).eval), s),
s,
error=error)
def xsingle(cell, rng):
if len(rng.ranges) == 1 and not rng.is_set and rng.value.shape[1] == 1:
rng = rng & Ranges((sh.combine_dicts(
rng.ranges[0], sh.selector(('r1', 'r2'), cell.ranges[0])), ))
if rng.ranges:
return rng
return Error.errors['#VALUE!']
def parse_dsp_solution(solution):
"""
Parses the co2mpas model results.
:param solution:
Co2mpas model after dispatching.
:type solution: schedula.Solution
:return:
Mapped outputs.
:rtype: dict[dict]
"""
res = {}
for k, v in solution.items():
sh.get_nested_dicts(res, *k.split('.'), default=co2_utl.ret_v(v))
for k, v in list(sh.stack_nested_keys(res, depth=3)):
n, k = k[:-1], k[-1]
if n == ('output', 'calibration') and k in ('wltp_l', 'wltp_h'):
v = sh.selector(('co2_emission_value', ), v, allow_miss=True)
if v:
d = sh.get_nested_dicts(res, 'target', 'prediction')
d[k] = sh.combine_dicts(v, d.get(k, {}))
res['pipe'] = solution.pipe
return res
def __and__(self, other): # Intersection.
r = []
for rng in other.ranges:
r.extend(
_intersect(rng, self.ranges, format_range=self.format_range))
values = sh.combine_dicts(self.values, other.values)
is_set = self.is_set or other.is_set
return Ranges(r, values, is_set, self.all_values and other.all_values)
def get_range(format_range, ref, context=None):
context = context or {}
m = _re_range.match(ref).groupdict().items()
m = {k: v for k, v in m if v is not None}
if 'ref' in m:
raise ValueError
i = sh.combine_dicts(context, m)
return dict(format_range(('name', 'n1', 'n2'), **i))
def write_to_excel(dfs, output_template):
"""
Writes DataFrames to excel.
:param dfs:
DataFrames of vehicle output report.
:type dfs: dict[str, pandas.DataFrame]
:param output_template:
Template output.
:type output_template: str
:return:
Excel output file.
:rtype: io.BytesIO
"""
import pandas as pd
log.debug('Writing into xl-file based on template(%s)...', output_template)
writer, fd = _clone_excel(output_template)
xlref, calculate_sheets, charts = [], sorted(writer.sheets), []
for k, v in sorted(dfs.items(), key=_sort_sheets):
if not k.startswith('graphs.'):
down = True
if k.endswith('pa'):
kw = {'named_ranges': ('rows', ), 'index': True, 'k0': 1}
elif k.endswith('ts'):
kw = {'named_ranges': ('columns', ), 'index': False, 'k0': 1}
elif k.endswith('proc_info'):
down = False
kw = {'named_ranges': ()}
else:
kw = {}
xlref.extend(_write_sheets(writer, k, v, down=down, **kw))
else:
try:
sheet = writer.book.add_worksheet(k)
except AttributeError:
sheet = writer.book.create_sheet(title=k)
charts.append((sheet, v))
for sheet, v in charts:
_chart2excel(writer, sheet, v)
if xlref:
xlref = sorted(sh.combine_dicts(*[x[1] for x in xlref]).items())
xlref = pd.DataFrame(xlref)
xlref.set_index([0], inplace=True)
_df2excel(writer,
'xlref',
xlref,
named_ranges=(),
index=True,
header=False)
writer.save()
return fd
def _err(model_id, model):
gears = func(inputs=sh.combine_dicts(inp, {
sgs: model_id,
model_id: model
}),
outputs=['gears'])['gears']
eng = calculate_gear_box_speeds_in(gears, vel, vsr, sv)
return calculate_error_coefficients(t_gears, gears, t_eng, eng, vel,
sv)
def fast_range2parts(**kw):
inputs = sh.selector(_keys, kw, allow_miss=True)
for func in (fast_range2parts_v1, fast_range2parts_v2, fast_range2parts_v3):
try:
return sh.combine_dicts(kw, base=func(**inputs))
except TypeError:
pass
else:
raise ValueError
def __or__(self, other): # Union.
base = self.ranges
for r0 in other.ranges:
stack = [r0]
for b in base:
s = stack.copy()
stack = []
for r in s:
stack.extend(_split(b, r, format_range=self.format_range))
base += tuple(stack)
values = sh.combine_dicts(self.values, other.values)
return Ranges(base, values, True, self.all_values and other.all_values)
def process(self, match, context=None):
d = super(Range, self).process(match)
if len(d) <= 1 and 'indirect' not in d and 'ref' in d:
try:
from .function import Function
if Function(self.source).name == d['ref']:
return {}
except TokenError:
pass
if 'ref' in d:
self.attr['is_reference'] = True
return range2parts(None, **sh.combine_dicts(context or {}, d))
def write_to_excel(data, output_file_name, template_file_name):
import pandas as pd
if template_file_name:
log.debug('Writing into xl-file(%s) based on template(%s)...',
output_file_name, template_file_name)
writer = clone_excel(template_file_name, output_file_name)
else:
log.debug('Writing into xl-file(%s)...', output_file_name)
writer = pd.ExcelWriter(output_file_name, engine='xlsxwriter')
xlref, calculate_sheets, charts = [], sorted(writer.sheets), []
for k, v in sorted(data.items(), key=_sort_sheets):
if not k.startswith('graphs.'):
down = True
if k.endswith('pa'):
kw = {'named_ranges': ('rows', ), 'index': True, 'k0': 1}
elif k.endswith('ts'):
kw = {'named_ranges': ('columns', ), 'index': False, 'k0': 1}
elif k.endswith('proc_info'):
down = False
kw = {'named_ranges': ()}
else:
kw = {}
xlref.extend(_write_sheets(writer, k, v, down=down, **kw))
else:
try:
sheet = writer.book.add_worksheet(k)
except AttributeError:
sheet = writer.book.create_sheet(title=k)
charts.append((sheet, v))
for sheet, v in charts:
_chart2excel(writer, sheet, v)
if xlref:
xlref = sorted(sh.combine_dicts(*[x[1] for x in xlref]).items())
xlref = pd.DataFrame(xlref)
xlref.set_index([0], inplace=True)
_df2excel(writer, 'xlref', xlref, 0, (), index=True, header=False)
if calculate_sheets:
import formulas
xl_model = formulas.ExcelModel()
context = xl_model.add_book(
writer.book, {'excel': osp.basename(output_file_name)})[1]
xl_model.pushes(*calculate_sheets,
context=context).finish().calculate()
xl_model.write(xl_model.books)
writer.save()
log.info('Written into xl-file(%s)...', output_file_name)
def _split(base, rng, intersect=None, format_range=range2parts):
z = _have_intersect(base, rng)
if not z:
return rng,
if intersect is not None:
intersect.update(z)
ranges = []
rng = sh.selector(('excel', 'sheet', 'n1', 'n2', 'r1', 'r2'), rng)
it = ('n1', 'n2', 1), ('n2', 'n1', -1), ('r1', 'r2', 1), ('r2', 'r1', -1)
for i, j, n in it:
if z[i] != rng[i]:
if j[0] == 'r':
r = sh.combine_dicts(rng, {j: str(int(z[i]) - n)})
else:
r = sh.combine_dicts(rng, {j: z[i] - n})
r = dict(format_range(('name', 'n1', 'n2'), **r))
ranges.append(r)
rng[i] = z[i]
return tuple(ranges)
def define_wltp_base_model(wltp_base_model):
"""
Defines WLTP base model.
:param wltp_base_model:
WLTP base model params.
:type wltp_base_model: dict
:return:
WLTP base model.
:rtype: dict
"""
import wltp.model as wltp_mdl
# noinspection PyProtectedMember
return sh.combine_dicts(wltp_mdl._get_model_base(), wltp_base_model)
def _yield_refs(book, context=None):
for n in book.defined_names.definedName:
if n.value == '#REF!':
continue
ref, i = n.name.upper(), n.localSheetId
rng = Ranges().push(n.value, context=context).ranges[0]['name']
sheet_names = book.sheetnames
if i is not None:
sheet_names = sheet_names[i:i + 1]
for sn in sheet_names:
name = range2parts(
None, **sh.combine_dicts(context, {
'sheet': sn,
'ref': ref
}))
yield name['name'], rng
def _parse_values(data, default=None, where=''):
default = default or {}
for k, v in data.items():
match = _re_params_name.match(k) if k is not None else None
if not match and default.get('scope') == 'meta':
match = _re_params_name.match('.'.join(
filter(bool, ('meta', default.get('meta'), k))))
if not match:
log.warning("Parameter '%s' %s cannot be parsed!", k, where)
continue
elif _isempty(v):
continue
match = {i: j.lower() for i, j in match.groupdict().items() if j}
for key in _parse_key(**sh.combine_dicts(default, match)):
yield key, v
def _convert_limits(lu, vsr, n_vsr, stop_velocity=dfl.values.stop_velocity):
_r, _l, _u = np.array(
sorted([vsr.get(k, 0)] + list(v) for k, v in lu.items())).T
nk, nr = np.array(sorted(sh.combine_dicts(n_vsr, base={0: 0}).items())).T
klu = sorted(
zip(nk.astype(int), _interp(_r[2:], _l[2:], nr),
_interp(_r[1:-1], _u[1:-1], nr)))
res, n0, n1 = {}, min(k for k in n_vsr if k > 0), max(n_vsr)
for k, l, u in klu:
if k == 0:
l, u = list(lu.get(k, [0, stop_velocity + 1]))
else:
if k == n0:
l = lu.get(k, (stop_velocity, ))[0]
if k == n1:
u = lu.get(k, (None, dfl.INF))[1]
res[k] = [l, u]
return res
def get_summary(solutions):
"""
Extract summary data from model solutions.
:param solutions:
All model solutions.
:type solutions: list[schedula.Solution]
:return:
Summary data.
:rtype: list
"""
return [sh.combine_dicts(
dict(sh.stack_nested_keys(sol.get('summary', {}), depth=4)), base={
'id': sol['vehicle_name'],
'base': sol['input_file_name']
}
) for sol in solutions]
def _get_theoretical(profile):
defaults = {
'cycle_type': 'WLTP',
'gear_box_type': 'manual',
'wltp_class': 'class3b',
'downscale_factor': 0
}
profile = {k: v for k, v in profile.items() if v}
profile = sh.combine_dicts(defaults, profile)
profile['cycle_type'] = profile['cycle_type'].upper()
profile['wltp_class'] = profile['wltp_class'].lower()
profile['gear_box_type'] = profile['gear_box_type'].lower()
from co2mpas.model.physical.cycle import cycle
res = cycle().dispatch(inputs=profile,
outputs=['times', 'velocities'],
shrink=True)
data = sh.selector(['times', 'velocities'], res, output_type='list')
return pd.DataFrame(data).T
def make_simulation_plan(plan, timestamp, variation, flag, model=None):
model, summary = model or batch.vehicle_processing_model(), {}
run_base = model.get_node('run_base')[0].dsp
run_modes = tuple(
run_base.get_sub_dsp_from_workflow(
('data', 'vehicle_name'), check_inputs=False,
graph=run_base.dmap).data_nodes) + ('start_time', 'vehicle_name')
var = json.dumps(variation, sort_keys=True)
o_cache, o_folder = flag['overwrite_cache'], flag['output_folder']
modelconf = flag.get('modelconf', None)
kw, bases = sh.combine_dicts(flag, {'run_base': True}), set()
for (i, base_fpath, run), p in tqdm.tqdm(plan, disable=False):
try:
base = get_results(model, o_cache, base_fpath, timestamp, run, var,
o_folder, modelconf)
except KeyError:
log.warning('Base model "%s" of variation "%s" cannot be parsed!',
base_fpath, i)
continue
name = base['vehicle_name']
if 'summary' in base and name not in bases:
batch._add2summary(summary, base['summary'])
bases.add(name)
name = '{}-{}'.format(name, i)
new_base, o = define_new_inputs(p, base)
inputs = batch.prepare_data(new_base, {}, base_fpath, o_cache,
o_folder, timestamp, False, modelconf)[0]
inputs.update(sh.selector(set(base).difference(run_modes), base))
inputs['vehicle_name'] = name
inputs.update(kw)
res = run_base.dispatch(inputs)
batch.notify_result_listener(plan_listener, {'solution': res})
s = filter_summary(p, o, res.get('summary', {}))
base_keys = {
'vehicle_name': (base_fpath, name, run),
}
batch._add2summary(summary, s, base_keys)
return summary
def add_cell(self,
cell,
context,
references=None,
formula_references=None,
formula_ranges=None,
external_links=None):
get_in = sh.get_nested_dicts
if formula_references is None:
formula_references = get_in(self.books, context['excel'], SHEETS,
context['sheet'], 'formula_references')
if formula_ranges is None:
formula_ranges = get_in(self.books, context['excel'], SHEETS,
context['sheet'], 'formula_ranges')
if references is None:
references = get_in(self.books, context['excel'], 'references')
if external_links is None:
external_links = get_in(self.books, context['excel'],
'external_links')
context = sh.combine_dicts(context,
base={'external_links': external_links})
crd = cell.coordinate
crd = formula_references.get(crd, crd)
#cell = Cell(crd, cell.value, context=context).compile()
try:
cell = Cell(crd, cell.value, context=context).compile()
except FormulaError:
# There was an error so set this value with the NA error using the excel function
cell = Cell(crd, "=NA()", context=context).compile()
if cell.output in self.cells:
return
if cell.value is not sh.EMPTY:
if any(not (cell.range - rng).ranges for rng in formula_ranges):
return
cell.update_inputs(references=references)
if cell.add(self.dsp, context=context):
self.cells[cell.output] = cell
return cell
def test_combine_dicts(self):
res = sh.combine_dicts({'a': 3, 'c': 3}, {'a': 1, 'b': 2})
self.assertEqual(res, {'a': 1, 'b': 2, 'c': 3})
