def eval_formula(expr: str, dataframe=None, context=None):
'''
expr: string
Symbolic expression to evaluate.
Example: `k(1)-delta*k(0)-i`
table: (optional) pandas dataframe
Each column is a time series, which can be indexed with dolo notations.
context: dict or CalibrationDict
'''
print("Evaluating: {}".format(expr))
if context is None:
dd = {} # context dictionary
elif isinstance(context, CalibrationDict):
dd = context.flat.copy()
else:
dd = context.copy()
# compat since normalize form for parameters doesn't match calib dict.
for k in [*dd.keys()]:
dd[stringify_symbol(k)] = dd[k]
expr_ast = parse_string(expr).value
variables = list_variables(expr_ast)
nexpr = stringify(expr_ast)
print(expr)
print(variables)
dd['log'] = log
dd['exp'] = exp
if dataframe is not None:
import pandas as pd
for (k, t) in variables:
dd[stringify_symbol((k, t))] = dataframe[k].shift(t)
dd['t'] = pd.Series(dataframe.index, index=dataframe.index)
expr = to_source(nexpr)
print(expr)
print(dd.keys())
res = eval(expr, dd)
return res
def make_method(equations,
arguments,
constants,
targets=None,
rhs_only=False,
definitions={},
funname='anonymous'):
compat = lambda s: s.replace("^", "**").replace('==', '=').replace(
'=', '==')
equations = [compat(eq) for eq in equations]
if isinstance(arguments, list):
arguments = OrderedDict([('arg_{}'.format(i), k)
for i, k in enumerate(arguments)])
## replace = by ==
known_variables = [a[0] for a in sum(arguments.values(), [])]
known_definitions = [a for a in definitions.keys()]
known_constants = [a[0] for a in constants]
all_variables = known_variables + known_definitions
known_functions = []
known_constants = []
if targets is not None:
all_variables.extend([o[0] for o in targets])
targets = [stringify(o) for o in targets]
else:
targets = ['_out_{}'.format(n) for n in range(len(equations))]
all_symbols = all_variables # + known_constants
equations = [parse(eq) for eq in equations]
definitions = {k: parse(v) for k, v in definitions.items()}
defs_incidence = {}
for sym, val in definitions.items():
lvars = list_variables(val, vars=known_definitions)
defs_incidence[(sym,
0)] = [v for v in lvars if v[0] in known_definitions]
# return defs_incidence
equations_incidence = {}
to_be_defined = set([])
for i, eq in enumerate(equations):
cn = CountNames(all_variables, known_functions, known_constants)
cn.visit(eq)
equations_incidence[i] = cn.variables
to_be_defined = to_be_defined.union(
[a for a in cn.variables if a[0] in known_definitions])
deps = []
for tv in to_be_defined:
ndeps = get_deps(defs_incidence, tv)
deps.extend(ndeps)
deps = [d for d in unique(deps)]
new_definitions = OrderedDict()
for k in deps:
val = definitions[k[0]]
nval = time_shift(val, k[1], all_variables)
new_definitions[stringify(k)] = normalize(nval, variables=all_symbols)
new_equations = []
for n, eq in enumerate(equations):
d = match(parse("_x == _y"), eq)
if d is not False:
lhs = d['_x']
rhs = d['_y']
if rhs_only:
val = rhs
else:
val = ast.BinOp(left=rhs, op=Sub(), right=lhs)
else:
val = eq
new_equations.append(normalize(val, variables=all_symbols))
# preambleIndex(Num(x))
preamble = []
for i, (arg_group_name, arg_group) in enumerate(arguments.items()):
for pos, t in enumerate(arg_group):
sym = stringify(t)
rhs = Subscript(value=Name(id=arg_group_name, ctx=Load()),
slice=Index(Num(pos)),
ctx=Load())
val = Assign(targets=[Name(id=sym, ctx=Store())], value=rhs)
preamble.append(val)
for pos, p in enumerate(constants):
sym = stringify(p)
rhs = Subscript(value=Name(id='p', ctx=Load()),
slice=Index(Num(pos)),
ctx=Load())
val = Assign(targets=[Name(id=sym, ctx=Store())], value=rhs)
preamble.append(val)
# now construct the function per se
body = []
for k, v in new_definitions.items():
line = Assign(targets=[Name(id=k, ctx=Store())], value=v)
body.append(line)
for n, neq in enumerate(new_equations):
line = Assign(targets=[Name(id=targets[n], ctx=Store())],
value=new_equations[n])
body.append(line)
for n, neq in enumerate(new_equations):
line = Assign(targets=[
Subscript(value=Name(id='out', ctx=Load()),
slice=Index(Num(n)),
ctx=Store())
],
value=Name(id=targets[n], ctx=Load()))
body.append(line)
from ast import arg, FunctionDef, Module
from ast import arguments as ast_arguments
f = FunctionDef(
name=funname,
args=ast_arguments(args=[arg(arg=a) for a in arguments.keys()] +
[arg(arg='p'), arg(arg='out')],
vararg=None,
kwarg=None,
kwonlyargs=[],
kw_defaults=[],
defaults=[]),
body=preamble + body,
decorator_list=[])
mod = Module(body=[f])
mod = ast.fix_missing_locations(mod)
return mod
def test_list_variables():
from dolang.symbolic import parse_string
e = parse_string('sin(a(1)+b+f(1)+f(+4)+sin(a)+k*cos(a(0)))')
list_variables(e)
assert (list_variables(e) == [('a', 1), ('f', 1), ('f', 4), ('a', 0)])
assert (list_variables(e, funs=['f']) == [('a', 1), ('a', 0)])
def test_list_variables():
from dolang.symbolic import parse_string
e = parse_string("sin(a(1)+b+f(1)+f(4)+sin(a)+k*cos(a(0)))")
list_variables(e)
assert list_variables(e) == [("a", 1), ("f", 1), ("f", 4), ("a", 0)]
