def display_ip(model, ivar):
var = ivar.upper()
a, fr, n, udtryk = split_frml(model.allvar[var]['frml'])
udtryk2 = re.sub(r'_', r'\_', udtryk[:-1]) # get rid of _
udtryk3 = re.sub(r'(?P<var>[a-zA-Z_]\w*)\((?P<lag>-[0-9]+)\)',
'\g<var>_{\g<lag>}', udtryk2)
udtryk4 = re.sub(r'=', r' & = &', udtryk3, 1)
totud = ['Formula:' + udtryk4 + r'\\']
for i in sorted(model.diffendocur[var].keys()):
ud = r'\frac{\partial ' + var + '}{\partial ' + i + '} = ' + str(
model.diffendocur[var][i])
ud = re.sub(r'_', r'\_', ud)
ud2 = re.sub(r'(?P<var>[a-zA-Z_]\w*)\((?P<lag>-[0-9]+)\)',
'\g<var>_{\g<lag>}', ud)
ud2 = re.sub(r'=', r' & = &', ud2, 1)
totud.append(r'' + ud2 + r'')
try:
totud.append(f'& = & {model.diffvalue[var][i]}')
except:
pass
# print(' ')
ud = r'\\'.join(totud)
# display(Latex(r'\begin{align}'+r' \\'+ud+r'\end{align}'))
display(Latex(r'\begin{eqnarray*}' + ud + r'\end{eqnarray*} '))
def modeldiff(model,
silent=False,
onlyendocur=False,
endovar=None,
maxdif=9999999999999999,
forcenum=False):
''' Differentiate all relations with respect to all variable
The result is placed in a dictory in the model instanse: model.diffendocur
'''
with mc.ttimer('Find espressions for partial derivatives', not silent):
model.diffendocur = {
} #defaultdict(defaultdict) #here we wanmt to store the derivativs
i = 0
for nvar, v in enumerate(model.endogene):
if nvar >= maxdif:
break
if not silent:
print(f'Now differentiating {v} {nvar}')
if onlyendocur:
endocur = findendocur(model, v)
else:
endocur = findallvar(model, v)
model.diffendocur[v] = {}
t = model.allvar[v]['frml'].upper()
a, fr, n, udtryk = split_frml(t)
udtryk = udtryk
udtryk = re.sub(r'LOG\(', 'log(',
udtryk) # sympy uses lover case for log and exp
udtryk = re.sub(r'EXP\(', 'exp(', udtryk)
lhs, rhs = udtryk.split('=', 1)
try:
kat = sympify(
rhs[0:-1], _clash
) # we take the the $ out _clash1 makes I is not taken as imiganary
except:
print('* Problem sympify ', lhs, '=', rhs[0:-1])
for rhv in endocur:
try:
if not forcenum:
ud = kat.diff(sympify(rhv, _clash))
if forcenum or 'Derivative(' in str(ud):
ud = numdif(model, v, rhv, silent=silent)
if not silent: print('numdif of {rhv}')
model.diffendocur[v.upper()][rhv.upper()] = str(ud)
except:
print('we have a serous problem deriving:', lhs, '|', rhv,
'\n', lhs, '=', rhs)
i += 1
if not silent:
print('Model :', model.name)
print('Number of variables :', len(model.allvar))
print('Number of endogeneus variables :', len(model.diffendocur))
print('Number of derivatives :', i)
def display_diff(model, bank, var=''):
temp = calculate_delta(bank)
l = model.maxnavlen
for v in sorted(model.diffvalue):
if var and not v == var: continue
f = re.sub(r' |\n', '', split_frml(model.allvar[v]['frml'])[3])
print(f)
for e, w in sorted(model.impact[v].items(), key=lambda x: x[1]):
evar = e.split('(')[0]
diffvalue = '{:12.3f}'.format(model.diffvalue[v][e])
delta = '{:12.3f}'.format(temp[evar])
timpact = '{:12.1f}% '.format(model.impact[v][e] / temp[v] * 100.)
print(v.ljust(l), e.ljust(l), diffvalue, delta, timpact,
model.diffendocur[v][e])
def display_ip_old(model, ivar):
var = ivar.upper()
a, fr, n, udtryk = split_frml(model.allvar[var]['frml'])
udtryk2 = re.sub(r'_', r'\_', udtryk[:-1]) # get rid of _
udtryk3 = re.sub(r'(?P<var>[a-zA-Z_]\w*)\((?P<lag>-[0-9]+)\)',
'\g<var>_{\g<lag>}', udtryk2)
udtryk3 = re.sub(r'delta_', r'\Delta ', udtryk3)
totud = [udtryk3]
for i in sorted(model.diffendocur[var].keys()):
ud = r'\frac{\partial ' + var + '}{\partial ' + i + '} = ' + str(
model.diffendocur[var][i])
ud = re.sub(r'delta_', r'\Delta ', ud)
ud = re.sub(r'_', r'\_', ud)
ud2 = re.sub(r'(?P<var>[a-zA-Z_]\w*)\((?P<lag>-[0-9]+)\)',
'\g<var>_{\g<lag>}', ud)
totud.append(r'' + ud2 + r'')
# print(' ')
ud = r'\\'.join(totud)
ud2 = re.sub(r'=', r' & = &', ud)
ud3 = r'$\begin{eqnarray}' + r' \\' + ud2 + r'\end{eqnarray}$'
return ud3
def modeldiff(model, silent=False, onlyendocur=False):
''' Differentiate all relations with respect to all variable
The result is placed in a dictory in the model instanse: model.diffendocur
'''
model.diffendocur = {
} #defaultdict(defaultdict) #here we wanmt to store the derivativs
i = 0
for v in model.endogene:
if onlyendocur:
endocur = findendocur(model, v)
else:
endocur = findallvar(model, v)
model.diffendocur[v] = {}
t = model.allvar[v]['frml'].upper()
a, fr, n, udtryk = split_frml(t)
udtryk = udtryk
udtryk = re.sub(r'LOG\(', 'log(',
udtryk) # sympy uses lover case for log and exp
udtryk = re.sub(r'EXP\(', 'exp(', udtryk)
lhs, rhs = udtryk.split('=', 1)
try:
kat = sympify(
rhs[0:-1], _clash
) # we take the the $ out _clash1 makes I is not taken as imiganary
except:
print('*', lhs, '=', rhs[0:-1])
for rhv in endocur:
try:
ud = kat.diff(sympify(rhv, _clash))
model.diffendocur[v.upper()][rhv.upper()] = ud
except:
print(lhs, '|', rhv, '\n', lhs, '=', rhs)
i += 1
if not silent:
print('Model :', model.name)
print('Number of variables :', len(model.allvar))
print('Number of endogeneus variables :', len(model.diffendocur))
print('Number of derivatives :', i)
def modeldiff(self):
''' Differentiate relations for self.enovar with respect to endogeneous variable
The result is placed in a dictory in the model instanse: model.diffendocur
'''
def numdif(model, v, rhv, delta=0.005, silent=True):
# print('**',model.allvar[v]['terms']['frml'])
def tout(t):
if t.lag:
return f'{t.var}({t.lag})'
return t.op + t.number + t.var
# breakpoint()
nt = model.allvar[v]['terms']
assignpos = nt.index(model.aequalterm) # find the position of =
rhsterms = nt[assignpos + 1:-1]
vterm = udtryk_parse(rhv)[0]
plusterm = udtryk_parse(f'({rhv}+{delta/2})', funks=model.funks)
minusterm = udtryk_parse(f'({rhv}-{delta/2})', funks=model.funks)
plus = itertools.chain.from_iterable(
[plusterm if t == vterm else [t] for t in rhsterms])
minus = itertools.chain.from_iterable(
[minusterm if t == vterm else [t] for t in rhsterms])
eplus = f'({"".join(tout(t) for t in plus)})'
eminus = f'({"".join(tout(t) for t in minus)})'
expression = f'({eplus}-{eminus})/{delta}'
if (not silent) and False:
print(expression)
return expression
def findallvar(model, v):
'''Finds all endogenous variables which is on the right side of = in the expresion for variable v
lagged variables are included if self.onlyendocur == False '''
# print(v)
terms = self.mmodel.allvar[v]['terms'][model.
allvar[v]['assigpos']:-1]
if self.endoandexo:
rhsvar = {
(nt.var + ('(' + nt.lag + ')' if nt.lag != '' else ''))
for nt in terms if nt.var
}
rhsvar = {tovarlag(nt.var, nt.lag) for nt in terms if nt.var}
else:
if self.onlyendocur:
rhsvar = {
tovarlag(nt.var, nt.lag)
for nt in terms if nt.var and nt.lag == ''
and nt.var in self.declared_endo_set
}
else:
rhsvar = {
tovarlag(nt.var, nt.lag)
for nt in terms
if nt.var and nt.var in self.declared_endo_set
}
var2 = sorted(list(rhsvar))
return var2
with ttimer('Find espressions for partial derivatives', self.timeit):
clash = {var: Symbol(var) for var in self.mmodel.allvar.keys()}
diffendocur = {
} #defaultdict(defaultdict) #here we wanmt to store the derivativs
i = 0
for nvar, v in enumerate(self.endovar):
if nvar >= self.maxdif:
break
if not self.silent and 0:
print(f'Now differentiating {v} {nvar}')
endocur = findallvar(self.mmodel, v)
diffendocur[v] = {}
t = self.mmodel.allvar[v]['frml'].upper()
a, fr, n, udtryk = split_frml(t)
udtryk = udtryk
udtryk = re.sub(
r'LOG\(', 'log(',
udtryk) # sympy uses lover case for log and exp
udtryk = re.sub(r'EXP\(', 'exp(', udtryk)
lhs, rhs = udtryk.split('=', 1)
try:
if not self.forcenum:
# kat=sympify(rhs[0:-1], md._clash) # we take the the $ out _clash1 makes I is not taken as imiganary
kat = sympify(
rhs[0:-1], clash
) # we take the the $ out _clash1 makes I is not taken as imiganary
except:
# breakpoint()
print('* Problem sympify ', lhs, '=', rhs[0:-1])
for rhv in endocur:
try:
# breakpoint()
if not self.forcenum:
# ud=str(kat.diff(sympify(rhv,md._clash)))
try:
ud = str(kat.diff(sympify(rhv, clash)))
ud = re.sub(
pt.namepat + r'(?:(\()([0-9])(\)))',
r'\g<1>\g<2>+\g<3>\g<4>', ud)
except:
ud = numdif(self.mmodel,
v,
rhv,
silent=self.silent)
if self.forcenum or 'Derivative(' in ud:
ud = numdif(self.mmodel,
v,
rhv,
silent=self.silent)
if not self.silent and 0: print('numdif of {rhv}')
diffendocur[v.upper()][rhv.upper()] = ud
except:
print('we have a serious problem deriving:', lhs, '|',
rhv, '\n', lhs, '=', rhs)
# breakpoint()
i += 1
if not self.silent:
print('Model :', self.mmodel.name)
print('Number of endogeneus variables :', len(diffendocur))
print('Number of derivatives :', i)
return diffendocur
def modeldiff(self):
''' Differentiate relations for self.enovar with respect to endogeneous variable
The result is placed in a dictory in the model instanse: model.diffendocur
'''
def findallvar(model, v):
'''Finds all endogenous variables which is on the right side of = in the expresion for variable v
lagged variables are included if self.onlyendocur == False '''
# print(v)
terms = self.mmodel.allvar[v]['terms'][model.
allvar[v]['assigpos']:-1]
if self.endoandexo:
rhsvar = {
(nt.var + ('(' + nt.lag + ')' if nt.lag != '' else ''))
for nt in terms if nt.var
}
rhsvar = {tovarlag(nt.var, nt.lag) for nt in terms if nt.var}
else:
if self.onlyendocur:
rhsvar = {
tovarlag(nt.var, nt.lag)
for nt in terms if nt.var and nt.lag == ''
and nt.var in self.declared_endo_set
}
else:
rhsvar = {
tovarlag(nt.var, nt.lag)
for nt in terms
if nt.var and nt.var in self.declared_endo_set
}
var2 = sorted(list(rhsvar))
return var2
with ttimer('Find espressions for partial derivatives', self.timeit):
diffendocur = {
} #defaultdict(defaultdict) #here we wanmt to store the derivativs
i = 0
for nvar, v in enumerate(self.endovar):
if nvar >= self.maxdif:
break
if not self.silent:
print(f'Now differentiating {v} {nvar}')
endocur = findallvar(self.mmodel, v)
diffendocur[v] = {}
t = self.mmodel.allvar[v]['frml'].upper()
a, fr, n, udtryk = split_frml(t)
udtryk = udtryk
udtryk = re.sub(
r'LOG\(', 'log(',
udtryk) # sympy uses lover case for log and exp
udtryk = re.sub(r'EXP\(', 'exp(', udtryk)
lhs, rhs = udtryk.split('=', 1)
try:
if not self.forcenum:
kat = sympify(
rhs[0:-1], md._clash
) # we take the the $ out _clash1 makes I is not taken as imiganary
except:
# breakpoint()
print('* Problem sympify ', lhs, '=', rhs[0:-1])
for rhv in endocur:
try:
if not self.forcenum:
ud = str(kat.diff(sympify(rhv, md._clash)))
ud = re.sub(pt.namepat + r'(?:(\()([0-9])(\)))',
r'\g<1>\g<2>+\g<3>\g<4>', ud)
if self.forcenum or 'Derivative(' in ud:
ud = md.numdif(self.mmodel,
v,
rhv,
silent=self.silent)
if not self.silent: print('numdif of {rhv}')
diffendocur[v.upper()][rhv.upper()] = ud
except:
print('we have a serous problem deriving:', lhs, '|',
rhv, '\n', lhs, '=', rhs)
breakpoint()
i += 1
if not self.silent:
print('Model :', self.mmodel.name)
print('Number of endogeneus variables :', len(diffendocur))
print('Number of derivatives :', i)
return diffendocur
