def getValidationData(vargs, data, debug):
"""
Structure data for validating the model. Modifies data['opts']
Args:
vargs: validation data valxdata, valzdata
data: shared alamo data options
debug: Additional options may be specified and will be applied
to the .alm
"""
if vargs != ():
debug["validation"] = True
xvaldata = vargs[0]
zvaldata = vargs[1]
temp = np.shape(xvaldata)
data["opts"]["nvaldata"] = temp[0]
if len(np.shape(zvaldata)) == 1:
zvaldata = np.reshape(zvaldata, (data["opts"]["nvaldata"], 1))
if temp[1] != data["opts"]["ninputs"]:
raise almerror.AlamoInputError(
"Number of input variables inconsistent between x and xval"
)
temp = np.shape(zvaldata)
if temp[0] != data["opts"]["nvaldata"] or temp[1] != data["opts"]["noutputs"]:
raise almerror.AlamoInputError(
"Problem with zval"
)
return xvaldata, zvaldata
def checkinput(data, debug, xdata, zdata, vargs, kwargs):
"""
Check the input data into doalamo for errors.
Args:
data/debug: shared default options for .alm file
xdata: (numpy.array or list[real])
zdata: (numpy.array or list[real)
vargs: Validation data
kwargs: Additional options may be specified and will be applied
to the .alm
"""
# t = np.zeros([1, 1])
kk = kwargs.keys()
# First check vargs for validation set viability
if len(vargs) > 0 and len(vargs) != 2:
raise almerror.AlamoInputError(
"Validation Xdata and Zdata must be"
"specified in the following form: "
"alamopy.doalamo(x,z,xval,zval)"
)
for arg in kk:
if arg not in data["pargs"]["opts"] + data["pargs"]["stropts"] + data["pargs"][
"lstopts"
] + data["pargs"]["set4"] + debug["pargs"] + ["xlabels", "zlabels"] + [
"xval",
"zval",
]:
raise almerror.AlamoInputError(
"The following argument given to"
"alamo() is not understood: {}".format(arg)
)
# read keys for debug
for key in debug["pargs"]:
if key in kk:
debug[key] = kwargs[key]
if kwargs.get("almname", None) is not None:
data["stropts"]["almname"] += ".alm"
def almplot(res, show=True):
try:
import matplotlib.pyplot as plt
import numpy as np
except Exception:
raise almerror.AlamoInputError(
"Cannot plot, possibly missing matplotlib package")
model = res['model']
if type(model) is dict:
firstKey = list(res['model'].keys())[0]
model = res['model'][firstKey].replace(' - ', ' + -')
else:
model = res['model'].replace(' - ', ' + -')
model = model.split('=')[1]
model = model.split(' + ')
if model[
0] == ' ': # if there are more than one terms, the first split is ' '
model = model[1:]
ndp = 100
t = np.linspace(0.08, 1.7, ndp)
out = np.zeros([3, ndp])
clo = np.zeros(ndp)
chi = np.zeros(ndp)
coeff = np.zeros(ndp)
for i in range(len(model)):
coeff[i] = float(model[i].split(' * ')[0])
if 'conf_inv' in res.keys():
if type(res['conf_inv']) is dict:
firstKey = list(res['conf_inv'].keys())[0]
clo[i] = coeff[i] - float(
res['conf_inv'][firstKey][i].split('+/-')[1])
chi[i] = coeff[i] + float(
res['conf_inv'][firstKey][i].split('+/-')[1])
else:
clo[i] = coeff[i] - float(res['conf_inv'][i].split('+/-')[1])
chi[i] = coeff[i] + float(res['conf_inv'][i].split('+/-')[1])
for i in range(ndp):
out[0, i] = float(coeff[0]) * t[i]**1.2 \
- float(coeff[1]) * t[i]**2 - float(coeff[2])
if 'conf_inv' in res.keys(): # If confidence intervals exist
out[1, i] = clo[0] * t[i]**1.2 - chi[1] * t[i]**2 - chi[2]
out[2, i] = chi[0] * t[i]**1.2 - clo[1] * t[i]**2 - clo[2]
plt.plot(t, out[0], 'b-')
if 'conf_inv' in res.keys():
plt.plot(t, out[1], 'r--', t, out[2], 'r--')
if show:
plt.show()
def buildSimWrapper(data, debug):
""" Builds an executable simulator to sample for data
Args:
data: shared alamo data options
debug: Additional options may be specified and will be applied
to the .alm
"""
if not isinstance(data["stropts"]["simulator"], type("string")):
try:
data["stropts"]["simulator"] = data["stropts"][
"simulator"].__name__
except Exception:
raise almerror.AlamoInputError(
"Simulator must be provided as a string"
"and obey ALAMOs simulator conventions"
"OR must be a python function whose name"
"can be obtained via .__name__")
data["stropts"]["simulator"] = alamopy.wrapwriter(
data["stropts"]["simulator"])
debug["simwrap"] = True
