class AnalysisMixins_test(unittest.TestCase):
"""Path to sample Data File"""
datadir = path.join(pth, "sample-data")
def setUp(self):
x_data = np.linspace(-10, 10, 101)
y_data = 0.01 * x_data**2 + 0.3 * x_data - 2
y_data *= np.random.normal(size=101, loc=1.0, scale=0.01)
x_data += np.random.normal(size=101, scale=0.02)
self.data = Data(x_data, y_data, column_headers=["X", "Y"])
self.data.setas = "xy"
def test_cuve_fit(self):
for output, fmt in zip(["fit", "row", "full", "dict", "data"],
[tuple, np.ndarray, tuple, dict, Data]):
res = self.data.curve_fit(fit, p0=[0.02, 0.2, 2], output=output)
self.assertTrue(
isinstance(res, fmt),
"Failed to get expected output from curve_fit for {} (got {})".
format(output, type(res)))
def test_lmfit(self):
for output, fmt in zip(["fit", "row", "full", "dict", "data"],
[tuple, np.ndarray, tuple, dict, Data]):
res = self.data.lmfit(fit, p0=[0.02, 0.2, 2], output=output)
self.assertTrue(
isinstance(res, fmt),
"Failed to get expected output from lmfit for {} (got {})".
format(output, type(res)))
def test_odr(self):
for output, fmt in zip(["fit", "row", "full", "dict", "data"],
[tuple, np.ndarray, tuple, dict, Data]):
res = self.data.odr(fit, p0=[0.02, 0.2, 2], output=output)
self.assertTrue(
isinstance(res, fmt),
"Failed to get expected output from idr for {} (got {})".
format(output, type(res)))
def test_differential_evolution(self):
for output, fmt in zip(["fit", "row", "full", "dict", "data"],
[tuple, np.ndarray, tuple, dict, Data]):
res = self.data.differential_evolution(fit,
p0=[0.02, 0.2, 2],
output=output)
self.assertTrue(
isinstance(res, fmt),
"Failed to get expected output from differential_evolution for {} (got {})"
.format(output, type(res)))
fit = SF.Arrhenius()
d.setas = "xye"
d.lmfit(fit, result=True, header="lmfit")
d.setas = "x...y"
d.plot(fmt="g-")
d.annotate_fit(
SF.Arrhenius,
x=0.5,
y=0.35,
prefix="Arrhenius",
mode="eng",
fontdict={"size": "x-small", "color": "green"},
)
d.setas = "xye"
res = d.odr(SF.Arrhenius, result=True, header="odr", prefix="ODR")
d.setas = "x....y"
d.plot(fmt="m-")
d.annotate_fit(
SF.Arrhenius,
x=0.5,
y=0.2,
prefix="ODR",
mode="eng",
fontdict={"size": "x-small", "color": "magenta"},
)
d.title = "Arrhenius Test Fit"
d.ylabel("Rate")
d.xlabel("Temperature (K)")
d.annotate_fit(
VFTEquation,
x=0.5,
y=0.35,
prefix="VFTEquation",
fontdict={
"size": "x-small",
"color": "green"
},
mode="eng",
)
# reset the columns for the fit
d.setas = "xye.."
# Now do the odr fit (will overwrite lmfit's metadata)
d.odr(VFTEquation, result=True)
d.setas = "x4.y"
# And plot and annotate
d.plot(fmt="m-", label="Orthogonal distance")
d.annotate_fit(
VFTEquation,
x=0.75,
y=0.35,
fontdict={
"size": "x-small",
"color": "magenta"
},
mode="eng",
)
#Make some data
x = linspace(0, 10.0, 101)
y = 2 + 4 * exp(-x / 1.7) + random.normal(scale=0.2, size=101)
d = Data(x, y, column_headers=["Time", "Signal"], setas="xy")
d.plot(fmt="ro") # plot our data
func = lambda x, A, B, C: A + B * exp(-x / C)
#Do the fitting and plot the result
fit = d.odr(func,
result=True,
header="Fit",
A=1,
B=1,
C=1,
prefix="Model",
residuals=True)
#Reset labels
d.labels = []
# Make nice two panel plot layout
ax = d.subplot2grid((3, 1), (2, 0))
d.setas = "x..y"
d.plot(fmt="g+")
d.title = ""
ax = d.subplot2grid((3, 1), (0, 0), rowspan=2)
d.setas = "xyy"
fontdict={"size": "x-small", "color": "blue"},
mode="eng",
)
d.annotate_fit(
VFTEquation,
x=0.5,
y=0.35,
prefix="VFTEquation",
fontdict={"size": "x-small", "color": "green"},
mode="eng",
)
# reset the columns for the fit
d.setas = "xye.."
# Now do the odr fit (will overwrite lmfit's metadata)
d.odr(VFTEquation, p0=p0)
d.setas = "x4.y"
# And plot and annotate
d.plot(fmt="m-", label="Orthogonal distance")
d.annotate_fit(
VFTEquation,
x=0.75,
y=0.35,
fontdict={"size": "x-small", "color": "magenta"},
mode="eng",
)
# Finally tidy up the plot a bit
d.yscale = "log"
d.ylim = (1e-35, 1e10)
fit = SF.Arrhenius()
d.setas = "xye"
d.lmfit(fit, result=True, header="lmfit")
d.setas = "x...y"
d.plot(fmt="g-")
d.annotate_fit(
SF.Arrhenius,
x=0.5,
y=0.35,
prefix="Arrhenius",
mode="eng",
fontdict={"size": "x-small", "color": "green"},
)
d.setas = "xye"
res = d.odr(SF.Arrhenius, result=True, header="odr", prefix="ODR")
d.setas = "x....y"
d.plot(fmt="m-")
d.annotate_fit(
SF.Arrhenius,
x=0.5,
y=0.2,
prefix="ODR",
mode="eng",
fontdict={"size": "x-small", "color": "magenta"},
)
d.title = "Arrhenius Test Fit"
d.ylabel("Rate")
d.xlabel("Temperature (K)")
from Stoner import Data
from numpy import linspace, exp, random
# Make some data
x = linspace(0, 10.0, 101)
y = 2 + 4 * exp(-x / 1.7) + random.normal(scale=0.2, size=101)
d = Data(x, y, column_headers=["Time", "Signal"], setas="xy")
d.plot(fmt="ro") # plot our data
func = lambda x, A, B, C: A + B * exp(-x / C)
# Do the fitting and plot the result
fit = d.odr(
func, result=True, header="Fit", A=1, B=1, C=1, prefix="Model", residuals=True
)
# Reset labels
d.labels = []
# Make nice two panel plot layout
ax = d.subplot2grid((3, 1), (2, 0))
d.setas = "x..y"
d.plot(fmt="g+")
d.title = ""
ax = d.subplot2grid((3, 1), (0, 0), rowspan=2)
d.setas = "xyy"
d.plot(fmt=["ro", "b-"])
d.xticklabels = [[]]
