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)))
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.differential_evolution(
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=["r.", "b-"])
d.xticklabels = [[]]
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.differential_evolution(
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)
