def norm_group(pos, _, **kargs):
"""Takes the drain current for each file in group and builds an analysis file and works out the mean drain"""
if "signal" in kargs:
signal = kargs["signal"]
else:
signal = "fluo"
lfit = kargs["lfit"]
rfit = kargs["rfit"]
posfile = Data()
posfile.metadata = pos[0].metadata
posfile = posfile & pos[0].column(0)
posfile.column_headers = ["Energy"]
for f in pos:
print(str(f["run"]) + str(f.find_col(signal)))
posfile = posfile & f.column(signal)
posfile.add_column(lambda r: np.mean(r[1:]), "mean drain")
ec = posfile.find_col("Energy")
md = posfile.find_col("mean drain")
linearfit = scipy.poly1d(
posfile.polyfit(ec, md, 1, lambda x, y: lfit[0] <= x <= lfit[1]))
posfile.add_column(lambda r: r[md] - linearfit(r[ec]), "minus linear")
highend = posfile.mean("minus", lambda r: rfit[0] <= r[ec] <= rfit[1])
ml = posfile.find_col("minus linear")
posfile.add_column(lambda r: r[ml] / highend, "normalised")
if "group_key" in kargs:
posfile[kargs["group_key"]] = pos.key
return posfile
def norm_group(pos,_,**kargs):
"""Takes the drain current for each file in group and builds an analysis file and works out the mean drain"""
if "signal" in kargs:
signal=kargs["signal"]
else:
signal="fluo"
lfit=kargs["lfit"]
rfit=kargs["rfit"]
posfile=Data()
posfile.metadata=pos[0].metadata
posfile=posfile&pos[0].column(0)
posfile.column_headers=['Energy']
for f in pos:
print(str(f["run"])+str(f.find_col(signal)))
posfile=posfile&f.column(signal)
posfile.add_column(lambda r:np.mean(r[1:]),"mean drain")
ec=posfile.find_col('Energy')
md=posfile.find_col('mean drain')
linearfit=scipy.poly1d(posfile.polyfit(ec,md,1,lambda x,y:lfit[0]<=x<=lfit[1]))
posfile.add_column(lambda r:r[md]-linearfit(r[ec]),'minus linear')
highend=posfile.mean('minus',lambda r:rfit[0]<=r[ec]<=rfit[1])
ml=posfile.find_col('minus linear')
posfile.add_column(lambda r:r[ml]/highend,"normalised")
if "group_key" in kargs:
posfile[kargs["group_key"]]=pos.key
return posfile
"""Decompose Example"""
# pylint: disable=invalid-name
from Stoner import Data
from Stoner.tools import format_val
from numpy import linspace, reshape, array
x = linspace(-10, 10, 201)
y = 0.3 * x**3 - 6 * x**2 + 11 * x - 20
d = Data(x, y, setas="xy", column_headers=["X", "Y"])
d.decompose()
d.setas = "xyyy"
coeffs = d.polyfit(polynomial_order=3)
str_coeffs = [format_val(c, mode="eng", places=1) for c in coeffs.ravel()]
str_coeffs = reshape(array(str_coeffs), coeffs.shape)
d.plot()
d.text(
-6,
-800,
"Coefficients\n{}".format(str_coeffs),
fontdict={"size": "x-small"},
)
d.ylabel = "Data"
d.title = "Decompose Example"
d.tight_layout()
from Stoner import Data
import Stoner.Fit as SF
from numpy import linspace
from numpy.random import normal
import matplotlib.pyplot as plt
# Make some data
x = linspace(-10, 10, 101)
y = SF.quadratic(x + normal(size=len(x), scale=0.1), 4, -2, 11) * normal(
size=len(x), scale=0.05, loc=1.0
)
s = y * 0.05
d = Data(x, y, s, setas="xye", column_headers=["X", "Y"])
d.plot(fmt="r.")
d.polyfit(result=True, header="Polyfit")
d.setas = "x..y"
d.plot(fmt="m-", label="Polyfit")
d.text(
-9,
450,
"Polynominal co-efficients\n{}".format(d["2nd-order polyfit coefficients"]),
fontdict={"size": "x-small", "color": "magenta"},
)
d.setas = "xy"
d.curve_fit(SF.quadratic, result=True, header="Curve-fit")
d.setas = "x...y"
d.plot(fmt="b-", label="curve-fit")
d.annotate_fit(
SF.quadratic,
"""Example of Quadratic Fit."""
from Stoner import Data
import Stoner.Fit as SF
from numpy import linspace
from numpy.random import normal
import matplotlib.pyplot as plt
# Make some data
x = linspace(-10, 10, 101)
y = SF.quadratic(x + normal(size=len(x), scale=0.1), 4, -2, 11) * normal(
size=len(x), scale=0.05, loc=1.0)
s = y * 0.05
d = Data(x, y, s, setas="xye", column_headers=["X", "Y"])
d.plot(fmt="r.")
d.polyfit(result=True, header="Polyfit")
d.setas = "x..y"
d.plot(fmt="m-", label="Polyfit")
d.text(
-9,
450,
"Polynominal co-efficients\n{}".format(
d["2nd-order polyfit coefficients"]),
fontdict={
"size": "x-small",
"color": "magenta"
},
)
d.setas = "xy"
d.curve_fit(SF.quadratic, result=True, header="Curve-fit")
"""Decompose Example"""
from Stoner import Data
from Stoner.tools import format_val
from numpy import linspace, reshape, array
x = linspace(-10, 10, 201)
y = 0.3 * x ** 3 - 6 * x ** 2 + 11 * x - 20
d = Data(x, y, setas="xy", column_headers=["X", "Y"])
d.decompose()
d.setas = "xyyy"
coeffs = d.polyfit(polynomial_order=3)
str_coeffs = [format_val(c, mode="eng") for c in coeffs.ravel()]
str_coeffs = reshape(array(str_coeffs), coeffs.shape)
d.plot()
d.text(-6, -800, "Coefficients\n{}".format(str_coeffs), fontdict={"size": "x-small"})
d.ylabel = "Data"
d.title = "Decompose Example"
d.tight_layout()
