本文整理汇总了Python中Stoner.Data.polyfit方法的典型用法代码示例。如果您正苦于以下问题：Python Data.polyfit方法的具体用法？Python Data.polyfit怎么用？Python Data.polyfit使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类Stoner.Data的用法示例。
在下文中一共展示了Data.polyfit方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: norm_group
# 需要导入模块: from Stoner import Data [as 别名]
# 或者: from Stoner.Data import polyfit [as 别名]
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示例2: linspace
# 需要导入模块: from Stoner import Data [as 别名]
# 或者: from Stoner.Data import polyfit [as 别名]
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,示例3: linspace
# 需要导入模块: from Stoner import Data [as 别名]
# 或者: from Stoner.Data import polyfit [as 别名]
"""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()