Python Data.figure方法代码示例

# 需要导入模块: from Stoner import Data [as 别名]
# 或者: from Stoner.Data import figure [as 别名]
"""Re-binning data example."""
from Stoner import Data
from Stoner.plot.utils import errorfill

d = Data("Noisy_Data.txt", setas="xy")

d.template.fig_height = 6
d.template.fig_width = 8
d.figure(figsize=(6, 8))
d.subplot(411)

e = d.bin(bins=0.05, mode="lin")
f = d.bin(bins=0.25, mode="lin")
g = d.bin(bins=0.05, mode="log")
h = d.bin(bins=50, mode="log")

for i, (binned, label) in enumerate(
    zip([e, f, g, h], ["0.05 Linear", "0.25 Linear", "0.05 Log", "50 log"])
):
    binned.subplot(411 + i)
    d.plot()
    binned.fig = d.fig
    binned.plot(plotter=errorfill, label=label)

    d.xlim = (1, 6)
    d.ylim(-0.1, 0.4)
    d.title = "Bin demo" if i == 0 else ""
d.tight_layout()

# 需要导入模块: from Stoner import Data [as 别名]
# 或者: from Stoner.Data import figure [as 别名]
def plane(coord, a, b, c):
    """Function to define a plane"""
    return c - (coord[0] * a + coord[1] * b)


coeefs = [1, -0.5, -1]
col = linspace(-10, 10, 6)
X, Y = meshgrid(col, col)
Z = plane((X, Y), *coeefs) + normal(size=X.shape, scale=7.0)
d = Data(
    column_stack((X.ravel(), Y.ravel(), Z.ravel())),
    filename="Fitting a Plane",
    setas="xyz",
)

d.column_headers = ["X", "Y", "Z"]
d.figure(projection="3d")
d.plot_xyz(plotter="scatter")

popt, pcov = d.curve_fit(plane, [0, 1], 2, result=True)
d.setas = "xy.z"

d.plot_xyz(linewidth=0, cmap=cmap.jet)

txt = "$z=c-ax+by$\n"
txt += "\n".join([d.format("plane:{}".format(k), latex=True) for k in ["a", "b", "c"]])

ax = plt.gca(projection="3d")
ax.text(15, 5, -50, txt)
d.draw()

# 需要导入模块: from Stoner import Data [as 别名]
# 或者: from Stoner.Data import figure [as 别名]
d = Data(filename, setas="xyy", template=DefaultPlotStyle)
d.normalise(2, scale=(0.0, 10.0))  # Just to keep the plot sane!

# Change the color and line style cycles
d.template["axes.prop_cycle"] = cycler(color=["Blue", "Purple"]) + cycler(
    linestyle=["-", "--"]
)

# Can also access as an attribute
d.template.template_lines__linewidth = 2.0

# Set the default figure size
d.template["figure.figsize"] = (6, 8)
d.template["figure.autolayout"] = True
# Make figure (before using subplot method) and select first subplot
d.figure()
d.subplot(211)
# Pkot with our customised defaults
d.plot()
d.grid(True, color="green", linestyle="-.")
d.title = "Customised Plot settings"
# Reset the template to defaults and switch to next subplot
d.template.clear()
d.subplot(212)
# Plot with defaults
d.plot()
d.title = "Style Default settings"
# Fixup layout
d.figwidth = 7  # Magic pass through attribute access

# 需要导入模块: from Stoner import Data [as 别名]
# 或者: from Stoner.Data import figure [as 别名]
t_col=": T2" # Temperature column label
r_cols=("Sample 4::R","Sample 7::R") #Resistance Column Labels
iterator="iterator" #Temperature ramp iteration column label
threshold=0.85 #Fraction of transition to fit to

data=Data(filename)  #Use FALSE to get a dialog box to the file containing Tc data

#Define my working x and y axes
#Split one file into a folder of two files by the iterator column
fldr=data.split(iterator)

result=Data()
for data in fldr: #For each iteration ramp in the Tc data
    row=[data.mean(iterator)]
    data.figure(figsize=(8,4))
    for i,r_col in enumerate(r_cols):
        data.setas(x=t_col,y=r_col)
        data.del_rows(isnan(data.y))

        #Normalise data on y axis between +/- 1
        data.normalise(base=(-1.,1.), replace=True)

        #Swap x and y axes around so that R is x and T is y
        data=~data

        #Curve fit a straight line, using only the central 90% of the resistance transition
        data.curve_fit(linear,bounds=lambda x,r:-threshold<x<threshold,result=True,p0=[7.0,0.0]) #result=True to record fit into metadata

        #Plot the results
        data.setas[-1]="y"

# 需要导入模块: from Stoner import Data [as 别名]
# 或者: from Stoner.Data import figure [as 别名]
    for c in [0, 2, 4, 6, 8, 9, 10]:
        result.data[:, c] = (resfldr[1][:, c] + resfldr[0][:, c]) / 2.0
    for c in [1, 3, 5, 7]:
        result.data[:, c] = gmean((resfldr[0][:, c], resfldr[1][:, c]), axis=0)

    # Doing the Kittel fit with an orthogonal distance regression as we have x errors not y errors
    p0 = [2, 200e3, 10e3]  # Some sensible guesses
    result.lmfit(
        Inverse_Kittel, p0=p0, result=True, header="Kittel Fit", output="report"
    )
    result.setas[-1] = "y"

    result.template.yformatter = TexEngFormatter
    result.template.xformatter = TexEngFormatter
    result.labels = None
    result.figure(figsize=(6, 8))
    result.subplot(211)
    result.plot(fmt=["r.", "b-"])
    result.annotate_fit(Inverse_Kittel, x=7e9, y=1e5, fontdict={"size": 8})
    result.ylabel = "$H_{res} \\mathrm{(Am^{-1})}$"
    result.title = "Inverse Kittel Fit"

    # Get alpha
    result.subplot(212)
    result.setas(y="Delta_H", e="Delta_H.stderr", x="Freq")
    result.y /= mu_0
    result.e /= mu_0
    result.lmfit(Linear, result=True, header="Width", output="report")
    result.setas[-1] = "y"
    result.plot(fmt=["r.", "b-"])
    result.annotate_fit(Linear, x=5.5e9, y=2.8e3, fontdict={"size": 8})