Imporve Makefile speed for black

Makefile

@@ -18,9 +18,9 @@ check:
 	prospector -E -0 --profile-path=. -P .landscape.yml Stoner > prospector-report.txt
 
 black:
-	find Stoner -name '*.py' -exec black -l 119 {} \;
-	find doc/samples -name '*.py' -exec black {} \;
-	find scripts -name '*.py' -exec black {} \;
+	find Stoner -name '*.py' | xargs -d "\n" black -l 119
+	find doc/samples -name '*.py' | xargs  -d "\n" black -l 80
+	find scripts -name '*.py' | xargs -d "\n" black -l 80
 
 commit: black
 	$(MAKE) -C doc readme

Stoner/analysis/fitting/mixins.py

@@ -1365,6 +1365,7 @@ def lmfit(self, model, xcol=None, ycol=None, p0=None, sigma=None, **kargs):
                 replace=replace,
                 output=output,
                 residuals=residuals,
+                **kargs
             )
         else:  # chi^2 mode
             pn = p0
@@ -1373,7 +1374,7 @@ def lmfit(self, model, xcol=None, ycol=None, p0=None, sigma=None, **kargs):
                 p0, single_fit = _prep_lmfit_p0(
                     model, data[1], data[0], pn_i, kargs
                 )  # model, data, params, prefix, columns, scale_covar,**kargs)
-                ret_val[i, :] = self.__lmfit_one(model, data, p0, prefix, _, scale_covar, output="row")
+                ret_val[i, :] = self.__lmfit_one(model, data, p0, prefix, _, scale_covar, output="row", **kargs)
             if output == "data":  # Create a data object and seet column headers etc correctly
                 ret = self.clone
                 ret.data = ret_val

doc/samples/3D_plot.py

@@ -6,7 +6,9 @@
 x, y = np.meshgrid(np.linspace(-2, 2, 100), np.linspace(-2, 2, 100))
 x = x.ravel()
 y = y.ravel()
-z = np.cos(4 * np.pi * np.sqrt(x ** 2 + y ** 2)) * np.exp(-np.sqrt(x ** 2 + y ** 2))
+z = np.cos(4 * np.pi * np.sqrt(x ** 2 + y ** 2)) * np.exp(
+    -np.sqrt(x ** 2 + y ** 2)
+)
 
 p = Data()
 p = p & x & y & z

doc/samples/Fitting/BDR.py

@@ -12,11 +12,17 @@
 # Curve fit
 d = Data(V, I, dI, setas="xye", column_headers=["Bias", "Current", "Noise"])
 
-d.curve_fit(SF.bdr, p0=[2.5, 3.2, 0.3, 15.0, 1.0], result=True, header="curve_fit")
+d.curve_fit(
+    SF.bdr, p0=[2.5, 3.2, 0.3, 15.0, 1.0], result=True, header="curve_fit"
+)
 d.setas = "xyey"
 d.plot(fmt=["r.", "b-"])
 d.annotate_fit(
-    SF.bdr, x=0.6, y=0.05, prefix="bdr", fontdict={"size": "x-small", "color": "blue"}
+    SF.bdr,
+    x=0.6,
+    y=0.05,
+    prefix="bdr",
+    fontdict={"size": "x-small", "color": "blue"},
 )
 
 # lmfit
@@ -35,7 +41,11 @@
 d.setas = "x...y"
 d.plot(fmt="g-")
 d.annotate_fit(
-    fit, x=0.2, y=0.05, prefix="BDR", fontdict={"size": "x-small", "color": "green"}
+    fit,
+    x=0.2,
+    y=0.05,
+    prefix="BDR",
+    fontdict={"size": "x-small", "color": "green"},
 )
 
 d.ylabel = "Current"

doc/samples/Fitting/FowlerNordheim.py

@@ -11,7 +11,9 @@
 
 d = Data(V, I, dI, setas="xye", column_headers=["Bias", "Current", "Noise"])
 
-d.curve_fit(SF.fowlerNordheim, p0=[2500, 3.2, 15.0], result=True, header="curve_fit")
+d.curve_fit(
+    SF.fowlerNordheim, p0=[2500, 3.2, 15.0], result=True, header="curve_fit"
+)
 d.setas = "xyey"
 d.plot(fmt=["r.", "b-"])
 d.annotate_fit(

doc/samples/Fitting/b_g.py

@@ -10,7 +10,13 @@
 params2 = deepcopy(params)
 G = SF.blochGrueneisen(T, *params) + normal(size=len(T), scale=5e-5)
 dG = ones_like(T) * 5e-5
-d = Data(T, G, dG, setas="xye", column_headers=["Temperature (K)", "Resistivity", "dR"])
+d = Data(
+    T,
+    G,
+    dG,
+    setas="xye",
+    column_headers=["Temperature (K)", "Resistivity", "dR"],
+)
 
 d.curve_fit(SF.blochGrueneisen, p0=params, result=True, header="curve_fit")
 

doc/samples/Fitting/f_s.py

@@ -25,7 +25,10 @@
 d.plot(fmt=["r.", "b-", "g-"])
 
 d.annotate_fit(
-    SF.fluchsSondheimer, x=0.2, y=0.6, fontdict={"size": "x-small", "color": "blue"}
+    SF.fluchsSondheimer,
+    x=0.2,
+    y=0.6,
+    fontdict={"size": "x-small", "color": "blue"},
 )
 d.annotate_fit(
     SF.FluchsSondheimer,

doc/samples/Fitting/langevin.py

@@ -9,7 +9,13 @@
 params = [1, 1.0e-11, 250]
 G = SF.langevin(B, *params) + normal(size=len(B), scale=5e-3)
 dG = ones_like(B) * 5e-3
-d = Data(B, G, dG, setas="xye", column_headers=["Field $\\mu_0H (T)$", "Moment", "dM"])
+d = Data(
+    B,
+    G,
+    dG,
+    setas="xye",
+    column_headers=["Field $\\mu_0H (T)$", "Moment", "dM"],
+)
 
 func = lambda H, M_s, m: SF.langevin(H, M_s, m, 250)
 
@@ -31,7 +37,11 @@
 d.plot(fmt=["r.", "b-", "g-"])
 
 d.annotate_fit(
-    func, x=0.1, y=0.5, fontdict={"size": "x-small", "color": "blue"}, mode="eng"
+    func,
+    x=0.1,
+    y=0.5,
+    fontdict={"size": "x-small", "color": "blue"},
+    mode="eng",
 )
 d.annotate_fit(
     SF.Langevin,

doc/samples/Fitting/modArrhenius.py

@@ -6,11 +6,15 @@
 
 # Make some data
 T = linspace(200, 350, 101)
-R = SF.modArrhenius(T, 1e6, 0.5, 1.5) * normal(scale=0.00005, loc=1.0, size=len(T))
+R = SF.modArrhenius(T, 1e6, 0.5, 1.5) * normal(
+    scale=0.00005, loc=1.0, size=len(T)
+)
 d = Data(T, R, setas="xy", column_headers=["T", "Rate"])
 
 # Curve_fit on its own
-d.curve_fit(SF.modArrhenius, p0=[1e6, 0.5, 1.5], result=True, header="curve_fit")
+d.curve_fit(
+    SF.modArrhenius, p0=[1e6, 0.5, 1.5], result=True, header="curve_fit"
+)
 d.setas = "xyy"
 d.plot(fmt=["r.", "b-"])
 d.annotate_fit(SF.modArrhenius, x=0.2, y=0.5)

doc/samples/Fitting/weak_localisation.py

@@ -25,7 +25,9 @@
 d.setas = "xyeyy"
 d.plot(fmt=["r.", "b-", "g-"])
 
-d.annotate_fit(SF.wlfit, x=0.05, y=0.75, fontdict={"size": "x-small", "color": "blue"})
+d.annotate_fit(
+    SF.wlfit, x=0.05, y=0.75, fontdict={"size": "x-small", "color": "blue"}
+)
 d.annotate_fit(
     SF.WLfit,
     x=0.05,

doc/samples/Vectorfield.py

@@ -3,7 +3,11 @@
 from os import path
 
 d = Data(path.join(__home__, "..", "sample-data", "OVF1.ovf"))
-e = d.select(Z__between=(10, 11)).select(X__between=(10, 18)).select(Y__between=(5, 13))
+e = (
+    d.select(Z__between=(10, 11))
+    .select(X__between=(10, 18))
+    .select(Y__between=(5, 13))
+)
 e.figure(figsize=(8, 4))
 
 # 2D vectors on a 2D Field

doc/samples/colormap_plot.py

@@ -5,7 +5,9 @@
 x, y = np.meshgrid(np.linspace(-2, 2, 100), np.linspace(-2, 2, 100))
 x = x.ravel()
 y = y.ravel()
-z = np.cos(4 * np.pi * np.sqrt(x ** 2 + y ** 2)) * np.exp(-np.sqrt(x ** 2 + y ** 2))
+z = np.cos(4 * np.pi * np.sqrt(x ** 2 + y ** 2)) * np.exp(
+    -np.sqrt(x ** 2 + y ** 2)
+)
 
 p = Data(np.column_stack((x, y, z)), column_headers=["X", "Y", "Z"])
 p.setas = "xyz"

doc/samples/contour_plot.py

@@ -5,7 +5,9 @@
 x, y = np.meshgrid(np.linspace(-2, 2, 100), np.linspace(-2, 2, 100))
 x = x.ravel()
 y = y.ravel()
-z = np.cos(4 * np.pi * np.sqrt(x ** 2 + y ** 2)) * np.exp(-np.sqrt(x ** 2 + y ** 2))
+z = np.cos(4 * np.pi * np.sqrt(x ** 2 + y ** 2)) * np.exp(
+    -np.sqrt(x ** 2 + y ** 2)
+)
 
 p = Data()
 p = p & x & y & z

doc/samples/curve_fit_line.py

@@ -9,7 +9,9 @@ def linear(x, m, c):
 
 d = Data("curve_fit_data.dat", setas="xye")
 d.plot(fmt="ro")
-fit = d.curve_fit(linear, result=True, replace=False, header="Fit", output="report")
+fit = d.curve_fit(
+    linear, result=True, replace=False, header="Fit", output="report"
+)
 d.setas = "x..y"
 d.plot(fmt="b-")
 d.annotate_fit(linear)

doc/samples/curve_fit_plane.py

@@ -32,7 +32,9 @@ def plane(coord, a, b, c):
 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"]])
+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)

doc/samples/curvefit_models.py

@@ -6,12 +6,18 @@
 
 # Make some data
 x = linspace(0, 10.0, 101)
-y = PowerLaw().func(x, 1e-1, 1.64) * random.normal(loc=1.0, scale=0.05, size=101)
+y = PowerLaw().func(x, 1e-1, 1.64) * random.normal(
+    loc=1.0, scale=0.05, size=101
+)
 d = Data(x, y, column_headers=["Time", "Signal"], setas="xy")
 
 # Do the fitting and plot the result
 fit = d.curve_fit(
-    PowerLaw, result=True, header="LM-Model Fit", residuals=True, output="report"
+    PowerLaw,
+    result=True,
+    header="LM-Model Fit",
+    residuals=True,
+    output="report",
 )
 
 ODRModel = odr_Model(PowerLaw, p0=(1, 1))

doc/samples/decompose.py

@@ -12,7 +12,12 @@
 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.text(
+    -6,
+    -800,
+    "Coefficients\n{}".format(str_coeffs),
+    fontdict={"size": "x-small"},
+)
 d.ylabel = "Data"
 d.title = "Decompose Example"
 d.tight_layout()

doc/samples/differential_evolution_simple.py

@@ -15,7 +15,14 @@
 
 # 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
+    func,
+    result=True,
+    header="Fit",
+    A=1,
+    B=1,
+    C=1,
+    prefix="Model",
+    residuals=True,
 )
 
 # Reset labels

doc/samples/extrapolate-demo.py

@@ -8,7 +8,11 @@
 x = linspace(1, 10, 101)
 d = Data(
     column_stack(
-        [x, (2 * x ** 2 - x + 2) + normal(size=len(x), scale=2.0), ones_like(x) * 2]
+        [
+            x,
+            (2 * x ** 2 - x + 2) + normal(size=len(x), scale=2.0),
+            ones_like(x) * 2,
+        ]
     ),
     setas="xye",
     column_headers=["x", "y"],
@@ -24,8 +28,16 @@
 d.title = "Extrapolation Demo"
 
 errorfill(
-    extra_x, y2[:, 0], color="green", yerr=y2[:, 1], label="Quadratic Extrapolation"
+    extra_x,
+    y2[:, 0],
+    color="green",
+    yerr=y2[:, 1],
+    label="Quadratic Extrapolation",
+)
+errorfill(
+    extra_x, y3[:, 0], color="red", yerr=y3[:, 1], label="Cubic Extrapolation"
+)
+errorfill(
+    extra_x, y1[:, 0], color="blue", yerr=y1[:, 1], label="Linear Extrapolation"
 )
-errorfill(extra_x, y3[:, 0], color="red", yerr=y3[:, 1], label="Cubic Extrapolation")
-errorfill(extra_x, y1[:, 0], color="blue", yerr=y1[:, 1], label="Linear Extrapolation")
 plt.legend(loc=2)

doc/samples/folder_fit.py

@@ -39,7 +39,9 @@
 # Have the amended results replace the existing data files
 fldr.each.odr(Quadratic, output="data")
 # Now take a slice through the metadata to get the files we want.
-result_2 = fldr.metadata.slice(["y", "Quadratic:b", "Quadratic:b err"], output="Data")
+result_2 = fldr.metadata.slice(
+    ["y", "Quadratic:b", "Quadratic:b err"], output="Data"
+)
 
 # Set the columns assignments and plot
 result_2.setas = "xye"

doc/samples/image/STXMIMage_Demo.py

@@ -11,7 +11,10 @@
 thisdir = dirname(__file__)
 
 imgs = ImageFolder(type=STXMImage)
-for fname in ["Sample_Image_2017-10-15_100.hdf5", "Sample_Image_2017-10-15_101.hdf5"]:
+for fname in [
+    "Sample_Image_2017-10-15_100.hdf5",
+    "Sample_Image_2017-10-15_101.hdf5",
+]:
     # Load the image
     img = STXMImage(join(thisdir, "..", "..", "..", "sample-data", fname))
     img.gridimage().crop(5, -15, 5, -5, _=True)  # regularise grid and crop

doc/samples/lmfit_demo.py

@@ -9,6 +9,7 @@
 datafile = join(__home__, "..", "sample-data", "PCAR Co Data.csv")
 
 d = cfg_data_from_ini(config, datafile)
+d.x += 0.244
 model, p0 = cfg_model_from_ini(config, data=d)
 
 fit = d.lmfit(model, result=True, header="lmfit", output="report")

doc/samples/lmfit_simple.py

@@ -11,7 +11,14 @@
 # Do the fitting and plot the result
 func = lambda x, A, B, C: A + B * exp(-x / C)
 fit = d.lmfit(
-    func, result=True, header="Fit", A=1, B=1, C=1, residuals=True, output="report"
+    func,
+    result=True,
+    header="Fit",
+    A=1,
+    B=1,
+    C=1,
+    residuals=True,
+    output="report",
 )
 
 # Reset labels

doc/samples/matrix_plot.py

@@ -3,7 +3,9 @@
 import numpy as np
 
 x, y = np.meshgrid(np.linspace(-2, 2, 101), np.linspace(-2, 2, 101))
-z = np.cos(4 * np.pi * np.sqrt(x ** 2 + y ** 2)) * np.exp(-np.sqrt(x ** 2 + y ** 2))
+z = np.cos(4 * np.pi * np.sqrt(x ** 2 + y ** 2)) * np.exp(
+    -np.sqrt(x ** 2 + y ** 2)
+)
 
 p = Data()
 p = p & np.linspace(-2, 2, 101) & z

doc/samples/odr_demo.py

@@ -20,9 +20,15 @@
 d.setas = "x.yyy"
 d.plot(fmt=["b-", "g-", "m-"], label=["lmfit", "odr", "diff.ev."])
 
-d.annotate_fit(model, x=0.05, y=0.75, fontdict={"size": "x-small", "color": "blue"})
 d.annotate_fit(
-    model, x=0.05, y=0.5, fontdict={"size": "x-small", "color": "green"}, prefix="odr"
+    model, x=0.05, y=0.75, fontdict={"size": "x-small", "color": "blue"}
+)
+d.annotate_fit(
+    model,
+    x=0.05,
+    y=0.5,
+    fontdict={"size": "x-small", "color": "green"},
+    prefix="odr",
 )
 d.annotate_fit(
     model,