Cleanup, import sort, formatting

pyzebra/__init__.py

@@ -1,8 +1,8 @@
-from pyzebra.anatric import *
-from pyzebra.h5 import *
-from pyzebra.xtal import *
-from pyzebra.load_1D import load_1D, parse_1D
-from pyzebra.ccl_findpeaks import ccl_findpeaks
-from pyzebra.fit2 import fitccl
-from pyzebra.comm_export import export_comm
 import pyzebra.ccl_dict_operation
+from pyzebra.anatric import *
+from pyzebra.ccl_findpeaks import ccl_findpeaks
+from pyzebra.comm_export import export_comm
+from pyzebra.fit2 import fitccl
+from pyzebra.h5 import *
+from pyzebra.load_1D import load_1D, parse_1D
+from pyzebra.xtal import *

pyzebra/app/save_load_dict.py

@@ -1,19 +0,0 @@
-import pickle
-
-
-def save_dict(obj, name):
-    """ saves dictionary as pickle file in binary format
-    :arg obj - object to save
-    :arg name - name of the file
-    NOTE: path should be added later"""
-    with open(name + '.pkl', 'wb') as f:
-        pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)
-
-
-def load_dict(name):
-    """load dictionary from picle file
-    :arg name - name of the file to load
-    NOTE: expect the file in the same folder, path should be added later
-    :return dictionary"""
-    with open(name + '.pkl', 'rb') as f:
-        return pickle.load(f)

pyzebra/ccl_dict_operation.py

@@ -1,5 +1,6 @@
 import numpy as np
 import uncertainties as u
+
 from .fit2 import create_uncertanities
 
 

pyzebra/fit2.py

@@ -1,16 +1,20 @@
 import numpy as np
+import uncertainties as u
 from lmfit import Model, Parameters
 from scipy.integrate import simps
-import uncertainties as u
+
 
 def bin_data(array, binsize):
     if isinstance(binsize, int) and 0 < binsize < len(array):
-        return [np.mean(array[binsize * i:binsize * i + binsize]) for i in range(int(
+        return [
-            np.ceil(len(array) / binsize)))]
+            np.mean(array[binsize * i : binsize * i + binsize])
+            for i in range(int(np.ceil(len(array) / binsize)))
+        ]
     else:
         print("Binsize need to be positive integer smaller than lenght of array")
         return array
 
+
 def find_nearest(array, value):
     # find nearest value and return index
     array = np.asarray(array)
@@ -36,7 +40,7 @@ def fitccl(
     constraints_max,
     numfit_min=None,
     numfit_max=None,
-    binning=None
+    binning=None,
 ):
     """Made for fitting of ccl date where 1 peak is expected. Allows for combination of gaussian and linear model combination
     :param data: dictionary after peak fining
@@ -75,17 +79,16 @@ def fitccl(
         x = bin_data(x, binning)
         y = list(meas["Counts"])
         y_err = list(np.sqrt(y)) if meas.get("sigma", None) is None else list(meas["sigma"])
-        combined = bin_data(create_uncertanities(y,y_err), binning)
+        combined = bin_data(create_uncertanities(y, y_err), binning)
         y = [combined[i].n for i in range(len(combined))]
         y_err = [combined[i].s for i in range(len(combined))]
 
-
     if len(meas["peak_indexes"]) == 0:
         # Case for no peak, gaussian in centre, sigma as 20% of range
         print("No peak")
         peak_index = find_nearest(x, np.mean(x))
         guess[0] = x[int(peak_index)] if guess[0] is None else guess[0]
-        guess[1] = (x[-1] - x[0])/5 if guess[1] is None else guess[1]
+        guess[1] = (x[-1] - x[0]) / 5 if guess[1] is None else guess[1]
         guess[2] = 50 if guess[2] is None else guess[2]
         guess[3] = 0 if guess[3] is None else guess[3]
         guess[4] = np.mean(y) if guess[4] is None else guess[4]
@@ -103,8 +106,6 @@ def fitccl(
         constraints_min[0] = np.min(x) if constraints_min[0] is None else constraints_min[0]
         constraints_max[0] = np.max(x) if constraints_max[0] is None else constraints_max[0]
 
-
-
     def gaussian(x, g_cen, g_width, g_amp):
         """1-d gaussian: gaussian(x, amp, cen, wid)"""
         return (g_amp / (np.sqrt(2 * np.pi) * g_width)) * np.exp(
@@ -119,45 +120,15 @@ def fitccl(
     params = Parameters()
     params.add_many(
         ("g_cen", x[int(peak_index)], bool(vary[0]), np.min(x), np.max(x), None, None),
-        (
+        ("g_width", guess[1], bool(vary[1]), constraints_min[1], constraints_max[1], None, None,),
-            "g_width",
+        ("g_amp", guess[2], bool(vary[2]), constraints_min[2], constraints_max[2], None, None,),
-            guess[1],
+        ("slope", guess[3], bool(vary[3]), constraints_min[3], constraints_max[3], None, None,),
-            bool(vary[1]),
+        ("intercept", guess[4], bool(vary[4]), constraints_min[4], constraints_max[4], None, None,),
-            constraints_min[1],
-            constraints_max[1],
-            None,
-            None,
-        ),
-        (
-            "g_amp",
-            guess[2],
-            bool(vary[2]),
-            constraints_min[2],
-            constraints_max[2],
-            None,
-            None,
-        ),
-        (
-            "slope",
-            guess[3],
-            bool(vary[3]),
-            constraints_min[3],
-            constraints_max[3],
-            None,
-            None,
-        ),
-        (
-            "intercept",
-            guess[4],
-            bool(vary[4]),
-            constraints_min[4],
-            constraints_max[4],
-            None,
-            None,
-        ),
     )
     # the weighted fit
-    result = mod.fit(y, params, weights=[np.abs(1/y_err[i]) for i in range(len(y_err))], x=x, calc_covar=True)
+    result = mod.fit(
+        y, params, weights=[np.abs(1 / y_err[i]) for i in range(len(y_err))], x=x, calc_covar=True
+    )
     # u.ufloat to work with uncertanities
     fit_area = u.ufloat(result.params["g_amp"].value, result.params["g_amp"].stderr)
     comps = result.eval_components()
@@ -183,7 +154,8 @@ def fitccl(
 
         it = -1
         while abs(numfit_max - numfit_min) < 3:
-            # in the case the peak is very thin and numerical integration would be on zero omega difference, finds closes values
+            # in the case the peak is very thin and numerical integration would be on zero omega
+            # difference, finds closes values
             it = it + 1
             numfit_min = find_nearest(
                 x,

pyzebra/h5.py

@@ -22,7 +22,7 @@ def parse_h5meta(file):
     for line in file:
         line = line.strip()
         if line.startswith("#begin "):
-            section = line[len("#begin "):]
+            section = line[len("#begin ") :]
             content[section] = []
 
         elif line.startswith("#end"):
@@ -64,20 +64,3 @@ def read_detector_data(filepath):
         det_data["temperature"] = h5f["/entry1/sample/temperature"][:]
 
     return det_data
-
-
-def open_h5meta(filepath):
-    """Open h5meta file like *.cami
-
-    Args:
-        filepath (str): File path of a h5meta file.
-
-    Returns:
-        dict: A dictionary with h5 names and their detector data and angles.
-    """
-    data = dict()
-    h5meta_content = read_h5meta(filepath)
-    for file in h5meta_content["filelist"]:
-        data[file] = read_detector_data(file)
-
-    return data

pyzebra/load_1D.py

@@ -97,7 +97,7 @@ def parse_1D(fileobj, data_type):
 
     # read data
     if data_type == ".ccl":
-        metadata['data_type'] = data_type
+        metadata["data_type"] = data_type
         measurements = {}
         decimal = list()
         data = fileobj.readlines()
@@ -162,7 +162,7 @@ def parse_1D(fileobj, data_type):
 
     elif data_type == ".dat":
         # skip the first 2 rows, the third row contans the column names
-        metadata['data_type'] = data_type
+        metadata["data_type"] = data_type
         next(fileobj)
         next(fileobj)
         col_names = next(fileobj).split()

pyzebra/param_study_moduls.py

@@ -1,10 +1,11 @@
-from load_1Dnn import load_1D
-from ccl_dict_operation import add_dict
-import pandas as pd
-from mpl_toolkits.mplot3d import Axes3D  # dont delete, otherwise waterfall wont work
-import matplotlib.pyplot as plt
 import matplotlib as mpl
+import matplotlib.pyplot as plt
 import numpy as np
+import pandas as pd
+from load_1Dnn import load_1D
+from mpl_toolkits.mplot3d import Axes3D  # dont delete, otherwise waterfall wont work
+
+from ccl_dict_operation import add_dict
 
 
 def load_dats(filepath):

pyzebra/visualize peak fitter.py

@@ -1,39 +0,0 @@
-plt.figure(figsize=(20, 10))
-plt.plot(x, y, "b-", label="Original data")
-plt.plot(x, comps["gaussian"], "r--", label="Gaussian component")
-plt.fill_between(x, comps["gaussian"], facecolor="red", alpha=0.4)
-plt.plot(x, comps["background"], "g--", label="Line component")
-plt.fill_between(x, comps["background"], facecolor="green", alpha=0.4)
-
-plt.fill_between(
-    x[numfit_min:numfit_max],
-    y[numfit_min:numfit_max],
-    facecolor="yellow",
-    alpha=0.4,
-    label="Integrated area", )
-
-plt.plot(x, result.best_fit, "k-", label="Best fit")
-plt.title(
-    "%s \n Gaussian: centre = %9.4f, sigma = %9.4f, area = %9.4f \n"
-    "background: slope = %9.4f, intercept = %9.4f \n"
-    "Int. area = %9.4f +/- %9.4f \n"
-    "fit area = %9.4f +/- %9.4f \n"
-    "ratio((fit-int)/fit)  = %9.4f"
-    % (
-        keys,
-        result.params["g_cen"].value,
-        result.params["g_width"].value,
-        result.params["g_amp"].value,
-        result.params["slope"].value,
-        result.params["intercept"].value,
-        int_area.n,
-        int_area.s,
-        result.params["g_amp"].value,
-        result.params["g_amp"].stderr,
-        (result.params["g_amp"].value - int_area.n) / result.params["g_amp"].value
-
-    )
-)
-
-plt.legend(loc="best")
-plt.show()