Merge branch 'det1d'

pyzebra/comm_export.py

@@ -67,8 +67,8 @@ def export_comm(data, path, lorentz=False):
             line = (
                 scan_number_str
                 + h_str
-                + l_str
                 + k_str
+                + l_str
                 + int_str
                 + sigma_str
                 + angle_str1

pyzebra/fitvol3.py

@@ -0,0 +1,167 @@
+import numpy as np
+from lmfit import Model, Parameters
+from scipy.integrate import simps
+import matplotlib.pyplot as plt
+import uncertainties as u
+from lmfit.models import GaussianModel
+from lmfit.models import VoigtModel
+from lmfit.models import PseudoVoigtModel
+
+
+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(np.ceil(len(array) / binsize)))
+        ]
+    else:
+        print("Binsize need to be positive integer smaller than lenght of array")
+        return array
+
+
+def create_uncertanities(y, y_err):
+    # create array with uncertanities for error propagation
+    combined = np.array([])
+    for i in range(len(y)):
+        part = u.ufloat(y[i], y_err[i])
+        combined = np.append(combined, part)
+    return combined
+
+
+def find_nearest(array, value):
+    # find nearest value and return index
+    array = np.asarray(array)
+    idx = (np.abs(array - value)).argmin()
+    return idx
+
+
+# predefined peak positions
+# peaks = [6.2,  8.1, 9.9, 11.5]
+peaks = [23.5, 24.5]
+# peaks = [24]
+def fitccl(scan, variable="om", peak_type="gauss", binning=None):
+
+    x = list(scan[variable])
+    y = list(scan["Counts"])
+    peak_centre = np.mean(x)
+    if binning is None or binning == 0 or binning == 1:
+        x = list(scan["om"])
+        y = list(scan["Counts"])
+        y_err = list(np.sqrt(y)) if scan.get("sigma", None) is None else list(scan["sigma"])
+        print(scan["peak_indexes"])
+        if not scan["peak_indexes"]:
+            peak_centre = np.mean(x)
+        else:
+            centre = x[int(scan["peak_indexes"])]
+    else:
+        x = list(scan["om"])
+        if not scan["peak_indexes"]:
+            peak_centre = np.mean(x)
+        else:
+            peak_centre = x[int(scan["peak_indexes"])]
+        x = bin_data(x, binning)
+        y = list(scan["Counts"])
+        y_err = list(np.sqrt(y)) if scan.get("sigma", None) is None else list(scan["sigma"])
+        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))]
+
+    def background(x, slope, intercept):
+        """background"""
+        return slope * (x - peak_centre) + intercept
+
+    def gaussian(x, center, g_sigma, amplitude):
+        """1-d gaussian: gaussian(x, amp, cen, wid)"""
+        return (amplitude / (np.sqrt(2.0 * np.pi) * g_sigma)) * np.exp(
+            -((x - center) ** 2) / (2 * g_sigma ** 2)
+        )
+
+    def lorentzian(x, center, l_sigma, amplitude):
+        """1d lorentzian"""
+        return (amplitude / (1 + ((1 * x - center) / l_sigma) ** 2)) / (np.pi * l_sigma)
+
+    def pseudoVoigt1(x, center, g_sigma, amplitude, l_sigma, fraction):
+        """PseudoVoight peak with different widths of lorenzian and gaussian"""
+        return (1 - fraction) * gaussian(x, center, g_sigma, amplitude) + fraction * (
+            lorentzian(x, center, l_sigma, amplitude)
+        )
+
+    mod = Model(background)
+    params = Parameters()
+    params.add_many(
+        ("slope", 0, True, None, None, None, None), ("intercept", 0, False, None, None, None, None)
+    )
+    for i in range(len(peaks)):
+        if peak_type == "gauss":
+            mod = mod + GaussianModel(prefix="p%d_" % (i + 1))
+            params.add(str("p%d_" % (i + 1) + "amplitude"), 20, True, 0, None, None)
+            params.add(str("p%d_" % (i + 1) + "center"), peaks[i], True, None, None, None)
+            params.add(str("p%d_" % (i + 1) + "sigma"), 0.2, True, 0, 5, None)
+        elif peak_type == "voigt":
+            mod = mod + VoigtModel(prefix="p%d_" % (i + 1))
+            params.add(str("p%d_" % (i + 1) + "amplitude"), 20, True, 0, None, None)
+            params.add(str("p%d_" % (i + 1) + "center"), peaks[i], True, None, None, None)
+            params.add(str("p%d_" % (i + 1) + "sigma"), 0.2, True, 0, 3, None)
+            params.add(str("p%d_" % (i + 1) + "gamma"), 0.2, True, 0, 5, None)
+        elif peak_type == "pseudovoigt":
+            mod = mod + PseudoVoigtModel(prefix="p%d_" % (i + 1))
+            params.add(str("p%d_" % (i + 1) + "amplitude"), 20, True, 0, None, None)
+            params.add(str("p%d_" % (i + 1) + "center"), peaks[i], True, None, None, None)
+            params.add(str("p%d_" % (i + 1) + "sigma"), 0.2, True, 0, 5, None)
+            params.add(str("p%d_" % (i + 1) + "fraction"), 0.5, True, -5, 5, None)
+        elif peak_type == "pseudovoigt1":
+            mod = mod + Model(pseudoVoigt1, prefix="p%d_" % (i + 1))
+            params.add(str("p%d_" % (i + 1) + "amplitude"), 20, True, 0, None, None)
+            params.add(str("p%d_" % (i + 1) + "center"), peaks[i], True, None, None, None)
+            params.add(str("p%d_" % (i + 1) + "g_sigma"), 0.2, True, 0, 5, None)
+            params.add(str("p%d_" % (i + 1) + "l_sigma"), 0.2, True, 0, 5, None)
+            params.add(str("p%d_" % (i + 1) + "fraction"), 0.5, True, 0, 1, None)
+    # add parameters
+
+    result = mod.fit(
+        y, params, weights=[np.abs(1 / y_err[i]) for i in range(len(y_err))], x=x, calc_covar=True
+    )
+
+    comps = result.eval_components()
+
+    reportstring = list()
+    for keys in result.params:
+        if result.params[keys].value is not None:
+            str2 = np.around(result.params[keys].value, 3)
+        else:
+            str2 = 0
+        if result.params[keys].stderr is not None:
+            str3 = np.around(result.params[keys].stderr, 3)
+        else:
+            str3 = 0
+        reportstring.append("%s = %2.3f +/- %2.3f" % (keys, str2, str3))
+
+    reportstring = "\n".join(reportstring)
+
+    plt.figure(figsize=(20, 10))
+    plt.plot(x, result.best_fit, "k-", label="Best fit")
+
+    plt.plot(x, y, "b-", label="Original data")
+    plt.plot(x, comps["background"], "g--", label="Line component")
+    for i in range(len(peaks)):
+        plt.plot(
+            x,
+            comps[str("p%d_" % (i + 1))],
+            "r--",
+        )
+        plt.fill_between(x, comps[str("p%d_" % (i + 1))], alpha=0.4, label=str("p%d_" % (i + 1)))
+    plt.legend()
+    plt.text(
+        np.min(x),
+        np.max(y),
+        reportstring,
+        fontsize=9,
+        verticalalignment="top",
+    )
+    plt.title(str(peak_type))
+
+    plt.xlabel("Omega [deg]")
+    plt.ylabel("Counts [a.u.]")
+    plt.show()
+
+    print(result.fit_report())

pyzebra/param_study_moduls.py

@@ -1,5 +1,4 @@
 from load_1D 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
@@ -7,6 +6,17 @@ import matplotlib as mpl
 import numpy as np
 import pickle
 import scipy.io as sio
+import uncertainties as u
+
+
+def create_tuples(x, y, y_err):
+    """creates tuples for sorting and merginng of the data
+    Counts need to be normalized to monitor before"""
+    t = list()
+    for i in range(len(x)):
+        tup = (x[i], y[i], y_err[i])
+        t.append(tup)
+    return t
 
 
 def load_dats(filepath):
@@ -200,3 +210,174 @@ def save_table(data, filetype, name, path=None):
         hdf.close()
     if filetype == "json":
         data.to_json((path + name + ".json"))
+
+    def normalize(dict, key, monitor):
+        """Normalizes the measurement to monitor, checks if sigma exists, otherwise creates it
+        :arg dict : dictionary to from which to tkae the scan
+        :arg key : which scan to normalize from dict1
+        :arg monitor : final monitor
+        :return counts - normalized counts
+        :return sigma - normalized sigma"""
+
+        counts = np.array(dict["scan"][key]["Counts"])
+        sigma = np.sqrt(counts) if "sigma" not in dict["scan"][key] else dict["scan"][key]["sigma"]
+        monitor_ratio = monitor / dict["scan"][key]["monitor"]
+        scaled_counts = counts * monitor_ratio
+        scaled_sigma = np.array(sigma) * monitor_ratio
+
+        return scaled_counts, scaled_sigma
+
+    def merge(dict1, dict2, scand_dict_result, keep=True, monitor=100000):
+        """merges the two tuples and sorts them, if om value is same, Counts value is average
+        averaging is propagated into sigma if dict1 == dict2, key[1] is deleted after merging
+        :arg dict1 : dictionary to which measurement will be merged
+        :arg dict2 : dictionary from which measurement will be merged
+        :arg scand_dict_result : result of scan_dict after auto function
+        :arg keep : if true, when monitors are same, does not change it, if flase, takes monitor
+        always
+        :arg monitor : final monitor after merging
+        note: dict1 and dict2 can be same dict
+        :return dict1 with merged scan"""
+        for keys in scand_dict_result:
+            for j in range(len(scand_dict_result[keys])):
+                first, second = scand_dict_result[keys][j][0], scand_dict_result[keys][j][1]
+                print(first, second)
+                if keep:
+                    if dict1["scan"][first]["monitor"] == dict2["scan"][second]["monitor"]:
+                        monitor = dict1["scan"][first]["monitor"]
+
+                # load om and Counts
+                x1, x2 = dict1["scan"][first]["om"], dict2["scan"][second]["om"]
+                cor_y1, y_err1 = normalize(dict1, first, monitor=monitor)
+                cor_y2, y_err2 = normalize(dict2, second, monitor=monitor)
+                # creates touples (om, Counts, sigma) for sorting and further processing
+                tuple_list = create_tuples(x1, cor_y1, y_err1) + create_tuples(x2, cor_y2, y_err2)
+                # Sort the list on om and add 0 0 0 tuple to the last position
+                sorted_t = sorted(tuple_list, key=lambda tup: tup[0])
+                sorted_t.append((0, 0, 0))
+                om, Counts, sigma = [], [], []
+                seen = list()
+                for i in range(len(sorted_t) - 1):
+                    if sorted_t[i][0] not in seen:
+                        if sorted_t[i][0] != sorted_t[i + 1][0]:
+                            om = np.append(om, sorted_t[i][0])
+                            Counts = np.append(Counts, sorted_t[i][1])
+                            sigma = np.append(sigma, sorted_t[i][2])
+                        else:
+                            om = np.append(om, sorted_t[i][0])
+                            counts1, counts2 = sorted_t[i][1], sorted_t[i + 1][1]
+                            sigma1, sigma2 = sorted_t[i][2], sorted_t[i + 1][2]
+                            count_err1 = u.ufloat(counts1, sigma1)
+                            count_err2 = u.ufloat(counts2, sigma2)
+                            avg = (count_err1 + count_err2) / 2
+                            Counts = np.append(Counts, avg.n)
+                            sigma = np.append(sigma, avg.s)
+                            seen.append(sorted_t[i][0])
+                    else:
+                        continue
+
+                if dict1 == dict2:
+                    del dict1["scan"][second]
+
+                note = (
+                    f"This measurement was merged with measurement {second} from "
+                    f'file {dict2["meta"]["original_filename"]} \n'
+                )
+                if "notes" not in dict1["scan"][first]:
+                    dict1["scan"][first]["notes"] = note
+                else:
+                    dict1["scan"][first]["notes"] += note
+
+                dict1["scan"][first]["om"] = om
+                dict1["scan"][first]["Counts"] = Counts
+                dict1["scan"][first]["sigma"] = sigma
+                dict1["scan"][first]["monitor"] = monitor
+                print("merging done")
+        return dict1
+
+
+def add_dict(dict1, dict2):
+    """adds two dictionaries, meta of the new is saved as meata+original_filename and
+    measurements are shifted to continue with numbering of first dict
+    :arg dict1 : dictionarry to add to
+    :arg dict2 : dictionarry from which to take the measurements
+    :return dict1 : combined dictionary
+    Note: dict1 must be made from ccl, otherwise we would have to change the structure of loaded
+    dat file"""
+    if dict1["meta"]["zebra_mode"] != dict2["meta"]["zebra_mode"]:
+        print("You are trying to add scans measured with different zebra modes")
+        return
+    max_measurement_dict1 = max([keys for keys in dict1["scan"]])
+    new_filenames = np.arange(
+        max_measurement_dict1 + 1, max_measurement_dict1 + 1 + len(dict2["scan"])
+    )
+    new_meta_name = "meta" + str(dict2["meta"]["original_filename"])
+    if new_meta_name not in dict1:
+        for keys, name in zip(dict2["scan"], new_filenames):
+            dict2["scan"][keys]["file_of_origin"] = str(dict2["meta"]["original_filename"])
+            dict1["scan"][name] = dict2["scan"][keys]
+
+        dict1[new_meta_name] = dict2["meta"]
+    else:
+        raise KeyError(
+            str(
+                "The file %s has alredy been added to %s"
+                % (dict2["meta"]["original_filename"], dict1["meta"]["original_filename"])
+            )
+        )
+    return dict1
+
+
+def auto(dict):
+    """takes just unique tuples from all tuples in dictionary returend by scan_dict
+    intendet for automatic merge if you doesent want to specify what scans to merge together
+    args: dict - dictionary from scan_dict function
+    :return dict - dict without repetitions"""
+    for keys in dict:
+        tuple_list = dict[keys]
+        new = list()
+        for i in range(len(tuple_list)):
+            if tuple_list[0][0] == tuple_list[i][0]:
+                new.append(tuple_list[i])
+        dict[keys] = new
+    return dict
+
+
+def scan_dict(dict, precision=0.5):
+    """scans dictionary for duplicate angles indexes
+    :arg dict : dictionary to scan
+    :arg precision : in deg, sometimes angles are zero so its easier this way, instead of
+    checking zero division
+    :return  dictionary with matching scans, if there are none, the dict is empty
+    note: can be checked by "not d", true if empty
+    """
+
+    if dict["meta"]["zebra_mode"] == "bi":
+        angles = ["twotheta_angle", "omega_angle", "chi_angle", "phi_angle"]
+    elif dict["meta"]["zebra_mode"] == "nb":
+        angles = ["gamma_angle", "omega_angle", "nu_angle"]
+    else:
+        print("Unknown zebra mode")
+        return
+
+    d = {}
+    for i in dict["scan"]:
+        for j in dict["scan"]:
+            if dict["scan"][i] != dict["scan"][j]:
+                itup = list()
+                for k in angles:
+                    itup.append(abs(abs(dict["scan"][i][k]) - abs(dict["scan"][j][k])))
+
+                if all(i <= precision for i in itup):
+                    if str([np.around(dict["scan"][i][k], 1) for k in angles]) not in d:
+                        d[str([np.around(dict["scan"][i][k], 1) for k in angles])] = list()
+                        d[str([np.around(dict["scan"][i][k], 1) for k in angles])].append((i, j))
+                    else:
+                        d[str([np.around(dict["scan"][i][k], 1) for k in angles])].append((i, j))
+
+                else:
+                    pass
+
+            else:
+                continue
+    return d