Update param_study_moduls.py

Updated the create_dataframe and added function called variables, which tries to decide which variables to plot in parametric study and q scans. Works good for primary variable (usually om), and reduces the secondary (slice variable, temperature, mag.field,...) variables to a few candidates from which one has to be picked. In one set for param study, it identified all parameters correctly, in q scan, the temperature varied as well as H index, so technically both could be used, but only one makes sense and that will have to be picked by user.

pyzebra/param_study_moduls.py

@@ -7,10 +7,10 @@ import pandas as pd
 import scipy.io as sio
 import uncertainties as u
 from mpl_toolkits.mplot3d import Axes3D  # dont delete, otherwise waterfall wont work
+import collections
 
 from .ccl_io import load_1D
 
-
 def create_tuples(x, y, y_err):
     """creates tuples for sorting and merginng of the data
     Counts need to be normalized to monitor before"""
@@ -49,45 +49,45 @@ def load_dats(filepath):
             if data_type == "txt":
                 dict1 = add_dict(dict1, load_1D(file_list[i][0]))
             else:
+
                 dict1 = add_dict(dict1, load_1D(file_list[i]))
         dict1["scan"][i + 1]["params"] = {}
         if data_type == "txt":
             for x in range(len(col_names) - 1):
-                dict1["scan"][i + 1]["params"][col_names[x + 1]] = file_list[i][x + 1]
-
+                dict1["scan"][i + 1]["params"][col_names[x + 1]] = float(file_list[i][x + 1])
     return dict1
 
 
-def create_dataframe(dict1):
+def create_dataframe(dict1, variables):
     """Creates pandas dataframe from the dictionary
     :arg ccl like dictionary
     :return pandas dataframe"""
     # create dictionary to which we pull only wanted items before transforming it to pd.dataframe
     pull_dict = {}
     pull_dict["filenames"] = list()
-    for key in dict1["scan"][1]["params"]:
-        pull_dict[key] = list()
-    pull_dict["temperature"] = list()
-    pull_dict["mag_field"] = list()
+    for keys in variables:
+        for item in variables[keys]:
+            pull_dict[item] = list()
     pull_dict["fit_area"] = list()
     pull_dict["int_area"] = list()
-    pull_dict["om"] = list()
     pull_dict["Counts"] = list()
 
+    for keys in pull_dict:
+        print(keys)
+
     # populate the dict
     for keys in dict1["scan"]:
         if "file_of_origin" in dict1["scan"][keys]:
             pull_dict["filenames"].append(dict1["scan"][keys]["file_of_origin"].split("/")[-1])
         else:
             pull_dict["filenames"].append(dict1["meta"]["original_filename"].split("/")[-1])
-        for key in dict1["scan"][keys]["params"]:
-            pull_dict[str(key)].append(float(dict1["scan"][keys]["params"][key]))
-        pull_dict["temperature"].append(dict1["scan"][keys]["temperature"])
-        pull_dict["mag_field"].append(dict1["scan"][keys]["mag_field"])
+
         pull_dict["fit_area"].append(dict1["scan"][keys]["fit"]["fit_area"])
         pull_dict["int_area"].append(dict1["scan"][keys]["fit"]["int_area"])
-        pull_dict["om"].append(dict1["scan"][keys]["om"])
         pull_dict["Counts"].append(dict1["scan"][keys]["Counts"])
+        for key in variables:
+            for i in variables[key]:
+                pull_dict[i].append(_finditem(dict1["scan"][keys], i))
 
     return pd.DataFrame(data=pull_dict)
 
@@ -213,7 +213,8 @@ def save_table(data, filetype, name, path=None):
     if filetype == "json":
         data.to_json((path + name + ".json"))
 
-def normalize(dict, key, monitor):
+
+def normalize(scan, 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
@@ -221,15 +222,16 @@ def normalize(dict, key, 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"]
+    counts = np.array(scan["Counts"])
+    sigma = np.sqrt(counts) if "sigma" not in scan else scan["sigma"]
+    monitor_ratio = monitor / scan["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):
+
+def merge(scan1, scan2, 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
@@ -240,62 +242,45 @@ def merge(dict1, dict2, scand_dict_result, keep=True, monitor=100000):
     :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
+    if keep:
+        if scan1["monitor"] == scan2["monitor"]:
+            monitor = scan1["monitor"]
+
+    # load om and Counts
+    x1, x2 = scan1["om"], scan2["om"]
+    cor_y1, y_err1 = normalize(scan1, monitor=monitor)
+    cor_y2, y_err2 = normalize(scan2, 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:
-                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
+                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
+    scan1["om"] = om
+    scan1["Counts"] = Counts
+    scan1["sigma"] = sigma
+    scan1["monitor"] = monitor
+    print("merging done")
 
 
 def add_dict(dict1, dict2):
@@ -306,9 +291,13 @@ def add_dict(dict1, dict2):
     :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
+    try:
+        if dict1["meta"]["zebra_mode"] != dict2["meta"]["zebra_mode"]:
+            print("You are trying to add scans measured with different zebra modes")
+            return
+    # this is for the qscan case
+    except KeyError:
+        print("Zebra mode not specified")
     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"])
@@ -371,6 +360,9 @@ def scan_dict(dict, precision=0.5):
                     itup.append(abs(abs(dict["scan"][i][k]) - abs(dict["scan"][j][k])))
 
                 if all(i <= precision for i in itup):
+                    print(itup)
+                    print([dict["scan"][i][k] for k in angles])
+                    print([dict["scan"][j][k] for k in angles])
                     if str([np.around(dict["scan"][i][k], 0) for k in angles]) not in d:
                         d[str([np.around(dict["scan"][i][k], 0) for k in angles])] = list()
                         d[str([np.around(dict["scan"][i][k], 0) for k in angles])].append((i, j))
@@ -382,4 +374,115 @@ def scan_dict(dict, precision=0.5):
 
             else:
                 continue
+
     return d
+
+
+def _finditem(obj, key):
+    if key in obj:
+        return obj[key]
+    for k, v in obj.items():
+        if isinstance(v, dict):
+            item = _finditem(v, key)
+            if item is not None:
+                return item
+
+
+def most_common(lst):
+    return max(set(lst), key=lst.count)
+
+
+def variables(dictionary):
+    """Funcrion to guess what variables will be used in the param study
+    i call pripary variable the one the array like variable, usually omega
+    and secondary the slicing variable, different for each scan,for example temperature"""
+    # find all variables that are in all scans
+    stdev_precision = 0.05
+    all_vars = list()
+    for keys in dictionary["scan"]:
+        all_vars.append([key for key in dictionary["scan"][keys] if key != "params"])
+        if dictionary["scan"][keys]["params"]:
+            all_vars.append(key for key in dictionary["scan"][keys]["params"])
+
+    all_vars = [i for sublist in all_vars for i in sublist]
+    # get the ones that are in all scans
+    b = collections.Counter(all_vars)
+    inall = [key for key in b if b[key] == len(dictionary["scan"])]
+    # delete those that are obviously wrong
+    wrong = [
+        "NP",
+        "Counts",
+        "Monitor1",
+        "Monitor2",
+        "Monitor3",
+        "h_index",
+        "l_index",
+        "k_index",
+        "number_of_measurements",
+        "monitor",
+        "Time",
+        "omega_angle",
+        "twotheta_angle",
+        "chi_angle",
+        "phi_angle",
+        "nu_angle",
+    ]
+    inall_red = [i for i in inall if i not in wrong]
+
+    # check for primary variable, needs to be list, we dont suspect the
+    # primary variable be as a parameter (be in scan[params])
+    primary_candidates = list()
+    for key in dictionary["scan"]:
+        for i in inall_red:
+            if isinstance(_finditem(dictionary["scan"][key], i), list):
+                if np.std(_finditem(dictionary["scan"][key], i)) > stdev_precision:
+                    primary_candidates.append(i)
+    # check which of the primary are in every scan
+    primary_candidates = collections.Counter(primary_candidates)
+    second_round_primary_candidates = [
+        key for key in primary_candidates if primary_candidates[key] == len(dictionary["scan"])
+    ]
+
+    if len(second_round_primary_candidates) == 1:
+        print("We've got a primary winner!", second_round_primary_candidates)
+    else:
+        print("Still not sure with primary:(", second_round_primary_candidates)
+
+    # check for secondary variable, we suspect a float\int or not changing array
+    # we dont need to check for primary ones
+    secondary_candidates = [i for i in inall_red if i not in second_round_primary_candidates]
+    # print("secondary candidates", secondary_candidates)
+    # select arrays and floats and ints
+    second_round_secondary_candidates = list()
+    for key in dictionary["scan"]:
+        for i in secondary_candidates:
+            if isinstance(_finditem(dictionary["scan"][key], i), float):
+                second_round_secondary_candidates.append(i)
+            elif isinstance(_finditem(dictionary["scan"][key], i), int):
+                second_round_secondary_candidates.append(i)
+            elif isinstance(_finditem(dictionary["scan"][key], i), list):
+                if np.std(_finditem(dictionary["scan"][key], i)) < stdev_precision:
+                    second_round_secondary_candidates.append(i)
+
+    second_round_secondary_candidates = collections.Counter(second_round_secondary_candidates)
+    second_round_secondary_candidates = [
+        key
+        for key in second_round_secondary_candidates
+        if second_round_secondary_candidates[key] == len(dictionary["scan"])
+    ]
+    # print("secondary candidates after second round", second_round_secondary_candidates)
+    # now we check if they vary between the scans
+    third_round_sec_candidates = list()
+    for i in second_round_secondary_candidates:
+        check_array = list()
+        for keys in dictionary["scan"]:
+            check_array.append(np.average(_finditem(dictionary["scan"][keys], i)))
+        # print(i, check_array, np.std(check_array))
+        if np.std(check_array) > stdev_precision:
+            third_round_sec_candidates.append(i)
+    if len(third_round_sec_candidates) == 1:
+        print("We've got a secondary winner!", third_round_sec_candidates)
+    else:
+        print("Still not sure with secondary :(", third_round_sec_candidates)
+
+    return {"primary": second_round_primary_candidates, "secondary": third_round_sec_candidates}