Schreiben der Codes für die Berechnungsvorschriften für die Ausarbeitung....

Schreiben der Codes für die Berechnungsvorschriften für die Ausarbeitung. Schreiben des Codes zum Plotten und Berechnung der Wärmekapazitäten.

functions/utility.py

-from typing import List, Tuple
+from typing import Dict, List, Tuple
+
 
 import h5py as h5
 import matplotlib.pyplot as plt
@@ -37,10 +38,8 @@ def plot_temp_over_time(
     for i in range(len(data)):
         # TODO: draw a plot using the ax.errorbar(...) function
         # Document: https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.errorbar.html
-
-        raise NotImplementedError(
-            "Delete these 3 lines if you have finished the code or want to test it.".upper()
-        )
+        #
+        ax.errorbar(time[i], data[i][0,:], data[i][1,:])
 
         # DONE #
 
@@ -50,9 +49,11 @@ def plot_temp_over_time(
 
     # TODO: set legend, x- and y- axis label.
 
-    raise NotImplementedError(
-        "Delete these 3 lines if you have finished the code or want to test it.".upper()
-    )
+    ax.set_ylabel(y_label)
+    
+    ax.set_xlabel(x_label)
+    
+    ax.legend(legend)
 
     # DONE #
 
@@ -61,7 +62,7 @@ def plot_temp_over_time(
 
 def get_plot_data_from_dataset(
     data_path: str, group_path: str
-) -> dict[str, np.ndarray]:
+) -> Dict[str, np.ndarray]:
     """Get the necessary data from the dataset to plot.
 
     This function returns the data in a HDF5 file in all subgroups of a group in 'group_path'
@@ -119,28 +120,45 @@ def get_plot_data_from_dataset(
                 continue
 
             # TODO: Find the start time point of the measurement.
+            
+            if start_time is None:
+                
+                start_time = dataset_start_time
+            
+            elif dataset_start_time < start_time:
+                
+                start_time = dataset_start_time
+
+            
+            
+            
+          
+        temperature = np.empty(shape=[len(subgroups), min_len])
+        time = np.empty(shape=[len(subgroups), min_len])
+        for i,subgroup in enumerate(subgroups):
 
-            raise NotImplementedError(
-                "Delete these 3 lines if you have finished the code or want to test it.".upper()
-            )
 
             # DONE #
 
-        for subgroup in subgroups:
+        
             # TODO: Save data in to the lists temperature, time and name.
             # Data for each sensor must have the same length because of np.ndarray will be use in the output.
 
-            raise NotImplementedError(
-                "Delete these 3 lines if you have finished the code or want to test it.".upper()
-            )
+            
+            time[i] = group[subgroup]["timestamp"][:min_len]
+            
+            temperature[i] = group[subgroup]["temperature"][:min_len]
+ 
+            name.append( group[subgroup].attrs["name"])
+
+
 
             # DONE #
 
     # TODO: return the output dict.
 
-    raise NotImplementedError(
-        "Delete these 3 lines if you have finished the code or want to test it.".upper()
-    )
+    return {"temperature": temperature, "timestamp": time, "name": name}
+
 
     # DONE #
 
@@ -159,9 +177,7 @@ def cal_mean_and_standard_deviation(data: np.ndarray) -> np.ndarray:
     # TODO: Calculate the mean and standard deviation of the first dimension and return the result as a
     # two-dimensional (2, n)-shaped ndarray.
 
-    raise NotImplementedError(
-        "Delete these 3 lines if you have finished the code or want to test it.".upper()
-    )
+    return np.array([np.mean(data, axis=0), np.std(data, axis=0, dtype=np.float32)])
 
     # DONE #
 
@@ -189,8 +205,15 @@ def get_start_end_temperature(
     # an idea that you can refer to. The goal of this function is to obtain from the data the high and
     # low temperatures necessary to calculate the heat capacity.
 
-    raise NotImplementedError(
-        "Delete these 3 lines if you have finished the code or want to test it.".upper()
-    )
+    abs_maximum = np.nanmax(temperature_data)
+    
+    max = np.mean([i for  i in np.nditer(temperature_data) if (abs_maximum-i) < threshold])
+    
+    abs_minimum = np.nanmin(temperature_data)
+    
+    min =  np.mean([i for  i in np.nditer(temperature_data) if (i-abs_minimum) < threshold])
+    
+    return (max, min)
+
 
     # DONE #