tweaks and fixed student ID

pyproject.toml

@@ -18,7 +18,7 @@ readme = {file = "README.md", content-type = "text/markdown"}
 dependencies = [
     "numpy",
     "scipy",
-    "lmfit",
+    "colorama",
     "matplotlib",
 ]
 

src/pycek_public/bomb_calorimetry.py

@@ -18,6 +18,7 @@ class bomb_calorimetry(cek.cek_labs):
         self.relaxation_time = 3
         self.number_of_values = 100
         self.noise_level = 0.1
+        self.precision = 2
 
         self.slope_before = np.random.uniform(0., self.noise_level) / 3
         self.slope_after = np.random.uniform(0., self.noise_level) / 3
@@ -97,6 +98,8 @@ class bomb_calorimetry(cek.cek_labs):
         x = np.linspace(0, self.number_of_values, self.number_of_values)
         y = np.random.normal(0, self.noise_level, self.number_of_values)
 
+        x = self._round_values(x,precision=0)
+
         dd = 0.
         T = self.temperature
         for i in range(self.number_of_values):
@@ -107,6 +110,7 @@ class bomb_calorimetry(cek.cek_labs):
                 dd = deltaT * (1 - np.exp( - (i - self.ignition_time) / self.relaxation_time) )
             y[i] += T + dd 
 
+        y = self._round_values(y)
         self.data = np.column_stack((x,y))
 
         return

src/pycek_public/cek_labs.py

@@ -19,7 +19,7 @@ class cek_labs(ABC):
         self.NA = 6.022e23
         self.temperature = 298
 
-        self.number_of_values = 100
+        self.number_of_values = 10
         self.output_file = None
         self.filename_gen = cek.TempFilenameGenerator()
 
@@ -29,6 +29,7 @@ class cek_labs(ABC):
             'output_file' : self.output_file,
         })
         
+        self.make_plots = False
         self.logger_level = "ERROR"
 
         # Define some lab specific parameters
@@ -50,6 +51,8 @@ class cek_labs(ABC):
         # Lab specific parameters
         self.setup_lab()
 
+        self.list_of_data_files = []
+
     def __str__(self):
         return f'CHEM2000 Lab: {self.__class__.__name__}'
 
@@ -57,6 +60,7 @@ class cek_labs(ABC):
         if isinstance(student_ID,int):
             self.student_ID = student_ID
         elif isinstance(student_ID,str):
+            student_ID = student_ID.strip()
             if student_ID.isdigit():
                 self.student_ID = int(student_ID)
             else:
@@ -65,7 +69,7 @@ class cek_labs(ABC):
             raise ValueError("student_ID must be an integer")
         np.random.seed(self.student_ID)
         self.update_metadata_from_attr()
-        self.logger.debug(f"Initial seed = {np.random.get_state()[1][0]}")
+        self.logger.critical(f"Initial seed = {np.random.get_state()[1][0]}")
 
     def set_token(self, token):
         self.token = token
@@ -181,6 +185,8 @@ class cek_labs(ABC):
         # Write the kwargs as metadata
         self.write_metadata(filename)
 
+        self.list_of_data_files.append( filename )
+
         return filename
 
     def read_data_file(self,filename=None):
@@ -226,17 +232,36 @@ class cek_labs(ABC):
                 key = key.replace("#","").strip()
                 metadata[key.strip()] = value.strip()
 
+        self.logger.debug("-"*50)
+        for k,v in metadata.items():
+            self.logger.debug(f"{k} = {v}")
+        self.logger.debug("-"*50)
         # Output results
         # print("Comments:")
         # print("\n".join(comments))
         # print("\nExtracted Data:")
         # print(data_array)
         return data_array, header, metadata
+    
+    def _cleanup(self, pattern=None):
+        from pathlib import Path
+        for ff in self.list_of_data_files:
+            # Check if file exists before deleting
+            file_path = Path(ff)
+            if file_path.exists():
+                file_path.unlink()
+            else:
+                print("The file does not exist")
+
+        # Delete multiple files using a pattern
+        if pattern is not None:
+            for file_path in Path('.').glob(pattern):
+                file_path.unlink()
 
-    def process_file(self, filename=None):
-        self.read_data(filename)
-        result = self.process_data()
-        return result
+    # def process_file(self, filename=None):
+    #     self.read_data(filename)
+    #     result = self.process_data()
+    #     return result
     
     def _valid_ID(self,ID):
         if ID in ["23745411"]:
@@ -355,7 +380,8 @@ class cek_labs(ABC):
             y += self._generate_noise(nvalues,noise_level)
         
         if positive:
-            y = np.abs(y)
+            eps = np.power(10.,-self.precision)
+            y = [ max(eps,np.abs(x)) for x in y ]
 
         # Note: weights parameter is currently unused
         if weights is not None:

src/pycek_public/crystal_violet.py

@@ -16,7 +16,8 @@ class crystal_violet(cek.cek_labs):
         self.expt_time = 1000
         self.number_of_values = 501
         self.noise_level = 0.1
-        self.precision = 4
+        self.precision = 6
+        self.background = 0.01
         
         self.activation_energy = 63e3 # J/mol
         self.prefactor = 5.9e9 # 1/M/s
@@ -68,7 +69,7 @@ class crystal_violet(cek.cek_labs):
                 xspacing = 'linear',
                 noise_level = self.noise_level,
                 positive = True,
-                background = 0.1
+                background = self.background,
                 )
         
         return 

src/pycek_public/plotting.py

@@ -27,8 +27,21 @@ class plotting():
             line = [line]
 
         # Plot the observed data points as a scatter plot
+        idx = 0
         for ds in scatter:
-            plt.scatter(ds[:,0],ds[:,1], color='blue', label='Data')
+            if ds is not None:
+                plt.scatter(ds[:,0],ds[:,1],label="Data "+str(idx)),
+                idx += 1
+            #     ymin = np.min(ds[:,1] - 0.1*np.abs(np.min(ds[:,1])))
+            #     ymax = np.max(ds[:,1] + 0.1*np.abs(np.max(ds[:,1])))
+            
+            # ax = plt.gca()        
+            # ax.set_ylim([ymin, ymax])
+
+        for ds in line:
+            if ds is not None:
+                plt.plot(ds[:,0],ds[:,1],color='red',label="Data "+str(idx)),
+                idx += 1
 
         # Add labels and title to the plot
         plt.xlabel(columns[0])

src/pycek_public/statistics_lab.py

@@ -11,6 +11,8 @@ class stats_lab(cek.cek_labs):
                           columns = ["X","Y"]
                           )
 
+        self.number_of_values = 10
+
         self.available_samples = [
             'Averages',
             'Propagation of uncertainty',
@@ -25,7 +27,8 @@ class stats_lab(cek.cek_labs):
                 (1.0, 0.1),
                 (12., 2.0)
             ],
-            'exp_values' : (1.0,9.0),
+            'exp_values' : (1.0,10.0),
+            "precision" : 3,
             }
         
         self.sample_parameters['Propagation of uncertainty'] = {
@@ -33,6 +36,7 @@ class stats_lab(cek.cek_labs):
                 (15.0, 1.0),
                 (133., 2.0)
             ],
+            "precision" : 3,
             }
 
         self.sample_parameters['Comparison of averages'] = {
@@ -40,19 +44,23 @@ class stats_lab(cek.cek_labs):
                 (15.0, 1.0),
                 (13.2, 2.0)
             ],
+            "precision" : 3,
             }
 
         self.sample_parameters['Linear fit'] = {
             "function" : lambda x,m,q: m*x + q, 
             "gen_values" : {'m':12.3 , 'q':1.0},
-            "xrange" : [0.0 , 10.0]
+            "xrange" : [0.0 , 10.0],
+            "exp_values" : 11.3,
+            "precision" : 3,
             }
 
         self.sample_parameters['Non linear fit'] = {
             "nval" : 10,
             "function" : lambda x,E0,K0,Kp,V0:  E0 + K0 * x / Kp * ( (V0/x)**Kp / (Kp-1)+1) - K0*V0/(Kp-1), 
             "gen_values" : {"E0":-634.2, "K0":12.43, "Kp":4.28, "V0":99.11},
-            "xrange" : [50 , 140]
+            "xrange" : [50 , 140],
+            "precision" : 3,
         }
         
         self.sample_parameters['Detection of outliers'] = {
@@ -60,6 +68,7 @@ class stats_lab(cek.cek_labs):
             "gen_values" : {'m':2.3 , 'q':0.1},
             "xrange" : [10.0 , 20.0],
             "shift" : 2,
+            "precision" : 3,
             }
         
     def create_data(self):
@@ -75,6 +84,9 @@ class stats_lab(cek.cek_labs):
             number_of_values = self.number_of_values,
             )
         
+        if "precision" in prm:
+            self.set_parameters( precision = prm["precision"] )
+
         if "noise" in prm:
             self.set_parameters( noise_level = prm["noise"] )
 
@@ -87,6 +99,8 @@ class stats_lab(cek.cek_labs):
             data = self._generate_normal_random(self.number_of_values, prm['gen_values'])
 
         elif self.sample in ["Linear fit"]:
+            self.noise_level = 5
+
             data = self.generate_data_from_function(
                 prm["function"], 
                 prm['gen_values'], 
@@ -96,6 +110,8 @@ class stats_lab(cek.cek_labs):
                 )
 
         elif self.sample in ["Non linear fit"]:
+            self.noise_level = 5
+
             data = self.generate_data_from_function(
                 prm["function"], 
                 prm['gen_values'], 
@@ -115,7 +131,6 @@ class stats_lab(cek.cek_labs):
             i = np.random.randint(self.number_of_values)
             data[i,1] += prm['shift']
 
-
         self.data = data
         return data            
 

src/pycek_public/surface_adsorption.py

@@ -25,7 +25,7 @@ class surface_adsorption(cek.cek_labs):
             }
 
         self.number_of_values = 100 
-        self.noise_level = 1e-6
+        self.noise_level = 0.5e-5
         self.precision = 10
 
     def create_data(self):
@@ -38,7 +38,7 @@ class surface_adsorption(cek.cek_labs):
             )
 
         self.add_metadata(**{
-            "Temperature (K)"    : self.temperature,
+            "Temperature (C)"    : self.temperature - 273.15,
             "Volume (L)"         : self.volume,
             "Molar mass (g/mol)" : self.sample_parameters["molarMass"],
             "MinDye (mg)"        : self.minDye,
@@ -65,19 +65,6 @@ class surface_adsorption(cek.cek_labs):
                 )
             
         self.data[:,0] *= conversion_factor
-        # grams of dye added
-        # x = np.linspace(self.minDye, self.maxDye, self.number_of_values) / 1000
-        # moles = x / self.params["molarMass"] # g
-        # initial_concentration = moles / self.volume # mol/L
-        # y = self.measure(K, self.params["Q"], initial_concentration)
-        
-        # # Because noise is added to the concentration in solution, but
-        # # the concentration on the surface is required in post-processing, which 
-        # # is very small, we divide by 1000
-
-        # y = cek.add_noise(y, self.uncertainty/1000)
-
-        # data_array = np.column_stack((x, y))
 
         return self.data