bugfix

marimo/docs/LabManualCHEM2000-1.pdf

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:f48be468f7985a539350b9bfdf596b3741b6282672e6377a53a09161e14d28eb
-size 4004997
+oid sha256:f364d817342f54021293b252d540252f283f25a0c17223106b23a1152192bf1d
+size 4005337

src/pycek_public/bomb_calorimetry.py

@@ -2,17 +2,17 @@ import pycek_public as cek
 import numpy as np
 import pprint as pp
 
+
 class bomb_calorimetry(cek.cek_labs):
     def setup_lab(self):
         """
         Define base information for the lab
         """
-        self.add_metadata( 
-            laboratory = 'Bomb Calorimetry',
-            columns = ["Time (s)","Temperature (K)"]
-            )
+        self.add_metadata(
+            laboratory="Bomb Calorimetry", columns=["Time (s)", "Temperature (K)"]
+        )
 
-        self.available_samples = ['benzoic', 'sucrose', 'naphthalene']
+        self.available_samples = ["benzoic", "sucrose", "naphthalene"]
 
         self.ignition_time = 20
         self.relaxation_time = 3
@@ -20,45 +20,45 @@ class bomb_calorimetry(cek.cek_labs):
         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
-        
+        self.slope_before = np.random.uniform(0.0, self.noise_level) / 3
+        self.slope_after = np.random.uniform(0.0, self.noise_level) / 3
+
         self.RT = self.R * self.temperature
-        
+
         # calorimeter constant (J/K)
-        self.calorimeter_constant = {'value':10135,'std_error':0.0}
+        self.calorimeter_constant = {"value": 10135, "std_error": 0.0}
         self.sample_parameters["co2"] = {
-            "mM" : 44.01,
-            "dH" : -393.51e3, # co2 enthapy of formation (J/mol/K)
+            "mM": 44.01,
+            "dH": -393.51e3,  # co2 enthapy of formation (J/mol/K)
         }
         self.sample_parameters["h2o"] = {
-            "mM" : 18.015,
-            "dH" : -285.83e3,# h2o enthapy of formation (J/mol/K)
-        }    
-            
+            "mM": 18.015,
+            "dH": -285.83e3,  # h2o enthapy of formation (J/mol/K)
+        }
+
         self.sample_parameters["benzoic"] = {
-            "mM" : 122.123,
-            "n1" : 7,
-            "n2" : 3,
-            "dn" : 7-15/2,
-            "dHf" : {'value':-384.8e3,'std_error':0.5e3},
-            "dHc" : {'value':-3227.26e3,'std_error':0.2e3},
+            "mM": 122.123,
+            "n1": 7,
+            "n2": 3,
+            "dn": 7 - 15 / 2,
+            "dHf": {"value": -384.8e3, "std_error": 0.5e3},
+            "dHc": {"value": -3227.26e3, "std_error": 0.2e3},
         }
         self.sample_parameters["sucrose"] = {
-            "mM" : 342.3,
-            "n1" : 12,
-            "n2" : 11,
-            "dn" : 0,
-            "dHf" : {'value':-2221.2e3,'std_error':0.2e3},
-            "dHc" : {'value':-5643.4e3,'std_error':1.8e3},
+            "mM": 342.3,
+            "n1": 12,
+            "n2": 11,
+            "dn": 0,
+            "dHf": {"value": -2221.2e3, "std_error": 0.2e3},
+            "dHc": {"value": -5643.4e3, "std_error": 1.8e3},
         }
         self.sample_parameters["naphthalene"] = {
-            "mM" : 128.17,
-            "n1" : 10,
-            "n2" : 4,
-            "dn" : 10 - 12,
-            "dHf" : {'value':77e3,'std_error':10.0e3},
-            "dHc" : {'value':-5160e3,'std_error':20.0e3},
+            "mM": 128.17,
+            "n1": 10,
+            "n2": 4,
+            "dn": 10 - 12,
+            "dHf": {"value": 77e3, "std_error": 10.0e3},
+            "dHc": {"value": -5160e3, "std_error": 20.0e3},
         }
 
     def create_data(self):
@@ -68,50 +68,56 @@ class bomb_calorimetry(cek.cek_labs):
         if self.sample is None:
             raise Exception("Sample not defined")
 
-        prm = self.sample_parameters[ self.sample ]
+        prm = self.sample_parameters[self.sample]
 
-        self.set_parameters( 
-            sample = self.sample,
-            number_of_values = self.number_of_values,
-            )
+        self.set_parameters(
+            sample=self.sample,
+            number_of_values=self.number_of_values,
+        )
 
         self.mass = np.random.normal(1000, 100)
-        self.add_metadata(**{
-            'Tablet mass (mg)': self.mass,
-            "Ignition time (s)" : self.ignition_time,
-            "Sample" : self.sample, 
-            })
+        self.add_metadata(
+            **{
+                "Tablet mass (mg)": f"{self.mass:.1f}",
+                "Ignition time (s)": self.ignition_time,
+                "Sample": self.sample,
+            }
+        )
 
         moles = self.mass / 1000 / prm["mM"]
 
         # combustion enthalpy
         # nH{co2} + mH{h2o} - H = DcH
-        DcH = prm["n1"] * self.sample_parameters["co2"]["dH"] + \
-              prm["n2"] * self.sample_parameters["h2o"]["dH"] - prm["dHf"]["value"]
+        DcH = (
+            prm["n1"] * self.sample_parameters["co2"]["dH"]
+            + prm["n2"] * self.sample_parameters["h2o"]["dH"]
+            - prm["dHf"]["value"]
+        )
 
         dH = DcH * moles
         dnrt = moles * self.RT * prm["dn"]
         dU = dH - dnrt
-        
-        deltaT = -dU / self.calorimeter_constant['value']
+
+        deltaT = -dU / self.calorimeter_constant["value"]
 
         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)
+        x = self._round_values(x, precision=0)
 
-        dd = 0.
+        dd = 0.0
         T = self.temperature
         for i in range(self.number_of_values):
             if i < self.ignition_time:
-                T += self.slope_before 
-            else:   
+                T += self.slope_before
+            else:
                 T += self.slope_after
-                dd = deltaT * (1 - np.exp( - (i - self.ignition_time) / self.relaxation_time) )
-            y[i] += T + dd 
+                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))
+        self.data = np.column_stack((x, y))
 
         return self.data
-