Making the analysis of data easier by including everything in a single dataframe. What remains is to clean up the remaining functions in the OFRAnalyser definition.

2026-07-07 22:01:39 +02:00
1 parent 09a245bc2f
commit 76b0129b6f
@@ -141,7 +141,7 @@ class OFRSample:
                        self.SOA_density = float(line.split(self.__data_separator)[1])
                        continue
        
-        self.sample_time = self.scan_up_time + self.scan_down_time
+        self.sampling_time = self.scan_up_time + self.scan_down_time
        data_lines.pop(-1)

        for line in data_lines:
@@ -169,7 +169,7 @@ class OFRSample:

        self.scan_up_time = None                        # Time to scan up the voltage range in DMA, in (s)
        self.scan_down_time = None                      # Time to scan down the voltage range in DMA, in(s)
-        self.sample_time = None                         # Total time to scan voltage up and back down in DMA, in (s)
+        self.sampling_time = None                       # Total time to scan voltage up and back down in DMA, in (s)

        self.lower_diameter_bound = None                # Lower Diameter bound of CPC, in (nm)
        self.upper_diameter_bound = None                # Upper Diameter bound of CPC, in (nm)
@@ -238,10 +238,10 @@ class OFRAnalyser:
        stopping_times          = []
        sampling_times          = []
        dry_air_flows           = []
-        ozone_flows             = []
        species_flows           = []
-        total_flows             = []
-        cnc_flows               = []
+        ozone_flows             = []
+        total_flows             = None
+        cpc_flows               = []

        for sample_number in self.sample_number_list:
            sample = OFRSample(source_file=self.data_source_file, sample_number = sample_number, data_separator = self.__data_separator)
@@ -256,63 +256,66 @@ class OFRAnalyser:

            if sample_number == 1:
                starting_times.append(0.00)
-                stopping_times.append(sample.sample_time)
+                stopping_times.append(sample.sampling_time)
            
-            starting_times.append(stopping_times[-1])
-            stopping_times.append(stopping_times[-1] + sample.sample_time)
+            elif sample_number >= 1:
+                starting_times.append(stopping_times[-1])
+                stopping_times.append(stopping_times[-1] + sample.sampling_time)

-        local_dry_air_flows     = []
-        local_ozone_flows = []
-        local_species_flows = []
-        local_cpc_flows = []
+            sampling_times.append(sample.sampling_time)

        out_flag = False

-        with open(self.flow_source_file, 'r', errors='ignore') as file:
+        with open(self.flows_source_file, 'r', errors='ignore') as file:
            for line_counter, line in enumerate(file.readlines()):

                if out_flag == True:
                    continue

-                i = 0
+                if line_counter >= 6:

-                if line_counter == 6:
+                    if line.startswith('-'):
+                        out_flag = True
+                        continue
+
+                    i = 0
                    items = line.split()

-                    while i <= items:
-                        pass
+                    while i < int(items[-1]):
+
+                        dry_air_flows.append(float(items[0]))
+                        species_flows.append(float(items[1]))
+                        ozone_flows.append(float(items[2]))
+                        cpc_flows.append(float(items[3]))
+
+                        i += 1

-                    dry_air_flows.append(float(items[0]))
-                    species_flows.append(float(items[1]))
-                    ozone_flows.append(float(items[2]))
-                    cnc_flows.append(float(items[-1]))
-                    starts.append(0)
-                    stops.append(float(items[3]) * float(items[4]))
-                    smpls.append(float(items[4]))
-                    i += 1
-                elif line_counter > 6 and not line.startswith('-'):
-                    items = line.split()
-                    dry_air_flows.append(float(items[0]))
-                    species_flows.append(float(items[1]))
-                    ozone_flows.append(float(items[2]))
-                    cnc_flows.append(float(items[-1]))
-                    starts.append(stops[i-1])
-                    stops.append(stops[i-1] + float(items[3]) * float(items[4]))
-                    smpls.append(float(items[4]))
-                    i += 1
-                if line_counter > 6 and line.startswith('-'):
-                    out_flag = True
                else:
                    continue

-        self.data = pd.DataFrame({'Sample': self.sample_number_list,
-                                  'Number (#/cm^3)': numbers,
-                                  'Length (mm/cm^3)': lengths,
-                                  'Surface (mm^2/cm^3)': surfaces,
-                                  'Volume (μm^3/cm^3)': volumes,
-                                  'Density (pg/μm^3)': densities,
-                                  'Mass (mg/cm^3)': masses,
-                                  'Time (s)': times})
+        total_flows = [(flow_1 + flow_2 + flow_3) * 1E3 for flow_1, flow_2, flow_3 in zip(dry_air_flows, species_flows, ozone_flows)]
+        mass_production_rate = [(mass * flow) for mass, flow in zip(mass_concentrations, total_flows)]
+        mass_produced = [(mpr * duration) for mpr, duration in zip(mass_production_rate, sampling_times)]
+        cumulative_mass_produced = np.cumsum(np.array(mass_produced)).tolist()
+
+        self.data = pd.DataFrame({'Sample (-)':             numbers_of_sample,
+                                  'Number (#/cm^3)':        number_concentrations,
+                                  'Length (mm/cm^3)':       length_concentrations,
+                                  'Surface (mm^2/cm^3)':    surface_concentrations,
+                                  'Volume (μm^3/cm^3)':     volume_concentrations,
+                                  'Density (pg/μm^3)':      aerosol_densities,
+                                  'Mass (mg/cm^3)':         mass_concentrations,
+                                  'Start (s)':              starting_times,
+                                  'Stop (s)':               stopping_times, 
+                                  'Duration (s)':           sampling_times,
+                                  'Dry Air Flow (L/min)':   dry_air_flows,
+                                  'Species Flow (L/min)':   species_flows,
+                                  'Ozone Flow (L/min)':     ozone_flows,
+                                  'Total Flow (cm^3/s)':     total_flows,
+                                  'CPC Flow (L/min)':       cpc_flows,
+                                  'Mass Production Rate (mg/s)': mass_production_rate,
+                                  'Mass Produced (mg)': mass_produced,
+                                  'Cumulative Mass Produced (mg)': cumulative_mass_produced})
        
    def GetPlottableData(self, which_data = 'Mass'):

@@ -10,7 +10,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
   "id": "8769766b",
   "metadata": {},
   "outputs": [],
@@ -26,8 +26,8 @@
    "import pandas as pd\n",
    "\n",
    "# Specify the source file for this analysis\n",
-    "source_file = Path(r\"Z:\\People\\David_Tschan\\data\\OFR_SMPS\\2026\\processed\\06_June\\20260612\\20260612_SET02_EXP01_full.txt\")\n",
-    "flow_source_file = Path(r\"Z:\\People\\David_Tschan\\data\\OFR_SMPS\\2026\\processed\\06_June\\20260612\\20260612_SET02_EXP01_full_flows.txt\")"
+    "source_file = Path(r\"C:\\Users\\tschan_d\\Documents\\data_local\\20260612_SET02_EXP01_full.txt\")\n",
+    "flows_source_file = Path(r\"C:\\Users\\tschan_d\\Documents\\data_local\\20260612_SET02_EXP01_full_flows.txt\")"
   ]
  },
  {
@@ -40,21 +40,10 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
   "id": "76a9b026",
   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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 truncated
-      "text/plain": [
-       "<Figure size 1000x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
   "source": [
    "# First load and get the data of sample 33 (arbitrary) and create a sample object\n",
    "sample_number = 33\n",
@@ -96,16 +85,17 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
   "id": "8db19b57",
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_number_list = [int(i) for i in range(1, 157)]\n",
    "\n",
-    "analyser = OFRAnalyser(data_source_file = source_file, sample_number_list=sample_number_list)\n",
+    "analyser = OFRAnalyser(data_source_file = source_file, flows_source_file = flows_source_file, sample_number_list = sample_number_list)\n",
    "analyser.GetData()\n",
-    "analyser.GetFlowData(flow_source_file = flow_source_file)"
+    "\n",
+    "analyser.data.to_csv(path_or_buf=r\"C:\\Users\\tschan_d\\Documents\\data_local\\data.csv\", index=False)"
   ]
  },
  {
@@ -118,40 +108,26 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
   "id": "a4477003",
   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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truncated
-      "text/plain": [
-       "<Figure size 1000x400 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
   "source": [
    "fig = plt.figure(figsize=(10,4), constrained_layout = True)\n",
    "ax = fig.add_subplot(111)\n",
    "ax_2 = plt.twinx(ax)\n",
    "\n",
-    "times, numbers = analyser.GetPlottableData(which_data='Number')\n",
-    "_, masses = analyser.GetPlottableData(which_data='Mass')\n",
-    "\n",
-    "ax.plot(times, numbers, color='red', label='Number')\n",
-    "ax_2.plot(times, masses, color='blue', label='Mass')\n",
+    "ax.plot(analyser.data['Sample (-)'], analyser.data['Number (#/cm^3)'], color='red', label='Number')\n",
+    "ax_2.plot(analyser.data['Sample (-)'], analyser.data['Mass (mg/cm^3)'], color='blue', label='Mass')\n",
    "\n",
    "ax.set_title('2026-06-12 SET02/EXP01: Experiment Overview')\n",
-    "ax.set_xlabel(r'Time $t$ $(\\unit{\\second})$')\n",
+    "ax.set_xlabel(r'Sample $(-)$')\n",
    "ax.set_ylabel(r'Number Density $N$ $(\\unit{\\per\\cubic\\centi\\meter})$')\n",
    "ax_2.set_ylabel(r'Mass Density $m$ $(\\unit{\\milli\\gram\\per\\cubic\\centi\\meter})$')\n",
-    "ax.xaxis.set_minor_locator(MultipleLocator(base=180, offset=0))\n",
-    "ax.xaxis.set_major_locator(MultipleLocator(base=1800, offset=0))\n",
+    "ax.xaxis.set_minor_locator(MultipleLocator(base=1, offset=0))\n",
+    "ax.xaxis.set_major_locator(MultipleLocator(base=10, offset=0))\n",
    "\n",
-    "ax.set_xlim([0, 30000])\n",
+    "ax.set_xlim([1, 156])\n",
    "ax.set_ylim([0, 1.2E6])\n",
    "ax_2.set_ylim([0, 15])\n",
    "\n",
@@ -173,43 +149,26 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
   "id": "a5af245c",
   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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truncated
-      "text/plain": [
-       "<Figure size 1000x250 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
   "source": [
-    "# Plotting the relevant data:\n",
-    "# Note that the dry air flow is not plotted because it is constant at 5 L/min.\n",
-    "\n",
-    "times, ozones = analyser.GetPlottableFlowData(which_flow_rate='Ozone')\n",
-    "_, species = analyser.GetPlottableFlowData(which_flow_rate='Species')\n",
-    "\n",
    "fig = plt.figure(figsize=(10,2.5), constrained_layout = True)\n",
    "ax = fig.add_subplot(111)\n",
-    "ax_2 = plt.twinx(ax)\n",
+    "ax_2 = plt.twinx(ax=ax)\n",
    "\n",
-    "ax.plot(times, ozones, color='red', linestyle='--', label='Ozone')\n",
-    "ax_2.plot(times, species, color='blue', linestyle='--', label=r'$\\alpha$-Pinene')\n",
+    "ax.step(analyser.data['Sample (-)'], analyser.data['Ozone Flow (L/min)'], color='forestgreen', linestyle='--', label='Ozone')\n",
+    "ax_2.step(analyser.data['Sample (-)'], analyser.data['Species Flow (L/min)'], color='magenta', linestyle='--', label=r'$\\alpha$-Pinene')\n",
    "\n",
    "ax.set_title('2026-06-12 SET02/EXP01: Flow Rates Overview ')\n",
-    "ax.set_xlabel(r'Time $t$ $(\\unit{\\second})$')\n",
+    "ax.set_xlabel(r'Sample $(-)$')\n",
    "ax.set_ylabel(r'Ozone Flow Rate $r_{\\ce{O3}}$ $(\\unit{\\liter\\per\\minute})$')\n",
    "ax_2.set_ylabel(r'$\\alpha$-Pinene Flow Rate $r_{\\alpha}$ $(\\unit{\\liter\\per\\minute})$')\n",
-    "ax.xaxis.set_minor_locator(MultipleLocator(base=180, offset=0))\n",
-    "ax.xaxis.set_major_locator(MultipleLocator(base=1800, offset=0))\n",
+    "ax.xaxis.set_minor_locator(MultipleLocator(base=1, offset=0))\n",
+    "ax.xaxis.set_major_locator(MultipleLocator(base=10, offset=0))\n",
    "\n",
-    "ax.set_xlim([0, 30000])\n",
+    "ax.set_xlim([1, 156])\n",
    "ax.set_ylim([0.6, 2.4])\n",
    "ax_2.set_ylim([0.0, 0.05])\n",
    "\n",
@@ -235,7 +194,32 @@
   "id": "e2ae6dbb",
   "metadata": {},
   "outputs": [],
-   "source": []
+   "source": [
+    "fig = plt.figure(figsize=(10,2.5), constrained_layout = True)\n",
+    "ax = fig.add_subplot(111)\n",
+    "ax_2 = plt.twinx(ax=ax)\n",
+    "\n",
+    "ax.step(analyser.data['Sample (-)'], analyser.data['Mass (mg/cm^3)'], color='forestgreen', linestyle='--', label='Ozone')\n",
+    "ax_2.step(analyser.data['Sample (-)'], analyser.data['Species Flow (L/min)'], color='magenta', linestyle='--', label=r'$\\alpha$-Pinene')\n",
+    "\n",
+    "ax.set_title('2026-06-12 SET02/EXP01: Flow Rates Overview ')\n",
+    "ax.set_xlabel(r'Sample $(-)$')\n",
+    "ax.set_ylabel(r'Ozone Flow Rate $r_{\\ce{O3}}$ $(\\unit{\\liter\\per\\minute})$')\n",
+    "ax_2.set_ylabel(r'$\\alpha$-Pinene Flow Rate $r_{\\alpha}$ $(\\unit{\\liter\\per\\minute})$')\n",
+    "ax.xaxis.set_minor_locator(MultipleLocator(base=1, offset=0))\n",
+    "ax.xaxis.set_major_locator(MultipleLocator(base=10, offset=0))\n",
+    "\n",
+    "ax.set_xlim([1, 156])\n",
+    "ax.set_ylim([0.6, 2.4])\n",
+    "ax_2.set_ylim([0.0, 0.05])\n",
+    "\n",
+    "lines_1, labels_1 = ax.get_legend_handles_labels()\n",
+    "lines_2, labels_2 = ax_2.get_legend_handles_labels()\n",
+    "\n",
+    "ax.legend(lines_1 + lines_2, labels_1 + labels_2, loc='upper left')\n",
+    "\n",
+    "plt.savefig(\"20260723_OFR_Analysis_01_Experiment_Overview_bottom.pdf\", format='pdf', dpi=400)"
+   ]
  }
 ],
 "metadata": {