Adding more analysis to the OFR Analysis
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tschan_d committed
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@@ -197,12 +197,13 @@ class OFRSample:
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class OFRAnalyser:
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def __init__(self, source_file, sample_number_list, data_separator = ',') -> None:
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def __init__(self, data_source_file, sample_number_list, data_separator = ',') -> None:
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self.source_file = source_file
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self.data_source_file = data_source_file
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self.flow_source_file = None
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self.__data_separator = data_separator
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with open(self.source_file, 'r', errors = 'ignore') as file:
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with open(self.data_source_file, 'r', errors = 'ignore') as file:
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for i, line in enumerate(file.readlines()):
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if i == 17:
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items = line.split(',')
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@@ -214,6 +215,7 @@ class OFRAnalyser:
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self.sample_number_list = sample_number_list
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self.data = None
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self.flow_data = None
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def __sample_number_list_check(self, sample_number_list) -> None:
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@@ -236,7 +238,7 @@ class OFRAnalyser:
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times = []
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for sample_number in self.sample_number_list:
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sample = OFRSample(source_file=self.source_file, sample_number = sample_number, data_separator = self.__data_separator)
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sample = OFRSample(source_file=self.data_source_file, sample_number = sample_number, data_separator = self.__data_separator)
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numbers.append(sample.Count.integrate())
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lengths.append(sample.Length.integrate() / 1E6)
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@@ -254,8 +256,58 @@ class OFRAnalyser:
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'Density (pg/μm^3)': densities,
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'Mass (mg/cm^3)': masses,
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'Time (s)': times})
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def GetFlowData(self, flow_source_file):
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# For a step diagram
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self.flow_source_file = flow_source_file
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dry_air_flows = []
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species_flows = []
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ozone_flows = []
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cnc_flows = []
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starts = []
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stops = []
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out_flag = False
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with open(self.flow_source_file, 'r', errors='ignore') as file:
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i = 0
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for line_counter, line in enumerate(file.readlines()):
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if out_flag == True:
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continue
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if line_counter == 2:
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items = line.split()
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start_time = float(items[-1])
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if line_counter == 6:
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items = line.split()
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dry_air_flows.append(float(items[0]))
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species_flows.append(float(items[1]))
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ozone_flows.append(float(items[2]))
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cnc_flows.append(float(items[-1]))
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starts.append(start_time)
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stops.append(float(items[3]) * float(items[4]) + start_time)
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i += 1
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elif line_counter > 6 and not line.startswith('-'):
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items = line.split()
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dry_air_flows.append(float(items[0]))
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species_flows.append(float(items[1]))
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ozone_flows.append(float(items[2]))
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cnc_flows.append(float(items[-1]))
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starts.append(stops[i-1])
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stops.append(stops[i-1] + float(items[3]) * float(items[4]))
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i += 1
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if line_counter > 6 and line.startswith('-'):
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out_flag = True
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else:
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continue
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self.flow_data = pd.DataFrame({'Dry Air (L/min)': dry_air_flows, 'Species (L/min)': species_flows, 'Ozone (L/min)': ozone_flows, 'Start (s)': starts, 'Stop (s)': stops, 'CNC Flow (L/min)': cnc_flows})
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def GetCumulativeMass(self):
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pass
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### ----------------------------------------------------------------------------------------------------------------------------------- ###
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###
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@@ -2,21 +2,17 @@
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#%%
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from defs.computing import OFRSample, OFRAnalyser
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import numpy as np
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from bokeh.plotting import figure, show
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from pathlib import Path
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# Path to the source file
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source_file = Path(r"C:\Users\tschan_d\Documents\data_local\20260612_SET02_EXP01_full.txt")
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flow_source_file = Path(r"C:\Users\tschan_d\Documents\data_local\20260612_SET02_EXP01_full_flows.txt")
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#%%
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# Plotting one of the spectra to show properties
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# first load and get the data of sample 57 (arbitrary)
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sample = OFRSample(source_file = source_file, sample_number = 150)
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# Now plot the spectrum
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p = figure(width = 600, height = 400, x_axis_type='log')
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p.line(x = sample.Count.var, y = sample.Count.fct)
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show(p)
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analyser = OFRAnalyser(data_source_file=source_file, sample_number_list=range(1, 157))
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analyser.Analyse()
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analyser.GetFlowData(flow_source_file=flow_source_file)
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print(analyser.flow_data)
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# %%
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@@ -10,7 +10,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 137,
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"execution_count": null,
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"id": "8769766b",
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"metadata": {},
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"outputs": [],
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@@ -26,7 +26,8 @@
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"import pandas as pd\n",
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"\n",
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"# Specify the source file for this analysis\n",
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"source_file = Path(r\"C:\\Users\\tschan_d\\Documents\\data_local\\20260612_SET02_EXP01_full.txt\")"
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"source_file = Path(r\"C:\\Users\\tschan_d\\Documents\\data_local\\20260612_SET02_EXP01_full.txt\")\n",
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"flow_source_file = Path(r\"C:\\Users\\tschan_d\\Documents\\data_local\\20260612_SET02_EXP01_full_flows.txt\")"
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]
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},
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{
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@@ -39,21 +40,10 @@
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},
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{
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"cell_type": "code",
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"execution_count": 114,
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"execution_count": null,
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"id": "76a9b026",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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truncated
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"text/plain": [
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"<Figure size 1000x600 with 2 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"outputs": [],
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"source": [
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"# First load and get the data of sample 33 (arbitrary) and create a sample object\n",
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"sample_number = 33\n",
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@@ -95,51 +85,16 @@
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},
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{
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"cell_type": "code",
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"execution_count": 115,
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"execution_count": null,
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"id": "8db19b57",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" Sample Number (#/cm^3) Length (mm/cm^3) Surface (mm^2/cm^3) \\\n",
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"0 1 3.090062e+00 0.001888 1.241420e-05 \n",
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"1 2 3.454977e+00 0.000126 3.560755e-08 \n",
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"2 3 9.325444e+00 0.001036 8.623624e-07 \n",
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"3 4 4.171921e+02 0.402549 2.812996e-03 \n",
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"4 5 1.052317e+05 6.641894 4.353249e-03 \n",
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".. ... ... ... ... \n",
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"151 152 9.529171e+05 177.681731 3.199811e-01 \n",
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"152 153 1.017064e+06 181.659088 3.188865e-01 \n",
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"153 154 1.041903e+06 186.626996 3.273445e-01 \n",
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"154 155 1.008587e+06 181.815120 3.214411e-01 \n",
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"155 156 9.957403e+05 182.039508 3.234802e-01 \n",
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"\n",
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" Volume (μm^3/cm^3) Density (pg/μm^3) Mass (mg/cm^3) Time (s) \n",
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"0 0.836666 1.2 1.003999e-03 180.0 \n",
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"1 0.000109 1.2 1.302891e-07 360.0 \n",
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"2 0.007271 1.2 8.725170e-06 540.0 \n",
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"3 196.905653 1.2 2.362868e-01 720.0 \n",
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"4 38.618096 1.2 4.634172e-02 900.0 \n",
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".. ... ... ... ... \n",
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"151 7645.085680 1.2 9.174103e+00 27360.0 \n",
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"152 7524.532677 1.2 9.029439e+00 27540.0 \n",
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"153 7732.212330 1.2 9.278655e+00 27720.0 \n",
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"154 7646.483862 1.2 9.175781e+00 27900.0 \n",
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"155 7694.993582 1.2 9.233992e+00 28080.0 \n",
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"\n",
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"[156 rows x 8 columns]\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"sample_number_list = [int(i) for i in range(1, 157)]\n",
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"\n",
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"analyser = OFRAnalyser(source_file = source_file, sample_number_list=sample_number_list)\n",
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"analyser = OFRAnalyser(data_source_file = source_file, sample_number_list=sample_number_list)\n",
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"analyser.Analyse()\n",
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"\n",
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"print(analyser.data)"
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"analyser.GetFlowData(flow_source_file = flow_source_file)"
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]
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},
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{
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@@ -152,21 +107,10 @@
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},
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{
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"cell_type": "code",
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"execution_count": 134,
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"execution_count": null,
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"id": "a4477003",
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"metadata": {},
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"outputs": [
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{
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"data": {
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||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA/MAAAGbCAYAAACIxMC9AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjksIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvJkbTWQAAAAlwSFlzAAAPYQAAD2EBqD+naQAA2nJJREFUeJzsnQd4FNXXxg9JIEASeu8gRTqiIBYUsQCiiAUFRVQUK4qinwULgoqi/BUbFgSxYQFsKIgKKKgUFUSKIL1KbwkkIZD5nvfe3M1ks7vZ3Wzf9/c8k92dnezOzM7sznvPOe8pYVmWJYQQQgghhBBCCIkaEsK9AoQQQgghhBBCCPENinlCCCGEEEIIISTKoJgnhBBCCCGEEEKiDIp5QgghhBBCCCEkyqCYJ4QQQgghhBBCogyKeUIIIYQQQgghJMqgmCeEEEIIIYQQQqIMinlCCCGEEEIIISTKoJgnhBBCCCGEEEKijLgW83PmzJGBAwfKySefLCkpKVK7dm257LLL5M8//3S5/JIlS+SCCy6Q1NRUqVChglxxxRWyYcOGAsv8+++/8sADD8ipp56qlqlUqZKcddZZMnXqVLfr8dVXX8m5554r5cqVU+vRsmVLefvtt73eDm/Wy7B582a1zbVq1ZLk5GS1zZdffrlX75Oeni4PPvigXHTRRVK1alUpUaKEPPnkk4WWO3HihLz44ovSvXt3qVOnjpQtW1aaN28uDz/8sBw8eNCr9zpy5IiMHj1a2rZtq/ZLWlqanHTSSXL11VfLzz//7Fjup59+Uuvhbpo0aZKaPC1jpgYNGjhed/fu3XLjjTdKlSpV1PqfccYZMnv27ALrePjwYXnmmWekS5cuUqNGDbX/W7durdY7KyvL7ba1b99ehgwZou7jPTytk+H9999Xj10dF7/99pskJiaq486AdbK/TpkyZdS+HDt2rOTm5hb4fxwrOGZw7GAbLrzwQnVMOYN16Nu3rzRr1kwSEhIK7C9/KeqzwecbyZjjL9LXc8aMGS7PVU9YliWTJ0+Wrl27SsWKFdX3RaNGjeSuu+6SrVu3SjSA8wBTuHn00UfllFNOUb8HpUuXVvvx1ltvVd/HdrBf8X2M5/FbUL58efV/r732mhw/fjxs608IIYSQyCRJ4pg33nhD9u3bp4RVixYtZM+ePfK///1POnXqJLNmzVIXsYbVq1eri8J27drJZ599psTaE088IZ07d5a//vpLiVvw/fffy7fffivXX3+9dOjQQV2Affrpp9KnTx8ZMWKE+h87zz33nLrQu/322+WRRx6RkiVLqvc6duyYV9vg7XqBFStWqGVxoThmzBgltP/77z+1rd6AfQUxCVHYu3dveeedd1wul5mZqYRDv3795JZbblGCGOLw6aeflunTp8sff/yhxKU7MBiAAYPly5fL//3f/0nHjh3V/LVr16r/nz9/vhr8sDNq1Cg577zzCr0WBgDAggULCsyHOL/qqqvk/vvvd8yDWAHZ2dly/vnnq4GHl19+WapVqyavv/66Gpz48ccfHe+9ZcsWJY7xWQ8dOlQJYawbtv2HH35Qk12Qg40bN8rSpUvV/xmwLzCw5IkBAwbI559/rtYX+8YIaQx63HDDDdK0aVO1f+3gc/7oo48cgxNvvvmm3Hfffeozx4ADwDGPYwVibeLEiUpoPPvss+o4+f3335VwN3zwwQeyc+dO9XlgQCAnJ0cCxbvvvqsG1ZzBeRnJYGAGx1akryfEPI5hbwU9Pt9rr71WfXfhPMagC4Tl33//LS+88IIS+d98840aqIxkxo0bJ5EAvkuwHzGoiYHJVatWqfP166+/lpUrV0rlypUd5zMGLx9//HGpV6+e+h3AZ3f33Xer73N337mEEEIIiVOsOGbXrl2F5qWnp1vVq1e3zj///ALz+/TpY1WpUsU6dOiQY96mTZuskiVLWg8++KBj3p49e6zc3NxCr9uzZ0+rbNmyVlZWlmPeH3/8YSUkJFijR4/2exu8XS+sU7t27dRkXwdfwGuYbcN24vAZPnx4oeWOHz9u7d27t9D8KVOmqP/54IMPPL7PnDlz1HITJ050+fyJEycc9+fOnauWxWv7Av7nrrvucvnc66+/rp7/7bffHPNycnKsFi1aWB07dnTMy8jIUJMzL7zwgvr/+fPnF3ru+eeft6pVq+bYhhtuuMFKSUnxap137txpVa5c2erSpYvjc7jjjjusxMREa9GiRQWWPffcc62WLVsWmHfs2DGrUaNG6jjEffB///d/6ljBMWPAsYRj6uqrr3a733E8169f3you7777rtpXv//+uxVNYP/hmIgWcKz78nU/atQotfxzzz3n8jjEZ4/vyQMHDlih5MiRI1asMGPGDLWPJ0yYUOSyOBeTkpL8/u4mhBBCSGwS12n2iLg6g+gqomz2NFJE1xGFuvLKK1XUxFC/fn0VDf7iiy8c8xCFdo7GAkQzjx49Kvv373fMQ+okosGIuviDL+s1b948Fdm59957HRFoX3FO/XYHUr5NpMmOibAXlaKLDABQs2ZNl88jxTuYYL8hIo3ovSEpKUn69+8vixcvlu3bt6t5SIPF5Mt2Tps2TaXR+rMN1atXV5FGpHS/+uqrKvKP7BKUL5j39ASyPlD+geMQEXmzrchAwTFjwLGEtHtkQdhTe4O93z3xySefqGMP54yd4cOHq+MN+wJs2rRJLff888+rEghEN5FtcNpppxUqkzDZHohA47sA5wUip4hgu0qlR2YCMiNQmoJl161b5zLNHqUT+B5B1ky3bt3UMYJjGVk4YOHChXL22Wer+cioeO+99wqtFzIgbrvtNpU9U6pUKWnYsKHK7LF/HmZbkWWDshYsg/fFcYv3sK+P2SZ7CQP+3xWIBiP6jn2BshpXxyGyN3bt2iUTJkxQ8/C9gu1B6Ykz11xzjfofeyYHIv5YT/wP1hn7CRkrdsx+RIYOslEQ0UbGjC/v5SrNHtuHqDgyQfA5InvppptucpwTABlByERAlpAB39PYb9g39u8qnBc4H33FZE3hu8WbZfE+ONYJIYQQQgxxLeZdcejQIZUSjrp1w/r161XqeJs2bQotj3m4qPdUIw3mzp2rLsjsAwgQ2LhghsCDeMSFGi7eIc68SbP3Zb3wXgAXxBdffLESOLhQvuSSS5ToCAUmldy+b10B4QXhifIHpIkjLdybtGAIHefJH1CO4G6fAqTF+rOd27ZtU4MBGHxxxtW6O9e2A3gGYEJJBtLrsU7OpRtFHTMQD0irx7GDx+62Fc+7814oCogeX2qVIZqct98upFCrj1IUiGmUaZj9DFE2bNgwVedvB6L/u+++U+UMH374oRJCPXr0KFBugVRnlMLg80Z5DQbGevbsKffcc48Szs5gn6O0AuUKGOhwNRhogJjEgAheD54YeG/8P9YVnxt8K8ygEUSr3afDlDKg/AWf7cyZM+Xmm29WAnrQoEGF3gtCHYMZ2FacL0jVxjmO7zKAlG2UlABsv5ncDZZhXQ4cOCC9evVyO3h36aWXqn1qBlGwPRgkQqmPc3o5th8DYTinTUkMUs4xaIrlMUgCPw6Ue+AzsYPvQawHBpzwOvhcfHkvZ3BOwRcFAysYxEFJFO5jO3C84pgH8CDBYAHOVwNKbFASY7YZYIAIiT5Y3htwXOM9MHCBQQkM5uA4cQaviWXxOWDgA2UOOPa9Ef6EEEIIiSPCnRoQaVx33XUqnREp8IZff/1VpUN+/PHHbtNRd+zY4fY1x48fr5Z5+eWXC8xPTk620tLSrIoVK1qvvfaaSi9/9NFHVdr0tddeW+S6+rJet912m3pcrlw56+abb7Z+/PFHle6OdFmkVHtaf1d4SrN3xbZt21Ra7mmnnVYgXdsdSD1NTU1V74GpZs2a1oABA6x58+YVWM6k2bubtm7d6nOaPdLOsb+cQdo9/m/y5Mlu13vZsmVWmTJlrMsvv7zQc2PHjlWftT09G2n27tbdudTDvi9RnoFl7MepqzR7vBcmfL4PP/yw+h+UZoDt27erx88++2yh/8c2Opca2CkqzR7HcNeuXS1v0+xdTXgNO0gxPuWUU6yGDRtaq1atUscTthNlHYaNGzeq/61Vq5aVmZnpmH/48GGrUqVK1gUXXOCY161bN6tOnToFSlTA4MGDrdKlS1v79+8vcIydc845hdbfPIdb58902rRpjnn4DKpWrarmL1myxDF/3759ajuHDh3qmIdjD8f+5s2bC7zXmDFj1P+vXLmywLa2bt26wD5YvHhxoe8FX9LsP/nkE7Xsm2++6XE57P/mzZs7Hrdv394688wzCywzbtw49VrLly9Xj7ds2aK+X+++++5C5U01atQoUNph9qOrchtv3gvg+MBkwD5x/mwAyjwwH69h0vlLlSpljRw50nHO4fmHHnpInd8m3X3QoEHqWPOG//77r8Dxffrpp6tz0BU4J81yJUqUUL8LhBBCCCHOMDJvAxEsRLZeeukllY7sjKcUc3fPIaoG92dExpzT6RElQkQKqdNYBqnxiDRiORhMIbJulnMXsfR2vUyUF6mtMFFCuioiWF9++aXs3bvXkYZrIkLFjW7bQWkBIoV4bUSZvEnXRvQNkWzsB0RK69atqyKsMJ+zp7kaYOgGwzbnCSm3/uDPZ420ZWQ6YF1dGVUhAwNRQefoGqJ9rtbdnXnXK6+8ovYlsEcJnUEGASKUmNC9ANHn6667TsaPH1/sbS0KHDeu0trdAad85+1ftGhRgWWQEo1oLFKbYTyHffDxxx+7TD1GtBPZJwZkpCCajAwVnD/IWMH6oeQB3QrsxzuOVTxvT1UHrjIqPO03vI4Bn3njxo1VNBzu5Aa4myPCb3c1R4YAvgvwmdnXC9F9YO/mABD9t+8Dk2nh7JQeaLD/7ccHUtXRWWHNmjUFjA2R/dCqVSv1GNkG2BYYOtq3DZ8Vzm1XXQFc7Xdv3ssV2Lfo2oBjwf7+MBBFRwrz/qaDBaLx5jzD/yH9HtkCv/zyi5qLine 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",
|
||||
@@ -175,7 +119,7 @@
|
||||
"ax.plot(analyser.data['Time (s)'], analyser.data['Number (#/cm^3)'], color='red', label='Number')\n",
|
||||
"ax_2.plot(analyser.data['Time (s)'], analyser.data['Mass (mg/cm^3)'], color='blue', label='Mass')\n",
|
||||
"\n",
|
||||
"ax.set_title('2026-06-12 SET02/EXP01: Experiment Overview ' + str(sample_number))\n",
|
||||
"ax.set_title('2026-06-12 SET02/EXP01: Experiment Overview')\n",
|
||||
"ax.set_xlabel('Time $(s)$')\n",
|
||||
"ax.set_ylabel('Number Density $(cm^{-3})$')\n",
|
||||
"ax_2.set_ylabel('Mass Denisty $(mg cm^{-3})$')\n",
|
||||
@@ -191,7 +135,7 @@
|
||||
"\n",
|
||||
"ax.legend(lines_1 + lines_2, labels_1 + labels_2, loc='upper left')\n",
|
||||
"\n",
|
||||
"plt.savefig(\"20260723_OFR_Analysis_01_Experiment_Overview.pdf\", format='pdf', dpi=400)"
|
||||
"plt.savefig(\"20260723_OFR_Analysis_01_Experiment_Overview_top.pdf\", format='pdf', dpi=400)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -202,68 +146,75 @@
|
||||
"Clearly, something is happening at around 5000 s, 12000s, and after 17000s. This corresponds to shifts in the flow rates... These are read from disk and depicted below:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 159,
|
||||
"id": "dec710d2",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Reading from disk\n",
|
||||
"\n",
|
||||
"source_file_for_flows = Path(r\"C:\\Users\\tschan_d\\Documents\\data_local\\20260612_SET02_EXP01_full_flows.txt\")\n",
|
||||
"\n",
|
||||
"dry_air_flows = []\n",
|
||||
"species_flows = []\n",
|
||||
"ozone_flows = []\n",
|
||||
"numbers_of_samples = []\n",
|
||||
"\n",
|
||||
"with open(source_file_for_flows, 'r', errors='ignore') as file:\n",
|
||||
" for i,line in enumerate(file.readlines()):\n",
|
||||
" if i >= 2:\n",
|
||||
" items = line.split()\n",
|
||||
" dry_air_flows.append(float(items[0]))\n",
|
||||
" species_flows.append(float(items[1]))\n",
|
||||
" ozone_flows.append(float(items[2]))\n",
|
||||
" numbers_of_samples.append(float(items[-1]))\n",
|
||||
"\n",
|
||||
"flow_data = pd.DataFrame({'Dry Air (L/min)': dry_air_flows, 'Species (L/min)': species_flows, 'Ozone (L/min)': ozone_flows, 'Samples': numbers_of_samples})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "75347e52",
|
||||
"id": "a5af245c",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"fig = plt.figure(figsize=(10,4), constrained_layout = True)\n",
|
||||
"# Obtaining the data to plot the flows correctly\n",
|
||||
"\n",
|
||||
"times = []\n",
|
||||
"dry_air = []\n",
|
||||
"species = []\n",
|
||||
"ozone = []\n",
|
||||
"\n",
|
||||
"for _, row in analyser.flow_data.iterrows():\n",
|
||||
" times.append(row['Start (s)'])\n",
|
||||
" times.append(row['Stop (s)'])\n",
|
||||
"\n",
|
||||
" # append it twice so that we can plot the data correctly\n",
|
||||
" i = 0\n",
|
||||
" while i < 2:\n",
|
||||
" dry_air.append(row['Dry Air (L/min)'])\n",
|
||||
" species.append(row['Species (L/min)'])\n",
|
||||
" ozone.append(row['Ozone (L/min)'])\n",
|
||||
" i += 1\n",
|
||||
"\n",
|
||||
"# Doing the actual plotting\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",
|
||||
"\n",
|
||||
"ax.plot(times, ozone, color='red', linestyle='--', label='Ozone')\n",
|
||||
"ax_2.plot(times, species, color='blue', linestyle='--', label=r'$\\alpha$-Pinene')\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"ax.plot([], analyser.data['Number (#/cm^3)'], color='red', label='Number')\n",
|
||||
"ax_2.plot(analyser.data['Time (s)'], analyser.data['Mass (mg/cm^3)'], color='blue', label='Mass')\n",
|
||||
"\n",
|
||||
"ax.set_title('2026-06-12 SET02/EXP01: Experiment Overview ' + str(sample_number))\n",
|
||||
"ax.set_title('2026-06-12 SET02/EXP01: Flow Rates Overview ')\n",
|
||||
"ax.set_xlabel('Time $(s)$')\n",
|
||||
"ax.set_ylabel('Number Density $(cm^{-3})$')\n",
|
||||
"ax_2.set_ylabel('Mass Denisty $(mg cm^{-3})$')\n",
|
||||
"ax.set_ylabel('Ozone Flow Rate $(L min^{-1})$')\n",
|
||||
"ax_2.set_ylabel(r'$\\alpha$-Pinene Flow Rate $(L min^{-1})$')\n",
|
||||
"ax.xaxis.set_minor_locator(MultipleLocator(base=180, offset=0))\n",
|
||||
"ax.xaxis.set_major_locator(MultipleLocator(base=1800, offset=0))\n",
|
||||
"\n",
|
||||
"ax.set_xlim([0, 30000])\n",
|
||||
"ax.set_ylim([0, 1.2E6])\n",
|
||||
"ax_2.set_ylim([0, 15])\n",
|
||||
"ax.set_ylim([0.6, 2.4])\n",
|
||||
"ax_2.set_ylim([0.015, 0.045])\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.pdf\", format='pdf', dpi=400)"
|
||||
"plt.savefig(\"20260723_OFR_Analysis_01_Experiment_Overview_bottom.pdf\", format='pdf', dpi=400)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "e6212394",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"To refine the analysis, we may also consider the production rates of SOA using the integral of the SOA production rate, obtained by multiplying the concentration with the flow rate with the sampling time for each sample:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "e2ae6dbb",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
|
||||
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Reference in new issue
Block a user