Developing OFR Analysis functions: issues with dimensions (nm -> cm)
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Vendored
+8
@@ -9,6 +9,14 @@
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"program": "${file}",
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"console": "integratedTerminal",
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"env": {"PYTHONPATH": "${workspaceFolder}/defs"}
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},
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{
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"name": "Python Debugger: debug_scrapyard.py",
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"type": "debugpy",
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"request": "launch",
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"program": "${workspaceFolder}/defs/debug_scrapyard.py",
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"console": "integratedTerminal",
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"env": {"PYTHONPATH": "${workspaceFolder}/defs"}
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}
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]
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}
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Vendored
+5
-1
@@ -46,5 +46,9 @@
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"strings": "off"
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},
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"editor.suggestOnTriggerCharacters": false,
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"window.zoomLevel": -1
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"window.zoomLevel": -1,
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"editor.wordWrap": "on",
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"editor.rulers": [140],
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"notebook.editorOptionsCustomizations": {"editor.rulers":[]},
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"editor.fontSize": 16
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}
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@@ -1,13 +1,14 @@
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### -------------------------------------------------------------------------------- ###
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### ----------------------------------------------------------------------------------------------------------------------------------- ###
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### SCRAPYARD TO DEBUG AND TEST FUNCTIONALITY
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import pandas as pd
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import numpy as np
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from defs import OFRAnalyser, OFRSample
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analyser = OFRAnalyser(sourcefile="Z:/People/David_Tschan/data/OFR_SMPS/2026/processed/06_June/20260612/20260612_SET02_EXP01_full.csv")
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analyser = OFRAnalyser(sourcefile="Z:/People/David_Tschan/data/OFR_SMPS/2026/processed/06_June/20260612/20260612_SET02_EXP01_full.csv",
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lower_bound=22.87, upper_bound=1000, density=102, scan_up=160, scan_down=20)
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samplelist = np.linspace(start=1, stop=analyser.max_samples, num=analyser.max_samples, dtype=int).tolist()
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analyser.GetTotalMass(samples=samplelist, plot=True)
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analyser.GetTotalMass(samples=[33], plot=False)
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print("Hello")
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print(analyser.total_mass)
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+59
-22
@@ -1,44 +1,70 @@
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### -------------------------------------------------------------------------------- ###
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### ----------------------------------------------------------------------------------------------------------------------------------- ###
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### IMPORTS AND CONFIGURATIONS
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import numpy as np
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import pandas as pd
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import csv
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import math
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import matplotlib.pyplot as plt
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### -------------------------------------------------------------------------------- ###
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### ----------------------------------------------------------------------------------------------------------------------------------- ###
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### OFR ANALYSIS
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class OFRSample:
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def __init__(self, number, sourcefile, headlines):
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def __init__(self, number, sourcefile, headlines, density, lower_bound, upper_bound, scan_up, scan_down):
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self.number = number
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self.sourcefile = sourcefile
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self.headlines = headlines
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self.diameters = np.empty(shape=(self.number, 1))
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self.counts = np.empty(shape=(self.number, 1))
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self.lower_bound = lower_bound
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self.upper_bound = upper_bound
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self.scan_up = scan_up
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self.scan_down = scan_down
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# in g/cc
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self.density = density
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self.diameters = None
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self.counts = None
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self.__get_data()
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def __get_data(self):
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columns = ['Diameter #' + str(self.number), 'Counts #' + str(self.number)]
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sample_data = pd.read_csv(filepath_or_buffer=self.sourcefile, header=self.headlines, usecols=columns, index=False).to_numpy()
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self.diameters = sample_data[:, 0]
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self.counts = sample_data[:, 1]
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def mass_integrator(self, method='trapz') -> None:
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self.__get_data()
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sample_data = pd.read_csv(filepath_or_buffer=self.sourcefile, header=self.headlines, usecols=columns, index_col=False,
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skipfooter=1, engine='python').to_numpy()
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filter_array = np.where((sample_data[:, 0] >= self.lower_bound) & (sample_data[:, 0] <= self.upper_bound), True, False)
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pass
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# Particle Diameter: (nm)
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self.diameters = sample_data[filter_array, 0]
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# Particle raw counts: (# / cc)
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self.counts = sample_data[filter_array, 1]
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def volume_integrator(self, input_prefix = 1E-9, output_prefix = 1E-2) -> np.float64:
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adjustment_factor = input_prefix / output_prefix
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#
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local_volumes = (math.pi / 6) * (self.diameters * adjustment_factor) ** 3
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local_volume = np.trapezoid(y=self.counts, x=local_volumes)
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return local_volume
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class OFRAnalyser:
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def __init__(self, sourcefile, headlines = 238) -> None:
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def __init__(self, sourcefile, headlines = 238, density=1.2, lower_bound = 0, upper_bound = 1896, scan_up=160, scan_down=20) -> None:
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self.sourcefile = sourcefile
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self.headlines = headlines
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self.density = density
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self.lower_bound = lower_bound
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self.upper_bound = upper_bound
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self.scan_up = scan_up
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self.scan_down = scan_down
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with open(self.sourcefile) as file:
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file_reader = csv.reader(file)
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@@ -50,8 +76,8 @@ class OFRAnalyser:
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self.max_samples = int((len(head_row) - 1) / 2)
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def __get_sample(self, sample_number) -> None:
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pass
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# total mass in mg
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self.total_mass = None
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def __sample_number_list_check(self, sample_number_list) -> None:
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@@ -66,11 +92,22 @@ class OFRAnalyser:
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masses = []
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for sample in samples:
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local_sample = OFRSample(number=sample, sourcefile=self.sourcefile, headlines=self.headlines)
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mass = local_sample.mass_integrator()
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masses.append(mass)
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local_sample = OFRSample(number=sample, sourcefile=self.sourcefile, headlines=self.headlines, density=self.density,
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lower_bound = self.lower_bound, upper_bound = self.upper_bound, scan_up = self.scan_up,
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scan_down = self.scan_down)
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# For python notebooks and debugging reasons
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if plot == True:
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fig = plt.figure()
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ax = fig.add_subplot(111)
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ax.plot(local_sample.diameters, local_sample.counts)
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plt.show()
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local_volume = local_sample.volume_integrator()
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masses.append(local_volume * local_sample.density)
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total_mass = sum(masses)
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self.total_mass = total_mass
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def PlotSampleDistribution(self, sample) -> None:
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pass
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@@ -78,5 +115,5 @@ class OFRAnalyser:
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def PlotAllDistributions(self):
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pass
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### -------------------------------------------------------------------------------- ###
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### ----------------------------------------------------------------------------------------------------------------------------------- ###
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###
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+40
-8
@@ -10,17 +10,19 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 1,
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"id": "ee443e0d",
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"metadata": {},
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"outputs": [],
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"source": [
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"from defs.defs import OFRSample, OFRAnalyser"
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"import pandas as pd\n",
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"import numpy as np\n",
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"from defs.defs import OFRAnalyser, OFRSample"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 8,
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"id": "83604ca62804491c",
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"metadata": {
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"ExecuteTime": {
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@@ -30,12 +32,12 @@
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},
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"outputs": [],
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"source": [
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"analyser = OFRAnalyser(sourcefile=\"Z:\\People\\David_Tschan\\data\\OFR_SMPS\\2026\\processed\\06_June\\20260612\\20260612_SET02_EXP01_full.csv\")"
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"analyser = OFRAnalyser(sourcefile=\"Z:/People/David_Tschan/data/OFR_SMPS/2026/processed/06_June/20260612/20260612_SET02_EXP01_full.csv\", lower_bound=22.87, upper_bound=1000, density=1.2, scan_up=160, scan_down=20)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 9,
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"id": "fc69589de06ec7c3",
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"metadata": {
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"ExecuteTime": {
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@@ -43,15 +45,45 @@
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"start_time": "2026-06-15T14:00:19.820656Z"
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}
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},
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"outputs": [],
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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": [
|
||||
"<Figure size 640x480 with 1 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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"source": [
|
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"analyser.sourcefile"
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"samplelist = np.linspace(start=1, stop=analyser.max_samples, num=analyser.max_samples, dtype=int).tolist()\n",
|
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"analyser.GetTotalMass(samples=[33], plot=True)"
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]
|
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},
|
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{
|
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"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"id": "a9eadad8",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
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"text": [
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"1.7670695718029623e-08\n"
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]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print(analyser.total_mass)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "a9eadad8",
|
||||
"id": "4475bcba",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
|
||||
Reference in new issue
Block a user