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Updating functions to work with the simplified data, but it's not qorking properly yet

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tschan_d committed 2026-07-02 18:09:40 +02:00
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.vscode/settings.json
Vendored
+1 -1
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@@ -45,7 +45,7 @@
"python.envFile": "${workspaceFolder}/.env",
"python.terminal.useEnvFile": true,
"editor.quickSuggestions": {
"other": "off",
"other": "on",
"comments": "off",
"strings": "off"
},
README.md
+24 -13
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@@ -7,19 +7,30 @@ Hello! This is the read-me file with information for the code project. Please ta
This code project is structured as follows:
```
Master_Thesis
+-- .git
+-- .vscode
+-- defs
+-- __init__.py
+-- defs.py
+-- vis
+-- __init__.py
+-- defs.py
+-- .env
+-- .gitignore
+-- .enviornment.yml
+-- main.py
+-- README.md
├───.git
| └───...
├───.vscode
| ├───settings.json
| └───launch.json
├───defs
| ├───__init__.py
| ├───computing.py
| └───visualising.py
├───docs
| └───defs
| ├───computing.html
| ├───index.html
| └───visualising.html
├───run
| ├───__init__.py
| ├───runfile_01.py
| └───...
├───.env
├───.gitignore
├───environment.yml
├───main.py
└───README.md
```
You are currently reading `README.md`, where the structure of this oroject is explained. You're in the right spot to start, then!
defs/computing.py
+132 -91
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@@ -3,7 +3,6 @@
import numpy as np
import pandas as pd
import csv
import math
import matplotlib.pyplot as plt
@@ -14,10 +13,13 @@ class OFRSample:
"""Object to represent a single sample from the OFR
Description: Bla
Description
-----------
Bla bla bla
Attributes
----------
number: int
The sample number
sourcefile: str
@@ -36,110 +38,151 @@ class OFRSample:
The time interval (s) between two measured data points of the raw count density
SOA_density: float
The density (kg/m3) of the SOA produced in the OFR
Methods
-------
__init__(number, sourcefile, headlines, density, lower_bound, upper_bound, scan_up, scan_down, delta_time) -> OFRSample:
The constructor of the Object
"""
def __init__(self, number, sourcefile, headlines, density, lower_bound, upper_bound, scan_up, scan_down, delta_time = 0.1):
"""Constructor of a OFRSample object
Description:
Parameters
----------
number: int
The sample number
Returns
-------
None: None
Does not have a return value
"""
def __init__(self, source_file, sample_number, data_separator):
# Sample information
self.number = number # sample number, i.e. 33
self.sourcefile = sourcefile # where the data is stored, "C:/Users/tschan_d/..."
self.headlines = headlines # Number of lines before raw data is reached, usually 238
self.lower_bound = lower_bound # Lower Diameter (nm) where CPC works
self.upper_bound = upper_bound # Upper Diameter (nm) where CPC works
self.scan_up = scan_up # Time to scan up the voltage range in DMA
self.scan_down = scan_down # Time to ramp the voltage back down in DMA
self.delta_time = delta_time # Measuring interval to get raw count density; (s)
self.SOA_density = density # Expected density of SOA (kg/m3)
self.source_file = source_file # where the data is stored, i.e. "C:/Users/tschan_d/..."
self.sample_number = sample_number # sample number, i.e. 33
self.__data_separator = data_separator
# Sample distributions
self.diameters = None # Diameters (nm)
self.count_density = None # Count Density (#/(m3*m*s))
self.surface_density = None # Surface Density (m2/(m3*m*s))
self.volume_density = None # Volume Density (m3/(m3*m*s))
self.mass_density = None # Mass Density (kg/(m3*m*s))
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.total_count = None # Total Count (#/(m3*s))
self.total_surface = None # Total Surface (m2/(m3*s))
self.total_volume = None # Total Volume (m3/(m3*s))
self.total_mass = None # Total Mass (kg/(m3*s))
self.sample_time = None
self.get_data()
self.lower_diameter_bound = None # Lower Diameter bound of CPC, in (nm)
self.upper_diameter_bound = None # Upper Diameter bound of CPC, in (nm)
self.aerosol_flow = None # Aerosol flow, in (m3/s)
self.SOA_density = None # Expected density of SOA, in (kg/m3)
def get_data(self):
# Sample Distributions
self.diameters = None # Bin Midpoint Diameter, in (m)
self.count_density = None # Count Density dN/dlnD, in (#/m3)
self.surface_density = None # Surface Density dS/dlnD, in (m2/(m3*m))
self.volume_density = None # Volume Density dV/dlnD, in (m3/(m3*m))
self.mass_density = None # Mass Density dm/dlnD, in (kg/(m3*m))
columns = ['Diameter #' + str(self.number), 'Counts #' + str(self.number)]
sample_data = pd.read_csv(filepath_or_buffer=self.sourcefile, header=self.headlines, usecols=columns, index_col=False,
skipfooter=1, engine='python').to_numpy()
# Distributed integrated along dlnD
self.sample_count_density = None # Total Count Density (#/(m3))
self.sample_surface_density = None # Total Surface Density (m2/(m3))
self.sample_volume_density = None # Total Volume Density (m3/(m3))
self.sample_mass_density = None # Total Mass Density (kg/(m3))
# Distributions integrated over sample
self.sample_count = None # Total Count (#/(m3))
self.sample_surface = None # Total Surface (m2/(m3))
self.sample_volume = None # Total Volume (m3/(m3))
self.sample_mass = None # Total Mass (kg/(m3))
self.__get_data()
self.__get_distributions()
self.__integrate_over_distributions()
self.__integrate_over_sample()
def __get_data(self):
footer_flag = False
footer_start_index = 0
data_flag = False
data_lines = []
midpoints = []
counts = []
with open(self.source_file, 'r') as file:
for i, line in enumerate(file.readlines()):
if i == 20:
data_flag = True
continue
if data_flag == True:
data_lines.append(line)
if line.split(self.__data_separator)[0] == 'Scan Up Time(s)':
data_flag = False
footer_flag = True
footer_start_index = i
self.scan_up_time = float(line.split(self.__data_separator)[1])
continue
if footer_flag == False:
continue
else:
if i == footer_start_index + 1:
self.scan_down_time = float(line.split(self.__data_separator)[1])
continue
elif i == footer_start_index + 6:
self.aerosol_flow = float(line.split(self.__data_separator)[1]) / 60000
continue
elif i == footer_start_index + 11:
self.lower_diameter_bound = float(line.split(self.__data_separator)[1])
continue
elif i == footer_start_index + 12:
self.upper_diameter_bound = float(line.split(self.__data_separator)[1])
continue
elif i == footer_start_index + 13:
self.SOA_density = float(line.split(self.__data_separator)[1]) * 1E3
continue
filter_array = np.where((sample_data[:, 0] >= self.lower_bound) & (sample_data[:, 0] <= self.upper_bound), True, False)
self.sample_time = self.scan_up_time + self.scan_down_time
data_lines.pop(-1)
# Particle Diameter: Raw data in (nm) -> Output data in (m): 1 nm = 1E-9 m
self.diameters = sample_data[filter_array, 0] * 1E-9
for line in data_lines:
items = line.split(self.__data_separator)
local_diameter_midpoint = float(items[0])
if self.lower_diameter_bound <= local_diameter_midpoint and local_diameter_midpoint <= self.upper_diameter_bound:
midpoints.append(local_diameter_midpoint)
items.pop(0)
counts.append(float(items[(self.sample_number - 1) * 2]))
else:
continue
# Input data from file is in (nm). Therefore, multiply with 1E-9 to get (m)
self.diameters = np.array(midpoints).reshape([-1, 1]) * 1E-9
# Particle Count Density: Raw data in (#/(cm3*nm*0.1s)) -> Output data in (#/(m3*m*s)): 1/(cm3*nm*s) = 1E16/(m3*m*s)
self.count_density = sample_data[filter_array, 1] * 1E16
# Input data from file is in (#/cm3). Therefore, multiply with 1E6 to get (#/m3)
self.count_density = np.array(counts).reshape([-1, 1]) * 1E6
self.surface_density = np.multiply(math.pi * self.diameters ** 2, self.count_density)
self.volume_density = np.multiply((math.pi / 6) * self.diameters ** 3, self.count_density)
self.mass_density = np.multiply(self.SOA_density, self.volume_density)
def __get_distributions(self):
self.surface_density = (math.pi * self.diameters ** 2) * self.count_density
self.volume_density = (math.pi * self.diameters ** 3) * self.count_density
self.mass_density = self.SOA_density * self.volume_density
self.total_count = self.__integrator(integrand = self.count_density)
self.total_surface = self.__integrator(integrand = self.surface_density)
self.total_volume = self.__integrator(integrand = self.volume_density)
self.total_mass = self.total_volume * self.SOA_density
def __integrate_over_distributions(self):
self.sample_count_density = self.__integrator(integrand=np.divide(self.count_density, self.diameters))
self.sample_surface_density = self.__integrator(integrand=np.divide(self.surface_density, self.diameters))
self.sample_volume_density = self.__integrator(integrand=np.divide(self.volume_density, self.diameters))
self.sample_mass_density = self.__integrator(integrand=np.divide(self.mass_density, self.diameters))
def __integrator(self, integrand: np.float64) -> np.float64:
return np.trapezoid(y = integrand, x = self.diameters)
return np.trapezoid(y = integrand.flatten(), x = self.diameters.flatten())
def __integrate_over_sample(self):
self.sample_count = self.sample_count_density * self.sample_time * self.aerosol_flow
self.sample_surface = self.sample_surface_density * self.sample_time * self.aerosol_flow
self.sample_volume = self.sample_volume_density * self.sample_time * self.aerosol_flow
self.sample_mass = self.sample_mass_density * self.sample_time * self.aerosol_flow
class OFRAnalyser:
def __init__(self, sourcefile, headlines = 238, density=1.2, lower_bound = 0, upper_bound = 1896, scan_up=160, scan_down=20,
flow_rate=5) -> None:
def __init__(self, source_file, data_separator = ',') -> None:
# where to take data from:
self.sourcefile = sourcefile
# how many lines to skip until the raw counts data section is reached:
self.headlines = headlines
# assumed SOA density
self.density = density
self.lower_bound = lower_bound
self.upper_bound = upper_bound
self.scan_up = scan_up
self.scan_down = scan_down
self.flow_rate = flow_rate
self.source_file = source_file
self.__data_separator = data_separator
with open(self.sourcefile) as file:
file_reader = csv.reader(file)
for index, row in enumerate(file_reader):
if index != self.headlines:
continue
with open(self.source_file, 'r') as file:
for i, line in enumerate(file.readlines()):
if i == 17:
items = line.split(',')
max_sample_number = int(len(items) / 2)
else:
head_row = row
continue
self.max_samples = int((len(head_row) - 1) / 2)
# total mass in mg
self.max_samples = max_sample_number
self.total_counts = None
self.total_surface = None
self.total_volume = None
@@ -162,16 +205,14 @@ class OFRAnalyser:
masses = []
for samplenumber in samples:
sample = OFRSample(number=samplenumber, sourcefile=self.sourcefile, headlines=self.headlines, density=self.density,
lower_bound = self.lower_bound, upper_bound = self.upper_bound, scan_up = self.scan_up,
scan_down = self.scan_down)
sample = OFRSample(source_file=self.source_file, sample_number = samplenumber, data_separator = self.__data_separator)
counts.append(sample.sample_count)
surfaces.append(sample.sample_surface)
volumes.append(sample.sample_volume)
masses.append(sample.sample_mass)
counts.append(sample.total_count)
surfaces.append(sample.total_surface)
volumes.append(sample.total_volume)
masses.append(sample.total_mass)
self.total_count = sum(counts)
self.total_counts = sum(counts)
self.total_surface = sum(surfaces)
self.total_volume = sum(volumes)
self.total_mass = sum(masses)
doc/defs/computing.html
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@@ -0,0 +1,338 @@
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</head>
<body>
<main>
<article id="content">
<header>
<h1 class="title">Module <code>defs.computing</code></h1>
</header>
<section id="section-intro">
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="defs.computing.OFRAnalyser"><code class="flex name class">
<span>class <span class="ident">OFRAnalyser</span></span>
<span>(</span><span>sourcefile, headlines=238, density=1.2, lower_bound=0, upper_bound=1896, scan_up=160, scan_down=20, flow_rate=5)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class OFRAnalyser:
def __init__(self, sourcefile, headlines = 238, density=1.2, lower_bound = 0, upper_bound = 1896, scan_up=160, scan_down=20,
flow_rate=5) -&gt; None:
# where to take data from:
self.sourcefile = sourcefile
# how many lines to skip until the raw counts data section is reached:
self.headlines = headlines
# assumed SOA density
self.density = density
self.lower_bound = lower_bound
self.upper_bound = upper_bound
self.scan_up = scan_up
self.scan_down = scan_down
self.filter_flow_rate = flow_rate # Flow rate of the OFR flow through the filer in (m3/s)
with open(self.sourcefile) as file:
file_reader = csv.reader(file)
for index, row in enumerate(file_reader):
if index != self.headlines:
continue
else:
head_row = row
self.max_samples = int((len(head_row) - 1) / 2)
# total mass in mg
self.total_counts = None
self.total_surface = None
self.total_volume = None
self.total_mass = None
def __sample_number_list_check(self, sample_number_list) -&gt; None:
last_sample = sample_number_list[-1]
if last_sample &gt; self.max_samples:
raise IndexError(&#34;The last sample(s) are out of range of the number of recorded samples&#34;)
def AggregateSamples(self, samples = [1]) -&gt; None:
self.__sample_number_list_check(samples)
counts = []
surfaces = []
volumes = []
masses = []
for samplenumber in samples:
sample = OFRSample(number=samplenumber, sourcefile=self.sourcefile, headlines=self.headlines, density=self.density,
lower_bound = self.lower_bound, upper_bound = self.upper_bound, scan_up = self.scan_up,
scan_down = self.scan_down)
counts.append(sample.total_count)
surfaces.append(sample.total_surface)
volumes.append(sample.total_volume)
masses.append(sample.total_mass)
self.total_count = sum(counts)
self.total_surface = sum(surfaces)
self.total_volume = sum(volumes)
self.total_mass = sum(masses)</code></pre>
</details>
<h3>Methods</h3>
<dl>
<dt id="defs.computing.OFRAnalyser.AggregateSamples"><code class="name flex">
<span>def <span class="ident">AggregateSamples</span></span>(<span>self, samples=[1]) ‑> None</span>
</code></dt>
<dd>
<div class="desc"></div>
</dd>
</dl>
</dd>
<dt id="defs.computing.OFRSample"><code class="flex name class">
<span>class <span class="ident">OFRSample</span></span>
<span>(</span><span>number, sourcefile, headlines, density, lower_bound, upper_bound, scan_up, scan_down, delta_time=0.1)</span>
</code></dt>
<dd>
<div class="desc"><p>Object to represent a single sample from the OFR</p>
<h2 id="description">Description</h2>
<p>Bla bla bla</p>
<h2 id="attributes">Attributes</h2>
<dl>
<dt><strong><code>number</code></strong> :&ensp;<code>int</code></dt>
<dd>The sample number</dd>
<dt><strong><code>sourcefile</code></strong> :&ensp;<code>str</code></dt>
<dd>The path to the data file</dd>
<dt><strong><code>headlines</code></strong> :&ensp;<code>int</code></dt>
<dd>The number of lines in the data file until raw data is reached</dd>
<dt><strong><code>lower_bound</code></strong> :&ensp;<code>float</code></dt>
<dd>The lower bound diameter (nm) of the SMPS</dd>
<dt><strong><code>upper_bound</code></strong> :&ensp;<code>float</code></dt>
<dd>The upper bound diameter (nm) of the SMPS</dd>
<dt><strong><code>scan_up</code></strong> :&ensp;<code>float</code></dt>
<dd>The time (s) to ramp up voltage in SMPS</dd>
<dt><strong><code>scan_down</code></strong> :&ensp;<code>float</code></dt>
<dd>The time (s) to ramp voltage back down in SMPS</dd>
<dt><strong><code>delta_time</code></strong> :&ensp;<code>float</code></dt>
<dd>The time interval (s) between two measured data points of the raw count density</dd>
<dt><strong><code>SOA_density</code></strong> :&ensp;<code>float</code></dt>
<dd>The density (kg/m3) of the SOA produced in the OFR</dd>
</dl></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class OFRSample:
&#34;&#34;&#34;Object to represent a single sample from the OFR
Description
-----------
Bla bla bla
Attributes
----------
number: int
The sample number
sourcefile: str
The path to the data file
headlines: int
The number of lines in the data file until raw data is reached
lower_bound: float
The lower bound diameter (nm) of the SMPS
upper_bound: float
The upper bound diameter (nm) of the SMPS
scan_up: float
The time (s) to ramp up voltage in SMPS
scan_down: float
The time (s) to ramp voltage back down in SMPS
delta_time: float
The time interval (s) between two measured data points of the raw count density
SOA_density: float
The density (kg/m3) of the SOA produced in the OFR
&#34;&#34;&#34;
def __init__(self, number, sourcefile, headlines, density, lower_bound, upper_bound, scan_up, scan_down, delta_time = 0.1):
# Sample information
self.number = number # sample number, i.e. 33
self.sourcefile = sourcefile # where the data is stored, i.e. &#34;C:/Users/tschan_d/...&#34;
self.headlines = headlines # Number of lines before raw data is reached, usually 238
self.lower_bound = lower_bound # Lower Diameter (nm) where CPC works
self.upper_bound = upper_bound # Upper Diameter (nm) where CPC works
self.scan_up = scan_up # Time to scan up the voltage range in DMA, in (s)
self.scan_down = scan_down # Time to scan down the voltage range in DMA, in(s)
self.delta_time = delta_time # Measuring interval to get raw count density, in (s)
self.SOA_density = density # Expected density of SOA (kg/m3)
# Sample distributions
self.diameters = None # Diameters (nm)
self.count_density = None # Count Density (#*s/(m3*m))
self.surface_density = None # Surface Density (m2*s/(m3*m))
self.volume_density = None # Volume Density (m3*s/(m3*m))
self.mass_density = None # Mass Density (kg*s/(m3*m))
self.total_count = None # Total Count (#*s/(m3))
self.total_surface = None # Total Surface (m2*s/(m3))
self.total_volume = None # Total Volume (m3*s/(m3))
self.total_mass = None # Total Mass (kg*s/(m3))
self.get_data()
def get_data(self):
r&#34;&#34;&#34;Method to load and process data from a file.
Detailed Description
--------------------
Load the raw data from a `.csv` file into the object instance. Raw data includes raw counts $c_{r}$
($\text{particles}/(cm^{3}nm$) and diameters $D_{r}$ ($nm$). Each data point in the raw counts, $c_{r,i}$, represents the
number of particles observed between $D_{r,i-1}$ and $D_{r,i}$ within the data point aqcuisition time of $\Delta t_{A}$, such that
$c_{r,i}$ represents the time-integrated quantity of the particle flux within $D_{r,i-1}\leq D\leq D_{r,i}$. The raw data is
filtered to include only data within $D_{L}\leq D_{r}\leq D_{u}$. Then, $c_{r}$ and $D_{r}$ are transformed to SI-Units, represented
by $c=1E15c_{0}$ ($\text{particles}s/(m^{3}m$) and $D=1E-9D_{r}$ ($m$), respectively. The final count data is then obtained by
integrating $c$ over the sampling time $\Delta t_{s}=t_{su}+t_{sd}$, where $t_{su}$ and $t_{sd}$ represent DMA voltage scan-up and
scan-down times, respectively.
Parameters
----------
a: None
This function has no paramters
Returns
-------
n: None
This function has no return value
&#34;&#34;&#34;
# The columns to use when reading the data file
columns = [&#39;Diameter #&#39; + str(self.number), &#39;Counts #&#39; + str(self.number)]
# Reading the data file
sample_data = pd.read_csv(filepath_or_buffer=self.sourcefile, header=self.headlines, usecols=columns, index_col=False,
skipfooter=1, engine=&#39;python&#39;).to_numpy()
# Filtering the data to include only lower_bound &lt;= diameter &lt;= upper_bound
filter_array = np.where((sample_data[:, 0] &gt;= self.lower_bound) &amp; (sample_data[:, 0] &lt;= self.upper_bound), True, False)
# Particle Diameter: Raw data in (nm) -&gt; Output data in (m): 1 nm = 1E-9 m
self.diameters = sample_data[filter_array, 0] * 1E-9
# Particle Count Density: Raw data in (#/(cm3*nm)) -&gt; Output data in (#/(m3*m)): 1/(cm3*nm) = 1E15/(m3*m*s). This is the inte-
# grated value of the density flux over Δt = time_delta. Furthermore, the count density is integrated over the sample scanning time
# of Δt = self.scan_up + self.scan_down. Then the time-integrated particle count density has units (#*s/(m3*m))
self.count_density = sample_data[filter_array, 1] * 1E15 * (self.scan_up + self.scan_down) # (#*s/(m3*m))
# Obtaining the corresponding surface-, volume- and mass densities
self.surface_density = np.multiply(math.pi * self.diameters ** 2, self.count_density) # (m2*s/(m3*m))
self.volume_density = np.multiply((math.pi / 6) * self.diameters ** 3, self.count_density) # (m3*s/(m3*m))
self.mass_density = np.multiply(self.SOA_density, self.volume_density) # (kg*s/(m3*m))
# Obtaining the corresponding quantities integrated over all diameters
self.total_count = self.__integrator(integrand = self.count_density) # (#*s/m3)
self.total_surface = self.__integrator(integrand = self.surface_density) # (m2*s/m3)
self.total_volume = self.__integrator(integrand = self.volume_density) # (m3*s/m3)
self.total_mass = self.total_volume * self.SOA_density # (kg*s/m3)
def __integrator(self, integrand: np.float64) -&gt; np.float64:
return np.trapezoid(y = integrand, x = self.diameters)</code></pre>
</details>
<h3>Methods</h3>
<dl>
<dt id="defs.computing.OFRSample.get_data"><code class="name flex">
<span>def <span class="ident">get_data</span></span>(<span>self)</span>
</code></dt>
<dd>
<div class="desc"><p>Method to load and process data from a file.</p>
<h2 id="detailed-description">Detailed Description</h2>
<p>Load the raw data from a <code>.csv</code> file into the object instance. Raw data includes raw counts $c_{r}$
($\text{particles}/(cm^{3}nm$) and diameters $D_{r}$ ($nm$). Each data point in the raw counts, $c_{r,i}$, represents the
number of particles observed between $D_{r,i-1}$ and $D_{r,i}$ within the data point aqcuisition time of $\Delta t_{A}$, such that
$c_{r,i}$ represents the time-integrated quantity of the particle flux within $D_{r,i-1}\leq D\leq D_{r,i}$. The raw data is
filtered to include only data within $D_{L}\leq D_{r}\leq D_{u}$. Then, $c_{r}$ and $D_{r}$ are transformed to SI-Units, represented
by $c=1E15c_{0}$ ($\text{particles}s/(m^{3}m$) and $D=1E-9D_{r}$ ($m$), respectively. The final count data is then obtained by
integrating $c$ over the sampling time $\Delta t_{s}=t_{su}+t_{sd}$, where $t_{su}$ and $t_{sd}$ represent DMA voltage scan-up and
scan-down times, respectively.</p>
<h2 id="parameters">Parameters</h2>
<dl>
<dt><strong><code>a</code></strong> :&ensp;<code>None</code></dt>
<dd>This function has no paramters</dd>
</dl>
<h2 id="returns">Returns</h2>
<dl>
<dt><strong><code>n</code></strong> :&ensp;<code>None</code></dt>
<dd>This function has no return value</dd>
</dl></div>
</dd>
</dl>
</dd>
</dl>
</section>
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<li><code><a title="defs" href="index.html">defs</a></code></li>
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<li><h3><a href="#header-classes">Classes</a></h3>
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<h4><code><a title="defs.computing.OFRAnalyser" href="#defs.computing.OFRAnalyser">OFRAnalyser</a></code></h4>
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<h1 class="title">Module <code>defs.visualising</code></h1>
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<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="defs.visualising.HelloWorld"><code class="flex name class">
<span>class <span class="ident">HelloWorld</span></span>
<span>(</span><span>word='Hellow')</span>
</code></dt>
<dd>
<div class="desc"><p>The docstring for this class</p>
<p>Description: Bla bla bla</p>
<h2 id="attributes">Attributes</h2>
<dl>
<dt><strong><code>word</code></strong> :&ensp;<code>str</code></dt>
<dd>A word</dd>
</dl>
<h2 id="methods">Methods</h2>
<p><strong>init</strong>: HelloWorld
The class constructor</p></div>
<details class="source">
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<span>Expand source code</span>
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<pre><code class="python">class HelloWorld():
&#34;&#34;&#34;The docstring for this class
Description: Bla bla bla
Attributes
----------
word: str
A word
Methods
-------
__init__: HelloWorld
The class constructor
&#34;&#34;&#34;
def __init__(self, word = &#34;Hellow&#34;):
self.word = word</code></pre>
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<li><h3>Super-module</h3>
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<h4><code><a title="defs.visualising.HelloWorld" href="#defs.visualising.HelloWorld">HelloWorld</a></code></h4>
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main.py
+3 -3
View File
@@ -1,10 +1,10 @@
#%%
from pathlib import Path
from defs.computing import OFRAnalyser
import numpy as np
#%%
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=1200, scan_up=160, scan_down=20)
analyser = OFRAnalyser(source_file=Path(r"Z:\People\David_Tschan\data\OFR_SMPS\2026\processed\06_June\20260612\20260612_SET02_EXP01_full.txt"))
#samplelist = np.linspace(start=1, stop=analyser.max_samples, num=analyser.max_samples, dtype=int).tolist()
@@ -12,5 +12,5 @@ analyser = OFRAnalyser(sourcefile="Z:/People/David_Tschan/data/OFR_SMPS/2026/pro
analyser.AggregateSamples(samples=[33])
#%%
print(analyser.total_count)
print(analyser.total_mass)
# %%