668 lines
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668 lines
31 KiB
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<article id="content">
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<header>
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<h1 class="title">Module <code>defs.SMPS</code></h1>
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</header>
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<section id="section-intro">
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<p>The docstring for this submodule</p>
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</section>
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<section>
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</section>
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<section>
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</section>
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<section>
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</section>
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<section>
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<h2 class="section-title" id="header-classes">Classes</h2>
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<dl>
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<dt id="defs.SMPS.Distribution"><code class="flex name class">
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<span>class <span class="ident">Distribution</span></span>
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<span>(</span><span>variable_array, function_array, form='log10')</span>
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</code></dt>
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<dd>
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<div class="desc"><p>Object representing a distribution.</p>
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<h2 id="description">Description</h2>
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<p>Bla Bla Bla</p>
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<h2 id="attributes">Attributes</h2>
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<dl>
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<dt><strong><code>var</code></strong> : <code>np.ndarray</code></dt>
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<dd>A 1D array containing the (independent) variable, for instance the diameter $D$.</dd>
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<dt><strong><code>fct</code></strong> : <code>np.ndarray</code></dt>
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<dd>rA 1D array containing the dependent functional value, for instance count density $\frac{d\log_{10}N}{d\log_{10}D}$</dd>
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</dl></div>
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<details class="source">
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<summary>
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<span>Expand source code</span>
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</summary>
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<pre><code class="python">class Distribution():
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r"""Object representing a distribution.
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Description
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-----------
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Bla Bla Bla
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Attributes
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----------
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var: np.ndarray
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A 1D array containing the (independent) variable, for instance the diameter $D$.
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fct: np.ndarray
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rA 1D array containing the dependent functional value, for instance count density $\frac{d\log_{10}N}{d\log_{10}D}$
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"""
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def __init__(self, variable_array, function_array, form = 'log10'):
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self.var = variable_array
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self.fct = function_array
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self.__form = form
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def transform(self, to = 'linear'):
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"""A method to transform distributions between different representations
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Description
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-----------
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Bla bla bla
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Parameters
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----------
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to: str
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Indicating into which representation the distribution should be transformed. Possible values are: `linear`, `ln` or `log10`
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"""
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if self.__form == to:
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warnings.warn("Already in desired format. Transformation has no effect.")
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return
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# TODO: check the transformations!
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# TODO: check the order of the transformation (first variable, then function, or vice versa) to avoid having a mess!
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match to:
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case 'linear':
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if self.__form == 'ln':
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self.var = np.exp(self.var)
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self.fct = np.divide(self.fct, self.var)
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self.__form = 'linear'
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elif self.__form == 'log10':
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self.var = np.power(10, self.var)
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self.fct = np.divide(self.fct, self.var * np.log(10))
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self.__form = 'linear'
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case 'ln':
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if self.__form == 'linear':
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self.var = np.log(self.var)
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self.fct = np.multiply(self.var, self.fct)
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self.__form = 'ln'
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elif self.__form == 'log10':
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self.var = np.log(np.power(10, self.var))
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self.fct = np.divide(self.fct, np.log(10))
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self.__form = 'ln'
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case 'log10':
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if self.__form == 'linear':
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self.fct = np.multiply(self.var, self.fct * np.log(10))
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self.var = np.log10(self.var)
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self.__form = 'log10'
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elif self.__form == 'ln':
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self.var = np.log10(np.exp(self.var))
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self.fct = np.multiply(self.fct, np.log(10))
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self.__form = 'log10'
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def update_units(self, new_var_units, new_fct_units):
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"""A method to update the units of the distribution
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Description
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-----------
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Bla Bla Bla
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Parameters
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----------
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new_var_units: float
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The SI factor used to transform the independent variable `self.var`
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new_fct_units: float
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The SI factor used to transform the dependent variable `self.fct`
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"""
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old_from = self.__form
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self.transform(to='linear')
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self.var = self.var * new_var_units
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self.fct = self.fct * new_fct_units
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self.transform(to=old_from)
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def integrate(self) -> np.float64:
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"""A method to integrate over the spectrum $dN/d f(D)$
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Description
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-----------
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Bla Bla Bla
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"""
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return np.trapezoid(y = self.fct, x = self.var)
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def median():
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# TODO: implement that method
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pass
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def mode():
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# TODO: implement that method
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pass
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def mean():
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# TODO: implement that method
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pass
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def variance():
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# TODO: implement that method
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pass</code></pre>
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</details>
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<h3>Methods</h3>
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<dl>
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<dt id="defs.SMPS.Distribution.integrate"><code class="name flex">
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<span>def <span class="ident">integrate</span></span>(<span>self) ‑> numpy.float64</span>
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</code></dt>
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<dd>
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<div class="desc"><p>A method to integrate over the spectrum $dN/d f(D)$</p>
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<h2 id="description">Description</h2>
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<p>Bla Bla Bla</p></div>
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</dd>
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<dt id="defs.SMPS.Distribution.mean"><code class="name flex">
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<span>def <span class="ident">mean</span></span>(<span>)</span>
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</code></dt>
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<dd>
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<div class="desc"></div>
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</dd>
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<dt id="defs.SMPS.Distribution.median"><code class="name flex">
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<span>def <span class="ident">median</span></span>(<span>)</span>
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</code></dt>
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<dd>
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<div class="desc"></div>
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</dd>
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<dt id="defs.SMPS.Distribution.mode"><code class="name flex">
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<span>def <span class="ident">mode</span></span>(<span>)</span>
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</code></dt>
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<dd>
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<div class="desc"></div>
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</dd>
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<dt id="defs.SMPS.Distribution.transform"><code class="name flex">
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<span>def <span class="ident">transform</span></span>(<span>self, to='linear')</span>
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</code></dt>
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<dd>
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<div class="desc"><p>A method to transform distributions between different representations</p>
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<h2 id="description">Description</h2>
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<p>Bla bla bla</p>
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<h2 id="parameters">Parameters</h2>
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<dl>
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<dt><strong><code>to</code></strong> : <code>str</code></dt>
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<dd>Indicating into which representation the distribution should be transformed. Possible values are: <code>linear</code>, <code>ln</code> or <code>log10</code></dd>
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</dl></div>
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</dd>
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<dt id="defs.SMPS.Distribution.update_units"><code class="name flex">
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<span>def <span class="ident">update_units</span></span>(<span>self, new_var_units, new_fct_units)</span>
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</code></dt>
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<dd>
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<div class="desc"><p>A method to update the units of the distribution</p>
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<h2 id="description">Description</h2>
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<p>Bla Bla Bla</p>
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<h2 id="parameters">Parameters</h2>
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<dl>
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<dt><strong><code>new_var_units</code></strong> : <code>float</code></dt>
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<dd>The SI factor used to transform the independent variable <code>self.var</code></dd>
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<dt><strong><code>new_fct_units</code></strong> : <code>float</code></dt>
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<dd>The SI factor used to transform the dependent variable <code>self.fct</code></dd>
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</dl></div>
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</dd>
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<dt id="defs.SMPS.Distribution.variance"><code class="name flex">
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<span>def <span class="ident">variance</span></span>(<span>)</span>
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</code></dt>
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<dd>
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<div class="desc"></div>
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</dd>
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</dl>
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</dd>
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<dt id="defs.SMPS.OFRAnalyser"><code class="flex name class">
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<span>class <span class="ident">OFRAnalyser</span></span>
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<span>(</span><span>data_source_file, flow_source_file, smpl_number_list, on_time_interval, data_separator=',')</span>
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</code></dt>
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<dd>
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<div class="desc"><p>An object representing an OFR experiment</p>
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<h2 id="description">Description</h2>
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<p>Bla Bla Bla</p>
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<h2 id="attributes">Attributes</h2>
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<dl>
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<dt><strong><code>data_source_file</code></strong> : <code>str</code></dt>
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<dd>A string pointing to the data source file</dd>
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<dt><strong><code>flow_source_file</code></strong> : <code>str</code></dt>
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<dd>A string pointing to the flow data source file</dd>
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<dt><strong><code>sample_number_list</code></strong> : <code>[int]</code></dt>
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<dd>A list of integers indicating which samples to consider</dd>
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<dt><strong><code>on_time_interval</code></strong> : <code>[pd.Timestamp]</code></dt>
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<dd>A list with two elements containing the pd.Timestamp objects when the VOC flow was switched on and off, respectively.</dd>
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<dt><strong><code>max_samples</code></strong> : <code>int</code></dt>
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<dd>The highest sample available in the data</dd>
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<dt><strong><code>data</code></strong> : <code>pandas.DataFrame</code></dt>
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<dd>A dataframe containing all the data</dd>
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</dl></div>
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<details class="source">
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<summary>
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<span>Expand source code</span>
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</summary>
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<pre><code class="python">class OFRAnalyser:
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"""An object representing an OFR experiment
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Description
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-----------
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Bla Bla Bla
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Attributes
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----------
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data_source_file: str
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A string pointing to the data source file
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flow_source_file: str
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A string pointing to the flow data source file
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sample_number_list: [int]
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A list of integers indicating which samples to consider
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on_time_interval: [pd.Timestamp]
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A list with two elements containing the pd.Timestamp objects when the VOC flow was switched on and off, respectively.
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max_samples: int
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The highest sample available in the data
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data: pandas.DataFrame
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A dataframe containing all the data
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"""
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def __init__(self, data_source_file, flow_source_file, smpl_number_list, on_time_interval, data_separator = ',') -> None:
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self.data_source_file = data_source_file
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self.flow_source_file = flow_source_file
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self.smpl_number_list = smpl_number_list
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self.on_time_interval = on_time_interval
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self.__data_separator = data_separator
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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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max_sample_number = int(len(items) / 2)
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else:
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continue
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self.max_samples = max_sample_number
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self.data = None
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def __smpl_number_list_check(self, smpl_number_list) -> None:
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# TODO: implement a similar check for the flow data file!
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last_sample = smpl_number_list[-1]
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if last_sample > self.max_samples:
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raise IndexError("The last sample(s) are out of range of the number of recorded samples")
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def GetData(self) -> None:
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"""Method to get the data
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Description
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-----------
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Bla Bla Bla
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"""
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# TODO make this work also for a specific sample!
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# TODO convert everything is SI units, making live a lot easier...
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self.__smpl_number_list_check(self.smpl_number_list)
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# Quantities of interest:
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numbers_of_sample = [] # (-)
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starting_times = [] # (timestamp)
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sampling_times = [] # (s)
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number_concentrations = [] # (#/m^3)
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length_concentrations = [] # (m/m^3)
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surface_concentrations = [] # (m^2/m^3)
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volume_concentrations = [] # (m^3/m^3)
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aerosol_densities = [] # (kg/m^3)
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mass_concentrations = [] # (kg/m^3)
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filter_flows = [] # (m^3/s)
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mass_production_rate = None # (kg/s)
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|
mass_produced = None # (kg)
|
|
cumulative_mass_produced = None # (kg)
|
|
|
|
for sample_number in self.smpl_number_list:
|
|
sample = OFRSample(source_file=self.data_source_file, sample_number = sample_number, data_separator = self.__data_separator)
|
|
|
|
numbers_of_sample.append(sample_number)
|
|
starting_times.append(sample.start_time)
|
|
|
|
number_concentrations.append(sample.Count.integrate())
|
|
length_concentrations.append(sample.Length.integrate())
|
|
surface_concentrations.append(sample.Surface.integrate())
|
|
volume_concentrations.append(sample.Volume.integrate())
|
|
aerosol_densities.append(sample.SOA_density)
|
|
mass_concentrations.append(sample.Mass.integrate())
|
|
|
|
sampling_times.append(sample.sampling_time)
|
|
|
|
out_flag = False
|
|
|
|
with open(self.flow_source_file, 'r', errors='ignore') as file:
|
|
|
|
j = 1
|
|
|
|
for line_counter, line in enumerate(file.readlines()):
|
|
|
|
if out_flag == True:
|
|
continue
|
|
|
|
if line_counter >= 6:
|
|
|
|
if line.startswith('-'):
|
|
out_flag = True
|
|
continue
|
|
|
|
i = 0
|
|
items = line.split()
|
|
|
|
while i < int(items[-1]):
|
|
|
|
if j in self.smpl_number_list:
|
|
filter_flows.append(float(items[0]) * (1E-3) / 60)
|
|
|
|
i += 1
|
|
j += 1
|
|
|
|
else:
|
|
continue
|
|
|
|
mass_production_rate = [(mass * flow) for mass, flow in zip(mass_concentrations, filter_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,
|
|
'Starting Time (-)': starting_times,
|
|
'Sampling Time (s)': sampling_times,
|
|
'Number (#/m^3)': number_concentrations,
|
|
'Length (m/m^3)': length_concentrations,
|
|
'Surface (m^2/m^3)': surface_concentrations,
|
|
'Volume (m^3/m^3)': volume_concentrations,
|
|
'Density (kg/m^3)': aerosol_densities,
|
|
'Mass (kg/m^3)': mass_concentrations,
|
|
'Filter Flow (m^3/s)': filter_flows,
|
|
'Mass Production Rate (kg/s)': mass_production_rate,
|
|
'Mass Produced (kg)': mass_produced,
|
|
'Cumulative Mass Produced (kg)': cumulative_mass_produced})</code></pre>
|
|
</details>
|
|
<h3>Methods</h3>
|
|
<dl>
|
|
<dt id="defs.SMPS.OFRAnalyser.GetData"><code class="name flex">
|
|
<span>def <span class="ident">GetData</span></span>(<span>self) ‑> None</span>
|
|
</code></dt>
|
|
<dd>
|
|
<div class="desc"><p>Method to get the data</p>
|
|
<h2 id="description">Description</h2>
|
|
<p>Bla Bla Bla</p></div>
|
|
</dd>
|
|
</dl>
|
|
</dd>
|
|
<dt id="defs.SMPS.OFRSample"><code class="flex name class">
|
|
<span>class <span class="ident">OFRSample</span></span>
|
|
<span>(</span><span>source_file, sample_number, data_separator=',')</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> : <code>int</code></dt>
|
|
<dd>The sample number</dd>
|
|
<dt><strong><code>start_time</code></strong> : <code>pd.datetime</code></dt>
|
|
<dd>The starting time of the sample recording</dd>
|
|
<dt><strong><code>sourcefile</code></strong> : <code>str</code></dt>
|
|
<dd>The path to the data file</dd>
|
|
<dt><strong><code>headlines</code></strong> : <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> : <code>float</code></dt>
|
|
<dd>The lower bound diameter (nm) of the SMPS</dd>
|
|
<dt><strong><code>upper_bound</code></strong> : <code>float</code></dt>
|
|
<dd>The upper bound diameter (nm) of the SMPS</dd>
|
|
<dt><strong><code>scan_up</code></strong> : <code>float</code></dt>
|
|
<dd>The time (s) to ramp up voltage in SMPS</dd>
|
|
<dt><strong><code>scan_down</code></strong> : <code>float</code></dt>
|
|
<dd>The time (s) to ramp voltage back down in SMPS</dd>
|
|
<dt><strong><code>delta_time</code></strong> : <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> : <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:
|
|
|
|
"""Object to represent a single sample from the OFR
|
|
|
|
Description
|
|
-----------
|
|
Bla bla bla
|
|
|
|
Attributes
|
|
----------
|
|
|
|
number: int
|
|
The sample number
|
|
start_time: pd.datetime
|
|
The starting time of the sample recording
|
|
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
|
|
"""
|
|
|
|
def __get_data(self) -> np.float64:
|
|
|
|
footer_flag = False
|
|
footer_start_index = 0
|
|
data_flag = False
|
|
data_lines = []
|
|
|
|
midpoints = []
|
|
counts = []
|
|
|
|
date_line = None
|
|
time_line = None
|
|
|
|
with open(self.source_file, 'r', errors = 'ignore') as file:
|
|
for i, line in enumerate(file.readlines()):
|
|
if i == 18:
|
|
date_line = line
|
|
continue
|
|
if i == 19:
|
|
time_line = line
|
|
continue
|
|
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])
|
|
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])
|
|
continue
|
|
|
|
self.sampling_time = self.scan_up_time + self.scan_down_time
|
|
data_lines.pop(-1)
|
|
|
|
# obtaining the starting time
|
|
raw_date_items = date_line.split(self.__data_separator)
|
|
raw_time_items = time_line.split(self.__data_separator)
|
|
del raw_date_items[0]
|
|
del raw_time_items[0]
|
|
|
|
raw_date = raw_date_items[(self.sample_number - 1) * 2].splitlines()[0]
|
|
raw_time = raw_time_items[(self.sample_number - 1) * 2].splitlines()[0]
|
|
self.start_time = pd.to_datetime(raw_date + ' ' + raw_time, format=r'%m/%d/%y %H:%M:%S')
|
|
|
|
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
|
|
|
|
self.aerosol_flow = self.aerosol_flow * 6E-4
|
|
self.SOA_density = self.SOA_density * 1E3
|
|
self.lower_diameter_bound = self.lower_diameter_bound * 1E-9
|
|
self.upper_diameter_bound = self.upper_diameter_bound * 1E-9
|
|
|
|
# Input data from file is in (nm)
|
|
return [np.array(midpoints), np.array(counts)]
|
|
|
|
def __init__(self, source_file, sample_number, data_separator = ','):
|
|
|
|
## VALUES READ IN FROM DISK
|
|
#--------------------------
|
|
|
|
# Sample information
|
|
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.start_time = None
|
|
self.__data_separator = data_separator
|
|
|
|
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.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)
|
|
self.aerosol_flow = None # Aerosol flow, in (L/min)
|
|
self.SOA_density = None # Expected density of SOA, in (g/cm3)
|
|
|
|
# Distributions of the relevant quantities (all relative to nanometer)
|
|
data = self.__get_data()
|
|
|
|
# Getting Distribution objects
|
|
self.Count = Distribution(variable_array = np.log10(data[0]), function_array = data[1], form='log10')
|
|
self.Count.update_units(new_fct_units=1E15, new_var_units=1E-9)
|
|
|
|
self.Length = Distribution(variable_array = np.log10(data[0]), function_array = data[0]*data[1], form = 'log10')
|
|
self.Length.update_units(new_fct_units=1E6, new_var_units=1E-9)
|
|
|
|
self.Surface = Distribution(variable_array = np.log(data[0]), function_array = ((math.pi)* data[0] ** 2) * data[1], form = 'log10')
|
|
self.Surface.update_units(new_fct_units=1E-3, new_var_units=1E-9)
|
|
|
|
self.Volume = Distribution(variable_array = np.log10(data[0]),function_array = ((math.pi / 6) * data[0] ** 3) * data[1], form = 'log10')
|
|
self.Volume.update_units(new_fct_units=1E-12, new_var_units=1E-9)
|
|
|
|
self.Mass = Distribution(variable_array = self.Volume.var, function_array = self.Volume.fct * self.SOA_density, form='log10')</code></pre>
|
|
</details>
|
|
</dd>
|
|
</dl>
|
|
</section>
|
|
</article>
|
|
<nav id="sidebar">
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<div class="toc">
|
|
<ul></ul>
|
|
</div>
|
|
<ul id="index">
|
|
<li><h3>Super-module</h3>
|
|
<ul>
|
|
<li><code><a title="defs" href="index.html">defs</a></code></li>
|
|
</ul>
|
|
</li>
|
|
<li><h3><a href="#header-classes">Classes</a></h3>
|
|
<ul>
|
|
<li>
|
|
<h4><code><a title="defs.SMPS.Distribution" href="#defs.SMPS.Distribution">Distribution</a></code></h4>
|
|
<ul class="two-column">
|
|
<li><code><a title="defs.SMPS.Distribution.integrate" href="#defs.SMPS.Distribution.integrate">integrate</a></code></li>
|
|
<li><code><a title="defs.SMPS.Distribution.mean" href="#defs.SMPS.Distribution.mean">mean</a></code></li>
|
|
<li><code><a title="defs.SMPS.Distribution.median" href="#defs.SMPS.Distribution.median">median</a></code></li>
|
|
<li><code><a title="defs.SMPS.Distribution.mode" href="#defs.SMPS.Distribution.mode">mode</a></code></li>
|
|
<li><code><a title="defs.SMPS.Distribution.transform" href="#defs.SMPS.Distribution.transform">transform</a></code></li>
|
|
<li><code><a title="defs.SMPS.Distribution.update_units" href="#defs.SMPS.Distribution.update_units">update_units</a></code></li>
|
|
<li><code><a title="defs.SMPS.Distribution.variance" href="#defs.SMPS.Distribution.variance">variance</a></code></li>
|
|
</ul>
|
|
</li>
|
|
<li>
|
|
<h4><code><a title="defs.SMPS.OFRAnalyser" href="#defs.SMPS.OFRAnalyser">OFRAnalyser</a></code></h4>
|
|
<ul class="">
|
|
<li><code><a title="defs.SMPS.OFRAnalyser.GetData" href="#defs.SMPS.OFRAnalyser.GetData">GetData</a></code></li>
|
|
</ul>
|
|
</li>
|
|
<li>
|
|
<h4><code><a title="defs.SMPS.OFRSample" href="#defs.SMPS.OFRSample">OFRSample</a></code></h4>
|
|
</li>
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</ul>
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</li>
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