0.5.1 documentation fixed

build/lib/crnpy/crnpy.py

@@ -1215,12 +1215,16 @@ def rover_centered_coordinates(x, y):
 def uncertainty_counts(raw_counts, metric="std", fp=1, fw=1, fi=1):
     """Function to estimate the uncertainty of raw counts.
 
-    Measurements of a proportional neutron detector system are governed by counting statistics that follow a Poissonian probability distribution (Zreda et al., 2012).
-    The expected uncertainty in the neutron count rate N is defined by the standard deviation $ \sqrt{N} $. (Jakobi et al., 2020)
-    It can be expressed as CV% as $ N^{-1/2} $
+    Measurements of proportional neutron detector systems are governed by counting statistics that follow a Poissonian probability distribution (Zreda et al., 2012).
+    The expected uncertainty in the neutron count rate $N$ is defined by the standard deviation $ \sqrt{N} $ (Jakobi et al., 2020).
+    The CV% can be expressed as $ N^{-1/2} $
 
     Args:
         raw_counts (array): Raw neutron counts.
+        metric (str): Either 'std' or 'cv' for standard deviation or coefficient of variation.
+        fp (float): Pressure correction factor.
+        fw (float): Humidity correction factor.
+        fi (float): Incoming neutron flux correction factor.
 
     Returns:
         uncertainty (float): Uncertainty of raw counts.
@@ -1248,18 +1252,18 @@ def uncertainty_vwc(raw_counts, N0, bulk_density, fp=1, fw=1, fi=1, a0=0.0808,a1
     r"""Function to estimate the uncertainty propagated to volumetric water content.
 
     The uncertainty of the volumetric water content is estimated by propagating the uncertainty of the raw counts.
-    Following Eq. 10 in Jakobi et al. (2020), the uncertainty of the volumetric water content is estimated as:
+    Following Eq. 10 in Jakobi et al. (2020), the uncertainty of the volumetric water content can be expressed as:
     $$
     \sigma_{\theta_g}(N) = \sigma_N \frac{a_0 N_0}{(N_{cor} - a_1 N_0)^4} \sqrt{(N_{cor} - a_1 N_0)^4 + 8 \sigma_N^2 (N_{cor} - a_1 N_0)^2 + 15 \sigma_N^4}
     $$
 
     Args:
         raw_counts (array): Raw neutron counts.
         N0 (float): Calibration parameter N0.
-        bulk_density (float): Bulk density in kg/m3.
-        fp (float): Calibration parameter fp.
-        fw (float): Calibration parameter fw.
-        fi (float): Calibration parameter fi.
+        bulk_density (float): Bulk density in g cm-3.
+        fp (float): Pressure correction factor.
+        fw (float): Humidity correction factor.
+        fi (float): Incoming neutron flux correction factor.
 
     Returns:
         sigma_VWC (float): Uncertainty in terms of volumetric water content.

docs/examples/calibration/calibration.ipynb

@@ -589,8 +589,6 @@
     }
    ],
    "source": [
-    "# Select station data only until the calibration date\n",
-    "\n",
     "# Define date in which the probe was deployed in the field (i.e., first record)\n",
     "deployment_date = df_station['timestamp'].iloc[0]\n",
     "\n",
@@ -764,7 +762,7 @@
    "source": [
     "# Incoming neutron flux correction\n",
     "\n",
-    "# Download data for the reference neutron monitor and add to the DataFrame\n",
+    "# Download data for the reference neutron monitor and add it to the DataFrame\n",
     "nmdb = crnpy.get_incoming_neutron_flux(deployment_date,\n",
     "                                        calibration_end,\n",
     "                                        station=\"DRBS\",\n",
@@ -797,7 +795,7 @@
    "source": [
     "### Determine field-average soil moisture and bulk density\n",
     "\n",
-    "Using the function [`nrad_weight()`](../../../reference/#crnpy.crnpy.nrad_weight) the weights corresponding to each soil sample will be computed considering air-humidity, sample depth, distance from station and bulk density.\n"
+    "Using the function [`nrad_weight()`](../../../reference/#crnpy.crnpy.nrad_weight) the weights corresponding to each soil sample will be computed considering air-humidity, sample depth, and distance from station and bulk density.\n"
    ]
   },
   {
@@ -846,7 +844,7 @@
    "source": [
     "## Solving for $N_0$\n",
     "\n",
-    "Previous steps estimated the a field volumetric water content of `0.263` and an average neutron count of `1542`. Using [`scipy.optimize.root()`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.root.html) $N_0$ is estimated given the observed value of $\\theta_v$ and neutron counts."
+    "Previous steps estimated the field volumetric water content of `0.263` and an average neutron count of `1542`. Using [`scipy.optimize.root()`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.root.html) $N_0$ is estimated given the observed value of $\\theta_v$ and neutron counts."
    ]
   },
   {

docs/examples/rover/Hydroinnova_rover_example.ipynb

@@ -519,7 +519,7 @@
     "# Estimate lattice water based on texture\n",
     "lattice_water = crnpy.lattice_water(clay_content=0.35)\n",
     "\n",
-    "# Estimate Soil Columetric Water Content\n",
+    "# Estimate Soil Volumetric Water Content\n",
     "df['VWC'] = crnpy.counts_to_vwc(df['corrected_neutrons_smoothed'], N0=550, bulk_density=1.3, Wlat=lattice_water, Wsoc=0.01)\n",
     "\n",
     "# Drop VWC NaN values before interpolating values\n",

site/examples/calibration/calibration/index.html

@@ -2097,9 +2097,7 @@ <h2 id="read-data-from-crnp">Read data from CRNP<a class="anchor-link" href="#re
                     </div>
                 </clipboard-copy>
             </div>
-            <div class="highlight-ipynb hl-python "><pre><span></span><span class="c1"># Select station data only until the calibration date</span>
-
-<span class="c1"># Define date in which the probe was deployed in the field (i.e., first record)</span>
+            <div class="highlight-ipynb hl-python "><pre><span></span><span class="c1"># Define date in which the probe was deployed in the field (i.e., first record)</span>
 <span class="n">deployment_date</span> <span class="o">=</span> <span class="n">df_station</span><span class="p">[</span><span class="s1">&#39;timestamp&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
 
 <span class="c1"># Filter station data from the first record to the end of the field survey calibration</span>
@@ -2108,9 +2106,7 @@ <h2 id="read-data-from-crnp">Read data from CRNP<a class="anchor-link" href="#re
 <span class="n">df_station</span> <span class="o">=</span> <span class="n">df_station</span><span class="p">[</span><span class="n">idx_period</span><span class="p">]</span>
 <span class="n">df_station</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
 </pre></div>
-<div id="cell-5" class="clipboard-copy-txt"># Select station data only until the calibration date
-
-# Define date in which the probe was deployed in the field (i.e., first record)
+<div id="cell-5" class="clipboard-copy-txt"># Define date in which the probe was deployed in the field (i.e., first record)
 deployment_date = df_station['timestamp'].iloc[0]
 
 # Filter station data from the first record to the end of the field survey calibration
@@ -2552,7 +2548,7 @@ <h2 id="correct-neutron-counts">Correct neutron counts<a class="anchor-link" hre
             </div>
             <div class="highlight-ipynb hl-python "><pre><span></span><span class="c1"># Incoming neutron flux correction</span>
 
-<span class="c1"># Download data for the reference neutron monitor and add to the DataFrame</span>
+<span class="c1"># Download data for the reference neutron monitor and add it to the DataFrame</span>
 <span class="n">nmdb</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">get_incoming_neutron_flux</span><span class="p">(</span><span class="n">deployment_date</span><span class="p">,</span>
                                         <span class="n">calibration_end</span><span class="p">,</span>
                                         <span class="n">station</span><span class="o">=</span><span class="s2">&quot;DRBS&quot;</span><span class="p">,</span>
@@ -2567,7 +2563,7 @@ <h2 id="correct-neutron-counts">Correct neutron counts<a class="anchor-link" hre
 </pre></div>
 <div id="cell-9" class="clipboard-copy-txt"># Incoming neutron flux correction
 
-# Download data for the reference neutron monitor and add to the DataFrame
+# Download data for the reference neutron monitor and add it to the DataFrame
 nmdb = crnpy.get_incoming_neutron_flux(deployment_date,
                                         calibration_end,
                                         station="DRBS",
@@ -2631,7 +2627,7 @@ <h2 id="correct-neutron-counts">Correct neutron counts<a class="anchor-link" hre
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
 </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
-<h3 id="determine-field-average-soil-moisture-and-bulk-density">Determine field-average soil moisture and bulk density<a class="anchor-link" href="#determine-field-average-soil-moisture-and-bulk-density">&#182;</a></h3><p>Using the function <a href="../../../reference/#crnpy.crnpy.nrad_weight"><code>nrad_weight()</code></a> the weights corresponding to each soil sample will be computed considering air-humidity, sample depth, distance from station and bulk density.</p>
+<h3 id="determine-field-average-soil-moisture-and-bulk-density">Determine field-average soil moisture and bulk density<a class="anchor-link" href="#determine-field-average-soil-moisture-and-bulk-density">&#182;</a></h3><p>Using the function <a href="../../../reference/#crnpy.crnpy.nrad_weight"><code>nrad_weight()</code></a> the weights corresponding to each soil sample will be computed considering air-humidity, sample depth, and distance from station and bulk density.</p>
 
 </div>
 </div>
@@ -2751,7 +2747,7 @@ <h3 id="determine-field-average-soil-moisture-and-bulk-density">Determine field-
 </div>
 <div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
 </div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput " data-mime-type="text/markdown">
-<h2 id="solving-for-n_0">Solving for $N_0$<a class="anchor-link" href="#solving-for-n_0">&#182;</a></h2><p>Previous steps estimated the a field volumetric water content of <code>0.263</code> and an average neutron count of <code>1542</code>. Using <a href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.root.html"><code>scipy.optimize.root()</code></a> $N_0$ is estimated given the observed value of $\theta_v$ and neutron counts.</p>
+<h2 id="solving-for-n_0">Solving for $N_0$<a class="anchor-link" href="#solving-for-n_0">&#182;</a></h2><p>Previous steps estimated the field volumetric water content of <code>0.263</code> and an average neutron count of <code>1542</code>. Using <a href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.root.html"><code>scipy.optimize.root()</code></a> $N_0$ is estimated given the observed value of $\theta_v$ and neutron counts.</p>
 
 </div>
 </div>

site/examples/rover/Hydroinnova_rover_example/index.html

@@ -2401,7 +2401,7 @@ <h4 id="atmospheric-correction">Atmospheric correction<a class="anchor-link" hre
 <span class="c1"># Estimate lattice water based on texture</span>
 <span class="n">lattice_water</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">lattice_water</span><span class="p">(</span><span class="n">clay_content</span><span class="o">=</span><span class="mf">0.35</span><span class="p">)</span>
 
-<span class="c1"># Estimate Soil Columetric Water Content</span>
+<span class="c1"># Estimate Soil Volumetric Water Content</span>
 <span class="n">df</span><span class="p">[</span><span class="s1">&#39;VWC&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">crnpy</span><span class="o">.</span><span class="n">counts_to_vwc</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;corrected_neutrons_smoothed&#39;</span><span class="p">],</span> <span class="n">N0</span><span class="o">=</span><span class="mi">550</span><span class="p">,</span> <span class="n">bulk_density</span><span class="o">=</span><span class="mf">1.3</span><span class="p">,</span> <span class="n">Wlat</span><span class="o">=</span><span class="n">lattice_water</span><span class="p">,</span> <span class="n">Wsoc</span><span class="o">=</span><span class="mf">0.01</span><span class="p">)</span>
 
 <span class="c1"># Drop VWC NaN values before interpolating values</span>
@@ -2422,7 +2422,7 @@ <h4 id="atmospheric-correction">Atmospheric correction<a class="anchor-link" hre
 # Estimate lattice water based on texture
 lattice_water = crnpy.lattice_water(clay_content=0.35)
 
-# Estimate Soil Columetric Water Content
+# Estimate Soil Volumetric Water Content
 df['VWC'] = crnpy.counts_to_vwc(df['corrected_neutrons_smoothed'], N0=550, bulk_density=1.3, Wlat=lattice_water, Wsoc=0.01)
 
 # Drop VWC NaN values before interpolating values