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Removed deprecated aliases
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@dineshpinto
dineshpinto committed Aug 17, 2023
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267 changes: 2 additions & 265 deletions docs/qudi_hira_analysis/analysis_logic.html
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Expand Up @@ -537,12 +537,7 @@ <h1 class="title">Module <code>qudi_hira_analysis.analysis_logic</code></h1>
pixel_avg = np.nanmean(image[row - 1:row + 2, col - 1:col + 2])

image[row, col] = pixel_avg
return image

# Aliases for backwards compatibility
analyse_mean = analyze_mean
analyse_mean_norm = analyze_mean_norm
analyse_mean_reference = analyze_mean_reference</code></pre>
return image</code></pre>
</details>
</section>
<section>
Expand Down Expand Up @@ -992,12 +987,7 @@ <h2 class="section-title" id="header-classes">Classes</h2>
pixel_avg = np.nanmean(image[row - 1:row + 2, col - 1:col + 2])

image[row, col] = pixel_avg
return image

# Aliases for backwards compatibility
analyse_mean = analyze_mean
analyse_mean_norm = analyze_mean_norm
analyse_mean_reference = analyze_mean_reference</code></pre>
return image</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
Expand Down Expand Up @@ -1113,256 +1103,6 @@ <h3>Class variables</h3>
</dl>
<h3>Static methods</h3>
<dl>
<dt id="qudi_hira_analysis.analysis_logic.AnalysisLogic.analyse_mean"><code class="name flex">
<span>def <span class="ident">analyse_mean</span></span>(<span>laser_data: np.ndarray, signal_start: float = 1e-07, signal_end: float = 3e-07, bin_width: float = 1e-09) ‑> Tuple[numpy.ndarray, numpy.ndarray]</span>
</code></dt>
<dd>
<div class="desc"><p>Calculate the mean of the signal window.</p>
<h2 id="args">Args</h2>
<dl>
<dt><strong><code>laser_data</code></strong></dt>
<dd>2D array of laser data</dd>
<dt><strong><code>signal_start</code></strong></dt>
<dd>start of the signal window in seconds</dd>
<dt><strong><code>signal_end</code></strong></dt>
<dd>end of the signal window in seconds</dd>
<dt><strong><code>bin_width</code></strong></dt>
<dd>width of a bin in seconds</dd>
</dl>
<h2 id="returns">Returns</h2>
<p>Mean of the signal window and measurement error</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">@staticmethod
def analyze_mean(
laser_data: np.ndarray,
signal_start: float = 100e-9,
signal_end: float = 300e-9,
bin_width: float = 1e-9
) -&gt; Tuple[np.ndarray, np.ndarray]:
&#34;&#34;&#34;
Calculate the mean of the signal window.

Args:
laser_data: 2D array of laser data
signal_start: start of the signal window in seconds
signal_end: end of the signal window in seconds
bin_width: width of a bin in seconds

Returns:
Mean of the signal window and measurement error
&#34;&#34;&#34;
# Get number of lasers
num_of_lasers = laser_data.shape[0]

if not isinstance(bin_width, float):
return np.zeros(num_of_lasers), np.zeros(num_of_lasers)

# Convert the times in seconds to bins (i.e. array indices)
signal_start_bin = round(signal_start / bin_width)
signal_end_bin = round(signal_end / bin_width)

# initialize data arrays for signal and measurement error
signal_data = np.empty(num_of_lasers, dtype=float)
error_data = np.empty(num_of_lasers, dtype=float)

# loop over all laser pulses and analyze them
for ii, laser_arr in enumerate(laser_data):
# calculate the mean of the data in the signal window
signal = laser_arr[signal_start_bin:signal_end_bin].mean()
signal_sum = laser_arr[signal_start_bin:signal_end_bin].sum()
signal_error = np.sqrt(signal_sum) / (signal_end_bin - signal_start_bin)

# Avoid numpy C type variables overflow and NaN values
if signal &lt; 0 or signal != signal:
signal_data[ii] = 0.0
error_data[ii] = 0.0
else:
signal_data[ii] = signal
error_data[ii] = signal_error

return signal_data, error_data</code></pre>
</details>
</dd>
<dt id="qudi_hira_analysis.analysis_logic.AnalysisLogic.analyse_mean_norm"><code class="name flex">
<span>def <span class="ident">analyse_mean_norm</span></span>(<span>laser_data: np.ndarray, signal_start: float = 1e-07, signal_end: float = 3e-07, norm_start: float = 1e-06, norm_end=2e-06, bin_width: float = 1e-09) ‑> Tuple[numpy.ndarray, numpy.ndarray]</span>
</code></dt>
<dd>
<div class="desc"><p>Divides the mean of the signal window from the mean of the reference window.</p>
<h2 id="args">Args</h2>
<dl>
<dt><strong><code>laser_data</code></strong></dt>
<dd>2D array of laser data</dd>
<dt><strong><code>signal_start</code></strong></dt>
<dd>start of the signal window in seconds</dd>
<dt><strong><code>signal_end</code></strong></dt>
<dd>end of the signal window in seconds</dd>
<dt><strong><code>norm_start</code></strong></dt>
<dd>start of the reference window in seconds</dd>
<dt><strong><code>norm_end</code></strong></dt>
<dd>end of the reference window in seconds</dd>
<dt><strong><code>bin_width</code></strong></dt>
<dd>width of a bin in seconds</dd>
</dl>
<h2 id="returns">Returns</h2>
<p>Normalized mean of the signal window and measurement error</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">@staticmethod
def analyze_mean_norm(
laser_data: np.ndarray,
signal_start: float = 100e-9,
signal_end: float = 300e-9,
norm_start: float = 1000e-9,
norm_end=2000e-9,
bin_width: float = 1e-9
) -&gt; Tuple[np.ndarray, np.ndarray]:
&#34;&#34;&#34;
Divides the mean of the signal window from the mean of the reference window.

Args:
laser_data: 2D array of laser data
signal_start: start of the signal window in seconds
signal_end: end of the signal window in seconds
norm_start: start of the reference window in seconds
norm_end: end of the reference window in seconds
bin_width: width of a bin in seconds

Returns:
Normalized mean of the signal window and measurement error
&#34;&#34;&#34;
# Get number of lasers
num_of_lasers = laser_data.shape[0]

if not isinstance(bin_width, float):
return np.zeros(num_of_lasers), np.zeros(num_of_lasers)

# Convert the times in seconds to bins (i.e. array indices)
signal_start_bin = round(signal_start / bin_width)
signal_end_bin = round(signal_end / bin_width)
norm_start_bin = round(norm_start / bin_width)
norm_end_bin = round(norm_end / bin_width)

# initialize data arrays for signal and measurement error
signal_data = np.empty(num_of_lasers, dtype=float)
error_data = np.empty(num_of_lasers, dtype=float)

# loop over all laser pulses and analyze them
for ii, laser_arr in enumerate(laser_data):
# calculate the sum and mean of the data in the normalization window
tmp_data = laser_arr[norm_start_bin:norm_end_bin]
reference_sum = np.sum(tmp_data)
reference_mean = (reference_sum / len(tmp_data)) if len(tmp_data) != 0 else 0.0

# calculate the sum and mean of the data in the signal window
tmp_data = laser_arr[signal_start_bin:signal_end_bin]
signal_sum = np.sum(tmp_data)
signal_mean = (signal_sum / len(tmp_data)) if len(tmp_data) != 0 else 0.0

# Calculate normalized signal while avoiding division by zero
if reference_mean &gt; 0 and signal_mean &gt;= 0:
signal_data[ii] = signal_mean / reference_mean
else:
signal_data[ii] = 0.0

# Calculate measurement error while avoiding division by zero
if reference_sum &gt; 0 and signal_sum &gt; 0:
# calculate with respect to gaussian error &#39;evolution&#39;
error_data[ii] = signal_data[ii] * np.sqrt(1 / signal_sum + 1 / reference_sum)
else:
error_data[ii] = 0.0

return signal_data, error_data</code></pre>
</details>
</dd>
<dt id="qudi_hira_analysis.analysis_logic.AnalysisLogic.analyse_mean_reference"><code class="name flex">
<span>def <span class="ident">analyse_mean_reference</span></span>(<span>laser_data: np.ndarray, signal_start: float = 1e-07, signal_end: float = 3e-07, norm_start: float = 1e-06, norm_end: float = 2e-06, bin_width: float = 1e-09) ‑> Tuple[numpy.ndarray, numpy.ndarray]</span>
</code></dt>
<dd>
<div class="desc"><p>Subtracts the mean of the signal window from the mean of the reference window.</p>
<h2 id="args">Args</h2>
<dl>
<dt><strong><code>laser_data</code></strong></dt>
<dd>2D array of laser data</dd>
<dt><strong><code>signal_start</code></strong></dt>
<dd>start of the signal window in seconds</dd>
<dt><strong><code>signal_end</code></strong></dt>
<dd>end of the signal window in seconds</dd>
<dt><strong><code>norm_start</code></strong></dt>
<dd>start of the reference window in seconds</dd>
<dt><strong><code>norm_end</code></strong></dt>
<dd>end of the reference window in seconds</dd>
<dt><strong><code>bin_width</code></strong></dt>
<dd>width of a bin in seconds</dd>
</dl>
<h2 id="returns">Returns</h2>
<p>Referenced mean of the signal window and measurement error</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">@staticmethod
def analyze_mean_reference(
laser_data: np.ndarray,
signal_start: float = 100e-9,
signal_end: float = 300e-9,
norm_start: float = 1000e-9,
norm_end: float = 2000e-9,
bin_width: float = 1e-9) -&gt; Tuple[np.ndarray, np.ndarray]:
&#34;&#34;&#34;
Subtracts the mean of the signal window from the mean of the reference window.

Args:
laser_data: 2D array of laser data
signal_start: start of the signal window in seconds
signal_end: end of the signal window in seconds
norm_start: start of the reference window in seconds
norm_end: end of the reference window in seconds
bin_width: width of a bin in seconds

Returns:
Referenced mean of the signal window and measurement error
&#34;&#34;&#34;
# Get number of lasers
num_of_lasers = laser_data.shape[0]

if not isinstance(bin_width, float):
return np.zeros(num_of_lasers), np.zeros(num_of_lasers)

# Convert the times in seconds to bins (i.e. array indices)
signal_start_bin = round(signal_start / bin_width)
signal_end_bin = round(signal_end / bin_width)
norm_start_bin = round(norm_start / bin_width)
norm_end_bin = round(norm_end / bin_width)

# initialize data arrays for signal and measurement error
signal_data = np.empty(num_of_lasers, dtype=float)
error_data = np.empty(num_of_lasers, dtype=float)

# loop over all laser pulses and analyze them
for ii, laser_arr in enumerate(laser_data):
# calculate the sum and mean of the data in the normalization window
tmp_data = laser_arr[norm_start_bin:norm_end_bin]
reference_sum = np.sum(tmp_data)
reference_mean = (reference_sum / len(tmp_data)) if len(tmp_data) != 0 else 0.0

# calculate the sum and mean of the data in the signal window
tmp_data = laser_arr[signal_start_bin:signal_end_bin]
signal_sum = np.sum(tmp_data)
signal_mean = (signal_sum / len(tmp_data)) if len(tmp_data) != 0 else 0.0

signal_data[ii] = signal_mean - reference_mean

# calculate with respect to gaussian error &#39;evolution&#39;
error_data[ii] = signal_data[ii] * np.sqrt(1 / abs(signal_sum) + 1 / abs(reference_sum))

return signal_data, error_data</code></pre>
</details>
</dd>
<dt id="qudi_hira_analysis.analysis_logic.AnalysisLogic.analyze_mean"><code class="name flex">
<span>def <span class="ident">analyze_mean</span></span>(<span>laser_data: np.ndarray, signal_start: float = 1e-07, signal_end: float = 3e-07, bin_width: float = 1e-09) ‑> Tuple[numpy.ndarray, numpy.ndarray]</span>
</code></dt>
Expand Down Expand Up @@ -2200,9 +1940,6 @@ <h1>Index</h1>
<li>
<h4><code><a title="qudi_hira_analysis.analysis_logic.AnalysisLogic" href="#qudi_hira_analysis.analysis_logic.AnalysisLogic">AnalysisLogic</a></code></h4>
<ul class="">
<li><code><a title="qudi_hira_analysis.analysis_logic.AnalysisLogic.analyse_mean" href="#qudi_hira_analysis.analysis_logic.AnalysisLogic.analyse_mean">analyse_mean</a></code></li>
<li><code><a title="qudi_hira_analysis.analysis_logic.AnalysisLogic.analyse_mean_norm" href="#qudi_hira_analysis.analysis_logic.AnalysisLogic.analyse_mean_norm">analyse_mean_norm</a></code></li>
<li><code><a title="qudi_hira_analysis.analysis_logic.AnalysisLogic.analyse_mean_reference" href="#qudi_hira_analysis.analysis_logic.AnalysisLogic.analyse_mean_reference">analyse_mean_reference</a></code></li>
<li><code><a title="qudi_hira_analysis.analysis_logic.AnalysisLogic.analyze_mean" href="#qudi_hira_analysis.analysis_logic.AnalysisLogic.analyze_mean">analyze_mean</a></code></li>
<li><code><a title="qudi_hira_analysis.analysis_logic.AnalysisLogic.analyze_mean_norm" href="#qudi_hira_analysis.analysis_logic.AnalysisLogic.analyze_mean_norm">analyze_mean_norm</a></code></li>
<li><code><a title="qudi_hira_analysis.analysis_logic.AnalysisLogic.analyze_mean_reference" href="#qudi_hira_analysis.analysis_logic.AnalysisLogic.analyze_mean_reference">analyze_mean_reference</a></code></li>
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