tem auswertung anfang

Lab_Report_LaTeX

@@ -1 +1 @@
-Subproject commit 6f7da8668c507f74760217c2e4c5f944f0d10b0d
+Subproject commit bc42f5fa1d5bdeddbe983f0fd653a5e0843c0f8f

tem/auswertung/out/figlist.txt

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+
+\begin{figure}[H]\centering
+  \input{../auswertung/figs/gold_hires/profile_1.pgf}
+  \caption{}
+  \label{fig:gold_hires-profile_1}
+\end{figure}
+    
+\begin{figure}[H]\centering
+  \input{../auswertung/figs/gold_hires/profile_4.pgf}
+  \caption{}
+  \label{fig:gold_hires-profile_4}
+\end{figure}
+    
+\begin{figure}[H]\centering
+  \input{../auswertung/figs/gold_hires/profile_6.pgf}
+  \caption{}
+  \label{fig:gold_hires-profile_6}
+\end{figure}
+    
+\begin{figure}[H]\centering
+  \input{../auswertung/figs/gold_hires/profile_10.pgf}
+  \caption{}
+  \label{fig:gold_hires-profile_10}
+\end{figure}
+    

tem/auswertung/utility.py

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+import numpy.fft as fft
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.optimize import root_scalar
+import matplotlib
+import matplotlib.ticker as ticker
+import os
+import re
+from SecondaryValue import SecondaryValue
+from scipy.optimize import curve_fit
+from scipy.signal import find_peaks
+
+###############################################################################
+#                                  Auxiliary                                  #
+###############################################################################
+
+
+def normalize(array):
+    tmp = array.copy()
+    tmp = tmp - tmp.min()
+    return tmp/tmp.max()
+
+def load_profiles(path):
+    """Parses the measured Gold profiles.
+
+    :param path: path to the log
+    :returns: numpy array, (x, amplitude)
+    """
+
+    skip = 0
+    with open(path, 'r') as f:
+        for line in f:
+            if 'Values' in line:
+                break
+            skip += 1
+
+    data = np.loadtxt(path, encoding='latin1', dtype=np.float, skiprows=skip + 1)
+
+    data[:, 1] = normalize(data[:, 1])
+    return data
+
+def scientific_round(val, *err):
+    """Scientifically rounds the values to the given errors."""
+    val, err = np.asarray(val), np.asarray(err)
+    err = err.T
+
+    if err.size == 1 and val.size > 1:
+        err = np.ones_like(val)*err
+
+    if len(err.shape) == 0:
+        err = np.array([err])
+
+    if val.size == 1 and err.shape[0] > 1:
+        val = np.ones_like(err)*val
+
+    i = np.floor(np.log10(err))
+    first_digit = (err // 10**i).astype(int)
+    prec = (-i + np.ones_like(err) * (first_digit <= 3)).astype(int)
+    prec = np.max(prec, axis=1)
+
+    def smart_round(value, precision):
+        value = np.round(value, precision)
+        if precision <= 0:
+            value = value.astype(int)
+        return value
+
+    if val.size > 1:
+        rounded = np.empty_like(val)
+        rounded_err = np.empty_like(err)
+        for n, (value, error, precision) in enumerate(zip(val, err, prec)):
+            rounded[n] = smart_round(value, precision)
+            rounded_err[n] = smart_round(error, precision)
+
+        return rounded, rounded_err
+    else:
+        prec = prec[0]
+        return smart_round(val, prec), smart_round(err, prec)[0]
+
+###############################################################################
+#                                  Plot Porn                                  #
+###############################################################################
+
+matplotlib.rcParams.update({
+    'font.family': 'serif',
+    'text.usetex': False,
+    'pgf.rcfonts': False,
+})
+
+def pinmp_ticks(axis, ticks):
+    axis.set_major_locator(ticker.MaxNLocator(ticks))
+    axis.set_minor_locator(ticker.MaxNLocator(ticks*10))
+    return axis
+
+def set_up_plot(ticks=10, pimp_top=True, subplot=111, fig=None):
+    if fig is None:
+        fig = plt.figure()
+    ax = fig.add_subplot(subplot)
+
+    pinmp_ticks(ax.xaxis, ticks)
+    pinmp_ticks(ax.yaxis, ticks)
+
+    ax.grid(which='minor', alpha=.3)
+    ax.grid(which='major', alpha=.8)
+
+
+    if pimp_top:
+        ax.tick_params(right=True, top=True, which='both')
+    else:
+        ax.tick_params(right=True, which='both')
+
+    return fig, ax
+
+def save_fig(fig, title, folder='unsorted', size=(5, 4)):
+    fig.set_size_inches(*size)
+    fig.tight_layout()
+    try:
+        os.makedirs(f'./figs/{folder}/')
+    except OSError as exc:
+        pass
+    fig.savefig(f'./figs/{folder}/{title}.pdf')
+    fig.savefig(f'./figs/{folder}/{title}.pgf')
+
+    with open('./out/figlist.txt', 'a') as f:
+        f.write(r'''
+\begin{figure}[H]\centering
+  \input{../auswertung/figs/'''
+  + f'{folder}/{title}.pgf' +
+  r'''}
+  \caption{}
+  \label{fig:''' + folder + '-' + title + r'''}
+\end{figure}
+    ''')
+
+def plot_profile(profile, **pyplot_args):
+    x, amp = profile.T
+    fig, ax = set_up_plot()
+
+    ax.step(x, amp, label='Intensität', **pyplot_args)
+    ax.set_xlim([x[0], x[-1]])
+    ax.set_ylabel('relative Intensit\"a')
+    ax.set_xlabel('x [nm]')
+
+    ax.legend()
+
+    return fig, ax
+
+
+def analyze_profile(profile, limits=(0, -1), save=None):
+    x, amp = profile.T
+    fig, ax = plot_profile(profile)
+
+    peaks, _ = find_peaks(amp[limits[0]:limits[1]])
+    peaks += limits[0]
+
+    ax.plot(x[peaks], amp[peaks], "x", label='Peaks')
+    ax.axvspan(x[limits[0]], x[limits[1]], color='gray', zorder=-1, alpha=.2,
+               label='Auswertungsbereich')
+    ax.legend()
+
+    dx = (x[1] - x[0])
+    numpeaks = peaks.size - 1
+    sigma = (x[peaks[1:]] - x[peaks[:-1]]).std()/np.sqrt(numpeaks)
+    l = (x[peaks[-1]] - x[peaks[0]])/numpeaks
+    dl = np.sqrt(2)*dx/numpeaks
+
+    if save:
+        save_fig(fig, *save)
+
+    return l, dl, sigma
+
+
+def evaluate_hypothesis(analyzed, maximum=10, gold=.4078):
+    diffs = np.empty((maximum, analyzed.shape[0]))
+
+    squared_ds = np.arange(1, maximum + 1, 1)
+    ds = np.sqrt(squared_ds)
+    a = analyzed[:,0][:, None] * ds[None, :]
+    diff = np.abs(a - gold)
+    mindiff = np.argmin(diff, axis=1)
+    return squared_ds[mindiff], analyzed[:]*ds[mindiff,None],  diff.min(axis=1)
+
+def generate_hypethsesis_table(squared, analyzed, residues):
+    out = ''
+    for square, value, residue in zip(squared, analyzed, residues):
+        #value = scientific_round(*value)
+        val, err = scientific_round(value[0], [value[1]], [value[2]])
+
+        out += rf'\(\sqrt{{{square}}}\) & {val} & ' \
+        + ' & '.join(err.astype(str)) + f' & {residue:.3f} \\\\\n'
+
+    return out