update to include datapoints

data/m04_cycledepends/processing_cycledepends.py

@@ -8,15 +8,12 @@ import VisTools.plotting as vt
 import uncertainties as unc
 import uncertainties.unumpy as unp
 
-import statsmodels.api as sm
-from statsmodels.stats.outliers_influence import summary_table as st
-
 plt.ioff()
 plt.style.use('bmh')
 fig, axarr = plt.subplots(
     2,
     1,
-    figsize=(9, 8),
+    figsize=(10, 8),
     gridspec_kw = {'height_ratios':[4,1]}
 )
 plt.subplots_adjust(hspace=.5)
@@ -26,6 +23,7 @@ fits = np.array([])
 errs = np.array([])
 colors = np.array([])
 
+data['dv_pit48/v'] /= 2
 data['Dv'] = data['v_pit48/v'] - data['v_set/v']
 data['Dvk'] = data['v_keith/v'] - data['v_set/v']
 print(data)
@@ -36,10 +34,10 @@ def plot_and_linfit(col: str,fil, l: str):
     p = axarr[0].errorbar(
         data[fil]['v_set/v'],
         data[fil][col],
-        alpha = 0,
+        alpha = 0.3,
         label=None,
-    #    fmt='.',
-    #    antialiased=True
+        fmt='.',
+        antialiased=True
     )
 
     vals = vt.lm_plot(
@@ -55,13 +53,16 @@ def plot_and_linfit(col: str,fil, l: str):
 
     return vals, p
 
-plot_and_linfit('Dvk', data.cycles == 0, 'ref')
 for i in range(8):
-    val, p = plot_and_linfit('Dv', data.cycles == i, 'scaler: {}'.format(i))
+    val, p = plot_and_linfit(
+        'Dv', 
+        data.cycles == i, 
+        '$f_{}={{m:.3f}}\\cdot x{{c:+.3f}}V$'.format(i))
     fits = np.append(fits, val[0])
     #errs = np.append(errs, np.sqrt(pcov[0][0]))
     #colors = np.append(colors, p[0].get_color() )
 
+plot_and_linfit('Dvk', data.cycles == 0, 'reference measurement')
 
 print(fits[0].n)
 for i in range(8):
@@ -77,14 +78,16 @@ axarr[0].set_xlabel('V$_{set}$/V')
 #axarr[0].set_title("PowerIt ADC Calibration: dependency on measurement cycles")
 axarr[0].set_ylabel('$\Delta$V$_{IN}$ / V')
 
-axarr[1].set_xlabel('sampleTicks scaler')
-axarr[1].set_ylabel('$\Delta(\Delta$V$_{IN})$')
-axarr[0].legend(bbox_to_anchor=(0, -.28,1, 1), loc="lower left",
-                mode="expand", borderaxespad=0, ncol=5)
-#plt.tight_layout()
-plt.savefig("./" + __file__[:-3].split("_")[1] + ".pdf", dpi=1000, bbox_inches='tight')
+axarr[1].set_xlabel('scaler value')
+axarr[1].set_ylabel('$m = \Delta$Gain')
+axarr[0].legend(
+    bbox_to_anchor=(0, -.29,1, 1), 
+    loc="lower left",
+    mode="expand", 
+    borderaxespad=0, 
+    ncol=3)
+plt.savefig(
+    "./" + __file__[:-3].split("_")[1] + ".pdf", 
+    dpi=1000, 
+    bbox_inches='tight')
 
-
-ss, d, ss2 = st(sm.OLS(data[data.cycles == 0]['v_pit48/v'], data[data.cycles == 0]['v_set/v']).fit(), alpha=.05)
-
-low_ci, high_ci = d[:,4:6].T