Revert "Merge branch 'develop_awgv1_devices' of https://github.com/Di…

…CarloLab-Delft/PycQED_py3 into develop_awgv1_devices"

This reverts commit 066b3c3, reversing
changes made to 31ee0be.

pycqed/analysis/analysis_toolbox.py

@@ -1950,19 +1950,22 @@ def plot_errorbars(x, y, ax=None, linewidth=2 ,markersize=2, marker='none'):
 
 
 def calculate_transmon_transitions(EC, EJ, asym=0, reduced_flux=0,
-                                   no_transitions=2, dim=None, ng=0):
+                                   no_transitions=2, dim=None):
     '''
     Calculates transmon energy levels from the full transmon qubit Hamiltonian.
     '''
     if dim is None:
         dim = no_transitions*20
 
     EJphi = EJ*np.sqrt(asym**2 + (1-asym**2)*np.cos(np.pi*reduced_flux)**2)
-    Ham = 4*EC*np.diag(np.arange(-dim-ng, dim-ng+1)**2) - EJphi/2 * \
+
+    Ham = 4*EC*np.diag(np.arange(-dim, dim+1)**2) - EJphi/2 * \
         (np.eye(2*dim+1, k=+1) + np.eye(2*dim+1, k=-1))
     HamEigs = np.linalg.eigvalsh(Ham)
     HamEigs.sort()
+
     transitions = HamEigs[1:]-HamEigs[:-1]
+
     return transitions[:no_transitions]
 
 

pycqed/analysis/decoupling/decoupling_analysis.py

@@ -47,7 +47,7 @@ def extract_data(self):
 
         # extracts the data
         for i, q_label in enumerate(self.qubit_scan_labels):
-            opt_dict = {'scan_label': '_spectroscopy_'+q_label,
+            opt_dict = {'scan_label': '__qubit_'+q_label,
                        'exact_label_match':True}
 
             self.spec_scans[i] = RA.quick_analysis(t_start=self.scan_start,

pycqed/analysis/fitting_models.py

@@ -3,8 +3,6 @@
 from scipy.special import erfc
 import lmfit
 import logging
-from pycqed.analysis import analysis_toolbox as a_tools
-from pycqed.analysis.tools import data_manipulation as dm_tools
 
 
 #################################
@@ -312,10 +310,33 @@ def HangerFuncAmplitude(f, f0, Q, Qe, A, theta):
     return abs(A * (1. - Q / Qe * np.exp(1.j * theta) / (1. + 2.j * Q * (f / 1.e9 - f0) / f0)))
 
 
+def HangerFuncComplex(f, pars):
+    '''
+    This is the complex function for a hanger which DOES NOT
+    take into account a possible slope
+    Input:
+        f = frequency
+        pars = parameters dictionary
+               f0, Q, Qe, A, theta, phi_v, phi_0
 
-def hanger_func_complex_SI(f, f0, Q, Qe,
-                           A, theta, phi_v, phi_0,
-                           slope =1):
+    Author: Stefano Poletto
+    '''
+    f0 = pars['f0']
+    Q = pars['Q']
+    Qe = pars['Qe']
+    A = pars['A']
+    theta = pars['theta']
+    phi_v = pars['phi_v']
+    phi_0 = pars['phi_0']
+
+    S21 = A * (1 - Q / Qe * np.exp(1j * theta) / (1 + 2.j * Q * (f / 1.e9 - f0) / f0)) * \
+          np.exp(1j * (phi_v * f + phi_0))
+
+    return S21
+
+def hanger_func_complex_SI(f: float, f0: float, Ql: float, Qe: float,
+                           A: float, theta: float, phi_v: float, phi_0: float,
+                           alpha:float =1):
     '''
     This is the complex function for a hanger (lamda/4 resonator).
     See equation 3.1 of the Asaad master thesis.
@@ -328,42 +349,22 @@ def hanger_func_complex_SI(f, f0, Q, Qe,
         f   : frequency
         f0  : resonance frequency
         A   : background transmission amplitude
-        Q  : loaded quality factor
+        Ql  : loaded quality factor
         Qe  : extrinsic quality factor
         theta:  phase of Qe (in rad)
         phi_v:  phase to account for propagation delay to sample
         phi_0:  phase to account for propagation delay from sample
-        slope:  slope of signal around the resonance
-
-    The complex hanger function that has a list of parameters as input
-    is now called hanger_func_complex_SI_pars
+        alpha:  slope of signal around the resonance
 
     '''
-    slope_corr = (1+slope*(f-f0)/f0)
+    slope_corr = (1+alpha*(f-f0)/f0)
     propagation_delay_corr = np.exp(1j * (phi_v * f + phi_0))
-    hanger_contribution = (1 - Q / Qe * np.exp(1j * theta)/
-                               (1 + 2.j * Q * (f  - f0) / f0))
+    hanger_contribution = (1 - Ql / Qe * np.exp(1j * theta)/
+                               (1 + 2.j * Ql * (f  - f0) / f0))
     S21 = A *  slope_corr * hanger_contribution * propagation_delay_corr
 
     return S21
 
-def hanger_func_complex_SI_pars(f,pars):
-    '''
-    This function is used in the minimization fitting which requires parameters.
-    It calls the function hanger_func_complex_SI, see there for details.
-    '''
-
-    f0 = pars['f0']
-    Ql = pars['Ql']
-    Qe = pars['Qe']
-    A = pars['A']
-    theta = pars['theta']
-    phi_v = pars['phi_v']
-    phi_0 = pars['phi_0']
-    alpha = pars['alpha']
-    return hanger_func_complex_SI(f, f0, Ql, Qe,
-                           A, theta, phi_v, phi_0, alpha)
-
 
 
 def PolyBgHangerFuncAmplitude(f, f0, Q, Qe, A, theta, poly_coeffs):
@@ -374,7 +375,6 @@ def PolyBgHangerFuncAmplitude(f, f0, Q, Qe, A, theta, poly_coeffs):
                   HangerFuncAmplitude(f, f0, Q, Qe, A, theta))
 
 
-
 def SlopedHangerFuncAmplitude(f, f0, Q, Qe, A, theta, slope):
     # This is the function for a hanger (lambda/4 resonator) which takes into
     # account a possible slope df
@@ -594,112 +594,6 @@ def exp_dec_guess(model, data, t):
     return params
 
 
-
-def SlopedHangerFuncAmplitudeGuess(data, f, fit_window=None):
-    xvals = f
-    peaks = a_tools.peak_finder(xvals, data)
-      # Search for peak
-    if peaks['dip'] is not None:    # look for dips first
-        f0 = peaks['dip']
-        amplitude_factor = -1.
-    elif peaks['peak'] is not None:  # then look for peaks
-        f0 = peaks['peak']
-        amplitude_factor = 1.
-    else:                                 # Otherwise take center of range
-        f0 = np.median(xvals)
-        amplitude_factor = -1.
-
-    min_index = np.argmin(data)
-    max_index = np.argmax(data)
-    min_frequency = xvals[min_index]
-    max_frequency = xvals[max_index]
-
-    amplitude_guess = max(dm_tools.reject_outliers(data))
-
-    # Creating parameters and estimations
-    S21min = (min(dm_tools.reject_outliers(data)) /
-              max(dm_tools.reject_outliers(data)))
-
-    Q = f0 / abs(min_frequency - max_frequency)
-
-    Qe = abs(Q / abs(1 - S21min))
-    guess_dict = {'f0': {'value': f0*1e-9,
-                         'min': min(xvals)*1e-9,
-                         'max': max(xvals)*1e-9},
-                  'A': {'value': amplitude_guess},
-                  'Q': {'value': Q,
-                        'min': 1,
-                        'max': 50e6},
-                  'Qe': {'value': Qe,
-                         'min': 1,
-                         'max': 50e6},
-                  'Qi': {'expr': 'abs(1./(1./Q-1./Qe*cos(theta)))',
-                         'vary': False},
-                  'Qc': {'expr': 'Qe/cos(theta)',
-                         'vary': False},
-                  'theta': {'value': 0,
-                            'min': -np.pi/2,
-                            'max': np.pi/2},
-                  'slope': {'value':0,
-                            'vary':True}}
-    return guess_dict
-
-
-
-def hanger_func_complex_SI_Guess(data, f, fit_window=None):
-    ## This is complete garbage, just to get some return value
-    xvals = f
-    abs_data = np.abs(data)
-    peaks = a_tools.peak_finder(xvals, abs_data)
-      # Search for peak
-    if peaks['dip'] is not None:    # look for dips first
-        f0 = peaks['dip']
-        amplitude_factor = -1.
-    elif peaks['peak'] is not None:  # then look for peaks
-        f0 = peaks['peak']
-        amplitude_factor = 1.
-    else:                                 # Otherwise take center of range
-        f0 = np.median(xvals)
-        amplitude_factor = -1.
-
-    min_index = np.argmin(abs_data)
-    max_index = np.argmax(abs_data)
-    min_frequency = xvals[min_index]
-    max_frequency = xvals[max_index]
-
-    amplitude_guess = max(dm_tools.reject_outliers(abs_data))
-
-    # Creating parameters and estimations
-    S21min = (min(dm_tools.reject_outliers(abs_data)) /
-              max(dm_tools.reject_outliers(abs_data)))
-
-    Q = f0 / abs(min_frequency - max_frequency)
-
-    Qe = abs(Q / abs(1 - S21min))
-    guess_dict = {'f0': {'value': f0*1e-9,
-                         'min': min(xvals)*1e-9,
-                         'max': max(xvals)*1e-9},
-                  'A': {'value': amplitude_guess},
-                  'Qe': {'value': Qe,
-                         'min': 1,
-                         'max': 50e6},
-                  'Ql': {'value': Q,
-                         'min': 1,
-                         'max': 50e6},
-                  'theta': {'value': 0,
-                            'min': -np.pi/2,
-                            'max': np.pi/2},
-                  'alpha': {'value':0,
-                            'vary':True},
-                  'phi_0': {'value':0,
-                            'vary':True},
-                  'phi_v': {'value':0,
-                            'vary':True}}
-
-    return guess_dict
-
-
-
 def group_consecutives(vals, step=1):
     """Return list of consecutive lists of numbers from vals (number list)."""
     run = []
@@ -1119,7 +1013,7 @@ def sum_int(x,y):
 HangerAmplitudeModel = lmfit.Model(HangerFuncAmplitude)
 SlopedHangerAmplitudeModel = lmfit.Model(SlopedHangerFuncAmplitude)
 PolyBgHangerAmplitudeModel = lmfit.Model(PolyBgHangerFuncAmplitude)
-HangerComplexModel = lmfit.Model(hanger_func_complex_SI)
+HangerComplexModel = lmfit.Model(HangerFuncComplex)
 SlopedHangerComplexModel = lmfit.Model(SlopedHangerFuncComplex)
 QubitFreqDacModel = lmfit.Model(QubitFreqDac)
 QubitFreqFluxModel = lmfit.Model(QubitFreqFlux)

pycqed/analysis/measurement_analysis.py

@@ -3668,7 +3668,7 @@ def plot_2D_histograms(self, shots_I_0, shots_Q_0, shots_I_1, shots_Q_1,
 
         axarray[0].set_title('2D histogram, pi pulse')
         im1 = axarray[0].imshow(np.transpose(H1), interpolation='nearest',
-                                origin='low', aspect='auto',
+                                origin='low',
                                 extent=[xedges1[0], xedges1[-1],
                                         yedges1[0], yedges1[-1]], cmap=cmap)
 
@@ -3684,7 +3684,7 @@ def plot_2D_histograms(self, shots_I_0, shots_Q_0, shots_I_1, shots_Q_1,
         # plotting 2D histograms of mmts with no pulse
         axarray[1].set_title('2D histogram, no pi pulse')
         im0 = axarray[1].imshow(np.transpose(H0), interpolation='nearest',
-                                origin='low', aspect='auto',
+                                origin='low',
                                 extent=[xedges0[0], xedges0[-1], yedges0[0],
                                         yedges0[-1]], cmap=cmap)
 

pycqed/analysis/tools/plotting.py

@@ -9,7 +9,6 @@
 import matplotlib.colors as col
 import hsluv
 
-
 def set_xlabel(axis, label, unit=None, **kw):
     """
     Takes in an axis object and add a unit aware label to it.
@@ -62,13 +61,12 @@ def set_ylabel(axis, label, unit=None, **kw):
     return axis
 
 
-SI_PREFIXES = dict(zip(range(-24, 25, 3), 'yzafpnμm kMGTPEZY'))
-SI_PREFIXES[0] = ""
 
-# N.B. not all of these are SI units, however, all of these support SI prefixes
-SI_UNITS = 'm,s,g,W,J,V,A,F,T,Hz,Ohm,S,N,C,px,b,B,K,Bar,Vpeak,Vpp,Vp,Vrms'.split(
-    ',')
+SI_PREFIXES_2 = dict(zip(range(-24, 25, 3), 'yzafpnμm kMGTPEZY'))
+# SI_PREFIXES_2[0] = ""
+#SI_PREFIXES = 'yzafpnμm kMGTPEZY'
 
+SI_UNITS = 'm,s,g,W,J,V,A,F,T,Hz,Ohm,S,N,C,px,b,B,K,Bar,Vpeak,Vpp,Vp,Vrms'.split(',')
 
 def SI_prefix_and_scale_factor(val, unit=None):
     """
@@ -86,12 +84,7 @@ def SI_prefix_and_scale_factor(val, unit=None):
         try:
             with np.errstate(all="ignore"):
                 prefix_power = np.log10(abs(val))//3 * 3
-                prefix = SI_PREFIXES[prefix_power]
-                # Greek symbols not supported in tex
-                if plt.rcParams['text.usetex'] and prefix == 'μ':
-                    prefix = r'$\mu$'
-
-            return 10 ** -prefix_power,  prefix + unit
+            return 10 ** -prefix_power, SI_PREFIXES_2[prefix_power] + unit
         except (KeyError, TypeError):
             pass
 
@@ -283,6 +276,7 @@ def flex_colormesh_plot_vs_xy(xvals, yvals, zvals, ax=None,
     xvals = np.array(xvals)
     yvals = np.array(yvals)
 
+
     # First, we need to sort the data as otherwise we get odd plotting
     # artefacts. An example is e.g., plotting a fourier transform
     sorted_x_arguments = xvals.argsort()
@@ -352,6 +346,7 @@ def autolabel_barplot(ax, rects, rotation=90):
                 ha='center', va='bottom', rotation=rotation)
 
 
+
 def set_axeslabel_color(ax, color):
     '''
     Ad hoc function to set the labels, ticks, ticklabels and title to a color.
@@ -366,7 +361,8 @@ def set_axeslabel_color(ax, color):
     plt.setp(ax.title, color=color)
 
 
-# generate custom colormaps
+
+##### generate custom colormaps
 def make_segmented_cmap():
     white = '#ffffff'
     black = '#000000'
@@ -376,24 +372,21 @@ def make_segmented_cmap():
         'anglemap', [black, red, white, blue, black], N=256, gamma=1)
     return anglemap
 
+def make_anglemap( N = 256, use_hpl = True ):
+    h = np.ones(N) # hue
+    h[:N//2] = 11.6 # red
+    h[N//2:] = 258.6 # blue
+    s = 100 # saturation
+    l = np.linspace(0, 100, N//2) # luminosity
+    l = np.hstack( (l,l[::-1] ) )
 
-def make_anglemap(N=256, use_hpl=True):
-    h = np.ones(N)  # hue
-    h[:N//2] = 11.6  # red
-    h[N//2:] = 258.6  # blue
-    s = 100  # saturation
-    l = np.linspace(0, 100, N//2)  # luminosity
-    l = np.hstack((l, l[::-1]))
-
-    colorlist = np.zeros((N, 3))
+    colorlist = np.zeros((N,3))
     for ii in range(N):
         if use_hpl:
-            colorlist[ii, :] = hsluv.hpluv_to_rgb((h[ii], s, l[ii]))
+            colorlist[ii,:] = hsluv.hpluv_to_rgb( (h[ii], s, l[ii]) )
         else:
-            colorlist[ii, :] = hsluv.hsluv_to_rgb((h[ii], s, l[ii]))
-    colorlist[colorlist > 1] = 1  # correct numeric errors
+            colorlist[ii,:] = hsluv.hsluv_to_rgb( (h[ii], s, l[ii]) )
+    colorlist[colorlist > 1] = 1 # correct numeric errors
     colorlist[colorlist < 0] = 0
-    return col.ListedColormap(colorlist)
-
-
-hsluv_anglemap = make_anglemap(use_hpl=False)
+    return col.ListedColormap( colorlist )
+hsluv_anglemap = make_anglemap( use_hpl = False )