#!/usr/bin/env python3 import numpy as np import sys ################################################################################ # BEWARE, FOR BEYOND THIS POINT THERE BE DRAGONS! THIS IS ONLY FOR EASE OF # GENERATING ACADEMIC PUBLICATIONS AND FIGURES, NEVER DO THIS SHIT! ################################################################################ def g1_map_default(system): # compute correction factor for g1 that will produce common gain at f0 g1_swp = system.g1 * np.sin(np.pi/2-system.phase_swp) / system.alpha_swp return g1_swp def g1_map_flat(system): return system.g1*np.ones(system.phase_swp.shape) def gamma_map_default(system): return np.cos(np.pi/2-system.phase_swp) / system.Q1 / system.alpha_swp def gamma_map_flat(system): return np.tan(np.pi/2-system.phase_swp) / system.Q1 / system.alpha_swp # Operating Enviornment ##### class ampSystem: f0 = 28 bw0 = 8 bw_plt = 3 """define global (hardware descriptive) variables for use in our system.""" def __init__(self, quiet=False): self.f0 = self.__class__.f0 # design frequency (GHz) self.bw0 = self.__class__.bw0 # assumed extreme tuning range (GHz) self.bw_plt = self.__class__.bw_plt # Plotting range (GHz) # Configuration Of Hardware ##### self.q1_L = 25 self.q1_C = 8 self.l1 = 140e-3 # nH self.gm1 = 2.5e-3 # S self._gamma_steps=8 self._gamma_cap_ratio = 0.997 self.alpha_min=1 if not quiet: ## Report System Descrption print(' L1 = %.3fpH, C1 = %.3ffF' % (1e3*self.l1, 1e6*self.c1)) print(' Rp = %.3f Ohm' % (1/self.g1)) print(' Q = %.1f' % (self.Q1)) self._gamma_warn = False self._g1_map_function = g1_map_default self._gamma_map_function = gamma_map_default @property def w0(self): return self.f0*2*np.pi @property def fbw(self): # fractional bandwidth return self.bw0/self.f0 # Compute system ##### @property def c1(self): return 1/(self.w0*self.w0*self.l1) @property def g1(self): g1_L = 1 / (self.q1_L*self.w0*self.l1) g1_C = self.w0 * self.c1 / self.q1_C return g1_L + g1_C @property def Q1(self): return np.sqrt(self.c1/self.l1)/self.g1 @property def phase_max(self): return np.pi/2 * (1 - 1/self.gamma_len) @property def gamma_len(self): return self._gamma_steps @property def gamma(self): gamma = 1 - np.power(self.f0 / (self.f0 + self.bw0/2),2) phase_limit_requested = (1-1/self.gamma_len)*np.pi/2 # Verify gamma is valid ##### gamma_max = 1/(np.min((1, self.alpha_min))*self.Q1) if gamma > (self._gamma_cap_ratio * gamma_max): if not self._gamma_warn: self._gamma_warn = True print("==> WARN: Gamma to large, reset to %.1f%% (was %.1f%%) <==" % \ (100*self._gamma_cap_ratio * gamma_max, 100*gamma)) gamma = self._gamma_cap_ratio * gamma_max return gamma @property def alpha_swp(self): range_partial = np.ceil(self.gamma_len/2) rhs = np.power(np.linspace(0,1,range_partial),2)*(self.alpha_min-1)+1 lhs = np.flip(rhs,0) #lhs = np.linspace(np.sqrt(self.alpha_min),1, range_partial) #rhs = np.flip(lhs,0) swp = np.concatenate((lhs,rhs[1:])) if np.mod(self.gamma_len,2) == 1 \ else np.concatenate((lhs,rhs)) #return np.power(swp,2) return swp def set_gamma_swp(self, gamma_swp_function): self._gamma_map_function = gamma_swp_function @property def gamma_swp(self): return self._gamma_map_function(self) @property def phase_swp(self): #def phaseSweepGenerate(g1, gamma, c, l, phase_extreme, phase_steps): # Linear PHASE gamma spacing # First compute the most extreme phase given the extreme gamma # if gamma is tuned to the limit, and we want to match the gain performance, # then this is the required tuned g1 value. gamma = self.gamma g1_limit = np.sqrt(np.power(self.g1,2) - np.power(gamma,2)*self.c1/self.l1) # This implies a Q in that particular setting Q_limit = self.Q1*self.g1/g1_limit # given this !, I compute the delta phase at that point. phase_limit = np.pi/2 - np.arctan(1/(Q_limit*gamma)) phase_swp = np.linspace(-1,1,self.gamma_len) * self.phase_max if phase_limit < self.phase_max: print( "==> ERROR: Phase Beyond bounds. Some states will be ignored") print( " %.3f requested\n" " %.3f hardware limit" % \ (180/np.pi*self.phase_max, 180/np.pi*abs(phase_limit))) print( " To increase tuning range, gamma must rise or native Q must rise") phase_swp = np.where(phase_swp > phase_limit, phase_swp, np.NaN) # This gives us our equal phase spacing points return phase_swp @property def c1_swp(self): return self.c1 * (1 + self.gamma_swp) def set_g1_swp(self, g1_swp_function): self._g1_map_function = g1_swp_function @property def g1_swp(self): return self._g1_map_function(self) def compute_block(self, f_dat): g1_swp = self.g1_swp c1_swp = self.c1_swp y_tank = np.zeros((self.gamma_len,f_dat.steps), dtype=complex) tf = np.zeros((self.gamma_len,f_dat.steps), dtype=complex) for itune,gamma_tune in enumerate(self.gamma_swp): c1_tune = c1_swp[itune] g1_tune = g1_swp[itune] y_tank[itune,:] = g1_tune + f_dat.jw*c1_tune + 1/(f_dat.jw * self.l1) tf[itune,:] = self.__class__.tf_compute(f_dat.delta, gamma_tune, g1_tune, self.gm1, self.l1, self.c1) tf = tf.T return (y_tank, tf) def compute_ref(self, f_dat): y_tank = self.g1 + f_dat.jw*self.c1 + 1/(f_dat.jw * self.l1) tf = self.__class__.tf_compute(f_dat.delta, 0, self.g1, self.gm1, self.l1, self.c1) return (y_tank, tf) @classmethod def tf_compute(cls, delta, gamma, gx, gm, l, c): Q = np.sqrt(c/l)/gx return gm / gx \ * 1j*(1+delta) \ / (1j*(1+delta) + Q*(1-np.power(1+delta,2)*(1+gamma))) # Operating Enviornment ##### class bufferSystem: """define global (hardware descriptive) variables for use in our system.""" def __init__(self, quiet=False): self.f0 = ampSystem.f0 # design frequency (GHz) self.bw0 = ampSystem.bw0 # assumed extreme tuning range (GHz) self.bw_plt = ampSystem.bw_plt # Plotting range (GHz) # Configuration Of Hardware ##### self.q2_L = 15 self.q2_C = 30 self.l2 = 140e-3 # nH self.gm2 = 4e-3 # S if not quiet: ## Report System Descrption print(' L2 = %.3fpH, C2 = %.3ffF' % (1e3*self.l2, 1e6*self.c2)) print(' Rp = %.3f Ohm' % (1/self.g2)) print(' Q = %.1f' % (self.Q2)) @property def w0(self): return self.f0*2*np.pi @property def fbw(self): # fractional bandwidth return self.bw0/self.f0 # Compute system ##### @property def c2(self): return 1/(self.w0*self.w0*self.l2) @property def g2(self): g2_L = 1 / (self.q2_L*self.w0*self.l2) g2_C = self.w0 * self.c2 / self.q2_C return g2_L + g2_C @property def Q2(self): return np.sqrt(self.c2/self.l2)/self.g2 def compute_ref(self, f_dat): y_tank = self.g2 + f_dat.jw*self.c2 + 1/(f_dat.jw * self.l2) tf = self.__class__.tf_compute(f_dat.delta, self.g2, self.gm2, self.l2, self.c2) return (y_tank, tf) @classmethod def tf_compute(cls, delta, gx, gm, l, c): Q = np.sqrt(c/l)/gx return gm / gx \ * 1j*(1+delta) \ / (1j*(1+delta) + Q*(1-np.power(1+delta,2)))