def shut_time_pdf(mec, tres, tmin=0.00001, tmax=1000, points=512, unit='ms'): """ Calculate ideal asymptotic and exact shut time distributions. Parameters ---------- mec : instance of type Mechanism tres : float Time resolution. tmin, tmax : floats Time range for burst length ditribution. points : int Number of points per plot. unit : str 'ms'- milliseconds. Returns ------- t : ndarray of floats, shape (num of points) Time in millisec. ipdf, epdf, apdf : ndarrays of floats, shape (num of points) Ideal, exact and asymptotic shut time distributions. """ open = False # Asymptotic pdf roots = scl.asymptotic_roots(tres, mec.QII, mec.QAA, mec.QIA, mec.QAI, mec.kI, mec.kA) tmax = (-1 / roots.max()) * 20 t = np.logspace(math.log10(tmin), math.log10(tmax), points) # Ideal pdf. eigs, w = scl.ideal_dwell_time_pdf_components(mec.QII, qml.phiF(mec)) fac = 1 / np.sum((w / eigs) * np.exp(-tres * eigs)) # Scale factor ipdf = t * pdfs.expPDF(t, 1 / eigs, w / eigs) * fac # Asymptotic pdf GAF, GFA = qml.iGs(mec.Q, mec.kA, mec.kI) areas = scl.asymptotic_areas(tres, roots, mec.QII, mec.QAA, mec.QIA, mec.QAI, mec.kI, mec.kA, GFA, GAF) apdf = scl.asymptotic_pdf(t, tres, -1 / roots, areas) # Exact pdf eigvals, gamma00, gamma10, gamma11 = scl.exact_GAMAxx(mec, tres, open) epdf = np.zeros(points) for i in range(points): epdf[i] = (t[i] * scl.exact_pdf(t[i], tres, roots, areas, eigvals, gamma00, gamma10, gamma11)) if unit == 'ms': t = t * 1000 # x scale in millisec return t, ipdf, epdf, apdf
def shut_time_pdf(mec, tres, tmin=0.00001, tmax=1000, points=512, unit='ms'): """ Calculate ideal asymptotic and exact shut time distributions. Parameters ---------- mec : instance of type Mechanism tres : float Time resolution. tmin, tmax : floats Time range for burst length ditribution. points : int Number of points per plot. unit : str 'ms'- milliseconds. Returns ------- t : ndarray of floats, shape (num of points) Time in millisec. ipdf, epdf, apdf : ndarrays of floats, shape (num of points) Ideal, exact and asymptotic shut time distributions. """ open = False # Asymptotic pdf roots = scl.asymptotic_roots(tres, mec.QII, mec.QAA, mec.QIA, mec.QAI, mec.kI, mec.kA) tmax = (-1 / roots.max()) * 20 t = np.logspace(math.log10(tmin), math.log10(tmax), points) # Ideal pdf. eigs, w = scl.ideal_dwell_time_pdf_components(mec.QII, qml.phiF(mec)) fac = 1 / np.sum((w / eigs) * np.exp(-tres * eigs)) # Scale factor ipdf = t * pdfs.expPDF(t, 1 / eigs, w / eigs) * fac # Asymptotic pdf GAF, GFA = qml.iGs(mec.Q, mec.kA, mec.kI) areas = scl.asymptotic_areas(tres, roots, mec.QII, mec.QAA, mec.QIA, mec.QAI, mec.kI, mec.kA, GFA, GAF) apdf = scl.asymptotic_pdf(t, tres, -1 / roots, areas) # Exact pdf eigvals, gamma00, gamma10, gamma11 = scl.exact_GAMAxx(mec, tres, open) epdf = np.zeros(points) for i in range(points): epdf[i] = (t[i] * scl.exact_pdf(t[i], tres, roots, areas, eigvals, gamma00, gamma10, gamma11)) if unit == 'ms': t = t * 1000 # x scale in millisec return t, ipdf, epdf, apdf
def subset_time_pdf(mec, tres, state1, state2, tmin=0.00001, tmax=1000, points=512, unit='ms'): """ Calculate ideal pdf of any subset dwell times. Parameters ---------- mec : instance of type Mechanism tres : float Time resolution. state1, state2 : ints tmin, tmax : floats Time range for burst length ditribution. points : int Number of points per plot. unit : str 'ms'- milliseconds. Returns ------- t : ndarray of floats, shape (num of points) Time in millisec. spdf : ndarray of floats, shape (num of points) Subset dwell time pdf. """ open = False if open: eigs, w = scl.ideal_dwell_time_pdf_components(mec.QAA, qml.phiA(mec)) else: eigs, w = scl.ideal_dwell_time_pdf_components(mec.QII, qml.phiF(mec)) tmax = tau.max() * 20 t = np.logspace(math.log10(tmin), math.log10(tmax), points) # Ideal pdf. fac = 1 / np.sum((w / eigs) * np.exp(-tres * eigs)) # Scale factor ipdf = t * pdfs.expPDF(t, 1 / eigs, w / eigs) * fac spdf = np.zeros(points) for i in range(points): spdf[i] = t[i] * scl.ideal_subset_time_pdf(mec.Q, state1, state2, t[i]) * fac if unit == 'ms': t = t * 1000 # x scale in millisec return t, ipdf, spdf
def subset_time_pdf(mec, tres, state1, state2, tmin=0.00001, tmax=1000, points=512, unit='ms'): """ Calculate ideal pdf of any subset dwell times. Parameters ---------- mec : instance of type Mechanism tres : float Time resolution. state1, state2 : ints tmin, tmax : floats Time range for burst length ditribution. points : int Number of points per plot. unit : str 'ms'- milliseconds. Returns ------- t : ndarray of floats, shape (num of points) Time in millisec. spdf : ndarray of floats, shape (num of points) Subset dwell time pdf. """ open = False if open: eigs, w = scl.ideal_dwell_time_pdf_components(mec.QAA, qml.phiA(mec)) else: eigs, w = scl.ideal_dwell_time_pdf_components(mec.QII, qml.phiF(mec)) tmax = tau.max() * 20 t = np.logspace(math.log10(tmin), math.log10(tmax), points) # Ideal pdf. fac = 1 / np.sum((w / eigs) * np.exp(-tres * eigs)) # Scale factor ipdf = t * pdfs.expPDF(t, 1 / eigs, w / eigs) * fac spdf = np.zeros(points) for i in range(points): spdf[i] = t[i] * scl.ideal_subset_time_pdf(mec.Q, state1, state2, t[i]) * fac if unit == 'ms': t = t * 1000 # x scale in millisec return t, ipdf, spdf
def corr_open_shut(mec, lag): """ Calculate data for the plot of open, shut and open-shut time correlations. Parameters ---------- mec : instance of type Mechanism lag : int Number of lags. Returns ------- c : ndarray of floats, shape (num of points,) Concentration in mikroM br : ndarray of floats, shape (num of points,) Mean burst length in millisec. brblk : ndarray of floats, shape (num of points,) Mean burst length in millisec corrected for fast block. """ kA, kF = mec.kA, mec.kI GAF, GFA = qml.iGs(mec.Q, kA, kF) XAA, XFF = np.dot(GAF, GFA), np.dot(GFA, GAF) phiA, phiF = qml.phiA(mec).reshape((1,kA)), qml.phiF(mec).reshape((1,kF)) varA = scl.corr_variance_A(phiA, mec.QAA, kA) varF = scl.corr_variance_A(phiF, mec.QII, kF) r = np.arange(1, lag + 1) roA, roF, roAF = np.zeros(lag), np.zeros(lag), np.zeros(lag) for i in range(lag): covA = scl.corr_covariance_A(i+1, phiA, mec.QAA, XAA, kA) roA[i] = scl.correlation_coefficient(covA, varA, varA) covF = scl.corr_covariance_A(i+1, phiF, mec.QII, XFF, kF) roF[i] = scl.correlation_coefficient(covF, varF, varF) covAF = scl.corr_covariance_AF(i+1, phiA, mec.QAA, mec.QII, XAA, GAF, kA, kF) roAF[i] = scl.correlation_coefficient(covAF, varA, varF) return r, roA, roF, roAF
def corr_open_shut(mec, lag): """ Calculate data for the plot of open, shut and open-shut time correlations. Parameters ---------- mec : instance of type Mechanism lag : int Number of lags. Returns ------- c : ndarray of floats, shape (num of points,) Concentration in mikroM br : ndarray of floats, shape (num of points,) Mean burst length in millisec. brblk : ndarray of floats, shape (num of points,) Mean burst length in millisec corrected for fast block. """ kA, kF = mec.kA, mec.kI GAF, GFA = qml.iGs(mec.Q, kA, kF) XAA, XFF = np.dot(GAF, GFA), np.dot(GFA, GAF) phiA, phiF = qml.phiA(mec).reshape((1, kA)), qml.phiF(mec).reshape((1, kF)) varA = scl.corr_variance_A(phiA, mec.QAA, kA) varF = scl.corr_variance_A(phiF, mec.QII, kF) r = np.arange(1, lag + 1) roA, roF, roAF = np.zeros(lag), np.zeros(lag), np.zeros(lag) for i in range(lag): covA = scl.corr_covariance_A(i + 1, phiA, mec.QAA, XAA, kA) roA[i] = scl.correlation_coefficient(covA, varA, varA) covF = scl.corr_covariance_A(i + 1, phiF, mec.QII, XFF, kF) roF[i] = scl.correlation_coefficient(covF, varF, varF) covAF = scl.corr_covariance_AF(i + 1, phiA, mec.QAA, mec.QII, XAA, GAF, kA, kF) roAF[i] = scl.correlation_coefficient(covAF, varA, varF) return r, roA, roF, roAF
def test_openshut(self): # # # Initial HJC vectors. expQFF = qml.expQt(self.mec.QFF, self.tres) expQAA = qml.expQt(self.mec.QAA, self.tres) GAF, GFA = qml.iGs(self.mec.Q, self.mec.kA, self.mec.kF) eGAF = qml.eGs(GAF, GFA, self.mec.kA, self.mec.kF, expQFF) eGFA = qml.eGs(GFA, GAF, self.mec.kF, self.mec.kA, expQAA) phiA = qml.phiHJC(eGAF, eGFA, self.mec.kA) phiF = qml.phiHJC(eGFA, eGAF, self.mec.kF) self.assertAlmostEqual(phiA[0], 0.153966, 6) self.assertAlmostEqual(phiA[1], 0.846034, 6) self.assertAlmostEqual(phiF[0], 0.530369, 6) self.assertAlmostEqual(phiF[1], 0.386116, 6) self.assertAlmostEqual(phiF[2], 0.0835153, 6) # Ideal shut time pdf eigs, w = scl.ideal_dwell_time_pdf_components(self.mec.QFF, qml.phiF(self.mec)) self.assertAlmostEqual(eigs[0], 0.263895, 6) self.assertAlmostEqual(eigs[1], 2062.93, 2) self.assertAlmostEqual(eigs[2], 19011.8, 1) self.assertAlmostEqual(w[0], 0.0691263, 6) self.assertAlmostEqual(w[1], 17.2607, 4) self.assertAlmostEqual(w[2], 13872.7, 1) # Asymptotic shut time pdf roots = scl.asymptotic_roots(self.tres, self.mec.QFF, self.mec.QAA, self.mec.QFA, self.mec.QAF, self.mec.kF, self.mec.kA) areas = scl.asymptotic_areas(self.tres, roots, self.mec.QFF, self.mec.QAA, self.mec.QFA, self.mec.QAF, self.mec.kF, self.mec.kA, GFA, GAF) mean = scl.exact_mean_time(self.tres, self.mec.QFF, self.mec.QAA, self.mec.QFA, self.mec.kF, self.mec.kA, GFA, GAF) self.assertAlmostEqual(-roots[0], 17090.2, 1) self.assertAlmostEqual(-roots[1], 2058.08, 2) self.assertAlmostEqual(-roots[2], 0.243565, 6) self.assertAlmostEqual(areas[0] * 100, 28.5815, 4) self.assertAlmostEqual(areas[1] * 100, 1.67311, 5) self.assertAlmostEqual(areas[2] * 100, 68.3542, 4) # Exact pdf eigvals, gamma00, gamma10, gamma11 = scl.exact_GAMAxx( self.mec, self.tres, False) self.assertAlmostEqual(gamma00[0], 0.940819, 6) self.assertAlmostEqual(gamma00[1], 117.816, 3) self.assertAlmostEqual(gamma00[2], 24.8962, 4) self.assertAlmostEqual(gamma00[3], 1.28843, 5) self.assertAlmostEqual(gamma00[4], 5370.18, 2) self.assertAlmostEqual(gamma10[0], 4.57792, 5) self.assertAlmostEqual(gamma10[1], 100.211, 3) self.assertAlmostEqual(gamma10[2], -5.49855, 4) self.assertAlmostEqual(gamma10[3], 0.671548, 6) self.assertAlmostEqual(gamma10[4], -99.9617, 4) self.assertAlmostEqual(gamma11[0], 0.885141, 6) self.assertAlmostEqual(gamma11[1], 43634.99, 1) self.assertAlmostEqual(gamma11[2], 718.068, 3) self.assertAlmostEqual(gamma11[3], -39.7437, 3) self.assertAlmostEqual(gamma11[4], -1.9832288e+06, 0)
def printout_correlations(mec, output=sys.stdout, eff='c'): """ """ str = ('\n\n*************************************\n' + 'CORRELATIONS\n') kA, kI = mec.kA, mec.kI str += ('kA, kF = {0:d}, {1:d}\n'.format(kA, kI)) GAF, GFA = qml.iGs(mec.Q, kA, kI) rGAF, rGFA = np.rank(GAF), np.rank(GFA) str += ('Ranks of GAF, GFA = {0:d}, {1:d}\n'.format(rGAF, rGFA)) XFF = np.dot(GFA, GAF) rXFF = np.rank(XFF) str += ('Rank of GFA * GAF = {0:d}\n'.format(rXFF)) ncF = rXFF - 1 eigXFF, AXFF = qml.eigs(XFF) str += ('Eigenvalues of GFA * GAF:\n') str1 = '' for i in range(kI): str1 += '\t{0:.5g}'.format(eigXFF[i]) str += str1 + '\n' XAA = np.dot(GAF, GFA) rXAA = np.rank(XAA) str += ('Rank of GAF * GFA = {0:d}\n'.format(rXAA)) ncA = rXAA - 1 eigXAA, AXAA = qml.eigs(XAA) str += ('Eigenvalues of GAF * GFA:\n') str1 = '' for i in range(kA): str1 += '\t{0:.5g}'.format(eigXAA[i]) str += str1 + '\n' phiA, phiF = qml.phiA(mec).reshape((1, kA)), qml.phiF(mec).reshape((1, kI)) varA = corr_variance_A(phiA, mec.QAA, kA) varF = corr_variance_A(phiF, mec.QII, kI) # open - open time correlations str += ('\n OPEN - OPEN TIME CORRELATIONS') str += ('Variance of open time = {0:.5g}\n'.format(varA)) SDA = sqrt(varA) str += ('SD of all open times = {0:.5g} ms\n'.format(SDA * 1000)) n = 50 SDA_mean_n = SDA / sqrt(float(n)) str += ('SD of means of {0:d} open times if'.format(n) + 'uncorrelated = {0:.5g} ms\n'.format(SDA_mean_n * 1000)) covAtot = 0 for i in range(1, n): covA = corr_covariance_A(i + 1, phiA, mec.QAA, XAA, kA) ro = correlation_coefficient(covA, varA, varA) covAtot += (n - i) * ro * varA vtot = n * varA + 2. * covAtot actSDA = sqrt(vtot / (n * n)) str += ('Actual SD of mean = {0:.5g} ms\n'.format(actSDA * 1000)) pA = 100 * (actSDA - SDA_mean_n) / SDA_mean_n str += ( 'Percent difference as result of correlation = {0:.5g}\n'.format(pA)) v2A = corr_limit_A(phiA, mec.QAA, AXAA, eigXAA, kA) pmaxA = 100 * (sqrt(1 + 2 * v2A / varA) - 1) str += ('Limiting value of percent difference for large n = {0:.5g}\n'. format(pmaxA)) str += ('Correlation coefficients, r(k), for up to lag k = 5:\n') for i in range(5): covA = corr_covariance_A(i + 1, phiA, mec.QAA, XAA, kA) ro = correlation_coefficient(covA, varA, varA) str += ('r({0:d}) = {1:.5g}\n'.format(i + 1, ro)) # shut - shut time correlations str += ('\n SHUT - SHUT TIME CORRELATIONS\n') str += ('Variance of shut time = {0:.5g}\n'.format(varF)) SDF = sqrt(varF) str += ('SD of all shut times = {0:.5g} ms\n'.format(SDF * 1000)) n = 50 SDF_mean_n = SDF / sqrt(float(n)) str += ('SD of means of {0:d} shut times if'.format(n) + 'uncorrelated = {0:.5g} ms\n'.format(SDF_mean_n * 1000)) covFtot = 0 for i in range(1, n): covF = corr_covariance_A(i + 1, phiF, mec.QII, XFF, kI) ro = correlation_coefficient(covF, varF, varF) covFtot += (n - i) * ro * varF vtotF = 50 * varF + 2. * covFtot actSDF = sqrt(vtotF / (50. * 50.)) str += ('Actual SD of mean = {0:.5g} ms\n'.format(actSDF * 1000)) pF = 100 * (actSDF - SDF_mean_n) / SDF_mean_n str += ( 'Percent difference as result of correlation = {0:.5g}\n'.format(pF)) v2F = corr_limit_A(phiF, mec.QII, AXFF, eigXFF, kI) pmaxF = 100 * (sqrt(1 + 2 * v2F / varF) - 1) str += ('Limiting value of percent difference for large n = {0:.5g}\n'. format(pmaxF)) str += ('Correlation coefficients, r(k), for up to k = 5 lags:\n') for i in range(5): covF = corr_covariance_A(i + 1, phiF, mec.QII, XFF, kI) ro = correlation_coefficient(covF, varF, varF) str += ('r({0:d}) = {1:.5g}\n'.format(i + 1, ro)) # open - shut time correlations str += ('\n OPEN - SHUT TIME CORRELATIONS\n') str += ('Correlation coefficients, r(k), for up to k= 5 lags:\n') for i in range(5): covAF = corr_covariance_AF(i + 1, phiA, mec.QAA, mec.QII, XAA, GAF, kA, kI) ro = correlation_coefficient(covAF, varA, varF) str += ('r({0:d}) = {1:.5g}\n'.format(i + 1, ro)) return str
def printout_distributions(mec, tres, eff='c'): """ """ str = '\n*******************************************\n' GAI, GIA = qml.iGs(mec.Q, mec.kA, mec.kI) # OPEN TIME DISTRIBUTIONS open = True # Ideal pdf eigs, w = ideal_dwell_time_pdf_components(mec.QAA, qml.phiA(mec)) str += 'IDEAL OPEN TIME DISTRIBUTION\n' str += pdfs.expPDF_printout(eigs, w) # Asymptotic pdf #roots = asymptotic_roots(mec, tres, open) roots = asymptotic_roots(tres, mec.QAA, mec.QII, mec.QAI, mec.QIA, mec.kA, mec.kI) #areas = asymptotic_areas(mec, tres, roots, open) areas = asymptotic_areas(tres, roots, mec.QAA, mec.QII, mec.QAI, mec.QIA, mec.kA, mec.kI, GAI, GIA) str += '\nASYMPTOTIC OPEN TIME DISTRIBUTION\n' str += 'term\ttau (ms)\tarea (%)\trate const (1/sec)\n' for i in range(mec.kA): str += ('{0:d}'.format(i + 1) + '\t{0:.5g}'.format(-1.0 / roots[i] * 1000) + '\t{0:.5g}'.format(areas[i] * 100) + '\t{0:.5g}\n'.format(-roots[i])) areast0 = np.zeros(mec.kA) for i in range(mec.kA): areast0[i] = areas[i] * np.exp(-tres * roots[i]) areast0 = areast0 / np.sum(areast0) str += ('Areas for asymptotic pdf renormalised for t=0 to\ infinity (and sum=1), so areas can be compared with ideal pdf.\n') for i in range(mec.kA): str += ('{0:d}'.format(i + 1) + '\t{0:.5g}\n'.format(areast0[i] * 100)) mean = exact_mean_time(tres, mec.QAA, mec.QII, mec.QAI, mec.kA, mec.kI, GAI, GIA) str += ('Mean open time (ms) = {0:.5g}\n'.format(mean * 1000)) # Exact pdf eigvals, gamma00, gamma10, gamma11 = exact_GAMAxx(mec, tres, open) str += ('\nEXACT OPEN TIME DISTRIBUTION\n') str += ('eigen\tg00(m)\tg10(m)\tg11(m)\n') for i in range(mec.k): str += ('{0:.5g}'.format(eigvals[i]) + '\t{0:.5g}'.format(gamma00[i]) + '\t{0:.5g}'.format(gamma10[i]) + '\t{0:.5g}\n'.format(gamma11[i])) str += ('\n\n*******************************************\n') # SHUT TIME DISTRIBUTIONS open = False # Ideal pdf eigs, w = ideal_dwell_time_pdf_components(mec.QII, qml.phiF(mec)) str += ('IDEAL SHUT TIME DISTRIBUTION\n') str += pdfs.expPDF_printout(eigs, w) # Asymptotic pdf #roots = asymptotic_roots(mec, tres, open) roots = asymptotic_roots(tres, mec.QII, mec.QAA, mec.QIA, mec.QAI, mec.kI, mec.kA) #areas = asymptotic_areas(mec, tres, roots, open) areas = asymptotic_areas(tres, roots, mec.QII, mec.QAA, mec.QIA, mec.QAI, mec.kI, mec.kA, GIA, GAI) str += ('\nASYMPTOTIC SHUT TIME DISTRIBUTION\n') str += ('term\ttau (ms)\tarea (%)\trate const (1/sec)\n') for i in range(mec.kI): str += ('{0:d}'.format(i + 1) + '\t{0:.5g}'.format(-1.0 / roots[i] * 1000) + '\t{0:.5g}'.format(areas[i] * 100) + '\t{0:.5g}\n'.format(-roots[i])) areast0 = np.zeros(mec.kI) for i in range(mec.kI): areast0[i] = areas[i] * np.exp(-tres * roots[i]) areast0 = areast0 / np.sum(areast0) str += ('Areas for asymptotic pdf renormalised for t=0 to\ infinity (and sum=1), so areas can be compared with ideal pdf.\n') for i in range(mec.kI): str += ('{0:d}'.format(i + 1) + '\t{0:.5g}\n'.format(areast0[i] * 100)) mean = exact_mean_time(tres, mec.QII, mec.QAA, mec.QIA, mec.kI, mec.kA, GIA, GAI) str += ('Mean shut time (ms) = {0:.6f}\n'.format(mean * 1000)) # Exact pdf eigvals, gamma00, gamma10, gamma11 = exact_GAMAxx(mec, tres, open) str += ('\nEXACT SHUT TIME DISTRIBUTION\n' + 'eigen\tg00(m)\tg10(m)\tg11(m)\n') for i in range(mec.k): str += ('{0:.5g}'.format(eigvals[i]) + '\t{0:.5g}'.format(gamma00[i]) + '\t{0:.5g}'.format(gamma10[i]) + '\t{0:.5g}\n'.format(gamma11[i])) # Transition probabilities pi = transition_probability(mec.Q) str += ('\nProbability of transitions regardless of time:\n') for i in range(mec.k): str1 = '[' for j in range(mec.k): str1 += '{0:.4g}\t'.format(pi[i, j]) str1 += ']\n' str += str1 # Transition frequency f = transition_frequency(mec.Q) str += ('\nFrequency of transitions (per second):\n') for i in range(mec.k): str1 = '[' for j in range(mec.k): str1 += '{0:.4g}\t'.format(f[i, j]) str1 += ']\n' str += str1 return str
def printout_tcrit(mec): """ Output calculations based on division into bursts by critical time (tcrit). Parameters ---------- mec : dcpyps.Mechanism The mechanism to be analysed. output : output device Default device: sys.stdout """ str = ('\n\n*******************************************\n' + 'CALCULATIONS BASED ON DIVISION INTO BURSTS BY' + ' tcrit- CRITICAL TIME.\n') # Ideal shut time pdf eigs, w = ideal_dwell_time_pdf_components(mec.QII, qml.phiF(mec)) str += ('\nIDEAL SHUT TIME DISTRIBUTION\n') str += pdfs.expPDF_printout(eigs, w) taus = 1 / eigs areas = w / eigs taus, areas = sortShell2(taus, areas) comps = taus.shape[0] - 1 tcrits = np.empty((3, comps)) for i in range(comps): str += ('\nCritical time between components {0:d} and {1:d}\n'.format( i + 1, i + 2) + '\nEqual % misclassified (DC criterion)\n') try: tcrit = so.bisect(pdfs.expPDF_tcrit_DC, taus[i], taus[i + 1], args=(taus, areas, i + 1)) enf, ens, pf, ps = pdfs.expPDF_misclassified( tcrit, taus, areas, i + 1) str += pdfs.expPDF_misclassified_printout(tcrit, enf, ens, pf, ps) except: str += ('Bisection with DC criterion failed.\n') tcrit = None tcrits[0, i] = tcrit str += ('\nEqual # misclassified (Clapham & Neher criterion)\n') try: tcrit = so.bisect(pdfs.expPDF_tcrit_CN, taus[i], taus[i + 1], args=(taus, areas, i + 1)) enf, ens, pf, ps = pdfs.expPDF_misclassified( tcrit, taus, areas, i + 1) str += pdfs.expPDF_misclassified_printout(tcrit, enf, ens, pf, ps) except: str += ('Bisection with Clapham & Neher criterion failed.\n') tcrit = None tcrits[1, i] = tcrit str += ('\nMinimum total # misclassified (Jackson et al criterion)') try: tcrit = so.bisect(pdfs.expPDF_tcrit_Jackson, taus[i], taus[i + 1], args=(taus, areas, i + 1)) enf, ens, pf, ps = pdfs.expPDF_misclassified( tcrit, taus, areas, i + 1) str += pdfs.expPDF_misclassified_printout(tcrit, enf, ens, pf, ps) except: str += ('\nBisection with Jackson et al criterion failed.') tcrit = None tcrits[2, i] = tcrit str += ('\nSUMMARY of tcrit values:\n' + 'Components DC\tC&N\tJackson\n') for i in range(comps): str += ('{0:d} to {1:d} '.format(i + 1, i + 2) + '\t{0:.5g}'.format(tcrits[0, i] * 1000) + '\t{0:.5g}'.format(tcrits[1, i] * 1000) + '\t{0:.5g}\n'.format(tcrits[2, i] * 1000)) return str
def printout_occupancies(mec, tres): """ """ str = ('\n\n\n*******************************************\n\n' + 'Open\tEquilibrium\tMean life\tMean latency (ms)\n' + 'state\toccupancy\t(ms)\tto next shutting\n' + '\t\t\tgiven start in this state\n') pinf = qml.pinf(mec.Q) for i in range(mec.k): if i == 0: mean_life_A = ideal_subset_mean_life_time(mec.Q, 1, mec.kA) str += ('Subset A ' + '\t{0:.5g}'.format(np.sum(pinf[:mec.kA])) + '\t{0:.5g}\n'.format(mean_life_A * 1000)) if i == mec.kA: mean_life_B = ideal_subset_mean_life_time(mec.Q, mec.kA + 1, mec.kE) str += ('\nShut\tEquilibrium\tMean life\tMean latency (ms)\n' + 'state\toccupancy\t(ms)\tto next opening\n' + '\t\t\tgiven start in this state\n' + 'Subset B ' + '\t{0:.5g}'.format(np.sum(pinf[mec.kA:mec.kE])) + '\t{0:.5g}\n'.format(mean_life_B * 1000)) if i == mec.kE: mean_life_C = ideal_subset_mean_life_time(mec.Q, mec.kE + 1, mec.kG) str += ('\nSubset C ' + '\t{0:.5g}'.format(np.sum(pinf[mec.kE:mec.kG])) + '\t{0:.5g}\n'.format(mean_life_C * 1000)) if i == mec.kG: mean_life_D = ideal_subset_mean_life_time(mec.Q, mec.kG + 1, mec.k) str += ('\nSubset D ' + '\t{0:.5g}'.format(np.sum(pinf[mec.kG:mec.k])) + '\t{0:.5g}\n'.format(mean_life_D * 1000)) mean = ideal_mean_latency_given_start_state(mec, i + 1) str += ('{0:d}'.format(i + 1) + '\t{0:.5g}'.format(pinf[i]) + '\t{0:.5g}'.format(-1 / mec.Q[i, i] * 1000) + '\t{0:.5g}\n'.format(mean * 1000)) expQFF = qml.expQt(mec.QFF, tres) expQAA = qml.expQt(mec.QAA, tres) GAF, GFA = qml.iGs(mec.Q, mec.kA, mec.kF) eGAF = qml.eGs(GAF, GFA, mec.kA, mec.kF, expQFF) eGFA = qml.eGs(GFA, GAF, mec.kF, mec.kA, expQAA) phiA = qml.phiHJC(eGAF, eGFA, mec.kA) phiF = qml.phiHJC(eGFA, eGAF, mec.kF) str += ('\n\nInitial vector for HJC openings phiOp =\n') for i in range(phiA.shape[0]): str += ('\t{0:.5g}'.format(phiA[i])) str += ('\nInitial vector for ideal openings phiOp =\n') phiAi = qml.phiA(mec) for i in range(phiA.shape[0]): str += ('\t{0:.5g}'.format(phiAi[i])) str += ('\nInitial vector for HJC shuttings phiSh =\n') for i in range(phiF.shape[0]): str += ('\t{0:.5g}'.format(phiF[i])) str += ('\nInitial vector for ideal shuttings phiSh =\n') phiFi = qml.phiF(mec) for i in range(phiF.shape[0]): str += ('\t{0:.5g}'.format(phiFi[i])) str += '\n' return str
def printout_correlations(mec, output=sys.stdout, eff='c'): """ """ str = ('\n\n*************************************\n' + 'CORRELATIONS\n') kA, kI = mec.kA, mec.kI str += ('kA, kF = {0:d}, {1:d}\n'.format(kA, kI)) GAF, GFA = qml.iGs(mec.Q, kA, kI) rGAF, rGFA = np.rank(GAF), np.rank(GFA) str += ('Ranks of GAF, GFA = {0:d}, {1:d}\n'.format(rGAF, rGFA)) XFF = np.dot(GFA, GAF) rXFF = np.rank(XFF) str += ('Rank of GFA * GAF = {0:d}\n'.format(rXFF)) ncF = rXFF - 1 eigXFF, AXFF = qml.eigs(XFF) str += ('Eigenvalues of GFA * GAF:\n') str1 = '' for i in range(kI): str1 += '\t{0:.5g}'.format(eigXFF[i]) str += str1 + '\n' XAA = np.dot(GAF, GFA) rXAA = np.rank(XAA) str += ('Rank of GAF * GFA = {0:d}\n'.format(rXAA)) ncA = rXAA - 1 eigXAA, AXAA = qml.eigs(XAA) str += ('Eigenvalues of GAF * GFA:\n') str1 = '' for i in range(kA): str1 += '\t{0:.5g}'.format(eigXAA[i]) str += str1 + '\n' phiA, phiF = qml.phiA(mec).reshape((1,kA)), qml.phiF(mec).reshape((1,kI)) varA = corr_variance_A(phiA, mec.QAA, kA) varF = corr_variance_A(phiF, mec.QII, kI) # open - open time correlations str += ('\n OPEN - OPEN TIME CORRELATIONS') str += ('Variance of open time = {0:.5g}\n'.format(varA)) SDA = sqrt(varA) str += ('SD of all open times = {0:.5g} ms\n'.format(SDA * 1000)) n = 50 SDA_mean_n = SDA / sqrt(float(n)) str += ('SD of means of {0:d} open times if'.format(n) + 'uncorrelated = {0:.5g} ms\n'.format(SDA_mean_n * 1000)) covAtot = 0 for i in range(1, n): covA = corr_covariance_A(i+1, phiA, mec.QAA, XAA, kA) ro = correlation_coefficient(covA, varA, varA) covAtot += (n - i) * ro * varA vtot = n * varA + 2. * covAtot actSDA = sqrt(vtot / (n * n)) str += ('Actual SD of mean = {0:.5g} ms\n'.format(actSDA * 1000)) pA = 100 * (actSDA - SDA_mean_n) / SDA_mean_n str += ('Percent difference as result of correlation = {0:.5g}\n'. format(pA)) v2A = corr_limit_A(phiA, mec.QAA, AXAA, eigXAA, kA) pmaxA = 100 * (sqrt(1 + 2 * v2A / varA) - 1) str += ('Limiting value of percent difference for large n = {0:.5g}\n'. format(pmaxA)) str += ('Correlation coefficients, r(k), for up to lag k = 5:\n') for i in range(5): covA = corr_covariance_A(i+1, phiA, mec.QAA, XAA, kA) ro = correlation_coefficient(covA, varA, varA) str += ('r({0:d}) = {1:.5g}\n'.format(i+1, ro)) # shut - shut time correlations str += ('\n SHUT - SHUT TIME CORRELATIONS\n') str += ('Variance of shut time = {0:.5g}\n'.format(varF)) SDF = sqrt(varF) str += ('SD of all shut times = {0:.5g} ms\n'.format(SDF * 1000)) n = 50 SDF_mean_n = SDF / sqrt(float(n)) str += ('SD of means of {0:d} shut times if'.format(n) + 'uncorrelated = {0:.5g} ms\n'.format(SDF_mean_n * 1000)) covFtot = 0 for i in range(1, n): covF = corr_covariance_A(i+1, phiF, mec.QII, XFF, kI) ro = correlation_coefficient(covF, varF, varF) covFtot += (n - i) * ro * varF vtotF = 50 * varF + 2. * covFtot actSDF = sqrt(vtotF / (50. * 50.)) str += ('Actual SD of mean = {0:.5g} ms\n'.format(actSDF * 1000)) pF = 100 * (actSDF - SDF_mean_n) / SDF_mean_n str += ('Percent difference as result of correlation = {0:.5g}\n'. format(pF)) v2F = corr_limit_A(phiF, mec.QII, AXFF, eigXFF, kI) pmaxF = 100 * (sqrt(1 + 2 * v2F / varF) - 1) str += ('Limiting value of percent difference for large n = {0:.5g}\n'. format(pmaxF)) str += ('Correlation coefficients, r(k), for up to k = 5 lags:\n') for i in range(5): covF = corr_covariance_A(i+1, phiF, mec.QII, XFF, kI) ro = correlation_coefficient(covF, varF, varF) str += ('r({0:d}) = {1:.5g}\n'.format(i+1, ro)) # open - shut time correlations str += ('\n OPEN - SHUT TIME CORRELATIONS\n') str += ('Correlation coefficients, r(k), for up to k= 5 lags:\n') for i in range(5): covAF = corr_covariance_AF(i+1, phiA, mec.QAA, mec.QII, XAA, GAF, kA, kI) ro = correlation_coefficient(covAF, varA, varF) str += ('r({0:d}) = {1:.5g}\n'.format(i+1, ro)) return str
def printout_tcrit(mec): """ Output calculations based on division into bursts by critical time (tcrit). Parameters ---------- mec : dcpyps.Mechanism The mechanism to be analysed. output : output device Default device: sys.stdout """ str = ('\n\n*******************************************\n' + 'CALCULATIONS BASED ON DIVISION INTO BURSTS BY' + ' tcrit- CRITICAL TIME.\n') # Ideal shut time pdf eigs, w = ideal_dwell_time_pdf_components(mec.QII, qml.phiF(mec)) str += ('\nIDEAL SHUT TIME DISTRIBUTION\n') str += pdfs.expPDF_printout(eigs, w) taus = 1 / eigs areas = w /eigs taus, areas = sortShell2(taus, areas) comps = taus.shape[0]-1 tcrits = np.empty((3, comps)) for i in range(comps): str += ('\nCritical time between components {0:d} and {1:d}\n'. format(i+1, i+2) + '\nEqual % misclassified (DC criterion)\n') try: tcrit = so.bisect(pdfs.expPDF_tcrit_DC, taus[i], taus[i+1], args=(taus, areas, i+1)) enf, ens, pf, ps = pdfs.expPDF_misclassified(tcrit, taus, areas, i+1) str += pdfs.expPDF_misclassified_printout(tcrit, enf, ens, pf, ps) except: str += ('Bisection with DC criterion failed.\n') tcrit = None tcrits[0, i] = tcrit str += ('\nEqual # misclassified (Clapham & Neher criterion)\n') try: tcrit = so.bisect(pdfs.expPDF_tcrit_CN, taus[i], taus[i+1], args=(taus, areas, i+1)) enf, ens, pf, ps = pdfs.expPDF_misclassified(tcrit, taus, areas, i+1) str += pdfs.expPDF_misclassified_printout(tcrit, enf, ens, pf, ps) except: str += ('Bisection with Clapham & Neher criterion failed.\n') tcrit = None tcrits[1, i] = tcrit str += ('\nMinimum total # misclassified (Jackson et al criterion)') try: tcrit = so.bisect(pdfs.expPDF_tcrit_Jackson, taus[i], taus[i+1], args=(taus, areas, i+1)) enf, ens, pf, ps = pdfs.expPDF_misclassified(tcrit, taus, areas, i+1) str += pdfs.expPDF_misclassified_printout(tcrit, enf, ens, pf, ps) except: str += ('\nBisection with Jackson et al criterion failed.') tcrit = None tcrits[2, i] = tcrit str += ('\nSUMMARY of tcrit values:\n' + 'Components DC\tC&N\tJackson\n') for i in range(comps): str += ('{0:d} to {1:d} '.format(i+1, i+2) + '\t{0:.5g}'.format(tcrits[0, i] * 1000) + '\t{0:.5g}'.format(tcrits[1, i] * 1000) + '\t{0:.5g}\n'.format(tcrits[2, i] * 1000)) return str
def printout_distributions(mec, tres, eff='c'): """ """ str = '\n*******************************************\n' GAI, GIA = qml.iGs(mec.Q, mec.kA, mec.kI) # OPEN TIME DISTRIBUTIONS open = True # Ideal pdf eigs, w = ideal_dwell_time_pdf_components(mec.QAA, qml.phiA(mec)) str += 'IDEAL OPEN TIME DISTRIBUTION\n' str += pdfs.expPDF_printout(eigs, w) # Asymptotic pdf #roots = asymptotic_roots(mec, tres, open) roots = asymptotic_roots(tres, mec.QAA, mec.QII, mec.QAI, mec.QIA, mec.kA, mec.kI) #areas = asymptotic_areas(mec, tres, roots, open) areas = asymptotic_areas(tres, roots, mec.QAA, mec.QII, mec.QAI, mec.QIA, mec.kA, mec.kI, GAI, GIA) str += '\nASYMPTOTIC OPEN TIME DISTRIBUTION\n' str += 'term\ttau (ms)\tarea (%)\trate const (1/sec)\n' for i in range(mec.kA): str += ('{0:d}'.format(i+1) + '\t{0:.5g}'.format(-1.0 / roots[i] * 1000) + '\t{0:.5g}'.format(areas[i] * 100) + '\t{0:.5g}\n'.format(- roots[i])) areast0 = np.zeros(mec.kA) for i in range(mec.kA): areast0[i] = areas[i] * np.exp(- tres * roots[i]) areast0 = areast0 / np.sum(areast0) str += ('Areas for asymptotic pdf renormalised for t=0 to\ infinity (and sum=1), so areas can be compared with ideal pdf.\n') for i in range(mec.kA): str += ('{0:d}'.format(i+1) + '\t{0:.5g}\n'.format(areast0[i] * 100)) mean = exact_mean_time(tres, mec.QAA, mec.QII, mec.QAI, mec.kA, mec.kI, GAI, GIA) str += ('Mean open time (ms) = {0:.5g}\n'.format(mean * 1000)) # Exact pdf eigvals, gamma00, gamma10, gamma11 = exact_GAMAxx(mec, tres, open) str += ('\nEXACT OPEN TIME DISTRIBUTION\n') str += ('eigen\tg00(m)\tg10(m)\tg11(m)\n') for i in range(mec.k): str += ('{0:.5g}'.format(eigvals[i]) + '\t{0:.5g}'.format(gamma00[i]) + '\t{0:.5g}'.format(gamma10[i]) + '\t{0:.5g}\n'.format(gamma11[i])) str += ('\n\n*******************************************\n') # SHUT TIME DISTRIBUTIONS open = False # Ideal pdf eigs, w = ideal_dwell_time_pdf_components(mec.QII, qml.phiF(mec)) str += ('IDEAL SHUT TIME DISTRIBUTION\n') str += pdfs.expPDF_printout(eigs, w) # Asymptotic pdf #roots = asymptotic_roots(mec, tres, open) roots = asymptotic_roots(tres, mec.QII, mec.QAA, mec.QIA, mec.QAI, mec.kI, mec.kA) #areas = asymptotic_areas(mec, tres, roots, open) areas = asymptotic_areas(tres, roots, mec.QII, mec.QAA, mec.QIA, mec.QAI, mec.kI, mec.kA, GIA, GAI) str += ('\nASYMPTOTIC SHUT TIME DISTRIBUTION\n') str += ('term\ttau (ms)\tarea (%)\trate const (1/sec)\n') for i in range(mec.kI): str += ('{0:d}'.format(i+1) + '\t{0:.5g}'.format(-1.0 / roots[i] * 1000) + '\t{0:.5g}'.format(areas[i] * 100) + '\t{0:.5g}\n'.format(- roots[i])) areast0 = np.zeros(mec.kI) for i in range(mec.kI): areast0[i] = areas[i] * np.exp(- tres * roots[i]) areast0 = areast0 / np.sum(areast0) str += ('Areas for asymptotic pdf renormalised for t=0 to\ infinity (and sum=1), so areas can be compared with ideal pdf.\n') for i in range(mec.kI): str += ('{0:d}'.format(i+1) + '\t{0:.5g}\n'.format(areast0[i] * 100)) mean = exact_mean_time(tres, mec.QII, mec.QAA, mec.QIA, mec.kI, mec.kA, GIA, GAI) str += ('Mean shut time (ms) = {0:.6f}\n'.format(mean * 1000)) # Exact pdf eigvals, gamma00, gamma10, gamma11 = exact_GAMAxx(mec, tres, open) str += ('\nEXACT SHUT TIME DISTRIBUTION\n' + 'eigen\tg00(m)\tg10(m)\tg11(m)\n') for i in range(mec.k): str += ('{0:.5g}'.format(eigvals[i]) + '\t{0:.5g}'.format(gamma00[i]) + '\t{0:.5g}'.format(gamma10[i]) + '\t{0:.5g}\n'.format(gamma11[i])) # Transition probabilities pi = transition_probability(mec.Q) str += ('\nProbability of transitions regardless of time:\n') for i in range(mec.k): str1 = '[' for j in range(mec.k): str1 += '{0:.4g}\t'.format(pi[i,j]) str1 += ']\n' str += str1 # Transition frequency f = transition_frequency(mec.Q) str += ('\nFrequency of transitions (per second):\n') for i in range(mec.k): str1 = '[' for j in range(mec.k): str1 += '{0:.4g}\t'.format(f[i,j]) str1 += ']\n' str += str1 return str
def printout_occupancies(mec, tres): """ """ str = ('\n\n\n*******************************************\n\n' + 'Open\tEquilibrium\tMean life\tMean latency (ms)\n' + 'state\toccupancy\t(ms)\tto next shutting\n' + '\t\t\tgiven start in this state\n') pinf = qml.pinf(mec.Q) for i in range(mec.k): if i == 0: mean_life_A = ideal_subset_mean_life_time(mec.Q, 1, mec.kA) str += ('Subset A ' + '\t{0:.5g}'.format(np.sum(pinf[:mec.kA])) + '\t{0:.5g}\n'.format(mean_life_A * 1000)) if i == mec.kA: mean_life_B = ideal_subset_mean_life_time(mec.Q, mec.kA + 1, mec.kE) str += ('\nShut\tEquilibrium\tMean life\tMean latency (ms)\n' + 'state\toccupancy\t(ms)\tto next opening\n' + '\t\t\tgiven start in this state\n' + 'Subset B ' + '\t{0:.5g}'.format(np.sum(pinf[mec.kA : mec.kE])) + '\t{0:.5g}\n'.format(mean_life_B * 1000)) if i == mec.kE: mean_life_C = ideal_subset_mean_life_time(mec.Q, mec.kE + 1, mec.kG) str += ('\nSubset C ' + '\t{0:.5g}'.format(np.sum(pinf[mec.kE : mec.kG])) + '\t{0:.5g}\n'.format(mean_life_C * 1000)) if i == mec.kG: mean_life_D = ideal_subset_mean_life_time(mec.Q, mec.kG + 1, mec.k) str += ('\nSubset D ' + '\t{0:.5g}'.format(np.sum(pinf[mec.kG : mec.k])) + '\t{0:.5g}\n'.format(mean_life_D * 1000)) mean = ideal_mean_latency_given_start_state(mec, i+1) str += ('{0:d}'.format(i+1) + '\t{0:.5g}'.format(pinf[i]) + '\t{0:.5g}'.format(-1 / mec.Q[i,i] * 1000) + '\t{0:.5g}\n'.format(mean * 1000)) expQFF = qml.expQt(mec.QFF, tres) expQAA = qml.expQt(mec.QAA, tres) GAF, GFA = qml.iGs(mec.Q, mec.kA, mec.kF) eGAF = qml.eGs(GAF, GFA, mec.kA, mec.kF, expQFF) eGFA = qml.eGs(GFA, GAF, mec.kF, mec.kA, expQAA) phiA = qml.phiHJC(eGAF, eGFA, mec.kA) phiF = qml.phiHJC(eGFA, eGAF, mec.kF) str += ('\n\nInitial vector for HJC openings phiOp =\n') for i in range(phiA.shape[0]): str += ('\t{0:.5g}'.format(phiA[i])) str += ('\nInitial vector for ideal openings phiOp =\n') phiAi = qml.phiA(mec) for i in range(phiA.shape[0]): str += ('\t{0:.5g}'.format(phiAi[i])) str += ('\nInitial vector for HJC shuttings phiSh =\n') for i in range(phiF.shape[0]): str += ('\t{0:.5g}'.format(phiF[i])) str += ('\nInitial vector for ideal shuttings phiSh =\n') phiFi = qml.phiF(mec) for i in range(phiF.shape[0]): str += ('\t{0:.5g}'.format(phiFi[i])) str += '\n' return str