Example #1
0
def get_response_content(fs):
    M, R = get_input_matrices(fs)
    # create the R table string and scripts
    headers = [
        't', 'mi.true.mut', 'mi.true.mutsel', 'mi.analog.mut',
        'mi.analog.mutsel'
    ]
    npoints = 100
    t_low = 0.0
    t_high = 5.0
    t_incr = (t_high - t_low) / (npoints - 1)
    t_values = [t_low + t_incr * i for i in range(npoints)]
    # get the data for the R table
    arr = []
    for t in t_values:
        mi_mut = ctmcmi.get_mutual_information(M, t)
        mi_mutsel = ctmcmi.get_mutual_information(R, t)
        mi_analog_mut = ctmcmi.get_ll_ratio_wrong(M, t)
        mi_analog_mutsel = ctmcmi.get_ll_ratio_wrong(R, t)
        row = [t, mi_mut, mi_mutsel, mi_analog_mut, mi_analog_mutsel]
        arr.append(row)
    # get the R table
    table_string = RUtil.get_table_string(arr, headers)
    # get the R script
    script = get_ggplot()
    # create the R plot image
    device_name = Form.g_imageformat_to_r_function[fs.imageformat]
    retcode, r_out, r_err, image_data = RUtil.run_plotter(
        table_string, script, device_name)
    if retcode:
        raise RUtil.RError(r_err)
    return image_data
Example #2
0
def get_response_content(fs):
    M, R = get_input_matrices(fs)
    # create the R table string and scripts
    headers = [
            't',
            'mi.true.mut',
            'mi.true.mutsel',
            'mi.analog.mut',
            'mi.analog.mutsel']
    npoints = 100
    t_low = 0.0
    t_high = 5.0
    t_incr = (t_high - t_low) / (npoints - 1)
    t_values = [t_low + t_incr*i for i in range(npoints)]
    # get the data for the R table
    arr = []
    for t in t_values:
        mi_mut = ctmcmi.get_mutual_information(M, t)
        mi_mutsel = ctmcmi.get_mutual_information(R, t)
        mi_analog_mut = ctmcmi.get_ll_ratio_wrong(M, t)
        mi_analog_mutsel = ctmcmi.get_ll_ratio_wrong(R, t)
        row = [t, mi_mut, mi_mutsel, mi_analog_mut, mi_analog_mutsel]
        arr.append(row)
    # get the R table
    table_string = RUtil.get_table_string(arr, headers)
    # get the R script
    script = get_ggplot()
    # create the R plot image
    device_name = Form.g_imageformat_to_r_function[fs.imageformat]
    retcode, r_out, r_err, image_data = RUtil.run_plotter(
            table_string, script, device_name)
    if retcode:
        raise RUtil.RError(r_err)
    return image_data
Example #3
0
def get_response_content(fs):
    M, R = get_input_matrices(fs)
    M_v = mrate.R_to_distn(M)
    R_v = mrate.R_to_distn(R)
    t = fs.t
    mi_mut = ctmcmi.get_mutual_information(M, t)
    mi_bal = ctmcmi.get_mutual_information(R, t)
    fi_mut = divtime.get_fisher_information(M, t)
    fi_bal = divtime.get_fisher_information(R, t)
    if fs.info_mut:
        information_sign = np.sign(mi_mut - mi_bal)
    elif fs.info_fis:
        information_sign = np.sign(fi_mut - fi_bal)
    out = StringIO()
    print >> out, '<html>'
    print >> out, '<body>'
    print >> out
    print >> out, '<pre>'
    print >> out, 'Explicitly computed answer',
    print >> out, '(not a heuristic but may be numerically imprecise):'
    if information_sign == 1:
        print >> out, '* pure mutation',
        print >> out, 'is more informative'
    elif information_sign == -1:
        print >> out, '* the balance of mutation and selection',
        print >> out, 'is more informative'
    else:
        print >> out, '  the information contents of the two processes',
        print >> out, 'are numerically indistinguishable'
    print >> out
    print >> out
    if fs.info_mut:
        print >> out, 'Mutual information properties',
        print >> out, 'at very small and very large times:'
        print >> out
        print >> out, get_mi_asymptotics(M, R)
        print >> out
        print >> out
    print >> out, 'Heuristics without regard to time or to the selected',
    print >> out, 'information variant (Fisher vs. mutual information):'
    print >> out
    print >> out, get_heuristics(M, R)
    print >> out
    print >> out
    print >> out, 'Input summary:'
    print >> out
    print >> out, 'mutation rate matrix:'
    print >> out, M
    print >> out
    print >> out, 'mutation process stationary distribution:'
    print >> out, M_v
    print >> out
    print >> out, 'mutation-selection balance rate matrix:'
    print >> out, R
    print >> out
    print >> out, 'mutation-selection balance stationary distribution:'
    print >> out, R_v
    print >> out
    print >> out, 'mutation process expected rate:'
    print >> out, mrate.Q_to_expected_rate(M)
    print >> out
    print >> out, 'mutation-selection balance expected rate:'
    print >> out, mrate.Q_to_expected_rate(R)
    print >> out
    print >> out
    print >> out, 'The following information calculations',
    print >> out, 'depend on t = %s:' % t
    print >> out
    print >> out, 'log(ratio(E(L))) for pure mutation:'
    print >> out, ctmcmi.get_ll_ratio_wrong(M, t)
    print >> out
    print >> out, 'log(ratio(E(L))) for mut-sel balance:'
    print >> out, ctmcmi.get_ll_ratio_wrong(R, t)
    print >> out
    print >> out, 'mutual information for pure mutation:'
    print >> out, mi_mut
    print >> out
    print >> out, 'mutual information for mut-sel balance:'
    print >> out, mi_bal
    print >> out
    print >> out, 'pinsker lower bound mi for pure mutation:'
    print >> out, ctmcmi.get_pinsker_lower_bound_mi(M, t)
    print >> out
    print >> out, 'pinsker lower bound mi for mut-sel balance:'
    print >> out, ctmcmi.get_pinsker_lower_bound_mi(R, t)
    print >> out
    print >> out, 'row based pinsker lower bound mi for pure mutation:'
    print >> out, ctmcmi.get_row_based_plb_mi(M, t)
    print >> out
    print >> out, 'row based pinsker lower bound mi for mut-sel balance:'
    print >> out, ctmcmi.get_row_based_plb_mi(R, t)
    print >> out
    print >> out, 'row based hellinger lower bound mi for pure mutation:'
    print >> out, ctmcmi.get_row_based_hellinger_lb_mi(M, t)
    print >> out
    print >> out, 'row based hellinger lower bound mi for mut-sel balance:'
    print >> out, ctmcmi.get_row_based_hellinger_lb_mi(R, t)
    print >> out
    print >> out, 'Fisher information for pure mutation:'
    print >> out, fi_mut
    print >> out
    print >> out, 'Fisher information for mut-sel balance:'
    print >> out, fi_bal
    print >> out
    print >> out, '</pre>'
    #
    # create the summaries
    summaries = (RateMatrixSummary(M), RateMatrixSummary(R))
    print >> out, get_html_table(summaries)
    print >> out
    print >> out, '<html>'
    print >> out, '<body>'
    return out.getvalue()
Example #4
0
def get_response_content(fs):
    M, R = get_input_matrices(fs)
    M_v = mrate.R_to_distn(M)
    R_v = mrate.R_to_distn(R)
    t = fs.t
    mi_mut = ctmcmi.get_mutual_information(M, t)
    mi_bal = ctmcmi.get_mutual_information(R, t)
    fi_mut = divtime.get_fisher_information(M, t)
    fi_bal = divtime.get_fisher_information(R, t)
    if fs.info_mut:
        information_sign = np.sign(mi_mut - mi_bal)
    elif fs.info_fis:
        information_sign = np.sign(fi_mut - fi_bal)
    out = StringIO()
    print >> out, '<html>'
    print >> out, '<body>'
    print >> out
    print >> out, '<pre>'
    print >> out, 'Explicitly computed answer',
    print >> out, '(not a heuristic but may be numerically imprecise):'
    if information_sign == 1:
        print >> out, '* pure mutation',
        print >> out, 'is more informative'
    elif information_sign == -1:
        print >> out, '* the balance of mutation and selection',
        print >> out, 'is more informative'
    else:
        print >> out, '  the information contents of the two processes',
        print >> out, 'are numerically indistinguishable'
    print >> out
    print >> out
    if fs.info_mut:
        print >> out, 'Mutual information properties',
        print >> out, 'at very small and very large times:'
        print >> out
        print >> out, get_mi_asymptotics(M, R)
        print >> out
        print >> out
    print >> out, 'Heuristics without regard to time or to the selected',
    print >> out, 'information variant (Fisher vs. mutual information):'
    print >> out
    print >> out, get_heuristics(M, R)
    print >> out
    print >> out
    print >> out, 'Input summary:'
    print >> out
    print >> out, 'mutation rate matrix:'
    print >> out, M
    print >> out
    print >> out, 'mutation process stationary distribution:'
    print >> out, M_v
    print >> out
    print >> out, 'mutation-selection balance rate matrix:'
    print >> out, R
    print >> out
    print >> out, 'mutation-selection balance stationary distribution:'
    print >> out, R_v
    print >> out
    print >> out, 'mutation process expected rate:'
    print >> out, mrate.Q_to_expected_rate(M)
    print >> out
    print >> out, 'mutation-selection balance expected rate:'
    print >> out, mrate.Q_to_expected_rate(R)
    print >> out
    print >> out
    print >> out, 'The following information calculations',
    print >> out, 'depend on t = %s:' % t
    print >> out
    print >> out, 'log(ratio(E(L))) for pure mutation:'
    print >> out, ctmcmi.get_ll_ratio_wrong(M, t)
    print >> out
    print >> out, 'log(ratio(E(L))) for mut-sel balance:'
    print >> out, ctmcmi.get_ll_ratio_wrong(R, t)
    print >> out
    print >> out, 'mutual information for pure mutation:'
    print >> out, mi_mut
    print >> out
    print >> out, 'mutual information for mut-sel balance:'
    print >> out, mi_bal
    print >> out
    print >> out, 'pinsker lower bound mi for pure mutation:'
    print >> out, ctmcmi.get_pinsker_lower_bound_mi(M, t)
    print >> out
    print >> out, 'pinsker lower bound mi for mut-sel balance:'
    print >> out, ctmcmi.get_pinsker_lower_bound_mi(R, t)
    print >> out
    print >> out, 'row based pinsker lower bound mi for pure mutation:'
    print >> out, ctmcmi.get_row_based_plb_mi(M, t)
    print >> out
    print >> out, 'row based pinsker lower bound mi for mut-sel balance:'
    print >> out, ctmcmi.get_row_based_plb_mi(R, t)
    print >> out
    print >> out, 'row based hellinger lower bound mi for pure mutation:'
    print >> out, ctmcmi.get_row_based_hellinger_lb_mi(M, t)
    print >> out
    print >> out, 'row based hellinger lower bound mi for mut-sel balance:'
    print >> out, ctmcmi.get_row_based_hellinger_lb_mi(R, t)
    print >> out
    print >> out, 'Fisher information for pure mutation:'
    print >> out, fi_mut
    print >> out
    print >> out, 'Fisher information for mut-sel balance:'
    print >> out, fi_bal
    print >> out
    print >> out, '</pre>'
    #
    # create the summaries
    summaries = (RateMatrixSummary(M), RateMatrixSummary(R))
    print >> out, get_html_table(summaries)
    print >> out
    print >> out, '<html>'
    print >> out, '<body>'
    return out.getvalue()