コード例 #1
0
def make_phi_hist_with_noise(rid, is_parallel, hist_dim, noise_stdev,
                             normalize):
    """Makes a histogram of the phi angle with added noise."""
    hist = TH1D("blurredHist" + str(rid), "blurredHist" + str(rid),
                hist_dim[0], hist_dim[1], hist_dim[2])

    if is_parallel:
        letter = 'P'
    else:
        letter = 'A'
    file_path = srkdata.srkglobal.results_dir + "Results_RID" + str(
        rid) + "_" + letter + ".root"

    f = ROOT.TFile.Open(file_path)
    phi_list = []
    for event in f.hitTree:
        phi_list.append(gRandom.Gaus(event.phi, noise_stdev))

    mean = srkmisc.reduce_periodics(phi_list)
    stdev = srkmisc.careful_std(phi_list)
    for x in phi_list:
        if normalize:
            hist.Fill((x - mean) / stdev)
        else:
            hist.Fill(x)

    f.Close()
    ROOT.SetOwnership(hist, True)
    return hist
コード例 #2
0
def make_alpha_vs_phi_plot(run_id, is_parallel):
    if is_parallel:
        letter = 'P'
    else:
        letter = 'A'
    file_path = srkdata.srkglobal.results_dir + "Results_RID" + str(
        run_id) + "_" + letter + ".root"

    if not srkmisc.file_exits_and_not_zombie(file_path):
        print file_path + " doesn't exist or is zombie."
        return {}

    root_file = TFile(file_path, "READ")
    hit_tree = gDirectory.Get('hitTree')
    gROOT.cd()
    num_events = hit_tree.GetEntries()

    phi_list = []
    alpha_list = []
    radius = srkdata.get_data_for_rids_from_database([run_id],
                                                     "ChamberRadius")[0][0]
    print radius
    for i in xrange(num_events):
        hit_tree.GetEntry(i)
        phi_list.append(hit_tree.phi)
        alpha = get_alpha_angle_2d(radius, hit_tree.pos0, hit_tree.vel0)
        alpha_list.append(alpha)
    root_file.Close()

    phi_mean = srkmisc.reduce_periodics(phi_list)

    delta_phi_list = map(lambda x: x - phi_mean, phi_list)

    plt.scatter(alpha_list, delta_phi_list)
コード例 #3
0
def calc_delta_stats_same_tracks(run_id, use_wrapping=True):
    """Calculates differences in orientation of fields when tracks are the same"""
    hit_trees = []
    root_files = []
    for is_parallel in [True, False]:
        if is_parallel:
            letter = 'P'
        else:
            letter = 'A'
        file_path = srkglobal.results_dir + "Results_RID" + str(
            run_id) + "_" + letter + ".root"

        if not srkmisc.file_exits_and_not_zombie(file_path):
            print file_path + " doesn't exist or is zombie."
            return {}

        root_files.append(TFile(file_path, "READ"))
        hit_trees.append(gDirectory.Get('hitTree'))

    gROOT.cd()

    delta_phi_list = []
    for i in xrange(hit_trees[0].GetEntries()):
        hit_trees[0].GetEntry(i)
        hit_trees[1].GetEntry(i)
        delta_phi = hit_trees[0].phi - hit_trees[1].phi
        delta_phi_list.append(delta_phi)
    if use_wrapping:
        delta_phi_mean = srkmisc.reduce_periodics(delta_phi_list)
    else:
        delta_phi_mean = srkmisc.careful_mean(delta_phi_list)
    delta_phi_std = srkmisc.careful_std(delta_phi_list)
    root_files[0].Close()
    root_files[1].Close()
    return [delta_phi_mean, delta_phi_std]
コード例 #4
0
ファイル: srkanalysis.py プロジェクト: nEDM-TUM/SRKAnalysis
def make_alpha_vs_phi_plot(run_id, is_parallel):
    if is_parallel:
        letter = 'P'
    else:
        letter = 'A'
    file_path = srkdata.srkglobal.results_dir + "Results_RID" + str(run_id) + "_" + letter + ".root"

    if not srkmisc.file_exits_and_not_zombie(file_path):
        print file_path + " doesn't exist or is zombie."
        return {}

    root_file = TFile(file_path, "READ")
    hit_tree = gDirectory.Get('hitTree')
    gROOT.cd()
    num_events = hit_tree.GetEntries()

    phi_list = []
    alpha_list = []
    radius = srkdata.get_data_for_rids_from_database([run_id], "ChamberRadius")[0][0]
    print radius
    for i in xrange(num_events):
        hit_tree.GetEntry(i)
        phi_list.append(hit_tree.phi)
        alpha = get_alpha_angle_2d(radius, hit_tree.pos0, hit_tree.vel0)
        alpha_list.append(alpha)
    root_file.Close()

    phi_mean = srkmisc.reduce_periodics(phi_list)

    delta_phi_list = map(lambda x: x - phi_mean, phi_list)

    plt.scatter(alpha_list, delta_phi_list)
コード例 #5
0
ファイル: srkanalysis.py プロジェクト: nEDM-TUM/SRKAnalysis
def make_phi_hist_with_noise(rid, is_parallel, hist_dim, noise_stdev, normalize):
    """Makes a histogram of the phi angle with added noise."""
    hist = TH1D("blurredHist" + str(rid), "blurredHist" + str(rid), hist_dim[0], hist_dim[1], hist_dim[2])

    if is_parallel:
        letter = 'P'
    else:
        letter = 'A'
    file_path = srkdata.srkglobal.results_dir + "Results_RID" + str(rid) + "_" + letter + ".root"

    f = ROOT.TFile.Open(file_path)
    phi_list = []
    for event in f.hitTree:
        phi_list.append(gRandom.Gaus(event.phi, noise_stdev))

    mean = srkmisc.reduce_periodics(phi_list)
    stdev = srkmisc.careful_std(phi_list)
    for x in phi_list:
        if normalize:
            hist.Fill((x - mean) / stdev)
        else:
            hist.Fill(x)

    f.Close()
    ROOT.SetOwnership(hist, True)
    return hist
コード例 #6
0
ファイル: srkanalysis.py プロジェクト: nEDM-TUM/SRKAnalysis
def calc_delta_stats_same_tracks(run_id, use_wrapping=True):
    """Calculates differences in orientation of fields when tracks are the same"""
    hit_trees = []
    root_files = []
    for is_parallel in [True, False]:
        if is_parallel:
            letter = 'P'
        else:
            letter = 'A'
        file_path = srkglobal.results_dir + "Results_RID" + str(run_id) + "_" + letter + ".root"

        if not srkmisc.file_exits_and_not_zombie(file_path):
            print file_path + " doesn't exist or is zombie."
            return {}

        root_files.append(TFile(file_path, "READ"))
        hit_trees.append(gDirectory.Get('hitTree'))

    gROOT.cd()

    delta_phi_list = []
    for i in xrange(hit_trees[0].GetEntries()):
        hit_trees[0].GetEntry(i)
        hit_trees[1].GetEntry(i)
        delta_phi = hit_trees[0].phi - hit_trees[1].phi
        delta_phi_list.append(delta_phi)
    if use_wrapping:
        delta_phi_mean = srkmisc.reduce_periodics(delta_phi_list)
    else:
        delta_phi_mean = srkmisc.careful_mean(delta_phi_list)
    delta_phi_std = srkmisc.careful_std(delta_phi_list)
    root_files[0].Close()
    root_files[1].Close()
    return [delta_phi_mean, delta_phi_std]
コード例 #7
0
def make_sz_prob_dist(run_id, is_parallel, use_wrapping=True):
    """Make a spin direction (after flip) detection probability histogram"""
    if is_parallel:
        letter = 'P'
    else:
        letter = 'A'

    file_path = srkdata.srkglobal.results_dir + "Results_RID" + str(
        run_id) + "_" + letter + ".root"

    if not srkmisc.file_exits_and_not_zombie(file_path):
        print file_path + " doesn't exist or is zombie."
        return []

    root_file = TFile(file_path, "READ")

    hit_tree = gDirectory.Get('hitTree')
    gROOT.cd()
    num_events = hit_tree.GetEntries()

    phi_list = []
    theta_list = []
    for i in xrange(num_events):
        hit_tree.GetEntry(i)
        phi_list.append(hit_tree.phi)
        theta_list.append(hit_tree.theta)
    root_file.Close()

    if use_wrapping:
        phi_mean = srkmisc.reduce_periodics(phi_list)
    else:
        phi_mean = srkmisc.careful_mean(phi_list)

    phi_std = srkmisc.careful_std(phi_list)

    theta_std = srkmisc.careful_std(theta_list)

    comb_std = np.sqrt(phi_std * phi_std + theta_std * theta_std)

    print "Combined Standard deviation: %e" % (comb_std)

    x = np.linspace(-30, 30, num=60)

    y = []
    for num_std_dev in x:
        sz_det_prob = 0
        for phi, theta in zip(phi_list, theta_list):
            sz_det_prob += calc_opposite_spin_prob(
                phi - phi_mean + comb_std * num_std_dev, theta)
        sz_det_prob /= num_events
        y.append(1 - sz_det_prob)

    return x, y
コード例 #8
0
ファイル: srkanalysis.py プロジェクト: nEDM-TUM/SRKAnalysis
def make_sz_prob_dist(run_id, is_parallel, use_wrapping=True):
    """Make a spin direction (after flip) detection probability histogram"""
    if is_parallel:
        letter = 'P'
    else:
        letter = 'A'

    file_path = srkdata.srkglobal.results_dir + "Results_RID" + str(run_id) + "_" + letter + ".root"

    if not srkmisc.file_exits_and_not_zombie(file_path):
        print file_path + " doesn't exist or is zombie."
        return []

    root_file = TFile(file_path, "READ")

    hit_tree = gDirectory.Get('hitTree')
    gROOT.cd()
    num_events = hit_tree.GetEntries()

    phi_list = []
    theta_list = []
    for i in xrange(num_events):
        hit_tree.GetEntry(i)
        phi_list.append(hit_tree.phi)
        theta_list.append(hit_tree.theta)
    root_file.Close()

    if use_wrapping:
        phi_mean = srkmisc.reduce_periodics(phi_list)
    else:
        phi_mean = srkmisc.careful_mean(phi_list)

    phi_std = srkmisc.careful_std(phi_list)

    theta_std = srkmisc.careful_std(theta_list)

    comb_std = np.sqrt(phi_std * phi_std + theta_std * theta_std)

    print "Combined Standard deviation: %e" % (comb_std)

    x = np.linspace(-30, 30, num=60)

    y = []
    for num_std_dev in x:
        sz_det_prob = 0
        for phi, theta in zip(phi_list, theta_list):
            sz_det_prob += calc_opposite_spin_prob(phi - phi_mean + comb_std * num_std_dev, theta)
        sz_det_prob /= num_events
        y.append(1 - sz_det_prob)

    return x, y
コード例 #9
0
def calc_stats_for_results_file(file_path, runtype="nedm", use_wrapping=False):
    """Calculates summary stats for a SRK results ROOT file."""
    stats = srkdata.default_file_stats(runtype)

    # Check if file exists and is valid
    if not srkmisc.file_exits_and_not_zombie(file_path):
        print file_path + " doesn't exist or is zombie."
        return {}

    root_file = TFile(file_path, "READ")

    hit_tree = gDirectory.Get('hitTree')
    gROOT.cd()
    stats['NumEventsRun'] = hit_tree.GetEntries()

    # Get arrays for phi and theta from file
    phi_array = np.empty(hit_tree.GetEntries())
    theta_array = np.empty(hit_tree.GetEntries())
    for i in xrange(stats['NumEventsRun']):
        hit_tree.GetEntry(i)
        phi_array[i] = hit_tree.phi
        theta_array[i] = hit_tree.theta
    root_file.Close()

    # Calculate the mean phi's and thetas.  Depending on purpose, wrap around at two pi depending on mean location.
    if use_wrapping:
        stats['PhiMean'] = srkmisc.reduce_periodics(phi_array)
        stats['ThetaMean'] = srkmisc.reduce_periodics(theta_array)
    else:
        stats['PhiMean'] = srkmisc.careful_mean(phi_array)
        stats['ThetaMean'] = srkmisc.careful_mean(theta_array)

    if runtype != "g2":
        # Calculate probability of detecting the spin in the opposite direction
        stats['SZDetProb'] = 0.
        for phi, theta in zip(phi_array, theta_array):
            stats['SZDetProb'] += calc_opposite_spin_prob(
                phi - stats['PhiMean'], theta)
        stats['SZDetProb'] /= stats['NumEventsRun']

    # Calculate other summary info
    stats['PhiStDev'] = srkmisc.careful_std(phi_array)
    stats['ThetaStDev'] = srkmisc.careful_std(theta_array)
    stats['PhiError'] = stats['PhiStDev'] / math.sqrt(len(phi_array))
    stats['ThetaError'] = stats['ThetaStDev'] / math.sqrt(len(theta_array))
    if runtype != "g2":
        stats['PhiKurtosis'] = kurtosis(phi_array)
        stats['ThetaKurtosis'] = kurtosis(theta_array)
        stats['PhiSkewness'] = skew(phi_array)
        stats['ThetaSkewness'] = skew(theta_array)
        print "Phi Kurtosis: %f" % stats['PhiKurtosis']

        percentile_lower, percentile_upper = np.percentile(
            phi_array, (16.5, 83.50))
        percentile_width = percentile_upper - percentile_lower
        stats['PhiPercentileWidth'] = percentile_width

        percentile_lower, percentile_upper = np.percentile(
            theta_array, (16.5, 83.5))
        percentile_width = percentile_upper - percentile_lower
        stats['ThetaPercentileWidth'] = percentile_width

        # Fit phi to Tsallis q-Gaussian function (old form)
        power, error = make_tsallis_fit(phi_array, stats['PhiMean'],
                                        stats['PhiStDev'])
        stats['PhiTsallisPower'] = power
        stats['PhiTsallisPowerError'] = error

        # Fit theta to Tsallis q-Gaussian function (old form)
        power, error = make_tsallis_fit(theta_array, stats['ThetaMean'],
                                        stats['ThetaStDev'])
        stats['ThetaTsallisPower'] = power
        stats['ThetaTsallisPowerError'] = error

        # Fit phi to Tsallis q-Gaussian function (new form)
        power, error = make_qgaussian_fit(phi_array, stats['PhiMean'],
                                          stats['PhiStDev'])
        stats['PhiQGaussianQ'] = power
        stats['PhiQGaussianQError'] = error

        # Fit theta to Tsallis q-Gaussian function (new form)
        power, error = make_qgaussian_fit(theta_array, stats['ThetaMean'],
                                          stats['ThetaStDev'])
        stats['ThetaQGaussianQ'] = power
        stats['ThetaQGaussianQError'] = error

    return stats
コード例 #10
0
ファイル: srkanalysis.py プロジェクト: nEDM-TUM/SRKAnalysis
def calc_stats_for_results_file(file_path, use_wrapping=False):
    """Calculates summary stats for a SRK results ROOT file."""
    stats = srkdata.default_file_stats()

    # Check if file exists and is valid
    if not srkmisc.file_exits_and_not_zombie(file_path):
        print file_path + " doesn't exist or is zombie."
        return {}

    root_file = TFile(file_path, "READ")

    hit_tree = gDirectory.Get('hitTree')
    gROOT.cd()
    stats['NumEventsRun'] = hit_tree.GetEntries()

    # Get arrays for phi and theta from file
    phi_array = np.empty(hit_tree.GetEntries())
    theta_array = np.empty(hit_tree.GetEntries())
    for i in xrange(stats['NumEventsRun']):
        hit_tree.GetEntry(i)
        phi_array[i] = hit_tree.phi
        theta_array[i] = hit_tree.theta
    root_file.Close()

    # Calculate the mean phi's and thetas.  Depending on purpose, wrap around at two pi depending on mean location.
    if use_wrapping:
        stats['PhiMean'] = srkmisc.reduce_periodics(phi_array)
        stats['ThetaMean'] = srkmisc.reduce_periodics(theta_array)
    else:
        stats['PhiMean'] = srkmisc.careful_mean(phi_array)
        stats['ThetaMean'] = srkmisc.careful_mean(theta_array)

    # Calculate probability of detecting the spin in the opposite direction
    stats['SZDetProb'] = 0.
    for phi, theta in zip(phi_array, theta_array):
        stats['SZDetProb'] += calc_opposite_spin_prob(phi - stats['PhiMean'], theta)
    stats['SZDetProb'] /= stats['NumEventsRun']

    # Calculate other summary info
    stats['PhiStDev'] = srkmisc.careful_std(phi_array)
    stats['ThetaStDev'] = srkmisc.careful_std(theta_array)
    stats['PhiError'] = stats['PhiStDev'] / math.sqrt(len(phi_array))
    stats['ThetaError'] = stats['ThetaStDev'] / math.sqrt(len(theta_array))
    stats['PhiKurtosis'] = kurtosis(phi_array)
    stats['ThetaKurtosis'] = kurtosis(theta_array)
    stats['PhiSkewness'] = skew(phi_array)
    stats['ThetaSkewness'] = skew(theta_array)
    print "Phi Kurtosis: %f" % stats['PhiKurtosis']

    percentile_lower, percentile_upper = np.percentile(phi_array, (16.5, 83.50))
    percentile_width = percentile_upper - percentile_lower
    stats['PhiPercentileWidth'] = percentile_width

    percentile_lower, percentile_upper = np.percentile(theta_array, (16.5, 83.5))
    percentile_width = percentile_upper - percentile_lower
    stats['ThetaPercentileWidth'] = percentile_width

    # Fit phi to Tsallis q-Gaussian function (old form)
    power, error = make_tsallis_fit(phi_array, stats['PhiMean'], stats['PhiStDev'])
    stats['PhiTsallisPower'] = power
    stats['PhiTsallisPowerError'] = error

    # Fit theta to Tsallis q-Gaussian function (old form)
    power, error = make_tsallis_fit(theta_array, stats['ThetaMean'], stats['ThetaStDev'])
    stats['ThetaTsallisPower'] = power
    stats['ThetaTsallisPowerError'] = error

    # Fit phi to Tsallis q-Gaussian function (new form)
    power, error = make_qgaussian_fit(phi_array, stats['PhiMean'], stats['PhiStDev'])
    stats['PhiQGaussianQ'] = power
    stats['PhiQGaussianQError'] = error

    # Fit theta to Tsallis q-Gaussian function (new form)
    power, error = make_qgaussian_fit(theta_array, stats['ThetaMean'], stats['ThetaStDev'])
    stats['ThetaQGaussianQ'] = power
    stats['ThetaQGaussianQError'] = error

    return stats