Пример #1
0
def plot_rdf(ax, dset_path):
    dset = dataset.get_dset(dset_path)
    vp, vp_err = dset.get_vp()
    if use_latex:
        label = r'$\phi = \SI{%.2g}{\percent}$' % vp
    else:
        label = r'$\phi = %.2g \%$' % vp
    rhos_norm, rhos_norm_err, R_edges_norm = dset.get_rhos_norm(dr)
    rhos_norm_err[np.isnan(rhos_norm_err)] = 0.0
    ax.errorbar(R_edges_norm[:-1], rhos_norm, yerr=rhos_norm_err, label=label)
def plot_rdf(ax, dset_path):
    dset = dataset.get_dset(dset_path)
    vp, vp_err = dset.get_vp()
    if use_latex:
        label = r'$\phi = \SI{%.2g}{\percent}$' % vp
    else:
        label = r'$\phi = %.2g \%$' % vp
    rhos_norm, rhos_norm_err, R_edges_norm = dset.get_rhos_norm(dr)
    rhos_norm_err[np.isnan(rhos_norm_err)] = 0.0
    ax.errorbar(R_edges_norm[:-1], rhos_norm, yerr=rhos_norm_err, label=label)
def plot_rdf(ax, dset_path, theta_max):
    dset = dataset.get_dset(dset_path, filter_z_flag=False, theta_max=theta_max)
    vp, vp_err = dset.get_vp()
    R = dset.R
    if use_latex:
        label = r"$\SI{" + "{:.3g}".format(R) + r"}{\um}$, " + r"$\SI{" + "{:.2g}".format(vp) + r"}{\percent}$"
    else:
        label = r"$" + "{:.3g}".format(R) + r"\mu m$, $" + "{:.2g}".format(vp) + r"\%$"
    rhos_norm, rhos_norm_err, R_edges_norm = dset.get_rhos_norm(dr)
    rhos_norm_err[np.isnan(rhos_norm_err)] = 0.0
    ax.errorbar(R_edges_norm[:-1], rhos_norm, yerr=rhos_norm_err, label=label)
def plot_rdf(ax, dset_path):
    dset = dataset.get_dset(dset_path)
    rhos_norm, rhos_norm_err, R_edges_norm = dset.get_rhos_norm(dr)
    vp, vp_err = dset.get_vp()
    R = dset.R
    if use_latex:
        label = (r'$\SI{' + '{:.3g}'.format(R) + r'}{\um}$, ' + r'$\SI{' +
                 '{:.2g}'.format(vp) + r'}{\percent}$')
    else:
        label = (r'$' + '{:.3g}'.format(R) + r'\mu m$, $' +
                 '{:.2g}'.format(vp) + r'\%$')
    import numpy as np
    rhos_norm_err[np.isnan(rhos_norm_err)] = 0.0
    ax.errorbar(R_edges_norm[:-1], rhos_norm, yerr=rhos_norm_err, label=label)
def plot_rdf(ax, dset_path):
    dset = dataset.get_dset(dset_path)
    rhos_norm, rhos_norm_err, R_edges_norm = dset.get_rhos_norm(dr)
    vp, vp_err = dset.get_vp()
    R = dset.R
    if use_latex:
        label = (r'$\SI{' + '{:.3g}'.format(R) + r'}{\um}$, ' +
                 r'$\SI{' + '{:.2g}'.format(vp) + r'}{\percent}$')
    else:
        label = (r'$' + '{:.3g}'.format(R) + r'\mu m$, $' +
                 '{:.2g}'.format(vp) + r'\%$')
    import numpy as np
    rhos_norm_err[np.isnan(rhos_norm_err)] = 0.0
    ax.errorbar(R_edges_norm[:-1], rhos_norm, yerr=rhos_norm_err,
                label=label)
def plot_rdf(ax, dset_path, theta_max):
    dset = dataset.get_dset(dset_path,
                            filter_z_flag=False,
                            theta_max=theta_max)
    vp, vp_err = dset.get_vp()
    R = dset.R
    if use_latex:
        label = (r'$\SI{' + '{:.3g}'.format(R) + r'}{\um}$, ' + r'$\SI{' +
                 '{:.2g}'.format(vp) + r'}{\percent}$')
    else:
        label = (r'$' + '{:.3g}'.format(R) + r'\mu m$, $' +
                 '{:.2g}'.format(vp) + r'\%$')
    rhos_norm, rhos_norm_err, R_edges_norm = dset.get_rhos_norm(dr)
    rhos_norm_err[np.isnan(rhos_norm_err)] = 0.0
    ax.errorbar(R_edges_norm[:-1], rhos_norm, yerr=rhos_norm_err, label=label)
Пример #7
0
def plot_rdf(ax, dset_path, smooth):
    dset = dataset.get_dset(dset_path)
    rhos_norm, rhos_norm_err, R_edges_norm = dset.get_rhos_norm(dr)
    vp, vp_err = dset.get_vp()
    R = dset.R
    if use_latex:
        label = r"$\SI{" + "{:.3g}".format(R) + r"}{\um}$, " + r"$\SI{" + "{:.2g}".format(vp) + r"}{\percent}$"
    else:
        label = r"$" + "{:.3g}".format(R) + r"\mu m$, $" + "{:.2g}".format(vp) + r"\%$"
    if smooth:
        label += r", Smooth swimming"
    else:
        label += r", Wild type"

    ax.errorbar(R_edges_norm[:-1], rhos_norm, yerr=rhos_norm_err, label=label)
def plot_rdf(ax, dset_path, i):
    dset = dataset.get_dset(dset_path)
    vp, vp_err = dset.get_vp()
    R = dset.R
    R_peak, R_peak_err = dset.get_R_peak(dr=dr, alg=alg)
    if use_latex:
        label = (r'$\SI{' + '{:.3g}'.format(R) + r'}{\um}$, ' +
                 r'$\SI{' + '{:.2g}'.format(vp) + r'}{\percent}$')
    else:
        label = (r'$' + '{:.3g}'.format(R) + r'\mu m$, $' +
                 '{:.2g}'.format(vp) + r'\%$')
    rhos_norm, rhos_norm_err, R_edges_norm = dset.get_rhos_norm(dr)
    rhos_norm_err[np.isnan(rhos_norm_err)] = 0.0
    ax.errorbar(R_edges_norm[:-1], rhos_norm, yerr=rhos_norm_err, label=label,
                c=ejm_rcparams.set2[i])
    ax.axvline(R_peak / R, c=ejm_rcparams.set2[i], ls='--')
Пример #9
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def plot_rdf(ax, dset_path, smooth):
    dset = dataset.get_dset(dset_path)
    rhos_norm, rhos_norm_err, R_edges_norm = dset.get_rhos_norm(dr)
    vp, vp_err = dset.get_vp()
    R = dset.R
    if use_latex:
        label = (r'$\SI{' + '{:.3g}'.format(R) + r'}{\um}$, ' +
                 r'$\SI{' + '{:.2g}'.format(vp) + r'}{\percent}$')
    else:
        label = (r'$' + '{:.3g}'.format(R) + r'\mu m$, $' +
                 '{:.2g}'.format(vp) + r'\%$')
    if smooth:
        label += r', Smooth swimming'
    else:
        label += r', Wild type'

    ax.errorbar(R_edges_norm[:-1], rhos_norm, yerr=rhos_norm_err,
                label=label)
def plot_rdf(ax, dset_path, i):
    dset = dataset.get_dset(dset_path)
    vp, vp_err = dset.get_vp()
    R = dset.R
    R_peak, R_peak_err = dset.get_R_peak(dr=dr, alg=alg)
    if use_latex:
        label = (r'$\SI{' + '{:.3g}'.format(R) + r'}{\um}$, ' + r'$\SI{' +
                 '{:.2g}'.format(vp) + r'}{\percent}$')
    else:
        label = (r'$' + '{:.3g}'.format(R) + r'\mu m$, $' +
                 '{:.2g}'.format(vp) + r'\%$')
    rhos_norm, rhos_norm_err, R_edges_norm = dset.get_rhos_norm(dr)
    rhos_norm_err[np.isnan(rhos_norm_err)] = 0.0
    ax.errorbar(R_edges_norm[:-1],
                rhos_norm,
                yerr=rhos_norm_err,
                label=label,
                c=ejm_rcparams.set2[i])
    ax.axvline(R_peak / R, c=ejm_rcparams.set2[i], ls='--')
Пример #11
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use_pgf = True

n_samples = 1e4
ds = 0.008
ds_exp = 0.008
s_close = 0.2
dr_peak = 0.7
alg = 'mean'

ejm_rcparams.set_pretty_plots(use_latex, use_pgf)

fig = plt.figure(figsize=(12, 12 * ejm_rcparams.golden_ratio))
ax = fig.add_subplot(111)
ejm_rcparams.prettify_axes(ax)

dset = dataset.get_dset(paths.correlation_exp_1_dset_path, filter_z_flag=True)
R_peak, R_peak_err = dset.get_R_peak(alg=alg, dr=dr_peak)
g, ge, s = dset.get_acf(ds_exp, n_samples, R_min=R_peak)
close = s < s_close
g = g[close]
ge = ge[close]
s = s[close]
ax.errorbar(s, g, yerr=ge, label='Experiment', c=color_exp)

dset = dataset.get_dset(paths.correlation_Drc_0_dset_path)
R_peak, R_peak_err = dset.get_R_peak(alg=alg, dr=dr_peak)
g, ge, s = dset.get_acf(ds, n_samples, R_min=R_peak)
close = s < s_close
g = g[close]
ge = ge[close]
s = s[close]
Пример #12
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    if exp:
        fname = 'grid_exp'
        color = color_exp
        dset_paths = paths.grid_exp_dset_paths
    else:
        fname = 'grid_sim'
        color = color_opt
        dset_paths = paths.grid_sim_dset_paths

    fig, axs = plt.subplots(3, 3, sharex=True, sharey=True, figsize=(16, 12))
    axs = axs.flatten()
    ejm_rcparams.prettify_axes(*axs)

    for i, ax in enumerate(axs):
        for dset_path in dset_paths:
            dset = dataset.get_dset(dset_path)
            rhos_norm, rhos_norm_err, R_edges_norm = dset.get_rhos_norm(dr)
            vp, vp_err = dset.get_vp()
            R = dset.R
            if use_latex:
                label = (r'$\SI{' + '{:.3g}'.format(R) + r'}{\um}$, ' +
                         r'$\SI{' + '{:.2g}'.format(vp) + r'}{\percent}$')
            else:
                label = (r'$' + '{:.3g}'.format(R) + r'\mu m$, $' +
                         '{:.2g}'.format(vp) + r'\%$')
            ax.errorbar(R_edges_norm[:-1],
                        rhos_norm,
                        yerr=rhos_norm_err,
                        label=label,
                        color=color)
        ax.text(0.07,
Пример #13
0
    if exp:
        fname = 'grid_exp'
        color = color_exp
        dset_paths = paths.grid_exp_dset_paths
    else:
        fname = 'grid_sim'
        color = color_opt
        dset_paths = paths.grid_sim_dset_paths

    fig, axs = plt.subplots(3, 3, sharex=True, sharey=True, figsize=(16, 12))
    axs = axs.flatten()
    ejm_rcparams.prettify_axes(*axs)

    for i, ax in enumerate(axs):
        for dset_path in dset_paths:
            dset = dataset.get_dset(dset_path)
            rhos_norm, rhos_norm_err, R_edges_norm = dset.get_rhos_norm(dr)
            vp, vp_err = dset.get_vp()
            R = dset.R
            if use_latex:
                label = (r'$\SI{' + '{:.3g}'.format(R) + r'}{\um}$, ' +
                         r'$\SI{' + '{:.2g}'.format(vp) + r'}{\percent}$')
            else:
                label = (r'$' + '{:.3g}'.format(R) + r'\mu m$, $' +
                         '{:.2g}'.format(vp) + r'\%$')
            ax.errorbar(R_edges_norm[:-1], rhos_norm, yerr=rhos_norm_err,
                        label=label, color=color)
        ax.text(0.07, 4.3, label, fontsize=32,
                horizontalalignment='left',
                verticalalignment='center')
Пример #14
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from utils import scatlyse

save_flag = True

use_latex = save_flag
use_pgf = True

ejm_rcparams.set_pretty_plots(use_latex, use_pgf)

t_steady = 50.0
dr = 0.7
n_samples = 1e2
alg = 'mean'

# Zero
d_0 = dataset.get_dset(paths.direct_Drc_0_dset_path)
Rps_0 = np.linspace(0.0, d_0.R, n_samples)
t_0, r1_0, r2_0 = d_0.get_direct()
r_0 = np.array([vector.vector_mag(r1_0), vector.vector_mag(r2_0)]).T
ps_0, ps_0_err = unzip([scatlyse(t_0, r_0, Rp, t_steady) for Rp in Rps_0])
ps_0 = np.array(ps_0)
ps_0_err = np.array(ps_0_err)
R_peak_0 = d_0.get_R_peak(alg=alg, dr=dr)[0]

# 10
d_10 = dataset.get_dset(paths.direct_Drc_10_dset_path)
Rps_10 = np.linspace(0.0, d_10.R, n_samples)
t_10, r1_10, r2_10 = d_10.get_direct()
r_10 = np.array([vector.vector_mag(r1_10), vector.vector_mag(r2_10)]).T
ps_10, ps_10_err = unzip([scatlyse(t_10, r_10, Rp, t_steady) for Rp in Rps_10])
ps_10 = np.array(ps_10)
Пример #15
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fig = plt.figure(figsize=(12, 12 * ejm_rcparams.golden_ratio))
ax = fig.add_subplot(111)

ejm_rcparams.prettify_axes(ax)

theta_factors = np.arange(1, 7)

for theta_factor in theta_factors:
    force_fullsphere = theta_factor == 1
    if theta_factor == 1:
        theta_max = np.pi / 2.0
    else:
        theta_max = np.pi / theta_factor

    dset = dataset.get_dset(paths.wholedrop_dset_path,
                            theta_max=theta_max,
                            force_fullsphere=force_fullsphere)

    rhos_norm, rhos_norm_err, R_edges_norm = dset.get_rhos_norm(dr)
    label = r'$\theta_\mathrm{max} = \pi'
    if theta_factor > 1:
        label += r' / {}'.format(theta_factor)
    label += r'$'

    ax.errorbar(R_edges_norm[:-1],
                rhos_norm,
                yerr=rhos_norm_err,
                label=label,
                lw=2)

ax.legend(loc='upper left', fontsize=24, ncol=2)
Пример #16
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from utils import scatlyse

save_flag = True

use_latex = save_flag
use_pgf = True

ejm_rcparams.set_pretty_plots(use_latex, use_pgf)

t_steady = 50.0
dr = 0.7
n_samples = 1e2
alg = 'mean'

# Zero
d_0 = dataset.get_dset(paths.direct_Drc_0_dset_path)
Rps_0 = np.linspace(0.0, d_0.R, n_samples)
t_0, r1_0, r2_0 = d_0.get_direct()
r_0 = np.array([vector.vector_mag(r1_0), vector.vector_mag(r2_0)]).T
ps_0, ps_0_err = unzip([scatlyse(t_0, r_0, Rp, t_steady) for Rp in Rps_0])
ps_0 = np.array(ps_0)
ps_0_err = np.array(ps_0_err)
R_peak_0 = d_0.get_R_peak(alg=alg, dr=dr)[0]

# 10
d_10 = dataset.get_dset(paths.direct_Drc_10_dset_path)
Rps_10 = np.linspace(0.0, d_10.R, n_samples)
t_10, r1_10, r2_10 = d_10.get_direct()
r_10 = np.array([vector.vector_mag(r1_10), vector.vector_mag(r2_10)]).T
ps_10, ps_10_err = unzip([scatlyse(t_10, r_10, Rp, t_steady) for Rp in Rps_10])
ps_10 = np.array(ps_10)
Пример #17
0
use_latex = save_flag
use_pgf = True

ejm_rcparams.set_pretty_plots(use_latex, use_pgf)

dr = 0.7

fig = plt.figure(figsize=(14, 6))

gridspec = GridSpec(1, 2)

ax = fig.add_subplot(111)

ejm_rcparams.prettify_axes(ax)

dset = dataset.get_dset(paths.alignment_yes_Drc_inf_dset_path)
rhos_norm, rhos_norm_err, R_edges_norm = dset.get_rhos_norm(dr)
vp, vp_err = dset.get_vp()
R = dset.R
rhos_norm_err[np.isnan(rhos_norm_err)] = 0.0
ax.errorbar(R_edges_norm[:-1], rhos_norm, yerr=rhos_norm_err, label='Align')

dset = dataset.get_dset(paths.alignment_no_Drc_inf_dset_path)
rhos_norm, rhos_norm_err, R_edges_norm = dset.get_rhos_norm(dr)
vp, vp_err = dset.get_vp()
R = dset.R
rhos_norm_err[np.isnan(rhos_norm_err)] = 0.0
ax.errorbar(R_edges_norm[:-1], rhos_norm, yerr=rhos_norm_err, label='No align')

ax.legend(loc='upper left', fontsize=24)
fig = plt.figure(figsize=(12, 12 * ejm_rcparams.golden_ratio))
ax = fig.add_subplot(111)

ejm_rcparams.prettify_axes(ax)

theta_factors = np.arange(1, 7)

for theta_factor in theta_factors:
    force_fullsphere = theta_factor == 1
    if theta_factor == 1:
        theta_max = np.pi / 2.0
    else:
        theta_max = np.pi / theta_factor

    dset = dataset.get_dset(paths.wholedrop_dset_path, theta_max=theta_max,
                            force_fullsphere=force_fullsphere)

    rhos_norm, rhos_norm_err, R_edges_norm = dset.get_rhos_norm(dr)
    label = r'$\theta_\mathrm{max} = \pi'
    if theta_factor > 1:
        label += r' / {}'.format(theta_factor)
    label += r'$'

    ax.errorbar(R_edges_norm[:-1], rhos_norm, yerr=rhos_norm_err,
                label=label, lw=2)

ax.legend(loc='upper left', fontsize=24, ncol=2)

ax.set_ylim(0.0, 2.0)
ax.set_xlim(0.0, 1.05)
ax.set_ylabel(r'$\rho(r) / \rho_0$', fontsize=35)