def displacement_grid(box_size, centre, Ncases, time, dt, w_traj, u_traj):
"""
Calculates displcament grid from square uniform coarse-graining.
Parameters
----------
box_size : float
Length of the considered system's square box.
centre : float array
Centre of the box.
Ncases : int
Number of boxes in each direction to compute the displacements.
time : int
Frame at which displacements will be calculated.
dt : int
Length of the interval of time for which the displacements are
calculated.
w_traj : active_particles.dat.Gsd
Wrapped trajectory object.
u_traj : active_particles.dat.Dat
Unwrapped trajectory object.
Returns
-------
ugrid : 2D array like
Displacement grid.
"""
return w_traj.to_grid(
time + dt*get_env('ENDPOINT', default=False, vartype=bool),
u_traj.displacement(time, time + dt),
Ncases=Ncases, box_size=box_size, centre=centre)
def __init__(self, u_traj, w_traj, frame, box_size, centre, arrow_width,
arrow_head_width, arrow_head_length, dt=0, **kwargs):
"""
Initialises and plots figure.
Parameters
----------
u_traj : active_particles.dat.Dat
Unwrapped trajectory object.
w_traj : active_particles.dat.Gsd
Wrapped trajectory object.
frame : int
Frame to render.
box_size : float
Length of the square box to render.
centre : 2-uple like
Centre of the box to render.
arrow_width : float
Width of the arrows.
arrow_head_width : float
Width of the arrows' head.
arrow_head_length : float
Length of the arrows' head.
dt : int
Lag time for displacement. (default: 0)
"""
super().__init__(w_traj, frame, box_size, centre,
arrow_width, arrow_head_width, arrow_head_length) # initialise superclass
global trajectory_tracer_particle # index of tracer particle
try:
if not(trajectory_tracer_particle in self.particles): # tracer particle not in frame
raise NameError # do as if tracer particle were not defined
except NameError: # tracer particle not defined
if get_env('TRACER_PARTICLE', default=True, vartype=bool): # TRACER_PARTICLE mode
trajectory_tracer_particle = np.argmin(
np.sum(self.positions**2, axis=-1)) # tracer particle at centre of frame
else:
trajectory_tracer_particle = -1 # there is no particle with index -1
self.displacements = u_traj.displacement(frame, frame + dt,
*self.particles) # particles' displacements between time and time + dt
self.draw()
def endpoint():
"""
Consider a variable A which depends on space, time and a lag time (e.g.,
displacement). It is possible to measure
(i) A between times t and t + dt for a particle which is at position r at
time t: A(r, t ; t, t + dt)
(ii) A between times t and t + dt for a particle which is at position r at
time t + dt: A(r, t + dt ; t, t + dt)
and calculate correlations over space and time in both cases.
We will add a 'b' following the name of the correlation for files which
have produced considering case (i).
e.g., for displacement correlations, filenames will begin with 'Cuub'
if correlations were calculated for displacements between times t and
t + dt between particles at position r at time t.
Case (i) corresponds to environment variable 'ENDPOINT' set as False or not
set, while case (ii) corresponds to environment variable 'ENDPOINT' set as
True.
"""
if get_env('ENDPOINT', default=False, vartype=bool): return ''
return 'b'
_slope0 = 0 # default initial slope for fitting line slider
_slope_min = -2 # minimum slope for fitting line slider
_slope_max = 2 # maximum slope for fitting line slider
_font_size = 10 # default font size
_marker_size = 20 # default marker size
_colormap = 'jet' # default plot colormap
# SCRIPT
if __name__ == '__main__': # executing as script
# VARIABLE DEFINITIONS
data_dir = get_env('DATA_DIRECTORY', default=getcwd()) # data directory
int_max = get_env(
'INTERVAL_MAXIMUM', default=1, vartype=int
) # maximum number of time snapshots taken for the calculation of the mean square displacement at each time
int_period = get_env(
'INTERVAL_PERIOD', default=1, vartype=int
) # period of time at which mean square displacement was calculated
parameters_file = get_env(
'PARAMETERS_FILE',
default=joinpath(data_dir,
naming.parameters_file)) # simulation parameters file
with open(parameters_file, 'rb') as param_file:
parameters = pickle.load(param_file) # parameters hash table
_, self.philocmax[dir], _ = max(histogram3D_dir,
key=lambda el: el[2])
self.histogram3D = np.transpose(self.histogram3D)
self.time_step_list = sorted(
OrderedDict.fromkeys(
self.time_step.values())) # list of time steps
# SCRIPT
if __name__ == '__main__': # executing as script
# VARIABLES DEFINITIONS
mode = get_env('VARIABLE', default='dr') # plotting variable
peclet = get_env(
'PECLET', default=True,
vartype=bool) # display Péclet number rather than mode variable
if mode == 'dr':
vzero = get_env('VZERO', default=_vzero,
vartype=float) # self-propulsion velocity
attributes = {'vzero': vzero} # attributes displayed in filenames
var = 'dr' # plot variable
var_min = get_env('DR_MIN', default=_dr_min,
vartype=float) # minimum rotation diffusion constant
var_max = get_env('DR_MAX', default=_dr_max,
vartype=float) # maximum rotation diffusion constant
box_size=box_size,
multiply_with_dr=multiply_with_dr)
self.init_frame_list = sorted(
OrderedDict.fromkeys([
init_frame for dir, init_frame in self.msd
])) # list of mean square displacements initial frames
# SCRIPT
if __name__ == '__main__': # executing as script
# VARIABLES DEFINITIONS
mode = get_env('VARIABLE', default='dr') # plotting variable
peclet = get_env(
'PECLET', default=True,
vartype=bool) # display Péclet number rather than mode variable
if mode == 'dr':
vzero = get_env('VZERO', default=_vzero,
vartype=float) # self-propelling velocity
attributes = {'vzero': vzero} # attributes displayed in filenames
var = 'dr' # plot variable
var_min = get_env('DR_MIN', default=_dr_min,
vartype=float) # minimum rotation diffusion constant
var_max = get_env('DR_MAX', default=_dr_max,
vartype=float) # maximum rotation diffusion constant
def plot_correlation(C, C2D, C1D, C1Dcor, C_min, C_max, naming_standard,
**directional_correlations):
"""
Plot correlations.
Parameters
----------
C : string
Correlation name.
C2D : 2D array
Correlation 2D grid.
C1D : 1D array
Correlation 1D average.
NOTE: This has to be of the form (r, C1D(r)) with C1D(r) the averaged
2D grid at radius r.
C1Dcor : 1D array
Correlation 1D average, correction with density correlation.
NOTE: This has to be of the form (r, C1D(r)) with C1D(r) the averaged
2D grid at radius r.
C_min : float
Correlation minimum for plot.
C_max : float
Correlation maximum for plot.
naming_standard : active_particles.naming standard
Standard naming object.
Optional keyword arguments
--------------------------
CL : float
Longitudinal correlation.
CT : float
Transversal correlation.
NOTE: These two variables have to be provided together.
Returns
-------
fig : matplotlib figure
Main figure.
axs : array of matplotlib axis
Main figure's axis.
gc [GRID_CIRCLE mode] : active_particles.plot.mpl_tools.GridCircle object
Grid circle object.
"""
cmap = plt.cm.jet
fig, axs = plt.subplots(2, 2)
fig.set_size_inches(16, 16)
fig.subplots_adjust(wspace=0.3)
fig.subplots_adjust(hspace=0.3)
suptitle = str(r'$N=%.2e, \phi=%1.2f, \tilde{v}=%.2e, \tilde{\nu}_r=%.2e$'
% (parameters['N'], parameters['density'], parameters['vzero'],
parameters['dr']) + '\n' +
r'$S_{init}=%.2e, \Delta t=%.2e$' % (init_frame,
dt*parameters['period_dump']*parameters['time_step']) +
r'$, S_{max}=%.2e, N_{cases}=%.2e$' % (int_max, Ncases))
fig.suptitle(suptitle)
# C2D
cgrid = CorGrid(C2D, box_size, display_size=2*r_max)
Cmin = np.min(C2D)
Cmax = np.max(C2D)
CvNorm = colors.Normalize(vmin=Cmin, vmax=Cmax)
CscalarMap = cmx.ScalarMappable(norm=CvNorm, cmap=cmap)
axs[0, 0].imshow(cgrid.display_grid.grid, cmap=cmap, norm=CvNorm,
extent=[-r_max, r_max, -r_max, r_max])
axs[0, 0].set_xlabel(r'$x$')
axs[0, 0].set_ylabel(r'$y$')
axs[0, 0].set_title('2D ' + r'$%s$' % C + ' ' +
(r'$(%s^T/%s^L(\frac{r}{a} = %.3e) = %.3e)$'
% (C, C, (box_size/Ncases)/parameters['a'],
directional_correlations['CT']/directional_correlations['CL'])
if 'CL' in directional_correlations
and 'CT' in directional_correlations else ''))
divider = make_axes_locatable(axs[0, 0])
cax = divider.append_axes("right", size="5%", pad=0.05)
cb = mpl.colorbar.ColorbarBase(cax, cmap=cmap, norm=CvNorm, orientation='vertical')
cb.set_label(r'$%s$' % C, labelpad=20, rotation=270)
# C1D shifted
fplot(axs[1, 0])(C1D[1:, 0], C1D[1:, 1]/Cnn1D[-1, 1])
axs[1, 0].set_xlabel(r'$r$')
axs[1, 0].set_ylabel(r'$%s$' % C + r'$/C_{\rho\rho}(r=r_{max})$')
axs[1, 0].set_title('radial ' + r'$%s$' % C + r'$/C_{\rho\rho}(r=r_{max})$'
+ ' ' + r'$(C_{\rho\rho}(r=r_{max}) = %.3e)$' % Cnn1D[-1, 1])
axs[1, 0].set_xlim(r_min, r_max)
axs[1, 0].set_ylim(C_min, C_max)
# Cnn1D and C1D
axs[0, 1].set_title('radial ' + r'$C_{\rho\rho}$' + ' and ' + r'$%s$' % C)
axs[0, 1].set_xlabel(r'$r$')
axs[0, 1].set_xlim(r_min, r_max)
axs[0, 1].plot(Cnn1D[1:, 0], Cnn1D[1:, 1], color='#1f77b4')
axs[0, 1].set_ylabel(r'$C_{\rho\rho}$', color='#1f77b4')
axs[0, 1].tick_params('y', colors='#1f77b4')
ax_right = axs[0, 1].twinx()
ax_right.semilogy(C1D[1:, 0], C1D[1:, 1], color='#ff7f0e')
ax_right.set_ylabel(r'$%s$' % C, color='#ff7f0e', rotation=270,
labelpad=10)
ax_right.tick_params('y', colors='#ff7f0e')
ax_right.set_ylim(C_min*Cnn1D[-1, 1], C_max*Cnn1D[-1, 1])
# C1D/Cnn
fplot(axs[1, 1])(C1Dcor[1:, 0], C1Dcor[1:, 1])
axs[1, 1].set_xlabel(r'$r$')
axs[1, 1].set_ylabel(r'$%s$' % C + r'$/C_{\rho\rho}$')
axs[1, 1].set_title('radial ' + r'$%s$' % C + r'$/C_{\rho\rho}$')
axs[1, 1].set_xlim(r_min, r_max)
axs[1, 1].set_ylim(C_min, C_max)
# SAVING
if get_env('SAVE', default=False, vartype=bool): # SAVE mode
image_name, = naming_standard.image().filename(**attributes)
fig.savefig(joinpath(data_dir, image_name))
# GRID CIRCLE
if get_env('GRID_CIRCLE', default=False, vartype=bool): # GRID_CIRCLE mode
ccorgrid = CorGrid(C2D/Cnn2D, box_size, display_size=2*r_max) # correlation corrected with density correlation
gc = GridCircle(ccorgrid.display_grid.grid,
extent=(-r_max, r_max, -r_max, r_max))
gc.fig.set_size_inches(fig.get_size_inches())
gc.fig.suptitle(suptitle)
gc.fig.subplots_adjust(wspace=0.4) # width space
gc.fig.subplots_adjust(hspace=0.3) # height space
gc.ax_grid.set_xlabel(r'$x$')
gc.ax_grid.set_ylabel(r'$y$')
gc.ax_grid.set_title('2D ' + r'$%s$' % C + ' ' +
(r'$(%s^T/%s^L(\frac{r}{a} = %.3e) = %.3e)$'
% (C, C, (box_size/Ncases)/parameters['a'],
directional_correlations['CT']/directional_correlations['CL'])
if 'CL' in directional_correlations
and 'CT' in directional_correlations else ''))
gc.colormap.set_label(r'$%s$' % C, labelpad=20, rotation=270)
gc.ax_plot.set_xlabel(r'$\theta$')
gc.ax_plot.set_ylabel(r'$%s(\theta)$' % C)
try:
return fig, axs, gc
except NameError: return fig, axs
from os.path import join as joinpath
from math import ceil
import numpy as np
import pickle
from operator import itemgetter
from collections import OrderedDict
from datetime import datetime
import matplotlib as mpl
if not(get_env('SHOW', default=False, vartype=bool)):
mpl.use('Agg') # avoids crash if launching without display
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import matplotlib.cm as cmx
from mpl_toolkits.axes_grid1 import make_axes_locatable
from active_particles.plot.mpl_tools import GridCircle
# DEFAULT VARIABLES
_r_min = 1 # default minimum radius for correlations plots
_r_max = 20 # default maximum radius for correlations plots
_Cuu_min = 1e-3 # default minimum displacement correlation for correlation plots
_Cuu_max = 1 # default maximum displacement correlation for correlation plots
from active_particles.exponents import float_to_letters, letters_to_float,\
significant_figures
from active_particles.init import get_env
from collections import OrderedDict
from itertools import chain
from copy import deepcopy
from os import getcwd
from os import environ as envvar
from os import listdir as ls
from os.path import join as joinpath
# DEFAULT NAMES
sim_directory = joinpath(get_env('HOME'),
'active_particles_data') # simulation data directory
out_directory = joinpath(sim_directory, 'out') # launch output directory
parameters_file = 'param.p' # simulation parameters file
log_file = 'log-output.log' # simulation log output file
wrapped_trajectory_file = 'trajectory.gsd' # wrapped trajectory file (with periodic boundary conditions)
unwrapped_trajectory_file = 'trajectory.dat' # unwrapped trajectory file (without periodic boundary conditions)
# GLOSSARY
class Glossary:
"""
This class references VarInfo objects.
"""
if dir in self.corT:
self.corT_max[dir] = self.corT[dir][
1, np.argmin(np.abs(self.corT[dir][0] - dtmax[dir]))]
if dir in self.ratioTL:
self.ratioTL_max[dir] = self.ratioTL[dir][
1, np.argmin(np.abs(self.ratioTL[dir][0] - dtmax[dir]))]
# SCRIPT
if __name__ == '__main__': # executing as script
# VARIABLES DEFINITIONS
mode = get_env('VARIABLE', default='dr') # plotting variable
peclet = get_env(
'PECLET', default=True,
vartype=bool) # display Péclet number rather than mode variable
if mode == 'dr':
vzero = get_env('VZERO', default=_vzero,
vartype=float) # self-propelling velocity
attributes = {'vzero': vzero} # attributes displayed in filenames
var = 'dr' # plot variable
var_min = get_env('DR_MIN', default=_dr_min,
vartype=float) # minimum rotation diffusion constant
var_max = get_env('DR_MAX', default=_dr_max,
vartype=float) # maximum rotation diffusion constant
_wspace = 0.4 # default plots width space
_hspace = 0.05 # default plots height space
_colormap = 'jet' # default plot colormap
_slope0 = 0 # default initial slope for fitting line slider
_slope_min = -2 # default minimum slope for fitting line slider
_slope_max = 2 # maximum slope for fitting line slider
# SCRIPT
if __name__ == '__main__': # executing as script
# VARIABLE DEFINITIONS
data_dir = get_env('DATA_DIRECTORY', default=getcwd()) # data directory
init_frame = get_env('INITIAL_FRAME', default=-1,
vartype=int) # frame to consider as initial
int_max = get_env(
'INTERVAL_MAXIMUM', default=1, vartype=int
) # maximum number of intervals of length dt considered in correlations calculations
parameters_file = get_env(
'PARAMETERS_FILE',
default=joinpath(data_dir,
naming.parameters_file)) # simulation parameters file
with open(parameters_file, 'rb') as param_file:
parameters = pickle.load(param_file) # parameters hash table
av_p_sep = parameters['box_size'] / np.sqrt(
from os import getcwd
from os import environ as envvar
from os.path import join as joinpath
import numpy as np
from math import ceil
import pickle
from collections import OrderedDict
from datetime import datetime
import matplotlib as mpl
if not (get_env('SHOW', default=False, vartype=bool)):
mpl.use('Agg') # avoids crash if launching without display
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import matplotlib.cm as cmx
from mpl_toolkits.axes_grid1 import make_axes_locatable
# DEFAULT VARIABLES
_init_frame = -1 # default frame to consider as initial
_int_max = 1 # default maximum number of frames on which to calculate densities
_box_size = 10 # default length of the square box in which particles are counted
_Nbins = 10 # default number of bins for the histogram
_phimax = 1 # default maximum local density for histogram
from active_particles.maths import wo_mean, mean_sterr, Histogram
from os import getcwd
from os import environ as envvar
from os.path import join as joinpath
import numpy as np
import pickle
from datetime import datetime
from collections import OrderedDict
import matplotlib as mpl
if not (get_env('SHOW', default=False, vartype=bool)):
mpl.use('Agg') # avoids crash if launching without display
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import matplotlib.cm as cmx
import matplotlib as mpl
from mpl_toolkits.axes_grid1 import make_axes_locatable
import matplotlib.pyplot as plt
from active_particles.plot.mpl_tools import FittingLine
# DEFAULT VARIABLES
_sq_disp_min = 1e-5 # default minimum included value of square displacement for histogram bins
_sq_disp_max = 1e5 # default maximum excluded value of square displacement for histogram bins
_Nbins = 100 # default number of histogram bins
_wspace = 0.2 # default plots width space
_hspace = 0.05 # default plots height space
_colormap = 'jet' # default plot colormap
_width_inset = 30 # default maximum C44 inset width in percentage of graph width
_height_inset = 30 # default maximum C44 inset height in percentage of graph height
# SCRIPT
if __name__ == '__main__': # executing as script
# VARIABLES DEFINITIONS
mode = get_env('MODE', default='real') # strain correlations computation mode
if mode in ('real', 'fourier'): naming_Css = naming.Css(mode=mode) # Css naming object
elif mode == 'cmsd':
naming_Ctt = naming.Ctt() # Ctt naming object
naming_Cll = naming.Cll() # Cll naming object
else: raise ValueError('Mode %s is not known.' % mode) # mode is not known
data_dir = get_env('DATA_DIRECTORY', default=getcwd()) # data directory
init_frame = get_env('INITIAL_FRAME', default=-1, vartype=int) # frame to consider as initial
int_max = get_env('INTERVAL_MAXIMUM', default=1, vartype=int) # maximum number of intervals of length dt considered in correlations calculations
parameters_file = get_env('PARAMETERS_FILE',
default=joinpath(data_dir, naming.parameters_file)) # simulation parameters file
with open(parameters_file, 'rb') as param_file:
from os import getcwd
from os import environ as envvar
from os.path import join as joinpath
import sys
from math import ceil
import pickle
import numpy as np
np.seterr(divide='ignore')
import matplotlib as mpl
if not(get_env('SHOW', default=False, vartype=bool)):
mpl.use('Agg') # avoids crash if launching without display
import matplotlib.pyplot as plt
from matplotlib.colors import Normalize as ColorsNormalise
from matplotlib.cm import ScalarMappable
from mpl_toolkits.axes_grid1 import make_axes_locatable
from datetime import datetime
from collections import OrderedDict
import subprocess
# DEFAULT VARIABLES
_frame_per = 1 # default frame rendering period
_pd2minmin = 1e-4 # default minimum D2min probability
_pd2minmax = 1e-1 # default maximum D2min probability
_contours = 20 # default contour level value
_font_size = 15 # default font size for the plot
_colormap = 'inferno' # default plot colormap
_colormap_label_pad = 20 # separation between label and colormap
# SCRIPT
if __name__ == '__main__': # executing as script
# VARIABLES DEFINITIONS
data_dir = get_env('DATA_DIRECTORY', default=getcwd()) # data directory
wrap_file_name = get_env(
'WRAPPED_FILE',
default=joinpath(
data_dir,
naming.wrapped_trajectory_file)) # wrapped trajectory file (.gsd)
init_frame = get_env('INITIAL_FRAME', default=-1,
vartype=int) # reference frame in D2min calculations
dt_min = get_env('DT_MIN', default=1, vartype=int) # minimum lag time
dt_max = get_env('DT_MAX', default=-1, vartype=int) # maximum lag time
int_max = get_env(
'INTERVAL_MAXIMUM', default=_int_max,