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manuscript_figures_micro.py
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manuscript_figures_micro.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@author: andreypoletaev
"""
# =============================================================================
# %% Block 1: initial imports & constants
# =============================================================================
import os, sys, re, freud
if os.path.join(os.path.abspath(os.getcwd()), "utils") not in sys.path :
sys.path.append(os.path.join(os.path.abspath(os.getcwd()), "utils"))
import numpy as np
import pandas as pd
import seaborn as sns
import hop_utils as hu
import crystal_utils as cu
from matplotlib import pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
from scipy.interpolate import interp1d
from datetime import datetime as dt
from itertools import cycle
from batlow import cm_data
batlow_cm = LinearSegmentedColormap.from_list('batlow', cm_data)
batlow_even = LinearSegmentedColormap.from_list('batlow_even', hu.batlow_lightness_scaled(0.4,0.6))
plt.rc('legend', fontsize=10)
plt.rc('axes', labelsize=16)
plt.rc('xtick', labelsize=12)
plt.rc('ytick', labelsize=12)
plt.rc('axes', titlesize=16)
plt.rc("savefig", format='pdf')
plt.rc('markers', fillstyle='full')
plt.rc('errorbar', capsize=3)
kb = 8.617e-5 ## [eV/K]
dddddd = dt.today().strftime('%y%m%d')
# =============================================================================
# Constants
# =============================================================================
## relevant_vars are actually used as of 2020/07/26
relevant_vars = ['metal','phase','T1','z','config','stoich','exclude']
## default number of sites per plane (beta)
num_polys = 200
## estimated maximum distances from oxygens based on stoichiometry
est_max_radii = {'100':0, '102':15, '106':9, '116':5, '120':5, '124':4, '130':4, 'unsym':6, 'symm':6, 'oh':6}
## all the possible mobile ions
mobile_ions = ['Na', 'K', 'Ag']
## accepted names for doubleprime
bdp_names = ['beta', 'doubleprime', 'bdp']
## doubleprime stoichiometries
bdp_stoichs = ['symm', 'unsym', 'oh', 'rand']
## figure display name of the distance metric by phase
distance_names = dict()
for n in bdp_names:
distance_names[n] = r'# Mg$_{Al}^\prime$ Neighbors'
distance_names['beta']=r'Distance to $O_i^{\prime\prime}$'
# empty placeholder
planes_data = None
## shorthands for labels in figures
bdp = r'$\beta^{\prime\prime}$'
beta = r'$\beta$'
phases = {'beta':beta, 'bdp':bdp}
## database of all the hops
try: all_planes = pd.read_csv('./sample_data/all_hop_planes.csv')
except: pass
## flag for loading atoms
frac = False
## flag for loading site-filling times (used in the PDF plotting block)
do_fill_times= False
## clear the data structure that holds the heavy hops dataframes
try: del planes_data
except : pass
# =============================================================================
# %% Block 2: Compile a database of files each of which stores hop events.
# ## This only screens for files that have 'plane.csv' in their name
# ## (Currently planes are organized into folders where they share a folder
# ## with only one lammps structure file. Crude, I know.)
# ##
# ## Currently this is pretty loose, but can be made stricter, e.g. looking for
# ## some identifiers to appear both in the filename AND in the folder name.
# =============================================================================
red_flags = ['grid', 'VACF', 'polymer', 'LAPC', 'legacy', 'verts',
'hop_planes', 'vanHove', 'BR', 'fill_times', 'actual',
'count', 'expected','Murch', 'paths', 'NN', 'counts', 'LAMMPS',
'oxygen_cells', 'edge', 'pre-update', 'jacf', 'vacf']
# list of dictionaries from which a pandas dataframe will be composed
dicts = []
for root, dirs, files in os.walk('./sample_data/'):
for f in files:
fp = os.path.join(root,f)
if f.endswith('plane.csv') and hu.which_one_in(red_flags, fp) is None :
# print(re.split('/|-', root), f)
## split stuff for finding all the specific variables
features = list(set(re.split('-|/',fp)))
## phase: beta or beta"
ph = hu.which_one_in(bdp_names, features)
if ph is None or not ph:
print(f'Phase unclear: {fp}')
continue
## temperature: look for decimals followed by 'K'
temps = set(re.findall(r'\d+K', fp))
if len(temps) != 1 :
print(f'Temperature unclear: {fp}')
continue
else:
# print(f'found temperature: {list(temps)[0]}')
this_temp = int(list(temps)[0][:-1])
## metal : look for there to be one mobile ion only, somewhere in the path
mobile_ion = hu.which_one_in(mobile_ions, features)
if mobile_ion is None or not mobile_ion:
print(f'Mobile ion unclear: {fp}')
continue
## stoichiometry: works for the beta phase 2020/07/26; bdp added 08/01
if ph == 'beta' :
stoichs = set(cu.flatten([re.findall(r'1\d\d_\d|1\d\d$|1\d\d_M\d.|1\d\d_M\d', feat) for feat in features]))
if len(stoichs) != 1:
print(f'Composition unclear: {fp}')
print(f'Culprit: {stoichs}')
continue
else:
lammps_stoich = list(stoichs)[0]
if '_' in lammps_stoich: st, ex = lammps_stoich.split('_')
else:
st = lammps_stoich
ex = 0
## for not-beta phase, assume it is beta-doubleprime
else:
stoich = [x for x in features if hu.which_one_in(bdp_stoichs, x)]
if len(stoich) > 1 :
print(f'Beta-doubleprime "rule" unclear: {stoich}.')
print('Recognized rules: "oh" for Al(4), "unsym" for un-symmetric Al(2),')
print(' "symm" (2Ms!) for any Al(2), "rand" for all sites.')
print('To fix this: rename the folders and/or the lammps file so that they all match.')
continue
else:
lammps_stoich = stoich[0]
if '_' in stoich[0]: st, ex = stoich[0].split('_')
else: st = stoich[0]; ex = 0
## find the lammps file: there should only be one
## this is not really robust as it does not test for whether the file is real
lf = '/'.join([x for x in root.split('/') if 'hops' not in x]) + '/'
lp = lf + mobile_ion + ph + lammps_stoich + '.lmp'
try:
with open(lp) : pass
except IOError :
try:
lp = lf + mobile_ion + '_' + ph + '_' + lammps_stoich + '.lmp'
except IOError :
print(f'Could not find lammps file: {lp}')
continue
## z of the plane.
## This is not great: it relies on having a temperature before z in the file name
## The value 4 is also not robust to beta vs doubleprime.
features2 = list(re.split('K_|-|/|\.',fp))
zs = [x.replace('plane','') for x in features2 if 'plane' in x]
if len(zs) != 1:
print(f'z-value unclear: {zs} in {fp}')
continue
else:
zs = zs[0]
if zs == 'z_all' :
_, _, _, atoms = cu.read_lmp(lp)
num_p = len(cu.get_conduction_planes(atoms,mobile_ion,
inexact=False if ph == 'beta' else True))
else: num_p = 1
## time length of simulation
hops = pd.read_csv(fp)
one_ion_hops = hops.query(f'ion == {hops.ion.min()}')
if len(one_ion_hops) > 1:
total_time = one_ion_hops.new_resid_time.sum() + one_ion_hops.old_resid_time.iloc[0]
else:
total_time = one_ion_hops.new_resid_time.values[0]
print(f'logged plane {fp}')
del hops, one_ion_hops
## put it all together
dicts.append(dict(phase=ph, stoich=st, exclude=ex, config=lammps_stoich, metal=mobile_ion,
T1=this_temp, z=zs, num_planes=num_p, total_time=total_time,
hop_path=fp, lammps_path=lp))
## sort and save
planes_df = pd.DataFrame(dicts).sort_values(by=['phase','metal','config','T1','z'], axis=0)
planes_df.to_csv('./sample_data/all_hop_planes.csv', index=False)
# =============================================================================
# %% Figure 5: pre-load data so it does not take time every time
# =============================================================================
planes_to_plot = all_planes.query('phase != "beta" & num_planes > 2 & config == "unsym_0" & T1 in [300,600]')
# planes_to_plot = all_planes.query('phase == "beta" & num_planes > 3 & config == "120_4" & T1 == 600')
planes_dicts = []
for plane in planes_to_plot.itertuples(index=False):
## load a dictionary with all the data and metadata
this_plane_data = hu.load_plane_with_atoms(plane, frac=False, do_fill_times=False)
## put it into a list
planes_dicts.append(this_plane_data)
planes_data = pd.DataFrame(planes_dicts)
# =============================================================================
# %% Figure 5: Plot semi-log of onward/reverse hopping ratios as correlation factor
# ## This is the direct comparison with Funke's model
# =============================================================================
variable = 'metal' ## << do pick this
## distance of the furthest sites away from defects that will be plotted
## (larger values will be ignored, if they are present)
max_r_to_plot = 4
clb_cut = 10 ## CLB: min Count (of hops) per Log Bin to plot; smaller counts (at longer times) get cut
old_site = 'all' ## plot for hops starting at sites: 'all', 'aBR', or 'BR'
full_label = False ## cosmetic changes to labels of lines
plot_total = True ## plot for all distances together
shade_range = True ## shade between top and bottom curves for each material
logbins = np.logspace(-0.7,4.3) ## sigma np.round(np.logspace(-0.7,4.3),2)
plot_gs = True ## plot relaxation times from van Hove function (self)
rs_list = [[0.01, 4.6]] ## default radii for van Hove decay points
plot_cdt = True ## add C_D values to the plot
# ========== automatic things below this line ==========
plt.rc('markers', fillstyle='full')
# ===== start figure =====
logcenters = np.sqrt(logbins[1:]*logbins[:-1])
var_values = sorted(set(planes_data[variable]))
fig, axes = plt.subplots(1,len(var_values),
sharex=True, sharey=True,figsize=(6*len(var_values),4.8))
if len(var_values) < 2 : axes = [axes]
# ===== iterate over the loaded planes =====
for var, ax in zip(var_values, axes):
# these_planes = planes_data.loc[planes_data[variable]==var]
these_planes = planes_data.query(f'{variable} == @var')
## start counters for colors and markers afresh for each panel
color_counter = 0
markers = cycle(['o', 's', 'v', 'd', '^','D'])
for i, plane in planes_data.iterrows():
if plane[variable] == var:
m = plane.metal; T1 = plane.T1; z = plane.z # ; z = 'BR-aBR'
ph = plane.phase; st = plane.stoich; cn = plane.config
folder = '/'.join(plane.hop_path.split('/')[:-2])
dist_col = 'new_ox_r' if ph == 'beta' else 'new_mg_count'
data2 = plane.hops.copy(deep=True); lat = ', all'
if old_site == 'BR' : data2 = data2.query('old_is_BR == True'); lat = r', BR$\rightarrow$aBR'
elif old_site == 'aBR' : data2 = data2.query('old_is_BR == False'); lat = r', aBR$\rightarrow$BR'
## if plotting shading, start min and max data structures
mins = 50*np.ones(len(logbins)-1)
maxes = np.zeros(len(logbins)-1)
# make a color map with a darker gray in the middle
num_curves = min(est_max_radii[st],max_r_to_plot)+1
# cmap = LinearSegmentedColormap('dark_cwb',segmentdata=hu.cdict, N=num_curves)
# colors = [cmap(i) for i in np.linspace(0,1, num_curves)]
colors = [batlow_even(i) for i in np.linspace(0,1, num_curves)]
## iterate marker for each set of data
this_marker = next(markers)
## calculate the all-sites curve
## Counts / Log bin Onward & Reverse
clb,_ = np.histogram(data2.new_resid_time[data2.rev_hop==True],bins=logbins)
clo,_ = np.histogram(data2.new_resid_time[data2.rev_hop==False],bins=logbins)
## calculate the ratio of onward / (onward + reverse)
## then multiply by 1.5 for the correlation factor
total_ratios = clo[clb>clb_cut]/(clb[clb>clb_cut]+clo[clb>clb_cut]) * 1.5
if plot_gs : int_fun = interp1d(logcenters[clb>clb_cut], total_ratios, fill_value=(-1,1), bounds_error=False)
if plot_total :
ax.plot(logcenters[clb>clb_cut], total_ratios, c='k', alpha=0.5,
ms=4, label=f'{T1}K', linestyle='-',marker=this_marker)
for r in range(num_curves) :
## select data
data3 = data2.query(f'{dist_col} == @r')
## Counts / Log bin Onward & Reverse
clb,_ = np.histogram(data3.new_resid_time[data3.rev_hop==True],bins=logbins)
clo,_ = np.histogram(data3.new_resid_time[data3.rev_hop==False],bins=logbins)
tag = f'{m} {T1}K, {z}' if full_label else f'{T1}K, {r}'
## make a special color for stoichiometric material
if st == '100' : this_color = 'tab:green'
else: this_color = colors[r]
## calculate the ratio of onward / (onward + reverse)
## then multiply by 1.5 for the correlation factor
ratios = clo[clb>clb_cut]/(clb[clb>clb_cut]+clo[clb>clb_cut]) * 1.5
## update mins and maxes
if shade_range and ratios.size > 0 :
# mins[(clb>clb_cut) & (mins[clb>clb_cut] > ratios)] = ratios[mins[clb>clb_cut] > ratios]
mins[clb>clb_cut] = np.minimum(mins[clb>clb_cut], ratios)
# maxes[(clb>clb_cut) & (maxes[clb>clb_cut] < ratios)] = ratios[maxes[clb>clb_cut] < ratios]
maxes[clb>clb_cut] = np.maximum(maxes[clb>clb_cut], ratios)
## Plot the ratio
if not plot_total :
ax.plot(logcenters[clb>clb_cut], ratios, ms=4, label=tag,
linestyle='--',marker=this_marker, c=this_color, mec=this_color, mfc='none')
if shade_range :
## Plot the shaded range
mins[mins == 50] = np.nan
maxes[maxes == 0] = np.nan
ax.fill_between(logcenters, mins, maxes, alpha=0.4, facecolor=hu.metal_colors[m])
## add hop relaxation rates from van Hove function
## only reasonable if
if plot_gs :
try:
gs = hu.load_gs(folder+f'/{m}-*-gs-{T1}K*ps.csv', option='Funke', radii=rs_list)
ax.plot(gs, int_fun(gs), marker=this_marker, mfc='yellow', mec='k', zorder=3, ls='')
except ValueError : print(f'could not do Gs decay for {m} {cn} {T1}. Check cell size.')
## plot approximately the timescale when C_D goes to zero
if plot_cdt :
ylevel = 1; cd = -100; errs = [[10],[10]]
if ph != 'beta' and T1 == 300 and m == 'K' :
cd = 4.5e3; errs = [[1000],[1500]]
elif ph != 'beta' and T1 == 600 and m == 'K' :
cd = 25; errs = [[10],[130]]
elif ph != 'beta' and T1 == 600 and m == 'Ag' :
cd = 140; errs = [[110],[45]]
elif ph != 'beta' and T1 == 600 and m == 'Na' :
cd = 45; errs = [[35],[150]]
elif ph == 'beta' and T1 == 600 and m == 'Ag' :
cd = 1e4; errs = [[3e3],[3e3]]
elif ph == 'beta' and T1 == 600 and m == 'Na' :
cd = 2e4; errs = [[5e3],[5e3]]
try :
ax.errorbar(x=cd, y=int_fun(cd), xerr=errs, marker=this_marker,
mfc='red', mec='k', zorder=3, ls='', c='red', ecolor='k')
except : ax.errorbar(x=cd, y=ylevel, xerr=errs, marker=this_marker,
mfc='red', mec='k', zorder=3, ls='', c='red', ecolor='k')
color_counter += 1
print(f'completed plane {m} {cn} {T1}')
axtitle = m + (r' $\beta$' if ph == 'beta' else r' $\beta^{\prime\prime}$') + ('\n'+ distance_names[ph] if not plot_total else '')
leg = ax.legend(title=f'{axtitle}', ncol = 2 if (not plot_total and len(these_planes) > 1) else 1, loc='lower right')
plt.setp(leg.get_title(), multialignment='center')
ax.plot([0,1e5], [1,1], ls=':', c='grey', lw=0.4)
ax.set(xscale='log', yscale='linear', ylim=[0,1.05],xlim=(0.25,25e3),xlabel=r'Hop Residence Time $\tau$, ps')
axes[0].set(ylabel='Correlation Factor $f$'+lat, yticks=[0,0.2,0.4,0.6,0.8,1.0])
fig.tight_layout(pad=0.5)
# =============================================================================
# %% Figure S2 : load planes
# =============================================================================
planes_to_plot = all_planes.query('phase != "beta" & metal == "Na" & config in ["unsym_0", "symm_1"] & T1 in [230,300,473,600] & z == "016"')
planes_to_plot = planes_to_plot.sort_values(by=['T1','z','metal','stoich'])
planes_dicts = []
for plane in planes_to_plot.itertuples(index=False):
## load a dictionary with all the data and metadata
this_plane_data = hu.load_plane_with_atoms(plane, frac, do_fill_times)
## put it into a list
planes_dicts.append(this_plane_data)
planes_data = pd.DataFrame(planes_dicts)
## labels based on defect placements
label_dict={'unsym_0':'not quenched', 'symm_1':'quenched'}
planes_data.config = [label_dict[x] for x in planes_data.config]
# =============================================================================
# %% Figure S2: by-site occupancy maps for individual planes & temperatures
# =============================================================================
# ========== automatic things below this line ==========
variable = 'T1'
var2 = 'config'
var_values = set(planes_data[variable])
planes_data.sort_values(by=[var2, variable], inplace=True)
fig, axes = plt.subplots(len(planes_data)//len(var_values),len(var_values),
sharex=True, sharey=True, figsize=(12,6))
if len(planes_data) < 2 or len(axes) < 2: axes = [axes]
else : axes = axes.reshape(-1)
site_pts_list = list()
sites_list = list()
polys_list = list()
mins_list = list()
maxs_list = list()
cell_num_list = list()
boxes_list = list()
voros_list = list()
## iterate through planes to compose lists of quantities to be plotted
for (i, plane), ax in zip(planes_data.iterrows(), axes):
data = plane.hops; tt = plane.total_time; ph = plane.phase
m = plane.metal; T1 = plane.T1; s = plane.stoich; z = plane.z
tt = plane.total_time
BR_sites = plane.BR_sites
sites_by_r = plane.sites_by_r
## dist_col is the dataframe column that has the distance index
dist_col = 'new_ox_r' if ph == 'beta' else 'new_mg_count'
if plane.num_planes > 1:
print('This block only works for single planes, not composite planes')
continue
## to plot grid maps, I need: site_pts, 2D box, a freud voronoi to continue
site_pts = np.copy(plane.site_pts)
site_pts[:,-1] = 0 ## flatten in case it has not done so yet.
box = freud.box.Box(Lx=plane.cell[0,0], Ly=plane.cell[1,1], is2D=True)
vor = freud.locality.Voronoi(box)
## destroy z-coordinate here, and make polygons
site_pts[:,-1] = 0
vor.compute((box,site_pts))
## occupancies
toplot_BR = pd.DataFrame(columns=['total',dist_col])
toplot_aBR = pd.DataFrame(columns=['total',dist_col])
for r in range(plane.max_r+1): ## range of distances to oxygens / counts of nearby defects
total_BR, total_aBR = hu.site_occupancies(data,set(sites_by_r[r]),BR_sites,tt,plane,r=r)
toplot_BR = toplot_BR.append(total_BR,sort=False)
toplot_aBR = toplot_aBR.append(total_aBR,sort=False)
sites = toplot_BR.append(toplot_aBR).total
polys_all = [ vor.polytopes[int(i)] for i in list(sites.index) ]
cell_nums = [ int(i) for i in list(sites.index) ]
## save limits and polygons for plotting
sites_list.append(sites)
polys_list.append(polys_all)
mins_list.append(min(sites))
maxs_list.append(max(sites))
cell_num_list.append(cell_nums)
## also save lists for freud tessellations
site_pts_list.append(site_pts)
boxes_list.append(box)
voros_list.append(vor)
## create bounds for coloring below
# bounds = [min([x if not np.isnan(x) else 0 for x in mins_list]), max([x if not np.isnan(x) else 0 for x in maxs_list])]
bounds = [0,1]
## this is the loop that actually creates the figure
for (i,plane), site_vals, polys, ax, site_pts, box, vor, cns \
in zip(planes_data.iterrows(), sites_list, polys_list, axes, site_pts_list,
boxes_list, voros_list, cell_num_list) :
plt.sca(ax)
v = str(plane[variable]); v2 = ['', str(plane[var2])][var2 != 'phase']
m = plane.metal; T1 = plane.T1; z = plane.z; ph = plane.phase
Lx = plane.cell[0,0]; Ly = plane.cell[1,1]
## make up axes labels for the plot
axt = v + ['','K'][variable=='T1']
axt2 = ', ' + ['','z='][var2=='z'] +v2+ ['','K'][var2 =='T1']
## plot the whole lattice, coloring by property chosen above
hu.draw_voronoi(box, site_pts, polys, draw_box=True, color_by_property=site_vals,
alpha=0.75, property_clim=bounds,cmap='YlOrRd') ## cell_numbers=cns
## set xy limits and the such
ax.set(aspect=1,xlim=(-0.55*Lx,0.55*Lx),ylim=(-0.55*Ly,0.55*Ly),
xticks=[], yticks=[], ylabel=axt2[2:])
## having pre-loaded the defect coordinates separately up top, plot them
if ph != 'beta' or 'M' in cn :
spec = {'facecolors':'tab:green', 'edgecolors':'k'}
half1 = plane.defects.query('-11 < z < -4')
ax.scatter(half1.x, half1.y, s=20, alpha=1, **spec)
half2 = plane.defects.query('-11 > z or z > -4')
ax.scatter(half2.x, half2.y, s=40, alpha=0.5, **spec)
else :
spec = {'facecolors':'tab:red', 'edgecolors':'k', 's':40}
ax.scatter(plane.defects.x, plane.defects.y, **spec)
ax.set(title=axt) ## +axt
## make labels: general 2D case
## make top, left, and bottom labels non-empty
for n, ax in enumerate(axes.flat):
if n >= len(var_values): ## not top row
ax.set(title='')
if n % len(var_values) != 0: ## not left column
ax.set(ylabel='')
if len(axes.flat) - n > len(var_values) : ## not bottom row
ax.set(xlabel='')
fig.tight_layout(pad=0.25)
# =============================================================================
# %% Figure 4 (+ Figure S6): pre-load data so it does not take time every time
# =============================================================================
planes_to_plot = all_planes.query('phase != "beta" & num_planes > 2 & config == "unsym_0" & T1 == 600')
# planes_to_plot = all_planes.query('phase == "beta" & num_planes > 3 & config == "120_4" & T1 == 1000')
planes_dicts = []
for plane in planes_to_plot.itertuples(index=False):
## load a dictionary with all the data and metadata
this_plane_data = hu.load_plane_with_atoms(plane, frac=False, do_fill_times=False)
## put it into a list
planes_dicts.append(this_plane_data)
planes_data = pd.DataFrame(planes_dicts)
# =============================================================================
# %% Figure 4b-d (+ Figure S6): block for 2D plot with squares (transition matrix)
# ## study hop characteristics by distances from oxygen defects
# ## 2020/04/07: for single planes site_pts_list, boxes_list, voros_list can be included
# ## these plots are resolved by from and to distances, i.e. these are scalars by edge
# =============================================================================
subject = 'Hops' ## Options: 'Hops' or 'Flybys' or '# Edges'
onw = True ## if True, track onward hops only
abr = True ## if True, track hops into aBR sites only (best with onward)
per_site = True ## if True, normalize by the number of edges between sites
write_max = False ## write out the max value on the plot
## assign the variable over which to plot multiple panels
variable = 'metal'
# ========== automatic things below this line ==========
var2 = 'phase' ## set a default, this gets assigned automatically
var_values = set(planes_data[variable])
## if a second variable is varied, then find the second variable
relevant_vars = ['config', 'metal','T1','z','stoich','exclude']
relevant_vars.remove(variable)
if len(planes_data) > len(var_values):
for rv in relevant_vars:
if len(set(planes_data[rv])) > 1: var2 = rv; break
fig, axes = plt.subplots(len(planes_data)//len(var_values),len(var_values),
sharey=True, sharex=True, figsize=(2+2*len(var_values),3.1))
## sort data so that it matches axes being cycled
planes_data.sort_values(by=[var2, variable], inplace=True)
if len(planes_data) < 2 or len(axes) < 2: axes = [axes]
else : axes = axes.reshape(-1)
## iterate to set common limits later
for ax, (i, plane) in zip(axes, planes_data.iterrows()):
plt.sca(ax)
m = plane.metal; T1 = plane.T1; z = plane.z; tt = plane.total_time/1000
v = str(plane[variable]); v2 = [str(plane[var2]), ''][var2 == 'phase']
max_r = plane.max_r; ph = plane.phase
## columns with distances : new and old
new_r_col = 'new_ox_r' if ph == 'beta' else 'new_mg_count'
old_r_col = 'old_ox_r' if ph == 'beta' else 'old_mg_count'
data = plane.hops.copy(deep=True).astype({old_r_col:'int32', new_r_col:'int32'})
edge_distances = plane.edge_distances
if subject == 'Flybys' : onw = True; data = data.query('new_resid_time < 1.1 & old_is_BR==False')
## apply the onwards flag
if onw : data = data.query('rev_hop == False')
if abr and ph == 'beta' : data = data.query('old_is_BR == True')
## check reasonableness and count using groupby
# print max(data.new_ox_r.unique()), max(data.new_ox_r.unique())
# cts = data.groupby([old_r_col,new_r_col]).size().reset_index()
cts = data.groupby(['old_cell','new_cell']).agg({'ion':'size',old_r_col:'mean',new_r_col:'mean'}) \
.reset_index(drop=True).groupby([old_r_col,new_r_col]).agg(['mean','std'])
# print cts.max(), cts.sum()
## transform to np 2D array for dividing by edge counts and meshgrid
cts_norm = np.zeros((int(data[old_r_col].max())+1, int(data[new_r_col].max())+1))
cts_stds = np.zeros((int(data[old_r_col].max())+1, int(data[new_r_col].max())+1))
for s1 in range(data[old_r_col].max()+1):
for s2 in range(data[new_r_col].max()+1):
try :
cts_norm[s1,s2] = cts.query(f'{old_r_col} == @s1 & {new_r_col} == @s2')[('ion', 'mean')] # /edge_distances[s1,s2]
cts_stds[s1,s2] = cts.query(f'{old_r_col} == @s1 & {new_r_col} == @s2')[('ion', 'std')]
# cts_norm[s1,s2] = cts.query(f'{old_r_col} == @s1 & {new_r_col} == @s2')[0]/edge_distances[s1,s2]
except : cts_norm[s1,s2] = 0
if subject == '# Edges': ## Plot connectivity
ax.pcolor(np.arange(cts[old_r_col].max()+2)-0.5,
np.arange(cts[new_r_col].max()+2)-0.5,
edge_distances,cmap='viridis')
maxval = str(int(edge_distances.max()))
# print(edge_distances)
elif subject == 'Hops' :
if not per_site: ## just plot all the hops
ax.hist2d(data[old_r_col],data[new_r_col],
bins=[max_r+1,max_r+1], range=[[-0.5,max_r+0.5],[-0.5,max_r+0.5]])
maxval=f'{cts[0].max()/tt:.2f} /ns'
else: ## normalize by # edges
ax.pcolor(np.arange(data[old_r_col].max()+2)-0.5,
np.arange(data[new_r_col].max()+2)-0.5,cts_norm.T,cmap='viridis')
maxval = f'{np.max(cts_norm)/tt:.1f}±{cts_stds[np.where(cts_norm == np.max(cts_norm))][0]/tt:.1f} /ns'
# print(cts_norm.round(1)/tt, cts_stds.round(1), tt)
elif subject == 'Flybys' :
if not per_site: ## just plot all the hops
ax.hist2d(data[old_r_col],data[new_r_col],bins=[max_r+1,max_r+1],
range=[[-0.5,max_r+0.5],[-0.5,max_r+0.5]])
maxval=f'{cts[0].max()/tt:.1f} /ns'
else: ## normalize by # edges
ax.pcolor(np.arange(cts[old_r_col].max()+2)-0.5,
np.arange(cts[new_r_col].max()+2)-0.5,
cts_norm.T,cmap='viridis')
maxval = f'{cts_norm.max()/tt:.2f} /ns'
## print matrices
print(f'=== {v} {v2}, {tt:.1f} ns ===')
print(cts_norm.round(1)/tt)
print(cts_stds.round(1)/tt)
## make up titles and stuff
axt = ', ' +v+ ['','K'][variable=='T1']
axt2 = ', ' +v2+ ['','K'][var2 =='T1']
# ax.set(title=axt[2:])
ax.set(xlabel=r'r$_{from}$', aspect=1,
ylabel=r'r$_{to}$'+['',axt2][var2 is not None])
ticklist = list(range(0,max_r+1,2))
ax.set_yticks(ticklist); ax.set_xticks(ticklist)
## label the plot with the max value
if write_max :
if subject == '# Edges':
ax.text(-0.25,max_r-1.125,f'max={maxval}',fontsize=10,color='tab:orange')
elif onw :
ax.text(-0.25,-0.125,f'max={maxval}',fontsize=10,color='tab:orange')
else :
ax.text(-0.25,max_r-1.125,f'max={maxval}',fontsize=10,color='tab:orange')
## write the x- and y- axes labels
if ph == 'beta' :
ax.set_xlabel(r'# Sites to $O_i^{\prime\prime}$, hop origin', fontsize=13.5)
ax.set_ylabel(r'# Sites to $O_i^{\prime\prime}$, destination', fontsize=13.5)
# ax.set(la)
else :
ax.set(xlabel = r'# Mg$_{Al}^\prime$ at hop origin',
ylabel = r'# Mg$_{Al}^\prime$ at destination')
del data
## make labels: general 2D case
## make top, left, and bottom labels non-empty
if len(axes) > 1:
for n, ax in enumerate(axes.flat):
if n >= len(var_values): ## not top row
ax.set(title='')
if n % len(var_values) != 0: ## not left column
ax.set(ylabel='')
if len(axes.flat) - n > len(var_values) : ## not bottom row
ax.set(xlabel='')
for i in range(5) : fig.tight_layout(pad=0.5, w_pad=0.25)
# =============================================================================
# %% Figure 4e-g, Figure S6e-g: Calc site energies from by-site occupancy
# ## here, non-hops automatically are 1, and empties automatically are 0
# =============================================================================
variable = 'metal' ## << do pick this
verbose = True ## flag for how much print() outputs to show. False is less.
plot_swarm = False ## for reducing clutter, skip plotting the swarm
plot_box = True ## for reducing clutter
# ========== automatic things below this line ==========
var_values = set(planes_data[variable])
fig, axes = plt.subplots(1,len(var_values),
sharex=True, sharey=True,figsize=(6*len(var_values),4.8 if not plot_swarm else 6))
if len(var_values) == 1 : axes = [axes]
gro = [] ## list of average triples: (degeneracy g, distance r, occupancy o)
site_gro = pd.DataFrame(columns=['o','r']) ## site-specific list of triples: (degeneracy g=1, distance r, occupancy o)
for var, ax in zip(sorted(var_values), axes):
Ts = []
energies = [[] for r in range(20)]
occs = [[] for r in range(20)]
for i, plane in planes_data.iterrows():
if plane[variable] == var:
gro = [] ## reset for the next plane
site_gro = pd.DataFrame(columns=['o','r']) ## reset for the next plane
mm = plane.metal; T1 = plane.T1; z = plane.z; ph = plane.phase
max_r = plane.max_r; tt = plane.total_time; cn = plane.config; st = plane.stoich
new_r_col = 'new_ox_r' if ph == 'beta' else 'new_mg_count'
data2 = plane.hops
BR_sites = plane.BR_sites
sites_by_r = plane.sites_by_r
Ts.append(T1) ## for later plotting
for r in range(min(max_r+1,20)) : ## for every distance
total_BR, total_aBR = hu.site_occupancies(data2,set(sites_by_r[r]),
BR_sites,tt,plane,r=r,verbose=False)
num_BR = len(total_BR); avg_BR = total_BR.total.mean()
num_aBR = len(total_aBR); avg_aBR = total_aBR.total.mean()
# quick display of how much the defect is dissociated
avg_r = (total_BR.total.sum() + total_aBR.total.sum()) / (num_BR + num_aBR) * 2
print(f'\n{m:2s} {cn} {T1:4d}K, r={r}, would-be stoichiometry: {avg_r:.2f}')
try:
## write occupancies for every-site energies
gro.append( (num_BR, r, avg_BR , 'B') )
gro.append( (num_aBR, r, avg_aBR, 'A') )
total = total_BR.append(total_aBR).rename(columns={'total':'o', new_r_col:'r'})
site_gro = site_gro.append(total)
## use nondegenerate method; you'd think num_BR==num_aBR, but NO.
EaBR = hu.two_state_nondeg_energy(num_BR, num_aBR,
num_BR*avg_BR, num_aBR*avg_aBR, T1, lb=-2)
EBR = hu.two_state_nondeg_energy(num_aBR, num_BR,
num_aBR*avg_aBR, num_BR*avg_BR, T1, lb=-2)
if verbose:
max_BR = total_BR.total.max(); max_aBR = total_aBR.total.max()
min_BR = total_BR.total.min(); min_aBR = total_aBR.total.min()
print(f' {m:2s} {T1:4d}K, r={r}, {len(set(sites_by_r[r])):3d} sites ({num_BR} BR and {num_aBR} a-BR):')
print(f' BR occup {avg_BR*100. :.2f}% ({min_BR*100. :.2f}-{max_BR*100. :.2f}), E_aBR {-EBR:.3f}eV')
print(f'a-BR occup {avg_aBR*100.:.2f}%, ({min_aBR*100. :.2f}-{max_aBR*100. :.2f}), E_aBR {EaBR:.3f}eV')
energies[r].append(np.mean([EaBR,-EBR]))
except (ValueError, AssertionError, IndexError) as error:
print(f'{m:2s} {T1:4d}K, r={r}, {len(set(sites_by_r[r])):3d} sites ({num_BR} BR and {num_aBR} a-BR):')
print('error:', error)
energies[r].append(np.nan)
# occs[r].append(occ_aBR)
## after all distances are done, compute full 10-site energies
if T1 > 200 and ( ph != 'beta') or (ph == 'beta' and hu.s2n(st) > 100) :
## (binning by distance to defects only works with Oi defects)
if ph == 'beta' and 'M' not in plane.exclude:
df = hu.multi_state_energies(gro, T1)
if ph != 'beta' : df.site = ['direct' if x == 'B' else 'offset' for x in df.site]
else : df.site = ['BR' if x == 'B' else 'aBR' for x in df.site]
df.es -= df.es.min()
df.sort_values(by=['site'])
## Plot one average point per distance
fig2, ax2 = plt.subplots()
sns.scatterplot(data=df, x='rs', y='es', style='site', ax=ax2, s=10) ## for the legend
ax2.scatter(df.query('site in ["BR","direct"]').rs, df.query('site in ["BR","direct"]').es, s=100, marker='o', c=hu.metal_colors[m])
ax2.scatter(df.query('site in ["aBR", "offset"]').rs, df.query('site in ["aBR", "offset"]').es, s=100, marker='X', c=hu.metal_colors[m])
ax2.set(title=f'{m} {T1}K', xlim=[min(df.rs)-0.25, max(df.rs)+0.25])
ax2.set(xlabel=distance_names[ph], ylabel='Site Energy, eV')
ax2.set_xticks(range(5))
fig2.tight_layout()
## calculate energies for every site
site_gro['g'] = 1
site_gro = hu.multi_site_es(site_gro,T1)
print('{:2s} {}K site energies: {:.3f}-{:.3f} eV'.format(m,T1,site_gro.e.min(), site_gro.e.max()))
if ph != 'beta' : site_gro.site = ['direct' if x == 'BR' else 'offset' for x in site_gro.site]
## plot box + swarm plots. Originally 1 figure per plane
# fig3, ax3 = plt.subplots(figsize=(9,8))
# sns.boxplot(ax=ax3, x='r', y='e', data=site_gro, hue='site', fliersize=0, sym='', palette='pastel')
# sns.swarmplot(ax=ax3, x='r', y='e', data=site_gro, hue='site', alpha=0.75, size=3)
# ax3.set(ylabel='Energy, eV', xlabel=distance_names[ph], title=f'{m} {T1}K')
# fig3.tight_layout()
if plot_box :
sns.boxplot(ax=ax, x='r', y='e', data=site_gro.sort_values(by='site'),
hue='site', fliersize=0, sym='', palette='pastel')
if plot_swarm :
sns.swarmplot(ax=ax, x='r', y='e', data=site_gro.sort_values(by='site'),
hue='site', alpha=0.75, size=3)
ax.set(xlabel=distance_names[ph], ylabel='')
# ax.set(title=f'{mm} {T1}K')
ax.legend(title=f'{mm} {phases[ph]} site:', fontsize=12, title_fontsize=12,
loc='lower left' if ph != 'beta' else 'upper center')
axes[0].set(ylabel='Relative Energy, eV')
fig.tight_layout(pad=0.5, w_pad=0.25)
# =============================================================================
# %% Figures S4-5: plot smoothed probability distribution functions (PDF's)
# =============================================================================
subject = 'PDF' ## 'PDF', 'CDF', 'fill'
variable = 'metal' ## Pick the 1st variable to plot << DO THIS
BR_disagg = False ## plot separate things for BR/aBR; assumes 'old_is_BR' is an index level
onw_disagg = False ## implemented, but not at the same time as BR/aBR
plot_onw = False ## (for BR-aBR) onward hops are plotted if true, back-hops if false. Fill: new ion
dist = 'new' ## 'new' or 'old' refer to new_cell/old_cell; plotting hops 'into' a distance uses 'new'
one_r = None ## if not None, then keep one value of 'dist' above, and disaggregate by a second distance
# r_to_plot = [0,1,2,3,4,5,6] ## if one_r is None, plot distances in this iterable rather than everything
r_to_plot = [] ## if one_r is None, plot distances in this iterable rather than everything
plot_total = True ## plot the full, all-distances distributions
per_site = False ## boolean flag; if True, then divide # of hops by # sites
downsample = True ## smooth with a gaussian window
hl = 200 ## [fs] sigma for a gaussian window
append_T = False ## cosmetic : temperature will be appended to plot labels
long_time_limit = 2000 ## [ps] upper limit on time for which to plot totals
guides = True ## plot power law guides
rs_list = [[0.01,1.7], [0.01,4.6]] ## for Gs markers
guide_v2 = 300 ## value of var2 at which to plot guidelines, typically temp
# ========== automatic things below this line ==========
## no need to smooth out CDF's
if subject == 'CDF' : downsample=False
var2 = None ## this gets determined later; leave as None
## get values that are varied in the current set of planes.
var_values = set(planes_data[variable])
## automatically find the second variable
relevant_vars = ['metal','T1','z','stoich','exclude', 'config']
relevant_vars.remove(variable)
if len(planes_data) > len(var_values): ## a second variable is varied
for rv in relevant_vars:
if len(set(planes_data[rv])) > 1: var2 = rv; break
if variable != 'T1' and var2 != 'T1': append_T = True
## sort planes by the two variables for easy plotting and switching between axes
if var2 is not None: planes_data = planes_data.sort_values(by=[variable, var2], axis=0)
else: planes_data = planes_data.sort_values(by=variable, axis=0)
## set up plots in the right dimensions
num_plots = max(1,2*BR_disagg+2*onw_disagg)
fig, axes = plt.subplots(num_plots, len(var_values), sharex=True,
figsize=(4.5*len(var_values),4.5*num_plots))
if not isinstance(axes, np.ndarray) : axes = [axes]
else : axes = axes.reshape(-1)
## Set up a cycler through axes, and a debugging counter.
## This helps to plot the right curves in the right axes
ax_iter = cycle(axes)
aa_iter = cycle(list(range(len(axes))))
## set up shared y scales by row of plots (all x scales are shared)
## for some reason only works upfront here, not after plotting
for n in range(num_plots) :
for i, ax in enumerate(axes[n*len(var_values)+1:(n+1)*len(var_values)]):
axes[n*len(var_values)]._shared_y_axes.join(ax,axes[n*len(var_values)])
## iterate over planes
for i, plane in planes_data.reset_index().iterrows():
## keep track of how many variables have switched between planes
## this helps to cycle to the correct axes later
switch_count = 0
if i != 0:
if v != str(plane[variable]) : switch_count += 1
if var2 is not None:
switch_count -= 1 if v2 == str(plane[var2]) else -1
else : switch_count += 1
else: ax = next(ax_iter)
## make shorthands for metadata
mm = plane.metal; T1 = plane.T1; z = plane.z; data = plane.hops.copy(deep=True) #.round({'new_resid_time':3})
v = str(plane[variable]); v2 = str(plane[var2]) if var2 is not None else 'no var2'
st = plane.stoich; ph = plane.phase; cn = plane.config
site_counts = [x.size for x in plane.sites_by_r]
folder = '/'.join(plane.hop_path.split('/')[:-2])
## round time points above
## columns with distances : new and old
new_r_col = 'new_ox_r' if ph == 'beta' else 'new_mg_count'
old_r_col = 'old_ox_r' if ph == 'beta' else 'old_mg_count'
## BR site column, reverse/onward column
BR_site_col = 'site_is_BR' if subject == 'fill' else 'old_is_BR'
rev_col = 'refill' if subject == 'fill' else 'rev_hop'
## adjust for the defect-free stoichiometries so they do not get skipped
## This is automatically beta
if cn == '100_0' and one_r is not None:
data.old_ox_r = one_r
data.new_ox_r = one_r
if cn == '100_0' :
site_counts = np.zeros(100).astype(int)
site_counts[-1] = len(plane.site_pts) * len(plane.site_pts[0])
if subject == 'fill' :
data = plane.fill_times.copy(deep=True)
# data.r_to_defect = one_r
new_r_col = 'r_to_defect'
old_r_col = 'r_to_defect'
## make a set of sub-divided DF's; then apply a map to them depending on options
data_aBR = data.query(f'{BR_site_col} == False')
data_BR = data.query(f'{BR_site_col} == True')
data_onw = data.query(f'{rev_col} == False') ## keep
data_rev = data.query(f'{rev_col} == True') ## keep
if plot_onw:
data_aBR = data_aBR.query(f'{rev_col} == False')