Module weac.plot
Plotting resources for the WEak Layer AntiCrack nucleation model.
Expand source code
"""Plotting resources for the WEak Layer AntiCrack nucleation model."""
# pylint: disable=invalid-name,too-many-locals,too-many-branches
# pylint: disable=too-many-arguments,too-many-statements
# Third party imports
from matplotlib.colors import Normalize
import numpy as np
import matplotlib.pyplot as plt
# Project imports
from weac.tools import isnotebook
# === SET PLOT STYLES =========================================================
def set_plotstyles():
"""Define styles plot markers, labels and colors."""
labelstyle = { # Text style of plot labels
'backgroundcolor': 'w',
'horizontalalignment': 'center',
'verticalalignment': 'center'}
# markerstyle = { # Style of plot markers
# 'marker': 'o',
# 'markersize': 5,
# 'markerfacecolor': 'w',
# 'zorder': 3}
colors = np.array([ # TUD color palette
['#DCDCDC', '#B5B5B5', '#898989', '#535353'], # gray
['#5D85C3', '#005AA9', '#004E8A', '#243572'], # blue
['#009CDA', '#0083CC', '#00689D', '#004E73'], # ocean
['#50B695', '#009D81', '#008877', '#00715E'], # teal
['#AFCC50', '#99C000', '#7FAB16', '#6A8B22'], # green
['#DDDF48', '#C9D400', '#B1BD00', '#99A604'], # lime
['#FFE05C', '#FDCA00', '#D7AC00', '#AE8E00'], # yellow
['#F8BA3C', '#F5A300', '#D28700', '#BE6F00'], # sand
['#EE7A34', '#EC6500', '#CC4C03', '#A94913'], # orange
['#E9503E', '#E6001A', '#B90F22', '#961C26'], # red
['#C9308E', '#A60084', '#951169', '#732054'], # magenta
['#804597', '#721085', '#611C73', '#4C226A']]) # puple
return labelstyle, colors
# === CONVENIENCE FUNCTIONS ===================================================
class MidpointNormalize(Normalize):
"""Colormap normalization to a specified midpoint. Default is 0."""
def __init__(self, vmin, vmax, midpoint=0, clip=False):
"""Inizialize normalization."""
self.midpoint = midpoint
Normalize.__init__(self, vmin, vmax, clip)
def __call__(self, value, clip=None):
"""Make instances callable as functions."""
normalized_min = max(0, 0.5*(1 - abs(
(self.midpoint - self.vmin)/(self.midpoint - self.vmax))))
normalized_max = min(1, 0.5*(1 + abs(
(self.vmax - self.midpoint)/(self.midpoint - self.vmin))))
normalized_mid = 0.5
x, y = [self.vmin, self.midpoint, self.vmax], [
normalized_min, normalized_mid, normalized_max]
return np.ma.masked_array(np.interp(value, x, y))
def outline(grid):
"""Extract outline values of a 2D array (matrix, grid)."""
top = grid[0, :-1]
right = grid[:-1, -1]
bot = grid[-1, :0:-1]
left = grid[::-1, 0]
return np.hstack([top, right, bot, left])
# === PLOT SLAB PROFILE =======================================================
def slab_profile(instance):
"""Create bar chart of slab profile."""
x = []
y = []
total_heigth = 0
for line in np.flipud(instance.slab):
x.append(line[0])
x.append(line[0])
y.append(total_heigth)
total_heigth = total_heigth + line[1]
y.append(total_heigth)
# Font setup
plt.rc('font', family='serif', size=7)
plt.rc('mathtext', fontset='cm')
# Create figure
fig = plt.figure(figsize=(2, 3))
plt.axis()
ax1 = fig.gca()
# Set axis labels
ax1.set_xlabel('Density (kg/m$^3$)')
ax1.set_ylabel('Height above weak layer (mm)')
ax1.set_xlim(500, 0)
ax1.fill_betweenx(y, 0, x)
plt.show()
# === DEFORMATION CONTOUR PLOT ================================================
def contours(instance, x, z, window=1e12, scale=100):
"""
Plot 2D deformation contours.
Arguments
---------
instance : object
Instance of layered class.
x : ndarray
Discretized x-coordinates (mm).
z : ndarray
Solution vectors at positions x as columns of matrix z.
window : int
Plot window (cm) around maximum vertical deflection.
scale : int
Scaling factor for the visualization of displacements.
"""
# Plot Setup
plt.rc('font', family='serif', size=10)
# Calculate top-to-bottom vertical positions (mm) in beam coordinate system
y = np.linspace(-instance.h/2, instance.h/2, num=21)
# Compute displacements on grid (cm)
U = 1e-1*np.vstack([instance.u(z, z0=y, unit='mm') for y in y])
W = 1e-1*np.vstack([instance.w(z, unit='mm') for _ in y])
# Compute grid coordinates with vertical origin at top surface (cm)
X, Y = np.meshgrid(1e-1*x, 1e-1*(y + instance.h/2))
# Plot outline
plt.plot(outline(X), outline(Y), 'k--', alpha=0.3, linewidth=1)
plt.plot(outline(X+scale*U), outline(Y+scale*W), 'k', linewidth=1)
# Get x-coordinate of maximum deflection w (cm) and derive plot limits
xfocus = x[np.max(np.argmax(W, axis=1))]/10
xmax = np.min([np.max(X+scale*U), xfocus + window/2])
xmin = np.max([np.min(X+scale*U), xfocus - window/2])
# Normalize colormap
norm = MidpointNormalize(vmin=1e3*np.min(U), vmax=1e3*np.max(U))
# Plot contours on deformed grid
plt.contourf(X+scale*U, Y+scale*W, 1e3*U,
norm=norm, cmap='RdBu_r', levels=100, alpha=0.2)
# Plot setup
plt.axis('scaled')
plt.xlim([xmin, xmax])
plt.gca().set_aspect(2)
plt.gca().invert_yaxis()
plt.gca().use_sticky_edges = False
# Plot labels
plt.gca().set_xlabel(r'lateral position $x$ (cm) $\longrightarrow$')
plt.gca().set_ylabel('depth below surface\n' r'$\longleftarrow $ $d$ (cm)')
plt.title(fr'${scale}\!\times\!$ scaled deformations (cm)', size=11)
# Colorbar
cbar = plt.colorbar(
shrink=0.5,
label=('lateral displacements\n'
r'$u$ ($\mu\mathrm{m}$) $\longrightarrow$'))
cbar.ax.tick_params(labelsize=9)
# === BASE PLOT FUNCTION ======================================================
def plot_data(
name, ax1data, ax1label,
ax2data=None, ax2label=None,
labelpos=None, vlines=True,
li=False, mi=False, ki=False,
xlabel=r'Horizontal position $x$ (cm)'):
"""Plot data. Base function."""
# Clear figure
plt.clf()
# Plot styles
labelstyle, colors = set_plotstyles()
# Font setup
plt.rc('font', family='serif', size=12)
plt.rc('mathtext', fontset='cm')
# Create figure
plt.axis()
ax1 = plt.gca()
# Axis limits
ax1.autoscale(axis='x', tight=True)
# Set axis labels
ax1.set_xlabel(xlabel + r' $\longrightarrow$')
ax1.set_ylabel(ax1label + r' $\longrightarrow$')
# Plot x-axis
ax1.axhline(0, linewidth=0.5, color='gray')
# Plot vertical separators
if vlines:
ax1.axvline(0, linewidth=0.5, color='gray')
for i, f in enumerate(ki):
if not f:
ax1.axvspan(sum(li[:i])/10, sum(li[:i+1])/10,
facecolor='gray', alpha=0.05, zorder=100)
for i, m in enumerate(mi, start=1):
if m > 0:
ax1.axvline(sum(li[:i])/10, linewidth=0.5, color='gray')
else:
ax1.autoscale(axis='y', tight=True)
# Calculate labelposition
if not labelpos:
x = ax1data[0][0]
labelpos = int(0.95*len(x[~np.isnan(x)]))
# Fill left y-axis
i = 0
for x, y, label in ax1data:
i += 1
if label == '' or 'FEA' in label:
# line, = ax1.plot(x, y, 'k:', linewidth=1)
ax1.plot(x, y, linewidth=3, color='white')
line, = ax1.plot(x, y, ':', linewidth=1) # , color='black'
thislabelpos = -2
x, y = x[~np.isnan(x)], y[~np.isnan(x)]
xtx = (x[thislabelpos - 1] + x[thislabelpos])/2
ytx = (y[thislabelpos - 1] + y[thislabelpos])/2
ax1.text(xtx, ytx, label, color=line.get_color(),
**labelstyle)
else:
# Plot line
ax1.plot(x, y, linewidth=3, color='white')
line, = ax1.plot(x, y, linewidth=1)
# Line label
x, y = x[~np.isnan(x)], y[~np.isnan(x)]
if len(x) > 0:
xtx = (x[labelpos - 10*i - 1] + x[labelpos - 10*i])/2
ytx = (y[labelpos - 10*i - 1] + y[labelpos - 10*i])/2
ax1.text(xtx, ytx, label, color=line.get_color(),
**labelstyle)
# Fill right y-axis
if ax2data:
# Create right y-axis
ax2 = ax1.twinx()
# Set axis label
ax2.set_ylabel(ax2label + r' $\longrightarrow$')
# Fill
for x, y, label in ax2data:
# Plot line
ax2.plot(x, y, linewidth=3, color='white')
line, = ax2.plot(x, y, linewidth=1, color=colors[8, 0])
# Line label
x, y = x[~np.isnan(x)], y[~np.isnan(x)]
xtx = (x[labelpos - 1] + x[labelpos])/2
ytx = (y[labelpos - 1] + y[labelpos])/2
ax2.text(xtx, ytx, label, color=line.get_color(),
**labelstyle)
# Save figure
filename = name + '.pdf'
if isnotebook():
plt.show()
else:
print('Rendering', filename, '...')
plt.savefig('plots/' + filename, bbox_inches='tight')
# === PLOT WRAPPERS ===========================================================
def displacements(instance, x, z, **segments):
"""Wrap for dispalcements plot."""
data = [
[x/10, instance.u(z, z0=0, unit='mm'), r'$u_0\ (\mathrm{mm})$'],
[x/10, -instance.w(z, unit='mm'), r'$-w\ (\mathrm{mm})$'],
[x/10, instance.psi(z, unit='degrees'), r'$\psi\ (^\circ)$ ']
]
plot_data(ax1label=r'Displacements', ax1data=data,
name='disp', **segments)
def section_forces(instance, x, z, **segments):
"""Wrap section forces plot."""
data = [
[x/10, instance.N(z), r'$N$'],
[x/10, instance.M(z), r'$M$'],
[x/10, instance.V(z), r'$V$']
]
plot_data(ax1label=r'Section forces', ax1data=data,
name='forc', **segments)
def stresses(instance, x, z, **segments):
"""Wrap stress plot."""
data = [
[x/10, instance.tau(z, unit='kPa'), r'$\tau$'],
[x/10, instance.sig(z, unit='kPa'), r'$\sigma$']
]
plot_data(ax1label=r'Stress (kPa)', ax1data=data,
name='stress', **segments)
def stress_criteria(x, stress, **segments):
"""Wrap plot of stress and energy criteria."""
data = [
[x/10, stress, r'$\sigma/\sigma_\mathrm{c}$']
]
plot_data(ax1label=r'Criteria', ax1data=data,
name='crit', **segments)
def err_comp(da, Gdif, Ginc, mode=0):
"""Wrap energy release rate plot."""
data = [
[da/10, 1e3*Gdif[mode, :], r'$\mathcal{G}$'],
[da/10, 1e3*Ginc[mode, :], r'$\bar{\mathcal{G}}$']
]
plot_data(
xlabel=r'Crack length $\Delta a$ (cm)',
ax1label=r'Energy release rate (J/m$^2$)',
ax1data=data, name='err', vlines=False)
def err_modes(da, G, kind='inc'):
"""Wrap energy release rate plot."""
label = r'$\bar{\mathcal{G}}$' if kind == 'inc' else r'$\mathcal{G}$'
data = [
[da/10, 1e3*G[2, :], label + r'$_\mathrm{I\!I}$'],
[da/10, 1e3*G[1, :], label + r'$_\mathrm{I}$'],
[da/10, 1e3*G[0, :], label + r'$_\mathrm{I+I\!I}$']
]
plot_data(
xlabel=r'Crack length $a$ (cm)',
ax1label=r'Energy release rate (J/m$^2$)',
ax1data=data, name='modes', vlines=False)
def fea_disp(instance, x, z, fea):
"""Wrap dispalcements plot."""
data = [
[fea[:, 0]/10, -np.flipud(fea[:, 1]), r'FEA $u_0$'],
[fea[:, 0]/10, np.flipud(fea[:, 2]), r'FEA $w_0$'],
# [fea[:, 0]/10, -np.flipud(fea[:, 3]), r'FEA $u(z=-h/2)$'],
# [fea[:, 0]/10, np.flipud(fea[:, 4]), r'FEA $w(z=-h/2)$'],
[fea[:, 0]/10,
np.flipud(np.rad2deg(fea[:, 5])), r'FEA $\psi$'],
[x/10, instance.u(z, z0=0), r'$u_0$'],
[x/10, -instance.w(z), r'$-w$'],
[x/10, np.rad2deg(instance.psi(z)), r'$\psi$']
]
plot_data(
ax1label=r'Displacements (mm)', ax1data=data, name='fea_disp',
labelpos=-50)
def fea_stress(instance, xb, zb, fea):
"""Wrap stress plot."""
data = [
[fea[:, 0]/10, 1e3*np.flipud(fea[:, 2]), r'FEA $\sigma_2$'],
[fea[:, 0]/10, 1e3*np.flipud(fea[:, 3]), r'FEA $\tau_{12}$'],
[xb/10, instance.tau(zb, unit='kPa'), r'$\tau$'],
[xb/10, instance.sig(zb, unit='kPa'), r'$\sigma$']
]
plot_data(ax1label=r'Stress (kPa)', ax1data=data, name='fea_stress',
labelpos=-50)Functions
def contours(instance, x, z, window=1000000000000.0, scale=100)-
Plot 2D deformation contours.
Arguments
instance:object- Instance of layered class.
x:ndarray- Discretized x-coordinates (mm).
z:ndarray- Solution vectors at positions x as columns of matrix z.
window:int- Plot window (cm) around maximum vertical deflection.
scale:int- Scaling factor for the visualization of displacements.
Expand source code
def contours(instance, x, z, window=1e12, scale=100): """ Plot 2D deformation contours. Arguments --------- instance : object Instance of layered class. x : ndarray Discretized x-coordinates (mm). z : ndarray Solution vectors at positions x as columns of matrix z. window : int Plot window (cm) around maximum vertical deflection. scale : int Scaling factor for the visualization of displacements. """ # Plot Setup plt.rc('font', family='serif', size=10) # Calculate top-to-bottom vertical positions (mm) in beam coordinate system y = np.linspace(-instance.h/2, instance.h/2, num=21) # Compute displacements on grid (cm) U = 1e-1*np.vstack([instance.u(z, z0=y, unit='mm') for y in y]) W = 1e-1*np.vstack([instance.w(z, unit='mm') for _ in y]) # Compute grid coordinates with vertical origin at top surface (cm) X, Y = np.meshgrid(1e-1*x, 1e-1*(y + instance.h/2)) # Plot outline plt.plot(outline(X), outline(Y), 'k--', alpha=0.3, linewidth=1) plt.plot(outline(X+scale*U), outline(Y+scale*W), 'k', linewidth=1) # Get x-coordinate of maximum deflection w (cm) and derive plot limits xfocus = x[np.max(np.argmax(W, axis=1))]/10 xmax = np.min([np.max(X+scale*U), xfocus + window/2]) xmin = np.max([np.min(X+scale*U), xfocus - window/2]) # Normalize colormap norm = MidpointNormalize(vmin=1e3*np.min(U), vmax=1e3*np.max(U)) # Plot contours on deformed grid plt.contourf(X+scale*U, Y+scale*W, 1e3*U, norm=norm, cmap='RdBu_r', levels=100, alpha=0.2) # Plot setup plt.axis('scaled') plt.xlim([xmin, xmax]) plt.gca().set_aspect(2) plt.gca().invert_yaxis() plt.gca().use_sticky_edges = False # Plot labels plt.gca().set_xlabel(r'lateral position $x$ (cm) $\longrightarrow$') plt.gca().set_ylabel('depth below surface\n' r'$\longleftarrow $ $d$ (cm)') plt.title(fr'${scale}\!\times\!$ scaled deformations (cm)', size=11) # Colorbar cbar = plt.colorbar( shrink=0.5, label=('lateral displacements\n' r'$u$ ($\mu\mathrm{m}$) $\longrightarrow$')) cbar.ax.tick_params(labelsize=9) def displacements(instance, x, z, **segments)-
Wrap for dispalcements plot.
Expand source code
def displacements(instance, x, z, **segments): """Wrap for dispalcements plot.""" data = [ [x/10, instance.u(z, z0=0, unit='mm'), r'$u_0\ (\mathrm{mm})$'], [x/10, -instance.w(z, unit='mm'), r'$-w\ (\mathrm{mm})$'], [x/10, instance.psi(z, unit='degrees'), r'$\psi\ (^\circ)$ '] ] plot_data(ax1label=r'Displacements', ax1data=data, name='disp', **segments) def err_comp(da, Gdif, Ginc, mode=0)-
Wrap energy release rate plot.
Expand source code
def err_comp(da, Gdif, Ginc, mode=0): """Wrap energy release rate plot.""" data = [ [da/10, 1e3*Gdif[mode, :], r'$\mathcal{G}$'], [da/10, 1e3*Ginc[mode, :], r'$\bar{\mathcal{G}}$'] ] plot_data( xlabel=r'Crack length $\Delta a$ (cm)', ax1label=r'Energy release rate (J/m$^2$)', ax1data=data, name='err', vlines=False) def err_modes(da, G, kind='inc')-
Wrap energy release rate plot.
Expand source code
def err_modes(da, G, kind='inc'): """Wrap energy release rate plot.""" label = r'$\bar{\mathcal{G}}$' if kind == 'inc' else r'$\mathcal{G}$' data = [ [da/10, 1e3*G[2, :], label + r'$_\mathrm{I\!I}$'], [da/10, 1e3*G[1, :], label + r'$_\mathrm{I}$'], [da/10, 1e3*G[0, :], label + r'$_\mathrm{I+I\!I}$'] ] plot_data( xlabel=r'Crack length $a$ (cm)', ax1label=r'Energy release rate (J/m$^2$)', ax1data=data, name='modes', vlines=False) def fea_disp(instance, x, z, fea)-
Wrap dispalcements plot.
Expand source code
def fea_disp(instance, x, z, fea): """Wrap dispalcements plot.""" data = [ [fea[:, 0]/10, -np.flipud(fea[:, 1]), r'FEA $u_0$'], [fea[:, 0]/10, np.flipud(fea[:, 2]), r'FEA $w_0$'], # [fea[:, 0]/10, -np.flipud(fea[:, 3]), r'FEA $u(z=-h/2)$'], # [fea[:, 0]/10, np.flipud(fea[:, 4]), r'FEA $w(z=-h/2)$'], [fea[:, 0]/10, np.flipud(np.rad2deg(fea[:, 5])), r'FEA $\psi$'], [x/10, instance.u(z, z0=0), r'$u_0$'], [x/10, -instance.w(z), r'$-w$'], [x/10, np.rad2deg(instance.psi(z)), r'$\psi$'] ] plot_data( ax1label=r'Displacements (mm)', ax1data=data, name='fea_disp', labelpos=-50) def fea_stress(instance, xb, zb, fea)-
Wrap stress plot.
Expand source code
def fea_stress(instance, xb, zb, fea): """Wrap stress plot.""" data = [ [fea[:, 0]/10, 1e3*np.flipud(fea[:, 2]), r'FEA $\sigma_2$'], [fea[:, 0]/10, 1e3*np.flipud(fea[:, 3]), r'FEA $\tau_{12}$'], [xb/10, instance.tau(zb, unit='kPa'), r'$\tau$'], [xb/10, instance.sig(zb, unit='kPa'), r'$\sigma$'] ] plot_data(ax1label=r'Stress (kPa)', ax1data=data, name='fea_stress', labelpos=-50) def outline(grid)-
Extract outline values of a 2D array (matrix, grid).
Expand source code
def outline(grid): """Extract outline values of a 2D array (matrix, grid).""" top = grid[0, :-1] right = grid[:-1, -1] bot = grid[-1, :0:-1] left = grid[::-1, 0] return np.hstack([top, right, bot, left]) def plot_data(name, ax1data, ax1label, ax2data=None, ax2label=None, labelpos=None, vlines=True, li=False, mi=False, ki=False, xlabel='Horizontal position $x$ (cm)')-
Plot data. Base function.
Expand source code
def plot_data( name, ax1data, ax1label, ax2data=None, ax2label=None, labelpos=None, vlines=True, li=False, mi=False, ki=False, xlabel=r'Horizontal position $x$ (cm)'): """Plot data. Base function.""" # Clear figure plt.clf() # Plot styles labelstyle, colors = set_plotstyles() # Font setup plt.rc('font', family='serif', size=12) plt.rc('mathtext', fontset='cm') # Create figure plt.axis() ax1 = plt.gca() # Axis limits ax1.autoscale(axis='x', tight=True) # Set axis labels ax1.set_xlabel(xlabel + r' $\longrightarrow$') ax1.set_ylabel(ax1label + r' $\longrightarrow$') # Plot x-axis ax1.axhline(0, linewidth=0.5, color='gray') # Plot vertical separators if vlines: ax1.axvline(0, linewidth=0.5, color='gray') for i, f in enumerate(ki): if not f: ax1.axvspan(sum(li[:i])/10, sum(li[:i+1])/10, facecolor='gray', alpha=0.05, zorder=100) for i, m in enumerate(mi, start=1): if m > 0: ax1.axvline(sum(li[:i])/10, linewidth=0.5, color='gray') else: ax1.autoscale(axis='y', tight=True) # Calculate labelposition if not labelpos: x = ax1data[0][0] labelpos = int(0.95*len(x[~np.isnan(x)])) # Fill left y-axis i = 0 for x, y, label in ax1data: i += 1 if label == '' or 'FEA' in label: # line, = ax1.plot(x, y, 'k:', linewidth=1) ax1.plot(x, y, linewidth=3, color='white') line, = ax1.plot(x, y, ':', linewidth=1) # , color='black' thislabelpos = -2 x, y = x[~np.isnan(x)], y[~np.isnan(x)] xtx = (x[thislabelpos - 1] + x[thislabelpos])/2 ytx = (y[thislabelpos - 1] + y[thislabelpos])/2 ax1.text(xtx, ytx, label, color=line.get_color(), **labelstyle) else: # Plot line ax1.plot(x, y, linewidth=3, color='white') line, = ax1.plot(x, y, linewidth=1) # Line label x, y = x[~np.isnan(x)], y[~np.isnan(x)] if len(x) > 0: xtx = (x[labelpos - 10*i - 1] + x[labelpos - 10*i])/2 ytx = (y[labelpos - 10*i - 1] + y[labelpos - 10*i])/2 ax1.text(xtx, ytx, label, color=line.get_color(), **labelstyle) # Fill right y-axis if ax2data: # Create right y-axis ax2 = ax1.twinx() # Set axis label ax2.set_ylabel(ax2label + r' $\longrightarrow$') # Fill for x, y, label in ax2data: # Plot line ax2.plot(x, y, linewidth=3, color='white') line, = ax2.plot(x, y, linewidth=1, color=colors[8, 0]) # Line label x, y = x[~np.isnan(x)], y[~np.isnan(x)] xtx = (x[labelpos - 1] + x[labelpos])/2 ytx = (y[labelpos - 1] + y[labelpos])/2 ax2.text(xtx, ytx, label, color=line.get_color(), **labelstyle) # Save figure filename = name + '.pdf' if isnotebook(): plt.show() else: print('Rendering', filename, '...') plt.savefig('plots/' + filename, bbox_inches='tight') def section_forces(instance, x, z, **segments)-
Wrap section forces plot.
Expand source code
def section_forces(instance, x, z, **segments): """Wrap section forces plot.""" data = [ [x/10, instance.N(z), r'$N$'], [x/10, instance.M(z), r'$M$'], [x/10, instance.V(z), r'$V$'] ] plot_data(ax1label=r'Section forces', ax1data=data, name='forc', **segments) def set_plotstyles()-
Define styles plot markers, labels and colors.
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def set_plotstyles(): """Define styles plot markers, labels and colors.""" labelstyle = { # Text style of plot labels 'backgroundcolor': 'w', 'horizontalalignment': 'center', 'verticalalignment': 'center'} # markerstyle = { # Style of plot markers # 'marker': 'o', # 'markersize': 5, # 'markerfacecolor': 'w', # 'zorder': 3} colors = np.array([ # TUD color palette ['#DCDCDC', '#B5B5B5', '#898989', '#535353'], # gray ['#5D85C3', '#005AA9', '#004E8A', '#243572'], # blue ['#009CDA', '#0083CC', '#00689D', '#004E73'], # ocean ['#50B695', '#009D81', '#008877', '#00715E'], # teal ['#AFCC50', '#99C000', '#7FAB16', '#6A8B22'], # green ['#DDDF48', '#C9D400', '#B1BD00', '#99A604'], # lime ['#FFE05C', '#FDCA00', '#D7AC00', '#AE8E00'], # yellow ['#F8BA3C', '#F5A300', '#D28700', '#BE6F00'], # sand ['#EE7A34', '#EC6500', '#CC4C03', '#A94913'], # orange ['#E9503E', '#E6001A', '#B90F22', '#961C26'], # red ['#C9308E', '#A60084', '#951169', '#732054'], # magenta ['#804597', '#721085', '#611C73', '#4C226A']]) # puple return labelstyle, colors def slab_profile(instance)-
Create bar chart of slab profile.
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def slab_profile(instance): """Create bar chart of slab profile.""" x = [] y = [] total_heigth = 0 for line in np.flipud(instance.slab): x.append(line[0]) x.append(line[0]) y.append(total_heigth) total_heigth = total_heigth + line[1] y.append(total_heigth) # Font setup plt.rc('font', family='serif', size=7) plt.rc('mathtext', fontset='cm') # Create figure fig = plt.figure(figsize=(2, 3)) plt.axis() ax1 = fig.gca() # Set axis labels ax1.set_xlabel('Density (kg/m$^3$)') ax1.set_ylabel('Height above weak layer (mm)') ax1.set_xlim(500, 0) ax1.fill_betweenx(y, 0, x) plt.show() def stress_criteria(x, stress, **segments)-
Wrap plot of stress and energy criteria.
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def stress_criteria(x, stress, **segments): """Wrap plot of stress and energy criteria.""" data = [ [x/10, stress, r'$\sigma/\sigma_\mathrm{c}$'] ] plot_data(ax1label=r'Criteria', ax1data=data, name='crit', **segments) def stresses(instance, x, z, **segments)-
Wrap stress plot.
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def stresses(instance, x, z, **segments): """Wrap stress plot.""" data = [ [x/10, instance.tau(z, unit='kPa'), r'$\tau$'], [x/10, instance.sig(z, unit='kPa'), r'$\sigma$'] ] plot_data(ax1label=r'Stress (kPa)', ax1data=data, name='stress', **segments)
Classes
class MidpointNormalize (vmin, vmax, midpoint=0, clip=False)-
Colormap normalization to a specified midpoint. Default is 0.
Inizialize normalization.
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class MidpointNormalize(Normalize): """Colormap normalization to a specified midpoint. Default is 0.""" def __init__(self, vmin, vmax, midpoint=0, clip=False): """Inizialize normalization.""" self.midpoint = midpoint Normalize.__init__(self, vmin, vmax, clip) def __call__(self, value, clip=None): """Make instances callable as functions.""" normalized_min = max(0, 0.5*(1 - abs( (self.midpoint - self.vmin)/(self.midpoint - self.vmax)))) normalized_max = min(1, 0.5*(1 + abs( (self.vmax - self.midpoint)/(self.midpoint - self.vmin)))) normalized_mid = 0.5 x, y = [self.vmin, self.midpoint, self.vmax], [ normalized_min, normalized_mid, normalized_max] return np.ma.masked_array(np.interp(value, x, y))Ancestors
- matplotlib.colors.Normalize