def make_pub_scatter_plots(limits, df, angle_label, ax_array=[],
symbol='o', i_color=0):
if len(ax_array) == 0:
fig = plt.figure(figsize=(3, 5))
gs = gridspec.GridSpec(2, 1, left=0.1, right=0.9, wspace=0, hspace=0)
ax_array = [plt.subplot(gs[0]), plt.subplot(gs[1])]
ax = ax_array[0]
x = 'zeta'
y = 'epsilon'
limit_patch('zeta2', 'epsilon2', limits, ax, b1=2)
limit_patch('zeta1', 'epsilon1', limits, ax, b1=1)
ax.plot(df[x], df[y], symbol, mec=gwp.qual_color(i_color),
mfc='none')
ax.set_xlabel(angle_label[x])
ax.set_ylabel(angle_label[y])
plt_format(ax)
ax.tick_params(labelbottom='off')# , labeltop='on', labelright='on')
ax = ax_array[1]
x = 'zeta'
y = 'alpha'
limit_patch('zeta2', 'alpha', limits, ax, b1=2)
limit_patch('zeta1', 'alpha', limits, ax, b1=1)
ax.plot(df[x][0:-1], df[y][1:], symbol, mec=gwp.qual_color(i_color),
mfc='none')
ax.set_xlabel(angle_label[x])
ax.set_ylabel(angle_label[y] + ' + 1')
plt_format(ax)
return ax_array
def compare_average_in_range(dcd_files, MD=True, show=False):
fig = plt.figure(figsize=(6, 4))
gs1 = gridspec.GridSpec(1,
1,
left=0.075,
right=0.75,
wspace=0.1,
hspace=0,
top=0.95)
ax1 = plt.subplot(gs1[:, 0])
labels = [
r'$\delta\theta=60$', r'$\delta\theta=50$', r'$\delta\theta=40$',
r'$\delta\theta=30$', r'$\delta\theta=20$', r'$\delta\theta=10$',
r'$\delta\theta=5$', r'$\delta\theta=1$'
]
plt.axhline(95, ls='--', c='DimGray', linewidth=2)
all_in_range = []
max_steps = []
min_steps = []
for (i, dcd_file) in enumerate(dcd_files):
all_in_range_file = os.path.join(
os.path.split(dcd_file)[0], 'average_in_range')
if MD:
all_in_range_file += '_MD'
all_in_range.append(np.loadtxt(all_in_range_file + '.out'))
max_steps.append(all_in_range[i][-1, 0])
min_steps.append(all_in_range[i][0, 0])
ax1.plot(all_in_range[i][:, 0],
all_in_range[i][:, 1] * 100,
'-',
c=gwp.qual_color(i),
label=labels[i])
ax1.set_xscale('log')
x_min = min(min_steps)
x_max = min(max_steps)
ax1.set_xlim([x_min, x_max])
ax1.set_ylim([73, 102])
ax1.set_ylabel(r'% in Range')
ax1.set_xlabel(r'Minimization Iterations')
default_fontsize = 12
lg = plt.legend(loc='lower left',
scatterpoints=1,
numpoints=1,
prop={'size': default_fontsize})
lg.draw_frame(False)
if show:
plt.show()
else:
save_name = '/home/schowell/data/myData/dihedrals/dsDNA_60bps/average_in_range'
if MD:
save_name += '_MD'
plt.savefig(save_name + '.eps', dpi=400, bbox_inches='tight')
plt.savefig(save_name + '.png', dpi=400, bbox_inches='tight')
print 'View figure: \nevince %s.eps &' % save_name
def plot_aez_dihedrals(dcd_files, key, max_frame=None, scale_spb=1,
used_goback=False, show=False, prefix=None):
angle_vs_steps = []
for dcd_file_name in dcd_files:
if max_frame:
npy_file = dcd_file_name[:-4] + '_%d.npy' % max_frame
else:
npy_file = dcd_file_name[:-3] + 'npy'
angle_vs_steps.append(np.load(npy_file))
print 'loaded percent of good angles file: %s' % npy_file
fig = plt.figure(figsize=(6.5, 10))
gs1 = gridspec.GridSpec(3, 1, left=0.1, right=0.75, wspace=0, hspace=0)
angle_labels = ['alpha', 'beta', 'gamma', 'delta', 'epsilon', 'zeta', 'chi']
plot_order = [1, 5, 6] # only plot alpha, beta, and gamma
default_fontsize = 12
for (i, i_angle) in enumerate(plot_order):
ax1 = plt.subplot(gs1[i])
x_max = []
for j in xrange(len(dcd_files)):
ax1.plot(angle_vs_steps[j][:, 0], angle_vs_steps[j][:, i_angle], '-',
c=gwp.qual_color(j), label=key[j])
x_max.append(angle_vs_steps[j][-1, 0])
ax1.set_ylim([0, 1.1])
ax1.set_ylabel(r'% in range')
ax1.set_xlim([0, min(x_max)])
if i < len(plot_order) - 1:
ax1.xaxis.set_ticklabels([])
# ax1.xaxis.set_ticks([])
else:
ax1.set_xlabel(r'steps per bp')
if i == 0:
lg = plt.legend(loc='upper left', bbox_to_anchor=(1, 1),
scatterpoints=1, numpoints=1,
prop={'size': default_fontsize})
ax1.set_title(angle_labels[i_angle-1], y=0.05)
if show:
plt.show()
else:
save_name = 'dsDNA_60bps/moved_in_range'
if prefix:
save_name = save_name + '_' + prefix
if max_frame:
save_name += '_' + str(max_frame)
plt.savefig(save_name + '.eps', dpi=400, bbox_inches='tight')
plt.savefig(save_name + '.png', dpi=400, bbox_inches='tight')
print 'saved figures to %s.eps and %s.png' % (save_name, save_name)
print 'done plotting'
def make_pub_scatter_plots(limits,
df,
angle_label,
ax_array=[],
symbol='o',
i_color=0):
if len(ax_array) == 0:
fig = plt.figure(figsize=(3, 5))
gs = gridspec.GridSpec(2, 1, left=0.1, right=0.9, wspace=0, hspace=0)
ax_array = [plt.subplot(gs[0]), plt.subplot(gs[1])]
ax = ax_array[0]
x = 'zeta'
y = 'epsilon'
limit_patch('zeta2', 'epsilon2', limits, ax, b1=2)
limit_patch('zeta1', 'epsilon1', limits, ax, b1=1)
ax.plot(df[x], df[y], symbol, mec=gwp.qual_color(i_color), mfc='none')
ax.set_xlabel(angle_label[x])
ax.set_ylabel(angle_label[y])
plt_format(ax)
ax.tick_params(labelbottom='off') # , labeltop='on', labelright='on')
ax = ax_array[1]
x = 'zeta'
y = 'alpha'
limit_patch('zeta2', 'alpha', limits, ax, b1=2)
limit_patch('zeta1', 'alpha', limits, ax, b1=1)
ax.plot(df[x][0:-1],
df[y][1:],
symbol,
mec=gwp.qual_color(i_color),
mfc='none')
ax.set_xlabel(angle_label[x])
ax.set_ylabel(angle_label[y] + ' + 1')
plt_format(ax)
return ax_array
def compare_average_in_range(dcd_files, MD=True, show=False):
fig = plt.figure(figsize=(6, 4))
gs1 = gridspec.GridSpec(1, 1, left=0.075, right=0.75, wspace=0.1,
hspace=0, top=0.95)
ax1 = plt.subplot(gs1[:, 0])
labels = [r'$\delta\theta=60$', r'$\delta\theta=50$', r'$\delta\theta=40$',
r'$\delta\theta=30$', r'$\delta\theta=20$', r'$\delta\theta=10$',
r'$\delta\theta=5$', r'$\delta\theta=1$']
plt.axhline(95, ls='--', c='DimGray', linewidth=2)
all_in_range = []
max_steps = []
min_steps = []
for (i, dcd_file) in enumerate(dcd_files):
all_in_range_file = os.path.join(os.path.split(dcd_file)[0],
'average_in_range')
if MD:
all_in_range_file += '_MD'
all_in_range.append(np.loadtxt(all_in_range_file + '.out'))
max_steps.append(all_in_range[i][-1, 0])
min_steps.append(all_in_range[i][0, 0])
ax1.plot(all_in_range[i][:, 0], all_in_range[i][:, 1]*100, '-',
c=gwp.qual_color(i), label=labels[i])
ax1.set_xscale('log')
x_min = min(min_steps)
x_max = min(max_steps)
ax1.set_xlim([x_min, x_max])
ax1.set_ylim([73, 102])
ax1.set_ylabel(r'% in Range')
ax1.set_xlabel(r'Minimization Iterations')
default_fontsize = 12
lg = plt.legend(loc='lower left', scatterpoints=1, numpoints=1,
prop={'size': default_fontsize})
lg.draw_frame(False)
if show:
plt.show()
else:
save_name = '/home/schowell/data/myData/dihedrals/dsDNA_60bps/average_in_range'
if MD:
save_name += '_MD'
plt.savefig(save_name + '.eps', dpi=400, bbox_inches='tight')
plt.savefig(save_name + '.png', dpi=400, bbox_inches='tight')
print 'View figure: \nevince %s.eps &' % save_name
def show_ncp_geometry(all_ncp_plot_vars):
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import x_dna.util.gw_plot as gwp
fig = plt.figure(figsize=(10, 6))
ax = fig.add_subplot(111, projection='3d')
n_ncps = len(all_ncp_plot_vars)
axis_mag = [90, 130]
i = 0
all_origins = [all_ncp_plot_vars[i].ncp_origin for i in xrange(n_ncps)]
all_origins = np.array(all_origins)
ncp_colors = [gwp.qual_color(0), gwp.qual_color(6),
gwp.qual_color(8), gwp.qual_color(2)]
axes_colors = [gwp.qual_color(9), gwp.qual_color(1)]
limits = []
for i in xrange(n_ncps):
ncp_origin = all_ncp_plot_vars[i].ncp_origin
ncp_axes = all_ncp_plot_vars[i].ncp_axes
# NCP axes
labels = ['Cylinder Axes', 'Dyad Axes']
for (j, axis) in enumerate(ncp_axes[[2, 0]]):
axes_vec = np.vstack((axis * (-axis_mag[j] / 2) + ncp_origin,
axis * (axis_mag[j] / 2) + ncp_origin))
limits.append(axes_vec)
if i == 0:
ax.plot(axes_vec[:, 0], axes_vec[:, 1], axes_vec[:, 2],
c=axes_colors[j], label=labels[j], linewidth=3)
ncp1_o = all_ncp_plot_vars[i].ncp_origin
if j == 1:
ncp2_o = all_ncp_plot_vars[i + 2].ncp_origin
stack_axis = ncp2_o - ncp1_o
stack_axis = stack_axis / \
np.sqrt(stack_axis.dot(stack_axis))
stack_vec = np.vstack((stack_axis * (-axis_mag[1] / 2) + ncp1_o,
stack_axis * (axis_mag[1] / 2) + ncp2_o))
limits.append(stack_vec)
ax.plot(stack_vec[:, 0], stack_vec[:, 1], stack_vec[:, 2],
c=gwp.qual_color(4), linewidth=3, label='Stack Axes')
ax.plot(all_origins[:, 0], all_origins[:, 1],
all_origins[:, 2], linewidth=2, c=gwp.qual_color(3),
label='Center-to-Center Segments')
else:
ax.plot(axes_vec[:, 0], axes_vec[:, 1], axes_vec[:, 2],
c=axes_colors[j], linewidth=3)
center = np.array(all_origins).mean(axis=0)
ax.plot([center[0]], [center[1]], [center[2]], marker='*',
c=gwp.qual_color(7), ms=10, label='Array Center', linestyle='None')
for i in xrange(n_ncps):
coor = all_ncp_plot_vars[i].coor
# plot the coordinates the cylinder was fit to
coor_label = "NCP %d" % (i + 1)
ax.plot(coor[:, 0], coor[:, 1], coor[:, 2], 'k', linewidth=5)
ax.plot(coor[:, 0], coor[:, 1], coor[:, 2], c=ncp_colors[i], linewidth=4,
label=coor_label)
i = 1
ncp1_o = all_ncp_plot_vars[i].ncp_origin
ncp2_o = all_ncp_plot_vars[i + 2].ncp_origin
stack_axis = ncp2_o - ncp1_o
stack_axis = stack_axis / np.sqrt(stack_axis.dot(stack_axis))
stack_vec = np.vstack((stack_axis * (-axis_mag[0] / 2) + ncp1_o,
stack_axis * (axis_mag[0] / 2) + ncp2_o))
limits.append(stack_vec)
ax.plot(stack_vec[:, 0], stack_vec[:, 1], stack_vec[:, 2],
c=gwp.qual_color(4), linewidth=3)
limits = np.array(limits).reshape(len(limits) * 2, 3)
extent = (limits.max(axis=0) - limits.min(axis=0)).max() / 2
ax.set_xlim([center[0] - extent, center[0] + extent])
ax.set_ylim([center[1] - extent, center[1] + extent])
ax.set_zlim([center[2] - extent, center[2] + extent])
# DO NOT RESIZE THE IMAGE: it messes up the scale
# Shrink current axis by 40% so the legend is visible
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.7, box.height])
# Put a legend
ax.set_axis_off()
# plt.title('Nucleosome Array Geometry Definitions')
lg = plt.legend(loc='upper left', numpoints=1, bbox_to_anchor=(1, 0.5))
lg.draw_frame(False)
plt.show()
return
def show_ncp_geometry(all_ncp_plot_vars):
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import x_dna.util.gw_plot as gwp
fig = plt.figure(figsize=(10, 6))
ax = fig.add_subplot(111, projection='3d')
n_ncps = len(all_ncp_plot_vars)
axis_mag = [90, 130]
i = 0
all_origins = [all_ncp_plot_vars[i].ncp_origin for i in xrange(n_ncps)]
all_origins = np.array(all_origins)
ncp_colors = [
gwp.qual_color(0),
gwp.qual_color(6),
gwp.qual_color(8),
gwp.qual_color(2)
]
axes_colors = [gwp.qual_color(9), gwp.qual_color(1)]
limits = []
for i in xrange(n_ncps):
ncp_origin = all_ncp_plot_vars[i].ncp_origin
ncp_axes = all_ncp_plot_vars[i].ncp_axes
# NCP axes
labels = ['Cylinder Axes', 'Dyad Axes']
for (j, axis) in enumerate(ncp_axes[[2, 0]]):
axes_vec = np.vstack((axis * (-axis_mag[j] / 2) + ncp_origin,
axis * (axis_mag[j] / 2) + ncp_origin))
limits.append(axes_vec)
if i == 0:
ax.plot(axes_vec[:, 0],
axes_vec[:, 1],
axes_vec[:, 2],
c=axes_colors[j],
label=labels[j],
linewidth=3)
ncp1_o = all_ncp_plot_vars[i].ncp_origin
if j == 1:
ncp2_o = all_ncp_plot_vars[i + 2].ncp_origin
stack_axis = ncp2_o - ncp1_o
stack_axis = stack_axis / \
np.sqrt(stack_axis.dot(stack_axis))
stack_vec = np.vstack(
(stack_axis * (-axis_mag[1] / 2) + ncp1_o,
stack_axis * (axis_mag[1] / 2) + ncp2_o))
limits.append(stack_vec)
ax.plot(stack_vec[:, 0],
stack_vec[:, 1],
stack_vec[:, 2],
c=gwp.qual_color(4),
linewidth=3,
label='Stack Axes')
ax.plot(all_origins[:, 0],
all_origins[:, 1],
all_origins[:, 2],
linewidth=2,
c=gwp.qual_color(3),
label='Center-to-Center Segments')
else:
ax.plot(axes_vec[:, 0],
axes_vec[:, 1],
axes_vec[:, 2],
c=axes_colors[j],
linewidth=3)
center = np.array(all_origins).mean(axis=0)
ax.plot([center[0]], [center[1]], [center[2]],
marker='*',
c=gwp.qual_color(7),
ms=10,
label='Array Center',
linestyle='None')
for i in xrange(n_ncps):
coor = all_ncp_plot_vars[i].coor
# plot the coordinates the cylinder was fit to
coor_label = "NCP %d" % (i + 1)
ax.plot(coor[:, 0], coor[:, 1], coor[:, 2], 'k', linewidth=5)
ax.plot(coor[:, 0],
coor[:, 1],
coor[:, 2],
c=ncp_colors[i],
linewidth=4,
label=coor_label)
i = 1
ncp1_o = all_ncp_plot_vars[i].ncp_origin
ncp2_o = all_ncp_plot_vars[i + 2].ncp_origin
stack_axis = ncp2_o - ncp1_o
stack_axis = stack_axis / np.sqrt(stack_axis.dot(stack_axis))
stack_vec = np.vstack((stack_axis * (-axis_mag[0] / 2) + ncp1_o,
stack_axis * (axis_mag[0] / 2) + ncp2_o))
limits.append(stack_vec)
ax.plot(stack_vec[:, 0],
stack_vec[:, 1],
stack_vec[:, 2],
c=gwp.qual_color(4),
linewidth=3)
limits = np.array(limits).reshape(len(limits) * 2, 3)
extent = (limits.max(axis=0) - limits.min(axis=0)).max() / 2
ax.set_xlim([center[0] - extent, center[0] + extent])
ax.set_ylim([center[1] - extent, center[1] + extent])
ax.set_zlim([center[2] - extent, center[2] + extent])
# DO NOT RESIZE THE IMAGE: it messes up the scale
# Shrink current axis by 40% so the legend is visible
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.7, box.height])
# Put a legend
ax.set_axis_off()
# plt.title('Nucleosome Array Geometry Definitions')
lg = plt.legend(loc='upper left', numpoints=1, bbox_to_anchor=(1, 0.5))
lg.draw_frame(False)
plt.show()
return
def make_selected_scatter_plots(limits, df, angle_label, ax_array=[],
symbol='x', i_color=0):
if len(ax_array) == 0:
fig, ax_array = plt.subplots(3, 3)
ax = ax_array[0, 0]
x = 'zeta'
y = 'alpha'
limit_patch('zeta1', 'alpha', limits, ax)
limit_patch('zeta2', 'alpha', limits, ax)
ax.plot(df[x][0:-1], df[y][1:], symbol, c=gwp.qual_color(i_color))
ax.set_xlabel(angle_label[x])
ax.set_ylabel(angle_label[y] + ' + 1')
plt_format(ax)
ax.tick_params(labelbottom='off') # , labeltop='on')
ax = ax_array[0, 1]
x = 'zeta'
y = 'beta'
limit_patch('zeta1', 'beta1', limits, ax)
limit_patch('zeta1', 'beta2', limits, ax)
limit_patch('zeta2', 'beta1', limits, ax)
limit_patch('zeta2', 'beta2', limits, ax)
ax.plot(df[x][0:-1], df[y][1:], symbol, c=gwp.qual_color(i_color))
ax.set_xlabel(angle_label[x])
ax.set_ylabel(angle_label[y] + ' + 1')
plt_format(ax)
ax.tick_params(labelleft='off', labelbottom='off') # , labeltop='on')
ax = ax_array[0, 2]
x = 'zeta'
y = 'epsilon'
limit_patch('zeta1', 'epsilon1', limits, ax)
limit_patch('zeta2', 'epsilon2', limits, ax)
ax.plot(df[x], df[y], symbol, c=gwp.qual_color(i_color))
ax.set_xlabel(angle_label[x])
ax.set_ylabel(angle_label[y])
plt_format(ax)
ax.tick_params(
labelleft='off', labelbottom='off')# , labeltop='on', labelright='on')
ax = ax_array[1, 0]
x = 'alpha'
y = 'gamma'
limit_patch('alpha', 'gamma', limits, ax)
ax.plot(df[x], df[y], symbol, c=gwp.qual_color(i_color))
ax.set_xlabel(angle_label[x])
ax.set_ylabel(angle_label[y])
plt_format(ax)
ax.tick_params(labelbottom='off')
ax = ax_array[1, 1]
x = 'zeta'
y = 'chi'
limit_patch('zeta1', 'chi1pu', limits, ax)
limit_patch('zeta1', 'chi1py', limits, ax)
limit_patch('zeta2', 'chi2', limits, ax)
ax.plot(df[x], df[y], symbol, c=gwp.qual_color(i_color))
ax.set_xlabel(angle_label[x])
ax.set_ylabel(angle_label[y])
plt_format(ax)
ax.tick_params(labelleft='off', labelbottom='off')
ax = ax_array[1, 2]
x = 'delta'
y = 'chi'
limit_patch('delta1', 'chi1pu', limits, ax)
limit_patch('delta1', 'chi1py', limits, ax)
limit_patch('delta2', 'chi2', limits, ax)
ax.plot(df[x], df[y], symbol, c=gwp.qual_color(i_color))
ax.set_xlabel(angle_label[x])
ax.set_ylabel(angle_label[y])
plt_format(ax)
# , labelright='on')
ax.tick_params(labelleft='off', labelbottom='off')
ax = ax_array[2, 0]
x = 'zeta'
y = 'zeta'
limit_patch('zeta1', 'zeta1', limits, ax)
limit_patch('zeta1', 'zeta2', limits, ax)
limit_patch('zeta2', 'zeta1', limits, ax)
# limit_patch('zeta2', 'zeta2', limits, ax)
ax.plot(df[x][:-1], df[y][1:], symbol, c=gwp.qual_color(i_color))
ax.set_xlabel(angle_label[x])
ax.set_ylabel(angle_label[y] + ' + 1')
plt_format(ax)
ax = ax_array[2, 1]
x = 'epsilon'
y = 'epsilon'
limit_patch('epsilon1', 'epsilon1', limits, ax)
limit_patch('epsilon1', 'epsilon2', limits, ax)
limit_patch('epsilon2', 'epsilon1', limits, ax)
# limit_patch('epsilon2', 'epsilon2', limits, ax)
ax.plot(df[x][:-1], df[y][1:], symbol, c=gwp.qual_color(i_color))
ax.set_xlabel(angle_label[x])
ax.set_ylabel(angle_label[y] + ' + 1')
plt_format(ax)
ax.tick_params(labelleft='off')
ax = ax_array[2, 2]
x = 'zeta'
y = 'delta'
limit_patch('zeta1', 'delta1', limits, ax)
limit_patch('zeta2', 'delta2', limits, ax)
ax.plot(df[x], df[y], symbol, c=gwp.qual_color(i_color))
ax.tick_params(labelleft='off') # , labelright='on')
ax.set_xlabel(angle_label[x])
ax.set_ylabel(angle_label[y])
plt_format(ax)
return ax_array
def compare_min_dihedrals(raw_dcd_files, min_dcd_files, key, max_frame=None,
scale_spb=1, used_goback=False, show=False, prefix=None):
raw_angle_vs_steps = []
min_angle_vs_steps = []
raw_all_in_range = []
min_all_in_range = []
for dcd_file_name in min_dcd_files:
if max_frame:
npy_file = dcd_file_name[:-4] + '_%d.npy' % max_frame
else:
npy_file = dcd_file_name[:-3] + 'npy'
all_in_range_file = dcd_file_name[:-4] + '_ave_in_range'
try:
if 'MD' in prefix:
npy_file = npy_file.replace('.npy', '_MD.npy')
all_in_range_file += '_MD'
except:
pass
min_angle_vs_steps.append(np.load(npy_file))
min_all_in_range.append(np.loadtxt(all_in_range_file + '.out'))
print 'loaded percent in range files: \n%s \n%s' % (npy_file,
all_in_range_file)
for dcd_file_name in raw_dcd_files:
if max_frame:
npy_file = dcd_file_name[:-4] + '_%d.npy' % max_frame
else:
npy_file = dcd_file_name[:-3] + 'npy'
all_in_range_file = dcd_file_name[:-4] + '_ave_in_range'
try:
if 'MD' in prefix:
npy_file = npy_file.replace('.npy', '_MD.npy')
all_in_range_file += '_MD'
except:
pass
raw_angle_vs_steps.append(np.load(npy_file))
raw_all_in_range.append(np.loadtxt(all_in_range_file + '.out'))
print 'loaded percent in range files: \n%s \n%s' % (npy_file,
all_in_range_file)
fig = plt.figure(figsize=(5, 12))
gs = gridspec.GridSpec(8, 2, left=0.1, right=0.75, wspace=0.07, hspace=0)
angle_labels = [r'$\alpha$', r'$\beta$', r'$\gamma$', r'$\delta$',
r'$\epsilon$', r'$\zeta$', r'$\chi$', r'All']
plot_order = [0, 2, 3, 4, 7, 1, 5, 6] # change to show select angles
default_fontsize = 12
def plot_settings(ax, x_min, x_max):
ax.set_ylim([71, 104])
ax.set_xscale('log')
ax.set_xlim([min(x_min), min(x_max)])
for (i, i_angle) in enumerate(plot_order):
ax = plt.subplot(gs[i, 0])
plt.axhline(95, ls='--', c='DimGray', linewidth=2)
x_max = []
x_min = []
for j in xrange(len(raw_dcd_files)):
if i_angle == 0:
ax.plot(raw_all_in_range[j][:, 0],
raw_all_in_range[j][:, 1]*100, '-',
c=gwp.qual_color(j), label=key[j])
x_max.append(raw_all_in_range[j][-1, 0])
x_min.append(raw_all_in_range[j][0, 0])
ax.text(18, 107, 'Raw')
else:
ax.plot(raw_angle_vs_steps[j][:, 0],
raw_angle_vs_steps[j][:, i_angle]*100, '-',
c=gwp.qual_color(j), label=key[j])
x_max.append(raw_angle_vs_steps[j][-1, 0])
x_min.append(raw_angle_vs_steps[j][0, 0])
plot_settings(ax, x_min, x_max)
ax.set_ylabel(r'% in range')
if i < len(plot_order) - 1:
ax.xaxis.set_ticklabels([])
# ax1.xaxis.set_ticks([])
else:
ax.set_xlabel(r'steps per bp')
ax.set_title(angle_labels[i_angle-1], y=0.03, x=0.12)
for (i, i_angle) in enumerate(plot_order):
ax = plt.subplot(gs[i, 1])
plt.axhline(95, ls='--', c='DimGray', linewidth=2)
for j in xrange(len(min_dcd_files)):
if i_angle == 0:
ax.plot(min_all_in_range[j][:, 0],
min_all_in_range[j][:, 1]*100, '-',
c=gwp.qual_color(j), label=key[j])
ax.text(9, 107, 'Minimized')
else:
ax.plot(min_angle_vs_steps[j][:, 0],
min_angle_vs_steps[j][:, i_angle]*100, '-',
c=gwp.qual_color(j), label=key[j])
plot_settings(ax, x_min, x_max)
ax.yaxis.set_ticklabels([])
if i < len(plot_order) - 1:
ax.xaxis.set_ticklabels([])
# ax1.xaxis.set_ticks([])
else:
ax.set_xlabel(r'steps per bp')
ax.set_title(angle_labels[i_angle-1], y=0.03, x=0.12)
if i == 0:
lg = plt.legend(loc='upper left', bbox_to_anchor=(1, 1),
scatterpoints=1, numpoints=1,
prop={'size': default_fontsize})
path = 'dsDNA_60bps/'
save_name = 'raw_min_percent_in_range'
if prefix:
save_name = prefix + '_' + save_name
if max_frame:
save_name += '_' + str(max_frame)
plt.savefig(path + save_name + '.eps', dpi=400, bbox_inches='tight')
plt.savefig(path + save_name + '.png', dpi=400, bbox_inches='tight')
print 'saved figures to %s.eps and %s.png' % (save_name, save_name)
if show:
plt.show()
print 'done plotting'
def make_selected_scatter_plots(limits,
df,
angle_label,
ax_array=[],
symbol='x',
i_color=0):
if len(ax_array) == 0:
fig, ax_array = plt.subplots(3, 3)
ax = ax_array[0, 0]
x = 'zeta'
y = 'alpha'
limit_patch('zeta1', 'alpha', limits, ax, b1=1)
limit_patch('zeta2', 'alpha', limits, ax, b1=2)
ax.plot(df[x][0:-1], df[y][1:], symbol, c=gwp.qual_color(i_color))
ax.set_xlabel(angle_label[x])
ax.set_ylabel(angle_label[y] + ' + 1')
plt_format(ax)
ax.tick_params(labelbottom='off') # , labeltop='on')
ax = ax_array[0, 1]
x = 'zeta'
y = 'beta'
limit_patch('zeta1', 'beta1', limits, ax, b1=1)
limit_patch('zeta1', 'beta2', limits, ax, b1=3)
limit_patch('zeta2', 'beta1', limits, ax, b1=3)
limit_patch('zeta2', 'beta2', limits, ax, b1=2)
ax.plot(df[x][0:-1], df[y][1:], symbol, c=gwp.qual_color(i_color))
ax.set_xlabel(angle_label[x])
ax.set_ylabel(angle_label[y] + ' + 1')
plt_format(ax)
ax.tick_params(labelleft='off', labelbottom='off') # , labeltop='on')
ax = ax_array[0, 2]
x = 'zeta'
y = 'epsilon'
limit_patch('zeta1', 'epsilon1', limits, ax, b1=1)
limit_patch('zeta2', 'epsilon2', limits, ax, b1=2)
ax.plot(df[x], df[y], symbol, c=gwp.qual_color(i_color))
ax.set_xlabel(angle_label[x])
ax.set_ylabel(angle_label[y])
plt_format(ax)
ax.tick_params(labelleft='off',
labelbottom='off') # , labeltop='on', labelright='on')
ax = ax_array[1, 0]
x = 'alpha'
y = 'gamma'
limit_patch('alpha', 'gamma', limits, ax, b1=1)
ax.plot(df[x], df[y], symbol, c=gwp.qual_color(i_color))
ax.set_xlabel(angle_label[x])
ax.set_ylabel(angle_label[y])
plt_format(ax)
ax.tick_params(labelbottom='off')
ax = ax_array[1, 1]
x = 'zeta'
y = 'chi'
limit_patch('zeta1', 'chi1pu', limits, ax, b1=1)
limit_patch('zeta1', 'chi1py', limits, ax, b1=1)
limit_patch('zeta2', 'chi2', limits, ax, b1=2)
ax.plot(df[x], df[y], symbol, c=gwp.qual_color(i_color))
ax.set_xlabel(angle_label[x])
ax.set_ylabel(angle_label[y])
plt_format(ax)
ax.tick_params(labelleft='off', labelbottom='off')
ax = ax_array[1, 2]
x = 'delta'
y = 'chi'
limit_patch('delta1', 'chi1pu', limits, ax, b1=1)
limit_patch('delta1', 'chi1py', limits, ax, b1=1)
limit_patch('delta2', 'chi2', limits, ax, b1=2)
ax.plot(df[x], df[y], symbol, c=gwp.qual_color(i_color))
ax.set_xlabel(angle_label[x])
ax.set_ylabel(angle_label[y])
plt_format(ax)
# , labelright='on')
ax.tick_params(labelleft='off', labelbottom='off')
ax = ax_array[2, 0]
x = 'zeta'
y = 'zeta'
limit_patch('zeta1', 'zeta1', limits, ax, b1=1)
limit_patch('zeta1', 'zeta2', limits, ax, b1=3)
limit_patch('zeta2', 'zeta1', limits, ax, b1=3)
# limit_patch('zeta2', 'zeta2', limits, ax, b1=True)
ax.plot(df[x][:-1], df[y][1:], symbol, c=gwp.qual_color(i_color))
ax.set_xlabel(angle_label[x])
ax.set_ylabel(angle_label[y] + ' + 1')
plt_format(ax)
ax = ax_array[2, 1]
x = 'epsilon'
y = 'epsilon'
limit_patch('epsilon1', 'epsilon1', limits, ax, b1=1)
limit_patch('epsilon1', 'epsilon2', limits, ax, b1=3)
limit_patch('epsilon2', 'epsilon1', limits, ax, b1=3)
# limit_patch('epsilon2', 'epsilon2', limits, ax, b1=True)
ax.plot(df[x][:-1], df[y][1:], symbol, c=gwp.qual_color(i_color))
ax.set_xlabel(angle_label[x])
ax.set_ylabel(angle_label[y] + ' + 1')
plt_format(ax)
ax.tick_params(labelleft='off')
ax = ax_array[2, 2]
x = 'zeta'
y = 'delta'
limit_patch('zeta1', 'delta1', limits, ax, b1=True)
limit_patch('zeta2', 'delta2', limits, ax, b1=True)
ax.plot(df[x], df[y], symbol, c=gwp.qual_color(i_color))
ax.tick_params(labelleft='off') # , labelright='on')
ax.set_xlabel(angle_label[x])
ax.set_ylabel(angle_label[y])
plt_format(ax)
return ax_array
def plot_good_dihedrals(dcd_file_name,
max_frame=None,
scale_spb=1,
used_goback=False,
show=False,
MD=True):
if MD:
if max_frame:
npy_file = dcd_file_name[:-4] + '_MD_%d.npy' % max_frame
else:
npy_file = dcd_file_name[:-4] + '_MD.npy'
else:
if max_frame:
npy_file = dcd_file_name[:-4] + '_%d.npy' % max_frame
else:
npy_file = dcd_file_name[:-4] + '.npy'
try:
angle_vs_steps = np.load(npy_file)
print 'loaded percent of good angles file: %s' % npy_file
except:
print 'failed to load percent of good angles file: %s' % npy_file
angles = pd.read_hdf(dcd_file_name[:-3] + 'hdf', 'angles')
angles['-90'] = np.abs(-90 - (angles['epsilon'] - angles['zeta']))
angles['+90'] = np.abs(90 - (angles['epsilon'] - angles['zeta']))
angles['min'] = angles.loc[:, ['+90', '-90']].min(axis=1)
angles['B-type'] = 'B'
angles.loc[angles['min'] == angles['+90'], 'B-type'] = 'BII'
angles.loc[angles['min'] == angles['-90'], 'B-type'] = 'BI'
try:
scale_spb == 1 # enforce there is not a scale
all_spb = np.loadtxt(dcd_file_name[:-3] + 'spb')
print('loaded steps/bead file: %s' % (dcd_file_name[:-3] + 'spb'))
except:
print(
'failed to load steps/bead file: %s \n'
'using %f step/s between frames' %
(dcd_file_name[:-3] + 'spb', scale_spb))
all_spb = np.unique(angles['frame'])
all_spb = all_spb.reshape(len(all_spb), 1)
all_spb = np.concatenate((all_spb, all_spb * scale_spb), axis=1)
if max_frame:
nf = max_frame
else:
nf = len(all_spb)
spb, indices = np.unique(all_spb[:nf, 1], return_inverse=True)
n_steps = len(spb)
angle_vs_steps = np.zeros((n_steps, 8)) # 7 angles
angle_vs_steps[:, 0] = spb
if MD:
limit_file = ('/home/schowell/data/myData/dihedrals/'
'dsDNA_60bps/md_angles.txt')
else:
limit_file = ('/home/schowell/data/myData/dihedrals/'
'dsDNA_60bps/dna_angles.txt')
limits = pd.read_csv(limit_file, index_col=0)
limits['low'] = limits['mean'] - 2 * limits['sd']
limits['high'] = limits['mean'] + 2 * limits['sd']
for (i, steps) in enumerate(spb):
if used_goback:
df, _ = get_step_i_dihedrals(i, indices, angles)
else:
df = angles[angles['frame'] == i + 1]
beta_good = sum(((df['beta'] > limits.loc['beta1']['low'])
& (df['beta'] < limits.loc['beta1']['high'])
& (df['B-type'] == 'BI'))
| ((df['beta'] > limits.loc['beta2']['low'])
& (df['beta'] < limits.loc['beta2']['high'])
& (df['B-type'] == 'BII')))
n_beta = df['beta'].notnull().sum() * 1.0
angle_vs_steps[i, 2] = beta_good / n_beta
gamma_good = sum((df['gamma'] > limits.loc['gamma']['low'])
& (df['gamma'] < limits.loc['gamma']['high']))
n_gamma = df['gamma'].notnull().sum() * 1.0
angle_vs_steps[i, 3] = gamma_good / n_gamma
delta_good = sum(((df['delta'] > limits.loc['delta1']['low'])
& (df['delta'] < limits.loc['delta1']['high'])
& (df['B-type'] == 'BI'))
| ((df['delta'] > limits.loc['delta2']['low'])
& (df['delta'] < limits.loc['delta2']['high'])
& (df['B-type'] == 'BII')))
n_delta = df['delta'].notnull().sum() * 1.0
angle_vs_steps[i, 4] = delta_good / n_delta
chi_good = sum((((df['resname'] == 'C') | (df['resname'] == 'T'))
& (df['chi'] > limits.loc['chi1py']['low'])
& (df['chi'] < limits.loc['chi1py']['high'])
& (df['B-type'] == 'BI'))
| (((df['resname'] == 'G') | (df['resname'] == 'A'))
& (df['chi'] > limits.loc['chi1pu']['low'])
& (df['chi'] < limits.loc['chi1pu']['high'])
& (df['B-type'] == 'BI'))
| ((df['chi'] > limits.loc['chi2']['low'])
& (df['chi'] < limits.loc['chi2']['high'])
& (df['B-type'] == 'BII')))
n_chi = df['chi'].notnull().sum() * 1.0
angle_vs_steps[i, 7] = chi_good / n_chi
alpha_good = sum((df['alpha'] > limits.loc['alpha']['low'])
& (df['alpha'] < limits.loc['alpha']['high']))
n_alpha = df['alpha'].notnull().sum() * 1.0
angle_vs_steps[i, 1] = alpha_good / n_alpha
epsilon_good = sum((
(df['epsilon'] > limits.loc['epsilon1']['low'])
& (df['epsilon'] < limits.loc['epsilon1']['high'])
& (df['B-type'] == 'BI')) | (
(df['epsilon'] > limits.loc['epsilon2']['low'])
& (df['epsilon'] < limits.loc['epsilon2']['high'])
& (df['B-type'] == 'BII')))
n_epsilon = df['epsilon'].notnull().sum() * 1.0
angle_vs_steps[i, 5] = epsilon_good / n_epsilon
zeta_good = sum(((df['zeta'] > limits.loc['zeta1']['low'])
& (df['zeta'] < limits.loc['zeta1']['high'])
& (df['B-type'] == 'BI'))
| ((df['zeta'] > limits.loc['zeta2']['low'])
& (df['zeta'] < limits.loc['zeta2']['high'])
& (df['B-type'] == 'BII')))
n_zeta = df['zeta'].notnull().sum() * 1.0
angle_vs_steps[i, 6] = zeta_good / n_zeta
np.save(npy_file, angle_vs_steps)
fig = plt.figure() # figsize=(6, 4))
gs1 = gridspec.GridSpec(1,
1,
left=0.075,
right=0.75,
wspace=0.1,
hspace=0,
top=0.95)
ax1 = plt.subplot(gs1[:, 0])
angle_labels = [
r'$\alpha$', r'$\beta$', r'$\gamma$', r'$\delta$', r'$\epsilon$',
r'$\zeta$', r'$\chi$'
]
plot_order = [2, 3, 4, 7, 1, 5, 6]
# plot_order = [1, 5, 6]
# plot_order = [2, 3, 4, 7]
ax1.axhline(95, ls='--', c='DimGray', linewidth=2)
# label=r'$2\sigma$ cutoff')
if len(angle_vs_steps) > 2:
for (c, i_angle) in enumerate(plot_order):
ax1.plot(angle_vs_steps[:, 0],
angle_vs_steps[:, i_angle] * 100,
'-',
c=gwp.qual_color(c),
label=angle_labels[i_angle - 1])
ax1.set_ylim([73, 102])
ax1.set_xscale('log')
x_max = angle_vs_steps[-1, 0]
x_min = angle_vs_steps[0, 0]
ax1.set_xlim([x_min, x_max])
else:
for (c, i_angle) in enumerate(plot_order):
ax1.plot(angle_vs_steps[:, 0],
angle_vs_steps[:, i_angle] * 100,
'-',
color=gwp.qual_color(c),
marker=gwp.symbol_order(c),
mec=gwp.qual_color(c),
label=angle_labels[i_angle - 1],
markersize=15,
mfc='none')
ax1.set_ylim([-1, 110])
ax1.set_ylabel(r'% of Dihedrals in $2\sigma$ Range')
ax1.set_xlabel(r'Minimization Iterations')
default_fontsize = 12
lg = plt.legend(loc='lower left',
scatterpoints=1,
numpoints=1,
prop={'size': default_fontsize})
lg.draw_frame(False)
if show:
plt.show()
else:
save_name = '%s_in_range' % dcd_file_name[:-4]
if MD:
save_name += '_MD'
if max_frame:
save_name += '_' + str(max_frame)
plt.savefig(save_name + '.eps', dpi=400, bbox_inches='tight')
plt.savefig(save_name + '.png', dpi=400, bbox_inches='tight')
print 'View figure: \nevince %s.eps &' % save_name
print 'done plotting'
all_in_range_file = dcd_file_name[:-4] + '_ave_in_range'
if MD:
all_in_range_file += '_MD'
all_in_range = np.copy(angle_vs_steps[:, :2])
all_in_range[:, 1] = angle_vs_steps[:, 1:].mean(axis=1)
np.savetxt(all_in_range_file + '.out', all_in_range)
def plot_good_dihedrals(dcd_file_name, max_frame=None, scale_spb=1,
used_goback=False, show=False, MD=True):
if MD:
if max_frame:
npy_file = dcd_file_name[:-4] + '_MD_%d.npy' % max_frame
else:
npy_file = dcd_file_name[:-4] + '_MD.npy'
else:
if max_frame:
npy_file = dcd_file_name[:-4] + '_%d.npy' % max_frame
else:
npy_file = dcd_file_name[:-4] + '.npy'
try:
angle_vs_steps = np.load(npy_file)
print 'loaded percent of good angles file: %s' % npy_file
except:
print 'failed to load percent of good angles file: %s' % npy_file
angles = pd.read_hdf(dcd_file_name[:-3] + 'hdf', 'angles')
angles['-90'] = np.abs(-90 - (angles['epsilon'] - angles['zeta']))
angles['+90'] = np.abs(90 - (angles['epsilon'] - angles['zeta']))
angles['min'] = angles.loc[:,['+90', '-90']].min(axis=1)
angles['B-type'] = 'B'
angles.loc[angles['min'] == angles['+90'], 'B-type'] = 'BII'
angles.loc[angles['min'] == angles['-90'], 'B-type'] = 'BI'
try:
scale_spb == 1 # enforce there is not a scale
all_spb = np.loadtxt(dcd_file_name[:-3] + 'spb')
print ('loaded steps/bead file: %s' %
(dcd_file_name[:-3] + 'spb'))
except:
print ('failed to load steps/bead file: %s \n'
'using %f step/s between frames' %
(dcd_file_name[:-3] + 'spb', scale_spb))
all_spb = np.unique(angles['frame'])
all_spb = all_spb.reshape(len(all_spb), 1)
all_spb = np.concatenate((all_spb, all_spb*scale_spb), axis=1)
if max_frame:
nf = max_frame
else:
nf = len(all_spb)
spb, indices = np.unique(all_spb[:nf, 1], return_inverse=True)
n_steps = len(spb)
angle_vs_steps = np.zeros((n_steps, 8)) # 7 angles
angle_vs_steps[:, 0] = spb
if MD:
limit_file = ('/home/schowell/data/myData/dihedrals/'
'dsDNA_60bps/md_angles.txt')
else:
limit_file = ('/home/schowell/data/myData/dihedrals/'
'dsDNA_60bps/dna_angles.txt')
limits = pd.read_csv(limit_file, index_col=0)
limits['low'] = limits['mean'] - 2 * limits['sd']
limits['high'] = limits['mean'] + 2 * limits['sd']
for (i, steps) in enumerate(spb):
if used_goback:
df, _ = get_step_i_dihedrals(i, indices, angles)
else:
df = angles[angles['frame'] == i+1]
beta_good = sum(
((df['beta'] > limits.loc['beta1']['low']) &
(df['beta'] < limits.loc['beta1']['high']) &
(df['B-type'] == 'BI')) |
((df['beta'] > limits.loc['beta2']['low']) &
(df['beta'] < limits.loc['beta2']['high']) &
(df['B-type'] == 'BII')))
n_beta = df['beta'].notnull().sum() * 1.0
angle_vs_steps[i, 2] = beta_good / n_beta
gamma_good = sum(
(df['gamma'] > limits.loc['gamma']['low']) &
(df['gamma'] < limits.loc['gamma']['high']))
n_gamma = df['gamma'].notnull().sum() * 1.0
angle_vs_steps[i, 3] = gamma_good / n_gamma
delta_good = sum(
((df['delta'] > limits.loc['delta1']['low']) &
(df['delta'] < limits.loc['delta1']['high']) &
(df['B-type'] == 'BI')) |
((df['delta'] > limits.loc['delta2']['low']) &
(df['delta'] < limits.loc['delta2']['high']) &
(df['B-type'] == 'BII')))
n_delta = df['delta'].notnull().sum() * 1.0
angle_vs_steps[i, 4] = delta_good / n_delta
chi_good = sum(
(((df['resname'] == 'C') | (df['resname'] == 'T')) &
(df['chi'] > limits.loc['chi1py']['low']) &
(df['chi'] < limits.loc['chi1py']['high']) &
(df['B-type'] == 'BI')) |
(((df['resname'] == 'G') | (df['resname'] == 'A')) &
(df['chi'] > limits.loc['chi1pu']['low']) &
(df['chi'] < limits.loc['chi1pu']['high']) &
(df['B-type'] == 'BI')) |
((df['chi'] > limits.loc['chi2']['low']) &
(df['chi'] < limits.loc['chi2']['high']) &
(df['B-type'] == 'BII')))
n_chi = df['chi'].notnull().sum() * 1.0
angle_vs_steps[i, 7] = chi_good / n_chi
alpha_good = sum(
(df['alpha'] > limits.loc['alpha']['low']) &
(df['alpha'] < limits.loc['alpha']['high']))
n_alpha = df['alpha'].notnull().sum() * 1.0
angle_vs_steps[i, 1] = alpha_good / n_alpha
epsilon_good = sum(
((df['epsilon'] > limits.loc['epsilon1']['low']) &
(df['epsilon'] < limits.loc['epsilon1']['high']) &
(df['B-type'] == 'BI')) |
((df['epsilon'] > limits.loc['epsilon2']['low']) &
(df['epsilon'] < limits.loc['epsilon2']['high']) &
(df['B-type'] == 'BII')))
n_epsilon = df['epsilon'].notnull().sum() * 1.0
angle_vs_steps[i, 5] = epsilon_good / n_epsilon
zeta_good = sum(
((df['zeta'] > limits.loc['zeta1']['low']) &
(df['zeta'] < limits.loc['zeta1']['high']) &
(df['B-type'] == 'BI')) |
((df['zeta'] > limits.loc['zeta2']['low']) &
(df['zeta'] < limits.loc['zeta2']['high']) &
(df['B-type'] == 'BII')))
n_zeta = df['zeta'].notnull().sum() * 1.0
angle_vs_steps[i, 6] = zeta_good / n_zeta
np.save(npy_file, angle_vs_steps)
fig = plt.figure() # figsize=(6, 4))
gs1 = gridspec.GridSpec(1, 1, left=0.075, right=0.75, wspace=0.1,
hspace=0, top=0.95)
ax1 = plt.subplot(gs1[:, 0])
angle_labels = [r'$\alpha$', r'$\beta$', r'$\gamma$', r'$\delta$',
r'$\epsilon$', r'$\zeta$', r'$\chi$']
plot_order = [2, 3, 4, 7, 1, 5, 6]
# plot_order = [1, 5, 6]
# plot_order = [2, 3, 4, 7]
ax1.axhline(95, ls='--', c='DimGray', linewidth=2)
# label=r'$2\sigma$ cutoff')
if len(angle_vs_steps)>2:
for (c, i_angle) in enumerate(plot_order):
ax1.plot(angle_vs_steps[:, 0], angle_vs_steps[:, i_angle]*100, '-',
c=gwp.qual_color(c), label=angle_labels[i_angle - 1])
ax1.set_ylim([73, 102])
ax1.set_xscale('log')
x_max = angle_vs_steps[-1, 0]
x_min = angle_vs_steps[0, 0]
ax1.set_xlim([x_min, x_max])
else:
for (c, i_angle) in enumerate(plot_order):
ax1.plot(angle_vs_steps[:, 0], angle_vs_steps[:, i_angle]*100, '-',
color=gwp.qual_color(c), marker=gwp.symbol_order(c),
mec=gwp.qual_color(c), label=angle_labels[i_angle - 1],
markersize=15, mfc='none')
ax1.set_ylim([-1, 110])
ax1.set_ylabel(r'% of Dihedrals in $2\sigma$ Range')
ax1.set_xlabel(r'Minimization Iterations')
default_fontsize = 12
lg = plt.legend(loc='lower left', scatterpoints=1, numpoints=1,
prop={'size': default_fontsize})
lg.draw_frame(False)
if show:
plt.show()
else:
save_name = '%s_in_range' % dcd_file_name[:-4]
if MD:
save_name += '_MD'
if max_frame:
save_name += '_' + str(max_frame)
plt.savefig(save_name + '.eps', dpi=400, bbox_inches='tight')
plt.savefig(save_name + '.png', dpi=400, bbox_inches='tight')
print 'View figure: \nevince %s.eps &' % save_name
print 'done plotting'
all_in_range_file = dcd_file_name[:-4] + '_ave_in_range'
if MD:
all_in_range_file += '_MD'
all_in_range = np.copy(angle_vs_steps[:,:2])
all_in_range[:,1] = angle_vs_steps[:,1:].mean(axis=1)
np.savetxt(all_in_range_file + '.out', all_in_range)
def plot_aez_dihedrals(dcd_files,
key,
max_frame=None,
scale_spb=1,
used_goback=False,
show=False,
prefix=None):
angle_vs_steps = []
for dcd_file_name in dcd_files:
if max_frame:
npy_file = dcd_file_name[:-4] + '_%d.npy' % max_frame
else:
npy_file = dcd_file_name[:-3] + 'npy'
angle_vs_steps.append(np.load(npy_file))
print 'loaded percent of good angles file: %s' % npy_file
fig = plt.figure(figsize=(6.5, 10))
gs1 = gridspec.GridSpec(3, 1, left=0.1, right=0.75, wspace=0, hspace=0)
angle_labels = [
'alpha', 'beta', 'gamma', 'delta', 'epsilon', 'zeta', 'chi'
]
plot_order = [1, 5, 6] # only plot alpha, beta, and gamma
default_fontsize = 12
for (i, i_angle) in enumerate(plot_order):
ax1 = plt.subplot(gs1[i])
x_max = []
for j in xrange(len(dcd_files)):
ax1.plot(angle_vs_steps[j][:, 0],
angle_vs_steps[j][:, i_angle],
'-',
c=gwp.qual_color(j),
label=key[j])
x_max.append(angle_vs_steps[j][-1, 0])
ax1.set_ylim([0, 1.1])
ax1.set_ylabel(r'% in range')
ax1.set_xlim([0, min(x_max)])
if i < len(plot_order) - 1:
ax1.xaxis.set_ticklabels([])
# ax1.xaxis.set_ticks([])
else:
ax1.set_xlabel(r'steps per bp')
if i == 0:
lg = plt.legend(loc='upper left',
bbox_to_anchor=(1, 1),
scatterpoints=1,
numpoints=1,
prop={'size': default_fontsize})
ax1.set_title(angle_labels[i_angle - 1], y=0.05)
if show:
plt.show()
else:
save_name = 'dsDNA_60bps/moved_in_range'
if prefix:
save_name = save_name + '_' + prefix
if max_frame:
save_name += '_' + str(max_frame)
plt.savefig(save_name + '.eps', dpi=400, bbox_inches='tight')
plt.savefig(save_name + '.png', dpi=400, bbox_inches='tight')
print 'saved figures to %s.eps and %s.png' % (save_name, save_name)
print 'done plotting'
def compare_min_dihedrals(raw_dcd_files,
min_dcd_files,
key,
max_frame=None,
scale_spb=1,
used_goback=False,
show=False,
prefix=None):
raw_angle_vs_steps = []
min_angle_vs_steps = []
raw_all_in_range = []
min_all_in_range = []
for dcd_file_name in min_dcd_files:
if max_frame:
npy_file = dcd_file_name[:-4] + '_%d.npy' % max_frame
else:
npy_file = dcd_file_name[:-3] + 'npy'
all_in_range_file = dcd_file_name[:-4] + '_ave_in_range'
try:
if 'MD' in prefix:
npy_file = npy_file.replace('.npy', '_MD.npy')
all_in_range_file += '_MD'
except:
pass
min_angle_vs_steps.append(np.load(npy_file))
min_all_in_range.append(np.loadtxt(all_in_range_file + '.out'))
print 'loaded percent in range files: \n%s \n%s' % (npy_file,
all_in_range_file)
for dcd_file_name in raw_dcd_files:
if max_frame:
npy_file = dcd_file_name[:-4] + '_%d.npy' % max_frame
else:
npy_file = dcd_file_name[:-3] + 'npy'
all_in_range_file = dcd_file_name[:-4] + '_ave_in_range'
try:
if 'MD' in prefix:
npy_file = npy_file.replace('.npy', '_MD.npy')
all_in_range_file += '_MD'
except:
pass
raw_angle_vs_steps.append(np.load(npy_file))
raw_all_in_range.append(np.loadtxt(all_in_range_file + '.out'))
print 'loaded percent in range files: \n%s \n%s' % (npy_file,
all_in_range_file)
fig = plt.figure(figsize=(5, 12))
gs = gridspec.GridSpec(8, 2, left=0.1, right=0.75, wspace=0.07, hspace=0)
angle_labels = [
r'$\alpha$', r'$\beta$', r'$\gamma$', r'$\delta$', r'$\epsilon$',
r'$\zeta$', r'$\chi$', r'All'
]
plot_order = [0, 2, 3, 4, 7, 1, 5, 6] # change to show select angles
default_fontsize = 12
def plot_settings(ax, x_min, x_max):
ax.set_ylim([71, 104])
ax.set_xscale('log')
ax.set_xlim([min(x_min), min(x_max)])
for (i, i_angle) in enumerate(plot_order):
ax = plt.subplot(gs[i, 0])
plt.axhline(95, ls='--', c='DimGray', linewidth=2)
x_max = []
x_min = []
for j in xrange(len(raw_dcd_files)):
if i_angle == 0:
ax.plot(raw_all_in_range[j][:, 0],
raw_all_in_range[j][:, 1] * 100,
'-',
c=gwp.qual_color(j),
label=key[j])
x_max.append(raw_all_in_range[j][-1, 0])
x_min.append(raw_all_in_range[j][0, 0])
ax.text(18, 107, 'Raw')
else:
ax.plot(raw_angle_vs_steps[j][:, 0],
raw_angle_vs_steps[j][:, i_angle] * 100,
'-',
c=gwp.qual_color(j),
label=key[j])
x_max.append(raw_angle_vs_steps[j][-1, 0])
x_min.append(raw_angle_vs_steps[j][0, 0])
plot_settings(ax, x_min, x_max)
ax.set_ylabel(r'% in range')
if i < len(plot_order) - 1:
ax.xaxis.set_ticklabels([])
# ax1.xaxis.set_ticks([])
else:
ax.set_xlabel(r'steps per bp')
ax.set_title(angle_labels[i_angle - 1], y=0.03, x=0.12)
for (i, i_angle) in enumerate(plot_order):
ax = plt.subplot(gs[i, 1])
plt.axhline(95, ls='--', c='DimGray', linewidth=2)
for j in xrange(len(min_dcd_files)):
if i_angle == 0:
ax.plot(min_all_in_range[j][:, 0],
min_all_in_range[j][:, 1] * 100,
'-',
c=gwp.qual_color(j),
label=key[j])
ax.text(9, 107, 'Minimized')
else:
ax.plot(min_angle_vs_steps[j][:, 0],
min_angle_vs_steps[j][:, i_angle] * 100,
'-',
c=gwp.qual_color(j),
label=key[j])
plot_settings(ax, x_min, x_max)
ax.yaxis.set_ticklabels([])
if i < len(plot_order) - 1:
ax.xaxis.set_ticklabels([])
# ax1.xaxis.set_ticks([])
else:
ax.set_xlabel(r'steps per bp')
ax.set_title(angle_labels[i_angle - 1], y=0.03, x=0.12)
if i == 0:
lg = plt.legend(loc='upper left',
bbox_to_anchor=(1, 1),
scatterpoints=1,
numpoints=1,
prop={'size': default_fontsize})
path = 'dsDNA_60bps/'
save_name = 'raw_min_percent_in_range'
if prefix:
save_name = prefix + '_' + save_name
if max_frame:
save_name += '_' + str(max_frame)
plt.savefig(path + save_name + '.eps', dpi=400, bbox_inches='tight')
plt.savefig(path + save_name + '.png', dpi=400, bbox_inches='tight')
print 'saved figures to %s.eps and %s.png' % (save_name, save_name)
if show:
plt.show()
print 'done plotting'
