def rectangle_around_axes(bottomleft_ax, topright_ax, pad, ls, label=None):
"""
pad: tuple, (left, right, bottom, top)
"""
left, bottom = bottomleft_ax.get_position().get_points()[0]
right, top = topright_ax.get_position().get_points()[1]
left -= pad[0]
right += pad[1]
bottom -= pad[2]
top += pad[3]
transf = plt.gcf().transFigure
rec = Rectangle((left, bottom),
right - left,
top - bottom,
transform=transf,
fill=False,
lw=2,
ls=ls)
rec = bottomleft_ax.add_patch(rec)
rec.set_clip_on(False)
if label:
middle = (top + bottom) / 2.
moreleft = left # pad[0]
bottomleft_ax.text(moreleft,
middle,
label,
transform=transf,
rotation='vertical',
fontstyle='oblique',
va='center',
ha='right')
def highlight_artist(self, val, artist=None):
# print val, artist
figure = self.get_figpage()._artists[0]
ax = self.get_figaxes()
if artist is None:
alist = self._artists
else:
alist = artist
if val == True:
container = self.get_container()
if container is None: return
de = self.get_data_extent()
from ifigure.matplotlib_mod.art3d_gl import AxesImageGL
if isinstance(alist[0], AxesImageGL):
hl = alist[0].add_hl_mask()
for item in hl:
alist[0].figobj_hl.append(item)
# hl = alist[0].make_hl_artist(container)
# rect_alpha = 0.0
else:
x = [de[0], de[1], de[1], de[0], de[0]]
y = [de[2], de[2], de[3], de[3], de[2]]
hl = container.plot(x,
y,
marker='s',
color='k',
linestyle='None',
markerfacecolor='None',
markeredgewidth=0.5,
scalex=False,
scaley=False)
rect_alpha = 0.3
hlp = Rectangle((de[0], de[2]),
de[1] - de[0],
de[3] - de[2],
alpha=rect_alpha,
facecolor='k',
figure=figure,
transform=container.transData)
if ax is not None:
x0, y0 = ax._artists[0].transAxes.transform((0, 0))
x1, y1 = ax._artists[0].transAxes.transform((1, 1))
bbox = Bbox([[x0, y0], [x1, y1]])
hlp.set_clip_box(bbox)
hlp.set_clip_on(True)
figure.patches.append(hlp)
for item in (hl[0], hlp):
alist[0].figobj_hl.append(item)
else:
for a in alist:
if len(a.figobj_hl) != 0:
a.figobj_hl[0].remove()
figure.patches.remove(a.figobj_hl[1])
a.figobj_hl = []
def plot_add_border(ax, offset=[0, 0], **rec_args):
xmin, xmax, ymin, ymax = ax.axis()
xmin, xmax, ymin, ymax = xmin - offset[0], xmax + offset[0], ymin - offset[
1], ymax + offset[1]
rec = Rectangle((xmin, ymin), (xmax - xmin), (ymax - ymin),
fill=False,
zorder=19,
**rec_args)
rec = ax.add_patch(rec)
rec.set_clip_on(False)
def boundary(sub1):
autoAxis = sub1.axis()
rec = Rectangle((autoAxis[0] - 0.6, autoAxis[2] - 0.4),
(autoAxis[1] - autoAxis[0]) + 1,
(autoAxis[3] - autoAxis[2]) + 1,
fill=False,
lw=2,
edgecolor='r')
rec = sub1.add_patch(rec)
rec.set_clip_on(False)
def highlight_artist(self, val, artist=None):
# print val, artist
figure=self.get_figpage()._artists[0]
ax = self.get_figaxes()
if artist is None:
alist=self._artists
else:
alist=artist
if val == True:
container = self.get_container()
if container is None: return
de = self.get_data_extent()
from ifigure.matplotlib_mod.art3d_gl import AxesImageGL
if isinstance(alist[0], AxesImageGL):
hl = alist[0].add_hl_mask()
for item in hl:
alist[0].figobj_hl.append(item)
# hl = alist[0].make_hl_artist(container)
# rect_alpha = 0.0
else:
x=[de[0],de[1],de[1],de[0],de[0]]
y=[de[2],de[2],de[3],de[3],de[2]]
hl= container.plot(x, y, marker='s',
color='k', linestyle='None',
markerfacecolor='None',
markeredgewidth = 0.5,
scalex=False, scaley=False)
rect_alpha = 0.3
hlp = Rectangle((de[0],de[2]),
de[1]-de[0],
de[3]-de[2],
alpha=rect_alpha, facecolor='k',
figure = figure,
transform= container.transData)
if ax is not None:
x0, y0 = ax._artists[0].transAxes.transform((0,0))
x1, y1 = ax._artists[0].transAxes.transform((1,1))
bbox = Bbox([[x0,y0],[x1,y1]])
hlp.set_clip_box(bbox)
hlp.set_clip_on(True)
figure.patches.append(hlp)
for item in (hl[0], hlp):
alist[0].figobj_hl.append(item)
else:
for a in alist:
if len(a.figobj_hl) != 0:
a.figobj_hl[0].remove()
figure.patches.remove(a.figobj_hl[1])
a.figobj_hl=[]
def draw_box_around_values(start, stop, ls):
deltaline = ty[start] - ty[start + 1]
top = ty[start] + deltaline # Draw the top border ABOVE the text
bottom = ty[stop]
rec = Rectangle((left - 0.1, bottom - 0.02),
right - left + 0.15,
top - bottom + 0.01,
fill=False,
lw=2,
transform=tax.transAxes,
ls=ls)
rec = tax.add_patch(rec)
rec.set_clip_on(False)
def on_click(event):
global prev_g_point
global prev_b_point
global dirty_type
global idx_seen
# print(idx_seen)
if event.button == 1:
plt.ion()
print('Your decision has been recorded!')
btn = event.inaxes
spn = (int(btn.get_title()) % 80) // 4
color_id = int(btn.get_title()) % 4
colors_temp = ['green', 'blue', 'gray', 'orange']
req_axis = subplot_axs_list[spn]
autoAxis = req_axis.axis()
rec = Rectangle((autoAxis[0] - 0.7, autoAxis[2] - 0.2),
(autoAxis[1] - autoAxis[0]) + 1,
(autoAxis[3] - autoAxis[2]) + 0.4,
fill=False,
lw=5,
color=colors_temp[color_id])
rec = req_axis.add_patch(rec)
rec.set_clip_on(False)
plt.draw()
print('subplot_title: {}'.format(req_axis.get_title()))
row_no = int(req_axis.get_title())
dirty_val = int(btn.get_title()) % 4
idx_seen[row_no] = str(dirty_val)
if dirty_val == 0:
prev_b_point = row_no
elif dirty_val == 3:
prev_g_point = row_no
dirty_type[row_no] = dirty_val
rk = spn
rv = dirty_val % 4
d = ['{}'.format(path[int(req_axis.get_title())]), '{}'.format(rv)]
field_names = ['path', 'dirty_value']
def highlight_artist(self, val, artist=None):
figure = self.get_figpage()._artists[0]
ax = self.get_figaxes()
if val:
if len(self._artists[0].figobj_hl) != 0:
return
box = self.get_artist_extent_all()
self._artist_extent = box
if box[0] is None:
return
x = [box[0], box[0], box[1], box[1], box[0]]
y = [box[3], box[2], box[2], box[3], box[3]]
hl = Line2D(x,
y,
marker='s',
color='k',
linestyle='None',
markerfacecolor='k',
markeredgewidth=0.5,
figure=figure,
alpha=0.3)
figure.lines.append(hl)
xy = (box[0], box[2])
w = box[1] - box[0]
h = box[3] - box[2]
hlp = Rectangle(xy, w, h, alpha=0.3, facecolor='k', figure=figure)
if ax is not None:
x0, y0 = ax._artists[0].transAxes.transform((0, 0))
x1, y1 = ax._artists[0].transAxes.transform((1, 1))
bbox = Bbox([[x0, y0], [x1, y1]])
hlp.set_clip_box(bbox)
hlp.set_clip_on(True)
figure.patches.append(hlp)
# if ax is not None and ax.get_3d():
# import mpl_toolkits.mplot3d.art3d as art3d
# art3d.patch_2d_to_3d(hl)
# if len(ax._artists) != 0:
# a.axes = self.get_figaxes()._artists[0]
self._artists[0].figobj_hl.extend([hl, hlp])
else:
if len(self._artists[0].figobj_hl) == 2:
figure.lines.remove(self._artists[0].figobj_hl[0])
figure.patches.remove(self._artists[0].figobj_hl[1])
self._artists[0].figobj_hl = []
def highlight_artist(self, val, artist=None):
figure=self.get_figpage()._artists[0]
ax = self.get_figaxes()
if val:
if len(self._artists[0].figobj_hl) != 0: return
box = self.get_artist_extent_all()
self._artist_extent = box
if box[0] is None: return
x = [box[0], box[0], box[1], box[1], box[0]]
y = [box[3], box[2], box[2], box[3], box[3]]
hl= Line2D(x, y, marker='s',
color='k', linestyle='None',
markerfacecolor='k',
markeredgewidth = 0.5,
figure=figure, alpha=0.3)
figure.lines.append(hl)
xy = (box[0], box[2])
w = box[1] - box[0]
h = box[3] - box[2]
hlp = Rectangle(xy, w, h, alpha=0.3, facecolor='k',
figure = figure)
if ax is not None:
x0, y0 = ax._artists[0].transAxes.transform((0,0))
x1, y1 = ax._artists[0].transAxes.transform((1,1))
bbox = Bbox([[x0,y0],[x1,y1]])
hlp.set_clip_box(bbox)
hlp.set_clip_on(True)
figure.patches.append(hlp)
# if ax is not None and ax.get_3d():
# import mpl_toolkits.mplot3d.art3d as art3d
# art3d.patch_2d_to_3d(hl)
# if len(ax._artists) != 0:
# a.axes = self.get_figaxes()._artists[0]
self._artists[0].figobj_hl.extend([hl, hlp])
else:
if len(self._artists[0].figobj_hl) == 2:
figure.lines.remove(self._artists[0].figobj_hl[0])
figure.patches.remove(self._artists[0].figobj_hl[1])
self._artists[0].figobj_hl = []
from pylab import *
from matplotlib.lines import Line2D
from matplotlib.patches import Rectangle
# build a rectangle in axes coords
left, width = .25, .5
bottom, height = .25, .5
right = left + width
top = bottom + height
ax = gca()
p = Rectangle((left, bottom), width, height,
fill=False,
)
p.set_transform(ax.transAxes)
p.set_clip_on(False)
ax.add_patch(p)
ax.text(left, bottom, 'left top',
horizontalalignment='left',
verticalalignment='top',
transform=ax.transAxes)
ax.text(left, bottom, 'left bottom',
horizontalalignment='left',
verticalalignment='bottom',
transform=ax.transAxes)
ax.text(right, top, 'right bottom',
horizontalalignment='right',
def render_profiles(num, seq, reactivity, stderr,
rx_rates, bg_rates, dc_rates,
rx_depth, bg_depth, dc_depth,
rx_simple_depth, bg_simple_depth, dc_simple_depth,
fileout, name, qc_pass):
# FIXME: this is fairly ugly code - should at least break each panel into a separate func
if rx_rates is None and bg_rates is None and dc_rates is None:
no_mapped = True
else:
no_mapped = False
legend_labels = []
if rx_rates is not None:
legend_labels.append("Modified")
rx_err = sqrt(rx_rates) / sqrt(rx_depth)
else:
rx_rates = np.zeros((len(rx_depth),))
rx_err = np.zeros((len(rx_depth),))
if bg_rates is not None:
legend_labels.append("Untreated")
bg_err = sqrt(bg_rates) / sqrt(bg_depth)
else:
bg_rates = np.zeros((len(rx_depth),))
bg_err = np.zeros((len(rx_depth),))
if dc_rates is not None:
legend_labels.append("Denatured")
dc_err = sqrt(dc_rates) / sqrt(dc_depth)
else:
dc_rates = np.zeros((len(rx_depth),))
dc_err = np.zeros((len(rx_depth),))
# Add a zeroeth nuc so axis numbering works correctly
# There's probably a better way to do this
num = np.append(0, num)
if reactivity is not None:
reactivity = np.append(0, reactivity)
stderr = np.append(0, stderr)
rx_depth = np.append(0, rx_depth)
bg_depth = np.append(0, bg_depth)
dc_depth = np.append(0, dc_depth)
rx_simple_depth = np.append(0, rx_simple_depth)
bg_simple_depth = np.append(0, bg_simple_depth)
dc_simple_depth = np.append(0, dc_simple_depth)
rx_rates = np.append(0, rx_rates)
bg_rates = np.append(0, bg_rates)
dc_rates = np.append(0, dc_rates)
rx_err = np.append(0, rx_err)
bg_err = np.append(0, bg_err)
dc_err = np.append(0, dc_err)
if reactivity is not None:
orange_thresh = 0.4
red_thresh = 0.85
gray_vals = []
gray_nums = []
gray_errs = []
black_vals = []
black_nums = []
black_errs = []
orange_vals = []
orange_nums = []
orange_errs = []
red_vals = []
red_nums = []
red_errs = []
for i in range(len(reactivity)):
if isnan(reactivity[i]):
gray_vals.append(-1)
gray_nums.append(num[i])
gray_errs.append(0)
elif reactivity[i] < orange_thresh:
black_vals.append(reactivity[i])
black_nums.append(num[i])
black_errs.append(stderr[i])
elif reactivity[i] < red_thresh:
orange_vals.append(reactivity[i])
orange_nums.append(num[i])
orange_errs.append(stderr[i])
else:
red_vals.append(reactivity[i])
red_nums.append(num[i])
red_errs.append(stderr[i])
yMin, ymax = (-0.5, 4)
left_inches = 0.9
right_inches = 0.4
sp_width = len(num)*0.032
fig_width = max(7,sp_width+left_inches+right_inches)
fig = plt.figure(figsize=(fig_width,8))
left_percent = left_inches/fig_width
right_percent = 1-right_inches/fig_width
ax1 = plt.subplot(311)
ax2 = plt.subplot(313)
ax3 = plt.subplot(312)
plt.subplots_adjust(hspace=0.5, left=left_percent,right=right_percent,top=0.94)
near_black = (0,0,1/255.0)
if reactivity is not None:
if len(gray_nums)>0:
ax1.bar(gray_nums,gray_vals,
align="center",
width=1.05, color="0.80", edgecolor="0.80",linewidth=0.0)
if len(black_nums)>0:
ax1.bar(black_nums,black_vals,
align="center",
width=1.05, color="black", edgecolor="black",linewidth=0.0,
yerr=black_errs,ecolor=near_black,capsize=1)
if len(orange_nums)>0:
ax1.bar(orange_nums,orange_vals,
align="center",
width=1.05, color="orange",edgecolor="orange",linewidth=0.0,
yerr=orange_errs,ecolor=near_black,capsize=1)
if len(red_nums)>0:
ax1.bar(red_nums,red_vals,
align="center",
width=1.05,color="red",edgecolor="red",linewidth=0.0,
yerr=red_errs,ecolor=near_black,capsize=1)
#print("title: "+name)
ax1title = ax1.set_title(name, horizontalalignment="left", fontsize=16)
x,y = ax1title.get_position()
ax1title.set_position((0,y))
ax1.set_ylim(yMin,ymax)
ax1.set_xlim(1,len(num))
#ax1.set_yticks(fontsize=9)
if not qc_pass:
msg = "Note: possible data quality issue - see log file"
return_flag = False
if no_mapped:
msg = "ERROR: no reads mapped to this RNA"
return_flag = True
txt = plt.text(30,573,
msg,
ha='left', va='top',
fontsize=16, color='red',
transform=mp.transforms.IdentityTransform())
if return_flag:
plt.savefig(fileout)
return
#tickNums = range(num[0]+10,num[-1]+1,10)
#tickPos = range(num[0]+9,num[-1],10)
#ax1.set_xticks(tickPos,tickNums,fontsize=9,rotation=30)
#ax1.set_xticks(fontsize=9)
ax1.yaxis.grid(True)
ax1.set_axisbelow(True)
ax1.spines["right"].set_visible(False)
ax1.spines["top"].set_visible(False)
for loc, spine in ax1.spines.items():
if loc == 'bottom':
spine.set_position(('outward', 6)) # move outward (down) 6 pts
spine.set_smart_bounds(True)
for loc, spine in ax1.spines.items():
if loc == 'left':
spine.set_position(('outward', 6)) # move outward (left) 6 pts
spine.set_smart_bounds(True)
# need to add labels after moving spines, otherwise they will disappear
ax1xlabel = ax1.set_xlabel("Nucleotide", horizontalalignment="left", fontsize=14, labelpad=0)
x,y = ax1xlabel.get_position()
ax1xlabel.set_position((0,y))
ax1ylabel = ax1.set_ylabel("Shape Reactivity", horizontalalignment="left", fontsize=14)
x,y = ax1ylabel.get_position()
ax1ylabel.set_position((x,0))
if reactivity is not None:
# add a SHAPE colorbar to the vertical axis
# uses a little transformation magic to place correctly
inv = ax1.transData.inverted()
for loc, spine in ax1.spines.items():
if loc == 'left':
trans = spine.get_transform()
pt = trans.transform_point([0,0])
pt2 = inv.transform_point(pt)
rectX = pt2[0]
ptA = (0,0)
ptB = (6,0)
ptA2 = inv.transform_point(ptA)
ptB2 = inv.transform_point(ptB)
rectW = ptB2[0]-ptA2[0]
rect = Rectangle((rectX,-0.5), rectW, orange_thresh+0.5, facecolor="black", edgecolor="none")
ax1.add_patch(rect)
rect.set_clip_on(False)
rect = Rectangle((rectX,orange_thresh), rectW, red_thresh-orange_thresh, facecolor="orange", edgecolor="none")
ax1.add_patch(rect)
rect.set_clip_on(False)
rect = Rectangle((rectX,red_thresh), rectW, 4-red_thresh, facecolor="red", edgecolor="none")
ax1.add_patch(rect)
rect.set_clip_on(False)
ax1.get_xaxis().tick_bottom() # remove unneeded ticks
ax1.get_yaxis().tick_left()
ax1.tick_params(axis='y',which='minor',left='off')
#ax1.tick_params(axis='x',which='minor')
ax1.minorticks_on()
yticks = ax1.get_yticks()
stripped_ticks = [str(val).rstrip('0').rstrip('.') for val in yticks]
ax1.set_yticklabels(stripped_ticks)
for line in ax1.get_yticklines():
line.set_markersize(6)
line.set_markeredgewidth(1)
for line in ax1.get_xticklines():
line.set_markersize(7)
line.set_markeredgewidth(2)
for line in ax1.xaxis.get_ticklines(minor=True):
line.set_markersize(5)
line.set_markeredgewidth(1)
# put nuc sequence below axis
font_prop = mp.font_manager.FontProperties(family = "monospace", style="normal",weight="bold",size="4.5")
for i in range(seq.shape[0]):
nuc = seq[i]
if nuc == "T":
nuc = "U"
color_dict = {"A": "#f20000",
"U": "#f28f00",
"G": "#00509d",
"C": "#00c200"}
if nuc in color_dict:
col = color_dict[nuc]
elif nuc.upper() in color_dict:
col = color_dict[nuc.upper()]
else:
col = "black"
ax1.annotate(nuc, xy=(i+1, -0.67),fontproperties=font_prop,color=col,annotation_clip=False, horizontalalignment="center")
handles = []
h, = ax2.plot(num, rx_simple_depth, linewidth = 1.5, color=rx_color, alpha=1.0)
ax2.plot(num, rx_depth, linewidth = 1.0, color=rx_color, alpha=0.3)
handles.append(h)
h, = ax2.plot(num, bg_simple_depth, linewidth = 1.5, color=bg_color, alpha=1.0)
ax2.plot(num, bg_depth, linewidth = 1.0, color=bg_color, alpha=0.3)
handles.append(h)
h, = ax2.plot(num, dc_simple_depth, linewidth = 1.5, color=dc_color, alpha=1.0)
ax2.plot(num, dc_depth, linewidth = 1.0, color=dc_color, alpha=0.3)
handles.append(h)
ax2.set_xlim(1,len(num))
#ax2.legend(["+Reagent","Background","Denatured"], bbox_to_anchor=(1.1,1.1))
leg = ax2.legend(handles, legend_labels, loc=2, borderpad=0.8, handletextpad=0.2, framealpha=0.75)
xmin, xmax, ymin, ymax = ax2.axis()
ax2.set_ylim(0,ymax)
#ax2.set_yscale('log')
#ax2.set_yscale('symlog')# useful, but disabled because of a matplotlib/pyparsing bug
ax2xlabel = ax2.set_xlabel("Nucleotide\n(note: effective read depths shown in lighter colors)", horizontalalignment="left", fontsize=14, labelpad=0)
x,y = ax2xlabel.get_position()
ax2xlabel.set_position((0,y))
ax2.spines["right"].set_visible(False)
ax2.spines["top"].set_visible(False)
ax2.get_xaxis().tick_bottom() # remove unneeded ticks
ax2.get_yaxis().tick_left()
ax2.minorticks_on()
ax2.tick_params(axis='y',which='minor',left='off')
#ax2.tick_params(axis='x',which='minor')
#xlabels = ["%.2f"%v for v in xticks]
#ax3.set_xticks(xticks)
#ax3.set_xticklabels(xlabels,rotation = -45, horizontalalignment='left')
yticks = [int(y) for y in ax2.get_yticks()]
formatted_ticks = []
for val in yticks:
formatted_ticks.append(metric_abbreviate(val))
ax2.set_yticklabels(formatted_ticks)
for line in ax2.get_yticklines():
line.set_markersize(6)
line.set_markeredgewidth(1)
for line in ax2.get_xticklines():
line.set_markersize(7)
line.set_markeredgewidth(2)
for line in ax2.xaxis.get_ticklines(minor=True):
line.set_markersize(5)
line.set_markeredgewidth(1)
ax2.yaxis.grid(True)
ax2.set_axisbelow(True)
ax2ylabel = ax2.set_ylabel("Read depth", horizontalalignment="left", fontsize=14)
x, y = ax2ylabel.get_position()
ax2ylabel.set_position((x, 0))
# tried to make an offset, smaller font note about effective depths,
# but couldn't get positioning/transforms to work properly.
# For now just putting in xaxis label
# choose a decent range for axis, excluding high-background positions
good_indices = []
for i in range(len(bg_rates)):
if bg_rates[i]<=0.05 or isnan(bg_rates[i]):
good_indices.append(i)
temp_rates = [rx_rates[i] for i in good_indices]
near_top_rate = percentile(temp_rates,98.0)
maxes = [0.32,0.16,0.08,0.04,0.02,0.01]
ymax = maxes[0]
for i in range(len(maxes)):
if near_top_rate<maxes[i]:
ymax = maxes[i]
rx_upper = rx_rates + rx_err
rx_lower = rx_rates - rx_err
bg_upper = bg_rates + bg_err
bg_lower = bg_rates - bg_err
dc_upper = dc_rates + dc_err
dc_lower = dc_rates - dc_err
ax3xlabel = ax3.set_xlabel("Nucleotide", horizontalalignment="left", fontsize=14, labelpad=0)
x,y = ax3xlabel.get_position()
ax3xlabel.set_position((0,y))
ax3ylabel = ax3.set_ylabel("Mutation rate (%)", horizontalalignment="left", fontsize=14)
x,y = ax3ylabel.get_position()
ax3ylabel.set_position((x,0))
ax3.plot(num, rx_rates, zorder=3, color=rx_color, linewidth=1.5)
ax3.plot(num, bg_rates, zorder=2, color=bg_color, linewidth=1.5)
ax3.plot(num, dc_rates, zorder=2, color=dc_color, linewidth=1.5)
ax3.fill_between(num, rx_lower, rx_upper, edgecolor="none", alpha=0.5, facecolor=rx_color)
ax3.fill_between(num, bg_lower, bg_upper, edgecolor="none", alpha=0.5, facecolor=bg_color)
ax3.fill_between(num, dc_lower, dc_upper, edgecolor="none", alpha=0.5, facecolor=dc_color)
ax3.legend(legend_labels, loc=2, borderpad=0.8, handletextpad=0.2, framealpha=0.75)
ax3.set_xlim((1,len(rx_rates)))
ax3.set_ylim((0,ymax))
ax3.spines["right"].set_visible(False)
ax3.spines["top"].set_visible(False)
ax3.get_xaxis().tick_bottom() # remove unneeded ticks
ax3.get_yaxis().tick_left()
ax3.minorticks_on()
ax3.tick_params(axis='y',which='minor',left='off')
ticks = [x*100 for x in ax3.get_yticks()]
ax3.set_yticklabels([str(val).rstrip('0').rstrip('.') for val in ticks])
for line in ax3.get_yticklines():
line.set_markersize(6)
line.set_markeredgewidth(1)
for line in ax3.get_xticklines():
line.set_markersize(7)
line.set_markeredgewidth(2)
for line in ax3.xaxis.get_ticklines(minor=True):
line.set_markersize(5)
line.set_markeredgewidth(1)
ax3.yaxis.grid(True)
ax3.set_axisbelow(True)
# TODO: add a tick for the first nuc - can't seem to add one without screwing
# up all the other ticks
plt.savefig(fileout)
def draw_border(axis):
from matplotlib.patches import Rectangle
rec = Rectangle((0, 0), 1, 1,
fill=False, lw=2, transform=axis.transAxes)
rec = axis.add_patch(rec)
rec.set_clip_on(False)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.legend(loc='lower left', bbox_to_anchor=(1, 0), frameon=False)
ax.set_title('Die Spannbreite der Ticketpreise',
size=18,
horizontalalignment='left')
ax.yaxis.set_ticks_position('left')
ax.xaxis.set_ticks_position('bottom')
ax.tick_params(axis='y', direction='out')
#add frames
autoAxis = ax.axis()
rec = Rectangle((autoAxis[0] - 5, autoAxis[2] - 10),
(autoAxis[1] - autoAxis[0]) + 50,
(autoAxis[3] - autoAxis[2]) + 30,
fill=False,
lw=1,
edgecolor="darkkhaki")
rec = ax.add_patch(rec)
rec.set_clip_on(False)
rec2 = Rectangle((autoAxis[0] - 5, autoAxis[2] + 142),
(autoAxis[1] - autoAxis[0]) + 50,
(autoAxis[3] - autoAxis[2]) - 120,
fill=True,
lw=1,
edgecolor="darkkhaki",
facecolor="darkkhaki")
rec2 = ax.add_patch(rec2)
rec2.set_clip_on(False)
#save the plot
fig.savefig("plot.png")
def writeFigures(num,reactivity,stdev,
rxRates,bgRates,dcRates,
rxErr,bgErr,dcErr,
rxDepth,bgDepth,dcDepth,
name,outputDir):
errFlag = False
try:
import matplotlib as mp
mp.rcParams["font.sans-serif"].insert(0,"Arial") # If matplotlib can find this font, then Illustrator
# should be able to open the correct font on PDF import
# rather than replacing it. Matplotlib generally falls
# back to Bitstream fonts, which Illustrator usually
# can't locate by default.
mp.rcParams["font.family"] = "sans-serif"
mp.rcParams["pdf.fonttype"] = 42 # use TrueType fonts when exporting pdfs (embeds most fonts)
mp.rcParams['xtick.direction'] = 'out'
mp.rcParams['ytick.direction'] = 'out'
mp.rcParams['legend.fontsize'] = 14
mp.rcParams['grid.color'] = ".8"
mp.rcParams['grid.linestyle'] = '-'
mp.rcParams['grid.linewidth'] = 1
mp.use('Agg')
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
except ImportError:
sys.stderr.write("Warning: matplotlib python module was not found, so no figures were rendered for %i.\n"%(name))
errFlag = True
version = [int(v) for v in mp.__version__.split('.')]
# Check that we have matplotlib version 1.2 or greater
if version[0]<1 or (version[0]==1 and version[1]<2):
sys.stderr.write("Warning: matplotlib version 1.2 or greater is recommended, but version %s was found, so no figures were rendered for %i.\n"%(mp.__version__,name))
errFlag = True
if errFlag==True:
return False
from numpy import percentile
# Add a zeroeth nuc so axis numbering works correctly
# There's probably a better way to do this
num = [0]+num
reactivity = [0]+reactivity
stdev = [0]+stdev
rxDepth = [0]+rxDepth
bgDepth = [0]+bgDepth
dcDepth = [0]+dcDepth
rxRates = [0]+rxRates
bgRates = [0]+bgRates
dcRates = [0]+dcRates
rxErr = [0]+rxErr
bgErr = [0]+bgErr
dcErr = [0]+dcErr
orangeThresh = 0.4
redThresh = 0.85
grayVals = []
grayNums = []
grayErrs = []
blackVals = []
blackNums = []
blackErrs = []
orangeVals = []
orangeNums = []
orangeErrs = []
redVals = []
redNums = []
redErrs = []
for i in range(len(reactivity)):
if reactivity[i] < -900:
grayVals.append(-1)
grayNums.append(num[i])
grayErrs.append(0)
elif reactivity[i] < orangeThresh:
blackVals.append(reactivity[i])
blackNums.append(num[i])
blackErrs.append(stdev[i])
elif reactivity[i] < redThresh:
orangeVals.append(reactivity[i])
orangeNums.append(num[i])
orangeErrs.append(stdev[i])
else:
redVals.append(reactivity[i])
redNums.append(num[i])
redErrs.append(stdev[i])
yMin, yMax = (-0.5, 4)
leftInches = 0.9
rightInches = 0.4
spWidth = len(num)*0.032
figWidth = max(7,spWidth+leftInches+rightInches)
fig = plt.figure(figsize=(figWidth,8))
leftPercent = leftInches/figWidth
rightPercent = 1-rightInches/figWidth
ax1 = plt.subplot(311)
ax2 = plt.subplot(313)
ax3 = plt.subplot(312)
plt.subplots_adjust(hspace=0.5, left=leftPercent,right=rightPercent,top=0.94)
nearBlack = (0,0,1/255.0)
if len(grayNums)>0:
ax1.bar(grayNums,grayVals,
align="center",
width=1.05, color="0.80", edgecolor="0.80",linewidth=0.0)
if len(blackNums)>0:
ax1.bar(blackNums,blackVals,
align="center",
width=1.05, color="black", edgecolor="black",linewidth=0.0,
yerr=blackErrs,ecolor=nearBlack,capsize=1)
if len(orangeNums)>0:
ax1.bar(orangeNums,orangeVals,
align="center",
width=1.05, color="orange",edgecolor="orange",linewidth=0.0,
yerr=orangeErrs,ecolor=nearBlack,capsize=1)
if len(redNums)>0:
ax1.bar(redNums,redVals,
align="center",
width=1.05,color="red",edgecolor="red",linewidth=0.0,
yerr=redErrs,ecolor=nearBlack,capsize=1)
ax1title = ax1.set_title(name, horizontalalignment="left", fontsize=16)
x,y = ax1title.get_position()
ax1title.set_position((0,y))
ax1.set_ylim(yMin,yMax)
ax1.set_xlim(1,len(num))
#ax1.set_yticks(fontsize=9)
#tickNums = range(num[0]+10,num[-1]+1,10)
#tickPos = range(num[0]+9,num[-1],10)
#ax1.set_xticks(tickPos,tickNums,fontsize=9,rotation=30)
#ax1.set_xticks(fontsize=9)
ax1.yaxis.grid(True)
ax1.set_axisbelow(True)
ax1.spines["right"].set_visible(False)
ax1.spines["top"].set_visible(False)
for loc, spine in ax1.spines.items():
if loc == 'bottom':
spine.set_position(('outward', 6)) # move outward (down) 6 pts
spine.set_smart_bounds(True)
for loc, spine in ax1.spines.items():
if loc == 'left':
spine.set_position(('outward', 6)) # move outward (left) 6 pts
spine.set_smart_bounds(True)
# need to add labels after moving spines, otherwise they will disappear
ax1xlabel = ax1.set_xlabel("Nucleotide", horizontalalignment="left", fontsize=14, labelpad=0)
x,y = ax1xlabel.get_position()
print str((x,y))
ax1xlabel.set_position((0,y))
ax1ylabel = ax1.set_ylabel("Shape Reactivity", horizontalalignment="left", fontsize=14)
x,y = ax1ylabel.get_position()
ax1ylabel.set_position((x,0))
# add a SHAPE colorbar to the vertical axis
# uses a little transformation magic to place correctly
inv = ax1.transData.inverted()
for loc, spine in ax1.spines.items():
if loc == 'left':
trans = spine.get_transform()
pt = trans.transform_point([0,0])
pt2 = inv.transform_point(pt)
rectX = pt2[0]
ptA = (0,0)
ptB = (6,0)
ptA2 = inv.transform_point(ptA)
ptB2 = inv.transform_point(ptB)
rectW = ptB2[0]-ptA2[0]
rect = Rectangle((rectX,-0.5), rectW, orangeThresh+0.5, facecolor="black", edgecolor="none")
ax1.add_patch(rect)
rect.set_clip_on(False)
rect = Rectangle((rectX,orangeThresh), rectW, redThresh-orangeThresh, facecolor="orange", edgecolor="none")
ax1.add_patch(rect)
rect.set_clip_on(False)
rect = Rectangle((rectX,redThresh), rectW, 4-redThresh, facecolor="red", edgecolor="none")
ax1.add_patch(rect)
rect.set_clip_on(False)
ax1.get_xaxis().tick_bottom() # remove unneeded ticks
ax1.get_yaxis().tick_left()
ax1.tick_params(axis='y',which='minor',left='off')
#ax1.tick_params(axis='x',which='minor')
ax1.minorticks_on()
yticks = ax1.get_yticks()
#print str(yticks)
strippedTicks = ["%d"%val if val==int(val) else "%s"%val for val in yticks]
ax1.set_yticklabels(strippedTicks)
for line in ax1.get_yticklines():
line.set_markersize(6)
line.set_markeredgewidth(1)
for line in ax1.get_xticklines():
line.set_markersize(7)
line.set_markeredgewidth(2)
for line in ax1.xaxis.get_ticklines(minor=True):
line.set_markersize(5)
line.set_markeredgewidth(1)
# put nuc sequence below axis
fontProp = mp.font_manager.FontProperties(family = "monospace", style="normal",weight="bold",size="4.5")
for i in range(len(seq)):
nuc = seq[i]
if nuc == "T":
nuc = "U"
colorDict = {"A":"#f20000",
"U":"#f28f00",
"G":"#00509d",
"C":"#00c200"}
if nuc in colorDict.keys():
col = colorDict[nuc]
else:
col = "black"
ax1.annotate(nuc, xy=(i+1, -0.67),fontproperties=fontProp,color=col,annotation_clip=False, horizontalalignment="center")
ax2.plot(num,rxDepth, linewidth = 1.5, color=rxColor)
ax2.plot(num,bgDepth, linewidth = 1.5, color=bgColor)
ax2.plot(num,dcDepth, linewidth = 1.5, color=dcColor)
ax2.set_xlim(1,len(num))
#ax2.legend(["+Reagent","Background","Denatured"], bbox_to_anchor=(1.1,1.1))
leg = ax2.legend(["Modified","Untreated","Denatured"], loc=2, borderpad=0.8, handletextpad=0.2, framealpha=0.75)
xmin, xmax, ymin, ymax = ax2.axis()
ax2.set_ylim(0,ymax)
#ax2.set_yscale('symlog')# useful but currently disabled because of a matplotlib/pyparsing bug
ax2xlabel = ax2.set_xlabel("Nucleotide", horizontalalignment="left", fontsize=14, labelpad=0)
x,y = ax2xlabel.get_position()
ax2xlabel.set_position((0,y))
ax2ylabel = ax2.set_ylabel("Read depth", horizontalalignment="left", fontsize=14)
x,y = ax2ylabel.get_position()
ax2ylabel.set_position((x,0))
#ax2title = ax2.set_title(name, horizontalalignment="left")
#x,y = ax2title.get_position()
#ax2title.set_position((0,y))
ax2.spines["right"].set_visible(False)
ax2.spines["top"].set_visible(False)
ax2.get_xaxis().tick_bottom() # remove unneeded ticks
ax2.get_yaxis().tick_left()
ax2.minorticks_on()
ax2.tick_params(axis='y',which='minor',left='off')
#ax2.tick_params(axis='x',which='minor')
#xlabels = ["%.2f"%v for v in xticks]
#ax3.set_xticks(xticks)
#ax3.set_xticklabels(xlabels,rotation = -45, horizontalalignment='left')
yticks = [int(y) for y in ax2.get_yticks()]
formattedTicks = []
for val in yticks:
formattedTicks.append(metricAbbreviate(val))
ax2.set_yticklabels(formattedTicks)
for line in ax2.get_yticklines():
line.set_markersize(6)
line.set_markeredgewidth(1)
for line in ax2.get_xticklines():
line.set_markersize(7)
line.set_markeredgewidth(2)
for line in ax2.xaxis.get_ticklines(minor=True):
line.set_markersize(5)
line.set_markeredgewidth(1)
ax2.yaxis.grid(True)
ax2.set_axisbelow(True)
# choose a decent range for axis, excluding high-background positions
goodIndices = []
for i in xrange(len(bgRates)):
if bgRates[i]<=conf.maxBackground:
goodIndices.append(i)
tempRates = [rxRates[i] for i in goodIndices]
nearTopRate = percentile(tempRates,98.0)
maxes = [0.32,0.16,0.08,0.04,0.02,0.01]
yMax = maxes[0]
for i in xrange(len(maxes)):
if nearTopRate<maxes[i]:
yMax = maxes[i]
rxUpper = [rxRates[i]+rxErr[i] for i in xrange(len(rxRates))]
rxLower = [rxRates[i]-rxErr[i] for i in xrange(len(rxRates))]
bgUpper = [bgRates[i]+bgErr[i] for i in xrange(len(bgRates))]
bgLower = [bgRates[i]-bgErr[i] for i in xrange(len(bgRates))]
dcUpper = [dcRates[i]+dcErr[i] for i in xrange(len(dcRates))]
dcLower = [dcRates[i]-dcErr[i] for i in xrange(len(dcRates))]
ax3xlabel = ax3.set_xlabel("Nucleotide", horizontalalignment="left", fontsize=14, labelpad=0)
x,y = ax3xlabel.get_position()
ax3xlabel.set_position((0,y))
ax3ylabel = ax3.set_ylabel("Mutation rate (%)", horizontalalignment="left", fontsize=14)
x,y = ax3ylabel.get_position()
ax3ylabel.set_position((x,0))
ax3.plot(rxRates, zorder=3,color=rxColor,linewidth=1.5)
ax3.plot(bgRates, zorder=2, color=bgColor, linewidth=1.5)
ax3.fill_between(num,rxLower,rxUpper,edgecolor="none",alpha=0.5, facecolor=rxColor)
ax3.fill_between(num,bgLower,bgUpper,edgecolor="none",alpha=0.5, facecolor=bgColor)
ax3.legend(["Modified","Untreated"], loc=2, borderpad=0.8, handletextpad=0.2, framealpha=0.75)
ax3.set_xlim((1,len(rxRates)))
ax3.set_ylim((0,yMax))
ax3.spines["right"].set_visible(False)
ax3.spines["top"].set_visible(False)
ax3.get_xaxis().tick_bottom() # remove unneeded ticks
ax3.get_yaxis().tick_left()
ax3.minorticks_on()
ax3.tick_params(axis='y',which='minor',left='off')
ticks = [x*100 for x in ax3.get_yticks()]
ax3.set_yticklabels(["%d"%val if val==int(val) else "%s"%val for val in ticks])
for line in ax3.get_yticklines():
line.set_markersize(6)
line.set_markeredgewidth(1)
for line in ax3.get_xticklines():
line.set_markersize(7)
line.set_markeredgewidth(2)
for line in ax3.xaxis.get_ticklines(minor=True):
line.set_markersize(5)
line.set_markeredgewidth(1)
ax3.yaxis.grid(True)
ax3.set_axisbelow(True)
# TODO: add a tick for the first nuc - can't seem to add one without screwing
# up all the other ticks
outPath = os.path.join(outputDir,"reactivity_profiles")
outPath = os.path.join(outPath,name+"_depth_and_reactivity.pdf")
plt.savefig(outPath)
return True
from matplotlib.patches import Rectangle
# build a rectangle in axes coords
left, width = .25, .5
bottom, height = .25, .5
right = left + width
top = bottom + height
ax = gca()
p = Rectangle(
(left, bottom),
width,
height,
fill=False,
)
p.set_transform(ax.transAxes)
p.set_clip_on(False)
ax.add_patch(p)
ax.text(left,
bottom,
'left top',
horizontalalignment='left',
verticalalignment='top',
transform=ax.transAxes)
ax.text(left,
bottom,
'left bottom',
horizontalalignment='left',
verticalalignment='bottom',
transform=ax.transAxes)
def plot_cnn_output(output,
path,
name,
title=None,
image=None,
video=False,
verbose=True,
highlight=None):
"""
This function creates a plot of the cnn output.
:param output: response from cnn (28,28,256)
:param path: folder where to save plot
:param name: name of plot
:param title: title of plot
:param image: if given, the image is displayed
:param video: if true an animation is created,
output shape should then be (#frames, 28,28,256) and image either none or (#frames)
:param verbose:
:param highlight: list of int, highlights the feature map at given indices by a red rectangle
:return:
"""
if verbose:
print("start plot_cnn_output of", name)
num_ft = output.shape[-1]
n_col = math.ceil(np.sqrt(num_ft))
n_rows = math.ceil(num_ft / n_col)
vmin, vmax = np.min(output), np.max(output)
fig, axs = plt.subplots(n_rows, n_col, figsize=(n_rows + 3, n_col))
# add entry to matrix if there's not enough columns/rows so np.ndenumerate can unpack two values
if n_col == 1 and n_rows == 1:
axs = [[axs]]
elif n_rows == 1:
axs = [axs]
plt.subplots_adjust(right=0.75, wspace=0.1, hspace=0.1)
# add title
if title is not None:
fig.suptitle(title)
ax_colorbar = fig.add_axes([0.83, 0.1, 0.02, 0.65])
# add image input
if image is not None:
ax_image = fig.add_axes([0.78, 0.78, .12, .12])
if np.max(image) > 1:
if verbose:
print("transform image data to RGB [0..1] from [0..255]")
image = image / 255
if not os.path.exists(path):
os.mkdir(path)
# add highlight
if highlight is not None:
for index_highlight in highlight:
multi_index_highlight = np.unravel_index(index_highlight,
(n_rows, n_col))
if video:
size_axis = output.shape[1]
else:
size_axis = output.shape[0]
rec = Rectangle((0, 0),
size_axis,
size_axis,
fill=False,
lw=size_axis * 0.15,
edgecolor='r')
rec = axs[multi_index_highlight].add_patch(rec)
rec.set_clip_on(False)
update_list = []
def update(n_frame):
if verbose and n_frame >= 0:
print("frame: {}/{}".format(n_frame, output.shape[0]), end="\r")
if not video:
data = output
else:
data = output[n_frame, ...]
# loop over all feature maps
for (i, j), ax in np.ndenumerate(axs):
n = i * n_col + j
try:
# old way, not sure to understand what Tim tried to do here
# ax.get_images()[0].set_data(data[..., n])
im = ax.imshow(data[..., n], vmin=vmin, vmax=vmax)
update_list.append(im)
except IndexError:
# old way, not sure to understand what Tim did here
# im = ax.imshow(data[...,n], vmin=vmin, vmax=vmax)
# update_list.append(im)
pass
ax.axis('off')
# add input image
if image is not None:
if video:
image_frame = image[n_frame]
else:
image_frame = image
try:
ax_image.get_images()[0].set_data(image_frame)
except IndexError:
im_image = ax_image.imshow(image_frame)
update_list.append(im_image)
ax_image.axis('off')
# display color bar
if n_frame <= 0:
cbar = fig.colorbar(im, cax=ax_colorbar)
return update_list
if not video:
update(-1)
fig.savefig(os.path.join(path, name))
else:
def init_func():
return []
anim = FuncAnimation(fig,
update,
frames=output.shape[0],
init_func=init_func,
blit=True,
interval=33)
writergif = animation.PillowWriter(fps=30)
anim.save(os.path.join(path, name), writer=writergif)
if verbose:
print("finished plot_cnn_output of", name)
def create_design_plot(self):
"""Creates a RNAi design plot."""
# Temp file for storing
temp_img_file = tempfile.mkstemp()
# Create main figure
fig, ax1 = plt.subplots(figsize=(15, 4))
off_target_dict, main_target_dict, efficient_dict, main_hits_histo = general_helpers.get_target_data(self.f_in, self.sirna_size)
# If there are no hits, we show only the efficient sirnas for designing a construct
if self.main_targets == []:
off_target_dict = {}
main_target_dict = {}
main_hits_histo = []
# Off-target position list
off_targets_pos = set()
for i in off_target_dict.values():
off_targets_pos = off_targets_pos | i
# Main-target position list
main_targets_plot = set()
for i in main_target_dict.values():
main_targets_plot = main_targets_plot | i
# Efficient position list
eff_sirna_plot = []
for i in efficient_dict.values():
eff_sirna_plot.extend(i)
eff_sirna_plot.sort()
# Draw efficiency plot
eff_sirna_histo = np.bincount(eff_sirna_plot, minlength=self.query_length)
ax1.plot(eff_sirna_histo, 'r-', label='Efficient siRNA hits')
# Draw main target histogram
main_histo = np.bincount(main_hits_histo, minlength=self.query_length)
ax1.plot(main_histo, 'b-', label='Main target siRNA hits')
# Draw main targets as green rectangles inside plot figure
for region in main_targets_plot:
someX, someY = region, 0.
currentAxis = fig.gca()
tri1 = Rectangle((someX, someY), 1, self.sirna_size + 3, color='g', alpha=0.2)#, label='green')
currentAxis.add_patch(tri1)
tri1.set_clip_on(False)
# Draw off-targets as red rectangles inside plot figure
for off_target in off_targets_pos:
someX, someY = off_target, 0.
currentAxis = fig.gca()
tri2 = Rectangle((someX, someY), 1, self.sirna_size + 3, color='r', alpha=0.2)#, label='red')
currentAxis.add_patch(tri2)
tri2.set_clip_on(False)
if max(eff_sirna_histo) > self.sirna_size:
max_y = max(eff_sirna_histo) + 3
else:
max_y = self.sirna_size + 3
ax1.set_ylim([0, max_y])
x_text = 'mRNA sequence position\n\n'
ax1.set_xlabel(x_text, fontsize=12)
ax1.xaxis.set_ticks(np.arange(0, self.query_length, 50))
ax1.set_ylabel('Nr of siRNAs', color='b', fontsize=12)
ax1.set_xlim([0, self.query_length])
plt.title("RNAi design plot\n\n", fontsize=14)
plt.tight_layout()
p1 = Rectangle((0, 0), 1, 1, fc="g", alpha=0.2)
p2 = Rectangle((0, 0), 1, 1, fc="r", alpha=0.2)
p3 = mlines.Line2D([], [], color='r')
p4 = mlines.Line2D([], [], color='b')
plt.legend([p1,p2, p3, p4], ["Main target", "Off target", 'Efficient siRNAs', 'All siRNAs'],
bbox_to_anchor=(0., 1.02, 1., .102), loc=4, ncol=4, mode="", borderaxespad=0.5, frameon=True)
plt.savefig(temp_img_file[1] + '.png')
#plt.show()
plt.close(fig)
return temp_img_file[1] + '.png', off_target_dict, main_target_dict
def highlight_artist(self, val, artist=None):
from ifigure.matplotlib_mod.art3d_gl import Poly3DCollectionGL
from ifigure.matplotlib_mod.art3d_gl import Line3DCollectionGL
figure = self.get_figpage()._artists[0]
ax = self.get_figaxes()
if artist is None:
alist = self._artists
else:
alist = artist
if val == True:
if self._parent is None: return
container = self.get_container()
if container is None: return
if isinstance(alist[0], Poly3DCollectionGL):
hl = alist[0].add_hl_mask()
for item in hl:
alist[0].figobj_hl.append(item)
else:
de = self.get_data_extent()
x = (de[0], de[1], de[1], de[0], de[0])
y = (de[2], de[2], de[3], de[3], de[2])
facecolor = 'k'
if isinstance(alist[0], Poly3DCollectionGL):
hl = alist[0].make_hl_artist(container)
facecolor = 'none'
self._hit_path = None
elif isinstance(alist[0], Line3DCollectionGL):
hl = alist[0].make_hl_artist(container)
facecolor = 'none'
self._hit_path = None
else:
hl = container.plot(x,
y,
marker='s',
color='k',
linestyle='None',
markerfacecolor='None',
markeredgewidth=0.5,
scalex=False,
scaley=False)
for item in hl:
alist[0].figobj_hl.append(item)
if self._hit_path is not None:
v = self._hit_path.vertices
hl = container.plot(v[:, 0],
v[:, 1],
marker='s',
color='k',
linestyle='None',
markerfacecolor='None',
markeredgewidth=0.5,
scalex=False,
scaley=False)
for item in hl:
alist[0].figobj_hl.append(item)
hlp = Rectangle((de[0], de[2]),
de[1] - de[0],
de[3] - de[2],
alpha=0.3,
facecolor=facecolor,
figure=figure,
transform=container.transData)
if ax is not None:
x0, y0 = ax._artists[0].transAxes.transform((0, 0))
x1, y1 = ax._artists[0].transAxes.transform((1, 1))
bbox = Bbox([[x0, y0], [x1, y1]])
hlp.set_clip_box(bbox)
hlp.set_clip_on(True)
figure.patches.append(hlp)
alist[0].figobj_hl.append(hlp)
else:
for a in alist:
if len(a.figobj_hl) == 0: continue
for hl in a.figobj_hl[:-1]:
hl.remove()
if isinstance(alist[0], Poly3DCollectionGL):
a.figobj_hl[-1].remove()
else:
figure.patches.remove(a.figobj_hl[-1])
a.figobj_hl = []
def generateGraph(self,
data=None,
outputFilename=None,
timeDivisions=6,
graphWidth=1920,
graphHeight=300,
darkMode=False,
rainVariance=False,
minMaxTemperature=False,
fontSize=12,
symbolZoom=1.0,
symbolDivision=1,
showCityName=None,
writeMetaData=None):
logging.debug("Initializing graph...")
if darkMode:
colors = self.colorsDarkMode
else:
colors = self.colorsLightMode
fig = plt.figure(0) # Main figure
rainAxis = fig.add_subplot(111)
# set font sizes
plt.rcParams.update({'font.size':
fontSize}) # Temperature Y axis and day names
rainAxis.tick_params(axis='y', labelsize=fontSize) # Rain Y axis
plt.xticks(fontsize=fontSize) # Time axis
if not graphWidth:
graphWidth = 1280
if not graphHeight:
graphHeight = 300
logging.debug("Graph size: %d x %d pixel" % (graphWidth, graphHeight))
fig.set_size_inches(
float(graphWidth) / fig.get_dpi(),
float(graphHeight) / fig.get_dpi())
# Plot dimension and borders
bbox = rainAxis.get_window_extent().transformed(
fig.dpi_scale_trans.inverted())
width, height = bbox.width * fig.dpi, bbox.height * fig.dpi # plot size in pixel
plt.margins(x=0)
rainAxis.margins(x=0)
plt.subplots_adjust(left=40 / width,
right=1 - 40 / width,
top=1 - 35 / height,
bottom=40 / height)
bbox = rainAxis.get_window_extent().transformed(
fig.dpi_scale_trans.inverted())
width, height = bbox.width * fig.dpi, bbox.height * fig.dpi # plot size in pixel
xPixelsPerDay = width / data["noOfDays"]
# Dimensions of the axis in pixel
firstDayX = math.ceil(bbox.x0 * fig.dpi)
firstDayY = math.ceil(bbox.y0 * fig.dpi)
dayWidth = math.floor((bbox.x1 - bbox.x0) * fig.dpi) / data["noOfDays"]
dayHeight = math.floor((bbox.y1 - bbox.y0) * fig.dpi)
# Show gray background on every 2nd day
for day in range(0, data["noOfDays"], 2):
plt.axvspan(data["timestamps"][0 + day * 24],
data["timestamps"][23 + day * 24] + 3600,
facecolor='gray',
alpha=0.2)
# Time axis and ticks
plt.xticks(data["timestamps"][::timeDivisions],
data["formatedTime"][::timeDivisions])
rainAxis.tick_params(axis='x', colors=colors["x-axis"])
# Rain (data gets splitted to stacked bars)
logging.debug("Creating rain plot...")
rainBars = [0] * len(self.rainColorSteps)
for i in range(0, len(self.rainColorSteps)):
rainBars[i] = []
for rain in data["rainfall"]:
for i in range(0, len(self.rainColorSteps)):
if rain > self.rainColorSteps[i]:
rainBars[i].append(self.rainColorStepSizes[i])
else:
if i > 0:
rainBars[i].append(
max(rain - self.rainColorSteps[i - 1], 0))
else:
rainBars[i].append(rain)
continue
rainAxis.bar(data["timestamps"],
rainBars[0],
width=3000,
color=self.rainColors[0],
align='edge')
bottom = [0] * len(rainBars[0])
for i in range(1, len(self.rainColorSteps)):
bottom = np.add(bottom, rainBars[i - 1]).tolist()
rainAxis.bar(data["timestamps"],
rainBars[i],
bottom=bottom,
width=3000,
color=self.rainColors[i],
align='edge')
rainAxis.tick_params(axis='y',
labelcolor=colors["rain-axis"],
width=0,
length=8)
rainYRange = plt.ylim()
rainScaleMax = max(
data["rainfall"]
) + 1 # Add a bit to make sure we do not bang our head
plt.ylim(0, rainScaleMax)
rainAxis.locator_params(axis='y', nbins=7)
# TODO find a better way than rounding
rainAxis.yaxis.set_major_formatter(FormatStrFormatter('%0.1f'))
# Rain color bar as y axis
plt.xlim(
data["timestamps"][0], data["timestamps"][-1] +
(data["timestamps"][1] - data["timestamps"][0]))
pixelToRainX = 1 / xPixelsPerDay * (data["timestamps"][23] -
data["timestamps"][0])
x = data["timestamps"][-1] + (
data["timestamps"][1] - data["timestamps"][0]) # end of x
w = 7 * pixelToRainX
for i in range(0, len(self.rainColorSteps)):
y = self.rainColorSteps[i] - self.rainColorStepSizes[i]
if y > rainScaleMax:
break
h = self.rainColorSteps[i] + self.rainColorStepSizes[i]
if y + h >= rainScaleMax: # reached top
h = rainScaleMax - y
rainScaleBar = Rectangle((x, y),
w,
h,
fc=self.rainColors[i],
alpha=1)
rainAxis.add_patch(rainScaleBar)
rainScaleBar.set_clip_on(False)
rainScaleBorder = Rectangle((x, 0),
w,
rainScaleMax,
fc="black",
fill=False,
alpha=1)
rainAxis.add_patch(rainScaleBorder)
rainScaleBorder.set_clip_on(False)
# Rain variance
if rainVariance:
rainfallVarianceAxis = rainAxis.twinx(
) # instantiate a second axes that shares the same x-axis
rainfallVarianceAxis.axes.yaxis.set_visible(False)
timestampsCentered = [i + 1500 for i in data["timestamps"]]
rainfallVarianceMin = np.subtract(
np.array(data["rainfall"]),
np.array(data["rainfallVarianceMin"]))
rainfallVarianceMax = np.subtract(
np.array(data["rainfallVarianceMax"]),
np.array(data["rainfall"]))
rainfallVarianceAxis.errorbar(
timestampsCentered,
data["rainfall"],
yerr=[rainfallVarianceMin, rainfallVarianceMax],
fmt="none",
elinewidth=1,
alpha=0.5,
ecolor='black',
capsize=3)
plt.ylim(0, rainScaleMax)
# Show when the model was last calculated
timestampLocal = data[
"modelCalculationTimestamp"] + self.utcOffset * 3600
l = mlines.Line2D([timestampLocal, timestampLocal],
[rainYRange[0], rainScaleMax])
rainAxis.add_line(l)
# Temperature
logging.debug("Creating temerature plot...")
temperatureAxis = rainAxis.twinx(
) # instantiate a second axes that shares the same x-axis
temperatureAxis.plot(data["timestamps"],
data["temperature"],
label="temperature",
color=self.temperatureColor,
linewidth=4)
#temperatureAxis.set_ylabel('Temperature', color=self.temperatureColor)
temperatureAxis.tick_params(axis='y',
labelcolor=colors["temperature-axis"])
temperatureAxis.grid(True)
# Position the Y Scales
temperatureAxis.yaxis.tick_left()
rainAxis.yaxis.tick_right()
# Make sure the temperature scaling has a gap of 45 pixel, so we can fit the labels
interimPixelToTemperature = (np.nanmax(data["temperature"]) -
np.nanmin(data["temperature"])) / height
temperatureScaleMin = np.nanmin(
data["temperature"]) - float(45) * interimPixelToTemperature
temperatureScaleMax = np.nanmax(
data["temperature"]) + float(45) * interimPixelToTemperature
plt.ylim(temperatureScaleMin, temperatureScaleMax)
temperatureAxis.locator_params(axis='y', nbins=6)
temperatureAxis.yaxis.set_major_formatter(FormatStrFormatter('%0.1f'))
pixelToTemperature = (temperatureScaleMax -
temperatureScaleMin) / height
# Temperature variance
temperatureVarianceAxis = temperatureAxis.twinx(
) # instantiate a second axes that shares the same x-axis
temperatureVarianceAxis.axes.yaxis.set_visible(False)
temperatureVarianceAxis.fill_between(data["timestamps"],
data["temperatureVarianceMin"],
data["temperatureVarianceMax"],
facecolor=self.temperatureColor,
alpha=0.2)
temperatureVarianceAxis.tick_params(axis='y',
labelcolor=self.temperatureColor)
plt.ylim(temperatureScaleMin, temperatureScaleMax)
logging.debug("Adding various additional information to the graph...")
# Mark min/max temperature per day
if minMaxTemperature:
da = DrawingArea(2, 2, 0, 0)
da.add_artist(
Circle((1, 1),
4,
color=self.temperatureColor,
fc="white",
lw=2))
for day in range(0, data["noOfDays"]):
dayXPixelMin = day * xPixelsPerDay
dayXPixelMax = (day + 1) * xPixelsPerDay - 1
maxTemperatureOfDay = {"data": -100, "timestamp": 0}
minTemperatureOfDay = {"data": +100, "timestamp": 0}
for h in range(0, 24):
if data["temperature"][day * 24 +
h] > maxTemperatureOfDay["data"]:
maxTemperatureOfDay["data"] = data["temperature"][day *
24 +
h]
maxTemperatureOfDay["timestamp"] = data["timestamps"][
day * 24 + h]
maxTemperatureOfDay["xpixel"] = (
data["timestamps"][day * 24 + h] -
data["timestamps"][0]) / (24 *
3600) * xPixelsPerDay
maxTemperatureOfDay["ypixel"] = (
data["temperature"][day * 24 + h] -
temperatureScaleMin) / (
temperatureScaleMax -
temperatureScaleMin) * height
if data["temperature"][day * 24 +
h] < minTemperatureOfDay["data"]:
minTemperatureOfDay["data"] = data["temperature"][day *
24 +
h]
minTemperatureOfDay["timestamp"] = data["timestamps"][
day * 24 + h]
minTemperatureOfDay["xpixel"] = (
data["timestamps"][day * 24 + h] -
data["timestamps"][0]) / (24 *
3600) * xPixelsPerDay
minTemperatureOfDay["ypixel"] = (
data["temperature"][day * 24 + h] -
temperatureScaleMin) / (
temperatureScaleMax -
temperatureScaleMin) * height
# Circles
temperatureVarianceAxis.add_artist(
AnnotationBbox(da, (maxTemperatureOfDay["timestamp"],
maxTemperatureOfDay["data"]),
xybox=(maxTemperatureOfDay["timestamp"],
maxTemperatureOfDay["data"]),
xycoords='data',
boxcoords=("data", "data"),
frameon=False))
temperatureVarianceAxis.add_artist(
AnnotationBbox(da, (minTemperatureOfDay["timestamp"],
minTemperatureOfDay["data"]),
xybox=(minTemperatureOfDay["timestamp"],
minTemperatureOfDay["data"]),
xycoords='data',
boxcoords=("data", "data"),
frameon=False))
# Max Temperature Labels
text = str(int(round(maxTemperatureOfDay["data"], 0))) + "°C"
f = plt.figure(
1
) # Temporary figure to get the dimensions of the text label
t = plt.text(0, 0, text, weight='bold')
temporaryLabel = t.get_window_extent(
renderer=f.canvas.get_renderer())
plt.figure(0) # Select Main figure again
# Check if text is fully within the day (x axis)
if maxTemperatureOfDay[
"xpixel"] - temporaryLabel.width / 2 < dayXPixelMin: # To far left
maxTemperatureOfDay[
"xpixel"] = dayXPixelMin + temporaryLabel.width / 2 + self.textShadowWidth / 2
if maxTemperatureOfDay[
"xpixel"] + temporaryLabel.width / 2 > dayXPixelMax: # To far right
maxTemperatureOfDay[
"xpixel"] = dayXPixelMax - temporaryLabel.width / 2 - self.textShadowWidth / 2
temperatureVarianceAxis.annotate(
text,
xycoords=('axes pixels'),
xy=(maxTemperatureOfDay["xpixel"],
maxTemperatureOfDay["ypixel"] + 8),
ha="center",
va="bottom",
color=colors["temperature-label"],
weight='bold',
path_effects=[
path_effects.withStroke(linewidth=self.textShadowWidth,
foreground="w")
])
# Min Temperature Labels
text = str(int(round(minTemperatureOfDay["data"], 0))) + "°C"
f = plt.figure(
1
) # Temporary figure to get the dimensions of the text label
t = plt.text(0, 0, text, weight='bold')
temporaryLabel = t.get_window_extent(
renderer=f.canvas.get_renderer())
plt.figure(0) # Select Main figure again
# Check if text is fully within the day (x axis)
if minTemperatureOfDay[
"xpixel"] - temporaryLabel.width / 2 < dayXPixelMin: # To far left
minTemperatureOfDay[
"xpixel"] = dayXPixelMin + temporaryLabel.width / 2 + self.textShadowWidth / 2
if minTemperatureOfDay[
"xpixel"] + temporaryLabel.width / 2 > dayXPixelMax: # To far right
minTemperatureOfDay[
"xpixel"] = dayXPixelMax - temporaryLabel.width / 2 - self.textShadowWidth / 2
temperatureVarianceAxis.annotate(
text,
xycoords=('axes pixels'),
xy=(minTemperatureOfDay["xpixel"],
minTemperatureOfDay["ypixel"] - 12),
ha="center",
va="top",
color=colors["temperature-label"],
weight='bold',
path_effects=[
path_effects.withStroke(linewidth=self.textShadowWidth,
foreground="w")
])
# Print day names
for day in range(0, data["noOfDays"]):
rainAxis.annotate(data['dayNames'][day],
xy=(day * xPixelsPerDay + xPixelsPerDay / 2,
-45),
xycoords='axes pixels',
ha="center",
weight='bold',
color=colors["x-axis"])
# Show y-axis units
rainAxis.annotate("mm\n/h",
linespacing=0.8,
xy=(width + 25, height + 12),
xycoords='axes pixels',
ha="center",
color=colors["rain-axis"])
rainAxis.annotate("°C",
xy=(-20, height + 10),
xycoords='axes pixels',
ha="center",
color=colors["temperature-axis"])
# Show Symbols above the graph
for i in range(0, len(data["symbols"]), symbolDivision):
symbolFile = os.path.dirname(
os.path.realpath(__file__)) + "/symbols/" + str(
data["symbols"][i]) + ".png"
if not os.path.isfile(symbolFile):
logging.warning(
"The symbol file %s seems to be missing. Please check the README.md!"
% symbolFile)
continue
symbolImage = mpimg.imread(symbolFile)
imagebox = OffsetImage(symbolImage, zoom=symbolZoom / 1.41 * 0.15)
xyPos = (
(data["symbolsTimestamps"][i] - data["symbolsTimestamps"][0]) /
(24 * 3600) + len(data["symbols"]) / 24 / 6 /
data["noOfDays"]) * xPixelsPerDay, height + 22
ab = AnnotationBbox(imagebox,
xy=xyPos,
xycoords='axes pixels',
frameon=False)
rainAxis.add_artist(ab)
# Show city name in graph
# TODO find a way to show the label in the background
if showCityName:
logging.debug("Adding city name to plot...")
text = fig.text(1 - 7 / width,
1 - 20 / height,
self.cityName,
color='gray',
ha='right',
transform=rainAxis.transAxes)
text.set_path_effects([
path_effects.Stroke(linewidth=self.textShadowWidth,
foreground='white'),
path_effects.Normal()
])
# Save the graph in a png image file
logging.debug("Saving graph to %s" % outputFilename)
plt.savefig(outputFilename, facecolor=colors["background"])
plt.close()
# Write Meta Data
if writeMetaData:
logging.debug("Saving Meta Data to %s" % writeMetaData)
metaData = {}
metaData['city'] = self.cityName
metaData['imageHeight'] = graphHeight
metaData['imageWidth'] = graphWidth
metaData['firstDayX'] = firstDayX
metaData['firstDayY'] = firstDayY
metaData['dayWidth'] = dayWidth
metaData['dayHeight'] = dayHeight
metaData['modelTimestamp'] = self.data[
"modelCalculationTimestamp"] # Seconds in UTC
with open(writeMetaData, 'w') as metaFile:
json.dump(metaData, metaFile)
def modelMap(grids, shakefile=None, suptitle=None, inventory_shapefile=None,
plotorder=None, maskthreshes=None, colormaps=None, boundaries=None,
zthresh=0, scaletype='continuous', lims=None, logscale=False,
ALPHA=0.7, maproads=True, mapcities=True, isScenario=False,
roadfolder=None, topofile=None, cityfile=None, oceanfile=None,
roadcolor='#6E6E6E', watercolor='#B8EEFF', countrycolor='#177F10',
outputdir=None, savepdf=True, savepng=True, showplots=False,
roadref='unknown', cityref='unknown', oceanref='unknown',
printparam=False, ds=True, dstype='mean', upsample=False):
"""
This function creates maps of mapio grid layers (e.g. liquefaction or
landslide models with their input layers)
All grids must use the same bounds
TO DO change so that all input layers do not have to have the same bounds,
test plotting multiple probability layers, and add option so that if PDF and
PNG aren't output, opens plot on screen using plt.show()
:param grids: Dictionary of N layers and metadata formatted like:
maplayers['layer name']={
'grid': mapio grid2D object,
'label': 'label for colorbar and top line of subtitle',
'type': 'output or input to model',
'description': 'detailed description of layer for subtitle'}.
Layer names must be unique.
:type name: Dictionary or Ordered dictionary - import collections;
grids = collections.OrderedDict()
:param shakefile: optional ShakeMap file (url or full file path) to extract information for labels and folder names
:type shakefile: Shakemap Event Dictionary
:param suptitle: This will be displayed at the top of the plots and in the
figure names
:type suptitle: string
:param plotorder: List of keys describing the order to plot the grids, if
None and grids is an ordered dictionary, it will use the order of the
dictionary, otherwise it will choose order which may be somewhat random
but it will always put a probability grid first
:type plotorder: list
:param maskthreshes: N x 1 array or list of lower thresholds for masking
corresponding to order in plotorder or order of OrderedDict if plotorder
is None. If grids is not an ordered dict and plotorder is not specified,
this will not work right. If None (default), nothing will be masked
:param colormaps: List of strings of matplotlib colormaps (e.g. cm.autumn_r)
corresponding to plotorder or order of dictionary if plotorder is None.
The list can contain both strings and None e.g. colormaps = ['cm.autumn',
None, None, 'cm.jet'] and None's will default to default colormap
:param boundaries: None to show entire study area, 'zoom' to zoom in on the
area of action (only works if there is a probability layer) using zthresh
as a threshold, or a dictionary defining lats and lons in the form of
boundaries.xmin = minlon, boundaries.xmax = maxlon, boundaries.ymin =
min lat, boundaries.ymax = max lat
:param zthresh: threshold for computing zooming bounds, only used if
boundaries = 'zoom'
:type zthresh: float
:param scaletype: Type of scale for plotting, 'continuous' or 'binned' -
will be reflected in colorbar
:type scaletype: string
:param lims: None or Nx1 list of tuples or numpy arrays corresponding to
plotorder defining the limits for saturating the colorbar (vmin, vmax) if
scaletype is continuous or the bins to use (clev) if scaletype if binned.
The list can contain tuples, arrays, and Nones, e.g. lims = [(0., 10.),
None, (0.1, 1.5), np.linspace(0., 1.5, 15)]. When None is specified, the
program will estimate the limits, when an array is specified but the scale
type is continuous, vmin will be set to min(array) and vmax will be set
to max(array)
:param lims: None or Nx1 list of Trues and Falses corresponding to
plotorder defining whether to use a linear or log scale (log10) for
plotting the layer. This will be reflected in the labels
:param ALPHA: Transparency for mapping, if there is a hillshade that will
plot below each layer, it is recommended to set this to at least 0.7
:type ALPHA: float
:param maproads: Whether to show roads or not, default True, but requires
that roadfile is specified and valid to work
:type maproads: boolean
:param mapcities: Whether to show cities or not, default True, but requires
that cityfile is specified and valid to work
:type mapcities: boolean
:param isScenario: Whether this is a scenario (True) or a real event (False)
(default False)
:type isScenario: boolean
:param roadfolder: Full file path to folder containing road shapefiles
:type roadfolder: string
:param topofile: Full file path to topography grid (GDAL compatible) - this
is only needed to make a hillshade if a premade hillshade is not specified
:type topofile: string
:param cityfile: Full file path to Pager file containing city & population
information
:type cityfile: string
:param roadcolor: Color to use for roads, if plotted, default #6E6E6E
:type roadcolor: Hex color or other matplotlib compatible way of defining
color
:param watercolor: Color to use for oceans, lakes, and rivers, default
#B8EEFF
:type watercolor: Hex color or other matplotlib compatible way of defining
color
:param countrycolor: Color for country borders, default #177F10
:type countrycolor: Hex color or other matplotlib compatible way of defining
color
:param outputdir: File path for outputting figures, if edict is defined, a
subfolder based on the event id will be created in this folder. If None,
will use current directory
:param savepdf: True to save pdf figure, False to not
:param savepng: True to save png figure, False to not
:param ds: True to allow downsampling for display (necessary when arrays
are quite large, False to not allow)
:param dstype: What function to use in downsampling, options are 'min',
'max', 'median', or 'mean'
:param upsample: True to upsample the layer to the DEM resolution for better
looking hillshades
:returns:
* PDF and/or PNG of map
* Downsampled and trimmed version of input grids. If no
modification was needed for plotting, this will be identical to grids but
without the metadata
"""
if suptitle is None:
suptitle = ' '
plt.ioff()
defaultcolormap = cm.jet
if shakefile is not None:
edict = ShakeGrid.load(shakefile, adjust='res').getEventDict()
temp = ShakeGrid.load(shakefile, adjust='res').getShakeDict()
edict['eventid'] = temp['shakemap_id']
edict['version'] = temp['shakemap_version']
else:
edict = None
# Get output file location
if outputdir is None:
print('No output location given, using current directory for outputs\n')
outputdir = os.getcwd()
if edict is not None:
outfolder = os.path.join(outputdir, edict['event_id'])
else:
outfolder = outputdir
if not os.path.isdir(outfolder):
os.makedirs(outfolder)
# Get plotting order, if not specified
if plotorder is None:
plotorder = list(grids.keys())
# Get boundaries to use for all plots
cut = True
if boundaries is None:
cut = False
keytemp = list(grids.keys())
boundaries = grids[keytemp[0]]['grid'].getGeoDict()
elif boundaries == 'zoom':
# Find probability layer (will just take the maximum bounds if there is
# more than one)
keytemp = list(grids.keys())
key1 = [key for key in keytemp if 'model' in key.lower()]
if len(key1) == 0:
print('Could not find model layer to use for zoom, using default boundaries')
keytemp = list(grids.keys())
boundaries = grids[keytemp[0]]['grid'].getGeoDict()
else:
lonmax = -1.e10
lonmin = 1.e10
latmax = -1.e10
latmin = 1.e10
for key in key1:
# get lat lons of areas affected and add, if no areas affected,
# switch to shakemap boundaries
temp = grids[key]['grid']
xmin, xmax, ymin, ymax = temp.getBounds()
lons = np.linspace(xmin, xmax, temp.getGeoDict().nx)
lats = np.linspace(ymax, ymin, temp.getGeoDict().ny) # backwards so it plots right
row, col = np.where(temp.getData() > float(zthresh))
lonmin = lons[col].min()
lonmax = lons[col].max()
latmin = lats[row].min()
latmax = lats[row].max()
# llons, llats = np.meshgrid(lons, lats) # make meshgrid
# llons1 = llons[temp.getData() > float(zthresh)]
# llats1 = llats[temp.getData() > float(zthresh)]
# if llons1.min() < lonmin:
# lonmin = llons1.min()
# if llons1.max() > lonmax:
# lonmax = llons1.max()
# if llats1.min() < latmin:
# latmin = llats1.min()
# if llats1.max() > latmax:
# latmax = llats1.max()
boundaries1 = {'dx': 100, 'dy': 100., 'nx': 100., 'ny': 100} # dummy fillers, only really care about bounds
if xmin < lonmin-0.15*(lonmax-lonmin):
boundaries1['xmin'] = lonmin-0.1*(lonmax-lonmin)
else:
boundaries1['xmin'] = xmin
if xmax > lonmax+0.15*(lonmax-lonmin):
boundaries1['xmax'] = lonmax+0.1*(lonmax-lonmin)
else:
boundaries1['xmax'] = xmax
if ymin < latmin-0.15*(latmax-latmin):
boundaries1['ymin'] = latmin-0.1*(latmax-latmin)
else:
boundaries1['ymin'] = ymin
if ymax > latmax+0.15*(latmax-latmin):
boundaries1['ymax'] = latmax+0.1*(latmax-latmin)
else:
boundaries1['ymax'] = ymax
boundaries = GeoDict(boundaries1, adjust='res')
else:
# SEE IF BOUNDARIES ARE SAME AS BOUNDARIES OF LAYERS
keytemp = list(grids.keys())
tempgdict = grids[keytemp[0]]['grid'].getGeoDict()
if np.abs(tempgdict.xmin-boundaries['xmin']) < 0.05 and \
np.abs(tempgdict.ymin-boundaries['ymin']) < 0.05 and \
np.abs(tempgdict.xmax-boundaries['xmax']) < 0.05 and \
np.abs(tempgdict.ymax - boundaries['ymax']) < 0.05:
print('Input boundaries are almost the same as specified boundaries, no cutting needed')
boundaries = tempgdict
cut = False
else:
try:
if boundaries['xmin'] > boundaries['xmax'] or \
boundaries['ymin'] > boundaries['ymax']:
print('Input boundaries are not usable, using default boundaries')
keytemp = list(grids.keys())
boundaries = grids[keytemp[0]]['grid'].getGeoDict()
cut = False
else:
# Build dummy GeoDict
boundaries = GeoDict({'xmin': boundaries['xmin'],
'xmax': boundaries['xmax'],
'ymin': boundaries['ymin'],
'ymax': boundaries['ymax'],
'dx': 100.,
'dy': 100.,
'ny': 100.,
'nx': 100.},
adjust='res')
except:
print('Input boundaries are not usable, using default boundaries')
keytemp = list(grids.keys())
boundaries = grids[keytemp[0]]['grid'].getGeoDict()
cut = False
# Pull out bounds for various uses
bxmin, bxmax, bymin, bymax = boundaries.xmin, boundaries.xmax, boundaries.ymin, boundaries.ymax
# Determine if need a single panel or multi-panel plot and if multi-panel,
# how many and how it will be arranged
fig = plt.figure()
numpanels = len(grids)
if numpanels == 1:
rowpan = 1
colpan = 1
# create the figure and axes instances.
fig.set_figwidth(5)
elif numpanels == 2 or numpanels == 4:
rowpan = np.ceil(numpanels/2.)
colpan = 2
fig.set_figwidth(13)
else:
rowpan = np.ceil(numpanels/3.)
colpan = 3
fig.set_figwidth(15)
if rowpan == 1:
fig.set_figheight(rowpan*6.0)
else:
fig.set_figheight(rowpan*5.3)
# Need to update naming to reflect the shakemap version once can get
# getHeaderData to work, add edict['version'] back into title, maybe
# shakemap id also?
fontsizemain = 14.
fontsizesub = 12.
fontsizesmallest = 10.
if rowpan == 1.:
fontsizemain = 12.
fontsizesub = 10.
fontsizesmallest = 8.
if edict is not None:
if isScenario:
title = edict['event_description']
else:
timestr = edict['event_timestamp'].strftime('%b %d %Y')
title = 'M%.1f %s v%i - %s' % (edict['magnitude'], timestr, edict['version'], edict['event_description'])
plt.suptitle(title+'\n'+suptitle, fontsize=fontsizemain)
else:
plt.suptitle(suptitle, fontsize=fontsizemain)
clear_color = [0, 0, 0, 0.0]
# Cut all of them and release extra memory
xbuff = (bxmax-bxmin)/10.
ybuff = (bymax-bymin)/10.
cutxmin = bxmin-xbuff
cutymin = bymin-ybuff
cutxmax = bxmax+xbuff
cutymax = bymax+ybuff
if cut is True:
newgrids = collections.OrderedDict()
for k, layer in enumerate(plotorder):
templayer = grids[layer]['grid']
try:
newgrids[layer] = {'grid': templayer.cut(cutxmin, cutxmax, cutymin, cutymax, align=True)}
except Exception as e:
print(('Cutting failed, %s, continuing with full layers' % e))
newgrids = grids
continue
del templayer
gc.collect()
else:
newgrids = grids
tempgdict = newgrids[list(grids.keys())[0]]['grid'].getGeoDict()
# Upsample layers to same as topofile if desired for better looking hillshades
if upsample is True and topofile is not None:
try:
topodict = GDALGrid.getFileGeoDict(topofile)
if topodict.dx >= tempgdict.dx or topodict.dy >= tempgdict.dy:
print('Upsampling not possible, resolution of results already smaller than DEM')
pass
else:
tempgdict1 = GeoDict({'xmin': tempgdict.xmin-xbuff,
'ymin': tempgdict.ymin-ybuff,
'xmax': tempgdict.xmax+xbuff,
'ymax': tempgdict.ymax+ybuff,
'dx': topodict.dx,
'dy': topodict.dy,
'nx': topodict.nx,
'ny': topodict.ny},
adjust='res')
tempgdict2 = tempgdict1.getBoundsWithin(tempgdict)
for k, layer in enumerate(plotorder):
newgrids[layer]['grid'] = newgrids[layer]['grid'].subdivide(tempgdict2)
except:
print('Upsampling failed, continuing')
# Downsample all of them for plotting, if needed, and replace them in
# grids (to save memory)
tempgrid = newgrids[list(grids.keys())[0]]['grid']
xsize = tempgrid.getGeoDict().nx
ysize = tempgrid.getGeoDict().ny
inchesx, inchesy = fig.get_size_inches()
divx = int(np.round(xsize/(500.*inchesx)))
divy = int(np.round(ysize/(500.*inchesy)))
xmin, xmax, ymin, ymax = tempgrid.getBounds()
gdict = tempgrid.getGeoDict() # Will be replaced if downsampled
del tempgrid
gc.collect()
if divx <= 1:
divx = 1
if divy <= 1:
divy = 1
if (divx > 1. or divy > 1.) and ds:
if dstype == 'max':
func = np.nanmax
elif dstype == 'min':
func = np.nanmin
elif dstype == 'med':
func = np.nanmedian
else:
func = np.nanmean
for k, layer in enumerate(plotorder):
layergrid = newgrids[layer]['grid']
dat = block_reduce(layergrid.getData().copy(),
block_size=(divy, divx),
cval=float('nan'),
func=func)
if k == 0:
lons = block_reduce(np.linspace(xmin, xmax, layergrid.getGeoDict().nx),
block_size=(divx,),
func=np.mean,
cval=float('nan'))
if math.isnan(lons[-1]):
lons[-1] = lons[-2] + (lons[1]-lons[0])
lats = block_reduce(np.linspace(ymax, ymin, layergrid.getGeoDict().ny),
block_size=(divy,),
func=np.mean,
cval=float('nan'))
if math.isnan(lats[-1]):
lats[-1] = lats[-2] + (lats[1]-lats[0])
gdict = GeoDict({'xmin': lons.min(),
'xmax': lons.max(),
'ymin': lats.min(),
'ymax': lats.max(),
'dx': np.abs(lons[1]-lons[0]),
'dy': np.abs(lats[1]-lats[0]),
'nx': len(lons),
'ny': len(lats)},
adjust='res')
newgrids[layer]['grid'] = Grid2D(dat, gdict)
del layergrid, dat
else:
lons = np.linspace(xmin, xmax, xsize)
lats = np.linspace(ymax, ymin, ysize) # backwards so it plots right side up
#make meshgrid
llons1, llats1 = np.meshgrid(lons, lats)
# See if there is an oceanfile for masking
bbox = PolygonSH(((cutxmin, cutymin), (cutxmin, cutymax), (cutxmax, cutymax), (cutxmax, cutymin)))
if oceanfile is not None:
try:
f = fiona.open(oceanfile)
oc = next(f)
f.close
shapes = shape(oc['geometry'])
# make boundaries into a shape
ocean = shapes.intersection(bbox)
except:
print('Not able to read specified ocean file, will use default ocean masking')
oceanfile = None
if inventory_shapefile is not None:
try:
f = fiona.open(inventory_shapefile)
invshp = list(f.items(bbox=(bxmin, bymin, bxmax, bymax)))
f.close()
inventory = [shape(inv[1]['geometry']) for inv in invshp]
except:
print('unable to read inventory shapefile specified, will not plot inventory')
inventory_shapefile = None
# # Find cities that will be plotted
if mapcities is True and cityfile is not None:
try:
mycity = BasemapCities.loadFromGeoNames(cityfile=cityfile)
bcities = mycity.limitByBounds((bxmin, bxmax, bymin, bymax))
#bcities = bcities.limitByPopulation(40000)
bcities = bcities.limitByGrid(nx=4, ny=4, cities_per_grid=2)
except:
print('Could not read in cityfile, not plotting cities')
mapcities = False
cityfile = None
# Load in topofile
if topofile is not None:
try:
topomap = GDALGrid.load(topofile, resample=True, method='linear', samplegeodict=gdict)
except:
topomap = GMTGrid.load(topofile, resample=True, method='linear', samplegeodict=gdict)
topodata = topomap.getData().copy()
# mask oceans if don't have ocean shapefile
if oceanfile is None:
topodata = maskoceans(llons1, llats1, topodata, resolution='h', grid=1.25, inlands=True)
else:
print('no hillshade is possible\n')
topomap = None
topodata = None
# Load in roads, if needed
if maproads is True and roadfolder is not None:
try:
roadslist = []
for folder in os.listdir(roadfolder):
road1 = os.path.join(roadfolder, folder)
shpfiles = glob.glob(os.path.join(road1, '*.shp'))
if len(shpfiles):
shpfile = shpfiles[0]
f = fiona.open(shpfile)
shapes = list(f.items(bbox=(bxmin, bymin, bxmax, bymax)))
for shapeid, shapedict in shapes:
roadslist.append(shapedict)
f.close()
except:
print('Not able to plot roads')
roadslist = None
val = 1
for k, layer in enumerate(plotorder):
layergrid = newgrids[layer]['grid']
if 'label' in list(grids[layer].keys()):
label1 = grids[layer]['label']
else:
label1 = layer
try:
sref = grids[layer]['description']['name']
except:
sref = None
ax = fig.add_subplot(rowpan, colpan, val)
val += 1
clat = bymin + (bymax-bymin)/2.0
clon = bxmin + (bxmax-bxmin)/2.0
# setup of basemap ('lcc' = lambert conformal conic).
# use major and minor sphere radii from WGS84 ellipsoid.
m = Basemap(llcrnrlon=bxmin, llcrnrlat=bymin, urcrnrlon=bxmax, urcrnrlat=bymax,
rsphere=(6378137.00, 6356752.3142),
resolution='l', area_thresh=1000., projection='lcc',
lat_1=clat, lon_0=clon, ax=ax)
x1, y1 = m(llons1, llats1) # get projection coordinates
axsize = ax.get_window_extent().transformed(fig.dpi_scale_trans.inverted())
if k == 0:
wid, ht = axsize.width, axsize.height
if colormaps is not None and \
len(colormaps) == len(newgrids) and \
colormaps[k] is not None:
palette = colormaps[k]
else: # Find preferred default color map for each type of layer
if 'prob' in layer.lower() or 'pga' in layer.lower() or \
'pgv' in layer.lower() or 'cohesion' in layer.lower() or \
'friction' in layer.lower() or 'fs' in layer.lower():
palette = cm.jet
elif 'slope' in layer.lower():
palette = cm.gnuplot2
elif 'precip' in layer.lower():
palette = cm2.s3pcpn
else:
palette = defaultcolormap
if topodata is not None:
if k == 0:
ptopo = m.transform_scalar(
np.flipud(topodata), lons+0.5*gdict.dx,
lats[::-1]-0.5*gdict.dy, np.round(300.*wid),
np.round(300.*ht), returnxy=False, checkbounds=False,
order=1, masked=False)
#use lightsource class to make our shaded topography
ls = LightSource(azdeg=135, altdeg=45)
ls1 = LightSource(azdeg=120, altdeg=45)
ls2 = LightSource(azdeg=225, altdeg=45)
intensity1 = ls1.hillshade(ptopo, fraction=0.25, vert_exag=1.)
intensity2 = ls2.hillshade(ptopo, fraction=0.25, vert_exag=1.)
intensity = intensity1*0.5 + intensity2*0.5
#hillshm_im = m.transform_scalar(np.flipud(hillshm), lons, lats[::-1], np.round(300.*wid), np.round(300.*ht), returnxy=False, checkbounds=False, order=0, masked=False)
#m.imshow(hillshm_im, cmap='Greys', vmin=0., vmax=3., zorder=1, interpolation='none') # vmax = 3 to soften colors to light gray
#m.pcolormesh(x1, y1, hillshm, cmap='Greys', linewidth=0., rasterized=True, vmin=0., vmax=3., edgecolors='none', zorder=1);
# plt.draw()
# Get the data
dat = layergrid.getData().copy()
# mask out anything below any specified thresholds
# Might need to move this up to before downsampling...might give illusion of no hazard in places where there is some that just got averaged out
if maskthreshes is not None and len(maskthreshes) == len(newgrids):
if maskthreshes[k] is not None:
dat[dat <= maskthreshes[k]] = float('NaN')
dat = np.ma.array(dat, mask=np.isnan(dat))
if logscale is not False and len(logscale) == len(newgrids):
if logscale[k] is True:
dat = np.log10(dat)
label1 = r'$log_{10}$(' + label1 + ')'
if scaletype.lower() == 'binned':
# Find order of range to know how to scale
order = np.round(np.log(np.nanmax(dat) - np.nanmin(dat)))
if order < 1.:
scal = 10**-order
else:
scal = 1.
if lims is None or len(lims) != len(newgrids):
clev = (np.linspace(np.floor(scal*np.nanmin(dat)), np.ceil(scal*np.nanmax(dat)), 10))/scal
else:
if lims[k] is None:
clev = (np.linspace(np.floor(scal*np.nanmin(dat)), np.ceil(scal*np.nanmax(dat)), 10))/scal
else:
clev = lims[k]
# Adjust to colorbar levels
dat[dat < clev[0]] = clev[0]
for j, level in enumerate(clev[:-1]):
dat[(dat >= clev[j]) & (dat < clev[j+1])] = clev[j]
# So colorbar saturates at top
dat[dat > clev[-1]] = clev[-1]
#panelhandle = m.contourf(x1, y1, datm, clev, cmap=palette, linewidth=0., alpha=ALPHA, rasterized=True)
vmin = clev[0]
vmax = clev[-1]
else:
if lims is not None and len(lims) == len(newgrids):
if lims[k] is None:
vmin = np.nanmin(dat)
vmax = np.nanmax(dat)
else:
vmin = lims[k][0]
vmax = lims[k][-1]
else:
vmin = np.nanmin(dat)
vmax = np.nanmax(dat)
# Mask out cells overlying oceans or block with a shapefile if available
if oceanfile is None:
dat = maskoceans(llons1, llats1, dat, resolution='h', grid=1.25, inlands=True)
else:
#patches = []
if type(ocean) is PolygonSH:
ocean = [ocean]
for oc in ocean:
patch = getProjectedPatch(oc, m, edgecolor="#006280", facecolor=watercolor, lw=0.5, zorder=4.)
#x, y = m(oc.exterior.xy[0], oc.exterior.xy[1])
#xy = zip(x, y)
#patch = Polygon(xy, facecolor=watercolor, edgecolor="#006280", lw=0.5, zorder=4.)
##patches.append(Polygon(xy, facecolor=watercolor, edgecolor=watercolor, zorder=500.))
ax.add_patch(patch)
##ax.add_collection(PatchCollection(patches))
if inventory_shapefile is not None:
for in1 in inventory:
if 'point' in str(type(in1)):
x, y = in1.xy
x = x[0]
y = y[0]
m.scatter(x, y, c='m', s=50, latlon=True, marker='^',
zorder=100001)
else:
x, y = m(in1.exterior.xy[0], in1.exterior.xy[1])
xy = list(zip(x, y))
patch = Polygon(xy, facecolor='none', edgecolor='k', lw=0.5, zorder=10.)
#patches.append(Polygon(xy, facecolor=watercolor, edgecolor=watercolor, zorder=500.))
ax.add_patch(patch)
palette.set_bad(clear_color, alpha=0.0)
# Plot it up
dat_im = m.transform_scalar(
np.flipud(dat), lons+0.5*gdict.dx, lats[::-1]-0.5*gdict.dy,
np.round(300.*wid), np.round(300.*ht), returnxy=False,
checkbounds=False, order=0, masked=True)
if topodata is not None: # Drape over hillshade
#turn data into an RGBA image
cmap = palette
#adjust data so scaled between vmin and vmax and between 0 and 1
dat1 = dat_im.copy()
dat1[dat1 < vmin] = vmin
dat1[dat1 > vmax] = vmax
dat1 = (dat1 - vmin)/(vmax-vmin)
rgba_img = cmap(dat1)
maskvals = np.dstack((dat1.mask, dat1.mask, dat1.mask))
rgb = np.squeeze(rgba_img[:, :, 0:3])
rgb[maskvals] = 1.
draped_hsv = ls.blend_hsv(rgb, np.expand_dims(intensity, 2))
m.imshow(draped_hsv, zorder=3., interpolation='none')
# This is just a dummy layer that will be deleted to make the
# colorbar look right
panelhandle = m.imshow(dat_im, cmap=palette, zorder=0.,
vmin=vmin, vmax=vmax)
else:
panelhandle = m.imshow(dat_im, cmap=palette, zorder=3.,
vmin=vmin, vmax=vmax, interpolation='none')
#panelhandle = m.pcolormesh(x1, y1, dat, linewidth=0., cmap=palette, vmin=vmin, vmax=vmax, alpha=ALPHA, rasterized=True, zorder=2.);
#panelhandle.set_edgecolors('face')
# add colorbar
cbfmt = '%1.1f'
if vmax is not None and vmin is not None:
if (vmax - vmin) < 1.:
cbfmt = '%1.2f'
elif vmax > 5.: # (vmax - vmin) > len(clev):
cbfmt = '%1.0f'
#norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)
if scaletype.lower() == 'binned':
cbar = fig.colorbar(panelhandle, spacing='proportional',
ticks=clev, boundaries=clev, fraction=0.036,
pad=0.04, format=cbfmt, extend='both')
#cbar1 = ColorbarBase(cbar.ax, cmap=palette, norm=norm, spacing='proportional', ticks=clev, boundaries=clev, fraction=0.036, pad=0.04, format=cbfmt, extend='both', extendfrac='auto')
else:
cbar = fig.colorbar(panelhandle, fraction=0.036, pad=0.04,
extend='both', format=cbfmt)
#cbar1 = ColorbarBase(cbar.ax, cmap=palette, norm=norm, fraction=0.036, pad=0.04, extend='both', extendfrac='auto', format=cbfmt)
if topodata is not None:
panelhandle.remove()
cbar.set_label(label1, fontsize=10)
cbar.ax.tick_params(labelsize=8)
parallels = m.drawparallels(getMapLines(bymin, bymax, 3),
labels=[1, 0, 0, 0], linewidth=0.5,
labelstyle='+/-', fontsize=9, xoffset=-0.8,
color='gray', zorder=100.)
m.drawmeridians(getMapLines(bxmin, bxmax, 3), labels=[0, 0, 0, 1],
linewidth=0.5, labelstyle='+/-', fontsize=9,
color='gray', zorder=100.)
for par in parallels:
try:
parallels[par][1][0].set_rotation(90)
except:
pass
#draw roads on the map, if they were provided to us
if maproads is True and roadslist is not None:
try:
for road in roadslist:
try:
xy = list(road['geometry']['coordinates'])
roadx, roady = list(zip(*xy))
mapx, mapy = m(roadx, roady)
m.plot(mapx, mapy, roadcolor, lw=0.5, zorder=9)
except:
continue
except Exception as e:
print(('Failed to plot roads, %s' % e))
#add city names to map
if mapcities is True and cityfile is not None:
try:
fontname = 'Arial'
fontsize = 8
if k == 0: # Only need to choose cities first time and then apply to rest
fcities = bcities.limitByMapCollision(
m, fontname=fontname, fontsize=fontsize)
ctlats, ctlons, names = fcities.getCities()
cxis, cyis = m(ctlons, ctlats)
for ctlat, ctlon, cxi, cyi, name in zip(ctlats, ctlons, cxis, cyis, names):
m.scatter(ctlon, ctlat, c='k', latlon=True, marker='.',
zorder=100000)
ax.text(cxi, cyi, name, fontname=fontname,
fontsize=fontsize, zorder=100000)
except Exception as e:
print('Failed to plot cities, %s' % e)
#draw star at epicenter
plt.sca(ax)
if edict is not None:
elat, elon = edict['lat'], edict['lon']
ex, ey = m(elon, elat)
plt.plot(ex, ey, '*', markeredgecolor='k', mfc='None', mew=1.0,
ms=15, zorder=10000.)
m.drawmapboundary(fill_color=watercolor)
m.fillcontinents(color=clear_color, lake_color=watercolor)
m.drawrivers(color=watercolor)
##m.drawcoastlines()
#draw country boundaries
m.drawcountries(color=countrycolor, linewidth=1.0)
#add map scale
m.drawmapscale((bxmax+bxmin)/2., (bymin+(bymax-bymin)/9.), clon, clat, np.round((((bxmax-bxmin)*111)/5)/10.)*10, barstyle='fancy', zorder=10)
# Add border
autoAxis = ax.axis()
rec = Rectangle((autoAxis[0]-0.7, autoAxis[2]-0.2), (autoAxis[1]-autoAxis[0])+1, (autoAxis[3]-autoAxis[2])+0.4, fill=False, lw=1, zorder=1e8)
rec = ax.add_patch(rec)
rec.set_clip_on(False)
plt.draw()
if sref is not None:
label2 = '%s\nsource: %s' % (label1, sref) # '%s\n' % label1 + r'{\fontsize{10pt}{3em}\selectfont{}%s}' % sref #
else:
label2 = label1
plt.title(label2, axes=ax, fontsize=fontsizesub)
#draw scenario watermark, if scenario
if isScenario:
plt.sca(ax)
cx, cy = m(clon, clat)
plt.text(cx, cy, 'SCENARIO', rotation=45, alpha=0.10, size=72, ha='center', va='center', color='red')
#if ds: # Could add this to print "downsampled" on map
# plt.text()
if k == 1 and rowpan == 1:
# adjust single level plot
axsize = ax.get_window_extent().transformed(fig.dpi_scale_trans.inverted())
ht2 = axsize.height
fig.set_figheight(ht2*1.6)
else:
plt.tight_layout()
# Make room for suptitle - tight layout doesn't account for it
plt.subplots_adjust(top=0.92)
if printparam is True:
try:
fig = plt.gcf()
dictionary = grids['model']['description']['parameters']
paramstring = 'Model parameters: '
halfway = np.ceil(len(dictionary)/2.)
for i, key in enumerate(dictionary):
if i == halfway and colpan == 1:
paramstring += '\n'
paramstring += ('%s = %s; ' % (key, dictionary[key]))
print(paramstring)
fig.text(0.01, 0.015, paramstring, fontsize=fontsizesmallest)
plt.draw()
except:
print('Could not display model parameters')
if edict is not None:
eventid = edict['eventid']
else:
eventid = ''
time1 = datetime.datetime.utcnow().strftime('%d%b%Y_%H%M')
outfile = os.path.join(outfolder, '%s_%s_%s.pdf' % (eventid, suptitle, time1))
pngfile = os.path.join(outfolder, '%s_%s_%s.png' % (eventid, suptitle, time1))
if savepdf is True:
print('Saving map output to %s' % outfile)
plt.savefig(outfile, dpi=300)
if savepng is True:
print('Saving map output to %s' % pngfile)
plt.savefig(pngfile)
if showplots is True:
plt.show()
else:
plt.close(fig)
return newgrids
def highlight_artist(self, val, artist=None):
from ifigure.matplotlib_mod.art3d_gl import Poly3DCollectionGL
from ifigure.matplotlib_mod.art3d_gl import Line3DCollectionGL
figure=self.get_figpage()._artists[0]
ax = self.get_figaxes()
if artist is None:
alist=self._artists
else:
alist=artist
if val == True:
if self._parent is None: return
container = self.get_container()
if container is None: return
if isinstance(alist[0], Poly3DCollectionGL):
hl = alist[0].add_hl_mask()
for item in hl:
alist[0].figobj_hl.append(item)
else:
de = self.get_data_extent()
x=(de[0], de[1],de[1],de[0],de[0])
y=(de[2], de[2],de[3],de[3],de[2])
facecolor='k'
if isinstance(alist[0], Poly3DCollectionGL):
hl = alist[0].make_hl_artist(container)
facecolor = 'none'
self._hit_path = None
elif isinstance(alist[0], Line3DCollectionGL):
hl = alist[0].make_hl_artist(container)
facecolor = 'none'
self._hit_path = None
else:
hl= container.plot(x, y, marker='s',
color='k', linestyle='None',
markerfacecolor='None',
markeredgewidth = 0.5,
scalex=False, scaley=False)
for item in hl:
alist[0].figobj_hl.append(item)
if self._hit_path is not None:
v = self._hit_path.vertices
hl= container.plot(v[:,0], v[:,1], marker='s',
color='k', linestyle='None',
markerfacecolor='None',
markeredgewidth = 0.5,
scalex=False, scaley=False)
for item in hl:
alist[0].figobj_hl.append(item)
hlp = Rectangle((de[0],de[2]),
de[1]-de[0],
de[3]-de[2],
alpha=0.3, facecolor=facecolor,
figure = figure,
transform= container.transData)
if ax is not None:
x0, y0 = ax._artists[0].transAxes.transform((0,0))
x1, y1 = ax._artists[0].transAxes.transform((1,1))
bbox = Bbox([[x0,y0],[x1,y1]])
hlp.set_clip_box(bbox)
hlp.set_clip_on(True)
figure.patches.append(hlp)
alist[0].figobj_hl.append(hlp)
else:
for a in alist:
if len(a.figobj_hl) == 0: continue
for hl in a.figobj_hl[:-1]:
hl.remove()
if isinstance(alist[0], Poly3DCollectionGL):
a.figobj_hl[-1].remove()
else:
figure.patches.remove(a.figobj_hl[-1])
a.figobj_hl=[]
def plot_beast_ifit(filters, waves, stats, pdf1d_hdu):
# setup the plot grid
gs = gridspec.GridSpec(4,
4,
height_ratios=[1.0, 1.0, 1.0, 1.0],
width_ratios=[1.0, 1.0, 1.0, 1.0])
ax = []
# plots for the 1D PDFs
for j in range(2):
for i in range(4):
ax.append(plt.subplot(gs[j + 2, i]))
# now for the big SED plot
ax.append(plt.subplot(gs[0:2, 0:3]))
# plot the SED
#print(np.sort(stats.colnames))
n_filters = len(filters)
# get the observations
waves *= 1e-4
obs_flux = np.empty((n_filters), dtype=np.float)
mod_flux = np.empty((n_filters, 3), dtype=np.float)
mod_flux_nd = np.empty((n_filters, 3), dtype=np.float)
mod_flux_wbias = np.empty((n_filters, 3), dtype=np.float)
k = starnum
c = ap_SkyCoord(ra=stats['RA'][k] * ap_units.degree,
dec=stats['DEC'][k] * ap_units.degree,
frame='icrs')
corname = ('PHAT J' + c.ra.to_string(unit=ap_units.hourangle,
sep="",
precision=2,
alwayssign=False,
pad=True) +
c.dec.to_string(sep="", precision=2, alwayssign=True, pad=True))
for i, cfilter in enumerate(filters):
obs_flux[i] = stats[cfilter][k]
mod_flux[i, 0] = np.power(10.0, stats['log' + cfilter + '_wd_p50'][k])
mod_flux[i, 1] = np.power(10.0, stats['log' + cfilter + '_wd_p16'][k])
mod_flux[i, 2] = np.power(10.0, stats['log' + cfilter + '_wd_p84'][k])
mod_flux_nd[i, 0] = np.power(10.0,
stats['log' + cfilter + '_nd_p50'][k])
mod_flux_nd[i, 1] = np.power(10.0,
stats['log' + cfilter + '_nd_p16'][k])
mod_flux_nd[i, 2] = np.power(10.0,
stats['log' + cfilter + '_nd_p84'][k])
if 'log' + cfilter + '_wd_bias_p50' in stats.colnames:
mod_flux_wbias[i, 0] = np.power(
10.0, stats['log' + cfilter + '_wd_bias_p50'][k])
mod_flux_wbias[i, 1] = np.power(
10.0, stats['log' + cfilter + '_wd_bias_p16'][k])
mod_flux_wbias[i, 2] = np.power(
10.0, stats['log' + cfilter + '_wd_bias_p84'][k])
ax[8].plot(waves, obs_flux, 'ko', label='observed')
if 'log' + filters[0] + '_wd_bias_p50' in stats.colnames:
ax[8].plot(waves,
mod_flux_wbias[:, 0],
'b-',
label='stellar+dust+bias')
ax[8].fill_between(waves,
mod_flux_wbias[:, 1],
mod_flux_wbias[:, 2],
color='b',
alpha=0.3)
ax[8].plot(waves, mod_flux[:, 0], 'r-', label='stellar+dust')
ax[8].fill_between(waves,
mod_flux[:, 1],
mod_flux[:, 2],
color='r',
alpha=0.2)
ax[8].plot(waves, mod_flux_nd[:, 0], 'y-', label='stellar only')
ax[8].fill_between(waves,
mod_flux_nd[:, 1],
mod_flux_nd[:, 2],
color='y',
alpha=0.1)
ax[8].legend(loc='upper right', bbox_to_anchor=(1.25, 1.025))
ax[8].set_ylabel(r'Flux [ergs s$^{-1}$ cm$^{-2}$ $\AA^{-1}$]')
ax[8].set_yscale('log')
ax[8].set_xscale('log')
ax[8].text(0.5,
-0.01,
r'$\lambda$ [$\AA$]',
transform=ax[8].transAxes,
va='top')
ax[8].set_xlim(0.2, 2.0)
ax[8].set_xticks([0.2, 0.3, 0.4, 0.5, 0.8, 0.9, 1.0, 2.0])
ax[8].get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())
ax[8].text(0.05,
0.95,
corname,
transform=ax[8].transAxes,
va='top',
ha='left')
# add the text results
keys = ['Av', 'M_ini', 'logA', 'Rv', 'f_A', 'Z', 'logT', 'logg', 'logL']
dispnames = [
'A(V)', 'log(M)', 'log(t)', 'R(V)', r'f$_\mathcal{A}$', 'Z',
r'log(T$_\mathrm{eff})$', 'log(g)', 'log(L)'
]
laby = 0.72
ty = np.linspace(laby - 0.07, 0.1, num=len(keys))
ty[3:] -= 0.025
ty[6:] -= 0.025
tx = [1.12, 1.2, 1.3]
for i in range(len(keys)):
ax[8].text(tx[0],
ty[i],
dispnames[i],
ha='right',
transform=ax[8].transAxes)
ax[8].text(tx[1],
ty[i],
disp_str(stats, starnum, keys[i]),
ha='center',
color='m',
transform=ax[8].transAxes)
best_val = stats[keys[i] + '_Best'][k]
if keys[i] == 'M_ini':
best_val = np.log10(best_val)
ax[8].text(tx[2],
ty[i],
'$' + "{0:.2f}".format(best_val) + '$',
ha='center',
color='c',
transform=ax[8].transAxes)
ax[8].text(tx[0], laby, 'Param', ha='right', transform=ax[8].transAxes)
ax[8].text(tx[1],
laby,
'50%$\pm$33%',
ha='center',
color='k',
transform=ax[8].transAxes)
ax[8].text(tx[2],
laby,
'Best',
color='k',
ha='center',
transform=ax[8].transAxes)
# now draw boxes around the different kinds of parameters
tax = ax[8]
# primary
rec = Rectangle((tx[0] - 0.1, ty[2] - 0.02),
tx[2] - tx[0] + 0.15, (ty[0] - ty[2]) * 1.5,
fill=False,
lw=2,
transform=tax.transAxes,
ls='dashed')
rec = tax.add_patch(rec)
rec.set_clip_on(False)
# secondary
rec = Rectangle((tx[0] - 0.1, ty[5] - 0.02),
tx[2] - tx[0] + 0.15, (ty[3] - ty[5]) * 1.5,
fill=False,
lw=2,
transform=tax.transAxes,
ls='dotted')
rec = tax.add_patch(rec)
rec.set_clip_on(False)
# derived
rec = Rectangle((tx[0] - 0.1, ty[8] - 0.02),
tx[2] - tx[0] + 0.15, (ty[6] - ty[8]) * 1.5,
fill=False,
lw=2,
transform=tax.transAxes,
ls='dashdot')
rec = tax.add_patch(rec)
rec.set_clip_on(False)
# padding for rectangles of 1D PDFs
pad = 0.1
# plot the primary parameter 1D PDFs
plot_1dpdf(ax[0], pdf1d_hdu, 'Av', 'A(V)', starnum, stats=stats)
plot_1dpdf(ax[1],
pdf1d_hdu,
'M_ini',
'log(M)',
starnum,
logx=True,
stats=stats)
plot_1dpdf(ax[2], pdf1d_hdu, 'logA', 'log(t)', starnum, stats=stats)
# draw a box around them and label
tax = ax[0]
rec = Rectangle((-1.75 * pad, -pad),
3 * (1.0 + pad) + 1.5 * pad,
1.0 + 1.5 * pad,
fill=False,
lw=2,
transform=tax.transAxes,
ls='dashed')
rec = tax.add_patch(rec)
rec.set_clip_on(False)
tax.text(-2. * pad,
0.5,
'Primary',
transform=tax.transAxes,
rotation='vertical',
fontstyle='oblique',
va='center',
ha='right')
tax.text(0.0,
0.5,
'Probability',
transform=tax.transAxes,
rotation='vertical',
va='center',
ha='right')
# plot the secondary parameter 1D PDFs
plot_1dpdf(ax[4], pdf1d_hdu, 'Rv', 'R(V)', starnum, stats=stats)
plot_1dpdf(ax[5],
pdf1d_hdu,
'f_A',
r'f$_\mathcal{A}$',
starnum,
stats=stats)
plot_1dpdf(ax[6], pdf1d_hdu, 'Z', 'Z', starnum, stats=stats)
# draw a box around them
tax = ax[4]
rec = Rectangle((-1.75 * pad, -pad),
3 * (1.0 + pad) + 1.5 * pad,
1.0 + 1.5 * pad,
fill=False,
lw=2,
transform=tax.transAxes,
ls='dotted')
rec = tax.add_patch(rec)
rec.set_clip_on(False)
tax.text(-2 * pad,
0.5,
'Secondary',
transform=tax.transAxes,
rotation='vertical',
fontstyle='oblique',
va='center',
ha='right')
tax.text(0.0,
0.5,
'Probability',
transform=tax.transAxes,
rotation='vertical',
va='center',
ha='right')
# plot the derived parameter 1D PDFs
plot_1dpdf(ax[3],
pdf1d_hdu,
'logT',
r'log(T$_\mathrm{eff})$',
starnum,
stats=stats)
plot_1dpdf(ax[7], pdf1d_hdu, 'logg', 'log(g)', starnum, stats=stats)
# draw a box around them
tax = ax[7]
rec = Rectangle((-0.25 * pad, -pad),
1.0 + 0.5 * pad,
2 * (1.0 + 2. * pad) - 0.125 * pad,
fill=False,
lw=2,
transform=tax.transAxes,
ls='dashdot')
rec = tax.add_patch(rec)
rec.set_clip_on(False)
tax.text(0.5,
2 * (1.0 + 2 * pad) - 1.0 * pad,
'Derived',
transform=tax.transAxes,
rotation='horizontal',
fontstyle='oblique',
va='bottom',
ha='center')
# optimize the figure layout
plt.tight_layout(h_pad=2.0, w_pad=1.0)
