def plot_prediction_over_epochs_plt():
"""
Plots the polygon generated by generate_vertices() using matplotlib.
"""
generate_vertices()
results_dir = '../results/UKDALE-RNN-lr=1e-5-2018-01-26 14:33:59'
verts = pickle.load(open(os.path.join(results_dir, 'vertices.pkl'), 'rb'))
zs = pickle.load(open(os.path.join(results_dir, 'zs.pkl'), 'rb'))
ys = pickle.load(open(os.path.join(results_dir, 'ys.pkl'), 'rb'))
fig = plt.figure(figsize=(18, 8))
ax = fig.gca(projection='3d')
poly = PolyCollection(verts[::], facecolors='w')
poly.set_edgecolor((0, 0, 0, .5))
poly.set_facecolor((.9, .9, 1, 0.3))
ax.add_collection3d(poly, zs=zs[::], zdir='y')
ax.set_xlabel('timestamps')
ax.set_xlim3d(0, ys.shape[0])
ax.set_ylabel('epochs')
ax.set_ylim3d(0, 320)
ax.set_zlabel('power')
ax.set_zlim3d(0, 2000)
ax.view_init(-40, -94)
plt.savefig(os.path.join(results_dir, 'prediction_over_epochs.png'))
def plotmesh_SubTh2simp2D(q, me, **kwargs):
lim = kwargs.pop('lim', True)
color = check_color(kwargs.pop('color', 'Blue'))
kwargs['edgecolor'] = check_color(kwargs.pop('edgecolor', color))
facecolor = check_color(kwargs.pop('facecolor', None))
if facecolor is None:
facecolor = 'none'
z = kwargs.pop('z', None)
if z is None:
fig = plt.gcf()
ax = fig.gca()
polys = PolyCollection(q[me], **kwargs)
polys.set_facecolor(facecolor)
coll = ax.add_collection(polys)
if lim:
box = np.array([
np.min(q[:, 0]),
np.max(q[:, 0]),
np.min(q[:, 1]),
np.max(q[:, 1])
])
set_axes(ax, box)
return coll
#return plt.triplot(q[:,0],q[:,1],triangles=me,**kwargs)
nq = q.shape[0]
assert isinstance(z, np.ndarray) and z.shape[0] == nq
z.resize((nq, 1))
return plotmesh_SubTh2simp3D(np.concatenate((q, z), axis=1),
me,
lim=lim,
**kwargs)
def plot_triangles(p,
adjustLowerLeft=False,
values=None,
values_cmap=matplotlib.cm.jet,
edgecolors='k'):
""" Add mesh triangles to a pyplot plot
@param p = object holding sww vertex information (from util.get_output)
@param adjustLowerLeft = if TRUE, use spatial coordinates, otherwise use ANUGA internal coordinates
@param values = list or array of length(p.vols), or None. All triangles are assigned this value (for face plotting colors).
@param values_cmap = colormap for faces [e.g. values_cmap = matplotlib.cm.get_cmap('spectral')]
@param edgecolors = edge color for polygons (using matplotlib.colors notation). Use 'none' for no color
"""
import matplotlib
from matplotlib import pyplot as pyplot
from matplotlib.collections import PolyCollection
x0 = p.xllcorner
y0 = p.yllcorner
# Make vertices for PolyCollection Object
vertices = []
for i in range(len(p.vols)):
k1 = p.vols[i][0]
k2 = p.vols[i][1]
k3 = p.vols[i][2]
tri_coords = numpy.array([[p.x[k1], p.y[k1]], [p.x[k2], p.y[k2]],
[p.x[k3], p.y[k3]]])
if adjustLowerLeft:
tri_coords[:, 0] = tri_coords[:, 0] + x0
tri_coords[:, 1] = tri_coords[:, 1] + y0
vertices.append(tri_coords)
# Make PolyCollection
if values is None:
all_poly = PolyCollection(vertices,
array=numpy.zeros(len(vertices)),
edgecolors=edgecolors)
all_poly.set_facecolor('none')
else:
try:
lv = len(values)
except:
values = numpy.array(len(p.vols) * [values])
lv = len(values)
msg = 'len(values) must be the same as len(p.vols) (or values can be a constant)'
assert lv == len(p.vols), msg
all_poly = PolyCollection(vertices,
array=values,
cmap=values_cmap,
edgecolors=edgecolors)
# Add to plot
# FIXME: To see the triangles, this might require that the user does
# something else to the plot?
pyplot.gca().add_collection(all_poly)
def plot_grid_2d(g, cell_value, ax, **kwargs):
"""
Plot the 2d grid g to the axis ax, with cell_value represented by the cell coloring.
keywargs: Keyword arguments:
color_map: Limits of the cell value color axis.
linewidth: Width of faces in 2d and edges in 3d.
rgb: Color map weights. Defaults to [1, 0, 0].
alpha: Transparency of the plot.
cells: boolean array with length number of cells. Only plot cells c
where cells[c]=True
"""
faces, _, _ = sps.find(g.cell_faces)
nodes, _, _ = sps.find(g.face_nodes)
alpha = kwargs.get("alpha", 1)
if kwargs.get("color_map"):
scalar_map = kwargs["color_map"]
@pp.time_logger(sections=module_sections)
def color_face(value):
return scalar_map.to_rgba(value, alpha)
else:
cell_value = np.zeros(g.num_cells)
rgb = kwargs.get("rgb", [1, 0, 0])
@pp.time_logger(sections=module_sections)
def color_face(value):
return np.r_[rgb, alpha]
cells = kwargs.get("cells", np.ones(g.num_cells, dtype=bool))
for c in np.arange(g.num_cells):
if not cells[c]:
continue
loc_f = slice(g.cell_faces.indptr[c], g.cell_faces.indptr[c + 1])
faces_loc = faces[loc_f]
loc_n = g.face_nodes.indptr[faces_loc]
pts_pairs = np.array([nodes[loc_n], nodes[loc_n + 1]])
sorted_nodes, _ = pp.utils.sort_points.sort_point_pairs(pts_pairs)
ordering = sorted_nodes[0, :]
pts = g.nodes[:, ordering]
linewidth = kwargs.get("linewidth", 1)
if kwargs.get("plot_2d", False):
poly = PolyCollection([pts[:2].T], linewidth=linewidth)
poly.set_edgecolor("k")
poly.set_facecolor(color_face(cell_value[c]))
ax.add_collection(poly)
else:
poly = Poly3DCollection([pts.T], linewidth=linewidth)
poly.set_edgecolor("k")
poly.set_facecolors(color_face(cell_value[c]))
ax.add_collection3d(poly)
if not kwargs.get("plot_2d", False):
ax.view_init(90, -90)
def plot_box(box, proc, fig, ax):
if sim.structure.gv.dim == 2:
low = mp.Vector3(box.low.x, box.low.y, box.low.z)
high = mp.Vector3(box.high.x, box.high.y, box.high.z)
points = [low, high]
x_len = mp.Vector3(high.x) - mp.Vector3(low.x)
y_len = mp.Vector3(y=high.y) - mp.Vector3(y=low.y)
xy_len = mp.Vector3(high.x, high.y) - mp.Vector3(low.x, low.y)
points += [low + x_len]
points += [low + y_len]
points += [low + xy_len]
points += [high - x_len]
points += [high - y_len]
points += [high - xy_len]
points = np.array([np.array(v) for v in points])
edges = [
[points[0], points[2], points[4], points[3]],
[points[1], points[5], points[7], points[6]],
[points[0], points[3], points[5], points[7]],
[points[1], points[4], points[2], points[6]],
[points[3], points[4], points[1], points[5]],
[points[0], points[7], points[6], points[2]]
]
faces = Poly3DCollection(edges, linewidths=1, edgecolors='k')
color_with_alpha = matplotlib.colors.to_rgba(chunk_colors[proc], alpha=0.2)
faces.set_facecolor(color_with_alpha)
ax.add_collection3d(faces)
# Plot the points themselves to force the scaling of the axes
ax.scatter(points[:, 0], points[:, 1], points[:, 2], s=0)
else:
low = mp.Vector3(box.low.x, box.low.y)
high = mp.Vector3(box.high.x, box.high.y)
points = [low, high]
x_len = mp.Vector3(high.x) - mp.Vector3(low.x)
y_len = mp.Vector3(y=high.y) - mp.Vector3(y=low.y)
points += [low + x_len]
points += [low + y_len]
points = np.array([np.array(v)[:-1] for v in points])
edges = [
[points[0], points[2], points[1], points[3]]
]
faces = PolyCollection(edges, linewidths=1, edgecolors='k')
color_with_alpha = matplotlib.colors.to_rgba(chunk_colors[proc])
faces.set_facecolor(color_with_alpha)
ax.add_collection(faces)
# Plot the points themselves to force the scaling of the axes
ax.scatter(points[:, 0], points[:, 1], s=0)
def plot_polys(verts, polys, facecolors=(1, 1, 1)):
collection = PolyCollection([verts[p, ::-1] for p in polys])
collection.set_facecolor(facecolors)
collection.set_edgecolor((0.8, 0.8, 0.8))
collection.set_linewidth(0.15)
ax = plt.gca()
ax.add_collection(collection)
xmin, xmax = np.amin(verts[:, 1]), np.amax(verts[:, 1])
ymin, ymax = np.amin(verts[:, 0]), np.amax(verts[:, 0])
ax.set_xlim([xmin - 1, xmax + 1])
ax.set_ylim([ymin - 1, ymax + 1])
def plot_triangles(p, adjustLowerLeft=False, values=None, values_cmap=matplotlib.cm.jet, edgecolors='k'):
""" Add mesh triangles to a pyplot plot
@param p = object holding sww vertex information (from util.get_output)
@param adjustLowerLeft = if TRUE, use spatial coordinates, otherwise use ANUGA internal coordinates
@param values = list or array of length(p.vols), or None. All triangles are assigned this value (for face plotting colors).
@param values_cmap = colormap for faces [e.g. values_cmap = matplotlib.cm.get_cmap('spectral')]
@param edgecolors = edge color for polygons (using matplotlib.colors notation). Use 'none' for no color
"""
import matplotlib
from matplotlib import pyplot as pyplot
from matplotlib.collections import PolyCollection
x0=p.xllcorner
y0=p.yllcorner
# Make vertices for PolyCollection Object
vertices = []
for i in range(len(p.vols)):
k1=p.vols[i][0]
k2=p.vols[i][1]
k3=p.vols[i][2]
tri_coords = numpy.array([ [p.x[k1], p.y[k1]], [p.x[k2], p.y[k2]], [p.x[k3], p.y[k3]] ])
if adjustLowerLeft:
tri_coords[:,0] = tri_coords[:,0] + x0
tri_coords[:,1] = tri_coords[:,1] + y0
vertices.append(tri_coords)
# Make PolyCollection
if values is None:
all_poly = PolyCollection( vertices, array = numpy.zeros(len(vertices)),
edgecolors=edgecolors)
all_poly.set_facecolor('none')
else:
try:
lv = len(values)
except:
values = numpy.array(len(p.vols)*[values])
lv = len(values)
msg = 'len(values) must be the same as len(p.vols) (or values can be a constant)'
assert lv==len(p.vols), msg
all_poly = PolyCollection( vertices, array = values, cmap = values_cmap,
edgecolors=edgecolors)
# Add to plot
# FIXME: To see the triangles, this might require that the user does
# something else to the plot?
pyplot.gca().add_collection(all_poly)
class ScatterPlotCC(ScatterPlot):
def get_polygon(self, x, y, c):
color_map = ('c', 'r', 'b', 'm', 'y', 'g')
d = defaultdict(list)
for p, cc in zip(zip(x, y), c):
d[cc].append(p)
polygons = []
colors = []
for k in set(c):
if len(d[k]) == 2:
pt1 = d[k][0]
pt2 = d[k][1]
dist = math.sqrt((pt1[0] - pt2[0]) ** 2 + (pt1[1] - pt2[1]) ** 2)
xmid = (pt1[0] + pt2[0]) / 2
ymid = (pt1[1] + pt2[1]) / 2
polygons.append(d[k])
colors.append(color_map[k])
elif len(d[k]) == 1:
pass
else:
ch = ConvexHull(d[k])
points = ch.points
pts = zip(points[ch.vertices, 0], points[ch.vertices, 1])
polygons.append(pts)
colors.append(color_map[k])
return polygons, colors
def setup_plot(self):
ax = plt.gca()
x, y, c = next(self.stream)
po, co = self.get_polygon(x, y, c)
self.poly = PolyCollection(po, facecolors = co, edgecolors='none')
ax.add_collection(self.poly)
self.scat = ax.scatter(x, y, c='k', marker = self.marker, s = 25)
return self.scat
def update_plot(self):
x, y, c = next(self.stream)
new_data = np.array(zip(x, y))
po, co = self.get_polygon(x, y, c)
self.poly.set_facecolor(co)
self.poly.set_verts(po)
self.scat.set_offsets(new_data)
return self.scat
def plot_polygons(verts, colors, ax=None, edgecolor=None):
"""
`verts` is a sequence of ( verts0, verts1, ...) where verts_i is a sequence of
xy tuples of vertices, or an equivalent numpy array of shape (nv, 2).
`c` is a sequence of (color0, color1, ...) where color_i is a color,
represented by a hex string (7 characters #xxxxxx).
Creates a PolygonCollection object in the axis `ax`."""
if ax is None:
fig = plt.gcf()
ax = fig.add_subplot(1,1,1)
pc = PolyCollection(verts)
pc.set_edgecolor(edgecolor)
pc.set_facecolor(colors)
ax.add_collection(pc)
ax.set_xlabel('X axis')
ax.set_ylabel('Y axis')
def plotmap(shpfile,color='0.5',fieldname='FID',convert=None,zone=15,\
scale=1.,offset=0.,subset=1):
"""
Plots a map layer from a polygon or multipolygon shapefile
Usage:
plotmap(shpfile,color='0.5',fieldname='FID',convert=None,zone=15)
convert = 'll2utm' or 'utm2ll'
zone = utm zone number
"""
from matplotlib.collections import PolyCollection
# Read the shapefile
xy, marker = readShpPoly(shpfile, FIELDNAME=fieldname)
if convert == 'utm2ll':
ll = []
for xytmp in xy:
ll.append(utm2ll(xytmp, zone, CS='WGS84', north=True))
xy = ll
elif convert == 'll2utm':
ll = []
for xytmp in xy:
ll.append(ll2utm(xytmp, zone, CS='WGS84', north=True))
xy = ll
for ii in range(len(xy)):
xy[ii] = xy[ii][::subset] * scale + offset
# Add the polygons to the current axis as a series of patches
fig = plt.gcf()
ax = fig.gca()
collection = PolyCollection(xy)
collection.set_facecolor(color)
#collection.set_rasterized(True)
ax.add_collection(collection)
#ax.axis('equal')
return ax, collection
def plotmap(shpfile,color='0.5',fieldname='FID',convert=None,zone=15,\
scale=1.,offset=0.,subset=1):
"""
Plots a map layer from a polygon or multipolygon shapefile
Usage:
plotmap(shpfile,color='0.5',fieldname='FID',convert=None,zone=15)
convert = 'll2utm' or 'utm2ll'
zone = utm zone number
"""
from matplotlib.collections import PolyCollection
# Read the shapefile
xy,marker = readShpPoly(shpfile,FIELDNAME=fieldname)
if convert=='utm2ll':
ll=[]
for xytmp in xy:
ll.append(utm2ll(xytmp,zone,CS='WGS84',north=True))
xy=ll
elif convert=='ll2utm':
ll=[]
for xytmp in xy:
ll.append(ll2utm(xytmp,zone,CS='WGS84',north=True))
xy=ll
for ii in range(len(xy)):
xy[ii] = xy[ii][::subset]*scale+offset
# Add the polygons to the current axis as a series of patches
fig = plt.gcf()
ax = fig.gca()
collection = PolyCollection(xy)
collection.set_facecolor(color)
#collection.set_rasterized(True)
ax.add_collection(collection)
#ax.axis('equal')
return ax, collection
def main_2d(vertices, faces, facecolors, edges, edgecolors, verts,
vertexcolors, args, canvassize=4):
fig = plt.figure()
fig.set_size_inches(canvassize, canvassize)
ax = fig.add_subplot(111)
ax.set_aspect("equal")
plt.axis('off')
these_faces = [vertices[i] for i in faces]
pd = PolyCollection(these_faces, edgecolors=edgecolors)
try:
pd.set_facecolor(facecolors)
except ValueError:
pd.set_array(np.array(facecolors))
pd.set_cmap(plt.get_cmap(args.cmap))
ax.add_collection(pd)
these_edges = vertices[edges]
ld = LineCollection(these_edges, edgecolor=edgecolors)
ax.add_collection(ld)
these_vertices = vertices[verts]
ax.scatter(these_vertices[..., 0], these_vertices[..., 1], c=vertexcolors)
def draw_colorbar(self, orientation):
self.axes.clear()
self.axes.set_axis_off()
if orientation is "vertical":
bin_height = 0.99 / len(self.color)
barverts = tuple(((0.89, i*bin_height+0.005), (0.99, i*bin_height+0.005), (0.99, (i+1)*bin_height+0.005), (0.89, (i+1)*bin_height+0.005)) for i in range(len(self.color)))
for i in range(len(self.bin)):
self.axes.text(1.00, (i+1)*bin_height - 0.01, str(self.bin[i]))
else:
bin_width = 0.99 / len(self.color)
barverts = tuple(((i*bin_width+0.005, 0.005), (i*bin_width+0.005, 0.105), ((i+1)*bin_width+0.005, 0.105), ((i+1)*bin_width+0.005, 0.005)) for i in range(len(self.color)))
for i in range(len(self.bin)):
self.axes.text((i+1)*bin_width - 0.015, -0.06, str(self.bin[i]))
bar = PolyCollection(barverts)
bar.set_edgecolor('black')
bar.set_facecolor(self.color)
bar.set_linewidth(0.5)
self.axes.add_collection(bar)
def swhlocal(swhs,
verts,
ncels,
colrs,
config,
mdlname='SMC',
datx='2018',
psfile='output.ps',
paprorn='portrait',
paprtyp='a3'):
## Import relevant modules and functions
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.collections import PolyCollection
from readcell import readtext
from colrboxy import colrboxy
from scale_swh import scale_swh
## Degree to radian conversion parameter.
d2rad = np.pi / 180.0
## Local plot configuration parameters
rdpols = config[0]
sztpxy = config[1]
rngsxy = config[2]
clrbxy = config[3]
## Maximum mapping radius.
radius = rdpols[0]
## Set up wave height scale and marks with colrs.N.
## colrs.N returns colrs' total number of colors 256.
waveht, factor, residu, marks, ncstr, nclrm = scale_swh(nclrm=colrs.N)
resmn1 = residu - 1.0
nswh0 = ncstr + int(factor * np.log(-resmn1 + residu))
# print ' nswh0 and marks = ', nswh0, marks
# print ' factor, residu, resmn1 = %f, %f, %f' % (factor, residu, resmn1)
## Some constant variables for plots.
xprop = {'xlim': rngsxy[0:2], 'xlabel': ''}
yprop = {'ylim': rngsxy[2:4], 'ylabel': ''}
## Use ijk to count how many times to draw.
ijk = 0
if True:
## Work out max and min values, excluding missing data (-999.0)
cmax = swhs.max()
cmin = swhs[swhs > -999.0].min()
print(' swh range %f, %f' % (cmin, cmax))
cmxs = 'SWHmx = %6.2f m' % cmax
cmns = 'SWHmn = %10.3E' % cmin
## Reset missing values (-999.0) to be -resmn1
swhs[swhs < -resmn1] = -resmn1
## Trim large values into plot range if any
swhs[swhs > 32.0] = 32.0
## Convert swhs with logarithm scale.
icnf = ncstr + np.rint(factor * np.log(swhs + residu))
nswh = np.array(icnf, dtype=np.int16)
print(" Drawing " + psfile)
## Set up first subplot and axis for northern hemisphere
fig = plt.figure(figsize=sztpxy[0:2])
ax = fig.add_subplot(1, 1, 1)
ax.set(**xprop)
ax.set(**yprop)
ax.set_aspect('equal')
ax.set_autoscale_on(False)
ax.set_axis_off()
plt.subplots_adjust(left=0.0, bottom=0.0, right=1.0, top=1.0)
## Prepare PolyCollection for this plot.
polynorth = PolyCollection(verts)
## Create color array for this plot
pcface = []
pcedge = []
for i in ncels:
if (nswh[i] != nswh0):
pcface.append(colrs(nswh[i]))
pcedge.append(colrs(nswh[i]))
else:
pcface.append(colrs(255))
pcedge.append(colrs(0))
# smcpoly.set_color(pcolr) ## This line defines both edge and face color.
polynorth.set_facecolor(pcface)
polynorth.set_edgecolor(pcedge)
polynorth.set_linewidth(0.2)
# print (" Drawing north hemisphere cells ... ")
ax.add_collection(polynorth)
## Draw colorbar inside plot.
xkeys, ykeys, clrply = colrboxy(clrbxy, colrs, marks)
ax.add_collection(clrply)
dkx = clrbxy[2]
dky = clrbxy[3]
for i in range(len(waveht)):
m = marks[i]
if (dkx < dky):
plt.text(xkeys[0] + 1.15 * dkx,
ykeys[m],
str(waveht[i]),
verticalalignment='center',
fontsize=11,
color='b')
else:
plt.text(xkeys[m],
ykeys[0] + 1.15 * dky,
str(waveht[i]),
horizontalalignment='center',
fontsize=11,
color='b')
if (dkx < dky):
plt.text(xkeys[0] + 2.0 * dkx,
ykeys[marks[3]],
'SWH m',
rotation=90,
verticalalignment='center',
fontsize=15,
color='k')
else:
plt.text(xkeys[marks[3]],
ykeys[0] + 2.0 * dky,
'SWH m',
rotation=0,
horizontalalignment='center',
fontsize=15,
color='k')
## Put cell information inside plot
tpx = sztpxy[2]
tpy = sztpxy[3]
plt.text(tpx,
tpy - 1.0,
mdlname,
horizontalalignment='center',
fontsize=17,
color='g')
plt.text(tpx,
tpy - 1.6,
datx,
horizontalalignment='center',
fontsize=15,
color='k')
plt.text(tpx,
tpy - 2.1,
cmxs,
horizontalalignment='center',
fontsize=13,
color='r')
plt.text(tpx,
tpy - 2.6,
cmns,
horizontalalignment='center',
fontsize=13,
color='b')
## Refresh subplots and save them.
plt.savefig(psfile,
dpi=None,
facecolor='w',
edgecolor='w',
orientation=paprorn,
papertype=paprtyp)
plt.close()
def plot_geocol_mpl(gc,
color=None,
facecolor='0.3',
edgecolor='0.7',
alpha=1.,
linewidth=0.2,
marker='o',
marker_size=20,
ax=None,
figsize=(9, 9)):
'''
Plot geographical data from the `geometry` column of a PySAL geotable to a
matplotlib backend.
...
Parameters
----------
gc : DataFrame
GeoCol with data to be plotted.
color : str/tuple/Series
[Optional. Default=None] Wrapper that sets both `facecolor`
and `edgecolor` at the same time. If set, `facecolor` and
`edgecolor` are ignored. It allows for either a single color
or a Series of the same length as `gc` with colors, indexed
on `gc.index`.
facecolor : str/tuple/Series
[Optional. Default='0.3'] Color for polygons and points. It
allows for either a single color or a Series of the same
length as `gc` with colors, indexed on `gc.index`.
edgecolor : str/tuple/Series
[Optional. Default='0.7'] Color for the polygon and point
edges. It allows for either a single color or a Series of
the same length as `gc` with colors, indexed on `gc.index`.
alpha : float/Series
[Optional. Default=1.] Transparency. It allows for either a
single value or a Series of the same length as `gc` with
colors, indexed on `gc.index`.
linewidth : float/Series
[Optional. Default=0.2] Width(s) of the lines in polygon and
line plotting (not applicable to points). It allows for
either a single value or a Series of the same length as `gc`
with colors, indexed on `gc.index`.
marker : 'o'
marker_size : int
ax : AxesSubplot
[Optional. Default=None] Pre-existing axes to which append the
collections and setup
figsize : tuple
w,h of figure
'''
geom = type(gc.iloc[0])
if color is not None:
facecolor = edgecolor = color
draw = False
if not ax:
f, ax = plt.subplots(1, figsize=figsize)
draw = True
# Geometry plotting
patches = []
ids = []
# Polygons
if geom == ps.cg.shapes.Polygon:
for id, shape in gc.iteritems():
for ring in shape.parts:
xy = np.array(ring)
patches.append(xy)
ids.append(id)
mpl_col = PolyCollection(patches)
# Lines
elif geom == ps.cg.shapes.Chain:
for id, shape in gc.iteritems():
for xy in shape.parts:
patches.append(xy)
ids.append(id)
mpl_col = LineCollection(patches)
facecolor = 'None'
# Points
elif geom == ps.cg.shapes.Point:
edgecolor = facecolor
xys = np.array(zip(*gc)).T
ax.scatter(xys[:, 0],
xys[:, 1],
marker=marker,
s=marker_size,
c=facecolor,
edgecolors=edgecolor,
linewidths=linewidth)
mpl_col = None
# Styling mpl collection (polygons & lines)
if mpl_col:
if type(facecolor) is pd.Series:
facecolor = facecolor.reindex(ids)
mpl_col.set_facecolor(facecolor)
if type(edgecolor) is pd.Series:
edgecolor = edgecolor.reindex(ids)
mpl_col.set_edgecolor(edgecolor)
if type(linewidth) is pd.Series:
linewidth = linewidth.reindex(ids)
mpl_col.set_linewidth(linewidth)
if type(alpha) is pd.Series:
alpha = alpha.reindex(ids)
mpl_col.set_alpha(alpha)
ax.add_collection(mpl_col, autolim=True)
ax.autoscale_view()
ax.set_axis_off()
if draw:
plt.axis('equal')
plt.show()
return None
def smcglobl(cel,
nvrts,
ncels,
svrts,
scels,
colrs,
config,
Arctic=None,
mdlname='SMC',
buoys=None,
psfile='output.ps',
paprtyp='a3'):
"""
## The smcglobl function plots a global view of a given smc grid.
## cel is the full cell array in shape(nc, 5).
## JGLi04Mar2019
"""
## Degree to radian conversion parameter.
d2rad = np.pi / 180.0
## Global plot configuration parameters.
rdpols = config[0]
sztpxy = config[1]
rngsxy = config[2]
clrbxy = config[3]
if (Arctic is not None): ncabgm = config[4]
## Maximum mapping radius.
radius = rdpols[0]
pangle = rdpols[1]
plon = rdpols[2]
plat = rdpols[3]
## Outline circle at equator
ciran = np.arange(1081) * d2rad / 3.0
xcirc = radius * np.cos(ciran)
ycirc = radius * np.sin(ciran)
## Define depth to color index conversion parameters and marks.
## Use only first 131 colors in colrs(0:255).
depth, factr, cstar, marks, ncstr, nclrm = scale_depth(nclrm=131)
## Cell color is decided by its depth value, dry cells use default 0 color.
nc = cel.shape[0]
ndeps = np.zeros((nc), dtype=np.int)
for j in range(nc):
if (cel[j, 4] > 0):
ndeps[j] = ncstr + np.rint(
(cstar - np.log10(cel[j, 4])) * factr).astype(np.int)
#ndeps = ncstr + np.rint( (cstar-np.log10(cel[:,4]))*factr ).astype(np.int)
if (Arctic is not None):
na = int(ncabgm[1])
nb = int(ncabgm[2])
jm = int(ncabgm[3])
## Set up first subplot and axis for northern hemisphere
print(" Drawing north hemisphere cells ... ")
fig = plt.figure(figsize=sztpxy[0:2])
ax1 = fig.add_subplot(1, 2, 1)
ax1.set_aspect('equal')
ax1.set_autoscale_on(False)
ax1.set_axis_off()
plt.subplots_adjust(left=0.02, bottom=0.02, right=0.98, top=0.95)
plt.subplots_adjust(wspace=0.02, hspace=0.01)
xprop = {'xlim': rngsxy[0:2], 'xlabel': 'Nothing'}
yprop = {'ylim': rngsxy[2:4], 'ylabel': 'Nothing'}
ax1.set(**xprop)
ax1.set(**yprop)
ax1.plot(xcirc, ycirc, 'b-')
## Select cells for one subplot and create verts and pcolr.
pcface = []
pcedge = []
for i in ncels:
if (Arctic is not None) and (cel[i, 1] == Arctic):
# Mark the arctic map-east reference region by first ring of its boundary cells
pcface.append(colrs(246))
pcedge.append(colrs(246))
#elif( Arctic is not None ) and ( cel[i,1] == jm ):
# # Mark the arctic cells
# pcface.append(colrs(168))
# pcedge.append(colrs(168))
else:
pcface.append(colrs(255))
pcedge.append(colrs(ndeps[i]))
## Draw this hemisphere cells
smcpoly = PolyCollection(nvrts)
smcpoly.set_facecolor(pcface)
smcpoly.set_edgecolor(pcedge)
smcpoly.set_linewidth(0.2)
ax1.add_collection(smcpoly)
## Draw colorbar for ax1.
xkeys, ykeys, clrply = colrboxy(clrbxy, colrs, marks, nclrm=nclrm)
ax1.add_collection(clrply)
for i in range(len(depth)):
m = marks[i]
plt.text(xkeys[m],
ykeys[0] + 1.15 * (ykeys[1] - ykeys[0]),
str(depth[i]),
horizontalalignment='center',
fontsize=11,
color='b')
#rotation=-90,verticalalignment='center', fontsize=11, color='b' )
plt.text(xkeys[marks[0]],
ykeys[0] + 2.2 * (ykeys[1] - ykeys[0]),
'Depth m',
horizontalalignment='left',
fontsize=15,
color='k')
#rotation=-90,verticalalignment='center', fontsize=15, color='k' )
# Overlay buoy sites on grid map if buoy file is provided.
if (buoys is not None):
hdr, buoyll = readtext(buoys)
nmbu = long(hdr[0])
buoyids = buoyll[:, 0].astype(str)
buoylat = buoyll[:, 1].astype(np.float)
buoylon = buoyll[:, 2].astype(np.float)
#; Convert slat slon to elat elon with given new pole
elat, elon, sxc, syc = steromap(buoylat,
buoylon,
plat,
plon,
Pangl=pangle)
#; Mark buoy position on map
print(' Selected buoys on north hemisphere ...')
for i in range(nmbu):
if ((elat[i] >= 0.0) and (rngsxy[0] < sxc[i] < rngsxy[1])
and (rngsxy[2] < syc[i] < rngsxy[3])):
print(' {:6} {:8.3f} {:8.3f}'.format(buoyids[i], buoylat[i],
buoylon[i]))
txtsz = int(abs(np.sin(elat[i] * d2rad) * 12.0))
plt.text(sxc[i],
syc[i],
'r',
fontsize=txtsz,
horizontalalignment='center',
color='r')
plt.text(sxc[i],
syc[i],
'.',
fontsize=txtsz * 2,
horizontalalignment='center',
color='r')
## Southern hemisphere
print(" Drawing south hemisphere cells ... ")
ax2 = fig.add_subplot(1, 2, 2)
ax2.set_aspect('equal')
ax2.axis('off')
plt.subplots_adjust(left=0.02, bottom=0.02, right=0.98, top=0.95)
plt.subplots_adjust(wspace=0.02, hspace=0.01)
ax2.set(**xprop)
ax2.set(**yprop)
ax2.plot(xcirc, ycirc, 'b-')
## Select cells for one subplot and create verts and pcolr.
pcface = []
pcedge = []
for i in scels:
pcface.append(colrs(255))
pcedge.append(colrs(ndeps[i]))
## Generate polygon collections for southern hemisphere
smcpoly = PolyCollection(svrts)
smcpoly.set_facecolor(pcface)
smcpoly.set_edgecolor(pcedge)
smcpoly.set_linewidth(0.2)
ax2.add_collection(smcpoly)
## Put cell information inside plot
tpx = sztpxy[2]
tpy = sztpxy[3]
dpy = 0.6
plt.text(tpx,
tpy + dpy * 0.5,
mdlname + ' Grid',
horizontalalignment='center',
fontsize=15,
color='k')
plt.text(tpx,
tpy + dpy * 2.0,
'NC=' + str(nc),
horizontalalignment='center',
fontsize=13,
color='r')
if (Arctic is not None):
plt.text(tpx,
tpy + dpy * 3.0,
'NA=' + str(na),
horizontalalignment='center',
fontsize=13,
color='r')
plt.text(tpx,
tpy + dpy * 4.0,
'NB=' + str(nb),
horizontalalignment='center',
fontsize=13,
color='r')
## Draw colorbar for ax2.
xkeys, ykeys, clrply = colrboxy(clrbxy, colrs, marks, nclrm=nclrm)
ax2.add_collection(clrply)
for i in range(len(depth)):
m = marks[i]
plt.text(xkeys[m],
ykeys[0] + 1.18 * (ykeys[1] - ykeys[0]),
str(depth[i]),
horizontalalignment='center',
fontsize=11,
color='b')
#verticalalignment='center', fontsize=11, color='b' )
#rotation=-90,verticalalignment='center', fontsize=11, color='b' )
plt.text(xkeys[marks[-1]],
ykeys[0] + 2.2 * (ykeys[1] - ykeys[0]),
'Depth m',
horizontalalignment='right',
fontsize=15,
color='k')
#rotation=-90,verticalalignment='center', fontsize=15, color='k' )
# Overlay buoy sites on grid map if buoy file is provided.
if (buoys is not None):
#; Mark buoy position on map
print(' Selected buoys on south hemisphere ...')
for i in range(nmbu):
if ((elat[i] < 0.0) and (rngsxy[0] < -sxc[i] < rngsxy[1])
and (rngsxy[2] < syc[i] < rngsxy[3])):
print(' {:6} {:8.3f} {:8.3f}'.format(buoyids[i], buoylat[i],
buoylon[i]))
txtsz = int(abs(np.sin(elat[i] * d2rad) * 12.0))
plt.text(-sxc[i],
syc[i],
'r',
fontsize=txtsz,
horizontalalignment='center',
color='r')
plt.text(-sxc[i],
syc[i],
'.',
fontsize=txtsz * 2,
horizontalalignment='center',
color='r')
## Refresh subplots and save them.
plt.subplots_adjust(wspace=0.02, hspace=0.01)
plt.savefig(psfile, dpi=None,facecolor='w',edgecolor='w', \
orientation='landscape',papertype=paprtyp,format='ps')
def smclocal(cel,
verts,
ncels,
colrs,
config,
Arctic=None,
mdlname='SMC',
buoys=None,
psfile='output.ps',
paprorn='portrait',
paprtyp='a3'):
"""
## The smclocal function plots a local region of a given smc grid.
## cel is the full cell array in shape(nc, 5).
## JGLi04Mar2019
"""
# Degree to radian conversion parameter.
d2rad = np.pi / 180.0
# Local plot configuration parameters
rdpols = config[0]
sztpxy = config[1]
rngsxy = config[2]
clrbxy = config[3]
if (Arctic): ncabgm = config[4]
# Maximum mapping radius.
radius = rdpols[0]
pangle = rdpols[1]
plon = rdpols[2]
plat = rdpols[3]
# Define depth to color index conversion parameters and marks.
# Use only first 131 colors in colrs(0:255).
depth, factr, cstar, marks, ncstr, nclrm = scale_depth(nclrm=136)
# Cell color is decided by its depth value.
nc = cel.shape[0]
ndeps = np.zeros((nc), dtype=np.int)
for j in range(nc):
if (cel[j, 4] > -11):
ndeps[j] = ncstr + np.rint(
(cstar - np.log10(cel[j, 4] + 11)) * factr).astype(np.int)
#ndeps = ncstr + np.rint( (cstar-np.log10(cel[:,4]))*factr ).astype(np.int)
if (Arctic is not None):
na = int(ncabgm[1])
nb = int(ncabgm[2])
jm = int(ncabgm[3])
# Workout output file format from its extension
#for i in np.arange(len(psfile))[::-1]:
# if( psfile[i] == '.' ):
# psfmt = psfile[i+1:]
# break
#print " Output file format will be "+psfmt
# Python plt.savefig will do this automatically.
# Use selected cells to draw the plot.
fig = plt.figure(figsize=sztpxy[0:2])
ax = fig.add_subplot(1, 1, 1)
yprop = {'ylim': rngsxy[2:4], 'ylabel': ''}
xprop = {'xlim': rngsxy[0:2], 'xlabel': ''}
ax.set(**xprop)
ax.set(**yprop)
ax.set_aspect('equal')
ax.set_autoscale_on(False)
ax.set_axis_off()
plt.subplots_adjust(left=0.0, bottom=0.0, right=1.0, top=1.0)
# Create color array for this plot
pcface = []
pcedge = []
for i in ncels:
if (Arctic is not None) and (cel[i, 1] == Arctic):
# Mark the arctic map-east reference region by first ring of its boundary cells
pcface.append(colrs(246))
pcedge.append(colrs(246))
#elif( Arctic is not None ) and ( cel[i,1] == jm ):
# # Mark the arctic cells
# pcface.append(colrs(168))
# pcedge.append(colrs(168))
else:
pcedge.append(colrs(ndeps[i] + 10))
pcface.append(colrs(ndeps[i]))
#pcface.append(colrs(255))
#pcedge.append(colrs(ndeps[i]))
# Create PolyCollection from selected verts and define edge and face color.
polynorth = PolyCollection(verts)
polynorth.set_facecolor(pcface)
polynorth.set_edgecolor(pcedge)
polynorth.set_linewidth(0.2)
# Draw the selected cells as colored polygons.
ax.add_collection(polynorth)
# Draw colorbar inside plot.
xkeys, ykeys, clrply = colrboxy(clrbxy, colrs, marks, nclrm=nclrm)
ax.add_collection(clrply)
dkx = clrbxy[2]
dky = clrbxy[3]
for i in range(len(depth)):
m = marks[i]
if (dkx < dky):
plt.text(xkeys[0] + 1.15 * dkx,
ykeys[m],
str(depth[i]),
verticalalignment='center',
fontsize=11,
color='b')
else:
plt.text(xkeys[m],
ykeys[0] + 1.15 * dky,
str(depth[i]),
horizontalalignment='center',
fontsize=11,
color='b')
if (dkx < dky):
plt.text(xkeys[0] + 1.9 * dkx,
ykeys[marks[2]],
'Depth m',
rotation=-90,
verticalalignment='center',
fontsize=15,
color='k')
else:
plt.text(xkeys[marks[2]],
ykeys[0] + 1.9 * dky,
'Depth m',
rotation=0,
horizontalalignment='center',
fontsize=15,
color='k')
# Put cell information inside plot
tpx = sztpxy[2]
tpy = sztpxy[3]
dpy = -0.6
plt.text(tpx,
tpy + dpy * 1,
mdlname + ' Grid',
horizontalalignment='center',
fontsize=15,
color='k')
plt.text(tpx,
tpy + dpy * 2,
'NC=' + str(nc),
horizontalalignment='center',
fontsize=13,
color='r')
if (Arctic is not None):
plt.text(tpx,
tpy + dpy * 3,
'NA=' + str(na),
horizontalalignment='center',
fontsize=13,
color='r')
plt.text(tpx,
tpy + dpy * 4,
'NB=' + str(nb),
horizontalalignment='center',
fontsize=13,
color='r')
# Overlay buoy sits on grid map if buoy file is provided.
if (buoys is not None):
hdr, buoyll = readtext(buoys)
nmbu = int(hdr[0])
buoyids = buoyll[:, 0].astype(str)
buoylat = buoyll[:, 1].astype(np.float)
buoylon = buoyll[:, 2].astype(np.float)
# Convert slat slon to elat elon with given new pole
elat, elon, sxc, syc = steromap(buoylat,
buoylon,
plat,
plon,
Pangl=pangle)
# Mark buoy position on map
print(' Selected buoys in this plot:')
for i in range(nmbu):
if ((elat[i] >= 25.0) and (rngsxy[0] < sxc[i] < rngsxy[1])
and (rngsxy[2] < syc[i] < rngsxy[3])):
print(' {:6} {:8.3f} {:8.3f}'.format(buoyids[i], buoylat[i],
buoylon[i]))
txtsz = int(abs(np.sin(elat[i] * d2rad) * 12.0))
plt.text(sxc[i],
syc[i],
'r',
fontsize=txtsz,
horizontalalignment='center',
color='r')
plt.text(sxc[i],
syc[i],
'.',
fontsize=txtsz * 2,
horizontalalignment='center',
color='r')
# Save plot as ps file
print(" Save the smc grid local plot ... ")
plt.savefig(psfile, dpi=None,facecolor='w',edgecolor='w', \
orientation=paprorn,papertype=paprtyp,format='ps')
def swhglobl(swhs,
nvrts,
ncels,
svrts,
scels,
colrs,
config,
mdlname='SMC',
datx='2018010106',
psfile='output.ps',
paprtyp='a3'):
"""
## Draw global swh plot with given polycollections and cell
## array. Output as A3 ps file. JGLi28Feb2019
##
"""
# Degree to radian conversion parameter.
d2rad = np.pi / 180.0
# Global plot configuration parameters.
rdpols = config[0]
sztpxy = config[1]
rngsxy = config[2]
clrbxy = config[3]
ncabgm = config[4]
# Maximum mapping radius.
radius = rdpols[0]
pangle = rdpols[1]
plon = rdpols[2]
plat = rdpols[3]
# Outline circle at equator
ciran = np.arange(1081) * d2rad / 3.0
xcirc = radius * np.cos(ciran)
ycirc = radius * np.sin(ciran)
# Set up wave height scale and marks with colrs.N.
# colrs.N returns colrs' total number of colors 256.
waveht, factor, residu, marks, ncstr, nclrm = scale_swh(nclrm=colrs.N)
resmn1 = residu - 1.0
nswh0 = ncstr + int(factor * np.log(-resmn1 + residu))
#print (' factor, residu, resmn1, nswh0 = {} {} {} {: d}'
#.format(factor, residu, resmn1, nswh0) )
# Some constant variables for plots.
xprop = {'xlim': rngsxy[0:2], 'xlabel': ''}
yprop = {'ylim': rngsxy[2:4], 'ylabel': ''}
if True:
# Work out max and min values, excluding missing data (-999.0)
cmax = swhs.max()
cmin = swhs[swhs > -999.0].min()
print(' swh range %f, %f' % (cmin, cmax))
cmxs = 'SWHmx = %6.2f m' % cmax
cmns = 'SWHmn = %10.3E' % cmin
# Reset missing values (-999.0) to be -resmn1
swhs[swhs < -resmn1] = -resmn1
# Trim large values into plot range if any
swhs[swhs > 32.0] = 32.0
# Convert swhs with logarithm scale.
icnf = np.rint(factor * np.log(swhs + residu))
nswh = np.array(icnf, dtype=np.int)
print(" Drawing " + psfile)
# Set up first subplot and axis for northern hemisphere
fig = plt.figure(figsize=sztpxy[0:2])
ax1 = fig.add_subplot(1, 2, 1)
ax1.set_aspect('equal')
ax1.set_autoscale_on(False)
ax1.set_axis_off()
plt.subplots_adjust(left=0.02, bottom=0.02, right=0.98, top=0.95)
plt.subplots_adjust(wspace=0.02, hspace=0.01)
ax1.set(**xprop)
ax1.set(**yprop)
ax1.plot(xcirc, ycirc, 'b-')
# Use loaded verts to setup PolyCollection.
polynorth = PolyCollection(nvrts)
# Create color array for this plot
pcface = []
pcedge = []
for i in ncels:
if (nswh[i] == nswh0):
pcface.append(colrs(255))
pcedge.append(colrs(0))
else:
pcface.append(colrs(nswh[i]))
pcedge.append(colrs(nswh[i]))
#smcpoly.set_color(pcolr) ## This line defines both edge and face color.
polynorth.set_facecolor(pcface)
polynorth.set_edgecolor(pcedge)
polynorth.set_linewidth(0.2)
ax1.add_collection(polynorth)
# Draw colorbar for ax1.
xkeys, ykeys, clrply = colrboxy(clrbxy, colrs, marks)
ax1.add_collection(clrply)
for i in range(len(waveht)):
m = marks[i]
plt.text(xkeys[m],
ykeys[0] + 1.18 * (ykeys[1] - ykeys[0]),
str(waveht[i]),
horizontalalignment='center',
fontsize=11,
color='b')
#rotation=-90,verticalalignment='center', fontsize=11, color='b' )
plt.text(xkeys[marks[0]],
ykeys[0] + 2.2 * (ykeys[1] - ykeys[0]),
'SWH m',
horizontalalignment='left',
fontsize=15,
color='k')
#rotation=-90,verticalalignment='center', fontsize=15, color='k' )
# Southern hemisphere subplot.
ax2 = fig.add_subplot(1, 2, 2)
ax2.set_aspect('equal')
ax2.axis('off')
plt.subplots_adjust(left=0.02, bottom=0.02, right=0.98, top=0.95)
plt.subplots_adjust(wspace=0.02, hspace=0.01)
ax2.set(**xprop)
ax2.set(**yprop)
ax2.plot(xcirc, ycirc, 'b-')
# Use loaded verts to set up PolyCollection.
polysouth = PolyCollection(svrts)
# Create color array for this plot
pcface = []
pcedge = []
for i in scels:
if (nswh[i] == nswh0):
pcface.append(colrs(255))
pcedge.append(colrs(0))
else:
pcface.append(colrs(nswh[i]))
pcedge.append(colrs(nswh[i]))
# smcpoly.set_color(pcolr) ## This line defines both edge and face color.
polysouth.set_facecolor(pcface)
polysouth.set_edgecolor(pcedge)
polysouth.set_linewidth(0.2)
ax2.add_collection(polysouth)
# Put statistic information inside subplot ax2
tpx = sztpxy[2]
tpy = sztpxy[3]
dpy = 0.6
plt.text(tpx,
9.0,
mdlname + ' SWH',
horizontalalignment='center',
fontsize=19,
color='r')
plt.text(tpx,
tpy + dpy * 1.0,
cmns,
horizontalalignment='center',
fontsize=15,
color='b')
plt.text(tpx,
tpy + dpy * 2.0,
cmxs,
horizontalalignment='center',
fontsize=15,
color='r')
plt.text(tpx,
tpy + dpy * 3.0,
datx,
horizontalalignment='center',
fontsize=17,
color='k')
# Draw colorbar for ax2.
xkeys, ykeys, clrply = colrboxy(clrbxy, colrs, marks)
ax2.add_collection(clrply)
for i in range(len(waveht)):
m = marks[i]
plt.text(xkeys[m],
ykeys[0] + 1.18 * (ykeys[1] - ykeys[0]),
str(waveht[i]),
horizontalalignment='center',
fontsize=13,
color='b')
#rotation=-90,verticalalignment='center', fontsize=11, color='b' )
plt.text(xkeys[marks[-1]],
ykeys[0] + 2.2 * (ykeys[1] - ykeys[0]),
'SWH m',
horizontalalignment='right',
fontsize=15,
color='k')
#rotation=-90,verticalalignment='center', fontsize=15, color='k' )
# Refresh subplots and save them.
plt.subplots_adjust(wspace=0.02, hspace=0.01)
plt.savefig(psfile,
dpi=None,
facecolor='w',
edgecolor='w',
orientation='landscape',
papertype=paprtyp,
format='ps')
plt.close()
def plot_3d_sequence(data_classes: List[SyncMeasData], long_mode: int,
trans_mode: Union[int, None]) -> plt.axis:
# Set 3D plot
fig = plt.figure()
axis = fig.gca(projection='3d')
# Store slices to ensure equally size arrays
cluster_arrays = []
q_mode_peaks = []
data_slices = []
data_slices_range = []
for data_class in data_classes:
collection = LabeledPeakCollection(identify_peaks(data_class))
try:
cluster_array, value_slice = collection.get_mode_sequence(
long_mode=long_mode, trans_mode=trans_mode)
cluster_arrays.append(cluster_array)
q_mode_peaks.append(collection.q_dict[long_mode])
except ValueError:
logging.warning(f'Longitudinal mode {long_mode} not well defined')
return axis
data_slice = get_value_to_data_slice(data_class=data_class,
value_slice=value_slice)
data_slices.append(data_slice)
data_slices_range.append(get_slice_range(data_slice)) # Store range
# Prepare plot data
leading_index = data_slices_range.index(max(data_slices_range))
leading_slice = data_slices[leading_index]
for i, slice in enumerate(data_slices):
range_diff = get_slice_range(leading_slice) - get_slice_range(slice)
padding = whole_integer_divider(num=range_diff, div=2)
data_slices[i] = (slice[0] - padding[0], slice[1] + padding[1])
sliced_xs = data_classes[leading_index].x_boundless_data[
leading_slice[0]:leading_slice[1]]
xs = np.arange(get_slice_range(leading_slice)) # sliced_xs #
zs = np.arange(len(data_slices))
verts = []
peaks = []
for i, slice in enumerate(data_slices):
data_class = data_classes[i]
data_class.slicer = slice
ys = data_class.y_data
verts.append(list(zip(xs, ys)))
# Peak scatter plot
peak_list = flatten_clusters(data=cluster_arrays[i])
peaks.append(peak_list) # Collect peaks for polarisation cross section
yp = [peak.get_y for peak in peak_list]
xp = [peak.get_relative_index for peak in peak_list]
zp = [zs[i] for j in range(len(peak_list))]
axis.scatter(xp, zp, yp, marker='o')
# Draw individual measurement polygons
poly = PolyCollection(verts)
poly.set_alpha(.7)
axis.add_collection3d(poly, zs=zs, zdir='y')
# Draw polarisation cross section
cross_section_count = len(peaks[0])
if all(len(peak_array) == cross_section_count
for peak_array in peaks): # Able to build consistent cross sections
cross_peaks = list(
map(list, zip(*peaks)
)) # Transposes peaks-list to allow for cross section ordering
xc = []
# Insert 0 bound values
zc = list(zs)
zc.insert(0, zc[0])
zc.append(zc[-1])
peak_verts = []
face_colors = [[v, .3, .3]
for v in np.linspace(.5, 1., len(cross_peaks))]
for i, cross_section in enumerate(cross_peaks):
yc = [peak.get_y for peak in cross_section]
# Insert 0 bound values
yc.insert(0, 0)
yc.append(0)
xc.append(
int(
np.mean([
peak.get_relative_index for peak in cross_section
]))) # np.mean([peak.get_x for peak in cross_section])) #
peak_verts.append(list(zip(zc, yc)))
poly = PolyCollection([list(zip(zc, yc))]) # peak_verts
poly.set_alpha(1)
poly.set_facecolor(face_colors[i])
axis.add_collection3d(poly, zs=xc[-1], zdir='x')
# poly = PolyCollection(peak_verts)
# poly.set_alpha(1)
# axis.add_collection3d(poly, zs=xc, zdir='x')
print('plotting')
else:
logging.warning(f'Cross section (peak) count is not consistent')
axis.set_xlabel('Relative cavity length [nm]')
axis.set_xlim3d(0, len(xs))
# axis.set_xticks(xs)
axis.set_ylabel('Polarisation [10 Degree]') # 'Measurement iterations')
axis.set_ylim3d(-1, len(zs) + 1)
# axis.set_yticks([str(10 * angle) for angle in zs])
axis.set_zlabel('Transmission [a.u.]')
axis.set_zlim3d(0, 1)
# Set viewport
axis.view_init(elev=22, azim=-15)
return axis
fig, ax = plt.subplots()
ax.set_xlim(left=-0.1, right=8)
ax.set_ylim(bottom=-0.1, top=5.8)
ax.set_aspect('equal')
points = ax.scatter(x, y, marker='x', color='black', zorder=2)
orig = ax.scatter(origin, origin, marker='+', color='black', zorder=2)
mid = ax.scatter(*mid, marker='.', color='black', zorder=2)
bounds = ax.scatter((img[0], img[2]), (img[1], img[3]), marker='x', color='black', zorder=2)
hex = Polygon(hex_coords, closed=True, edgecolor='red', fill=False, zorder=1)
r_line = Line2D(*R_line, zorder=1, linestyle='--')
d_line = Line2D(*d_line, zorder=1, linestyle='--')
img = Rectangle((img[0], img[1]), width=(img[2]-img[0]), height=(img[3]-img[1]), fill=False, edgecolor='black', linestyle=':')
hex_grid = PolyCollection(cells, closed=True, edgecolors='red')
hex_grid.set_facecolor(None)
ax.add_artist(hex)
ax.add_artist(img)
ax.add_artist(r_line)
ax.add_artist(d_line)
ax.add_artist(hex_grid)
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
for spine in ax.spines.values():
spine.set_visible(False)
for annotation in annotations:
def set_facecolor(self, colors):
PolyCollection.set_facecolor(self, colors)
self._facecolors3d = PolyCollection.get_facecolor(self)
def plot_geocol_mpl(gc, color=None, facecolor='0.3', edgecolor='0.7',
alpha=1., linewidth=0.2, marker='o', marker_size=20,
ax=None, figsize=(9,9)):
'''
Plot geographical data from the `geometry` column of a PySAL geotable to a
matplotlib backend.
...
Arguments
---------
gc : DataFrame
GeoCol with data to be plotted.
color : str/tuple/Series
[Optional. Default=None] Wrapper that sets both `facecolor`
and `edgecolor` at the same time. If set, `facecolor` and
`edgecolor` are ignored. It allows for either a single color
or a Series of the same length as `gc` with colors, indexed
on `gc.index`.
facecolor : str/tuple/Series
[Optional. Default='0.3'] Color for polygons and points. It
allows for either a single color or a Series of the same
length as `gc` with colors, indexed on `gc.index`.
edgecolor : str/tuple/Series
[Optional. Default='0.7'] Color for the polygon and point
edges. It allows for either a single color or a Series of
the same length as `gc` with colors, indexed on `gc.index`.
alpha : float/Series
[Optional. Default=1.] Transparency. It allows for either a
single value or a Series of the same length as `gc` with
colors, indexed on `gc.index`.
linewidth : float/Series
[Optional. Default=0.2] Width(s) of the lines in polygon and
line plotting (not applicable to points). It allows for
either a single value or a Series of the same length as `gc`
with colors, indexed on `gc.index`.
marker : 'o'
marker_size : int
ax : AxesSubplot
[Optional. Default=None] Pre-existing axes to which append the
collections and setup
figsize : tuple
w,h of figure
'''
geom = type(gc.iloc[0])
if color is not None:
facecolor = edgecolor = color
draw = False
if not ax:
f, ax = plt.subplots(1, figsize=figsize)
draw = True
# Geometry plotting
patches = []
ids = []
## Polygons
if geom == ps.cg.shapes.Polygon:
for id, shape in gc.iteritems():
for ring in shape.parts:
xy = np.array(ring)
patches.append(xy)
ids.append(id)
mpl_col = PolyCollection(patches)
## Lines
elif geom == ps.cg.shapes.Chain:
for id, shape in gc.iteritems():
for xy in shape.parts:
patches.append(xy)
ids.append(id)
mpl_col = LineCollection(patches)
facecolor = 'None'
## Points
elif geom == ps.cg.shapes.Point:
edgecolor = facecolor
xys = np.array(zip(*gc)).T
ax.scatter(xys[:, 0], xys[:, 1], marker=marker,
s=marker_size, c=facecolor, edgecolors=edgecolor,
linewidths=linewidth)
mpl_col = None
# Styling mpl collection (polygons & lines)
if mpl_col:
if type(facecolor) is pd.Series:
facecolor = facecolor.reindex(ids)
mpl_col.set_facecolor(facecolor)
if type(edgecolor) is pd.Series:
edgecolor = edgecolor.reindex(ids)
mpl_col.set_edgecolor(edgecolor)
if type(linewidth) is pd.Series:
linewidth = linewidth.reindex(ids)
mpl_col.set_linewidth(linewidth)
if type(alpha) is pd.Series:
alpha = alpha.reindex(ids)
mpl_col.set_alpha(alpha)
ax.add_collection(mpl_col, autolim=True)
ax.autoscale_view()
ax.set_axis_off()
if draw:
plt.axis('equal')
plt.show()
return None
def set_facecolor(self, colors):
PolyCollection.set_facecolor(self, colors)
self._facecolors3d = PolyCollection.get_facecolor(self)
else:
if lat_distance:
if geomag_distance:
target_mag=aacgm.Convert(target_lat,target_lon,0,year=2010,flag=0)
p1color_ax.plot([p.date2num(startday),p.date2num(endday)],
[target_mag[0],target_mag[0]],linewidth=4,linestyle="--",color="k",alpha=0.5)
else:
p1color_ax.plot([p.date2num(startday),p.date2num(endday)],
[target_lat,target_lat],linewidth=4,linestyle="--",color="k",alpha=0.5)
p.axes(p2color_ax)
pixs=N.array(p2pixels)
colors=p2values
coll = PolyCollection(pixs,edgecolors='none',linewidths=0.0,zorder=10)
coll.set_antialiased(False)
coll.set_facecolor(colors)
coll.set_alpha(1)
p2color_ax.add_collection(coll)
p2color_ax.autoscale_view()
p2color_ax.xaxis.set_major_formatter(dateFmt)
p2color_ax.set_xlim(p.date2num(startday), p.date2num(endday))
p2color_ax.set_ylim(plotdict["min_rang"],plotdict["max_rang"])
p2color_ax.xaxis.set_major_locator(tick2_minute_interval)
p2color_locator=MaxNLocator(nbins=6,prune='both',integer=True)
p2color_ax.yaxis.set_major_locator(p2color_locator)
p2color_ax.set_xticklabels([])
# p2color_ax.set_xticks([])
p2color_ax.yaxis.grid(True, linestyle='-', which='major', color='lightgrey',alpha=0.5)
p2color_ax.xaxis.grid(True, linestyle='-', which='major', color='lightgrey',alpha=0.5)
print "pcolor done"
