def draw_graph_on_map(g, node2weight, node2pos, pic_title, pic_area=[-130,10,140,70], output_fname_prefix=None, need_draw_edge=0):
"""
2007-09-13
identity_pair_ls is a list of pairs of strains (ecotype id as in table ecotype)
2007-10-08
correct a bug in 4*diameter_ls, diameter_ls has to be converted to array first.
sqrt the node weight, 8 times the original weight
"""
import os, sys
sys.stderr.write("Drawing graph on a map ...\n")
import pylab, math
from matplotlib.toolkits.basemap import Basemap
pylab.clf()
fig = pylab.figure()
fig.add_axes([0.05,0.05,0.9,0.9]) #[left, bottom, width, height]
m = Basemap(llcrnrlon=pic_area[0],llcrnrlat=pic_area[1],urcrnrlon=pic_area[2],urcrnrlat=pic_area[3],\
resolution='l',projection='mill', ax=pylab.gca())
sys.stderr.write("\tDrawing nodes ...")
euc_coord1_ls = []
euc_coord2_ls = []
diameter_ls = []
for n in g.nodes():
lat, lon = node2pos[n]
euc_coord1, euc_coord2 = m(lon, lat) #longitude first, latitude 2nd
euc_coord1_ls.append(euc_coord1)
euc_coord2_ls.append(euc_coord2)
diameter_ls.append(math.sqrt(node2weight[n]))
import numpy
diameter_ls = numpy.array(diameter_ls)
m.scatter(euc_coord1_ls, euc_coord2_ls, 8*diameter_ls, marker='o', color='r', alpha=0.4, zorder=12, faceted=False)
sys.stderr.write("Done.\n")
if need_draw_edge:
sys.stderr.write("\tDrawing edges ...")
ax=pylab.gca()
for popid1, popid2, no_of_connections in g.edges():
lat1, lon1 = node2pos[popid1]
lat2, lon2 = node2pos[popid2]
x1, y1 = m(lon1, lat1)
x2, y2 = m(lon2, lat2)
ax.plot([x1,x2],[y1,y2], 'g', linewidth=math.log(no_of_connections+1)/2, alpha=0.2, zorder=10)
sys.stderr.write("Done.\n")
#m.drawcoastlines()
m.drawparallels(pylab.arange(-90,90,30), labels=[1,1,0,1])
m.drawmeridians(pylab.arange(-180,180,30), labels=[1,1,0,1])
m.fillcontinents()
m.drawcountries()
m.drawstates()
pylab.title(pic_title)
if output_fname_prefix:
pylab.savefig('%s.eps'%output_fname_prefix, dpi=600)
pylab.savefig('%s.svg'%output_fname_prefix, dpi=600)
pylab.savefig('%s.png'%output_fname_prefix, dpi=600)
del fig, m, pylab
sys.stderr.write("Done.\n")
def draw_clustered_strain_location(self, label_ls, weighted_pos_ls, diameter_ls, label_type, label_type2label_name, pic_area=[-180,-90,180,90], output_fname_prefix=None, label_name=None):
"""
2007-07-11
draw populations derived from connected_components of the strain network
#each pie denotes a population, with diameter proportional to the size of the population
#each pie labeled with the number of strains in that population
2007-07-13
use popid as label
2007-07-17
no parallels, no meridians
2007-08-29
copied from CreatePopulation.py
2007-09-11
add label name
2007-10-14
correct a bug in 5*diameter_ls. diameter_ls has to an array
"""
sys.stderr.write("Drawing population map...")
import pylab
from matplotlib.toolkits.basemap import Basemap
pylab.clf()
fig = pylab.figure()
fig.add_axes([0.05,0.05,0.9,0.9]) #[left, bottom, width, height]
m = Basemap(llcrnrlon=pic_area[0],llcrnrlat=pic_area[1],urcrnrlon=pic_area[2],urcrnrlat=pic_area[3],\
resolution='l',projection='mill')
euc_coord1_ls = []
euc_coord2_ls = []
ax=pylab.gca()
for i in range(len(weighted_pos_ls)):
lat, lon = weighted_pos_ls[i]
euc_coord1, euc_coord2 = m(lon, lat) #longitude first, latitude 2nd
euc_coord1_ls.append(euc_coord1)
euc_coord2_ls.append(euc_coord2)
ax.text(euc_coord1, euc_coord2, str(label_ls[i]), size=5, alpha=0.5, horizontalalignment='center', verticalalignment='center', zorder=12)
import numpy
diameter_ls = numpy.array(diameter_ls)
m.scatter(euc_coord1_ls, euc_coord2_ls, 5*diameter_ls, marker='o', color='r', alpha=0.3, zorder=10, faceted=False)
#m.drawcoastlines()
m.drawparallels(pylab.arange(-90,90,30), labels=[1,1,0,1]) #labels intersect the left, right, top bottom of the plot
m.drawmeridians(pylab.arange(-180,180,30), labels=[1,1,0,1])
m.fillcontinents()
m.drawcountries()
m.drawstates()
pylab.title("worldwide distribution of %s populations, labeled by %s"%(len(weighted_pos_ls), label_type2label_name[label_type]))
if output_fname_prefix:
pylab.savefig('%s_pop_map.eps'%output_fname_prefix, dpi=300)
pylab.savefig('%s_pop_map.svg'%output_fname_prefix, dpi=300)
pylab.savefig('%s_pop_map.png'%output_fname_prefix, dpi=300)
del m, pylab, Basemap
sys.stderr.write("Done.\n")
def DrawSiteNetwork(self,
g,
node_label2pos_counts,
pic_area=[-180, -90, 180, 90],
output_fname_prefix=None):
"""
2007-07-17
put ax.plot() right after Basemap() but after m.xxx() so that it'll zoom in
use 'g' in ax.plot(), otherwise, ax.plot() alternates all colors.
no parallels, no meridians
2007-08-29 copied from CreatePopulation.py and renamed from DrawStrainNetwork
"""
sys.stderr.write("Drawing Site Network...")
import pylab
from matplotlib.toolkits.basemap import Basemap
pylab.clf()
fig = pylab.figure()
fig.add_axes([0.05, 0.05, 0.9, 0.9]) #[left, bottom, width, height]
m = Basemap(llcrnrlon=pic_area[0],llcrnrlat=pic_area[1],urcrnrlon=pic_area[2],urcrnrlat=pic_area[3],\
resolution='l',projection='mill')
ax = pylab.gca()
for e in g.edges():
lat1, lon1 = node_label2pos_counts[e[0]][0]
lat2, lon2 = node_label2pos_counts[e[1]][0]
x1, y1 = m(lon1, lat1)
x2, y2 = m(lon2, lat2)
ax.plot([x1, x2], [y1, y2], 'g', alpha=0.5, zorder=12)
#m.drawcoastlines()
m.drawparallels(pylab.arange(-90, 90, 30), labels=[1, 1, 0, 1])
m.drawmeridians(pylab.arange(-180, 180, 30), labels=[1, 1, 0, 1])
m.fillcontinents()
m.drawcountries()
m.drawstates()
pylab.title("Network of strains")
if output_fname_prefix:
pylab.savefig('%s_site_network.eps' % output_fname_prefix, dpi=300)
pylab.savefig('%s_site_network.svg' % output_fname_prefix, dpi=300)
pylab.savefig('%s_site_network.png' % output_fname_prefix, dpi=300)
del m, pylab, Basemap
sys.stderr.write("Done.\n")
def DrawSiteNetwork(self, g, node_label2pos_counts,pic_area=[-180,-90,180,90], output_fname_prefix=None):
"""
2007-07-17
put ax.plot() right after Basemap() but after m.xxx() so that it'll zoom in
use 'g' in ax.plot(), otherwise, ax.plot() alternates all colors.
no parallels, no meridians
2007-08-29 copied from CreatePopulation.py and renamed from DrawStrainNetwork
"""
sys.stderr.write("Drawing Site Network...")
import pylab
from matplotlib.toolkits.basemap import Basemap
pylab.clf()
fig = pylab.figure()
fig.add_axes([0.05,0.05,0.9,0.9]) #[left, bottom, width, height]
m = Basemap(llcrnrlon=pic_area[0],llcrnrlat=pic_area[1],urcrnrlon=pic_area[2],urcrnrlat=pic_area[3],\
resolution='l',projection='mill')
ax=pylab.gca()
for e in g.edges():
lat1, lon1 = node_label2pos_counts[e[0]][0]
lat2, lon2 = node_label2pos_counts[e[1]][0]
x1, y1 = m(lon1, lat1)
x2, y2 = m(lon2, lat2)
ax.plot([x1,x2],[y1,y2], 'g', alpha=0.5, zorder=12)
#m.drawcoastlines()
m.drawparallels(pylab.arange(-90,90,30), labels=[1,1,0,1])
m.drawmeridians(pylab.arange(-180,180,30), labels=[1,1,0,1])
m.fillcontinents()
m.drawcountries()
m.drawstates()
pylab.title("Network of strains")
if output_fname_prefix:
pylab.savefig('%s_site_network.eps'%output_fname_prefix, dpi=300)
pylab.savefig('%s_site_network.svg'%output_fname_prefix, dpi=300)
pylab.savefig('%s_site_network.png'%output_fname_prefix, dpi=300)
del m, pylab, Basemap
sys.stderr.write("Done.\n")
res = 'i'
map = Basemap(projection=prj,
llcrnrlon=lonmin,
llcrnrlat=latmin,
urcrnrlon=lonmax,
urcrnrlat=latmax,
resolution=res)
map.plot(lon, lat, 'r-')
map.plot(lon, lat, 'k+')
fs = 10
pylab.text(115.9,-31.9, "Perth", fontsize=fs, ha='left', va='top')
pylab.text(114.1,-21.9, "Exmouth", fontsize=fs, ha='left', va='top')
pylab.text(118.5,-20.4, "Port Hedland", fontsize=fs, ha='left', va='top')
pylab.text(130.8,-12.6, "Darwin", fontsize=fs, ha='left', va='top')
pylab.text(145.75,-16.9, "Cairns", fontsize=fs, ha='right', va='top')
pylab.text(149.2,-21.2, "Mackay", fontsize=fs, ha='right', va='top')
pylab.text(153.0,-27.5, "Brisbane", fontsize=fs, ha='right', va='top')
pylab.text(153.1,-30.25, "Coffs Harbour", fontsize=fs, ha='right', va='top')
pylab.text(151.1,-33.9, "Sydney", fontsize=fs, ha='right', va='top')
pylab.text(150.5,-35.3, "Ulladulla", fontsize=fs, ha='right', va='top')
map.fillcontinents(color='0.9')
map.drawcoastlines()
map.drawparallels(parallel, label=[1,0,0,1], fontsize=8)
map.drawmeridians(meridian, label=[1,0,0,1], fontsize=8)
pylab.show()
pylab.savefig("N:\\cyclone\\sandpits\\carthur\\landfall\\gates.v0.2.png")
res = 'i'
map = Basemap(projection=prj,
llcrnrlon=lonmin,
llcrnrlat=latmin,
urcrnrlon=lonmax,
urcrnrlat=latmax,
resolution=res)
map.plot(lon, lat, 'r-')
map.plot(lon, lat, 'k+')
fs = 10
pylab.text(115.9,-31.9, "Perth", fontsize=fs, ha='left', va='top')
pylab.text(114.1,-21.9, "Exmouth", fontsize=fs, ha='left', va='top')
pylab.text(118.5,-20.4, "Port Hedland", fontsize=fs, ha='left', va='top')
pylab.text(130.8,-12.6, "Darwin", fontsize=fs, ha='left', va='top')
pylab.text(145.75,-16.9, "Cairns", fontsize=fs, ha='right', va='top')
pylab.text(149.2,-21.2, "Mackay", fontsize=fs, ha='right', va='top')
pylab.text(153.0,-27.5, "Brisbane", fontsize=fs, ha='right', va='top')
pylab.text(153.1,-30.25, "Coffs Harbour", fontsize=fs, ha='right', va='top')
pylab.text(151.1,-33.9, "Sydney", fontsize=fs, ha='right', va='top')
pylab.text(150.5,-35.3, "Ulladulla", fontsize=fs, ha='right', va='top')
map.fillcontinents(color='0.9')
map.drawcoastlines()
map.drawparallels(parallel, label=[1,0,0,1], fontsize=8)
map.drawmeridians(meridian, label=[1,0,0,1], fontsize=8)
pylab.show()
pylab.savefig("N:\\cyclone\\sandpits\\carthur\\landfall\\gates.v0.2.png")
# set size of plot to match aspect ratio of map.
ax = gca()
l,b,w,h = ax.get_position()
b = 0.5 - 0.5*w*m.aspect; h = w*m.aspect
#l = 0.5 - 0.5*h/m.aspect; w = h/m.aspect
ax.set_position([l,b,w,h])
corners = ((m.xmin,m.ymin), (m.xmax,m.ymax))
ax.update_datalim( corners )
axis([m.xmin, m.xmax, m.ymin, m.ymax])
# draw map.
if plot in ['pcolor','imshow']:
m.drawcoastlines(ax)
else:
m.fillcontinents(ax,color='gray')
# draw parallels
delat = 30.
delon = 30.
circles = arange(0.,90.+delat,delat).tolist()+\
arange(-delat,-90.-delat,-delat).tolist()
m.drawparallels(ax,circles)
# draw meridians
meridians = arange(0.,360.,delon)
m.drawmeridians(ax,meridians)
ax.set_xticks([]) # no ticks
ax.set_yticks([])
title('500 hPa Height - '+plot)
nx = len(lons)
ny = int(80. * len(lats) / 90.)
lonsout, latsout = m.makegrid(nx, ny)
topodat = interp(topoin, lons, lats, lonsout, latsout)
xsize = rcParams['figure.figsize'][0]
fig = figure(figsize=(xsize, m.aspect * xsize))
fig.add_axes([0.1, 0.1, 0.8, 0.8])
im = imshow(topodat,
cm.jet,
extent=(m.llcrnrx, m.urcrnrx, m.llcrnry, m.urcrnry),
origin='lower')
ax = gca() # get current axis instance
m.drawcoastlines(ax)
m.drawcountries(ax)
m.drawstates(ax)
m.fillcontinents(ax)
# draw parallels
m.drawparallels(ax, circles)
# draw meridians
m.drawmeridians(ax, meridians)
ax.set_xticks([]) # no ticks
ax.set_yticks([])
title('Mercator')
print 'plotting Mercator example, close plot window to proceed ...'
show()
m = Basemap(-145.5,1.,-2.566,46.352,\
resolution='c',area_thresh=10000.,projection='lcc',\
lat_1=50.,lon_0=-107.)
# define grid (nx x ny regularly spaced native projection grid)
nx = int((m.xmax - m.xmin) / 40000.) + 1
m = Basemap(projection='lcc',
resolution='i',
llcrnrlon=5.0,
llcrnrlat= 52.,
urcrnrlon=35.5,
urcrnrlat=68.0,
lat_0=15.00,
lon_0=0.0,
suppress_ticks=False)
fig=pl.figure()
# background color will be used for 'wet' areas.
fig.add_axes([0.1,0.1,0.8,0.8],axisbg='azure')
m.drawcoastlines(linewidth=0.25)
m.fillcontinents(color='beige')
x, y, beta = zip(*[(241782.65384467551, 1019981.3539730886, 1.0),
(263546.12512432877, 686274.79435173667, 0),
(452162.87621465814, 207478.42619936226, 1.0),
(1431519.0837990609, 432367.62942244718, 1.0),
(1409755.6125194072, 816855.62202965701, -1.0),
(1612881.344462839, 816855.62202965701, 1.0),
(1620135.83488939, 1194089.1242103155, -1.0),
(2048150.7700559068, 1302906.4806085825, 1.0),
(1982860.3562169466, 1520541.1934051164, 1.0),
(1358974.1795335496, 1447996.2891396051, -1.0),
(1344465.1986804469, 1716412.4349219969, 0),
(1620135.83488939, 2318535.1403257404, 1.0),
(1199375.3901494243, 2383825.5541647007, 1.0),
(967231.6964997882, 1825229.7913202639, 0),
print shp_info
# find names of storms that reached Cat 4.
names = []
for shapedict in m.hurrtracks_info:
cat = shapedict['CATEGORY']
name = shapedict['NAME']
if cat in ['H4', 'H5'] and name not in names:
# only use named storms.
if name != 'NOT NAMED': names.append(name)
print names
print len(names)
# plot tracks of those storms.
for shapedict, shape in zip(m.hurrtracks_info, m.hurrtracks):
name = shapedict['NAME']
cat = shapedict['CATEGORY']
if name in names:
xx, yy = zip(*shape)
# show part of track where storm > Cat 4 as thick red.
if cat in ['H4', 'H5']:
p.plot(xx, yy, linewidth=1.5, color='r')
elif cat in ['H1', 'H2', 'H3']:
p.plot(xx, yy, color='k')
# draw coastlines, meridians and parallels.
m.drawcoastlines()
m.drawcountries()
m.fillcontinents(color='#cc9966')
m.drawparallels(p.arange(10, 70, 20), labels=[1, 1, 0, 0])
m.drawmeridians(p.arange(-100, 0, 20), labels=[0, 0, 0, 1])
p.title('Atlantic Hurricane Tracks (Storms Reaching Category 4, 1851-2004)')
p.show()
def draw_graph_on_map(g,
node2weight,
node2pos,
pic_title,
pic_area=[-130, 10, 140, 70],
output_fname_prefix=None,
need_draw_edge=0):
"""
2007-09-13
identity_pair_ls is a list of pairs of strains (ecotype id as in table ecotype)
2007-10-08
correct a bug in 4*diameter_ls, diameter_ls has to be converted to array first.
sqrt the node weight, 8 times the original weight
"""
import os, sys
sys.stderr.write("Drawing graph on a map ...\n")
import pylab, math
from matplotlib.toolkits.basemap import Basemap
pylab.clf()
fig = pylab.figure()
fig.add_axes([0.05, 0.05, 0.9, 0.9]) #[left, bottom, width, height]
m = Basemap(llcrnrlon=pic_area[0],llcrnrlat=pic_area[1],urcrnrlon=pic_area[2],urcrnrlat=pic_area[3],\
resolution='l',projection='mill', ax=pylab.gca())
sys.stderr.write("\tDrawing nodes ...")
euc_coord1_ls = []
euc_coord2_ls = []
diameter_ls = []
for n in g.nodes():
lat, lon = node2pos[n]
euc_coord1, euc_coord2 = m(lon, lat) #longitude first, latitude 2nd
euc_coord1_ls.append(euc_coord1)
euc_coord2_ls.append(euc_coord2)
diameter_ls.append(math.sqrt(node2weight[n]))
import numpy
diameter_ls = numpy.array(diameter_ls)
m.scatter(euc_coord1_ls,
euc_coord2_ls,
8 * diameter_ls,
marker='o',
color='r',
alpha=0.4,
zorder=12,
faceted=False)
sys.stderr.write("Done.\n")
if need_draw_edge:
sys.stderr.write("\tDrawing edges ...")
ax = pylab.gca()
for popid1, popid2, no_of_connections in g.edges():
lat1, lon1 = node2pos[popid1]
lat2, lon2 = node2pos[popid2]
x1, y1 = m(lon1, lat1)
x2, y2 = m(lon2, lat2)
ax.plot([x1, x2], [y1, y2],
'g',
linewidth=math.log(no_of_connections + 1) / 2,
alpha=0.2,
zorder=10)
sys.stderr.write("Done.\n")
#m.drawcoastlines()
m.drawparallels(pylab.arange(-90, 90, 30), labels=[1, 1, 0, 1])
m.drawmeridians(pylab.arange(-180, 180, 30), labels=[1, 1, 0, 1])
m.fillcontinents()
m.drawcountries()
m.drawstates()
pylab.title(pic_title)
if output_fname_prefix:
pylab.savefig('%s.eps' % output_fname_prefix, dpi=600)
pylab.savefig('%s.svg' % output_fname_prefix, dpi=600)
pylab.savefig('%s.png' % output_fname_prefix, dpi=600)
del fig, m, pylab
sys.stderr.write("Done.\n")
m = Basemap(-180.,-80.,180.,80.,\
resolution='c',area_thresh=10000.,projection='merc',\
lon_0=0.5*(lons[0]+lons[-1]),lat_ts=20.)
# transform to nx x ny regularly spaced native projection grid
nx = len(lons); ny = int(80.*len(lats)/90.)
topodat = m.transform_scalar(topoin,lons,lats,nx,ny)
# setup figure with same aspect ratio as map.
xsize = rcParams['figure.figsize'][0]
fig=figure(figsize=(xsize,m.aspect*xsize))
fig.add_axes([0.1,0.1,0.75,0.75])
# plot image over map.
im = m.imshow(topodat,cm.jet)
m.drawcoastlines()
m.drawcountries()
m.drawstates()
m.fillcontinents()
# draw parallels
m.drawparallels(circles,labels=[1,1,1,1])
# draw meridians
m.drawmeridians(meridians,labels=[1,1,1,1])
title('Mercator',y=1.1)
print 'plotting Mercator example, close plot window to proceed ...'
show()
# setup transverse mercator basemap.
m = Basemap(170.,-45,10.,45.,\
resolution='c',area_thresh=10000.,projection='tmerc',\
lat_0=0.,lon_0=-90.)
xsize = rcParams['figure.figsize'][0]
fig=figure(figsize=(xsize/m.aspect,xsize))
fig.add_axes([0.125,0.2,0.6,0.6])
def makeFigure(self):
"""
:todo: Uses pylab so may not be threadsafe.
"""
lon = self.grid.lon0 + N.arrayrange(self.grid.nlon) * self.grid.dlon
lat = self.grid.lat0 + N.arrayrange(self.grid.nlat) * self.grid.dlat
ll_lon, ll_lat = lon[0], lat[0]
ur_lon, ur_lat = lon[-1], lat[-1]
# Account for variable lat/lon axis ordering
#!TODO: Could we move this (along with the equivilent in render_imp.py into grid.py?
if self.grid.ilat < self.grid.ilon:
latLonOrdering = True
else:
latLonOrdering = False
if latLonOrdering:
var = self.grid.value
else:
var = MA.transpose(self.grid.value)
fig = p.figure()
map = Basemap(projection='cyl',
llcrnrlon=ll_lon,
llcrnrlat=ll_lat,
urcrnrlon=ur_lon,
urcrnrlat=ur_lat,
resolution='l')
## if self.grid.units:
## p.title("%s\n(%s)" % (self.grid.long_name, self.grid.units))
## else:
## p.title(self.grid.long_name)
p.title(self.grid.long_name)
if self.type == 'colour':
# transform_scalar doesn't support masked arrays so we must fill then replace the mask.
var_dat = map.transform_scalar(var.filled(1.0e20), lon, lat,
len(lon), len(lat))
var = MA.masked_values(var_dat, 1.0e20)
map.imshow(var,
cmap=self.cmap,
vmin=self.vmin,
vmax=self.vmax,
interpolation='nearest')
cbar = p.colorbar(orientation='horizontal', format='%.2g')
if self.grid.units:
cbar.ax.set_xlabel(self.grid.units)
else:
x, y = map(*p.meshgrid(lon, lat))
c = map.contour(x, y, var, 12, colors='black')
c.clabel(fontsize=8)
map.fillcontinents(color='#e0e0e0')
map.drawcoastlines(color='gray')
map.drawmeridians(p.arange(-180, 180, 30),
labels=[1, 0, 0, 1],
color='gray')
map.drawparallels(p.arange(-90, 90, 15),
labels=[1, 0, 0, 1],
color='gray')
# Wrap the caption
caption = word_wrap(self.caption, 80)
fig.text(0.1,
0.08,
caption,
fontsize=10,
horizontalalignment='left',
verticalalignment='top',
transform=fig.transFigure)
return fig
def test_drawMap(self):
sys.stderr.write("Drawing graph on a map ...\n")
#import pdb
#pdb.set_trace()
import StockDB, Stock_250kDB #StockDB has to be setup otherwise, StockDB.Ecotype.table is None in getEcotypeInfo()
hostname = 'papaya.usc.edu'
dbname = 'stock_250k'
db_user = '******'
db_passwd = ''
drivername = 'mysql'
schema = None
db = Stock_250kDB.Stock_250kDB(drivername=drivername,
username=db_user,
password=db_passwd,
hostname=hostname,
database=dbname,
schema=schema)
#doesn't matter which database to connect as far as StockDB is imported
#db = StockDB.StockDB(drivername=drivername, username=db_user,
# password=db_passwd, hostname=hostname, database=dbname, schema=schema)
db.setup(create_tables=False)
from common import getEcotypeInfo
ecotype_info = getEcotypeInfo(db)
from matplotlib.toolkits.basemap import Basemap
#from mpl_toolkits.basemap import Basemap
import pylab
from matplotlib import rcParams
rcParams['font.size'] = 6
rcParams['legend.fontsize'] = 6
#rcParams['text.fontsize'] = 6 #deprecated. use font.size instead
rcParams['axes.labelsize'] = 4
rcParams['axes.titlesize'] = 8
rcParams['xtick.labelsize'] = 4
rcParams['ytick.labelsize'] = 4
pylab.clf()
#fig = pylab.figure()
#fig.add_axes([0.05,0.05,0.9,0.9]) #[left, bottom, width, height]
axe_map = pylab.axes([0.5, 0.02, 0.4, 0.8], frameon=False)
axe_map.set_title("Global Arabidopsis Ecotype Distribution")
axe_map.set_xlabel('ecotype is drawn as circles.')
axe_map.set_ylabel('strains')
pic_area = [-140, -40, 140, 70]
m = Basemap(llcrnrlon=pic_area[0],llcrnrlat=pic_area[1],urcrnrlon=pic_area[2],urcrnrlat=pic_area[3],\
resolution='l',projection='mill', ax=axe_map)
"""
llcrnrx = -self.rmajor
llcrnry = -self.rmajor
urcrnrx = -llcrnrx
urcrnry = -llcrnry
"""
#m.drawcoastlines()
#m.bluemarble()
m.drawparallels(pylab.arange(-90, 90, 30),
labels=[1, 1, 0, 1],
size=4,
linewidth=0.1)
m.drawmeridians(pylab.arange(-180, 180, 30),
labels=[1, 1, 0, 1],
size=4,
linewidth=0.1)
m.fillcontinents()
m.drawcountries(linewidth=0.1)
#m.drawstates()
#m.drawlsmask((0,255,0,255), (0,0,255,255), lakes=True)
#m.drawlsmask('coral','aqua',lakes=True)
print "xlim:", axe_map.get_xlim()
print "ylim:", axe_map.get_ylim()
xlim = axe_map.get_xlim()
ylim = axe_map.get_ylim()
"""
for strain_id in StrainID2PCAPosInfo.strain_id_ls:
img_y_pos = StrainID2PCAPosInfo.strain_id2img_y_pos[strain_id]
phenotype_row_index = phenData.row_id2row_index[strain_id]
phenotype = phenData.data_matrix[phenotype_row_index][phenotype_col_index]
ecotype_id = int(strain_id)
ecotype_obj = ecotype_info.ecotype_id2ecotype_obj.get(ecotype_id)
if ecotype_obj:
lat, lon = ecotype_obj.latitude, ecotype_obj.longitude
else:
sys.stderr.write("Warning: Ecotype %s not in ecotype_info (fetched from stock db).\n"%ecotype_id)
continue
if lat and lon:
x, y = m(lon, lat)
color = cmap(norm(phenotype))
ax.plot([0, x], [img_y_pos, y], linestyle='--', alpha=0.2, linewidth=0.2)
ax.scatter([x],[y], s=10, linewidth=0, facecolor=color) #, zorder=10)
"""
#pylab.title("Global Arabidopsis Ecotype Distribution")
output_fname_prefix = '/tmp/map'
print "ylim:", axe_map.get_ylim()
axe_map.set_xlim(xlim)
axe_map.set_ylim(ylim)
axe_chromosome = pylab.axes([0.05, 0.02, 0.8, 0.8], frameon=False)
axe_chromosome.set_title("chromosome")
#fix the two transformations before doing cross-axe drawings
axe_map.transData.freeze() # eval the lazy objects
axe_map.transAxes.freeze()
axe_chromosome.transData.freeze() # eval the lazy objects
axe_chromosome.transAxes.freeze()
no_of_ecotypes = 200
ecotype_id_ls = ecotype_info.ecotype_id2ecotype_obj.keys(
)[:no_of_ecotypes]
no_of_ecotypes_drawn = 0
for i in range(no_of_ecotypes):
ecotype_id = ecotype_id_ls[i]
y_pos = i / float(no_of_ecotypes)
#y_pos = i/float(no_of_ecotypes)*ylim[1]
ecotype_obj = ecotype_info.ecotype_id2ecotype_obj.get(ecotype_id)
if ecotype_obj:
lat, lon = ecotype_obj.latitude, ecotype_obj.longitude
else:
sys.stderr.write(
"Warning: Ecotype %s not in ecotype_info (fetched from stock db).\n"
% ecotype_id)
continue
if lat and lon:
x, y = m(lon, lat)
#axe_map.plot([0, x], [y_pos, y], linestyle='--', alpha=0.2, linewidth=0.2)
axe_map.set_xlim(xlim)
axe_map.set_ylim(ylim)
axe_map.scatter([x], [y],
s=5,
linewidth=0,
facecolor='r',
alpha=0.2,
zorder=10)
canvas_x, canvas_y = axe_map.transData.xy_tup((x, y))
axe_chromosome_xy = axe_chromosome.transData.inverse_xy_tup(
(canvas_x, canvas_y))
axe_chromosome.plot([0, axe_chromosome_xy[0]],
[y_pos, axe_chromosome_xy[1]],
linestyle='--',
alpha=0.2,
linewidth=0.2)
no_of_ecotypes_drawn += 1
#release two transformations
axe_map.transData.thaw() # eval the lazy objects
axe_map.transAxes.thaw()
axe_chromosome.transData.thaw() # eval the lazy objects
axe_chromosome.transAxes.thaw()
#set to the same x/y_lim before cross-axe drawing
axe_map.set_xlim(xlim)
axe_map.set_ylim(ylim)
axe_chromosome.set_xlim([0, 1])
axe_chromosome.set_ylim([0, 1])
if output_fname_prefix:
pylab.savefig('%s.png' % output_fname_prefix, dpi=600)
pylab.savefig('%s.svg' % output_fname_prefix)
sys.stderr.write("%s ecotypes drawn. Done.\n" % (no_of_ecotypes_drawn))
# stack grids side-by-side (in longitiudinal direction), so
# any range of longitudes may be plotted on a world map.
tlon = N.concatenate((tlon, tlon + 360), 1)
tlat = N.concatenate((tlat, tlat), 1)
temp = MA.concatenate((temp, temp), 1)
tlon = tlon - 360.
pl.figure(figsize=(8.5, 11))
pl.subplot(2, 1, 1)
# subplot 1 just shows POP grid cells.
map = Basemap(projection='merc', lat_ts=20, llcrnrlon=-180, \
urcrnrlon=180, llcrnrlat=-84, urcrnrlat=84, resolution='c')
map.drawcoastlines()
map.fillcontinents(color='white')
x, y = map(tlon, tlat)
im = map.pcolor(x,y,MA.masked_array(N.zeros(temp.shape,'f'), temp.mask),\
shading='faceted',cmap=pl.cm.cool,vmin=0,vmax=0)
# disclaimer: these are not really the grid cells because of the
# way pcolor interprets the x and y args.
pl.title('(A) CCSM POP Grid Cells')
# subplot 2 is a contour plot of surface temperature from the
# CCSM ocean model.
pl.subplot(2, 1, 2)
map.drawcoastlines()
map.fillcontinents(color='white')
CS1 = map.contourf(x, y, temp, 15)
def draw_clustered_strain_location(self,
label_ls,
weighted_pos_ls,
diameter_ls,
label_type,
label_type2label_name,
pic_area=[-180, -90, 180, 90],
output_fname_prefix=None,
label_name=None):
"""
2007-07-11
draw populations derived from connected_components of the strain network
#each pie denotes a population, with diameter proportional to the size of the population
#each pie labeled with the number of strains in that population
2007-07-13
use popid as label
2007-07-17
no parallels, no meridians
2007-08-29
copied from CreatePopulation.py
2007-09-11
add label name
2007-10-14
correct a bug in 5*diameter_ls. diameter_ls has to an array
"""
sys.stderr.write("Drawing population map...")
import pylab
from matplotlib.toolkits.basemap import Basemap
pylab.clf()
fig = pylab.figure()
fig.add_axes([0.05, 0.05, 0.9, 0.9]) #[left, bottom, width, height]
m = Basemap(llcrnrlon=pic_area[0],llcrnrlat=pic_area[1],urcrnrlon=pic_area[2],urcrnrlat=pic_area[3],\
resolution='l',projection='mill')
euc_coord1_ls = []
euc_coord2_ls = []
ax = pylab.gca()
for i in range(len(weighted_pos_ls)):
lat, lon = weighted_pos_ls[i]
euc_coord1, euc_coord2 = m(lon,
lat) #longitude first, latitude 2nd
euc_coord1_ls.append(euc_coord1)
euc_coord2_ls.append(euc_coord2)
ax.text(euc_coord1,
euc_coord2,
str(label_ls[i]),
size=5,
alpha=0.5,
horizontalalignment='center',
verticalalignment='center',
zorder=12)
import numpy
diameter_ls = numpy.array(diameter_ls)
m.scatter(euc_coord1_ls,
euc_coord2_ls,
5 * diameter_ls,
marker='o',
color='r',
alpha=0.3,
zorder=10,
faceted=False)
#m.drawcoastlines()
m.drawparallels(pylab.arange(-90, 90, 30), labels=[
1, 1, 0, 1
]) #labels intersect the left, right, top bottom of the plot
m.drawmeridians(pylab.arange(-180, 180, 30), labels=[1, 1, 0, 1])
m.fillcontinents()
m.drawcountries()
m.drawstates()
pylab.title("worldwide distribution of %s populations, labeled by %s" %
(len(weighted_pos_ls), label_type2label_name[label_type]))
if output_fname_prefix:
pylab.savefig('%s_pop_map.eps' % output_fname_prefix, dpi=300)
pylab.savefig('%s_pop_map.svg' % output_fname_prefix, dpi=300)
pylab.savefig('%s_pop_map.png' % output_fname_prefix, dpi=300)
del m, pylab, Basemap
sys.stderr.write("Done.\n")
# stack grids side-by-side (in longitiudinal direction), so
# any range of longitudes may be plotted on a world map.
tlon = N.concatenate((tlon,tlon+360),1)
tlat = N.concatenate((tlat,tlat),1)
temp = MA.concatenate((temp,temp),1)
tlon = tlon-360.
pl.figure(figsize=(8.5,11))
pl.subplot(2,1,1)
# subplot 1 just shows POP grid cells.
map = Basemap(projection='merc', lat_ts=20, llcrnrlon=-180, \
urcrnrlon=180, llcrnrlat=-84, urcrnrlat=84, resolution='c')
map.drawcoastlines()
map.fillcontinents(color='white')
x, y = map(tlon,tlat)
im = map.pcolor(x,y,MA.masked_array(N.zeros(temp.shape,'f'), temp.mask),\
shading='faceted',cmap=pl.cm.cool,vmin=0,vmax=0)
# disclaimer: these are not really the grid cells because of the
# way pcolor interprets the x and y args.
pl.title('(A) CCSM POP Grid Cells')
# subplot 2 is a contour plot of surface temperature from the
# CCSM ocean model.
pl.subplot(2,1,2)
map.drawcoastlines()
map.fillcontinents(color='white')
CS1 = map.contourf(x,y,temp,15)
fig=figure()
# setup of sinusoidal basemap
m = Basemap(resolution='c',projection='sinu',lon_0=0)
ax = fig.add_axes([0.1,0.1,0.7,0.7])
# make a filled contour plot.
x, y = m(lons, lats)
CS = m.contour(x,y,hgt,15,linewidths=0.5,colors='k')
CS = m.contourf(x,y,hgt,15,cmap=cm.jet)
l,b,w,h=ax.get_position()
cax = axes([l+w+0.075, b, 0.05, h]) # setup colorbar axes
colorbar(drawedges=True, cax=cax) # draw colorbar
axes(ax) # make the original axes current again
# draw coastlines and political boundaries.
m.drawcoastlines()
m.drawmapboundary()
m.fillcontinents()
# draw parallels and meridians.
parallels = arange(-60.,90,30.)
m.drawparallels(parallels,labels=[1,0,0,0])
meridians = arange(-360.,360.,30.)
m.drawmeridians(meridians)
title('Sinusoidal Filled Contour Demo')
print 'plotting with sinusoidal basemap ...'
# create new figure
fig=figure()
# setup of mollweide basemap
m = Basemap(resolution='c',projection='moll',lon_0=0)
ax = fig.add_axes([0.1,0.1,0.7,0.7])
# make a filled contour plot.
x, y = m(lons, lats)
fig = figure(figsize=(xsize, m.aspect * xsize))
fig.add_axes([0.1, 0.1, 0.8, 0.8])
# nylat, nylon are lat/lon of New York
nylat = 40.78
nylon = -73.98
# lonlat, lonlon are lat/lon of London.
lonlat = 51.53
lonlon = 0.08
# find 1000 points along the great circle.
#x,y = m.gcpoints(nylon,nylat,lonlon,lonlat,1000)
# draw the great circle.
#m.plot(x,y,linewidth=2)
# drawgreatcircle performs the previous 2 steps in one call.
m.drawgreatcircle(nylon, nylat, lonlon, lonlat, linewidth=2, color='b')
m.drawcoastlines()
m.fillcontinents()
# draw parallels
circles = arange(10, 90, 20)
m.drawparallels(circles, labels=[1, 1, 0, 1])
# draw meridians
meridians = arange(-180, 180, 30)
m.drawmeridians(meridians, labels=[1, 1, 0, 1])
title('Great Circle from New York to London (Mercator)')
show()
# setup a gnomonic projection.
m = Basemap(llcrnrlon=-100.,llcrnrlat=20.,urcrnrlon=20.,urcrnrlat=60.,\
resolution='c',area_thresh=10000.,projection='gnom',\
lat_0=40.,lon_0=-45.)
# make figure with aspect ratio that matches map region.
xsize = rcParams['figure.figsize'][0]
resolution='c',area_thresh=10000.,projection='merc',\
lon_0=0.5*(lons[0]+lons[-1]),lat_ts=20.)
# transform to nx x ny regularly spaced native projection grid
nx = len(lons)
ny = int(80. * len(lats) / 90.)
topodat = m.transform_scalar(topoin, lons, lats, nx, ny)
# setup figure with same aspect ratio as map.
xsize = rcParams['figure.figsize'][0]
fig = figure(figsize=(xsize, m.aspect * xsize))
fig.add_axes([0.1, 0.1, 0.75, 0.75])
# plot image over map.
im = m.imshow(topodat, cm.jet)
m.drawcoastlines()
m.drawcountries()
m.drawstates()
m.fillcontinents()
# draw parallels
m.drawparallels(circles, labels=[1, 1, 1, 1])
# draw meridians
m.drawmeridians(meridians, labels=[1, 1, 1, 1])
title('Mercator', y=1.1)
print 'plotting Mercator example, close plot window to proceed ...'
show()
# setup transverse mercator basemap.
m = Basemap(170.,-45,10.,45.,\
resolution='c',area_thresh=10000.,projection='tmerc',\
lat_0=0.,lon_0=-90.)
xsize = rcParams['figure.figsize'][0]
fig = figure(figsize=(xsize / m.aspect, xsize))
fig.add_axes([0.125, 0.2, 0.6, 0.6])
def test_drawMap(self):
sys.stderr.write("Drawing graph on a map ...\n")
#import pdb
#pdb.set_trace()
import StockDB, Stock_250kDB #StockDB has to be setup otherwise, StockDB.Ecotype.table is None in getEcotypeInfo()
hostname='papaya.usc.edu'
dbname='stock_250k'
db_user='******'
db_passwd = ''
drivername='mysql'
schema = None
db = Stock_250kDB.Stock_250kDB(drivername=drivername, username=db_user,
password=db_passwd, hostname=hostname, database=dbname, schema=schema)
#doesn't matter which database to connect as far as StockDB is imported
#db = StockDB.StockDB(drivername=drivername, username=db_user,
# password=db_passwd, hostname=hostname, database=dbname, schema=schema)
db.setup(create_tables=False)
from common import getEcotypeInfo
ecotype_info = getEcotypeInfo(db)
from matplotlib.toolkits.basemap import Basemap
#from mpl_toolkits.basemap import Basemap
import pylab
from matplotlib import rcParams
rcParams['font.size'] = 6
rcParams['legend.fontsize'] = 6
#rcParams['text.fontsize'] = 6 #deprecated. use font.size instead
rcParams['axes.labelsize'] = 4
rcParams['axes.titlesize'] = 8
rcParams['xtick.labelsize'] = 4
rcParams['ytick.labelsize'] = 4
pylab.clf()
#fig = pylab.figure()
#fig.add_axes([0.05,0.05,0.9,0.9]) #[left, bottom, width, height]
axe_map = pylab.axes([0.5, 0.02, 0.4, 0.8], frameon=False)
axe_map.set_title("Global Arabidopsis Ecotype Distribution")
axe_map.set_xlabel('ecotype is drawn as circles.')
axe_map.set_ylabel('strains')
pic_area=[-140,-40,140,70]
m = Basemap(llcrnrlon=pic_area[0],llcrnrlat=pic_area[1],urcrnrlon=pic_area[2],urcrnrlat=pic_area[3],\
resolution='l',projection='mill', ax=axe_map)
"""
llcrnrx = -self.rmajor
llcrnry = -self.rmajor
urcrnrx = -llcrnrx
urcrnry = -llcrnry
"""
#m.drawcoastlines()
#m.bluemarble()
m.drawparallels(pylab.arange(-90,90,30), labels=[1,1,0,1], size=4, linewidth=0.1)
m.drawmeridians(pylab.arange(-180,180,30), labels=[1,1,0,1], size=4, linewidth=0.1)
m.fillcontinents()
m.drawcountries(linewidth=0.1)
#m.drawstates()
#m.drawlsmask((0,255,0,255), (0,0,255,255), lakes=True)
#m.drawlsmask('coral','aqua',lakes=True)
print "xlim:", axe_map.get_xlim()
print "ylim:", axe_map.get_ylim()
xlim = axe_map.get_xlim()
ylim = axe_map.get_ylim()
"""
for strain_id in StrainID2PCAPosInfo.strain_id_ls:
img_y_pos = StrainID2PCAPosInfo.strain_id2img_y_pos[strain_id]
phenotype_row_index = phenData.row_id2row_index[strain_id]
phenotype = phenData.data_matrix[phenotype_row_index][phenotype_col_index]
ecotype_id = int(strain_id)
ecotype_obj = ecotype_info.ecotype_id2ecotype_obj.get(ecotype_id)
if ecotype_obj:
lat, lon = ecotype_obj.latitude, ecotype_obj.longitude
else:
sys.stderr.write("Warning: Ecotype %s not in ecotype_info (fetched from stock db).\n"%ecotype_id)
continue
if lat and lon:
x, y = m(lon, lat)
color = cmap(norm(phenotype))
ax.plot([0, x], [img_y_pos, y], linestyle='--', alpha=0.2, linewidth=0.2)
ax.scatter([x],[y], s=10, linewidth=0, facecolor=color) #, zorder=10)
"""
#pylab.title("Global Arabidopsis Ecotype Distribution")
output_fname_prefix = '/tmp/map'
print "ylim:", axe_map.get_ylim()
axe_map.set_xlim(xlim)
axe_map.set_ylim(ylim)
axe_chromosome = pylab.axes([0.05, 0.02, 0.8, 0.8], frameon=False)
axe_chromosome.set_title("chromosome")
#fix the two transformations before doing cross-axe drawings
axe_map.transData.freeze() # eval the lazy objects
axe_map.transAxes.freeze()
axe_chromosome.transData.freeze() # eval the lazy objects
axe_chromosome.transAxes.freeze()
no_of_ecotypes = 200
ecotype_id_ls = ecotype_info.ecotype_id2ecotype_obj.keys()[:no_of_ecotypes]
no_of_ecotypes_drawn = 0
for i in range(no_of_ecotypes):
ecotype_id = ecotype_id_ls[i]
y_pos = i/float(no_of_ecotypes)
#y_pos = i/float(no_of_ecotypes)*ylim[1]
ecotype_obj = ecotype_info.ecotype_id2ecotype_obj.get(ecotype_id)
if ecotype_obj:
lat, lon = ecotype_obj.latitude, ecotype_obj.longitude
else:
sys.stderr.write("Warning: Ecotype %s not in ecotype_info (fetched from stock db).\n"%ecotype_id)
continue
if lat and lon:
x, y = m(lon, lat)
#axe_map.plot([0, x], [y_pos, y], linestyle='--', alpha=0.2, linewidth=0.2)
axe_map.set_xlim(xlim)
axe_map.set_ylim(ylim)
axe_map.scatter([x],[y], s=5, linewidth=0, facecolor='r', alpha=0.2, zorder=10)
canvas_x, canvas_y = axe_map.transData.xy_tup((x,y))
axe_chromosome_xy = axe_chromosome.transData.inverse_xy_tup((canvas_x,canvas_y))
axe_chromosome.plot([0,axe_chromosome_xy[0]], [y_pos, axe_chromosome_xy[1]], linestyle='--', alpha=0.2, linewidth=0.2)
no_of_ecotypes_drawn += 1
#release two transformations
axe_map.transData.thaw() # eval the lazy objects
axe_map.transAxes.thaw()
axe_chromosome.transData.thaw() # eval the lazy objects
axe_chromosome.transAxes.thaw()
#set to the same x/y_lim before cross-axe drawing
axe_map.set_xlim(xlim)
axe_map.set_ylim(ylim)
axe_chromosome.set_xlim([0,1])
axe_chromosome.set_ylim([0,1])
if output_fname_prefix:
pylab.savefig('%s.png'%output_fname_prefix, dpi=600)
pylab.savefig('%s.svg'%output_fname_prefix)
sys.stderr.write("%s ecotypes drawn. Done.\n"%(no_of_ecotypes_drawn))
# transform lons and lats to map coordinates.
x,y = m(array(lons_out)/d2r, array(lats_out)/d2r)
# find just those points in map region.
xx=[]
yy=[]
for xi,yi in zip(x,y):
if (xi>m.llcrnrx and xi<m.urcrnrx) and (yi>m.llcrnry and yi<m.urcrnry):
xx.append(xi)
yy.append(yi)
# plot them as filled circles on the map.
# first, create figure with same aspect ratio as map.
fig=m.createfigure()
# background color will be used for 'wet' areas.
fig.add_axes([0.1,0.1,0.8,0.8],axisbg='aqua')
# use zorder=10 to make sure markers are drawn last.
# (otherwise they are covered up when continents are filled)
m.scatter(xx,yy,marker='o',c='k',s=25,zorder=10)
# draw coasts and fill continents.
m.drawcoastlines(linewidth=0.5)
m.fillcontinents(color='coral')
# draw parallels and meridians.
delat = 20.
circles = arange(0.,90.,delat).tolist()+\
arange(-delat,-90,-delat).tolist()
m.drawparallels(circles)
delon = 45.
meridians = arange(0,360,delon)
m.drawmeridians(meridians,labels=[1,1,1,1])
title('Randomly Spaced Locations (Min Dist = %g km, %g points)'% (rcrit,len(lons_out)),y=1.075)
show()
# set size of plot to match aspect ratio of map.
ax = gca()
l, b, w, h = ax.get_position()
b = 0.5 - 0.5 * w * m.aspect
h = w * m.aspect
#l = 0.5 - 0.5*h/m.aspect; w = h/m.aspect
ax.set_position([l, b, w, h])
corners = ((m.xmin, m.ymin), (m.xmax, m.ymax))
ax.update_datalim(corners)
axis([m.xmin, m.xmax, m.ymin, m.ymax])
# draw map.
if plot in ['pcolor', 'imshow']:
m.drawcoastlines(ax)
else:
m.fillcontinents(ax, color='gray')
# draw parallels
delat = 30.
delon = 30.
circles = arange(0.,90.+delat,delat).tolist()+\
arange(-delat,-90.-delat,-delat).tolist()
m.drawparallels(ax, circles)
# draw meridians
meridians = arange(0., 360., delon)
m.drawmeridians(ax, meridians)
ax.set_xticks([]) # no ticks
ax.set_yticks([])
title('500 hPa Height - ' + plot)
from matplotlib.toolkits.basemap import Basemap
from pylab import *
# setup a mercator projection.
m = Basemap(-90.,30.,30.,60.,\
resolution='c',area_thresh=10000.,projection='merc',\
lat_ts=20.)
xsize = rcParams['figure.figsize'][0]
fig=figure(figsize=(xsize,m.aspect*xsize))
fig.add_axes([0.1,0.1,0.8,0.8])
ax = gca() # get current axis instance
ax.update_datalim(((m.llcrnrx, m.llcrnry),(m.urcrnrx,m.urcrnry)))
ax.set_xlim((m.llcrnrx, m.urcrnrx))
ax.set_ylim((m.llcrnry, m.urcrnry))
m.drawcoastlines(ax)
m.fillcontinents(ax)
# draw parallels
circles = [35,45,55]
m.drawparallels(ax,circles,labels=[1,1,0,1])
# draw meridians
meridians = [-90,-60,-30,0,30]
m.drawmeridians(ax,meridians,labels=[1,1,0,1])
# nylat, nylon are lat/lon of New York
nylat = 40.78
nylon = -73.98
# lonlat, lonlon are lat/lon of London.
lonlat = 51.53
lonlon = 0.08
# draw the great circle.
m.drawgreatcircle(ax,nylon,nylat,lonlon,lonlat,linewidth=2,color='b')
title('Great Circle from New York to London')
v = uniform(0.0, 1.0, size=npts)
z = uniform(0.0, 1.0, size=npts)
except: # this works for Numeric/numarray
u = uniform(0.0, 1.0, shape=npts)
v = uniform(0.0, 1.0, shape=npts)
z = uniform(0.0, 1.0, shape=npts)
lons = 360.0 * u
lats = (180.0 / math.pi) * arccos(2 * v - 1) - 90.0
# transform lons and lats to map coordinates.
x, y = m(lons, lats)
# plot them as filled circles on the map.
# first, create a figure.
fig = figure()
# background color will be used for 'wet' areas.
fig.add_axes([0.1, 0.1, 0.8, 0.8], axisbg="aqua")
# use zorder=10 to make sure markers are drawn last.
# (otherwise they are covered up when continents are filled)
m.scatter(x, y, 25, z, cmap=cm.jet, marker="o", faceted=False, zorder=10)
# draw coasts and fill continents.
m.drawcoastlines(linewidth=0.5)
m.fillcontinents(color="coral")
# draw parallels and meridians.
delat = 20.0
circles = arange(0.0, 90.0, delat).tolist() + arange(-delat, -90, -delat).tolist()
m.drawparallels(circles)
delon = 45.0
meridians = arange(0, 360, delon)
m.drawmeridians(meridians, labels=[1, 1, 1, 1])
title("Random Points", y=1.075)
show()
xx = []
yy = []
for xi, yi in zip(x, y):
if (xi > m.llcrnrx and xi < m.urcrnrx) and (yi > m.llcrnry
and yi < m.urcrnry):
xx.append(xi)
yy.append(yi)
# plot them as filled circles on the map.
# first, create figure with same aspect ratio as map.
fig = m.createfigure()
# background color will be used for 'wet' areas.
fig.add_axes([0.1, 0.1, 0.8, 0.8], axisbg='aqua')
# use zorder=10 to make sure markers are drawn last.
# (otherwise they are covered up when continents are filled)
m.scatter(xx, yy, marker='o', c='k', s=25, zorder=10)
# draw coasts and fill continents.
m.drawcoastlines(linewidth=0.5)
m.fillcontinents(color='coral')
# draw parallels and meridians.
delat = 20.
circles = arange(0.,90.,delat).tolist()+\
arange(-delat,-90,-delat).tolist()
m.drawparallels(circles)
delon = 45.
meridians = arange(0, 360, delon)
m.drawmeridians(meridians, labels=[1, 1, 1, 1])
title('Randomly Spaced Locations (Min Dist = %g km, %g points)' %
(rcrit, len(lons_out)),
y=1.075)
show()
print shp_info
# find names of storms that reached Cat 4.
names = []
for shapedict in m.hurrtracks_info:
cat = shapedict['CATEGORY']
name = shapedict['NAME']
if cat in ['H4','H5'] and name not in names:
# only use named storms.
if name != 'NOT NAMED': names.append(name)
print names
print len(names)
# plot tracks of those storms.
for shapedict,shape in zip(m.hurrtracks_info,m.hurrtracks):
name = shapedict['NAME']
cat = shapedict['CATEGORY']
if name in names:
xx,yy = zip(*shape)
# show part of track where storm > Cat 4 as thick red.
if cat in ['H4','H5']:
p.plot(xx,yy,linewidth=1.5,color='r')
elif cat in ['H1','H2','H3']:
p.plot(xx,yy,color='k')
# draw coastlines, meridians and parallels.
m.drawcoastlines()
m.drawcountries()
m.fillcontinents(color='#cc9966')
m.drawparallels(p.arange(10,70,20),labels=[1,1,0,0])
m.drawmeridians(p.arange(-100,0,20),labels=[0,0,0,1])
p.title('Atlantic Hurricane Tracks (Storms Reaching Category 4, 1851-2003)')
p.show()
