def main():
import sys
app = QtGui.QApplication(sys.argv)
data = load('PCA_Matrix_noProE.csv', delimiter = ',', skiprows = 1)
w = PCA_Widget(data)
#w.show()
sys.exit(app.exec_())
def getdrift_raw(filename,id3,interval,datetime_wanted):
# range_time is a number,unit by one day. datetime_wanted format is num
d=ml.load(filename)
lat1=d[:,8]
lon1=d[:,7]
idd=d[:,0]
year=[]
for n in range(len(idd)):
year.append(str(idd[n])[0:2])
h=d[:,4]
day=d[:,3]
month=d[:,2]
time1=[]
for i in range(len(idd)):
time1.append(date2num(datetime.datetime.strptime(str(int(h[i]))+' '+str(int(day[i]))+' '+str(int(month[i]))+' '+str(int(year[i])), "%H %d %m %y")))
idg1=list(ml.find(idd==id3))
idg2=list(ml.find(np.array(time1)<=datetime_wanted+interval/24))
"'0.25' means the usual Interval, It can be changed base on different drift data "
idg3=list(ml.find(np.array(time1)>=datetime_wanted-0.1))
idg23=list(set(idg2).intersection(set(idg3)))
# find which data we need
idg=list(set(idg23).intersection(set(idg1)))
print 'the length of drifter data is '+str(len(idg)),str(len(set(idg)))+' . if same, no duplicate'
lat,lon,time=[],[],[]
for x in range(len(idg)):
lat.append(round(lat1[idg[x]],4))
lon.append(round(lon1[idg[x]],4))
time.append(round(time1[idg[x]],4))
# time is num
return lat,lon,time
def getdrift_raw(filename,id3,interval,datetime_wanted):
# range_time is a number,unit by one day. datetime_wanted format is num
d=ml.load(filename)
lat1=d[:,8]
lon1=d[:,7]
idd=d[:,0]
year=[]
for n in range(len(idd)):
year.append(str(idd[n])[0:2])
h=d[:,4]
day=d[:,3]
month=d[:,2]
time1=[]
for i in range(len(idd)):
time1.append(date2num(datetime.datetime.strptime(str(int(h[i]))+' '+str(int(day[i]))+' '+str(int(month[i]))+' '+str(int(year[i])), "%H %d %m %y")))
idg1=list(ml.find(idd==id3))
idg2=list(ml.find(np.array(time1)<=datetime_wanted+interval/24))
"'0.25' means the usual Interval, It can be changed base on different drift data "
idg3=list(ml.find(np.array(time1)>=datetime_wanted-0.1))
idg23=list(set(idg2).intersection(set(idg3)))
# find which data we need
idg=list(set(idg23).intersection(set(idg1)))
print 'the length of drifter data is '+str(len(idg)),str(len(set(idg)))+' . if same, no duplicate'
lat,lon,time=[],[],[]
for x in range(len(idg)):
lat.append(round(lat1[idg[x]],4))
lon.append(round(lon1[idg[x]],4))
time.append(round(time1[idg[x]],4))
# time is num
return lat,lon,time
def range_latlon(filename,driftnumber): # function neede in case of "raw" drifter date d=ml.load(filename) id=ml.find(d[:,0]==int(driftnumber)) lat1=d[id,8] lon1=d[id,7] maxlon=max(lon1) minlon=min(lon1) maxlat=max(lat1) minlat=min(lat1) return maxlon,minlon,maxlat,minlat,lat1,lon1
def read_CSV_mlab(self, filename):
dirname, fname = os.path.split(str(filename))
name, ext = os.path.splitext(fname)
delim = mdictFileFormats[ext]
DataArray = []
try:
DataArray = load(filename, delimiter=delim, skiprows=1, dtype=str)
return DataArray.tolist()
except:
return []
def range_latlon(filename,
driftnumber): # function neede in case of "raw" drifter date
d = ml.load(filename)
id = ml.find(d[:, 0] == int(driftnumber))
lat1 = d[id, 8]
lon1 = d[id, 7]
maxlon = max(lon1)
minlon = min(lon1)
maxlat = max(lat1)
minlat = min(lat1)
return maxlon, minlon, maxlat, minlat, lat1, lon1
def __init__(self, ticker):
# load assumes floats unless you provide a converter
# dictionary keyed by column number
self.ticker = ticker
tickerfile = os.path.join(datadir, '%s.csv'%ticker)
# datestr2num is slow but it's easy
(self.date, self.open, self.high, self.low,
self.close, self.volume, self.adjclose) = load(
tickerfile, delimiter=',',
#converters={0:to_datenum2},
converters={0:datestr2num},
skiprows=1, unpack=True)
def __init__(self, ticker):
# load assumes floats unless you provide a converter
# dictionary keyed by column number
self.ticker = ticker
tickerfile = os.path.join(datadir, '%s.csv'%ticker)
# datestr2num is slow but it's easy
(self.date, self.open, self.high, self.low,
self.close, self.volume, self.adjclose) = load(
tickerfile, delimiter=',',
#converters={0:to_datenum2},
converters={0:datestr2num},
skiprows=1, unpack=True)
def getdrift_raw_range_latlon(filename, id3, interval, datetime_wanted_1, num,
step_size):
# this is for plot all the data in same range of lat and lon. id3 means int format of drift number
#'interval' means range of time, 'num' means how many pictures we will get
d = ml.load(filename)
lat1 = d[:, 8]
lon1 = d[:, 7]
idd = d[:, 0]
year = []
for n in range(len(idd)):
year.append(str(idd[n])[0:2])
h = d[:, 4]
day = d[:, 3]
month = d[:, 2]
time1 = []
for i in range(len(idd)):
time1.append(
date2num(
datetime.datetime.strptime(
str(int(h[i])) + ' ' + str(int(day[i])) + ' ' +
str(int(month[i])) + ' ' + str(int(year[i])),
"%H %d %m %y")))
idg1 = list(ml.find(idd == id3))
idg2 = list(
ml.find(
np.array(time1) <= datetime_wanted_1 + step_size / 24.0 *
(num - 1) + 0.25))
"'0.25' means the usual Interval, It can be changed base on different drift data "
idg3 = list(
ml.find(np.array(time1) >= datetime_wanted_1 - interval / 24.0))
idg23 = list(set(idg2).intersection(set(idg3)))
# find which data we need
idg = list(set(idg23).intersection(set(idg1)))
# print len(idg),len(set(idg))
lat, lon, time = [], [], []
for x in range(len(idg)):
lat.append(round(lat1[idg[x]], 4))
lon.append(round(lon1[idg[x]], 4))
maxlon = max(lon)
minlon = min(lon)
maxlat = max(lat)
minlat = min(lat)
# time is num
return maxlon, minlon, maxlat, minlat
def getdrift_raw_range_latlon(filename,id3,interval,datetime_wanted_1,num,step_size):
# this is for plot all the data in same range of lat and lon. id3 means int format of drift number
#'interval' means range of time, 'num' means how many pictures we will get
d=ml.load(filename)
lat1=d[:,8]
lon1=d[:,7]
idd=d[:,0]
year=[]
for n in range(len(idd)):
year.append(str(idd[n])[0:2])
h=d[:,4]
day=d[:,3]
month=d[:,2]
time1=[]
for i in range(len(idd)):
time1.append(date2num(datetime.datetime.strptime(str(int(h[i]))+' '+str(int(day[i]))+' '+str(int(month[i]))+' '+str(int(year[i])), "%H %d %m %y")))
idg1=list(ml.find(idd==id3))
idg2=list(ml.find(np.array(time1)<=datetime_wanted_1+step_size/24.0*(num-1)+0.25))
"'0.25' means the usual Interval, It can be changed base on different drift data "
idg3=list(ml.find(np.array(time1)>=datetime_wanted_1-interval/24.0))
idg23=list(set(idg2).intersection(set(idg3)))
# find which data we need
idg=list(set(idg23).intersection(set(idg1)))
# print len(idg),len(set(idg))
lat,lon,time=[],[],[]
for x in range(len(idg)):
lat.append(round(lat1[idg[x]],4))
lon.append(round(lon1[idg[x]],4))
maxlon=max(lon)
minlon=min(lon)
maxlat=max(lat)
minlat=min(lat)
# time is num
return maxlon,minlon,maxlat,minlat
for kind in ['P', 'C']:
for i in range(num+1):
plt.clf()
plt.axes([0.3,0.2,0.95-0.3,0.95-0.2])
if kind=='C' and i==0:
continue
if kind=='C' and len(data)>66000:
continue
data = mlab.load("%s_%s%d.dat" % (filename, kind, i))
num_elems = len(data)
t = np.arange(num_elems)
plt.hold(True)
max_val = max(abs(data)) * threshold
if kind == 'P':
size=4
width = 0.2
else:
from matplotlib.dates import strpdate2num
from matplotlib.mlab import load
from pylab import figure, show
dates, closes = load('data/msft.csv',
delimiter=',',
converters={0: strpdate2num('%d-%b-%y')},
skiprows=1,
usecols=(0, 2),
unpack=True)
fig = figure()
ax = fig.add_subplot(111)
ax.plot_date(dates, closes, '-')
fig.autofmt_xdate()
show()
from matplotlib.dates import num2date
import numpy as np
############
##HARDCODES
mthyr='feb2015' # mthyr of processing
############
f=open('/net/data5/jmanning/drift/drift2header_'+mthyr+'.out','w'); # opens output file
#pipe = subprocess.Popen(["perl", "/home3/ocn/jmanning/sql/getdrift_header.plx"
# this reads a list of ids that need to be loaded into drift_header table from a file like drift2header_'+mthyr+'mar2013.dat
# which is created by running the code "update_drift_header.py"
# It gets LAT_START,LON_START,BTM_DEPTH_START,START_DATE,DROGUE_DEPTH_START,
# DROGUE_DEPTH_END where start and end drogue depth is in the
# vertical dimension and the _data file list only the midvalue
#d=ml.load('/net/data5/jmanning/drift/sqldump_header.dat')
d=ml.load('/net/data5/jmanning/drift/sqldump_header.dat') # loads list of
codes=ml.load('/net/data5/jmanning/drift/codes.dat')
jj=0
for k in range(len(d)):
if len(set([d[k,0]]).intersection(set([138410703,146410701]))):
if (d[k,0]==138410703):
deployer='SEA ' # 12 character deployer name
notes='deployed by undergrad students, the aluminum surface drifter went silent on 10 September, recovered by Chathams Harbormaster Stuart Smith, and picked up by Miles Manning; the 12m drogue (25 Aug) dragged bottom on Tuckernuck Shoals until Dick Limeburner recovered it in mid-October'
typei='Irina '
droguetop=0
droguebot=1.0
droguedia=1.0
elif d[k,0]==146410701:
deployer='Tiejie'
notes='deployed by Hollings students, Conner and Ed; apparently lost drogue 3 weeks later, came ashore on Craigville Beach, possibly taken home by someone to Yarmouth'
typei='Drogue '
#from matplotlib.basemap import Basemap, shiftgrid #import matplotlib.numerix.ma as M import matplotlib.pyplot as plt import matplotlib.mlab #import matplotlib.colors as colors #from matplotlib import * #import asciidata #import MA mapfile = sys.argv[1]+'.lonlatT' lonfile = sys.argv[1]+'.lon' latfile = sys.argv[1]+'.lat' # load data map=np.array(mlab.load(mapfile)) #minv=MA.minimum(map) #maxv=MA.maximum(map) #print(minv,maxv) lons=np.array(mlab.load(lonfile)) lats=np.array(mlab.load(latfile)) #tmp=lons #tmp=lons[0] #lons=lons-tmp #lons[size(lons)-1]=360 print lons
def from_asc(fin,sep=' '): """read an array from an ascii file, return a numpy array""" from matplotlib.mlab import load return load(fin,delimiter=sep)
"""
parse and load ge.csv into a record array
"""
import time, datetime, csv
import dateutil.parser
import matplotlib.mlab as mlab
import matplotlib.dates as mdates
import matplotlib.cbook as cbook
import numpy as np
# this is how you can use the function np.loadtxt to do the same
# JDH TODO: this isn't working in mlab.load or np.loadtxt. Fix
#rows = np.loadtxt('data/ge.csv', skiprows=1, converters={0:mdates.date2num}, delimiter=',')
rows = mlab.load('data/ge.csv', skiprows=1, converters={0:mdates.date2num}, delimiter=',')
r = np.rec.fromrecords(rows, names='date,open,high,low,close,volume,adjclose')
# compute the average approximate dollars traded over the last 10 days
# hint: closing price * volume trades approx equals dollars trades
recent = r[-10:]
dollars = (recent.close * recent.volume).mean()
print '$%1.2fM'%(dollars/1e6)
# plot the adjusted closing price vs time since 2003 - hint, you must
# use date2num to convert the date to a float for mpl. Make two axes,
# one for price and one for volume. Use a bar chart for volume
import matplotlib.pyplot as plt
dates = mdates.num2date(r.date) # convert these to native datetime.date objects
mask = dates > datetime.date(2003,1,1)
price = r.adjclose[mask]
volume = r.volume[mask]
m = a*numpy.ones((len(t),), dtype=numpy.float_)
for k, tk in enumerate(t):
if k==0:
m[k] = a
else:
m[k] = m[k-1]*numpy.exp(-alpha*(t[k]-t[k-1])) + a
return m + 1.
# data files are located in 'data' and have filenames
# synapse_times.dat and synapse_data.dat, each of which contain a
# single column of ASCII floating point numbers. load each into an
# array t and s, where t is the array of times and s an equal length
# array of synapse amplitudes
t = load(os.path.join('data', 'synapse_times.dat'))
s = load(os.path.join('data', 'synapse_data.dat'))
guess = (-0.05, 0.3)
# create the model and do the best fit
model = Model(t, s, one_exponential)
bounds = [(None, 0.), (0., None)]
bestpars = model.fit(guess, bounds=bounds)
# plot the interspike-interval histogram, the actual data, and the best model fit
fig = figure()
ax1 = fig.add_subplot(311)
model.plot_isi(ax1)
from matplotlib import mlab filename = '/Users/miura/Dropbox/data/segmented.tif91_92.txt' x, y, ux1, uy1, mag1, ang1= mlab.load(filename, usecols=[0,1,2,3,4,5], unpack=True)
#data = csv.reader(open('/Users/miura/Dropbox/Mette/Tracks.csv'))
#p1 = []
#tA = []
#for row, points in enumerate(data):
# tA.append(points[2])
# p1.append(points[3:6])
# p2.append(points[3:])
#for d in tA:
# print d
from matplotlib import mlab as matp
ind, frame, gx, gy, gz, ux, vy, wz = matp.load(
filename,
skiprows=1,
delimiter=',',
usecols=[0, 1, 2, 3, 4, 5, 6, 7],
unpack=True)
#for d in frame:
#print d
#from mayavi.mlab import points3d
#from mayavi.mlab import plot3d
from mayavi.mlab import quiver3d
#from mayavi.mlab import flow
from mayavi import mlab as maya
##p1s = points3d(sx, sy, sz, scale_factor=.45, color=(1, 0, 0))
#trial with colorlist
#import csv
#data = csv.reader(open('C:/dropbox/My Dropbox/Pairs_NowCorrectDot.txt', 'rb'), delimiter='\t')
#p1 = []
#p2 = []
#for row, points in enumerate(data):
# p1.append(points[:3])
# p2.append(points[3:])
#for d in p2:
#print d
from matplotlib import mlab as matp
#filename = '/Users/miura/Dropbox/Pairs_NowCorrectDot.txt'
filename = 'c:/dropbox/My Dropbox/Wani_3D/Pairs_NowCorrectDot.txt'
x1, y1, z1, x2, y2, z2 = matp.load(filename,
usecols=[0, 1, 2, 3, 4, 5],
unpack=True)
#for d in z1:
#print d
#print x1[0]+x1[2]
#dotsfile = '/Users/miura/Dropbox/ProfileDiscdata.txt'
dotsfile = 'c:/dropbox/My Dropbox/Wani_3D/ProfileDiscdata.txt'
dx1, dy1, dz1 = matp.load(dotsfile, usecols=[0, 1, 2], unpack=True)
tabledots = matp.load(dotsfile)
xc1 = tabledots[0:(len(tabledots) / 2 - 1), 0]
xc2 = tabledots[(len(tabledots) / 2):, 0]
yc1 = tabledots[0:(len(tabledots) / 2 - 1), 1]
yc2 = tabledots[(len(tabledots) / 2):, 1]
zc1 = tabledots[0:(len(tabledots) / 2 - 1), 2]
zc2 = tabledots[(len(tabledots) / 2):, 2]
# exampleMayavi.py # Kota Miura ([email protected]) #example script to load coordinates data from tab-delimited file #and plot in 3D using mayavi2. #We assume that data in the file has a pair pf coordinates per line, #so 6 numbers are in one line separated by tab. from matplotlib import mlab as matp filename = '/Users/miura/data.txt' x1, y1, z1, x2, y2, z2 = matp.load(filename, usecols=[0, 1, 2, 3, 4, 5], unpack=True) from mayavi.mlab import points3d from mayavi.mlab import plot3d from mayavi import mlab as maya p1s = points3d(x1, y1, z1, scale_factor=.25, color=(0, 1, 1)) p2s = points3d(x2, y2, z2, scale_factor=.25, color=(1, 0, 0)) for idx, xval in enumerate(x1): plin1 = plot3d([x1[idx], x2[idx]], [y1[idx], y2[idx]], [z1[idx], z2[idx]], tube_radius=0.1, colormap='Spectral', color=(0, 0, 1)) maya.show()
# exampleMayavi.py # Kota Miura ([email protected]) #example script to load coordinates data from tab-delimited file #and plot in 3D using mayavi2. #We assume that data in the file has a pair pf coordinates per line, #so 6 numbers are in one line separated by tab. from matplotlib import mlab as matp filename = '/Users/miura/data.txt' x1, y1, z1, x2, y2, z2 = matp.load(filename, usecols=[0, 1, 2, 3, 4, 5], unpack=True) from mayavi.mlab import points3d from mayavi.mlab import plot3d from mayavi import mlab as maya p1s = points3d(x1, y1, z1, scale_factor=.25, color=(0, 1, 1)) p2s = points3d(x2, y2, z2, scale_factor=.25, color=(1, 0, 0)) for idx, xval in enumerate(x1): plin1 = plot3d([x1[idx], x2[idx]], [y1[idx], y2[idx]], [z1[idx], z2[idx]], tube_radius=0.1, colormap='Spectral', color=(0, 0, 1)) maya.show()
#data = csv.reader(open('/Users/miura/Dropbox/Mette/Tracks.csv'))
#p1 = []
#tA = []
#for row, points in enumerate(data):
# tA.append(points[2])
# p1.append(points[3:6])
# p2.append(points[3:])
#for d in tA:
# print d
from matplotlib import mlab as matp
ind, trajID, tA, x1, y1, z1, sx, sy, sz, ptID = matp.load(
filename,
skiprows=1,
delimiter=',',
usecols=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
unpack=True)
#for d in ind:
#print d
from mayavi.mlab import points3d
from mayavi.mlab import plot3d
from mayavi.mlab import quiver3d
from mayavi.mlab import flow
from mayavi import mlab as maya
p1s = points3d(sx, sy, sz, scale_factor=.45, color=(1, 0, 0))
curtid = -1.0
# pylab-free version of plotmap.py
##################################
# set backend to Agg.
import matplotlib
matplotlib.use('Agg')
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.toolkits.basemap import Basemap, shiftgrid
from matplotlib.figure import Figure
import matplotlib.numerix as NX
import matplotlib.cm as cm
from matplotlib.mlab import load
# read in topo data (on a regular lat/lon grid)
# longitudes go from 20 to 380.
topoin = load('etopo20data.gz')
lons = load('etopo20lons.gz')
lats = load('etopo20lats.gz')
# shift data so lons go from -180 to 180 instead of 20 to 380.
topoin, lons = shiftgrid(180., topoin, lons, start=False)
# create figure, axes instance.
fig = Figure()
canvas = FigureCanvas(fig)
ax = fig.add_axes([0.1, 0.1, 0.7, 0.7])
# setup of basemap ('lcc' = lambert conformal conic).
# use major and minor sphere radii from WGS84 ellipsoid.
# pass axes instance to Basemap constructor so pylab won't
# be imported.
m = Basemap(llcrnrlon=-145.5,llcrnrlat=1.,urcrnrlon=-2.566,urcrnrlat=46.352,\
#import csv
#data = csv.reader(open('C:/dropbox/My Dropbox/Pairs_NowCorrectDot.txt', 'rb'), delimiter='\t')
#p1 = []
#p2 = []
#for row, points in enumerate(data):
# p1.append(points[:3])
# p2.append(points[3:])
#for d in p2:
#print d
from matplotlib import mlab as matp
#filename = '/Users/miura/Dropbox/Pairs_NowCorrectDot.txt'
filename = 'c:/dropbox/My Dropbox/Wani_3D/Pairs_NowCorrectDot.txt'
x1, y1, z1, x2, y2, z2 = matp.load(filename, usecols=[0, 1, 2, 3, 4, 5], unpack=True)
#for d in z1:
#print d
#print x1[0]+x1[2]
#dotsfile = '/Users/miura/Dropbox/ProfileDiscdata.txt'
dotsfile = 'c:/dropbox/My Dropbox/Wani_3D/ProfileDiscdata.txt'
dx1, dy1, dz1 = matp.load(dotsfile, usecols=[0,1,2], unpack=True)
tabledots = matp.load(dotsfile)
xc1 = tabledots[0:(len(tabledots)/2 - 1), 0]
xc2 = tabledots[(len(tabledots)/2):, 0]
yc1 = tabledots[0:(len(tabledots)/2 - 1), 1]
yc2 = tabledots[(len(tabledots)/2):, 1]
zc1 = tabledots[0:(len(tabledots)/2 - 1), 2]
zc2 = tabledots[(len(tabledots)/2):, 2]
from mayavi.mlab import points3d
from mayavi.mlab import plot3d
"""
parse and load ge.csv into a record array
"""
import time, datetime, csv
import dateutil.parser
import matplotlib.mlab as mlab
import matplotlib.dates as mdates
import matplotlib.cbook as cbook
import numpy as np
# this is how you can use the function np.loadtxt to do the same
# JDH TODO: this isn't working in mlab.load or np.loadtxt. Fix
#rows = np.loadtxt('data/ge.csv', skiprows=1, converters={0:mdates.date2num}, delimiter=',')
rows = mlab.load('data/ge.csv',
skiprows=1,
converters={0: mdates.date2num},
delimiter=',')
r = np.rec.fromrecords(rows, names='date,open,high,low,close,volume,adjclose')
# compute the average approximate dollars traded over the last 10 days
# hint: closing price * volume trades approx equals dollars trades
recent = r[-10:]
dollars = (recent.close * recent.volume).mean()
print '$%1.2fM' % (dollars / 1e6)
# plot the adjusted closing price vs time since 2003 - hint, you must
# use date2num to convert the date to a float for mpl. Make two axes,
# one for price and one for volume. Use a bar chart for volume
import matplotlib.pyplot as plt
dates = mdates.num2date(
from matplotlib.dates import strpdate2num
from matplotlib.mlab import load
from pylab import figure, show
dates, closes = load(
'../data/msft.csv', delimiter=',',
converters={0:strpdate2num('%d-%b-%y')},
skiprows=1, usecols=(0,2), unpack=True)
fig = figure()
ax = fig.add_subplot(111)
ax.plot_date(dates, closes, '-')
fig.autofmt_xdate()
show()
#import csv
# data = csv.reader(open('C:/dropbox/My Dropbox/Pairs_NowCorrectDot.txt', 'rb'), delimiter='\t')
#data = csv.reader(open('/Users/miura/Dropbox/Mette/Tracks.csv'))
#p1 = []
#tA = []
#for row, points in enumerate(data):
# tA.append(points[2])
# p1.append(points[3:6])
# p2.append(points[3:])
#for d in tA:
# print d
from matplotlib import mlab as matp
ind, frame, gx, gy, gz, ux, vy, wz = matp.load(filename, skiprows=1, delimiter=',', usecols=[0, 1, 2, 3, 4, 5, 6, 7], unpack=True)
#for d in frame:
#print d
#from mayavi.mlab import points3d
#from mayavi.mlab import plot3d
from mayavi.mlab import quiver3d
from mayavi.mlab import flow
from mayavi import mlab as maya
import numpy as np
##p1s = points3d(sx, sy, sz, scale_factor=.45, color=(1, 0, 0))
#trial with colorlist
colorlist = []
# pylab-free version of simpletest.py
#####################################
# set backend to Agg.
import matplotlib
matplotlib.use('Agg')
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.toolkits.basemap import Basemap
from matplotlib.figure import Figure
from numpy import meshgrid
import matplotlib.numerix as nx
import matplotlib.cm as cm
from matplotlib.mlab import load
# read in topo data (on a regular lat/lon grid)
# longitudes go from 20 to 380.
etopo = load('etopo20data.gz')
lons = load('etopo20lons.gz')
lats = load('etopo20lats.gz')
# create figure.
fig = Figure()
canvas = FigureCanvas(fig)
# create axes instance, leaving room for colorbar at bottom.
ax = fig.add_axes([0.125,0.175,0.75,0.75])
# create Basemap instance for Robinson projection.
# set 'ax' keyword so pylab won't be imported.
m = Basemap(projection='robin',lon_0=0.5*(lons[0]+lons[-1]),ax=ax)
# make filled contour plot.
x, y = m(*meshgrid(lons, lats))
cs = m.contourf(x,y,etopo,30,cmap=cm.jet)
# draw coastlines.
m.drawcoastlines()
#import csv
# data = csv.reader(open('C:/dropbox/My Dropbox/Pairs_NowCorrectDot.txt', 'rb'), delimiter='\t')
#data = csv.reader(open('/Users/miura/Dropbox/Mette/Tracks.csv'))
#p1 = []
#tA = []
#for row, points in enumerate(data):
# tA.append(points[2])
# p1.append(points[3:6])
# p2.append(points[3:])
#for d in tA:
# print d
from matplotlib import mlab as matp
ind, trajID, tA, x1, y1, z1, sx, sy, sz, ptID = matp.load(filename, skiprows=1, delimiter=',', usecols=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], unpack=True)
#for d in ind:
#print d
from mayavi.mlab import points3d
from mayavi.mlab import plot3d
from mayavi.mlab import quiver3d
from mayavi.mlab import flow
from mayavi import mlab as maya
p1s = points3d(sx, sy, sz, scale_factor=.45, color=(1, 0, 0))
curtid = -1.0
cx = []
cy = []
