One more post about the OTN Gliders… Previous OTN glider posts include:
Below we use data from the OTN Ocean Glider Programme. We utilize the SCITOOLS Python Library for Scientific Computing to create the animated gif.
NOTE: Click on the image below to play the animation
CLICK on Image to play the ANIMATION
import csv
import numpy as np
import urllib2
import StringIO
import pickle
import scitools.easyviz as scit
from matplotlib.transforms import Bbox, TransformedBbox, \
blended_transform_factory
from mpl_toolkits.axes_grid1.inset_locator import BboxPatch, BboxConnector,\
BboxConnectorPatch
import matplotlib.pyplot as plt
# Open file
response = urllib2.urlopen('http://glider.ceotr.ca/data/live/sci_water_temp_live.csv')
data = response.read()
data = StringIO.StringIO(data)
# Read file
r = csv.DictReader(data)
# Initialize empty variables
date, lat, lon, depth, temp = [],[],[],[],[]
# Loop to parse data into our variables
for row in r:
date.append(float(row['unixtime']))
lat.append(float(row['lat']))
lon.append(float(row['lon']))
depth.append(float(row['depth']))
temp.append(float(row['sci_water_temp']))
# Estimate "distance along transect *******************************************
# First, design a function to estimate great-circle distance
def distance(origin, destination):
#Source: http://www.platoscave.net/blog/2009/oct/5/calculate-distance-latitude-longitude-python/
import math
lat1, lon1 = origin
lat2, lon2 = destination
radius = 6371 # km
dlat = math.radians(lat2-lat1)
dlon = math.radians(lon2-lon1)
a = math.sin(dlat/2) * math.sin(dlat/2) + math.cos(math.radians(lat1)) \
* math.cos(math.radians(lat2)) * math.sin(dlon/2) * math.sin(dlon/2)
c = 2 * math.atan2(math.sqrt(a), math.sqrt(1-a))
d = radius * c
return d
# Compute distance along transect
dist = np.zeros((np.size(lon)))
for i in range(2,np.size(lon)):
dist[i] = dist[i-1] + distance([lat[i-1], lon[i-1]], [lat[i], lon[i]])
# Estimate bathymetry **********************************************************
# Load bathymetry file created here: https://oceanpython.org/2013/03/21/bathymetry-topography-srtm30/
T = pickle.load(open('/home/diego/PythonTutorials/SRTM30/topo.p','rb'))
# Compute bathymetry
bathy = np.zeros((np.size(lon)))
for i in range(np.size(lon)):
cost_func = ((T['lons']-lon[i])**2) + ((T['lats']-lat[i])**2)
xmin, ymin = np.unravel_index(cost_func.argmin(), cost_func.shape)
bathy[i] = -T['topo'][xmin, ymin]
# Fuctions for zoom effect ****************************************************
# Source: http://matplotlib.org/dev/users/annotations_guide.html#zoom-effect-between-axes
def connect_bbox(bbox1, bbox2,
loc1a, loc2a, loc1b, loc2b,
prop_lines, prop_patches=None):
if prop_patches is None:
prop_patches = prop_lines.copy()
prop_patches["alpha"] = prop_patches.get("alpha", 1)*0.2
c1 = BboxConnector(bbox1, bbox2, loc1=loc1a, loc2=loc2a, **prop_lines)
c1.set_clip_on(False)
c2 = BboxConnector(bbox1, bbox2, loc1=loc1b, loc2=loc2b, **prop_lines)
c2.set_clip_on(False)
bbox_patch1 = BboxPatch(bbox1, **prop_patches)
bbox_patch2 = BboxPatch(bbox2, **prop_patches)
p = BboxConnectorPatch(bbox1, bbox2,
#loc1a=3, loc2a=2, loc1b=4, loc2b=1,
loc1a=loc1a, loc2a=loc2a, loc1b=loc1b, loc2b=loc2b,
**prop_patches)
p.set_clip_on(False)
return c1, c2, bbox_patch1, bbox_patch2, p
def zoom_effect(ax1, ax2, xmin, xmax, **kwargs):
"""
ax1 : the main axes
ax1 : the zoomed axes
(xmin,xmax) : the limits of the colored area in both plot axes.
connect ax1 & ax2. The x-range of (xmin, xmax) in both axes will
be marked. The keywords parameters will be used ti create
patches.
Source: http://matplotlib.org/dev/users/annotations_guide.html#zoom-effect-between-axes
"""
trans1 = blended_transform_factory(ax1.transData, ax1.transAxes)
trans2 = blended_transform_factory(ax2.transData, ax2.transAxes)
bbox = Bbox.from_extents(xmin, 0, xmax, 1)
mybbox1 = TransformedBbox(bbox, trans1)
mybbox2 = TransformedBbox(bbox, trans2)
prop_patches=kwargs.copy()
prop_patches["ec"]="r"
prop_patches["alpha"]=None
prop_patches["facecolor"]='none'
prop_patches["linewidth"]=2
c1, c2, bbox_patch1, bbox_patch2, p = \
connect_bbox(mybbox1, mybbox2,
loc1a=3, loc2a=2, loc1b=4, loc2b=1,
prop_lines=kwargs, prop_patches=prop_patches)
ax1.add_patch(bbox_patch1)
ax2.add_patch(bbox_patch2)
ax2.add_patch(c1)
ax2.add_patch(c2)
ax2.add_patch(p)
return c1, c2, bbox_patch1, bbox_patch2, p
# Make plots
nframes = 20
overlap = 0.95
window = np.floor(max(dist)-min(dist)) / (nframes - (nframes*overlap) + overlap)
xmin = 0
xmax = xmin + window
fig1 = plt.figure()
for i in range(0,nframes):
ax1 = plt.subplot(211)
plt.fill_between(dist,bathy,1000,color='k')
plt.scatter(dist,depth,s=15,c=temp,marker='o', edgecolor='none')
plt.ylim((-0.5,max(depth)+5))
ax1.set_ylim(ax1.get_ylim()[::-1])
cbar = plt.colorbar(orientation='vertical', extend='both')
cbar.ax.set_ylabel('Temperature ($^\circ$C)')
plt.title('OTN Glider transect')
plt.ylabel('Depth (m)')
ax1.set_xlim(min(dist),max(dist))
ax2 = plt.subplot(212)
plt.fill_between(dist,bathy,1000,color='k')
plt.scatter(dist,depth,s=15,c=temp,marker='o', edgecolor='none')
plt.ylim((-0.5,max(depth)+5))
ax2.set_ylim(ax2.get_ylim()[::-1])
plt.ylabel('Depth (m)')
plt.xlabel('Distance along transect (km)')
ax2.set_xlim(xmin,xmax)
zoom_effect(ax1, ax2,xmin,xmax)
# Save figure (without 'white' borders)
if i < 10:
plt.savefig('glider_0'+str(i)+'.png', bbox_inches='tight')
else:
plt.savefig('glider_'+str(i)+'.png', bbox_inches='tight')
plt.close()
xmin = xmax - np.floor(window*overlap)
xmax = xmin + window
# Make animated gif
scit.movie('glider_*.png',encoder='convert',output_file='glider_movie.gif',fps=4)
Ocean Python 