def showData(self,
axes=[],
lw=2,
onMap=True,
bins=100,
showVel=False,
centered=True,
showCenter=False):
"""Shows basic stats histograms and movement trajectories of all data sets.
.. note:: Creates new figure if axes is not specified.
Keyword Args:
ax (matplotlib.axes): Axes.
bins (int): Number of bins.
Returns:
list: List of modified axes.
"""
# Create axes if not specified
if len(axes) != 6:
fig, axes = pm.makeSubplot([3, 2])
if onMap:
ax = pm.drawMap(axes[0])
self.plotLonLatTraj(ax=ax, onMap=onMap, lw=lw)
self.plotCartTraj(ax=axes[1],
showVel=showVel,
centered=centered,
showCenter=showCenter)
self.showStats(axes=axes[2:])
return axes
def showClustersOnTracks(self,ax=None,centered=True,nbouts=3,showCenter=False): """Plots all trajectory of gps data colorized by kmeans-clustered groups. Keyword Args: ax (matplotlib.axes): Axes to plot in. nbouts (int): Number of bouts to cluster data in. centered (bool): Show centered coordinates. center (list): Center of plot. showCenter (bool): Display center. Returns: matplotlib.axes: Modified axes. """ # Create axes if necessary if ax==None: fig,axes=pm.makeSubplot([1,1]) ax=axes[0] #self.performKMeans(nbouts=nbouts) labels=self.kMeansLabels print labels colors=pm.getColors(len(np.unique(labels))) for i,w in enumerate(self.windows): w.plotCartTraj(ax=ax,vel=[],showVel=False,centered=centered,color=colors[labels[i]],center=[0,0,0],showCenter=showCenter)
def plotClusters(self,ax=None,var='dta',showLabels=False,cpick='jet',alg='kMeans',nbouts=3): """Shows scatter plot of gps data binned in windows. If algorithm is 'kMeans', performs additional kMeans and labels windows by bout. ``var`` defines which variables are shown on x/y/z-axis in the order they are specified. * 'd': distances * 't': deltaT * 'a': angles * 'v': velocities Keyword Args: ax (matplotlib.axes): Axes to plot in. var (str): Define which variables are plotted. showLabels (bool): Show labels. cpick (str): Colormap used for coloring. alg (str): Algorithm used for clustering. nbouts (int): Number of bouts to cluster data in. Returns: matplotlib.axes: Modified axes. """ idxs=[] # Figure out what to plot if 'd' in var: idxs.append(0) if 't' in var: idxs.append(1) if 'v' in var: idxs.append(2) if 'a' in var: idxs.append(3) # Cluster if alg=='none': labels=np.zeros(np.shape(self.stats)[0]) if alg=='kMeans': self.performKMeans(nbouts=nbouts) labels=self.kMeansLabels # Create axes if necessary if ax==None: fig,axes=pm.makeSubplot([1,1],proj=['3d']) ax=axes[0] # Plot pm.labeledScatter(self.stats[:,idxs[0]],self.stats[:,idxs[1]],self.stats[:,idxs[2]],labels,ax=ax,cmap=cpick) # Labels ax.set_xlabel(var[0]) ax.set_ylabel(var[1]) ax.set_zlabel(var[2]) ax.get_figure().canvas.draw() return ax
def showClusters(self, axes=None):
# Get number of window sets
N = len(self.Windows)
# Compute number of plots displayed
Nx = int(np.ceil(N / 2.))
Ny = int(np.floor(N / 2.))
# Show scatter plot for each window set
proj = N * ['3d']
fig, axes = pm.makeSubplot([Nx, Ny], proj=proj)
for i, w in enumerate(self.Windows):
w.plotClusters(ax=axes[i], var='dta')
# Show labeled trajectories for each set
fig, axes = pm.makeSubplot([Nx, Ny])
for i, w in enumerate(self.Windows):
w.showClustersOnTracks(ax=axes[i])
def showStats(self, axes=[], bins=100):
"""Creates four histograms showing overall movement stats of all dataset.
.. note:: Creates new figure if axes is not specified.
Keyword Args:
ax (matplotlib.axes): Axes.
bins (int): Number of bins.
Returns:
list: List of modified axes.
"""
# Create axes if not specified
if len(axes) != 4:
fig, axes = pm.makeSubplot([2, 2])
self.plotDHist(ax=axes[0], bins=bins)
self.plotVHist(ax=axes[1], bins=bins)
self.plotAnglesHist(ax=axes[2], bins=bins)
self.plotDeltaTHist(ax=axes[3], bins=bins)
return axes
w.showClustersOnTracks()
raw_input()
#for ID in g.IDs:
#g.data[ID].cleanUpData()
#g.data[ID].showAllPlots()
#stats_module.
raw_input()
# Create figure
fig, axes = pm.makeSubplot([2, 2])
# Draw map
m = pm.drawMap(axes[0])
# Plot location of sable island
#pm.plotSquareAroundCoordinate(m,sable,1)
# Plot trajectories into basemap
for ID in g.IDs:
g.data[ID].plotLonLatTraj(m)
# Plot trajectories in cartesian coordinates
for ID in g.IDs:
g.data[ID].plotCartTraj(axes[1])