def make_movie(datadir, deltaname, variable, framenum):
datafile = utils.datafiles[variable]
delta = utils.get_data(datadir, datafile, deltaname)
mp = utils.gridEdges(datadir)
cmap = utils.cmap[variable]
vmin = np.nanmin(delta["data"])
vmax = np.nanmax(delta["data"])
norm = colors.Normalize(vmin=vmin, vmax=vmax)
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
bm = utils.basemap(ax)
X, Y = bm(mp["lons"], mp["lats"])
print ax.get_xlim()
print ax.get_ylim()
ax.axis(utils.mapbounds[deltaname])
def updatefig(i):
mp["map"][delta["inds"][0], delta["inds"][1]] = delta["data"].iloc[i, :]
date = delta["data"].index[i].strftime("%Y-%m-%d")
im = bm.pcolormesh(X, Y, np.ma.masked_invalid(mp["map"]), cmap=cmap, norm=norm)
cbar = bm.colorbar(im, "bottom", cmap=cmap, norm=norm)
ax.set_title("{}: {}".format(utils.fullname[variable], date))
framenum = 5
ani = animation.FuncAnimation(fig, updatefig, frames=framenum)
ani.save("{}_{}_{}.mp4".format(utils.fullname[variable], deltaname, framenum))
corrs = []
for i, p in zip(idata.values.T, pmdata.values.T):
mask = (np.isfinite(i) & np.isfinite(p))
corrs.append(st.pearsonr(i[mask],p[mask])[0])
print len(corrs)
globe = utils.gridEdges('data')
delta = utils.get_data('data', 'delta_3B42_precip.pkl', 'Amazon')
fig = plt.figure()
ax1 = fig.add_subplot(1,1,1)
cmap = cm.GMT_drywet
vmax = np.nanmax(corrs)
# vmin = -vmax
vmin = 0
norm = colors.Normalize(vmin=vmin, vmax=vmax)
bm = utils.basemap(ax1)
X, Y = bm(globe['lons'], globe['lats'])
globe['map'][delta['inds'][0], delta['inds'][1]] = np.array(corrs)
im = bm.pcolormesh(X, Y, np.ma.masked_invalid(globe['map']),cmap=cmap, norm=norm)
cbar = bm.colorbar(im, "bottom", cmap=cmap, norm=norm)
ax1.axis(utils.mapbounds['Amazon'])
ax1.set_title("Ten Day Correlations")
# plt.show()
plt.savefig("./graphs/tendaystds.png")
#Array of nans the size of dataset based on size of map
#Array of bounding map edges
map = utils.gridEdges(datadir)
#Actual inundation data for specified river
delta = utils.get_data(datadir, datafile,deltaname)
cmap = cm.GMT_drywet
vmin = np.nanmin(delta['data'])
vmax = np.nanmax(delta['data'])
norm = colors.Normalize(vmin=vmin, vmax=vmax)
fig = plt.figure()
ax = fig.add_subplot(2,2,1)
#sets up basemap object to plot
bm = utils.basemap(ax)
X, Y = bm(map['lons'], map['lats'])
#sets axis for the river we are observing
ax.axis(utils.mapbounds['Amazon'])
#fills map with inundation data based on location of delta
map['map'][delta['inds'][0], delta['inds'][1]] = delta['data'].iloc[0,:]
date = delta['data'].index[0].strftime('%Y-%m-%d')
#sets up colormesh to display over map
im = bm.pcolormesh(X, Y, np.ma.masked_invalid(map['map']),cmap=cmap, norm=norm)
cbar = bm.colorbar(im, "bottom", cmap=cmap, norm=norm)
ax.set_title("Inundation: {}".format(date))
ax1 = fig.add_subplot(2,2,2)
