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))
import sys import utils """ python means.py [delta] """ deltaname = str(sys.argv[1]) datadir = 'data' datafile = 'inun_minremoved_v1v2.pkl' #Array of nans the size of dataset based on size of map #Array of bounding map edges globalMap = utils.gridEdges(datadir) #Actual inundation data for specified river delta = utils.get_data(datadir, datafile, deltaname) dates = delta['data'].index timeMean = delta['data'].mean(axis=1) locMean = delta['data'].mean(axis=0) cmap = cm.GMT_drywet vmin = np.amin(locMean) vmax = np.amax(locMean) norm = colors.Normalize(vmin=vmin, vmax=vmax) fig = plt.figure()
import matplotlib.pyplot as plt import pickle from mpl_toolkits.basemap import Basemap, cm import matplotlib.animation as animation from matplotlib import colors import utils import PCAmekong datadir = 'data' deltaname = 'Amazon' datafile = 'inun_minremoved_v1v2.pkl' #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'])
pmtm = pmdata.std(axis=1)
itm,pmtm = cleanNans(itm,pmtm)
slope, intercept, _, _, _ = st.linregress(itm, pmtm)
ax = addScatter(fig,plt,itm,pmtm,1,1,1)
ax.set_xlabel("Inundation")
ax.set_ylabel("Precipitation")
ax.plot(itm, slope*itm + intercept, color='k')
ax.set_title("Correlation is: {}".format(st.pearsonr(itm,pmtm)[0]),y=1.02)
# plt.savefig("./graphs/tendaystats.png")
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'])
datadir = 'data_old'
datafile = 'delta_3B42_precip.pkl'
delta = utils.get_data(datadir, datafile, deltaName)
results = get(analysisType,delta['data'],row);
f = open('output.txt','w')
for i in results:
f.write(str(i)+"\n")
f.close()
fig = plt.figure()
ax1 = fig.add_subplot(1,1,1)
# ax1.plot(np.arange(len(results)),results)
# i dont know what I am doing starting over here
globe = utils.gridEdges(datadir)
cmap = cm.GMT_drywet
# cmap = "copper"
vmin = np.nanmin(results.values)
vmax = np.nanmax(results.values)
norm = colors.Normalize(vmin=vmin, vmax=vmax)
bm = utils.basemap(ax1)
X, Y = bm(globe['lons'], globe['lats'])
#results = np.array(results)
ax1.axis(utils.mapbounds[deltaName])
ax1.set_title("Precipitation {} {}s by location".format(deltaName,analysisType))
globe['map'][delta['inds'][0], delta['inds'][1]] = results
