prl900

from keras.models import load_model, Sequential
from keras import layers
from keras.layers import Layer
from keras import models
import numpy as np
import matplotlib.pyplot as plt
import sys

x = np.load("/datasets/10zlevels.npy")
y = 1000*np.expand_dims(np.load("/datasets/1980-2016/full_tp_1980_2016.npy"), axis=3)

prl900

import pickle
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt

#hist = pickle.load(open("trainHistoryDict_0-5-7", 'rb'))
hist = pickle.load(open("trainHistoryDict_9", 'rb'))
print hist.keys()
plt.plot(hist['loss'])
plt.plot(hist['val_loss'])

prl900

prl900

prl900

import datacube as dc
import ctypes
from contextlib import closing
import numpy as np
from datacube.helpers import ga_pq_fuser
from datacube.storage import masking
import xarray as xr
import multiprocessing as mp

#N = 1001*1001

prl900

import datacube as dc

class BurnCube(dc.Datacube):
    def __init__(self):
        super(BurnCube, self).__init__(app='TreeMapping.getLandsatStack')
        self.measurements = ['red','green','blue','nir','swir1','swir2']
        self.res = (-100,100)
         
    def load_cube(self, lon, lat, period):
        query = {

prl900

package main

import (
	"fmt"
	"image"
	"image/color"
	"image/png"
	"os"
)

prl900

prl900

import gdal
from osgeo import osr
import numpy as np

ras = np.load("/Users/pablo/Downloads/test.npy")

drv = gdal.GetDriverByName("GTiff")
ds = drv.Create("/Users/pablo/Downloads/gbdx.tif", 8765, 10000, 1, gdal.GDT_Float32)
proj = osr.SpatialReference()
proj.ImportFromEPSG(4326)

prl900