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hmi-to-goes-forecast.py
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hmi-to-goes-forecast.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# 太陽磁場画像を取得するサンプルプログラムです
# 必要なライブラリのimport文です
import datetime, math,os, random,scipy.ndimage, StringIO, sys, urllib
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.dates as mdates
import pylab
from astropy.io import fits
from observational_data import *
import chainer
from chainer import computational_graph
from chainer import cuda
import chainer.functions as F
import chainer.links as L
from chainer import optimizers
from chainer import serializers
# パラメータ群
if len(sys.argv) < 2:
print "usage: {} data-folder-path".format(sys.argv[0])
exit(2)
set_data_path(sys.argv[1])
learning_batchsize = 10
learning_image_size = 256
initial_learn_count = 10000
predict_count = 365 * 24 * 4
learn_per_step = 1
global current_hour
current_hour = 365 * 24
workdir = 'hmi-to-goes-forecast'
try:
os.mkdir(workdir)
except Exception as e:
pass
model_filename = workdir + '/model.save'
state_filename = workdir + '/state.save'
# ConvolutionとBatchNormalizationをセットでやるchainです
class ConvBN(chainer.Chain):
def __init__ (self,in_nc, out_nc, ksize=3, stride=2):
super(ConvBN, self).__init__(
f = L.Convolution2D(in_nc, out_nc, ksize=ksize, stride=stride,
wscale=0.02*math.sqrt(ksize*ksize*in_nc)),
g = L.BatchNormalization(out_nc)
)
def __call__ (self, x):
return self.g(self.f(x))
# 256^2サイズの画像から１つの値を予測するニューラルネットワークです
class Predictor(chainer.Chain):
def __init__(self):
super(Predictor, self).__init__(
l1=ConvBN( 1, 16,3,stride=2),
l2=ConvBN( 16, 32,3,stride=2),
l3=ConvBN( 32, 64,3,stride=2),
l4=ConvBN( 64,128,3,stride=2),
l5=ConvBN(128,256,3,stride=2),
l6=ConvBN(256,512,3,stride=2),
l7=ConvBN(512,1024,3,stride=2),
l9=L.Linear(1024,1)
)
def __call__(self, x):
h = F.leaky_relu(self.l1(x))
h = F.leaky_relu(self.l2(h))
h = F.leaky_relu(self.l3(h))
h = F.leaky_relu(self.l4(h))
h = F.leaky_relu(self.l5(h))
h = F.leaky_relu(self.l6(h))
h = F.leaky_relu(self.l7(h))
return F.exp(self.l9(h)-13.0)
################################################################
# メインプログラム開始
################################################################
# ニューラルネットワークによるモデルと、モデルの最適化機を作ります
model = Predictor()
optimizer = optimizers.Adam(alpha = 0.0001)
optimizer.setup(model)
# セーブファイルが存在する場合は、セーブファイルから状態を読み込みます
if os.path.exists(model_filename) and os.path.exists(state_filename):
print 'Load model from', model_filename, state_filename
serializers.load_npz(model_filename, model)
serializers.load_npz(state_filename, optimizer)
# 現在までの学習状態をファイルに書き出す関数です。
def save():
print('save the model')
serializers.save_npz(model_filename, model)
print('save the optimizer')
serializers.save_npz(state_filename, optimizer)
print('saved.')
class InOutPair:
def visualize(self, fn_base):
hmi_fn = fn_base + '-hmi.png'
goes_fn = fn_base + '-goes.png'
img = np.arctan(self.hmi_img / 300.0)
img3 = np.zeros((1024,1024,3), dtype=np.float32)
img3[:,:,0] = np.minimum(1,np.maximum(-1,img))/2+0.5
img3[:,:,1] = np.minimum(1,np.maximum(-1,img))/2+0.5
img3[:,:,2] = np.minimum(1,np.maximum(-1,img))/2+0.5
pylab.rcParams['figure.figsize'] = (6.4,6.4)
pylab.clf()
pylab.gca().set_title('SDO/HMI Line-of-sight at {}(TAI)'.format(self.time))
pylab.imshow(img3)
pylab.savefig(hmi_fn)
pylab.close('all')
pylab.rcParams['figure.figsize'] = (6.4,4.8)
pylab.gca().set_yscale('log')
days = mdates.DayLocator() # every day
daysFmt = mdates.DateFormatter('%Y-%m-%d')
hours = mdates.HourLocator()
pylab.gca().xaxis.set_major_locator(days)
pylab.gca().xaxis.set_major_formatter(daysFmt)
pylab.gca().xaxis.set_minor_locator(hours)
pylab.gca().grid()
pylab.gcf().autofmt_xdate()
pylab.plot(self.goes_lightcurve_t, self.goes_lightcurve_y, 'b', zorder=300)
predict_t = [self.time, self.time+datetime.timedelta(days=1)]
predict_y = [self.goes_max_predict, self.goes_max_predict]
observe_t = [self.time, self.time+datetime.timedelta(days=1)]
observe_y = [self.goes_max, self.goes_max]
pylab.plot(predict_t, predict_y, color=(1,0,0), lw=1, zorder = 200,marker='o',linestyle='--')
pylab.plot(observe_t, observe_y, color=(1,0.66,0.66), lw=2, zorder = 100)
pylab.gca().set_xlabel('International Atomic Time')
pylab.gca().set_ylabel(u'GOES Long[1-8A] Xray Flux')
pylab.savefig(goes_fn)
pylab.close('all')
def visualize_log():
logs = []
predicts = []
observes = []
with open(workdir + '/log.txt','r') as fp:
for l in iter(fp.readline, ''):
ws = l.split()
t = datetime.datetime.strptime(ws[0],"%Y-%m-%dT%H:%M")
p = float(ws[1])
o = float(ws[2])
predicts.append(p)
observes.append(o)
pylab.rcParams['figure.figsize'] = (6.4,6.4)
pylab.gca().set_xscale('log')
pylab.gca().set_yscale('log')
pylab.scatter(predicts,observes, color=(1,0,0), zorder = 100,marker='.',s=1)
pylab.scatter(predicts[-100:],observes[-100:], color=(1,0,0), zorder = 200,marker='o',s=5)
pylab.scatter(predicts[-10:],observes[-10:], color=(1,0,0), zorder = 300,marker='o',s=10)
pylab.gca().set_xlabel('prediction')
pylab.gca().set_ylabel('observation')
pylab.gca().grid()
pylab.savefig(workdir+'/log.png')
pylab.close('all')
def predict(learn_mode = True):
global current_hour
batch = []
batchsize = learning_batchsize if learn_mode else 1
while len(batch) < batchsize:
if learn_mode:
# 学習モードの場合
# 2011年初頭からcurrent_hour - 24のあいだでランダムな時刻tを生成します
step = random.randrange(current_hour-24)
t = datetime.datetime(2011,1,1,0,0) + datetime.timedelta(hours=step)
else:
# 予測モードの場合
# 対象時刻はcurrent_hourです
t = datetime.datetime(2011,1,1,0,0) + datetime.timedelta(hours=current_hour)
# 時刻tのHMI画像の取得を試みます
img = get_hmi_image(t)
if img is None:
if learn_mode:
continue # だめだったら別のtを試す
else:
return None # あきらめる
# 時刻、画像、GOESライトカーブなどの情報を持ったInOutPairを作ります。
p = InOutPair()
p.time = t
p.hmi_img = img
p.goes_max = max(1e-8, get_goes_max(t, datetime.timedelta(days=1)))
p.goes_lightcurve_t = []
p.goes_lightcurve_y = []
t2 = t - datetime.timedelta(days=1)
while t2 < t + datetime.timedelta(days=2):
x2 = get_goes_flux(t2)
if x2 is not None:
p.goes_lightcurve_t.append(t2)
p.goes_lightcurve_y.append(x2)
t2 += datetime.timedelta(minutes=1)
batch.append(p)
# ニューラルネットワークに対する入力を作ります
input = np.ndarray((batchsize,1,learning_image_size,learning_image_size), dtype=np.float32)
for i in range(batchsize):
h,w = batch[i].hmi_img.shape
input[i,:,:,:] = scipy.ndimage.interpolation.zoom(batch[i].hmi_img,
(learning_image_size/float(h),learning_image_size/float(w)))
input_v = chainer.Variable(input)
predict_v = model(input_v)
predict = predict_v.data
for i in range(batchsize):
batch[i].goes_max_predict = predict[i,0]
if learn_mode:
# 学習モードの場合、正解とのずれを学習します
observe = np.ndarray((batchsize,1), dtype=np.float32)
for i in range(batchsize):
observe[i] = batch[i].goes_max
observe_v = chainer.Variable(observe)
def square_norm(x,y):
return F.sum((F.log(x)-F.log(y))**2)/batchsize
optimizer.update(square_norm, predict_v, observe_v)
# 何を学習しているかを可視化します。
for i in range(batchsize):
batch[i].visualize('{}/{:02}'.format(workdir,i))
else:
# 予報モードの場合、予報結果と正解を記録します。
with open(workdir + '/prediction-log.txt','a') as fp:
for p in batch:
fp.write(' '.join([p.time.strftime("%Y-%m-%dT%H:%M"),str(p.goes_max_predict),str(p.goes_max),"\n"]))
# まず、最初の1年間で練習します
for i in range(initial_learn_count):
print "learning: ", i, "/", initial_learn_count
try:
predict(learn_mode = True)
except Exception as e:
print str(e.message)
if i % 100 == 0:
save()
#visualize_log()
#時間を1時間づつ進めながら、予報実験をしていきます。
for t in range(predict_count):
print "predicting: ", t, "/", predict_count
try:
predict(learn_mode = False)
except Exception as e:
print str(e.message)
for i in range(learn_per_step):
try:
predict(learn_mode = True)
except Exception as e:
print str(e.message)
current_hour += 1