def isBalanced(root: TreeNode) -> bool:
if not root:
return True
if not isBalanced(root.left) or not isBalanced(root.right):
return False
return abs(depth(root.left) - depth(root.right)) <= 1
def depth(self, pair='btc_eur'):
if 'depth_count' in gv.keys():
count = int(gv['depth_count'])
else:
count = 20
if not pair in self.pairs.keys():
raise Exception('invalid pair', pair)
try:
s = self.api.query_public('Depth', {
'pair': self.pairs[pair],
'count': count
})
except Exception as e:
log.exception(e)
raise
if s['error']:
print("an error occured %s" % s['error'])
raise Exception(s['error'])
d = [depth(**v) for k, v in s['result'].items()][0]
self.curdepth[pair] = [d, time.time()]
return d
def depth(self, pair='btc_eur'):
#kwargs.setdefault('pair', 'btc_eur')
try:
s = self.api.get_depth({'pair': pair})
except Exception as e:
print(e)
d = depth(**s)
self.curdepth[pair] = [d, time.time()]
return d
def rendering(dir, eng):
# z的尺度与x和y相同，大小等同于测试图像大小，位置与测试图像像素点一一对应
# imgs为渲染结果，大小等同于测试图像大小，位置与测试图像像素点一一对应
vector, b = rebuild(dir)
#evaluate(b, dir) # 测试集上评估
z = depth(vector, eng)
imgs = render(b, dir)
#visualize(z, b, imgs) # 显示重建图像与深度
return z, imgs
def rendering(dir):
#z的尺度与x和y相同，大小等同于测试图像大小，位置与测试图像像素点一一对应
#imgs为渲染结果，大小等同于测试图像大小，位置与测试图像像素点一一对应
train_lvectors = np.zeros([7, 3]) # the direction of light
for line in open(dir + '/train.txt'):
i, ang1, ang2 = line.strip().split(",")
i = int(i)
ang1 = int(ang1)
ang2 = int(ang2)
train_lvectors[i - 1] = (np.sin(np.pi * ang1 / 180) *
np.cos(np.pi * ang2 / 180),
np.sin(np.pi * ang2 / 180),
np.cos(np.pi * ang1 / 180) *
np.cos(np.pi * ang2 / 180))
train_lvectors = -train_lvectors
test_lvectors = np.zeros([10, 3]) # the direction of light
for line in open(dir + '/test.txt'):
i, ang1, ang2 = line.strip().split(",")
i = int(i)
ang1 = int(ang1)
ang2 = int(ang2)
test_lvectors[i - 1] = (np.sin(np.pi * ang1 / 180) *
np.cos(np.pi * ang2 / 180),
np.sin(np.pi * ang2 / 180),
np.cos(np.pi * ang1 / 180) *
np.cos(np.pi * ang2 / 180))
test_lvectors = -test_lvectors
train_images = np.zeros([7, 168, 168])
for num in range(7):
image = Image.open(dir + '/train/' + str(num + 1) + '.bmp')
train_images[num] = np.asarray(image)
n_s = 3
alpha, beta, s, X, Y, Z, vector = rebuild(train_images, train_lvectors,
n_s)
evaluate(alpha, beta, s, X, Y, Z, n_s, train_lvectors, train_images)
imgs = render(alpha, beta, s, X, Y, Z, n_s, test_lvectors)
z = depth(vector)
return z, imgs
def load_molecuel(sequence):
"""
Prompts user to choose a method to loads molecule with chosen sequence, and
loads molecule via that method.
"""
# prompt user for molecule loading method and validate input
method = input("Molecule loading method (direct, acids, depth, random): ")
molecule = 0
if method == 'direct' or method == 'acids':
molecule = Molecule(sequence, method)
elif method == "depth":
molecule = depth(sequence)
elif method == "random":
molecule = Molecule(sequence, method)
else:
print('No valid loading method.')
return load_molecuel(sequence)
return molecule
def order():
ssl._create_default_https_context = ssl._create_unverified_context
url = config.api_url+"/v1/trade/orders"
buy_one_price,buy_one_amount,sell_one_price,sell_one_amount = depth.depth()
params = {}
params['amount'] = config.amount
params['orderType'] = 'BUY_LIMIT'
params['price'] = buy_one_price
params['symbol'] = config.symbol
param_list = ['%s=%s' % (k, v) for k, v in params.items()]
param_list.sort()
payload = ['POST', 'api.btcdo.com', '/v1/trade/orders', '&'.join(param_list)]
headers = {
'API-Key': config.api_key,
'API-Signature-Method': 'HmacSHA256',
'API-Signature-Version': '1',
'API-Timestamp': str(int(time.time() * 1000))
}
headers['User-Agent'] = 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.102 Safari/537.36'
headers['API-Unique-ID'] = uuid.uuid4().hex
headers_list = ['%s: %s' % (k.upper(), v) for k, v in headers.items()]
headers_list.sort()
payload.extend(headers_list)
payload.append(json.dumps(params))
payload_str = '\n'.join(payload)
# payload_str += '\n<json body data>'
# signature:
# signature = HmacSHA256(payload_str.encode("UTF-8"), config.secret_key)
sign = hmac.new(config.secret_key.encode('utf-8'), payload_str.encode('utf-8'), hashlib.sha256).hexdigest()
print('payload:\n----\n' + payload_str + '----\nsignature: ' + sign)
headers['API-Signature'] = sign
response = config.session.post(url=url,params=params,headers=headers)
print(response.text)
def depth(self, pair='btc_eur'):
if 'depth_count' in gv.keys():
count = int(gv['depth_count'])
else:
count = 20
try:
if pair == 'btc_ltc':
pair = 'ltc_btc'
s = self.api.get_param(pair, 'depth')
except Exception as e:
log.exception(e)
raise Exception('could not get depth')
d = depth(**s)
if pair == 'ltc_btc':
d.asks = list(map(lambda t: trade(1 / t.value, t.volume), d.asks))
d.bids = list(map(lambda t: trade(1 / t.value, t.volume), d.bids))
d.asks = d.asks[:count]
d.bids = d.bids[-count:]
btce.curdepth[pair] = [d, time.time()]
return d
def main(inlistrefl):
"""This is the main commandline program. It takes a text list of the surface reflectance Planet Dove mosaic tiles
(ls -1 L*.tif > inlistrefl.txt). The tiles are run to infer the Chl-a value. After removing spurious Chl-a values,
the average Chl-a of the tiles is used for all of the tiles that are processed in this batch. That Clh-a value is
then used in the subsequent depth and bottom reflectance steps.
"""
with open(inlistrefl, 'r') as f:
templist = f.readlines()
inlist = []
for thisfile in templist:
inlist.append(thisfile.strip())
del templist
chlavals = np.zeros(len(inlist), dtype=np.float32)
for k, infile in enumerate(inlist):
outhsvfile = os.path.splitext(os.path.basename(
infile.strip()))[0] + '_hsv'
myrgb2hsv(infile, outhsvfile)
print(("Processed RGB to HSV: %s") % (infile))
chlavals[k] = getchla(outhsvfile, infile)
print(("Chla: %7.4f for %s") % (chlavals[k], infile))
if (os.path.isfile(outhsvfile)):
os.remove(outhsvfile)
if (os.path.isfile(outhsvfile + ".hdr")):
os.remove(outhsvfile + ".hdr")
## filter out an Nans, negatives and values > 1.0 to get valid Chl-a values
good1 = np.less(chlavals, 1.0)
good2 = np.greater(chlavals, 0.0)
good3 = np.logical_not(np.isnan(chlavals))
goodchla = np.all(np.stack((good1, good2, good3)), axis=0)
try:
chla_global = np.nanmean(chlavals[goodchla])
except ValueError:
print("Problem with values in Chl-a array.")
print(("\nMean Chla for: %7.4f\n") % (chla_global))
## process the files to Depth and bottom reflectance using the global Chl-a value
for k, infile in enumerate(inlist):
depthfile = os.path.splitext(infile)[0] + "_depth.tif"
rbfile = os.path.splitext(infile)[0] + "_rb.tif"
try:
depth(infile, chla_global, depthfile)
except:
print("Error: Could not create Depth data.")
continue
print(("Processed Depth: %s") % (depthfile))
if (os.path.isfile(depthfile)):
try:
rb(infile, chla_global, depthfile, rbfile)
except:
print("Error: Could not create Rb data.")
continue
else:
print((
"Error: Could not find depth data file %s, so could not do bottom reflectance."
) % (depthfile))
continue
print(("Processed Rb: %s, %d of %d") % (infile, k, len(inlist)))
print(("All tiles done!"))
def main(ulx, uly, lrx, lry, outputdir):
ullatlon = []
lrlatlon = []
ullatlon.append(ulx)
ullatlon.append(uly)
lrlatlon.append(lrx)
lrlatlon.append(lry)
## done = download_planet(ullatlon, lrlatlon, outputdir)
## check to see how many files are there
imglist = []
rawlist = os.listdir(outputdir)
for filename in rawlist:
if (os.path.isfile(outputdir + filename)
and fnmatch.fnmatch(filename, '*_AnalyticMS.tif')):
imglist.append(filename)
numimgs = len(imglist)
## add stuff to build reflectance lookup tables and apply them
## success = subprocess.call(["extract_atmos_params_spatial_interp.py", outputdir, outputdir+"atmos_params.csv"])
##
f = open(outputdir + "atmos_params.csv", 'r')
x = f.readlines()
f.close()
##
## for row in x:
## vals = row.split(',')
## success = subprocess.call(["generate_rad_to_refl_lut.py", outputdir+vals[0].strip(), vals[1].strip(), vals[2].strip(), vals[3].strip(), outputdir])
##
refllist = []
##
## ## for each image, apply its lut to make reflectance image
##
for row in x:
vals = row.split(',')
inradfile = os.path.join(outputdir, vals[0].strip())
inlutfile = os.path.join(outputdir,
vals[0].split('.')[0] + "_luts.npz")
outreflfile = os.path.join(outputdir, vals[0].split('.')[0] + "_refl")
refllist.append(outreflfile)
## success = subprocess.call(["apply_refl_lut.py", inradfile, inlutfile, outreflfile])
##
drv = gdal.GetDriverByName('GTiff')
## for each reflectance image run the steps to get depth and bottom reflectance
## make and empty list that is same length to hold Chla values
chlavals = [None] * len(refllist)
for i, infile in enumerate(refllist):
outhsvfile = os.path.splitext(os.path.basename(
infile.strip()))[0] + '_hsv'
depthfile = os.path.splitext(infile)[0][0:-5] + "_depth.tif"
rbfile = os.path.splitext(infile)[0][0:-5] + "_rb.tif"
myrgb2hsv(infile, outhsvfile)
print(("Processed RGB to HSV: %s") % (infile))
chlavals[i] = getchla(outhsvfile, infile)
print(("Chla: %7.4f for %s") % (chlavals[i], infile))
if (os.path.isfile(outhsvfile)):
os.remove(outhsvfile)
if (os.path.isfile(outhsvfile + ".hdr")):
os.remove(outhsvfile + ".hdr")
## filter out an Nans, negatives and values > 1.0 to get valid Chl-a values
## good1 = np.less(chlavals, 1.0)
## good2 = np.greater(chlavals, 0.0)
## good3 = np.logical_not(np.isnan(chlavals))
## goodchla = np.all(np.stack((good1, good2, good3)), axis=0)
try:
chla_global = chlavals[i]
except ValueError:
print("Problem with value in Chl-a array.")
print(("\n%s Chla for: %7.4f\n") % (infile, chla_global))
try:
depth(infile, chla_global, depthfile)
except:
print("Error: Could not create Depth data.")
continue
print(("Processed Depth: %s") % (depthfile))
if (os.path.isfile(depthfile)):
try:
rb(infile, chla_global, depthfile, rbfile)
except:
print("Error: Could not create Rb data.")
continue
else:
print((
"Error: Could not find depth data file %s, so could not do bottom reflectance."
) % (depthfile))
continue
print(("Processed Bottom Reflectance: %s") % (rbfile))
print(("%d") % (i))
if (os.path.isfile(infile)):
# Open data
rasterDS = gdal.Open(infile, gdal.GA_ReadOnly)
else:
print(
("File: %s does not exist....skipping") % (outputdir + infile))
continue
# Get raster georeference info
gt = rasterDS.GetGeoTransform()
xOrigin = gt[0]
yOrigin = gt[3]
pixelWidth = gt[1]
pixelHeight = gt[5]
img_bounds = (gt[0], gt[0] + (rasterDS.RasterXSize * gt[1]),
gt[3] + (rasterDS.RasterYSize * gt[5]), gt[3])
x1, y1 = utm.from_latlon(ullatlon[1], ullatlon[0])[0:2]
x2, y2 = utm.from_latlon(lrlatlon[1], ullatlon[0])[0:2]
x3, y3 = utm.from_latlon(lrlatlon[1], lrlatlon[0])[0:2]
x4, y4 = utm.from_latlon(ullatlon[1], lrlatlon[0])[0:2]
xmin = min([x1, x2, x3, x4])
xmax = max([x1, x2, x3, x4])
ymin = min([y1, y2, y3, y4])
ymax = max([y1, y2, y3, y4])
focal_bounds = (xmin, xmax, ymin, ymax)
(focalinfo, imginfo) = get_overlap_info(focal_bounds, gt[1],
img_bounds, gt[1])
# Specify offset and rows and columns to read
xoff = int((xmin - xOrigin) / pixelWidth)
yoff = int((ymax - yOrigin) / pixelHeight)
xcount = int((xmax - xmin) / pixelWidth) + 1
ycount = int((ymin - ymax) / pixelHeight) + 1
## check to make sure we don't go out of bounds on image
## if (xoff < 0) or (yoff < 0) or ((xcount+xoff) > rasterDS.RasterXSize) or ((ycount+yoff) > rasterDS.RasterYSize):
## print("Skipping %s out of bounds for this polygon"% (infile))
## continue
ncols = rasterDS.RasterXSize
nrows = rasterDS.RasterYSize
## subset the water-leaving reflectance file
outDS = drv.Create(os.path.splitext(infile)[0]+"_subset.tif", xsize=xcount, \
ysize=ycount, bands=rasterDS.RasterCount, eType=rasterDS.GetRasterBand(1).DataType)
outDS.SetGeoTransform((xmin, gt[1], gt[2], ymax, gt[4], gt[5]))
outRasterSRS = osr.SpatialReference()
outRasterSRS.ImportFromWkt(rasterDS.GetProjectionRef())
outDS.SetProjection(outRasterSRS.ExportToWkt())
for bandnum in range(1, 5):
thisBand = rasterDS.GetRasterBand(bandnum)
thisData = thisBand.ReadAsArray(imginfo[0], imginfo[1], imginfo[2],
imginfo[3])
outBand = outDS.GetRasterBand(bandnum)
outBand.WriteArray(thisData)
outBand.FlushCache()
del outBand
inDS = None
outDS = None
rasterDS = None
## subset the bottom reflectance file
if (os.path.isfile(rbfile)):
# Open data
rasterDS = gdal.Open(rbfile, gdal.GA_ReadOnly)
else:
print(
("File: %s does not exist....skipping") % (outputdir + rbfile))
continue
rbDS = drv.Create(os.path.splitext(rbfile)[0]+"_subset.tif", xsize=xcount, \
ysize=ycount, bands=rasterDS.RasterCount, eType=rasterDS.GetRasterBand(1).DataType)
rbDS.SetGeoTransform((xmin, gt[1], gt[2], ymax, gt[4], gt[5]))
rbDS.SetProjection(outRasterSRS.ExportToWkt())
for bandnum in range(1, 4):
thisBand = rasterDS.GetRasterBand(bandnum)
thisData = thisBand.ReadAsArray(imginfo[0], imginfo[1], imginfo[2],
imginfo[3])
outBand = rbDS.GetRasterBand(bandnum)
outBand.WriteArray(thisData)
outBand.FlushCache()
del outBand
rasterDS = None
rbDS = None
## subset the depth file
if (os.path.isfile(depthfile)):
# Open data
rasterDS = gdal.Open(depthfile, gdal.GA_ReadOnly)
else:
print(("File: %s does not exist....skipping") %
(outputdir + depthfile))
continue
depthDS = drv.Create(os.path.splitext(depthfile)[0]+"_subset.tif", xsize=xcount, \
ysize=ycount, bands=1, eType=gdal.GDT_Float32)
depthDS.SetGeoTransform((xmin, gt[1], gt[2], ymax, gt[4], gt[5]))
depthDS.SetProjection(outRasterSRS.ExportToWkt())
thisBand = rasterDS.GetRasterBand(1)
thisData = thisBand.ReadAsArray(imginfo[0], imginfo[1], imginfo[2],
imginfo[3])
outBand = depthDS.GetRasterBand(1)
outBand.WriteArray(thisData)
outBand.FlushCache()
del outBand
rasterDS = None
depthDS = None
