所以我一个月前写了一些代码，并且一直在运行/更新它。我把最近的一个上传到GitHub，它很有效我知道它很有效，因为我在上传之前反复测试了它。然而，现在我打开了文件没有任何更改并提交了查询。。。不再有效，我所说的不再有效是指在150个查询中有2个成功。我有最近脚本中的数据，我104/150作品。有人知道为什么会这样吗？我的代码低于
"""
Imports needed for the code.
"""
"""
Script to get and clean data
"""
import numpy as np
import pandas as pd
from itertools import chain
from astroquery.gaia import Gaia
from pynverse import inversefunc
from astropy.io import ascii
import wget
import requests
import matplotlib.pyplot as plt
import numpy as np
import math
import pandas as pd
from sklearn.metrics import r2_score
from scipy import stats
import sklearn.metrics as sm
defaults = [0] * 3#needed for ignoring values that don't exsist
data = []#array for storing data
def reject_outliers(data):#Outlier Rejection Function
m = 2
u = np.mean(data)
s = np.std(data)
filtered = [e for e in data if (u - 2 * s < e < u + 2 * s)]
return filtered
def isNaN(num):#Checking if it is NaN(Not a Number)
return num != num
def HMS2deg(ra='', dec=''):#Convert from form RA to Degree RA(Gaia Form)
RA, DEC, rs, ds = '', '', 1, 1
if ra:
H, M, S, *_ = [float(i) for i in chain(ra.split(), defaults)]
if str(H)[0] == '-':
rs, H = -1, abs(H)
deg = (H*15) + (M/4)
RA = '{0}'.format(deg*rs)
if ra and dec:
return (RA, DEC)
else:
return RA or DEC
def HMS2degDEC(dec='', ra=''):#Convert from form Dec to Degree Dec(Gaia Form)
RA, DEC, rs, ds = '', '', 1, 1
if dec:
D, M, S, *_ = [float(i) for i in chain(dec.split(), defaults)]
S = S[0] if S else 0
if str(D)[0] == '-':
ds, D = -1, abs(D)
deg = D + (M/60) + (S/3600)
DEC = '{0}'.format(deg*ds)
if ra and dec:
return (RA, DEC)
else:
return RA or DEC
count=1
csv_file='test1.csv'#Data Storing File for Gaia
data = pd.read_csv(csv_file, error_bad_lines=False)#Ignore the bad lines
radata=data['R.A.']#get RA
decdata=data['Dec.']#get dec
agedata=data['Age(Myr)']#get Age
diamaterdata=data['Diameter']#get Diameter later converted to FOV
ra=[]#cleaned RA
dec=[]#cleaned Dec
age=[]#Cleaned age
csv_files=['M42.csv', 'Horsehead.csv', 'M93.csv', 'IrisTrain.csv']#Pre exsisting data
ages=[3, 6, 25, 0.055]#pre exsisting data's age
diameter=[]#Diameter cleaned data
gooddata=[]#Overall data storage for cleaned data
for i in range(len(radata)):#cleaning RA data and converting
if(isNaN(radata[i])):
ra.append(0)
else:
ra.append(HMS2deg(radata[i]))
print(ra)
for i in range(len(decdata)):#Cleaning Dec Data and converting
if(isNaN(decdata[i])):
dec.append(0)
else:
dec.append(HMS2degDEC(decdata[i]))
print(dec)
for i in range(len(diamaterdata)):#cleaning diameter data and converting to FOV
if(isNaN(diamaterdata[i])):
diameter.append(0)
else:
diameter.append(((diamaterdata[i])/3600)*100)
print(diameter)
for i in range(len(ra)):#Modified Query for each object
query1=""" SELECT bp_rp, parallax, pmra, pmdec, phot_g_mean_mag AS gp
FROM gaiadr2.gaia_source
WHERE 1 = CONTAINS(POINT('ICRS', ra, dec),
"""
query1=query1+" CIRCLE('ICRS'," +str(ra[i])+","+ str(dec[i])+","+str(diameter[i])+")"+")"
string2="""
AND phot_g_mean_flux_over_error > 50
AND phot_rp_mean_flux_over_error > 20
AND phot_bp_mean_flux_over_error > 20
AND visibility_periods_used > 8
"""
print(query1)
query1=query1+string2
try:#Try the following code
job = Gaia.launch_job(query1)#Launch query to gaia webpage
print(job)
results = job.get_results()#get results
ascii.write(results, 'values'+str(count)+'.csv', format='csv', fast_writer=False)
csv_files.append('values'+str(count)+'.csv')#store in CSV
ages.append(agedata[i])#append data
print(ages)
count+=1#avoid re-writing CSV file by creating different ones
except:#If the code throws any error, usually 'can't query' it will ignore the file, another filter to clean out any useless or bad data
continue
"""
End of Cleaning and Gathering Data
"""
"""
Training and Creating Model with the data
"""
arr2=[]
datasetY=[]
datasetX=[]
Y=[]
av=0
count=[]
count2=[]
MAD=[]
"""
def adjR(x, y, degree):
results = {}
coeffs = np.polyfit(x, y, degree)
p = np.poly1d(coeffs)
yhat = p(x)
ybar = np.sum(y)/len(y)
ssreg = np.sum((yhat-ybar)**2)
sstot = np.sum((y - ybar)**2)
results['r_squared'] = 1- (((1-(ssreg/sstot))*(len(y)-1))/(len(y)-degree-1)
return results
original accuracy calculation
"""
"""
def objective(x, a, b, c):
return a * x + b
needed for scipy modeling, polyfit was more accurate
"""
"""
Line 59-68 checks if CSV data is NAN if it is it will ignore the value and only take the data that can be used
"""
count=0
for i in range(len(csv_files)):
data=pd.read_csv(csv_files[i])
arr=data['gp']
arr2=data['bp_rp']
for i in range(len(arr2)):
if(isNaN(arr2[i])):
continue
elif(13<=arr[i]<=19):
datasetX.append(arr2[i])
datasetY.append(arr[i])
count+=1
mad=stats.median_absolute_deviation(datasetY)#Calculate MAD for Magnitude
mad2=stats.median_absolute_deviation(datasetX)#Calculate MAD for Color
madav=(mad+mad2)/2#Total MAD
MAD.append(count)#Appending to an Array for training and plotting
datasetX.clear()#Clearing for next Iteration
datasetY.clear()#Clearing for next Iteration
count=0
"""
Plotting data and Traning
"""
ages3=[]
MAD2=[]
ages2 = [4000 if math.isnan(i) else i for i in ages]#ignore any age nan values
print(len(ages3))
print(len(MAD))
MAD=[1.5 if math.isnan(i) else i for i in MAD]#ignore any MAD computation error values
for i in range(len(MAD)):
if(-500<=MAD[i]<=1500 and -25<=ages2[i]<170 or (100<=MAD[i]<=1262) and (278<=ages2[i]<=5067) or (-20<=MAD[i]<=20) and (3900<=ages2[i]<=4100) or (2642<=MAD[i]<=4750) and (0<=ages2[i]<=200) or (7800<=MAD[i]<=315800) and (0<=ages2[i]<=20)):
continue
else:
ages3.append(float(ages2[i]))
MAD2.append(float(MAD[i]))
fig = plt.figure()
ax1 = fig.add_subplot('111')
ax1.scatter(ages3, MAD2, color='blue')
plt.ylim(-7800,315800)
polyline = np.linspace(-5, 9000, 20)
mod1 = np.poly1d(np.polyfit(ages3, MAD2, 2))#Train for a function of degree 2
predict = np.poly1d(mod1)
ax1.plot(polyline,mod1(polyline), color='red')
print(np.interp(0.795, mod1(polyline),polyline))
print(mod1)#print model
plt.show()
"""
End of Training and Creating model/End of Script
"""
请关注这一部分，查询部分：
for i in range(len(ra)):#Modified Query for each object
query1=""" SELECT bp_rp, parallax, pmra, pmdec, phot_g_mean_mag AS gp
FROM gaiadr2.gaia_source
WHERE 1 = CONTAINS(POINT('ICRS', ra, dec),
"""
query1=query1+" CIRCLE('ICRS'," +str(ra[i])+","+ str(dec[i])+","+str(diameter[i])+")"+")"
string2="""
AND phot_g_mean_flux_over_error > 50
AND phot_rp_mean_flux_over_error > 20
AND phot_bp_mean_flux_over_error > 20
AND visibility_periods_used > 8
"""
print(query1)
query1=query1+string2
try:#Try the following code
job = Gaia.launch_job(query1)#Launch query to gaia webpage
print(job)
results = job.get_results()#get results
ascii.write(results, 'values'+str(count)+'.csv', format='csv', fast_writer=False)
csv_files.append('values'+str(count)+'.csv')#store in CSV
ages.append(agedata[i])#append data
print(ages)
count+=1#avoid re-writing CSV file by creating different ones
except:#If the code throws any error, usually 'can't query' it will ignore the file, another filter to clean out any useless or bad data
continue
谢谢你抽出时间。我知道这真的很不寻常。删除尝试后/除了这是错误：
Traceback (most recent call last):
File "read.py", line 120, in <module>
job = Gaia.launch_job(query1)#Launch query to gaia webpage
File "C:ProgramDataAnaconda3libsite-packagesastroquerygaiacore.py", line 846, in launch_job
return TapPlus.launch_job(self, query=query, name=name,
File "C:ProgramDataAnaconda3libsite-packagesastroqueryutilstapcore.py", line 344, in launch_job
results = utils.read_http_response(response, output_format)
File "C:ProgramDataAnaconda3libsite-packagesastroqueryutilstapxmlparserutils.py", line 42, in read_http_response
result = APTable.read(data, format=astropyFormat)
File "C:ProgramDataAnaconda3libsite-packagesastropytableconnect.py", line 61, in __call__
out = registry.read(cls, *args, **kwargs)
File "C:ProgramDataAnaconda3libsite-packagesastropyioregistry.py", line 520, in read
data = reader(*args, **kwargs)
File "C:ProgramDataAnaconda3libsite-packagesastropyiovotableconnect.py", line 116, in read_table_votable
raise ValueError("No table found")
ValueError: No table found
请注意，此问题已解决。原因在他们的网站上：https://www.cosmos.esa.int/web/gaia/news，计划维护。为了将来参考，如果你的代码停止工作，并且涉及查询，请前往他们的网站，他们可能已经发布了它