In [1]:
%matplotlib inline
import matplotlib.pyplot as plt
In [2]:
import os
In [3]:
dir(os)
Out[3]:
['CLD_CONTINUED', 'CLD_DUMPED', 'CLD_EXITED', 'CLD_TRAPPED', 'DirEntry', 'EX_CANTCREAT', 'EX_CONFIG', 'EX_DATAERR', 'EX_IOERR', 'EX_NOHOST', 'EX_NOINPUT', 'EX_NOPERM', 'EX_NOUSER', 'EX_OK', 'EX_OSERR', 'EX_OSFILE', 'EX_PROTOCOL', 'EX_SOFTWARE', 'EX_TEMPFAIL', 'EX_UNAVAILABLE', 'EX_USAGE', 'F_LOCK', 'F_OK', 'F_TEST', 'F_TLOCK', 'F_ULOCK', 'MutableMapping', 'NGROUPS_MAX', 'O_ACCMODE', 'O_APPEND', 'O_ASYNC', 'O_CLOEXEC', 'O_CREAT', 'O_DIRECT', 'O_DIRECTORY', 'O_DSYNC', 'O_EXCL', 'O_LARGEFILE', 'O_NDELAY', 'O_NOATIME', 'O_NOCTTY', 'O_NOFOLLOW', 'O_NONBLOCK', 'O_RDONLY', 'O_RDWR', 'O_RSYNC', 'O_SYNC', 'O_TRUNC', 'O_WRONLY', 'POSIX_FADV_DONTNEED', 'POSIX_FADV_NOREUSE', 'POSIX_FADV_NORMAL', 'POSIX_FADV_RANDOM', 'POSIX_FADV_SEQUENTIAL', 'POSIX_FADV_WILLNEED', 'PRIO_PGRP', 'PRIO_PROCESS', 'PRIO_USER', 'P_ALL', 'P_NOWAIT', 'P_NOWAITO', 'P_PGID', 'P_PID', 'P_WAIT', 'PathLike', 'RTLD_DEEPBIND', 'RTLD_GLOBAL', 'RTLD_LAZY', 'RTLD_LOCAL', 'RTLD_NODELETE', 'RTLD_NOLOAD', 'RTLD_NOW', 'R_OK', 'SCHED_BATCH', 'SCHED_FIFO', 'SCHED_IDLE', 'SCHED_OTHER', 'SCHED_RESET_ON_FORK', 'SCHED_RR', 'SEEK_CUR', 'SEEK_END', 'SEEK_SET', 'ST_APPEND', 'ST_MANDLOCK', 'ST_NOATIME', 'ST_NODEV', 'ST_NODIRATIME', 'ST_NOEXEC', 'ST_NOSUID', 'ST_RDONLY', 'ST_RELATIME', 'ST_SYNCHRONOUS', 'ST_WRITE', 'TMP_MAX', 'WCONTINUED', 'WCOREDUMP', 'WEXITED', 'WEXITSTATUS', 'WIFCONTINUED', 'WIFEXITED', 'WIFSIGNALED', 'WIFSTOPPED', 'WNOHANG', 'WNOWAIT', 'WSTOPPED', 'WSTOPSIG', 'WTERMSIG', 'WUNTRACED', 'W_OK', 'XATTR_CREATE', 'XATTR_REPLACE', 'XATTR_SIZE_MAX', 'X_OK', '_Environ', '__all__', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__spec__', '_execvpe', '_exists', '_exit', '_fspath', '_fwalk', '_get_exports_list', '_putenv', '_spawnvef', '_unsetenv', '_wrap_close', 'abc', 'abort', 'access', 'altsep', 'chdir', 'chmod', 'chown', 'chroot', 'close', 'closerange', 'confstr', 'confstr_names', 'cpu_count', 'ctermid', 'curdir', 'defpath', 'device_encoding', 'devnull', 'dup', 'dup2', 'environ', 'environb', 'error', 'execl', 'execle', 'execlp', 'execlpe', 'execv', 'execve', 'execvp', 'execvpe', 'extsep', 'fchdir', 'fchmod', 'fchown', 'fdatasync', 'fdopen', 'fork', 'forkpty', 'fpathconf', 'fsdecode', 'fsencode', 'fspath', 'fstat', 'fstatvfs', 'fsync', 'ftruncate', 'fwalk', 'get_blocking', 'get_exec_path', 'get_inheritable', 'get_terminal_size', 'getcwd', 'getcwdb', 'getegid', 'getenv', 'getenvb', 'geteuid', 'getgid', 'getgrouplist', 'getgroups', 'getloadavg', 'getlogin', 'getpgid', 'getpgrp', 'getpid', 'getppid', 'getpriority', 'getresgid', 'getresuid', 'getsid', 'getuid', 'getxattr', 'initgroups', 'isatty', 'kill', 'killpg', 'lchown', 'linesep', 'link', 'listdir', 'listxattr', 'lockf', 'lseek', 'lstat', 'major', 'makedev', 'makedirs', 'minor', 'mkdir', 'mkfifo', 'mknod', 'name', 'nice', 'open', 'openpty', 'pardir', 'path', 'pathconf', 'pathconf_names', 'pathsep', 'pipe', 'pipe2', 'popen', 'posix_fadvise', 'posix_fallocate', 'pread', 'preadv', 'putenv', 'pwrite', 'pwritev', 'read', 'readlink', 'readv', 'register_at_fork', 'remove', 'removedirs', 'removexattr', 'rename', 'renames', 'replace', 'rmdir', 'scandir', 'sched_get_priority_max', 'sched_get_priority_min', 'sched_getaffinity', 'sched_getparam', 'sched_getscheduler', 'sched_param', 'sched_rr_get_interval', 'sched_setaffinity', 'sched_setparam', 'sched_setscheduler', 'sched_yield', 'sendfile', 'sep', 'set_blocking', 'set_inheritable', 'setegid', 'seteuid', 'setgid', 'setgroups', 'setpgid', 'setpgrp', 'setpriority', 'setregid', 'setresgid', 'setresuid', 'setreuid', 'setsid', 'setuid', 'setxattr', 'spawnl', 'spawnle', 'spawnlp', 'spawnlpe', 'spawnv', 'spawnve', 'spawnvp', 'spawnvpe', 'st', 'stat', 'stat_result', 'statvfs', 'statvfs_result', 'strerror', 'supports_bytes_environ', 'supports_dir_fd', 'supports_effective_ids', 'supports_fd', 'supports_follow_symlinks', 'symlink', 'sync', 'sys', 'sysconf', 'sysconf_names', 'system', 'tcgetpgrp', 'tcsetpgrp', 'terminal_size', 'times', 'times_result', 'truncate', 'ttyname', 'umask', 'uname', 'uname_result', 'unlink', 'unsetenv', 'urandom', 'utime', 'wait', 'wait3', 'wait4', 'waitid', 'waitid_result', 'waitpid', 'walk', 'write', 'writev']
In [4]:
os.listdir()
Out[4]:
['.ipynb_checkpoints', '20.10.19', 'freddi.dat', 'diagram.csv', 'cat.jpg', 'V404Cyg.txt.gz', 'Untitled.html', 'io.html', 'test.fits', 'tmp.dat', 'cat.txt', 'cat.npy', 'cat.npz', 'io1.ipynb']
In [5]:
os.listdir('20.10.19')
Out[5]:
['mymodule.py', 'myscript.py', 'numpy-submodules.html', 'numpy-submodules.ipynb', 'other_script.py']
In [6]:
for root, dirs, files in os.walk('.'):
print(root, dirs, files)
. ['.ipynb_checkpoints', '20.10.19'] ['freddi.dat', 'diagram.csv', 'cat.jpg', 'V404Cyg.txt.gz', 'Untitled.html', 'io.html', 'test.fits', 'tmp.dat', 'cat.txt', 'cat.npy', 'cat.npz', 'io1.ipynb'] ./.ipynb_checkpoints [] ['numpy-submodules-checkpoint.ipynb', 'io1-checkpoint.ipynb'] ./20.10.19 [] ['mymodule.py', 'myscript.py', 'numpy-submodules.html', 'numpy-submodules.ipynb', 'other_script.py']
In [7]:
os.path
Out[7]:
<module 'posixpath' from '/opt/conda/lib/python3.7/posixpath.py'>
In [8]:
import pathlib
In [9]:
d = '20.10.19'
fname = 'other_script.py'
os.path.join(d, fname)
Out[9]:
'20.10.19/other_script.py'
In [10]:
# ПЛОХО
d + '/' + fname
Out[10]:
'20.10.19/other_script.py'
In [11]:
dir(os.path)
Out[11]:
['__all__', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__spec__', '_get_sep', '_joinrealpath', '_varprog', '_varprogb', 'abspath', 'altsep', 'basename', 'commonpath', 'commonprefix', 'curdir', 'defpath', 'devnull', 'dirname', 'exists', 'expanduser', 'expandvars', 'extsep', 'genericpath', 'getatime', 'getctime', 'getmtime', 'getsize', 'isabs', 'isdir', 'isfile', 'islink', 'ismount', 'join', 'lexists', 'normcase', 'normpath', 'os', 'pardir', 'pathsep', 'realpath', 'relpath', 'samefile', 'sameopenfile', 'samestat', 'sep', 'split', 'splitdrive', 'splitext', 'stat', 'supports_unicode_filenames', 'sys']
In [12]:
os.path.exists(os.path.join(d, fname))
Out[12]:
True
In [13]:
?os.removedirs
In [14]:
import shutil
In [15]:
?shutil.rmtree
In [16]:
shutil.copy
Out[16]:
<function shutil.copy(src, dst, *, follow_symlinks=True)>
In [17]:
shutil.move
Out[17]:
<function shutil.move(src, dst, copy_function=<function copy2 at 0x7fd1dddb1680>)>
In [18]:
!ls
20.10.19 cat.npz freddi.dat test.fits V404Cyg.txt.gz cat.jpg cat.txt io1.ipynb tmp.dat cat.npy diagram.csv io.html Untitled.html
In [19]:
fd = open('diagram.csv')
text = fd.read()
fd.close()
In [20]:
print(text[:100])
0.036950897,-19.812012 0.03500012,32.59825 0.2093362,42.75399 0.25276598,9.813249 0.2743527,-36.1325
In [21]:
# НЕПИТОНОВО
def read_file(file_path):
try:
fd = open(file_path)
text = fd.read()
x = text[100]
raise RuntimeError('ERROR')
# except RuntimeError as e:
# print(e)
# raise ValueError('Value Error') from e
finally:
fd.close()
return text
In [22]:
with open('diagram.csv') as fd:
text = fd.read()
fd.closed
Out[22]:
True
In [23]:
import contextlib
In [24]:
from astropy.io import fits
In [25]:
with fits.open('test.fits', mode='ostream') as f:
pass
In [26]:
# Учебный пример
def parse_csv(file_path):
x = []
y = []
with open(file_path) as fd:
for line in fd:
line = line.strip()
x_, y_ = map(float, line.split(','))
x.append(x_)
y.append(y_)
return x, y
x, y = parse_csv('diagram.csv')
plt.plot(x, y, 'x')
print(x)
[0.036950897, 0.03500012, 0.2093362, 0.25276598, 0.2743527, 0.27199373, 0.44834232, 0.50287396, 0.49865913, 0.6310321, 0.90020174, 0.9007238, 0.8012198, 0.89946604, 0.89804685, 1.0035977, 1.100515, 1.1055652, 1.3991741, 1.699523, 2.0069914, 1.9971569, 2.0035028, 1.9957709]
In [27]:
!head -n2 diagram.csv
0.036950897,-19.812012 0.03500012,32.59825
In [28]:
with open('diagram.csv', 'rb') as fd:
text = fd.read()
text[:100]
Out[28]:
b'0.036950897,-19.812012\n0.03500012,32.59825\n0.2093362,42.75399\n0.25276598,9.813249\n0.2743527,-36.1325'
In [29]:
with open('cat.jpg', 'rb') as fd:
data = fd.read(40)
data
Out[29]:
b'\xff\xd8\xff\xe0\x00\x10JFIF\x00\x01\x01\x01\x00H\x00H\x00\x00\xff\xe2\x0cXICC_PROFILE\x00\x01\x01\x00\x00'
In [30]:
from PIL import Image
import numpy as np
In [31]:
with Image.open('cat.jpg') as img:
a = np.array(img)
print(a)
a.dtype
[[[ 87 132 73] [ 90 128 67] [ 95 136 80] ... [ 83 125 49] [ 97 134 57] [101 135 61]] [[107 145 88] [107 138 70] [ 91 135 48] ... [ 76 123 43] [ 92 133 54] [104 145 67]] [[119 151 86] [119 149 79] [107 148 46] ... [ 74 123 44] [ 83 130 52] [ 81 129 55]] ... [[157 145 133] [142 146 129] [144 147 136] ... [180 159 164] [196 173 165] [113 101 89]] [[152 135 127] [111 108 119] [154 134 159] ... [121 102 106] [161 147 134] [210 193 183]] [[116 124 103] [ 86 90 93] [ 41 52 72] ... [161 160 156] [148 142 130] [191 173 153]]]
Out[31]:
dtype('uint8')
In [32]:
# img
In [33]:
plt.imshow(a)
Out[33]:
<matplotlib.image.AxesImage at 0x7fd1c14a8650>
In [34]:
os.listdir()
Out[34]:
['.ipynb_checkpoints', '20.10.19', 'freddi.dat', 'diagram.csv', 'cat.jpg', 'V404Cyg.txt.gz', 'Untitled.html', 'io.html', 'test.fits', 'tmp.dat', 'cat.txt', 'cat.npy', 'cat.npz', 'io1.ipynb']
In [35]:
import gzip
In [36]:
with gzip.open('V404Cyg.txt.gz') as f:
for i, line in zip(range(30), f):
print(f'{i}: {line.decode()}')
0: JD,Magnitude,Uncertainty,HQuncertainty,Band,Observer Code,Comment Code(s),Comp Star 1,Comp Star 2,Charts,Comments,Transfomed,Airmass,Validation Flag,Cmag,Kmag,HJD,Star Name,Observer Affiliation,Measurement Method,Grouping Method,ADS Reference,Digitizer,Credit 1: 2447674.538,11.9,,,Vis.,KRT,,,,,,,,V,,,,V404 CYG,,STD,,,, 2: 2447680.449,14.5,,,Vis.,BMU,,,,,,,,V,,,,V404 CYG,KNVWS,STD,,,, 3: 2447680.521,14.0,,,Vis.,KRT,,,,,,,,V,,,,V404 CYG,,STD,,,, 4: 2447685.05,14.2,,,Vis.,KOA,,,,,,,,V,,,,V404 CYG,,STD,,,, 5: 2447685.9,<14.0,,,Vis.,SCE,,,,,,,,V,,,,V404 CYG,,STD,,,, 6: 2447690.467,14.8,,,Vis.,KRT,,,,,,,,V,,,,V404 CYG,,STD,,,, 7: 2447691.449,15.0,,,Vis.,KRT,,,,,,,,V,,,,V404 CYG,,STD,,,, 8: 2447696.84,<13.3,,,Vis.,SCE,,,,,,,,V,,,,V404 CYG,,STD,,,, 9: 2447706.79,14.8,,,Vis.,SCE,,,,,,,,V,,,,V404 CYG,,STD,,,, 10: 2447717.7,<15.1,,,Vis.,GRI,,,,,,,,V,,,,V404 CYG,,STD,,,, 11: 2447717.7,15.3,,,Vis.,SCE,,,,,,,,V,,,,V404 CYG,,STD,,,, 12: 2447719.61,<14.0,,,Vis.,BRJ,,,,,,,,V,,,,V404 CYG,,STD,,,, 13: 2447736.62,<14.5,,,Vis.,BRJ,,,,,,,,V,,,,V404 CYG,,STD,,,, 14: 2447744.4868,<14.5,,,Vis.,MAR,,,,,,,,V,,,,V404 CYG,,STD,,,, 15: 2447748.4708,<14.5,,,Vis.,MAR,,,,,,,,V,,,,V404 CYG,,STD,,,, 16: 2447748.59,<14.3,,,Vis.,BRJ,,,,,,,,V,,,,V404 CYG,,STD,,,, 17: 2447762.37,<13.6,,,Vis.,SCZ,,,,,,,,V,,,,V404 CYG,AFOEV,STD,,,, 18: 2447763.61,<14.8,,,Vis.,BRJ,,,,,,,,V,,,,V404 CYG,,STD,,,, 19: 2447769.59,<14.5,,,Vis.,BRJ,,,,,,,,V,,,,V404 CYG,,STD,,,, 20: 2447772.59,<14.5,,,Vis.,BRJ,,,,,,,,V,,,,V404 CYG,,STD,,,, 21: 2447773.58,<14.8,,,Vis.,BRJ,,,,,,,,V,,,,V404 CYG,,STD,,,, 22: 2447774.58,<14.8,,,Vis.,BRJ,,,,,,,,V,,,,V404 CYG,,STD,,,, 23: 2447793.4,<14.0,,,Vis.,FRF,,,,,,,,V,,,,V404 CYG,MCSE,STD,,,, 24: 2447794.58,<14.5,,,Vis.,BRJ,,,,,,,,V,,,,V404 CYG,,STD,,,, 25: 2447804.66,<14.8,,,Vis.,BRJ,,,,,,,,V,,,,V404 CYG,,STD,,,, 26: 2447805.54,<14.8,,,Vis.,BRJ,,,,,,,,V,,,,V404 CYG,,STD,,,, 27: 2447823.55,<14.5,,,Vis.,BRJ,,,,,,,,V,,,,V404 CYG,,STD,,,, 28: 2447825.62,<14.5,,,Vis.,BRJ,,,,,,,,V,,,,V404 CYG,,STD,,,, 29: 2447848.55,<14.5,,,Vis.,BRJ,,,,,,,,V,,,,V404 CYG,,STD,,,,
In [37]:
np.loadtxt('diagram.csv', delimiter=',')
Out[37]:
array([[ 3.6950897e-02, -1.9812012e+01],
[ 3.5000120e-02, 3.2598250e+01],
[ 2.0933620e-01, 4.2753990e+01],
[ 2.5276598e-01, 9.8132490e+00],
[ 2.7435270e-01, -3.6132540e+01],
[ 2.7199373e-01, -7.7606960e+01],
[ 4.4834232e-01, 3.9542206e+02],
[ 5.0287396e-01, 4.1046777e+02],
[ 4.9865913e-01, 4.4323727e+02],
[ 6.3103210e-01, 3.2257190e+02],
[ 9.0020174e-01, 9.6505670e+01],
[ 9.0072380e-01, 2.1659071e+02],
[ 8.0121980e-01, 3.8078067e+02],
[ 8.9946604e-01, 4.2838660e+02],
[ 8.9804685e-01, 6.0306525e+02],
[ 1.0035977e+00, 8.2749520e+02],
[ 1.1005150e+00, 5.6943005e+02],
[ 1.1055652e+00, 7.2879663e+02],
[ 1.3991741e+00, 6.1179395e+02],
[ 1.6995230e+00, 1.0427968e+03],
[ 2.0069914e+00, 1.1069563e+03],
[ 1.9971569e+00, 8.4935736e+02],
[ 2.0035028e+00, 8.0566150e+02],
[ 1.9957709e+00, 5.3058610e+02]])
In [38]:
table = np.genfromtxt('diagram.csv', delimiter=',', names='x,y')
table['x']
table[2]
rec = np.rec.array(table)
rec.x
Out[38]:
array([0.0369509 , 0.03500012, 0.2093362 , 0.25276598, 0.2743527 ,
0.27199373, 0.44834232, 0.50287396, 0.49865913, 0.6310321 ,
0.90020174, 0.9007238 , 0.8012198 , 0.89946604, 0.89804685,
1.0035977 , 1.100515 , 1.1055652 , 1.3991741 , 1.699523 ,
2.0069914 , 1.9971569 , 2.0035028 , 1.9957709 ])
In [39]:
def convert_magn(b):
if b.startswith(b'<'):
return 999
return b
table = np.genfromtxt(
'V404Cyg.txt.gz',
delimiter=',',
usecols=(0, 1, 2),
names=True,
filling_values=0.0,
converters={1: convert_magn},
)
In [40]:
table[~np.isnan(table['Uncertainty'])]
Out[40]:
array([(2447674.538 , 11.9, 0.), (2447680.449 , 14.5, 0.),
(2447680.521 , 14. , 0.), ..., (2459142.88889, 999. , 0.),
(2459145.3431 , 999. , 0.), (2459146.84931, 999. , 0.)],
dtype=[('JD', '<f8'), ('Magnitude', '<f8'), ('Uncertainty', '<f8')])
In [41]:
plt.plot(table['JD'], table['Magnitude'], 'x')
plt.ylim([20, 10])
Out[41]:
(20.0, 10.0)
In [42]:
import pandas as pd
In [43]:
df = pd.read_csv('V404Cyg.txt.gz', low_memory=False)
print([df[column].dtype for column in df.columns])
df
[dtype('float64'), dtype('O'), dtype('float64'), dtype('float64'), dtype('O'), dtype('O'), dtype('O'), dtype('O'), dtype('O'), dtype('O'), dtype('O'), dtype('float64'), dtype('float64'), dtype('O'), dtype('float64'), dtype('float64'), dtype('float64'), dtype('O'), dtype('O'), dtype('O'), dtype('O'), dtype('float64'), dtype('float64'), dtype('float64')]
Out[43]:
| JD | Magnitude | Uncertainty | HQuncertainty | Band | Observer Code | Comment Code(s) | Comp Star 1 | Comp Star 2 | Charts | ... | Cmag | Kmag | HJD | Star Name | Observer Affiliation | Measurement Method | Grouping Method | ADS Reference | Digitizer | Credit | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2.447675e+06 | 11.9 | NaN | NaN | Vis. | KRT | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | V404 CYG | NaN | STD | NaN | NaN | NaN | NaN |
| 1 | 2.447680e+06 | 14.5 | NaN | NaN | Vis. | BMU | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | V404 CYG | KNVWS | STD | NaN | NaN | NaN | NaN |
| 2 | 2.447681e+06 | 14.0 | NaN | NaN | Vis. | KRT | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | V404 CYG | NaN | STD | NaN | NaN | NaN | NaN |
| 3 | 2.447685e+06 | 14.2 | NaN | NaN | Vis. | KOA | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | V404 CYG | NaN | STD | NaN | NaN | NaN | NaN |
| 4 | 2.447686e+06 | <14.0 | NaN | NaN | Vis. | SCE | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | V404 CYG | NaN | STD | NaN | NaN | NaN | NaN |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 80707 | 2.459137e+06 | <14.9 | NaN | NaN | Vis. | PYG | U | <149 | NaN | AAVSO 1346CXO | ... | NaN | NaN | NaN | V404 CYG | BAA-VSS | STD | NaN | NaN | NaN | NaN |
| 80708 | 2.459139e+06 | <14.4 | NaN | NaN | Vis. | MUY | NaN | 144 | 0 | X19001DUP | ... | NaN | NaN | NaN | V404 CYG | VVS | STD | NaN | NaN | NaN | NaN |
| 80709 | 2.459143e+06 | <15.6 | NaN | NaN | Vis. | LMK | NaN | 156 | NaN | 2169dbs | ... | NaN | NaN | NaN | V404 CYG | AAVSO | STD | NaN | NaN | NaN | NaN |
| 80710 | 2.459145e+06 | <14.4 | NaN | NaN | Vis. | MUY | NaN | 144 | 0 | X19001DUP | ... | NaN | NaN | NaN | V404 CYG | VVS | STD | NaN | NaN | NaN | NaN |
| 80711 | 2.459147e+06 | <15.2 | NaN | NaN | Vis. | LMK | NaN | 152 | NaN | 2169dbs | ... | NaN | NaN | NaN | V404 CYG | AAVSO | STD | NaN | NaN | NaN | NaN |
80712 rows × 24 columns
In [44]:
good = df[~df['Magnitude'].str.startswith('<')]
good.JD
Out[44]:
0 2.447675e+06
1 2.447680e+06
2 2.447681e+06
3 2.447685e+06
5 2.447690e+06
...
80702 2.459136e+06
80703 2.459137e+06
80704 2.459137e+06
80705 2.459137e+06
80706 2.459137e+06
Name: JD, Length: 73608, dtype: float64
In [45]:
good.loc[4]
--------------------------------------------------------------------------- KeyError Traceback (most recent call last) /opt/conda/lib/python3.7/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance) 2645 try: -> 2646 return self._engine.get_loc(key) 2647 except KeyError: pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc() pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc() pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item() pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item() KeyError: 4 During handling of the above exception, another exception occurred: KeyError Traceback (most recent call last) <ipython-input-45-9d10255a49ea> in <module> ----> 1 good.loc[4] /opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py in __getitem__(self, key) 1766 1767 maybe_callable = com.apply_if_callable(key, self.obj) -> 1768 return self._getitem_axis(maybe_callable, axis=axis) 1769 1770 def _is_scalar_access(self, key: Tuple): /opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py in _getitem_axis(self, key, axis) 1963 # fall thru to straight lookup 1964 self._validate_key(key, axis) -> 1965 return self._get_label(key, axis=axis) 1966 1967 /opt/conda/lib/python3.7/site-packages/pandas/core/indexing.py in _get_label(self, label, axis) 623 raise IndexingError("no slices here, handle elsewhere") 624 --> 625 return self.obj._xs(label, axis=axis) 626 627 def _get_loc(self, key: int, axis: int): /opt/conda/lib/python3.7/site-packages/pandas/core/generic.py in xs(self, key, axis, level, drop_level) 3535 loc, new_index = self.index.get_loc_level(key, drop_level=drop_level) 3536 else: -> 3537 loc = self.index.get_loc(key) 3538 3539 if isinstance(loc, np.ndarray): /opt/conda/lib/python3.7/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance) 2646 return self._engine.get_loc(key) 2647 except KeyError: -> 2648 return self._engine.get_loc(self._maybe_cast_indexer(key)) 2649 indexer = self.get_indexer([key], method=method, tolerance=tolerance) 2650 if indexer.ndim > 1 or indexer.size > 1: pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc() pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc() pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item() pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item() KeyError: 4
In [46]:
df.iloc[4]
Out[46]:
JD 2.44769e+06 Magnitude <14.0 Uncertainty NaN HQuncertainty NaN Band Vis. Observer Code SCE Comment Code(s) NaN Comp Star 1 NaN Comp Star 2 NaN Charts NaN Comments NaN Transfomed NaN Airmass NaN Validation Flag V Cmag NaN Kmag NaN HJD NaN Star Name V404 CYG Observer Affiliation NaN Measurement Method STD Grouping Method NaN ADS Reference NaN Digitizer NaN Credit NaN Name: 4, dtype: object
In [47]:
df.loc[[1, 2, 3, 10, 0, 1]]
Out[47]:
| JD | Magnitude | Uncertainty | HQuncertainty | Band | Observer Code | Comment Code(s) | Comp Star 1 | Comp Star 2 | Charts | ... | Cmag | Kmag | HJD | Star Name | Observer Affiliation | Measurement Method | Grouping Method | ADS Reference | Digitizer | Credit | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 2447680.449 | 14.5 | NaN | NaN | Vis. | BMU | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | V404 CYG | KNVWS | STD | NaN | NaN | NaN | NaN |
| 2 | 2447680.521 | 14.0 | NaN | NaN | Vis. | KRT | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | V404 CYG | NaN | STD | NaN | NaN | NaN | NaN |
| 3 | 2447685.050 | 14.2 | NaN | NaN | Vis. | KOA | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | V404 CYG | NaN | STD | NaN | NaN | NaN | NaN |
| 10 | 2447717.700 | 15.3 | NaN | NaN | Vis. | SCE | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | V404 CYG | NaN | STD | NaN | NaN | NaN | NaN |
| 0 | 2447674.538 | 11.9 | NaN | NaN | Vis. | KRT | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | V404 CYG | NaN | STD | NaN | NaN | NaN | NaN |
| 1 | 2447680.449 | 14.5 | NaN | NaN | Vis. | BMU | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | V404 CYG | KNVWS | STD | NaN | NaN | NaN | NaN |
6 rows × 24 columns
In [48]:
df['JD']
Out[48]:
0 2.447675e+06
1 2.447680e+06
2 2.447681e+06
3 2.447685e+06
4 2.447686e+06
...
80707 2.459137e+06
80708 2.459139e+06
80709 2.459143e+06
80710 2.459145e+06
80711 2.459147e+06
Name: JD, Length: 80712, dtype: float64
In [49]:
df[['JD', 'Magnitude']]
Out[49]:
| JD | Magnitude | |
|---|---|---|
| 0 | 2.447675e+06 | 11.9 |
| 1 | 2.447680e+06 | 14.5 |
| 2 | 2.447681e+06 | 14.0 |
| 3 | 2.447685e+06 | 14.2 |
| 4 | 2.447686e+06 | <14.0 |
| ... | ... | ... |
| 80707 | 2.459137e+06 | <14.9 |
| 80708 | 2.459139e+06 | <14.4 |
| 80709 | 2.459143e+06 | <15.6 |
| 80710 | 2.459145e+06 | <14.4 |
| 80711 | 2.459147e+06 | <15.2 |
80712 rows × 2 columns
In [50]:
def convert_magn(s):
if s.startswith('<'):
s = s[1:]
return float(s)
df['m'] = df.Magnitude.map(convert_magn)
df['is_upper_limit'] = df.Magnitude.str.startswith('<')
df.m
Out[50]:
0 11.9
1 14.5
2 14.0
3 14.2
4 14.0
...
80707 14.9
80708 14.4
80709 15.6
80710 14.4
80711 15.2
Name: m, Length: 80712, dtype: float64
In [51]:
df.is_upper_limit
Out[51]:
0 False
1 False
2 False
3 False
4 True
...
80707 True
80708 True
80709 True
80710 True
80711 True
Name: is_upper_limit, Length: 80712, dtype: bool
In [52]:
plt.plot(df.JD[~df.is_upper_limit], df.m[~df.is_upper_limit], 'x')
plt.plot(df.JD[df.is_upper_limit], df.m[df.is_upper_limit], 'v')
Out[52]:
[<matplotlib.lines.Line2D at 0x7fd1a946a450>]
In [53]:
with open('tmp.dat', 'x') as fh:
fh.write('hello\n')
--------------------------------------------------------------------------- FileExistsError Traceback (most recent call last) <ipython-input-53-7903543c5c48> in <module> ----> 1 with open('tmp.dat', 'x') as fh: 2 fh.write('hello\n') FileExistsError: [Errno 17] File exists: 'tmp.dat'
In [54]:
!cat tmp.dat
hello
In [55]:
with open('tmp.dat', 'w') as fh:
fh.write('hello\n')
In [56]:
np.savetxt('cat.txt', a.sum(axis=2))
In [57]:
!head -n1 cat.txt
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In [58]:
np.save('cat.npy', a)
In [59]:
b = np.load('cat.npy')
np.testing.assert_array_equal(a, b)
In [60]:
np.savez('cat.npz', a)
In [61]:
npz = np.load('cat.npz')
for b in npz.items():
print(b)
('arr_0', array([[[ 87, 132, 73],
[ 90, 128, 67],
[ 95, 136, 80],
...,
[ 83, 125, 49],
[ 97, 134, 57],
[101, 135, 61]],
[[107, 145, 88],
[107, 138, 70],
[ 91, 135, 48],
...,
[ 76, 123, 43],
[ 92, 133, 54],
[104, 145, 67]],
[[119, 151, 86],
[119, 149, 79],
[107, 148, 46],
...,
[ 74, 123, 44],
[ 83, 130, 52],
[ 81, 129, 55]],
...,
[[157, 145, 133],
[142, 146, 129],
[144, 147, 136],
...,
[180, 159, 164],
[196, 173, 165],
[113, 101, 89]],
[[152, 135, 127],
[111, 108, 119],
[154, 134, 159],
...,
[121, 102, 106],
[161, 147, 134],
[210, 193, 183]],
[[116, 124, 103],
[ 86, 90, 93],
[ 41, 52, 72],
...,
[161, 160, 156],
[148, 142, 130],
[191, 173, 153]]], dtype=uint8))
In [62]:
import pickle
In [ ]: