| from virgo_query import virgo_query | |
| simu = 'RefL0100N1504' | |
| name = 'Eagle' + simu | |
| snap = 28 | |
| file_save = 'data/{}_s{}_group.fits'.format(name, snap) | |
| print('saving %s' % file_save) | |
| query = """ |
| import numpy as np | |
| from numba import njit, prange | |
| import numba | |
| numba.config.NUMBA_NUM_THREADS = 20 | |
| numba.set_num_threads(20) | |
| @njit(parallel=True, fastmath=True) | |
| def binorm(xj, yj, x, y, xsig, ysig, nsig2_min=9.): | |
| r = np.zeros_like(x) |
| import glob | |
| import numpy as np | |
| import healpy | |
| from astropy.io import fits | |
| LEVEL_HPIX = 12 | |
| NSIDE_HPIX = 2**LEVEL_HPIX | |
| from astropy.io import ascii | |
| table = ascii.read('http://www.astro.uvic.ca/~alan/Nearby_Dwarf_Database_files/NearbyGalaxies.dat', delimiter=' ', guess=False, fast_reader=False, header_start=None, data_start=31, names="GalaxyName RA_h RA_m RA_s Dec_d Dec_m Dec_s E(B-V) distmod E_distmod e_distmod vh E_vh e_vh Vmag E_Vmag e_Vmag PA E_PA e_PA eps E_eps e_eps muV E_muV e_muV rh E_rh e_r vsig E_vsig e_vsig vrot E_vrot e_vrot MHI gvsig E_gvsig e_gvsig gvrot E_gvrot e_gvrot FeH E_FeH e_FeH Flag References".split()) |
| %pylab inline | |
| import numpy as np | |
| import pylab as plt | |
| import GPy | |
| import re | |
| def get_equation(kern): | |
| match = re.search(r'(math::)(\r\n|\r|\n)*(?P<equation>.*)(\r\n|\r|\n)*', kern.__doc__) | |
| return '' if match is None else match.group('equation').strip() |
| #!/usr/bin/env python3 | |
| # encoding=utf8 | |
| import os | |
| import re | |
| import unicodedata | |
| def slugify(value, max_length=50): | |
| """ |
tomboy2markdown: Convert Tomboy/Gnote notes to Markdown files.
It uses Markdown formatting when possible and creates subdirectories for notebooks.
This is a rewritten script of tomboy2text by Johannes H. Jensen with improved Markdown compatibility and customizability. Several known issues reported for tomboy2text are fixed. The output files are tested with Typora.
| def wstd(x, weights=None, axis=None, keepdims=False): | |
| x = np.asarray(x) | |
| if weights is not None: | |
| weights = np.asarray(weights) | |
| if weights.shape != x.shape: | |
| raise ValueError("weights must have same shape with x") | |
| else: | |
| weights = np.ones_like(x) | |
| w = weights / np.sum(weights, axis=axis, keepdims=True) |
| import GPy | |
| import numpy as np | |
| class HeteroNoise(GPy.kern.Kern): | |
| """ | |
| heteroscedastic white noise as function of X | |
| """ | |
| def __init__(self, input_dim, variance_function=1., active_dims=None): |
| from scipy.spatial import cKDTree | |
| transform = {0: lambda x: -x} | |
| a = np.random.randn(1000, 2) | |
| b = a.copy() | |
| for k in transform: | |
| b[:, k] = transform[k](b[:, k]) | |
| c = np.random.randn(1000, 2) |