python.snippets

# A valid snippet should starts with:
#
#		snippet trigger_word [ "description" [ options ] ]
#
# and end with:
#
#		endsnippet
#
# Snippet options:
#
#		b - Beginning of line.
#		i - In-word expansion.
#		w - Word boundary.
#		r - Regular expression
#		e - Custom context snippet
#		A - Snippet will be triggered automatically, when condition matches.
#
# Basic example:
#
#		snippet emitter "emitter properties" b
#		private readonly ${1} = new Emitter<$2>()
#		public readonly ${1/^_(.*)/$1/}: Event<$2> = this.$1.event
#		endsnippet
#
# Online reference: https://github.com/SirVer/ultisnips/blob/master/doc/UltiSnips.txt



snippet  simpimport


import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit

#from scipy.stats import chi2

import scipy.stats as scipstats

from scipy import signal as sp

import math as m


from scipy.odr import ODR, Model, Data, RealData
from random import gauss as ga

import pickle as pick

import random


from random import gauss

endsnippet


snippet singFig

mainplotFigure = plt.figure()

mp = mainplotFigure.add_axes([0,0,1,1])

mp.set_xlabel(r'')
mp.set_ylabel(r')')

mp.plot(xx, tt, ls='', marker = 'o', color = 'green', label = 'Experimental Data')

mp.ticklabel_format(style='sci',useMathText = True)

#mainplotFigure.savefig("figname.jpg", bbox_inches='tight', dpi = mainplotFigure.dpi)

endsnippet


snippet lambfunc
x = lambda a,b: a*b
endsnippet

snippet numpyLoad
= np.loadtxt( ${0:${VISUAL}} )
endsnippet