bhishanpdl

# Imports
from __future__ import print_function, division,with_statement
import sys
import numpy as np 
import matplotlib.pyplot as plt
import os

args = sys.argv[1]
ifile, c0, c1 = args.split(' ')
c0 = int(c0) 

bhishanpdl

import numpy as np
import pandas as pd

class display(object):
    """Display HTML representation of multiple objects
    Usage: display('df', "df.groupby('key').std()")
    """
    template = """<div style="float: left; padding: 10px;">
    <p style='font-family:"Courier New", Courier, monospace'>{0}</p>{1}
    </div>"""

bhishanpdl

import pandas as pd
import numpy as np

class display(object):
    """Display HTML representation of multiple objects"""
    template = """<div style="float: left; padding: 10px;">
    <p style='font-family:"Courier New", Courier, monospace'>{0}</p>{1}
    </div>"""
    def __init__(self, *args):
        self.args = args

bhishanpdl

# %%writefile compare_plots_for_txt_with_comment_head.py
# Plot the files
# Date: Jul 10, 2018
import pandas as pd 
import matplotlib.pyplot as plt
import numpy as np 
import os,sys,argparse
import glob

"""

bhishanpdl

The DMSTACK module obs_file will ingest a cluster fitsfile and do the CCD processing to give the fits table output src.fits with 77 parameters.

id (0)                                       coord_ra (1)
coord_dec (2)                                parent (3)                                   
deblend_nChild  (4)                          deblend_psfCenter_x (5)
deblend_psfCenter_y (6)                      deblend_psfFlux (7)                          
base_GaussianCentroid_x (8)                  base_GaussianCentroid_y (9)                  
base_NaiveCentroid_x  (10)                   base_NaiveCentroid_y (11)

bhishanpdl

tell application "System Events" to tell process "Google Chrome"
    perform action "AXRaise" of window 2
    set frontmost to true
end tell

bhishanpdl

def norm_scale(X, axis=0):
    mx = np.max(X, axis=axis)
    mi = np.min(X, axis=axis)
    epsilon = 10**-32
    return (X — mi) / (np.abs(mi) + mx + epsilon)
norm = norm_scale(X)
assert np.min(norm) >= 0
assert np.max(norm) <= 1

bhishanpdl

from sklearn.externals.joblib import Memory
memory = Memory(cachedir='/tmp', verbose=0)

@memory.cache
def computation(p1, p2):
    return p1 * p2
    
    
    
# example 2

bhishanpdl

from IPython.core.interactiveshell import InteractiveShell

# pretty print all cell's output and not just the last one
InteractiveShell.ast_node_interactivity = "all"

# pretty print only the last output of the cell
# InteractiveShell.ast_node_interactivity = "last_expr"