T. K. Daisy Leung astro313

## templates.ipynb

      
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                astro313
                / templates.ipynb
            
            
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              April 21, 2020 21:36
            
              
                Template for .ipynb, things to import or load at the beginning
              
          
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## nH.py

gridfile = '/mnt/home/daisyleung/Downloads/SIGAME_dev/sigame/temp/z6_data_files/cloud_models/GMC/grids/GMCgrid_abun_z6_em.models'

import glob
import pandas as pd
import numpy as np
gridsDF = pd.read_pickle(gridfile)
nHmw = gridsDF['nHmw']
nHmin = gridsDF['nHmin']
#

## DeepSpace.md

      
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                astro313
                / DeepSpace.md
            
            
              Created
              July 1, 2019 17:13
                — forked from jakgel/DeepSpace.md
            
              
                Some notes about marrying astrophysics with modern machine learning with particular focus on deep learning.
              
          
    Machine learning and deep learning in astronomy:

Machine learning has a long history in astronomy, but deep learning (DL) only got traction since arount 2016. Here I will list current DL effords:
The main field of ML applications in astrophysics is object classification. With source counts now ranging into the 10⁷-10⁸ for most surveys, machine learning is put to use to allow the classification of a large number of sources which would otherwise need an infeasible amount of manpower:

Classifying galaxies according to their HI content - SVM perform best in specisifity and is hence used over a DNN
Morphological classification of radio galaxies: Capsule Networks versus Convolutional Neural Networks *05/2019 - CNN show better performance than Capsule Networks, dropout provides more robusteness against image noise. Trained with LoTTs images.
[Optical Transient Object Classification in Wide Field Smal


## kde.py
import numpy as np
import matplotlib.pyplot as pl
import scipy.stats as st

data = np.random.multivariate_normal((0, 0), [[0.8, 0.05], [0.05, 0.7]], 100)
x = data[:, 0]
y = data[:, 1]
xmin, xmax = -3, 3
ymin, ymax = -3, 3

## .bashrc
if [ -f ~/.bash_profile ]; then
    . ~/.bash_profile
fi

## clump_handling.py
"""

DL and AP's edit to yt clump_finder to fix bugs and make it work, in order to do science with the Kavli project.

Last mod: July 17th 2018

PATH: blah blah (ipython installation path)/lib/python2.7/site-packages/yt/analysis_modules/level_sets/

Clump finding helper classes

## mpl_symbols.py

import matplotlib.cm as cm

max_color = 15.0 #Set maximum color index
color_gradient = cm.jet(arange(max_color)/max_color) #Set up color list from a canned color bar found in python
color_list = ['black','gray','darkorange','blue','red','green','orange','magenta','darkgoldenrod','purple','deeppink','darkolivegreen', 'cyan','yellow','beige']
symbol_list = ['o','v','8','x','s','*','h','D','^','8','1','o','o','o','o','o','o','o'] #Symbol list for rotation ladders on black and white Boltzmann plot
#for c in matplotlib.colors.cnames:
    #color_list.append(c)

## cloudMassCDF.py

# --- Cloud mass distribution, of clouds ID across all snapshots -----

fig = plt.figure()
ax = fig.add_subplot(111)

allmasses = []
for snap in ss.iterkeys():
    for snapleafs in ss[snap].iterkeys():
        allmasses.append(ss[snap][snapleafs].mass_Msun)

## massFunc.py
def mass_function(data, logged=False,
                  nbins=35, verbose=True):
    """
    Calculates a mass function from data, using 3 methods.

    Parameters
    ----------
    data: array

    logged: boolean

## price.rb

'''

from https://gist.github.com/IndianGuru/1192249


Working, as of 21 Oct 2017


'''

	gridfile = '/mnt/home/daisyleung/Downloads/SIGAME_dev/sigame/temp/z6_data_files/cloud_models/GMC/grids/GMCgrid_abun_z6_em.models'

	import glob
	import pandas as pd
	import numpy as np
	gridsDF = pd.read_pickle(gridfile)
	nHmw = gridsDF['nHmw']
	nHmin = gridsDF['nHmin']
	#
	import numpy as np
	import matplotlib.pyplot as pl
	import scipy.stats as st

	data = np.random.multivariate_normal((0, 0), [[0.8, 0.05], [0.05, 0.7]], 100)
	x = data[:, 0]
	y = data[:, 1]
	xmin, xmax = -3, 3
	ymin, ymax = -3, 3
	"""

	DL and AP's edit to yt clump_finder to fix bugs and make it work, in order to do science with the Kavli project.

	Last mod: July 17th 2018

	PATH: blah blah (ipython installation path)/lib/python2.7/site-packages/yt/analysis_modules/level_sets/

	Clump finding helper classes

	import matplotlib.cm as cm

	max_color = 15.0 #Set maximum color index
	color_gradient = cm.jet(arange(max_color)/max_color) #Set up color list from a canned color bar found in python
	color_list = ['black','gray','darkorange','blue','red','green','orange','magenta','darkgoldenrod','purple','deeppink','darkolivegreen', 'cyan','yellow','beige']
	symbol_list = ['o','v','8','x','s','*','h','D','^','8','1','o','o','o','o','o','o','o'] #Symbol list for rotation ladders on black and white Boltzmann plot
	#for c in matplotlib.colors.cnames:
	#color_list.append(c)

	# --- Cloud mass distribution, of clouds ID across all snapshots -----

	fig = plt.figure()
	ax = fig.add_subplot(111)

	allmasses = []
	for snap in ss.iterkeys():
	for snapleafs in ss[snap].iterkeys():
	allmasses.append(ss[snap][snapleafs].mass_Msun)
	def mass_function(data, logged=False,
	nbins=35, verbose=True):
	"""
	Calculates a mass function from data, using 3 methods.

	Parameters
	----------
	data: array

	logged: boolean

	'''

	from https://gist.github.com/IndianGuru/1192249


	Working, as of 21 Oct 2017


	'''