# Run this cell to load the slideshow css.
%run talktools

def fizzbuzz(number):
    if number % 3 == 0 and number % 5 == 0:
        return 'FizzBuzz'
    elif number % 3 == 0:
        return 'Fizz'
    elif number % 5 == 0:
        return 'Buzz'
    else:
        return number

fb = []
    
for number in range(1, 100):
    fb.append(fizzbuzz(number))

print fb

website('yt-project.org', height=600)

HTML('<iframe width="800" height="640" frameborder="0" allowFullScreen webkitallowfullscreen mozallowfullscreen src="https://sketchfab.com/models/486421c20a63434d9267020c11a4bc31/embed"></iframe><p style="font-size: 13px; font-weight: normal; margin: 5px; color: #4A4A4A;"><a href="https://sketchfab.com/models/486421c20a63434d9267020c11a4bc31" style="font-weight: bold; color: #1CAAD9;">Stellar Winds isodensity contour, rho=1e-24 g/cc</a>by <a href="https://sketchfab.com/jnaiman" style="font-weight: bold; color: #1CAAD9;">jnaiman</a>on <a href="https://sketchfab.com" style="font-weight: bold; color: #1CAAD9;">Sketchfab</a></p>')

website('http://yt-project.org/gallery.html')

# load yt, as simple as:
import yt

# yt.load() automagically figures out the output type
ds = yt.load('HiresGalaxy/DD0100/DD0100')

# Visualize a slice through the midplane of the galaxy
slc = yt.SlicePlot(ds, 'z', 'density', width=20*yt.units.kiloparsec)

# Customize the plot a bit
slc.set_figure_size(4)

import yt

ds = yt.load('big_cosmo/DD0020/DD0020')

print "Domain width is %s" % ds.domain_width.in_cgs()
print "Current redshift is %s" % ds.current_redshift

slc = yt.SlicePlot(ds, 0, 'density')

slc.set_figure_size(5)

# A data object that selects all of the data in the simulation, regardless of AMR level.
ad = ds.all_data()

# All of the gas density values, equivalent to ad['density']
dens =  ad[('gas', 'density')]

print dens

print len(dens)

print type(dens)

%matplotlib inline
from matplotlib import pyplot as plt

# Each value corresponds to a single cell - this dataset includes a large amount of data on 4 AMR levels
grid_level = ad['grid_level'].v

plt.hist(grid_level, bins=[-0.5, 0.5, 1.5, 2.5, 3.5, 4.5], log=True, facecolor='green')
plt.xlabel('AMR Level')
plt.ylabel('Number of cells')

max_val, max_loc = ds.find_max('matter_density')

print max_val
print max_loc

from yt.units import Mpc

sphere = ds.sphere(center=max_loc, radius=1*Mpc)

prj = yt.ProjectionPlot(ds, 0, ('gas', 'density'), center=max_loc, width=2.2*Mpc, data_source=sphere)

prj.set_zlim(('gas', 'density'), 1e-5, 1e-2)

prj.set_figure_size(5)

# Note: using a "deposit" field along with a weight field
# ('deposit', 'all_cic') is the cloud-in-cell deposition of all of the particles in the simulatin
prj = yt.ProjectionPlot(ds, 0, ('deposit', 'all_cic'), center=max_loc, width=2.2*Mpc, data_source=sphere, weight_field=('deposit', 'all_cic'))

prj.set_figure_size(5)

from pprint import pprint

ds = yt.load("GalaxyClusterMerger/fiducial_1to3_b0.273d_hdf5_plt_cnt_0175")

pprint(ds.field_list)

ds.derived_field_list

fi = ds.field_info[('gas', 'cell_mass')]
print fi.get_source()

print ds.field_info[('gas', 'specific_angular_momentum_x')].get_source()

def three_times_density(field, data):
    return 3*data['density']

ds = yt.load('HiresGalaxy/DD0100/DD0100')

ds.add_field(("gas", "three_times_density"), units="g/cm**3", function=three_times_density)

ad = ds.all_data()

print ad['density']
print ad['three_times_density']

from yt.units import cm, meter, kilogram, gram, second, joule

print kilogram

print type(kilogram)

print kilogram*meter**2/second**2

print kilogram*meter**2/second**2 == joule

print joule

print kilogram + gram

from yt.utilities.physical_constants import G, kboltz

print "Newton's constant: ", G
print "Newton's constant in MKS: ", G.in_mks(), "\n"

print "Boltzmann constant: ", kboltz
print "Boltzmann constant in MKS: ", kboltz.in_mks()

import numpy as np

rho = 1e-23*gram/cm**3

t_ff = np.sqrt(3*np.pi/(32*G*rho))

print t_ff

print t_ff.in_units('Myr')

ds = yt.load('big_cosmo/DD0020/DD0020')

prj = yt.ProjectionPlot(ds, 0, 'matter_density', width=(32, 'Mpccm/h'), weight_field='matter_density')
prj.set_figure_size(6)

prj.set_unit('matter_density', 'Msun/pc**3')

%matplotlib inline
import yt
import numpy as np
from yt.data_objects.particle_filters import add_particle_filter
from matplotlib import pyplot as plt
from pprint import pprint

ds = yt.load('HiresGalaxy/DD0100/DD0100')

pprint(sorted(ds.field_list))

def formed_star(pfilter, data):
    filter = data["all", "creation_time"] > 0
    return filter

add_particle_filter("formed_star", function=formed_star, filtered_type='all',
                    requires=["creation_time"])

# Load the data 
ds = yt.load("HiresGalaxy/DD0100/DD0100")
ds.add_particle_filter('formed_star')

# Create a selector to access the data
ad = ds.all_data()

# Access the creation time and particle mass datasets.
masses = ad['formed_star', 'particle_mass'].in_units('Msun')
formation_time = ad['formed_star', 'creation_time'].in_units('yr')

# Create a plot of the star formation rate as a function of time

time_range = [0, 1e8] # years
n_bins = 1000
hist, bins = np.histogram(formation_time, bins=n_bins, range=time_range,)

time = (bins[:-1] + bins[1:])/2

inds = np.digitize(formation_time, bins=bins)
sfr = np.array([masses[inds == j].sum()/(bins[j+1]-bins[j]) for j in range(len(time))])
sfr[sfr == 0] = np.nan

plt.plot(time/1e6, sfr)
plt.xlabel('Time  [Myr]')
plt.ylabel('SFR  [M$_\odot$ yr$^{-1}$]')

# Can create deposition fields based on these new particle types
yt.SlicePlot(ds, 2, ('deposit', 'formed_star_cic'), width=(20, 'kpc'))

from IPython.display import YouTubeVideo

YouTubeVideo('YWi4hO76Wss', width=1024, height=768)

YouTubeVideo('cyzzxkX4gjw', width=1024, height=768)

%%writefile parallel_test.py

import yt
yt.enable_parallelism()

ds = yt.load('big_cosmo/DD0020/DD0020')

ad = ds.all_data()

tm = ad.quantities.total_mass()

if yt.is_root():
    print tm

cat parallel_test.py

%%time

!python parallel_test.py

%%time

!mpirun -n 4 python parallel_test.py

website('http://yt-project.org/docs/dev-3.0/analyzing/parallel_computation.html')

import yt
import glob
from IPython.html.widgets import interact
from IPython.display import display
from yt.units import kpc

fns = sorted(glob.glob('/trove/goldbaum/nofeedback/DD????/DD????'))

time_series = yt.DatasetSeries(fns)

ds = time_series[-1]

prj = yt.ProjectionPlot(ds, 2, 'density')

@interact(width=[1, 100])
def show_plot(width=100):
    prj.set_width(width*kpc)
    display(prj)

@interact(index=[0, len(fns)-1])
def show_plot(index=57):
    ds = time_series[index]
    prj = yt.ProjectionPlot(ds, 2, 'density', width=(20, 'kpc'))
    prj.set_figure_size(5)
    prj.set_zlim('density', 1e-4, 1e-1)
    display(prj)