[yt-svn] commit/cookbook: brittonsmith: Moving more simple recipes to docs.
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Thu May 24 23:39:58 PDT 2012
1 new commit in cookbook:
https://bitbucket.org/yt_analysis/cookbook/changeset/58bafbdd2eac/
changeset: 58bafbdd2eac
user: brittonsmith
date: 2012-05-25 08:37:46
summary: Moving more simple recipes to docs.
affected #: 8 files
diff -r ac0619a863b2150dc5dadacebd3cbed6388471df -r 58bafbdd2eac05f6c6a26dc0ce13973baccebbb2 recipes/multi_width_save.py
--- a/recipes/multi_width_save.py
+++ /dev/null
@@ -1,39 +0,0 @@
-"""
-This recipe shows a slightly-fancy way to save a couple plots at a lot of
-different widths, ensuring that across the plots we have the same min/max for
-the colorbar.
-"""
-from yt.mods import *
-
-fn = "RedshiftOutput0005" # parameter file to load
-pf = load(fn) # load data
-
-pc = PlotCollection(pf, "c") # We get our Plot Collection object
-
-# Note that when we save, we will be using string formatting to change all of
-# the bits in here. You can add more, or remove some, if you like.
-fn = "%(bn)s_%(width)010i_%(unit)s" # template for image file names
-
-# Now let's set up the widths we want to use.
-widths = [ (2, "mpc"), (1000, 'kpc')]
-# We could add on more of these with:
-# widths += [ ... ]
-
-# Now we add a slice for x and y.
-pc.add_slice("Density", "x")
-pc.add_slice("Density", "y")
-
-# So for all of our widths, we will set the width of the plot and then make
-# sure that our limits for the colorbar are the min/max across the three plots.
-# Then we save! Each saved file will have a descriptive name, so we can tell
-# them apart.
-
-for width, unit in widths:
- pc.set_width(width,unit)
- vmin = min([p.norm.vmin for p in pc.plots])
- vmax = max([p.norm.vmax for p in pc.plots])
- pc.set_zlim(vmin,vmax)
- # This is the string formatting we talked about earlier
- d = {'bn':pf.basename, 'width':width, 'unit':unit}
- pc.save(fn % d)
-
diff -r ac0619a863b2150dc5dadacebd3cbed6388471df -r 58bafbdd2eac05f6c6a26dc0ce13973baccebbb2 recipes/offaxis_projection.py
--- a/recipes/offaxis_projection.py
+++ /dev/null
@@ -1,42 +0,0 @@
-"""
-This recipe shows how to make a projection of a dataset from an arbitrary
-projection angle (so you are not confined to the x, y, and z axes).
-See :ref:`volume_rendering` for more information.
-
-"""
-from yt.mods import * # set up our namespace
-
-fn = "RedshiftOutput0005" # parameter file to load
-
-pf = load(fn) # load data
-
-# Now we need a center of our volume to render. Here we'll just use
-# 0.5,0.5,0.5, because volume renderings are not periodic.
-c = [0.5, 0.5, 0.5]
-
-# Our image plane will be normal to some vector. For things like collapsing
-# objects, you could set it the way you would a cutting plane -- but for this
-# dataset, we'll just choose an off-axis value at random. This gets normalized
-# automatically.
-L = [0.5, 0.2, 0.7]
-
-# Our "width" is the width of the image plane as well as the depth -- so we set
-# it to be 0.8 so we get almost the whole domain. Note that corners may be
-# visible in the output image!
-W = 0.8
-
-# Now we decide how big an image we want. 512x512 should be sufficient.
-N = 512
-
-# Now we call the off_axis_projection function, which handles the rest.
-# Note that we set no_ghost equal to False, so that we *do* include ghost
-# zones in our data. This takes longer to calculate, but the results look
-# much cleaner than when you ignore the ghost zones.
-# Also note that we set the field which we want to project as "Density", but
-# really we could use any arbitrary field like "Temperature", "Metallicity"
-# or whatever.
-image = off_axis_projection(pf, c, L, W, N, "Density", no_ghost=False)
-
-# Image is now an NxN array representing the intensities of the various pixels.
-# And now, we call our direct image saver. We save the log of the result.
-write_image(na.log10(image), "%s_offaxis_projection.png" % pf)
diff -r ac0619a863b2150dc5dadacebd3cbed6388471df -r 58bafbdd2eac05f6c6a26dc0ce13973baccebbb2 recipes/overplot_particles.py
--- a/recipes/overplot_particles.py
+++ /dev/null
@@ -1,19 +0,0 @@
-"""
-This is a simple recipe to show how to open a dataset, plot a projection
-through it, and add particles on top. For more information see
-:ref:`callbacks`.
-"""
-from yt.mods import * # set up our namespace
-
-fn = "RedshiftOutput0005" # parameter file to load
-
-pf = load(fn) # load data
-pc = PlotCollection(pf, "c") # center at middle of domain
-p = pc.add_projection("Density", "x")
-p.modify["particles"](1.0) # 1.0 is the 'width' we want for our slab of
- # particles -- this governs the allowable locations
- # of particles that show up on the image
- # NOTE: we can also supply a *ptype* to cut based
- # on a given (integer) particle type
-pc.set_width(1.0, 'unitary') # change width of our plot to the full domain
-pc.save(fn) # save all plots
diff -r ac0619a863b2150dc5dadacebd3cbed6388471df -r 58bafbdd2eac05f6c6a26dc0ce13973baccebbb2 recipes/sum_mass_in_sphere.py
--- a/recipes/sum_mass_in_sphere.py
+++ /dev/null
@@ -1,19 +0,0 @@
-"""
-This recipe shows how to take a sphere, centered on the most dense point, and
-sum up the total mass in baryons and particles within that sphere. Note that
-this recipe will take advantage of multiple CPUs if executed with mpirun and
-supplied the --parallel command line argument. For more information, see
-:ref:`derived-quantities`.
-"""
-from yt.mods import * # set up our namespace
-
-fn = "RedshiftOutput0005" # parameter file to load
-
-pf = load(fn) # load data
-sp = pf.h.sphere("max", (1.0, "mpc"))
-
-baryon_mass, particle_mass = sp.quantities["TotalQuantity"](
- ["CellMassMsun", "ParticleMassMsun"])
-
-print "Total mass in sphere is %0.5e (gas = %0.5e / particles = %0.5e)" % \
- (baryon_mass + particle_mass, baryon_mass, particle_mass)
diff -r ac0619a863b2150dc5dadacebd3cbed6388471df -r 58bafbdd2eac05f6c6a26dc0ce13973baccebbb2 recipes/thin_slice_projection.py
--- a/recipes/thin_slice_projection.py
+++ /dev/null
@@ -1,19 +0,0 @@
-"""
-This is a simple recipe to show how to open a dataset and then take a
-weighted-average projection through it, but only through a very thin slice of
-the region. For more information see :ref:`methods-projections`.
-"""
-from yt.mods import * # set up our namespace
-
-fn = "RedshiftOutput0005" # parameter file to load
-
-pf = load(fn) # load data
-pc = PlotCollection(pf) # defaults to center at most dense point
-
-# The region object requires a center, a left edge, and a right edge. We want
-# a thin slice and we're projecting along x, so we'll create a region that
-# fully covers the domain in y and z, but only a portion of it in x.
-region = pf.h.region([0.3, 0.5, 0.5], [0.1, 0.0, 0.0], [0.5, 1.0, 1.0])
-
-pc.add_projection("Density", "x", weight_field="Density", data_source = region) # 0 = x-axis
-pc.save(fn) # save all plots
diff -r ac0619a863b2150dc5dadacebd3cbed6388471df -r 58bafbdd2eac05f6c6a26dc0ce13973baccebbb2 recipes/time_series_phase.py
--- a/recipes/time_series_phase.py
+++ /dev/null
@@ -1,39 +0,0 @@
-"""
-This is a recipe to sit inside a directory and plot a phase diagram for every
-one of the outputs in that directory.
-
-If run with mpirun and the --parallel flag, this will take advantage of
-multiple processors.
-"""
-from yt.mods import * # set up our namespace
-
-# this means get all the parameter files that it can autodetect and then supply
-# them as parameter file objects to the loop.
-for pf in all_pfs(max_depth=2):
- # We create a plot collection to hold our plot
- # If we don't specify the center, it will look for one -- but we don't
- # really care where it's centered for this plot.
- pc = PlotCollection(pf, "c")
-
- # Now we add a phase plot of a sphere with radius 1.0 in code units.
- # If your domain is not 0..1, then this may not cover it completely.
- p = pc.add_phase_sphere(1.0, '1', ["Density", "Temperature", "CellMassMsun"],
- weight=None,
- x_bins=128, x_bounds = (1e-32, 1e-24),
- y_bins=128, y_bounds = (1e2, 1e7))
- # We've over-specified things -- but this will help ensure we have constant
- # bounds. lazy_reader gives it the go-ahead to run in parallel, and we
- # have asked for 128 bins from 1e-32 .. 1e-24 in Density-space and 128 bins
- # between 1e2 and 1e7 in Temperature space. This will lead to very fine
- # points of much lower mass, which is okay. You can reduce the number of
- # bins to get more mass in each bin. Additionally, weight=None means that
- # no averaging is done -- it just gets summed up, so the value of each bin
- # will be all the mass residing within that bin.
-
- # Nowe let's add a title with some fun information. p is the plot we were
- # handed previously. We will add the name of the parameter file and the
- # current redshift.
- p.modify["title"]("%s (z = %0.2f)" % (pf, pf["CosmologyCurrentRedshift"]))
-
- # Now let's save it out.
- pc.save()#"%s" % pf)
diff -r ac0619a863b2150dc5dadacebd3cbed6388471df -r 58bafbdd2eac05f6c6a26dc0ce13973baccebbb2 recipes/time_series_quantity.py
--- a/recipes/time_series_quantity.py
+++ /dev/null
@@ -1,38 +0,0 @@
-"""
-This is a recipe to sit inside a directory and calculate a quantity for all of
-the outputs in that directory.
-
-If run with mpirun and the --parallel flag, this will take advantage of
-multiple processors.
-"""
-from yt.mods import * # set up our namespace
-
-# First set up our times and quantities lists
-times = []
-values = []
-
-# this means get all the parameter files that it can autodetect and then supply
-# them as parameter file objects to the loop.
-for pf in all_pfs(max_depth=2):
- # Get the current time, convert to years from code units
- times.append(pf["InitialTime"] * pf["years"])
-
- # Now get a box containing the entire dataset
- data = pf.h.all_data()
- # Now we calculate the average. The first argument is the quantity to
- # average, the second is the weight.
- # "lazy_reader" has two meanings -- the first is that it will try to
- # operate on each individual grid, rather than a flattened array of all the
- # data. The second is that it will also distribute grids across multiple
- # processors, if multiple processors are in use.
- val = data.quantities["WeightedAverageQuantity"](
- "Temperature", "CellVolume", lazy_reader=True)
- values.append(val)
-
-# Now we have our values and our time. We can plot this in pylab!
-
-import pylab
-pylab.semilogy(times, values, '-x')
-pylab.xlabel(r"$Time [years]$")
-pylab.ylabel(r"$\mathrm{H}^{+}\/\/\mathrm{Fraction}$")
-pylab.savefig("average_HII_fraction.png")
diff -r ac0619a863b2150dc5dadacebd3cbed6388471df -r 58bafbdd2eac05f6c6a26dc0ce13973baccebbb2 recipes/velocity_vectors_on_slice.py
--- a/recipes/velocity_vectors_on_slice.py
+++ /dev/null
@@ -1,16 +0,0 @@
-"""
-This is a simple recipe to show how to open a dataset, plot a slice
-through it, and add velocity vectors on top.
-"""
-from yt.mods import * # set up our namespace
-
-fn = "RedshiftOutput0005" # parameter file to load
-
-pf = load(fn) # load data
-pc = PlotCollection(pf) # defaults to center at most dense point
-p = pc.add_slice("Density", 0) # 0 = x-axis
-p.modify["velocity"]() # This takes a few arguments, but we'll use the defaults
- # here. You can control the 'skip' factor in the
- # vectors.
-pc.set_width(2.5, 'mpc') # change width of all plots in pc
-pc.save(fn) # save all plots
Repository URL: https://bitbucket.org/yt_analysis/cookbook/
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