[yt-svn] commit/yt-doc: brittonsmith: Fixing up simple recipes and moving to .py files.
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commits-noreply at bitbucket.org
Thu May 24 23:40:50 PDT 2012
1 new commit in yt-doc:
https://bitbucket.org/yt_analysis/yt-doc/changeset/64ee4579e450/
changeset: 64ee4579e450
user: brittonsmith
date: 2012-05-25 08:39:52
summary: Fixing up simple recipes and moving to .py files.
affected #: 16 files
diff -r 8715d11b3b597ca886cf769696a9157fcef2ab70 -r 64ee4579e450c47b17fb94a76df1d1a38b2f7f05 source/cookbook/arbitrary_vectors_on_slice.py
--- a/source/cookbook/arbitrary_vectors_on_slice.py
+++ /dev/null
@@ -1,23 +0,0 @@
-from yt.mods import *
-
-# Load the dataset.
-pf = load(RedshiftOutput0005)
-
-# Create a plot collection for the dataset.
-# With no additional arguments, the center will be
-# the densest point in the box.
-pc = PlotCollection(pf)
-
-# Make a density slice in the x axis.
-p = pc.add_slice("Density", "x")
-
-# Draw a velocity vector every 16 pixels.
-p.modify["velocity"](factor=16)
-
-# Change the width of the image.
-pc.set_width(2.5, 'mpc')
-
-# Save the image.
-# Optionally, give a string as an argument
-# to name files with a keyword.
-pc.save()
diff -r 8715d11b3b597ca886cf769696a9157fcef2ab70 -r 64ee4579e450c47b17fb94a76df1d1a38b2f7f05 source/cookbook/multi_width_image.py
--- /dev/null
+++ b/source/cookbook/multi_width_image.py
@@ -0,0 +1,32 @@
+from yt.mods import *
+
+# Load the dataset.
+pf = load("RedshiftOutput0005")
+
+# Create a plot collection for the dataset.
+# With no additional arguments, the center will be
+# the densest point in the box.
+pc = PlotCollection(pf)
+
+# Create a list of a couple of widths and units.
+widths = [(2, "mpc"),
+ (1000, 'kpc')]
+
+# Create some density slices.
+pc.add_slice("Density", "x")
+pc.add_slice("Density", "y")
+
+# Loop through the list of widths and units.
+for width, unit in widths:
+
+ # Set the width.
+ pc.set_width(width, unit)
+
+ # Get the min and max values within the image and
+ # set the limits accordingly.
+ 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)
+
+ # Write out the image with a unique name.
+ pc.save("%s_%010d_%s" % (pf, width, unit))
diff -r 8715d11b3b597ca886cf769696a9157fcef2ab70 -r 64ee4579e450c47b17fb94a76df1d1a38b2f7f05 source/cookbook/multi_width_save.inc
--- a/source/cookbook/multi_width_save.inc
+++ /dev/null
@@ -1,65 +0,0 @@
-.. _cookbook-multi_width_save:
-
-Multi width save
-----------------
-
-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.
-
-The latest version of this recipe can be downloaded here: http://hg.yt-project.org/cookbook/raw/tip/recipes/multi_width_save.py .
-
-.. code-block:: python
-
- from yt.mods import *
-
- fn = "RedshiftOutput0005" # parameter file to load
- pf = load(fn) # load data
-
- pc = PlotCollection(pf, center=[0.5, 0.5, 0.5]) # 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", 0)
- pc.add_slice("Density", 1)
-
- # 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)
-
-
-
-.. rubric:: Sample Output
-
-.. image:: _multi_width_save/multi_width_save_RedshiftOutput0005_0000000002_mpc_Slice_x_Density.png
- :width: 240
- :target: ../_images/multi_width_save_RedshiftOutput0005_0000000002_mpc_Slice_x_Density.png
-.. image:: _multi_width_save/multi_width_save_RedshiftOutput0005_0000000002_mpc_Slice_y_Density.png
- :width: 240
- :target: ../_images/multi_width_save_RedshiftOutput0005_0000000002_mpc_Slice_y_Density.png
-.. image:: _multi_width_save/multi_width_save_RedshiftOutput0005_0000001000_kpc_Slice_x_Density.png
- :width: 240
- :target: ../_images/multi_width_save_RedshiftOutput0005_0000001000_kpc_Slice_x_Density.png
-.. image:: _multi_width_save/multi_width_save_RedshiftOutput0005_0000001000_kpc_Slice_y_Density.png
- :width: 240
- :target: ../_images/multi_width_save_RedshiftOutput0005_0000001000_kpc_Slice_y_Density.png
-
-
diff -r 8715d11b3b597ca886cf769696a9157fcef2ab70 -r 64ee4579e450c47b17fb94a76df1d1a38b2f7f05 source/cookbook/offaxis_projection.py
--- /dev/null
+++ b/source/cookbook/offaxis_projection.py
@@ -0,0 +1,30 @@
+from yt.mods import *
+
+# Load the dataset.
+pf = load("RedshiftOutput0005")
+
+# Choose a center for the render.
+c = [0.5, 0.5, 0.5]
+
+# Choose a vector representing the viewing direction.
+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
+
+# The number of pixels along one side of the image.
+# The final image will have Npixel^2 pixels.
+Npixels = 512
+
+# Create the off axis projection.
+# Setting no_ghost to False speeds up the process, but makes a
+# slighly lower quality image.
+image = off_axis_projection(pf, c, L, W, N, "Density", no_ghost=False)
+
+# Write out the final image and give it a name
+# relating to what our dataset is called.
+# We save the log of the values so that the colors do not span
+# many orders of magnitude. Try it without and see what happens.
+write_image(na.log10(image), "%s_offaxis_projection.png" % pf)
diff -r 8715d11b3b597ca886cf769696a9157fcef2ab70 -r 64ee4579e450c47b17fb94a76df1d1a38b2f7f05 source/cookbook/overplot_particles.inc
--- a/source/cookbook/overplot_particles.inc
+++ /dev/null
@@ -1,36 +0,0 @@
-.. _cookbook-overplot_particles:
-
-Overplot particles
-------------------
-
-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`.
-
-The latest version of this recipe can be downloaded here: http://hg.yt-project.org/cookbook/raw/tip/recipes/overplot_particles.py .
-
-.. code-block:: python
-
- from yt.mods import * # set up our namespace
-
- fn = "RedshiftOutput0005" # parameter file to load
-
- pf = load(fn) # load data
- pc = PlotCollection(pf, center=[0.5,0.5,0.5]) # defaults to center at most dense point
- p = pc.add_projection("Density", 0) # 0 = x-axis
- 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, '1') # change width of our plot to the full domain
- pc.save(fn) # save all plots
-
-
-.. rubric:: Sample Output
-
-.. image:: _overplot_particles/overplot_particles_RedshiftOutput0005_Projection_x_Density.png
- :width: 240
- :target: ../_images/overplot_particles_RedshiftOutput0005_Projection_x_Density.png
-
-
diff -r 8715d11b3b597ca886cf769696a9157fcef2ab70 -r 64ee4579e450c47b17fb94a76df1d1a38b2f7f05 source/cookbook/overplot_particles.py
--- /dev/null
+++ b/source/cookbook/overplot_particles.py
@@ -0,0 +1,27 @@
+from yt.mods import *
+
+# Load the dataset.
+pf = load("RedshiftOutput0005")
+
+# Create a plot collection for the dataset.
+# With no additional arguments, the center will be
+# the densest point in the box.
+pc = PlotCollection(pf)
+
+# Make a density projection.
+p = pc.add_projection("Density", "x")
+
+# Modify the projection
+# The argument specifies the region along the line of sight
+# for which particles will be gathered.
+# 1.0 signifies the entire domain in the line of sight.
+p.modify["particles"](1.0)
+
+# Set the width of the plot to the whole volume.
+pc.set_width(1.0, 'unitary')
+
+# Save the image.
+# Optionally, give a string as an argument
+# to name files with a keyword.
+pc.save()
+
diff -r 8715d11b3b597ca886cf769696a9157fcef2ab70 -r 64ee4579e450c47b17fb94a76df1d1a38b2f7f05 source/cookbook/simple_volume_rendering.py
--- a/source/cookbook/simple_volume_rendering.py
+++ b/source/cookbook/simple_volume_rendering.py
@@ -3,11 +3,6 @@
# Load the dataset.
pf = load("RedshiftOutput0005")
-# Create a plot collection for the dataset.
-# With no additional arguments, the center will be
-# the densest point in the box.
-pc = PlotCollection(pf)
-
# Create a data container (like a sphere or region) that
# represents the entire domain.
dd = pf.h.all_data()
diff -r 8715d11b3b597ca886cf769696a9157fcef2ab70 -r 64ee4579e450c47b17fb94a76df1d1a38b2f7f05 source/cookbook/sum_mass_in_sphere.inc
--- a/source/cookbook/sum_mass_in_sphere.inc
+++ /dev/null
@@ -1,32 +0,0 @@
-.. _cookbook-sum_mass_in_sphere:
-
-Sum mass in sphere
-------------------
-
-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`.
-
-The latest version of this recipe can be downloaded here: http://hg.yt-project.org/cookbook/raw/tip/recipes/sum_mass_in_sphere.py .
-
-.. code-block:: python
-
- from yt.mods import * # set up our namespace
-
- fn = "RedshiftOutput0005" # parameter file to load
-
- pf = load(fn) # load data
- v, c = pf.h.find_max("Density")
- sp = pf.h.sphere(c, 1.0/pf["mpc"])
-
- baryon_mass, particle_mass = sp.quantities["TotalQuantity"](
- ["CellMassMsun", "ParticleMassMsun"], lazy_reader=True)
-
- print "Total mass in sphere is %0.5e (gas = %0.5e / particles = %0.5e)" % \
- (baryon_mass + particle_mass, baryon_mass, particle_mass)
-
-
-
-
diff -r 8715d11b3b597ca886cf769696a9157fcef2ab70 -r 64ee4579e450c47b17fb94a76df1d1a38b2f7f05 source/cookbook/sum_mass_in_sphere.py
--- /dev/null
+++ b/source/cookbook/sum_mass_in_sphere.py
@@ -0,0 +1,16 @@
+from yt.mods import *
+
+# Load the dataset.
+pf = load("RedshiftOutput0005")
+
+# Create a 1 Mpc radius sphere, centered on the max density.
+sp = pf.h.sphere("max", (1.0, "mpc"))
+
+# Use the TotalQuantity derived quantity to sum up the
+# values of the CellMassMsun and ParticleMassMsun fields
+# within the sphere.
+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 8715d11b3b597ca886cf769696a9157fcef2ab70 -r 64ee4579e450c47b17fb94a76df1d1a38b2f7f05 source/cookbook/thin_slice_projection.inc
--- a/source/cookbook/thin_slice_projection.inc
+++ /dev/null
@@ -1,36 +0,0 @@
-.. _cookbook-thin_slice_projection:
-
-Thin slice projection
----------------------
-
-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`.
-
-The latest version of this recipe can be downloaded here: http://hg.yt-project.org/cookbook/raw/tip/recipes/thin_slice_projection.py .
-
-.. code-block:: python
-
- 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", 0, weight_field="Density", data_source = region) # 0 = x-axis
- pc.save(fn) # save all plots
-
-
-.. rubric:: Sample Output
-
-.. image:: _thin_slice_projection/thin_slice_projection_RedshiftOutput0005_Projection_x_Density_Density.png
- :width: 240
- :target: ../_images/thin_slice_projection_RedshiftOutput0005_Projection_x_Density_Density.png
-
-
diff -r 8715d11b3b597ca886cf769696a9157fcef2ab70 -r 64ee4579e450c47b17fb94a76df1d1a38b2f7f05 source/cookbook/thin_slice_projection.py
--- /dev/null
+++ b/source/cookbook/thin_slice_projection.py
@@ -0,0 +1,23 @@
+from yt.mods import *
+
+# Load the dataset.
+pf = load("RedshiftOutput0005")
+
+# Create a plot collection for the dataset.
+# With no additional arguments, the center will be
+# the densest point in the box.
+pc = PlotCollection(pf)
+
+# Create a region that is a subset of the entire volume.
+center = [0.3, 0.5, 0.5]
+left_corner = [0.1, 0.0, 0.0]
+right_corner = [0.5, 1.0, 1.0]
+region = pf.h.region(center, left_corner, right_corner)
+
+# Create a density projection and supply the region we have just created.
+# Only cells within the region will be included in the projection.
+# Try with another data container, like a sphere or disk.
+pc.add_projection("Density", "x", weight_field="Density", data_source=region)
+
+# Save the image with the keyword.
+pc.save("Thin_Slice")
diff -r 8715d11b3b597ca886cf769696a9157fcef2ab70 -r 64ee4579e450c47b17fb94a76df1d1a38b2f7f05 source/cookbook/time_series_phase.inc
--- a/source/cookbook/time_series_phase.inc
+++ /dev/null
@@ -1,56 +0,0 @@
-.. _cookbook-time_series_phase:
-
-Time series phase
------------------
-
-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.
-
-The latest version of this recipe can be downloaded here: http://hg.yt-project.org/cookbook/raw/tip/recipes/time_series_phase.py .
-
-.. code-block:: python
-
- 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, center=[0.5, 0.5, 0.5])
-
- # 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, lazy_reader=True,
- 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)
-
-
-.. rubric:: Sample Output
-
-.. image:: _time_series_phase/time_series_phase_RedshiftOutput0005_Profile2D_0_Density_Temperature_CellMassMsun.png
- :width: 240
- :target: ../_images/time_series_phase_RedshiftOutput0005_Profile2D_0_Density_Temperature_CellMassMsun.png
-
-
diff -r 8715d11b3b597ca886cf769696a9157fcef2ab70 -r 64ee4579e450c47b17fb94a76df1d1a38b2f7f05 source/cookbook/time_series_phase.py
--- /dev/null
+++ b/source/cookbook/time_series_phase.py
@@ -0,0 +1,29 @@
+from yt.mods import *
+
+# Loop over a list of autodetected datasets.
+for pf in all_pfs(max_depth=2):
+
+ # Create a plot collection at the domain center.
+ pc = PlotCollection(pf, "c")
+
+ # Create a 2D profile of the total mass in bins of
+ # density and temperature.
+ # Setting weight to None will calculate a sum.
+ # Setting weight to a field will calculate an average
+ # weighted by that field.
+ # The radius of the sphere is set to be the size of the domain.
+ # This will make sure the entire volume is included in the calculation.
+ # The first and second arguments are radius and units. '1' means code units.
+ # We also manually set the number of bins and the range.
+ 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))
+
+ # Add a title to the image.
+ p.modify["title"]("%s (z = %0.2f)" % (pf, pf["CosmologyCurrentRedshift"]))
+
+ # Save the image.
+ # Optionally, give a string as an argument
+ # to name files with a keyword.
+ pc.save()
diff -r 8715d11b3b597ca886cf769696a9157fcef2ab70 -r 64ee4579e450c47b17fb94a76df1d1a38b2f7f05 source/cookbook/time_series_quantity.inc
--- a/source/cookbook/time_series_quantity.inc
+++ /dev/null
@@ -1,55 +0,0 @@
-.. _cookbook-time_series_quantity:
-
-Time series quantity
---------------------
-
-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.
-
-The latest version of this recipe can be downloaded here: http://hg.yt-project.org/cookbook/raw/tip/recipes/time_series_quantity.py .
-
-.. code-block:: python
-
- 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")
-
-
-.. rubric:: Sample Output
-
-.. image:: _time_series_quantity/time_series_quantity_average_HII_fraction.png
- :width: 240
- :target: ../_images/time_series_quantity_average_HII_fraction.png
-
-
diff -r 8715d11b3b597ca886cf769696a9157fcef2ab70 -r 64ee4579e450c47b17fb94a76df1d1a38b2f7f05 source/cookbook/time_series_quantity.py
--- /dev/null
+++ b/source/cookbook/time_series_quantity.py
@@ -0,0 +1,30 @@
+from yt.mods import *
+
+# Create lists for the times and values.
+times = []
+values = []
+
+# Loop over a list of autodetected datasets.
+for pf in all_pfs(max_depth=2):
+
+ # Get the current time, convert to years from code units
+ # Add it to the list.
+ times.append(pf["InitialTime"] * pf["years"])
+
+ # Create a container representing the entire domain.
+ data = pf.h.all_data()
+
+ # Use the WeightedAverageQuantity derived quantity to
+ # calculate the average temperature, weighted by volume.
+ val = data.quantities["WeightedAverageQuantity"](
+ "Temperature", "CellVolume")
+
+ # Add the value to the list.
+ values.append(val)
+
+# Use matplotlib to plot the values.
+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 8715d11b3b597ca886cf769696a9157fcef2ab70 -r 64ee4579e450c47b17fb94a76df1d1a38b2f7f05 source/cookbook/velocity_vectors_on_slice.py
--- /dev/null
+++ b/source/cookbook/velocity_vectors_on_slice.py
@@ -0,0 +1,23 @@
+from yt.mods import *
+
+# Load the dataset.
+pf = load(RedshiftOutput0005)
+
+# Create a plot collection for the dataset.
+# With no additional arguments, the center will be
+# the densest point in the box.
+pc = PlotCollection(pf)
+
+# Make a density slice in the x axis.
+p = pc.add_slice("Density", "x")
+
+# Draw a velocity vector every 16 pixels.
+p.modify["velocity"](factor=16)
+
+# Change the width of the image.
+pc.set_width(2.5, 'mpc')
+
+# Save the image.
+# Optionally, give a string as an argument
+# to name files with a keyword.
+pc.save()
Repository URL: https://bitbucket.org/yt_analysis/yt-doc/
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