[yt-svn] commit/yt: chummels: Merged in jzuhone/yt (pull request #1638)
commits-noreply at bitbucket.org
commits-noreply at bitbucket.org
Thu Jul 16 09:28:38 PDT 2015
1 new commit in yt:
https://bitbucket.org/yt_analysis/yt/commits/ad366f1dd932/
Changeset: ad366f1dd932
Branch: yt
User: chummels
Date: 2015-07-16 16:28:25+00:00
Summary: Merged in jzuhone/yt (pull request #1638)
[bugfix] Bugfixes for Python 3
Affected #: 23 files
diff -r b7b8cdbad2505dea1391bddbe3ab6e5d65f2e9b6 -r ad366f1dd932031937b1a7b59937fa3f0a278ecf doc/source/analyzing/analysis_modules/SZ_projections.ipynb
--- a/doc/source/analyzing/analysis_modules/SZ_projections.ipynb
+++ b/doc/source/analyzing/analysis_modules/SZ_projections.ipynb
@@ -1,7 +1,7 @@
{
"metadata": {
"name": "",
- "signature": "sha256:2cc168b2c1737c67647aa29892c0213e7a58233fa53c809f9cd975a4306e9bc8"
+ "signature": "sha256:487383ec23a092310522ec25bd02ad2eb16a3402c5ed3d2b103d33fe17697b3c"
},
"nbformat": 3,
"nbformat_minor": 0,
@@ -70,6 +70,13 @@
]
},
{
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<font color='red'>**NOTE**</font>: Currently, use of the SZpack library to create S-Z projections in yt is limited to Python 2.x."
+ ]
+ },
+ {
"cell_type": "heading",
"level": 2,
"metadata": {},
diff -r b7b8cdbad2505dea1391bddbe3ab6e5d65f2e9b6 -r ad366f1dd932031937b1a7b59937fa3f0a278ecf doc/source/analyzing/fields.rst
--- a/doc/source/analyzing/fields.rst
+++ b/doc/source/analyzing/fields.rst
@@ -174,7 +174,7 @@
Field plugins can be loaded dynamically, although at present this is not
particularly useful. Plans for extending field plugins to dynamically load, to
-enable simple definition of common types (gradient, divergence, etc), and to
+enable simple definition of common types (divergence, curl, etc), and to
more verbosely describe available fields, have been put in place for future
versions.
diff -r b7b8cdbad2505dea1391bddbe3ab6e5d65f2e9b6 -r ad366f1dd932031937b1a7b59937fa3f0a278ecf doc/source/cookbook/fit_spectrum.py
--- a/doc/source/cookbook/fit_spectrum.py
+++ b/doc/source/cookbook/fit_spectrum.py
@@ -10,10 +10,10 @@
def _OVI_number_density(field, data):
return data['H_number_density']*2.0
-# Define a function that will accept a ds and add the new field
+# Define a function that will accept a ds and add the new field
# defined above. This will be given to the LightRay below.
def setup_ds(ds):
- ds.add_field("O_p5_number_density",
+ ds.add_field(("gas","O_p5_number_density"),
function=_OVI_number_density,
units="cm**-3")
@@ -62,7 +62,7 @@
# Get all fields that need to be added to the light ray
fields = ['temperature']
-for s, params in species_dicts.iteritems():
+for s, params in species_dicts.items():
fields.append(params['field'])
# Make a light ray, and set njobs to -1 to use one core
@@ -79,7 +79,7 @@
sp = AbsorptionSpectrum(900.0, 1400.0, 50000)
# Iterate over species
-for s, params in species_dicts.iteritems():
+for s, params in species_dicts.items():
# Iterate over transitions for a single species
for i in range(params['numLines']):
# Add the lines to the spectrum
diff -r b7b8cdbad2505dea1391bddbe3ab6e5d65f2e9b6 -r ad366f1dd932031937b1a7b59937fa3f0a278ecf doc/source/cookbook/free_free_field.py
--- a/doc/source/cookbook/free_free_field.py
+++ /dev/null
@@ -1,105 +0,0 @@
-### THIS RECIPE IS CURRENTLY BROKEN IN YT-3.0
-### DO NOT TRUST THIS RECIPE UNTIL THIS LINE IS REMOVED
-
-import numpy as np
-import yt
-# Need to grab the proton mass from the constants database
-from yt.utilities.physical_constants import mp
-
-exit()
-# Define the emission field
-
-keVtoerg = 1.602e-9 # Convert energy in keV to energy in erg
-KtokeV = 8.617e-08 # Convert degrees Kelvin to degrees keV
-sqrt3 = np.sqrt(3.)
-expgamma = 1.78107241799 # Exponential of Euler's constant
-
-
-def _FreeFree_Emission(field, data):
-
- if data.has_field_parameter("Z"):
- Z = data.get_field_parameter("Z")
- else:
- Z = 1.077 # Primordial H/He plasma
-
- if data.has_field_parameter("mue"):
- mue = data.get_field_parameter("mue")
- else:
- mue = 1./0.875 # Primordial H/He plasma
-
- if data.has_field_parameter("mui"):
- mui = data.get_field_parameter("mui")
- else:
- mui = 1./0.8125 # Primordial H/He plasma
-
- if data.has_field_parameter("Ephoton"):
- Ephoton = data.get_field_parameter("Ephoton")
- else:
- Ephoton = 1.0 # in keV
-
- if data.has_field_parameter("photon_emission"):
- photon_emission = data.get_field_parameter("photon_emission")
- else:
- photon_emission = False # Flag for energy or photon emission
-
- n_e = data["density"]/(mue*mp)
- n_i = data["density"]/(mui*mp)
- kT = data["temperature"]*KtokeV
-
- # Compute the Gaunt factor
-
- g_ff = np.zeros(kT.shape)
- g_ff[Ephoton/kT > 1.] = np.sqrt((3./np.pi)*kT[Ephoton/kT > 1.]/Ephoton)
- g_ff[Ephoton/kT < 1.] = (sqrt3/np.pi)*np.log((4./expgamma) *
- kT[Ephoton/kT < 1.]/Ephoton)
-
- eps_E = 1.64e-20*Z*Z*n_e*n_i/np.sqrt(data["temperature"]) * \
- np.exp(-Ephoton/kT)*g_ff
-
- if photon_emission:
- eps_E /= (Ephoton*keVtoerg)
-
- return eps_E
-
-yt.add_field("FreeFree_Emission", function=_FreeFree_Emission)
-
-# Define the luminosity derived quantity
-def _FreeFreeLuminosity(data):
- return (data["FreeFree_Emission"]*data["cell_volume"]).sum()
-
-
-def _combFreeFreeLuminosity(data, luminosity):
- return luminosity.sum()
-
-yt.add_quantity("FreeFree_Luminosity", function=_FreeFreeLuminosity,
- combine_function=_combFreeFreeLuminosity, n_ret=1)
-
-ds = yt.load("GasSloshing/sloshing_nomag2_hdf5_plt_cnt_0150")
-
-sphere = ds.sphere(ds.domain_center, (100., "kpc"))
-
-# Print out the total luminosity at 1 keV for the sphere
-
-print("L_E (1 keV, primordial) = ", sphere.quantities["FreeFree_Luminosity"]())
-
-# The defaults for the field assume a H/He primordial plasma.
-# Let's set the appropriate parameters for a pure hydrogen plasma.
-
-sphere.set_field_parameter("mue", 1.0)
-sphere.set_field_parameter("mui", 1.0)
-sphere.set_field_parameter("Z", 1.0)
-
-print("L_E (1 keV, pure hydrogen) = ", sphere.quantities["FreeFree_Luminosity"]())
-
-# Now let's print the luminosity at an energy of E = 10 keV
-
-sphere.set_field_parameter("Ephoton", 10.0)
-
-print("L_E (10 keV, pure hydrogen) = ", sphere.quantities["FreeFree_Luminosity"]())
-
-# Finally, let's set the flag for photon emission, to get the total number
-# of photons emitted at this energy:
-
-sphere.set_field_parameter("photon_emission", True)
-
-print("L_ph (10 keV, pure hydrogen) = ", sphere.quantities["FreeFree_Luminosity"]())
diff -r b7b8cdbad2505dea1391bddbe3ab6e5d65f2e9b6 -r ad366f1dd932031937b1a7b59937fa3f0a278ecf doc/source/cookbook/simulation_analysis.py
--- a/doc/source/cookbook/simulation_analysis.py
+++ b/doc/source/cookbook/simulation_analysis.py
@@ -2,11 +2,11 @@
yt.enable_parallelism()
import collections
-# Enable parallelism in the script (assuming it was called with
+# Enable parallelism in the script (assuming it was called with
# `mpirun -np <n_procs>` )
yt.enable_parallelism()
-# By using wildcards such as ? and * with the load command, we can load up a
+# By using wildcards such as ? and * with the load command, we can load up a
# Time Series containing all of these datasets simultaneously.
ts = yt.load('enzo_tiny_cosmology/DD????/DD????')
@@ -16,7 +16,7 @@
# Create an empty dictionary
data = {}
-# Iterate through each dataset in the Time Series (using piter allows it
+# Iterate through each dataset in the Time Series (using piter allows it
# to happen in parallel automatically across available processors)
for ds in ts.piter():
ad = ds.all_data()
@@ -31,6 +31,6 @@
# Print out all the values we calculated.
print("Dataset Redshift Density Min Density Max")
print("---------------------------------------------------------")
-for key, val in od.iteritems():
+for key, val in od.items():
print("%s %05.3f %5.3g g/cm^3 %5.3g g/cm^3" % \
(key, val[1], val[0][0], val[0][1]))
diff -r b7b8cdbad2505dea1391bddbe3ab6e5d65f2e9b6 -r ad366f1dd932031937b1a7b59937fa3f0a278ecf doc/source/cookbook/time_series.py
--- a/doc/source/cookbook/time_series.py
+++ b/doc/source/cookbook/time_series.py
@@ -12,7 +12,7 @@
storage = {}
-# By using the piter() function, we can iterate on every dataset in
+# By using the piter() function, we can iterate on every dataset in
# the TimeSeries object. By using the storage keyword, we can populate
# a dictionary where the dataset is the key, and sto.result is the value
# for later use when the loop is complete.
@@ -25,13 +25,13 @@
sphere = ds.sphere("c", (100., "kpc"))
# Calculate the entropy within that sphere
entr = sphere["entropy"].sum()
- # Store the current time and sphere entropy for this dataset in our
+ # Store the current time and sphere entropy for this dataset in our
# storage dictionary as a tuple
store.result = (ds.current_time.in_units('Gyr'), entr)
# Convert the storage dictionary values to a Nx2 array, so the can be easily
# plotted
-arr = np.array(storage.values())
+arr = np.array(list(storage.values()))
# Plot up the results: time versus entropy
plt.semilogy(arr[:,0], arr[:,1], 'r-')
diff -r b7b8cdbad2505dea1391bddbe3ab6e5d65f2e9b6 -r ad366f1dd932031937b1a7b59937fa3f0a278ecf yt/analysis_modules/absorption_spectrum/absorption_spectrum_fit.py
--- a/yt/analysis_modules/absorption_spectrum/absorption_spectrum_fit.py
+++ b/yt/analysis_modules/absorption_spectrum/absorption_spectrum_fit.py
@@ -1011,7 +1011,7 @@
"""
f = h5py.File(file_name, 'w')
- for ion, params in lineDic.iteritems():
+ for ion, params in lineDic.items():
f.create_dataset("{0}/N".format(ion),data=params['N'])
f.create_dataset("{0}/b".format(ion),data=params['b'])
f.create_dataset("{0}/z".format(ion),data=params['z'])
diff -r b7b8cdbad2505dea1391bddbe3ab6e5d65f2e9b6 -r ad366f1dd932031937b1a7b59937fa3f0a278ecf yt/analysis_modules/cosmological_observation/light_cone/light_cone.py
--- a/yt/analysis_modules/cosmological_observation/light_cone/light_cone.py
+++ b/yt/analysis_modules/cosmological_observation/light_cone/light_cone.py
@@ -343,7 +343,7 @@
del output["object"]
# Combine results from each slice.
- all_slices = all_storage.keys()
+ all_slices = list(all_storage.keys())
all_slices.sort()
for my_slice in all_slices:
if save_slice_images:
diff -r b7b8cdbad2505dea1391bddbe3ab6e5d65f2e9b6 -r ad366f1dd932031937b1a7b59937fa3f0a278ecf yt/analysis_modules/halo_finding/halo_objects.py
--- a/yt/analysis_modules/halo_finding/halo_objects.py
+++ b/yt/analysis_modules/halo_finding/halo_objects.py
@@ -22,6 +22,7 @@
import glob
import os
import os.path as path
+from functools import cmp_to_key
from collections import defaultdict
from yt.extern.six import add_metaclass
from yt.extern.six.moves import zip as izip
@@ -39,7 +40,7 @@
TINY
from yt.utilities.physical_ratios import \
rho_crit_g_cm3_h2
-
+
from .hop.EnzoHop import RunHOP
from .fof.EnzoFOF import RunFOF
@@ -138,9 +139,9 @@
c[2] = self["particle_position_z"] - self.ds.domain_left_edge[2]
com = []
for i in range(3):
- # A halo is likely periodic around a boundary if the distance
+ # A halo is likely periodic around a boundary if the distance
# between the max and min particle
- # positions are larger than half the box.
+ # positions are larger than half the box.
# So skip the rest if the converse is true.
# Note we might make a change here when periodicity-handling is
# fully implemented.
@@ -444,7 +445,7 @@
Msun2g = mass_sun_cgs
rho_crit = rho_crit * ((1.0 + z) ** 3.0)
# Get some pertinent information about the halo.
- self.mass_bins = self.ds.arr(np.zeros(self.bin_count + 1,
+ self.mass_bins = self.ds.arr(np.zeros(self.bin_count + 1,
dtype='float64'),'Msun')
dist = np.empty(thissize, dtype='float64')
cen = self.center_of_mass()
@@ -475,7 +476,7 @@
self.overdensity = self.mass_bins * Msun2g / \
(4./3. * math.pi * rho_crit * \
(self.radial_bins )**3.0)
-
+
def _get_ellipsoid_parameters_basic(self):
np.seterr(all='ignore')
# check if there are 4 particles to form an ellipsoid
@@ -501,7 +502,7 @@
for axis in range(np.size(DW)):
cases = np.array([position[axis],
position[axis] + DW[axis],
- position[axis] - DW[axis]])
+ position[axis] - DW[axis]])
# pick out the smallest absolute distance from com
position[axis] = np.choose(np.abs(cases).argmin(axis=0), cases)
# find the furthest particle's index
@@ -571,7 +572,7 @@
_name = "RockstarHalo"
# See particle_mask
_radjust = 4.
-
+
def maximum_density(self):
r"""Not implemented."""
return -1
@@ -635,11 +636,11 @@
def get_ellipsoid_parameters(self):
r"""Calculate the parameters that describe the ellipsoid of
the particles that constitute the halo.
-
+
Parameters
----------
None
-
+
Returns
-------
tuple : (cm, mag_A, mag_B, mag_C, e0_vector, tilt)
@@ -650,7 +651,7 @@
#. mag_C as a float.
#. e0_vector as an array.
#. tilt as a float.
-
+
Examples
--------
>>> params = halos[0].get_ellipsoid_parameters()
@@ -662,22 +663,22 @@
basic_parameters[4], basic_parameters[5]]), basic_parameters[6]]
toreturn.extend(updated)
return tuple(toreturn)
-
+
def get_ellipsoid(self):
r"""Returns an ellipsoidal data object.
-
+
This will generate a new, empty ellipsoidal data object for this
halo.
-
+
Parameters
----------
None.
-
+
Returns
-------
ellipsoid : `yt.data_objects.data_containers.YTEllipsoidBase`
The ellipsoidal data object.
-
+
Examples
--------
>>> ell = halos[0].get_ellipsoid()
@@ -686,7 +687,7 @@
ell = self.data.ds.ellipsoid(ep[0], ep[1], ep[2], ep[3],
ep[4], ep[5])
return ell
-
+
class HOPHalo(Halo):
_name = "HOPHalo"
pass
@@ -763,14 +764,14 @@
self.size, key)
if field_data is not None:
if key == 'particle_index':
- #this is an index for turning data sorted by particle index
+ #this is an index for turning data sorted by particle index
#into the same order as the fields on disk
self._pid_sort = field_data.argsort().argsort()
#convert to YTArray using the data from disk
if key == 'particle_mass':
field_data = self.ds.arr(field_data, 'Msun')
else:
- field_data = self.ds.arr(field_data,
+ field_data = self.ds.arr(field_data,
self.ds._get_field_info('unknown',key).units)
self._saved_fields[key] = field_data
return self._saved_fields[key]
@@ -856,21 +857,21 @@
basic_parameters[4], basic_parameters[5]]), basic_parameters[6]]
toreturn.extend(updated)
return tuple(toreturn)
-
+
def get_ellipsoid(self):
- r"""Returns an ellipsoidal data object.
+ r"""Returns an ellipsoidal data object.
This will generate a new, empty ellipsoidal data object for this
halo.
-
+
Parameters
----------
None.
-
+
Returns
-------
ellipsoid : `yt.data_objects.data_containers.YTEllipsoidBase`
The ellipsoidal data object.
-
+
Examples
--------
>>> ell = halos[0].get_ellipsoid()
@@ -947,11 +948,11 @@
def maximum_density(self):
r"""Undefined for text halos."""
return -1
-
+
def maximum_density_location(self):
r"""Undefined, default to CoM"""
return self.center_of_mass()
-
+
def get_size(self):
# Have to just get it from the sphere.
return self["particle_position_x"].size
@@ -964,8 +965,8 @@
def __init__(self, data_source, dm_only=True, redshift=-1):
"""
Run hop on *data_source* with a given density *threshold*. If
- *dm_only* is True (default), only run it on the dark matter particles,
- otherwise on all particles. Returns an iterable collection of
+ *dm_only* is True (default), only run it on the dark matter particles,
+ otherwise on all particles. Returns an iterable collection of
*HopGroup* items.
"""
self._data_source = data_source
@@ -1051,7 +1052,7 @@
ellipsoid_data : bool.
Whether to print the ellipsoidal information to the file.
Default = False.
-
+
Examples
--------
>>> halos.write_out("HopAnalysis.out")
@@ -1144,10 +1145,10 @@
_halo_dt = np.dtype([('id', np.int64), ('pos', (np.float32, 6)),
('corevel', (np.float32, 3)), ('bulkvel', (np.float32, 3)),
('m', np.float32), ('r', np.float32), ('child_r', np.float32),
- ('vmax_r', np.float32),
+ ('vmax_r', np.float32),
('mgrav', np.float32), ('vmax', np.float32),
('rvmax', np.float32), ('rs', np.float32),
- ('klypin_rs', np.float32),
+ ('klypin_rs', np.float32),
('vrms', np.float32), ('J', (np.float32, 3)),
('energy', np.float32), ('spin', np.float32),
('alt_m', (np.float32, 4)), ('Xoff', np.float32),
@@ -1221,9 +1222,9 @@
"""
Read the out_*.list text file produced
by Rockstar into memory."""
-
+
ds = self.ds
- # In order to read the binary data, we need to figure out which
+ # In order to read the binary data, we need to figure out which
# binary files belong to this output.
basedir = os.path.dirname(self.out_list)
s = self.out_list.split('_')[-1]
@@ -1523,12 +1524,14 @@
id += 1
def haloCmp(h1, h2):
+ def cmp(a, b):
+ return (a > b) - (a < b)
c = cmp(h1.total_mass(), h2.total_mass())
if c != 0:
return -1 * c
if c == 0:
return cmp(h1.center_of_mass()[0], h2.center_of_mass()[0])
- self._groups.sort(haloCmp)
+ self._groups.sort(key=cmp_to_key(haloCmp))
sorted_max_dens = {}
for i, halo in enumerate(self._groups):
if halo.id in self._max_dens:
@@ -1873,7 +1876,7 @@
class LoadTextHaloes(GenericHaloFinder, TextHaloList):
r"""Load a text file of halos.
-
+
Like LoadHaloes, but when all that is available is a plain
text file. This assumes the text file has the 3-positions of halos
along with a radius. The halo objects created are spheres.
@@ -1882,7 +1885,7 @@
----------
fname : String
The name of the text file to read in.
-
+
columns : dict
A dict listing the column name : column number pairs for data
in the text file. It is zero-based (like Python).
@@ -1890,7 +1893,7 @@
Any column name outside of ['x', 'y', 'z', 'r'] will be attached
to each halo object in the supplementary dict 'supp'. See
example.
-
+
comment : String
If the first character of a line is equal to this, the line is
skipped. Default = "#".
@@ -1915,7 +1918,7 @@
Parameters
----------
fname : String
- The name of the Rockstar file to read in. Default =
+ The name of the Rockstar file to read in. Default =
"rockstar_halos/out_0.list'.
Examples
diff -r b7b8cdbad2505dea1391bddbe3ab6e5d65f2e9b6 -r ad366f1dd932031937b1a7b59937fa3f0a278ecf yt/analysis_modules/level_sets/clump_handling.py
--- a/yt/analysis_modules/level_sets/clump_handling.py
+++ b/yt/analysis_modules/level_sets/clump_handling.py
@@ -20,7 +20,8 @@
from yt.fields.derived_field import \
ValidateSpatial
from yt.funcs import mylog
-
+from yt.extern.six import string_types
+
from .clump_info_items import \
clump_info_registry
from .clump_validators import \
@@ -268,7 +269,7 @@
def write_clump_index(clump, level, fh):
top = False
- if not isinstance(fh, file):
+ if isinstance(fh, string_types):
fh = open(fh, "w")
top = True
for q in range(level):
@@ -285,7 +286,7 @@
def write_clumps(clump, level, fh):
top = False
- if not isinstance(fh, file):
+ if isinstance(fh, string_types):
fh = open(fh, "w")
top = True
if ((clump.children is None) or (len(clump.children) == 0)):
diff -r b7b8cdbad2505dea1391bddbe3ab6e5d65f2e9b6 -r ad366f1dd932031937b1a7b59937fa3f0a278ecf yt/data_objects/time_series.py
--- a/yt/data_objects/time_series.py
+++ b/yt/data_objects/time_series.py
@@ -130,7 +130,7 @@
def __new__(cls, outputs, *args, **kwargs):
if isinstance(outputs, string_types):
outputs = get_filenames_from_glob_pattern(outputs)
- ret = super(DatasetSeries, cls).__new__(cls, *args, **kwargs)
+ ret = super(DatasetSeries, cls).__new__(cls)
try:
ret._pre_outputs = outputs[:]
except TypeError:
diff -r b7b8cdbad2505dea1391bddbe3ab6e5d65f2e9b6 -r ad366f1dd932031937b1a7b59937fa3f0a278ecf yt/frontends/chombo/io.py
--- a/yt/frontends/chombo/io.py
+++ b/yt/frontends/chombo/io.py
@@ -76,7 +76,7 @@
for key, val in self._handle.attrs.items():
if key.startswith('component_'):
comp_number = int(re.match('component_(\d+)', key).groups()[0])
- field_dict[val] = comp_number
+ field_dict[val.decode('utf-8')] = comp_number
self._field_dict = field_dict
return self._field_dict
diff -r b7b8cdbad2505dea1391bddbe3ab6e5d65f2e9b6 -r ad366f1dd932031937b1a7b59937fa3f0a278ecf yt/frontends/fits/io.py
--- a/yt/frontends/fits/io.py
+++ b/yt/frontends/fits/io.py
@@ -32,7 +32,7 @@
def _read_particle_coords(self, chunks, ptf):
pdata = self.ds._handle[self.ds.first_image].data
assert(len(ptf) == 1)
- ptype = ptf.keys()[0]
+ ptype = list(ptf.keys())[0]
x = np.asarray(pdata.field("X"), dtype="=f8")
y = np.asarray(pdata.field("Y"), dtype="=f8")
z = np.ones(x.shape)
@@ -43,7 +43,7 @@
def _read_particle_fields(self, chunks, ptf, selector):
pdata = self.ds._handle[self.ds.first_image].data
assert(len(ptf) == 1)
- ptype = ptf.keys()[0]
+ ptype = list(ptf.keys())[0]
field_list = ptf[ptype]
x = np.asarray(pdata.field("X"), dtype="=f8")
y = np.asarray(pdata.field("Y"), dtype="=f8")
diff -r b7b8cdbad2505dea1391bddbe3ab6e5d65f2e9b6 -r ad366f1dd932031937b1a7b59937fa3f0a278ecf yt/frontends/fits/misc.py
--- a/yt/frontends/fits/misc.py
+++ b/yt/frontends/fits/misc.py
@@ -12,14 +12,18 @@
import numpy as np
import base64
-from yt.extern.six.moves import StringIO
+from yt.extern.six import PY3
from yt.fields.derived_field import ValidateSpatial
from yt.utilities.on_demand_imports import _astropy
from yt.funcs import mylog, get_image_suffix
from yt.visualization._mpl_imports import FigureCanvasAgg
from yt.units.yt_array import YTQuantity, YTArray
from yt.utilities.fits_image import FITSImageData
-
+if PY3:
+ from io import BytesIO as IO
+else:
+ from yt.extern.six.moves import StringIO as IO
+
import os
def _make_counts(emin, emax):
@@ -255,12 +259,12 @@
def _repr_html_(self):
ret = ''
- for k, v in self.plots.iteritems():
+ for k, v in self.plots.items():
canvas = FigureCanvasAgg(v)
- f = StringIO()
+ f = IO()
canvas.print_figure(f)
f.seek(0)
- img = base64.b64encode(f.read())
+ img = base64.b64encode(f.read()).decode()
ret += r'<img style="max-width:100%%;max-height:100%%;" ' \
r'src="data:image/png;base64,%s"><br>' % img
return ret
diff -r b7b8cdbad2505dea1391bddbe3ab6e5d65f2e9b6 -r ad366f1dd932031937b1a7b59937fa3f0a278ecf yt/frontends/halo_catalog/io.py
--- a/yt/frontends/halo_catalog/io.py
+++ b/yt/frontends/halo_catalog/io.py
@@ -39,7 +39,7 @@
data_files = set([])
# Only support halo reading for now.
assert(len(ptf) == 1)
- assert(ptf.keys()[0] == "halos")
+ assert(list(ptf.keys())[0] == "halos")
for chunk in chunks:
for obj in chunk.objs:
data_files.update(obj.data_files)
@@ -57,7 +57,7 @@
data_files = set([])
# Only support halo reading for now.
assert(len(ptf) == 1)
- assert(ptf.keys()[0] == "halos")
+ assert(list(ptf.keys())[0] == "halos")
for chunk in chunks:
for obj in chunk.objs:
data_files.update(obj.data_files)
diff -r b7b8cdbad2505dea1391bddbe3ab6e5d65f2e9b6 -r ad366f1dd932031937b1a7b59937fa3f0a278ecf yt/frontends/owls/io.py
--- a/yt/frontends/owls/io.py
+++ b/yt/frontends/owls/io.py
@@ -70,7 +70,7 @@
for chunk in chunks:
for obj in chunk.objs:
data_files.update(obj.data_files)
- for data_file in sorted(data_files):
+ for data_file in sorted(data_files, key=lambda x: x.filename):
f = _get_h5_handle(data_file.filename)
# This double-reads
for ptype, field_list in sorted(ptf.items()):
@@ -88,7 +88,7 @@
for chunk in chunks:
for obj in chunk.objs:
data_files.update(obj.data_files)
- for data_file in sorted(data_files):
+ for data_file in sorted(data_files, key=lambda x: x.filename):
f = _get_h5_handle(data_file.filename)
for ptype, field_list in sorted(ptf.items()):
if data_file.total_particles[ptype] == 0:
diff -r b7b8cdbad2505dea1391bddbe3ab6e5d65f2e9b6 -r ad366f1dd932031937b1a7b59937fa3f0a278ecf yt/frontends/ramses/io.py
--- a/yt/frontends/ramses/io.py
+++ b/yt/frontends/ramses/io.py
@@ -20,7 +20,12 @@
BaseIOHandler
from yt.utilities.logger import ytLogger as mylog
import yt.utilities.fortran_utils as fpu
-from yt.extern.six.moves import cStringIO
+from yt.extern.six import PY3
+
+if PY3:
+ from io import BytesIO as IO
+else:
+ from cStringIO import StringIO as IO
class IOHandlerRAMSES(BaseIOHandler):
_dataset_type = "ramses"
@@ -37,7 +42,7 @@
f = open(subset.domain.hydro_fn, "rb")
# This contains the boundary information, so we skim through
# and pick off the right vectors
- content = cStringIO(f.read())
+ content = IO(f.read())
rv = subset.fill(content, fields, selector)
for ft, f in fields:
d = rv.pop(f)
diff -r b7b8cdbad2505dea1391bddbe3ab6e5d65f2e9b6 -r ad366f1dd932031937b1a7b59937fa3f0a278ecf yt/frontends/rockstar/io.py
--- a/yt/frontends/rockstar/io.py
+++ b/yt/frontends/rockstar/io.py
@@ -28,6 +28,7 @@
from yt.utilities.lib.geometry_utils import compute_morton
from yt.geometry.oct_container import _ORDER_MAX
+from operator import attrgetter
class IOHandlerRockstarBinary(BaseIOHandler):
_dataset_type = "rockstar_binary"
@@ -45,12 +46,11 @@
data_files = set([])
# Only support halo reading for now.
assert(len(ptf) == 1)
- assert(ptf.keys()[0] == "halos")
+ assert(list(ptf.keys())[0] == "halos")
for chunk in chunks:
for obj in chunk.objs:
data_files.update(obj.data_files)
-
- for data_file in sorted(data_files):
+ for data_file in sorted(data_files,key=attrgetter("filename")):
pcount = data_file.header['num_halos']
with open(data_file.filename, "rb") as f:
f.seek(data_file._position_offset, os.SEEK_SET)
@@ -66,11 +66,11 @@
data_files = set([])
# Only support halo reading for now.
assert(len(ptf) == 1)
- assert(ptf.keys()[0] == "halos")
+ assert(list(ptf.keys())[0] == "halos")
for chunk in chunks:
for obj in chunk.objs:
data_files.update(obj.data_files)
- for data_file in sorted(data_files):
+ for data_file in sorted(data_files,key=attrgetter("filename")):
pcount = data_file.header['num_halos']
with open(data_file.filename, "rb") as f:
for ptype, field_list in sorted(ptf.items()):
diff -r b7b8cdbad2505dea1391bddbe3ab6e5d65f2e9b6 -r ad366f1dd932031937b1a7b59937fa3f0a278ecf yt/utilities/fits_image.py
--- a/yt/utilities/fits_image.py
+++ b/yt/utilities/fits_image.py
@@ -122,7 +122,7 @@
for key in fields:
if key not in exclude_fields:
if hasattr(img_data[key], "units"):
- self.field_units[key] = str(img_data[key].units)
+ self.field_units[key] = img_data[key].units
else:
self.field_units[key] = "dimensionless"
mylog.info("Making a FITS image of field %s" % key)
diff -r b7b8cdbad2505dea1391bddbe3ab6e5d65f2e9b6 -r ad366f1dd932031937b1a7b59937fa3f0a278ecf yt/visualization/color_maps.py
--- a/yt/visualization/color_maps.py
+++ b/yt/visualization/color_maps.py
@@ -11,7 +11,6 @@
# The full license is in the file COPYING.txt, distributed with this software.
#-----------------------------------------------------------------------------
import numpy as np
-from yt.extern.six.moves import zip as izip
import matplotlib
import matplotlib.colors as cc
@@ -86,9 +85,9 @@
194.5*_vs**2.88+99.72*np.exp(-77.24*(_vs-0.742)**2.0)
+ 45.40*_vs**0.089+10.0)/255.0
-cdict = {'red':zip(_vs,_kamae_red,_kamae_red),
- 'green':zip(_vs,_kamae_grn,_kamae_grn),
- 'blue':zip(_vs,_kamae_blu,_kamae_blu)}
+cdict = {'red':np.transpose([_vs,_kamae_red,_kamae_red]),
+ 'green':np.transpose([_vs,_kamae_grn,_kamae_grn]),
+ 'blue':np.transpose([_vs,_kamae_blu,_kamae_blu])}
add_cmap('kamae', cdict)
# This one is a simple black & green map
@@ -151,9 +150,9 @@
_vs = np.linspace(0,1,256)
for k,v in list(_cm.color_map_luts.items()):
if k not in yt_colormaps and k not in mcm.cmap_d:
- cdict = { 'red': zip(_vs,v[0],v[0]),
- 'green': zip(_vs,v[1],v[1]),
- 'blue': zip(_vs,v[2],v[2]) }
+ cdict = { 'red': np.transpose([_vs,v[0],v[0]]),
+ 'green': np.transpose([_vs,v[1],v[1]]),
+ 'blue': np.transpose([_vs,v[2],v[2]]) }
add_cmap(k, cdict)
def _extract_lookup_table(cmap_name):
@@ -393,9 +392,9 @@
# Second number is the (0..1) number to interpolate to when coming *from below*
# Third number is the (0..1) number to interpolate to when coming *from above*
_vs = np.linspace(0,1,256)
- cdict = {'red': zip(_vs, cmap[:,0], cmap[:,0]),
- 'green': zip(_vs, cmap[:,1], cmap[:,1]),
- 'blue': zip(_vs, cmap[:,2], cmap[:,2])}
+ cdict = {'red': np.transpose([_vs, cmap[:,0], cmap[:,0]]),
+ 'green': np.transpose([_vs, cmap[:,1], cmap[:,1]]),
+ 'blue': np.transpose([_vs, cmap[:,2], cmap[:,2]])}
if name is not None:
add_cmap(name, cdict)
diff -r b7b8cdbad2505dea1391bddbe3ab6e5d65f2e9b6 -r ad366f1dd932031937b1a7b59937fa3f0a278ecf yt/visualization/fixed_resolution.py
--- a/yt/visualization/fixed_resolution.py
+++ b/yt/visualization/fixed_resolution.py
@@ -27,7 +27,6 @@
import numpy as np
import weakref
import re
-import string
class FixedResolutionBuffer(object):
r"""
@@ -178,13 +177,13 @@
pstr = m.string[m.start()+1:m.end()-1]
segments = fname.split("_")
for i,s in enumerate(segments):
- segments[i] = string.capitalize(s)
+ segments[i] = s.capitalize()
if s == pstr:
ipstr = i
element = segments[ipstr-1]
roman = pnum2rom[pstr[1:]]
label = element + '\ ' + roman + '\ ' + \
- string.join(segments[ipstr+1:], '\ ')
+ '\ '.join(segments[ipstr+1:])
else:
label = fname
return label
diff -r b7b8cdbad2505dea1391bddbe3ab6e5d65f2e9b6 -r ad366f1dd932031937b1a7b59937fa3f0a278ecf yt/visualization/image_writer.py
--- a/yt/visualization/image_writer.py
+++ b/yt/visualization/image_writer.py
@@ -170,7 +170,7 @@
bitmap_array = np.concatenate([bitmap_array.astype('uint8'),
alpha_channel], axis=-1)
if transpose:
- bitmap_array = bitmap_array.swapaxes(0,1)
+ bitmap_array = bitmap_array.swapaxes(0,1).copy(order="C")
if filename is not None:
pw.write_png(bitmap_array, filename)
else:
diff -r b7b8cdbad2505dea1391bddbe3ab6e5d65f2e9b6 -r ad366f1dd932031937b1a7b59937fa3f0a278ecf yt/visualization/profile_plotter.py
--- a/yt/visualization/profile_plotter.py
+++ b/yt/visualization/profile_plotter.py
@@ -222,7 +222,7 @@
plot_spec = [plot_spec.copy() for p in profiles]
ProfilePlot._initialize_instance(self, profiles, label, plot_spec, y_log)
-
+
@validate_plot
def save(self, name=None, suffix=None):
r"""
@@ -530,15 +530,20 @@
xma = xmax
extrema = {p.x_field: ((xmi, str(p.x.units)), (xma, str(p.x.units)))}
units = {p.x_field: str(p.x.units)}
+ if self.x_log is None:
+ logs = None
+ else:
+ logs = {p.x_field: self.x_log}
for field in p.field_map.values():
units[field] = str(p.field_data[field].units)
self.profiles[i] = \
create_profile(p.data_source, p.x_field,
n_bins=len(p.x_bins)-1,
- fields=p.field_map.values(),
+ fields=list(p.field_map.values()),
weight_field=p.weight_field,
accumulation=p.accumulation,
fractional=p.fractional,
+ logs=logs,
extrema=extrema, units=units)
return self
@@ -1146,6 +1151,14 @@
extrema = {p.x_field: ((xmin, str(p.x.units)), (xmax, str(p.x.units))),
p.y_field: ((p.y_bins.min(), str(p.y.units)),
(p.y_bins.max(), str(p.y.units)))}
+ if self.x_log is not None or self.y_log is not None:
+ logs = {}
+ else:
+ logs = None
+ if self.x_log is not None:
+ logs[p.x_field] = self.x_log
+ if self.y_log is not None:
+ logs[p.y_field] = self.y_log
deposition = getattr(self.profile, "deposition", None)
if deposition is None:
additional_kwargs = {'accumulation': p.accumulation,
@@ -1155,11 +1168,12 @@
self.profile = create_profile(
p.data_source,
[p.x_field, p.y_field],
- p.field_map.values(),
+ list(p.field_map.values()),
n_bins=[len(p.x_bins)-1, len(p.y_bins)-1],
weight_field=p.weight_field,
units=units,
extrema=extrema,
+ logs=logs,
**additional_kwargs)
for field in zunits:
self.profile.set_field_unit(field, zunits[field])
@@ -1201,6 +1215,14 @@
extrema = {p.x_field: ((p.x_bins.min(), str(p.x.units)),
(p.x_bins.max(), str(p.x.units))),
p.y_field: ((ymin, str(p.y.units)), (ymax, str(p.y.units)))}
+ if self.x_log is not None or self.y_log is not None:
+ logs = {}
+ else:
+ logs = None
+ if self.x_log is not None:
+ logs[p.x_field] = self.x_log
+ if self.y_log is not None:
+ logs[p.y_field] = self.y_log
deposition = getattr(self.profile, "deposition", None)
if deposition is None:
additional_kwargs = {'accumulation': p.accumulation,
@@ -1210,11 +1232,12 @@
self.profile = create_profile(
p.data_source,
[p.x_field, p.y_field],
- p.field_map.values(),
+ list(p.field_map.values()),
n_bins=[len(p.x_bins)-1, len(p.y_bins)-1],
weight_field=p.weight_field,
units=units,
extrema=extrema,
+ logs=logs,
**additional_kwargs)
for field in zunits:
self.profile.set_field_unit(field, zunits[field])
Repository URL: https://bitbucket.org/yt_analysis/yt/
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