text
stringlengths 12
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stringlengths 5
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| path
stringlengths 4
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| language
stringclasses 1
value | license
stringclasses 15
values | size
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import vtk
def main():
colors = vtk.vtkNamedColors()
# Set the background color.
colors.SetColor("BkgColor", [0.3, 0.2, 0.1, 1.0])
input1 = vtk.vtkPolyData()
input2 = vtk.vtkPolyData()
sphereSource = vtk.vtkSphereSource()
sphereSource.SetCenter(5, 0, 0)
sphereSource.Update()
input1.ShallowCopy(sphereSource.GetOutput())
coneSource = vtk.vtkConeSource()
coneSource.Update()
input2.ShallowCopy(coneSource.GetOutput())
# Append the two meshes
appendFilter = vtk.vtkAppendPolyData()
appendFilter.AddInputData(input1)
appendFilter.AddInputData(input2)
appendFilter.Update()
# Remove any duplicate points.
cleanFilter = vtk.vtkCleanPolyData()
cleanFilter.SetInputConnection(appendFilter.GetOutputPort())
cleanFilter.Update()
# Create a mapper and actor
mapper = vtk.vtkPolyDataMapper()
mapper.SetInputConnection(cleanFilter.GetOutputPort())
actor = vtk.vtkActor()
actor.SetMapper(mapper)
# Create a renderer, render window, and interactor
renderer = vtk.vtkRenderer()
renderWindow = vtk.vtkRenderWindow()
renderWindow.AddRenderer(renderer)
renderWindowInteractor = vtk.vtkRenderWindowInteractor()
renderWindowInteractor.SetRenderWindow(renderWindow)
# Add the actors to the scene
renderer.AddActor(actor)
renderer.SetBackground(colors.GetColor3d("BkgColor"))
# Render and interact
renderWindowInteractor.Initialize()
renderWindow.Render()
renderer.GetActiveCamera().Zoom(0.9)
renderWindow.Render()
renderWindowInteractor.Start()
if __name__ == "__main__":
main()
|
lorensen/VTKExamples
|
src/Python/Filtering/CombinePolyData.py
|
Python
|
apache-2.0
| 1,645
|
[
"VTK"
] |
67b897da9c75130f240f33fe721f3faf84c63dddf5259492161bcbbf3964e1b4
|
"""
New version of bioconductor? This script goes through each current Bioconductor
recipe, finds the corresponding dependencies that are also in this repo, and
reports the [reverse toplogically sorted] set of Bioconductor packages that
should be updated using bioconductor-scraper.py.
In other words, the first items in the list should be updated and built first
since they are those that other packages are most dependent on.
Outputs strings of the format BioconductorName:recipename
"""
import glob
import yaml
import pyaml
import os
import networkx as nx
import itertools
from conda_build.metadata import MetaData
from conda import version
import sys
sys.path.insert(0, '..')
from bioconda_utils import utils
def bioc_name(recipe):
"""
Returns the Bioconductor name (rather than the sanitized lowercase bioconda
name) that can be provided to bioconda-scraper.py
"""
return MetaData(recipe).meta['source']['fn'].split('_')[0]
if __name__ == "__main__":
import argparse
ap = argparse.ArgumentParser()
ap.add_argument('--recipes', default='recipes', help='Recipes directory')
ap.add_argument('--config', default='recipes', help='Config YAML')
args = ap.parse_args()
recipes = list(utils.get_recipes(args.recipes, 'bioconductor-*'))
deps = itertools.chain(
itertools.chain(*(utils.get_deps(i, build=True, config=args.config) for i in recipes)),
itertools.chain(*(utils.get_deps(i, build=False, config=args.config) for i in recipes))
)
deps = list(filter(lambda x: x.startswith(('r-', 'bioconductor-')), deps))
bioconda_deps = list(
filter(
lambda x: os.path.isdir(x),
itertools.chain(*(utils.get_recipes(args.recipes, i) for i in deps))
)
)
dag, name2recipe = utils.get_dag(bioconda_deps, config=args.config)
def version_sort(x):
return version.VersionOrder(MetaData(x).meta['package']['version'])
for name in nx.topological_sort(dag):
recipe = sorted(name2recipe[name], key=version_sort)[-1]
print('{0}:{1}'.format(bioc_name(recipe), name))
|
instituteofpathologyheidelberg/bioconda-recipes
|
scripts/bioconductor/update_bioconductor_packages.py
|
Python
|
mit
| 2,111
|
[
"Bioconda",
"Bioconductor"
] |
6f804daf2de13699e634708c64c85e11717001ada1e536f77be331e8671accab
|
# Copyright (c) Pymatgen Development Team.
# Distributed under the terms of the MIT License.
"""
This module provides classes used to enumerate surface sites
and to find adsorption sites on slabs
"""
import itertools
import os
import numpy as np
from matplotlib import patches
from matplotlib.path import Path
from monty.serialization import loadfn
from scipy.spatial import Delaunay
from pymatgen import vis
from pymatgen.core.structure import Structure
from pymatgen.analysis.local_env import VoronoiNN
from pymatgen.analysis.structure_matcher import StructureMatcher
from pymatgen.core.operations import SymmOp
from pymatgen.core.surface import generate_all_slabs
from pymatgen.symmetry.analyzer import SpacegroupAnalyzer
from pymatgen.util.coord import in_coord_list_pbc
__author__ = "Joseph Montoya"
__copyright__ = "Copyright 2016, The Materials Project"
__version__ = "0.1"
__maintainer__ = "Joseph Montoya"
__credits__ = "Richard Tran"
__email__ = "montoyjh@lbl.gov"
__status__ = "Development"
__date__ = "December 2, 2015"
class AdsorbateSiteFinder:
"""
This class finds adsorbate sites on slabs and generates
adsorbate structures according to user-defined criteria.
The algorithm for finding sites is essentially as follows:
1. Determine "surface sites" by finding those within
a height threshold along the miller index of the
highest site
2. Create a network of surface sites using the Delaunay
triangulation of the surface sites
3. Assign on-top, bridge, and hollow adsorption sites
at the nodes, edges, and face centers of the Del.
Triangulation
4. Generate structures from a molecule positioned at
these sites
"""
def __init__(self, slab, selective_dynamics=False, height=0.9, mi_vec=None):
"""
Create an AdsorbateSiteFinder object.
Args:
slab (Slab): slab object for which to find adsorbate sites
selective_dynamics (bool): flag for whether to assign
non-surface sites as fixed for selective dynamics
height (float): height criteria for selection of surface sites
mi_vec (3-D array-like): vector corresponding to the vector
concurrent with the miller index, this enables use with
slabs that have been reoriented, but the miller vector
must be supplied manually
"""
# get surface normal from miller index
if mi_vec:
self.mvec = mi_vec
else:
self.mvec = get_mi_vec(slab)
slab = self.assign_site_properties(slab, height)
if selective_dynamics:
slab = self.assign_selective_dynamics(slab)
self.slab = slab
@classmethod
def from_bulk_and_miller(
cls,
structure,
miller_index,
min_slab_size=8.0,
min_vacuum_size=10.0,
max_normal_search=None,
center_slab=True,
selective_dynamics=False,
undercoord_threshold=0.09,
):
"""
This method constructs the adsorbate site finder from a bulk
structure and a miller index, which allows the surface sites
to be determined from the difference in bulk and slab coordination,
as opposed to the height threshold.
Args:
structure (Structure): structure from which slab
input to the ASF is constructed
miller_index (3-tuple or list): miller index to be used
min_slab_size (float): min slab size for slab generation
min_vacuum_size (float): min vacuum size for slab generation
max_normal_search (int): max normal search for slab generation
center_slab (bool): whether to center slab in slab generation
selective dynamics (bool): whether to assign surface sites
to selective dynamics
undercoord_threshold (float): threshold of "undercoordation"
to use for the assignment of surface sites. Default is
0.1, for which surface sites will be designated if they
are 10% less coordinated than their bulk counterpart
"""
# TODO: for some reason this works poorly with primitive cells
# may want to switch the coordination algorithm eventually
vnn_bulk = VoronoiNN(tol=0.05)
bulk_coords = [len(vnn_bulk.get_nn(structure, n)) for n in range(len(structure))]
struct = structure.copy(site_properties={"bulk_coordinations": bulk_coords})
slabs = generate_all_slabs(
struct,
max_index=max(miller_index),
min_slab_size=min_slab_size,
min_vacuum_size=min_vacuum_size,
max_normal_search=max_normal_search,
center_slab=center_slab,
)
slab_dict = {slab.miller_index: slab for slab in slabs}
if miller_index not in slab_dict:
raise ValueError("Miller index not in slab dict")
this_slab = slab_dict[miller_index]
vnn_surface = VoronoiNN(tol=0.05, allow_pathological=True)
surf_props, undercoords = [], []
this_mi_vec = get_mi_vec(this_slab)
mi_mags = [np.dot(this_mi_vec, site.coords) for site in this_slab]
average_mi_mag = np.average(mi_mags)
for n, site in enumerate(this_slab):
bulk_coord = this_slab.site_properties["bulk_coordinations"][n]
slab_coord = len(vnn_surface.get_nn(this_slab, n))
mi_mag = np.dot(this_mi_vec, site.coords)
undercoord = (bulk_coord - slab_coord) / bulk_coord
undercoords += [undercoord]
if undercoord > undercoord_threshold and mi_mag > average_mi_mag:
surf_props += ["surface"]
else:
surf_props += ["subsurface"]
new_site_properties = {
"surface_properties": surf_props,
"undercoords": undercoords,
}
new_slab = this_slab.copy(site_properties=new_site_properties)
return cls(new_slab, selective_dynamics)
def find_surface_sites_by_height(self, slab, height=0.9, xy_tol=0.05):
"""
This method finds surface sites by determining which sites are within
a threshold value in height from the topmost site in a list of sites
Args:
site_list (list): list of sites from which to select surface sites
height (float): threshold in angstroms of distance from topmost
site in slab along the slab c-vector to include in surface
site determination
xy_tol (float): if supplied, will remove any sites which are
within a certain distance in the miller plane.
Returns:
list of sites selected to be within a threshold of the highest
"""
# Get projection of coordinates along the miller index
m_projs = np.array([np.dot(site.coords, self.mvec) for site in slab.sites])
# Mask based on window threshold along the miller index.
mask = (m_projs - np.amax(m_projs)) >= -height
surf_sites = [slab.sites[n] for n in np.where(mask)[0]]
if xy_tol:
# sort surface sites by height
surf_sites = [s for (h, s) in zip(m_projs[mask], surf_sites)]
surf_sites.reverse()
unique_sites, unique_perp_fracs = [], []
for site in surf_sites:
this_perp = site.coords - np.dot(site.coords, self.mvec)
this_perp_frac = slab.lattice.get_fractional_coords(this_perp)
if not in_coord_list_pbc(unique_perp_fracs, this_perp_frac):
unique_sites.append(site)
unique_perp_fracs.append(this_perp_frac)
surf_sites = unique_sites
return surf_sites
def assign_site_properties(self, slab, height=0.9):
"""
Assigns site properties.
"""
if "surface_properties" in slab.site_properties.keys():
return slab
surf_sites = self.find_surface_sites_by_height(slab, height)
surf_props = ["surface" if site in surf_sites else "subsurface" for site in slab.sites]
return slab.copy(site_properties={"surface_properties": surf_props})
def get_extended_surface_mesh(self, repeat=(5, 5, 1)):
"""
Gets an extended surface mesh for to use for adsorption
site finding by constructing supercell of surface sites
Args:
repeat (3-tuple): repeat for getting extended surface mesh
"""
surf_str = Structure.from_sites(self.surface_sites)
surf_str.make_supercell(repeat)
return surf_str
@property
def surface_sites(self):
"""
convenience method to return a list of surface sites
"""
return [site for site in self.slab.sites if site.properties["surface_properties"] == "surface"]
def subsurface_sites(self):
"""
convenience method to return list of subsurface sites
"""
return [site for site in self.slab.sites if site.properties["surface_properties"] == "subsurface"]
def find_adsorption_sites(
self,
distance=2.0,
put_inside=True,
symm_reduce=1e-2,
near_reduce=1e-2,
positions=["ontop", "bridge", "hollow"],
no_obtuse_hollow=True,
):
"""
Finds surface sites according to the above algorithm. Returns
a list of corresponding cartesian coordinates.
Args:
distance (float): distance from the coordinating ensemble
of atoms along the miller index for the site (i. e.
the distance from the slab itself)
put_inside (bool): whether to put the site inside the cell
symm_reduce (float): symm reduction threshold
near_reduce (float): near reduction threshold
positions (list): which positions to include in the site finding
"ontop": sites on top of surface sites
"bridge": sites at edges between surface sites in Delaunay
triangulation of surface sites in the miller plane
"hollow": sites at centers of Delaunay triangulation faces
"subsurface": subsurface positions projected into miller plane
no_obtuse_hollow (bool): flag to indicate whether to include
obtuse triangular ensembles in hollow sites
"""
ads_sites = {k: [] for k in positions}
if "ontop" in positions:
ads_sites["ontop"] = [s.coords for s in self.surface_sites]
if "subsurface" in positions:
# Get highest site
ref = self.slab.sites[np.argmax(self.slab.cart_coords[:, 2])]
# Project diff between highest site and subs site into miller
ss_sites = [
self.mvec * np.dot(ref.coords - s.coords, self.mvec) + s.coords for s in self.subsurface_sites()
]
ads_sites["subsurface"] = ss_sites
if "bridge" in positions or "hollow" in positions:
mesh = self.get_extended_surface_mesh()
sop = get_rot(self.slab)
dt = Delaunay([sop.operate(m.coords)[:2] for m in mesh])
# TODO: refactor below to properly account for >3-fold
for v in dt.simplices:
if -1 not in v:
dots = []
for i_corner, i_opp in zip(range(3), ((1, 2), (0, 2), (0, 1))):
corner, opp = v[i_corner], [v[o] for o in i_opp]
vecs = [mesh[d].coords - mesh[corner].coords for d in opp]
vecs = [vec / np.linalg.norm(vec) for vec in vecs]
dots.append(np.dot(*vecs))
# Add bridge sites at midpoints of edges of D. Tri
if "bridge" in positions:
ads_sites["bridge"].append(self.ensemble_center(mesh, opp))
# Prevent addition of hollow sites in obtuse triangles
obtuse = no_obtuse_hollow and (np.array(dots) < 1e-5).any()
# Add hollow sites at centers of D. Tri faces
if "hollow" in positions and not obtuse:
ads_sites["hollow"].append(self.ensemble_center(mesh, v))
for key, sites in ads_sites.items():
# Pare off outer sites for bridge/hollow
if key in ["bridge", "hollow"]:
frac_coords = [self.slab.lattice.get_fractional_coords(ads_site) for ads_site in sites]
frac_coords = [
frac_coord
for frac_coord in frac_coords
if (frac_coord[0] > 1 and frac_coord[0] < 4 and frac_coord[1] > 1 and frac_coord[1] < 4)
]
sites = [self.slab.lattice.get_cartesian_coords(frac_coord) for frac_coord in frac_coords]
if near_reduce:
sites = self.near_reduce(sites, threshold=near_reduce)
if put_inside:
sites = [put_coord_inside(self.slab.lattice, coord) for coord in sites]
if symm_reduce:
sites = self.symm_reduce(sites, threshold=symm_reduce)
sites = [site + distance * self.mvec for site in sites]
ads_sites[key] = sites
ads_sites["all"] = sum(ads_sites.values(), [])
return ads_sites
def symm_reduce(self, coords_set, threshold=1e-6):
"""
Reduces the set of adsorbate sites by finding removing
symmetrically equivalent duplicates
Args:
coords_set: coordinate set in cartesian coordinates
threshold: tolerance for distance equivalence, used
as input to in_coord_list_pbc for dupl. checking
"""
surf_sg = SpacegroupAnalyzer(self.slab, 0.1)
symm_ops = surf_sg.get_symmetry_operations()
unique_coords = []
# Convert to fractional
coords_set = [self.slab.lattice.get_fractional_coords(coords) for coords in coords_set]
for coords in coords_set:
incoord = False
for op in symm_ops:
if in_coord_list_pbc(unique_coords, op.operate(coords), atol=threshold):
incoord = True
break
if not incoord:
unique_coords += [coords]
# convert back to cartesian
return [self.slab.lattice.get_cartesian_coords(coords) for coords in unique_coords]
def near_reduce(self, coords_set, threshold=1e-4):
"""
Prunes coordinate set for coordinates that are within
threshold
Args:
coords_set (Nx3 array-like): list or array of coordinates
threshold (float): threshold value for distance
"""
unique_coords = []
coords_set = [self.slab.lattice.get_fractional_coords(coords) for coords in coords_set]
for coord in coords_set:
if not in_coord_list_pbc(unique_coords, coord, threshold):
unique_coords += [coord]
return [self.slab.lattice.get_cartesian_coords(coords) for coords in unique_coords]
@classmethod
def ensemble_center(cls, site_list, indices, cartesian=True):
"""
Finds the center of an ensemble of sites selected from
a list of sites. Helper method for the find_adsorption_sites
algorithm.
Args:
site_list (list of sites): list of sites
indices (list of ints): list of ints from which to select
sites from site list
cartesian (bool): whether to get average fractional or
cartesian coordinate
"""
if cartesian:
return np.average([site_list[i].coords for i in indices], axis=0)
return np.average([site_list[i].frac_coords for i in indices], axis=0)
def add_adsorbate(self, molecule, ads_coord, repeat=None, translate=True, reorient=True):
"""
Adds an adsorbate at a particular coordinate. Adsorbate
represented by a Molecule object and is translated to (0, 0, 0) if
translate is True, or positioned relative to the input adsorbate
coordinate if translate is False.
Args:
molecule (Molecule): molecule object representing the adsorbate
ads_coord (array): coordinate of adsorbate position
repeat (3-tuple or list): input for making a supercell of slab
prior to placing the adsorbate
translate (bool): flag on whether to translate the molecule so
that its CoM is at the origin prior to adding it to the surface
reorient (bool): flag on whether to reorient the molecule to
have its z-axis concurrent with miller index
"""
molecule = molecule.copy()
if translate:
# Translate the molecule so that the center of mass of the atoms
# that have the most negative z coordinate is at (0, 0, 0)
front_atoms = molecule.copy()
front_atoms._sites = [s for s in molecule.sites if s.coords[2] == min(s.coords[2] for s in molecule.sites)]
x, y, z = front_atoms.center_of_mass
molecule.translate_sites(vector=[-x, -y, -z])
if reorient:
# Reorient the molecule along slab m_index
sop = get_rot(self.slab)
molecule.apply_operation(sop.inverse)
struct = self.slab.copy()
if repeat:
struct.make_supercell(repeat)
if "surface_properties" in struct.site_properties.keys():
molecule.add_site_property("surface_properties", ["adsorbate"] * molecule.num_sites)
if "selective_dynamics" in struct.site_properties.keys():
molecule.add_site_property("selective_dynamics", [[True, True, True]] * molecule.num_sites)
for site in molecule:
struct.append(
site.specie,
ads_coord + site.coords,
coords_are_cartesian=True,
properties=site.properties,
)
return struct
@classmethod
def assign_selective_dynamics(cls, slab):
"""
Helper function to assign selective dynamics site_properties
based on surface, subsurface site properties
Args:
slab (Slab): slab for which to assign selective dynamics
"""
sd_list = []
sd_list = [
[False, False, False] if site.properties["surface_properties"] == "subsurface" else [True, True, True]
for site in slab.sites
]
new_sp = slab.site_properties
new_sp["selective_dynamics"] = sd_list
return slab.copy(site_properties=new_sp)
def generate_adsorption_structures(
self,
molecule,
repeat=None,
min_lw=5.0,
translate=True,
reorient=True,
find_args=None,
):
"""
Function that generates all adsorption structures for a given
molecular adsorbate. Can take repeat argument or minimum
length/width of precursor slab as an input
Args:
molecule (Molecule): molecule corresponding to adsorbate
repeat (3-tuple or list): repeat argument for supercell generation
min_lw (float): minimum length and width of the slab, only used
if repeat is None
translate (bool): flag on whether to translate the molecule so
that its CoM is at the origin prior to adding it to the surface
reorient (bool): flag on whether or not to reorient adsorbate
along the miller index
find_args (dict): dictionary of arguments to be passed to the
call to self.find_adsorption_sites, e.g. {"distance":2.0}
"""
if repeat is None:
xrep = np.ceil(min_lw / np.linalg.norm(self.slab.lattice.matrix[0]))
yrep = np.ceil(min_lw / np.linalg.norm(self.slab.lattice.matrix[1]))
repeat = [xrep, yrep, 1]
structs = []
find_args = find_args or {}
for coords in self.find_adsorption_sites(**find_args)["all"]:
structs.append(
self.add_adsorbate(
molecule,
coords,
repeat=repeat,
translate=translate,
reorient=reorient,
)
)
return structs
def adsorb_both_surfaces(
self,
molecule,
repeat=None,
min_lw=5.0,
translate=True,
reorient=True,
find_args=None,
):
"""
Function that generates all adsorption structures for a given
molecular adsorbate on both surfaces of a slab. This is useful
for calculating surface energy where both surfaces need to be
equivalent or if we want to calculate nonpolar systems.
Args:
molecule (Molecule): molecule corresponding to adsorbate
repeat (3-tuple or list): repeat argument for supercell generation
min_lw (float): minimum length and width of the slab, only used
if repeat is None
reorient (bool): flag on whether or not to reorient adsorbate
along the miller index
find_args (dict): dictionary of arguments to be passed to the
call to self.find_adsorption_sites, e.g. {"distance":2.0}
"""
# Get the adsorbed surfaces first
find_args = find_args or {}
adslabs = self.generate_adsorption_structures(
molecule,
repeat=repeat,
min_lw=min_lw,
translate=translate,
reorient=reorient,
find_args=find_args,
)
new_adslabs = []
for adslab in adslabs:
# Find the adsorbate sites and indices in each slab
_, adsorbates, indices = False, [], []
for i, site in enumerate(adslab.sites):
if site.surface_properties == "adsorbate":
adsorbates.append(site)
indices.append(i)
# Start with the clean slab
adslab.remove_sites(indices)
slab = adslab.copy()
# For each site, we add it back to the slab along with a
# symmetrically equivalent position on the other side of
# the slab using symmetry operations
for adsorbate in adsorbates:
p2 = adslab.get_symmetric_site(adsorbate.frac_coords)
slab.append(adsorbate.specie, p2, properties={"surface_properties": "adsorbate"})
slab.append(
adsorbate.specie,
adsorbate.frac_coords,
properties={"surface_properties": "adsorbate"},
)
new_adslabs.append(slab)
return new_adslabs
def generate_substitution_structures(
self,
atom,
target_species=None,
sub_both_sides=False,
range_tol=1e-2,
dist_from_surf=0,
):
"""
Function that performs substitution-type doping on the surface and
returns all possible configurations where one dopant is substituted
per surface. Can substitute one surface or both.
Args:
atom (str): atom corresponding to substitutional dopant
sub_both_sides (bool): If true, substitute an equivalent
site on the other surface
target_species (list): List of specific species to substitute
range_tol (float): Find viable substitution sites at a specific
distance from the surface +- this tolerance
dist_from_surf (float): Distance from the surface to find viable
substitution sites, defaults to 0 to substitute at the surface
"""
target_species = target_species or []
# Get symmetrized structure in case we want to substitue both sides
sym_slab = SpacegroupAnalyzer(self.slab).get_symmetrized_structure()
# Define a function for substituting a site
def substitute(site, i):
slab = self.slab.copy()
props = self.slab.site_properties
if sub_both_sides:
# Find an equivalent site on the other surface
eq_indices = [indices for indices in sym_slab.equivalent_indices if i in indices][0]
for ii in eq_indices:
if "%.6f" % (sym_slab[ii].frac_coords[2]) != "%.6f" % (site.frac_coords[2]):
props["surface_properties"][ii] = "substitute"
slab.replace(ii, atom)
break
props["surface_properties"][i] = "substitute"
slab.replace(i, atom)
slab.add_site_property("surface_properties", props["surface_properties"])
return slab
# Get all possible substitution sites
substituted_slabs = []
# Sort sites so that we can define a range relative to the position of the
# surface atoms, i.e. search for sites above (below) the bottom (top) surface
sorted_sites = sorted(sym_slab, key=lambda site: site.frac_coords[2])
if sorted_sites[0].surface_properties == "surface":
d = sorted_sites[0].frac_coords[2] + dist_from_surf
else:
d = sorted_sites[-1].frac_coords[2] - dist_from_surf
for i, site in enumerate(sym_slab):
if d - range_tol < site.frac_coords[2] < d + range_tol:
if target_species and site.species_string in target_species:
substituted_slabs.append(substitute(site, i))
elif not target_species:
substituted_slabs.append(substitute(site, i))
matcher = StructureMatcher()
return [s[0] for s in matcher.group_structures(substituted_slabs)]
def get_mi_vec(slab):
"""
Convenience function which returns the unit vector aligned
with the miller index.
"""
mvec = np.cross(slab.lattice.matrix[0], slab.lattice.matrix[1])
return mvec / np.linalg.norm(mvec)
def get_rot(slab):
"""
Gets the transformation to rotate the z axis into the miller index
"""
new_z = get_mi_vec(slab)
a, b, c = slab.lattice.matrix
new_x = a / np.linalg.norm(a)
new_y = np.cross(new_z, new_x)
x, y, z = np.eye(3)
rot_matrix = np.array([np.dot(*el) for el in itertools.product([x, y, z], [new_x, new_y, new_z])]).reshape(3, 3)
rot_matrix = np.transpose(rot_matrix)
sop = SymmOp.from_rotation_and_translation(rot_matrix)
return sop
def put_coord_inside(lattice, cart_coordinate):
"""
converts a cartesian coordinate such that it is inside the unit cell.
"""
fc = lattice.get_fractional_coords(cart_coordinate)
return lattice.get_cartesian_coords([c - np.floor(c) for c in fc])
def reorient_z(structure):
"""
reorients a structure such that the z axis is concurrent with the
normal to the A-B plane
"""
struct = structure.copy()
sop = get_rot(struct)
struct.apply_operation(sop)
return struct
# Get color dictionary
colors = loadfn(os.path.join(os.path.dirname(vis.__file__), "ElementColorSchemes.yaml"))
color_dict = {el: [j / 256.001 for j in colors["Jmol"][el]] for el in colors["Jmol"].keys()}
def plot_slab(
slab,
ax,
scale=0.8,
repeat=5,
window=1.5,
draw_unit_cell=True,
decay=0.2,
adsorption_sites=True,
inverse=False,
):
"""
Function that helps visualize the slab in a 2-D plot, for
convenient viewing of output of AdsorbateSiteFinder.
Args:
slab (slab): Slab object to be visualized
ax (axes): matplotlib axes with which to visualize
scale (float): radius scaling for sites
repeat (int): number of repeating unit cells to visualize
window (float): window for setting the axes limits, is essentially
a fraction of the unit cell limits
draw_unit_cell (bool): flag indicating whether or not to draw cell
decay (float): how the alpha-value decays along the z-axis
inverse (bool): invert z axis to plot opposite surface
"""
orig_slab = slab.copy()
slab = reorient_z(slab)
orig_cell = slab.lattice.matrix.copy()
if repeat:
slab.make_supercell([repeat, repeat, 1])
coords = np.array(sorted(slab.cart_coords, key=lambda x: x[2]))
sites = sorted(slab.sites, key=lambda x: x.coords[2])
alphas = 1 - decay * (np.max(coords[:, 2]) - coords[:, 2])
alphas = alphas.clip(min=0)
corner = [0, 0, slab.lattice.get_fractional_coords(coords[-1])[-1]]
corner = slab.lattice.get_cartesian_coords(corner)[:2]
verts = orig_cell[:2, :2]
lattsum = verts[0] + verts[1]
# inverse coords, sites, alphas, to show other side of slab
if inverse:
alphas = np.array(reversed(alphas))
sites = list(reversed(sites))
coords = np.array(reversed(coords))
# Draw circles at sites and stack them accordingly
for n, coord in enumerate(coords):
r = sites[n].specie.atomic_radius * scale
ax.add_patch(patches.Circle(coord[:2] - lattsum * (repeat // 2), r, color="w", zorder=2 * n))
color = color_dict[sites[n].species_string]
ax.add_patch(
patches.Circle(
coord[:2] - lattsum * (repeat // 2),
r,
facecolor=color,
alpha=alphas[n],
edgecolor="k",
lw=0.3,
zorder=2 * n + 1,
)
)
# Adsorption sites
if adsorption_sites:
asf = AdsorbateSiteFinder(orig_slab)
if inverse:
inverse_slab = orig_slab.copy()
inverse_slab.make_supercell([1, 1, -1])
asf = AdsorbateSiteFinder(inverse_slab)
ads_sites = asf.find_adsorption_sites()["all"]
sop = get_rot(orig_slab)
ads_sites = [sop.operate(ads_site)[:2].tolist() for ads_site in ads_sites]
ax.plot(*zip(*ads_sites), color="k", marker="x", markersize=10, mew=1, linestyle="", zorder=10000)
# Draw unit cell
if draw_unit_cell:
verts = np.insert(verts, 1, lattsum, axis=0).tolist()
verts += [[0.0, 0.0]]
verts = [[0.0, 0.0]] + verts
codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO, Path.CLOSEPOLY]
verts = [(np.array(vert) + corner).tolist() for vert in verts]
path = Path(verts, codes)
patch = patches.PathPatch(path, facecolor="none", lw=2, alpha=0.5, zorder=2 * n + 2)
ax.add_patch(patch)
ax.set_aspect("equal")
center = corner + lattsum / 2.0
extent = np.max(lattsum)
lim_array = [center - extent * window, center + extent * window]
x_lim = [ele[0] for ele in lim_array]
y_lim = [ele[1] for ele in lim_array]
ax.set_xlim(x_lim)
ax.set_ylim(y_lim)
return ax
|
vorwerkc/pymatgen
|
pymatgen/analysis/adsorption.py
|
Python
|
mit
| 31,188
|
[
"Jmol",
"pymatgen"
] |
8b16062df9b0cba8adc2eb60fccc8aae0b0905251e4a17153852a265f6051e5b
|
#!/usr/bin/env python
########################################################################
# $HeadURL$
########################################################################
"""
Get the list of all the user files.
"""
__RCSID__ = "$Id$"
from DIRAC.Core.Base import Script
days = 0
months = 0
years = 0
wildcard = None
baseDir = ''
emptyDirsFlag = False
Script.registerSwitch( "D:", "Days=", "Match files older than number of days [%s]" % days )
Script.registerSwitch( "M:", "Months=", "Match files older than number of months [%s]" % months )
Script.registerSwitch( "Y:", "Years=", "Match files older than number of years [%s]" % years )
Script.registerSwitch( "w:", "Wildcard=", "Wildcard for matching filenames [All]" )
Script.registerSwitch( "b:", "BaseDir=", "Base directory to begin search (default /[vo]/user/[initial]/[username])" )
Script.registerSwitch( "e", "EmptyDirs", "Create a list of empty directories" )
Script.setUsageMessage( '\n'.join( [ __doc__.split( '\n' )[1],
'Usage:',
' %s [option|cfgfile] ...' % Script.scriptName, ] ) )
Script.parseCommandLine( ignoreErrors = False )
for switch in Script.getUnprocessedSwitches():
if switch[0] == "D" or switch[0].lower() == "days":
days = int( switch[1] )
if switch[0] == "M" or switch[0].lower() == "months":
months = int( switch[1] )
if switch[0] == "Y" or switch[0].lower() == "years":
years = int( switch[1] )
if switch[0].lower() == "w" or switch[0].lower() == "wildcard":
wildcard = switch[1]
if switch[0].lower() == "b" or switch[0].lower() == "basedir":
baseDir = switch[1]
if switch[0].lower() == "e" or switch[0].lower() == "emptydirs":
emptyDirsFlag = True
import DIRAC
from DIRAC import gLogger
from DIRAC.ConfigurationSystem.Client.Helpers.Registry import getVOForGroup
from DIRAC.Core.Security.ProxyInfo import getProxyInfo
from DIRAC.Resources.Catalog.FileCatalog import FileCatalog
from DIRAC.Core.Utilities.List import sortList
from datetime import datetime, timedelta
import sys, os, time, fnmatch
fc = FileCatalog()
def isOlderThan( cTimeStruct, days ):
timeDelta = timedelta( days = days )
maxCTime = datetime.utcnow() - timeDelta
if cTimeStruct < maxCTime:
return True
return False
withMetadata = False
if days or months or years:
withMetadata = True
totalDays = 0
if years:
totalDays += 365 * years
if months:
totalDays += 30 * months
if days:
totalDays += days
res = getProxyInfo( False, False )
if not res['OK']:
gLogger.error( "Failed to get client proxy information.", res['Message'] )
DIRAC.exit( 2 )
proxyInfo = res['Value']
username = proxyInfo['username']
vo = ''
if 'group' in proxyInfo:
vo = getVOForGroup( proxyInfo['group'] )
if not baseDir:
if not vo:
gLogger.error( 'Could not determine VO' )
Script.showHelp()
baseDir = '/%s/user/%s/%s' % ( vo, username[0], username )
baseDir = baseDir.rstrip( '/' )
gLogger.info( 'Will search for files in %s' % baseDir )
activeDirs = [baseDir]
allFiles = []
emptyDirs = []
while len( activeDirs ) > 0:
currentDir = activeDirs.pop()
res = fc.listDirectory( currentDir, withMetadata, timeout = 360 )
if not res['OK']:
gLogger.error( "Error retrieving directory contents", "%s %s" % ( currentDir, res['Message'] ) )
elif currentDir in res['Value']['Failed']:
gLogger.error( "Error retrieving directory contents", "%s %s" % ( currentDir, res['Value']['Failed'][currentDir] ) )
else:
dirContents = res['Value']['Successful'][currentDir]
subdirs = dirContents['SubDirs']
files = dirContents['Files']
if not subdirs and not files:
emptyDirs.append( currentDir )
gLogger.notice( '%s: empty directory' % currentDir )
else:
for subdir in sorted( subdirs, reverse = True ):
if ( not withMetadata ) or isOlderThan( subdirs[subdir]['CreationDate'], totalDays ):
activeDirs.append( subdir )
for filename in sorted( files ):
fileOK = False
if ( not withMetadata ) or isOlderThan( files[filename]['MetaData']['CreationDate'], totalDays ):
if wildcard is None or fnmatch.fnmatch( filename, wildcard ):
fileOK = True
if not fileOK:
files.pop( filename )
allFiles += sorted( files )
gLogger.notice( "%s: %d files%s, %d sub-directories" % ( currentDir, len( files ), ' matching' if withMetadata or wildcard else '', len( subdirs ) ) )
outputFileName = '%s.lfns' % baseDir.replace( '/%s' % vo, '%s' % vo ).replace( '/', '-' )
outputFile = open( outputFileName, 'w' )
for lfn in sortList( allFiles ):
outputFile.write( lfn + '\n' )
outputFile.close()
gLogger.notice( '%d matched files have been put in %s' % ( len( allFiles ), outputFileName ) )
if emptyDirsFlag:
outputFileName = '%s.emptydirs' % baseDir.replace( '/%s' % vo, '%s' % vo ).replace( '/', '-' )
outputFile = open( outputFileName, 'w' )
for dir in sortList( emptyDirs ):
outputFile.write( dir + '\n' )
outputFile.close()
gLogger.notice( '%d empty directories have been put in %s' % ( len( emptyDirs ), outputFileName ) )
DIRAC.exit( 0 )
|
vmendez/DIRAC
|
DataManagementSystem/scripts/dirac-dms-user-lfns.py
|
Python
|
gpl-3.0
| 5,259
|
[
"DIRAC"
] |
6f3950ff1a29dd4ed22ab56a7398d08e8f9848cc1213827b934f7d644e905601
|
import sys,os,hashlib,time
import json
SPLUNK_HOME = os.environ.get("SPLUNK_HOME")
#dynamically load in any eggs
EGG_DIR = SPLUNK_HOME + "/etc/apps/pubnub_alert/bin/"
for filename in os.listdir(EGG_DIR):
if filename.endswith(".egg"):
sys.path.append(EGG_DIR + filename)
from pubnubsdk import Pubnub
def callback(message):
print >> sys.stderr, "DEBUG Callback handler for sent message %s" % message
def send_message(settings):
print >> sys.stderr, "DEBUG Sending message to Pubnub with settings %s" % settings
pub_key = settings.get('pubkey')
sub_key = settings.get('subkey')
channel = settings.get('channel')
message = settings.get('message')
activation_key = settings.get('activationkey').strip()
app_name = "Pubnub Modular Alert"
if len(activation_key) > 32:
activation_hash = activation_key[:32]
activation_ts = activation_key[32:][::-1]
current_ts = time.time()
m = hashlib.md5()
m.update((app_name + activation_ts))
if not m.hexdigest().upper() == activation_hash.upper():
logging.error("FATAL Trial Activation key for App '%s' failed. Please ensure that you copy/pasted the key correctly." % app_name)
sys.exit(2)
if ((current_ts - long(activation_ts)) > 604800):
logging.error("FATAL Trial Activation key for App '%s' has now expired. Please visit http://www.baboonbones.com/#activation to purchase a non expiring key." % app_name)
sys.exit(2)
else:
m = hashlib.md5()
m.update((app_name))
if not m.hexdigest().upper() == activation_key.upper():
logging.error("FATAL Activation key for App '%s' failed. Please ensure that you copy/pasted the key correctly." % app_name)
sys.exit(2)
print >> sys.stderr, "INFO Sending message to Pubnub channel=%s with message=%s" % (channel,message)
try:
pubnub = Pubnub(publish_key=pub_key,subscribe_key=sub_key)
pubnub.publish(channel, message, callback=callback, error=callback)
print >> sys.stderr, "INFO Sent message to Pubnub : %s" % message
return True
except Exception as tre:
print >> sys.stderr,tre
return False
except:
e = sys.exc_info()[0]
print >> sys.stderr, "ERROR Error sending message to Pubnub: %s" % e
return False
if __name__ == "__main__":
if len(sys.argv) > 1 and sys.argv[1] == "--execute":
payload = json.loads(sys.stdin.read())
if not send_message(payload.get('configuration')):
print >> sys.stderr, "FATAL Failed trying to send Message to Pubnub"
sys.exit(2)
else:
print >> sys.stderr, "INFO Message successfully sent to Pubnub"
else:
print >> sys.stderr, "FATAL Unsupported execution mode (expected --execute flag)"
sys.exit(1)
|
damiendallimore/SplunkModularInputsPythonFramework
|
implementations/pubnub_alert/bin/pubnub.py
|
Python
|
apache-2.0
| 2,957
|
[
"VisIt"
] |
5c867adcf3372f9b7117cbefb99f0f9ce011e65e77eded11491ca6edfb9f197f
|
import os
import sys
import json
import numpy as np
# For python 2
import urllib2 as urllib
# For python 3
#from urllib import request as urllib
def format_url(server, full_size, group_list):
# Format the URL for butterfly
query_fmt = 'feature={}&id={}'
shape_fmt = 'depth={}&height={}&width={}&x=0&y=0&z=0'.format(*full_size)
group_fmt = 'experiment={}&sample={}&dataset={}&channel={}'.format(*group_list)
return '{}/api/entity_feature?{}&{}&{}'.format(server, query_fmt, shape_fmt, group_fmt)
def get_all_neurons(_fmt):
# Get all neurons
all_string = urllib.urlopen(_fmt.format('all_neurons', 0)).read()
return set( json.loads(all_string) )
def get_synapse_parent(_fmt, _id):
# Get neurons linked to a synapse
linked_string = urllib.urlopen(_fmt.format('synapse_parent', _id)).read()
return set(json.loads(linked_string).values()) - set([_id])
def get_neuron_children(_fmt, _id):
# Get synapses linked to a neuron
linked_string = urllib.urlopen(_fmt.format('neuron_children', _id)).read()
return set([ int(k) for k in json.loads(linked_string).keys() ])
def get_synapse_keypoint(_fmt, _id):
# Get neurons linked to a neuron
linked_string = urllib.urlopen(_fmt.format('synapse_keypoint', _id)).read()
return json.loads(linked_string)
def get_shared_synapse(_syn_file):
# Declare all parameters
full_size= [3394, 26624, 26624]
real_server = 'https://butterfly.rc.fas.harvard.edu'
group_list = [
'team1_waypoint_201610_full_May6',
'team1_waypoint_201610_full_May6',
'25k_201610_dataset',
'em',
]
fmt = format_url(real_server, full_size, group_list)
# Output array
all_shared = []
with open(_syn_file, 'r') as jf:
pairs = json.load(jf)
for p in pairs:
synapses = [get_neuron_children(fmt, i) for i in p]
# Take the set union of the two synapse sets
shared_set = synapses[0] & synapses[1]
print ("""
{} and {} share synapse {}
""".format(p[0],p[1], shared_set))
# Find the synapse keypoint for each of the shared synapses
shared_keypoints = [get_synapse_keypoint(fmt, i) for i in shared_set]
# Store to output
coordinates = dict(zip(shared_set, shared_keypoints))
all_shared.append(p + [coordinates])
with open('out.json','w') as jf:
json.dump(all_shared, jf)
def size_filter(count_file, count_min, banned_set=set()):
""" Filter by size and number of synapses
Arguments
----------
count_file : str
Path to file with id box-size on each line
count_min : int
The minimum number of boxes a neuron must cover
banned_set : set
The neurons never to use ever
Returns
--------
list
All neurons matching a given size
"""
# Output
out_list = []
# If no file
if not os.path.exists(count_file):
return out_list
# Load counts for all neurons
all_counts = np.loadtxt(count_file, dtype=np.uint32)
# Get indexes above threshold
all_big = np.where(all_counts >= count_min)[0]
return list(set(all_big) - banned_set)
def size_synapse_filter(count_file, count_min, banned_set=set(), two_way=False):
""" Filter by size and number of synapses
Arguments
----------
count_file : str
Path to file with id box-size on each line
count_min : int
The minimum number of boxes a neuron must cover
banned_set : set
The neurons never to use ever
two_way : bool
Whether linked neurons must also be >= min_size
Returns
--------
dict
Big neurons as keys with linked neurons as values
"""
# Declare all parameters
full_size= [1664, 14336, 14336]
real_server = 'http://localhost:8487'
group_list = [
'R0',
'2017_07_12',
'50_50_50',
'final_segmentation'
]
# Get the URL to the butterfly instance
fmt = format_url(real_server, full_size, group_list)
# Output
out_dict = {}
# If no file
if not os.path.exists(count_file):
return out_dict
# Load counts for all neurons
all_counts = np.loadtxt(count_file, dtype=np.uint32)
# Get indexes above threshold
all_big = np.where(all_counts >= count_min)[0]
all_big = list(set(all_big) - banned_set)
# Check if each index has synapse
for big_id in all_big:
# Make some temporary sets
this_id = set([big_id])
these_ids = set()
# Get all the synapses
id_syns = get_neuron_children(fmt, big_id)
# If there are synapses
for syn_id in id_syns:
# Get the neurons for the synapse
syn_neurons = get_synapse_parent(fmt, syn_id) - this_id
# Add the neuron to the final output
if len(syn_neurons):
new_id = list(syn_neurons)[0]
# Check if meets size criteria
if not two_way or new_id in all_big:
# Add to temp id set
these_ids.add(new_id)
# Add set to dictionary if not empty
if len(these_ids):
out_dict[big_id] = list(these_ids)
return out_dict
if __name__ == "__main__":
CASE = 0
if CASE == 0:
set_path = "/n/coxfs01/thejohnhoffer/R0/ids-2017-06-26_stitched/meshes/top_spread.txt"
count_path = "/n/coxfs01/thejohnhoffer/R0/ids-2017-06-26_stitched/meshes/spread_count.txt"
count_min = 20
all_neurons = size_filter(count_path, count_min)
# Write set
with open(set_path, 'w') as sf:
# Make set of all neurons
sf.write(':'.join(map(str,all_neurons)))
set_path = "/n/coxfs01/thejohnhoffer/R0/ids-2017-06-26_stitched/meshes/top_high.txt"
count_path = "/n/coxfs01/thejohnhoffer/R0/ids-2017-06-26_stitched/meshes/high_count.txt"
count_min = 7
all_neurons = size_filter(count_path, count_min)
# Write set
with open(set_path, 'w') as sf:
# Make set of all neurons
sf.write(':'.join(map(str,all_neurons)))
sys.exit()
# DEFAULT CASE
out_path = "/n/coxfs01/thejohnhoffer/R0/ids-2017-07-12_final/meshes/big_linked_nodes.json"
set_path = "/n/coxfs01/thejohnhoffer/R0/ids-2017-07-12_final/meshes/big_linked_nodes.txt"
count_path = "/n/coxfs01/thejohnhoffer/R0/ids-2017-07-12_final/meshes/spread_count.txt"
banned_set = set([418327,224632])
count_min = 4
two_way = True
# Get big neurons with synapses
neuron_dict = size_synapse_filter(count_path, count_min, banned_set, two_way)
# Write out
with open(out_path, 'w') as jf:
json.dump(neuron_dict, jf, indent=4)
# Write set
with open(set_path, 'w') as sf:
# Make set of all neurons
all_neurons = set(neuron_dict.keys())
for n_val in neuron_dict.values():
all_neurons = all_neurons | set(n_val)
# Make into a string
all_neurons = list(all_neurons)
sf.write(':'.join(map(str,all_neurons)))
|
Rhoana/butterfly
|
scripts/api_test/apiTest.py
|
Python
|
mit
| 7,118
|
[
"NEURON"
] |
dbb4fb2066db3b03a9ea5cbb76732aeeece18034f22d8d6c7573c96ee1540167
|
# Exercise 43: Basic Object-Oriented Analysis and Design
from sys import exit
from random import randint
class Scene(object):
def enter(self):
print "This scene is not yet configured. Subclass it and implement enter()."
exit(1)
class Engine(object):
def __init__(self, scene_map):
self.scene_map = scene_map
def play(self):
current_scene = self.scene_map.opening_scene()
last_scene = self.scene_map.next_scene('finished')
while current_scene != last_scene:
next_scene_name = current_scene.enter()
current_scene = self.scene_map.next_scene(next_scene_name)
# be sure to print out the last scene
current_scene.enter()
class Death(Scene):
quips = [
"You died. You kinda suck at this.",
"Your mom would be proud...if she were smarter.",
"Such a luser.",
"I have a small puppy that's better at this."
]
def enter(self):
print Death.quips[randint(0, len(self.quips)-1)]
exit(1)
class CentralCorridor(Scene):
def enter(self):
print "The Gothons of Planet Percal #25 have invaded your ship and destroyed"
print "your entire crew. You are the last surviving member and your last"
print "mission is to get the neutron destruct bomb from the Weapons Armory,"
print "put it in the bridge, and blow the ship up after getting into an "
print "escape pod."
print "\n"
print "You're running down the central corridor to the Weapons Armory when"
print "a Gothon jumps out, red scaly skin, dark grimy teeth, and evil clown costume"
print "flowing around his hate filled body. He's blocking the door to the"
print "Armory and about to pull a weapon to blast you."
action = raw_input("> ")
if action == "shoot!":
print "Quick on the draw you yank out your blaster and fire it at the Gothon."
print "His clown costume is flowing and moving around his body, which throws"
print "off your aim. Your laser hits his costume but misses him entirely. This"
print "completely ruins his brand new costume his mother bought him, which"
print "makes him fly into an insane rage and blast you repeatedly in the face until"
print "you are dead. Then he eats you."
return 'death'
elif action == "dodge!":
print "Like a world class boxer you dodge, weave, slip and slide right"
print "as the Gothon's blaster cranks a laser past your head."
print "In the middle of your artful dodge your foot slips and you"
print "bang your head on the metal wall and pass out."
print "You wake up shortly after only to die as the Gothon stomps on"
print "your head and eats you."
return 'death'
elif action == "tell a joke":
print "Lucky for you they made you learn Gothon insults in the academy."
print "You tell the one Gothon joke you know:"
print "Lbhe zbgure vf fb sng, jura fur fvgf nebhaq gur ubhfr, fur fvgf nebhaq gur ubhfr."
print "The Gothon stops, tries not to laugh, then busts out laughing and can't move."
print "While he's laughing you run up and shoot him square in the head"
print "putting him down, then jump through the Weapon Armory door."
return 'laser_weapon_armory'
else:
print "DOES NOT COMPUTE!"
return 'central_corridor'
class LaserWeaponArmory(Scene):
def enter(self):
print "You do a dive roll into the Weapon Armory, crouch and scan the room"
print "for more Gothons that might be hiding. It's dead quiet, too quiet."
print "You stand up and run to the far side of the room and find the"
print "neutron bomb in its container. There's a keypad lock on the box"
print "and you need the code to get the bomb out. If you get the code"
print "wrong 10 times then the lock closes forever and you can't"
print "get the bomb. The code is 3 digits."
code = "%d%d%d" % (randint(1,9), randint(1,9), randint(1,9))
guess = raw_input("[keypad]> ")
guesses = 0
while guess != code and guesses < 10:
print "BZZZZEDDD!"
guesses += 1
if guesses == 10:
break
print "guesses =", guesses
guess = raw_input("[keypad]> ")
if guess == code:
print "The container clicks open and the seal breaks, letting gas out."
print "You grab the neutron bomb and run as fast as you can to the"
print "bridge where you must place it in the right spot."
return 'the_bridge'
else:
print "The lock buzzes one last time and then you hear a sickening"
print "melting sound as the mechanism is fused together."
print "You decide to sit there, and finally the Gothons blow up the"
print "ship from their ship and you die."
return 'death'
class TheBridge(Scene):
def enter(self):
print "You burst onto the Bridge with the netron destruct bomb"
print "under your arm and surprise 5 Gothons who are trying to"
print "take control of the ship. Each of them has an even uglier"
print "clown costume than the last. They haven't pulled their"
print "weapons out yet, as they see the active bomb under your"
print "arm and don't want to set it off."
action = raw_input("> ")
if action == "throw the bomb":
print "In a panic you throw the bomb at the group of Gothons"
print "and make a leap for the door. Right as you drop it a"
print "Gothon shoots you right in the back killing you."
print "As you die you see another Gothon frantically try to disarm"
print "the bomb. You die knowing they will probably blow up when"
print "it goes off."
return 'death'
elif action == "slowly place the bomb":
print "You point your blaster at the bomb under your arm"
print "and the Gothons put their hands up and start to sweat."
print "You inch backward to the door, open it, and then carefully"
print "place the bomb on the floor, pointing your blaster at it."
print "You then jump back through the door, punch the close button"
print "and blast the lock so the Gothons can't get out."
print "Now that the bomb is placed you run to the escape pod to"
print "get off this tin can."
return 'escape_pod'
else:
print "DOES NOT COMPUTE!"
return "the_bridge"
class EscapePod(Scene):
def enter(self):
print "You rush through the ship desperately trying to make it to"
print "the escape pod before the whole ship explodes. It seems like"
print "hardly any Gothons are on the ship, so your run is clear of"
print "interference. You get to the chamber with the escape pods, and"
print "now need to pick one to take. Some of them could be damaged"
print "but you don't have time to look. There's 5 pods, which one"
print "do you take?"
good_pod = randint(1,5)
guess = raw_input("[pod #]> ")
if int(guess) != good_pod:
print "You jump into pod %s and hit the eject button." % guess
print "The pod escapes out into the void of space, then"
print "implodes as the hull ruptures, crushing your body"
print "into jam jelly."
return 'death'
else:
print "You jump into pod %s and hit the eject button." % guess
print "The pod easily slides out into space heading to"
print "the planet below. As it flies to the planet, you look"
print "back and see your ship implode then explode like a"
print "bright star, taking out the Gothon ship at the same"
print "time. You won!"
return 'finished'
class Finished(Scene):
def enter(self):
print "You won! Good job."
return 'finished'
class Map(object):
scenes = {
'central_corridor': CentralCorridor(),
'laser_weapon_armory': LaserWeaponArmory(),
'the_bridge': TheBridge(),
'escape_pod': EscapePod(),
'death': Death(),
'finished': Finished(),
}
def __init__(self, start_scene):
self.start_scene = start_scene
def next_scene(self, scene_name):
val = Map.scenes.get(scene_name)
return val
def opening_scene(self):
return self.next_scene(self.start_scene)
a_map = Map('laser_weapon_armory')
a_game = Engine(a_map)
a_game.play()
|
paulcarroty/Learn-Python-The-Hard-Way
|
ex43.py
|
Python
|
gpl-3.0
| 8,942
|
[
"BLAST"
] |
7c2fbaca882244d5c6eeffeb6c1637f1f3b8b88b7b5acce2a772ed0d766da2e1
|
# -*- coding: utf-8 -*-
"""
rigol.py
University of Oregon - Advanced Physics Lab
Built on top of rigolSkeleton.py to control the rigol ds 1000d/e
series oscilloscopes
Using this programming guide -> http://www.batronix.com/pdf/Rigol/ProgrammingGuide/DS1000DE_ProgrammingGuide_EN.pdf
sources I used
- http://scruss.com/blog/tag/usbtmc/
- http://www.righto.com/2013/07/rigol-oscilloscope-hacks-with-python.html
- http://www.cibomahto.com/2010/04/controlling-a-rigol-oscilloscope-using-linux-and-python/
"""
from __future__ import division
import usbcon as uc
import numpy as np
import ast
try:
import tkFileDialog as tkfd # python2
except:
import tkinter.filedialog as tkfd # python3
__author__ = "Brian Perrett"
class InvalidBackendException(Exception):
pass
class InvalidArgument(Exception):
pass
class Rigol:
backends = ["usbtmc"]
def __init__(self, backend, idProduct=None, idVendor=None):
"""
The volt1/2_scale attributes, along with other attributes defined here
should always be up to date if you are changing them solely with the methods
of this class. If you change the voltage scale on the oscilloscope manually,
you have to update these attributes manually also, or just use the "ask" methods
to query the volt scale/offset or time scale/offset.
"""
if backend == "usbtmc":
self.dev = uc.UsbCon(idProduct=idProduct, idVendor=idVendor)
else:
raise InvalidBackendException("Please specify a valid backend such as {}".format(self.backends))
delay = True if self.askTimebaseMode() == "DEL" else False
self.volt1_scale = self.askChannelScale(1)
self.volt1_offset = self.askChannelOffset(1)
self.volt2_scale = self.askChannelScale(2)
self.volt2_offset = self.askChannelOffset(2)
self.time_scale = self.askTimebaseScale(delayed=delay)
self.time_offset = self.askTimebaseOffset(delayed=delay)
def identify(self):
return self.dev.ask("*IDN?")
def reset(self):
self.dev.write("*RST")
def run(self):
self.dev.write(":RUN")
def stop(self):
"""
Stops data acquisition
"""
self.dev.write(":STOP")
def hardcopy(self):
"""
Apparently saves a bitmap of the screen somewhere, but I have no idea where.
"""
self.dev.write(":HARDcopy")
def auto(self):
self.dev.write(":AUTO")
def refreshAttributes(self):
"""
In case settings are changed manually on the oscilloscope, we can reset all
of our class attributes with this method.
"""
delay = True if self.askTimebaseMode() == "DEL" else False
self.volt1_scale = self.askChannelScale(1)
self.volt1_offset = self.askChannelOffset(1)
self.volt2_scale = self.askChannelScale(2)
self.volt2_offset = self.askChannelOffset(2)
self.time_scale = self.askTimebaseScale(delayed=delay)
self.time_offset = self.askTimebaseOffset(delayed=delay)
###########
# ACQUIRE #
###########
"""
fully implemented
1 - yes
2 - yes
3 - yes
4 - yes
5 - yes
"""
# ACQUIRE 1
def acquireType(self, typ):
"""
The commands set the current acquire type of the oscilloscope.
<type> could be NORM, AVER or PEAK.
"""
valid = ["NORM", "AVER", "PEAK"]
if typ not in valid:
raise InvalidArgument("Typ(e) argument must be one of {}.".format(valid))
msg = ":ACQ:TYPE {}".format(typ)
self.dev.write(msg)
def askAcquireType(self):
"""
Query acquire type of oscilloscope.
The query returns NORMAL, AVERAGE or PEAKDETECT
"""
msg = ":ACQ:TYPE?"
return self.dev.ask(msg)
# ACQUIRE 2
def acquireMode(self, mode):
"""
The commands set the current acquire mode of the oscilloscope.
<mode> could be RTIM (Real time Sampling) or ETIM (Equivalent Sampling).
"""
valid = ["RTIM", "ETIM"]
if mode not in valid:
raise InvalidArgument("Mode argument must be one of {}.".format(valid))
msg = ":ACQ:MODE {}".format(mode)
self.dev.write(msg)
def askAcquireMode(self):
"""
Query acquire mode for oscilloscope.
The query returns REAL_TIME or EQUAL_TIME.
"""
msg = ":ACQ:MODE?"
return self.dev.ask(msg)
# ACQUIRE 3
def acquireAverages(self, count):
"""
The commands set the average numbers in Average mode. <count>
could be and integer of 2 times the power of N within 2 and 256.
"""
valid = [2, 4, 8, 16, 32, 64, 128, 256]
if count not in valid:
raise InvalidArgument("Count argument must be one of {}.".format(valid))
msg = ":ACQ:AVER {}".format(count)
self.dev.write(msg)
def askAcquireAverages(self):
"""
The query returns 2, 4, 8, 16, 32, 64, 128 or 256.
"""
msg = ":ACQ:AVER?"
return self.dev.ask(msg)
# ACQUIRE 4
def askAcquireSamplingRate(self, channel):
"""
The command queries the current sampling rate of the analog channel or digital
channel (only for DS1000D series).
"""
valid = [1, 2]
if channel not in valid:
raise InvalidArgument("Channel argument must be one of {}.".format(channel))
msg = ":ACQ:SAMP? CHAN{}".format(channel)
return self.dev.ask(msg)
# ACQUIRE 5
def acquireMemDepth(self, depth):
"""
The commands set and query the memory depth of the oscilloscope. <depth>
could be LONG (long memory) or NORMal (normal memory)
"""
valid = ["LONG", "NORM"]
if depth not in valid:
raise InvalidArgument("Depth argument must be one of {}.".format(valid))
msg = ":ACQ:MEMD {}".format(depth)
self.dev.write(msg)
def askAcquireMemDepth(self):
"""
The query returns LONG or NORMAL.
"""
msg = ":ACQ:MEMD?"
return self.dev.ask(msg)
###########
# DISPLAY #
###########
"""
fully implemented
1 - yes
2 - yes
3 - yes
4 - yes
5 - yes
6 - yes
7 - yes
8 - yes
"""
# DISPLAY 1
def displayType(self, typ):
"""
The commands set the display type between sampling points. <type>
could be VECT (vector display) or DOTS (point display).
"""
valid = ["VECT", "DOTS"]
if typ not in valid:
raise InvalidArgument("Typ(e) argument must be one of {}.".format(valid))
msg = ":DISP:TYPE {}".format(typ)
self.dev.write(msg)
def askDisplayType(self):
"""
The query returns VECTORS or DOTS.
"""
msg = ":DISP:TYPE?"
return self.dev.ask(msg)
# DISPLAY 2
def displayGrid(self, grid):
"""
The commands set and query the state of the screen grid. <grid> could be FULL
(open the background grid and coordinates), HALF (turn off the background grid)
or NONE (turn off the background grid and coordinates).
"""
valid = ["FULL", "HALF", "NONE"]
if grid not in valid:
raise InvalidArgument("Grid argument must be one of {}.".format(valid))
msg = ":DISP:GRID {}".format(grid)
self.dev.write(msg)
def askDisplayGrid(self):
"""
The query returns FULL, HALF or NONE.
"""
msg = ":DISP:GRID?"
return self.dev.ask(msg)
# DISPLAY 3
def displayPersist(self, persist=True):
"""
The commands set and query the state of the waveform persist. “ON” denotes
the record points hold until disable the presist, “OFF” denotes the record point
varies in high refresh rate.
persist can take on boolean values True or False.
"""
msg = ":DISP:PERS {}".format("ON" if persist else "OFF")
self.dev.write(msg)
def askDisplayPersist(self):
"""
The query returns ON or OFF.
"""
msg = ":DISP:PERS?"
return self.dev.ask(msg)
# DISPLAY 4
def displayMnuDisplay(self, t):
"""
The commands set and query the time for hiding menus automatically. <time>
could be 1s, 2s, 5s, 10s, 20s or Infinite.
"""
valid = ["1", "2", "5", "10", "20", "Infinite"]
if str(t) not in valid:
raise InvalidArgument("t(ime) argument must be one of {}.".format(valid))
msg = ":DISP:MNUD {}".format(t)
self.dev.write(msg)
def askDisplayMnuDisplay(self):
"""
The query returns 1s, 2s, 5s, 10s, 20s or Infinite.
"""
msg = ":DISP:MNUD?"
return self.dev.ask(msg)
# DISPLAY 5
def displayMnuStatus(self, disp=True):
"""
The commands set and query the state of the operation menu.
disp can take boolean values True or False, to either display
menu status or not.
"""
msg = ":DISP:MNUS {}".format("ON" if disp else "OFF")
self.dev.write(msg)
def askDisplayMnuStatus(self):
"""
The query returns ON or OFF.
"""
msg = ":DISP:MNUS?"
return self.dev.ask(msg)
# DISPLAY 6
def displayClear(self):
"""
The command clears out of date waveforms on the screen during waveform
persist.
"""
msg = ":DISP:CLE"
self.dev.write(msg)
# DISPLAY 7
def displayBrightness(self, level):
"""
Changes the brightness level of the grid.
level - Brightness level from 0 to 32
"""
if level not in range(33):
raise InvalidArgument("Level argument must be between 0 and 32.")
self.dev.write(":DISP:BRIG {}".format(level))
def askDisplayBrightness(self):
"""
query the brightness of the grid
returns a string from 0 to 32
"""
return self.dev.ask(":DISP:BRIG?")
# DISPLAY 8
def displayIntensity(self, level):
"""
level - intensity level from 0 to 32
"""
if level < 0 or level > 32:
raise InvalidArgument("level argument must be between 0 and 8.")
self.dev.write(":DISP:INT {}".format(level))
def askDisplayIntensity(self):
"""
Returns waveform brightness from 0 to 32
"""
return self.dev.ask(":DISP:INT?")
############
# TIMEBASE #
############
"""
fully implemented
1 - yes
2 - yes
3 - yes
4 - yes
"""
# TIMEBASE 1
def timebaseMode(self, mode):
"""
The commands set and query the scan mode of horizontal timebase. <mode>
could be MAIN (main timebase) or DELayed (delayed scan).
"""
valid = ["MAIN", "DEL"]
if mode not in valid:
raise InvalidArgument("Mode argument must be one of {}.".format(valid))
msg = ":TIM:MODE {}".format(mode)
self.dev.write(msg)
def askTimebaseMode(self):
"""
The query returns MAIN or DELAYED.
"""
msg = ":TIM:MODE?"
return self.dev.ask(msg)
# TIMEBASE 2
def timebaseOffset(self, offset, delayed=False):
"""
The commands set and query the offset of the MAIN or DELayed timebase (that
is offset of the waveform position relative to the trigger midpoint.). Thereinto,
In NORMAL mode, the range of <scale_val> is 1s ~ end of the memory;
In STOP mode, the range of <scale_val> is -500s ~ +500s;
In SCAN mode, the range of <scale_val> is -6*Scale ~ +6*Scale; (Note: Scale
indicates the current horizontal scale, the unit is s/div.)
In MAIN state, the item [:DELayed] should be omitted.
"""
# not checking for valid input.
msg = ":TIM{}:OFFS {}".format(":DEL" if delayed else "", offset)
self.dev.write(msg)
self.time_offset = self.askTimebaseOffset(delayed=delayed)
def askTimebaseOffset(self, delayed=False):
"""
The query returns the setting value of the <offset> in s.
"""
msg = ":TIM{}:OFFS?".format(":DEL" if delayed else "")
offset = self.dev.ask(msg)
self.time_offset = float(offset)
return float(offset)
# TIMEBASE 3
def timebaseScale(self, scale, delayed=False):
"""
The commands set and query the horizontal scale for MAIN or DELayed
timebase, the unit is s/div (seconds/grid), thereinto:
In YT mode, the range of <scale_val> is 2ns - 50s;
In ROLL mode, the range of <scale_val> is 500ms - 50s;
In MAIN state, the item [:DELayed] should be omitted.
"""
msg = ":TIM{}:SCAL {}".format(":DEL" if delayed else "", scale)
self.dev.write(msg)
self.time_scale = self.askTimebaseScale(delayed=delayed)
def askTimebaseScale(self, delayed=False):
"""
The query returns the setting value of <scale_val> in s.
"""
msg = ":TIM{}:SCAL?".format(":DEL" if delayed else "")
scale = self.dev.ask(msg)
self.time_scale = float(scale)
return float(scale)
def timebaseFormat(self, format):
"""
The commands set and query the horizontal timebase. <value> could be XY, YT
or SCANning.
"""
valid = ["XY", "YT", "SCAN"]
if format not in valid:
raise InvalidArgument("Format argument must be one of {}".format(valid))
msg = ":TIM:FORM {}".format(format)
self.dev.write(msg)
def askTimebaseFormat(self):
"""
The query returns X-Y, Y-T or SCANNING.
"""
msg = ":TIM:FORM?"
return self.dev.ask(msg)
"""
__TRIGGER FUNCTIONS__
The trigger functions are separated into 8 sub-sections
1. Trigger Control
2. Edge Trigger
3. Pulse Trigger
4. Video Trigger
5. Slope Trigger
6. Pattern Trigger
7. Duration Trigger
8. Alternation Trigger
"""
######################
# 1. TRIGGER CONTROL #
######################
"""
Which trigger functions have been implemented.
Numbering is based off of numbering in the programming manual.
fully implemented
1 - yes
2 - yes
3 - yes
4 - yes
5 - yes
6 - yes
7 - yes
8 - yes
9 - yes
"""
# TRIGGER CONTROL 1
def triggerMode(self, mode):
"""
sets trigger mode
see valid_modes variable for valid mode values.
"""
valid_modes = ["EDGE", "PULS", "VIDEO", "SLOP", "PATT", "DUR", "ALT"]
if mode not in valid_modes:
raise InvalidArgument("Mode argument must be one of {}".format(valid_modes))
self.dev.write(":TRIG:MODE {}".format(mode))
def askTriggerMode(self):
"""
queries trigger mode
"""
return self.dev.ask(":TRIG:MODE?")
# TRIGGER CONTROL 2
def triggerSource(self, mode, source):
"""
sets trigger mode and source
"""
dig = ["DIG{}".format(x) for x in range(16)]
if mode == "EDGE":
valid_sources = ["CHAN1", "CHAN2", "EXT", "ACL"] + dig
elif mode == "PULSE":
valid_sources = ["CHAN1", "CHAN2", "EXT"] + dig
elif mode == "VIDEO":
valid_sources = ["CHAN1", "CHAN2", "EXT"]
elif mode == "SLOP":
valid_sources = ["CHAN1", "CHAN2", "EXT"]
elif mode in ["PATTERN", "DURATION", "ALTERNATION"]:
valid_sources = ["CHAN1", "CHAN2", "EXT", "ACL"] + dig
else:
raise InvalidArgument("Mode argument must be one of ".format(["EDGE", "PULS", "VIDEO", "SLOP", "PATT", "DUR", "ALT"]))
if source not in valid_sources:
raise InvalidArgument("Source argument must be one of {} for a trigger mode of {}".format(valid_sources, mode))
else:
msg = ":TRIG:{}:SOUR {}".format(mode, source)
# print("Writing {}".format(msg))
self.dev.write(msg)
def askTriggerSource(self, mode):
"""
query what trigger source is being used.
mode - can be any of ["EDGE", "PULS", "VIDEO", "SLOP", "PATT", "DUR", "ALT"]
"""
valid_modes = ["EDGE", "PULS", "VIDEO", "SLOP", "PATT", "DUR", "ALT"]
if mode not in valid_modes:
raise InvalidArgument("Mode argument must be one of {}.".format(valid_modes))
msg = ":TRIG:{}:SOUR?".format(mode)
# print(msg)
return self.dev.ask(msg)
# TRIGGER CONTROL 3
def triggerLevel(self, mode, level):
"""
The commands set and query the trigger level. <mode> could be :EDGE, :PULSe
or :VIDEO; the range of <level> is: -6*Scale~+6*Scale, Scale indicates the current vertical
scale, the unit is V/div.
"""
# We can check for valid inputs using the other methods. Implement this later.
valid_modes = ["EDGE", "PULS", "VIDEO"]
if mode not in valid_modes:
raise InvalidArgument("Mode argument must be one of {}.".format(valid_modes))
msg = ":TRIG{}:LEV {}".format(mode, level)
self.dev.write(msg)
def askTriggerLevel(self, mode):
"""
The query returns the setting value of <level> in V.
"""
valid_modes = ["EDGE", "PULS", "VIDEO"]
if mode not in valid_modes:
raise InvalidArgument("Mode argument must be one of {}.".format(valid_modes))
msg = ":TRIG{}:LEV?".format(mode)
return self.dev.ask(msg)
# TRIGGER CONTROL 4
def triggerSweep(self, mode, sweep):
"""
The commands set and query the trigger type. <mode>could be :EDGE,
:PULSe, :SLOPe, :PATTern or :DURation.
sweep can take values [AUTO, NORM, SING]
"""
valid_modes = ["EDGE", "PULS", "SLOP", "PATT", "DUR"]
valid_sweep = ["AUTO", "NORM", "SING"]
if mode not in valid_modes:
raise InvalidArgument("Mode argument must be one of {}.".format(valid_modes))
if sweep not in valid_sweep:
raise InvalidArgument("Sweep argument must be one of {}.".format(valid_sweep))
msg = ":TRIG:{}:SWE {}".format(mode, sweep)
self.dev.write(msg)
def askTriggerSweep(self, mode):
"""
The query returns AUTO, NORMAL or SINGLE.
"""
valid_modes = ["EDGE", "PULS", "SLOP", "PATT", "DUR"]
if mode not in valid_modes:
raise InvalidArgument("Mode argument must be one of {}.".format(valid_modes))
msg = ":TRIG:{}:SWE?".format(mode)
return self.dev.ask(msg)
# TRIGGER CONTROL 5
def triggerCoupling(self, mode, coupling):
"""
The commands set and query the coupling type. Thereinto,
DC: Allow all signals pass.
AC: Block DC signals and attenuate the signals lower than 10Hz.
HF: Reject high frequency signals (Higher than 150KHz).
LF: Reject DC signals and attenuate low frequency signals (Lower than 8KHz).
<mode> could be :EDGE, :PULSe or :SLOPe.
"""
valid_coupling = ["DC", "AC", "HF", "LF"]
valid_modes = ["EDGE", "PULS", "SLOP"]
if mode not in valid_modes:
raise InvalidArgument("Mode argument must be one of {}.".format(valid_modes))
if coupling not in valid_coupling:
raise InvalidArgument("Coupling argument must be one of {}.".format(valid_coupling))
msg = ":TRIG:{}:COUP {}".format(mode, coupling)
self.dev.write(msg)
def askTriggerCoupling(self, mode):
"""
The query returns DC, AC, HF or LF.
"""
valid_modes = ["EDGE", "PULS", "SLOP"]
if mode not in valid_modes:
raise InvalidArgument("Mode argument must be one of {}.".format(valid_modes))
msg = ":TRIG:{}:COUP?".format(mode)
return self.dev.ask(msg)
# TRIGGER CONTROL 6
def triggerHoldoff(self, count):
"""
The commands set and query the trigger holfoff time. The range of <count> is
<count>: 500ns~1.5s.
"""
if count < .0000005 or count > 1.5:
raise InvalidArgument("Count argument must be between 500ns and 1.5s")
msg = ":TRIG:HOLD {}".format(count)
self.dev.write(msg)
def askTriggerHoldoff(self):
"""
The query returns the setting value of <count> in s.
"""
msg = ":TRIG:HOLD?"
return self.dev.ask(msg)
# TRIGGER CONTROL 7
def askTriggerStatus(self):
"""
The command queries the operating status of the oscilloscope. The status could
be RUN, STOP, T`D, WAIT or AUTO.
"""
msg = ":TRIG:STAT?"
return self.dev.ask(msg)
# TRIGGER CONTROL 8
def trigger50(self):
"""
The command sets the trigger level to the vertical midpoint of amplitude.
"""
msg = ":TRIG%50"
self.dev.write(msg)
# TRIGGER CONTROL 9
def triggerForce(self):
"""
The command forces the oscilloscope to trigger signal, which is usually used in
“Normal” and “Single” mode.
"""
msg = ":FORC"
self.dev.write(msg)
###################
# 2. EDGE TRIGGER #
###################
"""
fully implemented
1 - yes
2 - yes
3 - yes
"""
# EDGE TRIGGER 1
def teSlope(self, positive=True):
"""
The commands set and query the type of edge trigger. The type could be
POSitive (Rising edge) or NEGative (Failing edge).
"""
msg = ":TRIG:EDGE:SLOP {}".format("POS" if positive else "NEG")
self.dev.write(msg)
def askTeSlope(self):
"""
The query returns POSITIVE or NEGATIVE.
"""
msg = ":TRIG:EDGE:SLOP?"
return self.dev.ask(msg)
# EDGE TRIGGER 2
def teSensitivity(self, count):
"""
The commands set and query the sensitive of edge trigger. The range of
<count> could be 0.1div~1div.
"""
if count < .1 or count > 1:
raise InvalidArgument("Count argument must be between .1 and 1")
msg = ":TRIG:EDGE:SENS {}".format(count)
self.dev.write(msg)
def askTeSensitivity(self):
"""
The query returns the setting value <count> in div.
"""
msg = ":TRIG:EDGE:SENS?"
return self.dev.ask(msg)
####################
# 3. PULSE TRIGGER #
####################
"""
fully implemented
1 - yes
2 - yes
3 - yes
"""
# PULSE TRIGGER 1
def tpMode(self, mode):
"""
The commands set and query the pulse condition. <mode> could be
+GREaterthan (positive pulse greater than), +LESSthan (positive pulse less
than), +EQUal (positive pulse equals to), -GREaterthan (negative pulse greater
than), -LESSthan (negative pulse less than) or –EQUal (negative pulse equals
to).
"""
valid = ["+GRE", "+LESS", "-GRE", "-LESS", "+EQU", "-EQU"]
if mode not in valid:
raise InvalidArgument("Mode argument must be one of {}".format(valid))
msg = ":TRIG:PULS:MODE {}".format(mode)
self.dev.write(msg)
def askTpMode(self):
"""
The query returns +GREATER THAN, +LESS THAN, +EQUAL, -GREATER THAN,
-LESS THAN or -EQUAL.
"""
msg = ":TRIG:PULS:MODE?"
return self.dev.ask(msg)
# PULSE TRIGGER 2
def tpSensitivity(self, count):
"""
The commands set and query the sensitive of pulse trigger. The range of
<count> could be 0.1div~1div.
"""
if count < .1 or count > 1:
raise InvalidArgument("Count argument must be between .1 and 1")
msg = ":TRIG:PULS:SENS {}".format(count)
self.dev.write(msg)
def askTpSensitivity(self):
"""
The query returns the setting value of <count> in div.
"""
msg = ":TRIG:PULS:SENS?"
return self.dev.ask(msg)
# PULSE TRIGGER 3
def tpWidth(self, width):
"""
The commands set and query the pulse width. The range of <wid> is 20ns ~
10s.
"""
if width < .00000002 or width > 10:
raise InvalidArgument("Width argument must be between 20ns and 10s")
msg = ":TRIG:PULS:WIDT {}".format(width)
self.dev.write(msg)
def askTpWidth(self):
"""
The query returns the setting value of the <wid> in s.
"""
msg = ":TRIG:PULS:WIDT?"
return self.dev.ask(msg)
####################
# 4. VIDEO TRIGGER #
####################
"""
fully implemented
1 - yes
2 - yes
3 - yes
4 - yes
5 - yes
"""
# VIDEO TRIGGER 1
def tvMode(self, mode):
"""
The commands set and query the synchronous mode of the video trigger.
<mode> could be ODDfield, EVENfield, LINE or ALLlines.
"""
valid = ["ODD", "EVEN", "LINE", "ALL"]
if mode not in valid:
raise InvalidArgument("Mode argument must be one of {}.".format(valid))
msg = ":TRIG:VIDEO:MODE {}".format(mode)
self.dev.write(msg)
def askTvMode(self):
"""
The query returns ODD FIELD, EVEN FIELD, LINE or ALL LINES.
"""
msg = ":TRIG:VIDEO:MODE?"
return self.dev.ask(msg)
# VIDEO TRIGGER 2
def tvPolarity(self, pos_polarity=True):
"""
The commands set and query the video polarity. The polarity could be POSitive
or NEGative.
"""
msg = ":TRIG:VIDEO:POL {}".format("POS" if pos_polarity else "NEG")
self.dev.write(msg)
def askTvPolarity(self):
"""
The query returns POSITIVE or NEGATIVE.
"""
msg = ":TRIG:VIDEO:POL?"
return self.dev.ask(msg)
# VIDEO TRIGGER 3
def tvStandard(self, ntsc=True):
"""
The commands set and query the type of video trigger standard.
"""
msg = ":TRIG:VIDEO:STAN {}".format("NTSC" if ntsc else "PALS")
self.dev.write(msg)
def askTvStandard(self):
"""
The query returns NTSC or PAL/SECAM.
"""
msg = ":TRIG:VIDEO:STAN?"
return self.dev.ask(msg)
# VIDEO TRIGGER 4
def tvLine(self, value):
"""
The commands set and query the number of specified line of synchronous. In
NTSC standard, the range of <value> is 1~525; in PAL/SECAM standard, the
range of <value> is 1~625.
"""
standard = self.askTvStandard()
if standard == "NTSC":
if value < 1 or value > 525:
raise InvalidArgument("When using {}, line value must be between 1 and 525.".format(standard))
if standard == "PAL/SECAM":
if value < 1 or value > 625:
raise InvalidArgument("When using {}, line value must be between 1 and 625.".format(standard))
msg = ":TRIG:VIDEO:LINE {}".format(value)
self.dev.write(msg)
def askTvLine(self):
"""
"""
msg = ":TRIG:VIDEO:LINE?"
return self.dev.ask(msg)
# VIDEO TRIGGER 5
def tvSensitivity(self, count):
"""
The commands set and query the trigger sensitive, the range of <count> is:
0.1div ~1div.
"""
if count < .1 or count > 1:
raise InvalidArgument("Count argument must be between .1 and 1.")
msg = ":TRIG:VIDEO:SENS {}".format(count)
self.dev.write(msg)
def askTvSensitivity(self):
"""
The query returns the setting value of <count> in div.
"""
msg = ":TRIG:VIDEO:SENS?"
return self.dev.ask(msg)
####################
# 5. SLOPE TRIGGER #
####################
"""
fully implemented
1 - yes
2 - yes
3 - yes
4 - yes
5 - yes
6 - yes
"""
# SLOPE TRIGGER 1
def tsTime(self, count):
"""
The commands set and query the time setting about slope trigger. The range of
<count> is 20ns~10s.
"""
if count < .00000002 or count > 10:
raise InvalidArgument("Count argument must be between 20ns and 10s.")
msg = ":TRIG:SLOP:TIME {}".format(count)
self.dev.write(msg)
def askTsTime(self):
"""
The query returns the setting value of <count> in s.
"""
msg = ":TRIG:SLOP:TIME?"
return self.dev.ask(msg)
# SLOPE TRIGGER 2
def tsSensitivity(self, count):
"""
The commands set and query the trigger sensitive. The range of <count> is:
0.1div ~1div.
"""
if count < .1 or count > 1:
raise InvalidArgument("Count argument must be between .1 and 1.")
msg = ":TRIG:SLOP:SENS {}".format(count)
self.dev.write(msg)
def askTsSensitivity(self):
"""
The query returns the setting value of <count> in div.
"""
msg = ":TRIG:SLOP:SENS?"
return self.dev.ask(msg)
# SLOPE TRIGGER 3
def tsMode(self, mode):
"""
The commands set and query the slope condition. <mode> could be
+GREaterthan (positive slope greater than), +LESSthan (positive slope less
than), + EQUal (positive slope equals to), -GREaterthan (negative slope greater
than), -LESSthan (negative slope less than) or –EQUal (negative slope equals
to).
"""
valid = ["+GRE", "+LESS", "+EQU", "-GRE", "-LESS", "-EQU"]
if mode not in valid:
raise InvalidArgument("Mode must be one of {}.".format(valid))
msg = ":TRIG:SLOP:MODE {}".format(mode)
self.dev.write(msg)
def askTsMode(self):
"""
The query returns +GREATER THAN, +LESS THAN, +EQUAL, -GREATER THAN,
-LESS THAN OR -EQUAL.
"""
msg = ":TRIG:SLOP:MODE?"
return self.dev.ask(msg)
# SLOPE TRIGGER 4
def tsWindow(self, count):
"""
The commands set and query the type of trigger level which can be adjusted by
the level knob on the oscilloscope.
When the slope condition is +GREaterthan, +LESSthan or + EQUal, <count>
could be PA (rising edge Level A), PB (rising edge Level B) or PAB (rising edge
Level AB);
When the slope condition is -GREaterthan, -LESSthan or –EQUal, <count> could
be NA (falling edge Level A), NB (falling edge LevelB) or NAB (falling edge
LevelAB).
"""
trig_mode = self.askTsMode()
if "+" in trig_mode:
valid = ["PA", "PB", "PAB"]
if count not in valid:
raise InvalidArgument("While trigger mode is {}, count argument must be one of {}.".format(valid))
if "-" in trig_mode:
valid = ["NA", "NB", "NAB"]
if count not in valid:
raise InvalidArgument("While trigger mode is {}, count argument must be one of {}.".format(valid))
msg = ":TRIG:SLOP:WIND {}".format(count)
self.dev.write(msg)
def askTsWindow(self):
"""
The query returns P_WIN_A, P_WIN_B, P_WIN_AB, N_WIN_A, N_WIN_B or
N_WIN_AB.
"""
msg = ":TRIG:SLOP:WIND?"
return self.dev.ask(msg)
# SLOPE TRIGGER 5
def tsLevelA(self, value):
"""
The commands set and query the upper boundary “Level A” of trigger level. The
range of <value> is LevelB~+ 6*Scale; Scale indicates the current vertical level,
the unit is V/div.
"""
# Not checking for valid inputs but should be implemented
msg = ":TRIG:SLOP:LEVA {}".format(value)
self.dev.write(msg)
def askTsLevelA(self):
"""
The query returns the setting value of level in V.
"""
msg = ":TRIG:SLOP:LEVA?"
return self.dev.ask(msg)
# SLOPE TRIGGER 6
def tsLevelB(self, value):
"""
The commands set and query the lower boundary “LEVel B” of trigger level. The
range of <value> is -6*Scale~LevelA; Scale indicates the current vertical level,
the unit is V/div.
"""
# Not checking for valid inputs but should be implemented
msg = ":TRIG:SLOP:LEVB {}".format(value)
self.dev.write(msg)
def askTsLevelB(self):
"""
The query returns the setting value of level in V.
Note: Level A (upper boundary) can not be less than the maximum of Level B
(lower boundary). """
msg = ":TRIG:SLOP:LEVB?"
return self.dev.ask(msg)
# PATTERN TRIGGER IS NOT A FEATURE ON THE DS1000E SERIES
# DURATION TRIGGER IS NOT A FEATURE ON THE DS1000E SERIES
##########################
# 8. ALTERNATION TRIGGER #
##########################
"""
not implemented
"""
########
# MATH #
########
"""
not implemented
"""
###########
# CHANNEL #
###########
"""
fully implemented
1 - yes
2 - yes
3 - yes
4 - yes
5 - yes
6 - yes
7 - yes
8 - yes
9 - yes
10 - yes
"""
# CHANNEL 1
def channelBwlimit(self, channel, on=True):
"""
The commands set and query the On/Off state of bandwidth limit. <n> could be
1 or 2.
"""
if channel not in [1, 2]:
raise InvalidArgument("Channel argument must be either {}.".format([1, 2]))
msg = ":CHAN{}:BWL {}".format(channel, "ON" if on else "OFF")
self.dev.write(msg)
def askChannelBwlimit(self, channel):
"""
The query returns ON or OFF.
"""
if channel not in [1, 2]:
raise InvalidArgument("Channel argument must be either {}.".format([1, 2]))
msg = ":CHAN{}:BWL?"
return self.ask(msg)
# CHANNEL 2
def channelCoupling(self, channel, coupling):
"""
The commands set and query the coupling mode of channel. DC indicates both
the AC and DC components passed from input signal; AC indicates the blocked
DC components; GND indicates to cut off the input of signal; <n> could be 1 or
2.
"""
valid = ["AC", "DC", "GND"]
if coupling not in valid:
raise InvalidArgument("Coupling argument must be one of {}.".format(valid))
if channel not in [1, 2]:
raise InvalidArgument("Channel argument must be one of {}.".format([1, 2]))
msg = ":CHAN{}:COUP {}".format(channel, coupling)
self.dev.write(msg)
def askChannelCoupling(self, channel):
"""
The query returns AC, DC or GND.
"""
if channel not in [1, 2]:
raise InvalidArgument("Channel argument must be one of {}.".format([1, 2]))
msg = ":CHAN{}:COUP?".format(channel)
return self.dev.ask(msg)
# CHANNEL 3
def channelDisplay(self, channel, on=True):
"""
channel - either 1 or 2
on - boolean value for turning the display on or off for the given channel.
"""
if channel not in [1, 2]:
raise InvalidArgument("Channel must take a value from {}.".format([1, 2]))
self.dev.write(":CHAN{}:DISP {}".format(channel, "ON" if on else "OFF"))
def askChannelDisplay(self, channel):
"""
The query returns ON or OFF.
"""
if channel not in [1, 2]:
raise InvalidArgument("Channel must take a value from {}.".format([1, 2]))
self.dev.write(":CHAN{}:DISP?".format(channel))
# CHANNEL 4
def channelInvert(self, channel, on=True):
"""
The commands set and query the On/Off state of the waveform inverted. <n>
could be 1 or 2.
"""
if channel not in [1, 2]:
raise InvalidArgument("Channel must take a value from {}.".format([1, 2]))
msg = ":CHAN{}:INV {}".format(channel, "ON" if on else "OFF")
self.dev.write(msg)
def askChannelInvert(self, channel):
"""
The query returns ON or OFF.
"""
if channel not in [1, 2]:
raise InvalidArgument("Channel must take a value from {}.".format([1, 2]))
msg = ":CHAN{}:INV?".format(channel)
return self.dev.ask(msg)
# CHANNEL 5
def channelOffset(self, channel, offset):
"""
The commands set and query the vertical offset. <n> could be 1 or 2.
When Scale≥250mV, the range of <offset>is -40V~+40V;
When Scale<250mV, the range of <offset>is -2V~+2V.
"""
# not checking for valid offset inputs
if channel not in [1, 2]:
raise InvalidArgument("Channel must take a value from {}.".format([1, 2]))
msg = ":CHAN{}:OFFS {}".format(channel, offset)
self.dev.write(msg)
if channel == 1:
self.volt1_offset = self.askChannelOffset(1)
elif channel == 2:
self.volt2_offset = self.askChannelOffset(2)
def askChannelOffset(self, channel):
"""
The query returns the setting value of <offset>.
"""
if channel not in [1, 2]:
raise InvalidArgument("Channel must take a value from {}.".format([1, 2]))
msg = ":CHAN{}:OFFS?".format(channel)
offset = float(self.dev.ask(msg))
if channel == 1:
self.volt1_offset = offset
elif channel == 2:
self.volt2_offset = offset
return float(offset)
# CHANNEL 6
def channelProbe(self, channel, attn):
"""
The commands set and query the attenuation factor of the probe. <n> could be
1 or 2; <attn> could be 1, 5, 10, 50, 100, 500 or 1000.
"""
if channel not in [1, 2]:
raise InvalidArgument("Channel must take a value from {}.".format([1, 2]))
valid_attn = [1, 5, 10, 50, 100, 500, 1000]
if attn not in valid_attn:
raise InvalidArgument("Attn argument must be one of {}".format(valid_attn))
msg = ":CHAN{}:PROB {}".format(channel, attn)
self.dev.write(msg)
def askChannelProbe(self, channel):
"""
The query returns the setting value of <attn>.
"""
if channel not in [1, 2]:
raise InvalidArgument("Channel must take a value from {}.".format([1, 2]))
msg = ":CHAN{}:PROB?".format(channel)
return self.dev.ask(msg)
# CHANNEL 7
def channelScale(self, channel, v):
"""
channel - Which channel to scale.
v - voltage scale
The commands set and query the vertical scale of waveform magnified by the
oscilloscope. <n> could be 1 or 2.
When the Probe is set to 1X, the range of <range> is 2mV ~ 10V;
When the Probe is set to 5X, the range of <range> is 10mV ~50V;
When the Probe is set to 10X, the range of <range> is 20mV ~ 100V;
When the Probe is set to 50X, the range of <range> is 100mV ~ 500V;
When the Probe is set to 100X, the range of <range> is 200mV ~ 1000V;
When the Probe is set to 500X, the range of <range> is 1V ~5000V;
When the Probe is set to 1000X, the range of <range> is 2V~ 10000V.
"""
# not error checking scale value
if channel not in [1, 2]:
raise InvalidArgument("Channel must take a value from {}.".format([1, 2]))
msg = ":CHAN{}:SCAL {}".format(channel, v)
self.dev.write(msg)
if channel == 1:
self.volt1_scale = self.askChannelScale(1)
elif channel == 2:
self.volt2_scale = self.askChannelScale(2)
def askChannelScale(self, channel):
"""
query what the scale is for channel <channel>
"""
if channel not in [1, 2]:
raise InvalidArgument("Channel must take a value from {}.".format([1, 2]))
msg = ":CHAN{}:SCAL?".format(channel)
scale = float(self.dev.ask(msg))
if channel == 1:
self.volt1_scale = scale
elif channel == 2:
self.volt2_scale = scale
return scale
# CHANNEL 8
def channelFilter(self, channel, on=True):
"""
The commands set and query the On/Off state of the filter. <n> could be 1 or 2.
"""
if channel not in [1, 2]:
raise InvalidArgument("Channel must take a value from {}.".format([1, 2]))
msg = "CHAN{}:FILT {}".format(channel, "ON" if on else "OFF")
self.dev.write(msg)
def askChannelFilter(self, channel):
"""
The query returns ON or OFF.
"""
if channel not in [1, 2]:
raise InvalidArgument("Channel must take a value from {}.".format([1, 2]))
msg = ":CHAN{}:FILT?".format(channel)
return self.dev.ask(msg)
# CHANNEL 9
def askChannelMemoryDepth(self, channel):
"""
The command queries the memory depth of the specified channel. <n> could
be 1 or 2.
In long memory, up to 1Mpts could be stored in single channel and 512kpts in
dual channels;
In common memory, up to 16kpts could be stored in single channel and 8kpts in
dual channels.
"""
if channel not in [1, 2]:
raise InvalidArgument("Channel must take a value from {}.".format([1, 2]))
msg = ":CHAN{}:MEMD?".format(channel)
return self.dev.ask(msg)
# CHANNEL 10
def channelVernier(self, channel, on=True):
"""
The commands set and query the adjusting mode of scale. ON denotes Fine,
OFF denotes Coarse; <n> could be 1 or 2.
"""
if channel not in [1, 2]:
raise InvalidArgument("Channel must take a value from {}.".format([1, 2]))
msg = ":CHAN{}:VERN {}".format(channel, "ON" if on else "OFF")
self.dev.write(msg)
def askChannelVernier(self, channel):
"""
The query returns Coarse or Fine.
"""
msg = ":CHAN{}:VERN?".format(channel)
return self.dev.ask(msg)
###########
# MEASURE #
###########
"""
partially implemented
1 - no
2 - yes
3 - no
.
.
.
23 - yes
"""
def measureVpp(self, channel):
"""
channel is either 1 or 2
returns peak to peak voltage in Volts.
"""
if channel not in [1, 2]:
raise InvalidArgument("Channel argument must be either 1 or 2")
msg = ":MEAS:VPP? CHAN{}".format(channel)
return float(self.dev.ask(msg))
############
# WAVEFORM #
############
"""
partially implemented
1 - yes
"""
# WAVEFORM 1
def askWaveformData(self, source):
"""
returns 1024 data for <source>. Raw Data.
run through numpy.frombuffer(data, "B") to get point data
First 10 bytes are apparently a header, so we can skip those.
"""
valid_sources = ["CHAN1", "CHAN2", "DIG", "MATH", "FFT"]
if source not in valid_sources:
raise InvalidArgument("Source argument must be one of {}".format(valid_sources))
msg = ":WAV:DATA? {}".format(source)
# self.dev.read()
# self.dev.write(msg)
# return self.dev.read_raw()
return self.dev.ask_raw(msg)[10:]
# WAVEFORM 2
def waveformPointsMode(self, mode):
"""
This command sets the mode of waveform points. <points_mode> can be:
NORMal, MAXimum or RAW.
"""
valid = ["NORM", "MAX", "RAW"]
if mode not in valid:
raise InvalidArgument("Mode argument must be one of {}.".format(valid))
msg = ":WAV:POIN:MODE {}".format(mode)
self.dev.write(msg)
def askWaveformPointsMode(self):
"""
The query returns NORMal, MAXimum or RAW.
"""
msg = ":WAV:POIN:MODE?"
return self.dev.ask(msg)
###################
# CUSTOM WAVEFORM #
###################
def getWaveform(self, source):
"""
The data that is extracted from the oscilloscope needs to be inverted,
multiplied by a constant and shifted by some voltage offset to get the
true values.
A custom method for retrieving the corrected voltage values out of the
oscilloscope.
"""
raw_data = self.askWaveformData(source)
raw_data = np.frombuffer(raw_data, "B")
data = self.convertVoltages(raw_data, source)
return data
def convertVoltages(self, data, source):
"""
data - numpy array of unconverted voltage values.
"""
if source == "CHAN1":
data = -data + 255
data = (data - 130.0 - self.volt1_offset/self.volt1_scale*25) / 25 * self.volt1_scale
elif source == "CHAN2":
data = -data + 255
data = (data - 130.0 - self.volt2_offset/self.volt2_scale*25) / 25 * self.volt2_scale
return data
def getTimebase(self):
"""
get correct x-values for plotting waveform
"""
time_axis = np.arange(-300.0/50*self.time_scale, 300.0/50*self.time_scale, self.time_scale/50.0)
return time_axis
#######
# KEY #
#######
"""
not enabled
"""
def keyLock(self, enable=True):
"""
This command is used to enable/disable the buttons function on the front panel
(except for “Force”).
"""
msg = ":KEY:LOCK {}".format("ENAB" if enable else "DIS")
self.dev.write(msg)
def askKeyLock(self):
"""
The query returns ENABLE or DISABLE.
"""
msg = "KEY:LOCK?"
return self.dev.ask(msg)
######################
# CUSTOM SAVE STATES #
######################
"""
Some methods to query many settings of the oscilloscope and then
load saved settings from the computer.
"""
# UNFINISHED
def saveState(self):
"""
Queries the state of the oscilloscope and saves it as a python list string
formatted file to a location of your choosing through tkFileDialog.
"""
save_location = tkfd.asksaveasfilename(defaultextension=".ros", filetypes=[("Rigol Oscilloscope Save", "*.ros")])
s = []
acquire_type = self.askAcquireType()[:4]
s.append(":ACQ:TYPE {}".format(acquire_type))
aquire_mode = self.askAquireMode()
aquire_mode = "RTIM" if aquire_mode[0] == "R" else "ETIM"
s.append(":ACQ:MODE {}".format(aquire_mode))
memdepth = self.askAcquireMemDepth()[:4]
s.append(":ACQ:MEMD {}".format(memdepth))
disptype = self.askDisplayType()[:4]
s.append(":DISP:TYPE {}".format(disptype))
dispgrid = self.askDisplayGrid()
s.append(":DISP:GRID {}".format(dispgrid))
disppersisty = self.askDisplayPersist()
s.append(":DISP:PERS {}".format(disppersisty))
dispbrightness = self.askDisplayBrightness()
s.append(":DISP:BRIG {}".format(dispbrightness))
dispintensity = self.askDisplayIntensity()
s.append(":DISP:INT {}".format(dispintensity))
timemode = self.askTimebaseMode()
timemode = "DEL" if timemode == "DELAYED" else timemode
s.append(":TIM:MODE {}".format(timemode))
delayed = True if timemode == "DEL" else False
timeoffset = self.askTimebaseMode(delayed=delayed)
s.append(":TIM{}:OFFS {}".format(":DEL" if delayed else "", timeoffset))
timescale = self.askTimebaseScale(delayed=delayed)
s.append(":TIM{}:SCAL {}".format(":DEL" if delayed else "", timescale))
timeformat = self.askTimebaseFormat()
s.append(":TIM:FORM {}".format(timeformat))
# continue with trigger settings
# Write file to save_location
with open(save_location, "w") as f:
f.write(str(s))
def loadState(self):
"""
writes the commands in the .ros save list.
"""
load_file = tkfd.askopenfilename(
defaultextension=".ros",
filetypes=[("Rigol Oscilloscope Save", "*.ros")],
title="Rigol Oscilloscope Save file to load"
)
with open(load_file, "r") as f:
state_str = f.read().strip()
state = ast.literal_eval(state_str)
for setting in state:
self.dev.write(setting)
print("{} save state has been loaded".format(load_file))
|
aplstudent/Rigol-DS1000DE
|
rigolds1000de/rigol.py
|
Python
|
mit
| 48,629
|
[
"Brian"
] |
bb088b7d5bfead89cbe95f29c3c13ec40916d66747ab089228f9ba94e32a6ae3
|
# start by importing version, because for some weird reason, I sometimes get
# networkx as over-riding the version here (no idea why...)
try:
# should work if installed through normal means: setup.py-based with
# pip, conda, easy_install, etc.
from . import version
except ImportError: # pragma: no cover
import os
# should work if someone just set the $PYTHONPATH to include OPS
directory = os.path.dirname(os.path.realpath(__file__))
prev_dir = os.path.split(directory)[0]
setupfile = os.path.join(prev_dir, "setup.py")
if not os.path.exists(setupfile):
# now we're screwed
raise ImportError("Unable to identify OPS version. " +
"OPS probably not installed correctly.")
# continue force-setting version based on `setup.py`
import imp # may be Py2 only!
ops_setup = imp.load_source("ops_setup", setupfile)
version = imp.new_module("openpathsampling.version")
version.version = ops_setup.preferences['version']
version.short_version = ops_setup.preferences['version']
version.git_version = ops_setup.get_git_version()
version.full_version = ops_setup.preferences['version']
if not ops_setup.preferences['released']:
version.full_version += ".dev-" + version.git_version[:7]
isrelease = str(ops_setup.preferences['released'])
from .high_level.move_scheme import (
MoveScheme, DefaultScheme, LockedMoveScheme, SRTISScheme,
OneWayShootingMoveScheme
)
from .high_level.transition import (
TISTransition, Transition, TPSTransition, FixedLengthTPSTransition
)
from .high_level.network import (
MSTISNetwork, TransitionNetwork, MISTISNetwork, TPSNetwork,
FixedLengthTPSNetwork
)
from .analysis.path_histogram import PathDensityHistogram
from .analysis.replica_network import (
ReplicaNetwork, trace_ensembles_for_replica,
trace_replicas_for_ensemble, condense_repeats,
ReplicaNetworkGraph
)
from .analysis.shooting_point_analysis import (
ShootingPointAnalysis, SnapshotByCoordinateDict
)
from .analysis.trajectory_transition_analysis import (
TrajectoryTransitionAnalysis,
TrajectorySegmentContainer
)
from .analysis.channel_analysis import ChannelAnalysis
from .bias_function import (
BiasFunction, BiasLookupFunction, BiasEnsembleTable,
SRTISBiasFromNetwork
)
from .collectivevariable import (
FunctionCV, MDTrajFunctionCV, MSMBFeaturizerCV,
InVolumeCV, CollectiveVariable, CoordinateGeneratorCV,
CoordinateFunctionCV, CallableCV, PyEMMAFeaturizerCV,
GeneratorCV)
from .ensemble import (
Ensemble, EnsembleCombination, EnsembleFactory, EntersXEnsemble,
EmptyEnsemble, ExitsXEnsemble, FullEnsemble, PartInXEnsemble,
AllInXEnsemble, AllOutXEnsemble, WrappedEnsemble,
SuffixTrajectoryEnsemble, PrefixTrajectoryEnsemble,
PartOutXEnsemble, LengthEnsemble, NegatedEnsemble,
ReversedTrajectoryEnsemble, SequentialEnsemble, VolumeEnsemble,
SequentialEnsemble, IntersectionEnsemble, UnionEnsemble,
SingleFrameEnsemble, MinusInterfaceEnsemble, TISEnsemble,
OptionalEnsemble, join_ensembles
)
from .high_level.interface_set import (
InterfaceSet, VolumeInterfaceSet, PeriodicVolumeInterfaceSet
)
from .high_level.ms_outer_interface import MSOuterTISInterface
from .step_visualizer_2D import StepVisualizer2D
from .movechange import (
EmptyMoveChange, ConditionalSequentialMoveChange,
MoveChange, PartialAcceptanceSequentialMoveChange,
RandomChoiceMoveChange, SampleMoveChange,
SequentialMoveChange, KeepLastSampleMoveChange,
FilterSamplesMoveChange,
PathSimulatorMoveChange, AcceptedSampleMoveChange,
RejectedSampleMoveChange, SubMoveChange,
FilterByEnsembleMoveChange, RejectedNaNSampleMoveChange,
RejectedMaxLengthSampleMoveChange
)
from .pathmover import Details, MoveDetails, SampleDetails
from .pathmover import (
RandomChoiceMover, PathMover, ConditionalSequentialMover,
PartialAcceptanceSequentialMover, BackwardShootMover, ForwardShootMover,
BackwardExtendMover, ForwardExtendMover, MinusMover,
SingleReplicaMinusMover, PathMoverFactory, PathReversalMover,
ReplicaExchangeMover, EnsembleHopMover,
SequentialMover, ConditionalMover,
PathSimulatorMover, PathReversalSet, NeighborEnsembleReplicaExchange,
SampleMover, StateSwapMover, FinalSubtrajectorySelectMover, EngineMover,
FirstSubtrajectorySelectMover, MultipleSetMinusMover,
OneWayShootingMover, RandomSubtrajectorySelectMover, SubPathMover,
EnsembleFilterMover, SelectionMover, FirstAllowedMover,
LastAllowedMover, OneWayExtendMover, SubtrajectorySelectMover,
IdentityPathMover, RandomAllowedChoiceMover,
TwoWayShootingMover, ForwardFirstTwoWayShootingMover,
BackwardFirstTwoWayShootingMover
)
from .pathsimulator import (
PathSimulator, FullBootstrapping, Bootstrapping, PathSampling, MCStep,
CommittorSimulation, DirectSimulation, ShootFromSnapshotsSimulation
)
from .sample import Sample, SampleSet
from .shooting import ShootingPointSelector, UniformSelector, \
GaussianBiasSelector, FirstFrameSelector, FinalFrameSelector
from .snapshot_modifier import (
NoModification, RandomVelocities, VelocityDirectionModifier,
SingleAtomVelocityDirectionModifier
)
from .storage.storage import Storage, AnalysisStorage
from .volume import (
Volume, VolumeCombination, VolumeFactory, VoronoiVolume,
EmptyVolume, FullVolume, CVDefinedVolume, PeriodicCVDefinedVolume,
IntersectionVolume, UnionVolume, SymmetricDifferenceVolume,
RelativeComplementVolume, join_volumes
)
from .high_level import move_strategy as strategies
import openpathsampling.numerics as numerics
from openpathsampling.engines import Trajectory, BaseSnapshot
import openpathsampling.engines.openmm as openmm
import openpathsampling.engines.toy as toy
def git_HEAD(): # pragma: no cover
from subprocess import check_output
import os.path
git_dir = os.path.dirname(os.path.realpath(__file__))
return check_output(["git", "-C", git_dir, "rev-parse", "HEAD"])[:-1]
# chops the newline at the end
import os.path
resources_directory = os.path.join(os.path.dirname(__file__), 'resources')
|
jhprinz/openpathsampling
|
openpathsampling/__init__.py
|
Python
|
lgpl-2.1
| 6,195
|
[
"OpenMM"
] |
f9a59ee3a9e8ca185d970da5257735d18f877aedd26803061826e2a228fdba84
|
#
# Copyright (c) 2015 nexB Inc. and others. All rights reserved.
# http://nexb.com and https://github.com/nexB/scancode-toolkit/
# The ScanCode software is licensed under the Apache License version 2.0.
# Data generated with ScanCode require an acknowledgment.
# ScanCode is a trademark of nexB Inc.
#
# You may not use this software except in compliance with the License.
# You may obtain a copy of the License at: http://apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software distributed
# under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
# CONDITIONS OF ANY KIND, either express or implied. See the License for the
# specific language governing permissions and limitations under the License.
#
# When you publish or redistribute any data created with ScanCode or any ScanCode
# derivative work, you must accompany this data with the following acknowledgment:
#
# Generated with ScanCode and provided on an "AS IS" BASIS, WITHOUT WARRANTIES
# OR CONDITIONS OF ANY KIND, either express or implied. No content created from
# ScanCode should be considered or used as legal advice. Consult an Attorney
# for any legal advice.
# ScanCode is a free software code scanning tool from nexB Inc. and others.
# Visit https://github.com/nexB/scancode-toolkit/ for support and download.
from __future__ import print_function, absolute_import
from collections import OrderedDict
"""
Main scanning functions.
Note: this API is unstable and still evolving.
"""
def extract_archives(location, recurse=True):
"""
Extract recursively any archives found at location and yield an iterable of
ExtractEvents.
If verbose is False, only the "done" event is returned at extraction
completion.
If verbose is True, both "start" and "done" events are returned.
"""
from extractcode.extract import extract
from extractcode import default_kinds
for xevent in extract(location, kinds=default_kinds, recurse=recurse):
yield xevent
def get_copyrights(location):
"""
Yield an iterable of dictionaries of copyright data detected in the file at
location. Each item contains a list of copyright statements and a start and
end line.
"""
from cluecode.copyrights import detect_copyrights
for copyrights, _, _, _, start_line, end_line in detect_copyrights(location):
if not copyrights:
continue
result = OrderedDict()
# FIXME: we should call this copyright instead, and yield one item per statement
result['statements'] = copyrights
result['start_line'] = start_line
result['end_line'] = end_line
yield result
DEJACODE_LICENSE_URL = 'https://enterprise.dejacode.com/license_library/Demo/{}/'
def get_licenses(location):
"""
Yield an iterable of dictionaries of license data detected in the file at
location for each detected license.
"""
from licensedcode.models import get_license
from licensedcode.detect import get_license_matches
for match in get_license_matches(location):
for license_key in match.rule.licenses:
lic = get_license(license_key)
result = OrderedDict()
result['key'] = lic.key
result['short_name'] = lic.short_name
result['category'] = lic.category
result['owner'] = lic.owner
result['homepage_url'] = lic.homepage_url
result['text_url'] = lic.text_urls[0] if lic.text_urls else ''
result['dejacode_url'] = DEJACODE_LICENSE_URL.format(lic.key)
result['spdx_license_key'] = lic.spdx_license_key
result['spdx_url'] = lic.spdx_url
result['start_line'] = match.query_position.start_line
result['end_line'] = match.query_position.end_line
yield result
def get_file_infos(location):
"""
Return a list of dictionaries of informations collected from the file or
directory at location.
"""
from commoncode import fileutils
from commoncode import filetype
from commoncode.hash import sha1, md5
from typecode import contenttype
T = contenttype.get_type(location)
is_file = T.is_file
is_dir = T.is_dir
infos = OrderedDict()
infos['type'] = filetype.get_type(location, short=False)
infos['name'] = fileutils.file_name(location)
infos['extension'] = is_file and fileutils.file_extension(location) or ''
infos['date'] = is_file and filetype.get_last_modified_date(location) or None
infos['size'] = T.size
infos['sha1'] = is_file and sha1(location) or None
infos['md5'] = is_file and md5(location) or None
infos['files_count'] = is_dir and filetype.get_file_count(location) or None
infos['mime_type'] = is_file and T.mimetype_file or None
infos['file_type'] = is_file and T.filetype_file or None
infos['programming_language'] = is_file and T.programming_language or None
infos['is_binary'] = is_file and T.is_binary or None
infos['is_text'] = is_file and T.is_text or None
infos['is_archive'] = is_file and T.is_archive or None
infos['is_media'] = is_file and T.is_media or None
infos['is_source'] = is_file and T.is_source or None
infos['is_script'] = is_file and T.is_script or None
return [infos]
def get_package_infos(location):
"""
Return a list of dictionaries of package information
collected from the location or an empty list.
"""
from packagedcode.recognize import recognize_packaged_archives
package = recognize_packaged_archives(location)
if not package:
return []
return [package.as_dict(simple=True)]
|
vinodpanicker/scancode-toolkit
|
src/scancode/api.py
|
Python
|
apache-2.0
| 5,688
|
[
"VisIt"
] |
14846e062a5419937875e0cb898b23e1c1644cbc9093ab09e4017a4f5884fe2a
|
""" PDP
PDP ( PolicyDecisionPoint ) is the back-end for the PolicySystem. It discovers
the policies, finds the best match, evaluates them, merges their results taking
the most penalizing one, computes the set of actions to be triggered and returns
all the information to the PEP which will enforce the actions.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from DIRAC import gLogger, S_OK, S_ERROR
from DIRAC.ResourceStatusSystem.PolicySystem.PolicyCaller import PolicyCaller
from DIRAC.ResourceStatusSystem.PolicySystem.StateMachine import RSSMachine
from DIRAC.ResourceStatusSystem.Utilities import RssConfiguration
from DIRAC.ResourceStatusSystem.Utilities.InfoGetter import getPolicyActionsThatApply, getPoliciesThatApply
__RCSID__ = '$Id$'
class PDP(object):
""" PDP ( Policy Decision Point )
"""
def __init__(self, clients=None):
""" Constructor.
examples:
>>> pdp = PDP( None )
>>> pdp1 = PDP( {} )
>>> pdp2 = PDP( { 'Client1' : Client1Object } )
:Parameters:
**clients** - [ None, `dict` ]
dictionary with Clients to be used in the Commands. If None, the Commands
will create their own clients.
"""
# decision parameters used to match policies and actions
self.decisionParams = None
# Helpers to discover policies and RSS metadata in CS
self.pCaller = PolicyCaller(clients)
# RSS State Machine, used to calculate most penalizing state while merging them
self.rssMachine = RSSMachine('Unknown')
self.log = gLogger.getSubLogger('PDP')
def setup(self, decisionParams=None):
""" method that sanitizes the decisionParams and ensures that at least it has
the keys in `standardParamsDict`. This will be relevant while doing the matching
with the RSS Policies configuration in the CS.
There is one key-value pair, `active` which is added on this method. This allows
policies to be de-activated from the CS, changing their active matchParam to
something else than `Active`.
examples:
>>> pdp.setup( None )
>>> self.decisionParams
{ 'element' : None, 'name' : None, ... }
>>> pdp.setup( { 'element' : 'AnElement' } )
>>> self.decisionParams
{ 'element' : 'AnElement', 'name' : None, ... }
>>> pdp.setup( { 'NonStandardKey' : 'Something' } )
>>> self.decisionParams
{ 'NonStandardKey' : 'Something', 'element' : None,... }
:Parameters:
**decisionParams** - [ None, `dict` ]
dictionary with the parameters to be matched with the RSS Policies configuration
in the CS.
"""
standardParamsDict = {'element': None,
'name': None,
'elementType': None,
'statusType': None,
'status': None,
'reason': None,
'tokenOwner': None,
# Last parameter allows policies to be de-activated
'active': 'Active'}
if decisionParams is not None:
standardParamsDict.update(decisionParams)
if standardParamsDict['element'] is not None:
self.log = gLogger.getSubLogger('PDP/%s' % standardParamsDict['element'])
if standardParamsDict['name'] is not None:
self.log = gLogger.getSubLogger('PDP/%s/%s' % (standardParamsDict['element'], standardParamsDict['name']))
self.log.verbose("Setup - statusType: %s, status: %s" % (standardParamsDict['statusType'],
standardParamsDict['status']))
self.decisionParams = standardParamsDict
def takeDecision(self):
""" main PDP method which does all the work. If firstly finds all the policies
defined in the CS that match <self.decisionParams> and runs them. Once it has
all the singlePolicyResults, it combines them. Next step is action discovery:
using a similar approach to the one used to discover the policies, but also
taking into account the single policy results and their combined result, finds
the actions to be triggered and returns.
examples:
>>> list(pdp.takeDecision()['Value'])
['singlePolicyResults', 'policyCombinedResult', 'decisionParams']
>>> pdp.takeDecision()['Value']['singlePolicyResults']
[ { 'Status' : 'Active',
'Reason' : 'blah',
'Policy' : { 'name' : 'AlwaysActiveForResource',
'type' : 'AlwaysActive',
'module' : 'AlwaysActivePolicy',
'description' : 'This is the AlwaysActive policy'
'command' : None,
'args' : {}
}
}, ... ]
>>> pdp.takeDecision()['Value']['policyCombinedResult']
{ 'Status' : 'Active',
'Reason' : 'blah ###',
'PolicyAction' : [ ( 'policyActionName1', 'policyActionType1' ), ... ]
}
:return: S_OK( { 'singlePolicyResults' : `list`,
'policyCombinedResult' : `dict`,
'decisionParams' : `dict` } ) / S_ERROR
"""
if self.decisionParams is None:
return S_OK({'singlePolicyResults': [],
'policyCombinedResult': {},
'decisionParams': self.decisionParams})
self.log.verbose("Taking decision")
# Policies..................................................................
# Get policies that match self.decisionParams
policiesThatApply = getPoliciesThatApply(self.decisionParams)
if not policiesThatApply['OK']:
return policiesThatApply
policiesThatApply = policiesThatApply['Value']
self.log.verbose("Policies that apply: %s" % ', '.join([po['name'] for po in policiesThatApply]))
# Evaluate policies
singlePolicyResults = self._runPolicies(policiesThatApply)
if not singlePolicyResults['OK']:
return singlePolicyResults
singlePolicyResults = singlePolicyResults['Value']
self.log.verbose("Single policy results: %s" % singlePolicyResults)
# Combine policies and get most penalizing status ( see RSSMachine )
policyCombinedResults = self._combineSinglePolicyResults(singlePolicyResults)
if not policyCombinedResults['OK']:
return policyCombinedResults
policyCombinedResults = policyCombinedResults['Value']
self.log.verbose("Combined policy result: %s" % policyCombinedResults)
# Actions...................................................................
policyActionsThatApply = getPolicyActionsThatApply(self.decisionParams,
singlePolicyResults,
policyCombinedResults)
if not policyActionsThatApply['OK']:
return policyActionsThatApply
policyActionsThatApply = policyActionsThatApply['Value']
self.log.verbose("Policy actions that apply: %s" % ','.join(pata[0] for pata in policyActionsThatApply))
policyCombinedResults['PolicyAction'] = policyActionsThatApply
return S_OK({'singlePolicyResults': singlePolicyResults,
'policyCombinedResult': policyCombinedResults,
'decisionParams': self.decisionParams})
def _runPolicies(self, policies):
""" Given a list of policy dictionaries, loads them making use of the PolicyCaller
and evaluates them. This method requires to have run setup previously.
examples:
>>> pdp._runPolicies([])['Value']
[]
>>> policyDict = { 'name' : 'AlwaysActiveResource',
'type' : 'AlwaysActive',
'args' : None,
'description' : 'This is the AlwaysActive policy',
'module' : 'AlwaysActivePolicy',
'command' : None }
>>> pdp._runPolicies([ policyDict, ... ] )['Value']
[ { 'Status' : 'Active', 'Reason' : 'blah', 'Policy' : policyDict }, ... ]
:Parameters:
**policies** - `list( dict )`
list of dictionaries containing the policies selected to be run. Check the
examples to get an idea of how the policy dictionaries look like.
:return: S_OK() / S_ERROR
"""
policyInvocationResults = []
# Gets all valid status for RSS to avoid misconfigured policies returning statuses
# that RSS does not understand.
validStatus = self.rssMachine.getStates()
for policyDict in policies:
# Load and evaluate policy described in <policyDict> for element described
# in <self.decisionParams>
policyInvocationResult = self.pCaller.policyInvocation(self.decisionParams,
policyDict)
if not policyInvocationResult['OK']:
# We should never enter this line ! Just in case there are policies
# missconfigured !
_msg = 'runPolicies no OK: %s' % policyInvocationResult
self.log.error(_msg)
return S_ERROR(_msg)
policyInvocationResult = policyInvocationResult['Value']
# Sanity Checks ( they should never happen ! )
if 'Status' not in policyInvocationResult:
_msg = 'runPolicies (no Status): %s' % policyInvocationResult
self.log.error(_msg)
return S_ERROR(_msg)
if not policyInvocationResult['Status'] in validStatus:
_msg = 'runPolicies ( not valid status ) %s' % policyInvocationResult['Status']
self.log.error(_msg)
return S_ERROR(_msg)
if 'Reason' not in policyInvocationResult:
_msg = 'runPolicies (no Reason): %s' % policyInvocationResult
self.log.error(_msg)
return S_ERROR(_msg)
policyInvocationResults.append(policyInvocationResult)
return S_OK(policyInvocationResults)
def _combineSinglePolicyResults(self, singlePolicyRes):
""" method that merges all the policies results into a combined one, which
will be the most penalizing status and the reasons of the single policy
results that returned the same penalizing status. All the rest, are ignored.
If there are no single policy results, it is returned `Unknown` state. While
combining policies, the ones containing the option `doNotCombine` are ignored.
examples:
>>> pdp._combineSingePolicyResults( [] )['Value']
{ 'Status' : 'Unknown', 'Reason' : 'No policy ..' }
>>> pdp._combineSingePolicyResults( [ { 'Status' : 'Active', 'Reason' : 'blah', 'Policy' : policyDict } ] )
{ 'Status' : 'Active', 'Reason' : 'blah' }
>>> pdp._combineSingePolicyResults( [ { 'Status' : 'Active', 'Reason' : 'blah', 'Policy' : policyDict },
{ 'Status' : 'Banned', 'Reason' : 'blah 2', 'Policy' : policyDict2 } ] )
{ 'Status' : 'Banned', 'Reason' : 'blah 2' }
>>> pdp._combineSingePolicyResults( [ { 'Status' : 'Active', 'Reason' : 'blah', 'Policy' : policyDict },
{ 'Status' : 'Active', 'Reason' : 'blah 2', 'Policy' : policyDict2 } ] )
{ 'Status' : 'Banned', 'Reason' : 'blah ### blah 2' }
:Parameters:
**singlePolicyRes** - `list( dict )`
list with every single policy result to be combined ( see _runPolicy for more details )
:return: S_OK( dict( Status, Reason ) | S_ERROR
"""
# Dictionary to be returned
policyCombined = {'Status': 'Unknown', # default, it should be overridden by the policies, if they exist
'Reason': ''}
# If there are no policyResults, we return Unknown
if not singlePolicyRes:
policyCombined['Reason'] = 'No policy applies to %(element)s, %(name)s, %(elementType)s' % self.decisionParams
self.log.warn(policyCombined['Reason'])
return S_OK(policyCombined)
# We set the rssMachine on the current state ( ensures it is a valid one )
# FIXME: probably this check can be done at takeDecision
machineStatus = self.rssMachine.setState(self.decisionParams['status'], noWarn=True)
if not machineStatus['OK']:
return machineStatus
# Discard all single policy results which belongs to policies that have set
# the option `doNotCombine` in the CS
policiesToCombine = self._findPoliciesToCombine(singlePolicyRes)
# Sort policy results using ther statuses by most restrictive ( lower level first )
self.rssMachine.orderPolicyResults(policiesToCombine)
# As they have been sorted by most restrictive status, the first one is going
# to be our candidate new state. Let's ask the RSSMachine if it allows us to
# make such transition.
candidateState = policiesToCombine[0]['Status']
nextState = self.rssMachine.getNextState(candidateState)
if not nextState['OK']:
return nextState
nextState = nextState['Value']
# most restrictive status defines the VO affected. VO='all' will affect all VOs
policyCombined['VO'] = policiesToCombine[0].get('VO', 'all')
# If the RssMachine does not accept the candidate, return forcing message
if candidateState != nextState:
policyCombined['Status'] = nextState
policyCombined['Reason'] = 'RssMachine forced status %s to %s' % (candidateState, nextState)
return S_OK(policyCombined)
# If the RssMachine accepts the candidate, just concatenate the reasons
for policyRes in policiesToCombine:
if policyRes['Status'] == nextState:
policyCombined['Reason'] += '%s ###' % policyRes['Reason']
policyCombined['Status'] = nextState
return S_OK(policyCombined)
def _findPoliciesToCombine(self, singlePolicyRes):
""" method that iterates over the single policy results and checks the CS
configuration of the policies looking for the option 'doNotCombine'. If it is
present, that single policy result is discarded.
:Parameters:
**singlePolicyRes** - `list( dict )`
list with every single policy result to be combined ( see _runPolicy for more details )
:return: `list( dict )`
"""
# Get policies configuration from the CS. We want to exclude the policies that
# have set the option `doNotCombine` from this process.
policiesConfiguration = RssConfiguration.getPolicies()
if not policiesConfiguration['OK']:
return policiesConfiguration
policiesConfiguration = policiesConfiguration['Value']
# Function that let's us know if we should combine the result of a single policy
# or not.
def combinePolicy(policyResult):
# Extract policy name from the dictionary returned by PolicyCaller
policyName = policyResult['Policy']['name']
try:
# If doNotCombineResult is defined, the policy is not taken into account
# to create the combined result. However, the single policy result remains
_ = policiesConfiguration[policyName]['doNotCombineResult']
return False
except KeyError:
return True
# Make a list of policies of which we want to merge their results
return [policyResult for policyResult in singlePolicyRes if combinePolicy(policyResult)]
# ...............................................................................
# EOF#EOF#EOF#EOF#EOF#EOF#EOF#EOF#EOF#EOF#EOF#EOF#EOF#EOF#EOF#EOF#EOF#EOF#EOF#EOF
|
yujikato/DIRAC
|
src/DIRAC/ResourceStatusSystem/PolicySystem/PDP.py
|
Python
|
gpl-3.0
| 15,481
|
[
"DIRAC"
] |
2950b7d94663e224bb6714c27a9356b9032a23c617ff958f3f4d94b0867d3653
|
# coding: utf-8
# Copyright (c) Pymatgen Development Team.
# Distributed under the terms of the MIT License.
import warnings
import os
import numpy as np
from monty.serialization import loadfn
from pymatgen.analysis.bond_valence import BVAnalyzer
from pymatgen.analysis.structure_matcher import StructureMatcher
from pymatgen.core import Structure
MODULE_DIR = os.path.dirname(os.path.abspath(__file__))
bond_params = loadfn(os.path.join(MODULE_DIR, 'DLS_bond_params.yaml'))
def is_ox(structure):
comp = structure.composition
for k in comp.keys():
try:
k.oxi_state
except AttributeError:
return False
return True
class RLSVolumePredictor:
"""
Reference lattice scaling (RLS) scheme that predicts the volume of a
structure based on a known crystal structure.
"""
def __init__(self, check_isostructural=True, radii_type="ionic-atomic",
use_bv=True):
"""
Args:
check_isostructural: Whether to test that the two structures are
isostructural. This algo works best for isostructural compounds.
Defaults to True.
radii_type (str): Types of radii to use. You can specify "ionic"
(only uses ionic radii), "atomic" (only uses atomic radii) or
"ionic-atomic" (uses either ionic or atomic radii, with a
preference for ionic where possible).
use_bv (bool): Whether to use BVAnalyzer to determine oxidation
states if not present.
"""
self.check_isostructural = check_isostructural
self.radii_type = radii_type
self.use_bv = use_bv
def predict(self, structure, ref_structure):
"""
Given a structure, returns the predicted volume.
Args:
structure (Structure): structure w/unknown volume
ref_structure (Structure): A reference structure with a similar
structure but different species.
Returns:
a float value of the predicted volume
"""
if self.check_isostructural:
m = StructureMatcher()
mapping = m.get_best_electronegativity_anonymous_mapping(
structure, ref_structure)
if mapping is None:
raise ValueError("Input structures do not match!")
if "ionic" in self.radii_type:
try:
# Use BV analyzer to determine oxidation states only if the
# oxidation states are not already specified in the structure
# and use_bv is true.
if (not is_ox(structure)) and self.use_bv:
a = BVAnalyzer()
structure = a.get_oxi_state_decorated_structure(structure)
if (not is_ox(ref_structure)) and self.use_bv:
a = BVAnalyzer()
ref_structure = a.get_oxi_state_decorated_structure(
ref_structure)
comp = structure.composition
ref_comp = ref_structure.composition
# Check if all the associated ionic radii are available.
if any([k.ionic_radius is None for k in list(comp.keys())]) or \
any([k.ionic_radius is None for k in
list(ref_comp.keys())]):
raise ValueError("Not all the ionic radii are available!")
numerator = 0
denominator = 0
# Here, the 1/3 factor on the composition accounts for atomic
# packing. We want the number per unit length.
for k, v in comp.items():
numerator += k.ionic_radius * v ** (1 / 3)
for k, v in ref_comp.items():
denominator += k.ionic_radius * v ** (1 / 3)
return ref_structure.volume * (numerator / denominator) ** 3
except Exception as ex:
warnings.warn("Exception occured. Will attempt atomic radii.")
# If error occurs during use of ionic radii scheme, pass
# and see if we can resolve it using atomic radii.
pass
if "atomic" in self.radii_type:
comp = structure.composition
ref_comp = ref_structure.composition
# Here, the 1/3 factor on the composition accounts for atomic
# packing. We want the number per unit length.
numerator = 0
denominator = 0
for k, v in comp.items():
numerator += k.atomic_radius * v ** (1 / 3)
for k, v in ref_comp.items():
denominator += k.atomic_radius * v ** (1 / 3)
return ref_structure.volume * (numerator / denominator) ** 3
raise ValueError("Cannot find volume scaling based on radii choices "
"specified!")
def get_predicted_structure(self, structure, ref_structure):
"""
Given a structure, returns back the structure scaled to predicted
volume.
Args:
structure (Structure): structure w/unknown volume
ref_structure (Structure): A reference structure with a similar
structure but different species.
Returns:
a Structure object with predicted volume
"""
new_structure = structure.copy()
new_structure.scale_lattice(self.predict(structure, ref_structure))
return new_structure
class DLSVolumePredictor:
"""
Data-mined lattice scaling (DLS) scheme that relies on data-mined bond
lengths to predict the crystal volume of a given structure.
As of 2/12/19, we suggest this method be used in conjunction with
min_scaling and max_scaling to prevent instances of very large, unphysical
predicted volumes found in a small subset of structures.
"""
def __init__(self, cutoff=4.0, min_scaling=0.5, max_scaling=1.5):
"""
Args:
cutoff (float): cutoff radius added to site radius for finding
site pairs. Necessary to increase only if your initial
structure guess is extremely bad (atoms way too far apart). In
all other instances, increasing cutoff gives same answer
but takes more time.
min_scaling (float): if not None, this will ensure that the new
volume is at least this fraction of the original (preventing
too-small volumes)
max_scaling (float): if not None, this will ensure that the new
volume is at most this fraction of the original (preventing
too-large volumes)
"""
self.cutoff = cutoff
self.min_scaling = min_scaling
self.max_scaling = max_scaling
def predict(self, structure, icsd_vol=False):
"""
Given a structure, returns the predicted volume.
Args:
structure (Structure) : a crystal structure with an unknown volume.
icsd_vol (bool) : True if the input structure's volume comes from
ICSD.
Returns:
a float value of the predicted volume.
"""
# Get standard deviation of electronnegativity in the structure.
std_x = np.std([site.specie.X for site in structure])
# Sites that have atomic radii
sub_sites = []
# Record the "DLS estimated radius" from bond_params.
bp_dict = {}
for sp in list(structure.composition.keys()):
if sp.atomic_radius:
sub_sites.extend([site for site in structure
if site.specie == sp])
else:
warnings.warn("VolumePredictor: no atomic radius data for "
"{}".format(sp))
if sp.symbol not in bond_params:
warnings.warn("VolumePredictor: bond parameters not found, "
"used atomic radii for {}".format(sp))
else:
r, k = bond_params[sp.symbol]["r"], bond_params[sp.symbol]["k"]
bp_dict[sp] = float(r) + float(k) * std_x
# Structure object that include only sites with known atomic radii.
reduced_structure = Structure.from_sites(sub_sites)
smallest_ratio = None
for site1 in reduced_structure:
sp1 = site1.specie
neighbors = reduced_structure.get_neighbors(site1,
sp1.atomic_radius +
self.cutoff)
for site2, dist in neighbors:
sp2 = site2.specie
if sp1 in bp_dict and sp2 in bp_dict:
expected_dist = bp_dict[sp1] + bp_dict[sp2]
else:
expected_dist = sp1.atomic_radius + sp2.atomic_radius
if not smallest_ratio or dist / expected_dist < smallest_ratio:
smallest_ratio = dist / expected_dist
if not smallest_ratio:
raise ValueError("Could not find any bonds within the given cutoff "
"in this structure.")
volume_factor = (1 / smallest_ratio) ** 3
# icsd volume fudge factor
if icsd_vol:
volume_factor *= 1.05
if self.min_scaling:
volume_factor = max(self.min_scaling, volume_factor)
if self.max_scaling:
volume_factor = min(self.max_scaling, volume_factor)
return structure.volume * volume_factor
def get_predicted_structure(self, structure, icsd_vol=False):
"""
Given a structure, returns back the structure scaled to predicted
volume.
Args:
structure (Structure): structure w/unknown volume
Returns:
a Structure object with predicted volume
"""
new_structure = structure.copy()
new_structure.scale_lattice(self.predict(structure, icsd_vol=icsd_vol))
return new_structure
|
dongsenfo/pymatgen
|
pymatgen/analysis/structure_prediction/volume_predictor.py
|
Python
|
mit
| 10,138
|
[
"CRYSTAL",
"pymatgen"
] |
6043741e6e50bf53a928d0ceaf22323a0e61e299aeccdf527f93c82225ce9f99
|
import numpy as np
from scipy.optimize import curve_fit
from scipy import asarray as ar,exp
from datetime import datetime
import matplotlib.pyplot as plt
import time
def gauss(x, A, mu, sigma):
'''Evaluates point on Gaussian Curve'''
return A*np.exp(-(x-mu)**2/(2.*sigma**2))
def _n64_to_string(x):
''' Takes numpy 64 datetime object and returns a string with month-day information encoded in it'''
return time.strftime("%b %d", time.gmtime(x.astype(int)/1000000000))
def plot_fit(times,values, standard_deviations = 3):
''' Takes time series data(list of values and list of times), fits a gaussian curve to it, then displays both the line-gra'''
x = range(len(times))
y = values
n = len(x)
mean = sum(x*y)/n
sigma = sum(y*(x-mean)**2)/n
popt,pcov = curve_fit(gauss,range(len(times)),values,p0=[1,mean,sigma])
plt.plot(range(len(times)),values, label = 'data')
plt.plot(x,gauss(x,*popt),color = 'red',label='fit')
margin = (standard_deviations* popt[2])
plt.axvline(x=popt[1] - margin, color = 'green', linestyle='--')
plt.axvline(x=popt[1] + margin, color = 'green', linestyle='--', label = str(standard_deviations) + '*std')
plt.legend()
plt.title('Fig. 3 - Fit for Time Constant')
plt.xlabel('Time')
_ticks = plt.xticks()[0]
_ticks = [t for t in _ticks if t >= 0 and t <= len(times)]
ticks = np.array(_ticks)
plt.xticks(ticks,[_n64_to_string(times[int(i)]) for i in ticks])
plt.ylabel('Precipitation')
plt.show()
## Get Dates
def get_bounds(times,values, standard_deviations = 1 ):
"Takes time series data, returns time "
x = range(len(times))
y = values
n = len(x)
mean = sum(x*y)/n
sigma = sum(y*(x-mean)**2)/n
stand = np.std(values)
popt,pcov = curve_fit(gauss,range(len(times)),values,p0=[1,mean,sigma])
start_index = int(popt[1] - (standard_deviations*popt[2]))
end_index = int(popt[1] + (standard_deviations*popt[2]))
return times[start_index], times[end_index]
|
ceos-seo/data_cube_notebooks
|
notebooks/demo/curve_fit_gaussian.py
|
Python
|
apache-2.0
| 2,230
|
[
"Gaussian"
] |
065d8be4551ff5c69a54e80b528bea1ba379ac6e5d3d3d641bb656a0d78942df
|
import numpy as np
def affine_forward(x, w, b):
"""
Computes the forward pass for an affine (fully-connected) layer.
The input x has shape (N, d_1, ..., d_k) and contains a minibatch of N
examples, where each example x[i] has shape (d_1, ..., d_k). We will
reshape each input into a vector of dimension D = d_1 * ... * d_k, and
then transform it to an output vector of dimension M.
Inputs:
- x: A numpy array containing input data, of shape (N, d_1, ..., d_k)
- w: A numpy array of weights, of shape (D, M)
- b: A numpy array of biases, of shape (M,)
Returns a tuple of:
- out: output, of shape (N, M)
- cache: (x, w, b)
"""
out = None
#############################################################################
# TODO: Implement the affine forward pass. Store the result in out. You #
# will need to reshape the input into rows. #
#############################################################################
pass
x_matrix = x.reshape((x.shape[0], np.prod(x.shape[1:])))
out = x_matrix.dot(w) + b
#############################################################################
# END OF YOUR CODE #
#############################################################################
cache = (x, w, b)
return out, cache
def affine_backward(dout, cache):
"""
Computes the backward pass for an affine layer.
Inputs:
- dout: Upstream derivative, of shape (N, M)
- cache: Tuple of:
- x: Input data, of shape (N, d_1, ... d_k)
- w: Weights, of shape (D, M)
Returns a tuple of:
- dx: Gradient with respect to x, of shape (N, d1, ..., d_k)
- dw: Gradient with respect to w, of shape (D, M)
- db: Gradient with respect to b, of shape (M,)
"""
x, w, b = cache
dx, dw, db = None, None, None
#############################################################################
# TODO: Implement the affine backward pass. #
#############################################################################
pass
N = dout.shape[0]
x_matrix = x.reshape((x.shape[0], np.prod(x.shape[1:])))
dx = dout.dot(w.T)
dx = dx.reshape(x.shape)
dw = x_matrix.T.dot(dout)
db = dout.sum(0)
# dw /= N
# db /= N
#############################################################################
# END OF YOUR CODE #
#############################################################################
return dx, dw, db
def relu_forward(x):
"""
Computes the forward pass for a layer of rectified linear units (ReLUs).
Input:
- x: Inputs, of any shape
Returns a tuple of:
- out: Output, of the same shape as x
- cache: x
"""
out = None
#############################################################################
# TODO: Implement the ReLU forward pass. #
#############################################################################
pass
out = np.maximum(x, 0)
#############################################################################
# END OF YOUR CODE #
#############################################################################
cache = x
return out, cache
def relu_backward(dout, cache):
"""
Computes the backward pass for a layer of rectified linear units (ReLUs).
Input:
- dout: Upstream derivatives, of any shape
- cache: Input x, of same shape as dout
Returns:
- dx: Gradient with respect to x
"""
dx, x = None, cache
#############################################################################
# TODO: Implement the ReLU backward pass. #
#############################################################################
pass
dx = dout * (x > 0)
#############################################################################
# END OF YOUR CODE #
#############################################################################
return dx
def batchnorm_forward(x, gamma, beta, bn_param):
"""
Forward pass for batch normalization.
During training the sample mean and (uncorrected) sample variance are
computed from minibatch statistics and used to normalize the incoming data.
During training we also keep an exponentially decaying running mean of the mean
and variance of each feature, and these averages are used to normalize data
at test-time.
At each timestep we update the running averages for mean and variance using
an exponential decay based on the momentum parameter:
running_mean = momentum * running_mean + (1 - momentum) * sample_mean
running_var = momentum * running_var + (1 - momentum) * sample_var
Note that the batch normalization paper suggests a different test-time
behavior: they compute sample mean and variance for each feature using a
large number of training images rather than using a running average. For
this implementation we have chosen to use running averages instead since
they do not require an additional estimation step; the torch7 implementation
of batch normalization also uses running averages.
Input:
- x: Data of shape (N, D)
- gamma: Scale parameter of shape (D,)
- beta: Shift parameter of shape (D,)
- bn_param: Dictionary with the following keys:
- mode: 'train' or 'test'; required
- eps: Constant for numeric stability
- momentum: Constant for running mean / variance.
- running_mean: Array of shape (D,) giving running mean of features
- running_var Array of shape (D,) giving running variance of features
Returns a tuple of:
- out: of shape (N, D)
- cache: A tuple of values needed in the backward pass
"""
mode = bn_param['mode']
eps = bn_param.get('eps', 1e-5)
momentum = bn_param.get('momentum', 0.9)
N, D = x.shape
running_mean = bn_param.get('running_mean', np.zeros(D, dtype=x.dtype))
running_var = bn_param.get('running_var', np.zeros(D, dtype=x.dtype))
out, cache = None, None
if mode == 'train':
#############################################################################
# TODO: Implement the training-time forward pass for batch normalization. #
# Use minibatch statistics to compute the mean and variance, use these #
# statistics to normalize the incoming data, and scale and shift the #
# normalized data using gamma and beta. #
# #
# You should store the output in the variable out. Any intermediates that #
# you need for the backward pass should be stored in the cache variable. #
# #
# You should also use your computed sample mean and variance together with #
# the momentum variable to update the running mean and running variance, #
# storing your result in the running_mean and running_var variables. #
#############################################################################
pass
sample_mean = x.mean(0)
sample_var = x.var(0)
running_mean = momentum * running_mean + (1 - momentum) * sample_mean
running_var = momentum * running_var + (1 - momentum) * sample_var
x_tilted = (x - sample_mean)/np.sqrt(sample_var + eps)
out = gamma * x_tilted + beta
cache = {'x': x, 'x_tilted': x_tilted, 'gamma': gamma, 'beta': beta,
'sample_mean': sample_mean, 'sample_var': sample_var, 'eps': eps}
#############################################################################
# END OF YOUR CODE #
#############################################################################
elif mode == 'test':
#############################################################################
# TODO: Implement the test-time forward pass for batch normalization. Use #
# the running mean and variance to normalize the incoming data, then scale #
# and shift the normalized data using gamma and beta. Store the result in #
# the out variable. #
#############################################################################
pass
x_tilted = (x - running_mean)/np.sqrt(running_var + eps)
out = gamma * x_tilted + beta
#############################################################################
# END OF YOUR CODE #
#############################################################################
else:
raise ValueError('Invalid forward batchnorm mode "%s"' % mode)
# Store the updated running means back into bn_param
bn_param['running_mean'] = running_mean
bn_param['running_var'] = running_var
return out, cache
def batchnorm_backward(dout, cache):
"""
Backward pass for batch normalization.
For this implementation, you should write out a computation graph for
batch normalization on paper and propagate gradients backward through
intermediate nodes.
Inputs:
- dout: Upstream derivatives, of shape (N, D)
- cache: Variable of intermediates from batchnorm_forward.
Returns a tuple of:
- dx: Gradient with respect to inputs x, of shape (N, D)
- dgamma: Gradient with respect to scale parameter gamma, of shape (D,)
- dbeta: Gradient with respect to shift parameter beta, of shape (D,)
"""
dx, dgamma, dbeta = None, None, None
#############################################################################
# TODO: Implement the backward pass for batch normalization. Store the #
# results in the dx, dgamma, and dbeta variables. #
#############################################################################
pass
gamma = cache['gamma']
x = cache['x']
mu_B = cache['sample_mean']
var_B = cache['sample_var']
eps = cache['eps']
N = x.shape[0]
dx1 = dout
dbeta = dout.sum(0)
dx_tilted = gamma * dx1
dgamma = (dx1 * cache['x_tilted']).sum(0)
x2 = x - mu_B
x5 = var_B + eps
x4 = np.sqrt(x5)
x3 = 1.0 / x4
# x_tilted left branch
dx2 = x3 * dx_tilted
dx = dx2
dmu_B = -dx2.sum(0)
# x_tilted right branch
dx3 = (x2 * dx_tilted).sum(0)
dx4 = -dx3 / x4 ** 2
dx5 = dx4 / (2 * np.sqrt(x5))
dvar_B = dx5
deps = dx5.sum()
# last, consider dmu, dvar influence on dx
dx += dmu_B / N
dx += 2 * (x - mu_B) * dvar_B / N
#############################################################################
# END OF YOUR CODE #
#############################################################################
return dx, dgamma, dbeta
def batchnorm_backward_alt(dout, cache):
"""
Alternative backward pass for batch normalization.
For this implementation you should work out the derivatives for the batch
normalizaton backward pass on paper and simplify as much as possible. You
should be able to derive a simple expression for the backward pass.
Note: This implementation should expect to receive the same cache variable
as batchnorm_backward, but might not use all of the values in the cache.
Inputs / outputs: Same as batchnorm_backward
"""
dx, dgamma, dbeta = None, None, None
#############################################################################
# TODO: Implement the backward pass for batch normalization. Store the #
# results in the dx, dgamma, and dbeta variables. #
# #
# After computing the gradient with respect to the centered inputs, you #
# should be able to compute gradients with respect to the inputs in a #
# single statement; our implementation fits on a single 80-character line. #
#############################################################################
pass
gamma = cache['gamma']
x = cache['x']
mu_B = cache['sample_mean']
var_B = cache['sample_var']
eps = cache['eps']
N = x.shape[0]
dx_tilted = dout * gamma
dvar_B = -(dx_tilted * (x - mu_B)).sum(0) * (var_B + eps) ** -1.5 / 2.0
dmu_B = -dx_tilted.sum(0) / np.sqrt(var_B + eps) # - 2 * dvar_B * (x - mu_B).sum(0) / N
dx = dx_tilted / np.sqrt(var_B + eps) + dmu_B / N + dvar_B * 2 * (x - mu_B) / N
dgamma = (dout * cache['x_tilted']).sum(0)
dbeta = dout.sum(0)
# from IPython.core.debugger import Tracer
# Tracer()()
#############################################################################
# END OF YOUR CODE #
#############################################################################
return dx, dgamma, dbeta
def dropout_forward(x, dropout_param):
"""
Performs the forward pass for (inverted) dropout.
Inputs:
- x: Input data, of any shape
- dropout_param: A dictionary with the following keys:
- p: Dropout parameter. We drop each neuron output with probability p.
- mode: 'test' or 'train'. If the mode is train, then perform dropout;
if the mode is test, then just return the input.
- seed: Seed for the random number generator. Passing seed makes this
function deterministic, which is needed for gradient checking but not in
real networks.
Outputs:
- out: Array of the same shape as x.
- cache: A tuple (dropout_param, mask). In training mode, mask is the dropout
mask that was used to multiply the input; in test mode, mask is None.
"""
p, mode = dropout_param['p'], dropout_param['mode']
if 'seed' in dropout_param:
np.random.seed(dropout_param['seed'])
mask = None
out = None
if mode == 'train':
###########################################################################
# TODO: Implement the training phase forward pass for inverted dropout. #
# Store the dropout mask in the mask variable. #
###########################################################################
pass
mask = (np.random.rand(*x.shape) < p) / p
out = x * mask
###########################################################################
# END OF YOUR CODE #
###########################################################################
elif mode == 'test':
###########################################################################
# TODO: Implement the test phase forward pass for inverted dropout. #
###########################################################################
pass
out = x
###########################################################################
# END OF YOUR CODE #
###########################################################################
cache = (dropout_param, mask)
out = out.astype(x.dtype, copy=False)
return out, cache
def dropout_backward(dout, cache):
"""
Perform the backward pass for (inverted) dropout.
Inputs:
- dout: Upstream derivatives, of any shape
- cache: (dropout_param, mask) from dropout_forward.
"""
dropout_param, mask = cache
mode = dropout_param['mode']
dx = None
if mode == 'train':
###########################################################################
# TODO: Implement the training phase backward pass for inverted dropout. #
###########################################################################
pass
dx = dout * mask
###########################################################################
# END OF YOUR CODE #
###########################################################################
elif mode == 'test':
dx = dout
return dx
def conv_forward_naive(x, w, b, conv_param):
"""
A naive implementation of the forward pass for a convolutional layer.
The input consists of N data points, each with C channels, height H and width
W. We convolve each input with F different filters, where each filter spans
all C channels and has height HH and width WW.
Input:
- x: Input data of shape (N, C, H, W)
- w: Filter weights of shape (F, C, HH, WW)
- b: Biases, of shape (F,)
- conv_param: A dictionary with the following keys:
- 'stride': The number of pixels between adjacent receptive fields in the
horizontal and vertical directions.
- 'pad': The number of pixels that will be used to zero-pad the input.
Returns a tuple of:
- out: Output data, of shape (N, F, H', W') where H' and W' are given by
H' = 1 + (H + 2 * pad - HH) / stride
W' = 1 + (W + 2 * pad - WW) / stride
- cache: (x, w, b, conv_param)
"""
out = None
#############################################################################
# TODO: Implement the convolutional forward pass. #
# Hint: you can use the function np.pad for padding. #
#############################################################################
pass
N, C, H, W = x.shape
F, _, HH, WW = w.shape
stride = conv_param['stride']
pad = conv_param['pad']
H_prime = 1 + (H + 2 * pad - HH) / stride
W_prime = 1 + (W + 2 * pad - WW) / stride
out = np.zeros((N, F, H_prime, W_prime))
for n in xrange(N):
x_n = np.pad(x[n], ((0,), (pad,), (pad,)), 'constant') # (C, H+pad, W+pad)
for i in xrange(H_prime):
h_start = i * stride
for j in xrange(W_prime):
w_start = j * stride
for f in xrange(F):
out[n, f, i, j] = (x_n[:, h_start:h_start + HH, w_start:w_start + WW] * w[f]).sum() + b[f]
#############################################################################
# END OF YOUR CODE #
#############################################################################
cache = (x, w, b, conv_param)
return out, cache
def conv_backward_naive(dout, cache):
"""
A naive implementation of the backward pass for a convolutional layer.
Inputs:
- dout: Upstream derivatives.
- cache: A tuple of (x, w, b, conv_param) as in conv_forward_naive
Returns a tuple of:
- dx: Gradient with respect to x
- dw: Gradient with respect to w
- db: Gradient with respect to b
"""
dx, dw, db = None, None, None
#############################################################################
# TODO: Implement the convolutional backward pass. #
#############################################################################
pass
(x, w, b, conv_param) = cache
N, C, H, W = x.shape
F, _, HH, WW = w.shape
dw, db = np.zeros_like(w), np.zeros_like(b)
stride = conv_param['stride']
pad = conv_param['pad']
dx = np.zeros((N, C, H + 2 * pad, W + 2 * pad))
H_prime = 1 + (H + 2 * pad - HH) / stride
W_prime = 1 + (W + 2 * pad - WW) / stride
for n in xrange(N):
x_n = np.pad(x[n], ((0,), (pad,), (pad,)), 'constant') # (C, H+pad, W+pad)
for i in xrange(H_prime):
h_start = i * stride
for j in xrange(W_prime):
w_start = j * stride
for f in xrange(F):
dx[n, :, h_start:h_start + HH, w_start:w_start + WW] += w[f] * dout[n, f, i, j]
dw[f] += x_n[:, h_start:h_start + HH, w_start:w_start + WW] * dout[n, f, i, j]
db = dout.sum((0, 2, 3))
dx = dx[:, :, pad:-pad, pad:-pad]
#############################################################################
# END OF YOUR CODE #
#############################################################################
return dx, dw, db
def max_pool_forward_naive(x, pool_param):
"""
A naive implementation of the forward pass for a max pooling layer.
Inputs:
- x: Input data, of shape (N, C, H, W)
- pool_param: dictionary with the following keys:
- 'pool_height': The height of each pooling region
- 'pool_width': The width of each pooling region
- 'stride': The distance between adjacent pooling regions
Returns a tuple of:
- out: Output data
- cache: (x, pool_param)
"""
out = None
#############################################################################
# TODO: Implement the max pooling forward pass #
#############################################################################
pass
N, C, H, W = x.shape
HH, WW, stride = pool_param['pool_height'], pool_param['pool_width'], pool_param['stride']
H_prime = (H - HH)/stride + 1
W_prime = (W - WW)/stride + 1
out = np.zeros((N, C, H_prime, W_prime))
for i in xrange(H_prime):
h_start = i * stride
for j in xrange(W_prime):
w_start = j * stride
out[:, :, i, j] = x[:, :, h_start:h_start + HH, w_start:w_start + WW].max((2, 3))
#############################################################################
# END OF YOUR CODE #
#############################################################################
cache = (x, pool_param)
return out, cache
def max_pool_backward_naive(dout, cache):
"""
A naive implementation of the backward pass for a max pooling layer.
Inputs:
- dout: Upstream derivatives
- cache: A tuple of (x, pool_param) as in the forward pass.
Returns:
- dx: Gradient with respect to x
"""
dx = None
#############################################################################
# TODO: Implement the max pooling backward pass #
#############################################################################
pass
(x, pool_param) = cache
N, C, H, W = x.shape
HH, WW, stride = pool_param['pool_height'], pool_param['pool_width'], pool_param['stride']
H_prime = (H - HH)/stride + 1
W_prime = (W - WW)/stride + 1
dx = np.zeros_like(x)
for n in xrange(N):
for c in xrange(C):
for i in xrange(H_prime):
h_start = i * stride
for j in xrange(W_prime):
w_start = j * stride
local_x = x[n, c, h_start:h_start + HH, w_start:w_start + WW]
h, w = np.unravel_index(local_x.argmax(), (HH, WW))
dx[n, c, h_start + h, w_start + w] += dout[n, c, i, j]
#############################################################################
# END OF YOUR CODE #
#############################################################################
return dx
def spatial_batchnorm_forward(x, gamma, beta, bn_param):
"""
Computes the forward pass for spatial batch normalization.
Inputs:
- x: Input data of shape (N, C, H, W)
- gamma: Scale parameter, of shape (C,)
- beta: Shift parameter, of shape (C,)
- bn_param: Dictionary with the following keys:
- mode: 'train' or 'test'; required
- eps: Constant for numeric stability
- momentum: Constant for running mean / variance. momentum=0 means that
old information is discarded completely at every time step, while
momentum=1 means that new information is never incorporated. The
default of momentum=0.9 should work well in most situations.
- running_mean: Array of shape (D,) giving running mean of features
- running_var Array of shape (D,) giving running variance of features
Returns a tuple of:
- out: Output data, of shape (N, C, H, W)
- cache: Values needed for the backward pass
"""
out, cache = None, None
#############################################################################
# TODO: Implement the forward pass for spatial batch normalization. #
# #
# HINT: You can implement spatial batch normalization using the vanilla #
# version of batch normalization defined above. Your implementation should #
# be very short; ours is less than five lines. #
#############################################################################
pass
N, C, H, W = x.shape
out = np.zeros((C, N, H * W))
cache = []
for c in xrange(C):
out[c], cache_c = batchnorm_forward(x[:, c].reshape((N, H * W)), gamma[c], beta[c], bn_param)
cache.append(cache_c)
out = np.swapaxes(out.reshape((C, N, H, W)), 0, 1)
#############################################################################
# END OF YOUR CODE #
#############################################################################
return out, cache
def spatial_batchnorm_backward(dout, cache):
"""
Computes the backward pass for spatial batch normalization.
Inputs:
- dout: Upstream derivatives, of shape (N, C, H, W)
- cache: Values from the forward pass
Returns a tuple of:
- dx: Gradient with respect to inputs, of shape (N, C, H, W)
- dgamma: Gradient with respect to scale parameter, of shape (C,)
- dbeta: Gradient with respect to shift parameter, of shape (C,)
"""
dx, dgamma, dbeta = None, None, None
#############################################################################
# TODO: Implement the backward pass for spatial batch normalization. #
# #
# HINT: You can implement spatial batch normalization using the vanilla #
# version of batch normalization defined above. Your implementation should #
# be very short; ours is less than five lines. #
#############################################################################
pass
N, C, H, W = dout.shape
dx = np.zeros((N, C, H * W))
dgamma = np.zeros((C, H * W))
dbeta = np.zeros((C, H * W))
for c in xrange(C):
dx[:, c], dgamma[c], dbeta[c] = batchnorm_backward_alt(dout[:, c].reshape((N, H * W)), cache[c])
dx = dx.reshape((N, C, H, W))
dgamma = dgamma.sum(1)
dbeta = dbeta.sum(1)
#############################################################################
# END OF YOUR CODE #
#############################################################################
return dx, dgamma, dbeta
def svm_loss(x, y):
"""
Computes the loss and gradient using for multiclass SVM classification.
Inputs:
- x: Input data, of shape (N, C) where x[i, j] is the score for the jth class
for the ith input.
- y: Vector of labels, of shape (N,) where y[i] is the label for x[i] and
0 <= y[i] < C
Returns a tuple of:
- loss: Scalar giving the loss
- dx: Gradient of the loss with respect to x
"""
N = x.shape[0]
correct_class_scores = x[np.arange(N), y]
margins = np.maximum(0, x - correct_class_scores[:, np.newaxis] + 1.0)
margins[np.arange(N), y] = 0
loss = np.sum(margins) / N
num_pos = np.sum(margins > 0, axis=1)
dx = np.zeros_like(x)
dx[margins > 0] = 1
dx[np.arange(N), y] -= num_pos
dx /= N
return loss, dx
def softmax_loss(x, y):
"""
Computes the loss and gradient for softmax classification.
Inputs:
- x: Input data, of shape (N, C) where x[i, j] is the score for the jth class
for the ith input.
- y: Vector of labels, of shape (N,) where y[i] is the label for x[i] and
0 <= y[i] < C
Returns a tuple of:
- loss: Scalar giving the loss
- dx: Gradient of the loss with respect to x
"""
probs = np.exp(x - np.max(x, axis=1, keepdims=True))
probs /= np.sum(probs, axis=1, keepdims=True)
N = x.shape[0]
loss = -np.sum(np.log(probs[np.arange(N), y])) / N
dx = probs.copy()
dx[np.arange(N), y] -= 1
dx /= N # I did not do this step in assignment 1, but it should have been done
return loss, dx
|
hanezu/cs231n-assignment
|
assignment2/cs231n/layers.py
|
Python
|
mit
| 27,957
|
[
"NEURON"
] |
2888afcf0e9d3098aa92bce602bd655db3c14dac356173048c1586ed41480eeb
|
# -*- coding: utf-8 -*-
# Copyright JS Foundation and other contributors, https://js.foundation/
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL <COPYRIGHT HOLDER> BE LIABLE FOR ANY
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
# THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
from __future__ import unicode_literals
from .nodes import Node
class NodeVisitor(object):
"""
A node visitor base class that walks the abstract syntax tree and calls a
visitor function for every node found. This function may return a value
which is forwarded by the `visit` method.
This class is meant to be subclassed, with the subclass adding visitor
methods.
Per default the visitor functions for the nodes are ``'visit_'`` +
class name of the node. So a `Module` node visit function would
be `visit_Module`. This behavior can be changed by overriding
the `visit` method. If no visitor function exists for a node
(return value `None`) the `generic_visit` visitor is used instead.
"""
def __call__(self, node, metadata):
return self.transform(node, metadata)
def transform(self, node, metadata):
"""Transform a node."""
if isinstance(node, Node):
method = 'transform_' + node.__class__.__name__
transformer = getattr(self, method, self.generic_transform)
new_node = transformer(node, metadata)
if new_node is not None:
node = new_node
return node
def generic_transform(self, node, metadata):
"""Called if no explicit transform function exists for a node."""
return node
def visit(self, node):
"""Visit a node."""
if isinstance(node, Node):
method = 'visit_' + node.__class__.__name__
visitor = getattr(self, method, self.generic_visit)
new_node = visitor(node)
if new_node is not None:
node = new_node
return node
def generic_visit(self, node):
"""Called if no explicit visitor function exists for a node."""
for field, value in list(node.__dict__.items()):
if not field.startswith('_'):
if isinstance(value, list):
for i, item in enumerate(value):
new_item = self.visit(item)
if new_item is not None and item is not new_item:
value[i] = new_item
else:
new_value = self.visit(value)
if new_value is not None and value is not new_value:
node.__dict__[field] = new_value
return node
|
mp-coder/translate-dev-tools
|
esprima/visitor.py
|
Python
|
mit
| 3,761
|
[
"VisIt"
] |
1c1de72ffc6346f98883cd2a63aaa64d1acb70ec085a8bf9bbef25acfe7b69c4
|
#!/usr/bin/env python
#
# denss.align_by_principal_axes.py
# A tool for aligning an electron density map to another electron density
# map based only on alignment of principal axes (no minimization).
#
# Part of DENSS
# DENSS: DENsity from Solution Scattering
# A tool for calculating an electron density map from solution scattering data
#
# Tested using Anaconda / Python 2.7
#
# Authors: Thomas D. Grant, Nhan D. Nguyen
# Email: <tgrant@hwi.buffalo.edu>, <ndnguyen20@wabash.edu>
# Copyright 2018 The Research Foundation for SUNY
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
from __future__ import print_function
import os, sys, logging
import numpy as np
import argparse
from saxstats._version import __version__
import saxstats.saxstats as saxs
parser = argparse.ArgumentParser(description="A tool for aligning an electron density map to another electron density map based only on alignment of principal axes (no minimization).", formatter_class=argparse.RawTextHelpFormatter)
parser.add_argument("--version", action="version",version="%(prog)s v{version}".format(version=__version__))
parser.add_argument("-f", "--file", type=str, help="MRC file for alignment to reference principal axes.")
parser.add_argument("-ref", "--ref", default = None, type=str, help="Reference (.mrc or .pdb file (map will be calculated from PDB))")
parser.add_argument("-o", "--output", default = None, type=str, help="output filename prefix")
parser.add_argument("-c_on", "--center_on", dest="center", action="store_true", help="Center PDB reference (default).")
parser.add_argument("-c_off", "--center_off", dest="center", action="store_false", help="Do not center PDB reference.")
parser.add_argument("-r", "--resolution", default=15.0, type=float, help="Desired resolution (i.e. Gaussian width sigma) of map calculated from PDB file.")
parser.set_defaults(center = True)
args = parser.parse_args()
if __name__ == "__main__":
if args.output is None:
fname_nopath = os.path.basename(args.file)
basename, ext = os.path.splitext(fname_nopath)
output = basename+"_alignedbyPA"
else:
output = args.output
logging.basicConfig(filename=output+'.log',level=logging.INFO,filemode='w',
format='%(asctime)s %(message)s', datefmt='%Y-%m-%d %I:%M:%S %p')
logging.info('BEGIN')
logging.info('Command: %s', ' '.join(sys.argv))
#logging.info('Script name: %s', sys.argv[0])
logging.info('DENSS Version: %s', __version__)
logging.info('Map filename(s): %s', args.file)
logging.info('Reference filename: %s', args.ref)
rho, side = saxs.read_mrc(args.file)
if args.ref is None:
print("Need reference file (.mrc or .pdb)")
sys.exit(1)
else:
if args.ref.endswith('.pdb'):
logging.info('Center PDB reference: %s', args.center)
logging.info('PDB reference map resolution: %.2f', args.resolution)
reffname_nopath = os.path.basename(args.ref)
refbasename, refext = os.path.splitext(reffname_nopath)
refoutput = refbasename+"_centered.pdb"
refside = side
voxel = (refside/movrho.shape)[0]
halfside = refside/2
n = int(refside/voxel)
dx = refside/n
x_ = np.linspace(-halfside,halfside,n)
x,y,z = np.meshgrid(x_,x_,x_,indexing='ij')
xyz = np.column_stack((x.ravel(),y.ravel(),z.ravel()))
pdb = saxs.PDB(args.ref)
if args.center:
pdb.coords -= pdb.coords.mean(axis=0)
pdb.write(filename=refoutput)
#use the new fastgauss function
#refrho = saxs.pdb2map_gauss(pdb,xyz=xyz,sigma=args.resolution)
refrho = saxs.pdb2map_fastgauss(pdb,x=x,y=y,z=z,sigma=args.resolution,r=args.resolution*2)
refrho = refrho*np.sum(movrho)/np.sum(refrho)
saxs.write_mrc(refrho,side,filename=refbasename+'_pdb.mrc')
if args.ref.endswith('.mrc'):
refrho, refside = saxs.read_mrc(args.ref)
if (not args.ref.endswith('.mrc')) and (not args.ref.endswith('.pdb')):
print("Invalid reference filename given. .mrc or .pdb file required")
sys.exit(1)
aligned = saxs.principal_axis_alignment(refrho,rho)
saxs.write_mrc(aligned, side, output+'.mrc')
print("%s.mrc written. " % (output,))
logging.info('END')
|
tdgrant1/denss
|
bin/denss.align_by_principal_axes.py
|
Python
|
gpl-3.0
| 5,070
|
[
"Gaussian"
] |
1e7a5cc6892568a505a319764b00e7b214654bdc78f5b253c19347f8f46c92d1
|
# copyright 2003-2010 LOGILAB S.A. (Paris, FRANCE), all rights reserved.
# contact http://www.logilab.fr/ -- mailto:contact@logilab.fr
#
# This file is part of logilab-common.
#
# logilab-common is free software: you can redistribute it and/or modify it under
# the terms of the GNU Lesser General Public License as published by the Free
# Software Foundation, either version 2.1 of the License, or (at your option) any
# later version.
#
# logilab-common is distributed in the hope that it will be useful, but WITHOUT
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
# FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more
# details.
#
# You should have received a copy of the GNU Lesser General Public License along
# with logilab-common. If not, see <http://www.gnu.org/licenses/>.
"""A generic visitor abstract implementation.
"""
__docformat__ = "restructuredtext en"
def no_filter(_):
return 1
# Iterators ###################################################################
class FilteredIterator(object):
def __init__(self, node, list_func, filter_func=None):
self._next = [(node, 0)]
if filter_func is None:
filter_func = no_filter
self._list = list_func(node, filter_func)
def next(self):
try:
return self._list.pop(0)
except :
return None
# Base Visitor ################################################################
class Visitor(object):
def __init__(self, iterator_class, filter_func=None):
self._iter_class = iterator_class
self.filter = filter_func
def visit(self, node, *args, **kargs):
"""
launch the visit on a given node
call 'open_visit' before the beginning of the visit, with extra args
given
when all nodes have been visited, call the 'close_visit' method
"""
self.open_visit(node, *args, **kargs)
return self.close_visit(self._visit(node))
def _visit(self, node):
iterator = self._get_iterator(node)
n = iterator.next()
while n:
result = n.accept(self)
n = iterator.next()
return result
def _get_iterator(self, node):
return self._iter_class(node, self.filter)
def open_visit(self, *args, **kargs):
"""
method called at the beginning of the visit
"""
pass
def close_visit(self, result):
"""
method called at the end of the visit
"""
return result
# standard visited mixin ######################################################
class VisitedMixIn(object):
"""
Visited interface allow node visitors to use the node
"""
def get_visit_name(self):
"""
return the visit name for the mixed class. When calling 'accept', the
method <'visit_' + name returned by this method> will be called on the
visitor
"""
try:
return self.TYPE.replace('-', '_')
except:
return self.__class__.__name__.lower()
def accept(self, visitor, *args, **kwargs):
func = getattr(visitor, 'visit_%s' % self.get_visit_name())
return func(self, *args, **kwargs)
def leave(self, visitor, *args, **kwargs):
func = getattr(visitor, 'leave_%s' % self.get_visit_name())
return func(self, *args, **kwargs)
|
dbbhattacharya/kitsune
|
vendor/packages/logilab-common/visitor.py
|
Python
|
bsd-3-clause
| 3,421
|
[
"VisIt"
] |
e24208c208f4c36ac94a6f47252a2f71f020f7a45254f5b7bf40eb14f6e6abc7
|
#!/usr/bin/env python
__author__ = 'waroquiers'
import unittest
import os
from pymatgen.analysis.chemenv.coordination_environments.coordination_geometry_finder import LocalGeometryFinder
from pymatgen.analysis.chemenv.coordination_environments.coordination_geometries import AllCoordinationGeometries
# from pymatgen.analysis.chemenv.coordination_environments.chemenv_strategies import SimplestChemenvStrategy
# from pymatgen.analysis.chemenv.coordination_environments.chemenv_strategies import SimpleAbundanceChemenvStrategy
# from pymatgen.core.structure import Structure
json_files_dir = os.path.join(os.path.dirname(__file__), "..", "..", "..", "..", "..",
'test_files', "chemenv", "json_test_files")
class CoordinationGeometryFinderTest(unittest.TestCase):
def setUp(self):
self.lgf = LocalGeometryFinder()
self.lgf.setup_parameters(centering_type='standard',
structure_refinement=self.lgf.STRUCTURE_REFINEMENT_NONE)
# self.strategies = [SimplestChemenvStrategy(), SimpleAbundanceChemenvStrategy()]
#
# def _strategy_test(self, strategy):
# files = []
# for (dirpath, dirnames, filenames) in os.walk(json_files_dir):
# files.extend(filenames)
# break
#
# for ifile, json_file in enumerate(files):
# with self.subTest(json_file=json_file):
# f = open("{}/{}".format(json_files_dir, json_file), 'r')
# dd = json.load(f)
# f.close()
#
# atom_indices = dd['atom_indices']
# expected_geoms = dd['expected_geoms']
#
# struct = Structure.from_dict(dd['structure'])
#
# struct = self.lgf.setup_structure(struct)
# se = self.lgf.compute_structure_environments_detailed_voronoi(only_indices=atom_indices,
# maximum_distance_factor=1.5)
#
# #All strategies should get the correct environment with their default parameters
# strategy.set_structure_environments(se)
# for ienv, isite in enumerate(atom_indices):
# ce = strategy.get_site_coordination_environment(struct[isite])
# try:
# coord_env = ce[0]
# except TypeError:
# coord_env = ce
# #Check that the environment found is the expected one
# self.assertEqual(coord_env, expected_geoms[ienv])
#
# def test_simplest_chemenv_strategy(self):
# strategy = SimplestChemenvStrategy()
# self._strategy_test(strategy)
#
# def test_simple_abundance_chemenv_strategy(self):
# strategy = SimpleAbundanceChemenvStrategy()
# self._strategy_test(strategy)
def test_perfect_environments(self):
allcg = AllCoordinationGeometries()
indices_CN = {1: [0],
2: [1, 0],
3: [1, 0, 2],
4: [2, 0, 3, 1],
5: [2, 3, 1, 0, 4],
6: [0, 2, 3, 1, 5, 4],
7: [2, 6, 0, 3, 4, 5, 1],
8: [1, 2, 6, 3, 7, 0, 4, 5],
9: [5, 2, 6, 0, 4, 7, 3, 8, 1],
10: [8, 5, 6, 3, 0, 7, 2, 4, 9, 1],
11: [7, 6, 4, 1, 2, 5, 0, 8, 9, 10, 3],
12: [5, 8, 9, 0, 3, 1, 4, 2, 6, 11, 10, 7],
13: [4, 11, 5, 12, 1, 2, 8, 3, 0, 6, 9, 7, 10],
}
for coordination in range(1, 14):
for mp_symbol in allcg.get_implemented_geometries(coordination=coordination,
returned='mp_symbol'):
cg = allcg.get_geometry_from_mp_symbol(mp_symbol=mp_symbol)
self.lgf.allcg = AllCoordinationGeometries(only_symbols=[mp_symbol])
self.lgf.setup_test_perfect_environment(mp_symbol, randomness=False,
indices=indices_CN[coordination],
random_translation='NONE', random_rotation='NONE',
random_scale='NONE')
se = self.lgf.compute_structure_environments(only_indices=[0],
maximum_distance_factor=1.01*cg.distfactor_max,
min_cn=cg.coordination_number,
max_cn=cg.coordination_number,
only_symbols=[mp_symbol]
)
self.assertAlmostEqual(se.get_csm(0, mp_symbol)['symmetry_measure'], 0.0, delta=1e-8,
msg='Failed to get perfect environment with mp_symbol {}'.format(mp_symbol))
if __name__ == "__main__":
unittest.main()
|
xhqu1981/pymatgen
|
pymatgen/analysis/chemenv/coordination_environments/tests/test_coordination_geometry_finder.py
|
Python
|
mit
| 5,197
|
[
"pymatgen"
] |
ed88fa8488d0d1b0995947bf33a8d91a0b533cdd302a8d289c5e8929deba3f94
|
# Copyright 2016-2020 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"). You
# may not use this file except in compliance with the License. A copy of
# the License is located at
#
# http://aws.amazon.com/apache2.0/
#
#
# or in the "license" file accompanying this file. This file is
# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
# ANY KIND, either express or implied. See the License for the specific
# language governing permissions and limitations under the License.
"""
Main module.
Functions:
None
Classes:
Main: Main, runnable driver for ec2rl
Exceptions:
MainError: base error class for this module
MainPrediagnosticFailure: raised when a prediagnostic check fails
MainFileCopyError: raised when a file copy fails
MainFileRemovalError: raised when removal of a file fails
MainInvalidVolumeSpecificationError: raised when an invalid volume ID or Linux block device is specified
MainDirectoryError: raised when a directory modification or creation fails
MainMissingRequiredArgument: raised when a subcommand is missing a required argument
MainVersionCheckTimeout: raised when a timeout occurs when trying to reach the VERSION S3 endpoint
"""
from __future__ import print_function
import datetime
import errno
import os
import platform
import re
import shutil
import sys
import logging
import ec2rlcore
import ec2rlcore.console_out
import ec2rlcore.constraint
import ec2rlcore.logutil
import ec2rlcore.module
import ec2rlcore.moduledir
import ec2rlcore.options
import ec2rlcore.paralleldiagnostics
import ec2rlcore.prediag
import ec2rlcore.programversion
import ec2rlcore.s3upload
import requests
import yaml
class Main(object):
"""
Main, runnable driver for ec2rl.
Attribute:
logger (Logger): logger object used to capture the different levels of logging message
console (Logger): logger object accessible via LogUtil.get_direct_console_logger() to output messages to stdout
directories (dict): contains all the various directory paths used by ec2rl
constraint (Constraint): dict-like object containing the constraint key:value pairs
_modules (ModuleDir): list-like object of the run modules
_prediags (ModuleDir): list-like object of the prediagnostic modules
_postdiags (ModuleDir): list-like object of the postdiagnostic modules
subcommand (str): subcommand to be executed (e.g. run, list)
options (Options): dict-like object of parsed command line arguments
Methods:
full_init: complete the lazy load of the modules
_setup_write_paths: create the directory structure necessary for ec2rl to run
_setup_environ: set environment variables to be passed to the modules
module: returns _modules, finishing the lazy load as needed
prediags returns _prediags, finishing the lazy load as needed
postdiag: returns _postdiags, finishing the lazy load as needed
get_help: returns the help message string for the given subcommand
list: print the list of available, applicable modules the user can run
help: print the help message for ec2rl or the specified items given in the args
save_config: use the provided arguments to create a ConfigParser configuration file, configuration.cfg
with all the module constraint:value pairs
menu_config: use a menu system to create a ConfigParser configuration file, configuration.cfg
__call__: run the subcommand calling getattr
version: display the version and licensing information
version_check: compares PROGRAM_VERSION against the upstream version and returns True if an update is available
software_check: check for software that is required by modules but not installed
bug_report: display version information relevant for inclusion in a bug report
upload: upload a tarball of a directory to S3 using either a presigned URL or an AWS-support provided URL
run: run one or more modules
_run_prunemodules: determines whether each module should be run and removes modules that should not be run from
the list of modules that will be run
_run_backup: creates a backup of the type appropriate to the parsed arguments
_run_prediagnostics: runs the prediagnostic modules
_run_diagmodules_parallel: runs the diagnostic modules in parallel
_run_postdiagnostics: runs the postdiagnostic modules
_summary: prints summary statistics of the diagnostic execution as well as other related messages
"""
# Implemented subcommands
subcommands = sorted(["run", "list", "help", "menu-config", "save-config", "upload", "version", "version-check",
"software-check", "bug-report"])
# Implemented meta options (long args)
__meta_options = ["--config-file", "--url", "--upload-directory"]
# Version number
PROGRAM_VERSION = ec2rlcore.programversion.ProgramVersion("1.1.5")
VERSION_ENDPOINT = "https://s3.amazonaws.com/ec2rescuelinux/VERSION"
def __init__(self, debug=False, full_init=False):
"""Perform minimal configuration of the object."""
self._write_initialized = False
self._full_initialized = False
# Empty initialization for heavier code paths which will be lazy executed when needed
self._modules = None # type: ec2rlcore.moduledir.ModuleDir
self._prediags = None # type: ec2rlcore.moduledir.ModuleDir
self._postdiags = None # type: ec2rlcore.moduledir.ModuleDir
self._modules_need_init = True
self._prediags_need_init = True
self._postdiags_need_init = True
self.pruned_modules = list()
self.prune_stats = dict()
self.directories = dict()
self.logger = ec2rlcore.logutil.LogUtil.get_root_logger()
self.console = ec2rlcore.logutil.LogUtil.set_direct_console_logger(logging.INFO)
# Parse the commandline options and set the subcommand to be run, if it was specified as an argument
self.options = ec2rlcore.options.Options(subcommands=Main.subcommands)
# if called with relative paths, build absolute path off current-working directory
_callp = sys.argv[0]
if not os.path.isabs(_callp):
_callp = os.path.abspath(_callp)
# The modules directory neighbors the executable
# (but the CWD/PWD will change, so we need a fully-specified path)
self.directories["CALLPATH"] = os.path.split(_callp)[0]
self.directories["LIBDIR"] = os.path.join(self.directories["CALLPATH"], "lib")
self.directories["WORKDIR"] = self._get_workdir(self.options.global_args)
if "--debug" in sys.argv or debug:
self.debug = True
self.logger.setLevel(logging.DEBUG)
self._setup_write_paths()
ec2rlcore.logutil.LogUtil.set_debug_log_handler(os.path.join(self.directories["RUNDIR"], "Debug.log"))
self.logger.debug("ec2rlcore.Main.__init__()")
else:
self.debug = False
self.logger.setLevel(logging.INFO)
# Help is the default subcommand
self.subcommand = "default_help"
self.constraint = ec2rlcore.constraint.Constraint()
# If the user specified a subcommand use that instead of the default.
if self.options.subcommand:
self.subcommand = self.options.subcommand
if full_init:
self.full_init()
def _get_workdir(self, global_args):
if "outputdir" in self.options.global_args and os.path.isdir(self.options.global_args["outputdir"]):
return self.options.global_args["outputdir"]
else:
return "/var/tmp/ec2rl"
def full_init(self):
"""Perform the rest of the init"""
# Only full init once!
if self._full_initialized:
return True
self._setup_write_paths()
# Configure the main log file
ec2rlcore.logutil.LogUtil.set_main_log_handler(os.path.join(self.directories["RUNDIR"], "Main.log"))
self.logger.debug("Added main log handler at {}".format(os.path.join(self.directories["RUNDIR"], "Main.log")))
ec2rlcore.logutil.LogUtil.set_console_log_handler(logging.WARNING)
self.logger.debug("Console logging for warning+ enabled")
self._setup_environ()
# Accessing the property will result in it being initialized
self.logger.debug("Initialized {} 'prediag' module(s)".format(len(self.prediags)))
self.logger.debug("Initialized {} 'run' module(s)".format(len(self.modules)))
self.logger.debug("Initialized {} 'postdiag' module(s)".format(len(self.postdiags)))
# Create the combined list of constraint:value pairs from all modules
for mod in self.modules:
self.constraint.update(mod.constraint.with_keys(["domain", "class", "distro", "software", "perfimpact"]))
self.logger.debug("my subcommand = {}".format(self.subcommand))
self.logger.debug("my constraints {}".format(self.constraint))
self.logger.debug("my global_args {}".format(self.options.global_args))
self._full_initialized = True
def _setup_write_paths(self):
"""
Create the directory structure necessary for ec2rl to run.
Returns:
True (bool)
"""
if self._write_initialized:
return True
# Each Run gets its own directory to hold output files
datetime_str = re.sub(":", "_", datetime.datetime.utcnow().isoformat())
self.directories["RUNDIR"] = os.path.join(self.directories["WORKDIR"], datetime_str)
# Store log files in the mod_out directory under RUNDIR
self.directories["LOGDIR"] = os.path.join(self.directories["RUNDIR"], "mod_out")
# Store gathered output log files in gathered_out directory under RUNDIR
self.directories["GATHEREDDIR"] = os.path.join(self.directories["RUNDIR"], "gathered_out")
# Getting the datetime_str for compression purposes
self.directories["SPECDIR"] = datetime_str
# Create working directory if it doesn't exist
try:
# Ensure the file is read/writeable by all users.
# Prevents permission issues when executed by root/sudo then a regular user
os.mkdir(self.directories["WORKDIR"])
# chmod is necessary as os.mkdir does not reliably set the permission mode
os.chmod(self.directories["WORKDIR"], 0o777)
except OSError as err:
if err.errno != errno.EEXIST:
raise MainDirectoryError(self.directories["WORKDIR"])
# create subdirectory for each tool run
try:
os.mkdir(self.directories["RUNDIR"], 0o700)
except OSError as err:
if err.errno != errno.EEXIST:
raise MainDirectoryError(self.directories["RUNDIR"])
# create the root subdirectory for the module logs
try:
os.mkdir(self.directories["LOGDIR"])
except OSError as err:
if err.errno != errno.EEXIST:
raise MainDirectoryError(self.directories["LOGDIR"])
# Create a subdirectory for each module placement
try:
os.mkdir("{}/prediagnostic".format(self.directories["LOGDIR"]))
except OSError as err:
if err.errno != errno.EEXIST:
raise MainDirectoryError("{}/prediagnostic".format(self.directories["LOGDIR"]))
try:
os.mkdir("{}/run".format(self.directories["LOGDIR"]))
except OSError as err:
if err.errno != errno.EEXIST:
raise MainDirectoryError("{}/run".format(self.directories["LOGDIR"]))
try:
os.mkdir("{}/postdiagnostic".format(self.directories["LOGDIR"]))
except OSError as err:
if err.errno != errno.EEXIST:
raise MainDirectoryError("{}/postdiagnostic".format(self.directories["LOGDIR"]))
# create subdirectory for the gathered output logs
try:
os.mkdir(self.directories["GATHEREDDIR"])
except OSError as err:
if err.errno != errno.EEXIST:
raise MainDirectoryError(self.directories["GATHEREDDIR"])
self._write_initialized = True
return True
def _setup_environ(self):
"""
Set environment variables to be passed to the modules.
Returns:
True (bool)
"""
self.logger.debug("ec2rlcore.Main._setup_environ()")
# LIBDIR: path to the vendored Python modules
# WORKDIR: path to the root directory used for data capture from all executions
# RUNDIR: path to the root directory for a particular execution
# LOGDIR: directory inside RUNDIR that contains module logs
# GATHEREDDIR: directory inside RUNDIR that contains files captured by the modules
# SPECDIR: root directory name for a particular execution (date + time)
os.environ["EC2RL_LIBDIR"] = self.directories["LIBDIR"]
os.environ["EC2RL_WORKDIR"] = self.directories["WORKDIR"]
os.environ["EC2RL_RUNDIR"] = self.directories["RUNDIR"]
os.environ["EC2RL_LOGDIR"] = self.directories["LOGDIR"]
os.environ["EC2RL_GATHEREDDIR"] = self.directories["GATHEREDDIR"]
os.environ["EC2RL_SPECDIR"] = self.directories["SPECDIR"]
os.environ["EC2RL_CALLPATH"] = self.directories["CALLPATH"]
if "perfimpact" in self.options.global_args and self.options.global_args["perfimpact"] == "true":
os.environ["EC2RL_PERFIMPACT"] = "True"
else:
os.environ["EC2RL_PERFIMPACT"] = "False"
return True
@property
def modules(self):
"""
Finish lazy load of self._modules and setup classes_to_run and domains_to_run, if needed, then return
self._modules.
Returns:
self._modules (ModuleDir): list-like object containing the run modules
"""
if self._modules_need_init:
self._modules = ec2rlcore.moduledir.ModuleDir("{}/mod.d".format(self.directories["CALLPATH"]))
self._modules_need_init = False
# Check if the user specified particular classes to run with --only-classes=
# Default to all classes if not specified
if "onlyclasses" in self.options.global_args.keys():
for module_class in self.options.global_args["onlyclasses"].rsplit(","):
if module_class and module_class not in self.options.classes_to_run:
self.options.classes_to_run.append(module_class)
else:
self.options.classes_to_run = self.modules.classes
# Check if the user specified particular domains to run with --only-domains=
# Default to all domains if not specified
if "onlydomains" in self.options.global_args.keys():
for module_domain in self.options.global_args["onlydomains"].rsplit(","):
if module_domain and module_domain not in self.options.domains_to_run:
self.options.domains_to_run.append(module_domain)
else:
self.options.domains_to_run = self.modules.domains
return self._modules
@property
def prediags(self):
"""
Finish lazy load of self._prediags, if needed, then return self._prediags.
Returns:
self._prediags (ModuleDir): list-like object containing the prediagnostic modules
"""
if self._prediags_need_init:
self._prediags = ec2rlcore.moduledir.ModuleDir("{}/pre.d".format(self.directories["CALLPATH"]))
self._prediags_need_init = False
return self._prediags
@property
def postdiags(self):
"""
Finish lazy load of self._prediags, if needed, then return self._postdiags.
Returns:
self._postdiags (ModuleDir): list-like object containing the postdiagnostic modules
"""
if self._postdiags_need_init:
self._postdiags = ec2rlcore.moduledir.ModuleDir("{}/post.d".format(self.directories["CALLPATH"]))
self._postdiags_need_init = False
self._postdiags_need_init = False
return self._postdiags
def get_help(self, help_arg=None):
"""
Return the help message matching the given subcommand.
Parameters:
help_arg (str): the subcommand whose help message is to be returned
Returns:
(str): the help message for the specified subcommand
"""
with open(self.directories["CALLPATH"] + "/ec2rlcore/help.yaml") as helpfile:
helpmessages = yaml.load(helpfile)
help_dict = {
"list": helpmessages["list_help"],
"run": helpmessages["run_help"],
"help": helpmessages["help_help"],
"menu-config": helpmessages["menu_config_help"],
"save-config": helpmessages["save_config_help"],
"upload": helpmessages["upload_help"],
"version": helpmessages["version_help"],
"version-check": helpmessages["version_check_help"],
"software-check": helpmessages["software_check_help"],
"bug-report": helpmessages["bug_report_help"]
}
# If the given subcommand is valid then return the matching help message
if help_arg in help_dict.keys():
help_message = str(help_dict[help_arg])
# If the given subcommand is not a supported subcommand then return a brief help/error message.
elif self.subcommand == "default_help":
help_message = helpmessages["help_header"]
help_message += "\nec2rl: missing subcommand operand"
else:
help_message = helpmessages["help_header"]
help_message += "\n"
for subcommand in Main.subcommands:
help_message += str(help_dict[subcommand])
help_message += "\n"
help_message += helpmessages["help_footer"]
return help_message
def list(self):
"""
Print the list of available, applicable modules the user can run.
Returns:
True (bool)
"""
self.logger.debug("ec2rlcore.Main.list()")
print("Here is a list of available modules that apply to the current host:\n")
print(ec2rlcore.module.Module.list_header)
for mod in self.modules:
if mod.applicable \
and set(mod.constraint["domain"]).intersection(self.options.domains_to_run) \
and set(mod.constraint["class"]).intersection(self.options.classes_to_run):
print(mod)
print("\nS: Requires sudo/root to run")
print("P: Requires --perfimpact=true to run (can potentially cause performance impact)")
print("R: Supports remediation if --remediate is given")
print("\nClasses refer to the type of task the module performs")
print(" Diagnose: success/fail/warn conditions determined by module.")
print(" Gather: create a copy of a local file for inspection.")
print(" Collect: collect command output")
print("\nDomains are defined per module and refer to the general area of investigation for the module.")
print("\nTo see module help, you can run:\n")
print("ec2rl help [MODULEa ... MODULEx]")
print("ec2rl help [--only-modules=MODULEa ... MODULEx] [--only-domains=DOMAINa ... DOMAINx]")
return True
def help(self):
"""
Print the help message for ec2rl or the specified items given in the args.
Returns:
True (bool)
"""
self.logger.debug("ec2rlcore.Main.help()")
args_to_help = []
output = ""
self.full_init()
# There are three ways to specify modules
# Option one
# ec2rl help --only-modules=telnetport
if "onlymodules" in self.options.global_args:
args_to_help = [mod_name for mod_name in self.options.global_args["onlymodules"].rsplit(",")]
# Option two
# ec2rl help --only-domains=net
elif "onlydomains" in self.options.global_args:
# Build the list of modules that match the domains given
for this_domain in self.options.domains_to_run:
if this_domain in self.modules.domain_map.keys():
for module_obj in self.modules.domain_map[this_domain]:
args_to_help.append(module_obj.name)
# Option three
# ec2rl help --only-classes=diagnose
elif "onlyclasses" in self.options.global_args:
for this_class in self.options.classes_to_run:
if this_class in self.modules.class_map.keys():
for module_obj in self.modules.class_map[this_class]:
args_to_help.append(module_obj.name)
# Or option four
# ec2rl help telnetport ex
elif len(sys.argv) >= 3:
for arg_num in range(2, len(sys.argv)):
# Filter out the arg if it isn't a subcommand or module name
if sys.argv[arg_num] in self.subcommands or sys.argv[arg_num] in self.modules.name_map.keys():
args_to_help.append(sys.argv[arg_num])
# Represents whether any of the args been matched to a module or subcommand
match = False
for arg in args_to_help:
# Only print two newlines for matches after the first
if match:
output += "\n\n"
# Handle cases where the arg is a subcommand
if arg in self.options.subcommand_list:
match = True
output += self.get_help(arg)
# Handle cases where the arg is a module name
elif arg in self.modules.name_map.keys():
match = True
output += self.modules.name_map[arg].help
# If neither modules nor subcommands match then use the default help/doc message
if not output:
# Default help
output = self.get_help()
print(output)
return True
def save_config(self):
"""
Add each module's constraints and any parsed arguments to a ConfigParser object and create a configuration
file from this object.
Returns:
True (bool)
"""
self.full_init()
self.logger.debug("ec2rlcore.Main.save_config()")
config_file = os.path.join(self.directories["RUNDIR"], "configuration.cfg")
self.options.write_config(config_file, self.modules)
ec2rlcore.dual_log("\n----------[Configuration File]----------\n")
ec2rlcore.dual_log("Configuration file saved:")
ec2rlcore.dual_log(config_file)
return True
def menu_config(self):
"""
Present the user with a curses-driven menu system for setting individual module options. Afterwards, either run
or save the configuration. The return is the either return value of run or save_config.
Returns:
(bool): whether the resulting subcommand was successful
"""
try:
import curses
except ImportError: # pragma: no coverage
print("ERROR:\tMissing Python module 'curses'.")
print("\tPlease install this module and rerun ec2rl")
sys.exit(1)
import ec2rlcore.menu_item
import ec2rlcore.menu_config
self.full_init()
self.logger.debug("ec2rlcore.Main.menu_config()")
# "Global" is reserved remove a module using that name
if "Global" in self.modules.name_map.keys():
self.modules.remove(self.modules.name_map["Global"])
# Create a menu-compatible OrderedDict from the data in self.modules
the_menu = ec2rlcore.menu_config.get_menu_config(self.modules)
the_menu()
# Copy the data entered into module_config back into options
module_menu = the_menu["View all modules"]
for module_name in module_menu.get_item_keys():
# Loop over the keys and remove any key:value pairs where the value is empty (unconfigured)
# This is two steps because we don't want to mutate the dict while iterating it
# Get the list of keys that need to be removed
keys_to_remove = []
for key in module_menu[module_name].get_item_keys():
if (isinstance(module_menu[module_name][key], ec2rlcore.menu_item.TextEntryItem)
and not module_menu[module_name][key].get_value()) \
or isinstance(module_menu[module_name][key], ec2rlcore.menu_item.ExitItem):
keys_to_remove.append(key)
# Delete each key whose value is empty
for key in keys_to_remove:
module_menu[module_name].remove(module_menu[module_name][key])
# Set the module's argument dict to point to the new OrderedDict
self.options.per_module_args[module_name] = module_menu[module_name].get_items_dict_copy()
# Add any new global key/value pairs to self.options.global_args, but skip items with empty values
global_menu = the_menu["Configure global module arguments"]
for key in global_menu.get_item_keys():
if isinstance(global_menu[key], (ec2rlcore.menu_item.ToggleItem, ec2rlcore.menu_item.TextEntryItem)) \
and global_menu[key].get_value():
self.options.global_args[key] = global_menu[key].get_value()
def update_only_global_arg(item_name):
"""
Helper function to update the only-* global arg values.
Parameters:
item_name (str): RHS of the hyphenated global_args key (e.g. classes in only-classes)
Returns:
(bool): represents whether an update was made
"""
num_items_to_run = 0
str_items_to_run = ""
for item in global_menu["only-{}".format(item_name)].get_items():
if item.get_value() == "True":
num_items_to_run += 1
if not str_items_to_run:
str_items_to_run = item.row_left
else:
str_items_to_run = ",".join((str_items_to_run, item.row_left))
if item_name == "modules" and 0 < num_items_to_run < len(self.modules) \
or item_name != "modules" and num_items_to_run < len(getattr(self.modules, item_name)):
self.options.global_args["only{}".format(item_name)] = str_items_to_run
else:
return False
return True
update_only_global_arg("classes")
update_only_global_arg("domains")
update_only_global_arg("modules")
# The subcommand key is set to "menuconfig" so change it to "run"
self.options.global_args["subcommand"] = "run"
# If the user selected "Run" from the menu then run the current configuration
if the_menu["Run this configuration"].get_value():
self.subcommand = "run"
return self()
# If the user did not select "Run" from the menu then just save the configuration
else:
# Create the actual configuration file
# Since the work is done in-place on per_module_args, a simple function call is all that is required
return self.save_config()
def __call__(self, subcommand=None):
"""
Call the subcommand function via getattr.
Parameters:
subcommand: subcommand to be executed (e.g. run, list)
Returns:
rv: return value of the subcommand function that is executed via getattr
"""
self.logger.debug("ec2rlcore.Main.__call__()")
if not subcommand and self.subcommand == "default_help":
subcommand = "help"
elif not subcommand:
subcommand = self.subcommand
# Replace the hyphens so the subcommand matches its method name
subcommand = subcommand.replace("-", "_")
# Subcommand return values represent function success/failure
return getattr(self, subcommand)()
def version(self):
"""Print the version and licensing information and return True."""
print("ec2rl {}".format(self.PROGRAM_VERSION))
print("Copyright 2016-2020 Amazon.com, Inc. or its affiliates. All rights reserved.")
print("This software is distributed under the Apache License, Version 2.0.")
print("")
print("This file is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, "
"either express or implied.")
return True
def version_check(self):
"""
Get the current upstream version, compare against this version, inform the user whether an update is available.
Returns:
True (bool)
"""
try:
upstream_version = ec2rlcore.programversion.ProgramVersion(requests.get(self.VERSION_ENDPOINT).text.strip())
except requests.exceptions.Timeout:
raise MainVersionCheckTimeout()
print("Running version: {}".format(self.PROGRAM_VERSION))
print("Upstream version: {}".format(upstream_version))
if upstream_version > self.PROGRAM_VERSION:
print("An update is available.")
else:
print("No update available.")
return True
def software_check(self):
"""
Check for software that is not installed but is required for a module, and inform the user on details about how
to obtain it if needed and return True.
Returns:
True (bool)
"""
packages_needed = set()
for mod in self.modules:
for software_constraint in iter(mod.constraint["software"]):
if not ec2rlcore.prediag.which(software_constraint):
packages_needed.update(set(mod.package))
packages_needed = [package for package in packages_needed if package]
if len(packages_needed) > 0:
print("One or more software packages required to run all modules are missing.\n"
"Information regarding these software packages can be found at the specified URLs below.\n")
for package in packages_needed:
modules = ",".join([mod.name for mod in self.modules.package_map[package]])
try:
package_name, package_link = package.split()
except ValueError:
raise MainSoftwareCheckPackageParsingFailure(package, modules)
print("{}: {}".format("Package-Name", package_name))
print("{}: {}".format("Package-URL", package_link))
print("{}: {}".format("Affected-Modules", modules))
print("")
else:
print("All test software requirements have been met.")
return True
def bug_report(self):
"""Print version information relevant for inclusion in a bug report and return True."""
print("ec2rl {}".format(self.PROGRAM_VERSION))
print("{}, {}".format(ec2rlcore.prediag.get_distro(), platform.release()))
print("Python {}, {}".format(platform.python_version(), sys.executable))
return True
def upload(self):
"""
Inspect the global_args and, if the url and upload directory were specified then tar the directory and upload
the resulting tarball to S3 using the provided url.
Returns:
True (bool)
"""
if "uploaddirectory" not in self.options.global_args.keys():
raise MainMissingRequiredArgument("The upload subcommand requires --upload-directory")
tgz_filename = "ec2rl-" + \
os.path.basename(os.path.normpath(self.options.global_args["uploaddirectory"])) + \
".tgz"
if "supporturl" in self.options.global_args.keys():
presigned_url = ec2rlcore.s3upload.get_presigned_url(self.options.global_args["supporturl"], tgz_filename)
elif "presignedurl" in self.options.global_args.keys():
presigned_url = self.options.global_args["presignedurl"]
else:
raise MainMissingRequiredArgument("The upload subcommand requires either --support-url or --presigned-url")
# Ensure the ssl module is a sufficiently high enough version to allow the upload to succeed.
# This check is placed here, rather than earlier, to let all the arg syntax checking to do its job first.
try:
from ssl import HAS_SNI
except ImportError:
raise MainUploadMissingSNISupport
try:
expanded_uploaddirectory = os.path.expanduser(
os.path.expandvars(
self.options.global_args["uploaddirectory"]))
ec2rlcore.s3upload.make_tarfile(tgz_filename, expanded_uploaddirectory)
ec2rlcore.s3upload.s3upload(presigned_url, tgz_filename)
finally:
# Always delete the temporary file created
try:
os.remove(tgz_filename)
except OSError:
raise MainFileRemovalError(tgz_filename)
return True
def run(self):
"""
Start the diagnostics run.
1. Determine what modules can be run.
2. Copy functions.bash to WORKDIR
3. "cd" to WORKDIR
4. Generate the run config
5. Setup logging for Main.log
6. Run prediagnostics
7. Prune modules not selected or not meeting constraints
8. Run all specified modules that are still applicable after constraint and prediagnostic checking
Returns:
mod_run_success (bool): True if all modules ran successfully else False
"""
self.logger.debug("ec2rlcore.Main.run()")
self.full_init()
# Move functions.bash to WORKDIR
_source = os.path.join(self.directories["CALLPATH"], "functions.bash")
_dest = os.path.join(self.directories["RUNDIR"], "functions.bash")
try:
shutil.copyfile(_source, _dest)
# This is typically OSError, but can also be IOError or SameFileError depending upon the version of Python
# Since the actual Exception is variable, catch any Exception
except Exception:
raise MainFileCopyError(_source, _dest)
# Change current working directory
os.chdir(self.directories["RUNDIR"])
# Only run the backup if the system is an instance
if self.options.global_args["notaninstance"] == "False":
self._run_backup()
self._run_prediagnostics()
self._run_prunemodules()
self.save_config()
ec2rlcore.dual_log("\n-------------[Output Logs]-------------\n")
ec2rlcore.dual_log("The output logs are located in:")
ec2rlcore.dual_log(self.directories["RUNDIR"])
# Parallel is now the default and only diagnostic module execution path.
# Setting concurrency=1 will simulate the serial execution mode by only running with one worker
mod_run_success = self._run_diagmodules_parallel()
self._run_postdiagnostics()
return mod_run_success
# Formerly flubber()
def _validate_constraints_have_args(self, mod, options, constraint, with_keys=None, without_keys=None):
"""
Validate whether a given module should be run given the constraints.
First, check to see if it is still applicable. Modules that have already been marked as not
applicable are skipped.
Second, the module is checked to see if it was excluded via the command line args.
Third, the constraint keys are filtered if with_keys or without_keys are provided. with_keys takes precedence
over without_keys if both are specified (without_keys is not evaluated).
Fourth, we attempt to match each constraint and constraint value to a corresponding constraint:value pair in
the options. The module-specific options are checked then the global options are checked.
constraint_retval is True when the constraint:value pair is found and False when it is not.
module_retval is initialized to True and ANDed with the resulting constraint_retval after each constraint is
checked. This keeps track of the overall success of the constraint checking.
Parameters:
mod (Module): the module being validated
options (Options): the Options instance containing the parsed constraints
constraint (Constraint): the Constraints instance containing
the combined constraint:value pairs of all modules
with_keys (list): key list to be used for filtering (filter to only these keys)
without_keys (list): key list to be used for filtering (filter to exclude these keys)
Returns:
module_retval (bool): whether the module can run
"""
self.logger.debug("moduledir.ModuleDir.validate_constraints_have_args()")
self.logger.debug("Validating module: {}".format(mod.name))
# module_retval (boolean), represents whether a module's constraints have values
module_retval = True
# Keep earlier decisions about module acceptance (and their message)
if not mod.applicable:
return False
# Check to see if module was excluded by name (--no=<module_name>)
if mod.name in options.global_args and options.global_args[mod.name] == "False":
self.logger.debug("module '{}' explicitly excluded with '--no={}'; skipping module".format(
mod.name, mod.name))
mod.applicable = False
mod.whyskipping = "explicitly excluded with '--no={}'.".format(mod.name)
return False
# Filter the constraints compared to include only the keys specified
if with_keys:
constraint_filtered_using = mod.constraint.with_keys
key_list = with_keys
# Filter the constraints compared to exclude the keys specified
elif without_keys:
constraint_filtered_using = mod.constraint.without_keys
key_list = without_keys
# All constraints will be compared
else:
constraint_filtered_using = mod.constraint.without_keys
key_list = []
self.logger.debug("key_list = {}".format(key_list))
# Check current module's constraints against options
for constraint_name in constraint_filtered_using(key_list).keys():
self.logger.debug("'{}' constraint:values = '{}:{}'".format(
mod.name, constraint_name, mod.constraint[constraint_name]))
# Skip optional constraints
if constraint_name == "optional":
continue
if constraint_name == "parallelexclusive":
continue
def check_per_module_args(this_constraint_value):
"""Check if this module has per-module args
Parameters:
this_constraint_value (str): the constraint whose value we ware looking for
Returns:
(bool): indicates whether a constraint value was found
"""
if mod.name in options.per_module_args:
self.logger.debug("....Checking per_module_args")
# Check if this module's per-module args contains this constraint name as well as a value
if this_constraint_value in options.per_module_args[mod.name] and \
options.per_module_args[mod.name][this_constraint_value]:
self.logger.debug("........FOUND Constraint value is present")
return True
else:
self.logger.debug("......UNFOUND Constraint value is False (bool) or absent from "
"options.per_module_args")
return False
return False
def check_global_constraints(this_constraint_value):
"""Check the global constraint:value pairs (options.global_args) for constraint:value pair
Parameters:
this_constraint_value (str): the constraint whose value we ware looking for
Returns:
(bool): indicates whether a constraint value was found
"""
if this_constraint_value in options.global_args:
self.logger.debug("....Checking global_args")
self.logger.debug("......FOUND constraint name present in global_args")
# Check if this key has a value
if options.global_args[this_constraint_value]:
self.logger.debug("........FOUND Constraint value is present")
return True
else:
self.logger.debug("......UNFOUND Constraint value is False (bool) or absent from "
"options.global_args")
return False
return False
# Check each value of the named-set against the run-constraints and run-options
for constraint_value in mod.constraint[constraint_name]:
self.logger.debug("Checking: {}:{}".format(constraint_name, constraint_value))
# constraint_retval (boolean) represents whether constraint has a value
if {constraint_name: constraint_value} in constraint:
self.logger.debug("..FOUND constraint:value pair present in the summation of constraints")
constraint_retval = True
module_retval = module_retval and constraint_retval
self.logger.debug("module_retval={}".format(module_retval))
# Found so go to the next constraint
continue
self.logger.debug("..UNFOUND constraint:value pair absent from summation of constraints")
# Run the additional checks
# Python short-circuits "and" and "or" operators so this is as efficient as a if/elif block while
# keeping the checks self-contained in their own functions
constraint_retval = check_per_module_args(constraint_value) or \
check_global_constraints(constraint_value)
# Add any new checks here, chaining off the constraint_retval assignment's "or" chain
# Handle cases where the constraint was found in neither options" global_args
# nor per_module_args
if not constraint_retval:
self.logger.debug("..UNFOUND {} absent from options".format(constraint_value))
mod.whyskipping = "Missing required argument '{}'.".format(constraint_value)
self.logger.debug("..constraint_retval={}".format(constraint_retval))
# AND the module and constraint return values
# (any constraint failure causes the module to fail where fail = False)
module_retval = module_retval and constraint_retval
self.logger.debug("module_retval={}".format(module_retval))
if not module_retval:
mod.applicable = False
return module_retval
def _run_prunemodules(self):
"""
Review the modules and set the applicable and whyskipping attributes to indicate whether the module should
be run. Once complete, remove any non-applicable modules from the module list so that they are not run.
"""
# Check if the user specified particular modules to run with --only-modules=
if "onlymodules" in self.options.global_args:
mods_to_run = [mod_name for mod_name in self.options.global_args["onlymodules"].rsplit(",")]
# If the user didn't specify modules with --only-modules then run all modules
else:
mods_to_run = "all"
self.logger.debug("mods_to_run={}".format(mods_to_run))
prune_list = []
for mod in self.modules:
prune = False
# If the module is supposed to run per the user's args, perform the additional prediagnostic checks
if (mods_to_run == "all" or mod.name in mods_to_run) \
and set(mod.constraint["domain"]).intersection(self.options.domains_to_run) \
and set(mod.constraint["class"]).intersection(self.options.classes_to_run):
# Flag the module for removal if the module requires the system be an EC2 instance and
# it is not an instance
if next(iter(mod.constraint["requires_ec2"])) == "True" \
and self.options.global_args["notaninstance"] == "True":
mod.whyskipping = "Module requires system be an EC2 instance."
prune = True
# Flag the module for removal if it is not applicable to the system's detected distro
elif os.environ["EC2RL_DISTRO"] not in list(mod.constraint["distro"]):
mod.whyskipping = "Not applicable to this distro."
prune = True
# Flag the module for removal if performance impacting but no okay given
elif next(iter(mod.constraint["perfimpact"])) == "True" and os.environ["EC2RL_PERFIMPACT"] == "False":
mod.whyskipping = "Requires performance impact okay, but not given."
prune = True
# Flag the module for removal if it requires root/sudo but this is not executing as such
elif next(iter(mod.constraint["sudo"])) == "True" and os.environ["EC2RL_SUDO"] == "False":
mod.whyskipping = "Requires sudo access, but not executing as root."
prune = True
# Flag the module for removal if validate_constraints_have_args found missing args (constraints)
elif not self._validate_constraints_have_args(mod=mod,
options=self.options,
constraint=self.constraint,
without_keys=["software",
"distro",
"sudo",
"requires_ec2"]):
# mod.whyskipping is set by _validate_constraints_have_args()
prune = True
# If the module isn't supposed to run, determine how it was excluded by the user's args
else:
prune = True
# Flag the module for removal of it was not specified or implied
if mods_to_run != "all" and mod.name not in mods_to_run:
mod.whyskipping = "Not specified to run."
# Flag the module for removal if its domain was not specified or implied
elif not set(mod.constraint["domain"]).intersection(self.options.domains_to_run):
mod.whyskipping = "Not in specified domain to run."
# else is equivalent of "not set(mod.constraint["class"]).intersection(self.options.classes_to_run):"
else:
mod.whyskipping = "Not in specified class to run."
# Check the software constraints against what can be found in PATH and is executable by this user, but
# don't bother checking these if the module is already flagged for removal
if not prune:
for software_constraint in iter(mod.constraint["software"]):
if not ec2rlcore.prediag.which(software_constraint, mode=os.X_OK):
mod.whyskipping = "Requires missing/non-executable software '{}'.".format(
software_constraint)
prune = True
# Create the list of modules to be removed
if prune:
self.logger.info("module {}/{}: Skipping. Reason: {}".format(mod.placement, mod.name, mod.whyskipping))
prune_list.append(mod)
else:
self.logger.info("module {}/{}: Passed prediagnostics validation.".format(mod.placement, mod.name))
# If module is flagged for pruning, add to the pruned list and remove from main module list
for mod in prune_list:
self._prune_module(mod)
def _prune_module(self, mod):
def add_to_prune_stats(reason):
if reason not in self.prune_stats:
self.prune_stats[reason] = 0
self.prune_stats[reason] += 1
# Only collect stats on the skip reasons we want to actually display
if mod.whyskipping.startswith("Requires performance impact okay, but not given."):
add_to_prune_stats(ec2rlcore.module.SkipReason.PERFORMANCE_IMPACT)
elif mod.whyskipping.startswith("Requires sudo access, but not executing as root."):
add_to_prune_stats(ec2rlcore.module.SkipReason.REQUIRES_SUDO)
elif mod.whyskipping.startswith("Requires missing/non-executable software"):
add_to_prune_stats(ec2rlcore.module.SkipReason.MISSING_SOFTWARE)
elif mod.whyskipping.startswith("Missing required argument"):
add_to_prune_stats(ec2rlcore.module.SkipReason.MISSING_ARGUMENT)
self.pruned_modules.append(mod)
self.modules.remove(mod)
def _run_backup(self):
"""
Runs the backup based on flags used
Returns:
True (bool)
"""
import ec2rlcore.awshelpers
import ec2rlcore.backup
# Catch exceptions raising up from AWS creds/boto/etc configuration
ec2rlcore.dual_log("\n-----------[Backup Creation]-----------\n")
# Creates an image of the current instance
if "backup" in self.options.global_args and self.options.global_args["backup"] == "ami":
ec2rlcore.backup.create_image(ec2rlcore.awshelpers.get_instance_id())
# Creates snapshots of all attached volumes
elif "backup" in self.options.global_args and self.options.global_args["backup"] == "allvolumes":
ec2rlcore.backup.create_all_snapshots(ec2rlcore.awshelpers.get_volume_ids())
elif "backup" in self.options.global_args:
for volume_name in self.options.global_args["backup"].rsplit(","):
if "vol" in volume_name:
ec2rlcore.backup.snapshot(volume_name)
else:
ec2rlcore.dual_log("Improper specification of volumes. Please verify you have specified a volume"
" such as vol-xxxxx.")
raise MainInvalidVolumeSpecificationError(volume_name)
else:
ec2rlcore.dual_log("No backup option selected. Please consider backing up your volumes or instance")
return True
def _run_prediagnostics(self):
"""
Run the selected diagnostic modules in the instance of Main.prediags.
Returns:
(bool): True if all prediagnostics modules ran successfully or there were none to run else False
Raises MainPrediagnosticFailure if any prediagnostic module fails
"""
# Start prediagnostics
self.logger.info("----------------------------------------")
self.logger.info("BEGIN PREDIAGNOSTICS")
self.logger.info("----------------------------------------")
# Get the system config using the functions in the prediag module
# Export these variables for the modules to use
os.environ["EC2RL_SUDO"] = str(ec2rlcore.prediag.check_root())
os.environ["EC2RL_DISTRO"] = ec2rlcore.prediag.get_distro()
os.environ["EC2RL_NET_DRIVER"] = ec2rlcore.prediag.get_net_driver()
if self.options.global_args["notaninstance"] == "True":
os.environ["EC2RL_VIRT_TYPE"] = "non-virtualized"
self.logger.info("prediagnostic/verify_metadata: not an instance; skipping")
# Prediagnostic checks that rely on the system being an EC2 instance go here
else:
if ec2rlcore.prediag.verify_metadata():
self.logger.info("prediagnostic/verify_metadata: can reach metadata server")
else:
ec2rlcore.dual_log("prediagnostic/verify_metadata: cannot reach metadata server")
raise MainPrediagnosticFailure("metadata server inaccessible")
os.environ["EC2RL_VIRT_TYPE"] = ec2rlcore.prediag.get_virt_type()
# Run each prediagnostic module
for mod in self.prediags:
if mod.applicable:
self.logger.info("module {}/{}: Running".format(mod.placement, mod.name))
self.logger.debug("module {}/{}: applicable = True".format(mod.placement, mod.name))
module_logger = ec2rlcore.logutil.LogUtil.create_module_logger(mod, self.directories["LOGDIR"])
module_logger.info(mod.run(options=self.options))
if mod.run_status == "FAILURE":
raise MainPrediagnosticFailure(mod.run_summary)
else:
self.logger.info("module {}/{}: Skipping. Reason: {}".format(mod.placement, mod.name, mod.whyskipping))
self.logger.debug("module '{}': skipping: {}".format(mod.name, mod.whyskipping))
return True
def _run_diagmodules_parallel(self):
"""
Run the selected diagnostic modules in parallel.
Returns:
(bool): True if all diagnostic modules ran successfully or none were run else False
"""
# If concurrency arg is set and is a number
if "concurrency" in self.options.global_args and self.options.global_args["concurrency"].isdigit():
# Concurrency has a minimum of 1 and a maximum of 100.
concurrency = min(max(1, int(self.options.global_args["concurrency"])), 100)
else:
# Default concurrency level is set here:
concurrency = 10
ec2rlcore.dual_log("\n--------------[Module Run]--------------\n")
# Sorting modules by class to attempt a consistent run order of classes
self.modules.sort(key=lambda mod: mod.constraint["class"][0])
ec2rlcore.paralleldiagnostics.parallel_run(self.modules,
self.directories["LOGDIR"],
concurrency=concurrency,
options=self.options)
ec2rlcore.dual_log("")
return self._summary()
def _run_postdiagnostics(self):
"""
Run the selected diagnostic modules in self.postdiags.
Returns:
(bool): True if all postdiagnostics modules ran successfully or there were none to run else False
"""
postdiag_success = True
# Start postdiagnostics
self.logger.info("----------------------------------------")
self.logger.info("BEGIN POSTDIAGNOSTICS")
self.logger.info("----------------------------------------")
# Run each postdiagnostic module
for mod in self.postdiags:
if mod.applicable:
self.logger.info("module {}/{}: Running".format(mod.placement, mod.name))
self.logger.debug("module {}/{}: applicable = True".format(mod.placement, mod.name))
module_logger = ec2rlcore.logutil.LogUtil.create_module_logger(mod, self.directories["LOGDIR"])
module_logger.info(mod.run(options=self.options))
if mod.run_status != "SUCCESS":
postdiag_success = False
else:
self.logger.info("module {}/{}: Skipping. Reason: {}".format(mod.placement, mod.name, mod.whyskipping))
self.logger.debug("module '{}': skipping: {}".format(mod.name, mod.whyskipping))
return postdiag_success
def _summary(self):
"""
Print the summary of diagnostic execution.
Returns:
(bool): True if all modules in the diagnose class ran successfully or none were run else False
"""
# Determine the execution results for the "diagnose" class of modules
diagnose_successes = 0
diagnose_failures = 0
diagnose_warnings = 0
diagnose_unknowns = 0
if "diagnose" in self.modules.class_map.keys():
ec2rlcore.dual_log("\n----------[Diagnostic Results]----------\n")
for module_obj in self.modules.class_map["diagnose"]:
if module_obj.run_status == "SUCCESS":
diagnose_successes += 1
elif module_obj.run_status == "FAILURE":
diagnose_failures += 1
elif module_obj.run_status == "WARN":
diagnose_warnings += 1
elif module_obj.run_status == "UNKNOWN":
diagnose_unknowns += 1
# Sort the list by the run_status string so that warnings, successes, etc are grouped.
for module_obj in sorted([module_obj for module_obj in self.modules.class_map["diagnose"]],
key=lambda mod: mod.run_status):
ec2rlcore.dual_log("{:32} {}".format("module " + module_obj.placement + "/" + module_obj.name,
module_obj.run_summary))
for detail in module_obj.run_status_details:
ec2rlcore.dual_log("{:32} {}".format(" ", detail))
# Output the execution results
ec2rlcore.dual_log("\n--------------[Run Stats]--------------\n")
ec2rlcore.dual_log("{:32} {}".format("Total modules run:", len(self.modules)))
if len(self.modules) > 0:
for class_name in self.modules.class_map.keys():
ec2rlcore.dual_log("{:32} {}".format("'{}' modules run:".format(class_name),
len(self.modules.class_map[class_name])))
if class_name == "diagnose":
ec2rlcore.dual_log("{:32} {}".format(" successes:", diagnose_successes))
ec2rlcore.dual_log("{:32} {}".format(" failures:", diagnose_failures))
ec2rlcore.dual_log("{:32} {}".format(" warnings:", diagnose_warnings))
ec2rlcore.dual_log("{:32} {}".format(" unknown:", diagnose_unknowns))
if len(self.prune_stats) > 0:
ec2rlcore.dual_log("\n{:32} {:<4} | {:<8} | {:<10} | {:<11}".format(
"Modules not run due to missing:", "sudo", "software", "parameters", "perf-impact"))
ec2rlcore.dual_log("{:32} {:>4} | {:>8} | {:>10} | {:>11}"
.format("",
self.prune_stats.get(ec2rlcore.module.SkipReason.REQUIRES_SUDO, 0),
self.prune_stats.get(ec2rlcore.module.SkipReason.MISSING_SOFTWARE, 0),
self.prune_stats.get(ec2rlcore.module.SkipReason.MISSING_ARGUMENT, 0),
self.prune_stats.get(ec2rlcore.module.SkipReason.PERFORMANCE_IMPACT, 0)))
if self.options.global_args["notaninstance"] == "True":
feedback_url = \
"https://aws.au1.qualtrics.com/jfe1/form/SV_3KrcrMZ2quIDzjn?InstanceID={}&Version={}\n".format(
"not_an_instance",
self.PROGRAM_VERSION)
else:
feedback_url = \
"https://aws.au1.qualtrics.com/jfe1/form/SV_3KrcrMZ2quIDzjn?InstanceID={}&Version={}\n".format(
ec2rlcore.awshelpers.get_instance_id(),
self.PROGRAM_VERSION)
ec2rlcore.dual_log(
"\n----------------[NOTICE]----------------\n"
"\nPlease note, this directory could contain sensitive data depending on modules run! "
"Please review its contents!\n"
"\n----------------[Upload]----------------\n"
"\nYou can upload results to AWS Support with the following, "
"or run 'help upload' for details on using an S3 presigned URL:\n"
"\n{}./ec2rl upload --upload-directory={} --support-url=\"URLProvidedByAWSSupport\" \n"
"\nThe quotation marks are required, and if you ran the tool with sudo, "
"you will also need to upload with sudo.\n"
"\n---------------[Feedback]---------------\n"
"\nWe appreciate your feedback. If you have any to give, please visit:\n{}".format(
"sudo " if os.environ["EC2RL_SUDO"] == "True" else "",
self.directories["RUNDIR"],
feedback_url))
if "diagnose" in self.modules.class_map.keys():
return len(self.modules.class_map["diagnose"]) == diagnose_successes
else:
return True
class MainError(Exception):
"""Base class for exceptions in this module."""
pass
class MainPrediagnosticFailure(MainError):
"""A prediagnostic check failed."""
def __init__(self, error_message, *args):
message = "Failed prediagnostic check: {}".format(error_message)
super(MainPrediagnosticFailure, self).__init__(message, *args)
class MainFileCopyError(MainError):
"""A file copy failed."""
def __init__(self, source, destination, *args):
message = "Failed to copy necessary file '{}' to '{}'".format(source, destination)
super(MainFileCopyError, self).__init__(message, *args)
class MainFileRemovalError(MainError):
"""Removal of a file failed."""
def __init__(self, filename, *args):
message = "Failed to remove file '{}'.".format(filename)
super(MainFileRemovalError, self).__init__(message, *args)
class MainInvalidVolumeSpecificationError(MainError):
"""An invalid EBS volume ID/Linux block device specified."""
def __init__(self, error_message, *args):
message = "Invalid EBS volume or block device specified: {}".format(error_message)
super(MainInvalidVolumeSpecificationError, self).__init__(message, *args)
class MainDirectoryError(MainError):
"""An error occurred while trying to create or modify a directory."""
def __init__(self, error_message, *args):
message = "Failed to create or modify directory: {}".format(error_message)
super(MainDirectoryError, self).__init__(message, *args)
class MainMissingRequiredArgument(MainError):
"""The subcommand was missing a required, complementary argument."""
def __init__(self, error_message, *args):
message = "Missing argument: {}".format(error_message)
super(MainMissingRequiredArgument, self).__init__(message, *args)
class MainVersionCheckTimeout(MainError):
"""A timeout occurred trying to connect to the S3 VERSION endpoint."""
def __init__(self):
message = "Connection timed out trying to obtain the latest version information."
super(MainVersionCheckTimeout, self).__init__(message)
class MainUploadMissingSNISupport(MainError):
"""Built-in ssl module does not support SNI which is necessary for uploading to function."""
def __init__(self):
message = "Python built-in ssl module is missing Server Name Indication (SNI) support. " \
"Uploading will not function properly without support for SNI. " \
"Upgrade Python to 2.7.9+ / 3.2+ or retry with the binary build of ec2rl."
super(MainUploadMissingSNISupport, self).__init__(message)
class MainSoftwareCheckPackageParsingFailure(MainError):
"""Failed to parse the package value into the name and URL."""
def __init__(self, package_string, modules):
message = "Failed to parse package string: '{}'. Malformed string present in the following modules: {}".format(
package_string, modules)
super(MainSoftwareCheckPackageParsingFailure, self).__init__(message)
|
Drudenhaus/aws-ec2rescue-linux
|
ec2rlcore/main.py
|
Python
|
apache-2.0
| 64,856
|
[
"VisIt"
] |
a8c4cdf3e978deaa8407539eacd5d4d9a30e46033f5144162045d49463ffc5ba
|
import os
import configparser
import io #We need to emulate a dummy [section] header for
#configparesr to underdstand our config.txt file.
#Some lists of options
options = ['arm_freq', 'gpu_freq', 'core_freq', 'h264_freq',
'isp_freq', 'v3d_freq', 'sdram_freq', 'over_voltage',
'over_voltage_sdram', 'over_voltage_sdram_c', 'over_voltage_sdram_i',
'over_voltage_sdram_p', 'sdtv_mode', 'sdtv_aspect', 'hdmi_mode',
'hdmi_drive', 'hdmi_boost', 'disable_overscan','overscan_left',
'overscan_right', 'overscan_top', 'overscan_bottom',
'framebuffer_width', 'framebuffer_height', 'test_mode', 'enable_l2cache']
sdtv_modes={'NTSC' : 0, 'NTSC-J' : 1, 'PAL' : 2, 'PAL-M' :3}
aspectratios={'4:3' : 1, '14:9' : 2, '16:9' : 3}
hdtv_resolutions=['VGA', '240p', '288p', '480i', '480p', '576i', \
'576p', '720p', '1080i', '1080p']
hdtv_modes={'VGA' : 1, '480p60' : 2, '480p60H' : 3, '720p60' : 4,
'1080i60' : 5, '480i60' : 6, '480i60H' : 7, '240p60' : 8,
'240p60H' : 9, '480i60_4x' : 10, '480i60_4xH' : 11, '240p60_4x' : 12,
'240p60_4xH' : 13, '480p60_2x' : 14, '480p60_2xH' : 15,
'1080p60' : 16, '576p50' : 17, '576p50H' : 18, '720p50' : 19,
'1080i50' : 20, '576i50' : 21, '576i50H' : 22, '288p50' : 23,
'288p50H' : 24, '576i50_4x' : 25, '576i50_4xH' : 26, '288p50_4x' : 27,
'288p50_4xH' : 28, '576p50_2x' : 29, '576p50_2xH' : 30,
'1080p50' : 31, '1080p24' : 32, '1080p25' : 33, '1080p30' : 34,
'480p60_4x' : 35, '480p60_4xH' : 36, '576p50_4x' : 37,
'576p50_4xH' : 38, '1080i50_rb' : 39, '1080i100' : 40, '720p100' : 41,
'576p100' : 42, '576p100H' : 43, '576i100' : 44, '576i100H' : 45,
'1080i120' : 46, '720p120' : 47, '480p120' : 48, '480p120H' : 49,
'480i120' : 50, '480i120H' : 51, '576p200' : 52, '576p200H' : 53,
'576i200' : 54, '576i200H' : 55, '480p240' : 56, '480p240H' : 57,
'480i240' : 58, '480i240H' : 59}
##Usable refreshrates by resolution
refreshrates = {
'refreshrates_240p' : ['60'] ,
'refreshrates_240p_advanced' : ['60', '60H', '60_4x', '60_4xH'] ,
'refreshrates_288p' : ['50'] ,
'refreshrates_288p_advanced' : ['50', '50H', '50_4x', '50_4xH'] ,
'refreshrates_480i' : ['60', '120', '200'] ,
'refreshrates_480i_advanced' : ['60', '60H', '60_4x', '60_4xH',
'120', '120H', '240', '240H'] ,
'refreshrates_480p' : ['60', '120', '200'] ,
'refreshrates_480p_advanced' : ['60', '60H', '60_2x', '60_2xH',
'60_4x', '60_4xH', '120', '120H',
'240', '240H'] ,
'refreshrates_576i' : ['50', '100', '200'] ,
'refreshrates_576i_advanced' : ['50', '50H', '50_4x', '50_4xH',
'100', '100H', '200', '200H'] ,
'refreshrates_576p' : ['50', '100', '200'] ,
'refreshrates_576p_advanced' : ['50', '50H', '50_2x', '50_2xH',
'50_4x', '50_4xH', '100', '100H',
'200', '200H'] ,
'refreshrates_720p' : ['50', '60', '100', '120'] ,
'refreshrates_720p_advanced' : ['50', '60', '100', '120'] ,
'refreshrates_1080i' : ['50', '60', '100', '120'] ,
'refreshrates_1080i_advanced' : ['50', '50_rb', '60', '100', '120'] ,
'refreshrates_1080p' : ['24', '25', '30', '50', '60'] ,
'refreshrates_1080p_advanced' : ['24', '25', '30', '50', '60'] }
####Configfile variables object/class
###Overclocking stuff
##Set voltage/frewuency variables to 0
##0 is a no go value for all of them, so
##we can exclude them from the saved file
##if == 0
class rpi_config:
####Start Variables###################################################
arm_freq = 0 #Arm CPU core frequency
#Default is 700
gpu_freq = 0 #Sets all 4 following at same time
#Defaults to 250
core_freq = 0 #General GPU core frequency
#Default is 250.
h264_freq = 0 #Sets the video decoder frequency
#Default is 250.
isp_freq = 0 #Image sensor pipeline frequency
#Default is 250.
v3d_freq = 0 #Frequency of the 3D builder
#Default 250.
sdram_freq = 0 #Frequency of the SDRAM memory chip
#Default 400.
over_voltage = 0 #ARM/GPU core voltage adjust.
#[-16,8] equates to [0.8V,1.4V].
#This rule applys to all of the voltages.
#Default 0 (1.2V)
over_voltage_sdram = 0 #Sets all voltage variables
#Default is 0 (1.2V)
over_voltage_sdram_c = 0 #SDRAM controller voltage adjust.
#Default is 0 (1.2V)
over_voltage_sdram_i = 0 #SDRAM I/O voltage adjust.
#Default is 0 (1.2V)
over_voltage_sdram_p = 0 #SDRAM phy voltage adjust.
#Default is 0 (1.2V)
###Video output stuff
sdtv_mode = 0 #composite tv mode. Default is 0 (NTSC)
sdtv_aspect = 0 #composite aspect ratio. Default is 1 (4:3)
hdmi_mode = 0 #hdmi mode. Default is negotiated with display.
hdmi_drive = 0 #hdmi port outputs HDMI(2)/DVI(1) signal
#Default to HDMI as it is HDMI port ^_^
hdmi_boost = 0 #Not documented offically anywhere. Use 0 and
#exclude from config at save time. HDMI signal power
disable_overscan = 0#Disables the overscanning when set to 1
overscan_left = 0 #number of pixels to skip on left
overscan_right = 0 #number of pixels to skip on right
overscan_top = 0 #number of pixels to skip on top
overscan_bottom = 0 #number of pixels to skip on bottom
#Assume we have a sane display,
#which doens't need any overscanning.
framebuffer_width = 0 #console framebuffer width in pixels.
#Default matches display.
framebuffer_height = 0 #console framebuffer height in pixels.
#Default matches display.
###Misc options
test_mode = 0 #enable test sound/image during boot
enable_l2cache = 0 #enable arm access to GPU's L2 cache.
#Needs corresponding L2 enabled kernel.Default 0
###Non options variables
configfile = '' #filename of the file to save into.
parser = configparser.ConfigParser()
#Write a header telling this app was used
generated_config = (
"#This config file was generated by rpiconf. rpiconf developper(s) may not\n\
#be held responsible for any damage caused by a malformated config file.\n\
#For more information visit our website \n\
#https://github.com/Huulivoide/rpiconf\n\n\n")
####End Variables#####################################################
####Start functions###################################################
def read_config(self):
# Thanks for user Tauran on stackowerflow for this hack
config_str = io.StringIO()
config_str.write("[dummy]\n")
if os.path.isfile(self.configfile):
config_str.write(open(self.configfile, 'r').read())
config_str.seek(0, os.SEEK_SET)
self.parser.read_file(config_str)
def overscan_state(self):
if self.overscan_bottom == 0 and self.overscan_top == 0 and \
self.overscan_left == 0 and self.overscan_right == 0:
self.disable_overscan = 1
else:
self.disable_overscan = 0
def use_unified_voltages(self, use_individual):
if use_individual == False:
self.over_voltage_sdram = 0
else:
self.over_voltage_sdram_c = 0
self.over_voltage_sdram_i = 0
self.over_voltage_sdram_p = 0
def use_unified_freq(self, use_individual):
if use_individual == False:
self.gpu_freq = 0
else:
self.core_freq = 0
self.h264_freq = 0
self.isp_freq = 0
self.v3d_freq = 0
def translate_hdmi_mode(self, raw_mode):
if raw_mode[0] == "AUTO":
self.hdmi_mode = 0
elif raw_mode[0] == "VGA":
self.hdmi_mode = 1
elif raw_mode[0] == "":
self.hdmi_mode = 0
elif raw_mode[0] != "":
self.hdmi_mode = hdtv_modes[str(raw_mode[0]) + \
str(raw_mode[1])]
#Check if config file was giveb as argument or if a /boot/config.txt
#exists. Return the filename as a string.
def get_configfile(self, _file):
BOOT_FILE_EXIST = os.path.isfile("/boot/config.txt")
FILE_EXISTS = os.path.isfile(_file)
if FILE_EXISTS:
self.configfile = _file
elif BOOT_FILE_EXIST:
self.configfile = "/boot/config.txt"
else:
self.configfile = "config.txt"
def include_option(self):
for option in options:
#Make 100% sure we get the value in string format
value = str(getattr(self, option))
if value != "0":
self.generated_config += (option + "=" + value + '\n')
else:
self.generated_config += ("#Value of '" + option
+ "' not defined! Exluding it from config file!\n")
####End functions#####################################################
|
Huulivoide/rpiconf
|
rpiconf.py
|
Python
|
bsd-2-clause
| 8,518
|
[
"VisIt"
] |
707fda4352d4a7abb7df804f9b5bc389fa5656e96c1d5f1110a43b863444c8f2
|
'''
Created on Aug 31, 2012
@author: eric
'''
import unittest
import os.path
import logging
import databundles.util
from testbundle.bundle import Bundle
from databundles.run import RunConfig
from test_base import TestBase
from databundles.client.rest import Rest #@UnresolvedImport
from databundles.library import QueryCommand, get_library
server_url = 'http://localhost:7979'
logger = databundles.util.get_logger(__name__)
logger.setLevel(logging.DEBUG)
class Test(TestBase):
def start_server(self, rc):
'''Run the Bottle server as a thread'''
from databundles.client.siesta import API
import databundles.server.main
from threading import Thread
import time
from functools import partial
logger.info("Starting library server")
# Give the server a new RunCOnfig, so we can use a different library.
server = Thread(target = partial(databundles.server.main.test_run, rc) )
server.setDaemon(True)
server.start()
#databundles.server.bottle.debug()
#
# Wait until the server responds to requests
#
a = API(server_url)
for i in range(1,10): #@UnusedVariable
try:
# An echo request to see if the server is running.
a.test.echo('foobar').get(bar='baz')
break
except:
logger.info( 'Server not started yet, waiting')
time.sleep(1)
def setUp(self):
import shutil,os
self.copy_or_build_bundle()
self.bundle_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)),'testbundle')
self.bundle = Bundle()
self.bundle_dir = self.bundle.bundle_dir
self.server_rc = RunConfig([os.path.join(self.bundle_dir,'server-test-config.yaml')])
self.client_rc = RunConfig([os.path.join(self.bundle_dir,'client-test-config.yaml')])
root = os.path.join(self.client_rc.filesystem.root_dir,'test')
shutil.rmtree(root)
#self.start_server(self.server_rc)
def get_library(self):
"""Clear out the database before the test run"""
l = get_library(self.client_rc, 'client')
l.database.clean()
l.logger.setLevel(logging.DEBUG)
return l
def tearDown(self):
'''Shutdown the server process by calling the close() API, then waiting for it
to stop serving requests '''
from databundles.client.siesta import API
import time
# Wait for the server to shutdown
a = API(server_url)
for i in range(1,10): #@UnusedVariable
try:
a.test.close.get()
#print 'Teardown: server still running, waiting'
time.sleep(1)
except:
break
def test_library_install(self):
'''Install the bundle and partitions, and check that they are
correctly installed. Check that installation is idempotent'''
l = self.get_library()
l.put(self.bundle)
l.put(self.bundle)
r = l.get(self.bundle.identity)
self.assertIsNotNone(r.bundle)
self.assertTrue(r.bundle is not False)
self.assertEquals(self.bundle.identity.id_, r.bundle.identity.id_)
print "Stored: ", r.bundle.identity.name
l.remove(self.bundle)
r = l.get(self.bundle.identity)
self.assertFalse(r)
#
# Same story, but push to remote first, so that the removed
# bundle will get loaded back rom the remote
#
l.put(self.bundle)
l.push()
r = l.get(self.bundle.identity)
self.assertIsNotNone(r.bundle)
l.remove(self.bundle)
r = l.get(self.bundle.identity)
self.assertIsNotNone(r.bundle)
self.assertTrue(r.bundle is not False)
self.assertEquals(self.bundle.identity.id_, r.bundle.identity.id_)
def suite():
suite = unittest.TestSuite()
suite.addTest(unittest.makeSuite(Test))
return suite
if __name__ == "__main__":
unittest.TextTestRunner().run(suite())
|
treyhunner/databundles
|
test/test_client.py
|
Python
|
bsd-3-clause
| 4,373
|
[
"SIESTA"
] |
60c876ba3cee9f1b6b8db989a6ddfdbb2b30c4c4b040998b10667bc99f47ce86
|
#!/usr/bin/env python
#python 2.7.5
#catcount.py
#Version 1. Adam Taranto, Feb 2017
#Contact, Adam Taranto, adam.taranto@anu.edu.au
#############################################
# Concatenate count tables from HTSeq-count #
#############################################
import csv
import sys
import os
import copy
import argparse
import pandas as pd
def tempPathCheck(args):
absOutDir = os.path.abspath(args.outDir)
if not os.path.isdir(absOutDir):
os.makedirs(absOutDir)
return absOutDir
def getbasename(path):
base = os.path.splitext(os.path.basename(path))[0]
return base
def fetchrows(indexlist,filepath,args):
''' Add rownames to indexlist, make unique '''
newrows = list()
with open(filepath) as f:
c = csv.reader(f, delimiter='\t')
for line in c:
if args.featureNames:
if line[0].startswith(args.featureNames):
newrows.append(line[0])
else:
newrows.append(line[0])
oldrows = copy.deepcopy(indexlist)
catlist = oldrows + newrows
uniq_rows = set(catlist)
return list(uniq_rows)
def makeblankdf(args):
index = list()
columns = list()
for sample in args.inFiles:
setcount = 0
for split_sample in sample.split(','):
setcount += 1
if setcount == 1:
columns = columns + [getbasename(split_sample)]
index = fetchrows(index,split_sample,args)
elif setcount > 1:
index = fetchrows(index,split_sample,args)
index.sort()
columns.sort()
df = pd.DataFrame(index=index, columns=columns)
df = df.fillna(0).astype(int)
return df
def readcounts(colID,filepath,args):
rowcounts = dict()
with open(filepath) as f:
c = csv.reader(f, delimiter='\t')
for line in c:
if args.featureNames:
if line[0].startswith(args.featureNames):
rowcounts[line[0]] = line[1]
else:
rowcounts[line[0]] = line[1]
counts_df = pd.DataFrame.from_dict(rowcounts, orient='index', dtype='int64').astype(int)
counts_df.columns = [colID]
return counts_df
def populatetable(mastertable,filelist,args):
filledtable = copy.deepcopy(mastertable)
for sample in filelist:
setcount = 0
for merge_sample in sample.split(','):
setcount += 1
if setcount == 1:
sample_name = getbasename(merge_sample)
temp_df = readcounts(sample_name,merge_sample,args)
filledtable = filledtable.add(temp_df, fill_value=0)
elif setcount > 1:
temp_df = readcounts(sample_name,merge_sample,args)
filledtable = filledtable.add(temp_df, fill_value=0)
return filledtable.astype(int)
def main(args):
if args.inFiles is None:
sys.exit('No input files provided')
if args.outDir:
outdir = tempPathCheck(args)
outpath = os.path.join(outdir,args.outName)
else:
outpath = args.outName
mastertable = makeblankdf(args)
filledtable = populatetable(mastertable,args.inFiles,args)
outopen = open(outpath, 'w')
filledtable.to_csv(path_or_buf=outopen, sep='\t', header=True, index=True, line_terminator='\n')
outopen.close()
if __name__== '__main__':
###Argument handling.
parser = argparse.ArgumentParser(
description='Takes a list of files containing counts by feature name and concatenates into single table. Can merge counts from comma separated pairs of file names.',
prog='catcount')
parser.add_argument("-i", "--inFiles",
type=str,
default=None,
nargs='+',
help="List of count files or pairs of files.")
parser.add_argument("-o", "--outName",
type=str,
default= "CatCounts.txt",
help="Write concatenated count table to this file.")
parser.add_argument("-d", "--outDir",
type=str,
default= None,
help="Directory for output file to be written to.")
parser.add_argument("-n", "--featureNames",
type=str,
default= None,
help="Keep feature names that start with this string. i.e. 'ge' will return 'gene_001'")
args = parser.parse_args()
main(args);
|
Adamtaranto/catcount
|
catcount.py
|
Python
|
mit
| 3,773
|
[
"HTSeq"
] |
73104eed5d8f2d7b56ebc295193628cdb52828a4b7c9079ef8a1d36c94f80790
|
"""Display the contents of the implementation cache."""
# Copyright (C) 2009, Thomas Leonard
# See the README file for details, or visit http://0install.net.
from __future__ import print_function
from zeroinstall import _
import os
import gtk
from zeroinstall.injector import namespaces, model
from zeroinstall.zerostore import BadDigest, manifest
from zeroinstall import support
from zeroinstall.support import basedir
from zeroinstall.gtkui import help_box, gtkutils
__all__ = ['CacheExplorer']
ROX_IFACE = 'http://rox.sourceforge.net/2005/interfaces/ROX-Filer'
# Tree view columns
class Column(object):
columns = []
def __init__(self, name, column_type, resizable=False, props={}, hide=False, markup=False):
self.idx = len(self.columns)
self.columns.append(self)
self.name = name
self.column_type = column_type
self.props = props
self.resizable = resizable
self.hide = hide
self.markup = markup
@classmethod
def column_types(cls):
return [col.column_type for col in cls.columns]
@classmethod
def add_all(cls, tree_view):
[col.add(tree_view) for col in cls.columns]
def get_cell(self):
cell = gtk.CellRendererText()
self.set_props(cell, self.props)
return cell
def set_props(self, obj, props):
for k,v in props.items():
obj.set_property(k, v)
def get_column(self):
if self.markup:
kwargs = {'markup': self.idx}
else:
kwargs = {'text': self.idx}
column = gtk.TreeViewColumn(self.name, self.get_cell(), **kwargs)
if 'xalign' in self.props:
self.set_props(column, {'alignment': self.props['xalign']})
return column
def add(self, tree_view):
if self.hide:
return
column = self.get_column()
if self.resizable: column.set_resizable(True)
tree_view.append_column(column)
NAME = Column(_('Name'), str, hide=True)
URI = Column(_('URI'), str, hide=True)
TOOLTIP = Column(_('Description'), str, hide=True)
ITEM_VIEW = Column(_('Item'), str, props={'ypad': 6, 'yalign': 0}, resizable=True, markup=True)
SELF_SIZE = Column(_('Self Size'), int, hide=True)
TOTAL_SIZE = Column(_('Total Size'), int, hide=True)
PRETTY_SIZE = Column(_('Size'), str, props={'xalign':1.0})
ITEM_OBJECT = Column(_('Object'), object, hide=True)
ACTION_REMOVE = object() # just make a unique value
class Section(object):
may_delete = False
def __init__(self, name, tooltip):
self.name = name
self.tooltip = tooltip
def append_to(self, model):
return model.append(None, extract_columns(
name=self.name,
tooltip=self.tooltip,
object=self,
))
SECTION_INTERFACES = Section(
_("Feeds"),
_("Feeds in the cache"))
SECTION_UNOWNED_IMPLEMENTATIONS = Section(
_("Unowned implementations and temporary files"),
_("These probably aren't needed any longer. You can delete them."))
SECTION_INVALID_INTERFACES = Section(
_("Invalid feeds (unreadable)"),
_("These feeds exist in the cache but cannot be read. You should probably delete them."))
import cgi
def extract_columns(**d):
vals = list(map(lambda x:None, Column.columns))
def setcol(column, val):
vals[column.idx] = val
name = d.get('name', None)
desc = d.get('desc', None)
uri = d.get('uri', None)
setcol(NAME, name)
setcol(URI, uri)
if name and uri:
setcol(ITEM_VIEW, '<span font-size="larger" weight="bold">%s</span>\n'
'<span color="#666666">%s</span>' % tuple(map(cgi.escape, (name, uri))))
else:
setcol(ITEM_VIEW, cgi.escape(name or desc))
size = d.get('size', 0)
setcol(SELF_SIZE, size)
setcol(TOTAL_SIZE, 0) # must be set to prevent type error
setcol(TOOLTIP, d.get('tooltip', None))
setcol(ITEM_OBJECT, d.get('object', None))
return vals
def popup_menu(bev, obj, model, path, cache_explorer):
menu = gtk.Menu()
for i in obj.menu_items:
if i is None:
item = gtk.SeparatorMenuItem()
else:
name, cb = i
item = gtk.MenuItem(name)
def _cb(item, cb=cb):
action_required = cb(obj, cache_explorer)
if action_required is ACTION_REMOVE:
model.remove(model.get_iter(path))
item.connect('activate', _cb)
item.show()
menu.append(item)
menu.popup(None, None, None, bev.button, bev.time)
def warn(message, parent=None):
"Present a blocking warning message with OK/Cancel buttons, and return True if OK was pressed"
dialog = gtk.MessageDialog(parent=parent, buttons=gtk.BUTTONS_OK_CANCEL, type=gtk.MESSAGE_WARNING)
dialog.set_property('text', message)
response = []
def _response(dialog, resp):
if resp == gtk.RESPONSE_OK:
response.append(True)
dialog.connect('response', _response)
dialog.run()
dialog.destroy()
return bool(response)
def size_if_exists(path):
"Get the size for a file, or 0 if it doesn't exist."
if path and os.path.isfile(path):
return os.path.getsize(path)
return 0
def get_size(path):
"Get the size for a directory tree. Get the size from the .manifest if possible."
man = os.path.join(path, '.manifest')
if os.path.exists(man):
size = os.path.getsize(man)
for line in open(man, 'rb'):
if line[:1] in "XF":
size += int(line.split(' ', 4)[3])
else:
size = 0
for root, dirs, files in os.walk(path):
for name in files:
size += os.path.getsize(os.path.join(root, name))
return size
def summary(feed):
if feed.summary:
return feed.get_name() + ' - ' + feed.summary
return feed.get_name()
def get_selected_paths(tree_view):
"GTK 2.0 doesn't have this built-in"
selection = tree_view.get_selection()
paths = []
def add(model, path, iter):
paths.append(path)
selection.selected_foreach(add)
return paths
def all_children(model, iter):
"make a python generator out of the children of `iter`"
iter = model.iter_children(iter)
while iter:
yield iter
iter = model.iter_next(iter)
# Responses
DELETE = 0
SAFE_MODE = False # really delete things
#SAFE_MODE = True # print deletes, instead of performing them
class CachedFeed(object):
def __init__(self, uri, size):
self.uri = uri
self.size = size
def delete(self):
if not os.path.isabs(self.uri):
cached_iface = basedir.load_first_cache(namespaces.config_site,
'interfaces', model.escape(self.uri))
if cached_iface:
if SAFE_MODE:
print("Delete", cached_iface)
else:
os.unlink(cached_iface)
user_overrides = basedir.load_first_config(namespaces.config_site,
namespaces.config_prog,
'interfaces', model._pretty_escape(self.uri))
if user_overrides:
if SAFE_MODE:
print("Delete", user_overrides)
else:
os.unlink(user_overrides)
def __cmp__(self, other):
return self.uri.__cmp__(other.uri)
class ValidFeed(CachedFeed):
def __init__(self, feed, size):
CachedFeed.__init__(self, feed.url, size)
self.feed = feed
self.in_cache = []
def delete_children(self):
deletable = self.deletable_children()
undeletable = list(filter(lambda child: not child.may_delete, self.in_cache))
# the only undeletable items we expect to encounter are LocalImplementations
unexpected_undeletable = list(filter(lambda child: not isinstance(child, LocalImplementation), undeletable))
assert not unexpected_undeletable, "unexpected undeletable items!: %r" % (unexpected_undeletable,)
[child.delete() for child in deletable]
def delete(self):
self.delete_children()
super(ValidFeed, self).delete()
def append_to(self, model, iter):
iter2 = model.append(iter, extract_columns(
name=self.feed.get_name(),
uri=self.uri,
tooltip=self.feed.summary,
object=self))
for cached_impl in self.in_cache:
cached_impl.append_to(model, iter2)
def launch(self, explorer):
os.spawnlp(os.P_NOWAIT, '0launch', '0launch', '--gui', self.uri)
def copy_uri(self, explorer):
clipboard = gtk.clipboard_get()
clipboard.set_text(self.uri)
primary = gtk.clipboard_get('PRIMARY')
primary.set_text(self.uri)
def deletable_children(self):
return list(filter(lambda child: child.may_delete, self.in_cache))
def prompt_delete(self, cache_explorer):
description = "\"%s\"" % (self.feed.get_name(),)
num_children = len(self.deletable_children())
if self.in_cache:
description += _(" (and %s %s)") % (num_children, _("implementation") if num_children == 1 else _("implementations"))
if warn(_("Really delete %s?") % (description,), parent=cache_explorer.window):
self.delete()
return ACTION_REMOVE
menu_items = [(_('Launch with GUI'), launch),
(_('Copy URI'), copy_uri),
(_('Delete'), prompt_delete)]
class RemoteFeed(ValidFeed):
may_delete = True
class LocalFeed(ValidFeed):
may_delete = False
class InvalidFeed(CachedFeed):
may_delete = True
def __init__(self, uri, ex, size):
CachedFeed.__init__(self, uri, size)
self.ex = ex
def append_to(self, model, iter):
model.append(iter, extract_columns(
name=self.uri.rsplit('/', 1)[-1],
uri=self.uri,
size=self.size,
tooltip=self.ex,
object=self))
class LocalImplementation:
may_delete = False
def __init__(self, impl):
self.impl = impl
def append_to(self, model, iter):
model.append(iter, extract_columns(
name=self.impl.local_path,
tooltip=_('This is a local version, not held in the cache.'),
object=self))
class CachedImplementation:
may_delete = True
def __init__(self, cache_dir, digest):
self.impl_path = os.path.join(cache_dir, digest)
self.size = get_size(self.impl_path)
self.digest = digest
def delete(self):
if SAFE_MODE:
print("Delete", self.impl_path)
else:
support.ro_rmtree(self.impl_path)
def open_rox(self, explorer):
os.spawnlp(os.P_WAIT, '0launch', '0launch', ROX_IFACE, '-d', self.impl_path)
def verify(self, explorer):
try:
manifest.verify(self.impl_path)
except BadDigest as ex:
box = gtk.MessageDialog(None, 0,
gtk.MESSAGE_WARNING, gtk.BUTTONS_OK, str(ex))
if ex.detail:
swin = gtk.ScrolledWindow()
buffer = gtk.TextBuffer()
mono = buffer.create_tag('mono', family = 'Monospace')
buffer.insert_with_tags(buffer.get_start_iter(), ex.detail, mono)
text = gtk.TextView(buffer)
text.set_editable(False)
text.set_cursor_visible(False)
swin.add(text)
swin.set_shadow_type(gtk.SHADOW_IN)
swin.set_border_width(4)
box.vbox.pack_start(swin)
swin.show_all()
box.set_resizable(True)
else:
box = gtk.MessageDialog(None, 0,
gtk.MESSAGE_INFO, gtk.BUTTONS_OK,
_('Contents match digest; nothing has been changed.'))
box.run()
box.destroy()
def prompt_delete(self, explorer):
if warn(_("Really delete implementation?"), parent=explorer.window):
self.delete()
return ACTION_REMOVE
menu_items = [(_('Open in ROX-Filer'), open_rox),
(_('Verify integrity'), verify),
(_('Delete'), prompt_delete)]
class UnusedImplementation(CachedImplementation):
def append_to(self, model, iter):
model.append(iter, extract_columns(
name=self.digest,
size=self.size,
tooltip=self.impl_path,
object=self))
class KnownImplementation(CachedImplementation):
def __init__(self, cached_iface, cache_dir, impl, impl_size, digest):
CachedImplementation.__init__(self, cache_dir, digest)
self.cached_iface = cached_iface
self.impl = impl
self.size = impl_size
def delete(self):
if SAFE_MODE:
print("Delete", self.impl)
else:
CachedImplementation.delete(self)
self.cached_iface.in_cache.remove(self)
def append_to(self, model, iter):
impl = self.impl
label = _('Version %(implementation_version)s (%(arch)s)') % {
'implementation_version': impl.get_version(),
'arch': impl.arch or 'any platform'}
model.append(iter, extract_columns(
name=label,
size=self.size,
tooltip=self.impl_path,
object=self))
def __cmp__(self, other):
if hasattr(other, 'impl'):
return self.impl.__cmp__(other.impl)
return -1
class CacheExplorer:
"""A graphical interface for viewing the cache and deleting old items."""
def __init__(self, iface_cache):
widgets = gtkutils.Template(os.path.join(os.path.dirname(__file__), 'cache.ui'), 'cache')
self.window = window = widgets.get_widget('cache')
window.set_default_size(gtk.gdk.screen_width() / 2, gtk.gdk.screen_height() / 2)
self.iface_cache = iface_cache
# Model
self.raw_model = gtk.TreeStore(*Column.column_types())
self.view_model = self.raw_model.filter_new()
self.model.set_sort_column_id(URI.idx, gtk.SORT_ASCENDING)
self.tree_view = widgets.get_widget('treeview')
self.tree_view.set_model(self.view_model)
Column.add_all(self.tree_view)
# Sort / Filter options:
def init_combo(combobox, items, on_select):
liststore = gtk.ListStore(str)
combobox.set_model(liststore)
cell = gtk.CellRendererText()
combobox.pack_start(cell, True)
combobox.add_attribute(cell, 'text', 0)
for item in items:
combobox.append_text(item[0])
combobox.set_active(0)
def _on_select(*a):
selected_item = combobox.get_active()
on_select(selected_item)
combobox.connect('changed', lambda *a: on_select(items[combobox.get_active()]))
def set_sort_order(sort_order):
#print "SORT: %r" % (sort_order,)
name, column, order = sort_order
self.model.set_sort_column_id(column.idx, order)
self.sort_combo = widgets.get_widget('sort_combo')
init_combo(self.sort_combo, SORT_OPTIONS, set_sort_order)
def set_filter(f):
#print "FILTER: %r" % (f,)
description, filter_func = f
self.view_model = self.model.filter_new()
self.view_model.set_visible_func(filter_func)
self.tree_view.set_model(self.view_model)
self.set_initial_expansion()
self.filter_combo = widgets.get_widget('filter_combo')
init_combo(self.filter_combo, FILTER_OPTIONS, set_filter)
def button_press(tree_view, bev):
if bev.button != 3:
return False
pos = tree_view.get_path_at_pos(int(bev.x), int(bev.y))
if not pos:
return False
path, col, x, y = pos
obj = self.model[path][ITEM_OBJECT.idx]
if obj and hasattr(obj, 'menu_items'):
popup_menu(bev, obj, model=self.model, path=path, cache_explorer=self)
self.tree_view.connect('button-press-event', button_press)
# Responses
window.set_default_response(gtk.RESPONSE_CLOSE)
selection = self.tree_view.get_selection()
def selection_changed(selection):
any_selected = False
for x in get_selected_paths(self.tree_view):
obj = self.model[x][ITEM_OBJECT.idx]
if obj is None or not obj.may_delete:
window.set_response_sensitive(DELETE, False)
return
any_selected = True
window.set_response_sensitive(DELETE, any_selected)
selection.set_mode(gtk.SELECTION_MULTIPLE)
selection.connect('changed', selection_changed)
selection_changed(selection)
def response(dialog, resp):
if resp == gtk.RESPONSE_CLOSE:
window.destroy()
elif resp == gtk.RESPONSE_HELP:
cache_help.display()
elif resp == DELETE:
self._delete()
window.connect('response', response)
@property
def model(self):
return self.view_model.get_model()
def _delete(self):
errors = []
model = self.model
paths = get_selected_paths(self.tree_view)
paths.reverse()
for path in paths:
item = model[path][ITEM_OBJECT.idx]
assert item.delete
try:
item.delete()
except OSError as ex:
errors.append(str(ex))
else:
model.remove(model.get_iter(path))
self._update_sizes()
if errors:
gtkutils.show_message_box(self.window, _("Failed to delete:\n%s") % '\n'.join(errors))
def show(self):
"""Display the window and scan the caches to populate it."""
self.window.show()
self.window.window.set_cursor(gtkutils.get_busy_pointer())
gtk.gdk.flush()
self._populate_model()
self.set_initial_expansion()
def set_initial_expansion(self):
model = self.model
try:
i = model.get_iter_root()
while i:
# expand only "Feeds"
if model[i][ITEM_OBJECT.idx] is SECTION_INTERFACES:
self.tree_view.expand_row(model.get_path(i), False)
i = model.iter_next(i)
finally:
self.window.window.set_cursor(None)
def _populate_model(self):
# Find cached implementations
unowned = {} # Impl ID -> Store
duplicates = [] # TODO
for s in self.iface_cache.stores.stores:
if os.path.isdir(s.dir):
for id in os.listdir(s.dir):
if id in unowned:
duplicates.append(id)
unowned[id] = s
ok_feeds = []
error_feeds = []
# Look through cached feeds for implementation owners
all_interfaces = self.iface_cache.list_all_interfaces()
all_feeds = {}
for uri in all_interfaces:
try:
iface = self.iface_cache.get_interface(uri)
except Exception as ex:
error_feeds.append((uri, str(ex), 0))
else:
all_feeds.update(self.iface_cache.get_feeds(iface))
for url, feed in all_feeds.iteritems():
if not feed: continue
feed_size = 0
try:
if url != feed.url:
# (e.g. for .new feeds)
raise Exception('Incorrect URL for feed (%s vs %s)' % (url, feed.url))
if os.path.isabs(url):
cached_feed = url
feed_type = LocalFeed
else:
feed_type = RemoteFeed
cached_feed = basedir.load_first_cache(namespaces.config_site,
'interfaces', model.escape(url))
user_overrides = basedir.load_first_config(namespaces.config_site,
namespaces.config_prog,
'interfaces', model._pretty_escape(url))
feed_size = size_if_exists(cached_feed) + size_if_exists(user_overrides)
except Exception as ex:
error_feeds.append((url, str(ex), feed_size))
else:
cached_feed = feed_type(feed, feed_size)
for impl in feed.implementations.values():
if impl.local_path:
cached_feed.in_cache.append(LocalImplementation(impl))
for digest in impl.digests:
if digest in unowned:
cached_dir = unowned[digest].dir
impl_path = os.path.join(cached_dir, digest)
impl_size = get_size(impl_path)
cached_feed.in_cache.append(KnownImplementation(cached_feed, cached_dir, impl, impl_size, digest))
del unowned[digest]
cached_feed.in_cache.sort()
ok_feeds.append(cached_feed)
if error_feeds:
iter = SECTION_INVALID_INTERFACES.append_to(self.raw_model)
for uri, ex, size in error_feeds:
item = InvalidFeed(uri, ex, size)
item.append_to(self.raw_model, iter)
unowned_sizes = []
local_dir = os.path.join(basedir.xdg_cache_home, '0install.net', 'implementations')
for id in unowned:
if unowned[id].dir == local_dir:
impl = UnusedImplementation(local_dir, id)
unowned_sizes.append((impl.size, impl))
if unowned_sizes:
iter = SECTION_UNOWNED_IMPLEMENTATIONS.append_to(self.raw_model)
for size, item in unowned_sizes:
item.append_to(self.raw_model, iter)
if ok_feeds:
iter = SECTION_INTERFACES.append_to(self.raw_model)
for item in ok_feeds:
item.append_to(self.raw_model, iter)
self._update_sizes()
def _update_sizes(self):
"""Set TOTAL_SIZE and PRETTY_SIZE to the total size, including all children."""
m = self.raw_model
def update(itr):
total = m[itr][SELF_SIZE.idx]
total += sum(map(update, all_children(m, itr)))
m[itr][PRETTY_SIZE.idx] = support.pretty_size(total) if total else '-'
m[itr][TOTAL_SIZE.idx] = total
return total
itr = m.get_iter_root()
while itr:
update(itr)
itr = m.iter_next(itr)
SORT_OPTIONS = [
('URI', URI, gtk.SORT_ASCENDING),
('Name', NAME, gtk.SORT_ASCENDING),
('Size', TOTAL_SIZE, gtk.SORT_DESCENDING),
]
def init_filters():
def filter_only(filterable_types, filter_func):
def _filter(model, iter):
obj = model.get_value(iter, ITEM_OBJECT.idx)
if any((isinstance(obj, t) for t in filterable_types)):
result = filter_func(model, iter)
return result
return True
return _filter
def not_(func):
return lambda *a: not func(*a)
def is_local_feed(model, iter):
return isinstance(model[iter][ITEM_OBJECT.idx], LocalFeed)
def has_implementations(model, iter):
return model.iter_has_child(iter)
return [
('All', lambda *a: True),
('Feeds with implementations', filter_only([ValidFeed], has_implementations)),
('Feeds without implementations', filter_only([ValidFeed], not_(has_implementations))),
('Local Feeds', filter_only([ValidFeed], is_local_feed)),
('Remote Feeds', filter_only([ValidFeed], not_(is_local_feed))),
]
FILTER_OPTIONS = init_filters()
cache_help = help_box.HelpBox(_("Cache Explorer Help"),
(_('Overview'), '\n' +
_("""When you run a program using Zero Install, it downloads the program's 'feed' file, \
which gives information about which versions of the program are available. This feed \
file is stored in the cache to save downloading it next time you run the program.
When you have chosen which version (implementation) of the program you want to \
run, Zero Install downloads that version and stores it in the cache too. Zero Install lets \
you have many different versions of each program on your computer at once. This is useful, \
since it lets you use an old version if needed, and different programs may need to use \
different versions of libraries in some cases.
The cache viewer shows you all the feeds and implementations in your cache. \
This is useful to find versions you don't need anymore, so that you can delete them and \
free up some disk space.""")),
(_('Invalid feeds'), '\n' +
_("""The cache viewer gets a list of all feeds in your cache. However, some may not \
be valid; they are shown in the 'Invalid feeds' section. It should be fine to \
delete these. An invalid feed may be caused by a local feed that no longer \
exists or by a failed attempt to download a feed (the name ends in '.new').""")),
(_('Unowned implementations and temporary files'), '\n' +
_("""The cache viewer searches through all the feeds to find out which implementations \
they use. If no feed uses an implementation, it is shown in the 'Unowned implementations' \
section.
Unowned implementations can result from old versions of a program no longer being listed \
in the feed file. Temporary files are created when unpacking an implementation after \
downloading it. If the archive is corrupted, the unpacked files may be left there. Unless \
you are currently unpacking new programs, it should be fine to delete everything in this \
section.""")),
(_('Feeds'), '\n' +
_("""All remaining feeds are listed in this section. You may wish to delete old versions of \
certain programs. Deleting a program which you may later want to run will require it to be downloaded \
again. Deleting a version of a program which is currently running may cause it to crash, so be careful!""")))
|
dabrahams/zeroinstall
|
zeroinstall/gtkui/cache.py
|
Python
|
lgpl-2.1
| 22,281
|
[
"VisIt"
] |
456ae1542a96329ad93007e8a01cb23c38e781ff8b8a8170b5a05ac754c1fd2d
|
# Orca
#
# Copyright 2011. Orca Team.
# Author: Joanmarie Diggs <joanmarie.diggs@gmail.com>
#
# This library is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
# License as published by the Free Software Foundation; either
# version 2.1 of the License, or (at your option) any later version.
#
# This library is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public
# License along with this library; if not, write to the
# Free Software Foundation, Inc., Franklin Street, Fifth Floor,
# Boston MA 02110-1301 USA.
__id__ = "$Id$"
__version__ = "$Revision$"
__date__ = "$Date$"
__copyright__ = "Copyright (c) 2011. Orca Team."
__license__ = "LGPL"
from gi.repository import GLib
import pyatspi
import queue
import threading
import time
from . import debug
from . import input_event
from . import messages
from . import orca_state
from . import script_manager
from . import settings
_scriptManager = script_manager.getManager()
class EventManager:
def __init__(self):
debug.println(debug.LEVEL_FINEST, 'INFO: Initializing event manager')
self._scriptListenerCounts = {}
self.registry = pyatspi.Registry
self._active = False
self._enqueueCount = 0
self._dequeueCount = 0
self._eventQueue = queue.Queue(0)
self._gidleId = 0
self._gidleLock = threading.Lock()
self.noFocusTimestamp = 0.0
debug.println(debug.LEVEL_FINEST, 'INFO: Event manager initialized')
def activate(self):
"""Called when this presentation manager is activated."""
debug.println(debug.LEVEL_FINEST, 'INFO: Activating event manager')
self._registerListener("window:activate")
self._registerListener("window:deactivate")
self._registerListener("object:children-changed")
self._registerListener("mouse:button")
self._active = True
debug.println(debug.LEVEL_FINEST, 'INFO: Event manager activated')
def deactivate(self):
"""Called when this event manager is deactivated."""
debug.println(debug.LEVEL_FINEST, 'INFO: Dectivating event manager')
self._active = False
for eventType in list(self._scriptListenerCounts.keys()):
self.registry.deregisterEventListener(self._enqueue, eventType)
self._scriptListenerCounts = {}
debug.println(debug.LEVEL_FINEST, 'INFO: Event manager deactivated')
def _ignore(self, event):
"""Returns True if this event should be ignored."""
if not self._active:
return True
ignoredList = ['object:state-changed:defunct',
'object:property-change:accessible-parent']
ignoredList.extend(settings.ignoredEventsList)
if list(filter(event.type.startswith, ignoredList)):
return True
# This should ultimately be changed as there are valid reasons
# to handle these events at the application level.
if event.type.startswith('object:children-changed:remove') \
and event.source != self.registry.getDesktop(0):
return True
return False
def _addToQueue(self, event, asyncMode):
debugging = settings.debugEventQueue
if debugging:
debug.println(debug.LEVEL_ALL, " acquiring lock...")
self._gidleLock.acquire()
if debugging:
debug.println(debug.LEVEL_ALL, " ...acquired")
debug.println(debug.LEVEL_ALL, " calling queue.put...")
debug.println(debug.LEVEL_ALL, " (full=%s)" \
% self._eventQueue.full())
self._eventQueue.put(event)
if debugging:
debug.println(debug.LEVEL_ALL, " ...put complete")
if asyncMode and not self._gidleId:
if settings.gilSleepTime:
time.sleep(settings.gilSleepTime)
self._gidleId = GLib.idle_add(self._dequeue)
if debugging:
debug.println(debug.LEVEL_ALL, " releasing lock...")
self._gidleLock.release()
if settings.debugEventQueue:
debug.println(debug.LEVEL_ALL, " ...released")
def _queuePrintln(self, e, isEnqueue=True):
"""Convenience method to output queue-related debugging info."""
if isinstance(e, input_event.KeyboardEvent):
data = "'%s' (%d)" % (e.event_string, e.hw_code)
elif isinstance(e, input_event.BrailleEvent):
data = "'%s'" % repr(e.event)
elif not debug.eventDebugFilter or debug.eventDebugFilter.match(e.type):
data = ""
else:
return
eType = str(e.type).upper()
if isEnqueue:
string = "----------> QUEUEING %s %s" % (eType, data)
else:
string = "DEQUEUED %s %s <----------" % (eType, data)
debug.println(debug.LEVEL_ALL, string)
def _enqueue(self, e):
"""Handles the enqueueing of all events destined for scripts.
Arguments:
- e: an at-spi event.
"""
if settings.debugEventQueue:
if self._enqueueCount:
msg = "_enqueue entered before exiting (count = %d)" \
% self._enqueueCount
debug.println(debug.LEVEL_ALL, msg)
self._enqueueCount += 1
inputEvents = (input_event.KeyboardEvent, input_event.BrailleEvent)
isObjectEvent = not isinstance(e, inputEvents)
if isObjectEvent and self._ignore(e):
if settings.debugEventQueue:
self._enqueueCount -= 1
return
self._queuePrintln(e)
asyncMode = settings.asyncMode
if isObjectEvent:
app = e.source.getApplication()
try:
toolkitName = app.toolkitName
except:
toolkitName = None
if toolkitName in settings.synchronousToolkits:
asyncMode = False
script = _scriptManager.getScript(app, e.source)
script.eventCache[e.type] = (e, time.time())
self._addToQueue(e, asyncMode)
if not asyncMode:
self._dequeue()
if settings.debugEventQueue:
self._enqueueCount -= 1
def _dequeue(self):
"""Handles all events destined for scripts. Called by the GTK
idle thread."""
rerun = True
if settings.debugEventQueue:
debug.println(debug.LEVEL_ALL,
"event_manager._dequeue %d" % self._dequeueCount)
self._dequeueCount += 1
try:
event = self._eventQueue.get_nowait()
self._queuePrintln(event, isEnqueue=False)
inputEvents = (input_event.KeyboardEvent, input_event.BrailleEvent)
if isinstance(event, inputEvents):
self._processInputEvent(event)
else:
orca_state.currentObjectEvent = event
debugging = not debug.eventDebugFilter \
or debug.eventDebugFilter.match(event.type)
if debugging:
startTime = time.time()
debug.println(debug.eventDebugLevel,
"\nvvvvv PROCESS OBJECT EVENT %s vvvvv" \
% event.type)
self._processObjectEvent(event)
if debugging:
debug.println(debug.eventDebugLevel,
"TOTAL PROCESSING TIME: %.4f" \
% (time.time() - startTime))
debug.println(debug.eventDebugLevel,
"^^^^^ PROCESS OBJECT EVENT %s ^^^^^\n" \
% event.type)
orca_state.currentObjectEvent = None
# [[[TODO: HACK - it would seem logical to only do this if we
# discover the queue is empty, but this inroduces a hang for
# some reason if done inside an acquire/release block for a
# lock. So...we do it here.]]]
#
try:
noFocus = \
not orca_state.activeScript \
or (not orca_state.locusOfFocus and \
self.noFocusTimestamp != orca_state.noFocusTimestamp)
except:
noFocus = True
self._gidleLock.acquire()
if self._eventQueue.empty():
if noFocus:
if settings.gilSleepTime:
time.sleep(settings.gilSleepTime)
fullMessage = messages.NO_FOCUS
defaultScript = _scriptManager.getDefaultScript()
defaultScript.presentMessage(fullMessage, '')
self.noFocusTimestamp = orca_state.noFocusTimestamp
self._gidleId = 0
rerun = False # destroy and don't call again
self._gidleLock.release()
except queue.Empty:
debug.println(debug.LEVEL_SEVERE,
"event_manager._dequeue: the event queue is empty!")
self._gidleId = 0
rerun = False # destroy and don't call again
except:
debug.printException(debug.LEVEL_SEVERE)
if settings.debugEventQueue:
self._dequeueCount -= 1
debug.println(debug.LEVEL_ALL,
"Leaving _dequeue. Count: %d" % self._dequeueCount)
return rerun
def _registerListener(self, eventType):
"""Tells this module to listen for the given event type.
Arguments:
- eventType: the event type.
"""
debug.println(debug.LEVEL_FINEST,
'INFO: Event manager registering listener for: %s' \
% eventType)
if eventType in self._scriptListenerCounts:
self._scriptListenerCounts[eventType] += 1
else:
self.registry.registerEventListener(self._enqueue, eventType)
self._scriptListenerCounts[eventType] = 1
def _deregisterListener(self, eventType):
"""Tells this module to stop listening for the given event type.
Arguments:
- eventType: the event type.
"""
debug.println(debug.LEVEL_FINEST,
'INFO: Event manager deregistering listener for: %s' \
% eventType)
if not eventType in self._scriptListenerCounts:
return
self._scriptListenerCounts[eventType] -= 1
if self._scriptListenerCounts[eventType] == 0:
self.registry.deregisterEventListener(self._enqueue, eventType)
del self._scriptListenerCounts[eventType]
def registerScriptListeners(self, script):
"""Tells the event manager to start listening for all the event types
of interest to the script.
Arguments:
- script: the script.
"""
debug.println(debug.LEVEL_FINEST,
'INFO: Event manager registering listeners for: %s' \
% script)
for eventType in list(script.listeners.keys()):
self._registerListener(eventType)
def deregisterScriptListeners(self, script):
"""Tells the event manager to stop listening for all the event types
of interest to the script.
Arguments:
- script: the script.
"""
debug.println(debug.LEVEL_FINEST,
'INFO: Event manager deregistering listeners for: %s' \
% script)
for eventType in list(script.listeners.keys()):
self._deregisterListener(eventType)
def registerModuleListeners(self, listeners):
"""Register the listeners on behalf of the caller."""
for eventType, function in list(listeners.items()):
self.registry.registerEventListener(function, eventType)
def deregisterModuleListeners(self, listeners):
"""Deegister the listeners on behalf of the caller."""
for eventType, function in list(listeners.items()):
self.registry.deregisterEventListener(function, eventType)
def registerKeystrokeListener(self, function, mask=None, kind=None):
"""Register the keystroke listener on behalf of the caller."""
debug.println(
debug.LEVEL_FINEST,
'INFO: Event manager registering keystroke listener function: %s' \
% function)
if mask == None:
mask = list(range(256))
if kind == None:
kind = (pyatspi.KEY_PRESSED_EVENT, pyatspi.KEY_RELEASED_EVENT)
self.registry.registerKeystrokeListener(function, mask=mask, kind=kind)
def deregisterKeystrokeListener(self, function, mask=None, kind=None):
"""Deregister the keystroke listener on behalf of the caller."""
debug.println(
debug.LEVEL_FINEST,
'INFO: Event manager deregistering keystroke listener function: %s'\
% function)
if mask == None:
mask = list(range(256))
if kind == None:
kind = (pyatspi.KEY_PRESSED_EVENT, pyatspi.KEY_RELEASED_EVENT)
self.registry.deregisterKeystrokeListener(
function, mask=mask, kind=kind)
def _processInputEvent(self, event):
"""Processes the given input event based on the keybinding from the
currently-active script.
Arguments:
- event: an instance of BrailleEvent or a KeyboardEvent
"""
if not orca_state.activeScript:
return
if isinstance(event, input_event.KeyboardEvent):
function = orca_state.activeScript.processKeyboardEvent
data = "'%s' (%d)" % (event.event_string, event.hw_code)
elif isinstance(event, input_event.BrailleEvent):
function = orca_state.activeScript.processBrailleEvent
data = "'%s'" % repr(event.event)
else:
return
eType = str(event.type).upper()
startTime = time.time()
debug.println(debug.eventDebugLevel,
"\nvvvvv PROCESS %s %s vvvvv" % (eType, data))
try:
function(event)
except:
debug.printException(debug.LEVEL_WARNING)
debug.printStack(debug.LEVEL_WARNING)
debug.println(debug.eventDebugLevel,
"TOTAL PROCESSING TIME: %.4f" \
% (time.time() - startTime))
debug.println(debug.eventDebugLevel,
"^^^^^ PROCESS %s %s ^^^^^\n" % (eType, data))
@staticmethod
def _getScriptForEvent(event):
"""Returns the script associated with event."""
if event.type.startswith("mouse:"):
return orca_state.activeScript
script = None
try:
app = event.host_application or event.source.getApplication()
if app and app.getState().contains(pyatspi.STATE_DEFUNCT):
msg = 'WARNING: App is defunct. Cannot get script for event.'
debug.println(debug.LEVEL_WARNING, msg)
return None
except:
msg = 'WARNING: Exception when getting script for event.'
debug.println(debug.LEVEL_WARNING, msg)
else:
script = _scriptManager.getScript(app, event.source)
return script
def _isActivatableEvent(self, event, script=None):
"""Determines if the event is one which should cause us to
change which script is currently active.
Returns a (boolean, string) tuple indicating whether or not
this is an activatable event, and our reason (for the purpose
of debugging).
"""
if not event.source:
return False, "event.source? What event.source??"
role = state = None
try:
role = event.source.getRole()
except (LookupError, RuntimeError):
return False, "Error getting event.source's role"
try:
state = event.source.getState()
except (LookupError, RuntimeError):
return False, "Error getting event.source's state"
if not script:
script = self._getScriptForEvent(event)
if not script:
return False, "There is no script for this event."
if script == orca_state.activeScript:
return False, "The script for this event is already active."
if not script.isActivatableEvent(event):
return False, "The script says not to activate for this event."
eType = event.type
if eType.startswith('window:activate'):
return True, "window:activate event"
if eType.startswith('focus') \
or (eType.startswith('object:state-changed:focused')
and event.detail1):
return True, "Event source claimed focus."
# This condition appears with gnome-screensave-dialog.
# See bug 530368.
if eType.startswith('object:state-changed:showing') \
and role == pyatspi.ROLE_PANEL \
and state.contains(pyatspi.STATE_MODAL):
return True, "Modal panel is showing."
return False, "No reason found to activate a different script."
def _processObjectEvent(self, event):
"""Handles all object events destined for scripts.
Arguments:
- e: an at-spi event.
"""
debug.printObjectEvent(debug.LEVEL_FINEST, event)
eType = event.type
if eType.startswith("object:children-changed:remove"):
try:
if event.source == self.registry.getDesktop(0):
_scriptManager.reclaimScripts()
return
except (LookupError, RuntimeError):
# If we got this error here, we'll get it again when we
# attempt to get the state, catch it, and clean up.
pass
except:
debug.printException(debug.LEVEL_WARNING)
return
# Clean up any flat review context so that Orca does not get
# confused (see bgo#609633)
#
if eType.startswith("window:deactivate") \
and orca_state.activeScript \
and orca_state.activeScript.flatReviewContext \
and orca_state.activeScript.app == event.host_application:
orca_state.activeScript.flatReviewContext = None
try:
state = event.source.getState()
except (LookupError, RuntimeError):
debug.println(debug.LEVEL_WARNING,
"Error while processing event: %s" % eType)
if eType.startswith("window:deactivate"):
orca_state.locusOfFocus = None
orca_state.activeWindow = None
return
except:
return
if state and state.contains(pyatspi.STATE_DEFUNCT):
debug.println(debug.LEVEL_FINEST, "IGNORING DEFUNCT OBJECT")
if eType.startswith("window:deactivate"):
orca_state.locusOfFocus = None
orca_state.activeWindow = None
return
if state and state.contains(pyatspi.STATE_ICONIFIED):
debug.println(debug.LEVEL_FINEST, "IGNORING ICONIFIED OBJECT")
return
if not debug.eventDebugFilter or debug.eventDebugFilter.match(eType) \
and not eType.startswith("mouse:"):
debug.printDetails(debug.LEVEL_FINEST, " ", event.source)
script = self._getScriptForEvent(event)
if not script:
return
debug.println(debug.LEVEL_FINEST, "Script for event: %s" % script.name)
setNewActiveScript, reason = self._isActivatableEvent(event, script)
if setNewActiveScript:
app = event.host_application or event.source.getApplication()
_scriptManager.setActiveScript(script, reason)
script.processObjectEvent(event)
def processKeyboardEvent(self, keyboardEvent):
"""Processes the given keyboard event based on the keybinding from the
currently active script. This method is called synchronously from the
at-spi registry and should be performant. In addition, it must return
True if it has consumed the event (and False if not).
Arguments:
- keyboardEvent: an instance of input_event.KeyboardEvent
Returns True if the event should be consumed.
"""
consume = False
if orca_state.activeScript \
and orca_state.activeScript.consumesKeyboardEvent(keyboardEvent):
consume = not orca_state.bypassNextCommand
if consume:
self._enqueue(keyboardEvent)
return consume
def processBrailleEvent(self, brailleEvent):
"""Called whenever a cursor key is pressed on the Braille display.
Arguments:
- brailleEvent: an instance of input_event.BrailleEvent
Returns True if the command was consumed; otherwise False
"""
if orca_state.activeScript \
and orca_state.activeScript.consumesBrailleEvent(brailleEvent):
self._enqueue(brailleEvent)
return True
else:
return False
_manager = EventManager()
def getManager():
return _manager
|
h4ck3rm1k3/orca-sonar
|
src/orca/event_manager.py
|
Python
|
lgpl-2.1
| 21,739
|
[
"ORCA"
] |
d9ecf13d9f532c2e4bdee3d649c3e9f69e626769fbc2a128d30dd46bb64e10ba
|
# Copyright 2013-2021 Lawrence Livermore National Security, LLC and other
# Spack Project Developers. See the top-level COPYRIGHT file for details.
#
# SPDX-License-Identifier: (Apache-2.0 OR MIT)
from spack import *
class RRrblup(RPackage):
"""Ridge Regression and Other Kernels for Genomic Selection
Software for genomic prediction with the RR-BLUP mixed model (Endelman
2011, <doi:10.3835/plantgenome2011.08.0024>). One application is to
estimate marker effects by ridge regression; alternatively, BLUPs can be
calculated based on an additive relationship matrix or a Gaussian
kernel."""
homepage = "https://potatobreeding.cals.wisc.edu/software"
url = "https://cloud.r-project.org/src/contrib/rrBLUP_4.6.tar.gz"
list_url = "https://cloud.r-project.org/src/contrib/Archive/rrBLUP"
version('4.6.1', sha256='e9230e74cc430a83ac5567071cb1c7f00b35c368f7d79bcc1cfde7225446c4db')
version('4.6', sha256='28b475a1466fcdc1780caace75cf34155338fda496cebd5799315598a4bc84af')
depends_on('r@2.14:', type=('build', 'run'))
|
LLNL/spack
|
var/spack/repos/builtin/packages/r-rrblup/package.py
|
Python
|
lgpl-2.1
| 1,071
|
[
"Gaussian"
] |
8d4f25a4864477d81a78b50d90c793b08efa84855e9aa87455e7ecbdd591dba7
|
from __future__ import division
import numpy as np
import scipy
from scipy.spatial.distance import cdist
from DensityEstimator import DensityEstimator
from VectorGaussianKernel import VectorGaussianKernel
from multi_flatten import multi_flatten
class WeightedKDE(DensityEstimator):
def __init__(self, d_points, num_subsamples=-1, num_pts_used=-1, d=None, kernel='gaussian', h=None, fixed_bandwidth=False, mode="FULL"):
super(WeightedKDE, self).__init__(d_points, num_subsamples, num_pts_used)
if not d:
if len(self.points.shape) < 2:
d = 1
else:
d = self.points.shape[1]
self.kernel = kernel
self.mode = mode
self.h = h
self.dim = d
if not self.h:
self.update_bandwidth(self.calculate_bandwidth(self.points))
else:
self._update_norm_consts()
self.xs = self.points
self.kde = self.good_kde
self.saved_results = {}
self.fixed_bandwidth = fixed_bandwidth
def __call__(self, query_points, weights=None, label=None, do_not_cache=False):
return self.kde(query_points, weights, label, do_not_cache)
def _update_norm_consts(self):
h = self.h
d = self.dim
if not h is None:
if self.mode == "SCALAR":
self._norm_const_1 = 1./(h**d * np.sqrt(2*np.pi))
else:
self._norm_const_1 = 1./(np.linalg.det(h) * (2*np.pi)**(d/2.))
def get_norm_consts(self):
return self._norm_const_1
def update_bandwidth(self, h):
self.h = h
self._update_norm_consts()
def chunk_process(self, chunk1, chunk2):
if self.mode == "SCALAR":
c2 = -0.5/self.h**2
distances = cdist(chunk1, chunk2, 'euclidean')**2
return np.exp(c2 * distances)
else:
HI = np.linalg.inv(self.h)
distances = cdist(chunk1, chunk2, 'mahalanobis', VI=HI**2)
return np.exp(-0.5 * distances)
def inner_loop(self, const1, weights, chunk, state_points, block_length):
M = state_points.shape[0]
j_results = []
start_j = 0
end_j = 0
for block_j in range(M // block_length):
start_j = block_j * block_length
end_j = start_j + block_length
cp = self.chunk_process(chunk, state_points[start_j:end_j])
j_results.append(cp)
""" compute remaining distances for j block """
cp = self.chunk_process(chunk, state_points[end_j:])
j_results.append(cp)
stacked = np.hstack(j_results)
return const1 * np.dot(stacked, weights), stacked
def good_kde(self, query_points, weights, label, do_not_cache):
xs = multi_flatten(self.xs)
query_points = multi_flatten(query_points)
N = query_points.shape[0]
M = xs.shape[0]
if self.h is None:
self.update_bandwidth(self.calculate_bandwidth(xs))
if weights is None:
weights = np.ones(M)
else:
weights = weights[self.sampled_indices]
const1 = self._norm_const_1
if self.fixed_bandwidth and label in self.saved_results:
stacks = self.saved_results[label]
all_results = [const1 * np.dot(s, weights) for s in stacks]
return 1./weights.sum() * np.hstack(all_results)
else:
block_length = 500
all_results = []
d_stacks = []
stacks = []
start_i = 0
end_i = 0
for block_i in range(N // block_length):
start_i = block_i * block_length
end_i = start_i + block_length
inner_result, stacked = self.inner_loop(const1, weights, query_points[start_i:end_i], xs, block_length)
all_results.append(inner_result)
if self.fixed_bandwidth:
stacks.append(stacked)
""" Compute for remaining block """
inner_result, stacked = self.inner_loop(const1, weights, query_points[end_i:], xs, block_length)
all_results.append(inner_result)
if self.fixed_bandwidth and not do_not_cache:
stacks.append(stacked)
self.saved_results[label] = stacks
return 1./weights.sum() * np.hstack(all_results)
def is_pos_semi_def(self, S):
return np.all(np.linalg.eigvals(S) >= 0)
def calculate_bandwidth(self, data):
""" Rule-of-Thumb """
data = multi_flatten(data)
n = data.shape[0]
d = data.shape[1]
a = 1. / (d + 4)
if self.mode == "FULL":
""" Full Covariance """
if d == 1:
s_mat = np.std(data, axis=0) * np.eye(d)
else:
cov = np.cov(data, rowvar=0)
if self.is_pos_semi_def(cov):
s_mat = scipy.linalg.sqrtm(cov)
else:
s_mat = np.std(data, axis=0) * np.eye(d)
return ((4./(d+2))**a) * s_mat * (n**-a)
elif self.mode in ("DIAG", "DIAGONAL"):
""" Diagonal Covariance """
cov_mat = np.std(data, axis=0) * np.eye(d)
return ((4./(d+2))**a) * cov_mat * (n**-a)
else:
""" Scalar Covariance """
stds = np.std(data, axis=0)
return ((4./(d+2))**a) * (np.mean(stds).item()) * (n**-a)
|
george-montanez/LICORScabinet
|
WeightedKDE.py
|
Python
|
gpl-2.0
| 5,589
|
[
"Gaussian"
] |
e9064b6add4878ca582c7096f8f4715f1ed485dedf8e928af4dde312ef214cbf
|
#
# Copyright 2016 The BigDL Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from optparse import OptionParser
from bigdl.dllib.nn.layer import Model
from pyspark.sql.functions import col, udf
from pyspark.sql.types import StringType, DoubleType
from bigdl.dllib.nncontext import *
from bigdl.dllib.feature.image import *
from bigdl.dllib.nnframes import *
from bigdl.orca.learn.bigdl.estimator import Estimator
from bigdl.orca import init_orca_context, stop_orca_context
def inference(image_path, model_path, batch_size, sc):
imageDF = NNImageReader.readImages(image_path, sc, resizeH=300, resizeW=300, image_codec=1)
getName = udf(lambda row: row[0], StringType())
transformer = ChainedPreprocessing(
[RowToImageFeature(), ImageResize(256, 256), ImageCenterCrop(224, 224),
ImageChannelNormalize(123.0, 117.0, 104.0), ImageMatToTensor(), ImageFeatureToTensor()])
model = Model.loadModel(model_path)
est = Estimator.from_bigdl(model=model,
feature_preprocessing=transformer)
predictionDF = est.predict(data=imageDF,
batch_size=batch_size,
feature_cols="image"
).withColumn("name", getName(col("image")))
return predictionDF
if __name__ == "__main__":
parser = OptionParser()
parser.add_option("-m", dest="model_path",
help="Required. pretrained model path.")
parser.add_option("-f", dest="image_path",
help="training data path.")
parser.add_option("--b", "--batch_size", type=int, dest="batch_size", default="56",
help="The number of samples per gradient update. Default is 56.")
parser.add_option('--cluster_mode', type=str, dest="cluster_mode", default="local",
help='The mode for the Spark cluster. local, yarn or spark-submit.')
(options, args) = parser.parse_args(sys.argv)
if not options.model_path:
parser.print_help()
parser.error('model_path is required')
if not options.image_path:
parser.print_help()
parser.error('image_path is required')
cluster_mode = options.cluster_mode
if cluster_mode == "local":
sc = init_orca_context(memory="3g")
elif cluster_mode.startswith("yarn"):
if cluster_mode == "yarn-client":
sc = init_orca_context(cluster_mode="yarn-client", num_nodes=2, memory="3g")
else:
sc = init_orca_context(cluster_mode="yarn-cluster", num_nodes=2, memory="3g")
elif cluster_mode == "spark-submit":
sc = init_orca_context(cluster_mode="spark-submit")
else:
print("init_orca_context failed. cluster_mode should be one of 'local', 'yarn' and 'spark-submit' but got "
+ cluster_mode)
image_path = options.image_path
model_path = options.model_path
batch_size = options.batch_size
get_most_possible = udf(lambda p: float(p.index(max(p))), DoubleType())
predictionDF = inference(image_path, model_path, batch_size, sc) \
.withColumn("prediction", get_most_possible(col("prediction")))
predictionDF.select("name", "prediction").orderBy("name").show(20, False)
print("finished...")
stop_orca_context()
|
intel-analytics/BigDL
|
python/orca/example/learn/bigdl/imageInference/imageInference.py
|
Python
|
apache-2.0
| 3,803
|
[
"ORCA"
] |
3fe07f7426220306186b252c4c6fe6a9299901f651d217ba05d5dee361e0dcf6
|
#
# Copyright (c) 2008--2017 Red Hat, Inc.
#
# This software is licensed to you under the GNU General Public License,
# version 2 (GPLv2). There is NO WARRANTY for this software, express or
# implied, including the implied warranties of MERCHANTABILITY or FITNESS
# FOR A PARTICULAR PURPOSE. You should have received a copy of GPLv2
# along with this software; if not, see
# http://www.gnu.org/licenses/old-licenses/gpl-2.0.txt.
#
# Red Hat trademarks are not licensed under GPLv2. No permission is
# granted to use or replicate Red Hat trademarks that are incorporated
# in this software or its documentation.
#
#
import time
import string
import rpm
import sys
try:
# python 2
import xmlrpclib
except ImportError:
# python3
import xmlrpc.client as xmlrpclib
from spacewalk.common.usix import IntType
# common module
from spacewalk.common.usix import raise_with_tb
from spacewalk.common import rhnCache, rhnFlags
from spacewalk.common.rhnConfig import CFG
from spacewalk.common.rhnLog import log_debug, log_error
from spacewalk.common.rhnException import rhnFault, rhnException
from spacewalk.common.rhnTranslate import _
# local module
import rhnUser
import rhnSQL
import rhnLib
class NoBaseChannelError(Exception):
pass
class InvalidServerArchError(Exception):
pass
class BaseChannelDeniedError(Exception):
pass
class ChannelException(Exception):
def __init__(self, channel_id=None, *args, **kwargs):
Exception.__init__(self, *args, **kwargs)
self.channel_id = channel_id
self.channel = None
class ModifiedError(ChannelException):
pass
class IncompatibilityError(Exception):
pass
class InvalidDataError(Exception):
pass
class ChannelNotFoundError(Exception):
pass
class NoToolsChannel(Exception):
pass
class NoChildChannels(Exception):
pass
class InvalidChannel(Exception):
pass
class BaseDatabaseObject:
def __init__(self):
self._row = None
def __getattr__(self, name):
if name.startswith('get_'):
return rhnLib.CallableObj(name[4:], self._get)
if name.startswith('set_'):
return rhnLib.CallableObj(name[4:], self._set)
raise AttributeError(name)
def _set(self, name, val):
self._new_row()
self._row[name] = val
def _get(self, name):
return self._row[name]
def _new_row(self):
raise NotImplementedError()
def save(self, with_updates=1):
try:
return self._save(with_updates=with_updates)
except:
rhnSQL.rollback()
raise
def _save(self, with_updates=1):
try:
self._row.save(with_updates=with_updates)
except rhnSQL.ModifiedRowError:
raise_with_tb(ModifiedError(self._row['id']), sys.exc_info()[2])
class BaseChannelObject(BaseDatabaseObject):
_table_name = None
_sequence_name = None
_generic_fields = []
def load_by_label(self, label):
self.__init__()
self._row = rhnSQL.Row(self._table_name, 'label')
self._row.load(label)
return self
def load_by_id(self, obj_id):
self.__init__()
self._row = rhnSQL.Row(self._table_name, 'id')
self._row.load(obj_id)
return self
def load_from_dict(self, dict):
# Re-init
self.__init__()
for f in self._generic_fields:
method = getattr(self, 'set_' + f)
method(dict.get(f))
self._load_rest(dict)
return self
def _load_rest(self, dict):
pass
def exists(self):
if not self._row:
return 0
return self._row.real
def get_org_id(self):
org_id = self._row['org_id']
if org_id is None:
return None
row = self._lookup_org_id(org_id)
if row.real:
return row['login']
return org_id
def set_org_id(self, val):
self._new_row()
if val is None or isinstance(val, IntType):
self._row['org_id'] = val
return
row = self._lookup_org_by_login(val)
if not row.real:
raise InvalidDataError("No such org", val)
self._row['org_id'] = row['org_id']
def _lookup_org_id(self, org_id):
row = rhnSQL.Row('web_contact', 'org_id')
row.load(org_id)
return row
def _lookup_org_by_login(self, login):
row = rhnSQL.Row('web_contact', 'login')
row.load(login)
return row
def _lookup_channel_family_by_id(self, channel_family_id):
row = rhnSQL.Row('rhnChannelFamily', 'id')
row.load(channel_family_id)
return row
def _lookup_channel_family_by_label(self, channel_family):
row = rhnSQL.Row('rhnChannelFamily', 'label')
row.load(channel_family)
return row
def _new_row(self):
if self._row is None:
self._row = rhnSQL.Row(self._table_name, 'id')
channel_id = rhnSQL.Sequence(self._sequence_name).next()
self._row.create(channel_id)
def as_dict(self):
ret = {}
for f in self._generic_fields:
method = getattr(self, 'get_' + f)
val = method()
ret[f] = val
return ret
# Channel creation
class Channel(BaseChannelObject):
_table_name = 'rhnChannel'
_sequence_name = 'rhn_channel_id_seq'
_generic_fields = ['label', 'name', 'summary', 'description', 'basedir',
'org_id', 'gpg_key_url', 'gpg_key_id', 'gpg_key_fp', 'end_of_life',
'channel_families', 'channel_arch', ]
def __init__(self):
BaseChannelObject.__init__(self)
self._channel_families = []
self._dists = {}
self._parent_channel_arch = None
def load_by_label(self, label):
BaseChannelObject.load_by_label(self, label)
self._load_channel_families()
self._load_dists()
return self
def load_by_id(self, label):
BaseChannelObject.load_by_id(self, label)
self._load_channel_families()
self._load_dists()
return self
def _load_rest(self, dict):
dists = dict.get('dists')
if not dists:
return
for dist in dists:
release = dist.get('release')
os = dist.get('os')
self._dists[release] = os
_query_get_db_channel_families = rhnSQL.Statement("""
select channel_family_id
from rhnChannelFamilyMembers
where channel_id = :channel_id
""")
def _get_db_channel_families(self, channel_id):
if channel_id is None:
return []
h = rhnSQL.prepare(self._query_get_db_channel_families)
h.execute(channel_id=channel_id)
return [x['channel_family_id'] for x in h.fetchall_dict() or []]
def _load_channel_families(self):
channel_id = self._row.get('id')
self._channel_families = self._get_db_channel_families(channel_id)
return 1
def _load_dists(self):
channel_id = self._row.get('id')
dists = self._get_db_dists(channel_id)
self.set_dists(dists)
_query_get_db_dists = rhnSQL.Statement("""
select os, release
from rhnDistChannelMap
where channel_id = :channel_id
and org_id is null
""")
def _get_db_dists(self, channel_id):
if channel_id is None:
return []
h = rhnSQL.prepare(self._query_get_db_dists)
h.execute(channel_id=channel_id)
return h.fetchall_dict() or []
# Setters
def set_channel_arch(self, val):
self._new_row()
arch = self._sanitize_arch(val)
row = self._lookup_channel_arch(arch)
if not row.real:
raise InvalidDataError("No such architecture", arch)
self._row['channel_arch_id'] = row['id']
def _sanitize_arch(self, arch):
if arch == 'i386':
return 'channel-ia32'
p = 'channel-'
if arch[:len(p)] != p:
return p + arch
return arch
def set_parent_channel(self, val):
self._new_row()
if val is None:
self._row['parent_channel'] = None
return
row = self._lookup_channel_by_label(val)
if not row.real:
raise InvalidDataError("Invalid parent channel", val)
self._row['parent_channel'] = row['id']
self._parent_channel_arch = row['channel_arch_id']
def set_channel_families(self, val):
self._new_row()
self._channel_families = []
for cf_label in val:
self.add_channel_family(cf_label)
def set_end_of_life(self, val):
self._new_row()
if val is None:
self._row['end_of_life'] = None
return
t = time.strptime(val, "%Y-%m-%d")
seconds = time.mktime(t)
t = rhnSQL.TimestampFromTicks(seconds)
self._row['end_of_life'] = t
def add_channel_family(self, name):
self._new_row()
cf = self._lookup_channel_family_by_label(name)
if not cf.real:
raise InvalidDataError("Invalid channel family", name)
self._channel_families.append(cf['id'])
def add_dist(self, release, os=None):
if os is None:
os = 'Red Hat Linux'
self._dists[release] = os
def set_dists(self, val):
self._dists.clear()
for h in val:
release = h['release']
os = h['os']
self.add_dist(release, os)
# Getters
def get_parent_channel(self):
pc_id = self._row['parent_channel']
if pc_id is None:
return None
return self._lookup_channel_by_id(pc_id)['label']
def get_channel_families(self):
cf_labels = []
for cf_id in self._channel_families:
row = self._lookup_channel_family_by_id(cf_id)
if row.real:
cf_labels.append(row['label'])
return cf_labels
def get_channel_arch(self):
channel_arch_id = self._row['channel_arch_id']
row = self._lookup_channel_arch_by_id(channel_arch_id)
assert row.real
return row['label']
def get_end_of_life(self):
date_obj = self._row['end_of_life']
if date_obj is None:
return None
return "%s-%02d-%02d %02d:%02d:%02d" % (
date_obj.year, date_obj.month, date_obj.day,
date_obj.hour, date_obj.minute, date_obj.second)
def get_dists(self):
ret = []
for release, os in self._dists.items():
ret.append({'release': release, 'os': os})
return ret
def _lookup_channel_by_id(self, channel_id):
row = rhnSQL.Row('rhnChannel', 'id')
row.load(channel_id)
return row
def _lookup_channel_by_label(self, channel):
row = rhnSQL.Row('rhnChannel', 'label')
row.load(channel)
return row
def _lookup_channel_arch(self, channel_arch):
row = rhnSQL.Row('rhnChannelArch', 'label')
row.load(channel_arch)
return row
def _lookup_channel_arch_by_id(self, channel_arch_id):
row = rhnSQL.Row('rhnChannelArch', 'id')
row.load(channel_arch_id)
return row
def _save(self, with_updates=1):
if self._parent_channel_arch:
if not self._compatible_channel_arches(self._parent_channel_arch,
self._row['channel_arch_id']):
raise IncompatibilityError("Incompatible channel arches")
BaseChannelObject._save(self, with_updates=with_updates)
# Save channel families now
self._save_channel_families()
self._save_dists()
_query_remove_channel_families = rhnSQL.Statement("""
delete from rhnChannelFamilyMembers
where channel_id = :channel_id
and channel_family_id = :channel_family_id
""")
_query_add_channel_families = rhnSQL.Statement("""
insert into rhnChannelFamilyMembers (channel_id, channel_family_id)
values (:channel_id, :channel_family_id)
""")
def _save_channel_families(self):
channel_id = self._row['id']
db_cfids = self._get_db_channel_families(channel_id)
h = {}
for db_cfid in db_cfids:
h[db_cfid] = None
to_add = []
for cfid in self._channel_families:
if cfid in h:
del h[cfid]
continue
to_add.append(cfid)
to_delete = list(h.keys())
if to_delete:
h = rhnSQL.prepare(self._query_remove_channel_families)
cids = [channel_id] * len(to_delete)
h.executemany(channel_id=cids, channel_family_id=to_delete)
if to_add:
h = rhnSQL.prepare(self._query_add_channel_families)
cids = [channel_id] * len(to_add)
h.executemany(channel_id=cids, channel_family_id=to_add)
def _save_dists(self):
channel_id = self._row['id']
db_dists = self._get_db_dists(channel_id)
d = self._dists.copy()
to_add = [[], []]
to_remove = []
to_update = [[], []]
for h in db_dists:
release = h['release']
os = h['os']
if release not in d:
to_remove.append(release)
continue
# Need to update?
m_os = d[release]
if m_os == os:
# Nothing to do
del d[release]
continue
to_update[0].append(release)
to_update[1].append(os)
# Everything else should be added
for release, os in list(d.items()):
to_add[0].append(release)
to_add[1].append(os)
self._remove_dists(to_remove)
self._update_dists(to_update[0], to_update[1])
self._add_dists(to_add[0], to_add[1])
_query_add_dists = rhnSQL.Statement("""
insert into rhnDistChannelMap
(channel_id, channel_arch_id, release, os, org_id)
values (:channel_id, :channel_arch_id, :release, :os, null)
""")
def _add_dists(self, releases, oses):
self._modify_dists(self._query_add_dists, releases, oses)
def _modify_dists(self, query, releases, oses):
if not releases:
return
count = len(releases)
channel_ids = [self._row['id']] * count
query_args = {'channel_id': channel_ids, 'release': releases}
if oses:
channel_arch_ids = [self._row['channel_arch_id']] * count
query_args.update({'channel_arch_id': channel_arch_ids,
'os': oses})
h = rhnSQL.prepare(query)
h.executemany(**query_args)
_query_update_dists = rhnSQL.Statement("""
update rhnDistChannelMap
set channel_arch_id = :channel_arch_id,
os = :os
where channel_id = :channel_id
and release = :release
and org_id is null
""")
def _update_dists(self, releases, oses):
self._modify_dists(self._query_update_dists, releases, oses)
_query_remove_dists = rhnSQL.Statement("""
delete from rhnDistChannelMap
where channel_id = :channel_id
and release = :release
and org_id is null
""")
def _remove_dists(self, releases):
self._modify_dists(self._query_remove_dists, releases, None)
def _compatible_channel_arches(self, parent_channel_arch, channel_arch):
# This could get more complicated later
return (parent_channel_arch == channel_arch)
def as_dict(self):
ret = BaseChannelObject.as_dict(self)
ret['dists'] = self.get_dists()
return ret
class ChannelFamily(BaseChannelObject):
_table_name = 'rhnChannelFamily'
_sequence_name = 'rhn_channel_family_id_seq'
_generic_fields = ['label', 'name', 'product_url']
def _load_by_id(query, item_object, pattern=None):
qargs = {}
if pattern:
query += "and label like :pattern"
qargs['pattern'] = pattern
h = rhnSQL.prepare(query)
h.execute(**qargs)
ret = []
while 1:
row = h.fetchone_dict()
if not row:
break
c = item_object.load_by_id(row['id'])
ret.append(c.as_dict())
return ret
def list_channel_families(pattern=None):
query = """
select id
from rhnChannelFamily
where org_id is null
"""
return _load_by_id(query, ChannelFamily(), pattern)
def list_channels(pattern=None):
query = """
select id
from rhnChannel
where 1=1
"""
return _load_by_id(query, Channel(), pattern)
# makes sure there are no None values in dictionaries, etc.
def __stringify(object):
if object is None:
return ''
if type(object) == type([]):
return list(map(__stringify, object))
# We need to know __stringify converts immutable types into immutable
# types
if type(object) == type(()):
return tuple(map(__stringify, object))
if type(object) == type({}):
ret = {}
for k, v in object.items():
ret[__stringify(k)] = __stringify(v)
return ret
# by default, we just str() it
return str(object)
# return the channel information
def channel_info(channel):
log_debug(3, channel)
# get the channel information
h = rhnSQL.prepare("""
select
ca.label arch,
c.id,
c.parent_channel,
c.org_id,
c.label,
c.name,
c.summary,
c.description,
to_char(c.last_modified, 'YYYYMMDDHH24MISS') last_modified
from
rhnChannel c,
rhnChannelArch ca
where
c.channel_arch_id = ca.id
and c.label = :channel
""")
h.execute(channel=str(channel))
ret = h.fetchone_dict()
return __stringify(ret)
# return information about a base channel for a server_id
def get_base_channel(server_id, none_ok=0):
log_debug(3, server_id)
h = rhnSQL.prepare("""
select
ca.label arch,
c.id,
c.parent_channel,
c.org_id,
c.label,
c.name,
c.summary,
c.description,
to_char(c.last_modified, 'YYYYMMDDHH24MISS') last_modified
from rhnChannel c, rhnChannelArch ca, rhnServerChannel sc
where sc.server_id = :server_id
and sc.channel_id = c.id
and c.channel_arch_id = ca.id
and c.parent_channel is NULL
""")
h.execute(server_id=str(server_id))
ret = h.fetchone_dict()
if not ret:
if not none_ok:
log_error("Server not subscribed to a base channel!", server_id)
return None
return __stringify(ret)
def channels_for_server(server_id):
"""channel info list for all channels accessible by this server.
list channels a server_id is subscribed to
We DO NOT want to cache this one because we depend on getting
accurate information and the caching would only introduce more
overhead on an otherwise very fast query
"""
log_debug(3, server_id)
try:
server_id = int(server_id)
except:
raise_with_tb(rhnFault(8, server_id), sys.exc_info()[2]) # Invalid rhnServer.id
# XXX: need to return unsubsubcribed channels and a way to indicate
# they arent already subscribed
# list all the channels this server is subscribed to. We also want
# to know if any of those channels has local packages in it... A
# local package has a org_id set.
h = rhnSQL.prepare("""
select
ca.label arch,
c.id,
c.parent_channel,
c.org_id,
c.label,
c.name,
c.summary,
c.description,
c.gpg_key_url,
case s.org_id when c.org_id then 1 else 0 end local_channel,
TO_CHAR(c.last_modified, 'YYYYMMDDHH24MISS') last_modified
from
rhnChannelArch ca,
rhnChannel c,
rhnServerChannel sc,
rhnServer s
where
c.id = sc.channel_id
and sc.server_id = :server_id
and s.id = :server_id
and ca.id = c.channel_arch_id
order by c.parent_channel nulls first
""")
h.execute(server_id=str(server_id))
channels = h.fetchall_dict()
if not channels:
log_error("Server not subscribed to any channels", server_id)
channels = []
return __stringify(channels)
def getSubscribedChannels(server_id):
"""
Format the response from channels_for_server in the way that the
handlers expect.
"""
channelList = channels_for_server(server_id)
channels = []
for each in channelList:
if 'last_modified' not in each:
# No last_modified attribute
# Probably an empty channel, so ignore
continue
channel = [each['label'], each['last_modified']]
# isBaseChannel
if each['parent_channel']:
flag = "0"
else:
flag = "1"
channel.append(flag)
# isLocalChannel
if each['local_channel']:
flag = "1"
else:
flag = "0"
channel.append(flag)
channels.append(channel)
return channels
def isCustomChannel(channel_id):
"""
Input: channel_id (from DB Table rhnChannel.id)
Returns: True if this is a custom channel
False if this is not a custom channel
"""
log_debug(3, channel_id)
h = rhnSQL.prepare("""
select
rcf.label
from
rhnChannelFamily rcf,
rhnChannelFamilyMembers rcfm
where
rcfm.channel_id = :channel_id
and rcfm.channel_family_id = rcf.id
and rcf.org_id is not null
""")
h.execute(channel_id=str(channel_id))
label = h.fetchone()
if label:
if label[0].startswith("private-channel-family"):
log_debug(3, channel_id, "is a custom channel")
return True
return False
# Fetch base channel for a given release and arch
def base_channel_for_rel_arch(release, server_arch, org_id=-1,
user_id=None):
log_debug(4, release, server_arch, org_id, user_id)
query = """
select ca.label arch,
c.id,
c.parent_channel,
c.org_id,
c.label,
c.name,
c.summary,
c.description,
to_char(c.last_modified, 'YYYYMMDDHH24MISS') last_modified
from rhnChannel c,
rhnChannelArch ca
where c.channel_arch_id = ca.id
and c.id = rhn_channel.base_channel_for_release_arch(
:release, :server_arch, :org_id, :user_id)
"""
rhnSQL.transaction("base_channel_for_rel_arch")
h = rhnSQL.prepare(query)
try:
h.execute(release=str(release), server_arch=str(server_arch),
org_id=org_id, user_id=user_id)
except rhnSQL.SQLSchemaError:
e = sys.exc_info()[1]
rhnSQL.rollback("base_channel_for_rel_arch")
if e.errno == 20263:
# Insufficient permissions for subscription
log_debug(4, 'BaseChannelDeniedError')
raise_with_tb(BaseChannelDeniedError(), sys.exc_info()[2])
if e.errno == 20244:
# Server architecture could not be found
log_debug(4, 'InvalidServerArchError')
raise_with_tb(InvalidServerArchError(str(server_arch)), sys.exc_info()[2])
# Re-raise unknown eceptions
log_debug(4, 'unkown exception')
raise
log_debug(4, 'got past exceptions')
return h.fetchone_dict()
def base_eus_channel_for_ver_rel_arch(version, release, server_arch,
org_id=-1, user_id=None):
"""
given a redhat-release version, release, and server arch, return a list
of dicts containing the details of the channel z streams either match the
version/release pair, or are greater.
"""
log_debug(4, version, release, server_arch, org_id, user_id)
eus_channels_query = """
select c.id,
c.label,
c.name,
rcm.release,
c.receiving_updates
from
rhnChannelPermissions cp,
rhnChannel c,
rhnServerArch sa,
rhnServerChannelArchCompat scac,
rhnReleaseChannelMap rcm
where
rcm.version = :version
and scac.server_arch_id = sa.id
and sa.label = :server_arch
and scac.channel_arch_id = rcm.channel_arch_id
and rcm.channel_id = c.id
and cp.channel_id = c.id
and cp.org_id = :org_id
and rhn_channel.loose_user_role_check(c.id, :user_id,
'subscribe') = 1
"""
eus_channels_prepared = rhnSQL.prepare(eus_channels_query)
eus_channels_prepared.execute(version=version,
server_arch=server_arch,
user_id=user_id,
org_id=org_id)
channels = []
while True:
channel = eus_channels_prepared.fetchone_dict()
if channel is None:
break
# the release part of redhat-release for rhel 4 is like
# 6.1 or 7; we just look at the first digit.
# for rhel 5 and up it's the full release number of rhel, followed by
# the true release number of the rpm, like 5.0.0.9 (for the 9th
# version of the redhat-release rpm, for RHEL GA)
db_release = channel['release']
if version in ['4AS', '4ES']:
parts = 1
else:
parts = 2
server_rel = '.'.join(release.split('-')[0].split('.')[:parts])
channel_rel = '.'.join(db_release.split('.')[:parts])
# XXX we're no longer using the is_default column from the db
if rpm.labelCompare(('0', server_rel, '0'),
('0', channel_rel, '0')) == 0:
channel['is_default'] = 'Y'
channels.append(channel)
if rpm.labelCompare(('0', server_rel, '0'),
('0', channel_rel, '0')) < 0:
channel['is_default'] = 'N'
channels.append(channel)
return channels
def get_channel_for_release_arch(release, server_arch, org_id=None):
log_debug(3, release, server_arch)
server_arch = rhnLib.normalize_server_arch(str(server_arch))
log_debug(3, 'normalized arch as %s' % server_arch)
if org_id is None:
query = """
select distinct
ca.label arch,
c.id,
c.parent_channel,
c.org_id,
c.label,
c.name,
c.summary,
c.description,
to_char(c.last_modified, 'YYYYMMDDHH24MISS') last_modified
from rhnDistChannelMap dcm,
rhnChannel c,
rhnChannelArch ca,
rhnServerChannelArchCompat scac,
rhnServerArch sa
where scac.server_arch_id = sa.id
and sa.label = :server_arch
and scac.channel_arch_id = dcm.channel_arch_id
and dcm.release = :release
and dcm.channel_id = c.id
and dcm.channel_arch_id = c.channel_arch_id
and dcm.org_id is null
and c.parent_channel is null
and c.org_id is null
and c.channel_arch_id = ca.id
"""
else:
query = """
select distinct
ca.label arch,
c.id,
c.parent_channel,
c.org_id,
c.label,
c.name,
c.summary,
c.description,
to_char(c.last_modified, 'YYYYMMDDHH24MISS') last_modified
from rhnOrgDistChannelMap odcm,
rhnChannel c,
rhnChannelArch ca,
rhnServerChannelArchCompat scac,
rhnServerArch sa
where scac.server_arch_id = sa.id
and sa.label = :server_arch
and scac.channel_arch_id = odcm.channel_arch_id
and odcm.release = :release
and odcm.channel_id = c.id
and odcm.channel_arch_id = c.channel_arch_id
and odcm.org_id = :org_id
and c.parent_channel is null
and c.org_id is null
and c.channel_arch_id = ca.id
"""
h = rhnSQL.prepare(query)
h.execute(release=str(release), server_arch=server_arch, org_id=org_id)
row = h.fetchone_dict()
if not row:
# No channles for this guy
log_debug(3, 'No channles for this guy')
return None
log_debug(3, 'row is %s' % str(row))
return row
def applet_channels_for_uuid(uuid):
log_debug(3, uuid)
query = """
select distinct
ca.label arch,
c.id,
c.parent_channel,
c.org_id,
c.label,
c.name,
c.summary,
c.description,
to_char(c.last_modified, 'YYYYMMDDHH24MISS') last_modified,
to_char(s.channels_changed, 'YYYYMMDDHH24MISS') server_channels_changed
from rhnChannelArch ca,
rhnChannel c,
rhnServerChannel sc,
rhnServer s,
rhnServerUuid su
where su.uuid = :uuid
and su.server_id = s.id
and su.server_id = sc.server_id
and sc.channel_id = c.id
and c.channel_arch_id = ca.id
"""
h = rhnSQL.prepare(query)
h.execute(uuid=uuid)
rows = h.fetchall_dict() or []
return rows
# retrieve a list of public channels for a given release and architecture
# we cannot cache this if it involves an org_id
# If a user_id is passed to this function, and all the available base channels
# for this server_arch/release combination are denied by the org admin, this
# function raises BaseChannelDeniedError
def channels_for_release_arch(release, server_arch, org_id=-1, user_id=None):
if not org_id:
org_id = -1
org_id = string.strip(str(org_id))
log_debug(3, release, server_arch, org_id)
# Can raise BaseChannelDeniedError or InvalidServerArchError
base_channel = base_channel_for_rel_arch(release, server_arch,
org_id=org_id, user_id=user_id)
if not base_channel:
raise NoBaseChannelError()
# At this point, base_channel is not null
# We assume here that subchannels are compatible with the base channels,
# so there would be no need to check for arch compatibility from this
# point
h = rhnSQL.prepare("""
select
ca.label arch,
c.id,
c.parent_channel,
c.org_id,
c.label,
c.name,
c.summary,
c.description,
to_char(c.last_modified, 'YYYYMMDDHH24MISS') last_modified,
-- If user_id is null, then the channel is subscribable
rhn_channel.loose_user_role_check(c.id, :user_id, 'subscribe')
subscribable
from
rhnChannelPermissions cp,
rhnOrgDistChannelMap odcm,
rhnChannel c,
rhnChannelArch ca
where
c.id = odcm.channel_id
and odcm.os in (
'Powertools'
)
and odcm.for_org_id = :org_id
and c.channel_arch_id = ca.id
and cp.channel_id = c.id
and cp.org_id = :org_id
and c.parent_channel = :parent_channel
""")
h.execute(org_id=org_id,
parent_channel=base_channel['id'], user_id=user_id)
channels = [base_channel]
while 1:
row = h.fetchone_dict()
if not row:
break
subscribable = row['subscribable']
del row['subscribable']
if not subscribable:
# Not allowed to subscribe to this channel
continue
channels.append(row)
return __stringify(channels)
_query_get_source_packages_from_ids = rhnSQL.Statement("""
select srpm.name
from rhnChannelPackage cp,
rhnPackage p,
rhnSourceRPM srpm
where cp.channel_id = :channel_id
and cp.package_id = p.id
and p.source_rpm_id = srpm.id
""")
def list_packages_source(channel_id):
ret = []
h = rhnSQL.prepare(_query_get_source_packages_from_ids)
h.execute(channel_id=channel_id)
results = h.fetchall_dict()
if results:
for r in results:
r = r['name']
if string.find(r, ".rpm") != -1:
r = string.replace(r, ".rpm", "")
new_evr = rhnLib.make_evr(r, source=1)
new_evr_list = [new_evr['name'], new_evr['version'], new_evr['release'], new_evr['epoch']]
ret.append(new_evr_list)
return ret
# the latest packages from the specified channel
_query_all_packages_from_channel_checksum = """
select
p.id,
pn.name,
pevr.version,
pevr.release,
pevr.epoch,
pa.label arch,
p.package_size,
ct.label as checksum_type,
c.checksum
from
rhnChannelPackage cp,
rhnPackage p,
rhnPackageName pn,
rhnPackageEVR pevr,
rhnPackageArch pa,
rhnChecksumType ct,
rhnChecksum c
where
cp.channel_id = :channel_id
and cp.package_id = p.id
and p.name_id = pn.id
and p.evr_id = pevr.id
and p.package_arch_id = pa.id
and p.checksum_id = c.id
and c.checksum_type_id = ct.id
order by pn.name, pevr.evr desc, pa.label
"""
# This function executes the SQL call for listing packages with checksum info
def list_all_packages_checksum_sql(channel_id):
log_debug(3, channel_id)
h = rhnSQL.prepare(_query_all_packages_from_channel_checksum)
h.execute(channel_id=str(channel_id))
ret = h.fetchall_dict()
if not ret:
return []
# process the results
ret = [(a["name"], a["version"], a["release"], a["epoch"],
a["arch"], a["package_size"], a['checksum_type'],
a['checksum']) for a in __stringify(ret)]
return ret
# This function executes the SQL call for listing latest packages with
# checksum info
def list_packages_checksum_sql(channel_id):
log_debug(3, channel_id)
# return the latest packages from the specified channel
query = """
select
pn.name,
pevr.version,
pevr.release,
pevr.epoch,
pa.label arch,
full_channel.package_size,
full_channel.checksum_type,
full_channel.checksum
from
rhnPackageArch pa,
( select
p.name_id,
max(pe.evr) evr
from
rhnChannelPackage cp,
rhnPackage p,
rhnPackageEVR pe
where
cp.channel_id = :channel_id
and cp.package_id = p.id
and p.evr_id = pe.id
group by p.name_id
) listall,
( select distinct
p.package_size,
p.name_id,
p.evr_id,
p.package_arch_id,
ct.label as checksum_type,
c.checksum
from
rhnChannelPackage cp,
rhnPackage p,
rhnChecksumType ct,
rhnChecksum c
where
cp.channel_id = :channel_id
and cp.package_id = p.id
and p.checksum_id = c.id
and c.checksum_type_id = ct.id
) full_channel,
-- Rank the package's arch
( select
package_arch_id,
count(*) rank
from
rhnServerPackageArchCompat
group by package_arch_id
) arch_rank,
rhnPackageName pn,
rhnPackageEVR pevr
where
pn.id = listall.name_id
-- link back to the specific package
and full_channel.name_id = listall.name_id
and full_channel.evr_id = pevr.id
and pevr.evr = listall.evr
and pa.id = full_channel.package_arch_id
and pa.id = arch_rank.package_arch_id
order by pn.name, arch_rank.rank desc
"""
h = rhnSQL.prepare(query)
h.execute(channel_id=str(channel_id))
ret = h.fetchall_dict()
if not ret:
return []
# process the results
ret = [(a["name"], a["version"], a["release"], a["epoch"],
a["arch"], a["package_size"], a['checksum_type'],
a['checksum']) for a in __stringify(ret)]
return ret
# This function executes the SQL call for listing packages
def _list_packages_sql(query, channel_id):
h = rhnSQL.prepare(query)
h.execute(channel_id=str(channel_id))
ret = h.fetchall_dict()
if not ret:
return []
# process the results
ret = [(a["name"], a["version"], a["release"], a["epoch"],
a["arch"], a["package_size"]) for a in __stringify(ret)]
return ret
def list_packages_sql(channel_id):
log_debug(3, channel_id)
# return the latest packages from the specified channel
query = """
select
pn.name,
pevr.version,
pevr.release,
pevr.epoch,
pa.label arch,
full_channel.package_size
from
rhnPackageArch pa,
( select
p.name_id,
max(pe.evr) evr
from
rhnChannelPackage cp,
rhnPackage p,
rhnPackageEVR pe
where
cp.channel_id = :channel_id
and cp.package_id = p.id
and p.evr_id = pe.id
group by p.name_id
) listall,
( select distinct
p.package_size,
p.name_id,
p.evr_id,
p.package_arch_id
from
rhnChannelPackage cp,
rhnPackage p
where
cp.channel_id = :channel_id
and cp.package_id = p.id
) full_channel,
-- Rank the package's arch
( select
package_arch_id,
count(*) rank
from
rhnServerPackageArchCompat
group by package_arch_id
) arch_rank,
rhnPackageName pn,
rhnPackageEVR pevr
where
pn.id = listall.name_id
-- link back to the specific package
and full_channel.name_id = listall.name_id
and full_channel.evr_id = pevr.id
and pevr.evr = listall.evr
and pa.id = full_channel.package_arch_id
and pa.id = arch_rank.package_arch_id
order by pn.name, arch_rank.rank desc
"""
return _list_packages_sql(query, channel_id)
# the latest packages from the specified channel
_query_latest_packages_from_channel = """
select
p.id,
pn.name,
pevr.version,
pevr.release,
pevr.epoch,
pa.label arch,
p.package_size
from
rhnChannelPackage cp,
rhnPackage p,
rhnPackageName pn,
rhnPackageEVR pevr,
rhnPackageArch pa
where
cp.channel_id = :channel_id
and cp.package_id = p.id
and p.name_id = pn.id
and p.evr_id = pevr.id
and p.package_arch_id = pa.id
order by pn.name, pevr.evr desc, pa.label
"""
# This function executes the SQL call for listing packages
def list_all_packages_sql(channel_id):
log_debug(3, channel_id)
return _list_packages_sql(_query_latest_packages_from_channel, channel_id)
# This function executes the SQL call for listing packages with all the
# dep information for each package also
def list_all_packages_complete_sql(channel_id):
log_debug(3, channel_id)
# return the latest packages from the specified channel
h = rhnSQL.prepare(_query_latest_packages_from_channel)
# This gathers the provides, requires, conflicts, obsoletes info
g = rhnSQL.prepare("""
select
pp.package_id,
'provides' as capability_type,
pp.capability_id,
pp.sense,
pc.name,
pc.version
from
rhnPackageProvides pp,
rhnPackageCapability pc
where
pp.package_id = :package_id
and pp.capability_id = pc.id
union all
select
pr.package_id,
'requires' as capability_type,
pr.capability_id,
pr.sense,
pc.name,
pc.version
from
rhnPackageRequires pr,
rhnPackageCapability pc
where
pr.package_id = :package_id
and pr.capability_id = pc.id
union all
select
prec.package_id,
'recommends' as capability_type,
prec.capability_id,
prec.sense,
pc.name,
pc.version
from
rhnPackageRecommends prec,
rhnPackageCapability pc
where
prec.package_id = :package_id
and prec.capability_id = pc.id
union all
select
sugg.package_id,
'suggests' as capability_type,
sugg.capability_id,
sugg.sense,
pc.name,
pc.version
from
rhnPackageSuggests sugg,
rhnPackageCapability pc
where
sugg.package_id = :package_id
and sugg.capability_id = pc.id
union all
select
supp.package_id,
'supplements' as capability_type,
supp.capability_id,
supp.sense,
pc.name,
pc.version
from
rhnPackageSupplements supp,
rhnPackageCapability pc
where
supp.package_id = :package_id
and supp.capability_id = pc.id
union all
select
enh.package_id,
'enhances' as capability_type,
enh.capability_id,
enh.sense,
pc.name,
pc.version
from
rhnPackageEnhances enh,
rhnPackageCapability pc
where
enh.package_id = :package_id
and enh.capability_id = pc.id
union all
select
pcon.package_id,
'conflicts' as capability_type,
pcon.capability_id,
pcon.sense,
pc.name,
pc.version
from
rhnPackageConflicts pcon,
rhnPackageCapability pc
where
pcon.package_id = :package_id
and pcon.capability_id = pc.id
union all
select
po.package_id,
'obsoletes' as capability_type,
po.capability_id,
po.sense,
pc.name,
pc.version
from
rhnPackageObsoletes po,
rhnPackageCapability pc
where
po.package_id = :package_id
and po.capability_id = pc.id
union all
select
brks.package_id,
'breaks' as capability_type,
brks.capability_id,
brks.sense,
pc.name,
pc.version
from
rhnPackageBreaks brks,
rhnPackageCapability pc
where
brks.package_id = :package_id
and brks.capability_id = pc.id
union all
select
pdep.package_id,
'predepends' as capability_type,
pdep.capability_id,
pdep.sense,
pc.name,
pc.version
from
rhnPackagePredepends pdep,
rhnPackageCapability pc
where
pdep.package_id = :package_id
and pdep.capability_id = pc.id
""")
h.execute(channel_id=str(channel_id))
# XXX This query has to order the architectures somehow; the 7.2 up2date
# client was broken and was selecting the wrong architecture if athlons
# are passed first. The rank ordering here should make sure that i386
# kernels appear before athlons.
ret = h.fetchall_dict()
if not ret:
return []
for pkgi in ret:
pkgi['provides'] = []
pkgi['requires'] = []
pkgi['conflicts'] = []
pkgi['obsoletes'] = []
pkgi['recommends'] = []
pkgi['suggests'] = []
pkgi['supplements'] = []
pkgi['enhances'] = []
pkgi['breaks'] = []
pkgi['predepends'] = []
g.execute(package_id=pkgi["id"])
deps = g.fetchall_dict() or []
for item in deps:
version = item['version'] or ""
relation = ""
if version:
sense = item['sense'] or 0
if sense & 2:
relation = relation + "<"
if sense & 4:
relation = relation + ">"
if sense & 8:
relation = relation + "="
if relation:
relation = " " + relation
if version:
version = " " + version
dep = item['name'] + relation + version
pkgi[item['capability_type']].append(dep)
# process the results
ret = [(a["name"], a["version"], a["release"], a["epoch"],
a["arch"], a["package_size"], a['provides'],
a['requires'], a['conflicts'], a['obsoletes'], a['recommends'], a['suggests'], a['supplements'], a['enhances'], a['breaks'], a['predepends']) for a in __stringify(ret)]
return ret
def list_packages_path(channel_id):
log_debug(3, channel_id)
# return the latest packages from the specified channel
h = rhnSQL.prepare("""
select
p.path
from
rhnPackage p,
rhnChannelPackage cp
where
cp.channel_id = :channel_id
and cp.package_id = p.id
""")
h.execute(channel_id=str(channel_id))
ret = h.fetchall()
if not ret:
return []
# process the results
# ret = map(lambda a: (a["path"]),
# __stringify(ret))
return ret
# list the latest packages for a channel
def list_packages(channel):
return _list_packages(channel, cache_prefix="list_packages",
function=list_packages_sql)
# list _all_ the packages for a channel
def list_all_packages(channel):
return _list_packages(channel, cache_prefix="list_all_packages",
function=list_all_packages_sql)
# list _all_ the packages for a channel, including checksum info
def list_all_packages_checksum(channel):
return _list_packages(channel, cache_prefix="list_all_packages_checksum",
function=list_all_packages_checksum_sql)
# list _all_ the packages for a channel
def list_all_packages_complete(channel):
return _list_packages(channel, cache_prefix="list_all_packages_complete",
function=list_all_packages_complete_sql)
# Common part of list_packages and list_all_packages*
# cache_prefix is the prefix for the file name we're caching this request as
# function is the generator function
def _list_packages(channel, cache_prefix, function):
log_debug(3, channel, cache_prefix)
# try the caching thing first
c_info = channel_info(channel)
if not c_info: # unknown channel
raise rhnFault(40, "could not find any data on channel '%s'" % channel)
cache_entry = "%s-%s" % (cache_prefix, channel)
ret = rhnCache.get(cache_entry, c_info["last_modified"])
if ret: # we scored a cache hit
log_debug(4, "Scored cache hit", channel)
# Mark the response as being already XMLRPC-encoded
rhnFlags.set("XMLRPC-Encoded-Response", 1)
return ret
ret = function(c_info["id"])
if not ret:
# we assume that channels with no packages are very fast to list,
# so we don't bother caching...
log_error("No packages found in channel",
c_info["id"], c_info["label"])
return []
# we need to append the channel label to the list
ret = list(map(lambda a, c=channel: a + (c,), ret))
ret = xmlrpclib.dumps((ret, ), methodresponse=1)
# Mark the response as being already XMLRPC-encoded
rhnFlags.set("XMLRPC-Encoded-Response", 1)
# set the cache
rhnCache.set(cache_entry, ret, c_info["last_modified"])
return ret
def getChannelInfoForKickstart(kickstart):
query = """
select c.label,
to_char(c.last_modified, 'YYYYMMDDHH24MISS') last_modified
from rhnChannel c,
rhnKickstartableTree kt
where c.id = kt.channel_id
and kt.label = :kickstart_label
"""
h = rhnSQL.prepare(query)
h.execute(kickstart_label=str(kickstart))
return h.fetchone_dict()
def getChannelInfoForKickstartOrg(kickstart, org_id):
query = """
select c.label,
to_char(c.last_modified, 'YYYYMMDDHH24MISS') last_modified
from rhnChannel c,
rhnKickstartableTree kt
where c.id = kt.channel_id
and kt.label = :kickstart_label
and kt.org_id = :org_id
"""
h = rhnSQL.prepare(query)
h.execute(kickstart_label=str(kickstart), org_id=int(org_id))
return h.fetchone_dict()
def getChannelInfoForKickstartSession(session):
# decode the session string
try:
session_id = int(session.split('x')[0].split(':')[0])
except Exception:
return None, None
query = """
select c.label,
to_char(c.last_modified, 'YYYYMMDDHH24MISS') last_modified
from rhnChannel c,
rhnKickstartableTree kt,
rhnKickstartSession ks
where c.id = kt.channel_id
and kt.id = ks.kstree_id
and ks.id = :session_id
"""
h = rhnSQL.prepare(query)
h.execute(session_id=session_id)
return h.fetchone_dict()
def getChildChannelInfoForKickstart(kickstart, child):
query = """
select c.label,
to_char(c.last_modified, 'YYYYMMDDHH24MISS') last_modified
from rhnChannel c,
rhnKickstartableTree kt,
rhnKickstartSession ks,
rhnChannel c2
where c2.id = kt.channel_id
and kt.label = :kickstart_label
and c.label = :child_label
and c.parent_channel = c2.id
"""
h = rhnSQL.prepare(query)
h.execute(kickstart_label=str(kickstart), child_label=str(child))
return h.fetchone_dict()
def getChannelInfoForTinyUrl(tinyurl):
query = """
select tu.url
from rhnTinyUrl tu
where tu.enabled = 'Y'
and tu.token = :tinyurl
"""
h = rhnSQL.prepare(query)
h.execute(tinyurl=str(tinyurl))
return h.fetchone_dict()
# list the obsoletes for a channel
def list_obsoletes(channel):
log_debug(3, channel)
# try the caching thing first
c_info = channel_info(channel)
if not c_info: # unknown channel
raise rhnFault(40, "could not find any data on channel '%s'" % channel)
cache_entry = "list_obsoletes-%s" % channel
ret = rhnCache.get(cache_entry, c_info["last_modified"])
if ret: # we scored a cache hit
log_debug(4, "Scored cache hit", channel)
return ret
# Get the obsoleted packages
h = rhnSQL.prepare("""
select distinct
pn.name,
pe.version, pe.release, pe.epoch,
pa.label arch,
pc.name obsolete_name,
pc.version obsolete_version,
p_info.sense
from rhnPackageCapability pc,
rhnPackageArch pa,
rhnPackageEVR pe,
rhnPackageName pn,
rhnPackage p,
( select cp.channel_id,
po.package_id, po.capability_id, po.sense
from rhnPackageObsoletes po,
rhnChannelPackage cp,
rhnChannel c
where 1=1
and c.label = :channel
and c.id = cp.channel_id
and cp.package_id = po.package_id
) p_info
where 1=1
and p_info.package_id = p.id
and p.name_id = pn.id
and p.evr_id = pe.id
and p.package_arch_id = pa.id
and p_info.capability_id = pc.id
""")
h.execute(channel=str(channel))
# Store stuff in a dictionary to makes things simpler
hash = {}
while 1:
row = h.fetchone_dict()
if not row:
break
row = __stringify(row)
key = (row['name'], row['version'], row['release'],
row["epoch"], row['arch'])
value = key + (row['obsolete_name'], row['obsolete_version'],
row['sense'])
if key not in hash:
hash[key] = []
hash[key].append(value)
# Now grab a listall and match it against what we got
pkglist = list_packages_sql(c_info["id"])
result = []
for pkg in pkglist:
key = tuple(pkg[:5])
if key in hash:
for p in hash[key]:
result.append(p)
# we can cache this now
rhnCache.set(cache_entry, result, c_info["last_modified"])
return result
def __auth_user(server_id, username, password):
""" Auth if user can add/remove channel from given server """
log_debug(3, server_id, username)
# check the username and password for compliance
user = rhnUser.auth_username_password(username, password)
# The user's password checks, verify that they have perms on that
# server.
h = rhnSQL.prepare("""
select 1
from rhnUserServerPerms usp
where usp.user_id = :user_id
and usp.server_id = :server_id
""")
h.execute(user_id=str(user.getid()), server_id=str(server_id))
res = h.fetchone_dict()
if not res:
# Not allowed to perform administrative tasks on this server
raise rhnFault(37)
return 1
# small wrapper around a PL/SQL function
def subscribe_sql(server_id, channel_id, commit=1):
log_debug(3, server_id, channel_id, commit)
subscribe_channel = rhnSQL.Procedure("rhn_channel.subscribe_server")
try:
# don't run the EC yet
subscribe_channel(server_id, channel_id, 0)
except rhnSQL.SQLSchemaError:
e = sys.exc_info()[1]
if e.errno == 20102: # channel_server_one_base
log_error("Channel subscribe failed, "
"%s already subscribed to %s (?)" % (server_id, channel_id))
raise_with_tb(rhnFault(38, "Server already subscribed to %s" % channel_id), sys.exc_info()[2])
# If we got here, it's an unknown error; ISE (for now)
log_error("SQLSchemaError", e)
raise_with_tb(rhnException(e), sys.exc_info()[2])
except rhnSQL.SQLError:
e = sys.exc_info()[1]
# If we got here, it's an unknown error; ISE (for now)
log_error("SQLError", e)
raise_with_tb(rhnException(e), sys.exc_info()[2])
if commit:
rhnSQL.commit()
return 1
_query_parent_channel_subscribed = rhnSQL.Statement("""
select 1
from rhnChannel c
join rhnServerChannel sc on c.parent_channel = sc.channel_id
where sc.server_id = :sid
and c.label = :channel
""")
_query_can_subscribe = rhnSQL.Statement("""
select rhn_channel.user_role_check(:cid, wc.id, 'subscribe') as can_subscribe
from web_contact wc
where wc.login_uc = upper(:username)
""")
# subscribe a server to a channel with authentication
def subscribe_channel(server_id, channel, username, password):
log_debug(3, server_id, channel, username)
# If auth doesn't blow up we're fine
__auth_user(server_id, username, password)
# get the channel_id
h = rhnSQL.prepare("select id from rhnChannel where label = :channel")
h.execute(channel=str(channel))
ret = h.fetchone_dict()
if not ret:
log_error("Channel %s does not exist?" % channel)
raise rhnFault(40, "Channel %s does not exist?" % channel)
channel_id = ret['id']
# check if server is subscribed to the parent of the given channel
h = rhnSQL.prepare(_query_parent_channel_subscribed)
h.execute(sid=server_id, channel=str(channel))
ret = h.fetchone_dict()
if not ret:
log_error("Parent of channel %s is not subscribed to server" % channel)
raise rhnFault(32, "Parent of channel %s is not subscribed to server" % channel)
# check specific channel subscription permissions
h = rhnSQL.prepare(_query_can_subscribe)
h.execute(cid=channel_id, username=username)
ret = h.fetchone_dict()
if ret and ret['can_subscribe']:
subscribe_sql(server_id, channel_id)
return 1
raise rhnFault(71)
# This class is only a convenient encapsulation of a server's attributes:
# server_id, org_id, release, arch, user_id. Sometimes we only pass the
# server_id, and later down the road we have to message "no channel for
# release foo, arch bar", but we don't know the release and arch anymore
class LiteServer:
_attributes = ['id', 'org_id', 'release', 'arch']
def __init__(self, **kwargs):
# Initialize attributes from **kwargs (set to None if value is not
# present)
for attr in self._attributes:
setattr(self, attr, kwargs.get(attr))
def init_from_server(self, server):
self.id = server.getid()
self.org_id = server.server['org_id']
self.release = server.server['release']
self.arch = server.archname
return self
def __repr__(self):
dict = {}
for attr in self._attributes:
dict[attr] = getattr(self, attr)
return "<%s instance at %s: attributes=%s>" % (
self.__class__.__name__, id(self), dict)
# If raise_exceptions is set, BaseChannelDeniedError, NoBaseChannelError are
# raised
def guess_channels_for_server(server, user_id=None, none_ok=0,
raise_exceptions=0):
log_debug(3, server)
if not isinstance(server, LiteServer):
raise rhnException("Server object is not a LiteServer")
if None in (server.org_id, server.release, server.arch):
# need to obtain the release and/or arch and/or org_id
h = rhnSQL.prepare("""
select s.org_id, s.release, sa.label arch
from rhnServer s, rhnServerArch sa
where s.id = :server_id and s.server_arch_id = sa.id
""")
h.execute(server_id=server.id)
ret = h.fetchone_dict()
if not ret:
log_error("Could not get the release/arch "
"for server %s" % server.id)
raise rhnFault(8, "Could not find the release/arch "
"for server %s" % server.id)
if server.org_id is None:
server.org_id = ret["org_id"]
if server.release is None:
server.release = ret["release"]
if server.arch is None:
server.arch = ret["arch"]
if raise_exceptions and not none_ok:
# Let exceptions pass through
return channels_for_release_arch(server.release, server.arch,
server.org_id, user_id=user_id)
try:
return channels_for_release_arch(server.release, server.arch,
server.org_id, user_id=user_id)
except NoBaseChannelError:
if none_ok:
return []
log_error("No available channels for (server, org)",
(server.id, server.org_id), server.release, server.arch)
msg = _("Your account does not have access to any channels matching "
"(release='%(release)s', arch='%(arch)s')%(www_activation)s")
error_strings = {
'release': server.release,
'arch': server.arch,
'www_activation': ''
}
if CFG.REFER_TO_WWW:
error_strings['www_activation'] = _("\nIf you have a "
"registration number, please register with it first at "
"http://www.redhat.com/apps/activate/ and then try again.\n\n")
raise_with_tb(rhnFault(19, msg % error_strings), sys.exc_info()[2])
except BaseChannelDeniedError:
if none_ok:
return []
raise raise_with_tb(rhnFault(71,
_("Insufficient subscription permissions for release (%s, %s")
% (server.release, server.arch)), sys.exc_info()[2])
# Subscribes the server to channels
# can raise BaseChannelDeniedError, NoBaseChannelError
# Only used for new server registrations
def subscribe_server_channels(server, user_id=None, none_ok=0):
s = LiteServer().init_from_server(server)
# bretm 02/19/2007 -- have to leave none_ok in here for now due to how
# the code is setup for reg token crap; it'd be very nice to clean up that
# path to eliminate any chance for a server to be registered and not have base
# channels, excluding expiration of channel entitlements
channels = guess_channels_for_server(s, user_id=user_id, none_ok=none_ok,
raise_exceptions=1)
rhnSQL.transaction('subscribe_server_channels')
for c in channels:
subscribe_sql(s.id, c["id"], 0)
return channels
# small wrapper around a PL/SQL function
def unsubscribe_sql(server_id, channel_id, commit=1):
log_debug(3, server_id, channel_id, commit)
unsubscribe_channel = rhnSQL.Procedure("rhn_channel.unsubscribe_server")
try:
# don't run the EC yet
unsubscribe_channel(server_id, channel_id, 0)
except rhnSQL.SQLError:
log_error("Channel unsubscribe from %s failed for %s" % (
channel_id, server_id))
return 0
if commit:
rhnSQL.commit()
return 1
# unsubscribe a server from a channel
def unsubscribe_channel(server_id, channel, username, password):
log_debug(3, server_id, channel, username)
# If auth doesn't blow up we're fine
__auth_user(server_id, username, password)
# now get the id of the channel
h = rhnSQL.prepare("""
select id, parent_channel from rhnChannel where label = :channel
""")
h.execute(channel=channel)
ret = h.fetchone_dict()
if not ret:
log_error("Asked to unsubscribe server %s from non-existent channel %s" % (
server_id, channel))
raise rhnFault(40, "The specified channel '%s' does not exist." % channel)
if not ret["parent_channel"]:
log_error("Cannot unsubscribe %s from base channel %s" % (
server_id, channel))
raise rhnFault(72, "You can not unsubscribe %s from base channel %s." % (
server_id, channel))
# check specific channel subscription permissions
channel_id = ret['id']
h = rhnSQL.prepare(_query_can_subscribe)
h.execute(cid=channel_id, username=username)
ret = h.fetchone_dict()
if ret and ret['can_subscribe']:
return unsubscribe_sql(server_id, channel_id)
raise rhnFault(71)
# unsubscribe from all channels
def unsubscribe_all_channels(server_id):
log_debug(3, server_id)
# We need to unsubscribe the children channels before the base ones.
rhnSQL.transaction("unsub_all_channels")
h = rhnSQL.prepare("""
select
sc.channel_id id
from
rhnChannel c,
rhnServerChannel sc
where
sc.server_id = :server_id
and sc.channel_id = c.id
order by c.parent_channel nulls last
""")
h.execute(server_id=str(server_id))
while 1:
c = h.fetchone_dict()
if not c:
break
ret = unsubscribe_sql(server_id, c["id"], 0)
if not ret:
rhnSQL.rollback("unsub_all_channels")
raise rhnFault(36, "Could not unsubscribe server %s "
"from existing channels" % (server_id,))
# finished unsubscribing
return 1
# Unsubscribe the server from the channels in the list
# A channel is a hash containing at least the keys:
# [id, label, parent_channel]
def unsubscribe_channels(server_id, channels):
log_debug(4, server_id, channels)
if not channels:
# Nothing to do
return 1
# We need to unsubscribe the children channels before the base ones.
rhnSQL.transaction("unsub_channels")
base_channels = [x for x in channels if not x['parent_channel']]
child_channels = [x for x in channels if x['parent_channel']]
for channel in child_channels + base_channels:
ret = unsubscribe_sql(server_id, channel["id"], 0)
if not ret:
rhnSQL.rollback("unsub_channels")
raise rhnFault(36, "Could not unsubscribe server %s "
"from channel %s" % (server_id, channel["label"]))
# finished unsubscribing
return 1
# Subscribe the server to the channels in the list
# A channel is a hash containing at least the keys:
# [id, label, parent_channel]
def subscribe_channels(server_id, channels):
log_debug(4, server_id, channels)
if not channels:
# Nothing to do
return 1
# We need to subscribe the base channel before the child ones.
base_channels = [x for x in channels if not x['parent_channel']]
child_channels = [x for x in channels if x['parent_channel']]
for channel in base_channels + child_channels:
subscribe_sql(server_id, channel["id"], 0)
# finished subscribing
return 1
# check if a server is subscribed to a channel
def is_subscribed(server_id, channel):
log_debug(3, server_id, channel)
h = rhnSQL.prepare("""
select 1 subscribed
from rhnServerChannel sc, rhnChannel c
where
sc.channel_id = c.id
and c.label = :channel
and sc.server_id = :server_id
""")
h.execute(server_id=str(server_id), channel=str(channel))
ret = h.fetchone_dict()
if not ret:
# System not subscribed to channel
return 0
return 1
# Returns 0, "", "" if system does not need any message, or
# (error_code, message_title, message) otherwise
def system_reg_message(server):
server_id = server.server['id']
# Is this system subscribed to a channel?
h = rhnSQL.prepare("""
select sc.channel_id
from rhnServerChannel sc
where sc.server_id = :server_id
""")
h.execute(server_id=server_id)
ret = h.fetchone_dict()
if not ret:
# System not subscribed to any channel
#
return (-1, s_invalid_channel_title,
s_invalid_channel_message %
(server.server["release"], server.archname))
# System does have a base channel; check entitlements
from rhnServer import server_lib # having this on top, cause TB due circular imports
entitlements = server_lib.check_entitlement(server_id)
if not entitlements:
# No entitlement
# We don't have an autoentitle preference for now, so display just one
# message
templates = rhnFlags.get('templateOverrides')
if templates and 'hostname' in templates:
hostname = templates['hostname']
else:
# Default to www
hostname = "rhn.redhat.com"
params = {
'entitlement_url': "https://%s"
"/rhn/systems/details/Edit.do?sid=%s" %
(hostname, server_id)
}
return -1, no_entitlement_title, no_entitlement_message % params
return 0, "", ""
def subscribe_to_tools_channel(server_id):
"""
Subscribes server_id to the RHN Tools channel associated with its base channel, if one exists.
"""
base_channel_dict = get_base_channel(server_id, none_ok=1)
if base_channel_dict is None:
raise NoBaseChannelError("Server %s has no base channel." %
str(server_id))
lookup_child_channels = rhnSQL.Statement("""
select id, label, parent_channel
from rhnChannel
where parent_channel = :id
""")
child_channel_data = rhnSQL.prepare(lookup_child_channels)
child_channel_data.execute(id=base_channel_dict['id'])
child_channels = child_channel_data.fetchall_dict()
if child_channels is None:
raise NoChildChannels("Base channel id %s has no child channels associated with it." %
base_channel_dict['id'])
tools_channel = None
for channel in child_channels:
if 'label' in channel:
if 'rhn-tools' in channel['label']:
tools_channel = channel
if tools_channel is None:
raise NoToolsChannel("Base channel id %s does not have a RHN Tools channel as a child channel." %
base_channel_dict['id'])
else:
if 'id' not in tools_channel:
raise InvalidChannel("RHN Tools channel has no id.")
if 'label' not in tools_channel:
raise InvalidChannel("RHN Tools channel has no label.")
if 'parent_channel' not in tools_channel:
raise InvalidChannel("RHN Tools channel has no parent_channel.")
subscribe_channels(server_id, [tools_channel])
# Various messages that can be reused
#
# bretm 02/07/2007 -- when we have better old-client documentation, probably
# will be safe to get rid of all this crap
h_invalid_channel_title = _("System Registered but Inactive")
h_invalid_channel_message = _("""
Invalid Architecture and OS release combination (%s, %s).
Your system has been registered, but will not receive updates
because it is not subscribed to a channel. If you have not yet
activated your product for service, please visit our website at:
http://www.redhat.com/apps/activate/
...to activate your product.""")
s_invalid_channel_title = _("System Registered but Inactive")
s_invalid_channel_message = _("""
Invalid Architecture and OS release combination (%s, %s).
Your system has been registered, but will not receive updates
because it could not be subscribed to a base channel.
Please contact your organization administrator for assistance.
""")
no_autoentitlement_message = _("""
This system has been successfully registered, but is not yet entitled
to service. To entitle this system to service, login to the web site at:
%(entitlement_url)s
""")
no_entitlement_title = _("System Registered but Inactive")
no_entitlement_message = _("""
This system has been successfully registered, but no service entitlements
were available. To entitle this system to service, login to the web site at:
%(entitlement_url)s
""")
|
jdobes/spacewalk
|
backend/server/rhnChannel.py
|
Python
|
gpl-2.0
| 70,185
|
[
"VisIt"
] |
6a3c6c3f315290e57b96510f06bc517e12dd8fcfa9292953e2cf561fb42b7de6
|
# Copyright 2013-2020 Lawrence Livermore National Security, LLC and other
# Spack Project Developers. See the top-level COPYRIGHT file for details.
#
# SPDX-License-Identifier: (Apache-2.0 OR MIT)
from spack import *
class RPreprocesscore(RPackage):
"""A collection of pre-processing functions.
A library of core preprocessing routines."""
homepage = "https://bioconductor.org/packages/preprocessCore"
git = "https://git.bioconductor.org/packages/preprocessCore.git"
version('1.46.0', commit='8cfc3938c1b08424587f437ed6cd2ec43512500e')
version('1.44.0', commit='dc1dc61fc562aaff3fd9b11ab0d48c2d6b3a5b81')
version('1.42.0', commit='2e3a8baeacfaa1526d51252642772ea951015bba')
version('1.40.0', commit='969bb0e5cbd63d569502ae4e6eaadc1e216646dd')
version('1.38.1', commit='c58cb4c720eda0f1c733b989b14912093a7c5fbc')
|
rspavel/spack
|
var/spack/repos/builtin/packages/r-preprocesscore/package.py
|
Python
|
lgpl-2.1
| 862
|
[
"Bioconductor"
] |
8cb4ce6a35c50e9799afdeb26a37a52c8654c8bb2ac97a693a4be33468811dd8
|
#! /usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2022 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import argparse
import os
import libcst as cst
import pathlib
import sys
from typing import (Any, Callable, Dict, List, Sequence, Tuple)
def partition(
predicate: Callable[[Any], bool],
iterator: Sequence[Any]
) -> Tuple[List[Any], List[Any]]:
"""A stable, out-of-place partition."""
results = ([], [])
for i in iterator:
results[int(predicate(i))].append(i)
# Returns trueList, falseList
return results[1], results[0]
class dataplexCallTransformer(cst.CSTTransformer):
CTRL_PARAMS: Tuple[str] = ('retry', 'timeout', 'metadata')
METHOD_TO_PARAMS: Dict[str, Tuple[str]] = {
'cancel_job': ('name', ),
'create_asset': ('parent', 'asset_id', 'asset', 'validate_only', ),
'create_content': ('parent', 'content', 'validate_only', ),
'create_entity': ('parent', 'entity', 'validate_only', ),
'create_environment': ('parent', 'environment_id', 'environment', 'validate_only', ),
'create_lake': ('parent', 'lake_id', 'lake', 'validate_only', ),
'create_partition': ('parent', 'partition', 'validate_only', ),
'create_task': ('parent', 'task_id', 'task', 'validate_only', ),
'create_zone': ('parent', 'zone_id', 'zone', 'validate_only', ),
'delete_asset': ('name', ),
'delete_content': ('name', ),
'delete_entity': ('name', 'etag', ),
'delete_environment': ('name', ),
'delete_lake': ('name', ),
'delete_partition': ('name', 'etag', ),
'delete_task': ('name', ),
'delete_zone': ('name', ),
'get_asset': ('name', ),
'get_content': ('name', 'view', ),
'get_entity': ('name', 'view', ),
'get_environment': ('name', ),
'get_job': ('name', ),
'get_lake': ('name', ),
'get_partition': ('name', ),
'get_task': ('name', ),
'get_zone': ('name', ),
'list_asset_actions': ('parent', 'page_size', 'page_token', ),
'list_assets': ('parent', 'page_size', 'page_token', 'filter', 'order_by', ),
'list_content': ('parent', 'page_size', 'page_token', 'filter', ),
'list_entities': ('parent', 'view', 'page_size', 'page_token', 'filter', ),
'list_environments': ('parent', 'page_size', 'page_token', 'filter', 'order_by', ),
'list_jobs': ('parent', 'page_size', 'page_token', ),
'list_lake_actions': ('parent', 'page_size', 'page_token', ),
'list_lakes': ('parent', 'page_size', 'page_token', 'filter', 'order_by', ),
'list_partitions': ('parent', 'page_size', 'page_token', 'filter', ),
'list_sessions': ('parent', 'page_size', 'page_token', ),
'list_tasks': ('parent', 'page_size', 'page_token', 'filter', 'order_by', ),
'list_zone_actions': ('parent', 'page_size', 'page_token', ),
'list_zones': ('parent', 'page_size', 'page_token', 'filter', 'order_by', ),
'update_asset': ('update_mask', 'asset', 'validate_only', ),
'update_content': ('update_mask', 'content', 'validate_only', ),
'update_entity': ('entity', 'validate_only', ),
'update_environment': ('update_mask', 'environment', 'validate_only', ),
'update_lake': ('update_mask', 'lake', 'validate_only', ),
'update_task': ('update_mask', 'task', 'validate_only', ),
'update_zone': ('update_mask', 'zone', 'validate_only', ),
}
def leave_Call(self, original: cst.Call, updated: cst.Call) -> cst.CSTNode:
try:
key = original.func.attr.value
kword_params = self.METHOD_TO_PARAMS[key]
except (AttributeError, KeyError):
# Either not a method from the API or too convoluted to be sure.
return updated
# If the existing code is valid, keyword args come after positional args.
# Therefore, all positional args must map to the first parameters.
args, kwargs = partition(lambda a: not bool(a.keyword), updated.args)
if any(k.keyword.value == "request" for k in kwargs):
# We've already fixed this file, don't fix it again.
return updated
kwargs, ctrl_kwargs = partition(
lambda a: a.keyword.value not in self.CTRL_PARAMS,
kwargs
)
args, ctrl_args = args[:len(kword_params)], args[len(kword_params):]
ctrl_kwargs.extend(cst.Arg(value=a.value, keyword=cst.Name(value=ctrl))
for a, ctrl in zip(ctrl_args, self.CTRL_PARAMS))
request_arg = cst.Arg(
value=cst.Dict([
cst.DictElement(
cst.SimpleString("'{}'".format(name)),
cst.Element(value=arg.value)
)
# Note: the args + kwargs looks silly, but keep in mind that
# the control parameters had to be stripped out, and that
# those could have been passed positionally or by keyword.
for name, arg in zip(kword_params, args + kwargs)]),
keyword=cst.Name("request")
)
return updated.with_changes(
args=[request_arg] + ctrl_kwargs
)
def fix_files(
in_dir: pathlib.Path,
out_dir: pathlib.Path,
*,
transformer=dataplexCallTransformer(),
):
"""Duplicate the input dir to the output dir, fixing file method calls.
Preconditions:
* in_dir is a real directory
* out_dir is a real, empty directory
"""
pyfile_gen = (
pathlib.Path(os.path.join(root, f))
for root, _, files in os.walk(in_dir)
for f in files if os.path.splitext(f)[1] == ".py"
)
for fpath in pyfile_gen:
with open(fpath, 'r') as f:
src = f.read()
# Parse the code and insert method call fixes.
tree = cst.parse_module(src)
updated = tree.visit(transformer)
# Create the path and directory structure for the new file.
updated_path = out_dir.joinpath(fpath.relative_to(in_dir))
updated_path.parent.mkdir(parents=True, exist_ok=True)
# Generate the updated source file at the corresponding path.
with open(updated_path, 'w') as f:
f.write(updated.code)
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description="""Fix up source that uses the dataplex client library.
The existing sources are NOT overwritten but are copied to output_dir with changes made.
Note: This tool operates at a best-effort level at converting positional
parameters in client method calls to keyword based parameters.
Cases where it WILL FAIL include
A) * or ** expansion in a method call.
B) Calls via function or method alias (includes free function calls)
C) Indirect or dispatched calls (e.g. the method is looked up dynamically)
These all constitute false negatives. The tool will also detect false
positives when an API method shares a name with another method.
""")
parser.add_argument(
'-d',
'--input-directory',
required=True,
dest='input_dir',
help='the input directory to walk for python files to fix up',
)
parser.add_argument(
'-o',
'--output-directory',
required=True,
dest='output_dir',
help='the directory to output files fixed via un-flattening',
)
args = parser.parse_args()
input_dir = pathlib.Path(args.input_dir)
output_dir = pathlib.Path(args.output_dir)
if not input_dir.is_dir():
print(
f"input directory '{input_dir}' does not exist or is not a directory",
file=sys.stderr,
)
sys.exit(-1)
if not output_dir.is_dir():
print(
f"output directory '{output_dir}' does not exist or is not a directory",
file=sys.stderr,
)
sys.exit(-1)
if os.listdir(output_dir):
print(
f"output directory '{output_dir}' is not empty",
file=sys.stderr,
)
sys.exit(-1)
fix_files(input_dir, output_dir)
|
googleapis/python-dataplex
|
scripts/fixup_dataplex_v1_keywords.py
|
Python
|
apache-2.0
| 8,657
|
[
"VisIt"
] |
ab01d10f87fd5f1074ebfb29a03c15ad3f115fb7714c533e8e7427e393d4adb2
|
# -*- coding: utf-8 -*-
# vi:si:et:sw=4:sts=4:ts=4
##
## Copyright (C) 2013 Async Open Source <http://www.async.com.br>
## All rights reserved
##
## This program is free software; you can redistribute it and/or modify
## it under the terms of the GNU General Public License as published by
## the Free Software Foundation; either version 2 of the License, or
## (at your option) any later version.
##
## This program is distributed in the hope that it will be useful,
## but WITHOUT ANY WARRANTY; without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
## GNU General Public License for more details.
##
## You should have received a copy of the GNU General Public License
## along with this program; if not, write to the Free Software
## Foundation, Inc., or visit: http://www.gnu.org/.
##
## Author(s): Stoq Team <stoq-devel@async.com.br>
##
import gtk
import mock
from stoqlib.domain.invoice import InvoiceLayout
from stoqlib.gui.dialogs.invoicedialog import InvoiceLayoutDialog
from stoqlib.gui.test.uitestutils import GUITest
class TestInvoiceLayoutListSlave(GUITest):
def test_show(self):
InvoiceLayout(description=u'Test Invoice', width=500, height=500)
dialog = InvoiceLayoutDialog(store=self.store)
self.check_dialog(dialog, 'invoice-layout-dialog-show')
def test_delete_model(self):
InvoiceLayout(store=self.store, description=u"Standard Invoice",
width=500, height=500)
dialog = InvoiceLayoutDialog(store=self.store, reuse_store=True)
item = dialog.list_slave.listcontainer.list[0]
dialog.list_slave.listcontainer.list.select(item)
with mock.patch('kiwi.ui.listdialog.yesno') as yesno:
yesno.side_effect = lambda *x, **y: gtk.RESPONSE_OK
self.click(dialog.list_slave.listcontainer.remove_button)
yesno.assert_called_with('Do you want to remove Standard Invoice ?',
buttons=(
('gtk-cancel', gtk.RESPONSE_CANCEL),
('gtk-remove', gtk.RESPONSE_OK)),
default=gtk.RESPONSE_OK, parent=None)
|
andrebellafronte/stoq
|
stoqlib/gui/test/test_invoicedialog.py
|
Python
|
gpl-2.0
| 2,237
|
[
"VisIt"
] |
2e26b290b98470c454a45db5add3d29d950817c3f78cd00564cdad1c5a755dc7
|
#coding=utf-8
import os
import io
import ast
import sys
import logging
import textwrap
import traceback
import collections
from opster import command, dispatch, Dispatcher
AUTO_PRINT = '#+'
def get_markup():
try:
return globals()['__MARKUP__']
except:
return None
def is_interactive():
if sys.flags.interactive:
return True
else:
try:
__IPYTHON__
return True
except:
return False
def get_figure(plot):
import matplotlib
if isinstance(plot,matplotlib.axes.Subplot):
return plot.get_figure()
elif isinstance(plot,matplotlib.figure.Figure):
return plot
FIGURE_LATEX = """\n\\begin{{figure}}[h]
\includegraphics[width=\\textwidth]{{{path}}}
\caption{{{caption}}}
\label{{{label}}}
\end{{figure}}\n"""
PREAMBLE = """\documentclass{article}
\\usepackage{fancyvrb}
\\usepackage{color}
\\usepackage[utf8]{inputenc}
\\usepackage{graphicx}
\\usepackage[margin=2cm]{geometry}
"""
def figure(plot,name,desc,float=True):
import matplotlib
if is_interactive():
matplotlib.interactive(True)
get_figure(plot).show()
else:
markup = get_markup()
fig = get_figure(plot)
fig.savefig(name)
matplotlib.pyplot.close(fig.number)
if markup == 'LATEX':
return FIGURE_LATEX.format(path=name,caption=desc,label=os.path.splitext(name)[0])
elif markup == 'MARKDOWN':
return '\n\n'.format(label=name,desc=desc,path=name)
class Visitor(ast.NodeTransformer):
""" special ast visitor, parses code chunks from string into single code
objects do not set maxdepth bigger than 1, except you know what you do, but
probaly the compilation will fail"""
def __init__(self,inputfilename,logger,maxdepth=1):
self.maxdepth = maxdepth
self.inputfilename = inputfilename
self.logger = logger
self.CodeChunk = collections.namedtuple(
'CodeChunk',['codeobject','source','mode'])
self.interactive = False
def _get_last_lineno(self,node):
maxlineno = 0
for x in ast.walk(node):
if hasattr(x,'lineno') and x.lineno > maxlineno:
maxlineno = x.lineno
return maxlineno
def _compile(self,node,mode='exec'):
try:
if mode == 'exec':
codeobject = compile(ast.Module([node]),self.inputfilename,'exec')
else:
codeobject = compile(ast.Expression(node),self.inputfilename,'eval')
return self.CodeChunk(codeobject,node.source,mode)
except:
self.logger.exception(
'failed to compile "{0}", {1}'.format(
node,traceback.format_exc()))
return self.CodeChunk(None,node.source,mode)
def _import_matplotlib(self,modpart):
if not 'matplotlib.pyplot' in sys.modules:
self.logger.info('detected "{0}" and imported matplotlib'
' before to choose backend "Agg"'.format(modpart))
return self.CodeChunk(
compile('import matplotlib;matplotlib.use("Agg")',
'<rstscript.dynamic>','exec'),'','exec')
else:
self.logger.info('detected import of matplotlib.pyplot, '
'but backend was already choosen')
def _detect_matplotlib(self,names,module=None):
if module:
for modpart in module.split('.'):
if modpart in ['pyplot','pylab','sympy']:
return self._import_matplotlib(modpart)
for name in names:
if name.name in ['pyplot','pylab','sympy']:
return self._import_matplotlib(name.name)
def visit_Import(self,node):
newnode = self._detect_matplotlib(node.names)
if newnode:
yield newnode
yield self._compile(node)
def visit_ImportFrom(self,node):
newnode = self._detect_matplotlib(node.names,module=node.module)
if newnode:
yield newnode
yield self._compile(node)
def visit_Expr(self,node):
if AUTO_PRINT in node.source:
nnode = node.value
nnode.source = node.source
nnode.lineno = node.lineno
yield self._compile(nnode,mode='eval')
else:
yield self._compile(node)
def visit_Assign(self,node):
yield self._compile(node)
if AUTO_PRINT in node.source:
if isinstance(node.targets[0],ast.Tuple):
for x in node.targets[0].elts:
x.source = x.id
x.lineno = node.lineno
x.ctx = ast.Load()
yield self._compile(x,mode='eval')
else:
x = node.targets[0]
x.source = x.id
x.lineno = node.lineno
x.ctx = ast.Load()
yield self._compile(x,mode='eval')
def visit(self, node, start_lineno,raw,depth=0):
"""Visit a node."""
if depth >= self.maxdepth:
method = 'visit_' + node.__class__.__name__
visitor = getattr(self, method, None)
# get source code of the node, must be before the next statement
startline = node.lineno-1
endline = self._get_last_lineno(node)
node.source = '\n'.join(raw[startline:endline])
#print('last line',self._get_last_lineno(node),'source',node.source)
node.lineno = node.lineno + start_lineno
if visitor:
yield from visitor(node)
else:
yield self._compile(node)
else:
depth += 1
for child in ast.iter_child_nodes(node):
yield from self.visit(child,start_lineno,raw,depth=depth)
class Litter(object):
def __init__(self,figdir='',stop_on_error=True,ipython_style=False,loglevel='WARNING',inputfilename='',inputfile=None,outputfilename='',outputfile=None,**kwargs):
from pygments import highlight
from pygments.lexers import PythonLexer
from pygments.formatters import LatexFormatter
self.lexer = PythonLexer()
self.formatter = LatexFormatter(full=False)
self.highlight = highlight
self.plt = None
if figdir:
self._figdir = figdir
else:
self._figdir = None
if inputfilename:
self.inputfile = open(inputfilename,'r')
elif inputfile:
self.inputfile = inputfile
self.inputfilename = self.inputfile.name
sys.path.append(os.path.abspath('.'))
if outputfilename:
self.outputfile = open(outputfilename,'wb')
elif outputfile:
self.outputfile = outputfile
self._raw = None
self.logger = logging.getLogger('litter')
self.logger.setLevel(loglevel)
handler = logging.StreamHandler(sys.stdout)
formatter = logging.Formatter('%(levelname)s - %(message)s')
handler.setFormatter(formatter)
handler.setLevel(loglevel)
self.logger.addHandler(handler)
self.visitor = Visitor(self.inputfile.name,self.logger)
self.globallocal = {}
self.autoprinting = True
self.ipythonstyle = ipython_style
self.stderr = io.StringIO()
self.stdout = io.StringIO()
self.traceback = io.StringIO()
self.stdout_sys = sys.stdout
self.stderr_sys = sys.stderr
self.ResultChunk = collections.namedtuple(
'ResultChunk',['codechunk','stderr','stdout','traceback','out'])
self.results = {}
self.stop_on_error = stop_on_error
self.reference = {}
def visit(self,tree,linenumber,source):
return self.visitor.visit(tree,linenumber,source.splitlines())
def chunks(self):
chunk = io.StringIO()
code = False
linenumber = 1
for i,line in enumerate(self.inputfile):
if not line.strip() or line.startswith('#'):
if code:
yield (linenumber,code,chunk.getvalue())
chunk.seek(0)
chunk.truncate()
code = not code
linenumber = i+1
if line.startswith('#coding'):
continue
elif line.startswith('#'):
yield (linenumber,code,line[1:])
else:
yield (linenumber,code,line)
else:
if not code:
yield (linenumber,code,chunk.getvalue())
chunk.seek(0)
chunk.truncate()
code = not code
linenumber = i+1
chunk.write(line)
yield (linenumber,code,chunk.getvalue())
@property
def figdir(self):
if not self._figdir:
self._figdir=os.path.abspath(os.path.dirname(self.outputfile.name))
return self._figdir
def _saveallfigures(self,result):
if not self.plt:
if 'matplotlib.pyplot' in sys.modules:
from matplotlib import pyplot
self.plt = pyplot
self.logger.info('imported matplotlib.pyplot')
else:
raise StopIteration
for num in self.plt.get_fignums():
if num > 1:
self.logger.error('there are several figures in this chunks, not supported so far')
else:
label,desc = self._get_label(result)
if label:
fig = self.plt.figure(num)
name = '{0}.pdf'.format(label)
figpath =os.path.join(self.figdir,name)
fig.savefig(figpath)
ref = '\n\n'.format(label=label,desc=desc,path=figpath)
self.logger.info('saved figure "{0}" to "{1}"'.format(label,figpath))
self.reference[label] = figpath
self.plt.close(num)
yield (False,ref)
def _get_label(self,result):
if '#:' in result.codechunk.source:
s = result.codechunk.source
t = s[s.find('#:')+2:]
if ':' in t:
return t.split(':')
else:
return t,t
else:
return None,None
def process(self):
for (linenumber,code,source) in self.chunks():
if code:
tree = ast.parse(source)
for chunk in self.visit(tree,linenumber-1,source):
result = self.execute(chunk)
yield (code,result)
yield from self._saveallfigures(result)
if result.traceback and self.stop_on_error:
raise StopIteration
elif source.startswith('latex') and globals()['__MARKUP__'] == 'MARKDOWN':
continue
elif source.startswith('latex'):
yield (code,source[5:])
elif source.startswith('litter'):
yield (code,source.format(self.globallocal))
else:
yield (code,source)
def inter(self):
from IPython.terminal.embed import InteractiveShellEmbed
from IPython.terminal.interactiveshell import TerminalMagics
inter = InteractiveShellEmbed()
magics = TerminalMagics(inter)
t = threading.Thread(target=inter)
t.start()
import time
time.sleep(2)
magics.store_or_execute('a=2\nprint(a)',None)
def format(self):
wascode = False
tmp = io.StringIO()
def writetmp(tmp):
val = tmp.getvalue()
if val:
if globals()['__MARKUP__'] == 'MARKDOWN':
self.outputfile.write(b'\n~~~python\n')
self.outputfile.write(val.encode('utf-8'))
self.outputfile.write(b'~~~\n\n')
elif globals()['__MARKUP__'] == 'LATEX':
self.outputfile.write(self.highlight(val, self.lexer, self.formatter).encode('utf-8'))
tmp.seek(0)
tmp.truncate()
if globals()['__MARKUP__'] == 'LATEX':
self.outputfile.write(PREAMBLE.encode('utf-8'))
self.outputfile.write(self.formatter.get_style_defs().encode('utf-8'))
self.outputfile.write(b'\\begin{document}')
for (code,result) in self.process():
if not code:
if wascode and tmp.tell():
writetmp(tmp)
wascode = False
if globals()['__MARKUP__'] == 'MARKDOWN':
self.outputfile.write(result.encode('utf-8'))
elif globals()['__MARKUP__'] == 'LATEX':
if result.startswith('###'):
self.outputfile.write(
('\\subsubsection*{%s}\n'%result[3:].strip()).encode('utf-8'))
elif result.startswith('##'):
self.outputfile.write(
('\\subsection*{%s}\n'%result[2:].strip()).encode('utf-8'))
elif result.startswith('#'):
self.outputfile.write(
('\\section*{%s}\n'%result[1:].strip()).encode('utf-8'))
else:
self.outputfile.write(result.encode('utf-8'))
elif code and not '#~' in result.codechunk.source:
wascode = True
self.format_result(tmp,result)
else:
if wascode and tmp.tell():
writetmp(tmp)
wascode = False
self.outputfile.write(result.out.encode('utf-8'))
self.outputfile.write(b'\n')
writetmp(tmp)
if globals()['__MARKUP__'] == 'LATEX':
self.outputfile.write(b'\end{document}')
for (label,ref) in self.reference.items():
self.outputfile.write('\n[{0}]: {1}\n'.format(label,ref).encode('utf-8'))
def format_result(self,out,res):
source = res.codechunk.source
if not '#>' in source and source:
if self.ipythonstyle:
out.write('In [{0}]: '.format(res.codechunk.codeobject.co_firstlineno))
out.write(source)
out.write('\n')
elif not res.codechunk.mode == 'eval':
out.write(source)
out.write('\n')
if res.stdout:
out.write(res.stdout)
out.write('\n')
if res.stderr:
out.write(res.stderr)
out.write('\n')
if res.traceback:
out.write(res.traceback)
out.write('\n')
if self.autoprinting and res.codechunk.mode == 'eval' and \
not ('#>' in source and not '#~' in source) and not '#:' in source:
line = res.codechunk.codeobject.co_firstlineno
if '#' in source:
coa = source[:source.find('#')]
result = '{0} {1}'.format(res.out,source[source.rfind('#')+2:])
else:
coa = source
result = res.out
if self.ipythonstyle:
try:
out.write('Out[{0}]: {1:.5g}'.format(line,result))
except:
out.write('Out[{0}]: {1}'.format(line,result))
else:
try:
out.write('{0} => {1:.5g}'.format(coa,result))
except:
out.write('{0} => {1}'.format(coa,result))
out.write('\n')
def execute(self,codechunk):
sys.stdout = self.stdout
sys.stderr = self.stderr
self.stdout.seek(0)
self.stderr.seek(0)
self.traceback.seek(0)
tr = ''
out = None
try:
if codechunk.mode == 'exec':
exec(codechunk.codeobject,self.globallocal,self.globallocal)
elif codechunk.mode == 'eval':
out=eval(codechunk.codeobject,self.globallocal,self.globallocal)
except:
tr = traceback.format_exc().strip()
# remove all line until a line containing rstscript.dynamic except
# the first
st = tr.find('\n')+1
en = tr.find('File "{0}"'.format(self.inputfilename))
self.traceback.write(tr[:st])
self.traceback.write(tr[en:])
finally:
sys.stdout = self.stdout_sys
sys.stderr = self.stderr_sys
if tr:
self.logger.warn(
'failed on line {0} with {1}'.format(
codechunk.codeobject.co_firstlineno,tr[tr.rfind('\n')+1:]))
self.stdout.truncate()
self.stderr.truncate()
rc = self.ResultChunk(
codechunk,self.stderr.getvalue(),
self.stdout.getvalue(),self.traceback.getvalue(),out,
)
self.stderr.seek(0)
self.stderr.truncate()
self.traceback.seek(0)
self.traceback.truncate()
self.stdout.seek(0)
self.stdout.truncate()
return rc
process = Dispatcher(globaloptions=[
('s','stop-on-error',False,'stop if an error occurs'),
('l','loglevel','WARNING','set the log level'),
('i','ipython-style',False,'ipython in out style'),
('h','highlighting-style','pygments','highlighting style'),
('v','version','0.0.1','version of the file'),
])
d = Dispatcher()
d.nest('process',process,'process the python input')
@process.command(name='md',usage='inputfile -o outpufile')
def process_md(inputfilename,outputfilename='',**kwargs):
global __MARKUP__
__MARKUP__ = 'MARKDOWN'
if not outputfilename:
with io.BytesIO() as tmp:
li = Litter(outputfile=tmp,inputfilename=inputfilename,figdir=os.path.abspath('.'),**kwargs)
li.format()
tmp.seek(0)
sys.stdout.write(tmp.read().decode('utf-8'))
else:
li = Litter(outputfilename=outputfilename,inputfilename=inputfilename,**kwargs)
li.format()
@process.command(name='pdf',usage='inputfile -o outpufile')
def process_pdf(inputfilename,outputfilename,**kwargs):
from sh import pandoc
import tempfile
global __MARKUP__
__MARKUP__ = 'MARKDOWN'
with tempfile.NamedTemporaryFile() as tmp:
li = Litter(outputfile=tmp.file,inputfilename=inputfilename,figdir=os.path.abspath(os.path.dirname(outputfilename)),**kwargs)
li.format()
tmp.file.flush()
kwargs.get('version')
pandoc(tmp.name,'-o',outputfilename,
'--template',os.path.join(
os.path.dirname(__file__),'data','template.tex'),'-V','version=%s'%kwargs.get('version'),'--highlight-style',kwargs.get('highlighting_style'))
@process.command(name='latex',usage='inputfile -o outpufile')
def process_latex(inputfilename,outputfilename,**kwargs):
global __MARKUP__
__MARKUP__ = 'LATEX'
if not outputfilename:
with io.BytesIO() as tmp:
li = Litter(outputfile=tmp,inputfilename=inputfilename,figdir=os.path.abspath('.'),**kwargs)
li.format()
tmp.seek(0)
sys.stdout.write(tmp.read().decode('utf-8'))
else:
li = Litter(outputfilename=outputfilename,inputfilename=inputfilename,**kwargs)
li.format()
@d.command(usage='input output')
def mdto(inputfilename,outputfilename,highlight='pygments'):
""" convert markdown to pdf using pandoc"""
from sh import pandoc
pandoc('-f','markdown',inputfilename,'-o',outputfilename,'--highlight-style',highlight,
'--template',os.path.join(
os.path.dirname(__file__),'data','template.tex'),'-V','version=0.0.1')
@d.command(usage='input')
def run(inputfilename):
pass
def main():
d.dispatch()
if __name__ == '__main__':
main()
|
mortbauer/pylitter
|
pylitter/__init__.py
|
Python
|
mit
| 20,027
|
[
"VisIt"
] |
77e916c364918e164fcd86532190f1241977de98ebd69f2e16d568d0a67e7783
|
# coding: utf-8
# Copyright (c) Pymatgen Development Team.
# Distributed under the terms of the MIT License.
import json
import os
import unittest
import numpy as np
import pandas as pd
from pymatgen.io.lammps.outputs import LammpsDump, parse_lammps_dumps, parse_lammps_log
from pymatgen.util.testing import PymatgenTest
test_dir = os.path.join(PymatgenTest.TEST_FILES_DIR, "lammps")
class LammpsDumpTest(unittest.TestCase):
@classmethod
def setUpClass(cls):
with open(os.path.join(test_dir, "dump.rdx_wc.100")) as f:
rdx_str = f.read()
cls.rdx = LammpsDump.from_string(string=rdx_str)
with open(os.path.join(test_dir, "dump.tatb")) as f:
tatb_str = f.read()
cls.tatb = LammpsDump.from_string(string=tatb_str)
def test_from_string(self):
self.assertEqual(self.rdx.timestep, 100)
self.assertEqual(self.rdx.natoms, 21)
np.testing.assert_array_equal(self.rdx.box.bounds, np.array([(35, 48)] * 3))
np.testing.assert_array_equal(self.rdx.data.columns, ["id", "type", "xs", "ys", "zs"])
rdx_data = self.rdx.data.iloc[-1]
rdx_data_target = [19, 2, 0.42369, 0.47347, 0.555425]
np.testing.assert_array_almost_equal(rdx_data, rdx_data_target)
self.assertEqual(self.tatb.timestep, 0)
self.assertEqual(self.tatb.natoms, 384)
bounds = [[0, 13.624], [0, 17.1149153805], [0, 15.1826391451]]
np.testing.assert_array_almost_equal(self.tatb.box.bounds, bounds)
tilt = [-5.75315630927, -6.325466, 7.4257288]
np.testing.assert_array_almost_equal(self.tatb.box.tilt, tilt)
np.testing.assert_array_equal(self.tatb.data.columns, ["id", "type", "q", "x", "y", "z"])
tatb_data = self.tatb.data.iloc[-1]
tatb_data_target = [356, 3, -0.482096, 2.58647, 12.9577, 14.3143]
np.testing.assert_array_almost_equal(tatb_data, tatb_data_target)
def test_json_dict(self):
encoded = json.dumps(self.rdx.as_dict())
decoded = json.loads(encoded)
rdx = LammpsDump.from_dict(decoded)
self.assertEqual(rdx.timestep, 100)
self.assertEqual(rdx.natoms, 21)
np.testing.assert_array_equal(rdx.box.bounds, np.array([(35, 48)] * 3))
pd.testing.assert_frame_equal(rdx.data, self.rdx.data)
class FuncTest(unittest.TestCase):
def test_parse_lammps_dumps(self):
# gzipped
rdx_10_pattern = os.path.join(test_dir, "dump.rdx.gz")
rdx_10 = list(parse_lammps_dumps(file_pattern=rdx_10_pattern))
timesteps_10 = [d.timestep for d in rdx_10]
np.testing.assert_array_equal(timesteps_10, np.arange(0, 101, 10))
self.assertTupleEqual(rdx_10[-1].data.shape, (21, 5))
# wildcard
rdx_25_pattern = os.path.join(test_dir, "dump.rdx_wc.*")
rdx_25 = list(parse_lammps_dumps(file_pattern=rdx_25_pattern))
timesteps_25 = [d.timestep for d in rdx_25]
np.testing.assert_array_equal(timesteps_25, np.arange(0, 101, 25))
self.assertTupleEqual(rdx_25[-1].data.shape, (21, 5))
def test_parse_lammps_log(self):
comb_file = "log.5Oct16.comb.Si.elastic.g++.1"
comb = parse_lammps_log(filename=os.path.join(test_dir, comb_file))
self.assertEqual(len(comb), 6)
# first comb run
comb0 = comb[0]
np.testing.assert_array_equal(["Step", "Temp", "TotEng", "PotEng", "E_vdwl", "E_coul"], comb0.columns)
self.assertEqual(len(comb0), 6)
comb0_data = [
[0, 1, -4.6295947, -4.6297237, -4.6297237, 0],
[5, 1, -4.6295965, -4.6297255, -4.6297255, 0],
]
np.testing.assert_array_almost_equal(comb0.iloc[[0, -1]], comb0_data)
# final comb run
comb_1 = comb[-1]
np.testing.assert_array_equal(
[
"Step",
"Lx",
"Ly",
"Lz",
"Xy",
"Xz",
"Yz",
"c_fxy[1]",
"c_fxy[2]",
"c_fxy[3]",
"c_fxy[4]",
"c_fxy[5]",
"c_fxy[6]",
],
comb_1.columns,
)
self.assertEqual(len(comb_1), 11)
comb_1_data = [[36, 5.1293854e-06], [46, 2192.8256]]
np.testing.assert_array_almost_equal(comb_1.iloc[[0, -1], [0, -3]], comb_1_data)
ehex_file = "log.13Oct16.ehex.g++.8"
ehex = parse_lammps_log(filename=os.path.join(test_dir, ehex_file))
self.assertEqual(len(ehex), 3)
ehex0, ehex1, ehex2 = ehex
# ehex run #1
np.testing.assert_array_equal(["Step", "Temp", "E_pair", "E_mol", "TotEng", "Press"], ehex0.columns)
self.assertEqual(len(ehex0), 11)
ehex0_data = [
[0, 1.35, -4.1241917, 0, -2.0994448, -3.1961612],
[1000, 1.3732017, -3.7100044, 0, -1.6504594, 0.83982701],
]
np.testing.assert_array_almost_equal(ehex0.iloc[[0, -1]], ehex0_data)
# ehex run #2
np.testing.assert_array_equal(["Step", "Temp", "c_Thot", "c_Tcold"], ehex1.columns)
self.assertEqual(len(ehex1), 11)
ehex1_data = [
[1000, 1.35, 1.431295, 1.2955644],
[11000, 1.3794051, 1.692299, 1.0515688],
]
np.testing.assert_array_almost_equal(ehex1.iloc[[0, -1]], ehex1_data)
# ehex run #3
np.testing.assert_array_equal(["Step", "Temp", "c_Thot", "c_Tcold", "v_tdiff", "f_ave"], ehex2.columns)
self.assertEqual(len(ehex2), 21)
ehex2_data = [
[11000, 1.3794051, 1.6903393, 1.0515688, 0, 0],
[31000, 1.3822489, 1.8220413, 1.0322271, -0.7550338, -0.76999077],
]
np.testing.assert_array_almost_equal(ehex2.iloc[[0, -1]], ehex2_data)
peptide_file = "log.5Oct16.peptide.g++.1"
peptide = parse_lammps_log(filename=os.path.join(test_dir, peptide_file))
peptide0 = peptide[0]
np.testing.assert_array_equal(
[
"Step",
"TotEng",
"KinEng",
"Temp",
"PotEng",
"E_bond",
"E_angle",
"E_dihed",
"E_impro",
"E_vdwl",
"E_coul",
"E_long",
"Press",
],
peptide0.columns,
)
self.assertEqual(len(peptide0), 7)
peptide0_select = peptide0.loc[[0, 6], ["Step", "TotEng", "Press"]]
peptide0_data = [[0, -5237.4580, -837.0112], [300, -5251.3637, -471.5505]]
np.testing.assert_array_almost_equal(peptide0_select, peptide0_data)
if __name__ == "__main__":
unittest.main()
|
richardtran415/pymatgen
|
pymatgen/io/lammps/tests/test_outputs.py
|
Python
|
mit
| 6,719
|
[
"LAMMPS",
"pymatgen"
] |
86da33fc1176a08608be3689f01a28fa98b7a9c812b404bdbd22cf31f905c59f
|
"""
@name: Modules/House/Lighting/utility.py
@author: D. Brian Kimmel
@contact: D.BrianKimmel@gmail.com
@copyright: (c) 2019-2020 by D. Brian Kimmel
@note: Created on Jan 20, 2019
@license: MIT License
@summary:
"""
__updated__ = '2020-01-18'
# Import system type stuff
# Import PyMh files
from Modules.Core.Utilities.debug_tools import PrettyFormatAny
from Modules.Core import logging_pyh as Logger
LOG = Logger.getLogger('PyHouse.LightUtility ')
class lightingUtility:
"""
"""
def _test_object_by_id(self, p_obj, name=None, key=None, UUID=None):
"""
Return the device object for a house using the given value.
A name, key or UUID may be used to identify the device.
@return: the Device object found or None.
"""
if name != None and p_obj.Name == name:
return p_obj
elif key != None and p_obj.Key == key:
return p_obj
elif UUID != None and p_obj.UUID == UUID:
return p_obj
elif UUID != None and p_obj.UUID == UUID:
return p_obj
return None
def get_object_by_id(self, p_objs, name=None, key=None, UUID=None):
"""
Return the device object from a dict of objects using the given value.
one of several things may be used for the lookup, a name,a key, or a UUID may be used to identify the object.
Only one object is returned.
@param p_objs: is the tree of lighting objects such as lights, buttons or controllers
@return: the object found or None.
"""
# LOG.debug(PrettyFormatAny.form(p_objs, 'Objs'))
for l_obj in p_objs.values():
# LOG.debug(PrettyFormatAny.form(l_obj, 'Obj'))
l_ret = self._test_object_by_id(l_obj, name, key, UUID)
if l_ret != None:
return l_obj
LOG.error('Light Lookup failed - arg error Name:{}, Key:{}, UUID:{}'.format(name, key, UUID))
return None
def get_object_type_by_id(self, p_objs, group=['Lights', 'Outlets'], name=None, key=None, UUID=None):
"""
Return the device object from a dict of objects using the given value.
one of several things may be used for the lookup, a name,a key, or a UUID may be used to identify the object.
Only one object is returned.
@param p_objs: is the tree of lighting objects such as lights, buttons or controllers
@return: the object found or None.
"""
# LOG.debug(PrettyFormatAny.form(p_objs, 'Objs'))
for l_class in group:
# LOG.debug('Class: "{}"'.format(l_class))
l_objs = getattr(p_objs, l_class)
# LOG.debug(PrettyFormatAny.form(l_objs, 'Objs'))
self.get_object_by_id(p_objs, name, key, UUID)
LOG.error('Light Lookup failed - arg error Name:{}, Key:{}, UUID:{}'.format(name, key, UUID))
return None
def get_controller_objs_by_family(self, p_objs, p_family):
""" Gets all the controllers for a device.
@param p_objs: a dict of objects to search - such as p_pyhouse_obj.House.Lighting.Controllers{}
@param p_family: the family to search for - 'Insteon'
@return: a list of controller objs that match the family
"""
l_ret = []
for l_obj in p_objs.values():
l_family = l_obj.Family.Name.lower()
if l_family == p_family.lower():
# LOG.info('Found Controller {}; Local:{}'.format(l_obj.Name, l_obj._isLocal))
if l_obj._isLocal:
l_ret.append(l_obj)
# LOG.debug(PrettyFormatAny.form(l_obj, 'Controller'))
if len(l_ret) == 0:
LOG.warning('Controller Lookup failed - arg error Family:{}'.format(p_family))
elif len(l_ret) > 1:
LOG.warning('Too many Controllers found {}'.format(len(l_ret)))
else:
# LOG.debug('Found {} active controller(s) for family {}'.format(len(l_ret), p_family))
pass
return l_ret
# ## END DBK
|
DBrianKimmel/PyHouse
|
Project/src/Modules/House/Lighting/utility.py
|
Python
|
mit
| 4,060
|
[
"Brian"
] |
d48672ce5483e0f3fa2f010608a108f2a317d343a50a2208de9a4aac776bbd57
|
import logging
import cgi
from dirac.lib.base import *
from pylons.controllers.util import redirect_to
import dirac.lib.helpers as helpers
import dirac.lib.credentials as credentials
from dirac.lib.webconfig import gWebConfig
from dirac.lib.webBase import defaultRedirect
from DIRAC import gLogger
log = logging.getLogger( __name__ )
class UserdataController( BaseController ):
def index( self ):
return defaultRedirect()
def __mapReferer( self ):
ref = request.environ[ 'HTTP_REFERER' ]
ref = ref[ ref.find( "/", 8 ) : ]
scriptName = request.environ[ 'SCRIPT_NAME' ]
if scriptName:
if scriptName[0] != "/":
scriptName = "/%s" % scriptName
if ref.find( scriptName ) == 0:
ref = ref[ len( scriptName ): ]
pI = ref.find( '?' );
if pI > -1:
params = ref[ pI + 1: ]
ref = ref[ :pI ]
else:
params = ""
pDict = dict( cgi.parse_qsl( params ) )
if not ref:
ref = "/"
return ( config[ 'routes.map' ].match( ref ), pDict )
def changeGroup( self ):
return self.__changeURLPropertyAndRedirect( 'dgroup', credentials.getAvailableGroups() )
def changeSetup( self ):
return self.__changeURLPropertyAndRedirect( 'dsetup', gWebConfig.getSetups() )
def __changeURLPropertyAndRedirect( self, propKey, validValues ):
requestedValue = request.environ[ 'pylons.routes_dict' ][ 'id' ]
redDict = False
if 'HTTP_REFERER' in request.environ:
refDict, paramsDict = self.__mapReferer()
if refDict:
redDict = paramsDict
for key in ( 'controller', 'action' ):
if key in refDict:
redDict[ key ] = refDict[ key ]
if 'id' in refDict:
redDict[ 'id' ] = refDict[ 'id' ]
else:
redDict[ 'id' ] = None
if 'controller' in redDict and 'action' in redDict and \
redDict[ 'controller' ] == 'template' and \
redDict[ 'action' ] == 'view':
redDict = False
if requestedValue in validValues:
request.environ[ 'pylons.routes_dict' ][ propKey ] = requestedValue
else:
gLogger.info( "Requested change to %s invalid %s" % ( requestedValue, validValues ) )
if redDict:
return redirect_to( **redDict )
return defaultRedirect()
def unauthorizedAction( self ):
c.error = "You're not authorized!"
return render( "/error.mako" )
|
DIRACGrid/DIRACWeb
|
dirac/controllers/web/userdata.py
|
Python
|
gpl-3.0
| 2,381
|
[
"DIRAC"
] |
0bf9ddd28356633f4fcfbbd72f502bc13b688be81d299078cb589b2638e1ec21
|
import xmlrpclib
import numpy as np
class ServerProxy(object):
def __init__(self, url):
self._xmlrpc_server_proxy = xmlrpclib.ServerProxy(url)
def __getattr__(self, name):
call_proxy = getattr(self._xmlrpc_server_proxy, name)
def _call(*args, **kwargs):
return call_proxy(args, kwargs)
return _call
if __name__ == "__main__":
lattice = [[10.00,0.00,0.00],[0.00,10.00,0.00],[0.00,0.00,10.00]]
atmlist = [1,2,2]
coord = [ \
[4.888906, 4.190531, 5.341819], \
[4.518590, 4.930775, 6.081417], \
[5.998905, 4.191843, 5.340974], \
]
na = 3
server = ServerProxy("http://localhost:8000")
exe = "/home/zxj/program/siesta-4.0b-485/Obj/siesta"
srcdir = "/home/zxj/program/siesta-4.0b-485/PIMD/svrtest"
tmpfolder = '/home/zxj/program/siesta-4.0b-485/PIMD/client/tmp'
for i in range(100):
output = server.calcEF(coord,lattice,atmlist,exe = exe,srcdir = srcdir, tmpfolder = tmpfolder)
energy,force,stress = output
coord = (np.array(coord) + 0.01 * np.array(force)).tolist()
#print np.array(force)
print max(np.abs(np.array(force)).flatten())
print output
|
zxjzxj9/MDTools
|
svrtest/PIclient.py
|
Python
|
bsd-3-clause
| 1,239
|
[
"SIESTA"
] |
189ad5288fcb169da4b32816ad8c8ff067b47fca4e1ddadc9645e4414e1b37c5
|
#!/usr/bin/env python
import unittest
from src.files import File
class TestFile(unittest.TestCase):
def setUp(self):
self.parentfile = File(name="/", parent=None)
self.childfile = File(name="home", parent=self.parentfile)
self.grandchildfile = File(name="brian", parent=self.childfile)
def test_get_absolute_path(self):
self.assertEqual("/home", self.childfile.get_absolute_path())
self.assertEqual("/home/brian", self.grandchildfile.get_absolute_path())
def test_get_absolute_path_root(self):
self.assertEqual("/", self.parentfile.get_absolute_path())
##########################
def suite():
suite = unittest.TestSuite()
suite.addTest(unittest.makeSuite(TestFile))
return suite
if __name__ == '__main__':
unittest.main()
|
bruab/Bashy_Prototype
|
test/files_tests.py
|
Python
|
mit
| 807
|
[
"Brian"
] |
9780debe73d8e9d77f5ca9e30a5d9cb9b05509a454a877a07a719e00de4f3931
|
#
# Copyright 2016 The BigDL Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from bigdl.orca.learn.metrics import Metric
from bigdl.orca.learn.utils import bigdl_metric_results_to_dict
from bigdl.dllib.nnframes import NNEstimator, NNModel
from bigdl.dllib.estimator import Estimator as SparkEstimator
from bigdl.orca.learn.spark_estimator import Estimator as OrcaSparkEstimator
from bigdl.orca.data import SparkXShards
from bigdl.dllib.optim.optimizer import MaxEpoch
from bigdl.dllib.feature.common import FeatureSet
from bigdl.orca.learn.metrics import Accuracy
from pyspark.sql.dataframe import DataFrame
class Estimator(object):
@staticmethod
def from_bigdl(*, model, loss=None, optimizer=None, metrics=None,
feature_preprocessing=None, label_preprocessing=None,
model_dir=None):
"""
Construct an Estimator with BigDL model, loss function and Preprocessing for feature and
label data.
:param model: BigDL Model to be trained.
:param loss: BigDL criterion.
:param optimizer: BigDL optimizer.
:param metrics: A evaluation metric or a list of evaluation metrics
:param feature_preprocessing: Used when data in `fit` and `predict` is a Spark DataFrame.
The param converts the data in feature column to a Tensor or to a Sample directly.
It expects a List of Int as the size of the converted Tensor, or a Preprocessing[F,
Tensor[T]]
If a List of Int is set as feature_preprocessing, it can only handle the case that
feature column contains the following data types:
Float, Double, Int, Array[Float], Array[Double], Array[Int] and MLlib Vector. The
feature data are converted to Tensors with the specified sizes before
sending to the model. Internally, a SeqToTensor is generated according to the
size, and used as the feature_preprocessing.
Alternatively, user can set feature_preprocessing as Preprocessing[F, Tensor[T]]
that transforms the feature data to a Tensor[T]. Some pre-defined Preprocessing are
provided in package bigdl.dllib.feature. Multiple Preprocessing can be combined as a
ChainedPreprocessing.
The feature_preprocessing will also be copied to the generated NNModel and applied
to feature column during transform.
:param label_preprocessing: Used when data in `fit` and `predict` is a Spark DataFrame.
similar to feature_preprocessing, but applies to Label data.
:param model_dir: The path to save model. During the training, if checkpoint_trigger is
defined and triggered, the model will be saved to model_dir.
:return:
"""
return BigDLEstimator(model=model, loss=loss, optimizer=optimizer,
metrics=metrics,
feature_preprocessing=feature_preprocessing,
label_preprocessing=label_preprocessing, model_dir=model_dir)
class BigDLEstimator(OrcaSparkEstimator):
def __init__(self, *, model, loss, optimizer=None, metrics=None,
feature_preprocessing=None, label_preprocessing=None, model_dir=None):
self.loss = loss
self.optimizer = optimizer
self.metrics = Metric.convert_metrics_list(metrics)
self.feature_preprocessing = feature_preprocessing
self.label_preprocessing = label_preprocessing
self.model_dir = model_dir
self.model = model
self.nn_model = NNModel(self.model, feature_preprocessing=self.feature_preprocessing)
self.nn_estimator = NNEstimator(self.model, self.loss, self.feature_preprocessing,
self.label_preprocessing)
if self.optimizer is None:
from bigdl.dllib.optim.optimizer import SGD
self.optimizer = SGD()
self.nn_estimator.setOptimMethod(self.optimizer)
self.estimator = SparkEstimator(self.model, self.optimizer, self.model_dir)
self.log_dir = None
self.app_name = None
self.is_nnframe_fit = False
def fit(self, data, epochs, batch_size=32, feature_cols="features", label_cols="label",
caching_sample=True, validation_data=None, validation_trigger=None,
checkpoint_trigger=None):
"""
Train this BigDL model with train data.
:param data: train data. It can be XShards or Spark DataFrame.
If data is XShards, each partition is a dictionary of {'x': feature,
'y': label}, where feature(label) is a numpy array or a list of numpy arrays.
:param epochs: Number of epochs to train the model.
:param batch_size: Batch size used for training. Default: 32.
:param feature_cols: Feature column name(s) of data. Only used when data is a Spark
DataFrame. Default: "features".
:param label_cols: Label column name(s) of data. Only used when data is a Spark DataFrame.
Default: "label".
:param caching_sample: whether to cache the Samples after preprocessing. Default: True
:param validation_data: Validation data. XShards and Spark DataFrame are supported.
If data is XShards, each partition is a dictionary of {'x': feature,
'y': label}, where feature(label) is a numpy array or a list of numpy arrays.
:param validation_trigger: Orca Trigger to trigger validation computation.
:param checkpoint_trigger: Orca Trigger to set a checkpoint.
:return:
"""
from bigdl.orca.learn.trigger import Trigger
assert batch_size > 0, "batch_size should be greater than 0"
if validation_data is not None:
assert self.metrics is not None, \
"You should provide metrics when creating this estimator if you provide " \
"validation_data."
if isinstance(data, DataFrame):
if isinstance(feature_cols, list):
data, validation_data, feature_cols = \
BigDLEstimator._combine_cols(data, feature_cols, col_name="features",
val_data=validation_data)
if isinstance(label_cols, list):
data, validation_data, label_cols = \
BigDLEstimator._combine_cols(data, label_cols, col_name="label",
val_data=validation_data)
self.nn_estimator.setBatchSize(batch_size).setMaxEpoch(epochs) \
.setCachingSample(caching_sample).setFeaturesCol(feature_cols) \
.setLabelCol(label_cols)
if validation_data is not None:
assert isinstance(validation_data, DataFrame), \
"validation_data should be a spark DataFrame."
assert validation_trigger is not None, \
"You should provide validation_trigger if you provide validation_data."
validation_trigger = Trigger.convert_trigger(validation_trigger)
self.nn_estimator.setValidation(validation_trigger, validation_data,
self.metrics, batch_size)
if self.log_dir is not None and self.app_name is not None:
from bigdl.dllib.optim.optimizer import TrainSummary
from bigdl.dllib.optim.optimizer import ValidationSummary
train_summary = TrainSummary(log_dir=self.log_dir, app_name=self.app_name)
self.nn_estimator.setTrainSummary(train_summary)
val_summary = ValidationSummary(log_dir=self.log_dir, app_name=self.app_name)
self.nn_estimator.setValidationSummary(val_summary)
if self.model_dir is not None and checkpoint_trigger is not None:
checkpoint_trigger = Trigger.convert_trigger(checkpoint_trigger)
self.nn_estimator.setCheckpoint(self.model_dir, checkpoint_trigger)
self.nn_model = self.nn_estimator.fit(data)
self.is_nnframe_fit = True
elif isinstance(data, SparkXShards):
from bigdl.orca.data.utils import xshard_to_sample
end_trigger = MaxEpoch(epochs)
checkpoint_trigger = Trigger.convert_trigger(checkpoint_trigger)
if isinstance(data, SparkXShards):
train_rdd = data.rdd.flatMap(xshard_to_sample)
train_feature_set = FeatureSet.sample_rdd(train_rdd)
if validation_data is None:
val_feature_set = None
else:
assert isinstance(validation_data, SparkXShards), \
"validation_data should be a XShards"
val_feature_set = FeatureSet.sample_rdd(
validation_data.rdd.flatMap(xshard_to_sample))
if self.log_dir is not None and self.app_name is not None:
self.estimator.set_tensorboard(self.log_dir, self.app_name)
self.estimator.train(train_feature_set, self.loss, end_trigger, checkpoint_trigger,
val_feature_set, self.metrics, batch_size)
self.is_nnframe_fit = False
else:
raise ValueError("Data and validation data should be XShards, but get " +
data.__class__.__name__)
else:
raise ValueError("Data should be XShards or Spark DataFrame, but get " +
data.__class__.__name__)
return self
def predict(self, data, batch_size=4, feature_cols="features", sample_preprocessing=None):
"""
Predict input data
:param data: predict input data. It can be XShards or Spark DataFrame.
If data is XShards, each partition is a dictionary of {'x': feature}, where feature
is a numpy array or a list of numpy arrays.
:param batch_size: Batch size used for inference. Default: 4.
:param feature_cols: Feature column name(s) of data. Only used when data is a Spark
DataFrame. Default: "features".
:param sample_preprocessing: Used when data is a Spark DataFrame. If the user want change
the default feature_preprocessing specified in Estimator.from_bigdl, the user can
pass the new sample_preprocessing methods.
:return: predicted result.
If input data is Spark DataFrame, the predict result is a DataFrame which includes
original columns plus 'prediction' column. The 'prediction' column can be
FloatType, VectorUDT or Array of VectorUDT depending on model outputs shape.
If input data is an XShards, the predict result is a XShards, each partition
of the XShards is a dictionary of {'prediction': result}, where result is a numpy
array or a list of numpy arrays.
"""
if isinstance(data, DataFrame):
if isinstance(feature_cols, list):
data, _, feature_cols = \
BigDLEstimator._combine_cols(data, feature_cols, col_name="features")
self.nn_model.setBatchSize(batch_size).setFeaturesCol(feature_cols)
if sample_preprocessing is not None:
self.nn_model.setSamplePreprocessing(sample_preprocessing)
return self.nn_model.transform(data)
elif isinstance(data, SparkXShards):
from bigdl.orca.data.utils import xshard_to_sample
from bigdl.orca.learn.utils import convert_predict_rdd_to_xshard
sample_rdd = data.rdd.flatMap(xshard_to_sample)
result_rdd = self.model.predict(sample_rdd)
return convert_predict_rdd_to_xshard(data, result_rdd)
else:
raise ValueError("Data should be XShards or Spark DataFrame, but get " +
data.__class__.__name__)
def evaluate(self, data, batch_size=32, feature_cols="features", label_cols="label"):
"""
Evaluate model.
:param data: validation data. It can be XShardsor or Spark DataFrame, each partition is
a dictionary of {'x': feature, 'y': label}, where feature(label) is a numpy array
or a list of numpy arrays.
:param batch_size: Batch size used for validation. Default: 32.
:param feature_cols: (Not supported yet) Feature column name(s) of data. Only used when
data is a Spark DataFrame. Default: None.
:param label_cols: (Not supported yet) Label column name(s) of data. Only used when data
is a Spark DataFrame. Default: None.
:return:
"""
assert data is not None, "validation data shouldn't be None"
assert self.metrics is not None, "metrics shouldn't be None, please specify the metrics" \
" argument when creating this estimator."
if isinstance(data, DataFrame):
if isinstance(feature_cols, list):
data, _, feature_cols = \
BigDLEstimator._combine_cols(data, [feature_cols], col_name="features")
if isinstance(label_cols, list):
data, _, label_cols = \
BigDLEstimator._combine_cols(data, label_cols, col_name="label")
self.nn_estimator._setNNBatchSize(batch_size)._setNNFeaturesCol(feature_cols) \
._setNNLabelCol(label_cols)
self.nn_estimator.setValidation(None, None,
self.metrics, batch_size)
if self.log_dir is not None and self.app_name is not None:
from bigdl.dllib.optim.optimizer import TrainSummary
from bigdl.dllib.optim.optimizer import ValidationSummary
val_summary = ValidationSummary(log_dir=self.log_dir, app_name=self.app_name)
self.nn_estimator.setValidationSummary(val_summary)
result = self.nn_estimator._eval(data)
elif isinstance(data, SparkXShards):
from bigdl.orca.data.utils import xshard_to_sample
val_feature_set = FeatureSet.sample_rdd(data.rdd.flatMap(xshard_to_sample))
result = self.estimator.evaluate(val_feature_set, self.metrics, batch_size)
else:
raise ValueError("Data should be XShards or Spark DataFrame, but get " +
data.__class__.__name__)
return bigdl_metric_results_to_dict(result)
def get_model(self):
"""
Get the trained BigDL model
:return: The trained BigDL model
"""
return self.model
def save(self, model_path):
"""
Save the BigDL model to model_path
:param model_path: path to save the trained model.
:return:
"""
try:
model = self.get_model()
model.saveModel(model_path + ".bigdl", model_path + ".bin", True)
except ValueError:
raise ValueError("You should fit before calling save")
def load(self, checkpoint, optimizer=None, loss=None, feature_preprocessing=None,
label_preprocessing=None, model_dir=None, is_checkpoint=False):
"""
Load existing BigDL model or checkpoint
:param checkpoint: Path to the existing model or checkpoint.
:param optimizer: BigDL optimizer.
:param loss: BigDL criterion.
:param feature_preprocessing: Used when data in `fit` and `predict` is a Spark DataFrame.
The param converts the data in feature column to a Tensor or to a Sample directly.
It expects a List of Int as the size of the converted Tensor, or a Preprocessing[F,
Tensor[T]]
If a List of Int is set as feature_preprocessing, it can only handle the case that
feature column contains the following data types:
Float, Double, Int, Array[Float], Array[Double], Array[Int] and MLlib Vector. The
feature data are converted to Tensors with the specified sizes before
sending to the model. Internally, a SeqToTensor is generated according to the
size, and used as the feature_preprocessing.
Alternatively, user can set feature_preprocessing as Preprocessing[F, Tensor[T]]
that transforms the feature data to a Tensor[T]. Some pre-defined Preprocessing are
provided in package bigdl.dllib.feature. Multiple Preprocessing can be combined as a
ChainedPreprocessing.
The feature_preprocessing will also be copied to the generated NNModel and applied
to feature column during transform.
:param label_preprocessing: Used when data in `fit` and `predict` is a Spark DataFrame.
similar to feature_preprocessing, but applies to Label data.
:param model_dir: The path to save model. During the training, if checkpoint_trigger is
defined and triggered, the model will be saved to model_dir.
:param is_checkpoint: Whether the path is a checkpoint or a saved BigDL model.
Default: False.
:return: The loaded estimator object.
"""
if loss is not None:
self.loss = loss
if optimizer is not None:
self.optimizer = optimizer
if feature_preprocessing is not None:
self.feature_preprocessing = feature_preprocessing
if label_preprocessing is not None:
self.label_preprocessing = label_preprocessing
if model_dir is not None:
self.model_dir = model_dir
if is_checkpoint:
self.load_orca_checkpoint(checkpoint)
else:
from bigdl.dllib.net import Net
self.model = Net.load_bigdl(checkpoint + ".bigdl", checkpoint + ".bin")
self.nn_estimator = NNEstimator(self.model, self.loss, self.feature_preprocessing,
self.label_preprocessing)
if self.optimizer is None:
from bigdl.dllib.optim.optimizer import SGD
self.optimizer = SGD()
self.nn_estimator.setOptimMethod(self.optimizer)
self.estimator = SparkEstimator(self.model, self.optimizer, self.model_dir)
self.nn_model = NNModel(self.model, feature_preprocessing=self.feature_preprocessing)
return self
def __load_bigdl_model(self, path, bigdl_type="float"):
from bigdl.dllib.utils.common import callBigDlFunc
from bigdl.dllib.nn.layer import Layer
jmodel = callBigDlFunc(bigdl_type, "loadBigDL", path)
return Layer.of(jmodel)
def load_orca_checkpoint(self, path, version=None, prefix=None):
"""
Load existing checkpoint. To load a specific checkpoint, please provide both `version`
and `perfix`. If `version` is None, then the latest checkpoint under the specified
directory will be loaded.
:param path: Path to the existing checkpoint (or directory containing Orca checkpoint
files).
:param version: checkpoint version, which is the suffix of model.* file, i.e., for
modle.4 file, the version is 4. If it is None, then load the latest checkpoint.
:param prefix: optimMethod prefix, for example 'optimMethod-Sequentialf53bddcc'
:return:
"""
from bigdl.dllib.nn.layer import Model, Container
from bigdl.dllib.optim.optimizer import OptimMethod
from bigdl.orca.learn.utils import find_latest_checkpoint
import os
if version is None:
path, prefix, version = find_latest_checkpoint(path, model_type="bigdl")
if path is None:
raise ValueError("Cannot find BigDL checkpoint, please check your checkpoint"
" path.")
else:
assert prefix is not None, "You should provide optimMethod prefix, " \
"for example 'optimMethod-TorchModelf53bddcc'"
try:
self.model = self.__load_bigdl_model(os.path.join(path, "model.{}".format(version)))
assert isinstance(self.model, Container), \
"The loaded model should be a Container, please check your checkpoint type."
self.optimizer = OptimMethod.load(os.path.join(path,
"{}.{}".format(prefix, version)))
except Exception:
raise ValueError("Cannot load BigDL checkpoint, please check your checkpoint path "
"and checkpoint type.")
self.estimator = SparkEstimator(self.model, self.optimizer, self.model_dir)
self.nn_estimator = NNEstimator(self.model, self.loss, self.feature_preprocessing,
self.label_preprocessing)
if self.optimizer is not None:
self.nn_estimator.setOptimMethod(self.optimizer)
self.nn_model = NNModel(self.model, feature_preprocessing=self.feature_preprocessing)
def clear_gradient_clipping(self):
"""
Clear gradient clipping parameters. In this case, gradient clipping will not be applied.
In order to take effect, it needs to be called before fit.
:return:
"""
self.nn_estimator.clearGradientClipping()
self.estimator.clear_gradient_clipping()
def set_constant_gradient_clipping(self, min, max):
"""
Set constant gradient clipping during the training process.
In order to take effect, it needs to be called before fit.
:param min: The minimum value to clip by.
:param max: The maximum value to clip by.
:return:
"""
self.nn_estimator.setConstantGradientClipping(min, max)
self.estimator.set_constant_gradient_clipping(min, max)
def set_l2_norm_gradient_clipping(self, clip_norm):
"""
Clip gradient to a maximum L2-Norm during the training process.
In order to take effect, it needs to be called before fit.
:param clip_norm: Gradient L2-Norm threshold.
:return:
"""
self.nn_estimator.setGradientClippingByL2Norm(clip_norm)
self.estimator.set_l2_norm_gradient_clipping(clip_norm)
def get_train_summary(self, tag=None):
"""
Get the scalar from model train summary.
This method will return a list of summary data of
[iteration_number, scalar_value, timestamp].
:param tag: The string variable represents the scalar wanted
"""
# Exception handle
if tag != "Loss" and tag != "LearningRate" and tag != "Throughput":
raise TypeError('Only "Loss", "LearningRate", "Throughput"'
+ 'are supported in train summary')
if self.is_nnframe_fit:
train_summary = self.nn_estimator.getTrainSummary()
return train_summary.read_scalar(tag=tag)
else:
return self.estimator.get_train_summary(tag=tag)
def get_validation_summary(self, tag=None):
"""
Get the scalar from model validation summary.
This method will return a list of summary data of
[iteration_number, scalar_value, timestamp].
Note that the metric and tag may not be consistent.
Please look up following form to pass tag parameter.
Left side is your metric during compile.
Right side is the tag you should pass.
>>> 'Accuracy' | 'Top1Accuracy'
>>> 'BinaryAccuracy' | 'Top1Accuracy'
>>> 'CategoricalAccuracy' | 'Top1Accuracy'
>>> 'SparseCategoricalAccuracy' | 'Top1Accuracy'
>>> 'AUC' | 'AucScore'
>>> 'HitRatio' | 'HitRate@k' (k is Top-k)
>>> 'Loss' | 'Loss'
>>> 'MAE' | 'MAE'
>>> 'NDCG' | 'NDCG'
>>> 'TFValidationMethod' | '${name + " " + valMethod.toString()}'
>>> 'Top5Accuracy' | 'Top5Accuracy'
>>> 'TreeNNAccuracy' | 'TreeNNAccuracy()'
>>> 'MeanAveragePrecision' | 'MAP@k' (k is Top-k) (BigDL)
>>> 'MeanAveragePrecision' | 'PascalMeanAveragePrecision' (Zoo)
>>> 'StatelessMetric' | '${name}'
:param tag: The string variable represents the scalar wanted
"""
if self.is_nnframe_fit:
assert tag is not None, "You should provide tag which should match the name of " \
"the ValidationMethod set into the optimizer. " \
"e.g.'MAE', 'Top1AccuracyLoss', 'Top1Accuracy' or " \
"'Top5Accuracy'."
val_summary = self.nn_estimator.getValidationSummary()
return val_summary.read_scalar(tag=tag)
else:
return self.estimator.get_validation_summary(tag=tag)
@staticmethod
def _combine_cols(data, cols, col_name="features", val_data=None):
if isinstance(cols, list):
if len(cols) == 1:
col_name = cols[0]
else:
from pyspark.ml.feature import VectorAssembler
assembler = VectorAssembler(
inputCols=cols,
outputCol=col_name)
data = assembler.transform(data)
if val_data is not None:
val_data = assembler.transform(val_data)
return data, val_data, col_name
|
intel-analytics/BigDL
|
python/orca/src/bigdl/orca/learn/bigdl/estimator.py
|
Python
|
apache-2.0
| 26,477
|
[
"ORCA"
] |
6f411a6236de019529d478ec75d478b1cfe8ff3484a9c0f6919ea19963d5dcd2
|
#!/usr/bin/python
#
# Copyright 2015 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the 'License');
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an 'AS IS' BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""This example adds two rule-based remarketing user lists.
Adds two rule-based remarketing user lists; one with no site visit date
restrictions and another that will only include users who visit your site in
the next six months.
The LoadFromStorage method is pulling credentials and properties from a
"googleads.yaml" file. By default, it looks for this file in your home
directory. For more information, see the "Caching authentication information"
section of our README.
"""
import calendar
from datetime import date
from datetime import datetime
from datetime import timedelta
from googleads import adwords
def main(client):
# Initialize appropriate service.
adwords_user_list_service = client.GetService(
'AdwordsUserListService', version='v201506')
# First rule item group - users who visited the checkout page and had more
# than one item in their shopping cart.
checkout_rule_item = {
'StringRuleItem': {
'key': {
'name': 'ecomm_pagetype'
},
'op': 'EQUALS',
'value': 'checkout'
}
}
cart_size_rule_item = {
'NumberRuleItem': {
'key': {
'name': 'cartsize'
},
'op': 'GREATER_THAN',
'value': '1.0'
}
}
# Combine the two rule items into a RuleItemGroup so AdWords will logically
# AND the rules together.
checkout_multiple_item_group = {
'items': [checkout_rule_item, cart_size_rule_item]
}
# Second rule item group - users who checked out within the next 3 months.
today = date.today()
start_date_rule_item = {
'DateRuleItem': {
'key': {
'name': 'checkoutdate'
},
'op': 'AFTER',
'value': today.strftime('%Y%m%d')
}
}
three_months_later = AddMonths(today, 3)
three_months_later_rule_item = {
'DateRuleItem': {
'key': {
'name': 'checkoutdate'
},
'op': 'BEFORE',
'value': three_months_later.strftime('%Y%m%d')
}
}
# Combine the date rule items into a RuleItemGroup
checked_out_date_range_item_group = {
'items': [start_date_rule_item, three_months_later_rule_item]
}
# Combine the rule item groups into a Rule so AdWords will logically OR the
# groups together.
rule = {
'groups': [
checkout_multiple_item_group,
checked_out_date_range_item_group
]
}
# Create the user list with no restrictions on site visit date.
expression_user_list = {
'xsi_type': 'ExpressionRuleUserList',
'name': 'Expression-based user list created at %s'
% datetime.today().strftime('%Y%m%d %H:%M:%S'),
'description': 'Users who checked out in three month window OR visited'
' the checkout page with more than one item in their'
' cart.',
'rule': rule
}
# Create the user list restricted to users who visit your site within the next
# six months.
end_date = AddMonths(today, 6)
date_user_list = {
'xsi_type': 'DateSpecificRuleUserList',
'name': 'Date rule user list created at %s'
% datetime.today().strftime('%Y%m%d %H:%M:%S'),
'description': 'Users who visited the site between %s and %s and checked'
' out in three month window OR visited the checkout page'
' with more than one item in their cart.'
% (today.strftime('%Y%m%d'), end_date.strftime('%Y%m%d')),
'rule': rule,
'startDate': today.strftime('%Y%m%d'),
'endDate': end_date.strftime('%Y%m%d')
}
# Create operations to add the user lists.
operations = [
{
'operand': user_list,
'operator': 'ADD',
} for user_list in [expression_user_list, date_user_list]
]
# Submit the operations.
user_lists = adwords_user_list_service.mutate(operations)
# Display results.
for user_list in user_lists['value']:
print (('User list added with ID %d, name "%s", status "%s", list type'
' "%s", accountUserListStatus "%s", description "%s".') %
(user_list['id'], user_list['name'],
user_list['status'], user_list['listType'],
user_list['accountUserListStatus'], user_list['description']))
def AddMonths(start_date, months):
"""A simple convenience utility for adding months to a given start date.
This increments the months by adding the number of days in the current month
to the current month, for each month.
Args:
start_date: date The date months are being added to.
months: int The number of months to add.
Returns:
A date equal to the start date incremented by the given number of months.
"""
current_date = start_date
i = 0
while i < months:
month_days = calendar.monthrange(current_date.year, current_date.month)[1]
current_date += timedelta(days=month_days)
i += 1
return current_date
if __name__ == '__main__':
# Initialize client object.
adwords_client = adwords.AdWordsClient.LoadFromStorage()
main(adwords_client)
|
wubr2000/googleads-python-lib
|
examples/adwords/v201506/remarketing/add_rule_based_user_lists.py
|
Python
|
apache-2.0
| 5,714
|
[
"VisIt"
] |
77cf0ef5c4c8d3c3f468257530d24506f3599df0bc9fdc589d64b0d642da6b23
|
# -*- coding: utf-8 -*-
#from django.shortcuts import render
#from django.views.generic import ListView
from django.utils import timezone
from django.http import HttpResponseRedirect
from django.contrib.auth.decorators import login_required
#from django.utils.translation import ugettext_lazy as _
from feincms.content.application.models import app_reverse
#from gauth.models import GUser
from gtag.models import Tag # , TaggedItem
from ask.models import Post
from ask.forms import QuestionForm, AnswerForm, CommentForm
#from django.contrib.contenttypes.models import ContentType
def tag_list():
#ct = ContentType.objects.get_for_model(Post)
#return TaggedItem.objects.filter(content_type=ct).select_related()
return Tag.objects.all().select_related()
def tag_post(request, tag):
qs = Post.objects.filter(tags__name__in=[tag]).select_related()
return 'ask/ask_tag_post.html', {
'qs': qs,
'tags': tag_list(),
}
def ask_index(request):
qs = Post.objects.filter(level=0).select_related()
return 'ask/ask_index.html', {
'qs': qs,
'tags': tag_list(),
}
def show_post(request, id):
""" Question display and quick reply. """
q = Post.objects.select_related().get(id=id)
form = AnswerForm
answers = q.get_descendants().select_related()
return 'ask/ask_detail.html', {
'q': q,
'nodes': answers,
'tags': tag_list(),
'form': form(),
}
#else:
# return 'ask/ask_no_permission.html', {
# 'error': _(u"You have no permission to view the question.")
# }
@login_required()
def reply_question(request, id):
form_class = AnswerForm
post = Post.objects.select_related().get(id=id)
if request.method == 'POST':
form = form_class(request.POST)
if form.is_valid():
answer = Post.objects.create(
author=request.user,
content=form.cleaned_data['content'],
title=post.title + '\'s answer by ' + request.user.username,
parent=Post.objects.select_related().get(id=id),
creation_date=timezone.now(),
lastedit_date=timezone.now(),
lastedit_user=request.user,
type=2
)
tags = form.cleaned_data['tags'].replace(u'，', ',').replace(' ', '').split(',')
for tag in tags:
answer.tags.add(tag)
answer.save()
return HttpResponseRedirect(app_reverse("ask-detail", 'ask.urls', args=id))
else:
return 'ask/ask_new_post.html', {
'form_error': form.errors,
'q': post,
'nodes': post.get_descendants().select_related(),
'tags': tag_list(),
'form': form_class(),
}
else:
form = form_class()
return 'ask/ask_new_post.html', {'form': form}
@login_required()
def comment_post(request, id):
form_class = CommentForm
post = Post.objects.select_related().get(id=id)
if request.method == 'POST':
form = form_class(request.POST)
if form.is_valid():
comment = Post.objects.create(
author=request.user,
content=form.cleaned_data['content'],
title=post.title + '\'s comment by ' + request.user.username,
parent=post,
creation_date=timezone.now(),
lastedit_date=timezone.now(),
lastedit_user=request.user,
type=3
)
tags = post.tags.all()
for tag in tags:
comment.tags.add(tag)
comment.save()
return HttpResponseRedirect(app_reverse("ask-detail", 'ask.urls'))
else:
return 'ask/ask_detail.html', {'form_errors': form.errors}
@login_required()
def ask_question(request):
form_class = QuestionForm
if request.method == 'POST':
form = form_class(request.POST)
if form.is_valid():
question = Post.objects.create(
author=request.user,
content=form.cleaned_data['content'],
title=form.cleaned_data['title'],
creation_date=timezone.now(),
lastedit_date=timezone.now(),
lastedit_user=request.user,
type=1
)
tags = form.cleaned_data['tags'].replace(u'，', ',').replace(' ', '').split(',')
for tag in tags:
question.tags.add(tag.replace(' ', ''))
question.save()
return HttpResponseRedirect(app_reverse("ask-index", 'ask.urls'))
else:
form = QuestionForm()
return 'ask/ask_new_post.html', {'form': form}
'''
def index(request):
""" QA landing page views. """
user = request.user
auth = user.is_authenticated()
# parse the date request
since = request.GET.get('since', DATE_FILTER[0]).lower()
# set the last active tab
sess.set_tab(tab)
# get the numerical value for these posts
post_type = POST_TYPE_MAP.get(tab, tab)
# override the sort order if the content so requires
sort_type = 'creation' if tab == 'recent' else sort_type
# this here needs to be reworked TODO
if tab == "best":
sort_type = "votes"
since = request.GET.get('since', 'this week')
messages.info(request, "Most <b>upvoted</b> active posts of <b>%s!</b>" % since)
elif tab == "bookmarked":
sort_type = "bookmark"
since = request.GET.get('since', 'this month')
messages.info(request, "Most <b>bookmarked</b> active posts of <b>%s!</b>" % since)
# the params object will carry
layout = const.USER_PILL_BAR if auth else const.ANON_PILL_BAR
# wether to show the type of the post
show_type = post_type in ('all', 'recent')
show_search = True
if tab in VALID_PILLS:
tab, pill = "posts", tab
else:
tab, pill = tab, ""
params = html.Params(
tab=tab,
pill=pill,
sort=sort_type,
sort_choices=SORT_CHOICES,
date_filter=DATE_FILTER,
since=since,
layout=layout,
show_type=show_type,
title="Bioinformatics Answers",
show_search=show_search)
# this will fill in the query (q) and the match (m)parameters
params.parse(request)
# returns the object manager that contains all or only visible posts
posts = get_post_manager(request)
# filter posts by type
posts = filter_by_type(request=request, posts=posts, post_type=post_type)
# apply date filtering
posts = filter_by_date(request=request, posts=posts, since=since)
# reduce SQL query count by preselecting data that will be displayed
posts = posts.select_related('author', 'author__profile', 'lastedit_user', 'lastedit_user__profile')
# sticky is not active on recent and all pages
sticky = (tab != 'recent') and (pill not in ('all', "best", "bookmarked"))
# order may change if it is invalid search
posts = apply_sort(request=request, posts=posts, order=sort_type, sticky=sticky)
# get the counts for the session
counts = sess.get_counts(post_type)
page = get_page(request, posts, per_page=const.POSTS_PER_PAGE)
# save the session
sess.save()
# try to set a more informative title
title_map = dict(
questions="Bioinformatics Questions",
unanswered="Unanswered Questions",
tutorials="Bioinformatics Tutorials",
jobs="Bioinformatics Jobs",
videos="Bioinformatics Videos",
news='Bioinformatics News',
tools="Bioinformatics Tools",
recent="Recent bioinformatics posts",
planet="Bioinformatics Planet",
galaxy="Galaxy on Biostar",
bookmarked="Most bookmarked",
)
params.title = title_map.get(pill) or title_map.get(tab, params.title)
return html.template(request, name='gqanda/index.html', page=page, params=params, counts=counts)
'''
|
indexofire/gork
|
src/gork/application/ask/views/main.py
|
Python
|
mit
| 8,076
|
[
"Galaxy"
] |
300ab7aede24e3bce6237325b603eef2c55f0d92771fb1feeddbae6701b9496e
|
from operator import *
import AST
from Exceptions import *
from Memory import *
from visit import *
operators = {}
operators['+'] = add
operators['-'] = sub
operators['*'] = mul
operators['/'] = div
operators['%'] = mod
operators['|'] = or_
operators['&'] = and_
operators['&&'] = and_
operators['||'] = or_
operators['<<'] = lshift
operators['>>'] = rshift
operators['=='] = eq
operators['!='] = ne
operators['>'] = lt
operators['<'] = gt
operators['<='] = le
operators['>='] = ge
class Interpreter(object):
def __init__(self, dex):
self.dex = dex
self.globalMemory = MemoryStack(Memory("global"))
self.functionMemories = []
@on('node')
def visit(self, node):
pass
@when(AST.Node)
def visit(self, node):
pass
@when(AST.Program)
def visit(self, node):
# print "PROGRAM"
node.declarations.accept(self)
node.fundefs.accept(self)
node.instructions.accept(self)
@when(AST.Declarations)
def visit(self, node):
# print "DECLARATIONS"
for declaration in node.declarations:
declaration.accept(self)
@when(AST.Declaration)
def visit(self, node):
# print "DECLARATION"
node.inits.accept(self)
@when(AST.Inits)
def visit(self, node):
# print "INITS"
for init in node.inits:
init.accept(self)
@when(AST.Init)
def visit(self, node):
# print "INIT"
if len(self.functionMemories) == 0:
self.globalMemory.put(node.id, node.expression.accept(self))
else:
self.functionMemories[len(self.functionMemories) - 1].put(node.id, node.expression.accept(self))
@when(AST.Instructions)
def visit(self, node):
# print "INSTRUCTIONS"
for instruction in node.instructions:
instruction.accept(self)
@when(AST.Instruction)
def visit(self, node):
pass
@when(AST.Print)
def visit(self, node):
# print "PRINT"
value = node.expression.accept(self)
if type(node.expression) is AST.Id:
value = self._get_id_value_from_memory(value)
print value
@when(AST.Labeled)
def visit(self, node):
node.instruction.accept(self)
@when(AST.Assignment)
def visit(self, node):
# print "ASSIGNMENT"
if len(self.functionMemories) == 0 or self.functionMemories[len(self.functionMemories) - 1].put_existing(
node.id, node.expression.accept(self)) is False:
self.globalMemory.put_existing(node.id, node.expression.accept(self))
@when(AST.Choice)
def visit(self, node):
# print "CHOICE"
if not node._if.accept(self):
node._else.accept(self) #return?
@when(AST.If)
def visit(self, node):
# print "IF"
if node.cond.accept(self):
node.statement.accept(self)
return True #co z returnem powyzszego, czy potrzebny?
else:
return False
@when(AST.Else)
def visit(self, node):
# print "ELSE"
return node.statement.accept(self) #czy tu potrzebny return?
@when(AST.While)
def visit(self, node):
# print "WHILE"
r = None
while node.cond.accept(self):
try:
r = node.statement.accept(self)
except BreakException:
break
except ContinueException:
continue
return r #co tutaj daje return?
@when(AST.RepeatUntil)
def visit(self, node):
# print "REPEAT"
r = None
try:
r = node.statement.accept(self)
except BreakException:
return r
except ContinueException:
pass
while node.cond.accept(self):
try:
r = node.statement.accept(self)
except BreakException:
break
except ContinueException:
continue
return r
@when(AST.Return)
def visit(self, node):
# print "RETURN"
raise ReturnValueException(node.expression.accept(self))
@when(AST.Continue)
def visit(self, node):
# print "CONTINUE"
raise ContinueException()
@when(AST.Break)
def visit(self, node):
# print "BREAK"
raise BreakException()
@when(AST.Compound)
def visit(self, node):
# print "COMPOUND"
function = False
if len(self.functionMemories) == 0:
self.globalMemory.push(Memory("compound"))
else:
function = True
self.functionMemories[len(self.functionMemories) - 1].push(Memory("compound"))
node.declarations.accept(self)
node.instructions.accept(self)
if function is False:
self.globalMemory.pop()
else:
self.functionMemories[len(self.functionMemories) - 1].pop()
@when(AST.Condition)
def visit(self, node):
pass
@when(AST.Expression)
def visit(self, node):
pass
@when(AST.Const)
def visit(self, node):
# print "CONST"
return self._value_as_proper_type(node.value)
def _value_as_proper_type(self, value):
for type_name in [int, float]:
try:
return type_name(value)
except Exception:
pass
return value
@when(AST.Id)
def visit(self, node):
# print "ID"
return node.id
@when(AST.BinExpr)
def visit(self, node):
# print "BINEXPR"
left = node.expr1.accept(self)
right = node.expr2.accept(self)
if type(node.expr1) is AST.Id:
left = self._get_id_value_from_memory(left)
# print "CONVERTED LEFT"
if type(node.expr2) is AST.Id:
right = self._get_id_value_from_memory(right)
# print "CONVERTED RIGHT"
# print type(node.expr1)
# print type(node.expr2)
# print " LEFT:", left, type(left)
# print " RIGHT:", right, type(right)
return operators[node.operator](left, right)
def _get_id_value_from_memory(self, id):
value = None
if self._currently_inside_a_function():
value = self.functionMemories[-1].get(id)
if value == None:
value = self.globalMemory.get(id)
return value
def _currently_inside_a_function(self):
return len(self.functionMemories) > 0
@when(AST.ExpressionInParentheses)
def visit(self, node):
# print "EXPRESSION IN PARENTHESES"
return node.expression.accept(self)
@when(AST.IdWithParentheses)
def visit(self, node):
# print "ID WITH PARENTHESES"
fundef = self.globalMemory.get(node.id)
functionMemory = MemoryStack(Memory(node.id))
map(lambda name, value: functionMemory.put(name, value), fundef.arglist.accept(self),
node.expression_list.accept(self))
self.functionMemories.append(functionMemory)
try:
fundef.accept(self)
self.functionMemories.pop()
except ReturnValueException as e:
self.functionMemories.pop()
return e.value
@when(AST.ExpressionList)
def visit(self, node):
# print "EXPRESSION LIST"
expressionResults = []
for expression in node.expressions:
expressionResults.append(expression.accept(self))
return expressionResults
@when(AST.FunctionDefinitions)
def visit(self, node):
# print "FUNCTION DEFINITIONS"
for fundef in node.fundefs:
self.globalMemory.put(fundef.id, fundef)
@when(AST.FunctionDefinition)
def visit(self, node):
# print "FUNCTION DEFINITION"
node.compound_instr.accept(self)
@when(AST.ArgumentList)
def visit(self, node):
# print "ARGUMENT LIST"
args = []
for arg in node.arg_list:
args.append(arg.accept(self))
return args
@when(AST.Argument)
def visit(self, node):
# print "ARGUMENT"
return node.id
|
PrzemekBurczyk/dalvik-compiler
|
src/Interpreter.py
|
Python
|
mit
| 8,180
|
[
"VisIt"
] |
354f2853b3eaa265314c685f5edaa67472e5e0144936807547b5843cac8bdda9
|
"""
Set of tools to analyse IRIS spectra.
"""
import numpy as np
import scipy.constants as const
from scipy import interpolate as interp
from scipy import ndimage
from pytp.math.utilsmath import peakdetect_lcl
from pytp.math.utilsfast import peakdetect, replace_nans
# pylint: disable=E1101
def iris_get_spec_features(wave_in, spec_in, wave_ref, vrange=(-60, 60),
lookahead=2, delta=2e-11, pmin=5, pfit=2,
gsigma=6, gnk=3):
"""
Extracts the line properties (blue peak, line centre, red peak)
of a Mg II line (h or k).
Parameters
----------
wave - 1D array
array of wavelengths/velocities
spec - nD array
spectral array, wavelength in last index
vrange - tuple or list
Doppler velocity limits around wave_ref, determing the range that
will be analysed
lookahead - integer
Parameter for peak finding, how many points to look ahead for peaks
delta - float
Minimum threshold for peak in spec units
pmin - integer
Number of points to find around the minimum
pfit - integer
Number of points to fit a parabola around the minimum
gsigma - integer
Gaussian sigma (in pixels) to smooth velocities and find outliers
gnk - integer
Kernel size for replace_nans
Returns
-------
results - tuple with results, bp, lc, rp
"""
# flatten spectrum
shape_in = spec_in.shape[:-1]
spec = np.reshape(spec_in, (np.prod(shape_in), spec_in.shape[-1]))
# Determine wavelength range to use
vaxis = const.c / 1e3 * (wave_in - wave_ref) / wave_ref
wvi = max(np.where(vaxis >= vrange[0])[0][0] - 1, 0)
wvf = min(np.where(vaxis <= vrange[1])[0][-1] + 1, vaxis.shape[0] - 1)
vel = vaxis[wvi:wvf]
spec = spec[:, wvi:wvf]
nspec = len(spec)
lc = np.ma.masked_all((2, nspec)) # line core (central reversal, k3/h3)
bp = np.ma.masked_all((2, nspec)) # blue peak
rp = np.ma.masked_all((2, nspec)) # red peak
# LINE CORE, main loop
for i in range(nspec):
guess = 5.
pts_min = pmin
lc[0, i], lc[1, i] = mg_single(vel, spec[i], guess, delta=delta,
lookahead=lookahead, ppts=pfit,
pts_min=pts_min, use_deriv=False)
# two iterations for cleaning up
for _nidx in range(2):
# put back in initial shape
lc = np.reshape(lc, (2,) + shape_in)
# find outliers,distant more than 3 km/s from a smoothed Gaussian
xpto = lc[0].data.copy()
xpto[lc[0].mask] = 0.
diff = np.abs(xpto - ndimage.gaussian_filter(xpto, gsigma,
mode='wrap'))
lc[:, diff > 3] = np.nan
lc.data[:, lc[0].mask] = np.nan # so that replace_nans will work
# perform inpainting on masked image to get next best guess
lc_guess = replace_nans(lc[0].data, 10, .5, gnk, 'localmean')
# put back into flat shape, redo loop with new guess
lc = np.reshape(lc, (2, np.prod(shape_in)))
lc_guess = np.reshape(lc_guess, (np.prod(shape_in)))
for i in range(nspec):
if not np.isfinite(lc[0, i]):
lc[0, i], lc[1, i] = \
mg_single(vel, spec[i], lc_guess[i], ppts=pfit,
lookahead=lookahead, delta=delta, pts_min=pmin,
force_guess=True)
print('Last run with use_deriv')
for i in range(nspec):
if not np.isfinite(lc[0, i]):
lc[0, i], lc[1, i] = \
mg_single(vel, spec[i], guess, delta=0., ppts=pfit,
lookahead=lookahead, pts_min=pts_min,
use_deriv=True)
lc = np.reshape(lc, (2,) + shape_in)
# clean up some isolated problem pixels
xpto = lc[0].data.copy()
xpto[lc[0].mask] = 40.
idx = np.abs(xpto - ndimage.gaussian_filter(xpto, 1., mode='wrap')) > 10
lc[:, idx] = np.nan
lc[0] = replace_nans(lc[0].data, 10, .5, gnk, 'localmean')
lc[1] = replace_nans(lc[1].data, 10, .5, gnk, 'localmean')
# get mask for bad values
bad_mask = ndimage.binary_fill_holes(np.abs(xpto -
ndimage.gaussian_filter(xpto, 1.7, mode='wrap')) > 6)
lc[:, bad_mask] = np.ma.masked
# get better lc estimate for peak detection
lc = np.reshape(lc, (2,) + shape_in)
xpto = lc[0].data.copy()
xpto[lc[0].mask] = 0.
diff = np.abs(xpto - ndimage.gaussian_filter(xpto, 10. * gsigma / 6,
mode='wrap'))
xpto[(diff > 4) | lc[0].mask] = np.nan
# perform inpainting on masked image to get next best guess
lc_guess = replace_nans(xpto, 10, .5, 2, 'localmean')
lc_guess = np.reshape(lc_guess, (np.prod(shape_in)))
# PEAKS, main loop
print('Peaks main loop...')
for i in range(nspec):
bp[:, i], rp[:, i] = mg_peaks_single(vel, spec[i], lc_guess[i],
lookahead=lookahead)
lc = np.reshape(lc, (2,) + shape_in)
bp = np.reshape(bp, (2,) + shape_in)
rp = np.reshape(rp, (2,) + shape_in)
return bp, lc, rp
# pylint: disable=E1101
def mg_single(vel, spec, guess, lookahead=2, pts_min=5, margin=15, ppts=2,
delta=5e-12, force_guess=False, use_deriv=False):
"""
Calculates the properties for a single Mg II spectrum. Similar to
specquant.linecentre, but also does peakdetect and everything more
streamlined.
For the conv spectra.
"""
if hasattr(spec[0], 'mask') or np.isnan(guess):
return np.ma.masked, np.ma.masked
deriv_flag = False
pmax, pmin = peakdetect(spec, vel, delta_max=delta, delta_min=delta,
lookahead_max=lookahead, lookahead_min=lookahead)
# Use peak detect unless forcing the use of supplied guess
if not force_guess:
if np.any(pmax) and np.any(pmin):
pmax = pmax[:, (pmax[0] > vel[0] + 10) & (pmax[0] < vel[-1] - 10.)]
pmin = pmin[:, (pmin[0] > vel[0] + 10) & (pmin[0] < vel[-1] - 10.)]
lpmax = len(pmax[0])
lpmin = len(pmin[0])
if (lpmin, lpmax) in [(1, 2), (3, 1), (3, 2), (3, 4), (5, 4),
(7, 6)]:
# most straightforward case: take the middle one
guess = pmin[0][lpmin // 2]
pts_min = pts_min // 2
elif (lpmin, lpmax) in [(2, 2), (2, 3), (3, 3), (4, 2), (4, 3),
(4, 4)]:
pts_min = pts_min // 2
# take the lowest minimum between the two largest maxima
# locations of two largest maxima
lmax = np.sort(pmax[0][np.argsort(pmax[1])[-2:]])
# minima inside the above window
idx = (pmin[0] > lmax[0]) & (pmin[0] < lmax[1])
if np.any(idx):
guess = pmin[0, idx][np.argmin(pmin[1, idx])]
elif lpmin == 2 and lpmax == 1: # was [1, 3]
if np.max(pmin[1]) / np.min(pmin[1]) > 1.3:
# use lowest minimum
guess = pmin[0, np.argmin(pmin[1])]
pts_min = pts_min // 2
else:
deriv_flag = True
elif lpmax == 1:
deriv_flag = True
elif lpmin == 1:
guess = pmin[0, 0]
pts_min = pts_min // 2
elif np.any(pmax):
lpmax = len(pmax[0])
if lpmax == 1:
deriv_flag = True
else:
if np.any(pmax):
pmax = pmax[:, (pmax[0] > vel[0] + 20) & (pmax[0] < vel[-1] - 20.)]
if np.any(pmax):
lpmax = len(pmax[0])
if lpmax == 1:
deriv_flag = True
if force_guess:
deriv_flag = False
if deriv_flag: # special case to use the derivatives
if not use_deriv:
return np.ma.masked, np.ma.masked
dd = np.abs(np.diff(spec))
# define the boundaries to inspect for peak asymmetry and derivative
incr = int(15. / (vel[1] - vel[0]))
idxm = np.argmin(np.abs(vel - pmax[0, 0]))
vv = [max(0, idxm - incr), min(idxm + incr, spec.shape[0] - 1)]
vv2 = [idxm - incr * 1.3, idxm + incr * 1.3]
vv2 = vv
if spec[vv[0]] > spec[vv[1]]: # k3/h3 peak on left side
try:
der = dd[vv2[0] + margin:idxm - margin]
pidx = vv2[0] + np.argmin(der) + margin + 1
except IndexError:
return np.ma.masked, np.ma.masked
else: # k3/h3 peak on right side
der = dd[idxm + margin:vv2[1] - margin]
pidx = idxm + np.argmin(der) + margin + 1
lc = vel[pidx]
lc_int = spec[pidx]
else:
# Approximate index of guess and of spectral minimum around it
try:
idg = np.argmin(np.abs(vel - guess))
ini = np.argmin(spec[idg - pts_min:idg + pts_min]) + idg - pts_min
except ValueError:
return np.ma.masked, np.ma.masked
# if no points, return masked
if len(vel[ini - ppts:ini + ppts + 1]) == 0:
return np.ma.masked, np.ma.masked
# Fit parabola
fit = np.polyfit(vel[ini - ppts:ini + ppts + 1],
spec[ini - ppts:ini + ppts + 1], 2)
# Convert poly to parabola coefficients
lc = -fit[1] / (2 * fit[0])
lc_int = fit[2] - fit[0] * lc ** 2
# If fitted minimum is furthen than 4 wavelenght points, mask
if np.abs(lc - vel[ini]) > 4 * (vel[1] - vel[0]):
lc = np.ma.masked
lc_int = np.ma.masked
return lc, lc_int
def mg_peaks_single(vel, spec, lc, lookahead=2):
"""
Calculate the intensity and velocity of the two peaks (h2v and h2r,
or k2v and k2r)
"""
bp = np.ma.masked_all(2)
rp = np.ma.masked_all(2)
if hasattr(spec[0], 'mask'):
return bp, rp
pmax, pmin = peakdetect_lcl(spec, vel, lookahead=lookahead)
if np.any(pmax):
# remove peaks more than 30 km/s from line centre
if np.isfinite(lc):
pmax = pmax[:, np.abs(pmax[0] - lc) < 30]
else:
pmax = pmax[:, np.abs(pmax[0] - 0) < 30]
lpmax = len(pmax[0])
lpmin = len(pmin[0])
# SELECTION OF TWO PEAKS
if lpmax > 4: # too many maxima, take inner 4
pmax = pmax[:, lpmax // 2 - 2: lpmax // 2 + 2]
lpmax = 4
if lpmax == 1:
if pmax[0, 0] > lc:
rp = pmax[:, 0]
else: # by default assign single max to blue peak if no core
bp = pmax[:, 0]
elif lpmax == 2:
bp = pmax[:, 0]
rp = pmax[:, 1]
elif lpmax == 3:
# take the one close to the line core, and from the
# remaining two chose the strongest
if pmax[0, 0] < lc < pmax[0, 1]:
bp = pmax[:, 0]
rp = pmax[:, np.argmax(pmax[1, 1:]) + 1]
elif pmax[0, 1] < lc < pmax[0, 2]:
bp = pmax[:, np.argmax(pmax[1, :-1])]
rp = pmax[:, -1]
else:
aa = pmax[:, pmax[1] != np.min(pmax[1])]
bp = aa[:, 0]
rp = aa[:, 1]
elif lpmax == 4:
# first look for special case when two close weak inner peaks
# are taken as peaks. In this case take the outer peaks.
sep_out = pmax[0, 3] - pmax[0, 0]
sep_in = pmax[0, 2] - pmax[0, 1]
rtt = (pmax[1, 0] > 1.06 * pmax[1, 1]) and \
(pmax[1, 3] > 1.06 * pmax[1, 2])
if (sep_out < 40) and (sep_in < 13) and rtt:
bp = pmax[:, 0]
rp = pmax[:, 3]
# if line core in the middle of the four, or outside the
# maxima region, take inner two maxima
elif (lc > pmax[0, 1]) and (lc < pmax[0, 2]):
bp = pmax[:, 1]
rp = pmax[:, 2]
elif (lc > pmax[0, 3]) or (lc < pmax[0, 0]):
# for now, just take inner two
bp = pmax[:, 1]
rp = pmax[:, 2]
elif lc < pmax[0, 1]: # proceed like for 3 maxima
bp = pmax[:, 0]
rp = pmax[:, np.argmax(pmax[1, 1:]) + 1]
elif lc < pmax[0, 3]:
bp = pmax[:, np.argmax(pmax[1, :3])]
rp = pmax[:, 3]
# interpolation for more precise peaks
if bp[0]:
idg = np.argmin(np.abs(vel - bp[0]))
llim = max(0, idg - 2)
hlim = min(idg + 2, spec.shape[0] - 1)
nvel = np.linspace(vel[llim], vel[hlim], 45)
llim = max(0, idg - 3)
hlim = min(idg + 4, spec.shape[0])
spl = interp.splrep(vel[llim:hlim], spec[llim:hlim], k=3, s=0)
nspec = interp.splev(nvel, spl, der=0)
midx = np.argmax(nspec)
bp[0] = nvel[midx]
bp[1] = nspec[midx]
if rp[0]:
idg = np.argmin(np.abs(vel - rp[0]))
llim = max(0, idg - 2)
hlim = min(idg + 2, spec.shape[0] - 1)
nvel = np.linspace(vel[llim], vel[hlim], 45)
llim = max(0, idg - 3)
hlim = min(idg + 4, spec.shape[0])
spl = interp.splrep(vel[llim:hlim], spec[llim:hlim], k=3, s=0)
nspec = interp.splev(nvel, spl, der=0)
midx = np.argmax(nspec)
rp[0] = nvel[midx]
rp[1] = nspec[midx]
return bp, rp
def getvel_centroid(wguess, wave, spec, spec_guess, pts=5):
"""
Calculates the centroid of the spectrum in 5 points around the line centre.
No intensity is obtained (perhaps use linear interpolation?)
"""
iwv = np.argmin(np.abs(wave - wguess))
iii = np.arange(iwv - pts, iwv + pts + 1)
iwv = spec_guess[..., iii].argmin(-1) + iwv - pts
vaxis = const.c / 1e3 * (wave - wguess) / wguess
vel = np.zeros(spec.shape[:-1], dtype='f')
for idx, value in np.ndenumerate(iwv):
nspec = spec[idx][value - pts:value + pts + 1]
nvaxis = vaxis[value - pts:value + pts + 1]
nspec = 1 - nspec / nspec[0]
vel[idx] = np.sum(nvaxis * nspec) / np.sum(nspec)
return vel
|
tiagopereira/pytp
|
pytp/spec/iris_spec.py
|
Python
|
bsd-3-clause
| 14,352
|
[
"Gaussian"
] |
941447a8f228567bf55c91dfd8b9e434e96bdaf154b99548b71e945aaff93d29
|
"""Testing for Gaussian process regression """
# Author: Jan Hendrik Metzen <jhm@informatik.uni-bremen.de>
# Licence: BSD 3 clause
import numpy as np
from scipy.optimize import approx_fprime
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels \
import RBF, ConstantKernel as C, WhiteKernel
from sklearn.utils.testing \
import (assert_true, assert_greater, assert_array_less,
assert_almost_equal, assert_equal)
def f(x):
return x * np.sin(x)
X = np.atleast_2d([1., 3., 5., 6., 7., 8.]).T
X2 = np.atleast_2d([2., 4., 5.5, 6.5, 7.5]).T
y = f(X).ravel()
fixed_kernel = RBF(length_scale=1.0, length_scale_bounds="fixed")
kernels = [RBF(length_scale=1.0), fixed_kernel,
RBF(length_scale=1.0, length_scale_bounds=(1e-3, 1e3)),
C(1.0, (1e-2, 1e2)) *
RBF(length_scale=1.0, length_scale_bounds=(1e-3, 1e3)),
C(1.0, (1e-2, 1e2)) *
RBF(length_scale=1.0, length_scale_bounds=(1e-3, 1e3)) +
C(1e-5, (1e-5, 1e2)),
C(0.1, (1e-2, 1e2)) *
RBF(length_scale=1.0, length_scale_bounds=(1e-3, 1e3)) +
C(1e-5, (1e-5, 1e2))]
def test_gpr_interpolation():
"""Test the interpolating property for different kernels."""
for kernel in kernels:
gpr = GaussianProcessRegressor(kernel=kernel).fit(X, y)
y_pred, y_cov = gpr.predict(X, return_cov=True)
assert_true(np.allclose(y_pred, y))
assert_true(np.allclose(np.diag(y_cov), 0.))
def test_lml_improving():
""" Test that hyperparameter-tuning improves log-marginal likelihood. """
for kernel in kernels:
if kernel == fixed_kernel:
continue
gpr = GaussianProcessRegressor(kernel=kernel).fit(X, y)
assert_greater(gpr.log_marginal_likelihood(gpr.kernel_.theta),
gpr.log_marginal_likelihood(kernel.theta))
def test_lml_precomputed():
""" Test that lml of optimized kernel is stored correctly. """
for kernel in kernels:
gpr = GaussianProcessRegressor(kernel=kernel).fit(X, y)
assert_equal(gpr.log_marginal_likelihood(gpr.kernel_.theta),
gpr.log_marginal_likelihood())
def test_converged_to_local_maximum():
""" Test that we are in local maximum after hyperparameter-optimization."""
for kernel in kernels:
if kernel == fixed_kernel:
continue
gpr = GaussianProcessRegressor(kernel=kernel).fit(X, y)
lml, lml_gradient = \
gpr.log_marginal_likelihood(gpr.kernel_.theta, True)
assert_true(np.all((np.abs(lml_gradient) < 1e-4) |
(gpr.kernel_.theta == gpr.kernel_.bounds[:, 0]) |
(gpr.kernel_.theta == gpr.kernel_.bounds[:, 1])))
def test_solution_inside_bounds():
""" Test that hyperparameter-optimization remains in bounds"""
for kernel in kernels:
if kernel == fixed_kernel:
continue
gpr = GaussianProcessRegressor(kernel=kernel).fit(X, y)
bounds = gpr.kernel_.bounds
max_ = np.finfo(gpr.kernel_.theta.dtype).max
tiny = 1e-10
bounds[~np.isfinite(bounds[:, 1]), 1] = max_
assert_array_less(bounds[:, 0], gpr.kernel_.theta + tiny)
assert_array_less(gpr.kernel_.theta, bounds[:, 1] + tiny)
def test_lml_gradient():
""" Compare analytic and numeric gradient of log marginal likelihood. """
for kernel in kernels:
gpr = GaussianProcessRegressor(kernel=kernel).fit(X, y)
lml, lml_gradient = gpr.log_marginal_likelihood(kernel.theta, True)
lml_gradient_approx = \
approx_fprime(kernel.theta,
lambda theta: gpr.log_marginal_likelihood(theta,
False),
1e-10)
assert_almost_equal(lml_gradient, lml_gradient_approx, 3)
def test_prior():
""" Test that GP prior has mean 0 and identical variances."""
for kernel in kernels:
gpr = GaussianProcessRegressor(kernel=kernel)
y_mean, y_cov = gpr.predict(X, return_cov=True)
assert_almost_equal(y_mean, 0, 5)
if len(gpr.kernel.theta) > 1:
# XXX: quite hacky, works only for current kernels
assert_almost_equal(np.diag(y_cov), np.exp(kernel.theta[0]), 5)
else:
assert_almost_equal(np.diag(y_cov), 1, 5)
def test_sample_statistics():
""" Test that statistics of samples drawn from GP are correct."""
for kernel in kernels:
gpr = GaussianProcessRegressor(kernel=kernel).fit(X, y)
y_mean, y_cov = gpr.predict(X2, return_cov=True)
samples = gpr.sample_y(X2, 300000)
# More digits accuracy would require many more samples
assert_almost_equal(y_mean, np.mean(samples, 1), 2)
assert_almost_equal(np.diag(y_cov) / np.diag(y_cov).max(),
np.var(samples, 1) / np.diag(y_cov).max(), 1)
def test_no_optimizer():
""" Test that kernel parameters are unmodified when optimizer is None."""
kernel = RBF(1.0)
gpr = GaussianProcessRegressor(kernel=kernel, optimizer=None).fit(X, y)
assert_equal(np.exp(gpr.kernel_.theta), 1.0)
def test_predict_cov_vs_std():
""" Test that predicted std.-dev. is consistent with cov's diagonal."""
for kernel in kernels:
gpr = GaussianProcessRegressor(kernel=kernel).fit(X, y)
y_mean, y_cov = gpr.predict(X2, return_cov=True)
y_mean, y_std = gpr.predict(X2, return_std=True)
assert_almost_equal(np.sqrt(np.diag(y_cov)), y_std)
def test_anisotropic_kernel():
""" Test that GPR can identify meaningful anisotropic length-scales. """
# We learn a function which varies in one dimension ten-times slower
# than in the other. The corresponding length-scales should differ by at
# least a factor 5
rng = np.random.RandomState(0)
X = rng.uniform(-1, 1, (50, 2))
y = X[:, 0] + 0.1 * X[:, 1]
kernel = RBF([1.0, 1.0])
gpr = GaussianProcessRegressor(kernel=kernel).fit(X, y)
assert_greater(np.exp(gpr.kernel_.theta[1]),
np.exp(gpr.kernel_.theta[0]) * 5)
def test_random_starts():
"""
Test that an increasing number of random-starts of GP fitting only
increases the log marginal likelihood of the chosen theta.
"""
n_samples, n_features = 25, 2
np.random.seed(0)
rng = np.random.RandomState(0)
X = rng.randn(n_samples, n_features) * 2 - 1
y = np.sin(X).sum(axis=1) + np.sin(3 * X).sum(axis=1) \
+ rng.normal(scale=0.1, size=n_samples)
kernel = C(1.0, (1e-2, 1e2)) \
* RBF(length_scale=[1.0] * n_features,
length_scale_bounds=[(1e-4, 1e+2)] * n_features) \
+ WhiteKernel(noise_level=1e-5, noise_level_bounds=(1e-5, 1e1))
last_lml = -np.inf
for n_restarts_optimizer in range(5):
gp = GaussianProcessRegressor(
kernel=kernel, n_restarts_optimizer=n_restarts_optimizer,
random_state=0,).fit(X, y)
lml = gp.log_marginal_likelihood(gp.kernel_.theta)
assert_greater(lml, last_lml - np.finfo(np.float32).eps)
last_lml = lml
def test_y_normalization():
""" Test normalization of the target values in GP
Fitting non-normalizing GP on normalized y and fitting normalizing GP
on unnormalized y should yield identical results
"""
y_mean = y.mean(0)
y_norm = y - y_mean
for kernel in kernels:
# Fit non-normalizing GP on normalized y
gpr = GaussianProcessRegressor(kernel=kernel)
gpr.fit(X, y_norm)
# Fit normalizing GP on unnormalized y
gpr_norm = GaussianProcessRegressor(kernel=kernel, normalize_y=True)
gpr_norm.fit(X, y)
# Compare predicted mean, std-devs and covariances
y_pred, y_pred_std = gpr.predict(X2, return_std=True)
y_pred = y_mean + y_pred
y_pred_norm, y_pred_std_norm = gpr_norm.predict(X2, return_std=True)
assert_almost_equal(y_pred, y_pred_norm)
assert_almost_equal(y_pred_std, y_pred_std_norm)
_, y_cov = gpr.predict(X2, return_cov=True)
_, y_cov_norm = gpr_norm.predict(X2, return_cov=True)
assert_almost_equal(y_cov, y_cov_norm)
def test_y_multioutput():
""" Test that GPR can deal with multi-dimensional target values"""
y_2d = np.vstack((y, y*2)).T
# Test for fixed kernel that first dimension of 2d GP equals the output
# of 1d GP and that second dimension is twice as large
kernel = RBF(length_scale=1.0)
gpr = GaussianProcessRegressor(kernel=kernel, optimizer=None,
normalize_y=False)
gpr.fit(X, y)
gpr_2d = GaussianProcessRegressor(kernel=kernel, optimizer=None,
normalize_y=False)
gpr_2d.fit(X, y_2d)
y_pred_1d, y_std_1d = gpr.predict(X2, return_std=True)
y_pred_2d, y_std_2d = gpr_2d.predict(X2, return_std=True)
_, y_cov_1d = gpr.predict(X2, return_cov=True)
_, y_cov_2d = gpr_2d.predict(X2, return_cov=True)
assert_almost_equal(y_pred_1d, y_pred_2d[:, 0])
assert_almost_equal(y_pred_1d, y_pred_2d[:, 1] / 2)
# Standard deviation and covariance do not depend on output
assert_almost_equal(y_std_1d, y_std_2d)
assert_almost_equal(y_cov_1d, y_cov_2d)
y_sample_1d = gpr.sample_y(X2, n_samples=10)
y_sample_2d = gpr_2d.sample_y(X2, n_samples=10)
assert_almost_equal(y_sample_1d, y_sample_2d[:, 0])
# Test hyperparameter optimization
for kernel in kernels:
gpr = GaussianProcessRegressor(kernel=kernel, normalize_y=True)
gpr.fit(X, y)
gpr_2d = GaussianProcessRegressor(kernel=kernel, normalize_y=True)
gpr_2d.fit(X, np.vstack((y, y)).T)
assert_almost_equal(gpr.kernel_.theta, gpr_2d.kernel_.theta, 4)
def test_custom_optimizer():
""" Test that GPR can use externally defined optimizers. """
# Define a dummy optimizer that simply tests 50 random hyperparameters
def optimizer(obj_func, initial_theta, bounds):
rng = np.random.RandomState(0)
theta_opt, func_min = \
initial_theta, obj_func(initial_theta, eval_gradient=False)
for _ in range(50):
theta = np.atleast_1d(rng.uniform(np.maximum(-2, bounds[:, 0]),
np.minimum(1, bounds[:, 1])))
f = obj_func(theta, eval_gradient=False)
if f < func_min:
theta_opt, func_min = theta, f
return theta_opt, func_min
for kernel in kernels:
if kernel == fixed_kernel:
continue
gpr = GaussianProcessRegressor(kernel=kernel, optimizer=optimizer)
gpr.fit(X, y)
# Checks that optimizer improved marginal likelihood
assert_greater(gpr.log_marginal_likelihood(gpr.kernel_.theta),
gpr.log_marginal_likelihood(gpr.kernel.theta))
def test_duplicate_input():
""" Test GPR can handle two different output-values for the same input. """
for kernel in kernels:
gpr_equal_inputs = \
GaussianProcessRegressor(kernel=kernel, alpha=1e-2)
gpr_similar_inputs = \
GaussianProcessRegressor(kernel=kernel, alpha=1e-2)
X_ = np.vstack((X, X[0]))
y_ = np.hstack((y, y[0] + 1))
gpr_equal_inputs.fit(X_, y_)
X_ = np.vstack((X, X[0] + 1e-15))
y_ = np.hstack((y, y[0] + 1))
gpr_similar_inputs.fit(X_, y_)
X_test = np.linspace(0, 10, 100)[:, None]
y_pred_equal, y_std_equal = \
gpr_equal_inputs.predict(X_test, return_std=True)
y_pred_similar, y_std_similar = \
gpr_similar_inputs.predict(X_test, return_std=True)
assert_almost_equal(y_pred_equal, y_pred_similar)
assert_almost_equal(y_std_equal, y_std_similar)
|
kjung/scikit-learn
|
sklearn/gaussian_process/tests/test_gpr.py
|
Python
|
bsd-3-clause
| 11,913
|
[
"Gaussian"
] |
6e2683ca4cd9aaeac738037d737e76ce18d694de13c521466143f88fba932f6f
|
# -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'settings_layout.ui'
#
# Created: Tue Mar 26 12:40:37 2013
# by: PyQt4 UI code generator 4.7.2
#
# WARNING! All changes made in this file will be lost!
from PyQt4 import QtCore, QtGui
class Ui_Dialog(object):
def setupUi(self, Dialog):
Dialog.setObjectName("Dialog")
Dialog.resize(445, 664)
self.gridLayout = QtGui.QGridLayout(Dialog)
self.gridLayout.setObjectName("gridLayout")
self.grid_delay_properties = QtGui.QGridLayout()
self.grid_delay_properties.setObjectName("grid_delay_properties")
self.box_word_select = QtGui.QDoubleSpinBox(Dialog)
self.box_word_select.setFocusPolicy(QtCore.Qt.ClickFocus)
self.box_word_select.setMaximum(1.0)
self.box_word_select.setSingleStep(0.05)
self.box_word_select.setProperty("value", 0.9)
self.box_word_select.setObjectName("box_word_select")
self.grid_delay_properties.addWidget(self.box_word_select, 16, 1, 1, 1)
self.label_seconds_delay = QtGui.QLabel(Dialog)
font = QtGui.QFont()
font.setWeight(50)
font.setUnderline(False)
font.setBold(False)
self.label_seconds_delay.setFont(font)
self.label_seconds_delay.setObjectName("label_seconds_delay")
self.grid_delay_properties.addWidget(self.label_seconds_delay, 2, 0, 1, 1)
self.box_seconds_delay = QtGui.QDoubleSpinBox(Dialog)
self.box_seconds_delay.setDecimals(3)
self.box_seconds_delay.setMinimum(-60.0)
self.box_seconds_delay.setMaximum(60.0)
self.box_seconds_delay.setSingleStep(0.05)
self.box_seconds_delay.setProperty("value", 0.2)
self.box_seconds_delay.setObjectName("box_seconds_delay")
self.grid_delay_properties.addWidget(self.box_seconds_delay, 2, 1, 1, 1)
self.label_deviation = QtGui.QLabel(Dialog)
font = QtGui.QFont()
font.setWeight(50)
font.setUnderline(False)
font.setBold(False)
self.label_deviation.setFont(font)
self.label_deviation.setObjectName("label_deviation")
self.grid_delay_properties.addWidget(self.label_deviation, 3, 0, 1, 1)
self.box_click_dev = QtGui.QDoubleSpinBox(Dialog)
self.box_click_dev.setFocusPolicy(QtCore.Qt.ClickFocus)
self.box_click_dev.setDecimals(3)
self.box_click_dev.setMinimum(0.01)
self.box_click_dev.setMaximum(1000.0)
self.box_click_dev.setSingleStep(0.01)
self.box_click_dev.setProperty("value", 0.05)
self.box_click_dev.setObjectName("box_click_dev")
self.grid_delay_properties.addWidget(self.box_click_dev, 3, 1, 1, 1)
self.box_enable_learning = QtGui.QCheckBox(Dialog)
self.box_enable_learning.setFocusPolicy(QtCore.Qt.ClickFocus)
self.box_enable_learning.setChecked(True)
self.box_enable_learning.setObjectName("box_enable_learning")
self.grid_delay_properties.addWidget(self.box_enable_learning, 4, 0, 1, 1)
self.box_learning_rate = QtGui.QDoubleSpinBox(Dialog)
self.box_learning_rate.setFocusPolicy(QtCore.Qt.ClickFocus)
self.box_learning_rate.setMaximum(1.0)
self.box_learning_rate.setSingleStep(0.05)
self.box_learning_rate.setProperty("value", 0.3)
self.box_learning_rate.setObjectName("box_learning_rate")
self.grid_delay_properties.addWidget(self.box_learning_rate, 4, 1, 1, 1)
self.label_delay = QtGui.QLabel(Dialog)
font = QtGui.QFont()
font.setFamily("Serif")
font.setWeight(75)
font.setUnderline(True)
font.setBold(True)
self.label_delay.setFont(font)
self.label_delay.setStatusTip("")
self.label_delay.setObjectName("label_delay")
self.grid_delay_properties.addWidget(self.label_delay, 1, 0, 1, 1)
self.label_fp_rate = QtGui.QLabel(Dialog)
self.label_fp_rate.setObjectName("label_fp_rate")
self.grid_delay_properties.addWidget(self.label_fp_rate, 8, 0, 1, 1)
self.box_fp_rate = QtGui.QDoubleSpinBox(Dialog)
self.box_fp_rate.setDecimals(4)
self.box_fp_rate.setMinimum(0.0)
self.box_fp_rate.setMaximum(3.0)
self.box_fp_rate.setSingleStep(0.01)
self.box_fp_rate.setProperty("value", 0.25)
self.box_fp_rate.setObjectName("box_fp_rate")
self.grid_delay_properties.addWidget(self.box_fp_rate, 8, 1, 1, 1)
self.label_fr = QtGui.QLabel(Dialog)
self.label_fr.setObjectName("label_fr")
self.grid_delay_properties.addWidget(self.label_fr, 9, 0, 1, 1)
self.label_switch_noise = QtGui.QLabel(Dialog)
font = QtGui.QFont()
font.setFamily("Serif")
font.setWeight(75)
font.setUnderline(True)
font.setBold(True)
self.label_switch_noise.setFont(font)
self.label_switch_noise.setObjectName("label_switch_noise")
self.grid_delay_properties.addWidget(self.label_switch_noise, 6, 0, 1, 1)
self.label_word_select_thresh = QtGui.QLabel(Dialog)
font = QtGui.QFont()
font.setFamily("Serif")
font.setWeight(50)
font.setBold(False)
self.label_word_select_thresh.setFont(font)
self.label_word_select_thresh.setObjectName("label_word_select_thresh")
self.grid_delay_properties.addWidget(self.label_word_select_thresh, 16, 0, 1, 1)
self.label_word_select = QtGui.QLabel(Dialog)
font = QtGui.QFont()
font.setFamily("Serif")
font.setWeight(75)
font.setUnderline(True)
font.setBold(True)
self.label_word_select.setFont(font)
self.label_word_select.setObjectName("label_word_select")
self.grid_delay_properties.addWidget(self.label_word_select, 15, 0, 1, 1)
self.label_error_correct = QtGui.QLabel(Dialog)
font = QtGui.QFont()
font.setWeight(75)
font.setUnderline(True)
font.setBold(True)
self.label_error_correct.setFont(font)
self.label_error_correct.setObjectName("label_error_correct")
self.grid_delay_properties.addWidget(self.label_error_correct, 10, 0, 1, 1)
self.label_undo_last = QtGui.QLabel(Dialog)
self.label_undo_last.setObjectName("label_undo_last")
self.grid_delay_properties.addWidget(self.label_undo_last, 11, 0, 1, 1)
self.box_undo = QtGui.QSpinBox(Dialog)
self.box_undo.setMinimum(3)
self.box_undo.setMaximum(100)
self.box_undo.setSingleStep(1)
self.box_undo.setProperty("value", 4)
self.box_undo.setObjectName("box_undo")
self.grid_delay_properties.addWidget(self.box_undo, 11, 1, 1, 1)
self.label_program_status = QtGui.QLabel(Dialog)
self.label_program_status.setObjectName("label_program_status")
self.grid_delay_properties.addWidget(self.label_program_status, 12, 0, 1, 1)
self.box_prog_status = QtGui.QSpinBox(Dialog)
self.box_prog_status.setMinimum(1)
self.box_prog_status.setMaximum(10)
self.box_prog_status.setProperty("value", 2)
self.box_prog_status.setObjectName("box_prog_status")
self.grid_delay_properties.addWidget(self.box_prog_status, 12, 1, 1, 1)
self.label_restart_word = QtGui.QLabel(Dialog)
self.label_restart_word.setObjectName("label_restart_word")
self.grid_delay_properties.addWidget(self.label_restart_word, 13, 0, 1, 1)
self.box_restart_word = QtGui.QSpinBox(Dialog)
self.box_restart_word.setMinimum(2)
self.box_restart_word.setMaximum(2000)
self.box_restart_word.setProperty("value", 6)
self.box_restart_word.setObjectName("box_restart_word")
self.grid_delay_properties.addWidget(self.box_restart_word, 13, 1, 1, 1)
self.label_switch_off = QtGui.QLabel(Dialog)
self.label_switch_off.setObjectName("label_switch_off")
self.grid_delay_properties.addWidget(self.label_switch_off, 14, 0, 1, 1)
self.box_shut_down = QtGui.QSpinBox(Dialog)
self.box_shut_down.setMinimum(4)
self.box_shut_down.setMaximum(2000)
self.box_shut_down.setProperty("value", 2000)
self.box_shut_down.setObjectName("box_shut_down")
self.grid_delay_properties.addWidget(self.box_shut_down, 14, 1, 1, 1)
self.label_config_settings = QtGui.QLabel(Dialog)
font = QtGui.QFont()
font.setFamily("Serif")
font.setWeight(75)
font.setUnderline(True)
font.setBold(True)
self.label_config_settings.setFont(font)
self.label_config_settings.setObjectName("label_config_settings")
self.grid_delay_properties.addWidget(self.label_config_settings, 17, 0, 1, 1)
self.label_file_length = QtGui.QLabel(Dialog)
self.label_file_length.setObjectName("label_file_length")
self.grid_delay_properties.addWidget(self.label_file_length, 21, 0, 1, 1)
self.box_file_length = QtGui.QDoubleSpinBox(Dialog)
self.box_file_length.setSingleStep(0.05)
self.box_file_length.setProperty("value", 0.21)
self.box_file_length.setObjectName("box_file_length")
self.grid_delay_properties.addWidget(self.box_file_length, 21, 1, 1, 1)
self.box_learn_delay = QtGui.QCheckBox(Dialog)
self.box_learn_delay.setChecked(True)
self.box_learn_delay.setObjectName("box_learn_delay")
self.grid_delay_properties.addWidget(self.box_learn_delay, 2, 2, 1, 1)
self.box_learn_std = QtGui.QCheckBox(Dialog)
self.box_learn_std.setChecked(True)
self.box_learn_std.setObjectName("box_learn_std")
self.grid_delay_properties.addWidget(self.box_learn_std, 3, 2, 1, 1)
self.box_learn_fp = QtGui.QCheckBox(Dialog)
self.box_learn_fp.setChecked(False)
self.box_learn_fp.setObjectName("box_learn_fp")
self.grid_delay_properties.addWidget(self.box_learn_fp, 8, 2, 1, 1)
self.box_learn_fr = QtGui.QCheckBox(Dialog)
self.box_learn_fr.setChecked(False)
self.box_learn_fr.setObjectName("box_learn_fr")
self.grid_delay_properties.addWidget(self.box_learn_fr, 9, 2, 1, 1)
self.label_channels = QtGui.QLabel(Dialog)
self.label_channels.setObjectName("label_channels")
self.grid_delay_properties.addWidget(self.label_channels, 18, 0, 1, 1)
self.box_channels = QtGui.QComboBox(Dialog)
self.box_channels.setMaxVisibleItems(14)
self.box_channels.setSizeAdjustPolicy(QtGui.QComboBox.AdjustToContentsOnFirstShow)
self.box_channels.setObjectName("box_channels")
self.box_channels.addItem("")
self.box_channels.addItem("")
self.box_channels.addItem("")
self.box_channels.addItem("")
self.box_channels.addItem("")
self.grid_delay_properties.addWidget(self.box_channels, 18, 1, 1, 1)
self.button_default = QtGui.QPushButton(Dialog)
self.button_default.setObjectName("button_default")
self.grid_delay_properties.addWidget(self.button_default, 0, 0, 1, 1)
self.box_fr = QtGui.QDoubleSpinBox(Dialog)
self.box_fr.setProperty("value", 5.0)
self.box_fr.setObjectName("box_fr")
self.grid_delay_properties.addWidget(self.box_fr, 9, 1, 1, 1)
self.button_gauss = QtGui.QPushButton(Dialog)
self.button_gauss.setObjectName("button_gauss")
self.grid_delay_properties.addWidget(self.button_gauss, 5, 0, 1, 1)
self.label_end_delay = QtGui.QLabel(Dialog)
self.label_end_delay.setObjectName("label_end_delay")
self.grid_delay_properties.addWidget(self.label_end_delay, 22, 0, 1, 1)
self.box_end_delay = QtGui.QDoubleSpinBox(Dialog)
self.box_end_delay.setSingleStep(0.1)
self.box_end_delay.setProperty("value", 0.3)
self.box_end_delay.setObjectName("box_end_delay")
self.grid_delay_properties.addWidget(self.box_end_delay, 22, 1, 1, 1)
self.gridLayout.addLayout(self.grid_delay_properties, 0, 0, 1, 1)
self.retranslateUi(Dialog)
self.box_channels.setCurrentIndex(4)
QtCore.QMetaObject.connectSlotsByName(Dialog)
def retranslateUi(self, Dialog):
Dialog.setWindowTitle(QtGui.QApplication.translate("Dialog", "Settings Editor", None, QtGui.QApplication.UnicodeUTF8))
self.label_seconds_delay.setToolTip(QtGui.QApplication.translate("Dialog", "The average click delay, measured in seconds", None, QtGui.QApplication.UnicodeUTF8))
self.label_seconds_delay.setText(QtGui.QApplication.translate("Dialog", "Average (seconds)", None, QtGui.QApplication.UnicodeUTF8))
self.label_deviation.setToolTip(QtGui.QApplication.translate("Dialog", "The standard deviation of click delay measured in seconds", None, QtGui.QApplication.UnicodeUTF8))
self.label_deviation.setText(QtGui.QApplication.translate("Dialog", "Deviation (seconds)", None, QtGui.QApplication.UnicodeUTF8))
self.box_enable_learning.setToolTip(QtGui.QApplication.translate("Dialog", "Adapt the click distribution according to previous word selections", None, QtGui.QApplication.UnicodeUTF8))
self.box_enable_learning.setText(QtGui.QApplication.translate("Dialog", "Learning rate (0.0 to 1.0)", None, QtGui.QApplication.UnicodeUTF8))
self.label_delay.setToolTip(QtGui.QApplication.translate("Dialog", "Click visualisation settings.", None, QtGui.QApplication.UnicodeUTF8))
self.label_delay.setText(QtGui.QApplication.translate("Dialog", "Click-time delay:", None, QtGui.QApplication.UnicodeUTF8))
self.label_fp_rate.setText(QtGui.QApplication.translate("Dialog", "Average number of false \n"
"positives per minute", None, QtGui.QApplication.UnicodeUTF8))
self.label_fr.setText(QtGui.QApplication.translate("Dialog", "Average percentage \n"
"false negatives (1-100)", None, QtGui.QApplication.UnicodeUTF8))
self.label_switch_noise.setText(QtGui.QApplication.translate("Dialog", "Switch Noise:", None, QtGui.QApplication.UnicodeUTF8))
self.label_word_select_thresh.setToolTip(QtGui.QApplication.translate("Dialog", "A word will be selected if its probability is above this value.", None, QtGui.QApplication.UnicodeUTF8))
self.label_word_select_thresh.setText(QtGui.QApplication.translate("Dialog", "Threshold", None, QtGui.QApplication.UnicodeUTF8))
self.label_word_select.setToolTip(QtGui.QApplication.translate("Dialog", "The speed at which the next letter will be displayed/pronounced.", None, QtGui.QApplication.UnicodeUTF8))
self.label_word_select.setText(QtGui.QApplication.translate("Dialog", "Word selection:", None, QtGui.QApplication.UnicodeUTF8))
self.label_error_correct.setText(QtGui.QApplication.translate("Dialog", "Error Correction:", None, QtGui.QApplication.UnicodeUTF8))
self.label_undo_last.setToolTip(QtGui.QApplication.translate("Dialog", "The number of clicks that will immediately result in an undo", None, QtGui.QApplication.UnicodeUTF8))
self.label_undo_last.setText(QtGui.QApplication.translate("Dialog", "Undo last letter \n"
"(number of clicks)", None, QtGui.QApplication.UnicodeUTF8))
self.label_program_status.setText(QtGui.QApplication.translate("Dialog", "Read program status \n"
"(number of alphabet repetions)", None, QtGui.QApplication.UnicodeUTF8))
self.label_restart_word.setText(QtGui.QApplication.translate("Dialog", "New word \n"
"(number of alphabet repetitions)", None, QtGui.QApplication.UnicodeUTF8))
self.label_switch_off.setText(QtGui.QApplication.translate("Dialog", "Shut down \n"
"(number of alphabet repetions)", None, QtGui.QApplication.UnicodeUTF8))
self.label_config_settings.setText(QtGui.QApplication.translate("Dialog", "Speed & Channel Settings:", None, QtGui.QApplication.UnicodeUTF8))
self.label_file_length.setText(QtGui.QApplication.translate("Dialog", "File length (seconds)", None, QtGui.QApplication.UnicodeUTF8))
self.box_learn_delay.setText(QtGui.QApplication.translate("Dialog", "Learn", None, QtGui.QApplication.UnicodeUTF8))
self.box_learn_std.setText(QtGui.QApplication.translate("Dialog", "Learn", None, QtGui.QApplication.UnicodeUTF8))
self.box_learn_fp.setText(QtGui.QApplication.translate("Dialog", "Learn", None, QtGui.QApplication.UnicodeUTF8))
self.box_learn_fr.setText(QtGui.QApplication.translate("Dialog", "Learn", None, QtGui.QApplication.UnicodeUTF8))
self.label_channels.setText(QtGui.QApplication.translate("Dialog", "Channels", None, QtGui.QApplication.UnicodeUTF8))
self.box_channels.setItemText(0, QtGui.QApplication.translate("Dialog", "1", None, QtGui.QApplication.UnicodeUTF8))
self.box_channels.setItemText(1, QtGui.QApplication.translate("Dialog", "2", None, QtGui.QApplication.UnicodeUTF8))
self.box_channels.setItemText(2, QtGui.QApplication.translate("Dialog", "3", None, QtGui.QApplication.UnicodeUTF8))
self.box_channels.setItemText(3, QtGui.QApplication.translate("Dialog", "4", None, QtGui.QApplication.UnicodeUTF8))
self.box_channels.setItemText(4, QtGui.QApplication.translate("Dialog", "5", None, QtGui.QApplication.UnicodeUTF8))
self.button_default.setText(QtGui.QApplication.translate("Dialog", "Load Default Settings", None, QtGui.QApplication.UnicodeUTF8))
self.button_gauss.setText(QtGui.QApplication.translate("Dialog", "Set Distribution to Gaussian", None, QtGui.QApplication.UnicodeUTF8))
self.label_end_delay.setText(QtGui.QApplication.translate("Dialog", "End delay (seconds)", None, QtGui.QApplication.UnicodeUTF8))
|
singleswitch/ticker
|
settings_layout.py
|
Python
|
mit
| 17,669
|
[
"Gaussian"
] |
eb2db13ebb4447ba262fc260043b7fe3e870ff571649be720a4516271397986f
|
'''
* Created by Zhenia Syryanyy (Yevgen Syryanyy)
* e-mail: yuginboy@gmail.com
* License: this code is under GPL license
* Last modified: 2017-08-24
'''
from feff.libs.class_Spectrum import Spectrum, GraphElement, TableData, BaseData
from feff.libs.class_for_parallel_comparison import Model_for_spectra, FTR_gulp_to_feff_A_model_base
from feff.libs.dir_and_file_operations import delete_all_subdirs_in_directory
import os
import datetime
from timeit import default_timer as timer
import copy
from shutil import copyfile
import numpy as np
# from joblib import Parallel, delayed
import pathos.multiprocessing as mp
def chunks(l, n):
"""Yield successive n-sized chunks from l."""
for i in range(0, len(l), n):
yield l[i:i + n]
def slice_list(input, size, whole_num=2):
# divide list into N equal parts which rounds to whole_num
# if input = [123456] but size=2, whole_num=2 we expect [1234], [56]
input_size = len(input)
slice_size = int(input_size / size)
while slice_size % whole_num:
slice_size = slice_size + 1
result = list(chunks (input, slice_size))
return result
def check_if_lengths_are_equal(input):
isEqual = True
L = len(input)
tmp = np.zeros(L)
for i in range(L):
tmp[i] = len(input[i])
for i in range(L-1):
if tmp[i] != tmp[i+1]:
isEqual = False
break
return isEqual
def slice_list_extend(input, size, whole_num=2):
lst_tmp = slice_list(input, size, whole_num=whole_num)
out = lst_tmp
if not check_if_lengths_are_equal(lst_tmp):
tmp_input = input
reduce_num = 0
N = len(input)
while not check_if_lengths_are_equal(lst_tmp):
reduce_num = reduce_num + whole_num
lst_tmp = slice_list(input[0:N - reduce_num], size, whole_num=whole_num)
out = lst_tmp
out.append(input[N - reduce_num: N])
return out
def one_thread_calculation(model_A_projectWorkingFEFFoutDirectory, model_A_listOfSnapshotFiles,
model_B_projectWorkingFEFFoutDirectory, model_B_listOfSnapshotFiles,
model_C_projectWorkingFEFFoutDirectory, model_C_listOfSnapshotFiles,
outDirectoryFor_3_type_ModelsFitResults, weight_R_factor_FTR=1.0, weight_R_factor_chi=0.0,
scale_theory_factor_FTR=0.81, scale_experiment_factor_FTR=1.0,
model_A_numberOfSerialEquivalentAtoms=2,
model_B_numberOfSerialEquivalentAtoms=2,
model_C_numberOfSerialEquivalentAtoms=2,
user='ID', sample_preparation_mode='AG', saveDataToDisk=True):
# create one thread of calculation by creating the object which will get to the input
# sliced list of files of model_A
a = FTR_gulp_to_feff_A_model_base()
a.weight_R_factor_FTR = weight_R_factor_FTR
a.weight_R_factor_chi = weight_R_factor_chi
a.scale_theory_factor_FTR = scale_theory_factor_FTR
a.scale_experiment_factor_FTR = scale_experiment_factor_FTR
a.model_A.numberOfSerialEquivalentAtoms = model_A_numberOfSerialEquivalentAtoms
a.model_B.numberOfSerialEquivalentAtoms = model_B_numberOfSerialEquivalentAtoms
a.model_C.numberOfSerialEquivalentAtoms = model_C_numberOfSerialEquivalentAtoms
# change the user name, which parameters for xftf transformation you want to use:
a.user = user
# change tha sample preparation method:
a.sample_preparation_mode = sample_preparation_mode
# for debug and profiling:
a.saveDataToDisk = saveDataToDisk
# if you want to find the minimum from the all snapshots do this:
a.calcAllSnapshotFilesFor_3_type_Models_parallel(
model_A_projectWorkingFEFFoutDirectory, model_A_listOfSnapshotFiles,
model_B_projectWorkingFEFFoutDirectory, model_B_listOfSnapshotFiles,
model_C_projectWorkingFEFFoutDirectory, model_C_listOfSnapshotFiles,
outDirectoryFor_3_type_ModelsFitResults)
return a.minimum
class FTR_gulp_to_feff_A_model(FTR_gulp_to_feff_A_model_base):
'''
Class to search optimal snapshot coordinates by compare chi(k) nad FTR(r) spectra between the snapshots and
average spectrum from all snapshots
'''
def findBestSnapshotsCombinationFrom_3_type_Models_parallel(self):
'''
searching procedure of 3 type Models (A - first, B - second, C - third) linear model:
a/n(A1 + A2 + .. + An) + b/m(B1 + B2 + .. + Bm) + c/l(C1 + C2 + .. + Cl)
a/n + b/m + c/l = 1
:return: a/n, b/m, c/l - coefficient which corresponds to concentration A,B,C phases in A-B-C compound
Parallel realization
'''
model_A_projectWorkingFEFFoutDirectory, model_A_listOfSnapshotFiles, \
model_B_projectWorkingFEFFoutDirectory, model_B_listOfSnapshotFiles, \
model_C_projectWorkingFEFFoutDirectory, model_C_listOfSnapshotFiles, \
outDirectoryForModelsFitResults = self.loadListOfFilesFor_3_type_Models()
def func(list_of_files):
return one_thread_calculation(model_A_projectWorkingFEFFoutDirectory, list_of_files,
model_B_projectWorkingFEFFoutDirectory, model_B_listOfSnapshotFiles,
model_C_projectWorkingFEFFoutDirectory, model_C_listOfSnapshotFiles,
outDirectoryForModelsFitResults,
weight_R_factor_FTR=self.weight_R_factor_FTR,
weight_R_factor_chi=self.weight_R_factor_chi,
scale_theory_factor_FTR=self.scale_theory_factor_FTR,
scale_experiment_factor_FTR=self.scale_experiment_factor_FTR,
model_A_numberOfSerialEquivalentAtoms=self.model_A.numberOfSerialEquivalentAtoms,
model_B_numberOfSerialEquivalentAtoms=self.model_B.numberOfSerialEquivalentAtoms,
model_C_numberOfSerialEquivalentAtoms=self.model_C.numberOfSerialEquivalentAtoms,
user=self.user,
sample_preparation_mode=self.sample_preparation_mode,
saveDataToDisk=self.saveDataToDisk)
start = timer()
number = self.model_A.numberOfSerialEquivalentAtoms
listOfIndexes = slice_list(model_A_listOfSnapshotFiles, size=self.parallel_job_numbers, whole_num=number)
print('*----'*10)
print('User calls {} number of threads'.format(self.parallel_job_numbers))
print('program define {} number of threads'.format(len(listOfIndexes)))
for idx, elem in enumerate(listOfIndexes):
print('Thread # {0} will be calculate {1} elements'.format(idx, len(elem)))
print('*----' * 10)
# # for debug
# for lst in listOfIndexes:
# result = func(lst)
# p = mp.Pool(self.parallel_job_numbers)
p = mp.Pool(len(listOfIndexes))
result = p.map(func, listOfIndexes)
# bar.update(i)
# bar.finish()
vec_Rtot = list((i.Rtot for i in result))
minIdx, = np.where(vec_Rtot == np.min(vec_Rtot))
Rtot = result[minIdx[0]].Rtot
snapshotName = result[minIdx[0]].snapshotName
obj = result[minIdx[0]]
# save ASCII column data:
if self.saveDataToDisk:
self.outMinValsDir = outDirectoryForModelsFitResults
if obj.indicator_minimum_from_FTRlinear_chi:
# if minimum have been found in FTRlinear_chi procedure:
obj.setOfSnapshotSpectra.saveSpectra_LinearComposition_FTR_from_linear_Chi_k(
output_dir=self.outMinValsDir)
# store model-A snapshots for this minimum case:
obj.model_A.saveSpectra_SimpleComposition(output_dir=self.outMinValsDir)
# store model-B snapshots for this minimum case:
obj.model_B.saveSpectra_SimpleComposition(output_dir=self.outMinValsDir)
# store model-C snapshots for this minimum case:
obj.model_C.saveSpectra_SimpleComposition(output_dir=self.outMinValsDir)
dst = os.path.join(self.outMinValsDir, os.path.basename(obj.pathToImage))
copyfile(obj.pathToImage, dst)
print('======'*10)
print('======'*10)
print('======'*10)
print('global minimum Rtot = {0}'.format(Rtot))
print('{0}'.format(snapshotName))
runtime = timer() - start
print('======'*10)
print("total runtime is {0:f} seconds".format(runtime))
txt = '======'*10
txt += '\n'
txt += 'global minimum Rtot = {0}'.format(Rtot)
txt += '\n'
txt += '{0}'.format(snapshotName)
txt += '\n'
txt += '======'*10
txt += '\n'
txt += "total runtime is {0:f} seconds".format(runtime)
txt += '\n'
txt_file_name = os.path.join(outDirectoryForModelsFitResults, 'result_info.txt')
f = open(txt_file_name, 'x')
f.write(txt)
print('Resulting information have been saved in: ', txt_file_name)
delete_all_subdirs_in_directory(outDirectoryForTowModelsFitResults)
if __name__ == '__main__':
print('-> you run ', __file__, ' file in a main mode')
# start global search of 3 type Models combination in Parallel mode:
a = FTR_gulp_to_feff_A_model()
a.weight_R_factor_FTR = 1.0
a.weight_R_factor_chi = 0.0
a.scale_theory_factor_FTR = 0.81
a.scale_experiment_factor_FTR = 1.0
a.model_A.numberOfSerialEquivalentAtoms = 1
a.model_B.numberOfSerialEquivalentAtoms = 2
a.model_C.numberOfSerialEquivalentAtoms = 3
# change the user name, which parameters for xftf transformation you want to use:
a.user = 'ID'
# change tha sample preparation method:
a.sample_preparation_mode = '450'
# if you want compare with the theoretical average, do this:
# a.calcAllSnapshotFiles()
# for debug and profiling:
a.saveDataToDisk = True
a.parallel_job_numbers = 10
# if you want to find the minimum from the all snapshots do this:
a.findBestSnapshotsCombinationFrom_3_type_Models_parallel()
|
yuginboy/from_GULP_to_FEFF
|
feff/libs/class_FTR_Spectrum_Compare_3_type_Models.py
|
Python
|
gpl-3.0
| 10,188
|
[
"FEFF"
] |
9bd4ac75f4c3c04c1c211661e4373cc274e0a0a79fec369c48ae0d5711ce9dff
|
import os
from uuid import uuid4
from datetime import datetime
from ..helpers import UniqueCourseTest
from ...fixtures.course import CourseFixture, XBlockFixtureDesc
from ...pages.lms.auto_auth import AutoAuthPage
from ...pages.lms.course_nav import CourseNavPage
from ...pages.lms.courseware import CoursewarePage
from ...pages.lms.edxnotes import EdxNotesUnitPage, EdxNotesPage, EdxNotesPageNoContent
from ...fixtures.edxnotes import EdxNotesFixture, Note, Range
class EdxNotesTestMixin(UniqueCourseTest):
"""
Creates a course with initial data and contains useful helper methods.
"""
def setUp(self):
"""
Initialize pages and install a course fixture.
"""
super(EdxNotesTestMixin, self).setUp()
self.courseware_page = CoursewarePage(self.browser, self.course_id)
self.course_nav = CourseNavPage(self.browser)
self.note_unit_page = EdxNotesUnitPage(self.browser, self.course_id)
self.notes_page = EdxNotesPage(self.browser, self.course_id)
self.username = str(uuid4().hex)[:5]
self.email = "{}@email.com".format(self.username)
self.selector = "annotate-id"
self.edxnotes_fixture = EdxNotesFixture()
self.course_fixture = CourseFixture(
self.course_info["org"], self.course_info["number"],
self.course_info["run"], self.course_info["display_name"]
)
self.course_fixture.add_advanced_settings({
u"edxnotes": {u"value": True}
})
self.course_fixture.add_children(
XBlockFixtureDesc("chapter", "Test Section 1").add_children(
XBlockFixtureDesc("sequential", "Test Subsection 1").add_children(
XBlockFixtureDesc("vertical", "Test Unit 1").add_children(
XBlockFixtureDesc(
"html",
"Test HTML 1",
data="""
<p><span class="{}">Annotate this text!</span></p>
<p>Annotate this text</p>
""".format(self.selector)
),
XBlockFixtureDesc(
"html",
"Test HTML 2",
data="""<p><span class="{}">Annotate this text!</span></p>""".format(self.selector)
),
),
XBlockFixtureDesc("vertical", "Test Unit 2").add_children(
XBlockFixtureDesc(
"html",
"Test HTML 3",
data="""<p><span class="{}">Annotate this text!</span></p>""".format(self.selector)
),
),
),
XBlockFixtureDesc("sequential", "Test Subsection 2").add_children(
XBlockFixtureDesc("vertical", "Test Unit 3").add_children(
XBlockFixtureDesc(
"html",
"Test HTML 4",
data="""
<p><span class="{}">Annotate this text!</span></p>
""".format(self.selector)
),
),
),
),
XBlockFixtureDesc("chapter", "Test Section 2").add_children(
XBlockFixtureDesc("sequential", "Test Subsection 3").add_children(
XBlockFixtureDesc("vertical", "Test Unit 4").add_children(
XBlockFixtureDesc(
"html",
"Test HTML 5",
data="""
<p><span class="{}">Annotate this text!</span></p>
""".format(self.selector)
),
XBlockFixtureDesc(
"html",
"Test HTML 6",
data="""<p><span class="{}">Annotate this text!</span></p>""".format(self.selector)
),
),
),
)).install()
self.addCleanup(self.edxnotes_fixture.cleanup)
AutoAuthPage(self.browser, username=self.username, email=self.email, course_id=self.course_id).visit()
def _add_notes(self):
xblocks = self.course_fixture.get_nested_xblocks(category="html")
notes_list = []
for index, xblock in enumerate(xblocks):
notes_list.append(
Note(
user=self.username,
usage_id=xblock.locator,
course_id=self.course_fixture._course_key,
ranges=[Range(startOffset=index, endOffset=index + 5)]
)
)
self.edxnotes_fixture.create_notes(notes_list)
self.edxnotes_fixture.install()
class EdxNotesDefaultInteractionsTest(EdxNotesTestMixin):
"""
Tests for creation, editing, deleting annotations inside annotatable components in LMS.
"""
def create_notes(self, components, offset=0):
self.assertGreater(len(components), 0)
index = offset
for component in components:
for note in component.create_note(".{}".format(self.selector)):
note.text = "TEST TEXT {}".format(index)
index += 1
def edit_notes(self, components, offset=0):
self.assertGreater(len(components), 0)
index = offset
for component in components:
self.assertGreater(len(component.notes), 0)
for note in component.edit_note():
note.text = "TEST TEXT {}".format(index)
index += 1
def edit_tags_in_notes(self, components, tags):
self.assertGreater(len(components), 0)
index = 0
for component in components:
self.assertGreater(len(component.notes), 0)
for note in component.edit_note():
note.tags = tags[index]
index += 1
self.assertEqual(index, len(tags), "Number of supplied tags did not match components")
def remove_notes(self, components):
self.assertGreater(len(components), 0)
for component in components:
self.assertGreater(len(component.notes), 0)
component.remove_note()
def assert_notes_are_removed(self, components):
for component in components:
self.assertEqual(0, len(component.notes))
def assert_text_in_notes(self, notes):
actual = [note.text for note in notes]
expected = ["TEST TEXT {}".format(i) for i in xrange(len(notes))]
self.assertEqual(expected, actual)
def assert_tags_in_notes(self, notes, expected_tags):
actual = [note.tags for note in notes]
expected = [expected_tags[i] for i in xrange(len(notes))]
self.assertEqual(expected, actual)
def test_can_create_notes(self):
"""
Scenario: User can create notes.
Given I have a course with 3 annotatable components
And I open the unit with 2 annotatable components
When I add 2 notes for the first component and 1 note for the second
Then I see that notes were correctly created
When I change sequential position to "2"
And I add note for the annotatable component on the page
Then I see that note was correctly created
When I refresh the page
Then I see that note was correctly stored
When I change sequential position to "1"
Then I see that notes were correctly stored on the page
"""
self.note_unit_page.visit()
components = self.note_unit_page.components
self.create_notes(components)
self.assert_text_in_notes(self.note_unit_page.notes)
self.course_nav.go_to_sequential_position(2)
components = self.note_unit_page.components
self.create_notes(components)
components = self.note_unit_page.refresh()
self.assert_text_in_notes(self.note_unit_page.notes)
self.course_nav.go_to_sequential_position(1)
components = self.note_unit_page.components
self.assert_text_in_notes(self.note_unit_page.notes)
def test_can_edit_notes(self):
"""
Scenario: User can edit notes.
Given I have a course with 3 components with notes
And I open the unit with 2 annotatable components
When I change text in the notes
Then I see that notes were correctly changed
When I change sequential position to "2"
And I change the note on the page
Then I see that note was correctly changed
When I refresh the page
Then I see that edited note was correctly stored
When I change sequential position to "1"
Then I see that edited notes were correctly stored on the page
"""
self._add_notes()
self.note_unit_page.visit()
components = self.note_unit_page.components
self.edit_notes(components)
self.assert_text_in_notes(self.note_unit_page.notes)
self.course_nav.go_to_sequential_position(2)
components = self.note_unit_page.components
self.edit_notes(components)
self.assert_text_in_notes(self.note_unit_page.notes)
components = self.note_unit_page.refresh()
self.assert_text_in_notes(self.note_unit_page.notes)
self.course_nav.go_to_sequential_position(1)
components = self.note_unit_page.components
self.assert_text_in_notes(self.note_unit_page.notes)
def test_can_delete_notes(self):
"""
Scenario: User can delete notes.
Given I have a course with 3 components with notes
And I open the unit with 2 annotatable components
When I remove all notes on the page
Then I do not see any notes on the page
When I change sequential position to "2"
And I remove all notes on the page
Then I do not see any notes on the page
When I refresh the page
Then I do not see any notes on the page
When I change sequential position to "1"
Then I do not see any notes on the page
"""
self._add_notes()
self.note_unit_page.visit()
components = self.note_unit_page.components
self.remove_notes(components)
self.assert_notes_are_removed(components)
self.course_nav.go_to_sequential_position(2)
components = self.note_unit_page.components
self.remove_notes(components)
self.assert_notes_are_removed(components)
components = self.note_unit_page.refresh()
self.assert_notes_are_removed(components)
self.course_nav.go_to_sequential_position(1)
components = self.note_unit_page.components
self.assert_notes_are_removed(components)
def test_can_create_note_with_tags(self):
"""
Scenario: a user of notes can define one with tags
Given I have a course with 3 annotatable components
And I open the unit with 2 annotatable components
When I add a note with tags for the first component
And I refresh the page
Then I see that note was correctly stored with its tags
"""
self.note_unit_page.visit()
components = self.note_unit_page.components
for note in components[0].create_note(".{}".format(self.selector)):
note.tags = ["fruit", "tasty"]
self.note_unit_page.refresh()
self.assertEqual(["fruit", "tasty"], self.note_unit_page.notes[0].tags)
def test_can_change_tags(self):
"""
Scenario: a user of notes can edit tags on notes
Given I have a course with 3 components with notes
When I open the unit with 2 annotatable components
And I edit tags on the notes for the 2 annotatable components
Then I see that the tags were correctly changed
And I again edit tags on the notes for the 2 annotatable components
And I refresh the page
Then I see that the tags were correctly changed
"""
self._add_notes()
self.note_unit_page.visit()
components = self.note_unit_page.components
self.edit_tags_in_notes(components, [["hard"], ["apple", "pear"]])
self.assert_tags_in_notes(self.note_unit_page.notes, [["hard"], ["apple", "pear"]])
self.edit_tags_in_notes(components, [[], ["avocado"]])
self.assert_tags_in_notes(self.note_unit_page.notes, [[], ["avocado"]])
self.note_unit_page.refresh()
self.assert_tags_in_notes(self.note_unit_page.notes, [[], ["avocado"]])
def test_sr_labels(self):
"""
Scenario: screen reader labels exist for text and tags fields
Given I have a course with 3 components with notes
When I open the unit with 2 annotatable components
And I open the editor for each note
Then the text and tags fields both have screen reader labels
"""
self._add_notes()
self.note_unit_page.visit()
# First note is in the first annotatable component, will have field indexes 0 and 1.
for note in self.note_unit_page.components[0].edit_note():
self.assertTrue(note.has_sr_label(0, 0, "Note"))
self.assertTrue(note.has_sr_label(1, 1, "Tags (space-separated)"))
# Second note is in the second annotatable component, will have field indexes 2 and 3.
for note in self.note_unit_page.components[1].edit_note():
self.assertTrue(note.has_sr_label(0, 2, "Note"))
self.assertTrue(note.has_sr_label(1, 3, "Tags (space-separated)"))
class EdxNotesPageTest(EdxNotesTestMixin):
"""
Tests for Notes page.
"""
def _add_notes(self, notes_list):
self.edxnotes_fixture.create_notes(notes_list)
self.edxnotes_fixture.install()
def _add_default_notes(self, tags=None):
"""
Creates 5 test notes. If tags are not specified, will populate the notes with some test tag data.
If tags are specified, they will be used for each of the 3 notes that have tags.
"""
xblocks = self.course_fixture.get_nested_xblocks(category="html")
self._add_notes([
Note(
usage_id=xblocks[4].locator,
user=self.username,
course_id=self.course_fixture._course_key,
text="First note",
quote="Annotate this text",
updated=datetime(2011, 1, 1, 1, 1, 1, 1).isoformat(),
),
Note(
usage_id=xblocks[2].locator,
user=self.username,
course_id=self.course_fixture._course_key,
text="",
quote=u"Annotate this text",
updated=datetime(2012, 1, 1, 1, 1, 1, 1).isoformat(),
tags=["Review", "cool"] if tags is None else tags
),
Note(
usage_id=xblocks[0].locator,
user=self.username,
course_id=self.course_fixture._course_key,
text="Third note",
quote="Annotate this text",
updated=datetime(2013, 1, 1, 1, 1, 1, 1).isoformat(),
ranges=[Range(startOffset=0, endOffset=18)],
tags=["Cool", "TODO"] if tags is None else tags
),
Note(
usage_id=xblocks[3].locator,
user=self.username,
course_id=self.course_fixture._course_key,
text="Fourth note",
quote="",
updated=datetime(2014, 1, 1, 1, 1, 1, 1).isoformat(),
tags=["review"] if tags is None else tags
),
Note(
usage_id=xblocks[1].locator,
user=self.username,
course_id=self.course_fixture._course_key,
text="Fifth note",
quote="Annotate this text",
updated=datetime(2015, 1, 1, 1, 1, 1, 1).isoformat(),
),
])
def assertNoteContent(self, item, text=None, quote=None, unit_name=None, time_updated=None, tags=None):
""" Verifies the expected properties of the note. """
self.assertEqual(text, item.text)
if item.quote is not None:
self.assertIn(quote, item.quote)
else:
self.assertIsNone(quote)
self.assertEqual(unit_name, item.unit_name)
self.assertEqual(time_updated, item.time_updated)
self.assertEqual(tags, item.tags)
def assertChapterContent(self, item, title=None, subtitles=None):
"""
Verifies the expected title and subsection titles (subtitles) for the given chapter.
"""
self.assertEqual(item.title, title)
self.assertEqual(item.subtitles, subtitles)
def assertGroupContent(self, item, title=None, notes=None):
"""
Verifies the expected title and child notes for the given group.
"""
self.assertEqual(item.title, title)
self.assertEqual(item.notes, notes)
def test_no_content(self):
"""
Scenario: User can see `No content` message.
Given I have a course without notes
When I open Notes page
Then I see only "You do not have any notes within the course." message
"""
notes_page_empty = EdxNotesPageNoContent(self.browser, self.course_id)
notes_page_empty.visit()
self.assertIn(
"You have not made any notes in this course yet. Other students in this course are using notes to:",
notes_page_empty.no_content_text)
def test_recent_activity_view(self):
"""
Scenario: User can view all notes by recent activity.
Given I have a course with 5 notes
When I open Notes page
Then I see 5 notes sorted by the updated date
And I see correct content in the notes
"""
self._add_default_notes()
self.notes_page.visit()
notes = self.notes_page.notes
self.assertEqual(len(notes), 5)
self.assertNoteContent(
notes[0],
quote=u"Annotate this text",
text=u"Fifth note",
unit_name="Test Unit 1",
time_updated="Jan 01, 2015 at 01:01 UTC"
)
self.assertNoteContent(
notes[1],
text=u"Fourth note",
unit_name="Test Unit 3",
time_updated="Jan 01, 2014 at 01:01 UTC",
tags=["review"]
)
self.assertNoteContent(
notes[2],
quote="Annotate this text",
text=u"Third note",
unit_name="Test Unit 1",
time_updated="Jan 01, 2013 at 01:01 UTC",
tags=["Cool", "TODO"]
)
self.assertNoteContent(
notes[3],
quote=u"Annotate this text",
unit_name="Test Unit 2",
time_updated="Jan 01, 2012 at 01:01 UTC",
tags=["Review", "cool"]
)
self.assertNoteContent(
notes[4],
quote=u"Annotate this text",
text=u"First note",
unit_name="Test Unit 4",
time_updated="Jan 01, 2011 at 01:01 UTC"
)
def test_course_structure_view(self):
"""
Scenario: User can view all notes by location in Course.
Given I have a course with 5 notes
When I open Notes page
And I switch to "Location in Course" view
Then I see 2 groups, 3 sections and 5 notes
And I see correct content in the notes and groups
"""
self._add_default_notes()
self.notes_page.visit().switch_to_tab("structure")
notes = self.notes_page.notes
groups = self.notes_page.chapter_groups
sections = self.notes_page.subsection_groups
self.assertEqual(len(notes), 5)
self.assertEqual(len(groups), 2)
self.assertEqual(len(sections), 3)
self.assertChapterContent(
groups[0],
title=u"Test Section 1",
subtitles=[u"Test Subsection 1", u"Test Subsection 2"]
)
self.assertGroupContent(
sections[0],
title=u"Test Subsection 1",
notes=[u"Fifth note", u"Third note", None]
)
self.assertNoteContent(
notes[0],
quote=u"Annotate this text",
text=u"Fifth note",
unit_name="Test Unit 1",
time_updated="Jan 01, 2015 at 01:01 UTC"
)
self.assertNoteContent(
notes[1],
quote=u"Annotate this text",
text=u"Third note",
unit_name="Test Unit 1",
time_updated="Jan 01, 2013 at 01:01 UTC",
tags=["Cool", "TODO"]
)
self.assertNoteContent(
notes[2],
quote=u"Annotate this text",
unit_name="Test Unit 2",
time_updated="Jan 01, 2012 at 01:01 UTC",
tags=["Review", "cool"]
)
self.assertGroupContent(
sections[1],
title=u"Test Subsection 2",
notes=[u"Fourth note"]
)
self.assertNoteContent(
notes[3],
text=u"Fourth note",
unit_name="Test Unit 3",
time_updated="Jan 01, 2014 at 01:01 UTC",
tags=["review"]
)
self.assertChapterContent(
groups[1],
title=u"Test Section 2",
subtitles=[u"Test Subsection 3"],
)
self.assertGroupContent(
sections[2],
title=u"Test Subsection 3",
notes=[u"First note"]
)
self.assertNoteContent(
notes[4],
quote=u"Annotate this text",
text=u"First note",
unit_name="Test Unit 4",
time_updated="Jan 01, 2011 at 01:01 UTC"
)
def test_tags_view(self):
"""
Scenario: User can view all notes by associated tags.
Given I have a course with 5 notes and I am viewing the Notes page
When I switch to the "Tags" view
Then I see 4 tag groups
And I see correct content in the notes and groups
"""
self._add_default_notes()
self.notes_page.visit().switch_to_tab("tags")
notes = self.notes_page.notes
groups = self.notes_page.tag_groups
self.assertEqual(len(notes), 7)
self.assertEqual(len(groups), 4)
# Tag group "cool"
self.assertGroupContent(
groups[0],
title=u"cool (2)",
notes=[u"Third note", None]
)
self.assertNoteContent(
notes[0],
quote=u"Annotate this text",
text=u"Third note",
unit_name="Test Unit 1",
time_updated="Jan 01, 2013 at 01:01 UTC",
tags=["Cool", "TODO"]
)
self.assertNoteContent(
notes[1],
quote=u"Annotate this text",
unit_name="Test Unit 2",
time_updated="Jan 01, 2012 at 01:01 UTC",
tags=["Review", "cool"]
)
# Tag group "review"
self.assertGroupContent(
groups[1],
title=u"review (2)",
notes=[u"Fourth note", None]
)
self.assertNoteContent(
notes[2],
text=u"Fourth note",
unit_name="Test Unit 3",
time_updated="Jan 01, 2014 at 01:01 UTC",
tags=["review"]
)
self.assertNoteContent(
notes[3],
quote=u"Annotate this text",
unit_name="Test Unit 2",
time_updated="Jan 01, 2012 at 01:01 UTC",
tags=["Review", "cool"]
)
# Tag group "todo"
self.assertGroupContent(
groups[2],
title=u"todo (1)",
notes=["Third note"]
)
self.assertNoteContent(
notes[4],
quote=u"Annotate this text",
text=u"Third note",
unit_name="Test Unit 1",
time_updated="Jan 01, 2013 at 01:01 UTC",
tags=["Cool", "TODO"]
)
# Notes with no tags
self.assertGroupContent(
groups[3],
title=u"[no tags] (2)",
notes=["Fifth note", "First note"]
)
self.assertNoteContent(
notes[5],
quote=u"Annotate this text",
text=u"Fifth note",
unit_name="Test Unit 1",
time_updated="Jan 01, 2015 at 01:01 UTC"
)
self.assertNoteContent(
notes[6],
quote=u"Annotate this text",
text=u"First note",
unit_name="Test Unit 4",
time_updated="Jan 01, 2011 at 01:01 UTC"
)
def test_easy_access_from_notes_page(self):
"""
Scenario: Ensure that the link to the Unit works correctly.
Given I have a course with 5 notes
When I open Notes page
And I click on the first unit link
Then I see correct text on the unit page
When go back to the Notes page
And I switch to "Location in Course" view
And I click on the second unit link
Then I see correct text on the unit page
When go back to the Notes page
And I run the search with "Fifth" query
And I click on the first unit link
Then I see correct text on the unit page
"""
def assert_page(note):
quote = note.quote
note.go_to_unit()
self.courseware_page.wait_for_page()
self.assertIn(quote, self.courseware_page.xblock_component_html_content())
self._add_default_notes()
self.notes_page.visit()
note = self.notes_page.notes[0]
assert_page(note)
self.notes_page.visit().switch_to_tab("structure")
note = self.notes_page.notes[1]
assert_page(note)
self.notes_page.visit().search("Fifth")
note = self.notes_page.notes[0]
assert_page(note)
def test_search_behaves_correctly(self):
"""
Scenario: Searching behaves correctly.
Given I have a course with 5 notes
When I open Notes page
When I run the search with " " query
Then I see the following error message "Please enter a term in the search field."
And I do not see "Search Results" tab
When I run the search with "note" query
Then I see that error message disappears
And I see that "Search Results" tab appears with 4 notes found
"""
self._add_default_notes()
self.notes_page.visit()
# Run the search with whitespaces only
self.notes_page.search(" ")
# Displays error message
self.assertTrue(self.notes_page.is_error_visible)
self.assertEqual(self.notes_page.error_text, u"Please enter a term in the search field.")
# Search results tab does not appear
self.assertNotIn(u"Search Results", self.notes_page.tabs)
# Run the search with correct query
self.notes_page.search("note")
# Error message disappears
self.assertFalse(self.notes_page.is_error_visible)
self.assertIn(u"Search Results", self.notes_page.tabs)
notes = self.notes_page.notes
self.assertEqual(len(notes), 4)
self.assertNoteContent(
notes[0],
quote=u"Annotate this text",
text=u"Fifth note",
unit_name="Test Unit 1",
time_updated="Jan 01, 2015 at 01:01 UTC"
)
self.assertNoteContent(
notes[1],
text=u"Fourth note",
unit_name="Test Unit 3",
time_updated="Jan 01, 2014 at 01:01 UTC",
tags=["review"]
)
self.assertNoteContent(
notes[2],
quote="Annotate this text",
text=u"Third note",
unit_name="Test Unit 1",
time_updated="Jan 01, 2013 at 01:01 UTC",
tags=["Cool", "TODO"]
)
self.assertNoteContent(
notes[3],
quote=u"Annotate this text",
text=u"First note",
unit_name="Test Unit 4",
time_updated="Jan 01, 2011 at 01:01 UTC"
)
def test_scroll_to_tag_recent_activity(self):
"""
Scenario: Can scroll to a tag group from the Recent Activity view (default view)
Given I have a course with 5 notes and I open the Notes page
When I click on a tag associated with a note
Then the Tags view tab gets focus and I scroll to the section of notes associated with that tag
"""
self._add_default_notes(["apple", "banana", "kiwi", "pear", "pumpkin", "squash", "zucchini"])
self.notes_page.visit()
self._scroll_to_tag_and_verify("pear", 3)
def test_scroll_to_tag_course_structure(self):
"""
Scenario: Can scroll to a tag group from the Course Structure view
Given I have a course with 5 notes and I open the Notes page and select the Course Structure view
When I click on a tag associated with a note
Then the Tags view tab gets focus and I scroll to the section of notes associated with that tag
"""
self._add_default_notes(["apple", "banana", "kiwi", "pear", "pumpkin", "squash", "zucchini"])
self.notes_page.visit().switch_to_tab("structure")
self._scroll_to_tag_and_verify("squash", 5)
def test_scroll_to_tag_search(self):
"""
Scenario: Can scroll to a tag group from the Search Results view
Given I have a course with 5 notes and I open the Notes page and perform a search
Then the Search view tab opens and gets focus
And when I click on a tag associated with a note
Then the Tags view tab gets focus and I scroll to the section of notes associated with that tag
"""
self._add_default_notes(["apple", "banana", "kiwi", "pear", "pumpkin", "squash", "zucchini"])
self.notes_page.visit().search("note")
self._scroll_to_tag_and_verify("pumpkin", 4)
def test_scroll_to_tag_from_tag_view(self):
"""
Scenario: Can scroll to a tag group from the Tags view
Given I have a course with 5 notes and I open the Notes page and select the Tag view
When I click on a tag associated with a note
Then I scroll to the section of notes associated with that tag
"""
self._add_default_notes(["apple", "banana", "kiwi", "pear", "pumpkin", "squash", "zucchini"])
self.notes_page.visit().switch_to_tab("tags")
self._scroll_to_tag_and_verify("kiwi", 2)
def _scroll_to_tag_and_verify(self, tag_name, group_index):
""" Helper method for all scroll to tag tests """
self.notes_page.notes[1].go_to_tag(tag_name)
# Because all the notes (with tags) have the same tags, they will end up ordered alphabetically.
pear_group = self.notes_page.tag_groups[group_index]
self.assertEqual(tag_name + " (3)", pear_group.title)
self.assertTrue(pear_group.scrolled_to_top(group_index))
def test_tabs_behaves_correctly(self):
"""
Scenario: Tabs behaves correctly.
Given I have a course with 5 notes
When I open Notes page
Then I see only "Recent Activity", "Location in Course", and "Tags" tabs
When I run the search with "note" query
And I see that "Search Results" tab appears with 4 notes found
Then I switch to "Recent Activity" tab
And I see all 5 notes
Then I switch to "Location in Course" tab
And I see all 2 groups and 5 notes
When I switch back to "Search Results" tab
Then I can still see 4 notes found
When I close "Search Results" tab
Then I see that "Recent Activity" tab becomes active
And "Search Results" tab disappears
And I see all 5 notes
"""
self._add_default_notes()
self.notes_page.visit()
# We're on Recent Activity tab.
self.assertEqual(len(self.notes_page.tabs), 3)
self.assertEqual([u"Recent Activity", u"Location in Course", u"Tags"], self.notes_page.tabs)
self.notes_page.search("note")
# We're on Search Results tab
self.assertEqual(len(self.notes_page.tabs), 4)
self.assertIn(u"Search Results", self.notes_page.tabs)
self.assertEqual(len(self.notes_page.notes), 4)
# We can switch on Recent Activity tab and back.
self.notes_page.switch_to_tab("recent")
self.assertEqual(len(self.notes_page.notes), 5)
self.notes_page.switch_to_tab("structure")
self.assertEqual(len(self.notes_page.chapter_groups), 2)
self.assertEqual(len(self.notes_page.notes), 5)
self.notes_page.switch_to_tab("search")
self.assertEqual(len(self.notes_page.notes), 4)
# Can close search results page
self.notes_page.close_tab()
self.assertEqual(len(self.notes_page.tabs), 3)
self.assertNotIn(u"Search Results", self.notes_page.tabs)
self.assertEqual(len(self.notes_page.notes), 5)
def test_open_note_when_accessed_from_notes_page(self):
"""
Scenario: Ensure that the link to the Unit opens a note only once.
Given I have a course with 2 sequentials that contain respectively one note and two notes
When I open Notes page
And I click on the first unit link
Then I see the note opened on the unit page
When I switch to the second sequential
I do not see any note opened
When I switch back to first sequential
I do not see any note opened
"""
xblocks = self.course_fixture.get_nested_xblocks(category="html")
self._add_notes([
Note(
usage_id=xblocks[1].locator,
user=self.username,
course_id=self.course_fixture._course_key,
text="Third note",
quote="Annotate this text",
updated=datetime(2012, 1, 1, 1, 1, 1, 1).isoformat(),
ranges=[Range(startOffset=0, endOffset=19)],
),
Note(
usage_id=xblocks[2].locator,
user=self.username,
course_id=self.course_fixture._course_key,
text="Second note",
quote="Annotate this text",
updated=datetime(2013, 1, 1, 1, 1, 1, 1).isoformat(),
ranges=[Range(startOffset=0, endOffset=19)],
),
Note(
usage_id=xblocks[0].locator,
user=self.username,
course_id=self.course_fixture._course_key,
text="First note",
quote="Annotate this text",
updated=datetime(2014, 1, 1, 1, 1, 1, 1).isoformat(),
ranges=[Range(startOffset=0, endOffset=19)],
),
])
self.notes_page.visit()
item = self.notes_page.notes[0]
item.go_to_unit()
self.courseware_page.wait_for_page()
note = self.note_unit_page.notes[0]
self.assertTrue(note.is_visible)
note = self.note_unit_page.notes[1]
self.assertFalse(note.is_visible)
self.course_nav.go_to_sequential_position(2)
note = self.note_unit_page.notes[0]
self.assertFalse(note.is_visible)
self.course_nav.go_to_sequential_position(1)
note = self.note_unit_page.notes[0]
self.assertFalse(note.is_visible)
class EdxNotesToggleSingleNoteTest(EdxNotesTestMixin):
"""
Tests for toggling single annotation.
"""
def setUp(self):
super(EdxNotesToggleSingleNoteTest, self).setUp()
self._add_notes()
self.note_unit_page.visit()
def test_can_toggle_by_clicking_on_highlighted_text(self):
"""
Scenario: User can toggle a single note by clicking on highlighted text.
Given I have a course with components with notes
When I click on highlighted text
And I move mouse out of the note
Then I see that the note is still shown
When I click outside the note
Then I see the the note is closed
"""
note = self.note_unit_page.notes[0]
note.click_on_highlight()
self.note_unit_page.move_mouse_to("body")
self.assertTrue(note.is_visible)
self.note_unit_page.click("body")
self.assertFalse(note.is_visible)
def test_can_toggle_by_clicking_on_the_note(self):
"""
Scenario: User can toggle a single note by clicking on the note.
Given I have a course with components with notes
When I click on the note
And I move mouse out of the note
Then I see that the note is still shown
When I click outside the note
Then I see the the note is closed
"""
note = self.note_unit_page.notes[0]
note.show().click_on_viewer()
self.note_unit_page.move_mouse_to("body")
self.assertTrue(note.is_visible)
self.note_unit_page.click("body")
self.assertFalse(note.is_visible)
def test_interaction_between_notes(self):
"""
Scenario: Interactions between notes works well.
Given I have a course with components with notes
When I click on highlighted text in the first component
And I move mouse out of the note
Then I see that the note is still shown
When I click on highlighted text in the second component
Then I see that the new note is shown
"""
note_1 = self.note_unit_page.notes[0]
note_2 = self.note_unit_page.notes[1]
note_1.click_on_highlight()
self.note_unit_page.move_mouse_to("body")
self.assertTrue(note_1.is_visible)
note_2.click_on_highlight()
self.assertFalse(note_1.is_visible)
self.assertTrue(note_2.is_visible)
class EdxNotesToggleNotesTest(EdxNotesTestMixin):
"""
Tests for toggling visibility of all notes.
"""
def setUp(self):
super(EdxNotesToggleNotesTest, self).setUp()
self._add_notes()
self.note_unit_page.visit()
def test_can_disable_all_notes(self):
"""
Scenario: User can disable all notes.
Given I have a course with components with notes
And I open the unit with annotatable components
When I click on "Show notes" checkbox
Then I do not see any notes on the sequential position
When I change sequential position to "2"
Then I still do not see any notes on the sequential position
When I go to "Test Subsection 2" subsection
Then I do not see any notes on the subsection
"""
# Disable all notes
self.note_unit_page.toggle_visibility()
self.assertEqual(len(self.note_unit_page.notes), 0)
self.course_nav.go_to_sequential_position(2)
self.assertEqual(len(self.note_unit_page.notes), 0)
self.course_nav.go_to_section(u"Test Section 1", u"Test Subsection 2")
self.assertEqual(len(self.note_unit_page.notes), 0)
def test_can_reenable_all_notes(self):
"""
Scenario: User can toggle notes visibility.
Given I have a course with components with notes
And I open the unit with annotatable components
When I click on "Show notes" checkbox
Then I do not see any notes on the sequential position
When I click on "Show notes" checkbox again
Then I see that all notes appear
When I change sequential position to "2"
Then I still can see all notes on the sequential position
When I go to "Test Subsection 2" subsection
Then I can see all notes on the subsection
"""
# Disable notes
self.note_unit_page.toggle_visibility()
self.assertEqual(len(self.note_unit_page.notes), 0)
# Enable notes to make sure that I can enable notes without refreshing
# the page.
self.note_unit_page.toggle_visibility()
self.assertGreater(len(self.note_unit_page.notes), 0)
self.course_nav.go_to_sequential_position(2)
self.assertGreater(len(self.note_unit_page.notes), 0)
self.course_nav.go_to_section(u"Test Section 1", u"Test Subsection 2")
self.assertGreater(len(self.note_unit_page.notes), 0)
|
antonve/s4-project-mooc
|
common/test/acceptance/tests/lms/test_lms_edxnotes.py
|
Python
|
agpl-3.0
| 40,727
|
[
"VisIt"
] |
c75a6b5cce2e109b453418bb75fa36c1760a92927d4e61b50b5ceacccb7f0b5a
|
# Copyright 2016 CERN. This software is distributed under the
# terms of the GNU General Public Licence version 3 (GPL Version 3),
# copied verbatim in the file LICENCE.md.
# In applying this licence, CERN does not waive the privileges and immunities
# granted to it by virtue of its status as an Intergovernmental Organization or
# submit itself to any jurisdiction.
# Project website: http://blond.web.cern.ch/
'''
**Class to choose plots and customize plot layout**
:Authors: **Helga Timko**
'''
from builtins import object
import os
import matplotlib.pyplot as plt
import h5py as hp
from ..plots.plot_beams import *
from ..plots.plot_slices import *
from ..plots.plot_llrf import *
def fig_folder(dirname):
'''
Create folder where plots will be stored.
'''
# Try to create directory
try:
os.makedirs(dirname)
# Check whether already exists/creation failed
except OSError:
if os.path.exists(dirname):
pass
else:
raise
class Plot(object):
def __init__(self, Ring, RFStation, Beam, dt_plot,
dt_bckp, xmin, xmax, ymin, ymax, xunit = 's', sampling = 1,
separatrix_plot = False, histograms_plot = True,
Profile = None, h5file = None, output_frequency = 1,
PhaseLoop = None, LHCNoiseFB = None, format_options = None):
'''
Define what plots should be plotted during the simulation. Passing only
basic objects, only phase space plot will be produced. Passing optional
objects, plots related to those objects will be produced as well.
For plots at a certain turn: use 'dt_plot' to set the plotting frequency
in units of time steps.
For plots as a function of time: use 'dt_bckp' to set plotting frequency
in units of time steps.
'''
#: | *Import Ring*
self.general_params = Ring
#: | *Import RFStation*
self.rf_params = RFStation
#: | *Import actual time step RFStation*
self.tstep = RFStation.counter
#: | *Import Beam*
self.beam = Beam
#: | *Plotting frequency in units of time steps*
self.dt_plot = dt_plot
self.dt_bckp = dt_bckp
#: | *Plot limit (where applicable) minimum on x-axis [xunit]*
self.xmin = xmin
#: | *Plot limit (where applicable) maximum on x-axis [xunit]*
self.xmax = xmax
#: | *Choice of x-axis unit (where applicable) 's' or 'rad'*
self.xunit = xunit
#: | *Plot limit (where applicable) minimum on y-axis [eV]*
self.ymin = ymin
#: | *Plot limit (where applicable) maximum on y-axis [eV]*
self.ymax = ymax
#: | *Sampling of large arrays (where applicable)*
self.sampling = sampling
#: | *Separatrix in phase space plot 'True' or 'False'*
self.separatix = separatrix_plot
#: | *Histogram in phase space plot 'True' or 'False'*
self.histogram = histograms_plot
#: | *Optional import of Profile*
self.profile = Profile
#: | *Optional import of Monitor file*
self.h5file = h5file
#: | *Optional sampling of monitored quantities*
self.dt_mon = output_frequency
#: | *Optional import of PhaseLoop*
self.PL = PhaseLoop
#: | *Optional import of LHCNoiseFB*
self.noiseFB = LHCNoiseFB
# Set plotting format
self.set_format(format_options)
# Track at initialisation
self.track()
def set_format(self, format_options):
'''
Initialize plot folder and custom plot formatting. For more options, see
http://matplotlib.org/1.3.1/users/customizing.html
'''
if format_options == None:
format_options = {'dummy': 0}
if 'dirname' not in format_options:
self.dirname = 'fig'
else:
self.dirname = format_options['dirname']
if 'linewidth' not in format_options:
self.lwidth = 2
else:
self.lwidth = format_options['linewidth']
if 'linestyle' not in format_options:
self.lstyle = '-'
else:
self.lstyle = format_options['linestyle']
if 'markersize' not in format_options:
self.msize = 6
else:
self.msize = format_options['markersize']
if 'alpha' not in format_options:
self.alpha = 0.05
else:
self.alpha = format_options['alpha']
if 'labelsize' not in format_options:
self.lsize = 18
else:
self.lsize = format_options['labelsize']
if 'fontfamily' not in format_options:
self.ffamily = 'sans-serif'
else:
self.ffamily = format_options['fontfamily']
if 'fontweight' not in format_options:
self.fweight = 'normal'
else:
self.fweight = format_options['fontweight']
if 'dpi' not in format_options:
self.dpi = 100
else:
self.dpi = format_options['dpi']
# Directory where longitudinal_plots will be stored
fig_folder(self.dirname)
# Ticksize
self.tsize = self.lsize - 2
# Set size of x- and y-grid numbers
plt.rc('xtick', labelsize=self.tsize)
plt.rc('ytick', labelsize=self.tsize)
# Set x- and y-grid labelsize and weight
plt.rc('axes', labelsize=self.lsize)
plt.rc('axes', labelweight=self.fweight)
# Set linewidth for continuous, markersize for discrete plotting
plt.rc('lines', linewidth=self.lwidth, markersize=self.msize)
# Set figure resolution, font
plt.rc('figure', dpi=self.dpi)
plt.rc('savefig', dpi=self.dpi)
plt.rc('font', family=self.ffamily)
def track(self):
'''
Plot in certain time steps and depending on imported objects
'''
# Snapshot-type plots
if (self.tstep[0] % self.dt_plot) == 0:
plot_long_phase_space(self.general_params, self.rf_params,
self.beam, self.xmin, self.xmax, self.ymin,
self.ymax, self.xunit,
sampling = self.sampling,
separatrix_plot = self.separatix,
histograms_plot = self.histogram,
dirname = self.dirname, alpha = self.alpha)
if self.profile:
plot_beam_profile(self.profile, self.tstep[0],
style = self.lstyle, dirname = self.dirname)
self.profile.beam_spectrum_freq_generation(self.profile.n_slices)
self.profile.beam_spectrum_generation(self.profile.n_slices)
plot_beam_spectrum(self.profile, self.tstep[0],
style = self.lstyle, dirname = self.dirname)
# Plots as a function of time
if (self.tstep[0] % self.dt_bckp) == 0 and self.h5file:
h5data = hp.File(self.h5file + '.h5', 'r')
plot_bunch_length_evol(self.rf_params, h5data,
output_freq = self.dt_mon,
dirname = self.dirname)
if self.profile and self.profile.fit_option == 'gaussian':
plot_bunch_length_evol_gaussian(self.rf_params, self.profile,
h5data,
output_freq = self.dt_mon,
dirname = self.dirname)
plot_position_evol(self.rf_params, h5data,
output_freq = self.dt_mon,
style = self.lstyle, dirname = self.dirname)
plot_energy_evol(self.rf_params, h5data, output_freq = self.dt_mon,
style = self.lstyle, dirname = self.dirname)
plot_COM_motion(self.general_params, self.rf_params, h5data,
output_freq = self.dt_mon, dirname = self.dirname)
plot_transmitted_particles(self.rf_params, h5data,
output_freq = self.dt_mon,
style = self.lstyle,
dirname = self.dirname)
if self.PL:
plot_PL_RF_freq(self.rf_params, h5data,
output_freq = self.dt_mon,
dirname = self.dirname)
plot_PL_RF_phase(self.rf_params, h5data,
output_freq = self.dt_mon,
dirname = self.dirname)
plot_PL_bunch_phase(self.rf_params, h5data,
output_freq = self.dt_mon,
dirname = self.dirname)
plot_PL_phase_corr(self.rf_params, h5data,
output_freq = self.dt_mon,
dirname = self.dirname)
plot_PL_freq_corr(self.rf_params, h5data,
output_freq = self.dt_mon,
dirname = self.dirname)
plot_RF_phase_error(self.rf_params, h5data,
output_freq = self.dt_mon,
dirname = self.dirname)
plot_RL_radial_error(self.rf_params, h5data,
output_freq = self.dt_mon,
dirname = self.dirname)
if self.noiseFB:
plot_LHCNoiseFB(self.rf_params, self.noiseFB, h5data,
output_freq = self.dt_mon,
dirname = self.dirname)
plot_LHCNoiseFB_FWHM(self.rf_params, self.noiseFB, h5data,
output_freq = self.dt_mon,
dirname = self.dirname)
if self.noiseFB.bl_meas_bbb != None:
plot_LHCNoiseFB_FWHM_bbb(self.rf_params, self.noiseFB,
h5data, output_freq = self.dt_mon,
dirname = self.dirname)
h5data.close()
def reset_frame(self, xmin, xmax, ymin, ymax):
self.xmin = xmin
self.xmax = xmax
self.ymin = ymin
self.ymax = ymax
|
dquartul/BLonD
|
blond/plots/plot.py
|
Python
|
gpl-3.0
| 11,025
|
[
"Gaussian"
] |
400fc717ac5d793d46be28d96ebb3d57b6618bce5d8b6a9a65d9ae6eca506d93
|
# Copyright (C) 2002, Thomas Hamelryck (thamelry@binf.ku.dk)
# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
# as part of this package.
"""mmCIF parsers"""
from __future__ import print_function
from string import ascii_letters
import numpy
import warnings
from Bio.File import as_handle
from Bio._py3k import range
from Bio.PDB.MMCIF2Dict import MMCIF2Dict
from Bio.PDB.StructureBuilder import StructureBuilder
from Bio.PDB.PDBExceptions import PDBConstructionException
from Bio.PDB.PDBExceptions import PDBConstructionWarning
class MMCIFParser(object):
"""Parse a mmCIF file and return a Structure object."""
def __init__(self, structure_builder=None, QUIET=False):
"""Create a PDBParser object.
The mmCIF parser calls a number of standard methods in an aggregated
StructureBuilder object. Normally this object is instanciated by the
MMCIParser object itself, but if the user provides his/her own
StructureBuilder object, the latter is used instead.
Arguments:
- structure_builder - an optional user implemented StructureBuilder class.
- QUIET - Evaluated as a Boolean. If true, warnings issued in constructing
the SMCRA data will be suppressed. If false (DEFAULT), they will be shown.
These warnings might be indicative of problems in the mmCIF file!
"""
if structure_builder is not None:
self._structure_builder = structure_builder
else:
self._structure_builder = StructureBuilder()
# self.header = None
# self.trailer = None
self.line_counter = 0
self.build_structure = None
self.QUIET = bool(QUIET)
# Public methods
def get_structure(self, structure_id, filename):
"""Return the structure.
Arguments:
- structure_id - string, the id that will be used for the structure
- filename - name of the mmCIF file OR an open filehandle
"""
with warnings.catch_warnings():
if self.QUIET:
warnings.filterwarnings("ignore", category=PDBConstructionWarning)
self._mmcif_dict = MMCIF2Dict(filename)
self._build_structure(structure_id)
return self._structure_builder.get_structure()
# Private methods
def _build_structure(self, structure_id):
mmcif_dict = self._mmcif_dict
atom_id_list = mmcif_dict["_atom_site.label_atom_id"]
residue_id_list = mmcif_dict["_atom_site.label_comp_id"]
try:
element_list = mmcif_dict["_atom_site.type_symbol"]
except KeyError:
element_list = None
seq_id_list = mmcif_dict["_atom_site.label_seq_id"]
chain_id_list = mmcif_dict["_atom_site.auth_asym_id"]
x_list = [float(x) for x in mmcif_dict["_atom_site.Cartn_x"]]
y_list = [float(x) for x in mmcif_dict["_atom_site.Cartn_y"]]
z_list = [float(x) for x in mmcif_dict["_atom_site.Cartn_z"]]
alt_list = mmcif_dict["_atom_site.label_alt_id"]
icode_list = mmcif_dict["_atom_site.pdbx_PDB_ins_code"]
b_factor_list = mmcif_dict["_atom_site.B_iso_or_equiv"]
occupancy_list = mmcif_dict["_atom_site.occupancy"]
fieldname_list = mmcif_dict["_atom_site.group_PDB"]
try:
serial_list = [int(n) for n in mmcif_dict["_atom_site.pdbx_PDB_model_num"]]
except KeyError:
# No model number column
serial_list = None
except ValueError:
# Invalid model number (malformed file)
raise PDBConstructionException("Invalid model number")
try:
aniso_u11 = mmcif_dict["_atom_site.aniso_U[1][1]"]
aniso_u12 = mmcif_dict["_atom_site.aniso_U[1][2]"]
aniso_u13 = mmcif_dict["_atom_site.aniso_U[1][3]"]
aniso_u22 = mmcif_dict["_atom_site.aniso_U[2][2]"]
aniso_u23 = mmcif_dict["_atom_site.aniso_U[2][3]"]
aniso_u33 = mmcif_dict["_atom_site.aniso_U[3][3]"]
aniso_flag = 1
except KeyError:
# no anisotropic B factors
aniso_flag = 0
# if auth_seq_id is present, we use this.
# Otherwise label_seq_id is used.
if "_atom_site.auth_seq_id" in mmcif_dict:
seq_id_list = mmcif_dict["_atom_site.auth_seq_id"]
else:
seq_id_list = mmcif_dict["_atom_site.label_seq_id"]
# Now loop over atoms and build the structure
current_chain_id = None
current_residue_id = None
structure_builder = self._structure_builder
structure_builder.init_structure(structure_id)
structure_builder.init_seg(" ")
# Historically, Biopython PDB parser uses model_id to mean array index
# so serial_id means the Model ID specified in the file
current_model_id = -1
current_serial_id = -1
for i in range(0, len(atom_id_list)):
# set the line_counter for 'ATOM' lines only and not
# as a global line counter found in the PDBParser()
# this number should match the '_atom_site.id' index in the MMCIF
structure_builder.set_line_counter(i)
x = x_list[i]
y = y_list[i]
z = z_list[i]
resname = residue_id_list[i]
chainid = chain_id_list[i]
altloc = alt_list[i]
if altloc == ".":
altloc = " "
int_resseq = int(seq_id_list[i])
icode = icode_list[i]
if icode == "?":
icode = " "
name = atom_id_list[i]
# occupancy & B factor
try:
tempfactor = float(b_factor_list[i])
except ValueError:
raise PDBConstructionException("Invalid or missing B factor")
try:
occupancy = float(occupancy_list[i])
except ValueError:
raise PDBConstructionException("Invalid or missing occupancy")
fieldname = fieldname_list[i]
if fieldname == "HETATM":
hetatm_flag = "H"
else:
hetatm_flag = " "
resseq = (hetatm_flag, int_resseq, icode)
if serial_list is not None:
# model column exists; use it
serial_id = serial_list[i]
if current_serial_id != serial_id:
# if serial changes, update it and start new model
current_serial_id = serial_id
current_model_id += 1
structure_builder.init_model(current_model_id, current_serial_id)
current_chain_id = None
current_residue_id = None
else:
# no explicit model column; initialize single model
structure_builder.init_model(current_model_id)
if current_chain_id != chainid:
current_chain_id = chainid
structure_builder.init_chain(current_chain_id)
current_residue_id = None
if current_residue_id != resseq:
current_residue_id = resseq
structure_builder.init_residue(resname, hetatm_flag, int_resseq, icode)
coord = numpy.array((x, y, z), 'f')
element = element_list[i] if element_list else None
structure_builder.init_atom(name, coord, tempfactor, occupancy, altloc,
name, element=element)
if aniso_flag == 1:
u = (aniso_u11[i], aniso_u12[i], aniso_u13[i],
aniso_u22[i], aniso_u23[i], aniso_u33[i])
mapped_anisou = [float(x) for x in u]
anisou_array = numpy.array(mapped_anisou, 'f')
structure_builder.set_anisou(anisou_array)
# Now try to set the cell
try:
a = float(mmcif_dict["_cell.length_a"])
b = float(mmcif_dict["_cell.length_b"])
c = float(mmcif_dict["_cell.length_c"])
alpha = float(mmcif_dict["_cell.angle_alpha"])
beta = float(mmcif_dict["_cell.angle_beta"])
gamma = float(mmcif_dict["_cell.angle_gamma"])
cell = numpy.array((a, b, c, alpha, beta, gamma), 'f')
spacegroup = mmcif_dict["_symmetry.space_group_name_H-M"]
spacegroup = spacegroup[1:-1] # get rid of quotes!!
if spacegroup is None:
raise Exception
structure_builder.set_symmetry(spacegroup, cell)
except Exception:
pass # no cell found, so just ignore
class FastMMCIFParser(object):
"""Parse an MMCIF file and return a Structure object."""
def __init__(self, structure_builder=None, QUIET=False):
"""Create a FastMMCIFParser object.
The mmCIF parser calls a number of standard methods in an aggregated
StructureBuilder object. Normally this object is instanciated by the
parser object itself, but if the user provides his/her own
StructureBuilder object, the latter is used instead.
The main difference between this class and the regular MMCIFParser is
that only 'ATOM' and 'HETATM' lines are parsed here. Use if you are
interested only in coordinate information.
Arguments:
- structure_builder - an optional user implemented StructureBuilder class.
- QUIET - Evaluated as a Boolean. If true, warnings issued in constructing
the SMCRA data will be suppressed. If false (DEFAULT), they will be shown.
These warnings might be indicative of problems in the mmCIF file!
"""
if structure_builder is not None:
self._structure_builder = structure_builder
else:
self._structure_builder = StructureBuilder()
self.line_counter = 0
self.build_structure = None
self.QUIET = bool(QUIET)
# Public methods
def get_structure(self, structure_id, filename):
"""Return the structure.
Arguments:
- structure_id - string, the id that will be used for the structure
- filename - name of the mmCIF file OR an open filehandle
"""
with warnings.catch_warnings():
if self.QUIET:
warnings.filterwarnings("ignore", category=PDBConstructionWarning)
with as_handle(filename) as handle:
self._build_structure(structure_id, handle)
return self._structure_builder.get_structure()
# Private methods
def _build_structure(self, structure_id, filehandle):
# Read only _atom_site. and atom_site_anisotrop entries
read_atom, read_aniso = False, False
_fields, _records = [], []
_anisof, _anisors = [], []
for line in filehandle:
if line.startswith('_atom_site.'):
read_atom = True
_fields.append(line.strip())
elif line.startswith('_atom_site_anisotrop.'):
read_aniso = True
_anisof.append(line.strip())
elif read_atom and line.startswith('#'):
read_atom = False
elif read_aniso and line.startswith('#'):
read_aniso = False
elif read_atom:
_records.append(line.strip())
elif read_aniso:
_anisors.append(line.strip())
# Dumping the shlex module here since this particular
# category should be rather straightforward.
# Quite a performance boost..
_record_tbl = zip(*map(str.split, _records))
_anisob_tbl = zip(*map(str.split, _anisors))
mmcif_dict = dict(zip(_fields, _record_tbl))
mmcif_dict.update(dict(zip(_anisof, _anisob_tbl)))
# Build structure object
atom_id_list = mmcif_dict["_atom_site.label_atom_id"]
residue_id_list = mmcif_dict["_atom_site.label_comp_id"]
try:
element_list = mmcif_dict["_atom_site.type_symbol"]
except KeyError:
element_list = None
seq_id_list = mmcif_dict["_atom_site.label_seq_id"]
chain_id_list = mmcif_dict["_atom_site.auth_asym_id"]
x_list = [float(x) for x in mmcif_dict["_atom_site.Cartn_x"]]
y_list = [float(x) for x in mmcif_dict["_atom_site.Cartn_y"]]
z_list = [float(x) for x in mmcif_dict["_atom_site.Cartn_z"]]
alt_list = mmcif_dict["_atom_site.label_alt_id"]
icode_list = mmcif_dict["_atom_site.pdbx_PDB_ins_code"]
b_factor_list = mmcif_dict["_atom_site.B_iso_or_equiv"]
occupancy_list = mmcif_dict["_atom_site.occupancy"]
fieldname_list = mmcif_dict["_atom_site.group_PDB"]
try:
serial_list = [int(n) for n in mmcif_dict["_atom_site.pdbx_PDB_model_num"]]
except KeyError:
# No model number column
serial_list = None
except ValueError:
# Invalid model number (malformed file)
raise PDBConstructionException("Invalid model number")
try:
aniso_u11 = mmcif_dict["_atom_site.aniso_U[1][1]"]
aniso_u12 = mmcif_dict["_atom_site.aniso_U[1][2]"]
aniso_u13 = mmcif_dict["_atom_site.aniso_U[1][3]"]
aniso_u22 = mmcif_dict["_atom_site.aniso_U[2][2]"]
aniso_u23 = mmcif_dict["_atom_site.aniso_U[2][3]"]
aniso_u33 = mmcif_dict["_atom_site.aniso_U[3][3]"]
aniso_flag = 1
except KeyError:
# no anisotropic B factors
aniso_flag = 0
# if auth_seq_id is present, we use this.
# Otherwise label_seq_id is used.
if "_atom_site.auth_seq_id" in mmcif_dict:
seq_id_list = mmcif_dict["_atom_site.auth_seq_id"]
else:
seq_id_list = mmcif_dict["_atom_site.label_seq_id"]
# Now loop over atoms and build the structure
current_chain_id = None
current_residue_id = None
structure_builder = self._structure_builder
structure_builder.init_structure(structure_id)
structure_builder.init_seg(" ")
# Historically, Biopython PDB parser uses model_id to mean array index
# so serial_id means the Model ID specified in the file
current_model_id = -1
current_serial_id = -1
for i in range(0, len(atom_id_list)):
# set the line_counter for 'ATOM' lines only and not
# as a global line counter found in the PDBParser()
# this number should match the '_atom_site.id' index in the MMCIF
structure_builder.set_line_counter(i)
x = x_list[i]
y = y_list[i]
z = z_list[i]
resname = residue_id_list[i]
chainid = chain_id_list[i]
altloc = alt_list[i]
if altloc == ".":
altloc = " "
int_resseq = int(seq_id_list[i])
icode = icode_list[i]
if icode == "?":
icode = " "
name = atom_id_list[i].strip('"') # Remove occasional " from quoted atom names (e.g. xNA)
# occupancy & B factor
try:
tempfactor = float(b_factor_list[i])
except ValueError:
raise PDBConstructionException("Invalid or missing B factor")
try:
occupancy = float(occupancy_list[i])
except ValueError:
raise PDBConstructionException("Invalid or missing occupancy")
fieldname = fieldname_list[i]
if fieldname == "HETATM":
hetatm_flag = "H"
else:
hetatm_flag = " "
resseq = (hetatm_flag, int_resseq, icode)
if serial_list is not None:
# model column exists; use it
serial_id = serial_list[i]
if current_serial_id != serial_id:
# if serial changes, update it and start new model
current_serial_id = serial_id
current_model_id += 1
structure_builder.init_model(current_model_id, current_serial_id)
current_chain_id = None
current_residue_id = None
else:
# no explicit model column; initialize single model
structure_builder.init_model(current_model_id)
if current_chain_id != chainid:
current_chain_id = chainid
structure_builder.init_chain(current_chain_id)
current_residue_id = None
if current_residue_id != resseq:
current_residue_id = resseq
structure_builder.init_residue(resname, hetatm_flag, int_resseq, icode)
coord = numpy.array((x, y, z), 'f')
element = element_list[i] if element_list else None
structure_builder.init_atom(name, coord, tempfactor, occupancy, altloc,
name, element=element)
if aniso_flag == 1:
u = (aniso_u11[i], aniso_u12[i], aniso_u13[i],
aniso_u22[i], aniso_u23[i], aniso_u33[i])
mapped_anisou = [float(x) for x in u]
anisou_array = numpy.array(mapped_anisou, 'f')
structure_builder.set_anisou(anisou_array)
if __name__ == "__main__":
import sys
if len(sys.argv) != 2:
print("Usage: python MMCIFparser.py filename")
raise SystemExit
filename = sys.argv[1]
p = MMCIFParser()
structure = p.get_structure("test", filename)
for model in structure.get_list():
print(model)
for chain in model.get_list():
print(chain)
print("Found %d residues." % len(chain.get_list()))
|
zjuchenyuan/BioWeb
|
Lib/Bio/PDB/MMCIFParser.py
|
Python
|
mit
| 17,902
|
[
"Biopython"
] |
a76b937f6c862ce1456ba1eeca1b358ce983829b85b3c711c5036291385e6ea1
|
"""
API operations on annotations.
"""
import logging
from galaxy import web
from galaxy.web.base.controller import BaseAPIController, UsesHistoryMixin, UsesLibraryMixinItems, UsesHistoryDatasetAssociationMixin, UsesStoredWorkflowMixin, UsesExtendedMetadataMixin, HTTPNotImplemented
log = logging.getLogger( __name__ )
class BaseExtendedMetadataController( BaseAPIController, UsesExtendedMetadataMixin, UsesHistoryMixin, UsesLibraryMixinItems, UsesHistoryDatasetAssociationMixin, UsesStoredWorkflowMixin ):
@web.expose_api
def index( self, trans, **kwd ):
idnum = kwd[self.exmeta_item_id]
item = self._get_item_from_id(trans, idnum, check_writable=False)
if item is not None:
ex_meta = self.get_item_extended_metadata_obj( trans, item )
if ex_meta is not None:
return ex_meta.data
@web.expose_api
def create( self, trans, payload, **kwd ):
idnum = kwd[self.exmeta_item_id]
item = self._get_item_from_id(trans, idnum, check_writable=True)
if item is not None:
ex_obj = self.get_item_extended_metadata_obj(trans, item)
if ex_obj is not None:
self.unset_item_extended_metadata_obj(trans, item)
self.delete_extended_metadata(trans, ex_obj)
ex_obj = self.create_extended_metadata(trans, payload)
self.set_item_extended_metadata_obj(trans, item, ex_obj)
@web.expose_api
def delete( self, trans, **kwd ):
idnum = kwd[self.tagged_item_id]
item = self._get_item_from_id(trans, idnum, check_writable=True)
if item is not None:
ex_obj = self.get_item_extended_metadata_obj(trans, item)
if ex_obj is not None:
self.unset_item_extended_metadata_obj(trans, item)
self.delete_extended_metadata(trans, ex_obj)
@web.expose_api
def undelete( self, trans, **kwd ):
raise HTTPNotImplemented()
class LibraryDatasetExtendMetadataController(BaseExtendedMetadataController):
controller_name = "library_dataset_extended_metadata"
exmeta_item_id = "library_content_id"
def _get_item_from_id(self, trans, idstr, check_writable=True):
if check_writable:
item = self.get_library_dataset_dataset_association( trans, idstr)
if trans.app.security_agent.can_modify_library_item( trans.get_current_user_roles(), item ):
return item
else:
item = self.get_library_dataset_dataset_association( trans, idstr)
if trans.app.security_agent.can_access_library_item( trans.get_current_user_roles(), item, trans.user ):
return item
return None
class HistoryDatasetExtendMetadataController(BaseExtendedMetadataController):
controller_name = "history_dataset_extended_metadata"
exmeta_item_id = "history_content_id"
def _get_item_from_id(self, trans, idstr, check_writable=True):
if check_writable:
return self.get_dataset( trans, idstr, check_ownership=True, check_accessible=True, check_state=True )
else:
return self.get_dataset( trans, idstr, check_ownership=False, check_accessible=True, check_state=True )
|
mikel-egana-aranguren/SADI-Galaxy-Docker
|
galaxy-dist/lib/galaxy/webapps/galaxy/api/extended_metadata.py
|
Python
|
gpl-3.0
| 3,233
|
[
"Galaxy"
] |
c373dd034fe55b9b79c0e36a80133279cb02e4283d3c3dcdd34b49562a7bc3bf
|
"""
Created on Mon Dec 02 2019
@author: Pedram Tavadze
"""
#import pychemia
import xml.etree.ElementTree as ET
from numpy import array
def text_to_bool(text):
"""boolians in vaspxml are stores as T or F in str format, this function coverts them to python boolians """
text = text.strip(' ')
if text == 'T' or text == '.True.' or text == '.TRUE.':
return True
else:
return False
def conv(ele, _type):
"""This function converts the xml text to the type specified in the attrib of xml tree """
if _type == 'string':
return ele.strip()
elif _type == 'int':
return int(ele)
elif _type == 'logical':
return text_to_bool(ele)
elif _type == 'float':
if "*" in ele:
return None
return float(ele)
def get_varray(xml_tree):
"""Returns an array for each varray tag in vaspxml """
ret = []
for ielement in xml_tree:
ret.append([conv(x, 'float') for x in ielement.text.split()])
return ret
def get_params(xml_tree, dest):
"""dest should be a dictionary
This function is recurcive #check spelling"""
for ielement in xml_tree:
if ielement.tag == 'separator':
dest[ielement.attrib['name'].strip()] = {}
dest[ielement.attrib['name'].strip()] = get_params(ielement, dest[ielement.attrib['name']])
else :
if 'type' in ielement.attrib:
_type = ielement.attrib['type']
else :
_type = 'float'
if ielement.text is None:
dest[ielement.attrib['name'].strip()] = None
elif len(ielement.text.split()) > 1:
dest[ielement.attrib['name'].strip()] = [conv(x, _type) for x in ielement.text.split()]
else :
dest[ielement.attrib['name'].strip()] = conv(ielement.text, _type)
return dest
def get_structure(xml_tree):
"""Returns a dictionary of the structure """
ret = {}
for ielement in xml_tree:
if ielement.tag == 'crystal':
for isub in ielement:
if isub.attrib['name'] == 'basis':
ret['cell'] = get_varray(isub)
elif isub.attrib['name'] == 'volume':
ret['volume'] = float(isub.text)
elif isub.attrib['name'] == 'rec_basis':
ret['rec_cell'] = get_varray(isub)
elif ielement.tag == 'varray':
if ielement.attrib['name'] == 'positions':
ret['reduced'] = get_varray(ielement)
return ret
def get_scstep(xml_tree):
"""This function extracts the self-consistent step information """
scstep = {'time': {}, 'energy': {}}
for isub in xml_tree:
if isub.tag=='time':
scstep['time'][isub.attrib['name']] = [conv(x, 'float') for x in isub.text.split()]
elif isub.tag == 'energy':
for ienergy in isub:
scstep['energy'][ienergy.attrib['name']] = conv(ienergy.text, 'float')
return scstep
def get_set(xml_tree, ret):
""" This function will extract any element taged set recurcively"""
if xml_tree[0].tag == 'r':
ret[xml_tree.attrib['comment']] = get_varray(xml_tree)
return ret
else:
ret[xml_tree.attrib['comment']] = {}
for ielement in xml_tree:
if ielement.tag == 'set' :
ret[xml_tree.attrib['comment']][ielement.attrib['comment']] = {}
ret[xml_tree.attrib['comment']][ielement.attrib['comment']] = get_set(ielement, ret[xml_tree.attrib['comment']][ielement.attrib['comment']])
return ret
def get_general(xml_tree, ret):
""" This function will parse any element in calculatio other than the structures, scsteps"""
if 'dimension' in [x.tag for x in xml_tree]:
ret['info'] = []
ret['data'] = {}
for ielement in xml_tree:
if ielement.tag == 'field':
ret['info'].append(ielement.text.strip(' '))
elif ielement.tag == 'set':
for iset in ielement:
ret['data'] = get_set(iset, ret['data'])
return ret
else :
for ielement in xml_tree:
if ielement.tag == 'i':
if 'name' in ielement.attrib:
if ielement.attrib['name'] == 'efermi':
ret['efermi'] = float(ielement.text)
continue
ret[ielement.tag] = {}
ret[ielement.tag] = get_general(ielement, ret[ielement.tag])
return ret
def parse_vasprun(vasprun):
tree = ET.parse(vasprun)
root = tree.getroot()
calculation = []
structures = []
forces = []
stresses = []
orbital_magnetization = {}
run_info = {}
incar = {}
general = {}
kpoints_info = {}
vasp_params = {}
kpoints_list = []
k_weights = []
atom_info = {}
for ichild in root:
if ichild.tag == 'generator':
for ielement in ichild:
run_info[ielement.attrib['name']] = ielement.text
elif ichild.tag == 'incar':
incar = get_params(ichild, incar)
## Skipping 1st structure which is primitive cell
elif ichild.tag == 'kpoints':
for ielement in ichild:
if ielement.items()[0][0] == 'param':
kpoints_info['mode'] = ielement.items()[0][1]
if kpoints_info['mode'] == 'listgenerated':
kpoints_info['kpoint_vertices'] = []
for isub in ielement:
if isub.attrib == 'divisions':
kpoints_info['ndivision'] = int(isub.text)
else:
if len(isub.text.split()) !=3:
continue
kpoints_info['kpoint_vertices'].append([float(x) for x in isub.text.split()])
else :
for isub in ielement:
if isub.attrib['name'] == 'divisions':
kpoints_info['kgrid'] = [int(x) for x in isub.text.split()]
elif isub.attrib['name'] == 'usershift':
kpoints_info['user_shift'] = [float(x) for x in isub.text.split()]
elif isub.attrib['name'] == 'genvec1':
kpoints_info['genvec1'] = [float(x) for x in isub.text.split()]
elif isub.attrib['name'] == 'genvec2':
kpoints_info['genvec2'] = [float(x) for x in isub.text.split()]
elif isub.attrib['name'] == 'genvec3':
kpoints_info['genvec3'] = [float(x) for x in isub.text.split()]
elif isub.attrib['name'] == 'shift':
kpoints_info['shift'] = [float(x) for x in isub.text.split()]
elif ielement.items()[0][1] == 'kpointlist':
for ik in ielement:
kpoints_list.append([float(x) for x in ik.text.split()])
kpoints_list = array(kpoints_list)
elif ielement.items()[0][1] == 'weights':
for ik in ielement:
k_weights.append(float(ik.text))
k_weights = array(k_weights)
## Vasp Parameters
elif ichild.tag == 'parameters':
vasp_params = get_params(ichild,vasp_params)
## Atom info
elif ichild.tag == 'atominfo':
for ielement in ichild:
if ielement.tag == 'atoms':
atom_info['natom'] = int(ielement.text)
elif ielement.tag == 'types':
atom_info['nspecies'] = int(ielement.text)
elif ielement.tag == 'array':
if ielement.attrib['name'] == 'atoms':
for isub in ielement:
if isub.tag == 'set':
atom_info['symbols'] = []
for isym in isub :
atom_info['symbols'].append(isym[0].text)
elif ielement.attrib['name'] == 'atomtypes':
atom_info['atom_types'] = {}
for isub in ielement:
if isub.tag == 'set':
for iatom in isub:
atom_info['atom_types'][iatom[1].text] = {}
atom_info['atom_types'][iatom[1].text]['natom_per_specie'] = int(iatom[0].text)
atom_info['atom_types'][iatom[1].text]['mass'] = float(iatom[2].text)
atom_info['atom_types'][iatom[1].text]['valance'] = float(iatom[3].text)
atom_info['atom_types'][iatom[1].text]['pseudopotential'] = iatom[4].text.strip()
elif ichild.tag == 'structure':
if ichild.attrib['name'] == 'initialpos':
initial_pos = get_structure(ichild)
elif ichild.attrib['name'] == 'finalpos':
final_pos = get_structure(ichild)
elif ichild.tag == 'calculation':
for ielement in ichild:
if ielement.tag == 'scstep':
calculation.append(get_scstep(ielement))
elif ielement.tag == 'structure':
structures.append(get_structure(ielement))
elif ielement.tag == 'varray':
if ielement.attrib['name'] == 'forces':
forces.append(get_varray(ielement))
elif ielement.attrib['name'] == 'stress':
stresses.append(get_varray(ielement))
# elif ielement.tag == 'eigenvalues':
# for isub in ielement[0] :
# if isub.tag == 'set':
# for iset in isub :
# eigen_values[iset.attrib['comment']] = {}
# for ikpt in iset :
# eigen_values[iset.attrib['comment']][ikpt.attrib['comment']] = get_varray(ikpt)
elif ielement.tag == 'separator':
if ielement.attrib['name'] == "orbital magnetization":
for isub in ielement:
orbital_magnetization[isub.attrib['name']] = [float(x) for x in isub.text.split()]
# elif ielement.tag == 'dos':
# for isub in ielement :
# if 'name' in isub.attrib:
# if isub.attrib['name'] == 'efermi' :
# dos['efermi'] = float(isub.text)
# else :
# dos[isub.tag] = {}
# dos[isub.tag]['info'] = []
# for iset in isub[0] :
# if iset.tag == 'set' :
# for isub_set in iset:
# dos[isub.tag] = get_set(isub_set,dos[isub.tag])
# elif iset.tag == 'field' :
# dos[isub.tag]['info'].append(iset.text.strip(' '))
else:
general[ielement.tag] = {}
general[ielement.tag] = get_general(ielement, general[ielement.tag])
# NEED TO ADD ORBITAL MAGNETIZATION
return {'calculation': calculation, 'structures': structures, 'forces': forces, 'run_info': run_info,
'incar': incar, 'general': general, 'kpoints_info': kpoints_info, 'vasp_params': vasp_params,
'kpoints': {'kpoints_list': kpoints_list, 'k_weights': k_weights}, 'atom_info': atom_info}
|
MaterialsDiscovery/PyChemia
|
pychemia/code/vasp/xml_output.py
|
Python
|
mit
| 12,302
|
[
"CRYSTAL",
"VASP"
] |
32bd72a3063cafe51d1fb40c1b6fbf453ae88d6a9aec434a566b7f88afaf5949
|
# -*- coding: utf-8 -*-
# vim: autoindent shiftwidth=4 expandtab textwidth=120 tabstop=4 softtabstop=4
###############################################################################
# OpenLP - Open Source Lyrics Projection #
# --------------------------------------------------------------------------- #
# Copyright (c) 2008-2013 Raoul Snyman #
# Portions copyright (c) 2008-2013 Tim Bentley, Gerald Britton, Jonathan #
# Corwin, Samuel Findlay, Michael Gorven, Scott Guerrieri, Matthias Hub, #
# Meinert Jordan, Armin Köhler, Erik Lundin, Edwin Lunando, Brian T. Meyer. #
# Joshua Miller, Stevan Pettit, Andreas Preikschat, Mattias Põldaru, #
# Christian Richter, Philip Ridout, Simon Scudder, Jeffrey Smith, #
# Maikel Stuivenberg, Martin Thompson, Jon Tibble, Dave Warnock, #
# Frode Woldsund, Martin Zibricky, Patrick Zimmermann #
# --------------------------------------------------------------------------- #
# This program is free software; you can redistribute it and/or modify it #
# under the terms of the GNU General Public License as published by the Free #
# Software Foundation; version 2 of the License. #
# #
# This program is distributed in the hope that it will be useful, but WITHOUT #
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or #
# FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for #
# more details. #
# #
# You should have received a copy of the GNU General Public License along #
# with this program; if not, write to the Free Software Foundation, Inc., 59 #
# Temple Place, Suite 330, Boston, MA 02111-1307 USA #
###############################################################################
hiddenimports = ['openlp.plugins.presentations.lib.impresscontroller',
'openlp.plugins.presentations.lib.powerpointcontroller',
'openlp.plugins.presentations.lib.pptviewcontroller']
|
marmyshev/item_title
|
resources/pyinstaller/hook-openlp.plugins.presentations.presentationplugin.py
|
Python
|
gpl-2.0
| 2,323
|
[
"Brian"
] |
b59c3b7b9e19365caa2e6d4c4329a526f86ab6b6a42e993095acf61fedb5a43e
|
# class generated by DeVIDE::createDeVIDEModuleFromVTKObject
from module_kits.vtk_kit.mixins import SimpleVTKClassModuleBase
import vtk
class vtkExtractSelectedLocations(SimpleVTKClassModuleBase):
def __init__(self, module_manager):
SimpleVTKClassModuleBase.__init__(
self, module_manager,
vtk.vtkExtractSelectedLocations(), 'Processing.',
('vtkDataSet', 'vtkSelection'), ('vtkDataSet',),
replaceDoc=True,
inputFunctions=None, outputFunctions=None)
|
nagyistoce/devide
|
modules/vtk_basic/vtkExtractSelectedLocations.py
|
Python
|
bsd-3-clause
| 522
|
[
"VTK"
] |
54f0acfe8f227c79306ef5270c6b40ddc205f0b86110a4e53b92641aa128edc8
|
r"""
The transient advection-diffusion equation with a given divergence-free
advection velocity.
Find :math:`u` such that:
.. math::
\int_{\Omega} s \pdiff{u}{t}
+ \int_{\Omega} s \nabla \cdot \left(\ul{v} u \right)
+ \int_{\Omega} D \nabla s \cdot \nabla u
= 0
\;, \quad \forall s \;.
View the results using::
python postproc.py square_tri2.*.vtk -b --wireframe
"""
from __future__ import absolute_import
from sfepy import data_dir
filename_mesh = data_dir + '/meshes/2d/square_tri2.mesh'
regions = {
'Omega' : 'all', # or 'cells of group 6'
'Gamma_Left' : ('vertices in (x < -0.99999)', 'facet'),
'Gamma_Right' : ('vertices in (x > 0.99999)', 'facet'),
}
fields = {
'concentration' : ('real', 1, 'Omega', 1),
}
variables = {
'u' : ('unknown field', 'concentration', 0, 1),
's' : ('test field', 'concentration', 'u'),
}
ebcs = {
'u1' : ('Gamma_Left', {'u.0' : 2.0}),
'u2' : ('Gamma_Right', {'u.0' : 0.0}),
}
# Units: D: 0.0001 m^2 / day, v: [0.1, 0] m / day -> time in days.
materials = {
'm' : ({'D' : 0.0001, 'v' : [[0.1], [0.0]]},),
}
integrals = {
'i' : 2,
}
equations = {
'advection-diffusion' :
"""
dw_dot.i.Omega(s, du/dt)
+ dw_advect_div_free.i.Omega(m.v, s, u)
+ dw_laplace.i.Omega(m.D, s, u)
= 0
"""
}
solvers = {
'ts' : ('ts.simple', {
't0' : 0.0,
't1' : 10.0,
'dt' : None,
'n_step' : 11, # Has precedence over dt.
'verbose' : 1,
}),
'newton' : ('nls.newton', {
'i_max' : 1,
'eps_a' : 1e-10,
}),
'ls' : ('ls.scipy_direct', {}),
}
options = {
'ts' : 'ts',
'nls' : 'newton',
'ls' : 'ls',
'save_times' : 'all',
}
|
vlukes/sfepy
|
examples/diffusion/time_advection_diffusion.py
|
Python
|
bsd-3-clause
| 1,739
|
[
"VTK"
] |
a8d82a42cf1404fe43963ab216337898f3784d9445ef3e2973eb58672dc0a722
|
# Copyright 2012-2014 Brian May
#
# This file is part of python-tldap.
#
# python-tldap is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# python-tldap is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with python-tldap If not, see <http://www.gnu.org/licenses/>.
from __future__ import absolute_import
import django.conf
from tldap import setup
from tldap.utils import DEFAULT_LDAP_ALIAS
# For backwards compatibility - Port any old database settings over to
# the new values.
if not hasattr(django.conf.settings, 'LDAP'):
django.conf.settings.LDAP = {}
# ok to use django settings
if not django.conf.settings.LDAP and hasattr(django.conf.settings, 'LDAP_URL'):
django.conf.settings.LDAP[DEFAULT_LDAP_ALIAS] = {
'ENGINE': 'tldap.backend.fake_transactions',
'URI': django.conf.settings.LDAP_URL,
'USER': django.conf.settings.LDAP_ADMIN_USER,
'PASSWORD': django.conf.settings.LDAP_ADMIN_PASSWORD,
'START_TLS': False,
'TLS_CA': None,
'LDAP_ACCOUNT_BASE': django.conf.settings.LDAP_USER_BASE,
'LDAP_GROUP_BASE': django.conf.settings.LDAP_GROUP_BASE,
}
if hasattr(django.conf.settings, 'LDAP_USE_TLS'):
django.conf.settings.LDAP[DEFAULT_LDAP_ALIAS]["START_TLS"] = (
django.conf.settings.LDAP_USE_TLS)
django.conf.settings.LDAP[DEFAULT_LDAP_ALIAS]["TLS_CA"] = (
django.conf.settings.LDAP_TLS_CA)
setup(django.conf.settings.LDAP)
|
brianmay/python-tldap-debian
|
tldap/django.py
|
Python
|
gpl-3.0
| 1,882
|
[
"Brian"
] |
40513e6f35b75487d223367a4d70cd78c86c83f32995375ad9abdfc5056d4ebe
|
# coding=utf-8
# Copyright 2022 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Lint as: python2, python3
"""Tests for research.biology.chemgraph.py.molecules."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl.testing import parameterized
from rdkit import Chem
import tensorflow.compat.v1 as tf
from mol_dqn.chemgraph.dqn.py import molecules
class MoleculesTest(parameterized.TestCase, tf.test.TestCase):
def setUp(self):
super(MoleculesTest, self).setUp()
self.atom_types = ['H', 'C', 'N', 'O', 'F']
def test_atom_valences(self):
self.assertEqual([1, 4, 3, 2, 1], molecules.atom_valences(self.atom_types))
def test_get_scaffold(self):
self.assertEqual('', molecules.get_scaffold(Chem.MolFromSmiles('CCO')))
self.assertEqual('c1ccccc1',
molecules.get_scaffold(Chem.MolFromSmiles('CC1=CC=CC=C1')))
# Note that the carbonyl is included in the scaffold...
self.assertEqual(
'O=C(c1ccccc1)c1ccccc1',
molecules.get_scaffold(
Chem.MolFromSmiles('C1=CC=C(C=C1C(C2=CC(=CC=C2)C(C)C)=O)C(C)C')))
# ...but the hydroxyl is not.
self.assertEqual(
'c1ccc(Cc2ccccc2)cc1',
molecules.get_scaffold(
Chem.MolFromSmiles('C1=CC=C(C=C1C(C2=CC(=CC=C2)C(C)C)O[H])C(C)C')))
@parameterized.parameters(
('c1ccccc1', 'c1ccccc1', True),
('c1ccccc1CC', 'c1ccccc1', True),
('C1=CC=C2C=CC=CC2=C1', 'c1ccccc1', True),
('C1=CC=CC=C1CCC2=CC=CC=C2', 'c1ccccc1', True),
('C1CCCCC1', 'c1ccccc1', False),
('C1CCCC1', 'c1ccccc1', False),
)
def test_contains_scaffold(self, smiles, scaffold, expected):
self.assertEqual(
expected,
molecules.contains_scaffold(Chem.MolFromSmiles(smiles), scaffold))
@parameterized.parameters(('C1CCC1', 4), ('C1CCCCC1', 6), ('C1CCCCCC1', 7),
('c2ccc1ccccc1c2', 6), ('C2CCC1CCCC1CC2', 7),
('CC1CC2CCCCCC3CC(C1)C23', 8))
def test_get_ring_size(self, mol, size):
mol = Chem.MolFromSmiles(mol)
self.assertEqual(molecules.get_largest_ring_size(mol), size)
@parameterized.parameters(
('C1CCC1', 0.5604),
('C1CCCCC1', 1.3406),
('C1CCCCCC1', 0.7307),
# Make sure they are consistent with reported values.
# https://github.com/wengong-jin/icml18-jtnn/blob/master/data/opt.test.logP-SA
('COc1cc2c(cc1OC)CC([NH3+])C2', -2.50504567445),
('OC[C@@H](Br)C(F)(F)Br', -2.45357941743),
('NC(=O)C1(N2CCCC2)CC[NH2+]CC1', -5.375513278))
def test_penalized_logp(self, mol, score):
mol = Chem.MolFromSmiles(mol)
self.assertAlmostEqual(molecules.penalized_logp(mol), score)
if __name__ == '__main__':
tf.test.main()
|
google-research/google-research
|
mol_dqn/chemgraph/dqn/py/molecules_test.py
|
Python
|
apache-2.0
| 3,310
|
[
"RDKit"
] |
21ad5963c7cebee1abd41a02fbd965051d797e0a2326c9622fdee61a9a935fdf
|
#!/usr/bin/env python
__author__ = 'Danelle Cline'
__copyright__ = '2016'
__license__ = 'GPL v3'
__contact__ = 'dcline at mbari.org'
__doc__ = '''
Utility class to convert netCDF4 to netCDFs in a format compatible with STOQs loading.
This basically captures the independently sampled variables captured in the LRAUV and
(that have their own time base) and interpolates them onto a common lat/lon/depth/time
trajectory.
@var __date__: Date of last svn commit
@undocumented: __doc__ parser
@status: production
@license: GPL
'''
import matplotlib as mpl
mpl.use('Agg') # Force matplotlib to not use any Xwindows backend
import matplotlib.pyplot as plt
import sys
import os
import errno
# Add grandparent dir to pythonpath so that we can see the CANON and toNetCDF modules
sys.path.insert(0, os.path.join(os.path.dirname(__file__), "../../") )
import pdb
import netCDF4
import numpy as np
import pandas as pd
import time
import datetime as dt
from time import mktime
from CANON.toNetCDF import BaseWriter
from netCDF4 import Dataset
import pydap.client
import numpy
import DAPloaders
import logging
import socket
import json
# Map common LRAUV variable names to CF standard names: http://cfconventions.org/standard-names.html
sn_lookup = {
'bin_mean_temperature': 'sea_water_temperature',
'bin_mean_salinity': 'sea_water_salinity',
'bin_mean_chlorophyll': 'mass_concentration_of_chlorophyll_in_sea_water',
}
class InterpolatorWriter(BaseWriter):
logger = logging.getLogger('lrauvNc4ToNetcdf')
fh = logging.StreamHandler()
f = logging.Formatter("%(levelname)s %(asctime)sZ %(filename)s %(funcName)s():%(lineno)d %(message)s")
fh.setFormatter(f)
logger.addHandler(fh)
logger.setLevel(logging.DEBUG)
df = []
all_sub_ts = {}
all_coord = {}
all_attrib = {}
def reset(self):
self.df = []
self.all_sub_ts = {}
self.all_coord = {}
self.all_attrib = {}
def write_netcdf(self, out_file, in_url):
# Check parent directory and create if needed
dirName = os.path.dirname(out_file)
try:
os.makedirs(dirName)
except OSError as e:
if e.errno != errno.EEXIST:
raise
# Create the NetCDF file
self.logger.debug("Creating netCDF file %s", out_file)
self.ncFile = Dataset(out_file, 'w')
# If specified on command line override the default generic title with what is specified
self.ncFile.title = 'LRAUV interpolated data'
# Combine any summary text specified on command line with the generic summary stating the original source file
self.ncFile.summary = 'Observational oceanographic data translated with modification from original data file %s' % in_url
# add in time dimensions first
ts_key = []
for key in list(self.all_sub_ts.keys()):
if key.find('time') != -1:
ts = self.all_sub_ts[key]
if not ts.empty:
self.logger.debug("Adding in record variable %s", key)
v = self.initRecordVariable(key)
v[:] = self.all_sub_ts[key].values
else:
ts_key.append(key)
# add in other remaining time series
for key in ts_key:
ts = self.all_sub_ts[key]
if not ts.empty:
try:
logging.debug("Adding in record variable %s", key)
v = self.initRecordVariable(key)
v[:] = self.all_sub_ts[key].values
except Exception as e:
self.logger.error(e)
continue
self.logger.debug("Adding in global metadata")
self.add_global_metadata()
self.ncFile.close()
# End write_netcdf()
def interpolate(self, data, times):
x = np.asarray(times,dtype=np.float64)
xp = np.asarray(data.index,dtype=np.float64)
fp = np.asarray(data)
ts = pd.Series(index=times)
# interpolate to get data onto spacing of datetimes in times variable
# this can be irregularly spaced
ts[:] = np.interp(x,xp,fp)
return ts
# End interpolate
def createSeriesPydap(self, name, tname):
v = self.df[name]
v_t = self.df[tname]
data = np.asarray(v_t)
data[data/1e10 < -1.] = 'NaN'
data[data/1e10 > 1.] ='NaN'
v_time_epoch = data
v_time = pd.to_datetime(v_time_epoch[:],unit='s')
v_time_series = pd.Series(v[:],index=v_time)
return v_time_series
# End createSeriesPydap
def createSeries(self, subgroup, name, tname):
v = subgroup[name]
v_t = subgroup[tname]
v_time_epoch = v_t
v_time = pd.to_datetime(v_time_epoch[:],unit='s')
v_time_series = pd.Series(v[:],index=v_time)
return v_time_series
# End createSeries
def initRecordVariable(self, key, units=None):
# Create record variable to store in nc file
v = self.all_sub_ts[key]
if key.find('time') != -1:
# convert time to epoch seconds
esec_list = v.index.values.astype(dt.datetime)/1E9
# trajectory dataset, time is the only netCDF dimension
self.ncFile.createDimension(key, len(esec_list))
rc = self.ncFile.createVariable(key, 'float64', (key,), fill_value='NaN')
rc.standard_name = 'time'
rc.units = 'seconds since 1970-01-01 00:00:00'
# Used in global metadata
if key == 'time':
self.time = rc
return rc
elif key.find('latitude') != -1:
# Record Variables - coordinates for trajectory - save in the instance and use for metadata generation
c = self.all_coord[key]
rc = self.ncFile.createVariable(key, 'float64', (c['time'],), fill_value='NaN')
rc.long_name = 'LATITUDE'
rc.standard_name = 'latitude'
rc.units = 'degree_north'
rc[:] = self.all_sub_ts[key].values
# Used in global metadata
if key == 'latitude':
self.latitude = rc
return rc
elif key.find('longitude') != -1:
c = self.all_coord[key]
rc = self.ncFile.createVariable(key, 'float64', (c['time'],), fill_value='NaN')
rc.long_name = 'LONGITUDE'
rc.standard_name = 'longitude'
rc.units = 'degree_east'
rc[:] = self.all_sub_ts[key].values
# Used in global metadata
if key == 'longitude':
self.longitude = rc
return rc
elif key.find('depth') != -1:
c = self.all_coord[key]
rc = self.ncFile.createVariable(key, 'float64', (c['time'],), fill_value='NaN')
rc.long_name = 'DEPTH'
rc.standard_name = 'depth'
rc.units = 'm'
rc[:] = self.all_sub_ts[key].values
# Used in global metadata
if key == 'depth':
self.depth = rc
return rc
else:
a = self.all_attrib[key]
c = self.all_coord[key]
rc = self.ncFile.createVariable(key, 'float64', (c['time'],), fill_value='NaN')
if 'long_name' in a:
rc.long_name = a['long_name']
if 'standard_name' in a:
rc.standard_name = a['standard_name']
elif key in sn_lookup.keys():
rc.standard_name = sn_lookup[key]
rc.standard_name = key
rc.coordinates = ' '.join(list(c.values()))
if units is None:
if 'units' in a:
rc.units = a['units']
else:
rc.units = ''
else:
rc.units = units
if key.find('pitch') != -1 or key.find('roll') != -1 or key.find('yaw') != -1 or key.find('angle') != -1 or key.find('rate') != -1:
if 'rad/s' in rc.units:
rc.units = 'degree/s'
else:
rc.units = 'degree'
return rc
# End initRecordVariable
def getValidTimeRange(self, ts):
start = ts.index[0]
end = ts.index[-1]
if pd.isnull(start) or pd.isnull(end):
self.logger.info('Invalid starting or ending time found. Searching for valid time range')
selector = np.where(~pd.isnull(ts.index))
if len(selector) > 2:
start = ts[selector[0]]
end = ts[selector[-1]]
# If still can't find a valid time, then raise exception here
if pd.isnull(start) or pd.isnull(end):
raise Exception('Cannot find a valid time range')
return (start, end)
return(start, end)
# End getValidTimeRange
def process(self, url, out_file, parm, interp_key):
self.df = []
self.all_sub_ts = {}
self.all_coord = {}
self.all_attrib = {}
coord = ['latitude','longitude','depth']
all_ts = {}
parm_valid = []
try:
self.df = pydap.client.open_url(url)
except socket.error as e:
self.logger.error('Failed in attempt to open_url(%s)', url)
raise e
# Create pandas time series for each parameter and store attributes
for key in parm:
try:
ts = self.createSeriesPydap(key, key + '_time')
if ts.size == 0:
continue
self.all_attrib[key] = self.df[key].attributes
self.all_attrib[key + '_i'] = self.df[key].attributes
self.all_coord[key] = {'time':'time','depth':'depth','latitude':'latitude','longitude':'longitude'}
parm_valid.append(key)
all_ts[key] = ts
self.logger.info('Found parameter ' + key)
except KeyError as e:
self.logger.info('Key error on parameter ' + key)
continue
# Create another pandas time series for each coordinate
for key in coord:
try:
ts = self.createSeriesPydap(key, key + '_time')
all_ts[key] = ts
except KeyError as e:
self.logger.info('Key error on coordinate ' + key)
raise e
# create independent lat/lon/depth profiles for each parameter
for key in parm_valid:
# TODO: add try catch block on this
# Get independent parameter to interpolate on
t = pd.Series(index = all_ts[key].index)
# Store the parameter as-is - this is the raw data
self.all_sub_ts[key] = pd.Series(all_ts[key])
self.all_coord[key] = { 'time': key+'_time', 'depth': key+'_depth', 'latitude': key+'_latitude', 'longitude':key+'_longitude'}
# interpolate each coordinate to the time of the parameter
# key looks like sea_water_temperature_depth, sea_water_temperature_lat, sea_water_temperature_lon, etc.
for c in coord:
# get coordinate
ts = all_ts[c]
# and interpolate using parameter time
if not ts.empty:
i = self.interpolate(ts, t.index)
self.all_sub_ts[key + '_' + c] = i
self.all_coord[key + '_' + c] = { 'time': key+'_time', 'depth': key+' _depth', 'latitude': key+'_latitude', 'longitude':key+'_longitude'}
# add in time coordinate separately
v_time = all_ts[key].index
esec_list = v_time.values.astype(dt.datetime)/1E9
self.all_sub_ts[key + '_time'] = pd.Series(esec_list,index=v_time)
# TODO: add try catch block on this
# Get independent parameter to interpolate on
t = pd.Series(index = all_ts[interp_key].index)
# store time using interpolation parameter
v_time = all_ts[interp_key].index
esec_list = v_time.values.astype(dt.datetime)/1E9
self.all_sub_ts['time'] = pd.Series(esec_list,index=v_time)
# interpolate all parameters and coordinates
for key in parm_valid:
value = all_ts[key]
if not value.empty :
i = self.interpolate(value, t.index)
self.all_sub_ts[key + '_i'] = i
else:
self.all_sub_ts[key + '_i'] = value
self.all_coord[key + '_i'] = { 'time': 'time', 'depth': 'depth', 'latitude':'latitude', 'longitude':'longitude'}
for key in coord:
value = all_ts[key]
self.all_sub_ts[key] = value
if not value.empty :
i = self.interpolate(value, t.index)
self.all_sub_ts[key] = i
else:
self.all_sub_ts[key] = value
self.all_coord[key] = { 'time': 'time', 'depth': 'depth', 'latitude':'latitude', 'longitude':'longitude'}
self.logger.info("%s", list(self.all_sub_ts.keys()))
# Write data to the file
self.write_netcdf(out_file, url)
self.logger.info('Wrote ' + out_file)
# End processSingleParm
def createCoord(self, coord):
all_ts = {}
for v in self.df.variables:
g = None
if any(v in s for s in coord):
# Create pandas time series for each coordinate and store attributes
for c in coord:
try:
ts = self.createSeries(self.df.variables, c, c+'_'+'time')
all_ts[c] = ts
except Exception as e:
self.logger.error(e)
continue
return all_ts
# End createCoord
def processNc4FileDecimated(self, url, in_file, out_file, parms, group_parms, interp_key):
self.reset()
parm_valid = []
coord = ['latitude','longitude','depth']
self.df = netCDF4.Dataset(in_file, mode='r')
coord_ts = self.createCoord(coord)
# Create pandas time series for each parameter and store attributes
for key in parms:
try:
ts = self.createSeriesPydap(key, key + '_time')
if ts.size == 0:
self.logger.info('Variable ' + key + ' empty so skipping')
continue
attr = {}
for name in self.df[key].ncattrs():
attr[name]=getattr(self.df[key],name)
self.all_attrib[key] = attr
self.all_coord[key] = {'time': 'time', 'depth': 'depth', 'latitude': 'latitude', 'longitude': 'longitude'}
parm_valid.append(key)
self.all_sub_ts[key] = ts
self.logger.info('Found parameter ' + key)
except Exception as e:
self.logger.error(e)
continue
# Create pandas time series for each parameter in each group and store attributes
for group in self.df.groups:
g = None
variables = None
if group in group_parms.keys():
g = group
else:
continue
times = []
subgroup = None
pkeys = None
try:
subgroup = self.df.groups[g]
pkeys = group_parms[g]
except Exception as e:
self.logger.error(e)
continue
# Create pandas time series for each parameter and store attributes
if subgroup is not None and pkeys is not None:
for p in pkeys:
try:
key = p["rename"]
var = p["name"]
ts = self.createSeries(subgroup.variables, var, var+'_'+'time')
attr = {}
# don't store or try to interpolate empty time series
if ts.size == 0:
self.logger.info('Variable ' + var + ' empty so skipping')
continue
for name in subgroup.variables[var].ncattrs():
attr[name] = getattr(subgroup.variables[var],name)
# Potential override of attributes from json data
for name in ('units', 'standard_name'):
try:
attr[name] = p[name]
except KeyError:
continue
self.all_attrib[key] = attr
if key.find('pitch') != -1 or key.find('roll') != -1 or key.find('yaw') != -1 or key.find('angle') != -1 or key.find('rate') != -1:
ts = ts * 180.0 / numpy.pi
# store for later processing into the netCDF
self.all_sub_ts[key] = ts
self.all_coord[key] = { 'time':'time', 'depth':'depth', 'latitude':'latitude', 'longitude':'longitude'}
self.logger.info('Found in group ' + group + ' parameter ' + var + ' renaming to ' + key)
parm_valid.append(key)
except KeyError as e:
self.logger.error(e)
continue
except Exception as e:
self.logger.error(e)
continue
# create independent lat/lon/depth profiles for each parameter
for key in parm_valid:
# Get independent parameter to interpolate on
t = pd.Series(index = self.all_sub_ts[key].index)
self.all_coord[key] = { 'time': key+'_time', 'depth': key+'_depth', 'latitude': key+'_latitude', 'longitude':key+'_longitude'}
# interpolate each coordinate to the time of the parameter
# key looks like sea_water_temperature_depth, sea_water_temperature_lat, sea_water_temperature_lon, etc.
for c in coord:
# get coordinate
ts = coord_ts[c]
# and interpolate using parameter time
if not ts.empty:
i = self.interpolate(ts, t.index)
self.all_sub_ts[key + '_' + c] = i
self.all_coord[key + '_' + c] = { 'time': key+'_time', 'depth': key+' _depth', 'latitude': key+'_latitude', 'longitude':key+'_longitude'}
# add in time coordinate separately
v_time = self.all_sub_ts[key].index
esec_list = v_time.values.astype(dt.datetime)/1E9
self.all_sub_ts[key + '_time'] = pd.Series(esec_list,index=v_time)
# Get independent parameter to interpolate on
t = pd.Series(index = self.all_sub_ts[interp_key].index)
# store time using interpolation parameter
v_time = self.all_sub_ts[interp_key].index
esec_list = v_time.values.astype(dt.datetime)/1E9
self.all_sub_ts['time'] = pd.Series(esec_list,index=v_time)
for key in coord:
value = coord_ts[key]
self.all_sub_ts[key] = value
if not value.empty :
i = self.interpolate(value, t.index)
self.all_sub_ts[key] = i
else:
self.all_sub_ts[key] = value
self.all_coord[key] = { 'time': 'time', 'depth': 'depth', 'latitude':'latitude', 'longitude':'longitude'}
self.logger.info("%s", self.all_sub_ts.keys())
# Write data to the file
self.write_netcdf(out_file, url)
self.logger.info('Wrote ' + out_file)
# End processSingleParm
def processNc4File(self, in_file, out_file, parm, resampleFreq):
self.reset()
all_ts = {}
start_times = []
end_times = []
coord = ["latitude", "longitude", "depth", "time"]
self.df = netCDF4.Dataset(in_file, mode='r')
all_ts = self.createCoord(coord)
for group in self.df.groups:
g = None
variables = None
if any(group in s for s in list(parm.keys())):
g = group
else:
continue
times = []
subgroup = None
pkeys = None
# either a subgroup or a list of variables
try:
for key in subgroup:
subgroup = self.df.groups[g].group[key]
pkeys = list(parm[g][key].keys())
break
except Exception as e:
self.logger.error(e)
self.logger.warn('falling back to main group %s' % group)
subgroup = self.df.groups[g]
pkeys = parm[g]
if subgroup is not None and pkeys is not None:
# Create pandas time series for each parameter and store attributes
for v in pkeys:
try:
key = group + '_' + v # prepend the group name to the variable name to make it unique
ts = self.createSeries(subgroup.variables, v, v+'_'+'time')
# don't store or try to interpolate empty time series
if ts.size == 0:
self.logger.info('Variable ' + v + ' empty so skipping')
continue
attr = {}
for name in subgroup.variables[v].ncattrs():
attr[name]=getattr(subgroup.variables[v],name)
self.all_attrib[key] = attr
# resample using the mean
ts_resample = ts.resample(resampleFreq).mean()[:]
self.all_sub_ts[key] = ts_resample
self.all_coord[key] = { 'time': key+'_time', 'depth': key+' _depth', 'latitude': key+'_latitude', 'longitude':key+'_longitude'}
# create independent lat/lon/depth profiles for each parameter
# interpolate each coordinate to the time of the param
# key looks like sea_water_temperature_depth, sea_water_temperature_lat, sea_water_temperature_lon, etc.
for c in coord:
i = self.interpolate(ts, ts_resample.index)
self.all_sub_ts[key + '_' + c] = i
self.all_coord[key + '_' + c] = { 'time': key+'_time', 'depth': key+' _depth', 'latitude': key+
'_latitude', 'longitude':key+'_longitude'}
self.logger.info('Found parameter ' + key)
except KeyError as e:
self.logger.error(e)
continue
except Exception as e:
self.logger.error(e)
continue
# Get time parameter and align other coordinates to this
t = pd.Series(index = all_ts['time'].index)
# resample
t_resample = t.resample(resampleFreq)[:]
# add in coordinates
for key in coord:
value = all_ts[key]
i = self.interpolate(value, t_resample.index)
self.all_sub_ts[key] = i
self.all_coord[key] = { 'time': 'time', 'depth': 'depth', 'latitude':'latitude', 'longitude':'longitude'}
self.logger.info("%s", list(self.all_sub_ts.keys()))
# Write data to the file
self.write_netcdf(out_file, in_file)
self.logger.info('Wrote ' + out_file)
# End processNc4
def processResampleNc4File(self, in_file, out_file, parm, resampleFreq, rad_to_deg):
self.reset()
coord_ts = {}
start_times = []
end_times = []
coord = ["latitude", "longitude", "depth", "time"]
self.logger.info('Reading %s file...' % in_file)
self.df = netCDF4.Dataset(in_file, mode='r')
coord_ts = self.createCoord(coord)
# Get time parameter and align everything to this
t = pd.Series(index = coord_ts['time'].index)
# resample
t_resample = t.resample(resampleFreq).asfreq()[:]
for group in self.df.groups:
g = None
variables = None
if group in list(parm.keys()):
g = group
else:
continue
times = []
subgroup = None
pkeys = None
try:
subgroup = self.df.groups[g]
pkeys = parm[g]
except Exception as e:
self.logger.error(e)
raise e
if subgroup is not None and pkeys is not None:
# Create pandas time series for each parameter and store attributes
for p in pkeys:
try:
key = p["rename"]
var = p["name"]
ts = self.createSeries(subgroup.variables, var, var+'_'+'time')
attr = {}
# don't store or try to interpolate empty time series
if ts.size == 0:
self.logger.info('Variable ' + var + ' empty so skipping')
continue
for name in subgroup.variables[var].ncattrs():
attr[name] = getattr(subgroup.variables[var],name)
# Potential override of attributes from json data
for name in ('units', 'standard_name'):
try:
attr[name] = p[name]
except KeyError:
continue
self.all_attrib[key] = attr
# resample using the mean then interpolate on to the time dimension
ts_resample = ts.resample(resampleFreq).mean()[:]
i = self.interpolate(ts_resample, t_resample.index)
if key.find('pitch') != -1 or key.find('roll') != -1 or key.find('yaw') != -1 or key.find('angle') != -1 or key.find('rate') != -1:
i = i * 180.0 / numpy.pi
# store for later processing into the netCDF
self.all_sub_ts[key] = i
self.all_coord[key] = { 'time':'time', 'depth':'depth', 'latitude':'latitude', 'longitude':'longitude'}
# plotting for debugging
'''fig, axes = plt.subplots(3)
plt.legend(loc='best')
axes[0].set_title('raw ' + var + ' data')
ts.plot(ax=axes[0],color='r')
axes[1].set_title('resampled')
ts_resample.plot(ax=axes[1],color='g')
axes[2].set_title('interpolated')
i.plot(ax=axes[2],color='b')
plt.show()'''
self.logger.info('Found in group ' + group + ' parameter ' + var + ' renaming to ' + key)
except KeyError as e:
self.logger.error(e)
continue
except Exception as e:
self.logger.error(e)
continue
# add in coordinates
for key in coord:
try:
value = coord_ts[key]
if rad_to_deg:
if key.find('latitude') != -1 or key.find('longitude') != -1:
value = value * 180.0/ numpy.pi
i = self.interpolate(value, t_resample.index)
self.all_sub_ts[key] = i
self.all_coord[key] = { 'time': 'time', 'depth': 'depth', 'latitude':'latitude', 'longitude':'longitude'}
except Exception as e:
self.logger.error(e)
raise e
self.logger.info("%s", list(self.all_sub_ts.keys()))
self.write_netcdf(out_file, in_file)
self.logger.info('Wrote ' + out_file)
# End processResampleNc4File
def processResample(self, url, out_file, parm, interpFreq, resampleFreq):
self.reset()
esec_list = []
self.parm = ['latitude','longitude','depth'] + parm
all_ts = {}
start_times = []
end_times = []
try:
self.df = pydap.client.open_url(url)
except socket.error as e:
self.logger.error('Failed in attempt to open_url(%s)', url)
raise e
except ValueError as e:
self.logger.error('Value error when opening open_url(%s)', url)
raise e
# Create pandas time series and get sampling metric for each
for key, value in list(parm.items()):
try:
p_ts = self.createSeriesPydap(key)
except KeyError as e:
p_ts = pd.Series()
self.logger.info('Key error on ' + key)
raise e
all_ts[key] = p_ts
try:
(start,end) = self.getValidTimeRange(p_ts)
start_times.append(start)
end_times.append(end)
except Exception:
self.logger.info('Start/end ' + parm + ' time range invalid')
# the full range should span all the time series data to store
start_time = min(start_times)
end_time = max(end_times)
full_range = pd.date_range(start_time,end_time,freq=interpFreq)
t = pd.Series(index = full_range)
ts = t.index.values
# convert time to epoch seconds
esec_list = t.resample(resampleFreq).index.values.astype(dt.datetime)/1E9
for key, value in list(all_ts.items()):
if not value.empty :
# swap byte order and create a new series
values = value
newvalues = values.byteswap().newbyteorder()
pr = pd.Series(newvalues, index=value.index)
# reindex to the full range that covers all data
# forward fill
pr.reindex(index = full_range, method='ffill')
# interpolate onto regular time scale
i = self.interpolate(pr, ts)
try:
isub = i.resample(resampleFreq)[:]
# plotting for debugging
'''fig, axes = plt.subplots(4)
plt.legend(loc='best')
axes[0].set_title('raw ' + self.parm[j] + ' data')
p.plot(ax=axes[0],color='r')
axes[1].set_title('reindexed')
pr.plot(ax=axes[1],color='g')
axes[2].set_title('interpolated')
i.plot(ax=axes[2],color='b')
axes[3].set_title('resampled')
isub.plot(ax=axes[3],color='y')
plt.show()'''
except IndexError as e:
self.logger.error(e)
raise e
self.all_sub_ts[key] = isub
else:
self.all_sub_ts[key] = pd.Series()
# Write data to the file
self.write_netcdf(out_file, url)
self.logger.info('Wrote ' + out_file)
# End processResample
if __name__ == '__main__':
pw = InterpolatorWriter()
pw.process_command_line()
nc4_file='/home/vagrant/LRAUV/daphne/missionlogs/2015/20150930_20151008/20151006T201728/201510062017_201510062027.nc4'
nc4_file='/mbari/LRAUV/opah/missionlogs/2017/20170502_20170508/20170508T185643/201705081856_201705090002.nc4'
nc4_file='/mbari/LRAUV/makai/missionlogs/2018/20180603_20180611/20180604T235249/201806042353_201806050437.nc4'
outDir = '/tmp/'
resample_freq='10S'
rad_to_deg = True
parm = '{' \
'"CTD_NeilBrown": [ ' \
'{ "name":"sea_water_salinity" , "rename":"salinity" }, ' \
'{ "name":"sea_water_temperature" , "rename":"temperature" } ' \
'],' \
'"WetLabsBB2FL": [ ' \
'{ "name":"mass_concentration_of_chlorophyll_in_sea_water", "rename":"chlorophyll" }, ' \
'{ "name":"Output470", "rename":"bbp470" }, ' \
'{ "name":"Output650", "rename":"bbp650" } ' \
'],' \
'"PAR_Licor": [ ' \
'{ "name":"downwelling_photosynthetic_photon_flux_in_sea_water", "rename":"PAR" } ' \
'],' \
'"ISUS" : [ ' \
'{ "name":"mole_concentration_of_nitrate_in_sea_water", "rename":"nitrate" } ' \
'],' \
'"Aanderaa_O2": [ ' \
'{ "name":"mass_concentration_of_oxygen_in_sea_water", "rename":"oxygen" } ' \
'] }'
# Formulate new filename from the url. Should be the same name as the .nc4 specified in the url
# with resample appended to indicate it has resampled data and is now in .nc format
f = nc4_file.rsplit('/',1)[1]
out_file = outDir + '.'.join(f.split('.')[:-1]) + '_' + resample_freq + '.nc'
pw.processResampleNc4File(nc4_file, out_file, json.loads(parm),resample_freq, rad_to_deg)
print('Done.')
|
danellecline/stoqs
|
stoqs/loaders/CANON/toNetCDF/lrauvNc4ToNetcdf.py
|
Python
|
gpl-3.0
| 33,496
|
[
"NetCDF"
] |
d9ecb3a9184d916a13bd751a47fbde9dd9a12ffc965d90d8145df743d10727d0
|
#!/usr/bin/env python
"""droplet.py: Routines for working with the CO2-IL droplet XYZ
files."""
from __future__ import print_function
import os
from glob import glob
from itertools import count
from itertools import cycle
from math import sqrt
import numpy as np
from sympy import S
import periodictable
import mbe
from mbe.utils import pad_left_zeros
# These are the total charges for the each of the ionic liquid
# components.
MAP_CHARGES = {
'C8H15N2': +1,
'F6P': -1,
'CO2': 0,
}
def rename_old_droplet_files():
"""Rename all the original droplet files from 'XXX_drop.xyz' to
'drop_XXX.xyz', where the number is left-padded with zeros.
"""
filenames = glob('*_drop.xyz')
print(filenames)
# Store the maximum length of the internal number.
maxlen = 0
# Unfortunately, two full traversals need to be performed.
# 1. Find the maximum length of the internal number and store it.
for oldfilename in filenames:
newlen = len(oldfilename.split('_')[0])
if newlen > maxlen:
maxlen = newlen
newfilenames = []
# 2. Go through each file again and rename.
for oldfilename in filenames:
splitname = oldfilename.split('_')
filenumlen = len(splitname[0])
if filenumlen < maxlen:
splitname[0] = pad_left_zeros(splitname[0], maxlen)
newfilename = 'drop_{}.xyz'.format(splitname[0])
os.rename(oldfilename, newfilename)
print(oldfilename + ' -> ' + newfilename)
newfilenames.append(newfilename)
return newfilenames
def rename_new_droplet_files():
"""Rename all the original droplet files from 'clusterXXX.xyz' to
'drop_XXX.xyz', where the number is left-padded with zeros.
"""
filenames = glob('cluster*.xyz')
print(filenames)
# Store the maximum length of the internal number.
maxlen = 0
# Unfortunately, two full traversals need to be performed.
# 1. Find the maximum length of the internal number and store it.
for oldfilename in filenames:
numstr = oldfilename[7:-4]
newlen = len(numstr)
if newlen > maxlen:
maxlen = newlen
newfilenames = []
# 2. Go through each file again and rename.
for oldfilename in filenames:
numstr = oldfilename[7:-4]
filenumlen = len(numstr)
if filenumlen < maxlen:
numstr = pad_left_zeros(numstr, maxlen)
newfilename = 'drop_{}.xyz'.format(numstr)
os.rename(oldfilename, newfilename)
print(oldfilename + ' -> ' + newfilename)
newfilenames.append(newfilename)
return newfilenames
# def get_bond_graph(obmol):
# """Test a few possible ways for getting the bond_connectivities using
# Open Babel. Not used for the droplets.
# """
# bond_pair_indices1 = []
# bond_pair_indices2 = []
# for obbond in ob.OBMolBondIter(obmol):
# bond_pair_indices1.append((obbond.GetBeginAtomIdx(),
# obbond.GetEndAtomIdx(),
# obbond.GetBondOrder()))
# for obbond in ob.OBMolBondIter(obmol):
# bond_pair_indices2.append((obbond.GetBeginAtom().GetIndex(),
# obbond.GetEndAtom().GetIndex(),
# obbond.GetBondOrder()))
# bonds = [{'atoms': [bond.GetBeginAtom().GetIndex(),
# bond.GetEndAtom().GetIndex()],
# 'order': bond.GetBondOrder(),
# 'symbols': [pt.Element[bond.GetBeginAtom().GetAtomicNum()],
# pt.Element[bond.GetEndAtom().GetAtomicNum()]]}
# for bond in ob.OBMolBondIter(obmol)]
# print('bond pair indices (1)')
# print(bond_pair_indices1)
# print('bond pair indices (2)')
# print(bond_pair_indices2)
# print('bond pair indices (3)')
# for bond in bonds:
# print(bond)
# def make_obmol_from_file(filename, obconv):
# """Make an Open Babel molecule from a file, using an OBConv instance
# with the correct conversion type already set.
# """
# print(filename)
# obmol = ob.OBMol()
# obconv.ReadFile(obmol, filename)
# obmol.ConnectTheDots()
# obmol.PerceiveBondOrders()
# return obmol
# def make_ob_fragments_from_obmol(obmol):
# """Make a list of Open Babel molecules (fragments) from an OBMol
# containing multiple (non-bonded) molecules.
# """
# return obmol.Separate()
def determine_fragment_grouping(atoms):
"""From the main list of atoms (probably right from an input file),
return a list of lists, where each list contains the indices for atoms
on distinct molecules.
"""
grouping_anions = []
grouping_cations = []
grouping_CO2 = []
# We have some a priori knowledge of the ordering of atoms in each
# of the molecules that can be used to our advantage.
ordering_anion = ['F', 'F', 'F', 'F', 'F', 'F', 'P']
ordering_cation = ['N', 'C', 'N', 'C', 'C', 'H', 'H', 'H', 'C', 'H', 'H',
'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C', 'H', 'H', 'C',
'H', 'H', 'H']
ordering_CO2 = ['C', 'O', 'O']
# An iterator over each ordering that will never run out.
possible_orderings = cycle((ordering_anion, ordering_cation, ordering_CO2))
start = 0
while start < len(atoms):
# For each possible ordering (representing a single molecule),
# make a "window" over the atom ordering from the total input,
# seeing if they match.
for possible_ordering in possible_orderings:
# The end of the window always needs to be updated.
end = start + len(possible_ordering)
if atoms[start:end] == []:
break
# If we have a match, add its indices to a list as a
# "group" representing a molecule. Only update the start
# of the window when we have a match.
if possible_ordering == atoms[start:end]:
grouping = list(range(start, end))
if possible_ordering == ordering_anion:
grouping_anions.append(grouping)
elif possible_ordering == ordering_cation:
grouping_cations.append(grouping)
elif possible_ordering == ordering_CO2:
grouping_CO2.append(grouping)
else:
raise Exception
start = end
# A list of lists, where each list contains the indices for atoms
# on distinct molecules.
return grouping_anions, grouping_cations, grouping_CO2
def make_fragments_from_grouping(atoms, coords, grouping, start=1, pc=None, pc_addition='every'):
"""Given a set of atoms (list of atomic symbols), coordinates (list of
length 3 lists), and a grouping of atoms into molecules, make a list
of Fragment objects corresponding to the grouping.
Optionally, deal with point charge business.
"""
fragments = []
for i, group in zip(count(start=start), grouping):
start, end = group[0], group[-1] + 1
fragment = mbe.fragment.Fragment()
fragment.atoms = atoms[start:end]
fragment.formula_string = fragment._make_canonical_formula_string()
fragment.coords = coords[start:end]
fragment.charge = MAP_CHARGES[fragment.formula_string]
fragment.nfragments = 1
fragment.comment = ' '.join([fragment.formula_string, '({})'.format(i)])
fragment.name = 'f{}'.format(i)
fragment.symbol_repr = {S(fragment.name)}
if pc is not None:
if pc_addition == 'every':
fragment.pointcharges = pc
elif pc_addition == 'unique':
fragment.pointcharges = pc[start:end]
else:
pass
fragments.append(fragment)
return fragments
def combine_fragments_for_covp(fragments):
"""COVP analysis with Q-Chem can only be performed with two
fragments. Given a list of N fragments, combine the first N-1 into
one, leaving the last one uncombined.
"""
return [mbe.fragment.combine_fragment_sequence(fragments[:-1]), fragments[-1]]
def get_point_charges_qmout(pc_file_path, pc_type):
"""Use cclib to parse a QM output file for point charges (Mulliken,
Lowdin, CHELPG, ...).
"""
from cclib.parser import ccopen
job = ccopen(pc_file_path)
data = job.parse()
# pylint: disable=E1101
return data.atomcharges[pc_type]
def get_point_charges_txt(pc_file_path):
"""Extract point charges from a plain text file, where the charges
themselves are in a column (assumed to be the last).
"""
pointcharges = []
with open(pc_file_path) as pc_file:
for line in pc_file:
pointcharges.append(float(line.split()[-1]))
return pointcharges
def distance_twopoint(l1, l2):
"""Return the distance between two Cartesian points (given as
lists).
"""
return sqrt(((l1[0] - l2[0])**2) + ((l1[1] - l2[1])**2) + ((l1[2] - l2[2])**2))
def distance_atomic(fragment1, fragment2):
"""Return the distance between the atom in fragment 1 and the atom in
fragment 2 that are closest to each other.
"""
shortest_distance = 1.0e20
for c1 in fragment1.coords:
for c2 in fragment2.coords:
current_distance = distance_twopoint(c1, c2)
if current_distance < shortest_distance:
shortest_distance = current_distance
return shortest_distance
distance_atomic_shortest = distance_atomic
def distance_atomic_longest(fragment1, fragment2):
"""Return the distance between the atom in fragment 1 and the atom in
fragment 2 that are furthest from each other.
"""
longest_distance = -1.0e20
for c1 in fragment1.coords:
for c2 in fragment2.coords:
current_distance = distance_twopoint(c1, c2)
if current_distance > longest_distance:
longest_distance = current_distance
return longest_distance
def distance_centerofmass(fragment1, fragment2):
"""Return the distance between the center of mass of fragment 1 and
the center of mass of fragment 2."""
COM_fragment1 = fragment_centerofmass(fragment1)
COM_fragment2 = fragment_centerofmass(fragment2)
return distance_twopoint(COM_fragment1, COM_fragment2)
def fragment_centerofmass(fragment):
"""Calculate the center of mass (COM) of a fragment."""
masses = np.array([pt.Mass[element] for element in fragment.atoms])
mass_sum = np.sum(masses)
coords = np.array(fragment.coords)
COMx = np.sum(coords[:, 0] * masses) / mass_sum
COMy = np.sum(coords[:, 1] * masses) / mass_sum
COMz = np.sum(coords[:, 2] * masses) / mass_sum
return np.array([COMx, COMy, COMz])
def get_n_closest_fragments(n, target_fragment, other_fragments, method='atomic'):
"""Return the n closest fragments to the target fragment, based on
distance between centers of mass or pairwise between atoms.
"""
closest_fragments = []
distances = []
if method == 'centerofmass':
COM_target = fragment_centerofmass(target_fragment)
# Calculate the distance between the target fragment and the other
# fragments.
for fragidx, other_fragment in enumerate(other_fragments):
if method == 'atomic':
distance = distance_atomic(target_fragment, other_fragment)
elif method == 'centerofmass':
COM_other = fragment_centerofmass(other_fragment)
distance = distance_twopoint(COM_target, COM_other)
else:
raise NotImplementedError
distances.append((fragidx, distance))
# Now that all the distances are calculated, sort them in
# increasing order.
distances = sorted(distances, key=lambda x: x[1])
# Return the n closest fragments by:
# 1. Get the index of the ith fragment.
# 2. Look into all other fragments for it.
# 3. Append the Fragment object to the list.
for i in range(n):
closest_fragments.append(other_fragments[distances[i][0]])
return closest_fragments
def get_n_closest_pairs(n, fragments_anions, fragments_cations, fragment_CO2, method='atomic'):
"""Return the n closest ionic liquid pairs to the CO2, given lists of
Fragment objects of each.
"""
closest_fragments_anions = get_n_closest_fragments(n, fragment_CO2, fragments_anions, method)
closest_fragments_cations = get_n_closest_fragments(n, fragment_CO2, fragments_cations, method)
return closest_fragments_anions, closest_fragments_cations
if __name__ == '__main__':
import argparse
import sys
# import logging
parser = argparse.ArgumentParser()
parser.add_argument('--verbose',
action='store_true',
help="""Print more information to stdout.""")
parser.add_argument('--debug',
action='store_true',
help="""Print debug-level information to stdout.""")
parser.add_argument('--rename-old',
action='store_true',
help="""whether or not to run the rename method on the \
old set of droplet XYZ files (only for the original files!)""")
parser.add_argument('--rename-new',
action='store_true',
help="""whether or not to run the rename method on the \
new set of droplet XYZ files (only for the original files!)""")
parser.add_argument('--single',
help="""pass a single droplet XYZ file to only \
operate on that file""")
parser.add_argument('--path',
default='.',
help="""the path all of the droplet coordinate files \
are contained (can be relative or absolute)""")
parser.add_argument('--write-fragment-input-qchem',
action='store_true',
help="""Write the new fragments from each droplet to \
disk as a single fragment input file (Q-Chem style).""")
parser.add_argument('--print-fragment-input-qchem',
action='store_true',
help="""Print the fragment input (Q-Chem style).""")
parser.add_argument('--write-fragment-input-psi',
action='store_true',
help="""Write the new fragments from each droplet to \
disk as a single fragment input file (Psi style).""")
parser.add_argument('--print-fragment-input-psi',
action='store_true',
help="""Print the fragment input (Psi style).""")
parser.add_argument('--write-input-sections-qchem',
action='store_true',
help="""Same as --write-fragment-input-xxx, but with \
possible point charges (Q-Chem style).""")
parser.add_argument('--print-input-sections-qchem',
action='store_true',
help="""Same as --print-fragment-input-xxx, but with \
possible point charges (Q-Chem style).""")
parser.add_argument('--mbe-order',
type=int,
default=0,
help="""Order of the many-body expansion to go up to.""")
parser.add_argument('--point-charge-type',
choices=('mulliken', 'lowdin', 'chelpg', 'hirshfeld'),
default='mulliken',
help="""The type of point charges to extract from \
an output file.""")
parser.add_argument('--point-charge-file-type',
choices=('qmout', 'txt'),
default='txt',
help="""If 'qmout', the files containing point charges \
are QM outputs to be parsed by cclib. If 'txt', they \
are two columns, the first containing atomic symbols, \
the second containing the charge; --point-charge-type \
doesn't matter.""")
parser.add_argument('--point-charge-output-cation',
help="""An output file containing point charges \
that will be applied to every cation.""")
parser.add_argument('--point-charge-output-anion',
help="""An output file containing point charges \
that will be applied to every anion.""")
parser.add_argument('--point-charge-output-unique',
help="""An output file containing unique point charges \
for every atom.""")
parser.add_argument('--num-closest-pairs-qm',
type=int,
default=0,
help="""The number of closest ionic liquid pairs to the \
CO2 that will be treated quantum mechanically.""")
parser.add_argument('--num-closest-pairs-mm',
type=int,
default=0,
help="""The number of closest ionic liquid pairs to the \
CO2 that will be treated as point charges. If any pairs \
are treated using QM, these are the closest outside the \
QM region.""")
parser.add_argument('--distance-metric',
choices=('centerofmass', 'atomic'),
default='atomic',
help="""The metric for calculating distances between \
fragments. 'atomic' is ...""")
parser.add_argument('--all-pairs-qm',
action='store_true',
help="""Treat all ionic liquid pairs quantum mechanically.""")
parser.add_argument('--all-other-pairs-mm',
action='store_true',
help="""Treat all possible ionic liquid pairs as point \
charges. If any pairs are treated using QM, treat all \
others as point charges.""")
parser.add_argument('--make-supersystem',
action='store_true',
help="""When writing or printing fragment inputs, \
combine all fragments into a single block, making a \
'traditional' molecule input. For Q-Chem, this is \
required when only working with a single fragment.""")
parser.add_argument('--qchem-covp',
action='store_true',
help="""If writing a fragment (non-supersystem) input for Q-Chem, combine
all the fragments except the last, so COVP analysis can be
performed.""")
args = parser.parse_args()
if args.debug:
args.verbose = True
# if args.verbose:
# level = logging.INFO
# if args.debug:
# level = logging.DEBUG
# else:
# level = logging.WARNING
# logging.basicConfig(level=level)
# If we want to rename all the files, be safe and don't try any
# other operations.
if args.rename_old:
rename_old_droplet_files()
sys.exit(0)
if args.rename_new:
rename_new_droplet_files()
sys.exit(0)
if args.single:
filenames = [args.single]
else:
filenames = [name for name in glob(os.path.join(args.path, 'drop_*.xyz'))]
if args.debug:
print(filenames)
# obconv = ob.OBConversion()
# obconv.SetInAndOutFormats('xyz', 'xyz')
for filename in filenames:
# Here is how it might be done with Open Babel as a more general case..
# obmol_droplet = make_obmol_from_file(filename, obconv)
# obmol_fragments = make_ob_fragments_from_obmol(obmol_droplet)
# fragments = []
# for i, obmol_fragment in zip(count(start=1), obmol_fragments):
# pbmol_fragment = pb.Molecule(obmol_fragment)
# atoms = [pt.Element[atom.atomicnum] for atom in pbmol_fragment.atoms]
# coords = [atom.coords for atom in pbmol_fragment.atoms]
# formula = pbmol_fragment.formula
# if formula not in MAP_CHARGES.keys():
# print(formula)
# if formula in MAP_CHARGES.keys():
# charge = MAP_CHARGES[formula]
# else:
# charge = -999
# fragment = mbe.fragment.Fragment()
# fragment.atoms = atoms
# fragment.coords = coords
# fragment.charge = charge
# fragment.nfragments = 1
# fragment.comment = ' '.join([formula, '({})'.format(i)])
# fragment.name = fragment.comment
# print(fragment)
# fragments.append(fragment)
basename = os.path.splitext(os.path.basename(filename))[0]
# Read in the entire XYZ file before breaking it apart into fragments.
natoms, comment, atoms, coords = mbe.xyz_operations.read_xyz(filename)
if args.verbose:
print(basename, natoms)
fragments = []
# Determine which atoms from the XYZ file belong to individual
# anions, cations, and the CO2, returning a list of lists,
# each containing the indices of an atom.
grouping_anions, grouping_cations, grouping_CO2 = determine_fragment_grouping(atoms)
grouping = grouping_anions + grouping_cations + grouping_CO2
if args.debug:
print('grouping of anions:')
print(grouping_anions)
print('grouping of cations:')
print(grouping_cations)
print('grouping of CO2:')
print(grouping_CO2)
print('all groupings:')
print(grouping)
# Figure out whether or not we need to add point charges to
# fragments based on if certain command-line arguments were
# passed.
if args.point_charge_file_type == 'qmout':
if args.point_charge_output_unique:
# There is a unique point charge for every atom in the
# input XYZ file.
pointcharges_unique = get_point_charges_qmout(args.point_charge_output_unique, args.point_charge_type)
elif args.point_charge_output_anion and args.point_charge_output_cation:
# There are common point charges for each anion and cation
# in the XYZ file.
pointcharges_anion = get_point_charges_qmout(args.point_charge_output_anion, args.point_charge_type)
pointcharges_cation = get_point_charges_qmout(args.point_charge_output_cation, args.point_charge_type)
else:
# Don't worry about adding point charges.
pass
else:
# Must be the simple format.
if args.point_charge_output_unique:
pointcharges_unique = get_point_charges_txt(args.point_charge_output_unique)
elif args.point_charge_output_anion and args.point_charge_output_cation:
pointcharges_anion = get_point_charges_txt(args.point_charge_output_anion)
pointcharges_cation = get_point_charges_txt(args.point_charge_output_cation)
# From the indices, ...
if args.point_charge_output_unique:
start = 1
fragments_anions = make_fragments_from_grouping(atoms, coords, grouping_anions, start=start, pc=pointcharges_unique, pc_addition='unique')
start = len(fragments_anions)
fragments_cations = make_fragments_from_grouping(atoms, coords, grouping_cations, start=start, pc=pointcharges_unique, pc_addition='unique')
start = len(fragments_anions) + len(fragments_cations)
fragment_CO2 = make_fragments_from_grouping(atoms, coords, grouping_CO2, start=start)[0]
elif args.point_charge_output_anion and args.point_charge_output_cation:
start = 1
fragments_anions = make_fragments_from_grouping(atoms, coords, grouping_anions, start=start, pc=pointcharges_anion, pc_addition='every')
start = len(fragments_anions)
fragments_cations = make_fragments_from_grouping(atoms, coords, grouping_cations, start=start, pc=pointcharges_cation, pc_addition='every')
start = len(fragments_anions) + len(fragments_cations)
fragment_CO2 = make_fragments_from_grouping(atoms, coords, grouping_CO2, start=start)[0]
else:
start = 1
fragments_anions = make_fragments_from_grouping(atoms, coords, grouping_anions, start=start)
start = len(fragments_anions)
fragments_cations = make_fragments_from_grouping(atoms, coords, grouping_cations, start=start)
start = len(fragments_anions) + len(fragments_cations)
fragment_CO2 = make_fragments_from_grouping(atoms, coords, grouping_CO2, start=start)[0]
if args.debug:
if args.point_charge_output_unique:
for f in fragments_anions:
print(f.pointcharges)
for f in fragments_cations:
print(f.pointcharges)
elif args.point_charge_output_cation and args.point_charge_output_anion:
for f in fragments_anions:
print(f.pointcharges)
for f in fragments_cations:
print(f.pointcharges)
else:
pass
fragments = fragments_anions + fragments_cations + [fragment_CO2]
disk_fragments_qm = []
disk_fragments_mm = []
# Control flow:
# 1. all fragments are QM
# 2. some fragments are QM
# remainder: all are MM
# remainder: some are MM
# remainder: ignore
# 3. all other than CO2 are MM
# 4. some fragments are MM
# 5. CO2 alone
if args.all_pairs_qm:
disk_fragments_qm = fragments
elif args.num_closest_pairs_qm:
n_qm = args.num_closest_pairs_qm
closest_pairs_qm = get_n_closest_pairs(n_qm, fragments_anions, fragments_cations, fragment_CO2, method=args.distance_metric)
closest_anions_qm = closest_pairs_qm[0]
closest_cations_qm = closest_pairs_qm[1]
all_other_anions = [f for f in fragments_anions if f not in closest_anions_qm]
all_other_cations = [f for f in fragments_cations if f not in closest_cations_qm]
if args.all_other_pairs_mm:
disk_fragments_mm = all_other_anions + all_other_cations
elif args.num_closest_pairs_mm:
n_mm = args.num_closest_pairs_mm
closest_pairs_mm = get_n_closest_pairs(n_mm, all_other_anions, all_other_cations, fragment_CO2, method=args.distance_metric)
closest_anions_mm = closest_pairs_mm[0]
closest_cations_mm = closest_pairs_mm[1]
disk_fragments_mm = closest_anions_mm + closest_cations_mm
else:
pass
disk_fragments_qm = closest_anions_qm + closest_cations_qm + [fragment_CO2]
elif args.all_other_pairs_mm:
disk_fragments_qm = [fragment_CO2]
disk_fragments_mm = fragments_anions + fragments_cations
elif args.num_closest_pairs_mm:
n_mm = args.num_closest_pairs_mm
closest_pairs_mm = get_n_closest_pairs(n_mm, fragments_anions, fragments_cations, fragment_CO2, method=args.distance_metric)
closest_anions_mm = closest_pairs_mm[0]
closest_cations_mm = closest_pairs_mm[1]
disk_fragments_mm = closest_anions_mm + closest_cations_mm
disk_fragments_qm = [fragment_CO2]
else:
disk_fragments_qm = [fragment_CO2]
# This needs to come before any possible fragment
# recombination, otherwise the count will be wrong (always 0)!
# The format of the filename could be change to specify both #
# of fragments and # of "pairs", but not for now...
n_qm = (len(disk_fragments_qm) - 1) // 2
# Do any of the fragments need to be recombined for any reason
# (such as COVP analysis)?
if args.qchem_covp:
disk_fragments_qm = combine_fragments_for_covp(disk_fragments_qm)
# For now, we do nothing with the many-body expansion other
# than generate and print it out if requested.
if args.mbe_order > 0:
monomer_symbols = []
for monomer in fragments:
for symbol in monomer.symbol_repr:
monomer_symbols.append(symbol)
if args.debug:
print('Symbolic representation of monomers:')
print(monomer_symbols)
mbe_expression = mbe.expressions.MBEn(monomer_symbols, args.mbe_order)
if args.debug:
print('Full MBE{} expression:'.format(args.mbe_order))
print(mbe_expression)
# Write or print full Q-Chem $molecule/$external_charges sections.
if args.write_input_sections_qchem:
n_mm = len(disk_fragments_mm) // 2
filename = '{}_{}qm_{}mm'.format(os.path.splitext(os.path.basename(filename))[0], n_qm, n_mm)
mbe.xyz_operations.write_input_sections_qchem(disk_fragments_qm, disk_fragments_mm, supersystem=args.make_supersystem, filename=filename, stdout=False)
if args.print_input_sections_qchem:
mbe.xyz_operations.write_input_sections_qchem(disk_fragments_qm, disk_fragments_mm, supersystem=args.make_supersystem)
# Write fragments to disk or print them to stdout.
if args.write_fragment_input_qchem:
filename = 'frag_{}'.format(os.path.basename(filename))
mbe.xyz_operations.write_fragment_section_qchem(disk_fragments_qm, filename=filename)
if args.write_fragment_input_psi:
filename = 'frag_{}'.format(os.path.basename(filename))
mbe.xyz_operations.write_fragment_section_psi(disk_fragments_qm, filename=filename)
if args.print_fragment_input_qchem:
mbe.xyz_operations.write_fragment_section_qchem(disk_fragments_qm)
if args.print_fragment_input_psi:
mbe.xyz_operations.write_fragment_section_psi(disk_fragments_qm)
|
berquist/mbe
|
mbe/examples/droplet.py
|
Python
|
mpl-2.0
| 30,616
|
[
"Open Babel",
"Q-Chem",
"cclib"
] |
adcb802eb9804799cd31a38c856aec683666498092d355f12c5b49e437375f5d
|
# Copyright 2013-2020 Lawrence Livermore National Security, LLC and other
# Spack Project Developers. See the top-level COPYRIGHT file for details.
#
# SPDX-License-Identifier: (Apache-2.0 OR MIT)
# Legal Notice
# ------------
# OPENFOAM is a trademark owned by OpenCFD Ltd
# (producer and distributor of the OpenFOAM software via www.openfoam.com).
# The trademark information must remain visible and unadulterated in this
# file and via the "spack info" and comply with the term set by
# http://openfoam.com/legal/trademark-policy.php
#
# This file is not part of OpenFOAM, nor does it constitute a component of an
# OpenFOAM distribution.
#
##############################################################################
#
# Notes
# - The openfoam-org package is a modified version of the openfoam package.
# If changes are needed here, consider if they should also be applied there.
#
# - Building with boost/cgal is not included, since some of the logic is not
# entirely clear and thus untested.
# - Resolution of flex, zlib needs more attention (within OpenFOAM)
#
# - mpi handling: WM_MPLIB=SYSTEMMPI and use spack to populate prefs.sh for it.
# Provide wmake rules for special purpose 'USER' and 'USERMPI'
# mpi implementations, in case these are required.
#
# Known issues
# - Combining +zoltan with +int64 has not been tested, but probably won't work.
# - Combining +mgridgen with +int64 or +float32 probably won't work.
#
##############################################################################
import glob
import re
import os
import llnl.util.tty as tty
from spack import *
from spack.pkg.builtin.openfoam import add_extra_files
from spack.pkg.builtin.openfoam import write_environ
from spack.pkg.builtin.openfoam import rewrite_environ_files
from spack.pkg.builtin.openfoam import mplib_content
from spack.pkg.builtin.openfoam import OpenfoamArch
from spack.util.environment import EnvironmentModifications
class OpenfoamOrg(Package):
"""OpenFOAM is a GPL-opensource C++ CFD-toolbox.
The openfoam.org release is managed by the OpenFOAM Foundation Ltd as
a licensee of the OPENFOAM trademark.
This offering is not approved or endorsed by OpenCFD Ltd,
producer and distributor of the OpenFOAM software via www.openfoam.com,
and owner of the OPENFOAM trademark.
"""
homepage = "http://www.openfoam.org/"
baseurl = "https://github.com/OpenFOAM"
url = "https://github.com/OpenFOAM/OpenFOAM-4.x/archive/version-4.1.tar.gz"
git = "https://github.com/OpenFOAM/OpenFOAM-dev.git"
version('develop', branch='master')
version('7', sha256='12389cf092dc032372617785822a597aee434a50a62db2a520ab35ba5a7548b5',
url=baseurl + '/OpenFOAM-7/archive/version-7.tar.gz')
version('6', sha256='32a6af4120e691ca2df29c5b9bd7bc7a3e11208947f9bccf6087cfff5492f025',
url=baseurl + '/OpenFOAM-6/archive/version-6.tar.gz')
version('5.0', sha256='9057d6a8bb9fa18802881feba215215699065e0b3c5cdd0c0e84cb29c9916c89',
url=baseurl + '/OpenFOAM-5.x/archive/version-5.0.tar.gz')
version('4.1', sha256='2de18de64e7abdb1b649ad8e9d2d58b77a2b188fb5bcb6f7c2a038282081fd31',
url=baseurl + '/OpenFOAM-4.x/archive/version-4.1.tar.gz')
version('2.4.0', sha256='9529aa7441b64210c400c019dcb2e0410fcfd62a6f62d23b6c5994c4753c4465',
url=baseurl + '/OpenFOAM-2.4.x/archive/version-2.4.0.tar.gz')
variant('int64', default=False,
description='Compile with 64-bit label')
variant('float32', default=False,
description='Compile with 32-bit scalar (single-precision)')
variant('source', default=True,
description='Install library/application sources and tutorials')
variant('metis', default=False,
description='With metis decomposition')
depends_on('mpi')
depends_on('zlib')
depends_on('flex')
depends_on('cmake', type='build')
# Require scotch with ptscotch - corresponds to standard OpenFOAM setup
depends_on('scotch~metis+mpi~int64', when='~int64')
depends_on('scotch~metis+mpi+int64', when='+int64')
depends_on('metis@5:', when='+metis')
depends_on('metis+int64', when='+metis+int64')
# General patches - foamEtcFile as per openfoam.com (robuster)
common = ['spack-Allwmake', 'README-spack']
assets = ['bin/foamEtcFile']
# Version-specific patches
patch('https://github.com/OpenFOAM/OpenFOAM-7/commit/ef33cf38ac9b811072a8970c71fbda35a90f6641.patch',
sha256='73103e6b1bdbf3b1e0d517cbbd11562e98c6e9464df5f43e5125e9a5b457d1c5', when='@7')
patch('50-etc.patch', when='@5.0:5.9')
patch('41-etc.patch', when='@4.1')
patch('41-site.patch', when='@4.1:')
patch('240-etc.patch', when='@2.4.0')
# The openfoam architecture, compiler information etc
_foam_arch = None
# Content for etc/prefs.{csh,sh}
etc_prefs = {}
# Content for etc/config.{csh,sh}/ files
etc_config = {}
phases = ['configure', 'build', 'install']
build_script = './spack-Allwmake' # <- Added by patch() method.
#
# - End of definitions / setup -
#
# Some user config settings
@property
def config(self):
settings = {
# Use system mpi for spack
'mplib': 'SYSTEMMPI',
# Add links into bin/, lib/ (eg, for other applications)
'link': False,
}
# OpenFOAM v2.4 and earlier lacks WM_LABEL_OPTION
if self.spec.satisfies('@:2.4'):
settings['label-size'] = False
return settings
def setup_run_environment(self, env):
bashrc = self.prefix.etc.bashrc
try:
env.extend(EnvironmentModifications.from_sourcing_file(
bashrc, clean=True
))
except Exception as e:
msg = 'unexpected error when sourcing OpenFOAM bashrc [{0}]'
tty.warn(msg.format(str(e)))
def setup_dependent_build_environment(self, env, dependent_spec):
"""Location of the OpenFOAM project directory.
This is identical to the WM_PROJECT_DIR value, but we avoid that
variable since it would mask the normal OpenFOAM cleanup of
previous versions.
"""
env.set('FOAM_PROJECT_DIR', self.projectdir)
def setup_dependent_run_environment(self, env, dependent_spec):
"""Location of the OpenFOAM project directory.
This is identical to the WM_PROJECT_DIR value, but we avoid that
variable since it would mask the normal OpenFOAM cleanup of
previous versions.
"""
env.set('FOAM_PROJECT_DIR', self.projectdir)
@property
def projectdir(self):
"""Absolute location of project directory: WM_PROJECT_DIR/"""
return self.prefix # <- install directly under prefix
@property
def foam_arch(self):
if not self._foam_arch:
self._foam_arch = OpenfoamArch(self.spec, **self.config)
return self._foam_arch
@property
def archbin(self):
"""Relative location of architecture-specific executables"""
return join_path('platforms', self.foam_arch, 'bin')
@property
def archlib(self):
"""Relative location of architecture-specific libraries"""
return join_path('platforms', self.foam_arch, 'lib')
def rename_source(self):
"""This is fairly horrible.
The github tarfiles have weird names that do not correspond to the
canonical name. We need to rename these, but leave a symlink for
spack to work with.
"""
# Note that this particular OpenFOAM requires absolute directories
# to build correctly!
parent = os.path.dirname(self.stage.source_path)
original = os.path.basename(self.stage.source_path)
target = 'OpenFOAM-{0}'.format(self.version)
# Could also grep through etc/bashrc for WM_PROJECT_VERSION
with working_dir(parent):
if original != target and not os.path.lexists(target):
os.rename(original, target)
os.symlink(target, original)
tty.info('renamed {0} -> {1}'.format(original, target))
def patch(self):
"""Adjust OpenFOAM build for spack.
Where needed, apply filter as an alternative to normal patching."""
self.rename_source()
add_extra_files(self, self.common, self.assets)
# Avoid WM_PROJECT_INST_DIR for ThirdParty, site or jobControl.
# Use openfoam-site.patch to handle jobControl, site.
#
# Filtering: bashrc,cshrc (using a patch is less flexible)
edits = {
'WM_THIRD_PARTY_DIR':
r'$WM_PROJECT_DIR/ThirdParty #SPACK: No separate third-party',
'WM_VERSION': str(self.version), # consistency
'FOAMY_HEX_MESH': '', # This is horrible (unset variable?)
}
rewrite_environ_files( # Adjust etc/bashrc and etc/cshrc
edits,
posix=join_path('etc', 'bashrc'),
cshell=join_path('etc', 'cshrc'))
def configure(self, spec, prefix):
"""Make adjustments to the OpenFOAM configuration files in their various
locations: etc/bashrc, etc/config.sh/FEATURE and customizations that
don't properly fit get placed in the etc/prefs.sh file (similiarly for
csh).
"""
# Filtering bashrc, cshrc
edits = {}
edits.update(self.foam_arch.foam_dict())
rewrite_environ_files( # Adjust etc/bashrc and etc/cshrc
edits,
posix=join_path('etc', 'bashrc'),
cshell=join_path('etc', 'cshrc'))
# MPI content, with absolute paths
user_mpi = mplib_content(spec)
# Content for etc/prefs.{csh,sh}
self.etc_prefs = {
r'MPI_ROOT': spec['mpi'].prefix, # Absolute
r'MPI_ARCH_FLAGS': '"%s"' % user_mpi['FLAGS'],
r'MPI_ARCH_INC': '"%s"' % user_mpi['PINC'],
r'MPI_ARCH_LIBS': '"%s"' % user_mpi['PLIBS'],
}
# Content for etc/config.{csh,sh}/ files
self.etc_config = {
'CGAL': {},
'scotch': {},
'metis': {},
'paraview': [],
'gperftools': [], # Currently unused
}
if True:
self.etc_config['scotch'] = {
'SCOTCH_ARCH_PATH': spec['scotch'].prefix,
# For src/parallel/decompose/Allwmake
'SCOTCH_VERSION': 'scotch-{0}'.format(spec['scotch'].version),
}
if '+metis' in spec:
self.etc_config['metis'] = {
'METIS_ARCH_PATH': spec['metis'].prefix,
}
# Write prefs files according to the configuration.
# Only need prefs.sh for building, but install both for end-users
if self.etc_prefs:
write_environ(
self.etc_prefs,
posix=join_path('etc', 'prefs.sh'),
cshell=join_path('etc', 'prefs.csh'))
# Adjust components to use SPACK variants
for component, subdict in self.etc_config.items():
# Versions up to 3.0 used an etc/config/component.sh naming
# convention instead of etc/config.sh/component
if spec.satisfies('@:3.0'):
write_environ(
subdict,
posix=join_path('etc', 'config', component) + '.sh',
cshell=join_path('etc', 'config', component) + '.csh')
else:
write_environ(
subdict,
posix=join_path('etc', 'config.sh', component),
cshell=join_path('etc', 'config.csh', component))
def build(self, spec, prefix):
"""Build using the OpenFOAM Allwmake script, with a wrapper to source
its environment first.
Only build if the compiler is known to be supported.
"""
self.foam_arch.has_rule(self.stage.source_path)
self.foam_arch.create_rules(self.stage.source_path, self)
args = []
if self.parallel: # Build in parallel? - pass via the environment
os.environ['WM_NCOMPPROCS'] = str(make_jobs)
builder = Executable(self.build_script)
builder(*args)
def install(self, spec, prefix):
"""Install under the projectdir"""
mkdirp(self.projectdir)
projdir = os.path.basename(self.projectdir)
# Filtering: bashrc, cshrc
edits = {
'WM_PROJECT_INST_DIR': os.path.dirname(self.projectdir),
'WM_PROJECT_DIR': join_path('$WM_PROJECT_INST_DIR', projdir),
}
# All top-level files, except spack build info and possibly Allwmake
if '+source' in spec:
ignored = re.compile(r'^spack-.*')
else:
ignored = re.compile(r'^(Allwmake|spack-).*')
files = [
f for f in glob.glob("*")
if os.path.isfile(f) and not ignored.search(f)
]
for f in files:
install(f, self.projectdir)
# Having wmake and ~source is actually somewhat pointless...
# Install 'etc' before 'bin' (for symlinks)
# META-INFO for 1812 and later (or backported)
dirs = ['META-INFO', 'etc', 'bin', 'wmake']
if '+source' in spec:
dirs.extend(['applications', 'src', 'tutorials'])
for d in dirs:
if os.path.isdir(d):
install_tree(
d,
join_path(self.projectdir, d),
symlinks=True)
dirs = ['platforms']
if '+source' in spec:
dirs.extend(['doc'])
# Install platforms (and doc) skipping intermediate targets
relative_ignore_paths = ['src', 'applications', 'html', 'Guides']
ignore = lambda p: p in relative_ignore_paths
for d in dirs:
install_tree(
d,
join_path(self.projectdir, d),
ignore=ignore,
symlinks=True)
etc_dir = join_path(self.projectdir, 'etc')
rewrite_environ_files( # Adjust etc/bashrc and etc/cshrc
edits,
posix=join_path(etc_dir, 'bashrc'),
cshell=join_path(etc_dir, 'cshrc'))
self.install_links()
def install_links(self):
"""Add symlinks into bin/, lib/ (eg, for other applications)"""
# Make build log visible - it contains OpenFOAM-specific information
with working_dir(self.projectdir):
os.symlink(
join_path(os.path.relpath(self.install_log_path)),
join_path('log.' + str(self.foam_arch)))
if not self.config['link']:
return
# ln -s platforms/linux64GccXXX/lib lib
with working_dir(self.projectdir):
if os.path.isdir(self.archlib):
os.symlink(self.archlib, 'lib')
# (cd bin && ln -s ../platforms/linux64GccXXX/bin/* .)
with working_dir(join_path(self.projectdir, 'bin')):
for f in [
f for f in glob.glob(join_path('..', self.archbin, "*"))
if os.path.isfile(f)
]:
os.symlink(f, os.path.basename(f))
|
rspavel/spack
|
var/spack/repos/builtin/packages/openfoam-org/package.py
|
Python
|
lgpl-2.1
| 15,258
|
[
"ParaView"
] |
ac1f0bc4fe7a1eed2ad7a0aab3e9d216353230dac11cdd2bfe7da2513853cea4
|
# This file contains waf optimisations for Samba
# most of these optimisations are possible because of the restricted build environment
# that Samba has. For example, Samba doesn't attempt to cope with Win32 paths during the
# build, and Samba doesn't need build varients
# overall this makes some build tasks quite a bit faster
import os
import Build, Utils, Node
from TaskGen import feature, after, before
import preproc
@feature('c', 'cc', 'cxx')
@after('apply_type_vars', 'apply_lib_vars', 'apply_core')
def apply_incpaths(self):
lst = []
try:
kak = self.bld.kak
except AttributeError:
kak = self.bld.kak = {}
# TODO move the uselib processing out of here
for lib in self.to_list(self.uselib):
for path in self.env['CPPPATH_' + lib]:
if not path in lst:
lst.append(path)
if preproc.go_absolute:
for path in preproc.standard_includes:
if not path in lst:
lst.append(path)
for path in self.to_list(self.includes):
if not path in lst:
if preproc.go_absolute or path[0] != '/': # os.path.isabs(path):
lst.append(path)
else:
self.env.prepend_value('CPPPATH', path)
for path in lst:
node = None
if path[0] == '/': # os.path.isabs(path):
if preproc.go_absolute:
node = self.bld.root.find_dir(path)
elif path[0] == '#':
node = self.bld.srcnode
if len(path) > 1:
try:
node = kak[path]
except KeyError:
kak[path] = node = node.find_dir(path[1:])
else:
try:
node = kak[(self.path.id, path)]
except KeyError:
kak[(self.path.id, path)] = node = self.path.find_dir(path)
if node:
self.env.append_value('INC_PATHS', node)
@feature('c', 'cc')
@after('apply_incpaths')
def apply_obj_vars_cc(self):
"""after apply_incpaths for INC_PATHS"""
env = self.env
app = env.append_unique
cpppath_st = env['CPPPATH_ST']
lss = env['_CCINCFLAGS']
try:
cac = self.bld.cac
except AttributeError:
cac = self.bld.cac = {}
# local flags come first
# set the user-defined includes paths
for i in env['INC_PATHS']:
try:
lss.extend(cac[i.id])
except KeyError:
cac[i.id] = [cpppath_st % i.bldpath(env), cpppath_st % i.srcpath(env)]
lss.extend(cac[i.id])
env['_CCINCFLAGS'] = lss
# set the library include paths
for i in env['CPPPATH']:
app('_CCINCFLAGS', cpppath_st % i)
import Node, Environment
def vari(self):
return "default"
Environment.Environment.variant = vari
def variant(self, env):
if not env: return 0
elif self.id & 3 == Node.FILE: return 0
else: return "default"
Node.Node.variant = variant
import TaskGen, Task
def create_task(self, name, src=None, tgt=None):
task = Task.TaskBase.classes[name](self.env, generator=self)
if src:
task.set_inputs(src)
if tgt:
task.set_outputs(tgt)
return task
TaskGen.task_gen.create_task = create_task
def hash_constraints(self):
a = self.attr
sum = hash((str(a('before', '')),
str(a('after', '')),
str(a('ext_in', '')),
str(a('ext_out', '')),
self.__class__.maxjobs))
return sum
Task.TaskBase.hash_constraints = hash_constraints
def hash_env_vars(self, env, vars_lst):
idx = str(id(env)) + str(vars_lst)
try:
return self.cache_sig_vars[idx]
except KeyError:
pass
m = Utils.md5()
m.update(''.join([str(env[a]) for a in vars_lst]))
ret = self.cache_sig_vars[idx] = m.digest()
return ret
Build.BuildContext.hash_env_vars = hash_env_vars
def store_fast(self, filename):
file = open(filename, 'wb')
data = self.get_merged_dict()
try:
Build.cPickle.dump(data, file, -1)
finally:
file.close()
Environment.Environment.store_fast = store_fast
def load_fast(self, filename):
file = open(filename, 'rb')
try:
data = Build.cPickle.load(file)
finally:
file.close()
self.table.update(data)
Environment.Environment.load_fast = load_fast
def is_this_a_static_lib(self, name):
try:
cache = self.cache_is_this_a_static_lib
except AttributeError:
cache = self.cache_is_this_a_static_lib = {}
try:
return cache[name]
except KeyError:
ret = cache[name] = 'cstaticlib' in self.bld.get_tgen_by_name(name).features
return ret
TaskGen.task_gen.is_this_a_static_lib = is_this_a_static_lib
def shared_ancestors(self):
try:
cache = self.cache_is_this_a_static_lib
except AttributeError:
cache = self.cache_is_this_a_static_lib = {}
try:
return cache[id(self)]
except KeyError:
ret = []
if 'cshlib' in self.features: # or 'cprogram' in self.features:
if getattr(self, 'uselib_local', None):
lst = self.to_list(self.uselib_local)
ret = [x for x in lst if not self.is_this_a_static_lib(x)]
cache[id(self)] = ret
return ret
TaskGen.task_gen.shared_ancestors = shared_ancestors
@feature('c', 'cc', 'cxx')
@after('apply_link', 'init_cc', 'init_cxx', 'apply_core')
def apply_lib_vars(self):
"""after apply_link because of 'link_task'
after default_cc because of the attribute 'uselib'"""
# after 'apply_core' in case if 'cc' if there is no link
env = self.env
app = env.append_value
seen_libpaths = set([])
# OPTIMIZATION 1: skip uselib variables already added (700ms)
seen_uselib = set([])
# 1. the case of the libs defined in the project (visit ancestors first)
# the ancestors external libraries (uselib) will be prepended
self.uselib = self.to_list(self.uselib)
names = self.to_list(self.uselib_local)
seen = set([])
tmp = Utils.deque(names) # consume a copy of the list of names
while tmp:
lib_name = tmp.popleft()
# visit dependencies only once
if lib_name in seen:
continue
y = self.get_tgen_by_name(lib_name)
if not y:
raise Utils.WafError('object %r was not found in uselib_local (required by %r)' % (lib_name, self.name))
y.post()
seen.add(lib_name)
# OPTIMIZATION 2: pre-compute ancestors shared libraries (100ms)
tmp.extend(y.shared_ancestors())
# link task and flags
if getattr(y, 'link_task', None):
link_name = y.target[y.target.rfind('/') + 1:]
if 'cstaticlib' in y.features:
app('STATICLIB', link_name)
elif 'cshlib' in y.features or 'cprogram' in y.features:
# WARNING some linkers can link against programs
app('LIB', link_name)
# the order
self.link_task.set_run_after(y.link_task)
# for the recompilation
dep_nodes = getattr(self.link_task, 'dep_nodes', [])
self.link_task.dep_nodes = dep_nodes + y.link_task.outputs
# OPTIMIZATION 3: reduce the amount of function calls
# add the link path too
par = y.link_task.outputs[0].parent
if id(par) not in seen_libpaths:
seen_libpaths.add(id(par))
tmp_path = par.bldpath(self.env)
if not tmp_path in env['LIBPATH']:
env.prepend_value('LIBPATH', tmp_path)
# add ancestors uselib too - but only propagate those that have no staticlib
for v in self.to_list(y.uselib):
if v not in seen_uselib:
seen_uselib.add(v)
if not env['STATICLIB_' + v]:
if not v in self.uselib:
self.uselib.insert(0, v)
# 2. the case of the libs defined outside
for x in self.uselib:
for v in self.p_flag_vars:
val = self.env[v + '_' + x]
if val:
self.env.append_value(v, val)
@feature('cprogram', 'cshlib', 'cstaticlib')
@after('apply_lib_vars')
@before('apply_obj_vars')
def samba_before_apply_obj_vars(self):
"""before apply_obj_vars for uselib, this removes the standard pathes"""
def is_standard_libpath(env, path):
for _path in env.STANDARD_LIBPATH:
if _path == os.path.normpath(path):
return True
return False
v = self.env
for i in v['RPATH']:
if is_standard_libpath(v, i):
v['RPATH'].remove(i)
for i in v['LIBPATH']:
if is_standard_libpath(v, i):
v['LIBPATH'].remove(i)
|
abartlet/samba
|
buildtools/wafsamba/samba_optimisation.py
|
Python
|
gpl-3.0
| 8,773
|
[
"VisIt"
] |
79422bf253a09d6311564f526c1982118aaae58d69ed63d5a859f16ef420717d
|
"""
================================
Workshop: Dartmouth College 2010
================================
First lets go to the directory with the data we'll be working on and start the interactive python interpreter
(with some nipype specific configuration). Note that nipype does not need to be run through ipython - it is
just much nicer to do interactive work in it.
.. sourcecode:: bash
cd $TDPATH
ipython -p nipype
For every neuroimaging procedure supported by nipype there exists a wrapper - a small piece of code managing
the underlying software (FSL, SPM, AFNI etc.). We call those interfaces. They are standarised so we can hook them up
together. Lets have a look at some of them.
.. sourcecode:: ipython
In [1]: import nipype.interfaces.fsl as fsl
In [2]: fsl.BET.help()
Inputs
------
Mandatory:
in_file: input file to skull strip
Optional:
args: Additional parameters to the command
center: center of gravity in voxels
environ: Environment variables (default={})
frac: fractional intensity threshold
functional: apply to 4D fMRI data
mutually exclusive: functional, reduce_bias
mask: create binary mask image
mesh: generate a vtk mesh brain surface
no_output: Don't generate segmented output
out_file: name of output skull stripped image
outline: create surface outline image
output_type: FSL output type
radius: head radius
reduce_bias: bias field and neck cleanup
mutually exclusive: functional, reduce_bias
skull: create skull image
threshold: apply thresholding to segmented brain image and mask
vertical_gradient: vertical gradient in fractional intensity threshold (-1, 1)
Outputs
-------
mask_file: path/name of binary brain mask (if generated)
meshfile: path/name of vtk mesh file (if generated)
out_file: path/name of skullstripped file
outline_file: path/name of outline file (if generated)
In [3]: import nipype.interfaces.freesurfer as fs
In [4]: fs.Smooth.help()
Inputs
------
Mandatory:
in_file: source volume
num_iters: number of iterations instead of fwhm
mutually exclusive: surface_fwhm
reg_file: registers volume to surface anatomical
surface_fwhm: surface FWHM in mm
mutually exclusive: num_iters
requires: reg_file
Optional:
args: Additional parameters to the command
environ: Environment variables (default={})
proj_frac: project frac of thickness a long surface normal
mutually exclusive: proj_frac_avg
proj_frac_avg: average a long normal min max delta
mutually exclusive: proj_frac
smoothed_file: output volume
subjects_dir: subjects directory
vol_fwhm: volumesmoothing outside of surface
Outputs
-------
args: Additional parameters to the command
environ: Environment variables
smoothed_file: smoothed input volume
subjects_dir: subjects directory
You can read about all of the interfaces implemented in nipype at our online documentation at http://nipy.sourceforge.net/nipype/documentation.html#documentation .
Check it out now.
Using interfaces
----------------
Having interfaces allows us to use third party software (like FSL BET) as function. Look how simple it is.
"""
import nipype.interfaces.fsl as fsl
result = fsl.BET(in_file='data/s1/struct.nii').run()
print result
"""
Running a single program is not much of a breakthrough. Lets run motion correction followed by smoothing
(isotropic - in other words not using SUSAN). Notice that in the first line we are setting the output data type
for all FSL interfaces.
"""
fsl.FSLCommand.set_default_output_type('NIFTI_GZ')
result1 = fsl.MCFLIRT(in_file='data/s1/f3.nii').run()
result2 = fsl.Smooth(in_file='f3_mcf.nii.gz', fwhm=6).run()
"""
Simple workflow
---------------
In the previous example we knew that fsl.MCFLIRT will produce a file called f3_mcf.nii.gz and we have hard coded
this as an input to fsl.Smooth. This is quite limited, but luckily nipype supports joining interfaces in pipelines.
This way output of one interface will be used as an input of another without having to hard code anything. Before
connecting Interfaces we need to put them into (separate) Nodes and give them unique names. This way every interface will
process data in a separate folder.
"""
import nipype.pipeline.engine as pe
import os
motion_correct = pe.Node(interface=fsl.MCFLIRT(in_file=os.path.abspath('data/s1/f3.nii')),
name="motion_correct")
smooth = pe.Node(interface=fsl.Smooth(fwhm=6), name="smooth")
motion_correct_and_smooth = pe.Workflow(name="motion_correct_and_smooth")
motion_correct_and_smooth.base_dir = os.path.abspath('.') # define where will be the root folder for the workflow
motion_correct_and_smooth.connect([
(motion_correct, smooth, [('out_file', 'in_file')])
])
# we are connecting 'out_file' output of motion_correct to 'in_file' input of smooth
motion_correct_and_smooth.run()
"""
Another workflow
----------------
Another example of a simple workflow (calculate the mean of fMRI signal and subtract it).
This time we'll be assigning inputs after defining the workflow.
"""
calc_mean = pe.Node(interface=fsl.ImageMaths(), name="calc_mean")
calc_mean.inputs.op_string = "-Tmean"
subtract = pe.Node(interface=fsl.ImageMaths(), name="subtract")
subtract.inputs.op_string = "-sub"
demean = pe.Workflow(name="demean")
demean.base_dir = os.path.abspath('.')
demean.connect([
(calc_mean, subtract, [('out_file', 'in_file2')])
])
demean.inputs.calc_mean.in_file = os.path.abspath('data/s1/f3.nii')
demean.inputs.subtract.in_file = os.path.abspath('data/s1/f3.nii')
demean.run()
"""
Reusing workflows
-----------------
The beauty of the workflows is that they are reusable. We can just import a workflow made by someone
else and feed it with our data.
"""
from fmri_fsl import preproc
preproc.base_dir = os.path.abspath('.')
preproc.inputs.inputspec.func = os.path.abspath('data/s1/f3.nii')
preproc.inputs.inputspec.struct = os.path.abspath('data/s1/struct.nii')
preproc.run()
"""
... and we can run it again and it won't actually rerun anything because none of
the parameters have changed.
"""
preproc.run()
"""
... and we can change a parameter and run it again. Only the dependent nodes
are rerun and that too only if the input state has changed.
"""
preproc.inputs.meanfuncmask.frac = 0.5
preproc.run()
"""
Visualizing workflows 1
-----------------------
So what did we run in this precanned workflow
"""
preproc.write_graph()
"""
Datasink
--------
Datasink is a special interface for copying and arranging results.
"""
import nipype.interfaces.io as nio
preproc.inputs.inputspec.func = os.path.abspath('data/s1/f3.nii')
preproc.inputs.inputspec.struct = os.path.abspath('data/s1/struct.nii')
datasink = pe.Node(interface=nio.DataSink(),name='sinker')
preprocess = pe.Workflow(name='preprocout')
preprocess.base_dir = os.path.abspath('.')
preprocess.connect([
(preproc, datasink, [('meanfunc2.out_file', 'meanfunc'),
('maskfunc3.out_file', 'funcruns')])
])
preprocess.run()
"""
Datagrabber
-----------
Datagrabber is (surprise, surprise) an interface for collecting files from hard drive. It is very flexible and
supports almost any file organisation of your data you can imagine.
"""
datasource1 = nio.DataGrabber()
datasource1.inputs.template = 'data/s1/f3.nii'
datasource1.inputs.sort_filelist = True
results = datasource1.run()
print results.outputs
datasource2 = nio.DataGrabber()
datasource2.inputs.template = 'data/s*/f*.nii'
datasource2.inputs.sort_filelist = True
results = datasource2.run()
print results.outputs
datasource3 = nio.DataGrabber(infields=['run'])
datasource3.inputs.template = 'data/s1/f%d.nii'
datasource3.inputs.sort_filelist = True
datasource3.inputs.run = [3, 7]
results = datasource3.run()
print results.outputs
datasource4 = nio.DataGrabber(infields=['subject_id', 'run'])
datasource4.inputs.template = 'data/%s/f%d.nii'
datasource4.inputs.sort_filelist = True
datasource4.inputs.run = [3, 7]
datasource4.inputs.subject_id = ['s1', 's3']
results = datasource4.run()
print results.outputs
"""
Iterables
---------
Iterables is a special field of the Node class that enables to iterate all workfloes/nodes connected to it over
some parameters. Here we'll use it to iterate over two subjects.
"""
import nipype.interfaces.utility as util
infosource = pe.Node(interface=util.IdentityInterface(fields=['subject_id']),
name="infosource")
infosource.iterables = ('subject_id', ['s1', 's3'])
datasource = pe.Node(nio.DataGrabber(infields=['subject_id'], outfields=['func', 'struct']), name="datasource")
datasource.inputs.template = '%s/%s.nii'
datasource.inputs.base_directory = os.path.abspath('data')
datasource.inputs.template_args = dict(func=[['subject_id','f3']], struct=[['subject_id','struct']])
datasource.inputs.sort_filelist = True
my_workflow = pe.Workflow(name="my_workflow")
my_workflow.base_dir = os.path.abspath('.')
my_workflow.connect([(infosource, datasource, [('subject_id', 'subject_id')]),
(datasource, preproc, [('func', 'inputspec.func'),
('struct', 'inputspec.struct')])])
my_workflow.run()
"""
and we can change a node attribute and run it again
"""
smoothnode = my_workflow.get_node('preproc.smooth')
assert(str(smoothnode)=='preproc.smooth')
smoothnode.iterables = ('fwhm', [5.,10.])
my_workflow.run()
"""
Visualizing workflows 2
-----------------------
In the case of nested workflows, we might want to look at expanded forms of the workflow.
"""
|
rameshvs/nipype
|
examples/workshop_dartmouth_2010.py
|
Python
|
bsd-3-clause
| 9,842
|
[
"VTK"
] |
0792ebc0c2ff6e387fa9f3771deebaac8e0fd843d12f65a9bbc7ea37e46c2a1c
|
# coding=utf-8
# Copyright 2016 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Visitor class for traversing Python statements."""
from __future__ import unicode_literals
import string
import textwrap
from grumpy.compiler import block
from grumpy.compiler import expr
from grumpy.compiler import expr_visitor
from grumpy.compiler import imputil
from grumpy.compiler import util
from grumpy.pythonparser import algorithm
from grumpy.pythonparser import ast
_NATIVE_TYPE_PREFIX = 'type_'
# Partial list of known vcs for go module import
# Full list can be found at https://golang.org/src/cmd/go/vcs.go
# TODO: Use official vcs.go module instead of partial list
_KNOWN_VCS = [
'golang.org', 'github.com', 'bitbucket.org', 'git.apache.org',
'git.openstack.org', 'launchpad.net'
]
_nil_expr = expr.nil_expr
class StatementVisitor(algorithm.Visitor):
"""Outputs Go statements to a Writer for the given Python nodes."""
# pylint: disable=invalid-name,missing-docstring
def __init__(self, block_, future_node=None):
self.block = block_
self.future_node = future_node
self.writer = util.Writer()
self.expr_visitor = expr_visitor.ExprVisitor(self)
def generic_visit(self, node):
msg = 'node not yet implemented: {}'.format(type(node).__name__)
raise util.ParseError(node, msg)
def visit_expr(self, node):
return self.expr_visitor.visit(node)
def visit_Assert(self, node):
self._write_py_context(node.lineno)
# TODO: Only evaluate msg if cond is false.
with self.visit_expr(node.msg) if node.msg else _nil_expr as msg,\
self.visit_expr(node.test) as cond:
self.writer.write_checked_call1(
'πg.Assert(πF, {}, {})', cond.expr, msg.expr)
def visit_AugAssign(self, node):
op_type = type(node.op)
if op_type not in StatementVisitor._AUG_ASSIGN_TEMPLATES:
fmt = 'augmented assignment op not implemented: {}'
raise util.ParseError(node, fmt.format(op_type.__name__))
self._write_py_context(node.lineno)
with self.visit_expr(node.target) as target,\
self.visit_expr(node.value) as value,\
self.block.alloc_temp() as temp:
self.writer.write_checked_call2(
temp, StatementVisitor._AUG_ASSIGN_TEMPLATES[op_type],
lhs=target.expr, rhs=value.expr)
self._assign_target(node.target, temp.expr)
def visit_Assign(self, node):
self._write_py_context(node.lineno)
with self.visit_expr(node.value) as value:
for target in node.targets:
self._tie_target(target, value.expr)
def visit_Break(self, node):
if not self.block.loop_stack:
raise util.ParseError(node, "'break' not in loop")
self._write_py_context(node.lineno)
self.writer.write_tmpl(textwrap.dedent("""\
$breakvar = true
continue"""), breakvar=self.block.top_loop().breakvar.name)
def visit_ClassDef(self, node):
# Since we only care about global vars, we end up throwing away the locals
# collected by BlockVisitor. But use it anyway since it buys us detection of
# assignment to vars that are later declared global.
block_visitor = block.BlockVisitor()
for child in node.body:
block_visitor.visit(child)
global_vars = {v.name for v in block_visitor.vars.values()
if v.type == block.Var.TYPE_GLOBAL}
# Visit all the statements inside body of the class definition.
body_visitor = StatementVisitor(block.ClassBlock(
self.block, node.name, global_vars), self.future_node)
# Indent so that the function body is aligned with the goto labels.
with body_visitor.writer.indent_block():
body_visitor._visit_each(node.body) # pylint: disable=protected-access
self._write_py_context(node.lineno)
with self.block.alloc_temp('*πg.Dict') as cls, \
self.block.alloc_temp() as mod_name, \
self.block.alloc_temp('[]*πg.Object') as bases, \
self.block.alloc_temp() as meta:
self.writer.write('{} = make([]*πg.Object, {})'.format(
bases.expr, len(node.bases)))
for i, b in enumerate(node.bases):
with self.visit_expr(b) as b:
self.writer.write('{}[{}] = {}'.format(bases.expr, i, b.expr))
self.writer.write('{} = πg.NewDict()'.format(cls.name))
self.writer.write_checked_call2(
mod_name, 'πF.Globals().GetItem(πF, {}.ToObject())',
self.block.root.intern('__name__'))
self.writer.write_checked_call1(
'{}.SetItem(πF, {}.ToObject(), {})',
cls.expr, self.block.root.intern('__module__'), mod_name.expr)
tmpl = textwrap.dedent("""
_, πE = πg.NewCode($name, $filename, nil, 0, func(πF *πg.Frame, _ []*πg.Object) (*πg.Object, *πg.BaseException) {
\tπClass := $cls
\t_ = πClass""")
self.writer.write_tmpl(tmpl, name=util.go_str(node.name),
filename=util.go_str(self.block.root.filename),
cls=cls.expr)
with self.writer.indent_block():
self.writer.write_temp_decls(body_visitor.block)
self.writer.write_block(body_visitor.block,
body_visitor.writer.getvalue())
self.writer.write('return nil, nil')
tmpl = textwrap.dedent("""\
}).Eval(πF, πF.Globals(), nil, nil)
if πE != nil {
\tcontinue
}
if $meta, πE = $cls.GetItem(πF, $metaclass_str.ToObject()); πE != nil {
\tcontinue
}
if $meta == nil {
\t$meta = πg.TypeType.ToObject()
}""")
self.writer.write_tmpl(
tmpl, meta=meta.name, cls=cls.expr,
metaclass_str=self.block.root.intern('__metaclass__'))
with self.block.alloc_temp() as type_:
type_expr = ('{}.Call(πF, []*πg.Object{{πg.NewStr({}).ToObject(), '
'πg.NewTuple({}...).ToObject(), {}.ToObject()}}, nil)')
self.writer.write_checked_call2(
type_, type_expr, meta.expr,
util.go_str(node.name), bases.expr, cls.expr)
self.block.bind_var(self.writer, node.name, type_.expr)
def visit_Continue(self, node):
if not self.block.loop_stack:
raise util.ParseError(node, "'continue' not in loop")
self._write_py_context(node.lineno)
self.writer.write('continue')
def visit_Delete(self, node):
self._write_py_context(node.lineno)
for target in node.targets:
if isinstance(target, ast.Attribute):
with self.visit_expr(target.value) as t:
self.writer.write_checked_call1(
'πg.DelAttr(πF, {}, {})', t.expr,
self.block.root.intern(target.attr))
elif isinstance(target, ast.Name):
self.block.del_var(self.writer, target.id)
elif isinstance(target, ast.Subscript):
with self.visit_expr(target.value) as t,\
self.visit_expr(target.slice) as index:
self.writer.write_checked_call1('πg.DelItem(πF, {}, {})',
t.expr, index.expr)
else:
msg = 'del target not implemented: {}'.format(type(target).__name__)
raise util.ParseError(node, msg)
def visit_Expr(self, node):
self._write_py_context(node.lineno)
self.visit_expr(node.value).free()
def visit_For(self, node):
with self.block.alloc_temp() as i:
with self.visit_expr(node.iter) as iter_expr:
self.writer.write_checked_call2(i, 'πg.Iter(πF, {})', iter_expr.expr)
def testfunc(testvar):
with self.block.alloc_temp() as n:
self.writer.write_tmpl(textwrap.dedent("""\
if $n, πE = πg.Next(πF, $i); πE != nil {
\tisStop, exc := πg.IsInstance(πF, πE.ToObject(), πg.StopIterationType.ToObject())
\tif exc != nil {
\t\tπE = exc
\t} else if isStop {
\t\tπE = nil
\t\tπF.RestoreExc(nil, nil)
\t}
\t$testvar = !isStop
} else {
\t$testvar = true"""), n=n.name, i=i.expr, testvar=testvar.name)
with self.writer.indent_block():
self._tie_target(node.target, n.expr)
self.writer.write('}')
self._visit_loop(testfunc, node)
def visit_FunctionDef(self, node):
self._write_py_context(node.lineno + len(node.decorator_list))
func = self.visit_function_inline(node)
self.block.bind_var(self.writer, node.name, func.expr)
while node.decorator_list:
decorator = node.decorator_list.pop()
wrapped = ast.Name(id=node.name)
decorated = ast.Call(func=decorator, args=[wrapped], keywords=[],
starargs=None, kwargs=None)
target = ast.Assign(targets=[wrapped], value=decorated, loc=node.loc)
self.visit_Assign(target)
def visit_Global(self, node):
self._write_py_context(node.lineno)
def visit_If(self, node):
# Collect the nodes for each if/elif/else body and write the dispatching
# switch statement.
bodies = []
# An elif clause is represented as a single If node within the orelse
# section of the previous If node. Thus this loop terminates once we are
# done all the elif clauses at which time the orelse var will contain the
# nodes (if any) for the else clause.
orelse = [node]
while len(orelse) == 1 and isinstance(orelse[0], ast.If):
ifnode = orelse[0]
with self.visit_expr(ifnode.test) as cond:
label = self.block.genlabel()
# We goto the body of the if statement instead of executing it inline
# because the body itself may be a goto target and Go does not support
# jumping to targets inside a block.
with self.block.alloc_temp('bool') as is_true:
self.writer.write_tmpl(textwrap.dedent("""\
if $is_true, πE = πg.IsTrue(πF, $cond); πE != nil {
\tcontinue
}
if $is_true {
\tgoto Label$label
}"""), is_true=is_true.name, cond=cond.expr, label=label)
bodies.append((label, ifnode.body, ifnode.lineno))
orelse = ifnode.orelse
default_label = end_label = self.block.genlabel()
if orelse:
end_label = self.block.genlabel()
# The else is not represented by ast and thus there is no lineno.
bodies.append((default_label, orelse, None))
self.writer.write('goto Label{}'.format(default_label))
# Write the body of each clause.
for label, body, lineno in bodies:
if lineno:
self._write_py_context(lineno)
self.writer.write_label(label)
self._visit_each(body)
self.writer.write('goto Label{}'.format(end_label))
self.writer.write_label(end_label)
def visit_Import(self, node):
self._write_py_context(node.lineno)
for imp in self.block.root.importer.visit(node):
self._import_and_bind(imp)
def visit_ImportFrom(self, node):
self._write_py_context(node.lineno)
if node.module == '__future__' and node != self.future_node:
raise util.LateFutureError(node)
for imp in self.block.root.importer.visit(node):
self._import_and_bind(imp)
def visit_Module(self, node):
self._visit_each(node.body)
def visit_Pass(self, node):
self._write_py_context(node.lineno)
def visit_Print(self, node):
if self.block.root.future_features.print_function:
raise util.ParseError(node, 'syntax error (print is not a keyword)')
self._write_py_context(node.lineno)
with self.block.alloc_temp('[]*πg.Object') as args:
self.writer.write('{} = make([]*πg.Object, {})'.format(
args.expr, len(node.values)))
for i, v in enumerate(node.values):
with self.visit_expr(v) as arg:
self.writer.write('{}[{}] = {}'.format(args.expr, i, arg.expr))
self.writer.write_checked_call1('πg.Print(πF, {}, {})', args.expr,
'true' if node.nl else 'false')
def visit_Raise(self, node):
with self.visit_expr(node.exc) if node.exc else _nil_expr as t,\
self.visit_expr(node.inst) if node.inst else _nil_expr as inst,\
self.visit_expr(node.tback) if node.tback else _nil_expr as tb:
if node.inst:
assert node.exc, 'raise had inst but no type'
if node.tback:
assert node.inst, 'raise had tback but no inst'
self._write_py_context(node.lineno)
self.writer.write('πE = πF.Raise({}, {}, {})'.format(
t.expr, inst.expr, tb.expr))
self.writer.write('continue')
def visit_Return(self, node):
assert isinstance(self.block, block.FunctionBlock)
self._write_py_context(node.lineno)
if self.block.is_generator and node.value:
raise util.ParseError(node, 'returning a value in a generator function')
if node.value:
with self.visit_expr(node.value) as value:
self.writer.write('πR = {}'.format(value.expr))
else:
self.writer.write('πR = πg.None')
self.writer.write('continue')
def visit_Try(self, node):
# The general structure generated by this method is shown below:
#
# checkpoints.Push(Except)
# <try body>
# Checkpoints.Pop()
# <else body>
# goto Finally
# Except:
# <dispatch table>
# Handler1:
# <handler 1 body>
# Checkpoints.Pop() // Finally
# goto Finally
# Handler2:
# <handler 2 body>
# Checkpoints.Pop() // Finally
# goto Finally
# ...
# Finally:
# <finally body>
#
# The dispatch table maps the current exception to the appropriate handler
# label according to the exception clauses.
# Write the try body.
self._write_py_context(node.lineno)
finally_label = self.block.genlabel(is_checkpoint=bool(node.finalbody))
if node.finalbody:
self.writer.write('πF.PushCheckpoint({})'.format(finally_label))
except_label = None
if node.handlers:
except_label = self.block.genlabel(is_checkpoint=True)
self.writer.write('πF.PushCheckpoint({})'.format(except_label))
self._visit_each(node.body)
if except_label:
self.writer.write('πF.PopCheckpoint()') # except_label
if node.orelse:
self._visit_each(node.orelse)
if node.finalbody:
self.writer.write('πF.PopCheckpoint()') # finally_label
self.writer.write('goto Label{}'.format(finally_label))
with self.block.alloc_temp('*πg.BaseException') as exc:
if except_label:
with self.block.alloc_temp('*πg.Traceback') as tb:
self.writer.write_label(except_label)
self.writer.write_tmpl(textwrap.dedent("""\
if πE == nil {
continue
}
πE = nil
$exc, $tb = πF.ExcInfo()"""), exc=exc.expr, tb=tb.expr)
handler_labels = self._write_except_dispatcher(
exc.expr, tb.expr, node.handlers)
# Write the bodies of each of the except handlers.
for handler_label, except_node in zip(handler_labels, node.handlers):
self._write_except_block(handler_label, exc.expr, except_node)
if node.finalbody:
self.writer.write('πF.PopCheckpoint()') # finally_label
self.writer.write('goto Label{}'.format(finally_label))
# Write the finally body.
self.writer.write_label(finally_label)
if node.finalbody:
with self.block.alloc_temp('*πg.Traceback') as tb:
self.writer.write('{}, {} = πF.RestoreExc(nil, nil)'.format(
exc.expr, tb.expr))
self._visit_each(node.finalbody)
self.writer.write_tmpl(textwrap.dedent("""\
if $exc != nil {
\tπE = πF.Raise($exc.ToObject(), nil, $tb.ToObject())
\tcontinue
}
if πR != nil {
\tcontinue
}"""), exc=exc.expr, tb=tb.expr)
def visit_While(self, node):
self._write_py_context(node.lineno)
def testfunc(testvar):
with self.visit_expr(node.test) as cond:
self.writer.write_checked_call2(
testvar, 'πg.IsTrue(πF, {})', cond.expr)
self._visit_loop(testfunc, node)
def visit_With(self, node):
assert len(node.items) == 1, 'multiple items in a with not yet supported'
item = node.items[0]
self._write_py_context(node.loc.line())
# mgr := EXPR
with self.visit_expr(item.context_expr) as mgr,\
self.block.alloc_temp() as exit_func,\
self.block.alloc_temp() as value:
# The code here has a subtle twist: It gets the exit function attribute
# from the class, not from the object. This matches the pseudo code from
# PEP 343 exactly, and is very close to what CPython actually does. (The
# CPython implementation actually uses a special lookup which is performed
# on the object, but skips the instance dictionary: see ceval.c and
# lookup_maybe in typeobject.c.)
# exit := type(mgr).__exit__
self.writer.write_checked_call2(
exit_func, 'πg.GetAttr(πF, {}.Type().ToObject(), {}, nil)',
mgr.expr, self.block.root.intern('__exit__'))
# value := type(mgr).__enter__(mgr)
self.writer.write_checked_call2(
value, 'πg.GetAttr(πF, {}.Type().ToObject(), {}, nil)',
mgr.expr, self.block.root.intern('__enter__'))
self.writer.write_checked_call2(
value, '{}.Call(πF, πg.Args{{{}}}, nil)',
value.expr, mgr.expr)
finally_label = self.block.genlabel(is_checkpoint=True)
self.writer.write('πF.PushCheckpoint({})'.format(finally_label))
if item.optional_vars:
self._tie_target(item.optional_vars, value.expr)
self._visit_each(node.body)
self.writer.write('πF.PopCheckpoint()')
self.writer.write_label(finally_label)
with self.block.alloc_temp() as swallow_exc,\
self.block.alloc_temp('bool') as swallow_exc_bool,\
self.block.alloc_temp('*πg.BaseException') as exc,\
self.block.alloc_temp('*πg.Traceback') as tb,\
self.block.alloc_temp('*πg.Type') as t:
# temp := exit(mgr, *sys.exec_info())
tmpl = """\
$exc, $tb = nil, nil
if πE != nil {
\t$exc, $tb = πF.ExcInfo()
}
if $exc != nil {
\t$t = $exc.Type()
\tif $swallow_exc, πE = $exit_func.Call(πF, πg.Args{$mgr, $t.ToObject(), $exc.ToObject(), $tb.ToObject()}, nil); πE != nil {
\t\tcontinue
\t}
} else {
\tif $swallow_exc, πE = $exit_func.Call(πF, πg.Args{$mgr, πg.None, πg.None, πg.None}, nil); πE != nil {
\t\tcontinue
\t}
}
"""
self.writer.write_tmpl(
textwrap.dedent(tmpl), exc=exc.expr, tb=tb.expr, t=t.name,
mgr=mgr.expr, exit_func=exit_func.expr,
swallow_exc=swallow_exc.name)
# if Exc != nil && swallow_exc != true {
# Raise(nil, nil)
# }
self.writer.write_checked_call2(
swallow_exc_bool, 'πg.IsTrue(πF, {})', swallow_exc.expr)
self.writer.write_tmpl(textwrap.dedent("""\
if $exc != nil && $swallow_exc != true {
\tπE = πF.Raise(nil, nil, nil)
\tcontinue
}
if πR != nil {
\tcontinue
}"""), exc=exc.expr, swallow_exc=swallow_exc_bool.expr)
def visit_function_inline(self, node):
"""Returns an GeneratedExpr for a function with the given body."""
# First pass collects the names of locals used in this function. Do this in
# a separate pass so that we know whether to resolve a name as a local or a
# global during the second pass.
func_visitor = block.FunctionBlockVisitor(node)
for child in node.body:
func_visitor.visit(child)
func_block = block.FunctionBlock(self.block, node.name, func_visitor.vars,
func_visitor.is_generator)
visitor = StatementVisitor(func_block, self.future_node)
# Indent so that the function body is aligned with the goto labels.
with visitor.writer.indent_block():
visitor._visit_each(node.body) # pylint: disable=protected-access
result = self.block.alloc_temp()
with self.block.alloc_temp('[]πg.Param') as func_args:
args = node.args
argc = len(args.args)
self.writer.write('{} = make([]πg.Param, {})'.format(
func_args.expr, argc))
# The list of defaults only contains args for which a default value is
# specified so pad it with None to make it the same length as args.
defaults = [None] * (argc - len(args.defaults)) + args.defaults
for i, (a, d) in enumerate(zip(args.args, defaults)):
with self.visit_expr(d) if d else expr.nil_expr as default:
tmpl = '$args[$i] = πg.Param{Name: $name, Def: $default}'
self.writer.write_tmpl(tmpl, args=func_args.expr, i=i,
name=util.go_str(a.arg), default=default.expr)
flags = []
if args.vararg:
flags.append('πg.CodeFlagVarArg')
if args.kwarg:
flags.append('πg.CodeFlagKWArg')
# The function object gets written to a temporary writer because we need
# it as an expression that we subsequently bind to some variable.
self.writer.write_tmpl(
'$result = πg.NewFunction(πg.NewCode($name, $filename, $args, '
'$flags, func(πF *πg.Frame, πArgs []*πg.Object) '
'(*πg.Object, *πg.BaseException) {',
result=result.name, name=util.go_str(node.name),
filename=util.go_str(self.block.root.filename), args=func_args.expr,
flags=' | '.join(flags) if flags else 0)
with self.writer.indent_block():
for var in func_block.vars.values():
if var.type != block.Var.TYPE_GLOBAL:
fmt = 'var {0} *πg.Object = {1}; _ = {0}'
self.writer.write(fmt.format(
util.adjust_local_name(var.name), var.init_expr))
self.writer.write_temp_decls(func_block)
self.writer.write('var πR *πg.Object; _ = πR')
self.writer.write('var πE *πg.BaseException; _ = πE')
if func_block.is_generator:
self.writer.write(
'return πg.NewGenerator(πF, func(πSent *πg.Object) '
'(*πg.Object, *πg.BaseException) {')
with self.writer.indent_block():
self.writer.write_block(func_block, visitor.writer.getvalue())
self.writer.write('return nil, πE')
self.writer.write('}).ToObject(), nil')
else:
self.writer.write_block(func_block, visitor.writer.getvalue())
self.writer.write(textwrap.dedent("""\
if πE != nil {
\tπR = nil
} else if πR == nil {
\tπR = πg.None
}
return πR, πE"""))
self.writer.write('}), πF.Globals()).ToObject()')
return result
_AUG_ASSIGN_TEMPLATES = {
ast.Add: 'πg.IAdd(πF, {lhs}, {rhs})',
ast.BitAnd: 'πg.IAnd(πF, {lhs}, {rhs})',
ast.Div: 'πg.IDiv(πF, {lhs}, {rhs})',
ast.FloorDiv: 'πg.IFloorDiv(πF, {lhs}, {rhs})',
ast.LShift: 'πg.ILShift(πF, {lhs}, {rhs})',
ast.Mod: 'πg.IMod(πF, {lhs}, {rhs})',
ast.Mult: 'πg.IMul(πF, {lhs}, {rhs})',
ast.BitOr: 'πg.IOr(πF, {lhs}, {rhs})',
ast.Pow: 'πg.IPow(πF, {lhs}, {rhs})',
ast.RShift: 'πg.IRShift(πF, {lhs}, {rhs})',
ast.Sub: 'πg.ISub(πF, {lhs}, {rhs})',
ast.BitXor: 'πg.IXor(πF, {lhs}, {rhs})',
}
def _assign_target(self, target, value):
if isinstance(target, ast.Name):
self.block.bind_var(self.writer, target.id, value)
elif isinstance(target, ast.Attribute):
with self.visit_expr(target.value) as obj:
self.writer.write_checked_call1(
'πg.SetAttr(πF, {}, {}, {})', obj.expr,
self.block.root.intern(target.attr), value)
elif isinstance(target, ast.Subscript):
with self.visit_expr(target.value) as mapping,\
self.visit_expr(target.slice) as index:
self.writer.write_checked_call1('πg.SetItem(πF, {}, {}, {})',
mapping.expr, index.expr, value)
else:
msg = 'assignment target not yet implemented: ' + type(target).__name__
raise util.ParseError(target, msg)
def _build_assign_target(self, target, assigns):
if isinstance(target, (ast.Tuple, ast.List)):
children = []
for elt in target.elts:
children.append(self._build_assign_target(elt, assigns))
tmpl = 'πg.TieTarget{Children: []πg.TieTarget{$children}}'
return string.Template(tmpl).substitute(children=', '.join(children))
temp = self.block.alloc_temp()
assigns.append((target, temp))
tmpl = 'πg.TieTarget{Target: &$temp}'
return string.Template(tmpl).substitute(temp=temp.name)
def _import_and_bind(self, imp):
"""Generates code that imports a module and binds it to a variable.
Args:
imp: Import object representing an import of the form "import x.y.z" or
"from x.y import z". Expects only a single binding.
"""
# Acquire handles to the Code objects in each Go package and call
# ImportModule to initialize all modules.
with self.block.alloc_temp() as mod, \
self.block.alloc_temp('[]*πg.Object') as mod_slice:
self.writer.write_checked_call2(
mod_slice, 'πg.ImportModule(πF, {})', util.go_str(imp.name))
# Bind the imported modules or members to variables in the current scope.
for binding in imp.bindings:
if binding.bind_type == imputil.Import.MODULE:
self.writer.write('{} = {}[{}]'.format(
mod.name, mod_slice.expr, binding.value))
self.block.bind_var(self.writer, binding.alias, mod.expr)
else:
self.writer.write('{} = {}[{}]'.format(
mod.name, mod_slice.expr, imp.name.count('.')))
# Binding a member of the imported module.
with self.block.alloc_temp() as member:
self.writer.write_checked_call2(
member, 'πg.GetAttr(πF, {}, {}, nil)',
mod.expr, self.block.root.intern(binding.value))
self.block.bind_var(self.writer, binding.alias, member.expr)
def _tie_target(self, target, value):
if isinstance(target, ast.Name):
self._assign_target(target, value)
return
assigns = []
self.writer.write_checked_call1(
'πg.Tie(πF, {}, {})',
self._build_assign_target(target, assigns), value)
for t, temp in assigns:
self._assign_target(t, temp.expr)
self.block.free_temp(temp)
def _visit_each(self, nodes):
for node in nodes:
self.visit(node)
def _visit_loop(self, testfunc, node):
start_label = self.block.genlabel(is_checkpoint=True)
else_label = self.block.genlabel(is_checkpoint=True)
end_label = self.block.genlabel()
with self.block.alloc_temp('bool') as breakvar:
self.block.push_loop(breakvar)
self.writer.write('πF.PushCheckpoint({})'.format(else_label))
self.writer.write('{} = false'.format(breakvar.name))
self.writer.write_label(start_label)
self.writer.write_tmpl(textwrap.dedent("""\
if πE != nil || πR != nil {
\tcontinue
}
if $breakvar {
\tπF.PopCheckpoint()
\tgoto Label$end_label
}"""), breakvar=breakvar.expr, end_label=end_label)
with self.block.alloc_temp('bool') as testvar:
testfunc(testvar)
self.writer.write_tmpl(textwrap.dedent("""\
if πE != nil || !$testvar {
\tcontinue
}
πF.PushCheckpoint($start_label)\
"""), testvar=testvar.name, start_label=start_label)
self._visit_each(node.body)
self.writer.write('continue')
# End the loop so that break applies to an outer loop if present.
self.block.pop_loop()
self.writer.write_label(else_label)
self.writer.write(textwrap.dedent("""\
if πE != nil || πR != nil {
\tcontinue
}"""))
if node.orelse:
self._visit_each(node.orelse)
self.writer.write_label(end_label)
def _write_except_block(self, label, exc, except_node):
self._write_py_context(except_node.lineno)
self.writer.write_label(label)
if except_node.name:
self.block.bind_var(self.writer, except_node.name.id,
'{}.ToObject()'.format(exc))
self._visit_each(except_node.body)
self.writer.write('πF.RestoreExc(nil, nil)')
def _write_except_dispatcher(self, exc, tb, handlers):
"""Outputs a Go code that jumps to the appropriate except handler.
Args:
exc: Go variable holding the current exception.
tb: Go variable holding the current exception's traceback.
handlers: A list of ast.ExceptHandler nodes.
Returns:
A list of Go labels indexes corresponding to the exception handlers.
Raises:
ParseError: Except handlers are in an invalid order.
"""
handler_labels = []
for i, except_node in enumerate(handlers):
handler_labels.append(self.block.genlabel())
if except_node.type:
with self.visit_expr(except_node.type) as type_,\
self.block.alloc_temp('bool') as is_inst:
self.writer.write_checked_call2(
is_inst, 'πg.IsInstance(πF, {}.ToObject(), {})', exc, type_.expr)
self.writer.write_tmpl(textwrap.dedent("""\
if $is_inst {
\tgoto Label$label
}"""), is_inst=is_inst.expr, label=handler_labels[-1])
else:
# This is a bare except. It should be the last handler.
if i != len(handlers) - 1:
msg = "default 'except:' must be last"
raise util.ParseError(except_node, msg)
self.writer.write('goto Label{}'.format(handler_labels[-1]))
if handlers[-1].type:
# There's no bare except, so the fallback is to re-raise.
self.writer.write(
'πE = πF.Raise({}.ToObject(), nil, {}.ToObject())'.format(exc, tb))
self.writer.write('continue')
return handler_labels
def _write_py_context(self, lineno):
if lineno:
line = self.block.root.buffer.source_line(lineno).strip()
self.writer.write('// line {}: {}'.format(lineno, line))
self.writer.write('πF.SetLineno({})'.format(lineno))
|
google/grumpy
|
compiler/stmt.py
|
Python
|
apache-2.0
| 31,128
|
[
"VisIt"
] |
2cd12362085a37d8a4ef8f24c7b3ddd62cfb87784be368d780f35a1f4b6e47e2
|
#!/usr/bin/env python
###
### Animation example: http://matplotlib.org/examples/animation/dynamic_image.html
###
from pylab import *
import matplotlib.animation as animation
interpolation='nearest'
#interpolation='gaussian'
#interpolation=None
#cmap=plt.get_cmap('bwr')
#cmap=plt.get_cmap('seismic_r')
cmap=plt.get_cmap('coolwarm_r')
### --- Parameters ---
#fig, ax = subplots(figsize=(6,6))
fig, ax = subplots(figsize=(12,12))
#subplots_adjust(left=0, right=1, bottom=0, top=1)
mx=magdata[0,1:Nx+1,1:Ny+1,0]
my=magdata[0,1:Nx+1,1:Ny+1,1]
mz=magdata[0,1:Nx+1,1:Ny+1,2]
im=ax.imshow(mz.T,interpolation=interpolation, cmap = cmap, origin='lower',vmin=-1,vmax=1,zorder=1)
#im=ax.imshow(mz.T,interpolation=interpolation, cmap = cmap, origin='lower',extent=[1,Nx,1,Ny],vmin=-1,vmax=1,zorder=1)
#im=imshow(magdata[0,:,:,2].T,interpolation=interpolation, cmap = cmap, origin='lower',vmin=-1,vmax=1)
#width=0.0016
#scale=1
#width=0.0012
#scale=0.8
width=0.0015
scale=1.1
#X, Y = meshgrid(np.arange(1,Nx+1),np.arange(1,Ny+1))
#Q = ax.quiver(X, Y, mx.T,my.T,pivot='mid',zorder=2,width=width, scale=scale, scale_units='x')
Q = ax.quiver(mx.T,my.T,pivot='mid',zorder=2,width=width, scale=scale, scale_units='x')
#Q = ax.quiver(X, Y, U, V, pivot='mid', color='r', units='inches')
mt = text(.5, .5, 't=%.2f' % 0., fontsize=15)
#mt = text(1.5, 1.5, 't=%.2f' % 0., fontsize=15)
#time_text = text(.5, .5, '', fontsize=15)
def init():
return updatefig(0)
def updatefig(frame):
data=magdata[frame,1:Nx+1,1:Ny+1,2].T
im.set_array(data)
Q.set_UVC(magdata[frame,1:Nx+1,1:Ny+1,0].T, magdata[frame,1:Nx+1,1:Ny+1,1].T)
mt.set_text('t=%.2f' % (frame*countout*dt))
return im,Q,mt,
def animate_as_gif(frame):
return updatefig(frame)
#export = True
export = False
if(export==True):
anim = animation.FuncAnimation(fig, animate_as_gif, np.arange(0, Nframes), init_func=init, interval=100, blit=True, repeat=False)
anim.save('animation.gif', writer='imagemagick')
else:
#anim = animation.FuncAnimation(fig, updatefig, np.arange(1, Nframes), init_func=init, interval=500, blit=True, repeat=False)
anim = animation.FuncAnimation(fig, updatefig, np.arange(0, Nframes), init_func=init, interval=100, blit=True, repeat=False)
# anim = animation.FuncAnimation(fig, updatefig, np.arange(0, Nframes,100), init_func=init, interval=100, blit=True, repeat=False)
fig.tight_layout()
gca().set_aspect('equal', adjustable='box')
#axis('off')
show()
|
drgulevich/microLLG
|
anim.py
|
Python
|
gpl-3.0
| 2,478
|
[
"Gaussian"
] |
4abafcf0038528cc8ceaf61552023ac3d28cedcadf3e9fc29d65198419d19449
|
# -*- coding: utf-8 -*-
# 已知bug 必须先行引入这两个库，否则会报错
import numpy
import matplotlib.image as mpimg
import sys
import tensorflow as tf
from tensorflow.python import control_flow_ops
import math
import os
import csv
from tqdm import tqdm
# 读取数据
import input_data
# 设置gpu使用数量
tf.app.flags.DEFINE_integer("num_gpus", 2, "How many GPUs to use.")
# 每一层的神经元数量设定，为全链接层
# 第一层为输入层，28*28
# 最后一层为分类输出层，有10个分类
layer_sizes = [784, 1000, 500, 250, 250, 250, 10]
# 层数，大L
L = len(layer_sizes) - 1
# 总样本数：0有15000个，1-9个有5000个。
num_unlabeled_samples = 60000
num_labeled_samples = 1000
num_examples = num_unlabeled_samples + num_labeled_samples
# 全样本扫描循环次数
# 样本数量庞大，通过设定mini batch的大小分批扫描，所有样本都扫描一次算一次全样本扫描
num_epochs = 150
# 类别数量
num_labeled = 10
# 冷启动的lr值
starter_learning_rate = 0.02
# 经历15次全样本扫描后，lr值开始衰减
# epoch after which to begin learning rate decay
decay_after = 15
# mini batch的大小
batch_size = 100
# ( 总样本数 / mini_batch = 一次全样本扫描所需要的批次 ) * 全样本扫描次数 = 总的循环次数
# number of loop iterations
num_iter = (num_examples / batch_size) * num_epochs
# 为输入值的张量分配一块内存区域，类型为float32，shape为一维数组，数组长度为输入层神经元个数
inputs = tf.placeholder(tf.float32, shape=(None, layer_sizes[0]))
# 为输出值的张量分配一块内存区域，大小暂时未定
outputs = tf.placeholder(tf.float32)
# 创建两个lambda，用于初始化偏置参数与权重
# 偏置项的值由传入参数矢量化确定
bi = lambda inits, size, name: tf.Variable(inits * tf.ones([size]), name=name)
# 权值由随机生成的正态分布确定
wi = lambda shape, name: tf.Variable(tf.random_normal(shape, name=name)) / math.sqrt(shape[0])
# 制作层依赖pair making
# shapes of linear layers
shapes = zip(layer_sizes[:-1], layer_sizes[1:])
# 编码器（有监督学习）权值 W
# 解码器（无监督学习）全职 V
# batch norn中用于重构变换过程的beta（可学习）
# batch norn中用于重构变换过程的gamma（可学习）
weights = {'W': [wi(s, "W") for s in shapes], # Encoder weights
'V': [wi(s[::-1], "V") for s in shapes], # Decoder weights
'beta': [bi(0.0, layer_sizes[l+1], "beta") for l in range(L)], # batch normalization parameter to shift the normalized value
'gamma': [bi(1.0, layer_sizes[l+1], "beta") for l in range(L)]} # batch normalization parameter to scale the normalized value
# 调整噪点影响程度的scale值
noise_std = 0.3
# 去噪用的cost的超参数，用于控制每一层的重要度（importance）
denoising_cost = [1000.0, 10.0, 0.10, 0.10, 0.10, 0.10, 0.10] # hyperparameters that denote the importance of each layer
# 合并两个二维tensor，0代表行合并，1代表列合并
join = lambda l, u: tf.concat(0, [l, u])
# 切片处理
# 切出前batch_size个样本作为标记数据
labeled = lambda x: tf.slice(x, [0, 0], [batch_size, -1]) if x is not None else x
# 切出batch_size到结束个样本作为未标记数据
unlabeled = lambda x: tf.slice(x, [batch_size, 0], [-1, -1]) if x is not None else x
# pair
split_lu = lambda x: (labeled(x), unlabeled(x))
# 留下一个bool位
is_training = tf.placeholder(tf.bool)
print(denoising_cost)
exit()
# 设置衰减value，用于维持参数的移动平均（moving average）
# decay是指新的一轮数据（l层数据）进入时，旧的数据（l-1层数的数据）权重降低的百分比
ewma = tf.train.ExponentialMovingAverage(decay=0.99)
# this list stores the updates to be made to average mean and variance
bn_assigns = []
# batch norm处理
def batch_normalization(batch, mean=None, var=None):
if mean == None or var == None:
# 计算batch的均值与方差
mean, var = tf.nn.moments(batch, axes=[0])
# 对batch中的每一个数值进行基于逆标准差的normalization处理
return (batch - mean) / tf.sqrt(var + tf.constant(1e-10))
# average mean and variance of all layers
# 为每一层的running_mean和running_var分配空间并初始化
running_mean = [tf.Variable(tf.constant(0.0, shape=[l]), trainable=False) for l in layer_sizes[1:]]
running_var = [tf.Variable(tf.constant(1.0, shape=[l]), trainable=False) for l in layer_sizes[1:]]
# 更新batch normalization
def update_mean_var_and_batch_normalization(batch, l):
# 一个batch更新一次，而非一个epoch
"batch normalize + update average mean and variance of layer l"
mean, var = tf.nn.moments(batch, axes=[0])
# 设定上一层的running_mean为mean值
assign_mean = running_mean[l-1].assign(mean)
assign_var = running_var[l-1].assign(var)
# 应用衰减
bn_assigns.append(ewma.apply([running_mean[l-1], running_var[l-1]]))
# 优先计算玩assign_mean和assign_var后在计算bn值。是一种强制控制计算先后顺序的方法
with tf.control_dependencies([assign_mean, assign_var]):
return (batch - mean) / tf.sqrt(var + 1e-10)
# 编码器
def encoder(inputs, noise_std):
# 生成正太分布的随机噪点，乘以noise_std调整噪点的权重
h = inputs + tf.random_normal(tf.shape(inputs)) * noise_std
# d用来储存 激活前的值 激活后的值 平均值 方差
# to store the pre-activation, activation, mean and variance for each layer
d = {}
# 把数据集分别切到两个（标记，未标记）序列中
# The data for labeled and unlabeled examples are stored separately
d['labeled'] = {'z': {}, 'm': {}, 'v': {}, 'h': {}}
d['unlabeled'] = {'z': {}, 'm': {}, 'v': {}, 'h': {}}
# 设定第0层的值
d['labeled']['z'][0], d['unlabeled']['z'][0] = split_lu(h)
# 逐层迭代
for l in range(1, L+1):
# logic layer start at 1
current_logic_layer = l
# data layer start at 0
current_data_layer = current_logic_layer - 1
# next data layer
next_data_layer = current_data_layer + 1
print "Current Layer ", current_logic_layer, " : ", layer_sizes[current_data_layer], " -> to next layer : ", layer_sizes[next_data_layer]
d['labeled']['h'][current_data_layer], d['unlabeled']['h'][current_data_layer] = split_lu(h)
# matmul 矩阵乘法，激活之前的运算
# pre-activation
z_pre = tf.matmul(h, weights['W'][l-1])
# 算完继续分开
z_pre_l, z_pre_u = split_lu(z_pre) # split labeled and unlabeled examples
# 计算非标记数据的均值与方差
m, v = tf.nn.moments(z_pre_u, axes=[0])
# bn算法训练流程
def training_batch_norm():
# 训练两组encoder，一个是加入噪点的，一个是不加入噪点的
# 且batch normalization中标记数据和未标记数据分开处理
if noise_std > 0:
# 对标记数据和非标记数据分别进行batch_norm，然后合并
z = join(batch_normalization(z_pre_l), batch_normalization(z_pre_u, m, v))
# 加入噪点，生成一个与z_pre同样大小的向量，用随机数填充，然后乘以随机噪点权重
z += tf.random_normal(tf.shape(z_pre)) * noise_std
else:
# Clean encoder
# batch normalization + update the average mean and variance using batch mean and variance of labeled examples
# 如果要训练干净的编码器，并不需要加入随机噪点
z = join(update_mean_var_and_batch_normalization(z_pre_l, l), batch_normalization(z_pre_u, m, v))
return z
#else:
# 进入评估分支
def eval_batch_norm():
# Evaluation batch normalization
# obtain average mean and variance and use it to normalize the batch
mean = ewma.average(running_mean[l-1])
var = ewma.average(running_var[l-1])
z = batch_normalization(z_pre, mean, var)
# Instead of the above statement, the use of the following 2 statements containing a typo
# consistently produces a 0.2% higher accuracy for unclear reasons.
# m_l, v_l = tf.nn.moments(z_pre_l, axes=[0])
# z = join(batch_normalization(z_pre_l, m_l, mean, var), batch_normalization(z_pre_u, mean, var))
return z
# perform batch normalization according to value of boolean "training" placeholder:
# training是一个bool值，根据改值的设定，确定是进入训练还是评价流程
z = control_flow_ops.cond(is_training, training_batch_norm, eval_batch_norm)
# 如果是输出层（最后一层），应用softmax函数
if l == L:
# use softmax activation in output layer
h = tf.nn.softmax(weights['gamma'][l-1] * (z + weights["beta"][l-1]))
# 如果不是输出层，使用ReLU激活函数
else:
# use ReLU activation in hidden layers
h = tf.nn.relu(z + weights["beta"][l-1])
d['labeled']['z'][l], d['unlabeled']['z'][l] = split_lu(z)
d['unlabeled']['m'][l], d['unlabeled']['v'][l] = m, v # save mean and variance of unlabeled examples for decoding
d['labeled']['h'][l], d['unlabeled']['h'][l] = split_lu(h)
return h, d
print "=== Corrupted Encoder ==="
y_c, corr = encoder(inputs, noise_std)
print "=== Clean Encoder ==="
# 设置noise_std为0训练一个clean encoder
y, clean = encoder(inputs, 0.0)
print "=== Decoder ==="
# 定义高斯去噪器，输入z corr，输出去噪后的预测值
# 论文17页
def g_gauss(z_c, u, size):
"gaussian denoising function proposed in the original paper"
wi = lambda inits, name: tf.Variable(inits * tf.ones([size]), name=name)
a1 = wi(0., 'a1')
a2 = wi(1., 'a2')
a3 = wi(0., 'a3')
a4 = wi(0., 'a4')
a5 = wi(0., 'a5')
a6 = wi(0., 'a6')
a7 = wi(1., 'a7')
a8 = wi(0., 'a8')
a9 = wi(0., 'a9')
a10 = wi(0., 'a10')
mu = a1 * tf.sigmoid(a2 * u + a3) + a4 * u + a5
v = a6 * tf.sigmoid(a7 * u + a8) + a9 * u + a10
z_est = (z_c - mu) * v + mu
return z_est
# Decoder
z_est = {}
d_cost = [] # to store the denoising cost of all layers
# 从第L层开始，迭代到第0层
for l in range(L, -1, -1):
print "Layer ", l, ": ", layer_sizes[l+1] if l+1 < len(layer_sizes) else None, " -> ", layer_sizes[l], ", denoising cost: ", denoising_cost[l]
# 取出每一层激活后的值（最终结果）
z, z_c = clean['unlabeled']['z'][l], corr['unlabeled']['z'][l]
# get 没有的时候返回缺省值
m, v = clean['unlabeled']['m'].get(l, 0), clean['unlabeled']['v'].get(l, 1-1e-10)
if l == L:
u = unlabeled(y_c)
else:
# ？？
u = tf.matmul(z_est[l+1], weights['V'][l])
u = batch_normalization(u)
# z_est是根据z(l+1)的数据还原出来的z的预测值（去噪后）
z_est[l] = g_gauss(z_c, u, layer_sizes[l])
# batch norm处理
z_est_bn = (z_est[l] - m) / v
# append the cost of this layer to d_cost
# 逐层计算cost：z_est_bn - z
# reduce_sum 跨越维度的计算sum值
# reduce_mean 跨越维度的计算均值
# 方差 square(z_est_bn - z)
# 维度加法 reduce_sum(方差,1)
d_cost.append((tf.reduce_mean(tf.reduce_sum(tf.square(z_est_bn - z), 1)) / layer_sizes[l]) * denoising_cost[l])
# calculate total unsupervised cost by adding the denoising cost of all layers
#
u_cost = tf.add_n(d_cost)
y_N = labeled(y_c)
# 负对数概率之和
cost = -tf.reduce_mean(tf.reduce_sum(outputs*tf.log(y_N), 1)) # supervised cost
# cost 叠加
loss = cost + u_cost # total cost
# ground truth的cost
pred_cost = -tf.reduce_mean(tf.reduce_sum(outputs*tf.log(y), 1)) # cost used for prediction
# tf.argmax 返回最大值的索引
# 正确预测
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(outputs, 1)) # no of correct predictions
# 精度
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) * tf.constant(100.0)
learning_rate = tf.Variable(starter_learning_rate, trainable=False)
# 使用adam算法动态调整步长，基于最初设定的learning_rate
train_step = tf.train.AdamOptimizer(learning_rate).minimize(loss)
# add the updates of batch normalization statistics to train_step
bn_updates = tf.group(*bn_assigns)
with tf.control_dependencies([train_step]):
train_step = tf.group(bn_updates)
print "=== Loading Data ==="
mnist = input_data.read_data_sets("MNIST_data", num_labeled=num_labeled, one_hot=True)
# num_labeled = num_labeled
# num_labeled = 1002
saver = tf.train.Saver()
print "=== Starting Session ==="
sess = tf.Session()
i_iter = 0
ckpt = tf.train.get_checkpoint_state('checkpoints/') # get latest checkpoint (if any)
if ckpt and ckpt.model_checkpoint_path:
# if checkpoint exists, restore the parameters and set epoch_n and i_iter
saver.restore(sess, ckpt.model_checkpoint_path)
epoch_n = int(ckpt.model_checkpoint_path.split('-')[1])
i_iter = (epoch_n+1) * (num_examples/batch_size)
print "Restored Epoch ", epoch_n
else:
# no checkpoint exists. create checkpoints directory if it does not exist.
if not os.path.exists('checkpoints'):
os.makedirs('checkpoints')
init = tf.initialize_all_variables()
sess.run(init)
print "=== Training ==="
print "Initial Accuracy: ", sess.run(accuracy, feed_dict={inputs: mnist.test.images, outputs: mnist.test.labels, is_training: False}), "%"
for i in tqdm(range(i_iter, num_iter)):
images, labels = mnist.train.next_batch(batch_size)
sess.run(train_step, feed_dict={inputs: images, outputs: labels, is_training: True})
if (i > 1) and ((i+1) % (num_iter/num_epochs) == 0):
epoch_n = i/(num_examples/batch_size)
if (epoch_n+1) >= decay_after:
# decay learning rate
# learning_rate = starter_learning_rate * ((num_epochs - epoch_n) / (num_epochs - decay_after))
ratio = 1.0 * (num_epochs - (epoch_n+1)) # epoch_n + 1 because learning rate is set for next epoch
ratio = max(0, ratio / (num_epochs - decay_after))
sess.run(learning_rate.assign(starter_learning_rate * ratio))
saver.save(sess, 'checkpoints/model.ckpt', epoch_n)
# print "Epoch ", epoch_n, ", Accuracy: ", sess.run(accuracy, feed_dict={inputs: mnist.test.images, outputs:mnist.test.labels, training: False}), "%"
with open('train_log', 'ab') as train_log:
# write test accuracy to file "train_log"
train_log_w = csv.writer(train_log)
log_i = [epoch_n] + sess.run([accuracy], feed_dict={inputs: mnist.test.images, outputs:mnist.test.labels, is_training: False})
train_log_w.writerow(log_i)
print "Final Accuracy: ", sess.run(accuracy, feed_dict={inputs: mnist.test.images, outputs: mnist.test.labels, is_training: False}), "%"
sess.close()
|
ryukinkou/ladder_customized
|
ladder_customized_tf/ladder.py
|
Python
|
mit
| 15,125
|
[
"Gaussian"
] |
d1d24febed9b80a8a816c39ff063ef296bb6a7d852e83416e8448c22f7cdfd5a
|
#!/usr/bin/env python3
# ver 0.1 - copy from rdf_itf.py (v0.1) and modify codes on 2/3/2018
import argparse
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description='calculation scaling of Ree of single chain')
## args
parser.add_argument('-i', '--input', default='traj.trr', nargs='?',
help='input trajectory file')
parser.add_argument('-s', '--structure', default='topol.tpr', nargs='?',
help='.tpr structure file')
parser.add_argument('-select', '--select', nargs='?',
help='selection of each molecule')
parser.add_argument('-nmol', '--nmol', nargs='?', type=int,
help='# molecules')
parser.add_argument('-cutoff', '--cutoff', default=0.0, nargs='?', type=float,
help='cut-off checking distance between atoms in a molecule (d_cutoff < d_neighbor_atoms: stop)')
parser.add_argument('-b', '--begin', default=-1, nargs='?', type=int,
help='begining frame (-1: last half trajectory)')
parser.add_argument('-o', '--output', default='pol.ree.scal', nargs='?',
help='output filename for scaling of Ree file')
parser.add_argument('args', nargs=argparse.REMAINDER)
parser.add_argument('-v', '--version', action='version', version='%(prog)s 0.1')
## read args
args = parser.parse_args()
## Check arguments for log
print(" input arguments: {0}".format(args))
## import modules
import sys
sys.path.append('/home/htjung/Utility/python/')
import hjung
from hjung import *
import MDAnalysis as mda
from MDAnalysis.analysis import distances
import numpy as np
from scipy.spatial.distance import pdist
## timer
start_proc, start_prof = hjung.time.init()
## read files
u = mda.Universe(args.structure,args.input)
n_frames = len(u.trajectory)
if args.begin == -1:
skip_frames = int(n_frames/2)
print(" skip {} frames".format(skip_frames))
else:
skip_frames = args.begin
if args.begin >= n_frames:
raise ValueError("wrong args.begin because of > n_frames")
n_frames = n_frames - skip_frames
atomtxt = open(args.select).read()
#hjung.polymer.check_traj_connectivity(u,str(atomtxt),args.nmol,args.cutoff,'simple')
## data setting
select_mol = u.select_atoms(str(atomtxt))
if len(select_mol)%args.nmol != 0:
raise ValueError("wrong # molecules, (args.nmol, select_mol) {} {} ".format(args.nmol, len(select_mol)))
n_deg = int(len(select_mol)/args.nmol)
print("assume all molecules has {} atoms".format(n_deg))
data_ree = np.zeros((args.nmol,n_deg-1))
#data_ree_vec = np.zeros((args.nmol,n_deg,3))
# make a list, the indices which are in the same lag
def list_idx_decr(start,end,init_step):
#print("in {} {} {}".format(start,end,init_step))
list_i = []
while start < end:
if start < 0:
print(" something wrong (bug?)!")
break
list_i.append(int(start))
init_step = init_step - 1
start = start + init_step
return list_i
# make list set (list of list of indices) where the elements (in fact, list of indices) are grouped by lag
# pair_data_size number of pairs between any two points excluding duplicates
# which is the same as the length of result array in scipy.spatial.distance.pdist
# n_data_points number of datas you used for scipy.spatial.distance.pdist
# which is the same as the length of argument in scipy.spatial.distance.pdist
def main_list_idx(pair_data_size,n_data_points):
# check validity of arguments
#print(" main_list_idx:")
expect_size = int((n_data_points-1)*n_data_points/2)
if int(pair_data_size) != expect_size:
raise ValueError(" Your arugments are wrong because {}(input) != {}(expect) based on {} ".format(pair_data_size,expect_size,n_data_points))
list_set = []
i_end = pair_data_size
max_lag = n_data_points
for i_start in range(n_data_points):
#print(" lag: {}".format(i_start))
i_end = i_end - i_start
if i_end < i_start:
break
list_set.append(list_idx_decr(i_start,i_end,max_lag))
return list_set
## read trajectory
i_frame = 0
imod = hjung.time.process_init()
list_sets = main_list_idx(int((n_deg-1)*n_deg/2),n_deg)
#print(list_sets)
print(len(list_sets))
for ts in u.trajectory[skip_frames:]:
for i_mol in range(args.nmol):
pair_dist = pdist(select_mol.positions[i_mol*n_deg:(i_mol+1)*n_deg],metric='euclidean')
#print(pair_dist[list_sets[len(list_sets)-1]])
#print(np.mean(pair_dist[list_sets[len(list_sets)-1]]))
for i_lag in range(len(list_sets)):
data_ree[i_mol,i_lag] = data_ree[i_mol,i_lag] + np.mean(pair_dist[list_sets[i_lag]])
i_frame = i_frame + 1
imod = hjung.time.process_print(i_frame, n_frames, imod)
#print(float(n_frames))
norm_data_ree = data_ree/float(n_frames)
norm_data_ree = np.transpose(norm_data_ree)
# save raw rg data file
np.savetxt(args.output, norm_data_ree,
header='time-averaged scaling of Ree of {} single chains'.format(args.nmol), fmt='%e', comments='# ')
np.save(args.output, norm_data_ree)
# save avg file
if args.nmol > 1:
data_ree_avg = np.column_stack((np.mean(norm_data_ree, axis=1),np.std(norm_data_ree, axis=1)))
np.savetxt(args.output+'.avg', data_ree_avg,
header='molecule-averaged scaling of Ree of {} chains'.format(args.nmol), fmt='%e', comments='# ')
print(" saved average Ree files")
## timer
hjung.time.end_print(start_proc, start_prof)
|
jht0664/Utility_python_gromacs
|
python/scaling_ree.py
|
Python
|
mit
| 5,304
|
[
"MDAnalysis"
] |
838a8cfc14ef2527f4effc43c8374f96f6e98ebafe4ea6356e4536cde80f7092
|
#!/usr/bin/env python
import vtk
from vtk.test import Testing
from vtk.util.misc import vtkGetDataRoot
VTK_DATA_ROOT = vtkGetDataRoot()
# create tensor ellipsoids
# Create the RenderWindow, Renderer and interactive renderer
#
ren1 = vtk.vtkRenderer()
renWin = vtk.vtkRenderWindow()
renWin.AddRenderer(ren1)
iren = vtk.vtkRenderWindowInteractor()
iren.SetRenderWindow(renWin)
ptLoad = vtk.vtkPointLoad()
ptLoad.SetLoadValue(100.0)
ptLoad.SetSampleDimensions(30,30,30)
ptLoad.ComputeEffectiveStressOn()
ptLoad.SetModelBounds(-10,10,-10,10,-10,10)
extractTensor = vtk.vtkExtractTensorComponents()
extractTensor.SetInputConnection(ptLoad.GetOutputPort())
extractTensor.ScalarIsEffectiveStress()
extractTensor.ScalarIsComponent()
extractTensor.ExtractScalarsOn()
extractTensor.ExtractVectorsOn()
extractTensor.ExtractNormalsOff()
extractTensor.ExtractTCoordsOn()
contour = vtk.vtkContourFilter()
contour.SetInputConnection(extractTensor.GetOutputPort())
contour.SetValue(0,0)
probe = vtk.vtkProbeFilter()
probe.SetInputConnection(contour.GetOutputPort())
probe.SetSourceConnection(ptLoad.GetOutputPort())
su = vtk.vtkLoopSubdivisionFilter()
su.SetInputConnection(probe.GetOutputPort())
su.SetNumberOfSubdivisions(1)
s1Mapper = vtk.vtkPolyDataMapper()
s1Mapper.SetInputConnection(probe.GetOutputPort())
# s1Mapper SetInputConnection [su GetOutputPort]
s1Actor = vtk.vtkActor()
s1Actor.SetMapper(s1Mapper)
#
# plane for context
#
g = vtk.vtkImageDataGeometryFilter()
g.SetInputConnection(ptLoad.GetOutputPort())
g.SetExtent(0,100,0,100,0,0)
g.Update()
#for scalar range
gm = vtk.vtkPolyDataMapper()
gm.SetInputConnection(g.GetOutputPort())
gm.SetScalarRange(g.GetOutput().GetScalarRange())
ga = vtk.vtkActor()
ga.SetMapper(gm)
s1Mapper.SetScalarRange(g.GetOutput().GetScalarRange())
#
# Create outline around data
#
outline = vtk.vtkOutlineFilter()
outline.SetInputConnection(ptLoad.GetOutputPort())
outlineMapper = vtk.vtkPolyDataMapper()
outlineMapper.SetInputConnection(outline.GetOutputPort())
outlineActor = vtk.vtkActor()
outlineActor.SetMapper(outlineMapper)
outlineActor.GetProperty().SetColor(0,0,0)
#
# Create cone indicating application of load
#
coneSrc = vtk.vtkConeSource()
coneSrc.SetRadius(.5)
coneSrc.SetHeight(2)
coneMap = vtk.vtkPolyDataMapper()
coneMap.SetInputConnection(coneSrc.GetOutputPort())
coneActor = vtk.vtkActor()
coneActor.SetMapper(coneMap)
coneActor.SetPosition(0,0,11)
coneActor.RotateY(90)
coneActor.GetProperty().SetColor(1,0,0)
camera = vtk.vtkCamera()
camera.SetFocalPoint(0.113766,-1.13665,-1.01919)
camera.SetPosition(-29.4886,-63.1488,26.5807)
camera.SetViewAngle(24.4617)
camera.SetViewUp(0.17138,0.331163,0.927879)
camera.SetClippingRange(1,100)
ren1.AddActor(s1Actor)
ren1.AddActor(outlineActor)
ren1.AddActor(coneActor)
ren1.AddActor(ga)
ren1.SetBackground(1.0,1.0,1.0)
ren1.SetActiveCamera(camera)
renWin.SetSize(300,300)
renWin.Render()
# prevent the tk window from showing up then start the event loop
# --- end of script --
|
HopeFOAM/HopeFOAM
|
ThirdParty-0.1/ParaView-5.0.1/VTK/Filters/Extraction/Testing/Python/ExtractTensors.py
|
Python
|
gpl-3.0
| 2,970
|
[
"VTK"
] |
a9b1bd7514a719514a6ca78d6ee3fb2817ecf785ef56cb3f3408e792972f6cf0
|
#!/usr/bin/env python
# This simple example shows how to render a very large image (i.e.
# one that cannot fit on the screen).
import vtk
from vtk.util.misc import vtkGetDataRoot
VTK_DATA_ROOT = vtkGetDataRoot()
# We'll import some data to start. Since we are using an importer,
# we've got to give it a render window and such. Note that the render
# window size is set fairly small.
ren = vtk.vtkRenderer()
ren.SetBackground(0.1, 0.2, 0.4)
renWin = vtk.vtkRenderWindow()
renWin.AddRenderer(ren)
renWin.SetSize(125, 125)
iren = vtk.vtkRenderWindowInteractor()
iren.SetRenderWindow(renWin)
importer = vtk.vtk3DSImporter()
importer.SetRenderWindow(renWin)
importer.SetFileName(VTK_DATA_ROOT + "/Data/Viewpoint/iflamigm.3ds")
importer.ComputeNormalsOn()
importer.Read()
# We'll set up the view we want.
ren.GetActiveCamera().SetPosition(0, 1, 0)
ren.GetActiveCamera().SetFocalPoint(0, 0, 0)
ren.GetActiveCamera().SetViewUp(0, 0, 1)
# Let the renderer compute a good position and focal point.
ren.ResetCamera()
ren.GetActiveCamera().Dolly(1.4)
ren.ResetCameraClippingRange()
renderLarge = vtk.vtkRenderLargeImage()
renderLarge.SetInput(ren)
renderLarge.SetMagnification(4)
# We write out the image which causes the rendering to occur. If you
# watch your screen you might see the pieces being rendered right
# after one another.
writer = vtk.vtkPNGWriter()
writer.SetInputConnection(renderLarge.GetOutputPort())
writer.SetFileName("largeImage.png")
writer.Write()
|
HopeFOAM/HopeFOAM
|
ThirdParty-0.1/ParaView-5.0.1/VTK/Examples/Rendering/Python/RenderLargeImage.py
|
Python
|
gpl-3.0
| 1,468
|
[
"VTK"
] |
dc782529f10359979983681dd4120215d7590300d9770b4fc91c25dcc666ac65
|
# flake8: noqa E122
# 2^16 English wordlist. Derived from
# https://github.com/diracdeltas/niceware/blob/master/lib/wordlist.js
# which in turn, is derived from
# http://www-01.sil.org/linguistics/wordlists/english/.
# Originally compiled for the Yahoo End-to-End project.
# https://github.com/yahoo/end-to-end
WORD_LIST = [
'a',
'aah',
'aardvark',
'aardwolf',
'academia',
'academic',
'academical',
'academician',
'academicianship',
'academicism',
'academy',
'acadia',
'acapulco',
'ace',
'aced',
'acerb',
'acerbate',
'acerber',
'acerbest',
'acerbic',
'acerbity',
'acerola',
'acerose',
'acetate',
'acetic',
'acetified',
'acetify',
'acetifying',
'acetone',
'acetonic',
'ache',
'ached',
'achene',
'achenial',
'achier',
'achiest',
'achievable',
'achieve',
'achieved',
'achievement',
'achiever',
'achieving',
'aching',
'achoo',
'achordate',
'achromat',
'achromatic',
'achromatism',
'achy',
'acid',
'acidhead',
'acidic',
'acidifiable',
'acidification',
'acidified',
'acidifier',
'acidify',
'acidifying',
'acidity',
'acidly',
'acidotic',
'acidulate',
'acidulation',
'acidulously',
'acidy',
'acing',
'acknowledge',
'acknowledgeable',
'acknowledgement',
'acknowledger',
'acknowledging',
'acknowledgment',
'aclu',
'acme',
'acne',
'acned',
'acoin',
'acolyte',
'aconite',
'acorn',
'acoustic',
'acoustical',
'acquaint',
'acquaintance',
'acquaintanceship',
'acquainted',
'acquainting',
'acquiesce',
'acquiesced',
'acquiescence',
'acquiescent',
'acquiescently',
'acquiescing',
'acquiesence',
'acquirable',
'acquire',
'acquirement',
'acquirer',
'acquiring',
'acquisition',
'acquisitive',
'acquit',
'acquittal',
'acquitted',
'acquitter',
'acquitting',
'acre',
'acreage',
'acrid',
'acrider',
'acridest',
'acridity',
'acridly',
'acrimoniously',
'acrimony',
'acrobat',
'acrobatic',
'acromegalic',
'acromegaly',
'acronym',
'acrophobia',
'acrostic',
'acrylate',
'acrylic',
'act',
'actable',
'acted',
'actin',
'acting',
'actinic',
'actinide',
'actinism',
'actinium',
'action',
'actionability',
'actionable',
'activate',
'activation',
'active',
'activism',
'activist',
'activistic',
'activity',
'actomyosin',
'actorish',
'actual',
'actuality',
'actualization',
'actualize',
'actualized',
'actualizing',
'actuarial',
'actuary',
'actuate',
'actuation',
'acuity',
'acupuncture',
'acupuncturist',
'acute',
'acutely',
'acuter',
'acutest',
'ad',
'adage',
'adagial',
'adagio',
'adam',
'adamance',
'adamancy',
'adamant',
'adamantine',
'adamantly',
'adapt',
'adaptability',
'adaptable',
'adaptation',
'adapted',
'adapter',
'adapting',
'adaption',
'adaptive',
'adaptometer',
'adhere',
'adherence',
'adherent',
'adherer',
'adhering',
'adhesion',
'adhesional',
'adhesive',
'adiabatic',
'adiathermancy',
'adieu',
'adieux',
'adipose',
'adiposity',
'adjacency',
'adjacent',
'adjacently',
'adjectival',
'adjective',
'adjoin',
'adjoined',
'adjoining',
'adjoint',
'adjourn',
'adjourned',
'adjourning',
'adjournment',
'adjudge',
'adjudging',
'adjudicate',
'adjudication',
'adjunct',
'adjunctive',
'adjunctly',
'adjuration',
'adjuratory',
'adjure',
'adjurer',
'adjuring',
'adjuror',
'adjust',
'adjustable',
'adjusted',
'adjuster',
'adjusting',
'adjustment',
'adjutancy',
'adjutant',
'admin',
'administer',
'administerial',
'administering',
'administrable',
'administrant',
'administrate',
'administration',
'administrational',
'administrative',
'administratrix',
'adminstration',
'admirable',
'admirably',
'admiral',
'admiralship',
'admiralty',
'admiration',
'admire',
'admirer',
'admiring',
'admissability',
'admissable',
'admissibility',
'admissible',
'admissibly',
'admission',
'admissive',
'admit',
'admittance',
'admitted',
'admitter',
'admitting',
'admonish',
'admonished',
'admonisher',
'admonishing',
'admonishment',
'admonition',
'admonitory',
'ado',
'adobe',
'adolescence',
'adolescent',
'adolescently',
'adolf',
'adolph',
'adopt',
'adoptability',
'adoptable',
'adopted',
'adoptee',
'adopter',
'adopting',
'adoption',
'adoptive',
'adorability',
'adorable',
'adorably',
'adoration',
'adore',
'adorer',
'adoring',
'adorn',
'adorned',
'adorner',
'adorning',
'adornment',
'adoze',
'adrenal',
'adrenalin',
'adrenaline',
'adrenocortical',
'adriatic',
'adrift',
'adroit',
'adroiter',
'adroitest',
'adroitly',
'adsorb',
'adsorbable',
'adsorbate',
'adsorbed',
'adsorbent',
'adsorbing',
'adsorption',
'adsorptive',
'adulate',
'adulation',
'adulatory',
'adult',
'adulterant',
'adulterate',
'adulteration',
'adulterer',
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'worthing',
'worthlessly',
'worthwhile',
'worthy',
'wotted',
'wotting',
'would',
'wouldest',
'wouldst',
'wound',
'wounding',
'wove',
'woven',
'wow',
'wowed',
'wowing',
'wowser',
'wrack',
'wrackful',
'wracking',
'wraith',
'wrang',
'wrangle',
'wrangled',
'wrangler',
'wrangling',
'wrap',
'wraparound',
'wrapper',
'wrapping',
'wrapt',
'wrasse',
'wrastle',
'wrastled',
'wrath',
'wrathed',
'wrathful',
'wrathfully',
'wrathier',
'wrathiest',
'wrathily',
'wrathing',
'wrathy',
'wreak',
'wreaked',
'wreaker',
'wreaking',
'wreath',
'wreathe',
'wreathed',
'wreathing',
'wreathy',
'wreck',
'wreckage',
'wrecker',
'wreckful',
'wrecking',
'wren',
'wrench',
'wrenched',
'wrenching',
'wrest',
'wrested',
'wrester',
'wresting',
'wrestle',
'wrestled',
'wrestler',
'wrestling',
'wretch',
'wretched',
'wretcheder',
'wried',
'wrier',
'wriest',
'wriggle',
'wriggled',
'wriggler',
'wrigglier',
'wriggliest',
'wriggling',
'wriggly',
'wright',
'wrigley',
'wring',
'wringer',
'wringing',
'wrinkle',
'wrinkled',
'wrinklier',
'wrinkliest',
'wrinkling',
'wrinkly',
'wrist',
'wristband',
'wristdrop',
'wristiest',
'wristlet',
'wristwatch',
'wristy',
'writ',
'writable',
'write',
'writeoff',
'writer',
'writhe',
'writhed',
'writher',
'writhing',
'writing',
'written',
'wrong',
'wrongdoer',
'wrongdoing',
'wronger',
'wrongest',
'wrongful',
'wrongfully',
'wronging',
'wrongly',
'wrote',
'wroth',
'wrothful',
'wrought',
'wrung',
'wry',
'wryer',
'wryest',
'wrying',
'wryly',
'wryneck',
'wurst',
'wurzel',
'wye',
'wyoming',
'wyomingite',
'wyvern',
'xanthate',
'xanthic',
'xanthin',
'xanthine',
'xanthippe',
'xanthochroid',
'xanthoma',
'xanthophyll',
'xebec',
'xenia',
'xenic',
'xenobiology',
'xenocryst',
'xenogamy',
'xenograft',
'xenolith',
'xenolithic',
'xenon',
'xenophobe',
'xenophobia',
'xenophobic',
'xeric',
'xeroderma',
'xerographic',
'xerography',
'xerophthalmia',
'xerophyte',
'xerox',
'xeroxed',
'xeroxing',
'xiphoid',
'xiphosuran',
'xylan',
'xylem',
'xylene',
'xylidine',
'xylitol',
'xylograph',
'xylography',
'xyloid',
'xylophone',
'xylophonist',
'xylose',
'xylotomy',
'xyster',
'yabber',
'yacht',
'yachted',
'yachter',
'yachting',
'yachtman',
'yachtsman',
'yachtsmanship',
'yachtswoman',
'yack',
'yacking',
'yahoo',
'yahooism',
'yahooligan',
'yahooligans',
'yahweh',
'yak',
'yakked',
'yakking',
'yale',
'yam',
'yammer',
'yammerer',
'yammering',
'yamun',
'yang',
'yangtze',
'yank',
'yanked',
'yankee',
'yanking',
'yanqui',
'yap',
'yapper',
'yapping',
'yard',
'yardage',
'yardarm',
'yardbird',
'yarding',
'yardman',
'yardmaster',
'yardstick',
'yare',
'yarely',
'yarer',
'yarest',
'yarmulke',
'yarn',
'yarned',
'yarning',
'yarrow',
'yashmac',
'yashmak',
'yaw',
'yawed',
'yawing',
'yawl',
'yawled',
'yawling',
'yawn',
'yawned',
'yawner',
'yawning',
'yawp',
'yawped',
'yawper',
'yawping',
'yay',
'ycleped',
'yclept',
'ye',
'yea',
'yeah',
'year',
'yearbook',
'yearling',
'yearlong',
'yearly',
'yearn',
'yearned',
'yearner',
'yearning',
'yeast',
'yeasted',
'yeastier',
'yeastiest',
'yeastily',
'yeasting',
'yeasty',
'yegg',
'yeggman',
'yell',
'yelled',
'yeller',
'yelling',
'yellow',
'yellowbellied',
'yellowbelly',
'yellowed',
'yellower',
'yellowest',
'yellowing',
'yellowish',
'yellowknife',
'yellowly',
'yellowy',
'yelp',
'yelped',
'yelper',
'yelping',
'yemenite',
'yen',
'yenned',
'yenning',
'yenta',
'yeoman',
'yeomanly',
'yeomanry',
'yep',
'yerba',
'yeshiva',
'yeshivah',
'yeshivoth',
'yessed',
'yessing',
'yester',
'yesterday',
'yesteryear',
'yet',
'yeti',
'yew',
'yid',
'yield',
'yielder',
'yielding',
'yin',
'yip',
'yipe',
'yippee',
'yippie',
'yipping',
'ymca',
'yod',
'yodel',
'yodeled',
'yodeler',
'yodeling',
'yodelled',
'yodeller',
'yodelling',
'yodle',
'yodled',
'yodler',
'yodling',
'yoga',
'yogee',
'yoghurt',
'yogi',
'yogic',
'yogin',
'yogini',
'yogurt',
'yoke',
'yoked',
'yokel',
'yokelish',
'yokemate',
'yoking',
'yokohama',
'yolk',
'yolked',
'yolkier',
'yolky',
'yon',
'yond',
'yonder',
'yoni',
'yonker',
'yore',
'york',
'yorker',
'yosemite',
'you',
'young',
'younger',
'youngest',
'youngish',
'youngling',
'youngster',
'youngstown',
'younker',
'your',
'yourn',
'yourself',
'youse',
'youth',
'youthen',
'youthened',
'youthening',
'youthful',
'youthfully',
'yow',
'yowed',
'yowie',
'yowing',
'yowl',
'yowled',
'yowler',
'yowling',
'ytterbic',
'ytterbium',
'yttria',
'yttric',
'yttrium',
'yuan',
'yucca',
'yugoslav',
'yugoslavia',
'yugoslavian',
'yuk',
'yukked',
'yukking',
'yukon',
'yule',
'yuletide',
'yummier',
'yummiest',
'yummy',
'yup',
'yuppie',
'yurt',
'ywca',
'zabaione',
'zachariah',
'zag',
'zagging',
'zaire',
'zairian',
'zambezi',
'zambia',
'zambian',
'zanier',
'zaniest',
'zanily',
'zany',
'zanyish',
'zanzibar',
'zap',
'zapping',
'zazen',
'zeal',
'zealand',
'zealander',
'zealot',
'zealotry',
'zealously',
'zebeck',
'zebra',
'zebraic',
'zebrine',
'zebroid',
'zebu',
'zed',
'zee',
'zeitgeist',
'zen',
'zenana',
'zendo',
'zenith',
'zenithal',
'zeolite',
'zephyr',
'zeppelin',
'zero',
'zeroed',
'zeroing',
'zest',
'zested',
'zestful',
'zestfully',
'zestier',
'zestiest',
'zesting',
'zesty',
'zeta',
'zig',
'zigging',
'ziggurat',
'zigzag',
'zigzagging',
'zikurat',
'zilch',
'zillion',
'zillionth',
'zimbabwe',
'zinc',
'zincate',
'zinced',
'zincic',
'zincified',
'zincify',
'zincing',
'zincite',
'zincking',
'zincky',
'zincoid',
'zincy',
'zing',
'zinger',
'zingier',
'zingiest',
'zinging',
'zingy',
'zinkify',
'zinky',
'zinnia',
'zion',
'zionism',
'zionist',
'zip',
'zipper',
'zippering',
'zippier',
'zippiest',
'zipping',
'zippy',
'zircon',
'zirconic',
'zirconium',
'zither',
'zitherist',
'zithern',
'zizzle',
'zizzled',
'zizzling',
'zodiac',
'zodiacal',
'zombie',
'zonal',
'zonation',
'zone',
'zoner',
'zonetime',
'zoning',
'zonked',
'zoo',
'zoologist',
'zoology',
'zoom',
'zoomed',
'zooming',
'zooparasitic',
'zoopathology',
'zoophobia',
'zoophyte',
'zooplankton',
'zori',
'zoroaster',
'zoroastrian',
'zoroastrianism',
'zoster',
'zouave',
'zowie',
'zoysia',
'zucchetto',
'zucchini',
'zulu',
'zuni',
'zurich',
'zwieback',
'zygote',
'zygotic',
'zymase',
'zymogenic',
'zymology',
'zymoplastic',
'zymoscope',
'zymurgy',
'zyzzyva',
];
|
moreati/python-niceware
|
niceware/wordlist.py
|
Python
|
mit
| 804,814
|
[
"Amber",
"Avogadro",
"BLAST",
"Brian",
"CASINO",
"CRYSTAL",
"ESPResSo",
"Elk",
"Firefly",
"GULP",
"Galaxy",
"Jaguar",
"MOOSE",
"NEURON",
"ORCA",
"SIESTA",
"TINKER",
"VisIt",
"exciting"
] |
6c5806b51e4462817039ca71152344bd830075075873fa551bfa7107db58cc29
|
# -*- coding: utf-8 -*-
# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
"""The fsl module provides classes for interfacing with the `FSL
<http://www.fmrib.ox.ac.uk/fsl/index.html>`_ command line tools. This
was written to work with FSL version 4.1.4.
Examples
--------
See the docstrings of the individual classes for examples.
.. testsetup::
# Change directory to provide relative paths for doctests
import os
filepath = os.path.dirname(os.path.realpath( __file__ ))
datadir = os.path.realpath(os.path.join(filepath, '../testing/data'))
os.chdir(datadir)
"""
from __future__ import print_function, division, unicode_literals, absolute_import
from builtins import map, range
import os
import os.path as op
import re
from glob import glob
import tempfile
import numpy as np
from ...utils.filemanip import (load_json, save_json, split_filename,
fname_presuffix)
from ..base import (traits, TraitedSpec, OutputMultiPath, File,
CommandLine, CommandLineInputSpec, isdefined)
from .base import FSLCommand, FSLCommandInputSpec, Info
class CopyGeomInputSpec(FSLCommandInputSpec):
in_file = File(exists=True, mandatory=True, argstr="%s", position=0,
desc="source image")
dest_file = File(exists=True, mandatory=True, argstr="%s", position=1,
desc="destination image", copyfile=True,
output_name='out_file',
name_source='dest_file', name_template='%s')
ignore_dims = traits.Bool(desc='Do not copy image dimensions',
argstr='-d', position="-1")
class CopyGeomOutputSpec(TraitedSpec):
out_file = File(exists=True, desc="image with new geometry header")
class CopyGeom(FSLCommand):
"""Use fslcpgeom to copy the header geometry information to another image.
Copy certain parts of the header information (image dimensions, voxel
dimensions, voxel dimensions units string, image orientation/origin or
qform/sform info) from one image to another. Note that only copies from
Analyze to Analyze or Nifti to Nifti will work properly. Copying from
different files will result in loss of information or potentially incorrect
settings.
"""
_cmd = "fslcpgeom"
input_spec = CopyGeomInputSpec
output_spec = CopyGeomOutputSpec
class RobustFOVInputSpec(FSLCommandInputSpec):
in_file = File(exists=True,
desc='input filename',
argstr='-i %s', position=0, mandatory=True)
out_roi = File(desc="ROI volume output name", argstr="-r %s",
name_source=['in_file'], hash_files=False,
name_template='%s_ROI')
class RobustFOVOutputSpec(TraitedSpec):
out_roi = File(exists=True, desc="ROI volume output name")
class RobustFOV(FSLCommand):
_cmd = 'robustfov'
input_spec = RobustFOVInputSpec
output_spec = RobustFOVOutputSpec
class ImageMeantsInputSpec(FSLCommandInputSpec):
in_file = File(exists=True,
desc='input file for computing the average timeseries',
argstr='-i %s', position=0, mandatory=True)
out_file = File(desc='name of output text matrix',
argstr='-o %s', genfile=True, hash_files=False)
mask = File(exists=True, desc='input 3D mask', argstr='-m %s')
spatial_coord = traits.List(traits.Int,
desc=('<x y z> requested spatial coordinate '
'(instead of mask)'),
argstr='-c %s')
use_mm = traits.Bool(desc=('use mm instead of voxel coordinates (for -c '
'option)'), argstr='--usemm')
show_all = traits.Bool(desc=('show all voxel time series (within mask) '
'instead of averaging'), argstr='--showall')
eig = traits.Bool(
desc=('calculate Eigenvariate(s) instead of mean (output will have 0 '
'mean)'),
argstr='--eig')
order = traits.Int(1, desc='select number of Eigenvariates',
argstr='--order=%d', usedefault=True)
nobin = traits.Bool(desc=('do not binarise the mask for calculation of '
'Eigenvariates'), argstr='--no_bin')
transpose = traits.Bool(
desc=('output results in transpose format (one row per voxel/mean)'),
argstr='--transpose')
class ImageMeantsOutputSpec(TraitedSpec):
out_file = File(exists=True, desc="path/name of output text matrix")
class ImageMeants(FSLCommand):
""" Use fslmeants for printing the average timeseries (intensities) to
the screen (or saves to a file). The average is taken over all voxels
in the mask (or all voxels in the image if no mask is specified)
"""
_cmd = 'fslmeants'
input_spec = ImageMeantsInputSpec
output_spec = ImageMeantsOutputSpec
def _list_outputs(self):
outputs = self.output_spec().get()
outputs['out_file'] = self.inputs.out_file
if not isdefined(outputs['out_file']):
outputs['out_file'] = self._gen_fname(self.inputs.in_file,
suffix='_ts',
ext='.txt',
change_ext=True)
outputs['out_file'] = os.path.abspath(outputs['out_file'])
return outputs
def _gen_filename(self, name):
if name == 'out_file':
return self._list_outputs()[name]
return None
class SmoothInputSpec(FSLCommandInputSpec):
in_file = File(exists=True, argstr="%s", position=0, mandatory=True)
sigma = traits.Float(
argstr="-kernel gauss %.03f -fmean", position=1, xor=['fwhm'],
mandatory=True,
desc='gaussian kernel sigma in mm (not voxels)')
fwhm = traits.Float(
argstr="-kernel gauss %.03f -fmean", position=1, xor=['sigma'],
mandatory=True,
desc=('gaussian kernel fwhm, will be converted to sigma in mm '
'(not voxels)'))
smoothed_file = File(
argstr="%s", position=2, name_source=['in_file'],
name_template='%s_smooth', hash_files=False)
class SmoothOutputSpec(TraitedSpec):
smoothed_file = File(exists=True)
class Smooth(FSLCommand):
"""
Use fslmaths to smooth the image
Examples
--------
Setting the kernel width using sigma:
>>> sm = Smooth()
>>> sm.inputs.output_type = 'NIFTI_GZ'
>>> sm.inputs.in_file = 'functional2.nii'
>>> sm.inputs.sigma = 8.0
>>> sm.cmdline # doctest: +ELLIPSIS +IGNORE_UNICODE
'fslmaths functional2.nii -kernel gauss 8.000 -fmean functional2_smooth.nii.gz'
Setting the kernel width using fwhm:
>>> sm = Smooth()
>>> sm.inputs.output_type = 'NIFTI_GZ'
>>> sm.inputs.in_file = 'functional2.nii'
>>> sm.inputs.fwhm = 8.0
>>> sm.cmdline # doctest: +ELLIPSIS +IGNORE_UNICODE
'fslmaths functional2.nii -kernel gauss 3.397 -fmean functional2_smooth.nii.gz'
One of sigma or fwhm must be set:
>>> from nipype.interfaces.fsl import Smooth
>>> sm = Smooth()
>>> sm.inputs.output_type = 'NIFTI_GZ'
>>> sm.inputs.in_file = 'functional2.nii'
>>> sm.cmdline #doctest: +ELLIPSIS
Traceback (most recent call last):
...
ValueError: Smooth requires a value for one of the inputs ...
"""
input_spec = SmoothInputSpec
output_spec = SmoothOutputSpec
_cmd = 'fslmaths'
def _format_arg(self, name, trait_spec, value):
if name == 'fwhm':
sigma = float(value) / np.sqrt(8 * np.log(2))
return super(Smooth, self)._format_arg(name, trait_spec, sigma)
return super(Smooth, self)._format_arg(name, trait_spec, value)
class MergeInputSpec(FSLCommandInputSpec):
in_files = traits.List(File(exists=True), argstr="%s", position=2,
mandatory=True)
dimension = traits.Enum('t', 'x', 'y', 'z', 'a', argstr="-%s", position=0,
desc=("dimension along which to merge, optionally "
"set tr input when dimension is t"),
mandatory=True)
tr = traits.Float(position=-1, argstr='%.2f',
desc=('use to specify TR in seconds (default is 1.00 '
'sec), overrides dimension and sets it to tr'))
merged_file = File(argstr="%s", position=1, name_source='in_files',
name_template='%s_merged', hash_files=False)
class MergeOutputSpec(TraitedSpec):
merged_file = File(exists=True)
class Merge(FSLCommand):
"""Use fslmerge to concatenate images
Images can be concatenated across time, x, y, or z dimensions. Across the
time (t) dimension the TR is set by default to 1 sec.
Note: to set the TR to a different value, specify 't' for dimension and
specify the TR value in seconds for the tr input. The dimension will be
automatically updated to 'tr'.
Examples
--------
>>> from nipype.interfaces.fsl import Merge
>>> merger = Merge()
>>> merger.inputs.in_files = ['functional2.nii', 'functional3.nii']
>>> merger.inputs.dimension = 't'
>>> merger.inputs.output_type = 'NIFTI_GZ'
>>> merger.cmdline # doctest: +IGNORE_UNICODE
'fslmerge -t functional2_merged.nii.gz functional2.nii functional3.nii'
>>> merger.inputs.tr = 2.25
>>> merger.cmdline # doctest: +IGNORE_UNICODE
'fslmerge -tr functional2_merged.nii.gz functional2.nii functional3.nii 2.25'
"""
_cmd = 'fslmerge'
input_spec = MergeInputSpec
output_spec = MergeOutputSpec
def _format_arg(self, name, spec, value):
if name == 'tr':
if self.inputs.dimension != 't':
raise ValueError('When TR is specified, dimension must be t')
return spec.argstr % value
if name == 'dimension':
if isdefined(self.inputs.tr):
return '-tr'
return spec.argstr % value
return super(Merge, self)._format_arg(name, spec, value)
class ExtractROIInputSpec(FSLCommandInputSpec):
in_file = File(exists=True, argstr="%s",
position=0, desc="input file", mandatory=True)
roi_file = File(argstr="%s", position=1,
desc="output file", genfile=True, hash_files=False)
x_min = traits.Int(argstr="%d", position=2)
x_size = traits.Int(argstr="%d", position=3)
y_min = traits.Int(argstr="%d", position=4)
y_size = traits.Int(argstr="%d", position=5)
z_min = traits.Int(argstr="%d", position=6)
z_size = traits.Int(argstr="%d", position=7)
t_min = traits.Int(argstr="%d", position=8)
t_size = traits.Int(argstr="%d", position=9)
_crop_xor = ['x_min', 'x_size', 'y_min',
'y_size', 'z_min', 'z_size', 't_min', 't_size']
crop_list = traits.List(traits.Tuple(traits.Int, traits.Int),
argstr="%s", position=2, xor=_crop_xor,
desc="list of two tuples specifying crop options")
class ExtractROIOutputSpec(TraitedSpec):
roi_file = File(exists=True)
class ExtractROI(FSLCommand):
"""Uses FSL Fslroi command to extract region of interest (ROI)
from an image.
You can a) take a 3D ROI from a 3D data set (or if it is 4D, the
same ROI is taken from each time point and a new 4D data set is
created), b) extract just some time points from a 4D data set, or
c) control time and space limits to the ROI. Note that the
arguments are minimum index and size (not maximum index). So to
extract voxels 10 to 12 inclusive you would specify 10 and 3 (not
10 and 12).
Examples
--------
>>> from nipype.interfaces.fsl import ExtractROI
>>> from nipype.testing import anatfile
>>> fslroi = ExtractROI(in_file=anatfile, roi_file='bar.nii', t_min=0,
... t_size=1)
>>> fslroi.cmdline == 'fslroi %s bar.nii 0 1' % anatfile
True
"""
_cmd = 'fslroi'
input_spec = ExtractROIInputSpec
output_spec = ExtractROIOutputSpec
def _format_arg(self, name, spec, value):
if name == "crop_list":
return " ".join(map(str, sum(list(map(list, value)), [])))
return super(ExtractROI, self)._format_arg(name, spec, value)
def _list_outputs(self):
"""Create a Bunch which contains all possible files generated
by running the interface. Some files are always generated, others
depending on which ``inputs`` options are set.
Returns
-------
outputs : Bunch object
Bunch object containing all possible files generated by
interface object.
If None, file was not generated
Else, contains path, filename of generated outputfile
"""
outputs = self._outputs().get()
outputs['roi_file'] = self.inputs.roi_file
if not isdefined(outputs['roi_file']):
outputs['roi_file'] = self._gen_fname(self.inputs.in_file,
suffix='_roi')
outputs['roi_file'] = os.path.abspath(outputs['roi_file'])
return outputs
def _gen_filename(self, name):
if name == 'roi_file':
return self._list_outputs()[name]
return None
class SplitInputSpec(FSLCommandInputSpec):
in_file = File(exists=True, argstr="%s", position=0, mandatory=True,
desc="input filename")
out_base_name = traits.Str(argstr="%s", position=1, desc="outputs prefix")
dimension = traits.Enum(
't', 'x', 'y', 'z', argstr="-%s", position=2,
mandatory=True,
desc="dimension along which the file will be split")
class SplitOutputSpec(TraitedSpec):
out_files = OutputMultiPath(File(exists=True))
class Split(FSLCommand):
"""Uses FSL Fslsplit command to separate a volume into images in
time, x, y or z dimension.
"""
_cmd = 'fslsplit'
input_spec = SplitInputSpec
output_spec = SplitOutputSpec
def _list_outputs(self):
"""Create a Bunch which contains all possible files generated
by running the interface. Some files are always generated, others
depending on which ``inputs`` options are set.
Returns
-------
outputs : Bunch object
Bunch object containing all possible files generated by
interface object.
If None, file was not generated
Else, contains path, filename of generated outputfile
"""
outputs = self._outputs().get()
ext = Info.output_type_to_ext(self.inputs.output_type)
outbase = 'vol*'
if isdefined(self.inputs.out_base_name):
outbase = '%s*' % self.inputs.out_base_name
outputs['out_files'] = sorted(glob(os.path.join(os.getcwd(),
outbase + ext)))
return outputs
class ImageMathsInputSpec(FSLCommandInputSpec):
in_file = File(exists=True, argstr="%s", mandatory=True, position=1)
in_file2 = File(exists=True, argstr="%s", position=3)
out_file = File(argstr="%s", position=4, genfile=True, hash_files=False)
op_string = traits.Str(argstr="%s", position=2,
desc="string defining the operation, i. e. -add")
suffix = traits.Str(desc="out_file suffix")
out_data_type = traits.Enum('char', 'short', 'int', 'float', 'double',
'input', argstr="-odt %s", position=5,
desc=("output datatype, one of (char, short, "
"int, float, double, input)"))
class ImageMathsOutputSpec(TraitedSpec):
out_file = File(exists=True)
class ImageMaths(FSLCommand):
"""Use FSL fslmaths command to allow mathematical manipulation of images
`FSL info <http://www.fmrib.ox.ac.uk/fslcourse/lectures/practicals/intro/index.htm#fslutils>`_
Examples
--------
>>> from nipype.interfaces import fsl
>>> from nipype.testing import anatfile
>>> maths = fsl.ImageMaths(in_file=anatfile, op_string= '-add 5',
... out_file='foo_maths.nii')
>>> maths.cmdline == 'fslmaths %s -add 5 foo_maths.nii' % anatfile
True
"""
input_spec = ImageMathsInputSpec
output_spec = ImageMathsOutputSpec
_cmd = 'fslmaths'
def _gen_filename(self, name):
if name == 'out_file':
return self._list_outputs()[name]
return None
def _parse_inputs(self, skip=None):
return super(ImageMaths, self)._parse_inputs(skip=['suffix'])
def _list_outputs(self):
suffix = '_maths' # ohinds: build suffix
if isdefined(self.inputs.suffix):
suffix = self.inputs.suffix
outputs = self._outputs().get()
outputs['out_file'] = self.inputs.out_file
if not isdefined(outputs['out_file']):
outputs['out_file'] = self._gen_fname(self.inputs.in_file,
suffix=suffix)
outputs['out_file'] = os.path.abspath(outputs['out_file'])
return outputs
class FilterRegressorInputSpec(FSLCommandInputSpec):
in_file = File(exists=True, argstr="-i %s",
desc="input file name (4D image)", mandatory=True,
position=1)
out_file = File(argstr="-o %s",
desc="output file name for the filtered data",
genfile=True, position=2, hash_files=False)
design_file = File(exists=True, argstr="-d %s", position=3, mandatory=True,
desc=("name of the matrix with time courses (e.g. GLM "
"design or MELODIC mixing matrix)"))
filter_columns = traits.List(
traits.Int, argstr="-f '%s'",
xor=["filter_all"], mandatory=True,
position=4,
desc=("(1-based) column indices to filter out of the data"))
filter_all = traits.Bool(mandatory=True, argstr="-f '%s'",
xor=["filter_columns"], position=4,
desc=("use all columns in the design file in "
"denoising"))
mask = File(exists=True, argstr="-m %s", desc="mask image file name")
var_norm = traits.Bool(argstr="--vn",
desc="perform variance-normalization on data")
out_vnscales = traits.Bool(argstr="--out_vnscales",
desc=("output scaling factors for variance "
"normalization"))
class FilterRegressorOutputSpec(TraitedSpec):
out_file = File(exists=True, desc="output file name for the filtered data")
class FilterRegressor(FSLCommand):
"""Data de-noising by regressing out part of a design matrix
Uses simple OLS regression on 4D images
"""
input_spec = FilterRegressorInputSpec
output_spec = FilterRegressorOutputSpec
_cmd = 'fsl_regfilt'
def _format_arg(self, name, trait_spec, value):
if name == 'filter_columns':
return trait_spec.argstr % ",".join(map(str, value))
elif name == "filter_all":
design = np.loadtxt(self.inputs.design_file)
try:
n_cols = design.shape[1]
except IndexError:
n_cols = 1
return trait_spec.argstr % ",".join(
map(str, list(range(1, n_cols + 1))))
return super(FilterRegressor, self)._format_arg(
name, trait_spec, value)
def _list_outputs(self):
outputs = self.output_spec().get()
outputs['out_file'] = self.inputs.out_file
if not isdefined(outputs['out_file']):
outputs['out_file'] = self._gen_fname(
self.inputs.in_file, suffix='_regfilt')
outputs['out_file'] = os.path.abspath(outputs['out_file'])
return outputs
def _gen_filename(self, name):
if name == 'out_file':
return self._list_outputs()[name]
return None
class ImageStatsInputSpec(FSLCommandInputSpec):
split_4d = traits.Bool(argstr='-t', position=1,
desc=('give a separate output line for each 3D '
'volume of a 4D timeseries'))
in_file = File(exists=True, argstr="%s", mandatory=True, position=2,
desc='input file to generate stats of')
op_string = traits.Str(argstr="%s", mandatory=True, position=3,
desc=("string defining the operation, options are "
"applied in order, e.g. -M -l 10 -M will "
"report the non-zero mean, apply a threshold "
"and then report the new nonzero mean"))
mask_file = File(exists=True, argstr="",
desc='mask file used for option -k %s')
class ImageStatsOutputSpec(TraitedSpec):
out_stat = traits.Any(desc='stats output')
class ImageStats(FSLCommand):
"""Use FSL fslstats command to calculate stats from images
`FSL info
<http://www.fmrib.ox.ac.uk/fslcourse/lectures/practicals/intro/index.htm#fslutils>`_
Examples
--------
>>> from nipype.interfaces.fsl import ImageStats
>>> from nipype.testing import funcfile
>>> stats = ImageStats(in_file=funcfile, op_string= '-M')
>>> stats.cmdline == 'fslstats %s -M'%funcfile
True
"""
input_spec = ImageStatsInputSpec
output_spec = ImageStatsOutputSpec
_cmd = 'fslstats'
def _format_arg(self, name, trait_spec, value):
if name == 'mask_file':
return ''
if name == 'op_string':
if '-k %s' in self.inputs.op_string:
if isdefined(self.inputs.mask_file):
return self.inputs.op_string % self.inputs.mask_file
else:
raise ValueError(
'-k %s option in op_string requires mask_file')
return super(ImageStats, self)._format_arg(name, trait_spec, value)
def aggregate_outputs(self, runtime=None, needed_outputs=None):
outputs = self._outputs()
# local caching for backward compatibility
outfile = os.path.join(os.getcwd(), 'stat_result.json')
if runtime is None:
try:
out_stat = load_json(outfile)['stat']
except IOError:
return self.run().outputs
else:
out_stat = []
for line in runtime.stdout.split('\n'):
if line:
values = line.split()
if len(values) > 1:
out_stat.append([float(val) for val in values])
else:
out_stat.extend([float(val) for val in values])
if len(out_stat) == 1:
out_stat = out_stat[0]
save_json(outfile, dict(stat=out_stat))
outputs.out_stat = out_stat
return outputs
class AvScaleInputSpec(CommandLineInputSpec):
all_param = traits.Bool(False, argstr='--allparams')
mat_file = File(exists=True, argstr='%s',
desc='mat file to read', position=-2)
ref_file = File(exists=True, argstr='%s', position=-1,
desc='reference file to get center of rotation')
class AvScaleOutputSpec(TraitedSpec):
rotation_translation_matrix = traits.List(
traits.List(traits.Float), desc='Rotation and Translation Matrix')
scales = traits.List(traits.Float, desc='Scales (x,y,z)')
skews = traits.List(traits.Float, desc='Skews')
average_scaling = traits.Float(desc='Average Scaling')
determinant = traits.Float(desc='Determinant')
forward_half_transform = traits.List(
traits.List(traits.Float), desc='Forward Half Transform')
backward_half_transform = traits.List(
traits.List(traits.Float), desc='Backwards Half Transform')
left_right_orientation_preserved = traits.Bool(
desc='True if LR orientation preserved')
rot_angles = traits.List(traits.Float, desc='rotation angles')
translations = traits.List(traits.Float, desc='translations')
class AvScale(CommandLine):
"""Use FSL avscale command to extract info from mat file output of FLIRT
Examples
--------
>>> avscale = AvScale()
>>> avscale.inputs.mat_file = 'flirt.mat'
>>> res = avscale.run() # doctest: +SKIP
"""
input_spec = AvScaleInputSpec
output_spec = AvScaleOutputSpec
_cmd = 'avscale'
def _run_interface(self, runtime):
runtime = super(AvScale, self)._run_interface(runtime)
expr = re.compile(
'Rotation\ &\ Translation\ Matrix:\n(?P<rot_tran_mat>[0-9\.\ \n-]+)[\s\n]*'
'(Rotation\ Angles\ \(x,y,z\)\ \[rads\]\ =\ (?P<rot_angles>[0-9\.\ -]+))?[\s\n]*'
'(Translations\ \(x,y,z\)\ \[mm\]\ =\ (?P<translations>[0-9\.\ -]+))?[\s\n]*'
'Scales\ \(x,y,z\)\ =\ (?P<scales>[0-9\.\ -]+)[\s\n]*'
'Skews\ \(xy,xz,yz\)\ =\ (?P<skews>[0-9\.\ -]+)[\s\n]*'
'Average\ scaling\ =\ (?P<avg_scaling>[0-9\.-]+)[\s\n]*'
'Determinant\ =\ (?P<determinant>[0-9\.-]+)[\s\n]*'
'Left-Right\ orientation:\ (?P<lr_orientation>[A-Za-z]+)[\s\n]*'
'Forward\ half\ transform\ =[\s]*\n'
'(?P<fwd_half_xfm>[0-9\.\ \n-]+)[\s\n]*'
'Backward\ half\ transform\ =[\s]*\n'
'(?P<bwd_half_xfm>[0-9\.\ \n-]+)[\s\n]*')
out = expr.search(runtime.stdout).groupdict()
outputs = {}
outputs['rotation_translation_matrix'] = [[
float(v) for v in r.strip().split(' ')]
for r in out['rot_tran_mat'].strip().split('\n')]
outputs['scales'] = [
float(s) for s in out['scales'].strip().split(' ')]
outputs['skews'] = [float(s) for s in out['skews'].strip().split(' ')]
outputs['average_scaling'] = float(out['avg_scaling'].strip())
outputs['determinant'] = float(out['determinant'].strip())
outputs['left_right_orientation_preserved'] = out[
'lr_orientation'].strip() == 'preserved'
outputs['forward_half_transform'] = [[
float(v) for v in r.strip().split(' ')]
for r in out['fwd_half_xfm'].strip().split('\n')]
outputs['backward_half_transform'] = [[
float(v) for v in r.strip().split(' ')]
for r in out['bwd_half_xfm'].strip().split('\n')]
if self.inputs.all_param:
outputs['rot_angles'] = [
float(r) for r in out['rot_angles'].strip().split(' ')]
outputs['translations'] = [
float(r) for r in out['translations'].strip().split(' ')]
setattr(self, '_results', outputs)
return runtime
def _list_outputs(self):
return self._results
class OverlayInputSpec(FSLCommandInputSpec):
transparency = traits.Bool(desc='make overlay colors semi-transparent',
position=1, argstr='%s', usedefault=True,
default_value=True)
out_type = traits.Enum('float', 'int', position=2, usedefault=True,
argstr='%s',
desc='write output with float or int')
use_checkerboard = traits.Bool(desc='use checkerboard mask for overlay',
argstr='-c', position=3)
background_image = File(exists=True, position=4, mandatory=True,
argstr='%s', desc='image to use as background')
_xor_inputs = ('auto_thresh_bg', 'full_bg_range', 'bg_thresh')
auto_thresh_bg = traits.Bool(
desc=('automatically threshold the background image'),
argstr='-a', position=5,
xor=_xor_inputs, mandatory=True)
full_bg_range = traits.Bool(desc='use full range of background image',
argstr='-A', position=5, xor=_xor_inputs,
mandatory=True)
bg_thresh = traits.Tuple(
traits.Float, traits.Float, argstr='%.3f %.3f',
position=5,
desc='min and max values for background intensity',
xor=_xor_inputs, mandatory=True)
stat_image = File(exists=True, position=6, mandatory=True, argstr='%s',
desc='statistical image to overlay in color')
stat_thresh = traits.Tuple(traits.Float, traits.Float, position=7,
mandatory=True, argstr='%.2f %.2f',
desc=('min and max values for the statistical '
'overlay'))
show_negative_stats = traits.Bool(desc=('display negative statistics in '
'overlay'), xor=['stat_image2'],
argstr='%s', position=8)
stat_image2 = File(exists=True, position=9, xor=['show_negative_stats'],
argstr='%s',
desc='second statistical image to overlay in color')
stat_thresh2 = traits.Tuple(traits.Float, traits.Float, position=10,
desc=('min and max values for second '
'statistical overlay'),
argstr='%.2f %.2f')
out_file = File(desc='combined image volume',
position=-1, argstr='%s', genfile=True, hash_files=False)
class OverlayOutputSpec(TraitedSpec):
out_file = File(exists=True, desc='combined image volume')
class Overlay(FSLCommand):
""" Use FSL's overlay command to combine background and statistical images
into one volume
Examples
--------
>>> from nipype.interfaces import fsl
>>> combine = fsl.Overlay()
>>> combine.inputs.background_image = 'mean_func.nii.gz'
>>> combine.inputs.auto_thresh_bg = True
>>> combine.inputs.stat_image = 'zstat1.nii.gz'
>>> combine.inputs.stat_thresh = (3.5, 10)
>>> combine.inputs.show_negative_stats = True
>>> res = combine.run() #doctest: +SKIP
"""
_cmd = 'overlay'
input_spec = OverlayInputSpec
output_spec = OverlayOutputSpec
def _format_arg(self, name, spec, value):
if name == 'transparency':
if value:
return '1'
else:
return '0'
if name == 'out_type':
if value == 'float':
return '0'
else:
return '1'
if name == 'show_negative_stats':
return '%s %.2f %.2f' % (self.inputs.stat_image,
self.inputs.stat_thresh[0] * -1,
self.inputs.stat_thresh[1] * -1)
return super(Overlay, self)._format_arg(name, spec, value)
def _list_outputs(self):
outputs = self._outputs().get()
out_file = self.inputs.out_file
if not isdefined(out_file):
if isdefined(self.inputs.stat_image2) and (
not isdefined(self.inputs.show_negative_stats) or not
self.inputs.show_negative_stats):
stem = "%s_and_%s" % (
split_filename(self.inputs.stat_image)[1],
split_filename(self.inputs.stat_image2)[1])
else:
stem = split_filename(self.inputs.stat_image)[1]
out_file = self._gen_fname(stem, suffix='_overlay')
outputs['out_file'] = os.path.abspath(out_file)
return outputs
def _gen_filename(self, name):
if name == 'out_file':
return self._list_outputs()['out_file']
return None
class SlicerInputSpec(FSLCommandInputSpec):
in_file = File(exists=True, position=1, argstr='%s', mandatory=True,
desc='input volume')
image_edges = File(exists=True, position=2, argstr='%s',
desc=('volume to display edge overlay for (useful for '
'checking registration'))
label_slices = traits.Bool(
position=3, argstr='-L', desc='display slice number',
usedefault=True, default_value=True)
colour_map = File(exists=True, position=4, argstr='-l %s',
desc=('use different colour map from that stored in '
'nifti header'))
intensity_range = traits.Tuple(traits.Float, traits.Float, position=5,
argstr='-i %.3f %.3f',
desc='min and max intensities to display')
threshold_edges = traits.Float(position=6, argstr='-e %.3f',
desc='use threshold for edges')
dither_edges = traits.Bool(position=7, argstr='-t',
desc=('produce semi-transparent (dithered) '
'edges'))
nearest_neighbour = traits.Bool(position=8, argstr='-n',
desc=('use nearest neighbor interpolation '
'for output'))
show_orientation = traits.Bool(position=9, argstr='%s', usedefault=True,
default_value=True,
desc='label left-right orientation')
_xor_options = ('single_slice', 'middle_slices', 'all_axial',
'sample_axial')
single_slice = traits.Enum(
'x', 'y', 'z', position=10, argstr='-%s',
xor=_xor_options, requires=['slice_number'],
desc=('output picture of single slice in the x, y, or z plane'))
slice_number = traits.Int(position=11, argstr='-%d',
desc='slice number to save in picture')
middle_slices = traits.Bool(position=10, argstr='-a', xor=_xor_options,
desc=('output picture of mid-sagittal, axial, '
'and coronal slices'))
all_axial = traits.Bool(position=10, argstr='-A', xor=_xor_options,
requires=['image_width'],
desc='output all axial slices into one picture')
sample_axial = traits.Int(position=10, argstr='-S %d',
xor=_xor_options, requires=['image_width'],
desc=('output every n axial slices into one '
'picture'))
image_width = traits.Int(
position=-2, argstr='%d', desc='max picture width')
out_file = File(position=-1, genfile=True, argstr='%s',
desc='picture to write', hash_files=False)
scaling = traits.Float(position=0, argstr='-s %f', desc='image scale')
class SlicerOutputSpec(TraitedSpec):
out_file = File(exists=True, desc='picture to write')
class Slicer(FSLCommand):
"""Use FSL's slicer command to output a png image from a volume.
Examples
--------
>>> from nipype.interfaces import fsl
>>> from nipype.testing import example_data
>>> slice = fsl.Slicer()
>>> slice.inputs.in_file = example_data('functional.nii')
>>> slice.inputs.all_axial = True
>>> slice.inputs.image_width = 750
>>> res = slice.run() #doctest: +SKIP
"""
_cmd = 'slicer'
input_spec = SlicerInputSpec
output_spec = SlicerOutputSpec
def _format_arg(self, name, spec, value):
if name == 'show_orientation':
if value:
return ''
else:
return '-u'
elif name == "label_slices":
if value:
return '-L'
else:
return ''
return super(Slicer, self)._format_arg(name, spec, value)
def _list_outputs(self):
outputs = self._outputs().get()
out_file = self.inputs.out_file
if not isdefined(out_file):
out_file = self._gen_fname(self.inputs.in_file, ext='.png')
outputs['out_file'] = os.path.abspath(out_file)
return outputs
def _gen_filename(self, name):
if name == 'out_file':
return self._list_outputs()['out_file']
return None
class PlotTimeSeriesInputSpec(FSLCommandInputSpec):
in_file = traits.Either(File(exists=True), traits.List(File(exists=True)),
mandatory=True, argstr="%s", position=1,
desc=("file or list of files with columns of "
"timecourse information"))
plot_start = traits.Int(argstr="--start=%d", xor=("plot_range",),
desc="first column from in-file to plot")
plot_finish = traits.Int(argstr="--finish=%d", xor=("plot_range",),
desc="final column from in-file to plot")
plot_range = traits.Tuple(traits.Int, traits.Int, argstr="%s",
xor=("plot_start", "plot_finish"),
desc=("first and last columns from the in-file "
"to plot"))
title = traits.Str(argstr="%s", desc="plot title")
legend_file = File(exists=True, argstr="--legend=%s", desc="legend file")
labels = traits.Either(traits.Str, traits.List(traits.Str),
argstr="%s", desc="label or list of labels")
y_min = traits.Float(argstr="--ymin=%.2f", desc="minumum y value",
xor=("y_range",))
y_max = traits.Float(argstr="--ymax=%.2f", desc="maximum y value",
xor=("y_range",))
y_range = traits.Tuple(traits.Float, traits.Float, argstr="%s",
xor=("y_min", "y_max"),
desc="min and max y axis values")
x_units = traits.Int(
argstr="-u %d", usedefault=True, default_value=1,
desc=("scaling units for x-axis (between 1 and length of in file)"))
plot_size = traits.Tuple(traits.Int, traits.Int, argstr="%s",
desc="plot image height and width")
x_precision = traits.Int(argstr="--precision=%d",
desc="precision of x-axis labels")
sci_notation = traits.Bool(argstr="--sci",
desc="switch on scientific notation")
out_file = File(argstr="-o %s", genfile=True,
desc="image to write", hash_files=False)
class PlotTimeSeriesOutputSpec(TraitedSpec):
out_file = File(exists=True, desc='image to write')
class PlotTimeSeries(FSLCommand):
"""Use fsl_tsplot to create images of time course plots.
Examples
--------
>>> import nipype.interfaces.fsl as fsl
>>> plotter = fsl.PlotTimeSeries()
>>> plotter.inputs.in_file = 'functional.par'
>>> plotter.inputs.title = 'Functional timeseries'
>>> plotter.inputs.labels = ['run1', 'run2']
>>> plotter.run() #doctest: +SKIP
"""
_cmd = "fsl_tsplot"
input_spec = PlotTimeSeriesInputSpec
output_spec = PlotTimeSeriesOutputSpec
def _format_arg(self, name, spec, value):
if name == "in_file":
if isinstance(value, list):
args = ",".join(value)
return "-i %s" % args
else:
return "-i %s" % value
elif name == "labels":
if isinstance(value, list):
args = ",".join(value)
return "-a %s" % args
else:
return "-a %s" % value
elif name == "title":
return "-t \'%s\'" % value
elif name == "plot_range":
return "--start=%d --finish=%d" % value
elif name == "y_range":
return "--ymin=%d --ymax=%d" % value
elif name == "plot_size":
return "-h %d -w %d" % value
return super(PlotTimeSeries, self)._format_arg(name, spec, value)
def _list_outputs(self):
outputs = self._outputs().get()
out_file = self.inputs.out_file
if not isdefined(out_file):
if isinstance(self.inputs.in_file, list):
infile = self.inputs.in_file[0]
else:
infile = self.inputs.in_file
out_file = self._gen_fname(infile, ext='.png')
outputs['out_file'] = os.path.abspath(out_file)
return outputs
def _gen_filename(self, name):
if name == 'out_file':
return self._list_outputs()['out_file']
return None
class PlotMotionParamsInputSpec(FSLCommandInputSpec):
in_file = traits.Either(File(exists=True), traits.List(File(exists=True)),
mandatory=True, argstr="%s", position=1,
desc="file with motion parameters")
in_source = traits.Enum("spm", "fsl", mandatory=True,
desc=("which program generated the motion "
"parameter file - fsl, spm"))
plot_type = traits.Enum("rotations", "translations", "displacement",
argstr="%s", mandatory=True,
desc=("which motion type to plot - rotations, "
"translations, displacement"))
plot_size = traits.Tuple(traits.Int, traits.Int, argstr="%s",
desc="plot image height and width")
out_file = File(argstr="-o %s", genfile=True,
desc="image to write", hash_files=False)
class PlotMotionParamsOutputSpec(TraitedSpec):
out_file = File(exists=True, desc='image to write')
class PlotMotionParams(FSLCommand):
"""Use fsl_tsplot to plot the estimated motion parameters from a
realignment program.
Examples
--------
>>> import nipype.interfaces.fsl as fsl
>>> plotter = fsl.PlotMotionParams()
>>> plotter.inputs.in_file = 'functional.par'
>>> plotter.inputs.in_source = 'fsl'
>>> plotter.inputs.plot_type = 'rotations'
>>> res = plotter.run() #doctest: +SKIP
Notes
-----
The 'in_source' attribute determines the order of columns that are expected
in the source file. FSL prints motion parameters in the order rotations,
translations, while SPM prints them in the opposite order. This interface
should be able to plot timecourses of motion parameters generated from
other sources as long as they fall under one of these two patterns. For
more flexibilty, see the :class:`fsl.PlotTimeSeries` interface.
"""
_cmd = 'fsl_tsplot'
input_spec = PlotMotionParamsInputSpec
output_spec = PlotMotionParamsOutputSpec
def _format_arg(self, name, spec, value):
if name == "plot_type":
source = self.inputs.in_source
if self.inputs.plot_type == 'displacement':
title = '-t \'MCFLIRT estimated mean displacement (mm)\''
labels = '-a abs,rel'
return '%s %s' % (title, labels)
# Get the right starting and ending position depending on source
# package
sfdict = dict(fsl_rot=(1, 3), fsl_tra=(
4, 6), spm_rot=(4, 6), spm_tra=(1, 3))
# Format the title properly
sfstr = "--start=%d --finish=%d" % sfdict[
"%s_%s" % (source, value[:3])]
titledict = dict(fsl="MCFLIRT", spm="Realign")
unitdict = dict(rot="radians", tra="mm")
title = "\'%s estimated %s (%s)\'" % (
titledict[source], value, unitdict[value[:3]])
return "-t %s %s -a x,y,z" % (title, sfstr)
elif name == "plot_size":
return "-h %d -w %d" % value
elif name == "in_file":
if isinstance(value, list):
args = ",".join(value)
return "-i %s" % args
else:
return "-i %s" % value
return super(PlotMotionParams, self)._format_arg(name, spec, value)
def _list_outputs(self):
outputs = self._outputs().get()
out_file = self.inputs.out_file
if not isdefined(out_file):
if isinstance(self.inputs.in_file, list):
infile = self.inputs.in_file[0]
else:
infile = self.inputs.in_file
plttype = dict(rot="rot", tra="trans", dis="disp")[
self.inputs.plot_type[:3]]
out_file = fname_presuffix(
infile, suffix="_%s.png" % plttype, use_ext=False)
outputs['out_file'] = os.path.abspath(out_file)
return outputs
def _gen_filename(self, name):
if name == 'out_file':
return self._list_outputs()['out_file']
return None
class ConvertXFMInputSpec(FSLCommandInputSpec):
in_file = File(exists=True, mandatory=True, argstr="%s", position=-1,
desc="input transformation matrix")
in_file2 = File(
exists=True, argstr="%s", position=-2,
desc="second input matrix (for use with fix_scale_skew or concat_xfm)")
_options = ["invert_xfm", "concat_xfm", "fix_scale_skew"]
invert_xfm = traits.Bool(argstr="-inverse", position=-3, xor=_options,
desc="invert input transformation")
concat_xfm = traits.Bool(argstr="-concat", position=-3, xor=_options,
requires=["in_file2"],
desc=("write joint transformation of two input "
"matrices"))
fix_scale_skew = traits.Bool(argstr="-fixscaleskew", position=-3,
xor=_options, requires=["in_file2"],
desc=("use secondary matrix to fix scale and "
"skew"))
out_file = File(genfile=True, argstr="-omat %s", position=1,
desc="final transformation matrix", hash_files=False)
class ConvertXFMOutputSpec(TraitedSpec):
out_file = File(exists=True, desc="output transformation matrix")
class ConvertXFM(FSLCommand):
"""Use the FSL utility convert_xfm to modify FLIRT transformation matrices.
Examples
--------
>>> import nipype.interfaces.fsl as fsl
>>> invt = fsl.ConvertXFM()
>>> invt.inputs.in_file = "flirt.mat"
>>> invt.inputs.invert_xfm = True
>>> invt.inputs.out_file = 'flirt_inv.mat'
>>> invt.cmdline # doctest: +IGNORE_UNICODE
'convert_xfm -omat flirt_inv.mat -inverse flirt.mat'
"""
_cmd = "convert_xfm"
input_spec = ConvertXFMInputSpec
output_spec = ConvertXFMOutputSpec
def _list_outputs(self):
outputs = self._outputs().get()
outfile = self.inputs.out_file
if not isdefined(outfile):
_, infile1, _ = split_filename(self.inputs.in_file)
if self.inputs.invert_xfm:
outfile = fname_presuffix(infile1,
suffix="_inv.mat",
newpath=os.getcwd(),
use_ext=False)
else:
if self.inputs.concat_xfm:
_, infile2, _ = split_filename(self.inputs.in_file2)
outfile = fname_presuffix("%s_%s" % (infile1, infile2),
suffix=".mat",
newpath=os.getcwd(),
use_ext=False)
else:
outfile = fname_presuffix(infile1,
suffix="_fix.mat",
newpath=os.getcwd(),
use_ext=False)
outputs["out_file"] = os.path.abspath(outfile)
return outputs
def _gen_filename(self, name):
if name == "out_file":
return self._list_outputs()["out_file"]
return None
class SwapDimensionsInputSpec(FSLCommandInputSpec):
in_file = File(exists=True, mandatory=True, argstr="%s", position="1",
desc="input image")
_dims = ["x", "-x", "y", "-y", "z",
"-z", "RL", "LR", "AP", "PA", "IS", "SI"]
new_dims = traits.Tuple(traits.Enum(_dims), traits.Enum(_dims),
traits.Enum(_dims), argstr="%s %s %s",
mandatory=True,
desc="3-tuple of new dimension order")
out_file = File(genfile=True, argstr="%s",
desc="image to write", hash_files=False)
class SwapDimensionsOutputSpec(TraitedSpec):
out_file = File(exists=True, desc="image with new dimensions")
class SwapDimensions(FSLCommand):
"""Use fslswapdim to alter the orientation of an image.
This interface accepts a three-tuple corresponding to the new
orientation. You may either provide dimension ids in the form of
(-)x, (-)y, or (-z), or nifti-syle dimension codes
(RL, LR, AP, PA, IS, SI).
"""
_cmd = "fslswapdim"
input_spec = SwapDimensionsInputSpec
output_spec = SwapDimensionsOutputSpec
def _list_outputs(self):
outputs = self._outputs().get()
outputs["out_file"] = self.inputs.out_file
if not isdefined(self.inputs.out_file):
outputs["out_file"] = self._gen_fname(self.inputs.in_file,
suffix='_newdims')
outputs["out_file"] = os.path.abspath(outputs["out_file"])
return outputs
def _gen_filename(self, name):
if name == "out_file":
return self._list_outputs()["out_file"]
return None
class PowerSpectrumInputSpec(FSLCommandInputSpec):
# We use position args here as list indices - so a negative number
# will put something on the end
in_file = File(exists=True,
desc="input 4D file to estimate the power spectrum",
argstr='%s', position=0, mandatory=True)
out_file = File(desc='name of output 4D file for power spectrum',
argstr='%s', position=1, genfile=True, hash_files=False)
class PowerSpectrumOutputSpec(TraitedSpec):
out_file = File(
exists=True, desc="path/name of the output 4D power spectrum file")
class PowerSpectrum(FSLCommand):
"""Use FSL PowerSpectrum command for power spectrum estimation.
Examples
--------
>>> from nipype.interfaces import fsl
>>> pspec = fsl.PowerSpectrum()
>>> pspec.inputs.in_file = 'functional.nii'
>>> res = pspec.run() # doctest: +SKIP
"""
_cmd = 'fslpspec'
input_spec = PowerSpectrumInputSpec
output_spec = PowerSpectrumOutputSpec
def _gen_outfilename(self):
out_file = self.inputs.out_file
if not isdefined(out_file) and isdefined(self.inputs.in_file):
out_file = self._gen_fname(self.inputs.in_file,
suffix='_ps')
return out_file
def _list_outputs(self):
outputs = self.output_spec().get()
outputs['out_file'] = os.path.abspath(self._gen_outfilename())
return outputs
def _gen_filename(self, name):
if name == 'out_file':
return self._gen_outfilename()
return None
class SigLossInputSpec(FSLCommandInputSpec):
in_file = File(mandatory=True,
exists=True,
argstr='-i %s',
desc='b0 fieldmap file')
out_file = File(argstr='-s %s',
desc='output signal loss estimate file',
genfile=True)
mask_file = File(exists=True,
argstr='-m %s',
desc='brain mask file')
echo_time = traits.Float(argstr='--te=%f',
desc='echo time in seconds')
slice_direction = traits.Enum('x', 'y', 'z',
argstr='-d %s',
desc='slicing direction')
class SigLossOuputSpec(TraitedSpec):
out_file = File(exists=True,
desc='signal loss estimate file')
class SigLoss(FSLCommand):
"""Estimates signal loss from a field map (in rad/s)
Examples
--------
>>> sigloss = SigLoss()
>>> sigloss.inputs.in_file = "phase.nii"
>>> sigloss.inputs.echo_time = 0.03
>>> res = sigloss.run() # doctest: +SKIP
"""
input_spec = SigLossInputSpec
output_spec = SigLossOuputSpec
_cmd = 'sigloss'
def _list_outputs(self):
outputs = self.output_spec().get()
outputs['out_file'] = self.inputs.out_file
if not isdefined(outputs['out_file']) and \
isdefined(self.inputs.in_file):
outputs['out_file'] = self._gen_fname(self.inputs.in_file,
suffix='_sigloss')
return outputs
def _gen_filename(self, name):
if name == 'out_file':
return self._list_outputs()['out_file']
return None
class Reorient2StdInputSpec(FSLCommandInputSpec):
in_file = File(exists=True, mandatory=True, argstr="%s")
out_file = File(genfile=True, hash_files=False, argstr="%s")
class Reorient2StdOutputSpec(TraitedSpec):
out_file = File(exists=True)
class Reorient2Std(FSLCommand):
"""fslreorient2std is a tool for reorienting the image to match the
approximate orientation of the standard template images (MNI152).
Examples
--------
>>> reorient = Reorient2Std()
>>> reorient.inputs.in_file = "functional.nii"
>>> res = reorient.run() # doctest: +SKIP
"""
_cmd = 'fslreorient2std'
input_spec = Reorient2StdInputSpec
output_spec = Reorient2StdOutputSpec
def _gen_filename(self, name):
if name == 'out_file':
return self._gen_fname(self.inputs.in_file,
suffix="_reoriented")
return None
def _list_outputs(self):
outputs = self.output_spec().get()
if not isdefined(self.inputs.out_file):
outputs['out_file'] = self._gen_filename('out_file')
else:
outputs['out_file'] = os.path.abspath(self.inputs.out_file)
return outputs
class InvWarpInputSpec(FSLCommandInputSpec):
warp = File(exists=True, argstr='--warp=%s', mandatory=True,
desc=('Name of file containing warp-coefficients/fields. This '
'would typically be the output from the --cout switch of'
' fnirt (but can also use fields, like the output from '
'--fout).'))
reference = File(exists=True, argstr='--ref=%s', mandatory=True,
desc=('Name of a file in target space. Note that the '
'target space is now different from the target '
'space that was used to create the --warp file. It '
'would typically be the file that was specified '
'with the --in argument when running fnirt.'))
inverse_warp = File(argstr='--out=%s', name_source=['warp'],
hash_files=False, name_template='%s_inverse',
desc=('Name of output file, containing warps that are '
'the "reverse" of those in --warp. This will be '
'a field-file (rather than a file of spline '
'coefficients), and it will have any affine '
'component included as part of the '
'displacements.'))
absolute = traits.Bool(argstr='--abs', xor=['relative'],
desc=('If set it indicates that the warps in --warp'
' should be interpreted as absolute, provided'
' that it is not created by fnirt (which '
'always uses relative warps). If set it also '
'indicates that the output --out should be '
'absolute.'))
relative = traits.Bool(argstr='--rel', xor=['absolute'],
desc=('If set it indicates that the warps in --warp'
' should be interpreted as relative. I.e. the'
' values in --warp are displacements from the'
' coordinates in the --ref space. If set it '
'also indicates that the output --out should '
'be relative.'))
niter = traits.Int(argstr='--niter=%d',
desc=('Determines how many iterations of the '
'gradient-descent search that should be run.'))
regularise = traits.Float(argstr='--regularise=%f',
desc='Regularization strength (deafult=1.0).')
noconstraint = traits.Bool(argstr='--noconstraint',
desc='Do not apply Jacobian constraint')
jacobian_min = traits.Float(argstr='--jmin=%f',
desc=('Minimum acceptable Jacobian value for '
'constraint (default 0.01)'))
jacobian_max = traits.Float(argstr='--jmax=%f',
desc=('Maximum acceptable Jacobian value for '
'constraint (default 100.0)'))
class InvWarpOutputSpec(TraitedSpec):
inverse_warp = File(exists=True,
desc=('Name of output file, containing warps that are '
'the "reverse" of those in --warp.'))
class InvWarp(FSLCommand):
"""
Use FSL Invwarp to invert a FNIRT warp
Examples
--------
>>> from nipype.interfaces.fsl import InvWarp
>>> invwarp = InvWarp()
>>> invwarp.inputs.warp = "struct2mni.nii"
>>> invwarp.inputs.reference = "anatomical.nii"
>>> invwarp.inputs.output_type = "NIFTI_GZ"
>>> invwarp.cmdline # doctest: +IGNORE_UNICODE
'invwarp --out=struct2mni_inverse.nii.gz --ref=anatomical.nii --warp=struct2mni.nii'
>>> res = invwarp.run() # doctest: +SKIP
"""
input_spec = InvWarpInputSpec
output_spec = InvWarpOutputSpec
_cmd = 'invwarp'
class ComplexInputSpec(FSLCommandInputSpec):
complex_in_file = File(exists=True, argstr="%s", position=2)
complex_in_file2 = File(exists=True, argstr="%s", position=3)
real_in_file = File(exists=True, argstr="%s", position=2)
imaginary_in_file = File(exists=True, argstr="%s", position=3)
magnitude_in_file = File(exists=True, argstr="%s", position=2)
phase_in_file = File(exists=True, argstr='%s', position=3)
_ofs = ['complex_out_file',
'magnitude_out_file', 'phase_out_file',
'real_out_file', 'imaginary_out_file']
_conversion = ['real_polar', 'real_cartesian',
'complex_cartesian', 'complex_polar',
'complex_split', 'complex_merge', ]
complex_out_file = File(genfile=True, argstr="%s", position=-3,
xor=_ofs + _conversion[:2])
magnitude_out_file = File(genfile=True, argstr="%s", position=-4,
xor=_ofs[:1] + _ofs[3:] + _conversion[1:])
phase_out_file = File(genfile=True, argstr="%s", position=-3,
xor=_ofs[:1] + _ofs[3:] + _conversion[1:])
real_out_file = File(genfile=True, argstr="%s", position=-4,
xor=_ofs[:3] + _conversion[:1] + _conversion[2:])
imaginary_out_file = File(genfile=True, argstr="%s", position=-3,
xor=_ofs[:3] + _conversion[:1] + _conversion[2:])
start_vol = traits.Int(position=-2, argstr='%d')
end_vol = traits.Int(position=-1, argstr='%d')
real_polar = traits.Bool(
argstr='-realpolar', xor=_conversion, position=1,)
# requires=['complex_in_file','magnitude_out_file','phase_out_file'])
real_cartesian = traits.Bool(
argstr='-realcartesian', xor=_conversion, position=1,)
# requires=['complex_in_file','real_out_file','imaginary_out_file'])
complex_cartesian = traits.Bool(
argstr='-complex', xor=_conversion, position=1,)
# requires=['real_in_file','imaginary_in_file','complex_out_file'])
complex_polar = traits.Bool(
argstr='-complexpolar', xor=_conversion, position=1,)
# requires=['magnitude_in_file','phase_in_file',
# 'magnitude_out_file','phase_out_file'])
complex_split = traits.Bool(
argstr='-complexsplit', xor=_conversion, position=1,)
# requires=['complex_in_file','complex_out_file'])
complex_merge = traits.Bool(
argstr='-complexmerge', xor=_conversion + ['start_vol', 'end_vol'],
position=1,)
# requires=['complex_in_file','complex_in_file2','complex_out_file'])
class ComplexOuputSpec(TraitedSpec):
magnitude_out_file = File()
phase_out_file = File()
real_out_file = File()
imaginary_out_file = File()
complex_out_file = File()
class Complex(FSLCommand):
"""fslcomplex is a tool for converting complex data
Examples
--------
>>> cplx = Complex()
>>> cplx.inputs.complex_in_file = "complex.nii"
>>> cplx.real_polar = True
>>> res = cplx.run() # doctest: +SKIP
"""
_cmd = 'fslcomplex'
input_spec = ComplexInputSpec
output_spec = ComplexOuputSpec
def _parse_inputs(self, skip=None):
if skip is None:
skip = []
if self.inputs.real_cartesian:
skip += self.inputs._ofs[:3]
elif self.inputs.real_polar:
skip += self.inputs._ofs[:1] + self.inputs._ofs[3:]
else:
skip += self.inputs._ofs[1:]
return super(Complex, self)._parse_inputs(skip)
def _gen_filename(self, name):
if name == 'complex_out_file':
if self.inputs.complex_cartesian:
in_file = self.inputs.real_in_file
elif self.inputs.complex_polar:
in_file = self.inputs.magnitude_in_file
elif self.inputs.complex_split or self.inputs.complex_merge:
in_file = self.inputs.complex_in_file
else:
return None
return self._gen_fname(in_file, suffix="_cplx")
elif name == 'magnitude_out_file':
return self._gen_fname(self.inputs.complex_in_file, suffix="_mag")
elif name == 'phase_out_file':
return self._gen_fname(
self.inputs.complex_in_file, suffix="_phase")
elif name == 'real_out_file':
return self._gen_fname(self.inputs.complex_in_file, suffix="_real")
elif name == 'imaginary_out_file':
return self._gen_fname(self.inputs.complex_in_file, suffix="_imag")
return None
def _get_output(self, name):
output = getattr(self.inputs, name)
if not isdefined(output):
output = self._gen_filename(name)
return os.path.abspath(output)
def _list_outputs(self):
outputs = self.output_spec().get()
if self.inputs.complex_cartesian or self.inputs.complex_polar or \
self.inputs.complex_split or self.inputs.complex_merge:
outputs['complex_out_file'] = self._get_output('complex_out_file')
elif self.inputs.real_cartesian:
outputs['real_out_file'] = self._get_output('real_out_file')
outputs['imaginary_out_file'] = self._get_output(
'imaginary_out_file')
elif self.inputs.real_polar:
outputs['magnitude_out_file'] = self._get_output(
'magnitude_out_file')
outputs['phase_out_file'] = self._get_output('phase_out_file')
return outputs
class WarpUtilsInputSpec(FSLCommandInputSpec):
in_file = File(
exists=True, argstr='--in=%s', mandatory=True,
desc=('Name of file containing warp-coefficients/fields. This '
'would typically be the output from the --cout switch of '
'fnirt (but can also use fields, like the output from '
'--fout).'))
reference = File(exists=True, argstr='--ref=%s', mandatory=True,
desc=('Name of a file in target space. Note that the '
'target space is now different from the target '
'space that was used to create the --warp file. It '
'would typically be the file that was specified '
'with the --in argument when running fnirt.'))
out_format = traits.Enum(
'spline', 'field', argstr='--outformat=%s',
desc=('Specifies the output format. If set to field (default) '
'the output will be a (4D) field-file. If set to spline '
'the format will be a (4D) file of spline coefficients.'))
warp_resolution = traits.Tuple(
traits.Float, traits.Float, traits.Float,
argstr='--warpres=%0.4f,%0.4f,%0.4f',
desc=('Specifies the resolution/knot-spacing of the splines pertaining'
' to the coefficients in the --out file. This parameter is only '
'relevant if --outformat is set to spline. It should be noted '
'that if the --in file has a higher resolution, the resulting '
'coefficients will pertain to the closest (in a least-squares'
' sense) file in the space of fields with the --warpres'
' resolution. It should also be noted that the resolution '
'will always be an integer multiple of the voxel '
'size.'))
knot_space = traits.Tuple(
traits.Int, traits.Int, traits.Int,
argstr='--knotspace=%d,%d,%d',
desc=('Alternative (to --warpres) specification of the resolution of '
'the output spline-field.'))
out_file = File(
argstr='--out=%s', position=-1, name_source=['in_file'],
output_name='out_file',
desc=('Name of output file. The format of the output depends on what '
'other parameters are set. The default format is a (4D) '
'field-file. If the --outformat is set to spline the format '
'will be a (4D) file of spline coefficients.'))
write_jacobian = traits.Bool(
False, mandatory=True, usedefault=True,
desc='Switch on --jac flag with automatically generated filename')
out_jacobian = File(
argstr='--jac=%s',
desc=('Specifies that a (3D) file of Jacobian determinants '
'corresponding to --in should be produced and written to '
'filename.'))
with_affine = traits.Bool(
False, argstr='--withaff',
desc=('Specifies that the affine transform (i.e. that which was '
'specified for the --aff parameter in fnirt) should be '
'included as displacements in the --out file. That can be '
'useful for interfacing with software that cannot decode '
'FSL/fnirt coefficient-files (where the affine transform is '
'stored separately from the displacements).'))
class WarpUtilsOutputSpec(TraitedSpec):
out_file = File(
desc=('Name of output file, containing the warp as field or '
'coefficients.'))
out_jacobian = File(
desc=('Name of output file, containing the map of the determinant of '
'the Jacobian'))
class WarpUtils(FSLCommand):
"""Use FSL `fnirtfileutils <http://fsl.fmrib.ox.ac.uk/fsl/fsl-4.1.9/fnirt/warp_utils.html>`_
to convert field->coefficients, coefficients->field, coefficients->other_coefficients etc
Examples
--------
>>> from nipype.interfaces.fsl import WarpUtils
>>> warputils = WarpUtils()
>>> warputils.inputs.in_file = "warpfield.nii"
>>> warputils.inputs.reference = "T1.nii"
>>> warputils.inputs.out_format = 'spline'
>>> warputils.inputs.warp_resolution = (10,10,10)
>>> warputils.inputs.output_type = "NIFTI_GZ"
>>> warputils.cmdline # doctest: +ELLIPSIS +IGNORE_UNICODE
'fnirtfileutils --in=warpfield.nii --outformat=spline --ref=T1.nii --warpres=10.0000,10.0000,10.0000 --out=warpfield_coeffs.nii.gz'
>>> res = invwarp.run() # doctest: +SKIP
"""
input_spec = WarpUtilsInputSpec
output_spec = WarpUtilsOutputSpec
_cmd = 'fnirtfileutils'
def _parse_inputs(self, skip=None):
if skip is None:
skip = []
suffix = 'field'
if (isdefined(self.inputs.out_format) and
self.inputs.out_format == 'spline'):
suffix = 'coeffs'
trait_spec = self.inputs.trait('out_file')
trait_spec.name_template = "%s_" + suffix
if self.inputs.write_jacobian:
if not isdefined(self.inputs.out_jacobian):
jac_spec = self.inputs.trait('out_jacobian')
jac_spec.name_source = ['in_file']
jac_spec.name_template = '%s_jac'
jac_spec.output_name = 'out_jacobian'
else:
skip += ['out_jacobian']
skip += ['write_jacobian']
return super(WarpUtils, self)._parse_inputs(skip=skip)
class ConvertWarpInputSpec(FSLCommandInputSpec):
reference = File(
exists=True, argstr='--ref=%s', mandatory=True, position=1,
desc='Name of a file in target space of the full transform.')
out_file = File(
argstr='--out=%s', position=-1, name_source=['reference'],
name_template='%s_concatwarp', output_name='out_file',
desc=('Name of output file, containing warps that are the combination '
'of all those given as arguments. The format of this will be a '
'field-file (rather than spline coefficients) with any affine '
'components included.'))
premat = File(exists=True, argstr='--premat=%s',
desc='filename for pre-transform (affine matrix)')
warp1 = File(exists=True, argstr='--warp1=%s',
desc='Name of file containing initial '
'warp-fields/coefficients (follows premat). This could '
'e.g. be a fnirt-transform from a subjects structural '
'scan to an average of a group of subjects.')
midmat = File(exists=True, argstr="--midmat=%s",
desc="Name of file containing mid-warp-affine transform")
warp2 = File(
exists=True, argstr='--warp2=%s',
desc='Name of file containing secondary warp-fields/coefficients '
'(after warp1/midmat but before postmat). This could e.g. be a '
'fnirt-transform from the average of a group of subjects to some '
'standard space (e.g. MNI152).')
postmat = File(
exists=True, argstr='--postmat=%s',
desc='Name of file containing an affine transform (applied last). It '
'could e.g. be an affine transform that maps the MNI152-space '
'into a better approximation to the Talairach-space (if indeed '
'there is one).')
shift_in_file = File(
exists=True, argstr='--shiftmap=%s',
desc='Name of file containing a "shiftmap", a non-linear transform '
'with displacements only in one direction (applied first, before '
'premat). This would typically be a fieldmap that has been '
'pre-processed using fugue that maps a subjects functional (EPI) '
'data onto an undistorted space (i.e. a space that corresponds '
'to his/her true anatomy).')
shift_direction = traits.Enum(
'y-', 'y', 'x', 'x-', 'z', 'z-',
argstr="--shiftdir=%s", requires=['shift_in_file'],
desc='Indicates the direction that the distortions from '
'--shiftmap goes. It depends on the direction and '
'polarity of the phase-encoding in the EPI sequence.')
cons_jacobian = traits.Bool(
False, argstr='--constrainj',
desc='Constrain the Jacobian of the warpfield to lie within specified '
'min/max limits.')
jacobian_min = traits.Float(argstr='--jmin=%f',
desc='Minimum acceptable Jacobian value for '
'constraint (default 0.01)')
jacobian_max = traits.Float(argstr='--jmax=%f',
desc='Maximum acceptable Jacobian value for '
'constraint (default 100.0)')
abswarp = traits.Bool(
argstr='--abs', xor=['relwarp'],
desc='If set it indicates that the warps in --warp1 and --warp2 should'
' be interpreted as absolute. I.e. the values in --warp1/2 are '
'the coordinates in the next space, rather than displacements. '
'This flag is ignored if --warp1/2 was created by fnirt, which '
'always creates relative displacements.')
relwarp = traits.Bool(
argstr='--rel', xor=['abswarp'],
desc='If set it indicates that the warps in --warp1/2 should be '
'interpreted as relative. I.e. the values in --warp1/2 are '
'displacements from the coordinates in the next space.')
out_abswarp = traits.Bool(
argstr='--absout', xor=['out_relwarp'],
desc='If set it indicates that the warps in --out should be absolute, '
'i.e. the values in --out are displacements from the coordinates '
'in --ref.')
out_relwarp = traits.Bool(
argstr='--relout', xor=['out_abswarp'],
desc='If set it indicates that the warps in --out should be relative, '
'i.e. the values in --out are displacements from the coordinates '
'in --ref.')
class ConvertWarpOutputSpec(TraitedSpec):
out_file = File(
exists=True,
desc='Name of output file, containing the warp as field or '
'coefficients.')
class ConvertWarp(FSLCommand):
"""Use FSL `convertwarp <http://fsl.fmrib.ox.ac.uk/fsl/fsl-4.1.9/fnirt/warp_utils.html>`_
for combining multiple transforms into one.
Examples
--------
>>> from nipype.interfaces.fsl import ConvertWarp
>>> warputils = ConvertWarp()
>>> warputils.inputs.warp1 = "warpfield.nii"
>>> warputils.inputs.reference = "T1.nii"
>>> warputils.inputs.relwarp = True
>>> warputils.inputs.output_type = "NIFTI_GZ"
>>> warputils.cmdline # doctest: +ELLIPSIS +IGNORE_UNICODE
'convertwarp --ref=T1.nii --rel --warp1=warpfield.nii --out=T1_concatwarp.nii.gz'
>>> res = warputils.run() # doctest: +SKIP
"""
input_spec = ConvertWarpInputSpec
output_spec = ConvertWarpOutputSpec
_cmd = 'convertwarp'
class WarpPointsBaseInputSpec(CommandLineInputSpec):
in_coords = File(exists=True, position=-1, argstr='%s', mandatory=True,
desc='filename of file containing coordinates')
xfm_file = File(exists=True, argstr='-xfm %s', xor=['warp_file'],
desc='filename of affine transform (e.g. source2dest.mat)')
warp_file = File(exists=True, argstr='-warp %s', xor=['xfm_file'],
desc='filename of warpfield (e.g. '
'intermediate2dest_warp.nii.gz)')
coord_vox = traits.Bool(True, argstr='-vox', xor=['coord_mm'],
desc='all coordinates in voxels - default')
coord_mm = traits.Bool(False, argstr='-mm', xor=['coord_vox'],
desc='all coordinates in mm')
out_file = File(name_source='in_coords',
name_template='%s_warped', output_name='out_file',
desc='output file name')
class WarpPointsInputSpec(WarpPointsBaseInputSpec):
src_file = File(exists=True, argstr='-src %s', mandatory=True,
desc='filename of source image')
dest_file = File(exists=True, argstr='-dest %s', mandatory=True,
desc='filename of destination image')
class WarpPointsOutputSpec(TraitedSpec):
out_file = File(
exists=True,
desc='Name of output file, containing the warp as field or '
'coefficients.')
class WarpPoints(CommandLine):
"""Use FSL `img2imgcoord <http://fsl.fmrib.ox.ac.uk/fsl/fsl-4.1.9/flirt/overview.html>`_
to transform point sets. Accepts plain text files and vtk files.
.. Note:: transformation of TrackVis trk files is not yet implemented
Examples
--------
>>> from nipype.interfaces.fsl import WarpPoints
>>> warppoints = WarpPoints()
>>> warppoints.inputs.in_coords = 'surf.txt'
>>> warppoints.inputs.src_file = 'epi.nii'
>>> warppoints.inputs.dest_file = 'T1.nii'
>>> warppoints.inputs.warp_file = 'warpfield.nii'
>>> warppoints.inputs.coord_mm = True
>>> warppoints.cmdline # doctest: +ELLIPSIS +IGNORE_UNICODE
'img2imgcoord -mm -dest T1.nii -src epi.nii -warp warpfield.nii surf.txt'
>>> res = warppoints.run() # doctest: +SKIP
"""
input_spec = WarpPointsInputSpec
output_spec = WarpPointsOutputSpec
_cmd = 'img2imgcoord'
_terminal_output = 'stream'
def __init__(self, command=None, **inputs):
self._tmpfile = None
self._in_file = None
self._outformat = None
super(WarpPoints, self).__init__(command=command, **inputs)
def _format_arg(self, name, trait_spec, value):
if name == 'out_file':
return ''
return super(WarpPoints, self)._format_arg(name, trait_spec, value)
def _parse_inputs(self, skip=None):
fname, ext = op.splitext(self.inputs.in_coords)
setattr(self, '_in_file', fname)
setattr(self, '_outformat', ext[1:])
first_args = super(WarpPoints, self)._parse_inputs(
skip=['in_coords', 'out_file'])
second_args = fname + '.txt'
if ext in ['.vtk', '.trk']:
if self._tmpfile is None:
self._tmpfile = tempfile.NamedTemporaryFile(
suffix='.txt', dir=os.getcwd(), delete=False).name
second_args = self._tmpfile
return first_args + [second_args]
def _vtk_to_coords(self, in_file, out_file=None):
from ..vtkbase import tvtk
from ...interfaces import vtkbase as VTKInfo
if VTKInfo.no_tvtk():
raise ImportError(
'TVTK is required and tvtk package was not found')
reader = tvtk.PolyDataReader(file_name=in_file + '.vtk')
reader.update()
mesh = VTKInfo.vtk_output(reader)
points = mesh.points
if out_file is None:
out_file, _ = op.splitext(in_file) + '.txt'
np.savetxt(out_file, points)
return out_file
def _coords_to_vtk(self, points, out_file):
from ..vtkbase import tvtk
from ...interfaces import vtkbase as VTKInfo
if VTKInfo.no_tvtk():
raise ImportError(
'TVTK is required and tvtk package was not found')
reader = tvtk.PolyDataReader(file_name=self.inputs.in_file)
reader.update()
mesh = VTKInfo.vtk_output(reader)
mesh.points = points
writer = tvtk.PolyDataWriter(file_name=out_file)
VTKInfo.configure_input_data(writer, mesh)
writer.write()
def _trk_to_coords(self, in_file, out_file=None):
from nibabel.trackvis import TrackvisFile
trkfile = TrackvisFile.from_file(in_file)
streamlines = trkfile.streamlines
if out_file is None:
out_file, _ = op.splitext(in_file)
np.savetxt(streamlines, out_file + '.txt')
return out_file + '.txt'
def _coords_to_trk(self, points, out_file):
raise NotImplementedError('trk files are not yet supported')
def _overload_extension(self, value, name):
if name == 'out_file':
return '%s.%s' % (value, getattr(self, '_outformat'))
def _run_interface(self, runtime):
fname = getattr(self, '_in_file')
outformat = getattr(self, '_outformat')
tmpfile = None
if outformat == 'vtk':
tmpfile = self._tmpfile
self._vtk_to_coords(fname, out_file=tmpfile)
elif outformat == 'trk':
tmpfile = self._tmpfile
self._trk_to_coords(fname, out_file=tmpfile)
runtime = super(WarpPoints, self)._run_interface(runtime)
newpoints = np.fromstring(
'\n'.join(runtime.stdout.split('\n')[1:]), sep=' ')
if tmpfile is not None:
try:
os.remove(tmpfile.name)
except:
pass
out_file = self._filename_from_source('out_file')
if outformat == 'vtk':
self._coords_to_vtk(newpoints, out_file)
elif outformat == 'trk':
self._coords_to_trk(newpoints, out_file)
else:
np.savetxt(out_file, newpoints.reshape(-1, 3))
return runtime
class WarpPointsToStdInputSpec(WarpPointsBaseInputSpec):
img_file = File(exists=True, argstr='-img %s', mandatory=True,
desc=('filename of input image'))
std_file = File(exists=True, argstr='-std %s', mandatory=True,
desc=('filename of destination image'))
premat_file = File(exists=True, argstr='-premat %s',
desc=('filename of pre-warp affine transform '
'(e.g. example_func2highres.mat)'))
class WarpPointsToStd(WarpPoints):
"""
Use FSL `img2stdcoord <http://fsl.fmrib.ox.ac.uk/fsl/fsl-4.1.9/flirt/overview.html>`_
to transform point sets to standard space coordinates. Accepts plain text
files and vtk files.
.. Note:: transformation of TrackVis trk files is not yet implemented
Examples
--------
>>> from nipype.interfaces.fsl import WarpPointsToStd
>>> warppoints = WarpPointsToStd()
>>> warppoints.inputs.in_coords = 'surf.txt'
>>> warppoints.inputs.img_file = 'T1.nii'
>>> warppoints.inputs.std_file = 'mni.nii'
>>> warppoints.inputs.warp_file = 'warpfield.nii'
>>> warppoints.inputs.coord_mm = True
>>> warppoints.cmdline # doctest: +ELLIPSIS +IGNORE_UNICODE
'img2stdcoord -mm -img T1.nii -std mni.nii -warp warpfield.nii surf.txt'
>>> res = warppoints.run() # doctest: +SKIP
"""
input_spec = WarpPointsToStdInputSpec
output_spec = WarpPointsOutputSpec
_cmd = 'img2stdcoord'
class MotionOutliersInputSpec(FSLCommandInputSpec):
in_file = File(
exists=True, mandatory=True, desc="unfiltered 4D image",
argstr="-i %s")
out_file = File(argstr="-o %s", name_source='in_file',
name_template='%s_outliers.txt',
keep_extension=True, desc='output outlier file name',
hash_files=False)
mask = File(
exists=True, argstr="-m %s", desc="mask image for calculating metric")
metric = traits.Enum(
'refrms', ['refrms', 'dvars', 'refmse', 'fd', 'fdrms'], argstr="--%s",
desc='metrics: refrms - RMS intensity difference to reference volume '
'as metric [default metric], refmse - Mean Square Error version '
'of refrms (used in original version of fsl_motion_outliers), '
'dvars - DVARS, fd - frame displacement, fdrms - FD with RMS '
'matrix calculation')
threshold = traits.Float(argstr="--thresh=%g",
desc=("specify absolute threshold value "
"(otherwise use box-plot cutoff = P75 + "
"1.5*IQR)"))
no_motion_correction = traits.Bool(
argstr="--nomoco",
desc="do not run motion correction (assumed already done)")
dummy = traits.Int(
argstr="--dummy=%d",
desc='number of dummy scans to delete (before running anything and '
'creating EVs)')
out_metric_values = File(
argstr="-s %s", name_source='in_file', name_template='%s_metrics.txt',
keep_extension=True,
desc='output metric values (DVARS etc.) file name', hash_files=False)
out_metric_plot = File(
argstr="-p %s", name_source='in_file', name_template='%s_metrics.png',
hash_files=False, keep_extension=True,
desc='output metric values plot (DVARS etc.) file name')
class MotionOutliersOutputSpec(TraitedSpec):
out_file = File(exists=True)
out_metric_values = File(exists=True)
out_metric_plot = File(exists=True)
class MotionOutliers(FSLCommand):
"""
Use FSL fsl_motion_outliers`http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSLMotionOutliers`_ to find outliers in timeseries (4d) data.
Examples
--------
>>> from nipype.interfaces.fsl import MotionOutliers
>>> mo = MotionOutliers()
>>> mo.inputs.in_file = "epi.nii"
>>> mo.cmdline # doctest: +ELLIPSIS +IGNORE_UNICODE
'fsl_motion_outliers -i epi.nii -o epi_outliers.txt -p epi_metrics.png -s epi_metrics.txt'
>>> res = mo.run() # doctest: +SKIP
"""
input_spec = MotionOutliersInputSpec
output_spec = MotionOutliersOutputSpec
_cmd = 'fsl_motion_outliers'
|
carolFrohlich/nipype
|
nipype/interfaces/fsl/utils.py
|
Python
|
bsd-3-clause
| 85,154
|
[
"Gaussian",
"VTK"
] |
24919461526849a1b87fe0c6317a522faec6dce847069e6d0460c374f3be3d36
|
"""
Copyright (c) 2013-2014 Benedicte Ofstad
Distributed under the GNU Lesser General Public License v3.0.
For full terms see the file LICENSE.md.
"""
"""
Module for reading DALTON input
The functions of this module is:
read_mol_quad(filename, nm)
read_magnet(filename, nm)
def read_polari(filename, nm)
def read_spinrot(filename, natom, nm)
read_nucquad(filename, natom, nm)
def read_nucquad(filename, natom, nm)
def read_optrot(filename, nm)
def read_2d_input(filename, nm)
def read_3d_input(filename, nm)
read_4d_input(filename, natom, nm)
read_quartic_force_field(filename, n_cord)
read_DALTON_values_4d_reduced
read_DALTON_values_4d_full
read_DALTON_values_3d_reduced(filename)
read_DALTON_values_3d_full(filename)
read_cubic_force_field(filename, n_cord)
read_cubic_force_field_chiral(filename, n_cord)
write_to_file(molecule, property_type, results, n_atom = None)
"""
from numpy import array, zeros, add, subtract, diag, double
import numpy as np
import re
import pydoc
def read_molecule(filename):
"""Reads the MOLECULE.INP file generated be DALTON, and extracts the
information we need from it.
filename: The name of the MOLECULE.INP file as saved in the input
directory
return: The cartessian coordinates of the molecule as a list
The masses of the atoms as a list
The number of atoms as a list
The charge of the atoms as a list
The number of atoms in the molecule as an int
The atoms making up the molecule as a list
"""
f = open(filename, 'r')
charge_list = []
num_atoms_list = []
atom_list =[]
atomicmass1 = {'O': 15.9994, 'H': 1.00794, 'C':12.0107, 'D':2.013553212724,'T':3.0160492, 'F':18.998403, 'Cl': 34.96885268, 'N': 14.00674}
coordinates = [] # contains the [x,y,z] coordinates of the input atoms
mass = [] # contains the corresponding masses of the atoms
finished = 0
while (finished == 0):
mline = re.search('(?<=ypes\=)\w+',f.readline())
if mline:
atomtypes = int(mline.group(0)) #The input file specifies how many different types of atoms are present
finished = 1
while(atomtypes > 0):
mline = f.readline()
charge = int(re.search('(?<=arge\=)\w+',mline).group(0))
num_atoms = int(re.search('(?<=toms\=)\w+',mline).group(0))
charge_list.append(charge)
num_atoms_list.append(num_atoms)
for i in range(num_atoms):
mline = f.readline().split()
atom_list.append(mline[0])
mass.append(atomicmass1[mline[0]])
coordinates.append(mline[1:4])
atomtypes -= 1
f.close()
coordinates = array(coordinates, double)
return coordinates, mass, num_atoms_list, charge_list, sum(num_atoms_list), atom_list
def read_hessian(filename, n_coords):
"""Reads a hessian from file.
filename: The name of the file the hessian is contained in.
n_coords: The number of cartessian coordinates needed to express
the location of the molecule ie. 3 * number of atoms
return: A 2-dimensional np.array containg the hessian
"""
dummy = []
f = open(filename, 'r')
hessian = zeros((n_coords, n_coords))
#dummy = f.readline()
for i in range(n_coords):
a = f.readline().split()
for j in range(n_coords):
hessian[i,j] = float(a[j])
f.close()
return hessian
def read_MOLQUAD(filename, nm):
"""Reads and returns the second derivative of the property"""
f = open(filename, 'r')
second_deriv = zeros((nm,3,3))
property_type = "Molecular Quadrupole Moment"
dummy = []
finished = 0
while (finished == 0):
cur_line = f.readline()
if re.search('Molecular quadrupole moment second derivatives',cur_line):
finished = 1
dummy = f.readline()
dummy = f.readline()
dummy = f.readline()
for mode in range(nm):
mline = f.readline()
mline = mline.split()
second_deriv[mode][0][0]= mline[1]
second_deriv[mode][0][1]= mline[2]
second_deriv[mode][1][1]= mline[3]
second_deriv[mode][0][2]= mline[4]
second_deriv[mode][1][2]= mline[5]
second_deriv[mode][2][2]= mline[6]
#second_deriv_t = second_deriv[mode].transpose()
#second_deriv_temp = add(second_deriv[mode], second_deriv_t)
#second_deriv[mode] = subtract(second_deriv_temp , diag(second_deriv[mode].diagonal()))
f.close()
return second_deriv
def read_MAGNET(filename, nm):
"""Reads and returns the second derivative of the property"""
f = open(filename, 'r')
second_deriv_magnet = zeros((nm,3,3))
second_deriv_g = zeros((nm,3,3))
property_type = "Magnetizability"
dummy = []
finished = 0
while (finished == 0):
cur_line = f.readline()
if re.search('Magnetizability tensor second derivatives',cur_line):
finished = 1
dummy = f.readline()
dummy = f.readline()
dummy = f.readline()
for mode in range(nm):
mline = f.readline()
mline = mline.split()
second_deriv_magnet[mode][0][0]= mline[1]
second_deriv_magnet[mode][0][1]= mline[2]
second_deriv_magnet[mode][1][1]= mline[3]
second_deriv_magnet[mode][0][2]= mline[4]
second_deriv_magnet[mode][1][2]= mline[5]
second_deriv_magnet[mode][2][2]= mline[6]
dummy = f.readline()
dummy = f.readline()
dummy = f.readline()
dummy = f.readline()
dummy = f.readline()
for mode in range(nm):
mline = f.readline()
mline = mline.split()
second_deriv_g[mode][0][0]= mline[1]
second_deriv_g[mode][0][1]= mline[2]
second_deriv_g[mode][1][1]= mline[3]
second_deriv_g[mode][0][2]= mline[4]
second_deriv_g[mode][1][2]= mline[5]
second_deriv_g[mode][2][2]= mline[6]
second_deriv_t = second_deriv_g[mode].transpose()
second_deriv_temp = add(second_deriv_g[mode], second_deriv_t)
second_deriv_g[mode] = subtract(second_deriv_temp, diag(second_deriv_g[mode].diagonal()))
f.close()
return second_deriv_magnet
def read_GFACTOR(filename, nm):
"""Reads and returns the second derivative of the property"""
f = open(filename, 'r')
second_deriv = zeros((nm,3,3))
property_type = "Polarizability"
dummy = []
finished = 0
while (finished == 0):
cur_line = f.readline()
if re.search('ty second derivatives',cur_line):
line_split = cur_line.split()
finished = 1
dummy = f.readline()
dummy = f.readline()
dummy = f.readline()
for mode in range(nm):
mline = f.readline()
mline = mline.split()
second_deriv_magnet[mode][0][0]= mline[1]
second_deriv_magnet[mode][0][1]= mline[2]
second_deriv_magnet[mode][1][1]= mline[3]
second_deriv_magnet[mode][0][2]= mline[4]
second_deriv_magnet[mode][1][2]= mline[5]
second_deriv_magnet[mode][2][2]= mline[6]
dummy = f.readline()
dummy = f.readline()
dummy = f.readline()
dummy = f.readline()
dummy = f.readline()
for mode in range(nm):
mline = f.readline()
mline = mline.split()
second_deriv_g[mode][0][0]= mline[1]
second_deriv_g[mode][0][1]= mline[2]
second_deriv_g[mode][1][1]= mline[3]
second_deriv_g[mode][0][2]= mline[4]
second_deriv_g[mode][1][2]= mline[5]
second_deriv_g[mode][2][2]= mline[6]
second_deriv_t = second_deriv_g[mode].transpose()
second_deriv_temp = add(second_deriv_g[mode], second_deriv_t)
second_deriv_g[mode] = subtract(second_deriv_temp, diag(second_deriv_g[mode].diagonal()))
f.close()
return second_deriv_g
def read_POLARI(filename, nm):
"""Reads and returns the second derivative of the property"""
f = open(filename, 'r')
second_deriv = zeros((nm,3,3))
property_type = 0
dummy = []
finished = 0
while (finished == 0):
cur_line = f.readline()
if re.search('ty second derivatives',cur_line):
line_split = cur_line.split()
property_type = line_split[0] + line_split[1]
finished = 1
dummy = f.readline()
dummy = f.readline()
dummy = f.readline()
for mode in range(nm):
mline = f.readline()
mline = mline.split()
second_deriv[mode][0][0]= mline[1]
second_deriv[mode][0][1]= mline[2]
second_deriv[mode][1][1]= mline[3]
second_deriv[mode][0][2]= mline[4]
second_deriv[mode][1][2]= mline[5]
second_deriv[mode][2][2]= mline[6]
f.close()
return second_deriv
def read_SPINROT(filename, natom, nm):
"""Reads and returns the second derivative of the property"""
f = open(filename, 'r')
second_deriv = zeros((natom,nm,3,3))
property_type = "Spin-Rotation Constants"
dummy = []
values = zeros((9))
finished = 0
while (finished == 0):
cur_line = f.readline()
if re.search('second derivatives for',cur_line):
line_split = cur_line.split()
finished = 1
for atom in range(natom):
dummy = f.readline()
dummy = f.readline()
dummy = f.readline()
if atom != 0:
dummy = f.readline()
dummy = f.readline()
for mode in range(nm):
mline = f.readline()
mline = mline.replace('********', '0.000') #This is just a theory
mline = mline.replace(' ', '')
index1 = 1 #Before the decimal place
index2 = 0 #Which value we are at
for i in range(len(mline)):
if (mline[i] == '.'):
values[index2] = mline[index1:i+4]
index2 = index2 + 1
index1 = i+4
second_deriv[atom][mode][0][0]= values[0]
second_deriv[atom][mode][0][1]= values[1]
second_deriv[atom][mode][0][2]= values[2]
second_deriv[atom][mode][1][0]= values[3]
second_deriv[atom][mode][1][1]= values[4]
second_deriv[atom][mode][1][2]= values[5]
second_deriv[atom][mode][2][0]= values[6]
second_deriv[atom][mode][2][1]= values[7]
second_deriv[atom][mode][2][2]= values[8]
f.close()
return second_deriv, property_type
def read_NUCQUAD(filename, natom, nm):
"""Reads and returns the second derivative of the property"""
"""Imports things needed from the DALTON.OUT that I need"""
f = open(filename, 'r')
second_deriv = zeros((natom,nm,3,3))
property_type = "Nuclear Quadrupole Moment"
dummy = []
finished = 0
while (finished == 0):
cur_line = f.readline()
if re.search('second derivatives for:',cur_line):
line_split = cur_line.split()
finished = 1
for atom in range(natom):
dummy = f.readline()
dummy = f.readline()
dummy = f.readline()
if atom != 0:
dummy = f.readline()
dummy = f.readline()
for mode in range(nm):
mline = f.readline()
mline = mline.split()
second_deriv[atom][mode][0][0]= mline[1]
second_deriv[atom][mode][0][1]= mline[2]
second_deriv[atom][mode][0][2]= mline[3]
second_deriv[atom][mode][1][1]= mline[4]
second_deriv[atom][mode][1][2]= mline[5]
second_deriv[atom][mode][2][2]= mline[6]
f.close()
return second_deriv, property_type
def read_OPTROT(filename, nm):
"""Reads and returns the second derivative of the property"""
f = open(filename, 'r')
second_deriv_optrot = zeros((nm,3,3))
second_deriv_optrot1 = zeros((nm,3,3))
second_deriv_optrot2 = zeros((nm,3,3))
dummy = []
finished = 0
while (finished != 2):
cur_line = f.readline()
if re.search('second derivatives',cur_line):
finished = finished + 1
dummy = f.readline()
dummy = f.readline()
dummy = f.readline()
for mode in range(nm):
mline = f.readline()
mline = mline.split()
second_deriv_optrot[mode][0][0]= mline[1]
second_deriv_optrot[mode][0][1]= mline[2]
second_deriv_optrot[mode][1][1]= mline[3]
second_deriv_optrot[mode][0][2]= mline[4]
second_deriv_optrot[mode][1][2]= mline[5]
second_deriv_optrot[mode][2][2]= mline[6]
i = 0
while(i < 19):
dummy = f.readline()
i = i+1
for mode in range(nm):
mline = f.readline()
mline = mline.split()
second_deriv_optrot1[mode][0][0]= mline[1]
second_deriv_optrot1[mode][0][1]= mline[2]
second_deriv_optrot1[mode][1][1]= mline[3]
second_deriv_optrot1[mode][0][2]= mline[4]
second_deriv_optrot1[mode][1][2]= mline[5]
second_deriv_optrot1[mode][2][2]= mline[6]
i = 0
while(i < 19):
dummy = f.readline()
i = i+1
for mode in range(nm):
mline = f.readline()
mline = mline.split()
second_deriv_optrot2[mode][0][0]= mline[1]
second_deriv_optrot2[mode][0][1]= mline[2]
second_deriv_optrot2[mode][1][1]= mline[3]
second_deriv_optrot2[mode][0][2]= mline[4]
second_deriv_optrot2[mode][1][2]= mline[5]
second_deriv_optrot2[mode][2][2]= mline[6]
f.close()
print second_deriv_optrot[0]
print second_deriv_optrot1[0]
print second_deriv_optrot2[8]
return second_deriv_optrot, second_deriv_optrot1, second_deriv_optrot2
def read_2d_input(filename, nm):
"""Reads and returns the second derivative of the property"""
f = open(filename, 'r')
second_deriv = zeros((6,3))
property_type = 0
dummy = []
finished = 0
while (finished == 0):
cur_line = f.readline()
if re.search('second derivatives',cur_line):
finished = 1
dummy = f.readline()
dummy = f.readline()
for mode in range(nm):
mline = f.readline()
mline = mline.split()
second_deriv[mode][0] = mline[1]
second_deriv[mode][1] = mline[2]
second_deriv[mode][2] = mline[3]
f.close()
return second_deriv
def read_SHIELD(filename, natom, nm):
"""Reads and returns the second derivative of the property"""
f = open(filename, 'r')
second_deriv = zeros((natom,nm,3,3))
property_type = "Nuclear Shieldings"
dummy = []
finished = 0
while (finished == 0):
cur_line = f.readline()
if re.search('second derivatives for:',cur_line):
line_split = cur_line.split()
finished = 1
for atom in range(natom):
dummy = f.readline()
dummy = f.readline()
dummy = f.readline()
if atom != 0:
dummy = f.readline()
dummy = f.readline()
for mode in range(nm):
mline = f.readline()
mline = mline.split()
second_deriv[atom][mode][0][0]= mline[1]
second_deriv[atom][mode][0][1]= mline[2]
second_deriv[atom][mode][0][2]= mline[3]
second_deriv[atom][mode][1][0]= mline[4]
second_deriv[atom][mode][1][1]= mline[5]
second_deriv[atom][mode][1][2]= mline[6]
second_deriv[atom][mode][2][0]= mline[7]
second_deriv[atom][mode][2][1]= mline[8]
second_deriv[atom][mode][2][2]= mline[9]
f.close()
return second_deriv, property_type
def read_quartic_force_field(filename, n_cord):
"""Reads and returns the quartic force field in cartessian coordinates
as a 4 dimensional np.array"""
f = open(filename, 'r')
quartic_force_field = zeros((n_cord, n_cord, n_cord, n_cord))
dummy = []
for D4 in range(n_cord):
dummy = f.readline()
for D3 in range(n_cord):
dummy = f.readline()
for D2 in range(n_cord):
mline = f.readline()
mline = mline.split()
for D1 in range(n_cord):
quartic_force_field[D4][D3][D2][D1] = mline[D1]
f.close()
return quartic_force_field
def read_DALTON_NUCQUAD(filename, natom):
""" For testing purposes, extracts the correct values from DALTON"""
f = open(filename, 'r')
uncorrected_values = zeros((natom,3,3))
corrections = zeros((natom,3,3))
corrected_values = zeros((natom,3,3))
dummy = []
finished = 0
while (finished != 2):
cur_line = f.readline()
if not cur_line: raise Exception("Dalton file does not contain the values looked for")
if re.search('Vibrationally corrected',cur_line):
finished = finished + 1
for atom in range(natom):
dummy = f.readline()
dummy = f.readline()
if atom != 0:
dummy = f.readline()
dummy = f.readline()
dummy = f.readline()
dummy = f.readline()
dummy = f.readline()
mline = f.readline()
mline = mline.split()
uncorrected_values[atom][0][0] = mline[1]
corrections[atom][0][0] = mline[2]
corrected_values[atom][0][0] = mline[3]
mline = f.readline()
mline = mline.split()
uncorrected_values[atom][0][1] = mline[1]
corrections[atom][0][1] = mline[2]
corrected_values[atom][0][1] = mline[3]
mline = f.readline()
mline = mline.split()
uncorrected_values[atom][1][1] = mline[1]
corrections[atom][1][1] = mline[2]
corrected_values[atom][1][1] = mline[3]
mline = f.readline()
mline = mline.split()
uncorrected_values[atom][0][2] = mline[1]
corrections[atom][0][2] = mline[2]
corrected_values[atom][0][2] = mline[3]
mline = f.readline()
mline = mline.split()
uncorrected_values[atom][1][2] = mline[1]
corrections[atom][1][2] = mline[2]
corrected_values[atom][1][2] = mline[3]
mline = f.readline()
mline = mline.split()
uncorrected_values[atom][2][2] = mline[1]
corrections[atom][2][2] = mline[2]
corrected_values[atom][2][2] = mline[3]
return uncorrected_values, corrections, corrected_values
def read_DALTON_SHIELD(filename, natom):
""" For testing purposes, extracts the correct values from DALTON"""
f = open(filename, 'r')
uncorrected_values = zeros((natom,3,3))
corrections = zeros((natom,3,3))
corrected_values = zeros((natom,3,3))
dummy = []
finished = 0
while (finished != 2):
cur_line = f.readline()
if not cur_line: raise Exception("Dalton file does not contain the values looked for")
if re.search('Vibrationally corrected',cur_line):
finished = finished + 1
for atom in range(natom):
dummy = f.readline()
dummy = f.readline()
if atom != 0:
dummy = f.readline()
dummy = f.readline()
dummy = f.readline()
for i in range(3):
mline = f.readline()
mline = mline.split()
uncorrected_values[atom][0][i] = mline[1]
corrections[atom][0][i] = mline[2]
corrected_values[atom][0][i] = mline[3]
mline = f.readline()
mline = mline.split()
uncorrected_values[atom][1][i] = mline[1]
corrections[atom][1][i] = mline[2]
corrected_values[atom][1][i] = mline[3]
mline = f.readline()
mline = mline.split()
uncorrected_values[atom][2][i] = mline[1]
corrections[atom][2][i] = mline[2]
corrected_values[atom][2][i] = mline[3]
return uncorrected_values, corrections, corrected_values
def read_DALTON_SPINROT(filename, natom):
""" For testing purposes, extracts the correct values from DALTON"""
f = open(filename, 'r')
uncorrected_values = zeros((natom,3,3))
corrections = zeros((natom,3,3))
corrected_values = zeros((natom,3,3))
dummy = []
finished = 0
while (finished != 2):
cur_line = f.readline()
if not cur_line: raise Exception("Dalton file does not contain the values looked for")
if re.search('Vibrationally corrected',cur_line):
finished = finished + 1
for atom in range(natom):
dummy = f.readline()
dummy = f.readline()
if atom != 0:
dummy = f.readline()
dummy = f.readline()
dummy = f.readline()
for i in range(3):
mline = f.readline()
mline = mline.split()
uncorrected_values[atom][0][i] = mline[1]
corrections[atom][0][i] = mline[2]
corrected_values[atom][0][i] = mline[3]
mline = f.readline()
mline = mline.split()
uncorrected_values[atom][1][i] = mline[1]
corrections[atom][1][i] = mline[2]
corrected_values[atom][1][i] = mline[3]
mline = f.readline()
mline = mline.split()
uncorrected_values[atom][2][i] = mline[1]
corrections[atom][2][i] = mline[2]
corrected_values[atom][2][i] = mline[3]
return uncorrected_values, corrections, corrected_values
def read_DALTON_MAGNET(filename):
""" For testing purposes, extracts the correct values from DALTON"""
f = open(filename, 'r')
uncorrected_values = zeros((3,3))
corrections = zeros((3,3))
corrected_values = zeros((3,3))
dummy = []
finished = 0
while (finished != 2):
cur_line = f.readline()
if not cur_line: raise Exception("Dalton file does not contain the values looked for")
if re.search('Vibrationally corrected',cur_line):
finished = finished + 1
dummy = f.readline()
dummy = f.readline()
mline = f.readline()
mline = mline.split()
uncorrected_values[0][0] = mline[1]
corrections[0][0] = mline[2]
corrected_values[0][0] = mline[3]
mline = f.readline()
mline = mline.split()
uncorrected_values[0][1] = mline[1]
corrections[0][1] = mline[2]
corrected_values[0][1] = mline[3]
mline = f.readline()
mline = mline.split()
uncorrected_values[0][2] = mline[1]
corrections[0][2] = mline[2]
corrected_values[0][2] = mline[3]
mline = f.readline()
mline = mline.split()
uncorrected_values[1][1] = mline[1]
corrections[1][1] = mline[2]
corrected_values[1][1] = mline[3]
mline = f.readline()
mline = mline.split()
uncorrected_values[1][2] = mline[1]
corrections[1][2] = mline[2]
corrected_values[1][2] = mline[3]
mline = f.readline()
mline = mline.split()
uncorrected_values[2][2] = mline[1]
corrections[2][2] = mline[2]
corrected_values[2][2] = mline[3]
return uncorrected_values, corrections, corrected_values
def read_DALTON_OPTROT(filename):
""" For testing purposes, extracts the correct values from DALTON"""
f = open(filename, 'r')
uncorrected_values = zeros((3,3,3))
corrections = zeros((3,3,3))
corrected_values = zeros((3,3,3))
dummy = []
finished = 0
while (finished != 2):
cur_line = f.readline()
if not cur_line: raise Exception("Dalton file does not contain the values looked for")
if re.search('Vibrationally corrected',cur_line):
finished = finished + 1
for i in range(3):
dummy = f.readline()
dummy = f.readline()
mline = f.readline()
mline = mline.split()
uncorrected_values[i][0][0] = mline[1]
corrections[i][0][0] = mline[2]
corrected_values[i][0][0] = mline[3]
mline = f.readline()
mline = mline.split()
uncorrected_values[i][0][1] = mline[1]
corrections[i][0][1] = mline[2]
corrected_values[i][0][1] = mline[3]
mline = f.readline()
mline = mline.split()
uncorrected_values[i][0][2] = mline[1]
corrections[i][0][2] = mline[2]
corrected_values[i][0][2] = mline[3]
mline = f.readline()
mline = mline.split()
uncorrected_values[i][1][1] = mline[1]
corrections[i][1][1] = mline[2]
corrected_values[i][1][1] = mline[3]
mline = f.readline()
mline = mline.split()
uncorrected_values[i][1][2] = mline[1]
corrections[i][1][2] = mline[2]
corrected_values[i][1][2] = mline[3]
mline = f.readline()
mline = mline.split()
uncorrected_values[i][2][2] = mline[1]
corrections[i][2][2] = mline[2]
corrected_values[i][2][2] = mline[3]
dummy = f.readline()
dummy = f.readline()
dummy = f.readline()
dummy = f.readline()
dummy = f.readline()
return uncorrected_values, corrections, corrected_values
def read_DALTON_MOLQUAD(filename):
""" For testing purposes, extracts the correct values from DALTON"""
f = open(filename, 'r')
uncorrected_values = zeros((3,3))
corrections = zeros((3,3))
corrected_values = zeros((3,3))
dummy = []
finished = 0
while (finished != 2):
cur_line = f.readline()
if not cur_line: raise Exception("Dalton file does not contain the values looked for")
if re.search('Vibrationally corrected',cur_line):
finished = finished + 1
dummy = f.readline()
dummy = f.readline()
mline = f.readline()
mline = mline.split()
uncorrected_values[0][0] = mline[1]
corrections[0][0] = mline[2]
corrected_values[0][0] = mline[3]
mline = f.readline()
mline = mline.split()
uncorrected_values[0][1] = mline[1]
corrections[0][1] = mline[2]
corrected_values[0][1] = mline[3]
mline = f.readline()
mline = mline.split()
uncorrected_values[0][2] = mline[1]
corrections[0][2] = mline[2]
corrected_values[0][2] = mline[3]
mline = f.readline()
mline = mline.split()
uncorrected_values[1][1] = mline[1]
corrections[1][1] = mline[2]
corrected_values[1][1] = mline[3]
mline = f.readline()
mline = mline.split()
uncorrected_values[1][2] = mline[1]
corrections[1][2] = mline[2]
corrected_values[1][2] = mline[3]
mline = f.readline()
mline = mline.split()
uncorrected_values[2][2] = mline[1]
corrections[2][2] = mline[2]
corrected_values[2][2] = mline[3]
return uncorrected_values, corrections, corrected_values
def read_DALTON_POLARI(filename):
""" For testing purposes, extracts the correct values from DALTON"""
f = open(filename, 'r')
uncorrected_values = zeros((3,3))
corrections = zeros((3,3))
corrected_values = zeros((3,3))
dummy = []
finished = 0
while (finished != 2):
cur_line = f.readline()
if not cur_line: raise Exception("Dalton file does not contain the values looked for")
if re.search('Vibrationally corrected',cur_line):
finished = finished + 1
dummy = f.readline()
dummy = f.readline()
mline = f.readline()
mline = mline.split()
uncorrected_values[0][0] = mline[1]
corrections[0][0] = mline[2]
corrected_values[0][0] = mline[3]
mline = f.readline()
mline = mline.split()
uncorrected_values[0][1] = mline[1]
corrections[0][1] = mline[2]
corrected_values[0][1] = mline[3]
mline = f.readline()
mline = mline.split()
uncorrected_values[0][2] = mline[1]
corrections[0][2] = mline[2]
corrected_values[0][2] = mline[3]
mline = f.readline()
mline = mline.split()
uncorrected_values[1][1] = mline[1]
corrections[1][1] = mline[2]
corrected_values[1][1] = mline[3]
mline = f.readline()
mline = mline.split()
uncorrected_values[1][2] = mline[1]
corrections[1][2] = mline[2]
corrected_values[1][2] = mline[3]
mline = f.readline()
mline = mline.split()
uncorrected_values[2][2] = mline[1]
corrections[2][2] = mline[2]
corrected_values[2][2] = mline[3]
return uncorrected_values, corrections, corrected_values
def read_DALTON_GFACTOR(filename):
""" For testing purposes, extracts the correct values from DALTON"""
f = open(filename, 'r')
uncorrected_values = zeros((3,3))
corrections = zeros((3,3))
corrected_values = zeros((3,3))
dummy = []
finished = 0
while (finished != 3):
cur_line = f.readline()
if not cur_line: raise Exception("Dalton file does not contain the values looked for")
if re.search('Vibrationally corrected',cur_line):
finished = finished + 1
dummy = f.readline()
dummy = f.readline()
for i in range(3):
mline = f.readline()
mline = mline.split()
uncorrected_values[0][i] = mline[1]
corrections[0][i] = mline[2]
corrected_values[0][i] = mline[3]
mline = f.readline()
mline = mline.split()
uncorrected_values[1][i] = mline[1]
corrections[1][i] = mline[2]
corrected_values[1][i] = mline[3]
mline = f.readline()
mline = mline.split()
uncorrected_values[2][i] = mline[1]
corrections[2][i] = mline[2]
corrected_values[2][i] = mline[3]
return uncorrected_values, corrections, corrected_values
def read_DALTON_values_2d(filename):
""" For testing purposes, extracts the correct values from DALTON"""
f = open(filename, 'r')
uncorrected_values = zeros((3))
corrections = zeros((3))
corrected_values = zeros((3))
dummy = []
finished = 0
while (finished == 0):
cur_line = f.readline()
if not cur_line: raise Exception("Dalton file does not contain the values looked for")
if re.search('Vibrationally corrected',cur_line):
finished = finished + 1
dummy = f.readline()
dummy = f.readline()
for i in range(3):
mline = f.readline()
mline = mline.split()
uncorrected_values[i] = mline[1]
corrections[i] = mline[2]
corrected_values[i] = mline[3]
return uncorrected_values, corrections, corrected_values
def read_eigenvector(filename, n_cord):
""" For testing purposes, extracts the correct values from DALTON"""
f = open(filename, 'r')
eigenvector = zeros((n_cord, n_cord))
dummy = []
counter = n_cord # Make an if counting, and thus reading correctly..
finished = 0
while (finished == 0):
cur_line = f.readline()
if re.search('The eigenvectors',cur_line):
finished = 1
dummy = f.readline()
dummy = f.readline()
j = 0
while(j < n_cord):
for D2 in range(n_cord):
mline = f.readline()
mline = mline.split()
for D1 in range(min(5,n_cord - j)):
eigenvector[D2][D1 + j] = mline[D1 + 1]
dummy = f.readline()
dummy = f.readline()
j = j + 5
f.close()
return eigenvector
def read_cubic_force_field(filename, n_cord):
"""Reads and returns the cubic force field in cartessian coordinates
as a 4 dimensional np.array"""
f = open(filename, 'r')
cubic_force_field = zeros((n_cord, n_cord, n_cord))
dummy = []
counter = n_cord # Make an if counting, and thus reading correctly..
finished = 0
while (finished == 0):
cur_line = f.readline()
if re.search('Anharmonic force constants',cur_line):
finished = 1
for D3 in range(n_cord):
dummy = f.readline()
dummy = f.readline()
dummy = f.readline()
if(D3 != 0):
dummy = f.readline()
j = 0
while(j < n_cord):
for D2 in range(n_cord):
mline = f.readline()
mline = mline.split()
for D1 in range(min(5,n_cord - j)):
cubic_force_field[D3][D2][D1 + j] = mline[D1 + 1]
dummy = f.readline()
dummy = f.readline()
j = j + 5
f.close()
return cubic_force_field
def read_cubic_force_field_anal(filename, n_cord):
"""Reads and returns the cubic force field in cartessian coordinates
as a 3 dimensional np.array"""
f = open(filename, 'r')
cubic_force_field = zeros((n_cord, n_cord, n_cord))
dummy = []
for D3 in range(n_cord):
dummy = f.readline()
for D2 in range(n_cord):
mline = f.readline()
mline = mline.split()
for D1 in range(n_cord):
cubic_force_field[D3][D2][D1] = mline[D1]
f.close()
return cubic_force_field
def read_dipole_gradient(filename, n_cord):
"""Reads and returns the dipole gradient in cartessian coordinates
as a 3 dimensional np.array"""
f = open(filename, 'r')
dipole_gradient = zeros((n_cord, 3))
dummy = []
dummy = f.readline()
for D2 in range(n_cord):
mline = f.readline()
mline = mline.split()
for D1 in range(3):
dipole_gradient[D2][D1] = mline[D1]
f.close()
return dipole_gradient
def read_dipole_hessian(filename, n_cord):
"""Reads and returns the dipole second derivative in cartessian coordinates
as a 3 dimensional np.array"""
f = open(filename, 'r')
dipole_hessian = zeros((n_cord, n_cord, 3))
dummy = []
for D3 in range(n_cord):
dummy = f.readline()
for D2 in range(n_cord):
mline = f.readline()
mline = mline.split()
for D1 in range(3):
dipole_hessian[D3][D2][D1] = mline[D1]
f.close()
return dipole_hessian
def read_polari_gradient(filename, n_cord):
"""Reads and returns the polarizability gradient in cartessian coordinates
as a 3 dimensional np.array"""
f = open(filename, 'r')
polari_gradient = zeros((n_cord, 3, 3))
dummy = []
for D3 in range(n_cord):
dummy = f.readline()
for D2 in range(3):
mline = f.readline()
mline = mline.split()
for D1 in range(3):
polari_gradient[D3][D2][D1] = mline[D1]
f.close()
return polari_gradient
def read_polari_hessian(filename, n_cord):
"""Reads and returns the polarizability second derivative in cartessian coordinates
as a 3 dimensional np.array"""
f = open(filename, 'r')
dipole_hessian = zeros((n_cord, n_cord, 3, 3))
dummy = []
for D4 in range(n_cord):
dummy = f.readline()
for D3 in range(n_cord):
dummy = f.readline()
for D2 in range(3):
mline = f.readline()
mline = mline.split()
for D1 in range(3):
dipole_hessian[D4][D3][D2][D1] = mline[D1]
f.close()
return dipole_hessian
def read_cubic_force_field_norm(filename, n_nm):
""" For testing purposes, extracts the correct values from DALTON"""
f = open(filename, 'r')
cubic_force_field = zeros((n_m))
dummy = []
counter = n_cord # Make an if counting, and thus reading correctly..
finished = 0
while (finished == 0):
cur_line = f.readline()
if re.search('Anharmonic force constants',cur_line):
finished = 1
for D3 in range(n_cord):
dummy = f.readline()
dummy = f.readline()
dummy = f.readline()
if(D3 != 0):
dummy = f.readline()
j = 0
while(j < n_cord):
for D2 in range(n_cord):
mline = f.readline()
mline = mline.split()
for D1 in range(min(5,n_cord - j)):
cubic_force_field[D3][D2][D1 + j] = mline[D1 + 1]
dummy = f.readline()
dummy = f.readline()
j = j + 5
f.close()
return cubic_force_field
|
Benedicte/vibrational_motion
|
read_input.py
|
Python
|
lgpl-3.0
| 37,852
|
[
"Dalton"
] |
9b77c9196cd22c7c87ebfdff5f71dc30347047b2a417049fb2c5a6aa89614139
|
# Copyright 2020 The GPflow Contributors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import abc
from typing import Any, Callable, Iterable, Tuple, Union
import tensorflow as tf
from ..base import TensorType
class GaussianQuadrature:
"""
Abstract class implementing quadrature methods to compute Gaussian Expectations.
Inheriting classes must provide the method _build_X_W to create points and weights
to be used for quadrature.
"""
@abc.abstractmethod
def _build_X_W(self, mean: TensorType, var: TensorType) -> Tuple[tf.Tensor, tf.Tensor]:
raise NotImplementedError
def __call__(
self,
fun: Union[Callable[..., tf.Tensor], Iterable[Callable[..., tf.Tensor]]],
mean: TensorType,
var: TensorType,
*args: Any,
**kwargs: Any,
) -> tf.Tensor:
r"""
Compute the Gaussian Expectation of a function f:
X ~ N(mean, var)
E[f(X)] = ∫f(x, *args, **kwargs)p(x)dx
Using the formula:
E[f(X)] = sum_{i=1}^{N_quad_points} f(x_i) * w_i
where x_i, w_i must be provided by the inheriting class through self._build_X_W.
The computations broadcast along batch-dimensions, represented by [b1, b2, ..., bX].
:param fun: Callable or Iterable of Callables that operates elementwise, with
signature f(X, *args, **kwargs). Moreover, it must satisfy the shape-mapping:
X shape: [N_quad_points, b1, b2, ..., bX, d],
usually [N_quad_points, N, d]
f(X) shape: [N_quad_points, b1, b2, ...., bX, d'],
usually [N_quad_points, N, 1] or [N_quad_points, N, d]
In most cases, f should only operate over the last dimension of X
:param mean: Array/Tensor with shape [b1, b2, ..., bX, d], usually [N, d],
representing the mean of a d-Variate Gaussian distribution
:param var: Array/Tensor with shape b1, b2, ..., bX, d], usually [N, d],
representing the variance of a d-Variate Gaussian distribution
:param *args: Passed to fun
:param **kargs: Passed to fun
:return: Array/Tensor with shape [b1, b2, ...., bX, d'],
usually [N, d] or [N, 1]
"""
X, W = self._build_X_W(mean, var)
if isinstance(fun, Iterable):
return [tf.reduce_sum(f(X, *args, **kwargs) * W, axis=0) for f in fun]
return tf.reduce_sum(fun(X, *args, **kwargs) * W, axis=0)
def logspace(
self,
fun: Union[Callable[..., tf.Tensor], Iterable[Callable[..., tf.Tensor]]],
mean: TensorType,
var: TensorType,
*args: Any,
**kwargs: Any,
) -> tf.Tensor:
r"""
Compute the Gaussian log-Expectation of a the exponential of a function f:
X ~ N(mean, var)
log E[exp[f(X)]] = log ∫exp[f(x, *args, **kwargs)]p(x)dx
Using the formula:
log E[exp[f(X)]] = log sum_{i=1}^{N_quad_points} exp[f(x_i) + log w_i]
where x_i, w_i must be provided by the inheriting class through self._build_X_W.
The computations broadcast along batch-dimensions, represented by [b1, b2, ..., bX].
:param fun: Callable or Iterable of Callables that operates elementwise, with
signature f(X, *args, **kwargs). Moreover, it must satisfy the shape-mapping:
X shape: [N_quad_points, b1, b2, ..., bX, d],
usually [N_quad_points, N, d]
f(X) shape: [N_quad_points, b1, b2, ...., bX, d'],
usually [N_quad_points, N, 1] or [N_quad_points, N, d]
In most cases, f should only operate over the last dimension of X
:param mean: Array/Tensor with shape [b1, b2, ..., bX, d], usually [N, d],
representing the mean of a d-Variate Gaussian distribution
:param var: Array/Tensor with shape b1, b2, ..., bX, d], usually [N, d],
representing the variance of a d-Variate Gaussian distribution
:param *args: Passed to fun
:param **kargs: Passed to fun
:return: Array/Tensor with shape [b1, b2, ...., bX, d'],
usually [N, d] or [N, 1]
"""
X, W = self._build_X_W(mean, var)
logW = tf.math.log(W)
if isinstance(fun, Iterable):
return [tf.reduce_logsumexp(f(X, *args, **kwargs) + logW, axis=0) for f in fun]
return tf.reduce_logsumexp(fun(X, *args, **kwargs) + logW, axis=0)
|
GPflow/GPflow
|
gpflow/quadrature/base.py
|
Python
|
apache-2.0
| 5,025
|
[
"Gaussian"
] |
ff662a36d0e0ad6f53bdd65677d0eec79f1eec9f617a186a17329f98b886e0d1
|
# Copyright (c) Charl P. Botha, TU Delft.
# All rights reserved.
# See COPYRIGHT for details.
import cStringIO
from vtk.wx.wxVTKRenderWindowInteractor import wxVTKRenderWindowInteractor
import wx
# wxPython 2.8.8.1 wx.aui bugs severely on GTK. See:
# http://trac.wxwidgets.org/ticket/9716
# Until this is fixed, use this PyAUI to which I've added a
# wx.aui compatibility layer.
if wx.Platform == "__WXGTK__":
from external import PyAUI
wx.aui = PyAUI
else:
import wx.aui
# one could have loaded a wxGlade created resource like this:
#from resources.python import DICOMBrowserPanels
#reload(DICOMBrowserPanels)
class SkeletonAUIViewerFrame(wx.Frame):
"""wx.Frame child class used by SkeletonAUIViewer for its
interface.
This is an AUI-managed window, so we create the top-level frame,
and then populate it with AUI panes.
"""
def __init__(self, parent, id=-1, title="", name=""):
wx.Frame.__init__(self, parent, id=id, title=title,
pos=wx.DefaultPosition, size=(800,800), name=name)
self.menubar = wx.MenuBar()
self.SetMenuBar(self.menubar)
file_menu = wx.Menu()
self.id_file_open = wx.NewId()
file_menu.Append(self.id_file_open, "&Open\tCtrl-O",
"Open a file", wx.ITEM_NORMAL)
self.menubar.Append(file_menu, "&File")
views_menu = wx.Menu()
views_default_id = wx.NewId()
views_menu.Append(views_default_id, "&Default\tCtrl-0",
"Activate default view layout.", wx.ITEM_NORMAL)
views_max_image_id = wx.NewId()
views_menu.Append(views_max_image_id, "&Maximum image size\tCtrl-1",
"Activate maximum image view size layout.",
wx.ITEM_NORMAL)
self.menubar.Append(views_menu, "&Views")
# tell FrameManager to manage this frame
self._mgr = wx.aui.AuiManager()
self._mgr.SetManagedWindow(self)
self._mgr.AddPane(self._create_series_pane(), wx.aui.AuiPaneInfo().
Name("series").Caption("Series").Top().
BestSize(wx.Size(600, 100)).
CloseButton(False).MaximizeButton(True))
self._mgr.AddPane(self._create_files_pane(), wx.aui.AuiPaneInfo().
Name("files").Caption("Image Files").
Left().
BestSize(wx.Size(200,400)).
CloseButton(False).MaximizeButton(True))
self._mgr.AddPane(self._create_meta_pane(), wx.aui.AuiPaneInfo().
Name("meta").Caption("Image Metadata").
Left().
BestSize(wx.Size(200,400)).
CloseButton(False).MaximizeButton(True))
self._mgr.AddPane(self._create_rwi_pane(), wx.aui.AuiPaneInfo().
Name("rwi").Caption("3D Renderer").
Center().
BestSize(wx.Size(400,400)).
CloseButton(False).MaximizeButton(True))
self.SetMinSize(wx.Size(400, 300))
# first we save this default perspective with all panes
# visible
self._perspectives = {}
self._perspectives['default'] = self._mgr.SavePerspective()
# then we hide all of the panes except the renderer
self._mgr.GetPane("series").Hide()
self._mgr.GetPane("files").Hide()
self._mgr.GetPane("meta").Hide()
# save the perspective again
self._perspectives['max_image'] = self._mgr.SavePerspective()
# and put back the default perspective / view
self._mgr.LoadPerspective(self._perspectives['default'])
# finally tell the AUI manager to do everything that we've
# asked
self._mgr.Update()
# we bind the views events here, because the functionality is
# completely encapsulated in the frame and does not need to
# round-trip to the DICOMBrowser main module.
self.Bind(wx.EVT_MENU, self._handler_default_view,
id=views_default_id)
self.Bind(wx.EVT_MENU, self._handler_max_image_view,
id=views_max_image_id)
def close(self):
self.Destroy()
def _create_files_pane(self):
sl = wx.ListCtrl(self, -1,
style=wx.LC_REPORT)
sl.InsertColumn(0, "Full name")
# we'll autosize this column later
sl.SetColumnWidth(0, 300)
#sl.InsertColumn(SeriesColumns.modality, "Modality")
self.files_lc = sl
return sl
def _create_rwi_pane(self):
panel = wx.Panel(self, -1)
self.rwi = wxVTKRenderWindowInteractor(panel, -1, (400,400))
self.button1 = wx.Button(panel, -1, "Add Superquadric")
self.button2 = wx.Button(panel, -1, "Reset Camera")
self.button3 = wx.Button(panel, -1, "Start Timer Event")
button_sizer = wx.BoxSizer(wx.HORIZONTAL)
button_sizer.Add(self.button1)
button_sizer.Add(self.button2)
button_sizer.Add(self.button3)
sizer1 = wx.BoxSizer(wx.VERTICAL)
sizer1.Add(self.rwi, 1, wx.EXPAND|wx.BOTTOM, 7)
sizer1.Add(button_sizer)
tl_sizer = wx.BoxSizer(wx.VERTICAL)
tl_sizer.Add(sizer1, 1, wx.ALL|wx.EXPAND, 7)
panel.SetSizer(tl_sizer)
tl_sizer.Fit(panel)
return panel
def _create_meta_pane(self):
ml = wx.ListCtrl(self, -1,
style=wx.LC_REPORT |
wx.LC_HRULES | wx.LC_VRULES |
wx.LC_SINGLE_SEL)
ml.InsertColumn(0, "Key")
ml.SetColumnWidth(0, 70)
ml.InsertColumn(1, "Value")
ml.SetColumnWidth(1, 70)
self.meta_lc = ml
return ml
def _create_series_pane(self):
sl = wx.ListCtrl(self, -1,
style=wx.LC_REPORT | wx.LC_HRULES | wx.LC_SINGLE_SEL,
size=(600,120))
sl.InsertColumn(0, "Description")
sl.SetColumnWidth(1, 170)
sl.InsertColumn(2, "Modality")
sl.InsertColumn(3, "# Images")
sl.InsertColumn(4, "Size")
self.series_lc = sl
return sl
def render(self):
"""Update embedded RWI, i.e. update the image.
"""
self.rwi.Render()
def _handler_default_view(self, event):
"""Event handler for when the user selects View | Default from
the main menu.
"""
self._mgr.LoadPerspective(
self._perspectives['default'])
def _handler_max_image_view(self, event):
"""Event handler for when the user selects View | Max Image
from the main menu.
"""
self._mgr.LoadPerspective(
self._perspectives['max_image'])
|
chrisidefix/devide
|
modules/viewers/SkeletonAUIViewerFrame.py
|
Python
|
bsd-3-clause
| 6,877
|
[
"VTK"
] |
356263091d9c466fe11e79ef6f969b48e9be1c0fd8e43a0fe7717bddd9b8ad9c
|
#!/usr/bin/python
from multiprocessing import Pool
import time
import os
import sys
import argparse
from Bio import SeqIO
from Bio.Seq import Seq
from Bio.Alphabet import IUPAC
from Bio.SeqUtils import GC
from Bio.SeqRecord import SeqRecord
#from Bio.Seq import Seq
#from Bio.Alphabet import generic_dna
# Copyright(C) 2015 David Ream
# Released under GPL version 3 licence. http://www.gnu.org/licenses/lgpl.html
# Do not remove this comment
# This exists to make the main function easier to read. It contains code to run the argument parser, and does nothing else.
def parser_code():
parser = argparse.ArgumentParser(description='Convert all genbank files found in a specified folder, and optionally a file containing the accessions that you wish to include, and create BLAST searchable databases from them.')
#parser.add_argument("-i", "--infolder", dest="infolder", metavar="DIRECTORY", default='./genomes/',
# help="Folder containing all genbank files for use by the program.")
parser.add_argument("-G", "--genbank_directory", dest="genbank_directory", metavar="DIRECTORY", default='./genomes/',
help="Folder containing all genbank files for use by the program.")
parser.add_argument("-o", "--outfolder", dest="outfolder", metavar="DIRECTORY", default='./db/',
help="Folder where the BLAST searchable databases will be stored.")
parser.add_argument("-f", "--filter", dest="filter", metavar="FILE", default='',
help="File restrictiong which accession numbers this script will process. If no file is provided, filtering is not performed.")
parser.add_argument("-n", "--num_proc", dest="num_proc", metavar="INT", default = os.sysconf("SC_NPROCESSORS_CONF"), type=int,
help="Number of processors that you want this script to run on. The default is every CPU that the system has.")
parser.add_argument("-p", "--protein", dest="protein", default=True, action='store_false',
help="Flag to toggle mode from protein to DNA sequences for use as database construction. The default is protein.")
parser.add_argument("-q", "--quiet", dest="quiet", action="store_true", default=False,
help="Suppresses most program text outputs.")
return parser.parse_args()
def check_options(parsed_args):
'''
# section of code that checks the infolder entry
if os.path.isdir(parsed_args.infolder):
infolder = parsed_args.infolder
else:
print "The folder %s does not exist." % parsed_args.infolder
sys.exit()
'''
# check the genbank folder
if os.path.isdir(parsed_args.genbank_directory):
genbank_directory = parsed_args.genbank_directory
else:
print "The folder %s does not exist." % parsed_args.genbank_directory
sys.exit()
# if the directory that the user specifies does not exist, then the program makes it for them.
if not os.path.isdir(parsed_args.outfolder):
os.makedirs(parsed_args.outfolder)
outfolder = parsed_args.outfolder
if outfolder[-1] != '/':
outfolder = outfolder + '/'
# Check the filter file
if parsed_args.filter == '' or os.path.exists(parsed_args.filter):
filter_file = parsed_args.filter
else:
print "The file %s does not exist." % parsed_args.filter
sys.exit()
# section of code that deals determining the number of CPU cores that will be used by the program
if parsed_args.num_proc > os.sysconf("SC_NPROCESSORS_CONF"):
num_proc = os.sysconf("SC_NPROCESSORS_CONF")
elif parsed_args.num_proc < 1:
num_proc = 1
else:
num_proc = int(parsed_args.num_proc)
do_protein = parsed_args.protein
quiet = parsed_args.quiet
#return infolder, outfolder, filter_file, num_proc, do_protein
return genbank_directory, outfolder, filter_file, num_proc, do_protein, quiet
#this function will return all of the files that are in a directory. os.walk is recursive traversal.
def returnRecursiveDirFiles(root_dir):
result = []
for path, dir_name, flist in os.walk(root_dir):
for f in flist:
fname = os.path.join(path, f)
if os.path.isfile(fname):
result.append(fname)
return result
#################################################################
#### so yeah.... not gonna do this right now... #####
#### I feel there is no reason to, but keeping #####
#### the code in here just in case i am wrong about that #####
#################################################################
# I need to do an analysis on gc content, and gc skew.
# currently this will return the organism gc, (mean, SD, varience) of the GC in coding regions
# the results will we retained in a file the calling program opens tab delimited
def GCAnalysis(NC, organism, gc_list, seq, outfile):
handle = open(outfile, 'a')
organism_gc = "%3.2f" % GC(seq)
mean = "%3.2f" % numpy.mean(gc_list)
#mode = "%5.2f" % numpy.mode(gc_list)
var = "%5.2f" % numpy.var(gc_list)
std = "%5.2f" % numpy.std(gc_list)
handle.write('\t'.join([NC, organism, organism_gc, mean, var, std]) + NEW_LINE)
# take the genbank file specified by genbank path, and save the customized result file in the db_directory folder
#def convert_genbank(genbank_path, db_directory, error_fname): #, gc_outfile = 'gc_analysis.txt'):
def convert_genbank(genbank_tuple):
genbank_path, db_directory, error_fname, do_protein = genbank_tuple
record_list = []
seq_record = SeqIO.parse(open(genbank_path), "genbank").next()
accession = seq_record.annotations['accessions'][0]
organism = seq_record.annotations['organism'].replace(' ', '_')
err_log = []
gc_list = [] # no need for this right now, but leaving in
# loop over the genbank file
for fnum, feature in enumerate(seq_record.features):
err_flag = False
error_in_field = False
if feature.type == 'CDS':
#print dir(feature.location)
try:
start = int(feature.location.start)
stop = int(feature.location.end)
except:
error_in_field = True
strand = feature.strand
dna_seq = seq_record.seq[start:stop]
#print "dna_seq", type(dna_seq), dna_seq
gc = GC(dna_seq)
gc_list.append(gc)
gc = "%3.2f" % gc
try:
locus = feature.qualifiers['locus_tag'][0]
except:
try:
locus = feature.qualifiers['gene'][0]
except:
locus = 'error'
print "Error in the organism %s with NC # %s" % (organism, accession)
err_flag = True
err_log.append([organism, accession])
if do_protein:
#seq = seq.translate()
#print type(seq)
#print feature.qualifiers.keys()
#seq = dir(feature)
try:
if 'translation' in feature.qualifiers.keys():
prot_seq = Seq(''.join(feature.qualifiers['translation']), IUPAC.protein)
#print "prot_seq", type(prot_seq), prot_seq
if 'gene' in feature.qualifiers:
gene = feature.qualifiers['gene'][0]
#record_list.append(SeqRecord(prot_seq, id = '|'.join([accession, organism, locus, gene, str(start), str(stop), str(strand), gc]).replace(' ', ''), description = ''))
seq_rec_to_store = SeqRecord(prot_seq, id = '|'.join([accession, organism, locus, gene, str(start), str(stop), str(strand), gc]).replace(' ', ''), description = '')
else:
#record_list.append(SeqRecord(prot_seq, id = '|'.join([accession, organism, locus, 'unknown', str(start), str(stop), str(strand), gc]).replace(' ', ''),description = ''))
seq_rec_to_store = SeqRecord(prot_seq, id = '|'.join([accession, organism, locus, 'unknown', str(start), str(stop), str(strand), gc]).replace(' ', ''),description = '')
#print prot_seq
else:
pass
#print "This was not a protein sequence"
except:
print "Error in function convert_genbank(genbank_tuple) from the format_db.py script, unhandled error in the genbank parse."
else:
# put something in here that will deal with RNA later, if we plan to go that route.
pass
if not error_in_field:
record_list.append(seq_rec_to_store)
else:
print "a record was omitted"
'''
#print len(seq)
if len(seq) < 2:
#pass
print "len seq", len(seq)
elif do_protein:
if 'gene' in feature.qualifiers:
gene = feature.qualifiers['gene'][0]
record_list.append(SeqRecord(seq, id = '|'.join([accession, organism, locus, gene, str(start), str(stop), str(strand), gc]).replace(' ', ''),
description = ''))
else:
record_list.append(
SeqRecord(seq, id = '|'.join([accession, organism, locus, 'unknown', str(start), str(stop), str(strand), gc]).replace(' ', ''),
description = ''))
else:
if 'gene' in feature.qualifiers:
gene = feature.qualifiers['gene'][0]
record_list.append(SeqRecord(seq, id = '|'.join([accession, organism, locus, gene, str(start), str(stop), str(strand), gc]).replace(' ', ''),
description = ''))
else:
record_list.append(
SeqRecord(seq, id = '|'.join([accession, organism, locus, 'unknown', str(start), str(stop), str(strand), gc]).replace(' ', ''),
description = ''))
'''
#if os.path.isfile(gc_outfile):
# os.remove(gc_outfile)
#GCAnalysis(accession, organism, gc_list, seq_record.seq, gc_outfile)
handle = open(error_fname, 'a')
for i in err_log:
handle.write('\t'.join(i) + '\n')
handle.close()
if not err_flag:
outpath = db_directory + os.path.splitext(os.path.basename(genbank_path))[0] + '.ffc'
#print outpath
out_handle = open(outpath,"w")
SeqIO.write(record_list, out_handle, "fasta")
out_handle.close()
if do_protein:
cmd = "formatdb -i %s -p T -o F" % (outpath)
#print "got here"
else:
cmd = "formatdb -i %s -p F -o F" % (outpath)
os.system(cmd)
#print "Passed main loop"
return outpath, err_flag
def parallel_convert_genbank(file_list, outfolder, num_proc, do_protein, error_fname = "./error_log.txt"):
# Make sure that we have a new error log each time the program is run
if os.path.isfile(error_fname):
os.remove(error_fname)
# Package the variables for the convert_genbank function so everything can be run in parallel
tuple_list = [(i, outfolder, error_fname, do_protein) for i in file_list]
pool = Pool(processes = num_proc)
result = dict(pool.map(convert_genbank, tuple_list))
def main():
start = time.time()
parsed_args = parser_code()
#infolder, outfolder, filter_file, num_proc, do_protein = check_options(parsed_args)
genbank_directory, outfolder, filter_file, num_proc, do_protein, quiet = check_options(parsed_args)
#flist = returnRecursiveDirFiles(infolder)
flist = returnRecursiveDirFiles(genbank_directory)
if filter_file != '':
filter_list = [i.strip() for i in open(filter_file).readlines()]
file_list = [i for i in flist if i.split('/')[-1].split('.')[0] in filter_list]
else:
file_list = flist
#print "do_protein", do_protein
parallel_convert_genbank(file_list, outfolder, num_proc, do_protein)
if not quiet:
print time.time() - start
# A successful command could look like this:
# ./format_db.py -f ./phylo_order.txt
# ./format_db.py -i /home/dave/Desktop/all_genbank -o ./db1/ -f ./phylo_order.txt
if __name__ == '__main__':
main()
|
nguyenngochuy91/Ancestral-Blocks-Reconstruction
|
format_db1.py
|
Python
|
gpl-3.0
| 12,782
|
[
"BLAST"
] |
71a91fd3ffb64e6654087a4aeb3618a0f4a4e60f8a3694e36fd11b29234dfc9d
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# TAMkin is a post-processing toolkit for normal mode analysis, thermochemistry
# and reaction kinetics.
# Copyright (C) 2008-2012 Toon Verstraelen <Toon.Verstraelen@UGent.be>, An Ghysels
# <An.Ghysels@UGent.be> and Matthias Vandichel <Matthias.Vandichel@UGent.be>
# Center for Molecular Modeling (CMM), Ghent University, Ghent, Belgium; all
# rights reserved unless otherwise stated.
#
# This file is part of TAMkin.
#
# TAMkin is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License
# as published by the Free Software Foundation; either version 3
# of the License, or (at your option) any later version.
#
# In addition to the regulations of the GNU General Public License,
# publications and communications based in parts on this program or on
# parts of this program are required to cite the following article:
#
# "TAMkin: A Versatile Package for Vibrational Analysis and Chemical Kinetics",
# An Ghysels, Toon Verstraelen, Karen Hemelsoet, Michel Waroquier and Veronique
# Van Speybroeck, Journal of Chemical Information and Modeling, 2010, 50,
# 1736-1750W
# http://dx.doi.org/10.1021/ci100099g
#
# TAMkin is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, see <http://www.gnu.org/licenses/>
#
#--
from __future__ import print_function
# Import the tamkin libarary.
from tamkin import *
# Import unit conversin factors
from molmod import *
# Import numpy stuff
from numpy import *
# Load the gaussian data.
molecule = load_molecule_g03fchk("gaussian.fchk")
print("Energy [kJ/mol]:", molecule.energy/kjmol)
# A) Detect all blocks such that only the dihedral angles remain free, and apply
# MBH
# detect the graph based on inter-atomic distances
molecule.set_default_graph()
# define a block for each atom and its neighbors (if it has more than one neighbor)
blocks = []
for central, neighbors in molecule.graph.neighbors.items():
if len(neighbors) > 1:
block = list(neighbors)
block.append(central)
blocks.append(block)
print("Blocks:", blocks)
nma = NMA(molecule, MBH(blocks))
print("The zero eigenmodes: %s" % nma.zeros)
# write the modes to file
dump_modes_gaussian("molden.vib", nma)
# B) Construct a partition function with the typical gas phase contributions.
pf = PartFun(nma, [ExtTrans(), ExtRot()])
print("Heat capacity at 300K, constant pressure [J/(mol*K)]:", pf.heat_capacity(300*kelvin)/(joule/mol/kelvin))
# Write some general information about the molecule and the partition function
# to a file.
pf.write_to_file("partfun.txt")
# Write an extensive overview of the thermodynamic properties to a file:
ta = ThermoAnalysis(pf, arange(300,601,5))
ta.write_to_file("thermo.csv")
|
molmod/tamkin
|
tamkin/examples/014_pentane_mbh/thermo.py
|
Python
|
gpl-3.0
| 3,015
|
[
"Gaussian"
] |
7dbf4d37873a5e42d6125b9d1c32b499fc68ec485e1f7dfadcd2ec65d68921ab
|
"""Group velocity calculation."""
# Copyright (C) 2013 Atsushi Togo
# All rights reserved.
#
# This file is part of phonopy.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in
# the documentation and/or other materials provided with the
# distribution.
#
# * Neither the name of the phonopy project nor the names of its
# contributors may be used to endorse or promote products derived
# from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
# COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
import warnings
from typing import Optional, Union
import numpy as np
from phonopy.harmonic.derivative_dynmat import DerivativeOfDynamicalMatrix
from phonopy.harmonic.dynamical_matrix import DynamicalMatrix, DynamicalMatrixNAC
from phonopy.harmonic.force_constants import similarity_transformation
from phonopy.phonon.degeneracy import degenerate_sets
from phonopy.structure.symmetry import Symmetry
from phonopy.units import VaspToTHz
class GroupVelocity:
r"""Class to calculate group velocities of phonons.
d omega ----
------- = \ / omega
d q \/q
Gradient of omega in reciprocal space, which is calculated here by
1 d D(q)
------- <e(q,nu)|------|e(q,nu)>
2 omega d q
Attributes
----------
group_velocity : ndarray
Group velocities at q-points.
shape=(q-points, num_band, 3), dtype='double', order='C'
q_length : float
Distance in reciprocal space used to calculate finite difference of
dynamcial matrix.
"""
Default_q_length = 1e-5
def __init__(
self,
dynamical_matrix: Union[DynamicalMatrix, DynamicalMatrixNAC],
q_length=None,
symmetry: Optional[Symmetry] = None,
frequency_factor_to_THz=VaspToTHz,
cutoff_frequency=1e-4,
):
"""Init method.
dynamical_matrix : DynamicalMatrix or DynamicalMatrixNAC
Dynamical matrix class instance.
q_length : float
This is used such as D(q + q_length) - D(q - q_length) for
calculating finite difference of dynamical matrix.
Default is None, which gives 1e-5.
symmetry : Symmetry
This is used to symmetrize group velocity at each q-points.
Default is None, which means no symmetrization.
frequency_factor_to_THz : float
Unit conversion factor to convert to THz. Default is VaspToTHz.
cutoff_frequency : float
Group velocity is set zero if phonon frequency is below this value.
"""
self._dynmat = dynamical_matrix
primitive = dynamical_matrix.primitive
self._reciprocal_lattice_inv = primitive.cell
self._reciprocal_lattice = np.linalg.inv(self._reciprocal_lattice_inv)
self._q_length = q_length
if self._dynmat.is_nac() and self._dynmat.nac_method == "gonze":
if self._q_length is None:
self._q_length = self.Default_q_length
self._ddm: Optional[DerivativeOfDynamicalMatrix]
if self._q_length is None:
self._ddm = DerivativeOfDynamicalMatrix(dynamical_matrix)
else:
self._ddm = None
self._symmetry = symmetry
self._factor = frequency_factor_to_THz
self._cutoff_frequency = cutoff_frequency
self._directions = np.array(
[[1, 2, 3], [1, 0, 0], [0, 1, 0], [0, 0, 1]], dtype="double"
)
self._directions[0] /= np.linalg.norm(self._directions[0])
self._q_points = None
self._group_velocities = None
self._perturbation = None
def run(self, q_points, perturbation=None):
"""Group velocities are computed at q-points.
Calculated group velocities are stored in self._group_velocities.
Parameters
----------
q_points : array-like
List of q-points such as [[0, 0, 0], [0.1, 0.2, 0.3], ...].
perturbation : array-like
Direction in fractional coordinates of reciprocal space.
"""
self._q_points = q_points
self._perturbation = perturbation
if perturbation is None:
# Give an random direction to break symmetry
self._directions[0] = np.array([1, 2, 3])
else:
self._directions[0] = np.dot(self._reciprocal_lattice, perturbation)
self._directions[0] /= np.linalg.norm(self._directions[0])
gv = [self._calculate_group_velocity_at_q(q) for q in self._q_points]
self._group_velocities = np.array(gv, dtype="double", order="C")
@property
def q_length(self):
"""Setter an getter of q_length."""
return self._q_length
@q_length.setter
def q_length(self, q_length):
self._q_length = q_length
def get_q_length(self):
"""Return q_length."""
warnings.warn(
"GroupVelocity.get_q_length() is deprecated. "
"Use q_length attribute instead.",
DeprecationWarning,
)
return self.q_length
def set_q_length(self, q_length):
"""Set q_length."""
warnings.warn(
"GroupVelocity.set_q_length() is deprecated. "
"Use q_length attribute instead.",
DeprecationWarning,
)
self.q_length = q_length
@property
def group_velocities(self):
"""Return group velocities."""
return self._group_velocities
def get_group_velocity(self):
"""Return group velocities."""
warnings.warn(
"GroupVelocity.get_group_velocity() is deprecated. "
"Use group_velocities attribute instead.",
DeprecationWarning,
)
return self.group_velocities
def _calculate_group_velocity_at_q(self, q):
self._dynmat.run(q)
dm = self._dynmat.dynamical_matrix
eigvals, eigvecs = np.linalg.eigh(dm)
eigvals = eigvals.real
freqs = np.sqrt(abs(eigvals)) * np.sign(eigvals) * self._factor
gv = np.zeros((len(freqs), 3), dtype="double", order="C")
deg_sets = degenerate_sets(freqs)
ddms = self._get_dD(np.array(q))
pos = 0
for deg in deg_sets:
gv[pos : pos + len(deg)] = self._perturb_D(ddms, eigvecs[:, deg])
pos += len(deg)
for i, f in enumerate(freqs):
if f > self._cutoff_frequency:
gv[i, :] *= self._factor ** 2 / f / 2
else:
gv[i, :] = 0
if self._perturbation is None:
if self._symmetry is None:
return gv
else:
return self._symmetrize_group_velocity(gv, q)
else:
return gv
def _symmetrize_group_velocity(self, gv, q):
"""Symmetrize obtained group velocities using site symmetries."""
rotations = []
for r in self._symmetry.reciprocal_operations:
q_in_BZ = q - np.rint(q)
diff = q_in_BZ - np.dot(r, q_in_BZ)
if (np.abs(diff) < self._symmetry.tolerance).all():
rotations.append(r)
gv_sym = np.zeros_like(gv)
for r in rotations:
r_cart = similarity_transformation(self._reciprocal_lattice, r)
gv_sym += np.dot(r_cart, gv.T).T
return gv_sym / len(rotations)
def _get_dD(self, q):
"""Compute derivative or finite difference of dynamcial matrices."""
if self._q_length is None:
return self._get_dD_analytical(q)
else:
return self._get_dD_FD(q)
def _get_dD_FD(self, q):
"""Compute finite difference of dynamcial matrices."""
ddm = []
for dqc in self._directions * self._q_length:
dq = np.dot(self._reciprocal_lattice_inv, dqc)
ddm.append(
_delta_dynamical_matrix(q, dq, self._dynmat) / self._q_length / 2
)
return np.array(ddm)
def _get_dD_analytical(self, q):
"""Compute derivative of dynamcial matrices."""
self._ddm.run(q)
ddm = self._ddm.get_derivative_of_dynamical_matrix()
dtype = "c%d" % (np.dtype("double").itemsize * 2)
ddm_dirs = np.zeros((len(self._directions),) + ddm.shape[1:], dtype=dtype)
for i, dq in enumerate(self._directions):
for j in range(3):
ddm_dirs[i] += dq[j] * ddm[j]
return ddm_dirs
def _perturb_D(self, ddms, eigsets):
"""Treat degeneracy.
Group velocities are calculated using analytical continuation using
specified directions (self._directions) in reciprocal space.
ddms : array-like
List of delta (derivative or finite difference) of dynamical
matrices along several q-directions for perturbation.
eigsets : array-like
List of phonon eigenvectors of degenerate bands.
"""
_, eigvecs = np.linalg.eigh(np.dot(eigsets.T.conj(), np.dot(ddms[0], eigsets)))
gv = []
rot_eigsets = np.dot(eigsets, eigvecs)
for ddm in ddms[1:]:
gv.append(
np.diag(np.dot(rot_eigsets.T.conj(), np.dot(ddm, rot_eigsets))).real
)
return np.transpose(gv)
def get_group_velocity(
q, # q-point
dynamical_matrix,
q_length=None, # finite distance in q
symmetry=None,
frequency_factor_to_THz=VaspToTHz,
):
"""Return group velocity at a q-point."""
gv = GroupVelocity(
dynamical_matrix,
q_length=q_length,
symmetry=symmetry,
frequency_factor_to_THz=frequency_factor_to_THz,
)
gv.run([q])
return gv.group_velocity[0]
def _delta_dynamical_matrix(q, delta_q, dynmat):
dynmat.run(q - delta_q)
dm1 = dynmat.dynamical_matrix
dynmat.run(q + delta_q)
dm2 = dynmat.dynamical_matrix
return dm2 - dm1
|
atztogo/phonopy
|
phonopy/phonon/group_velocity.py
|
Python
|
bsd-3-clause
| 10,970
|
[
"phonopy"
] |
472bdc52deb07e1756b2fa0026bfa7407d24e7ba62113ddba7fbb09ccc1fbb9f
|
# Copyright (C) 2003-2005 Peter J. Verveer
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#
# 1. Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above
# copyright notice, this list of conditions and the following
# disclaimer in the documentation and/or other materials provided
# with the distribution.
#
# 3. The name of the author may not be used to endorse or promote
# products derived from this software without specific prior
# written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS
# OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE
# GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
from collections.abc import Iterable
import warnings
import numpy
import operator
from numpy.core.multiarray import normalize_axis_index
from . import _ni_support
from . import _nd_image
from . import _ni_docstrings
__all__ = ['correlate1d', 'convolve1d', 'gaussian_filter1d', 'gaussian_filter',
'prewitt', 'sobel', 'generic_laplace', 'laplace',
'gaussian_laplace', 'generic_gradient_magnitude',
'gaussian_gradient_magnitude', 'correlate', 'convolve',
'uniform_filter1d', 'uniform_filter', 'minimum_filter1d',
'maximum_filter1d', 'minimum_filter', 'maximum_filter',
'rank_filter', 'median_filter', 'percentile_filter',
'generic_filter1d', 'generic_filter']
def _invalid_origin(origin, lenw):
return (origin < -(lenw // 2)) or (origin > (lenw - 1) // 2)
@_ni_docstrings.docfiller
def correlate1d(input, weights, axis=-1, output=None, mode="reflect",
cval=0.0, origin=0):
"""Calculate a 1-D correlation along the given axis.
The lines of the array along the given axis are correlated with the
given weights.
Parameters
----------
%(input)s
weights : array
1-D sequence of numbers.
%(axis)s
%(output)s
%(mode)s
%(cval)s
%(origin)s
Examples
--------
>>> from scipy.ndimage import correlate1d
>>> correlate1d([2, 8, 0, 4, 1, 9, 9, 0], weights=[1, 3])
array([ 8, 26, 8, 12, 7, 28, 36, 9])
"""
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
raise TypeError('Complex type not supported')
output = _ni_support._get_output(output, input)
weights = numpy.asarray(weights, dtype=numpy.float64)
if weights.ndim != 1 or weights.shape[0] < 1:
raise RuntimeError('no filter weights given')
if not weights.flags.contiguous:
weights = weights.copy()
axis = normalize_axis_index(axis, input.ndim)
if _invalid_origin(origin, len(weights)):
raise ValueError('Invalid origin; origin must satisfy '
'-(len(weights) // 2) <= origin <= '
'(len(weights)-1) // 2')
mode = _ni_support._extend_mode_to_code(mode)
_nd_image.correlate1d(input, weights, axis, output, mode, cval,
origin)
return output
@_ni_docstrings.docfiller
def convolve1d(input, weights, axis=-1, output=None, mode="reflect",
cval=0.0, origin=0):
"""Calculate a 1-D convolution along the given axis.
The lines of the array along the given axis are convolved with the
given weights.
Parameters
----------
%(input)s
weights : ndarray
1-D sequence of numbers.
%(axis)s
%(output)s
%(mode)s
%(cval)s
%(origin)s
Returns
-------
convolve1d : ndarray
Convolved array with same shape as input
Examples
--------
>>> from scipy.ndimage import convolve1d
>>> convolve1d([2, 8, 0, 4, 1, 9, 9, 0], weights=[1, 3])
array([14, 24, 4, 13, 12, 36, 27, 0])
"""
weights = weights[::-1]
origin = -origin
if not len(weights) & 1:
origin -= 1
return correlate1d(input, weights, axis, output, mode, cval, origin)
def _gaussian_kernel1d(sigma, order, radius):
"""
Computes a 1-D Gaussian convolution kernel.
"""
if order < 0:
raise ValueError('order must be non-negative')
exponent_range = numpy.arange(order + 1)
sigma2 = sigma * sigma
x = numpy.arange(-radius, radius+1)
phi_x = numpy.exp(-0.5 / sigma2 * x ** 2)
phi_x = phi_x / phi_x.sum()
if order == 0:
return phi_x
else:
# f(x) = q(x) * phi(x) = q(x) * exp(p(x))
# f'(x) = (q'(x) + q(x) * p'(x)) * phi(x)
# p'(x) = -1 / sigma ** 2
# Implement q'(x) + q(x) * p'(x) as a matrix operator and apply to the
# coefficients of q(x)
q = numpy.zeros(order + 1)
q[0] = 1
D = numpy.diag(exponent_range[1:], 1) # D @ q(x) = q'(x)
P = numpy.diag(numpy.ones(order)/-sigma2, -1) # P @ q(x) = q(x) * p'(x)
Q_deriv = D + P
for _ in range(order):
q = Q_deriv.dot(q)
q = (x[:, None] ** exponent_range).dot(q)
return q * phi_x
@_ni_docstrings.docfiller
def gaussian_filter1d(input, sigma, axis=-1, order=0, output=None,
mode="reflect", cval=0.0, truncate=4.0):
"""1-D Gaussian filter.
Parameters
----------
%(input)s
sigma : scalar
standard deviation for Gaussian kernel
%(axis)s
order : int, optional
An order of 0 corresponds to convolution with a Gaussian
kernel. A positive order corresponds to convolution with
that derivative of a Gaussian.
%(output)s
%(mode)s
%(cval)s
truncate : float, optional
Truncate the filter at this many standard deviations.
Default is 4.0.
Returns
-------
gaussian_filter1d : ndarray
Examples
--------
>>> from scipy.ndimage import gaussian_filter1d
>>> gaussian_filter1d([1.0, 2.0, 3.0, 4.0, 5.0], 1)
array([ 1.42704095, 2.06782203, 3. , 3.93217797, 4.57295905])
>>> gaussian_filter1d([1.0, 2.0, 3.0, 4.0, 5.0], 4)
array([ 2.91948343, 2.95023502, 3. , 3.04976498, 3.08051657])
>>> import matplotlib.pyplot as plt
>>> np.random.seed(280490)
>>> x = np.random.randn(101).cumsum()
>>> y3 = gaussian_filter1d(x, 3)
>>> y6 = gaussian_filter1d(x, 6)
>>> plt.plot(x, 'k', label='original data')
>>> plt.plot(y3, '--', label='filtered, sigma=3')
>>> plt.plot(y6, ':', label='filtered, sigma=6')
>>> plt.legend()
>>> plt.grid()
>>> plt.show()
"""
sd = float(sigma)
# make the radius of the filter equal to truncate standard deviations
lw = int(truncate * sd + 0.5)
# Since we are calling correlate, not convolve, revert the kernel
weights = _gaussian_kernel1d(sigma, order, lw)[::-1]
return correlate1d(input, weights, axis, output, mode, cval, 0)
@_ni_docstrings.docfiller
def gaussian_filter(input, sigma, order=0, output=None,
mode="reflect", cval=0.0, truncate=4.0):
"""Multidimensional Gaussian filter.
Parameters
----------
%(input)s
sigma : scalar or sequence of scalars
Standard deviation for Gaussian kernel. The standard
deviations of the Gaussian filter are given for each axis as a
sequence, or as a single number, in which case it is equal for
all axes.
order : int or sequence of ints, optional
The order of the filter along each axis is given as a sequence
of integers, or as a single number. An order of 0 corresponds
to convolution with a Gaussian kernel. A positive order
corresponds to convolution with that derivative of a Gaussian.
%(output)s
%(mode_multiple)s
%(cval)s
truncate : float
Truncate the filter at this many standard deviations.
Default is 4.0.
Returns
-------
gaussian_filter : ndarray
Returned array of same shape as `input`.
Notes
-----
The multidimensional filter is implemented as a sequence of
1-D convolution filters. The intermediate arrays are
stored in the same data type as the output. Therefore, for output
types with a limited precision, the results may be imprecise
because intermediate results may be stored with insufficient
precision.
Examples
--------
>>> from scipy.ndimage import gaussian_filter
>>> a = np.arange(50, step=2).reshape((5,5))
>>> a
array([[ 0, 2, 4, 6, 8],
[10, 12, 14, 16, 18],
[20, 22, 24, 26, 28],
[30, 32, 34, 36, 38],
[40, 42, 44, 46, 48]])
>>> gaussian_filter(a, sigma=1)
array([[ 4, 6, 8, 9, 11],
[10, 12, 14, 15, 17],
[20, 22, 24, 25, 27],
[29, 31, 33, 34, 36],
[35, 37, 39, 40, 42]])
>>> from scipy import misc
>>> import matplotlib.pyplot as plt
>>> fig = plt.figure()
>>> plt.gray() # show the filtered result in grayscale
>>> ax1 = fig.add_subplot(121) # left side
>>> ax2 = fig.add_subplot(122) # right side
>>> ascent = misc.ascent()
>>> result = gaussian_filter(ascent, sigma=5)
>>> ax1.imshow(ascent)
>>> ax2.imshow(result)
>>> plt.show()
"""
input = numpy.asarray(input)
output = _ni_support._get_output(output, input)
orders = _ni_support._normalize_sequence(order, input.ndim)
sigmas = _ni_support._normalize_sequence(sigma, input.ndim)
modes = _ni_support._normalize_sequence(mode, input.ndim)
axes = list(range(input.ndim))
axes = [(axes[ii], sigmas[ii], orders[ii], modes[ii])
for ii in range(len(axes)) if sigmas[ii] > 1e-15]
if len(axes) > 0:
for axis, sigma, order, mode in axes:
gaussian_filter1d(input, sigma, axis, order, output,
mode, cval, truncate)
input = output
else:
output[...] = input[...]
return output
@_ni_docstrings.docfiller
def prewitt(input, axis=-1, output=None, mode="reflect", cval=0.0):
"""Calculate a Prewitt filter.
Parameters
----------
%(input)s
%(axis)s
%(output)s
%(mode_multiple)s
%(cval)s
Examples
--------
>>> from scipy import ndimage, misc
>>> import matplotlib.pyplot as plt
>>> fig = plt.figure()
>>> plt.gray() # show the filtered result in grayscale
>>> ax1 = fig.add_subplot(121) # left side
>>> ax2 = fig.add_subplot(122) # right side
>>> ascent = misc.ascent()
>>> result = ndimage.prewitt(ascent)
>>> ax1.imshow(ascent)
>>> ax2.imshow(result)
>>> plt.show()
"""
input = numpy.asarray(input)
axis = normalize_axis_index(axis, input.ndim)
output = _ni_support._get_output(output, input)
modes = _ni_support._normalize_sequence(mode, input.ndim)
correlate1d(input, [-1, 0, 1], axis, output, modes[axis], cval, 0)
axes = [ii for ii in range(input.ndim) if ii != axis]
for ii in axes:
correlate1d(output, [1, 1, 1], ii, output, modes[ii], cval, 0,)
return output
@_ni_docstrings.docfiller
def sobel(input, axis=-1, output=None, mode="reflect", cval=0.0):
"""Calculate a Sobel filter.
Parameters
----------
%(input)s
%(axis)s
%(output)s
%(mode_multiple)s
%(cval)s
Examples
--------
>>> from scipy import ndimage, misc
>>> import matplotlib.pyplot as plt
>>> fig = plt.figure()
>>> plt.gray() # show the filtered result in grayscale
>>> ax1 = fig.add_subplot(121) # left side
>>> ax2 = fig.add_subplot(122) # right side
>>> ascent = misc.ascent()
>>> result = ndimage.sobel(ascent)
>>> ax1.imshow(ascent)
>>> ax2.imshow(result)
>>> plt.show()
"""
input = numpy.asarray(input)
axis = normalize_axis_index(axis, input.ndim)
output = _ni_support._get_output(output, input)
modes = _ni_support._normalize_sequence(mode, input.ndim)
correlate1d(input, [-1, 0, 1], axis, output, modes[axis], cval, 0)
axes = [ii for ii in range(input.ndim) if ii != axis]
for ii in axes:
correlate1d(output, [1, 2, 1], ii, output, modes[ii], cval, 0)
return output
@_ni_docstrings.docfiller
def generic_laplace(input, derivative2, output=None, mode="reflect",
cval=0.0,
extra_arguments=(),
extra_keywords=None):
"""
N-D Laplace filter using a provided second derivative function.
Parameters
----------
%(input)s
derivative2 : callable
Callable with the following signature::
derivative2(input, axis, output, mode, cval,
*extra_arguments, **extra_keywords)
See `extra_arguments`, `extra_keywords` below.
%(output)s
%(mode_multiple)s
%(cval)s
%(extra_keywords)s
%(extra_arguments)s
"""
if extra_keywords is None:
extra_keywords = {}
input = numpy.asarray(input)
output = _ni_support._get_output(output, input)
axes = list(range(input.ndim))
if len(axes) > 0:
modes = _ni_support._normalize_sequence(mode, len(axes))
derivative2(input, axes[0], output, modes[0], cval,
*extra_arguments, **extra_keywords)
for ii in range(1, len(axes)):
tmp = derivative2(input, axes[ii], output.dtype, modes[ii], cval,
*extra_arguments, **extra_keywords)
output += tmp
else:
output[...] = input[...]
return output
@_ni_docstrings.docfiller
def laplace(input, output=None, mode="reflect", cval=0.0):
"""N-D Laplace filter based on approximate second derivatives.
Parameters
----------
%(input)s
%(output)s
%(mode_multiple)s
%(cval)s
Examples
--------
>>> from scipy import ndimage, misc
>>> import matplotlib.pyplot as plt
>>> fig = plt.figure()
>>> plt.gray() # show the filtered result in grayscale
>>> ax1 = fig.add_subplot(121) # left side
>>> ax2 = fig.add_subplot(122) # right side
>>> ascent = misc.ascent()
>>> result = ndimage.laplace(ascent)
>>> ax1.imshow(ascent)
>>> ax2.imshow(result)
>>> plt.show()
"""
def derivative2(input, axis, output, mode, cval):
return correlate1d(input, [1, -2, 1], axis, output, mode, cval, 0)
return generic_laplace(input, derivative2, output, mode, cval)
@_ni_docstrings.docfiller
def gaussian_laplace(input, sigma, output=None, mode="reflect",
cval=0.0, **kwargs):
"""Multidimensional Laplace filter using Gaussian second derivatives.
Parameters
----------
%(input)s
sigma : scalar or sequence of scalars
The standard deviations of the Gaussian filter are given for
each axis as a sequence, or as a single number, in which case
it is equal for all axes.
%(output)s
%(mode_multiple)s
%(cval)s
Extra keyword arguments will be passed to gaussian_filter().
Examples
--------
>>> from scipy import ndimage, misc
>>> import matplotlib.pyplot as plt
>>> ascent = misc.ascent()
>>> fig = plt.figure()
>>> plt.gray() # show the filtered result in grayscale
>>> ax1 = fig.add_subplot(121) # left side
>>> ax2 = fig.add_subplot(122) # right side
>>> result = ndimage.gaussian_laplace(ascent, sigma=1)
>>> ax1.imshow(result)
>>> result = ndimage.gaussian_laplace(ascent, sigma=3)
>>> ax2.imshow(result)
>>> plt.show()
"""
input = numpy.asarray(input)
def derivative2(input, axis, output, mode, cval, sigma, **kwargs):
order = [0] * input.ndim
order[axis] = 2
return gaussian_filter(input, sigma, order, output, mode, cval,
**kwargs)
return generic_laplace(input, derivative2, output, mode, cval,
extra_arguments=(sigma,),
extra_keywords=kwargs)
@_ni_docstrings.docfiller
def generic_gradient_magnitude(input, derivative, output=None,
mode="reflect", cval=0.0,
extra_arguments=(), extra_keywords=None):
"""Gradient magnitude using a provided gradient function.
Parameters
----------
%(input)s
derivative : callable
Callable with the following signature::
derivative(input, axis, output, mode, cval,
*extra_arguments, **extra_keywords)
See `extra_arguments`, `extra_keywords` below.
`derivative` can assume that `input` and `output` are ndarrays.
Note that the output from `derivative` is modified inplace;
be careful to copy important inputs before returning them.
%(output)s
%(mode_multiple)s
%(cval)s
%(extra_keywords)s
%(extra_arguments)s
"""
if extra_keywords is None:
extra_keywords = {}
input = numpy.asarray(input)
output = _ni_support._get_output(output, input)
axes = list(range(input.ndim))
if len(axes) > 0:
modes = _ni_support._normalize_sequence(mode, len(axes))
derivative(input, axes[0], output, modes[0], cval,
*extra_arguments, **extra_keywords)
numpy.multiply(output, output, output)
for ii in range(1, len(axes)):
tmp = derivative(input, axes[ii], output.dtype, modes[ii], cval,
*extra_arguments, **extra_keywords)
numpy.multiply(tmp, tmp, tmp)
output += tmp
# This allows the sqrt to work with a different default casting
numpy.sqrt(output, output, casting='unsafe')
else:
output[...] = input[...]
return output
@_ni_docstrings.docfiller
def gaussian_gradient_magnitude(input, sigma, output=None,
mode="reflect", cval=0.0, **kwargs):
"""Multidimensional gradient magnitude using Gaussian derivatives.
Parameters
----------
%(input)s
sigma : scalar or sequence of scalars
The standard deviations of the Gaussian filter are given for
each axis as a sequence, or as a single number, in which case
it is equal for all axes.
%(output)s
%(mode_multiple)s
%(cval)s
Extra keyword arguments will be passed to gaussian_filter().
Returns
-------
gaussian_gradient_magnitude : ndarray
Filtered array. Has the same shape as `input`.
Examples
--------
>>> from scipy import ndimage, misc
>>> import matplotlib.pyplot as plt
>>> fig = plt.figure()
>>> plt.gray() # show the filtered result in grayscale
>>> ax1 = fig.add_subplot(121) # left side
>>> ax2 = fig.add_subplot(122) # right side
>>> ascent = misc.ascent()
>>> result = ndimage.gaussian_gradient_magnitude(ascent, sigma=5)
>>> ax1.imshow(ascent)
>>> ax2.imshow(result)
>>> plt.show()
"""
input = numpy.asarray(input)
def derivative(input, axis, output, mode, cval, sigma, **kwargs):
order = [0] * input.ndim
order[axis] = 1
return gaussian_filter(input, sigma, order, output, mode,
cval, **kwargs)
return generic_gradient_magnitude(input, derivative, output, mode,
cval, extra_arguments=(sigma,),
extra_keywords=kwargs)
def _correlate_or_convolve(input, weights, output, mode, cval, origin,
convolution):
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
raise TypeError('Complex type not supported')
origins = _ni_support._normalize_sequence(origin, input.ndim)
weights = numpy.asarray(weights, dtype=numpy.float64)
wshape = [ii for ii in weights.shape if ii > 0]
if len(wshape) != input.ndim:
raise RuntimeError('filter weights array has incorrect shape.')
if convolution:
weights = weights[tuple([slice(None, None, -1)] * weights.ndim)]
for ii in range(len(origins)):
origins[ii] = -origins[ii]
if not weights.shape[ii] & 1:
origins[ii] -= 1
for origin, lenw in zip(origins, wshape):
if _invalid_origin(origin, lenw):
raise ValueError('Invalid origin; origin must satisfy '
'-(weights.shape[k] // 2) <= origin[k] <= '
'(weights.shape[k]-1) // 2')
if not weights.flags.contiguous:
weights = weights.copy()
output = _ni_support._get_output(output, input)
if not isinstance(mode, str) and isinstance(mode, Iterable):
raise RuntimeError("A sequence of modes is not supported")
mode = _ni_support._extend_mode_to_code(mode)
_nd_image.correlate(input, weights, output, mode, cval, origins)
return output
@_ni_docstrings.docfiller
def correlate(input, weights, output=None, mode='reflect', cval=0.0,
origin=0):
"""
Multidimensional correlation.
The array is correlated with the given kernel.
Parameters
----------
%(input)s
weights : ndarray
array of weights, same number of dimensions as input
%(output)s
%(mode)s
%(cval)s
%(origin_multiple)s
Returns
-------
result : ndarray
The result of correlation of `input` with `weights`.
See Also
--------
convolve : Convolve an image with a kernel.
Examples
--------
Correlation is the process of moving a filter mask often referred to
as kernel over the image and computing the sum of products at each location.
>>> from scipy.ndimage import correlate
>>> input_img = np.arange(25).reshape(5,5)
>>> print(input_img)
[[ 0 1 2 3 4]
[ 5 6 7 8 9]
[10 11 12 13 14]
[15 16 17 18 19]
[20 21 22 23 24]]
Define a kernel (weights) for correlation. In this example, it is for sum of
center and up, down, left and right next elements.
>>> weights = [[0, 1, 0],
... [1, 1, 1],
... [0, 1, 0]]
We can calculate a correlation result:
For example, element ``[2,2]`` is ``7 + 11 + 12 + 13 + 17 = 60``.
>>> correlate(input_img, weights)
array([[ 6, 10, 15, 20, 24],
[ 26, 30, 35, 40, 44],
[ 51, 55, 60, 65, 69],
[ 76, 80, 85, 90, 94],
[ 96, 100, 105, 110, 114]])
"""
return _correlate_or_convolve(input, weights, output, mode, cval,
origin, False)
@_ni_docstrings.docfiller
def convolve(input, weights, output=None, mode='reflect', cval=0.0,
origin=0):
"""
Multidimensional convolution.
The array is convolved with the given kernel.
Parameters
----------
%(input)s
weights : array_like
Array of weights, same number of dimensions as input
%(output)s
%(mode)s
cval : scalar, optional
Value to fill past edges of input if `mode` is 'constant'. Default
is 0.0
%(origin_multiple)s
Returns
-------
result : ndarray
The result of convolution of `input` with `weights`.
See Also
--------
correlate : Correlate an image with a kernel.
Notes
-----
Each value in result is :math:`C_i = \\sum_j{I_{i+k-j} W_j}`, where
W is the `weights` kernel,
j is the N-D spatial index over :math:`W`,
I is the `input` and k is the coordinate of the center of
W, specified by `origin` in the input parameters.
Examples
--------
Perhaps the simplest case to understand is ``mode='constant', cval=0.0``,
because in this case borders (i.e., where the `weights` kernel, centered
on any one value, extends beyond an edge of `input`) are treated as zeros.
>>> a = np.array([[1, 2, 0, 0],
... [5, 3, 0, 4],
... [0, 0, 0, 7],
... [9, 3, 0, 0]])
>>> k = np.array([[1,1,1],[1,1,0],[1,0,0]])
>>> from scipy import ndimage
>>> ndimage.convolve(a, k, mode='constant', cval=0.0)
array([[11, 10, 7, 4],
[10, 3, 11, 11],
[15, 12, 14, 7],
[12, 3, 7, 0]])
Setting ``cval=1.0`` is equivalent to padding the outer edge of `input`
with 1.0's (and then extracting only the original region of the result).
>>> ndimage.convolve(a, k, mode='constant', cval=1.0)
array([[13, 11, 8, 7],
[11, 3, 11, 14],
[16, 12, 14, 10],
[15, 6, 10, 5]])
With ``mode='reflect'`` (the default), outer values are reflected at the
edge of `input` to fill in missing values.
>>> b = np.array([[2, 0, 0],
... [1, 0, 0],
... [0, 0, 0]])
>>> k = np.array([[0,1,0], [0,1,0], [0,1,0]])
>>> ndimage.convolve(b, k, mode='reflect')
array([[5, 0, 0],
[3, 0, 0],
[1, 0, 0]])
This includes diagonally at the corners.
>>> k = np.array([[1,0,0],[0,1,0],[0,0,1]])
>>> ndimage.convolve(b, k)
array([[4, 2, 0],
[3, 2, 0],
[1, 1, 0]])
With ``mode='nearest'``, the single nearest value in to an edge in
`input` is repeated as many times as needed to match the overlapping
`weights`.
>>> c = np.array([[2, 0, 1],
... [1, 0, 0],
... [0, 0, 0]])
>>> k = np.array([[0, 1, 0],
... [0, 1, 0],
... [0, 1, 0],
... [0, 1, 0],
... [0, 1, 0]])
>>> ndimage.convolve(c, k, mode='nearest')
array([[7, 0, 3],
[5, 0, 2],
[3, 0, 1]])
"""
return _correlate_or_convolve(input, weights, output, mode, cval,
origin, True)
@_ni_docstrings.docfiller
def uniform_filter1d(input, size, axis=-1, output=None,
mode="reflect", cval=0.0, origin=0):
"""Calculate a 1-D uniform filter along the given axis.
The lines of the array along the given axis are filtered with a
uniform filter of given size.
Parameters
----------
%(input)s
size : int
length of uniform filter
%(axis)s
%(output)s
%(mode)s
%(cval)s
%(origin)s
Examples
--------
>>> from scipy.ndimage import uniform_filter1d
>>> uniform_filter1d([2, 8, 0, 4, 1, 9, 9, 0], size=3)
array([4, 3, 4, 1, 4, 6, 6, 3])
"""
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
raise TypeError('Complex type not supported')
axis = normalize_axis_index(axis, input.ndim)
if size < 1:
raise RuntimeError('incorrect filter size')
output = _ni_support._get_output(output, input)
if (size // 2 + origin < 0) or (size // 2 + origin >= size):
raise ValueError('invalid origin')
mode = _ni_support._extend_mode_to_code(mode)
_nd_image.uniform_filter1d(input, size, axis, output, mode, cval,
origin)
return output
@_ni_docstrings.docfiller
def uniform_filter(input, size=3, output=None, mode="reflect",
cval=0.0, origin=0):
"""Multidimensional uniform filter.
Parameters
----------
%(input)s
size : int or sequence of ints, optional
The sizes of the uniform filter are given for each axis as a
sequence, or as a single number, in which case the size is
equal for all axes.
%(output)s
%(mode_multiple)s
%(cval)s
%(origin_multiple)s
Returns
-------
uniform_filter : ndarray
Filtered array. Has the same shape as `input`.
Notes
-----
The multidimensional filter is implemented as a sequence of
1-D uniform filters. The intermediate arrays are stored
in the same data type as the output. Therefore, for output types
with a limited precision, the results may be imprecise because
intermediate results may be stored with insufficient precision.
Examples
--------
>>> from scipy import ndimage, misc
>>> import matplotlib.pyplot as plt
>>> fig = plt.figure()
>>> plt.gray() # show the filtered result in grayscale
>>> ax1 = fig.add_subplot(121) # left side
>>> ax2 = fig.add_subplot(122) # right side
>>> ascent = misc.ascent()
>>> result = ndimage.uniform_filter(ascent, size=20)
>>> ax1.imshow(ascent)
>>> ax2.imshow(result)
>>> plt.show()
"""
input = numpy.asarray(input)
output = _ni_support._get_output(output, input)
sizes = _ni_support._normalize_sequence(size, input.ndim)
origins = _ni_support._normalize_sequence(origin, input.ndim)
modes = _ni_support._normalize_sequence(mode, input.ndim)
axes = list(range(input.ndim))
axes = [(axes[ii], sizes[ii], origins[ii], modes[ii])
for ii in range(len(axes)) if sizes[ii] > 1]
if len(axes) > 0:
for axis, size, origin, mode in axes:
uniform_filter1d(input, int(size), axis, output, mode,
cval, origin)
input = output
else:
output[...] = input[...]
return output
@_ni_docstrings.docfiller
def minimum_filter1d(input, size, axis=-1, output=None,
mode="reflect", cval=0.0, origin=0):
"""Calculate a 1-D minimum filter along the given axis.
The lines of the array along the given axis are filtered with a
minimum filter of given size.
Parameters
----------
%(input)s
size : int
length along which to calculate 1D minimum
%(axis)s
%(output)s
%(mode)s
%(cval)s
%(origin)s
Notes
-----
This function implements the MINLIST algorithm [1]_, as described by
Richard Harter [2]_, and has a guaranteed O(n) performance, `n` being
the `input` length, regardless of filter size.
References
----------
.. [1] http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.42.2777
.. [2] http://www.richardhartersworld.com/cri/2001/slidingmin.html
Examples
--------
>>> from scipy.ndimage import minimum_filter1d
>>> minimum_filter1d([2, 8, 0, 4, 1, 9, 9, 0], size=3)
array([2, 0, 0, 0, 1, 1, 0, 0])
"""
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
raise TypeError('Complex type not supported')
axis = normalize_axis_index(axis, input.ndim)
if size < 1:
raise RuntimeError('incorrect filter size')
output = _ni_support._get_output(output, input)
if (size // 2 + origin < 0) or (size // 2 + origin >= size):
raise ValueError('invalid origin')
mode = _ni_support._extend_mode_to_code(mode)
_nd_image.min_or_max_filter1d(input, size, axis, output, mode, cval,
origin, 1)
return output
@_ni_docstrings.docfiller
def maximum_filter1d(input, size, axis=-1, output=None,
mode="reflect", cval=0.0, origin=0):
"""Calculate a 1-D maximum filter along the given axis.
The lines of the array along the given axis are filtered with a
maximum filter of given size.
Parameters
----------
%(input)s
size : int
Length along which to calculate the 1-D maximum.
%(axis)s
%(output)s
%(mode)s
%(cval)s
%(origin)s
Returns
-------
maximum1d : ndarray, None
Maximum-filtered array with same shape as input.
None if `output` is not None
Notes
-----
This function implements the MAXLIST algorithm [1]_, as described by
Richard Harter [2]_, and has a guaranteed O(n) performance, `n` being
the `input` length, regardless of filter size.
References
----------
.. [1] http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.42.2777
.. [2] http://www.richardhartersworld.com/cri/2001/slidingmin.html
Examples
--------
>>> from scipy.ndimage import maximum_filter1d
>>> maximum_filter1d([2, 8, 0, 4, 1, 9, 9, 0], size=3)
array([8, 8, 8, 4, 9, 9, 9, 9])
"""
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
raise TypeError('Complex type not supported')
axis = normalize_axis_index(axis, input.ndim)
if size < 1:
raise RuntimeError('incorrect filter size')
output = _ni_support._get_output(output, input)
if (size // 2 + origin < 0) or (size // 2 + origin >= size):
raise ValueError('invalid origin')
mode = _ni_support._extend_mode_to_code(mode)
_nd_image.min_or_max_filter1d(input, size, axis, output, mode, cval,
origin, 0)
return output
def _min_or_max_filter(input, size, footprint, structure, output, mode,
cval, origin, minimum):
if (size is not None) and (footprint is not None):
warnings.warn("ignoring size because footprint is set", UserWarning, stacklevel=3)
if structure is None:
if footprint is None:
if size is None:
raise RuntimeError("no footprint provided")
separable = True
else:
footprint = numpy.asarray(footprint, dtype=bool)
if not footprint.any():
raise ValueError("All-zero footprint is not supported.")
if footprint.all():
size = footprint.shape
footprint = None
separable = True
else:
separable = False
else:
structure = numpy.asarray(structure, dtype=numpy.float64)
separable = False
if footprint is None:
footprint = numpy.ones(structure.shape, bool)
else:
footprint = numpy.asarray(footprint, dtype=bool)
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
raise TypeError('Complex type not supported')
output = _ni_support._get_output(output, input)
origins = _ni_support._normalize_sequence(origin, input.ndim)
if separable:
sizes = _ni_support._normalize_sequence(size, input.ndim)
modes = _ni_support._normalize_sequence(mode, input.ndim)
axes = list(range(input.ndim))
axes = [(axes[ii], sizes[ii], origins[ii], modes[ii])
for ii in range(len(axes)) if sizes[ii] > 1]
if minimum:
filter_ = minimum_filter1d
else:
filter_ = maximum_filter1d
if len(axes) > 0:
for axis, size, origin, mode in axes:
filter_(input, int(size), axis, output, mode, cval, origin)
input = output
else:
output[...] = input[...]
else:
fshape = [ii for ii in footprint.shape if ii > 0]
if len(fshape) != input.ndim:
raise RuntimeError('footprint array has incorrect shape.')
for origin, lenf in zip(origins, fshape):
if (lenf // 2 + origin < 0) or (lenf // 2 + origin >= lenf):
raise ValueError('invalid origin')
if not footprint.flags.contiguous:
footprint = footprint.copy()
if structure is not None:
if len(structure.shape) != input.ndim:
raise RuntimeError('structure array has incorrect shape')
if not structure.flags.contiguous:
structure = structure.copy()
if not isinstance(mode, str) and isinstance(mode, Iterable):
raise RuntimeError(
"A sequence of modes is not supported for non-separable "
"footprints")
mode = _ni_support._extend_mode_to_code(mode)
_nd_image.min_or_max_filter(input, footprint, structure, output,
mode, cval, origins, minimum)
return output
@_ni_docstrings.docfiller
def minimum_filter(input, size=None, footprint=None, output=None,
mode="reflect", cval=0.0, origin=0):
"""Calculate a multidimensional minimum filter.
Parameters
----------
%(input)s
%(size_foot)s
%(output)s
%(mode_multiple)s
%(cval)s
%(origin_multiple)s
Returns
-------
minimum_filter : ndarray
Filtered array. Has the same shape as `input`.
Notes
-----
A sequence of modes (one per axis) is only supported when the footprint is
separable. Otherwise, a single mode string must be provided.
Examples
--------
>>> from scipy import ndimage, misc
>>> import matplotlib.pyplot as plt
>>> fig = plt.figure()
>>> plt.gray() # show the filtered result in grayscale
>>> ax1 = fig.add_subplot(121) # left side
>>> ax2 = fig.add_subplot(122) # right side
>>> ascent = misc.ascent()
>>> result = ndimage.minimum_filter(ascent, size=20)
>>> ax1.imshow(ascent)
>>> ax2.imshow(result)
>>> plt.show()
"""
return _min_or_max_filter(input, size, footprint, None, output, mode,
cval, origin, 1)
@_ni_docstrings.docfiller
def maximum_filter(input, size=None, footprint=None, output=None,
mode="reflect", cval=0.0, origin=0):
"""Calculate a multidimensional maximum filter.
Parameters
----------
%(input)s
%(size_foot)s
%(output)s
%(mode_multiple)s
%(cval)s
%(origin_multiple)s
Returns
-------
maximum_filter : ndarray
Filtered array. Has the same shape as `input`.
Notes
-----
A sequence of modes (one per axis) is only supported when the footprint is
separable. Otherwise, a single mode string must be provided.
Examples
--------
>>> from scipy import ndimage, misc
>>> import matplotlib.pyplot as plt
>>> fig = plt.figure()
>>> plt.gray() # show the filtered result in grayscale
>>> ax1 = fig.add_subplot(121) # left side
>>> ax2 = fig.add_subplot(122) # right side
>>> ascent = misc.ascent()
>>> result = ndimage.maximum_filter(ascent, size=20)
>>> ax1.imshow(ascent)
>>> ax2.imshow(result)
>>> plt.show()
"""
return _min_or_max_filter(input, size, footprint, None, output, mode,
cval, origin, 0)
@_ni_docstrings.docfiller
def _rank_filter(input, rank, size=None, footprint=None, output=None,
mode="reflect", cval=0.0, origin=0, operation='rank'):
if (size is not None) and (footprint is not None):
warnings.warn("ignoring size because footprint is set", UserWarning, stacklevel=3)
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
raise TypeError('Complex type not supported')
origins = _ni_support._normalize_sequence(origin, input.ndim)
if footprint is None:
if size is None:
raise RuntimeError("no footprint or filter size provided")
sizes = _ni_support._normalize_sequence(size, input.ndim)
footprint = numpy.ones(sizes, dtype=bool)
else:
footprint = numpy.asarray(footprint, dtype=bool)
fshape = [ii for ii in footprint.shape if ii > 0]
if len(fshape) != input.ndim:
raise RuntimeError('filter footprint array has incorrect shape.')
for origin, lenf in zip(origins, fshape):
if (lenf // 2 + origin < 0) or (lenf // 2 + origin >= lenf):
raise ValueError('invalid origin')
if not footprint.flags.contiguous:
footprint = footprint.copy()
filter_size = numpy.where(footprint, 1, 0).sum()
if operation == 'median':
rank = filter_size // 2
elif operation == 'percentile':
percentile = rank
if percentile < 0.0:
percentile += 100.0
if percentile < 0 or percentile > 100:
raise RuntimeError('invalid percentile')
if percentile == 100.0:
rank = filter_size - 1
else:
rank = int(float(filter_size) * percentile / 100.0)
if rank < 0:
rank += filter_size
if rank < 0 or rank >= filter_size:
raise RuntimeError('rank not within filter footprint size')
if rank == 0:
return minimum_filter(input, None, footprint, output, mode, cval,
origins)
elif rank == filter_size - 1:
return maximum_filter(input, None, footprint, output, mode, cval,
origins)
else:
output = _ni_support._get_output(output, input)
if not isinstance(mode, str) and isinstance(mode, Iterable):
raise RuntimeError(
"A sequence of modes is not supported by non-separable rank "
"filters")
mode = _ni_support._extend_mode_to_code(mode)
_nd_image.rank_filter(input, rank, footprint, output, mode, cval,
origins)
return output
@_ni_docstrings.docfiller
def rank_filter(input, rank, size=None, footprint=None, output=None,
mode="reflect", cval=0.0, origin=0):
"""Calculate a multidimensional rank filter.
Parameters
----------
%(input)s
rank : int
The rank parameter may be less then zero, i.e., rank = -1
indicates the largest element.
%(size_foot)s
%(output)s
%(mode)s
%(cval)s
%(origin_multiple)s
Returns
-------
rank_filter : ndarray
Filtered array. Has the same shape as `input`.
Examples
--------
>>> from scipy import ndimage, misc
>>> import matplotlib.pyplot as plt
>>> fig = plt.figure()
>>> plt.gray() # show the filtered result in grayscale
>>> ax1 = fig.add_subplot(121) # left side
>>> ax2 = fig.add_subplot(122) # right side
>>> ascent = misc.ascent()
>>> result = ndimage.rank_filter(ascent, rank=42, size=20)
>>> ax1.imshow(ascent)
>>> ax2.imshow(result)
>>> plt.show()
"""
rank = operator.index(rank)
return _rank_filter(input, rank, size, footprint, output, mode, cval,
origin, 'rank')
@_ni_docstrings.docfiller
def median_filter(input, size=None, footprint=None, output=None,
mode="reflect", cval=0.0, origin=0):
"""
Calculate a multidimensional median filter.
Parameters
----------
%(input)s
%(size_foot)s
%(output)s
%(mode)s
%(cval)s
%(origin_multiple)s
Returns
-------
median_filter : ndarray
Filtered array. Has the same shape as `input`.
Examples
--------
>>> from scipy import ndimage, misc
>>> import matplotlib.pyplot as plt
>>> fig = plt.figure()
>>> plt.gray() # show the filtered result in grayscale
>>> ax1 = fig.add_subplot(121) # left side
>>> ax2 = fig.add_subplot(122) # right side
>>> ascent = misc.ascent()
>>> result = ndimage.median_filter(ascent, size=20)
>>> ax1.imshow(ascent)
>>> ax2.imshow(result)
>>> plt.show()
"""
return _rank_filter(input, 0, size, footprint, output, mode, cval,
origin, 'median')
@_ni_docstrings.docfiller
def percentile_filter(input, percentile, size=None, footprint=None,
output=None, mode="reflect", cval=0.0, origin=0):
"""Calculate a multidimensional percentile filter.
Parameters
----------
%(input)s
percentile : scalar
The percentile parameter may be less then zero, i.e.,
percentile = -20 equals percentile = 80
%(size_foot)s
%(output)s
%(mode)s
%(cval)s
%(origin_multiple)s
Returns
-------
percentile_filter : ndarray
Filtered array. Has the same shape as `input`.
Examples
--------
>>> from scipy import ndimage, misc
>>> import matplotlib.pyplot as plt
>>> fig = plt.figure()
>>> plt.gray() # show the filtered result in grayscale
>>> ax1 = fig.add_subplot(121) # left side
>>> ax2 = fig.add_subplot(122) # right side
>>> ascent = misc.ascent()
>>> result = ndimage.percentile_filter(ascent, percentile=20, size=20)
>>> ax1.imshow(ascent)
>>> ax2.imshow(result)
>>> plt.show()
"""
return _rank_filter(input, percentile, size, footprint, output, mode,
cval, origin, 'percentile')
@_ni_docstrings.docfiller
def generic_filter1d(input, function, filter_size, axis=-1,
output=None, mode="reflect", cval=0.0, origin=0,
extra_arguments=(), extra_keywords=None):
"""Calculate a 1-D filter along the given axis.
`generic_filter1d` iterates over the lines of the array, calling the
given function at each line. The arguments of the line are the
input line, and the output line. The input and output lines are 1-D
double arrays. The input line is extended appropriately according
to the filter size and origin. The output line must be modified
in-place with the result.
Parameters
----------
%(input)s
function : {callable, scipy.LowLevelCallable}
Function to apply along given axis.
filter_size : scalar
Length of the filter.
%(axis)s
%(output)s
%(mode)s
%(cval)s
%(origin)s
%(extra_arguments)s
%(extra_keywords)s
Notes
-----
This function also accepts low-level callback functions with one of
the following signatures and wrapped in `scipy.LowLevelCallable`:
.. code:: c
int function(double *input_line, npy_intp input_length,
double *output_line, npy_intp output_length,
void *user_data)
int function(double *input_line, intptr_t input_length,
double *output_line, intptr_t output_length,
void *user_data)
The calling function iterates over the lines of the input and output
arrays, calling the callback function at each line. The current line
is extended according to the border conditions set by the calling
function, and the result is copied into the array that is passed
through ``input_line``. The length of the input line (after extension)
is passed through ``input_length``. The callback function should apply
the filter and store the result in the array passed through
``output_line``. The length of the output line is passed through
``output_length``. ``user_data`` is the data pointer provided
to `scipy.LowLevelCallable` as-is.
The callback function must return an integer error status that is zero
if something went wrong and one otherwise. If an error occurs, you should
normally set the python error status with an informative message
before returning, otherwise a default error message is set by the
calling function.
In addition, some other low-level function pointer specifications
are accepted, but these are for backward compatibility only and should
not be used in new code.
"""
if extra_keywords is None:
extra_keywords = {}
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
raise TypeError('Complex type not supported')
output = _ni_support._get_output(output, input)
if filter_size < 1:
raise RuntimeError('invalid filter size')
axis = normalize_axis_index(axis, input.ndim)
if (filter_size // 2 + origin < 0) or (filter_size // 2 + origin >=
filter_size):
raise ValueError('invalid origin')
mode = _ni_support._extend_mode_to_code(mode)
_nd_image.generic_filter1d(input, function, filter_size, axis, output,
mode, cval, origin, extra_arguments,
extra_keywords)
return output
@_ni_docstrings.docfiller
def generic_filter(input, function, size=None, footprint=None,
output=None, mode="reflect", cval=0.0, origin=0,
extra_arguments=(), extra_keywords=None):
"""Calculate a multidimensional filter using the given function.
At each element the provided function is called. The input values
within the filter footprint at that element are passed to the function
as a 1-D array of double values.
Parameters
----------
%(input)s
function : {callable, scipy.LowLevelCallable}
Function to apply at each element.
%(size_foot)s
%(output)s
%(mode)s
%(cval)s
%(origin_multiple)s
%(extra_arguments)s
%(extra_keywords)s
Notes
-----
This function also accepts low-level callback functions with one of
the following signatures and wrapped in `scipy.LowLevelCallable`:
.. code:: c
int callback(double *buffer, npy_intp filter_size,
double *return_value, void *user_data)
int callback(double *buffer, intptr_t filter_size,
double *return_value, void *user_data)
The calling function iterates over the elements of the input and
output arrays, calling the callback function at each element. The
elements within the footprint of the filter at the current element are
passed through the ``buffer`` parameter, and the number of elements
within the footprint through ``filter_size``. The calculated value is
returned in ``return_value``. ``user_data`` is the data pointer provided
to `scipy.LowLevelCallable` as-is.
The callback function must return an integer error status that is zero
if something went wrong and one otherwise. If an error occurs, you should
normally set the python error status with an informative message
before returning, otherwise a default error message is set by the
calling function.
In addition, some other low-level function pointer specifications
are accepted, but these are for backward compatibility only and should
not be used in new code.
"""
if (size is not None) and (footprint is not None):
warnings.warn("ignoring size because footprint is set", UserWarning, stacklevel=2)
if extra_keywords is None:
extra_keywords = {}
input = numpy.asarray(input)
if numpy.iscomplexobj(input):
raise TypeError('Complex type not supported')
origins = _ni_support._normalize_sequence(origin, input.ndim)
if footprint is None:
if size is None:
raise RuntimeError("no footprint or filter size provided")
sizes = _ni_support._normalize_sequence(size, input.ndim)
footprint = numpy.ones(sizes, dtype=bool)
else:
footprint = numpy.asarray(footprint, dtype=bool)
fshape = [ii for ii in footprint.shape if ii > 0]
if len(fshape) != input.ndim:
raise RuntimeError('filter footprint array has incorrect shape.')
for origin, lenf in zip(origins, fshape):
if (lenf // 2 + origin < 0) or (lenf // 2 + origin >= lenf):
raise ValueError('invalid origin')
if not footprint.flags.contiguous:
footprint = footprint.copy()
output = _ni_support._get_output(output, input)
mode = _ni_support._extend_mode_to_code(mode)
_nd_image.generic_filter(input, function, footprint, output, mode,
cval, origins, extra_arguments, extra_keywords)
return output
|
person142/scipy
|
scipy/ndimage/filters.py
|
Python
|
bsd-3-clause
| 51,486
|
[
"Gaussian"
] |
d999dcdaed3be1df9c8145b456797327cc03685ca2f34c42f774bd9692a3acc3
|
import unittest
from util import meanclip
import numpy as np
class TestMeanclip(unittest.TestCase):
"""
Generate 1000 gaussian normal numbers with three outliers. Check
that meanclip does the right thing
"""
def testSimple(self):
mu = 100
sig = 10
s = np.random.normal(mu, sig, 1000)
s[400] = mu+13*sig
s[500] = mu+15*sig
s[654] = mu-15*sig
mean,sigma,nSurvived = meanclip.meanclip(s, clipsig=9.0)
self.assertEqual(nSurvived, 997, \
"three should get cut nSurived=%d" % nSurvived)
self.assertAlmostEqual(mu, mean, delta=0.7, \
msg="mu=%f mean=%f" % (mu,mean))
self.assertAlmostEqual(sig, sigma, delta=0.6, \
msg="sig=%f sigma=%f" % (sig,sigma))
if __name__ == '__main__':
unittest.main()
|
bmazin/ARCONS-pipeline
|
util/test/TestMeanClip.py
|
Python
|
gpl-2.0
| 893
|
[
"Gaussian"
] |
71355dd4f86e891fd330cc800d7fda8351c64545bc1b4ed6ea2a8b00654216e7
|
"""
A script to convert the drosophila connectome into SpineML
This build upon the pure data to add in the required infered network components:
# Install libSpineML from source
# https://github.com/AdamRTomkins/libSpineML
"""
from __future__ import division
from libSpineML import smlExperiment as exp
from libSpineML import smlNetwork as net
from libSpineML import smlComponent as com
from libSpineML.smlUtils import create_spineml_network
import csv
import sys
import cStringIO
import graphviz as gv
import matplotlib.pyplot as plt
import networkx as nx
import numpy as np
import copy
neuron_fieldnames = ['neuron_name', 'innv_neuropil', 'mem_model', 'resting_pot', 'reset_pot', 'threshold_pot', 'rfact_period', 'Cm', 'tm']
neuron_property_list = ['resting_pot', 'reset_pot', 'threshold_pot', 'rfact_period', 'Cm', 'tm']
default_neuron_models ={}
default_neuron_models['LIF'] = {'resting_pot' :-60,
'reset_pot' :-70,
'threshold_pot' :-10,
'rfact_period' :0,
'Cm' :10,
'tm' :10
}
synapse_fieldnames = ['pre-neuron', 'post-neuron', 'neuropil', 'weight', 'type', 'reversal_pot', 'txt', 'tst', 'pv', 'tD', 'tF', 'tLP', 'PosISI', 'NegISI']
def main(neuron_file,synapse_file):
""" Process the parameter files and create a SpineML network """
print "Processing Files..."
neurons, populations, projections = process_files(neuron_file,synapse_file)
print "Creating SpineML representation..."
create_spineml_network(neurons, populations,
projections,output_filename='network.xml',project_name= 'drosophila')
print "Creating Graph Visualisation..."
create_graphviz_graph(populations,projections)
def process_neuroarch_files(neuron_file, info_dict, neuron_params):
""" Convert the neuroarch files into populations, projections and neurons
neuron_file : A csv of neuron pre and post names, with the synapse count
lpu_dict : a dictionary of neuron names : LPU
"""
# Process the text files
neuroarch_fieldnames = ['PreSynaptic Neuron', 'PostSynaptic Neuron', 'N', 'Inferred']
neuron_reader = csv.DictReader(open(neuron_file), fieldnames=neuroarch_fieldnames,delimiter=',')
lpu_dict = info_dict['neurons']
neurons = {}
populations = {}
projections = {}
skip = True
ns = set()
for row in neuron_reader:
if skip:
skip = False
continue
pre_name = row['PreSynaptic Neuron']
post_name = row['PostSynaptic Neuron']
ns.add(pre_name)
ns.add(post_name)
try:
pre_lpu = lpu_dict[pre_name]
except:
pre_lpu = pre_name
try:
post_lpu = lpu_dict[post_name]
except:
post_lpu = post_name
# NEURONS IS WRONG
#assert False
if pre_name not in neurons.keys():
if pre_lpu not in populations:
populations[pre_lpu] = [pre_name]
else:
populations[pre_lpu].append(pre_name)
neurons[pre_name] = neuron_params.copy()
neurons[pre_name]['name'] = pre_name
neurons[pre_name]['pre'] = pre_lpu
neurons[pre_name]['index']= len(populations[pre_lpu])-1
if post_name not in neurons:
if post_lpu not in populations:
populations[post_lpu] = [post_name]
else:
populations[post_lpu].append(post_name)
neurons[post_name] = neuron_params.copy()
neurons[post_name]['name'] = post_name
neurons[post_name]['index']= len(populations[post_lpu])-1
print 'POST: %s <%s> %s' %(post_name, post_lpu, neurons[post_name]['index'])
print ns
print len(list(ns))
neuron_reader = csv.DictReader(open(neuron_file), fieldnames=neuroarch_fieldnames,delimiter=',')
skip = True
for row in neuron_reader:
if skip:
skip = False
continue
pre_neuron = row['PreSynaptic Neuron']
post_neuron = row['PostSynaptic Neuron']
synapse_number = row['N']
# get the LPU of the pre neuron
try:
assert 'lpus' in lpu_data
pre_lpu = lpu_dict[pre_neuron]
except:
pre_lpu = pre_neuron
# get the LPU index of the pre neuron
pre_index = neurons[pre_neuron]['index']
# get the LPU of the post neuron
try:
post_lpu = lpu_dict[post_neuron]
except:
post_lpu = post_neuron
# get the LPU index of the post neuron
post_index = neurons[post_neuron]['index']
if pre_lpu not in projections:
projections[pre_lpu] = {}
if post_lpu not in projections[pre_lpu]:
projections[pre_lpu][post_lpu] = []
projections[pre_lpu][post_lpu].append((pre_index,post_index,int(synapse_number)))
return (neurons, populations, projections)
if __name__ == "__main__":
#main('../data/flycircuit1.2/CUNeuParsV1-2.txt','../data/flycircuit1.2/CUSynParsV1-2.txt')
#neurons, populations, projections = process_files('../data/flycircuit1.2/CUNeuParsV1-2.txt','../data/flycircuit1.2/CUSynParsV1-2.txt')
import pickle
info_dict = pickle.load(( open( "lpu_dicts.p", "rb" ) ))
#neurons, populations, projections = process_neuroarch_files('../ffbo_connectivity.csv',info_dict,{'mem_model':'LIF'})
neurons, populations, projections = process_neuroarch_files('vision_connectivity.csv',info_dict,{'mem_model':'LIF'})
#neurons, populations, projections = process_neuroarch_files('larval.csv',info_dict,{'mem_model':'LIF'})
data = {"neurons": neurons,"population": populations, "projections":projections}
pickle.dump( data, open( "tmp_data.p", "wb" ) )
print "Creating SpineML representation..."
create_spineml_network(neurons, populations,
projections,output_filename='neuroarch_network.xml',project_name= 'neuroarch')
#network = create_networkx_graph(populations,projections,prune=0)
|
AdamRTomkins/libSpineML
|
libSpineML/examples/neuroarch_example/spineml/create_executable_drosophila_network.py
|
Python
|
gpl-3.0
| 6,554
|
[
"NEURON"
] |
741e20e246750cf6b60209c2b81850194f83025f9fb66db43881d7000a29d1b5
|
"""
Implementation of logistic regression for chapter 3 of "Python Machine Learning".
This is adapted from chapter 2's implementation of ADAptive LInear NEuron (Adaline).
"""
import numpy as np
def homegrown_lr(observations, labels, learning_rate=0.02, max_training_iterations=100):
"""
Trains a (binary) perceptron, returning a function that can predict / classify
given a new observations, as well as insight into how the training progressed via
a log of the weights and squared errors for each iteration.
:param observations: array of rows
:param labels: correct label classification for each row: [1, -1, 1, 1, ...]
:param learning_rate: how fast to update weights
:param max_training_iterations: max number of times to iterate through observations
:return: (prediction_fn, weights_log, errors_log)
"""
the_weights = np.zeros(1 + observations.shape[1])
weights_log = []
num_errors_log = []
squared_error_log = []
def sigmoid(z):
return 1.0 / (1.0 + np.exp(-z))
def net_input(observations, weights):
return np.dot(observations, weights[1:]) + weights[0]
def activation(observations, weights):
return sigmoid(net_input(observations, weights))
def quantized_output(output):
return np.where(output >= 0.5, 1, 0)
def predict(observations, weights=the_weights):
return quantized_output(activation(observations, weights))
for _ in range(max_training_iterations):
weights_log.append(np.copy(the_weights))
raw_outputs = activation(observations, the_weights)
errors = labels - raw_outputs
weight_deltas = learning_rate * np.dot(observations.transpose(), errors)
the_weights[1:] += weight_deltas
the_weights[0] += learning_rate * np.sum(errors)
squared_errors = (errors ** 2).sum() / 2.0
num_errors = (quantized_output(raw_outputs) != labels).sum()
squared_error_log.append(squared_errors)
num_errors_log.append(num_errors)
if num_errors == 0:
break
return predict, weights_log, squared_error_log, num_errors_log
|
krosaen/ml-study
|
python-ml-book/ch03/homegrown_lr.py
|
Python
|
mit
| 2,141
|
[
"NEURON"
] |
f25898b6da31d97d48a12ce8d10fd388171cf3eb4fa5c8f69b601676765a4461
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
setup.py
~~~~~~~~
installation script
:copyright: (c) 2013-2015 by Jauhien Piatlicki
:copyright: (c) 2014 by Brian Dolbec
(code for conditional module installation
is taken from the layman project)
:license: GPL-2, see LICENSE for more details.
"""
import os
from distutils.core import setup
SELECTABLE = {'bson': 'file_bson', 'git': 'git_syncer'}
use_defaults = ' '.join(list(SELECTABLE))
USE = os.environ.get("USE", use_defaults).split()
optional_modules = []
for mod in SELECTABLE:
if mod in USE:
optional_modules.append('g_sorcery.%s' % SELECTABLE[mod])
setup(name = 'g-sorcery',
version = '0.2.1',
description = 'framework for automated ebuild generators',
author = 'Jauhien Piatlicki',
author_email = 'jauhien@gentoo.org',
packages = ['g_sorcery', 'gs_db_tool'] + optional_modules,
package_data = {'g_sorcery': ['data/*']},
scripts = ['bin/g-sorcery', 'bin/gs-db-tool'],
data_files = [('/etc/g-sorcery/', ['g-sorcery.cfg'])],
license = 'GPL-2',
)
|
jauhien/g-sorcery
|
setup.py
|
Python
|
gpl-2.0
| 1,194
|
[
"Brian"
] |
171ad94183d6f77c4f252d0a117df54a65dc38732026058b1207fb325ef09b96
|
""" Helper for /Registry section
"""
import errno
from DIRAC import S_OK, S_ERROR
from DIRAC.Core.Utilities import DErrno
from DIRAC.ConfigurationSystem.Client.Config import gConfig
from DIRAC.ConfigurationSystem.Client.Helpers.CSGlobals import getVO
__RCSID__ = "$Id$"
# pylint: disable=missing-docstring
gBaseRegistrySection = "/Registry"
def getUsernameForDN(dn, usersList=False):
if not usersList:
retVal = gConfig.getSections("%s/Users" % gBaseRegistrySection)
if not retVal['OK']:
return retVal
usersList = retVal['Value']
for username in usersList:
if dn in gConfig.getValue("%s/Users/%s/DN" % (gBaseRegistrySection, username), []):
return S_OK(username)
return S_ERROR("No username found for dn %s" % dn)
def getDNForUsername(username):
dnList = gConfig.getValue("%s/Users/%s/DN" % (gBaseRegistrySection, username), [])
if dnList:
return S_OK(dnList)
return S_ERROR("No DN found for user %s" % username)
def getDNForHost(host):
dnList = gConfig.getValue("%s/Hosts/%s/DN" % (gBaseRegistrySection, host), [])
if dnList:
return S_OK(dnList)
return S_ERROR("No DN found for host %s" % host)
def getGroupsForDN(dn):
retVal = getUsernameForDN(dn)
if not retVal['OK']:
return retVal
return getGroupsForUser(retVal['Value'])
def __getGroupsWithAttr(attrName, value):
retVal = gConfig.getSections("%s/Groups" % gBaseRegistrySection)
if not retVal['OK']:
return retVal
groupsList = retVal['Value']
groups = []
for group in groupsList:
if value in gConfig.getValue("%s/Groups/%s/%s" % (gBaseRegistrySection, group, attrName), []):
groups.append(group)
if not groups:
return S_ERROR("No groups found for %s=%s" % (attrName, value))
groups.sort()
return S_OK(groups)
def getGroupsForUser(username):
return __getGroupsWithAttr('Users', username)
def getGroupsForVO(vo):
if getVO():
return gConfig.getSections("%s/Groups" % gBaseRegistrySection)
return __getGroupsWithAttr('VO', vo)
def getGroupsWithProperty(propName):
return __getGroupsWithAttr("Properties", propName)
def getHostnameForDN(dn):
retVal = gConfig.getSections("%s/Hosts" % gBaseRegistrySection)
if not retVal['OK']:
return retVal
hostList = retVal['Value']
for hostname in hostList:
if dn in gConfig.getValue("%s/Hosts/%s/DN" % (gBaseRegistrySection, hostname), []):
return S_OK(hostname)
return S_ERROR("No hostname found for dn %s" % dn)
def getDefaultUserGroup():
return gConfig.getValue("/%s/DefaultGroup" % gBaseRegistrySection, "user")
def findDefaultGroupForDN(dn):
result = getUsernameForDN(dn)
if not result['OK']:
return result
return findDefaultGroupForUser(result['Value'])
def findDefaultGroupForUser(userName):
userDefGroups = getUserOption(userName, "DefaultGroup", [])
defGroups = userDefGroups + gConfig.getValue("%s/DefaultGroup" % gBaseRegistrySection, ["user"])
result = getGroupsForUser(userName)
if not result['OK']:
return result
userGroups = result['Value']
for group in defGroups:
if group in userGroups:
return S_OK(group)
if userGroups:
return S_OK(userGroups[0])
return S_ERROR("User %s has no groups" % userName)
def getAllUsers():
retVal = gConfig.getSections("%s/Users" % gBaseRegistrySection)
if not retVal['OK']:
return []
return retVal['Value']
def getAllGroups():
retVal = gConfig.getSections("%s/Groups" % gBaseRegistrySection)
if not retVal['OK']:
return []
return retVal['Value']
def getUsersInGroup(groupName, defaultValue=None):
if defaultValue is None:
defaultValue = []
option = "%s/Groups/%s/Users" % (gBaseRegistrySection, groupName)
return gConfig.getValue(option, defaultValue)
def getUsersInVO(vo, defaultValue=None):
if defaultValue is None:
defaultValue = []
result = getGroupsForVO(vo)
if not result['OK']:
return defaultValue
groups = result['Value']
if not groups:
return defaultValue
userList = []
for group in groups:
userList += getUsersInGroup(group)
return userList
def getDNsInVO(vo):
DNs = []
for user in getUsersInVO(vo):
result = getDNForUsername(user)
if result['OK']:
DNs.extend(result['Value'])
return DNs
def getDNsInGroup(groupName):
DNs = []
for user in getUsersInGroup(groupName):
result = getDNForUsername(user)
if result['OK']:
DNs.extend(result['Value'])
return DNs
def getPropertiesForGroup(groupName, defaultValue=None):
if defaultValue is None:
defaultValue = []
option = "%s/Groups/%s/Properties" % (gBaseRegistrySection, groupName)
return gConfig.getValue(option, defaultValue)
def getPropertiesForHost(hostName, defaultValue=None):
if defaultValue is None:
defaultValue = []
option = "%s/Hosts/%s/Properties" % (gBaseRegistrySection, hostName)
return gConfig.getValue(option, defaultValue)
def getPropertiesForEntity(group, name="", dn="", defaultValue=None):
if defaultValue is None:
defaultValue = []
if group == 'hosts':
if not name:
result = getHostnameForDN(dn)
if not result['OK']:
return defaultValue
name = result['Value']
return getPropertiesForHost(name, defaultValue)
else:
return getPropertiesForGroup(group, defaultValue)
def __matchProps(sProps, rProps):
foundProps = []
for prop in sProps:
if prop in rProps:
foundProps.append(prop)
return foundProps
def groupHasProperties(groupName, propList):
if isinstance(propList, basestring):
propList = [propList]
return __matchProps(propList, getPropertiesForGroup(groupName))
def hostHasProperties(hostName, propList):
if isinstance(propList, basestring):
propList = [propList]
return __matchProps(propList, getPropertiesForHost(hostName))
def getUserOption(userName, optName, defaultValue=""):
return gConfig.getValue("%s/Users/%s/%s" % (gBaseRegistrySection, userName, optName), defaultValue)
def getGroupOption(groupName, optName, defaultValue=""):
return gConfig.getValue("%s/Groups/%s/%s" % (gBaseRegistrySection, groupName, optName), defaultValue)
def getHostOption(hostName, optName, defaultValue=""):
return gConfig.getValue("%s/Hosts/%s/%s" % (gBaseRegistrySection, hostName, optName), defaultValue)
def getHosts():
return gConfig.getSections('%s/Hosts' % gBaseRegistrySection)
def getVOOption(voName, optName, defaultValue=""):
return gConfig.getValue("%s/VO/%s/%s" % (gBaseRegistrySection, voName, optName), defaultValue)
def getBannedIPs():
return gConfig.getValue("%s/BannedIPs" % gBaseRegistrySection, [])
def getVOForGroup(group):
voName = getVO()
if voName:
return voName
return gConfig.getValue("%s/Groups/%s/VO" % (gBaseRegistrySection, group), "")
def getDefaultVOMSAttribute():
return gConfig.getValue("%s/DefaultVOMSAttribute" % gBaseRegistrySection, "")
def getVOMSAttributeForGroup(group):
return gConfig.getValue("%s/Groups/%s/VOMSRole" % (gBaseRegistrySection, group), getDefaultVOMSAttribute())
def getDefaultVOMSVO():
vomsVO = gConfig.getValue("%s/DefaultVOMSVO" % gBaseRegistrySection, "")
if vomsVO:
return vomsVO
return getVO()
def getVOMSVOForGroup(group):
vomsVO = gConfig.getValue("%s/Groups/%s/VOMSVO" % (gBaseRegistrySection, group), getDefaultVOMSVO())
if not vomsVO:
vo = getVOForGroup(group)
vomsVO = getVOOption(vo, 'VOMSName', '')
return vomsVO
def getGroupsWithVOMSAttribute(vomsAttr):
retVal = gConfig.getSections("%s/Groups" % (gBaseRegistrySection))
if not retVal['OK']:
return []
groups = []
for group in retVal['Value']:
if vomsAttr == gConfig.getValue("%s/Groups/%s/VOMSRole" % (gBaseRegistrySection, group), ""):
groups.append(group)
return groups
def getVOs():
""" Get all the configured VOs
"""
voName = getVO()
if voName:
return S_OK([voName])
return gConfig.getSections('%s/VO' % gBaseRegistrySection)
def getVOMSServerInfo(requestedVO=''):
""" Get information on VOMS servers for the given VO or for all of them
"""
vomsDict = {}
result = getVOs()
if result['OK']:
voNames = result['Value']
for vo in voNames:
if requestedVO and vo != requestedVO:
continue
vomsName = getVOOption(vo, 'VOMSName', '')
if not vomsName:
continue
vomsDict.setdefault(vo, {})
vomsDict[vo]['VOMSName'] = getVOOption(vo, 'VOMSName', '')
result = gConfig.getSections('%s/VO/%s/VOMSServers' % (gBaseRegistrySection, vo))
if result['OK']:
serverList = result['Value']
vomsDict[vo].setdefault("Servers", {})
for server in serverList:
vomsDict[vo]['Servers'].setdefault(server, {})
DN = gConfig.getValue('%s/VO/%s/VOMSServers/%s/DN' % (gBaseRegistrySection, vo, server), '')
CA = gConfig.getValue('%s/VO/%s/VOMSServers/%s/CA' % (gBaseRegistrySection, vo, server), '')
port = gConfig.getValue('%s/VO/%s/VOMSServers/%s/Port' % (gBaseRegistrySection, vo, server), 0)
vomsDict[vo]['Servers'][server]['DN'] = DN
vomsDict[vo]['Servers'][server]['CA'] = CA
vomsDict[vo]['Servers'][server]['Port'] = port
return S_OK(vomsDict)
def getVOMSRoleGroupMapping(vo=''):
""" Get mapping of the VOMS role to the DIRAC group
:param str vo: perform the operation for the given VO
:return: standard structure with two mappings: VOMS-DIRAC { <VOMS_Role>: [<DIRAC_Group>] }
and DIRAC-VOMS { <DIRAC_Group>: <VOMS_Role> } and a list of DIRAC groups without mapping
"""
result = getGroupsForVO(vo)
if not result['OK']:
return result
groupList = result['Value']
vomsGroupDict = {}
groupVomsDict = {}
noVOMSGroupList = []
noVOMSSyncGroupList = []
for group in groupList:
vomsRole = getGroupOption(group, 'VOMSRole')
if vomsRole:
vomsGroupDict.setdefault(vomsRole, [])
vomsGroupDict[vomsRole].append(group)
groupVomsDict[group] = vomsRole
syncVOMS = getGroupOption(group, 'AutoSyncVOMS', True)
if not syncVOMS:
noVOMSSyncGroupList.append(group)
for group in groupList:
if group not in groupVomsDict:
noVOMSGroupList.append(group)
return S_OK({"VOMSDIRAC": vomsGroupDict,
"DIRACVOMS": groupVomsDict,
"NoVOMS": noVOMSGroupList,
"NoSyncVOMS": noVOMSSyncGroupList})
def getUsernameForID(ID, usersList=None):
""" Get DIRAC user name by ID
:param basestring ID: user ID
:param list usersList: list of DIRAC user names
:return: S_OK(basestring)/S_ERROR()
"""
if not usersList:
retVal = gConfig.getSections("%s/Users" % gBaseRegistrySection)
if not retVal['OK']:
return retVal
usersList = retVal['Value']
for username in usersList:
if ID in gConfig.getValue("%s/Users/%s/ID" % (gBaseRegistrySection, username), []):
return S_OK(username)
return S_ERROR("No username found for ID %s" % ID)
def getCAForUsername(username):
""" Get CA option by user name
:param basestring username: user name
:return: S_OK(basestring)/S_ERROR()
"""
dnList = gConfig.getValue("%s/Users/%s/CA" % (gBaseRegistrySection, username), [])
if dnList:
return S_OK(dnList)
return S_ERROR("No CA found for user %s" % username)
def __getDNSectionName(userDN):
""" Change user DN string by replacing special symbol that not used in
a section names, e.g.: "/O=O_test/OU=OU_test/F=F_test"
will replace to: "-O_O_test-OU_OU_test-F_F_test"
:param basestring userDN: user DN
:return: basestring that can be use as a section name
"""
return userDN.replace('/', '-').replace('=', '_')
def getDNProperty(userDN, value):
""" Get property from DNProperties section by user DN
:param basestring userDN: user DN
:param basestring value: option that need to get
:return: S_OK(basestring,list)/S_ERROR() -- basestring or list that contain option value
"""
result = getUsernameForDN(userDN)
if not result['OK']:
return result
secDN = __getDNSectionName(userDN)
return S_OK(gConfig.getValue("%s/Users/%s/DNProperties/%s/%s" %
(gBaseRegistrySection, result['Value'], secDN, value)))
def getProxyProvidersForDN(userDN):
""" Get proxy providers by user DN
:param basestring userDN: user DN
:return: S_OK(list)/S_ERROR()
"""
result = getDNProperty(userDN, 'ProxyProviders')
if not result['OK']:
return result
ppList = result['Value'] or []
if not isinstance(ppList, list):
ppList = ppList.split()
return S_OK(ppList)
def getDNFromProxyProviderForUserID(proxyProvider, userID):
""" Get groups by user DN in DNProperties
:param basestring proxyProvider: proxy provider name
:param basestring userID: user identificator
:return: S_OK(basestring)/S_ERROR()
"""
# Get user name
result = getUsernameForID(userID)
if not result['OK']:
return result
# Get DNs from user
result = getDNForUsername(result['Value'])
if not result['OK']:
return result
for DN in result['Value']:
result = getProxyProvidersForDN(DN)
if not result['OK']:
return result
if proxyProvider in result['Value']:
return S_OK(DN)
return S_ERROR(errno.ENODATA,
"No DN found for %s proxy provider for user ID %s" % (proxyProvider, userID))
def isDownloadableGroup(groupName):
""" Get permission to download proxy with group in a argument
:params basestring groupName: DIRAC group
:return: boolean
"""
if getGroupOption(groupName, 'DownloadableProxy') in [False, 'False', 'false', 'no']:
return False
return True
|
chaen/DIRAC
|
ConfigurationSystem/Client/Helpers/Registry.py
|
Python
|
gpl-3.0
| 13,634
|
[
"DIRAC"
] |
2692948660ac2b5035ee06fd09aa995f53b1cf5239bcff722bdc24f2ba573a1c
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# For UTF-8 see: http://www.python.org/dev/peps/pep-0263/
# PYTHONIOENCODING=utf_8
# Copyright 2015 Actian Corporation
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Add:V4
## Add test on not easily convertable datatypes (MSSQL : sql_variant, xml, byte(s), image, ...)
import sys, os, traceback
import getopt, re # Import basic modules
import warnings
import codecs
import time
import xml.dom.minidom
from xml.dom.minidom import Node
from string import Template
from copy import deepcopy
from driverTools import dbconnector
reload(sys)
sys.setdefaultencoding('utf-8')
# Program parameters
g_pars = [ 'src=' , 'dest=' , 'loadata' , 'cretab', 'creview', 'creall', 'loaddl', 'loadtest', 'batchsize=',
'truncate', 'parfile=', 'fdelim=', 'unload', 'translation=', 'quote=', 'cmdsep=', 'charmax=',
'creindex', 'ownsrc=', 'owntgt=',
'add_drop' ]
g_trnm = {} # Global translation table for names
g_trty = {} # Global translation table for datatypes
g_quote = None
g_cmdsep = ''
g_bin = os.path.dirname(__file__)
g_bin = "." if g_bin == "" else g_bin
g_prg = os.path.basename(__file__).split('.')[0] # The short name of the script (__file__= sys.argv[0])
XMLINI = "%s/../etc/%s.xml" % (g_bin, g_prg)
## Remaining Pg datatypes:
## REM: The postgres datatype with default precision to NULL exceeds the maximum Vector's precision.
## REM: hence, the datatype is converted to a DOUBLE PRECISION which might not fit expectations.
## bytea binary data ("byte array")
## cidr IPv4 or IPv6 network address
## circle circle on a plane
## inet IPv4 or IPv6 host address
## line infinite line on a plane
## lseg line segment on a plane
## macaddr MAC (Media Access Control) address
## path geometric path on a plane
## point geometric point on a plane
## polygon closed geometric path on a plane
## tsquery text search query
## tsvector text search document
## txid_snapshot user-level transaction ID snapshot
## uuid universally unique identifier
pg2vw = {
"INT" : ("INTEGER" , "<COLNAME>", "<VALUE>" ), # Signed 4 bytes integer (-2^31; 2^31 - 1 (2,147,483,647))
"INT4" : ("INTEGER" , "<COLNAME>", "<VALUE>" ), # Signed 4 bytes integer (-2^31; 2^31 - 1 (2,147,483,647))
"INTEGER" : ("INTEGER" , "<COLNAME>", "<VALUE>" ), # Signed 4 bytes integer (-2^31; 2^31 - 1 (2,147,483,647))
"SERIAL" : ("INTEGER" , "<COLNAME>", "<VALUE>" ), # 4 Bytes autoincrement N/A in VW
"SERIAL4" : ("INTEGER" , "<COLNAME>", "<VALUE>" ), # 4 Bytes autoincrement N/A in VW
"NUMERIC" : ("DECIMAL(<PRECISION>,<SCALE>)", "<COLNAME>", "<VALUE>" ), #
"DECIMAL" : ("DECIMAL(<PRECISION>,<SCALE>)", "<COLNAME>", "<VALUE>" ),
"MONEY" : ("DECIMAL(19,4)" , "<COLNAME>", "<VALUE>" ), # NUMERIC(19,4) ?? To check
"SMALLINT" : ("SMALLINT" , "<COLNAME>", "<VALUE>" ), # 2 Bytes [-32768:32767]-
"INT2" : ("SMALLINT" , "<COLNAME>", "<VALUE>" ), # 2 Bytes [-32768:32767]-
"BIGINT" : ("BIGINT" , "<COLNAME>", "<VALUE>" ), # 8 Bytes : -2^63 ;+2^63-1
"INT8" : ("BIGINT" , "<COLNAME>", "<VALUE>" ), # 8 Bytes : -2^63 ;+2^63-1
"BIGSERIAL" : ("BIGINT" , "<COLNAME>", "<VALUE>" ), # 8 Bytes autoincrement N/A in VW
"SERIAL8" : ("BIGINT" , "<COLNAME>", "<VALUE>" ), # 8 Bytes autoincrement N/A in VW
"DOUBLE PRECISION" : ("FLOAT8" , "<COLNAME>", "<VALUE>" ), # 8 Bytes
"FLOAT8" : ("FLOAT8" , "<COLNAME>", "<VALUE>" ), # 8 Bytes
"FLOAT4" : ("FLOAT4" , "<COLNAME>", "'<VALUE>'"), # 4 bytes
"REAL" : ("FLOAT4" , "<COLNAME>", "'<VALUE>'"), # 4 bytes
"TIMESTAMP" : ("TIMESTAMP" , "<COLNAME>", "'<VALUE>'"), # OR: [0-9] (def: 6) => Add precision VW [max 9]
"TIMESTAMP WITHOUT TIME ZONE" : ("TIMESTAMP" , "<COLNAME>", "'<VALUE>'"), #
"TIMESTAMP WITH TIME ZONE" : ("TIMESTAMP WITH TIME ZONE", "<COLNAME>", "'<VALUE>'"), #
"TIMESTAMPTZ" : ("TIMESTAMP WITH TIME ZONE" , "<COLNAME>", "'<VALUE>'"), #
"DATE" : ("ANSIDATE" , "<COLNAME>", "'<VALUE>'"), # 1999-01-08
"TIME%" : ("TIME(<SCALE>)" , "<COLNAME>", "'<VALUE>'"), # OR: [0-9] (def: 6) => Add precision VW [max 6]
"TIME % WITH TIME ZONE" : ("TIME(<SCALE>) WITH TIME ZONE", "<COLNAME>", "'<VALUE>'"), #
"TIMETZ%" : ("TIME(<SCALE>) WITH TIME ZONE", "<COLNAME>", "'<VALUE>'"), #
"INTERVAL" : ("INTERVAL" , "<COLNAME>", "'<VALUE>'"), # To check
"CHAR" : ("CHAR(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"), #
"CHARACTER" : ("CHAR(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"), #
"VARCHAR" : ("VARCHAR(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"), #
"CHARACTER VARYING": ("VARCHAR(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"), #
"TEXT" : ("VARCHAR(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"), #
"BYTEA" : ("VARCHAR(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"), # N/A in VW / To do something, we convert to VARCHAR
"ARRAY" : ("ARRAY(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"), # N/A in VW / Need to converted to an equivalent table
"BIT" : ("BIT(<PRECISION>)" , "<COLNAME>", "<VALUE>" ), # N/A IN VW
"BIT VARYING" : ("BIT VARYING(<PRECISION>)" , "<COLNAME>", "<VALUE>" ), # N/A IN VW
"BOOLEAN" : ("BOOLEAN" , "<COLNAME>", "<VALUE>" ), # N/A IN VW
"BOOL" : ("BOOLEAN" , "<COLNAME>", "<VALUE>" ), # N/A IN VW
"VARBINARY" : ("VARBINARY(<PRECISION>)" , "<COLNAME>", "<VALUE>" ), # N/A IN VW
"IMAGE" : ("BLOB" , "<COLNAME>", "<VALUE>" ), # N/A IN VW
"BIT" : ("BOOLEAN" , "<COLNAME>", "<VALUE>" ), #
"XML" : ("CLOB" , "CAST(<COLNAME> AS VARCHAR(8000))", "'<VALUE>'")
}
'''
Global translation table for datatype conversion from MS*SQL to SYBASE ASE
Sybase default values for NUMERIC and DECIMAL :
In Adaptive Server IQ, the default precision is 126 and the default scale is 38.
In Adaptive Server Enterprise, the default precision is 18 and the default scale is 0.
In Adaptive Server Anywhere, the default precision is 30 and the default scale is 6.
IQ: MONEY, SMALLMONEY = NUMERIC(19,4), NUMERIC(10,4).
IQ: FLOAT(p) is a synonym for REAL ou DOUBLE depending of precision
IQ: REAL is a sigle precision floting point number stored in 4 bytes
IQ: SMALLDATETIME = DATETIME => 8 Bytes
Global translation table for datatype conversion from MS*SQL to INGRES VW
VW : not compressed datatypes : DECIMAL with precision > 18, float, float4
Unsupported datatypes : *BYTES, INGRESDATE, BOOLEAN, UUID, TABLE_KEY, OBJECT_KEY, * WITH TIMEZONE
'''
iq2vw = {
"INT" : ("INTEGER" , "<COLNAME>", "<VALUE>" ), # Signed 4 bytes integer (-2^31; 2^31 - 1 (2,147,483,647))
"INTEGER" : ("INTEGER" , "<COLNAME>", "<VALUE>" ), # Signed 4 bytes integer (-2^31; 2^31 - 1 (2,147,483,647))
"NUMERIC" : ("DECIMAL(<PRECISION>,<SCALE>)", "<COLNAME>", "<VALUE>" ), #
"DECIMAL" : ("DECIMAL(<PRECISION>,<SCALE>)", "<COLNAME>", "<VALUE>" ),
"MONEY" : ("DECIMAL(19,4)" , "<COLNAME>", "<VALUE>" ), # IQ : NUMERIC(19,4)
"SMALLMONEY" : ("DECIMAL(10,4)" , "<COLNAME>", "<VALUE>" ), # IQ : NUMERIC(10,4)
"TINYINT" : ("SMALLINT" , "<COLNAME>", "<VALUE>" ), # IQ: [O:255] VW(TINYINT): -128:+127
"SMALLINT" : ("SMALLINT" , "<COLNAME>", "<VALUE>" ), # 2 Bytes [-32768:32767]-
"BIGINT" : ("BIGINT" , "<COLNAME>", "<VALUE>" ), # 8 Bytes : -2^63 ;+2^63-1
"DOUBLE" : ("FLOAT8" , "<COLNAME>", "<VALUE>" ), # 8 Bytes
"FLOAT" : ("FLOAT8" , "<COLNAME>", "'<VALUE>'"), # IQ: 8 Bytes/4 Bytes
"REAL" : ("FLOAT4" , "<COLNAME>", "'<VALUE>'"), # IQ: 4 bytes
"TIMESTAMP" : ("TIMESTAMP" , "<COLNAME>", "'<VALUE>'"), # IQ: 1999-01-08 04:05:06.xxxxxx
"DATETIME" : ("TIMESTAMP" , "<COLNAME>", "'<VALUE>'"), # IQ: 1999-01-08 04:05:06.xxxxxx
"SMALLDATETIME" : ("TIMESTAMP" , "<COLNAME>", "'<VALUE>'"), # IQ: 1999-01-08 04:05:06.xxxxxx
"DATE" : ("ANSIDATE" , "<COLNAME>", "'<VALUE>'"), # IQ: 1999-01-08
"TIME" : ("TIME WITHOUT TIMEZONE" , "<COLNAME>", "'<VALUE>'"), # RIQ 04:05:00.xxxxxx
"CHAR" : ("CHAR(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"), #
"CHARACTER" : ("CHAR(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"), #
"VARCHAR" : ("VARCHAR(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"), #
"CHARACTER VARYING": ("VARCHAR(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"), #
"TEXT" : ("CLOB" , "<COLNAME>", "'<VALUE>'"), # N/A IN VW
"NTEXT" : ("CLOB" , "<COLNAME>", "'<VALUE>'"), # N/A IN VW
"UNIQUEIDENTIFIER" : ("UNIQUEIDENTIFIER" , "<COLNAME>", "'<VALUE>'"), # N/A IN VW
"BINARY" : ("BINARY(<PRECISION>)" , "<COLNAME>", "<VALUE>" ), # N/A IN VW
"VARBINARY" : ("VARBINARY(<PRECISION>)" , "<COLNAME>", "<VALUE>" ), # N/A IN VW
"IMAGE" : ("BLOB" , "<COLNAME>", "<VALUE>" ), # N/A IN VW
"BIT" : ("TINYINT" , "<COLNAME>", "<VALUE>" ), # IQ:0/1/NULL
"XML" : ("CLOB" , "CAST(<COLNAME> AS VARCHAR(8000))", "'<VALUE>'")
}
'''
Global translation table for datatype conversion from MS*SQL to MYSQL
'''
ms2my = {
"INT" : ("INT" , "<COLNAME>", "<VALUE>" ),
"NUMERIC" : ("NUMERIC(<PRECISION>,<SCALE>)" , "<COLNAME>", "<VALUE>" ),
"DECIMAL" : ("DECIMAL(<PRECISION>,<SCALE>)" , "<COLNAME>", "<VALUE>" ),
"DATETIME" : ("TIMESTAMP" , "CONVERT(VARCHAR,<COLNAME>,121)", "'<VALUE>'"),
"NVARCHAR" : ("VARCHAR(<PRECISION>) CHARACTER SET UTF8", "CAST(<COLNAME> AS TEXT)", "'<VALUE>'"), # Should be NTEXT but pb driver MSSQL
"VARCHAR" : ("VARCHAR(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"),
"UNIQUEIDENTIFIER" : ("VARCHAR(64)" , "<COLNAME>", "'<VALUE>'"),
"NCHAR" : ("NCHAR(<PRECISION>) CHARACTER SET UTF8" , "CAST(<COLNAME> AS TEXT)", "'<VALUE>'"),
"CHAR" : ("CHAR(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"),
"BINARY" : ("BINARY(<PRECISION>)" , "<COLNAME>", "<VALUE>" ),
"VARBINARY" : ("VARBINARY(<PRECISION>)" , "<COLNAME>", "<VALUE>" ),
"SMALLDATETIME" : ("DATETIME" , "CONVERT(VARCHAR,<COLNAME>,120)", "'<VALUE>'"),
"BIT" : ("BIT(1)" , "<COLNAME>", "<VALUE>" ),
"MONEY" : ("DOUBLE" , "<COLNAME>", "<VALUE>" ),
"SMALLMONEY" : ("FLOAT" , "<COLNAME>", "<VALUE>" ),
"TINYINT" : ("TINYINT" , "<COLNAME>", "<VALUE>" ), # [O:255] - My: [0:255] or [-128:127]
"SMALLINT" : ("SMALLINT" , "<COLNAME>", "<VALUE>" ), # [-32768:32767]-
"FLOAT" : ("FLOAT(52,10)" , "<COLNAME>", "'<VALUE>'"), # 8 bytes - 8 (precision suppressed here)
"XML" : ("TEXT" , "CAST(<COLNAME> AS VARCHAR(8000))", "'<VALUE>'")
}
'''
Global translation table for datatype conversion from MS*SQL to SYBASE ASE
'''
ms2ase = {
"INT" : ("INT" , "<COLNAME>", "<VALUE>" ),
"NUMERIC" : ("NUMERIC(<PRECISION>,<SCALE>)" , "<COLNAME>", "<VALUE>" ),
"DECIMAL" : ("DECIMAL(<PRECISION>,<SCALE>)" , "<COLNAME>", "<VALUE>" ),
"DATETIME" : ("DATETIME" , "<COLNAME>", "'<VALUE>'"), # In Sybase "timestamp" is only lowercase
"TIMESTAMP" : ("timestamp" , "<COLNAME>", "'<VALUE>'"),
"NVARCHAR" : ("NVARCHAR(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"),
"VARCHAR" : ("VARCHAR(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"),
"UNIQUEIDENTIFIER" : ("VARCHAR(64)" , "<COLNAME>", "'<VALUE>'"),
"NCHAR" : ("NCHAR(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"),
"CHAR" : ("CHAR(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"),
"BINARY" : ("BINARY(<PRECISION>)" , "<COLNAME>", "<VALUE>" ),
"VARBINARY" : ("VARBINARY(<PRECISION>)" , "<COLNAME>", "<VALUE>" ),
"SMALLDATETIME" : ("SMALLDATETIME" , "<COLNAME>", "'<VALUE>'"),
"BIT" : ("BIT" , "<COLNAME>", "<VALUE>" ),
"MONEY" : ("MONEY" , "<COLNAME>", "<VALUE>" ),
"SMALLMONEY" : ("SMALLMONEY" , "<COLNAME>", "<VALUE>" ),
"TINYINT" : ("TINYINT" , "<COLNAME>", "<VALUE>" ), # [O:255] - My: [0:255] or [-128:127]
"SMALLINT" : ("SMALLINT" , "<COLNAME>", "<VALUE>" ), # [-32768:32767]-
"FLOAT" : ("FLOAT" , "<COLNAME>", "<VALUE>"), # 8 bytes - 8 (precision suppressed here)
"XML" : ("TEXT" , "CAST(<COLNAME> AS VARCHAR(8000))", "'<VALUE>'")
}
'''
Global translation table for datatype conversion from MS*SQL to SYBASE ASE
Sybase default values for NUMERIC and DECIMAL :
In Adaptive Server IQ, the default precision is 126 and the default scale is 38.
In Adaptive Server Enterprise, the default precision is 18 and the default scale is 0.
In Adaptive Server Anywhere, the default precision is 30 and the default scale is 6.
IQ: MONEY, SMALLMONEY = NUMERIC(19,4), NUMERIC(10,4).
IQ: SMALLDATETIME = DATETIME => 8 Bytes
'''
ms2iq = {
"INT" : ("INTEGER" , "<COLNAME>", "<VALUE>" ), # Signed 4 bytes integer (-2^31; 2^31 - 1 (2,147,483,647))
"NUMERIC" : ("NUMERIC(<PRECISION>,<SCALE>)", "<COLNAME>", "<VALUE>" ), # MSSQL: -10^38 +1 through 10^38 –1.
"DECIMAL" : ("DECIMAL(<PRECISION>,<SCALE>)", "<COLNAME>", "<VALUE>" ),
"MONEY" : ("NUMERIC(19,4)" , "<COLNAME>", "<VALUE>" ), # MSSQL : NUMERIC(19,4)
"SMALLMONEY" : ("NUMERIC(10,4)" , "<COLNAME>", "<VALUE>" ), # MSSQL : NUMERIC(10,4)
"TINYINT" : ("TINYINT" , "<COLNAME>", "<VALUE>" ), # SQL: [O:255]
"SMALLINT" : ("SMALLINT" , "<COLNAME>", "<VALUE>" ), # 2 Bytes [-32768:32767]-
"BIGINT" : ("BIGINT" , "<COLNAME>", "<VALUE>" ), # 8 Bytes : -2^63 ;+2^63-1
"FLOAT" : ("FLOAT(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"), # MS(def) = float(53) precision(4 or 8)
"REAL" : ("FLOAT(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"), # MS(def) = FLOAT(24)
"ROWVERSION" : ("VARBINARY(8)" , "<COLNAME>", "'<VALUE>'"), # MS : An incremental number for versionning
"TIMESTAMP" : ("VARBINARY(8)" , "<COLNAME>", "'<VALUE>'"), # MS = ROWVERSION
"DATETIME" : ("DATETIME" , "CONVERT(VARCHAR,<COLNAME>,121)", "'<VALUE>'"), # MS: 1999-01-08 04:05:06.xxxx
"SMALLDATETIME" : ("SMALLDATETIME" , "CONVERT(VARCHAR,<COLNAME>,120)", "'<VALUE>'"), # MS: 1999-01-08 04:05:00
"NCHAR" : ("CHAR(<PRECISION>)" , "CAST(<COLNAME> AS TEXT)", "'<VALUE>'"),
"CHAR" : ("CHAR(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"),
"NVARCHAR" : ("VARCHAR(<PRECISION>)" , "CAST(<COLNAME> AS TEXT)", "'<VALUE>'"), # Should be NTEXT but pb driver MSSQL
"VARCHAR" : ("VARCHAR(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"),
"TEXT" : ("CLOB" , "<COLNAME>", "'<VALUE>'"), # MS: 2^31 Chars
"NTEXT" : ("CLOB" , "<COLNAME>", "'<VALUE>'"), # MS: 2^31/2 UTF8 Chars
"UNIQUEIDENTIFIER" : ("UNIQUEIDENTIFIER" , "<COLNAME>", "'<VALUE>'"),
"BINARY" : ("BINARY(<PRECISION>)" , "<COLNAME>", "<VALUE>" ), # MS: [1:8000] (Fixed size)
"VARBINARY" : ("VARBINARY(<PRECISION>)" , "<COLNAME>", "<VALUE>" ), # MS: [1:8000] (Var size)
"IMAGE" : ("BLOB" , "<COLNAME>", "<VALUE>" ), # MS: 2^31 bytes
"BIT" : ("BIT" , "<COLNAME>", "<VALUE>" ), # SQL:0/1; Ingres: -128:+127
"XML" : ("CLOB" , "CAST(<COLNAME> AS VARCHAR(8000))", "'<VALUE>'")
}
'''
Global translation table for datatype conversion from MS*SQL to POSTGRES
'''
ms2pg = {
"INT" : ("INTEGER" , "<COLNAME>", "<VALUE>" ), # Signed 4 bytes integer
"NUMERIC" : ("NUMERIC(<PRECISION>,<SCALE>)", "<COLNAME>", "<VALUE>" ),
"DECIMAL" : ("DECIMAL(<PRECISION>,<SCALE>)", "<COLNAME>", "<VALUE>" ),
"DATETIME" : ("TIMESTAMP" , "CONVERT(VARCHAR,<COLNAME>,121)", "'<VALUE>'"), # MS: 1999-01-08 04:05:06.xxxx
"NVARCHAR" : ("VARCHAR(<PRECISION>)" , "CAST(<COLNAME> AS TEXT)", "'<VALUE>'"), # Should be NTEXT but pb driver MSSQL
"VARCHAR" : ("VARCHAR(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"),
"UNIQUEIDENTIFIER" : ("VARCHAR(64)" , "<COLNAME>", "'<VALUE>'"),
"NCHAR" : ("CHAR(<PRECISION>)" , "CAST(<COLNAME> AS TEXT)", "'<VALUE>'"),
"CHAR" : ("CHAR(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"),
"BINARY" : ("BITEA" , "<COLNAME>", "<VALUE>" ),
"VARBINARY" : ("BITEA" , "<COLNAME>", "<VALUE>" ),
"SMALLDATETIME" : ("TIMESTAMP" , "CONVERT(VARCHAR,<COLNAME>,120)", "'<VALUE>'"),
"BIT" : ("BIT" , "<COLNAME>", "<VALUE>" ),
"MONEY" : ("DOUBLE PRECISION" , "<COLNAME>", "<VALUE>" ), # MSSQL : NUMERIC(19,4)
"SMALLMONEY" : ("MONEY" , "<COLNAME>", "<VALUE>" ), # MSSQL : NUMERIC(10,4)
"TINYINT" : ("SMALLINT" , "<COLNAME>", "<VALUE>" ), # [O:255]
"SMALLINT" : ("SMALLINT" , "<COLNAME>", "<VALUE>" ), # 2 Bytes [-32768:32767]-
"FLOAT" : ("DOUBLE PRECISION" , "<COLNAME>", "'<VALUE>'"), # 8 bytes - 8 (precision suppressed here)
"XML" : ("TEXT" , "CAST(<COLNAME> AS VARCHAR(8000))", "'<VALUE>'")
}
'''
Global translation table for datatype conversion from MS*SQL to MATRIX
'''
ms2mx = {
"INT" : ("INTEGER" , "<COLNAME>", "<VALUE>" ), # Signed 4 bytes integer
"NUMERIC" : ("NUMERIC(<PRECISION>,<SCALE>)", "<COLNAME>", "<VALUE>" ),
"DECIMAL" : ("DECIMAL(<PRECISION>,<SCALE>)", "<COLNAME>", "<VALUE>" ),
"DATETIME" : ("TIMESTAMP" , "CONVERT(VARCHAR,<COLNAME>,121)", "'<VALUE>'"), # MS: 1999-01-08 04:05:06.xxxx
"NVARCHAR" : ("VARCHAR(<PRECISION>)" , "CAST(<COLNAME> AS TEXT)", "'<VALUE>'"), # Should be NTEXT but pb driver MSSQL
"VARCHAR" : ("VARCHAR(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"),
"UNIQUEIDENTIFIER" : ("VARCHAR(64)" , "<COLNAME>", "'<VALUE>'"),
"NCHAR" : ("CHAR(<PRECISION>)" , "CAST(<COLNAME> AS TEXT)", "'<VALUE>'"),
"CHAR" : ("CHAR(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"),
"BINARY" : ("BITEA" , "<COLNAME>", "<VALUE>" ),
"VARBINARY" : ("BITEA" , "<COLNAME>", "<VALUE>" ),
"SMALLDATETIME" : ("TIMESTAMP" , "CONVERT(VARCHAR,<COLNAME>,120)", "'<VALUE>'"),
"BIT" : ("BIT" , "<COLNAME>", "<VALUE>" ),
"MONEY" : ("NUMERIC(19,4)" , "<COLNAME>", "<VALUE>" ), # MSSQL : NUMERIC(19,4)
"SMALLMONEY" : ("NUMERIC(10,4)" , "<COLNAME>", "<VALUE>" ), # MSSQL : NUMERIC(10,4)
"TINYINT" : ("SMALLINT" , "<COLNAME>", "<VALUE>" ), # [O:255]
"SMALLINT" : ("SMALLINT" , "<COLNAME>", "<VALUE>" ), # 2 Bytes [-32768:32767]-
"FLOAT" : ("DOUBLE PRECISION" , "<COLNAME>", "'<VALUE>'"), # 8 bytes - 8 (precision suppressed here)
"XML" : ("TEXT" , "CAST(<COLNAME> AS VARCHAR(8000))", "'<VALUE>'")
}
'''
Global translation table for datatype conversion from MS*SQL to INGRES DB
ii : Text = VARCHAR(32000) 16000 if UTF8
'''
ms2ii = {
"INT" : ("INTEGER" , "<COLNAME>", "<VALUE>" ), # Signed 4 bytes integer (-2^31; 2^31 - 1 (2,147,483,647))
'''
added the full INTEGER value
'''
"INTEGER" : ("INTEGER" , "<COLNAME>", "<VALUE>" ), # Signed 4 bytes integer (-2^31; 2^31 - 1 (2,147,483,647))
"NUMERIC" : ("NUMERIC(<PRECISION>,<SCALE>)", "<COLNAME>", "<VALUE>" ), # MSSQL: -10^38 +1 through 10^38 –1.
"DECIMAL" : ("DECIMAL(<PRECISION>,<SCALE>)", "<COLNAME>", "<VALUE>" ),
"BIT" : ("BOOLEAN" , "<COLNAME>", "<VALUE>" ), # SQL:0/1;
"TINYINT" : ("SMALLINT" , "<COLNAME>", "<VALUE>" ), # SQL: [O:255] ; Ingres: -128:+127
"SMALLINT" : ("SMALLINT" , "<COLNAME>", "<VALUE>" ), # 2 Bytes [-32768:32767]-
"BIGINT" : ("BIGINT" , "<COLNAME>", "<VALUE>" ), # 8 Bytes : -2^63 ;+2^63-1
"FLOAT" : ("FLOAT(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"), # MSSQL: Storage depends of precision(4 or 8). We take 8
"REAL" : ("FLOAT(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"), # MSSQL: = FLOAT
"UNIQUEIDENTIFIER" : ("UUID" , "<COLNAME>", "'<VALUE>'"), # DEFAULT is automatic as UUID_CREATE()
"ROWVERSION" : ("BIGINT" , "<COLNAME>", "'<VALUE>'"), # MS : An incremental number for versionning
"MONEY" : ("NUMERIC(19,4)" , "<COLNAME>", "<VALUE>" ), # MSSQL : NUMERIC(19,4) 8 bytes
"SMALLMONEY" : ("NUMERIC(10,4)" , "<COLNAME>", "<VALUE>" ), # MSSQL : NUMERIC(10,4) 4 bytes
"TIMESTAMP" : ("BIGINT" , "<COLNAME>", "'<VALUE>'"), # MS = ROWVERSION
"DATETIME" : ("TIMESTAMP" , "CONVERT(VARCHAR,<COLNAME>,121)", "'<VALUE>'"), # SQL: 1999-01-08 04:05:06; ii: sweden
"SMALLDATETIME" : ("TIMESTAMP" , "CONVERT(VARCHAR,<COLNAME>,120)", "'<VALUE>'"), # depricate the INGRESDATE
"NCHAR" : ("NCHAR(<PRECISION>)" , "CAST(<COLNAME> AS TEXT)", "'<VALUE>'"),
"CHAR" : ("CHAR(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"),
"NVARCHAR" : ("NVARCHAR(<PRECISION>)" , "CAST(<COLNAME> AS TEXT)", "'<VALUE>'"), # Should be NTEXT but pb driver MSSQL
"VARCHAR" : ("VARCHAR(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"),
"TEXT" : ("LONG VARCHAR" , "<COLNAME>", "'<VALUE>'"), # 2^31 Chars
"NTEXT" : ("LONG NVARCHAR" , "<COLNAME>", "'<VALUE>'"), # 2^31/2 UTF8 Chars
"BINARY" : ("BINARY(<PRECISION>)" , "<COLNAME>", "<VALUE>" ), # SQL: 8000 Max (Ingres: max configured row size)
"VARBINARY" : ("VARBINARY(<PRECISION>)" , "<COLNAME>", "<VALUE>" ), # SQL: 8000 Max (Ingres: max configured row size)
"IMAGE" : ("LONG BYTE" , "<COLNAME>", "<VALUE>" ), # SQL: 2^31 Max (Ingres: max configured row size)
"XML" : ("LONG NVARCHAR" , "CAST(<COLNAME> AS NVARCHAR(max))", "'<VALUE>'") # MSSQL : 2 GB
}
'''
Global translation table for datatype conversion from MS*SQL to INGRES VW
VW : not compressed datatypes : DECIMAL with precision > 18, float, float4
Keith Bolam - these are old definitions exclusions
Unsupported datatypes : *BYTES, INGRESDATE, BOOLEAN, UUID, TABLE_KEY, OBJECT_KEY, * WITH TIMEZONE
Updated list
Unsupported datatypes : *BYTES, INGRESDATE, TABLE_KEY, OBJECT_KEY
The PLACEHOLDER was added due to a bug with the parsing of the arrary - perhaps introduced by the comment??
'''
ms2vw = {
"INT" : ("INTEGER" , "<COLNAME>", "<VALUE>" ), # Signed 4 bytes integer (-2^31; 2^31 - 1 (2,147,483,647))
"INTEGER" : ("INTEGER" , "<COLNAME>", "<VALUE>" ), # Signed 4 bytes integer (-2^31; 2^31 - 1 (2,147,483,647))
'''
added the INTEGER
'''
"PLACEHOLDER" : ("PLACEHOLDER" , "<COLNAME>", "<VALUE>" ), # MSSQL : NUMERIC(10,4)
"NUMERIC" : ("NUMERIC(<PRECISION>,<SCALE>)", "<COLNAME>", "<VALUE>" ), # MSSQL: -10^38 +1 through 10^38 –1.
"DECIMAL" : ("DECIMAL(<PRECISION>,<SCALE>)", "<COLNAME>", "<VALUE>" ),
"SMALLMONEY" : ("DECIMAL(10,4)" , "<COLNAME>", "<VALUE>" ), # MSSQL : NUMERIC(10,4)
"MONEY" : ("DECIMAL(19,4)" , "<COLNAME>", "<VALUE>" ), # MSSQL : NUMERIC(19,4)
"TINYINT" : ("SMALLINT" , "<COLNAME>", "<VALUE>" ), # SQL: [O:255] - VW(TINYINT): -128:+127
"SMALLINT" : ("SMALLINT" , "<COLNAME>", "<VALUE>" ), # 2 Bytes [-32768:32767]-
"BIGINT" : ("BIGINT" , "<COLNAME>", "<VALUE>" ), # 8 Bytes : -2^63 ;+2^63-1
"FLOAT" : ("FLOAT(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"), # MS(def) = float(53) precision(4 or 8)
"REAL" : ("FLOAT(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"), # MS(def) = FLOAT(24)
"ROWVERSION" : ("BIGINT" , "<COLNAME>", "'<VALUE>'"), # MS : An incremental number for versionning
"TIMESTAMP" : ("BIGINT" , "<COLNAME>", "'<VALUE>'"), # MS = ROWVERSION
"DATETIME" : ("TIMESTAMP" , "CONVERT(VARCHAR,<COLNAME>,121)", "'<VALUE>'"), # MS: 1999-01-08 04:05:06.xxxx
"SMALLDATETIME" : ("TIMESTAMP(0)" , "CONVERT(VARCHAR,<COLNAME>,120)", "'<VALUE>'"), # MS: 1999-01-08 04:05:00
"TIME" : ("TIME" , "CONVERT(VARCHAR,<COLNAME>,120)", "'<VALUE>'"), # MS: 1999-01-08 04:05:00
"DATE" : ("ANSIDATE" , "CONVERT(VARCHAR,<COLNAME>,120)", "'<VALUE>'"), # MS: 1999-01-08
"NCHAR" : ("NCHAR(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"),
"CHAR" : ("CHAR(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"),
"NVARCHAR" : ("NVARCHAR(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"),
"VARCHAR" : ("VARCHAR(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"),
"TEXT" : ("VARCHAR(4000)" , "CAST(<COLNAME> AS VARCHAR(4000))", "'<VALUE>'"), # MS: 2^31 Chars
"NTEXT" : ("NVARCHAR(4000)" , "CAST(<COLNAME> AS NVARCHAR(4000))", "'<VALUE>'"), # MS: 2^31/2 UTF8 Chars
"UNIQUEIDENTIFIER" : ("UUID" , "<COLNAME>", "'<VALUE>'"), # ADed UUID in Vector 5.0
#the following are not supported in any shape in Vector currently - this can change in Vector 5.1 where the BINARY columns can be put into a hash/heap table and join back the main table
"BINARY" : ("BINARY(<PRECISION>)" , "CAST(<COLNAME> AS NVARCHAR(4000))", "'<VALUE>'" ), # MS: [1:8000] (Fixed size)
"VARBINARY" : ("CHARACTER VARYING(4000)" , "CASE WHEN <COLNAME> IS NOT NULL THEN '--IMAGE--' END", "'<VALUE>'" ), # MS: [1:8000] (Var size)
"IMAGE" : ("LONG BYTE" , "<COLNAME>", "<VALUE>" ), # MS: 2^31 bytes
"BIT" : ("TINYINT" , "CAST(<COLNAME> AS TINYINT)", "<VALUE>" ), # SQL:0/1;
"XML" : ("NVARCHAR(4000)" , "CAST(<COLNAME> AS NVARCHAR(4000))", "'<VALUE>'"), # MSSQL : 2 GB
"HIERARCHYID" : ("NVARCHAR(4000)" , "CAST(<COLNAME> AS NVARCHAR(4000))", "'<VALUE>'"),
"GEOMETRY" : ("NVARCHAR(4000)" , "CAST(<COLNAME> AS NVARCHAR(4000))", "'<VALUE>'"),
"GEOGRAPHY" : ("NVARCHAR(4000)" , "CAST(<COLNAME> AS NVARCHAR(4000))", "'<VALUE>'"),
"BINARY" : ("BINARY(<PRECISION>)" , "CAST(<COLNAME> AS NVARCHAR(4000))", "'<VALUE>'" ), # MS: [1:8000] (Fixed size)
"VARBINARY" : ("CHARACTER VARYING(4000)" , "CASE WHEN <COLNAME> IS NOT NULL THEN '--IMAGE--' END", "'<VALUE>'" ), # MS: [1:8000] (Var size)
# "BIT" : ("BOOLEAN" , "CAST(<COLNAME> AS TINYINT)", "<VALUE>" ), # SQL:0/1;
"BIT" : ("BOOLEAN" , "<COLNAME>", "'<VALUE>'") # SQL:0/1;
}
ms2vw_default = {
"DEFAULT newid" : "",
"DEFAULT getdate" : "DEFAULT CURRENT_TIMESTAMP",
"IDENTITY(1,1)" : "GENERATED BY DEFAULT AS IDENTITY ",
"COMMA" : ""","""
}
ms2vw_view = [
["[" , "\""],
["]" , "\""],
["CONVERT(money" , "money"], ## poor mans awk/substitution/translation
["(money,(" , "((money("], ## money and other items are available as automatic CAST operators
["0101" , "-01-01"], ## some rudimentory massage of the dates - shoudle
["1231" , "-12-31"],
["WITH SCHEMABINDING", ""],
["LAST_ITEM_IS_8"]
]
'''
Global translation table for datatype conversion from MS*SQL to TERADATA
'''
ms2td = {
"INT" : ("INTEGER" , "<COLNAME>", "<VALUE>" ), # Signed 4 bytes integer
"NUMERIC" : ("NUMERIC(<PRECISION>,<SCALE>)", "<COLNAME>", "<VALUE>" ), # td <PRECISION> [1-18], <SCALE>[0-<PRECISION>]
"DECIMAL" : ("DECIMAL(<PRECISION>,<SCALE>)", "<COLNAME>", "<VALUE>" ), # td: id NUMERIC
"DATETIME" : ("TIMESTAMP" , "CONVERT(VARCHAR,<COLNAME>,120)", "'<VALUE>'"), # 1999-01-08 04:05:06
"TIMESTAMP" : ("TIMESTAMP" , "CONVERT(VARCHAR,<COLNAME>,121)", "'<VALUE>'"), # 1999-01-08 04:05:06
"NVARCHAR" : ("VARCHAR(<PRECISION>)" , "CAST(<COLNAME> AS TEXT)", "'<VALUE>'"), # Should be NTEXT but pb driver MSSQL
"VARCHAR" : ("VARCHAR(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"),
"UNIQUEIDENTIFIER" : ("VARCHAR(64)" , "<COLNAME>", "'<VALUE>'"), # td: 64000 or 32000 (unicode)
"NCHAR" : ("CHAR(<PRECISION>)" , "CAST(<COLNAME> AS TEXT)", "'<VALUE>'"),
"CHAR" : ("CHAR(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"),
"BINARY" : ("BITEA" , "<COLNAME>", "<VALUE>" ),
"VARBINARY" : ("BITEA" , "<COLNAME>", "<VALUE>" ),
"SMALLDATETIME" : ("TIMESTAMP" , "CONVERT(VARCHAR,<COLNAME>,120)", "'<VALUE>'"),
"BIT" : ("BYTEINT" , "<COLNAME>", "<VALUE>" ), # td: [-128,127]
"MONEY" : ("BIGINT" , "<COLNAME>", "<VALUE>" ), # Signed 8 bytes integer
"SMALLMONEY" : ("INTEGER" , "<COLNAME>", "<VALUE>" ), # Signed 4 bytes integer
"TINYINT" : ("SMALLINT" , "<COLNAME>", "<VALUE>" ), # [O:255]
"SMALLINT" : ("SMALLINT" , "<COLNAME>", "<VALUE>" ), # 2 Bytes [-32768:32767]-
"FLOAT" : ("FLOAT" , "<COLNAME>", "'<VALUE>'"), # 8 bytes
"SQL_VARIANT" : ("FLOAT" , "<COLNAME>", "'<VALUE>'"), # 8 bytes
"XML" : ("CLOB" , "CAST(<COLNAME> AS VARCHAR(8000))", "'<VALUE>'")
}
'''
Global translation table for datatype conversion from MS*SQL to PROGRESS (SQL-92)
'''
ms2pr = {
"INT" : ("INTEGER" , "<COLNAME>", "<VALUE>" ), # Signed 4 bytes integer
"NUMERIC" : ("NUMERIC(<PRECISION>,<SCALE>)", "<COLNAME>", "<VALUE>" ), # (pr) Default prec=32; scale=0
"DECIMAL" : ("DECIMAL(<PRECISION>,<SCALE>)", "<COLNAME>", "<VALUE>" ), #
"DATETIME" : ("TIMESTAMP" , "CONVERT(VARCHAR,<COLNAME>,121)", "{ts '<VALUE>'}"), # yyyy-mm-dd hh:mi:ss.mmm
"NVARCHAR" : ("VARCHAR(<PRECISION>)" , "CAST(<COLNAME> AS TEXT)", "'<VALUE>'"), # Should be NTEXT but pb driver MSSQL
"VARCHAR" : ("VARCHAR(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"), # Max : 31995 (pr)
"UNIQUEIDENTIFIER" : ("VARCHAR(64)" , "<COLNAME>", "'<VALUE>'"), #
"NCHAR" : ("CHAR(<PRECISION>)" , "CAST(<COLNAME> AS TEXT)", "'<VALUE>'"), # Comment : See char datatype
"CHAR" : ("CHAR(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"), # Char(Max) = 2000 (pr); 8000 mssql=>Overflow may occured
"BINARY" : ("VARBINARY(<PRECISION>)" , "<COLNAME>", "<VALUE>" ), # binary(Max) = 2000 bytes (pr) ; 8000 mssql
"VARBINARY" : ("VARBINARY(<PRECISION>)" , "<COLNAME>", "<VALUE>" ), # Max : 31995 bytes (pr); 8000 mssql
"SMALLDATETIME" : ("TIMESTAMP" , "CONVERT(VARCHAR,<COLNAME>,120)", "{ts '<VALUE>'}"),
"BIT" : ("BIT" , "<COLNAME>", "<VALUE>" ), # Single bit 0,1
"MONEY" : ("FLOAT" , "<COLNAME>", "<VALUE>" ), # Signed 8 bytes integer
"SMALLMONEY" : ("INTEGER" , "<COLNAME>", "<VALUE>" ), # Signed 4 bytes integer
"TINYINT" : ("SMALLINT" , "<COLNAME>", "<VALUE>" ), # [O:255] (ms) - TINYINT [-127:128] (pr)
"SMALLINT" : ("SMALLINT" , "<COLNAME>", "<VALUE>" ), # 2 Bytes [-32768:32767]-
"FLOAT" : ("FLOAT" , "<COLNAME>", "'<VALUE>'"), # Signed 8 bytes
"XML" : ("VARCHAR(8000)" , "CAST(<COLNAME> AS VARCHAR(8000))", "'<VALUE>'")
}
'''
Global translation table for datatype conversion from MS*SQL to MAX DB
DOUBLE PRECISION = FLOAT(38)
'''
ms2md = {
"INT" : ("INTEGER" , "<COLNAME>", "<VALUE>" ), # Signed 4 bytes integer
"NUMERIC" : ("NUMERIC(<PRECISION>,<SCALE>)", "<COLNAME>", "<VALUE>" ), # td <PRECISION> [1-18], <SCALE>[0-<PRECISION>]
"DECIMAL" : ("DECIMAL(<PRECISION>,<SCALE>)", "<COLNAME>", "<VALUE>" ), # td: id NUMERIC
"BIT" : ("BOOLEAN" , "<COLNAME>", "<VALUE>" ), #
"MONEY" : ("NUMERIC(19,4)" , "<COLNAME>", "<VALUE>" ), # Signed 8 bytes integer
"SMALLMONEY" : ("NUMERIC(10,4)" , "<COLNAME>", "<VALUE>" ), # Signed 4 bytes integer
"TINYINT" : ("SMALLINT" , "<COLNAME>", "<VALUE>" ), # [O:255]
"SMALLINT" : ("SMALLINT" , "<COLNAME>", "<VALUE>" ), # 2 Bytes [-32768:32767]-
"FLOAT" : ("FLOAT(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"), # 8 bytes
"BIGINT" : ("DOUBLE PRECISION" , "<COLNAME>", "<VALUE>" ), # 8 Bytes : -2^63 ;+2^63-1
"ROWVERSION" : ("DOUBLE PRECISION" , "<COLNAME>", "'<VALUE>'"), # MS : An incremental number for versionning
"TIMESTAMP" : ("DOUBLE PRECISION" , "<COLNAME>", "'<VALUE>'"), # MS = ROWVERSION
"DATETIME" : ("TIMESTAMP" , "CONVERT(VARCHAR,<COLNAME>,121)", "'<VALUE>'"), # 1999-01-08 04:05:06
"SMALLDATETIME" : ("TIMESTAMP" , "CONVERT(VARCHAR,<COLNAME>,120)", "'<VALUE>'"),
"NVARCHAR" : ("NVARCHAR(<PRECISION>)" , "CAST(<COLNAME> AS TEXT)", "'<VALUE>'"), # Should be NTEXT but pb driver MSSQL
"VARCHAR" : ("VARCHAR(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"),
"UNIQUEIDENTIFIER" : ("VARCHAR(64)" , "<COLNAME>", "'<VALUE>'"), # td: 64000 or 32000 (unicode)
"NCHAR" : ("NCHAR(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"),
"CHAR" : ("CHAR(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"),
"BINARY" : ("BINARY(<PRECISION>)" , "<COLNAME>", "<VALUE>" ),
"VARBINARY" : ("VARBINARY(<PRECISION>)" , "<COLNAME>", "<VALUE>" ),
"XML" : ("CLOB" , "CAST(<COLNAME> AS NVARCHAR(max))", "'<VALUE>'")
}
'''
Global translation table for datatype conversion from MS*SQL to HANA NEW DB
'''
ms2ha = {
"INT" : ("INTEGER" , "<COLNAME>", "<VALUE>" ), # Signed 4 bytes integer
"NUMERIC" : ("NUMERIC(<PRECISION>,<SCALE>)", "<COLNAME>", "<VALUE>" ), # td <PRECISION> [1-18], <SCALE>[0-<PRECISION>]
"BIT" : ("TINYINT" , "<COLNAME>", "<VALUE>" ), #
"MONEY" : ("NUMERIC(19,4)" , "<COLNAME>", "<VALUE>" ), # Signed 8 bytes integer
"SMALLMONEY" : ("NUMERIC(10,4)" , "<COLNAME>", "<VALUE>" ), # Signed 4 bytes integer
"TINYINT" : ("TINYINT" , "<COLNAME>", "<VALUE>" ), # [O:255]
"SMALLINT" : ("SMALLINT" , "<COLNAME>", "<VALUE>" ), # 2 Bytes [-32768:32767]-
"FLOAT" : ("FLOAT(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"), # 8 bytes
"BIGINT" : ("DOUBLE PRECISION" , "<COLNAME>", "<VALUE>" ), # 8 Bytes : -2^63 ;+2^63-1
"DECIMAL" : ("DECIMAL(<PRECISION>,<SCALE>)", "<COLNAME>", "<VALUE>" ), # td: id NUMERIC
"ROWVERSION" : ("DOUBLE PRECISION" , "<COLNAME>", "'<VALUE>'"), # MS : An incremental number for versionning
"TIMESTAMP" : ("DOUBLE PRECISION" , "<COLNAME>", "'<VALUE>'"), # MS = ROWVERSION
"DATETIME" : ("TIMESTAMP" , "CONVERT(VARCHAR,<COLNAME>,121)", "'<VALUE>'"), # 1999-01-08 04:05:06
"SMALLDATETIME" : ("TIMESTAMP" , "CONVERT(VARCHAR,<COLNAME>,120)", "'<VALUE>'"),
"NVARCHAR" : ("NVARCHAR(<PRECISION>)" , "CAST(<COLNAME> AS TEXT)", "'<VALUE>'"), # Should be NTEXT but pb driver MSSQL
"VARCHAR" : ("VARCHAR(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"),
"UNIQUEIDENTIFIER" : ("VARCHAR(64)" , "<COLNAME>", "'<VALUE>'"), # td: 64000 or 32000 (unicode)
"NCHAR" : ("NCHAR(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"),
"CHAR" : ("CHAR(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"),
"BINARY" : ("BINARY(<PRECISION>)" , "<COLNAME>", "<VALUE>" ),
"VARBINARY" : ("VARBINARY(<PRECISION>)" , "<COLNAME>", "<VALUE>" ),
"XML" : ("CLOB" , "CAST(<COLNAME> AS NVARCHAR(max))", "'<VALUE>'") # MSSQL : 2GB
}
'''
Global translation table for datatype conversion from MYSQL to INGRES VW
VW : not compressed datatypes : DECIMAL with precision > 18, float, float4
Unsupported datatypes : *BYTES, INGRESDATE, BOOLEAN, UUID, TABLE_KEY, OBJECT_KEY, * WITH TIMEZONE
'''
my2vw = {
"INT" : ("INTEGER" , "<COLNAME>", "<VALUE>" ), # Signed 4 bytes integer (-2^31; 2^31 - 1 (2,147,483,647))
"INTEGER" : ("INTEGER" , "<COLNAME>", "<VALUE>" ), # Signed 4 bytes integer (-2^31; 2^31 - 1 (2,147,483,647))
"NUMERIC" : ("DECIMAL(<PRECISION>,<SCALE>)", "<COLNAME>", "<VALUE>" ), # MSSQL: -10^38 +1 through 10^38 –1.
"DECIMAL" : ("DECIMAL(<PRECISION>,<SCALE>)", "<COLNAME>", "<VALUE>" ), # MSSQL: -10^38 +1 through 10^38 –1.
"DEC" : ("DECIMAL(<PRECISION>,<SCALE>)", "<COLNAME>", "<VALUE>" ), # MSSQL: -10^38 +1 through 10^38 –1.
"FIXED" : ("DECIMAL(<PRECISION>,<SCALE>)", "<COLNAME>", "<VALUE>" ),
"BIT" : ("TINYINT" , "<COLNAME>", "<VALUE>" ), # MY: Bit values
"TINYINT" : ("TINYINT" , "<COLNAME>", "<VALUE>" ), # MY: -128:127 - VW: -128:+127
"SMALLINT" : ("SMALLINT" , "<COLNAME>", "<VALUE>" ), # 2 Bytes [-32768:32767]-
"MEDIUMINT" : ("INTEGER" , "<COLNAME>", "<VALUE>" ), # MY: 3 Signed bytes
"BIGINT" : ("BIGINT" , "<COLNAME>", "<VALUE>" ), # 8 Bytes : -2^63 ;+2^63-1
"DOUBLE" : ("DECIMAL(<PRECISION>,<SCALE>)", "<COLNAME>", "<VALUE>" ), # 8 Bytes : -2^63 ;+2^63-1
"DOUBLE PRECISION" : ("DECIMAL(<PRECISION>,<SCALE>)", "<COLNAME>", "<VALUE>" ), # 8 Bytes : -2^63 ;+2^63-1
"FLOAT" : ("DECIMAL(<PRECISION>,<SCALE>)", "<COLNAME>", "'<VALUE>'"), # MS(def) = float(53) precision(4 or 8)
"REAL" : ("DECIMAL(<PRECISION>,<SCALE>)", "<COLNAME>", "'<VALUE>'"), # MS(def) = FLOAT(24)
"ROWVERSION" : ("BIGINT" , "<COLNAME>", "'<VALUE>'"), # MS : An incremental number for versionning
"TIMESTAMP" : ("BIGINT" , "<COLNAME>", "'<VALUE>'"), # MS = ROWVERSION
"DATE" : ("ANSIDATE" , "<COLNAME>", "'<VALUE>'"), # MY: 1999-01-08
"DATETIME" : ("TIMESTAMP(0)" , "<COLNAME>", "'<VALUE>'"), # MY: 1999-01-08 04:05:06
"TIMESTAMP" : ("TIMESTAMP" , "<COLNAME>", "'<VALUE>'"), # MY: 1999-01-08 04:05:00???
"TIME" : ("TIME WITHOUT TIME ZONE" , "<COLNAME>", "'<VALUE>'"), # MY: HH:MI:SS
"YEAR" : ("TINYINT" , "<COLNAME>", "<VALUE>" ), # MY: 1901:2155, 0000
"NCHAR" : ("NCHAR(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"),
"CHAR" : ("CHAR(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"),
"NVARCHAR" : ("NVARCHAR(<PRECISION>)" , "CAST(<COLNAME> AS VARCHAR(max))", "'<VALUE>'"),
"VARCHAR" : ("VARCHAR(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"),
"TEXT" : ("VARCHAR(16000)" , "<COLNAME>", "'<VALUE>'"), # MS: 2^31 Chars
"LONGTEXT" : ("VARCHAR(16000)" , "<COLNAME>", "'<VALUE>'"), # MS: 2^31 Chars
"LONGBLOB" : ("VARCHAR(16000)" , "<COLNAME>", "'<VALUE>'"), # MS: 2^31 Chars
"BINARY" : ("BINARY(<PRECISION>)" , "<COLNAME>", "<VALUE>" ), # MS: [1:8000] (Fixed size)
"VARBINARY" : ("VARBINARY(<PRECISION>)" , "<COLNAME>", "<VALUE>" ), # MS: [1:8000] (Var size)
}
'''
Global translation table for datatype conversion from db2 to db2
'''
d22d2 = {
"INTEGER" : ("INTEGER" , "<COLNAME>", "<VALUE>" ),
"BIGINT" : ("BIGINT" , "<COLNAME>", "<VALUE>" ),
"BINARY" : ("BINARY" , "<COLNAME>", "<VALUE>" ),
"BLOB" : ("BLOB" , "<COLNAME>", "<VALUE>" ),
"CHAR" : ("CHAR(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"),
"CLOB" : ("CLOB" , "<COLNAME>", "'<VALUE>'"),
"DATE" : ("DATE" , "<COLNAME>", "'<VALUE>'"),
"DBCLOB" : ("DBCLOB" , "<COLNAME>", "<VALUE>" ),
"SDECIMAL" : ("SDECIMAL" , "<COLNAME>", "<VALUE>" ),
"DOUBLE" : ("DOUBLE" , "<COLNAME>", "<VALUE>" ),
"FLOAT" : ("FLOAT" , "<COLNAME>", "<VALUE>" ),
"GRAPHIC" : ("GRAPHIC" , "<COLNAME>", "<VALUE>" ),
"INTEGER" : ("INTEGER" , "<COLNAME>", "<VALUE>" ),
"LONGVARCHAR" : ("LONGVARCHAR" , "<COLNAME>", "'<VALUE>'"),
"LONGVAR" : ("LONGVARCHAR" , "<COLNAME>", "'<VALUE>'"),
"LONGVARBINARY" : ("LONGVARBINARY" , "<COLNAME>", "<VALUE>" ),
"LONGVARGRAPHIC" : ("LONGVARGRAPHIC" , "<COLNAME>", "<VALUE>" ),
"NUMERIC" : ("NUMERIC(<PRECISION>,<SCALE>)", "<COLNAME>", "<VALUE>" ),
"DECIMAL" : ("DECIMAL(<PRECISION>,<SCALE>)", "<COLNAME>", "<VALUE>" ),
"REAL" : ("REAL" , "<COLNAME>", "<VALUE>" ),
"SMALLINT" : ("SMALLINT" , "<COLNAME>", "<VALUE>" ),
"TIME" : ("TIME" , "<COLNAME>", "'<VALUE>'"),
"TIMESTAMP" : ("TIMESTAMP" , "<COLNAME>", "'<VALUE>'"),
"TIMESTMP" : ("TIMESTAMP" , "<COLNAME>", "'<VALUE>'"),
"VARCHAR" : ("VARCHAR(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"),
"VARBINARY" : ("VARBINARY" , "<COLNAME>", "<VALUE>" ),
"VARGRAPHIC" : ("VARGRAPHIC" , "<COLNAME>", "<VALUE>" )
}
'''
Global translation table for datatype conversion from db2 to Vector
VW : not compressed datatypes : DECIMAL with precision > 18, float, float4
Unsupported datatypes : *BYTES, INGRESDATE, BOOLEAN, UUID, TABLE_KEY, OBJECT_KEY, * WITH TIMEZONE
'''
d22vw = {
"INT" : ("INTEGER" , "<COLNAME>", "<VALUE>" ), # d2: [-2,147,483,648, +2,147,483,647]
"INTEGER" : ("INTEGER" , "<COLNAME>", "<VALUE>" ), # d2: [-2,147,483,648, +2,147,483,647]
"SMALLINT" : ("SMALLINT" , "<COLNAME>", "<VALUE>" ), # d2: [32,768, +32,767]
"BIGINT" : ("BIGINT" , "<COLNAME>", "<VALUE>" ),
"NUMERIC" : ("DECIMAL(<PRECISION>,<SCALE>)", "<COLNAME>", "<VALUE>" ), # d2: (31,31)
"DECIMAL" : ("DECIMAL(<PRECISION>,<SCALE>)", "<COLNAME>", "<VALUE>" ),
"REAL" : ("FLOAT4" , "<COLNAME>", "<VALUE>" ), # d2: -7.2E+75 to 7.2E+75
"DOUBLE" : ("FLOAT8" , "<COLNAME>", "<VALUE>" ),
"FLOAT" : ("FLOAT8" , "<COLNAME>", "<VALUE>" ), # d2: 8 Bits
"TIME" : ("TIME" , "<COLNAME>", "'<VALUE>'"),
"TIMESTAMP" : ("TIMESTAMP" , "<COLNAME>", "'<VALUE>'"),
"TIMESTMP" : ("TIMESTAMP" , "<COLNAME>", "'<VALUE>'"),
"VARCHAR" : ("VARCHAR(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"),
"CHAR" : ("CHAR(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"),
"BINARY" : ("BINARY" , "<COLNAME>", "<VALUE>" ),
"BLOB" : ("BLOB" , "<COLNAME>", "<VALUE>" ),
"CLOB" : ("CLOB" , "<COLNAME>", "'<VALUE>'"),
"DATE" : ("DATE" , "<COLNAME>", "'<VALUE>'"),
"DBCLOB" : ("DBCLOB" , "<COLNAME>", "<VALUE>" ),
"SDECIMAL" : ("SDECIMAL" , "<COLNAME>", "<VALUE>" ),
"GRAPHIC" : ("GRAPHIC" , "<COLNAME>", "<VALUE>" ),
"LONGVARCHAR" : ("LONGVARCHAR" , "<COLNAME>", "'<VALUE>'"),
"LONGVAR" : ("LONGVARCHAR" , "<COLNAME>", "'<VALUE>'"),
"LONGVARBINARY" : ("LONGVARBINARY" , "<COLNAME>", "<VALUE>" ),
"LONGVARGRAPHIC" : ("LONGVARGRAPHIC" , "<COLNAME>", "<VALUE>" ),
"VARBINARY" : ("VARBINARY" , "<COLNAME>", "<VALUE>" ),
"VARGRAPHIC" : ("VARGRAPHIC" , "<COLNAME>", "<VALUE>" )
}
'''
Global translation table for datatype conversion from TERADATA to INGRES VW
NEED TERADATA DATATYPE AS DEFINED IN TABLE : DBC.Columns(c.ColumnType)
VW : not compressed datatypes : DECIMAL with precision > 18, float, float4
Unsupported datatypes : *BYTES, INGRESDATE, BOOLEAN, UUID, TABLE_KEY, OBJECT_KEY, * WITH TIMEZONE
'''
td2vw = {
"INT" : ("INTEGER" , "<COLNAME>", "<VALUE>" ), # Signed 4 bytes integer (-2^31; 2^31 - 1 (2,147,483,647))
"NUMERIC" : ("DECIMAL(<PRECISION>,<SCALE>)", "<COLNAME>", "<VALUE>" ), # MSSQL: -10^38 +1 through 10^38 –1.
"DECIMAL" : ("DECIMAL(<PRECISION>,<SCALE>)", "<COLNAME>", "<VALUE>" ),
"MONEY" : ("FLOAT8" , "<COLNAME>", "<VALUE>" ), # MSSQL : NUMERIC(19,4) - VW > 18 not compressed => FLOAT8
"SMALLMONEY" : ("NUMERIC(10,4)" , "<COLNAME>", "<VALUE>" ), # MSSQL : NUMERIC(10,4)
"TINYINT" : ("SMALLINT" , "<COLNAME>", "<VALUE>" ), # SQL: [O:255] - VW(TINYINT): -128:+127
"SMALLINT" : ("SMALLINT" , "<COLNAME>", "<VALUE>" ), # 2 Bytes [-32768:32767]-
"BIGINT" : ("BIGINT" , "<COLNAME>", "<VALUE>" ), # 8 Bytes : -2^63 ;+2^63-1
"FLOAT" : ("FLOAT(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"), # MS(def) = float(53) precision(4 or 8)
"REAL" : ("FLOAT(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"), # MS(def) = FLOAT(24)
"ROWVERSION" : ("BIGINT" , "<COLNAME>", "'<VALUE>'"), # MS : An incremental number for versionning
"TIMESTAMP" : ("BIGINT" , "<COLNAME>", "'<VALUE>'"), # MS = ROWVERSION
"DATETIME" : ("TIMESTAMP" , "CONVERT(VARCHAR,<COLNAME>,121)", "'<VALUE>'"), # MS: 1999-01-08 04:05:06.xxxx
"SMALLDATETIME" : ("TIMESTAMP(0)" , "CONVERT(VARCHAR,<COLNAME>,120)", "'<VALUE>'"), # MS: 1999-01-08 04:05:00
"NCHAR" : ("NCHAR(<PRECISION>)" , "CAST(<COLNAME> AS NVARCHAR(max))", "'<VALUE>'"),
"CHAR" : ("CHAR(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"),
"NVARCHAR" : ("NVARCHAR(<PRECISION>)" , "CAST(<COLNAME> AS VARCHAR(max))", "'<VALUE>'"),
"VARCHAR" : ("VARCHAR(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"),
"TEXT" : ("LONG VARCHAR" , "<COLNAME>", "'<VALUE>'"), # MS: 2^31 Chars
"NTEXT" : ("LONG NVARCHAR" , "<COLNAME>", "'<VALUE>'"), # MS: 2^31/2 UTF8 Chars
"UNIQUEIDENTIFIER" : ("VARCHAR(64)" , "<COLNAME>", "'<VALUE>'"),
"BINARY" : ("BINARY(<PRECISION>)" , "<COLNAME>", "<VALUE>" ), # MS: [1:8000] (Fixed size)
"VARBINARY" : ("VARBINARY(<PRECISION>)" , "<COLNAME>", "<VALUE>" ), # MS: [1:8000] (Var size)
"IMAGE" : ("LONG BYTE" , "<COLNAME>", "<VALUE>" ), # MS: 2^31 bytes
"BIT" : ("TINYINT" , "<COLNAME>", "<VALUE>" ), # SQL:0/1;
"XML" : ("LONG NVARCHAR" , "CAST(<COLNAME> AS NVARCHAR(max))", "'<VALUE>'") # MSSQL : 2 GB
}
'''
Global translation table for datatype conversion from ORACLE to INGRES VW
VW : not compressed datatypes : DECIMAL with precision > 18, float, float4
Unsupported datatypes : *BYTES, INGRESDATE, BOOLEAN, UUID, TABLE_KEY, OBJECT_KEY, * WITH TIMEZONE
OR : NUMBER(s,p) s. The precision p can range from 1 to 38. The scale s can range from -84 to 127
TIMESTAMP(x) : [0-9] (def: 6)
'''
or2vw = {
"INT" : ("DECIMAL(38)" , "<COLNAME>", "<VALUE>" ), # OR: DECIMAL(38)
"INTEGER" : ("DECIMAL(38)" , "<COLNAME>", "<VALUE>" ), # OR: DECIMAL(38)
"SMALLINT" : ("DECIMAL(38)" , "<COLNAME>", "<VALUE>" ), # OR: DECIMAL(38)
"NUMBER" : ("DECIMAL(<PRECISION>,<SCALE>)", "<COLNAME>", "<VALUE>" ), # OR: p[1,38]; s[84,127]; 1,22 Bytes VW: ?
"DECIMAL" : ("DECIMAL(<PRECISION>,<SCALE>)", "<COLNAME>", "<VALUE>" ), # OR: p[1,38]; s[84,127]; 1,22 Bytes VW: ?
"FLOAT" : ("FLOAT8" , "<COLNAME>", "<VALUE>" ), # OR: p[1,38]; s[84,127]; 1,22 Bytes VW: ?
"BINARY_DOUBLE" : ("FLOAT8" , "<COLNAME>", "<VALUE>" ), # OR:8 bytes float
"BINARY_FLOAT" : ("FLOAT4" , "<COLNAME>", "'<VALUE>'"), # OR:4 bytes float
"TIMESTAMP%" : ("TIMESTAMP(<SCALE>)" , "<COLNAME>", "'<VALUE>'"), # OR: [0-9] (def: 6) => Add precision VW [max 6]
"TIMESTAMP % WITH TIME ZONE" : ("TIMESTAMP(<SCALE>)", "<COLNAME>", "'<VALUE>'"), # Not implemented yet
"TIMESTAMP % WITH LOCAL TIME ZONE": ("TIMESTAMP(<SCALE>)", "<COLNAME>", "'<VALUE>'"), # Not implemented yet
"DATE" : ("TIMESTAMP(0)" , "TO_CHAR(<COLNAME>,'HH24-MM-DD HH24:MI:SS')", "'<VALUE>'"), # OR: 1999-01-08 04:05:00
"CHAR" : ("CHAR(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"), # OR [1:2000]
"VARCHAR" : ("VARCHAR(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"),
"VARCHAR2" : ("VARCHAR(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"),
"CLOB" : ("VARCHAR(<PRECISION>)" , "CAST(<COLNAME> AS VARCHAR(max))", "'<VALUE>'"), # OR: 4GB-1*db_block_size VW: Not implemented
"NVARCHAR" : ("NVARCHAR(<PRECISION>)" , "CAST(<COLNAME> AS VARCHAR(max))", "'<VALUE>'"),
"NCHAR" : ("NCHAR(<PRECISION>)" , "CAST(<COLNAME> AS NVARCHAR(max))", "'<VALUE>'"),
"NCLOB" : ("NVARCHAR(<PRECISION>)" , "CAST(<COLNAME> AS VARCHAR(max))", "'<VALUE>'"), # OR: 4GB-1*db_block_size
"LONG" : ("LONG VARCHAR" , "<COLNAME>", "'<VALUE>'"), # OR: 2^31 Chars # Add test on unsupported datatypes
"ROWID" : ("VARCHAR(64)" , "<COLNAME>", "'<VALUE>'"), #
"UROWID" : ("VARCHAR(<PRECISION>)" , "<COLNAME>", "'<VALUE>'"), # OR: [1:4000]
"RAW" : ("BINARY(<PRECISION>)" , "<COLNAME>", "<VALUE>" ), # OR [1:2000]
"LONG RAW" : ("LONG BYTE" , "<COLNAME>", "<VALUE>" ), # OR: 2^31 bytes
"BLOB" : ("NVARCHAR(<PRECISION>)" , "CAST(<COLNAME> AS VARCHAR(max))", "'<VALUE>'"), # OR: 4GB-1*db_block_size
"BFILE" : ("VARCHAR(<PRECISION>)" , "<COLNAME>", "'<VALUE>'") # OR: 4GB
}
'''
Translation table for equivalent datatype
Ex: line 1: To transfert data from "mssql" to "netezza" we can use the postgres matrix "ms2pg"
To transfert data from "mssql" to "teradata" we use table "ms2td"
'''
g_trmxty = { "mssql" : {"mysql" : ms2my,
"postgres" : ms2pg,
"netezza" : ms2pg,
"greenplum" : ms2pg,
"teradata" : ms2td,
"progress" : ms2pr,
"maxdb" : ms2md,
"matrix" : ms2mx,
"ase" : ms2ase,
"ingres" : ms2ii,
"vector" : ms2vw,
"vectorh" : ms2vw,
"iq" : ms2iq,
"hana" : ms2ha
},
"db2" : {"db2" : d22d2,
"vector" : d22vw,
},
"oracle" : {"vector" : or2vw
},
"mysql" : {"vector" : my2vw
},
"iq" : {"vector" : iq2vw
},
"postgres" : {"vector" : pg2vw
},
"teradata" : {"vector" : td2vw
}
}
'''
Print usage and exit
'''
def usage():
print "Usage : "
print g_pars
sys.exit(1)
'''
Get SQL definition from XML file. SQL is retrieved by using keywords passed as function parameters
1: Database type "mysql, mssql, ..."
2: Sqltype : Select, Create, ...
3: Unique Identifier
'''
def getXMLdata(p_dbtype, p_sql, p_id):
rc=""
xmldoc = xml.dom.minidom.parse(XMLINI)
for node in xmldoc.getElementsByTagName(p_dbtype)[0].getElementsByTagName(p_sql):
if node.getAttribute("id") == p_id:
for child in node.childNodes:
if child.nodeType == Node.TEXT_NODE:
rc = child.data
return(rc)
''' Populate g_trnm which is a 2 dimension array built with values of parametre --translation
Exemple : --translation=scname:dbo,demo,test,toto;iscname:dbo,demo2
=> g_trnm['scname']['dbo'] = demo
g_trnm['scname']['test'] = toto
g_trnm['iscname']['dbo'] = demo2
=> iscname, scname are the column names in the header of an object description query
'''
def init_g_trnm(p_s):
h2 = {}
for line in p_s.split(';'):
t1=line.split(':')
key1 = t1[0].strip()
values_key1 = t1[1]
t2=values_key1.split(',')
for i in range(len(t2)/2):
key2 = t2[i*2].strip()
value_key2 = t2[i*2+1].strip()
h2[key2] = value_key2
g_trnm[key1]= deepcopy(h2)
''' Function which translate tuple (key1, key2) with values inserted in dictionnary g_trnm
Input : key1, key2
if datum is not found key2 is returned.
'''
def tr(p_key1, p_key2):
rc = p_key2
if g_trnm.has_key(p_key1) and g_trnm[p_key1].has_key(p_key2): rc = g_trnm[p_key1][p_key2]
return(rc)
'''
Strip values of a row and try to return encoded unicode string.
If failed return ascii format
'''
def strip_row (p_row):
row = []
for v in p_row:
if type(v) == str:
v = v.strip()
try: v = v.decode('utf_8')
except UnicodeDecodeError:
pass
row.append(v)
return(row)
'''
Add Quote or not according to value of global variable :
'''
def quote(p_str):
s = p_str
if g_quote is not None:
s = g_quote + s + g_quote
return(s)
'''
Generate tables based on src database and convert table to match the destination database format.
'''
def generateTb():
scname_src = ""
scname = ""
tbname = ""
isnewtb = True
isnewsch= True
s = ""
rls = []
tmp_tgt = ""
drp = s.split('\n');
ddl = s.split('\n');
sql = getXMLdata(p_dbtype=g_srcdbtype, p_sql="select", p_id="tbDefinition")
s = getXMLdata(p_dbtype=g_destdbtype, p_sql="create", p_id="tb").strip()
ddl += s.split('\n')
s = getXMLdata(p_dbtype=g_destdbtype, p_sql="create", p_id="drop" ).strip()
drp += s.split('\n')
cur = g_srcdb.execute(sql)
for line in cur:
row = strip_row(line)
'''
An attempt to get one schema only loaded
'''
if (p_ownsrc) == (""):
scname_src = row[0]
p_ownsrc == row[0]
else:
scname_src = p_ownsrc
if (row[0],row[1]) != (scname_src, tbname):
#xxxx
if row[0] != scname: isnewsch = True;
if (scname, tbname) != ("", ""):
s += ddl[3]
rls.append(s+g_cmdsep+"\n")
'''
an attempt to load a specific table
'''
if (p_owntgt) == (""):
scname = row[0]
p_owntgt == row[0]
else:
scname = p_owntgt
tbname = row[1]
isnewtb = True;
if isnewsch:
s = Template(getXMLdata(p_dbtype=g_destdbtype, p_sql="create", p_id="sch").strip())
s = s.substitute(scname=quote(tr('scname',scname)) )
isnewsch = False
if ( s!= "" ):
rls.append(s+g_cmdsep+"\n")
else:
rls.append(s+"\n");
if isnewtb:
if p_addrop:
s = Template(drp[1]) # The drop table statement
s = s.substitute(scname=quote(tr('scname',scname)), tbname=quote(tbname) )
if ( s!= "" ):
rls.append(s+g_cmdsep+"\n")
s = Template(ddl[1]) # The create table statement
s = s.substitute(scname=quote(tr('scname',scname)), tbname=quote(tbname) )
isnewtb = False
else:
s += ','
s += "\n"
clname = row[2]
tyname = row[3]
precision = row[4] if row[4] > 0 else p_charmax
scale = 0 if row[5] is None else row[5]
isnull = '' if row[6] is None else row[6]
dfval = '' if row[7] is None else "DEFAULT " + row[7]
if dfval in ms2vw_default:
dfval = ms2vw_default[dfval]
if "NEXT VALUE FOR" in dfval: dfval = ''
(new_type, select_cast, insert_cast) = g_trty[tyname.upper()] # Substitute datatype by equivalent datatype
s += Template(ddl[2]).substitute( clname=quote(clname), tyname=new_type, isnull=isnull, dfval=dfval)
s = s.replace('<PRECISION>', str(precision) )
s = s.replace('<SCALE>' , str(scale) )
s += ddl[3]
rls.append(s+g_cmdsep+"\n")
return(rls)
'''
Generate views based on src database and convert view to match the destination database format.
SELECT s.table_schema as scname, s.table_name as viwname, s.view_definition as viwdef, s.check_option as viwchk , s.is_updateable viwupd
'''
def generateViw():
scname = ""
scname_src = ""
viwname = ""
viwdef = ""
viwchk = ""
viwupd = ""
tbname = ""
isnewviw = True
isnewsch = True
s = ""
rls = []
sql = getXMLdata(p_dbtype=g_srcdbtype, p_sql="select", p_id="viwDefinition")
s = getXMLdata(p_dbtype=g_destdbtype, p_sql="create", p_id="viw" ).strip()
ddl = s.split(' ')
# print "VIEW NAME>>>>"
# print sql
# print "VIEW NAME <<<<"
'''
START View
'''
cur = g_srcdb.execute(sql)
for line in cur:
row = strip_row(line)
print line
'''
An attempt to get one schema only loaded
'''
if (p_ownsrc) == (""):
scname_src = row[0]
p_ownsrc == row[0]
else:
scname_src = p_ownsrc
if (row[0],row[1]) != (scname_src, viwname):
if row[0] != scname:
isnewsch =True;
if (scname, viwname) != ("", ""):
rls.append(s+g_cmdsep+"\n")
scname = row[0]
viwname = row[1]
viwdef = row[2]
if (p_owntgt) == (""):
scname = row[0]
else:
scname = p_owntgt
scname_src = row[0]
# print "VIEW DEF>>>>"
# print p_owntgt
# print scname_src
# print scname
# print viwdef
# print "VIEW DEF updated<<<<"
# Tried regex to swap CONVERT to CAST, but it's detecting too much of the text
#regex = re.compile(r'CONVERT\((?P<type>[^,]*),(?P<value>[^,]*)\)', re.MULTILINE)
#viwdef = regex.sub(r'CAST(\g<value> AS \g<type>)', viwdef)
for i in range(0,len(ms2vw_view)-1):
viwdef = viwdef.replace(ms2vw_view[i][0], ms2vw_view[i][1])
viwdef = viwdef.replace(quote(p_ownsrc), quote(p_owntgt))
viwdef = viwdef.replace('['+p_ownsrc+']', quote(p_owntgt))
# print viwdef
# print '['+p_ownsrc+']'
# print "VIEW DEF end<<<<"
isnewviw = True
if isnewsch:
s = Template(getXMLdata(p_dbtype=g_destdbtype, p_sql="create", p_id="sch").strip())
s = s.substitute(scname=quote(tr('scname',scname)) )
isnewsch = False
if ( s!= "" ):
rls.append(s+g_cmdsep+"\n")
else:
rls.append(s+"\n")
else:
print "Already got"
if isnewviw:
s = viwdef # The create view statement
isnewviw= False
else:
s += ""
s += "\n"
viwdef = row[4]
# print "VIEW NAME>>>>"
# print viwname
# print s
# print "VIEW NAME <<<<"
# print "VIEW DEF>>>>"
# print viwdef
# print row[1]
# print "VIEW DEF<<<<"
# s += viwdef
# rls.append(s+g_cmdsep+"\n")
return(rls)
'''
end View
'''
'''
Generate unique constraints (also inclue PrimaryKeys since a PK is also a unique constraint)
based on src database and convert unique constraint to the new database format.
'''
def generateUk():
scname_src = ""
scname = ""
tbname = ""
csname = ""
cstype = ""
isnewcs = True
s = ""
msk = '","'
t = ""
rls = [] # A returned list which contains the results of the function
sql = getXMLdata(p_dbtype=g_srcdbtype , p_sql="select", p_id="ukDefinition")
ddl = getXMLdata(p_dbtype=g_destdbtype, p_sql="create", p_id="uk" ).strip()
cur = g_srcdb.execute(sql)
for line in cur:
row = strip_row(line)
'''
An attempt to get one schema only loaded
'''
if (p_ownsrc) == (""):
scname_src = row[0]
p_ownsrc == row[0]
else:
scname_src = p_ownsrc
'''
Constraint type
Column Names
'''
cstype = row[3]
clname = row[4]
if (row[0], row[1], row[2]) != (scname_src, tbname, csname):
if (scname, tbname, csname) != ("", "", ""): # We pass to the next index definition
s = Template(s).substitute(clname='')
rls.append(s+g_cmdsep+"\n")
# scname = row[0]
tbname = row[1]
csname = row[2]+p_idxsep+row[1]
isnewcs = True
'''
an attempt to load a specific table
'''
if (p_owntgt) == (""):
scname = row[0]
scname_src = row[0]
else:
scname = p_owntgt
scname_src = p_ownsrc
s = Template(ddl)
s = s.substitute(scname=quote(tr('scname',scname)), tbname=quote(tbname),
csname=quote(csname), cstype=cstype,
clname=quote(clname)+r'${clname}')
if isnewcs:
isnewcs = False
else:
s = Template(s).substitute(clname=','+quote(clname)+r'${clname}')
s = Template(s).substitute(clname='') # End Uk definition
rls.append(s+g_cmdsep+"\n")
return(rls)
'''
Generate foreign key based on src database and convert fk to the new database format.
'''
def generateFk():
scname = ""
scname_src = ""
tbname = ""
csname = ""
isnewcs = True
s = ""
rls = []
sql = getXMLdata(p_dbtype=g_srcdbtype , p_sql="select", p_id="fkDefinition")
ddl = getXMLdata(p_dbtype=g_destdbtype, p_sql="create", p_id="fk" ).strip()
cur = g_srcdb.execute(sql)
for line in cur:
row = strip_row(line)
'''
An attempt to get one schema only loaded
'''
if (p_ownsrc) == (""):
scname_src = row[0]
p_ownsrc == row[0]
else:
scname_src = p_ownsrc
clname = row[3]
rclname = row[6]
if (row[0], row[1], row[2]) != (scname, tbname, csname):
if (scname, tbname, csname) != ("", "", ""): # We pass to the next index definition
s = Template(s).substitute(clname='', rclname='')
rls.append(s+g_cmdsep+"\n")
scname = row[0]
tbname = row[1]
'''
an attempt to load a specific table
'''
if (p_owntgt) == (""):
scname = row[0]
## scname_src = row[0]
else:
scname = p_owntgt
rscname = p_owntgt
## scname_src = p_ownsrc
'''
Constraint names( Objects names ) must be unique in a schema
'''
csname = row[2]+p_idxsep+row[1]
'''
Resource owner ...
ALTER TABLE "dbo"."Order Details" ADD CONSTRAINT "FK_Order_Details_Orders" FOREIGN KEY ( "OrderID" )
REFERENCES "dbo_rsc"."Orders_rtb" ( "OrderID" )
'''
rscname = row[4]
'''
ALTER TABLE "dbo"."CustomerCustomerDemo" ADD CONSTRAINT "FK_CustomerCustomerDemo" FOREIGN KEY ( "CustomerTypeID" )
REFERENCES "dbo"."CustomerDemographics_rtb" ( "CustomerTypeID" )
'''
rtbname = row[5]
isnewcs = True
s = Template(ddl)
s = s.substitute( scname =quote(tr('scname' ,scname)) , tbname=quote(tbname),
csname =quote(csname) , clname=quote(clname)+r'${clname}',
rscname=quote(tr('rscname',scname)), rtbname=quote(rtbname),
rclname=quote(rclname)+r'${rclname}')
if isnewcs:
isnewcs = False
else:
s = Template(s).substitute(clname=','+quote(clname)+r'${clname}', rclname=','+quote(rclname)+r'${rclname}')
s = Template(s).substitute(clname='', rclname='') # End constraint definition
rls.append(s+g_cmdsep+"\n")
return(rls)
'''
Generate indexes based on src database and convert indexes to match the destination database format.
'''
def generateIx():
scname = ""
scname_src = ""
tbname = ""
ixname = ""
isnewix = True
s = ""
rls = []
sql = getXMLdata(p_dbtype=g_srcdbtype , p_sql="select", p_id="ixDefinition")
ddl = getXMLdata(p_dbtype=g_destdbtype, p_sql="create", p_id="ix" ).strip()
# print "INDEX NAME>>>>"
# print sql
# print "INDEX NAME <<<<"
cur = g_srcdb.execute(sql)
for line in cur:
# print line
row = strip_row(line)
'''
An attempt to get one schema only loaded
'''
if (p_ownsrc) == (""):
scname_src = row[0]
p_ownsrc == row[0]
else:
scname_src = p_ownsrc
clname = row[6]
# print "CLNAME >>>"
# print clname
# print "CLNAME >>>"
if (row[0], row[1], row[2]) != (scname, tbname, ixname):
if (scname, tbname, ixname) != ("", "", ""): # We print last index and we pass to the next index definition
s = Template(s).substitute(clname='')
rls.append(s+"\n")
if ixtype != 'BTREE':
print "Warning : Unknown %s index type %s. Following command has been skipped\n%s " % (g_srcdbtype, str(ixtype), s )
scname = row[0]
tbname = row[1]
'''
an attempt to load a specific table
'''
if (p_owntgt) == (""):
scname = row[0]
scname_src = row[0]
else:
scname = p_owntgt
rscname = p_owntgt
scname_src = p_ownsrc
'''
Constraint names( Objects names ) must be unique in a schema
'''
iscname = row[2]
ixname = row[3]+p_idxsep+row[1]
ixtype = row[4]
ixuniq = row[5]
isnewix = True
s = Template(ddl)
s = s.substitute(ixuniq = '' if ixuniq is None else ixuniq,
iscname = quote(tr('iscname', iscname)),
ixname = quote(ixname),
scname = quote(tr('scname',scname)),
tbname = quote(tbname),
# clname = quote(clname))
clname = quote(clname)+r'${clname}')
print s
if isnewix:
isnewix = False
else:
s = Template(s).substitute(clname=','+quote(clname)+r'${clname}')
print "ADDING ONE"
s = Template(s).substitute(clname='') # End index definition
rls.append(s+"\n")
print s
return(rls)
'''
Extract data from src db and load data to dest db
'''
def unloadData(p_fdelim):
scname = ""
tbname = ""
insert = ""
fname = ""
fdelim = p_fdelim
counter = 0
select = ""
sqls = []
s = ""
colnum = 0
sql = getXMLdata(p_dbtype=g_srcdbtype, p_sql="select", p_id="tbDefinition")
cursrc = g_srcdb.execute(sql)
# Iterate to prepare statements select, insert
for line in cursrc:
row = strip_row(line)
if (row[0],row[1]) != (scname, tbname):
if (scname, tbname) != ("", ""):
select += selfrom
sqls.append( (fname, colnum, select, insert))
scname = row[0]
tbname = row[1]
s = quote(tbname) if scname is None else quote(scname) + '.' + quote(tbname)
select = 'SELECT '
selfrom= ' FROM ' + s
s = quote(tbname) if scname is None else quote(tr('scname',scname)) + '_' + quote(tbname) + '.txt'
fname = s
insert = ""
colnum = 0
if colnum > 0:
insert += fdelim
select += ","
clname = row[2]
tyname = row[3]
(new_type, select_cast, insert_cast) = g_trty[tyname.upper()] # Translate datatypes according to translation table
select += select_cast
select = select.replace('<COLNAME>', '"'+clname+'"')
insert += insert_cast
insert = insert.replace('<VALUE>', '<V'+str(colnum)+'>')
colnum += 1
select += selfrom
sqls.append( (fname, colnum, select, insert) )
# Iterate to select, bind and insert data
for (fname, colnum, select, insert) in sqls:
try:
print select
cursrc = g_srcdb.execute(select)
counter = 0
f = codecs.open(fname, encoding='utf-8', mode= 'w')
sz = 0.0
t1 = time.time()
for line in cursrc: ## Read source cursor (SELECT)
row = strip_row(line)
s = insert
for i in range(0,colnum): ## Prepare INSERT
value = row[i]
if value is None: # When Null
s = s.replace("'<V" + str(i) + ">'", '' ) # Replace NULL value for strings and dates
s = s.replace("<V" + str(i) + ">" , '' ) # Replace NULL value for integers or floats
sz+= 1
elif type(value) == unicode: # When string (unicode)
value = value.replace("'", "''") # Replace simple ' by '' in insert command
s = s.replace("<V" + unicode(str(i), 'utf-8') + ">", value)
sz += len(value)
else: # When not string
try:
s = s.replace("<V" + str(i) + ">", str(value) )
sz += len(str(value)) # Not right but gives an idea of the size of numbers
except UnicodeDecodeError: # String has an unknown character
print "UnicodeDecodeError for column <%d> value = " % (i),
print value
value = value.replace("'", "''") # Replace simple ' by '' in insert command
s = s.replace("<V" + str(i) + ">", unicode(value, 'utf-8', 'ignore') )
sz += len(value)
finally:
pass
try: ## Write line
f.write(s + "\n")
counter += 1
except Exception:
print "%s" % (s)
traceback.print_exc(file=sys.stdout)
finally:
pass
except Exception:
print "%s" % (s)
traceback.print_exc(file=sys.stdout)
finally:
t2 = time.time()
f.close()
print "Rows extracted: %d - Elapsed time(s): %f - Mean data size(MB): %f\n" % (counter, (t2-t1), sz/1024/1024)
'''
Loads a lot of data in a single predefined table.
'''
def loadTest():
# Run precondition script if found (e.g. this can be used to setup session authorization)
pres = getXMLdata(p_dbtype=g_destdbtype, p_sql="create", p_id="pre")
pre = Template(pres).substitute(scname=quote(tr('scname',"dbo")))
g_destdb.execute(pre)
# Iterate to select, bind and insert data
s = ""
total = 10000
try:
counter = 0
currentCounter = 0
inserts = []
t1 = time.time()
for j in range (0, total/p_batchsize):
currentCounter = 0
inserts = []
for i in range (0, p_batchsize):
s = "INSERT INTO dbo.Territories VALUES (%d,'%s',%d)" % (i, "Teritory%d" % i, 2*i)
inserts.append(s)
t2 = time.time()
currentCounter += len(inserts) * insertSQL(g_destdb, ";\n".join(inserts))
counter += currentCounter
print "[%d] Batch inserted: %d - Elapsed time(s): %f" % (j, currentCounter, (t2-t1))
t2 = time.time()
print "Total rows inserted: %d - Elapsed time(s): %f" % (counter, (t2-t1))
except Exception:
print s
traceback.print_exc(file=sys.stdout)
finally:
pass
'''
Extract data from src db and load data to dest db
@param truncate: Decides if the existing data should be erased before import
'''
def loadData(truncate):
scname = ""
tbname = ""
insert = ""
counter = 0
select = ""
sqls = []
s = ""
colnum = 0
table_name = ""
sql = getXMLdata(p_dbtype=g_srcdbtype, p_sql="select", p_id="tbDefinition")
cursrc = g_srcdb.execute(sql)
# Iterate to prepare statements select, insert
for line in cursrc:
row = strip_row(line)
if (row[0],row[1]) != (scname, tbname):
if (scname, tbname) != ("", ""):
insert += ")"
select += selfrom
sqls.append( (colnum, select, insert, quote(tbname) if scname is None else quote(tr('scname',scname)) + '.' + quote(tbname)))
scname = row[0]
tbname = row[1]
s = quote(tbname) if scname is None else quote(scname) + '.' + quote(tbname)
select = 'SELECT '
selfrom= ' FROM ' + s
s = quote(tbname) if scname is None else quote(tr('scname',scname)) + '.' + quote(tbname)
table_name = s
insert = 'INSERT INTO ' + s + ' VALUES ('
colnum = 0
if colnum > 0:
insert += ","
select += ","
clname = row[2]
tyname = row[3]
(new_type, select_cast, insert_cast) = g_trty[tyname.upper()] # Translate datatypes according to translation table
select += select_cast
select = select.replace('<COLNAME>', '"'+clname+'"')
insert += insert_cast
insert = insert.replace('<VALUE>', '<V'+str(colnum)+'>')
colnum += 1
insert += ")"
select += selfrom
sqls.append( (colnum, select, insert, table_name))
# Run precondition script if found (e.g. this can be used to setup session authorization)
pres = getXMLdata(p_dbtype=g_destdbtype, p_sql="create", p_id="pre")
pre = Template(pres).substitute(scname=quote(tr('scname',scname)))
for pre_line in pre.split(';'):
g_destdb.execute(pre_line)
# Iterate to select, bind and insert data
for (colnum, select, insert, table_name) in sqls:
try:
print select
cursrc = g_srcdb.execute(select)
counter = 0
currentCounter = 0
inserts = []
print "Loading ..."
sz = 0.0
t1 = time.time()
# If truncate was specified remove existing rows from the destination table.
if truncate and table_name !='' :
g_destdb.execute('MODIFY %s TO TRUNCATED' % table_name)
isFirstInsert = True
for line in cursrc: ## Read source cursor (SELECT)
row = strip_row(line)
s = insert
for i in range(0,colnum): ## Prepare INSERT
value = row[i]
if value is None: # When Null
s = s.replace("'<V" + str(i) + ">'", 'NULL' ) # Replace NULL value for strings and dates
s = s.replace("<V" + str(i) + ">" , 'NULL' ) # Replace NULL value for integers or floats
sz+= 1
elif type(value) == unicode: # When string (unicode)
value = value.replace("'", "''") # Replace simple ' by '' in insert command
s = s.replace("<V" + unicode(str(i), 'utf-8') + ">", value)
sz += len(value)
else: # When not string
try:
s = s.replace("<V" + str(i) + ">", str(value) )
sz += len(str(value)) # Not right but gives an idea of the size of numbers
except UnicodeDecodeError: # String has an unknown character
print "UnicodeDecodeError for column <%d> value = " % (i),
print value
value = value.replace("'", "''") # Replace simple ' by '' in insert command
s = s.replace("<V" + str(i) + ">", unicode(value, 'utf-8', 'ignore') )
sz += len(value)
print "- Resulting DML : %s " % (s)
finally:
pass
if isFirstInsert:
inserts.append(s)
isFirstInsert = False
else:
# Get the string after VAULES, e.g. ('<V0>','<V1>',<V2>)
s = s.encode("utf-8")
values = s[s.index("VALUES") + 6:]
inserts.append(values)
currentCounter = len(inserts)
if currentCounter>= p_batchsize:
counter += currentCounter * insertSQL(g_destdb, ",".join(inserts))
inserts = []
t2 = time.time()
print "Batch inserted: %d - Elapsed time(s): %f - Estimated size(MB): %f\n" % (currentCounter, (t2-t1), sz/1024/1024)
currentCounter = 0
isFirstInsert = True
if currentCounter > 0:
counter += currentCounter * insertSQL(g_destdb, ",".join(inserts))
t2 = time.time()
print "Total Rows inserted: %d - Elapsed time(s): %f - Estimated size(MB): %f\n" % (counter, (t2-t1), sz/1024/1024)
except Exception:
print s
traceback.print_exc(file=sys.stdout)
finally:
pass
'''
call db sql script with exception wrap
'''
def insertSQL(db, sql):
try: ## Execute INSERT
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
if len(w) > 0: print len(w)
db.execute(sql)
db.commit ()
if len(w) > 0:
print sql
print str(w[-1].message)
return 1
except Exception:
print sql
traceback.print_exc(file=sys.stdout)
finally:
pass
return 0
# -------------------------------------------------------------------------------------------
# MAIN
# -------------------------------------------------------------------------------------------
p_loadata = False
p_cretab = False
p_creall = False
p_creview = False
p_creindex = False
p_addrop = False
p_loaddl = False
p_unload = False
p_fdelim = "\t"
p_src = None
p_dest = None
p_quote = None
p_cmdsep = None
p_batchsize = 1000
p_charmax = 6400
p_loadtest = False
p_truncate = False
global p_ownsrc
global p_owntgt
fname = ""
tbs = []
uks = []
ixs = []
fks = []
viw = []
if sys.argv[1:] == []: usage() ## Print usage and exit
try:
opts, args = getopt.getopt(sys.argv[1:], '', g_pars )
except getopt.GetoptError, err:
print str(err)
for s in g_pars: print s
sys.exit(2)
####generateViw
p_owntgt = ""
p_ownsrc = ""
for opt, arg in opts:
if opt == "--src" : p_src = arg.strip()
elif opt == "--dest" : p_dest = arg.strip()
elif opt == "--quote" : p_quote = arg.strip()
elif opt == "--cmdsep" : p_cmdsep = arg.strip()
elif opt == "--loadata": p_loadata= True
elif opt == "--cretab" : p_cretab = True
elif opt == "--creindex" : p_creindex = True
elif opt == "--add_drop" : p_addrop = True
elif opt == "--creall" : p_creall = True
elif opt == "--creview" : p_creview = True
elif opt == "--unload" : p_unload = True
elif opt == "--fdelim" : p_fdelim = arg.strip()
elif opt == "--loaddl" : p_loaddl = True
elif opt == "--batchsize": p_batchsize= int(arg.strip())
elif opt == "--charmax" : p_charmax= int(arg.strip())
elif opt == "--ownsrc" : p_ownsrc= arg.strip()
elif opt == "--owntgt" : p_owntgt= arg.strip()
elif opt == "--loadtest": p_loadtest= True
elif opt == "--truncate": p_truncate= True
elif opt == "--parfile":
fname = arg
if fname != "":
for line in open(fname, 'r'):
if re.match("^--.*=.*", line):
(param, value) = line.split('=')
param = param.strip()
value = value.strip()
if param == "--src" : p_src = value
elif param == "--dest" : p_dest = value
elif param == "--quote" : p_quote = value
elif param == "--cmdsep": p_cmdsep= value
elif param == "--fdelim": p_fdelim= value
elif param == "--translation": init_g_trnm(value)
elif param == "--owntgt" : p_owntgt = value
elif param == "--ownsrc" : p_ownsrc = value
else:
assert False, "unhandled option"
if p_dest == "vector":
p_idsep = "_x100"
elif p_dest == "vectorh":
p_idsep = "_x100"
else:
p_idxsep = "_ax11_"
print "Src = %s" % (p_src)
print "Dest = %s" % (p_dest)
print "New Target Owner = %s" % (p_owntgt)
print "Single Source Owner = %s" % (p_ownsrc)
g_srcdbtype = p_src.split(':')[0].split('-')[0]
g_destdbtype = p_dest.split(':')[0].split('-')[0]
g_trty = g_trmxty[g_srcdbtype][g_destdbtype]
g_srcdb = dbconnector(p_src)
if p_loaddl or p_loadata or p_loadtest: g_destdb = dbconnector(p_dest)
if p_quote is not None: g_quote = p_quote
if p_cmdsep is not None: g_cmdsep = p_cmdsep
if p_cretab:
tbs = generateTb()
fname = g_prg+'_tab.txt'
f = codecs.open(fname, encoding='utf-8', mode= 'w')
for s in tbs: f.write(s)
f.close()
if p_creview:
viw = generateViw()
fname = g_prg+'_viw.txt'
f = codecs.open(fname, encoding='utf-8', mode= 'w')
for s in viw: f.write(s)
f.close()
if p_loaddl and p_cretab:
for s in tbs:
try:
g_destdb.execute(s)
print s
except Exception:
print s
print sys.exc_info()
if p_loaddl and p_creview:
for s in viw:
try:
g_destdb.execute(s)
print s
except Exception:
print s
print sys.exc_info()
if p_loadata: loadData(p_truncate)
if p_unload : unloadData(p_fdelim)
if p_loadtest: loadTest()
if p_creall:
uks = generateUk()
ixs = generateIx()
fks = generateFk()
viw = generateViw()
fname = g_prg+'_all.txt'
f = codecs.open(fname, encoding='utf-8', mode= 'w')
for s in uks: f.write(s)
for s in ixs: f.write(s)
for s in fks: f.write(s)
for s in viw: f.write(s)
f.close()
if p_creindex:
uks = generateUk()
ixs = generateIx()
fks = generateFk()
fname = g_prg+'_index.txt'
f = codecs.open(fname, encoding='utf-8', mode= 'w')
for s in uks: f.write(s)
for s in ixs: f.write(s)
for s in fks: f.write(s)
f.close()
if p_loaddl and p_creall:
for s in uks:
print s
try: g_destdb.execute(s)
except Exception, e: print "Exception : ", e
for s in ixs:
print s
try: g_destdb.execute(s)
except Exception, e: print "Exception : ", e
for s in fks:
print s
try: g_destdb.execute(s)
except Exception, e: print "Exception : ", e
for s in viw:
print s
try: g_destdb.execute(s)
except Exception, e: print "Exception : ", e
if p_loaddl and p_creindex:
for s in uks:
print s
try: g_destdb.execute(s)
except Exception, e: print "Exception : ", e
for s in ixs:
try: g_destdb.execute(s)
except Exception, e: print "Exception : ", e
for s in fks:
print s
try: g_destdb.execute(s)
except Exception, e: print "Exception : ", e
if p_loaddl or p_loadata: g_destdb.close()
g_srcdb.close()
|
ActianCorp/dbmv
|
bin/dbmv.py
|
Python
|
apache-2.0
| 94,490
|
[
"ASE"
] |
8adc3f3a4ebee99cfd8f71beb6c988c911bf0ec81aeb6af5d79898395ee72e88
|
# -*- coding: utf-8 -*-
#
# test_growth_curves.py
#
# This file is part of NEST.
#
# Copyright (C) 2004 The NEST Initiative
#
# NEST is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, or
# (at your option) any later version.
#
# NEST is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with NEST. If not, see <http://www.gnu.org/licenses/>.
from scipy.integrate import quad
import math
import numpy
from numpy import testing
import pylab
import unittest
import nest
from nest import raster_plot
import time
HAVE_OPENMP = nest.sli_func("is_threaded")
class SynapticElementIntegrator(object):
"""
Generic class which describes how to compute the number of
Synaptic Element based on Ca value
Each derived class should overwrite the get_se(self, t) method
"""
def __init__(self, tau_ca=10000.0, beta_ca=0.001):
"""
Constructor
:param tau_ca (float): time constant of Ca decay
:param beta_ca (float): each spike increase Ca value by this value
"""
self.tau_ca = tau_ca
self.beta_ca = beta_ca
self.t_minus = 0
self.ca_minus = 0
self.se_minus = 0
def reset(self):
self.t_minus = 0
self.ca_minus = 0
self.se_minus = 0
def handle_spike(self, t):
"""
Add beta_ca to the value of Ca at t = spike time
Also update the number of synaptic element
:param t (float): spike time
"""
assert t >= self.t_minus
# Update the number of synaptic element
self.se_minus = self.get_se(t)
# update Ca value
self.ca_minus = self.get_ca(t) + self.beta_ca
self.t_minus = t
def get_ca(self, t):
"""
:param t (float): current time
:return: Ca value
"""
assert t >= self.t_minus
ca = self.ca_minus * math.exp((self.t_minus - t) / self.tau_ca)
if ca > 0:
return ca
else:
return 0
def get_se(self, t):
"""
:param t (float): current time
:return: Number of synaptic element
Should be overwritten
"""
return 0.0
class LinearExactSEI(SynapticElementIntegrator):
"""
Compute the number of synaptic element corresponding to a
linear growth curve
dse/dCa = nu * (1 - Ca/eps)
Use the exact solution
"""
def __init__(self, eps=0.7, growth_rate=1.0, *args, **kwargs):
"""
Constructor
:param eps: fix point
:param growth_rate: scaling of the growth curve
.. seealso:: SynapticElementIntegrator()
"""
super(LinearExactSEI, self).__init__(*args, **kwargs)
self.eps = eps
self.growth_rate = growth_rate
def get_se(self, t):
"""
:param t (float): current time
:return: Number of synaptic element
"""
assert t >= self.t_minus
se = 1 / self.eps * (
self.growth_rate * self.tau_ca * (
self.get_ca(t) - self.ca_minus
) + self.growth_rate * self.eps * (t - self.t_minus)
) + self.se_minus
if se > 0:
return se
else:
return 0
class LinearNumericSEI(SynapticElementIntegrator):
"""
Compute the number of synaptic element corresponding to a
linear growth curve
dse/dCa = nu * (1 - Ca/eps)
Use numerical integration (see scipy.integrate.quad)
"""
def __init__(self, eps=0.7, growth_rate=1.0, *args, **kwargs):
"""
Constructor
:param eps: fix point
:param growth_rate: scaling of the growth curve
.. seealso:: SynapticElementIntegrator()
"""
super(LinearNumericSEI, self).__init__(*args, **kwargs)
self.eps = eps
self.growth_rate = growth_rate
def get_se(self, t):
"""
:param t (float): current time
:return: Number of synaptic element
"""
assert t >= self.t_minus
se = self.se_minus + quad(self.growth_curve, self.t_minus, t)[0]
if se > 0:
return se
else:
return 0
def growth_curve(self, t):
return self.growth_rate * (1.0 - (self.get_ca(t) / self.eps))
class GaussianNumericSEI(SynapticElementIntegrator):
"""
Compute the number of synaptic element corresponding to a
linear growth curve
dse/dCa = nu * (2 * exp( ((Ca - xi)/zeta)^2 ) - 1)
with:
xi = (eta + eps) / 2.0
zeta = (eta - eps) / (2.0 * sqrt(ln(2.0)))
Use numerical integration (see scipy.integrate.quad)
"""
def __init__(self, eta=0.1, eps=0.7, growth_rate=1.0, *args, **kwargs):
"""
Constructor
:param eps: low fix point
:param eta: high fix point
:param growth_rate: scaling of the growth curve
.. seealso:: SynapticElementIntegrator()
"""
super(GaussianNumericSEI, self).__init__(*args, **kwargs)
self.zeta = (eta - eps) / (2.0 * math.sqrt(math.log(2.0)))
self.xi = (eta + eps) / 2.0
self.growth_rate = growth_rate
def get_se(self, t):
"""
:param t (float): current time
:return: Number of synaptic element
"""
assert t >= self.t_minus
se = self.se_minus + quad(self.growth_curve, self.t_minus, t)[0]
if se > 0:
return se
else:
return 0
def growth_curve(self, t):
return self.growth_rate * (
2 * math.exp(
- math.pow((self.get_ca(t) - self.xi) / self.zeta, 2)
) - 1
)
class SigmoidNumericSEI(SynapticElementIntegrator):
"""
Compute the number of synaptic element corresponding to a
sigmoid growth curve
dse/dCa = nu * ((2.0 / exp( (Ca - eps)/psi)) - 1.0)
Use numerical integration (see scipy.integrate.quad)
"""
def __init__(self, eps=0.7, growth_rate=1.0, psi=0.1, *args, **kwargs):
"""
Constructor
:param eps: set point
:param psi: controls width of growth curve
:param growth_rate: scaling of the growth curve
.. seealso:: SynapticElementIntegrator()
"""
super(SigmoidNumericSEI, self).__init__(*args, **kwargs)
self.eps = eps
self.psi = psi
self.growth_rate = growth_rate
def get_se(self, t):
"""
:param t (float): current time
:return: Number of synaptic element
"""
assert t >= self.t_minus
se = self.se_minus + quad(self.growth_curve, self.t_minus, t)[0]
if se > 0:
return se
else:
return 0
def growth_curve(self, t):
return self.growth_rate * (
(2.0 / (1.0 + math.exp(
(self.get_ca(t) - self.eps) / self.psi
))) - 1.0
)
@unittest.skipIf(not HAVE_OPENMP, 'NEST was compiled without multi-threading')
class TestGrowthCurve(unittest.TestCase):
"""
Unittest class to test the GrowthCurve used with nest
"""
def setUp(self):
nest.ResetKernel()
nest.SetKernelStatus({"total_num_virtual_procs": 4})
nest.ResetNetwork()
nest.set_verbosity('M_DEBUG')
self.sim_time = 10000
self.sim_step = 100
nest.SetKernelStatus(
{'structural_plasticity_update_interval': self.sim_time + 1})
self.se_integrator = []
self.sim_steps = None
self.ca_nest = None
self.ca_python = None
self.se_nest = None
self.se_python = None
# build
self.pop = nest.Create('iaf_neuron', 10)
self.local_nodes = nest.GetNodes([0], {'model': 'iaf_neuron'}, True)[0]
self.spike_detector = nest.Create('spike_detector')
nest.Connect(self.pop, self.spike_detector, 'all_to_all')
noise = nest.Create('poisson_generator')
nest.SetStatus(noise, {"rate": 800000.0})
nest.Connect(noise, self.pop, 'all_to_all')
def simulate(self):
self.sim_steps = numpy.arange(0, self.sim_time, self.sim_step)
self.ca_nest = numpy.zeros(
(len(self.local_nodes), len(self.sim_steps)))
self.ca_python = numpy.zeros(
(len(self.se_integrator), len(self.sim_steps)))
self.se_nest = numpy.zeros(
(len(self.local_nodes), len(self.sim_steps)))
self.se_python = numpy.zeros(
(len(self.se_integrator), len(self.sim_steps)))
start = time.clock()
for t_i, t in enumerate(self.sim_steps):
for n_i, n in enumerate(self.local_nodes):
self.ca_nest[n_i][t_i], synaptic_elements = nest.GetStatus(
[n], ('Ca', 'synaptic_elements'))[0]
self.se_nest[n_i][t_i] = synaptic_elements['se']['z']
nest.Simulate(self.sim_step)
start = time.clock()
tmp = nest.GetStatus(self.spike_detector, 'events')[0]
spikes_all = tmp['times']
senders_all = tmp['senders']
for n_i, n in enumerate(self.local_nodes):
spikes = spikes_all[senders_all == n]
[sei.reset() for sei in self.se_integrator]
spike_i = 0
for t_i, t in enumerate(self.sim_steps):
while spike_i < len(spikes) and spikes[spike_i] <= t:
[sei.handle_spike(spikes[spike_i])
for sei in self.se_integrator]
spike_i += 1
for sei_i, sei in enumerate(self.se_integrator):
self.ca_python[sei_i, t_i] = sei.get_ca(t)
self.se_python[sei_i, t_i] = sei.get_se(t)
for sei_i, sei in enumerate(self.se_integrator):
testing.assert_almost_equal(
self.ca_nest[n_i], self.ca_python[sei_i], decimal=5)
testing.assert_almost_equal(
self.se_nest[n_i], self.se_python[sei_i], decimal=5)
def plot(self):
pylab.ion()
for i, sei in enumerate(self.se_integrator):
pylab.figure()
pylab.subplot(1, 2, 1)
pylab.title('Ca')
pylab.plot(self.sim_steps, self.ca_nest[0, :])
pylab.plot(self.sim_steps, self.ca_python[i])
pylab.legend(('nest', sei.__class__.__name__))
pylab.subplot(1, 2, 2)
pylab.title('Synaptic Element')
pylab.plot(self.sim_steps, self.se_nest[0, :])
pylab.plot(self.sim_steps, self.se_python[i])
pylab.legend(('nest', sei.__class__.__name__))
pylab.savefig('sp' + sei.__class__.__name__ + '.png')
raster_plot.from_device(self.spike_detector)
pylab.savefig('sp_raster_plot.png')
def test_linear_growth_curve(self):
beta_ca = 0.0001
tau_ca = 10000.0
growth_rate = 0.0001
eps = 0.10
nest.SetStatus(
self.local_nodes,
{
'beta_Ca': beta_ca,
'tau_Ca': tau_ca,
'synaptic_elements': {
'se': {
'growth_curve': 'linear',
'growth_rate': growth_rate,
'eps': eps,
'z': 0.0
}
}
}
)
self.se_integrator.append(LinearExactSEI(
tau_ca=tau_ca, beta_ca=beta_ca, eps=eps, growth_rate=growth_rate))
self.se_integrator.append(LinearNumericSEI(
tau_ca=tau_ca, beta_ca=beta_ca, eps=eps, growth_rate=growth_rate))
self.simulate()
# check that we got the same values from one run to another
# expected = self.se_nest[:, 10]
# print(self.se_nest[:, 10].__repr__())
expected = numpy.array([
0.08376263, 0.08374046, 0.08376031, 0.08376756, 0.08375428,
0.08378699, 0.08376784, 0.08369779, 0.08374215, 0.08370484
])
for n in self.pop:
if n in self.local_nodes:
testing.assert_almost_equal(
self.se_nest[self.local_nodes.index(n), 10], expected[
self.pop.index(n)],
decimal=8)
def test_gaussian_growth_curve(self):
beta_ca = 0.0001
tau_ca = 10000.0
growth_rate = 0.0001
eta = 0.05
eps = 0.10
nest.SetStatus(
self.local_nodes,
{
'beta_Ca': beta_ca,
'tau_Ca': tau_ca,
'synaptic_elements': {
'se': {
'growth_curve': 'gaussian',
'growth_rate': growth_rate,
'eta': eta, 'eps': eps, 'z': 0.0
}
}
}
)
self.se_integrator.append(
GaussianNumericSEI(tau_ca=tau_ca, beta_ca=beta_ca,
eta=eta, eps=eps, growth_rate=growth_rate))
self.simulate()
# check that we got the same values from one run to another
# expected = self.se_nest[:, 30]
# print(self.se_nest[:, 30].__repr__())
expected = numpy.array([
0.10044035, 0.10062526, 0.1003149, 0.10046311, 0.1005713,
0.10031755, 0.10032216, 0.10040191, 0.10058179, 0.10068598
])
for n in self.pop:
if n in self.local_nodes:
testing.assert_almost_equal(
self.se_nest[self.local_nodes.index(n), 30], expected[
self.pop.index(n)],
decimal=5)
def test_sigmoid_growth_curve(self):
beta_ca = 0.0001
tau_ca = 10000.0
growth_rate = 0.0001
eps = 0.10
psi = 0.10
nest.SetStatus(
self.local_nodes,
{
'beta_Ca': beta_ca,
'tau_Ca': tau_ca,
'synaptic_elements': {
'se': {
'growth_curve': 'sigmoid',
'growth_rate': growth_rate,
'eps': eps, 'psi': 0.1, 'z': 0.0
}
}
}
)
self.se_integrator.append(
SigmoidNumericSEI(tau_ca=tau_ca, beta_ca=beta_ca,
eps=eps, psi=psi, growth_rate=growth_rate))
self.simulate()
# check that we got the same values from one run to another
# expected = self.se_nest[:, 30]
# print self.se_nest[:, 30].__repr__()
expected = numpy.array([
0.07801164, 0.07796841, 0.07807825, 0.07797382, 0.07802574,
0.07805961, 0.07808139, 0.07794451, 0.07799474, 0.07794458
])
for n in self.pop:
if n in self.local_nodes:
testing.assert_almost_equal(
self.se_nest[self.local_nodes.index(n), 30], expected[
self.pop.index(n)],
decimal=5)
def tearDown(self):
# uncomment this line if you want to plot values
# self.plot()
return
def suite():
test_suite = unittest.makeSuite(TestGrowthCurve, 'test')
return test_suite
if __name__ == '__main__':
unittest.main()
|
drodarie/nest-simulator
|
pynest/nest/tests/test_sp/test_growth_curves.py
|
Python
|
gpl-2.0
| 15,637
|
[
"Gaussian"
] |
aedfd719f48eeb3a45d962da39d8383653c89334b1345a85e5188cf70ba1380e
|
"""Standalone Python tool which lets you preview TrueType fonts as they would be
rendered on a pinball DMD."""
# font_tester.py
# Mission Pinball Framework
# Written by Brian Madden & Gabe Knuth
# Released under the MIT License. (See license info at the end of this file.)
# Documentation and more info at http://missionpinball.com/mpf
"""
Requirements
------------
* Requires Python & Pygame. Any version of either should be fine?
* Works on Windows, Mac, Linux
* Does *not* require the Mission Pinball Framework. This tool is completely
standalone.
Running the tool
----------------
Run this tool by specifying the path to a folder which has TrueType font files
(.ttf) you want to test. You can optionally specify a file name too which is the
first font it will load. Otherwise it just starts at the beginning of the
alphabet. For example:
python font_tester.py c:\Windows\Fonts
or
python font_tester.py ../mpf.fonts/pixelmix.ttf
Instructions
------------
Once the window pops up:
* Type any characters to have them appear in the window.
* Left & Right arrows cycle through the different fonts in the folder.
* Up & Down arrows increase & decrease the size of the font.
* Shift+Up & Shift+Down adjust the vertical placement of the font
* CTRL+A (or CMD+A on a Mac) toggles Antialias mode
* CTRL+B (or CMD+B) toggles the green Bounding Box.
* CTRL+S (or CMD+S) takes a snapshot of the screen and saves it to the
'font_snapshots' folder
Limitations (which we will address soon):
* Only works with TrueType fonts with .ttf extensions.
* Doesn't work with shift symbols. (i.e. SHIFT+1 shows "1" and not "!")
"""
import os
import sys
from optparse import OptionParser
import pygame
import pygame.locals
# CONFIGURATION SETTINGS: You can change these if you want
# ------------------------------------------------------------------------------
window_size = (800, 600) # pixel size of the main window
dmd_size = (128, 32) # pixel size of the native DMD
dmd_screen_size = (640, 160) # pixel size of the on screen DMD
pixel_color = (255, 85, 0) # R, G, B colors of the font pixels
dark_color = (34, 17, 0) # R, G, B colors of the 'off' pixels
pixel_spacing = 2 # Pixel spacing between dots
loop_ms = 100 # How many ms it waits per cycle
font_bg_color = (0, 200, 0) # R, G, B color of the CTRL+B box
max_chars = 20 # How many characters are displayed
snapshot_folder = "font_snapshots" # Path of the CTRL+S screenshots
prefer_uncompressed_snapshots = False # Do you want uncompressed BMPs?
snapshot_flash_brightness = 255 # Color of the snapshot flash
snapshot_flash_steps = 5 # Steps for the flash to be done
text_string = "HELLO" # Initial text
font_size = 10 # Initial size
antialias = False # Initial antialias setting
bounding_box = False # Initial bounding box settings
# ------------------------------------------------------------------------------
# END OF CONFIGURATION SETTINGS. Don't change anything below here
font_path = None
font_list = list()
font_index = 0
char = None
y_offset = 0
snapshot_flash_index = 0
def load_font():
global font
global font_list
global font_index
global font_size
global font_path
font = pygame.font.Font(os.path.join(font_path, font_list[font_index]),
font_size)
def change_font_size(direction):
global font_size
if direction == 'up':
font_size += 1
elif direction == 'down' and font_size > 1:
font_size -= 1
load_font()
update_screen()
def change_font(direction):
global font_list
global font_index
if direction == 'left':
font_index -= 1
elif direction == 'right':
font_index += 1
if font_index < 0:
font_index = len(font_list) - 1
elif font_index > len(font_list) - 1:
font_index = 0
load_font()
update_screen()
def change_y_offset(direction):
global y_offset
if direction == 'up':
y_offset -= 1
elif direction == 'down':
y_offset += 1
def main_loop():
global snapshot_flash_index
for event in pygame.event.get():
if event.type == pygame.QUIT:
sys.exit()
elif event.type == pygame.KEYDOWN:
process_key(event.key, event.mod)
pygame.time.delay(loop_ms)
if snapshot_flash_index:
snapshot_flash_index -= 1
update_screen()
def process_key(key, mods):
global text_string
global max_chars
ctrl = False
shift = False
# Calculate mods
if (mods & pygame.locals.KMOD_SHIFT) or (mods & pygame.locals.KMOD_CAPS):
shift = True
if (mods & pygame.locals.KMOD_META) or (mods & pygame.locals.KMOD_CTRL):
ctrl = True
if key == pygame.locals.K_ESCAPE:
sys.exit()
else:
char = pygame.key.name(key)
if ctrl and char == 's':
screen_snap()
elif ctrl and char == 'a':
flip_antialias()
elif ctrl and char == 'b':
flip_bounding_box()
elif shift and (char == 'up' or char == 'down'):
change_y_offset(char)
elif char == 'up' or char == 'down':
change_font_size(char)
elif char == 'left' or char == 'right':
change_font(char)
else:
text_string += get_char(char, shift)
if len(text_string) > max_chars:
text_string = text_string[-max_chars:]
update_screen()
def update_screen():
global font
global antialias
global text_string
global pixel_color
global dark_color
global screen
global dmd_size
global dmd_screen_size
global pixel_spacing
global font_size
global font_index
global font_list
global font_bg_color
global bounding_box
global snapshot_flash_index
global snapshot_flash_brightness
global snapshot_flash_steps
global y_offset
# render the text
if bounding_box:
text_surface = font.render(text_string, antialias, pixel_color,
font_bg_color)
else:
text_surface = font.render(text_string, antialias, pixel_color)
# figure out the x, y position to center this text in the DMD
x = (dmd_size[0] - text_surface.get_width()) / 2
y = ((dmd_size[1] - text_surface.get_height()) / 2) + y_offset
# put that text on our 'dmd' surface in the way it will be in MPF
dmd_surface = pygame.Surface((dmd_size))
dmd_surface.fill(dark_color)
dmd_surface.blit(text_surface, (x, y))
# scale it
dmd_screen_surface = pygame.transform.scale(dmd_surface, (dmd_screen_size))
# pixelize it
if pixel_spacing:
ratio = dmd_screen_surface.get_width() / float(dmd_surface.get_width())
for row in range(dmd_surface.get_height() + 1):
pygame.draw.line(dmd_screen_surface, (0, 0, 0), (0, row*ratio),
(dmd_screen_surface.get_width()-1, row*ratio),
pixel_spacing)
for col in range(dmd_surface.get_width() + 1):
pygame.draw.line(dmd_screen_surface, (0, 0, 0), (col*ratio, 0),
(col*ratio, dmd_screen_surface.get_height()-1),
pixel_spacing)
# Create the surface for the font name
info_font = pygame.font.Font(None, 50)
fontname_surface = info_font.render(font_list[font_index], True,
(255, 255, 255))
fontsize_surface = info_font.render("Font Size: " + str(font_size), True,
(255, 255, 255))
if antialias:
aa_string = "ON"
else:
aa_string = "OFF"
antialias_surface = info_font.render("Antialias: " + aa_string, True,
(255, 255, 255))
screen.fill((0,0,0))
# center the DMD screen on the display surface
x = (screen.get_width() - dmd_screen_surface.get_width()) / 2
y = (screen.get_height() - dmd_screen_surface.get_height()) / 2
# draw a box around the DMD
pygame.draw.rect(screen, (255, 255, 255),
(x-1, y-1,dmd_screen_surface.get_width() + 2,
dmd_screen_surface.get_height() + 2), 1)
screen.blit(dmd_screen_surface, (x, y))
screen.blit(fontname_surface, (10,10))
x = screen.get_width() - fontsize_surface.get_width() - 10
screen.blit(fontsize_surface, (x, 10))
y = screen.get_height() - antialias_surface.get_height() - 10
screen.blit(antialias_surface, (10, y))
if snapshot_flash_index:
value = int(snapshot_flash_brightness * snapshot_flash_index /
float(snapshot_flash_steps))
screen.fill((value, value, value))
pygame.display.update()
def screen_snap():
global snapshot_folder
global font_list
global font_index
global font_size
global antialias
global prefer_uncompressed_snapshots
global bounding_box
global snapshot_flash_index
global snapshot_flash_steps
global y_offset
# make sure we have our folder
if not os.path.isdir(snapshot_folder):
os.mkdir(snapshot_folder)
surface = pygame.display.get_surface()
filename = font_list[font_index].split('.')[0] + '-' + str(font_size)
if antialias:
filename += '-aa'
if bounding_box:
filename += '-bb'
if y_offset:
filename += '-y' + str(y_offset)
if prefer_uncompressed_snapshots or not pygame.image.get_extended():
filename += '.bmp'
else:
filename += '.png'
filename = os.path.join(snapshot_folder, filename)
pygame.image.save(surface, filename)
snapshot_flash_index = snapshot_flash_steps
def flip_antialias():
global antialias
antialias ^= 1
def flip_bounding_box():
global bounding_box
bounding_box ^= 1
def get_char(char, shift):
if char == 'space':
return ' '
if len(char) == 1:
if char.isalpha() and shift:
return char.upper()
else:
return char
return ''
def setup_file_list(font_string):
global font_file
global font_path
global font_list
global font_index
if os.path.isdir(font_string):
font_path = font_string
font_file = None
elif os.path.isfile(font_string):
font_path, font_file = os.path.split(font_string)
else:
print "ERROR: Parameter passed isn't a valid path or file name."
sys.exit()
# Find all the fonts in this folder and add them to the list
for item in os.walk(font_path):
for file_name in item[2]:
if file_name.upper().endswith('.TTF'):
font_list.append(file_name)
# figure out which one is ours
if font_file:
font_index = font_list.index(font_file)
def main():
global screen
# Get command line input
parser = OptionParser()
(options, args) = parser.parse_args()
options = vars(options)
if len(args) != 1:
print "Error. This tool requires a font filename as a command line parameter"
sys.exit()
else:
setup_file_list(args[0])
pygame.init()
# Set up the window
flags = 0
flags = flags | pygame.locals.RESIZABLE
screen = pygame.display.set_mode(window_size, flags)
pygame.display.set_caption("Mission Pinball Framework Font Tester")
load_font()
update_screen()
while 1:
main_loop()
if __name__ == "__main__":
main()
# The MIT License (MIT)
# Copyright (c) 2013-2015 Brian Madden and Gabe Knuth
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
|
jabdoa2/mpf
|
tools/font_tester.py
|
Python
|
mit
| 12,828
|
[
"Brian"
] |
d1e1eb0b3913ce29ac84b6d3d68901154b17867e9db869d2f5cda9faf99a18cf
|
#!/usr/bin/env python2.6
"""
Tue Nov 27 14:50:38 PST 2012
Parse blastclust output files and call the different cluster types.
This is applied in searching for similar (but not identical) clones to the expanded clones.
The input blast was done on the top clones of each patients:
For each V-J recombination:
Blast the top clones with that V-J against all the clones carrying that V-J
In a way, we use each top clone as a seed and look for similar sequences using that seed. The hits from the blast that pass a certain similarity cutoff form the cluster.
We distinguish 5 types of cluster:
#Types: 1: "Multiple expanded clones from multiple samples"
# 2: "Multiple expanded clones from only one sample"
# 3: "Expanded clones that do not cluster with any other sequence"
# 4: "Non-expanded clones that do not cluster with any other sequence"
# 5: "Others"
Return a summary of:
nknguyen at soe ucsc edu
"""
import os, sys, re
from Bio.Blast import NCBIXML
import immunoseq.lib.immunoseqLib as iseqlib
def getCloneInfo(clonestr):
#as11D;183042;size=8925
items = clonestr.lstrip('>').split(';')
sample = items[0]
size = int(items[-1].lstrip("size="))
id = items[1]
return sample, id, size
class Clone():
def __init__(self, clonestr):
sample, id, size = getCloneInfo(clonestr)
self.desc = clonestr.lstrip('>')
self.sample = sample
self.id = id
self.size = size
def setFreq(self, total):
if total == 0:
raise ValueError("Error: Total sequences of sample %s is 0." %(self.sample))
else:
self.freq = 100.0*self.size/total
class Cluster():
def __init__(self, line, vj, sample2total):
self.vj = vj
items = line.strip().split()
clones = [Clone(item) for item in items]
#set clone frequency:
for clone in clones:
if sample2total:
if clone.sample in sample2total:
clone.setFreq(sample2total[clone.sample])
else:
raise ValueError("The total number of sequences is required but not found for sample %s\n" %clone.sample)
self.clones = sorted(clones, key=lambda c: c.size, reverse=True)
self.totalReads = sum([c.size for c in clones])
self.numClones = len(self.clones)
def getDesc(self):
return "%s;%d;%d" %(self.vj, self.numClones, self.totalReads)
def readBlastclustOutfiles(infile, vjname, options):
#minPos, minLen, minExpSize, minExpClones, minMotifClones, minExpFreq, sample2total
#Read in blastclust output file:
f = open(infile, 'r')
clusters = []
for line in f:
cluster = Cluster(line, vjname, options.sample2total)
clusters.append(cluster)
f.close()
return clusters
def getClusterType(cluster, minExpFreq, minExpSize, minExpClones):
#Types: 1: "Multiple expanded clones from multiple samples"
# 2: "Multiple expanded clones from only one sample"
# 3: "Expanded clones that do not cluster with any other sequence"
# 4: "Non-expanded clones that do not cluster with any other sequence"
# 5: "Others"
numExp = 0
expSamples = [] #samples with expanded clones besides seed sample
clones = {} #non-redundant clone of the current cluster, key=cloneStr, value = clone sequence
type = 5
if cluster.numClones == 1: #type 3 or 4
clone = cluster.clones[0]
if clone.size >= minExpSize and clone.freq >= minExpFreq: #expanded clone, type 3
numExp += 1
type = 3
else: #non-expanded clone, type 4
type = 4
else:
for clone in cluster.clones:
#expanded
if clone.size >= minExpSize and clone.freq >= minExpFreq: #expanded clone
numExp += 1
if clone.sample not in expSamples:
expSamples.append(clone.sample)
if numExp >= minExpClones: #type 1 or 2
if len(expSamples) == 1: #no other sample has expanded clones
type = 2
else:
type = 1
return type, numExp
def getFh(type, fh1, fh2, fh3, fh4, fh5):
if type == 1:
return fh1
elif type == 2:
return fh2
elif type == 3:
return fh3
elif type == 4:
return fh4
else:
return fh5
def printCluster(cluster, fh):
fh.write("\n>%s\n" %cluster.getDesc())
fh.write("\t%s\n" %("\n\t".join([clone.desc for clone in cluster.clones])) )
def clusterId2info():
id2info = { 1: "Multiple expanded clones from multiple samples",
2: "Multiple expanded clones from only one sample",
3: "Expanded clones that do not cluster with any other sequence",
4: "Non-expanded clones that do not cluster with any other sequence",
5: "Others",
}
return id2info
def separateClusters(clusters, minExpFreq, minExpSize, minExpClones, fh1, fh2, fh3, fh4, fh5, type2count, type2clusters):
#Types: 1: "Multiple expanded clones from multiple samples"
# 2: "Multiple expanded clones from only one sample"
# 3: "Expanded clones that do not cluster with any other sequence"
# 4: "Non-expanded clones that do not cluster with any other sequence"
# 5: "Others"
totalseq = 0
expseq = 0
for cluster in clusters:
type, numExp = getClusterType(cluster, minExpFreq, minExpSize, minExpClones)
expseq += numExp
totalseq += cluster.numClones
fh = getFh(type, fh1, fh2, fh3, fh4, fh5)
printCluster(cluster, fh)
#Update type count
if type not in type2count:
type2count[type] = 1
else:
type2count[type] += 1
#Update type2clusters:
if type not in type2clusters:
type2clusters[type] = [cluster]
else:
type2clusters[type].append(cluster)
return totalseq, expseq
def getPc(count, total):
if total == 0:
return 0
return 100.0*count/total
def readFaFile(file):
header2seq = {}
f = open(file, 'r')
header = ''
seq = ''
for line in f:
line = line.strip()
if len(line) == 0:
continue
if line[0] == '>':
if header != '' and header not in header2seq:
header2seq[header] = seq
header = line.lstrip('>')
seq = ''
else:
seq += line
if header != '' and seq != '' and header not in header2seq:
header2seq[header] = seq
f.close()
return header2seq
def readSample2total(file):
sample2total = {}
f = open(file, 'r')
for line in f:
items = line.strip().split()
sample2total[items[0]] = int(items[1])
f.close()
return sample2total
def addOptions(parser):
parser.add_option('-i', '--indir', dest='indir', help='Input directory, containing blastclust output files')
parser.add_option('-f', '--fasta', dest='fadir', help='Fasta directory, containing fasta files')
parser.add_option('-o', '--outdir', dest='outdir', help='Output directory')
parser.add_option('-b', '--basename', dest='basename', default='out', help='Output files basename. Default=%default')
parser.add_option('-S', '--minExpandedSize', dest='minExpSize', type='int', default=1000, help='Minimum number of reads for a clone to be called "expanded". Default=%default')
parser.add_option('-C', '--minExpandedClones', dest='minExpClones', type='int', default=2, help='Minimum number of expanded clones for a cluster to be classified as type 1. Default=%default')
parser.add_option('-F', '--minExpandedFreq', dest='minExpFreq', type='float', default=0.0, help='Minimun frequency (ranging from 0 - 100%) for a clone to be called "expanded". Default=%default')
parser.add_option('--sample2total', dest='sample2total', help='Required if --minExpandedFreq is larger than 0. Format: <sample> <totalCount>')
parser.add_option('-v', '--removeV', dest='vfile', help='If specified, remove the rest of V gene and only print out the CDR3 region of each sequence in the output fasta files. Default = None')
#parser.add_option('-p', '--positive', dest='minPos', type='float', default=0.9, help='Minimum portion of positive matches. Default=%default')
#parser.add_option('-l', '--len', dest='minLen', type='int', default=10, help='Minimum sequence length to be included in the output. Default=%default')
#parser.add_option('-c', '--minMotifClones', dest='minMotifClones', type='int', default=10, help='Minimum number of clones carrying the same motif for a seed and its cluster to be classified as type 2. Default=%default ')
def main():
parser = iseqlib.initOptions()
addOptions(parser)
options, args = parser.parse_args()
if options.minExpFreq > 0 and not options.sample2total:
parser.error("--sample2total is required as --minExpandedFreq > 0\n")
if options.sample2total:
options.sample2total = readSample2total(options.sample2total)
ext = 'txt'
infiles = iseqlib.getfiles(options.indir, ext)
outbasename = os.path.join(options.outdir, options.basename)
fh1 = open("%s-1.txt" %outbasename, 'w')
fh2 = open("%s-2.txt" %outbasename, 'w')
fh3 = open("%s-3.txt" %outbasename, 'w')
fh4 = open("%s-4.txt" %outbasename, 'w')
fh5 = open("%s-5.txt" %outbasename, 'w')
type2count = {}
type2clusters = {}
totalseq = 0
expseq = 0
for file in infiles:
infile = os.path.join(options.indir, file)
vjname = file.rstrip(ext).rstrip('.')
clusters = readBlastclustOutfiles(infile, vjname, options)
total, exp = separateClusters(clusters, options.minExpFreq, options.minExpSize, options.minExpClones, fh1, fh2, fh3, fh4, fh5, type2count, type2clusters)
totalseq += total
expseq += exp
fh1.close()
fh2.close()
fh3.close()
fh4.close()
fh5.close()
#summary stats:
summaryFile = "%s-summary.txt" %outbasename
fh = open(summaryFile, 'w')
fh.write("#min size to be called expanded clones: %d\n#min number of expanded clones required for type 1: %d\n" %(options.minExpSize, options.minExpClones))
id2info = clusterId2info()
fh.write("Total number of clones: %d\n" %totalseq)
fh.write("Total number of expanded clones: %d\n" %expseq)
for type in sorted(type2count.keys()):
fh.write("%d\t%s\t%d\n" %(type, id2info[type], type2count[type]))
fh.close()
#Print type2clones:
#Read v files:
v2seq = {}
if options.vfile:
v2seq = readFaFile(options.vfile)
#Read fasta files:
ext = 'fa'
infiles = iseqlib.getfiles(options.fadir, ext)
vj2id2seq = {}
for file in infiles:
infile = os.path.join(options.fadir, file)
vjname = file.rstrip(ext).rstrip('.')
vj2id2seq[vjname] = readFaFile(infile)
for type, clusters in type2clusters.iteritems():
outfile = "%s-%d.fa" %(outbasename, type)
f = open(outfile, 'w')
f.write("#Type: %s\n" %type)
clusters = sorted(clusters, key=lambda c: c.totalReads, reverse=True)
for cluster in clusters:
f.write("#Cluster %s\n" %cluster.getDesc())
id2seq = vj2id2seq[cluster.vj]
v = cluster.vj.split(".")[0]
vseq = ""
if v in v2seq:
vseq = v2seq[v]
for clone in cluster.clones:
seq = id2seq[ clone.desc ]
cdr3seq = seq[ len(vseq): ]
header = "%s;%s;%d;%s" %(clone.sample, cdr3seq, clone.size, cluster.vj)
f.write(">%s\n" %header)
f.write("%s\n" %seq)
f.close()
if __name__ == '__main__':
main()
|
ngannguyen/immunoseq
|
src/clusterType_blastclust.py
|
Python
|
mit
| 11,873
|
[
"BLAST"
] |
025d794255e063730197be7f95a0862d4cde7bc303f0c20d532aadb4d17912b0
|
##############################################################################
# adaptiveMD: A Python Framework to Run Adaptive Molecular Dynamics (MD)
# Simulations on HPC Resources
# Copyright 2017 FU Berlin and the Authors
#
# Authors: Jan-Hendrik Prinz
# Contributors:
#
# `adaptiveMD` is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as
# published by the Free Software Foundation, either version 2.1
# of the License, or (at your option) any later version.
#
# This library is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public
# License along with MDTraj. If not, see <http://www.gnu.org/licenses/>.
##############################################################################
from __future__ import print_function, absolute_import
from itertools import chain
from .condition import Condition
from .task import Task
class Event(object):
"""
Class describing the condition and function execution of some code
"""
_skip_done_conditions = True
_wait_for_completion = True
def __init__(self, when=None):
"""
Parameters
----------
when : `Condition`
the callable that determines when an Event should be executed
"""
self._on = None
self._until = None
self._finish_conditions = []
self._active_tasks = []
self._generator = None
self._current_when = None
self._current_iter = None
if when is not None:
self.on(when)
def on(self, when):
"""
Specify a list of conditions when the event is to be executed
Parameters
----------
when : (list of) callable -> bool
a condition (a function the returns a bool) that is tested for
and if it is true (once trigger is called) the event will
be executed and the next condition will be waited for.
Returns
-------
self
return the object itself for chaining
"""
self._on = when
if hasattr(self._on, '__iter__'):
# there are multiple conditions so pick first
self._current_iter = iter(self._on)
else:
self._current_iter = iter([self._on])
self._advance()
return self
def _advance(self):
try:
# there are multiple conditions so pick first
self._current_when = next(self._current_iter)
except StopIteration:
self._current_when = None
def _update_conditions(self):
self._active_tasks = [x for x in self._active_tasks if not x.is_done()]
self._finish_conditions = [x for x in self._finish_conditions if x()]
@property
def active_tasks(self):
"""
Returns
-------
list of `Task`
the list of currently active tasks in this event
"""
self._update_conditions()
return self._active_tasks
@property
def has_running_tasks(self):
self._update_conditions()
return len(self._finish_conditions) > 0
# return len(self._active_tasks) > 0
def __bool__(self):
self._update_conditions()
return self._current_when is not None or self.has_running_tasks
def _generate(self, scheduler):
if self._generator is not None:
# todo: this should be cleaner and not guess the number of args
try:
return self._generator(scheduler)
except TypeError:
return self._generator()
else:
return []
def __call__(self, scheduler):
generated = self._generate(scheduler)
tasks = scheduler.submit(generated)
for t in tasks:
if isinstance(t, Task):
self._active_tasks.append(t)
self._finish_conditions.append(t.is_done)
# self._active_tasks.extend(tasks)
return generated
def trigger(self, scheduler):
"""
Test conditions and trigger execution if they are fulfilled
Parameters
----------
scheduler : `Scheduler`
the scheduler which will handle submission of tasks if desired
Returns
-------
list of `Task`
a list of new tasks that should be submitted
"""
if self:
if not self.has_running_tasks or not self._wait_for_completion:
if self._until is not None and self._until():
self._current_when = None
if self._current_when and self._current_when():
tasks = self(scheduler)
self._advance()
if self._skip_done_conditions:
while self._current_when is not None and \
self._current_when():
self._advance()
return tasks
return []
def __str__(self):
return '%s(%s, %s[%s])' % (
'active' if self else '------',
self.__class__.__name__,
str(self._current_when),
str(self._current_when()) if self._current_when else 'None',
)
def repeat(self, times=None):
"""
Set the event iter to restart if its tasks are finished
Notes
-----
This overrides potential multiple trigger events and just repeats when
finished until the stop condition is met
Returns
-------
self
"""
if times is None:
self._current_iter = chain(
self._current_iter,
(TasksFinished(self) for _ in iter(int, 1)))
else:
self._current_iter = chain(
self._current_iter,
(TasksFinished(self) for _ in range(times)))
return self
def until(self, repeat):
"""
Set the event iter to restart if its tasks are finished
Notes
-----
This overrides potential multiple trigger events and just repeats when
finished until the stop condition is met
Returns
-------
self
"""
self._until = repeat
return self
def do(self, generator):
"""
Set the task generator to be used once a condition is met
Parameters
----------
generator : function -> list of `Task`
Returns
-------
self
"""
self._generator = generator
return self
def cancel(self):
"""
Stop execution of future events
"""
self._current_when = None
@property
def on_done(self):
"""
Return a `Condition` that is True once the event is finished
Returns
-------
"""
return TasksFinished(self)
class TasksFinished(Condition):
"""
Condition to represent the completion of an event
"""
def __init__(self, event):
super(TasksFinished, self).__init__()
self.event = event
def check(self):
return not bool(self.event)
class StopEvent(Event):
"""
Event that represents the termination of the used scheduler
"""
def __call__(self, scheduler):
return StopIteration
|
jrossyra/adaptivemd
|
adaptivemd/event.py
|
Python
|
lgpl-2.1
| 7,626
|
[
"MDTraj"
] |
9f48d4024470ac5837641f40aed5d5fa802df74824d19fd5e85d869631d7985b
|
# -*- coding: utf-8 -*-
#
# Copyright (C) 2016-2017 Bitergia
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software
# Foundation, 51 Franklin Street, Fifth Floor, Boston, MA 02110-1335, USA.
#
# Authors:
# Alberto Martín <alberto.martin@bitergia.com>
# Santiago Dueñas <sduenas@bitergia.com>
#
import json
import logging
import time
from grimoirelab.toolkit.datetime import datetime_to_utc
from grimoirelab.toolkit.uris import urijoin
from ...backend import (Backend,
BackendCommand,
BackendCommandArgumentParser)
from ...client import HttpClient
from ...utils import DEFAULT_DATETIME
MAX_QUESTIONS = 100 # Maximum number of reviews per query
logger = logging.getLogger(__name__)
class StackExchange(Backend):
"""StackExchange backend for Perceval.
This class retrieves the questions stored in any of the
StackExchange sites. To initialize this class the
site must be provided.
:param site: StackExchange site
:param tagged: filter items by question Tag
:param api_token: StackExchange access_token for the API
:param max_questions: max of questions per page retrieved
:param tag: label used to mark the data
:param archive: archive to store/retrieve items
"""
version = '0.9.0'
def __init__(self, site, tagged=None, api_token=None,
max_questions=None, tag=None, archive=None):
origin = site
super().__init__(origin, tag=tag, archive=archive)
self.site = site
self.api_token = api_token
self.tagged = tagged
self.max_questions = max_questions
self.client = None
def fetch(self, from_date=DEFAULT_DATETIME):
"""Fetch the questions from the site.
The method retrieves, from a StackExchange site, the
questions updated since the given date.
:param from_date: obtain questions updated since this date
:returns: a generator of questions
"""
if not from_date:
from_date = DEFAULT_DATETIME
from_date = datetime_to_utc(from_date)
kwargs = {'from_date': from_date}
items = super().fetch("question", **kwargs)
return items
def fetch_items(self, **kwargs):
"""Fetch the questions"""
from_date = kwargs['from_date']
logger.info("Looking for questions at site '%s', with tag '%s' and updated from '%s'",
self.site, self.tagged, str(from_date))
whole_pages = self.client.get_questions(from_date)
for whole_page in whole_pages:
questions = self.parse_questions(whole_page)
for question in questions:
yield question
@classmethod
def has_archiving(cls):
"""Returns whether it supports archiving items on the fetch process.
:returns: this backend supports items archive
"""
return True
@classmethod
def has_resuming(cls):
"""Returns whether it supports to resume the fetch process.
:returns: this backend supports items resuming
"""
return True
@staticmethod
def metadata_id(item):
"""Extracts the identifier from a StackExchange item."""
return str(item['question_id'])
@staticmethod
def metadata_updated_on(item):
"""Extracts the update time from a StackExchange item.
The timestamp is extracted from 'last_activity_date' field.
This date is a UNIX timestamp but needs to be converted to
a float value.
:param item: item generated by the backend
:returns: a UNIX timestamp
"""
return float(item['last_activity_date'])
@staticmethod
def metadata_category(item):
"""Extracts the category from a StackExchange item.
This backend only generates one type of item which is
'question'.
"""
return 'question'
@staticmethod
def parse_questions(raw_page):
"""Parse a StackExchange API raw response.
The method parses the API response retrieving the
questions from the received items
:param items: items from where to parse the questions
:returns: a generator of questions
"""
raw_questions = json.loads(raw_page)
questions = raw_questions['items']
for question in questions:
yield question
def _init_client(self, from_archive=False):
"""Init client"""
return StackExchangeClient(self.site, self.tagged, self.api_token, self.max_questions,
self.archive, from_archive)
class StackExchangeClient(HttpClient):
"""StackExchange API client.
This class implements a simple client to retrieve questions from
any Stackexchange site.
:param site: URL of the Bugzilla server
:param tagged: filter items by question Tag
:param token: StackExchange access_token for the API
:param max_questions: max number of questions per query
:param archive: an archive to store/read fetched data
:param from_archive: it tells whether to write/read the archive
:raises HTTPError: when an error occurs doing the request
"""
# Filters are immutable and non-expiring. This filter allows to retrieve all
# the information regarding Each question. To know more, visit
# https://api.stackexchange.com/docs/questions and paste the filter in the
# whitebox filter. It will display a list of checkboxes with the selected
# values for the filter provided.
QUESTIONS_FILTER = 'Bf*y*ByQD_upZqozgU6lXL_62USGOoV3)MFNgiHqHpmO_Y-jHR'
STACKEXCHANGE_API_URL = 'https://api.stackexchange.com'
VERSION_API = '2.2'
def __init__(self, site, tagged, token, max_questions, archive=None, from_archive=False):
super().__init__(self.STACKEXCHANGE_API_URL, archive=archive, from_archive=from_archive)
self.site = site
self.tagged = tagged
self.token = token
self.max_questions = max_questions
def __build_payload(self, page, from_date, order='desc', sort='activity'):
payload = {'page': page,
'pagesize': self.max_questions,
'order': order,
'sort': sort,
'tagged': self.tagged,
'site': self.site,
'key': self.token,
'filter': self.QUESTIONS_FILTER}
if from_date:
timestamp = int(from_date.timestamp())
payload['min'] = timestamp
return payload
def __log_status(self, quota_remaining, quota_max, page_size, total):
logger.debug("Rate limit: %s/%s" % (quota_remaining,
quota_max))
if (total != 0):
nquestions = min(page_size, total)
logger.info("Fetching questions: %s/%s" % (nquestions,
total))
else:
logger.info("No questions were found.")
def get_questions(self, from_date):
"""Retrieve all the questions from a given date.
:param from_date: obtain questions updated since this date
"""
page = 1
url = urijoin(self.base_url, self.VERSION_API, "questions")
req = self.fetch(url, payload=self.__build_payload(page, from_date))
questions = req.text
data = req.json()
tquestions = data['total']
nquestions = data['page_size']
self.__log_status(data['quota_remaining'],
data['quota_max'],
nquestions,
tquestions)
while questions:
yield questions
questions = None
if data['has_more']:
page += 1
backoff = data.get('backoff', None)
if backoff:
logger.debug("Expensive query. Wait %s secs to send a new request",
backoff)
time.sleep(float(backoff))
req = self.fetch(url, payload=self.__build_payload(page, from_date))
data = req.json()
questions = req.text
nquestions += data['page_size']
self.__log_status(data['quota_remaining'],
data['quota_max'],
nquestions,
tquestions)
class StackExchangeCommand(BackendCommand):
"""Class to run StackExchange backend from the command line."""
BACKEND = StackExchange
@staticmethod
def setup_cmd_parser():
"""Returns the StackExchange argument parser."""
parser = BackendCommandArgumentParser(from_date=True,
token_auth=True,
archive=True)
# StackExchange options
group = parser.parser.add_argument_group('StackExchange arguments')
group.add_argument('--site', dest='site',
required=True,
help="StackExchange site")
group.add_argument('--tagged', dest='tagged',
help="filter items by question Tag")
group.add_argument('--max-questions', dest='max_questions',
type=int, default=MAX_QUESTIONS,
help="Maximum number of questions requested in the same query")
return parser
|
sduenas/perceval
|
perceval/backends/core/stackexchange.py
|
Python
|
gpl-3.0
| 10,117
|
[
"VisIt"
] |
b14796107d139f6004e53cf3ea093d13b3ac24b4a77c7fd0ed85550eb28be465
|
#!/usr/bin/python
# -*- coding: utf-8 -*-
# (c) 2017, Brian Coca <bcoca@ansible.com>
# (c) 2017, Adam Miller <admiller@redhat.com>
# (c) 2017 Ansible Project
# GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt)
from __future__ import absolute_import, division, print_function
__metaclass__ = type
ANSIBLE_METADATA = {
'metadata_version': '1.1',
'status': ['preview'],
'supported_by': 'core'
}
DOCUMENTATION = '''
module: sysvinit
author:
- "Ansible Core Team"
version_added: "2.6"
short_description: Manage SysV services.
description:
- Controls services on target hosts that use the SysV init system.
options:
name:
required: true
description:
- Name of the service.
aliases: ['service']
state:
choices: [ 'started', 'stopped', 'restarted', 'reloaded' ]
description:
- C(started)/C(stopped) are idempotent actions that will not run commands unless necessary.
Not all init scripts support C(restarted) nor C(reloaded) natively, so these will both trigger a stop and start as needed.
enabled:
type: bool
description:
- Whether the service should start on boot. B(At least one of state and enabled are required.)
sleep:
default: 1
description:
- If the service is being C(restarted) or C(reloaded) then sleep this many seconds between the stop and start command.
This helps to workaround badly behaving services.
pattern:
description:
- A substring to look for as would be found in the output of the I(ps) command as a stand-in for a status result.
- If the string is found, the service will be assumed to be running.
- "This option is mainly for use with init scripts that don't support the 'status' option."
runlevels:
description:
- The runlevels this script should be enabled/disabled from.
- Use this to override the defaults set by the package or init script itself.
arguments:
description:
- Additional arguments provided on the command line that some init scripts accept.
aliases: [ 'args' ]
daemonize:
type: bool
description:
- Have the module daemonize as the service itself might not do so properly.
- This is useful with badly written init scripts or deamons, which
commonly manifests as the task hanging as it is still holding the
tty or the service dying when the task is over as the connection
closes the session.
default: no
notes:
- One option other than name is required.
requirements:
- That the service managed has a corresponding init script.
'''
EXAMPLES = '''
- name: make sure apache2 is started
sysvinit:
name: apache2
state: started
enabled: yes
- name: make sure apache2 is started on runlevels 3 and 5
sysvinit:
name: apache2
state: started
enabled: yes
runlevels:
- 3
- 5
'''
RETURN = '''
results:
description: results from actions taken
returned: always
type: complex
contains:
"attempts": 1
"changed": true
"name": "apache2"
"status": {
"enabled": {
"changed": true,
"rc": 0,
"stderr": "",
"stdout": ""
},
"stopped": {
"changed": true,
"rc": 0,
"stderr": "",
"stdout": "Stopping web server: apache2.\n"
}
}
'''
import re
from time import sleep
from ansible.module_utils.basic import AnsibleModule
from ansible.module_utils.service import sysv_is_enabled, get_sysv_script, sysv_exists, fail_if_missing, get_ps, daemonize
def main():
module = AnsibleModule(
argument_spec=dict(
name=dict(required=True, type='str', aliases=['service']),
state=dict(choices=['started', 'stopped', 'restarted', 'reloaded'], type='str'),
enabled=dict(type='bool'),
sleep=dict(type='int', default=1),
pattern=dict(type='str'),
arguments=dict(type='str', aliases=['args']),
runlevels=dict(type='list'),
daemonize=dict(type='bool', default=False),
),
supports_check_mode=True,
required_one_of=[['state', 'enabled']],
)
name = module.params['name']
action = module.params['state']
enabled = module.params['enabled']
runlevels = module.params['runlevels']
pattern = module.params['pattern']
sleep_for = module.params['sleep']
rc = 0
out = err = ''
result = {
'name': name,
'changed': False,
'status': {}
}
# ensure service exists, get script name
fail_if_missing(module, sysv_exists(name), name)
script = get_sysv_script(name)
# locate binaries for service management
paths = ['/sbin', '/usr/sbin', '/bin', '/usr/bin']
binaries = ['chkconfig', 'update-rc.d', 'insserv', 'service']
# Keeps track of the service status for various runlevels because we can
# operate on multiple runlevels at once
runlevel_status = {}
location = {}
for binary in binaries:
location[binary] = module.get_bin_path(binary, opt_dirs=paths)
# figure out enable status
if runlevels:
for rl in runlevels:
runlevel_status.setdefault(rl, {})
runlevel_status[rl]["enabled"] = sysv_is_enabled(name, runlevel=rl)
else:
runlevel_status["enabled"] = sysv_is_enabled(name)
# figure out started status, everyone does it different!
is_started = False
worked = False
# user knows other methods fail and supplied pattern
if pattern:
worked = is_started = get_ps(module, pattern)
else:
if location.get('service'):
# standard tool that has been 'destandarized' by reimplementation in other OS/distros
cmd = '%s %s status' % (location['service'], name)
elif script:
# maybe script implements status (not LSB)
cmd = '%s status' % script
else:
module.fail_json(msg="Unable to determine service status")
(rc, out, err) = module.run_command(cmd)
if not rc == -1:
# special case
if name == 'iptables' and "ACCEPT" in out:
worked = True
is_started = True
# check output messages, messy but sadly more reliable than rc
if not worked and out.count('\n') <= 1:
cleanout = out.lower().replace(name.lower(), '')
for stopped in ['stop', 'is dead ', 'dead but ', 'could not access pid file', 'inactive']:
if stopped in cleanout:
worked = True
break
if not worked:
for started_status in ['run', 'start', 'active']:
if started_status in cleanout and "not " not in cleanout:
is_started = True
worked = True
break
# hope rc is not lying to us, use often used 'bad' returns
if not worked and rc in [1, 2, 3, 4, 69]:
worked = True
if not worked:
# hail mary
if rc == 0:
is_started = True
worked = True
# ps for luck, can only assure positive match
elif get_ps(module, name):
is_started = True
worked = True
module.warn("Used ps output to match service name and determine it is up, this is very unreliable")
if not worked:
module.warn("Unable to determine if service is up, assuming it is down")
###########################################################################
# BEGIN: Enable/Disable
result['status'].setdefault('enabled', {})
result['status']['enabled']['changed'] = False
result['status']['enabled']['rc'] = None
result['status']['enabled']['stdout'] = None
result['status']['enabled']['stderr'] = None
if runlevels:
result['status']['enabled']['runlevels'] = runlevels
for rl in runlevels:
if enabled != runlevel_status[rl]["enabled"]:
result['changed'] = True
result['status']['enabled']['changed'] = True
if not module.check_mode and result['changed']:
# Perform enable/disable here
if enabled:
if location.get('update-rc.d'):
(rc, out, err) = module.run_command("%s %s enable %s" % (location['update-rc.d'], name, ' '.join(runlevels)))
elif location.get('chkconfig'):
(rc, out, err) = module.run_command("%s --level %s %s on" % (location['chkconfig'], ''.join(runlevels), name))
else:
if location.get('update-rc.d'):
(rc, out, err) = module.run_command("%s %s disable %s" % (location['update-rc.d'], name, ' '.join(runlevels)))
elif location.get('chkconfig'):
(rc, out, err) = module.run_command("%s --level %s %s off" % (location['chkconfig'], ''.join(runlevels), name))
else:
if enabled is not None and enabled != runlevel_status["enabled"]:
result['changed'] = True
result['status']['enabled']['changed'] = True
if not module.check_mode and result['changed']:
# Perform enable/disable here
if enabled:
if location.get('update-rc.d'):
(rc, out, err) = module.run_command("%s %s defaults" % (location['update-rc.d'], name))
elif location.get('chkconfig'):
(rc, out, err) = module.run_command("%s %s on" % (location['chkconfig'], name))
else:
if location.get('update-rc.d'):
(rc, out, err) = module.run_command("%s %s disable" % (location['update-rc.d'], name))
elif location.get('chkconfig'):
(rc, out, err) = module.run_command("%s %s off" % (location['chkconfig'], name))
# Assigned above, might be useful is something goes sideways
if not module.check_mode and result['status']['enabled']['changed']:
result['status']['enabled']['rc'] = rc
result['status']['enabled']['stdout'] = out
result['status']['enabled']['stderr'] = err
rc, out, err = None, None, None
if "illegal runlevel specified" in result['status']['enabled']['stderr']:
module.fail_json(msg="Illegal runlevel specified for enable operation on service %s" % name, **result)
# END: Enable/Disable
###########################################################################
###########################################################################
# BEGIN: state
result['status'].setdefault(module.params['state'], {})
result['status'][module.params['state']]['changed'] = False
result['status'][module.params['state']]['rc'] = None
result['status'][module.params['state']]['stdout'] = None
result['status'][module.params['state']]['stderr'] = None
if action:
action = re.sub(r'p?ed$', '', action.lower())
def runme(doit):
args = module.params['arguments']
cmd = "%s %s %s" % (script, doit, "" if args is None else args)
# how to run
if module.params['daemonize']:
(rc, out, err) = daemonize(cmd)
else:
(rc, out, err) = module.run_command(cmd)
# FIXME: ERRORS
if rc != 0:
module.fail_json(msg="Failed to %s service: %s" % (action, name), rc=rc, stdout=out, stderr=err)
return (rc, out, err)
if action == 'restart':
result['changed'] = True
result['status'][module.params['state']]['changed'] = True
if not module.check_mode:
# cannot rely on existing 'restart' in init script
for dothis in ['stop', 'start']:
(rc, out, err) = runme(dothis)
if sleep_for:
sleep(sleep_for)
elif is_started != (action == 'start'):
result['changed'] = True
result['status'][module.params['state']]['changed'] = True
if not module.check_mode:
rc, out, err = runme(action)
elif is_started == (action == 'stop'):
result['changed'] = True
result['status'][module.params['state']]['changed'] = True
if not module.check_mode:
rc, out, err = runme(action)
if not module.check_mode and result['status'][module.params['state']]['changed']:
result['status'][module.params['state']]['rc'] = rc
result['status'][module.params['state']]['stdout'] = out
result['status'][module.params['state']]['stderr'] = err
rc, out, err = None, None, None
# END: state
###########################################################################
module.exit_json(**result)
if __name__ == '__main__':
main()
|
pilou-/ansible
|
lib/ansible/modules/system/sysvinit.py
|
Python
|
gpl-3.0
| 13,409
|
[
"Brian"
] |
8540b1cb277ef6b9b2e28f5add608acfcdb3030a210d8d84c64ec02c235b4c12
|
import threading
import os
import functools
import sublime
from .show_error import show_error
from .console_write import console_write
from .unicode import unicode_from_os
from .clear_directory import clear_directory, delete_directory, clean_old_files
from .automatic_upgrader import AutomaticUpgrader
from .package_manager import PackageManager
from .open_compat import open_compat
from .package_io import package_file_exists
from .settings import preferences_filename, pc_settings_filename, load_list_setting, save_list_setting
from . import loader, text, __version__
from .providers.release_selector import is_compatible_version
class PackageCleanup(threading.Thread):
"""
Cleans up folders for packages that were removed, but that still have files
in use.
"""
def __init__(self):
self.manager = PackageManager()
settings = sublime.load_settings(pc_settings_filename())
# We no longer use the installed_dependencies setting because it is not
# necessary and created issues with settings shared across operating systems
if settings.get('installed_dependencies'):
settings.erase('installed_dependencies')
sublime.save_settings(pc_settings_filename())
self.original_installed_packages = load_list_setting(settings, 'installed_packages')
self.remove_orphaned = settings.get('remove_orphaned', True)
threading.Thread.__init__(self)
def run(self):
# Ensure we record the installation of Package Control itself
if 'Package Control' not in self.original_installed_packages:
params = {
'package': 'Package Control',
'operation': 'install',
'version': __version__
}
self.manager.record_usage(params)
self.original_installed_packages.append('Package Control')
found_packages = []
installed_packages = list(self.original_installed_packages)
found_dependencies = []
installed_dependencies = self.manager.list_dependencies()
# We scan the Installed Packages folder in ST3 before we check for
# dependencies since some dependencies might be specified by a
# .sublime-package-new that has not yet finished being installed.
if int(sublime.version()) >= 3000:
installed_path = sublime.installed_packages_path()
for file in os.listdir(installed_path):
# If there is a package file ending in .sublime-package-new, it
# means that the .sublime-package file was locked when we tried
# to upgrade, so the package was left in ignored_packages and
# the user was prompted to restart Sublime Text. Now that the
# package is not loaded, we can replace the old version with the
# new one.
if file[-20:] == '.sublime-package-new' and file != loader.loader_package_name + '.sublime-package-new':
package_name = file.replace('.sublime-package-new', '')
package_file = os.path.join(installed_path, package_name + '.sublime-package')
if os.path.exists(package_file):
os.remove(package_file)
os.rename(os.path.join(installed_path, file), package_file)
console_write(
u'''
Finished replacing %s.sublime-package
''',
package_name
)
continue
if file[-16:] != '.sublime-package':
continue
package_name = file.replace('.sublime-package', '')
if package_name == loader.loader_package_name:
found_dependencies.append(package_name)
continue
# Cleanup packages that were installed via Package Control, but
# we removed from the "installed_packages" list - usually by
# removing them from another computer and the settings file
# being synced.
if self.remove_orphaned and package_name not in self.original_installed_packages and package_file_exists(package_name, 'package-metadata.json'):
# Since Windows locks the .sublime-package files, we must
# do a dance where we disable the package first, which has
# to be done in the main Sublime Text thread.
package_filename = os.path.join(installed_path, file)
# We use a functools.partial to generate the on-complete callback in
# order to bind the current value of the parameters, unlike lambdas.
sublime.set_timeout(functools.partial(self.remove_package_file, package_name, package_filename), 10)
else:
found_packages.append(package_name)
required_dependencies = set(self.manager.find_required_dependencies())
extra_dependencies = list(set(installed_dependencies) - required_dependencies)
# Clean up unneeded dependencies so that found_dependencies will only
# end up having required dependencies added to it
for dependency in extra_dependencies:
dependency_dir = os.path.join(sublime.packages_path(), dependency)
if delete_directory(dependency_dir):
console_write(
u'''
Removed directory for unneeded dependency %s
''',
dependency
)
else:
cleanup_file = os.path.join(dependency_dir, 'package-control.cleanup')
if not os.path.exists(cleanup_file):
open_compat(cleanup_file, 'w').close()
console_write(
u'''
Unable to remove directory for unneeded dependency %s -
deferring until next start
''',
dependency
)
# Make sure when cleaning up the dependency files that we remove the loader for it also
loader.remove(dependency)
for package_name in os.listdir(sublime.packages_path()):
found = True
package_dir = os.path.join(sublime.packages_path(), package_name)
if not os.path.isdir(package_dir):
continue
clean_old_files(package_dir)
# Cleanup packages/dependencies that could not be removed due to in-use files
cleanup_file = os.path.join(package_dir, 'package-control.cleanup')
if os.path.exists(cleanup_file):
if delete_directory(package_dir):
console_write(
u'''
Removed old directory %s
''',
package_name
)
found = False
else:
if not os.path.exists(cleanup_file):
open_compat(cleanup_file, 'w').close()
console_write(
u'''
Unable to remove old directory %s - deferring until next
start
''',
package_name
)
# Finish reinstalling packages that could not be upgraded due to
# in-use files
reinstall = os.path.join(package_dir, 'package-control.reinstall')
if os.path.exists(reinstall):
metadata_path = os.path.join(package_dir, 'package-metadata.json')
if not clear_directory(package_dir, [metadata_path]):
if not os.path.exists(reinstall):
open_compat(reinstall, 'w').close()
def show_still_locked(package_name):
show_error(
u'''
An error occurred while trying to finish the upgrade of
%s. You will most likely need to restart your computer
to complete the upgrade.
''',
package_name
)
# We use a functools.partial to generate the on-complete callback in
# order to bind the current value of the parameters, unlike lambdas.
sublime.set_timeout(functools.partial(show_still_locked, package_name), 10)
else:
self.manager.install_package(package_name)
if package_file_exists(package_name, 'package-metadata.json'):
# This adds previously installed packages from old versions of
# PC. As of PC 3.0, this should basically never actually be used
# since installed_packages was added in late 2011.
if not self.original_installed_packages:
installed_packages.append(package_name)
params = {
'package': package_name,
'operation': 'install',
'version':
self.manager.get_metadata(package_name).get('version')
}
self.manager.record_usage(params)
# Cleanup packages that were installed via Package Control, but
# we removed from the "installed_packages" list - usually by
# removing them from another computer and the settings file
# being synced.
elif self.remove_orphaned and package_name not in self.original_installed_packages:
self.manager.backup_package_dir(package_name)
if delete_directory(package_dir):
console_write(
u'''
Removed directory for orphaned package %s
''',
package_name
)
found = False
else:
if not os.path.exists(cleanup_file):
open_compat(cleanup_file, 'w').close()
console_write(
u'''
Unable to remove directory for orphaned package %s -
deferring until next start
''',
package_name
)
if package_name[-20:] == '.package-control-old':
console_write(
u'''
Removed old directory %s
''',
package_name
)
delete_directory(package_dir)
# Skip over dependencies since we handle them separately
if (package_file_exists(package_name, 'dependency-metadata.json') or package_file_exists(package_name, '.sublime-dependency')) and (package_name == loader.loader_package_name or loader.exists(package_name)):
found_dependencies.append(package_name)
continue
if found:
found_packages.append(package_name)
invalid_packages = []
invalid_dependencies = []
# Check metadata to verify packages were not improperly installed
for package in found_packages:
if package == 'User':
continue
metadata = self.manager.get_metadata(package)
if metadata:
if not self.is_compatible(metadata):
invalid_packages.append(package)
# Make sure installed dependencies are not improperly installed
for dependency in found_dependencies:
metadata = self.manager.get_metadata(dependency, is_dependency=True)
if metadata and not self.is_compatible(metadata):
invalid_dependencies.append(package)
if invalid_packages or invalid_dependencies:
def show_sync_error():
message = u''
if invalid_packages:
package_s = 's were' if len(invalid_packages) != 1 else ' was'
message += text.format(
u'''
The following incompatible package%s found installed:
%s
''',
(package_s, '\n'.join(invalid_packages))
)
if invalid_dependencies:
dependency_s = 'ies were' if len(invalid_dependencies) != 1 else 'y was'
message += text.format(
u'''
The following incompatible dependenc%s found installed:
%s
''',
(dependency_s, '\n'.join(invalid_dependencies))
)
message += text.format(
u'''
This is usually due to syncing packages across different
machines in a way that does not check package metadata for
compatibility.
Please visit https://packagecontrol.io/docs/syncing for
information about how to properly sync configuration and
packages across machines.
To restore package functionality, please remove each listed
package and reinstall it.
'''
)
show_error(message)
sublime.set_timeout(show_sync_error, 100)
sublime.set_timeout(lambda: self.finish(installed_packages, found_packages, found_dependencies), 10)
def remove_package_file(self, name, filename):
"""
On Windows, .sublime-package files are locked when imported, so we must
disable the package, delete it and then re-enable the package.
:param name:
The name of the package
:param filename:
The filename of the package
"""
def do_remove():
try:
os.remove(filename)
console_write(
u'''
Removed orphaned package %s
''',
name
)
except (OSError) as e:
console_write(
u'''
Unable to remove orphaned package %s - deferring until
next start: %s
''',
(name, unicode_from_os(e))
)
finally:
# Always re-enable the package so it doesn't get stuck
pref_filename = preferences_filename()
settings = sublime.load_settings(pref_filename)
ignored = load_list_setting(settings, 'ignored_packages')
new_ignored = list(ignored)
try:
new_ignored.remove(name)
except (ValueError):
pass
save_list_setting(settings, pref_filename, 'ignored_packages', new_ignored, ignored)
# Disable the package so any filesystem locks are released
pref_filename = preferences_filename()
settings = sublime.load_settings(pref_filename)
ignored = load_list_setting(settings, 'ignored_packages')
new_ignored = list(ignored)
new_ignored.append(name)
save_list_setting(settings, pref_filename, 'ignored_packages', new_ignored, ignored)
sublime.set_timeout(do_remove, 700)
def is_compatible(self, metadata):
"""
Detects if a package is compatible with the current Sublime Text install
:param metadata:
A dict from a metadata file
:return:
If the package is compatible
"""
sublime_text = metadata.get('sublime_text')
platforms = metadata.get('platforms', [])
# This indicates the metadata is old, so we assume a match
if not sublime_text and not platforms:
return True
if not is_compatible_version(sublime_text):
return False
if not isinstance(platforms, list):
platforms = [platforms]
platform_selectors = [sublime.platform() + '-' + sublime.arch(),
sublime.platform(), '*']
for selector in platform_selectors:
if selector in platforms:
return True
return False
def finish(self, installed_packages, found_packages, found_dependencies):
"""
A callback that can be run the main UI thread to perform saving of the
Package Control.sublime-settings file. Also fires off the
:class:`AutomaticUpgrader`.
:param installed_packages:
A list of the string package names of all "installed" packages,
even ones that do not appear to be in the filesystem.
:param found_packages:
A list of the string package names of all packages that are
currently installed on the filesystem.
:param found_dependencies:
A list of the string package names of all dependencies that are
currently installed on the filesystem.
"""
# Make sure we didn't accidentally ignore packages because something
# was interrupted before it completed.
pc_filename = pc_settings_filename()
pc_settings = sublime.load_settings(pc_filename)
in_process = load_list_setting(pc_settings, 'in_process_packages')
if in_process:
filename = preferences_filename()
settings = sublime.load_settings(filename)
ignored = load_list_setting(settings, 'ignored_packages')
new_ignored = list(ignored)
for package in in_process:
if package in new_ignored:
# This prevents removing unused dependencies from being messed up by
# the functionality to re-enable packages that were left disabled
# by an error.
if loader.loader_package_name == package and loader.is_swapping():
continue
console_write(
u'''
The package %s is being re-enabled after a Package
Control operation was interrupted
''',
package
)
new_ignored.remove(package)
save_list_setting(settings, filename, 'ignored_packages', new_ignored, ignored)
save_list_setting(pc_settings, pc_filename, 'in_process_packages', [])
save_list_setting(pc_settings, pc_filename, 'installed_packages',
installed_packages, self.original_installed_packages)
AutomaticUpgrader(found_packages, found_dependencies).start()
|
herove/dotfiles
|
sublime/Packages/Package Control/package_control/package_cleanup.py
|
Python
|
mit
| 19,285
|
[
"VisIt"
] |
dc618a8624443b86f3d27d7d53fe2aecd1a1c4bf7b5adf171838a0f3d5d3289c
|
# Copyright 2019 DeepMind Technologies Limited. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""JAX functions for working with probability distributions.
Reinforcement learning algorithms often require to estimate suitably
parametrized probability distributions. In this subpackage a distribution is
represented as a collection of functions that may be used to sample from a
distribution, to evaluate its probability mass (or density) function, and
to compute statistics such as its entropy.
"""
import collections
import chex
import distrax
import jax
import jax.numpy as jnp
Array = chex.Array
Numeric = chex.Numeric
DiscreteDistribution = collections.namedtuple(
"DiscreteDistribution", ["sample", "probs", "logprob", "entropy", "kl"])
ContinuousDistribution = collections.namedtuple(
"ContinuousDistribution", ["sample", "prob", "logprob", "entropy",
"kl_to_standard_normal", "kl"])
def categorical_sample(key, probs):
"""Sample from a set of discrete probabilities."""
return distrax.Categorical(probs=probs).sample(seed=key)
def softmax(temperature=1.):
"""A softmax distribution."""
def sample_fn(key: Array, logits: Array):
return distrax.Softmax(logits, temperature).sample(seed=key)
def probs_fn(logits: Array):
return distrax.Softmax(logits, temperature).probs
def logprob_fn(sample: Array, logits: Array):
return distrax.Softmax(logits, temperature).log_prob(sample)
def entropy_fn(logits: Array):
return distrax.Softmax(logits, temperature).entropy()
def kl_fn(p_logits: Array, q_logits: Array):
return categorical_kl_divergence(p_logits, q_logits, temperature)
return DiscreteDistribution(sample_fn, probs_fn, logprob_fn, entropy_fn,
kl_fn)
def clipped_entropy_softmax(temperature=1., entropy_clip=1.):
"""A softmax distribution with clipped entropy (1 is eq to not clipping)."""
def sample_fn(key: Array, logits: Array, action_spec=None):
del action_spec
return distrax.Softmax(logits, temperature).sample(seed=key)
def probs_fn(logits: Array, action_spec=None):
del action_spec
return distrax.Softmax(logits, temperature).probs
def logprob_fn(sample: Array, logits: Array, action_spec=None):
del action_spec
return distrax.Softmax(logits, temperature).log_prob(sample)
def entropy_fn(logits: Array):
return jnp.minimum(
distrax.Softmax(logits, temperature).entropy(),
entropy_clip * jnp.log(logits.shape[-1]))
def kl_fn(p_logits: Array, q_logits: Array):
return categorical_kl_divergence(p_logits, q_logits, temperature)
return DiscreteDistribution(sample_fn, probs_fn, logprob_fn, entropy_fn,
kl_fn)
def _mix_with_uniform(probs, epsilon):
"""Mix an arbitrary categorical distribution with a uniform distribution."""
num_actions = probs.shape[-1]
uniform_probs = jnp.ones_like(probs) / num_actions
return (1 - epsilon) * probs + epsilon * uniform_probs
def epsilon_softmax(epsilon, temperature):
"""An epsilon-softmax distribution."""
def sample_fn(key: Array, logits: Array):
probs = distrax.Softmax(logits=logits, temperature=temperature).probs
return distrax.Categorical(
probs=_mix_with_uniform(probs, epsilon)).sample(seed=key)
def probs_fn(logits: Array):
probs = distrax.Softmax(logits=logits, temperature=temperature).probs
return distrax.Categorical(
probs=_mix_with_uniform(probs, epsilon)).probs
def log_prob_fn(sample: Array, logits: Array):
probs = distrax.Softmax(logits=logits, temperature=temperature).probs
return distrax.Categorical(
probs=_mix_with_uniform(probs, epsilon)).log_prob(sample)
def entropy_fn(logits: Array):
probs = distrax.Softmax(logits=logits, temperature=temperature).probs
return distrax.Categorical(
probs=_mix_with_uniform(probs, epsilon)).entropy()
def kl_fn(p_logits: Array, q_logits: Array):
return categorical_kl_divergence(p_logits, q_logits, temperature)
return DiscreteDistribution(sample_fn, probs_fn, log_prob_fn, entropy_fn,
kl_fn)
def greedy():
"""A greedy distribution."""
def sample_fn(key: Array, preferences: Array):
return distrax.Greedy(preferences).sample(seed=key)
def probs_fn(preferences: Array):
return distrax.Greedy(preferences).probs
def log_prob_fn(sample: Array, preferences: Array):
return distrax.Greedy(preferences).log_prob(sample)
def entropy_fn(preferences: Array):
return distrax.Greedy(preferences).entropy()
return DiscreteDistribution(sample_fn, probs_fn, log_prob_fn, entropy_fn,
None)
def epsilon_greedy(epsilon=None):
"""An epsilon-greedy distribution."""
def sample_fn(key: Array, preferences: Array, epsilon=epsilon):
return distrax.EpsilonGreedy(preferences, epsilon).sample(seed=key)
def probs_fn(preferences: Array, epsilon=epsilon):
return distrax.EpsilonGreedy(preferences, epsilon).probs
def logprob_fn(sample: Array, preferences: Array, epsilon=epsilon):
return distrax.EpsilonGreedy(preferences, epsilon).log_prob(sample)
def entropy_fn(preferences: Array, epsilon=epsilon):
return distrax.EpsilonGreedy(preferences, epsilon).entropy()
return DiscreteDistribution(sample_fn, probs_fn, logprob_fn, entropy_fn, None)
def safe_epsilon_softmax(epsilon, temperature):
"""Tolerantly handles the temperature=0 case."""
egreedy = epsilon_greedy(epsilon)
unsafe = epsilon_softmax(epsilon, temperature)
def sample_fn(key: Array, logits: Array):
return jax.lax.cond(temperature > 0,
(key, logits), lambda tup: unsafe.sample(*tup),
(key, logits), lambda tup: egreedy.sample(*tup))
def probs_fn(logits: Array):
return jax.lax.cond(temperature > 0,
logits, unsafe.probs,
logits, egreedy.probs)
def log_prob_fn(sample: Array, logits: Array):
return jax.lax.cond(temperature > 0,
(sample, logits), lambda tup: unsafe.logprob(*tup),
(sample, logits), lambda tup: egreedy.logprob(*tup))
def entropy_fn(logits: Array):
return jax.lax.cond(temperature > 0,
logits, unsafe.entropy,
logits, egreedy.entropy)
def kl_fn(p_logits: Array, q_logits: Array):
return categorical_kl_divergence(p_logits, q_logits, temperature)
return DiscreteDistribution(sample_fn, probs_fn, log_prob_fn, entropy_fn,
kl_fn)
def _add_gaussian_noise(key, sample, sigma):
noise = jax.random.normal(key, shape=sample.shape) * sigma
return sample + noise
def gaussian_diagonal(sigma=None):
"""A gaussian distribution with diagonal covariance matrix."""
def sample_fn(key: Array, mu: Array, sigma: Array = sigma):
return distrax.MultivariateNormalDiag(
mu, jnp.ones_like(mu) * sigma).sample(seed=key)
def prob_fn(sample: Array, mu: Array, sigma: Array = sigma):
return distrax.MultivariateNormalDiag(
mu, jnp.ones_like(mu) * sigma).prob(sample)
def logprob_fn(sample: Array, mu: Array, sigma: Array = sigma):
return distrax.MultivariateNormalDiag(
mu, jnp.ones_like(mu) * sigma).log_prob(sample)
def entropy_fn(mu: Array, sigma: Array = sigma):
return distrax.MultivariateNormalDiag(
mu, jnp.ones_like(mu) * sigma).entropy()
def kl_to_standard_normal_fn(mu: Array, sigma: Array = sigma):
return distrax.MultivariateNormalDiag(
mu, jnp.ones_like(mu) * sigma).kl_divergence(
distrax.MultivariateNormalDiag(
jnp.zeros_like(mu), jnp.ones_like(mu)))
def kl_fn(mu_0: Array, sigma_0: Numeric, mu_1: Array, sigma_1: Numeric):
return distrax.MultivariateNormalDiag(
mu_0, jnp.ones_like(mu_0) * sigma_0).kl_divergence(
distrax.MultivariateNormalDiag(mu_1, jnp.ones_like(mu_1) * sigma_1))
return ContinuousDistribution(sample_fn, prob_fn, logprob_fn, entropy_fn,
kl_to_standard_normal_fn, kl_fn)
def squashed_gaussian(sigma_min=-4, sigma_max=0.):
"""A squashed gaussian distribution with diagonal covariance matrix."""
def minmaxvals(a, action_spec):
# broadcasts action spec to action shape
min_shape = action_spec.minimum.shape or (1,)
max_shape = action_spec.maximum.shape or (1,)
min_vals = jnp.broadcast_to(action_spec.minimum, a.shape[:-1] + min_shape)
max_vals = jnp.broadcast_to(action_spec.maximum, a.shape[:-1] + max_shape)
return min_vals, max_vals
def sigma_activation(sigma, sigma_min=sigma_min, sigma_max=sigma_max):
return jnp.exp(sigma_min + 0.5 * (sigma_max - sigma_min) *
(jnp.tanh(sigma) + 1.))
def mu_activation(mu):
return jnp.tanh(mu)
def transform(a, action_spec):
min_vals, max_vals = minmaxvals(a, action_spec)
scale = (max_vals - min_vals) * 0.5
actions = (jnp.tanh(a) + 1.0) * scale + min_vals
return actions
def inv_transform(a, action_spec):
min_vals, max_vals = minmaxvals(a, action_spec)
scale = (max_vals - min_vals) * 0.5
actions_tanh = (a - min_vals) / scale - 1.
return jnp.arctanh(actions_tanh)
def log_det_jacobian(a, action_spec):
min_vals, max_vals = minmaxvals(a, action_spec)
scale = (max_vals - min_vals) * 0.5
log_ = jnp.sum(jnp.log(scale))
# computes sum log (1-tanh(a)**2)
log_ += jnp.sum(2. * (jnp.log(2.) - a - jax.nn.softplus(-2. * a)))
return log_
def sample_fn(key: Array,
mu: Array,
sigma: Array,
action_spec,
):
mu = mu_activation(mu)
sigma = sigma_activation(sigma)
action = _add_gaussian_noise(key, mu, sigma)
return transform(action, action_spec)
def prob_fn(sample: Array, mu: Array, sigma: Array, action_spec):
# Support scalar and vector `sigma`. If vector, mu.shape==sigma.shape.
mu = mu_activation(mu)
sigma = sigma_activation(sigma)
# Compute pdf for multivariate gaussian.
d = mu.shape[-1]
det = jnp.prod(sigma**2, axis=-1)
z = ((2 * jnp.pi)**(0.5 * d)) * (det**0.5)
exp = jnp.exp(-0.5 * jnp.sum(
((mu - inv_transform(sample, action_spec)) / sigma)**2, axis=-1))
det_jacobian = jnp.prod(jnp.clip(1 - sample**2, 0., 1.) + 1e-6)
return exp / (z * det_jacobian)
def logprob_fn(sample: Array, mu: Array, sigma: Array, action_spec):
# Support scalar and vector `sigma`. If vector, mu.shape==sigma.shape.
mu = mu_activation(mu)
sigma = sigma_activation(sigma)
# Compute logpdf for multivariate gaussian in a numerically safe way.
d = mu.shape[-1]
half_logdet = jnp.sum(jnp.log(sigma), axis=-1)
logz = half_logdet + 0.5 * d * jnp.log(2 * jnp.pi)
logexp = -0.5 * jnp.sum(
((mu - inv_transform(sample, action_spec)) / sigma)**2, axis=-1)
return logexp - logz - log_det_jacobian(sample, action_spec)
def entropy_fn(mu: Array, sigma: Array):
# Support scalar and vector `sigma`. If vector, mu.shape==sigma.shape.
mu = mu_activation(mu)
sigma = sigma_activation(sigma)
# Compute entropy in a numerically safe way.
d = mu.shape[-1]
half_logdet = jnp.sum(jnp.log(sigma), axis=-1)
return half_logdet + 0.5 * d * (1 + jnp.log(2 * jnp.pi))
def kl_to_standard_normal_fn(mu: Array,
sigma: Array,
per_dimension: bool = False):
mu = mu_activation(mu)
sigma = sigma_activation(sigma)
v = jnp.clip(sigma**2, 1e-6, 1e6)
kl = 0.5 * (v + mu**2 - jnp.ones_like(mu) - jnp.log(v))
if not per_dimension:
kl = jnp.sum(kl, axis=-1)
return kl
def kl_fn(mu_0: Array, sigma_0: Numeric, mu_1: Array, sigma_1: Numeric):
sigma_0 = sigma_activation(sigma_0)
mu_0 = mu_activation(mu_0)
sigma_1 = sigma_activation(sigma_1)
mu_1 = mu_activation(mu_1)
return multivariate_normal_kl_divergence(mu_0, sigma_0, mu_1, sigma_1)
return ContinuousDistribution(sample_fn, prob_fn, logprob_fn, entropy_fn,
kl_to_standard_normal_fn, kl_fn)
def categorical_importance_sampling_ratios(pi_logits_t: Array,
mu_logits_t: Array,
a_t: Array) -> Array:
"""Compute importance sampling ratios from logits.
Args:
pi_logits_t: unnormalized logits at time t for the target policy.
mu_logits_t: unnormalized logits at time t for the behavior policy.
a_t: actions at time t.
Returns:
importance sampling ratios.
"""
return distrax.importance_sampling_ratios(distrax.Categorical(
pi_logits_t), distrax.Categorical(mu_logits_t), a_t)
def categorical_cross_entropy(
labels: Array,
logits: Array
) -> Array:
"""Computes the softmax cross entropy between sets of logits and labels.
See "Deep Learning" by Goodfellow et al.
(http://www.deeplearningbook.org/contents/prob.html).
Args:
labels: a valid probability distribution (non-negative, sum to 1).
logits: unnormalized log probabilities.
Returns:
a scalar loss.
"""
chex.assert_rank([logits, labels], 1)
return -jnp.sum(labels * jax.nn.log_softmax(logits))
def categorical_kl_divergence(
p_logits: Array,
q_logits: Array,
temperature: float = 1.
) -> Array:
"""Compute the KL between two categorical distributions from their logits.
Args:
p_logits: unnormalized logits for the first distribution.
q_logits: unnormalized logits for the second distribution.
temperature: the temperature for the softmax distribution, defaults at 1.
Returns:
the kl divergence between the distributions.
"""
return distrax.Softmax(p_logits, temperature).kl_divergence(
distrax.Softmax(q_logits, temperature))
def multivariate_normal_kl_divergence(
mu_0: Array, sigma_0: Numeric, mu_1: Array, sigma_1: Numeric,
) -> Array:
"""Compute the KL between 2 gaussian distrs with diagonal covariance matrices.
Args:
mu_0: array like of mean values for policy 0
sigma_0: array like of std values for policy 0
mu_1: array like of mean values for policy 1
sigma_1: array like of std values for policy 1
Returns:
the kl divergence between the distributions.
"""
return distrax.MultivariateNormalDiag(mu_0, sigma_0).kl_divergence(
distrax.MultivariateNormalDiag(mu_1, sigma_1))
|
deepmind/rlax
|
rlax/_src/distributions.py
|
Python
|
apache-2.0
| 15,076
|
[
"Gaussian"
] |
a66f2686fa5e409200901f0c1312acd4486b321621ebd2718c6b429281034085
|
#!/usr/bin/env python
#20-11-2017
#Authors:Sebastian ECHEVERRI RESTREPO,
# sebastian.echeverri.restrepo@skf.com, sebastianecheverrir@gmail.com
# James EWEN
# j.ewen14@imperial.ac.uk, jimmyewen@gmail.com
#################################################################################3
# This file generates all the input files needed by moltemplate
# (.lt extension) and calls it to generate the input files needed
# by lammps
#################################################################################3
import os
import sys
sys.path.append("root")
from Rough import Rough
from AddEAM import AddEAM
from Fe2O3 import Fe2O3
from AddFe2O3 import AddFe2O3
def lopls(xlo,xhi,ylo,yhi,zlo,zhi,OFMn_x,OFMn_y,nAlkane, Alkanen_x,\
Alkanen_y, Alkanen_z, Alkane, BZBZ, BZBZn_x, BZBZn_y, BZBZn_z,\
Squalane, Squalanen_x, Squalanen_y, Squalanen_z, OFM ,OFMtype, Surfaces,\
FractalLevels,RMSin,H,boxLenghtX,boxLenghtY,boxLenghtZ,aFe,Separation):
f = open('lopls.lt','wr+')
#############################################################
f.write('import "root/loplsaaMETAL.lt"')
f.write("\n")
if Surfaces == 1:
f.write('import "WEA.lt"')
if Surfaces == 2:
f.write('import "Fe2O3.lt"')
## This part builds the basic CH, CH2, CH3, COOH, CONH2, RCOOR
f.write("\n")
f.write("\n")
f.write('CH inherits LOPLSAA {')
f.write("\n")
f.write(' write("Data Atoms") {')
f.write("\n")
f.write(' $atom:C $mol:... @atom:87 0.00 0.000 0.000 0.000')
f.write("\n")
f.write(' $atom:H $mol:... @atom:89 0.00 0.0 1.0 0.000')
f.write("\n")
f.write(' }')
f.write("\n")
f.write("\n")
f.write(' write(\'Data Bond List\') {')
f.write("\n")
f.write(' $bond:CH $atom:C $atom:H')
f.write("\n")
f.write('}')
f.write("\n")
f.write(' } #CH')
f.write("\n")
f.write("\n")
f.write('CH2 inherits LOPLSAA {')
f.write("\n")
f.write(' write("Data Atoms") {')
f.write("\n")
f.write(' $atom:C $mol:... @atom:81 0.00 0.000 0.000 0.000')
f.write("\n")
f.write(' $atom:H1 $mol:... @atom:8500 0.00 0.892430762954 0.63104384422426 0.000')
f.write("\n")
f.write(' $atom:H2 $mol:... @atom:8500 0.00 -0.892430762954 0.63104384422426 -0.000')
f.write("\n")
f.write(' }')
f.write("\n")
f.write("\n")
f.write(' write(\'Data Bond List\') {')
f.write("\n")
f.write(' $bond:CH1 $atom:C $atom:H1')
f.write("\n")
f.write(' $bond:CH2 $atom:C $atom:H2')
f.write("\n")
f.write('}')
f.write("\n")
f.write(' } #CH2')
f.write("\n")
f.write("\n")
f.write('CH3 inherits LOPLSAA {')
f.write("\n")
f.write(' write("Data Atoms") {')
f.write("\n")
f.write(' $atom:C $mol:... @atom:80 0.00 0.000000 0.000000 0.000000')
f.write("\n")
f.write(' $atom:H1 $mol:... @atom:85 0.00 0.8924307629540046 0.6310438442242609 0.000000')
f.write("\n")
f.write(' $atom:H2 $mol:... @atom:85 0.00 -0.8924307629540046 0.6310438442242609 -0.000000')
f.write("\n")
f.write(' $atom:H3 $mol:... @atom:85 0.00 -0.000000 -0.6310438442242609 -0.8924307629540046')
f.write("\n")
f.write(' }')
f.write("\n")
f.write("\n")
f.write(' write(\'Data Bond List\') {')
f.write("\n")
f.write(' $bond:CH1 $atom:C $atom:H1')
f.write("\n")
f.write(' $bond:CH2 $atom:C $atom:H2')
f.write("\n")
f.write(' $bond:CH3 $atom:C $atom:H3')
f.write("\n")
f.write(' }')
f.write("\n")
f.write("\n")
f.write('} # CH3')
f.write("\n")
f.write("\n")
f.write('COOH inherits LOPLSAA {')
f.write("\n")
f.write(' write("Data Atoms") {')
f.write("\n")
f.write(' $atom:C $mol:... @atom:209 0.00 0.000000 0.000000 0.000000')
f.write("\n")
f.write(' $atom:O $mol:... @atom:210 0.00 0.00000 1.000000 0.000000')
f.write("\n")
f.write(' $atom:OH $mol:... @atom:211 0.00 -0.000000 0.000000 -1.000000')
f.write("\n")
f.write(' $atom:HO $mol:... @atom:212 0.00 -0.000000 0.400000 -2.00000')
f.write("\n")
f.write(' }')
f.write("\n")
f.write("\n")
f.write(' write(\'Data Bond List\') {')
f.write("\n")
f.write(' $bond:CO $atom:C $atom:O')
f.write("\n")
f.write(' $bond:COH $atom:C $atom:OH')
f.write("\n")
f.write(' $bond:OHHO $atom:OH $atom:HO')
f.write("\n")
f.write(' }')
f.write("\n")
f.write("\n")
f.write('} # COOH')
f.write("\n")
f.write("\n")
f.write('CONH2 inherits LOPLSAA {')
f.write("\n")
f.write(' write("Data Atoms") {')
f.write("\n")
f.write(' $atom:C $mol:... @atom:177 0.00 0.000000 0.000000 0.000000')
f.write("\n")
f.write(' $atom:O $mol:... @atom:178 0.00 0.00000 1.000000 0.000000')
f.write("\n")
f.write(' $atom:N $mol:... @atom:179 0.00 -0.000000 0.000000 -1.000000')
f.write("\n")
f.write(' $atom:H1 $mol:... @atom:182 0.00 -0.000000 -1.00000 -1.00000')
f.write("\n")
f.write(' $atom:H2 $mol:... @atom:182 0.00 -0.000000 0.000000 -2.00000')
f.write("\n")
f.write(' }')
f.write("\n")
f.write("\n")
f.write(' write(\'Data Bond List\') {')
f.write("\n")
f.write(' $bond:CO $atom:C $atom:O')
f.write("\n")
f.write(' $bond:CN $atom:C $atom:N')
f.write("\n")
f.write(' $bond:NH1 $atom:N $atom:H1')
f.write("\n")
f.write(' $bond:NH2 $atom:N $atom:H2')
f.write("\n")
f.write(' }')
f.write("\n")
f.write("\n")
f.write('} # CONH2')
f.write("\n")
f.write("\n")
f.write('RCOOR inherits LOPLSAA {')
f.write("\n")
f.write(' write("Data Atoms") {')
f.write("\n")
f.write(' $atom:C $mol:... @atom:406 0.00 0.000000 0.000000 0.000000')
f.write("\n")
f.write(' $atom:O $mol:... @atom:407 0.00 0.00000 1.000000 0.000000')
f.write("\n")
f.write(' $atom:OS $mol:... @atom:408 0.00 -0.000000 0.000000 -1.000000')
f.write("\n")
f.write(' $atom:CT $mol:... @atom:409 0.00 -0.000000 0.00000 -2.00000')
f.write("\n")
f.write(' $atom:H1 $mol:... @atom:8500 0.00 0.892430762954 0.63104384422426 -2.000')
f.write("\n")
f.write(' $atom:H2 $mol:... @atom:8500 0.00 -0.892430762954 0.63104384422426 -2.000')
f.write("\n")
f.write(' $atom:CT2 $mol:... @atom:100 0.00 -0.000000 0.00000 -3.00000')
f.write("\n")
f.write(' $atom:HC $mol:... @atom:8500 0.00 -0.892430762954 -0.63104384422426 -3.000')
# f.write(' $atom:HC $mol:... @atom:89 0.00 -0.892430762954 -0.63104384422426 -3.000')
f.write("\n")
f.write(' $atom:OH $mol:... @atom:96 0.00 0.892430762954 -0.63104384422426 -3.000')
f.write("\n")
f.write(' $atom:HO $mol:... @atom:97 0.00 1.6 -1.2 -3.000')
f.write("\n")
f.write(' $atom:CT3 $mol:... @atom:99 0.00 -0.000000 0.00000 -4.00000')
f.write("\n")
f.write(' $atom:H3 $mol:... @atom:8500 0.00 0.892430762954 0.63104384422426 -4.000')
f.write("\n")
f.write(' $atom:H4 $mol:... @atom:8500 0.00 -0.892430762954 0.63104384422426 -4.000')
f.write("\n")
f.write(' $atom:OH2 $mol:... @atom:96 0.00 -0.000000 0.00000 -5.00000')
f.write("\n")
f.write(' $atom:HO2 $mol:... @atom:97 0.00 -0.000000 0.00000 -6.00000')
f.write("\n")
f.write(' }')
f.write("\n")
f.write("\n")
f.write(' write(\'Data Bond List\') {')
f.write("\n")
f.write(' $bond:CO $atom:C $atom:O')
f.write("\n")
f.write(' $bond:COS $atom:C $atom:OS')
f.write("\n")
f.write(' $bond:OSCT $atom:OS $atom:CT')
f.write("\n")
f.write(' $bond:CTH1 $atom:CT $atom:H1')
f.write("\n")
f.write(' $bond:CTH2 $atom:CT $atom:H2')
f.write("\n")
f.write(' $bond:CTCT2 $atom:CT $atom:CT2')
f.write("\n")
f.write(' $bond:CT2HC $atom:CT2 $atom:HC')
f.write("\n")
f.write(' $bond:CT2OH $atom:CT2 $atom:OH')
f.write("\n")
f.write(' $bond:OHHO $atom:OH $atom:HO')
f.write("\n")
f.write(' $bond:CT2CT3 $atom:CT2 $atom:CT3')
f.write("\n")
f.write(' $bond:CT3H3 $atom:CT3 $atom:H3')
f.write("\n")
f.write(' $bond:CT3H4 $atom:CT3 $atom:H4')
f.write("\n")
f.write(' $bond:CT3OH2 $atom:CT3 $atom:OH2')
f.write("\n")
f.write(' $bond:OH2HO2 $atom:OH2 $atom:HO2')
f.write("\n")
f.write(' }')
f.write("\n")
f.write("\n")
f.write('} # RCOOR')
######################################################################
# This part ensembles the CH2 and CH3 into polymers, import is the data on line 95 which makes sure the final molecule is in the right direction
#the OFM SA
f.write("\n")
f.write("\n")
f.write('SA inherits LOPLSAA {')
f.write("\n")
f.write("\n")
f.write(' create_var {$mol} ')
f.write("\n")
f.write(' OFMpolymers = new CH2 [')
n_OFMpolymers = str(18)
f.write(n_OFMpolymers)
f.write('].rot(180,0,0,1).move(0,0,1.2533223)')
f.write("\n")
f.write(' delete OFMpolymers[0]')
f.write("\n")
f.write(' delete OFMpolymers[')
OFMn_1= (17)
f.write(str(OFMn_1))
f.write("]")
f.write("\n")
f.write(' OFMpolymers[0] = new CH3')
f.write("\n")
f.write(' OFMpolymers[')
f.write(str(OFMn_1))
f.write('] = new COOH ')
f.write("\n")
f.write(' OFMpolymers[')
f.write(str(OFMn_1))
f.write("].rot(180,1,")
f.write('0')
f.write(',0).move(0,0,')
OFMd_n_1 = (OFMn_1)*1.2533223
f.write(str(OFMd_n_1))
f.write(')')
f.write("\n")
f.write("\n")
f.write(" write('Data Bond List') {")
f.write("\n")
for a in range (0, (17)):
f.write(" $bond:b")
f.write(str(a+1))
f.write(" $atom:OFMpolymers[")
f.write(str(a))
f.write("]/C $atom:OFMpolymers[")
f.write(str(a+1))
f.write("]/C")
f.write("\n")
f.write("\n")
f.write(" }")
f.write("\n")
f.write("} # SA")
f.write("\n")
###############################################################
#the OFM SAm
f.write("\n")
f.write("\n")
f.write('SAm inherits LOPLSAA {')
f.write("\n")
f.write("\n")
f.write(' create_var {$mol} ')
f.write("\n")
f.write(' OFMpolymers = new CH2 [')
n_OFMpolymers = str(18)
f.write(n_OFMpolymers)
f.write('].rot(180,0,0,1).move(0,0,1.2533223)')
f.write("\n")
f.write(' delete OFMpolymers[0]')
f.write("\n")
f.write(' delete OFMpolymers[')
OFMn_1= (17)
f.write(str(OFMn_1))
f.write("]")
f.write("\n")
f.write(' OFMpolymers[0] = new CH3')
f.write("\n")
f.write(' OFMpolymers[')
f.write(str(OFMn_1))
f.write('] = new CONH2 ')
f.write("\n")
f.write(' OFMpolymers[')
f.write(str(OFMn_1))
f.write("].rot(180,1,")
f.write('0')
f.write(',0).move(0,0,')
OFMd_n_1 = (OFMn_1)*1.2533223
f.write(str(OFMd_n_1))
f.write(')')
f.write("\n")
f.write("\n")
f.write(" write('Data Bond List') {")
f.write("\n")
for a in range (0, (17)):
f.write(" $bond:b")
f.write(str(a+1))
f.write(" $atom:OFMpolymers[")
f.write(str(a))
f.write("]/C $atom:OFMpolymers[")
f.write(str(a+1))
f.write("]/C")
f.write("\n")
f.write("\n")
f.write(" }")
f.write("\n")
f.write("} # SAm")
f.write("\n")
###############################################################
#the OFM GMS
f.write("\n")
f.write("\n")
f.write('GMS inherits LOPLSAA {')
f.write("\n")
f.write("\n")
f.write(' create_var {$mol} ')
f.write("\n")
f.write(' OFMpolymers = new CH2 [')
n_OFMpolymers = str(18)
f.write(n_OFMpolymers)
f.write('].rot(180,0,0,1).move(0,0,1.2533223)')
f.write("\n")
f.write(' delete OFMpolymers[0]')
f.write("\n")
f.write(' delete OFMpolymers[')
OFMn_1= (17)
f.write(str(OFMn_1))
f.write("]")
f.write("\n")
f.write(' OFMpolymers[0] = new CH3')
f.write("\n")
f.write(' OFMpolymers[')
f.write(str(OFMn_1))
f.write('] = new RCOOR ')
f.write("\n")
f.write(' OFMpolymers[')
f.write(str(OFMn_1))
f.write("].rot(180,1,")
f.write('0')
f.write(',0).move(0,0,')
OFMd_n_1 = (OFMn_1)*1.2533223
f.write(str(OFMd_n_1))
f.write(')')
f.write("\n")
f.write("\n")
f.write(" write('Data Bond List') {")
f.write("\n")
for a in range (0, (17)):
f.write(" $bond:b")
f.write(str(a+1))
f.write(" $atom:OFMpolymers[")
f.write(str(a))
f.write("]/C $atom:OFMpolymers[")
f.write(str(a+1))
f.write("]/C")
f.write("\n")
f.write("\n")
f.write(" }")
f.write("\n")
f.write("} # GMS")
f.write("\n")
###############################################################
#the OFM OA
f.write("\n")
f.write("\n")
f.write('OA inherits LOPLSAA {')
f.write("\n")
f.write("\n")
f.write(' create_var {$mol} ')
f.write("\n")
f.write(' OFMpolymers = new CH2 [')
n_OFMpolymers = str(8)
f.write(n_OFMpolymers)
f.write('].rot(180,0,0,1).move(0,0,1.2533223)')
f.write("\n")
f.write(' delete OFMpolymers[0]')
f.write("\n")
f.write(' OFMpolymers[0] = new CH3')
f.write("\n")
f.write(' OFMpolymers[8] = new CH')
f.write("\n")
f.write(' OFMpolymers[8].rot(45,1,0,0).move(0,0,10.0)')
f.write("\n")
f.write(' OFMpolymers[9] = new CH')
f.write("\n")
f.write(' OFMpolymers[9].rot(45,1,0,0).move(0,0,10.0).move(0,-0.8,0.8)')
f.write("\n")
f.write(' OFMpolymers2 = new CH2 [7].rot(180,0,0,1).move(0,0,1.2533223)')
f.write("\n")
f.write(' OFMpolymers2[].rot(180,0,0,1).rot(45,1,0,0).move(0,0,10.0).move(0,-1.6,1.6)')
f.write("\n")
f.write(' OFMpolymers2[7] = new COOH')
f.write("\n")
f.write(' OFMpolymers2[7].rot(-180,0,0,1).rot(-125,1,0,0).move(0,0,10.0).move(0,-7.8,7.8)')
f.write("\n")
f.write("\n")
f.write(" write('Data Bond List') {")
f.write("\n")
for a in range (0, (9)):
f.write(" $bond:b")
f.write(str(a+1))
f.write(" $atom:OFMpolymers[")
f.write(str(a))
f.write("]/C $atom:OFMpolymers[")
f.write(str(a+1))
f.write("]/C")
f.write("\n")
f.write(' $bond:bc $atom:OFMpolymers[9]/C $atom:OFMpolymers2[0]/C ')
f.write("\n")
for a in range (0, (7)):
f.write(" $bond:c")
f.write(str(a+1))
f.write(" $atom:OFMpolymers2[")
f.write(str(a))
f.write("]/C $atom:OFMpolymers2[")
f.write(str(a+1))
f.write("]/C")
f.write("\n")
f.write("\n")
f.write(" }")
f.write("\n")
f.write("} # OA")
f.write("\n")
###############################################################
#the OFM OAm
f.write("\n")
f.write("\n")
f.write('OAm inherits LOPLSAA {')
f.write("\n")
f.write("\n")
f.write(' create_var {$mol} ')
f.write("\n")
f.write(' OFMpolymers = new CH2 [')
n_OFMpolymers = str(8)
f.write(n_OFMpolymers)
f.write('].rot(180,0,0,1).move(0,0,1.2533223)')
f.write("\n")
f.write(' delete OFMpolymers[0]')
f.write("\n")
f.write(' OFMpolymers[0] = new CH3')
f.write("\n")
f.write(' OFMpolymers[8] = new CH')
f.write("\n")
f.write(' OFMpolymers[8].rot(45,1,0,0).move(0,0,10.0)')
f.write("\n")
f.write(' OFMpolymers[9] = new CH')
f.write("\n")
f.write(' OFMpolymers[9].rot(45,1,0,0).move(0,0,10.0).move(0,-0.8,0.8)')
f.write("\n")
f.write(' OFMpolymers2 = new CH2 [7].rot(180,0,0,1).move(0,0,1.2533223)')
f.write("\n")
f.write(' OFMpolymers2[].rot(180,0,0,1).rot(45,1,0,0).move(0,0,10.0).move(0,-1.6,1.6)')
f.write("\n")
f.write(' OFMpolymers2[7] = new CONH2')
f.write("\n")
f.write(' OFMpolymers2[7].rot(-180,0,0,1).rot(-125,1,0,0).move(0,0,10.0).move(0,-7.8,7.8)')
f.write("\n")
f.write("\n")
f.write(" write('Data Bond List') {")
f.write("\n")
for a in range (0, (9)):
f.write(" $bond:b")
f.write(str(a+1))
f.write(" $atom:OFMpolymers[")
f.write(str(a))
f.write("]/C $atom:OFMpolymers[")
f.write(str(a+1))
f.write("]/C")
f.write("\n")
f.write(' $bond:bc $atom:OFMpolymers[9]/C $atom:OFMpolymers2[0]/C ')
f.write("\n")
for a in range (0, (7)):
f.write(" $bond:c")
f.write(str(a+1))
f.write(" $atom:OFMpolymers2[")
f.write(str(a))
f.write("]/C $atom:OFMpolymers2[")
f.write(str(a+1))
f.write("]/C")
f.write("\n")
f.write("\n")
f.write(" }")
f.write("\n")
f.write("} # OAm")
f.write("\n")
###############################################################
###############################################################
#the OFM GMO
f.write("\n")
f.write("\n")
f.write('GMO inherits LOPLSAA {')
f.write("\n")
f.write("\n")
f.write(' create_var {$mol} ')
f.write("\n")
f.write(' OFMpolymers = new CH2 [')
n_OFMpolymers = str(8)
f.write(n_OFMpolymers)
f.write('].rot(180,0,0,1).move(0,0,1.2533223)')
f.write("\n")
f.write(' delete OFMpolymers[0]')
f.write("\n")
f.write(' OFMpolymers[0] = new CH3')
f.write("\n")
f.write(' OFMpolymers[8] = new CH')
f.write("\n")
f.write(' OFMpolymers[8].rot(45,1,0,0).move(0,0,10.0)')
f.write("\n")
f.write(' OFMpolymers[9] = new CH')
f.write("\n")
f.write(' OFMpolymers[9].rot(45,1,0,0).move(0,0,10.0).move(0,-0.8,0.8)')
f.write("\n")
f.write(' OFMpolymers2 = new CH2 [7].rot(180,0,0,1).move(0,0,1.2533223)')
f.write("\n")
f.write(' OFMpolymers2[].rot(180,0,0,1).rot(45,1,0,0).move(0,0,10.0).move(0,-1.6,1.6)')
f.write("\n")
f.write(' OFMpolymers2[7] = new RCOOR')
f.write("\n")
f.write(' OFMpolymers2[7].rot(-180,0,0,1).rot(-125,1,0,0).move(0,0,10.0).move(0,-7.8,7.8)')
f.write("\n")
f.write("\n")
f.write(" write('Data Bond List') {")
f.write("\n")
for a in range (0, (9)):
f.write(" $bond:b")
f.write(str(a+1))
f.write(" $atom:OFMpolymers[")
f.write(str(a))
f.write("]/C $atom:OFMpolymers[")
f.write(str(a+1))
f.write("]/C")
f.write("\n")
f.write(' $bond:bc $atom:OFMpolymers[9]/C $atom:OFMpolymers2[0]/C ')
f.write("\n")
for a in range (0, (7)):
f.write(" $bond:c")
f.write(str(a+1))
f.write(" $atom:OFMpolymers2[")
f.write(str(a))
f.write("]/C $atom:OFMpolymers2[")
f.write(str(a+1))
f.write("]/C")
f.write("\n")
f.write("\n")
f.write(" }")
f.write("\n")
f.write("} # GMO")
f.write("\n")
###############################################################
###############################################################
####
#The AlKane
f.write("\n")
f.write("\n")
f.write('Hexadecane inherits LOPLSAA {')
f.write("\n")
f.write("\n")
f.write(' create_var {$mol} ')
f.write("\n")
f.write(' AlkanePolymer = new CH2 [')
n_AlkanePolymer = str(nAlkane)
f.write(n_AlkanePolymer)
f.write('].rot(180,0,0,1).move(0,0,1.2533223)')
f.write("\n")
f.write(' delete AlkanePolymer[0]')
f.write("\n")
f.write(' delete AlkanePolymer[')
Alkanen_1= (nAlkane-1)
f.write(str(Alkanen_1))
f.write("]")
f.write("\n")
f.write(' AlkanePolymer[0] = new CH3')
f.write("\n")
f.write(' AlkanePolymer[')
f.write(str(Alkanen_1))
f.write('] = new CH3 ')
f.write("\n")
f.write(' AlkanePolymer[')
f.write(str(Alkanen_1))
f.write("]")
if nAlkane%2==0:
f.write('.rot(180,0,0,1).rot(180,0,1,0).')
else:
f.write('.rot(180,0,1,0).')
f.write('move(0,0,')
Alkaned_n_1 = (Alkanen_1)*1.2533223
f.write(str(Alkaned_n_1))
f.write(')')
f.write("\n")
f.write("\n")
f.write(" write('Data Bond List') {")
f.write("\n")
for a in range (0, (nAlkane-1)):
f.write(" $bond:b")
f.write(str(a+1))
f.write(" $atom:AlkanePolymer[")
f.write(str(a))
f.write("]/C $atom:AlkanePolymer[")
f.write(str(a+1))
f.write("]/C")
f.write("\n")
f.write("\n")
f.write(" }")
f.write("\n")
f.write("} # Hexadecane")
f.write("\n")
###############################################################
####
#Benzyl Benzoate
f.write("BZBZ inherits LOPLSAA {\n")
f.write(" # atomID molID atomType charge X Y Z\n")
f.write(" write('Data Atoms') {\n")
f.write(" $atom:C1 $mol:... @atom:90 0.00 1.617 2.278 2.109 \n")
f.write(" $atom:C2 $mol:... @atom:90 0.00 2.453 2.282 0.996 \n")
f.write(" $atom:C3 $mol:... @atom:90 0.00 3.836 2.153 1.161 \n")
f.write(" $atom:C4 $mol:... @atom:90 0.00 4.380 2.026 2.443 \n")
f.write(" $atom:C5 $mol:... @atom:90 0.00 3.546 2.027 3.561 \n")
f.write(" $atom:C6 $mol:... @atom:842 0.00 2.158 2.153 3.398 \n")
f.write(" $atom:C7 $mol:... @atom:40600 0.00 1.215 2.158 4.553 \n")
f.write(" $atom:C8 $mol:... @atom:81 0.00 0.980 2.119 6.918 \n")
f.write(" $atom:C9 $mol:... @atom:90 0.00 2.283 3.365 8.682 \n")
f.write(" $atom:C10 $mol:... @atom:90 0.00 1.854 2.146 8.142 \n")
f.write(" $atom:C11 $mol:... @atom:90 0.00 2.261 0.951 8.751 \n")
f.write(" $atom:C12 $mol:... @atom:90 0.00 3.083 0.974 9.879 \n")
f.write(" $atom:C13 $mol:... @atom:90 0.00 3.507 2.195 10.410 \n")
f.write(" $atom:C14 $mol:... @atom:90 0.00 3.105 3.392 9.810 \n")
f.write(" $atom:H1 $mol:... @atom:91 0.00 0.541 2.374 2.000 \n")
f.write(" $atom:H2 $mol:... @atom:91 0.00 2.030 2.381 0.000 \n")
f.write(" $atom:H3 $mol:... @atom:91 0.00 4.490 2.152 0.293 \n")
f.write(" $atom:H4 $mol:... @atom:91 0.00 5.454 1.927 2.572 \n")
f.write(" $atom:H5 $mol:... @atom:91 0.00 3.965 1.933 4.557 \n")
f.write(" $atom:H6 $mol:... @atom:8500 0.00 0.349 1.226 6.888 \n")
f.write(" $atom:H7 $mol:... @atom:8500 0.00 0.332 2.997 6.858 \n")
f.write(" $atom:H8 $mol:... @atom:91 0.00 1.968 4.298 8.219 \n")
f.write(" $atom:H9 $mol:... @atom:91 0.00 1.928 0.000 8.341 \n")
f.write(" $atom:H10 $mol:... @atom:91 0.00 3.388 0.040 10.346 \n")
f.write(" $atom:H11 $mol:... @atom:91 0.00 4.143 2.214 11.292 \n")
f.write(" $atom:H12 $mol:... @atom:91 0.00 3.429 4.344 10.223 \n")
f.write(" $atom:O1 $mol:... @atom:40700 0.00 0.000 2.208 4.444 \n")
f.write(" $atom:O2 $mol:... @atom:40800 0.00 1.844 2.107 5.743 \n")
f.write(" }\n")
f.write(" write('Data Bond List') {\n")
f.write(" # Aromatic Carbon Cycles\n")
f.write(" $bond:CC1 $atom:C1 $atom:C2\n")
f.write(" $bond:CC2 $atom:C2 $atom:C3\n")
f.write(" $bond:CC3 $atom:C3 $atom:C4\n")
f.write(" $bond:CC4 $atom:C4 $atom:C5\n")
f.write(" $bond:CC5 $atom:C5 $atom:C6\n")
f.write(" $bond:CC6 $atom:C6 $atom:C1\n")
f.write(" $bond:CC7 $atom:C9 $atom:C10\n")
f.write(" $bond:CC8 $atom:C10 $atom:C11\n")
f.write(" $bond:CC9 $atom:C11 $atom:C12\n")
f.write(" $bond:CC10 $atom:C12 $atom:C13\n")
f.write(" $bond:CC11 $atom:C13 $atom:C14\n")
f.write(" $bond:CC12 $atom:C14 $atom:C9\n")
f.write("# Non Aromatic Carbons\n")
f.write(" $bond:CC13 $atom:C7 $atom:C6\n")
f.write(" $bond:CC14 $atom:C8 $atom:C10\n")
f.write("# Hydrogen - Aromatic Carbon\n")
f.write(" $bond:CH1 $atom:C1 $atom:H1\n")
f.write(" $bond:CH2 $atom:C2 $atom:H2\n")
f.write(" $bond:CH3 $atom:C3 $atom:H3\n")
f.write(" $bond:CH4 $atom:C4 $atom:H4\n")
f.write(" $bond:CH5 $atom:C5 $atom:H5\n")
f.write(" $bond:CH6 $atom:C9 $atom:H8\n")
f.write(" $bond:CH7 $atom:C11 $atom:H9\n")
f.write(" $bond:CH8 $atom:C12 $atom:H10\n")
f.write(" $bond:CH9 $atom:C13 $atom:H11\n")
f.write(" $bond:CH10 $atom:C14 $atom:H12\n")
f.write("# Hydrogen - Non aromatic Carbon\n")
f.write(" $bond:CH11 $atom:C8 $atom:H6\n")
f.write(" $bond:CH12 $atom:C8 $atom:H7\n")
f.write("# Oxygen\n")
f.write(" $bond:CO1 $atom:C7 $atom:O1\n")
f.write(" $bond:CO2 $atom:C7 $atom:O2\n")
f.write(" $bond:CO3 $atom:C8 $atom:O2\n")
f.write(" }\n")
f.write("}\n")
###############################################################
####
#Squalane
f.write("squalane inherits LOPLSAA { \n")
f.write(" # atomID molID atomType charge X Y Z\n")
f.write(" write('Data Atoms') {\n")
f.write(" $atom:H33 $mol:... @atom:85 0.00 -13.888 -2.118 0.507\n")
f.write(" $atom:C1 $mol:... @atom:80 0.00 -13.557 -2.018 -0.555\n")
f.write(" $atom:H31 $mol:... @atom:85 0.00 -14.464 -1.954 -1.204\n")
f.write(" $atom:H32 $mol:... @atom:85 0.00 -12.986 -2.935 -0.835\n")
f.write(" $atom:C2 $mol:... @atom:8100 0.00 -12.698 -0.782 -0.729\n")
f.write(" $atom:H34 $mol:... @atom:8500 0.00 -12.351 -0.740 -1.801\n")
f.write(" $atom:C3 $mol:... @atom:80 0.00 -13.512 0.462 -0.441\n")
f.write(" $atom:H35 $mol:... @atom:85 0.00 -13.877 0.457 0.614\n")
f.write(" $atom:H36 $mol:... @atom:85 0.00 -12.896 1.380 -0.598\n")
f.write(" $atom:H37 $mol:... @atom:85 0.00 -14.397 0.510 -1.121\n")
f.write(" $atom:C4 $mol:... @atom:81 0.00 -11.472 -0.866 0.169\n")
f.write(" $atom:H38 $mol:... @atom:8500 0.00 -11.770 -0.645 1.228\n")
f.write(" $atom:H39 $mol:... @atom:8500 0.00 -11.073 -1.915 0.148\n")
f.write(" $atom:C5 $mol:... @atom:81 0.00 -10.377 0.088 -0.262\n")
f.write(" $atom:H40 $mol:... @atom:8500 0.00 -10.070 -0.144 -1.315\n")
f.write(" $atom:H41 $mol:... @atom:8500 0.00 -10.769 1.139 -0.254\n")
f.write(" $atom:C6 $mol:... @atom:81 0.00 -9.176 -0.018 0.654\n")
f.write(" $atom:H42 $mol:... @atom:8500 0.00 -9.473 0.252 1.701\n")
f.write(" $atom:H43 $mol:... @atom:8500 0.00 -8.829 -1.086 0.672\n")
f.write(" $atom:C7 $mol:... @atom:8100 0.00 -8.025 0.876 0.209\n")
f.write(" $atom:H44 $mol:... @atom:8500 0.00 -7.883 0.745 -0.902\n")
f.write(" $atom:C8 $mol:... @atom:80 0.00 -8.336 2.333 0.485\n")
f.write(" $atom:H45 $mol:... @atom:85 0.00 -8.488 2.503 1.578\n")
f.write(" $atom:H46 $mol:... @atom:85 0.00 -7.496 2.984 0.140\n")
f.write(" $atom:H47 $mol:... @atom:85 0.00 -9.263 2.644 -0.054\n")
f.write(" $atom:C9 $mol:... @atom:81 0.00 -6.741 0.452 0.910\n")
f.write(" $atom:H48 $mol:... @atom:8500 0.00 -6.777 0.778 1.982\n")
f.write(" $atom:H49 $mol:... @atom:8500 0.00 -6.671 -0.669 0.907\n")
f.write(" $atom:C10 $mol:... @atom:81 0.00 -5.505 1.020 0.245\n")
f.write(" $atom:H50 $mol:... @atom:8500 0.00 -5.489 0.723 -0.837\n")
f.write(" $atom:H51 $mol:... @atom:8500 0.00 -5.537 2.141 0.280\n")
f.write(" $atom:C11 $mol:... @atom:81 0.00 -4.247 0.523 0.926\n")
f.write(" $atom:H52 $mol:... @atom:8500 0.00 -4.258 0.825 2.006\n")
f.write(" $atom:H53 $mol:... @atom:8500 0.00 -4.238 -0.599 0.895\n")
f.write(" $atom:C12 $mol:... @atom:8100 0.00 -2.982 1.057 0.265\n")
f.write(" $atom:H54 $mol:... @atom:8500 0.00 -3.107 0.981 -0.853\n")
f.write(" $atom:C13 $mol:... @atom:80 0.00 -2.757 2.511 0.626\n")
f.write(" $atom:H55 $mol:... @atom:85 0.00 -2.590 2.622 1.725\n")
f.write(" $atom:H56 $mol:... @atom:85 0.00 -1.864 2.913 0.090\n")
f.write(" $atom:H57 $mol:... @atom:85 0.00 -3.644 3.126 0.340\n")
f.write(" $atom:C14 $mol:... @atom:81 0.00 -1.789 0.203 0.672\n")
f.write(" $atom:H58 $mol:... @atom:8500 0.00 -1.543 0.396 1.749\n")
f.write(" $atom:H59 $mol:... @atom:8500 0.00 -2.064 -0.882 0.584\n")
f.write(" $atom:C15 $mol:... @atom:81 0.00 -0.569 0.468 -0.184\n")
f.write(" $atom:H60 $mol:... @atom:8500 0.00 -0.830 0.332 -1.267\n")
f.write(" $atom:H61 $mol:... @atom:8500 0.00 -0.236 1.532 -0.055\n")
f.write(" $atom:C16 $mol:... @atom:81 0.00 0.568 -0.463 0.183\n")
f.write(" $atom:H62 $mol:... @atom:8500 0.00 0.830 -0.327 1.266\n")
f.write(" $atom:H63 $mol:... @atom:8500 0.00 0.236 -1.526 0.054\n")
f.write(" $atom:C17 $mol:... @atom:81 0.00 1.788 -0.197 -0.673\n")
f.write(" $atom:H64 $mol:... @atom:8500 0.00 1.542 -0.389 -1.751\n")
f.write(" $atom:H65 $mol:... @atom:8500 0.00 2.064 0.887 -0.584\n")
f.write(" $atom:C18 $mol:... @atom:8100 0.00 2.982 -1.053 -0.267\n")
f.write(" $atom:H66 $mol:... @atom:8500 0.00 3.105 -0.979 0.851\n")
f.write(" $atom:C19 $mol:... @atom:80 0.00 2.756 -2.505 -0.631\n")
f.write(" $atom:H67 $mol:... @atom:85 0.00 2.591 -2.615 -1.730\n")
f.write(" $atom:H68 $mol:... @atom:85 0.00 3.642 -3.122 -0.345\n")
f.write(" $atom:H69 $mol:... @atom:85 0.00 1.862 -2.908 -0.097\n")
f.write(" $atom:C20 $mol:... @atom:81 0.00 4.247 -0.518 -0.927\n")
f.write(" $atom:H70 $mol:... @atom:8500 0.00 4.257 -0.816 -2.008\n")
f.write(" $atom:H71 $mol:... @atom:8500 0.00 4.239 0.604 -0.892\n")
f.write(" $atom:C21 $mol:... @atom:81 0.00 5.505 -1.018 -0.248\n")
f.write(" $atom:H72 $mol:... @atom:8500 0.00 5.489 -0.725 0.836\n")
f.write(" $atom:H73 $mol:... @atom:8500 0.00 5.537 -2.138 -0.287\n")
f.write(" $atom:C22 $mol:... @atom:81 0.00 6.741 -0.447 -0.910\n")
f.write(" $atom:H74 $mol:... @atom:8500 0.00 6.777 -0.768 -1.984\n")
f.write(" $atom:H75 $mol:... @atom:8500 0.00 6.672 0.673 -0.901\n")
f.write(" $atom:C23 $mol:... @atom:8100 0.00 8.025 -0.877 -0.211\n")
f.write(" $atom:H76 $mol:... @atom:8500 0.00 7.883 -0.752 0.900\n")
f.write(" $atom:C24 $mol:... @atom:80 0.00 8.334 -2.332 -0.496\n")
f.write(" $atom:H77 $mol:... @atom:85 0.00 9.261 -2.647 0.041\n")
f.write(" $atom:H78 $mol:... @atom:85 0.00 7.494 -2.984 -0.156\n")
f.write(" $atom:H79 $mol:... @atom:85 0.00 8.487 -2.495 -1.590\n")
f.write(" $atom:C25 $mol:... @atom:81 0.00 9.176 0.020 -0.651\n")
f.write(" $atom:H80 $mol:... @atom:8500 0.00 9.472 -0.243 -1.700\n")
f.write(" $atom:H81 $mol:... @atom:8500 0.00 8.829 1.088 -0.662\n")
f.write(" $atom:C26 $mol:... @atom:81 0.00 10.378 -0.093 0.263\n")
f.write(" $atom:H82 $mol:... @atom:8500 0.00 10.071 0.131 1.319\n")
f.write(" $atom:H83 $mol:... @atom:8500 0.00 10.769 -1.144 0.248\n")
f.write(" $atom:C27 $mol:... @atom:81 0.00 11.473 0.863 -0.162\n")
f.write(" $atom:H84 $mol:... @atom:8500 0.00 11.770 0.650 -1.222\n")
f.write(" $atom:H85 $mol:... @atom:8500 0.00 11.074 1.912 -0.133\n")
f.write(" $atom:C28 $mol:... @atom:8100 0.00 12.699 0.773 0.735\n")
f.write(" $atom:H86 $mol:... @atom:8500 0.00 12.353 0.723 1.807\n")
f.write(" $atom:C29 $mol:... @atom:80 0.00 13.513 -0.470 0.438\n")
f.write(" $atom:H87 $mol:... @atom:85 0.00 14.398 -0.523 1.116\n")
f.write(" $atom:H88 $mol:... @atom:85 0.00 12.897 -1.388 0.587\n")
f.write(" $atom:H89 $mol:... @atom:85 0.00 13.877 -0.457 -0.618\n")
f.write(" $atom:C30 $mol:... @atom:80 0.00 13.559 2.009 0.570\n")
f.write(" $atom:H90 $mol:... @atom:85 0.00 12.988 2.925 0.857\n")
f.write(" $atom:H91 $mol:... @atom:85 0.00 14.466 1.940 1.217\n")
f.write(" $atom:H92 $mol:... @atom:85 0.00 13.889 2.117 -0.492\n")
f.write(" }\n")
f.write(" write('Data Bond List') {\n")
f.write(" $bond:B1 $atom:H33 $atom:C1\n")
f.write(" $bond:B2 $atom:C1 $atom:H31\n")
f.write(" $bond:B3 $atom:C1 $atom:H32\n")
f.write(" $bond:B4 $atom:C1 $atom:C2\n")
f.write(" $bond:B5 $atom:C2 $atom:H34\n")
f.write(" $bond:B6 $atom:C2 $atom:C3\n")
f.write(" $bond:B7 $atom:C2 $atom:C4\n")
f.write(" $bond:B8 $atom:C3 $atom:H35\n")
f.write(" $bond:B9 $atom:C3 $atom:H36\n")
f.write(" $bond:B10 $atom:C3 $atom:H37\n")
f.write(" $bond:B11 $atom:C4 $atom:H38\n")
f.write(" $bond:B12 $atom:C4 $atom:H39\n")
f.write(" $bond:B13 $atom:C4 $atom:C5\n")
f.write(" $bond:B14 $atom:C5 $atom:H40\n")
f.write(" $bond:B15 $atom:C5 $atom:H41\n")
f.write(" $bond:B16 $atom:C5 $atom:C6\n")
f.write(" $bond:B17 $atom:C6 $atom:H42\n")
f.write(" $bond:B18 $atom:C6 $atom:H43\n")
f.write(" $bond:B19 $atom:C6 $atom:C7\n")
f.write(" $bond:B20 $atom:C7 $atom:H44\n")
f.write(" $bond:B21 $atom:C7 $atom:C8\n")
f.write(" $bond:B22 $atom:C7 $atom:C9\n")
f.write(" $bond:B23 $atom:C8 $atom:H45\n")
f.write(" $bond:B24 $atom:C8 $atom:H46\n")
f.write(" $bond:B25 $atom:C8 $atom:H47\n")
f.write(" $bond:B26 $atom:C9 $atom:H48\n")
f.write(" $bond:B27 $atom:C9 $atom:H49\n")
f.write(" $bond:B28 $atom:C9 $atom:C10\n")
f.write(" $bond:B29 $atom:C10 $atom:H50\n")
f.write(" $bond:B30 $atom:C10 $atom:H51\n")
f.write(" $bond:B31 $atom:C10 $atom:C11\n")
f.write(" $bond:B32 $atom:C11 $atom:H52\n")
f.write(" $bond:B33 $atom:C11 $atom:H53\n")
f.write(" $bond:B34 $atom:C11 $atom:C12\n")
f.write(" $bond:B35 $atom:C12 $atom:H54\n")
f.write(" $bond:B36 $atom:C12 $atom:C13\n")
f.write(" $bond:B37 $atom:C12 $atom:C14\n")
f.write(" $bond:B38 $atom:C13 $atom:H55\n")
f.write(" $bond:B39 $atom:C13 $atom:H56\n")
f.write(" $bond:B40 $atom:C13 $atom:H57\n")
f.write(" $bond:B41 $atom:C14 $atom:H58\n")
f.write(" $bond:B42 $atom:C14 $atom:H59\n")
f.write(" $bond:B43 $atom:C14 $atom:C15\n")
f.write(" $bond:B44 $atom:C15 $atom:H60\n")
f.write(" $bond:B45 $atom:C15 $atom:H61\n")
f.write(" $bond:B46 $atom:C15 $atom:C16\n")
f.write(" $bond:B47 $atom:C16 $atom:H62\n")
f.write(" $bond:B48 $atom:C16 $atom:H63\n")
f.write(" $bond:B49 $atom:C16 $atom:C17\n")
f.write(" $bond:B50 $atom:C17 $atom:H64\n")
f.write(" $bond:B51 $atom:C17 $atom:H65\n")
f.write(" $bond:B52 $atom:C17 $atom:C18\n")
f.write(" $bond:B53 $atom:C18 $atom:H66\n")
f.write(" $bond:B54 $atom:C18 $atom:C19\n")
f.write(" $bond:B55 $atom:C18 $atom:C20\n")
f.write(" $bond:B56 $atom:C19 $atom:H67\n")
f.write(" $bond:B57 $atom:C19 $atom:H68\n")
f.write(" $bond:B58 $atom:C19 $atom:H69\n")
f.write(" $bond:B59 $atom:C20 $atom:H70\n")
f.write(" $bond:B60 $atom:C20 $atom:H71\n")
f.write(" $bond:B61 $atom:C20 $atom:C21\n")
f.write(" $bond:B62 $atom:C21 $atom:H72\n")
f.write(" $bond:B63 $atom:C21 $atom:H73\n")
f.write(" $bond:B64 $atom:C21 $atom:C22\n")
f.write(" $bond:B65 $atom:C22 $atom:H74\n")
f.write(" $bond:B66 $atom:C22 $atom:H75\n")
f.write(" $bond:B67 $atom:C22 $atom:C23\n")
f.write(" $bond:B68 $atom:C23 $atom:H76\n")
f.write(" $bond:B69 $atom:C23 $atom:C24\n")
f.write(" $bond:B70 $atom:C23 $atom:C25\n")
f.write(" $bond:B71 $atom:C24 $atom:H77\n")
f.write(" $bond:B72 $atom:C24 $atom:H78\n")
f.write(" $bond:B73 $atom:C24 $atom:H79\n")
f.write(" $bond:B74 $atom:C25 $atom:H80\n")
f.write(" $bond:B75 $atom:C25 $atom:H81\n")
f.write(" $bond:B76 $atom:C25 $atom:C26\n")
f.write(" $bond:B77 $atom:C26 $atom:H82\n")
f.write(" $bond:B78 $atom:C26 $atom:H83\n")
f.write(" $bond:B79 $atom:C26 $atom:C27\n")
f.write(" $bond:B80 $atom:C27 $atom:H84\n")
f.write(" $bond:B81 $atom:C27 $atom:H85\n")
f.write(" $bond:B82 $atom:C27 $atom:C28\n")
f.write(" $bond:B83 $atom:C28 $atom:H86\n")
f.write(" $bond:B84 $atom:C28 $atom:C29\n")
f.write(" $bond:B85 $atom:C28 $atom:C30\n")
f.write(" $bond:B86 $atom:C29 $atom:H87\n")
f.write(" $bond:B87 $atom:C29 $atom:H88\n")
f.write(" $bond:B88 $atom:C29 $atom:H89\n")
f.write(" $bond:B89 $atom:C30 $atom:H90\n")
f.write(" $bond:B90 $atom:C30 $atom:H91\n")
f.write(" $bond:B91 $atom:C30 $atom:H92\n")
f.write(" }\n")
f.write("}\n")
######################################################################
#rough Iron surfaces
if Surfaces == 1:
Rough(FractalLevels,RMSin,H,boxLenghtX,boxLenghtY,boxLenghtZ,aFe,Separation)
######################################################################
#Flat Fe2O3 surfaces
if Surfaces == 2:
Fe2O3(FractalLevels,RMSin,H,boxLenghtX,boxLenghtY,boxLenghtZ,aFe,Separation)
######################################################################
# This calculates the amount of molecules in each direction and the total amount of molecules
####
#The OFM
#OFMn_x = int((xhi-xlo)/OFMs_x)
#OFMn_y = int((yhi-ylo)/OFMs_y)
OFMn_z = 1 #int((zhi-zlo)/OFMs_z)
#N_total = OFMn_x*OFMn_y*OFMn_z
# This determines how far apart all OFMpolymers will be placed
OFMs_x = (xhi-xlo)/OFMn_x
OFMs_y = (yhi-ylo)/OFMn_y
OFMs_z = 0 #(1.5*n)+3
####
#The AlKane
# This determines how far apart all Alkanes polymers will be placed
Alkanes_x = (xhi-xlo)/Alkanen_x #(1.2533223*(nAlkane-1))+5
Alkanes_y = (yhi-ylo)/Alkanen_y
if Alkanen_z == 1:
Alkanes_z = 0.0
else:
Alkanes_z = ((zhi-23.3065-4)-(zlo+23.3065+4))/(Alkanen_z-1)
####
#BZBZ
# This determines how far apart all BZBZ molecules will be placed
BZBZ_x = (xhi-xlo)/BZBZn_x #(1.2533223*(nAlkane-1))+5
BZBZ_y = (yhi-ylo)/BZBZn_y
if BZBZn_z == 1:
BZBZ_z = 0.0
else:
BZBZ_z = ((zhi-23.3065-5)-(zlo+23.3065+5))/(BZBZn_z-1)
####
#Squalane
# This determines how far apart all Squalane molecules will be placed
Squalane_x = (xhi-xlo)/Squalanen_x #(1.2533223*(nAlkane-1))+5
Squalane_y = (yhi-ylo)/Squalanen_y
if Squalanen_z == 1:
Squalane_z = 0.0
else:
Squalane_z = ((zhi-23.3065-5)-(zlo+23.3065+5))/(Squalanen_z-1)
######################################################################
#Placing the polymers in the box
f.write("\n")
f.write("# Periodic boundary conditions:")
f.write("\n")
f.write("write_once(\"Data Boundary\") {")
f.write("\n")
f.write(str(xlo)+" "+str(xhi))
f.write(" xlo xhi")
f.write("\n")
f.write(str(ylo)+" "+str(yhi))
f.write(" ylo yhi")
f.write("\n")
#rough Iron surfaces
if Surfaces == 1 or Surfaces == 0 :
f.write(str(zlo-boxLenghtZ*aFe-20)+" "+str(zhi+boxLenghtZ*aFe+20))
f.write(" zlo zhi")
f.write("\n")
f.write("}")
#Flat Fe2O3 surfaces
if Surfaces == 2 :
f.write(str(zlo-boxLenghtZ*13.730-20)+" "+str(zhi+boxLenghtZ*13.730+20))
f.write(" zlo zhi")
f.write("\n")
f.write("}")
#####
#The OFMs
if OFM == 1:
# Here the OFMpolymers are placed, using the number of OFMpolymers in each direction and the set distance
f.write("\n")
f.write("\n")
if OFMtype == 'SA':
f.write("molecules = new SA [")
elif OFMtype == 'SAm':
f.write("molecules = new SAm [")
elif OFMtype == 'GMS':
f.write("molecules = new GMS [")
elif OFMtype == 'OA':
f.write("molecules = new OA [")
elif OFMtype == 'OAm':
f.write("molecules = new OAm [")
elif OFMtype == 'GMO':
f.write("molecules = new GMO [")
f.write(str(OFMn_z))
f.write("].move(0, 0,")
f.write(str(OFMs_z))
f.write(")")
f.write("\n")
f.write(" [")
f.write(str(OFMn_y))
f.write("].move(0, ")
f.write(str(OFMs_y))
f.write(", 0)")
f.write("\n")
f.write(" [")
f.write(str(OFMn_x))
f.write("].move(")
f.write(str(OFMs_x))
f.write(", 0, 0)")
f.write("\n")
f.write("molecules[*][*][*].rot(180,1,0,0).move("+str(xlo)+","+str(ylo+(OFMn_y-1)*OFMs_y)+","+str(zlo+23.3065)+")")
f.write("\n")
f.write("\n")
if OFMtype == 'SA':
f.write("molecules2 = new SA [")
elif OFMtype == 'SAm':
f.write("molecules2 = new SAm [")
elif OFMtype == 'GMS':
f.write("molecules2 = new GMS [")
elif OFMtype == 'OA':
f.write("molecules2 = new OA [")
elif OFMtype == 'OAm':
f.write("molecules2 = new OAm [")
elif OFMtype == 'GMO':
f.write("molecules2 = new GMO [")
f.write(str(OFMn_z))
f.write("].move(0, 0,")
f.write(str(OFMs_z))
f.write(")")
f.write("\n")
f.write(" [")
f.write(str(OFMn_y))
f.write("].move(0, ")
f.write(str(OFMs_y))
f.write(", 0)")
f.write("\n")
f.write(" [")
f.write(str(OFMn_x))
f.write("].move(")
f.write(str(OFMs_x))
f.write(", 0, 0)")
f.write("\n")
f.write("molecules2[*][*][*].move("+str(xlo)+","+str(ylo)+","+str(zlo+(zhi-zlo)-23.3065)+")")
#####
#The Alkanes
if Alkane == 1:
f.write("\n")
f.write("\n")
f.write("molecules3 = new Hexadecane.rot(90, 0, 1, 0) [")
f.write(str(Alkanen_z))
f.write("].move(0, 0,")
f.write(str(Alkanes_z))
f.write(")")
f.write("\n")
f.write(" [")
f.write(str(Alkanen_y))
f.write("].move(0, ")
f.write(str(Alkanes_y))
f.write(", 0)")
f.write("\n")
f.write(" [")
f.write(str(Alkanen_x))
f.write("].move(")
f.write(str(Alkanes_x))
f.write(", 0, 0)")
f.write("\n")
f.write("molecules3[*][*][*].move("+str(xlo)+","+str(ylo)+","+str(zlo+23.3065+4)+")")
######
#BZBZ
if BZBZ == 1:
f.write("\n")
f.write("\n")
f.write("molecules4 = new BZBZ.rot(90, 0, 1, 0) [")
f.write(str(BZBZn_z))
f.write("].move(0, 0,")
f.write(str(BZBZ_z))
f.write(")")
f.write("\n")
f.write(" [")
f.write(str(BZBZn_y))
f.write("].move(0, ")
f.write(str(BZBZ_y))
f.write(", 0)")
f.write("\n")
f.write(" [")
f.write(str(BZBZn_x))
f.write("].move(")
f.write(str(BZBZ_x))
f.write(", 0, 0)")
f.write("\n")
f.write("molecules4[*][*][*].move("+str(xlo)+","+str(ylo)+","+str(zlo+23.3065+8)+")")
#####
#Squalane
if Squalane == 1:
f.write("\n")
f.write("\n")
f.write("molecules6 = new squalane.move(15, 0, 0) [")
f.write(str(Squalanen_z))
f.write("].move(0, 0,")
f.write(str(Squalane_z))
f.write(")")
f.write("\n")
f.write(" [")
f.write(str(Squalanen_y))
f.write("].move(0, ")
f.write(str(Squalane_y))
f.write(", 0)")
f.write("\n")
f.write(" [")
f.write(str(Squalanen_x))
f.write("].move(")
f.write(str(Squalane_x))
f.write(", 0, 0)")
f.write("\n")
f.write("molecules6[*][*][*].move("+str(xlo)+","+str(ylo)+","+str(zlo+23.3065+4)+")")
######
#The Surfaces
if Surfaces == 1:
f.write("\n")
f.write("\n")
f.write("molecules5 = new FESurface.move("+str(xlo)+","+str(ylo)+","+str(zlo-boxLenghtZ*aFe-1)+")")
f.write("\n")
f.write("\n")
if Surfaces == 2:
f.write("\n")
f.write("\n")
f.write("molecules5 = new FESurface.move("+str(xlo)+","+str(ylo)+","+str(zlo-boxLenghtZ*13.730-1)+")")
f.write("\n")
f.write("\n")
#f.write("molecules4 = new FESurface.move("+str(xlo)+","+str(ylo)+","+str(zlo-boxLenghtZ*aFe-1)+")")
f.close()
# Creates the name for the all the imput files
name = 'lopls' #+str(N_total)
os.rename('lopls.lt',name+'.lt')
#runs Moltemplate
os.system('moltemplate.sh '+name+'.lt')
#os.system('moltemplate_2016-12-18.sh '+name+'.lt')
#os.system('moltemplate_2017-2-10.sh '+name+'.lt')
# Builds the .in file
########################################################################
f = open('in.'+name,'wr+')
f.write("# ------------------------------- Initialization Section --------------------")
f.write("\n")
f.write("include "+name+".in.init")
f.write("\n")
f.write("read_data " + name +".data")
f.write("\n")
f.write("include "+name+".in.settings")
f.write("\n")
f.write("include "+name+".in.charges")
f.write("\n")
f.write("\n")
f.write("dump dump1 all atom 1000 "+name+".dump")
f.write("\n")
f.write("thermo_style custom step lx ly lz density temp press etotal")
f.write("\n")
f.write("thermo 1")
f.write("\n")
f.write("write_data "+ name +"Initial.data")
f.write("\n")
f.write("\n")
f.write("# ------------------Run Equilibriation ---------------------------")
f.write("\n")
f.write("\n")
f.write("min_style cg")
f.write("\n")
f.write("minimize 0.0 0.0 100000 100000")
f.write("\n")
f.write("\n")
f.close()
############################################################################################################
if Surfaces == 1:
AddEAM()
os.system('rm WEA.lt')
if Surfaces == 2:
AddFe2O3(name)
os.system('rm Fe2O3.lt')
# Moves all files to a seperate folder
os.system('rm -r lopls')
os.system('mkdir lopls')
os.system('rm -r output_ttree')
os.system('rm '+name+'.in')
os.system('mv '+name+'.in.init lopls')
os.system('mv '+name+'.in.CreateBonds lopls')
os.system('mv '+name+'.in.settings lopls')
os.system('mv '+name+'.in.charges lopls')
os.system('rm '+name+'.lt')
os.system('mv '+name+'.data lopls')
# moves all new input files to the folder
os.system("mv in."+name+" lopls")
|
JE1314/LAMMPS_builder
|
root/lopls.py
|
Python
|
gpl-3.0
| 49,103
|
[
"LAMMPS"
] |
0ec2fb3f638777031ebe17f36b365ec3737c51531c424070bff25b9d6e27e5cc
|
#
# QAPI event generator
#
# Copyright (c) 2014 Wenchao Xia
# Copyright (c) 2015-2016 Red Hat Inc.
#
# Authors:
# Wenchao Xia <wenchaoqemu@gmail.com>
# Markus Armbruster <armbru@redhat.com>
#
# This work is licensed under the terms of the GNU GPL, version 2.
# See the COPYING file in the top-level directory.
from qapi import *
def gen_event_send_proto(name, arg_type, boxed):
return 'void qapi_event_send_%(c_name)s(%(param)s)' % {
'c_name': c_name(name.lower()),
'param': gen_params(arg_type, boxed, 'Error **errp')}
def gen_event_send_decl(name, arg_type, boxed):
return mcgen('''
%(proto)s;
''',
proto=gen_event_send_proto(name, arg_type, boxed))
# Declare and initialize an object 'qapi' using parameters from gen_params()
def gen_param_var(typ):
assert not typ.variants
ret = mcgen('''
%(c_name)s param = {
''',
c_name=typ.c_name())
sep = ' '
for memb in typ.members:
ret += sep
sep = ', '
if memb.optional:
ret += 'has_' + c_name(memb.name) + sep
if memb.type.name == 'str':
# Cast away const added in gen_params()
ret += '(char *)'
ret += c_name(memb.name)
ret += mcgen('''
};
''')
if not typ.is_implicit():
ret += mcgen('''
%(c_name)s *arg = ¶m;
''',
c_name=typ.c_name())
return ret
def gen_event_send(name, arg_type, boxed):
# FIXME: Our declaration of local variables (and of 'errp' in the
# parameter list) can collide with exploded members of the event's
# data type passed in as parameters. If this collision ever hits in
# practice, we can rename our local variables with a leading _ prefix,
# or split the code into a wrapper function that creates a boxed
# 'param' object then calls another to do the real work.
ret = mcgen('''
%(proto)s
{
QDict *qmp;
Error *err = NULL;
QMPEventFuncEmit emit;
''',
proto=gen_event_send_proto(name, arg_type, boxed))
if arg_type and not arg_type.is_empty():
ret += mcgen('''
QObject *obj;
Visitor *v;
''')
if not boxed:
ret += gen_param_var(arg_type)
else:
assert not boxed
ret += mcgen('''
emit = qmp_event_get_func_emit();
if (!emit) {
return;
}
qmp = qmp_event_build_dict("%(name)s");
''',
name=name)
if arg_type and not arg_type.is_empty():
ret += mcgen('''
v = qobject_output_visitor_new(&obj);
''')
if not arg_type.is_implicit():
ret += mcgen('''
visit_type_%(c_name)s(v, "%(name)s", &arg, &err);
''',
name=name, c_name=arg_type.c_name())
else:
ret += mcgen('''
visit_start_struct(v, "%(name)s", NULL, 0, &err);
if (err) {
goto out;
}
visit_type_%(c_name)s_members(v, ¶m, &err);
if (!err) {
visit_check_struct(v, &err);
}
visit_end_struct(v, NULL);
''',
name=name, c_name=arg_type.c_name())
ret += mcgen('''
if (err) {
goto out;
}
visit_complete(v, &obj);
qdict_put_obj(qmp, "data", obj);
''')
ret += mcgen('''
emit(%(c_enum)s, qmp, &err);
''',
c_enum=c_enum_const(event_enum_name, name))
if arg_type and not arg_type.is_empty():
ret += mcgen('''
out:
visit_free(v);
''')
ret += mcgen('''
error_propagate(errp, err);
QDECREF(qmp);
}
''')
return ret
class QAPISchemaGenEventVisitor(QAPISchemaVisitor):
def __init__(self):
self.decl = None
self.defn = None
self._event_names = None
def visit_begin(self, schema):
self.decl = ''
self.defn = ''
self._event_names = []
def visit_end(self):
self.decl += gen_enum(event_enum_name, self._event_names)
self.defn += gen_enum_lookup(event_enum_name, self._event_names)
self._event_names = None
def visit_event(self, name, info, arg_type, boxed):
self.decl += gen_event_send_decl(name, arg_type, boxed)
self.defn += gen_event_send(name, arg_type, boxed)
self._event_names.append(name)
(input_file, output_dir, do_c, do_h, prefix, dummy) = parse_command_line()
c_comment = '''
/*
* schema-defined QAPI event functions
*
* Copyright (c) 2014 Wenchao Xia
*
* Authors:
* Wenchao Xia <wenchaoqemu@gmail.com>
*
* This work is licensed under the terms of the GNU LGPL, version 2.1 or later.
* See the COPYING.LIB file in the top-level directory.
*
*/
'''
h_comment = '''
/*
* schema-defined QAPI event functions
*
* Copyright (c) 2014 Wenchao Xia
*
* Authors:
* Wenchao Xia <wenchaoqemu@gmail.com>
*
* This work is licensed under the terms of the GNU LGPL, version 2.1 or later.
* See the COPYING.LIB file in the top-level directory.
*
*/
'''
(fdef, fdecl) = open_output(output_dir, do_c, do_h, prefix,
'qapi-event.c', 'qapi-event.h',
c_comment, h_comment)
fdef.write(mcgen('''
#include "qemu/osdep.h"
#include "qemu-common.h"
#include "%(prefix)sqapi-event.h"
#include "%(prefix)sqapi-visit.h"
#include "qapi/qobject-output-visitor.h"
#include "qapi/qmp-event.h"
''',
prefix=prefix))
fdecl.write(mcgen('''
#include "qapi/error.h"
#include "qapi/qmp/qdict.h"
#include "%(prefix)sqapi-types.h"
''',
prefix=prefix))
event_enum_name = c_name(prefix + 'QAPIEvent', protect=False)
schema = QAPISchema(input_file)
gen = QAPISchemaGenEventVisitor()
schema.visit(gen)
fdef.write(gen.defn)
fdecl.write(gen.decl)
close_output(fdef, fdecl)
|
J-Liu/qemu
|
scripts/qapi-event.py
|
Python
|
gpl-2.0
| 5,702
|
[
"VisIt"
] |
7bd96bb70a7612c2a038313157e2aa106d3a09c693ffb122437185358ffc2acd
|
# -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'vizParasDialogue.ui'
#
# Created: Thu May 19 00:20:13 2011
# by: PyQt4 UI code generator 4.8.3
#
# WARNING! All changes made in this file will be lost!
#Author:Chaitanya CH
#FileName: vizParasDialogue.py
#This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License as
# published by the Free Software Foundation; either version 3, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; see the file COPYING. If not, write to
# the Free Software Foundation, Inc., 51 Franklin Street, Fifth
# Floor, Boston, MA 02110-1301, USA.
from PyQt4 import QtCore, QtGui
try:
_fromUtf8 = QtCore.QString.fromUtf8
except AttributeError:
_fromUtf8 = lambda s: s
class Ui_Dialog(object):
def setupUi(self, Dialog):
Dialog.setObjectName(_fromUtf8("Dialog"))
Dialog.resize(493, 574)
self.label = QtGui.QLabel(Dialog)
self.label.setGeometry(QtCore.QRect(230, 235, 67, 17))
self.label.setObjectName(_fromUtf8("label"))
self.label_2 = QtGui.QLabel(Dialog)
self.label_2.setGeometry(QtCore.QRect(50, 240, 67, 17))
self.label_2.setObjectName(_fromUtf8("label_2"))
self.label_3 = QtGui.QLabel(Dialog)
self.label_3.setGeometry(QtCore.QRect(100, 295, 111, 17))
self.label_3.setObjectName(_fromUtf8("label_3"))
self.label_4 = QtGui.QLabel(Dialog)
self.label_4.setGeometry(QtCore.QRect(280, 295, 121, 17))
self.label_4.setObjectName(_fromUtf8("label_4"))
self.variable = QtGui.QLineEdit(Dialog)
self.variable.setGeometry(QtCore.QRect(100, 235, 113, 27))
self.variable.setObjectName(_fromUtf8("variable"))
self.moosepath = QtGui.QLineEdit(Dialog)
self.moosepath.setGeometry(QtCore.QRect(280, 235, 113, 27))
self.moosepath.setObjectName(_fromUtf8("moosepath"))
self.vizMinVal = QtGui.QLineEdit(Dialog)
self.vizMinVal.setGeometry(QtCore.QRect(100, 315, 113, 27))
self.vizMinVal.setToolTip(_fromUtf8(""))
self.vizMinVal.setObjectName(_fromUtf8("vizMinVal"))
self.vizMaxVal = QtGui.QLineEdit(Dialog)
self.vizMaxVal.setGeometry(QtCore.QRect(280, 315, 113, 27))
self.vizMaxVal.setToolTip(_fromUtf8(""))
self.vizMaxVal.setObjectName(_fromUtf8("vizMaxVal"))
self.label_5 = QtGui.QLabel(Dialog)
self.label_5.setGeometry(QtCore.QRect(142, 370, 81, 17))
self.label_5.setObjectName(_fromUtf8("label_5"))
self.acceptButton = QtGui.QPushButton(Dialog)
self.acceptButton.setGeometry(QtCore.QRect(360, 530, 95, 27))
self.acceptButton.setObjectName(_fromUtf8("acceptButton"))
self.resetButton = QtGui.QPushButton(Dialog)
self.resetButton.setGeometry(QtCore.QRect(210, 530, 95, 27))
self.resetButton.setObjectName(_fromUtf8("resetButton"))
self.label_6 = QtGui.QLabel(Dialog)
self.label_6.setGeometry(QtCore.QRect(120, 260, 261, 31))
self.label_6.setObjectName(_fromUtf8("label_6"))
self.mtree = MooseTreeWidget(Dialog)
self.mtree.setGeometry(QtCore.QRect(30, 30, 191, 131))
self.mtree.setObjectName(_fromUtf8("mtree"))
self.vizCells = QtGui.QListWidget(Dialog)
self.vizCells.setGeometry(QtCore.QRect(280, 30, 181, 131))
self.vizCells.setObjectName(_fromUtf8("vizCells"))
self.label_8 = QtGui.QLabel(Dialog)
self.label_8.setGeometry(QtCore.QRect(30, 10, 101, 17))
self.label_8.setObjectName(_fromUtf8("label_8"))
self.label_9 = QtGui.QLabel(Dialog)
self.label_9.setGeometry(QtCore.QRect(280, 10, 121, 17))
self.label_9.setObjectName(_fromUtf8("label_9"))
self.closeButton = QtGui.QPushButton(Dialog)
self.closeButton.setGeometry(QtCore.QRect(50, 530, 95, 27))
self.closeButton.setObjectName(_fromUtf8("closeButton"))
self.addCellButton = QtGui.QToolButton(Dialog)
self.addCellButton.setGeometry(QtCore.QRect(240, 30, 24, 25))
self.addCellButton.setObjectName(_fromUtf8("addCellButton"))
self.removeCellButton = QtGui.QToolButton(Dialog)
self.removeCellButton.setGeometry(QtCore.QRect(240, 80, 24, 25))
self.removeCellButton.setObjectName(_fromUtf8("removeCellButton"))
self.allCellsButton = QtGui.QToolButton(Dialog)
self.allCellsButton.setGeometry(QtCore.QRect(240, 130, 24, 25))
self.allCellsButton.setObjectName(_fromUtf8("allCellsButton"))
self.styleComboBox = QtGui.QComboBox(Dialog)
self.styleComboBox.setGeometry(QtCore.QRect(100, 180, 111, 31))
self.styleComboBox.setObjectName(_fromUtf8("styleComboBox"))
self.styleComboBox.addItem(_fromUtf8(""))
self.styleComboBox.addItem(_fromUtf8(""))
self.styleComboBox.addItem(_fromUtf8(""))
self.styleComboBox.addItem(_fromUtf8(""))
self.label_10 = QtGui.QLabel(Dialog)
self.label_10.setGeometry(QtCore.QRect(50, 190, 67, 17))
self.label_10.setObjectName(_fromUtf8("label_10"))
self.colorMapComboBox = QtGui.QComboBox(Dialog)
self.colorMapComboBox.setGeometry(QtCore.QRect(230, 360, 101, 31))
self.colorMapComboBox.setObjectName(_fromUtf8("colorMapComboBox"))
self.colorMapComboBox.addItem(_fromUtf8(""))
self.colorMapComboBox.addItem(_fromUtf8(""))
self.colorMapComboBox.addItem(_fromUtf8(""))
self.colorMapComboBox.addItem(_fromUtf8(""))
self.colorMapComboBox.addItem(_fromUtf8(""))
self.colorMapComboBox.addItem(_fromUtf8(""))
self.specificCompartmentName = QtGui.QLineEdit(Dialog)
self.specificCompartmentName.setEnabled(False)
self.specificCompartmentName.setGeometry(QtCore.QRect(280, 182, 113, 27))
self.specificCompartmentName.setObjectName(_fromUtf8("specificCompartmentName"))
self.label_7 = QtGui.QLabel(Dialog)
self.label_7.setEnabled(False)
self.label_7.setGeometry(QtCore.QRect(230, 188, 67, 17))
self.label_7.setObjectName(_fromUtf8("label_7"))
self.label_11 = QtGui.QLabel(Dialog)
self.label_11.setEnabled(False)
self.label_11.setGeometry(QtCore.QRect(230, 424, 67, 17))
self.label_11.setObjectName(_fromUtf8("label_11"))
self.label_12 = QtGui.QLabel(Dialog)
self.label_12.setEnabled(False)
self.label_12.setGeometry(QtCore.QRect(50, 425, 67, 17))
self.label_12.setObjectName(_fromUtf8("label_12"))
self.label_13 = QtGui.QLabel(Dialog)
self.label_13.setEnabled(False)
self.label_13.setGeometry(QtCore.QRect(280, 455, 131, 17))
self.label_13.setObjectName(_fromUtf8("label_13"))
self.label_14 = QtGui.QLabel(Dialog)
self.label_14.setEnabled(False)
self.label_14.setGeometry(QtCore.QRect(100, 455, 121, 17))
self.label_14.setObjectName(_fromUtf8("label_14"))
self.vizMinVal_2 = QtGui.QLineEdit(Dialog)
self.vizMinVal_2.setEnabled(False)
self.vizMinVal_2.setGeometry(QtCore.QRect(100, 475, 113, 27))
self.vizMinVal_2.setText(_fromUtf8(""))
self.vizMinVal_2.setObjectName(_fromUtf8("vizMinVal_2"))
self.vizMaxVal_2 = QtGui.QLineEdit(Dialog)
self.vizMaxVal_2.setEnabled(False)
self.vizMaxVal_2.setGeometry(QtCore.QRect(280, 475, 113, 27))
self.vizMaxVal_2.setText(_fromUtf8(""))
self.vizMaxVal_2.setObjectName(_fromUtf8("vizMaxVal_2"))
self.variable_2 = QtGui.QLineEdit(Dialog)
self.variable_2.setEnabled(False)
self.variable_2.setGeometry(QtCore.QRect(100, 420, 113, 27))
self.variable_2.setText(_fromUtf8(""))
self.variable_2.setObjectName(_fromUtf8("variable_2"))
self.moosepath_2 = QtGui.QLineEdit(Dialog)
self.moosepath_2.setEnabled(False)
self.moosepath_2.setGeometry(QtCore.QRect(280, 420, 113, 27))
self.moosepath_2.setObjectName(_fromUtf8("moosepath_2"))
self.retranslateUi(Dialog)
self.styleComboBox.setCurrentIndex(2)
QtCore.QObject.connect(self.closeButton, QtCore.SIGNAL(_fromUtf8("clicked()")), Dialog.reject)
QtCore.QMetaObject.connectSlotsByName(Dialog)
Dialog.setTabOrder(self.styleComboBox, self.specificCompartmentName)
Dialog.setTabOrder(self.specificCompartmentName, self.variable)
Dialog.setTabOrder(self.variable, self.moosepath)
Dialog.setTabOrder(self.moosepath, self.vizMinVal)
Dialog.setTabOrder(self.vizMinVal, self.vizMaxVal)
Dialog.setTabOrder(self.vizMaxVal, self.colorMapComboBox)
Dialog.setTabOrder(self.colorMapComboBox, self.variable_2)
Dialog.setTabOrder(self.variable_2, self.moosepath_2)
Dialog.setTabOrder(self.moosepath_2, self.vizMinVal_2)
Dialog.setTabOrder(self.vizMinVal_2, self.vizMaxVal_2)
Dialog.setTabOrder(self.vizMaxVal_2, self.closeButton)
Dialog.setTabOrder(self.closeButton, self.resetButton)
Dialog.setTabOrder(self.resetButton, self.acceptButton)
Dialog.setTabOrder(self.acceptButton, self.addCellButton)
Dialog.setTabOrder(self.addCellButton, self.removeCellButton)
Dialog.setTabOrder(self.removeCellButton, self.allCellsButton)
Dialog.setTabOrder(self.allCellsButton, self.vizCells)
def retranslateUi(self, Dialog):
Dialog.setWindowTitle(QtGui.QApplication.translate("Dialog", "Visualization Parameters", None, QtGui.QApplication.UnicodeUTF8))
self.label.setText(QtGui.QApplication.translate("Dialog", "Path", None, QtGui.QApplication.UnicodeUTF8))
self.label_2.setText(QtGui.QApplication.translate("Dialog", "Field*", None, QtGui.QApplication.UnicodeUTF8))
self.label_3.setText(QtGui.QApplication.translate("Dialog", "Minimum Value*", None, QtGui.QApplication.UnicodeUTF8))
self.label_4.setText(QtGui.QApplication.translate("Dialog", "Maximum Value*", None, QtGui.QApplication.UnicodeUTF8))
self.variable.setToolTip(QtGui.QApplication.translate("Dialog", "Name of the field to be visualized (/Cell/Compartment/Field)", None, QtGui.QApplication.UnicodeUTF8))
self.variable.setText(QtGui.QApplication.translate("Dialog", "Vm", None, QtGui.QApplication.UnicodeUTF8))
self.moosepath.setToolTip(QtGui.QApplication.translate("Dialog", "Name of the moose path relative to compartment of the field (/Cell/Compartment/Path/Field)", None, QtGui.QApplication.UnicodeUTF8))
self.vizMinVal.setText(QtGui.QApplication.translate("Dialog", "-0.1", None, QtGui.QApplication.UnicodeUTF8))
self.vizMaxVal.setText(QtGui.QApplication.translate("Dialog", "0.07", None, QtGui.QApplication.UnicodeUTF8))
self.label_5.setText(QtGui.QApplication.translate("Dialog", "Color Map", None, QtGui.QApplication.UnicodeUTF8))
self.acceptButton.setText(QtGui.QApplication.translate("Dialog", "OK", None, QtGui.QApplication.UnicodeUTF8))
self.resetButton.setText(QtGui.QApplication.translate("Dialog", "Reset", None, QtGui.QApplication.UnicodeUTF8))
self.label_6.setText(QtGui.QApplication.translate("Dialog", "(path relative to compartment path)", None, QtGui.QApplication.UnicodeUTF8))
self.mtree.setToolTip(QtGui.QApplication.translate("Dialog", "Select cells to visualize, double click to add cell OR select and click \'+\' button, click \'A\' button to draw all cells", None, QtGui.QApplication.UnicodeUTF8))
self.vizCells.setToolTip(QtGui.QApplication.translate("Dialog", "List of cells to visualize", None, QtGui.QApplication.UnicodeUTF8))
self.label_8.setText(QtGui.QApplication.translate("Dialog", "Moose Tree", None, QtGui.QApplication.UnicodeUTF8))
self.label_9.setText(QtGui.QApplication.translate("Dialog", "Cells to visualize*", None, QtGui.QApplication.UnicodeUTF8))
self.closeButton.setText(QtGui.QApplication.translate("Dialog", "Close", None, QtGui.QApplication.UnicodeUTF8))
self.addCellButton.setToolTip(QtGui.QApplication.translate("Dialog", "Add selected cell to the visualization list", None, QtGui.QApplication.UnicodeUTF8))
self.addCellButton.setText(QtGui.QApplication.translate("Dialog", "+", None, QtGui.QApplication.UnicodeUTF8))
self.removeCellButton.setToolTip(QtGui.QApplication.translate("Dialog", "Remove selected cell from visualization", None, QtGui.QApplication.UnicodeUTF8))
self.removeCellButton.setText(QtGui.QApplication.translate("Dialog", "-", None, QtGui.QApplication.UnicodeUTF8))
self.allCellsButton.setToolTip(QtGui.QApplication.translate("Dialog", "Add all cells to the visualization list", None, QtGui.QApplication.UnicodeUTF8))
self.allCellsButton.setText(QtGui.QApplication.translate("Dialog", "A", None, QtGui.QApplication.UnicodeUTF8))
self.styleComboBox.setItemText(0, QtGui.QApplication.translate("Dialog", "Compartment Disk", None, QtGui.QApplication.UnicodeUTF8))
self.styleComboBox.setItemText(1, QtGui.QApplication.translate("Dialog", "Ball & Stick", None, QtGui.QApplication.UnicodeUTF8))
self.styleComboBox.setItemText(2, QtGui.QApplication.translate("Dialog", "True Dim", None, QtGui.QApplication.UnicodeUTF8))
self.styleComboBox.setItemText(3, QtGui.QApplication.translate("Dialog", "Grid View", None, QtGui.QApplication.UnicodeUTF8))
self.label_10.setText(QtGui.QApplication.translate("Dialog", "Style", None, QtGui.QApplication.UnicodeUTF8))
self.colorMapComboBox.setItemText(0, QtGui.QApplication.translate("Dialog", "jet", None, QtGui.QApplication.UnicodeUTF8))
self.colorMapComboBox.setItemText(1, QtGui.QApplication.translate("Dialog", "grey", None, QtGui.QApplication.UnicodeUTF8))
self.colorMapComboBox.setItemText(2, QtGui.QApplication.translate("Dialog", "redhot", None, QtGui.QApplication.UnicodeUTF8))
self.colorMapComboBox.setItemText(3, QtGui.QApplication.translate("Dialog", "fire", None, QtGui.QApplication.UnicodeUTF8))
self.colorMapComboBox.setItemText(4, QtGui.QApplication.translate("Dialog", "greenfire", None, QtGui.QApplication.UnicodeUTF8))
self.colorMapComboBox.setItemText(5, QtGui.QApplication.translate("Dialog", "heat", None, QtGui.QApplication.UnicodeUTF8))
self.specificCompartmentName.setToolTip(QtGui.QApplication.translate("Dialog", "Compartment Name. Default is \'soma\'", None, QtGui.QApplication.UnicodeUTF8))
self.label_7.setText(QtGui.QApplication.translate("Dialog", "Name", None, QtGui.QApplication.UnicodeUTF8))
self.label_11.setText(QtGui.QApplication.translate("Dialog", "Path2", None, QtGui.QApplication.UnicodeUTF8))
self.label_12.setText(QtGui.QApplication.translate("Dialog", "Field2", None, QtGui.QApplication.UnicodeUTF8))
self.label_13.setText(QtGui.QApplication.translate("Dialog", "Maximum Value2", None, QtGui.QApplication.UnicodeUTF8))
self.label_14.setText(QtGui.QApplication.translate("Dialog", "Minimum Value2", None, QtGui.QApplication.UnicodeUTF8))
self.variable_2.setToolTip(QtGui.QApplication.translate("Dialog", "Name of the field to be visualized as changing disk size (/Cell/Compartment/Field)", None, QtGui.QApplication.UnicodeUTF8))
self.moosepath_2.setToolTip(QtGui.QApplication.translate("Dialog", "Name of the moose path relative to compartment of the field (/Cell/Compartment/Path/Field)", None, QtGui.QApplication.UnicodeUTF8))
from moosetree import MooseTreeWidget
|
BhallaLab/moose-thalamocortical
|
pymoose/gui/qt/vizParasDialogue.py
|
Python
|
lgpl-2.1
| 15,864
|
[
"MOOSE"
] |
372a06d645755aa6a21437babda06d43ffb405950590e1f343c0767cae1a3a5c
|
from __future__ import annotations
import copy
import logging
import os
import dxtbx.model
import libtbx.phil
from cctbx.miller import map_to_asu
from rstbx.cftbx.coordinate_frame_helpers import align_reference_frame
from scitbx import matrix
from dials.array_family import flex
from dials.util import Sorry
from dials.util.filter_reflections import (
FilteringReductionMethods,
filter_reflection_table,
)
try:
from typing import Tuple
except ImportError:
pass
logger = logging.getLogger(__name__)
def export_xds_ascii(integrated_data, experiment_list, params, var_model=(1, 0)):
"""Export data from integrated_data corresponding to experiment_list to
an XDS_ASCII.HKL formatted text file."""
if len(experiment_list) == 1:
experiment_data = integrated_data.select(integrated_data["id"] >= 0)
_export_experiment(
params.xds_ascii.hklout,
experiment_data,
experiment_list[0],
params,
var_model,
)
else:
for i, experiment in enumerate(experiment_list):
experiment_data = integrated_data.select(integrated_data["id"] == i)
name, ext = os.path.splitext(params.xds_ascii.hklout)
filename = name + f"_{i}" + ext
_export_experiment(filename, experiment_data, experiment, params, var_model)
def _export_experiment(filename, integrated_data, experiment, params, var_model=(1, 0)):
# type: (str, flex.reflection_table, dxtbx.model.Experiment, libtbx.phil.scope_extract, Tuple)
"""Export a single experiment to an XDS_ASCII.HKL format file.
Args:
filename: The file to write to
integrated_data: The reflection table, pre-selected to one experiment
experiment: The experiment list entry to export
params: The PHIL configuration object
var_model:
"""
# export for xds_ascii should only be for non-scaled reflections
assert any(
i in integrated_data for i in ["intensity.sum.value", "intensity.prf.value"]
)
# Handle requesting profile intensities (default via auto) but no column
if "profile" in params.intensity and "intensity.prf.value" not in integrated_data:
raise Sorry(
"Requested profile intensity data but only summed present. Use intensity=sum."
)
integrated_data = filter_reflection_table(
integrated_data,
intensity_choice=params.intensity,
partiality_threshold=params.mtz.partiality_threshold,
combine_partials=params.mtz.combine_partials,
min_isigi=params.mtz.min_isigi,
filter_ice_rings=params.mtz.filter_ice_rings,
d_min=params.mtz.d_min,
)
# calculate the scl = lp/dqe correction for outputting but don't apply it as
# it has already been applied in filter_reflection_table
(
integrated_data,
scl,
) = FilteringReductionMethods.calculate_lp_qe_correction_and_filter(integrated_data)
# sort data before output
nref = len(integrated_data["miller_index"])
indices = flex.size_t_range(nref)
unique = copy.deepcopy(integrated_data["miller_index"])
map_to_asu(experiment.crystal.get_space_group().type(), False, unique)
perm = sorted(indices, key=lambda k: unique[k])
integrated_data = integrated_data.select(flex.size_t(perm))
if experiment.goniometer is None:
print("Warning: No goniometer. Experimentally exporting with (1 0 0) axis")
unit_cell = experiment.crystal.get_unit_cell()
if experiment.scan is None:
print("Warning: No Scan. Experimentally exporting no-oscillation values")
image_range = (1, 1)
phi_start, phi_range = 0.0, 0.0
else:
image_range = experiment.scan.get_image_range()
phi_start, phi_range = experiment.scan.get_image_oscillation(image_range[0])
# gather the required information for the reflection file
nref = len(integrated_data["miller_index"])
miller_index = integrated_data["miller_index"]
# profile correlation
if "profile.correlation" in integrated_data:
prof_corr = 100.0 * integrated_data["profile.correlation"]
else:
prof_corr = flex.double(nref, 100.0)
# partiality
if "partiality" in integrated_data:
partiality = 100 * integrated_data["partiality"]
else:
prof_corr = flex.double(nref, 100.0)
if "intensity.sum.value" in integrated_data:
I = integrated_data["intensity.sum.value"]
V = integrated_data["intensity.sum.variance"]
assert V.all_gt(0)
V = var_model[0] * (V + var_model[1] * I * I)
sigI = flex.sqrt(V)
else:
I = integrated_data["intensity.prf.value"]
V = integrated_data["intensity.prf.variance"]
assert V.all_gt(0)
V = var_model[0] * (V + var_model[1] * I * I)
sigI = flex.sqrt(V)
fout = open(filename, "w")
# first write the header - in the "standard" coordinate frame...
panel = experiment.detector[0]
fast = panel.get_fast_axis()
slow = panel.get_slow_axis()
Rd = align_reference_frame(fast, (1, 0, 0), slow, (0, 1, 0))
print("Coordinate change:")
print("%5.2f %5.2f %5.2f\n%5.2f %5.2f %5.2f\n%5.2f %5.2f %5.2f\n" % Rd.elems)
fast = Rd * fast
slow = Rd * slow
qx, qy = panel.get_pixel_size()
nx, ny = panel.get_image_size()
distance = matrix.col(Rd * panel.get_origin()).dot(
matrix.col(Rd * panel.get_normal())
)
org = Rd * (
matrix.col(panel.get_origin()) - distance * matrix.col(panel.get_normal())
)
orgx = -org.dot(fast) / qx
orgy = -org.dot(slow) / qy
UB = Rd * matrix.sqr(experiment.crystal.get_A())
real_space_ABC = UB.inverse().elems
if experiment.goniometer is not None:
axis = Rd * experiment.goniometer.get_rotation_axis()
else:
axis = Rd * (1, 0, 0)
beam = Rd * experiment.beam.get_s0()
cell_fmt = "%9.3f %9.3f %9.3f %7.3f %7.3f %7.3f"
axis_fmt = "%9.3f %9.3f %9.3f"
fout.write(
"\n".join(
[
"!FORMAT=XDS_ASCII MERGE=FALSE FRIEDEL'S_LAW=TRUE",
"!Generated by dials.export",
"!DATA_RANGE= %d %d" % image_range,
"!ROTATION_AXIS= %9.6f %9.6f %9.6f" % axis.elems,
"!OSCILLATION_RANGE= %f" % phi_range,
"!STARTING_ANGLE= %f" % phi_start,
"!STARTING_FRAME= %d" % image_range[0],
"!SPACE_GROUP_NUMBER= %d"
% experiment.crystal.get_space_group().type().number(),
f"!UNIT_CELL_CONSTANTS= {cell_fmt % unit_cell.parameters()}",
f"!UNIT_CELL_A-AXIS= {axis_fmt % real_space_ABC[0:3]}",
f"!UNIT_CELL_B-AXIS= {axis_fmt % real_space_ABC[3:6]}",
f"!UNIT_CELL_C-AXIS= {axis_fmt % real_space_ABC[6:9]}",
f"!X-RAY_WAVELENGTH= {experiment.beam.get_wavelength():f}",
"!INCIDENT_BEAM_DIRECTION= %f %f %f" % beam.elems,
"!NX= %d NY= %d QX= %f QY= %f" % (nx, ny, qx, qy),
f"!ORGX= {orgx:9.2f} ORGY= {orgy:9.2f}",
f"!DETECTOR_DISTANCE= {distance:8.3f}",
"!DIRECTION_OF_DETECTOR_X-AXIS= %9.5f %9.5f %9.5f" % fast.elems,
"!DIRECTION_OF_DETECTOR_Y-AXIS= %9.5f %9.5f %9.5f" % slow.elems,
"!VARIANCE_MODEL= %7.3e %7.3e" % var_model,
"!NUMBER_OF_ITEMS_IN_EACH_DATA_RECORD=12",
"!ITEM_H=1",
"!ITEM_K=2",
"!ITEM_L=3",
"!ITEM_IOBS=4",
"!ITEM_SIGMA(IOBS)=5",
"!ITEM_XD=6",
"!ITEM_YD=7",
"!ITEM_ZD=8",
"!ITEM_RLP=9",
"!ITEM_PEAK=10",
"!ITEM_CORR=11",
"!ITEM_PSI=12",
"!END_OF_HEADER",
"",
]
)
)
# then write the data records
s0 = Rd * matrix.col(experiment.beam.get_s0())
for j in range(nref):
x, y, z = integrated_data["xyzcal.px"][j]
phi = phi_start + z * phi_range
h, k, l = miller_index[j]
X = (UB * (h, k, l)).rotate(axis, phi, deg=True)
s = s0 + X
g = s.cross(s0).normalize()
# find component of beam perpendicular to f, e
e = -(s + s0).normalize()
if h == k and k == l:
u = (h, -h, 0)
else:
u = (k - l, l - h, h - k)
q = (
(matrix.col(u).transpose() * UB.inverse())
.normalize()
.transpose()
.rotate(axis, phi, deg=True)
)
psi = q.angle(g, deg=True)
if q.dot(e) < 0:
psi *= -1
fout.write(
"%d %d %d %f %f %f %f %f %f %.1f %.1f %f\n"
% (
h,
k,
l,
I[j],
sigI[j],
x,
y,
z,
scl[j],
partiality[j],
prof_corr[j],
psi,
)
)
fout.write("!END_OF_DATA\n")
fout.close()
logger.info("Output %d reflections to %s", nref, filename)
|
dials/dials
|
util/export_xds_ascii.py
|
Python
|
bsd-3-clause
| 9,243
|
[
"CRYSTAL"
] |
953540117dfdf41ca2a0723315204fd237531004f396eb7b143500660f265f29
|
#!/usr/bin/env python
"""
register DCommands session environment variables
"""
import os
from COMDIRAC.Interfaces import critical
from COMDIRAC.Interfaces import DSession
if __name__ == "__main__":
import sys
from DIRAC.Core.Base import Script
Script.setUsageMessage( '\n'.join( [ __doc__.split( '\n' )[1],
'Usage:',
' %s [[section.]option=value]...' % Script.scriptName,
'Arguments:',
' section: section (defaults to "session:environment")',
' option: option name',
' value: value to be set',] )
)
Script.parseCommandLine( ignoreErrors = True )
args = Script.getPositionalArgs()
session = DSession( )
modified = False
for arg in args:
section = None
option = None
arg, value = arg.split( "=", 1 )
if "." in arg:
section, option = arg.split( ".", 1 )
else:
option = arg
if section:
session.set( section, option, value )
else:
session.setEnv( option, value )
modified = True
if modified:
session.write( )
|
pigay/COMDIRAC
|
Interfaces/scripts/dsetenv.py
|
Python
|
gpl-3.0
| 1,274
|
[
"DIRAC"
] |
ccda6e737735efd0a5dd9271a1f3fec1f67ce97ff113c7d562e2a304906e4d80
|
"""HTML character entity references."""
# maps the HTML entity name to the Unicode code point
name2codepoint = {
'AElig': 0x00c6, # latin capital letter AE = latin capital ligature AE, U+00C6 ISOlat1
'Aacute': 0x00c1, # latin capital letter A with acute, U+00C1 ISOlat1
'Acirc': 0x00c2, # latin capital letter A with circumflex, U+00C2 ISOlat1
'Agrave': 0x00c0, # latin capital letter A with grave = latin capital letter A grave, U+00C0 ISOlat1
'Alpha': 0x0391, # greek capital letter alpha, U+0391
'Aring': 0x00c5, # latin capital letter A with ring above = latin capital letter A ring, U+00C5 ISOlat1
'Atilde': 0x00c3, # latin capital letter A with tilde, U+00C3 ISOlat1
'Auml': 0x00c4, # latin capital letter A with diaeresis, U+00C4 ISOlat1
'Beta': 0x0392, # greek capital letter beta, U+0392
'Ccedil': 0x00c7, # latin capital letter C with cedilla, U+00C7 ISOlat1
'Chi': 0x03a7, # greek capital letter chi, U+03A7
'Dagger': 0x2021, # double dagger, U+2021 ISOpub
'Delta': 0x0394, # greek capital letter delta, U+0394 ISOgrk3
'ETH': 0x00d0, # latin capital letter ETH, U+00D0 ISOlat1
'Eacute': 0x00c9, # latin capital letter E with acute, U+00C9 ISOlat1
'Ecirc': 0x00ca, # latin capital letter E with circumflex, U+00CA ISOlat1
'Egrave': 0x00c8, # latin capital letter E with grave, U+00C8 ISOlat1
'Epsilon': 0x0395, # greek capital letter epsilon, U+0395
'Eta': 0x0397, # greek capital letter eta, U+0397
'Euml': 0x00cb, # latin capital letter E with diaeresis, U+00CB ISOlat1
'Gamma': 0x0393, # greek capital letter gamma, U+0393 ISOgrk3
'Iacute': 0x00cd, # latin capital letter I with acute, U+00CD ISOlat1
'Icirc': 0x00ce, # latin capital letter I with circumflex, U+00CE ISOlat1
'Igrave': 0x00cc, # latin capital letter I with grave, U+00CC ISOlat1
'Iota': 0x0399, # greek capital letter iota, U+0399
'Iuml': 0x00cf, # latin capital letter I with diaeresis, U+00CF ISOlat1
'Kappa': 0x039a, # greek capital letter kappa, U+039A
'Lambda': 0x039b, # greek capital letter lambda, U+039B ISOgrk3
'Mu': 0x039c, # greek capital letter mu, U+039C
'Ntilde': 0x00d1, # latin capital letter N with tilde, U+00D1 ISOlat1
'Nu': 0x039d, # greek capital letter nu, U+039D
'OElig': 0x0152, # latin capital ligature OE, U+0152 ISOlat2
'Oacute': 0x00d3, # latin capital letter O with acute, U+00D3 ISOlat1
'Ocirc': 0x00d4, # latin capital letter O with circumflex, U+00D4 ISOlat1
'Ograve': 0x00d2, # latin capital letter O with grave, U+00D2 ISOlat1
'Omega': 0x03a9, # greek capital letter omega, U+03A9 ISOgrk3
'Omicron': 0x039f, # greek capital letter omicron, U+039F
'Oslash': 0x00d8, # latin capital letter O with stroke = latin capital letter O slash, U+00D8 ISOlat1
'Otilde': 0x00d5, # latin capital letter O with tilde, U+00D5 ISOlat1
'Ouml': 0x00d6, # latin capital letter O with diaeresis, U+00D6 ISOlat1
'Phi': 0x03a6, # greek capital letter phi, U+03A6 ISOgrk3
'Pi': 0x03a0, # greek capital letter pi, U+03A0 ISOgrk3
'Prime': 0x2033, # double prime = seconds = inches, U+2033 ISOtech
'Psi': 0x03a8, # greek capital letter psi, U+03A8 ISOgrk3
'Rho': 0x03a1, # greek capital letter rho, U+03A1
'Scaron': 0x0160, # latin capital letter S with caron, U+0160 ISOlat2
'Sigma': 0x03a3, # greek capital letter sigma, U+03A3 ISOgrk3
'THORN': 0x00de, # latin capital letter THORN, U+00DE ISOlat1
'Tau': 0x03a4, # greek capital letter tau, U+03A4
'Theta': 0x0398, # greek capital letter theta, U+0398 ISOgrk3
'Uacute': 0x00da, # latin capital letter U with acute, U+00DA ISOlat1
'Ucirc': 0x00db, # latin capital letter U with circumflex, U+00DB ISOlat1
'Ugrave': 0x00d9, # latin capital letter U with grave, U+00D9 ISOlat1
'Upsilon': 0x03a5, # greek capital letter upsilon, U+03A5 ISOgrk3
'Uuml': 0x00dc, # latin capital letter U with diaeresis, U+00DC ISOlat1
'Xi': 0x039e, # greek capital letter xi, U+039E ISOgrk3
'Yacute': 0x00dd, # latin capital letter Y with acute, U+00DD ISOlat1
'Yuml': 0x0178, # latin capital letter Y with diaeresis, U+0178 ISOlat2
'Zeta': 0x0396, # greek capital letter zeta, U+0396
'aacute': 0x00e1, # latin small letter a with acute, U+00E1 ISOlat1
'acirc': 0x00e2, # latin small letter a with circumflex, U+00E2 ISOlat1
'acute': 0x00b4, # acute accent = spacing acute, U+00B4 ISOdia
'aelig': 0x00e6, # latin small letter ae = latin small ligature ae, U+00E6 ISOlat1
'agrave': 0x00e0, # latin small letter a with grave = latin small letter a grave, U+00E0 ISOlat1
'alefsym': 0x2135, # alef symbol = first transfinite cardinal, U+2135 NEW
'alpha': 0x03b1, # greek small letter alpha, U+03B1 ISOgrk3
'amp': 0x0026, # ampersand, U+0026 ISOnum
'and': 0x2227, # logical and = wedge, U+2227 ISOtech
'ang': 0x2220, # angle, U+2220 ISOamso
'aring': 0x00e5, # latin small letter a with ring above = latin small letter a ring, U+00E5 ISOlat1
'asymp': 0x2248, # almost equal to = asymptotic to, U+2248 ISOamsr
'atilde': 0x00e3, # latin small letter a with tilde, U+00E3 ISOlat1
'auml': 0x00e4, # latin small letter a with diaeresis, U+00E4 ISOlat1
'bdquo': 0x201e, # double low-9 quotation mark, U+201E NEW
'beta': 0x03b2, # greek small letter beta, U+03B2 ISOgrk3
'brvbar': 0x00a6, # broken bar = broken vertical bar, U+00A6 ISOnum
'bull': 0x2022, # bullet = black small circle, U+2022 ISOpub
'cap': 0x2229, # intersection = cap, U+2229 ISOtech
'ccedil': 0x00e7, # latin small letter c with cedilla, U+00E7 ISOlat1
'cedil': 0x00b8, # cedilla = spacing cedilla, U+00B8 ISOdia
'cent': 0x00a2, # cent sign, U+00A2 ISOnum
'chi': 0x03c7, # greek small letter chi, U+03C7 ISOgrk3
'circ': 0x02c6, # modifier letter circumflex accent, U+02C6 ISOpub
'clubs': 0x2663, # black club suit = shamrock, U+2663 ISOpub
'cong': 0x2245, # approximately equal to, U+2245 ISOtech
'copy': 0x00a9, # copyright sign, U+00A9 ISOnum
'crarr': 0x21b5, # downwards arrow with corner leftwards = carriage return, U+21B5 NEW
'cup': 0x222a, # union = cup, U+222A ISOtech
'curren': 0x00a4, # currency sign, U+00A4 ISOnum
'dArr': 0x21d3, # downwards double arrow, U+21D3 ISOamsa
'dagger': 0x2020, # dagger, U+2020 ISOpub
'darr': 0x2193, # downwards arrow, U+2193 ISOnum
'deg': 0x00b0, # degree sign, U+00B0 ISOnum
'delta': 0x03b4, # greek small letter delta, U+03B4 ISOgrk3
'diams': 0x2666, # black diamond suit, U+2666 ISOpub
'divide': 0x00f7, # division sign, U+00F7 ISOnum
'eacute': 0x00e9, # latin small letter e with acute, U+00E9 ISOlat1
'ecirc': 0x00ea, # latin small letter e with circumflex, U+00EA ISOlat1
'egrave': 0x00e8, # latin small letter e with grave, U+00E8 ISOlat1
'empty': 0x2205, # empty set = null set = diameter, U+2205 ISOamso
'emsp': 0x2003, # em space, U+2003 ISOpub
'ensp': 0x2002, # en space, U+2002 ISOpub
'epsilon': 0x03b5, # greek small letter epsilon, U+03B5 ISOgrk3
'equiv': 0x2261, # identical to, U+2261 ISOtech
'eta': 0x03b7, # greek small letter eta, U+03B7 ISOgrk3
'eth': 0x00f0, # latin small letter eth, U+00F0 ISOlat1
'euml': 0x00eb, # latin small letter e with diaeresis, U+00EB ISOlat1
'euro': 0x20ac, # euro sign, U+20AC NEW
'exist': 0x2203, # there exists, U+2203 ISOtech
'fnof': 0x0192, # latin small f with hook = function = florin, U+0192 ISOtech
'forall': 0x2200, # for all, U+2200 ISOtech
'frac12': 0x00bd, # vulgar fraction one half = fraction one half, U+00BD ISOnum
'frac14': 0x00bc, # vulgar fraction one quarter = fraction one quarter, U+00BC ISOnum
'frac34': 0x00be, # vulgar fraction three quarters = fraction three quarters, U+00BE ISOnum
'frasl': 0x2044, # fraction slash, U+2044 NEW
'gamma': 0x03b3, # greek small letter gamma, U+03B3 ISOgrk3
'ge': 0x2265, # greater-than or equal to, U+2265 ISOtech
'gt': 0x003e, # greater-than sign, U+003E ISOnum
'hArr': 0x21d4, # left right double arrow, U+21D4 ISOamsa
'harr': 0x2194, # left right arrow, U+2194 ISOamsa
'hearts': 0x2665, # black heart suit = valentine, U+2665 ISOpub
'hellip': 0x2026, # horizontal ellipsis = three dot leader, U+2026 ISOpub
'iacute': 0x00ed, # latin small letter i with acute, U+00ED ISOlat1
'icirc': 0x00ee, # latin small letter i with circumflex, U+00EE ISOlat1
'iexcl': 0x00a1, # inverted exclamation mark, U+00A1 ISOnum
'igrave': 0x00ec, # latin small letter i with grave, U+00EC ISOlat1
'image': 0x2111, # blackletter capital I = imaginary part, U+2111 ISOamso
'infin': 0x221e, # infinity, U+221E ISOtech
'int': 0x222b, # integral, U+222B ISOtech
'iota': 0x03b9, # greek small letter iota, U+03B9 ISOgrk3
'iquest': 0x00bf, # inverted question mark = turned question mark, U+00BF ISOnum
'isin': 0x2208, # element of, U+2208 ISOtech
'iuml': 0x00ef, # latin small letter i with diaeresis, U+00EF ISOlat1
'kappa': 0x03ba, # greek small letter kappa, U+03BA ISOgrk3
'lArr': 0x21d0, # leftwards double arrow, U+21D0 ISOtech
'lambda': 0x03bb, # greek small letter lambda, U+03BB ISOgrk3
'lang': 0x2329, # left-pointing angle bracket = bra, U+2329 ISOtech
'laquo': 0x00ab, # left-pointing double angle quotation mark = left pointing guillemet, U+00AB ISOnum
'larr': 0x2190, # leftwards arrow, U+2190 ISOnum
'lceil': 0x2308, # left ceiling = apl upstile, U+2308 ISOamsc
'ldquo': 0x201c, # left double quotation mark, U+201C ISOnum
'le': 0x2264, # less-than or equal to, U+2264 ISOtech
'lfloor': 0x230a, # left floor = apl downstile, U+230A ISOamsc
'lowast': 0x2217, # asterisk operator, U+2217 ISOtech
'loz': 0x25ca, # lozenge, U+25CA ISOpub
'lrm': 0x200e, # left-to-right mark, U+200E NEW RFC 2070
'lsaquo': 0x2039, # single left-pointing angle quotation mark, U+2039 ISO proposed
'lsquo': 0x2018, # left single quotation mark, U+2018 ISOnum
'lt': 0x003c, # less-than sign, U+003C ISOnum
'macr': 0x00af, # macron = spacing macron = overline = APL overbar, U+00AF ISOdia
'mdash': 0x2014, # em dash, U+2014 ISOpub
'micro': 0x00b5, # micro sign, U+00B5 ISOnum
'middot': 0x00b7, # middle dot = Georgian comma = Greek middle dot, U+00B7 ISOnum
'minus': 0x2212, # minus sign, U+2212 ISOtech
'mu': 0x03bc, # greek small letter mu, U+03BC ISOgrk3
'nabla': 0x2207, # nabla = backward difference, U+2207 ISOtech
'nbsp': 0x00a0, # no-break space = non-breaking space, U+00A0 ISOnum
'ndash': 0x2013, # en dash, U+2013 ISOpub
'ne': 0x2260, # not equal to, U+2260 ISOtech
'ni': 0x220b, # contains as member, U+220B ISOtech
'not': 0x00ac, # not sign, U+00AC ISOnum
'notin': 0x2209, # not an element of, U+2209 ISOtech
'nsub': 0x2284, # not a subset of, U+2284 ISOamsn
'ntilde': 0x00f1, # latin small letter n with tilde, U+00F1 ISOlat1
'nu': 0x03bd, # greek small letter nu, U+03BD ISOgrk3
'oacute': 0x00f3, # latin small letter o with acute, U+00F3 ISOlat1
'ocirc': 0x00f4, # latin small letter o with circumflex, U+00F4 ISOlat1
'oelig': 0x0153, # latin small ligature oe, U+0153 ISOlat2
'ograve': 0x00f2, # latin small letter o with grave, U+00F2 ISOlat1
'oline': 0x203e, # overline = spacing overscore, U+203E NEW
'omega': 0x03c9, # greek small letter omega, U+03C9 ISOgrk3
'omicron': 0x03bf, # greek small letter omicron, U+03BF NEW
'oplus': 0x2295, # circled plus = direct sum, U+2295 ISOamsb
'or': 0x2228, # logical or = vee, U+2228 ISOtech
'ordf': 0x00aa, # feminine ordinal indicator, U+00AA ISOnum
'ordm': 0x00ba, # masculine ordinal indicator, U+00BA ISOnum
'oslash': 0x00f8, # latin small letter o with stroke, = latin small letter o slash, U+00F8 ISOlat1
'otilde': 0x00f5, # latin small letter o with tilde, U+00F5 ISOlat1
'otimes': 0x2297, # circled times = vector product, U+2297 ISOamsb
'ouml': 0x00f6, # latin small letter o with diaeresis, U+00F6 ISOlat1
'para': 0x00b6, # pilcrow sign = paragraph sign, U+00B6 ISOnum
'part': 0x2202, # partial differential, U+2202 ISOtech
'permil': 0x2030, # per mille sign, U+2030 ISOtech
'perp': 0x22a5, # up tack = orthogonal to = perpendicular, U+22A5 ISOtech
'phi': 0x03c6, # greek small letter phi, U+03C6 ISOgrk3
'pi': 0x03c0, # greek small letter pi, U+03C0 ISOgrk3
'piv': 0x03d6, # greek pi symbol, U+03D6 ISOgrk3
'plusmn': 0x00b1, # plus-minus sign = plus-or-minus sign, U+00B1 ISOnum
'pound': 0x00a3, # pound sign, U+00A3 ISOnum
'prime': 0x2032, # prime = minutes = feet, U+2032 ISOtech
'prod': 0x220f, # n-ary product = product sign, U+220F ISOamsb
'prop': 0x221d, # proportional to, U+221D ISOtech
'psi': 0x03c8, # greek small letter psi, U+03C8 ISOgrk3
'quot': 0x0022, # quotation mark = APL quote, U+0022 ISOnum
'rArr': 0x21d2, # rightwards double arrow, U+21D2 ISOtech
'radic': 0x221a, # square root = radical sign, U+221A ISOtech
'rang': 0x232a, # right-pointing angle bracket = ket, U+232A ISOtech
'raquo': 0x00bb, # right-pointing double angle quotation mark = right pointing guillemet, U+00BB ISOnum
'rarr': 0x2192, # rightwards arrow, U+2192 ISOnum
'rceil': 0x2309, # right ceiling, U+2309 ISOamsc
'rdquo': 0x201d, # right double quotation mark, U+201D ISOnum
'real': 0x211c, # blackletter capital R = real part symbol, U+211C ISOamso
'reg': 0x00ae, # registered sign = registered trade mark sign, U+00AE ISOnum
'rfloor': 0x230b, # right floor, U+230B ISOamsc
'rho': 0x03c1, # greek small letter rho, U+03C1 ISOgrk3
'rlm': 0x200f, # right-to-left mark, U+200F NEW RFC 2070
'rsaquo': 0x203a, # single right-pointing angle quotation mark, U+203A ISO proposed
'rsquo': 0x2019, # right single quotation mark, U+2019 ISOnum
'sbquo': 0x201a, # single low-9 quotation mark, U+201A NEW
'scaron': 0x0161, # latin small letter s with caron, U+0161 ISOlat2
'sdot': 0x22c5, # dot operator, U+22C5 ISOamsb
'sect': 0x00a7, # section sign, U+00A7 ISOnum
'shy': 0x00ad, # soft hyphen = discretionary hyphen, U+00AD ISOnum
'sigma': 0x03c3, # greek small letter sigma, U+03C3 ISOgrk3
'sigmaf': 0x03c2, # greek small letter final sigma, U+03C2 ISOgrk3
'sim': 0x223c, # tilde operator = varies with = similar to, U+223C ISOtech
'spades': 0x2660, # black spade suit, U+2660 ISOpub
'sub': 0x2282, # subset of, U+2282 ISOtech
'sube': 0x2286, # subset of or equal to, U+2286 ISOtech
'sum': 0x2211, # n-ary summation, U+2211 ISOamsb
'sup': 0x2283, # superset of, U+2283 ISOtech
'sup1': 0x00b9, # superscript one = superscript digit one, U+00B9 ISOnum
'sup2': 0x00b2, # superscript two = superscript digit two = squared, U+00B2 ISOnum
'sup3': 0x00b3, # superscript three = superscript digit three = cubed, U+00B3 ISOnum
'supe': 0x2287, # superset of or equal to, U+2287 ISOtech
'szlig': 0x00df, # latin small letter sharp s = ess-zed, U+00DF ISOlat1
'tau': 0x03c4, # greek small letter tau, U+03C4 ISOgrk3
'there4': 0x2234, # therefore, U+2234 ISOtech
'theta': 0x03b8, # greek small letter theta, U+03B8 ISOgrk3
'thetasym': 0x03d1, # greek small letter theta symbol, U+03D1 NEW
'thinsp': 0x2009, # thin space, U+2009 ISOpub
'thorn': 0x00fe, # latin small letter thorn with, U+00FE ISOlat1
'tilde': 0x02dc, # small tilde, U+02DC ISOdia
'times': 0x00d7, # multiplication sign, U+00D7 ISOnum
'trade': 0x2122, # trade mark sign, U+2122 ISOnum
'uArr': 0x21d1, # upwards double arrow, U+21D1 ISOamsa
'uacute': 0x00fa, # latin small letter u with acute, U+00FA ISOlat1
'uarr': 0x2191, # upwards arrow, U+2191 ISOnum
'ucirc': 0x00fb, # latin small letter u with circumflex, U+00FB ISOlat1
'ugrave': 0x00f9, # latin small letter u with grave, U+00F9 ISOlat1
'uml': 0x00a8, # diaeresis = spacing diaeresis, U+00A8 ISOdia
'upsih': 0x03d2, # greek upsilon with hook symbol, U+03D2 NEW
'upsilon': 0x03c5, # greek small letter upsilon, U+03C5 ISOgrk3
'uuml': 0x00fc, # latin small letter u with diaeresis, U+00FC ISOlat1
'weierp': 0x2118, # script capital P = power set = Weierstrass p, U+2118 ISOamso
'xi': 0x03be, # greek small letter xi, U+03BE ISOgrk3
'yacute': 0x00fd, # latin small letter y with acute, U+00FD ISOlat1
'yen': 0x00a5, # yen sign = yuan sign, U+00A5 ISOnum
'yuml': 0x00ff, # latin small letter y with diaeresis, U+00FF ISOlat1
'zeta': 0x03b6, # greek small letter zeta, U+03B6 ISOgrk3
'zwj': 0x200d, # zero width joiner, U+200D NEW RFC 2070
'zwnj': 0x200c, # zero width non-joiner, U+200C NEW RFC 2070
}
# maps the HTML5 named character references to the equivalent Unicode character(s)
html5 = {
'Aacute': '\xc1',
'aacute': '\xe1',
'Aacute;': '\xc1',
'aacute;': '\xe1',
'Abreve;': '\u0102',
'abreve;': '\u0103',
'ac;': '\u223e',
'acd;': '\u223f',
'acE;': '\u223e\u0333',
'Acirc': '\xc2',
'acirc': '\xe2',
'Acirc;': '\xc2',
'acirc;': '\xe2',
'acute': '\xb4',
'acute;': '\xb4',
'Acy;': '\u0410',
'acy;': '\u0430',
'AElig': '\xc6',
'aelig': '\xe6',
'AElig;': '\xc6',
'aelig;': '\xe6',
'af;': '\u2061',
'Afr;': '\U0001d504',
'afr;': '\U0001d51e',
'Agrave': '\xc0',
'agrave': '\xe0',
'Agrave;': '\xc0',
'agrave;': '\xe0',
'alefsym;': '\u2135',
'aleph;': '\u2135',
'Alpha;': '\u0391',
'alpha;': '\u03b1',
'Amacr;': '\u0100',
'amacr;': '\u0101',
'amalg;': '\u2a3f',
'AMP': '&',
'amp': '&',
'AMP;': '&',
'amp;': '&',
'And;': '\u2a53',
'and;': '\u2227',
'andand;': '\u2a55',
'andd;': '\u2a5c',
'andslope;': '\u2a58',
'andv;': '\u2a5a',
'ang;': '\u2220',
'ange;': '\u29a4',
'angle;': '\u2220',
'angmsd;': '\u2221',
'angmsdaa;': '\u29a8',
'angmsdab;': '\u29a9',
'angmsdac;': '\u29aa',
'angmsdad;': '\u29ab',
'angmsdae;': '\u29ac',
'angmsdaf;': '\u29ad',
'angmsdag;': '\u29ae',
'angmsdah;': '\u29af',
'angrt;': '\u221f',
'angrtvb;': '\u22be',
'angrtvbd;': '\u299d',
'angsph;': '\u2222',
'angst;': '\xc5',
'angzarr;': '\u237c',
'Aogon;': '\u0104',
'aogon;': '\u0105',
'Aopf;': '\U0001d538',
'aopf;': '\U0001d552',
'ap;': '\u2248',
'apacir;': '\u2a6f',
'apE;': '\u2a70',
'ape;': '\u224a',
'apid;': '\u224b',
'apos;': "'",
'ApplyFunction;': '\u2061',
'approx;': '\u2248',
'approxeq;': '\u224a',
'Aring': '\xc5',
'aring': '\xe5',
'Aring;': '\xc5',
'aring;': '\xe5',
'Ascr;': '\U0001d49c',
'ascr;': '\U0001d4b6',
'Assign;': '\u2254',
'ast;': '*',
'asymp;': '\u2248',
'asympeq;': '\u224d',
'Atilde': '\xc3',
'atilde': '\xe3',
'Atilde;': '\xc3',
'atilde;': '\xe3',
'Auml': '\xc4',
'auml': '\xe4',
'Auml;': '\xc4',
'auml;': '\xe4',
'awconint;': '\u2233',
'awint;': '\u2a11',
'backcong;': '\u224c',
'backepsilon;': '\u03f6',
'backprime;': '\u2035',
'backsim;': '\u223d',
'backsimeq;': '\u22cd',
'Backslash;': '\u2216',
'Barv;': '\u2ae7',
'barvee;': '\u22bd',
'Barwed;': '\u2306',
'barwed;': '\u2305',
'barwedge;': '\u2305',
'bbrk;': '\u23b5',
'bbrktbrk;': '\u23b6',
'bcong;': '\u224c',
'Bcy;': '\u0411',
'bcy;': '\u0431',
'bdquo;': '\u201e',
'becaus;': '\u2235',
'Because;': '\u2235',
'because;': '\u2235',
'bemptyv;': '\u29b0',
'bepsi;': '\u03f6',
'bernou;': '\u212c',
'Bernoullis;': '\u212c',
'Beta;': '\u0392',
'beta;': '\u03b2',
'beth;': '\u2136',
'between;': '\u226c',
'Bfr;': '\U0001d505',
'bfr;': '\U0001d51f',
'bigcap;': '\u22c2',
'bigcirc;': '\u25ef',
'bigcup;': '\u22c3',
'bigodot;': '\u2a00',
'bigoplus;': '\u2a01',
'bigotimes;': '\u2a02',
'bigsqcup;': '\u2a06',
'bigstar;': '\u2605',
'bigtriangledown;': '\u25bd',
'bigtriangleup;': '\u25b3',
'biguplus;': '\u2a04',
'bigvee;': '\u22c1',
'bigwedge;': '\u22c0',
'bkarow;': '\u290d',
'blacklozenge;': '\u29eb',
'blacksquare;': '\u25aa',
'blacktriangle;': '\u25b4',
'blacktriangledown;': '\u25be',
'blacktriangleleft;': '\u25c2',
'blacktriangleright;': '\u25b8',
'blank;': '\u2423',
'blk12;': '\u2592',
'blk14;': '\u2591',
'blk34;': '\u2593',
'block;': '\u2588',
'bne;': '=\u20e5',
'bnequiv;': '\u2261\u20e5',
'bNot;': '\u2aed',
'bnot;': '\u2310',
'Bopf;': '\U0001d539',
'bopf;': '\U0001d553',
'bot;': '\u22a5',
'bottom;': '\u22a5',
'bowtie;': '\u22c8',
'boxbox;': '\u29c9',
'boxDL;': '\u2557',
'boxDl;': '\u2556',
'boxdL;': '\u2555',
'boxdl;': '\u2510',
'boxDR;': '\u2554',
'boxDr;': '\u2553',
'boxdR;': '\u2552',
'boxdr;': '\u250c',
'boxH;': '\u2550',
'boxh;': '\u2500',
'boxHD;': '\u2566',
'boxHd;': '\u2564',
'boxhD;': '\u2565',
'boxhd;': '\u252c',
'boxHU;': '\u2569',
'boxHu;': '\u2567',
'boxhU;': '\u2568',
'boxhu;': '\u2534',
'boxminus;': '\u229f',
'boxplus;': '\u229e',
'boxtimes;': '\u22a0',
'boxUL;': '\u255d',
'boxUl;': '\u255c',
'boxuL;': '\u255b',
'boxul;': '\u2518',
'boxUR;': '\u255a',
'boxUr;': '\u2559',
'boxuR;': '\u2558',
'boxur;': '\u2514',
'boxV;': '\u2551',
'boxv;': '\u2502',
'boxVH;': '\u256c',
'boxVh;': '\u256b',
'boxvH;': '\u256a',
'boxvh;': '\u253c',
'boxVL;': '\u2563',
'boxVl;': '\u2562',
'boxvL;': '\u2561',
'boxvl;': '\u2524',
'boxVR;': '\u2560',
'boxVr;': '\u255f',
'boxvR;': '\u255e',
'boxvr;': '\u251c',
'bprime;': '\u2035',
'Breve;': '\u02d8',
'breve;': '\u02d8',
'brvbar': '\xa6',
'brvbar;': '\xa6',
'Bscr;': '\u212c',
'bscr;': '\U0001d4b7',
'bsemi;': '\u204f',
'bsim;': '\u223d',
'bsime;': '\u22cd',
'bsol;': '\\',
'bsolb;': '\u29c5',
'bsolhsub;': '\u27c8',
'bull;': '\u2022',
'bullet;': '\u2022',
'bump;': '\u224e',
'bumpE;': '\u2aae',
'bumpe;': '\u224f',
'Bumpeq;': '\u224e',
'bumpeq;': '\u224f',
'Cacute;': '\u0106',
'cacute;': '\u0107',
'Cap;': '\u22d2',
'cap;': '\u2229',
'capand;': '\u2a44',
'capbrcup;': '\u2a49',
'capcap;': '\u2a4b',
'capcup;': '\u2a47',
'capdot;': '\u2a40',
'CapitalDifferentialD;': '\u2145',
'caps;': '\u2229\ufe00',
'caret;': '\u2041',
'caron;': '\u02c7',
'Cayleys;': '\u212d',
'ccaps;': '\u2a4d',
'Ccaron;': '\u010c',
'ccaron;': '\u010d',
'Ccedil': '\xc7',
'ccedil': '\xe7',
'Ccedil;': '\xc7',
'ccedil;': '\xe7',
'Ccirc;': '\u0108',
'ccirc;': '\u0109',
'Cconint;': '\u2230',
'ccups;': '\u2a4c',
'ccupssm;': '\u2a50',
'Cdot;': '\u010a',
'cdot;': '\u010b',
'cedil': '\xb8',
'cedil;': '\xb8',
'Cedilla;': '\xb8',
'cemptyv;': '\u29b2',
'cent': '\xa2',
'cent;': '\xa2',
'CenterDot;': '\xb7',
'centerdot;': '\xb7',
'Cfr;': '\u212d',
'cfr;': '\U0001d520',
'CHcy;': '\u0427',
'chcy;': '\u0447',
'check;': '\u2713',
'checkmark;': '\u2713',
'Chi;': '\u03a7',
'chi;': '\u03c7',
'cir;': '\u25cb',
'circ;': '\u02c6',
'circeq;': '\u2257',
'circlearrowleft;': '\u21ba',
'circlearrowright;': '\u21bb',
'circledast;': '\u229b',
'circledcirc;': '\u229a',
'circleddash;': '\u229d',
'CircleDot;': '\u2299',
'circledR;': '\xae',
'circledS;': '\u24c8',
'CircleMinus;': '\u2296',
'CirclePlus;': '\u2295',
'CircleTimes;': '\u2297',
'cirE;': '\u29c3',
'cire;': '\u2257',
'cirfnint;': '\u2a10',
'cirmid;': '\u2aef',
'cirscir;': '\u29c2',
'ClockwiseContourIntegral;': '\u2232',
'CloseCurlyDoubleQuote;': '\u201d',
'CloseCurlyQuote;': '\u2019',
'clubs;': '\u2663',
'clubsuit;': '\u2663',
'Colon;': '\u2237',
'colon;': ':',
'Colone;': '\u2a74',
'colone;': '\u2254',
'coloneq;': '\u2254',
'comma;': ',',
'commat;': '@',
'comp;': '\u2201',
'compfn;': '\u2218',
'complement;': '\u2201',
'complexes;': '\u2102',
'cong;': '\u2245',
'congdot;': '\u2a6d',
'Congruent;': '\u2261',
'Conint;': '\u222f',
'conint;': '\u222e',
'ContourIntegral;': '\u222e',
'Copf;': '\u2102',
'copf;': '\U0001d554',
'coprod;': '\u2210',
'Coproduct;': '\u2210',
'COPY': '\xa9',
'copy': '\xa9',
'COPY;': '\xa9',
'copy;': '\xa9',
'copysr;': '\u2117',
'CounterClockwiseContourIntegral;': '\u2233',
'crarr;': '\u21b5',
'Cross;': '\u2a2f',
'cross;': '\u2717',
'Cscr;': '\U0001d49e',
'cscr;': '\U0001d4b8',
'csub;': '\u2acf',
'csube;': '\u2ad1',
'csup;': '\u2ad0',
'csupe;': '\u2ad2',
'ctdot;': '\u22ef',
'cudarrl;': '\u2938',
'cudarrr;': '\u2935',
'cuepr;': '\u22de',
'cuesc;': '\u22df',
'cularr;': '\u21b6',
'cularrp;': '\u293d',
'Cup;': '\u22d3',
'cup;': '\u222a',
'cupbrcap;': '\u2a48',
'CupCap;': '\u224d',
'cupcap;': '\u2a46',
'cupcup;': '\u2a4a',
'cupdot;': '\u228d',
'cupor;': '\u2a45',
'cups;': '\u222a\ufe00',
'curarr;': '\u21b7',
'curarrm;': '\u293c',
'curlyeqprec;': '\u22de',
'curlyeqsucc;': '\u22df',
'curlyvee;': '\u22ce',
'curlywedge;': '\u22cf',
'curren': '\xa4',
'curren;': '\xa4',
'curvearrowleft;': '\u21b6',
'curvearrowright;': '\u21b7',
'cuvee;': '\u22ce',
'cuwed;': '\u22cf',
'cwconint;': '\u2232',
'cwint;': '\u2231',
'cylcty;': '\u232d',
'Dagger;': '\u2021',
'dagger;': '\u2020',
'daleth;': '\u2138',
'Darr;': '\u21a1',
'dArr;': '\u21d3',
'darr;': '\u2193',
'dash;': '\u2010',
'Dashv;': '\u2ae4',
'dashv;': '\u22a3',
'dbkarow;': '\u290f',
'dblac;': '\u02dd',
'Dcaron;': '\u010e',
'dcaron;': '\u010f',
'Dcy;': '\u0414',
'dcy;': '\u0434',
'DD;': '\u2145',
'dd;': '\u2146',
'ddagger;': '\u2021',
'ddarr;': '\u21ca',
'DDotrahd;': '\u2911',
'ddotseq;': '\u2a77',
'deg': '\xb0',
'deg;': '\xb0',
'Del;': '\u2207',
'Delta;': '\u0394',
'delta;': '\u03b4',
'demptyv;': '\u29b1',
'dfisht;': '\u297f',
'Dfr;': '\U0001d507',
'dfr;': '\U0001d521',
'dHar;': '\u2965',
'dharl;': '\u21c3',
'dharr;': '\u21c2',
'DiacriticalAcute;': '\xb4',
'DiacriticalDot;': '\u02d9',
'DiacriticalDoubleAcute;': '\u02dd',
'DiacriticalGrave;': '`',
'DiacriticalTilde;': '\u02dc',
'diam;': '\u22c4',
'Diamond;': '\u22c4',
'diamond;': '\u22c4',
'diamondsuit;': '\u2666',
'diams;': '\u2666',
'die;': '\xa8',
'DifferentialD;': '\u2146',
'digamma;': '\u03dd',
'disin;': '\u22f2',
'div;': '\xf7',
'divide': '\xf7',
'divide;': '\xf7',
'divideontimes;': '\u22c7',
'divonx;': '\u22c7',
'DJcy;': '\u0402',
'djcy;': '\u0452',
'dlcorn;': '\u231e',
'dlcrop;': '\u230d',
'dollar;': '$',
'Dopf;': '\U0001d53b',
'dopf;': '\U0001d555',
'Dot;': '\xa8',
'dot;': '\u02d9',
'DotDot;': '\u20dc',
'doteq;': '\u2250',
'doteqdot;': '\u2251',
'DotEqual;': '\u2250',
'dotminus;': '\u2238',
'dotplus;': '\u2214',
'dotsquare;': '\u22a1',
'doublebarwedge;': '\u2306',
'DoubleContourIntegral;': '\u222f',
'DoubleDot;': '\xa8',
'DoubleDownArrow;': '\u21d3',
'DoubleLeftArrow;': '\u21d0',
'DoubleLeftRightArrow;': '\u21d4',
'DoubleLeftTee;': '\u2ae4',
'DoubleLongLeftArrow;': '\u27f8',
'DoubleLongLeftRightArrow;': '\u27fa',
'DoubleLongRightArrow;': '\u27f9',
'DoubleRightArrow;': '\u21d2',
'DoubleRightTee;': '\u22a8',
'DoubleUpArrow;': '\u21d1',
'DoubleUpDownArrow;': '\u21d5',
'DoubleVerticalBar;': '\u2225',
'DownArrow;': '\u2193',
'Downarrow;': '\u21d3',
'downarrow;': '\u2193',
'DownArrowBar;': '\u2913',
'DownArrowUpArrow;': '\u21f5',
'DownBreve;': '\u0311',
'downdownarrows;': '\u21ca',
'downharpoonleft;': '\u21c3',
'downharpoonright;': '\u21c2',
'DownLeftRightVector;': '\u2950',
'DownLeftTeeVector;': '\u295e',
'DownLeftVector;': '\u21bd',
'DownLeftVectorBar;': '\u2956',
'DownRightTeeVector;': '\u295f',
'DownRightVector;': '\u21c1',
'DownRightVectorBar;': '\u2957',
'DownTee;': '\u22a4',
'DownTeeArrow;': '\u21a7',
'drbkarow;': '\u2910',
'drcorn;': '\u231f',
'drcrop;': '\u230c',
'Dscr;': '\U0001d49f',
'dscr;': '\U0001d4b9',
'DScy;': '\u0405',
'dscy;': '\u0455',
'dsol;': '\u29f6',
'Dstrok;': '\u0110',
'dstrok;': '\u0111',
'dtdot;': '\u22f1',
'dtri;': '\u25bf',
'dtrif;': '\u25be',
'duarr;': '\u21f5',
'duhar;': '\u296f',
'dwangle;': '\u29a6',
'DZcy;': '\u040f',
'dzcy;': '\u045f',
'dzigrarr;': '\u27ff',
'Eacute': '\xc9',
'eacute': '\xe9',
'Eacute;': '\xc9',
'eacute;': '\xe9',
'easter;': '\u2a6e',
'Ecaron;': '\u011a',
'ecaron;': '\u011b',
'ecir;': '\u2256',
'Ecirc': '\xca',
'ecirc': '\xea',
'Ecirc;': '\xca',
'ecirc;': '\xea',
'ecolon;': '\u2255',
'Ecy;': '\u042d',
'ecy;': '\u044d',
'eDDot;': '\u2a77',
'Edot;': '\u0116',
'eDot;': '\u2251',
'edot;': '\u0117',
'ee;': '\u2147',
'efDot;': '\u2252',
'Efr;': '\U0001d508',
'efr;': '\U0001d522',
'eg;': '\u2a9a',
'Egrave': '\xc8',
'egrave': '\xe8',
'Egrave;': '\xc8',
'egrave;': '\xe8',
'egs;': '\u2a96',
'egsdot;': '\u2a98',
'el;': '\u2a99',
'Element;': '\u2208',
'elinters;': '\u23e7',
'ell;': '\u2113',
'els;': '\u2a95',
'elsdot;': '\u2a97',
'Emacr;': '\u0112',
'emacr;': '\u0113',
'empty;': '\u2205',
'emptyset;': '\u2205',
'EmptySmallSquare;': '\u25fb',
'emptyv;': '\u2205',
'EmptyVerySmallSquare;': '\u25ab',
'emsp13;': '\u2004',
'emsp14;': '\u2005',
'emsp;': '\u2003',
'ENG;': '\u014a',
'eng;': '\u014b',
'ensp;': '\u2002',
'Eogon;': '\u0118',
'eogon;': '\u0119',
'Eopf;': '\U0001d53c',
'eopf;': '\U0001d556',
'epar;': '\u22d5',
'eparsl;': '\u29e3',
'eplus;': '\u2a71',
'epsi;': '\u03b5',
'Epsilon;': '\u0395',
'epsilon;': '\u03b5',
'epsiv;': '\u03f5',
'eqcirc;': '\u2256',
'eqcolon;': '\u2255',
'eqsim;': '\u2242',
'eqslantgtr;': '\u2a96',
'eqslantless;': '\u2a95',
'Equal;': '\u2a75',
'equals;': '=',
'EqualTilde;': '\u2242',
'equest;': '\u225f',
'Equilibrium;': '\u21cc',
'equiv;': '\u2261',
'equivDD;': '\u2a78',
'eqvparsl;': '\u29e5',
'erarr;': '\u2971',
'erDot;': '\u2253',
'Escr;': '\u2130',
'escr;': '\u212f',
'esdot;': '\u2250',
'Esim;': '\u2a73',
'esim;': '\u2242',
'Eta;': '\u0397',
'eta;': '\u03b7',
'ETH': '\xd0',
'eth': '\xf0',
'ETH;': '\xd0',
'eth;': '\xf0',
'Euml': '\xcb',
'euml': '\xeb',
'Euml;': '\xcb',
'euml;': '\xeb',
'euro;': '\u20ac',
'excl;': '!',
'exist;': '\u2203',
'Exists;': '\u2203',
'expectation;': '\u2130',
'ExponentialE;': '\u2147',
'exponentiale;': '\u2147',
'fallingdotseq;': '\u2252',
'Fcy;': '\u0424',
'fcy;': '\u0444',
'female;': '\u2640',
'ffilig;': '\ufb03',
'fflig;': '\ufb00',
'ffllig;': '\ufb04',
'Ffr;': '\U0001d509',
'ffr;': '\U0001d523',
'filig;': '\ufb01',
'FilledSmallSquare;': '\u25fc',
'FilledVerySmallSquare;': '\u25aa',
'fjlig;': 'fj',
'flat;': '\u266d',
'fllig;': '\ufb02',
'fltns;': '\u25b1',
'fnof;': '\u0192',
'Fopf;': '\U0001d53d',
'fopf;': '\U0001d557',
'ForAll;': '\u2200',
'forall;': '\u2200',
'fork;': '\u22d4',
'forkv;': '\u2ad9',
'Fouriertrf;': '\u2131',
'fpartint;': '\u2a0d',
'frac12': '\xbd',
'frac12;': '\xbd',
'frac13;': '\u2153',
'frac14': '\xbc',
'frac14;': '\xbc',
'frac15;': '\u2155',
'frac16;': '\u2159',
'frac18;': '\u215b',
'frac23;': '\u2154',
'frac25;': '\u2156',
'frac34': '\xbe',
'frac34;': '\xbe',
'frac35;': '\u2157',
'frac38;': '\u215c',
'frac45;': '\u2158',
'frac56;': '\u215a',
'frac58;': '\u215d',
'frac78;': '\u215e',
'frasl;': '\u2044',
'frown;': '\u2322',
'Fscr;': '\u2131',
'fscr;': '\U0001d4bb',
'gacute;': '\u01f5',
'Gamma;': '\u0393',
'gamma;': '\u03b3',
'Gammad;': '\u03dc',
'gammad;': '\u03dd',
'gap;': '\u2a86',
'Gbreve;': '\u011e',
'gbreve;': '\u011f',
'Gcedil;': '\u0122',
'Gcirc;': '\u011c',
'gcirc;': '\u011d',
'Gcy;': '\u0413',
'gcy;': '\u0433',
'Gdot;': '\u0120',
'gdot;': '\u0121',
'gE;': '\u2267',
'ge;': '\u2265',
'gEl;': '\u2a8c',
'gel;': '\u22db',
'geq;': '\u2265',
'geqq;': '\u2267',
'geqslant;': '\u2a7e',
'ges;': '\u2a7e',
'gescc;': '\u2aa9',
'gesdot;': '\u2a80',
'gesdoto;': '\u2a82',
'gesdotol;': '\u2a84',
'gesl;': '\u22db\ufe00',
'gesles;': '\u2a94',
'Gfr;': '\U0001d50a',
'gfr;': '\U0001d524',
'Gg;': '\u22d9',
'gg;': '\u226b',
'ggg;': '\u22d9',
'gimel;': '\u2137',
'GJcy;': '\u0403',
'gjcy;': '\u0453',
'gl;': '\u2277',
'gla;': '\u2aa5',
'glE;': '\u2a92',
'glj;': '\u2aa4',
'gnap;': '\u2a8a',
'gnapprox;': '\u2a8a',
'gnE;': '\u2269',
'gne;': '\u2a88',
'gneq;': '\u2a88',
'gneqq;': '\u2269',
'gnsim;': '\u22e7',
'Gopf;': '\U0001d53e',
'gopf;': '\U0001d558',
'grave;': '`',
'GreaterEqual;': '\u2265',
'GreaterEqualLess;': '\u22db',
'GreaterFullEqual;': '\u2267',
'GreaterGreater;': '\u2aa2',
'GreaterLess;': '\u2277',
'GreaterSlantEqual;': '\u2a7e',
'GreaterTilde;': '\u2273',
'Gscr;': '\U0001d4a2',
'gscr;': '\u210a',
'gsim;': '\u2273',
'gsime;': '\u2a8e',
'gsiml;': '\u2a90',
'GT': '>',
'gt': '>',
'GT;': '>',
'Gt;': '\u226b',
'gt;': '>',
'gtcc;': '\u2aa7',
'gtcir;': '\u2a7a',
'gtdot;': '\u22d7',
'gtlPar;': '\u2995',
'gtquest;': '\u2a7c',
'gtrapprox;': '\u2a86',
'gtrarr;': '\u2978',
'gtrdot;': '\u22d7',
'gtreqless;': '\u22db',
'gtreqqless;': '\u2a8c',
'gtrless;': '\u2277',
'gtrsim;': '\u2273',
'gvertneqq;': '\u2269\ufe00',
'gvnE;': '\u2269\ufe00',
'Hacek;': '\u02c7',
'hairsp;': '\u200a',
'half;': '\xbd',
'hamilt;': '\u210b',
'HARDcy;': '\u042a',
'hardcy;': '\u044a',
'hArr;': '\u21d4',
'harr;': '\u2194',
'harrcir;': '\u2948',
'harrw;': '\u21ad',
'Hat;': '^',
'hbar;': '\u210f',
'Hcirc;': '\u0124',
'hcirc;': '\u0125',
'hearts;': '\u2665',
'heartsuit;': '\u2665',
'hellip;': '\u2026',
'hercon;': '\u22b9',
'Hfr;': '\u210c',
'hfr;': '\U0001d525',
'HilbertSpace;': '\u210b',
'hksearow;': '\u2925',
'hkswarow;': '\u2926',
'hoarr;': '\u21ff',
'homtht;': '\u223b',
'hookleftarrow;': '\u21a9',
'hookrightarrow;': '\u21aa',
'Hopf;': '\u210d',
'hopf;': '\U0001d559',
'horbar;': '\u2015',
'HorizontalLine;': '\u2500',
'Hscr;': '\u210b',
'hscr;': '\U0001d4bd',
'hslash;': '\u210f',
'Hstrok;': '\u0126',
'hstrok;': '\u0127',
'HumpDownHump;': '\u224e',
'HumpEqual;': '\u224f',
'hybull;': '\u2043',
'hyphen;': '\u2010',
'Iacute': '\xcd',
'iacute': '\xed',
'Iacute;': '\xcd',
'iacute;': '\xed',
'ic;': '\u2063',
'Icirc': '\xce',
'icirc': '\xee',
'Icirc;': '\xce',
'icirc;': '\xee',
'Icy;': '\u0418',
'icy;': '\u0438',
'Idot;': '\u0130',
'IEcy;': '\u0415',
'iecy;': '\u0435',
'iexcl': '\xa1',
'iexcl;': '\xa1',
'iff;': '\u21d4',
'Ifr;': '\u2111',
'ifr;': '\U0001d526',
'Igrave': '\xcc',
'igrave': '\xec',
'Igrave;': '\xcc',
'igrave;': '\xec',
'ii;': '\u2148',
'iiiint;': '\u2a0c',
'iiint;': '\u222d',
'iinfin;': '\u29dc',
'iiota;': '\u2129',
'IJlig;': '\u0132',
'ijlig;': '\u0133',
'Im;': '\u2111',
'Imacr;': '\u012a',
'imacr;': '\u012b',
'image;': '\u2111',
'ImaginaryI;': '\u2148',
'imagline;': '\u2110',
'imagpart;': '\u2111',
'imath;': '\u0131',
'imof;': '\u22b7',
'imped;': '\u01b5',
'Implies;': '\u21d2',
'in;': '\u2208',
'incare;': '\u2105',
'infin;': '\u221e',
'infintie;': '\u29dd',
'inodot;': '\u0131',
'Int;': '\u222c',
'int;': '\u222b',
'intcal;': '\u22ba',
'integers;': '\u2124',
'Integral;': '\u222b',
'intercal;': '\u22ba',
'Intersection;': '\u22c2',
'intlarhk;': '\u2a17',
'intprod;': '\u2a3c',
'InvisibleComma;': '\u2063',
'InvisibleTimes;': '\u2062',
'IOcy;': '\u0401',
'iocy;': '\u0451',
'Iogon;': '\u012e',
'iogon;': '\u012f',
'Iopf;': '\U0001d540',
'iopf;': '\U0001d55a',
'Iota;': '\u0399',
'iota;': '\u03b9',
'iprod;': '\u2a3c',
'iquest': '\xbf',
'iquest;': '\xbf',
'Iscr;': '\u2110',
'iscr;': '\U0001d4be',
'isin;': '\u2208',
'isindot;': '\u22f5',
'isinE;': '\u22f9',
'isins;': '\u22f4',
'isinsv;': '\u22f3',
'isinv;': '\u2208',
'it;': '\u2062',
'Itilde;': '\u0128',
'itilde;': '\u0129',
'Iukcy;': '\u0406',
'iukcy;': '\u0456',
'Iuml': '\xcf',
'iuml': '\xef',
'Iuml;': '\xcf',
'iuml;': '\xef',
'Jcirc;': '\u0134',
'jcirc;': '\u0135',
'Jcy;': '\u0419',
'jcy;': '\u0439',
'Jfr;': '\U0001d50d',
'jfr;': '\U0001d527',
'jmath;': '\u0237',
'Jopf;': '\U0001d541',
'jopf;': '\U0001d55b',
'Jscr;': '\U0001d4a5',
'jscr;': '\U0001d4bf',
'Jsercy;': '\u0408',
'jsercy;': '\u0458',
'Jukcy;': '\u0404',
'jukcy;': '\u0454',
'Kappa;': '\u039a',
'kappa;': '\u03ba',
'kappav;': '\u03f0',
'Kcedil;': '\u0136',
'kcedil;': '\u0137',
'Kcy;': '\u041a',
'kcy;': '\u043a',
'Kfr;': '\U0001d50e',
'kfr;': '\U0001d528',
'kgreen;': '\u0138',
'KHcy;': '\u0425',
'khcy;': '\u0445',
'KJcy;': '\u040c',
'kjcy;': '\u045c',
'Kopf;': '\U0001d542',
'kopf;': '\U0001d55c',
'Kscr;': '\U0001d4a6',
'kscr;': '\U0001d4c0',
'lAarr;': '\u21da',
'Lacute;': '\u0139',
'lacute;': '\u013a',
'laemptyv;': '\u29b4',
'lagran;': '\u2112',
'Lambda;': '\u039b',
'lambda;': '\u03bb',
'Lang;': '\u27ea',
'lang;': '\u27e8',
'langd;': '\u2991',
'langle;': '\u27e8',
'lap;': '\u2a85',
'Laplacetrf;': '\u2112',
'laquo': '\xab',
'laquo;': '\xab',
'Larr;': '\u219e',
'lArr;': '\u21d0',
'larr;': '\u2190',
'larrb;': '\u21e4',
'larrbfs;': '\u291f',
'larrfs;': '\u291d',
'larrhk;': '\u21a9',
'larrlp;': '\u21ab',
'larrpl;': '\u2939',
'larrsim;': '\u2973',
'larrtl;': '\u21a2',
'lat;': '\u2aab',
'lAtail;': '\u291b',
'latail;': '\u2919',
'late;': '\u2aad',
'lates;': '\u2aad\ufe00',
'lBarr;': '\u290e',
'lbarr;': '\u290c',
'lbbrk;': '\u2772',
'lbrace;': '{',
'lbrack;': '[',
'lbrke;': '\u298b',
'lbrksld;': '\u298f',
'lbrkslu;': '\u298d',
'Lcaron;': '\u013d',
'lcaron;': '\u013e',
'Lcedil;': '\u013b',
'lcedil;': '\u013c',
'lceil;': '\u2308',
'lcub;': '{',
'Lcy;': '\u041b',
'lcy;': '\u043b',
'ldca;': '\u2936',
'ldquo;': '\u201c',
'ldquor;': '\u201e',
'ldrdhar;': '\u2967',
'ldrushar;': '\u294b',
'ldsh;': '\u21b2',
'lE;': '\u2266',
'le;': '\u2264',
'LeftAngleBracket;': '\u27e8',
'LeftArrow;': '\u2190',
'Leftarrow;': '\u21d0',
'leftarrow;': '\u2190',
'LeftArrowBar;': '\u21e4',
'LeftArrowRightArrow;': '\u21c6',
'leftarrowtail;': '\u21a2',
'LeftCeiling;': '\u2308',
'LeftDoubleBracket;': '\u27e6',
'LeftDownTeeVector;': '\u2961',
'LeftDownVector;': '\u21c3',
'LeftDownVectorBar;': '\u2959',
'LeftFloor;': '\u230a',
'leftharpoondown;': '\u21bd',
'leftharpoonup;': '\u21bc',
'leftleftarrows;': '\u21c7',
'LeftRightArrow;': '\u2194',
'Leftrightarrow;': '\u21d4',
'leftrightarrow;': '\u2194',
'leftrightarrows;': '\u21c6',
'leftrightharpoons;': '\u21cb',
'leftrightsquigarrow;': '\u21ad',
'LeftRightVector;': '\u294e',
'LeftTee;': '\u22a3',
'LeftTeeArrow;': '\u21a4',
'LeftTeeVector;': '\u295a',
'leftthreetimes;': '\u22cb',
'LeftTriangle;': '\u22b2',
'LeftTriangleBar;': '\u29cf',
'LeftTriangleEqual;': '\u22b4',
'LeftUpDownVector;': '\u2951',
'LeftUpTeeVector;': '\u2960',
'LeftUpVector;': '\u21bf',
'LeftUpVectorBar;': '\u2958',
'LeftVector;': '\u21bc',
'LeftVectorBar;': '\u2952',
'lEg;': '\u2a8b',
'leg;': '\u22da',
'leq;': '\u2264',
'leqq;': '\u2266',
'leqslant;': '\u2a7d',
'les;': '\u2a7d',
'lescc;': '\u2aa8',
'lesdot;': '\u2a7f',
'lesdoto;': '\u2a81',
'lesdotor;': '\u2a83',
'lesg;': '\u22da\ufe00',
'lesges;': '\u2a93',
'lessapprox;': '\u2a85',
'lessdot;': '\u22d6',
'lesseqgtr;': '\u22da',
'lesseqqgtr;': '\u2a8b',
'LessEqualGreater;': '\u22da',
'LessFullEqual;': '\u2266',
'LessGreater;': '\u2276',
'lessgtr;': '\u2276',
'LessLess;': '\u2aa1',
'lesssim;': '\u2272',
'LessSlantEqual;': '\u2a7d',
'LessTilde;': '\u2272',
'lfisht;': '\u297c',
'lfloor;': '\u230a',
'Lfr;': '\U0001d50f',
'lfr;': '\U0001d529',
'lg;': '\u2276',
'lgE;': '\u2a91',
'lHar;': '\u2962',
'lhard;': '\u21bd',
'lharu;': '\u21bc',
'lharul;': '\u296a',
'lhblk;': '\u2584',
'LJcy;': '\u0409',
'ljcy;': '\u0459',
'Ll;': '\u22d8',
'll;': '\u226a',
'llarr;': '\u21c7',
'llcorner;': '\u231e',
'Lleftarrow;': '\u21da',
'llhard;': '\u296b',
'lltri;': '\u25fa',
'Lmidot;': '\u013f',
'lmidot;': '\u0140',
'lmoust;': '\u23b0',
'lmoustache;': '\u23b0',
'lnap;': '\u2a89',
'lnapprox;': '\u2a89',
'lnE;': '\u2268',
'lne;': '\u2a87',
'lneq;': '\u2a87',
'lneqq;': '\u2268',
'lnsim;': '\u22e6',
'loang;': '\u27ec',
'loarr;': '\u21fd',
'lobrk;': '\u27e6',
'LongLeftArrow;': '\u27f5',
'Longleftarrow;': '\u27f8',
'longleftarrow;': '\u27f5',
'LongLeftRightArrow;': '\u27f7',
'Longleftrightarrow;': '\u27fa',
'longleftrightarrow;': '\u27f7',
'longmapsto;': '\u27fc',
'LongRightArrow;': '\u27f6',
'Longrightarrow;': '\u27f9',
'longrightarrow;': '\u27f6',
'looparrowleft;': '\u21ab',
'looparrowright;': '\u21ac',
'lopar;': '\u2985',
'Lopf;': '\U0001d543',
'lopf;': '\U0001d55d',
'loplus;': '\u2a2d',
'lotimes;': '\u2a34',
'lowast;': '\u2217',
'lowbar;': '_',
'LowerLeftArrow;': '\u2199',
'LowerRightArrow;': '\u2198',
'loz;': '\u25ca',
'lozenge;': '\u25ca',
'lozf;': '\u29eb',
'lpar;': '(',
'lparlt;': '\u2993',
'lrarr;': '\u21c6',
'lrcorner;': '\u231f',
'lrhar;': '\u21cb',
'lrhard;': '\u296d',
'lrm;': '\u200e',
'lrtri;': '\u22bf',
'lsaquo;': '\u2039',
'Lscr;': '\u2112',
'lscr;': '\U0001d4c1',
'Lsh;': '\u21b0',
'lsh;': '\u21b0',
'lsim;': '\u2272',
'lsime;': '\u2a8d',
'lsimg;': '\u2a8f',
'lsqb;': '[',
'lsquo;': '\u2018',
'lsquor;': '\u201a',
'Lstrok;': '\u0141',
'lstrok;': '\u0142',
'LT': '<',
'lt': '<',
'LT;': '<',
'Lt;': '\u226a',
'lt;': '<',
'ltcc;': '\u2aa6',
'ltcir;': '\u2a79',
'ltdot;': '\u22d6',
'lthree;': '\u22cb',
'ltimes;': '\u22c9',
'ltlarr;': '\u2976',
'ltquest;': '\u2a7b',
'ltri;': '\u25c3',
'ltrie;': '\u22b4',
'ltrif;': '\u25c2',
'ltrPar;': '\u2996',
'lurdshar;': '\u294a',
'luruhar;': '\u2966',
'lvertneqq;': '\u2268\ufe00',
'lvnE;': '\u2268\ufe00',
'macr': '\xaf',
'macr;': '\xaf',
'male;': '\u2642',
'malt;': '\u2720',
'maltese;': '\u2720',
'Map;': '\u2905',
'map;': '\u21a6',
'mapsto;': '\u21a6',
'mapstodown;': '\u21a7',
'mapstoleft;': '\u21a4',
'mapstoup;': '\u21a5',
'marker;': '\u25ae',
'mcomma;': '\u2a29',
'Mcy;': '\u041c',
'mcy;': '\u043c',
'mdash;': '\u2014',
'mDDot;': '\u223a',
'measuredangle;': '\u2221',
'MediumSpace;': '\u205f',
'Mellintrf;': '\u2133',
'Mfr;': '\U0001d510',
'mfr;': '\U0001d52a',
'mho;': '\u2127',
'micro': '\xb5',
'micro;': '\xb5',
'mid;': '\u2223',
'midast;': '*',
'midcir;': '\u2af0',
'middot': '\xb7',
'middot;': '\xb7',
'minus;': '\u2212',
'minusb;': '\u229f',
'minusd;': '\u2238',
'minusdu;': '\u2a2a',
'MinusPlus;': '\u2213',
'mlcp;': '\u2adb',
'mldr;': '\u2026',
'mnplus;': '\u2213',
'models;': '\u22a7',
'Mopf;': '\U0001d544',
'mopf;': '\U0001d55e',
'mp;': '\u2213',
'Mscr;': '\u2133',
'mscr;': '\U0001d4c2',
'mstpos;': '\u223e',
'Mu;': '\u039c',
'mu;': '\u03bc',
'multimap;': '\u22b8',
'mumap;': '\u22b8',
'nabla;': '\u2207',
'Nacute;': '\u0143',
'nacute;': '\u0144',
'nang;': '\u2220\u20d2',
'nap;': '\u2249',
'napE;': '\u2a70\u0338',
'napid;': '\u224b\u0338',
'napos;': '\u0149',
'napprox;': '\u2249',
'natur;': '\u266e',
'natural;': '\u266e',
'naturals;': '\u2115',
'nbsp': '\xa0',
'nbsp;': '\xa0',
'nbump;': '\u224e\u0338',
'nbumpe;': '\u224f\u0338',
'ncap;': '\u2a43',
'Ncaron;': '\u0147',
'ncaron;': '\u0148',
'Ncedil;': '\u0145',
'ncedil;': '\u0146',
'ncong;': '\u2247',
'ncongdot;': '\u2a6d\u0338',
'ncup;': '\u2a42',
'Ncy;': '\u041d',
'ncy;': '\u043d',
'ndash;': '\u2013',
'ne;': '\u2260',
'nearhk;': '\u2924',
'neArr;': '\u21d7',
'nearr;': '\u2197',
'nearrow;': '\u2197',
'nedot;': '\u2250\u0338',
'NegativeMediumSpace;': '\u200b',
'NegativeThickSpace;': '\u200b',
'NegativeThinSpace;': '\u200b',
'NegativeVeryThinSpace;': '\u200b',
'nequiv;': '\u2262',
'nesear;': '\u2928',
'nesim;': '\u2242\u0338',
'NestedGreaterGreater;': '\u226b',
'NestedLessLess;': '\u226a',
'NewLine;': '\n',
'nexist;': '\u2204',
'nexists;': '\u2204',
'Nfr;': '\U0001d511',
'nfr;': '\U0001d52b',
'ngE;': '\u2267\u0338',
'nge;': '\u2271',
'ngeq;': '\u2271',
'ngeqq;': '\u2267\u0338',
'ngeqslant;': '\u2a7e\u0338',
'nges;': '\u2a7e\u0338',
'nGg;': '\u22d9\u0338',
'ngsim;': '\u2275',
'nGt;': '\u226b\u20d2',
'ngt;': '\u226f',
'ngtr;': '\u226f',
'nGtv;': '\u226b\u0338',
'nhArr;': '\u21ce',
'nharr;': '\u21ae',
'nhpar;': '\u2af2',
'ni;': '\u220b',
'nis;': '\u22fc',
'nisd;': '\u22fa',
'niv;': '\u220b',
'NJcy;': '\u040a',
'njcy;': '\u045a',
'nlArr;': '\u21cd',
'nlarr;': '\u219a',
'nldr;': '\u2025',
'nlE;': '\u2266\u0338',
'nle;': '\u2270',
'nLeftarrow;': '\u21cd',
'nleftarrow;': '\u219a',
'nLeftrightarrow;': '\u21ce',
'nleftrightarrow;': '\u21ae',
'nleq;': '\u2270',
'nleqq;': '\u2266\u0338',
'nleqslant;': '\u2a7d\u0338',
'nles;': '\u2a7d\u0338',
'nless;': '\u226e',
'nLl;': '\u22d8\u0338',
'nlsim;': '\u2274',
'nLt;': '\u226a\u20d2',
'nlt;': '\u226e',
'nltri;': '\u22ea',
'nltrie;': '\u22ec',
'nLtv;': '\u226a\u0338',
'nmid;': '\u2224',
'NoBreak;': '\u2060',
'NonBreakingSpace;': '\xa0',
'Nopf;': '\u2115',
'nopf;': '\U0001d55f',
'not': '\xac',
'Not;': '\u2aec',
'not;': '\xac',
'NotCongruent;': '\u2262',
'NotCupCap;': '\u226d',
'NotDoubleVerticalBar;': '\u2226',
'NotElement;': '\u2209',
'NotEqual;': '\u2260',
'NotEqualTilde;': '\u2242\u0338',
'NotExists;': '\u2204',
'NotGreater;': '\u226f',
'NotGreaterEqual;': '\u2271',
'NotGreaterFullEqual;': '\u2267\u0338',
'NotGreaterGreater;': '\u226b\u0338',
'NotGreaterLess;': '\u2279',
'NotGreaterSlantEqual;': '\u2a7e\u0338',
'NotGreaterTilde;': '\u2275',
'NotHumpDownHump;': '\u224e\u0338',
'NotHumpEqual;': '\u224f\u0338',
'notin;': '\u2209',
'notindot;': '\u22f5\u0338',
'notinE;': '\u22f9\u0338',
'notinva;': '\u2209',
'notinvb;': '\u22f7',
'notinvc;': '\u22f6',
'NotLeftTriangle;': '\u22ea',
'NotLeftTriangleBar;': '\u29cf\u0338',
'NotLeftTriangleEqual;': '\u22ec',
'NotLess;': '\u226e',
'NotLessEqual;': '\u2270',
'NotLessGreater;': '\u2278',
'NotLessLess;': '\u226a\u0338',
'NotLessSlantEqual;': '\u2a7d\u0338',
'NotLessTilde;': '\u2274',
'NotNestedGreaterGreater;': '\u2aa2\u0338',
'NotNestedLessLess;': '\u2aa1\u0338',
'notni;': '\u220c',
'notniva;': '\u220c',
'notnivb;': '\u22fe',
'notnivc;': '\u22fd',
'NotPrecedes;': '\u2280',
'NotPrecedesEqual;': '\u2aaf\u0338',
'NotPrecedesSlantEqual;': '\u22e0',
'NotReverseElement;': '\u220c',
'NotRightTriangle;': '\u22eb',
'NotRightTriangleBar;': '\u29d0\u0338',
'NotRightTriangleEqual;': '\u22ed',
'NotSquareSubset;': '\u228f\u0338',
'NotSquareSubsetEqual;': '\u22e2',
'NotSquareSuperset;': '\u2290\u0338',
'NotSquareSupersetEqual;': '\u22e3',
'NotSubset;': '\u2282\u20d2',
'NotSubsetEqual;': '\u2288',
'NotSucceeds;': '\u2281',
'NotSucceedsEqual;': '\u2ab0\u0338',
'NotSucceedsSlantEqual;': '\u22e1',
'NotSucceedsTilde;': '\u227f\u0338',
'NotSuperset;': '\u2283\u20d2',
'NotSupersetEqual;': '\u2289',
'NotTilde;': '\u2241',
'NotTildeEqual;': '\u2244',
'NotTildeFullEqual;': '\u2247',
'NotTildeTilde;': '\u2249',
'NotVerticalBar;': '\u2224',
'npar;': '\u2226',
'nparallel;': '\u2226',
'nparsl;': '\u2afd\u20e5',
'npart;': '\u2202\u0338',
'npolint;': '\u2a14',
'npr;': '\u2280',
'nprcue;': '\u22e0',
'npre;': '\u2aaf\u0338',
'nprec;': '\u2280',
'npreceq;': '\u2aaf\u0338',
'nrArr;': '\u21cf',
'nrarr;': '\u219b',
'nrarrc;': '\u2933\u0338',
'nrarrw;': '\u219d\u0338',
'nRightarrow;': '\u21cf',
'nrightarrow;': '\u219b',
'nrtri;': '\u22eb',
'nrtrie;': '\u22ed',
'nsc;': '\u2281',
'nsccue;': '\u22e1',
'nsce;': '\u2ab0\u0338',
'Nscr;': '\U0001d4a9',
'nscr;': '\U0001d4c3',
'nshortmid;': '\u2224',
'nshortparallel;': '\u2226',
'nsim;': '\u2241',
'nsime;': '\u2244',
'nsimeq;': '\u2244',
'nsmid;': '\u2224',
'nspar;': '\u2226',
'nsqsube;': '\u22e2',
'nsqsupe;': '\u22e3',
'nsub;': '\u2284',
'nsubE;': '\u2ac5\u0338',
'nsube;': '\u2288',
'nsubset;': '\u2282\u20d2',
'nsubseteq;': '\u2288',
'nsubseteqq;': '\u2ac5\u0338',
'nsucc;': '\u2281',
'nsucceq;': '\u2ab0\u0338',
'nsup;': '\u2285',
'nsupE;': '\u2ac6\u0338',
'nsupe;': '\u2289',
'nsupset;': '\u2283\u20d2',
'nsupseteq;': '\u2289',
'nsupseteqq;': '\u2ac6\u0338',
'ntgl;': '\u2279',
'Ntilde': '\xd1',
'ntilde': '\xf1',
'Ntilde;': '\xd1',
'ntilde;': '\xf1',
'ntlg;': '\u2278',
'ntriangleleft;': '\u22ea',
'ntrianglelefteq;': '\u22ec',
'ntriangleright;': '\u22eb',
'ntrianglerighteq;': '\u22ed',
'Nu;': '\u039d',
'nu;': '\u03bd',
'num;': '#',
'numero;': '\u2116',
'numsp;': '\u2007',
'nvap;': '\u224d\u20d2',
'nVDash;': '\u22af',
'nVdash;': '\u22ae',
'nvDash;': '\u22ad',
'nvdash;': '\u22ac',
'nvge;': '\u2265\u20d2',
'nvgt;': '>\u20d2',
'nvHarr;': '\u2904',
'nvinfin;': '\u29de',
'nvlArr;': '\u2902',
'nvle;': '\u2264\u20d2',
'nvlt;': '<\u20d2',
'nvltrie;': '\u22b4\u20d2',
'nvrArr;': '\u2903',
'nvrtrie;': '\u22b5\u20d2',
'nvsim;': '\u223c\u20d2',
'nwarhk;': '\u2923',
'nwArr;': '\u21d6',
'nwarr;': '\u2196',
'nwarrow;': '\u2196',
'nwnear;': '\u2927',
'Oacute': '\xd3',
'oacute': '\xf3',
'Oacute;': '\xd3',
'oacute;': '\xf3',
'oast;': '\u229b',
'ocir;': '\u229a',
'Ocirc': '\xd4',
'ocirc': '\xf4',
'Ocirc;': '\xd4',
'ocirc;': '\xf4',
'Ocy;': '\u041e',
'ocy;': '\u043e',
'odash;': '\u229d',
'Odblac;': '\u0150',
'odblac;': '\u0151',
'odiv;': '\u2a38',
'odot;': '\u2299',
'odsold;': '\u29bc',
'OElig;': '\u0152',
'oelig;': '\u0153',
'ofcir;': '\u29bf',
'Ofr;': '\U0001d512',
'ofr;': '\U0001d52c',
'ogon;': '\u02db',
'Ograve': '\xd2',
'ograve': '\xf2',
'Ograve;': '\xd2',
'ograve;': '\xf2',
'ogt;': '\u29c1',
'ohbar;': '\u29b5',
'ohm;': '\u03a9',
'oint;': '\u222e',
'olarr;': '\u21ba',
'olcir;': '\u29be',
'olcross;': '\u29bb',
'oline;': '\u203e',
'olt;': '\u29c0',
'Omacr;': '\u014c',
'omacr;': '\u014d',
'Omega;': '\u03a9',
'omega;': '\u03c9',
'Omicron;': '\u039f',
'omicron;': '\u03bf',
'omid;': '\u29b6',
'ominus;': '\u2296',
'Oopf;': '\U0001d546',
'oopf;': '\U0001d560',
'opar;': '\u29b7',
'OpenCurlyDoubleQuote;': '\u201c',
'OpenCurlyQuote;': '\u2018',
'operp;': '\u29b9',
'oplus;': '\u2295',
'Or;': '\u2a54',
'or;': '\u2228',
'orarr;': '\u21bb',
'ord;': '\u2a5d',
'order;': '\u2134',
'orderof;': '\u2134',
'ordf': '\xaa',
'ordf;': '\xaa',
'ordm': '\xba',
'ordm;': '\xba',
'origof;': '\u22b6',
'oror;': '\u2a56',
'orslope;': '\u2a57',
'orv;': '\u2a5b',
'oS;': '\u24c8',
'Oscr;': '\U0001d4aa',
'oscr;': '\u2134',
'Oslash': '\xd8',
'oslash': '\xf8',
'Oslash;': '\xd8',
'oslash;': '\xf8',
'osol;': '\u2298',
'Otilde': '\xd5',
'otilde': '\xf5',
'Otilde;': '\xd5',
'otilde;': '\xf5',
'Otimes;': '\u2a37',
'otimes;': '\u2297',
'otimesas;': '\u2a36',
'Ouml': '\xd6',
'ouml': '\xf6',
'Ouml;': '\xd6',
'ouml;': '\xf6',
'ovbar;': '\u233d',
'OverBar;': '\u203e',
'OverBrace;': '\u23de',
'OverBracket;': '\u23b4',
'OverParenthesis;': '\u23dc',
'par;': '\u2225',
'para': '\xb6',
'para;': '\xb6',
'parallel;': '\u2225',
'parsim;': '\u2af3',
'parsl;': '\u2afd',
'part;': '\u2202',
'PartialD;': '\u2202',
'Pcy;': '\u041f',
'pcy;': '\u043f',
'percnt;': '%',
'period;': '.',
'permil;': '\u2030',
'perp;': '\u22a5',
'pertenk;': '\u2031',
'Pfr;': '\U0001d513',
'pfr;': '\U0001d52d',
'Phi;': '\u03a6',
'phi;': '\u03c6',
'phiv;': '\u03d5',
'phmmat;': '\u2133',
'phone;': '\u260e',
'Pi;': '\u03a0',
'pi;': '\u03c0',
'pitchfork;': '\u22d4',
'piv;': '\u03d6',
'planck;': '\u210f',
'planckh;': '\u210e',
'plankv;': '\u210f',
'plus;': '+',
'plusacir;': '\u2a23',
'plusb;': '\u229e',
'pluscir;': '\u2a22',
'plusdo;': '\u2214',
'plusdu;': '\u2a25',
'pluse;': '\u2a72',
'PlusMinus;': '\xb1',
'plusmn': '\xb1',
'plusmn;': '\xb1',
'plussim;': '\u2a26',
'plustwo;': '\u2a27',
'pm;': '\xb1',
'Poincareplane;': '\u210c',
'pointint;': '\u2a15',
'Popf;': '\u2119',
'popf;': '\U0001d561',
'pound': '\xa3',
'pound;': '\xa3',
'Pr;': '\u2abb',
'pr;': '\u227a',
'prap;': '\u2ab7',
'prcue;': '\u227c',
'prE;': '\u2ab3',
'pre;': '\u2aaf',
'prec;': '\u227a',
'precapprox;': '\u2ab7',
'preccurlyeq;': '\u227c',
'Precedes;': '\u227a',
'PrecedesEqual;': '\u2aaf',
'PrecedesSlantEqual;': '\u227c',
'PrecedesTilde;': '\u227e',
'preceq;': '\u2aaf',
'precnapprox;': '\u2ab9',
'precneqq;': '\u2ab5',
'precnsim;': '\u22e8',
'precsim;': '\u227e',
'Prime;': '\u2033',
'prime;': '\u2032',
'primes;': '\u2119',
'prnap;': '\u2ab9',
'prnE;': '\u2ab5',
'prnsim;': '\u22e8',
'prod;': '\u220f',
'Product;': '\u220f',
'profalar;': '\u232e',
'profline;': '\u2312',
'profsurf;': '\u2313',
'prop;': '\u221d',
'Proportion;': '\u2237',
'Proportional;': '\u221d',
'propto;': '\u221d',
'prsim;': '\u227e',
'prurel;': '\u22b0',
'Pscr;': '\U0001d4ab',
'pscr;': '\U0001d4c5',
'Psi;': '\u03a8',
'psi;': '\u03c8',
'puncsp;': '\u2008',
'Qfr;': '\U0001d514',
'qfr;': '\U0001d52e',
'qint;': '\u2a0c',
'Qopf;': '\u211a',
'qopf;': '\U0001d562',
'qprime;': '\u2057',
'Qscr;': '\U0001d4ac',
'qscr;': '\U0001d4c6',
'quaternions;': '\u210d',
'quatint;': '\u2a16',
'quest;': '?',
'questeq;': '\u225f',
'QUOT': '"',
'quot': '"',
'QUOT;': '"',
'quot;': '"',
'rAarr;': '\u21db',
'race;': '\u223d\u0331',
'Racute;': '\u0154',
'racute;': '\u0155',
'radic;': '\u221a',
'raemptyv;': '\u29b3',
'Rang;': '\u27eb',
'rang;': '\u27e9',
'rangd;': '\u2992',
'range;': '\u29a5',
'rangle;': '\u27e9',
'raquo': '\xbb',
'raquo;': '\xbb',
'Rarr;': '\u21a0',
'rArr;': '\u21d2',
'rarr;': '\u2192',
'rarrap;': '\u2975',
'rarrb;': '\u21e5',
'rarrbfs;': '\u2920',
'rarrc;': '\u2933',
'rarrfs;': '\u291e',
'rarrhk;': '\u21aa',
'rarrlp;': '\u21ac',
'rarrpl;': '\u2945',
'rarrsim;': '\u2974',
'Rarrtl;': '\u2916',
'rarrtl;': '\u21a3',
'rarrw;': '\u219d',
'rAtail;': '\u291c',
'ratail;': '\u291a',
'ratio;': '\u2236',
'rationals;': '\u211a',
'RBarr;': '\u2910',
'rBarr;': '\u290f',
'rbarr;': '\u290d',
'rbbrk;': '\u2773',
'rbrace;': '}',
'rbrack;': ']',
'rbrke;': '\u298c',
'rbrksld;': '\u298e',
'rbrkslu;': '\u2990',
'Rcaron;': '\u0158',
'rcaron;': '\u0159',
'Rcedil;': '\u0156',
'rcedil;': '\u0157',
'rceil;': '\u2309',
'rcub;': '}',
'Rcy;': '\u0420',
'rcy;': '\u0440',
'rdca;': '\u2937',
'rdldhar;': '\u2969',
'rdquo;': '\u201d',
'rdquor;': '\u201d',
'rdsh;': '\u21b3',
'Re;': '\u211c',
'real;': '\u211c',
'realine;': '\u211b',
'realpart;': '\u211c',
'reals;': '\u211d',
'rect;': '\u25ad',
'REG': '\xae',
'reg': '\xae',
'REG;': '\xae',
'reg;': '\xae',
'ReverseElement;': '\u220b',
'ReverseEquilibrium;': '\u21cb',
'ReverseUpEquilibrium;': '\u296f',
'rfisht;': '\u297d',
'rfloor;': '\u230b',
'Rfr;': '\u211c',
'rfr;': '\U0001d52f',
'rHar;': '\u2964',
'rhard;': '\u21c1',
'rharu;': '\u21c0',
'rharul;': '\u296c',
'Rho;': '\u03a1',
'rho;': '\u03c1',
'rhov;': '\u03f1',
'RightAngleBracket;': '\u27e9',
'RightArrow;': '\u2192',
'Rightarrow;': '\u21d2',
'rightarrow;': '\u2192',
'RightArrowBar;': '\u21e5',
'RightArrowLeftArrow;': '\u21c4',
'rightarrowtail;': '\u21a3',
'RightCeiling;': '\u2309',
'RightDoubleBracket;': '\u27e7',
'RightDownTeeVector;': '\u295d',
'RightDownVector;': '\u21c2',
'RightDownVectorBar;': '\u2955',
'RightFloor;': '\u230b',
'rightharpoondown;': '\u21c1',
'rightharpoonup;': '\u21c0',
'rightleftarrows;': '\u21c4',
'rightleftharpoons;': '\u21cc',
'rightrightarrows;': '\u21c9',
'rightsquigarrow;': '\u219d',
'RightTee;': '\u22a2',
'RightTeeArrow;': '\u21a6',
'RightTeeVector;': '\u295b',
'rightthreetimes;': '\u22cc',
'RightTriangle;': '\u22b3',
'RightTriangleBar;': '\u29d0',
'RightTriangleEqual;': '\u22b5',
'RightUpDownVector;': '\u294f',
'RightUpTeeVector;': '\u295c',
'RightUpVector;': '\u21be',
'RightUpVectorBar;': '\u2954',
'RightVector;': '\u21c0',
'RightVectorBar;': '\u2953',
'ring;': '\u02da',
'risingdotseq;': '\u2253',
'rlarr;': '\u21c4',
'rlhar;': '\u21cc',
'rlm;': '\u200f',
'rmoust;': '\u23b1',
'rmoustache;': '\u23b1',
'rnmid;': '\u2aee',
'roang;': '\u27ed',
'roarr;': '\u21fe',
'robrk;': '\u27e7',
'ropar;': '\u2986',
'Ropf;': '\u211d',
'ropf;': '\U0001d563',
'roplus;': '\u2a2e',
'rotimes;': '\u2a35',
'RoundImplies;': '\u2970',
'rpar;': ')',
'rpargt;': '\u2994',
'rppolint;': '\u2a12',
'rrarr;': '\u21c9',
'Rrightarrow;': '\u21db',
'rsaquo;': '\u203a',
'Rscr;': '\u211b',
'rscr;': '\U0001d4c7',
'Rsh;': '\u21b1',
'rsh;': '\u21b1',
'rsqb;': ']',
'rsquo;': '\u2019',
'rsquor;': '\u2019',
'rthree;': '\u22cc',
'rtimes;': '\u22ca',
'rtri;': '\u25b9',
'rtrie;': '\u22b5',
'rtrif;': '\u25b8',
'rtriltri;': '\u29ce',
'RuleDelayed;': '\u29f4',
'ruluhar;': '\u2968',
'rx;': '\u211e',
'Sacute;': '\u015a',
'sacute;': '\u015b',
'sbquo;': '\u201a',
'Sc;': '\u2abc',
'sc;': '\u227b',
'scap;': '\u2ab8',
'Scaron;': '\u0160',
'scaron;': '\u0161',
'sccue;': '\u227d',
'scE;': '\u2ab4',
'sce;': '\u2ab0',
'Scedil;': '\u015e',
'scedil;': '\u015f',
'Scirc;': '\u015c',
'scirc;': '\u015d',
'scnap;': '\u2aba',
'scnE;': '\u2ab6',
'scnsim;': '\u22e9',
'scpolint;': '\u2a13',
'scsim;': '\u227f',
'Scy;': '\u0421',
'scy;': '\u0441',
'sdot;': '\u22c5',
'sdotb;': '\u22a1',
'sdote;': '\u2a66',
'searhk;': '\u2925',
'seArr;': '\u21d8',
'searr;': '\u2198',
'searrow;': '\u2198',
'sect': '\xa7',
'sect;': '\xa7',
'semi;': ';',
'seswar;': '\u2929',
'setminus;': '\u2216',
'setmn;': '\u2216',
'sext;': '\u2736',
'Sfr;': '\U0001d516',
'sfr;': '\U0001d530',
'sfrown;': '\u2322',
'sharp;': '\u266f',
'SHCHcy;': '\u0429',
'shchcy;': '\u0449',
'SHcy;': '\u0428',
'shcy;': '\u0448',
'ShortDownArrow;': '\u2193',
'ShortLeftArrow;': '\u2190',
'shortmid;': '\u2223',
'shortparallel;': '\u2225',
'ShortRightArrow;': '\u2192',
'ShortUpArrow;': '\u2191',
'shy': '\xad',
'shy;': '\xad',
'Sigma;': '\u03a3',
'sigma;': '\u03c3',
'sigmaf;': '\u03c2',
'sigmav;': '\u03c2',
'sim;': '\u223c',
'simdot;': '\u2a6a',
'sime;': '\u2243',
'simeq;': '\u2243',
'simg;': '\u2a9e',
'simgE;': '\u2aa0',
'siml;': '\u2a9d',
'simlE;': '\u2a9f',
'simne;': '\u2246',
'simplus;': '\u2a24',
'simrarr;': '\u2972',
'slarr;': '\u2190',
'SmallCircle;': '\u2218',
'smallsetminus;': '\u2216',
'smashp;': '\u2a33',
'smeparsl;': '\u29e4',
'smid;': '\u2223',
'smile;': '\u2323',
'smt;': '\u2aaa',
'smte;': '\u2aac',
'smtes;': '\u2aac\ufe00',
'SOFTcy;': '\u042c',
'softcy;': '\u044c',
'sol;': '/',
'solb;': '\u29c4',
'solbar;': '\u233f',
'Sopf;': '\U0001d54a',
'sopf;': '\U0001d564',
'spades;': '\u2660',
'spadesuit;': '\u2660',
'spar;': '\u2225',
'sqcap;': '\u2293',
'sqcaps;': '\u2293\ufe00',
'sqcup;': '\u2294',
'sqcups;': '\u2294\ufe00',
'Sqrt;': '\u221a',
'sqsub;': '\u228f',
'sqsube;': '\u2291',
'sqsubset;': '\u228f',
'sqsubseteq;': '\u2291',
'sqsup;': '\u2290',
'sqsupe;': '\u2292',
'sqsupset;': '\u2290',
'sqsupseteq;': '\u2292',
'squ;': '\u25a1',
'Square;': '\u25a1',
'square;': '\u25a1',
'SquareIntersection;': '\u2293',
'SquareSubset;': '\u228f',
'SquareSubsetEqual;': '\u2291',
'SquareSuperset;': '\u2290',
'SquareSupersetEqual;': '\u2292',
'SquareUnion;': '\u2294',
'squarf;': '\u25aa',
'squf;': '\u25aa',
'srarr;': '\u2192',
'Sscr;': '\U0001d4ae',
'sscr;': '\U0001d4c8',
'ssetmn;': '\u2216',
'ssmile;': '\u2323',
'sstarf;': '\u22c6',
'Star;': '\u22c6',
'star;': '\u2606',
'starf;': '\u2605',
'straightepsilon;': '\u03f5',
'straightphi;': '\u03d5',
'strns;': '\xaf',
'Sub;': '\u22d0',
'sub;': '\u2282',
'subdot;': '\u2abd',
'subE;': '\u2ac5',
'sube;': '\u2286',
'subedot;': '\u2ac3',
'submult;': '\u2ac1',
'subnE;': '\u2acb',
'subne;': '\u228a',
'subplus;': '\u2abf',
'subrarr;': '\u2979',
'Subset;': '\u22d0',
'subset;': '\u2282',
'subseteq;': '\u2286',
'subseteqq;': '\u2ac5',
'SubsetEqual;': '\u2286',
'subsetneq;': '\u228a',
'subsetneqq;': '\u2acb',
'subsim;': '\u2ac7',
'subsub;': '\u2ad5',
'subsup;': '\u2ad3',
'succ;': '\u227b',
'succapprox;': '\u2ab8',
'succcurlyeq;': '\u227d',
'Succeeds;': '\u227b',
'SucceedsEqual;': '\u2ab0',
'SucceedsSlantEqual;': '\u227d',
'SucceedsTilde;': '\u227f',
'succeq;': '\u2ab0',
'succnapprox;': '\u2aba',
'succneqq;': '\u2ab6',
'succnsim;': '\u22e9',
'succsim;': '\u227f',
'SuchThat;': '\u220b',
'Sum;': '\u2211',
'sum;': '\u2211',
'sung;': '\u266a',
'sup1': '\xb9',
'sup1;': '\xb9',
'sup2': '\xb2',
'sup2;': '\xb2',
'sup3': '\xb3',
'sup3;': '\xb3',
'Sup;': '\u22d1',
'sup;': '\u2283',
'supdot;': '\u2abe',
'supdsub;': '\u2ad8',
'supE;': '\u2ac6',
'supe;': '\u2287',
'supedot;': '\u2ac4',
'Superset;': '\u2283',
'SupersetEqual;': '\u2287',
'suphsol;': '\u27c9',
'suphsub;': '\u2ad7',
'suplarr;': '\u297b',
'supmult;': '\u2ac2',
'supnE;': '\u2acc',
'supne;': '\u228b',
'supplus;': '\u2ac0',
'Supset;': '\u22d1',
'supset;': '\u2283',
'supseteq;': '\u2287',
'supseteqq;': '\u2ac6',
'supsetneq;': '\u228b',
'supsetneqq;': '\u2acc',
'supsim;': '\u2ac8',
'supsub;': '\u2ad4',
'supsup;': '\u2ad6',
'swarhk;': '\u2926',
'swArr;': '\u21d9',
'swarr;': '\u2199',
'swarrow;': '\u2199',
'swnwar;': '\u292a',
'szlig': '\xdf',
'szlig;': '\xdf',
'Tab;': '\t',
'target;': '\u2316',
'Tau;': '\u03a4',
'tau;': '\u03c4',
'tbrk;': '\u23b4',
'Tcaron;': '\u0164',
'tcaron;': '\u0165',
'Tcedil;': '\u0162',
'tcedil;': '\u0163',
'Tcy;': '\u0422',
'tcy;': '\u0442',
'tdot;': '\u20db',
'telrec;': '\u2315',
'Tfr;': '\U0001d517',
'tfr;': '\U0001d531',
'there4;': '\u2234',
'Therefore;': '\u2234',
'therefore;': '\u2234',
'Theta;': '\u0398',
'theta;': '\u03b8',
'thetasym;': '\u03d1',
'thetav;': '\u03d1',
'thickapprox;': '\u2248',
'thicksim;': '\u223c',
'ThickSpace;': '\u205f\u200a',
'thinsp;': '\u2009',
'ThinSpace;': '\u2009',
'thkap;': '\u2248',
'thksim;': '\u223c',
'THORN': '\xde',
'thorn': '\xfe',
'THORN;': '\xde',
'thorn;': '\xfe',
'Tilde;': '\u223c',
'tilde;': '\u02dc',
'TildeEqual;': '\u2243',
'TildeFullEqual;': '\u2245',
'TildeTilde;': '\u2248',
'times': '\xd7',
'times;': '\xd7',
'timesb;': '\u22a0',
'timesbar;': '\u2a31',
'timesd;': '\u2a30',
'tint;': '\u222d',
'toea;': '\u2928',
'top;': '\u22a4',
'topbot;': '\u2336',
'topcir;': '\u2af1',
'Topf;': '\U0001d54b',
'topf;': '\U0001d565',
'topfork;': '\u2ada',
'tosa;': '\u2929',
'tprime;': '\u2034',
'TRADE;': '\u2122',
'trade;': '\u2122',
'triangle;': '\u25b5',
'triangledown;': '\u25bf',
'triangleleft;': '\u25c3',
'trianglelefteq;': '\u22b4',
'triangleq;': '\u225c',
'triangleright;': '\u25b9',
'trianglerighteq;': '\u22b5',
'tridot;': '\u25ec',
'trie;': '\u225c',
'triminus;': '\u2a3a',
'TripleDot;': '\u20db',
'triplus;': '\u2a39',
'trisb;': '\u29cd',
'tritime;': '\u2a3b',
'trpezium;': '\u23e2',
'Tscr;': '\U0001d4af',
'tscr;': '\U0001d4c9',
'TScy;': '\u0426',
'tscy;': '\u0446',
'TSHcy;': '\u040b',
'tshcy;': '\u045b',
'Tstrok;': '\u0166',
'tstrok;': '\u0167',
'twixt;': '\u226c',
'twoheadleftarrow;': '\u219e',
'twoheadrightarrow;': '\u21a0',
'Uacute': '\xda',
'uacute': '\xfa',
'Uacute;': '\xda',
'uacute;': '\xfa',
'Uarr;': '\u219f',
'uArr;': '\u21d1',
'uarr;': '\u2191',
'Uarrocir;': '\u2949',
'Ubrcy;': '\u040e',
'ubrcy;': '\u045e',
'Ubreve;': '\u016c',
'ubreve;': '\u016d',
'Ucirc': '\xdb',
'ucirc': '\xfb',
'Ucirc;': '\xdb',
'ucirc;': '\xfb',
'Ucy;': '\u0423',
'ucy;': '\u0443',
'udarr;': '\u21c5',
'Udblac;': '\u0170',
'udblac;': '\u0171',
'udhar;': '\u296e',
'ufisht;': '\u297e',
'Ufr;': '\U0001d518',
'ufr;': '\U0001d532',
'Ugrave': '\xd9',
'ugrave': '\xf9',
'Ugrave;': '\xd9',
'ugrave;': '\xf9',
'uHar;': '\u2963',
'uharl;': '\u21bf',
'uharr;': '\u21be',
'uhblk;': '\u2580',
'ulcorn;': '\u231c',
'ulcorner;': '\u231c',
'ulcrop;': '\u230f',
'ultri;': '\u25f8',
'Umacr;': '\u016a',
'umacr;': '\u016b',
'uml': '\xa8',
'uml;': '\xa8',
'UnderBar;': '_',
'UnderBrace;': '\u23df',
'UnderBracket;': '\u23b5',
'UnderParenthesis;': '\u23dd',
'Union;': '\u22c3',
'UnionPlus;': '\u228e',
'Uogon;': '\u0172',
'uogon;': '\u0173',
'Uopf;': '\U0001d54c',
'uopf;': '\U0001d566',
'UpArrow;': '\u2191',
'Uparrow;': '\u21d1',
'uparrow;': '\u2191',
'UpArrowBar;': '\u2912',
'UpArrowDownArrow;': '\u21c5',
'UpDownArrow;': '\u2195',
'Updownarrow;': '\u21d5',
'updownarrow;': '\u2195',
'UpEquilibrium;': '\u296e',
'upharpoonleft;': '\u21bf',
'upharpoonright;': '\u21be',
'uplus;': '\u228e',
'UpperLeftArrow;': '\u2196',
'UpperRightArrow;': '\u2197',
'Upsi;': '\u03d2',
'upsi;': '\u03c5',
'upsih;': '\u03d2',
'Upsilon;': '\u03a5',
'upsilon;': '\u03c5',
'UpTee;': '\u22a5',
'UpTeeArrow;': '\u21a5',
'upuparrows;': '\u21c8',
'urcorn;': '\u231d',
'urcorner;': '\u231d',
'urcrop;': '\u230e',
'Uring;': '\u016e',
'uring;': '\u016f',
'urtri;': '\u25f9',
'Uscr;': '\U0001d4b0',
'uscr;': '\U0001d4ca',
'utdot;': '\u22f0',
'Utilde;': '\u0168',
'utilde;': '\u0169',
'utri;': '\u25b5',
'utrif;': '\u25b4',
'uuarr;': '\u21c8',
'Uuml': '\xdc',
'uuml': '\xfc',
'Uuml;': '\xdc',
'uuml;': '\xfc',
'uwangle;': '\u29a7',
'vangrt;': '\u299c',
'varepsilon;': '\u03f5',
'varkappa;': '\u03f0',
'varnothing;': '\u2205',
'varphi;': '\u03d5',
'varpi;': '\u03d6',
'varpropto;': '\u221d',
'vArr;': '\u21d5',
'varr;': '\u2195',
'varrho;': '\u03f1',
'varsigma;': '\u03c2',
'varsubsetneq;': '\u228a\ufe00',
'varsubsetneqq;': '\u2acb\ufe00',
'varsupsetneq;': '\u228b\ufe00',
'varsupsetneqq;': '\u2acc\ufe00',
'vartheta;': '\u03d1',
'vartriangleleft;': '\u22b2',
'vartriangleright;': '\u22b3',
'Vbar;': '\u2aeb',
'vBar;': '\u2ae8',
'vBarv;': '\u2ae9',
'Vcy;': '\u0412',
'vcy;': '\u0432',
'VDash;': '\u22ab',
'Vdash;': '\u22a9',
'vDash;': '\u22a8',
'vdash;': '\u22a2',
'Vdashl;': '\u2ae6',
'Vee;': '\u22c1',
'vee;': '\u2228',
'veebar;': '\u22bb',
'veeeq;': '\u225a',
'vellip;': '\u22ee',
'Verbar;': '\u2016',
'verbar;': '|',
'Vert;': '\u2016',
'vert;': '|',
'VerticalBar;': '\u2223',
'VerticalLine;': '|',
'VerticalSeparator;': '\u2758',
'VerticalTilde;': '\u2240',
'VeryThinSpace;': '\u200a',
'Vfr;': '\U0001d519',
'vfr;': '\U0001d533',
'vltri;': '\u22b2',
'vnsub;': '\u2282\u20d2',
'vnsup;': '\u2283\u20d2',
'Vopf;': '\U0001d54d',
'vopf;': '\U0001d567',
'vprop;': '\u221d',
'vrtri;': '\u22b3',
'Vscr;': '\U0001d4b1',
'vscr;': '\U0001d4cb',
'vsubnE;': '\u2acb\ufe00',
'vsubne;': '\u228a\ufe00',
'vsupnE;': '\u2acc\ufe00',
'vsupne;': '\u228b\ufe00',
'Vvdash;': '\u22aa',
'vzigzag;': '\u299a',
'Wcirc;': '\u0174',
'wcirc;': '\u0175',
'wedbar;': '\u2a5f',
'Wedge;': '\u22c0',
'wedge;': '\u2227',
'wedgeq;': '\u2259',
'weierp;': '\u2118',
'Wfr;': '\U0001d51a',
'wfr;': '\U0001d534',
'Wopf;': '\U0001d54e',
'wopf;': '\U0001d568',
'wp;': '\u2118',
'wr;': '\u2240',
'wreath;': '\u2240',
'Wscr;': '\U0001d4b2',
'wscr;': '\U0001d4cc',
'xcap;': '\u22c2',
'xcirc;': '\u25ef',
'xcup;': '\u22c3',
'xdtri;': '\u25bd',
'Xfr;': '\U0001d51b',
'xfr;': '\U0001d535',
'xhArr;': '\u27fa',
'xharr;': '\u27f7',
'Xi;': '\u039e',
'xi;': '\u03be',
'xlArr;': '\u27f8',
'xlarr;': '\u27f5',
'xmap;': '\u27fc',
'xnis;': '\u22fb',
'xodot;': '\u2a00',
'Xopf;': '\U0001d54f',
'xopf;': '\U0001d569',
'xoplus;': '\u2a01',
'xotime;': '\u2a02',
'xrArr;': '\u27f9',
'xrarr;': '\u27f6',
'Xscr;': '\U0001d4b3',
'xscr;': '\U0001d4cd',
'xsqcup;': '\u2a06',
'xuplus;': '\u2a04',
'xutri;': '\u25b3',
'xvee;': '\u22c1',
'xwedge;': '\u22c0',
'Yacute': '\xdd',
'yacute': '\xfd',
'Yacute;': '\xdd',
'yacute;': '\xfd',
'YAcy;': '\u042f',
'yacy;': '\u044f',
'Ycirc;': '\u0176',
'ycirc;': '\u0177',
'Ycy;': '\u042b',
'ycy;': '\u044b',
'yen': '\xa5',
'yen;': '\xa5',
'Yfr;': '\U0001d51c',
'yfr;': '\U0001d536',
'YIcy;': '\u0407',
'yicy;': '\u0457',
'Yopf;': '\U0001d550',
'yopf;': '\U0001d56a',
'Yscr;': '\U0001d4b4',
'yscr;': '\U0001d4ce',
'YUcy;': '\u042e',
'yucy;': '\u044e',
'yuml': '\xff',
'Yuml;': '\u0178',
'yuml;': '\xff',
'Zacute;': '\u0179',
'zacute;': '\u017a',
'Zcaron;': '\u017d',
'zcaron;': '\u017e',
'Zcy;': '\u0417',
'zcy;': '\u0437',
'Zdot;': '\u017b',
'zdot;': '\u017c',
'zeetrf;': '\u2128',
'ZeroWidthSpace;': '\u200b',
'Zeta;': '\u0396',
'zeta;': '\u03b6',
'Zfr;': '\u2128',
'zfr;': '\U0001d537',
'ZHcy;': '\u0416',
'zhcy;': '\u0436',
'zigrarr;': '\u21dd',
'Zopf;': '\u2124',
'zopf;': '\U0001d56b',
'Zscr;': '\U0001d4b5',
'zscr;': '\U0001d4cf',
'zwj;': '\u200d',
'zwnj;': '\u200c',
}
# maps the Unicode code point to the HTML entity name
codepoint2name = {}
# maps the HTML entity name to the character
# (or a character reference if the character is outside the Latin-1 range)
entitydefs = {}
for (name, codepoint) in name2codepoint.items():
codepoint2name[codepoint] = name
entitydefs[name] = chr(codepoint)
del name, codepoint
|
technologiescollege/Blockly-rduino-communication
|
scripts_XP/Lib/html/entities.py
|
Python
|
gpl-3.0
| 75,243
|
[
"Bowtie"
] |
2e10df5a59f46c50814d424f21e20512e3430bb40af1576a688c6c83776c3b70
|
# Copyright 2014-2018 The PySCF Developers. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Author: Qiming Sun <osirpt.sun@gmail.com>
#
import unittest
import tempfile
import numpy
from pyscf import lib
from pyscf import gto
from pyscf import scf
from pyscf import df
class KnownValues(unittest.TestCase):
def test_aug_etb(self):
mol = gto.M(
verbose = 0,
atom = '''O 0 0. 0.
1 0 -0.757 0.587
1 0 0.757 0.587''',
basis = 'cc-pvdz',
)
df.addons.aug_etb(mol)
mol = gto.M(
verbose = 0,
atom = '''O 0 0. 0.
1 0 -0.757 0.587
1 0 0.757 0.587''',
basis = ('cc-pvdz', [[4, (1., 1.)]])
)
auxbasis = df.addons.aug_etb(mol)
self.assertEqual(len(auxbasis['O']), 36)
self.assertEqual(len(auxbasis['H']), 12)
def test_make_auxbasis(self):
mol = gto.M(
verbose = 0,
atom = '''O 0 0. 0.
1 0 -0.757 0.587
1 0 0.757 0.587''',
basis = {'default': 'cc-pvdz', 'O':'631g'}
)
auxmol = df.addons.make_auxmol(mol, auxbasis={'default':'ccpvdz-fit'})
self.assertEqual(auxmol.nao_nr(), 146)
auxbasis = df.addons.make_auxbasis(mol, mp2fit=True)
self.assertEqual(auxbasis['O'], 'cc-pvdz-ri')
self.assertEqual(auxbasis['H'], 'cc-pvdz-ri')
mol = gto.M(
verbose = 0,
atom = '''O 0 0. 0.
1 0 -0.757 0.587
1 0 0.757 0.587''',
basis = {'H': [[0,(1.,1.)], [1, (.5, 1.)]],
'O': ('631g', [[0, 0, (1., 1.)]])}
)
auxbasis = df.addons.make_auxbasis(mol)
self.assertEqual(len(auxbasis['O']), 32)
self.assertEqual(len(auxbasis['H']), 3)
def test_default_auxbasis(self):
mol = gto.M(atom='He 0 0 0; O 0 0 1', basis='ccpvdz')
auxbasis = df.addons.make_auxbasis(mol)
self.assertTrue(auxbasis['O'] == 'cc-pvdz-jkfit')
self.assertTrue(isinstance(auxbasis['He'], list))
if __name__ == "__main__":
print("Full Tests for df.addons")
unittest.main()
|
gkc1000/pyscf
|
pyscf/df/test/test_addons.py
|
Python
|
apache-2.0
| 2,911
|
[
"PySCF"
] |
93d1b56f5607b666df616251e670170c848d7449d5d46e0b293f160c48f8486c
|
"""
PySCeS - Python Simulator for Cellular Systems (http://pysces.sourceforge.net)
Copyright (C) 2004-2020 B.G. Olivier, J.M. Rohwer, J.-H.S Hofmeyr all rights reserved,
Brett G. Olivier (bgoli@users.sourceforge.net)
Triple-J Group for Molecular Cell Physiology
Stellenbosch University, South Africa.
Permission to use, modify, and distribute this software is given under the
terms of the PySceS (BSD style) license. See LICENSE.txt that came with
this distribution for specifics.
NO WARRANTY IS EXPRESSED OR IMPLIED. USE AT YOUR OWN RISK.
Brett G. Olivier
"""
from __future__ import division, print_function
from __future__ import absolute_import
from __future__ import unicode_literals
from .version import __version__
# Structural Analysis module
from pysces.PyscesStoich import Stoich
from .PyscesCore2 import StructMatrix
class PyscesEnhancedStoich(Stoich):
"""PySCeS stoichiometry class for use with core2"""
N = None
Nr = None
K = None
K0 = None
L = None
L0 = None
Gamma = None
def __init__(self, core):
Stoich.__init__(self, core.stoichiometric_matrix.array)
self.species = core.stoichiometric_matrix.row
self.reactions = core.stoichiometric_matrix.col
def getNullSpaces(self):
self.AnalyseK()
self.AnalyseL()
def testNullSpaces(self):
# TODO: build in nullspace validity checks from PyscesModel
pass
def setStructMatrices(self):
self.N = StructMatrix(self.nmatrix, self.nmatrix_row, self.nmatrix_col)
self.N.setRow(self.species)
self.N.setCol(self.reactions)
self.Nr = StructMatrix(self.nrmatrix, self.nrmatrix_row, self.nrmatrix_col)
self.Nr.setRow(self.species)
self.Nr.setCol(self.reactions)
self.K = StructMatrix(self.kmatrix, self.kmatrix_row, self.kmatrix_col)
self.K.setRow(self.reactions)
self.K.setCol(self.reactions)
self.K0 = StructMatrix(
self.kzeromatrix, self.kzeromatrix_row, self.kzeromatrix_col
)
self.K0.setRow(self.reactions)
self.K0.setCol(self.reactions)
self.L = StructMatrix(self.lmatrix, self.lmatrix_row, self.lmatrix_col)
self.L.setRow(self.species)
self.L.setCol(self.species)
self.L0 = StructMatrix(
self.lzeromatrix, self.lzeromatrix_row, self.lzeromatrix_col
)
self.L0.setRow(self.species)
self.L0.setCol(self.species)
if self.info_moiety_conserve:
self.Gamma = StructMatrix(
self.conservation_matrix,
self.conservation_matrix_row,
self.conservation_matrix_col,
)
self.Gamma.setRow(self.species)
self.Gamma.setCol(self.species)
class StructuralModule(object):
core = None
struct = None
def setCore(self, core):
self.core = core
self.struct = None
if self.core.stoichiometric_matrix == None:
print("StructuralModule building stoichiometric matrix ...")
self.core.setStoichiometricMatrix()
def getCore(self):
self.core.struct = self.struct
return self.core
def analyseStoichiometry(self):
self.struct = PyscesEnhancedStoich(self.core)
self.struct.getNullSpaces()
self.struct.setStructMatrices()
# Integration Module
import numpy
"""
class StateDataObj(object):
flux = None
flux_labels = None
species = None
species_labels = None
valid = True
_suffix = None
_prefix = None
def __init__(self):
self.species_labels = []
self.flux_labels = []
def setSpecies(self, name, value, suffix=None):
if suffix != None:
name = name + suffix
if name not in self.species_labels:
self.species_labels.append(name)
self._suffix = suffix
setattr(self, name, value)
def setFlux(self, name, value, prefix=None):
if prefix != None:
name = prefix + name
if name not in self.flux_labels:
self.flux_labels.append(name)
self._prefix = prefix
setattr(self, name, value)
def setAllSpecies(self, species_labels, species, suffix=None):
assert len(species_labels) == len(species), '\nThis aint gonna work1'
self.species_labels = []
# self.species_labels = tuple(species_labels)
self.species = species.copy()
for S in range(len(species_labels)):
self.setSpecies(species_labels[S], species[S], suffix)
def setAllFluxes(self, flux_labels, flux, prefix=None):
assert len(flux_labels) == len(flux), '\nThis aint gonna work2'
self.flux_labels = []
# self.flux_labels = tuple(flux_labels)
self.flux = flux.copy()
for J in range(len(flux_labels)):
self.setFlux(flux_labels[J], flux[J], prefix)
def getFlux(self, name):
if prefix != None:
name = prefix + name
return getattr(self, name)
def getSpecies(self, name):
if suffix != None:
name = name + suffix
return getattr(self, name)
"""
class StateDataObj(object):
"""
New class used to store steady-state data.
"""
fluxes = None
species = None
rules = None
xdata = None
flux_labels = None
species_labels = None
rules_labels = None
xdata_labels = None
HAS_FLUXES = False
HAS_SPECIES = False
HAS_RULES = False
HAS_XDATA = False
IS_VALID = True
def setSpecies(self, species, lbls=None):
"""Set the species array"""
self.species = species
self.HAS_SPECIES = True
if lbls != None:
self.species_labels = lbls
for s in range(len(self.species_labels)):
setattr(self, self.species_labels[s], self.species[s])
def setFluxes(self, fluxes, lbls=None):
"""set the flux array"""
self.fluxes = fluxes
self.HAS_FLUXES = True
if lbls != None:
self.flux_labels = lbls
for f in range(len(self.flux_labels)):
setattr(self, self.flux_labels[f], self.fluxes[f])
def setRules(self, rules, lbls=None):
"""Set the results of rate rules"""
self.rules = rules
self.HAS_RULES = True
if lbls != None:
self.rules_labels = lbls
for r in range(len(self.rules_labels)):
setattr(self, self.rules_labels[r], self.rules[r])
def setXData(self, xdata, lbls=None):
"""Sets extra simulation data"""
self.xdata = xdata
self.HAS_XDATA = True
if lbls != None:
self.xdata_labels = lbls
for x in range(len(self.xdata_labels)):
setattr(self, self.xdata_labels[x], self.xdata[x])
def getSpecies(self, lbls=False):
"""return species array"""
output = None
if self.HAS_SPECIES:
output = self.species
if not lbls:
return output
else:
return output, self.species_labels
def getFluxes(self, lbls=False):
"""return flux array"""
output = None
if self.HAS_FLUXES:
output = self.fluxes
if not lbls:
return output
else:
return output, self.flux_labels
def getRules(self, lbls=False):
"""Return rule array"""
output = None
if self.HAS_RULES:
output = self.rules
if not lbls:
return output
else:
return output, self.rules_labels
def getXData(self, lbls=False):
"""Return xdata array"""
output = None
if self.HAS_XDATA:
output = self.xdata
if not lbls:
return output
else:
return output, self.xdata_labels
def getAllStateData(self, lbls=False):
"""
Return all available data as species+fluxes+rules
if lbls=True returns (array,labels) else just array
"""
labels = []
output = None
if self.HAS_SPECIES:
output = self.species
labels += self.species_labels
if self.HAS_FLUXES:
if output == None:
output = self.fluxes
else:
output = numpy.hstack((output, self.fluxes))
labels += self.flux_labels
if self.HAS_RULES:
if output == None:
output = self.rules
else:
output = numpy.hstack((output, self.rules))
labels += self.rules_labels
if self.HAS_XDATA:
if output == None:
output = self.xdata
else:
output = numpy.hstack((output, self.xdata))
labels += self.xdata_labels
if not lbls:
return output
else:
return output, labels
def getStateData(self, *args, **kwargs):
"""getSimData(\*args) feed this method species/rate labels and it
will return an array of [time, sp1, r1, ....]
"""
if 'lbls' in kwargs:
lbls = kwargs['lbls']
else:
lbls = False
lout = []
output = []
for roc in args:
if self.HAS_SPECIES and roc in self.species_labels:
lout.append(roc)
output.append(self.species[self.species_labels.index(roc)])
elif self.HAS_FLUXES and roc in self.flux_labels:
lout.append(roc)
output.append(self.fluxes[self.flux_labels.index(roc)])
elif self.HAS_RULES and roc in self.rules_labels:
lout.append(roc)
output.append(self.rules[self.rules_labels.index(roc)])
elif self.HAS_XDATA and roc in self.xdata_labels:
lout.append(roc)
output.append(self.xdata[self.xdata_labels.index(roc)])
else:
print('I don\'t have an attribute %s ... ignoring.' % roc)
if not lbls:
return output
else:
return numpy.array(output), lout
'''
class IntegrationDataObj(object):
"""
This class is specifically designed to store the results of a time simulation
It has methods for setting the Time, Labels, Species and Rate data and
getting Time, Species and Rate (including time) arrays. However, of more use:
- getOutput(*arg) feed this method species/rate labels and it will return
an array of [time, sp1, r1, ....]
- getDataAtTime(time) the data generated at time point "time".
- getDataInTimeInterval(time, bounds=None) more intelligent version of the above
returns an array of all data points where: time-bounds <= time <= time+bounds
where bounds defaults to stepsize.
time = None
rates = None
species = None
rate_labels = None
species_labels = None
def setLabels(self, species, rates):
"""set the species and rate label lists"""
self.species_labels = species
self.rate_labels = rates
def setTime(self, time):
"""Set the time vector"""
self.time = time.reshape(len(time), 1)
def setSpecies(self, species):
"""Set the species array"""
self.species = species
def setRates(self, rates):
"""set the rate array"""
self.rates = rates
def getTime(self):
"""return the time vector"""
assert self.time != None, "\nNo time"
return self.time.reshape(len(self.time),)
def getSpecies(self):
"""return time+species array"""
assert self.species != None, "\nNo species"
return numpy.hstack((self.time, self.species))
def getRates(self):
"""return time+rate array"""
assert self.rates != None, "\nNo rates"
return numpy.hstack((self.time, self.rates))
def getDataAtTime(self, time):
"""Return all data generated at "time" """
t = None
sp = None
ra = None
temp_t = self.time.reshape(len(self.time),)
for tt in range(len(temp_t)):
if temp_t[tt] == time:
t = tt
if self.species is not None:
sp = self.species.take([tt], axis=0)
if self.rates is not None:
ra = self.rates.take([tt], axis=0)
break
output = None
if t is not None:
output = numpy.array([[temp_t[t]]])
if sp is not None:
output = numpy.hstack((output,sp))
if ra is not None:
output = numpy.hstack((output,ra))
return output
def getDataInTimeInterval(self, time, bounds=None):
"""
getDataInTimeInterval(time, bounds=None) returns an array of all
data points where: time-bounds <= time <= time+bounds
where bound defaults to stepsize
"""
temp_t = self.time.reshape(len(self.time),)
if bounds == None:
bounds = temp_t[1] - temp_t[0]
c1 = (temp_t >= time-bounds)
c2 = (temp_t <= time+bounds)
print 'Searching (%s:%s:%s)' % (time-bounds, time, time+bounds)
t = []
sp = None
ra = None
for tt in range(len(c1)):
if c1[tt] and c2[tt]:
t.append(tt)
output = None
if len(t) > 0:
output = self.time.take(t)
output = output.reshape(len(output),1)
if self.species is not None:
output = numpy.hstack((output, self.species.take(t, axis=0)))
if self.rates is not None:
output = numpy.hstack((output, self.rates.take(t, axis=0)))
return output
def getOutput(self, *args):
"""getOutput(*arg) feed this method species/rate labels and it
will return an array of [time, sp1, r1, ....]
"""
output = self.time
for roc in args:
if roc in self.species_labels:
assert self.species != None, "\nNo species"
output = numpy.hstack((output, self.species.take([self.species_labels.index(roc)], axis=-1)))
if roc in self.rate_labels:
assert self.rates != None, "\nNo rates"
output = numpy.hstack((output, self.rates.take([self.rate_labels.index(roc)], axis=-1)))
return output
'''
class IntegrationDataObj(object):
"""
This class is specifically designed to store the results of a time simulation
It has methods for setting the Time, Labels, Species and Rate data and
getting Time, Species and Rate (including time) arrays. However, of more use:
- getOutput(\*args) feed this method species/rate labels and it will return
an array of [time, sp1, r1, ....]
- getDataAtTime(time) the data generated at time point "time".
- getDataInTimeInterval(time, bounds=None) more intelligent version of the above
returns an array of all data points where: time-bounds <= time <= time+bounds
"""
time = None
rates = None
species = None
rules = None
xdata = None
time_label = 'Time'
rate_labels = None
species_labels = None
rules_labels = None
xdata_labels = None
HAS_SPECIES = False
HAS_RATES = False
HAS_RULES = False
HAS_TIME = False
HAS_XDATA = False
IS_VALID = True
TYPE_INFO = 'Deterministic'
def setLabels(self, species=None, rates=None, rules=None):
"""set the species, rate and rule label lists"""
if species != None:
self.species_labels = species
if rates != None:
self.rate_labels = rates
if rules != None:
self.rules_labels = rules
def setTime(self, time, lbl=None):
"""Set the time vector"""
self.time = time.reshape(len(time), 1)
self.HAS_TIME = True
if lbl != None:
self.time_label = lbl
def setSpecies(self, species, lbls=None):
"""Set the species array"""
self.species = species
self.HAS_SPECIES = True
if lbls != None:
self.species_labels = lbls
def setRates(self, rates, lbls=None):
"""set the rate array"""
self.rates = rates
self.HAS_RATES = True
if lbls != None:
self.rate_labels = lbls
def setRules(self, rules, lbls=None):
"""Set the results of rate rules"""
self.rules = rules
self.HAS_RULES = True
if lbls != None:
self.rules_labels = lbls
def setXData(self, xdata, lbls=None):
"""Sets extra simulation data"""
self.xdata = xdata
self.HAS_XDATA = True
if lbls != None:
self.xdata_labels = lbls
def getTime(self, lbls=False):
"""return the time vector"""
output = None
if self.HAS_TIME:
output = self.time.reshape(len(self.time),)
if not lbls:
return output
else:
return output, [self.time_label]
def getSpecies(self, lbls=False):
"""return time+species array"""
output = None
if self.HAS_SPECIES:
output = numpy.hstack((self.time, self.species))
labels = [self.time_label] + self.species_labels
else:
output = self.time
labels = [self.time_label]
if not lbls:
return output
else:
return output, labels
def getRates(self, lbls=False):
"""return time+rate array"""
output = None
if self.HAS_RATES:
output = numpy.hstack((self.time, self.rates))
labels = [self.time_label] + self.rate_labels
else:
output = self.time
labels = [self.time_label]
if not lbls:
return output
else:
return output, labels
def getRules(self, lbls=False):
"""Return time+rule array"""
## assert self.rules != None, "\nNo rules"
output = None
if self.HAS_RULES:
output = numpy.hstack((self.time, self.rules))
labels = [self.time_label] + self.rules_labels
else:
output = self.time
labels = [self.time_label]
if not lbls:
return output
else:
return output, labels
def getXData(self, lbls=False):
"""Return time+xdata array"""
## assert self.rules != None, "\nNo rules"
output = None
if self.HAS_XDATA:
output = numpy.hstack((self.time, self.xdata))
labels = [self.time_label] + self.xdata_labels
else:
output = self.time
labels = [self.time_label]
if not lbls:
return output
else:
return output, labels
def getDataAtTime(self, time):
"""Return all data generated at "time" """
# TODO add rate rule data
t = None
sp = None
ra = None
ru = None
xd = None
temp_t = self.time.reshape(len(self.time),)
for tt in range(len(temp_t)):
if temp_t[tt] == time:
t = tt
if self.HAS_SPECIES:
sp = self.species.take([tt], axis=0)
if self.HAS_RATES:
ra = self.rates.take([tt], axis=0)
if self.HAS_RULES:
ru = self.rules.take([tt], axis=0)
if self.HAS_XDATA:
xd = self.xdata.take([tt], axis=0)
break
output = None
if t is not None:
output = numpy.array([[temp_t[t]]])
if sp is not None:
output = numpy.hstack((output, sp))
if ra is not None:
output = numpy.hstack((output, ra))
if ru is not None:
output = numpy.hstack((output, ru))
if xd is not None:
output = numpy.hstack((output, xd))
return output
def getDataInTimeInterval(self, time, bounds=None):
"""
getDataInTimeInterval(time, bounds=None) returns an array of all
data points where: time-bounds <= time <= time+bounds
where bound defaults to stepsize
"""
# TODO add rate rule data
temp_t = self.time.reshape(len(self.time),)
if bounds == None:
bounds = temp_t[1] - temp_t[0]
c1 = temp_t >= time - bounds
c2 = temp_t <= time + bounds
print('Searching (%s:%s:%s)' % (time - bounds, time, time + bounds))
t = []
sp = None
ra = None
for tt in range(len(c1)):
if c1[tt] and c2[tt]:
t.append(tt)
output = None
if len(t) > 0:
output = self.time.take(t)
output = output.reshape(len(output), 1)
if self.HAS_SPECIES and self.HAS_TIME:
output = numpy.hstack((output, self.species.take(t, axis=0)))
if self.HAS_RATES:
output = numpy.hstack((output, self.rates.take(t, axis=0)))
if self.HAS_RULES:
output = numpy.hstack((output, self.rules.take(t, axis=0)))
if self.HAS_XDATA:
output = numpy.hstack((output, self.xdata.take(t, axis=0)))
return output
def getOutput(self, *args):
"""
Old alias for getSimData()
getOutput(\*args) feed this method species/rate labels and it
will return an array of [time, sp1, r1, ....]
"""
return self.getSimData(*args)
def getAllSimData(self, lbls=False):
"""
Return all available data as time+species+rates+rules
if lbls=True returns (array,lables) else just array
"""
labels = [self.time_label]
if self.HAS_SPECIES and self.HAS_TIME:
output = numpy.hstack((self.time, self.species))
labels += self.species_labels
if self.HAS_RATES:
output = numpy.hstack((output, self.rates))
labels += self.rate_labels
if self.HAS_RULES:
output = numpy.hstack((output, self.rules))
labels += self.rules_labels
if self.HAS_XDATA:
output = numpy.hstack((output, self.xdata))
labels += self.xdata_labels
if not lbls:
return output
else:
return output, labels
def getSimData(self, *args, **kwargs):
"""getSimData(\*args) feed this method species/rate labels and it
will return an array of [time, sp1, r1, ....]
"""
output = self.time
## print argimrgs
if 'lbls' in kwargs:
lbls = kwargs['lbls']
else:
lbls = False
lout = [self.time_label]
for roc in args:
if self.HAS_SPECIES and roc in self.species_labels:
lout.append(roc)
output = numpy.hstack(
(
output,
self.species.take([self.species_labels.index(roc)], axis=-1),
)
)
if self.HAS_RATES and roc in self.rate_labels:
lout.append(roc)
output = numpy.hstack(
(output, self.rates.take([self.rate_labels.index(roc)], axis=-1))
)
if self.HAS_RULES and roc in self.rules_labels:
lout.append(roc)
output = numpy.hstack(
(output, self.rules.take([self.rules_labels.index(roc)], axis=-1))
)
if self.HAS_XDATA and roc in self.xdata_labels:
lout.append(roc)
output = numpy.hstack(
(output, self.xdata.take([self.xdata_labels.index(roc)], axis=-1))
)
if not lbls:
return output
else:
return output, lout
# TODO:
class IntegrationBase(object):
name = None
core = None
data = None
sim_start = None
sim_end = None
sim_point = None
initial_value_vector = None
def setName(self, name):
self.name = name
def getName(self):
return self.name
def setCore(self, core):
self.core = core
self.data = IntegrationData()
self.data.setLabels(self.core.hasVariableSpecies(), self.core.hasReactions())
def getCore(self):
return self.core
def getData(self):
return self.data
|
bgoli/pysces
|
pysces/core2/PyscesCore2Modules.py
|
Python
|
bsd-3-clause
| 24,562
|
[
"PySCeS"
] |
20b409d0ebeb896d21427019e1054a7356c2c3b01d7aca1cb4f9cc0a77cdb39e
|
# -*- coding: utf-8 -*-
# Copyright 2014 splinter authors. All rights reserved.
# Use of this source code is governed by a BSD-style
# license that can be found in the LICENSE file.
from __future__ import with_statement
import os.path
import re
import time
import sys
import lxml.etree
import lxml.html
from lxml.cssselect import CSSSelector
from splinter.driver import DriverAPI, ElementAPI
from splinter.driver.element_present import ElementPresentMixIn
from splinter.element_list import ElementList
from splinter.exceptions import ElementDoesNotExist
class LxmlDriver(ElementPresentMixIn, DriverAPI):
_response = ""
_url = ""
def __init__(self, user_agent=None, wait_time=2):
self.wait_time = wait_time
self._history = []
self._last_urls = []
self._forms = {}
def __enter__(self):
return self
def __exit__(self, exc_type, exc_value, traceback):
pass
def _do_method(self, action, url, data=None):
raise NotImplementedError(
"%s doesn't support doing http methods." % self.driver_name
)
def visit(self, url):
self._do_method("get", url)
def serialize(self, form):
data = {}
for key in form.inputs.keys():
input = form.inputs[key]
if getattr(input, "type", "") == "submit":
try:
form.remove(input)
# Issue 595: throws ValueError: Element not child of this node
except ValueError:
pass
for k, v in form.fields.items():
if v is None:
continue
if isinstance(v, lxml.html.MultipleSelectOptions):
data[k] = [val for val in v]
else:
data[k] = v
for key in form.inputs.keys():
input = form.inputs[key]
if getattr(input, "type", "") == "file" and key in data:
data[key] = open(data[key], "rb")
return data
def submit(self, form):
method = form.attrib.get("method", "get").lower()
action = form.attrib.get("action", "")
if action.strip() != ".":
url = os.path.join(self._url, action)
else:
url = self._url
self._url = url
data = self.serialize(form)
self._do_method(method, url, data=data)
return self._response
def submit_data(self, form):
raise NotImplementedError(
"%s doesn't support submitting then getting the data." % self.driver_name
)
def back(self):
self._last_urls.insert(0, self.url)
self.visit(self._last_urls[1])
def forward(self):
try:
self.visit(self._last_urls.pop())
except IndexError:
pass
def reload(self):
self.visit(self._url)
def quit(self):
pass
@property
def htmltree(self):
try:
return self._html
except AttributeError:
self._html = lxml.html.fromstring(self.html)
return self._html
@property
def title(self):
html = self.htmltree
return html.xpath("//title")[0].text_content().strip()
@property
def html(self):
raise NotImplementedError(
"%s doesn't support getting the html of the response." % self.driver_name
)
@property
def url(self):
return self._url
def find_option_by_value(self, value):
html = self.htmltree
element = html.xpath('//option[@value="%s"]' % value)[0]
control = LxmlControlElement(element.getparent(), self)
return ElementList(
[LxmlOptionElement(element, control)], find_by="value", query=value
)
def find_option_by_text(self, text):
html = self.htmltree
element = html.xpath('//option[normalize-space(text())="%s"]' % text)[0]
control = LxmlControlElement(element.getparent(), self)
return ElementList(
[LxmlOptionElement(element, control)], find_by="text", query=text
)
def find_by_css(self, selector):
xpath = CSSSelector(selector).path
return self.find_by_xpath(
xpath, original_find="css", original_selector=selector
)
def find_by_xpath(self, xpath, original_find=None, original_selector=None):
html = self.htmltree
elements = []
for xpath_element in html.xpath(xpath):
if self._element_is_link(xpath_element):
return self._find_links_by_xpath(xpath)
elif self._element_is_control(xpath_element):
elements.append((LxmlControlElement, xpath_element))
else:
elements.append((LxmlElement, xpath_element))
find_by = original_find or "xpath"
query = original_selector or xpath
return ElementList(
[element_class(element, self) for element_class, element in elements],
find_by=find_by,
query=query,
)
def find_by_tag(self, tag):
return self.find_by_xpath(
"//%s" % tag, original_find="tag", original_selector=tag
)
def find_by_value(self, value):
return self.find_by_xpath(
'//*[@value="%s"]' % value, original_find="value", original_selector=value
)
def find_by_text(self, text):
return self.find_by_xpath(
'//*[text()="%s"]' % text, original_find="text", original_selector=text
)
def find_by_id(self, id_value):
return self.find_by_xpath(
'//*[@id="%s"][1]' % id_value,
original_find="id",
original_selector=id_value,
)
def find_by_name(self, name):
html = self.htmltree
xpath = '//*[@name="%s"]' % name
elements = []
for xpath_element in html.xpath(xpath):
elements.append(xpath_element)
find_by = "name"
query = xpath
return ElementList(
[LxmlControlElement(element, self) for element in elements],
find_by=find_by,
query=query,
)
def find_link_by_text(self, text):
return self._find_links_by_xpath("//a[text()='%s']" % text)
def find_link_by_href(self, href):
return self._find_links_by_xpath("//a[@href='%s']" % href)
def find_link_by_partial_href(self, partial_href):
return self._find_links_by_xpath("//a[contains(@href, '%s')]" % partial_href)
def find_link_by_partial_text(self, partial_text):
return self._find_links_by_xpath(
"//a[contains(normalize-space(.), '%s')]" % partial_text
)
def fill(self, name, value):
self.find_by_name(name=name).first.fill(value)
def fill_form(self, field_values, form_id=None, name=None):
form = None
if name is not None:
form = self.find_by_name(name)
if form_id is not None:
form = self.find_by_id(form_id)
for name, value in field_values.items():
if form:
element = form.find_by_name(name)
control = element.first._element
else:
element = self.find_by_name(name)
control = element.first._control
control_type = control.get("type")
if control_type == "checkbox":
if value:
control.value = value # control.options
else:
control.value = []
elif control_type == "radio":
control.value = (
value
) # [option for option in control.options if option == value]
elif control_type == "select":
if isinstance(value, list):
control.value = value
else:
control.value = [value]
else:
# text, textarea, password, tel
control.value = value
def choose(self, name, value):
self.find_by_name(name).first._control.value = value
def check(self, name):
control = self.find_by_name(name).first._control
control.value = ["checked"]
def uncheck(self, name):
control = self.find_by_name(name).first._control
control.value = []
def attach_file(self, name, file_path):
control = self.find_by_name(name).first._control
control.value = file_path
def _find_links_by_xpath(self, xpath):
html = self.htmltree
links = html.xpath(xpath)
return ElementList(
[LxmlLinkElement(link, self) for link in links],
find_by="xpath",
query=xpath,
)
def select(self, name, value):
self.find_by_name(name).first._control.value = value
def is_text_present(self, text, wait_time=None):
wait_time = wait_time or self.wait_time
end_time = time.time() + wait_time
while time.time() < end_time:
if self._is_text_present(text):
return True
return False
def _is_text_present(self, text):
try:
body = self.find_by_tag("body").first
return text in body.text
except ElementDoesNotExist:
# This exception will be thrown if the body tag isn't present
# This has occasionally been observed. Assume that the
# page isn't fully loaded yet
return False
def is_text_not_present(self, text, wait_time=None):
wait_time = wait_time or self.wait_time
end_time = time.time() + wait_time
while time.time() < end_time:
if not self._is_text_present(text):
return True
return False
def _element_is_link(self, element):
return element.tag == "a"
def _element_is_control(self, element):
return element.tag in ["button", "input", "textarea"]
@property
def cookies(self):
return self._cookie_manager
class LxmlElement(ElementAPI):
def __init__(self, element, parent):
self._element = element
self.parent = parent
def __getitem__(self, attr):
return self._element.attrib[attr]
def find_by_css(self, selector):
elements = self._element.cssselect(selector)
return ElementList([self.__class__(element, self) for element in elements])
def find_by_xpath(self, selector):
elements = self._element.xpath(selector)
return ElementList([self.__class__(element, self) for element in elements])
def find_by_name(self, name):
elements = self._element.cssselect('[name="%s"]' % name)
return ElementList([self.__class__(element, self) for element in elements])
def find_by_tag(self, name):
elements = self._element.cssselect(name)
return ElementList([self.__class__(element, self) for element in elements])
def find_by_value(self, value):
elements = self._element.cssselect('[value="%s"]' % value)
return ElementList([self.__class__(element, self) for element in elements])
def find_by_text(self, text):
return self.find_by_xpath('./*[text()="%s"]' % text)
def find_by_id(self, id):
elements = self._element.cssselect("#%s" % id)
return ElementList([self.__class__(element, self) for element in elements])
@property
def value(self):
return self._element.text_content()
@property
def text(self):
return self.value
@property
def outer_html(self):
return lxml.html.tostring(self._element, encoding="unicode").strip()
@property
def html(self):
return re.match(r"^<[^<>]+>(.*)</[^<>]+>$", self.outer_html, re.MULTILINE | re.DOTALL).group(1)
def has_class(self, class_name):
return len(self._element.find_class(class_name)) > 0
class LxmlLinkElement(LxmlElement):
def __init__(self, element, parent):
super(LxmlLinkElement, self).__init__(element, parent)
self._browser = parent
def __getitem__(self, attr):
return super(LxmlLinkElement, self).__getitem__(attr)
def click(self):
return self._browser.visit(self["href"])
class LxmlControlElement(LxmlElement):
def __init__(self, control, parent):
self._control = control
self.parent = parent
def __getitem__(self, attr):
return self._control.attrib[attr]
@property
def value(self):
return self._control.value
@property
def checked(self):
return bool(self._control.value)
def click(self):
parent_form = self._get_parent_form()
if self._control.get("type") == "submit":
name = self._control.get("name")
if name:
value = self._control.get("value", "")
parent_form.append(
lxml.html.Element("input", name=name, value=value, type="hidden")
)
return self.parent.submit_data(parent_form)
def fill(self, value):
parent_form = self._get_parent_form()
if sys.version_info[0] > 2:
parent_form.fields[self["name"]] = value
else:
if not isinstance(value, unicode):
value = value.decode("utf-8")
parent_form.fields[self["name"]] = value
def select(self, value):
self._control.value = value
def _get_parent_form(self):
parent_form = next(self._control.iterancestors("form"))
return self.parent._forms.setdefault(parent_form._name(), parent_form)
class LxmlOptionElement(LxmlElement):
def __init__(self, control, parent):
self._control = control
self.parent = parent
def __getitem__(self, attr):
return self._control.attrib[attr]
@property
def text(self):
return self._control.text
@property
def value(self):
return self._control.attrib["value"]
@property
def selected(self):
return self.parent.value == self.value
|
bmcculley/splinter
|
splinter/driver/lxmldriver.py
|
Python
|
bsd-3-clause
| 14,070
|
[
"VisIt"
] |
e6ac5a9621967f465e6c91f25eb6524d555c274d941c441402b7341727544350
|
from ase.md.velocitydistribution import MaxwellBoltzmannDistribution
from ase.lattice.cubic import FaceCenteredCubic
atoms = FaceCenteredCubic(size=(50,50,50), symbol="Cu", pbc=False)
print "Number of atoms:", len(atoms)
MaxwellBoltzmannDistribution(atoms, 0.1)
temp = atoms.get_kinetic_energy() / (1.5 * len(atoms))
print "Temperature", temp, " (should be 0.1)"
assert abs(temp - 0.1) < 1e-3
|
grhawk/ASE
|
tools/ase/test/maxwellboltzmann.py
|
Python
|
gpl-2.0
| 395
|
[
"ASE"
] |
b3c0690aee2569dee290559ff4bbaafc49581ae77da8382e7f3371fbc8aab0cd
|
# region gplv3preamble
# The Medical Simulation Markup Language (MSML) - Simplifying the biomechanical modeling workflow
#
# MSML has been developed in the framework of 'SFB TRR 125 Cognition-Guided Surgery'
#
# If you use this software in academic work, please cite the paper:
# S. Suwelack, M. Stoll, S. Schalck, N.Schoch, R. Dillmann, R. Bendl, V. Heuveline and S. Speidel,
# The Medical Simulation Markup Language (MSML) - Simplifying the biomechanical modeling workflow,
# Medicine Meets Virtual Reality (MMVR) 2014
#
# Copyright (C) 2013-2014 see Authors.txt
#
# If you have any questions please feel free to contact us at suwelack@kit.edu
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
# endregion
__author__ = 'Alexander Weigl'
from stub import *
# #############################################
simulation = SimulationBuilder()
input_vol_mesh = simulation.Variable(value="bunnyVolumeMesh.vtk",
logical="Mesh",
physical="str")
input_surf_mesh = simulation.Variable(value="Bunny6000Surface.vtk", logical="Mesh",
physical="file.vtk")
wf = WorkflowBuilder()
bunnyVolumeMesher = wf.TetgenCreateVolumeMesh(
meshFilename=input_vol_mesh,
surfaceMesh=input_surf_mesh,
preserveBoundary=False)
bodyToIndexGroup = wf.boxROIToIndexOperator(
box=(-0.1, -0.03, -0.07, 0.06, 0.19, 0.06),
mesh=bunnyVolumeMesher.mesh,
select="elements")
bottomToIndexGroup = wf.boxROIToIndexOperator(
box=(-0.1, 0.03, -0.07, 0.07, 0.035, 0.06),
mesh=bunnyVolumeMesher.mesh,
select="points")
simulation.workflow = wf.workflow
## Scene
#
bunny = simulation.SceneObject(
mesh=Mesh(type=MeshTypes.LinearTetraheder, value=bunnyVolumeMesher.mesh),
material=Material(
Region(bodyToIndexGroup.indices, None,
linearElasticMaterial(0.49, 80000),
mass(1000))
),
constraints=[
Constraints(0, fixedConstraint(bottomToIndexGroup.indices))
],
output=[
displacement(1)
]
)
env = simulation.Environment(None, Step(0.05, 100))
run(simulation.msml_file, filename = __file__)
# ############################################
|
CognitionGuidedSurgery/msml
|
examples/BunnyExample/bunny.py
|
Python
|
gpl-3.0
| 2,784
|
[
"VTK"
] |
293316364fb1860f729b3252acb5b7dc8f658c2ed4de72f6b5ac67692a396de8
|
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