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py | 1a310ab0a721ed99d6df375ddfdf50875f5fd8f6 | class CogTV:
def __init__(self):
pass
def setScreen(self, scene):
pass
|
py | 1a310b0d553f5abe9491d815ab4b0c628638105a | #!/usr/bin/python2.7
# -*- coding: UTF-8 -*-
'''
Created on 2018年6月15日
@author: zhaohongxing
'''
import os
from PyQt5.Qt import Qt
from PyQt5.Qt import QIcon,QStandardItemModel,QStandardItem
'''
from PyQt5 import QtGui
'''
from PyQt5.QtWidgets import QTableView,QVBoxLayout,QDialog,QPushButton
from PyQt5.QtCore import QSize, pyqtSignal
import wechatutil
class ContactListWindow(QDialog):
WIDTH = 600
membersConfirmed = pyqtSignal(str)
def __init__(self,member_list,parent = None):
super(ContactListWindow,self).__init__(parent)
self.setModal(True)
self.user_home = os.path.expanduser('~')
self.app_home = self.user_home + '/.wechat/'
self.head_home = ("%s/heads"%(self.app_home))
self.cache_home = ("%s/cache/"%(self.app_home))
self.cache_image_home = "%s/image/"%(self.cache_home)
self.contact_head_home = ("%s/contact/"%(self.head_home))
self.default_head_icon = './resource/images/default.png'
self.members = member_list
self.membersTable = QTableView()
self.membersTable.horizontalHeader().setStretchLastSection(True)
self.membersTable.verticalHeader().setDefaultSectionSize(60)
#self.membersTable.horizontalHeader().setDefaultSectionSize(60)
self.membersTable.setColumnWidth(0, 10);
self.membersTable.setColumnWidth(1, 60);
self.membersTable.verticalHeader().setVisible(False)
self.membersTable.horizontalHeader().setVisible(False)
#confirm
self.confirm = QPushButton(wechatutil.unicode("確定"),self)
self.membersTableModel = QStandardItemModel(0,2)
self.membersTableModel.itemChanged.connect(self.itemChanged)
self.initinal_member_list_widget()
mainLayout=QVBoxLayout()
mainLayout.addWidget(self.membersTable)
mainLayout.addWidget(self.confirm)
self.setLayout(mainLayout)
#self.membersTable.clicked.connect(self.contact_item_clicked)
self.confirm.clicked.connect(self.do_confirm)
self.selectedRowCount = 0
def itemChanged(self,item):
if item.checkState() == Qt.Checked:
self.selectedRowCount += 1
else:
self.selectedRowCount -= 1
if self.selectedRowCount > 0:
self.confirm.setText(wechatutil.unicode("確定(%d)"%(self.selectedRowCount)))
else:
self.confirm.setText(wechatutil.unicode("確定"))
def initinal_member_list_widget(self):
self.append_row(self.members, self.membersTableModel)
self.membersTable.setModel(self.membersTableModel)
self.membersTable.setIconSize(QSize(40,40))
def append_row(self,members,data_model):
for (i,member) in enumerate(members):
cells = []
user_name = member['UserName']
user_name_cell = QStandardItem(user_name)
user_name_cell.setCheckable(True)
cells.append(user_name_cell)
user_avatar = self.contact_head_home + member['UserName']+".jpg"
if not os.path.exists(user_avatar):
user_avatar = self.default_head_icon
dn = member['DisplayName'] or member['NickName']
if not dn:
dn = member['NickName']
item = QStandardItem(QIcon(user_avatar),wechatutil.unicode(dn))
cells.append(item)
data_model.appendRow(cells)
def do_confirm(self):
rowCount = self.membersTableModel.rowCount()
selected_user_names = ""
for row in range(rowCount):
item = self.membersTableModel.item(row,0)
if item.checkState() == Qt.Checked:
index = self.membersTableModel.index(row,0)
user_name_obj = self.membersTableModel.data(index)
if user_name_obj:
user_name = user_name_obj
user = {}
user['UserName']=user_name
selected_user_names=selected_user_names+(user_name)
selected_user_names=selected_user_names+(";")
if len(selected_user_names) > 0:
dictt = {}
dictt['UserNames']=selected_user_names
self.membersConfirmed.emit(selected_user_names)
self.close() |
py | 1a310b66a9b824753a91af32fceeba35240da5ec | """
This module contains pdsolve() and different helper functions that it
uses. It is heavily inspired by the ode module and hence the basic
infrastructure remains the same.
**Functions in this module**
These are the user functions in this module:
- pdsolve() - Solves PDE's
- classify_pde() - Classifies PDEs into possible hints for dsolve().
- pde_separate() - Separate variables in partial differential equation either by
additive or multiplicative separation approach.
These are the helper functions in this module:
- pde_separate_add() - Helper function for searching additive separable solutions.
- pde_separate_mul() - Helper function for searching multiplicative
separable solutions.
**Currently implemented solver methods**
The following methods are implemented for solving partial differential
equations. See the docstrings of the various pde_hint() functions for
more information on each (run help(pde)):
- 1st order linear homogeneous partial differential equations
with constant coefficients.
- 1st order linear general partial differential equations
with constant coefficients.
- 1st order linear partial differential equations with
variable coefficients.
"""
from functools import reduce
from itertools import combinations_with_replacement
from sympy.simplify import simplify # type: ignore
from sympy.core import Add, S
from sympy.core.function import Function, expand, AppliedUndef, Subs
from sympy.core.relational import Equality, Eq
from sympy.core.symbol import Symbol, Wild, symbols
from sympy.functions import exp
from sympy.integrals.integrals import Integral, integrate
from sympy.utilities.iterables import has_dups, is_sequence
from sympy.utilities.misc import filldedent
from sympy.solvers.deutils import _preprocess, ode_order, _desolve
from sympy.solvers.solvers import solve
from sympy.simplify.radsimp import collect
import operator
allhints = (
"1st_linear_constant_coeff_homogeneous",
"1st_linear_constant_coeff",
"1st_linear_constant_coeff_Integral",
"1st_linear_variable_coeff"
)
def pdsolve(eq, func=None, hint='default', dict=False, solvefun=None, **kwargs):
"""
Solves any (supported) kind of partial differential equation.
**Usage**
pdsolve(eq, f(x,y), hint) -> Solve partial differential equation
eq for function f(x,y), using method hint.
**Details**
``eq`` can be any supported partial differential equation (see
the pde docstring for supported methods). This can either
be an Equality, or an expression, which is assumed to be
equal to 0.
``f(x,y)`` is a function of two variables whose derivatives in that
variable make up the partial differential equation. In many
cases it is not necessary to provide this; it will be autodetected
(and an error raised if it couldn't be detected).
``hint`` is the solving method that you want pdsolve to use. Use
classify_pde(eq, f(x,y)) to get all of the possible hints for
a PDE. The default hint, 'default', will use whatever hint
is returned first by classify_pde(). See Hints below for
more options that you can use for hint.
``solvefun`` is the convention used for arbitrary functions returned
by the PDE solver. If not set by the user, it is set by default
to be F.
**Hints**
Aside from the various solving methods, there are also some
meta-hints that you can pass to pdsolve():
"default":
This uses whatever hint is returned first by
classify_pde(). This is the default argument to
pdsolve().
"all":
To make pdsolve apply all relevant classification hints,
use pdsolve(PDE, func, hint="all"). This will return a
dictionary of hint:solution terms. If a hint causes
pdsolve to raise the NotImplementedError, value of that
hint's key will be the exception object raised. The
dictionary will also include some special keys:
- order: The order of the PDE. See also ode_order() in
deutils.py
- default: The solution that would be returned by
default. This is the one produced by the hint that
appears first in the tuple returned by classify_pde().
"all_Integral":
This is the same as "all", except if a hint also has a
corresponding "_Integral" hint, it only returns the
"_Integral" hint. This is useful if "all" causes
pdsolve() to hang because of a difficult or impossible
integral. This meta-hint will also be much faster than
"all", because integrate() is an expensive routine.
See also the classify_pde() docstring for more info on hints,
and the pde docstring for a list of all supported hints.
**Tips**
- You can declare the derivative of an unknown function this way:
>>> from sympy import Function, Derivative
>>> from sympy.abc import x, y # x and y are the independent variables
>>> f = Function("f")(x, y) # f is a function of x and y
>>> # fx will be the partial derivative of f with respect to x
>>> fx = Derivative(f, x)
>>> # fy will be the partial derivative of f with respect to y
>>> fy = Derivative(f, y)
- See test_pde.py for many tests, which serves also as a set of
examples for how to use pdsolve().
- pdsolve always returns an Equality class (except for the case
when the hint is "all" or "all_Integral"). Note that it is not possible
to get an explicit solution for f(x, y) as in the case of ODE's
- Do help(pde.pde_hintname) to get help more information on a
specific hint
Examples
========
>>> from sympy.solvers.pde import pdsolve
>>> from sympy import Function, Eq
>>> from sympy.abc import x, y
>>> f = Function('f')
>>> u = f(x, y)
>>> ux = u.diff(x)
>>> uy = u.diff(y)
>>> eq = Eq(1 + (2*(ux/u)) + (3*(uy/u)), 0)
>>> pdsolve(eq)
Eq(f(x, y), F(3*x - 2*y)*exp(-2*x/13 - 3*y/13))
"""
if not solvefun:
solvefun = Function('F')
# See the docstring of _desolve for more details.
hints = _desolve(eq, func=func, hint=hint, simplify=True,
type='pde', **kwargs)
eq = hints.pop('eq', False)
all_ = hints.pop('all', False)
if all_:
# TODO : 'best' hint should be implemented when adequate
# number of hints are added.
pdedict = {}
failed_hints = {}
gethints = classify_pde(eq, dict=True)
pdedict.update({'order': gethints['order'],
'default': gethints['default']})
for hint in hints:
try:
rv = _helper_simplify(eq, hint, hints[hint]['func'],
hints[hint]['order'], hints[hint][hint], solvefun)
except NotImplementedError as detail:
failed_hints[hint] = detail
else:
pdedict[hint] = rv
pdedict.update(failed_hints)
return pdedict
else:
return _helper_simplify(eq, hints['hint'], hints['func'],
hints['order'], hints[hints['hint']], solvefun)
def _helper_simplify(eq, hint, func, order, match, solvefun):
"""Helper function of pdsolve that calls the respective
pde functions to solve for the partial differential
equations. This minimizes the computation in
calling _desolve multiple times.
"""
if hint.endswith("_Integral"):
solvefunc = globals()[
"pde_" + hint[:-len("_Integral")]]
else:
solvefunc = globals()["pde_" + hint]
return _handle_Integral(solvefunc(eq, func, order,
match, solvefun), func, order, hint)
def _handle_Integral(expr, func, order, hint):
r"""
Converts a solution with integrals in it into an actual solution.
Simplifies the integral mainly using doit()
"""
if hint.endswith("_Integral"):
return expr
elif hint == "1st_linear_constant_coeff":
return simplify(expr.doit())
else:
return expr
def classify_pde(eq, func=None, dict=False, *, prep=True, **kwargs):
"""
Returns a tuple of possible pdsolve() classifications for a PDE.
The tuple is ordered so that first item is the classification that
pdsolve() uses to solve the PDE by default. In general,
classifications near the beginning of the list will produce
better solutions faster than those near the end, though there are
always exceptions. To make pdsolve use a different classification,
use pdsolve(PDE, func, hint=<classification>). See also the pdsolve()
docstring for different meta-hints you can use.
If ``dict`` is true, classify_pde() will return a dictionary of
hint:match expression terms. This is intended for internal use by
pdsolve(). Note that because dictionaries are ordered arbitrarily,
this will most likely not be in the same order as the tuple.
You can get help on different hints by doing help(pde.pde_hintname),
where hintname is the name of the hint without "_Integral".
See sympy.pde.allhints or the sympy.pde docstring for a list of all
supported hints that can be returned from classify_pde.
Examples
========
>>> from sympy.solvers.pde import classify_pde
>>> from sympy import Function, Eq
>>> from sympy.abc import x, y
>>> f = Function('f')
>>> u = f(x, y)
>>> ux = u.diff(x)
>>> uy = u.diff(y)
>>> eq = Eq(1 + (2*(ux/u)) + (3*(uy/u)), 0)
>>> classify_pde(eq)
('1st_linear_constant_coeff_homogeneous',)
"""
if func and len(func.args) != 2:
raise NotImplementedError("Right now only partial "
"differential equations of two variables are supported")
if prep or func is None:
prep, func_ = _preprocess(eq, func)
if func is None:
func = func_
if isinstance(eq, Equality):
if eq.rhs != 0:
return classify_pde(eq.lhs - eq.rhs, func)
eq = eq.lhs
f = func.func
x = func.args[0]
y = func.args[1]
fx = f(x,y).diff(x)
fy = f(x,y).diff(y)
# TODO : For now pde.py uses support offered by the ode_order function
# to find the order with respect to a multi-variable function. An
# improvement could be to classify the order of the PDE on the basis of
# individual variables.
order = ode_order(eq, f(x,y))
# hint:matchdict or hint:(tuple of matchdicts)
# Also will contain "default":<default hint> and "order":order items.
matching_hints = {'order': order}
if not order:
if dict:
matching_hints["default"] = None
return matching_hints
else:
return ()
eq = expand(eq)
a = Wild('a', exclude = [f(x,y)])
b = Wild('b', exclude = [f(x,y), fx, fy, x, y])
c = Wild('c', exclude = [f(x,y), fx, fy, x, y])
d = Wild('d', exclude = [f(x,y), fx, fy, x, y])
e = Wild('e', exclude = [f(x,y), fx, fy])
n = Wild('n', exclude = [x, y])
# Try removing the smallest power of f(x,y)
# from the highest partial derivatives of f(x,y)
reduced_eq = None
if eq.is_Add:
var = set(combinations_with_replacement((x,y), order))
dummyvar = var.copy()
power = None
for i in var:
coeff = eq.coeff(f(x,y).diff(*i))
if coeff != 1:
match = coeff.match(a*f(x,y)**n)
if match and match[a]:
power = match[n]
dummyvar.remove(i)
break
dummyvar.remove(i)
for i in dummyvar:
coeff = eq.coeff(f(x,y).diff(*i))
if coeff != 1:
match = coeff.match(a*f(x,y)**n)
if match and match[a] and match[n] < power:
power = match[n]
if power:
den = f(x,y)**power
reduced_eq = Add(*[arg/den for arg in eq.args])
if not reduced_eq:
reduced_eq = eq
if order == 1:
reduced_eq = collect(reduced_eq, f(x, y))
r = reduced_eq.match(b*fx + c*fy + d*f(x,y) + e)
if r:
if not r[e]:
## Linear first-order homogeneous partial-differential
## equation with constant coefficients
r.update({'b': b, 'c': c, 'd': d})
matching_hints["1st_linear_constant_coeff_homogeneous"] = r
else:
if r[b]**2 + r[c]**2 != 0:
## Linear first-order general partial-differential
## equation with constant coefficients
r.update({'b': b, 'c': c, 'd': d, 'e': e})
matching_hints["1st_linear_constant_coeff"] = r
matching_hints[
"1st_linear_constant_coeff_Integral"] = r
else:
b = Wild('b', exclude=[f(x, y), fx, fy])
c = Wild('c', exclude=[f(x, y), fx, fy])
d = Wild('d', exclude=[f(x, y), fx, fy])
r = reduced_eq.match(b*fx + c*fy + d*f(x,y) + e)
if r:
r.update({'b': b, 'c': c, 'd': d, 'e': e})
matching_hints["1st_linear_variable_coeff"] = r
# Order keys based on allhints.
retlist = []
for i in allhints:
if i in matching_hints:
retlist.append(i)
if dict:
# Dictionaries are ordered arbitrarily, so make note of which
# hint would come first for pdsolve(). Use an ordered dict in Py 3.
matching_hints["default"] = None
matching_hints["ordered_hints"] = tuple(retlist)
for i in allhints:
if i in matching_hints:
matching_hints["default"] = i
break
return matching_hints
else:
return tuple(retlist)
def checkpdesol(pde, sol, func=None, solve_for_func=True):
"""
Checks if the given solution satisfies the partial differential
equation.
pde is the partial differential equation which can be given in the
form of an equation or an expression. sol is the solution for which
the pde is to be checked. This can also be given in an equation or
an expression form. If the function is not provided, the helper
function _preprocess from deutils is used to identify the function.
If a sequence of solutions is passed, the same sort of container will be
used to return the result for each solution.
The following methods are currently being implemented to check if the
solution satisfies the PDE:
1. Directly substitute the solution in the PDE and check. If the
solution hasn't been solved for f, then it will solve for f
provided solve_for_func hasn't been set to False.
If the solution satisfies the PDE, then a tuple (True, 0) is returned.
Otherwise a tuple (False, expr) where expr is the value obtained
after substituting the solution in the PDE. However if a known solution
returns False, it may be due to the inability of doit() to simplify it to zero.
Examples
========
>>> from sympy import Function, symbols
>>> from sympy.solvers.pde import checkpdesol, pdsolve
>>> x, y = symbols('x y')
>>> f = Function('f')
>>> eq = 2*f(x,y) + 3*f(x,y).diff(x) + 4*f(x,y).diff(y)
>>> sol = pdsolve(eq)
>>> assert checkpdesol(eq, sol)[0]
>>> eq = x*f(x,y) + f(x,y).diff(x)
>>> checkpdesol(eq, sol)
(False, (x*F(4*x - 3*y) - 6*F(4*x - 3*y)/25 + 4*Subs(Derivative(F(_xi_1), _xi_1), _xi_1, 4*x - 3*y))*exp(-6*x/25 - 8*y/25))
"""
# Converting the pde into an equation
if not isinstance(pde, Equality):
pde = Eq(pde, 0)
# If no function is given, try finding the function present.
if func is None:
try:
_, func = _preprocess(pde.lhs)
except ValueError:
funcs = [s.atoms(AppliedUndef) for s in (
sol if is_sequence(sol, set) else [sol])]
funcs = set().union(funcs)
if len(funcs) != 1:
raise ValueError(
'must pass func arg to checkpdesol for this case.')
func = funcs.pop()
# If the given solution is in the form of a list or a set
# then return a list or set of tuples.
if is_sequence(sol, set):
return type(sol)([checkpdesol(
pde, i, func=func,
solve_for_func=solve_for_func) for i in sol])
# Convert solution into an equation
if not isinstance(sol, Equality):
sol = Eq(func, sol)
elif sol.rhs == func:
sol = sol.reversed
# Try solving for the function
solved = sol.lhs == func and not sol.rhs.has(func)
if solve_for_func and not solved:
solved = solve(sol, func)
if solved:
if len(solved) == 1:
return checkpdesol(pde, Eq(func, solved[0]),
func=func, solve_for_func=False)
else:
return checkpdesol(pde, [Eq(func, t) for t in solved],
func=func, solve_for_func=False)
# try direct substitution of the solution into the PDE and simplify
if sol.lhs == func:
pde = pde.lhs - pde.rhs
s = simplify(pde.subs(func, sol.rhs).doit())
return s is S.Zero, s
raise NotImplementedError(filldedent('''
Unable to test if %s is a solution to %s.''' % (sol, pde)))
def pde_1st_linear_constant_coeff_homogeneous(eq, func, order, match, solvefun):
r"""
Solves a first order linear homogeneous
partial differential equation with constant coefficients.
The general form of this partial differential equation is
.. math:: a \frac{\partial f(x,y)}{\partial x}
+ b \frac{\partial f(x,y)}{\partial y} + c f(x,y) = 0
where `a`, `b` and `c` are constants.
The general solution is of the form:
.. math::
f(x, y) = F(- a y + b x ) e^{- \frac{c (a x + b y)}{a^2 + b^2}}
and can be found in SymPy with ``pdsolve``::
>>> from sympy.solvers import pdsolve
>>> from sympy.abc import x, y, a, b, c
>>> from sympy import Function, pprint
>>> f = Function('f')
>>> u = f(x,y)
>>> ux = u.diff(x)
>>> uy = u.diff(y)
>>> genform = a*ux + b*uy + c*u
>>> pprint(genform)
d d
a*--(f(x, y)) + b*--(f(x, y)) + c*f(x, y)
dx dy
>>> pprint(pdsolve(genform))
-c*(a*x + b*y)
---------------
2 2
a + b
f(x, y) = F(-a*y + b*x)*e
Examples
========
>>> from sympy import pdsolve
>>> from sympy import Function, pprint
>>> from sympy.abc import x,y
>>> f = Function('f')
>>> pdsolve(f(x,y) + f(x,y).diff(x) + f(x,y).diff(y))
Eq(f(x, y), F(x - y)*exp(-x/2 - y/2))
>>> pprint(pdsolve(f(x,y) + f(x,y).diff(x) + f(x,y).diff(y)))
x y
- - - -
2 2
f(x, y) = F(x - y)*e
References
==========
- Viktor Grigoryan, "Partial Differential Equations"
Math 124A - Fall 2010, pp.7
"""
# TODO : For now homogeneous first order linear PDE's having
# two variables are implemented. Once there is support for
# solving systems of ODE's, this can be extended to n variables.
f = func.func
x = func.args[0]
y = func.args[1]
b = match[match['b']]
c = match[match['c']]
d = match[match['d']]
return Eq(f(x,y), exp(-S(d)/(b**2 + c**2)*(b*x + c*y))*solvefun(c*x - b*y))
def pde_1st_linear_constant_coeff(eq, func, order, match, solvefun):
r"""
Solves a first order linear partial differential equation
with constant coefficients.
The general form of this partial differential equation is
.. math:: a \frac{\partial f(x,y)}{\partial x}
+ b \frac{\partial f(x,y)}{\partial y}
+ c f(x,y) = G(x,y)
where `a`, `b` and `c` are constants and `G(x, y)` can be an arbitrary
function in `x` and `y`.
The general solution of the PDE is:
.. math::
f(x, y) = \left. \left[F(\eta) + \frac{1}{a^2 + b^2}
\int\limits^{a x + b y} G\left(\frac{a \xi + b \eta}{a^2 + b^2},
\frac{- a \eta + b \xi}{a^2 + b^2} \right)
e^{\frac{c \xi}{a^2 + b^2}}\, d\xi\right]
e^{- \frac{c \xi}{a^2 + b^2}}
\right|_{\substack{\eta=- a y + b x\\ \xi=a x + b y }}\, ,
where `F(\eta)` is an arbitrary single-valued function. The solution
can be found in SymPy with ``pdsolve``::
>>> from sympy.solvers import pdsolve
>>> from sympy.abc import x, y, a, b, c
>>> from sympy import Function, pprint
>>> f = Function('f')
>>> G = Function('G')
>>> u = f(x,y)
>>> ux = u.diff(x)
>>> uy = u.diff(y)
>>> genform = a*ux + b*uy + c*u - G(x,y)
>>> pprint(genform)
d d
a*--(f(x, y)) + b*--(f(x, y)) + c*f(x, y) - G(x, y)
dx dy
>>> pprint(pdsolve(genform, hint='1st_linear_constant_coeff_Integral'))
// a*x + b*y \
|| / |
|| | |
|| | c*xi |
|| | ------- |
|| | 2 2 |
|| | /a*xi + b*eta -a*eta + b*xi\ a + b |
|| | G|------------, -------------|*e d(xi)|
|| | | 2 2 2 2 | |
|| | \ a + b a + b / |
|| | |
|| / |
|| |
f(x, y) = ||F(eta) + -------------------------------------------------------|*
|| 2 2 |
\\ a + b /
<BLANKLINE>
\|
||
||
||
||
||
||
||
||
-c*xi ||
-------||
2 2||
a + b ||
e ||
||
/|eta=-a*y + b*x, xi=a*x + b*y
Examples
========
>>> from sympy.solvers.pde import pdsolve
>>> from sympy import Function, pprint, exp
>>> from sympy.abc import x,y
>>> f = Function('f')
>>> eq = -2*f(x,y).diff(x) + 4*f(x,y).diff(y) + 5*f(x,y) - exp(x + 3*y)
>>> pdsolve(eq)
Eq(f(x, y), (F(4*x + 2*y)*exp(x/2) + exp(x + 4*y)/15)*exp(-y))
References
==========
- Viktor Grigoryan, "Partial Differential Equations"
Math 124A - Fall 2010, pp.7
"""
# TODO : For now homogeneous first order linear PDE's having
# two variables are implemented. Once there is support for
# solving systems of ODE's, this can be extended to n variables.
xi, eta = symbols("xi eta")
f = func.func
x = func.args[0]
y = func.args[1]
b = match[match['b']]
c = match[match['c']]
d = match[match['d']]
e = -match[match['e']]
expterm = exp(-S(d)/(b**2 + c**2)*xi)
functerm = solvefun(eta)
solvedict = solve((b*x + c*y - xi, c*x - b*y - eta), x, y)
# Integral should remain as it is in terms of xi,
# doit() should be done in _handle_Integral.
genterm = (1/S(b**2 + c**2))*Integral(
(1/expterm*e).subs(solvedict), (xi, b*x + c*y))
return Eq(f(x,y), Subs(expterm*(functerm + genterm),
(eta, xi), (c*x - b*y, b*x + c*y)))
def pde_1st_linear_variable_coeff(eq, func, order, match, solvefun):
r"""
Solves a first order linear partial differential equation
with variable coefficients. The general form of this partial
differential equation is
.. math:: a(x, y) \frac{\partial f(x, y)}{\partial x}
+ b(x, y) \frac{\partial f(x, y)}{\partial y}
+ c(x, y) f(x, y) = G(x, y)
where `a(x, y)`, `b(x, y)`, `c(x, y)` and `G(x, y)` are arbitrary
functions in `x` and `y`. This PDE is converted into an ODE by
making the following transformation:
1. `\xi` as `x`
2. `\eta` as the constant in the solution to the differential
equation `\frac{dy}{dx} = -\frac{b}{a}`
Making the previous substitutions reduces it to the linear ODE
.. math:: a(\xi, \eta)\frac{du}{d\xi} + c(\xi, \eta)u - G(\xi, \eta) = 0
which can be solved using ``dsolve``.
>>> from sympy.abc import x, y
>>> from sympy import Function, pprint
>>> a, b, c, G, f= [Function(i) for i in ['a', 'b', 'c', 'G', 'f']]
>>> u = f(x,y)
>>> ux = u.diff(x)
>>> uy = u.diff(y)
>>> genform = a(x, y)*u + b(x, y)*ux + c(x, y)*uy - G(x,y)
>>> pprint(genform)
d d
-G(x, y) + a(x, y)*f(x, y) + b(x, y)*--(f(x, y)) + c(x, y)*--(f(x, y))
dx dy
Examples
========
>>> from sympy.solvers.pde import pdsolve
>>> from sympy import Function, pprint
>>> from sympy.abc import x,y
>>> f = Function('f')
>>> eq = x*(u.diff(x)) - y*(u.diff(y)) + y**2*u - y**2
>>> pdsolve(eq)
Eq(f(x, y), F(x*y)*exp(y**2/2) + 1)
References
==========
- Viktor Grigoryan, "Partial Differential Equations"
Math 124A - Fall 2010, pp.7
"""
from sympy.solvers.ode import dsolve
xi, eta = symbols("xi eta")
f = func.func
x = func.args[0]
y = func.args[1]
b = match[match['b']]
c = match[match['c']]
d = match[match['d']]
e = -match[match['e']]
if not d:
# To deal with cases like b*ux = e or c*uy = e
if not (b and c):
if c:
try:
tsol = integrate(e/c, y)
except NotImplementedError:
raise NotImplementedError("Unable to find a solution"
" due to inability of integrate")
else:
return Eq(f(x,y), solvefun(x) + tsol)
if b:
try:
tsol = integrate(e/b, x)
except NotImplementedError:
raise NotImplementedError("Unable to find a solution"
" due to inability of integrate")
else:
return Eq(f(x,y), solvefun(y) + tsol)
if not c:
# To deal with cases when c is 0, a simpler method is used.
# The PDE reduces to b*(u.diff(x)) + d*u = e, which is a linear ODE in x
plode = f(x).diff(x)*b + d*f(x) - e
sol = dsolve(plode, f(x))
syms = sol.free_symbols - plode.free_symbols - {x, y}
rhs = _simplify_variable_coeff(sol.rhs, syms, solvefun, y)
return Eq(f(x, y), rhs)
if not b:
# To deal with cases when b is 0, a simpler method is used.
# The PDE reduces to c*(u.diff(y)) + d*u = e, which is a linear ODE in y
plode = f(y).diff(y)*c + d*f(y) - e
sol = dsolve(plode, f(y))
syms = sol.free_symbols - plode.free_symbols - {x, y}
rhs = _simplify_variable_coeff(sol.rhs, syms, solvefun, x)
return Eq(f(x, y), rhs)
dummy = Function('d')
h = (c/b).subs(y, dummy(x))
sol = dsolve(dummy(x).diff(x) - h, dummy(x))
if isinstance(sol, list):
sol = sol[0]
solsym = sol.free_symbols - h.free_symbols - {x, y}
if len(solsym) == 1:
solsym = solsym.pop()
etat = (solve(sol, solsym)[0]).subs(dummy(x), y)
ysub = solve(eta - etat, y)[0]
deq = (b*(f(x).diff(x)) + d*f(x) - e).subs(y, ysub)
final = (dsolve(deq, f(x), hint='1st_linear')).rhs
if isinstance(final, list):
final = final[0]
finsyms = final.free_symbols - deq.free_symbols - {x, y}
rhs = _simplify_variable_coeff(final, finsyms, solvefun, etat)
return Eq(f(x, y), rhs)
else:
raise NotImplementedError("Cannot solve the partial differential equation due"
" to inability of constantsimp")
def _simplify_variable_coeff(sol, syms, func, funcarg):
r"""
Helper function to replace constants by functions in 1st_linear_variable_coeff
"""
eta = Symbol("eta")
if len(syms) == 1:
sym = syms.pop()
final = sol.subs(sym, func(funcarg))
else:
for key, sym in enumerate(syms):
final = sol.subs(sym, func(funcarg))
return simplify(final.subs(eta, funcarg))
def pde_separate(eq, fun, sep, strategy='mul'):
"""Separate variables in partial differential equation either by additive
or multiplicative separation approach. It tries to rewrite an equation so
that one of the specified variables occurs on a different side of the
equation than the others.
:param eq: Partial differential equation
:param fun: Original function F(x, y, z)
:param sep: List of separated functions [X(x), u(y, z)]
:param strategy: Separation strategy. You can choose between additive
separation ('add') and multiplicative separation ('mul') which is
default.
Examples
========
>>> from sympy import E, Eq, Function, pde_separate, Derivative as D
>>> from sympy.abc import x, t
>>> u, X, T = map(Function, 'uXT')
>>> eq = Eq(D(u(x, t), x), E**(u(x, t))*D(u(x, t), t))
>>> pde_separate(eq, u(x, t), [X(x), T(t)], strategy='add')
[exp(-X(x))*Derivative(X(x), x), exp(T(t))*Derivative(T(t), t)]
>>> eq = Eq(D(u(x, t), x, 2), D(u(x, t), t, 2))
>>> pde_separate(eq, u(x, t), [X(x), T(t)], strategy='mul')
[Derivative(X(x), (x, 2))/X(x), Derivative(T(t), (t, 2))/T(t)]
See Also
========
pde_separate_add, pde_separate_mul
"""
do_add = False
if strategy == 'add':
do_add = True
elif strategy == 'mul':
do_add = False
else:
raise ValueError('Unknown strategy: %s' % strategy)
if isinstance(eq, Equality):
if eq.rhs != 0:
return pde_separate(Eq(eq.lhs - eq.rhs, 0), fun, sep, strategy)
else:
return pde_separate(Eq(eq, 0), fun, sep, strategy)
if eq.rhs != 0:
raise ValueError("Value should be 0")
# Handle arguments
orig_args = list(fun.args)
subs_args = []
for s in sep:
for j in range(0, len(s.args)):
subs_args.append(s.args[j])
if do_add:
functions = reduce(operator.add, sep)
else:
functions = reduce(operator.mul, sep)
# Check whether variables match
if len(subs_args) != len(orig_args):
raise ValueError("Variable counts do not match")
# Check for duplicate arguments like [X(x), u(x, y)]
if has_dups(subs_args):
raise ValueError("Duplicate substitution arguments detected")
# Check whether the variables match
if set(orig_args) != set(subs_args):
raise ValueError("Arguments do not match")
# Substitute original function with separated...
result = eq.lhs.subs(fun, functions).doit()
# Divide by terms when doing multiplicative separation
if not do_add:
eq = 0
for i in result.args:
eq += i/functions
result = eq
svar = subs_args[0]
dvar = subs_args[1:]
return _separate(result, svar, dvar)
def pde_separate_add(eq, fun, sep):
"""
Helper function for searching additive separable solutions.
Consider an equation of two independent variables x, y and a dependent
variable w, we look for the product of two functions depending on different
arguments:
`w(x, y, z) = X(x) + y(y, z)`
Examples
========
>>> from sympy import E, Eq, Function, pde_separate_add, Derivative as D
>>> from sympy.abc import x, t
>>> u, X, T = map(Function, 'uXT')
>>> eq = Eq(D(u(x, t), x), E**(u(x, t))*D(u(x, t), t))
>>> pde_separate_add(eq, u(x, t), [X(x), T(t)])
[exp(-X(x))*Derivative(X(x), x), exp(T(t))*Derivative(T(t), t)]
"""
return pde_separate(eq, fun, sep, strategy='add')
def pde_separate_mul(eq, fun, sep):
"""
Helper function for searching multiplicative separable solutions.
Consider an equation of two independent variables x, y and a dependent
variable w, we look for the product of two functions depending on different
arguments:
`w(x, y, z) = X(x)*u(y, z)`
Examples
========
>>> from sympy import Function, Eq, pde_separate_mul, Derivative as D
>>> from sympy.abc import x, y
>>> u, X, Y = map(Function, 'uXY')
>>> eq = Eq(D(u(x, y), x, 2), D(u(x, y), y, 2))
>>> pde_separate_mul(eq, u(x, y), [X(x), Y(y)])
[Derivative(X(x), (x, 2))/X(x), Derivative(Y(y), (y, 2))/Y(y)]
"""
return pde_separate(eq, fun, sep, strategy='mul')
def _separate(eq, dep, others):
"""Separate expression into two parts based on dependencies of variables."""
# FIRST PASS
# Extract derivatives depending our separable variable...
terms = set()
for term in eq.args:
if term.is_Mul:
for i in term.args:
if i.is_Derivative and not i.has(*others):
terms.add(term)
continue
elif term.is_Derivative and not term.has(*others):
terms.add(term)
# Find the factor that we need to divide by
div = set()
for term in terms:
ext, sep = term.expand().as_independent(dep)
# Failed?
if sep.has(*others):
return None
div.add(ext)
# FIXME: Find lcm() of all the divisors and divide with it, instead of
# current hack :(
# https://github.com/sympy/sympy/issues/4597
if len(div) > 0:
final = 0
for term in eq.args:
eqn = 0
for i in div:
eqn += term / i
final += simplify(eqn)
eq = final
# SECOND PASS - separate the derivatives
div = set()
lhs = rhs = 0
for term in eq.args:
# Check, whether we have already term with independent variable...
if not term.has(*others):
lhs += term
continue
# ...otherwise, try to separate
temp, sep = term.expand().as_independent(dep)
# Failed?
if sep.has(*others):
return None
# Extract the divisors
div.add(sep)
rhs -= term.expand()
# Do the division
fulldiv = reduce(operator.add, div)
lhs = simplify(lhs/fulldiv).expand()
rhs = simplify(rhs/fulldiv).expand()
# ...and check whether we were successful :)
if lhs.has(*others) or rhs.has(dep):
return None
return [lhs, rhs]
|
py | 1a310bca53a1b5e608a90067a9814f4172b33a1f | from notifications.signals import notify
def notify_answer(request, topico, resposta):
recipient = resposta.parent.user if resposta.parent else topico.user
verb = 'responder'
description = f'{recipient} respondeu seu post em {topico.titulo}.'
url = topico.get_absolute_url() + f'#post{resposta.pk}'
if request.user.pk != recipient.pk:
notify.send(sender=request.user, recipient=recipient, target=topico,
action_object=resposta, verb=verb, description=description, url=url) |
py | 1a310c17812bdb0293f2b1dbb41749e21abbc495 | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import torchvision
from torch.autograd import Variable
import itertools
import operator
from itertools import islice
from collections import OrderedDict
def to_var(x, requires_grad=True):
if torch.cuda.is_available():
x = x.cuda()
return Variable(x, requires_grad=requires_grad)
class MetaModule(nn.Module):
# adopted from: Adrien Ecoffet https://github.com/AdrienLE
def parameters(self):
for name, param in self.named_params(self):
yield param
def named_parameters(self):
for name, param in self.named_params(self):
yield name, param
def named_leaves(self):
return []
def named_submodules(self):
return []
def named_params(self, curr_module=None, memo=None, prefix=''):
if memo is None:
memo = set()
if hasattr(curr_module, 'named_leaves'):
for name, p in curr_module.named_leaves():
if p is not None and p not in memo:
memo.add(p)
yield prefix + ('.' if prefix else '') + name, p
else:
for name, p in curr_module._parameters.items():
if p is not None and p not in memo:
memo.add(p)
yield prefix + ('.' if prefix else '') + name, p
for mname, module in curr_module.named_children():
submodule_prefix = prefix + ('.' if prefix else '') + mname
for name, p in self.named_params(module, memo, submodule_prefix):
yield name, p
def update_params(self, lr_inner, first_order=False, source_params=None, detach=False):
if source_params is not None:
for tgt, src in zip(self.named_params(self), source_params):
name_t, param_t = tgt
# name_s, param_s = src
# grad = param_s.grad
# name_s, param_s = src
grad = src
if first_order:
grad = to_var(grad.detach().data)
tmp = param_t - lr_inner * grad
self.set_param(self, name_t, tmp)
else:
for name, param in self.named_params(self):
if not detach:
grad = param.grad
if first_order:
grad = to_var(grad.detach().data)
tmp = param - lr_inner * grad
self.set_param(self, name, tmp)
else:
param = param.detach_()
self.set_param(self, name, param)
def set_param(self,curr_mod, name, param):
if '.' in name:
n = name.split('.')
module_name = n[0]
rest = '.'.join(n[1:])
for name, mod in curr_mod.named_children():
if module_name == name:
self.set_param(mod, rest, param)
break
else:
setattr(curr_mod, name, param)
def detach_params(self):
for name, param in self.named_params(self):
self.set_param(self, name, param.detach())
def copy(self, other, same_var=False):
for name, param in other.named_params():
if not same_var:
param = to_var(param.data.clone(), requires_grad=True)
self.set_param(name, param)
class MetaLinear(MetaModule):
def __init__(self, *args, **kwargs):
super().__init__()
ignore = nn.Linear(*args, **kwargs)
self.register_buffer('weight', to_var(ignore.weight.data, requires_grad=True))
self.register_buffer('bias', to_var(ignore.bias.data, requires_grad=True))
self.in_features = ignore.weight.size(1)
self.out_features = ignore.weight.size(0)
def forward(self, x):
return F.linear(x, self.weight, self.bias)
def named_leaves(self):
return [('weight', self.weight), ('bias', self.bias)]
class MetaSequential(MetaModule):
r"""A sequential container.
Modules will be added to it in the order they are passed in the constructor.
Alternatively, an ordered dict of modules can also be passed in.
To make it easier to understand, here is a small example::
# Example of using Sequential
model = MetaSequential(
MetaConv2d(1,20,5),
nn.ReLU(),
MetaConv2d(20,64,5),
nn.ReLU()
)
# Example of using Sequential with OrderedDict
model = MetaSequential(OrderedDict([
('conv1', MetaConv2d(1,20,5)),
('relu1', nn.ReLU()),
('conv2', MetaConv2d(20,64,5)),
('relu2', nn.ReLU())
]))
"""
def __init__(self, *args):
super(MetaSequential, self).__init__()
if len(args) == 1 and isinstance(args[0], OrderedDict):
for key, module in args[0].items():
self.add_module(key, module)
else:
for idx, module in enumerate(args):
self.add_module(str(idx), module)
def _get_item_by_idx(self, iterator, idx):
"""Get the idx-th item of the iterator"""
size = len(self)
idx = operator.index(idx)
if not -size <= idx < size:
raise IndexError('index {} is out of range'.format(idx))
idx %= size
return next(islice(iterator, idx, None))
def __getitem__(self, idx):
if isinstance(idx, slice):
return self.__class__(OrderedDict(list(self._modules.items())[idx]))
else:
return self._get_item_by_idx(self._modules.values(), idx)
def __setitem__(self, idx, module):
key = self._get_item_by_idx(self._modules.keys(), idx)
return setattr(self, key, module)
def __delitem__(self, idx):
if isinstance(idx, slice):
for key in list(self._modules.keys())[idx]:
delattr(self, key)
else:
key = self._get_item_by_idx(self._modules.keys(), idx)
delattr(self, key)
def __len__(self):
return len(self._modules)
def __dir__(self):
keys = super(Sequential, self).__dir__()
keys = [key for key in keys if not key.isdigit()]
return keys
def forward(self, input):
for module in self._modules.values():
input = module(input)
return input
class MetaModuleList(MetaModule):
r"""Holds submodules in a list.
:class:`~MetaModuleList` can be indexed like a regular Python list, but
modules it contains are properly registered, and will be visible by all
:class:`~MetaModule` methods.
Arguments:
modules (iterable, optional): an iterable of modules to add
Example::
class MyModule(MetaModule):
def __init__(self):
super(MyModule, self).__init__()
self.linears = MetaModuleList([MetaLinear(10, 10) for i in range(10)])
def forward(self, x):
# ModuleList can act as an iterable, or be indexed using ints
for i, l in enumerate(self.linears):
x = self.linears[i // 2](x) + l(x)
return x
"""
def __init__(self, modules=None):
super(MetaModuleList, self).__init__()
if modules is not None:
self += modules
def _get_abs_string_index(self, idx):
"""Get the absolute index for the list of modules"""
idx = operator.index(idx)
if not (-len(self) <= idx < len(self)):
raise IndexError('index {} is out of range'.format(idx))
if idx < 0:
idx += len(self)
return str(idx)
def __getitem__(self, idx):
if isinstance(idx, slice):
return self.__class__(list(self._modules.values())[idx])
else:
return self._modules[self._get_abs_string_index(idx)]
def __setitem__(self, idx, module):
idx = self._get_abs_string_index(idx)
return setattr(self, str(idx), module)
def __delitem__(self, idx):
if isinstance(idx, slice):
for k in range(len(self._modules))[idx]:
delattr(self, str(k))
else:
delattr(self, self._get_abs_string_index(idx))
# To preserve numbering, self._modules is being reconstructed with modules after deletion
str_indices = [str(i) for i in range(len(self._modules))]
self._modules = OrderedDict(list(zip(str_indices, self._modules.values())))
def __len__(self):
return len(self._modules)
def __iter__(self):
return iter(self._modules.values())
def __iadd__(self, modules):
return self.extend(modules)
def __dir__(self):
keys = super(ModuleList, self).__dir__()
keys = [key for key in keys if not key.isdigit()]
return keys
def insert(self, index, module):
r"""Insert a given module before a given index in the list.
Arguments:
index (int): index to insert.
module (MetaModule): module to insert
"""
for i in range(len(self._modules), index, -1):
self._modules[str(i)] = self._modules[str(i - 1)]
self._modules[str(index)] = module
def append(self, module):
r"""Appends a given module to the end of the list.
Arguments:
module (MetaModule): module to append
"""
self.add_module(str(len(self)), module)
return self
def extend(self, modules):
r"""Appends modules from a Python iterable to the end of the list.
Arguments:
modules (iterable): iterable of modules to append
"""
if not isinstance(modules, container_abcs.Iterable):
raise TypeError("ModuleList.extend should be called with an "
"iterable, but got " + type(modules).__name__)
offset = len(self)
for i, module in enumerate(modules):
self.add_module(str(offset + i), module)
return self
class ModuleDict(MetaModule):
r"""Holds submodules in a dictionary.
:class:`~MetaModuleDict` can be indexed like a regular Python dictionary,
but modules it contains are properly registered, and will be visible by all
:class:`~MetaModule` methods.
:class:`~MetaModuleDict` is an **ordered** dictionary that respects
* the order of insertion, and
* in :meth:`~MetaModuleDict.update`, the order of the merged ``OrderedDict``
or another :class:`~MetaModuleDict` (the argument to :meth:`~MetaModuleDict.update`).
Note that :meth:`~MetaModuleDict.update` with other unordered mapping
types (e.g., Python's plain ``dict``) does not preserve the order of the
merged mapping.
Arguments:
modules (iterable, optional): a mapping (dictionary) of (string: module)
or an iterable of key-value pairs of type (string, module)
Example::
class MyModule(MetaModule):
def __init__(self):
super(MyModule, self).__init__()
self.choices = MetaModuleDict({
'conv': MetaConv2d(10, 10, 3),
'pool': nn.MaxPool2d(3)
})
self.activations = MetaModuleDict([
['lrelu', nn.LeakyReLU()],
['prelu', nn.PReLU()]
])
def forward(self, x, choice, act):
x = self.choices[choice](x)
x = self.activations[act](x)
return x
"""
def __init__(self, modules=None):
super(MetaModuleDict, self).__init__()
if modules is not None:
self.update(modules)
def __getitem__(self, key):
return self._modules[key]
def __setitem__(self, key, module):
self.add_module(key, module)
def __delitem__(self, key):
del self._modules[key]
def __len__(self):
return len(self._modules)
def __iter__(self):
return iter(self._modules)
def __contains__(self, key):
return key in self._modules
def clear(self):
"""Remove all items from the ModuleDict.
"""
self._modules.clear()
def pop(self, key):
r"""Remove key from the ModuleDict and return its module.
Arguments:
key (string): key to pop from the ModuleDict
"""
v = self[key]
del self[key]
return v
def keys(self):
r"""Return an iterable of the ModuleDict keys.
"""
return self._modules.keys()
def items(self):
r"""Return an iterable of the ModuleDict key/value pairs.
"""
return self._modules.items()
def values(self):
r"""Return an iterable of the ModuleDict values.
"""
return self._modules.values()
def update(self, modules):
r"""Update the :class:`~MetaModuleDict` with the key-value pairs from a
mapping or an iterable, overwriting existing keys.
.. note::
If :attr:`modules` is an ``OrderedDict``, a :class:`~MetaModuleDict`, or
an iterable of key-value pairs, the order of new elements in it is preserved.
Arguments:
modules (iterable): a mapping (dictionary) from string to :class:`~MetaModule`,
or an iterable of key-value pairs of type (string, :class:`~MetaModule`)
"""
if not isinstance(modules, container_abcs.Iterable):
raise TypeError("ModuleDict.update should be called with an "
"iterable of key/value pairs, but got " +
type(modules).__name__)
if isinstance(modules, container_abcs.Mapping):
if isinstance(modules, (OrderedDict, ModuleDict)):
for key, module in modules.items():
self[key] = module
else:
for key, module in sorted(modules.items()):
self[key] = module
else:
for j, m in enumerate(modules):
if not isinstance(m, container_abcs.Iterable):
raise TypeError("ModuleDict update sequence element "
"#" + str(j) + " should be Iterable; is" +
type(m).__name__)
if not len(m) == 2:
raise ValueError("ModuleDict update sequence element "
"#" + str(j) + " has length " + str(len(m)) +
"; 2 is required")
self[m[0]] = m[1]
def forward(self):
raise NotImplementedError()
class LeNet(MetaModule):
def __init__(self, n_out):
super(LeNet, self).__init__()
layers = []
layers.append(MetaConv2d(1, 6, kernel_size=5))
layers.append(nn.ReLU(inplace=True))
layers.append(nn.MaxPool2d(kernel_size=2,stride=2))
layers.append(MetaConv2d(6, 16, kernel_size=5))
layers.append(nn.ReLU(inplace=True))
layers.append(nn.MaxPool2d(kernel_size=2,stride=2))
layers.append(MetaConv2d(16, 120, kernel_size=5))
layers.append(nn.ReLU(inplace=True))
self.main = nn.Sequential(*layers)
layers = []
layers.append(MetaLinear(120, 84))
layers.append(nn.ReLU(inplace=True))
layers.append(MetaLinear(84, n_out))
self.fc_layers = nn.Sequential(*layers)
def forward(self, x):
x = self.main(x)
x = x.view(-1, 120)
return self.fc_layers(x).squeeze()
|
py | 1a310d07ee210eb8ddec1a5bbd788d533dde86d5 | #!/usr/bin/env python
# Software License Agreement (Apache License 2.0)
#
# Copyright 2017 Florian Kromer
#
# 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 rospy
class ParameterContractViolation(Exception):
"""
Basic exception for contract violations raised by parameters.
"""
def __init__(self, name, msg=None):
if msg is None:
# default error message
msg = "Contract violation of parameter %s" % name
super(ParameterContractViolation, self).__init__(msg)
# make name accessible for exception handling
self.name = name
class ParameterValueViolation(ParameterContractViolation):
"""
Exception for value contract violations raised by parameters.
"""
def __init__(self, name, value):
super(ParameterValueViolation, self).__init__(
name, msg="Parameter %s violated contract with value %s" % (name, value))
self.value = value
def _check_parameter_exists(name):
"""
Checks if a parameter exists.
Args:
name (string): Name of the parameter.
Returns:
bool: True if existing, False if not existing.
"""
if rospy.has_param(name):
return True
return False
def assert_parameter_exists(name):
"""
Indicates a contract violation if the parameter is expected to exist but if
it does not exist by raising an exception.
Args:
name (string): Name of the parameter.
Raises:
ParameterContractViolation: Raised if parameter is not existing.
"""
if not _check_parameter_exists(name):
raise ParameterContractViolation(name, "Parameter %s not existing" % (name))
def enforce_parameter_exists(name):
"""
Indicates a contract violation if the parameter is expected to exist but if
it does not exist by logging or diagnostics.
Args:
name (string): Name of the parameter.
"""
if not _check_parameter_exists(name):
rospy.logwarn("Parameter %s not existing" % (name))
def assert_parameter_not_exists(name):
"""
Indicates a contract violation if the parameter is expected to not exist but
if it does exist.
Args:
name (string): Name of the parameter.
Raises:
ParameterContractViolation: Raised if parameter is existing.
"""
if rospy.has_param(name):
raise ParameterContractViolation(name, "Parameter %s existing" % (name))
def assert_parameter_has_value(name, value):
"""
Indicates a contract violation if it is expected that the parameter has a
specific value but if it has not.
Args:
name (string): Name of the parameter.
value (depends on the parameter type): Value of the parameter.
Raises:
ParameterValueViolation: Raised if parameter value is not like expected.
"""
if rospy.has_param(name):
observed_value = rospy.get_param(name)
if value != observed_value:
ParameterValueViolation(name, value)
else:
raise ParameterContractViolation(name, "Parameter %s not existing" % (name))
def assert_parameter_in_range(name, lower_bound, upper_bound):
"""
Indicates a contract violation if it is expected that a parameter value of
type 32-bit integers has a value within a defined range but if it has not.
Args:
name (string): Name of the parameter.
Raises:
ParameterValueViolation: Raised if parameter value is not in the range.
ParameterContractViolation: Raised if parameter does not exist.
"""
if rospy.has_param(name):
value = rospy.get_param(name)
if lower_bound > value > upper_bound:
raise ParameterValueViolation(name, value)
else:
raise ParameterContractViolation(name, "Parameter %s not existing" % (name))
def assert_parameter_out_range(name, lower_bound, upper_bound):
"""
Indicates a contract violation if it is expected that a parameter value of
type 32-bit integers has a value outside a defined range but if it has not.
Args:
name (string): Name of the parameter.
Raises:
ParameterValueViolation: Raised if parameter value is not outside the range.
ParameterContractViolation: Raised if parameter does not exist.
"""
if rospy.has_param(name):
value = rospy.get_param(name)
if lower_bound > value > upper_bound:
raise ParameterValueViolation(name, value)
else:
raise ParameterContractViolation(name, "Parameter %s not existing" % (name))
|
py | 1a310d12195444f1e28dfdb77c76764ad77f7c0a | # -*- coding: utf-8 -*-
# Copyright (C) 2012 Yahoo! 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.
from taskflow import engines
from taskflow.engines.action_engine import compiler
from taskflow import exceptions as exc
from taskflow.patterns import graph_flow as gf
from taskflow.patterns import linear_flow as lf
from taskflow.patterns import unordered_flow as uf
from taskflow import retry
from taskflow import test
from taskflow.tests import utils as test_utils
def _replicate_graph_with_names(compilation):
# Turn a graph of nodes into a graph of names only so that
# testing can use those names instead of having to use the exact
# node objects themselves (which is problematic for any end nodes that
# are added into the graph *dynamically*, and are not there in the
# original/source flow).
g = compilation.execution_graph
n_g = g.__class__(name=g.name)
for node, node_data in g.nodes_iter(data=True):
n_g.add_node(node.name, attr_dict=node_data)
for u, v, u_v_data in g.edges_iter(data=True):
n_g.add_edge(u.name, v.name, attr_dict=u_v_data)
return n_g
class PatternCompileTest(test.TestCase):
def test_task(self):
task = test_utils.DummyTask(name='a')
g = _replicate_graph_with_names(
compiler.PatternCompiler(task).compile())
self.assertEqual(['a'], list(g.nodes()))
self.assertEqual([], list(g.edges()))
def test_retry(self):
r = retry.AlwaysRevert('r1')
self.assertRaises(TypeError, compiler.PatternCompiler(r).compile)
def test_wrong_object(self):
msg_regex = '^Unknown object .* requested to compile'
self.assertRaisesRegex(TypeError, msg_regex,
compiler.PatternCompiler(42).compile)
def test_empty(self):
flo = lf.Flow("test")
compiler.PatternCompiler(flo).compile()
def test_linear(self):
a, b, c, d = test_utils.make_many(4)
flo = lf.Flow("test")
flo.add(a, b, c)
inner_flo = lf.Flow("sub-test")
inner_flo.add(d)
flo.add(inner_flo)
g = _replicate_graph_with_names(
compiler.PatternCompiler(flo).compile())
self.assertEqual(8, len(g))
order = g.topological_sort()
self.assertEqual(['test', 'a', 'b', 'c',
"sub-test", 'd', "sub-test[$]",
'test[$]'], order)
self.assertTrue(g.has_edge('c', "sub-test"))
self.assertTrue(g.has_edge("sub-test", 'd'))
self.assertEqual({'invariant': True},
g.get_edge_data("sub-test", 'd'))
self.assertEqual(['test[$]'], list(g.no_successors_iter()))
self.assertEqual(['test'], list(g.no_predecessors_iter()))
def test_invalid(self):
a, b, c = test_utils.make_many(3)
flo = lf.Flow("test")
flo.add(a, b, c)
flo.add(flo)
self.assertRaises(ValueError,
compiler.PatternCompiler(flo).compile)
def test_unordered(self):
a, b, c, d = test_utils.make_many(4)
flo = uf.Flow("test")
flo.add(a, b, c, d)
g = _replicate_graph_with_names(
compiler.PatternCompiler(flo).compile())
self.assertEqual(6, len(g))
self.assertItemsEqual(g.edges(), [
('test', 'a'),
('test', 'b'),
('test', 'c'),
('test', 'd'),
('a', 'test[$]'),
('b', 'test[$]'),
('c', 'test[$]'),
('d', 'test[$]'),
])
self.assertEqual(set(['test']), set(g.no_predecessors_iter()))
def test_linear_nested(self):
a, b, c, d = test_utils.make_many(4)
flo = lf.Flow("test")
flo.add(a, b)
inner_flo = uf.Flow("test2")
inner_flo.add(c, d)
flo.add(inner_flo)
g = _replicate_graph_with_names(
compiler.PatternCompiler(flo).compile())
self.assertEqual(8, len(g))
sub_g = g.subgraph(['a', 'b'])
self.assertFalse(sub_g.has_edge('b', 'a'))
self.assertTrue(sub_g.has_edge('a', 'b'))
self.assertEqual({'invariant': True}, sub_g.get_edge_data("a", "b"))
sub_g = g.subgraph(['c', 'd'])
self.assertEqual(0, sub_g.number_of_edges())
# This ensures that c and d do not start executing until after b.
self.assertTrue(g.has_edge('b', 'test2'))
self.assertTrue(g.has_edge('test2', 'c'))
self.assertTrue(g.has_edge('test2', 'd'))
def test_unordered_nested(self):
a, b, c, d = test_utils.make_many(4)
flo = uf.Flow("test")
flo.add(a, b)
flo2 = lf.Flow("test2")
flo2.add(c, d)
flo.add(flo2)
g = _replicate_graph_with_names(
compiler.PatternCompiler(flo).compile())
self.assertEqual(8, len(g))
self.assertItemsEqual(g.edges(), [
('test', 'a'),
('test', 'b'),
('test', 'test2'),
('test2', 'c'),
('c', 'd'),
('d', 'test2[$]'),
('test2[$]', 'test[$]'),
('a', 'test[$]'),
('b', 'test[$]'),
])
def test_unordered_nested_in_linear(self):
a, b, c, d = test_utils.make_many(4)
inner_flo = uf.Flow('ut').add(b, c)
flo = lf.Flow('lt').add(a, inner_flo, d)
g = _replicate_graph_with_names(
compiler.PatternCompiler(flo).compile())
self.assertEqual(8, len(g))
self.assertItemsEqual(g.edges(), [
('lt', 'a'),
('a', 'ut'),
('ut', 'b'),
('ut', 'c'),
('b', 'ut[$]'),
('c', 'ut[$]'),
('ut[$]', 'd'),
('d', 'lt[$]'),
])
def test_graph(self):
a, b, c, d = test_utils.make_many(4)
flo = gf.Flow("test")
flo.add(a, b, c, d)
compilation = compiler.PatternCompiler(flo).compile()
self.assertEqual(6, len(compilation.execution_graph))
self.assertEqual(8, compilation.execution_graph.number_of_edges())
def test_graph_nested(self):
a, b, c, d, e, f, g = test_utils.make_many(7)
flo = gf.Flow("test")
flo.add(a, b, c, d)
flo2 = lf.Flow('test2')
flo2.add(e, f, g)
flo.add(flo2)
g = _replicate_graph_with_names(
compiler.PatternCompiler(flo).compile())
self.assertEqual(11, len(g))
self.assertItemsEqual(g.edges(), [
('test', 'a'),
('test', 'b'),
('test', 'c'),
('test', 'd'),
('a', 'test[$]'),
('b', 'test[$]'),
('c', 'test[$]'),
('d', 'test[$]'),
('test', 'test2'),
('test2', 'e'),
('e', 'f'),
('f', 'g'),
('g', 'test2[$]'),
('test2[$]', 'test[$]'),
])
def test_graph_nested_graph(self):
a, b, c, d, e, f, g = test_utils.make_many(7)
flo = gf.Flow("test")
flo.add(a, b, c, d)
flo2 = gf.Flow('test2')
flo2.add(e, f, g)
flo.add(flo2)
g = _replicate_graph_with_names(
compiler.PatternCompiler(flo).compile())
self.assertEqual(11, len(g))
self.assertItemsEqual(g.edges(), [
('test', 'a'),
('test', 'b'),
('test', 'c'),
('test', 'd'),
('test', 'test2'),
('test2', 'e'),
('test2', 'f'),
('test2', 'g'),
('e', 'test2[$]'),
('f', 'test2[$]'),
('g', 'test2[$]'),
('test2[$]', 'test[$]'),
('a', 'test[$]'),
('b', 'test[$]'),
('c', 'test[$]'),
('d', 'test[$]'),
])
def test_graph_links(self):
a, b, c, d = test_utils.make_many(4)
flo = gf.Flow("test")
flo.add(a, b, c, d)
flo.link(a, b)
flo.link(b, c)
flo.link(c, d)
g = _replicate_graph_with_names(
compiler.PatternCompiler(flo).compile())
self.assertEqual(6, len(g))
self.assertItemsEqual(g.edges(data=True), [
('test', 'a', {'invariant': True}),
('a', 'b', {'manual': True}),
('b', 'c', {'manual': True}),
('c', 'd', {'manual': True}),
('d', 'test[$]', {'invariant': True}),
])
self.assertItemsEqual(['test'], g.no_predecessors_iter())
self.assertItemsEqual(['test[$]'], g.no_successors_iter())
def test_graph_dependencies(self):
a = test_utils.ProvidesRequiresTask('a', provides=['x'], requires=[])
b = test_utils.ProvidesRequiresTask('b', provides=[], requires=['x'])
flo = gf.Flow("test").add(a, b)
g = _replicate_graph_with_names(
compiler.PatternCompiler(flo).compile())
self.assertEqual(4, len(g))
self.assertItemsEqual(g.edges(data=True), [
('test', 'a', {'invariant': True}),
('a', 'b', {'reasons': set(['x'])}),
('b', 'test[$]', {'invariant': True}),
])
self.assertItemsEqual(['test'], g.no_predecessors_iter())
self.assertItemsEqual(['test[$]'], g.no_successors_iter())
def test_graph_nested_requires(self):
a = test_utils.ProvidesRequiresTask('a', provides=['x'], requires=[])
b = test_utils.ProvidesRequiresTask('b', provides=[], requires=[])
c = test_utils.ProvidesRequiresTask('c', provides=[], requires=['x'])
inner_flo = lf.Flow("test2").add(b, c)
flo = gf.Flow("test").add(a, inner_flo)
g = _replicate_graph_with_names(
compiler.PatternCompiler(flo).compile())
self.assertEqual(7, len(g))
self.assertItemsEqual(g.edges(data=True), [
('test', 'a', {'invariant': True}),
('test2', 'b', {'invariant': True}),
('a', 'test2', {'reasons': set(['x'])}),
('b', 'c', {'invariant': True}),
('c', 'test2[$]', {'invariant': True}),
('test2[$]', 'test[$]', {'invariant': True}),
])
self.assertItemsEqual(['test'], list(g.no_predecessors_iter()))
self.assertItemsEqual(['test[$]'], list(g.no_successors_iter()))
def test_graph_nested_provides(self):
a = test_utils.ProvidesRequiresTask('a', provides=[], requires=['x'])
b = test_utils.ProvidesRequiresTask('b', provides=['x'], requires=[])
c = test_utils.ProvidesRequiresTask('c', provides=[], requires=[])
inner_flo = lf.Flow("test2").add(b, c)
flo = gf.Flow("test").add(a, inner_flo)
g = _replicate_graph_with_names(
compiler.PatternCompiler(flo).compile())
self.assertEqual(7, len(g))
self.assertItemsEqual(g.edges(data=True), [
('test', 'test2', {'invariant': True}),
('a', 'test[$]', {'invariant': True}),
# The 'x' requirement is produced out of test2...
('test2[$]', 'a', {'reasons': set(['x'])}),
('test2', 'b', {'invariant': True}),
('b', 'c', {'invariant': True}),
('c', 'test2[$]', {'invariant': True}),
])
self.assertItemsEqual(['test'], g.no_predecessors_iter())
self.assertItemsEqual(['test[$]'], g.no_successors_iter())
def test_empty_flow_in_linear_flow(self):
flo = lf.Flow('lf')
a = test_utils.ProvidesRequiresTask('a', provides=[], requires=[])
b = test_utils.ProvidesRequiresTask('b', provides=[], requires=[])
empty_flo = gf.Flow("empty")
flo.add(a, empty_flo, b)
g = _replicate_graph_with_names(
compiler.PatternCompiler(flo).compile())
self.assertItemsEqual(g.edges(), [
("lf", "a"),
("a", "empty"),
("empty", "empty[$]"),
("empty[$]", "b"),
("b", "lf[$]"),
])
def test_many_empty_in_graph_flow(self):
flo = gf.Flow('root')
a = test_utils.ProvidesRequiresTask('a', provides=[], requires=[])
flo.add(a)
b = lf.Flow('b')
b_0 = test_utils.ProvidesRequiresTask('b.0', provides=[], requires=[])
b_1 = lf.Flow('b.1')
b_2 = lf.Flow('b.2')
b_3 = test_utils.ProvidesRequiresTask('b.3', provides=[], requires=[])
b.add(b_0, b_1, b_2, b_3)
flo.add(b)
c = lf.Flow('c')
c_0 = lf.Flow('c.0')
c_1 = lf.Flow('c.1')
c_2 = lf.Flow('c.2')
c.add(c_0, c_1, c_2)
flo.add(c)
d = test_utils.ProvidesRequiresTask('d', provides=[], requires=[])
flo.add(d)
flo.link(b, d)
flo.link(a, d)
flo.link(c, d)
g = _replicate_graph_with_names(
compiler.PatternCompiler(flo).compile())
self.assertTrue(g.has_edge('root', 'a'))
self.assertTrue(g.has_edge('root', 'b'))
self.assertTrue(g.has_edge('root', 'c'))
self.assertTrue(g.has_edge('b.0', 'b.1'))
self.assertTrue(g.has_edge('b.1[$]', 'b.2'))
self.assertTrue(g.has_edge('b.2[$]', 'b.3'))
self.assertTrue(g.has_edge('c.0[$]', 'c.1'))
self.assertTrue(g.has_edge('c.1[$]', 'c.2'))
self.assertTrue(g.has_edge('a', 'd'))
self.assertTrue(g.has_edge('b[$]', 'd'))
self.assertTrue(g.has_edge('c[$]', 'd'))
self.assertEqual(20, len(g))
def test_empty_flow_in_nested_flow(self):
flow = lf.Flow('lf')
a = test_utils.ProvidesRequiresTask('a', provides=[], requires=[])
b = test_utils.ProvidesRequiresTask('b', provides=[], requires=[])
flow2 = lf.Flow("lf-2")
c = test_utils.ProvidesRequiresTask('c', provides=[], requires=[])
d = test_utils.ProvidesRequiresTask('d', provides=[], requires=[])
empty_flow = gf.Flow("empty")
flow2.add(c, empty_flow, d)
flow.add(a, flow2, b)
g = _replicate_graph_with_names(
compiler.PatternCompiler(flow).compile())
for u, v in [('lf', 'a'), ('a', 'lf-2'),
('lf-2', 'c'), ('c', 'empty'),
('empty[$]', 'd'), ('d', 'lf-2[$]'),
('lf-2[$]', 'b'), ('b', 'lf[$]')]:
self.assertTrue(g.has_edge(u, v))
def test_empty_flow_in_graph_flow(self):
flow = lf.Flow('lf')
a = test_utils.ProvidesRequiresTask('a', provides=['a'], requires=[])
b = test_utils.ProvidesRequiresTask('b', provides=[], requires=['a'])
empty_flow = lf.Flow("empty")
flow.add(a, empty_flow, b)
compilation = compiler.PatternCompiler(flow).compile()
g = compilation.execution_graph
self.assertTrue(g.has_edge(flow, a))
self.assertTrue(g.has_edge(a, empty_flow))
empty_flow_successors = g.successors(empty_flow)
self.assertEqual(1, len(empty_flow_successors))
empty_flow_terminal = empty_flow_successors[0]
self.assertIs(empty_flow, empty_flow_terminal.flow)
self.assertEqual(compiler.FLOW_END,
g.node[empty_flow_terminal]['kind'])
self.assertTrue(g.has_edge(empty_flow_terminal, b))
def test_empty_flow_in_graph_flow_linkage(self):
flow = gf.Flow('lf')
a = test_utils.ProvidesRequiresTask('a', provides=[], requires=[])
b = test_utils.ProvidesRequiresTask('b', provides=[], requires=[])
empty_flow = lf.Flow("empty")
flow.add(a, empty_flow, b)
flow.link(a, b)
compilation = compiler.PatternCompiler(flow).compile()
g = compilation.execution_graph
self.assertTrue(g.has_edge(a, b))
self.assertTrue(g.has_edge(flow, a))
self.assertTrue(g.has_edge(flow, empty_flow))
def test_checks_for_dups(self):
flo = gf.Flow("test").add(
test_utils.DummyTask(name="a"),
test_utils.DummyTask(name="a")
)
e = engines.load(flo)
self.assertRaisesRegex(exc.Duplicate,
'^Atoms with duplicate names',
e.compile)
def test_checks_for_dups_globally(self):
flo = gf.Flow("test").add(
gf.Flow("int1").add(test_utils.DummyTask(name="a")),
gf.Flow("int2").add(test_utils.DummyTask(name="a")))
e = engines.load(flo)
self.assertRaisesRegex(exc.Duplicate,
'^Atoms with duplicate names',
e.compile)
def test_retry_in_linear_flow(self):
flo = lf.Flow("test", retry.AlwaysRevert("c"))
compilation = compiler.PatternCompiler(flo).compile()
self.assertEqual(3, len(compilation.execution_graph))
self.assertEqual(2, compilation.execution_graph.number_of_edges())
def test_retry_in_unordered_flow(self):
flo = uf.Flow("test", retry.AlwaysRevert("c"))
compilation = compiler.PatternCompiler(flo).compile()
self.assertEqual(3, len(compilation.execution_graph))
self.assertEqual(2, compilation.execution_graph.number_of_edges())
def test_retry_in_graph_flow(self):
flo = gf.Flow("test", retry.AlwaysRevert("c"))
compilation = compiler.PatternCompiler(flo).compile()
g = compilation.execution_graph
self.assertEqual(3, len(g))
self.assertEqual(2, g.number_of_edges())
def test_retry_in_nested_flows(self):
c1 = retry.AlwaysRevert("c1")
c2 = retry.AlwaysRevert("c2")
inner_flo = lf.Flow("test2", c2)
flo = lf.Flow("test", c1).add(inner_flo)
g = _replicate_graph_with_names(
compiler.PatternCompiler(flo).compile())
self.assertEqual(6, len(g))
self.assertItemsEqual(g.edges(data=True), [
('test', 'c1', {'invariant': True}),
('c1', 'test2', {'invariant': True, 'retry': True}),
('test2', 'c2', {'invariant': True}),
('c2', 'test2[$]', {'invariant': True}),
('test2[$]', 'test[$]', {'invariant': True}),
])
self.assertIs(c1, g.node['c2']['retry'])
self.assertItemsEqual(['test'], list(g.no_predecessors_iter()))
self.assertItemsEqual(['test[$]'], list(g.no_successors_iter()))
def test_retry_in_linear_flow_with_tasks(self):
c = retry.AlwaysRevert("c")
a, b = test_utils.make_many(2)
flo = lf.Flow("test", c).add(a, b)
g = _replicate_graph_with_names(
compiler.PatternCompiler(flo).compile())
self.assertEqual(5, len(g))
self.assertItemsEqual(g.edges(data=True), [
('test', 'c', {'invariant': True}),
('a', 'b', {'invariant': True}),
('c', 'a', {'invariant': True, 'retry': True}),
('b', 'test[$]', {'invariant': True}),
])
self.assertItemsEqual(['test'], g.no_predecessors_iter())
self.assertItemsEqual(['test[$]'], g.no_successors_iter())
self.assertIs(c, g.node['a']['retry'])
self.assertIs(c, g.node['b']['retry'])
def test_retry_in_unordered_flow_with_tasks(self):
c = retry.AlwaysRevert("c")
a, b = test_utils.make_many(2)
flo = uf.Flow("test", c).add(a, b)
g = _replicate_graph_with_names(
compiler.PatternCompiler(flo).compile())
self.assertEqual(5, len(g))
self.assertItemsEqual(g.edges(data=True), [
('test', 'c', {'invariant': True}),
('c', 'a', {'invariant': True, 'retry': True}),
('c', 'b', {'invariant': True, 'retry': True}),
('b', 'test[$]', {'invariant': True}),
('a', 'test[$]', {'invariant': True}),
])
self.assertItemsEqual(['test'], list(g.no_predecessors_iter()))
self.assertItemsEqual(['test[$]'], list(g.no_successors_iter()))
self.assertIs(c, g.node['a']['retry'])
self.assertIs(c, g.node['b']['retry'])
def test_retry_in_graph_flow_with_tasks(self):
r = retry.AlwaysRevert("r")
a, b, c = test_utils.make_many(3)
flo = gf.Flow("test", r).add(a, b, c).link(b, c)
g = _replicate_graph_with_names(
compiler.PatternCompiler(flo).compile())
self.assertItemsEqual(g.edges(data=True), [
('test', 'r', {'invariant': True}),
('r', 'a', {'invariant': True, 'retry': True}),
('r', 'b', {'invariant': True, 'retry': True}),
('b', 'c', {'manual': True}),
('a', 'test[$]', {'invariant': True}),
('c', 'test[$]', {'invariant': True}),
])
self.assertItemsEqual(['test'], g.no_predecessors_iter())
self.assertItemsEqual(['test[$]'], g.no_successors_iter())
self.assertIs(r, g.node['a']['retry'])
self.assertIs(r, g.node['b']['retry'])
self.assertIs(r, g.node['c']['retry'])
def test_retries_hierarchy(self):
c1 = retry.AlwaysRevert("c1")
c2 = retry.AlwaysRevert("c2")
a, b, c, d = test_utils.make_many(4)
inner_flo = lf.Flow("test2", c2).add(b, c)
flo = lf.Flow("test", c1).add(a, inner_flo, d)
g = _replicate_graph_with_names(
compiler.PatternCompiler(flo).compile())
self.assertEqual(10, len(g))
self.assertItemsEqual(g.edges(data=True), [
('test', 'c1', {'invariant': True}),
('c1', 'a', {'invariant': True, 'retry': True}),
('a', 'test2', {'invariant': True}),
('test2', 'c2', {'invariant': True}),
('c2', 'b', {'invariant': True, 'retry': True}),
('b', 'c', {'invariant': True}),
('c', 'test2[$]', {'invariant': True}),
('test2[$]', 'd', {'invariant': True}),
('d', 'test[$]', {'invariant': True}),
])
self.assertIs(c1, g.node['a']['retry'])
self.assertIs(c1, g.node['d']['retry'])
self.assertIs(c2, g.node['b']['retry'])
self.assertIs(c2, g.node['c']['retry'])
self.assertIs(c1, g.node['c2']['retry'])
self.assertIsNone(g.node['c1'].get('retry'))
def test_retry_subflows_hierarchy(self):
c1 = retry.AlwaysRevert("c1")
a, b, c, d = test_utils.make_many(4)
inner_flo = lf.Flow("test2").add(b, c)
flo = lf.Flow("test", c1).add(a, inner_flo, d)
g = _replicate_graph_with_names(
compiler.PatternCompiler(flo).compile())
self.assertEqual(9, len(g))
self.assertItemsEqual(g.edges(data=True), [
('test', 'c1', {'invariant': True}),
('c1', 'a', {'invariant': True, 'retry': True}),
('a', 'test2', {'invariant': True}),
('test2', 'b', {'invariant': True}),
('b', 'c', {'invariant': True}),
('c', 'test2[$]', {'invariant': True}),
('test2[$]', 'd', {'invariant': True}),
('d', 'test[$]', {'invariant': True}),
])
self.assertIs(c1, g.node['a']['retry'])
self.assertIs(c1, g.node['d']['retry'])
self.assertIs(c1, g.node['b']['retry'])
self.assertIs(c1, g.node['c']['retry'])
self.assertIsNone(g.node['c1'].get('retry'))
|
py | 1a310d14fbb512fb7d3d75bf99d7b451b11e34bf | #!/usr/bin/env python
command += testshade ("-g 128 128 --layer testlay -param:lockgeom=0 scale 5.0 test -iters 2 -reparam testlay scale 15.0 -od uint8 -o Cout out.tif")
outputs = [ "out.txt", "out.tif" ]
# expect a few LSB failures
failthresh = 0.004
failpercent = 0.05
|
py | 1a310d203832ad7c7c8a40c8d3a6aa8c2ea91551 | #!/usr/bin/python
from pwn import *
import sys
sys.path.append('/home/ww9210/develop/vminstance')
sys.path.append('/home/ww9210/develop/dataflowanalyzer')
import vminstance
import dataflowanalyzer
import subprocess
import re
import random
def test_0728(gdb_port=random.randint(15000,16000)):
context.update(arch = 'amd64')
start_time = time.time()
vm = vminstance.vmInstance(qemu_config_file = \
'/home/ww9210/develop/kuafffp/poc_qemu_config/cve-2016-0728-nokasan.cfg' \
, gdb_deamon_port = gdb_port\
, log_filename = 'vm_log_0728_ce.txt'\
, start_grace_time = 5\
, enable_kvm = True\
, two_cpus = True\
)
vm.run_gdb_deamon()
vm.run()
vm.connect()
vm.s.put('exp_boost')
sh = vm.s.shell('/bin/sh')
sh.sendline('chmod +x ./exp_boost')
sleep(1)
gdb = dataflowanalyzer.DebuggerGdb(gdbport = gdb_port, qemu_gdbport = vm.qemu_config.gdb_port\
, vmlinux_path = vm.qemu_config.vmlinux_path)
gdb.connectGdb()
gdb.loadVmlinux()
gdb.connectQemu()
gdb.b(0xffffffff812dcca8)
"""
0xffffffff812dcca2 <join_session_keyring+114>: mov rsi,r13
0xffffffff812dcca5 <join_session_keyring+117>: mov rdi,r12
0xffffffff812dcca8 <join_session_keyring+120>: call 0xffffffff812dc8b0 <install_session_keyring_to_cred>
"""
gdb.b('wait_for_key_construction')
gdb.c()
sh.sendline('./exp_boost ww9210')
print 'catching...'
gdb.catch()
obj_base = gdb.get_reg('rsi')
print 'object base is: ', hex(obj_base)
gdb.delete(1)
gdb.cmd('watch *'+hex(obj_base))
for i in range(5):
gdb.c()
cont = gdb.catch()
print cont
if 'key_put' in cont and '0xffffffff812d8d28' in cont and gdb.xgx(obj_base)&0xffffffff == 2:
gdb.set_int(obj_base, 1)
gdb.delete(2)
gdb.delete(3)
gdb.b(0xffffffff812d8bd8)
gdb.c()
break
print 'object base', hex(obj_base)
gdb.Gdbinteractive()
sh.interactive()
vm.shutdown()
if __name__ == '__main__':
test_0728()
|
py | 1a310d4d247ce5efed3b60e838880a331fd5168d | from graphs.graph import Graph
def get_edges(g,verticies_list):
sum=0
for i in range(0, len(verticies_list)-1):
found=False
for neighbor in g.get_neighbors(verticies_list[i]):
if verticies_list[i+1] == neighbor[0]:
sum+=neighbor[1]
found=True
if found == False :
return(False,0)
return (True, sum)
|
py | 1a310e449d16efbc2d2233e2f79f55b5a509bd02 | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
import os
import pathlib
import sys
# The version info for the project you're documenting, acts as replacement for
# |version| and |release|, also used in various other places throughout the
# built documents.
# see https://pypi.org/project/setuptools-scm/ for details
from pkg_resources import get_distribution
print("python exec:", sys.executable)
print("sys.path:", sys.path)
root = pathlib.Path(__file__).parent.parent.absolute()
os.environ["PYTHONPATH"] = str(root)
sys.path.insert(0, str(root))
import ragmac_xdem # isort:skip
# -- Project information -----------------------------------------------------
project = "ragmac_xdem"
copyright = "2021, Amaury Dehecq"
author = "Amaury Dehecq"
release = get_distribution("ragmac_xdem").version
# for example take major/minor
version = ".".join(release.split(".")[:2])
# -- General configuration ---------------------------------------------------
# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.
extensions = [
"sphinx.ext.autodoc",
"sphinx.ext.viewcode",
"sphinx.ext.napoleon",
"nbsphinx",
"recommonmark",
"sphinx.ext.mathjax",
"sphinx.ext.autosummary",
"sphinx.ext.extlinks",
"sphinx.ext.intersphinx",
"numpydoc",
"nbsphinx",
"IPython.sphinxext.ipython_directive",
"IPython.sphinxext.ipython_console_highlighting",
"sphinxcontrib.srclinks",
]
# Add any paths that contain templates here, relative to this directory.
templates_path = ["_templates"]
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
# This pattern also affects html_static_path and html_extra_path.
exclude_patterns = ["_build", "**.ipynb_checkpoints", "Thumbs.db", ".DS_Store"]
# -- Options for HTML output -------------------------------------------------
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
#
html_theme = "pangeo"
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ["_static"]
# -- nbsphinx specific options ----------------------------------------------
# this allows notebooks to be run even if they produce errors.
nbsphinx_allow_errors = True
|
py | 1a310ec87ab9e27b22a71ab673690cc8ac515bfb | # coding=utf-8
# Copyright 2022 The TensorFlow Datasets 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.
"""WSC273 dataset."""
from tensorflow_datasets.text.wsc273.wsc273 import Wsc273
|
py | 1a310f857a6be17bfe2bdc86377fd0c3a7e31ad9 | from webdnn.graph.attribute import Attribute
class Input(Attribute):
"""Input
Attribute for input variable of graph
"""
pass
|
py | 1a310f8c3a5bd83b913257ee39d720b1b8f07b5a | """
===============
Subplots Adjust
===============
Adjusting the spacing of margins and subplots using
:func:`~matplotlib.pyplot.subplots_adjust`.
"""
import matplotlib.pyplot as plt
import numpy as np
# Fixing random state for reproducibility
np.random.seed(19680801)
plt.subplot(211)
plt.imshow(np.random.random((100, 100)), cmap=plt.cm.BuPu_r)
plt.subplot(212)
plt.imshow(np.random.random((100, 100)), cmap=plt.cm.BuPu_r)
plt.subplots_adjust(bottom=0.1, right=0.8, top=0.9)
cax = plt.axes([0.85, 0.1, 0.075, 0.8])
plt.colorbar(cax=cax)
plt.show()
|
py | 1a311019e40440aa46c857ec175ac1bb91f27758 | #! /usr/bin/python2
#
# Copyright (c) 2017 Intel Corporation
#
# SPDX-License-Identifier: Apache-2.0
#
import codecs
import os
import shutil
import socket
import string
import subprocess
import sys
import telnetlib
import tempfile
import time
import serial
import commonl
import ttbl
import ttbl.cm_loopback
import ttbl.cm_serial
import ttbl.config
import ttbl.pc_ykush
import ttbl.tt_qemu
class tt_serial(
ttbl.test_target,
ttbl.tt_power_control_mixin,
ttbl.cm_serial.cm_serial):
"""A generic test target, power switched with a pluggable power
control implementation and with one or more serial ports.
Example configuration::
>>> ttbl.config.target_add(
>>> tt_serial(
>>> "minnow-01",
>>> power_control = ttbl.pc.dlwps7("http://URL"),
>>> serial_ports = [
>>> { "port": "/dev/tty-minnow-01", "baudrate": 115200 }
>>> ]),
>>> tags = {
>>> 'build_only': True,
>>> 'bsp_models': { 'x86': None },
>>> 'bsps': {
>>> 'x86': dict(board = 'minnowboard',
>>> console = "")
>>> }
>>> },
>>> target_type = "minnow_max")
With a udev configuration that generated the ``/dev/tty-minnow-01``
name such as ``/etc/udev/rules.d/SOMETHING.rules``::
SUBSYSTEM == "tty", ENV{ID_SERIAL_SHORT} == "SERIALNUMBER", \
GROUP = "SOMEGROUP", MODE = "0660", \
SYMLINK += "tty-minnow-01"
:param power_control: an instance of an implementation
of the power_control_mixin used to implement power control for
the target. Use ttbl.pc.manual() for manual power control that
requires user interaction.
:param serial_ports: list of serial port dictionaries, specified
as for :func:`serial.serial_for_url` with a couple of extras as
specified in :class:`ttbl.cm_serial`.
"""
def __init__(self, id, power_control, serial_ports,
_tags = None, target_type = None):
ttbl.test_target.__init__(self, id, _tags = _tags, _type = target_type)
ttbl.tt_power_control_mixin.__init__(self, power_control)
ttbl.cm_serial.cm_serial.__init__(self, self.state_dir, serial_ports)
class tt_power(
ttbl.test_target,
ttbl.tt_power_control_mixin):
def __init__(self, id, power_control, power = None):
"""
A generic test target for just power control
>>> ttbl.config.target_add(
>>> ttbl.tt.tt_power(name, ttbl.pc.dlwps7(URL), power = None),
>>> tags = dict(idle_poweroff = 0))
:param bool power: if specified, switch the power of the target
upon initialization; *True* powers it on, *False* powers it
off, *None* does nothing.
"""
assert isinstance(id, basestring)
ttbl.test_target.__init__(self, id)
ttbl.tt_power_control_mixin.__init__(self, power_control)
if power == True:
self.log.info("Powering on per configuration")
self._power_on_do()
elif power == False:
self.log.info("Powering off per configuration")
self._power_off_do()
class tt_power_lc(
ttbl.test_target,
ttbl.cm_loopback.cm_loopback,
ttbl.tt_power_control_mixin):
def __init__(self, id, power_control, power = None, consoles = None):
"""
A generic test target for just power control and fake loopback consoles
>>> ttbl.config.target_add(
>>> ttbl.tt.tt_power(name, ttbl.pc.dlwps7(URL), power = None))
:param bool power: if specified, switch the power of the target
upon initialization; *True* powers it on, *False* powers it
off, *None* does nothing.
:param consoles: see :class:`ttbl.cm_loopback.cm_loopback`.
"""
ttbl.test_target.__init__(self, id)
ttbl.tt_power_control_mixin.__init__(self, power_control)
ttbl.cm_loopback.cm_loopback.__init__(self, self.state_dir, consoles)
if power == True:
self.log.info("Powering on per configuration")
self._power_on_do()
elif power == False:
self.log.info("Powering off per configuration")
self._power_off_do()
class tt_arduino2(
ttbl.test_target,
ttbl.test_target_images_mixin,
ttbl.tt_power_control_mixin,
ttbl.cm_serial.cm_serial):
#: Command to call to execute the BOSSA command line flasher
bossac_cmd = "bossac"
def __init__(self, _id, serial_port,
power_control = None,
bossac_cmd = "bossac"):
"""Test target for a target flashable with the bossac tool (mostly
Arduino Due)
*Requirements*
- Needs a connection to the USB programming port
- Uses the bossac utility built on the *arduino* branch from
https://github.com/shumatech/BOSSA/tree/arduino; requires it
to be installed in the path ``bossac_cmd`` (defaults to sytem
path). Supports ``kernel{,-arm}`` images::
$ git clone https://github.com/shumatech/BOSSA.git bossac.git
$ cd bossac.git
$ make -k
$ sudo install -o root -g root bin/bossac /usr/local/bin
- TTY devices need to be properly configured permission wise for
bossac and serial console to work; for such, choose a Unix group
which can get access to said devices and add udev rules such as::
# Arduino2 boards: allow reading USB descriptors
SUBSYSTEM=="usb", ATTR{idVendor}=="2a03", ATTR{idProduct}=="003d", \
GROUP="GROUPNAME", MODE = "660"
# Arduino2 boards: allow reading serial port
SUBSYSTEM == "tty", ENV{ID_SERIAL_SHORT} == "SERIALNUMBER", \
GROUP = "GROUPNAME", MODE = "0660", \
SYMLINK += "tty-TARGETNAME"
The theory of operation is quite simple. According to
https://www.arduino.cc/en/Guide/ArduinoDue#toc4, the Due will
erase the flash if you open the programming port at 1200bps
and then start a reset process and launch the flash when you
open the port at 115200. This is not so clear in the URL
above, but this is what expermientation found.
So for flashing, we'll take over the console, set the serial
port to 1200bps, wait a wee bit and then call bossac.
We need power control to fully reset the Arduino Due when it
gets in a tight spot (and to save power when not using it).
There is no reset, we just power cycle -- found no way to do a
reset in SW without erasing the flash.
:param str _id: name identifying the target
:param str serial_port: File name of the device node
representing the serial port this device is connected to.
:param ttbl.tt_power_control_impl power_control: power controller
(if any)
:param bossac_cmd: Path and file where to find the `bossac`
utility.
"""
self.serial_port = serial_port
self.serial_port_basename = os.path.basename(serial_port)
#:param power_url: http://USER:PASSWORD@HOST:PORT/OUTLETNUMBER
ttbl.test_target.__init__(self, _id)
ttbl.test_target_images_mixin.__init__(self)
ttbl.tt_power_control_mixin.__init__(self, power_control)
ttbl.cm_serial.cm_serial.__init__(
self, self.state_dir,
[
"pc",
{ 'port': serial_port, 'baudrate': 115200 }
])
self.bossac_cmd = bossac_cmd
def image_do_set(self, image_type, image_name):
"""Just validates the image types are ok. The flashing happens in
images_do_set().
:param str image_type: Type of the image supported
:param str image_name: Name of image file in the daemon
storage space for the user
:raises: Any exception on failure
"""
if image_type != "kernel" and image_type != "kernel-arm":
raise self.unsupported_image_e("%s: image type not supported "
"(only kernel or kernel-arm)"
% image_type)
self.power_on(self.owner_get())
with self.console_takeover():
# erase the flash by opening the serial port at 1200bps
self.log.info("Erasing the flash")
eo = serial.Serial(port = self.serial_port, baudrate = 1200)
time.sleep(0.25)
eo.close()
self.log.debug("Erased the flash")
# now write it
cmdline = [ self.bossac_cmd,
"-p", self.serial_port_basename,
"-e", # Erase current
"-w", # Write a new one
"-v", # Verify,
"-b", # Boot from Flash
image_name ]
self.log.info("flashing image with: %s" % " ".join(cmdline))
so = commonl.logfile_open("bossac", type(self), True, 0)
s = subprocess.Popen(
cmdline, stdin = None, cwd = "/tmp",
stdout = so, stderr = subprocess.STDOUT)
self.log.info("running %s" % (" ".join(cmdline)))
r = s.wait()
del s
so.seek(0)
# Say what happened
if r != 0:
self.log.error("flashing failed")
m = ""
with codecs.open(so.name, "r", encoding = 'utf-8') as so_r:
for line in so_r:
line = line.decode('utf-8').strip()
self.log.error("flashing output: " + line)
m += "flashing output: " + line + "\n"
raise Exception("Flashing failed\n" + m)
# Check the log, if it does not say "Verify succesful", it didn't work
with codecs.open(so.name, "r", encoding = 'utf-8') as so_r:
m = ""
for line in so_r:
line = line.decode('utf-8').strip()
if line.endswith("Verify successful"):
break
m += "flashing output: " + line + "\n"
else:
raise Exception(
"Flashing failed (can't find 'Verify syccessful')\n" + m)
self.log.info("flashing succeeded")
with codecs.open(so.name, "r", encoding = 'utf-8') as so_r:
for line in so_r:
line = line.strip()
self.log.debug("flashing: " + line)
def images_do_set(self, images):
pass
class tt_esp32(
ttbl.test_target,
ttbl.tt_power_control_mixin,
ttbl.cm_serial.cm_serial,
ttbl.test_target_images_mixin):
esptool_path = "__unconfigured__tt_esp32.esptool_path__"
def __init__(self, _id, serial_number,
power_control, serial_port):
"""\
Test target ESP32 Tensilica based MCUs that use the ESP-IDF framework
:param str _id: name identifying the target
:param str serial_number: Unique USB serial number of the device (can
be updated with http://cp210x-program.sourceforge.net/)
:param power_control: Power control implementation or rail
(:class:`ttbl.tt_power_control_impl` or list of such)
:param str serial_port: Device name of the serial port where
the console will be found. This can be set with udev to be a
constant name.
The base code will convert the *ELF* image to the required
*bin* image using the ``esptool.py`` script. Then it will
flash it via the serial port.
*Requirements*
- The ESP-IDK framework, of which ``esptool.py`` is used to
flash the target; to install::
$ cd /opt
$ git clone --recursive https://github.com/espressif/esp-idf.git
(note the ``--recursive``!! it is needed so all the
submodules are picked up)
configure path to it globally by setting
:attr:`esptool_path` in a /etc/ttbd-production/conf_*.py file:
.. code-block:: python
import ttbl.tt
ttbl.tt.tt_esp32.esptool_path = "/opt/esp-idf/components/esptool_py/esptool/esptool.py"
Note you will also most likely need this in the client to
compile code for the board.
- Permissions to use USB devices in */dev/bus/usb* are needed;
*ttbd* usually roots with group *root*, which shall be
enough.
- Needs power control for proper operation; FIXME: pending to
make it operate without power control, using ``esptool.py``.
"""
assert isinstance(_id, basestring)
assert isinstance(serial_number, basestring)
assert isinstance(power_control, ttbl.tt_power_control_impl) \
or isinstance(power_control, list)
self.serial_number = serial_number
ttbl.test_target.__init__(self, _id)
ttbl.tt_power_control_mixin.__init__(self, power_control)
ttbl.test_target_images_mixin.__init__(self)
self.serial_port = serial_port
ttbl.cm_serial.cm_serial.__init__(
self, self.state_dir,
[
"pc",
{ 'port': serial_port, 'baudrate': 115200 }
])
def images_do_set(self, images):
# We implement image_do_set(), as there is only one image to set
pass
def image_do_set(self, image_type, image_name):
"""Just validates the image types are ok. The flashing happens in
images_do_set().
:param str image_type: Type of the image supported
:param str image_name: Name of image file in the daemon
storage space for the user
:raises: Any exception on failure
"""
cmdline_convert = [
self.esptool_path,
"--chip", "esp32",
"elf2image",
]
cmdline_flash = [
self.esptool_path,
"--chip", "esp32",
"--port", self.serial_port,
"--baud", "921600",
"--before", "default_reset",
"write_flash", "-u",
"--flash_mode", "dio",
"--flash_freq", "40m",
"--flash_size", "detect",
"0x1000",
]
if image_type == "kernel":
image_type = "kernel-xternsa"
if not image_type.startswith("kernel-"):
raise RuntimeError(
"Unknown image type '%s' (valid: kernel-{%s})"
% (image_type, ",".join(self.tags['bsps'].keys())))
image_name_bin = image_name + ".bin"
try:
cmdline = cmdline_convert + [ image_name,
"--output", image_name_bin ]
self.log.info("converting with %s" % " ".join(cmdline))
s = subprocess.check_output(cmdline, cwd = "/tmp",
stderr = subprocess.STDOUT)
except subprocess.CalledProcessError as e:
self.log.error("converting image with %s failed: (%d) %s"
% (" ".join(cmdline), e.returncode, e.output))
raise
self._power_cycle_do()
with self.console_takeover(): # give up the serial port
try:
cmdline = cmdline_flash + [ image_name_bin ]
self.log.info("flashing with %s" % " ".join(cmdline))
s = subprocess.check_output(cmdline, cwd = "/tmp",
stderr = subprocess.STDOUT)
self.log.info("flashed with %s: %s" % (" ".join(cmdline), s))
except subprocess.CalledProcessError as e:
self.log.error("flashing with %s failed: (%d) %s"
% (" ".join(cmdline), e.returncode, e.output))
raise
self._power_off_do()
self.log.info("flashing succeeded")
class tt_flasher(
ttbl.test_target,
ttbl.test_target_images_mixin,
ttbl.tt_power_control_mixin,
ttbl.tt_debug_mixin,
ttbl.cm_serial.cm_serial):
class error(RuntimeError):
pass
def __init__(self, _id, serial_ports,
flasher, power_control):
"""Test target flashable, power switchable with debuggin
Any target which supports the :class:`ttbl.flasher.flasher_c`
interface can be used, mostly OpenOCD targets.
How we use this, is for example:
>>> flasher_openocd = ttbl.flasher.openocd_c("frdm_k64f", FRDM_SERIAL,
>>> openocd10_path, openocd10_scripts)
>>> ttbl.config.target_add(
>>> ttbl.tt.tt_flasher(
>>> NAME,
>>> serial_ports = [
>>> "pc",
>>> dict(port = "/dev/tty-NAME", baudrate = 115200)
>>> ],
>>> flasher = flasher_obj,
>>> power_control = [
>>> ttbl.pc_ykush.ykush(YKUSH_SERIAL, YKUSH_PORT)
>>> # delay until device comes up
>>> ttbl.pc.delay_til_usb_device(FRDM_SERIAL),
>>> ttbl.cm_serial.pc(), # Connect serial ports
>>> flasher_openocd, # Start / stop OpenOCD
>>> ]
>>> ),
>>> tags = {
>>> 'bsp_models' : { 'arm': None },
>>> 'bsps' : {
>>> "arm": dict(board = "frdm_k64f", kernelname = 'zephyr.bin',
>>> kernel = [ "micro", "nano" ],
>>> console = "", quark_se_stub = "no"),
>>> },
>>> 'slow_flash_factor': 5, # Flash verification slow
>>> 'flash_verify': 'False', # Or disable it ...
>>> },
>>> target_type = "frdm_k64f")
.. note: the power for this target is a normal power control
implementation, HOWEVER, the power rail also contains
the OpenOCD flasher to start/stop the daemon once the
board is powered up.
:param str _id: target name
:param serial_ports: list of serial port dictionaries,
specified as for :func:`serial.serial_for_url` with a couple
of extras as specified in :class:`ttbl.cm_serial`.
:param ttbl.flasher.flasher_c flasher: flashing object that
provides access to deploy images and debug control
:param power_control: an instance of an implementation
of the power_control_mixin used to implement power control for
the target. Use ttbl.pc.manual() for manual power control that
requires user interaction.
"""
ttbl.test_target.__init__(self, _id)
ttbl.test_target_images_mixin.__init__(self)
ttbl.tt_power_control_mixin.__init__(self, power_control)
ttbl.tt_debug_mixin.__init__(self)
ttbl.cm_serial.cm_serial.__init__(self, self.state_dir, serial_ports)
self.flasher = flasher
self.flasher.test_target_link(self)
self.power_on_post_fns.append(self.power_on_do_post)
self.power_off_pre_fns.append(self.power_off_do_pre)
# Debugging interface
#
# We don't do much other than resuming the target if we stop
# debugging
def debug_do_start(self, tt_ignored):
pass
def debug_do_halt(self, _):
if self.flasher:
self.flasher.target_halt(for_what = "debug_halt")
def debug_do_reset(self, _):
if self.flasher:
self.flasher.target_reset_halt(for_what = "debug_reset")
def debug_do_reset_halt(self, _):
if self.flasher:
self.flasher.target_reset_halt(for_what = "debug_reset_halt")
def debug_do_resume(self, _):
if self.flasher:
self.flasher.target_resume(for_what = "debug_resume")
def debug_do_stop(self, _):
if self.flasher:
self.flasher.target_resume()
def debug_do_info(self, _):
# FIXME: self.flasher should be providing this information, this
# is breaking segmentation
count = 2 # port #0 is for telnet, #1 for TCL
tcp_port_base_s = self.fsdb.get("openocd.port")
if tcp_port_base_s == None:
return "Debugging information not available, power on?"
tcp_port_base = int(tcp_port_base_s)
s = "OpenOCD telnet server: %s %d\n" \
% (socket.getfqdn('0.0.0.0'), tcp_port_base)
for target in self.flasher.board['targets']:
s += "GDB server: %s: tcp:%s:%d\n" % (target,
socket.getfqdn('0.0.0.0'),
tcp_port_base + count)
count +=1
if self.fsdb.get('powered') != None:
s += "Debugging available as target is ON"
else:
s += "Debugging not available as target is OFF"
return s
def debug_do_openocd(self, _, command):
return self.flasher.openocd_cmd(command)
# Wrap actual reset with retries
def target_reset_halt(self, for_what = ""):
tries = 1
tries_max = 2
# FIXME: current limitation, can't access the tags from the
# constructor as the ones we add in target_add() aren't there
# yet.
wait = \
float(self.tags.get('hard_recover_rest_time', 2))
while tries <= tries_max:
# The Arduino101 get's so stuck sometimes
try:
self.flasher.target_reset_halt(for_what)
break
except self.flasher.error:
pass
try_s = "%d/%d" % (tries, tries_max)
time.sleep(2)
try:
self.flasher.target_reset("[recover reset #1 %s] " % try_s
+ for_what)
except self.flasher.error:
pass
try:
self.flasher.target_reset_halt("[retry %s] " % try_s
+ for_what)
break
except self.flasher.error:
pass
# In some targets, this fails because maybe we just
# power-cycled and the JTAG said it was ready but it
# is really not ready...when that happens, just
# power-cycle again.
# well, that didn't work either; bring the big guns,
# power cycle it and try the whole thing again
wait_s = (1 + 2.0 * tries/tries_max) * wait
self.log.info("Failed to reset/halt, power-cycle (%.2fs) "
"and retrying (try %d/%d)"
% (wait_s, tries, tries_max))
self.power_cycle(self.owner_get(), wait_s)
tries += 1
else:
# FIXME: pass the exception we get or the log or something
raise self.error("Can't reset/halt the target")
def target_reset(self, for_what = ""):
tries = 1
tries_max = 5
# FIXME: current limitation, can't access the tags from the
# constructor as the ones we add in target_add() aren't there
# yet.
wait = \
float(self.tags.get('hard_recover_rest_time', 10))
while tries <= tries_max:
# The Arduino101 get's so stuck sometimes
try:
self.flasher.target_reset(for_what)
break
except self.flasher.error:
pass
# Try again
try:
self.flasher.target_reset(for_what)
break
except self.flasher.error:
pass
# Bring the big guns, power cycle it
if wait != None:
wait_s = tries * wait
self.log.info("Failed to reset/run, power-cycle (%.2fs) "
"and retrying (try %d/%d)"
% (wait_s, tries, tries_max))
self.power_cycle(self.owner_get(), wait_s)
tries += 1
else:
# FIXME: pass the exception we get or the log or something
raise self.error("Can't reset/run the target")
# Power interface
#
# Fire up the flasher when we power the target up, so it can
# access the JTAG
def power_on_do_post(self):
self.flasher.start()
def power_off_do_pre(self):
self.flasher.stop()
def reset_do(self, _):
# We halt first so we can stop recording from the serial ports
# and then restart wihout getting any trash; we use reset_halt
# because it is a single command for all targets (halt needs
# to select each target).
self.flasher.target_reset_halt()
self.consoles_reset()
# When we reset, if we are debugging we need to halt the target as
# soon as it starts. Otherwise, we reset it normally. These
# are atomic (they act on all the targets at the same time..in
# theory)
if self.fsdb.get("debug") != None:
self.flasher.target_reset_halt()
else:
self.flasher.target_reset()
# Flashing interface -- quite simple, we need the target on and
# then just flash the image in.
def image_do_set(self, image_type, image_name):
pass
def images_do_set(self, images):
# FIXME: current limitation, can't access the tags from the
# constructor as the ones we add in target_add() aren't there
# yet.
wait = \
float(self.tags.get('hard_recover_rest_time', 10))
if self.fsdb.get("disable_power_cycle_before_flash") != 'True':
# Make sure the target is really fresh before flashing it
try:
# See the documentation for this on class flasher_c
# for why we have to do it.
self.flasher.hack_reset_after_power_on = True
self.power_cycle(self.owner_get(), wait = wait)
finally:
self.flasher.hack_reset_after_power_on = False
self.log.info("sleeping 2s after power cycle")
# HACK: For whatever the reason, we need to sleep before
# resetting/halt, seems some of the targets are not ready
# inmediately after
time.sleep(2)
self.target_reset_halt(for_what = "for image flashing")
timeout_factor = self.tags.get('slow_flash_factor', 1)
verify = self.tags.get('flash_verify', 'True') == 'True'
# FIXME: replace this check for verifying which image types
# the flasher supports
for t, n in images.iteritems():
if t == "kernel-x86":
it = "x86"
elif t == "kernel":
it = "x86"
elif t == "kernel-arc":
it = "arc"
elif t == "kernel-arm":
it = "arm"
elif t == "rom":
it = "rom"
elif t == "bootloader":
it = "bootloader"
else:
raise self.unsupported_image_e(
"%s: Unknown image type (expected "
"kernel|kernel-(x86,arc,arm), rom)"
% t)
try:
self.flasher.image_write(it, n, timeout_factor, verify)
except ValueError as e:
self.log.exception("flashing got exception: %s", e)
raise self.unsupported_image_e(e.message)
class tt_dfu(
ttbl.test_target,
ttbl.tt_power_control_mixin,
ttbl.cm_serial.cm_serial,
ttbl.test_target_images_mixin):
def __init__(self, _id, serial_number,
power_control, power_control_board,
serial_ports = None):
"""Test target for a flashable with DFU Utils
*Requirements*
- Needs a connection to the USB port that exposes a DFU
interface upon boot
- Uses the dfu-utils utility, available for most (if not all)
Linux distributions
- Permissions to use USB devices in */dev/bus/usb* are needed;
*ttbd* usually roots with group *root*, which shall be
enough.
- Needs power control for proper operation
:param str _id: name identifying the target
:param power_control: Power control implementation or rail
(:class:`ttbl.tt_power_control_impl` or list of such)
:param ttbl.tt_power_control_impl power_control: power controller
*just* for the board--this is the component in the power
control rail that controls the board only (versus other
parts such as serial ports or pseudo-power-controllers that
wait for the USB device to pop up.
Note the tags to the target must include, on each supported
BSP, a tag named *dfu_interface_name* listing the name of the
*altsetting* of the DFU interface to which the image for said
BSP needs to be flashed.
This can be found, when the device exposes the DFU interfaces
with the *lsusb -v* command; for example, for a tinyTILE
(output summarized for clarity)::
$ lsusb -v
...
Bus 002 Device 110: ID 8087:0aba Intel Corp.
Device Descriptor:
bLength 18
bDescriptorType 1
...
Interface Descriptor:
bInterfaceClass 254 Application Specific Interface
bInterfaceSubClass 1 Device Firmware Update...
iInterface 4 x86_rom
Interface Descriptor:
bInterfaceClass 254 Application Specific Interface
bInterfaceSubClass 1 Device Firmware Update...
iInterface 5 x86_boot
Interface Descriptor:
bInterfaceClass 254 Application Specific Interface
bInterfaceSubClass 1 Device Firmware Update
iInterface 6 x86_app
Interface Descriptor:
bInterfaceClass 254 Application Specific Interface
bInterfaceSubClass 1 Device Firmware Update
iInterface 7 config
Interface Descriptor:
bInterfaceClass 254 Application Specific Interface
bInterfaceSubClass 1 Device Firmware Update
iInterface 8 panic
Interface Descriptor:
bInterfaceClass 254 Application Specific Interface
bInterfaceSubClass 1 Device Firmware Update
iInterface 9 events
Interface Descriptor:
bInterfaceClass 254 Application Specific Interface
bInterfaceSubClass 1 Device Firmware Update
iInterface 10 logs
Interface Descriptor:
bInterfaceClass 254 Application Specific Interface
bInterfaceSubClass 1 Device Firmware Update
iInterface 11 sensor_core
Interface Descriptor:
bInterfaceClass 254 Application Specific Interface
bInterfaceSubClass 1 Device Firmware Update
iInterface 12 ble_core
In this case, the three cores available are x86 (x86_app), arc
(sensor_core) and ARM (ble_core).
*Example*
A Tiny Tile can be connected, without exposing a serial console:
>>> pc_board = ttbl.pc_ykush.ykush("YK22909", 1)
>>>
>>> ttbl.config.target_add(
>>> tt_dfu("ti-01",
>>> serial_number = "5614010001031629",
>>> power_control = [
>>> pc_board,
>>> ttbl.pc.delay_til_usb_device("5614010001031629"),
>>> ],
>>> power_control_board = pc_board),
>>> tags = {
>>> 'bsp_models': { 'x86+arc': ['x86', 'arc'], 'x86': None, 'arc': None},
>>> 'bsps' : {
>>> "x86": dict(zephyr_board = "tinytile",
>>> zephyr_kernelname = 'zephyr.bin',
>>> dfu_interface_name = "x86_app",
>>> console = ""),
>>> "arm": dict(zephyr_board = "arduino_101_ble",
>>> zephyr_kernelname = 'zephyr.bin',
>>> dfu_interface_name = "ble_core",
>>> console = ""),
>>> "arc": dict(zephyr_board = "arduino_101_sss",
>>> zephyr_kernelname = 'zephyr.bin',
>>> dfu_interface_name = 'sensor_core',
>>> console = "")
>>> },
>>>
>>> },
>>> target_type = "tile"
>>> )
"""
assert isinstance(_id, basestring)
assert isinstance(serial_number, basestring)
assert isinstance(power_control, ttbl.tt_power_control_impl) \
or isinstance(power_control, list)
self.serial_number = serial_number
self.pc_board = power_control_board
self.pc_usb = ttbl.pc.delay_til_usb_device(serial_number)
ttbl.test_target.__init__(self, _id)
ttbl.tt_power_control_mixin.__init__(self, power_control)
ttbl.test_target_images_mixin.__init__(self)
ttbl.cm_serial.cm_serial.__init__(self, self.state_dir, serial_ports)
def images_do_set(self, images):
"""Just validates the image types are ok. The flashing happens in
images_do_set().
:param str image_type: Type of the image supported
:param str image_name: Name of image file in the daemon
storage space for the user
:raises: Any exception on failure
"""
# Power cycle the board so it goes into DFU mode; it then
# stays there for five seconds
self.pc_board.power_cycle_raw(self, 5)
self.pc_usb.power_on_do(self)
cmdline = [
"/usr/bin/dfu-util",
"-S", self.serial_number
]
for image_type, image_name in images.iteritems():
if image_type == "kernel":
image_type = "kernel-x86"
if not image_type.startswith("kernel-"):
raise RuntimeError(
"Unknown image type '%s' (valid: kernel-{%s})"
% (image_type, ",".join(self.tags['bsps'].keys())))
bsp = image_type[len("kernel-"):]
tags_bsp = self.tags.get('bsps', {}).get(bsp, None)
if tags_bsp == None:
raise RuntimeError(
"Unknown BSP %s from image type '%s' (valid: %s)"
% (bsp, image_type, " ".join(self.tags['bsps'].keys())))
dfu_if_name = tags_bsp.get('dfu_interface_name', None)
if dfu_if_name == None:
raise RuntimeError(
"Misconfigured target: image type %s (BSP %s) has "
"no 'dfu_interface_name' key to indicate which DFU "
"interface shall it flash"
% (image_type, bsp))
# now write it
cmdline += [
"-a", dfu_if_name,
"-D", image_name,
]
try:
self.log.info("flashing with %s" % (" ".join(cmdline)))
s = subprocess.check_output(cmdline, cwd = "/tmp",
stderr = subprocess.STDOUT)
self.log.info("flashed with %s: %s" % (" ".join(cmdline), s))
except subprocess.CalledProcessError as e:
self.log.error("flashing with %s failed: (%d) %s" %
(" ".join(cmdline), e.returncode, e.output))
raise
self.log.info("flashing succeeded")
self.pc_board.power_off_do(self)
def image_do_set(self, t, n):
pass
class tt_max10(
ttbl.test_target,
ttbl.tt_power_control_mixin,
ttbl.cm_serial.cm_serial,
ttbl.test_target_images_mixin):
"""
Test target for an Altera MAX10
This allows to flash images to an Altera MAX10, using the Quartus
tools, freely downloadable from http://dl.altera.com.
Exports the following interfaces:
- power control (using any AC power switch, such as the
:class:`Digital Web Power Switch 7 <ttbl.pc.dlwps7>`)
- serial console
- image (in hex format) flashing (using the Quartus Prime tools
package)
Multiple instances at the same time are supported; however, due to
the JTAG interface not exporting a serial number, addressing has
to be done by USB path, which is risky (as it will change when the
cable is plugged to another port or might be enumerated in a
different number).
Note that:
- when flashing LED1 blinks green/blue
- the blue power switch must be pressed, to ensure the board is
*ON* when we switch the AC power to the power brick on
- SW2 DIP bank on the back of the board has to be all OFF (down)
except for 3, that has to be ON (this comes from the Zephyr
Altera MAX10 configuration)
- J7 (at the front of the board, next to the coaxial connectors)
has to be open
Pending:
- CPU design hardcoded to use Zephyr's -- it shall be possible to
flash it
"""
#: Path where the Quartus Programmer binaries have been installed
#:
#: 1. Download Quartus Prime Programmer and Tools from
#: http://dl.altera.com/17.1/?edition=lite&platform=linux&download_manager=direct
#: 2. Install to e.g `/opt/intelFPGA/17.1/qprogrammer/bin`.
#: 3. Configure in /etc/ttbd-production/conf_00_max10.py::
#:
#: .. code-block: python
#:
#: import ttbl.tt
#: ttbl.tt.tt_max10.quartus_path = "/opt/intelFPGA/17.1/qprogrammer/bin"
quartus_path = "__unconfigured__tt_max10.quartus_path__"
#: Path to where the NIOS Zephyr CPU image has been installed
#:
#: 1. Download the CPU image to `/var/lib/ttbd`::
#:
#: $ wget -O /var/lib/ttbd/ghrd_10m50da.sof \
#: https://github.com/zephyrproject-rtos/zephyr/raw/master/arch/nios2/soc/nios2f-zephyr/cpu/ghrd_10m50da.sof
#:
#: 3. Configure in /etc/ttbd-production/conf_00_max10.py:
#:
#: .. code-block: python
#:
#: import ttbl.tt
#: ttbl.tt.tt_max10.input_sof = "/var/lib/ttbd/ghrd_10m50da.sof"
input_sof = "__unconfigured__tt_max10.input_sof__"
def __init__(self, _id, device_id,
power_control, serial_port = None):
assert isinstance(_id, basestring)
assert isinstance(device_id, basestring)
assert isinstance(power_control, ttbl.tt_power_control_impl) \
or isinstance(power_control, list)
self.device_id = device_id
ttbl.test_target.__init__(self, _id)
ttbl.tt_power_control_mixin.__init__(self, power_control)
ttbl.test_target_images_mixin.__init__(self)
self.serial_port = serial_port
if serial_port:
ttbl.cm_serial.cm_serial.__init__(
self, self.state_dir,
[
"pc",
{ 'port': serial_port, 'baudrate': 115200 }
])
else:
ttbl.cm_serial.cm_serial.__init__(self, self.state_dir, [])
quartus_cpf_template = """\
<?xml version="1.0" encoding="US-ASCII" standalone="yes"?>
<cof>
<output_filename>${OUTPUT_FILENAME}</output_filename>
<n_pages>1</n_pages>
<width>1</width>
<mode>14</mode>
<sof_data>
<user_name>Page_0</user_name>
<page_flags>1</page_flags>
<bit0>
<sof_filename>${SOF_FILENAME}<compress_bitstream>1</compress_bitstream></sof_filename>
</bit0>
</sof_data>
<version>10</version>
<create_cvp_file>0</create_cvp_file>
<create_hps_iocsr>0</create_hps_iocsr>
<auto_create_rpd>0</auto_create_rpd>
<rpd_little_endian>1</rpd_little_endian>
<options>
<map_file>1</map_file>
</options>
<MAX10_device_options>
<por>0</por>
<io_pullup>1</io_pullup>
<config_from_cfm0_only>0</config_from_cfm0_only>
<isp_source>0</isp_source>
<verify_protect>0</verify_protect>
<epof>0</epof>
<ufm_source>2</ufm_source>
<ufm_filepath>${KERNEL_FILENAME}</ufm_filepath>
</MAX10_device_options>
<advanced_options>
<ignore_epcs_id_check>2</ignore_epcs_id_check>
<ignore_condone_check>2</ignore_condone_check>
<plc_adjustment>0</plc_adjustment>
<post_chain_bitstream_pad_bytes>-1</post_chain_bitstream_pad_bytes>
<post_device_bitstream_pad_bytes>-1</post_device_bitstream_pad_bytes>
<bitslice_pre_padding>1</bitslice_pre_padding>
</advanced_options>
</cof>
"""
# XXX Do we care about FileRevision, DefaultMfr, PartName? Do they need
# to be parameters? So far seems to work across 2 different boards, leave
# this alone for now.
quartus_pgm_template = """\
/* Quartus Prime Version 16.0.0 Build 211 04/27/2016 SJ Lite Edition */
JedecChain;
FileRevision(JESD32A);
DefaultMfr(6E);
P ActionCode(Cfg)
Device PartName(10M50DAF484ES) Path("${POF_DIR}/") File("${POF_FILE}") MfrSpec(OpMask(1));
ChainEnd;
AlteraBegin;
ChainType(JTAG);
AlteraEnd;"""
def _create_pof(self, output_pof, input_sof, kernel_hex):
t = string.Template(self.quartus_cpf_template)
input_sof = os.path.abspath(input_sof)
kernel_hex = os.path.abspath(kernel_hex)
# These tools are very stupid and freak out if the desired filename
# extensions are used. The kernel image must have extension .hex
with tempfile.NamedTemporaryFile(dir = self.state_dir,
suffix = ".cof") as temp_xml:
xml = t.substitute(SOF_FILENAME = input_sof,
OUTPUT_FILENAME = output_pof.name,
KERNEL_FILENAME = kernel_hex)
temp_xml.write(xml)
temp_xml.flush()
try:
cmd = [
os.path.join(self.quartus_path, "quartus_cpf"),
"-c", temp_xml.name
]
subprocess.check_output(cmd)
except OSError as e:
raise RuntimeError("Failed to create POF file w/ %s: %s"
% (" ".join(cmd), e))
except subprocess.CalledProcessError as cpe:
raise RuntimeError("Failed to create POF file: %s"
% cpe.output.decode("UTF-8"))
return output_pof
def images_do_set(self, images):
# We implement image_do_set(), as there is only one image to set
pass
# FIXME: limitation: SOF image is fixed, should be possible to
# upload it and default to built-in? Problem is we need to fixup
# the build instructions so they understand they need to upload
# the SOF too
# FIXME: also, the SOF is kinda big, 3M
def image_do_set(self, image_type, image_name):
if image_type == "kernel":
image_type = "kernel-max10"
if not image_type.startswith("kernel-"):
raise RuntimeError(
"Unknown image type '%s' (valid: kernel-{%s})"
% (image_type, ",".join(self.tags['bsps'].keys())))
self._power_cycle_do()
# This code snippet lifted from Zephyr's
# scripts/support/quartus-flash.py -- thx
# Minimum changes to place files in directories and wipe them
# upon context exit, match local style .
# def _flash_kernel(device_id, input_sof, kernel_hex):
self.log.info("Flashing %s:%s" % (image_type, image_name))
with tempfile.NamedTemporaryFile(dir = self.state_dir,
suffix = ".pof") as output_pof, \
tempfile.NamedTemporaryFile(dir = self.state_dir,
suffix = ".hex") as kernel_hex, \
tempfile.NamedTemporaryFile(dir = self.state_dir,
suffix = ".cdf") as temp_cdf:
# Apparently, the tools get freaked out by our largish
# file names, so just make it a temp with a short sweet name
shutil.copyfile(image_name, kernel_hex.name)
pof_file = self._create_pof(output_pof, self.input_sof, kernel_hex.name)
dname, fname = os.path.split(pof_file.name)
t = string.Template(self.quartus_pgm_template)
cdf = t.substitute(POF_DIR = dname, POF_FILE = fname)
temp_cdf.write(cdf)
temp_cdf.flush()
try:
output = subprocess.check_output([
os.path.join(self.quartus_path, "quartus_pgm"),
"--quiet",
"-c", self.device_id,
temp_cdf.name
])
except subprocess.CalledProcessError as cpe:
raise RuntimeError("Failed to flash image: %s"
% cpe.output.decode("UTF-8"))
self.log.info("Flashed %s:%s; output:\n%s"
% (image_type, image_name, output))
self._power_off_do()
self.log.info("flashing succeeded")
class grub2elf(tt_serial, ttbl.test_target_images_mixin):
"""Boot anything that can take an ELF image with grub2
**Overview**
A platform that can EFI boot off a multiplexed boot USB drive;
this drive:
- when connected to the target, acts as boot drive which boots
into grub2 which multiboots into whatever ELF binary we gave it
- when connected to the server, we partition, format, install
grub2 and the ELF kernel to be booted.
An eight-port USBRLY8 relay bank acting as a USB switcher, each
relay switching one of the four USB lines from target to server,
using :class:`ttbl.usbrly08b.plugger`:
- the USB-A female cable is connected to the C relay terminals
- the USB-A male cable for the server is connected to the NC relay
terminals
- the USB-A male cable for the client is connected to the NO relay
terminal
- a target that EFI/boots and can boot off a USB drive
Limitations:
- kinda hardcoded x86-64, shall be easy to fix
**Methodology**
The power rail for the target ensures that when the target is
powered on, the USB boot drive is connected to the target by the
USB multiplexor. When the target is off, the USB boot drive is
connected to the server.
The imaging process in :meth:`image_do_set` will make sure the USB
drive is connected to the server (by powering off the target) and
then use the helper script ``/usr/share/tcf/setup-efi-grub2-elf.sh``
to flash the ELF kernel to the drive (as well, will create the
grub2 boot structure)--for this we need the drive's USB serial
number and the ELF file to boot.
Upon boot, the boot drive will be detected and booted by default,
as the grub configuration is set to just boot that ELF kernel.
For cases where BIOS interaction with the console might be
necessary, a boot coercer can be implemented in the form of a
power control implementation that in its `power_on_do()` method
talks to the serial port to do whatever is needed. See for example
:class:`conf_00_lib.minnowboard_EFI_boot_grub_pc` which does so
for Minnowboards.
**Setup**
- the helper script ``/usr/share/tcf/setup-efi-grub2-elf.sh`` is
used to partition, configure and setup the USB drive--it
is run with *sudo* (via the sudo configurations script
:download:`/etc/sudoers.d/ttbd_sudo <../ttbd/ttbd_sudo>`)
- The daemon will require specific capabilities for being able to
run *sudo* (*CAP_SETGID*, *CAP_SETUID*, *CAP_SYS_ADMIN*,
*CAP_FOWNER*, *CAP_DAC_OVERRIDE*) setup in
:download:`/etc/systemd/system/[email protected]
<../ttbd/[email protected]>`.
- Ensure the following packages are available in the system:
* parted
* dosfstools
* grub2-efi-x64-cdboot and grub2-efi-x64-modules
* util-linux
- Identify the serial number for the USB drive; plug it to a
machine and issue::
$ lsblk -o "NAME,SERIAL,VENDOR,MODEL"
NAME SERIAL VENDOR MODEL
sdb AOJROZB8 JetFlash Transcend 8GB
sdj 76508A8E JetFlash Transcend 8GB
...
(for this example, ours is *76508A8E*, `/dev/sdj`)
blank the USB drive (**NOTE!!!** This will destroy the drive's
contents)::
$ dd if=/dev/zero of=/dev/sdj
- Create a power controller
- Setup the target's BIOS to boot by default off the USB drive
See :func:`conf_00_lib.minnowboard_add` for an example instantiation.
"""
def __init__(self, _id,
power_controller,
usb_drive_serial,
usbrly08b_serial, usbrly08b_bank,
serial_port,
boot_coercer = None):
power_control = [
# Ensure the USB dongle is / has been connected to the server
ttbl.pc.delay_til_usb_device(usb_drive_serial,
when_powering_on = False,
want_connected = True),
ttbl.usbrly08b.plugger(usbrly08b_serial, usbrly08b_bank),
# let the dongle power up, otherwise it won't be seen
ttbl.pc.delay(2),
ttbl.pc.delay_til_usb_device(usb_drive_serial,
when_powering_on = True,
want_connected = False),
ttbl.pc.delay(2), # let USB dongle settle to the target
ttbl.cm_serial.pc(), # Let it open and close ports
power_controller,
ttbl.pc.delay(2), # board powers up...
]
# A boot coercer is a PCI that talks to the target to get it to
# boot right, so it only implements power_on_do() to do that,
# power_off_do() has only a pass and power_get_do() returns
# True.
# This is eg needed if we need to tell the bios to do this, do
# that -- in the case of Minnowboard, tell the EFI shell to
# run grub (sometimes).
if boot_coercer:
assert isinstance(boot_coercer, ttbl.tt_power_control_impl)
power_control.append(boot_coercer)
self.usb_drive_serial = usb_drive_serial
tt_serial.__init__(
self,
_id,
power_control,
serial_ports = [
"pc",
{ "port": serial_port, "baudrate": 115200 }
])
ttbl.test_target_images_mixin.__init__(self)
image_types_valid = ("kernel", "kernel-x86")
def image_do_set(self, image_type, image_name):
if image_type not in self.image_types_valid:
raise self.unsupported_image_e(
"%s: image type not supported (valid: %s)"
% (image_type, ", ".join(self.image_types_valid)))
# power off the board to flash, this will redirect the USB
# drive to be connected to the server
self.power_off(self.owner_get())
# We don't verify image_name is an ELF file so that we can
# also use this to flash other stuff and it's up to the Grub
# bootloader to interpret it.
# We need an image with a bootloader, we use grub2 and we
# share the setup-efi-grub2-elf.sh implementation from
# simics and others
cmd_path = commonl.ttbd_locate_helper("setup-efi-grub2-elf.sh",
log = self.log)
# Yeah, sudo ... it kinda sucks, but it is the best way to
# isolate it -- could run from the daemon, then it'd have too
# many permissions--nope. file ./ttbd.sudo contains the config
# to put in /etc/sudoers.d for this to work.
cmdline = [ "sudo", "-n", cmd_path, self.usb_drive_serial,
image_name, "x86_64" ]
try:
self.log.debug("flashing with command '%s'" % " ".join(cmdline))
output = subprocess.check_output(cmdline,
stderr = subprocess.STDOUT)
except subprocess.CalledProcessError as cpe:
msg = "flashing with command '%s' failed: %s" \
% (" ".join(cpe.cmd), cpe.output)
self.log.error(msg)
raise RuntimeError(msg)
self.log.debug("flashed with command '%s': %s"
% (" ".join(cmdline), output))
def images_do_set(self, images):
# No need to set multiple images at the same time
pass
class simics(
ttbl.test_target,
ttbl.tt_power_control_mixin,
ttbl.tt_power_control_impl,
ttbl.test_target_images_mixin,
ttbl.test_target_console_mixin):
"""
Driver for a target based on Simics simulation of a platform
Currently this driver is quite basic and supports only the image
and console management interfaces:
- images are only supported as an ELF file that is booted by
*grub2* when simics boots from a hard disk image generated on
the fly.
- the only supported console is a serial output (no input)
**System setup**
1. In a configuration file (e.g. */etc/environment*), set the base
package for Simics::
SIMICS_BASE_PACKAGE=/opt/simics/5.0/simics-5.0.136
note that all the packages and extensions installed in there
must have been registered with the global Simics configuration,
as it will execute under the user as which the daemon is run
(usually *ttbd*).
Note that the installation of Simics and any extra packages
needed can be done automagically with::
$ destdir=/opt/simics/5.0
$ mkdir -p $destdir
# --batch: no questions asked, just proceed
# -a: auto select packages and register them
$ ./install-simics.pl --batch -a --prefix $destdir \\
package-1000-5.0.136-linux64.tar.gz.aes KEY-1000 \\
package-1001-5.0.54-linux64.tar.gz.aes KEY-1001 \\
package-1010-5.0.59-linux64.tar.gz.aes KEY-1010 \\
package-1012-5.0.24-linux64.tar.gz.aes KEY-1012 \\
package-2018-5.0.31-linux64.tar.gz.aes KEY-2018 \\
package-2075-5.0.50-linux64.tar.gz.aes KEY-2075
"""
class error_e(Exception): # pylint: disable = missing-docstring
pass
class simics_start_e(error_e): # pylint: disable = missing-docstring
pass
#: location of the base Simics installation in the file system; by
#: default this taken from the *SIMICS_BASE_PACKAGE* environment
#: variable, if it exists; it can also be set in a configuration
#: file as:
#:
#: >>> ttbl.tt.simics.base_package = "/some/path/simics-5.0.136"
base_package = os.environ.get('SIMICS_BASE_PACKAGE', None)
def __init__(self, _id, simics_cmds, _tags = None,
image_size_mb = 100):
assert isinstance(_id, basestring)
assert isinstance(simics_cmds, basestring)
assert image_size_mb > 0
if self.base_package == None:
raise RuntimeError(
"Simics not yet configured, either define environment "
"variable SIMICS_BASE_PACKAGE or configuration "
"ttbl.tt.simics.base_package")
ttbl.test_target.__init__(self, _id, _tags = _tags)
ttbl.tt_power_control_mixin.__init__(self)
ttbl.tt_power_control_impl.__init__(self)
ttbl.test_target_images_mixin.__init__(self)
ttbl.test_target_console_mixin.__init__(self)
self.simics_path = os.path.join(self.base_package, "bin/simics")
self.simics_check_path = os.path.join(self.base_package,
"linux64/bin/simics-common")
self.simics_cmds = simics_cmds
#: Variables that can be expanded in the Simics configuration
#: script passed as an argument
self.simics_vars = dict(
simics_workspace = os.path.join(self.state_dir,
"simics.workspace"),
simics_pidfile = os.path.join(self.state_dir, "simics.pid"),
simics_console = os.path.join(self.state_dir,
"simics-console.read"),
simics_hd0 = os.path.join(self.state_dir, "simics-hd0.img"),
simics_hd0_size = image_size_mb,
)
self.logfile_name = os.path.join(self.state_dir, "simics.log")
self.telnet = None
# FIXME: verify the BSP is kosher? generate command line from it?
image_types_valid = ( "kernel", "kernel-x86" )
# Image management interface
def image_do_set(self, image_type, image_name):
if image_type not in self.image_types_valid:
raise self.unsupported_image_e(
"%s: image type not supported (valid: %s)"
% (image_type, ", ".join(self.image_types_valid)))
# power off the target to flash, so in case simics is running
# on the image/files, it is stopped and we won't conflict /
# corrupt anything.
self.power_off(self.owner_get())
# Remove old image and create a new one, just writing one byte
# at the end to create a shallow file.
commonl.rm_f(self.simics_vars['simics_hd0'])
with open(self.simics_vars['simics_hd0'], "w") as f:
f.seek(self.simics_vars['simics_hd0_size'] * 1024 * 1024 - 1)
f.write('0')
# We don't verify image_name is an ELF file so that we can
# also use this to flash other stuff and it's up to the Grub
# bootloader to interpret it.
# Simics needs an image with a bootloader, we use grub2 and we
# share the setup-efi-grub2-elf.sh implementation from
# grub2elf.
cmd_path = commonl.ttbd_locate_helper("setup-efi-grub2-elf.sh",
log = self.log)
# Yeah, sudo ... it kinda sucks, but it is the best way to
# isolate it -- could run from the daemon, then it'd have too
# many permissions--nope. file ./ttbd_sudo contains the config
# to put in /etc/sudoers.d for this to work. Also note the
# systemd configuration requires us to have permission to
# regain certain capabilities.
cmdline = [ "sudo", "-n", cmd_path, self.simics_vars['simics_hd0'],
image_name, "i386" ]
try:
self.log.debug("flashing with '%s'" % " ".join(cmdline))
output = subprocess.check_output(cmdline,
stderr = subprocess.STDOUT)
except subprocess.CalledProcessError as cpe:
msg = "flashing with command '%s' failed: %s" \
% (" ".join(cpe.cmd), cpe.output)
self.log.error(msg)
raise RuntimeError(msg)
self.log.debug("flashed with command '%s': %s"
% (" ".join(cmdline), output))
def images_do_set(self, images):
pass
# power control interface
def _simics_launch(self, _target):
# Note this function will be called again if there is a
# resource conflict because simics will fail to start and
# _power_on_do() will detect it.
cmd_file_name = os.path.join(self.state_dir, "commands")
# clean up old state, but NOT the hd, as we probably created
# the image with images_do_set() before
commonl.rm_f(cmd_file_name)
if self.fsdb.get("debug") != None: # if debugging, keep log
commonl.rm_f(self.logfile_name)
commonl.rm_f(self.simics_vars['simics_console'])
commonl.rm_f(self.simics_vars['simics_pidfile'])
try:
# Create a fresh Simics workspace
shutil.rmtree(self.simics_vars['simics_workspace'],
ignore_errors = True)
cmdline = [
os.path.join(self.base_package, "bin/project-setup"),
"--ignore-existing-files",
self.simics_vars['simics_workspace'] ]
self.log.info("creating workspace with %s" % " ".join(cmdline))
subprocess.check_output(cmdline, shell = False,
stderr = subprocess.STDOUT)
except subprocess.CalledProcessError as e:
self.log.error("failed to create workspace: %s" % e.output)
except OSError as e:
self.log.error("failed to create workspace: %s" % e)
# Write the command script here, in case anything changes in
# the interpretation of the fields
simics_console_port = commonl.tcp_port_assigner(1)
with open(cmd_file_name, "w") as cmd_file:
simics_vars = dict(self.simics_vars)
simics_vars['simics_console_port'] = simics_console_port
cmd_file.write(self.simics_cmds % simics_vars)
cmdline = [ self.simics_path, "-no-gui" ]
if self.fsdb.get("debug"): # if debugging, be verbose
cmdline += [ "-verbose", "-verbose" ]
cmdline += [
"-project", self.simics_vars['simics_workspace'], cmd_file_name
]
# Fire up simics, redirecting all the output (stdout, stderr,
# traces) to a log file
logfile = open(self.logfile_name, "ab")
try:
env = dict(os.environ)
env['SIMICS_BASE_PACKAGE'] = self.base_package
self.log.info("Starting simics with: %s" % " ".join(cmdline))
p = subprocess.Popen(
cmdline, shell = False, cwd = self.state_dir, env = env,
close_fds = True, stdout = logfile, stderr = subprocess.STDOUT)
except OSError as e:
raise self.simics_start_e("Simics failed to start: %s" % e)
with open(self.simics_vars['simics_pidfile'], "w") as pidfilef:
pidfilef.write("%d" % p.pid)
pid = commonl.process_started( # Verify it started
self.simics_vars['simics_pidfile'],
self.simics_check_path,
verification_f = os.path.exists,
verification_f_args = (self.simics_vars['simics_console'],),
timeout = 20, tag = "simics", log = self.log)
if pid == None:
raise self.simics_start_e("Simics failed to start after 5s")
self.fsdb.set('simics_console_port', "%d" %
simics_console_port)
def power_on_do(self, target):
# try to start qemu, retrying if we have to
for cnt in range(5):
try:
self._simics_launch(target)
break
except self.error_e:
with open(self.logfile_name) as logfile:
for line in logfile:
if 'Address already in use' in line:
# Ops, port we took for the console is
# taken, try again with another port
self.log.info("%d/5: port conflict, trying again"
% cnt)
self.power_off_do(target)
continue
else:
raise RuntimeError("simis: did not start after 5 tries")
def power_off_do(self, _target):
self.fsdb.set('simics_console_port', None)
commonl.process_terminate(self.simics_vars['simics_pidfile'],
tag = "simics",
path = self.simics_check_path)
def power_get_do(self, _target):
pid = commonl.process_alive(self.simics_vars['simics_pidfile'],
self.simics_check_path)
return pid != None
# Console mixin
# Any file SOMETHING-console.read describes a console that is available.
def console_do_list(self):
consoles = []
for filename in os.listdir(self.state_dir):
if filename.endswith("-console.read"):
console_name = filename[:-len("-console.read")]
consoles.append(console_name)
return consoles
def console_do_read(self, console_id = None, offset = 0):
if console_id == None:
console_id = 'simics'
if console_id != 'simics':
raise RuntimeError("console ID '%s' not found" % console_id)
# Reading is simple -- simics pipes all the output to a file
# called simics-console.read
consolefname = os.path.join(self.state_dir,
"%s-console.read" % console_id)
if os.path.isfile(consolefname):
# don't open codecs.open() UTF-8, as that will trip Flask
# when passing the generator up to serve to the client
ifd = open(consolefname, "rb")
if offset > 0:
ifd.seek(offset)
return ifd
else:
return iter(())
def console_do_write(self, _data, _console_id = None):
_simics_console_port = self.fsdb.get('simics_console_port')
if _simics_console_port == None:
raise RuntimeError("target is off, cannot write to it")
simics_console_port = int(_simics_console_port)
# re-create it for every write -- yeah, overkill, but this
# runs across multiple servers, so we don't know if it was
# power cycled and thus the port is still valid/open..
# FIXME: hack, should cache
telnet = telnetlib.Telnet('127.0.0.1', simics_console_port)
# KLUDGE, workaround
# So this C-like loop (because I want it to be clearer
# than hidden iterator pythonic stuff) it is chunking
# the data to be sent to the VM's serial console
# and doing a short sleep in between. Why?
# Because by observation we've seen data being lost
# when sending it to the sock that represents the
# input. Chunking it up and giving it a breather
# alleviated it.
chunk_size = 8
count = 0
l = len(_data)
while l > 0:
if l >= chunk_size:
chunk_data = _data[count:count + chunk_size]
else:
chunk_data = _data[count:count + l]
# FIXME: I seriously don't have any idea of what am I doing
# here; this Python2 string decoding/encoding stuff is
# utterly confusing -- but this is how it works :/
telnet.write(chunk_data.decode('latin1').encode('utf-8'))
time.sleep(0.15)
l -= chunk_size
count += chunk_size
|
py | 1a31104b11cdd902cc971f225584b84aa89cde71 | #!/usr/bin/python
# -*- coding: utf-8 -*-
def warn(*args, **kwargs):
pass
from django.shortcuts import render
from django.core.files.storage import FileSystemStorage
from django.http import HttpResponse, JsonResponse
from django.db.models import Q
from .models import *
def search(request):
try:
query = request.GET['search']
query = str(query).lower()
mydict = {
"urls" : Url.objects.all().filter(Q(link__contains=query) | Q(result__contains=query) | Q(created_at__contains=query) |
Q(rank__contains=query) | Q(dom__contains=query) | Q(country__contains=query) | Q(state__contains=query) | Q(emails__contains=query) |
Q(add__contains=query) | Q(org__contains=query) | Q(city__contains=query)
).order_by('-created_at')
}
return render(request,'list.html',context=mydict)
except:
return render(request,'404.html')
def error_404_view(request, exception):
return render(request,'404.html')
def index(request):
try:
return render(request, '404.html')
except:
return render(request, '404.html')
from requests import get
import json
from dateutil import parser as dateparser
from django.http import HttpResponse
from django.shortcuts import render
def result(request):
#text=request.GET['nm'].strip()
#http://127.0.0.1:8000/result?uniqueid=12&nm=hi&phonenumber=12321&time=21%2F12%2F1998+12%3A12%3A12
#result="booked"
lat=request.GET['LAT']
lon=request.GET['LON']
import reverse_geocoder as rg
coordinates = (lat,lon)
#print (rg.search(coordinates)[0]['name'], rg.search(coordinates)[0]['admin1'])
mydict = {
"query" : f"{lat},{lon}",
"city" : rg.search(coordinates)[0]['name'],
"state" : rg.search(coordinates)[0]['admin1']
}
response = JsonResponse(mydict)
return response
|
py | 1a3110a46fac47e9441195ccd606cb148fac6b9a | # Copyright (c) 2021 PaddlePaddle Authors. 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 os
import time
import yaml
import cv2
import re
import numpy as np
from collections import defaultdict
import paddle
from paddle.inference import Config
from paddle.inference import create_predictor
from picodet_postprocess import PicoDetPostProcess
from utils import argsparser, Timer, get_current_memory_mb, _is_valid_video, video2frames
from det_infer import Detector, DetectorPicoDet, get_test_images, print_arguments, PredictConfig
from det_infer import load_predictor
from benchmark_utils import PaddleInferBenchmark
from visualize import plot_tracking
from mot.tracker import DeepSORTTracker
from mot.utils import MOTTimer, write_mot_results, flow_statistic, scale_coords, clip_box, preprocess_reid
from mot.mtmct.utils import parse_bias
from mot.mtmct.postprocess import trajectory_fusion, sub_cluster, gen_res, print_mtmct_result
from mot.mtmct.postprocess import get_mtmct_matching_results, save_mtmct_crops, save_mtmct_vis_results
# Global dictionary
MOT_SUPPORT_MODELS = {'DeepSORT'}
def bench_log(detector, img_list, model_info, batch_size=1, name=None):
mems = {
'cpu_rss_mb': detector.cpu_mem / len(img_list),
'gpu_rss_mb': detector.gpu_mem / len(img_list),
'gpu_util': detector.gpu_util * 100 / len(img_list)
}
perf_info = detector.det_times.report(average=True)
data_info = {
'batch_size': batch_size,
'shape': "dynamic_shape",
'data_num': perf_info['img_num']
}
log = PaddleInferBenchmark(detector.config, model_info, data_info,
perf_info, mems)
log(name)
class SDE_Detector(Detector):
"""
Detector of SDE methods
Args:
pred_config (object): config of model, defined by `Config(model_dir)`
model_dir (str): root path of model.pdiparams, model.pdmodel and infer_cfg.yml
device (str): Choose the device you want to run, it can be: CPU/GPU/XPU, default is CPU
run_mode (str): mode of running(fluid/trt_fp32/trt_fp16)
batch_size (int): size of per batch in inference, default is 1 in tracking models
trt_min_shape (int): min shape for dynamic shape in trt
trt_max_shape (int): max shape for dynamic shape in trt
trt_opt_shape (int): opt shape for dynamic shape in trt
trt_calib_mode (bool): If the model is produced by TRT offline quantitative
calibration, trt_calib_mode need to set True
cpu_threads (int): cpu threads
enable_mkldnn (bool): whether to open MKLDNN
"""
def __init__(self,
pred_config,
model_dir,
device='CPU',
run_mode='fluid',
batch_size=1,
trt_min_shape=1,
trt_max_shape=1088,
trt_opt_shape=608,
trt_calib_mode=False,
cpu_threads=1,
enable_mkldnn=False):
super(SDE_Detector, self).__init__(
pred_config=pred_config,
model_dir=model_dir,
device=device,
run_mode=run_mode,
batch_size=batch_size,
trt_min_shape=trt_min_shape,
trt_max_shape=trt_max_shape,
trt_opt_shape=trt_opt_shape,
trt_calib_mode=trt_calib_mode,
cpu_threads=cpu_threads,
enable_mkldnn=enable_mkldnn)
assert batch_size == 1, "The detector of tracking models only supports batch_size=1 now"
self.pred_config = pred_config
def postprocess(self,
boxes,
ori_image_shape,
threshold,
inputs,
scaled=False):
over_thres_idx = np.nonzero(boxes[:, 1:2] >= threshold)[0]
if len(over_thres_idx) == 0:
pred_dets = np.zeros((1, 6), dtype=np.float32)
pred_xyxys = np.zeros((1, 4), dtype=np.float32)
return pred_dets, pred_xyxys
else:
boxes = boxes[over_thres_idx]
if not scaled:
# scaled means whether the coords after detector outputs
# have been scaled back to the original image, set True
# in general detector, set False in JDE YOLOv3.
input_shape = inputs['image'].shape[2:]
im_shape = inputs['im_shape'][0]
scale_factor = inputs['scale_factor'][0]
pred_bboxes = scale_coords(boxes[:, 2:], input_shape, im_shape,
scale_factor)
else:
pred_bboxes = boxes[:, 2:]
pred_xyxys, keep_idx = clip_box(pred_bboxes, ori_image_shape)
if len(keep_idx[0]) == 0:
pred_dets = np.zeros((1, 6), dtype=np.float32)
pred_xyxys = np.zeros((1, 4), dtype=np.float32)
return pred_dets, pred_xyxys
pred_scores = boxes[:, 1:2][keep_idx[0]]
pred_cls_ids = boxes[:, 0:1][keep_idx[0]]
pred_tlwhs = np.concatenate(
(pred_xyxys[:, 0:2], pred_xyxys[:, 2:4] - pred_xyxys[:, 0:2] + 1),
axis=1)
pred_dets = np.concatenate(
(pred_tlwhs, pred_scores, pred_cls_ids), axis=1)
return pred_dets, pred_xyxys
def predict(self,
image_path,
ori_image_shape,
threshold=0.5,
scaled=False,
repeats=1,
add_timer=True):
'''
Args:
image_path (list[str]): path of images, only support one image path
(batch_size=1) in tracking model
ori_image_shape (list[int]: original image shape
threshold (float): threshold of predicted box' score
scaled (bool): whether the coords after detector outputs are scaled,
default False in jde yolov3, set True in general detector.
repeats (int): repeat number for prediction
add_timer (bool): whether add timer during prediction
Returns:
pred_dets (np.ndarray, [N, 6]): 'x,y,w,h,score,cls_id'
pred_xyxys (np.ndarray, [N, 4]): 'x1,y1,x2,y2'
'''
# preprocess
if add_timer:
self.det_times.preprocess_time_s.start()
inputs = self.preprocess(image_path)
input_names = self.predictor.get_input_names()
for i in range(len(input_names)):
input_tensor = self.predictor.get_input_handle(input_names[i])
input_tensor.copy_from_cpu(inputs[input_names[i]])
if add_timer:
self.det_times.preprocess_time_s.end()
self.det_times.inference_time_s.start()
# model prediction
for i in range(repeats):
self.predictor.run()
output_names = self.predictor.get_output_names()
boxes_tensor = self.predictor.get_output_handle(output_names[0])
boxes = boxes_tensor.copy_to_cpu()
if add_timer:
self.det_times.inference_time_s.end(repeats=repeats)
self.det_times.postprocess_time_s.start()
# postprocess
if len(boxes) == 0:
pred_dets = np.zeros((1, 6), dtype=np.float32)
pred_xyxys = np.zeros((1, 4), dtype=np.float32)
else:
pred_dets, pred_xyxys = self.postprocess(
boxes, ori_image_shape, threshold, inputs, scaled=scaled)
if add_timer:
self.det_times.postprocess_time_s.end()
self.det_times.img_num += 1
return pred_dets, pred_xyxys
class SDE_DetectorPicoDet(DetectorPicoDet):
"""
PicoDet of SDE methods, the postprocess of PicoDet has not been exported as
other detectors, so do postprocess here.
Args:
pred_config (object): config of model, defined by `Config(model_dir)`
model_dir (str): root path of model.pdiparams, model.pdmodel and infer_cfg.yml
device (str): Choose the device you want to run, it can be: CPU/GPU/XPU, default is CPU
run_mode (str): mode of running(fluid/trt_fp32/trt_fp16)
batch_size (int): size of per batch in inference, default is 1 in tracking models
trt_min_shape (int): min shape for dynamic shape in trt
trt_max_shape (int): max shape for dynamic shape in trt
trt_opt_shape (int): opt shape for dynamic shape in trt
trt_calib_mode (bool): If the model is produced by TRT offline quantitative
calibration, trt_calib_mode need to set True
cpu_threads (int): cpu threads
enable_mkldnn (bool): whether to open MKLDNN
"""
def __init__(self,
pred_config,
model_dir,
device='CPU',
run_mode='fluid',
batch_size=1,
trt_min_shape=1,
trt_max_shape=1088,
trt_opt_shape=608,
trt_calib_mode=False,
cpu_threads=1,
enable_mkldnn=False):
super(SDE_DetectorPicoDet, self).__init__(
pred_config=pred_config,
model_dir=model_dir,
device=device,
run_mode=run_mode,
batch_size=batch_size,
trt_min_shape=trt_min_shape,
trt_max_shape=trt_max_shape,
trt_opt_shape=trt_opt_shape,
trt_calib_mode=trt_calib_mode,
cpu_threads=cpu_threads,
enable_mkldnn=enable_mkldnn)
assert batch_size == 1, "The detector of tracking models only supports batch_size=1 now"
self.pred_config = pred_config
def postprocess(self, boxes, ori_image_shape, threshold):
over_thres_idx = np.nonzero(boxes[:, 1:2] >= threshold)[0]
if len(over_thres_idx) == 0:
pred_dets = np.zeros((1, 6), dtype=np.float32)
pred_xyxys = np.zeros((1, 4), dtype=np.float32)
return pred_dets, pred_xyxys
else:
boxes = boxes[over_thres_idx]
pred_bboxes = boxes[:, 2:]
pred_xyxys, keep_idx = clip_box(pred_bboxes, ori_image_shape)
if len(keep_idx[0]) == 0:
pred_dets = np.zeros((1, 6), dtype=np.float32)
pred_xyxys = np.zeros((1, 4), dtype=np.float32)
return pred_dets, pred_xyxys
pred_scores = boxes[:, 1:2][keep_idx[0]]
pred_cls_ids = boxes[:, 0:1][keep_idx[0]]
pred_tlwhs = np.concatenate(
(pred_xyxys[:, 0:2], pred_xyxys[:, 2:4] - pred_xyxys[:, 0:2] + 1),
axis=1)
pred_dets = np.concatenate(
(pred_tlwhs, pred_scores, pred_cls_ids), axis=1)
return pred_dets, pred_xyxys
def predict(self,
image_path,
ori_image_shape,
threshold=0.5,
scaled=False,
repeats=1,
add_timer=True):
'''
Args:
image_path (list[str]): path of images, only support one image path
(batch_size=1) in tracking model
ori_image_shape (list[int]: original image shape
threshold (float): threshold of predicted box' score
scaled (bool): whether the coords after detector outputs are scaled,
default False in jde yolov3, set True in general detector.
repeats (int): repeat number for prediction
add_timer (bool): whether add timer during prediction
Returns:
pred_dets (np.ndarray, [N, 6]): 'x,y,w,h,score,cls_id'
pred_xyxys (np.ndarray, [N, 4]): 'x1,y1,x2,y2'
'''
# preprocess
if add_timer:
self.det_times.preprocess_time_s.start()
inputs = self.preprocess(image_path)
input_names = self.predictor.get_input_names()
for i in range(len(input_names)):
input_tensor = self.predictor.get_input_handle(input_names[i])
input_tensor.copy_from_cpu(inputs[input_names[i]])
if add_timer:
self.det_times.preprocess_time_s.end()
self.det_times.inference_time_s.start()
# model prediction
for i in range(repeats):
self.predictor.run()
np_score_list.clear()
np_boxes_list.clear()
output_names = self.predictor.get_output_names()
num_outs = int(len(output_names) / 2)
for out_idx in range(num_outs):
np_score_list.append(
self.predictor.get_output_handle(output_names[out_idx])
.copy_to_cpu())
np_boxes_list.append(
self.predictor.get_output_handle(output_names[
out_idx + num_outs]).copy_to_cpu())
if add_timer:
self.det_times.inference_time_s.end(repeats=repeats)
self.det_times.postprocess_time_s.start()
# postprocess
self.picodet_postprocess = PicoDetPostProcess(
inputs['image'].shape[2:],
inputs['im_shape'],
inputs['scale_factor'],
strides=self.pred_config.fpn_stride,
nms_threshold=self.pred_config.nms['nms_threshold'])
boxes, boxes_num = self.picodet_postprocess(np_score_list,
np_boxes_list)
if len(boxes) == 0:
pred_dets = np.zeros((1, 6), dtype=np.float32)
pred_xyxys = np.zeros((1, 4), dtype=np.float32)
else:
pred_dets, pred_xyxys = self.postprocess(boxes, ori_image_shape,
threshold)
if add_timer:
self.det_times.postprocess_time_s.end()
self.det_times.img_num += 1
return pred_dets, pred_xyxys
class SDE_ReID(object):
"""
ReID of SDE methods
Args:
pred_config (object): config of model, defined by `Config(model_dir)`
model_dir (str): root path of model.pdiparams, model.pdmodel and infer_cfg.yml
device (str): Choose the device you want to run, it can be: CPU/GPU/XPU, default is CPU
run_mode (str): mode of running(fluid/trt_fp32/trt_fp16)
batch_size (int): size of per batch in inference, default 50 means at most
50 sub images can be made a batch and send into ReID model
trt_min_shape (int): min shape for dynamic shape in trt
trt_max_shape (int): max shape for dynamic shape in trt
trt_opt_shape (int): opt shape for dynamic shape in trt
trt_calib_mode (bool): If the model is produced by TRT offline quantitative
calibration, trt_calib_mode need to set True
cpu_threads (int): cpu threads
enable_mkldnn (bool): whether to open MKLDNN
"""
def __init__(self,
pred_config,
model_dir,
device='CPU',
run_mode='fluid',
batch_size=50,
trt_min_shape=1,
trt_max_shape=1088,
trt_opt_shape=608,
trt_calib_mode=False,
cpu_threads=1,
enable_mkldnn=False):
self.pred_config = pred_config
self.predictor, self.config = load_predictor(
model_dir,
run_mode=run_mode,
batch_size=batch_size,
min_subgraph_size=self.pred_config.min_subgraph_size,
device=device,
use_dynamic_shape=self.pred_config.use_dynamic_shape,
trt_min_shape=trt_min_shape,
trt_max_shape=trt_max_shape,
trt_opt_shape=trt_opt_shape,
trt_calib_mode=trt_calib_mode,
cpu_threads=cpu_threads,
enable_mkldnn=enable_mkldnn)
self.det_times = Timer()
self.cpu_mem, self.gpu_mem, self.gpu_util = 0, 0, 0
self.batch_size = batch_size
assert pred_config.tracker, "Tracking model should have tracker"
pt = pred_config.tracker
max_age = pt['max_age'] if 'max_age' in pt else 30
max_iou_distance = pt[
'max_iou_distance'] if 'max_iou_distance' in pt else 0.7
self.tracker = DeepSORTTracker(
max_age=max_age, max_iou_distance=max_iou_distance)
def get_crops(self, xyxy, ori_img):
w, h = self.tracker.input_size
self.det_times.preprocess_time_s.start()
crops = []
xyxy = xyxy.astype(np.int64)
ori_img = ori_img.transpose(1, 0, 2) # [h,w,3]->[w,h,3]
for i, bbox in enumerate(xyxy):
crop = ori_img[bbox[0]:bbox[2], bbox[1]:bbox[3], :]
crops.append(crop)
crops = preprocess_reid(crops, w, h)
self.det_times.preprocess_time_s.end()
return crops
def preprocess(self, crops):
# to keep fast speed, only use topk crops
crops = crops[:self.batch_size]
inputs = {}
inputs['crops'] = np.array(crops).astype('float32')
return inputs
def postprocess(self, pred_dets, pred_embs):
tracker = self.tracker
tracker.predict()
online_targets = tracker.update(pred_dets, pred_embs)
online_tlwhs, online_scores, online_ids = [], [], []
for t in online_targets:
if not t.is_confirmed() or t.time_since_update > 1:
continue
tlwh = t.to_tlwh()
tscore = t.score
tid = t.track_id
if tlwh[2] * tlwh[3] <= tracker.min_box_area:
continue
if tracker.vertical_ratio > 0 and tlwh[2] / tlwh[
3] > tracker.vertical_ratio:
continue
online_tlwhs.append(tlwh)
online_scores.append(tscore)
online_ids.append(tid)
tracking_outs = {
'online_tlwhs': online_tlwhs,
'online_scores': online_scores,
'online_ids': online_ids,
}
return tracking_outs
def postprocess_mtmct(self, pred_dets, pred_embs, frame_id, seq_name):
tracker = self.tracker
tracker.predict()
online_targets = tracker.update(pred_dets, pred_embs)
online_tlwhs, online_scores, online_ids = [], [], []
online_tlbrs, online_feats = [], []
for t in online_targets:
if not t.is_confirmed() or t.time_since_update > 1:
continue
tlwh = t.to_tlwh()
tscore = t.score
tid = t.track_id
if tlwh[2] * tlwh[3] <= tracker.min_box_area:
continue
if tracker.vertical_ratio > 0 and tlwh[2] / tlwh[
3] > tracker.vertical_ratio:
continue
online_tlwhs.append(tlwh)
online_scores.append(tscore)
online_ids.append(tid)
online_tlbrs.append(t.to_tlbr())
online_feats.append(t.feat)
tracking_outs = {
'online_tlwhs': online_tlwhs,
'online_scores': online_scores,
'online_ids': online_ids,
'feat_data': {},
}
for _tlbr, _id, _feat in zip(online_tlbrs, online_ids, online_feats):
feat_data = {}
feat_data['bbox'] = _tlbr
feat_data['frame'] = f"{frame_id:06d}"
feat_data['id'] = _id
_imgname = f'{seq_name}_{_id}_{frame_id}.jpg'
feat_data['imgname'] = _imgname
feat_data['feat'] = _feat
tracking_outs['feat_data'].update({_imgname: feat_data})
return tracking_outs
def predict(self,
crops,
pred_dets,
repeats=1,
add_timer=True,
MTMCT=False,
frame_id=0,
seq_name=''):
# preprocess
if add_timer:
self.det_times.preprocess_time_s.start()
inputs = self.preprocess(crops)
input_names = self.predictor.get_input_names()
for i in range(len(input_names)):
input_tensor = self.predictor.get_input_handle(input_names[i])
input_tensor.copy_from_cpu(inputs[input_names[i]])
if add_timer:
self.det_times.preprocess_time_s.end()
self.det_times.inference_time_s.start()
# model prediction
for i in range(repeats):
self.predictor.run()
output_names = self.predictor.get_output_names()
feature_tensor = self.predictor.get_output_handle(output_names[0])
pred_embs = feature_tensor.copy_to_cpu()
if add_timer:
self.det_times.inference_time_s.end(repeats=repeats)
self.det_times.postprocess_time_s.start()
# postprocess
if MTMCT == False:
tracking_outs = self.postprocess(pred_dets, pred_embs)
else:
tracking_outs = self.postprocess_mtmct(pred_dets, pred_embs,
frame_id, seq_name)
if add_timer:
self.det_times.postprocess_time_s.end()
self.det_times.img_num += 1
return tracking_outs
def predict_image(detector, reid_model, image_list):
image_list.sort()
for i, img_file in enumerate(image_list):
frame = cv2.imread(img_file)
ori_image_shape = list(frame.shape[:2])
if FLAGS.run_benchmark:
# warmup
pred_dets, pred_xyxys = detector.predict(
[img_file],
ori_image_shape,
FLAGS.threshold,
FLAGS.scaled,
repeats=10,
add_timer=False)
# run benchmark
pred_dets, pred_xyxys = detector.predict(
[img_file],
ori_image_shape,
FLAGS.threshold,
FLAGS.scaled,
repeats=10,
add_timer=True)
cm, gm, gu = get_current_memory_mb()
detector.cpu_mem += cm
detector.gpu_mem += gm
detector.gpu_util += gu
print('Test iter {}, file name:{}'.format(i, img_file))
else:
pred_dets, pred_xyxys = detector.predict(
[img_file], ori_image_shape, FLAGS.threshold, FLAGS.scaled)
if len(pred_dets) == 1 and np.sum(pred_dets) == 0:
print('Frame {} has no object, try to modify score threshold.'.
format(i))
online_im = frame
else:
# reid process
crops = reid_model.get_crops(pred_xyxys, frame)
if FLAGS.run_benchmark:
# warmup
tracking_outs = reid_model.predict(
crops, pred_dets, repeats=10, add_timer=False)
# run benchmark
tracking_outs = reid_model.predict(
crops, pred_dets, repeats=10, add_timer=True)
else:
tracking_outs = reid_model.predict(crops, pred_dets)
online_tlwhs = tracking_outs['online_tlwhs']
online_scores = tracking_outs['online_scores']
online_ids = tracking_outs['online_ids']
online_im = plot_tracking(
frame, online_tlwhs, online_ids, online_scores, frame_id=i)
if FLAGS.save_images:
if not os.path.exists(FLAGS.output_dir):
os.makedirs(FLAGS.output_dir)
img_name = os.path.split(img_file)[-1]
out_path = os.path.join(FLAGS.output_dir, img_name)
cv2.imwrite(out_path, online_im)
print("save result to: " + out_path)
def predict_video(detector, reid_model, camera_id):
if camera_id != -1:
capture = cv2.VideoCapture(camera_id)
video_name = 'mot_output.mp4'
else:
capture = cv2.VideoCapture(FLAGS.video_file)
video_name = os.path.split(FLAGS.video_file)[-1]
# Get Video info : resolution, fps, frame count
width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = int(capture.get(cv2.CAP_PROP_FPS))
frame_count = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))
print("fps: %d, frame_count: %d" % (fps, frame_count))
if not os.path.exists(FLAGS.output_dir):
os.makedirs(FLAGS.output_dir)
out_path = os.path.join(FLAGS.output_dir, video_name)
if not FLAGS.save_images:
video_format = 'mp4v'
fourcc = cv2.VideoWriter_fourcc(*video_format)
writer = cv2.VideoWriter(out_path, fourcc, fps, (width, height))
frame_id = 0
timer = MOTTimer()
results = defaultdict(list)
id_set = set()
interval_id_set = set()
in_id_list = list()
out_id_list = list()
prev_center = dict()
records = list()
entrance = [0, height / 2., width, height / 2.]
video_fps = fps
while (1):
ret, frame = capture.read()
if not ret:
break
timer.tic()
ori_image_shape = list(frame.shape[:2])
pred_dets, pred_xyxys = detector.predict([frame], ori_image_shape,
FLAGS.threshold, FLAGS.scaled)
if len(pred_dets) == 1 and np.sum(pred_dets) == 0:
print('Frame {} has no object, try to modify score threshold.'.
format(frame_id))
timer.toc()
im = frame
else:
# reid process
crops = reid_model.get_crops(pred_xyxys, frame)
tracking_outs = reid_model.predict(crops, pred_dets)
online_tlwhs = tracking_outs['online_tlwhs']
online_scores = tracking_outs['online_scores']
online_ids = tracking_outs['online_ids']
results[0].append(
(frame_id + 1, online_tlwhs, online_scores, online_ids))
# NOTE: just implement flow statistic for one class
result = (frame_id + 1, online_tlwhs, online_scores, online_ids)
statistic = flow_statistic(
result, FLAGS.secs_interval, FLAGS.do_entrance_counting,
video_fps, entrance, id_set, interval_id_set, in_id_list,
out_id_list, prev_center, records)
id_set = statistic['id_set']
interval_id_set = statistic['interval_id_set']
in_id_list = statistic['in_id_list']
out_id_list = statistic['out_id_list']
prev_center = statistic['prev_center']
records = statistic['records']
timer.toc()
fps = 1. / timer.duration
im = plot_tracking(
frame,
online_tlwhs,
online_ids,
online_scores,
frame_id=frame_id,
fps=fps,
do_entrance_counting=FLAGS.do_entrance_counting,
entrance=entrance)
if FLAGS.save_images:
save_dir = os.path.join(FLAGS.output_dir, video_name.split('.')[-2])
if not os.path.exists(save_dir):
os.makedirs(save_dir)
cv2.imwrite(
os.path.join(save_dir, '{:05d}.jpg'.format(frame_id)), im)
else:
writer.write(im)
frame_id += 1
print('detect frame:%d, fps: %f' % (frame_id, fps))
if camera_id != -1:
cv2.imshow('Tracking Detection', im)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
if FLAGS.save_mot_txts:
result_filename = os.path.join(FLAGS.output_dir,
video_name.split('.')[-2] + '.txt')
write_mot_results(result_filename, results)
result_filename = os.path.join(
FLAGS.output_dir, video_name.split('.')[-2] + '_flow_statistic.txt')
f = open(result_filename, 'w')
for line in records:
f.write(line)
print('Flow statistic save in {}'.format(result_filename))
f.close()
if FLAGS.save_images:
save_dir = os.path.join(FLAGS.output_dir, video_name.split('.')[-2])
cmd_str = 'ffmpeg -f image2 -i {}/%05d.jpg {}'.format(save_dir,
out_path)
os.system(cmd_str)
print('Save video in {}.'.format(out_path))
else:
writer.release()
def predict_mtmct_seq(detector, reid_model, seq_name, output_dir):
fpath = os.path.join(FLAGS.mtmct_dir, seq_name)
if os.path.exists(os.path.join(fpath, 'img1')):
fpath = os.path.join(fpath, 'img1')
assert os.path.isdir(fpath), '{} should be a directory'.format(fpath)
image_list = os.listdir(fpath)
image_list.sort()
assert len(image_list) > 0, '{} has no images.'.format(fpath)
results = defaultdict(list)
mot_features_dict = {} # cid_tid_fid feats
print('Totally {} frames found in seq {}.'.format(
len(image_list), seq_name))
for frame_id, img_file in enumerate(image_list):
if frame_id % 40 == 0:
print('Processing frame {} of seq {}.'.format(frame_id, seq_name))
frame = cv2.imread(os.path.join(fpath, img_file))
ori_image_shape = list(frame.shape[:2])
frame_path = os.path.join(fpath, img_file)
pred_dets, pred_xyxys = detector.predict([frame_path], ori_image_shape,
FLAGS.threshold, FLAGS.scaled)
if len(pred_dets) == 1 and np.sum(pred_dets) == 0:
print('Frame {} has no object, try to modify score threshold.'.
format(frame_id))
online_im = frame
else:
# reid process
crops = reid_model.get_crops(pred_xyxys, frame)
tracking_outs = reid_model.predict(
crops,
pred_dets,
MTMCT=True,
frame_id=frame_id,
seq_name=seq_name)
feat_data_dict = tracking_outs['feat_data']
mot_features_dict = dict(mot_features_dict, **feat_data_dict)
online_tlwhs = tracking_outs['online_tlwhs']
online_scores = tracking_outs['online_scores']
online_ids = tracking_outs['online_ids']
online_im = plot_tracking(frame, online_tlwhs, online_ids,
online_scores, frame_id)
results[0].append(
(frame_id + 1, online_tlwhs, online_scores, online_ids))
if FLAGS.save_images:
save_dir = os.path.join(output_dir, seq_name)
if not os.path.exists(save_dir): os.makedirs(save_dir)
img_name = os.path.split(img_file)[-1]
out_path = os.path.join(save_dir, img_name)
cv2.imwrite(out_path, online_im)
if FLAGS.save_mot_txts:
result_filename = os.path.join(output_dir, seq_name + '.txt')
write_mot_results(result_filename, results)
return mot_features_dict
def predict_mtmct(detector, reid_model, mtmct_dir, mtmct_cfg):
MTMCT = mtmct_cfg['MTMCT']
assert MTMCT == True, 'predict_mtmct should be used for MTMCT.'
cameras_bias = mtmct_cfg['cameras_bias']
cid_bias = parse_bias(cameras_bias)
scene_cluster = list(cid_bias.keys())
# 1.zone releated parameters
use_zone = mtmct_cfg['use_zone']
zone_path = mtmct_cfg['zone_path']
# 2.tricks parameters, can be used for other mtmct dataset
use_ff = mtmct_cfg['use_ff']
use_rerank = mtmct_cfg['use_rerank']
# 3.camera releated parameters
use_camera = mtmct_cfg['use_camera']
use_st_filter = mtmct_cfg['use_st_filter']
# 4.zone releated parameters
use_roi = mtmct_cfg['use_roi']
roi_dir = mtmct_cfg['roi_dir']
mot_list_breaks = []
cid_tid_dict = dict()
output_dir = FLAGS.output_dir
if not os.path.exists(output_dir): os.makedirs(output_dir)
seqs = os.listdir(mtmct_dir)
seqs.sort()
for seq in seqs:
fpath = os.path.join(mtmct_dir, seq)
if os.path.isfile(fpath) and _is_valid_video(fpath):
ext = seq.split('.')[-1]
seq = seq.split('.')[-2]
print('ffmpeg processing of video {}'.format(fpath))
frames_path = video2frames(
video_path=fpath, outpath=mtmct_dir, frame_rate=25)
fpath = os.path.join(mtmct_dir, seq)
if os.path.isdir(fpath) == False:
print('{} is not a image folder.'.format(fpath))
continue
mot_features_dict = predict_mtmct_seq(detector, reid_model, seq,
output_dir)
cid = int(re.sub('[a-z,A-Z]', "", seq))
tid_data, mot_list_break = trajectory_fusion(
mot_features_dict,
cid,
cid_bias,
use_zone=use_zone,
zone_path=zone_path)
mot_list_breaks.append(mot_list_break)
# single seq process
for line in tid_data:
tracklet = tid_data[line]
tid = tracklet['tid']
if (cid, tid) not in cid_tid_dict:
cid_tid_dict[(cid, tid)] = tracklet
map_tid = sub_cluster(
cid_tid_dict,
scene_cluster,
use_ff=use_ff,
use_rerank=use_rerank,
use_camera=use_camera,
use_st_filter=use_st_filter)
pred_mtmct_file = os.path.join(output_dir, 'mtmct_result.txt')
if use_camera:
gen_res(pred_mtmct_file, scene_cluster, map_tid, mot_list_breaks)
else:
gen_res(
pred_mtmct_file,
scene_cluster,
map_tid,
mot_list_breaks,
use_roi=use_roi,
roi_dir=roi_dir)
if FLAGS.save_images:
camera_results, cid_tid_fid_res = get_mtmct_matching_results(
pred_mtmct_file)
crops_dir = os.path.join(output_dir, 'mtmct_crops')
save_mtmct_crops(
cid_tid_fid_res, images_dir=mtmct_dir, crops_dir=crops_dir)
save_dir = os.path.join(output_dir, 'mtmct_vis')
save_mtmct_vis_results(
camera_results,
images_dir=mtmct_dir,
save_dir=save_dir,
save_videos=FLAGS.save_images)
# evalution metrics
data_root_gt = os.path.join(mtmct_dir, '..', 'gt', 'gt.txt')
if os.path.exists(data_root_gt):
print_mtmct_result(data_root_gt, pred_mtmct_file)
def main():
pred_config = PredictConfig(FLAGS.model_dir)
detector_func = 'SDE_Detector'
if pred_config.arch == 'PicoDet':
detector_func = 'SDE_DetectorPicoDet'
detector = eval(detector_func)(pred_config,
FLAGS.model_dir,
device=FLAGS.device,
run_mode=FLAGS.run_mode,
batch_size=FLAGS.batch_size,
trt_min_shape=FLAGS.trt_min_shape,
trt_max_shape=FLAGS.trt_max_shape,
trt_opt_shape=FLAGS.trt_opt_shape,
trt_calib_mode=FLAGS.trt_calib_mode,
cpu_threads=FLAGS.cpu_threads,
enable_mkldnn=FLAGS.enable_mkldnn)
pred_config = PredictConfig(FLAGS.reid_model_dir)
reid_model = SDE_ReID(
pred_config,
FLAGS.reid_model_dir,
device=FLAGS.device,
run_mode=FLAGS.run_mode,
batch_size=FLAGS.reid_batch_size,
trt_min_shape=FLAGS.trt_min_shape,
trt_max_shape=FLAGS.trt_max_shape,
trt_opt_shape=FLAGS.trt_opt_shape,
trt_calib_mode=FLAGS.trt_calib_mode,
cpu_threads=FLAGS.cpu_threads,
enable_mkldnn=FLAGS.enable_mkldnn)
# predict from video file or camera video stream
if FLAGS.video_file is not None or FLAGS.camera_id != -1:
predict_video(detector, reid_model, FLAGS.camera_id)
elif FLAGS.mtmct_dir is not None:
mtmct_cfg_file = FLAGS.mtmct_cfg
with open(mtmct_cfg_file) as f:
mtmct_cfg = yaml.safe_load(f)
predict_mtmct(detector, reid_model, FLAGS.mtmct_dir, mtmct_cfg)
else:
# predict from image
img_list = get_test_images(FLAGS.image_dir, FLAGS.image_file)
predict_image(detector, reid_model, img_list)
if not FLAGS.run_benchmark:
detector.det_times.info(average=True)
reid_model.det_times.info(average=True)
else:
mode = FLAGS.run_mode
det_model_dir = FLAGS.model_dir
det_model_info = {
'model_name': det_model_dir.strip('/').split('/')[-1],
'precision': mode.split('_')[-1]
}
bench_log(detector, img_list, det_model_info, name='Det')
reid_model_dir = FLAGS.reid_model_dir
reid_model_info = {
'model_name': reid_model_dir.strip('/').split('/')[-1],
'precision': mode.split('_')[-1]
}
bench_log(reid_model, img_list, reid_model_info, name='ReID')
if __name__ == '__main__':
paddle.enable_static()
parser = argsparser()
FLAGS = parser.parse_args()
print_arguments(FLAGS)
FLAGS.device = FLAGS.device.upper()
assert FLAGS.device in ['CPU', 'GPU', 'XPU'
], "device should be CPU, GPU or XPU"
main()
|
py | 1a311118f207b109f72b04552b596740dc758fae | #!usr/bin/env python3
# -*- coding:utf-8 _*-
from setuptools import setup
setup(name='font-converter',
version='0.1',
description='A font converter script',
url='http://github.com/5uw1st/font-converter',
author='5uw1st',
author_email='[email protected]',
license='MIT',
packages=['font_converter'],
python_requires=">=3.6",
install_requires=[
'requests==2.20.1',
'redis==3.3.8',
'pytesseract==0.3.0',
'Pillow==6.1.0',
],
zip_safe=False)
|
py | 1a3111bea352c6ee26d9cdeef4033302e248a38e | # coding: utf-8
import os
import sys
import re
import time
import pickle
import shutil
import random
import argparse
from darknet_util import *
from darknet import Darknet
from preprocess import prep_image, process_img, inp_to_image
from dataset import color_attrs, direction_attrs, type_attrs
import torch
import torchvision
import paramiko
import cv2
import numpy as np
import PIL
from PIL import Image
from matplotlib import pyplot as plt
from matplotlib.widgets import Cursor
from matplotlib.image import AxesImage
from scipy.spatial.distance import cityblock
from tqdm import tqdm
# -------------------------------------
# for matplotlib to displacy chinese characters correctly
from pylab import *
mpl.rcParams['font.sans-serif'] = ['SimHei']
use_cuda = True # True
os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
device = torch.device(
'cuda: 0' if torch.cuda.is_available() and use_cuda else 'cpu')
if use_cuda:
torch.manual_seed(0)
torch.cuda.manual_seed_all(0)
print('=> device: ', device)
local_model_path = './checkpoints/epoch_39.pth'
local_car_cfg_path = './car.cfg'
local_car_det_weights_path = './car_detect.weights'
class Cls_Net(torch.nn.Module):
"""
vehicle multilabel classification model
"""
def __init__(self, num_cls, input_size):
"""
network definition
:param is_freeze:
"""
torch.nn.Module.__init__(self)
# output channels
self._num_cls = num_cls
# input image size
self.input_size = input_size
# delete original FC and add custom FC
self.features = torchvision.models.resnet18(pretrained=True)
del self.features.fc
# print('feature extractor:\n', self.features)
self.features = torch.nn.Sequential(
*list(self.features.children()))
self.fc = torch.nn.Linear(512 ** 2, num_cls) # 输出类别数
# print('=> fc layer:\n', self.fc)
def forward(self, X):
"""
:param X:
:return:
"""
N = X.size()[0]
X = self.features(X) # extract features
X = X.view(N, 512, 1 ** 2)
X = torch.bmm(X, torch.transpose(X, 1, 2)) / (1 ** 2) # Bi-linear CNN
X = X.view(N, 512 ** 2)
X = torch.sqrt(X + 1e-5)
X = torch.nn.functional.normalize(X)
X = self.fc(X)
assert X.size() == (N, self._num_cls)
return X
# ------------------------------------- vehicle detection model
class Car_Classifier(object):
"""
vehicle detection model mabager
"""
def __init__(self,
num_cls,
model_path=local_model_path):
"""
load model and initialize
"""
# define model and load weights
self.net = Cls_Net(num_cls=num_cls, input_size=224).to(device)
# self.net = torch.nn.DataParallel(Net(num_cls=20, input_size=224),
# device_ids=[0]).to(device)
self.net.load_state_dict(torch.load(model_path))
print('=> vehicle classifier loaded from %s' % model_path)
# set model to eval mode
self.net.eval()
# test data transforms
self.transforms = torchvision.transforms.Compose([
torchvision.transforms.Resize(size=224),
torchvision.transforms.CenterCrop(size=224),
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize(mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225))
])
# split each label
self.color_attrs = color_attrs
print('=> color_attrs:\n', self.color_attrs)
self.direction_attrs = direction_attrs
print('=> direction attrs:\n', self.direction_attrs)
self.type_attrs = type_attrs
print('=> type_attrs:\n', self.type_attrs)
def get_predict(self, output):
"""
get prediction from output
"""
# get each label's prediction from output
output = output.cpu() # fetch data from gpu
pred_color = output[:, :9]
pred_direction = output[:, 9:11]
pred_type = output[:, 11:]
color_idx = pred_color.max(1, keepdim=True)[1]
direction_idx = pred_direction.max(1, keepdim=True)[1]
type_idx = pred_type.max(1, keepdim=True)[1]
pred = torch.cat((color_idx, direction_idx, type_idx), dim=1)
return pred
def pre_process(self, image):
"""
image formatting
:rtype: PIL.JpegImagePlugin.JpegImageFile
"""
# image data formatting
if type(image) == np.ndarray:
if image.shape[2] == 3: # turn all 3 channels to RGB format
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
elif image.shape[2] == 1: # turn 1 channel to RGB
image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)
# turn numpy.ndarray into PIL.Image
image = Image.fromarray(image)
elif type(image) == PIL.JpegImagePlugin.JpegImageFile:
if image.mode == 'L' or image.mode == 'I': # turn 8bits or 32bits into 3 channels RGB
image = image.convert('RGB')
return image
def predict(self, img):
"""
predict vehicle attributes by classifying
:return: vehicle color, direction and type
"""
# image pre-processing
img = self.transforms(img)
img = img.view(1, 3, 224, 224)
# put image data into device
img = img.to(device)
# calculating inference
output = self.net.forward(img)
# get result
# self.get_predict_ce, return pred to host side(cpu)
pred = self.get_predict(output)
color_name = self.color_attrs[pred[0][0]]
direction_name = self.direction_attrs[pred[0][1]]
type_name = self.type_attrs[pred[0][2]]
return color_name, direction_name, type_name
class Car_DC():
def __init__(self,
src_dir,
dst_dir,
car_cfg_path=local_car_cfg_path,
car_det_weights_path=local_car_det_weights_path,
inp_dim=768,
prob_th=0.2,
nms_th=0.4,
num_classes=1):
"""
model initialization
"""
# super parameters
self.inp_dim = inp_dim
self.prob_th = prob_th
self.nms_th = nms_th
self.num_classes = num_classes
self.dst_dir = dst_dir
# clear dst_dir
if os.path.exists(self.dst_dir):
for x in os.listdir(self.dst_dir):
if x.endswith('.jpg'):
os.remove(self.dst_dir + '/' + x)
else:
os.makedirs(self.dst_dir)
# initialize vehicle detection model
self.detector = Darknet(car_cfg_path)
self.detector.load_weights(car_det_weights_path)
# set input dimension of image
self.detector.net_info['height'] = self.inp_dim
self.detector.to(device)
self.detector.eval() # evaluation mode
print('=> car detection model initiated.')
# initiate multilabel classifier
self.classifier = Car_Classifier(num_cls=19,
model_path=local_model_path)
# initiate imgs_path
self.imgs_path = [os.path.join(src_dir, x) for x in os.listdir(
src_dir) if x.endswith('.jpg')]
def cls_draw_bbox(self, output, orig_img):
"""
1. predict vehicle's attributes based on bbox of vehicle
2. draw bbox to orig_img
"""
labels = []
pt_1s = []
pt_2s = []
# 1
for det in output:
# rectangle points
pt_1 = tuple(det[1:3].int()) # the left-up point
pt_2 = tuple(det[3:5].int()) # the right down point
pt_1s.append(pt_1)
pt_2s.append(pt_2)
# turn BGR back to RGB
ROI = Image.fromarray(
orig_img[pt_1[1]: pt_2[1],
pt_1[0]: pt_2[0]][:, :, ::-1])
# ROI.show()
# call classifier to predict
car_color, car_direction, car_type = self.classifier.predict(ROI)
label = str(car_color + ' ' + car_direction + ' ' + car_type)
labels.append(label)
print('=> predicted label: ', label)
# 2
color = (0, 215, 255)
for i, det in enumerate(output):
pt_1 = pt_1s[i]
pt_2 = pt_2s[i]
# draw bounding box
cv2.rectangle(orig_img, pt_1, pt_2, color, thickness=2)
# get str text size
txt_size = cv2.getTextSize(
label, cv2.FONT_HERSHEY_PLAIN, 2, 2)[0]
# pt_2 = pt_1[0] + txt_size[0] + 3, pt_1[1] + txt_size[1] + 5
pt_2 = pt_1[0] + txt_size[0] + 3, pt_1[1] - txt_size[1] - 5
# draw text background rect
cv2.rectangle(orig_img, pt_1, pt_2, color, thickness=-1) # text
# draw text
cv2.putText(orig_img, labels[i], (pt_1[0], pt_1[1]), # pt_1[1] + txt_size[1] + 4
cv2.FONT_HERSHEY_PLAIN, 2, [225, 255, 255], 2)
def process_predict(self,
prediction,
prob_th,
num_cls,
nms_th,
inp_dim,
orig_img_size):
"""
processing detections
"""
scaling_factor = min([inp_dim / float(x)
for x in orig_img_size]) # W, H scaling factor
output = post_process(prediction,
prob_th,
num_cls,
nms=True,
nms_conf=nms_th,
CUDA=True) # post-process such as nms
if type(output) != int:
output[:, [1, 3]] -= (inp_dim - scaling_factor *
orig_img_size[0]) / 2.0 # x, w
output[:, [2, 4]] -= (inp_dim - scaling_factor *
orig_img_size[1]) / 2.0 # y, h
output[:, 1:5] /= scaling_factor
for i in range(output.shape[0]):
output[i, [1, 3]] = torch.clamp(
output[i, [1, 3]], 0.0, orig_img_size[0])
output[i, [2, 4]] = torch.clamp(
output[i, [2, 4]], 0.0, orig_img_size[1])
return output
def detect_classify(self):
"""
detect and classify
"""
for x in self.imgs_path:
# read image data
img = Image.open(x)
img2det = process_img(img, self.inp_dim)
img2det = img2det.to(device) # put image data to device
# vehicle detection
prediction = self.detector.forward(img2det, CUDA=True)
# calculating scaling factor
orig_img_size = list(img.size)
output = self.process_predict(prediction,
self.prob_th,
self.num_classes,
self.nms_th,
self.inp_dim,
orig_img_size)
orig_img = cv2.cvtColor(np.asarray(
img), cv2.COLOR_RGB2BGR) # RGB => BGR
if type(output) != int:
self.cls_draw_bbox(output, orig_img)
dst_path = self.dst_dir + '/' + os.path.split(x)[1]
if not os.path.exists(dst_path):
cv2.imwrite(dst_path, orig_img)
# -----------------------------------------------------------
parser = argparse.ArgumentParser(description='Detect and classify cars.')
parser.add_argument('-src-dir',
type=str,
default='./test_imgs',
help='source directory of images')
parser.add_argument('-dst-dir',
type=str,
default='./test_result',
help='destination directory of images to store results.')
if __name__ == '__main__':
# ---------------------------- Car detect and classify
# DR_model = Car_DC(src_dir='./test_imgs',
# dst_dir='./test_result')
# DR_model.detect_classify()
args = parser.parse_args()
DR_model = Car_DC(src_dir=args.src_dir, dst_dir=args.dst_dir)
DR_model.detect_classify()
|
py | 1a311287858742e4089ce0d0057dc5ae29d8da25 | """
SimplePose for COCO Keypoint, implemented in TensorFlow.
Original paper: 'Simple Baselines for Human Pose Estimation and Tracking,' https://arxiv.org/abs/1804.06208.
"""
__all__ = ['SimplePose', 'simplepose_resnet18_coco', 'simplepose_resnet50b_coco', 'simplepose_resnet101b_coco',
'simplepose_resnet152b_coco', 'simplepose_resneta50b_coco', 'simplepose_resneta101b_coco',
'simplepose_resneta152b_coco']
import os
import tensorflow as tf
tf.random.set_seed(3)
import tensorflow.keras.layers as nn
from .common import get_activation_layer, BatchNorm, conv1x1, HeatmapMaxDetBlock, is_channels_first
from .resnet import resnet18, resnet50b, resnet101b, resnet152b
from .resneta import resneta50b, resneta101b, resneta152b
class Deconv2d(nn.Layer):
"""
Standard deconvolution layer.
Parameters:
----------
in_channels : int
Number of input channels.
out_channels : int
Number of output channels.
kernel_size : int or tuple/list of 2 int
Convolution window size.
strides : int or tuple/list of 2 int, default 1
Strides of the convolution.
padding : int or tuple/list of 2 int, default 0
Padding value for convolution layer.
out_padding : int or tuple/list of 2 int, default 0
Output padding value for deconvolution layer.
dilation : int or tuple/list of 2 int, default 1
Dilation value for convolution layer.
groups : int, default 1
Number of groups.
use_bias : bool, default True
Whether the layer uses a bias vector.
data_format : str, default 'channels_last'
The ordering of the dimensions in tensors.
"""
def __init__(self,
in_channels,
out_channels,
kernel_size,
strides=1,
padding=0,
out_padding=0,
dilation=1,
groups=1,
use_bias=True,
data_format="channels_last",
**kwargs):
super(Deconv2d, self).__init__(**kwargs)
assert (dilation == 1)
assert (groups == 1)
assert (in_channels is not None)
if isinstance(padding, int):
padding = (padding, padding)
self.use_crop = (padding[0] > 0) or (padding[1] > 0)
if self.use_crop:
self.crop = nn.Cropping2D(
cropping=padding,
data_format=data_format,
name="crop")
self.conv = nn.Conv2DTranspose(
filters=out_channels,
kernel_size=kernel_size,
strides=strides,
padding="valid",
output_padding=out_padding,
data_format=data_format,
dilation_rate=dilation,
use_bias=use_bias,
name="conv")
def call(self, x):
x = self.conv(x)
if self.use_crop:
x = self.crop(x)
return x
class DeconvBlock(nn.Layer):
"""
Deconvolution block with batch normalization and activation.
Parameters:
----------
in_channels : int
Number of input channels.
out_channels : int
Number of output channels.
kernel_size : int or tuple/list of 2 int
Convolution window size.
strides : int or tuple/list of 2 int
Strides of the deconvolution.
padding : int or tuple/list of 2 int
Padding value for deconvolution layer.
out_padding : int or tuple/list of 2 int, default 0
Output padding value for deconvolution layer.
dilation : int or tuple/list of 2 int, default 1
Dilation value for deconvolution layer.
groups : int, default 1
Number of groups.
use_bias : bool, default False
Whether the layer uses a bias vector.
use_bn : bool, default True
Whether to use BatchNorm layer.
bn_eps : float, default 1e-5
Small float added to variance in Batch norm.
activation : function or str or None, default 'relu'
Activation function or name of activation function.
data_format : str, default 'channels_last'
The ordering of the dimensions in tensors.
"""
def __init__(self,
in_channels,
out_channels,
kernel_size,
strides,
padding,
out_padding=0,
dilation=1,
groups=1,
use_bias=False,
use_bn=True,
bn_eps=1e-5,
activation="relu",
data_format="channels_last",
**kwargs):
super(DeconvBlock, self).__init__(**kwargs)
assert (in_channels is not None)
self.activate = (activation is not None)
self.use_bn = use_bn
self.conv = Deconv2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
strides=strides,
padding=padding,
out_padding=out_padding,
dilation=dilation,
groups=groups,
use_bias=use_bias,
data_format=data_format,
name="conv")
if self.use_bn:
self.bn = BatchNorm(
epsilon=bn_eps,
data_format=data_format,
name="bn")
if self.activate:
self.activ = get_activation_layer(activation)
def call(self, x, training=None):
x = self.conv(x)
if self.use_bn:
x = self.bn(x, training=training)
if self.activate:
x = self.activ(x)
return x
class SimplePose(tf.keras.Model):
"""
SimplePose model from 'Simple Baselines for Human Pose Estimation and Tracking,' https://arxiv.org/abs/1804.06208.
Parameters:
----------
backbone : nn.Sequential
Feature extractor.
backbone_out_channels : int
Number of output channels for the backbone.
channels : list of int
Number of output channels for each decoder unit.
return_heatmap : bool, default False
Whether to return only heatmap.
in_channels : int, default 3
Number of input channels.
in_size : tuple of two ints, default (256, 192)
Spatial size of the expected input image.
keypoints : int, default 17
Number of keypoints.
data_format : str, default 'channels_last'
The ordering of the dimensions in tensors.
"""
def __init__(self,
backbone,
backbone_out_channels,
channels,
return_heatmap=False,
in_channels=3,
in_size=(256, 192),
keypoints=17,
data_format="channels_last",
**kwargs):
super(SimplePose, self).__init__(**kwargs)
assert (in_channels == 3)
self.in_size = in_size
self.keypoints = keypoints
self.return_heatmap = return_heatmap
self.data_format = data_format
self.backbone = backbone
self.backbone._name = "backbone"
self.decoder = tf.keras.Sequential(name="decoder")
in_channels = backbone_out_channels
for i, out_channels in enumerate(channels):
self.decoder.add(DeconvBlock(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=4,
strides=2,
padding=1,
data_format=data_format,
name="unit{}".format(i + 1)))
in_channels = out_channels
self.decoder.add(conv1x1(
in_channels=in_channels,
out_channels=keypoints,
use_bias=True,
data_format=data_format,
name="final_block"))
self.heatmap_max_det = HeatmapMaxDetBlock(
data_format=data_format,
name="heatmap_max_det")
def call(self, x, training=None):
x = self.backbone(x, training=training)
heatmap = self.decoder(x, training=training)
if self.return_heatmap or not tf.executing_eagerly():
return [heatmap]
else:
keypoints = self.heatmap_max_det(heatmap)
return keypoints
def get_simplepose(backbone,
backbone_out_channels,
keypoints,
model_name=None,
data_format="channels_last",
pretrained=False,
root=os.path.join("~", ".tensorflow", "models"),
channels=[256, 256, 256],
**kwargs):
"""
Create SimplePose model with specific parameters.
Parameters:
----------
backbone : nn.Sequential
Feature extractor.
backbone_out_channels : int
Number of output channels for the backbone.
keypoints : int
Number of keypoints.
model_name : str or None, default None
Model name for loading pretrained model.
data_format : str, default 'channels_last'
The ordering of the dimensions in tensors.
pretrained : bool, default False
Whether to load the pretrained weights for model.
root : str, default '~/.tensorflow/models'
Location for keeping the model parameters.
"""
net = SimplePose(
backbone=backbone,
backbone_out_channels=backbone_out_channels,
channels=channels,
keypoints=keypoints,
data_format=data_format,
**kwargs)
if pretrained:
if (model_name is None) or (not model_name):
raise ValueError("Parameter `model_name` should be properly initialized for loading pretrained model.")
from .model_store import get_model_file
in_channels = kwargs["in_channels"] if ("in_channels" in kwargs) else 3
input_shape = (1,) + (in_channels,) + net.in_size if net.data_format == "channels_first" else\
(1,) + net.in_size + (in_channels,)
net.build(input_shape=input_shape)
net.load_weights(
filepath=get_model_file(
model_name=model_name,
local_model_store_dir_path=root))
return net
def simplepose_mobilenetv2_coco(mv2_alpha=1.0, keypoints=17, data_format="channels_last", **kwargs):
backbone = tf.keras.applications.MobileNetV2(include_top=False, alpha=mv2_alpha)
return get_simplepose(backbone, backbone_out_channels=512, keypoints=keypoints,
model_name="simplepose_mobilenetv2_coco", data_format=data_format, **kwargs)
def simplepose_mv2_coco(keypoints=17, data_format="channels_last", **kwargs):
from .mv2_cpm import MobileNetV2
backbone = MobileNetV2()
return get_simplepose(backbone, backbone_out_channels=256, keypoints=keypoints,
model_name="simplepose_mv2_coco", data_format=data_format, channels=[256, 256], **kwargs)
def simplepose_resnet18_coco(pretrained_backbone=False, keypoints=17, data_format="channels_last", **kwargs):
"""
SimplePose model on the base of ResNet-18 for COCO Keypoint from 'Simple Baselines for Human Pose Estimation and
Tracking,' https://arxiv.org/abs/1804.06208.
Parameters:
----------
pretrained_backbone : bool, default False
Whether to load the pretrained weights for feature extractor.
keypoints : int, default 17
Number of keypoints.
data_format : str, default 'channels_last'
The ordering of the dimensions in tensors.
pretrained : bool, default False
Whether to load the pretrained weights for model.
root : str, default '~/.tensorflow/models'
Location for keeping the model parameters.
"""
backbone = resnet18(pretrained=pretrained_backbone, data_format=data_format).features
backbone._layers.pop()
return get_simplepose(backbone=backbone, backbone_out_channels=512, keypoints=keypoints,
model_name="simplepose_resnet18_coco", data_format=data_format, **kwargs)
def simplepose_resnet50b_coco(pretrained_backbone=False, keypoints=17, data_format="channels_last", **kwargs):
"""
SimplePose model on the base of ResNet-50b for COCO Keypoint from 'Simple Baselines for Human Pose Estimation and
Tracking,' https://arxiv.org/abs/1804.06208.
Parameters:
----------
pretrained_backbone : bool, default False
Whether to load the pretrained weights for feature extractor.
keypoints : int, default 17
Number of keypoints.
data_format : str, default 'channels_last'
The ordering of the dimensions in tensors.
pretrained : bool, default False
Whether to load the pretrained weights for model.
root : str, default '~/.tensorflow/models'
Location for keeping the model parameters.
"""
backbone = resnet50b(pretrained=pretrained_backbone, data_format=data_format).features
backbone._layers.pop()
return get_simplepose(backbone=backbone, backbone_out_channels=2048, keypoints=keypoints,
model_name="simplepose_resnet50b_coco", data_format=data_format, **kwargs)
def simplepose_resnet101b_coco(pretrained_backbone=False, keypoints=17, data_format="channels_last", **kwargs):
"""
SimplePose model on the base of ResNet-101b for COCO Keypoint from 'Simple Baselines for Human Pose Estimation
and Tracking,' https://arxiv.org/abs/1804.06208.
Parameters:
----------
pretrained_backbone : bool, default False
Whether to load the pretrained weights for feature extractor.
keypoints : int, default 17
Number of keypoints.
data_format : str, default 'channels_last'
The ordering of the dimensions in tensors.
pretrained : bool, default False
Whether to load the pretrained weights for model.
root : str, default '~/.tensorflow/models'
Location for keeping the model parameters.
"""
backbone = resnet101b(pretrained=pretrained_backbone, data_format=data_format).features
backbone._layers.pop()
return get_simplepose(backbone=backbone, backbone_out_channels=2048, keypoints=keypoints,
model_name="simplepose_resnet101b_coco", data_format=data_format, **kwargs)
def simplepose_resnet152b_coco(pretrained_backbone=False, keypoints=17, data_format="channels_last", **kwargs):
"""
SimplePose model on the base of ResNet-152b for COCO Keypoint from 'Simple Baselines for Human Pose Estimation
and Tracking,' https://arxiv.org/abs/1804.06208.
Parameters:
----------
pretrained_backbone : bool, default False
Whether to load the pretrained weights for feature extractor.
keypoints : int, default 17
Number of keypoints.
data_format : str, default 'channels_last'
The ordering of the dimensions in tensors.
pretrained : bool, default False
Whether to load the pretrained weights for model.
root : str, default '~/.tensorflow/models'
Location for keeping the model parameters.
"""
backbone = resnet152b(pretrained=pretrained_backbone, data_format=data_format).features
backbone._layers.pop()
return get_simplepose(backbone=backbone, backbone_out_channels=2048, keypoints=keypoints,
model_name="simplepose_resnet152b_coco", data_format=data_format, **kwargs)
def simplepose_resneta50b_coco(pretrained_backbone=False, keypoints=17, data_format="channels_last", **kwargs):
"""
SimplePose model on the base of ResNet(A)-50b for COCO Keypoint from 'Simple Baselines for Human Pose Estimation
and Tracking,' https://arxiv.org/abs/1804.06208.
Parameters:
----------
pretrained_backbone : bool, default False
Whether to load the pretrained weights for feature extractor.
keypoints : int, default 17
Number of keypoints.
data_format : str, default 'channels_last'
The ordering of the dimensions in tensors.
pretrained : bool, default False
Whether to load the pretrained weights for model.
root : str, default '~/.tensorflow/models'
Location for keeping the model parameters.
"""
backbone = resneta50b(pretrained=pretrained_backbone, data_format=data_format).features
backbone._layers.pop()
return get_simplepose(backbone=backbone, backbone_out_channels=2048, keypoints=keypoints,
model_name="simplepose_resneta50b_coco", data_format=data_format, **kwargs)
def simplepose_resneta101b_coco(pretrained_backbone=False, keypoints=17, data_format="channels_last", **kwargs):
"""
SimplePose model on the base of ResNet(A)-101b for COCO Keypoint from 'Simple Baselines for Human Pose Estimation
and Tracking,' https://arxiv.org/abs/1804.06208.
Parameters:
----------
pretrained_backbone : bool, default False
Whether to load the pretrained weights for feature extractor.
keypoints : int, default 17
Number of keypoints.
data_format : str, default 'channels_last'
The ordering of the dimensions in tensors.
pretrained : bool, default False
Whether to load the pretrained weights for model.
root : str, default '~/.tensorflow/models'
Location for keeping the model parameters.
"""
backbone = resneta101b(pretrained=pretrained_backbone, data_format=data_format).features
backbone._layers.pop()
return get_simplepose(backbone=backbone, backbone_out_channels=2048, keypoints=keypoints,
model_name="simplepose_resneta101b_coco", data_format=data_format, **kwargs)
def simplepose_resneta152b_coco(pretrained_backbone=False, keypoints=17, data_format="channels_last", **kwargs):
"""
SimplePose model on the base of ResNet(A)-152b for COCO Keypoint from 'Simple Baselines for Human Pose Estimation
and Tracking,' https://arxiv.org/abs/1804.06208.
Parameters:
----------
pretrained_backbone : bool, default False
Whether to load the pretrained weights for feature extractor.
keypoints : int, default 17
Number of keypoints.
data_format : str, default 'channels_last'
The ordering of the dimensions in tensors.
pretrained : bool, default False
Whether to load the pretrained weights for model.
root : str, default '~/.tensorflow/models'
Location for keeping the model parameters.
"""
backbone = resneta152b(pretrained=pretrained_backbone, data_format=data_format).features
backbone._layers.pop()
return get_simplepose(backbone=backbone, backbone_out_channels=2048, keypoints=keypoints,
model_name="simplepose_resneta152b_coco", data_format=data_format, **kwargs)
def _test():
import numpy as np
import tensorflow.keras.backend as K
data_format = "channels_last"
# data_format = "channels_first"
in_size = (256, 192)
keypoints = 17
return_heatmap = False
pretrained = False
models = [
simplepose_resnet18_coco,
simplepose_resnet50b_coco,
simplepose_resnet101b_coco,
simplepose_resnet152b_coco,
simplepose_resneta50b_coco,
simplepose_resneta101b_coco,
simplepose_resneta152b_coco,
]
for model in models:
net = model(pretrained=pretrained, in_size=in_size, return_heatmap=return_heatmap, data_format=data_format)
batch = 14
x = tf.random.normal((batch, 3, in_size[0], in_size[1]) if is_channels_first(data_format) else
(batch, in_size[0], in_size[1], 3))
y = net(x)
assert (y.shape[0] == batch)
if return_heatmap:
if is_channels_first(data_format):
assert ((y.shape[1] == keypoints) and (y.shape[2] == x.shape[2] // 4) and
(y.shape[3] == x.shape[3] // 4))
else:
assert ((y.shape[3] == keypoints) and (y.shape[1] == x.shape[1] // 4) and
(y.shape[2] == x.shape[2] // 4))
else:
assert ((y.shape[1] == keypoints) and (y.shape[2] == 3))
weight_count = sum([np.prod(K.get_value(w).shape) for w in net.trainable_weights])
print("m={}, {}".format(model.__name__, weight_count))
assert (model != simplepose_resnet18_coco or weight_count == 15376721)
assert (model != simplepose_resnet50b_coco or weight_count == 33999697)
assert (model != simplepose_resnet101b_coco or weight_count == 52991825)
assert (model != simplepose_resnet152b_coco or weight_count == 68635473)
assert (model != simplepose_resneta50b_coco or weight_count == 34018929)
assert (model != simplepose_resneta101b_coco or weight_count == 53011057)
assert (model != simplepose_resneta152b_coco or weight_count == 68654705)
if __name__ == "__main__":
_test()
|
py | 1a311573372ab9525196d1108623bb785e20284c | """Selector and proactor event loops for Windows."""
import _overlapped
import _winapi
import errno
import math
import msvcrt
import socket
import struct
import time
import weakref
from . import events
from . import base_subprocess
from . import futures
from . import exceptions
from . import proactor_events
from . import selector_events
from . import tasks
from . import windows_utils
from .log import logger
__all__ = (
'SelectorEventLoop', 'ProactorEventLoop', 'IocpProactor',
'DefaultEventLoopPolicy', 'WindowsSelectorEventLoopPolicy',
'WindowsProactorEventLoopPolicy',
)
NULL = 0
INFINITE = 0xffffffff
ERROR_CONNECTION_REFUSED = 1225
ERROR_CONNECTION_ABORTED = 1236
# Initial delay in seconds for connect_pipe() before retrying to connect
CONNECT_PIPE_INIT_DELAY = 0.001
# Maximum delay in seconds for connect_pipe() before retrying to connect
CONNECT_PIPE_MAX_DELAY = 0.100
class _OverlappedFuture(futures.Future):
"""Subclass of Future which represents an overlapped operation.
Cancelling it will immediately cancel the overlapped operation.
"""
def __init__(self, ov, *, loop=None):
super().__init__(loop=loop)
if self._source_traceback:
del self._source_traceback[-1]
self._ov = ov
def _repr_info(self):
info = super()._repr_info()
if self._ov is not None:
state = 'pending' if self._ov.pending else 'completed'
info.insert(1, f'overlapped=<{state}, {self._ov.address:#x}>')
return info
def _cancel_overlapped(self):
if self._ov is None:
return
try:
self._ov.cancel()
except OSError as exc:
context = {
'message': 'Cancelling an overlapped future failed',
'exception': exc,
'future': self,
}
if self._source_traceback:
context['source_traceback'] = self._source_traceback
self._loop.call_exception_handler(context)
self._ov = None
def cancel(self, msg=None):
self._cancel_overlapped()
return super().cancel(msg=msg)
def set_exception(self, exception):
super().set_exception(exception)
self._cancel_overlapped()
def set_result(self, result):
super().set_result(result)
self._ov = None
class _BaseWaitHandleFuture(futures.Future):
"""Subclass of Future which represents a wait handle."""
def __init__(self, ov, handle, wait_handle, *, loop=None):
super().__init__(loop=loop)
if self._source_traceback:
del self._source_traceback[-1]
# Keep a reference to the Overlapped object to keep it alive until the
# wait is unregistered
self._ov = ov
self._handle = handle
self._wait_handle = wait_handle
# Should we call UnregisterWaitEx() if the wait completes
# or is cancelled?
self._registered = True
def _poll(self):
# non-blocking wait: use a timeout of 0 millisecond
return (_winapi.WaitForSingleObject(self._handle, 0) ==
_winapi.WAIT_OBJECT_0)
def _repr_info(self):
info = super()._repr_info()
info.append(f'handle={self._handle:#x}')
if self._handle is not None:
state = 'signaled' if self._poll() else 'waiting'
info.append(state)
if self._wait_handle is not None:
info.append(f'wait_handle={self._wait_handle:#x}')
return info
def _unregister_wait_cb(self, fut):
# The wait was unregistered: it's not safe to destroy the Overlapped
# object
self._ov = None
def _unregister_wait(self):
if not self._registered:
return
self._registered = False
wait_handle = self._wait_handle
self._wait_handle = None
try:
_overlapped.UnregisterWait(wait_handle)
except OSError as exc:
if exc.winerror != _overlapped.ERROR_IO_PENDING:
context = {
'message': 'Failed to unregister the wait handle',
'exception': exc,
'future': self,
}
if self._source_traceback:
context['source_traceback'] = self._source_traceback
self._loop.call_exception_handler(context)
return
# ERROR_IO_PENDING means that the unregister is pending
self._unregister_wait_cb(None)
def cancel(self, msg=None):
self._unregister_wait()
return super().cancel(msg=msg)
def set_exception(self, exception):
self._unregister_wait()
super().set_exception(exception)
def set_result(self, result):
self._unregister_wait()
super().set_result(result)
class _WaitCancelFuture(_BaseWaitHandleFuture):
"""Subclass of Future which represents a wait for the cancellation of a
_WaitHandleFuture using an event.
"""
def __init__(self, ov, event, wait_handle, *, loop=None):
super().__init__(ov, event, wait_handle, loop=loop)
self._done_callback = None
def cancel(self):
raise RuntimeError("_WaitCancelFuture must not be cancelled")
def set_result(self, result):
super().set_result(result)
if self._done_callback is not None:
self._done_callback(self)
def set_exception(self, exception):
super().set_exception(exception)
if self._done_callback is not None:
self._done_callback(self)
class _WaitHandleFuture(_BaseWaitHandleFuture):
def __init__(self, ov, handle, wait_handle, proactor, *, loop=None):
super().__init__(ov, handle, wait_handle, loop=loop)
self._proactor = proactor
self._unregister_proactor = True
self._event = _overlapped.CreateEvent(None, True, False, None)
self._event_fut = None
def _unregister_wait_cb(self, fut):
if self._event is not None:
_winapi.CloseHandle(self._event)
self._event = None
self._event_fut = None
# If the wait was cancelled, the wait may never be signalled, so
# it's required to unregister it. Otherwise, IocpProactor.close() will
# wait forever for an event which will never come.
#
# If the IocpProactor already received the event, it's safe to call
# _unregister() because we kept a reference to the Overlapped object
# which is used as a unique key.
self._proactor._unregister(self._ov)
self._proactor = None
super()._unregister_wait_cb(fut)
def _unregister_wait(self):
if not self._registered:
return
self._registered = False
wait_handle = self._wait_handle
self._wait_handle = None
try:
_overlapped.UnregisterWaitEx(wait_handle, self._event)
except OSError as exc:
if exc.winerror != _overlapped.ERROR_IO_PENDING:
context = {
'message': 'Failed to unregister the wait handle',
'exception': exc,
'future': self,
}
if self._source_traceback:
context['source_traceback'] = self._source_traceback
self._loop.call_exception_handler(context)
return
# ERROR_IO_PENDING is not an error, the wait was unregistered
self._event_fut = self._proactor._wait_cancel(self._event,
self._unregister_wait_cb)
class PipeServer(object):
"""Class representing a pipe server.
This is much like a bound, listening socket.
"""
def __init__(self, address):
self._address = address
self._free_instances = weakref.WeakSet()
# initialize the pipe attribute before calling _server_pipe_handle()
# because this function can raise an exception and the destructor calls
# the close() method
self._pipe = None
self._accept_pipe_future = None
self._pipe = self._server_pipe_handle(True)
def _get_unconnected_pipe(self):
# Create new instance and return previous one. This ensures
# that (until the server is closed) there is always at least
# one pipe handle for address. Therefore if a client attempt
# to connect it will not fail with FileNotFoundError.
tmp, self._pipe = self._pipe, self._server_pipe_handle(False)
return tmp
def _server_pipe_handle(self, first):
# Return a wrapper for a new pipe handle.
if self.closed():
return None
flags = _winapi.PIPE_ACCESS_DUPLEX | _winapi.FILE_FLAG_OVERLAPPED
if first:
flags |= _winapi.FILE_FLAG_FIRST_PIPE_INSTANCE
h = _winapi.CreateNamedPipe(
self._address, flags,
_winapi.PIPE_TYPE_MESSAGE | _winapi.PIPE_READMODE_MESSAGE |
_winapi.PIPE_WAIT,
_winapi.PIPE_UNLIMITED_INSTANCES,
windows_utils.BUFSIZE, windows_utils.BUFSIZE,
_winapi.NMPWAIT_WAIT_FOREVER, _winapi.NULL)
pipe = windows_utils.PipeHandle(h)
self._free_instances.add(pipe)
return pipe
def closed(self):
return (self._address is None)
def close(self):
if self._accept_pipe_future is not None:
self._accept_pipe_future.cancel()
self._accept_pipe_future = None
# Close all instances which have not been connected to by a client.
if self._address is not None:
for pipe in self._free_instances:
pipe.close()
self._pipe = None
self._address = None
self._free_instances.clear()
__del__ = close
class _WindowsSelectorEventLoop(selector_events.BaseSelectorEventLoop):
"""Windows version of selector event loop."""
class ProactorEventLoop(proactor_events.BaseProactorEventLoop):
"""Windows version of proactor event loop using IOCP."""
def __init__(self, proactor=None):
if proactor is None:
proactor = IocpProactor()
super().__init__(proactor)
def run_forever(self):
try:
assert self._self_reading_future is None
self.call_soon(self._loop_self_reading)
super().run_forever()
finally:
if self._self_reading_future is not None:
ov = self._self_reading_future._ov
self._self_reading_future.cancel()
# self_reading_future was just cancelled so if it hasn't been
# finished yet, it never will be (it's possible that it has
# already finished and its callback is waiting in the queue,
# where it could still happen if the event loop is restarted).
# Unregister it otherwise IocpProactor.close will wait for it
# forever
if ov is not None:
self._proactor._unregister(ov)
self._self_reading_future = None
async def create_pipe_connection(self, protocol_factory, address):
f = self._proactor.connect_pipe(address)
pipe = await f
protocol = protocol_factory()
trans = self._make_duplex_pipe_transport(pipe, protocol,
extra={'addr': address})
return trans, protocol
async def start_serving_pipe(self, protocol_factory, address):
server = PipeServer(address)
def loop_accept_pipe(f=None):
pipe = None
try:
if f:
pipe = f.result()
server._free_instances.discard(pipe)
if server.closed():
# A client connected before the server was closed:
# drop the client (close the pipe) and exit
pipe.close()
return
protocol = protocol_factory()
self._make_duplex_pipe_transport(
pipe, protocol, extra={'addr': address})
pipe = server._get_unconnected_pipe()
if pipe is None:
return
f = self._proactor.accept_pipe(pipe)
except OSError as exc:
if pipe and pipe.fileno() != -1:
self.call_exception_handler({
'message': 'Pipe accept failed',
'exception': exc,
'pipe': pipe,
})
pipe.close()
elif self._debug:
logger.warning("Accept pipe failed on pipe %r",
pipe, exc_info=True)
except exceptions.CancelledError:
if pipe:
pipe.close()
else:
server._accept_pipe_future = f
f.add_done_callback(loop_accept_pipe)
self.call_soon(loop_accept_pipe)
return [server]
async def _make_subprocess_transport(self, protocol, args, shell,
stdin, stdout, stderr, bufsize,
extra=None, **kwargs):
waiter = self.create_future()
transp = _WindowsSubprocessTransport(self, protocol, args, shell,
stdin, stdout, stderr, bufsize,
waiter=waiter, extra=extra,
**kwargs)
try:
await waiter
except (SystemExit, KeyboardInterrupt):
raise
except BaseException:
transp.close()
await transp._wait()
raise
return transp
class IocpProactor:
"""Proactor implementation using IOCP."""
def __init__(self, concurrency=0xffffffff):
self._loop = None
self._results = []
self._iocp = _overlapped.CreateIoCompletionPort(
_overlapped.INVALID_HANDLE_VALUE, NULL, 0, concurrency)
self._cache = {}
self._registered = weakref.WeakSet()
self._unregistered = []
self._stopped_serving = weakref.WeakSet()
def _check_closed(self):
if self._iocp is None:
raise RuntimeError('IocpProactor is closed')
def __repr__(self):
info = ['overlapped#=%s' % len(self._cache),
'result#=%s' % len(self._results)]
if self._iocp is None:
info.append('closed')
return '<%s %s>' % (self.__class__.__name__, " ".join(info))
def set_loop(self, loop):
self._loop = loop
def select(self, timeout=None):
if not self._results:
self._poll(timeout)
tmp = self._results
self._results = []
return tmp
def _result(self, value):
fut = self._loop.create_future()
fut.set_result(value)
return fut
def recv(self, conn, nbytes, flags=0):
self._register_with_iocp(conn)
ov = _overlapped.Overlapped(NULL)
try:
if isinstance(conn, socket.socket):
ov.WSARecv(conn.fileno(), nbytes, flags)
else:
ov.ReadFile(conn.fileno(), nbytes)
except BrokenPipeError:
return self._result(b'')
def finish_recv(trans, key, ov):
try:
return ov.getresult()
except OSError as exc:
if exc.winerror in (_overlapped.ERROR_NETNAME_DELETED,
_overlapped.ERROR_OPERATION_ABORTED):
raise ConnectionResetError(*exc.args)
else:
raise
return self._register(ov, conn, finish_recv)
def recv_into(self, conn, buf, flags=0):
self._register_with_iocp(conn)
ov = _overlapped.Overlapped(NULL)
try:
if isinstance(conn, socket.socket):
ov.WSARecvInto(conn.fileno(), buf, flags)
else:
ov.ReadFileInto(conn.fileno(), buf)
except BrokenPipeError:
return self._result(0)
def finish_recv(trans, key, ov):
try:
return ov.getresult()
except OSError as exc:
if exc.winerror in (_overlapped.ERROR_NETNAME_DELETED,
_overlapped.ERROR_OPERATION_ABORTED):
raise ConnectionResetError(*exc.args)
else:
raise
return self._register(ov, conn, finish_recv)
def recvfrom(self, conn, nbytes, flags=0):
self._register_with_iocp(conn)
ov = _overlapped.Overlapped(NULL)
try:
ov.WSARecvFrom(conn.fileno(), nbytes, flags)
except BrokenPipeError:
return self._result((b'', None))
def finish_recv(trans, key, ov):
try:
return ov.getresult()
except OSError as exc:
if exc.winerror in (_overlapped.ERROR_NETNAME_DELETED,
_overlapped.ERROR_OPERATION_ABORTED):
raise ConnectionResetError(*exc.args)
else:
raise
return self._register(ov, conn, finish_recv)
def sendto(self, conn, buf, flags=0, addr=None):
self._register_with_iocp(conn)
ov = _overlapped.Overlapped(NULL)
ov.WSASendTo(conn.fileno(), buf, flags, addr)
def finish_send(trans, key, ov):
try:
return ov.getresult()
except OSError as exc:
if exc.winerror in (_overlapped.ERROR_NETNAME_DELETED,
_overlapped.ERROR_OPERATION_ABORTED):
raise ConnectionResetError(*exc.args)
else:
raise
return self._register(ov, conn, finish_send)
def send(self, conn, buf, flags=0):
self._register_with_iocp(conn)
ov = _overlapped.Overlapped(NULL)
if isinstance(conn, socket.socket):
ov.WSASend(conn.fileno(), buf, flags)
else:
ov.WriteFile(conn.fileno(), buf)
def finish_send(trans, key, ov):
try:
return ov.getresult()
except OSError as exc:
if exc.winerror in (_overlapped.ERROR_NETNAME_DELETED,
_overlapped.ERROR_OPERATION_ABORTED):
raise ConnectionResetError(*exc.args)
else:
raise
return self._register(ov, conn, finish_send)
def accept(self, listener):
self._register_with_iocp(listener)
conn = self._get_accept_socket(listener.family)
ov = _overlapped.Overlapped(NULL)
ov.AcceptEx(listener.fileno(), conn.fileno())
def finish_accept(trans, key, ov):
ov.getresult()
# Use SO_UPDATE_ACCEPT_CONTEXT so getsockname() etc work.
buf = struct.pack('@P', listener.fileno())
conn.setsockopt(socket.SOL_SOCKET,
_overlapped.SO_UPDATE_ACCEPT_CONTEXT, buf)
conn.settimeout(listener.gettimeout())
return conn, conn.getpeername()
async def accept_coro(future, conn):
# Coroutine closing the accept socket if the future is cancelled
try:
await future
except exceptions.CancelledError:
conn.close()
raise
future = self._register(ov, listener, finish_accept)
coro = accept_coro(future, conn)
tasks.ensure_future(coro, loop=self._loop)
return future
def connect(self, conn, address):
if conn.type == socket.SOCK_DGRAM:
# WSAConnect will complete immediately for UDP sockets so we don't
# need to register any IOCP operation
_overlapped.WSAConnect(conn.fileno(), address)
fut = self._loop.create_future()
fut.set_result(None)
return fut
self._register_with_iocp(conn)
# The socket needs to be locally bound before we call ConnectEx().
try:
_overlapped.BindLocal(conn.fileno(), conn.family)
except OSError as e:
if e.winerror != errno.WSAEINVAL:
raise
# Probably already locally bound; check using getsockname().
if conn.getsockname()[1] == 0:
raise
ov = _overlapped.Overlapped(NULL)
ov.ConnectEx(conn.fileno(), address)
def finish_connect(trans, key, ov):
ov.getresult()
# Use SO_UPDATE_CONNECT_CONTEXT so getsockname() etc work.
conn.setsockopt(socket.SOL_SOCKET,
_overlapped.SO_UPDATE_CONNECT_CONTEXT, 0)
return conn
return self._register(ov, conn, finish_connect)
def sendfile(self, sock, file, offset, count):
self._register_with_iocp(sock)
ov = _overlapped.Overlapped(NULL)
offset_low = offset & 0xffff_ffff
offset_high = (offset >> 32) & 0xffff_ffff
ov.TransmitFile(sock.fileno(),
msvcrt.get_osfhandle(file.fileno()),
offset_low, offset_high,
count, 0, 0)
def finish_sendfile(trans, key, ov):
try:
return ov.getresult()
except OSError as exc:
if exc.winerror in (_overlapped.ERROR_NETNAME_DELETED,
_overlapped.ERROR_OPERATION_ABORTED):
raise ConnectionResetError(*exc.args)
else:
raise
return self._register(ov, sock, finish_sendfile)
def accept_pipe(self, pipe):
self._register_with_iocp(pipe)
ov = _overlapped.Overlapped(NULL)
connected = ov.ConnectNamedPipe(pipe.fileno())
if connected:
# ConnectNamePipe() failed with ERROR_PIPE_CONNECTED which means
# that the pipe is connected. There is no need to wait for the
# completion of the connection.
return self._result(pipe)
def finish_accept_pipe(trans, key, ov):
ov.getresult()
return pipe
return self._register(ov, pipe, finish_accept_pipe)
async def connect_pipe(self, address):
delay = CONNECT_PIPE_INIT_DELAY
while True:
# Unfortunately there is no way to do an overlapped connect to
# a pipe. Call CreateFile() in a loop until it doesn't fail with
# ERROR_PIPE_BUSY.
try:
handle = _overlapped.ConnectPipe(address)
break
except OSError as exc:
if exc.winerror != _overlapped.ERROR_PIPE_BUSY:
raise
# ConnectPipe() failed with ERROR_PIPE_BUSY: retry later
delay = min(delay * 2, CONNECT_PIPE_MAX_DELAY)
await tasks.sleep(delay)
return windows_utils.PipeHandle(handle)
def wait_for_handle(self, handle, timeout=None):
"""Wait for a handle.
Return a Future object. The result of the future is True if the wait
completed, or False if the wait did not complete (on timeout).
"""
return self._wait_for_handle(handle, timeout, False)
def _wait_cancel(self, event, done_callback):
fut = self._wait_for_handle(event, None, True)
# add_done_callback() cannot be used because the wait may only complete
# in IocpProactor.close(), while the event loop is not running.
fut._done_callback = done_callback
return fut
def _wait_for_handle(self, handle, timeout, _is_cancel):
self._check_closed()
if timeout is None:
ms = _winapi.INFINITE
else:
# RegisterWaitForSingleObject() has a resolution of 1 millisecond,
# round away from zero to wait *at least* timeout seconds.
ms = math.ceil(timeout * 1e3)
# We only create ov so we can use ov.address as a key for the cache.
ov = _overlapped.Overlapped(NULL)
wait_handle = _overlapped.RegisterWaitWithQueue(
handle, self._iocp, ov.address, ms)
if _is_cancel:
f = _WaitCancelFuture(ov, handle, wait_handle, loop=self._loop)
else:
f = _WaitHandleFuture(ov, handle, wait_handle, self,
loop=self._loop)
if f._source_traceback:
del f._source_traceback[-1]
def finish_wait_for_handle(trans, key, ov):
# Note that this second wait means that we should only use
# this with handles types where a successful wait has no
# effect. So events or processes are all right, but locks
# or semaphores are not. Also note if the handle is
# signalled and then quickly reset, then we may return
# False even though we have not timed out.
return f._poll()
self._cache[ov.address] = (f, ov, 0, finish_wait_for_handle)
return f
def _register_with_iocp(self, obj):
# To get notifications of finished ops on this objects sent to the
# completion port, were must register the handle.
if obj not in self._registered:
self._registered.add(obj)
_overlapped.CreateIoCompletionPort(obj.fileno(), self._iocp, 0, 0)
# XXX We could also use SetFileCompletionNotificationModes()
# to avoid sending notifications to completion port of ops
# that succeed immediately.
def _register(self, ov, obj, callback):
self._check_closed()
# Return a future which will be set with the result of the
# operation when it completes. The future's value is actually
# the value returned by callback().
f = _OverlappedFuture(ov, loop=self._loop)
if f._source_traceback:
del f._source_traceback[-1]
if not ov.pending:
# The operation has completed, so no need to postpone the
# work. We cannot take this short cut if we need the
# NumberOfBytes, CompletionKey values returned by
# PostQueuedCompletionStatus().
try:
value = callback(None, None, ov)
except OSError as e:
f.set_exception(e)
else:
f.set_result(value)
# Even if GetOverlappedResult() was called, we have to wait for the
# notification of the completion in GetQueuedCompletionStatus().
# Register the overlapped operation to keep a reference to the
# OVERLAPPED object, otherwise the memory is freed and Windows may
# read uninitialized memory.
# Register the overlapped operation for later. Note that
# we only store obj to prevent it from being garbage
# collected too early.
self._cache[ov.address] = (f, ov, obj, callback)
return f
def _unregister(self, ov):
"""Unregister an overlapped object.
Call this method when its future has been cancelled. The event can
already be signalled (pending in the proactor event queue). It is also
safe if the event is never signalled (because it was cancelled).
"""
self._check_closed()
self._unregistered.append(ov)
def _get_accept_socket(self, family):
s = socket.socket(family)
s.settimeout(0)
return s
def _poll(self, timeout=None):
if timeout is None:
ms = INFINITE
elif timeout < 0:
raise ValueError("negative timeout")
else:
# GetQueuedCompletionStatus() has a resolution of 1 millisecond,
# round away from zero to wait *at least* timeout seconds.
ms = math.ceil(timeout * 1e3)
if ms >= INFINITE:
raise ValueError("timeout too big")
while True:
status = _overlapped.GetQueuedCompletionStatus(self._iocp, ms)
if status is None:
break
ms = 0
err, transferred, key, address = status
try:
f, ov, obj, callback = self._cache.pop(address)
except KeyError:
if self._loop.get_debug():
self._loop.call_exception_handler({
'message': ('GetQueuedCompletionStatus() returned an '
'unexpected event'),
'status': ('err=%s transferred=%s key=%#x address=%#x'
% (err, transferred, key, address)),
})
# key is either zero, or it is used to return a pipe
# handle which should be closed to avoid a leak.
if key not in (0, _overlapped.INVALID_HANDLE_VALUE):
_winapi.CloseHandle(key)
continue
if obj in self._stopped_serving:
f.cancel()
# Don't call the callback if _register() already read the result or
# if the overlapped has been cancelled
elif not f.done():
try:
value = callback(transferred, key, ov)
except OSError as e:
f.set_exception(e)
self._results.append(f)
else:
f.set_result(value)
self._results.append(f)
# Remove unregistered futures
for ov in self._unregistered:
self._cache.pop(ov.address, None)
self._unregistered.clear()
def _stop_serving(self, obj):
# obj is a socket or pipe handle. It will be closed in
# BaseProactorEventLoop._stop_serving() which will make any
# pending operations fail quickly.
self._stopped_serving.add(obj)
def close(self):
if self._iocp is None:
# already closed
return
# Cancel remaining registered operations.
for address, (fut, ov, obj, callback) in list(self._cache.items()):
if fut.cancelled():
# Nothing to do with cancelled futures
pass
elif isinstance(fut, _WaitCancelFuture):
# _WaitCancelFuture must not be cancelled
pass
else:
try:
fut.cancel()
except OSError as exc:
if self._loop is not None:
context = {
'message': 'Cancelling a future failed',
'exception': exc,
'future': fut,
}
if fut._source_traceback:
context['source_traceback'] = fut._source_traceback
self._loop.call_exception_handler(context)
# Wait until all cancelled overlapped complete: don't exit with running
# overlapped to prevent a crash. Display progress every second if the
# loop is still running.
msg_update = 1.0
start_time = time.monotonic()
next_msg = start_time + msg_update
while self._cache:
if next_msg <= time.monotonic():
logger.debug('%r is running after closing for %.1f seconds',
self, time.monotonic() - start_time)
next_msg = time.monotonic() + msg_update
# handle a few events, or timeout
self._poll(msg_update)
self._results = []
_winapi.CloseHandle(self._iocp)
self._iocp = None
def __del__(self):
self.close()
class _WindowsSubprocessTransport(base_subprocess.BaseSubprocessTransport):
def _start(self, args, shell, stdin, stdout, stderr, bufsize, **kwargs):
self._proc = windows_utils.Popen(
args, shell=shell, stdin=stdin, stdout=stdout, stderr=stderr,
bufsize=bufsize, **kwargs)
def callback(f):
returncode = self._proc.poll()
self._process_exited(returncode)
f = self._loop._proactor.wait_for_handle(int(self._proc._handle))
f.add_done_callback(callback)
SelectorEventLoop = _WindowsSelectorEventLoop
class WindowsSelectorEventLoopPolicy(events.BaseDefaultEventLoopPolicy):
_loop_factory = SelectorEventLoop
class WindowsProactorEventLoopPolicy(events.BaseDefaultEventLoopPolicy):
_loop_factory = ProactorEventLoop
DefaultEventLoopPolicy = WindowsProactorEventLoopPolicy
|
py | 1a31167fc6d4ad18de810b003a848158d49dfdf9 | from django.urls import path, include
from comment.api.views import CommentCreateApiView, CommentListApiView, CommentValidateApiView
app_name = "comment"
urlpatterns = [
path('create/', CommentCreateApiView.as_view(), name='create'),
path('list/', CommentListApiView.as_view(), name='list'),
path('validate/<pk>', CommentValidateApiView.as_view(), name='validate'),
]
|
py | 1a31173f08fa3cd58402e85b4441216381a3771e | def readline(f, newline):
buf = ""
while True:
while newline in buf:
pos = buf.index(newline)
yield buf[:pos]
buf = buf[pos + len(newline):]
chunk = f.read(4096 * 10)
if not chunk:
yield buf
break
buf += chunk
with open("index.txt") as f:
for line in readline(f, "{|}"):
print(line)
|
py | 1a311761b476828346ffb5aebe93651f0964e2fc | # Generated by Django 3.0 on 2021-01-05 14:01
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
]
operations = [
migrations.CreateModel(
name='Action',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('verb', models.CharField(max_length=255)),
('created', models.DateTimeField(auto_now_add=True, db_index=True)),
('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='actions', to=settings.AUTH_USER_MODEL)),
],
options={
'ordering': ('-created',),
},
),
]
|
py | 1a3118ff32aac1c9165b9f878ca8b8f876541a2e | """The module defines the abstract interface for resolving container images for tool execution."""
from abc import (
ABCMeta,
abstractmethod,
abstractproperty,
)
from galaxy.util.bunch import Bunch
from galaxy.util.dictifiable import Dictifiable
class ResolutionCache(Bunch):
"""Simple cache for duplicated computation created once per set of requests (likely web request in Galaxy context).
This should not be assumed to be thread safe - resolution using a given cache should all occur
one resolution at a time in a single thread.
"""
mulled_resolution_cache = None
class ContainerResolver(Dictifiable, metaclass=ABCMeta):
"""Description of a technique for resolving container images for tool execution."""
# Keys for dictification.
dict_collection_visible_keys = ["resolver_type", "can_uninstall_dependencies", "builds_on_resolution"]
can_uninstall_dependencies = False
builds_on_resolution = False
read_only = True # not used for containers, but set for when they are used like dependency resolvers
def __init__(self, app_info=None, **kwds):
"""Default initializer for ``ContainerResolver`` subclasses."""
self.app_info = app_info
self.resolver_kwds = kwds
def _get_config_option(self, key, default=None):
"""Look in resolver-specific settings for option and then fallback to
global settings.
"""
if self.app_info and hasattr(self.app_info, key):
return getattr(self.app_info, key)
else:
return default
@abstractmethod
def resolve(self, enabled_container_types, tool_info, resolution_cache=None, **kwds):
"""Find a container matching all supplied requirements for tool.
The supplied argument is a :class:`galaxy.tool_util.deps.containers.ToolInfo` description
of the tool and its requirements.
"""
@abstractproperty
def resolver_type(self):
"""Short label for the type of container resolution."""
def _container_type_enabled(self, container_description, enabled_container_types):
"""Return a boolean indicating if the specified container type is enabled."""
return container_description.type in enabled_container_types
def __str__(self):
return f"{self.__class__.__name__}[]"
|
py | 1a31192a5e77861da5836b222bf9b668132d1eac | import warnings
from itertools import islice
from types import GeneratorType
from typing import (
TYPE_CHECKING,
AbstractSet,
Any,
Callable,
Dict,
Generator,
Iterator,
List,
Optional,
Set,
Tuple,
Type,
TypeVar,
Union,
no_type_check,
)
from .typing import AnyType, display_as_type
from .version import version_info
if TYPE_CHECKING:
from inspect import Signature
from .main import BaseModel, BaseConfig # noqa: F401
from .typing import AbstractSetIntStr, DictIntStrAny, IntStr, MappingIntStrAny, ReprArgs # noqa: F401
from .fields import ModelField # noqa: F401
from .dataclasses import DataclassType # noqa: F401
__all__ = (
'import_string',
'sequence_like',
'validate_field_name',
'lenient_issubclass',
'in_ipython',
'deep_update',
'update_not_none',
'almost_equal_floats',
'get_model',
'to_camel',
'PyObjectStr',
'Representation',
'GetterDict',
'ValueItems',
'version_info', # required here to match behaviour in v1.3
)
def import_string(dotted_path: str) -> Any:
"""
Stolen approximately from django. Import a dotted module path and return the attribute/class designated by the
last name in the path. Raise ImportError if the import fails.
"""
from importlib import import_module
try:
module_path, class_name = dotted_path.strip(' ').rsplit('.', 1)
except ValueError as e:
raise ImportError(f'"{dotted_path}" doesn\'t look like a module path') from e
module = import_module(module_path)
try:
return getattr(module, class_name)
except AttributeError as e:
raise ImportError(f'Module "{module_path}" does not define a "{class_name}" attribute') from e
def truncate(v: Union[str], *, max_len: int = 80) -> str:
"""
Truncate a value and add a unicode ellipsis (three dots) to the end if it was too long
"""
warnings.warn('`truncate` is no-longer used by pydantic and is deprecated', DeprecationWarning)
if isinstance(v, str) and len(v) > (max_len - 2):
# -3 so quote + string + … + quote has correct length
return (v[: (max_len - 3)] + '…').__repr__()
try:
v = v.__repr__()
except TypeError:
v = v.__class__.__repr__(v) # in case v is a type
if len(v) > max_len:
v = v[: max_len - 1] + '…'
return v
def sequence_like(v: AnyType) -> bool:
return isinstance(v, (list, tuple, set, frozenset, GeneratorType))
def validate_field_name(bases: List[Type['BaseModel']], field_name: str) -> None:
"""
Ensure that the field's name does not shadow an existing attribute of the model.
"""
for base in bases:
if getattr(base, field_name, None):
raise NameError(
f'Field name "{field_name}" shadows a BaseModel attribute; '
f'use a different field name with "alias=\'{field_name}\'".'
)
def lenient_issubclass(cls: Any, class_or_tuple: Union[AnyType, Tuple[AnyType, ...]]) -> bool:
return isinstance(cls, type) and issubclass(cls, class_or_tuple)
def in_ipython() -> bool:
"""
Check whether we're in an ipython environment, including jupyter notebooks.
"""
try:
eval('__IPYTHON__')
except NameError:
return False
else: # pragma: no cover
return True
KeyType = TypeVar('KeyType')
def deep_update(mapping: Dict[KeyType, Any], updating_mapping: Dict[KeyType, Any]) -> Dict[KeyType, Any]:
updated_mapping = mapping.copy()
for k, v in updating_mapping.items():
if k in mapping and isinstance(mapping[k], dict) and isinstance(v, dict):
updated_mapping[k] = deep_update(mapping[k], v)
else:
updated_mapping[k] = v
return updated_mapping
def update_not_none(mapping: Dict[Any, Any], **update: Any) -> None:
mapping.update({k: v for k, v in update.items() if v is not None})
def almost_equal_floats(value_1: float, value_2: float, *, delta: float = 1e-8) -> bool:
"""
Return True if two floats are almost equal
"""
return abs(value_1 - value_2) <= delta
def generate_model_signature(
init: Callable[..., None], fields: Dict[str, 'ModelField'], config: Type['BaseConfig']
) -> 'Signature':
"""
Generate signature for model based on its fields
"""
from inspect import Parameter, Signature, signature
present_params = signature(init).parameters.values()
merged_params: Dict[str, Parameter] = {}
var_kw = None
use_var_kw = False
for param in islice(present_params, 1, None): # skip self arg
if param.kind is param.VAR_KEYWORD:
var_kw = param
continue
merged_params[param.name] = param
if var_kw: # if custom init has no var_kw, fields which are not declared in it cannot be passed through
allow_names = config.allow_population_by_field_name
for field_name, field in fields.items():
param_name = field.alias
if field_name in merged_params or param_name in merged_params:
continue
elif not param_name.isidentifier():
if allow_names and field_name.isidentifier():
param_name = field_name
else:
use_var_kw = True
continue
# TODO: replace annotation with actual expected types once #1055 solved
kwargs = {'default': field.default} if not field.required else {}
merged_params[param_name] = Parameter(
param_name, Parameter.KEYWORD_ONLY, annotation=field.outer_type_, **kwargs
)
if config.extra is config.extra.allow:
use_var_kw = True
if var_kw and use_var_kw:
# Make sure the parameter for extra kwargs
# does not have the same name as a field
default_model_signature = [
('__pydantic_self__', Parameter.POSITIONAL_OR_KEYWORD),
('data', Parameter.VAR_KEYWORD),
]
if [(p.name, p.kind) for p in present_params] == default_model_signature:
# if this is the standard model signature, use extra_data as the extra args name
var_kw_name = 'extra_data'
else:
# else start from var_kw
var_kw_name = var_kw.name
# generate a name that's definitely unique
while var_kw_name in fields:
var_kw_name += '_'
merged_params[var_kw_name] = var_kw.replace(name=var_kw_name)
return Signature(parameters=list(merged_params.values()), return_annotation=None)
def get_model(obj: Union[Type['BaseModel'], Type['DataclassType']]) -> Type['BaseModel']:
from .main import BaseModel # noqa: F811
try:
model_cls = obj.__pydantic_model__ # type: ignore
except AttributeError:
model_cls = obj
if not issubclass(model_cls, BaseModel):
raise TypeError('Unsupported type, must be either BaseModel or dataclass')
return model_cls
def to_camel(string: str) -> str:
return ''.join(word.capitalize() for word in string.split('_'))
class PyObjectStr(str):
"""
String class where repr doesn't include quotes. Useful with Representation when you want to return a string
representation of something that valid (or pseudo-valid) python.
"""
def __repr__(self) -> str:
return str(self)
class Representation:
"""
Mixin to provide __str__, __repr__, and __pretty__ methods. See #884 for more details.
__pretty__ is used by [devtools](https://python-devtools.helpmanual.io/) to provide human readable representations
of objects.
"""
__slots__: Tuple[str, ...] = tuple()
def __repr_args__(self) -> 'ReprArgs':
"""
Returns the attributes to show in __str__, __repr__, and __pretty__ this is generally overridden.
Can either return:
* name - value pairs, e.g.: `[('foo_name', 'foo'), ('bar_name', ['b', 'a', 'r'])]`
* or, just values, e.g.: `[(None, 'foo'), (None, ['b', 'a', 'r'])]`
"""
attrs = ((s, getattr(self, s)) for s in self.__slots__)
return [(a, v) for a, v in attrs if v is not None]
def __repr_name__(self) -> str:
"""
Name of the instance's class, used in __repr__.
"""
return self.__class__.__name__
def __repr_str__(self, join_str: str) -> str:
return join_str.join(repr(v) if a is None else f'{a}={v!r}' for a, v in self.__repr_args__())
def __pretty__(self, fmt: Callable[[Any], Any], **kwargs: Any) -> Generator[Any, None, None]:
"""
Used by devtools (https://python-devtools.helpmanual.io/) to provide a human readable representations of objects
"""
yield self.__repr_name__() + '('
yield 1
for name, value in self.__repr_args__():
if name is not None:
yield name + '='
yield fmt(value)
yield ','
yield 0
yield -1
yield ')'
def __str__(self) -> str:
return self.__repr_str__(' ')
def __repr__(self) -> str:
return f'{self.__repr_name__()}({self.__repr_str__(", ")})'
class GetterDict(Representation):
"""
Hack to make object's smell just enough like dicts for validate_model.
We can't inherit from Mapping[str, Any] because it upsets cython so we have to implement all methods ourselves.
"""
__slots__ = ('_obj',)
def __init__(self, obj: Any):
self._obj = obj
def __getitem__(self, key: str) -> Any:
try:
return getattr(self._obj, key)
except AttributeError as e:
raise KeyError(key) from e
def get(self, key: Any, default: Any = None) -> Any:
return getattr(self._obj, key, default)
def extra_keys(self) -> Set[Any]:
"""
We don't want to get any other attributes of obj if the model didn't explicitly ask for them
"""
return set()
def keys(self) -> List[Any]:
"""
Keys of the pseudo dictionary, uses a list not set so order information can be maintained like python
dictionaries.
"""
return list(self)
def values(self) -> List[Any]:
return [self[k] for k in self]
def items(self) -> Iterator[Tuple[str, Any]]:
for k in self:
yield k, self.get(k)
def __iter__(self) -> Iterator[str]:
for name in dir(self._obj):
if not name.startswith('_'):
yield name
def __len__(self) -> int:
return sum(1 for _ in self)
def __contains__(self, item: Any) -> bool:
return item in self.keys()
def __eq__(self, other: Any) -> bool:
return dict(self) == dict(other.items()) # type: ignore
def __repr_args__(self) -> 'ReprArgs':
return [(None, dict(self))] # type: ignore
def __repr_name__(self) -> str:
return f'GetterDict[{display_as_type(self._obj)}]'
class ValueItems(Representation):
"""
Class for more convenient calculation of excluded or included fields on values.
"""
__slots__ = ('_items', '_type')
def __init__(self, value: Any, items: Union['AbstractSetIntStr', 'MappingIntStrAny']) -> None:
if TYPE_CHECKING:
self._items: Union['AbstractSetIntStr', 'MappingIntStrAny']
self._type: Type[Union[set, dict]] # type: ignore
# For further type checks speed-up
if isinstance(items, dict):
self._type = dict
elif isinstance(items, AbstractSet):
self._type = set
else:
raise TypeError(f'Unexpected type of exclude value {items.__class__}')
if isinstance(value, (list, tuple)):
try:
items = self._normalize_indexes(items, len(value))
except TypeError as e:
raise TypeError(
'Excluding fields from a sequence of sub-models or dicts must be performed index-wise: '
'expected integer keys or keyword "__all__"'
) from e
self._items = items
@no_type_check
def is_excluded(self, item: Any) -> bool:
"""
Check if item is fully excluded
(value considered excluded if self._type is set and item contained in self._items
or self._type is dict and self._items.get(item) is ...
:param item: key or index of a value
"""
if self._type is set:
return item in self._items
return self._items.get(item) is ...
@no_type_check
def is_included(self, item: Any) -> bool:
"""
Check if value is contained in self._items
:param item: key or index of value
"""
return item in self._items
@no_type_check
def for_element(self, e: 'IntStr') -> Optional[Union['AbstractSetIntStr', 'MappingIntStrAny']]:
"""
:param e: key or index of element on value
:return: raw values for elemet if self._items is dict and contain needed element
"""
if self._type is dict:
item = self._items.get(e)
return item if item is not ... else None
return None
@no_type_check
def _normalize_indexes(
self, items: Union['AbstractSetIntStr', 'MappingIntStrAny'], v_length: int
) -> Union['AbstractSetIntStr', 'DictIntStrAny']:
"""
:param items: dict or set of indexes which will be normalized
:param v_length: length of sequence indexes of which will be
>>> self._normalize_indexes({0, -2, -1}, 4)
{0, 2, 3}
>>> self._normalize_indexes({'__all__'}, 4)
{0, 1, 2, 3}
"""
if self._type is set:
if '__all__' in items:
if items != {'__all__'}:
raise ValueError('set with keyword "__all__" must not contain other elements')
return {i for i in range(v_length)}
return {v_length + i if i < 0 else i for i in items}
else:
normalized_items = {v_length + i if i < 0 else i: v for i, v in items.items() if i != '__all__'}
all_set = items.get('__all__')
if all_set:
for i in range(v_length):
normalized_items.setdefault(i, set()).update(all_set)
return normalized_items
def __repr_args__(self) -> 'ReprArgs':
return [(None, self._items)]
|
py | 1a3119501271b19d5d2595d31873bd7a4496c209 | #!/usr/bin/env python3
# -*- encoding: utf-8 -*-
from flask import Blueprint, render_template
about_blueprint = Blueprint('about', __name__)
@about_blueprint.route("/catalog/about")
def about():
"""Show about page."""
return render_template("about.html")
|
py | 1a311b204dd69d09d8dc8a2fc1e8e572b9ab0cf8 | # _____ ______ _____
# / ____/ /\ | ____ | __ \
# | | / \ | |__ | |__) | Caer - Modern Computer Vision
# | | / /\ \ | __| | _ / Languages: Python, C, C++, Cuda
# | |___ / ____ \ | |____ | | \ \ http://github.com/jasmcaus/caer
# \_____\/_/ \_ \______ |_| \_\
# Licensed under the MIT License <http://opensource.org/licenses/MIT>
# SPDX-License-Identifier: MIT
# Copyright (c) 2020-2021 The Caer Authors <http://github.com/jasmcaus>
from threading import Thread
import time
import math
from queue import Queue
import cv2 as cv
from .constants import FRAME_COUNT, FPS
__all__ = [
'GPUFileStream'
]
class GPUFileStream:
r"""
This is an auxiliary class that enables Video Streaming using the GPU for caer with minimalistic latency, and at the expense of little to no additional computational requirements.
The basic idea behind it is to tracks and save the salient feature array for the given number of frames and then uses these anchor point to cancel out all perturbations relative to it for the incoming frames in the queue. This class relies heavily on **Threaded Queue mode** for error-free & ultra-fast frame handling.
Args:
source (int, str): Source path for the video. If ``source=0``, the default camera device is used. For
multiple external camera devices, use incremented values. For eg: ``source=1`` represents the second camera device on your system.
qsize (int): Default queue size for handling the video streams. Default: 128.
"""
def __init__(self, source, qsize=128):
"""
Source must be a path to a video file
Utilizes your system's GPU to process the stream
"""
if not isinstance(source, str):
raise ValueError(f'Expected either a filepath. Got {type(source)}. Consider using VideoStream which supports both live video as well as pre-existing videos')
# initialize the file video stream along with the boolean
# used to indicate if the thread should be stopped or not
self.stream = cv.VideoCapture(source)
self.kill_stream = False
self.count = 0
# initialize the queue to store frames
self.Q = Queue(maxsize=qsize)
self.width = int(self.stream.get(cv.CAP_PROP_FRAME_WIDTH))
self.height = int(self.stream.get(cv.CAP_PROP_FRAME_HEIGHT))
self.res = (self.width, self.height)
self.fps = math.ceil(self.stream.get(FPS))
self.frames = int(self.stream.get(FRAME_COUNT))
# since we use UMat to store the images to
# we need to initialize them beforehand
self.qframes = [0] * qsize
for ii in range(qsize):
self.qframes[ii] = cv.UMat(self.height, self.width, cv.CV_8UC3)
def begin_stream(self):
# start a thread to read frames from the file video stream
t = Thread(target=self.update, args=())
t.daemon = True
t.start()
return self
def update(self):
# keep looping infinitely
while True:
if self.kill_stream:
return
# otherwise, ensure the queue has room in it
if not self.Q.full():
self.count += 1
target = (self.count-1) % self.Q.maxsize
ret = self.stream.grab()
if not ret:
self.release()
return
self.stream.retrieve(self.qframes[target])
# add the frame to the queue
self.Q.put(target)
def read(self):
while (not self.more() and self.kill_stream):
time.sleep(0.1)
# return next frame in the queue
return self.qframes[self.Q.get()]
def more(self):
# return True if there are still frames in the queue
return self.Q.qsize() > 0
def release(self):
self.kill_stream = True
# wait until stream resources are released
self.thread.join()
# Gets frame count
def count_frames(self):
if not self.kill_stream and not self.live_video:
return self.frames
# if get_opencv_version() == '2':
# return int(self.stream.get(FRAME_COUNT_DEPR))
# else:
# return int(self.stream.get(FRAME_COUNT))
if self.live_video:
print('[WARNING] Frames cannot be computed on live streams')
return -1
# Gets FPS count
def get_fps(self):
if not self.kill_stream:
return self.fps
# Get frame dimensions
def get_res(self):
return self.res |
py | 1a311b8d1c0f185243f288a50fdddfc7fdeb848f | # -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models, migrations
class Migration(migrations.Migration):
dependencies = [
('api', '0007_apirequest_extra'),
]
operations = [
migrations.AddField(
model_name='apirequest',
name='api_client',
field=models.CharField(null=True, editable=False, max_length=50),
preserve_default=True,
),
]
|
py | 1a311cc6d7ebf838a7a232773174002e36b9c602 | #-*- coding:utf-8 -*-
import pytest
import random
import string
import itertools as itt
import collections
import spiceminer as sm
import spiceminer.kernel.lowlevel as lowlevel
### Helpers ###
def rstrings(max_size):
while True:
yield ''.join(random.sample(string.lowercase, random.randint(1, max_size)))
class FakeKernel(object):
def __init__(self, path):
self.path = path
self.loaded = True
def _unload(self):
self.loaded = False
@pytest.fixture(scope='function')
def fake_LOADED(kernelfiles, monkeypatch):
available = random.sample(kernelfiles, random.randint(0, len(kernelfiles)))
substitute = {FakeKernel(path) for path in available}
monkeypatch.setattr(lowlevel, 'LOADED_KERNELS', substitute)
return substitute
@pytest.fixture(scope='function')
def fake_loaders(monkeypatch):
'''Patch lowlevel loader functions to rerturn dummy results.'''
def fake_loader(path):
windows = {'ABC': 'Test'}
return windows
for func in ('_load_sp', '_load_pc', '_load_c', '_load_f'):
monkeypatch.setattr(lowlevel, func, fake_loader)
@pytest.fixture(scope='function')
def fake_furnsh(monkeypatch):
monkeypatch.setattr(lowlevel.spice, 'furnsh', lambda x: None)
monkeypatch.setattr(lowlevel.spice, 'unload', lambda x: None)
### Tests ###
class TestKernelProperties(object):
def test_kp_good(self, kernelfile):
kprops = lowlevel.kernel_properties(kernelfile)
assert kprops.path == kernelfile
assert kprops.arch in lowlevel.ARCH
assert kprops.type in lowlevel.KTYPE
def test_kp_bad(self, nonkernelfile):
with pytest.raises((ValueError, sm.SpiceError)):
kprops = lowlevel.kernel_properties(nonkernelfile)
@pytest.mark.parametrize('ktype', list(lowlevel.KTYPE) + list(
set(itt.islice(rstrings(10), 5)) - lowlevel.KTYPE
))
def test_info_type(ktype):
info = lowlevel._info_type(ktype)
if ktype in lowlevel.KTYPE:
assert info in ('pos', 'rot', 'none')
else:
assert info == None
xValueError = pytest.mark.xfail(raises=ValueError)
@pytest.mark.parametrize('arch', list(lowlevel.ARCH) + [xValueError('?')])
@pytest.mark.parametrize('ktype', list(lowlevel.KTYPE) + [
xValueError(next(rstrings(10)))
])
def test_validate(arch, ktype):
lowlevel._validate('Test', arch, ktype)
@pytest.mark.parametrize('recursive', [True, False])
def test_icollect_kprops(datadir, kernelfiles, recursive):
paths = set(kp.path for kp in lowlevel.icollect_kprops(datadir, recursive, False))
if not recursive:
assert len(paths) < len(kernelfiles)
assert paths - set(kernelfiles) == set()
def test_ifilter_kprops(kernelfiles, fake_LOADED):
kp = collections.namedtuple('KernelPath', 'path')
result = lowlevel.ifilter_kprops(kp(path) for path in kernelfiles)
result_paths = set(kprops.path for kprops in result)
fake_paths = set(k.path for k in fake_LOADED)
assert result_paths.symmetric_difference(fake_paths) == set(kernelfiles)
def test_iunload_kprops(kernelfiles, fake_LOADED):
kp = collections.namedtuple('KernelPath', 'path')
result = lowlevel.iunload_kprops(kp(path) for path in kernelfiles)
result_paths = set(kprops.path for kprops in result)
assert result_paths == set(kernelfiles)
unloaded_paths = {k.path for k in fake_LOADED if not k.loaded}
assert len(unloaded_paths) == len(fake_LOADED)
@pytest.mark.parametrize('types', [
[random.choice(list(lowlevel.KTYPE)) for i in range(10)]
])
def test_split_kprops(types):
kt = collections.namedtuple('KernelType', 'type')
kpmisc, kpbody = lowlevel.split_kprops(kt(t) for t in types)
body_types = {kt.type for kt in kpbody}
misc_types = {kt.type for kt in kpmisc}
assert body_types.union(lowlevel.KTYPE_BODY) == lowlevel.KTYPE_BODY
assert misc_types.intersection(lowlevel.KTYPE_BODY) == set()
assert body_types.union(misc_types) == set(types)
@pytest.mark.usefixtures('fake_loaders', 'fake_furnsh')
def test_load_any(kernelfile):
kp = collections.namedtuple('KernelProperties', ['path', 'type'])
kprops = kp(kernelfile, random.choice(list(lowlevel.KTYPE)))
time_window_map = lowlevel.load_any(kprops)
if kprops.type in lowlevel.KTYPE_BODY:
assert time_window_map == {'ABC': 'Test'}
else:
assert time_window_map == {}
def test_unload_any():
pass
@pytest.mark.parametrize('path', ['.'])
def test_load_dummy(path):
assert lowlevel._load_dummy(path) == {}
@pytest.mark.usefixtures('with_leapseconds')
def test_load_sp(spfile):
time_window_map = lowlevel._load_sp(spfile)
assert time_window_map != {}
@pytest.mark.usefixtures('with_leapseconds', 'with_spacecraftclock')
def test_load_c(cfile):
time_window_map = lowlevel._load_c(cfile)
assert time_window_map != {}
@pytest.mark.usefixtures('with_leapseconds')
def test_load_pc(pcfile):
time_window_map = lowlevel._load_pc(pcfile)
assert time_window_map != {}
@pytest.mark.usefixtures('with_leapseconds')
def test_load_f(ffile):
time_window_map = lowlevel._load_f(ffile)
assert time_window_map != {}
|
py | 1a311d152bedbd39e32d9587e27a033a2aa95be0 | def myprint():
print('myprint: ' + s)
s = 'I am global variable'
print(s)
myprint() |
py | 1a311d7c0922e41259749d2de354c8d21f97df0b | from typing import Any, Dict
from dbt.contracts.connection import HasCredentials
from dbt.context.base import (
BaseContext, contextproperty
)
class TargetContext(BaseContext):
# subclass is ConfiguredContext
def __init__(self, config: HasCredentials, cli_vars: Dict[str, Any]):
super().__init__(cli_vars=cli_vars)
self.config = config
@contextproperty
def target(self) -> Dict[str, Any]:
"""`target` contains information about your connection to the warehouse
(specified in profiles.yml). Some configs are shared between all
adapters, while others are adapter-specific.
Common:
|----------|-----------|------------------------------------------|
| Variable | Example | Description |
|----------|-----------|------------------------------------------|
| name | dev | Name of the active target |
|----------|-----------|------------------------------------------|
| schema | dbt_alice | Name of the dbt schema (or, dataset on |
| | | BigQuery) |
|----------|-----------|------------------------------------------|
| type | postgres | The active adapter being used. |
|----------|-----------|------------------------------------------|
| threads | 4 | The number of threads in use by dbt |
|----------|-----------|------------------------------------------|
Snowflake:
|----------|-----------|------------------------------------------|
| Variable | Example | Description |
|----------|-----------|------------------------------------------|
| database | RAW | The active target's database. |
|----------|-----------|------------------------------------------|
| warehouse| TRANSFORM | The active target's warehouse. |
|----------|-----------|------------------------------------------|
| user | USERNAME | The active target's user |
|----------|-----------|------------------------------------------|
| role | ROLENAME | The active target's role |
|----------|-----------|------------------------------------------|
| account | abc123 | The active target's account |
|----------|-----------|------------------------------------------|
Postgres/Redshift:
|----------|-------------------|----------------------------------|
| Variable | Example | Description |
|----------|-------------------|----------------------------------|
| dbname | analytics | The active target's database. |
|----------|-------------------|----------------------------------|
| host | abc123.us-west-2. | The active target's host. |
| | redshift.amazonaws| |
| | .com | |
|----------|-------------------|----------------------------------|
| user | dbt_user | The active target's user |
|----------|-------------------|----------------------------------|
| port | 5439 | The active target's port |
|----------|-------------------|----------------------------------|
BigQuery:
|----------|-----------|------------------------------------------|
| Variable | Example | Description |
|----------|-----------|------------------------------------------|
| project | abc-123 | The active target's project. |
|----------|-----------|------------------------------------------|
"""
return self.config.to_target_dict()
def generate_target_context(
config: HasCredentials, cli_vars: Dict[str, Any]
) -> Dict[str, Any]:
ctx = TargetContext(config, cli_vars)
return ctx.to_dict()
|
py | 1a311dcf3bca504bc6bedd9da792a511696e9de5 | import json
import datetime
from django.utils import timezone
from django.core.exceptions import PermissionDenied
from rest_framework import permissions, generics
from resources.models import Unit, Reservation, Resource, ResourceType
from hmlvaraus.models.hml_reservation import HMLReservation
from hmlvaraus.models.berth import Berth
from django.contrib.gis.geos import GEOSGeometry
from rest_framework import status
from rest_framework.response import Response
from django.utils.dateparse import parse_datetime
import pytz
class ImporterView(generics.CreateAPIView):
base_name = 'importer'
permission_classes = [permissions.IsAuthenticated]
def post(self, request):
request_user = request.user
if not request_user.is_staff:
raise PermissionDenied()
uploaded_file = request.data['file']
data = uploaded_file.read().decode("utf-8")
data_rows = data.split('\n')
# Kohteet
if data_rows[0][0] == '1':
del data_rows[1]
del data_rows[0]
for row in data_rows:
fields = row.split(';')
try:
print('Kohdedataa')
a = fields[5]
except:
continue
location = None
if fields[5] and fields[5] != '':
location = fields[5].split(',')
coordinates = []
for coord in location:
coord = coord.strip()
coord = float(coord)
coordinates = [coord] + coordinates
location = GEOSGeometry(json.dumps({'type': 'Point', 'coordinates': coordinates}))
Unit.objects.get_or_create(name=fields[0], street_address=fields[1], address_zip=fields[2], email=fields[3], phone=fields[4], location=location, description=fields[6])
# Venepaikat
if data_rows[0][0] == '2':
del data_rows[1]
del data_rows[0]
for row in data_rows:
fields = row.split(';')
try:
print('Venepaikkadataa, Kohde:', fields[0])
unit = Unit.objects.get(name=fields[0]);
except:
continue
resource_types = ResourceType.objects.all();
for resource_type in resource_types:
if 'vene' in resource_type.name.lower() or 'boat' in resource_type.name.lower():
type_instance = resource_type
resource = Resource.objects.get_or_create(unit=unit, name=fields[1], description=fields[2], type=type_instance, reservable=True)[0]
is_disabled = False
if fields[3] == 'kyllä':
is_disabled = True
price = 0
if fields[4]:
price = fields[4].replace(',', '.')
price = float(price)
type_mapping = {
'numero': 'number',
'laituri': 'dock',
'poletti': 'ground'
}
length = 0
width = 0
depth = 0
if fields[5] and fields[5] != '':
length = int(fields[5])
if fields[6] and fields[6] != '':
width = int(fields[6])
if fields[7] and fields[7] != '':
depth = int(fields[7])
berth_type = type_mapping.get(fields[8].lower(), None)
Berth.objects.get_or_create(resource=resource, is_disabled=is_disabled, price=price, length_cm=length, width_cm=width, depth_cm=depth, type=berth_type)
# Varaukset
if data_rows[0][0] == '3':
del data_rows[1]
del data_rows[0]
for i, row in enumerate(data_rows):
fields = row.split(';')
try:
print(i, 'Varausdataa, Kohde:', fields[1])
unit = Unit.objects.get(name=fields[1])
resource = Resource.objects.get(unit=unit, name=str(fields[0]), description=str(fields[4]))
except:
continue
resource.reservable = False
berth = Berth.objects.get(resource=resource)
begin = parse_datetime(str(fields[2]) + ' 00:00:00')
begin = pytz.timezone("Europe/Helsinki").localize(begin, is_dst=None)
end = parse_datetime(str(fields[3]) + ' 00:00:00')
end = pytz.timezone("Europe/Helsinki").localize(end, is_dst=None)
state = 'confirmed'
state_updated_at = timezone.now()
is_paid = False
is_paid_at = None
if fields[5] and fields[5].strip() != '':
state_updated_at = datetime.datetime.strptime(fields[5], "%d.%m.%Y %H:%M")
state = 'cancelled'
if fields[6] and fields[6].strip() != '':
is_paid_at = datetime.datetime.strptime(fields[6], "%d.%m.%Y %H:%M")
is_paid = True
reservation = Reservation.objects.create(
resource=resource,
begin=begin,
end=end,
event_description=fields[4] or '',
state=state,
reserver_name=fields[7] or '',
reserver_email_address=fields[8] or '',
reserver_phone_number=fields[9] or '',
reserver_address_street=fields[10] or '',
reserver_address_city=fields[11] or '',
reserver_address_zip=fields[12] or '',
)
HMLReservation.objects.get_or_create(reservation=reservation, berth=berth, state_updated_at=state_updated_at, is_paid_at=is_paid_at, is_paid=is_paid)
resource.save()
return Response(
status=status.HTTP_201_CREATED
)
|
py | 1a311def484d912295ffd2dc48724e092b31100f | #!/usr/bin/env python3
from training import *
from datasets import load_mnist_data as load_data
def make_tiny_model (input_shape, **kwds):
return tf.keras.models.Sequential([
tf.keras.layers.Conv2D(6, (3, 3), input_shape = input_shape),
tf.keras.layers.Activation('relu'),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(32),
tf.keras.layers.Activation('relu'),
tf.keras.layers.Dense(10),
tf.keras.layers.Activation('softmax'),
], **kwds)
def make_small_model (input_shape, **kwds):
return tf.keras.models.Sequential([
tf.keras.layers.Conv2D(8, (3, 3), input_shape = input_shape),
tf.keras.layers.Activation('relu'),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(42),
tf.keras.layers.Activation('relu'),
tf.keras.layers.Dense(10),
tf.keras.layers.Activation('softmax'),
], **kwds)
def make_small_maxp_model (input_shape, **kwds):
return tf.keras.models.Sequential([
tf.keras.layers.Conv2D(8, (3, 3), input_shape = input_shape),
tf.keras.layers.Activation('relu'),
tf.keras.layers.MaxPooling2D(),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(42),
tf.keras.layers.Activation('relu'),
tf.keras.layers.Dense(10),
tf.keras.layers.Activation('softmax'),
], **kwds)
def make_medium_model (input_shape, **kwds):
return tf.keras.models.Sequential([
tf.keras.layers.Conv2D(5, (3, 3), input_shape = input_shape),
tf.keras.layers.Activation('relu'),
tf.keras.layers.Conv2D(5, (5, 5)),
tf.keras.layers.Activation('relu'),
tf.keras.layers.Conv2D(3, (7, 7)),
tf.keras.layers.Activation('relu'),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(128),
tf.keras.layers.Activation('relu'),
tf.keras.layers.Dense(64),
tf.keras.layers.Activation('relu'),
tf.keras.layers.Dense(10),
tf.keras.layers.Activation('softmax'),
], **kwds)
def make_large_model (input_shape, **kwds):
return tf.keras.models.Sequential([
tf.keras.layers.Conv2D(32, (3, 3), input_shape = input_shape),
tf.keras.layers.Activation('relu'),
tf.keras.layers.Conv2D(16, (5, 5)),
tf.keras.layers.Activation('relu'),
tf.keras.layers.Conv2D(8, (7, 7)),
tf.keras.layers.Activation('relu'),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(256),
tf.keras.layers.Activation('relu'),
tf.keras.layers.Dense(64),
tf.keras.layers.Activation('relu'),
tf.keras.layers.Dense(10),
tf.keras.layers.Activation('softmax'),
], **kwds)
# classifier (load_data, make_tiny_model,
# model_name = 'mnist_tiny',
# epochs = 20)
# classifier (load_data, make_small_model,
# model_name = 'mnist_small',
# epochs = 20)
# classifier (load_data, make_small_model,
# model_name = 'mnist_small_overfitting',
# early_stopping = False,
# epochs = 50)
classifier (load_data, make_small_maxp_model,
model_name = 'mnist_small_maxp',
epochs = 20)
# classifier (load_data, make_medium_model,
# model_name = 'mnist_medium',
# epochs = 20)
# classifier (load_data, make_large_model,
# model_name = 'mnist_overfitting',
# early_stopping = False,
# epochs = 20)
|
py | 1a311f8c96d103e999b09e38d691bbd0be6cbbb1 | """Gaussian MLP Policy.
A policy represented by a Gaussian distribution
which is parameterized by a multilayer perceptron (MLP).
"""
# pylint: disable=wrong-import-order
import akro
import numpy as np
import tensorflow as tf
from garage.tf.models import GaussianMLPModel
from garage.tf.policies.policy import StochasticPolicy
class GaussianMLPPolicy(StochasticPolicy):
"""Gaussian MLP Policy.
A policy represented by a Gaussian distribution
which is parameterized by a multilayer perceptron (MLP).
Args:
env_spec (garage.envs.env_spec.EnvSpec): Environment specification.
name (str): Model name, also the variable scope.
hidden_sizes (list[int]): Output dimension of dense layer(s) for
the MLP for mean. For example, (32, 32) means the MLP consists
of two hidden layers, each with 32 hidden units.
hidden_nonlinearity (callable): Activation function for intermediate
dense layer(s). It should return a tf.Tensor. Set it to
None to maintain a linear activation.
hidden_w_init (callable): Initializer function for the weight
of intermediate dense layer(s). The function should return a
tf.Tensor.
hidden_b_init (callable): Initializer function for the bias
of intermediate dense layer(s). The function should return a
tf.Tensor.
output_nonlinearity (callable): Activation function for output dense
layer. It should return a tf.Tensor. Set it to None to
maintain a linear activation.
output_w_init (callable): Initializer function for the weight
of output dense layer(s). The function should return a
tf.Tensor.
output_b_init (callable): Initializer function for the bias
of output dense layer(s). The function should return a
tf.Tensor.
learn_std (bool): Is std trainable.
adaptive_std (bool): Is std a neural network. If False, it will be a
parameter.
std_share_network (bool): Boolean for whether mean and std share
the same network.
init_std (float): Initial value for std.
std_hidden_sizes (list[int]): Output dimension of dense layer(s) for
the MLP for std. For example, (32, 32) means the MLP consists
of two hidden layers, each with 32 hidden units.
min_std (float): If not None, the std is at least the value of min_std,
to avoid numerical issues.
max_std (float): If not None, the std is at most the value of max_std,
to avoid numerical issues.
std_hidden_nonlinearity (callable): Nonlinearity for each hidden layer
in the std network. The function should return a tf.Tensor.
std_output_nonlinearity (callable): Nonlinearity for output layer in
the std network. The function should return a
tf.Tensor.
std_parameterization (str): How the std should be parametrized. There
are a few options:
- exp: the logarithm of the std will be stored, and applied a
exponential transformation
- softplus: the std will be computed as log(1+exp(x))
layer_normalization (bool): Bool for using layer normalization or not.
"""
def __init__(self,
env_spec,
name='GaussianMLPPolicy',
hidden_sizes=(32, 32),
hidden_nonlinearity=tf.nn.tanh,
hidden_w_init=tf.initializers.glorot_uniform(),
hidden_b_init=tf.zeros_initializer(),
output_nonlinearity=None,
output_w_init=tf.initializers.glorot_uniform(),
output_b_init=tf.zeros_initializer(),
learn_std=True,
adaptive_std=False,
std_share_network=False,
init_std=1.0,
min_std=1e-6,
max_std=None,
std_hidden_sizes=(32, 32),
std_hidden_nonlinearity=tf.nn.tanh,
std_output_nonlinearity=None,
std_parameterization='exp',
layer_normalization=False):
if not isinstance(env_spec.action_space, akro.Box):
raise ValueError('GaussianMLPPolicy only works with '
'akro.Box action space, but not {}'.format(
env_spec.action_space))
super().__init__(name, env_spec)
self._obs_dim = env_spec.observation_space.flat_dim
self._action_dim = env_spec.action_space.flat_dim
self._hidden_sizes = hidden_sizes
self._hidden_nonlinearity = hidden_nonlinearity
self._hidden_w_init = hidden_w_init
self._hidden_b_init = hidden_b_init
self._output_nonlinearity = output_nonlinearity
self._output_w_init = output_w_init
self._output_b_init = output_b_init
self._learn_std = learn_std
self._adaptive_std = adaptive_std
self._std_share_network = std_share_network
self._init_std = init_std
self._min_std = min_std
self._max_std = max_std
self._std_hidden_sizes = std_hidden_sizes
self._std_hidden_nonlinearity = std_hidden_nonlinearity
self._std_output_nonlinearity = std_output_nonlinearity
self._std_parameterization = std_parameterization
self._layer_normalization = layer_normalization
self._f_dist = None
self._dist = None
self.model = GaussianMLPModel(
output_dim=self._action_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
hidden_w_init=hidden_w_init,
hidden_b_init=hidden_b_init,
output_nonlinearity=output_nonlinearity,
output_w_init=output_w_init,
output_b_init=output_b_init,
learn_std=learn_std,
adaptive_std=adaptive_std,
std_share_network=std_share_network,
init_std=init_std,
min_std=min_std,
max_std=max_std,
std_hidden_sizes=std_hidden_sizes,
std_hidden_nonlinearity=std_hidden_nonlinearity,
std_output_nonlinearity=std_output_nonlinearity,
std_parameterization=std_parameterization,
layer_normalization=layer_normalization,
name='GaussianMLPModel')
self._initialize()
def _initialize(self):
"""Initialize policy."""
with tf.compat.v1.variable_scope(self.name) as vs:
self._variable_scope = vs
state_input = tf.compat.v1.placeholder(tf.float32,
shape=(None, None,
self._obs_dim))
self._dist, mean, log_std = self.model.build(state_input).outputs
self._f_dist = tf.compat.v1.get_default_session().make_callable(
[self._dist.sample(), mean, log_std], feed_list=[state_input])
@property
def input_dim(self):
"""int: Dimension of the policy input."""
return self._obs_dim
def build(self, state_input, name=None):
"""Build policy.
Args:
state_input (tf.Tensor) : State input.
name (str): Name of the policy, which is also the name scope.
Returns:
tfp.distributions.MultivariateNormalDiag: Distribution.
tf.tensor: Mean.
tf.Tensor: Log of standard deviation.
"""
with tf.compat.v1.variable_scope(self._variable_scope):
return self.model.build(state_input, name=name)
@property
def vectorized(self):
"""Vectorized or not.
Returns:
Bool: True if primitive supports vectorized operations.
"""
return True
def get_action(self, observation):
"""Get single action from this policy for the input observation.
Args:
observation (numpy.ndarray): Observation from environment.
Returns:
numpy.ndarray: Actions
dict: Predicted action and agent information.
Note:
It returns an action and a dict, with keys
- mean (numpy.ndarray): Mean of the distribution.
- log_std (numpy.ndarray): Log standard deviation of the
distribution.
"""
sample, mean, log_std = self._f_dist(np.expand_dims([observation], 1))
sample = self.action_space.unflatten(np.squeeze(sample, 1)[0])
mean = self.action_space.unflatten(np.squeeze(mean, 1)[0])
log_std = self.action_space.unflatten(np.squeeze(log_std, 1)[0])
return sample, dict(mean=mean, log_std=log_std)
def get_actions(self, observations):
"""Get multiple actions from this policy for the input observations.
Args:
observations (numpy.ndarray): Observations from environment.
Returns:
numpy.ndarray: Actions
dict: Predicted action and agent information.
Note:
It returns actions and a dict, with keys
- mean (numpy.ndarray): Means of the distribution.
- log_std (numpy.ndarray): Log standard deviations of the
distribution.
"""
samples, means, log_stds = self._f_dist(np.expand_dims(
observations, 1))
samples = self.action_space.unflatten_n(np.squeeze(samples, 1))
means = self.action_space.unflatten_n(np.squeeze(means, 1))
log_stds = self.action_space.unflatten_n(np.squeeze(log_stds, 1))
return samples, dict(mean=means, log_std=log_stds)
@property
def distribution(self):
"""Policy distribution.
Returns:
tfp.Distribution.MultivariateNormalDiag: Policy distribution.
"""
return self._dist
def clone(self, name):
"""Return a clone of the policy.
It only copies the configuration of the primitive,
not the parameters.
Args:
name (str): Name of the newly created policy. It has to be
different from source policy if cloned under the same
computational graph.
Returns:
garage.tf.policies.GaussianMLPPolicy: Newly cloned policy.
"""
return self.__class__(
name=name,
env_spec=self._env_spec,
hidden_sizes=self._hidden_sizes,
hidden_nonlinearity=self._hidden_nonlinearity,
hidden_w_init=self._hidden_w_init,
hidden_b_init=self._hidden_b_init,
output_nonlinearity=self._output_nonlinearity,
output_w_init=self._output_w_init,
output_b_init=self._output_b_init,
learn_std=self._learn_std,
adaptive_std=self._adaptive_std,
std_share_network=self._std_share_network,
init_std=self._init_std,
min_std=self._min_std,
max_std=self._max_std,
std_hidden_sizes=self._std_hidden_sizes,
std_hidden_nonlinearity=self._std_hidden_nonlinearity,
std_output_nonlinearity=self._std_output_nonlinearity,
std_parameterization=self._std_parameterization,
layer_normalization=self._layer_normalization)
def __getstate__(self):
"""Object.__getstate__.
Returns:
dict: the state to be pickled for the instance.
"""
new_dict = super().__getstate__()
del new_dict['_f_dist']
del new_dict['_dist']
return new_dict
def __setstate__(self, state):
"""Object.__setstate__.
Args:
state (dict): Unpickled state.
"""
super().__setstate__(state)
self._initialize()
|
py | 1a3120b9c15a59170503cbe409ce3994c30fddc4 | # never name this package "types", or mypy will crash in ugly ways
from hologram import (
FieldEncoder, JsonSchemaMixin, JsonDict, ValidationError
)
from datetime import timedelta
from typing import NewType
Port = NewType('Port', int)
class PortEncoder(FieldEncoder):
@property
def json_schema(self):
return {'type': 'integer', 'minimum': 0, 'maximum': 65535}
class TimeDeltaFieldEncoder(FieldEncoder[timedelta]):
"""Encodes timedeltas to dictionaries"""
def to_wire(self, value: timedelta) -> float:
return value.total_seconds()
def to_python(self, value) -> timedelta:
if isinstance(value, timedelta):
return value
try:
return timedelta(seconds=value)
except TypeError:
raise ValidationError(
'cannot encode {} into timedelta'.format(value)
) from None
@property
def json_schema(self) -> JsonDict:
return {'type': 'number'}
JsonSchemaMixin.register_field_encoders({
Port: PortEncoder(),
timedelta: TimeDeltaFieldEncoder()
})
|
py | 1a31220ea5ef7ebf8b9ed118138ac41a58285c32 |
# Copyright (C) 2019 The Raphielscape Company LLC.
#
# Licensed under the Raphielscape Public License, Version 1.d (the "License");
# you may not use this file except in compliance with the License.
#
# Original source for the deepfrying code (used under the following
# license): https://github.com/Ovyerus/deeppyer
# MIT License
#
# Copyright (c) 2017 Ovyerus
#
# 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.
# Ported from Xtra-telegram by @heyworld
# KING USERBOT
import os
from telethon.errors.rpcerrorlist import YouBlockedUserError
from userbot.events import register
from userbot import bot, TEMP_DOWNLOAD_DIRECTORY, CMD_HELP
@register(outgoing=True, pattern=r'^.kekuatan(:? |$)([1-8])?')
async def _(fry):
await fry.edit("`King Mengaktifkan Kekuatan Telegram...⚡`")
level = fry.pattern_match.group(2)
if fry.fwd_from:
return
if not fry.reply_to_msg_id:
await fry.edit("`Mohon Balas Di Sticker King`")
return
reply_message = await fry.get_reply_message()
if not reply_message.media:
await fry.edit("`Gambar tidak di dukung`")
return
if reply_message.sender.bot:
await fry.edit("`Mohon Balas Di Sticker King`")
return
chat = "@image_deepfrybot"
message_id_to_reply = fry.message.reply_to_msg_id
async with fry.client.conversation(chat) as conv:
try:
msg = await conv.send_message(reply_message)
if level:
m = f"/deepfry {level}"
msg_level = await conv.send_message(
m,
reply_to=msg.id)
r = await conv.get_response()
response = await conv.get_response()
else:
response = await conv.get_response()
""" - don't spam notif - """
await bot.send_read_acknowledge(conv.chat_id)
except YouBlockedUserError:
await fry.reply("`King Mohon Unblock` @image_deepfrybot`...`")
return
if response.text.startswith("Forward"):
await fry.edit("`King Mohon Matikan Setelan Forward Privasi...`")
else:
downloaded_file_name = await fry.client.download_media(
response.media,
TEMP_DOWNLOAD_DIRECTORY
)
await fry.client.send_file(
fry.chat_id,
downloaded_file_name,
force_document=False,
reply_to=message_id_to_reply
)
""" - cleanup chat after completed - """
try:
msg_level
except NameError:
await fry.client.delete_messages(conv.chat_id,
[msg.id, response.id])
else:
await fry.client.delete_messages(
conv.chat_id,
[msg.id, response.id, r.id, msg_level.id])
await fry.delete()
return os.remove(downloaded_file_name)
@register(outgoing=True, pattern=r'^.df(:? |$)([1-8])?')
async def _(fry):
await fry.edit("`Sedang Dalam Proses......`")
level = fry.pattern_match.group(2)
if fry.fwd_from:
return
if not fry.reply_to_msg_id:
await fry.edit("`Mohon Balas Di Sticker King`")
return
reply_message = await fry.get_reply_message()
if not reply_message.media:
await fry.edit("`Mohon Balas Di Sticker King`")
return
if reply_message.sender.bot:
await fry.edit("`Mohon Balas Di Sticker King`")
return
chat = "@image_deepfrybot"
message_id_to_reply = fry.message.reply_to_msg_id
async with fry.client.conversation(chat) as conv:
try:
msg = await conv.send_message(reply_message)
if level:
m = f"/deepfry {level}"
msg_level = await conv.send_message(
m,
reply_to=msg.id)
r = await conv.get_response()
response = await conv.get_response()
else:
response = await conv.get_response()
""" - don't spam notif - """
await bot.send_read_acknowledge(conv.chat_id)
except YouBlockedUserError:
await fry.reply("`King Mohon Unblock` @image_deepfrybot`...`")
return
if response.text.startswith("Forward"):
await fry.edit("`King Mohon Matikan Setelan Privasi Forward...`")
else:
downloaded_file_name = await fry.client.download_media(
response.media,
TEMP_DOWNLOAD_DIRECTORY
)
await fry.client.send_file(
fry.chat_id,
downloaded_file_name,
force_document=False,
reply_to=message_id_to_reply
)
""" - cleanup chat after completed - """
try:
msg_level
except NameError:
await fry.client.delete_messages(conv.chat_id,
[msg.id, response.id])
else:
await fry.client.delete_messages(
conv.chat_id,
[msg.id, response.id, r.id, msg_level.id])
await fry.delete()
return os.remove(downloaded_file_name)
CMD_HELP.update({
"kekuatan":
"**Modules: __Kekuatan__\n\n⚡𝘾𝙈𝘿⚡: `.kekuatan` / `.kekuatan [level(1-8)]`"
"\n**Penjelasan:** untuk mengubah foto/sticker."
})
|
py | 1a312242c7dee2b9a7e6b1e22412161f90d85d84 | from collections import Counter
class Solution:
def removeDuplicateLetters(self, s: str) -> str:
counter = Counter(s)
seen = set()
stack = []
for letter in s:
counter[letter] -= 1
if letter in seen:
continue
while stack and stack[-1] > letter and counter[stack[-1]] > 0:
seen.remove(stack.pop())
stack.append(letter)
seen.add(letter)
return "".join(stack)
|
py | 1a31236e0d3942882dcfcbd16cb03a0df595a349 | #!/usr/bin/env python
import argparse
import six
import requests
NAMED_URL_RES_DILIMITER = "--"
NAMED_URL_RES_INNER_DILIMITER = "-"
NAMED_URL_RES_DILIMITER_ENCODE = "%2D"
URL_PATH_RESERVED_CHARSET = {}
for c in ';/?:@=&[]':
URL_PATH_RESERVED_CHARSET[c] = six.moves.urllib.parse.quote(c, safe='')
def _get_named_url_graph(url, auth):
"""Get the graph data structure Tower used to manage all named URLs.
Args:
url: String representing the URL of tower configuration endpoint where
to fetch graph information.
auth: Tuple of username + password to authenticate connection to Tower.
Return:
A dict of graph nodes that in ensembly represent the graph structure. Each
node is represented as a dict of 'fields' and 'adj_list'.
Raises:
N/A
"""
r = requests.get(url, auth=auth, verify=False)
ret = r.json()['NAMED_URL_GRAPH_NODES']
return ret
def _encode_uri(text):
"""Properly encode input text to make it satisfy named URL convention.
Args:
text: the original string to be encoded.
Return:
The encoded string
Raises:
N/A
"""
for c in URL_PATH_RESERVED_CHARSET:
if c in text:
text = text.replace(c, URL_PATH_RESERVED_CHARSET[c])
text = text.replace(NAMED_URL_RES_INNER_DILIMITER,
'[%s]' % NAMED_URL_RES_INNER_DILIMITER)
return text
def _generate_identifier_component(response, fields):
"""Generate an individual component of named URL identifier.
Args:
response: JSON containing the details of a particular resource object.
fields: name of resource object fields needed to generate a named URL
identifier component.
Return:
A string representing generated identifier component.
Raises:
N/A
"""
ret = []
for field_name in fields:
ret.append(_encode_uri(response[field_name]))
return NAMED_URL_RES_INNER_DILIMITER.join(ret)
def _get_named_url_identifier(url, named_url_graph, resource, tower_host, auth, ret):
"""DFS the named URL graph structure to generate identifier for a resource object.
Args:
url: A string used to access a particular resource object to generate identifier
component from.
named_url_graph: The graph structure used to DFS against.
resource: Key name of the current graph node.
tower_host: String representing the host name of Tower backend.
auth: Tuple of username + password to authenticate connection to Tower.
ret: list of strings storing components that would later be joined into
the final named URL identifier.
Return:
None. Note the actual outcome is stored in argument ret due to the recursive
nature of this function.
Raises:
"""
r = requests.get(url, auth=auth, verify=False).json()
ret.append(_generate_identifier_component(r, named_url_graph[resource]['fields']))
for next_ in named_url_graph[resource]['adj_list']:
next_fk, next_res = tuple(next_)
if next_fk in r['related']:
_get_named_url_identifier(tower_host.strip('/') + r['related'][next_fk],
named_url_graph, next_res, tower_host, auth, ret)
else:
ret.append('')
def main(username=None, password=None, tower_host=None, resource=None, pk=None):
"""Main function for generating and printing named URL of a resource object given its pk.
Args:
username: String representing the username needed to authenticating Tower.
password: String representing the password needed to authenticating Tower.
tower_host: String representing the host name of Tower backend.
resource: REST API name of a specific resource, e.g. name for resource inventory
is 'inventories'.
pk: Primary key of the resource object whose named URL will be derived.
Returns:
None
Raises:
N/A
"""
start_url = '%s/api/v2/%s/%s/' % (tower_host.strip('/'), resource.strip('/'), pk)
conf_url = '%s/api/v2/settings/named-url/' % tower_host.strip('/')
auth = (username, password)
named_url_graph = _get_named_url_graph(conf_url, auth)
named_url_identifier = []
_get_named_url_identifier(start_url, named_url_graph, resource,
tower_host, auth, named_url_identifier)
print('%s/api/v2/%s/%s/' % (tower_host.strip('/'), resource.strip('/'),
NAMED_URL_RES_DILIMITER.join(named_url_identifier)))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--username', type=str, required=True,
help='Name of the Tower user for making requests',
dest='username', metavar='STR')
parser.add_argument('--password', type=str, required=True,
help='Password of the Tower user for making requests',
dest='password', metavar='STR')
parser.add_argument('--tower-host', type=str, required=True,
help='Tower host name, like "http://127.0.0.1"',
dest='tower_host', metavar='STR')
parser.add_argument('--resource', type=str, required=True,
help='Name of the resource in REST endpoints',
dest='resource', metavar='STR')
parser.add_argument('--pk', type=int, required=True,
help='Primary key of resource object whose named URL will be derived',
dest='pk', metavar='INT')
main(**vars(parser.parse_args()))
|
py | 1a31245a4a0cb45323077cf7454190ca2e1a537d | # coding=utf-8
# --------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
#
# Code generated by Microsoft (R) AutoRest Code Generator.
# Changes may cause incorrect behavior and will be lost if the code is
# regenerated.
# --------------------------------------------------------------------------
from msrest.serialization import Model
class ConnectionMonitorResult(Model):
"""Information about the connection monitor.
Variables are only populated by the server, and will be ignored when
sending a request.
All required parameters must be populated in order to send to Azure.
:ivar name: Name of the connection monitor.
:vartype name: str
:ivar id: ID of the connection monitor.
:vartype id: str
:param etag: Default value: "A unique read-only string that changes
whenever the resource is updated." .
:type etag: str
:ivar type: Connection monitor type.
:vartype type: str
:param location: Connection monitor location.
:type location: str
:param tags: Connection monitor tags.
:type tags: dict[str, str]
:param source: Required.
:type source:
~azure.mgmt.network.v2017_11_01.models.ConnectionMonitorSource
:param destination: Required.
:type destination:
~azure.mgmt.network.v2017_11_01.models.ConnectionMonitorDestination
:param auto_start: Determines if the connection monitor will start
automatically once created. Default value: True .
:type auto_start: bool
:param monitoring_interval_in_seconds: Monitoring interval in seconds.
Default value: 60 .
:type monitoring_interval_in_seconds: int
:param provisioning_state: The provisioning state of the connection
monitor. Possible values include: 'Succeeded', 'Updating', 'Deleting',
'Failed'
:type provisioning_state: str or
~azure.mgmt.network.v2017_11_01.models.ProvisioningState
:param start_time: The date and time when the connection monitor was
started.
:type start_time: datetime
:param monitoring_status: The monitoring status of the connection monitor.
:type monitoring_status: str
"""
_validation = {
'name': {'readonly': True},
'id': {'readonly': True},
'type': {'readonly': True},
'source': {'required': True},
'destination': {'required': True},
}
_attribute_map = {
'name': {'key': 'name', 'type': 'str'},
'id': {'key': 'id', 'type': 'str'},
'etag': {'key': 'etag', 'type': 'str'},
'type': {'key': 'type', 'type': 'str'},
'location': {'key': 'location', 'type': 'str'},
'tags': {'key': 'tags', 'type': '{str}'},
'source': {'key': 'properties.source', 'type': 'ConnectionMonitorSource'},
'destination': {'key': 'properties.destination', 'type': 'ConnectionMonitorDestination'},
'auto_start': {'key': 'properties.autoStart', 'type': 'bool'},
'monitoring_interval_in_seconds': {'key': 'properties.monitoringIntervalInSeconds', 'type': 'int'},
'provisioning_state': {'key': 'properties.provisioningState', 'type': 'str'},
'start_time': {'key': 'properties.startTime', 'type': 'iso-8601'},
'monitoring_status': {'key': 'properties.monitoringStatus', 'type': 'str'},
}
def __init__(self, **kwargs):
super(ConnectionMonitorResult, self).__init__(**kwargs)
self.name = None
self.id = None
self.etag = kwargs.get('etag', "A unique read-only string that changes whenever the resource is updated.")
self.type = None
self.location = kwargs.get('location', None)
self.tags = kwargs.get('tags', None)
self.source = kwargs.get('source', None)
self.destination = kwargs.get('destination', None)
self.auto_start = kwargs.get('auto_start', True)
self.monitoring_interval_in_seconds = kwargs.get('monitoring_interval_in_seconds', 60)
self.provisioning_state = kwargs.get('provisioning_state', None)
self.start_time = kwargs.get('start_time', None)
self.monitoring_status = kwargs.get('monitoring_status', None)
|
py | 1a31246fb0511e1af29e081496c47cec931f0079 | from django.db.models.signals import pre_save, post_delete
from django.dispatch import receiver
from .serializers import XXTMP_PO_HEADERS, ElasticPO_headersSerializer
@receiver(pre_save, sender=XXTMP_PO_HEADERS, dispatch_uid="update_record")
def update_es_record(sender, instance, **kwargs):
obj = ElasticPO_headersSerializer(instance)
obj.save()
@receiver(post_delete, sender=XXTMP_PO_HEADERS, dispatch_uid="delete_record")
def delete_es_record(sender, instance, *args, **kwargs):
obj = ElasticPO_headersSerializer(instance)
obj.delete(ignore=404)
|
py | 1a3124710842e08574b7d0b22373bdfce04a0c52 | # Generated by Django 2.2.13 on 2020-07-21 23:29
import datetime
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('scales', '0001_initial'),
]
operations = [
migrations.AlterField(
model_name='scale',
name='created_at',
field=models.TimeField(default=datetime.datetime(2020, 7, 21, 23, 29, 11, 517956), editable=False),
),
migrations.AlterField(
model_name='scale',
name='created_when',
field=models.DateField(default=datetime.datetime(2020, 7, 21, 23, 29, 11, 517998), editable=False),
),
]
|
py | 1a31249dd4025a966d8f9e01d3235e3a9810453b | # This file is MACHINE GENERATED! Do not edit.
# Generated by: tensorflow/python/tools/api/generator/create_python_api.py script.
"""Public API for tf.keras.applications.densenet namespace.
"""
from __future__ import print_function as _print_function
import sys as _sys
from keras.applications.densenet import DenseNet121
from keras.applications.densenet import DenseNet169
from keras.applications.densenet import DenseNet201
from keras.applications.densenet import decode_predictions
from keras.applications.densenet import preprocess_input
del _print_function
|
bzl | 1a312549920f465562521a5e820a0bcecda9f0bf | """Implementation of core Haskell rules"""
load("@bazel_skylib//lib:dicts.bzl", "dicts")
load(
":providers.bzl",
"C2hsLibraryInfo",
"HaddockInfo",
"HaskellInfo",
"HaskellLibraryInfo",
"HaskellToolchainLibraryInfo",
"all_dependencies_package_ids",
)
load(":cc.bzl", "cc_interop_info")
load(
":private/actions/info.bzl",
"compile_info_output_groups",
"library_info_output_groups",
)
load(
":private/actions/link.bzl",
"link_binary",
"link_library_dynamic",
"link_library_static",
)
load(":private/actions/package.bzl", "package")
load(":private/plugins.bzl", "resolve_plugin_tools")
load(":private/actions/runghc.bzl", "build_haskell_runghc")
load(":private/context.bzl", "haskell_context")
load(":private/dependencies.bzl", "gather_dep_info")
load(":private/expansions.bzl", "expand_make_variables", "haskell_library_extra_label_attrs")
load(":private/java.bzl", "java_interop_info")
load(":private/mode.bzl", "is_profiling_enabled")
load(
":private/path_utils.bzl",
"determine_module_names",
"get_dynamic_hs_lib_name",
"get_lib_extension",
"get_static_hs_lib_name",
"infer_main_module",
"ln",
"match_label",
"parse_pattern",
)
load(":private/pkg_id.bzl", "pkg_id")
load(":private/set.bzl", "set")
load(":private/list.bzl", "list")
load(":private/version_macros.bzl", "generate_version_macros")
load(":providers.bzl", "GhcPluginInfo", "HaskellCoverageInfo")
load("@bazel_skylib//lib:paths.bzl", "paths")
load("@bazel_skylib//lib:collections.bzl", "collections")
load("@bazel_skylib//lib:shell.bzl", "shell")
load("@rules_cc//cc:find_cc_toolchain.bzl", "find_cc_toolchain")
load("//haskell/experimental:providers.bzl", "HaskellModuleInfo")
load("//haskell/experimental/private:module.bzl", "build_haskell_modules", "get_module_path_from_target")
# Note [Empty Libraries]
#
# GHC 8.10.x wants to load the shared libraries corresponding to packages needed
# for running TemplateHaskell splices. It wants to do this even when all the
# necessary object files are passed in the command line.
#
# In order to satisfy GHC, and yet avoid passing the linked library as input, we
# create a ficticious package which points to an empty shared library. The
# ficticious and the real package share the same interface files.
#
# Avoiding to pass the real shared library as input is necessary when building
# individual modules with haskell_module, otherwise building the module would
# need to wait until all of the modules of library dependencies have been built.
#
# See Note [Narrowed Dependencies] for an overview of what this feature is
# needed for.
def _prepare_srcs(srcs):
srcs_files = []
import_dir_map = {}
for src in srcs:
# If it has the "files" attribute, it must be a Target
if hasattr(src, "files"):
if C2hsLibraryInfo in src:
srcs_files += src.files.to_list()
for f in src.files.to_list():
import_dir_map[f] = src[C2hsLibraryInfo].import_dir
else:
srcs_files += src.files.to_list()
# otherwise it's just a file
else:
srcs_files.append(src)
return srcs_files, import_dir_map
def haskell_test_impl(ctx):
return _haskell_binary_common_impl(ctx, is_test = True)
def haskell_binary_impl(ctx):
return _haskell_binary_common_impl(ctx, is_test = False)
def _should_inspect_coverage(ctx, hs, is_test):
return hs.coverage_enabled and is_test
def _coverage_enabled_for_target(coverage_source_patterns, label):
for pat in coverage_source_patterns:
if match_label(pat, label):
return True
return False
# Mix files refer to genfile srcs including their root. Therefore, we
# must condition the src filepaths passed in for coverage to match.
def _condition_coverage_src(hs, src):
if not src.path.startswith(hs.genfiles_dir.path):
return src
""" Genfiles have the genfile directory as part of their path,
so declaring a file with the sample path actually makes the new
file double-qualified by the genfile directory.
This is necessary because mix files capture the genfile
path before compilation, and then expect those files to be
qualified by the genfile directory when `hpc report` or
`hpc markup` are used. But, genfiles included as runfiles
are no longer qualified. So, double-qualifying them results in
only one level of qualification as runfiles.
"""
conditioned_src = hs.actions.declare_file(src.path)
hs.actions.run_shell(
inputs = [src],
outputs = [conditioned_src],
arguments = [
src.path,
conditioned_src.path,
],
command = """
mkdir -p $(dirname "$2") && cp "$1" "$2"
""",
)
return conditioned_src
def _resolve_preprocessors(ctx, preprocessors):
if not hasattr(ctx, "resolve_tools"):
# No resolve_tools when ctx is faked (see protobuf.bzl).
return struct(
inputs = depset(),
input_manifests = [],
)
(inputs, input_manifests) = ctx.resolve_tools(tools = preprocessors)
return struct(
inputs = inputs,
input_manifests = input_manifests,
)
def _expand_make_variables(name, ctx, strings):
# All labels in all attributes should be location-expandable.
return expand_make_variables(name, ctx, strings, haskell_library_extra_label_attrs(ctx.attr))
def haskell_module_from_target(m):
""" Produces the module name from a HaskellModuleInfo """
return paths.split_extension(get_module_path_from_target(m))[0].replace("/", ".")
def is_main_as_haskell_module(modules, main_function):
main_module = infer_main_module(main_function).replace(".", "/")
for m in modules:
if haskell_module_from_target(m) == main_module:
return True
return False
def _haskell_binary_common_impl(ctx, is_test):
hs = haskell_context(ctx)
deps = ctx.attr.deps + ctx.attr.narrowed_deps
dep_info = gather_dep_info(ctx.attr.name, ctx.attr.deps)
all_deps_info = gather_dep_info(ctx.attr.name, deps)
modules = ctx.attr.modules
if modules and ctx.files.srcs:
fail("""Only one of "srcs" or "modules" attributes must be specified in {}""".format(ctx.label))
if not modules and ctx.attr.narrowed_deps:
fail("""The attribute "narrowed_deps" can only be used if "modules" is specified in {}""".format(ctx.label))
# Note [Plugin order]
plugin_decl = reversed(ctx.attr.plugins)
non_default_plugin_decl = reversed(ctx.attr.non_default_plugins)
all_plugin_decls = plugin_decl + non_default_plugin_decl
plugin_dep_info = gather_dep_info(
ctx.attr.name,
[dep for plugin in all_plugin_decls for dep in plugin[GhcPluginInfo].deps],
)
package_ids = all_dependencies_package_ids(deps)
# Add any interop info for other languages.
cc = cc_interop_info(
ctx,
override_cc_toolchain = hs.tools_config.maybe_exec_cc_toolchain,
)
java = java_interop_info(deps)
# Make shell tools available.
posix = ctx.toolchains["@rules_sh//sh/posix:toolchain_type"]
# Determine file directories.
interfaces_dir = paths.join("_iface", hs.name)
objects_dir = paths.join("_obj", hs.name)
with_profiling = is_profiling_enabled(hs)
srcs_files, import_dir_map = _prepare_srcs(ctx.attr.srcs)
main_as_haskell_module = is_main_as_haskell_module(modules, ctx.attr.main_function)
module_map = determine_module_names(srcs_files, not main_as_haskell_module, ctx.attr.main_function, ctx.file.main_file)
inspect_coverage = _should_inspect_coverage(ctx, hs, is_test)
dynamic = not ctx.attr.linkstatic
if with_profiling or hs.toolchain.static_runtime:
# NOTE We can't have profiling and dynamic code at the
# same time, see:
# https://ghc.haskell.org/trac/ghc/ticket/15394
# Also, static GHC doesn't support dynamic code
dynamic = False
module_outputs = build_haskell_modules(ctx, hs, cc, posix, "", with_profiling, dynamic, interfaces_dir, objects_dir)
plugins = [resolve_plugin_tools(ctx, plugin[GhcPluginInfo]) for plugin in plugin_decl]
non_default_plugins = [resolve_plugin_tools(ctx, plugin[GhcPluginInfo]) for plugin in non_default_plugin_decl]
preprocessors = _resolve_preprocessors(ctx, ctx.attr.tools)
user_compile_flags = _expand_make_variables("ghcopts", ctx, ctx.attr.ghcopts)
c = hs.toolchain.actions.compile_binary(
hs,
cc,
java,
posix,
dep_info,
plugin_dep_info,
srcs = srcs_files,
module_map = module_map,
import_dir_map = import_dir_map,
extra_srcs = depset(ctx.files.extra_srcs),
user_compile_flags = user_compile_flags,
dynamic = dynamic,
with_profiling = with_profiling,
interfaces_dir = interfaces_dir,
objects_dir = objects_dir,
main_function = ctx.attr.main_function,
version = ctx.attr.version,
inspect_coverage = inspect_coverage,
plugins = plugins,
non_default_plugins = non_default_plugins,
preprocessors = preprocessors,
)
# gather intermediary code coverage instrumentation data
coverage_data = c.coverage_data
for dep in deps:
if HaskellCoverageInfo in dep:
coverage_data += dep[HaskellCoverageInfo].coverage_data
coverage_data = list.dedup_on(_get_mix_filepath, coverage_data)
user_compile_flags = _expand_make_variables("ghcopts", ctx, ctx.attr.ghcopts)
(binary, solibs) = link_binary(
hs,
cc,
posix,
all_deps_info,
ctx.files.extra_srcs,
user_compile_flags,
c.object_files + c.dyn_object_files,
module_outputs.os,
dynamic = dynamic,
with_profiling = with_profiling,
version = ctx.attr.version,
)
hs_info = HaskellInfo(
package_databases = all_deps_info.package_databases,
version_macros = set.empty(),
source_files = c.source_files,
boot_files = c.boot_files,
extra_source_files = c.extra_source_files,
import_dirs = c.import_dirs,
hs_libraries = all_deps_info.hs_libraries,
deps_hs_libraries = all_deps_info.deps_hs_libraries,
interface_dirs = all_deps_info.interface_dirs,
deps_interface_dirs = all_deps_info.deps_interface_dirs,
compile_flags = c.compile_flags,
user_compile_flags = user_compile_flags,
user_repl_flags = _expand_make_variables("repl_ghci_args", ctx, ctx.attr.repl_ghci_args),
)
cc_info = cc_common.merge_cc_infos(
cc_infos = [dep[CcInfo] for dep in deps if CcInfo in dep],
)
target_files = depset([binary])
user_compile_flags = _expand_make_variables("ghcopts", ctx, ctx.attr.ghcopts)
extra_args = _expand_make_variables("runcompile_flags", ctx, ctx.attr.runcompile_flags)
build_haskell_runghc(
hs,
cc,
posix,
runghc_wrapper = ctx.file._ghci_repl_wrapper,
extra_args = extra_args,
user_compile_flags = user_compile_flags,
output = ctx.outputs.runghc,
package_databases = all_deps_info.package_databases,
version = ctx.attr.version,
hs_info = hs_info,
)
executable = binary
extra_runfiles = []
if inspect_coverage:
binary_path = paths.join(ctx.workspace_name, binary.short_path)
hpc_path = paths.join(ctx.workspace_name, hs.toolchain.tools.hpc.short_path)
tix_file_path = hs.label.name + ".tix"
mix_file_paths = [
paths.join(ctx.workspace_name, datum.mix_file.short_path)
for datum in coverage_data
]
mix_file_paths = collections.uniq(mix_file_paths) # remove duplicates
# find which modules to exclude from coverage analysis, by using the specified source patterns
raw_coverage_source_patterns = ctx.attr.experimental_coverage_source_patterns
coverage_source_patterns = [parse_pattern(ctx, pat) for pat in raw_coverage_source_patterns]
modules_to_exclude = [paths.split_extension(datum.mix_file.basename)[0] for datum in coverage_data if not _coverage_enabled_for_target(coverage_source_patterns, datum.target_label)]
modules_to_exclude = collections.uniq(modules_to_exclude) # remove duplicates
expected_covered_expressions_percentage = ctx.attr.expected_covered_expressions_percentage
expected_uncovered_expression_count = ctx.attr.expected_uncovered_expression_count
strict_coverage_analysis = ctx.attr.strict_coverage_analysis
coverage_report_format = ctx.attr.coverage_report_format
if coverage_report_format != "text" and coverage_report_format != "html":
fail("""haskell_test attribute "coverage_report_format" must be one of "text" or "html".""")
wrapper = hs.actions.declare_file("{}_coverage/coverage_wrapper.sh".format(ctx.label.name))
ctx.actions.expand_template(
template = ctx.file._coverage_wrapper_template,
output = wrapper,
substitutions = {
"{binary_path}": shell.quote(binary_path),
"{hpc_path}": shell.quote(hpc_path),
"{tix_file_path}": shell.quote(tix_file_path),
"{expected_covered_expressions_percentage}": shell.quote(str(expected_covered_expressions_percentage)),
"{expected_uncovered_expression_count}": shell.quote(str(expected_uncovered_expression_count)),
"{mix_file_paths}": shell.array_literal(mix_file_paths),
"{modules_to_exclude}": shell.array_literal(modules_to_exclude),
"{strict_coverage_analysis}": str(strict_coverage_analysis),
"{coverage_report_format}": shell.quote(ctx.attr.coverage_report_format),
"{package_path}": shell.quote(ctx.label.package),
},
is_executable = True,
)
executable = wrapper
mix_runfiles = [datum.mix_file for datum in coverage_data]
srcs_runfiles = [_condition_coverage_src(hs, datum.src_file) for datum in coverage_data]
extra_runfiles = [
ctx.file._bash_runfiles,
hs.toolchain.tools.hpc,
binary,
] + mix_runfiles + srcs_runfiles + java.inputs.to_list()
return [
hs_info,
cc_info,
DefaultInfo(
executable = executable,
files = target_files,
runfiles = ctx.runfiles(
files = extra_runfiles + solibs,
collect_data = True,
),
),
OutputGroupInfo(**compile_info_output_groups(
name = ctx.label.name,
workspace_name = ctx.workspace_name,
hs = hs,
cc = cc,
c = c,
posix = posix,
runfiles = ctx.runfiles(collect_data = True).files,
)),
]
def _create_empty_library(hs, cc, posix, my_pkg_id, with_shared, with_profiling, empty_libs_dir):
"""See Note [Empty Libraries]"""
dep_info = gather_dep_info("haskell_module-empty_lib", [])
empty_c = hs.actions.declare_file("empty.c")
hs.actions.write(empty_c, "")
static_library = link_library_static(
hs,
cc,
posix,
dep_info,
depset([empty_c]),
my_pkg_id,
with_profiling = with_profiling,
libdir = empty_libs_dir,
)
libs = [static_library]
if with_shared:
dynamic_library = link_library_dynamic(
hs,
cc,
posix,
dep_info,
depset(),
depset([empty_c]),
my_pkg_id,
[],
empty_libs_dir,
)
libs = [dynamic_library, static_library]
return libs
def haskell_library_impl(ctx):
hs = haskell_context(ctx)
deps = ctx.attr.deps + ctx.attr.exports + ctx.attr.narrowed_deps
dep_info = gather_dep_info(ctx.attr.name, ctx.attr.deps + ctx.attr.exports)
narrowed_deps_info = gather_dep_info(ctx.attr.name, ctx.attr.narrowed_deps)
all_deps_info = gather_dep_info(ctx.attr.name, deps)
all_plugins = ctx.attr.plugins + ctx.attr.non_default_plugins
plugin_dep_info = gather_dep_info(
ctx.attr.name,
[dep for plugin in all_plugins for dep in plugin[GhcPluginInfo].deps],
)
package_ids = all_dependencies_package_ids(deps)
modules = ctx.attr.modules
if modules and ctx.files.srcs:
fail("""Only one of "srcs" or "modules" attributes must be specified in {}""".format(ctx.label))
if not modules and ctx.attr.narrowed_deps:
fail("""The attribute "narrowed_deps" is enabled only if "modules" is specified in {}""".format(ctx.label))
# Add any interop info for other languages.
cc = cc_interop_info(
ctx,
override_cc_toolchain = hs.tools_config.maybe_exec_cc_toolchain,
)
java = java_interop_info(ctx.attr.deps + ctx.attr.narrowed_deps)
# Make shell tools available.
posix = ctx.toolchains["@rules_sh//sh/posix:toolchain_type"]
with_profiling = is_profiling_enabled(hs)
srcs_files, import_dir_map = _prepare_srcs(ctx.attr.srcs)
module_map = determine_module_names(srcs_files)
package_name = getattr(ctx.attr, "package_name", None)
version = getattr(ctx.attr, "version", None)
my_pkg_id = pkg_id.new(ctx.label, package_name, version)
# If we're compiling a package, put the interfaces inside the
# package directory.
interfaces_dir = paths.join(pkg_id.to_string(my_pkg_id), "_iface")
objects_dir = paths.join("_obj", hs.name)
non_empty = srcs_files or modules
with_shared = not ctx.attr.linkstatic
if with_profiling or hs.toolchain.static_runtime:
# NOTE We can't have profiling and dynamic code at the
# same time, see:
# https://ghc.haskell.org/trac/ghc/ticket/15394
# Also, static GHC doesn't support dynamic code
with_shared = False
module_outputs = build_haskell_modules(ctx, hs, cc, posix, pkg_id.to_string(my_pkg_id), with_profiling, with_shared, interfaces_dir, objects_dir)
plugins = [resolve_plugin_tools(ctx, plugin[GhcPluginInfo]) for plugin in ctx.attr.plugins]
non_default_plugins = [resolve_plugin_tools(ctx, plugin[GhcPluginInfo]) for plugin in ctx.attr.non_default_plugins]
preprocessors = _resolve_preprocessors(ctx, ctx.attr.tools)
user_compile_flags = _expand_make_variables("ghcopts", ctx, ctx.attr.ghcopts)
c = hs.toolchain.actions.compile_library(
hs,
cc,
java,
posix,
dep_info,
plugin_dep_info,
srcs = srcs_files,
module_map = module_map,
import_dir_map = import_dir_map,
extra_srcs = depset(ctx.files.extra_srcs),
user_compile_flags = user_compile_flags,
with_shared = with_shared,
with_profiling = with_profiling,
interfaces_dir = interfaces_dir,
objects_dir = objects_dir,
my_pkg_id = my_pkg_id,
plugins = plugins,
non_default_plugins = non_default_plugins,
preprocessors = preprocessors,
)
other_modules = ctx.attr.hidden_modules
exposed_modules_reexports = _exposed_modules_reexports(ctx.attr.reexported_modules)
haskell_module_names = [haskell_module_from_target(m) for m in modules]
exposed_modules = set.from_list(module_map.keys() + exposed_modules_reexports + haskell_module_names)
set.mutable_difference(exposed_modules, set.from_list(other_modules))
exposed_modules = set.to_list(exposed_modules)
if non_empty:
static_library = link_library_static(
hs,
cc,
posix,
all_deps_info,
depset(c.object_files, transitive = [module_outputs.os]),
my_pkg_id,
with_profiling = with_profiling,
)
else:
static_library = None
if with_shared and non_empty:
dynamic_library = link_library_dynamic(
hs,
cc,
posix,
all_deps_info,
depset(ctx.files.extra_srcs),
depset(c.dyn_object_files, transitive = [module_outputs.dyn_os]),
my_pkg_id,
user_compile_flags,
)
else:
dynamic_library = None
conf_file, cache_file = package(
hs,
cc,
posix,
all_deps_info,
with_shared,
exposed_modules,
other_modules,
my_pkg_id,
non_empty,
)
empty_libs_dir = "empty_libs"
conf_file_empty, cache_file_empty = package(
hs,
cc,
posix,
all_deps_info,
with_shared,
exposed_modules,
other_modules,
my_pkg_id,
non_empty,
empty_libs_dir,
)
interface_dirs = depset(
direct = c.interface_files,
transitive = [all_deps_info.interface_dirs, module_outputs.his, module_outputs.dyn_his],
)
version_macros = set.empty()
if version:
package_name = hs.name
if hasattr(ctx.attr, "package_name") and ctx.attr.package_name:
package_name = ctx.attr.package_name
version_macros = set.singleton(
generate_version_macros(ctx, package_name, version),
)
empty_libs = _create_empty_library(hs, cc, posix, my_pkg_id, with_shared, with_profiling, empty_libs_dir)
export_infos = gather_dep_info(ctx.attr.name, ctx.attr.exports)
hs_info = HaskellInfo(
package_databases = depset([cache_file], transitive = [all_deps_info.package_databases]),
empty_lib_package_databases = depset(
direct = [cache_file_empty],
transitive = [
dep_info.package_databases,
narrowed_deps_info.empty_lib_package_databases,
export_infos.empty_lib_package_databases,
],
),
version_macros = version_macros,
source_files = c.source_files,
boot_files = c.boot_files,
extra_source_files = c.extra_source_files,
import_dirs = set.mutable_union(c.import_dirs, export_infos.import_dirs),
hs_libraries = depset(
direct = [lib for lib in [static_library, dynamic_library] if lib],
transitive = [all_deps_info.hs_libraries],
),
deps_hs_libraries = depset(
transitive = [dep_info.hs_libraries, narrowed_deps_info.deps_hs_libraries],
),
empty_hs_libraries = depset(
direct = empty_libs,
transitive = [all_deps_info.empty_hs_libraries, export_infos.empty_hs_libraries],
),
interface_dirs = depset(transitive = [interface_dirs, export_infos.interface_dirs]),
deps_interface_dirs = depset(transitive = [dep_info.interface_dirs, narrowed_deps_info.deps_interface_dirs]),
compile_flags = c.compile_flags,
user_compile_flags = user_compile_flags,
user_repl_flags = _expand_make_variables("repl_ghci_args", ctx, ctx.attr.repl_ghci_args),
per_module_transitive_interfaces = module_outputs.per_module_transitive_interfaces,
per_module_transitive_objects = module_outputs.per_module_transitive_objects,
)
exports = [
reexp[HaskellLibraryInfo]
for reexp in ctx.attr.exports
if HaskellCoverageInfo in reexp
]
lib_info = HaskellLibraryInfo(
package_id = pkg_id.to_string(my_pkg_id),
version = version,
exports = exports,
)
dep_coverage_data = []
for dep in deps:
if HaskellCoverageInfo in dep:
dep_coverage_data += dep[HaskellCoverageInfo].coverage_data
coverage_data = dep_coverage_data + c.coverage_data
coverage_data = list.dedup_on(_get_mix_filepath, coverage_data)
coverage_info = HaskellCoverageInfo(
coverage_data = coverage_data,
)
target_files = depset([file for file in [static_library, dynamic_library] if file])
if hasattr(ctx, "outputs"):
extra_args = _expand_make_variables("runcompile_flags", ctx, ctx.attr.runcompile_flags)
user_compile_flags = _expand_make_variables("ghcopts", ctx, ctx.attr.ghcopts)
build_haskell_runghc(
hs,
cc,
posix,
runghc_wrapper = ctx.file._ghci_repl_wrapper,
extra_args = extra_args,
user_compile_flags = user_compile_flags,
output = ctx.outputs.runghc,
package_databases = all_deps_info.package_databases,
version = ctx.attr.version,
hs_info = hs_info,
lib_info = lib_info,
)
default_info = None
if hasattr(ctx, "runfiles"):
default_info = DefaultInfo(
files = target_files,
runfiles = ctx.runfiles(transitive_files = java.inputs, collect_data = True),
)
else:
default_info = DefaultInfo(
files = target_files,
)
# Create a CcInfo provider so that CC rules can work with
# a haskell library as if it was a regular CC one.
# XXX: protobuf is passing a "patched ctx"
# which includes the real ctx as "real_ctx"
real_ctx = getattr(ctx, "real_ctx", ctx)
cc_toolchain = find_cc_toolchain(real_ctx)
feature_configuration = cc_common.configure_features(
ctx = real_ctx,
cc_toolchain = cc_toolchain,
requested_features = ctx.features,
unsupported_features = ctx.disabled_features,
)
if dynamic_library or static_library:
linker_inputs = [
cc_common.create_linker_input(
owner = ctx.label,
libraries = depset(direct = [
cc_common.create_library_to_link(
actions = ctx.actions,
feature_configuration = feature_configuration,
dynamic_library = dynamic_library,
dynamic_library_symlink_path = dynamic_library.basename if dynamic_library else "",
static_library = static_library,
cc_toolchain = cc_toolchain,
),
]),
),
]
else:
linker_inputs = []
compilation_context = cc_common.create_compilation_context()
linking_context = cc_common.create_linking_context(
linker_inputs = depset(direct = linker_inputs),
)
out_cc_info = cc_common.merge_cc_infos(
cc_infos = [
CcInfo(
compilation_context = compilation_context,
linking_context = linking_context,
),
] + [dep[CcInfo] for dep in deps if CcInfo in dep],
)
return [
hs_info,
out_cc_info,
coverage_info,
default_info,
lib_info,
OutputGroupInfo(**dicts.add(
compile_info_output_groups(
# For haskell_proto_aspect, which doesn't have a ctx.workspace_name,
# just set it to "". It won't matter in practice because those rules don't
# have runfiles and won't be compiled directly anyway.
workspace_name = getattr(ctx, "workspace_name", ""),
hs = hs,
cc = cc,
name = ctx.label.name,
c = c,
posix = posix,
runfiles = default_info.default_runfiles.files if getattr(default_info, "default_runfiles", None) else depset(),
),
library_info_output_groups(
name = ctx.label.name,
hs = hs,
hs_info = hs_info,
lib_info = lib_info,
),
)),
]
# We should not need this provider. It exists purely as a workaround
# for https://github.com/bazelbuild/bazel/issues/8129.
#
# TODO Get rid of this by computing a CcInfo in haskell_import
# instead. Currently blocked on upstream.
HaskellImportHack = provider()
HaskellToolchainLibraries = provider()
def haskell_toolchain_library_impl(ctx):
hs = haskell_context(ctx)
if ctx.attr.package:
package = ctx.attr.package
else:
package = ctx.label.name
libraries = ctx.attr._toolchain_libraries[HaskellToolchainLibraries].libraries
target = libraries.get(package)
if not target:
fail(
"""
{} is not a toolchain library.
Check that it ships with your version of GHC.
The following toolchain libraries are available:
{}
""".format(package, libraries),
)
return [
target.default_info,
target.hs_info,
target.hs_lib_info,
target.cc_info,
target.haddock_info,
HaskellToolchainLibraryInfo(),
OutputGroupInfo(**library_info_output_groups(
hs = hs,
name = ctx.label.name,
hs_info = target.hs_info,
lib_info = target.hs_lib_info,
)),
]
def _toolchain_library_symlink(dynamic_library):
prefix = dynamic_library.owner.workspace_root.replace("_", "_U").replace("/", "_S")
basename = dynamic_library.basename
return paths.join(prefix, basename)
def haskell_toolchain_libraries_impl(ctx):
hs = haskell_context(ctx)
with_profiling = is_profiling_enabled(hs)
with_threaded = "-threaded" in hs.toolchain.ghcopts
cc_toolchain = find_cc_toolchain(ctx)
feature_configuration = cc_common.configure_features(
ctx = ctx,
cc_toolchain = cc_toolchain,
requested_features = ctx.features,
unsupported_features = ctx.disabled_features,
)
libraries = hs.toolchain.libraries
# List of library in left-to-right post-ordering
# Meaning, if package B depends on package A, then A will appear before B.
ordered = depset(transitive = [
target[HaskellImportHack].transitive_depends
for target in hs.toolchain.libraries.values()
])
library_dict = {}
for package in ordered.to_list():
target = libraries[package]
# Construct CcInfo
additional_link_inputs = []
if with_profiling:
# GHC does not provide dynamic profiling mode libraries. The dynamic
# libraries that are available are missing profiling symbols, that
# other profiling mode build results will reference. Therefore, we
# don't import dynamic libraries in profiling mode.
libs = {
get_static_hs_lib_name(hs.toolchain.version, lib): {"static": lib}
for lib in target[HaskellImportHack].static_profiling_libraries.to_list()
}
else:
# Workaround for https://github.com/tweag/rules_haskell/issues/881
# Static and dynamic libraries don't necessarily pair up 1 to 1.
# E.g. the rts package in the Unix GHC bindist contains the
# dynamic libHSrts and the static libCffi and libHSrts.
libs = {}
for lib in target[HaskellImportHack].dynamic_libraries.to_list():
libname = get_dynamic_hs_lib_name(hs.toolchain.version, lib)
if libname == "ffi" and libname in libs:
# Make sure that the file of libffi matching its soname
# ends up in target runfiles. Otherwise, execution will
# fail with "cannot open shared object file" errors.
# On Linux libffi comes in three shapes:
# libffi.so, libffi.so.7, libffi.so.7.1.0
# (version numbers may vary)
# The soname is then libffi.so.7, meaning, at runtime the
# dynamic linker will look for libffi.so.7. So, that file
# should be the LibraryToLink.dynamic_library.
ext_components = get_lib_extension(lib).split(".")
if len(ext_components) == 2 and ext_components[0] == "so":
libs[libname]["dynamic"] = lib
else:
libs[libname] = {"dynamic": lib}
for lib in target[HaskellImportHack].static_libraries.to_list():
name = get_static_hs_lib_name(with_profiling, lib)
entry = libs.get(name, {})
entry["static"] = lib
libs[name] = entry
# Avoid duplicate runtime and ffi libraries. These libraries come
# in threaded and non-threaded flavors. Depending on the
# compilation mode we want to forward only one or the other.
# XXX: Threaded mode should be a per-target property. Use Bazel
# build configurations and transitions to select the threaded or
# non-threaded runtime and ffi on a per-target basis.
if "HSrts_thr" in libs:
if with_threaded:
libs["HSrts"] = libs["HSrts_thr"]
libs.pop("HSrts_thr")
if "Cffi_thr" in libs:
if with_threaded:
libs["ffi"]["static"] = libs["Cffi_thr"]["static"]
libs.pop("Cffi_thr")
linker_inputs = [
cc_common.create_linker_input(
owner = ctx.label,
libraries = depset(direct = [
cc_common.create_library_to_link(
actions = ctx.actions,
feature_configuration = feature_configuration,
dynamic_library = lib.get("dynamic", None),
dynamic_library_symlink_path =
_toolchain_library_symlink(lib["dynamic"]) if lib.get("dynamic") else "",
static_library = lib.get("static", None),
cc_toolchain = cc_toolchain,
)
for lib in libs.values()
]),
user_link_flags = depset(direct = target[HaskellImportHack].linkopts),
),
]
compilation_context = cc_common.create_compilation_context(
headers = target[HaskellImportHack].headers,
includes = target[HaskellImportHack].includes,
)
linking_context = cc_common.create_linking_context(
linker_inputs = depset(direct = linker_inputs),
)
cc_info = CcInfo(
compilation_context = compilation_context,
linking_context = linking_context,
)
library_dict[package] = struct(
default_info = target[DefaultInfo],
hs_info = target[HaskellInfo],
hs_lib_info = target[HaskellLibraryInfo],
cc_info = cc_common.merge_cc_infos(cc_infos = [cc_info] + [
library_dict[dep].cc_info
for dep in target[HaskellImportHack].depends
]),
haddock_info = target[HaddockInfo],
)
return [HaskellToolchainLibraries(libraries = library_dict)]
haskell_toolchain_libraries = rule(
haskell_toolchain_libraries_impl,
attrs = {
"_cc_toolchain": attr.label(
default = Label("@rules_cc//cc:current_cc_toolchain"),
),
},
toolchains = [
"@rules_cc//cc:toolchain_type",
"@rules_haskell//haskell:toolchain",
],
fragments = ["cpp"],
)
"""Generate Haskell toolchain libraries.
This is an internal rule and should not be user facing.
This rule is a work-around for toolchain transitions not being implemented,
yet. See
https://github.com/bazelbuild/proposals/blob/master/designs/2019-02-12-toolchain-transitions.md
This will need to be revisited once that proposal is implemented.
"""
def haskell_import_impl(ctx):
# The `allow_files` attribute of `rule` cannot define patterns of accepted
# file extensions like `.so.*`. Instead, we check for the correct file
# extensions here.
for lib in ctx.files.shared_libraries:
msg = "in shared_libraries attribute of haskell_import rule {}: " + \
"source file '{}' is misplaced here " + \
"(expected .dll, .dylib, .so or .so.*)"
ext = get_lib_extension(lib)
if not (ext in ["dll", "dylib", "so"] or ext.startswith("so.")):
fail(msg.format(str(ctx.label), str(lib.short_path)))
id = ctx.attr.id or ctx.attr.name
target_files = [
file
for file in ctx.files.static_libraries + ctx.files.shared_libraries
]
version_macros = set.empty()
if ctx.attr.version != None:
version_macros = set.singleton(
generate_version_macros(ctx, ctx.label.name, ctx.attr.version),
)
hs_info = HaskellInfo(
# XXX Empty set of conf and cache files only works for global db.
package_databases = depset(),
empty_lib_package_databases = depset(),
version_macros = version_macros,
source_files = depset(),
boot_files = depset(),
extra_source_files = depset(),
import_dirs = set.empty(),
hs_libraries = depset(),
deps_hs_libraries = depset(),
empty_hs_libraries = depset(),
interface_dirs = depset(),
deps_interface_dirs = depset(),
compile_flags = [],
user_compile_flags = [],
user_repl_flags = [],
)
import_info = HaskellImportHack(
# Make sure we're using the same order for dynamic_libraries,
# static_libraries.
dynamic_libraries = depset(ctx.files.shared_libraries),
static_libraries = depset(ctx.files.static_libraries, order = "topological"),
# NOTE: haskell_import is evaluated as a toolchain rule. Even if we
# bazel build with -c dbg, this rule is still executed with
# ctx.var["COMPILATION_MODE"] == "opt". Therefore, we need to carry
# both profiling and non-profiling libraries forward so that a later
# haskell_toolchain_library can select the appropriate artifacts.
static_profiling_libraries = depset(ctx.files.static_profiling_libraries, order = "topological"),
headers = depset(ctx.files.hdrs),
includes = depset(ctx.attr.includes),
linkopts = ctx.attr.linkopts,
depends = [dep.label.name for dep in ctx.attr.deps],
transitive_depends = depset(
direct = [ctx.attr.name],
transitive = [dep[HaskellImportHack].transitive_depends for dep in ctx.attr.deps],
order = "postorder",
),
)
coverage_info = HaskellCoverageInfo(coverage_data = [])
lib_info = HaskellLibraryInfo(
package_id = id,
version = ctx.attr.version,
exports = [],
)
default_info = DefaultInfo(
files = depset(target_files),
)
# This package haddock informations
transitive_html = {id: ctx.file.haddock_html} if ctx.file.haddock_html else {}
transitive_haddocks = {id: ctx.files.haddock_interfaces}
# Add dependencies haddock informations
for dep in ctx.attr.deps:
transitive_html.update(dep[HaddockInfo].transitive_html)
transitive_haddocks.update(dep[HaddockInfo].transitive_haddocks)
haddock_info = HaddockInfo(
package_id = id,
transitive_html = transitive_html,
transitive_haddocks = transitive_haddocks,
)
return [
hs_info,
import_info,
coverage_info,
default_info,
lib_info,
haddock_info,
]
def _exposed_modules_reexports(reexported_modules):
"""Creates a ghc-pkg-compatible list of reexport declarations.
A ghc-pkg registration file declares reexports as part of the
exposed-modules field in the following format:
exposed-modules: A, B, C from pkg-c:C, D from pkg-d:Original.D
Here, the Original.D module from pkg-d is renamed by virtue of a
different name being used before the "from" keyword.
This function creates a ghc-pkg-compatible list of reexport declarations
(as shown above) from a dictionary mapping package targets to "Cabal-style"
reexported-modules declarations. That is, something like:
{
":pkg-c": "C",
":pkg-d": "Original.D as D",
":pkg-e": "E1, Original.E2 as E2",
}
Args:
reexported_modules: a dictionary mapping package targets to "Cabal-style"
reexported-modules declarations.
Returns:
a ghc-pkg-compatible list of reexport declarations.
"""
exposed_reexports = []
for dep, cabal_decls in reexported_modules.items():
for cabal_decl in cabal_decls.split(","):
stripped_cabal_decl = cabal_decl.strip()
cabal_decl_parts = stripped_cabal_decl.split(" as ")
original = cabal_decl_parts[0]
if len(cabal_decl_parts) == 2:
reexported = cabal_decl_parts[1]
else:
reexported = cabal_decl_parts[0]
if HaskellLibraryInfo in dep:
pkg = dep[HaskellLibraryInfo].package_id
exposed_reexport = "{reexported} from {pkg}:{original}".format(
reexported = reexported,
pkg = pkg,
original = original,
)
exposed_reexports.append(exposed_reexport)
return exposed_reexports
def _get_mix_filepath(coverage_datum):
""" Extracts mix file path from a coverage datum.
"""
return coverage_datum.mix_file.short_path
|
py | 1a3125d04d238348d257b41efc876f7e003b2f23 | import os
import numpy as np
import json
from itertools import product
class Node():
'''
Class for representing a node in the ImageNet/WordNet hierarchy.
'''
def __init__(self, wnid, parent_wnid=None, name=""):
"""
Args:
wnid (str) : WordNet ID for synset represented by node
parent_wnid (str) : WordNet ID for synset of node's parent
name (str) : word/human-interpretable description of synset
"""
self.wnid = wnid
self.name = name
self.class_num = -1
self.parent_wnid = parent_wnid
self.descendant_count_in = 0
self.descendants_all = set()
def add_child(self, child):
"""
Add child to given node.
Args:
child (Node) : Node object for child
"""
child.parent_wnid = self.wnid
def __str__(self):
return f'Name: ({self.name}), ImageNet Class: ({self.class_num}), Descendants: ({self.descendant_count_in})'
def __repr__(self):
return f'Name: ({self.name}), ImageNet Class: ({self.class_num}), Descendants: ({self.descendant_count_in})'
class ImageNetHierarchy():
'''
Class for representing ImageNet/WordNet hierarchy.
'''
def __init__(self, ds_path, ds_info_path):
"""
Args:
ds_path (str) : Path to ImageNet dataset
ds_info_path (str) : Path to supplementary files for the ImageNet dataset
('wordnet.is_a.txt', 'words.txt' and 'imagenet_class_index.json')
which can be obtained from http://image-net.org/download-API.
"""
self.tree = {}
ret = self.load_imagenet_info(ds_path, ds_info_path)
self.in_wnids, self.wnid_to_name, self.wnid_to_num, self.num_to_name = ret
with open(os.path.join(ds_info_path, 'wordnet.is_a.txt'), 'r') as f:
for line in f.readlines():
parent_wnid, child_wnid = line.strip('\n').split(' ')
parentNode = self.get_node(parent_wnid)
childNode = self.get_node(child_wnid)
parentNode.add_child(childNode)
for wnid in self.in_wnids:
self.tree[wnid].descendant_count_in = 0
self.tree[wnid].class_num = self.wnid_to_num[wnid]
for wnid in self.in_wnids:
node = self.tree[wnid]
while node.parent_wnid is not None:
self.tree[node.parent_wnid].descendant_count_in += 1
self.tree[node.parent_wnid].descendants_all.update(node.descendants_all)
self.tree[node.parent_wnid].descendants_all.add(node.wnid)
node = self.tree[node.parent_wnid]
del_nodes = [wnid for wnid in self.tree \
if (self.tree[wnid].descendant_count_in == 0 and self.tree[wnid].class_num == -1)]
for d in del_nodes:
self.tree.pop(d, None)
assert all([k.descendant_count_in > 0 or k.class_num != -1 for k in self.tree.values()])
self.wnid_sorted = sorted(sorted([(k, v.descendant_count_in, len(v.descendants_all)) \
for k, v in self.tree.items()
],
key=lambda x: x[2],
reverse=True
),
key=lambda x: x[1],
reverse=True
)
@staticmethod
def load_imagenet_info(ds_path, ds_info_path):
"""
Get information about mapping between ImageNet wnids/class numbers/class names.
Args:
ds_path (str) : Path to ImageNet dataset
ds_info_path (str) : Path to supplementary files for the ImageNet dataset
('wordnet.is_a.txt', 'words.txt', 'imagenet_class_index.json')
which can be obtained from http://image-net.org/download-API.
"""
files = os.listdir(os.path.join(ds_path, 'train'))
in_wnids = [f for f in files if f[0]=='n']
f = open(os.path.join(ds_info_path, 'words.txt'))
wnid_to_name = [l.strip() for l in f.readlines()]
wnid_to_name = {l.split('\t')[0]: l.split('\t')[1] \
for l in wnid_to_name}
with open(os.path.join(ds_info_path, 'imagenet_class_index.json'), 'r') as f:
base_map = json.load(f)
wnid_to_num = {v[0]: int(k) for k, v in base_map.items()}
num_to_name = {int(k): v[1] for k, v in base_map.items()}
return in_wnids, wnid_to_name, wnid_to_num, num_to_name
def get_node(self, wnid):
"""
Add node to tree.
Args:
wnid (str) : WordNet ID for synset represented by node
Returns:
A node object representing the specified wnid.
"""
if wnid not in self.tree:
self.tree[wnid] = Node(wnid, name=self.wnid_to_name[wnid])
return self.tree[wnid]
def is_ancestor(self, ancestor_wnid, child_wnid):
"""
Check if a node is an ancestor of another.
Args:
ancestor_wnid (str) : WordNet ID for synset represented by ancestor node
child_wnid (str) : WordNet ID for synset represented by child node
Returns:
A boolean variable indicating whether or not the node is an ancestor
"""
return (child_wnid in self.tree[ancestor_wnid].descendants_all)
def get_descendants(self, node_wnid, in_imagenet=False):
"""
Get all descendants of a given node.
Args:
node_wnid (str) : WordNet ID for synset for node
in_imagenet (bool) : If True, only considers descendants among
ImageNet synsets, else considers all possible
descendants in the WordNet hierarchy
Returns:
A set of wnids corresponding to all the descendants
"""
if in_imagenet:
return set([self.wnid_to_num[ww] for ww in self.tree[node_wnid].descendants_all
if ww in set(self.in_wnids)])
else:
return self.tree[node_wnid].descendants_all
def get_superclasses(self, n_superclasses,
ancestor_wnid=None, superclass_lowest=None,
balanced=True):
"""
Get superclasses by grouping together classes from the ImageNet dataset.
Args:
n_superclasses (int) : Number of superclasses desired
ancestor_wnid (str) : (optional) WordNet ID that can be used to specify
common ancestor for the selected superclasses
superclass_lowest (set of str) : (optional) Set of WordNet IDs of nodes
that shouldn't be further sub-classes
balanced (bool) : If True, all the superclasses will have the same number
of ImageNet subclasses
Returns:
superclass_wnid (list): List of WordNet IDs of superclasses
class_ranges (list of sets): List of ImageNet subclasses per superclass
label_map (dict): Mapping from class number to human-interpretable description
for each superclass
"""
assert superclass_lowest is None or \
not any([self.is_ancestor(s1, s2) for s1, s2 in product(superclass_lowest, superclass_lowest)])
superclass_info = []
for (wnid, ndesc_in, ndesc_all) in self.wnid_sorted:
if len(superclass_info) == n_superclasses:
break
if ancestor_wnid is None or self.is_ancestor(ancestor_wnid, wnid):
keep_wnid = [True] * (len(superclass_info) + 1)
superclass_info.append((wnid, ndesc_in))
for ii, (w, d) in enumerate(superclass_info):
if self.is_ancestor(w, wnid):
if superclass_lowest and w in superclass_lowest:
keep_wnid[-1] = False
else:
keep_wnid[ii] = False
for ii in range(len(superclass_info) - 1, -1, -1):
if not keep_wnid[ii]:
superclass_info.pop(ii)
superclass_wnid = [w for w, _ in superclass_info]
class_ranges, label_map = self.get_subclasses(superclass_wnid,
balanced=balanced)
return superclass_wnid, class_ranges, label_map
def get_subclasses(self, superclass_wnid, balanced=True):
"""
Get ImageNet subclasses for a given set of superclasses from the WordNet
hierarchy.
Args:
superclass_wnid (list): List of WordNet IDs of superclasses
balanced (bool) : If True, all the superclasses will have the same number
of ImageNet subclasses
Returns:
class_ranges (list of sets): List of ImageNet subclasses per superclass
label_map (dict): Mapping from class number to human-interpretable description
for each superclass
"""
ndesc_min = min([self.tree[w].descendant_count_in for w in superclass_wnid])
class_ranges, label_map = [], {}
for ii, w in enumerate(superclass_wnid):
descendants = self.get_descendants(w, in_imagenet=True)
if balanced and len(descendants) > ndesc_min:
descendants = set([dd for ii, dd in enumerate(sorted(list(descendants))) if ii < ndesc_min])
class_ranges.append(descendants)
label_map[ii] = self.tree[w].name
for i in range(len(class_ranges)):
for j in range(i + 1, len(class_ranges)):
assert(len(class_ranges[i].intersection(class_ranges[j])) == 0)
return class_ranges, label_map
def common_superclass_wnid(group_name):
"""
Get WordNet IDs of common superclasses.
Args:
group_name (str): Name of group
Returns:
superclass_wnid (list): List of WordNet IDs of superclasses
"""
common_groups = {
# ancestor_wnid = 'n00004258'
'living_9': ['n02084071', #dog, domestic dog, Canis familiaris
'n01503061', # bird
'n01767661', # arthropod
'n01661091', # reptile, reptilian
'n02469914', # primate
'n02512053', # fish
'n02120997', # feline, felid
'n02401031', # bovid
'n01627424', # amphibian
],
'mixed_10': [
'n02084071', #dog,
'n01503061', #bird
'n02159955', #insect
'n02484322', #monkey
'n02958343', #car
'n02120997', #feline
'n04490091', #truck
'n13134947', #fruit
'n12992868', #fungus
'n02858304', #boat
],
'mixed_13': ['n02084071', #dog,
'n01503061', #bird (52)
'n02159955', #insect (27)
'n03405725', #furniture (21)
'n02512053', #fish (16),
'n02484322', #monkey (13)
'n02958343', #car (10)
'n02120997', #feline (8),
'n04490091', #truck (7)
'n13134947', #fruit (7)
'n12992868', #fungus (7)
'n02858304', #boat (6)
'n03082979', #computer(6)
],
# Dataset from Geirhos et al., 2018: arXiv:1811.12231
'geirhos_16': ['n02686568', #aircraft (3)
'n02131653', #bear (3)
'n02834778', #bicycle (2)
'n01503061', #bird (52)
'n02858304', #boat (6)
'n02876657', #bottle (7)
'n02958343', #car (10)
'n02121808', #cat (5)
'n03001627', #char (4)
'n03046257', #clock (3)
'n02084071', #dog (116)
'n02503517', #elephant (2)
'n03614532', #keyboard (3)
'n03623556', #knife (2)
'n03862676', #oven (2)
'n04490091', #truck (7)
],
'big_12': ['n02084071', #dog (100+)
'n04341686', #structure (55)
'n01503061', #bird (52)
'n03051540', #clothing (48)
'n04576211', #wheeled vehicle
'n01661091', #reptile, reptilian (36)
'n02075296', #carnivore
'n02159955', #insect (27)
'n03800933', #musical instrument (26)
'n07555863', #food (24)
'n03405725', #furniture (21)
'n02469914', #primate (20)
],
'mid_12': ['n02084071', #dog (100+)
'n01503061', #bird (52)
'n04576211', #wheeled vehicle
'n01661091', #reptile, reptilian (36)
'n02075296', #carnivore
'n02159955', #insect (27)
'n03800933', #musical instrument (26)
'n07555863', #food (24)
'n03419014', #garment (24)
'n03405725', #furniture (21)
'n02469914', #primate (20)
'n02512053', #fish (16)
]
}
if group_name in common_groups:
superclass_wnid = common_groups[group_name]
return superclass_wnid
else:
raise ValueError("Custom group does not exist")
|
py | 1a31263932a29b4ff86adc4d6d9bc6d8ae06de64 | import Sofa
import sys
def createScene(rootNode):
rootNode.createObject('PythonScriptController', filename=__file__, classname="AllocationTestController")
return rootNode
class AllocationTestController(Sofa.PythonScriptController):
node = None
subnode = None
def onLoaded(self,node):
self.node = node
def add(self):
# adding components in the scene, that increment a counter at each construction
for i in xrange(10):
self.node.createObject('PythonTestAllocationCounter')
def remove(self):
# removing all components, the destructor should be called, decrementing the counter
for o in self.node.getObjects():
self.node.removeObject(o)
def addSub(self):
# adding components in subnode, that increment a counter at each construction
self.subnode = self.node.createChild('subnode')
for i in xrange(10):
self.subnode.createObject('PythonTestAllocationCounter')
def removeSub(self):
# removing subnode should remove all its components
self.node.removeChild(self.subnode)
self.subnode = None # this is mandatory not to keep any pointer on the python side
def detachSub(self):
self.subnode.detachFromGraph()
self.subnode = None # this is mandatory not to keep any pointer on the python side |
py | 1a31268410de4ce6cdd92db7096e9f1ed022a5b7 | #!/usr/bin/env python
"""Django's command-line utility for administrative tasks."""
import os
import sys
def main():
"""Run administrative tasks."""
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'product_catalog.settings')
try:
from django.core.management import execute_from_command_line
except ImportError as exc:
raise ImportError(
"Couldn't import Django. Are you sure it's installed and "
"available on your PYTHONPATH environment variable? Did you "
"forget to activate a virtual environment?"
) from exc
execute_from_command_line(sys.argv)
if __name__ == '__main__':
main()
|
py | 1a3127916955bafed5ddcc1a0a682fe2be048bd0 | import sys
from pbsuite.utils.FileHandlers import FastqFile, M5File
from pbsuite.jelly.Support import AlignmentConnector, SUPPORTFLAGS
"""
Need to do work here
"""
if __name__ == '__main__':
connector = AlignmentConnector()
aligns = connector.parseAlignments(M5File(sys.argv[1]))
reads = FastqFile(sys.argv[2])
bestScore = None
best = None
fout = open("reads.fastq",'w')
spanCount = 0
for readGroup in aligns:
if readGroup[0].qname.startswith("ref"):
continue
if len(readGroup) == 2:
r1, r2 = readGroup
a = connector.extendsTarget(r1)
b = connector.extendsTarget(r2)
if a != SUPPORTFLAGS.none and b != SUPPORTFLAGS.none:
spanCount += 1
print r1.qname, "spans"
rStart = min(r1.qend, r2.qend)
rEnd = max(r1.qstart, r2.qstart)
t = reads[r1.qname].subSeq(rStart, rEnd)
fout.write(str(t))
gout = open("seed%d.fasta" % spanCount, 'w')
gout.write(">%s\n%s\n" % (t.name, t.seq))
gout.close()
if bestScore is None:
bestScore = r1.score + r2.score
best = reads[r1.qname].subSeq(rStart, rEnd)
else:
if (r1.score + r2.score) < bestScore:
best = reads[r1.qname].subSeq(rStart, rEnd)
else:
a = readGroup[0]
if a.tname.endswith('e5'):
fout.write(str(reads[a.qname].subSeq(0, a.qstart)))
elif a.tname.endswith('e3'):
fout.write(str(reads[a.qname].subSeq(a.qend, a.qseqlength)))
fout.close()
print "%d spans" % spanCount
fout = open("seed.fasta",'w')
fout.write(">%s\n%s\n" % (best.name, best.seq))
fout.close()
|
py | 1a3128972ac26439b859069f69b68f28340ea86b | from math import *
from prettytable import PrettyTable
def func(x, y):
return x * x + y * y
def main():
mas_x = []; mas_y = []
tmp_x = []; tmp_y = []; tmp_y2 = []
tmp_x3 = []; tmp_y3 = []
matrix = []
beg = 0; end = 10
N = abs(end - beg) - 1
eps = 1e-5
for i in range(beg, end):
tmp_x.append(i)
tmp_y.append(i)
matrix = create_new_matrix(func, tmp_x, tmp_y)
print_matrix(tmp_x, tmp_y, matrix)
n_X = int(input("input n for X: "))
n_Y = int(input("input n for Y: "))
x = float(input("input x: "))
y = float(input("input y: "))
mas_x = create_new_x_y(x, n_X, N, tmp_x)
mas_y = create_new_x_y(y, n_Y, N, tmp_y)
matrix = create_new_matrix(func, mas_x, mas_y)
new_x = []
for i in range(len(mas_x)):
new_x.append(interpolation(y, n_Y, mas_y, matrix[i]))
answer = interpolation(x, n_X, mas_x, new_x)
print("\nF(x, y) = ", answer)
def print_matrix(tmp_x, tmp_y, matrix):
print("|X|Y|", end = " ")
for i in range(0, len(tmp_x)):
print("{:5d}".format(tmp_x[i]), end = " ")
print()
for i in range(0, len(tmp_x)):
print("{:3d}".format(tmp_x[i])," ", end = " ")
for j in range(0, len(tmp_y)):
print( "{:5d}".format(matrix[i][j]), end = " ")
print()
print()
def create_new_matrix(f, tmp_x, tmp_y):
matrix = []
for i in range(0, len(tmp_x)):
matrix.append([])
for j in range(0, len(tmp_y)):
matrix[i].append(f(tmp_x[i], tmp_y[j]))
return matrix
def create_new_x_y(x, n, N, tmp_x):
mas_x = []
if (x <= tmp_x[0]):
for i in range(0, n + 1):
mas_x.append(tmp_x[i])
elif (x >= tmp_x[N]):
for i in range(len(tmp_x) - (n + 1), len(tmp_x)):
mas_x.append(tmp_x[i])
else:
back = 0; up = 0
for i in range(1, N):
if((tmp_x[i - 1] <= x) and (tmp_x[i] > x)):
up = i; back = i - 1
for k in range(0, n + 1):
if (k % 2 == 0):
if (up < len(tmp_x)):
mas_x.append(tmp_x[up])
up += 1
elif (back >= 0):
mas_x.insert(0, tmp_x[back])
back -= 1
else:
if (back >= 0):
mas_x.insert(0, tmp_x[back])
back -= 1
elif(up < len(tmp_x)):
mas_x.append(tmp_x[up])
up += 1
return mas_x
def interpolation(x, n, mas_x, mas_y):
matrix = []
matrix.append([])
for i in range(0, n):
matrix[0].append((mas_y[i] - mas_y[i + 1])/(mas_x[i] - mas_x[i + 1]))
m = n - 1
for i in range(1, n):
matrix.append([])
for j in range(0, m):
matrix[i].append(((matrix[i - 1][j] - matrix[i - 1][j + 1]))/(mas_x[j] - mas_x[j + 2]))
m -= 1
y = mas_y[0]
fact = 1
for i in range(0, n):
fact *= (x - mas_x[i])
y += matrix[i][0] * fact
return y
if __name__ == "__main__":
main();
|
py | 1a312a27eaa0f5dcdf0b5e6df7c8fd74dbb94b86 | # vim: tabstop=4 shiftwidth=4 softtabstop=4
# Copyright 2012 OpenStack 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.
from __future__ import absolute_import
try:
import pam
except ImportError:
pam = None
import PAM
from keystone import identity
def PAM_authenticate(username, password):
def _pam_conv(auth, query_list):
resp = []
for query, q_type in query_list:
if q_type in [PAM.PAM_PROMPT_ECHO_ON, PAM.PAM_PROMPT_ECHO_OFF]:
resp.append((password, 0))
elif q_type in [PAM.PAM_PROMPT_ERROR_MSG,
PAM.PAM_PROMPT_TEXT_INFO]:
resp.append(('', 0))
return resp
auth = PAM.pam()
auth.start('passwd')
auth.set_item(PAM.PAM_USER, username)
auth.set_item(PAM.PAM_CONV, _pam_conv)
try:
auth.authenticate()
auth.acct_mgmt()
except PAM.error:
raise AssertionError('Invalid user / password')
return True
class PamIdentity(identity.Driver):
"""Very basic identity based on PAM.
Tenant is always the same as User, root user has admin role.
"""
def authenticate(self, user_id, tenant_id, password):
auth = pam.authenticate if pam else PAM_authenticate
if auth(user_id, password):
metadata = {}
if user_id == 'root':
metadata['is_admin'] = True
tenant = {'id': user_id, 'name': user_id}
user = {'id': user_id, 'name': user_id}
return (user, tenant, metadata)
def get_project(self, tenant_id):
return {'id': tenant_id, 'name': tenant_id}
def get_project_by_name(self, tenant_name, domain_id):
# TODO(henry-nash): Used domain_id once domains are implemented
# in LDAP backend
return {'id': tenant_name, 'name': tenant_name}
def get_user(self, user_id):
return {'id': user_id, 'name': user_id}
def get_user_by_name(self, user_name, domain_id):
# TODO(henry-nash): Used domain_id once domains are implemented
# in LDAP backend
return {'id': user_name, 'name': user_name}
def get_role(self, role_id):
raise NotImplementedError()
def list_users(self):
raise NotImplementedError()
def list_roles(self):
raise NotImplementedError()
def add_user_to_project(self, tenant_id, user_id):
pass
def remove_user_from_project(self, tenant_id, user_id):
pass
def get_projects_for_user(self, user_id):
return [user_id]
def get_roles_for_user_and_project(self, user_id, tenant_id):
raise NotImplementedError()
def add_role_to_user_and_project(self, user_id, tenant_id, role_id):
raise NotImplementedError()
def remove_role_from_user_and_project(self, user_id, tenant_id, role_id):
raise NotImplementedError()
def create_user(self, user_id, user):
raise NotImplementedError()
def update_user(self, user_id, user):
raise NotImplementedError()
def delete_user(self, user_id):
raise NotImplementedError()
def create_project(self, tenant_id, tenant):
raise NotImplementedError()
def update_project(self, tenant_id, tenant):
raise NotImplementedError()
def delete_project(self, tenant_id, tenant):
raise NotImplementedError()
def get_metadata(self, user_id, tenant_id):
metadata = {}
if user_id == 'root':
metadata['is_admin'] = True
return metadata
def create_metadata(self, user_id, tenant_id, metadata):
raise NotImplementedError()
def update_metadata(self, user_id, tenant_id, metadata):
raise NotImplementedError()
def create_role(self, role_id, role):
raise NotImplementedError()
def update_role(self, role_id, role):
raise NotImplementedError()
def delete_role(self, role_id):
raise NotImplementedError()
|
py | 1a312b6673a7488eb5e3b7368a7e25de24c8c521 | import configparser
import os
from compute.config import AlgorithmConfig
import numpy as np
from train.utils import TrainConfig
class StatusUpdateTool(object):
@classmethod
def clear_config(cls):
config_file = os.path.join(os.path.dirname(__file__), 'global.ini')
config = configparser.ConfigParser()
config.read(config_file)
config.write(open(config_file, 'w'))
@classmethod
def __write_ini_file(cls, section, key, value):
config_file = os.path.join(os.path.dirname(__file__), 'global.ini')
config = configparser.ConfigParser()
config.read(config_file)
config.set(section, key, value)
config.write(open(config_file, 'w'))
@classmethod
def __read_ini_file(cls, section, key):
config_file = os.path.join(os.path.dirname(__file__), 'global.ini')
config = configparser.ConfigParser()
config.read(config_file)
return config.get(section, key)
@classmethod
def get_num_class(cls):
return TrainConfig.get_out_cls_num()
@classmethod
def get_input_weight(cls):
rs = TrainConfig.get_data_input_size()
return rs[0]
@classmethod
def get_input_height(cls):
rs = TrainConfig.get_data_input_size()
return rs[1]
@classmethod
def get_input_channel(cls):
rs = TrainConfig.get_data_input_size()
return rs[2]
@classmethod
def get_init_params(cls):
g = AlgorithmConfig()
pop_size = int(g.read_ini_file('pop_size'))
max_gen = int(g.read_ini_file('max_gen'))
params = {}
params['pop_size'] = pop_size
params['max_gen'] = max_gen
return params
@classmethod
def begin_evolution(cls):
section = 'evolution_status'
key = 'IS_RUNNING'
cls.__write_ini_file(section, key, "1")
@classmethod
def end_evolution(cls):
section = 'evolution_status'
key = 'IS_RUNNING'
cls.__write_ini_file(section, key, "0")
@classmethod
def is_evolution_running(cls):
rs = cls.__read_ini_file('evolution_status', 'IS_RUNNING')
if rs == '1':
return True
else:
return False
|
py | 1a312c1d48f208b3f96681ebd098e5fa3d2b98c4 | #!/usr/bin/env python
"""Django's command-line utility for administrative tasks."""
import os
import sys
def main():
"""Run administrative tasks."""
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'buttonpython.settings')
try:
from django.core.management import execute_from_command_line
except ImportError as exc:
raise ImportError(
"Couldn't import Django. Are you sure it's installed and "
"available on your PYTHONPATH environment variable? Did you "
"forget to activate a virtual environment?"
) from exc
execute_from_command_line(sys.argv)
if __name__ == '__main__':
main()
|
py | 1a312c35f581b6098df5839f4d1517eba53dae60 | import cv2
import random
import numpy as np
import skimage.transform
from typing import Union, Optional, Sequence, Tuple, Dict
from . import functional as F
from ...core.transforms_interface import DualTransform, to_tuple
__all__ = ["ShiftScaleRotate", "ElasticTransform", "Perspective", "Affine", "PiecewiseAffine"]
class ShiftScaleRotate(DualTransform):
"""Randomly apply affine transforms: translate, scale and rotate the input.
Args:
shift_limit ((float, float) or float): shift factor range for both height and width. If shift_limit
is a single float value, the range will be (-shift_limit, shift_limit). Absolute values for lower and
upper bounds should lie in range [0, 1]. Default: (-0.0625, 0.0625).
scale_limit ((float, float) or float): scaling factor range. If scale_limit is a single float value, the
range will be (-scale_limit, scale_limit). Default: (-0.1, 0.1).
rotate_limit ((int, int) or int): rotation range. If rotate_limit is a single int value, the
range will be (-rotate_limit, rotate_limit). Default: (-45, 45).
interpolation (OpenCV flag): flag that is used to specify the interpolation algorithm. Should be one of:
cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4.
Default: cv2.INTER_LINEAR.
border_mode (OpenCV flag): flag that is used to specify the pixel extrapolation method. Should be one of:
cv2.BORDER_CONSTANT, cv2.BORDER_REPLICATE, cv2.BORDER_REFLECT, cv2.BORDER_WRAP, cv2.BORDER_REFLECT_101.
Default: cv2.BORDER_REFLECT_101
value (int, float, list of int, list of float): padding value if border_mode is cv2.BORDER_CONSTANT.
mask_value (int, float,
list of int,
list of float): padding value if border_mode is cv2.BORDER_CONSTANT applied for masks.
shift_limit_x ((float, float) or float): shift factor range for width. If it is set then this value
instead of shift_limit will be used for shifting width. If shift_limit_x is a single float value,
the range will be (-shift_limit_x, shift_limit_x). Absolute values for lower and upper bounds should lie in
the range [0, 1]. Default: None.
shift_limit_y ((float, float) or float): shift factor range for height. If it is set then this value
instead of shift_limit will be used for shifting height. If shift_limit_y is a single float value,
the range will be (-shift_limit_y, shift_limit_y). Absolute values for lower and upper bounds should lie
in the range [0, 1]. Default: None.
p (float): probability of applying the transform. Default: 0.5.
Targets:
image, mask, keypoints
Image types:
uint8, float32
"""
def __init__(
self,
shift_limit=0.0625,
scale_limit=0.1,
rotate_limit=45,
interpolation=cv2.INTER_LINEAR,
border_mode=cv2.BORDER_REFLECT_101,
value=None,
mask_value=None,
shift_limit_x=None,
shift_limit_y=None,
always_apply=False,
p=0.5,
):
super(ShiftScaleRotate, self).__init__(always_apply, p)
self.shift_limit_x = to_tuple(shift_limit_x if shift_limit_x is not None else shift_limit)
self.shift_limit_y = to_tuple(shift_limit_y if shift_limit_y is not None else shift_limit)
self.scale_limit = to_tuple(scale_limit, bias=1.0)
self.rotate_limit = to_tuple(rotate_limit)
self.interpolation = interpolation
self.border_mode = border_mode
self.value = value
self.mask_value = mask_value
def apply(self, img, angle=0, scale=0, dx=0, dy=0, interpolation=cv2.INTER_LINEAR, **params):
return F.shift_scale_rotate(img, angle, scale, dx, dy, interpolation, self.border_mode, self.value)
def apply_to_mask(self, img, angle=0, scale=0, dx=0, dy=0, **params):
return F.shift_scale_rotate(img, angle, scale, dx, dy, cv2.INTER_NEAREST, self.border_mode, self.mask_value)
def apply_to_keypoint(self, keypoint, angle=0, scale=0, dx=0, dy=0, rows=0, cols=0, **params):
return F.keypoint_shift_scale_rotate(keypoint, angle, scale, dx, dy, rows, cols)
def get_params(self):
return {
"angle": random.uniform(self.rotate_limit[0], self.rotate_limit[1]),
"scale": random.uniform(self.scale_limit[0], self.scale_limit[1]),
"dx": random.uniform(self.shift_limit_x[0], self.shift_limit_x[1]),
"dy": random.uniform(self.shift_limit_y[0], self.shift_limit_y[1]),
}
def apply_to_bbox(self, bbox, angle, scale, dx, dy, **params):
return F.bbox_shift_scale_rotate(bbox, angle, scale, dx, dy, **params)
def get_transform_init_args(self):
return {
"shift_limit_x": self.shift_limit_x,
"shift_limit_y": self.shift_limit_y,
"scale_limit": to_tuple(self.scale_limit, bias=-1.0),
"rotate_limit": self.rotate_limit,
"interpolation": self.interpolation,
"border_mode": self.border_mode,
"value": self.value,
"mask_value": self.mask_value,
}
class ElasticTransform(DualTransform):
"""Elastic deformation of images as described in [Simard2003]_ (with modifications).
Based on https://gist.github.com/ernestum/601cdf56d2b424757de5
.. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for
Convolutional Neural Networks applied to Visual Document Analysis", in
Proc. of the International Conference on Document Analysis and
Recognition, 2003.
Args:
alpha (float):
sigma (float): Gaussian filter parameter.
alpha_affine (float): The range will be (-alpha_affine, alpha_affine)
interpolation (OpenCV flag): flag that is used to specify the interpolation algorithm. Should be one of:
cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4.
Default: cv2.INTER_LINEAR.
border_mode (OpenCV flag): flag that is used to specify the pixel extrapolation method. Should be one of:
cv2.BORDER_CONSTANT, cv2.BORDER_REPLICATE, cv2.BORDER_REFLECT, cv2.BORDER_WRAP, cv2.BORDER_REFLECT_101.
Default: cv2.BORDER_REFLECT_101
value (int, float, list of ints, list of float): padding value if border_mode is cv2.BORDER_CONSTANT.
mask_value (int, float,
list of ints,
list of float): padding value if border_mode is cv2.BORDER_CONSTANT applied for masks.
approximate (boolean): Whether to smooth displacement map with fixed kernel size.
Enabling this option gives ~2X speedup on large images.
same_dxdy (boolean): Whether to use same random generated shift for x and y.
Enabling this option gives ~2X speedup.
Targets:
image, mask
Image types:
uint8, float32
"""
def __init__(
self,
alpha=1,
sigma=50,
alpha_affine=50,
interpolation=cv2.INTER_LINEAR,
border_mode=cv2.BORDER_REFLECT_101,
value=None,
mask_value=None,
always_apply=False,
approximate=False,
same_dxdy=False,
p=0.5,
):
super(ElasticTransform, self).__init__(always_apply, p)
self.alpha = alpha
self.alpha_affine = alpha_affine
self.sigma = sigma
self.interpolation = interpolation
self.border_mode = border_mode
self.value = value
self.mask_value = mask_value
self.approximate = approximate
self.same_dxdy = same_dxdy
def apply(self, img, random_state=None, interpolation=cv2.INTER_LINEAR, **params):
return F.elastic_transform(
img,
self.alpha,
self.sigma,
self.alpha_affine,
interpolation,
self.border_mode,
self.value,
np.random.RandomState(random_state),
self.approximate,
self.same_dxdy,
)
def apply_to_mask(self, img, random_state=None, **params):
return F.elastic_transform(
img,
self.alpha,
self.sigma,
self.alpha_affine,
cv2.INTER_NEAREST,
self.border_mode,
self.mask_value,
np.random.RandomState(random_state),
self.approximate,
self.same_dxdy,
)
def get_params(self):
return {"random_state": random.randint(0, 10000)}
def get_transform_init_args_names(self):
return (
"alpha",
"sigma",
"alpha_affine",
"interpolation",
"border_mode",
"value",
"mask_value",
"approximate",
"same_dxdy",
)
class Perspective(DualTransform):
"""Perform a random four point perspective transform of the input.
Args:
scale (float or (float, float)): standard deviation of the normal distributions. These are used to sample
the random distances of the subimage's corners from the full image's corners.
If scale is a single float value, the range will be (0, scale). Default: (0.05, 0.1).
keep_size (bool): Whether to resize image’s back to their original size after applying the perspective
transform. If set to False, the resulting images may end up having different shapes
and will always be a list, never an array. Default: True
pad_mode (OpenCV flag): OpenCV border mode.
pad_val (int, float, list of int, list of float): padding value if border_mode is cv2.BORDER_CONSTANT.
Default: 0
mask_pad_val (int, float, list of int, list of float): padding value for mask
if border_mode is cv2.BORDER_CONSTANT. Default: 0
fit_output (bool): If True, the image plane size and position will be adjusted to still capture
the whole image after perspective transformation. (Followed by image resizing if keep_size is set to True.)
Otherwise, parts of the transformed image may be outside of the image plane.
This setting should not be set to True when using large scale values as it could lead to very large images.
Default: False
p (float): probability of applying the transform. Default: 0.5.
Targets:
image, mask, keypoints, bboxes
Image types:
uint8, float32
"""
def __init__(
self,
scale=(0.05, 0.1),
keep_size=True,
pad_mode=cv2.BORDER_CONSTANT,
pad_val=0,
mask_pad_val=0,
fit_output=False,
interpolation=cv2.INTER_LINEAR,
always_apply=False,
p=0.5,
):
super().__init__(always_apply, p)
self.scale = to_tuple(scale, 0)
self.keep_size = keep_size
self.pad_mode = pad_mode
self.pad_val = pad_val
self.mask_pad_val = mask_pad_val
self.fit_output = fit_output
self.interpolation = interpolation
def apply(self, img, matrix=None, max_height=None, max_width=None, **params):
return F.perspective(
img, matrix, max_width, max_height, self.pad_val, self.pad_mode, self.keep_size, params["interpolation"]
)
def apply_to_bbox(self, bbox, matrix=None, max_height=None, max_width=None, **params):
return F.perspective_bbox(bbox, params["rows"], params["cols"], matrix, max_width, max_height, self.keep_size)
def apply_to_keypoint(self, keypoint, matrix=None, max_height=None, max_width=None, **params):
return F.perspective_keypoint(
keypoint, params["rows"], params["cols"], matrix, max_width, max_height, self.keep_size
)
@property
def targets_as_params(self):
return ["image"]
def get_params_dependent_on_targets(self, params):
h, w = params["image"].shape[:2]
scale = np.random.uniform(*self.scale)
points = np.random.normal(0, scale, [4, 2])
points = np.mod(np.abs(points), 1)
# top left -- no changes needed, just use jitter
# top right
points[1, 0] = 1.0 - points[1, 0] # w = 1.0 - jitter
# bottom right
points[2] = 1.0 - points[2] # w = 1.0 - jitt
# bottom left
points[3, 1] = 1.0 - points[3, 1] # h = 1.0 - jitter
points[:, 0] *= w
points[:, 1] *= h
# Obtain a consistent order of the points and unpack them individually.
# Warning: don't just do (tl, tr, br, bl) = _order_points(...)
# here, because the reordered points is used further below.
points = self._order_points(points)
tl, tr, br, bl = points
# compute the width of the new image, which will be the
# maximum distance between bottom-right and bottom-left
# x-coordiates or the top-right and top-left x-coordinates
min_width = None
max_width = None
while min_width is None or min_width < 2:
width_top = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))
width_bottom = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
max_width = int(max(width_top, width_bottom))
min_width = int(min(width_top, width_bottom))
if min_width < 2:
step_size = (2 - min_width) / 2
tl[0] -= step_size
tr[0] += step_size
bl[0] -= step_size
br[0] += step_size
# compute the height of the new image, which will be the maximum distance between the top-right
# and bottom-right y-coordinates or the top-left and bottom-left y-coordinates
min_height = None
max_height = None
while min_height is None or min_height < 2:
height_right = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))
height_left = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))
max_height = int(max(height_right, height_left))
min_height = int(min(height_right, height_left))
if min_height < 2:
step_size = (2 - min_height) / 2
tl[1] -= step_size
tr[1] -= step_size
bl[1] += step_size
br[1] += step_size
# now that we have the dimensions of the new image, construct
# the set of destination points to obtain a "birds eye view",
# (i.e. top-down view) of the image, again specifying points
# in the top-left, top-right, bottom-right, and bottom-left order
# do not use width-1 or height-1 here, as for e.g. width=3, height=2
# the bottom right coordinate is at (3.0, 2.0) and not (2.0, 1.0)
dst = np.array([[0, 0], [max_width, 0], [max_width, max_height], [0, max_height]], dtype=np.float32)
# compute the perspective transform matrix and then apply it
m = cv2.getPerspectiveTransform(points, dst)
if self.fit_output:
m, max_width, max_height = self._expand_transform(m, (h, w))
return {"matrix": m, "max_height": max_height, "max_width": max_width, "interpolation": self.interpolation}
@classmethod
def _expand_transform(cls, matrix, shape):
height, width = shape
# do not use width-1 or height-1 here, as for e.g. width=3, height=2, max_height
# the bottom right coordinate is at (3.0, 2.0) and not (2.0, 1.0)
rect = np.array([[0, 0], [width, 0], [width, height], [0, height]], dtype=np.float32)
dst = cv2.perspectiveTransform(np.array([rect]), matrix)[0]
# get min x, y over transformed 4 points
# then modify target points by subtracting these minima => shift to (0, 0)
dst -= dst.min(axis=0, keepdims=True)
dst = np.around(dst, decimals=0)
matrix_expanded = cv2.getPerspectiveTransform(rect, dst)
max_width, max_height = dst.max(axis=0)
return matrix_expanded, int(max_width), int(max_height)
@staticmethod
def _order_points(pts: np.ndarray) -> np.ndarray:
pts = np.array(sorted(pts, key=lambda x: x[0]))
left = pts[:2] # points with smallest x coordinate - left points
right = pts[2:] # points with greatest x coordinate - right points
if left[0][1] < left[1][1]:
tl, bl = left
else:
bl, tl = left
if right[0][1] < right[1][1]:
tr, br = right
else:
br, tr = right
return np.array([tl, tr, br, bl], dtype=np.float32)
def get_transform_init_args_names(self):
return ("scale", "keep_size", "pad_mode", "pad_val", "mask_pad_val", "fit_output", "interpolation")
class Affine(DualTransform):
"""Augmentation to apply affine transformations to images.
This is mostly a wrapper around the corresponding classes and functions in OpenCV.
Affine transformations involve:
- Translation ("move" image on the x-/y-axis)
- Rotation
- Scaling ("zoom" in/out)
- Shear (move one side of the image, turning a square into a trapezoid)
All such transformations can create "new" pixels in the image without a defined content, e.g.
if the image is translated to the left, pixels are created on the right.
A method has to be defined to deal with these pixel values.
The parameters `cval` and `mode` of this class deal with this.
Some transformations involve interpolations between several pixels
of the input image to generate output pixel values. The parameters `interpolation` and
`mask_interpolation` deals with the method of interpolation used for this.
Args:
scale (number, tuple of number or dict): Scaling factor to use, where ``1.0`` denotes "no change" and
``0.5`` is zoomed out to ``50`` percent of the original size.
* If a single number, then that value will be used for all images.
* If a tuple ``(a, b)``, then a value will be uniformly sampled per image from the interval ``[a, b]``.
That value will be used identically for both x- and y-axis.
* If a dictionary, then it is expected to have the keys ``x`` and/or ``y``.
Each of these keys can have the same values as described above.
Using a dictionary allows to set different values for the two axis and sampling will then happen
*independently* per axis, resulting in samples that differ between the axes.
translate_percent (None, number, tuple of number or dict): Translation as a fraction of the image height/width
(x-translation, y-translation), where ``0`` denotes "no change"
and ``0.5`` denotes "half of the axis size".
* If ``None`` then equivalent to ``0.0`` unless `translate_px` has a value other than ``None``.
* If a single number, then that value will be used for all images.
* If a tuple ``(a, b)``, then a value will be uniformly sampled per image from the interval ``[a, b]``.
That sampled fraction value will be used identically for both x- and y-axis.
* If a dictionary, then it is expected to have the keys ``x`` and/or ``y``.
Each of these keys can have the same values as described above.
Using a dictionary allows to set different values for the two axis and sampling will then happen
*independently* per axis, resulting in samples that differ between the axes.
translate_px (None, int, tuple of int or dict): Translation in pixels.
* If ``None`` then equivalent to ``0`` unless `translate_percent` has a value other than ``None``.
* If a single int, then that value will be used for all images.
* If a tuple ``(a, b)``, then a value will be uniformly sampled per image from
the discrete interval ``[a..b]``. That number will be used identically for both x- and y-axis.
* If a dictionary, then it is expected to have the keys ``x`` and/or ``y``.
Each of these keys can have the same values as described above.
Using a dictionary allows to set different values for the two axis and sampling will then happen
*independently* per axis, resulting in samples that differ between the axes.
rotate (number or tuple of number): Rotation in degrees (**NOT** radians), i.e. expected value range is
around ``[-360, 360]``. Rotation happens around the *center* of the image,
not the top left corner as in some other frameworks.
* If a number, then that value will be used for all images.
* If a tuple ``(a, b)``, then a value will be uniformly sampled per image from the interval ``[a, b]``
and used as the rotation value.
shear (number, tuple of number or dict): Shear in degrees (**NOT** radians), i.e. expected value range is
around ``[-360, 360]``, with reasonable values being in the range of ``[-45, 45]``.
* If a number, then that value will be used for all images as
the shear on the x-axis (no shear on the y-axis will be done).
* If a tuple ``(a, b)``, then two value will be uniformly sampled per image
from the interval ``[a, b]`` and be used as the x- and y-shear value.
* If a dictionary, then it is expected to have the keys ``x`` and/or ``y``.
Each of these keys can have the same values as described above.
Using a dictionary allows to set different values for the two axis and sampling will then happen
*independently* per axis, resulting in samples that differ between the axes.
interpolation (int): OpenCV interpolation flag.
mask_interpolation (int): OpenCV interpolation flag.
cval (number or sequence of number): The constant value to use when filling in newly created pixels.
(E.g. translating by 1px to the right will create a new 1px-wide column of pixels
on the left of the image).
The value is only used when `mode=constant`. The expected value range is ``[0, 255]`` for ``uint8`` images.
cval_mask (number or tuple of number): Same as cval but only for masks.
mode (int): OpenCV border flag.
fit_output (bool): Whether to modify the affine transformation so that the whole output image is always
contained in the image plane (``True``) or accept parts of the image being outside
the image plane (``False``). This can be thought of as first applying the affine transformation
and then applying a second transformation to "zoom in" on the new image so that it fits the image plane,
This is useful to avoid corners of the image being outside of the image plane after applying rotations.
It will however negate translation and scaling.
p (float): probability of applying the transform. Default: 0.5.
Targets:
image, mask, keypoints, bboxes
Image types:
uint8, float32
"""
def __init__(
self,
scale: Optional[Union[float, Sequence[float], dict]] = None,
translate_percent: Optional[Union[float, Sequence[float], dict]] = None,
translate_px: Optional[Union[int, Sequence[int], dict]] = None,
rotate: Optional[Union[float, Sequence[float]]] = None,
shear: Optional[Union[float, Sequence[float], dict]] = None,
interpolation: int = cv2.INTER_LINEAR,
mask_interpolation: int = cv2.INTER_NEAREST,
cval: Union[int, float, Sequence[int], Sequence[float]] = 0,
cval_mask: Union[int, float, Sequence[int], Sequence[float]] = 0,
mode: int = cv2.BORDER_CONSTANT,
fit_output: bool = False,
always_apply: bool = False,
p: float = 0.5,
):
super().__init__(always_apply=always_apply, p=p)
params = [scale, translate_percent, translate_px, rotate, shear]
if all([p is None for p in params]):
scale = {"x": (0.9, 1.1), "y": (0.9, 1.1)}
translate_percent = {"x": (-0.1, 0.1), "y": (-0.1, 0.1)}
rotate = (-15, 15)
shear = {"x": (-10, 10), "y": (-10, 10)}
else:
scale = scale if scale is not None else 1.0
rotate = rotate if rotate is not None else 0.0
shear = shear if shear is not None else 0.0
self.interpolation = interpolation
self.mask_interpolation = mask_interpolation
self.cval = cval
self.cval_mask = cval_mask
self.mode = mode
self.scale = self._handle_dict_arg(scale, "scale")
self.translate_percent, self.translate_px = self._handle_translate_arg(translate_px, translate_percent)
self.rotate = to_tuple(rotate, rotate)
self.fit_output = fit_output
self.shear = self._handle_dict_arg(shear, "shear")
def get_transform_init_args_names(self):
return (
"interpolation",
"mask_interpolation",
"cval",
"mode",
"scale",
"translate_percent",
"translate_px",
"rotate",
"fit_output",
"shear",
"cval_mask",
)
@staticmethod
def _handle_dict_arg(val: Union[float, Sequence[float], dict], name: str):
if isinstance(val, dict):
if "x" not in val and "y" not in val:
raise ValueError(
f'Expected {name} dictionary to contain at least key "x" or ' 'key "y". Found neither of them.'
)
x = val.get("x", 1.0)
y = val.get("y", 1.0)
return {"x": to_tuple(x, x), "y": to_tuple(y, y)}
return {"x": to_tuple(val, val), "y": to_tuple(val, val)}
@classmethod
def _handle_translate_arg(
cls,
translate_px: Optional[Union[float, Sequence[float], dict]],
translate_percent: Optional[Union[float, Sequence[float], dict]],
):
if translate_percent is None and translate_px is None:
translate_px = 0
if translate_percent is not None and translate_px is not None:
raise ValueError(
"Expected either translate_percent or translate_px to be " "provided, " "but neither of them was."
)
if translate_percent is not None:
# translate by percent
return cls._handle_dict_arg(translate_percent, "translate_percent"), translate_px
if translate_px is None:
raise ValueError("translate_px is None.")
# translate by pixels
return translate_percent, cls._handle_dict_arg(translate_px, "translate_px")
def apply(
self,
img: np.ndarray,
matrix: skimage.transform.ProjectiveTransform = None,
output_shape: Sequence[int] = None,
**params
) -> np.ndarray:
return F.warp_affine(
img,
matrix,
interpolation=self.interpolation,
cval=self.cval,
mode=self.mode,
output_shape=output_shape,
)
def apply_to_mask(
self,
img: np.ndarray,
matrix: skimage.transform.ProjectiveTransform = None,
output_shape: Sequence[int] = None,
**params
) -> np.ndarray:
return F.warp_affine(
img,
matrix,
interpolation=self.mask_interpolation,
cval=self.cval_mask,
mode=self.mode,
output_shape=output_shape,
)
def apply_to_bbox(
self,
bbox: Sequence[float],
matrix: skimage.transform.ProjectiveTransform = None,
rows: int = 0,
cols: int = 0,
output_shape: Sequence[int] = (),
**params
) -> Sequence[float]:
return F.bbox_affine(bbox, matrix, rows, cols, output_shape)
def apply_to_keypoint(
self,
keypoint: Sequence[float],
matrix: skimage.transform.ProjectiveTransform = None,
scale: dict = None,
**params
) -> Sequence[float]:
return F.keypoint_affine(keypoint, matrix=matrix, scale=scale)
@property
def targets_as_params(self):
return ["image"]
def get_params_dependent_on_targets(self, params: dict) -> dict:
h, w = params["image"].shape[:2]
translate: Dict[str, Union[int, float]]
if self.translate_px is not None:
translate = {key: random.randint(*value) for key, value in self.translate_px.items()}
elif self.translate_percent is not None:
translate = {key: random.uniform(*value) for key, value in self.translate_percent.items()}
translate["x"] = translate["x"] * w
translate["y"] = translate["y"] * h
else:
translate = {"x": 0, "y": 0}
shear = {key: random.uniform(*value) for key, value in self.shear.items()}
scale = {key: random.uniform(*value) for key, value in self.scale.items()}
rotate = random.uniform(*self.rotate)
# for images we use additional shifts of (0.5, 0.5) as otherwise
# we get an ugly black border for 90deg rotations
shift_x = w / 2 - 0.5
shift_y = h / 2 - 0.5
matrix_to_topleft = skimage.transform.SimilarityTransform(translation=[-shift_x, -shift_y])
matrix_shear_y_rot = skimage.transform.AffineTransform(rotation=-np.pi / 2)
matrix_shear_y = skimage.transform.AffineTransform(shear=np.deg2rad(shear["y"]))
matrix_shear_y_rot_inv = skimage.transform.AffineTransform(rotation=np.pi / 2)
matrix_transforms = skimage.transform.AffineTransform(
scale=(scale["x"], scale["y"]),
translation=(translate["x"], translate["y"]),
rotation=np.deg2rad(rotate),
shear=np.deg2rad(shear["x"]),
)
matrix_to_center = skimage.transform.SimilarityTransform(translation=[shift_x, shift_y])
matrix = (
matrix_to_topleft
+ matrix_shear_y_rot
+ matrix_shear_y
+ matrix_shear_y_rot_inv
+ matrix_transforms
+ matrix_to_center
)
if self.fit_output:
matrix, output_shape = self._compute_affine_warp_output_shape(matrix, params["image"].shape)
else:
output_shape = params["image"].shape
return {
"rotate": rotate,
"scale": scale,
"matrix": matrix,
"output_shape": output_shape,
}
@staticmethod
def _compute_affine_warp_output_shape(
matrix: skimage.transform.ProjectiveTransform, input_shape: Sequence[int]
) -> Tuple[skimage.transform.ProjectiveTransform, Sequence[int]]:
height, width = input_shape[:2]
if height == 0 or width == 0:
return matrix, input_shape
# determine shape of output image
corners = np.array([[0, 0], [0, height - 1], [width - 1, height - 1], [width - 1, 0]])
corners = matrix(corners)
minc = corners[:, 0].min()
minr = corners[:, 1].min()
maxc = corners[:, 0].max()
maxr = corners[:, 1].max()
out_height = maxr - minr + 1
out_width = maxc - minc + 1
if len(input_shape) == 3:
output_shape = np.ceil((out_height, out_width, input_shape[2]))
else:
output_shape = np.ceil((out_height, out_width))
output_shape_tuple = tuple([int(v) for v in output_shape.tolist()])
# fit output image in new shape
translation = (-minc, -minr)
matrix_to_fit = skimage.transform.SimilarityTransform(translation=translation)
matrix = matrix + matrix_to_fit
return matrix, output_shape_tuple
class PiecewiseAffine(DualTransform):
"""Apply affine transformations that differ between local neighbourhoods.
This augmentation places a regular grid of points on an image and randomly moves the neighbourhood of these point
around via affine transformations. This leads to local distortions.
This is mostly a wrapper around scikit-image's ``PiecewiseAffine``.
See also ``Affine`` for a similar technique.
Note:
This augmenter is very slow. Try to use ``ElasticTransformation`` instead, which is at least 10x faster.
Note:
For coordinate-based inputs (keypoints, bounding boxes, polygons, ...),
this augmenter still has to perform an image-based augmentation,
which will make it significantly slower and not fully correct for such inputs than other transforms.
Args:
scale (float, tuple of float): Each point on the regular grid is moved around via a normal distribution.
This scale factor is equivalent to the normal distribution's sigma.
Note that the jitter (how far each point is moved in which direction) is multiplied by the height/width of
the image if ``absolute_scale=False`` (default), so this scale can be the same for different sized images.
Recommended values are in the range ``0.01`` to ``0.05`` (weak to strong augmentations).
* If a single ``float``, then that value will always be used as the scale.
* If a tuple ``(a, b)`` of ``float`` s, then a random value will
be uniformly sampled per image from the interval ``[a, b]``.
nb_rows (int, tuple of int): Number of rows of points that the regular grid should have.
Must be at least ``2``. For large images, you might want to pick a higher value than ``4``.
You might have to then adjust scale to lower values.
* If a single ``int``, then that value will always be used as the number of rows.
* If a tuple ``(a, b)``, then a value from the discrete interval
``[a..b]`` will be uniformly sampled per image.
nb_cols (int, tuple of int): Number of columns. Analogous to `nb_rows`.
interpolation (int): The order of interpolation. The order has to be in the range 0-5:
- 0: Nearest-neighbor
- 1: Bi-linear (default)
- 2: Bi-quadratic
- 3: Bi-cubic
- 4: Bi-quartic
- 5: Bi-quintic
mask_interpolation (int): same as interpolation but for mask.
cval (number): The constant value to use when filling in newly created pixels.
cval_mask (number): Same as cval but only for masks.
mode (str): {'constant', 'edge', 'symmetric', 'reflect', 'wrap'}, optional
Points outside the boundaries of the input are filled according
to the given mode. Modes match the behaviour of `numpy.pad`.
absolute_scale (bool): Take `scale` as an absolute value rather than a relative value.
keypoints_threshold (float): Used as threshold in conversion from distance maps to keypoints.
The search for keypoints works by searching for the
argmin (non-inverted) or argmax (inverted) in each channel. This
parameters contains the maximum (non-inverted) or minimum (inverted) value to accept in order to view a hit
as a keypoint. Use ``None`` to use no min/max. Default: 0.01
Targets:
image, mask, keypoints, bboxes
Image types:
uint8, float32
"""
def __init__(
self,
scale: Union[float, Sequence[float]] = (0.03, 0.05),
nb_rows: Union[int, Sequence[int]] = 4,
nb_cols: Union[int, Sequence[int]] = 4,
interpolation: int = 1,
mask_interpolation: int = 0,
cval: int = 0,
cval_mask: int = 0,
mode: str = "constant",
absolute_scale: bool = False,
always_apply: bool = False,
keypoints_threshold: float = 0.01,
p: float = 0.5,
):
super(PiecewiseAffine, self).__init__(always_apply, p)
self.scale = to_tuple(scale, scale)
self.nb_rows = to_tuple(nb_rows, nb_rows)
self.nb_cols = to_tuple(nb_cols, nb_cols)
self.interpolation = interpolation
self.mask_interpolation = mask_interpolation
self.cval = cval
self.cval_mask = cval_mask
self.mode = mode
self.absolute_scale = absolute_scale
self.keypoints_threshold = keypoints_threshold
def get_transform_init_args_names(self):
return (
"scale",
"nb_rows",
"nb_cols",
"interpolation",
"mask_interpolation",
"cval",
"cval_mask",
"mode",
"absolute_scale",
"keypoints_threshold",
)
@property
def targets_as_params(self):
return ["image"]
def get_params_dependent_on_targets(self, params) -> dict:
h, w = params["image"].shape[:2]
nb_rows = np.clip(random.randint(*self.nb_rows), 2, None)
nb_cols = np.clip(random.randint(*self.nb_cols), 2, None)
nb_cells = nb_cols * nb_rows
scale = random.uniform(*self.scale)
state = np.random.RandomState(random.randint(0, 1 << 31))
jitter = state.normal(0, scale, (nb_cells, 2))
if not np.any(jitter > 0):
return {"matrix": None}
y = np.linspace(0, h, nb_rows)
x = np.linspace(0, w, nb_cols)
# (H, W) and (H, W) for H=rows, W=cols
xx_src, yy_src = np.meshgrid(x, y)
# (1, HW, 2) => (HW, 2) for H=rows, W=cols
points_src = np.dstack([yy_src.flat, xx_src.flat])[0]
if self.absolute_scale:
jitter[:, 0] = jitter[:, 0] / h if h > 0 else 0.0
jitter[:, 1] = jitter[:, 1] / w if w > 0 else 0.0
jitter[:, 0] = jitter[:, 0] * h
jitter[:, 1] = jitter[:, 1] * w
points_dest = np.copy(points_src)
points_dest[:, 0] = points_dest[:, 0] + jitter[:, 0]
points_dest[:, 1] = points_dest[:, 1] + jitter[:, 1]
# Restrict all destination points to be inside the image plane.
# This is necessary, as otherwise keypoints could be augmented
# outside of the image plane and these would be replaced by
# (-1, -1), which would not conform with the behaviour of the other augmenters.
points_dest[:, 0] = np.clip(points_dest[:, 0], 0, h - 1)
points_dest[:, 1] = np.clip(points_dest[:, 1], 0, w - 1)
matrix = skimage.transform.PiecewiseAffineTransform()
matrix.estimate(points_src[:, ::-1], points_dest[:, ::-1])
return {
"matrix": matrix,
}
def apply(
self, img: np.ndarray, matrix: skimage.transform.PiecewiseAffineTransform = None, **params
) -> np.ndarray:
return F.piecewise_affine(img, matrix, self.interpolation, self.mode, self.cval)
def apply_to_mask(
self, img: np.ndarray, matrix: skimage.transform.PiecewiseAffineTransform = None, **params
) -> np.ndarray:
return F.piecewise_affine(img, matrix, self.mask_interpolation, self.mode, self.cval_mask)
def apply_to_bbox(
self,
bbox: Sequence[float],
rows: int = 0,
cols: int = 0,
matrix: skimage.transform.PiecewiseAffineTransform = None,
**params
) -> Sequence[float]:
return F.bbox_piecewise_affine(bbox, matrix, rows, cols, self.keypoints_threshold)
def apply_to_keypoint(
self,
keypoint: Sequence[float],
rows: int = 0,
cols: int = 0,
matrix: skimage.transform.PiecewiseAffineTransform = None,
**params
):
return F.keypoint_piecewise_affine(keypoint, matrix, rows, cols, self.keypoints_threshold)
|
py | 1a312d2507aa36ff7f594066f5f8334771b8fc17 | from sympy import S, Rational
from sympy.external import import_module
from sympy.stats import Binomial, sample, Die, FiniteRV, DiscreteUniform, Bernoulli, BetaBinomial, Hypergeometric, \
Rademacher
from sympy.testing.pytest import skip, raises
def test_given_sample():
X = Die('X', 6)
scipy = import_module('scipy')
if not scipy:
skip('Scipy is not installed. Abort tests')
assert sample(X, X > 5) == 6
def test_sample_numpy():
distribs_numpy = [
Binomial("B", 5, 0.4),
]
size = 3
numpy = import_module('numpy')
if not numpy:
skip('Numpy is not installed. Abort tests for _sample_numpy.')
else:
for X in distribs_numpy:
samps = sample(X, size=size, library='numpy')
for sam in samps:
assert sam in X.pspace.domain.set
raises(NotImplementedError,
lambda: sample(Die("D"), library='numpy'))
raises(NotImplementedError,
lambda: Die("D").pspace.sample(library='tensorflow'))
def test_sample_scipy():
distribs_scipy = [
FiniteRV('F', {1: S.Half, 2: Rational(1, 4), 3: Rational(1, 4)}),
DiscreteUniform("Y", list(range(5))),
Die("D"),
Bernoulli("Be", 0.3),
Binomial("Bi", 5, 0.4),
BetaBinomial("Bb", 2, 1, 1),
Hypergeometric("H", 1, 1, 1),
Rademacher("R")
]
size = 3
scipy = import_module('scipy')
if not scipy:
skip('Scipy not installed. Abort tests for _sample_scipy.')
else:
for X in distribs_scipy:
samps = sample(X, size=size)
samps2 = sample(X, size=(2, 2))
for sam in samps:
assert sam in X.pspace.domain.set
for i in range(2):
for j in range(2):
assert samps2[i][j] in X.pspace.domain.set
def test_sample_pymc3():
distribs_pymc3 = [
Bernoulli('B', 0.2),
Binomial('N', 5, 0.4)
]
size = 3
pymc3 = import_module('pymc3')
if not pymc3:
skip('PyMC3 is not installed. Abort tests for _sample_pymc3.')
else:
for X in distribs_pymc3:
samps = sample(X, size=size, library='pymc3')
for sam in samps:
assert sam in X.pspace.domain.set
raises(NotImplementedError,
lambda: (sample(Die("D"), library='pymc3')))
def test_sample_seed():
F = FiniteRV('F', {1: S.Half, 2: Rational(1, 4), 3: Rational(1, 4)})
size = 10
libraries = ['scipy', 'numpy', 'pymc3']
for lib in libraries:
try:
imported_lib = import_module(lib)
if imported_lib:
s0 = sample(F, size=size, library=lib, seed=0)
s1 = sample(F, size=size, library=lib, seed=0)
s2 = sample(F, size=size, library=lib, seed=1)
assert all(s0 == s1)
assert not all(s1 == s2)
except NotImplementedError:
continue
|
py | 1a312d664f95be9471d53c006c172531cdd91ca1 | # 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.
from protocall.proto import protocall_pb2
from value import value
def print_(arguments, symbols):
for arg in arguments:
name = arg[0]
atom = arg[1]
print "%s: %s" % (name, value(atom))
print
return None
def double(arguments, symbols):
arg = arguments[0]
name = arg[0]
atom = arg[1]
a = protocall_pb2.Atom()
a.literal.integer.value = atom.literal.integer.value * 2
return a
def append(arguments, symbols):
list_ = arguments[0]
item = arguments[1]
e = list_[1].literal.array.element.add()
e.atom.CopyFrom(item[1])
return e
|
py | 1a312d8e7908a6ff686681158b2b3737cdb976c6 | # -*- coding: utf-8 -*-
# PLEASE DO NOT EDIT THIS FILE, IT IS GENERATED AND WILL BE OVERWRITTEN:
# https://github.com/ccxt/ccxt/blob/master/CONTRIBUTING.md#how-to-contribute-code
from ccxt.base.exchange import Exchange
# -----------------------------------------------------------------------------
try:
basestring # Python 3
except NameError:
basestring = str # Python 2
import math
from ccxt.base.errors import ExchangeError
from ccxt.base.errors import AuthenticationError
from ccxt.base.errors import PermissionDenied
from ccxt.base.errors import AccountSuspended
from ccxt.base.errors import ArgumentsRequired
from ccxt.base.errors import BadRequest
from ccxt.base.errors import BadSymbol
from ccxt.base.errors import InsufficientFunds
from ccxt.base.errors import InvalidAddress
from ccxt.base.errors import InvalidOrder
from ccxt.base.errors import OrderNotFound
from ccxt.base.errors import NotSupported
from ccxt.base.errors import RateLimitExceeded
from ccxt.base.errors import ExchangeNotAvailable
from ccxt.base.errors import InvalidNonce
from ccxt.base.decimal_to_precision import ROUND
from ccxt.base.decimal_to_precision import TRUNCATE
from ccxt.base.decimal_to_precision import TICK_SIZE
class bitmart(Exchange):
def describe(self):
return self.deep_extend(super(bitmart, self).describe(), {
'id': 'bitmart',
'name': 'BitMart',
'countries': ['US', 'CN', 'HK', 'KR'],
'rateLimit': 250, # a bit slower than 50 times per second ~40 times per second
'version': 'v1',
'certified': True,
'pro': True,
'has': {
'cancelAllOrders': True,
'cancelOrder': True,
'cancelOrders': True,
'createOrder': True,
'fetchBalance': True,
'fetchCanceledOrders': True,
'fetchClosedOrders': True,
'fetchCurrencies': True,
'fetchDepositAddress': True,
'fetchDeposits': True,
'fetchFundingFee': True,
'fetchMarkets': True,
'fetchMyTrades': True,
'fetchOHLCV': True,
'fetchOpenOrders': True,
'fetchOrder': True,
'fetchOrderBook': True,
'fetchOrders': True,
'fetchOrderTrades': True,
'fetchStatus': True,
'fetchTicker': True,
'fetchTickers': True,
'fetchTime': True,
'fetchTrades': True,
'fetchWithdrawals': True,
'withdraw': True,
},
'hostname': 'bitmart.com', # bitmart.info, bitmart.news for Hong Kong users
'urls': {
'logo': 'https://user-images.githubusercontent.com/1294454/129991357-8f47464b-d0f4-41d6-8a82-34122f0d1398.jpg',
'api': {
'rest': 'https://api-cloud.{hostname}', # bitmart.info for Hong Kong users
},
'www': 'https://www.bitmart.com/',
'doc': 'https://developer-pro.bitmart.com/',
'referral': {
'url': 'http://www.bitmart.com/?r=rQCFLh',
'discount': 0.3,
},
'fees': 'https://www.bitmart.com/fee/en',
},
'requiredCredentials': {
'apiKey': True,
'secret': True,
'uid': True,
},
'api': {
'public': {
'system': {
'get': {
'time': 5, # https://api-cloud.bitmart.com/system/time
'service': 5, # https://api-cloud.bitmart.com/system/service
},
},
'account': {
'get': {
'currencies': 10, # https://api-cloud.bitmart.com/account/v1/currencies
},
},
'spot': {
'get': {
'currencies': 1,
'symbols': 1,
'symbols/details': 1,
'ticker': 1, # ?symbol=BTC_USDT
'steps': 1, # ?symbol=BMX_ETH
'symbols/kline': 1, # ?symbol=BMX_ETH&step=15&from=1525760116&to=1525769116
'symbols/book': 1, # ?symbol=BMX_ETH&precision=6
'symbols/trades': 1, # ?symbol=BMX_ETH
},
},
'contract': {
'get': {
'tickers': 0.5,
},
},
},
'private': {
'account': {
'get': {
'wallet': 0.5, # ?account_type=1
'deposit/address': 1, # ?currency=USDT-TRC20
'withdraw/charge': 1, # ?currency=BTC
'deposit-withdraw/history': 1, # ?limit=10&offset=1&operationType=withdraw
'deposit-withdraw/detail': 1, # ?id=1679952
},
'post': {
'withdraw/apply': 1,
},
},
'spot': {
'get': {
'wallet': 0.5,
'order_detail': 0.1,
'orders': 0.5,
'trades': 0.5,
},
'post': {
'submit_order': 0.1, # https://api-cloud.bitmart.com/spot/v1/submit_order
'cancel_order': 0.1, # https://api-cloud.bitmart.com/spot/v2/cancel_order
'cancel_orders': 0.1,
},
},
},
},
'timeframes': {
'1m': 1,
'3m': 3,
'5m': 5,
'15m': 15,
'30m': 30,
'45m': 45,
'1h': 60,
'2h': 120,
'3h': 180,
'4h': 240,
'1d': 1440,
'1w': 10080,
'1M': 43200,
},
'fees': {
'trading': {
'tierBased': True,
'percentage': True,
'taker': self.parse_number('0.0025'),
'maker': self.parse_number('0.0025'),
'tiers': {
'taker': [
[self.parse_number('0'), self.parse_number('0.0020')],
[self.parse_number('10'), self.parse_number('0.18')],
[self.parse_number('50'), self.parse_number('0.0016')],
[self.parse_number('250'), self.parse_number('0.0014')],
[self.parse_number('1000'), self.parse_number('0.0012')],
[self.parse_number('5000'), self.parse_number('0.0010')],
[self.parse_number('25000'), self.parse_number('0.0008')],
[self.parse_number('50000'), self.parse_number('0.0006')],
],
'maker': [
[self.parse_number('0'), self.parse_number('0.001')],
[self.parse_number('10'), self.parse_number('0.0009')],
[self.parse_number('50'), self.parse_number('0.0008')],
[self.parse_number('250'), self.parse_number('0.0007')],
[self.parse_number('1000'), self.parse_number('0.0006')],
[self.parse_number('5000'), self.parse_number('0.0005')],
[self.parse_number('25000'), self.parse_number('0.0004')],
[self.parse_number('50000'), self.parse_number('0.0003')],
],
},
},
},
'precisionMode': TICK_SIZE,
'exceptions': {
'exact': {
# general errors
'30000': ExchangeError, # 404, Not found
'30001': AuthenticationError, # 401, Header X-BM-KEY is empty
'30002': AuthenticationError, # 401, Header X-BM-KEY not found
'30003': AccountSuspended, # 401, Header X-BM-KEY has frozen
'30004': AuthenticationError, # 401, Header X-BM-SIGN is empty
'30005': AuthenticationError, # 401, Header X-BM-SIGN is wrong
'30006': AuthenticationError, # 401, Header X-BM-TIMESTAMP is empty
'30007': AuthenticationError, # 401, Header X-BM-TIMESTAMP range. Within a minute
'30008': AuthenticationError, # 401, Header X-BM-TIMESTAMP invalid format
'30010': PermissionDenied, # 403, IP is forbidden. We recommend enabling IP whitelist for API trading. After that reauth your account
'30011': AuthenticationError, # 403, Header X-BM-KEY over expire time
'30012': AuthenticationError, # 403, Header X-BM-KEY is forbidden to request it
'30013': RateLimitExceeded, # 429, Request too many requests
'30014': ExchangeNotAvailable, # 503, Service unavailable
# funding account errors
'60000': BadRequest, # 400, Invalid request(maybe the body is empty, or the int parameter passes string data)
'60001': BadRequest, # 400, Asset account type does not exist
'60002': BadRequest, # 400, currency does not exist
'60003': ExchangeError, # 400, Currency has been closed recharge channel, if there is any problem, please consult customer service
'60004': ExchangeError, # 400, Currency has been closed withdraw channel, if there is any problem, please consult customer service
'60005': ExchangeError, # 400, Minimum amount is %s
'60006': ExchangeError, # 400, Maximum withdraw precision is %d
'60007': InvalidAddress, # 400, Only withdrawals from added addresses are allowed
'60008': InsufficientFunds, # 400, Balance not enough
'60009': ExchangeError, # 400, Beyond the limit
'60010': ExchangeError, # 400, Withdraw id or deposit id not found
'60011': InvalidAddress, # 400, Address is not valid
'60012': ExchangeError, # 400, This action is not supported in self currency(If IOTA, HLX recharge and withdraw calls are prohibited)
'60020': PermissionDenied, # 403, Your account is not allowed to recharge
'60021': PermissionDenied, # 403, Your account is not allowed to withdraw
'60022': PermissionDenied, # 403, No withdrawals for 24 hours
'60030': BadRequest, # 405, Method Not Allowed
'60031': BadRequest, # 415, Unsupported Media Type
'60050': ExchangeError, # 500, User account not found
'60051': ExchangeError, # 500, Internal Server Error
# spot errors
'50000': BadRequest, # 400, Bad Request
'50001': BadSymbol, # 400, Symbol not found
'50002': BadRequest, # 400, From Or To format error
'50003': BadRequest, # 400, Step format error
'50004': BadRequest, # 400, Kline size over 500
'50005': OrderNotFound, # 400, Order Id not found
'50006': InvalidOrder, # 400, Minimum size is %s
'50007': InvalidOrder, # 400, Maximum size is %s
'50008': InvalidOrder, # 400, Minimum price is %s
'50009': InvalidOrder, # 400, Minimum count*price is %s
'50010': InvalidOrder, # 400, RequestParam size is required
'50011': InvalidOrder, # 400, RequestParam price is required
'50012': InvalidOrder, # 400, RequestParam notional is required
'50013': InvalidOrder, # 400, Maximum limit*offset is %d
'50014': BadRequest, # 400, RequestParam limit is required
'50015': BadRequest, # 400, Minimum limit is 1
'50016': BadRequest, # 400, Maximum limit is %d
'50017': BadRequest, # 400, RequestParam offset is required
'50018': BadRequest, # 400, Minimum offset is 1
'50019': BadRequest, # 400, Maximum price is %s
# '50019': ExchangeError, # 400, Invalid status. validate status is [1=Failed, 2=Success, 3=Frozen Failed, 4=Frozen Success, 5=Partially Filled, 6=Fully Fulled, 7=Canceling, 8=Canceled
'50020': InsufficientFunds, # 400, Balance not enough
'50021': BadRequest, # 400, Invalid %s
'50022': ExchangeNotAvailable, # 400, Service unavailable
'50023': BadSymbol, # 400, This Symbol can't place order by api
'50029': InvalidOrder, # {"message":"param not match : size * price >=1000","code":50029,"trace":"f931f030-b692-401b-a0c5-65edbeadc598","data":{}}
'50030': InvalidOrder, # {"message":"Order is already canceled","code":50030,"trace":"8d6f64ee-ad26-45a4-9efd-1080f9fca1fa","data":{}}
'53000': AccountSuspended, # 403, Your account is frozen due to security policies. Please contact customer service
'53001': AccountSuspended, # {"message":"Your kyc country is restricted. Please contact customer service.","code":53001,"trace":"8b445940-c123-4de9-86d7-73c5be2e7a24","data":{}}
'57001': BadRequest, # 405, Method Not Allowed
'58001': BadRequest, # 415, Unsupported Media Type
'59001': ExchangeError, # 500, User account not found
'59002': ExchangeError, # 500, Internal Server Error
# contract errors
'40001': ExchangeError, # 400, Cloud account not found
'40002': ExchangeError, # 400, out_trade_no not found
'40003': ExchangeError, # 400, out_trade_no already existed
'40004': ExchangeError, # 400, Cloud account count limit
'40005': ExchangeError, # 400, Transfer vol precision error
'40006': PermissionDenied, # 400, Invalid ip error
'40007': BadRequest, # 400, Parse parameter error
'40008': InvalidNonce, # 400, Check nonce error
'40009': BadRequest, # 400, Check ver error
'40010': BadRequest, # 400, Not found func error
'40011': BadRequest, # 400, Invalid request
'40012': ExchangeError, # 500, System error
'40013': ExchangeError, # 400, Access too often" CLIENT_TIME_INVALID, "Please check your system time.
'40014': BadSymbol, # 400, This contract is offline
'40015': BadSymbol, # 400, This contract's exchange has been paused
'40016': InvalidOrder, # 400, This order would trigger user position liquidate
'40017': InvalidOrder, # 400, It is not possible to open and close simultaneously in the same position
'40018': InvalidOrder, # 400, Your position is closed
'40019': ExchangeError, # 400, Your position is in liquidation delegating
'40020': InvalidOrder, # 400, Your position volume is not enough
'40021': ExchangeError, # 400, The position is not exsit
'40022': ExchangeError, # 400, The position is not isolated
'40023': ExchangeError, # 400, The position would liquidate when sub margin
'40024': ExchangeError, # 400, The position would be warnning of liquidation when sub margin
'40025': ExchangeError, # 400, The position’s margin shouldn’t be lower than the base limit
'40026': ExchangeError, # 400, You cross margin position is in liquidation delegating
'40027': InsufficientFunds, # 400, You contract account available balance not enough
'40028': PermissionDenied, # 400, Your plan order's count is more than system maximum limit.
'40029': InvalidOrder, # 400, The order's leverage is too large.
'40030': InvalidOrder, # 400, The order's leverage is too small.
'40031': InvalidOrder, # 400, The deviation between current price and trigger price is too large.
'40032': InvalidOrder, # 400, The plan order's life cycle is too long.
'40033': InvalidOrder, # 400, The plan order's life cycle is too short.
'40034': BadSymbol, # 400, This contract is not found
},
'broad': {},
},
'commonCurrencies': {
'COT': 'Community Coin',
'CPC': 'CPCoin',
'DMS': 'DimSum', # conflict with Dragon Mainland Shards
'FOX': 'Fox Finance',
'GDT': 'Gorilla Diamond',
'$HERO': 'Step Hero',
'$PAC': 'PAC',
'MIM': 'MIM Swarm',
'MVP': 'MVP Coin',
'ONE': 'Menlo One',
'PLA': 'Plair',
'TCT': 'TacoCat Token',
'TRU': 'Truebit', # conflict with TrueFi
},
'options': {
'networks': {
'TRX': 'TRC20',
'ETH': 'ERC20',
},
'defaultNetworks': {
'USDT': 'ERC20',
},
'defaultType': 'spot', # 'spot', 'swap'
'fetchBalance': {
'type': 'spot', # 'spot', 'swap', 'contract', 'account'
},
'createMarketBuyOrderRequiresPrice': True,
},
})
def fetch_time(self, params={}):
response = self.publicSystemGetTime(params)
#
# {
# "message":"OK",
# "code":1000,
# "trace":"c4e5e5b7-fe9f-4191-89f7-53f6c5bf9030",
# "data":{
# "server_time":1599843709578
# }
# }
#
data = self.safe_value(response, 'data', {})
return self.safe_integer(data, 'server_time')
def fetch_status(self, params={}):
options = self.safe_value(self.options, 'fetchBalance', {})
defaultType = self.safe_string(self.options, 'defaultType')
type = self.safe_string(options, 'type', defaultType)
type = self.safe_string(params, 'type', type)
params = self.omit(params, 'type')
response = self.publicSystemGetService(params)
#
# {
# "code": 1000,
# "trace":"886fb6ae-456b-4654-b4e0-d681ac05cea1",
# "message": "OK",
# "data": {
# "serivce":[
# {
# "title": "Spot API Stop",
# "service_type": "spot",
# "status": "2",
# "start_time": 1527777538000,
# "end_time": 1527777538000
# },
# {
# "title": "Contract API Stop",
# "service_type": "contract",
# "status": "2",
# "start_time": 1527777538000,
# "end_time": 1527777538000
# }
# ]
# }
# }
#
data = self.safe_value(response, 'data', {})
services = self.safe_value(data, 'service', [])
servicesByType = self.index_by(services, 'service_type')
if (type == 'swap') or (type == 'future'):
type = 'contract'
service = self.safe_value(servicesByType, type)
status = None
eta = None
if service is not None:
statusCode = self.safe_integer(service, 'status')
if statusCode == 2:
status = 'ok'
else:
status = 'maintenance'
eta = self.safe_integer(service, 'end_time')
self.status = self.extend(self.status, {
'status': status,
'updated': self.milliseconds(),
'eta': eta,
})
return self.status
def fetch_spot_markets(self, params={}):
response = self.publicSpotGetSymbolsDetails(params)
#
# {
# "message":"OK",
# "code":1000,
# "trace":"a67c9146-086d-4d3f-9897-5636a9bb26e1",
# "data":{
# "symbols":[
# {
# "symbol":"PRQ_BTC",
# "symbol_id":1232,
# "base_currency":"PRQ",
# "quote_currency":"BTC",
# "quote_increment":"1.0000000000",
# "base_min_size":"1.0000000000",
# "base_max_size":"10000000.0000000000",
# "price_min_precision":8,
# "price_max_precision":10,
# "expiration":"NA",
# "min_buy_amount":"0.0001000000",
# "min_sell_amount":"0.0001000000"
# },
# ]
# }
# }
#
data = self.safe_value(response, 'data', {})
symbols = self.safe_value(data, 'symbols', [])
result = []
for i in range(0, len(symbols)):
market = symbols[i]
id = self.safe_string(market, 'symbol')
numericId = self.safe_integer(market, 'symbol_id')
baseId = self.safe_string(market, 'base_currency')
quoteId = self.safe_string(market, 'quote_currency')
base = self.safe_currency_code(baseId)
quote = self.safe_currency_code(quoteId)
symbol = base + '/' + quote
#
# https://github.com/bitmartexchange/bitmart-official-api-docs/blob/master/rest/public/symbols_details.md#response-details
# from the above API doc:
# quote_increment Minimum order price as well as the price increment
# price_min_precision Minimum price precision(digit) used to query price and kline
# price_max_precision Maximum price precision(digit) used to query price and kline
#
# the docs are wrong: https://github.com/ccxt/ccxt/issues/5612
#
pricePrecision = self.safe_integer(market, 'price_max_precision')
precision = {
'amount': self.safe_number(market, 'base_min_size'),
'price': self.parse_number(self.decimal_to_precision(math.pow(10, -pricePrecision), ROUND, 14)),
}
minBuyCost = self.safe_number(market, 'min_buy_amount')
minSellCost = self.safe_number(market, 'min_sell_amount')
minCost = max(minBuyCost, minSellCost)
limits = {
'amount': {
'min': self.safe_number(market, 'base_min_size'),
'max': self.safe_number(market, 'base_max_size'),
},
'price': {
'min': None,
'max': None,
},
'cost': {
'min': minCost,
'max': None,
},
}
result.append({
'id': id,
'numericId': numericId,
'symbol': symbol,
'base': base,
'quote': quote,
'baseId': baseId,
'quoteId': quoteId,
'type': 'spot',
'spot': True,
'future': False,
'swap': False,
'precision': precision,
'limits': limits,
'info': market,
'active': True,
})
return result
def fetch_contract_markets(self, params={}):
response = self.publicContractGetContracts(params)
#
# {
# "errno":"OK",
# "message":"OK",
# "code":1000,
# "trace":"7fcedfb5-a660-4780-8a7a-b36a9e2159f7",
# "data":{
# "contracts":[
# {
# "contract":{
# "contract_id":1,
# "index_id":1,
# "name":"BTCUSDT",
# "display_name":"BTCUSDT永续合约",
# "display_name_en":"BTCUSDT_SWAP",
# "contract_type":1,
# "base_coin":"BTC",
# "quote_coin":"USDT",
# "price_coin":"BTC",
# "exchange":"*",
# "contract_size":"0.0001",
# "begin_at":"2018-08-17T04:00:00Z",
# "delive_at":"2020-08-15T12:00:00Z",
# "delivery_cycle":28800,
# "min_leverage":"1",
# "max_leverage":"100",
# "price_unit":"0.1",
# "vol_unit":"1",
# "value_unit":"0.0001",
# "min_vol":"1",
# "max_vol":"300000",
# "liquidation_warn_ratio":"0.85",
# "fast_liquidation_ratio":"0.8",
# "settgle_type":1,
# "open_type":3,
# "compensate_type":1,
# "status":3,
# "block":1,
# "rank":1,
# "created_at":"2018-07-12T19:16:57Z",
# "depth_bord":"1.001",
# "base_coin_zh":"比特币",
# "base_coin_en":"Bitcoin",
# "max_rate":"0.00375",
# "min_rate":"-0.00375"
# },
# "risk_limit":{"contract_id":1,"base_limit":"1000000","step":"500000","maintenance_margin":"0.005","initial_margin":"0.01"},
# "fee_config":{"contract_id":1,"maker_fee":"-0.0003","taker_fee":"0.001","settlement_fee":"0","created_at":"2018-07-12T20:47:22Z"},
# "plan_order_config":{"contract_id":0,"min_scope":"0.001","max_scope":"2","max_count":10,"min_life_cycle":24,"max_life_cycle":168}
# },
# ]
# }
# }
#
data = self.safe_value(response, 'data', {})
contracts = self.safe_value(data, 'contracts', [])
result = []
for i in range(0, len(contracts)):
market = contracts[i]
contract = self.safe_value(market, 'contract', {})
id = self.safe_string(contract, 'contract_id')
numericId = self.safe_integer(contract, 'contract_id')
baseId = self.safe_string(contract, 'base_coin')
quoteId = self.safe_string(contract, 'quote_coin')
base = self.safe_currency_code(baseId)
quote = self.safe_currency_code(quoteId)
symbol = self.safe_string(contract, 'name')
#
# https://github.com/bitmartexchange/bitmart-official-api-docs/blob/master/rest/public/symbols_details.md#response-details
# from the above API doc:
# quote_increment Minimum order price as well as the price increment
# price_min_precision Minimum price precision(digit) used to query price and kline
# price_max_precision Maximum price precision(digit) used to query price and kline
#
# the docs are wrong: https://github.com/ccxt/ccxt/issues/5612
#
amountPrecision = self.safe_number(contract, 'vol_unit')
pricePrecision = self.safe_number(contract, 'price_unit')
precision = {
'amount': amountPrecision,
'price': pricePrecision,
}
limits = {
'amount': {
'min': self.safe_number(contract, 'min_vol'),
'max': self.safe_number(contract, 'max_vol'),
},
'price': {
'min': None,
'max': None,
},
'cost': {
'min': None,
'max': None,
},
}
contractType = self.safe_value(contract, 'contract_type')
future = False
swap = False
type = 'contract'
if contractType == 1:
type = 'swap'
swap = True
elif contractType == 2:
type = 'future'
future = True
feeConfig = self.safe_value(market, 'fee_config', {})
maker = self.safe_number(feeConfig, 'maker_fee')
taker = self.safe_number(feeConfig, 'taker_fee')
result.append({
'id': id,
'numericId': numericId,
'symbol': symbol,
'base': base,
'quote': quote,
'baseId': baseId,
'quoteId': quoteId,
'maker': maker,
'taker': taker,
'type': type,
'spot': False,
'future': future,
'swap': swap,
'precision': precision,
'limits': limits,
'info': market,
'active': None,
})
return result
def fetch_markets(self, params={}):
return self.fetch_spot_markets()
def fetch_funding_fee(self, code, params={}):
self.load_markets()
currency = self.currency(code)
request = {
'currency': currency['id'],
}
response = self.privateAccountGetWithdrawCharge(self.extend(request, params))
#
# {
# message: 'OK',
# code: '1000',
# trace: '3ecc0adf-91bd-4de7-aca1-886c1122f54f',
# data: {
# today_available_withdraw_BTC: '100.0000',
# min_withdraw: '0.005',
# withdraw_precision: '8',
# withdraw_fee: '0.000500000000000000000000000000'
# }
# }
#
data = response['data']
withdrawFees = {}
withdrawFees[code] = self.safe_number(data, 'withdraw_fee')
return {
'info': response,
'withdraw': withdrawFees,
'deposit': {},
}
def parse_ticker(self, ticker, market=None):
#
# spot
#
# {
# "symbol":"ETH_BTC",
# "last_price":"0.036037",
# "quote_volume_24h":"4380.6660000000",
# "base_volume_24h":"159.3582006712",
# "high_24h":"0.036972",
# "low_24h":"0.035524",
# "open_24h":"0.036561",
# "close_24h":"0.036037",
# "best_ask":"0.036077",
# "best_ask_size":"9.9500",
# "best_bid":"0.035983",
# "best_bid_size":"4.2792",
# "fluctuation":"-0.0143",
# "s_t": "1630981727", # ws only
# "url":"https://www.bitmart.com/trade?symbol=ETH_BTC"
# }
#
# contract
#
# {
# contract_symbol: "DGBUSDT",
# last_price: "0.05759",
# index_price: "0.05757755",
# last_funding_rate: "0.00010000",
# price_change_percent_24h: "0.244",
# volume_24h: "64303817.028126",
# url: "https://futures.bitmart.com/en?symbol=DGBUSDT"
# }
#
timestamp = self.safe_timestamp_2(ticker, 'timestamp', 's_t', self.milliseconds())
marketId = self.safe_string_2(ticker, 'symbol', 'contract_id')
marketId = self.safe_string(ticker, 'contract_symbol', marketId)
symbol = self.safe_symbol(marketId, market)
last = self.safe_number_2(ticker, 'close_24h', 'last_price')
percentage = self.safe_number_2(ticker, 'fluctuation', 'rise_fall_rate')
if percentage is not None:
percentage *= 100
if percentage is None:
percentage = self.safe_number(ticker, 'price_change_percent_24h')
baseVolume = self.safe_number_2(ticker, 'base_coin_volume', 'base_volume_24h')
quoteVolume = self.safe_number_2(ticker, 'quote_coin_volume', 'quote_volume_24h')
quoteVolume = self.safe_number(ticker, 'volume_24h', quoteVolume)
open = self.safe_number_2(ticker, 'open_24h', 'open')
average = None
if (last is not None) and (open is not None):
average = self.sum(last, open) / 2
average = self.safe_number(ticker, 'avg_price', average)
price = self.safe_value(ticker, 'depth_price', ticker)
return {
'symbol': symbol,
'timestamp': timestamp,
'datetime': self.iso8601(timestamp),
'high': self.safe_number_2(ticker, 'high', 'high_24h'),
'low': self.safe_number_2(ticker, 'low', 'low_24h'),
'bid': self.safe_number_2(price, 'best_bid', 'bid_price'),
'bidVolume': self.safe_number(ticker, 'best_bid_size'),
'ask': self.safe_number_2(price, 'best_ask', 'ask_price'),
'askVolume': self.safe_number(ticker, 'best_ask_size'),
'vwap': None,
'open': self.safe_number(ticker, 'open_24h'),
'close': last,
'last': last,
'previousClose': None,
'change': None,
'percentage': percentage,
'average': average,
'baseVolume': baseVolume,
'quoteVolume': quoteVolume,
'info': ticker,
}
def fetch_ticker(self, symbol, params={}):
self.load_markets()
market = self.market(symbol)
request = {}
method = None
if market['swap'] or market['future']:
method = 'publicContractGetTickers'
request['contractID'] = market['id']
elif market['spot']:
method = 'publicSpotGetTicker'
request['symbol'] = market['id']
response = getattr(self, method)(self.extend(request, params))
#
# spot
#
# {
# "message":"OK",
# "code":1000,
# "trace":"6aa5b923-2f57-46e3-876d-feca190e0b82",
# "data":{
# "tickers":[
# {
# "symbol":"ETH_BTC",
# "last_price":"0.036037",
# "quote_volume_24h":"4380.6660000000",
# "base_volume_24h":"159.3582006712",
# "high_24h":"0.036972",
# "low_24h":"0.035524",
# "open_24h":"0.036561",
# "close_24h":"0.036037",
# "best_ask":"0.036077",
# "best_ask_size":"9.9500",
# "best_bid":"0.035983",
# "best_bid_size":"4.2792",
# "fluctuation":"-0.0143",
# "url":"https://www.bitmart.com/trade?symbol=ETH_BTC"
# }
# ]
# }
# }
#
# contract
#
# {
# message: "OK",
# code: "1000",
# trace: "84a0dc44-b395-4bae-a1b7-fe1201defd51",
# data: {
# tickers: [
# {
# contract_symbol: "DGBUSDT",
# last_price: "0.05759",
# index_price: "0.05757755",
# last_funding_rate: "0.00010000",
# price_change_percent_24h: "0.244",
# volume_24h: "64303817.028126",
# url: "https://futures.bitmart.com/en?symbol=DGBUSDT"
# },
# ],
# },
# }
#
data = self.safe_value(response, 'data', {})
tickers = self.safe_value(data, 'tickers', [])
tickersById = self.index_by(tickers, 'symbol')
ticker = self.safe_value(tickersById, market['id'])
return self.parse_ticker(ticker, market)
def fetch_tickers(self, symbols=None, params={}):
self.load_markets()
defaultType = self.safe_string(self.options, 'defaultType', 'spot')
type = self.safe_string(params, 'type', defaultType)
params = self.omit(params, 'type')
method = None
if (type == 'swap') or (type == 'future'):
method = 'publicContractGetTickers'
elif type == 'spot':
method = 'publicSpotGetTicker'
response = getattr(self, method)(params)
data = self.safe_value(response, 'data', {})
tickers = self.safe_value(data, 'tickers', [])
result = {}
for i in range(0, len(tickers)):
ticker = self.parse_ticker(tickers[i])
symbol = ticker['symbol']
result[symbol] = ticker
return self.filter_by_array(result, 'symbol', symbols)
def fetch_currencies(self, params={}):
response = self.publicAccountGetCurrencies(params)
#
# {
# "message":"OK",
# "code":1000,
# "trace":"8c768b3c-025f-413f-bec5-6d6411d46883",
# "data":{
# "currencies":[
# {"currency":"MATIC","name":"Matic Network","withdraw_enabled":true,"deposit_enabled":true},
# {"currency":"KTN","name":"Kasoutuuka News","withdraw_enabled":true,"deposit_enabled":false},
# {"currency":"BRT","name":"Berith","withdraw_enabled":true,"deposit_enabled":true},
# ]
# }
# }
#
data = self.safe_value(response, 'data', {})
currencies = self.safe_value(data, 'currencies', [])
result = {}
for i in range(0, len(currencies)):
currency = currencies[i]
id = self.safe_string(currency, 'currency')
code = self.safe_currency_code(id)
name = self.safe_string(currency, 'name')
withdrawEnabled = self.safe_value(currency, 'withdraw_enabled')
depositEnabled = self.safe_value(currency, 'deposit_enabled')
active = withdrawEnabled and depositEnabled
result[code] = {
'id': id,
'code': code,
'name': name,
'info': currency, # the original payload
'active': active,
'fee': None,
'precision': None,
'limits': {
'amount': {'min': None, 'max': None},
'withdraw': {'min': None, 'max': None},
},
}
return result
def fetch_order_book(self, symbol, limit=None, params={}):
self.load_markets()
market = self.market(symbol)
request = {}
method = None
if market['spot']:
method = 'publicSpotGetSymbolsBook'
request['symbol'] = market['id']
if limit is not None:
request['size'] = limit # default 50, max 200
# request['precision'] = 4 # optional price precision / depth level whose range is defined in symbol details
elif market['swap'] or market['future']:
method = 'publicContractGetDepth'
request['contractID'] = market['id']
if limit is not None:
request['count'] = limit # returns all records if size is omitted
response = getattr(self, method)(self.extend(request, params))
#
# spot
#
# {
# "message":"OK",
# "code":1000,
# "trace":"8254f8fc-431d-404f-ad9a-e716339f66c7",
# "data":{
# "buys":[
# {"amount":"4.7091","total":"4.71","price":"0.034047","count":"1"},
# {"amount":"5.7439","total":"10.45","price":"0.034039","count":"1"},
# {"amount":"2.5249","total":"12.98","price":"0.032937","count":"1"},
# ],
# "sells":[
# {"amount":"41.4365","total":"41.44","price":"0.034174","count":"1"},
# {"amount":"4.2317","total":"45.67","price":"0.034183","count":"1"},
# {"amount":"0.3000","total":"45.97","price":"0.034240","count":"1"},
# ]
# }
# }
#
# contract
#
# {
# "errno":"OK",
# "message":"OK",
# "code":1000,
# "trace":"c330dfca-ca5b-4f15-b350-9fef3f049b4f",
# "data":{
# "sells":[
# {"price":"347.6","vol":"6678"},
# {"price":"347.7","vol":"3452"},
# {"price":"347.8","vol":"6331"},
# ],
# "buys":[
# {"price":"347.5","vol":"6222"},
# {"price":"347.4","vol":"20979"},
# {"price":"347.3","vol":"15179"},
# ]
# }
# }
#
data = self.safe_value(response, 'data', {})
if market['spot']:
return self.parse_order_book(data, symbol, None, 'buys', 'sells', 'price', 'amount')
elif market['swap'] or market['future']:
return self.parse_order_book(data, symbol, None, 'buys', 'sells', 'price', 'vol')
def parse_trade(self, trade, market=None):
#
# public fetchTrades spot( amount = count * price )
#
# {
# "amount": "818.94",
# "order_time": "1637601839035", # ETH/USDT
# "price": "4221.99",
# "count": "0.19397",
# "type": "buy"
# }
#
# public fetchTrades contract, private fetchMyTrades contract
#
# {
# "order_id":109159616160,
# "trade_id":109159616197,
# "contract_id":2,
# "deal_price":"347.6",
# "deal_vol":"5623",
# "make_fee":"-5.8636644",
# "take_fee":"9.772774",
# "created_at":"2020-09-09T11:49:50.749170536Z",
# "way":1,
# "fluctuation":"0"
# }
#
# private fetchMyTrades spot
#
# {
# "detail_id":256348632,
# "order_id":2147484350,
# "symbol":"BTC_USDT",
# "create_time":1590462303000,
# "side":"buy",
# "fees":"0.00001350",
# "fee_coin_name":"BTC",
# "notional":"88.00000000",
# "price_avg":"8800.00",
# "size":"0.01000",
# "exec_type":"M"
# }
#
id = self.safe_string_2(trade, 'trade_id', 'detail_id')
timestamp = self.safe_integer_2(trade, 'order_time', 'create_time')
if timestamp is None:
timestamp = self.safe_timestamp(trade, 's_t')
if timestamp is None:
timestamp = self.parse8601(self.safe_string(trade, 'created_at'))
type = None
way = self.safe_integer(trade, 'way')
side = self.safe_string_lower_2(trade, 'type', 'side')
if (side is None) and (way is not None):
if way < 5:
side = 'buy'
else:
side = 'sell'
takerOrMaker = None
execType = self.safe_string(trade, 'exec_type')
if execType is not None:
takerOrMaker = 'maker' if (execType == 'M') else 'taker'
priceString = self.safe_string_2(trade, 'price', 'deal_price')
priceString = self.safe_string(trade, 'price_avg', priceString)
amountString = self.safe_string_2(trade, 'count', 'deal_vol')
amountString = self.safe_string(trade, 'size', amountString)
costString = self.safe_string_2(trade, 'amount', 'notional')
orderId = self.safe_integer(trade, 'order_id')
marketId = self.safe_string_2(trade, 'contract_id', 'symbol')
symbol = self.safe_symbol(marketId, market, '_')
feeCostString = self.safe_string(trade, 'fees')
fee = None
if feeCostString is not None:
feeCurrencyId = self.safe_string(trade, 'fee_coin_name')
feeCurrencyCode = self.safe_currency_code(feeCurrencyId)
if (feeCurrencyCode is None) and (market is not None):
feeCurrencyCode = market['base'] if (side == 'buy') else market['quote']
fee = {
'cost': feeCostString,
'currency': feeCurrencyCode,
}
return self.safe_trade({
'info': trade,
'id': id,
'order': orderId,
'timestamp': timestamp,
'datetime': self.iso8601(timestamp),
'symbol': symbol,
'type': type,
'side': side,
'price': priceString,
'amount': amountString,
'cost': costString,
'takerOrMaker': takerOrMaker,
'fee': fee,
}, market)
def fetch_trades(self, symbol, since=None, limit=None, params={}):
self.load_markets()
market = self.market(symbol)
request = {
'symbol': market['id'],
}
method = None
if market['spot']:
request['symbol'] = market['id']
method = 'publicSpotGetSymbolsTrades'
elif market['swap'] or market['future']:
method = 'publicContractGetTrades'
request['contractID'] = market['id']
response = getattr(self, method)(self.extend(request, params))
#
# spot
#
# {
# "message":"OK",
# "code":1000,
# "trace":"222d74c0-8f6d-49d9-8e1b-98118c50eeba",
# "data":{
# "trades":[
# {
# "amount":"0.005703",
# "order_time":1599652045394,
# "price":"0.034029",
# "count":"0.1676",
# "type":"sell"
# },
# ]
# }
# }
#
# contract
#
# {
# "errno":"OK",
# "message":"OK",
# "code":1000,
# "trace":"782bc746-b86e-43bf-8d1a-c68b479c9bdd",
# "data":{
# "trades":[
# {
# "order_id":109159616160,
# "trade_id":109159616197,
# "contract_id":2,
# "deal_price":"347.6",
# "deal_vol":"5623",
# "make_fee":"-5.8636644",
# "take_fee":"9.772774",
# "created_at":"2020-09-09T11:49:50.749170536Z",
# "way":1,
# "fluctuation":"0"
# }
# ]
# }
# }
#
data = self.safe_value(response, 'data', {})
trades = self.safe_value(data, 'trades', [])
return self.parse_trades(trades, market, since, limit)
def parse_ohlcv(self, ohlcv, market=None):
#
# spot
#
# {
# "last_price":"0.034987",
# "timestamp":1598787420,
# "volume":"1.0198",
# "open":"0.035007",
# "close":"0.034987",
# "high":"0.035007",
# "low":"0.034986"
# }
#
# contract
#
# {
# "low":"404.4",
# "high":"404.4",
# "open":"404.4",
# "close":"404.4",
# "last_price":"404.4",
# "avg_price":"404.4",
# "volume":"7670",
# "timestamp":1598758441,
# "rise_fall_rate":"0",
# "rise_fall_value":"0",
# "base_coin_volume":"76.7",
# "quote_coin_volume":"31017.48"
# }
#
# ws
#
# [
# 1631056350, # timestamp
# '46532.83', # oopen
# '46555.71', # high
# '46511.41', # low
# '46555.71', # close
# '0.25', # volume
# ]
#
if isinstance(ohlcv, list):
return [
self.safe_timestamp(ohlcv, 0),
self.safe_number(ohlcv, 1),
self.safe_number(ohlcv, 2),
self.safe_number(ohlcv, 3),
self.safe_number(ohlcv, 4),
self.safe_number(ohlcv, 5),
]
else:
return [
self.safe_timestamp(ohlcv, 'timestamp'),
self.safe_number(ohlcv, 'open'),
self.safe_number(ohlcv, 'high'),
self.safe_number(ohlcv, 'low'),
self.safe_number(ohlcv, 'close'),
self.safe_number(ohlcv, 'volume'),
]
def fetch_ohlcv(self, symbol, timeframe='1m', since=None, limit=None, params={}):
self.load_markets()
market = self.market(symbol)
type = market['type']
method = None
request = {}
duration = self.parse_timeframe(timeframe)
if type == 'spot':
method = 'publicSpotGetSymbolsKline'
request['symbol'] = market['id']
request['step'] = self.timeframes[timeframe]
# the exchange will return an empty array if more than 500 candles is requested
maxLimit = 500
if limit is None:
limit = maxLimit
limit = min(maxLimit, limit)
if since is None:
end = int(self.milliseconds() / 1000)
start = end - limit * duration
request['from'] = start
request['to'] = end
else:
start = int(since / 1000)
end = self.sum(start, limit * duration)
request['from'] = start
request['to'] = end
elif (type == 'swap') or (type == 'future'):
method = 'publicContractGetQuote'
request['contractID'] = market['id']
defaultLimit = 500
if limit is None:
limit = defaultLimit
if since is None:
end = int(self.milliseconds() / 1000)
start = end - limit * duration
request['startTime'] = start
request['endTime'] = end
else:
start = int(since / 1000)
end = self.sum(start, limit * duration)
request['startTime'] = start
request['endTime'] = end
request['unit'] = self.timeframes[timeframe]
request['resolution'] = 'M'
response = getattr(self, method)(self.extend(request, params))
#
# spot
#
# {
# "message":"OK",
# "code":1000,
# "trace":"80d86378-ab4e-4c70-819e-b42146cf87ad",
# "data":{
# "klines":[
# {"last_price":"0.034987","timestamp":1598787420,"volume":"1.0198","open":"0.035007","close":"0.034987","high":"0.035007","low":"0.034986"},
# {"last_price":"0.034986","timestamp":1598787480,"volume":"0.3959","open":"0.034982","close":"0.034986","high":"0.034986","low":"0.034980"},
# {"last_price":"0.034978","timestamp":1598787540,"volume":"0.3259","open":"0.034987","close":"0.034978","high":"0.034987","low":"0.034977"},
# ]
# }
# }
#
# swap
#
# {
# "errno":"OK",
# "message":"OK",
# "code":1000,
# "trace":"32965074-5804-4655-b693-e953e36026a0",
# "data":[
# {"low":"404.4","high":"404.4","open":"404.4","close":"404.4","last_price":"404.4","avg_price":"404.4","volume":"7670","timestamp":1598758441,"rise_fall_rate":"0","rise_fall_value":"0","base_coin_volume":"76.7","quote_coin_volume":"31017.48"},
# {"low":"404.1","high":"404.4","open":"404.4","close":"404.1","last_price":"404.1","avg_price":"404.15881086","volume":"12076","timestamp":1598758501,"rise_fall_rate":"-0.000741839762611276","rise_fall_value":"-0.3","base_coin_volume":"120.76","quote_coin_volume":"48806.2179994536"},
# {"low":"404","high":"404.3","open":"404.1","close":"404","last_price":"404","avg_price":"404.08918918","volume":"740","timestamp":1598758561,"rise_fall_rate":"-0.000247463499133878","rise_fall_value":"-0.1","base_coin_volume":"7.4","quote_coin_volume":"2990.259999932"},
# ]
# }
#
data = self.safe_value(response, 'data', {})
if isinstance(data, list):
return self.parse_ohlcvs(data, market, timeframe, since, limit)
else:
klines = self.safe_value(data, 'klines', [])
return self.parse_ohlcvs(klines, market, timeframe, since, limit)
def fetch_my_trades(self, symbol=None, since=None, limit=None, params={}):
if symbol is None:
raise ArgumentsRequired(self.id + ' fetchMyTrades() requires a symbol argument')
self.load_markets()
market = self.market(symbol)
method = None
request = {}
if market['spot']:
request['symbol'] = market['id']
request['offset'] = 1 # max offset * limit < 500
if limit is None:
limit = 100 # max 100
request['limit'] = limit
method = 'privateSpotGetTrades'
elif market['swap'] or market['future']:
request['contractID'] = market['id']
# request['offset'] = 1
if limit is not None:
request['size'] = limit # max 60
method = 'privateContractGetUserTrades'
response = getattr(self, method)(self.extend(request, params))
#
# spot
#
# {
# "message":"OK",
# "code":1000,
# "trace":"a06a5c53-8e6f-42d6-8082-2ff4718d221c",
# "data":{
# "current_page":1,
# "trades":[
# {
# "detail_id":256348632,
# "order_id":2147484350,
# "symbol":"BTC_USDT",
# "create_time":1590462303000,
# "side":"buy",
# "fees":"0.00001350",
# "fee_coin_name":"BTC",
# "notional":"88.00000000",
# "price_avg":"8800.00",
# "size":"0.01000",
# "exec_type":"M"
# },
# ]
# }
# }
#
# contract
#
# {
# "code": 1000,
# "trace":"886fb6ae-456b-4654-b4e0-d681ac05cea1",
# "message": "OK",
# "data": {
# "trades": [
# {
# "order_id": 10116361,
# "trade_id": 10116363,
# "contract_id": 1,
# "deal_price": "16",
# "deal_vol": "10",
# "make_fee": "0.04",
# "take_fee": "0.12",
# "created_at": null,
# "way": 5,
# "fluctuation": "0"
# }
# ]
# }
# }
#
data = self.safe_value(response, 'data', {})
trades = self.safe_value(data, 'trades', [])
return self.parse_trades(trades, market, since, limit)
def fetch_order_trades(self, id, symbol=None, since=None, limit=None, params={}):
if symbol is None:
raise ArgumentsRequired(self.id + ' fetchOrderTrades() requires a symbol argument')
self.load_markets()
market = self.market(symbol)
method = None
request = {}
if market['spot']:
request['symbol'] = market['id']
request['order_id'] = id
method = 'privateSpotGetTrades'
elif market['swap'] or market['future']:
request['contractID'] = market['id']
request['orderID'] = id
method = 'privateContractGetOrderTrades'
response = getattr(self, method)(self.extend(request, params))
#
# spot
#
# {
# "message":"OK",
# "code":1000,
# "trace":"a06a5c53-8e6f-42d6-8082-2ff4718d221c",
# "data":{
# "current_page":1,
# "trades":[
# {
# "detail_id":256348632,
# "order_id":2147484350,
# "symbol":"BTC_USDT",
# "create_time":1590462303000,
# "side":"buy",
# "fees":"0.00001350",
# "fee_coin_name":"BTC",
# "notional":"88.00000000",
# "price_avg":"8800.00",
# "size":"0.01000",
# "exec_type":"M"
# },
# ]
# }
# }
#
# contract
#
# {
# "code": 1000,
# "trace":"886fb6ae-456b-4654-b4e0-d681ac05cea1",
# "message": "OK",
# "data": {
# "trades": [
# {
# "order_id": 10116361,
# "trade_id": 10116363,
# "contract_id": 1,
# "deal_price": "16",
# "deal_vol": "10",
# "make_fee": "0.04",
# "take_fee": "0.12",
# "created_at": null,
# "way": 5,
# "fluctuation": "0"
# }
# ]
# }
# }
#
data = self.safe_value(response, 'data', {})
trades = self.safe_value(data, 'trades', [])
return self.parse_trades(trades, market, since, limit)
def fetch_balance(self, params={}):
self.load_markets()
method = None
options = self.safe_value(self.options, 'fetchBalance', {})
defaultType = self.safe_string(self.options, 'defaultType', 'spot')
type = self.safe_string(options, 'type', defaultType)
type = self.safe_string(params, 'type', type)
params = self.omit(params, 'type')
if type == 'spot':
method = 'privateSpotGetWallet'
elif type == 'account':
method = 'privateAccountGetWallet'
elif (type == 'swap') or (type == 'future') or (type == 'contract'):
method = 'privateContractGetAccounts'
response = getattr(self, method)(params)
#
# spot
#
# {
# "message":"OK",
# "code":1000,
# "trace":"39069916-72f9-44c7-acde-2ad5afd21cad",
# "data":{
# "wallet":[
# {"id":"BTC","name":"Bitcoin","available":"0.00000062","frozen":"0.00000000"},
# {"id":"ETH","name":"Ethereum","available":"0.00002277","frozen":"0.00000000"},
# {"id":"BMX","name":"BitMart Token","available":"0.00000000","frozen":"0.00000000"}
# ]
# }
# }
#
# account
#
# {
# "message":"OK",
# "code":1000,
# "trace":"5c3b7fc7-93b2-49ef-bb59-7fdc56915b59",
# "data":{
# "wallet":[
# {"currency":"BTC","name":"Bitcoin","available":"0.00000062","frozen":"0.00000000"},
# {"currency":"ETH","name":"Ethereum","available":"0.00002277","frozen":"0.00000000"}
# ]
# }
# }
#
# contract
#
# {
# "code": 1000,
# "trace":"886fb6ae-456b-4654-b4e0-d681ac05cea1",
# "message": "OK",
# "data": {
# "accounts": [
# {
# "account_id": 10,
# "coin_code": "USDT",
# "freeze_vol": "1201.8",
# "available_vol": "8397.65",
# "cash_vol": "0",
# "realised_vol": "-0.5",
# "unrealised_vol": "-0.5",
# "earnings_vol": "-0.5",
# "created_at": "2018-07-13T16:48:49+08:00",
# "updated_at": "2018-07-13T18:34:45.900387+08:00"
# }
# ]
# }
# }
#
data = self.safe_value(response, 'data', {})
wallet = self.safe_value_2(data, 'wallet', 'accounts', [])
result = {'info': response}
for i in range(0, len(wallet)):
balance = wallet[i]
currencyId = self.safe_string_2(balance, 'id', 'currency')
currencyId = self.safe_string(balance, 'coin_code', currencyId)
code = self.safe_currency_code(currencyId)
account = self.account()
account['free'] = self.safe_string_2(balance, 'available', 'available_vol')
account['used'] = self.safe_string_2(balance, 'frozen', 'freeze_vol')
result[code] = account
return self.safe_balance(result)
def parse_order(self, order, market=None):
#
# createOrder
#
# {
# "order_id": 2707217580
# }
#
# cancelOrder
#
# '2707217580' # order id
#
# spot fetchOrder, fetchOrdersByStatus, fetchOpenOrders, fetchClosedOrders
#
# {
# "order_id":1736871726781,
# "symbol":"BTC_USDT",
# "create_time":1591096004000,
# "side":"sell",
# "type":"market",
# "price":"0.00",
# "price_avg":"0.00",
# "size":"0.02000",
# "notional":"0.00000000",
# "filled_notional":"0.00000000",
# "filled_size":"0.00000",
# "status":"8"
# }
#
# contract fetchOrder, fetchOrdersByStatus, fetchOpenOrders, fetchClosedOrders, fetchOrders
#
# {
# "order_id": 10539098,
# "contract_id": 1,
# "position_id": 10539088,
# "account_id": 10,
# "price": "16",
# "vol": "1",
# "done_avg_price": "16",
# "done_vol": "1",
# "way": 3,
# "category": 1,
# "open_type": 2,
# "make_fee": "0.00025",
# "take_fee": "0.012",
# "origin": "",
# "created_at": "2018-07-23T11:55:56.715305Z",
# "finished_at": "2018-07-23T11:55:56.763941Z",
# "status": 4,
# "errno": 0
# }
#
id = None
if isinstance(order, basestring):
id = order
order = {}
id = self.safe_string(order, 'order_id', id)
timestamp = self.parse8601(self.safe_string(order, 'created_at'))
timestamp = self.safe_integer(order, 'create_time', timestamp)
marketId = self.safe_string_2(order, 'symbol', 'contract_id')
symbol = self.safe_symbol(marketId, market, '_')
status = None
if market is not None:
status = self.parse_order_status_by_type(market['type'], self.safe_string(order, 'status'))
amount = self.safe_string_2(order, 'size', 'vol')
filled = self.safe_string_2(order, 'filled_size', 'done_vol')
average = self.safe_string_2(order, 'price_avg', 'done_avg_price')
price = self.safe_string(order, 'price')
side = self.safe_string_2(order, 'way', 'side')
# 1 = Open long
# 2 = Close short
# 3 = Close long
# 4 = Open short
category = self.safe_integer(order, 'category')
type = self.safe_string(order, 'type')
if category == 1:
type = 'limit'
elif category == 2:
type = 'market'
return self.safe_order2({
'id': id,
'clientOrderId': None,
'info': order,
'timestamp': timestamp,
'datetime': self.iso8601(timestamp),
'lastTradeTimestamp': None,
'symbol': symbol,
'type': type,
'timeInForce': None,
'postOnly': None,
'side': side,
'price': price,
'stopPrice': None,
'amount': amount,
'cost': None,
'average': average,
'filled': filled,
'remaining': None,
'status': status,
'fee': None,
'trades': None,
}, market)
def parse_order_status_by_type(self, type, status):
statusesByType = {
'spot': {
'1': 'failed', # Order failure
'2': 'open', # Placing order
'3': 'failed', # Order failure, Freeze failure
'4': 'open', # Order success, Pending for fulfilment
'5': 'open', # Partially filled
'6': 'closed', # Fully filled
'7': 'canceling', # Canceling
'8': 'canceled', # Canceled
},
'swap': {
'1': 'open', # Submitting
'2': 'open', # Commissioned
'4': 'closed', # Completed
},
}
statuses = self.safe_value(statusesByType, type, {})
return self.safe_string(statuses, status, status)
def create_order(self, symbol, type, side, amount, price=None, params={}):
self.load_markets()
market = self.market(symbol)
request = {}
method = None
if market['spot']:
request['symbol'] = market['id']
request['side'] = side
request['type'] = type
method = 'privateSpotPostSubmitOrder'
if type == 'limit':
request['size'] = self.amount_to_precision(symbol, amount)
request['price'] = self.price_to_precision(symbol, price)
elif type == 'market':
# for market buy it requires the amount of quote currency to spend
if side == 'buy':
notional = self.safe_number(params, 'notional')
createMarketBuyOrderRequiresPrice = self.safe_value(self.options, 'createMarketBuyOrderRequiresPrice', True)
if createMarketBuyOrderRequiresPrice:
if price is not None:
if notional is None:
notional = amount * price
elif notional is None:
raise InvalidOrder(self.id + " createOrder() requires the price argument with market buy orders to calculate total order cost(amount to spend), where cost = amount * price. Supply a price argument to createOrder() call if you want the cost to be calculated for you from price and amount, or, alternatively, add .options['createMarketBuyOrderRequiresPrice'] = False and supply the total cost value in the 'amount' argument or in the 'notional' extra parameter(the exchange-specific behaviour)")
else:
notional = amount if (notional is None) else notional
precision = market['precision']['price']
request['notional'] = self.decimal_to_precision(notional, TRUNCATE, precision, self.precisionMode)
elif side == 'sell':
request['size'] = self.amount_to_precision(symbol, amount)
elif market['swap'] or market['future']:
method = 'privateContractPostSubmitOrder'
request['contractID'] = market['id']
if type == 'limit':
request['category'] = 1
elif type == 'market':
request['category'] = 2
request['way'] = side # 1 = open long, 2 = close short, 3 = close long, 4 = open short
request['custom_id'] = self.nonce()
request['open_type'] = 1 # 1 = cross margin, 2 = fixed margin
request['leverage'] = 1 # must meet the effective range of leverage configured in the contract
request['price'] = self.price_to_precision(symbol, price)
request['vol'] = self.amount_to_precision(symbol, amount)
response = getattr(self, method)(self.extend(request, params))
#
# spot and contract
#
# {
# "code": 1000,
# "trace":"886fb6ae-456b-4654-b4e0-d681ac05cea1",
# "message": "OK",
# "data": {
# "order_id": 2707217580
# }
# }
#
data = self.safe_value(response, 'data', {})
return self.parse_order(data, market)
def cancel_order(self, id, symbol=None, params={}):
if symbol is None:
raise ArgumentsRequired(self.id + ' cancelOrder() requires a symbol argument')
self.load_markets()
market = self.market(symbol)
request = {}
method = None
if market['spot']:
method = 'privateSpotPostCancelOrder'
request['order_id'] = int(id)
request['symbol'] = market['id']
elif market['swap'] or market['future']:
method = 'privateContractPostCancelOrders'
request['contractID'] = market['id']
request['orders'] = [int(id)]
response = getattr(self, method)(self.extend(request, params))
#
# spot
#
# {
# "code": 1000,
# "trace":"886fb6ae-456b-4654-b4e0-d681ac05cea1",
# "message": "OK",
# "data": {
# "result": True
# }
# }
#
# spot alternative
#
# {
# "code": 1000,
# "trace":"886fb6ae-456b-4654-b4e0-d681ac05cea1",
# "message": "OK",
# "data": True
# }
#
# contract
#
# {
# "code": 1000,
# "trace":"886fb6ae-456b-4654-b4e0-d681ac05cea1",
# "message": "OK",
# "data": {
# "succeed": [
# 2707219612
# ],
# "failed": []
# }
# }
#
data = self.safe_value(response, 'data')
if data is True:
return self.parse_order(id, market)
succeeded = self.safe_value(data, 'succeed')
if succeeded is not None:
id = self.safe_string(succeeded, 0)
if id is None:
raise InvalidOrder(self.id + ' cancelOrder() failed to cancel ' + symbol + ' order id ' + id)
else:
result = self.safe_value(data, 'result')
if not result:
raise InvalidOrder(self.id + ' cancelOrder() ' + symbol + ' order id ' + id + ' is filled or canceled')
order = self.parse_order(id, market)
return self.extend(order, {'id': id})
def cancel_all_orders(self, symbol=None, params={}):
if symbol is None:
raise ArgumentsRequired(self.id + ' cancelAllOrders() requires a symbol argument')
side = self.safe_string(params, 'side')
if side is None:
raise ArgumentsRequired(self.id + " cancelAllOrders() requires a `side` parameter('buy' or 'sell')")
self.load_markets()
market = self.market(symbol)
if not market['spot']:
raise NotSupported(self.id + ' cancelAllOrders() does not support ' + market['type'] + ' orders, only spot orders are accepted')
request = {
'symbol': market['id'],
'side': side, # 'buy' or 'sell'
}
response = self.privateSpotPostCancelOrders(self.extend(request, params))
#
# {
# "code": 1000,
# "trace":"886fb6ae-456b-4654-b4e0-d681ac05cea1",
# "message": "OK",
# "data": {}
# }
#
return response
def cancel_orders(self, ids, symbol=None, params={}):
if symbol is None:
raise ArgumentsRequired(self.id + ' canelOrders() requires a symbol argument')
self.load_markets()
market = self.market(symbol)
if not market['spot']:
raise NotSupported(self.id + ' cancelOrders() does not support ' + market['type'] + ' orders, only contract orders are accepted')
orders = []
for i in range(0, len(ids)):
orders.append(int(ids[i]))
request = {
'orders': orders,
}
response = self.privateContractPostCancelOrders(self.extend(request, params))
#
# spot
#
# {
# "code": 1000,
# "trace":"886fb6ae-456b-4654-b4e0-d681ac05cea1",
# "message": "OK",
# "data": {
# "result": True
# }
# }
#
# contract
#
# {
# "code": 1000,
# "trace":"886fb6ae-456b-4654-b4e0-d681ac05cea1",
# "message": "OK",
# "data": {
# "succeed": [
# 2707219612
# ],
# "failed": []
# }
# }
#
return response
def fetch_orders_by_status(self, status, symbol=None, since=None, limit=None, params={}):
if symbol is None:
raise ArgumentsRequired(self.id + ' fetchOrdersByStatus() requires a symbol argument')
self.load_markets()
market = self.market(symbol)
request = {}
method = None
if market['spot']:
method = 'privateSpotGetOrders'
request['symbol'] = market['id']
request['offset'] = 1 # max offset * limit < 500
request['limit'] = 100 # max limit is 100
# 1 = Order failure
# 2 = Placing order
# 3 = Order failure, Freeze failure
# 4 = Order success, Pending for fulfilment
# 5 = Partially filled
# 6 = Fully filled
# 7 = Canceling
# 8 = Canceled
# 9 = Outstanding(4 and 5)
# 10 = 6 and 8
if status == 'open':
request['status'] = 9
elif status == 'closed':
request['status'] = 6
elif status == 'canceled':
request['status'] = 8
else:
request['status'] = status
elif market['swap'] or market['future']:
method = 'privateContractGetUserOrders'
request['contractID'] = market['id']
# request['offset'] = 1
if limit is not None:
request['size'] = limit # max 60
# 0 = All
# 1 = Submitting
# 2 = Commissioned
# 3 = 1 and 2
# 4 = Completed
if status == 'open':
request['status'] = 3
elif status == 'closed':
request['status'] = 4
else:
request['status'] = status
response = getattr(self, method)(self.extend(request, params))
#
# spot
#
# {
# "message":"OK",
# "code":1000,
# "trace":"70e7d427-7436-4fb8-8cdd-97e1f5eadbe9",
# "data":{
# "current_page":1,
# "orders":[
# {
# "order_id":2147601241,
# "symbol":"BTC_USDT",
# "create_time":1591099963000,
# "side":"sell",
# "type":"limit",
# "price":"9000.00",
# "price_avg":"0.00",
# "size":"1.00000",
# "notional":"9000.00000000",
# "filled_notional":"0.00000000",
# "filled_size":"0.00000",
# "status":"4"
# }
# ]
# }
# }
#
# contract
#
# {
# "code": 1000,
# "trace":"886fb6ae-456b-4654-b4e0-d681ac05cea1",
# "message": "OK",
# "data": {
# "orders": [
# {
# "order_id": 10284160,
# "contract_id": 1,
# "price": "8",
# "vol": "4",
# "done_avg_price": "0",
# "done_vol": "0",
# "way": 1,
# "category": 1,
# "open_type": 2,
# "make_fee": "0",
# "take_fee": "0",
# "origin": "",
# "created_at": "2018-07-17T07:24:13.410507Z",
# "finished_at": null,
# "status": 2,
# "errno": 0
# }
# ]
# }
# }
#
data = self.safe_value(response, 'data', {})
orders = self.safe_value(data, 'orders', [])
return self.parse_orders(orders, market, since, limit)
def fetch_open_orders(self, symbol=None, since=None, limit=None, params={}):
return self.fetch_orders_by_status('open', symbol, since, limit, params)
def fetch_closed_orders(self, symbol=None, since=None, limit=None, params={}):
return self.fetch_orders_by_status('closed', symbol, since, limit, params)
def fetch_canceled_orders(self, symbol=None, since=None, limit=None, params={}):
return self.fetch_orders_by_status('canceled', symbol, since, limit, params)
def fetch_orders(self, symbol=None, since=None, limit=None, params={}):
if symbol is None:
raise ArgumentsRequired(self.id + ' fetchOrders() requires a symbol argument')
self.load_markets()
market = self.market(symbol)
if not (market['swap'] or market['future']):
raise NotSupported(self.id + ' fetchOrders does not support ' + market['type'] + ' markets, only contracts are supported')
return self.fetch_orders_by_status(0, symbol, since, limit, params)
def fetch_order(self, id, symbol=None, params={}):
if symbol is None:
raise ArgumentsRequired(self.id + ' fetchOrder() requires a symbol argument')
self.load_markets()
request = {}
market = self.market(symbol)
method = None
if not isinstance(id, basestring):
id = str(id)
if market['spot']:
request['symbol'] = market['id']
request['order_id'] = id
method = 'privateSpotGetOrderDetail'
elif market['swap'] or market['future']:
request['contractID'] = market['id']
request['orderID'] = id
method = 'privateContractGetUserOrderInfo'
response = getattr(self, method)(self.extend(request, params))
#
# spot
#
# {
# "message":"OK",
# "code":1000,
# "trace":"a27c2cb5-ead4-471d-8455-1cfeda054ea6",
# "data": {
# "order_id":1736871726781,
# "symbol":"BTC_USDT",
# "create_time":1591096004000,
# "side":"sell",
# "type":"market",
# "price":"0.00",
# "price_avg":"0.00",
# "size":"0.02000",
# "notional":"0.00000000",
# "filled_notional":"0.00000000",
# "filled_size":"0.00000",
# "status":"8"
# }
# }
#
# contract
#
# {
# "code": 1000,
# "trace":"886fb6ae-456b-4654-b4e0-d681ac05cea1",
# "message": "OK",
# "data": {
# "orders": [
# {
# "order_id": 10539098,
# "contract_id": 1,
# "position_id": 10539088,
# "account_id": 10,
# "price": "16",
# "vol": "1",
# "done_avg_price": "16",
# "done_vol": "1",
# "way": 3,
# "category": 1,
# "make_fee": "0.00025",
# "take_fee": "0.012",
# "origin": "",
# "created_at": "2018-07-23T11:55:56.715305Z",
# "finished_at": "2018-07-23T11:55:56.763941Z",
# "status": 4,
# "errno": 0
# }
# ]
# }
# }
#
data = self.safe_value(response, 'data')
if 'orders' in data:
orders = self.safe_value(data, 'orders', [])
firstOrder = self.safe_value(orders, 0)
if firstOrder is None:
raise OrderNotFound(self.id + ' fetchOrder() could not find ' + symbol + ' order id ' + id)
return self.parse_order(firstOrder, market)
else:
return self.parse_order(data, market)
def fetch_deposit_address(self, code, params={}):
self.load_markets()
currency = self.currency(code)
request = {
'currency': currency['id'],
}
if code == 'USDT':
defaultNetworks = self.safe_value(self.options, 'defaultNetworks')
defaultNetwork = self.safe_string_upper(defaultNetworks, code)
networks = self.safe_value(self.options, 'networks', {})
network = self.safe_string_upper(params, 'network', defaultNetwork) # self line allows the user to specify either ERC20 or ETH
network = self.safe_string(networks, network, network) # handle ERC20>ETH alias
if network is not None:
request['currency'] += '-' + network # when network the currency need to be changed to currency + '-' + network https://developer-pro.bitmart.com/en/account/withdraw_apply.html on the end of page
params = self.omit(params, 'network')
response = self.privateAccountGetDepositAddress(self.extend(request, params))
#
# {
# "message":"OK",
# "code":1000,
# "trace":"0e6edd79-f77f-4251-abe5-83ba75d06c1a",
# "data":{
# "currency":"USDT-TRC20",
# "chain":"USDT-TRC20",
# "address":"TGR3ghy2b5VLbyAYrmiE15jasR6aPHTvC5",
# "address_memo":""
# }
# }
#
data = self.safe_value(response, 'data', {})
address = self.safe_string(data, 'address')
tag = self.safe_string(data, 'address_memo')
self.check_address(address)
return {
'currency': code,
'address': address,
'tag': tag,
'network': None, # TODO: parse
'info': response,
}
def withdraw(self, code, amount, address, tag=None, params={}):
tag, params = self.handle_withdraw_tag_and_params(tag, params)
self.check_address(address)
self.load_markets()
currency = self.currency(code)
request = {
'currency': currency['id'],
'amount': amount,
'destination': 'To Digital Address', # To Digital Address, To Binance, To OKEX
'address': address,
}
if tag is not None:
request['address_memo'] = tag
if code == 'USDT':
defaultNetworks = self.safe_value(self.options, 'defaultNetworks')
defaultNetwork = self.safe_string_upper(defaultNetworks, code)
networks = self.safe_value(self.options, 'networks', {})
network = self.safe_string_upper(params, 'network', defaultNetwork) # self line allows the user to specify either ERC20 or ETH
network = self.safe_string(networks, network, network) # handle ERC20>ETH alias
if network is not None:
request['currency'] += '-' + network # when network the currency need to be changed to currency + '-' + network https://developer-pro.bitmart.com/en/account/withdraw_apply.html on the end of page
params = self.omit(params, 'network')
response = self.privateAccountPostWithdrawApply(self.extend(request, params))
#
# {
# "code": 1000,
# "trace":"886fb6ae-456b-4654-b4e0-d681ac05cea1",
# "message": "OK",
# "data": {
# "withdraw_id": "121212"
# }
# }
#
data = self.safe_value(response, 'data')
transaction = self.parse_transaction(data, currency)
return self.extend(transaction, {
'code': code,
'address': address,
'tag': tag,
})
def fetch_transactions_by_type(self, type, code=None, since=None, limit=None, params={}):
self.load_markets()
if limit is None:
limit = 50 # max 50
request = {
'operation_type': type, # deposit or withdraw
'offset': 1,
'limit': limit,
}
currency = None
if code is not None:
currency = self.currency(code)
request['currency'] = currency['id']
response = self.privateAccountGetDepositWithdrawHistory(self.extend(request, params))
#
# {
# "message":"OK",
# "code":1000,
# "trace":"142bf92a-fc50-4689-92b6-590886f90b97",
# "data":{
# "records":[
# {
# "withdraw_id":"1679952",
# "deposit_id":"",
# "operation_type":"withdraw",
# "currency":"BMX",
# "apply_time":1588867374000,
# "arrival_amount":"59.000000000000",
# "fee":"1.000000000000",
# "status":0,
# "address":"0xe57b69a8776b37860407965B73cdFFBDFe668Bb5",
# "address_memo":"",
# "tx_id":""
# },
# ]
# }
# }
#
data = self.safe_value(response, 'data', {})
records = self.safe_value(data, 'records', [])
return self.parse_transactions(records, currency, since, limit)
def fetch_deposits(self, code=None, since=None, limit=None, params={}):
return self.fetch_transactions_by_type('deposit', code, since, limit, params)
def fetch_withdrawals(self, code=None, since=None, limit=None, params={}):
return self.fetch_transactions_by_type('withdraw', code, since, limit, params)
def parse_transaction_status(self, status):
statuses = {
'0': 'pending', # Create
'1': 'pending', # Submitted, waiting for withdrawal
'2': 'pending', # Processing
'3': 'ok', # Success
'4': 'canceled', # Cancel
'5': 'failed', # Fail
}
return self.safe_string(statuses, status, status)
def parse_transaction(self, transaction, currency=None):
#
# withdraw
#
# {
# "withdraw_id": "121212"
# }
#
# fetchDeposits, fetchWithdrawals
#
# {
# "withdraw_id":"1679952",
# "deposit_id":"",
# "operation_type":"withdraw",
# "currency":"BMX",
# "apply_time":1588867374000,
# "arrival_amount":"59.000000000000",
# "fee":"1.000000000000",
# "status":0,
# "address":"0xe57b69a8776b37860407965B73cdFFBDFe668Bb5",
# "address_memo":"",
# "tx_id":""
# }
#
id = None
withdrawId = self.safe_string(transaction, 'withdraw_id')
depositId = self.safe_string(transaction, 'deposit_id')
type = None
if (withdrawId is not None) and (withdrawId != ''):
type = 'withdraw'
id = withdrawId
elif (depositId is not None) and (depositId != ''):
type = 'deposit'
id = depositId
amount = self.safe_number(transaction, 'arrival_amount')
timestamp = self.safe_integer(transaction, 'apply_time')
currencyId = self.safe_string(transaction, 'currency')
code = self.safe_currency_code(currencyId, currency)
status = self.parse_transaction_status(self.safe_string(transaction, 'status'))
feeCost = self.safe_number(transaction, 'fee')
fee = None
if feeCost is not None:
fee = {
'cost': feeCost,
'currency': code,
}
txid = self.safe_string(transaction, 'tx_id')
if txid == '':
txid = None
address = self.safe_string(transaction, 'address')
tag = self.safe_string(transaction, 'address_memo')
return {
'info': transaction,
'id': id,
'currency': code,
'amount': amount,
'address': address,
'addressFrom': None,
'addressTo': None,
'tag': tag,
'tagFrom': None,
'tagTo': None,
'status': status,
'type': type,
'updated': None,
'txid': txid,
'timestamp': timestamp,
'datetime': self.iso8601(timestamp),
'fee': fee,
}
def nonce(self):
return self.milliseconds()
def sign(self, path, api='public', method='GET', params={}, headers=None, body=None):
access = self.safe_string(api, 0)
type = self.safe_string(api, 1)
baseUrl = self.implode_hostname(self.urls['api']['rest'])
url = baseUrl + '/' + type
if type != 'system':
url += '/' + self.version
url += '/' + self.implode_params(path, params)
query = self.omit(params, self.extract_params(path))
if type == 'system':
if query:
# print(query)
url += '?' + self.urlencode(query)
elif access == 'public':
if query:
# print(query)
url += '?' + self.urlencode(query)
elif access == 'private':
self.check_required_credentials()
timestamp = str(self.milliseconds())
queryString = ''
headers = {
'X-BM-KEY': self.apiKey,
'X-BM-TIMESTAMP': timestamp,
}
if (method == 'POST') or (method == 'PUT'):
headers['Content-Type'] = 'application/json'
body = self.json(query)
queryString = body
else:
if query:
queryString = self.urlencode(query)
url += '?' + queryString
auth = timestamp + '#' + self.uid + '#' + queryString
signature = self.hmac(self.encode(auth), self.encode(self.secret))
headers['X-BM-SIGN'] = signature
return {'url': url, 'method': method, 'body': body, 'headers': headers}
def handle_errors(self, code, reason, url, method, headers, body, response, requestHeaders, requestBody):
if response is None:
return
#
# spot
#
# {"message":"Bad Request [to is empty]","code":50000,"trace":"f9d46e1b-4edb-4d07-a06e-4895fb2fc8fc","data":{}}
# {"message":"Bad Request [from is empty]","code":50000,"trace":"579986f7-c93a-4559-926b-06ba9fa79d76","data":{}}
# {"message":"Kline size over 500","code":50004,"trace":"d625caa8-e8ca-4bd2-b77c-958776965819","data":{}}
# {"message":"Balance not enough","code":50020,"trace":"7c709d6a-3292-462c-98c5-32362540aeef","data":{}}
#
# contract
#
# {"errno":"OK","message":"INVALID_PARAMETER","code":49998,"trace":"eb5ebb54-23cd-4de2-9064-e090b6c3b2e3","data":null}
#
message = self.safe_string(response, 'message')
errorCode = self.safe_string(response, 'code')
if ((errorCode is not None) and (errorCode != '1000')) or ((message is not None) and (message != 'OK')):
feedback = self.id + ' ' + body
self.throw_exactly_matched_exception(self.exceptions['exact'], errorCode, feedback)
self.throw_broadly_matched_exception(self.exceptions['broad'], errorCode, feedback)
self.throw_exactly_matched_exception(self.exceptions['exact'], message, feedback)
self.throw_broadly_matched_exception(self.exceptions['broad'], message, feedback)
raise ExchangeError(feedback) # unknown message
|
py | 1a312e02e13b66b2b41f27a3b5a39a6a2c01bd9a | """
Writes out submission datetime details (when it was submitted, how long it was in grading
process, etc) to a history.json file which is a list of all grading attempts for a
particular submission (including initial grading of it and all regrades).
"""
import os
import sys
import collections
import json
from datetime import datetime
from submitty_utils import dateutils
import fcntl
import traceback
import zipfile
import stat
import subprocess
import shutil
import codecs
import glob
import docker
from typing import Optional
class Logger:
"""Specialized logger class that accumulates stack traces."""
def __init__(
self, *,
log_dir: str,
stack_trace_dir: str,
capture_traces: bool = False,
# This used to be "UNKNOWN", but "NO JOB" better describes the circumstances.
job_id: str = "NO JOB",
):
self.log_dir = log_dir
self.stack_trace_dir = stack_trace_dir
self.capture_traces = capture_traces
self.accumulated_traces = []
self.job_id = job_id
def _log_filename(self) -> str:
"""Get the name of the file that should be logged into.
Currently, this is in the format YYYYMMDD.txt.
"""
now = dateutils.get_current_time()
return f'{datetime.strftime(now, "%Y%m%d")}.txt'
@property
def log_path(self) -> str:
"""Get the full path to the regular logging file."""
return os.path.join(self.log_dir, self._log_filename())
@property
def stack_trace_path(self) -> str:
"""Get the full path to the stack trace logging file."""
return os.path.join(self.stack_trace_dir, self._log_filename())
def log_message(
self, message: str, *,
is_batch: bool = False,
which_untrusted: str = "",
jobname: str = "",
timelabel: str = "",
elapsed_time: Optional[int] = None,
job_id: Optional[str] = None
):
"""Log a message to this logger's configured log directory."""
now = dateutils.get_current_time()
easy_to_read_date = dateutils.write_submitty_date(now, True)
batch_string = "BATCH" if is_batch else ""
if elapsed_time is None:
elapsed_time = -1
elapsed_time_string = "" if elapsed_time < 0 else '{:9.3f}'.format(elapsed_time)
time_unit = "" if elapsed_time < 0 else "sec"
job_id = job_id or self.job_id
parts = (easy_to_read_date, f"{job_id:>6s}", f"{batch_string:>5s}", f"{which_untrusted:>11s}",
f"{jobname:75s}", f"{timelabel:6s} {elapsed_time_string:>9s} {time_unit:>3s}", message)
write_to_log(self.log_path, ' | '.join((str(x) for x in parts)))
def log_stack_trace(
self, trace: str, *,
is_batch: bool = False,
which_untrusted: str = '',
job_id: Optional[str] = None,
jobname: str = "",
echo_source: Optional[str] = None,
):
"""Log a stack trace to this logger's configured stack trace directory."""
job_id = job_id or self.job_id
# Save the parameters to this trace so we can duplicate these on the
# shipper's end once the job finishes.
#
# TODO: Maybe we want to store time info too? Might need to think a bit
# more in terms of the stack traces log file format.
if self.capture_traces:
self.accumulated_traces.append({
'trace': trace,
'is_batch': is_batch,
'which_untrusted': which_untrusted,
'job_id': job_id,
'jobname': jobname,
})
# Always run this since this could be deleted without us knowing
os.makedirs(self.stack_trace_dir, exist_ok=True)
now = dateutils.get_current_time()
easy_to_read_date = dateutils.write_submitty_date(now, True)
message = f"[{easy_to_read_date}][{job_id:>6s}]\n"
if echo_source is not None:
message += f"== (Echoed from {echo_source})\n"
message += f"== Batch? {is_batch}\n"
message += f"== Which: {which_untrusted}\n"
message += f"== Job: {jobname}\n"
for line in trace.splitlines():
message += f"== {line}\n"
message = message.strip()
write_to_log(self.stack_trace_path, message)
def just_write_grade_history(json_file,assignment_deadline,submission_time,seconds_late,
first_access_time,access_duration,queue_time,batch_regrade,grading_began,
wait_time,grading_finished,grade_time,autograde_total,
revision):
#####################################
# LOAD THE PREVIOUS HISTORY
if os.path.isfile(json_file):
with open(json_file, 'r') as infile:
obj = json.load(infile, object_pairs_hook=collections.OrderedDict)
else:
obj = []
#####################################
# CREATE THE NEWEST INFO BLOB
blob = collections.OrderedDict()
blob["assignment_deadline"] = assignment_deadline
blob["submission_time"] = submission_time
seconds_late = seconds_late
if seconds_late > 0:
minutes_late = int((seconds_late+60-1) / 60)
hours_late = int((seconds_late+60*60-1) / (60*60))
days_late = int((seconds_late+60*60*24-1) / (60*60*24))
blob["days_late_before_extensions"] = days_late
blob["queue_time"] = queue_time
blob["batch_regrade"] = True if batch_regrade == "BATCH" else False
blob["first_access_time"] = first_access_time
blob["access_duration"] = access_duration
blob["grading_began"] = grading_began
blob["wait_time"] = wait_time
blob["grading_finished"] = grading_finished
blob["grade_time"] = grade_time
blob["autograde_result"] = autograde_total
autograde_array = str.split(autograde_total)
if len(autograde_array) > 0 and autograde_array[0] == "Automatic":
blob["autograde_total"] = int(autograde_array[3])
if len(autograde_array) == 6:
blob["autograde_max_possible"] = int(autograde_array[5])
if revision:
blob["revision"] = revision
#####################################
# ADD IT TO THE HISTORY
obj.append(blob)
with open(json_file, 'w') as outfile:
json.dump(obj, outfile, indent=4, separators=(',', ': '))
# ==================================================================================
#
# LOGGING FUNCTIONS
#
# ==================================================================================
def log_container_meta(log_path, event="", name="", container="", time=0):
""" Given a log file, create or append container meta data to a log file. """
now = dateutils.get_current_time()
easy_to_read_date = dateutils.write_submitty_date(now, True)
time_unit = "sec"
parts = (easy_to_read_date, name, container, event, f"{time:.3f}", time_unit)
write_to_log(log_path, ' | '.join(parts))
def write_to_log(log_path, message):
""" Given a log file, create or append message to log file"""
with open(log_path, 'a+') as log_file:
try:
fcntl.flock(log_file, fcntl.LOCK_EX | fcntl.LOCK_NB)
print(message, file=log_file)
fcntl.flock(log_file, fcntl.LOCK_UN)
except:
print("Could not gain a lock on the log file.")
# ==================================================================================
#
# VALIDATION FUNCTIONS
#
# ==================================================================================
def setup_for_validation(config, working_directory, complete_config, is_vcs, testcases, job_id):
""" Prepare a directory for validation by copying in and permissioning the required files. """
tmp_submission = os.path.join(working_directory,"TMP_SUBMISSION")
tmp_work = os.path.join(working_directory,"TMP_WORK")
tmp_results = os.path.join(working_directory,"TMP_RESULTS")
submission_path = os.path.join(tmp_submission, "submission")
checkout_subdirectory = complete_config["autograding"].get("use_checkout_subdirectory","")
tmp_logs = os.path.join(working_directory,"TMP_SUBMISSION","tmp_logs")
tmp_work_test_output = os.path.join(tmp_work, "test_output")
tmp_work_generated_output = os.path.join(tmp_work, "generated_output")
tmp_work_instructor_solution = os.path.join(tmp_work, "instructor_solution")
tmp_autograding = os.path.join(working_directory,"TMP_AUTOGRADING")
os.mkdir(tmp_work_test_output)
os.mkdir(tmp_work_generated_output)
os.mkdir(tmp_work_instructor_solution)
patterns = complete_config['autograding']
# Add all permissions to tmp_work
add_permissions_recursive(tmp_work,
stat.S_IRUSR | stat.S_IWUSR | stat.S_IXUSR | stat.S_IRGRP | stat.S_IWGRP | stat.S_IXGRP | stat.S_IROTH | stat.S_IWOTH | stat.S_IXOTH,
stat.S_IRUSR | stat.S_IWUSR | stat.S_IXUSR | stat.S_IRGRP | stat.S_IWGRP | stat.S_IXGRP | stat.S_IROTH | stat.S_IWOTH | stat.S_IXOTH,
stat.S_IRUSR | stat.S_IWUSR | stat.S_IXUSR | stat.S_IRGRP | stat.S_IWGRP | stat.S_IXGRP | stat.S_IROTH | stat.S_IWOTH | stat.S_IXOTH)
# Copy required submission/checkout files
pattern_copy("submission_to_validation", patterns['submission_to_validation'], submission_path, tmp_work, tmp_logs)
checkout_subdir_path = os.path.join(tmp_submission, 'checkout', checkout_subdirectory)
if os.path.exists(checkout_subdir_path):
pattern_copy("checkout_to_validation", patterns['submission_to_validation'],checkout_subdir_path,tmp_work,tmp_logs)
for c in testcases:
if c.type == 'Compilation':
pattern_copy("compilation_to_validation", patterns['compilation_to_validation'], c.secure_environment.directory, tmp_work, tmp_logs)
# Copy expected files into the tmp_work_test_output path
test_output_path = os.path.join(tmp_autograding, 'test_output')
copy_contents_into(config, job_id, test_output_path, tmp_work_test_output, tmp_logs)
generated_output_path = os.path.join(tmp_autograding, 'generated_output')
copy_contents_into(config, job_id, generated_output_path, tmp_work_generated_output, tmp_logs)
# Copy in instructor solution code.
instructor_solution = os.path.join(tmp_autograding, 'instructor_solution')
copy_contents_into(config, job_id, instructor_solution, tmp_work_instructor_solution, tmp_logs)
# Copy any instructor custom validation code into the tmp work directory
custom_validation_code_path = os.path.join(tmp_autograding, 'custom_validation_code')
copy_contents_into(config, job_id, custom_validation_code_path, tmp_work, tmp_logs)
# Copy the .submit.notebook to tmp_work for validation
submit_notebook_path = os.path.join(tmp_submission, 'submission', ".submit.notebook")
if os.path.exists(submit_notebook_path):
shutil.copy(
submit_notebook_path,
os.path.join(tmp_work, '.submit.notebook')
)
# Copy the validation script into this directory.
bin_runner = os.path.join(tmp_autograding, "bin","validate.out")
my_runner = os.path.join(tmp_work, "my_validator.out")
shutil.copy(bin_runner, my_runner)
add_permissions_recursive(tmp_work,
stat.S_IROTH | stat.S_IWOTH | stat.S_IXOTH,
stat.S_IROTH | stat.S_IWOTH | stat.S_IXOTH,
stat.S_IROTH | stat.S_IWOTH | stat.S_IXOTH)
add_permissions(my_runner, stat.S_IXUSR | stat.S_IXGRP |stat.S_IROTH | stat.S_IWOTH | stat.S_IXOTH)
# ==================================================================================
#
# ARCHIVAL AND PERMISSIONS FUNCTIONS
#
# ==================================================================================
def add_all_permissions(path):
""" Recursively chmod a directory or file 777. """
if os.path.isdir(path):
add_permissions_recursive(path,
stat.S_IRUSR | stat.S_IWUSR | stat.S_IXUSR | stat.S_IRGRP | stat.S_IWGRP | stat.S_IXGRP | stat.S_IROTH | stat.S_IWOTH | stat.S_IXOTH,
stat.S_IRUSR | stat.S_IWUSR | stat.S_IXUSR | stat.S_IRGRP | stat.S_IWGRP | stat.S_IXGRP | stat.S_IROTH | stat.S_IWOTH | stat.S_IXOTH,
stat.S_IRUSR | stat.S_IWUSR | stat.S_IXUSR | stat.S_IRGRP | stat.S_IWGRP | stat.S_IXGRP | stat.S_IROTH | stat.S_IWOTH | stat.S_IXOTH)
elif os.path.isfile(path):
add_permissions(path, stat.S_IRUSR | stat.S_IWUSR | stat.S_IXUSR | stat.S_IRGRP | stat.S_IWGRP | stat.S_IXGRP | stat.S_IROTH | stat.S_IWOTH | stat.S_IXOTH)
def lock_down_folder_permissions(top_dir):
# Chmod a directory to take away group and other rwx.
os.chmod(top_dir,os.stat(top_dir).st_mode & ~stat.S_IRGRP & ~stat.S_IWGRP & ~stat.S_IXGRP & ~stat.S_IROTH & ~stat.S_IWOTH & ~stat.S_IXOTH)
def cleanup_stale_containers(user_id_of_runner, my_log_function):
# Remove any docker containers left over from past runs.
client = docker.from_env(timeout=60)
try:
# Get all containers (running or not) with user_id_of_runner in their name
# sparse=True gets containers without fully evaluating them. This is important,
# as race conditions with other grading threads can cause this call to fail otherwise.
old_containers = client.containers.list(all=True, filters={"name":user_id_of_runner}, sparse=True)
for old_container in old_containers:
try:
my_log_function(f'Removing stale container {old_container.name}')
old_container.remove(force=True)
except docker.errors.NotFound:
# This is an expected case which does not constitute an error, caused
# by the use of sparse=True
pass
except Exception:
my_log_function("ERROR: Could not remove docker container")
# Get all networks with user_id_of_runner in their name
old_networks = client.networks.list(filters={"name":user_id_of_runner})
for old_network in old_networks:
try:
my_log_function(f'Removing stale network {old_network.name}')
old_network.remove()
except Exception:
my_log_function("ERROR: Could not remove docker network")
finally:
client.close()
def prepare_directory_for_autograding(config, working_directory, user_id_of_runner, autograding_zip_file, submission_zip_file, is_test_environment):
"""
Given a working directory, set up that directory for autograding by creating the required subdirectories
and configuring permissions.
"""
# If an old (stale) version of the working directory exists, we need to remove it.
if os.path.exists(working_directory):
# Make certain we can remove old instances of the working directory.
if not is_test_environment:
untrusted_grant_rwx_access(
config.submitty['submitty_install_dir'], user_id_of_runner, working_directory
)
add_all_permissions(working_directory)
shutil.rmtree(working_directory,ignore_errors=True)
# Create the working directory
os.mkdir(working_directory)
# Important directory variables.
tmp_autograding = os.path.join(working_directory,"TMP_AUTOGRADING")
tmp_submission = os.path.join(working_directory,"TMP_SUBMISSION")
tmp_work = os.path.join(working_directory,"TMP_WORK")
tmp_logs = os.path.join(working_directory,"TMP_SUBMISSION","tmp_logs")
submission_path = os.path.join(tmp_submission, "submission")
tmp_work_test_input = os.path.join(tmp_work, "test_input")
os.mkdir(tmp_work)
os.mkdir(tmp_work_test_input)
# Unzip the autograding and submission folders
unzip_this_file(autograding_zip_file,tmp_autograding)
unzip_this_file(submission_zip_file,tmp_submission)
with open(os.path.join(tmp_autograding, "complete_config.json"), 'r') as infile:
complete_config_obj = json.load(infile)
# Handle the case where a student errantly submits to multiple parts of a one part only gradeable.
if complete_config_obj.get('one_part_only', False) == True:
allow_only_one_part(submission_path, log_path=os.path.join(tmp_logs, "overall.txt"))
with open(os.path.join(tmp_submission,"queue_file.json"), 'r') as infile:
queue_obj = json.load(infile)
job_id = queue_obj["job_id"]
# copy output files
test_input_path = os.path.join(tmp_autograding, 'test_input')
# Copy test input files into tmp_work_test_input.
copy_contents_into(config, job_id, test_input_path, tmp_work_test_input, tmp_logs)
# Lock down permissions on the unzipped folders/test input folder to stop untrusted users from gaining access.
lock_down_folder_permissions(tmp_work_test_input)
lock_down_folder_permissions(tmp_autograding)
lock_down_folder_permissions(tmp_submission)
def archive_autograding_results(
config,
working_directory: os.PathLike,
job_id: str,
which_untrusted: str,
is_batch_job: bool,
complete_config_obj: dict,
gradeable_config_obj: dict,
queue_obj: dict,
is_test_environment: bool
):
""" After grading is finished, archive the results. """
tmp_autograding = os.path.join(working_directory,"TMP_AUTOGRADING")
tmp_submission = os.path.join(working_directory,"TMP_SUBMISSION")
tmp_work = os.path.join(working_directory,"TMP_WORK")
tmp_logs = os.path.join(working_directory,"TMP_SUBMISSION","tmp_logs")
tmp_results = os.path.join(working_directory,"TMP_RESULTS")
submission_path = os.path.join(tmp_submission, "submission")
random_output_path = os.path.join(tmp_work, 'random_output')
if "generate_output" not in queue_obj:
partial_path = os.path.join(queue_obj["gradeable"],queue_obj["who"],str(queue_obj["version"]))
item_name = os.path.join(queue_obj["semester"],queue_obj["course"],"submissions",partial_path)
elif queue_obj["generate_output"]:
item_name = os.path.join(queue_obj["semester"],queue_obj["course"],"generated_output",queue_obj["gradeable"])
results_public_dir = os.path.join(tmp_results,"results_public")
results_details_dir = os.path.join(tmp_results, "details")
patterns = complete_config_obj['autograding']
# Copy work to details
pattern_copy("work_to_details", patterns['work_to_details'], tmp_work, results_details_dir, tmp_logs)
# Copy work to public
if 'work_to_public' in patterns:
pattern_copy("work_to_public", patterns['work_to_public'], tmp_work, results_public_dir, tmp_logs)
if os.path.exists(random_output_path):
pattern_copy("work_to_random_output", [os.path.join(random_output_path, '**', '*.txt'),], tmp_work, tmp_results, tmp_logs)
# timestamp of first access to the gradeable page
first_access_string = ""
# grab the submission time
if "generate_output" in queue_obj and queue_obj["generate_output"]:
submission_string = ""
else:
with open(os.path.join(tmp_submission, 'submission' ,".submit.timestamp"), 'r') as submission_time_file:
submission_string = submission_time_file.read().rstrip()
# grab the first access to the gradeable page (if it exists)
user_assignment_access_filename = os.path.join(tmp_submission, ".user_assignment_access.json")
if os.path.exists(user_assignment_access_filename):
with open(user_assignment_access_filename, 'r') as access_file:
obj = json.load(access_file)
first_access_string = obj[0]["timestamp"]
history_file_tmp = os.path.join(tmp_submission,"history.json")
history_file = os.path.join(tmp_results,"history.json")
if os.path.isfile(history_file_tmp) and not is_test_environment:
shutil.move(history_file_tmp, history_file)
# fix permissions
ta_group_id = os.stat(tmp_results).st_gid
os.chown(history_file, int(config.submitty_users['daemon_uid']),ta_group_id)
add_permissions(history_file, stat.S_IRGRP)
grading_finished = dateutils.get_current_time()
grade_result = ""
if "generate_output" not in queue_obj:
try:
shutil.copy(os.path.join(tmp_work, "grade.txt"), tmp_results)
with open(os.path.join(tmp_work,"grade.txt")) as f:
lines = f.readlines()
for line in lines:
line = line.rstrip('\n')
if line.startswith("Automatic grading total:"):
grade_result = line
except:
with open(os.path.join(tmp_logs,"overall.txt"),'a') as f:
f.write(f"\n\nERROR: Grading incomplete -- Could not process {os.path.join(tmp_work,'grade.txt')}")
config.logger.log_message(
"ERROR: could not process grade.txt. See stack trace entry for more details.",
job_id=job_id,
is_batch=is_batch_job,
which_untrusted=which_untrusted,
jobname=item_name,
)
config.logger.log_stack_trace(
traceback.format_exc(),
job_id=job_id,
is_batch=is_batch_job,
which_untrusted=which_untrusted,
jobname=item_name,
)
gradeable_deadline_string = gradeable_config_obj["date_due"]
submission_datetime = dateutils.read_submitty_date(submission_string)
gradeable_deadline_datetime = dateutils.read_submitty_date(gradeable_deadline_string)
gradeable_deadline_longstring = dateutils.write_submitty_date(gradeable_deadline_datetime)
submission_longstring = dateutils.write_submitty_date(submission_datetime)
seconds_late = int((submission_datetime-gradeable_deadline_datetime).total_seconds())
# compute the access duration in seconds (if it exists)
access_duration = -1
if first_access_string != "":
first_access_datetime = dateutils.read_submitty_date(first_access_string)
access_duration = int((submission_datetime-first_access_datetime).total_seconds())
# note: negative = not late
grading_finished_longstring = dateutils.write_submitty_date(grading_finished)
with open(os.path.join(tmp_submission,".grading_began"), 'r') as f:
grading_began_longstring = f.read()
grading_began = dateutils.read_submitty_date(grading_began_longstring)
gradingtime = (grading_finished - grading_began).total_seconds()
queue_obj["gradingtime"]=gradingtime
queue_obj["grade_result"]=grade_result
queue_obj["which_untrusted"]=which_untrusted
waittime = queue_obj["waittime"]
try:
# Make certain results.json is utf-8 encoded.
results_json_path = os.path.join(tmp_work, 'results.json')
with codecs.open(results_json_path, 'r', encoding='utf-8', errors='ignore') as infile:
results_str = "".join(line.rstrip() for line in infile)
results_obj = json.loads(results_str)
with open(results_json_path, 'w') as outfile:
json.dump(results_obj, outfile, indent=4)
shutil.move(results_json_path, os.path.join(tmp_results, "results.json"))
except:
with open(os.path.join(tmp_logs,"overall.txt"),'a') as f:
f.write(f"\n\nERROR: Grading incomplete -- Could not open/write {os.path.join(tmp_work,'results.json')}")
config.logger.log_message(
"ERROR: results.json read/write error",
job_id=job_id,
is_batch=is_batch_job,
which_untrusted=which_untrusted,
jobname=item_name,
)
config.logger.log_stack_trace(
traceback.format_exc(),
job_id=job_id,
is_batch=is_batch_job,
which_untrusted=which_untrusted,
jobname=item_name,
)
# Rescue custom validator files
custom_validator_output_directory = os.path.join(tmp_results, "custom_validator_output")
pattern_copy("rescue_custom_validator_validation_jsons", [os.path.join(tmp_work, 'validation_results_*.json'),], tmp_work, custom_validator_output_directory, tmp_logs)
pattern_copy("rescue_custom_validator_logs", [os.path.join(tmp_work, 'validation_logfile_*.txt'),], tmp_work, custom_validator_output_directory, tmp_logs)
pattern_copy("rescue_custom_validator_errors", [os.path.join(tmp_work, 'validation_stderr_*.txt'),], tmp_work, custom_validator_output_directory, tmp_logs)
just_write_grade_history(history_file,
gradeable_deadline_longstring,
submission_longstring,
seconds_late,
first_access_string,
access_duration,
queue_obj["queue_time"],
"BATCH" if is_batch_job else "INTERACTIVE",
grading_began_longstring,
int(waittime),
grading_finished_longstring,
int(gradingtime),
grade_result,
queue_obj.get("revision", None))
with open(os.path.join(tmp_logs,"overall.txt"),'a') as f:
f.write("FINISHED GRADING!\n")
config.logger.log_message(
grade_result,
job_id=job_id,
is_batch=is_batch_job,
which_untrusted=which_untrusted,
jobname=item_name,
timelabel="grade:",
elapsed_time=gradingtime
)
with open(os.path.join(tmp_results,"queue_file.json"),'w') as outfile:
json.dump(queue_obj,outfile,sort_keys=True,indent=4,separators=(',', ': '))
# save the logs!
shutil.copytree(tmp_logs,os.path.join(tmp_results,"logs"))
# Save the .submit.notebook
# Copy the .submit.notebook to tmp_work for validation
submit_notebook_path = os.path.join(tmp_submission, 'submission', ".submit.notebook")
if os.path.exists(submit_notebook_path):
shutil.copy(
submit_notebook_path,
os.path.join(tmp_results, ".submit.notebook")
)
def allow_only_one_part(path, log_path=os.devnull):
"""
Given a path to a directory, iterate through the directory and detect folders that start with
"part". If there is more than one and they have files, then delete all of the part folders except
for the first one that has files.
An example would be if you had the folder structure:
part1/
test.py
part2/
test.cpp
Then the part2 folder would be deleted, leaving just the part1 folder.
:param path: string filepath to directory to scan for parts in
:param log_path: string filepath to file to write print statements to
"""
if not os.path.isdir(path):
return
with open(log_path, 'a') as log:
clean_directories = []
print('Clean up multiple parts')
log.flush()
for entry in sorted(os.listdir(path)):
full_path = os.path.join(path, entry)
if not os.path.isdir(full_path) or not entry.startswith('part'):
continue
count = len(os.listdir(full_path))
print('{}: {}'.format(entry, count))
if count > 0:
clean_directories.append(full_path)
if len(clean_directories) > 1:
print("Student submitted to multiple parts in violation of instructions.\n"
"Removing files from all but first non empty part.")
for i in range(1, len(clean_directories)):
print("REMOVE: {}".format(clean_directories[i]))
for entry in os.listdir(clean_directories[i]):
print(" -> {}".format(entry))
shutil.rmtree(clean_directories[i])
# go through the testcase folder (e.g. test01/) and remove anything
# that matches the test input (avoid archiving copies of these files!)
def remove_test_input_files(overall_log, test_input_path, testcase_folder):
for path, subdirs, files in os.walk(test_input_path):
for name in files:
relative = path[len(test_input_path)+1:]
my_file = os.path.join(testcase_folder, relative, name)
if os.path.isfile(my_file):
print ("removing (likely) stale test_input file: ", my_file, file=overall_log)
overall_log.flush()
os.remove(my_file)
def add_permissions(item,perms):
if os.getuid() == os.stat(item).st_uid:
os.chmod(item,os.stat(item).st_mode | perms)
# else, can't change permissions on this file/directory!
def add_permissions_recursive(top_dir,root_perms,dir_perms,file_perms):
for root, dirs, files in os.walk(top_dir):
add_permissions(root,root_perms)
for d in dirs:
add_permissions(os.path.join(root, d),dir_perms)
for f in files:
add_permissions(os.path.join(root, f),file_perms)
# copy the files & directories from source to target
# it will create directories as needed
# it's ok if the target directory or subdirectories already exist
# it will overwrite files with the same name if they exist
def copy_contents_into(config, job_id, source, target, tmp_logs):
if not os.path.isdir(target):
config.logger.log_message(
"ERROR: Could not copy contents. The target directory does not exist: " + target,
job_id=job_id
)
raise RuntimeError("ERROR: the target directory does not exist: '", target, "'")
if os.path.isdir(source):
for item in os.listdir(source):
if os.path.isdir(os.path.join(source,item)):
if os.path.isdir(os.path.join(target,item)):
# recurse
copy_contents_into(config, job_id,os.path.join(source,item),os.path.join(target,item),tmp_logs)
elif os.path.isfile(os.path.join(target,item)):
config.logger.log_message(
"ERROR: the target subpath is a file not a directory "
f"'{os.path.join(target,item)}'",
job_id=job_id,
)
raise RuntimeError("ERROR: the target subpath is a file not a directory '", os.path.join(target,item), "'")
else:
# copy entire subtree
shutil.copytree(os.path.join(source,item),os.path.join(target,item))
else:
if os.path.exists(os.path.join(target,item)):
with open(os.path.join(tmp_logs,"overall.txt"),'a') as f:
print ("\nWARNING: REMOVING DESTINATION FILE" , os.path.join(target,item),
" THEN OVERWRITING: ", os.path.join(source,item), "\n", file=f)
os.remove(os.path.join(target,item))
try:
shutil.copy(os.path.join(source,item),target)
except:
config.logger.log_stack_trace(traceback.format_exc(), job_id=job_id)
return
else:
print(f'{source} is not a directory')
# copy files that match one of the patterns from the source directory
# to the target directory.
def pattern_copy(what, patterns, source, target, tmp_logs):
with open(os.path.join(tmp_logs,"overall.txt"),'a') as f:
print (what," pattern copy ", patterns, " from ", source, " -> ", target, file=f)
for pattern in patterns:
for my_file in glob.glob(os.path.join(source,pattern),recursive=True):
if (os.path.isfile(my_file)):
# grab the matched name
relpath = os.path.relpath(my_file,source)
# make the necessary directories leading to the file
os.makedirs(os.path.join(target,os.path.dirname(relpath)),exist_ok=True)
# copy the file
shutil.copy(my_file,os.path.join(target,relpath))
print (" COPY ",my_file,
" -> ",os.path.join(target,relpath), file=f)
else:
print ("skip this directory (will recurse into it later)", my_file, file=f)
# give permissions to all created files to the DAEMON_USER
def untrusted_grant_rwx_access(SUBMITTY_INSTALL_DIR, which_untrusted, my_dir):
subprocess.call([os.path.join(SUBMITTY_INSTALL_DIR, "sbin", "untrusted_execute"),
which_untrusted,
"/usr/bin/find",
my_dir,
"-user",
which_untrusted,
"-exec",
"/bin/chmod",
"ugo+rwx",
"{}",
";"])
# Used by packer unpacker
def zip_my_directory(path,zipfilename):
zipf = zipfile.ZipFile(zipfilename,'w',zipfile.ZIP_DEFLATED)
for root,dirs,files in os.walk(path):
for my_file in files:
relpath = root[len(path)+1:]
zipf.write(os.path.join(root,my_file),os.path.join(relpath,my_file))
zipf.close()
# Used by packer unpacker
def unzip_this_file(zipfilename,path):
if not os.path.exists(zipfilename):
raise RuntimeError("ERROR: zip file does not exist '", zipfilename, "'")
zip_ref = zipfile.ZipFile(zipfilename,'r')
zip_ref.extractall(path)
zip_ref.close()
# ==================================================================================
#
# PRE- AND POST-COMMAND FUNCTIONS
#
# ==================================================================================
def pre_command_copy_file(config, source_testcase, source_directory, destination_testcase, destination, job_id, tmp_logs):
""" Handles the cp pre_command. """
source_testcase = os.path.join(str(os.getcwd()), source_testcase)
if not os.path.isdir(source_testcase):
raise RuntimeError("ERROR: The directory {0} does not exist.".format(source_testcase))
if not os.path.isdir(destination_testcase):
raise RuntimeError("ERROR: The directory {0} does not exist.".format(destination_testcase))
source = os.path.join(source_testcase, source_directory)
target = os.path.join(destination_testcase, destination)
# The target without the potential executable.
target_base = '/'.join(target.split('/')[:-1])
# If the source is a directory, we copy the entire thing into the
# target.
if os.path.isdir(source):
# We must copy from directory to directory
copy_contents_into(config, job_id, source, target, tmp_logs)
# Separate ** and * for simplicity.
elif not '**' in source:
# Grab all of the files that match the pattern
files = glob.glob(source, recursive=True)
# The target base must exist in order for a copy to occur
if target_base != '' and not os.path.isdir(target_base):
raise RuntimeError("ERROR: The directory {0} does not exist.".format(target_base))
# Copy every file. This works whether target exists (is a directory) or does not (is a target file)
for file in files:
try:
shutil.copy(file, target)
except Exception as e:
traceback.print_exc()
config.logger.log_message(
f"Pre Command could not perform copy: {file} -> {target}",
job_id=job_id
)
else:
# Everything after the first **.
source_base = source[:source.find('**')]
# The full target must exist (we must be moving to a directory.)
if not os.path.isdir(target):
raise RuntimeError("ERROR: The directory {0} does not exist.".format(target))
# Grab all of the files that match the pattern.
files = glob.glob(source, recursive=True)
# For every file matched
for file_source in files:
file_target = os.path.join(target, file_source.replace(source_base,''))
# Remove the file path.
file_target_dir = '/'.join(file_target.split('/')[:-1])
# If the target directory doesn't exist, create it.
if not os.path.isdir(file_target_dir):
os.makedirs(file_target_dir)
# Copy.
try:
shutil.copy(file_source, file_target)
except Exception as e:
traceback.print_exc()
config.logger.log_message(
f"Pre Command could not perform copy: {file_source} -> {file_target}",
job_id=job_id
)
|
py | 1a312e1a1308dd2843df3b2ba6b71ce916acac0e | from __future__ import absolute_import
from django.http import Http404
from django.core.cache import cache
from django.test import TestCase
from django.test.client import RequestFactory
from django.test.utils import override_settings
from django_dynamic_fixture import get, new
from corsheaders.middleware import CorsMiddleware
from mock import patch
from readthedocs.core.middleware import SubdomainMiddleware
from readthedocs.projects.models import Project, ProjectRelationship, Domain
from readthedocs.rtd_tests.utils import create_user
@override_settings(USE_SUBDOMAIN=True)
class MiddlewareTests(TestCase):
def setUp(self):
self.factory = RequestFactory()
self.middleware = SubdomainMiddleware()
self.url = '/'
self.owner = create_user(username='owner', password='test')
self.pip = get(Project, slug='pip', users=[self.owner], privacy_level='public')
def test_failey_cname(self):
request = self.factory.get(self.url, HTTP_HOST='my.host.com')
with self.assertRaises(Http404):
self.middleware.process_request(request)
self.assertEqual(request.cname, True)
@override_settings(PRODUCTION_DOMAIN='readthedocs.org')
def test_proper_subdomain(self):
request = self.factory.get(self.url, HTTP_HOST='pip.readthedocs.org')
self.middleware.process_request(request)
self.assertEqual(request.urlconf, 'readthedocs.core.urls.subdomain')
self.assertEqual(request.subdomain, True)
self.assertEqual(request.slug, 'pip')
@override_settings(PRODUCTION_DOMAIN='prod.readthedocs.org')
def test_subdomain_different_length(self):
request = self.factory.get(self.url, HTTP_HOST='pip.prod.readthedocs.org')
self.middleware.process_request(request)
self.assertEqual(request.urlconf, 'readthedocs.core.urls.subdomain')
self.assertEqual(request.subdomain, True)
self.assertEqual(request.slug, 'pip')
def test_domain_object(self):
self.domain = get(Domain, domain='docs.foobar.com', project=self.pip)
request = self.factory.get(self.url, HTTP_HOST='docs.foobar.com')
self.middleware.process_request(request)
self.assertEqual(request.urlconf, 'readthedocs.core.urls.subdomain')
self.assertEqual(request.domain_object, True)
self.assertEqual(request.slug, 'pip')
def test_domain_object_missing(self):
self.domain = get(Domain, domain='docs.foobar2.com', project=self.pip)
request = self.factory.get(self.url, HTTP_HOST='docs.foobar.com')
with self.assertRaises(Http404):
self.middleware.process_request(request)
def test_proper_cname(self):
cache.get = lambda x: 'my_slug'
request = self.factory.get(self.url, HTTP_HOST='my.valid.homename')
self.middleware.process_request(request)
self.assertEqual(request.urlconf, 'readthedocs.core.urls.subdomain')
self.assertEqual(request.cname, True)
self.assertEqual(request.slug, 'my_slug')
def test_request_header(self):
request = self.factory.get(self.url, HTTP_HOST='some.random.com', HTTP_X_RTD_SLUG='pip')
self.middleware.process_request(request)
self.assertEqual(request.urlconf, 'readthedocs.core.urls.subdomain')
self.assertEqual(request.cname, True)
self.assertEqual(request.rtdheader, True)
self.assertEqual(request.slug, 'pip')
@override_settings(PRODUCTION_DOMAIN='readthedocs.org')
def test_proper_cname_uppercase(self):
cache.get = lambda x: x.split('.')[0]
request = self.factory.get(self.url, HTTP_HOST='PIP.RANDOM.COM')
self.middleware.process_request(request)
self.assertEqual(request.urlconf, 'readthedocs.core.urls.subdomain')
self.assertEqual(request.cname, True)
self.assertEqual(request.slug, 'pip')
def test_request_header_uppercase(self):
request = self.factory.get(self.url, HTTP_HOST='some.random.com', HTTP_X_RTD_SLUG='PIP')
self.middleware.process_request(request)
self.assertEqual(request.urlconf, 'readthedocs.core.urls.subdomain')
self.assertEqual(request.cname, True)
self.assertEqual(request.rtdheader, True)
self.assertEqual(request.slug, 'pip')
@override_settings(USE_SUBDOMAIN=True)
# no need to do a real dns query so patch cname_to_slug
@patch('readthedocs.core.middleware.cname_to_slug', new=lambda x: 'doesnt')
def test_use_subdomain_on(self):
request = self.factory.get(self.url, HTTP_HOST='doesnt.really.matter')
ret_val = self.middleware.process_request(request)
self.assertIsNone(ret_val, None)
class TestCORSMiddleware(TestCase):
def setUp(self):
self.factory = RequestFactory()
self.middleware = CorsMiddleware()
self.url = '/api/v2/search'
self.owner = create_user(username='owner', password='test')
self.project = get(
Project, slug='pip',
users=[self.owner], privacy_level='public',
mail_language_project=None
)
self.subproject = get(
Project,
users=[self.owner],
privacy_level='public',
mail_language_project=None,
)
self.relationship = get(
ProjectRelationship,
parent=self.project,
child=self.subproject
)
self.domain = get(Domain, domain='my.valid.domain', project=self.project)
def test_proper_domain(self):
request = self.factory.get(
self.url,
{'project': self.project.slug},
HTTP_ORIGIN='http://my.valid.domain',
)
resp = self.middleware.process_response(request, {})
self.assertIn('Access-Control-Allow-Origin', resp)
def test_invalid_domain(self):
request = self.factory.get(
self.url,
{'project': self.project.slug},
HTTP_ORIGIN='http://invalid.domain',
)
resp = self.middleware.process_response(request, {})
self.assertNotIn('Access-Control-Allow-Origin', resp)
def test_invalid_project(self):
request = self.factory.get(
self.url,
{'project': 'foo'},
HTTP_ORIGIN='http://my.valid.domain',
)
resp = self.middleware.process_response(request, {})
self.assertNotIn('Access-Control-Allow-Origin', resp)
def test_valid_subproject(self):
self.assertTrue(
Project.objects.filter(
pk=self.project.pk,
subprojects__child=self.subproject
).exists()
)
request = self.factory.get(
self.url,
{'project': self.subproject.slug},
HTTP_ORIGIN='http://my.valid.domain',
)
resp = self.middleware.process_response(request, {})
self.assertIn('Access-Control-Allow-Origin', resp)
|
py | 1a312ebad291791ff00ecf8ec50a32d5858a499f | from __future__ import unicode_literals
import fnmatch
import logging
import os
import re
import shutil
import subprocess
import tempfile
from difflib import SequenceMatcher
from functools import cmp_to_key
from django.core.exceptions import ObjectDoesNotExist
from django.utils import six
from django.utils.encoding import force_text
from django.utils.translation import ugettext as _
from djblets.log import log_timed
from djblets.siteconfig.models import SiteConfiguration
from djblets.util.compat.python.past import cmp
from djblets.util.contextmanagers import controlled_subprocess
from reviewboard.deprecation import RemovedInReviewBoard50Warning
from reviewboard.diffviewer.commit_utils import exclude_ancestor_filediffs
from reviewboard.diffviewer.errors import DiffTooBigError, PatchError
from reviewboard.scmtools.core import PRE_CREATION, HEAD
CHUNK_RANGE_RE = re.compile(
br'^@@ -(?P<orig_start>\d+)(,(?P<orig_len>\d+))? '
br'\+(?P<modified_start>\d+)(,(?P<modified_len>\d+))? @@',
re.M)
NEWLINE_CONVERSION_BYTES_RE = re.compile(br'\r(\r?\n)?')
NEWLINE_CONVERSION_UNICODE_RE = re.compile(r'\r(\r?\n)?')
NEWLINE_BYTES_RE = re.compile(br'(?:\n|\r(?:\r?\n)?)')
NEWLINE_UNICODE_RE = re.compile(r'(?:\n|\r(?:\r?\n)?)')
_PATCH_GARBAGE_INPUT = 'patch: **** Only garbage was found in the patch input.'
def convert_to_unicode(s, encoding_list):
"""Return the passed string as a unicode object.
If conversion to unicode fails, we try the user-specified encoding, which
defaults to ISO 8859-15. This can be overridden by users inside the
repository configuration, which gives users repository-level control over
file encodings.
Ideally, we'd like to have per-file encodings, but this is hard. The best
we can do now is a comma-separated list of things to try.
Returns the encoding type which was used and the decoded unicode object.
Args:
s (bytes or bytearray or unicode):
The string to convert to Unicode.
encoding_list (list of unicode):
The list of encodings to try.
Returns:
tuple:
A tuple with the following information:
1. A compatible encoding (:py:class:`unicode`).
2. The Unicode data (:py:class:`unicode`).
Raises:
TypeError:
The provided value was not a Unicode string, byte string, or
a byte array.
UnicodeDecodeError:
None of the encoding types were valid for the provided string.
"""
if isinstance(s, bytearray):
# Some SCMTool backends return file data as a bytearray instead of
# bytes.
s = bytes(s)
if isinstance(s, six.text_type):
# Nothing to do
return 'utf-8', s
elif isinstance(s, bytes):
try:
# First try strict utf-8
enc = 'utf-8'
return enc, six.text_type(s, enc)
except UnicodeError:
# Now try any candidate encodings
for e in encoding_list:
try:
return e, six.text_type(s, e)
except (UnicodeError, LookupError):
pass
# Finally, try to convert to unicode and replace all unknown
# characters.
try:
enc = 'utf-8'
return enc, six.text_type(s, enc, errors='replace')
except UnicodeError:
raise UnicodeDecodeError(
_("Diff content couldn't be converted to unicode using "
"the following encodings: %s")
% (['utf-8'] + encoding_list))
else:
raise TypeError('Value to convert is unexpected type %s', type(s))
def convert_line_endings(data):
r"""Convert line endings in a file.
Some types of repositories provide files with a single trailing Carriage
Return (``\r``), even if the rest of the file used a CRLF (``\r\n``)
throughout. In these cases, GNU diff will add a ``\ No newline at end of
file`` to the end of the diff, which GNU patch understands and will apply
to files with just a trailing ``\r``.
However, we normalize ``\r`` to ``\n``, which breaks GNU patch in these
cases. This function works around this by removing the last ``\r`` and
then converting standard types of newlines to a ``\n``.
This is not meant for use in providing byte-compatible versions of files,
but rather to help with comparing lines-for-lines in situations where
two versions of a file may come from different platforms with different
newlines.
Args:
data (bytes or unicode):
A string to normalize. This supports either byte strings or
Unicode strings.
Returns:
bytes or unicode:
The data with newlines converted, in the original string type.
Raises:
TypeError:
The ``data`` argument provided is not a byte string or Unicode
string.
"""
# See https://www.reviewboard.org/bugs/386/ and
# https://reviews.reviewboard.org/r/286/ for the rationale behind the
# normalization.
if data:
if isinstance(data, bytes):
cr = b'\r'
lf = b'\n'
newline_re = NEWLINE_CONVERSION_BYTES_RE
elif isinstance(data, six.text_type):
cr = '\r'
lf = '\n'
newline_re = NEWLINE_CONVERSION_UNICODE_RE
else:
raise TypeError(
_('%s is not a valid string type for convert_line_endings.')
% type(data))
if data.endswith(cr):
data = data[:-1]
data = newline_re.sub(lf, data)
return data
def split_line_endings(data):
"""Split a string into lines while preserving all non-CRLF characters.
Unlike :py:meth:`str.splitlines`, this will only split on the following
character sequences: ``\n``, ``\r``, ``\r\n``, and ``\r\r\n``.
This is needed to prevent the sort of issues encountered with
Unicode strings when calling :py:meth:`str.splitlines``, which is that form
feed characters would be split. :program:`patch` and :program:`diff` accept
form feed characters as valid characters in diffs, and doesn't treat them
as newlines, but :py:meth:`str.splitlines` will treat it as a newline
anyway.
Args:
data (bytes or unicode):
The data to split into lines.
Returns:
list of bytes or unicode:
The list of lines.
"""
if isinstance(data, bytes):
lines = NEWLINE_BYTES_RE.split(data)
elif isinstance(data, six.text_type):
lines = NEWLINE_UNICODE_RE.split(data)
else:
raise TypeError('data must be a bytes or unicode string, not %s'
% type(data))
# splitlines() would chop off the last entry, if the string ends with
# a newline. split() doesn't do this. We need to retain that same
# behavior by chopping it off ourselves.
if not lines[-1]:
lines = lines[:-1]
return lines
def patch(diff, orig_file, filename, request=None):
"""Apply a diff to a file.
This delegates out to ``patch`` because noone except Larry Wall knows how
to patch.
Args:
diff (bytes):
The contents of the diff to apply.
orig_file (bytes):
The contents of the original file.
filename (unicode):
The name of the file being patched.
request (django.http.HttpRequest, optional):
The HTTP request, for use in logging.
Returns:
bytes:
The contents of the patched file.
Raises:
reviewboard.diffutils.errors.PatchError:
An error occurred when trying to apply the patch.
"""
log_timer = log_timed('Patching file %s' % filename, request=request)
if not diff.strip():
# Someone uploaded an unchanged file. Return the one we're patching.
return orig_file
# Prepare the temporary directory if none is available
tempdir = tempfile.mkdtemp(prefix='reviewboard.')
try:
orig_file = convert_line_endings(orig_file)
diff = convert_line_endings(diff)
(fd, oldfile) = tempfile.mkstemp(dir=tempdir)
f = os.fdopen(fd, 'w+b')
f.write(orig_file)
f.close()
newfile = '%s-new' % oldfile
process = subprocess.Popen(['patch', '-o', newfile, oldfile],
stdin=subprocess.PIPE, stdout=subprocess.PIPE,
stderr=subprocess.PIPE, cwd=tempdir)
with controlled_subprocess('patch', process) as p:
stdout, stderr = p.communicate(diff)
failure = p.returncode
try:
with open(newfile, 'rb') as f:
new_file = f.read()
except Exception:
new_file = None
if failure:
rejects_file = '%s.rej' % newfile
try:
with open(rejects_file, 'rb') as f:
rejects = f.read()
except Exception:
rejects = None
error_output = force_text(stderr.strip() or stdout.strip())
# Munge the output to show the filename instead of
# randomly-generated tempdir locations.
base_filename = os.path.basename(filename)
error_output = (
error_output
.replace(rejects_file, '%s.rej' % base_filename)
.replace(oldfile, base_filename)
)
raise PatchError(filename=filename,
error_output=error_output,
orig_file=orig_file,
new_file=new_file,
diff=diff,
rejects=rejects)
return new_file
finally:
shutil.rmtree(tempdir)
log_timer.done()
def get_original_file_from_repo(filediff, request=None, encoding_list=None):
"""Return the pre-patched file for the FileDiff from the repository.
The parent diff will be applied if it exists.
Version Added:
4.0
Args:
filediff (reviewboard.diffviewer.models.filediff.FileDiff):
The FileDiff to retrieve the pre-patch file for.
request (django.http.HttpRequest, optional):
The HTTP request from the client.
encoding_list (list of unicode, optional):
A custom list of encodings to try when processing the file. This
will override the encoding list normally retrieved from the
FileDiff and repository.
If there's already a known valid encoding for the file, it will be
used instead.
This is here for compatibility and will be removed in Review Board
5.0.
Returns:
bytes:
The pre-patched file.
Raises:
UnicodeDecodeError:
The source file was not compatible with any of the available
encodings.
reviewboard.diffutils.errors.PatchError:
An error occurred when trying to apply the patch.
reviewboard.scmtools.errors.SCMError:
An error occurred while computing the pre-patch file.
"""
data = b''
extra_data = filediff.extra_data or {}
# If the file has a parent source filename/revision recorded, we're
# going to need to fetch that, since that'll be (potentially) the
# latest commit in the repository.
#
# This information was added in Review Board 3.0.19. Prior versions
# stored the parent source revision as filediff.source_revision
# (rather than leaving that as identifying information for the actual
# file being shown in the review). It did not store the parent
# filename at all (which impacted diffs that contained a moved/renamed
# file on any type of repository that required a filename for lookup,
# such as Mercurial -- Git was not affected, since it only needs
# blob SHAs).
#
# If we're not working with a parent diff, or this is a FileDiff
# with legacy parent diff information, we just use the FileDiff
# FileDiff filename/revision fields as normal.
source_filename = extra_data.get('parent_source_filename',
filediff.source_file)
source_revision = extra_data.get('parent_source_revision',
filediff.source_revision)
if source_revision != PRE_CREATION:
repository = filediff.get_repository()
data = repository.get_file(
source_filename,
source_revision,
base_commit_id=filediff.diffset.base_commit_id,
request=request)
# Convert to unicode before we do anything to manipulate the string.
encoding_list = get_filediff_encodings(filediff, encoding_list)
encoding, data = convert_to_unicode(data, encoding_list)
# Repository.get_file doesn't know or care about how we need line
# endings to work. So, we'll just transform every time.
#
# This is mostly only a problem if the diff chunks aren't in the
# cache, though if several people are working off the same file,
# we'll be doing extra work to convert those line endings for each
# of those instead of once.
#
# Only other option is to cache the resulting file, but then we're
# duplicating the cached contents.
data = convert_line_endings(data)
# Convert back to bytes using whichever encoding we used to decode.
data = data.encode(encoding)
if not filediff.encoding:
# Now that we know an encoding that works, remember it for next
# time.
filediff.extra_data['encoding'] = encoding
filediff.save(update_fields=('extra_data',))
# If there's a parent diff set, apply it to the buffer.
if (filediff.parent_diff and
not filediff.is_parent_diff_empty(cache_only=True)):
try:
data = patch(diff=filediff.parent_diff,
orig_file=data,
filename=source_filename,
request=request)
except PatchError as e:
# patch(1) cannot process diff files that contain no diff sections.
# We are going to check and see if the parent diff contains no diff
# chunks.
if (e.error_output == _PATCH_GARBAGE_INPUT and
not filediff.is_parent_diff_empty()):
raise
return data
def get_original_file(filediff, request=None, encoding_list=None):
"""Return the pre-patch file of a FileDiff.
Version Changed:
4.0:
The ``encoding_list`` parameter should no longer be provided by
callers. Encoding lists are now calculated automatically. Passing
a custom list will override the calculated one.
Args:
filediff (reviewboard.diffviewer.models.filediff.FileDiff):
The FileDiff to retrieve the pre-patch file for.
request (django.http.HttpRequest, optional):
The HTTP request from the client.
encoding_list (list of unicode, optional):
A custom list of encodings to try when processing the file. This
will override the encoding list normally retrieved from the
FileDiff and repository.
If there's already a known valid encoding for the file, it will be
used instead.
Returns:
bytes:
The pre-patch file.
Raises:
UnicodeDecodeError:
The source file was not compatible with any of the available
encodings.
reviewboard.diffutils.errors.PatchError:
An error occurred when trying to apply the patch.
reviewboard.scmtools.errors.SCMError:
An error occurred while computing the pre-patch file.
"""
if encoding_list:
RemovedInReviewBoard50Warning.warn(
'The encoding_list parameter passed to get_original_file() is '
'deprecated and will be removed in Review Board 5.0.')
data = b''
# If the FileDiff has a parent diff, it must be the case that it has no
# ancestor FileDiffs. We can fall back to the no history case here.
if filediff.parent_diff:
return get_original_file_from_repo(filediff=filediff,
request=request,
encoding_list=encoding_list)
# Otherwise, there may be one or more ancestors that we have to apply.
ancestors = filediff.get_ancestors(minimal=True)
if ancestors:
oldest_ancestor = ancestors[0]
# If the file was created outside this history, fetch it from the
# repository and apply the parent diff if it exists.
if not oldest_ancestor.is_new:
data = get_original_file_from_repo(filediff=oldest_ancestor,
request=request,
encoding_list=encoding_list)
if not oldest_ancestor.is_diff_empty:
data = patch(diff=oldest_ancestor.diff,
orig_file=data,
filename=oldest_ancestor.source_file,
request=request)
for ancestor in ancestors[1:]:
# TODO: Cache these results so that if this ``filediff`` is an
# ancestor of another FileDiff, computing that FileDiff's original
# file will be cheaper. This will also allow an ancestor filediff's
# original file to be computed cheaper.
data = patch(diff=ancestor.diff,
orig_file=data,
filename=ancestor.source_file,
request=request)
elif not filediff.is_new:
data = get_original_file_from_repo(filediff=filediff,
request=request,
encoding_list=encoding_list)
return data
def get_patched_file(source_data, filediff, request=None):
"""Return the patched version of a file.
This will normalize the patch, applying any changes needed for the
repository, and then patch the provided data with the patch contents.
Args:
source_data (bytes):
The file contents to patch.
filediff (reviewboard.diffviewer.models.filediff.FileDiff):
The FileDiff representing the patch.
request (django.http.HttpClient, optional):
The HTTP request from the client.
Returns:
bytes:
The patched file contents.
"""
repository = filediff.get_repository()
diff = repository.normalize_patch(patch=filediff.diff,
filename=filediff.source_file,
revision=filediff.source_revision)
return patch(diff=diff,
orig_file=source_data,
filename=filediff.dest_file,
request=request)
def get_revision_str(revision):
if revision == HEAD:
return "HEAD"
elif revision == PRE_CREATION:
return ""
else:
return _("Revision %s") % revision
def get_filenames_match_patterns(patterns, filenames):
"""Return whether any of the filenames match any of the patterns.
This is used to compare a list of filenames to a list of
:py:mod:`patterns <fnmatch>`. The patterns are case-sensitive.
Args:
patterns (list of unicode):
The list of patterns to match against.
filename (list of unicode):
The list of filenames.
Returns:
bool:
``True`` if any filenames match any patterns. ``False`` if none match.
"""
for pattern in patterns:
for filename in filenames:
if fnmatch.fnmatchcase(filename, pattern):
return True
return False
def get_filediff_encodings(filediff, encoding_list=None):
"""Return a list of encodings to try for a FileDiff's source text.
If the FileDiff already has a known encoding stored, then it will take
priority. The provided encoding list, or the repository's list of
configured encodingfs, will be provided as fallbacks.
Args:
filediff (reviewboard.diffviewer.models.filediff.FileDiff):
The FileDiff to return encodings for.
encoding_list (list of unicode, optional):
An explicit list of encodings to try. If not provided, the
repository's list of encodings will be used instead (which is
generally preferred).
Returns:
list of unicode:
The list of encodings to try for the source file.
"""
filediff_encoding = filediff.encoding
encodings = []
if encoding_list is None:
encoding_list = filediff.get_repository().get_encoding_list()
if filediff_encoding:
encodings.append(filediff_encoding)
encodings += [
encoding
for encoding in encoding_list
if encoding != filediff_encoding
]
else:
encodings += encoding_list
return encodings
def get_matched_interdiff_files(tool, filediffs, interfilediffs):
"""Generate pairs of matched files for display in interdiffs.
This compares a list of filediffs and a list of interfilediffs, attempting
to best match up the files in both for display in the diff viewer.
This will prioritize matches that share a common source filename,
destination filename, and new/deleted state. Failing that, matches that
share a common source filename are paired off.
Any entries in ``interfilediffs` that don't have any match in ``filediffs``
are considered new changes in the interdiff, and any entries in
``filediffs`` that don't have entries in ``interfilediffs`` are considered
reverted changes.
Args:
tool (reviewboard.scmtools.core.SCMTool)
The tool used for all these diffs.
filediffs (list of reviewboard.diffviewer.models.filediff.FileDiff):
The list of filediffs on the left-hand side of the diff range.
interfilediffs (list of reviewboard.diffviewer.models.filediff.
FileDiff):
The list of filediffs on the right-hand side of the diff range.
Yields:
tuple:
A paired off filediff match. This is a tuple containing two entries,
each a :py:class:`~reviewboard.diffviewer.models.filediff.FileDiff` or
``None``.
"""
parser = tool.get_parser(b'')
_normfile = parser.normalize_diff_filename
def _make_detail_key(filediff):
return (_normfile(filediff.source_file),
_normfile(filediff.dest_file),
filediff.is_new,
filediff.deleted)
# In order to support interdiffs properly, we need to display diffs on
# every file in the union of both diffsets. Iterating over one diffset
# or the other doesn't suffice. We also need to be careful to handle
# things like renamed/moved files, particularly when there are multiple
# of them with the same source filename.
#
# This is done in four stages:
#
# 1. Build up maps and a set for keeping track of possible
# interfilediff candidates for future stages.
#
# 2. Look for any files that are common between the two diff revisions
# that have the same source filename, same destination filename, and
# the same new/deleted states.
#
# Unless a diff is hand-crafted, there should never be more than one
# match here.
#
# 3. Look for any files that are common between the two diff revisions
# that have the same source filename and new/deleted state. These will
# ignore the destination filename, helping to match cases where diff 1
# modifies a file and diff 2 modifies + renames/moves it.
#
# 4. Add any remaining files from diff 2 that weren't found in diff 1.
#
# We don't have to worry about things like the order of matched diffs.
# That will be taken care of at the end of the function.
detail_interdiff_map = {}
simple_interdiff_map = {}
remaining_interfilediffs = set()
# Stage 1: Build up the maps/set of interfilediffs.
for interfilediff in interfilediffs:
source_file = _normfile(interfilediff.source_file)
detail_key = _make_detail_key(interfilediff)
# We'll store this interfilediff in three spots: The set of
# all interfilediffs, the detail map (for source + dest +
# is_new file comparisons), and the simple map (for direct
# source_file comparisons). These will be used for the
# different matching stages.
remaining_interfilediffs.add(interfilediff)
detail_interdiff_map[detail_key] = interfilediff
simple_interdiff_map.setdefault(source_file, set()).add(interfilediff)
# Stage 2: Look for common files with the same source/destination
# filenames and new/deleted states.
#
# There will only be one match per filediff, at most. Any filediff or
# interfilediff that we find will be excluded from future stages.
remaining_filediffs = []
for filediff in filediffs:
source_file = _normfile(filediff.source_file)
try:
interfilediff = detail_interdiff_map.pop(
_make_detail_key(filediff))
except KeyError:
remaining_filediffs.append(filediff)
continue
yield filediff, interfilediff
if interfilediff:
remaining_interfilediffs.discard(interfilediff)
try:
simple_interdiff_map.get(source_file, []).remove(interfilediff)
except ValueError:
pass
# Stage 3: Look for common files with the same source/destination
# filenames (when they differ).
#
# Any filediff from diff 1 not already processed in stage 2 will be
# processed here. We'll look for any filediffs from diff 2 that were
# moved/copied from the same source to the same destination. This is one
# half of the detailed file state we checked in stage 2.
new_remaining_filediffs = []
for filediff in remaining_filediffs:
source_file = _normfile(filediff.source_file)
found_interfilediffs = [
temp_interfilediff
for temp_interfilediff in simple_interdiff_map.get(source_file, [])
if (temp_interfilediff.dest_file == filediff.dest_file and
filediff.source_file != filediff.dest_file)
]
if found_interfilediffs:
remaining_interfilediffs.difference_update(found_interfilediffs)
for interfilediff in found_interfilediffs:
simple_interdiff_map[source_file].remove(interfilediff)
yield filediff, interfilediff
else:
new_remaining_filediffs.append(filediff)
remaining_filediffs = new_remaining_filediffs
# Stage 4: Look for common files with the same source filenames and
# new/deleted states.
#
# Any filediff from diff 1 not already processed in stage 3 will be
# processed here. We'll look for any filediffs from diff 2 that match
# the source filename and the new/deleted state. Any that we find will
# be matched up.
new_remaining_filediffs = []
for filediff in remaining_filediffs:
source_file = _normfile(filediff.source_file)
found_interfilediffs = [
temp_interfilediff
for temp_interfilediff in simple_interdiff_map.get(source_file, [])
if (temp_interfilediff.is_new == filediff.is_new and
temp_interfilediff.deleted == filediff.deleted)
]
if found_interfilediffs:
remaining_interfilediffs.difference_update(found_interfilediffs)
for interfilediff in found_interfilediffs:
simple_interdiff_map[source_file].remove(interfilediff)
yield filediff, interfilediff
else:
new_remaining_filediffs.append(filediff)
remaining_filediffs = new_remaining_filediffs
# Stage 5: Look for common files with the same source filenames and
# compatible new/deleted states.
#
# This will help catch files that were marked as new in diff 1 but not in
# diff 2, or deleted in diff 2 but not in diff 1. (The inverse for either
# is NOT matched!). This is important because if a file is introduced in a
# parent diff, the file can end up showing up as new itself (which is a
# separate bug).
#
# Even if that bug did not exist, it's still possible for a file to be new
# in one revision but committed separately (by that user or another), so we
# need these matched.
#
# Any files not found with a matching interdiff will simply be yielded.
# This is the last stage dealing with the filediffs in the first revision.
for filediff in remaining_filediffs:
source_file = _normfile(filediff.source_file)
found_interfilediffs = [
temp_interfilediff
for temp_interfilediff in simple_interdiff_map.get(source_file, [])
if (((filediff.is_new or not temp_interfilediff.is_new) or
(not filediff.is_new and temp_interfilediff.is_new and
filediff.dest_detail == temp_interfilediff.dest_detail)) and
(not filediff.deleted or temp_interfilediff.deleted))
]
if found_interfilediffs:
remaining_interfilediffs.difference_update(found_interfilediffs)
for interfilediff in found_interfilediffs:
# NOTE: If more stages are ever added that deal with
# simple_interdiff_map, then we'll need to remove
# interfilediff from that map here.
yield filediff, interfilediff
else:
yield filediff, None
# Stage 6: Add any remaining files from the interdiff.
#
# We've removed everything that we've already found. What's left are
# interdiff files that are new. They have no file to diff against.
#
# The end result is going to be a view that's the same as when you're
# viewing a standard diff. As such, we can pretend the interdiff is
# the source filediff and not specify an interdiff. Keeps things
# simple, code-wise, since we really have no need to special-case
# this.
for interfilediff in remaining_interfilediffs:
yield None, interfilediff
def get_filediffs_match(filediff1, filediff2):
"""Return whether two FileDiffs effectively match.
This is primarily checking that the patched version of two files are going
to be basically the same.
This will first check that we even have both FileDiffs. Assuming we have
both, this will check the diff for equality. If not equal, we at least
check that both files were deleted (which is equivalent to being equal).
The patched SHAs are then checked. These would be generated as part of the
diff viewing process, so may not be available. We prioritize the SHA256
hashes (introduced in Review Board 4.0), and fall back on SHA1 hashes if
not present.
Args:
filediff1 (reviewboard.diffviewer.models.filediff.FileDiff):
The first FileDiff to compare.
filediff2 (reviewboard.diffviewer.models.filediff.FileDiff):
The second FileDiff to compare.
Returns:
bool:
``True`` if both FileDiffs effectively match. ``False`` if they do
not.
Raises:
ValueError:
``None`` was provided for both ``filediff1`` and ``filediff2``.
"""
if filediff1 is None and filediff2 is None:
raise ValueError('filediff1 and filediff2 cannot both be None')
# For the hash comparisons, there's a chance we won't have any SHA1 (RB
# 2.0+) or SHA256 (RB 4.0+) hashes, so we have to check for them. We want
# to prioritize SHA256 hashes, but if the filediff or interfilediff lacks
# a SHA256 hash, we want to fall back to SHA1.
return (filediff1 is not None and filediff2 is not None and
(filediff1.diff == filediff2.diff or
(filediff1.deleted and filediff2.deleted) or
(filediff1.patched_sha256 is not None and
filediff1.patched_sha256 == filediff2.patched_sha256) or
((filediff1.patched_sha256 is None or
filediff2.patched_sha256 is None) and
filediff1.patched_sha1 is not None and
filediff1.patched_sha1 == filediff2.patched_sha1)))
def get_diff_files(diffset, filediff=None, interdiffset=None,
interfilediff=None, base_filediff=None, request=None,
filename_patterns=None, base_commit=None, tip_commit=None):
"""Return a list of files that will be displayed in a diff.
This will go through the given diffset/interdiffset, or a given filediff
within that diffset, and generate the list of files that will be
displayed. This file list will contain a bunch of metadata on the files,
such as the index, original/modified names, revisions, associated
filediffs/diffsets, and so on.
This can be used along with :py:func:`populate_diff_chunks` to build a full
list containing all diff chunks used for rendering a side-by-side diff.
Args:
diffset (reviewboard.diffviewer.models.diffset.DiffSet):
The diffset containing the files to return.
filediff (reviewboard.diffviewer.models.filediff.FileDiff, optional):
A specific file in the diff to return information for.
interdiffset (reviewboard.diffviewer.models.diffset.DiffSet, optional):
A second diffset used for an interdiff range.
interfilediff (reviewboard.diffviewer.models.filediff.FileDiff,
optional):
A second specific file in ``interdiffset`` used to return
information for. This should be provided if ``filediff`` and
``interdiffset`` are both provided. If it's ``None`` in this
case, then the diff will be shown as reverted for this file.
This may not be provided if ``base_filediff`` is provided.
base_filediff (reviewbaord.diffviewer.models.filediff.FileDiff,
optional):
The base FileDiff to use.
This may only be provided if ``filediff`` is provided and
``interfilediff`` is not.
filename_patterns (list of unicode, optional):
A list of filenames or :py:mod:`patterns <fnmatch>` used to
limit the results. Each of these will be matched against the
original and modified file of diffs and interdiffs.
base_commit (reviewboard.diffviewer.models.diffcommit.DiffCommit,
optional):
An optional base commit. No :py:class:`FileDiffs
<reviewboard.diffviewer.models.filediff.FileDiff>` from commits
before that commit will be included in the results.
This argument only applies to :py:class:`DiffSets
<reviewboard.diffviewer.models.diffset.DiffSet>` with
:py:class:`DiffCommits <reviewboard.diffviewer.models.diffcommit
.DiffCommit>`.
tip_commit (reviewboard.diffviewer.models.diffcommit.DiffSet,
optional):
An optional tip commit. No :py:class:`FileDiffs
<reviewboard.diffviewer.models.filediff.FileDiff>` from commits
after that commit will be included in the results.
This argument only applies to :py:class:`DiffSets
<reviewboard.diffviewer.models.diffset.DiffSet>` with
:py:class:`DiffCommits <reviewboard.diffviewer.models.diffcommit
.DiffCommit>`.
Returns:
list of dict:
A list of dictionaries containing information on the files to show
in the diff, in the order in which they would be shown.
"""
# It is presently not supported to do an interdiff with commit spans. It
# would require base/tip commits for the interdiffset as well.
assert not interdiffset or (base_commit is None and tip_commit is None)
assert base_filediff is None or interfilediff is None
if (diffset.commit_count > 0 and
base_commit and
tip_commit and
base_commit.pk > tip_commit.pk):
# If the base commit is more recent than the tip commit the interval
# **must** be empty.
return []
per_commit_filediffs = None
requested_base_filediff = base_filediff
if filediff:
filediffs = [filediff]
if interdiffset:
log_timer = log_timed("Generating diff file info for "
"interdiffset ids %s-%s, filediff %s" %
(diffset.id, interdiffset.id, filediff.id),
request=request)
else:
log_timer = log_timed("Generating diff file info for "
"diffset id %s, filediff %s" %
(diffset.id, filediff.id),
request=request)
if (diffset.commit_count > 0 and
((base_commit and filediff.commit_id <= base_commit.pk) or
(tip_commit and filediff.commit_id > tip_commit.pk))):
# The requested FileDiff is outside the requested commit range.
return []
else:
if (diffset.commit_count > 0 and
(base_commit is not None or tip_commit is not None)):
# Even if we have base_commit, we need to query for all FileDiffs
# so that we can do ancestor computations.
filediffs = per_commit_filediffs = diffset.per_commit_files
if base_commit:
base_commit_id = base_commit.pk
else:
base_commit_id = 0
if tip_commit:
tip_commit_id = tip_commit.pk
else:
tip_commit_id = None
filediffs = [
f
for f in filediffs
if (f.commit_id > base_commit_id and
(not tip_commit_id or
f.commit_id <= tip_commit_id))
]
filediffs = exclude_ancestor_filediffs(filediffs,
per_commit_filediffs)
else:
filediffs = diffset.cumulative_files
if interdiffset:
log_timer = log_timed("Generating diff file info for "
"interdiffset ids %s-%s" %
(diffset.id, interdiffset.id),
request=request)
else:
log_timer = log_timed("Generating diff file info for "
"diffset id %s" % diffset.id,
request=request)
# Filediffs that were created with leading slashes stripped won't match
# those created with them present, so we need to compare them without in
# order for the filenames to match up properly.
tool = diffset.repository.get_scmtool()
if interdiffset:
if not filediff:
if interdiffset.commit_count > 0:
# Currently, only interdiffing between cumulative diffs is
# supported.
interfilediffs = interdiffset.cumulative_files
else:
interfilediffs = list(interdiffset.files.all())
elif interfilediff:
interfilediffs = [interfilediff]
else:
interfilediffs = []
filediff_parts = []
matched_filediffs = get_matched_interdiff_files(
tool=tool,
filediffs=filediffs,
interfilediffs=interfilediffs)
for temp_filediff, temp_interfilediff in matched_filediffs:
if temp_filediff:
filediff_parts.append((temp_filediff, temp_interfilediff,
True))
elif temp_interfilediff:
filediff_parts.append((temp_interfilediff, None, False))
else:
logging.error(
'get_matched_interdiff_files returned an entry with an '
'empty filediff and interfilediff for diffset=%r, '
'interdiffset=%r, filediffs=%r, interfilediffs=%r',
diffset, interdiffset, filediffs, interfilediffs)
raise ValueError(
'Internal error: get_matched_interdiff_files returned an '
'entry with an empty filediff and interfilediff! Please '
'report this along with information from the server '
'error log.')
else:
# We're not working with interdiffs. We can easily create the
# filediff_parts directly.
filediff_parts = [
(temp_filediff, None, False)
for temp_filediff in filediffs
]
# Now that we have all the bits and pieces we care about for the filediffs,
# we can start building information about each entry on the diff viewer.
files = []
for parts in filediff_parts:
filediff, interfilediff, force_interdiff = parts
newfile = filediff.is_new
if interdiffset:
# First, find out if we want to even process this one.
# If the diffs are identical, or the patched files are identical,
# or if the files were deleted in both cases, then we can be
# absolutely sure that there's nothing interesting to show to
# the user.
if get_filediffs_match(filediff, interfilediff):
continue
source_revision = _('Diff Revision %s') % diffset.revision
else:
source_revision = get_revision_str(filediff.source_revision)
if interfilediff:
dest_revision = _('Diff Revision %s') % interdiffset.revision
else:
if force_interdiff:
dest_revision = (_('Diff Revision %s - File Reverted') %
interdiffset.revision)
elif newfile:
dest_revision = _('New File')
else:
dest_revision = _('New Change')
if interfilediff:
raw_depot_filename = filediff.dest_file
raw_dest_filename = interfilediff.dest_file
else:
raw_depot_filename = filediff.source_file
raw_dest_filename = filediff.dest_file
depot_filename = tool.normalize_path_for_display(raw_depot_filename)
dest_filename = tool.normalize_path_for_display(raw_dest_filename)
if filename_patterns:
if dest_filename == depot_filename:
filenames = [dest_filename]
else:
filenames = [dest_filename, depot_filename]
if not get_filenames_match_patterns(patterns=filename_patterns,
filenames=filenames):
continue
base_filediff = None
if filediff.commit_id:
# If we pre-computed this above (or before) and we have all
# FileDiffs, this will cost no additional queries.
#
# Otherwise this will cost up to
# ``1 + len(diffset.per_commit_files.count())`` queries.
ancestors = filediff.get_ancestors(minimal=False,
filediffs=per_commit_filediffs)
if ancestors:
if requested_base_filediff:
assert len(filediffs) == 1
if requested_base_filediff in ancestors:
base_filediff = requested_base_filediff
else:
raise ValueError(
'Invalid base_filediff (ID %d) for filediff (ID '
'%d)'
% (requested_base_filediff.pk, filediff.pk))
elif base_commit:
base_filediff = filediff.get_base_filediff(
base_commit=base_commit,
ancestors=ancestors)
f = {
'depot_filename': depot_filename,
'dest_filename': dest_filename or depot_filename,
'revision': source_revision,
'dest_revision': dest_revision,
'filediff': filediff,
'interfilediff': interfilediff,
'force_interdiff': force_interdiff,
'binary': filediff.binary,
'deleted': filediff.deleted,
'moved': filediff.moved,
'copied': filediff.copied,
'moved_or_copied': filediff.moved or filediff.copied,
'newfile': newfile,
'is_symlink': filediff.extra_data.get('is_symlink', False),
'index': len(files),
'chunks_loaded': False,
'is_new_file': (
(newfile or
(base_filediff is not None and
base_filediff.is_new)) and
not interfilediff and
not filediff.parent_diff
),
'base_filediff': base_filediff,
}
# When displaying an interdiff, we do not want to display the
# revision of the base filediff. Instead, we will display the diff
# revision as computed above.
if base_filediff and not interdiffset:
f['revision'] = get_revision_str(base_filediff.source_revision)
f['depot_filename'] = tool.normalize_path_for_display(
base_filediff.source_file)
if force_interdiff:
f['force_interdiff_revision'] = interdiffset.revision
files.append(f)
log_timer.done()
if len(files) == 1:
return files
else:
return get_sorted_filediffs(
files,
key=lambda f: f['interfilediff'] or f['filediff'])
def populate_diff_chunks(files, enable_syntax_highlighting=True,
request=None):
"""Populates a list of diff files with chunk data.
This accepts a list of files (generated by get_diff_files) and generates
diff chunk data for each file in the list. The chunk data is stored in
the file state.
"""
from reviewboard.diffviewer.chunk_generator import get_diff_chunk_generator
for diff_file in files:
generator = get_diff_chunk_generator(
request,
diff_file['filediff'],
diff_file['interfilediff'],
diff_file['force_interdiff'],
enable_syntax_highlighting,
base_filediff=diff_file.get('base_filediff'))
chunks = list(generator.get_chunks())
diff_file.update({
'chunks': chunks,
'num_chunks': len(chunks),
'changed_chunk_indexes': [],
'whitespace_only': len(chunks) > 0,
})
for j, chunk in enumerate(chunks):
chunk['index'] = j
if chunk['change'] != 'equal':
diff_file['changed_chunk_indexes'].append(j)
meta = chunk.get('meta', {})
if not meta.get('whitespace_chunk', False):
diff_file['whitespace_only'] = False
diff_file.update({
'num_changes': len(diff_file['changed_chunk_indexes']),
'chunks_loaded': True,
})
def get_file_from_filediff(context, filediff, interfilediff):
"""Return the files that corresponds to the filediff/interfilediff.
This is primarily intended for use with templates. It takes a
RequestContext for looking up the user and for caching file lists,
in order to improve performance and reduce lookup times for files that have
already been fetched.
This function returns either exactly one file or ``None``.
"""
interdiffset = None
key = "_diff_files_%s_%s" % (filediff.diffset.id, filediff.id)
if interfilediff:
key += "_%s" % (interfilediff.id)
interdiffset = interfilediff.diffset
if key in context:
files = context[key]
else:
assert 'user' in context
request = context.get('request', None)
files = get_diff_files(filediff.diffset, filediff, interdiffset,
interfilediff=interfilediff,
request=request)
populate_diff_chunks(files, get_enable_highlighting(context['user']),
request=request)
context[key] = files
if not files:
return None
assert len(files) == 1
return files[0]
def get_last_line_number_in_diff(context, filediff, interfilediff):
"""Determine the last virtual line number in the filediff/interfilediff.
This returns the virtual line number to be used in expandable diff
fragments.
"""
f = get_file_from_filediff(context, filediff, interfilediff)
last_chunk = f['chunks'][-1]
last_line = last_chunk['lines'][-1]
return last_line[0]
def _get_last_header_in_chunks_before_line(chunks, target_line):
"""Find the last header in the list of chunks before the target line."""
def find_last_line_numbers(lines):
"""Return a tuple of the last line numbers in the given list of lines.
The last line numbers are not always contained in the last element of
the ``lines`` list. This is the case when dealing with interdiffs that
have filtered out opcodes.
See :py:func:`get_chunks_in_range` for a description of what is
contained in each element of ``lines``.
"""
last_left = None
last_right = None
for line in reversed(lines):
if not last_right and line[4]:
last_right = line[4]
if not last_left and line[1]:
last_left = line[1]
if last_left and last_right:
break
return last_left, last_right
def find_header(headers, offset, last_line):
"""Return the last header that occurs before a line.
The offset parameter is the difference between the virtual number and
and actual line number in the chunk. This is required because the
header line numbers are original or patched line numbers, not virtual
line numbers.
"""
# In the case of interdiffs, it is possible that there will be headers
# in the chunk that don't belong to it, but were put there due to
# chunks being merged together. We must therefore ensure that the
# header we're looking at is actually in the chunk.
end_line = min(last_line, target_line)
for header in reversed(headers):
virtual_line = header[0] + offset
if virtual_line < end_line:
return {
'line': virtual_line,
'text': header[1]
}
# The most up-to-date header information
header = {
'left': None,
'right': None
}
for chunk in chunks:
lines = chunk['lines']
virtual_first_line = lines[0][0]
if virtual_first_line <= target_line:
if virtual_first_line == target_line:
# The given line number is the first line of a new chunk so
# there can't be any relevant header information here.
break
last_left, last_right = find_last_line_numbers(lines)
if 'left_headers' in chunk['meta'] and lines[0][1]:
offset = virtual_first_line - lines[0][1]
left_header = find_header(chunk['meta']['left_headers'],
offset, last_left + offset)
header['left'] = left_header or header['left']
if 'right_headers' in chunk['meta'] and lines[0][4]:
offset = virtual_first_line - lines[0][4]
right_header = find_header(chunk['meta']['right_headers'],
offset, last_right + offset)
header['right'] = right_header or header['right']
else:
# We've gone past the given line number.
break
return header
def get_last_header_before_line(context, filediff, interfilediff, target_line):
"""Get the last header that occurs before the given line.
This returns a dictionary of ``left`` header and ``right`` header. Each
header is either ``None`` or a dictionary with the following fields:
======== ==============================================================
Field Description
======== ==============================================================
``line`` Virtual line number (union of the original and patched files)
``text`` The header text
======== ==============================================================
"""
f = get_file_from_filediff(context, filediff, interfilediff)
return _get_last_header_in_chunks_before_line(f['chunks'], target_line)
def get_file_chunks_in_range(context, filediff, interfilediff,
first_line, num_lines):
"""Generate the chunks within a range of lines in the specified filediff.
This is primarily intended for use with templates. It takes a
RequestContext for looking up the user and for caching file lists,
in order to improve performance and reduce lookup times for files that have
already been fetched.
See :py:func:`get_chunks_in_range` for information on the returned state
of the chunks.
"""
f = get_file_from_filediff(context, filediff, interfilediff)
if f:
return get_chunks_in_range(f['chunks'], first_line, num_lines)
else:
return []
def get_chunks_in_range(chunks, first_line, num_lines):
"""Generate the chunks within a range of lines of a larger list of chunks.
This takes a list of chunks, computes a subset of those chunks from the
line ranges provided, and generates a new set of those chunks.
Each returned chunk is a dictionary with the following fields:
============= ========================================================
Variable Description
============= ========================================================
``change`` The change type ("equal", "replace", "insert", "delete")
``numlines`` The number of lines in the chunk.
``lines`` The list of lines in the chunk.
``meta`` A dictionary containing metadata on the chunk
============= ========================================================
Each line in the list of lines is an array with the following data:
======== =============================================================
Index Description
======== =============================================================
0 Virtual line number (union of the original and patched files)
1 Real line number in the original file
2 HTML markup of the original file
3 Changed regions of the original line (for "replace" chunks)
4 Real line number in the patched file
5 HTML markup of the patched file
6 Changed regions of the patched line (for "replace" chunks)
7 True if line consists of only whitespace changes
======== =============================================================
"""
for i, chunk in enumerate(chunks):
lines = chunk['lines']
if lines[-1][0] >= first_line >= lines[0][0]:
start_index = first_line - lines[0][0]
if first_line + num_lines <= lines[-1][0]:
last_index = start_index + num_lines
else:
last_index = len(lines)
new_chunk = {
'index': i,
'lines': chunk['lines'][start_index:last_index],
'numlines': last_index - start_index,
'change': chunk['change'],
'meta': chunk.get('meta', {}),
}
yield new_chunk
first_line += new_chunk['numlines']
num_lines -= new_chunk['numlines']
assert num_lines >= 0
if num_lines == 0:
break
def get_enable_highlighting(user):
user_syntax_highlighting = True
if user.is_authenticated():
try:
profile = user.get_profile()
user_syntax_highlighting = profile.syntax_highlighting
except ObjectDoesNotExist:
pass
siteconfig = SiteConfiguration.objects.get_current()
return (siteconfig.get('diffviewer_syntax_highlighting') and
user_syntax_highlighting)
def get_line_changed_regions(oldline, newline):
"""Returns regions of changes between two similar lines."""
if oldline is None or newline is None:
return None, None
# Use the SequenceMatcher directly. It seems to give us better results
# for this. We should investigate steps to move to the new differ.
differ = SequenceMatcher(None, oldline, newline)
# This thresholds our results -- we don't want to show inter-line diffs
# if most of the line has changed, unless those lines are very short.
# FIXME: just a plain, linear threshold is pretty crummy here. Short
# changes in a short line get lost. I haven't yet thought of a fancy
# nonlinear test.
if differ.ratio() < 0.6:
return None, None
oldchanges = []
newchanges = []
back = (0, 0)
for tag, i1, i2, j1, j2 in differ.get_opcodes():
if tag == 'equal':
if (i2 - i1 < 3) or (j2 - j1 < 3):
back = (j2 - j1, i2 - i1)
continue
oldstart, oldend = i1 - back[0], i2
newstart, newend = j1 - back[1], j2
if oldchanges and oldstart <= oldchanges[-1][1] < oldend:
oldchanges[-1] = (oldchanges[-1][0], oldend)
elif not oldline[oldstart:oldend].isspace():
oldchanges.append((oldstart, oldend))
if newchanges and newstart <= newchanges[-1][1] < newend:
newchanges[-1] = (newchanges[-1][0], newend)
elif not newline[newstart:newend].isspace():
newchanges.append((newstart, newend))
back = (0, 0)
return oldchanges, newchanges
def get_sorted_filediffs(filediffs, key=None):
"""Sorts a list of filediffs.
The list of filediffs will be sorted first by their base paths in
ascending order.
Within a base path, they'll be sorted by base name (minus the extension)
in ascending order.
If two files have the same base path and base name, we'll sort by the
extension in descending order. This will make :file:`*.h` sort ahead of
:file:`*.c`/:file:`*.cpp`, for example.
If the list being passed in is actually not a list of FileDiffs, it
must provide a callable ``key`` parameter that will return a FileDiff
for the given entry in the list. This will only be called once per
item.
"""
def cmp_filediffs(filediff1, filediff2):
x = make_key(filediff1)
y = make_key(filediff2)
# Sort based on basepath in ascending order.
if x[0] != y[0]:
a = x[0]
b = y[0]
else:
# Sort based on filename in ascending order, then based on
# the extension in descending order, to make *.h sort ahead of
# *.c/cpp.
x_file, x_ext = os.path.splitext(x[1])
y_file, y_ext = os.path.splitext(y[1])
if x_file == y_file:
a = y_ext
b = x_ext
else:
a = x_file
b = y_file
return cmp(a, b)
def make_key(filediff):
if key:
filediff = key(filediff)
filename = filediff.dest_file
i = filename.rfind('/')
if i == -1:
return '', filename
else:
return filename[:i], filename[i + 1:]
return sorted(filediffs, key=cmp_to_key(cmp_filediffs))
def get_displayed_diff_line_ranges(chunks, first_vlinenum, last_vlinenum):
"""Return the displayed line ranges based on virtual line numbers.
This takes the virtual line numbers (the index in the side-by-side diff
lines) and returns the human-readable line numbers, the chunks they're in,
and mapped virtual line numbers.
A virtual line range may start or end in a chunk not containing displayed
line numbers (such as an "original" range starting/ending in an "insert"
chunk). The resulting displayed line ranges will exclude these chunks.
Args:
chunks (list of dict):
The list of chunks for the diff.
first_vlinenum (int):
The first virtual line number. This uses 1-based indexes.
last_vlinenum (int):
The last virtual line number. This uses 1-based indexes.
Returns:
tuple:
A tuple of displayed line range information, containing 2 items.
Each item will either be a dictionary of information, or ``None``
if there aren't any displayed lines to show.
The dictionary contains the following keys:
``display_range``:
A tuple containing the displayed line range.
``virtual_range``:
A tuple containing the virtual line range that ``display_range``
maps to.
``chunk_range``:
A tuple containing the beginning/ending chunks that
``display_range`` maps to.
Raises:
ValueError:
The range provided was invalid.
"""
if first_vlinenum < 0:
raise ValueError('first_vlinenum must be >= 0')
if last_vlinenum < first_vlinenum:
raise ValueError('last_vlinenum must be >= first_vlinenum')
orig_start_linenum = None
orig_end_linenum = None
orig_start_chunk = None
orig_last_valid_chunk = None
patched_start_linenum = None
patched_end_linenum = None
patched_start_chunk = None
patched_last_valid_chunk = None
for chunk in chunks:
lines = chunk['lines']
if not lines:
logging.warning('get_displayed_diff_line_ranges: Encountered '
'empty chunk %r',
chunk)
continue
first_line = lines[0]
last_line = lines[-1]
chunk_first_vlinenum = first_line[0]
chunk_last_vlinenum = last_line[0]
if first_vlinenum > chunk_last_vlinenum:
# We're too early. There won't be anything of interest here.
continue
if last_vlinenum < chunk_first_vlinenum:
# We're not going to find anything useful at this point, so bail.
break
change = chunk['change']
valid_for_orig = (change != 'insert' and first_line[1])
valid_for_patched = (change != 'delete' and first_line[4])
if valid_for_orig:
orig_last_valid_chunk = chunk
if not orig_start_chunk:
orig_start_chunk = chunk
if valid_for_patched:
patched_last_valid_chunk = chunk
if not patched_start_chunk:
patched_start_chunk = chunk
if chunk_first_vlinenum <= first_vlinenum <= chunk_last_vlinenum:
# This chunk contains the first line that can possibly be used for
# the comment range. We know the start and end virtual line numbers
# in the range, so we can compute the proper offset.
offset = first_vlinenum - chunk_first_vlinenum
if valid_for_orig:
orig_start_linenum = first_line[1] + offset
orig_start_vlinenum = first_line[0] + offset
if valid_for_patched:
patched_start_linenum = first_line[4] + offset
patched_start_vlinenum = first_line[0] + offset
elif first_vlinenum < chunk_first_vlinenum:
# One side of the the comment range may not have started in a valid
# chunk (this would happen if a comment began in an insert or
# delete chunk). If that happened, we may not have been able to set
# the beginning of the range in the condition above. Check for this
# and try setting it now.
if orig_start_linenum is None and valid_for_orig:
orig_start_linenum = first_line[1]
orig_start_vlinenum = first_line[0]
if patched_start_linenum is None and valid_for_patched:
patched_start_linenum = first_line[4]
patched_start_vlinenum = first_line[0]
# Figure out the end ranges, now that we know the valid ending chunks of
# each. We're going to try to get the line within the chunk that represents
# the end, if within the chunk, capping it to the last line in the chunk.
#
# If a particular range did not have a valid chunk anywhere in that range,
# we're going to invalidate the entire range.
if orig_last_valid_chunk:
lines = orig_last_valid_chunk['lines']
first_line = lines[0]
last_line = lines[-1]
offset = last_vlinenum - first_line[0]
orig_end_linenum = min(last_line[1], first_line[1] + offset)
orig_end_vlinenum = min(last_line[0], first_line[0] + offset)
assert orig_end_linenum >= orig_start_linenum
assert orig_end_vlinenum >= orig_start_vlinenum
orig_range_info = {
'display_range': (orig_start_linenum, orig_end_linenum),
'virtual_range': (orig_start_vlinenum, orig_end_vlinenum),
'chunk_range': (orig_start_chunk, orig_last_valid_chunk),
}
else:
orig_range_info = None
if patched_last_valid_chunk:
lines = patched_last_valid_chunk['lines']
first_line = lines[0]
last_line = lines[-1]
offset = last_vlinenum - first_line[0]
patched_end_linenum = min(last_line[4], first_line[4] + offset)
patched_end_vlinenum = min(last_line[0], first_line[0] + offset)
assert patched_end_linenum >= patched_start_linenum
assert patched_end_vlinenum >= patched_start_vlinenum
patched_range_info = {
'display_range': (patched_start_linenum, patched_end_linenum),
'virtual_range': (patched_start_vlinenum, patched_end_vlinenum),
'chunk_range': (patched_start_chunk, patched_last_valid_chunk),
}
else:
patched_range_info = None
return orig_range_info, patched_range_info
def get_diff_data_chunks_info(diff):
"""Return information on each chunk in a diff.
This will scan through a unified diff file, looking for each chunk in the
diff and returning information on their ranges and lines of context. This
can be used to generate statistics on diffs and help map changed regions
in diffs to lines of source files.
Args:
diff (bytes):
The diff data to scan.
Returns:
list of dict:
A list of chunk information dictionaries. Each entry has an ``orig``
and ``modified` dictionary containing the following keys:
``chunk_start`` (``int``):
The starting line number of the chunk shown in the diff, including
any lines of context. This is 0-based.
``chunk_len`` (``int``):
The length of the chunk shown in the diff, including any lines of
context.
``changes_start`` (``int``):
The starting line number of a range of changes shown in a chunk in
the diff.
This is after any lines of context and is 0-based.
``changes_len`` (``int``):
The length of the changes shown in a chunk in the diff, excluding
any lines of context.
``pre_lines_of_context`` (``int``):
The number of lines of context before any changes in a chunk. If
the chunk doesn't have any changes, this will contain all lines of
context otherwise shown around changes in the other region in this
entry.
``post_lines_of_context`` (``int``):
The number of lines of context after any changes in a chunk. If
the chunk doesn't have any changes, this will be 0.
"""
def _finalize_result():
if not cur_result:
return
for result_dict, unchanged_lines in ((cur_result_orig,
orig_unchanged_lines),
(cur_result_modified,
modified_unchanged_lines)):
result_dict['changes_len'] -= unchanged_lines
if result_dict['changes_len'] == 0:
assert result_dict['pre_lines_of_context'] == 0
result_dict['pre_lines_of_context'] = unchanged_lines
else:
result_dict['post_lines_of_context'] = unchanged_lines
process_orig_changes = False
process_modified_changes = False
results = []
cur_result = None
cur_result_orig = None
cur_result_modified = None
orig_unchanged_lines = 0
modified_unchanged_lines = 0
# Look through the chunks of the diff, trying to find the amount
# of context shown at the beginning of each chunk. Though this
# will usually be 3 lines, it may be fewer or more, depending
# on file length and diff generation settings.
for i, line in enumerate(split_line_endings(diff.strip())):
if line.startswith(b'-'):
if process_orig_changes:
# We've found the first change in the original side of the
# chunk. We now know how many lines of context we have here.
#
# We reduce the indexes by 1 because the chunk ranges
# in diffs start at 1, and we want a 0-based index.
cur_result_orig['pre_lines_of_context'] = orig_unchanged_lines
cur_result_orig['changes_start'] += orig_unchanged_lines
cur_result_orig['changes_len'] -= orig_unchanged_lines
process_orig_changes = False
orig_unchanged_lines = 0
elif line.startswith(b'+'):
if process_modified_changes:
# We've found the first change in the modified side of the
# chunk. We now know how many lines of context we have here.
#
# We reduce the indexes by 1 because the chunk ranges
# in diffs start at 1, and we want a 0-based index.
cur_result_modified['pre_lines_of_context'] = \
modified_unchanged_lines
cur_result_modified['changes_start'] += \
modified_unchanged_lines
cur_result_modified['changes_len'] -= modified_unchanged_lines
process_modified_changes = False
modified_unchanged_lines = 0
elif line.startswith(b' '):
# We might be before a group of changes, inside a group of changes,
# or after a group of changes. Either way, we want to track these
# values.
orig_unchanged_lines += 1
modified_unchanged_lines += 1
else:
# This was not a change within a chunk, or we weren't processing,
# so check to see if this is a chunk header instead.
m = CHUNK_RANGE_RE.match(line)
if m:
# It is a chunk header. Start by updating the previous range
# to factor in the lines of trailing context.
_finalize_result()
# Next, reset the state for the next range, and pull the line
# numbers and lengths from the header. We'll also normalize
# the starting locations to be 0-based.
orig_start = int(m.group('orig_start')) - 1
orig_len = int(m.group('orig_len') or '1')
modified_start = int(m.group('modified_start')) - 1
modified_len = int(m.group('modified_len') or '1')
cur_result_orig = {
'pre_lines_of_context': 0,
'post_lines_of_context': 0,
'chunk_start': orig_start,
'chunk_len': orig_len,
'changes_start': orig_start,
'changes_len': orig_len,
}
cur_result_modified = {
'pre_lines_of_context': 0,
'post_lines_of_context': 0,
'chunk_start': modified_start,
'chunk_len': modified_len,
'changes_start': modified_start,
'changes_len': modified_len,
}
cur_result = {
'orig': cur_result_orig,
'modified': cur_result_modified,
}
results.append(cur_result)
process_orig_changes = True
process_modified_changes = True
orig_unchanged_lines = 0
modified_unchanged_lines = 0
else:
logging.warning('Unexpected content on line %d of diff: "%s"',
i, line)
# We need to adjust the last range, if we're still processing
# trailing context.
_finalize_result()
return results
def check_diff_size(diff_file, parent_diff_file=None):
"""Check the size of the given diffs against the maximum allowed size.
If either of the provided diffs are too large, an exception will be raised.
Args:
diff_file (django.core.files.uploadedfile.UploadedFile):
The diff file.
parent_diff_file (django.core.files.uploadedfile.UploadedFile,
optional):
The parent diff file, if any.
Raises:
reviewboard.diffviewer.errors.DiffTooBigError:
The supplied files are too big.
"""
siteconfig = SiteConfiguration.objects.get_current()
max_diff_size = siteconfig.get('diffviewer_max_diff_size')
if max_diff_size > 0:
if diff_file.size > max_diff_size:
raise DiffTooBigError(
_('The supplied diff file is too large.'),
max_diff_size=max_diff_size)
if parent_diff_file and parent_diff_file.size > max_diff_size:
raise DiffTooBigError(
_('The supplied parent diff file is too large.'),
max_diff_size=max_diff_size)
def get_total_line_counts(files_qs):
"""Return the total line counts of all given FileDiffs.
Args:
files_qs (django.db.models.query.QuerySet):
The queryset descripting the :py:class:`FileDiffs
<reviewboard.diffviewer.models.filediff.FileDiff>`.
Returns:
dict:
A dictionary with the following keys:
* ``raw_insert_count``
* ``raw_delete_count``
* ``insert_count``
* ``delete_count``
* ``replace_count``
* ``equal_count``
* ``total_line_count``
Each entry maps to the sum of that line count type for all
:py:class:`FileDiffs
<reviewboard.diffviewer.models.filediff.FileDiff>`.
"""
counts = {
'raw_insert_count': 0,
'raw_delete_count': 0,
'insert_count': 0,
'delete_count': 0,
'replace_count': None,
'equal_count': None,
'total_line_count': None,
}
for filediff in files_qs:
for key, value in six.iteritems(filediff.get_line_counts()):
if value is not None:
if counts[key] is None:
counts[key] = value
else:
counts[key] += value
return counts
|
py | 1a312eda6ac15637aaa3e935fa431e5e20ceeeaf | #!/usr/bin/env python
#
# Copyright 2015 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
"""Creates an AndroidManifest.xml for an APK split.
Given the manifest file for the main APK, generates an AndroidManifest.xml with
the value required for a Split APK (package, versionCode, etc).
"""
import lxml.etree
import optparse
from util import build_utils
MANIFEST_TEMPLATE = """<?xml version="1.0" encoding="utf-8"?>
<manifest
xmlns:android="http://schemas.android.com/apk/res/android"
package="%(package)s"
split="%(split)s">
<uses-sdk android:minSdkVersion="21" />
<application android:hasCode="%(has_code)s">
</application>
</manifest>
"""
def ParseArgs():
"""Parses command line options.
Returns:
An options object as from optparse.OptionsParser.parse_args()
"""
parser = optparse.OptionParser()
build_utils.AddDepfileOption(parser)
parser.add_option('--main-manifest', help='The main manifest of the app')
parser.add_option('--out-manifest', help='The output manifest')
parser.add_option('--split', help='The name of the split')
parser.add_option(
'--has-code',
action='store_true',
default=False,
help='Whether the split will contain a .dex file')
(options, args) = parser.parse_args()
if args:
parser.error('No positional arguments should be given.')
# Check that required options have been provided.
required_options = ('main_manifest', 'out_manifest', 'split')
build_utils.CheckOptions(options, parser, required=required_options)
return options
def Build(main_manifest, split, has_code):
"""Builds a split manifest based on the manifest of the main APK.
Args:
main_manifest: the XML manifest of the main APK as a string
split: the name of the split as a string
has_code: whether this split APK will contain .dex files
Returns:
The XML split manifest as a string
"""
doc = lxml.etree.fromstring(main_manifest)
package = doc.xpath('/manifest/@package')[0]
return MANIFEST_TEMPLATE % {
'package': package,
'split': split.replace('-', '_'),
'has_code': str(has_code).lower()
}
def main():
options = ParseArgs()
main_manifest = file(options.main_manifest).read()
split_manifest = Build(
main_manifest,
options.split,
options.has_code)
with file(options.out_manifest, 'w') as f:
f.write(split_manifest)
if options.depfile:
build_utils.WriteDepfile(
options.depfile,
[main_manifest] + build_utils.GetPythonDependencies())
if __name__ == '__main__':
main()
|
py | 1a312eebb329222b44e1ab3e86642b05d53bed49 | # -*- coding: utf-8 -*-
# Resource object code
#
# Created: Mon Dec 9 12:39:51 2019
# by: The Resource Compiler for PySide2 (Qt v5.13.0)
#
# WARNING! All changes made in this file will be lost!
from PySide2 import QtCore
qt_resource_data = b"\
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<\
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-8\x22 standalone=\x22\
no\x22?>\x0a<svg\x0a xm\
lns:dc=\x22http://p\
url.org/dc/eleme\
nts/1.1/\x22\x0a xml\
ns:cc=\x22http://cr\
eativecommons.or\
g/ns#\x22\x0a xmlns:\
rdf=\x22http://www.\
w3.org/1999/02/2\
2-rdf-syntax-ns#\
\x22\x0a xmlns:svg=\x22\
http://www.w3.or\
g/2000/svg\x22\x0a x\
mlns=\x22http://www\
.w3.org/2000/svg\
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odi=\x22http://sodi\
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i-0.dtd\x22\x0a xmln\
s:inkscape=\x22http\
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nkscape\x22\x0a widt\
h=\x2248\x22\x0a height\
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rdf:about=\x22\x22>\x0a\
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def qCleanupResources():
QtCore.qUnregisterResourceData(0x01, qt_resource_struct, qt_resource_name, qt_resource_data)
qInitResources()
|
py | 1a313097295e5d2b5e64ec97ffdbc0634712a8db | # pylint: disable=unused-import
try:
import json
except ImportError:
try:
import simplejson as json
except ImportError as ie:
raise ImportError(
'No json library installed.'
' Try running `pip install simplejson` to install a compatible json library.'
) from ie
|
py | 1a3131e465579d8b854f24dd4633a3d07fe8600b | """Define mapping of litex components to generated zephyr output"""
class Mapping:
"""Mapping from litex component to generated zephyr output
:param name: Name of the mapping, only used during debugging
:type name: str
"""
def __init__(self, name):
self.name = name
|
py | 1a31324f25e519d75abd088f1d1fb58983c3cab4 | """
We introduce some useful functions to print attributes of target object.
"""
import sys
import functools
import itertools
__all__ = ['display', 'camel_attr_print', 'trace']
def trace(func):
@functools.wraps(func)
def wrapper(*args, **kwargs):
result = func(*args, **kwargs)
print('%s(%r, %r) -> %r' % (func.__name__, args, kwargs, result))
return result
return wrapper
def _decorator_write_to_file(file):
def wrapper(func):
if not _validate_file_object(file):
print("File doesn't have write() method. Print to sys.stdout\n")
return func
else:
@functools.wraps(func)
def nfunc(*args, **kwargs):
return func(*args, file=file, **kwargs)
return nfunc
return wrapper
def _validate_file_object(file):
if not hasattr(file, 'write'):
return False
return True
def display(obj, tp, *args, file=sys.stdout, **kwargs):
"""
:param obj: specify your input object. It can be anything available in Python.
:param tp: specify the type of what you want to print. It can be 'state', 'attr', 'func', 'item'.
:param file: print the output to the file. the file must have write() method.
>>>import numpy as np
>>>display(np, 'attr', 'array')
object: <module 'numpy' from 'D:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\__init__.py'>
attribute name: array
attribute: <built-in function array>
>>>display(np, 'func', 'array', [313])
object: <module 'numpy' from 'D:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\__init__.py'>
function: array
function in: ([313],) {}
function return: [313]
>>>display('1 in [1,32,4]', 'state')
statement: 1 in [1,32,4]
return: True
>>>display('1 in a', 'state', {'a':[1,2,3,4]})
statement: 1 in a
return: True
>>>display([1,3,23], 'item', 0)
object: [1, 3, 23]
item sequence: (0,)
item return: 1
>>>display([[23,424],3,23], 'item', 0, 1)
object: [[23, 424], 3, 23]
item sequence: (0, 1)
item return: 424
"""
if file != sys.stdout:
mprint = _decorator_write_to_file(file)(print)
else:
mprint = print
if tp == 'item':
_display_item(obj, *args, mprint=mprint)
elif tp == 'attr':
_display_attr(obj, *args, mprint=mprint)
elif tp == 'func':
_display_func(obj, *args, mprint=mprint, **kwargs)
elif tp == 'state':
_display_state(obj, *args, mprint=mprint)
else:
print('Sorry, please choose a type "item", "attr", "func", "state"')
def camel_attr_print(obj, file=sys.stdout):
"""
:param obj: specify your input object. It can be anything available in Python.
:param file: print the output to the file. the file must have write() method.
>>>import numpy as np
>>>camel_attr_print(np)
object: <module 'numpy' from 'D:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\__init__.py'>
attribute name: __NUMPY_SETUP__
attribute: False
object: <module 'numpy' from 'D:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\__init__.py'>
attribute name: __all__
...
"""
if not _validate_file_object(file):
print("File doesn't have write() method. Print to sys.stdout\n")
file = sys.stdout
i = itertools.filterfalse(_find_not_camel, dir(obj))
display(obj, 'attr', *i, file=file)
def _find_not_camel(s):
if s.startswith('__') and s not in ('__doc__', '__builtins__'):
return False
else:
return True
def _display_attr(obj, *attrs, mprint=print):
mprint('object:', obj)
for attr in attrs:
mprint('attribute name:', attr)
attr = getattr(obj, attr)
mprint('attribute:', attr)
mprint('')
def _display_func(obj, func, *args, mprint=print, **kwargs):
mprint('object:', obj)
mprint('function:', func)
attr = getattr(obj, func)
mprint('function in:', args, kwargs)
mprint('function return:', attr(*args, **kwargs))
mprint('')
def _display_item(obj, *items, mprint=print):
mprint('object:', obj)
mprint('item sequence:', items)
r = obj
for item in items:
r = r[item]
mprint('item return:', r)
mprint('')
def _display_state(statement, *args, mprint=print):
mprint('statement:', statement)
mprint('return:', eval(statement, *args))
mprint('')
|
py | 1a3132fb9d9999df45804cb13b6a7594d0adf92a | #!/usr/bin/env python3
#
# Synthesis-based resolution of features/enforcers interactions in CPS
# Copyright 2020 Carnegie Mellon University.
# NO WARRANTY. THIS CARNEGIE MELLON UNIVERSITY AND SOFTWARE ENGINEERING
# INSTITUTE MATERIAL IS FURNISHED ON AN "AS-IS" BASIS. CARNEGIE MELLON
# UNIVERSITY MAKES NO WARRANTIES OF ANY KIND, EITHER EXPRESSED OR IMPLIED,
# AS TO ANY MATTER INCLUDING, BUT NOT LIMITED TO, WARRANTY OF FITNESS FOR
# PURPOSE OR MERCHANTABILITY, EXCLUSIVITY, OR RESULTS OBTAINED FROM USE OF
# THE MATERIAL. CARNEGIE MELLON UNIVERSITY DOES NOT MAKE ANY WARRANTY OF ANY
# KIND WITH RESPECT TO FREEDOM FROM PATENT, TRADEMARK, OR COPYRIGHT
# INFRINGEMENT.
# Released under a BSD (SEI)-style license, please see license.txt or contact
# [email protected] for full terms.
# [DISTRIBUTION STATEMENT A] This material has been approved for public
# release and unlimited distribution. Please see Copyright notice for
# non-US Government use and distribution.
# This Software includes and/or makes use of the following Third-Party Software
# subject to its own license:
# 1. JsonCpp
# (https://github.com/open-source-parsers/jsoncpp/blob/master/LICENSE)
# Copyright 2010 Baptiste Lepilleur and The JsonCpp Authors.
# DM20-0762
#
import sys
import os
import numpy as np
import pathlib
import itertools
import random
import json
# Assume ego starts 0,0 always
# Just relative to ego at varying distances
enemy_start_positions = [
'5,5',
'5,0',
'0,5',
'-5,5',
'5,-5',
'-5,-5',
'-5,0',
'0,-5',
'1,1',
'1,0',
'0,1',
'-1,1',
'1,-1',
'-1,-1',
'-1,0',
'0,-1',
'10,10',
'10,0',
'0,10',
'-10,10',
'10,-10',
'-10,-10',
'-10,0',
'0,-10',
]
# Config values that will be changed
config_vals = {
'ENEMY_DRONE_SPEED' : [x for x in np.arange(1.2, 2.1, 0.1)],
'WAYPOINT_SEED' : [x for x in range(0, 999)], # Kinda dumb way to do this space-wise but it's fine
'BOUNDARY_SIZE' : [x for x in np.arange(10, 30, 1)],
# 'SUGGEST_ACTION_RANGE' : [0,1]
}
# FLIGHT_WEIGHT stays constant
weight_vals = [
# Equal weights
{'BOUNDARY_WEIGHT' : 1,'RUNAWAY_WEIGHT' : 1, 'MISSILE_WEIGHT' : 1},
# 1 : 1.5 : 2
{'BOUNDARY_WEIGHT' : 1,'RUNAWAY_WEIGHT' : 1.5,'MISSILE_WEIGHT' : 2},
{'BOUNDARY_WEIGHT' : 1,'MISSILE_WEIGHT' : 1.5,'RUNAWAY_WEIGHT' : 2},
{'RUNAWAY_WEIGHT' : 1,'BOUNDARY_WEIGHT' : 1.5,'MISSILE_WEIGHT' : 2},
{'RUNAWAY_WEIGHT' : 1,'MISSILE_WEIGHT' : 1.5,'BOUNDARY_WEIGHT' : 2},
{'MISSILE_WEIGHT' : 1,'RUNAWAY_WEIGHT' : 1.5,'BOUNDARY_WEIGHT' : 2},
{'MISSILE_WEIGHT' : 1,'BOUNDARY_WEIGHT' : 1.5,'RUNAWAY_WEIGHT' : 2},
# 1 : 2 : 3
{'BOUNDARY_WEIGHT' : 1,'RUNAWAY_WEIGHT' : 2,'MISSILE_WEIGHT' : 3},
{'BOUNDARY_WEIGHT' : 1,'MISSILE_WEIGHT' : 2,'RUNAWAY_WEIGHT' : 3},
{'RUNAWAY_WEIGHT' : 1,'BOUNDARY_WEIGHT' : 2,'MISSILE_WEIGHT' : 3},
{'RUNAWAY_WEIGHT' : 1,'MISSILE_WEIGHT' : 2,'BOUNDARY_WEIGHT' : 3},
{'MISSILE_WEIGHT' : 1,'RUNAWAY_WEIGHT' : 2,'BOUNDARY_WEIGHT' : 3},
{'MISSILE_WEIGHT' : 1,'BOUNDARY_WEIGHT' : 2,'RUNAWAY_WEIGHT' : 3},
]
def make_config_file(base_config_file, outfile, vals):
# Open input and output file
with open(base_config_file, 'r') as base, open(outfile, 'w') as out:
# Convert to list by space delim
for line in base:
line_lst = line.split(' ')
# If this var is one we change, then write what's stored in vars
if(line_lst[0] in vals):
# Handle the case that it's a float differently bc annoying precision
if isinstance(line_lst[0], np.float64):
out.write(line_lst[0] + ' ' + '{:.2f}'.format(vals[line_lst[0]]) + '\n')
else:
out.write(line_lst[0] + ' ' + str(vals[line_lst[0]]) + '\n')
# If this var is not one we change, write it as is
else:
out.write(line)
def default(o):
if isinstance(o, np.int64): return int(o)
raise TypeError
def make_files(config, rootdir, enemy_start_positions, num_configurations):
newdir = ''
# They're all the same at this point -- just get the vals for any coordinator
vals = config["RobustnessCoordinator"]
# Get all the combinations of variable-values we have
combinations = [dict((zip(vals.keys(), t))) for t in itertools.product(*vals.values())]
sample_size = num_configurations
# Get a random sample of the combinations
comb_sample = random.sample(combinations, sample_size)
# Randomly assign weights to each case
for entry in comb_sample:
weights = random.choice(weight_vals)
entry.update(weights)
for coordinator in config:
try:
newdir = rootdir+'/'+coordinator
os.makedirs(newdir, exist_ok=True)
except OSError:
print("Failed to create directory: %s" % newdir)
i=0
# Create a directory and a corresponding config file for each test case
for entry in comb_sample:
# Everything else is random so this is fine
enemy_start_pos_str = enemy_start_positions[i%len(enemy_start_positions)]
i = i+1
# Need to add this manually bc it's not in config file params
controlled_vars = entry.copy();
controlled_vars["enemy_strt_pos"] = enemy_start_pos_str;
dirname = ''
for name in entry:
if isinstance(entry[name], np.float64):
dirname+=name+'{:.2f}'.format(entry[name])+'-'
else:
dirname+=name+str(entry[name])+'-'
# trim the hyphen off the end
dirname = dirname[0:-1]
try:
os.makedirs(rootdir+'/'+coordinator+'/'+dirname, exist_ok=True)
except OSError:
print("Failed to create directory: %s" % rootdir+'/'+coordinator+'/'+dirname)
# Write config file
make_config_file('./drone.cfg', rootdir+coordinator+'/'+dirname+'/'+'drone.cfg', entry)
with open(rootdir+coordinator+'/'+dirname+'/'+'controlled_vars.json', 'w') as controlled_varsfile:
json.dump(controlled_vars, controlled_varsfile, default=default)
# Write positions
with open(rootdir+coordinator+'/'+dirname+'/'+'enemy_start_pos', 'wb') as posfile:
posfile.write(enemy_start_pos_str.encode('utf-8'))
def main():
if(len(sys.argv) != 3):
print("Usage: generate_tests.py <test_dir> <num_configurations>")
exit()
cwd = os.getcwd()
os.makedirs(sys.argv[1], exist_ok=True)
num_configurations = int(sys.argv[2])
rootdir = cwd+'/'+sys.argv[1]+'/'
if((cwd.split('/'))[-1] != 'missionapp'):
print("Must be in missionapp directory to use this script. Given %s\n" % cwd)
print(cwd.split('/'))
exit()
base_config_file = 'drone.cfg'
coordinators = ['PriorityCoordinator', 'RobustnessCoordinator', 'SynthRobustnessCoordinator']
config = { coord : config_vals.copy() for coord in coordinators }
make_files(config, rootdir, enemy_start_positions, num_configurations)
if __name__ == "__main__":
main()
|
py | 1a313475913ca2ebb1ad14d72612228b49fbce63 | def test_dummy():
assert 1 == 1
# this is just a dummy test
# add meaningful tests |
py | 1a3135009e8a95578bfe5f70efc5e350b6a417e8 | #!/usr/bin/env python
"""Django's command-line utility for administrative tasks."""
import os
import sys
def main():
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'photo_frame.settings')
try:
from django.core.management import execute_from_command_line
except ImportError as exc:
raise ImportError(
"Couldn't import Django. Are you sure it's installed and "
"available on your PYTHONPATH environment variable? Did you "
"forget to activate a virtual environment?"
) from exc
execute_from_command_line(sys.argv)
if __name__ == '__main__':
main()
|
py | 1a3137073d8f07d24e2551a07bd100679fdf6451 | from time import sleep
import datetime
import getpass
import keyring
import traceback
from github import Github
DEBUG = False
earliest = datetime.datetime(2012,1,1)
def getAERepos(username):
count = 0
try:
g = Github(username, getGithubPassword(username))
for repo in g.legacy_search_repos('app engine'):
count += 1
try:
#if repo.updated_at > earliest or repo.pushed_at > earliest:
if repo.pushed_at > earliest:
try:
print '{0};{1};{2};{3};{4};{5};{6};{7};{8}'.format(
repo.name,
repo.created_at.date(),
repo.updated_at.date(),
repo.pushed_at.date(),
repo.owner.login,
repo.language,
repo.forks,
repo.watchers,
repo.description)
except:
print 'ERROR unable to print description of repo {0}'.format(repo.name)
if DEBUG and count > 10:
break
if 'appscale' in repo.name.lower():
print '\tFound AppScale!'
except:
print 'ERROR1 unable to get repo'
sleep(2)
except:
print 'ERROR2 unable to get anything'
def printRepository(username):
g = Github(username, getGithubPassword(username))
user = g.get_user()
repositories = user.get_repos()
for repository in repositories:
print repository.name
printBranches(repository)
def printBranches(repository):
for branch in repository.get_branches():
print ' ', branch.name
tree = branch.commit.commit.tree
printTree(repository, tree, ' ')
def printTree(repository, tree, indent):
for element in tree.tree:
print indent, element.path
if element.type == 'tree':
printTree(repository, repository.get_git_tree(element.sha), indent + ' ')
def getGithubPassword(username):
service = 'github'
password = keyring.get_password(service, username)
if password == None:
print "Enter password for user", username
password = getpass.getpass()
keyring.set_password(service, username, password)
return password
# Pass your Github username as a parameter
#printRepository('ckrintz')
# step through the repos with keyword 'app engine'
getAERepos('ckrintz')
|
py | 1a3137092d366e49dc9666d6765222f9382bd4cb | from chainer import cuda
from chainer import function
from chainer import variable
class _DummyFunction(function.Function):
def __init__(self, grads):
self.grads = grads
def forward(self, inputs):
xp = cuda.get_array_module(*inputs)
return xp.array(0),
def backward(self, inputs, outputs):
return self.grads
class Forget(function.Function):
def __init__(self, func):
if not callable(func):
raise TypeError('func must be callable')
self.func = func
def _call_func(self, xs):
outs = self.func(*xs)
if isinstance(outs, tuple):
for i, out in enumerate(outs):
if isinstance(out, variable.Variable):
continue
n = i + 1
suffix = {1: 'st', 2: 'nd', 3: 'rd'}.get(
n if n < 20 else n % 10, 'th')
msg = ('{}{} element of a returned tuple is not Variable, '
'but is {}').format(n, suffix, type(out))
raise RuntimeError(msg)
elif isinstance(outs, variable.Variable):
outs = (outs,)
else:
msg = ('A tuple of Variables or a Variable are expected, but {} '
'is returned.'.format(type(outs)))
raise RuntimeError(msg)
return outs
def forward(self, inputs):
xs = [variable.Variable(x, volatile=True) for x in inputs]
outs = self._call_func(xs)
return tuple(out.data for out in outs)
def backward(self, inputs, grads):
xs = [variable.Variable(x, volatile=False) for x in inputs]
outs = self._call_func(xs)
_DummyFunction(grads)(*outs).backward()
return tuple(x.grad for x in xs)
def forget(func, *xs):
"""Call a function without storing internal results.
On a forward propagation Chainer stores all internal results of
:class:`Function` on a computational graph as they are required on
backward-propagation. These results consume too much memory when the
internal results are too large. This method **forgets** such internal
results on forward propagation, and still supports back-propagation with
recalculation.
In a forward propagation, this method calls a given function with given
variables without creating a computational graph. That means, no internal
results are stored. In a backward propagation this method calls the given
function again to create a computational graph to execute back-propagation.
This method reduces internal memory usage. Instead it requires more
calculation time as it calls the function twice.
.. admonition:: Example
Let ``f`` be a function defined as:
>>> def f(a, b):
... return a + b + a
and, ``x`` and ``y`` be :class:`~chainer.Variable`:
>>> x = chainer.Variable(np.random.uniform(-1, 1, 5).astype('f'))
>>> y = chainer.Variable(np.random.uniform(-1, 1, 5).astype('f'))
When ``z`` is calculated as ``z = f(x, y)``, its internal result
``x + y`` is stored in memory. Instead if you call ``f`` with
:meth:`forget`:
>>> z = F.forget(f, x, y)
internal ``x + y`` is forgotten.
.. note::
The method does not support functions behaving randomly, such as
:meth:`~chainer.functions.dropout` and
:meth:`~chainer.functions.negative_sampling`. It is because first results
of these function differ from the second one.
Args:
func (callable): A function to call. It needs to be called with
:class:`~chainer.Variable` object(s) and to return a
:class:`~chainer.Variable` object or a tuple of
:class:`~chainer.Variable` objects.
xs (~chainer.Variable): Argument variables of the function.
Returns:
~chainer.Variable: A variable ``func`` returns. If it returns a tuple,
the method returns a tuple too.
"""
return Forget(func)(*xs)
|
py | 1a3137dba4cd2e166fcbfa7612dd486edeeb7ff7 |
""" tpm.py
Wrapper classes for swtpm
"""
# pylint: disable=R0902,R0913,R0914,C0302,W0703
#
# swtpm_setup.py
#
# Authors: Stefan Berger <[email protected]>
#
# (c) Copyright IBM Corporation 2020
#
import os
import socket
import struct
import subprocess
import time
# TPM1.2 imports
from cryptography.hazmat.backends import default_backend
from cryptography.hazmat.primitives import hashes, hmac
from cryptography.hazmat.primitives.asymmetric import padding
from cryptography.hazmat.primitives.asymmetric.rsa import RSAPublicNumbers
from py_swtpm_setup.swtpm_utils import logit, logerr, sha1
CMD_INIT = 0x2
CMD_SHUTDOWN = 0x3
CMD_GET_INFO = 0x12
TPMLIB_INFO_TPMSPECIFICATION = 1
TPMLIB_INFO_TPMATTRIBUTES = 2
#
# swtpm base class for TPM 1.2 and TPM 2.0
#
class Swtpm:
""" Swtpm is the base class for usage of swtpm as TPM 1.2 or TPM 2 """
def __init__(self, swtpm_exec_l, state_path, keyopt, logfile, fds_to_pass, is_tpm2=False):
""" Class constructor
swtpm_exec_l is a list like ["swtpm", "socket"]
"""
self.swtpm_exec_l = swtpm_exec_l
self.state_path = state_path
self.keyopt = keyopt
self.logfile = logfile
self.fds_to_pass = fds_to_pass
self.is_tpm2 = is_tpm2
self.pidfile = None
self.swtpm_proc = None
self.data_client_socket = None
self.data_swtpm_socket = None
self.ctrl_client_socket = None
self.ctrl_swtpm_socket = None
# Probe the socket domain; Linux only has socket.AF_UNIX, Cygwin AF_INET
self.socket_domain = socket.AF_UNIX
try:
s1, s2 = socket.socketpair(self.socket_domain)
s1.close()
s2.close()
except ValueError: # Cygwin gives a ValueError
self.socket_domain = socket.AF_INET
def start(self):
""" The start method starts the TPM 2 """
self.pidfile = os.path.join(self.state_path, ".swtpm_setup.pidfile")
cmdline = self.swtpm_exec_l.copy()
if self.is_tpm2:
cmdline.extend(["--tpm2"])
if self.keyopt:
cmdline.extend(["--key", self.keyopt])
cmdline.extend(["--flags", "not-need-init",
"--tpmstate", "dir=%s" % self.state_path,
"--pid", "file=%s" % self.pidfile])
# cmdline.extend(["--log", "file=/tmp/log,level=20"])
ctr = 0
while ctr < 100:
self.data_client_socket, self.data_swtpm_socket = socket.socketpair(self.socket_domain,
socket.SOCK_STREAM)
os.set_inheritable(self.data_swtpm_socket.fileno(), True)
self.ctrl_client_socket, self.ctrl_swtpm_socket = socket.socketpair(self.socket_domain,
socket.SOCK_STREAM)
os.set_inheritable(self.ctrl_swtpm_socket.fileno(), True)
r_cmdline = cmdline.copy()
r_cmdline.extend(["--server", "type=tcp,fd=%d" % self.data_swtpm_socket.fileno(),
"--ctrl", "type=unixio,clientfd=%d" %
self.ctrl_swtpm_socket.fileno()])
self.remove_pidfile()
# print("starting swtpm: %s\n" % r_cmdline)
try:
pass_fds = [self.data_swtpm_socket.fileno(),
self.ctrl_swtpm_socket.fileno()]
pass_fds.extend(self.fds_to_pass)
self.swtpm_proc = subprocess.Popen(r_cmdline, stdout=subprocess.PIPE,
stderr=subprocess.STDOUT, pass_fds=pass_fds)
except Exception as err:
logerr(self.logfile,
"Failed to start swtpm %s: %s\n" % (" ".join(self.swtpm_exec_l), str(err)))
ctr += 1
ctr2 = 0
while True:
# Is it still running?
if self.swtpm_proc.poll():
stderr = self.swtpm_proc.communicate()[0]
print("TPM died? %s\n" % stderr)
self.stop()
break
if os.path.exists(self.pidfile):
print("TPM is listening on Unix socket.")
return 0
ctr2 += 1
time.sleep(0.05)
if ctr2 == 40:
self.stop()
break
return 1
def remove_pidfile(self):
""" Remove the pidfile if it exists """
if self.pidfile:
try:
os.remove(self.pidfile)
except Exception:
pass
def stop(self):
""" Stop the running swtpm instance """
if self.swtpm_proc:
if not self.swtpm_proc.poll():
self.ctrl_shutdown()
try:
self.swtpm_proc.wait(timeout=0.5)
except subprocess.TimeoutExpired:
self.swtpm_proc.kill()
self.swtpm_proc.wait()
self.swtpm_proc = None
self.remove_pidfile()
for sock in [self.data_client_socket, self.data_swtpm_socket,
self.ctrl_client_socket, self.ctrl_swtpm_socket]:
if sock:
sock.close()
self.data_client_socket = None
self.data_swtpm_socket = None
self.ctrl_client_socket = None
self.ctrl_swtpm_socket = None
def destroy(self):
""" Destroy the running swtpm instance """
self.stop()
def transfer(self, req, cmdname, use_ctrl=False):
""" Send a command to swtpm and receive a response """
if use_ctrl:
sock = self.ctrl_client_socket
offset = 0
else:
sock = self.data_client_socket
offset = 6
try:
sock.sendall(req)
rsp = sock.recv(4096)
except Exception as err:
logerr(self.logfile, "transfer error: %s\n" % str(err))
return None, 1
if not use_ctrl:
if len(rsp) < 10:
logerr(self.logfile,
"Response for %s has only %d bytes.\n" % (cmdname, len(rsp)))
return None, 1
returncode = struct.unpack(">I", rsp[offset:offset+4])[0]
if returncode != 0:
logerr(self.logfile, "%s failed: 0x%x\n" % (cmdname, returncode))
return None, 1
return rsp, 0
def ctrl_init(self):
""" Send an Init over the control channel """
req = struct.pack(">I I", CMD_INIT, 0)
_, ret = self.transfer(req, "CMD_INIT", use_ctrl=True)
return ret
def ctrl_shutdown(self):
""" Send an Init over the control channel """
req = struct.pack(">I", CMD_SHUTDOWN)
_, ret = self.transfer(req, "CMD_SHUTDOWN", use_ctrl=True)
return ret
def ctrl_get_tpm_specs_and_attrs(self):
""" Get the TPM specification parameters over the control channel """
req = struct.pack(">I QII", CMD_GET_INFO,
TPMLIB_INFO_TPMSPECIFICATION | TPMLIB_INFO_TPMATTRIBUTES, 0, 0)
rsp, ret = self.transfer(req, "CMD_GET_INFO", use_ctrl=True)
if ret != 0:
return "", 1
length = struct.unpack(">I", rsp[8:12])[0]
# compensate for null-terminated string
length -= 1
data = struct.unpack("%ds" % length, rsp[12:12+length])[0]
return data.decode(), 0
#
# TPM 2 support
#
TPM2_ST_NO_SESSIONS = 0x8001
TPM2_ST_SESSIONS = 0x8002
TPM2_CC_EVICTCONTROL = 0x00000120
TPM2_CC_NV_DEFINESPACE = 0x0000012a
TPM2_CC_PCR_ALLOCATE = 0x0000012b
TPM2_CC_CREATEPRIMARY = 0x00000131
TPM2_CC_NV_WRITE = 0x00000137
TPM2_CC_NV_WRITELOCK = 0x00000138
TPM2_CC_STARTUP = 0x00000144
TPM2_CC_SHUTDOWN = 0x00000145
TPM2_CC_GETCAPABILITY = 0x0000017a
TPM2_SU_CLEAR = 0x0000
TPM2_RH_OWNER = 0x40000001
TPM2_RS_PW = 0x40000009
TPM2_RH_ENDORSEMENT = 0x4000000b
TPM2_RH_PLATFORM = 0x4000000c
TPM2_ALG_RSA = 0x0001
TPM2_ALG_SHA1 = 0x0004
TPM2_ALG_AES = 0x0006
TPM2_ALG_SHA256 = 0x000b
TPM2_ALG_SHA384 = 0x000c
TPM2_ALG_SHA512 = 0x000d
TPM2_ALG_SHA3_256 = 0x0027
TPM2_ALG_SHA3_384 = 0x0028
TPM2_ALG_SHA3_512 = 0x0028
TPM2_ALG_NULL = 0x0010
TPM2_ALG_SM3 = 0x0012
TPM2_ALG_ECC = 0x0023
TPM2_ALG_CFB = 0x0043
TPM2_CAP_PCRS = 0x00000005
TPM2_ECC_NIST_P384 = 0x0004
TPMA_NV_PLATFORMCREATE = 0x40000000
TPMA_NV_AUTHREAD = 0x40000
TPMA_NV_NO_DA = 0x2000000
TPMA_NV_PPWRITE = 0x1
TPMA_NV_PPREAD = 0x10000
TPMA_NV_OWNERREAD = 0x20000
TPMA_NV_WRITEDEFINE = 0x2000
# Use standard EK Cert NVRAM, EK and SRK handles per IWG spec.
# "TCG TPM v2.0 Provisioning Guide"; Version 1.0, Rev 1.0, March 15, 2017
# Table 2
TPM2_NV_INDEX_RSA2048_EKCERT = 0x01c00002
TPM2_NV_INDEX_RSA2048_EKTEMPLATE = 0x01c00004
TPM2_NV_INDEX_RSA3072_HI_EKCERT = 0x01c0001c
TPM2_NV_INDEX_RSA3072_HI_EKTEMPLATE = 0x01c0001d
# For ECC follow "TCG EK Credential Profile For TPM Family 2.0; Level 0"
# Specification Version 2.1; Revision 13; 10 December 2018
TPM2_NV_INDEX_PLATFORMCERT = 0x01c08000
TPM2_NV_INDEX_ECC_SECP384R1_HI_EKCERT = 0x01c00016
TPM2_NV_INDEX_ECC_SECP384R1_HI_EKTEMPLATE = 0x01c00017
TPM2_EK_RSA_HANDLE = 0x81010001
TPM2_EK_RSA3072_HANDLE = 0x8101001c
TPM2_EK_ECC_SECP384R1_HANDLE = 0x81010016
TPM2_SPK_HANDLE = 0x81000001
NONCE_EMPTY = struct.pack('>H', 0)
NONCE_RSA2048 = struct.pack('>H256s', 0x100, ('\0' * 0x100).encode())
NONCE_RSA3072 = struct.pack('>H384s', 0x180, ('\0' * 0x180).encode())
NONCE_ECC_384 = struct.pack('>H48s', 0x30, ('\0' * 0x30).encode())
PCR_BANKS_TO_NAMES = {
TPM2_ALG_SHA1: "sha1",
TPM2_ALG_SHA256: "sha256",
TPM2_ALG_SHA384: "sha384",
TPM2_ALG_SHA512: "sha512",
TPM2_ALG_SM3: "sm3-256",
TPM2_ALG_SHA3_256: "sha3-256",
TPM2_ALG_SHA3_384: "sha3-384",
TPM2_ALG_SHA3_512: "sha3-512",
}
BANK_NAMES_TO_ALGID = {
"sha1": TPM2_ALG_SHA1,
"sha256": TPM2_ALG_SHA256,
"sha384": TPM2_ALG_SHA384,
"sha512": TPM2_ALG_SHA512,
"sm3-256": TPM2_ALG_SM3,
"sha3-256": TPM2_ALG_SHA3_256,
"sha3-384": TPM2_ALG_SHA3_384,
"sha3-512": TPM2_ALG_SHA3_512,
}
class Swtpm2(Swtpm):
""" Class for manufacturing a swtpm TPM 2 """
def __init__(self, swtpm_exec_l, state_path, keyopt, logfile, fds_to_pass):
""" Class constructor
swtpm_exec_l is a list like ["swtpm", "socket"]
"""
super(Swtpm2, self).__init__(swtpm_exec_l, state_path, keyopt, logfile, fds_to_pass,
is_tpm2=True)
def shutdown(self):
""" Shut down the TPM 2 """
fmt = ">HII H"
req = struct.pack(fmt,
TPM2_ST_NO_SESSIONS, struct.calcsize(fmt), TPM2_CC_SHUTDOWN,
TPM2_SU_CLEAR)
_, ret = self.transfer(req, "TPM2_Shutdown")
return ret
def run_swtpm_bios(self):
""" Startup the TPM 2 """
fmt = '>HII H'
req = struct.pack(fmt,
TPM2_ST_NO_SESSIONS, struct.calcsize(fmt), TPM2_CC_STARTUP,
TPM2_SU_CLEAR)
_, ret = self.transfer(req, "TPM2_Startup")
return ret
def get_all_pcr_banks(self):
""" Get all available PCR banks """
fmt = '>HII III'
req = struct.pack(fmt,
TPM2_ST_NO_SESSIONS, struct.calcsize(fmt), TPM2_CC_GETCAPABILITY,
TPM2_CAP_PCRS, 0, 64)
rsp, ret = self.transfer(req, "TPM2_GetCapability")
if ret != 0:
return [], 1
count = struct.unpack('>H', rsp[17:19])[0]
offset = 19
res = []
for _ in range(count):
bank, length = struct.unpack('>HB', rsp[offset:offset+3])
name = PCR_BANKS_TO_NAMES[bank]
if name:
res.append(name)
else:
res.append('%02x' % bank)
offset += 2 + 1 + length
return res, 0
def set_active_pcr_banks(self, pcr_banks, all_pcr_banks):
""" Set the list of active PCR banks to the one the user wants """
pcrselects = "".encode()
count = 0
active = []
# enable the ones the user wants
for pcr_bank in pcr_banks:
if pcr_bank not in all_pcr_banks:
# Skip if not even available
continue
try:
hashalg = BANK_NAMES_TO_ALGID[pcr_bank]
except KeyError:
continue
active.insert(0, pcr_bank)
pcrselects += struct.pack('>H BBBB', hashalg, 3, 0xff, 0xff, 0xff)
#print("activate hashalg = %d\n" % hashalg)
count += 1
if len(active) == 0:
logerr(self.logfile,
"No PCR banks could be allocated. None of the selected algorithms are "
"supported.\n")
return [], 1
# disable the rest
for pcr_bank in all_pcr_banks:
if pcr_bank in pcr_banks:
# Skip if to activate
continue
try:
hashalg = BANK_NAMES_TO_ALGID[pcr_bank]
except KeyError:
continue
#print("deactivate hashalg = %d\n" % hashalg)
pcrselects += struct.pack('>H BBBB', hashalg, 3, 0, 0, 0)
count += 1
authblock = struct.pack('>I HBH', TPM2_RS_PW, 0, 0, 0)
fmt = '>HII I I%ds I %ds' % (len(authblock), len(pcrselects))
req = struct.pack(fmt,
TPM2_ST_SESSIONS, struct.calcsize(fmt), TPM2_CC_PCR_ALLOCATE,
TPM2_RH_PLATFORM,
len(authblock), authblock,
count,
pcrselects)
_, ret = self.transfer(req, "TPM2_PCR_Allocate")
return active, ret
def evictcontrol(self, curr_handle, perm_handle):
""" Make object at the curr_handler permanent with the perm_handle """
authblock = struct.pack('>IHBH', TPM2_RS_PW, 0, 0, 0)
fmt = '>HII II I%ds I' % len(authblock)
req = struct.pack(fmt,
TPM2_ST_SESSIONS, struct.calcsize(fmt), TPM2_CC_EVICTCONTROL,
TPM2_RH_OWNER, curr_handle,
len(authblock), authblock,
perm_handle)
_, ret = self.transfer(req, "TPM2_EvictControl")
return ret
def createprimary_ek_rsa(self, rsa_keysize, allowsigning, decryption):
""" Create an RSA Ek """
if rsa_keysize == 2048:
authpolicy = b'\x83\x71\x97\x67\x44\x84\xb3\xf8\x1a\x90\xcc\x8d' \
b'\x46\xa5\xd7\x24\xfd\x52\xd7\x6e\x06\x52\x0b\x64' \
b'\xf2\xa1\xda\x1b\x33\x14\x69\xaa'
keyflags = 0
symkeylen = 128
havenonce = True
addlen = 0
elif rsa_keysize == 3072:
authpolicy = b'\xB2\x6E\x7D\x28\xD1\x1A\x50\xBC\x53\xD8\x82\xBC' \
b'\xF5\xFD\x3A\x1A\x07\x41\x48\xBB\x35\xD3\xB4\xE4' \
b'\xCB\x1C\x0A\xD9\xBD\xE4\x19\xCA\xCB\x47\xBA\x09' \
b'\x69\x96\x46\x15\x0F\x9F\xC0\x00\xF3\xF8\x0E\x12'
keyflags = 0x40
symkeylen = 256
havenonce = False
addlen = 16
if allowsigning and decryption:
# keyflags: fixedTPM, fixedParent, sensitiveDatOrigin,
# adminWithPolicy, sign, decrypt
keyflags = keyflags | 0x000600b2
# symmetric: TPM_ALG_NULL
symkeydata = struct.pack(">H", TPM2_ALG_NULL)
off = 72 + addlen
elif allowsigning:
# keyflags: fixedTPM, fixedParent, sensitiveDatOrigin,
# adminWithPolicy, sign
keyflags = keyflags | 0x000400b2
# symmetric: TPM_ALG_NULL
symkeydata = struct.pack(">H", TPM2_ALG_NULL)
off = 72 + addlen
else:
# keyflags: fixedTPM, fixedParent, sensitiveDatOrigin,
# adminWithPolicy, restricted, decrypt
keyflags = keyflags | 0x000300b2
# symmetric: TPM_ALG_AES, 128bit or 256bit, TPM_ALG_CFB
symkeydata = struct.pack(">HHH", TPM2_ALG_AES, symkeylen, TPM2_ALG_CFB)
off = 76 + addlen
return self._createprimary_rsa(TPM2_RH_ENDORSEMENT, keyflags, symkeydata, authpolicy,
rsa_keysize, havenonce, off)
def _createprimary_rsa(self, primaryhandle, keyflags, symkeydata, authpolicy,
rsa_keysize, havenonce, off):
""" Create an RSA key with the given parameters """
if rsa_keysize == 2048:
nonce = NONCE_RSA2048
hashalg = TPM2_ALG_SHA256
elif rsa_keysize == 3072:
if not havenonce:
nonce = NONCE_EMPTY
else:
nonce = NONCE_RSA3072
hashalg = TPM2_ALG_SHA384
else:
logerr(self.logfile, "Unsupported keysize %d\n" % rsa_keysize)
return b'', "", 0, 1
authblock = struct.pack('>IHBH', TPM2_RS_PW, 0, 0, 0)
fmt = '>HHI H%ds %ds HH I %ds' % \
(len(authpolicy), len(symkeydata), len(nonce))
public = struct.pack(fmt,
TPM2_ALG_RSA, hashalg, keyflags,
len(authpolicy), authpolicy,
symkeydata,
TPM2_ALG_NULL, rsa_keysize,
0,
nonce)
ek_template = public
fmt = ">HII I I%ds HI H%ds IH" % (len(authblock), len(public))
req = struct.pack(fmt,
TPM2_ST_SESSIONS, struct.calcsize(fmt), TPM2_CC_CREATEPRIMARY,
primaryhandle,
len(authblock), authblock,
4, 0,
len(public), public,
0, 0)
rsp, ret = self.transfer(req, "TPM2_CreatePrimary")
if ret != 0:
return b'', "", 0, 1
handle = struct.unpack(">I", rsp[10:14])[0]
modlen = struct.unpack(">H", rsp[off:off+2])[0]
if modlen != rsa_keysize >> 3:
logerr(self.logfile, "RSA key: Getting modulus from wrong offset %d\n" % off)
return b'', "", 0, 1
off += 2
ekparam = struct.unpack(">%ds" % modlen, rsp[off:off+modlen])[0].hex()
return ek_template, ekparam, handle, 0
def _createprimary_ecc(self, primaryhandle, keyflags, symkeydata, authpolicy,
curveid, hashalg, nonce, off):
""" Create an ECC key with the given parameters """
authblock = struct.pack('>IHBH', TPM2_RS_PW, 0, 0, 0)
fmt = '>HHI H%ds %ds HH H %ds%ds' % \
(len(authpolicy), len(symkeydata), len(nonce), len(nonce))
public = struct.pack(fmt,
TPM2_ALG_ECC, hashalg, keyflags,
len(authpolicy), authpolicy,
symkeydata,
TPM2_ALG_NULL, curveid,
TPM2_ALG_NULL,
nonce, nonce)
ek_template = public
fmt = '>HII I I%ds HI H%ds IH' % (len(authblock), len(public))
req = struct.pack(fmt,
TPM2_ST_SESSIONS, struct.calcsize(fmt), TPM2_CC_CREATEPRIMARY,
primaryhandle,
len(authblock), authblock,
4, 0,
len(public), public,
0, 0)
rsp, ret = self.transfer(req, "TPM2_CreatePrimary")
if ret != 0:
return b'', "", 0, 1
handle = struct.unpack('>I', rsp[10:14])[0]
if curveid == TPM2_ECC_NIST_P384:
exp_ksize = 48
cid = "secp384r1"
else:
logerr(self.logfile, "Unknown curveid 0x%x\n" % curveid)
return b'', "", 0, 1
ksize1 = struct.unpack('>H', rsp[off:off+2])[0]
off2 = off + 2 + ksize1
ksize2 = struct.unpack('>H', rsp[off2:off2+2])[0]
if ksize1 != exp_ksize or ksize2 != exp_ksize:
logerr(self.logfile, "ECC: Getting key parameters from wrong offset\n")
return b'', "", 0, 1
off += 2
xparam = struct.unpack(">%ds" % ksize1, rsp[off:off+ksize1])[0]
off2 += 2
yparam = struct.unpack(">%ds" % ksize2, rsp[off2:off2+ksize2])[0]
ekparam = "x=%s,y=%s,id=%s" % (xparam.hex(), yparam.hex(), cid)
return ek_template, ekparam, handle, 0
def createprimary_spk_ecc_nist_p384(self):
""" Create a NIST p384 ECC SPK """
keyflags = 0x00030472
symkeydata = struct.pack('>HHH', TPM2_ALG_AES, 256, TPM2_ALG_CFB)
authpolicy = b''
off = 42
return self._createprimary_ecc(TPM2_RH_OWNER, keyflags, symkeydata, authpolicy,
TPM2_ECC_NIST_P384, TPM2_ALG_SHA384, NONCE_ECC_384, off)
def createprimary_spk_rsa(self, rsa_keysize):
""" Create a primary RSA key with the given keysize """
keyflags = 0x00030472
authpolicy = ''.encode()
if rsa_keysize == 2048:
symkeylen = 128
elif rsa_keysize == 3072:
symkeylen = 256
symkeydata = struct.pack('>HHH', TPM2_ALG_AES, symkeylen, TPM2_ALG_CFB)
off = 44
return self._createprimary_rsa(TPM2_RH_OWNER, keyflags, symkeydata, authpolicy,
rsa_keysize, True, off)
def create_spk(self, isecc, rsa_keysize):
""" Create either an ECC or RSA storage primary key """
if isecc:
_, _, handle, ret = self.createprimary_spk_ecc_nist_p384()
else:
_, _, handle, ret = self.createprimary_spk_rsa(rsa_keysize)
if ret != 0:
return 1
ret = self.evictcontrol(handle, TPM2_SPK_HANDLE)
if ret == 0:
logit(self.logfile,
"Successfully created storage primary key with handle 0x%x.\n" % TPM2_SPK_HANDLE)
return ret
def createprimary_ek_ecc_nist_p384(self, allowsigning, decryption):
""" Create en ECC EK key that may be allowed to sign and/or decrypt """
if allowsigning and decryption:
# keyflags: fixedTPM, fixedParent, sensitiveDatOrigin,
# userWithAuth, adminWithPolicy, sign, decrypt
keyflags = 0x000600f2
# symmetric: TPM_ALG_NULL
symkeydata = struct.pack(">H", TPM2_ALG_NULL)
off = 86
elif allowsigning:
# keyflags: fixedTPM, fixedParent, sensitiveDatOrigin,
# userWithAuth, adminWithPolicy, sign
keyflags = 0x000400f2
# symmetric: TPM_ALG_NULL
symkeydata = struct.pack(">H", TPM2_ALG_NULL)
off = 86
else:
# keyflags: fixedTPM, fixedParent, sensitiveDatOrigin,
# userWithAuth, adminWithPolicy, restricted, decrypt
keyflags = 0x000300f2
# symmetric: TPM_ALG_AES, 256bit, TPM_ALG_CFB
symkeydata = struct.pack(">HHH", TPM2_ALG_AES, 256, TPM2_ALG_CFB)
off = 90
# authPolicy from Ek Credential Profile; Spec v 2.1; rev12; p. 43
authpolicy = b'\xB2\x6E\x7D\x28\xD1\x1A\x50\xBC\x53\xD8\x82\xBC' \
b'\xF5\xFD\x3A\x1A\x07\x41\x48\xBB\x35\xD3\xB4\xE4' \
b'\xCB\x1C\x0A\xD9\xBD\xE4\x19\xCA\xCB\x47\xBA\x09' \
b'\x69\x96\x46\x15\x0F\x9F\xC0\x00\xF3\xF8\x0E\x12'
ek_template, ekparam, handle, ret = \
self._createprimary_ecc(TPM2_RH_ENDORSEMENT, keyflags, symkeydata, authpolicy,
TPM2_ECC_NIST_P384, TPM2_ALG_SHA384, NONCE_EMPTY, off)
if ret != 0:
logerr(self.logfile, "create_spk_ecc failed\n")
return ek_template, ekparam, handle, ret
def create_ek(self, isecc, rsa_keysize, allowsigning, decryption, lock_nvram):
""" Create an ECC or RSA EK """
if isecc:
tpm2_ek_handle = TPM2_EK_ECC_SECP384R1_HANDLE
keytype = "ECC"
nvindex = TPM2_NV_INDEX_ECC_SECP384R1_HI_EKTEMPLATE
else:
if rsa_keysize == 2048:
tpm2_ek_handle = TPM2_EK_RSA_HANDLE
nvindex = TPM2_NV_INDEX_RSA2048_EKTEMPLATE
elif rsa_keysize == 3072:
tpm2_ek_handle = TPM2_EK_RSA3072_HANDLE
nvindex = TPM2_NV_INDEX_RSA3072_HI_EKTEMPLATE
keytype = "RSA %d" % rsa_keysize
if isecc:
ek_template, ekparam, handle, ret = \
self.createprimary_ek_ecc_nist_p384(allowsigning, decryption)
else:
ek_template, ekparam, handle, ret = \
self.createprimary_ek_rsa(rsa_keysize, allowsigning, decryption)
if ret == 0:
ret = self.evictcontrol(handle, tpm2_ek_handle)
if ret != 0:
logerr(self.logfile, "create_ek failed\n")
return "", 1
logit(self.logfile,
"Successfully created %s EK with handle 0x%x.\n" % (keytype, tpm2_ek_handle))
if allowsigning:
nvindexattrs = TPMA_NV_PLATFORMCREATE | \
TPMA_NV_AUTHREAD | \
TPMA_NV_OWNERREAD | \
TPMA_NV_PPREAD | \
TPMA_NV_PPWRITE | \
TPMA_NV_NO_DA | \
TPMA_NV_WRITEDEFINE
ret = self.write_nvram(nvindex, nvindexattrs, ek_template, lock_nvram, "EK template")
if ret == 0:
logit(self.logfile,
"Successfully created NVRAM area 0x%x for %s EK template.\n" %
(nvindex, keytype))
return ekparam, ret
def nv_definespace(self, nvindex, nvindexattrs, size):
""" Define an NVIndex with attributes and given size """
authblock = struct.pack(">IHBH", TPM2_RS_PW, 0, 0, 0)
nvpublic = struct.pack('>IHI H H',
nvindex, TPM2_ALG_SHA256, nvindexattrs,
0,
size)
fmt = ">HII I I%ds H H%ds" % (len(authblock), len(nvpublic))
req = struct.pack(fmt,
TPM2_ST_SESSIONS, struct.calcsize(fmt), TPM2_CC_NV_DEFINESPACE,
TPM2_RH_PLATFORM,
len(authblock), authblock,
0,
len(nvpublic), nvpublic)
_, ret = self.transfer(req, "TPM2_NV_DefineSpace")
return ret
def nv_write(self, nvindex, data):
""" Write the data into the given NVIndex """
authblock = struct.pack(">IHBH", TPM2_RS_PW, 0, 0, 0)
offset = 0
stepsize = 1024
while offset < len(data):
if offset + stepsize < len(data):
buf = data[offset : offset + stepsize]
else:
buf = data[offset : len(data)]
fmt = ">HII II I%ds H%dsH" % (len(authblock), len(buf))
req = struct.pack(fmt,
TPM2_ST_SESSIONS, struct.calcsize(fmt), TPM2_CC_NV_WRITE,
TPM2_RH_PLATFORM, nvindex,
len(authblock), authblock,
len(buf), buf, offset)
_, ret = self.transfer(req, "TPM2_NV_Write")
if ret != 0:
return 1
offset += stepsize
return 0
def nv_writelock(self, nvindex):
""" Lock the given index """
authblock = struct.pack(">IHBH", TPM2_RS_PW, 0, 0, 0)
fmt = ">HII II I%ds" % (len(authblock))
req = struct.pack(fmt,
TPM2_ST_SESSIONS, struct.calcsize(fmt), TPM2_CC_NV_WRITELOCK,
TPM2_RH_PLATFORM, nvindex,
len(authblock), authblock)
_, ret = self.transfer(req, "TPM2_NV_WriteLock")
return ret
def write_nvram(self, nvindex, nvindexattrs, data, lock_nvram, purpose):
""" Define NVRAM space, write data to it and lock it if wanted """
ret = self.nv_definespace(nvindex, nvindexattrs, len(data))
if ret != 0:
logerr(self.logfile, "Could not create NVRAM area 0x%x for %s.\n" % (nvindex, purpose))
return 1
ret = self.nv_write(nvindex, data)
if ret != 0:
logerr(self.logfile,
"Could not write %s into NVRAM area 0x%x.\n" % (purpose, nvindex))
return 1
if lock_nvram:
ret = self.nv_writelock(nvindex)
if ret != 0:
logerr(self.logfile, "Could not lock EK template NVRAM area 0x%x.\n" % nvindex)
return 1
return ret
def write_ek_cert_nvram(self, isecc, rsa_keysize, lock_nvram, ekcert):
""" Write the given ekcert into an NVRAM area appropriate for the key type and size """
if not isecc:
if rsa_keysize == 2048:
nvindex = TPM2_NV_INDEX_RSA2048_EKCERT
elif rsa_keysize == 3072:
nvindex = TPM2_NV_INDEX_RSA3072_HI_EKCERT
keytype = "RSA %d" % rsa_keysize
else:
nvindex = TPM2_NV_INDEX_ECC_SECP384R1_HI_EKCERT
keytype = "ECC"
nvindexattrs = TPMA_NV_PLATFORMCREATE | \
TPMA_NV_AUTHREAD | \
TPMA_NV_OWNERREAD | \
TPMA_NV_PPREAD | \
TPMA_NV_PPWRITE | \
TPMA_NV_NO_DA | \
TPMA_NV_WRITEDEFINE
ret = self.write_nvram(nvindex, nvindexattrs, ekcert, lock_nvram, "EK Certificate")
if ret == 0:
logit(self.logfile,
"Successfully created NVRAM area 0x%x for %s EK certificate.\n" %
(nvindex, keytype))
else:
logerr(self.logfile,
"Could not create NVRAM area 0x%x for %s EK certificate.\n" %
(nvindex, keytype))
return ret
def write_platform_cert_nvram(self, lock_nvram, platformcert):
""" Write the platform certificate into an NVRAM area """
nvindex = TPM2_NV_INDEX_PLATFORMCERT
nvindexattrs = TPMA_NV_PLATFORMCREATE | \
TPMA_NV_AUTHREAD | \
TPMA_NV_OWNERREAD | \
TPMA_NV_PPREAD | \
TPMA_NV_PPWRITE | \
TPMA_NV_NO_DA | \
TPMA_NV_WRITEDEFINE
ret = self.write_nvram(nvindex, nvindexattrs, platformcert, lock_nvram,
"Platform Certificate")
if ret == 0:
logit(self.logfile,
"Successfully created NVRAM area 0x%x for platform certificate.\n" % nvindex)
else:
logerr(self.logfile,
"Could not create NVRAM area 0x%x for platform certificate.\n" % nvindex)
return ret
#
# TPM 1.2 support
#
TPM_TAG_RQU_COMMAND = 0x00c1
TPM_TAG_RQU_AUTH1_COMMAND = 0x00c2
TPM_ORD_OIAP = 0x0000000A
TPM_ORD_OSAP = 0x0000000B
TPM_ORD_TAKE_OWNERSHIP = 0x0000000D
TPM_ORD_OWNER_CLEAR = 0x0000005B
TPM_ORD_PHYSICAL_ENABLE = 0x0000006F
TPM_ORD_PHYSICAL_SET_DEACTIVATED = 0x00000072
TPM_ORD_STARTUP = 0x00000099
TPM_ORD_NV_DEFINE_SPACE = 0x000000CC
TPM_ORD_NV_WRITE_VALUE = 0x000000CD
TSC_ORD_PHYSICAL_PRESENCE = 0x4000000A
TPM_ST_CLEAR = 0x0001
TPM_PHYSICAL_PRESENCE_CMD_ENABLE = 0x0020
TPM_PHYSICAL_PRESENCE_PRESENT = 0x0008
TPM_ALG_RSA = 0x00000001
TPM_KEY_STORAGE = 0x0011
TPM_AUTH_ALWAYS = 0x01
TPM_PID_OWNER = 0x0005
TPM_ES_RSAESOAEP_SHA1_MGF1 = 0x0003
TPM_SS_NONE = 0x0001
TPM_TAG_PCR_INFO_LONG = 0x0006
TPM_TAG_NV_ATTRIBUTES = 0x0017
TPM_TAG_NV_DATA_PUBLIC = 0x0018
TPM_TAG_KEY12 = 0x0028
TPM_LOC_ZERO = 0x01
TPM_LOC_ALL = 0x1f
TPM_NV_INDEX_D_BIT = 0x10000000
TPM_NV_INDEX_EKCERT = 0xF000
TPM_NV_INDEX_PLATFORMCERT = 0xF002
TPM_NV_INDEX_LOCK = 0xFFFFFFFF
TPM_NV_PER_OWNERREAD = 0x00020000
TPM_NV_PER_OWNERWRITE = 0x00000002
TPM_ET_OWNER = 0x02
TPM_ET_NV = 0x0b
TPM_KH_EK = 0x40000006
class Swtpm12(Swtpm):
""" Class for manufacturing a swtpm TPM 1.2 """
def __init__(self, swtpm_exec_l, state_path, keyopt, logfile, fds_to_pass):
""" Class constructor
swtpm_exec_l is a list like ["swtpm", "socket"]
"""
super(Swtpm12, self).__init__(swtpm_exec_l, state_path, keyopt, logfile, fds_to_pass)
def startup(self, startup_type):
""" Run TPM_Startup() """
fmt = ">HII H"
req = struct.pack(fmt,
TPM_TAG_RQU_COMMAND, struct.calcsize(fmt), TPM_ORD_STARTUP,
startup_type)
_, ret = self.transfer(req, "TPM_Startup")
return ret
def tsc_physicalpresence(self, physicalpresence):
""" Run TSC_PhysicalPresence """
fmt = ">HII H"
req = struct.pack(fmt,
TPM_TAG_RQU_COMMAND, struct.calcsize(fmt), TSC_ORD_PHYSICAL_PRESENCE,
physicalpresence)
_, ret = self.transfer(req, "TSC_PhysicalPresence")
return ret
def physical_enable(self):
""" Run TPM_PhysicalEnable """
fmt = ">HII"
req = struct.pack(fmt,
TPM_TAG_RQU_COMMAND, struct.calcsize(fmt), TPM_ORD_PHYSICAL_ENABLE)
_, ret = self.transfer(req, "TSC_PhysicalEnable")
return ret
def physical_set_deactivated(self, state):
""" Run TPM_PhysicalSetDeactivated """
fmt = ">HI I B"
req = struct.pack(fmt,
TPM_TAG_RQU_COMMAND, struct.calcsize(fmt),
TPM_ORD_PHYSICAL_SET_DEACTIVATED,
state)
_, ret = self.transfer(req, "TPM_PhysiclaSetDaectivated")
return ret
def run_swtpm_bios(self):
""" Initialize the swtpm """
if self.startup(TPM_ST_CLEAR) or \
self.tsc_physicalpresence(TPM_PHYSICAL_PRESENCE_CMD_ENABLE) or \
self.tsc_physicalpresence(TPM_PHYSICAL_PRESENCE_PRESENT) or \
self.physical_enable() or \
self.physical_set_deactivated(0):
return 1
return 0
def create_endorsement_key_pair(self):
""" Create an endorsement key for the TPM 1.2 """
req = b'\x00\xc1\x00\x00\x00\x36\x00\x00\x00\x78\x38\xf0\x30\x81\x07\x2b' \
b'\x0c\xa9\x10\x98\x08\xc0\x4B\x05\x11\xc9\x50\x23\x52\xc4\x00\x00' \
b'\x00\x01\x00\x03\x00\x02\x00\x00\x00\x0c\x00\x00\x08\x00\x00\x00' \
b'\x00\x02\x00\x00\x00\x00'
rsp, ret = self.transfer(req, "TPM_CreateEndorsementKeyPair")
if ret != 0:
return b'', 1
length = struct.unpack(">I", rsp[34:38])[0]
if length != 256:
logerr(self.logfile, "Offset to EK Public key is wrong.\n")
return b'', 1
pubek = struct.unpack("256s", rsp[38:38+256])[0]
return pubek, 0
def oiap(self):
""" Create an OIAP session """
fmt = ">HII"
req = struct.pack(fmt,
TPM_TAG_RQU_COMMAND, struct.calcsize(fmt), TPM_ORD_OIAP)
rsp, ret = self.transfer(req, "TPM_OIAP")
if ret != 0:
return b'', 0, 1
authhandle = struct.unpack(">I", rsp[10:14])[0]
nonce_even = struct.unpack("20s", rsp[14:34])[0]
return nonce_even, authhandle, 0
def take_ownership(self, ownerpass_digest, srkpass_digest, pubek):
""" Run TPM_TakeOwernship """
exponent = int('10001', 16)
modulus = int(pubek.hex(), 16)
pubekkey = RSAPublicNumbers(exponent, modulus).public_key(backend=default_backend())
oaep = padding.OAEP(
mgf=padding.MGF1(algorithm=hashes.SHA1()),
algorithm=hashes.SHA1(),
label="TCPA".encode()
)
enc_owner_auth = pubekkey.encrypt(ownerpass_digest, oaep)
enc_srk_auth = pubekkey.encrypt(srkpass_digest, oaep)
nonce_even, auth_handle, ret = self.oiap()
if ret != 0:
return 1
tpm_rsa_key_parms = struct.pack(">III",
2048, # keyLength
2, # numPrimes
0) # exponentSize
tpm_key_parms = struct.pack(">I HH I%ds" % (len(tpm_rsa_key_parms)),
TPM_ALG_RSA, # algorithmId
TPM_ES_RSAESOAEP_SHA1_MGF1, # encScheme
TPM_SS_NONE, # sigScheme
len(tpm_rsa_key_parms), tpm_rsa_key_parms)
tpm_key12 = struct.pack(">HH HIB %ds I I I" %
(len(tpm_key_parms)),
TPM_TAG_KEY12, 0,
TPM_KEY_STORAGE, # keyUsage
0, # keyFlags
TPM_AUTH_ALWAYS, # authDataUsage
tpm_key_parms,
0,
0,
0)
fmt_auth = ">I20sB20s"
fmt = ">HII H I256s I256s %ds" % len(tpm_key12)
nonce_odd = os.urandom(20)
req = struct.pack(fmt,
TPM_TAG_RQU_AUTH1_COMMAND,
struct.calcsize(fmt) + struct.calcsize(fmt_auth),
TPM_ORD_TAKE_OWNERSHIP,
TPM_PID_OWNER,
len(enc_owner_auth), enc_owner_auth,
len(enc_srk_auth), enc_srk_auth,
tpm_key12)
# req needs authhandle, nonceodd & ownerAuth appended
shainput = struct.unpack("%ds" % (len(req) - 6), req[6:len(req)])[0]
in_param_digest = sha1(shainput)
continue_auth_session = 0
in_auth_setup_params = struct.pack(">20s20sB", nonce_even, nonce_odd, continue_auth_session)
macinput = struct.pack(">20s %ds" % len(in_auth_setup_params),
in_param_digest, in_auth_setup_params)
myhmac = hmac.HMAC(ownerpass_digest, hashes.SHA1(), backend=default_backend())
myhmac.update(macinput)
owner_auth = myhmac.finalize()
req += struct.pack(fmt_auth, auth_handle, nonce_odd, continue_auth_session, owner_auth)
_, ret = self.transfer(req, "TPM_TakeOwnership")
return ret
def ownerclear(self, ownerpass_digest):
""" clear TPM ownership """
nonce_even, auth_handle, ret = self.oiap()
if ret != 0:
return 1
nonce_odd = os.urandom(20)
fmt_auth = ">I20sB20s"
fmt = ">H II"
req = struct.pack(fmt,
TPM_TAG_RQU_AUTH1_COMMAND,
struct.calcsize(fmt) + struct.calcsize(fmt_auth), TPM_ORD_OWNER_CLEAR)
shainput = struct.unpack("%ds" % (len(req) - 6), req[6:len(req)])[0]
in_param_digest = sha1(shainput)
continue_auth_session = 0
in_auth_setup_params = struct.pack(">20s20sB", nonce_even, nonce_odd, continue_auth_session)
macinput = struct.pack(">20s %ds" % len(in_auth_setup_params),
in_param_digest, in_auth_setup_params)
myhmac = hmac.HMAC(ownerpass_digest, hashes.SHA1(), backend=default_backend())
myhmac.update(macinput)
owner_auth = myhmac.finalize()
req += struct.pack(fmt_auth, auth_handle, nonce_odd, continue_auth_session, owner_auth)
_, ret = self.transfer(req, "TPM_ClearOwner")
return ret
def nv_define_space(self, nvindex, nvindexattrs, size):
""" Define an nvindex with the given permissions and size """
pcr_info_short = struct.pack(">HBBB B 20s",
3, 0, 0, 0,
TPM_LOC_ALL,
('\x00' * 20).encode())
fmt = ">HI %ds%ds HI BBBI" % (len(pcr_info_short), len(pcr_info_short))
nv_data_public = struct.pack(fmt,
TPM_TAG_NV_DATA_PUBLIC, nvindex,
pcr_info_short, pcr_info_short,
TPM_TAG_NV_ATTRIBUTES, nvindexattrs,
0, 0, 0, size)
fmt = ">HII %ds 20s" % len(nv_data_public)
req = struct.pack(fmt,
TPM_TAG_RQU_COMMAND, struct.calcsize(fmt), TPM_ORD_NV_DEFINE_SPACE,
nv_data_public,
('\x00' * 20).encode())
_, ret = self.transfer(req, "TPM_NV_DefineSpace")
return ret
def nv_write_value(self, nvindex, data):
""" Write data to an index """
fmt = ">HII III%ds" % len(data)
req = struct.pack(fmt,
TPM_TAG_RQU_COMMAND, struct.calcsize(fmt), TPM_ORD_NV_WRITE_VALUE,
nvindex, 0, len(data), data)
_, ret = self.transfer(req, "TPM_NV_WriteValue")
return ret
def write_ek_cert_nvram(self, data):
""" Write the EK Certificate into NVRAM """
nvindex = TPM_NV_INDEX_EKCERT|TPM_NV_INDEX_D_BIT
ret = self.nv_define_space(nvindex, TPM_NV_PER_OWNERREAD|TPM_NV_PER_OWNERWRITE, len(data))
if ret != 0:
return 1
ret = self.nv_write_value(nvindex, data)
if ret != 0:
return 1
return 0
def write_platform_cert_nvram(self, data):
""" Write the Platform Certificate into NVRAM """
nvindex = TPM_NV_INDEX_PLATFORMCERT|TPM_NV_INDEX_D_BIT
ret = self.nv_define_space(nvindex, TPM_NV_PER_OWNERREAD|TPM_NV_PER_OWNERWRITE, len(data))
if ret != 0:
return 1
return self.nv_write_value(nvindex, data)
def nv_lock(self):
""" Lock the NVRAM """
return self.nv_define_space(TPM_NV_INDEX_LOCK, 0, 0)
|
py | 1a3138de56d4b949e2c7c498e3ecc4e2e51a877d | import logging
from spaceone.core.manager import BaseManager
from spaceone.core.connector.space_connector import SpaceConnector
from spaceone.core import cache
from spaceone.cost_analysis.error import *
_LOGGER = logging.getLogger(__name__)
class IdentityManager(BaseManager):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.identity_connector: SpaceConnector = self.locator.get_connector('SpaceConnector', service='identity')
def list_projects(self, query, domain_id):
return self.identity_connector.dispatch('Project.list', {'query': query, 'domain_id': domain_id})
def get_project(self, project_id, domain_id):
return self.identity_connector.dispatch('Project.get', {'project_id': project_id, 'domain_id': domain_id})
def list_project_groups(self, query, domain_id):
return self.identity_connector.dispatch('ProjectGroup.list', {'query': query, 'domain_id': domain_id})
def get_project_group(self, project_group_id, domain_id):
return self.identity_connector.dispatch('ProjectGroup.get', {'project_group_id': project_group_id,
'domain_id': domain_id})
def list_projects_in_project_group(self, project_group_id, domain_id, recursive=False, query=None):
request = {
'project_group_id': project_group_id,
'domain_id': domain_id,
'recursive': recursive
}
if query:
request['query'] = query
return self.identity_connector.dispatch('ProjectGroup.list_projects', request)
def get_service_account(self, service_account_id, domain_id):
return self.identity_connector.dispatch('ServiceAccount.get', {'service_account_id': service_account_id,
'domain_id': domain_id})
def list_service_accounts(self, query, domain_id):
return self.identity_connector.dispatch('ServiceAccount.list', {'query': query, 'domain_id': domain_id})
|
py | 1a31390df8157aa287258bfabdd4571c504538bf | # -*- coding: utf-8 -*-
# @Time : 2019-02-25 09:53
# @Author : EchoShoot
# @Email : [email protected]
# @URL : https://github.com/EchoShoot
# @File : Login.py
# @Explain : 整合登录逻辑
import logging
import shelve
import time
from selenium import webdriver
from SmartLogin import finder
from SmartLogin import monitor
from SmartLogin import Errors
logger = logging.getLogger(__name__)
# logger.disabled = True
class Login(object):
def __init__(self, login_url, target_page, driver=None):
self.login_page = login_url # 登录地址
self.target_page = target_page # 目标地址
self.cookie_db = shelve.open('Cookie', writeback=True) # 存储Cookie
self.driver = driver
if self.driver is None:
from pkg_resources import resource_filename, Requirement
driverpath = resource_filename(Requirement.parse('SmartLogin'), 'SmartLogin/resource/chromedriver')
logger.info("use default chrome driver from path: {}".format(driverpath))
self.driver = webdriver.Chrome(driverpath)
def auto_login(self, username, password, click_xpath=None, update=False):
""" 自动登录到页面, 如果页面存在登录记录, 则跳过登录过程. """
cookies = self.cookie_db.get(self.login_page, None) # 提取 cookie
if update or cookies is None: # 如果 cookie 不存在
cookies = self.login(username, password, click_xpath) # 模拟登录来获取 cookie
self.cookie_db[self.login_page] = cookies # 存入 cookie
else:
# 一定是先访问网页,才能添加 Cookie 不报错
self.driver.get(self.target_page)
self.driver.delete_all_cookies() # 清空 Cookie 排除干扰
for cookie in cookies:
cookie.pop('expiry', None) # 弹出过期时间,让所有 Cookie 不会过期
self.driver.add_cookie(cookie)
self.driver.refresh() # 刷新使得 Cookie 效果生效
return cookies
def login(self, username, password, click_xpath=None):
""" 自动切换到登录框, 输入账号密码 """
try:
finder.smart_get(self.driver, self.login_page, 30) # 限制跳转到某个网页的加载时间
finder.auto_switch_to_LoginForm(self.driver) # 自动切换到有密码输入框的地方
if click_xpath: # 有些网页需要点击一下才会切换到密码输入框
self.driver.find_element_by_xpath(click_xpath).click()
finder.fill_username_and_password(self.driver, username, password) # 输入账号密码
monitor.until_login_page_switch(self.driver) # 等待页面发生跳转
cookies = self.driver.get_cookies() # 获取 Cookie 的操作
except Errors.NoSuchWindowException as e:
raise Errors.BrowsersMayClosed("浏览器可能被关闭了.") from e
else:
return cookies
def close(self):
self.cookie_db.close()
self.driver.quit()
if __name__ == '__main__':
login = Login(
target_page='https://i.qq.com',
login_url='https://i.qq.com',
)
cookie = login.auto_login('username', 'password',
click_xpath='//a[@id="switcher_plogin"]', # 若登录前不需要点击某处可以赋值为None
update=True)
print('拦截到 Cookie: {}'.format(cookie))
time.sleep(4) # 延迟4秒便于观察 |
py | 1a313918b965ac86982ab93c6537411317e9b272 | # from test import test
from senti19.senti19.test import test_name
# test.test_name()
# Tests().test_print_name() |
py | 1a313aa0e9949cfc731f21228a3dabd743eebfd9 | # -*- coding: utf-8 -*-
# Copyright 2020 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 proto # type: ignore
from google.ads.googleads.v8.resources.types import payments_account
__protobuf__ = proto.module(
package="google.ads.googleads.v8.services",
marshal="google.ads.googleads.v8",
manifest={"ListPaymentsAccountsRequest", "ListPaymentsAccountsResponse",},
)
class ListPaymentsAccountsRequest(proto.Message):
r"""Request message for fetching all accessible payments
accounts.
Attributes:
customer_id (str):
Required. The ID of the customer to apply the
PaymentsAccount list operation to.
"""
customer_id = proto.Field(proto.STRING, number=1,)
class ListPaymentsAccountsResponse(proto.Message):
r"""Response message for
[PaymentsAccountService.ListPaymentsAccounts][google.ads.googleads.v8.services.PaymentsAccountService.ListPaymentsAccounts].
Attributes:
payments_accounts (Sequence[google.ads.googleads.v8.resources.types.PaymentsAccount]):
The list of accessible payments accounts.
"""
payments_accounts = proto.RepeatedField(
proto.MESSAGE, number=1, message=payments_account.PaymentsAccount,
)
__all__ = tuple(sorted(__protobuf__.manifest))
|
py | 1a313ac2eaaccddc98520dc5be17257f4fbe712c | """This is used to patch the QApplication style sheet.
It reads the current stylesheet, appends our modifications and sets the new stylesheet.
"""
from PyQt5 import QtWidgets
def patch_qt_stylesheet(use_dark_theme: bool) -> None:
if not use_dark_theme:
return
app = QtWidgets.QApplication.instance()
style_sheet = app.styleSheet()
style_sheet = style_sheet + '''
/* PayToEdit text was being clipped */
QAbstractScrollArea {
padding: 0px;
}
/* In History tab, labels while edited were being clipped (Windows) */
QAbstractItemView QLineEdit {
padding: 0px;
}
'''
app.setStyleSheet(style_sheet)
|
py | 1a313c2bf66324aaa926e2f79e23257b013f52ea | """
Deutscher Wetterdienst: API
Aktuelle Wetterdaten von allen Deutschen Wetterstationen # noqa: E501
The version of the OpenAPI document: 1.0.0
Generated by: https://openapi-generator.tech
"""
import re # noqa: F401
import sys # noqa: F401
from deutschland.dwd.exceptions import ApiAttributeError
from deutschland.dwd.model_utils import ApiTypeError # noqa: F401
from deutschland.dwd.model_utils import (
ModelComposed,
ModelNormal,
ModelSimple,
cached_property,
change_keys_js_to_python,
convert_js_args_to_python_args,
date,
datetime,
file_type,
none_type,
validate_get_composed_info,
)
from ..model_utils import OpenApiModel
class StationOverview10865Forecast2(ModelNormal):
"""NOTE: This class is auto generated by OpenAPI Generator.
Ref: https://openapi-generator.tech
Do not edit the class manually.
Attributes:
allowed_values (dict): The key is the tuple path to the attribute
and the for var_name this is (var_name,). The value is a dict
with a capitalized key describing the allowed value and an allowed
value. These dicts store the allowed enum values.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
discriminator_value_class_map (dict): A dict to go from the discriminator
variable value to the discriminator class name.
validations (dict): The key is the tuple path to the attribute
and the for var_name this is (var_name,). The value is a dict
that stores validations for max_length, min_length, max_items,
min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum,
inclusive_minimum, and regex.
additional_properties_type (tuple): A tuple of classes accepted
as additional properties values.
"""
allowed_values = {}
validations = {}
@cached_property
def additional_properties_type():
"""
This must be a method because a model may have properties that are
of type self, this must run after the class is loaded
"""
return (
bool,
date,
datetime,
dict,
float,
int,
list,
str,
none_type,
) # noqa: E501
_nullable = False
@cached_property
def openapi_types():
"""
This must be a method because a model may have properties that are
of type self, this must run after the class is loaded
Returns
openapi_types (dict): The key is attribute name
and the value is attribute type.
"""
return {
"station_id": (str,), # noqa: E501
"start": (int,), # noqa: E501
"time_step": (int,), # noqa: E501
"temperature": ([float],), # noqa: E501
"temperature_std": ([float],), # noqa: E501
"wind_speed": (
str,
none_type,
), # noqa: E501
"wind_direction": (
str,
none_type,
), # noqa: E501
"wind_gust": (
str,
none_type,
), # noqa: E501
"icon": ([int],), # noqa: E501
"precipitation_total": ([int],), # noqa: E501
"precipitation_probablity": (
str,
none_type,
), # noqa: E501
"precipitation_probablity_index": (
str,
none_type,
), # noqa: E501
}
@cached_property
def discriminator():
return None
attribute_map = {
"station_id": "stationId", # noqa: E501
"start": "start", # noqa: E501
"time_step": "timeStep", # noqa: E501
"temperature": "temperature", # noqa: E501
"temperature_std": "temperatureStd", # noqa: E501
"wind_speed": "windSpeed", # noqa: E501
"wind_direction": "windDirection", # noqa: E501
"wind_gust": "windGust", # noqa: E501
"icon": "icon", # noqa: E501
"precipitation_total": "precipitationTotal", # noqa: E501
"precipitation_probablity": "precipitationProbablity", # noqa: E501
"precipitation_probablity_index": "precipitationProbablityIndex", # noqa: E501
}
read_only_vars = {}
_composed_schemas = {}
@classmethod
@convert_js_args_to_python_args
def _from_openapi_data(cls, *args, **kwargs): # noqa: E501
"""StationOverview10865Forecast2 - a model defined in OpenAPI
Keyword Args:
_check_type (bool): if True, values for parameters in openapi_types
will be type checked and a TypeError will be
raised if the wrong type is input.
Defaults to True
_path_to_item (tuple/list): This is a list of keys or values to
drill down to the model in received_data
when deserializing a response
_spec_property_naming (bool): True if the variable names in the input data
are serialized names, as specified in the OpenAPI document.
False if the variable names in the input data
are pythonic names, e.g. snake case (default)
_configuration (Configuration): the instance to use when
deserializing a file_type parameter.
If passed, type conversion is attempted
If omitted no type conversion is done.
_visited_composed_classes (tuple): This stores a tuple of
classes that we have traveled through so that
if we see that class again we will not use its
discriminator again.
When traveling through a discriminator, the
composed schema that is
is traveled through is added to this set.
For example if Animal has a discriminator
petType and we pass in "Dog", and the class Dog
allOf includes Animal, we move through Animal
once using the discriminator, and pick Dog.
Then in Dog, we will make an instance of the
Animal class but this time we won't travel
through its discriminator because we passed in
_visited_composed_classes = (Animal,)
station_id (str): [optional] # noqa: E501
start (int): [optional] # noqa: E501
time_step (int): [optional] # noqa: E501
temperature ([float]): [optional] # noqa: E501
temperature_std ([float]): [optional] # noqa: E501
wind_speed (str, none_type): [optional] # noqa: E501
wind_direction (str, none_type): [optional] # noqa: E501
wind_gust (str, none_type): [optional] # noqa: E501
icon ([int]): [optional] # noqa: E501
precipitation_total ([int]): [optional] # noqa: E501
precipitation_probablity (str, none_type): [optional] # noqa: E501
precipitation_probablity_index (str, none_type): [optional] # noqa: E501
"""
_check_type = kwargs.pop("_check_type", True)
_spec_property_naming = kwargs.pop("_spec_property_naming", False)
_path_to_item = kwargs.pop("_path_to_item", ())
_configuration = kwargs.pop("_configuration", None)
_visited_composed_classes = kwargs.pop("_visited_composed_classes", ())
self = super(OpenApiModel, cls).__new__(cls)
if args:
raise ApiTypeError(
"Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments."
% (
args,
self.__class__.__name__,
),
path_to_item=_path_to_item,
valid_classes=(self.__class__,),
)
self._data_store = {}
self._check_type = _check_type
self._spec_property_naming = _spec_property_naming
self._path_to_item = _path_to_item
self._configuration = _configuration
self._visited_composed_classes = _visited_composed_classes + (self.__class__,)
for var_name, var_value in kwargs.items():
if (
var_name not in self.attribute_map
and self._configuration is not None
and self._configuration.discard_unknown_keys
and self.additional_properties_type is None
):
# discard variable.
continue
setattr(self, var_name, var_value)
return self
required_properties = set(
[
"_data_store",
"_check_type",
"_spec_property_naming",
"_path_to_item",
"_configuration",
"_visited_composed_classes",
]
)
@convert_js_args_to_python_args
def __init__(self, *args, **kwargs): # noqa: E501
"""StationOverview10865Forecast2 - a model defined in OpenAPI
Keyword Args:
_check_type (bool): if True, values for parameters in openapi_types
will be type checked and a TypeError will be
raised if the wrong type is input.
Defaults to True
_path_to_item (tuple/list): This is a list of keys or values to
drill down to the model in received_data
when deserializing a response
_spec_property_naming (bool): True if the variable names in the input data
are serialized names, as specified in the OpenAPI document.
False if the variable names in the input data
are pythonic names, e.g. snake case (default)
_configuration (Configuration): the instance to use when
deserializing a file_type parameter.
If passed, type conversion is attempted
If omitted no type conversion is done.
_visited_composed_classes (tuple): This stores a tuple of
classes that we have traveled through so that
if we see that class again we will not use its
discriminator again.
When traveling through a discriminator, the
composed schema that is
is traveled through is added to this set.
For example if Animal has a discriminator
petType and we pass in "Dog", and the class Dog
allOf includes Animal, we move through Animal
once using the discriminator, and pick Dog.
Then in Dog, we will make an instance of the
Animal class but this time we won't travel
through its discriminator because we passed in
_visited_composed_classes = (Animal,)
station_id (str): [optional] # noqa: E501
start (int): [optional] # noqa: E501
time_step (int): [optional] # noqa: E501
temperature ([float]): [optional] # noqa: E501
temperature_std ([float]): [optional] # noqa: E501
wind_speed (str, none_type): [optional] # noqa: E501
wind_direction (str, none_type): [optional] # noqa: E501
wind_gust (str, none_type): [optional] # noqa: E501
icon ([int]): [optional] # noqa: E501
precipitation_total ([int]): [optional] # noqa: E501
precipitation_probablity (str, none_type): [optional] # noqa: E501
precipitation_probablity_index (str, none_type): [optional] # noqa: E501
"""
_check_type = kwargs.pop("_check_type", True)
_spec_property_naming = kwargs.pop("_spec_property_naming", False)
_path_to_item = kwargs.pop("_path_to_item", ())
_configuration = kwargs.pop("_configuration", None)
_visited_composed_classes = kwargs.pop("_visited_composed_classes", ())
if args:
raise ApiTypeError(
"Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments."
% (
args,
self.__class__.__name__,
),
path_to_item=_path_to_item,
valid_classes=(self.__class__,),
)
self._data_store = {}
self._check_type = _check_type
self._spec_property_naming = _spec_property_naming
self._path_to_item = _path_to_item
self._configuration = _configuration
self._visited_composed_classes = _visited_composed_classes + (self.__class__,)
for var_name, var_value in kwargs.items():
if (
var_name not in self.attribute_map
and self._configuration is not None
and self._configuration.discard_unknown_keys
and self.additional_properties_type is None
):
# discard variable.
continue
setattr(self, var_name, var_value)
if var_name in self.read_only_vars:
raise ApiAttributeError(
f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate "
f"class with read only attributes."
)
|
py | 1a313ca05b5deda64a1cca510515f2bd994f226c | import unittest
from clpy import testing
@testing.parameterize(
{'shape': (2, 3, 4), 'transpose': None, 'indexes': (1, 0, 2)},
{'shape': (2, 3, 4), 'transpose': None, 'indexes': (-1, 0, -2)},
{'shape': (2, 3, 4), 'transpose': (2, 0, 1), 'indexes': (1, 0, 2)},
{'shape': (2, 3, 4), 'transpose': (2, 0, 1), 'indexes': (-1, 0, -2)},
{'shape': (2, 3, 4), 'transpose': None,
'indexes': (slice(None), slice(None, 1), slice(2))},
{'shape': (2, 3, 4), 'transpose': None,
'indexes': (slice(None), slice(None, -1), slice(-2))},
{'shape': (2, 3, 4), 'transpose': (2, 0, 1),
'indexes': (slice(None), slice(None, 1), slice(2))},
{'shape': (2, 3, 5), 'transpose': None,
'indexes': (slice(None, None, -1), slice(1, None, -1), slice(4, 1, -2))},
{'shape': (2, 3, 5), 'transpose': (2, 0, 1),
'indexes': (slice(4, 1, -2), slice(None, None, -1), slice(1, None, -1))},
{'shape': (2, 3, 4), 'transpose': None, 'indexes': (Ellipsis, 2)},
{'shape': (2, 3, 4), 'transpose': None, 'indexes': (1, Ellipsis)},
{'shape': (2, 3, 4, 5), 'transpose': None, 'indexes': (1, Ellipsis, 3)},
{'shape': (2, 3, 4), 'transpose': None,
'indexes': (1, None, slice(2), None, 2)},
{'shape': (2, 3), 'transpose': None, 'indexes': (None,)},
{'shape': (2,), 'transpose': None, 'indexes': (slice(None,), None)},
{'shape': (), 'transpose': None, 'indexes': (None,)},
{'shape': (), 'transpose': None, 'indexes': (None, None)},
{'shape': (10,), 'transpose': None, 'indexes': (slice(10, -9, -1),)},
{'shape': (10,), 'transpose': None, 'indexes': (slice(-9, -10, -1),)},
{'shape': (10,), 'transpose': None, 'indexes': (slice(-1, -10, -1),)},
{'shape': (10,), 'transpose': None, 'indexes': (slice(-1, -11, -1),)},
{'shape': (10,), 'transpose': None, 'indexes': (slice(-11, -11, -1),)},
{'shape': (10,), 'transpose': None, 'indexes': (slice(10, -9, -3),)},
{'shape': (10,), 'transpose': None, 'indexes': (slice(-1, -11, -3),)},
{'shape': (10,), 'transpose': None, 'indexes': (slice(1, -5, -1),)},
{'shape': (10,), 'transpose': None, 'indexes': (slice(0, -5, -1),)},
{'shape': (10,), 'transpose': None, 'indexes': (slice(-1, -5, -1),)},
{'shape': (10,), 'transpose': None, 'indexes': (slice(-4, -5, -1),)},
{'shape': (10,), 'transpose': None, 'indexes': (slice(-5, -5, -1),)},
{'shape': (10,), 'transpose': None, 'indexes': (slice(-6, -5, -1),)},
{'shape': (10,), 'transpose': None, 'indexes': (slice(-10, -5, -1),)},
{'shape': (10,), 'transpose': None, 'indexes': (slice(-11, -5, -1),)},
{'shape': (10,), 'transpose': None, 'indexes': (slice(-12, -5, -1),)},
{'shape': (10,), 'transpose': None, 'indexes': (slice(-5, 1, -1),)},
{'shape': (10,), 'transpose': None, 'indexes': (slice(-5, 0, -1),)},
{'shape': (10,), 'transpose': None, 'indexes': (slice(-5, -1, -1),)},
{'shape': (10,), 'transpose': None, 'indexes': (slice(-5, -4, -1),)},
{'shape': (10,), 'transpose': None, 'indexes': (slice(-5, -5, -1),)},
{'shape': (10,), 'transpose': None, 'indexes': (slice(-5, -6, -1),)},
{'shape': (10,), 'transpose': None, 'indexes': (slice(-5, -10, -1),)},
{'shape': (10,), 'transpose': None, 'indexes': (slice(-5, -11, -1),)},
{'shape': (10,), 'transpose': None, 'indexes': (slice(-5, -12, -1),)},
# reversing indexing on empty dimension
{'shape': (0,), 'transpose': None, 'indexes': (slice(None, None, -1),)},
{'shape': (0, 0), 'transpose': None,
'indexes': (slice(None, None, -1), slice(None, None, -1))},
{'shape': (0, 0), 'transpose': None,
'indexes': (None, slice(None, None, -1))},
{'shape': (0, 0), 'transpose': None,
'indexes': (slice(None, None, -1), None)},
{'shape': (0, 1), 'transpose': None,
'indexes': (slice(None, None, -1), None)},
{'shape': (1, 0), 'transpose': None,
'indexes': (None, slice(None, None, -1))},
{'shape': (1, 0, 1), 'transpose': None,
'indexes': (None, slice(None, None, -1), None)},
#
{'shape': (2, 0), 'transpose': None,
'indexes': (1, slice(None, None, None))},
)
@testing.gpu
class TestArrayIndexingParameterized(unittest.TestCase):
_multiprocess_can_split_ = True
@testing.for_all_dtypes()
@testing.numpy_clpy_array_equal()
def test_getitem(self, xp, dtype):
a = testing.shaped_arange(self.shape, xp, dtype)
if self.transpose:
a = a.transpose(self.transpose)
return a[self.indexes]
@testing.parameterize(
{'shape': (), 'transpose': None, 'indexes': 0},
{'shape': (), 'transpose': None, 'indexes': (slice(0, 1, 0),)},
{'shape': (2, 3), 'transpose': None, 'indexes': (0, 0, 0)},
{'shape': (2, 3, 4), 'transpose': None, 'indexes': -3},
{'shape': (2, 3, 4), 'transpose': (2, 0, 1), 'indexes': -5},
{'shape': (2, 3, 4), 'transpose': None, 'indexes': 3},
{'shape': (2, 3, 4), 'transpose': (2, 0, 1), 'indexes': 5},
{'shape': (2, 3, 4), 'transpose': None,
'indexes': (slice(0, 1, 0), )},
{'shape': (2, 3, 4), 'transpose': None,
'indexes': (slice((0, 0), None, None), )},
{'shape': (2, 3, 4), 'transpose': None,
'indexes': (slice(None, (0, 0), None), )},
{'shape': (2, 3, 4), 'transpose': None,
'indexes': (slice(None, None, (0, 0)), )},
)
@testing.with_requires('numpy>=1.12.0')
@testing.gpu
class TestArrayInvalidIndex(unittest.TestCase):
@testing.for_all_dtypes()
@testing.numpy_clpy_raises()
def test_invalid_getitem(self, xp, dtype):
a = testing.shaped_arange(self.shape, xp, dtype)
if self.transpose:
a = a.transpose(self.transpose)
a[self.indexes]
@testing.gpu
class TestArrayIndex(unittest.TestCase):
_multiprocess_can_split_ = True
@testing.for_all_dtypes()
@testing.numpy_clpy_array_equal()
def test_setitem_constant(self, xp, dtype):
a = xp.zeros((2, 3, 4), dtype=dtype)
a[:] = 1
return a
@testing.for_all_dtypes()
@testing.numpy_clpy_array_equal()
def test_setitem_partial_constant(self, xp, dtype):
a = xp.zeros((2, 3, 4), dtype=dtype)
a[1, 1:3] = 1
return a
@testing.for_all_dtypes()
@testing.numpy_clpy_array_equal()
def test_setitem_copy(self, xp, dtype):
a = xp.zeros((2, 3, 4), dtype=dtype)
b = testing.shaped_arange((2, 3, 4), xp, dtype)
a[:] = b
return a
@testing.for_all_dtypes()
@testing.numpy_clpy_array_equal()
def test_setitem_partial_copy(self, xp, dtype):
a = xp.zeros((2, 3, 4), dtype=dtype)
b = testing.shaped_arange((3, 2), xp, dtype)
a[1, ::-1, 1:4:2] = b
return a
@testing.numpy_clpy_array_equal()
def test_T(self, xp):
a = testing.shaped_arange((2, 3, 4), xp)
return a.T
@testing.numpy_clpy_array_equal()
def test_T_vector(self, xp):
a = testing.shaped_arange((4,), xp)
return a.T
|
py | 1a313cea86a931fe57a3cc2a2237e526ec797163 | # app
from ..constants import FORMATS, LOG_FORMATTERS, LOG_LEVELS, REPOSITORIES, STRATEGIES, VERSION_SCHEMES
env_help = (
'Pipenv has 2 envs in same file: main and dev. '
'For poetry you can also use main-opt and dev-opt '
'that indicates to install optional requirements '
'from given env.'
)
def build_config(parser):
config_group = parser.add_argument_group('Configuration file')
config_group.add_argument('-c', '--config', help='path to config file.')
config_group.add_argument('-e', '--env', default='main', help='environment in config.')
def build_from(parser):
from_group = parser.add_argument_group('Input file')
from_group.add_argument('--from', help='path or format for reading requirements.')
from_group.add_argument('--from-format', choices=FORMATS, help='format for reading requirements.')
from_group.add_argument('--from-path', help='path to input file.')
def build_to(parser):
to_group = parser.add_argument_group('Output file')
to_group.add_argument('--to', help='path or format for writing requirements.')
to_group.add_argument('--to-format', choices=FORMATS, help='output requirements file format.')
to_group.add_argument('--to-path', help='path to output file.')
def build_resolver(parser):
resolver_group = parser.add_argument_group('Resolver rules')
resolver_group.add_argument('--strategy', choices=STRATEGIES, help='Algorithm to select best release.')
resolver_group.add_argument('--prereleases', action='store_true', help='Allow prereleases')
resolver_group.add_argument('--mutations', type=int, help='Maximum mutations limit')
def build_api(parser):
api_group = parser.add_argument_group('APIs endpoints')
api_group.add_argument('--warehouse', help='warehouse API URL.')
api_group.add_argument('--bitbucket', help='bitbucket API URL.')
api_group.add_argument('--repo', choices=REPOSITORIES, help='force repository for first-level deps.')
def build_output(parser):
output_group = parser.add_argument_group('Console output')
output_group.add_argument('--format', choices=LOG_FORMATTERS, help='output format.')
output_group.add_argument('--level', choices=LOG_LEVELS, help='minimal level for log messages.')
output_group.add_argument('--nocolors', action='store_true', help='do not color output.')
output_group.add_argument('--silent', action='store_true', help='suppress any output except errors.')
output_group.add_argument('--traceback', action='store_true', help='show traceback for exceptions.')
output_group.add_argument('--filter', help='filter for JSON output.')
def build_venv(parser):
venv_group = parser.add_argument_group('Virtual environment')
venv_group.add_argument('--venv', help='path to venv directory for project.')
venv_group.add_argument('--python', help='python version for venv.')
def build_other(parser):
other_group = parser.add_argument_group('Other')
other_group.add_argument('--cache-path', help='path to dephell cache')
other_group.add_argument('--cache-ttl', type=int, help='Time to live for releases list cache')
other_group.add_argument('--project', help='path to the current project')
other_group.add_argument('--bin', help='path to the dir for installing scripts')
other_group.add_argument('--envs', nargs='*', help='environments (main, dev) or extras to install')
other_group.add_argument('--tests', nargs='*', help='paths to test files')
other_group.add_argument('--versioning', choices=sorted(VERSION_SCHEMES), help='versioning scheme for project')
|
py | 1a313ed7e184e08a66366fe00edbf9c4be0d4f6a | import json
import os
import re
import urllib.request
import warnings
from typing import Optional, Union, Tuple, Dict
import ee
import pkg_resources
from ee_extra.STAC.utils import _get_platform_STAC
from ee_extra.utils import _load_JSON
def _get_expression_map(img: ee.Image, platformDict: dict) -> dict:
"""Gets the dictionary required for the map parameter i n ee.Image.expression() method.
Args:
img : Image to get the dictionary from.
platformDict : Dictionary retrieved from the _get_STAC_platform() method.
Returns:
Map dictionary for the ee.Image.expression() method.
"""
def lookupS2(img):
return {
"A": img.select("B1"),
"B": img.select("B2"),
"G": img.select("B3"),
"R": img.select("B4"),
"RE1": img.select("B5"),
"RE2": img.select("B6"),
"RE3": img.select("B7"),
"N": img.select("B8"),
"N2": img.select("B8A"),
"WV": img.select("B9"),
"S1": img.select("B11"),
"S2": img.select("B12"),
"lambdaG": 559.8,
"lambdaR": 664.6,
"lambdaN": 832.8,
}
def lookupL8(img):
return {
"A": img.select("B1"),
"B": img.select("B2"),
"G": img.select("B3"),
"R": img.select("B4"),
"N": img.select("B5"),
"S1": img.select("B6"),
"S2": img.select("B7"),
"T1": img.select("B10"),
"T2": img.select("B11"),
"lambdaG": 560.0,
"lambdaR": 655.0,
"lambdaN": 865.0,
}
def lookupL8C2(img):
return {
"A": img.select("SR_B1"),
"B": img.select("SR_B2"),
"G": img.select("SR_B3"),
"R": img.select("SR_B4"),
"N": img.select("SR_B5"),
"S1": img.select("SR_B6"),
"S2": img.select("SR_B7"),
"T1": img.select("ST_B10"),
"lambdaG": 560.0,
"lambdaR": 655.0,
"lambdaN": 865.0,
}
def lookupL45(img):
return {
"B": img.select("B1"),
"G": img.select("B2"),
"R": img.select("B3"),
"N": img.select("B4"),
"S1": img.select("B5"),
"T1": img.select("B6"),
"S2": img.select("B7"),
"lambdaG": 560.0,
"lambdaR": 660.0,
"lambdaN": 830.0,
}
def lookupL45C2(img):
return {
"B": img.select("SR_B1"),
"G": img.select("SR_B2"),
"R": img.select("SR_B3"),
"N": img.select("SR_B4"),
"S1": img.select("SR_B5"),
"T1": img.select("ST_B6"),
"S2": img.select("SR_B7"),
"lambdaG": 560.0,
"lambdaR": 660.0,
"lambdaN": 830.0,
}
def lookupL7(img):
return {
"B": img.select("B1"),
"G": img.select("B2"),
"R": img.select("B3"),
"N": img.select("B4"),
"S1": img.select("B5"),
"T1": img.select("B6"),
"S2": img.select("B7"),
"lambdaG": 560.0,
"lambdaR": 660.0,
"lambdaN": 835.0,
}
def lookupL7C2(img):
return {
"B": img.select("SR_B1"),
"G": img.select("SR_B2"),
"R": img.select("SR_B3"),
"N": img.select("SR_B4"),
"S1": img.select("SR_B5"),
"T1": img.select("ST_B6"),
"S2": img.select("SR_B7"),
"lambdaG": 560.0,
"lambdaR": 660.0,
"lambdaN": 835.0,
}
def lookupMOD09GQ(img):
return {
"R": img.select("sur_refl_b01"),
"N": img.select("sur_refl_b02"),
"lambdaR": 645.0,
"lambdaN": 858.5,
}
def lookupMOD09GA(img):
return {
"B": img.select("sur_refl_b03"),
"G": img.select("sur_refl_b04"),
"R": img.select("sur_refl_b01"),
"N": img.select("sur_refl_b02"),
"S1": img.select("sur_refl_b06"),
"S2": img.select("sur_refl_b07"),
"lambdaG": 555.0,
"lambdaR": 645.0,
"lambdaN": 858.5,
}
def lookupMCD43A4(img):
return {
"B": img.select("Nadir_Reflectance_Band3"),
"G": img.select("Nadir_Reflectance_Band4"),
"R": img.select("Nadir_Reflectance_Band1"),
"N": img.select("Nadir_Reflectance_Band2"),
"S1": img.select("Nadir_Reflectance_Band6"),
"S2": img.select("Nadir_Reflectance_Band7"),
"lambdaG": 555.0,
"lambdaR": 645.0,
"lambdaN": 858.5,
}
lookupPlatform = {
"COPERNICUS/S2": lookupS2,
"COPERNICUS/S2_SR": lookupS2,
"LANDSAT/LC08/C01/T1_SR": lookupL8,
"LANDSAT/LC08/C01/T2_SR": lookupL8,
"LANDSAT/LC08/C02/T1_L2": lookupL8C2,
"LANDSAT/LC08/C02/T2_L2": lookupL8C2,
"LANDSAT/LC09/C02/T1_L2": lookupL8C2,
"LANDSAT/LC09/C02/T2_L2": lookupL8C2,
"LANDSAT/LE07/C01/T1_SR": lookupL7,
"LANDSAT/LE07/C01/T2_SR": lookupL7,
"LANDSAT/LE07/C02/T1_L2": lookupL7C2,
"LANDSAT/LE07/C02/T2_L2": lookupL7C2,
"LANDSAT/LT05/C01/T1_SR": lookupL45,
"LANDSAT/LT05/C01/T2_SR": lookupL45,
"LANDSAT/LT05/C02/T1_L2": lookupL45C2,
"LANDSAT/LT05/C02/T2_L2": lookupL45C2,
"LANDSAT/LT04/C01/T1_SR": lookupL45,
"LANDSAT/LT04/C01/T2_SR": lookupL45,
"LANDSAT/LT04/C02/T1_L2": lookupL45C2,
"LANDSAT/LT04/C02/T2_L2": lookupL45C2,
"MODIS/006/MOD09GQ": lookupMOD09GQ,
"MODIS/006/MYD09GQ": lookupMOD09GQ,
"MODIS/006/MOD09GA": lookupMOD09GA,
"MODIS/006/MYD09GA": lookupMOD09GA,
"MODIS/006/MOD09Q1": lookupMOD09GQ,
"MODIS/006/MYD09Q1": lookupMOD09GQ,
"MODIS/006/MOD09A1": lookupMOD09GA,
"MODIS/006/MYD09A1": lookupMOD09GA,
"MODIS/006/MCD43A4": lookupMCD43A4,
}
if platformDict["platform"] not in list(lookupPlatform.keys()):
raise Exception(
"Sorry, satellite platform not supported for index computation!"
)
return lookupPlatform[platformDict["platform"]](img)
def _get_indices(online: bool) -> dict:
"""Retrieves the dictionary of indices used for the index() method in ee.Image and ee.ImageCollection classes.
Args:
online : Wheter to retrieve the most recent list of indices directly from the GitHub repository and not from the local copy.
Returns:
Indices.
"""
if online:
with urllib.request.urlopen(
"https://raw.githubusercontent.com/davemlz/awesome-ee-spectral-indices/main/output/spectral-indices-dict.json"
) as url:
indices = json.loads(url.read().decode())
else:
indices = _load_JSON("spectral-indices-dict.json")
return indices["SpectralIndices"]
def _get_kernel_image(
img: ee.Image, lookup: dict, kernel: str, sigma: Union[str, float], a: str, b: str
) -> ee.Image:
"""Creates an ee.Image representing a kernel computed on bands [a] and [b].
Args:
img : Image to compute the kernel on.
lookup : Dictionary retrieved from _get_expression_map().
kernel : Kernel to use.
sigma : Length-scale parameter. Used for kernel = 'RBF'.
a : Key of the first band to use.
b : Key of the second band to use.
Returns:
Kernel image.
"""
if a not in list(lookup.keys()) or b not in list(lookup.keys()):
return None
else:
lookupab = {"a": lookup[a], "b": lookup[b]}
if isinstance(sigma, str):
lookup = {**lookup, **lookupab, "sigma": img.expression(sigma, lookupab)}
else:
lookup = {**lookup, **lookupab, "sigma": sigma}
kernels = {
"linear": "a * b",
"RBF": "exp((-1.0 * (a - b) ** 2.0)/(2.0 * sigma ** 2.0))",
"poly": "((a * b) + c) ** p",
}
return img.expression(kernels[kernel], lookup)
def _remove_none_dict(dictionary: dict) -> dict:
"""Removes elements from a dictionary with None values.
Args:
dictionary : Dictionary to remove None values.
Returns:
Curated dictionary.
"""
newDictionary = dict(dictionary)
for key in dictionary.keys():
if dictionary[key] is None:
del newDictionary[key]
return newDictionary
def _get_kernel_parameters(
img: ee.Image, lookup: dict, kernel: str, sigma: Union[str, float]
) -> dict:
"""Gets the additional kernel parameters to compute kernel indices.
Args:
img : Image to compute the kernel parameters on.
lookup : Dictionary retrieved from _get_expression_map().
kernel : Kernel to use.
sigma : Length-scale parameter. Used for kernel = 'RBF'.
Returns:
Kernel parameters.
"""
kernelParameters = {
"kNN": _get_kernel_image(img, lookup, kernel, sigma, "N", "N"),
"kNR": _get_kernel_image(img, lookup, kernel, sigma, "N", "R"),
"kNB": _get_kernel_image(img, lookup, kernel, sigma, "N", "B"),
"kNL": _get_kernel_image(img, lookup, kernel, sigma, "N", "L"),
"kGG": _get_kernel_image(img, lookup, kernel, sigma, "G", "G"),
"kGR": _get_kernel_image(img, lookup, kernel, sigma, "G", "R"),
"kGB": _get_kernel_image(img, lookup, kernel, sigma, "G", "B"),
"kBB": _get_kernel_image(img, lookup, kernel, sigma, "B", "B"),
"kBR": _get_kernel_image(img, lookup, kernel, sigma, "B", "R"),
"kBL": _get_kernel_image(img, lookup, kernel, sigma, "B", "L"),
"kRR": _get_kernel_image(img, lookup, kernel, sigma, "R", "R"),
"kRB": _get_kernel_image(img, lookup, kernel, sigma, "R", "B"),
"kRL": _get_kernel_image(img, lookup, kernel, sigma, "R", "L"),
"kLL": _get_kernel_image(img, lookup, kernel, sigma, "L", "L"),
}
return kernelParameters
def _get_tc_coefficients(platform: str) -> dict:
"""Gets the platform-specific coefficient dictionary required for tasseled cap
transformation.
Platform matching is strict, meaning that data must be at the processing level
specified by the reference literature that coefficients were sourced from, e.g.
Landsat 8 SR cannot be transformed with Landsat 8 TOA coefficients.
Args:
platform : Platform name retrieved from the STAC.
Returns:
Map dictionary with band names and corresponding coefficients for brightness
greenness, and wetness.
Raises:
Exception : If the platform has no supported coefficients.
"""
SENTINEL2_1C = {
"bands": (
"B1",
"B2",
"B3",
"B4",
"B5",
"B6",
"B7",
"B8",
"B8A",
"B9",
"B10",
"B11",
"B12",
),
"TCB": (
0.2381,
0.2569,
0.2934,
0.3020,
0.3099,
0.3740,
0.4180,
0.3580,
0.3834,
0.0103,
0.0020,
0.0896,
0.0780,
),
"TCG": (
-0.2266,
-0.2818,
-0.3020,
-0.4283,
-0.2959,
0.1602,
0.3127,
0.3138,
0.4261,
0.1454,
-0.0017,
-0.1341,
-0.2538,
),
"TCW": (
0.1825,
0.1763,
0.1615,
0.0486,
0.0170,
0.0223,
0.0219,
-0.0755,
-0.0910,
-0.1369,
0.0003,
-0.7701,
-0.5293,
),
}
# Zhai et al. 2022 also provide coefficients with the blue band, but
# recommend omitting it due to difficulties in atmospheric correction.
LANDSAT8_SR = {
"bands": ("SR_B3", "SR_B4", "SR_B5", "SR_B6", "SR_B7"),
"TCB": (0.4596, 0.5046, 0.5458, 0.4114, 0.2589),
"TCG": (-0.3374, -0.4901, 0.7909, 0.0177, -0.1416),
"TCW": (0.2254, 0.3681, 0.2250, -0.6053, -0.6298)
}
# Zhai et al. 2022 coefficients were included for L8 TOA over the Baig
# et al. 2014 coefficients for consistency with the L8 SR coefficients,
# which were not calculated by Baig et al.
LANDSAT8_TOA = {
"bands": ("B3", "B4", "B5", "B6", "B7"),
"TCB": (0.4321, 0.4971, 0.5695, 0.4192, 0.2569),
"TCG": (-0.3318, -0.4844, 0.7856, -0.0331, -0.1923),
"TCW": (0.2633, 0.3945, 0.1801, -0.6121, -0.6066)
}
# Coefficients for Landsat 8 OLI are usable for Landsat 9 OLI-2, per
# Zhai et al. 2022
LANDSAT9_SR = LANDSAT8_SR
LANDSAT9_TOA = LANDSAT8_TOA
LANDSAT7_TOA = {
"bands": ("B1", "B2", "B3", "B4", "B5", "B7"),
"TCB": (0.3561, 0.3972, 0.3904, 0.6966, 0.2286, 0.1596),
"TCG": (-0.3344, -0.3544, -0.4556, 0.6966, -0.0242, -0.2630),
"TCW": (0.2626, 0.2141, 0.0926, 0.0656, -0.7629, -0.5388),
}
LANDSAT4_DN = {
"bands": ("B1", "B2", "B3", "B4", "B5", "B7"),
"TCB": (0.3037, 0.2793, 0.4743, 0.5585, 0.5082, 0.1863),
"TCG": (-0.2848, -0.2435, -0.5435, 0.7243, 0.0840, -0.1800),
"TCW": (0.1509, 0.1973, 0.3279, 0.3406, -0.7112, -0.4572),
}
LANDSAT4_SR = {
"bands": ("SR_B1", "SR_B2", "SR_B3", "SR_B4", "SR_B5", "SR_B7"),
"TCB": (0.2043, 0.4158, 0.5524, 0.5741, 0.3124, 0.2303),
"TCG": (-0.1603, -0.2819, -0.4934, 0.7940, -0.0002, -0.1446),
"TCW": (0.0315, 0.2021, 0.3102, 0.1594, -0.6806, -0.6109),
}
LANDSAT5_DN = {
"bands": ("B1", "B2", "B3", "B4", "B5", "B7"),
"TCB": (0.2909, 0.2493, 0.4806, 0.5568, 0.4438, 0.1706),
"TCG": (-0.2728, -0.2174, -0.5508, 0.7221, 0.0733, -0.1648),
"TCW": (0.1446, 0.1761, 0.3322, 0.3396, -0.6210, -0.4186),
}
MODIS_NBAR = {
"bands": (
"Nadir_Reflectance_Band1",
"Nadir_Reflectance_Band2",
"Nadir_Reflectance_Band3",
"Nadir_Reflectance_Band4",
"Nadir_Reflectance_Band5",
"Nadir_Reflectance_Band6",
"Nadir_Reflectance_Band7",
),
"TCB": (0.4395, 0.5945, 0.2460, 0.3918, 0.3506, 0.2136, 0.2678),
"TCG": (-0.4064, 0.5129, -0.2744, -0.2893, 0.4882, -0.0036, -0.4169),
"TCW": (0.1147, 0.2489, 0.2408, 0.3132, -0.3122, -0.6416, -0.5087),
}
platformCoeffs = {
"COPERNICUS/S2": SENTINEL2_1C,
"MODIS/006/MCD43A4": MODIS_NBAR,
"LANDSAT/LC09/C02/T1_L2": LANDSAT9_SR,
"LANDSAT/LC09/C02/T1_TOA": LANDSAT9_TOA,
"LANDSAT/LC08/C02/T1_L2": LANDSAT8_SR,
"LANDSAT/LC08/C02/T2_L2": LANDSAT8_SR,
"LANDSAT/LC08/C01/T1_TOA": LANDSAT8_TOA,
"LANDSAT/LC08/C01/T1_RT_TOA": LANDSAT8_TOA,
"LANDSAT/LC08/C01/T2_TOA": LANDSAT8_TOA,
"LANDSAT/LE07/C01/T1_TOA": LANDSAT7_TOA,
"LANDSAT/LE07/C01/T1_RT_TOA": LANDSAT7_TOA,
"LANDSAT/LE07/C01/T2_TOA": LANDSAT7_TOA,
"LANDSAT/LT05/C01/T1": LANDSAT5_DN,
"LANDSAT/LT05/C01/T2": LANDSAT5_DN,
"LANDSAT/LT04/C02/T1_L2": LANDSAT4_SR,
"LANDSAT/LT04/C02/T2_L2": LANDSAT4_SR,
"LANDSAT/LT04/C01/T1": LANDSAT4_DN,
"LANDSAT/LT04/C01/T2": LANDSAT4_DN,
}
if platform not in list(platformCoeffs.keys()):
raise Exception(
"Sorry, satellite platform not supported for tasseled cap transformation! Use one of "
+ str(list(platformCoeffs.keys()))
)
return platformCoeffs[platform]
def _match_histogram(
source: ee.Image,
target: ee.Image,
bands: Optional[Dict[str, str]],
geometry: Optional[ee.Geometry],
maxBuckets: int,
) -> ee.Image:
"""Adjust the histogram of an image to match a target image.
Args:
source : Image to adjust.
target : Image to use as the histogram reference.
bands : An optional dictionary of band names to match, with source bands as keys
and target bands as values. If none is provided, bands will be matched by name.
Any bands not included here will be dropped.
geometry : The optional region to match histograms in that overlaps both images.
If none is provided, the geometry of the source image will be used. If the
source image is unbounded and no geometry is provided, histogram matching will
fail.
maxBuckets : The maximum number of buckets to use when building histograms. More
buckets will require more memory and time but will generate more accurate
results. The number of buckets will be rounded to the nearest power of 2.
Returns:
The adjusted image containing the matched source bands.
"""
def histogram_lookup(
source_hist: ee.Array, target_hist: ee.Array
) -> Tuple[ee.List, ee.List]:
"""Build a list of target values with corresponding counts to source values from a source and target histogram.
Args:
source_hist : A histogram for a source image returned by ee.Reducer.autoHistogram
target_hist : A histogram for a target image returned by ee.Reducer.autoHistogram
Returns:
Source histogram values and target histogram values with corresponding counts.
"""
source_vals = source_hist.slice(1, 0, 1).project([0])
source_counts = source_hist.slice(1, 1, 2).project([0])
source_counts = source_counts.divide(source_counts.get([-1]))
target_vals = target_hist.slice(1, 0, 1).project([0])
target_counts = target_hist.slice(1, 1, 2).project([0])
target_counts = target_counts.divide(target_counts.get([-1]))
def lookup_value(n):
"""Find the first target value with at least n counts."""
index = target_counts.gte(n).argmax()
return target_vals.get(index)
target_lookup_vals = source_counts.toList().map(lookup_value)
return (source_vals.toList(), target_lookup_vals)
geometry = ee.Element.geometry(source) if geometry is None else geometry
source_bands = source.bandNames() if bands is None else list(bands.keys())
target_bands = source.bandNames() if bands is None else list(bands.values())
bands = ee.Dictionary.fromLists(source_bands, target_bands)
source = source.select(source_bands)
target = target.select(target_bands)
source_histogram = source.reduceRegion(
reducer=ee.Reducer.autoHistogram(maxBuckets=maxBuckets, cumulative=True),
geometry=geometry,
scale=30,
maxPixels=1e13,
bestEffort=True,
)
target_histogram = target.updateMask(source.mask()).reduceRegion(
reducer=ee.Reducer.autoHistogram(maxBuckets=maxBuckets, cumulative=True),
geometry=geometry,
scale=30,
maxPixels=1e13,
bestEffort=True,
)
def match_bands(source_band: ee.String, target_band: ee.String) -> ee.Image:
"""Match the histogram of one source band to a target band.
Args:
source_band : The name of a band in the source image to adjust.
target_band : The name of a corresponding band in the target image to match to.
Returns:
The source band image histogram-matched to the target band.
"""
x, y = histogram_lookup(
source_histogram.getArray(source_band),
target_histogram.getArray(target_band),
)
matched = source.select([source_band]).interpolate(x, y)
return matched
matched = (
ee.ImageCollection(bands.map(match_bands).values())
.toBands()
.rename(bands.keys())
)
# Preserve the metadata, band types, and band order of the source image.
matched = ee.Image(matched.copyProperties(source, source.propertyNames()))
matched = matched.cast(source.bandTypes(), source.bandNames())
matched = matched.set("ee_extra:HISTOGRAM_TARGET", target)
# If the source image was bounded, clip the matched output to its bounds. If the source
# image doesn't have a `geometry` this will fail, but that seems exceptionally rare.
matched = ee.Algorithms.If(
source.geometry().isUnbounded(),
matched,
matched.clip(source.geometry().bounds()),
)
return ee.Image(matched)
|
py | 1a31402e86d05dd5dfa3c2d92780e0a2d814ef76 | # coding: utf-8
# Copyright (c) 2016, 2021, Oracle and/or its affiliates. All rights reserved.
# This software is dual-licensed to you under the Universal Permissive License (UPL) 1.0 as shown at https://oss.oracle.com/licenses/upl or Apache License 2.0 as shown at http://www.apache.org/licenses/LICENSE-2.0. You may choose either license.
from .update_data_asset_details import UpdateDataAssetDetails
from oci.util import formatted_flat_dict, NONE_SENTINEL, value_allowed_none_or_none_sentinel # noqa: F401
from oci.decorators import init_model_state_from_kwargs
@init_model_state_from_kwargs
class UpdateDataAssetFromAtp(UpdateDataAssetDetails):
"""
Details for the Autonomous Transaction Processing data asset type.
"""
def __init__(self, **kwargs):
"""
Initializes a new UpdateDataAssetFromAtp object with values from keyword arguments. The default value of the :py:attr:`~oci.data_integration.models.UpdateDataAssetFromAtp.model_type` attribute
of this class is ``ORACLE_ATP_DATA_ASSET`` and it should not be changed.
The following keyword arguments are supported (corresponding to the getters/setters of this class):
:param model_type:
The value to assign to the model_type property of this UpdateDataAssetFromAtp.
Allowed values for this property are: "ORACLE_DATA_ASSET", "ORACLE_OBJECT_STORAGE_DATA_ASSET", "ORACLE_ATP_DATA_ASSET", "ORACLE_ADWC_DATA_ASSET", "MYSQL_DATA_ASSET", "GENERIC_JDBC_DATA_ASSET", "FUSION_APP_DATA_ASSET", "AMAZON_S3_DATA_ASSET"
:type model_type: str
:param key:
The value to assign to the key property of this UpdateDataAssetFromAtp.
:type key: str
:param model_version:
The value to assign to the model_version property of this UpdateDataAssetFromAtp.
:type model_version: str
:param name:
The value to assign to the name property of this UpdateDataAssetFromAtp.
:type name: str
:param description:
The value to assign to the description property of this UpdateDataAssetFromAtp.
:type description: str
:param object_status:
The value to assign to the object_status property of this UpdateDataAssetFromAtp.
:type object_status: int
:param object_version:
The value to assign to the object_version property of this UpdateDataAssetFromAtp.
:type object_version: int
:param identifier:
The value to assign to the identifier property of this UpdateDataAssetFromAtp.
:type identifier: str
:param external_key:
The value to assign to the external_key property of this UpdateDataAssetFromAtp.
:type external_key: str
:param asset_properties:
The value to assign to the asset_properties property of this UpdateDataAssetFromAtp.
:type asset_properties: dict(str, str)
:param registry_metadata:
The value to assign to the registry_metadata property of this UpdateDataAssetFromAtp.
:type registry_metadata: oci.data_integration.models.RegistryMetadata
:param service_name:
The value to assign to the service_name property of this UpdateDataAssetFromAtp.
:type service_name: str
:param driver_class:
The value to assign to the driver_class property of this UpdateDataAssetFromAtp.
:type driver_class: str
:param credential_file_content:
The value to assign to the credential_file_content property of this UpdateDataAssetFromAtp.
:type credential_file_content: str
:param wallet_secret:
The value to assign to the wallet_secret property of this UpdateDataAssetFromAtp.
:type wallet_secret: oci.data_integration.models.SensitiveAttribute
:param wallet_password_secret:
The value to assign to the wallet_password_secret property of this UpdateDataAssetFromAtp.
:type wallet_password_secret: oci.data_integration.models.SensitiveAttribute
:param region_id:
The value to assign to the region_id property of this UpdateDataAssetFromAtp.
:type region_id: str
:param tenancy_id:
The value to assign to the tenancy_id property of this UpdateDataAssetFromAtp.
:type tenancy_id: str
:param compartment_id:
The value to assign to the compartment_id property of this UpdateDataAssetFromAtp.
:type compartment_id: str
:param autonomous_db_id:
The value to assign to the autonomous_db_id property of this UpdateDataAssetFromAtp.
:type autonomous_db_id: str
:param default_connection:
The value to assign to the default_connection property of this UpdateDataAssetFromAtp.
:type default_connection: oci.data_integration.models.UpdateConnectionFromAtp
"""
self.swagger_types = {
'model_type': 'str',
'key': 'str',
'model_version': 'str',
'name': 'str',
'description': 'str',
'object_status': 'int',
'object_version': 'int',
'identifier': 'str',
'external_key': 'str',
'asset_properties': 'dict(str, str)',
'registry_metadata': 'RegistryMetadata',
'service_name': 'str',
'driver_class': 'str',
'credential_file_content': 'str',
'wallet_secret': 'SensitiveAttribute',
'wallet_password_secret': 'SensitiveAttribute',
'region_id': 'str',
'tenancy_id': 'str',
'compartment_id': 'str',
'autonomous_db_id': 'str',
'default_connection': 'UpdateConnectionFromAtp'
}
self.attribute_map = {
'model_type': 'modelType',
'key': 'key',
'model_version': 'modelVersion',
'name': 'name',
'description': 'description',
'object_status': 'objectStatus',
'object_version': 'objectVersion',
'identifier': 'identifier',
'external_key': 'externalKey',
'asset_properties': 'assetProperties',
'registry_metadata': 'registryMetadata',
'service_name': 'serviceName',
'driver_class': 'driverClass',
'credential_file_content': 'credentialFileContent',
'wallet_secret': 'walletSecret',
'wallet_password_secret': 'walletPasswordSecret',
'region_id': 'regionId',
'tenancy_id': 'tenancyId',
'compartment_id': 'compartmentId',
'autonomous_db_id': 'autonomousDbId',
'default_connection': 'defaultConnection'
}
self._model_type = None
self._key = None
self._model_version = None
self._name = None
self._description = None
self._object_status = None
self._object_version = None
self._identifier = None
self._external_key = None
self._asset_properties = None
self._registry_metadata = None
self._service_name = None
self._driver_class = None
self._credential_file_content = None
self._wallet_secret = None
self._wallet_password_secret = None
self._region_id = None
self._tenancy_id = None
self._compartment_id = None
self._autonomous_db_id = None
self._default_connection = None
self._model_type = 'ORACLE_ATP_DATA_ASSET'
@property
def service_name(self):
"""
Gets the service_name of this UpdateDataAssetFromAtp.
The Autonomous Transaction Processing instance service name.
:return: The service_name of this UpdateDataAssetFromAtp.
:rtype: str
"""
return self._service_name
@service_name.setter
def service_name(self, service_name):
"""
Sets the service_name of this UpdateDataAssetFromAtp.
The Autonomous Transaction Processing instance service name.
:param service_name: The service_name of this UpdateDataAssetFromAtp.
:type: str
"""
self._service_name = service_name
@property
def driver_class(self):
"""
Gets the driver_class of this UpdateDataAssetFromAtp.
The Autonomous Transaction Processing driver class
:return: The driver_class of this UpdateDataAssetFromAtp.
:rtype: str
"""
return self._driver_class
@driver_class.setter
def driver_class(self, driver_class):
"""
Sets the driver_class of this UpdateDataAssetFromAtp.
The Autonomous Transaction Processing driver class
:param driver_class: The driver_class of this UpdateDataAssetFromAtp.
:type: str
"""
self._driver_class = driver_class
@property
def credential_file_content(self):
"""
Gets the credential_file_content of this UpdateDataAssetFromAtp.
The credential file content from an Autonomous Transaction Processing wallet.
:return: The credential_file_content of this UpdateDataAssetFromAtp.
:rtype: str
"""
return self._credential_file_content
@credential_file_content.setter
def credential_file_content(self, credential_file_content):
"""
Sets the credential_file_content of this UpdateDataAssetFromAtp.
The credential file content from an Autonomous Transaction Processing wallet.
:param credential_file_content: The credential_file_content of this UpdateDataAssetFromAtp.
:type: str
"""
self._credential_file_content = credential_file_content
@property
def wallet_secret(self):
"""
Gets the wallet_secret of this UpdateDataAssetFromAtp.
:return: The wallet_secret of this UpdateDataAssetFromAtp.
:rtype: oci.data_integration.models.SensitiveAttribute
"""
return self._wallet_secret
@wallet_secret.setter
def wallet_secret(self, wallet_secret):
"""
Sets the wallet_secret of this UpdateDataAssetFromAtp.
:param wallet_secret: The wallet_secret of this UpdateDataAssetFromAtp.
:type: oci.data_integration.models.SensitiveAttribute
"""
self._wallet_secret = wallet_secret
@property
def wallet_password_secret(self):
"""
Gets the wallet_password_secret of this UpdateDataAssetFromAtp.
:return: The wallet_password_secret of this UpdateDataAssetFromAtp.
:rtype: oci.data_integration.models.SensitiveAttribute
"""
return self._wallet_password_secret
@wallet_password_secret.setter
def wallet_password_secret(self, wallet_password_secret):
"""
Sets the wallet_password_secret of this UpdateDataAssetFromAtp.
:param wallet_password_secret: The wallet_password_secret of this UpdateDataAssetFromAtp.
:type: oci.data_integration.models.SensitiveAttribute
"""
self._wallet_password_secret = wallet_password_secret
@property
def region_id(self):
"""
Gets the region_id of this UpdateDataAssetFromAtp.
The Autonomous Data Warehouse instance region Id.
:return: The region_id of this UpdateDataAssetFromAtp.
:rtype: str
"""
return self._region_id
@region_id.setter
def region_id(self, region_id):
"""
Sets the region_id of this UpdateDataAssetFromAtp.
The Autonomous Data Warehouse instance region Id.
:param region_id: The region_id of this UpdateDataAssetFromAtp.
:type: str
"""
self._region_id = region_id
@property
def tenancy_id(self):
"""
Gets the tenancy_id of this UpdateDataAssetFromAtp.
The Autonomous Data Warehouse instance tenancy Id.
:return: The tenancy_id of this UpdateDataAssetFromAtp.
:rtype: str
"""
return self._tenancy_id
@tenancy_id.setter
def tenancy_id(self, tenancy_id):
"""
Sets the tenancy_id of this UpdateDataAssetFromAtp.
The Autonomous Data Warehouse instance tenancy Id.
:param tenancy_id: The tenancy_id of this UpdateDataAssetFromAtp.
:type: str
"""
self._tenancy_id = tenancy_id
@property
def compartment_id(self):
"""
Gets the compartment_id of this UpdateDataAssetFromAtp.
The Autonomous Data Warehouse instance compartment Id.
:return: The compartment_id of this UpdateDataAssetFromAtp.
:rtype: str
"""
return self._compartment_id
@compartment_id.setter
def compartment_id(self, compartment_id):
"""
Sets the compartment_id of this UpdateDataAssetFromAtp.
The Autonomous Data Warehouse instance compartment Id.
:param compartment_id: The compartment_id of this UpdateDataAssetFromAtp.
:type: str
"""
self._compartment_id = compartment_id
@property
def autonomous_db_id(self):
"""
Gets the autonomous_db_id of this UpdateDataAssetFromAtp.
Tha Autonomous Database Id
:return: The autonomous_db_id of this UpdateDataAssetFromAtp.
:rtype: str
"""
return self._autonomous_db_id
@autonomous_db_id.setter
def autonomous_db_id(self, autonomous_db_id):
"""
Sets the autonomous_db_id of this UpdateDataAssetFromAtp.
Tha Autonomous Database Id
:param autonomous_db_id: The autonomous_db_id of this UpdateDataAssetFromAtp.
:type: str
"""
self._autonomous_db_id = autonomous_db_id
@property
def default_connection(self):
"""
Gets the default_connection of this UpdateDataAssetFromAtp.
:return: The default_connection of this UpdateDataAssetFromAtp.
:rtype: oci.data_integration.models.UpdateConnectionFromAtp
"""
return self._default_connection
@default_connection.setter
def default_connection(self, default_connection):
"""
Sets the default_connection of this UpdateDataAssetFromAtp.
:param default_connection: The default_connection of this UpdateDataAssetFromAtp.
:type: oci.data_integration.models.UpdateConnectionFromAtp
"""
self._default_connection = default_connection
def __repr__(self):
return formatted_flat_dict(self)
def __eq__(self, other):
if other is None:
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
return not self == other
|
py | 1a31404749354acdb2af34a31902f9dc5c7a0c8a | #!/usr/bin/env python3
import argparse
import json
import sys
import traceback
import re
from sonic_py_common import device_info, logger
from swsscommon.swsscommon import SonicV2Connector, ConfigDBConnector, SonicDBConfig
INIT_CFG_FILE = '/etc/sonic/init_cfg.json'
SYSLOG_IDENTIFIER = 'db_migrator'
# Global logger instance
log = logger.Logger(SYSLOG_IDENTIFIER)
class DBMigrator():
def __init__(self, namespace, socket=None):
"""
Version string format:
version_<major>_<minor>_<build>
major: starting from 1, sequentially incrementing in master
branch.
minor: in github branches, minor version stays in 0. This minor
version creates space for private branches derived from
github public branches. These private branches shall use
none-zero values.
build: sequentially increase within a minor version domain.
"""
self.CURRENT_VERSION = 'version_2_0_0'
self.TABLE_NAME = 'VERSIONS'
self.TABLE_KEY = 'DATABASE'
self.TABLE_FIELD = 'VERSION'
db_kwargs = {}
if socket:
db_kwargs['unix_socket_path'] = socket
if namespace is None:
self.configDB = ConfigDBConnector(**db_kwargs)
else:
self.configDB = ConfigDBConnector(use_unix_socket_path=True, namespace=namespace, **db_kwargs)
self.configDB.db_connect('CONFIG_DB')
self.appDB = SonicV2Connector(host='127.0.0.1')
if self.appDB is not None:
self.appDB.connect(self.appDB.APPL_DB)
self.stateDB = SonicV2Connector(host='127.0.0.1')
if self.stateDB is not None:
self.stateDB.connect(self.stateDB.STATE_DB)
version_info = device_info.get_sonic_version_info()
asic_type = version_info.get('asic_type')
self.asic_type = asic_type
if asic_type == "mellanox":
from mellanox_buffer_migrator import MellanoxBufferMigrator
self.mellanox_buffer_migrator = MellanoxBufferMigrator(self.configDB)
def migrate_pfc_wd_table(self):
'''
Migrate all data entries from table PFC_WD_TABLE to PFC_WD
'''
data = self.configDB.get_table('PFC_WD_TABLE')
for key in data:
self.configDB.set_entry('PFC_WD', key, data[key])
self.configDB.delete_table('PFC_WD_TABLE')
def is_ip_prefix_in_key(self, key):
'''
Function to check if IP address is present in the key. If it
is present, then the key would be a tuple or else, it shall be
be string
'''
return (isinstance(key, tuple))
def migrate_interface_table(self):
'''
Migrate all data from existing INTERFACE table with IP Prefix
to have an additional ONE entry without IP Prefix. For. e.g, for an entry
"Vlan1000|192.168.0.1/21": {}", this function shall add an entry without
IP prefix as ""Vlan1000": {}". This is for VRF compatibility.
'''
if_db = []
if_tables = {
'INTERFACE',
'PORTCHANNEL_INTERFACE',
'VLAN_INTERFACE',
'LOOPBACK_INTERFACE'
}
for table in if_tables:
data = self.configDB.get_table(table)
for key in data:
if not self.is_ip_prefix_in_key(key):
if_db.append(key)
continue
for table in if_tables:
data = self.configDB.get_table(table)
for key in data:
if not self.is_ip_prefix_in_key(key) or key[0] in if_db:
continue
log.log_info('Migrating interface table for ' + key[0])
self.configDB.set_entry(table, key[0], data[key])
if_db.append(key[0])
def migrate_intf_table(self):
'''
Migrate all data from existing INTF table in APP DB during warmboot with IP Prefix
to have an additional ONE entry without IP Prefix. For. e.g, for an entry
"Vlan1000:192.168.0.1/21": {}", this function shall add an entry without
IP prefix as ""Vlan1000": {}". This also migrates 'lo' to 'Loopback0' interface
'''
if self.appDB is None:
return
data = self.appDB.keys(self.appDB.APPL_DB, "INTF_TABLE:*")
if data is None:
return
if_db = []
for key in data:
if_name = key.split(":")[1]
if if_name == "lo":
self.appDB.delete(self.appDB.APPL_DB, key)
key = key.replace(if_name, "Loopback0")
log.log_info('Migrating lo entry to ' + key)
self.appDB.set(self.appDB.APPL_DB, key, 'NULL', 'NULL')
if '/' not in key:
if_db.append(key.split(":")[1])
continue
data = self.appDB.keys(self.appDB.APPL_DB, "INTF_TABLE:*")
for key in data:
if_name = key.split(":")[1]
if if_name in if_db:
continue
log.log_info('Migrating intf table for ' + if_name)
table = "INTF_TABLE:" + if_name
self.appDB.set(self.appDB.APPL_DB, table, 'NULL', 'NULL')
if_db.append(if_name)
def migrate_copp_table(self):
'''
Delete the existing COPP table
'''
if self.appDB is None:
return
keys = self.appDB.keys(self.appDB.APPL_DB, "COPP_TABLE:*")
if keys is None:
return
for copp_key in keys:
self.appDB.delete(self.appDB.APPL_DB, copp_key)
def migrate_config_db_buffer_tables_for_dynamic_calculation(self, speed_list, cable_len_list, default_dynamic_th, default_lossless_profiles, abandon_method, append_item_method):
'''
Migrate buffer tables to dynamic calculation mode
parameters
@speed_list - list of speed supported
@cable_len_list - list of cable length supported
@default_dynamic_th - default dynamic th
@default_lossless_profiles - default lossless profiles from the previous image
@abandon_method - a function which is called to abandon the migration and keep the current configuration
if the current one doesn't match the default one
@append_item_method - a function which is called to append an item to the list of pending commit items
any update to buffer configuration will be pended and won't be applied until
all configuration is checked and aligns with the default one
1. Buffer profiles for lossless PGs in BUFFER_PROFILE table will be removed
if their names have the convention of pg_lossless_<speed>_<cable_length>_profile
where the speed and cable_length belongs speed_list and cable_len_list respectively
and the dynamic_th is equal to default_dynamic_th
2. Insert tables required for dynamic buffer calculation
- DEFAULT_LOSSLESS_BUFFER_PARAMETER|AZURE: {'default_dynamic_th': default_dynamic_th}
- LOSSLESS_TRAFFIC_PATTERN|AZURE: {'mtu': '1500', 'small_packet_percentage': '100'}
3. For lossless dynamic PGs, remove the explicit referencing buffer profiles
Before: BUFFER_PG|<port>|3-4: {'profile': 'BUFFER_PROFILE|pg_lossless_<speed>_<cable_length>_profile'}
After: BUFFER_PG|<port>|3-4: {'profile': 'NULL'}
'''
# Migrate BUFFER_PROFILEs, removing dynamically generated profiles
dynamic_profile = self.configDB.get_table('BUFFER_PROFILE')
profile_pattern = 'pg_lossless_([1-9][0-9]*000)_([1-9][0-9]*m)_profile'
for name, info in dynamic_profile.items():
m = re.search(profile_pattern, name)
if not m:
continue
speed = m.group(1)
cable_length = m.group(2)
if speed in speed_list and cable_length in cable_len_list:
log.log_info("current profile {} {}".format(name, info))
log.log_info("default profile {} {}".format(name, default_lossless_profiles.get(name)))
default_profile = default_lossless_profiles.get(name);
if info.get("xon") == default_profile.get("xon") and info.get("size") == default_profile.get("size") and info.get('dynamic_th') == default_dynamic_th:
append_item_method(('BUFFER_PROFILE', name, None))
log.log_info("Lossless profile {} has been removed".format(name))
else:
log.log_notice("Lossless profile {} doesn't match the default configuration, keep using traditional buffer calculation mode")
abandon_method()
return True
# Migrate BUFFER_PGs, removing the explicit designated profiles
buffer_pgs = self.configDB.get_table('BUFFER_PG')
ports = self.configDB.get_table('PORT')
all_cable_lengths = self.configDB.get_table('CABLE_LENGTH')
if not buffer_pgs or not ports or not all_cable_lengths:
log.log_notice("At lease one of tables BUFFER_PG, PORT and CABLE_LENGTH hasn't been defined, skip following migration")
abandon_method()
return True
cable_lengths = all_cable_lengths[list(all_cable_lengths.keys())[0]]
for name, profile in buffer_pgs.items():
# do the db migration
port, pg = name
if pg != '3-4':
continue
try:
profile_name = profile['profile'][1:-1].split('|')[1]
m = re.search(profile_pattern, profile_name)
except Exception:
continue
if not m:
continue
speed = m.group(1)
cable_length = m.group(2)
try:
if speed == ports[port]['speed'] and cable_length == cable_lengths[port]:
append_item_method(('BUFFER_PG', name, {'profile': 'NULL'}))
else:
log.log_notice("Lossless PG profile {} for port {} doesn't match its speed {} or cable length {}, keep using traditional buffer calculation mode".format(
profile_name, port, speed, cable_length))
abandon_method()
return True
except Exception:
continue
# Insert other tables required for dynamic buffer calculation
metadata = self.configDB.get_entry('DEVICE_METADATA', 'localhost')
metadata['buffer_model'] = 'dynamic'
append_item_method(('DEVICE_METADATA', 'localhost', metadata))
append_item_method(('DEFAULT_LOSSLESS_BUFFER_PARAMETER', 'AZURE', {'default_dynamic_th': default_dynamic_th}))
append_item_method(('LOSSLESS_TRAFFIC_PATTERN', 'AZURE', {'mtu': '1500', 'small_packet_percentage': '100'}))
return True
def prepare_dynamic_buffer_for_warm_reboot(self, buffer_pools = None, buffer_profiles = None, buffer_pgs = None):
'''
This is the very first warm reboot of buffermgrd (dynamic) if the system reboot from old image by warm-reboot
In this case steps need to be taken to get buffermgrd prepared (for warm reboot)
During warm reboot, buffer tables should be installed in the first place.
However, it isn't able to achieve that when system is warm-rebooted from an old image
without dynamic buffer supported, because the buffer info wasn't in the APPL_DB in the old image.
The solution is to copy that info from CONFIG_DB into APPL_DB in db_migrator.
During warm-reboot, db_migrator adjusts buffer info in CONFIG_DB by removing some fields
according to requirement from dynamic buffer calculation.
The buffer info before that adjustment needs to be copied to APPL_DB.
1. set WARM_RESTART_TABLE|buffermgrd as {restore_count: 0}
2. Copy the following tables from CONFIG_DB into APPL_DB in case of warm reboot
The separator in fields that reference objects in other table needs to be updated from '|' to ':'
- BUFFER_POOL
- BUFFER_PROFILE, separator updated for field 'pool'
- BUFFER_PG, separator updated for field 'profile'
- BUFFER_QUEUE, separator updated for field 'profile
- BUFFER_PORT_INGRESS_PROFILE_LIST, separator updated for field 'profile_list'
- BUFFER_PORT_EGRESS_PROFILE_LIST, separator updated for field 'profile_list'
'''
warmreboot_state = self.stateDB.get(self.stateDB.STATE_DB, 'WARM_RESTART_ENABLE_TABLE|system', 'enable')
mmu_size = self.stateDB.get(self.stateDB.STATE_DB, 'BUFFER_MAX_PARAM_TABLE|global', 'mmu_size')
if warmreboot_state == 'true' and not mmu_size:
log.log_notice("This is the very first run of buffermgrd (dynamic), prepare info required from warm reboot")
else:
return True
buffer_table_list = [
('BUFFER_POOL', buffer_pools, None),
('BUFFER_PROFILE', buffer_profiles, 'pool'),
('BUFFER_PG', buffer_pgs, 'profile'),
('BUFFER_QUEUE', None, 'profile'),
('BUFFER_PORT_INGRESS_PROFILE_LIST', None, 'profile_list'),
('BUFFER_PORT_EGRESS_PROFILE_LIST', None, 'profile_list')
]
for pair in buffer_table_list:
keys_copied = []
keys_ignored = []
table_name, entries, reference_field_name = pair
app_table_name = table_name + "_TABLE"
if not entries:
entries = self.configDB.get_table(table_name)
for key, items in entries.items():
# copy items to appl db
if reference_field_name:
confdb_ref = items.get(reference_field_name)
if not confdb_ref or confdb_ref == "NULL":
keys_ignored.append(key)
continue
items_referenced = confdb_ref.split(',')
appdb_ref = ""
first_item = True
for item in items_referenced:
if first_item:
first_item = False
else:
appdb_ref += ','
subitems = item.split('|')
first_key = True
for subitem in subitems:
if first_key:
appdb_ref += subitem + '_TABLE'
first_key = False
else:
appdb_ref += ':' + subitem
items[reference_field_name] = appdb_ref
keys_copied.append(key)
if type(key) is tuple:
appl_db_key = app_table_name + ':' + ':'.join(key)
else:
appl_db_key = app_table_name + ':' + key
for field, data in items.items():
self.appDB.set(self.appDB.APPL_DB, appl_db_key, field, data)
if keys_copied:
log.log_info("The following items in table {} in CONFIG_DB have been copied to APPL_DB: {}".format(table_name, keys_copied))
if keys_ignored:
log.log_info("The following items in table {} in CONFIG_DB have been ignored: {}".format(table_name, keys_copied))
return True
def version_unknown(self):
"""
version_unknown tracks all SONiC versions that doesn't have a version
string defined in config_DB.
Nothing can be assumped when migrating from this version to the next
version.
Any migration operation needs to test if the DB is in expected format
before migrating date to the next version.
"""
log.log_info('Handling version_unknown')
# NOTE: Uncomment next 3 lines of code when the migration code is in
# place. Note that returning specific string is intentional,
# here we only intended to migrade to DB version 1.0.1.
# If new DB version is added in the future, the incremental
# upgrade will take care of the subsequent migrations.
self.migrate_pfc_wd_table()
self.migrate_interface_table()
self.migrate_intf_table()
self.set_version('version_1_0_2')
return 'version_1_0_2'
def version_1_0_1(self):
"""
Version 1_0_1.
"""
log.log_info('Handling version_1_0_1')
self.migrate_interface_table()
self.migrate_intf_table()
self.set_version('version_1_0_2')
return 'version_1_0_2'
def version_1_0_2(self):
"""
Version 1_0_2.
"""
log.log_info('Handling version_1_0_2')
# Check ASIC type, if Mellanox platform then need DB migration
if self.asic_type == "mellanox":
if self.mellanox_buffer_migrator.mlnx_migrate_buffer_pool_size('version_1_0_2', 'version_1_0_3') \
and self.mellanox_buffer_migrator.mlnx_flush_new_buffer_configuration():
self.set_version('version_1_0_3')
else:
self.set_version('version_1_0_3')
return 'version_1_0_3'
def version_1_0_3(self):
"""
Version 1_0_3.
"""
log.log_info('Handling version_1_0_3')
# Check ASIC type, if Mellanox platform then need DB migration
if self.asic_type == "mellanox":
if self.mellanox_buffer_migrator.mlnx_migrate_buffer_pool_size('version_1_0_3', 'version_1_0_4') \
and self.mellanox_buffer_migrator.mlnx_migrate_buffer_profile('version_1_0_3', 'version_1_0_4') \
and self.mellanox_buffer_migrator.mlnx_flush_new_buffer_configuration():
self.set_version('version_1_0_4')
else:
self.set_version('version_1_0_4')
return 'version_1_0_4'
def version_1_0_4(self):
"""
Current latest version. Nothing to do here.
"""
log.log_info('Handling version_1_0_4')
# Check ASIC type, if Mellanox platform then need DB migration
if self.asic_type == "mellanox":
speed_list = self.mellanox_buffer_migrator.default_speed_list
cable_len_list = self.mellanox_buffer_migrator.default_cable_len_list
buffer_pools = self.configDB.get_table('BUFFER_POOL')
buffer_profiles = self.configDB.get_table('BUFFER_PROFILE')
buffer_pgs = self.configDB.get_table('BUFFER_PG')
default_lossless_profiles = self.mellanox_buffer_migrator.mlnx_get_default_lossless_profile('version_1_0_4')
abandon_method = self.mellanox_buffer_migrator.mlnx_abandon_pending_buffer_configuration
append_method = self.mellanox_buffer_migrator.mlnx_append_item_on_pending_configuration_list
if self.mellanox_buffer_migrator.mlnx_migrate_buffer_pool_size('version_1_0_4', 'version_2_0_0') \
and self.mellanox_buffer_migrator.mlnx_migrate_buffer_profile('version_1_0_4', 'version_2_0_0') \
and self.migrate_config_db_buffer_tables_for_dynamic_calculation(speed_list, cable_len_list, '0', default_lossless_profiles,
abandon_method, append_method) \
and self.mellanox_buffer_migrator.mlnx_flush_new_buffer_configuration() \
and self.prepare_dynamic_buffer_for_warm_reboot(buffer_pools, buffer_profiles, buffer_pgs):
metadata = self.configDB.get_entry('DEVICE_METADATA', 'localhost')
if not metadata.get('buffer_model'):
metadata['buffer_model'] = 'traditional'
self.configDB.set_entry('DEVICE_METADATA', 'localhost', metadata)
log.log_notice('Setting buffer_model to traditional')
else:
log.log_notice('Got buffer_model {}'.format(metadata.get('buffer_model')))
self.set_version('version_2_0_0')
else:
self.prepare_dynamic_buffer_for_warm_reboot()
metadata = self.configDB.get_entry('DEVICE_METADATA', 'localhost')
metadata['buffer_model'] = 'traditional'
self.configDB.set_entry('DEVICE_METADATA', 'localhost', metadata)
log.log_notice('Setting buffer_model to traditional')
self.set_version('version_2_0_0')
return 'version_2_0_0'
def version_2_0_0(self):
"""
Current latest version. Nothing to do here.
"""
log.log_info('Handling version_2_0_0')
return None
def get_version(self):
version = self.configDB.get_entry(self.TABLE_NAME, self.TABLE_KEY)
if version and version[self.TABLE_FIELD]:
return version[self.TABLE_FIELD]
return 'version_unknown'
def set_version(self, version=None):
if not version:
version = self.CURRENT_VERSION
log.log_info('Setting version to ' + version)
entry = { self.TABLE_FIELD : version }
self.configDB.set_entry(self.TABLE_NAME, self.TABLE_KEY, entry)
def common_migration_ops(self):
try:
with open(INIT_CFG_FILE) as f:
init_db = json.load(f)
except Exception as e:
raise Exception(str(e))
for init_cfg_table, table_val in init_db.items():
data = self.configDB.get_table(init_cfg_table)
if data:
# Ignore overriding the values that pre-exist in configDB
continue
log.log_info("Migrating table {} from INIT_CFG to config_db".format(init_cfg_table))
# Update all tables that do not exist in configDB but are present in INIT_CFG
for init_table_key, init_table_val in table_val.items():
self.configDB.set_entry(init_cfg_table, init_table_key, init_table_val)
self.migrate_copp_table()
def migrate(self):
version = self.get_version()
log.log_info('Upgrading from version ' + version)
while version:
next_version = getattr(self, version)()
if next_version == version:
raise Exception('Version migrate from %s stuck in same version' % version)
version = next_version
# Perform common migration ops
self.common_migration_ops()
def main():
try:
parser = argparse.ArgumentParser()
parser.add_argument('-o',
dest='operation',
metavar='operation (migrate, set_version, get_version)',
type = str,
required = False,
choices=['migrate', 'set_version', 'get_version'],
help = 'operation to perform [default: get_version]',
default='get_version')
parser.add_argument('-s',
dest='socket',
metavar='unix socket',
type = str,
required = False,
help = 'the unix socket that the desired database listens on',
default = None )
parser.add_argument('-n',
dest='namespace',
metavar='asic namespace',
type = str,
required = False,
help = 'The asic namespace whose DB instance we need to connect',
default = None )
args = parser.parse_args()
operation = args.operation
socket_path = args.socket
namespace = args.namespace
if args.namespace is not None:
SonicDBConfig.load_sonic_global_db_config(namespace=args.namespace)
if socket_path:
dbmgtr = DBMigrator(namespace, socket=socket_path)
else:
dbmgtr = DBMigrator(namespace)
result = getattr(dbmgtr, operation)()
if result:
print(str(result))
except Exception as e:
log.log_error('Caught exception: ' + str(e))
traceback.print_exc()
print(str(e))
parser.print_help()
sys.exit(1)
if __name__ == "__main__":
main()
|
py | 1a3140c3dd840c57a081c358620998829408e123 | # Copyright 2022 The TensorFlow Authors. 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.
"""Downloads and prepares TriviaQA dataset."""
from unittest import mock
from absl import app
from absl import flags
from absl import logging
import apache_beam as beam
import tensorflow_datasets as tfds
from official.projects.triviaqa import dataset # pylint: disable=unused-import
flags.DEFINE_integer('sequence_length', 4096, 'Max number of tokens.')
flags.DEFINE_integer(
'global_sequence_length', None,
'Max number of question tokens plus sentences. If not set, defaults to '
'sequence_length // 16 + 64.')
flags.DEFINE_integer(
'stride', 3072,
'For documents longer than `sequence_length`, where to split them.')
flags.DEFINE_string(
'sentencepiece_model_path', None,
'SentencePiece model to use for tokenization.')
flags.DEFINE_string('data_dir', None, 'Data directory for TFDS.')
flags.DEFINE_string('runner', 'DirectRunner', 'Beam runner to use.')
FLAGS = flags.FLAGS
def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
builder = tfds.builder(
'bigbird_trivia_qa/rc_wiki.preprocessed',
data_dir=FLAGS.data_dir,
sentencepiece_model_path=FLAGS.sentencepiece_model_path,
sequence_length=FLAGS.sequence_length,
global_sequence_length=FLAGS.global_sequence_length,
stride=FLAGS.stride)
download_config = tfds.download.DownloadConfig(
beam_options=beam.options.pipeline_options.PipelineOptions(flags=[
f'--runner={FLAGS.runner}',
'--direct_num_workers=8',
'--direct_running_mode=multi_processing',
]))
with mock.patch('tensorflow_datasets.core.download.extractor._normpath',
new=lambda x: x):
builder.download_and_prepare(download_config=download_config)
logging.info(builder.info.splits)
if __name__ == '__main__':
flags.mark_flag_as_required('sentencepiece_model_path')
app.run(main)
|
py | 1a3140d183ab009c8d4d030d35870b9d71da2093 |
import os
import click
import shutil
import subprocess
import pkg_resources
import sys
import errno
import traceback
from monitor.logs import init_logging, logger
class ValidationExceptionBinaryNotFound(Exception):
pass
class NotRunningRoot(Exception):
pass
@click.group()
def cli():
click.echo("FileWave Monitor v13 configuration.")
delay_30m = 60 * 30
def run_root_command(cmd_array, **kwargs):
try:
os.rename('/etc/foo', '/etc/bar')
except IOError as e:
if (e == errno.EPERM):
return False
proc = subprocess.Popen(cmd_array, stdout=subprocess.PIPE, **kwargs)
return proc.communicate()[0].decode('utf-8')
def run_root_commands(commands):
for c in commands:
run_root_command(c, shell=True)
def running_on_a_fwxserver_host(exist_func=os.path.exists):
'''
Check directories exist to see if we are running on a FileWave server host installation
This should return True if we are, regardless of being Mac/Linux/Docker etc.
'''
dirs_that_must_exist = ["bin", "certs",
"django", "log"]
main_filewave_dir = os.path.join("/usr/local", "filewave")
if not exist_func(main_filewave_dir):
return False
for f in [os.path.join(main_filewave_dir, d) for d in dirs_that_must_exist]:
if not exist_func(f):
return False
return True
@cli.command('integrate', help="Integrates the module assuming you are running this on the FileWave Server")
def install_into_environment():
init_logging()
if running_on_a_fwxserver_host():
if run_root_command(["ls", "-l"]) is False:
logger.info(
"provisioning is requested - but I've detected you are not running as root - aborting")
raise NotRunningRoot(
"provisioning is requested - but I've detected you are not running as root - aborting")
try:
provision_postgres_wal_interval()
provision_apache_mod_status()
provision_prometheus_binary()
provision_mtail_binary()
provision_exporters()
provision_supervisord_conf()
validate_provisioning()
logger.info("Looks like everything is configured, please restart the server now, then validate installation.")
except Exception as e:
logger.error("Error during provisioning, are you using sudo?")
logger.error(e)
traceback.print_exc(file=sys.stdout)
return
else:
logger.info("Didn't detect a FileWave Server host - configuration aborted")
def provision_postgres_wal_interval():
# /usr/local/filewave/fwxserver/DB/pg_data/postgresql.conf
# log_min_duration_statement = 200
#
cmds = [
"sed -i 's/log_min_duration_statement = 10000/log_min_duration_statement = 200/g' /usr/local/filewave/fwxserver/DB/pg_data/postgresql.conf"
]
return run_root_commands(cmds)
def provision_prometheus_binary():
cmds = [
"wget https://github.com/prometheus/prometheus/releases/download/v2.19.2/prometheus-2.19.2.linux-amd64.tar.gz",
"tar xzf prometheus-2.19.2.linux-amd64.tar.gz",
"mkdir -p /usr/local/etc/filewave/prometheus",
"mkdir -p /usr/local/etc/filewave/prometheus/conf.d/rules",
"mkdir -p /usr/local/etc/filewave/prometheus/conf.d/alerts",
"mkdir -p /usr/local/filewave/prometheus/",
"mv prometheus-2.19.2.linux-amd64/prometheus /usr/local/sbin/",
"mv prometheus-2.19.2.linux-amd64/promtool /usr/local/sbin/",
"mv prometheus-2.19.2.linux-amd64/tsdb /usr/local/sbin/",
"mv prometheus-2.19.2.linux-amd64/console_libraries /usr/local/filewave/prometheus/",
"mv prometheus-2.19.2.linux-amd64/consoles /usr/local/filewave/prometheus/",
"mkdir -p /usr/local/etc/filewave/prometheus/conf.d/jobs/http",
"chown -R root:root /usr/local/filewave/prometheus/",
"rm -rf prometheus-2.19.2.linux-amd64"
]
run_root_commands(cmds)
prom_file = "prometheus.yml"
data = pkg_resources.resource_string("monitor.config", prom_file).decode('utf-8')
provisioning_file = os.path.join("/usr/local/etc/filewave/prometheus", prom_file)
with open(provisioning_file, 'w') as f:
f.write(data)
shutil.chown(provisioning_file, user="root", group="root")
shutil.chown("/usr/local/filewave/prometheus", user="root", group="root")
def provision_apache_mod_status():
'''
#LoadModule status_module modules/mod_status.so
# Uncomment following lines to enable mod status = and connect to https://localhost:20443/server-status?refresh=5 to see server status
# Used by the prometheus apache_exporter. Works only on localhost (intentional to reduce security exposure).
<IfModule status_module>
<Location /server-status>
SetHandler server-status
Order Deny,Allow
Deny from all
Allow from 127.0.0.1 ::1
</Location>
ExtendedStatus On
</IfModule>
'''
cmds = [
"sed -i 's/#LoadModule status_module modules\/mod_status\.so/LoadModule status_module modules\/mod_status\.so/g' /usr/local/filewave/apache/conf/httpd.conf"
]
run_root_commands(cmds)
def provision_mtail_binary():
logger.info("downloading mtail...")
# mtail binary: 15th Jul 2020
# https://github.com/google/mtail/releases/download/v3.0.0-rc36/mtail_v3.0.0-rc36_linux_amd64
cmds = [
"mkdir -p /usr/local/etc/filewave/mtail/progs",
"chown -R root:root /usr/local/etc/filewave/mtail",
"wget https://github.com/google/mtail/releases/download/v3.0.0-rc36/mtail_v3.0.0-rc36_linux_amd64",
"cp mtail_v3.0.0-rc36_linux_amd64 /usr/local/sbin/mtail",
"chmod +x /usr/local/sbin/mtail",
"firewall-cmd --zone=public --add-port=21090/tcp --permanent",
"firewall-cmd --reload"
]
run_root_commands(cmds)
# write .mtail programs into /usr/local/etc/filewave/mtail/progs
for mtail_file in pkg_resources.resource_listdir("monitor", "config"):
if mtail_file.endswith(".mtail"):
logger.info(f"writing with: {mtail_file}")
data = pkg_resources.resource_string("monitor.config", mtail_file).decode('utf-8')
provisioning_file = os.path.join("/usr/local/etc/filewave/mtail/progs", mtail_file)
with open(provisioning_file, 'w') as f:
f.write(data)
shutil.chown(provisioning_file, user="root", group="root")
def provision_exporters():
logger.info("downloading postgres exporter...")
# from https://github.com/wrouesnel/postgres_exporter/releases/download/v0.8.0/postgres_exporter_v0.8.0_linux-amd64.tar.gz
cmds = [
"wget https://github.com/wrouesnel/postgres_exporter/releases/download/v0.8.0/postgres_exporter_v0.8.0_linux-amd64.tar.gz",
"tar xzf postgres_exporter_v0.8.0_linux-amd64.tar.gz",
"mv -f postgres_exporter_v0.8.0_linux-amd64/postgres_exporter /usr/local/sbin/ && rm -rf postgres_exporter_v0.8.0_linux-amd64"
]
run_root_commands(cmds)
logger.info("downloading apache exporter...")
cmds = [
"wget https://github.com/Lusitaniae/apache_exporter/releases/download/v0.8.0/apache_exporter-0.8.0.linux-amd64.tar.gz",
"tar xzf apache_exporter-0.8.0.linux-amd64.tar.gz",
"mv -f apache_exporter-0.8.0.linux-amd64/apache_exporter /usr/local/sbin/ && rm -rf apache_exporter-0.8.0.linux-amd64"
]
run_root_commands(cmds)
logger.info("downloading node_exporter")
cmds = [
"wget https://github.com/prometheus/node_exporter/releases/download/v1.0.1/node_exporter-1.0.1.linux-amd64.tar.gz",
"tar xzf node_exporter-1.0.1.linux-amd64.tar.gz",
"mv -f node_exporter-1.0.1.linux-amd64/node_exporter /usr/local/sbin/ && rm -rf node_exporter-1.0.1.linux-amd64"
]
run_root_commands(cmds)
def provision_supervisord_conf():
cmds = [
"sed -i 's/\; port\=\*\:9001/port=127\.0\.0\.1\:9001/g' /usr/local/etc/filewave/supervisor/supervisord-server.conf",
"sed -i 's/\; \[inet_http_server\]/\[inet_http_server\]/g' /usr/local/etc/filewave/supervisor/supervisord-server.conf",
"sed -i 's/\;\[include\]/\[include\]/g' /usr/local/etc/filewave/supervisor/supervisord-server.conf",
"sed -i 's/\;files = relative\/directory\/\*\.ini/files=extras\/\*\.conf/g' /usr/local/etc/filewave/supervisor/supervisord-server.conf",
]
supervisord_dir = os.path.join("/usr/local/etc/filewave/supervisor/", "extras")
if not os.path.exists(supervisord_dir):
os.makedirs(supervisord_dir)
data = pkg_resources.resource_string("monitor.config", "monitor-v13.conf").decode('utf-8')
provisioning_file = os.path.join(supervisord_dir, "monitor-v13.conf")
with open(provisioning_file, "w") as f:
f.write(data)
run_root_commands(cmds)
def validate_provisioning():
binaries = [ "node_exporter", "apache_exporter", "mtail", "postgres_exporter", "prometheus", "promtool", "tsdb" ]
for b in binaries:
f = os.path.join("/usr/local/sbin", b)
if not os.path.exists(f):
raise ValidationExceptionBinaryNotFound(f"failed to find required binary: {f}")
else:
logger.info(f"OK: {f}")
shutil.chown(f, user="root", group="root")
|
py | 1a3142240fb52c5c55bc9219738b50d1d08e6c6d | #Rebecca Schuetz
#May 25, 2016
#Homework 2
#1) Make a list of the following numbers: 22, 90, 0, -10, 3, 22, and 48
numbers = [22, 90, 0, -10, 3, 22, 48]
#1) Display the number of elements in the list
print(len(numbers))
#2) Display the 4th element of this list.
print(numbers[3])
#3) Display the sum of the 2nd and 4th element of the list.
print(numbers[1] + numbers[3])
#4) Display the 2nd-largest value in the list.
print(sorted(numbers)[-2])
#5) Display the last element of the original unsorted list
print(list(numbers)[-1])
#6) For each number, display a number: if your original number is less than 10,
#multiply it by thirty. If it's also even, add six.
#If it's greater than 50 subtract ten.
#If it's not negative ten, subtract one.
#(For example: 2 is less than 10, so 2 * 30 = 60, 2 is also even,
#so 60 + 6 = 66, 2 is not negative ten, so 66 - 1 = 65.)
#print('The answers I know are right')
#for number in numbers:
# if number < 10:
# number_less_than_10 = number * 30
# if number % 2 == 0:
# if number == -10:
# print(number_less_than_10 + 6)
# else:
# print(number_less_than_10 + 5)
# else:
# print(number_less_than_10 - 1)
# elif number > 50:
# print(number - 11)
# else:
# print(number - 1)
#print('A way of doing it without the awkward minus ones')
for number in numbers:
newnumber = number
if number < 10:
newnumber = number * 30
if number % 2 == 0:
newnumber = newnumber + 6
elif number > 50:
newnumber = number - 10
if number == -10:
print(newnumber)
else:
print(newnumber - 1)
#7) Sum the result of each of the numbers divided by two.
print(sum(numbers) / 2)
#DICTIONARIES
#8) Sometimes dictionaries are used to describe multiple aspects of a single object.
#Like, say, a movie. Define a dictionary called movie that works with the following code.
movie = {'title': 'The Life Aquatic', 'year': 2004, 'director': 'Wes Anderson',
'budget': 50000000, 'revenue': 34806726}
print("My favorite movie is", movie['title'], "which was released in", movie['year'],
"and was directed by", movie['director'])
#9) Add entries to the movie dictionary for budget and revenue
#(you'll use code like movie['budget'] = 1000), and print out the difference between the two.
#10) If the movie cost more to make than it made in theaters, print "It was a flop".
#If the film's revenue was more than five times the amount it cost to make, print "That was a good investment."
if movie['revenue'] < movie['budget']:
print("It was a flop.")
if movie['revenue'] > (movie['budget'] * 5):
print("That was a good investment.")
#11) Sometimes dictionaries are used to describe the same aspects of many different objects.
#Make ONE dictionary that describes the population of the boroughs of NYC.
#Manhattan has 1.6 million residents,
#Brooklyn has 2.6m,
#Bronx has 1.4m,
#Queens has 2.3m and
#Staten Island has 470,000.
#(Tip: keeping it all in either millions or thousands is a good idea)
population = {'Manhattan': 1.6, 'Brooklyn': 2.6, 'Bronx': 1.4, 'Queens': 2.3,
'Staten Island': .47 }
#12) Display the population of Brooklyn.
print("Brooklyn has", population['Brooklyn'], 'million people.')
#13) Display the combined population of all five boroughs.
print("All five buroughs have", round(sum(population.values()),2), 'million people.')
#14) Display what percent of NYC's population lives in Manhattan.
print(round(population['Manhattan'] / sum(population.values()) * 100,2), "percent of NYC's population lives in Manhattan.")
|
py | 1a3142cff2943e228f408b8354cbe67d1b3270ed | import sys
import csv
import get_info
def main(argv):
skip = int(argv[1])
with open('final_movie_upload_data.csv', mode='r') as csv_file:
csv_reader = csv.DictReader(csv_file)
for i in range(0, skip):
next(csv_reader)
count = 0
new_data = []
for row in csv_reader:
idx = row["idx"]
title = row["title"]
#if line_count == 0:
# print(f'Column names are {", ".join(row)}')
# line_count += 1
# continue
print(f'\t{idx} {title}')
data = get_info.search(title)
if "title" in data and "description" in data:
data["idx"] = idx
data["title"] = f"{data['title']}({title})"
new_data.append(data)
count += 1
if count == 3 :
break;
print(f'Processed {count} lines.')
append(new_data)
def append(data):
field_names = ['idx','title','description']
with open('movie_info.csv', 'a+', newline='') as write_obj:
dict_writer = csv.DictWriter(write_obj, fieldnames=field_names)
for row in data:
dict_writer.writerow(row)
if __name__ == "__main__":
main(sys.argv)
|
py | 1a31442230ddacbd68261d47f35e92cb2940da0f |
from tensorflow.keras.models import model_from_json
import numpy as np
import cv2
import math
import tensorflow as tf
from tensorflow.keras.preprocessing import image
facec = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
from matplotlib import pyplot as plt
import os
import shutil
from skimage.measure import compare_ssim
with open("model.json", "r") as json_file: #Loading the saved model
loaded_model_json = json_file.read()
loaded_model = model_from_json(loaded_model_json)
loaded_model.load_weights("model_weights.h5")
loaded_model._make_predict_function()
label_to_text = {0:'angry', 1:'disgust', 2:'fear', 3:'happy', 4: 'sad'}
def pred(img_path):
label_to_text = {0:'angry', 1:'disgust', 2:'fear', 3:'happy', 4: 'sad'}
img=cv2.imread(img_path) #read Image
gray_fr = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) #covert image to grayscale
faces_rects = facec.detectMultiScale(gray_fr, scaleFactor = 1.2, minNeighbors = 5) #opencv's cascade classifier will be used for detecting the face
if len(faces_rects)!=0:
for (x, y, w, h) in faces_rects:
fc = gray_fr[y:y+h, x:x+w] #extracting only the face part
roi = cv2.resize(fc, (48, 48)) #resizing it according to the image that are acceptable by our model
img = image.img_to_array(roi)
img = img/255
img = np.expand_dims(img, axis=0)
return label_to_text[np.argmax(loaded_model.predict(img))],img #model.predict is used for predicting the emotion
else:
return 0,0 #return 0 if the face is not found
def removeout():
shutil.rmtree('output/') #remove output folder
def vidframe(vidname):
if vidname==0:
cap = cv2.VideoCapture(0)
# Define the codec and create VideoWriter object
fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter('output.mp4',fourcc, 20.0, (640,480))
while(cap.isOpened()):
ret, frame = cap.read()
if ret==True:
out.write(frame)
cv2.imshow('frame',frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
else:
break
# Release everything if job is finished
cap.release()
out.release()
cv2.destroyAllWindows()
vidname="output.mp4"
if os.path.exists('output'): #if output folder is present then delete it
removeout() #create Output folder for storing frame
os.mkdir('output')
cap = cv2.VideoCapture(vidname) #capture video
frameRate=cap.get(5)
count = 0
while(cap.isOpened()): #store the frames in output folder
frameId = cap.get(1)
ret, frame = cap.read()
if (ret != True):
break
if (frameId % math.floor(frameRate) == 0):
filename ="output/frame%d.jpg" % count;count+=1
cv2.imwrite(filename, frame)
cap.release()
result=[] # used for storing emotion
face=[] #used for storing face images
for filename in os.listdir("output"): #loop through each frame
a,b = pred("output/"+filename) #run pred function to get emotion and face images
result.append(a)
face.append(b)
removeout()
result=[x for x in result if x!=0] #removing null prediction
face=[x for x in face if len(str(x))>1]
return result, face
def ssimscore1(im1,im2):
im1=im1.reshape(48, 48, 1).astype('float32') #reshaping the flattened image array
im2=im2.reshape(48, 48, 1).astype('float32')
(score, diff) = compare_ssim(im1, im2, full=True,multichannel=True) #comparing the image for finding difference using compare_ssim function
return score
|
py | 1a31448f1f90b075db6dd6164c277187bb7926fc |
from operator import attrgetter
import pyangbind.lib.xpathhelper as xpathhelper
from pyangbind.lib.yangtypes import RestrictedPrecisionDecimalType, RestrictedClassType, TypedListType
from pyangbind.lib.yangtypes import YANGBool, YANGListType, YANGDynClass, ReferenceType
from pyangbind.lib.base import PybindBase
from decimal import Decimal
from bitarray import bitarray
import __builtin__
class cpu_info_state(PybindBase):
"""
This class was auto-generated by the PythonClass plugin for PYANG
from YANG module brocade-qos-operational - based on the path /cpu-info-state. Each member element of
the container is represented as a class variable - with a specific
YANG type.
YANG Description: CPU EGID and Group ID mapping
"""
__slots__ = ('_pybind_generated_by', '_path_helper', '_yang_name', '_rest_name', '_extmethods', '__name','__egid','__group_id','__description',)
_yang_name = 'cpu-info-state'
_rest_name = 'cpu-info-state'
_pybind_generated_by = 'container'
def __init__(self, *args, **kwargs):
path_helper_ = kwargs.pop("path_helper", None)
if path_helper_ is False:
self._path_helper = False
elif path_helper_ is not None and isinstance(path_helper_, xpathhelper.YANGPathHelper):
self._path_helper = path_helper_
elif hasattr(self, "_parent"):
path_helper_ = getattr(self._parent, "_path_helper", False)
self._path_helper = path_helper_
else:
self._path_helper = False
extmethods = kwargs.pop("extmethods", None)
if extmethods is False:
self._extmethods = False
elif extmethods is not None and isinstance(extmethods, dict):
self._extmethods = extmethods
elif hasattr(self, "_parent"):
extmethods = getattr(self._parent, "_extmethods", None)
self._extmethods = extmethods
else:
self._extmethods = False
self.__group_id = YANGDynClass(base=RestrictedClassType(base_type=int, restriction_dict={'range': ['-128..127']}, int_size=8), is_leaf=True, yang_name="group-id", rest_name="group-id", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-qos-operational', defining_module='brocade-qos-operational', yang_type='int8', is_config=False)
self.__egid = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="egid", rest_name="egid", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-qos-operational', defining_module='brocade-qos-operational', yang_type='uint32', is_config=False)
self.__name = YANGDynClass(base=unicode, is_leaf=True, yang_name="name", rest_name="name", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-qos-operational', defining_module='brocade-qos-operational', yang_type='string', is_config=False)
self.__description = YANGDynClass(base=unicode, is_leaf=True, yang_name="description", rest_name="description", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-qos-operational', defining_module='brocade-qos-operational', yang_type='string', is_config=False)
load = kwargs.pop("load", None)
if args:
if len(args) > 1:
raise TypeError("cannot create a YANG container with >1 argument")
all_attr = True
for e in self._pyangbind_elements:
if not hasattr(args[0], e):
all_attr = False
break
if not all_attr:
raise ValueError("Supplied object did not have the correct attributes")
for e in self._pyangbind_elements:
nobj = getattr(args[0], e)
if nobj._changed() is False:
continue
setmethod = getattr(self, "_set_%s" % e)
if load is None:
setmethod(getattr(args[0], e))
else:
setmethod(getattr(args[0], e), load=load)
def _path(self):
if hasattr(self, "_parent"):
return self._parent._path()+[self._yang_name]
else:
return [u'cpu-info-state']
def _rest_path(self):
if hasattr(self, "_parent"):
if self._rest_name:
return self._parent._rest_path()+[self._rest_name]
else:
return self._parent._rest_path()
else:
return [u'cpu-info-state']
def _get_name(self):
"""
Getter method for name, mapped from YANG variable /cpu_info_state/name (string)
YANG Description: CPU EGID name
"""
return self.__name
def _set_name(self, v, load=False):
"""
Setter method for name, mapped from YANG variable /cpu_info_state/name (string)
If this variable is read-only (config: false) in the
source YANG file, then _set_name is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_name() directly.
YANG Description: CPU EGID name
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=unicode, is_leaf=True, yang_name="name", rest_name="name", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-qos-operational', defining_module='brocade-qos-operational', yang_type='string', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """name must be of a type compatible with string""",
'defined-type': "string",
'generated-type': """YANGDynClass(base=unicode, is_leaf=True, yang_name="name", rest_name="name", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-qos-operational', defining_module='brocade-qos-operational', yang_type='string', is_config=False)""",
})
self.__name = t
if hasattr(self, '_set'):
self._set()
def _unset_name(self):
self.__name = YANGDynClass(base=unicode, is_leaf=True, yang_name="name", rest_name="name", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-qos-operational', defining_module='brocade-qos-operational', yang_type='string', is_config=False)
def _get_egid(self):
"""
Getter method for egid, mapped from YANG variable /cpu_info_state/egid (uint32)
YANG Description: CPU EGID value
"""
return self.__egid
def _set_egid(self, v, load=False):
"""
Setter method for egid, mapped from YANG variable /cpu_info_state/egid (uint32)
If this variable is read-only (config: false) in the
source YANG file, then _set_egid is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_egid() directly.
YANG Description: CPU EGID value
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="egid", rest_name="egid", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-qos-operational', defining_module='brocade-qos-operational', yang_type='uint32', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """egid must be of a type compatible with uint32""",
'defined-type': "uint32",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="egid", rest_name="egid", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-qos-operational', defining_module='brocade-qos-operational', yang_type='uint32', is_config=False)""",
})
self.__egid = t
if hasattr(self, '_set'):
self._set()
def _unset_egid(self):
self.__egid = YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['0..4294967295']}, int_size=32), is_leaf=True, yang_name="egid", rest_name="egid", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-qos-operational', defining_module='brocade-qos-operational', yang_type='uint32', is_config=False)
def _get_group_id(self):
"""
Getter method for group_id, mapped from YANG variable /cpu_info_state/group_id (int8)
YANG Description: CPU Group ID
"""
return self.__group_id
def _set_group_id(self, v, load=False):
"""
Setter method for group_id, mapped from YANG variable /cpu_info_state/group_id (int8)
If this variable is read-only (config: false) in the
source YANG file, then _set_group_id is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_group_id() directly.
YANG Description: CPU Group ID
"""
parent = getattr(self, "_parent", None)
if parent is not None and load is False:
raise AttributeError("Cannot set keys directly when" +
" within an instantiated list")
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=RestrictedClassType(base_type=int, restriction_dict={'range': ['-128..127']}, int_size=8), is_leaf=True, yang_name="group-id", rest_name="group-id", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-qos-operational', defining_module='brocade-qos-operational', yang_type='int8', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """group_id must be of a type compatible with int8""",
'defined-type': "int8",
'generated-type': """YANGDynClass(base=RestrictedClassType(base_type=int, restriction_dict={'range': ['-128..127']}, int_size=8), is_leaf=True, yang_name="group-id", rest_name="group-id", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-qos-operational', defining_module='brocade-qos-operational', yang_type='int8', is_config=False)""",
})
self.__group_id = t
if hasattr(self, '_set'):
self._set()
def _unset_group_id(self):
self.__group_id = YANGDynClass(base=RestrictedClassType(base_type=int, restriction_dict={'range': ['-128..127']}, int_size=8), is_leaf=True, yang_name="group-id", rest_name="group-id", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, is_keyval=True, namespace='urn:brocade.com:mgmt:brocade-qos-operational', defining_module='brocade-qos-operational', yang_type='int8', is_config=False)
def _get_description(self):
"""
Getter method for description, mapped from YANG variable /cpu_info_state/description (string)
YANG Description: Description of CPU EGID
"""
return self.__description
def _set_description(self, v, load=False):
"""
Setter method for description, mapped from YANG variable /cpu_info_state/description (string)
If this variable is read-only (config: false) in the
source YANG file, then _set_description is considered as a private
method. Backends looking to populate this variable should
do so via calling thisObj._set_description() directly.
YANG Description: Description of CPU EGID
"""
if hasattr(v, "_utype"):
v = v._utype(v)
try:
t = YANGDynClass(v,base=unicode, is_leaf=True, yang_name="description", rest_name="description", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-qos-operational', defining_module='brocade-qos-operational', yang_type='string', is_config=False)
except (TypeError, ValueError):
raise ValueError({
'error-string': """description must be of a type compatible with string""",
'defined-type': "string",
'generated-type': """YANGDynClass(base=unicode, is_leaf=True, yang_name="description", rest_name="description", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-qos-operational', defining_module='brocade-qos-operational', yang_type='string', is_config=False)""",
})
self.__description = t
if hasattr(self, '_set'):
self._set()
def _unset_description(self):
self.__description = YANGDynClass(base=unicode, is_leaf=True, yang_name="description", rest_name="description", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-qos-operational', defining_module='brocade-qos-operational', yang_type='string', is_config=False)
name = __builtin__.property(_get_name)
egid = __builtin__.property(_get_egid)
group_id = __builtin__.property(_get_group_id)
description = __builtin__.property(_get_description)
_pyangbind_elements = {'name': name, 'egid': egid, 'group_id': group_id, 'description': description, }
|
py | 1a3147e48ad4eeca45f01c46703c327d79896717 | # --------------------------------------------------------
# Tensorflow Faster R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Jiasen Lu, Jianwei Yang, based on code from Ross Girshick
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import _init_paths
import os
import sys
import numpy as np
import argparse
import pprint
import pdb
import time
import cv2
import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.optim as optim
import xml.etree.ElementTree as ET
import torchvision.transforms as transforms
import torchvision.datasets as dset
from scipy.misc import imread
from roi_data_layer.roidb import combined_roidb
from roi_data_layer.roibatchLoader import roibatchLoader
from model.utils.config import cfg, cfg_from_file, cfg_from_list, get_output_dir
from model.rpn.bbox_transform import clip_boxes
from model.nms.nms_wrapper import nms
from model.rpn.bbox_transform import bbox_transform_inv
from model.utils.net_utils import save_net, load_net, vis_detections
from model.utils.blob import im_list_to_blob
from model.faster_rcnn.vgg16 import vgg16
from model.faster_rcnn.resnet import resnet
from model.faster_rcnn.prefood_res50 import PreResNet50
from datasets.food_category import get_categories
from datasets.id2name import id2eng, id2chn
try:
xrange # Python 2
except NameError:
xrange = range # Python 3
beehoonid2name = {'1': 'bee hoon', '2': 'fried noodles', '3': 'kway teow', '4': 'kway teow, yellow noodles mix', '5': 'rice', '51': 'fried rice', '7': 'hokkien mee', '8': 'maggie noodle', '9': 'Glutinous rice', '10': 'beehoon and noodle mix', '110': 'stir fry mee tai mak', '11': 'fried egg', '12': 'scrambled egg', '13': 'cabbage', '131': 'hairy gourd with egg', '14': 'french bean/long bean', '141': 'broccoli', '142': 'celery', '143': 'beansprout', '15': 'deep fried beancurd skin', '16':
'fried beancurd/taukwa', '17': 'taupok', '171': 'braised taupok', '18': 'Acar', '181': 'Stir fried eggplant', '19': 'cucumber', '21': 'luncheon meat', '22': 'hashbrown', '23': 'ngoh hiang', '24': 'begedil', '25': 'spring roll', '31': 'otah', '32': 'fish ball/sotong ball', '33': 'white, yellow fish fillet', '331': 'orange, red fish fillet', '34': 'fish cake', '341': 'ngoh hiang fish cake', '35': 'kuning fish (fried small fish)', '351': 'fried fish steak', '36': 'siew mai',
'41': 'hotdog/taiwan sausage', '42': 'seaweed chicken', '43': 'chicken nugget', '44': 'fried chicken / chicken wings', '441': 'fried chicken chopped up', '45': 'fried chicken cutlet (not ground meat)', '55': 'curry mixed veg', '551': 'curry chicken and potato', '61': 'ikan bilis', '62': 'chilli paste', '63': 'green chilli', '64': 'peanut', '65': 'Sweet Sauce', '66': 'red chilli chopped', '71': 'deep fried fish', '91': 'Butter cereal chicken', '92': 'fried wanton/ dumpling', '93':
'Vegetarian meat', '94': 'Fried onions', '95': 'Crabstick'}
#id2chn = beehoonid2name
def parse_rec(filename):
""" Parse a PASCAL VOC xml file """
tree = ET.parse(filename)
objects = []
for obj in tree.findall('object'):
obj_struct = {}
obj_struct['name'] = obj.find('name').text
obj_struct['pose'] = obj.find('pose').text
obj_struct['truncated'] = int(obj.find('truncated').text)
obj_struct['difficult'] = int(obj.find('difficult').text)
bbox = obj.find('bndbox')
obj_struct['bbox'] = [int(bbox.find('xmin').text),
int(bbox.find('ymin').text),
int(bbox.find('xmax').text),
int(bbox.find('ymax').text)]
objects.append(obj_struct)
return objects
def parse_args():
"""
Parse input arguments
"""
parser = argparse.ArgumentParser(description='Train a Fast R-CNN network')
parser.add_argument('--dataset', dest='dataset',
help='training dataset',
default='pascal_voc', type=str)
parser.add_argument('--cfg', dest='cfg_file',
help='optional config file',
default='cfgs/vgg16.yml', type=str)
parser.add_argument('--net', dest='net',
help='vgg16, res50, res101, res152',
default='res101', type=str)
parser.add_argument('--set', dest='set_cfgs',
help='set config keys', default=None,
nargs=argparse.REMAINDER)
parser.add_argument('--load_dir', dest='load_dir',
help='directory to load models',
default="/srv/share/jyang375/models")
parser.add_argument('--image_dir', dest='image_dir',
help='directory to load images for demo',
default="images")
parser.add_argument('--cuda', dest='cuda',
help='whether use CUDA',
action='store_true')
parser.add_argument('--mGPUs', dest='mGPUs',
help='whether use multiple GPUs',
action='store_true')
parser.add_argument('--cag', dest='class_agnostic',
help='whether perform class_agnostic bbox regression',
action='store_true')
parser.add_argument('--parallel_type', dest='parallel_type',
help='which part of model to parallel, 0: all, 1: model before roi pooling',
default=0, type=int)
parser.add_argument('--checksession', dest='checksession',
help='checksession to load model',
default=1, type=int)
parser.add_argument('--checkepoch', dest='checkepoch',
help='checkepoch to load network',
default=1, type=int)
parser.add_argument('--checkpoint', dest='checkpoint',
help='checkpoint to load network',
default=10021, type=int)
parser.add_argument('--bs', dest='batch_size',
help='batch_size',
default=1, type=int)
parser.add_argument('--vis', dest='vis',
help='visualization mode',
action='store_true')
parser.add_argument('--webcam_num', dest='webcam_num',
help='webcam ID number',
default=-1, type=int)
args = parser.parse_args()
return args
lr = cfg.TRAIN.LEARNING_RATE
momentum = cfg.TRAIN.MOMENTUM
weight_decay = cfg.TRAIN.WEIGHT_DECAY
def _get_image_blob(im):
"""Converts an image into a network input.
Arguments:
im (ndarray): a color image in BGR order
Returns:
blob (ndarray): a data blob holding an image pyramid
im_scale_factors (list): list of image scales (relative to im) used
in the image pyramid
"""
im_orig = im.astype(np.float32, copy=True)
im_orig -= cfg.PIXEL_MEANS
im_shape = im_orig.shape
im_size_min = np.min(im_shape[0:2])
im_size_max = np.max(im_shape[0:2])
processed_ims = []
im_scale_factors = []
for target_size in cfg.TEST.SCALES:
im_scale = float(target_size) / float(im_size_min)
# Prevent the biggest axis from being more than MAX_SIZE
if np.round(im_scale * im_size_max) > cfg.TEST.MAX_SIZE:
im_scale = float(cfg.TEST.MAX_SIZE) / float(im_size_max)
im = cv2.resize(im_orig, None, None, fx=im_scale, fy=im_scale,
interpolation=cv2.INTER_LINEAR)
im_scale_factors.append(im_scale)
processed_ims.append(im)
# Create a blob to hold the input images
blob = im_list_to_blob(processed_ims)
return blob, np.array(im_scale_factors)
if __name__ == '__main__':
args = parse_args()
print('Called with args:')
print(args)
if args.cfg_file is not None:
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
cfg.USE_GPU_NMS = args.cuda
print('Using config:')
pprint.pprint(cfg)
np.random.seed(cfg.RNG_SEED)
# train set
# -- Note: Use validation set and disable the flipped to enable faster loading.
input_dir = args.load_dir + "/" + args.net + "/" + args.dataset
if not os.path.exists(input_dir):
raise Exception(
'There is no input directory for loading network from ' + input_dir)
load_name = os.path.join(input_dir,
'faster_rcnn_{}_{}_{}.pth'.format(args.checksession, args.checkepoch, args.checkpoint))
#pascal_classes = np.asarray(get_categories("EconomicBeeHoon_train"))
pascal_classes = get_categories('All_train_mt10')
# initilize the network here.
if args.net == 'vgg16':
fasterRCNN = vgg16(pascal_classes, pretrained=False,
class_agnostic=args.class_agnostic)
elif args.net == 'res101':
fasterRCNN = resnet(pascal_classes, 101, pretrained=False,
class_agnostic=args.class_agnostic)
elif args.net == 'res50':
fasterRCNN = resnet(pascal_classes, 50, pretrained=False,
class_agnostic=args.class_agnostic)
elif args.net == 'res152':
fasterRCNN = resnet(pascal_classes, 152, pretrained=False,
class_agnostic=args.class_agnostic)
elif args.net == 'foodres50':
fasterRCNN = PreResNet50(pascal_classes, pretrained=False,
class_agnostic=args.class_agnostic)
else:
print("network is not defined")
pdb.set_trace()
fasterRCNN.create_architecture()
print("load checkpoint %s" % (load_name))
if args.cuda > 0:
checkpoint = torch.load(load_name)
else:
checkpoint = torch.load(
load_name, map_location=(lambda storage, loc: storage))
fasterRCNN.load_state_dict(checkpoint['model'])
if 'pooling_mode' in checkpoint.keys():
cfg.POOLING_MODE = checkpoint['pooling_mode']
print('load model successfully!')
# pdb.set_trace()
print("load checkpoint %s" % (load_name))
# initilize the tensor holder here.
im_data = torch.FloatTensor(1)
im_info = torch.FloatTensor(1)
num_boxes = torch.LongTensor(1)
gt_boxes = torch.FloatTensor(1)
# ship to cuda
if args.cuda > 0:
im_data = im_data.cuda()
im_info = im_info.cuda()
num_boxes = num_boxes.cuda()
gt_boxes = gt_boxes.cuda()
# make variable
im_data = Variable(im_data, volatile=True)
im_info = Variable(im_info, volatile=True)
num_boxes = Variable(num_boxes, volatile=True)
gt_boxes = Variable(gt_boxes, volatile=True)
if args.cuda > 0:
cfg.CUDA = True
if args.cuda > 0:
fasterRCNN.cuda()
fasterRCNN.eval()
start = time.time()
max_per_image = 100
thresh = 0.05
vis = True
webcam_num = args.webcam_num
# Set up webcam or get image directories
if webcam_num >= 0:
cap = cv2.VideoCapture(webcam_num)
num_images = 0
else:
imglist = os.listdir(args.image_dir)
num_images = len(imglist)
print('Loaded Photo: {} images.'.format(num_images))
while (num_images >= 0):
total_tic = time.time()
if webcam_num < 0:
num_images -= 1
# Get image from the webcam
if webcam_num >= 0:
if not cap.isOpened():
raise RuntimeError(
"Webcam could not open. Please check connection.")
ret, frame = cap.read()
im_in = np.array(frame)
# Load the demo image
else:
im_file = os.path.join(args.image_dir, imglist[num_images])
# im = cv2.imread(im_file)
im_in = np.array(imread(im_file))
if len(im_in.shape) == 2:
im_in = im_in[:, :, np.newaxis]
im_in = np.concatenate((im_in, im_in, im_in), axis=2)
# rgb -> bgr
im = im_in[:, :, ::-1]
blobs, im_scales = _get_image_blob(im)
assert len(im_scales) == 1, "Only single-image batch implemented"
im_blob = blobs
im_info_np = np.array(
[[im_blob.shape[1], im_blob.shape[2], im_scales[0]]], dtype=np.float32)
im_data_pt = torch.from_numpy(im_blob)
im_data_pt = im_data_pt.permute(0, 3, 1, 2)
im_info_pt = torch.from_numpy(im_info_np)
im_data.data.resize_(im_data_pt.size()).copy_(im_data_pt)
im_info.data.resize_(im_info_pt.size()).copy_(im_info_pt)
gt_boxes.data.resize_(1, 1, 5).zero_()
num_boxes.data.resize_(1).zero_()
# pdb.set_trace()
det_tic = time.time()
rois, cls_prob, bbox_pred, \
rpn_loss_cls, rpn_loss_box, \
RCNN_loss_cls, RCNN_loss_bbox, \
rois_label = fasterRCNN(im_data, im_info, gt_boxes, num_boxes)
scores = cls_prob.data
boxes = rois.data[:, :, 1:5]
if cfg.TEST.BBOX_REG:
# Apply bounding-box regression deltas
box_deltas = bbox_pred.data
if cfg.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED:
# Optionally normalize targets by a precomputed mean and stdev
if args.class_agnostic:
if args.cuda > 0:
box_deltas = box_deltas.view(-1, 4) * torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_STDS).cuda() \
+ torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_MEANS).cuda()
else:
box_deltas = box_deltas.view(-1, 4) * torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_STDS) \
+ torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_MEANS)
box_deltas = box_deltas.view(1, -1, 4)
else:
if args.cuda > 0:
box_deltas = box_deltas.view(-1, 4) * torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_STDS).cuda() \
+ torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_MEANS).cuda()
else:
box_deltas = box_deltas.view(-1, 4) * torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_STDS) \
+ torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_MEANS)
box_deltas = box_deltas.view(
1, -1, 4 * len(pascal_classes))
pred_boxes = bbox_transform_inv(boxes, box_deltas, 1)
pred_boxes = clip_boxes(pred_boxes, im_info.data, 1)
else:
# Simply repeat the boxes, once for each class
pred_boxes = np.tile(boxes, (1, scores.shape[1]))
pred_boxes /= im_scales[0]
scores = scores.squeeze()
pred_boxes = pred_boxes.squeeze()
det_toc = time.time()
detect_time = det_toc - det_tic
misc_tic = time.time()
# get gt
# 1. read xml
if vis:
im2show = np.copy(im)
for j in xrange(1, len(pascal_classes)):
inds = torch.nonzero(scores[:, j] > thresh).view(-1)
# if there is det
if inds.numel() > 0:
cls_scores = scores[:, j][inds]
_, order = torch.sort(cls_scores, 0, True)
if args.class_agnostic:
cls_boxes = pred_boxes[inds, :]
else:
cls_boxes = pred_boxes[inds][:, j * 4:(j + 1) * 4]
cls_dets = torch.cat((cls_boxes, cls_scores.unsqueeze(1)), 1)
# cls_dets = torch.cat((cls_boxes, cls_scores), 1)
cls_dets = cls_dets[order]
keep = nms(cls_dets, cfg.TEST.NMS,
force_cpu=not cfg.USE_GPU_NMS)
cls_dets = cls_dets[keep.view(-1).long()]
if vis:
im2show = vis_detections(
im2show, id2eng[pascal_classes[j]], cls_dets.cpu().numpy(), 0.5)
misc_toc = time.time()
nms_time = misc_toc - misc_tic
if webcam_num == -1:
sys.stdout.write('im_detect: {:d}/{:d} {:.3f}s {:.3f}s \r'
.format(num_images + 1, len(imglist), detect_time, nms_time))
sys.stdout.flush()
if vis and webcam_num == -1:
# cv2.imshow('test', im2show)
# cv2.waitKey(0)
result_path = os.path.join(
args.image_dir, imglist[num_images][:-4] + "_det.jpg")
cv2.imwrite(result_path, im2show)
else:
#im2showRGB = cv2.cvtColor(im2show, cv2.COLOR_BGR2RGB)
im2showRGB = im2show
cv2.namedWindow("frame", 0)
cv2.resizeWindow("frame", 800, 800)
cv2.imshow("frame", im2showRGB)
total_toc = time.time()
total_time = total_toc - total_tic
frame_rate = 1 / total_time
print('Frame rate:', frame_rate)
if cv2.waitKey(5000) & 0xFF == ord('q'):
break
if webcam_num >= 0:
cap.release()
cv2.destroyAllWindows()
|
py | 1a31482ab1b64d2f5e340b0b5127bb2b142fc900 |
__author__ = "Timothy Tickle"
__copyright__ = "Copyright 2015"
__credits__ = [ "Timothy Tickle", "Brian Haas" ]
__license__ = "MIT"
__maintainer__ = "Timothy Tickle"
__email__ = "[email protected]"
__status__ = "Development"
import Commandline
import os
import ParentPipelineTester
import unittest
class ScriptTester( ParentPipelineTester.ParentPipelineTester ):
"""
Testing for scripts, starting at command line.
"""
str_script_dir = "src"
str_test_data_dir = "test_data"
str_test_data_dir_working = os.path.join( str_test_data_dir, "active_testing_script_tester" )
# combine_vchk.py
def test_combine_vchk_for_1_file( self ):
"""
Test combining VCHK files when only one is given
"""
# Create test environment
str_combine_script = os.path.join( self.str_script_dir, "combine_vchk.py" )
str_vchk_dir = os.path.join( self.str_test_data_dir, "test_combine_vchk_for_1_file" )
str_output_dir = os.path.join( self.str_test_data_dir_working, "test_combine_vchk_for_1_file" )
str_substitutions_dis_json = os.path.join( self.str_test_data_dir, "test_combine_vchk_1_distributions_substitutions_ANSWER.json" )
str_substitutions_dis_json_test = os.path.join( str_output_dir, "Distributions_substitutions.json" )
str_substitutions_dis_pdf_test = os.path.join( str_output_dir, "Distributions_substitutions.pdf" )
str_substitions_total_json = os.path.join( self.str_test_data_dir, "test_combine_vchk_for_1_total_substitutions_ANSWER.json" )
str_substitions_total_json_test = os.path.join( str_output_dir, "Total_substitutions.json" )
str_substitions_total_pdf_test = os.path.join( str_output_dir, "Total_substitutions.pdf" )
self.func_make_dummy_dir( self.str_test_data_dir_working )
self.func_make_dummy_dir( str_output_dir )
# Call Example script
str_command = " ".join( [ str_combine_script, "--input_dir", str_vchk_dir, "--output_dir", str_output_dir ] )
Commandline.Commandline().func_CMD( str_command )
# Check test environment for results
f_success = self.func_are_files_equivalent( str_substitutions_dis_json, str_substitutions_dis_json_test )
f_success = f_success and self.func_are_files_equivalent( str_substitions_total_json, str_substitions_total_json_test )
# Destroy environment
self.func_remove_files( [ str_substitutions_dis_json_test, str_substitions_total_json_test ] )
self.func_remove_files( [ str_substitutions_dis_pdf_test, str_substitions_total_pdf_test ] )
self.func_remove_dirs( [ str_output_dir ] )
# Evaluate
self.func_test_true( f_success )
def test_combine_vchk_for_2_file( self ):
"""
Test combining VCHK files when two are given
"""
# Create test environment
str_combine_script = os.path.join( self.str_script_dir, "combine_vchk.py" )
str_vchk_dir = os.path.join( self.str_test_data_dir, "test_combine_vchk_for_2_file" )
str_output_dir = os.path.join( self.str_test_data_dir_working, "test_combine_vchk_for_2_file" )
str_substitutions_dis_json = os.path.join( self.str_test_data_dir, "test_combine_vchk_2_distributions_substitutions_ANSWER.json" )
str_substitutions_dis_json_test = os.path.join( str_output_dir, "Distributions_substitutions.json" )
str_substitutions_dis_pdf_test = os.path.join( str_output_dir, "Distributions_substitutions.pdf" )
str_substitions_total_json = os.path.join( self.str_test_data_dir, "test_combine_vchk_for_2_total_substitutions_ANSWER.json" )
str_substitions_total_json_test = os.path.join( str_output_dir, "Total_substitutions.json" )
str_substitions_total_pdf_test = os.path.join( str_output_dir, "Total_substitutions.pdf" )
self.func_make_dummy_dir( self.str_test_data_dir_working )
self.func_make_dummy_dir( str_output_dir )
# Call Example script
str_command = " ".join( [ str_combine_script, "--input_dir", str_vchk_dir, "--output_dir", str_output_dir ] )
Commandline.Commandline().func_CMD( str_command )
# Check test environment for results
f_success = self.func_are_files_equivalent( str_substitutions_dis_json, str_substitutions_dis_json_test )
f_success = f_success and self.func_are_files_equivalent( str_substitions_total_json, str_substitions_total_json_test )
# Destroy environment
self.func_remove_files( [ str_substitutions_dis_json_test, str_substitions_total_json_test ] )
self.func_remove_files( [ str_substitutions_dis_pdf_test, str_substitions_total_pdf_test ] )
self.func_remove_dirs( [ str_output_dir ] )
# Evaluate
self.func_test_true( f_success )
def test_combine_vchk_for_3_file( self ):
"""
Test combining VCHK files when three are given
"""
# Create test environment
str_combine_script = os.path.join( self.str_script_dir, "combine_vchk.py" )
str_vchk_dir = os.path.join( self.str_test_data_dir, "test_combine_vchk_for_3_file" )
str_output_dir = os.path.join( self.str_test_data_dir_working, "test_combine_vchk_for_3_file" )
str_substitutions_dis_json = os.path.join( self.str_test_data_dir, "test_combine_vchk_3_distributions_substitutions_ANSWER.json" )
str_substitutions_dis_json_test = os.path.join( str_output_dir, "Distributions_substitutions.json" )
str_substitutions_dis_pdf_test = os.path.join( str_output_dir, "Distributions_substitutions.pdf" )
str_substitions_total_json = os.path.join( self.str_test_data_dir, "test_combine_vchk_for_3_total_substitutions_ANSWER.json" )
str_substitions_total_json_test = os.path.join( str_output_dir, "Total_substitutions.json" )
str_substitions_total_pdf_test = os.path.join( str_output_dir, "Total_substitutions.pdf" )
self.func_make_dummy_dir( self.str_test_data_dir_working )
self.func_make_dummy_dir( str_output_dir )
# Call Example script
str_command = " ".join( [ str_combine_script, "--input_dir", str_vchk_dir, "--output_dir", str_output_dir ] )
Commandline.Commandline().func_CMD( str_command )
# Check test environment for results
f_success = self.func_are_files_equivalent( str_substitutions_dis_json, str_substitutions_dis_json_test )
f_success = f_success and self.func_are_files_equivalent( str_substitions_total_json, str_substitions_total_json_test )
# Destroy environment
self.func_remove_files( [ str_substitutions_dis_json_test, str_substitions_total_json_test ] )
self.func_remove_files( [ str_substitutions_dis_pdf_test, str_substitions_total_pdf_test ] )
self.func_remove_dirs( [ str_output_dir ] )
# Evaluate
self.func_test_true( f_success )
# comfirm_maf_mutations.py
# filter_snps_rna_editing.py
def test_filter_snps_rna_editing_no_resources( self ):
"""
Test filter_snps_rna_editing with data no resources for filtering
"""
# Create test environment
str_filter_script = os.path.join( self.str_script_dir, "filter_snps_rna_editing.py" )
str_test_file = os.path.join( self.str_test_data_dir, "test_filter_snps_rna_editing_no_resources.vcf" )
str_answer_file = os.path.join( self.str_test_data_dir, "test_filter_snps_rna_editing_no_resources_ANSWER.vcf" )
str_result_file = os.path.join( self.str_test_data_dir_working, "test_filter_snps_rna_editing_no_resources.vcf" )
self.func_make_dummy_dir( self.str_test_data_dir_working )
self.func_make_dummy_dir( self.str_test_data_dir )
# Call Example script
str_command = " ".join( [ str_filter_script, str_test_file, str_result_file ] )
Commandline.Commandline().func_CMD( str_command )
# Check test environment for results
f_success = self.func_are_files_equivalent( str_answer_file, str_result_file )
# Destroy environment
#self.func_remove_files( [ str_result_file ] )
# Evaluate
self.func_test_true( f_success )
def test_filter_snps_rna_editing_with_darned( self ):
"""
Test filter_snps_rna_editing with data darned resources for filtering
"""
# Create test environment
str_filter_script = os.path.join( self.str_script_dir, "filter_snps_rna_editing.py" )
str_test_file = os.path.join( self.str_test_data_dir, "test_filter_snps_rna_editing_darned.vcf" )
str_darned_file = os.path.join( self.str_test_data_dir, "test_filter_snps_rna_editing_darned.tab" )
str_answer_file = os.path.join( self.str_test_data_dir, "test_filter_snps_rna_editing_darned_ANSWER.vcf" )
str_result_file = os.path.join( self.str_test_data_dir_working, "test_filter_snps_rna_editing_darned.vcf" )
self.func_make_dummy_dir( self.str_test_data_dir_working )
self.func_make_dummy_dir( self.str_test_data_dir )
# Call Example script
str_command = " ".join( [ str_filter_script, "--darned", str_darned_file, str_test_file, str_result_file ] )
Commandline.Commandline().func_CMD( str_command )
# Check test environment for results
f_success = self.func_are_files_equivalent( str_answer_file, str_result_file )
# Destroy environment
#self.func_remove_files( [ str_result_file ] )
# Evaluate
self.func_test_true( f_success )
def test_filter_snps_rna_editing_with_radar( self ):
"""
Test filter_snps_rna_editing with data radar resources for filtering
"""
# Create test environment
str_filter_script = os.path.join( self.str_script_dir, "filter_snps_rna_editing.py" )
str_test_file = os.path.join( self.str_test_data_dir, "test_filter_snps_rna_editing_radar.vcf" )
str_radar_file = os.path.join( self.str_test_data_dir, "test_filter_snps_rna_editing_radar.tab" )
str_answer_file = os.path.join( self.str_test_data_dir, "test_filter_snps_rna_editing_radar_ANSWER.vcf" )
str_result_file = os.path.join( self.str_test_data_dir_working, "test_filter_snps_rna_editing_radar.vcf" )
self.func_make_dummy_dir( self.str_test_data_dir_working )
self.func_make_dummy_dir( self.str_test_data_dir )
# Call Example script
str_command = " ".join( [ str_filter_script, "--radar", str_radar_file, str_test_file, str_result_file ] )
Commandline.Commandline().func_CMD( str_command )
# Check test environment for results
f_success = self.func_are_files_equivalent( str_answer_file, str_result_file )
# Destroy environment
#self.func_remove_files( [ str_result_file ] )
# Evaluate
self.func_test_true( f_success )
def test_filter_snps_rna_editing_with_darned_radar( self ):
"""
Test filter_snps_rna_editing with darned and radar data resources for filtering
"""
# Create test environment
str_filter_script = os.path.join( self.str_script_dir, "filter_snps_rna_editing.py" )
str_test_file = os.path.join( self.str_test_data_dir, "test_filter_snps_rna_editing_radar_darned.vcf" )
str_radar_file = os.path.join( self.str_test_data_dir, "test_filter_snps_rna_editing_radar.tab" )
str_darned_file = os.path.join( self.str_test_data_dir, "test_filter_snps_rna_editing_darned.tab" )
str_answer_file = os.path.join( self.str_test_data_dir, "test_filter_snps_rna_editing_radar_darned_ANSWER.vcf" )
str_result_file = os.path.join( self.str_test_data_dir_working, "test_filter_snps_rna_editing_radar_darned.vcf" )
self.func_make_dummy_dir( self.str_test_data_dir_working )
self.func_make_dummy_dir( self.str_test_data_dir )
# Call Example script
str_command = " ".join( [ str_filter_script, "--radar", str_radar_file, "--darned", str_darned_file, str_test_file, str_result_file ] )
Commandline.Commandline().func_CMD( str_command )
# Check test environment for results
f_success = self.func_are_files_equivalent( str_answer_file, str_result_file )
# Destroy environment
#self.func_remove_files( [ str_result_file ] )
# Evaluate
self.func_test_true( f_success )
# filter_variant_clusters.py
def test_filter_clusters_for_no_filtering( self ):
"""
Test filter_variant_cluster with data that does not need filtering
"""
# Create test environment
str_filter_script = os.path.join( self.str_script_dir, "filter_variant_clusters.py" )
str_output_dir = os.path.join( self.str_test_data_dir_working, "test_filter_clusters" )
str_test_file = os.path.join( self.str_test_data_dir, "test_filter_clusters.vcf" )
str_answer_file = os.path.join( self.str_test_data_dir, "test_filter_clusters_for_no_filtering_ANSWER.vcf" )
str_result_file = os.path.join( str_output_dir, "test_filter_clusters_for_no_filtering.vcf" )
self.func_make_dummy_dir( self.str_test_data_dir_working )
self.func_make_dummy_dir( self.str_test_data_dir )
self.func_make_dummy_dir( str_output_dir )
# Call Example script
str_command = " ".join( [ str_filter_script, "--window", "35", "--cluster", "34", str_test_file, str_result_file ] )
Commandline.Commandline().func_CMD( str_command )
# Check test environment for results
f_success = self.func_are_files_equivalent( str_test_file, str_result_file )
# Destroy environment
self.func_remove_files( [ str_result_file ] )
self.func_remove_dirs( [ str_output_dir ] )
# Evaluate
self.func_test_true( f_success )
def test_filter_clusters_for_all_filtering( self ):
"""
Test filter_variant_cluster with data that will be completely filtered
"""
# Create test environment
str_filter_script = os.path.join( self.str_script_dir, "filter_variant_clusters.py" )
str_output_dir = os.path.join( self.str_test_data_dir_working, "test_filter_clusters" )
str_test_file = os.path.join( self.str_test_data_dir, "test_filter_clusters.vcf" )
str_answer_file = os.path.join( self.str_test_data_dir, "test_filter_clusters_for_all_filtering_ANSWER.vcf" )
str_result_file = os.path.join( str_output_dir, "test_filter_clusters_for_all_filtering.vcf" )
self.func_make_dummy_dir( self.str_test_data_dir_working )
self.func_make_dummy_dir( self.str_test_data_dir )
self.func_make_dummy_dir( str_output_dir )
# Call Example script
str_command = " ".join( [ str_filter_script, "--window", "1", "--cluster", "1", str_test_file, str_result_file ] )
Commandline.Commandline().func_CMD( str_command )
# Check test environment for results
f_success = self.func_are_files_equivalent( str_answer_file, str_result_file )
# Destroy environment
self.func_remove_files( [ str_result_file ] )
self.func_remove_dirs( [ str_output_dir ] )
# Evaluate
self.func_test_true( f_success )
def test_filter_clusters_for_mild_filtering( self ):
"""
Test filter_variant_cluster with mild filtering
"""
# Create test environment
str_filter_script = os.path.join( self.str_script_dir, "filter_variant_clusters.py" )
str_output_dir = os.path.join( self.str_test_data_dir_working, "test_filter_clusters" )
str_test_file = os.path.join( self.str_test_data_dir, "test_filter_clusters.vcf" )
str_answer_file = os.path.join( self.str_test_data_dir, "test_filter_clusters_for_mild_filtering_ANSWER.vcf" )
str_result_file = os.path.join( str_output_dir, "test_filter_clusters_for_mild_filtering.vcf" )
self.func_make_dummy_dir( self.str_test_data_dir_working )
self.func_make_dummy_dir( self.str_test_data_dir )
self.func_make_dummy_dir( str_output_dir )
# Call Example script
str_command = " ".join( [ str_filter_script, "--window", "35", "--cluster", "2", str_test_file, str_result_file ] )
Commandline.Commandline().func_CMD( str_command )
# Check test environment for results
f_success = self.func_are_files_equivalent( str_answer_file, str_result_file )
# Destroy environment
self.func_remove_files( [ str_result_file ] )
self.func_remove_dirs( [ str_output_dir ] )
# Evaluate
self.func_test_true( f_success )
# filter_vcf_for_cancer.py
def test_filter_vcf_for_cancer_for_COMMON_filtering( self ):
"""
Test filter_vcf_for_cancer for COMMON features.
"""
# Create test environment
str_filter_cancer_script = os.path.join( self.str_script_dir, "filter_vcf_for_cancer.py" )
str_test_file = os.path.join( self.str_test_data_dir, "test_filter_vcf_for_cancer_COMMON.vcf" )
str_answer_file = os.path.join( self.str_test_data_dir, "test_filter_vcf_for_cancer_COMMON_ANSWER.vcf" )
str_result_file = os.path.join( self.str_test_data_dir_working, "test_filter_for_cancer_COMMON.vcf" )
self.func_make_dummy_dir( self.str_test_data_dir_working )
self.func_make_dummy_dir( self.str_test_data_dir )
# Call Example script
str_command = " ".join( [ str_filter_cancer_script, str_test_file, str_result_file ] )
Commandline.Commandline().func_CMD( str_command )
# Check test environment for results
f_success = self.func_are_files_equivalent( str_answer_file, str_result_file )
# Destroy environment
self.func_remove_files( [ str_result_file ] )
# Evaluate
self.func_test_true( f_success )
def test_filter_vcf_for_cancer_for_DP_filtering( self ):
"""
Test filter_vcf_for_cancer for DP features.
"""
# Create test environment
str_filter_cancer_script = os.path.join( self.str_script_dir, "filter_vcf_for_cancer.py" )
str_test_file = os.path.join( self.str_test_data_dir, "test_filter_vcf_for_cancer_DP.vcf" )
str_answer_file = os.path.join( self.str_test_data_dir, "test_filter_vcf_for_cancer_DP_ANSWER.vcf" )
str_result_file = os.path.join( self.str_test_data_dir_working, "test_filter_for_cancer_DP.vcf" )
self.func_make_dummy_dir( self.str_test_data_dir_working )
self.func_make_dummy_dir( self.str_test_data_dir )
# Call Example script
str_command = " ".join( [ str_filter_cancer_script, str_test_file, str_result_file ] )
Commandline.Commandline().func_CMD( str_command )
# Check test environment for results
f_success = self.func_are_files_equivalent( str_answer_file, str_result_file )
# Destroy environment
self.func_remove_files( [ str_result_file ] )
# Evaluate
self.func_test_true( f_success )
def test_filter_vcf_for_cancer_for_SAO_filtering( self ):
"""
Test filter_vcf_for_cancer for SAO features.
"""
# Create test environment
str_filter_cancer_script = os.path.join( self.str_script_dir, "filter_vcf_for_cancer.py" )
str_test_file = os.path.join( self.str_test_data_dir, "test_filter_vcf_for_cancer_SAO.vcf" )
str_answer_file = os.path.join( self.str_test_data_dir, "test_filter_vcf_for_cancer_SAO_ANSWER.vcf" )
str_result_file = os.path.join( self.str_test_data_dir_working, "test_filter_for_cancer_SAO.vcf" )
self.func_make_dummy_dir( self.str_test_data_dir_working )
self.func_make_dummy_dir( self.str_test_data_dir )
# Call Example script
str_command = " ".join( [ str_filter_cancer_script, str_test_file, str_result_file ] )
Commandline.Commandline().func_CMD( str_command )
# Check test environment for results
f_success = self.func_are_files_equivalent( str_answer_file, str_result_file )
# Destroy environment
self.func_remove_files( [ str_result_file ] )
# Evaluate
self.func_test_true( f_success )
def test_filter_vcf_for_cancer_for_FATHMM_filtering( self ):
"""
Test filter_vcf_for_cancer for FATHMM features.
"""
# Create test environment
str_filter_cancer_script = os.path.join( self.str_script_dir, "filter_vcf_for_cancer.py" )
str_test_file = os.path.join( self.str_test_data_dir, "test_filter_vcf_for_cancer_FATHMM.vcf" )
str_answer_file = os.path.join( self.str_test_data_dir, "test_filter_vcf_for_cancer_FATHMM_ANSWER.vcf" )
str_result_file = os.path.join( self.str_test_data_dir_working, "test_filter_for_cancer_FATHMM.vcf" )
self.func_make_dummy_dir( self.str_test_data_dir_working )
self.func_make_dummy_dir( self.str_test_data_dir )
# Call Example script
str_command = " ".join( [ str_filter_cancer_script, str_test_file, str_result_file ] )
Commandline.Commandline().func_CMD( str_command )
# Check test environment for results
f_success = self.func_are_files_equivalent( str_answer_file, str_result_file )
# Destroy environment
self.func_remove_files( [ str_result_file ] )
# Evaluate
self.func_test_true( f_success )
def test_filter_vcf_for_cancer_for_ALL_filtering( self ):
"""
Test filter_vcf_for_cancer for ALL possible features.
"""
# Create test environment
str_filter_cancer_script = os.path.join( self.str_script_dir, "filter_vcf_for_cancer.py" )
str_test_file = os.path.join( self.str_test_data_dir, "test_filter_vcf_for_cancer_ALL.vcf" )
str_answer_file = os.path.join( self.str_test_data_dir, "test_filter_vcf_for_cancer_ALL_ANSWER.vcf" )
str_result_file = os.path.join( self.str_test_data_dir_working, "test_filter_for_cancer_ALL.vcf" )
self.func_make_dummy_dir( self.str_test_data_dir_working )
self.func_make_dummy_dir( self.str_test_data_dir )
# Call Example script
str_command = " ".join( [ str_filter_cancer_script, str_test_file, str_result_file ] )
Commandline.Commandline().func_CMD( str_command )
# Check test environment for results
f_success = self.func_are_files_equivalent( str_answer_file, str_result_file )
# Destroy environment
self.func_remove_files( [ str_result_file ] )
# Evaluate
self.func_test_true( f_success )
# groom_cravat_annotation.py
def test_groom_cravat_annotation_for_coding_variants_tab( self ):
"""
Test groom_cravat_annotation for a coding variants tab file.
"""
# Create test environment
str_groom_script = os.path.join( self.str_script_dir, "groom_cravat_annotation.py" )
str_test_file = os.path.join( self.str_test_data_dir, "test_groom_cravat_annotations_coding.tab" )
str_answer_file = os.path.join( self.str_test_data_dir, "test_groom_cravat_annotations_coding_ANSWER.tab" )
str_result_file = os.path.join( self.str_test_data_dir_working, "test_groom_cravat_annotations_coding.tab" )
self.func_make_dummy_dir( self.str_test_data_dir_working )
self.func_make_dummy_dir( self.str_test_data_dir )
# Call Example script
str_command = " ".join( [ str_groom_script, str_test_file, str_result_file ] )
Commandline.Commandline().func_CMD( str_command )
# Check test environment for results
f_success = self.func_are_files_equivalent( str_answer_file, str_result_file )
# Destroy environment
self.func_remove_files( [ str_result_file ] )
# Evaluate
self.func_test_true( f_success )
def test_groom_cravat_annotation_for_noncoding_variants_tab( self ):
"""
Test groom_cravat_annotation for a noncoding variants tab file.
"""
# Create test environment
str_groom_script = os.path.join( self.str_script_dir, "groom_cravat_annotation.py" )
str_test_file = os.path.join( self.str_test_data_dir, "test_groom_cravat_annotations_noncoding.tab" )
str_answer_file = os.path.join( self.str_test_data_dir, "test_groom_cravat_annotations_noncoding_ANSWER.tab" )
str_result_file = os.path.join( self.str_test_data_dir_working, "test_groom_cravat_annotations_noncoding.tab" )
self.func_make_dummy_dir( self.str_test_data_dir_working )
self.func_make_dummy_dir( self.str_test_data_dir )
# Call Example script
str_command = " ".join( [ str_groom_script, str_test_file, str_result_file ] )
Commandline.Commandline().func_CMD( str_command )
# Check test environment for results
f_success = self.func_are_files_equivalent( str_answer_file, str_result_file )
# Destroy environment
self.func_remove_files( [ str_result_file ] )
# Evaluate
self.func_test_true( f_success )
# groom_vcf_gatk.py
def test_groom_vcf_gatk_for_good_vcf( self ):
"""
Test filter_groom_vcf_gatk for a vcf not needed to be filtered
"""
# Create test environment
str_groom_script = os.path.join( self.str_script_dir, "groom_vcf.py" )
str_test_file = os.path.join( self.str_test_data_dir, "test_groom_vcf_gatk.vcf" )
str_answer_file = os.path.join( self.str_test_data_dir, "test_groom_vcf_gatk_for_good_vcf_ANSWER.vcf" )
str_result_file = os.path.join( self.str_test_data_dir_working, "test_groom_vcf_gatk_for_good_vcf.vcf" )
self.func_make_dummy_dir( self.str_test_data_dir_working )
self.func_make_dummy_dir( self.str_test_data_dir )
# Call Example script
str_command = " ".join( [ str_groom_script, str_test_file, str_result_file ] )
Commandline.Commandline().func_CMD( str_command )
# Check test environment for results
f_success = self.func_are_files_equivalent( str_answer_file, str_result_file )
# Destroy environment
self.func_remove_files( [ str_result_file ] )
# Evaluate
self.func_test_true( f_success )
def test_groom_vcf_gatk_for_remove_spaces_vcf( self ):
"""
Test filter_groom_vcf_gatk for a vcf which needs spaces removed.
"""
# Create test environment
str_groom_script = os.path.join( self.str_script_dir, "groom_vcf.py" )
str_test_file = os.path.join( self.str_test_data_dir, "test_groom_vcf_gatk_spaces.vcf" )
str_answer_file = os.path.join( self.str_test_data_dir, "test_groom_vcf_gatk_for_remove_spaces_vcf_ANSWER.vcf" )
str_result_file = os.path.join( self.str_test_data_dir_working, "test_groom_vcf_gatk_for_remove_spaces_vcf.vcf" )
self.func_make_dummy_dir( self.str_test_data_dir_working )
self.func_make_dummy_dir( self.str_test_data_dir )
# Call Example script
str_command = " ".join( [ str_groom_script, str_test_file, str_result_file ] )
Commandline.Commandline().func_CMD( str_command )
# Check test environment for results
f_success = self.func_are_files_equivalent( str_answer_file, str_result_file )
# Destroy environment
self.func_remove_files( [ str_result_file ] )
# Evaluate
self.func_test_true( f_success )
def test_groom_vcf_gatk_for_42features_vcf( self ):
"""
Test filter_groom_vcf_gatk for a vcf with VCF 4.2 features
"""
# Create test environment
str_groom_script = os.path.join( self.str_script_dir, "groom_vcf.py" )
str_test_file = os.path.join( self.str_test_data_dir, "test_groom_vcf_gatk_42features.vcf" )
str_answer_file = os.path.join( self.str_test_data_dir, "test_groom_vcf_gatk_for_42features_vcf_ANSWER.vcf" )
str_result_file = os.path.join( self.str_test_data_dir_working, "test_groom_vcf_gatk_for_42features_vcf.vcf" )
self.func_make_dummy_dir( self.str_test_data_dir_working )
self.func_make_dummy_dir( self.str_test_data_dir )
# Call Example script
str_command = " ".join( [ str_groom_script, str_test_file, str_result_file ] )
Commandline.Commandline().func_CMD( str_command )
# Check test environment for results
f_success = self.func_are_files_equivalent( str_answer_file, str_result_file )
# Destroy environment
self.func_remove_files( [ str_result_file ] )
# Evaluate
self.func_test_true( f_success )
# reduce_vcf_to_snps.py
def test_reduce_vcf_to_snp_for_small_file_no_filter( self ):
"""
Test reducing the vcf file to snps for a file that is small (less than 100) and should not be filtered.
"""
# Create test environment
str_filtered_vcf_script = os.path.join( self.str_script_dir, "reduce_vcf_to_snps.py" )
str_filtered_vcf_test_file = os.path.join( self.str_test_data_dir, "test_reduce_vcf_to_snp_for_small_file_no_filter.vcf" )
str_filtered_vcf = os.path.join( self.str_test_data_dir_working, "test_reduce_vcf_to_snp_for_small_file_no_filter_RESULT.vcf" )
self.func_make_dummy_dir( self.str_test_data_dir_working )
# Call Example script
str_command = " ".join( [ str_filtered_vcf_script, "--reference", str_filtered_vcf_test_file, str_filtered_vcf ] )
Commandline.Commandline().func_CMD( str_command )
# Check test environment for results
f_success = self.func_are_files_equivalent( str_filtered_vcf, str_filtered_vcf_test_file )
# Destroy environment
self.func_remove_files( [ str_filtered_vcf ] )
# Evaluate
self.func_test_true( f_success )
def test_reduce_vcf_to_snp_for_small_file_filter_reference( self ):
"""
Test reducing the vcf file to snps for a file that is small (less than 100) and should be filtered.
When filtering as a reference, it will not have PASS info so this is ignored.
"""
# Create test environment
str_filtered_vcf_script = os.path.join( self.str_script_dir, "reduce_vcf_to_snps.py" )
str_filtered_vcf_test_file = os.path.join( self.str_test_data_dir, "test_reduce_vcf_to_snp_for_small_file_filter.vcf" )
str_filtered_vcf_answer = os.path.join( self.str_test_data_dir, "test_reduce_vcf_to_snp_for_small_file_filter_reference_ANSWER.vcf" )
str_filtered_vcf_result = os.path.join( self.str_test_data_dir_working, "test_reduce_vcf_to_snp_for_small_file_filter_reference_RESULT.vcf" )
self.func_make_dummy_dir( self.str_test_data_dir_working )
# Call Example script
str_command = " ".join( [ str_filtered_vcf_script, "--reference", str_filtered_vcf_test_file, str_filtered_vcf_result ] )
Commandline.Commandline().func_CMD( str_command )
# Check test environment for results
f_success = self.func_are_files_equivalent( str_filtered_vcf_answer, str_filtered_vcf_result )
# Destroy environment
self.func_remove_files( [ str_filtered_vcf_result ] )
# Evaluate
self.func_test_true( f_success )
def test_reduce_vcf_to_snp_for_small_file_filter( self ):
"""
Test reducing the vcf file to snps for a file that is small (less than 100) and should be filtered.
"""
# Create test environment
str_filtered_vcf_script = os.path.join( self.str_script_dir, "reduce_vcf_to_snps.py" )
str_filtered_vcf_test_file = os.path.join( self.str_test_data_dir, "test_reduce_vcf_to_snp_for_small_file_filter.vcf" )
str_filtered_vcf_answer = os.path.join( self.str_test_data_dir, "test_reduce_vcf_to_snp_for_small_file_filter_ANSWER.vcf" )
str_filtered_vcf_result = os.path.join( self.str_test_data_dir_working, "test_reduce_vcf_to_snp_for_small_file_filter_RESULT.vcf" )
self.func_make_dummy_dir( self.str_test_data_dir_working )
# Call Example script
str_command = " ".join( [ str_filtered_vcf_script, str_filtered_vcf_test_file, str_filtered_vcf_result ] )
Commandline.Commandline().func_CMD( str_command )
# Check test environment for results
f_success = self.func_are_files_equivalent( str_filtered_vcf_answer, str_filtered_vcf_result )
# Destroy environment
self.func_remove_files( [ str_filtered_vcf_result ] )
# Evaluate
self.func_test_true( f_success )
# vcfs_to_snp_calls_tab.py
def test_vcfs_to_snp_calls_tab_filter_maf_vcf( self ):
"""
Test vcfs_to_snp_calls_tab.py with filtering. Inputs are maf and vcf files.
"""
# Create test environment
str_snp_calls_script = os.path.join( self.str_script_dir, "vcfs_to_snp_calls_tab.py" )
str_snp_calls_input_file_1 = os.path.join( self.str_test_data_dir, "vcfs_to_snp_calls_tab_filter.maf" )
str_maf_tumor_key = "test"
str_snp_calls_input_file_2 = os.path.join( self.str_test_data_dir, "vcfs_to_snp_calls_tab_1_filter.vcf" )
str_snp_calls_input_depth_1 = os.path.join( self.str_test_data_dir, "vcfs_to_snp_calls_tab_1_filter.depth" )
str_snp_calls_input_depth_2 = os.path.join( self.str_test_data_dir, "vcfs_to_snp_calls_tab_1_filter.depth" )
str_snp_calls_answer = os.path.join( self.str_test_data_dir, "vcfs_to_snp_calls_tab_filter_maf_vcf_1_ANSWER_sorted.tab" )
str_snp_calls_result = os.path.join( self.str_test_data_dir_working, "vcfs_to_snp_calls_tab_filter_maf_vcf_1_RESULT.tab" )
self.func_make_dummy_dir( self.str_test_data_dir_working )
# Call Example script
str_command = " ".join( [ str_snp_calls_script, "--maf_reference", str_snp_calls_input_file_1, "--tumor", str_maf_tumor_key,
"--vcf", str_snp_calls_input_file_2, "--count_reference", str_snp_calls_input_depth_1,
"--count", str_snp_calls_input_depth_2, str_snp_calls_result ] )
Commandline.Commandline().func_CMD( str_command )
# Check test environment for results
lstr_answer_lines = None
with open( str_snp_calls_answer, "r" ) as hndl_answer:
lstr_answer_lines = [ str_line for str_line in hndl_answer.read().split("\n") if str_line ]
lstr_answer_lines.sort()
lstr_result_lines = None
with open( str_snp_calls_result, "r" ) as hndl_result:
lstr_result_lines = [ str_line for str_line in hndl_result.read().split("\n") if str_line ]
lstr_result_lines.sort()
# Destroy environment
self.func_remove_files( [ str_snp_calls_result ] )
# Evaluate
self.func_test_equals( "\n".join( lstr_answer_lines), "\n".join( lstr_result_lines ) )
def test_vcfs_to_snp_calls_tab_filter_maf_vcf_2( self ):
"""
Test vcfs_to_snp_calls_tab.py with filtering. Inputs are maf and vcf 2 files.
"""
# Create test environment
str_snp_calls_script = os.path.join( self.str_script_dir, "vcfs_to_snp_calls_tab.py" )
str_snp_calls_input_file_1 = os.path.join( self.str_test_data_dir, "vcfs_to_snp_calls_tab_filter.maf" )
str_maf_tumor_key = "test"
str_snp_calls_input_file_2 = os.path.join( self.str_test_data_dir, "vcfs_to_snp_calls_tab_2_filter.vcf" )
str_snp_calls_input_depth_1 = os.path.join( self.str_test_data_dir, "vcfs_to_snp_calls_tab_1_filter.depth" )
str_snp_calls_input_depth_2 = os.path.join( self.str_test_data_dir, "vcfs_to_snp_calls_tab_2_filter.depth" )
str_snp_calls_answer = os.path.join( self.str_test_data_dir, "vcfs_to_snp_calls_tab_filter_maf_vcf_2_ANSWER_sorted.tab" )
str_snp_calls_result = os.path.join( self.str_test_data_dir_working, "vcfs_to_snp_calls_tab_filter_maf_vcf_2_RESULT.tab" )
self.func_make_dummy_dir( self.str_test_data_dir_working )
# Call Example script
str_command = " ".join( [ str_snp_calls_script, "--maf_reference", str_snp_calls_input_file_1, "--tumor", str_maf_tumor_key,
"--vcf", str_snp_calls_input_file_2, "--count_reference", str_snp_calls_input_depth_1,
"--count", str_snp_calls_input_depth_2, str_snp_calls_result ] )
Commandline.Commandline().func_CMD( str_command )
# Check test environment for results
lstr_answer_lines = None
with open( str_snp_calls_answer, "r" ) as hndl_answer:
lstr_answer_lines = [ str_line for str_line in hndl_answer.read().split("\n") if str_line ]
lstr_answer_lines.sort()
lstr_result_lines = None
with open( str_snp_calls_result, "r" ) as hndl_result:
lstr_result_lines = [ str_line for str_line in hndl_result.read().split("\n") if str_line ]
lstr_result_lines.sort()
# Destroy environment
self.func_remove_files( [ str_snp_calls_result ] )
# Evaluate
self.func_test_equals( "\n".join( lstr_answer_lines), "\n".join( lstr_result_lines ) )
def test_vcfs_to_snp_calls_tab_filter_vcf_1_2( self ):
"""
Test vcfs_to_snp_calls_tab.py with filtering. Inputs are and vcf 1 and 2 files.
"""
# Create test environment
str_snp_calls_script = os.path.join( self.str_script_dir, "vcfs_to_snp_calls_tab.py" )
str_snp_calls_input_file_1 = os.path.join( self.str_test_data_dir, "vcfs_to_snp_calls_tab_1_filter.vcf" )
str_snp_calls_input_file_2 = os.path.join( self.str_test_data_dir, "vcfs_to_snp_calls_tab_2_filter.vcf" )
str_snp_calls_input_depth_1 = os.path.join( self.str_test_data_dir, "vcfs_to_snp_calls_tab_1_filter.depth" )
str_snp_calls_input_depth_2 = os.path.join( self.str_test_data_dir, "vcfs_to_snp_calls_tab_2_filter.depth" )
str_snp_calls_answer = os.path.join( self.str_test_data_dir, "vcfs_to_snp_calls_tab_filter_vcf_1_2_ANSWER_sorted.tab" )
str_snp_calls_result = os.path.join( self.str_test_data_dir_working, "vcfs_to_snp_calls_tab_filter_vcf_1_2_RESULT.tab" )
self.func_make_dummy_dir( self.str_test_data_dir_working )
# Call Example script
str_command = " ".join( [ str_snp_calls_script, "--vcf_reference", str_snp_calls_input_file_1,
"--vcf", str_snp_calls_input_file_2, "--count_reference", str_snp_calls_input_depth_1,
"--count", str_snp_calls_input_depth_2, str_snp_calls_result ] )
Commandline.Commandline().func_CMD( str_command )
# Check test environment for results
lstr_answer_lines = None
with open( str_snp_calls_answer, "r" ) as hndl_answer:
lstr_answer_lines = [ str_line for str_line in hndl_answer.read().split("\n") if str_line ]
lstr_answer_lines.sort()
lstr_result_lines = None
with open( str_snp_calls_result, "r" ) as hndl_result:
lstr_result_lines = [ str_line for str_line in hndl_result.read().split("\n") if str_line ]
lstr_result_lines.sort()
# Destroy environment
self.func_remove_files( [ str_snp_calls_result ] )
# Evaluate
self.func_test_equals( "\n".join( lstr_answer_lines), "\n".join( lstr_result_lines ) )
# vcfs_to_genotype_matrix.py
# This is for validation only, not for the pipeline runs.
# Under development.
def not_test_vcfs_to_genotype_matrix_1_file( self ):
"""
Test vcfs_to_genotype_matrix.py with one input file.
"""
# Create test environment
str_genotype_script = os.path.join( self.str_script_dir, "vcfs_to_genotype_matrix.py" )
str_vcf_directory = os.path.join( self.str_test_data_dir, "test_vcf_genotype_matrix_dir_1" )
str_genotype_answer = os.path.join( self.str_test_data_dir, "vcfs_to_genotype_matrix_1_ANSWER.txt" )
str_genotype_result = os.path.join( self.str_test_data_dir_working, "vcfs_to_genotype_matrix_1_RESULT.txt" )
self.func_make_dummy_dir( self.str_test_data_dir_working )
# Call Example script
str_command = " ".join( [ str_genotype_script, "--matrix", str_genotype_result, str_vcf_directory ] )
Commandline.Commandline().func_CMD( str_command )
# Check test environment for results
f_success = self.func_are_files_equivalent( str_genotype_answer, str_genotype_result )
# Destroy environment
self.func_remove_files( [ str_genotype_result ] )
# Evaluate
self.func_test_true( f_success )
def not_test_vcfs_to_genotype_matrix_3_file( self ):
"""
Test vcfs_to_genotype_matrix.py with one input file in one directory and 2 in another.
"""
# Create test environment
str_genotype_script = os.path.join( self.str_script_dir, "vcfs_to_genotype_matrix.py" )
str_vcf_directory_1 = os.path.join( self.str_test_data_dir, "test_vcf_genotype_matrix_dir_1" )
str_vcf_directory_2 = os.path.join( self.str_test_data_dir, "test_vcf_genotype_matrix_dir_2" )
str_genotype_answer = os.path.join( self.str_test_data_dir, "vcfs_to_genotype_matrix_3_ANSWER.txt" )
str_genotype_result = os.path.join( self.str_test_data_dir_working, "vcfs_to_genotype_matrix_3_RESULT.txt" )
self.func_make_dummy_dir( self.str_test_data_dir_working )
# Call Example script
str_command = " ".join( [ str_genotype_script, "--matrix", str_genotype_result, str_vcf_directory_1, str_vcf_directory_2 ] )
Commandline.Commandline().func_CMD( str_command )
# Check test environment for results
f_success = self.func_are_files_equivalent( str_genotype_answer, str_genotype_result )
# Destroy environment
self.func_remove_files( [ str_genotype_result ] )
# Evaluate
self.func_test_true( f_success )
# visualize_mutation_depth_tab_files.R
# This is for validation only, not for the pipeline runs.
# Under development.
def not_test_visualize_mutation_depth_tab_files_for_error_counts_opt( self ):
"""
Tests to make sure the TP, FP, FN, senstivity, and specificity measurements are correct from a test data set.
This is testing output that has a changing feature space (optimization figure) and not the "ROC" plot.
"""
# Create environment
str_vis_script = os.path.join( self.str_script_dir, "visualize_mutation_depth_tab_files.R" )
str_test_input_file = os.path.join( self.str_test_data_dir, "test_visualize_tab.tab" )
str_answer_file = os.path.join( self.str_test_data_dir, "test_visualize_mutation_depth_tab_files_for_error_counts_opt_ANSWER.txt" )
str_result_file = os.path.join( self.str_test_data_dir_working, "test_visualize_tab.tab_data.txt" )
self.func_make_dummy_dir( self.str_test_data_dir_working )
# Call example script
str_command = " ".join( [ str_vis_script, "-o", self.str_test_data_dir_working, "-k RNA_DNA", str_test_input_file ])
Commandline.Commandline().func_CMD( str_command )
# Check for sucess
f_success = self.func_are_files_equivalent( str_answer_file, str_result_file )
# Destroy environment
self.func_remove_files( [ str_result_file, os.path.join( self.str_test_data_dir_working, "test_visualize_tab.tab_depth_distributions.pdf" ),
os.path.join( self.str_test_data_dir_working, "test_visualize_tab.tab_fdr_min_read_coverage_norm.pdf" ),
os.path.join( self.str_test_data_dir_working, "test_visualize_tab.tab_data_truth_held.txt" ),
os.path.join( self.str_test_data_dir_working, "test_visualize_tab.tab_optimize_detail_validation.pdf" ),
os.path.join( self.str_test_data_dir_working, "test_visualize_tab.tab_raw_class_distributions_detail_validation.pdf" ),
os.path.join( self.str_test_data_dir_working, "test_visualize_tab.tab_roc_detail_validation.pdf" ),
os.path.join( self.str_test_data_dir_working, "test_visualize_tab.tab_sensitivity_min_read_coverage_norm.pdf" ) ] )
self.func_remove_dirs( [ self.str_test_data_dir_working ] )
# Evaluate
self.func_test_true( f_success )
def not_test_visualize_mutation_depth_tab_files_for_error_counts_roc( self ):
"""
Tests to make sure the TP, FP, FN, senstivity, and specificity measurements are correct from a test data set.
This is testing output that has a set feature space (the "ROC" plot) and not the optimization plot.
"""
# Create environment
str_vis_script = os.path.join( self.str_script_dir, "visualize_mutation_depth_tab_files.R" )
str_test_input_file = os.path.join( self.str_test_data_dir, "test_visualize_tab_roc.tab" )
str_answer_file_1 = os.path.join( self.str_test_data_dir, "test_visualize_tab_roc.tab_data_roc_1_answer.txt" )
str_answer_file_2 = os.path.join( self.str_test_data_dir, "test_visualize_tab_roc.tab_data_roc_2_answer.txt" )
str_answer_file_3 = os.path.join( self.str_test_data_dir, "test_visualize_tab_roc.tab_data_roc_3_answer.txt" )
str_answer_file_4 = os.path.join( self.str_test_data_dir, "test_visualize_tab_roc.tab_data_roc_4_answer.txt" )
str_result_file_1 = os.path.join( self.str_test_data_dir_working, "test_visualize_tab_roc.tab_data_roc_1.txt" )
str_result_file_2 = os.path.join( self.str_test_data_dir_working, "test_visualize_tab_roc.tab_data_roc_2.txt" )
str_result_file_3 = os.path.join( self.str_test_data_dir_working, "test_visualize_tab_roc.tab_data_roc_3.txt" )
str_result_file_4 = os.path.join( self.str_test_data_dir_working, "test_visualize_tab_roc.tab_data_roc_4.txt" )
self.func_make_dummy_dir( self.str_test_data_dir_working )
# Call example script
str_command = " ".join( [ str_vis_script, "-o", self.str_test_data_dir_working, "-k RNA_DNA", str_test_input_file ])
Commandline.Commandline().func_CMD( str_command )
# Check for sucess
f_success_1 = self.func_are_files_equivalent( str_answer_file_1, str_result_file_1 )
f_success_2 = self.func_are_files_equivalent( str_answer_file_2, str_result_file_2 )
f_success_3 = self.func_are_files_equivalent( str_answer_file_3, str_result_file_3 )
f_success_4 = self.func_are_files_equivalent( str_answer_file_4, str_result_file_4 )
# Destroy environment
# self.func_remove_files( [ os.path.join( self.str_test_data_dir_working, "test_visualize_tab_roc.tab_depth_distributions.pdf" ),
# os.path.join( self.str_test_data_dir_working, "test_visualize_tab_roc.tab_fdr_min_read_coverage_norm.pdf" ),
# os.path.join( self.str_test_data_dir_working, "test_visualize_tab_roc.tab_data.txt" ),
# os.path.join( self.str_test_data_dir_working, "test_visualize_tab_roc.tab_raw_class_distributions_detail_validation.pdf" ),
# os.path.join( self.str_test_data_dir_working, "test_visualize_tab_roc.tab_roc.pdf" ),
# os.path.join( self.str_test_data_dir_working, "test_visualize_tab_roc.tab_optimize_detail_validation.pdf" ),
# os.path.join( str_result_file_1 ),
# os.path.join( str_result_file_2 ),
# os.path.join( str_result_file_3 ),
# os.path.join( str_result_file_4 ),
# os.path.join( self.str_test_data_dir_working, "test_visualize_tab_roc.tab_sensitivity_min_read_coverage_norm.pdf" ) ] )
# self.func_remove_dirs( [ self.str_test_data_dir_working ] )
# Evaluate
self.func_test_true( f_success_1 and f_success_2 and f_success_3 and f_success_4 )
def not_test_visualize_mutation_depth_tab_files_for_roc_like_rnaseq( self ):
"""
Tests to make sure the TP, FP, FN, senstivity, and specificity measurements are correct from a test data set.
This is testing output that has a set feature space (the "ROC" plot) and not the optimization plot.
The other test uses a simple input data set similar to traditional ROC data, this one have varying RNA seq depth and
such that allows a more authentic test.
"""
# Create environment
str_vis_script = os.path.join( self.str_script_dir, "visualize_mutation_depth_tab_files.R" )
str_test_input_file = os.path.join( self.str_test_data_dir, "test_visualize_tab_roc_like_rnaseq.tab" )
str_answer_file_1 = os.path.join( self.str_test_data_dir, "test_visualize_tab_roc.tab_data_roc_like_rnaseq_1_answer.txt" )
str_answer_file_2 = os.path.join( self.str_test_data_dir, "test_visualize_tab_roc.tab_data_roc_like_ranseq_2_answer.txt" )
str_answer_file_3 = os.path.join( self.str_test_data_dir, "test_visualize_tab_roc.tab_data_roc_like_rnaseq_3_answer.txt" )
str_answer_file_4 = os.path.join( self.str_test_data_dir, "test_visualize_tab_roc.tab_data_roc_like_rnaseq_4_answer.txt" )
str_result_file_1 = os.path.join( self.str_test_data_dir_working, "test_visualize_tab_roc.tab_data_roc_like_rnaseq_1.txt" )
str_result_file_2 = os.path.join( self.str_test_data_dir_working, "test_visualize_tab_roc.tab_data_roc_like_rnaseq_2.txt" )
str_result_file_3 = os.path.join( self.str_test_data_dir_working, "test_visualize_tab_roc.tab_data_roc_like_rnaseq_3.txt" )
str_result_file_4 = os.path.join( self.str_test_data_dir_working, "test_visualize_tab_roc.tab_data_roc_like_rnaseq_4.txt" )
self.func_make_dummy_dir( self.str_test_data_dir_working )
# Call example script
str_command = " ".join( [ str_vis_script, "-o", self.str_test_data_dir_working, "-k RNA_DNA", str_test_input_file ])
Commandline.Commandline().func_CMD( str_command )
# Check for sucess
f_success_1 = self.func_are_files_equivalent( str_answer_file_1, str_result_file_1 )
f_success_2 = self.func_are_files_equivalent( str_answer_file_2, str_result_file_2 )
f_success_3 = self.func_are_files_equivalent( str_answer_file_3, str_result_file_3 )
f_success_4 = self.func_are_files_equivalent( str_answer_file_4, str_result_file_4 )
# Destroy environment
# self.func_remove_files( [ os.path.join( self.str_test_data_dir_working, "test_visualize_tab_roc_like_rnaseq.tab_depth_distributions.pdf" ),
# os.path.join( self.str_test_data_dir_working, "test_visualize_tab_roc_like_rnaseq.tab_fdr_min_read_coverage_norm.pdf" ),
# os.path.join( self.str_test_data_dir_working, "test_visualize_tab_roc_like_rnaseq.tab_data.txt" ),
# os.path.join( self.str_test_data_dir_working, "test_visualize_tab_roc_like_rnaseq.tab_raw_class_distributions_detail_validation.pdf" ),
# os.path.join( self.str_test_data_dir_working, "test_visualize_tab_roc_like_rnaseq.tab_roc.pdf" ),
# os.path.join( self.str_test_data_dir_working, "test_visualize_tab_roc_like_rnaseq.tab_optimize_detail_validation.pdf" ),
# os.path.join( str_result_file_1 ),
# os.path.join( str_result_file_2 ),
# os.path.join( str_result_file_3 ),
# os.path.join( str_result_file_4 ),
# os.path.join( self.str_test_data_dir_working, "test_visualize_tab_roc_like_rnaseq.tab_sensitivity_min_read_coverage_norm.pdf" ) ] )
# self.func_remove_dirs( [ self.str_test_data_dir_working ] )
# Evaluate
self.func_test_true( f_success_1 and f_success_2 and f_success_3 and f_success_4 )
# Creates a suite of tests
def suite():
return unittest.TestLoader().loadTestsFromTestCase( ScriptTester )
|
py | 1a31482b056d765cca1ff45d2969e3da585ccfb8 | from flask import Flask
from config import config_options
from flask_bootstrap import Bootstrap
from flask_sqlalchemy import SQLAlchemy
from flask_login import LoginManager
from flask_uploads import UploadSet,configure_uploads,IMAGES
from flask_mail import Mail
bootstrap = Bootstrap()
db = SQLAlchemy()
login_manager = LoginManager()
login_manager.session_protection = 'strong'
login_manager.login_view = 'auth.login'
photos = UploadSet('photos',IMAGES)
mail = Mail()
def create_app(config_name):
app = Flask(__name__)
# Creating the app configurations
app.config.from_object(config_options[config_name])
# Initializing flask extensions
bootstrap.init_app(app)
db.init_app(app)
login_manager.init_app(app)
mail.init_app(app)
# configure UploadSet
configure_uploads(app,photos)
# Registering the blueprints
from .rental_hub import rental_hub as rental_hub_blueprint
app.register_blueprint(rental_hub_blueprint)
from .auth import auth as auth_blueprint
app.register_blueprint(auth_blueprint,url_prefix = '/authenticate')
return app
|
py | 1a314856063667ae07023040d95eb91d76c46511 | """Test of Ray-tune without RLLib"""
from ray import tune
def objective(step, alpha, beta):
return (0.1 + alpha * step / 100)**(-1) + beta * 0.1
def train(config):
alpha, beta = config["alpha"], config["beta"]
for step in range(10):
score = objective(step, alpha, beta)
tune.report(mean_loss=score)
analysis = tune.run(
train,
config={
"alpha": tune.grid_search([0.001, 0.01]),
"beta": tune.choice([1, 2])
})
print("Best config: ", analysis.get_best_config(metric="mean_loss", mode="min"))
|
py | 1a314b307aa544252aa42c8ee155003b1a28b4aa | from concurrent import futures
from bs4 import BeautifulSoup
import pprint
import concurrent
import requests
import re
import time
import sys
MILE_TO_KM = 1.60934
CINEMA_MATCH_REGEX = r"(?P<cinema>[^,]*)"
NUM_OF_CINEMAS = 25
# TODO: PLEASE DON'T USE THESE METHODS TOO MUCH. THEY ACTUALLY QUERY THE API.
# IF YOU'RE GOING TO USE A LOT, SAVE THE DATA YOURSELF FOR TESTING.
CINEMAS = []
CINEMA_CID = {}
CINEMA_DIST = {}
F_TO_CINEMAS = {}
FCID_TO_TIMES = {}
F_CINEMA_TO_TIMES = {}
class DataParser:
def get_cinemas_latlong(self, latitude, longitude):
"""
Give this function a longitude and latitude and CINEMAS, CINEMA_IDS and
DISTANCES lists are populated with (up to) 5 results.
"""
print latitude, longitude
sys.stdout.flush()
global CINEMAS, CINEMA_CID, CINEMA_DIST
film_names = requests.get(
"https://api.cinelist.co.uk/search/cinemas/coordinates/{}/{}".
format(latitude, longitude))
cinemas = film_names.json()["cinemas"][:NUM_OF_CINEMAS]
for i in cinemas:
# Runs regex over cinemas to remove the location
cinema_name = re.match(CINEMA_MATCH_REGEX, i['name']).group("cinema")
# Dict storing {cinema name: cinema ID}
CINEMA_CID[cinema_name] = i['id']
# Converts distance from mile to km and rounds to 3dp.
# Dict storing {cinema name: distance}
CINEMA_DIST[cinema_name] = round(i['distance'] * MILE_TO_KM, 3)
def get_latlong(self, postcode):
"""
Give this function a postcode and get the corresponding latitude and
longitude.
"""
location_data = requests.get(
"http://api.postcodes.io/postcodes/{}".format(postcode))
location_data = location_data.json()
latitude = location_data['result']['latitude']
longitude = location_data['result']['longitude']
return round(latitude, 6), round(longitude, 6)
def get_cinema_url(self, cinema):
search_url = 'https://www.google.co.uk/search?q=' + cinema
res = requests.get(search_url)
soup = BeautifulSoup(res.text, "lxml")
# Parsing the html page to get the first url link in the google search
# results, which will be the wikipedia page link
g_search_res = soup.select('.r a')
if not g_search_res:
print 'VERY BAD: No google search results could be obtained'
sys.stdout.flush()
return ''
fst_ref_url = g_search_res[0].get('href')
if not fst_ref_url:
print 'RED ALERT: First google search result has no href tag'
sys.stdout.flush()
return ''
cinema_url = fst_ref_url.split('=')[1].split('&')[0]
print(cinema_url)
sys.stdout.flush()
return cinema_url
def fast_get_film_info(self, film_name):
"""
Given FILM_NAME, this will find the corresponding movie poster and
return the image url for the movie poster.
"""
error_url = 'https://literalminded.files.wordpress.com' \
'/2010/11/image-unavailable1.png'
error_overview = ''
if '&' in film_name:
film_name = 'and'.join(film_name.split('&'))
film_name = film_name.encode('utf-8')
api_url = 'https://api.themoviedb.org/3/search/movie?api_key=' \
'ab499564677631cc1c25f6749d42a16e' \
'&language=en-US&query={}'.format(film_name)
res = requests.get(api_url).json()
if res['total_results'] == 0:
return error_url, error_overview
first_result = res['results'][0]
poster_path = first_result['poster_path']
if poster_path is None:
img_url = error_url
else:
img_url = 'http://image.tmdb.org/t/p/w154' + poster_path
overview = first_result['overview']
if overview is None:
overview = error_overview
else:
groups = overview.split('.')
overview = '.'.join(groups[:2])
if overview[-1] != '.':
overview += '.'
return img_url, overview
def get_films_for_cinemas(self, date):
"""
Give this function a cinema ID and day and we can populate FILMS with
all film showings and times.
:param date: Date to get cinema films for.
:return:
"""
global CINEMA_CID, CINEMA_DIST
local_data = {}
def get_films_for_cinema(cinema):
# Get the cinema ID for a given cinema,
# E.g. Cineworld London - Enfield: 10477
cinema_id = CINEMA_CID[cinema]
# Get list of films showing at this cinema
url = "https://mysterious-eyrie-40497.herokuapp.com/cinemas/{}/" \
"showings/{}".format(cinema_id, date)
#url = "http://moviesapi.herokuapp.com/cinemas/{}/" \
# "showings/{}".format(cinema_id, date)
films = requests.get(url)
# Create a JSON object storing film name, cinema, showtimes and
# distance to the cinema.
films_json = films.json()
for i in films_json:
filmname = i["title"]
times = i['time']
if filmname in local_data:
local_data[filmname]["cinema"].append(
{cinema: [{"showtimes": times}]})
else:
local_data[filmname] = {}
poster, overview = self.fast_get_film_info(filmname)
local_data[filmname]["image"] = poster
local_data[filmname]["overview"] = overview
local_data[filmname]["cinema"] = \
[{cinema: [{"showtimes": times}]}]
executor = concurrent.futures.ThreadPoolExecutor(NUM_OF_CINEMAS)
futures = [executor.submit(get_films_for_cinema, cinema_name)
for cinema_name in CINEMA_CID.keys()]
concurrent.futures.wait(futures)
return local_data
def parse_date(self, day, month, year):
"""Convert date into a suitable format for use by the external API."""
day = str(day)
month = str(month)
year = str(year)
if len(day) == 1:
day = "0" + day
if len(month) == 1:
month = "0" + month
return year + "-" + month + "-" + day
def get_local_data(self, day, month, year, latitude, longitude):
"""Get all film data for a given location."""
self.get_cinemas_latlong(latitude, longitude)
formatted_date = self.parse_date(day, month, year)
return self.get_films_for_cinemas(formatted_date)
if __name__ == '__main__':
dParser = DataParser()
# print dParser.get_latlong("en12lz")
start_time = time.time()
pprint.PrettyPrinter(indent=4).pprint(
dParser.get_local_data(25, 6, 2017, 51.636743, -0.069069))
print dParser.get_cinema_url('Cineworld fullham road')
print time.time() - start_time
|
py | 1a314c00e6c61d3956b76b8d40b25886d14a7cfb | # Copyright 2012 New Dream Network, LLC (DreamHost)
#
# 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 contextlib
import mock
import testtools
import webob
from neutron.agent.linux import utils as agent_utils
from neutron.agent.metadata import agent
from neutron.agent import metadata_agent
from neutron.common import constants
from neutron.common import utils
from neutron.tests import base
class FakeConf(object):
admin_user = 'neutron'
admin_password = 'password'
admin_tenant_name = 'tenant'
auth_url = 'http://127.0.0.1'
auth_strategy = 'keystone'
auth_region = 'region'
auth_insecure = False
auth_ca_cert = None
endpoint_type = 'adminURL'
nova_metadata_ip = '9.9.9.9'
nova_metadata_port = 8775
metadata_proxy_shared_secret = 'secret'
nova_metadata_protocol = 'http'
nova_metadata_insecure = True
nova_client_cert = 'nova_cert'
nova_client_priv_key = 'nova_priv_key'
cache_url = ''
class FakeConfCache(FakeConf):
cache_url = 'memory://?default_ttl=5'
class TestMetadataProxyHandlerBase(base.BaseTestCase):
fake_conf = FakeConf
def setUp(self):
super(TestMetadataProxyHandlerBase, self).setUp()
self.log_p = mock.patch.object(agent, 'LOG')
self.log = self.log_p.start()
self.handler = agent.MetadataProxyHandler(self.fake_conf)
self.handler.plugin_rpc = mock.Mock()
self.handler.context = mock.Mock()
class TestMetadataProxyHandlerRpc(TestMetadataProxyHandlerBase):
def test_get_port_filters(self):
router_id = 'test_router_id'
ip = '1.2.3.4'
networks = ('net_id1', 'net_id2')
expected = {'device_id': [router_id],
'device_owner': constants.ROUTER_INTERFACE_OWNERS,
'network_id': networks,
'fixed_ips': {'ip_address': [ip]}}
actual = self.handler._get_port_filters(router_id, ip, networks)
self.assertEqual(expected, actual)
def test_get_router_networks(self):
router_id = 'router-id'
expected = ('network_id1', 'network_id2')
ports = [{'network_id': 'network_id1', 'something': 42},
{'network_id': 'network_id2', 'something_else': 32}]
self.handler.plugin_rpc.get_ports.return_value = ports
networks = self.handler._get_router_networks(router_id)
self.assertEqual(expected, networks)
def test_get_ports_for_remote_address(self):
ip = '1.1.1.1'
networks = ('network_id1', 'network_id2')
expected = [{'port_id': 'port_id1'},
{'port_id': 'port_id2'}]
self.handler.plugin_rpc.get_ports.return_value = expected
ports = self.handler._get_ports_for_remote_address(ip, networks)
self.assertEqual(expected, ports)
def test_get_ports_using_rpc_fallback_to_client(self):
ip = '1.1.1.1'
networks = ('network_id1', 'network_id2')
self.handler.plugin_rpc.get_ports.side_effect = AttributeError
with mock.patch('neutronclient.v2_0.client.Client') as neutron_client:
mock_list_ports = neutron_client.return_value.list_ports
expected_ports = {'ports': ['expected_port']}
mock_list_ports.return_value = expected_ports
ports = self.handler._get_ports_from_server(ip_address=ip,
networks=networks)
self.assertEqual(expected_ports['ports'], ports)
class TestMetadataProxyHandlerCache(TestMetadataProxyHandlerBase):
fake_conf = FakeConfCache
def setUp(self):
super(TestMetadataProxyHandlerCache, self).setUp()
self.qclient_p = mock.patch('neutronclient.v2_0.client.Client')
self.qclient = self.qclient_p.start()
self.handler.use_rpc = False
def test_call(self):
req = mock.Mock()
with mock.patch.object(self.handler,
'_get_instance_and_tenant_id') as get_ids:
get_ids.return_value = ('instance_id', 'tenant_id')
with mock.patch.object(self.handler, '_proxy_request') as proxy:
proxy.return_value = 'value'
retval = self.handler(req)
self.assertEqual(retval, 'value')
def test_call_no_instance_match(self):
req = mock.Mock()
with mock.patch.object(self.handler,
'_get_instance_and_tenant_id') as get_ids:
get_ids.return_value = None, None
retval = self.handler(req)
self.assertIsInstance(retval, webob.exc.HTTPNotFound)
def test_call_internal_server_error(self):
req = mock.Mock()
with mock.patch.object(self.handler,
'_get_instance_and_tenant_id') as get_ids:
get_ids.side_effect = Exception
retval = self.handler(req)
self.assertIsInstance(retval, webob.exc.HTTPInternalServerError)
self.assertEqual(len(self.log.mock_calls), 2)
def test_get_router_networks(self):
router_id = 'router-id'
expected = ('network_id1', 'network_id2')
ports = {'ports': [{'network_id': 'network_id1', 'something': 42},
{'network_id': 'network_id2',
'something_else': 32}],
'not_used': [1, 2, 3]}
mock_list_ports = self.qclient.return_value.list_ports
mock_list_ports.return_value = ports
networks = self.handler._get_router_networks(router_id)
mock_list_ports.assert_called_once_with(
device_id=router_id,
device_owner=constants.ROUTER_INTERFACE_OWNERS)
self.assertEqual(expected, networks)
def _test_get_router_networks_twice_helper(self):
router_id = 'router-id'
ports = {'ports': [{'network_id': 'network_id1', 'something': 42}],
'not_used': [1, 2, 3]}
expected_networks = ('network_id1',)
with mock.patch(
'oslo_utils.timeutils.utcnow_ts', return_value=0):
mock_list_ports = self.qclient.return_value.list_ports
mock_list_ports.return_value = ports
networks = self.handler._get_router_networks(router_id)
mock_list_ports.assert_called_once_with(
device_id=router_id,
device_owner=constants.ROUTER_INTERFACE_OWNERS)
self.assertEqual(expected_networks, networks)
networks = self.handler._get_router_networks(router_id)
def test_get_router_networks_twice(self):
self._test_get_router_networks_twice_helper()
self.assertEqual(
1, self.qclient.return_value.list_ports.call_count)
def _get_ports_for_remote_address_cache_hit_helper(self):
remote_address = 'remote_address'
networks = ('net1', 'net2')
fixed_ips = ["ip_address=%s" % remote_address]
mock_list_ports = self.qclient.return_value.list_ports
mock_list_ports.return_value = {'ports': [{'network_id': 'net1',
'something': 42}]}
self.handler._get_ports_for_remote_address(remote_address, networks)
mock_list_ports.assert_called_once_with(
network_id=networks, fixed_ips=fixed_ips)
self.assertEqual(1, mock_list_ports.call_count)
self.handler._get_ports_for_remote_address(remote_address,
networks)
def test_get_ports_for_remote_address_cache_hit(self):
self._get_ports_for_remote_address_cache_hit_helper()
self.assertEqual(
1, self.qclient.return_value.list_ports.call_count)
def test_get_ports_network_id(self):
network_id = 'network-id'
router_id = 'router-id'
remote_address = 'remote-address'
expected = ['port1']
networks = (network_id,)
with contextlib.nested(
mock.patch.object(self.handler, '_get_ports_for_remote_address'),
mock.patch.object(self.handler, '_get_router_networks')
) as (mock_get_ip_addr, mock_get_router_networks):
mock_get_ip_addr.return_value = expected
ports = self.handler._get_ports(remote_address, network_id,
router_id)
mock_get_ip_addr.assert_called_once_with(remote_address,
networks)
self.assertFalse(mock_get_router_networks.called)
self.assertEqual(expected, ports)
def test_get_ports_router_id(self):
router_id = 'router-id'
remote_address = 'remote-address'
expected = ['port1']
networks = ('network1', 'network2')
with contextlib.nested(
mock.patch.object(self.handler,
'_get_ports_for_remote_address',
return_value=expected),
mock.patch.object(self.handler,
'_get_router_networks',
return_value=networks)
) as (mock_get_ip_addr, mock_get_router_networks):
ports = self.handler._get_ports(remote_address,
router_id=router_id)
mock_get_router_networks.called_once_with(router_id)
mock_get_ip_addr.assert_called_once_with(remote_address, networks)
self.assertEqual(expected, ports)
def test_get_ports_no_id(self):
self.assertRaises(TypeError, self.handler._get_ports, 'remote_address')
def _get_instance_and_tenant_id_helper(self, headers, list_ports_retval,
networks=None, router_id=None):
remote_address = '192.168.1.1'
headers['X-Forwarded-For'] = remote_address
req = mock.Mock(headers=headers)
def mock_list_ports(*args, **kwargs):
return {'ports': list_ports_retval.pop(0)}
self.qclient.return_value.list_ports.side_effect = mock_list_ports
self.qclient.return_value.get_auth_info.return_value = {
'auth_token': None, 'endpoint_url': None}
instance_id, tenant_id = self.handler._get_instance_and_tenant_id(req)
new_qclient_call = mock.call(
username=FakeConf.admin_user,
tenant_name=FakeConf.admin_tenant_name,
region_name=FakeConf.auth_region,
auth_url=FakeConf.auth_url,
password=FakeConf.admin_password,
auth_strategy=FakeConf.auth_strategy,
token=None,
insecure=FakeConf.auth_insecure,
ca_cert=FakeConf.auth_ca_cert,
endpoint_url=None,
endpoint_type=FakeConf.endpoint_type)
expected = []
if router_id:
expected.extend([
new_qclient_call,
mock.call().list_ports(
device_id=router_id,
device_owner=constants.ROUTER_INTERFACE_OWNERS
),
mock.call().get_auth_info()
])
expected.extend([
new_qclient_call,
mock.call().list_ports(
network_id=networks, fixed_ips=['ip_address=192.168.1.1']),
mock.call().get_auth_info()
])
self.qclient.assert_has_calls(expected)
return (instance_id, tenant_id)
def test_get_instance_id_router_id(self):
router_id = 'the_id'
headers = {
'X-Neutron-Router-ID': router_id
}
networks = ('net1', 'net2')
ports = [
[{'network_id': 'net1'}, {'network_id': 'net2'}],
[{'device_id': 'device_id', 'tenant_id': 'tenant_id',
'network_id': 'net1'}]
]
self.assertEqual(
self._get_instance_and_tenant_id_helper(headers, ports,
networks=networks,
router_id=router_id),
('device_id', 'tenant_id')
)
def test_get_instance_id_router_id_no_match(self):
router_id = 'the_id'
headers = {
'X-Neutron-Router-ID': router_id
}
networks = ('net1', 'net2')
ports = [
[{'network_id': 'net1'}, {'network_id': 'net2'}],
[]
]
self.assertEqual(
self._get_instance_and_tenant_id_helper(headers, ports,
networks=networks,
router_id=router_id),
(None, None)
)
def test_get_instance_id_network_id(self):
network_id = 'the_id'
headers = {
'X-Neutron-Network-ID': network_id
}
ports = [
[{'device_id': 'device_id',
'tenant_id': 'tenant_id',
'network_id': 'the_id'}]
]
self.assertEqual(
self._get_instance_and_tenant_id_helper(headers, ports,
networks=('the_id',)),
('device_id', 'tenant_id')
)
def test_get_instance_id_network_id_no_match(self):
network_id = 'the_id'
headers = {
'X-Neutron-Network-ID': network_id
}
ports = [[]]
self.assertEqual(
self._get_instance_and_tenant_id_helper(headers, ports,
networks=('the_id',)),
(None, None)
)
def test_auth_info_cache(self):
router_id = 'the_id'
list_ports = [
[{'network_id': 'net1'}],
[{'device_id': 'did', 'tenant_id': 'tid', 'network_id': 'net1'}]]
def update_get_auth_info(*args, **kwargs):
self.qclient.return_value.get_auth_info.return_value = {
'auth_token': 'token', 'endpoint_url': 'uri'}
return {'ports': list_ports.pop(0)}
self.qclient.return_value.list_ports.side_effect = update_get_auth_info
new_qclient_call = mock.call(
username=FakeConf.admin_user,
tenant_name=FakeConf.admin_tenant_name,
region_name=FakeConf.auth_region,
auth_url=FakeConf.auth_url,
password=FakeConf.admin_password,
auth_strategy=FakeConf.auth_strategy,
token=None,
insecure=FakeConf.auth_insecure,
ca_cert=FakeConf.auth_ca_cert,
endpoint_url=None,
endpoint_type=FakeConf.endpoint_type)
cached_qclient_call = mock.call(
username=FakeConf.admin_user,
tenant_name=FakeConf.admin_tenant_name,
region_name=FakeConf.auth_region,
auth_url=FakeConf.auth_url,
password=FakeConf.admin_password,
auth_strategy=FakeConf.auth_strategy,
token='token',
insecure=FakeConf.auth_insecure,
ca_cert=FakeConf.auth_ca_cert,
endpoint_url='uri',
endpoint_type=FakeConf.endpoint_type)
headers = {'X-Forwarded-For': '192.168.1.10',
'X-Neutron-Router-ID': router_id}
req = mock.Mock(headers=headers)
self.handler._get_instance_and_tenant_id(req)
expected = [
new_qclient_call,
mock.call().list_ports(
device_id=router_id,
device_owner=constants.ROUTER_INTERFACE_OWNERS
),
mock.call().get_auth_info(),
cached_qclient_call,
mock.call().list_ports(network_id=('net1',),
fixed_ips=['ip_address=192.168.1.10']),
mock.call().get_auth_info(),
]
self.qclient.assert_has_calls(expected)
def _proxy_request_test_helper(self, response_code=200, method='GET'):
hdrs = {'X-Forwarded-For': '8.8.8.8'}
body = 'body'
req = mock.Mock(path_info='/the_path', query_string='', headers=hdrs,
method=method, body=body)
resp = mock.MagicMock(status=response_code)
req.response = resp
with mock.patch.object(self.handler, '_sign_instance_id') as sign:
sign.return_value = 'signed'
with mock.patch('httplib2.Http') as mock_http:
resp.__getitem__.return_value = "text/plain"
mock_http.return_value.request.return_value = (resp, 'content')
retval = self.handler._proxy_request('the_id', 'tenant_id',
req)
mock_http.assert_called_once_with(
ca_certs=None, disable_ssl_certificate_validation=True)
mock_http.assert_has_calls([
mock.call().add_certificate(
FakeConf.nova_client_priv_key,
FakeConf.nova_client_cert,
"%s:%s" % (FakeConf.nova_metadata_ip,
FakeConf.nova_metadata_port)
),
mock.call().request(
'http://9.9.9.9:8775/the_path',
method=method,
headers={
'X-Forwarded-For': '8.8.8.8',
'X-Instance-ID-Signature': 'signed',
'X-Instance-ID': 'the_id',
'X-Tenant-ID': 'tenant_id'
},
body=body
)]
)
return retval
def test_proxy_request_post(self):
response = self._proxy_request_test_helper(method='POST')
self.assertEqual(response.content_type, "text/plain")
self.assertEqual(response.body, 'content')
def test_proxy_request_200(self):
response = self._proxy_request_test_helper(200)
self.assertEqual(response.content_type, "text/plain")
self.assertEqual(response.body, 'content')
def test_proxy_request_400(self):
self.assertIsInstance(self._proxy_request_test_helper(400),
webob.exc.HTTPBadRequest)
def test_proxy_request_403(self):
self.assertIsInstance(self._proxy_request_test_helper(403),
webob.exc.HTTPForbidden)
def test_proxy_request_404(self):
self.assertIsInstance(self._proxy_request_test_helper(404),
webob.exc.HTTPNotFound)
def test_proxy_request_409(self):
self.assertIsInstance(self._proxy_request_test_helper(409),
webob.exc.HTTPConflict)
def test_proxy_request_500(self):
self.assertIsInstance(self._proxy_request_test_helper(500),
webob.exc.HTTPInternalServerError)
def test_proxy_request_other_code(self):
with testtools.ExpectedException(Exception):
self._proxy_request_test_helper(302)
def test_sign_instance_id(self):
self.assertEqual(
self.handler._sign_instance_id('foo'),
'773ba44693c7553d6ee20f61ea5d2757a9a4f4a44d2841ae4e95b52e4cd62db4'
)
class TestMetadataProxyHandlerNoCache(TestMetadataProxyHandlerCache):
fake_conf = FakeConf
def test_get_router_networks_twice(self):
self._test_get_router_networks_twice_helper()
self.assertEqual(
2, self.qclient.return_value.list_ports.call_count)
def test_get_ports_for_remote_address_cache_hit(self):
self._get_ports_for_remote_address_cache_hit_helper()
self.assertEqual(
2, self.qclient.return_value.list_ports.call_count)
class TestUnixDomainMetadataProxy(base.BaseTestCase):
def setUp(self):
super(TestUnixDomainMetadataProxy, self).setUp()
self.cfg_p = mock.patch.object(agent, 'cfg')
self.cfg = self.cfg_p.start()
looping_call_p = mock.patch(
'neutron.openstack.common.loopingcall.FixedIntervalLoopingCall')
self.looping_mock = looping_call_p.start()
self.cfg.CONF.metadata_proxy_socket = '/the/path'
self.cfg.CONF.metadata_workers = 0
self.cfg.CONF.metadata_backlog = 128
@mock.patch.object(agent_utils, 'ensure_dir')
def test_init_doesnot_exists(self, ensure_dir):
agent.UnixDomainMetadataProxy(mock.Mock())
ensure_dir.assert_called_once_with('/the')
def test_init_exists(self):
with mock.patch('os.path.isdir') as isdir:
with mock.patch('os.unlink') as unlink:
isdir.return_value = True
agent.UnixDomainMetadataProxy(mock.Mock())
unlink.assert_called_once_with('/the/path')
def test_init_exists_unlink_no_file(self):
with mock.patch('os.path.isdir') as isdir:
with mock.patch('os.unlink') as unlink:
with mock.patch('os.path.exists') as exists:
isdir.return_value = True
exists.return_value = False
unlink.side_effect = OSError
agent.UnixDomainMetadataProxy(mock.Mock())
unlink.assert_called_once_with('/the/path')
def test_init_exists_unlink_fails_file_still_exists(self):
with mock.patch('os.path.isdir') as isdir:
with mock.patch('os.unlink') as unlink:
with mock.patch('os.path.exists') as exists:
isdir.return_value = True
exists.return_value = True
unlink.side_effect = OSError
with testtools.ExpectedException(OSError):
agent.UnixDomainMetadataProxy(mock.Mock())
unlink.assert_called_once_with('/the/path')
@mock.patch.object(agent, 'MetadataProxyHandler')
@mock.patch.object(agent_utils, 'UnixDomainWSGIServer')
@mock.patch.object(agent_utils, 'ensure_dir')
def test_run(self, ensure_dir, server, handler):
p = agent.UnixDomainMetadataProxy(self.cfg.CONF)
p.run()
ensure_dir.assert_called_once_with('/the')
server.assert_has_calls([
mock.call('neutron-metadata-agent'),
mock.call().start(handler.return_value,
'/the/path', workers=0,
backlog=128),
mock.call().wait()]
)
def test_main(self):
with mock.patch.object(agent, 'UnixDomainMetadataProxy') as proxy:
with mock.patch.object(metadata_agent, 'config') as config:
with mock.patch.object(metadata_agent, 'cfg') as cfg:
with mock.patch.object(utils, 'cfg'):
metadata_agent.main()
self.assertTrue(config.setup_logging.called)
proxy.assert_has_calls([
mock.call(cfg.CONF),
mock.call().run()]
)
def test_init_state_reporting(self):
with mock.patch('os.makedirs'):
proxy = agent.UnixDomainMetadataProxy(mock.Mock())
self.looping_mock.assert_called_once_with(proxy._report_state)
self.looping_mock.return_value.start.assert_called_once_with(
interval=mock.ANY)
def test_report_state(self):
with mock.patch('neutron.agent.rpc.PluginReportStateAPI') as state_api:
with mock.patch('os.makedirs'):
proxy = agent.UnixDomainMetadataProxy(mock.Mock())
self.assertTrue(proxy.agent_state['start_flag'])
proxy._report_state()
self.assertNotIn('start_flag', proxy.agent_state)
state_api_inst = state_api.return_value
state_api_inst.report_state.assert_called_once_with(
proxy.context, proxy.agent_state, use_call=True)
|
py | 1a314c6e2c20b483b369cb85d8c4a4a95c519d14 | from .simulator import *
def is_available():
"""Returns a boolean to indicate the availability of a CUDA GPU.
"""
# Simulator is always available
return True
def cuda_error():
"""Returns None or an exception if the CUDA driver fails to initialize.
"""
# Simulator never fails to initialize
return None
|
py | 1a314cf82308bb579c801cad55065a85d1e0e2e0 | """
OpenAPI definition
No description provided (generated by Openapi Generator https://github.com/openapitools/openapi-generator) # noqa: E501
The version of the OpenAPI document: v0
Contact: [email protected]
Generated by: https://openapi-generator.tech
"""
import re # noqa: F401
import sys # noqa: F401
from gooddata_metadata_client.model_utils import ( # noqa: F401
ApiTypeError,
ModelComposed,
ModelNormal,
ModelSimple,
cached_property,
change_keys_js_to_python,
convert_js_args_to_python_args,
date,
datetime,
file_type,
none_type,
validate_get_composed_info,
OpenApiModel
)
from gooddata_metadata_client.exceptions import ApiAttributeError
def lazy_import():
from gooddata_metadata_client.model.json_api_metric_patch_attributes import JsonApiMetricPatchAttributes
globals()['JsonApiMetricPatchAttributes'] = JsonApiMetricPatchAttributes
class JsonApiMetricPatch(ModelNormal):
"""NOTE: This class is auto generated by OpenAPI Generator.
Ref: https://openapi-generator.tech
Do not edit the class manually.
Attributes:
allowed_values (dict): The key is the tuple path to the attribute
and the for var_name this is (var_name,). The value is a dict
with a capitalized key describing the allowed value and an allowed
value. These dicts store the allowed enum values.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
discriminator_value_class_map (dict): A dict to go from the discriminator
variable value to the discriminator class name.
validations (dict): The key is the tuple path to the attribute
and the for var_name this is (var_name,). The value is a dict
that stores validations for max_length, min_length, max_items,
min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum,
inclusive_minimum, and regex.
additional_properties_type (tuple): A tuple of classes accepted
as additional properties values.
"""
allowed_values = {
('type',): {
'METRIC': "metric",
},
}
validations = {
('id',): {
'regex': {
'pattern': r'^((?!\.)[.A-Za-z0-9_-]{1,255}:)?(?!\.)[.A-Za-z0-9_-]{1,255}$', # noqa: E501
},
},
}
@cached_property
def additional_properties_type():
"""
This must be a method because a model may have properties that are
of type self, this must run after the class is loaded
"""
lazy_import()
return (bool, date, datetime, dict, float, int, list, str, none_type,) # noqa: E501
_nullable = False
@cached_property
def openapi_types():
"""
This must be a method because a model may have properties that are
of type self, this must run after the class is loaded
Returns
openapi_types (dict): The key is attribute name
and the value is attribute type.
"""
lazy_import()
return {
'attributes': (JsonApiMetricPatchAttributes,), # noqa: E501
'id': (str,), # noqa: E501
'type': (str,), # noqa: E501
}
@cached_property
def discriminator():
return None
attribute_map = {
'attributes': 'attributes', # noqa: E501
'id': 'id', # noqa: E501
'type': 'type', # noqa: E501
}
read_only_vars = {
}
_composed_schemas = {}
@classmethod
@convert_js_args_to_python_args
def _from_openapi_data(cls, attributes, id, *args, **kwargs): # noqa: E501
"""JsonApiMetricPatch - a model defined in OpenAPI
Args:
attributes (JsonApiMetricPatchAttributes):
id (str): API identifier of an object
Keyword Args:
type (str): Object type. defaults to "metric", must be one of ["metric", ] # noqa: E501
_check_type (bool): if True, values for parameters in openapi_types
will be type checked and a TypeError will be
raised if the wrong type is input.
Defaults to True
_path_to_item (tuple/list): This is a list of keys or values to
drill down to the model in received_data
when deserializing a response
_spec_property_naming (bool): True if the variable names in the input data
are serialized names, as specified in the OpenAPI document.
False if the variable names in the input data
are pythonic names, e.g. snake case (default)
_configuration (Configuration): the instance to use when
deserializing a file_type parameter.
If passed, type conversion is attempted
If omitted no type conversion is done.
_visited_composed_classes (tuple): This stores a tuple of
classes that we have traveled through so that
if we see that class again we will not use its
discriminator again.
When traveling through a discriminator, the
composed schema that is
is traveled through is added to this set.
For example if Animal has a discriminator
petType and we pass in "Dog", and the class Dog
allOf includes Animal, we move through Animal
once using the discriminator, and pick Dog.
Then in Dog, we will make an instance of the
Animal class but this time we won't travel
through its discriminator because we passed in
_visited_composed_classes = (Animal,)
"""
type = kwargs.get('type', "metric")
_check_type = kwargs.pop('_check_type', True)
_spec_property_naming = kwargs.pop('_spec_property_naming', True)
_path_to_item = kwargs.pop('_path_to_item', ())
_configuration = kwargs.pop('_configuration', None)
_visited_composed_classes = kwargs.pop('_visited_composed_classes', ())
self = super(OpenApiModel, cls).__new__(cls)
if args:
for arg in args:
if isinstance(arg, dict):
kwargs.update(arg)
else:
raise ApiTypeError(
"Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % (
args,
self.__class__.__name__,
),
path_to_item=_path_to_item,
valid_classes=(self.__class__,),
)
self._data_store = {}
self._check_type = _check_type
self._spec_property_naming = _spec_property_naming
self._path_to_item = _path_to_item
self._configuration = _configuration
self._visited_composed_classes = _visited_composed_classes + (self.__class__,)
self.attributes = attributes
self.id = id
self.type = type
for var_name, var_value in kwargs.items():
if var_name not in self.attribute_map and \
self._configuration is not None and \
self._configuration.discard_unknown_keys and \
self.additional_properties_type is None:
# discard variable.
continue
setattr(self, var_name, var_value)
return self
required_properties = set([
'_data_store',
'_check_type',
'_spec_property_naming',
'_path_to_item',
'_configuration',
'_visited_composed_classes',
])
@convert_js_args_to_python_args
def __init__(self, attributes, id, *args, **kwargs): # noqa: E501
"""JsonApiMetricPatch - a model defined in OpenAPI
Args:
attributes (JsonApiMetricPatchAttributes):
id (str): API identifier of an object
Keyword Args:
type (str): Object type. defaults to "metric", must be one of ["metric", ] # noqa: E501
_check_type (bool): if True, values for parameters in openapi_types
will be type checked and a TypeError will be
raised if the wrong type is input.
Defaults to True
_path_to_item (tuple/list): This is a list of keys or values to
drill down to the model in received_data
when deserializing a response
_spec_property_naming (bool): True if the variable names in the input data
are serialized names, as specified in the OpenAPI document.
False if the variable names in the input data
are pythonic names, e.g. snake case (default)
_configuration (Configuration): the instance to use when
deserializing a file_type parameter.
If passed, type conversion is attempted
If omitted no type conversion is done.
_visited_composed_classes (tuple): This stores a tuple of
classes that we have traveled through so that
if we see that class again we will not use its
discriminator again.
When traveling through a discriminator, the
composed schema that is
is traveled through is added to this set.
For example if Animal has a discriminator
petType and we pass in "Dog", and the class Dog
allOf includes Animal, we move through Animal
once using the discriminator, and pick Dog.
Then in Dog, we will make an instance of the
Animal class but this time we won't travel
through its discriminator because we passed in
_visited_composed_classes = (Animal,)
"""
type = kwargs.get('type', "metric")
_check_type = kwargs.pop('_check_type', True)
_spec_property_naming = kwargs.pop('_spec_property_naming', False)
_path_to_item = kwargs.pop('_path_to_item', ())
_configuration = kwargs.pop('_configuration', None)
_visited_composed_classes = kwargs.pop('_visited_composed_classes', ())
if args:
for arg in args:
if isinstance(arg, dict):
kwargs.update(arg)
else:
raise ApiTypeError(
"Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % (
args,
self.__class__.__name__,
),
path_to_item=_path_to_item,
valid_classes=(self.__class__,),
)
self._data_store = {}
self._check_type = _check_type
self._spec_property_naming = _spec_property_naming
self._path_to_item = _path_to_item
self._configuration = _configuration
self._visited_composed_classes = _visited_composed_classes + (self.__class__,)
self.attributes = attributes
self.id = id
self.type = type
for var_name, var_value in kwargs.items():
if var_name not in self.attribute_map and \
self._configuration is not None and \
self._configuration.discard_unknown_keys and \
self.additional_properties_type is None:
# discard variable.
continue
setattr(self, var_name, var_value)
if var_name in self.read_only_vars:
raise ApiAttributeError(f"`{var_name}` is a read-only attribute. Use `from_openapi_data` to instantiate "
f"class with read only attributes.")
|
py | 1a314d37754d75b258b2bd144ecbd45e4d9d7b60 | import tensorflow as tf
import os
import shutil
from tensorflow.python.saved_model import tag_constants
from tensorflow.python import ops
def get_graph_def_from_file(graph_filepath):
tf.compat.v1.reset_default_graph()
with ops.Graph().as_default():
with tf.compat.v1.gfile.GFile(graph_filepath, 'rb') as f:
graph_def = tf.compat.v1.GraphDef()
graph_def.ParseFromString(f.read())
return graph_def
def convert_graph_def_to_saved_model(export_dir, graph_filepath, input_name, outputs):
graph_def = get_graph_def_from_file(graph_filepath)
with tf.compat.v1.Session(graph=tf.Graph()) as session:
tf.import_graph_def(graph_def, name='')
tf.compat.v1.saved_model.simple_save(
session,
export_dir,# change input_image to node.name if you know the name
inputs={input_name: session.graph.get_tensor_by_name('{}:0'.format(node.name))
for node in graph_def.node if node.op=='Placeholder'},
outputs={t.rstrip(":0"):session.graph.get_tensor_by_name(t) for t in outputs}
)
print('Optimized graph converted to SavedModel!')
tf.compat.v1.enable_eager_execution()
# convert this to a TF Serving compatible mode
shutil.rmtree('./saved_model', ignore_errors=True)
convert_graph_def_to_saved_model('./saved_model', './v3-large_224_1.0_float.pb', 'input', ['MobilenetV3/Predictions/Softmax:0'])
|
py | 1a314e28488f9b07a7c7751d6a0cd3b1d438f780 | """
Contains settings used elsewhere in the library.
"""
from typing import Literal
from pathlib import Path
from pydantic import BaseSettings, DirectoryPath, FilePath
from dotenv import load_dotenv, find_dotenv
# find the first file named ".env" in the current directory or a parent directory
load_dotenv(dotenv_path=find_dotenv(filename='.env'))
ENCOMP_BASE = Path(__file__).parent.resolve()
class Settings(BaseSettings):
"""
Settings class.
Use an ``.env``-file to override the defaults.
The ``.env``-file is located using ``dotenv.find_dotenv(filename='.env')``, this will find a
file in the directory of the running Python process or in a parent directory.
The variables in the ``.env``-file have the same names (not case-sensitive)
as the attributes of this class, with the additional prefix ``ENCOMP_``.
In case of invalid values in the ``.env``-file or environment variables,
a ``ValidationError`` is raised.
.. note::
Names that are defined as global environment variables (either on the system
or user level) take precedence over names in the ``.env``-file.
The global environment variables are loaded even if no ``.env``-file was found.
* ``DATA_DIRECTORY``: path to a directory with auxiliary data
* ``ADDITIONAL_UNITS``: path to a file with additional unit definitions for ``pint``
* ``TYPE_CHECKING``: whether to check parameter and return value types of the core
library function. This does not impact user-defined functions, the
``typeguard.typechecked`` decorator must be used explicitly
* ``TYPESET_SYMBOL_SCRIPTS``: whether to typeset Sympy symbol sub- and superscripts
* ``IGNORE_NDARRAY_UNIT_STRIPPED_WARNING``: whether to suppress the ``pint`` warning
when converting Quantity to Numpy array.
* ``MATPLOTLIB_NOTEBOOK_FORMAT``: figure format for Matplotlib figures in Jupyter Notebooks
* ``AUTOCONVERT_OFFSET_TO_BASEUNIT``: whether to automatically convert offset units in calculations. If this is False, °C must be converted to K before multiplication (for example)
* ``DEFAULT_UNIT_FORMAT``: default unit format for ``Quantity`` objects: one of ``~P`` (compact), ``~L`` (Latex), ``~H`` (HTML), ``~Lx`` (Latex with SIUNITX package)
.. note::
All names are case-insensitive.
"""
data_directory: DirectoryPath = ENCOMP_BASE / 'data'
additional_units: FilePath = data_directory / 'additional-units.txt'
type_checking: bool = False
typeset_symbol_scripts: bool = True
ignore_ndarray_unit_stripped_warning: bool = True
matplotlib_notebook_format: Literal['retina', 'png', 'svg'] = 'retina'
autoconvert_offset_to_baseunit: bool = True
default_unit_format: Literal['~P', '~L', '~H', '~Lx'] = '~P'
class Config:
env_prefix = 'ENCOMP_'
env_file = '.env'
env_file_encoding = 'utf-8'
case_sensitive = False
# the settings object is initialized the first time the library loads
# settings can be changed during runtime by setting attributes on this instance
SETTINGS = Settings()
|
py | 1a314e5da8e0a761183714313da99566115a9b63 | # -*- coding: utf-8 -*-
from matplotlib.patches import Patch
from matplotlib.pyplot import axis, legend
from ....Functions.init_fig import init_fig
from ....definitions import config_dict
MAGNET_COLOR = config_dict["PLOT"]["COLOR_DICT"]["MAGNET_COLOR"]
def plot(self, fig=None, display_magnet=True):
"""Plot the Hole in a matplotlib fig
Parameters
----------
self : Hole
A Hole object
fig :
if None, open a new fig and plot, else add to the current
one (Default value = None)
display_magnet : bool
if True, plot the magnet inside the hole, if there is any (Default value = True)
Returns
-------
None
"""
display = fig is None
if display:
color = "k"
else:
color = "w"
surf_hole = self.build_geometry()
patches = list()
for surf in surf_hole:
if "Magnet" in surf.label and display_magnet:
patches.extend(surf.get_patches(color=MAGNET_COLOR))
else:
patches.extend(surf.get_patches(color=color))
# Display the result
(fig, axes, patch_leg, label_leg) = init_fig(fig)
axes.set_xlabel("(m)")
axes.set_ylabel("(m)")
axes.set_title("Hole")
# Add all the hole (and magnet) to fig
for patch in patches:
axes.add_patch(patch)
# Axis Setup
axis("equal")
Lim = self.get_Rbo() * 1.2
axes.set_xlim(-Lim, Lim)
axes.set_ylim(-Lim, Lim)
if display_magnet and "Magnet" in [surf.label for surf in surf_hole]:
patch_leg.append(Patch(color=MAGNET_COLOR))
label_leg.append("Magnet")
legend(patch_leg, label_leg)
fig.show()
|
py | 1a314ed7e98779b54d32d41ecc3dcb9d4a4be264 | from jesse.helpers import get_candle_source, slice_candles, np_shift, same_length
import numpy as np
from numba import njit,jit
import talib
from typing import Union
from jesse.helpers import get_config
from collections import namedtuple
import tulipy as ti
import math
"""
https://www.tradingview.com/script/sxZRzQzQ-Divergence-Indicator-any-oscillator/#chart-view-comment-form
Possibly Accurate, needs more testing
"""
#jesse backtest '2021-01-03' '2021-03-02'
DIVERGENCES = namedtuple('Divergences',['bearCond', 'bullCond', 'hiddenBullCond','hiddenBearCond'])
def divergence(candles: np.ndarray, lbR:int=2, lbL:int=2, rangeUpper:int=200, rangeLower:int=0,source_type: str = "close", sequential: bool = False) -> DIVERGENCES:
candles = slice_candles(candles, sequential)
source1 = get_candle_source(candles, source_type=source_type)
bearCond, bullCond, hiddenBullCond, hiddenBearCond = fast_div(source,source,candles,lbR,lbL,rangeUpper,rangeLower)
if sequential:
return DIVERGENCES(bearCond,bullCond,hiddenBearCond,hiddenBullCond)
else:
return DIVERGENCES(bearCond[-1],bullCond[-1],hiddenBearCond[-1],hiddenBullCond[-1])
def fast_div(source1,source,candles,r,l,rangeUpper,rangeLower):
highmiddlesource = np.full_like(source1,0)
lowmiddlesource = np.full_like(source1,0)
pivothigh = np.full_like(source1,0)
pivotlow = np.full_like(source1,0)
lastpivothighprice = np.full_like(source1,0)
lastpivotlowprice = np.full_like(source1,0)
priceslowest = np.full_like(source1,np.nan)
priceshighest = np.full_like(source1,np.nan)
priceshigh = np.full_like(source1,np.nan)
priceslow = np.full_like(source1,np.nan)
highindices = np.full_like(source1,np.nan)
lowindices = np.full_like(source1,np.nan)
ivar = np.full_like(source1,0)
for i in range(source1.shape[0]):
highmiddlesource[i] = source[i-r]
lowmiddlesource[i] = source[i-l]
if (np.all(highmiddlesource[i] >= source[i-(l+r):i-(r)]) and np.all(highmiddlesource[i] > source[i-(r-1):i+1])):
pivothigh[i] = 1
lastpivothighprice[i] = highmiddlesource[i]
else:
pivothigh[i] = 0
lastpivothighprice[i] = lastpivothighprice[i-1]
if (np.all(lowmiddlesource[i] <= source[i-(l+r):i-(r)]) and np.all(lowmiddlesource[i] < source[i-(r-1):i+1])):
pivotlow[i] = 1
lastpivotlowprice[i] = lowmiddlesource[i]
else:
pivotlow[i] = 0
lastpivotlowprice[i] = lastpivotlowprice[i-1]
if pivothigh[i] == 1:
priceshigh[i] = source[i-r]
priceshighest[i] = candles[:,3][i-r]
highindices[i] = (i-r)
if pivotlow[i] == 1:
priceslow[i] = source[i-l]
priceslowest[i] = candles[:,4][i-l]
lowindices[i] = (i-l)
ivar[i] = i
ivar1 = int(ivar[-1])
priceshigh = priceshigh[~np.isnan(priceshigh)]
priceshigh = np.concatenate((np.full((source.shape[0] - priceshigh.shape[0]), np.nan), priceshigh))
priceshighest = priceshighest[~np.isnan(priceshighest)]
priceshighest = np.concatenate((np.full((source.shape[0] - priceshighest.shape[0]), np.nan), priceshighest))
priceslow = priceslow[~np.isnan(priceslow)]
priceslow = np.concatenate((np.full((source.shape[0] - priceslow.shape[0]), np.nan), priceslow))
priceslowest = priceslowest[~np.isnan(priceslowest)]
priceslowest = np.concatenate((np.full((source.shape[0] - priceslowest.shape[0]), np.nan), priceslowest))
highindices = highindices[~np.isnan(highindices)]
highindices = np.concatenate((np.full((source.shape[0] - highindices.shape[0]), np.nan), highindices))
lowindices = lowindices[~np.isnan(lowindices)]
lowindices = np.concatenate((np.full((source.shape[0] - lowindices.shape[0]), np.nan), lowindices))
oscHL = 1 if source[-(r+1)] > priceslow[-2] and (np.abs(lowindices[-2]-ivar1) >= rangeLower and np.abs(lowindices[-2]-ivar1) <= rangeUpper) else 0
priceLL = 1 if candles[:,4][-(r+1)] < priceslowest[-2] else 0
bullCond = 1 if priceLL == 1 and oscHL == 1 and pivotlow[-1] == 1 else 0
oscLL = 1 if (source[-(r+1)] < priceslow[-2] and np.abs(lowindices[-2]-ivar1) >= rangeLower and np.abs(lowindices[-2]-ivar1) <= rangeUpper) else 0
priceHL = 1 if candles[:,4][-(r+1)] > priceslowest[-2] else 0
hiddenBullCond = 1 if priceHL == 1 and oscLL == 1 and pivotlow[-1] == 1 else 0
oscLH = 1 if source[-(r+1)] < priceshigh[-2] and (np.abs(highindices[-2]-ivar1) >= rangeLower and np.abs(highindices[-2]-ivar1) <= rangeUpper) else 0
priceHH = 1 if candles[:,3][-(r+1)] > priceshighest[-2] else 0
bearCond = 1 if priceHH == 1 and oscLH == 1 and pivothigh[-1] == 1 else 0
oscHH = 1 if source[-(r+1)] > priceshigh[-2] and (np.abs(highindices[-2]-ivar1) >= rangeLower and np.abs(highindices[-2]-ivar1) <= rangeUpper) else 0
priceLH = 1 if candles[:,3][-(r+1)] < priceshighest[-2] else 0
hiddenBearCond = 1 if priceLH == 1 and oscHH == 1 and pivothigh[-1] == 1 else 0
return bearCond, bullCond, hiddenBullCond, hiddenBearCond
|
py | 1a314f29f5c821bd4517f8b3972bb56000ac2620 | from enum import Enum
from itertools import takewhile
from grid import Grid, Point
import grid_utils
class DiscState(Enum):
empty = 0
red = 1
black = 2
class Game(object):
def __init__(self, initial_grid=None):
self.restart(initial_grid)
def restart(self, initial_grid=None):
if initial_grid is None:
self.grid = Grid(6, # Rows
7, # Cols
initial_value=DiscState.empty)
else:
self.grid = initial_grid
self.current_player = DiscState.red
self.winner = None
self.is_end = False
def try_turn(self, color, col_index):
added_point = self.try_move(color, col_index)
if added_point is not None:
winner = self.get_winner(added_point, self.current_player)
if winner:
self.winner = winner
self.is_end = True
return True
else:
if not self.is_board_full():
self.switch_player()
else:
# Tie game
self.is_end = True
return True
return False
def try_move(self, color, col_index):
if self.current_player is not color:
return None
if not self.can_add_disc(col_index):
return None
return self.add_disc(col_index, self.current_player)
def switch_player(self):
if self.current_player is DiscState.red:
self.current_player = DiscState.black
else:
self.current_player = DiscState.red
def is_board_full(self):
for col_index in range(self.grid.width):
if self.can_add_disc(col_index):
return False
return True
def can_add_disc(self, col_index):
if col_index >= self.grid.width:
return False
return self.grid[-1][col_index] is DiscState.empty
def add_disc(self, col_index, color):
for row_index in range(self.grid.height):
if self.grid[row_index][col_index] is DiscState.empty:
self.grid[row_index][col_index] = color
return Point(row_index, col_index)
break
else:
raise ValueError("column %i is full" % col_index)
def get_winner(self, last_move, current_player, row_size=4):
assert self.grid.at(last_move) is not DiscState.empty
if grid_utils.is_in_row_run(self.grid, last_move, row_size) or \
grid_utils.is_in_col_run(self.grid, last_move, row_size) or \
grid_utils.is_in_diag_down_run(self.grid, last_move, row_size) or \
grid_utils.is_in_diag_up_run(self.grid, last_move, row_size):
return current_player
return None
def render_board(self):
str_repr = ["Current board state:\n"]
str_repr += [" %i " % col_index for col_index in range(self.grid.width)] + ["\n"]
for row in reversed(self.grid):
row_repr = []
for disc_value in row:
if disc_value is DiscState.empty:
row_repr.append("| |")
elif disc_value is DiscState.red:
row_repr.append("|O|")
else: # disc_value is black
row_repr.append("|X|")
row_repr.append("\n")
str_repr += row_repr
print("".join(str_repr))
|
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