fumiyau/python_comments_free_500000 · Datasets at Hugging Face

1c43bcddd351165d681ed76a72205a2d674eab7f

4,026

py

Python

cdtcommon/calculator.py

Just-Jojo/mcoc-v3

41c69960c8aff2dbbfd5d11ecc17e7af73e1e305

[ "MIT" ]

3

2020-08-09T03:03:20.000Z

2020-12-13T19:01:07.000Z

cdtcommon/calculator.py

Just-Jojo/mcoc-v3

41c69960c8aff2dbbfd5d11ecc17e7af73e1e305

[ "MIT" ]

19

2020-07-24T00:37:51.000Z

2021-06-18T17:22:14.000Z

cdtcommon/calculator.py

Just-Jojo/mcoc-v3

41c69960c8aff2dbbfd5d11ecc17e7af73e1e305

[ "MIT" ]

7

2020-06-30T20:09:08.000Z

2021-02-20T03:48:09.000Z

import math import re import discord from redbot.core import checks, commands from redbot.core.config import Config from .cdtcommon import CdtCommon from .cdtembed import Embed class Calculator(commands.Cog): """Calculator""" def __init__(self, bot): self.bot = bot self.thumbnail = "https://www.ebuyer.com/blog/wp-content/uploads/2014/07/buttons-on-a-calculator-header1.jpg" @commands.command(pass_context=True, name="calculator", aliases=("calc",)) async def _calc(self, ctx, *, m): """Math is fun! Type math, get fun.""" m = "".join(m) math_filter = re.findall( r"[\[\]\-()*+/0-9=.,% ]|>|<|==|>=|<=|\||&|~|!=|^|sum" + "|range|random|randint|choice|randrange|True|False|if|and|or|else" + "|is|not|for|in|acos|acosh|asin|asinh|atan|atan2|atanh|ceil" + "|copysign|cos|cosh|degrees|e|erf|erfc|exp|expm1|fabs|factorial" + "|floor|fmod|frexp|fsum|gamma|gcd|hypot|inf|isclose|isfinite" + "|isinf|isnan|ldexp|lgamma|log|log10|log1p|log2|modf|nan|pi" + "|pow|radians|sin|sinh|sqrt|tan|tanh|round", m, ) calculate_stuff = eval("".join(math_filter)) if len(str(calculate_stuff)) > 0: em = await Embed.create( ctx, title="CollectorDevTeam Calculator", thumbnail=self.thumbnail, description="**Input**\n`{}`\n\n**Result**\n`{}`".format(m, calculate_stuff), ) em.add_field(name="Type Math", value="Get Fun") await ctx.send(embed=em) @commands.command( aliases=[ "p2f", ], hidden=True, ) async def per2flat(self, ctx, per: float, ch_rating: int = 100): """Convert Percentage to MCOC Flat Value""" await ctx.send(CdtCommon.to_flat(per, ch_rating)) # , aliases=('f2p')) --> this was translating as "flat | f | 2 | p" @commands.command(pass_context=True, name="flat") async def flat2per(self, ctx, *, m): """Convert MCOC Flat Value to Percentge <equation> [challenger rating = 100]""" if " " in m: m, cr = m.rsplit(" ", 1) challenger_rating = int(cr) else: challenger_rating = 100 m = "".join(m) math_filter = re.findall( r"[\[\]\-()*+/0-9=.,% ]" + r"|acos|acosh|asin|asinh" + r"|atan|atan2|atanh|ceil|copysign|cos|cosh|degrees|e|erf|erfc|exp" + r"|expm1|fabs|factorial|floor|fmod|frexp|fsum|gamma|gcd|hypot|inf" + r"|isclose|isfinite|isinf|isnan|round|ldexp|lgamma|log|log10|log1p" + r"|log2|modf|nan|pi|pow|radians|sin|sinh|sqrt|tan|tanh", m, ) flat_val = eval("".join(math_filter)) p = CdtCommon.from_flat(flat_val, challenger_rating) em = await Embed.create( ctx, color=discord.Color.gold(), title="FlatValue:", thumbnail=self.thumbnail, description="{}".format(flat_val), ) em.add_field(name="Percentage:", value="{}\%".format(p)) await ctx.send(embed=em) @commands.command(aliases=["compf", "cfrac"], hidden=True) async def compound_frac(self, ctx, base: float, exp: int): # On second thought, I'm not gonna touch this # - Jojo """Calculate multiplicative compounded fractions""" if base > 1: base = base / 100 compound = 1 - (1 - base) ** exp em = await Embed.create( ctx, color=discord.Color.gold(), title="Compounded Fractions", thumbnail=self.thumbnail, description="{:.2%} compounded {} times".format(base, exp), ) em.add_field(name="Expected Chance", value="{:.2%}".format(compound)) await ctx.send(embed=em)

38.711538

118

0.544709

import math import re import discord from redbot.core import checks, commands from redbot.core.config import Config from .cdtcommon import CdtCommon from .cdtembed import Embed class Calculator(commands.Cog): def __init__(self, bot): self.bot = bot self.thumbnail = "https://www.ebuyer.com/blog/wp-content/uploads/2014/07/buttons-on-a-calculator-header1.jpg" @commands.command(pass_context=True, name="calculator", aliases=("calc",)) async def _calc(self, ctx, *, m): m = "".join(m) math_filter = re.findall( r"[\[\]\-()*+/0-9=.,% ]|>|<|==|>=|<=|\||&|~|!=|^|sum" + "|range|random|randint|choice|randrange|True|False|if|and|or|else" + "|is|not|for|in|acos|acosh|asin|asinh|atan|atan2|atanh|ceil" + "|copysign|cos|cosh|degrees|e|erf|erfc|exp|expm1|fabs|factorial" + "|floor|fmod|frexp|fsum|gamma|gcd|hypot|inf|isclose|isfinite" + "|isinf|isnan|ldexp|lgamma|log|log10|log1p|log2|modf|nan|pi" + "|pow|radians|sin|sinh|sqrt|tan|tanh|round", m, ) calculate_stuff = eval("".join(math_filter)) if len(str(calculate_stuff)) > 0: em = await Embed.create( ctx, title="CollectorDevTeam Calculator", thumbnail=self.thumbnail, description="**Input**\n`{}`\n\n**Result**\n`{}`".format(m, calculate_stuff), ) em.add_field(name="Type Math", value="Get Fun") await ctx.send(embed=em) @commands.command( aliases=[ "p2f", ], hidden=True, ) async def per2flat(self, ctx, per: float, ch_rating: int = 100): await ctx.send(CdtCommon.to_flat(per, ch_rating)) @commands.command(pass_context=True, name="flat") async def flat2per(self, ctx, *, m): if " " in m: m, cr = m.rsplit(" ", 1) challenger_rating = int(cr) else: challenger_rating = 100 m = "".join(m) math_filter = re.findall( r"[\[\]\-()*+/0-9=.,% ]" + r"|acos|acosh|asin|asinh" + r"|atan|atan2|atanh|ceil|copysign|cos|cosh|degrees|e|erf|erfc|exp" + r"|expm1|fabs|factorial|floor|fmod|frexp|fsum|gamma|gcd|hypot|inf" + r"|isclose|isfinite|isinf|isnan|round|ldexp|lgamma|log|log10|log1p" + r"|log2|modf|nan|pi|pow|radians|sin|sinh|sqrt|tan|tanh", m, ) flat_val = eval("".join(math_filter)) p = CdtCommon.from_flat(flat_val, challenger_rating) em = await Embed.create( ctx, color=discord.Color.gold(), title="FlatValue:", thumbnail=self.thumbnail, description="{}".format(flat_val), ) em.add_field(name="Percentage:", value="{}\%".format(p)) await ctx.send(embed=em) @commands.command(aliases=["compf", "cfrac"], hidden=True) async def compound_frac(self, ctx, base: float, exp: int): # - Jojo if base > 1: base = base / 100 compound = 1 - (1 - base) ** exp em = await Embed.create( ctx, color=discord.Color.gold(), title="Compounded Fractions", thumbnail=self.thumbnail, description="{:.2%} compounded {} times".format(base, exp), ) em.add_field(name="Expected Chance", value="{:.2%}".format(compound)) await ctx.send(embed=em)

true

1c43bd3bebe28921f0af5e1ac829a25b07e7fc62

10,912

py

Python

qiskit/pulse/pulse_lib/samplers/decorators.py

lerongil/qiskit-terra

a25af2a2378bc3d4f5ec73b948d048d1b707454c

[ "Apache-2.0" ]

3

2019-05-19T17:39:38.000Z

2020-01-28T19:59:18.000Z

qiskit/pulse/pulse_lib/samplers/decorators.py

lerongil/qiskit-terra

a25af2a2378bc3d4f5ec73b948d048d1b707454c

[ "Apache-2.0" ]

4

2019-05-13T15:28:46.000Z

2019-12-19T20:47:02.000Z

qiskit/pulse/pulse_lib/samplers/decorators.py

lerongil/qiskit-terra

a25af2a2378bc3d4f5ec73b948d048d1b707454c

[ "Apache-2.0" ]

1

2021-07-07T16:55:41.000Z

# -*- coding: utf-8 -*- # This code is part of Qiskit. # # (C) Copyright IBM 2017, 2019. # # This code is licensed under the Apache License, Version 2.0. You may # obtain a copy of this license in the LICENSE.txt file in the root directory # of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. # # Any modifications or derivative works of this code must retain this # copyright notice, and modified files need to carry a notice indicating # that they have been altered from the originals. # pylint: disable=missing-return-doc """Sampler decorator module for sampling of continuous pulses to discrete pulses to be exposed to user. Some atypical boilerplate has been added to solve the problem of decorators not preserving their wrapped function signatures. Below we explain the problem that samplers solve and how we implement this. A sampler is a function that takes an continuous pulse function with signature: ```python def f(times: np.ndarray, *args, **kwargs) -> np.ndarray: ... ``` and returns a new function: def f(duration: int, *args, **kwargs) -> SamplePulse: ... Samplers are used to build up pulse commands from continuous pulse functions. In Python the creation of a dynamic function that wraps another function will cause the underlying signature and documentation of the underlying function to be overwritten. In order to circumvent this issue the Python standard library provides the decorator `functools.wraps` which allows the programmer to expose the names and signature of the wrapped function as those of the dynamic function. Samplers are implemented by creating a function with signature @sampler def left(continuous_pulse: Callable, duration: int, *args, **kwargs) ... This will create a sampler function for `left`. Since it is a dynamic function it would not have the docstring of `left` available too `help`. This could be fixed by wrapping with `functools.wraps` in the `sampler`, but this would then cause the signature to be that of the sampler function which is called on the continuous pulse, below: `(continuous_pulse: Callable, duration: int, *args, **kwargs)`` This is not correct for the sampler as the output sampled functions accept only a function. For the standard sampler we get around this by not using `functools.wraps` and explicitly defining our samplers such as `left`, `right` and `midpoint` and calling `sampler` internally on the function that implements the sampling schemes such as `left_sample`, `right_sample` and `midpoint_sample` respectively. See `left` for an example of this. In this way our standard samplers will expose the proper help signature, but a user can still create their own sampler with @sampler def custom_sampler(time, *args, **kwargs): ... However, in this case it will be missing documentation of the underlying sampling methods. We believe that the definition of custom samplers will be rather infrequent. However, users will frequently apply sampler instances too continuous pulses. Therefore, a different approach was required for sampled continuous functions (the output of an continuous pulse function decorated by a sampler instance). A sampler instance is a decorator that may be used to wrap continuous pulse functions such as linear below: ```python @left def linear(times: np.ndarray, m: float, b: float) -> np.ndarray: ```Linear test function Args: times: Input times. m: Slope. b: Intercept Returns: np.ndarray ``` return m*times+b ``` Which after decoration may be called with a duration rather than an array of times ```python duration = 10 pulse_command = linear(10, 0.1, 0.1) ``` If one calls help on `linear` they will find ``` linear(duration:int, *args, **kwargs) -> numpy.ndarray Discretized continuous pulse function: `linear` using sampler: `_left`. The first argument (time) of the continuous pulse function has been replaced with a discretized `duration` of type (int). Args: duration (int) *args: Remaining arguments of continuous pulse function. See continuous pulse function documentation below. **kwargs: Remaining kwargs of continuous pulse function. See continuous pulse function documentation below. Sampled continuous function: function linear in module test.python.pulse.test_samplers linear(x:numpy.ndarray, m:float, b:float) -> numpy.ndarray Linear test function Args: x: Input times. m: Slope. b: Intercept Returns: np.ndarray ``` This is partly because `functools.wraps` has been used on the underlying function. This in itself is not sufficient as the signature of the sampled function has `duration`, whereas the signature of the continuous function is `time`. This is achieved by removing `__wrapped__` set by `functools.wraps` in order to preserve the correct signature and also applying `_update_annotations` and `_update_docstring` to the generated function which corrects the function annotations and adds an informative docstring respectively. The user therefore has access to the correct sampled function docstring in its entirety, while still seeing the signature for the continuous pulse function and all of its arguments. """ import functools from typing import Callable import textwrap import pydoc import numpy as np import qiskit.pulse.commands as commands from . import strategies def _update_annotations(discretized_pulse: Callable) -> Callable: """Update annotations of discretized continuous pulse function with duration. Args: discretized_pulse: Discretized decorated continuous pulse. """ undecorated_annotations = list(discretized_pulse.__annotations__.items()) decorated_annotations = undecorated_annotations[1:] decorated_annotations.insert(0, ('duration', int)) discretized_pulse.__annotations__ = dict(decorated_annotations) return discretized_pulse def _update_docstring(discretized_pulse: Callable, sampler_inst: Callable) -> Callable: """Update annotations of discretized continuous pulse function. Args: discretized_pulse: Discretized decorated continuous pulse. sampler_inst: Applied sampler. """ wrapped_docstring = pydoc.render_doc(discretized_pulse, '%s') header, body = wrapped_docstring.split('\n', 1) body = textwrap.indent(body, ' ') wrapped_docstring = header+body updated_ds = """ Discretized continuous pulse function: `{continuous_name}` using sampler: `{sampler_name}`. The first argument (time) of the continuous pulse function has been replaced with a discretized `duration` of type (int). Args: duration (int) *args: Remaining arguments of continuous pulse function. See continuous pulse function documentation below. **kwargs: Remaining kwargs of continuous pulse function. See continuous pulse function documentation below. Sampled continuous function: {continuous_doc} """.format(continuous_name=discretized_pulse.__name__, sampler_name=sampler_inst.__name__, continuous_doc=wrapped_docstring) discretized_pulse.__doc__ = updated_ds return discretized_pulse def sampler(sample_function: Callable) -> Callable: """Sampler decorator base method. Samplers are used for converting an continuous function to a discretized pulse. They operate on a function with the signature: `def f(times: np.ndarray, *args, **kwargs) -> np.ndarray` Where `times` is a numpy array of floats with length n_times and the output array is a complex numpy array with length n_times. The output of the decorator is an instance of `FunctionalPulse` with signature: `def g(duration: int, *args, **kwargs) -> SamplePulse` Note if your continuous pulse function outputs a `complex` scalar rather than a `np.ndarray`, you should first vectorize it before applying a sampler. This class implements the sampler boilerplate for the sampler. Args: sample_function: A sampler function to be decorated. """ def generate_sampler(continuous_pulse: Callable) -> Callable: """Return a decorated sampler function.""" @functools.wraps(continuous_pulse) def call_sampler(duration: int, *args, **kwargs) -> commands.SamplePulse: """Replace the call to the continuous function with a call to the sampler applied to the analytic pulse function.""" sampled_pulse = sample_function(continuous_pulse, duration, *args, **kwargs) return np.asarray(sampled_pulse, dtype=np.complex_) # Update type annotations for wrapped continuous function to be discrete call_sampler = _update_annotations(call_sampler) # Update docstring with that of the sampler and include sampled function documentation. call_sampler = _update_docstring(call_sampler, sample_function) # Unset wrapped to return base sampler signature # but still get rest of benefits of wraps # such as __name__, __qualname__ call_sampler.__dict__.pop('__wrapped__') # wrap with functional pulse return commands.functional_pulse(call_sampler) return generate_sampler def left(continuous_pulse: Callable) -> Callable: r"""Left sampling strategy decorator. See `pulse.samplers.sampler` for more information. For `duration`, return: $$\{f(t) \in \mathbb{C} | t \in \mathbb{Z} \wedge 0<=t<\texttt{duration}\}$$ Args: continuous_pulse: To sample. """ return sampler(strategies.left_sample)(continuous_pulse) def right(continuous_pulse: Callable) -> Callable: r"""Right sampling strategy decorator. See `pulse.samplers.sampler` for more information. For `duration`, return: $$\{f(t) \in \mathbb{C} | t \in \mathbb{Z} \wedge 0<t<=\texttt{duration}\}$$ Args: continuous_pulse: To sample. """ return sampler(strategies.right_sample)(continuous_pulse) def midpoint(continuous_pulse: Callable) -> Callable: r"""Midpoint sampling strategy decorator. See `pulse.samplers.sampler` for more information. For `duration`, return: $$\{f(t+0.5) \in \mathbb{C} | t \in \mathbb{Z} \wedge 0<=t<\texttt{duration}\}$$ Args: continuous_pulse: To sample. """ return sampler(strategies.midpoint_sample)(continuous_pulse)

38.971429

100

0.701155

import functools from typing import Callable import textwrap import pydoc import numpy as np import qiskit.pulse.commands as commands from . import strategies def _update_annotations(discretized_pulse: Callable) -> Callable: undecorated_annotations = list(discretized_pulse.__annotations__.items()) decorated_annotations = undecorated_annotations[1:] decorated_annotations.insert(0, ('duration', int)) discretized_pulse.__annotations__ = dict(decorated_annotations) return discretized_pulse def _update_docstring(discretized_pulse: Callable, sampler_inst: Callable) -> Callable: wrapped_docstring = pydoc.render_doc(discretized_pulse, '%s') header, body = wrapped_docstring.split('\n', 1) body = textwrap.indent(body, ' ') wrapped_docstring = header+body updated_ds = """ Discretized continuous pulse function: `{continuous_name}` using sampler: `{sampler_name}`. The first argument (time) of the continuous pulse function has been replaced with a discretized `duration` of type (int). Args: duration (int) *args: Remaining arguments of continuous pulse function. See continuous pulse function documentation below. **kwargs: Remaining kwargs of continuous pulse function. See continuous pulse function documentation below. Sampled continuous function: {continuous_doc} """.format(continuous_name=discretized_pulse.__name__, sampler_name=sampler_inst.__name__, continuous_doc=wrapped_docstring) discretized_pulse.__doc__ = updated_ds return discretized_pulse def sampler(sample_function: Callable) -> Callable: def generate_sampler(continuous_pulse: Callable) -> Callable: @functools.wraps(continuous_pulse) def call_sampler(duration: int, *args, **kwargs) -> commands.SamplePulse: sampled_pulse = sample_function(continuous_pulse, duration, *args, **kwargs) return np.asarray(sampled_pulse, dtype=np.complex_) call_sampler = _update_annotations(call_sampler) call_sampler = _update_docstring(call_sampler, sample_function) call_sampler.__dict__.pop('__wrapped__') return commands.functional_pulse(call_sampler) return generate_sampler def left(continuous_pulse: Callable) -> Callable: return sampler(strategies.left_sample)(continuous_pulse) def right(continuous_pulse: Callable) -> Callable: return sampler(strategies.right_sample)(continuous_pulse) def midpoint(continuous_pulse: Callable) -> Callable: return sampler(strategies.midpoint_sample)(continuous_pulse)

true

1c43be1b50051126e52c822f3f6ff0e79cfbfacb

3,517

py

Python

simplex_method.py

dvapan/simplex_method

dcc930b092dffa2e55162ea035f43d85572c8568

[ "MIT" ]

null

simplex_method.py

dvapan/simplex_method

dcc930b092dffa2e55162ea035f43d85572c8568

[ "MIT" ]

null

simplex_method.py

dvapan/simplex_method

dcc930b092dffa2e55162ea035f43d85572c8568

[ "MIT" ]

null

# coding=utf-8 __author__ = 'dvapan' import scipy as sc import scipy.linalg as lin import pprint # # c = sc.matrix([2.0, 3.0]).transpose() # A = sc.matrix([[-10.0, 5.0], [6.0, 20.0], [8.0, 15.0]]) # b = sc.matrix([600.0, 600.0, 600.0]).transpose() # I = [2, 3, 4] def transform_to_classic(A,b,c): count_vars = A.shape[1] addition_vars = A.shape[0] count_all_vars = count_vars + addition_vars _A = sc.resize(A, (A.shape[0], count_all_vars)) _A[:, :count_vars] = A _A[:, count_vars:] = sc.eye(addition_vars) _c = sc.resize(c, (count_all_vars, 1)) _c[count_vars:, :] = sc.zeros((addition_vars, 1)) I = range(count_vars, count_vars+addition_vars) return _A, b, _c, I # A = sc.matrix([[1, 1, -1, 1], # [1, 14, 10, -10]]) # b = sc.matrix([2, 24]).transpose() # c = sc.matrix([1, 2, 3, -4]).transpose() def get_point_from_basis(A, b, I): B_sigma = A[:, I] x_sigma = lin.solve(B_sigma, b) x = sc.zeros(A.shape[1]) #print x_sigma x[I] = x_sigma return x def simplex_method(A, b, c, I, eps): count_all_vars = A.shape[1] q = 50 while q > 0: B_sigma = A[:, I] c_sigma = c[I, :] x_sigma = lin.solve(B_sigma, b) y = lin.solve(B_sigma.transpose(), c_sigma) D = sc.matrix(A).transpose()*y - c non_base_I = [e for e in range(count_all_vars) if e not in I] q-=1 finish = reduce(lambda x, y: x and y, map(lambda x: x > -eps, D[non_base_I]), True) # print I # print D.transpose().tolist()[0], get_point_from_basis(A, b, I) if finish: x = get_point_from_basis(A, b, I) return x, I, (sc.matrix(x)*sc.matrix(c))[0, 0] k = min([i for i in non_base_I if D[i] < 0]) lmd_k = lin.solve(B_sigma, A[:, k]) finish = reduce(lambda x, y: x and y, map(lambda x: x < 0, lmd_k),True) if finish: return None, None, sc.nan tmp = sc.array(x_sigma.transpose())[0].tolist() min_i = 0 while lmd_k[min_i] <= 0: min_i += 1 for i in xrange(len(lmd_k)): if lmd_k[i] > 0 and tmp[i]/lmd_k[i] < tmp[min_i]/lmd_k[min_i]: min_i = i s = min_i I[s] = k return None,None,None def artificial_basis_method(A, b, c, eps): count_vars = A.shape[1] addition_vars = A.shape[0] count_all_vars = count_vars + addition_vars _A = sc.resize(A, (A.shape[0], count_all_vars)) _A[:, :count_vars] = A _A[:, count_vars:] = sc.eye(addition_vars) _c = sc.resize(c, (count_all_vars, 1)) _c[:count_vars, :] = sc.zeros((count_vars, 1)) _c[count_vars:, :] = sc.full((addition_vars, 1), -1) # if I is None: I = range(count_vars, count_vars+addition_vars) # pprint.pprint((_A, b, _c ,I)) Res = simplex_method(_A, b, _c, I, eps) if Res[2] < -eps: return None, None, None Real_I = [i for i in range(count_vars) if i not in Res[1]] for i in range(len(Res[1])): if Res[1][i] >= count_vars: Res[1][i] = Real_I.pop(0) return Res def double_phase_simplex_method(A, b, c, eps): Res = artificial_basis_method(A, b, c, eps) # while Res[1] is not None and len(filter(lambda x: x >= A.shape[1], Res[1])) > 0: # print "NEED NEXT ITER OF FIRST PHASE" # Res = artificial_basis_method(A, b, c, eps, Res[1]) if Res[1] is not None: return simplex_method(A, b, c, Res[1], eps) else: return None, None, None

29.308333

91

0.562411

__author__ = 'dvapan' import scipy as sc import scipy.linalg as lin import pprint def transform_to_classic(A,b,c): count_vars = A.shape[1] addition_vars = A.shape[0] count_all_vars = count_vars + addition_vars _A = sc.resize(A, (A.shape[0], count_all_vars)) _A[:, :count_vars] = A _A[:, count_vars:] = sc.eye(addition_vars) _c = sc.resize(c, (count_all_vars, 1)) _c[count_vars:, :] = sc.zeros((addition_vars, 1)) I = range(count_vars, count_vars+addition_vars) return _A, b, _c, I def get_point_from_basis(A, b, I): B_sigma = A[:, I] x_sigma = lin.solve(B_sigma, b) x = sc.zeros(A.shape[1]) x[I] = x_sigma return x def simplex_method(A, b, c, I, eps): count_all_vars = A.shape[1] q = 50 while q > 0: B_sigma = A[:, I] c_sigma = c[I, :] x_sigma = lin.solve(B_sigma, b) y = lin.solve(B_sigma.transpose(), c_sigma) D = sc.matrix(A).transpose()*y - c non_base_I = [e for e in range(count_all_vars) if e not in I] q-=1 finish = reduce(lambda x, y: x and y, map(lambda x: x > -eps, D[non_base_I]), True) if finish: x = get_point_from_basis(A, b, I) return x, I, (sc.matrix(x)*sc.matrix(c))[0, 0] k = min([i for i in non_base_I if D[i] < 0]) lmd_k = lin.solve(B_sigma, A[:, k]) finish = reduce(lambda x, y: x and y, map(lambda x: x < 0, lmd_k),True) if finish: return None, None, sc.nan tmp = sc.array(x_sigma.transpose())[0].tolist() min_i = 0 while lmd_k[min_i] <= 0: min_i += 1 for i in xrange(len(lmd_k)): if lmd_k[i] > 0 and tmp[i]/lmd_k[i] < tmp[min_i]/lmd_k[min_i]: min_i = i s = min_i I[s] = k return None,None,None def artificial_basis_method(A, b, c, eps): count_vars = A.shape[1] addition_vars = A.shape[0] count_all_vars = count_vars + addition_vars _A = sc.resize(A, (A.shape[0], count_all_vars)) _A[:, :count_vars] = A _A[:, count_vars:] = sc.eye(addition_vars) _c = sc.resize(c, (count_all_vars, 1)) _c[:count_vars, :] = sc.zeros((count_vars, 1)) _c[count_vars:, :] = sc.full((addition_vars, 1), -1) I = range(count_vars, count_vars+addition_vars) Res = simplex_method(_A, b, _c, I, eps) if Res[2] < -eps: return None, None, None Real_I = [i for i in range(count_vars) if i not in Res[1]] for i in range(len(Res[1])): if Res[1][i] >= count_vars: Res[1][i] = Real_I.pop(0) return Res def double_phase_simplex_method(A, b, c, eps): Res = artificial_basis_method(A, b, c, eps) if Res[1] is not None: return simplex_method(A, b, c, Res[1], eps) else: return None, None, None

true

1c43beac38a9be1ebc96e5f4db2e17a1f69ebb88

24,223

py

Python

python/process_content.py

tdjames1/TMA-data-extraction

03af0ef3b61df5486f6f061e4e3b62de2e238476

[ "BSD-3-Clause" ]

null

python/process_content.py

tdjames1/TMA-data-extraction

03af0ef3b61df5486f6f061e4e3b62de2e238476

[ "BSD-3-Clause" ]

5

2021-01-05T12:14:53.000Z

2021-08-23T09:18:11.000Z

python/process_content.py

tdjames1/TMA-data-extraction

03af0ef3b61df5486f6f061e4e3b62de2e238476

[ "BSD-3-Clause" ]

1

2021-02-18T14:59:42.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- """tma_process_content .. module:: TMA-data-extraction :synopis: Scripts and functions for extracting weather alert data from Tanzanian Meteorological Authority "Five days Severe weather impact-based forecasts" PDFs. .. moduleauthor: Tamora D. James <t.d.james1@leeds.ac.uk>, CEMAC (UoL) .. description: This module was developed by CEMAC as part of the GCRF African Swift Project. This script processes page contents and metadata extracted from Tanzanian Meteorological Authority "Five days Severe weather impact-based forecasts" PDFs and produces a netCDF4 file containing gridded weather alert data. :copyright: © 2020 University of Leeds. :license: BSD 3-clause (see LICENSE) Example: To use:: ./tma_process_content <path/to/page2_content.txt> <path/to/metadata.csv> <path/to/page2_content.txt> - Path to content extracted from page 2 of TMA weather forecast PDF <path/to/metadata.csv> - Path to CSV containing text metadata extracted from page 2 of TMA weather forecast PDF .. CEMAC_cemac_generic: https://github.com/cemac/cemac_generic """ import sys import argparse import os import numpy as np import numpy.linalg as LA import bezier import matplotlib.pyplot as plt import matplotlib.patches as mpatches from matplotlib.path import Path import cartopy.crs as ccrs import cartopy.feature as cfeature import cartopy.io.shapereader as shpreader import skimage.draw import xarray as xr import pandas as pd PDF_GS_OPS = { 'g': 'setgray (nonstroke)', 'G': 'setgray (stroke)', 'gs': 'setgraphicsstate', 'j': 'setlinejoin', 'M': 'setmiterlimit', 'rg': 'setrgbcolor (nonstroke)', 'RG': 'setrgbcolor (stroke)', 'q': 'gsave', 'Q': 'grestore', 'w': 'setlinewidth', 'W': 'clip', 'W*': 'eoclip', } MAP_IMG = "../resources/TZA_map.png" # Extent of original map image when matched to PlateCarree projection extent_MAP_IMG = [28.405, 41.475, -12., -0.745] def readFile(fp): with open(fp) as f: lines = [line.rstrip() for line in f] return lines def extractGraphics(lines): path_ops = {'m', 'c', 'l'} term_ops = {'f*', 'S', 'n'} col_ops = {'rg', 'RG', 'g', 'G'} block = [] graphics = [] images = [] col = None # Iterate over the lines for line in lines: if line.endswith(tuple(path_ops)): #print("got path operator") block.append(line) elif line.endswith(tuple(term_ops)): #print("got terminating path operator") block.append(line) path = processBlock(list(block)) if len(path['contour']): graphics.append({'path': path, 'colour': col}) del block[:] elif line.endswith(tuple(col_ops)): block.append(line) col = processColour(line) elif "Do" in line: #print("got image operator") block.append(line) image = processImage(list(block)) if len(image): images.append(image) del block[:] else: block.append(line) # print(len(graphics)) # print(len(graphics[0]['path'])) # print(len(images)) return [images, graphics] def appendCurve(start, controls): nodes = np.concatenate((start, controls)) nodes = nodes.reshape(len(nodes)//2,2).transpose() #print(nodes) curve = bezier.Curve.from_nodes(nodes) return curve def getCentroid(vertices): #print(vertices) if len(vertices): v = np.array(vertices) return np.mean(v, axis = 0) def processBlock(lines): #print(list(line.rstrip() for line in lines)) path_is_open = False start_xy = [] current = [] next_xy = [] controls = [] vertices = [] line_collection = [] draw_filled_area = True for line in lines: s = line.split() if not len(s): continue #print(s[-1]) op = s[-1] if op == "m": path_is_open = True if (len(s) > 3): s = s[len(s)-3:] start_xy = current = np.array(s[:-1], dtype = float) vertices.append(current) print("[PATH] start point:", start_xy) elif op == "c": if path_is_open: #print("append bezier curve") controls = np.array(s[:-1], dtype = float) print("[PATH] bezier curve, control points:", controls) curve = appendCurve(current, controls) line_collection.append(curve) current = controls[-2:] vertices.append(current) else: print("[PATH] current path is not open to append bezier curve") elif op == "l": if path_is_open: print("[PATH] append line segment") next_xy = np.array(s[:-1], dtype = float) curve = appendCurve(current, next_xy) line_collection.append(curve) current = next_xy vertices.append(current) else: print("[PATH] current path is not open to append line segment") elif op == "f*": print("[PATH] fill region") path_is_open = False if not draw_filled_area: del line_collection[:] break elif op == "S": print("[PATH] stroke region") path_is_open = False break elif op == "n": print("[PATH] end path without fill or stroke") path_is_open = False del line_collection[:] break elif op == "h": print("[PATH] close subpath") if path_is_open: if (current - start_xy).any(): print("[PATH] append line segment to close subpath") line = appendCurve(current, start_xy) line_collection.append(line) current = start_xy vertices.append(current) path_is_open = False else: print("[PATH] current path is not open to close path") else: if op in PDF_GS_OPS.keys(): print("[PATH] got operator: " + op + " = " + PDF_GS_OPS[op]) else: print("[PATH] unknown operator: " + op) centroid = getCentroid(vertices) return {'contour': line_collection, 'centroid': centroid} def processColour(line): col = None s = line.split() if not len(s): return op = s[-1] if op.lower() == "rg": print("[COLOUR] got set RGB colour operator", op) print(s) if (len(s) > 4): s = s[len(s)-4:] otype = 'stroke' if op == "RG" else 'fill' col = {'type': otype, 'col_spec': 'rgb', 'val': np.array(s[:-1], dtype = float) } elif op.lower() == "g": print("[PATH] got set gray operator", op) print(s) if (len(s) > 2): s = s[len(s)-2:] otype = 'stroke' if op == "G" else 'fill' col = { 'type': otype, 'col_spec': 'gs', 'val': np.array(s[:-1], dtype = float) } return col def processImage(lines): #print(list(line.rstrip() for line in lines)) img_collection = [] rect = [] ctm = [] name = "" for line in lines: s = line.split() if not len(s): continue #print(s[-1]) op = s[-1] if op == "q": print("[IMG] start image") elif op == "re": rect = np.array(s[:-1], dtype = float) elif op == "cm": try: ctm = np.array(s[-7:-1], dtype = float) print("[IMG] ctm:", ctm) except ValueError as e: print("Error setting CTM from", s, ": ", e) elif op == "Q": if s[-2] == "Do": name = s[-3] img_collection.append({'name': name, 'clip': rect, 'ctm': ctm}) elif op == "n": print("[IMG] end path") else: if op in PDF_GS_OPS.keys(): print("[IMG] got operator: " + op + " = " + PDF_GS_OPS[op]) else: print("[IMG] unknown operator: " + op) return img_collection def createPlot(images, contours): fig, ax = plt.subplots() # Create a figure containing a single axis n_col = len(plt.rcParams['axes.prop_cycle']) for i in range(len(contours)): for curve in contours[i]['contour']: _ = curve.plot(num_pts = 256, color = "C" + str(i%n_col), ax = ax) # plot centroid i cx, cy = contours[i]['centroid'] plt.plot(cx, cy, "o") for i in range(len(images)): for img in images[i]: print("Image:", img['name']) ## x y w h re # xy = img[1][:2] # wh = img[1][2:] # w, h = img[1][2:] # print(xy) # print(wh) ## w 0 0 h x y cm ctm = img['ctm'].reshape(2,3) scale = [img['ctm'][0], img['ctm'][3]] position = img['ctm'][4:] w, h = scale xy = position print("Position:", xy) print("Size:", w, "x", h) pos_check = xy[1] + h < 450 size_check = w > 120 if pos_check & size_check: rect = mpatches.Rectangle(tuple(xy), w, h, fc="none", ec="green") ax.add_patch(rect) arr_img = plt.imread(MAP_IMG, format='png') ax.imshow(arr_img, interpolation='none', origin='lower', extent=[xy[0], xy[0]+w, xy[1]+h, xy[1]], clip_on=True) _ = ax.set_xlim(0, 842) _ = ax.set_ylim(0, 595) #_ = ax.set_xlim(150, 650) #_ = ax.set_ylim(200, 400) _ = ax.set_aspect(1) plt.show() def getMapGroups(images, graphics): map_groups = [] for i in range(len(images)): for img in images[i]: #print("Image:", img['name']) # w 0 0 h x y cm ctm = img['ctm'].reshape(2,3) w, h = [img['ctm'][0], img['ctm'][3]] x, y = img['ctm'][4:] #print("Position: ", x, ",", y) #print("Size: ", w, "x", h) # Identify map images by location/size pos_check = y + h < 450 size_check = w > 120 if pos_check & size_check: # Get graphics within map boundary graphics_dict = {} for gfx in graphics: ix, iy = gfx['path']['centroid'] #print("Centroid: ", ix, ",", iy) x_check = (x < ix) & (ix < x + w) y_check = (y < iy) & (iy < y + h) if x_check & y_check: print("Centroid: ", ix, ",", iy) if (ix, iy) not in graphics_dict.keys(): graphics_dict[(ix, iy)] = [ gfx ] else: # Check whether colour and contour are the # same as previously stored graphics found_match = False nodes = np.hstack([np.hstack(c.nodes) for c in gfx['path']['contour']]) for g in graphics_dict[(ix, iy)]: n = np.hstack([np.hstack(c.nodes) for c in g['path']['contour']]) if (nodes == n).all(): # nodes match, what about colours? col = gfx['colour'] c = g['colour'] if col['col_spec'] == c['col_spec'] and np.array_equal(getColourValue(col), getColourValue(c)): found_match = True break if not found_match: graphics_dict[(ix, iy)].append(gfx) print("Graphics with distinct centroids:", len(graphics_dict)) map_groups.append((img, graphics_dict)) def getXPos(mg): return mg[0]['ctm'][4] map_groups.sort(key = getXPos) return map_groups def getColourValue(col): if col is not None: return tuple(col['val']) def transformMapGroup(map_group): img, graphics_dict = map_group print("Image:", img['name']) # Construct current transformation matrix for image # a b 0 # c d 0 # e f 1 m1 = np.hstack((img['ctm'].reshape(3,2), np.array([[0],[0],[1]]))) try: m1_inv = LA.inv(m1) except LinAlgError: sys.exit("Could not invert transformation matrix") # Create transformation matrix to map from canonical image coords # to extent of original map image matched to PlateCarree projection lon_min, lon_max, lat_min, lat_max = extent_MAP_IMG tm = np.array([lon_max - lon_min, 0, 0, lat_max - lat_min, lon_min, lat_min]) m2 = np.hstack((tm.reshape(3,2), np.array([[0],[0],[1]]))) # Pre-multiply transformation matrices m = np.matmul(m1_inv, m2) graphics_list = [] print("Processing graphics:", len(graphics_dict)) for z, graphics in graphics_dict.items(): print("Got", len(graphics), "graphics objects with centroid:", z) stroke_col = None fill_col = None #breakpoint() for i in range(len(graphics)): col = graphics[i]['colour'] if col is not None: print("got colour state:", col) # Get stroke colour specification if col['type'] == "stroke": stroke_col = getColourValue(col) # Get fill colour specification if col['type'] == "fill": fill_col = getColourValue(col) contour = [] for curve in graphics[i]['path']['contour']: ## Relocate curve according to new coordinate system nodes = curve.nodes nodes_new = [] for j in range(len(nodes.T)): # Multiply node by combined transformation matrix m to # get coordinates with respect to image space and # transform from canonical image coords to PlateCarree # map projection v = np.matmul(np.append(nodes.T[j], 1), m) nodes_new.append(v[:-1]) nodes = np.array(nodes_new).T curve_new = bezier.Curve.from_nodes(nodes) contour.append(curve_new) # Relocate centroid i centroid = np.matmul(np.append(graphics[i]['path']['centroid'], 1), m)[:-1] path = { 'colour': { 'stroke': stroke_col, 'fill': fill_col }, 'contour': contour, 'centroid': centroid } graphics_list.append({ 'path': path}) return (img, graphics_list) ## end of transformMapGroup() def plotMapGroup(map_group, ax): _, graphics = map_group n_col = len(plt.rcParams['axes.prop_cycle']) print("Processing graphics:", len(graphics)) for i in range(len(graphics)): col = "C" + str(i%n_col) if graphics[i]['path']['colour'] is not None: col = graphics[i]['path']['colour'] for curve in graphics[i]['path']['contour']: _ = curve.plot(num_pts = 256, color = col, ax = ax) # plot centroid i cx, cy = graphics[i]['path']['centroid'] ax.plot(cx, cy, "o") ## end of plotMapGroup() def getAlertMasks(map_group): _, graphics = map_group # mask will have shape defined by the image map extent divided # into 0.1 degree grid res = 0.1 # lon_min, lon_max, lat_min, lat_max = [round(x, 1) for x in extent_MAP_IMG] # nx, ny = np.array([lon_max - lon_min, lat_max - lat_min])/res # img_shape = (round(nx), round(ny), 3) lon_min, lon_max, lat_min, lat_max = extent_MAP_IMG x = np.arange(lon_min, lon_max, res) # [round(x,1) for x in x] y = np.arange(lat_min, lat_max, res) # [round(y,1) for y in y] xx, yy = np.meshgrid(x, y) xy = np.vstack((xx.ravel(), yy.ravel())).T # Create transformation matrix tm = np.array([res, 0, 0, res, lon_min, lat_min]) m = np.hstack((tm.reshape(3,2), np.array([[0],[0],[1]]))) try: m_inv = LA.inv(m) except LinAlgError: sys.exit("Could not invert transformation matrix") mask_list = [] for i in range(len(graphics)): col = graphics[i]['path']['colour'] print(col) if col['stroke'] is not None and col['stroke'].count(col['stroke'][0]) != 3: # got a contour with RGB colour alert_val = 0 r, g, b = col if col == (0.0, 0.0, 0.0): print("colour: black") elif col == (1.0, 1.0, 0.0): print("colour: yellow") alert_val = 1 elif g > 0.33 and g < 0.66: # (0.89, 0.424, 0.0392) # (0.969, 0.588, 0.275) print("colour: orange") alert_val = 2 elif g < 0.33: print("colour: red") alert_val = 3 else: print("colour: other") #img = np.zeros(img_shape, dtype = np.double) img2 = np.array([alert_val]*xx.size).reshape(xx.shape) img = np.zeros(xx.shape, dtype = np.double) # nodes_new = [] # for curve in graphics[i]['path']['contour']: # # transform curve to grid coords # nodes = curve.nodes # for i in range(len(nodes.T)): # # Multiply node by transformation matrix m to # # get grid coordinates # v = np.matmul(np.append(nodes.T[i], 1), m_inv) # nodes_new.append(v[:-1]) # nodes = np.array([node.round() for node in nodes_new]) # mask = skimage.draw.polygon2mask(img_shape[:-1], nodes) # img[mask] = col #mask_list.append(img) ## alternative approach vv = np.vstack([curve.nodes.T for curve in graphics[i]['path']['contour']]) # construct a Path from the vertices pth = Path(vv, closed=False) # test which pixels fall within the path mask = pth.contains_points(xy) # reshape to the same size as the grid mask = mask.reshape(xx.shape) # create a masked array masked = np.ma.masked_array(img2, ~mask) # or simply set values for masked pixels img[mask] = alert_val # combine with coords... am = np.stack((xx, yy, img)) mask_list.append(am) return mask_list ## end def createGriddedData(map_groups, alert_data, file_path=None): # container for gridded data layers vars = {} # data will have shape defined by the image map extent divided # into 0.1 degree grid res = 0.1 lon_min, lon_max, lat_min, lat_max = extent_MAP_IMG x = np.arange(lon_min, lon_max, res) # [round(x,1) for x in x] y = np.arange(lat_min, lat_max, res) # [round(y,1) for y in y] xx, yy = np.meshgrid(x, y) xy = np.vstack((xx.ravel(), yy.ravel())).T # Create transformation matrix tm = np.array([res, 0, 0, res, lon_min, lat_min]) m = np.hstack((tm.reshape(3,2), np.array([[0],[0],[1]]))) try: m_inv = LA.inv(m) except LinAlgError: sys.exit("Could not invert transformation matrix") for i, mg in enumerate(map_groups): _, graphics = mg print(i) # count arrays added for this group n = 0 for j, gfx in enumerate(graphics): colour = gfx['path']['colour'] print(colour) col = None if colour['stroke'] is not None and len(colour['stroke']) == 3: col = colour['stroke'] elif colour['fill'] is not None and len(colour['fill']) == 3: col = colour['fill'] if col is not None: # got a contour with associated RGB colour print(col) alert_val = 0 r, g, b = col if col == (0.0, 0.0, 0.0): print("colour: black") elif col == (1.0, 1.0, 0.0): print("colour: yellow") alert_val = 1 elif col == (1.0, 0.0, 0.0): print("colour: red") alert_val = 3 elif g > 0.25 and g < 0.66: # (0.89, 0.424, 0.0392) # (0.969, 0.588, 0.275) # (0.596, 0.282, 0.0275) print("colour: orange") alert_val = 2 elif r > 0.9 and g < 0.25: print("colour: red") alert_val = 3 else: print("colour: other") img = np.zeros(xx.shape, dtype = np.double) # get nodes for the alert area vv = np.vstack([curve.nodes.T for curve in gfx['path']['contour']]) # construct a Path from the vertices pth = Path(vv, closed=False) # test which pixels fall within the path mask = pth.contains_points(xy) # reshape to the same size as the grid mask = mask.reshape(xx.shape) # set values for masked pixels img[mask] = alert_val da = xr.DataArray(data=img, dims=["lat", "lon"], coords=[y, x]) da.attrs = { 'issue_date': alert_data.loc[i,'issue_date'], 'alert_date': alert_data.loc[i,'date'], 'alert_day': alert_data.loc[i,'day'], 'alert_weekday': alert_data.loc[i,'weekday'], 'alert_id': n+1, 'alert_type': '', 'alert_text': alert_data.loc[i,'alert_text'], } var_name = '_'.join(['alert', 'day'+str(da.attrs['alert_day']), str(da.attrs['alert_id'])]) vars[var_name] = da n += 1 # combine data arrays into data set issue_date = alert_data.loc[0, 'issue_date'] ds = xr.Dataset(data_vars=vars, attrs={ 'title': 'TMA weather warnings for ' + issue_date, 'issue_date': issue_date, }) if file_path is None: file_path = 'TMA_weather_warning_'+issue_date+'.nc' ds.to_netcdf(file_path) ## end # Main def main(): parser = argparse.ArgumentParser(description='Process TMA PDF contents') parser.add_argument('filepath', nargs=1, type=str) parser.add_argument('metadata', nargs=1, type=str) args = parser.parse_args() try: # read lines from input file lines = readFile(args.filepath[0]) except: # IOError print("Input file not found:", args.filepath[0]) sys.exit(4) images, graphics = extractGraphics(lines) mgroups = getMapGroups(images, graphics) mgroups = [transformMapGroup(mg) for mg in mgroups] try: # Get associated data - one row per forecast date alert_data = pd.read_csv(args.metadata[0]) except FileNotFoundError: print("Couldn't read metadata file:", args.metadata[0]) else: file_name = os.path.basename(args.metadata[0]).split(".")[0] + ".nc" createGriddedData(mgroups, alert_data, file_name) ## end of main() # Run main if __name__ == "__main__": main()

35.054993

131

0.509103

import sys import argparse import os import numpy as np import numpy.linalg as LA import bezier import matplotlib.pyplot as plt import matplotlib.patches as mpatches from matplotlib.path import Path import cartopy.crs as ccrs import cartopy.feature as cfeature import cartopy.io.shapereader as shpreader import skimage.draw import xarray as xr import pandas as pd PDF_GS_OPS = { 'g': 'setgray (nonstroke)', 'G': 'setgray (stroke)', 'gs': 'setgraphicsstate', 'j': 'setlinejoin', 'M': 'setmiterlimit', 'rg': 'setrgbcolor (nonstroke)', 'RG': 'setrgbcolor (stroke)', 'q': 'gsave', 'Q': 'grestore', 'w': 'setlinewidth', 'W': 'clip', 'W*': 'eoclip', } MAP_IMG = "../resources/TZA_map.png" extent_MAP_IMG = [28.405, 41.475, -12., -0.745] def readFile(fp): with open(fp) as f: lines = [line.rstrip() for line in f] return lines def extractGraphics(lines): path_ops = {'m', 'c', 'l'} term_ops = {'f*', 'S', 'n'} col_ops = {'rg', 'RG', 'g', 'G'} block = [] graphics = [] images = [] col = None for line in lines: if line.endswith(tuple(path_ops)): block.append(line) elif line.endswith(tuple(term_ops)): block.append(line) path = processBlock(list(block)) if len(path['contour']): graphics.append({'path': path, 'colour': col}) del block[:] elif line.endswith(tuple(col_ops)): block.append(line) col = processColour(line) elif "Do" in line: block.append(line) image = processImage(list(block)) if len(image): images.append(image) del block[:] else: block.append(line) return [images, graphics] def appendCurve(start, controls): nodes = np.concatenate((start, controls)) nodes = nodes.reshape(len(nodes)//2,2).transpose() curve = bezier.Curve.from_nodes(nodes) return curve def getCentroid(vertices): if len(vertices): v = np.array(vertices) return np.mean(v, axis = 0) def processBlock(lines): path_is_open = False start_xy = [] current = [] next_xy = [] controls = [] vertices = [] line_collection = [] draw_filled_area = True for line in lines: s = line.split() if not len(s): continue op = s[-1] if op == "m": path_is_open = True if (len(s) > 3): s = s[len(s)-3:] start_xy = current = np.array(s[:-1], dtype = float) vertices.append(current) print("[PATH] start point:", start_xy) elif op == "c": if path_is_open: controls = np.array(s[:-1], dtype = float) print("[PATH] bezier curve, control points:", controls) curve = appendCurve(current, controls) line_collection.append(curve) current = controls[-2:] vertices.append(current) else: print("[PATH] current path is not open to append bezier curve") elif op == "l": if path_is_open: print("[PATH] append line segment") next_xy = np.array(s[:-1], dtype = float) curve = appendCurve(current, next_xy) line_collection.append(curve) current = next_xy vertices.append(current) else: print("[PATH] current path is not open to append line segment") elif op == "f*": print("[PATH] fill region") path_is_open = False if not draw_filled_area: del line_collection[:] break elif op == "S": print("[PATH] stroke region") path_is_open = False break elif op == "n": print("[PATH] end path without fill or stroke") path_is_open = False del line_collection[:] break elif op == "h": print("[PATH] close subpath") if path_is_open: if (current - start_xy).any(): print("[PATH] append line segment to close subpath") line = appendCurve(current, start_xy) line_collection.append(line) current = start_xy vertices.append(current) path_is_open = False else: print("[PATH] current path is not open to close path") else: if op in PDF_GS_OPS.keys(): print("[PATH] got operator: " + op + " = " + PDF_GS_OPS[op]) else: print("[PATH] unknown operator: " + op) centroid = getCentroid(vertices) return {'contour': line_collection, 'centroid': centroid} def processColour(line): col = None s = line.split() if not len(s): return op = s[-1] if op.lower() == "rg": print("[COLOUR] got set RGB colour operator", op) print(s) if (len(s) > 4): s = s[len(s)-4:] otype = 'stroke' if op == "RG" else 'fill' col = {'type': otype, 'col_spec': 'rgb', 'val': np.array(s[:-1], dtype = float) } elif op.lower() == "g": print("[PATH] got set gray operator", op) print(s) if (len(s) > 2): s = s[len(s)-2:] otype = 'stroke' if op == "G" else 'fill' col = { 'type': otype, 'col_spec': 'gs', 'val': np.array(s[:-1], dtype = float) } return col def processImage(lines): img_collection = [] rect = [] ctm = [] name = "" for line in lines: s = line.split() if not len(s): continue op = s[-1] if op == "q": print("[IMG] start image") elif op == "re": rect = np.array(s[:-1], dtype = float) elif op == "cm": try: ctm = np.array(s[-7:-1], dtype = float) print("[IMG] ctm:", ctm) except ValueError as e: print("Error setting CTM from", s, ": ", e) elif op == "Q": if s[-2] == "Do": name = s[-3] img_collection.append({'name': name, 'clip': rect, 'ctm': ctm}) elif op == "n": print("[IMG] end path") else: if op in PDF_GS_OPS.keys(): print("[IMG] got operator: " + op + " = " + PDF_GS_OPS[op]) else: print("[IMG] unknown operator: " + op) return img_collection def createPlot(images, contours): fig, ax = plt.subplots() n_col = len(plt.rcParams['axes.prop_cycle']) for i in range(len(contours)): for curve in contours[i]['contour']: _ = curve.plot(num_pts = 256, color = "C" + str(i%n_col), ax = ax) cx, cy = contours[i]['centroid'] plt.plot(cx, cy, "o") for i in range(len(images)): for img in images[i]: print("Image:", img['name']) = img['ctm'].reshape(2,3) scale = [img['ctm'][0], img['ctm'][3]] position = img['ctm'][4:] w, h = scale xy = position print("Position:", xy) print("Size:", w, "x", h) pos_check = xy[1] + h < 450 size_check = w > 120 if pos_check & size_check: rect = mpatches.Rectangle(tuple(xy), w, h, fc="none", ec="green") ax.add_patch(rect) arr_img = plt.imread(MAP_IMG, format='png') ax.imshow(arr_img, interpolation='none', origin='lower', extent=[xy[0], xy[0]+w, xy[1]+h, xy[1]], clip_on=True) _ = ax.set_xlim(0, 842) _ = ax.set_ylim(0, 595) _ = ax.set_aspect(1) plt.show() def getMapGroups(images, graphics): map_groups = [] for i in range(len(images)): for img in images[i]: ctm = img['ctm'].reshape(2,3) w, h = [img['ctm'][0], img['ctm'][3]] x, y = img['ctm'][4:] pos_check = y + h < 450 size_check = w > 120 if pos_check & size_check: graphics_dict = {} for gfx in graphics: ix, iy = gfx['path']['centroid'] x_check = (x < ix) & (ix < x + w) y_check = (y < iy) & (iy < y + h) if x_check & y_check: print("Centroid: ", ix, ",", iy) if (ix, iy) not in graphics_dict.keys(): graphics_dict[(ix, iy)] = [ gfx ] else: found_match = False nodes = np.hstack([np.hstack(c.nodes) for c in gfx['path']['contour']]) for g in graphics_dict[(ix, iy)]: n = np.hstack([np.hstack(c.nodes) for c in g['path']['contour']]) if (nodes == n).all(): col = gfx['colour'] c = g['colour'] if col['col_spec'] == c['col_spec'] and np.array_equal(getColourValue(col), getColourValue(c)): found_match = True break if not found_match: graphics_dict[(ix, iy)].append(gfx) print("Graphics with distinct centroids:", len(graphics_dict)) map_groups.append((img, graphics_dict)) def getXPos(mg): return mg[0]['ctm'][4] map_groups.sort(key = getXPos) return map_groups def getColourValue(col): if col is not None: return tuple(col['val']) def transformMapGroup(map_group): img, graphics_dict = map_group print("Image:", img['name']) m1 = np.hstack((img['ctm'].reshape(3,2), np.array([[0],[0],[1]]))) try: m1_inv = LA.inv(m1) except LinAlgError: sys.exit("Could not invert transformation matrix") lon_min, lon_max, lat_min, lat_max = extent_MAP_IMG tm = np.array([lon_max - lon_min, 0, 0, lat_max - lat_min, lon_min, lat_min]) m2 = np.hstack((tm.reshape(3,2), np.array([[0],[0],[1]]))) m = np.matmul(m1_inv, m2) graphics_list = [] print("Processing graphics:", len(graphics_dict)) for z, graphics in graphics_dict.items(): print("Got", len(graphics), "graphics objects with centroid:", z) stroke_col = None fill_col = None for i in range(len(graphics)): col = graphics[i]['colour'] if col is not None: print("got colour state:", col) if col['type'] == "stroke": stroke_col = getColourValue(col) if col['type'] == "fill": fill_col = getColourValue(col) contour = [] for curve in graphics[i]['path']['contour']: nodes_new = [] for j in range(len(nodes.T)): v = np.matmul(np.append(nodes.T[j], 1), m) nodes_new.append(v[:-1]) nodes = np.array(nodes_new).T curve_new = bezier.Curve.from_nodes(nodes) contour.append(curve_new) centroid = np.matmul(np.append(graphics[i]['path']['centroid'], 1), m)[:-1] path = { 'colour': { 'stroke': stroke_col, 'fill': fill_col }, 'contour': contour, 'centroid': centroid } graphics_list.append({ 'path': path}) return (img, graphics_list) , ax): _, graphics = map_group n_col = len(plt.rcParams['axes.prop_cycle']) print("Processing graphics:", len(graphics)) for i in range(len(graphics)): col = "C" + str(i%n_col) if graphics[i]['path']['colour'] is not None: col = graphics[i]['path']['colour'] for curve in graphics[i]['path']['contour']: _ = curve.plot(num_pts = 256, color = col, ax = ax) cx, cy = graphics[i]['path']['centroid'] ax.plot(cx, cy, "o") _group): _, graphics = map_group res = 0.1 lon_min, lon_max, lat_min, lat_max = extent_MAP_IMG x = np.arange(lon_min, lon_max, res) y = np.arange(lat_min, lat_max, res) xx, yy = np.meshgrid(x, y) xy = np.vstack((xx.ravel(), yy.ravel())).T tm = np.array([res, 0, 0, res, lon_min, lat_min]) m = np.hstack((tm.reshape(3,2), np.array([[0],[0],[1]]))) try: m_inv = LA.inv(m) except LinAlgError: sys.exit("Could not invert transformation matrix") mask_list = [] for i in range(len(graphics)): col = graphics[i]['path']['colour'] print(col) if col['stroke'] is not None and col['stroke'].count(col['stroke'][0]) != 3: alert_val = 0 r, g, b = col if col == (0.0, 0.0, 0.0): print("colour: black") elif col == (1.0, 1.0, 0.0): print("colour: yellow") alert_val = 1 elif g > 0.33 and g < 0.66: print("colour: orange") alert_val = 2 elif g < 0.33: print("colour: red") alert_val = 3 else: print("colour: other") img2 = np.array([alert_val]*xx.size).reshape(xx.shape) img = np.zeros(xx.shape, dtype = np.double) stack([curve.nodes.T for curve in graphics[i]['path']['contour']]) pth = Path(vv, closed=False) mask = pth.contains_points(xy) mask = mask.reshape(xx.shape) masked = np.ma.masked_array(img2, ~mask) img[mask] = alert_val am = np.stack((xx, yy, img)) mask_list.append(am) return mask_list createGriddedData(map_groups, alert_data, file_path=None): vars = {} res = 0.1 lon_min, lon_max, lat_min, lat_max = extent_MAP_IMG x = np.arange(lon_min, lon_max, res) y = np.arange(lat_min, lat_max, res) xx, yy = np.meshgrid(x, y) xy = np.vstack((xx.ravel(), yy.ravel())).T tm = np.array([res, 0, 0, res, lon_min, lat_min]) m = np.hstack((tm.reshape(3,2), np.array([[0],[0],[1]]))) try: m_inv = LA.inv(m) except LinAlgError: sys.exit("Could not invert transformation matrix") for i, mg in enumerate(map_groups): _, graphics = mg print(i) n = 0 for j, gfx in enumerate(graphics): colour = gfx['path']['colour'] print(colour) col = None if colour['stroke'] is not None and len(colour['stroke']) == 3: col = colour['stroke'] elif colour['fill'] is not None and len(colour['fill']) == 3: col = colour['fill'] if col is not None: print(col) alert_val = 0 r, g, b = col if col == (0.0, 0.0, 0.0): print("colour: black") elif col == (1.0, 1.0, 0.0): print("colour: yellow") alert_val = 1 elif col == (1.0, 0.0, 0.0): print("colour: red") alert_val = 3 elif g > 0.25 and g < 0.66: print("colour: orange") alert_val = 2 elif r > 0.9 and g < 0.25: print("colour: red") alert_val = 3 else: print("colour: other") img = np.zeros(xx.shape, dtype = np.double) vv = np.vstack([curve.nodes.T for curve in gfx['path']['contour']]) pth = Path(vv, closed=False) mask = pth.contains_points(xy) mask = mask.reshape(xx.shape) img[mask] = alert_val da = xr.DataArray(data=img, dims=["lat", "lon"], coords=[y, x]) da.attrs = { 'issue_date': alert_data.loc[i,'issue_date'], 'alert_date': alert_data.loc[i,'date'], 'alert_day': alert_data.loc[i,'day'], 'alert_weekday': alert_data.loc[i,'weekday'], 'alert_id': n+1, 'alert_type': '', 'alert_text': alert_data.loc[i,'alert_text'], } var_name = '_'.join(['alert', 'day'+str(da.attrs['alert_day']), str(da.attrs['alert_id'])]) vars[var_name] = da n += 1 issue_date = alert_data.loc[0, 'issue_date'] ds = xr.Dataset(data_vars=vars, attrs={ 'title': 'TMA weather warnings for ' + issue_date, 'issue_date': issue_date, }) if file_path is None: file_path = 'TMA_weather_warning_'+issue_date+'.nc' ds.to_netcdf(file_path) f main(): parser = argparse.ArgumentParser(description='Process TMA PDF contents') parser.add_argument('filepath', nargs=1, type=str) parser.add_argument('metadata', nargs=1, type=str) args = parser.parse_args() try: lines = readFile(args.filepath[0]) except: print("Input file not found:", args.filepath[0]) sys.exit(4) images, graphics = extractGraphics(lines) mgroups = getMapGroups(images, graphics) mgroups = [transformMapGroup(mg) for mg in mgroups] try: alert_data = pd.read_csv(args.metadata[0]) except FileNotFoundError: print("Couldn't read metadata file:", args.metadata[0]) else: file_name = os.path.basename(args.metadata[0]).split(".")[0] + ".nc" createGriddedData(mgroups, alert_data, file_name) ## end of main() # Run main if __name__ == "__main__": main()

true

1c43c028cb0bd6fa2468ee77c1143f9fda2d934e

66,798

py

Python

nipyapi/registry/apis/bundles_api.py

iMajna/nipyapi

5480af8fe8c6b470249837835cb1a067abb6678e

[ "Apache-2.0" ]

null

nipyapi/registry/apis/bundles_api.py

iMajna/nipyapi

5480af8fe8c6b470249837835cb1a067abb6678e

[ "Apache-2.0" ]

1

2020-03-16T10:02:46.000Z

2020-03-16T13:37:42.000Z

nipyapi/registry/apis/bundles_api.py

iMajna/nipyapi

5480af8fe8c6b470249837835cb1a067abb6678e

[ "Apache-2.0" ]

null

# coding: utf-8 """ Apache NiFi Registry REST API The REST API provides an interface to a registry with operations for saving, versioning, reading NiFi flows and components. OpenAPI spec version: 0.7.0 Contact: dev@nifi.apache.org Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import sys import os import re # python 2 and python 3 compatibility library from six import iteritems from ..configuration import Configuration from ..api_client import ApiClient class BundlesApi(object): """ NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. Ref: https://github.com/swagger-api/swagger-codegen """ def __init__(self, api_client=None): config = Configuration() if api_client: self.api_client = api_client else: if not config.api_client: config.api_client = ApiClient() self.api_client = config.api_client def get_bundle_version_extension_additional_details_docs(self, bundle_id, version, name, **kwargs): """ Get bundle version extension docs details Gets the additional details documentation for the given extension in the given extension bundle version. NOTE: This endpoint is subject to change as NiFi Registry and its REST API evolve. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response. >>> def callback_function(response): >>> pprint(response) >>> >>> thread = api.get_bundle_version_extension_additional_details_docs(bundle_id, version, name, callback=callback_function) :param callback function: The callback function for asynchronous request. (optional) :param str bundle_id: The extension bundle identifier (required) :param str version: The version of the bundle (required) :param str name: The fully qualified name of the extension (required) :return: str If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('callback'): return self.get_bundle_version_extension_additional_details_docs_with_http_info(bundle_id, version, name, **kwargs) else: (data) = self.get_bundle_version_extension_additional_details_docs_with_http_info(bundle_id, version, name, **kwargs) return data def get_bundle_version_extension_additional_details_docs_with_http_info(self, bundle_id, version, name, **kwargs): """ Get bundle version extension docs details Gets the additional details documentation for the given extension in the given extension bundle version. NOTE: This endpoint is subject to change as NiFi Registry and its REST API evolve. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response. >>> def callback_function(response): >>> pprint(response) >>> >>> thread = api.get_bundle_version_extension_additional_details_docs_with_http_info(bundle_id, version, name, callback=callback_function) :param callback function: The callback function for asynchronous request. (optional) :param str bundle_id: The extension bundle identifier (required) :param str version: The version of the bundle (required) :param str name: The fully qualified name of the extension (required) :return: str If the method is called asynchronously, returns the request thread. """ all_params = ['bundle_id', 'version', 'name'] all_params.append('callback') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_bundle_version_extension_additional_details_docs" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'bundle_id' is set if ('bundle_id' not in params) or (params['bundle_id'] is None): raise ValueError("Missing the required parameter `bundle_id` when calling `get_bundle_version_extension_additional_details_docs`") # verify the required parameter 'version' is set if ('version' not in params) or (params['version'] is None): raise ValueError("Missing the required parameter `version` when calling `get_bundle_version_extension_additional_details_docs`") # verify the required parameter 'name' is set if ('name' not in params) or (params['name'] is None): raise ValueError("Missing the required parameter `name` when calling `get_bundle_version_extension_additional_details_docs`") collection_formats = {} path_params = {} if 'bundle_id' in params: path_params['bundleId'] = params['bundle_id'] if 'version' in params: path_params['version'] = params['version'] if 'name' in params: path_params['name'] = params['name'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.\ select_header_accept(['text/html']) # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.\ select_header_content_type(['*/*']) # Authentication setting auth_settings = ['tokenAuth', 'Authorization'] return self.api_client.call_api('/bundles/{bundleId}/versions/{version}/extensions/{name}/docs/additional-details', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='str', auth_settings=auth_settings, callback=params.get('callback'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_bundle_version_extension_docs(self, bundle_id, version, name, **kwargs): """ Get bundle version extension docs Gets the documentation for the given extension in the given extension bundle version. NOTE: This endpoint is subject to change as NiFi Registry and its REST API evolve. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response. >>> def callback_function(response): >>> pprint(response) >>> >>> thread = api.get_bundle_version_extension_docs(bundle_id, version, name, callback=callback_function) :param callback function: The callback function for asynchronous request. (optional) :param str bundle_id: The extension bundle identifier (required) :param str version: The version of the bundle (required) :param str name: The fully qualified name of the extension (required) :return: str If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('callback'): return self.get_bundle_version_extension_docs_with_http_info(bundle_id, version, name, **kwargs) else: (data) = self.get_bundle_version_extension_docs_with_http_info(bundle_id, version, name, **kwargs) return data def get_bundle_version_extension_docs_with_http_info(self, bundle_id, version, name, **kwargs): """ Get bundle version extension docs Gets the documentation for the given extension in the given extension bundle version. NOTE: This endpoint is subject to change as NiFi Registry and its REST API evolve. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response. >>> def callback_function(response): >>> pprint(response) >>> >>> thread = api.get_bundle_version_extension_docs_with_http_info(bundle_id, version, name, callback=callback_function) :param callback function: The callback function for asynchronous request. (optional) :param str bundle_id: The extension bundle identifier (required) :param str version: The version of the bundle (required) :param str name: The fully qualified name of the extension (required) :return: str If the method is called asynchronously, returns the request thread. """ all_params = ['bundle_id', 'version', 'name'] all_params.append('callback') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_bundle_version_extension_docs" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'bundle_id' is set if ('bundle_id' not in params) or (params['bundle_id'] is None): raise ValueError("Missing the required parameter `bundle_id` when calling `get_bundle_version_extension_docs`") # verify the required parameter 'version' is set if ('version' not in params) or (params['version'] is None): raise ValueError("Missing the required parameter `version` when calling `get_bundle_version_extension_docs`") # verify the required parameter 'name' is set if ('name' not in params) or (params['name'] is None): raise ValueError("Missing the required parameter `name` when calling `get_bundle_version_extension_docs`") collection_formats = {} path_params = {} if 'bundle_id' in params: path_params['bundleId'] = params['bundle_id'] if 'version' in params: path_params['version'] = params['version'] if 'name' in params: path_params['name'] = params['name'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.\ select_header_accept(['text/html']) # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.\ select_header_content_type(['*/*']) # Authentication setting auth_settings = ['tokenAuth', 'Authorization'] return self.api_client.call_api('/bundles/{bundleId}/versions/{version}/extensions/{name}/docs', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='str', auth_settings=auth_settings, callback=params.get('callback'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_bundle_versions(self, **kwargs): """ Get all bundle versions Gets the metadata about extension bundle versions across all authorized buckets with optional filters applied. If the user is not authorized to any buckets, an empty list will be returned. NOTE: This endpoint is subject to change as NiFi Registry and its REST API evolve. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response. >>> def callback_function(response): >>> pprint(response) >>> >>> thread = api.get_bundle_versions(callback=callback_function) :param callback function: The callback function for asynchronous request. (optional) :param str group_id: Optional groupId to filter results. The value may be an exact match, or a wildcard, such as 'com.%' to select all bundle versions where the groupId starts with 'com.'. :param str artifact_id: Optional artifactId to filter results. The value may be an exact match, or a wildcard, such as 'nifi-%' to select all bundle versions where the artifactId starts with 'nifi-'. :param str version: Optional version to filter results. The value maye be an exact match, or a wildcard, such as '1.0.%' to select all bundle versions where the version starts with '1.0.'. :return: list[BundleVersionMetadata] If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('callback'): return self.get_bundle_versions_with_http_info(**kwargs) else: (data) = self.get_bundle_versions_with_http_info(**kwargs) return data def get_bundle_versions_with_http_info(self, **kwargs): """ Get all bundle versions Gets the metadata about extension bundle versions across all authorized buckets with optional filters applied. If the user is not authorized to any buckets, an empty list will be returned. NOTE: This endpoint is subject to change as NiFi Registry and its REST API evolve. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response. >>> def callback_function(response): >>> pprint(response) >>> >>> thread = api.get_bundle_versions_with_http_info(callback=callback_function) :param callback function: The callback function for asynchronous request. (optional) :param str group_id: Optional groupId to filter results. The value may be an exact match, or a wildcard, such as 'com.%' to select all bundle versions where the groupId starts with 'com.'. :param str artifact_id: Optional artifactId to filter results. The value may be an exact match, or a wildcard, such as 'nifi-%' to select all bundle versions where the artifactId starts with 'nifi-'. :param str version: Optional version to filter results. The value maye be an exact match, or a wildcard, such as '1.0.%' to select all bundle versions where the version starts with '1.0.'. :return: list[BundleVersionMetadata] If the method is called asynchronously, returns the request thread. """ all_params = ['group_id', 'artifact_id', 'version'] all_params.append('callback') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_bundle_versions" % key ) params[key] = val del params['kwargs'] collection_formats = {} path_params = {} query_params = [] if 'group_id' in params: query_params.append(('groupId', params['group_id'])) if 'artifact_id' in params: query_params.append(('artifactId', params['artifact_id'])) if 'version' in params: query_params.append(('version', params['version'])) header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.\ select_header_accept(['application/json']) # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.\ select_header_content_type(['*/*']) # Authentication setting auth_settings = ['tokenAuth', 'Authorization'] return self.api_client.call_api('/bundles/versions', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[BundleVersionMetadata]', auth_settings=auth_settings, callback=params.get('callback'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_bundles(self, **kwargs): """ Get all bundles Gets the metadata for all bundles across all authorized buckets with optional filters applied. The returned results will include only items from buckets for which the user is authorized. If the user is not authorized to any buckets, an empty list will be returned. NOTE: This endpoint is subject to change as NiFi Registry and its REST API evolve. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response. >>> def callback_function(response): >>> pprint(response) >>> >>> thread = api.get_bundles(callback=callback_function) :param callback function: The callback function for asynchronous request. (optional) :param str bucket_name: Optional bucket name to filter results. The value may be an exact match, or a wildcard, such as 'My Bucket%' to select all bundles where the bucket name starts with 'My Bucket'. :param str group_id: Optional groupId to filter results. The value may be an exact match, or a wildcard, such as 'com.%' to select all bundles where the groupId starts with 'com.'. :param str artifact_id: Optional artifactId to filter results. The value may be an exact match, or a wildcard, such as 'nifi-%' to select all bundles where the artifactId starts with 'nifi-'. :return: list[ExtensionBundle] If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('callback'): return self.get_bundles_with_http_info(**kwargs) else: (data) = self.get_bundles_with_http_info(**kwargs) return data def get_bundles_with_http_info(self, **kwargs): """ Get all bundles Gets the metadata for all bundles across all authorized buckets with optional filters applied. The returned results will include only items from buckets for which the user is authorized. If the user is not authorized to any buckets, an empty list will be returned. NOTE: This endpoint is subject to change as NiFi Registry and its REST API evolve. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response. >>> def callback_function(response): >>> pprint(response) >>> >>> thread = api.get_bundles_with_http_info(callback=callback_function) :param callback function: The callback function for asynchronous request. (optional) :param str bucket_name: Optional bucket name to filter results. The value may be an exact match, or a wildcard, such as 'My Bucket%' to select all bundles where the bucket name starts with 'My Bucket'. :param str group_id: Optional groupId to filter results. The value may be an exact match, or a wildcard, such as 'com.%' to select all bundles where the groupId starts with 'com.'. :param str artifact_id: Optional artifactId to filter results. The value may be an exact match, or a wildcard, such as 'nifi-%' to select all bundles where the artifactId starts with 'nifi-'. :return: list[ExtensionBundle] If the method is called asynchronously, returns the request thread. """ all_params = ['bucket_name', 'group_id', 'artifact_id'] all_params.append('callback') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_bundles" % key ) params[key] = val del params['kwargs'] collection_formats = {} path_params = {} query_params = [] if 'bucket_name' in params: query_params.append(('bucketName', params['bucket_name'])) if 'group_id' in params: query_params.append(('groupId', params['group_id'])) if 'artifact_id' in params: query_params.append(('artifactId', params['artifact_id'])) header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.\ select_header_accept(['application/json']) # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.\ select_header_content_type(['*/*']) # Authentication setting auth_settings = ['tokenAuth', 'Authorization'] return self.api_client.call_api('/bundles', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[ExtensionBundle]', auth_settings=auth_settings, callback=params.get('callback'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def global_delete_bundle_version(self, bundle_id, version, **kwargs): """ Delete bundle version Deletes the given extension bundle version and it's associated binary content. NOTE: This endpoint is subject to change as NiFi Registry and its REST API evolve. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response. >>> def callback_function(response): >>> pprint(response) >>> >>> thread = api.global_delete_bundle_version(bundle_id, version, callback=callback_function) :param callback function: The callback function for asynchronous request. (optional) :param str bundle_id: The extension bundle identifier (required) :param str version: The version of the bundle (required) :return: BundleVersion If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('callback'): return self.global_delete_bundle_version_with_http_info(bundle_id, version, **kwargs) else: (data) = self.global_delete_bundle_version_with_http_info(bundle_id, version, **kwargs) return data def global_delete_bundle_version_with_http_info(self, bundle_id, version, **kwargs): """ Delete bundle version Deletes the given extension bundle version and it's associated binary content. NOTE: This endpoint is subject to change as NiFi Registry and its REST API evolve. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response. >>> def callback_function(response): >>> pprint(response) >>> >>> thread = api.global_delete_bundle_version_with_http_info(bundle_id, version, callback=callback_function) :param callback function: The callback function for asynchronous request. (optional) :param str bundle_id: The extension bundle identifier (required) :param str version: The version of the bundle (required) :return: BundleVersion If the method is called asynchronously, returns the request thread. """ all_params = ['bundle_id', 'version'] all_params.append('callback') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method global_delete_bundle_version" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'bundle_id' is set if ('bundle_id' not in params) or (params['bundle_id'] is None): raise ValueError("Missing the required parameter `bundle_id` when calling `global_delete_bundle_version`") # verify the required parameter 'version' is set if ('version' not in params) or (params['version'] is None): raise ValueError("Missing the required parameter `version` when calling `global_delete_bundle_version`") collection_formats = {} path_params = {} if 'bundle_id' in params: path_params['bundleId'] = params['bundle_id'] if 'version' in params: path_params['version'] = params['version'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.\ select_header_accept(['application/json']) # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.\ select_header_content_type(['*/*']) # Authentication setting auth_settings = ['tokenAuth', 'Authorization'] return self.api_client.call_api('/bundles/{bundleId}/versions/{version}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='BundleVersion', auth_settings=auth_settings, callback=params.get('callback'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def global_delete_extension_bundle(self, bundle_id, **kwargs): """ Delete bundle Deletes the given extension bundle and all of it's versions. NOTE: This endpoint is subject to change as NiFi Registry and its REST API evolve. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response. >>> def callback_function(response): >>> pprint(response) >>> >>> thread = api.global_delete_extension_bundle(bundle_id, callback=callback_function) :param callback function: The callback function for asynchronous request. (optional) :param str bundle_id: The extension bundle identifier (required) :return: ExtensionBundle If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('callback'): return self.global_delete_extension_bundle_with_http_info(bundle_id, **kwargs) else: (data) = self.global_delete_extension_bundle_with_http_info(bundle_id, **kwargs) return data def global_delete_extension_bundle_with_http_info(self, bundle_id, **kwargs): """ Delete bundle Deletes the given extension bundle and all of it's versions. NOTE: This endpoint is subject to change as NiFi Registry and its REST API evolve. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response. >>> def callback_function(response): >>> pprint(response) >>> >>> thread = api.global_delete_extension_bundle_with_http_info(bundle_id, callback=callback_function) :param callback function: The callback function for asynchronous request. (optional) :param str bundle_id: The extension bundle identifier (required) :return: ExtensionBundle If the method is called asynchronously, returns the request thread. """ all_params = ['bundle_id'] all_params.append('callback') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method global_delete_extension_bundle" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'bundle_id' is set if ('bundle_id' not in params) or (params['bundle_id'] is None): raise ValueError("Missing the required parameter `bundle_id` when calling `global_delete_extension_bundle`") collection_formats = {} path_params = {} if 'bundle_id' in params: path_params['bundleId'] = params['bundle_id'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.\ select_header_accept(['application/json']) # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.\ select_header_content_type(['*/*']) # Authentication setting auth_settings = ['tokenAuth', 'Authorization'] return self.api_client.call_api('/bundles/{bundleId}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='ExtensionBundle', auth_settings=auth_settings, callback=params.get('callback'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def global_get_bundle_version(self, bundle_id, version, **kwargs): """ Get bundle version Gets the descriptor for the given version of the given extension bundle. NOTE: This endpoint is subject to change as NiFi Registry and its REST API evolve. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response. >>> def callback_function(response): >>> pprint(response) >>> >>> thread = api.global_get_bundle_version(bundle_id, version, callback=callback_function) :param callback function: The callback function for asynchronous request. (optional) :param str bundle_id: The extension bundle identifier (required) :param str version: The version of the bundle (required) :return: BundleVersion If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('callback'): return self.global_get_bundle_version_with_http_info(bundle_id, version, **kwargs) else: (data) = self.global_get_bundle_version_with_http_info(bundle_id, version, **kwargs) return data def global_get_bundle_version_with_http_info(self, bundle_id, version, **kwargs): """ Get bundle version Gets the descriptor for the given version of the given extension bundle. NOTE: This endpoint is subject to change as NiFi Registry and its REST API evolve. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response. >>> def callback_function(response): >>> pprint(response) >>> >>> thread = api.global_get_bundle_version_with_http_info(bundle_id, version, callback=callback_function) :param callback function: The callback function for asynchronous request. (optional) :param str bundle_id: The extension bundle identifier (required) :param str version: The version of the bundle (required) :return: BundleVersion If the method is called asynchronously, returns the request thread. """ all_params = ['bundle_id', 'version'] all_params.append('callback') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method global_get_bundle_version" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'bundle_id' is set if ('bundle_id' not in params) or (params['bundle_id'] is None): raise ValueError("Missing the required parameter `bundle_id` when calling `global_get_bundle_version`") # verify the required parameter 'version' is set if ('version' not in params) or (params['version'] is None): raise ValueError("Missing the required parameter `version` when calling `global_get_bundle_version`") collection_formats = {} path_params = {} if 'bundle_id' in params: path_params['bundleId'] = params['bundle_id'] if 'version' in params: path_params['version'] = params['version'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.\ select_header_accept(['application/json']) # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.\ select_header_content_type(['*/*']) # Authentication setting auth_settings = ['tokenAuth', 'Authorization'] return self.api_client.call_api('/bundles/{bundleId}/versions/{version}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='BundleVersion', auth_settings=auth_settings, callback=params.get('callback'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def global_get_bundle_version_content(self, bundle_id, version, **kwargs): """ Get bundle version content Gets the binary content for the given version of the given extension bundle. NOTE: This endpoint is subject to change as NiFi Registry and its REST API evolve. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response. >>> def callback_function(response): >>> pprint(response) >>> >>> thread = api.global_get_bundle_version_content(bundle_id, version, callback=callback_function) :param callback function: The callback function for asynchronous request. (optional) :param str bundle_id: The extension bundle identifier (required) :param str version: The version of the bundle (required) :return: list[str] If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('callback'): return self.global_get_bundle_version_content_with_http_info(bundle_id, version, **kwargs) else: (data) = self.global_get_bundle_version_content_with_http_info(bundle_id, version, **kwargs) return data def global_get_bundle_version_content_with_http_info(self, bundle_id, version, **kwargs): """ Get bundle version content Gets the binary content for the given version of the given extension bundle. NOTE: This endpoint is subject to change as NiFi Registry and its REST API evolve. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response. >>> def callback_function(response): >>> pprint(response) >>> >>> thread = api.global_get_bundle_version_content_with_http_info(bundle_id, version, callback=callback_function) :param callback function: The callback function for asynchronous request. (optional) :param str bundle_id: The extension bundle identifier (required) :param str version: The version of the bundle (required) :return: list[str] If the method is called asynchronously, returns the request thread. """ all_params = ['bundle_id', 'version'] all_params.append('callback') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method global_get_bundle_version_content" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'bundle_id' is set if ('bundle_id' not in params) or (params['bundle_id'] is None): raise ValueError("Missing the required parameter `bundle_id` when calling `global_get_bundle_version_content`") # verify the required parameter 'version' is set if ('version' not in params) or (params['version'] is None): raise ValueError("Missing the required parameter `version` when calling `global_get_bundle_version_content`") collection_formats = {} path_params = {} if 'bundle_id' in params: path_params['bundleId'] = params['bundle_id'] if 'version' in params: path_params['version'] = params['version'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.\ select_header_accept(['application/octet-stream']) # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.\ select_header_content_type(['*/*']) # Authentication setting auth_settings = ['tokenAuth', 'Authorization'] return self.api_client.call_api('/bundles/{bundleId}/versions/{version}/content', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[str]', auth_settings=auth_settings, callback=params.get('callback'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def global_get_bundle_version_extension(self, bundle_id, version, name, **kwargs): """ Get bundle version extension Gets the metadata about the extension with the given name in the given extension bundle version. NOTE: This endpoint is subject to change as NiFi Registry and its REST API evolve. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response. >>> def callback_function(response): >>> pprint(response) >>> >>> thread = api.global_get_bundle_version_extension(bundle_id, version, name, callback=callback_function) :param callback function: The callback function for asynchronous request. (optional) :param str bundle_id: The extension bundle identifier (required) :param str version: The version of the bundle (required) :param str name: The fully qualified name of the extension (required) :return: list[Extension] If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('callback'): return self.global_get_bundle_version_extension_with_http_info(bundle_id, version, name, **kwargs) else: (data) = self.global_get_bundle_version_extension_with_http_info(bundle_id, version, name, **kwargs) return data def global_get_bundle_version_extension_with_http_info(self, bundle_id, version, name, **kwargs): """ Get bundle version extension Gets the metadata about the extension with the given name in the given extension bundle version. NOTE: This endpoint is subject to change as NiFi Registry and its REST API evolve. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response. >>> def callback_function(response): >>> pprint(response) >>> >>> thread = api.global_get_bundle_version_extension_with_http_info(bundle_id, version, name, callback=callback_function) :param callback function: The callback function for asynchronous request. (optional) :param str bundle_id: The extension bundle identifier (required) :param str version: The version of the bundle (required) :param str name: The fully qualified name of the extension (required) :return: list[Extension] If the method is called asynchronously, returns the request thread. """ all_params = ['bundle_id', 'version', 'name'] all_params.append('callback') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method global_get_bundle_version_extension" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'bundle_id' is set if ('bundle_id' not in params) or (params['bundle_id'] is None): raise ValueError("Missing the required parameter `bundle_id` when calling `global_get_bundle_version_extension`") # verify the required parameter 'version' is set if ('version' not in params) or (params['version'] is None): raise ValueError("Missing the required parameter `version` when calling `global_get_bundle_version_extension`") # verify the required parameter 'name' is set if ('name' not in params) or (params['name'] is None): raise ValueError("Missing the required parameter `name` when calling `global_get_bundle_version_extension`") collection_formats = {} path_params = {} if 'bundle_id' in params: path_params['bundleId'] = params['bundle_id'] if 'version' in params: path_params['version'] = params['version'] if 'name' in params: path_params['name'] = params['name'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.\ select_header_accept(['application/json']) # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.\ select_header_content_type(['*/*']) # Authentication setting auth_settings = ['tokenAuth', 'Authorization'] return self.api_client.call_api('/bundles/{bundleId}/versions/{version}/extensions/{name}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[Extension]', auth_settings=auth_settings, callback=params.get('callback'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def global_get_bundle_version_extensions(self, bundle_id, version, **kwargs): """ Get bundle version extensions Gets the metadata about the extensions in the given extension bundle version. NOTE: This endpoint is subject to change as NiFi Registry and its REST API evolve. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response. >>> def callback_function(response): >>> pprint(response) >>> >>> thread = api.global_get_bundle_version_extensions(bundle_id, version, callback=callback_function) :param callback function: The callback function for asynchronous request. (optional) :param str bundle_id: The extension bundle identifier (required) :param str version: The version of the bundle (required) :return: list[ExtensionMetadata] If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('callback'): return self.global_get_bundle_version_extensions_with_http_info(bundle_id, version, **kwargs) else: (data) = self.global_get_bundle_version_extensions_with_http_info(bundle_id, version, **kwargs) return data def global_get_bundle_version_extensions_with_http_info(self, bundle_id, version, **kwargs): """ Get bundle version extensions Gets the metadata about the extensions in the given extension bundle version. NOTE: This endpoint is subject to change as NiFi Registry and its REST API evolve. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response. >>> def callback_function(response): >>> pprint(response) >>> >>> thread = api.global_get_bundle_version_extensions_with_http_info(bundle_id, version, callback=callback_function) :param callback function: The callback function for asynchronous request. (optional) :param str bundle_id: The extension bundle identifier (required) :param str version: The version of the bundle (required) :return: list[ExtensionMetadata] If the method is called asynchronously, returns the request thread. """ all_params = ['bundle_id', 'version'] all_params.append('callback') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method global_get_bundle_version_extensions" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'bundle_id' is set if ('bundle_id' not in params) or (params['bundle_id'] is None): raise ValueError("Missing the required parameter `bundle_id` when calling `global_get_bundle_version_extensions`") # verify the required parameter 'version' is set if ('version' not in params) or (params['version'] is None): raise ValueError("Missing the required parameter `version` when calling `global_get_bundle_version_extensions`") collection_formats = {} path_params = {} if 'bundle_id' in params: path_params['bundleId'] = params['bundle_id'] if 'version' in params: path_params['version'] = params['version'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.\ select_header_accept(['application/json']) # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.\ select_header_content_type(['*/*']) # Authentication setting auth_settings = ['tokenAuth', 'Authorization'] return self.api_client.call_api('/bundles/{bundleId}/versions/{version}/extensions', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[ExtensionMetadata]', auth_settings=auth_settings, callback=params.get('callback'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def global_get_bundle_versions(self, bundle_id, **kwargs): """ Get bundle versions Gets the metadata for the versions of the given extension bundle. NOTE: This endpoint is subject to change as NiFi Registry and its REST API evolve. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response. >>> def callback_function(response): >>> pprint(response) >>> >>> thread = api.global_get_bundle_versions(bundle_id, callback=callback_function) :param callback function: The callback function for asynchronous request. (optional) :param str bundle_id: The extension bundle identifier (required) :return: list[BundleVersionMetadata] If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('callback'): return self.global_get_bundle_versions_with_http_info(bundle_id, **kwargs) else: (data) = self.global_get_bundle_versions_with_http_info(bundle_id, **kwargs) return data def global_get_bundle_versions_with_http_info(self, bundle_id, **kwargs): """ Get bundle versions Gets the metadata for the versions of the given extension bundle. NOTE: This endpoint is subject to change as NiFi Registry and its REST API evolve. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response. >>> def callback_function(response): >>> pprint(response) >>> >>> thread = api.global_get_bundle_versions_with_http_info(bundle_id, callback=callback_function) :param callback function: The callback function for asynchronous request. (optional) :param str bundle_id: The extension bundle identifier (required) :return: list[BundleVersionMetadata] If the method is called asynchronously, returns the request thread. """ all_params = ['bundle_id'] all_params.append('callback') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method global_get_bundle_versions" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'bundle_id' is set if ('bundle_id' not in params) or (params['bundle_id'] is None): raise ValueError("Missing the required parameter `bundle_id` when calling `global_get_bundle_versions`") collection_formats = {} path_params = {} if 'bundle_id' in params: path_params['bundleId'] = params['bundle_id'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.\ select_header_accept(['application/json']) # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.\ select_header_content_type(['*/*']) # Authentication setting auth_settings = ['tokenAuth', 'Authorization'] return self.api_client.call_api('/bundles/{bundleId}/versions', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[BundleVersionMetadata]', auth_settings=auth_settings, callback=params.get('callback'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def global_get_extension_bundle(self, bundle_id, **kwargs): """ Get bundle Gets the metadata about an extension bundle. NOTE: This endpoint is subject to change as NiFi Registry and its REST API evolve. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response. >>> def callback_function(response): >>> pprint(response) >>> >>> thread = api.global_get_extension_bundle(bundle_id, callback=callback_function) :param callback function: The callback function for asynchronous request. (optional) :param str bundle_id: The extension bundle identifier (required) :return: ExtensionBundle If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True if kwargs.get('callback'): return self.global_get_extension_bundle_with_http_info(bundle_id, **kwargs) else: (data) = self.global_get_extension_bundle_with_http_info(bundle_id, **kwargs) return data def global_get_extension_bundle_with_http_info(self, bundle_id, **kwargs): """ Get bundle Gets the metadata about an extension bundle. NOTE: This endpoint is subject to change as NiFi Registry and its REST API evolve. This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please define a `callback` function to be invoked when receiving the response. >>> def callback_function(response): >>> pprint(response) >>> >>> thread = api.global_get_extension_bundle_with_http_info(bundle_id, callback=callback_function) :param callback function: The callback function for asynchronous request. (optional) :param str bundle_id: The extension bundle identifier (required) :return: ExtensionBundle If the method is called asynchronously, returns the request thread. """ all_params = ['bundle_id'] all_params.append('callback') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method global_get_extension_bundle" % key ) params[key] = val del params['kwargs'] # verify the required parameter 'bundle_id' is set if ('bundle_id' not in params) or (params['bundle_id'] is None): raise ValueError("Missing the required parameter `bundle_id` when calling `global_get_extension_bundle`") collection_formats = {} path_params = {} if 'bundle_id' in params: path_params['bundleId'] = params['bundle_id'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.\ select_header_accept(['application/json']) # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.\ select_header_content_type(['*/*']) # Authentication setting auth_settings = ['tokenAuth', 'Authorization'] return self.api_client.call_api('/bundles/{bundleId}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='ExtensionBundle', auth_settings=auth_settings, callback=params.get('callback'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats)

47.918221

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0.600243

from __future__ import absolute_import import sys import os import re from six import iteritems from ..configuration import Configuration from ..api_client import ApiClient class BundlesApi(object): def __init__(self, api_client=None): config = Configuration() if api_client: self.api_client = api_client else: if not config.api_client: config.api_client = ApiClient() self.api_client = config.api_client def get_bundle_version_extension_additional_details_docs(self, bundle_id, version, name, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('callback'): return self.get_bundle_version_extension_additional_details_docs_with_http_info(bundle_id, version, name, **kwargs) else: (data) = self.get_bundle_version_extension_additional_details_docs_with_http_info(bundle_id, version, name, **kwargs) return data def get_bundle_version_extension_additional_details_docs_with_http_info(self, bundle_id, version, name, **kwargs): all_params = ['bundle_id', 'version', 'name'] all_params.append('callback') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_bundle_version_extension_additional_details_docs" % key ) params[key] = val del params['kwargs'] if ('bundle_id' not in params) or (params['bundle_id'] is None): raise ValueError("Missing the required parameter `bundle_id` when calling `get_bundle_version_extension_additional_details_docs`") if ('version' not in params) or (params['version'] is None): raise ValueError("Missing the required parameter `version` when calling `get_bundle_version_extension_additional_details_docs`") if ('name' not in params) or (params['name'] is None): raise ValueError("Missing the required parameter `name` when calling `get_bundle_version_extension_additional_details_docs`") collection_formats = {} path_params = {} if 'bundle_id' in params: path_params['bundleId'] = params['bundle_id'] if 'version' in params: path_params['version'] = params['version'] if 'name' in params: path_params['name'] = params['name'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None header_params['Accept'] = self.api_client.\ select_header_accept(['text/html']) header_params['Content-Type'] = self.api_client.\ select_header_content_type(['*/*']) auth_settings = ['tokenAuth', 'Authorization'] return self.api_client.call_api('/bundles/{bundleId}/versions/{version}/extensions/{name}/docs/additional-details', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='str', auth_settings=auth_settings, callback=params.get('callback'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_bundle_version_extension_docs(self, bundle_id, version, name, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('callback'): return self.get_bundle_version_extension_docs_with_http_info(bundle_id, version, name, **kwargs) else: (data) = self.get_bundle_version_extension_docs_with_http_info(bundle_id, version, name, **kwargs) return data def get_bundle_version_extension_docs_with_http_info(self, bundle_id, version, name, **kwargs): all_params = ['bundle_id', 'version', 'name'] all_params.append('callback') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_bundle_version_extension_docs" % key ) params[key] = val del params['kwargs'] if ('bundle_id' not in params) or (params['bundle_id'] is None): raise ValueError("Missing the required parameter `bundle_id` when calling `get_bundle_version_extension_docs`") if ('version' not in params) or (params['version'] is None): raise ValueError("Missing the required parameter `version` when calling `get_bundle_version_extension_docs`") if ('name' not in params) or (params['name'] is None): raise ValueError("Missing the required parameter `name` when calling `get_bundle_version_extension_docs`") collection_formats = {} path_params = {} if 'bundle_id' in params: path_params['bundleId'] = params['bundle_id'] if 'version' in params: path_params['version'] = params['version'] if 'name' in params: path_params['name'] = params['name'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None header_params['Accept'] = self.api_client.\ select_header_accept(['text/html']) header_params['Content-Type'] = self.api_client.\ select_header_content_type(['*/*']) auth_settings = ['tokenAuth', 'Authorization'] return self.api_client.call_api('/bundles/{bundleId}/versions/{version}/extensions/{name}/docs', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='str', auth_settings=auth_settings, callback=params.get('callback'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_bundle_versions(self, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('callback'): return self.get_bundle_versions_with_http_info(**kwargs) else: (data) = self.get_bundle_versions_with_http_info(**kwargs) return data def get_bundle_versions_with_http_info(self, **kwargs): all_params = ['group_id', 'artifact_id', 'version'] all_params.append('callback') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_bundle_versions" % key ) params[key] = val del params['kwargs'] collection_formats = {} path_params = {} query_params = [] if 'group_id' in params: query_params.append(('groupId', params['group_id'])) if 'artifact_id' in params: query_params.append(('artifactId', params['artifact_id'])) if 'version' in params: query_params.append(('version', params['version'])) header_params = {} form_params = [] local_var_files = {} body_params = None header_params['Accept'] = self.api_client.\ select_header_accept(['application/json']) header_params['Content-Type'] = self.api_client.\ select_header_content_type(['*/*']) auth_settings = ['tokenAuth', 'Authorization'] return self.api_client.call_api('/bundles/versions', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[BundleVersionMetadata]', auth_settings=auth_settings, callback=params.get('callback'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def get_bundles(self, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('callback'): return self.get_bundles_with_http_info(**kwargs) else: (data) = self.get_bundles_with_http_info(**kwargs) return data def get_bundles_with_http_info(self, **kwargs): all_params = ['bucket_name', 'group_id', 'artifact_id'] all_params.append('callback') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method get_bundles" % key ) params[key] = val del params['kwargs'] collection_formats = {} path_params = {} query_params = [] if 'bucket_name' in params: query_params.append(('bucketName', params['bucket_name'])) if 'group_id' in params: query_params.append(('groupId', params['group_id'])) if 'artifact_id' in params: query_params.append(('artifactId', params['artifact_id'])) header_params = {} form_params = [] local_var_files = {} body_params = None header_params['Accept'] = self.api_client.\ select_header_accept(['application/json']) header_params['Content-Type'] = self.api_client.\ select_header_content_type(['*/*']) auth_settings = ['tokenAuth', 'Authorization'] return self.api_client.call_api('/bundles', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[ExtensionBundle]', auth_settings=auth_settings, callback=params.get('callback'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def global_delete_bundle_version(self, bundle_id, version, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('callback'): return self.global_delete_bundle_version_with_http_info(bundle_id, version, **kwargs) else: (data) = self.global_delete_bundle_version_with_http_info(bundle_id, version, **kwargs) return data def global_delete_bundle_version_with_http_info(self, bundle_id, version, **kwargs): all_params = ['bundle_id', 'version'] all_params.append('callback') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method global_delete_bundle_version" % key ) params[key] = val del params['kwargs'] if ('bundle_id' not in params) or (params['bundle_id'] is None): raise ValueError("Missing the required parameter `bundle_id` when calling `global_delete_bundle_version`") if ('version' not in params) or (params['version'] is None): raise ValueError("Missing the required parameter `version` when calling `global_delete_bundle_version`") collection_formats = {} path_params = {} if 'bundle_id' in params: path_params['bundleId'] = params['bundle_id'] if 'version' in params: path_params['version'] = params['version'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None header_params['Accept'] = self.api_client.\ select_header_accept(['application/json']) header_params['Content-Type'] = self.api_client.\ select_header_content_type(['*/*']) auth_settings = ['tokenAuth', 'Authorization'] return self.api_client.call_api('/bundles/{bundleId}/versions/{version}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='BundleVersion', auth_settings=auth_settings, callback=params.get('callback'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def global_delete_extension_bundle(self, bundle_id, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('callback'): return self.global_delete_extension_bundle_with_http_info(bundle_id, **kwargs) else: (data) = self.global_delete_extension_bundle_with_http_info(bundle_id, **kwargs) return data def global_delete_extension_bundle_with_http_info(self, bundle_id, **kwargs): all_params = ['bundle_id'] all_params.append('callback') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method global_delete_extension_bundle" % key ) params[key] = val del params['kwargs'] if ('bundle_id' not in params) or (params['bundle_id'] is None): raise ValueError("Missing the required parameter `bundle_id` when calling `global_delete_extension_bundle`") collection_formats = {} path_params = {} if 'bundle_id' in params: path_params['bundleId'] = params['bundle_id'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None header_params['Accept'] = self.api_client.\ select_header_accept(['application/json']) header_params['Content-Type'] = self.api_client.\ select_header_content_type(['*/*']) auth_settings = ['tokenAuth', 'Authorization'] return self.api_client.call_api('/bundles/{bundleId}', 'DELETE', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='ExtensionBundle', auth_settings=auth_settings, callback=params.get('callback'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def global_get_bundle_version(self, bundle_id, version, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('callback'): return self.global_get_bundle_version_with_http_info(bundle_id, version, **kwargs) else: (data) = self.global_get_bundle_version_with_http_info(bundle_id, version, **kwargs) return data def global_get_bundle_version_with_http_info(self, bundle_id, version, **kwargs): all_params = ['bundle_id', 'version'] all_params.append('callback') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method global_get_bundle_version" % key ) params[key] = val del params['kwargs'] if ('bundle_id' not in params) or (params['bundle_id'] is None): raise ValueError("Missing the required parameter `bundle_id` when calling `global_get_bundle_version`") if ('version' not in params) or (params['version'] is None): raise ValueError("Missing the required parameter `version` when calling `global_get_bundle_version`") collection_formats = {} path_params = {} if 'bundle_id' in params: path_params['bundleId'] = params['bundle_id'] if 'version' in params: path_params['version'] = params['version'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None header_params['Accept'] = self.api_client.\ select_header_accept(['application/json']) header_params['Content-Type'] = self.api_client.\ select_header_content_type(['*/*']) auth_settings = ['tokenAuth', 'Authorization'] return self.api_client.call_api('/bundles/{bundleId}/versions/{version}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='BundleVersion', auth_settings=auth_settings, callback=params.get('callback'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def global_get_bundle_version_content(self, bundle_id, version, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('callback'): return self.global_get_bundle_version_content_with_http_info(bundle_id, version, **kwargs) else: (data) = self.global_get_bundle_version_content_with_http_info(bundle_id, version, **kwargs) return data def global_get_bundle_version_content_with_http_info(self, bundle_id, version, **kwargs): all_params = ['bundle_id', 'version'] all_params.append('callback') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method global_get_bundle_version_content" % key ) params[key] = val del params['kwargs'] if ('bundle_id' not in params) or (params['bundle_id'] is None): raise ValueError("Missing the required parameter `bundle_id` when calling `global_get_bundle_version_content`") if ('version' not in params) or (params['version'] is None): raise ValueError("Missing the required parameter `version` when calling `global_get_bundle_version_content`") collection_formats = {} path_params = {} if 'bundle_id' in params: path_params['bundleId'] = params['bundle_id'] if 'version' in params: path_params['version'] = params['version'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None header_params['Accept'] = self.api_client.\ select_header_accept(['application/octet-stream']) header_params['Content-Type'] = self.api_client.\ select_header_content_type(['*/*']) auth_settings = ['tokenAuth', 'Authorization'] return self.api_client.call_api('/bundles/{bundleId}/versions/{version}/content', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[str]', auth_settings=auth_settings, callback=params.get('callback'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def global_get_bundle_version_extension(self, bundle_id, version, name, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('callback'): return self.global_get_bundle_version_extension_with_http_info(bundle_id, version, name, **kwargs) else: (data) = self.global_get_bundle_version_extension_with_http_info(bundle_id, version, name, **kwargs) return data def global_get_bundle_version_extension_with_http_info(self, bundle_id, version, name, **kwargs): all_params = ['bundle_id', 'version', 'name'] all_params.append('callback') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method global_get_bundle_version_extension" % key ) params[key] = val del params['kwargs'] if ('bundle_id' not in params) or (params['bundle_id'] is None): raise ValueError("Missing the required parameter `bundle_id` when calling `global_get_bundle_version_extension`") if ('version' not in params) or (params['version'] is None): raise ValueError("Missing the required parameter `version` when calling `global_get_bundle_version_extension`") if ('name' not in params) or (params['name'] is None): raise ValueError("Missing the required parameter `name` when calling `global_get_bundle_version_extension`") collection_formats = {} path_params = {} if 'bundle_id' in params: path_params['bundleId'] = params['bundle_id'] if 'version' in params: path_params['version'] = params['version'] if 'name' in params: path_params['name'] = params['name'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None header_params['Accept'] = self.api_client.\ select_header_accept(['application/json']) header_params['Content-Type'] = self.api_client.\ select_header_content_type(['*/*']) auth_settings = ['tokenAuth', 'Authorization'] return self.api_client.call_api('/bundles/{bundleId}/versions/{version}/extensions/{name}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[Extension]', auth_settings=auth_settings, callback=params.get('callback'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def global_get_bundle_version_extensions(self, bundle_id, version, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('callback'): return self.global_get_bundle_version_extensions_with_http_info(bundle_id, version, **kwargs) else: (data) = self.global_get_bundle_version_extensions_with_http_info(bundle_id, version, **kwargs) return data def global_get_bundle_version_extensions_with_http_info(self, bundle_id, version, **kwargs): all_params = ['bundle_id', 'version'] all_params.append('callback') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method global_get_bundle_version_extensions" % key ) params[key] = val del params['kwargs'] if ('bundle_id' not in params) or (params['bundle_id'] is None): raise ValueError("Missing the required parameter `bundle_id` when calling `global_get_bundle_version_extensions`") if ('version' not in params) or (params['version'] is None): raise ValueError("Missing the required parameter `version` when calling `global_get_bundle_version_extensions`") collection_formats = {} path_params = {} if 'bundle_id' in params: path_params['bundleId'] = params['bundle_id'] if 'version' in params: path_params['version'] = params['version'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None header_params['Accept'] = self.api_client.\ select_header_accept(['application/json']) header_params['Content-Type'] = self.api_client.\ select_header_content_type(['*/*']) auth_settings = ['tokenAuth', 'Authorization'] return self.api_client.call_api('/bundles/{bundleId}/versions/{version}/extensions', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[ExtensionMetadata]', auth_settings=auth_settings, callback=params.get('callback'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def global_get_bundle_versions(self, bundle_id, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('callback'): return self.global_get_bundle_versions_with_http_info(bundle_id, **kwargs) else: (data) = self.global_get_bundle_versions_with_http_info(bundle_id, **kwargs) return data def global_get_bundle_versions_with_http_info(self, bundle_id, **kwargs): all_params = ['bundle_id'] all_params.append('callback') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method global_get_bundle_versions" % key ) params[key] = val del params['kwargs'] if ('bundle_id' not in params) or (params['bundle_id'] is None): raise ValueError("Missing the required parameter `bundle_id` when calling `global_get_bundle_versions`") collection_formats = {} path_params = {} if 'bundle_id' in params: path_params['bundleId'] = params['bundle_id'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None header_params['Accept'] = self.api_client.\ select_header_accept(['application/json']) header_params['Content-Type'] = self.api_client.\ select_header_content_type(['*/*']) auth_settings = ['tokenAuth', 'Authorization'] return self.api_client.call_api('/bundles/{bundleId}/versions', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[BundleVersionMetadata]', auth_settings=auth_settings, callback=params.get('callback'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats) def global_get_extension_bundle(self, bundle_id, **kwargs): kwargs['_return_http_data_only'] = True if kwargs.get('callback'): return self.global_get_extension_bundle_with_http_info(bundle_id, **kwargs) else: (data) = self.global_get_extension_bundle_with_http_info(bundle_id, **kwargs) return data def global_get_extension_bundle_with_http_info(self, bundle_id, **kwargs): all_params = ['bundle_id'] all_params.append('callback') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') params = locals() for key, val in iteritems(params['kwargs']): if key not in all_params: raise TypeError( "Got an unexpected keyword argument '%s'" " to method global_get_extension_bundle" % key ) params[key] = val del params['kwargs'] if ('bundle_id' not in params) or (params['bundle_id'] is None): raise ValueError("Missing the required parameter `bundle_id` when calling `global_get_extension_bundle`") collection_formats = {} path_params = {} if 'bundle_id' in params: path_params['bundleId'] = params['bundle_id'] query_params = [] header_params = {} form_params = [] local_var_files = {} body_params = None header_params['Accept'] = self.api_client.\ select_header_accept(['application/json']) header_params['Content-Type'] = self.api_client.\ select_header_content_type(['*/*']) auth_settings = ['tokenAuth', 'Authorization'] return self.api_client.call_api('/bundles/{bundleId}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='ExtensionBundle', auth_settings=auth_settings, callback=params.get('callback'), _return_http_data_only=params.get('_return_http_data_only'), _preload_content=params.get('_preload_content', True), _request_timeout=params.get('_request_timeout'), collection_formats=collection_formats)

true

1c43c04f2267901e6a6439f5190e528fae312122

23,661

py

Python

vt/client.py

kesh-stripe/vt-py

00ec3743cfc8649c84d3aabc45986177f468bd71

[ "Apache-2.0" ]

null

vt/client.py

kesh-stripe/vt-py

00ec3743cfc8649c84d3aabc45986177f468bd71

[ "Apache-2.0" ]

null

vt/client.py

kesh-stripe/vt-py

00ec3743cfc8649c84d3aabc45986177f468bd71

[ "Apache-2.0" ]

null

# Copyright © 2019 The vt-py 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 aiohttp import asyncio import base64 import json from .error import APIError from .feed import Feed from .object import Object from .iterator import Iterator from .version import __version__ __all__ = [ 'Client', 'ClientResponse', 'url_id'] _API_HOST = 'https://www.virustotal.com' # All API endpoints start with this prefix, you don't need to include the # prefix in the paths you request as it's prepended automatically. _ENDPOINT_PREFIX = '/api/v3' # AppEngine server decides whether or not it should serve gzipped content # based on Accept-Encoding and User-Agent. Non-standard UAs are not served # with gzipped content unless it contains the string "gzip" somewhere. # See: https://cloud.google.com/appengine/kb/#compression _USER_AGENT_FMT = '{agent}; vtpy {version}; gzip' def _make_sync(future): """Utility function that waits for an async call, making it sync.""" try: event_loop = asyncio.get_event_loop() except RuntimeError: # Generate an event loop if there isn't any. event_loop = asyncio.new_event_loop() asyncio.set_event_loop(event_loop) return event_loop.run_until_complete(future) def url_id(url): """Generates the object ID for an URL. The ID generated by this function can be used in calls that expect a URL ID like `client.get_object('/urls/<id>')` """ return base64.urlsafe_b64encode(url.encode()).decode().strip("=") class ClientResponse: """Class representing the HTTP responses returned by the client. This class is just a thing wrapper around `aiohttp.ClientResponse <https://aiohttp.readthedocs.io/en/stable/client_reference.html#aiohttp.ClientResponse>`_ that allows using it in both asynchronous and synchronous mode. Instances of this class have all the attributes that you can find in `aiohttp.ClientResponse`, like `version`, `status`, `method`, `url`, and so on. Methods in `aiohttp.ClientResponse` that return a coroutine have two flavors in this class: synchronous and asynchronous. For example, `aiohttp.ClientResponse.read()` becomes `vt.ClientResponse.read_async()`, and `vt.ClientResponse.read()` is the synchronous version of `vt.ClientResponse.read_async()`. Find more information about attributes and methods in `aiohttp.ClientResponse` in: https://aiohttp.readthedocs.io/en/stable/client_reference.html#aiohttp.ClientResponse """ def __init__(self, aiohttp_resp): self._aiohttp_resp = aiohttp_resp def __getattr__(self, attr): return getattr(self._aiohttp_resp, attr) @property def content(self): return StreamReader(self._aiohttp_resp.content) async def read_async(self): return await self._aiohttp_resp.read() def read(self): return _make_sync(self.read_async()) async def json_async(self): return await self._aiohttp_resp.json() def json(self): return _make_sync(self.json_async()) async def text_async(self): return await self._aiohttp_resp.text() def text(self): return _make_sync(self.text_async()) class StreamReader: """Class representing the HTTP responses returned by the client. This class is just a thing wrapper around `aiohttp.StreamReader <https://aiohttp.readthedocs.io/en/stable/streams.html#aiohttp.StreamReader>`_ that allows using it in both asynchronous and synchronous mode. Instances of this class have all the methods that you can find in `aiohttp.StreamReader`, like `readany()`, `readany()`, etc. Methods in `aiohttp.StreamReader` come in two flavors in this class: synchronous and asynchronous. For example, `read()` and `read_async`, where `read` is the synchronous one and `read_async` is the asynchronous. Find more information about attributes and methods in `aiohttp.StreamReader` in: https://aiohttp.readthedocs.io/en/stable/streams.html#aiohttp.StreamReader """ def __init__(self, aiohttp_stream_reader): self._aiohttp_stream_reader = aiohttp_stream_reader def __getattr__(self, attr): return getattr(self._aiohttp_stream_reader, attr) async def read_async(self, n=-1): return await self._aiohttp_stream_reader.read(n) def read(self, n=-1): return _make_sync(self.read_async(n)) async def readany_async(self): return await self._aiohttp_stream_reader.readany() def readany(self): return _make_sync(self.readany_async()) async def readexactly_async(self, n): return await self._aiohttp_stream_reader.readexactly(n) def readexactly(self, n): return _make_sync(self.readexactly_async(n)) async def readline_async(self): return await self._aiohttp_stream_reader.readline() def readline(self): return _make_sync(self.readline_async()) async def readchunk_async(self): return await self._aiohttp_stream_reader.readchunk() def readchunk(self): return _make_sync(self.readchunk_async()) class Client: """Client for interacting with VirusTotal. :param apikey: Your VirusTotal API key. :param agent: A string that identifies your application. :param host: By default https://www.virustotal.com, it can be changed for testing purposes. :type apikey: str :type agent: str :type host: str """ def __init__(self, apikey, agent="unknown", host=None): """Intialize the client with the provided API key.""" if not isinstance(apikey, str): raise ValueError('API key must be a string') if not apikey: raise ValueError('API key can not be an empty string') self._host = host or _API_HOST self._apikey = apikey self._agent = agent self._session = None def _full_url(self, path, *args): try: path = path.format(*args) except IndexError: raise ValueError('Not enough arguments to fill all placeholders in path') if path.startswith('http'): return path return self._host + _ENDPOINT_PREFIX + path def _get_session(self): if not self._session: self._session = aiohttp.ClientSession( connector=aiohttp.TCPConnector(ssl=False), headers={ 'X-Apikey': self._apikey, 'Accept-Encoding': 'gzip', 'User-Agent': _USER_AGENT_FMT.format_map({ 'agent': self._agent, 'version': __version__})}) return self._session async def __aenter__(self): return self async def __aexit__(self, type, value, traceback): await self.close_async() def __enter__(self): return self def __exit__(self, type, value, traceback): self.close() def _extract_data_from_json(self, json_response): if not 'data' in json_response: raise ValueError('response does not returns a data field') return json_response['data'] async def _response_to_json(self, response): error = await self.get_error_async(response) if error: raise error return await response.json_async() async def _response_to_object(self, response): json_response = await self._response_to_json(response) try: return Object.from_dict(self._extract_data_from_json(json_response)) except ValueError as err: raise ValueError('response is not an object: {}'.format(err)) async def close_async(self): """Like :func:`close` but returns a coroutine.""" if self._session: await self._session.close() self._session = None def close(self): """Closes the client. When the client is not needed anymore it should be closed for releasing resources like TCP connections. """ return _make_sync(self.close_async( )) def delete(self, path, *path_args): """Sends a DELETE request to a given API endpoint. :param path: Path to API endpoint, can contain format placeholders {}. :param path_args: A variable number of arguments that are put into any placeholders used in path. :type path: str :returns: An instance of :class:`ClientResponse`. """ return _make_sync(self.delete_async(path, *path_args)) async def delete_async(self, path, *path_args): """Like :func:`delete` but returns a coroutine.""" return ClientResponse( await self._get_session().delete(self._full_url(path, *path_args))) def download_file(self, hash, file): """Downloads a file given its hash (SHA-256, SHA-1 or MD5). The file indentified by the hash will be written to the provided file object. The file object must be opened in write binary mode ('wb'). :param hash: File hash. :param file: A file object where the downloaded file will be written to. :type hash: str :type file: file-like object """ return _make_sync(self.download_file_async(hash, file)) async def download_file_async(self, hash, file): """Like :func:`download_file` but returns a coroutine.""" response = await self.get_async('/files/{}/download'.format(hash)) while True: chunk = await response.content.read_async(1024*1024) if not chunk: break file.write(chunk) def feed(self, feed_type, cursor=None): """Returns an iterator for a VirusTotal feed. This functions returns an iterator that allows to retrieve a continuous stream of files as they are scanned by VirusTotal. See the documentation for the :class:`Feed` class for more details. :param feed_type: One of the supported feed types enumerated in :class:`FeedType`. :param cursor: An optional cursor indicating where to start. This argument can be a string in the format 'YYYMMDDhhmm' indicating the date and time of the first package that will be retrieved. :type hash: :class:`vt.FeedType` :type cursor: str """ return Feed(self, feed_type, cursor=cursor) def get(self, path, *path_args, params=None): """Sends a GET request to a given API endpoint. This is a low-level function that returns a raw HTTP response, no error checking nor response parsing is performed. See :func:`get_json`, :func:`get_data` and :func:`get_object` for higher-level functions. :param path: Path to API endpoint, can contain format placeholders {}. :param path_args: A variable number of arguments that are put into any placeholders used in path. :param params: Parameters sent in the request. :type path: str :type params: dict :returns: An instance of :class:`ClientResponse`. """ return _make_sync(self.get_async(path, *path_args, params=params)) async def get_async(self, path, *path_args, params=None): """Like :func:`get` but returns a coroutine.""" return ClientResponse( await self._get_session().get( self._full_url(path, *path_args), params=params)) def get_data(self, path, *path_args, params=None): """Sends a GET request to a given API endpoint and returns response's data. Most VirusTotal API responses are JSON-encoded with the following format:: {"data": <response data>} This function parses the server's response and return only the data, if the response is not in the expected format an exception is raised. For endpoints where the data is a VirusTotal object you can use :func:`get_object` instead. :param path: Path to API endpoint, can contain format placeholders {}. :param path_args: A variable number of arguments that are put into any placeholders used in path. :param params: Parameters sent in the request. :type path: str :type params: dict :returns: Whatever the server returned in the response's data field, it may be a dict, list, string or some other Python type, depending on the endpoint called. """ return _make_sync(self.get_data_async(path, *path_args, params=params)) async def get_data_async(self, path, *path_args, params=None): """Like :func:`get_data` but returns a coroutine.""" json_response = await self.get_json_async(path, *path_args, params=params) return self._extract_data_from_json(json_response) async def get_error_async(self, response): """Given a :class:`ClientResponse` returns a :class:`APIError` This function checks if the response from the VirusTotal backend was an error and returns the appropiate :class:`APIError` or None if no error occurred. :param response: A :class:`ClientResponse` instance. :returns: An instance of :class:`APIError` or None. """ if response.status == 200: return None if response.status >= 400 and response.status <= 499: if response.content_type == 'application/json': json_response = await response.json_async() error = json_response.get('error') if error: return APIError.from_dict(error) return APIError('ClientError', await response.text_async()) return APIError('ServerError', await response.text_async()) def get_json(self, path, *path_args, params=None): """Sends a GET request to a given API endpoint and parses the response. Most VirusTotal API responses are JSON-encoded. This function parses the JSON, check for errors, and return the server response as a dictionary. :param path: Path to API endpoint, can contain format placeholders {}. :param path_args: A variable number of arguments that are put into any placeholders used in path. :param params: Parameters sent in the request. :type path: str :type params: dict :returns: A dictionary with the backend's response. """ return _make_sync(self.get_json_async(path, *path_args, params=params)) async def get_json_async(self, path, *path_args, params=None): """Like :func:`get_json` but returns a coroutine.""" response = await self.get_async(path, *path_args, params=params) return await self._response_to_json(response) def get_object(self, path, *path_args, params=None): """Sends a GET request to a given API endpoint and returns an object. The endpoint specified must return an object, not a collection. This means that get_object can be used with endpoints like /files/{file_id} and /urls/{url_id}, which return an individual object but not with /comments, which returns a collection of objects. :param path: Path to API endpoint, can contain format placeholders {}. :param path_args: A variable number of arguments that are put into any placeholders used in path. :param params: Parameters sent in the request. :type path: str :type params: dict :returns: An instance of :class:`Object`. """ return _make_sync(self.get_object_async(path, *path_args, params=params)) async def get_object_async(self, path, *path_args, params=None): """Like :func:`get_object` but returns a coroutine.""" response = await self.get_async(path, *path_args, params=params) return await self._response_to_object(response) def patch(self, path, *path_args, data=None): """Sends a PATCH request to a given API endpoint. This is a low-level function that returns a raw HTTP response, no error checking nor response parsing is performed. See :func:`patch_object` for a higher-level function. :param path: Path to API endpoint, can contain format placeholders {}. :param path_args: A variable number of arguments that are put into any placeholders used in path. :param data: Data sent in the request body. :type path: str :type data: A string or bytes :returns: An instance of :class:`ClientResponse`. """ return _make_sync(self.patch_async(path, *path_args, data)) async def patch_async(self, path, *path_args, data=None): """Like :func:`patch` but returns a coroutine.""" return ClientResponse( await self._get_session().patch( self._full_url(path, *path_args), data=data)) def patch_object(self, path, *path_args, obj): """Sends a PATCH request for modifying an object. This function modifies an object. The endpoint must be one that identifies an object, like /intelligence/hunting_rulesets/{id}. :param path: Path to API endpoint, can contain format placeholders {}. :param path_args: A variable number of arguments that are put into any placeholders used in path. :param obj: Object that has been modified. :type path: str :type obj: :class:`Object` :returns: An instance of :class:`Object` representing the same object after the changes has been applied. """ return _make_sync(self.patch_object_async(path, *path_args, obj=obj)) async def patch_object_async(self, path, *path_args, obj): """Like :func:`patch_object` but returns a coroutine.""" data = json.dumps({'data': obj.to_dict(modified_attributes_only=True)}) response = await self.patch_async(path, *path_args, data=data) return await self._response_to_object(response) def post(self, path, *path_args, data=None): """Sends a POST request to a given API endpoint. This is a low-level function that returns a raw HTTP response, no error checking nor response parsing is performed. See :func:`post_object` for a higher-level function. :param path: Path to API endpoint, can contain format placeholders {}. :param path_args: A variable number of arguments that are put into any placeholders used in path. :param data: Data sent in the request body. :type path: str :type data: A string or bytes :returns: An instance of :class:`ClientResponse`. """ return _make_sync(self.post_async(path, *path_args, data=data)) async def post_async(self, path, *path_args, data=None): """Like :func:`post` but returns a coroutine.""" return ClientResponse( await self._get_session().post( self._full_url(path, *path_args), data=data)) def post_object(self, path, *path_args, obj): """Sends a POST request for creating an object. This function create a new object. The endpoint must be one that identifies a collection, like /intelligence/hunting_rulesets. :param path: Path to API endpoint. :param path_args: A variable number of arguments that are put into any placeholders used in path. :param obj: Instance :class:`Object` whith the type expected by the API endpoint. :type path: str :type obj: :class:`Object` :returns: An instance of :class:`Object` representing the new object. """ return _make_sync(self.post_object_async(path, *path_args, obj=obj)) async def post_object_async(self, path, *path_args, obj): """Like :func:`post_object` but returns a coroutine.""" data = json.dumps({'data': obj.to_dict()}) response = await self.post_async(path, *path_args, data=data) return await self._response_to_object(response) def iterator(self, path, *path_args, params=None, cursor=None, limit=None, batch_size=0): """Returns an iterator for the collection specified by the given path. The endpoint specified by path must return a collection of objects. An example of such an endpoint are /comments and /intelligence/search. :param path: Path to API endpoint returning a collection. :param path_args: A variable number of arguments that are put into any placeholders used in path. :param params: Additional parameters passed to the endpoint. :param cursor: Cursor for resuming the iteration at the point it was left previously. A cursor can be obtained with Iterator.cursor(). This cursor is not the same one returned by the VirusTotal API. :param limit: Maximum number of objects that will be returned by the iterator. If a limit is not provided the iterator continues until it reaches the last object in the collection. :param batch_size: Maximum number of objects retrieved on each call to the endpoint. If not provided the server will decide how many objects to return. :type path: str :type params: dict :type cursor: str :type limit: int :type batch_size: int :returns: An instance of :class:`Iterator`. """ return Iterator(self, self._full_url(path, *path_args), params=params, cursor=cursor, limit=limit, batch_size=batch_size) def scan_file(self, file, wait_for_completion=False): """Scans a file. :param file: File to be scanned. :param wait_for_completion: If True the function doesn't return until the analysis has been completed. :type file: File-like object. :type wait_for_completion: bool :returns: An instance of :class:`Object` of analysis type. """ return _make_sync(self.scan_file_async( file, wait_for_completion=wait_for_completion)) async def scan_file_async(self, file, wait_for_completion=False): """Like :func:`scan_file` but returns a coroutine.""" # The snippet below could be replaced with this simpler code: # # form_data = aiohttp.FormData() # form_data.add_field('file', file) # # However, aiohttp.FormData assumes that the server supports RFC 5987 and # send a Content-Disposition like: # # 'form-data; name="file"; filename="foobar"; filename*=UTF-8''foobar # # AppEngine's upload handler doesn't like the filename*=UTF-8''foobar field # and fails with this Content-Disposition header. part = aiohttp.get_payload(file) filename = file.name if hasattr(file, 'name') else 'unknown' disposition = 'form-data; name="file"; filename="{}"'.format(filename) part.headers['Content-Disposition'] = disposition form_data = aiohttp.MultipartWriter('form-data') form_data.append_payload(part) upload_url = await self.get_data_async('/files/upload_url') response = ClientResponse( await self._get_session().post(upload_url, data=form_data)) analysis = await self._response_to_object(response) if wait_for_completion: analysis = await self._wait_for_analysis_completion(analysis) return analysis def scan_url(self, url, wait_for_completion=False): """Scans a URL. :param url: The URL to be scanned. :param wait_for_completion: If True the function doesn't return until the analysis has been completed. :type url: str :type wait_for_completion: bool :returns: An instance of :class:`Object` of analysis type. """ return _make_sync(self.scan_url_async( url, wait_for_completion=wait_for_completion)) async def scan_url_async(self, url, wait_for_completion=False): """Like :func:`scan_url` but returns a coroutine.""" form_data = aiohttp.FormData() form_data.add_field('url', url) response = ClientResponse( await self._get_session().post(self._full_url('/urls'), data=form_data)) analysis = await self._response_to_object(response) if wait_for_completion: analysis = await self._wait_for_analysis_completion(analysis) return analysis async def _wait_for_analysis_completion(self, analysis): while True: analysis = await self.get_object_async('/analyses/{}', analysis.id) if analysis.status == 'completed': break await asyncio.sleep(20) return analysis

37.261417

91

0.711677

import aiohttp import asyncio import base64 import json from .error import APIError from .feed import Feed from .object import Object from .iterator import Iterator from .version import __version__ __all__ = [ 'Client', 'ClientResponse', 'url_id'] _API_HOST = 'https://www.virustotal.com' # prefix in the paths you request as it's prepended automatically. _ENDPOINT_PREFIX = '/api/v3' _FMT = '{agent}; vtpy {version}; gzip' def _make_sync(future): try: event_loop = asyncio.get_event_loop() except RuntimeError: event_loop = asyncio.new_event_loop() asyncio.set_event_loop(event_loop) return event_loop.run_until_complete(future) def url_id(url): return base64.urlsafe_b64encode(url.encode()).decode().strip("=") class ClientResponse: def __init__(self, aiohttp_resp): self._aiohttp_resp = aiohttp_resp def __getattr__(self, attr): return getattr(self._aiohttp_resp, attr) @property def content(self): return StreamReader(self._aiohttp_resp.content) async def read_async(self): return await self._aiohttp_resp.read() def read(self): return _make_sync(self.read_async()) async def json_async(self): return await self._aiohttp_resp.json() def json(self): return _make_sync(self.json_async()) async def text_async(self): return await self._aiohttp_resp.text() def text(self): return _make_sync(self.text_async()) class StreamReader: def __init__(self, aiohttp_stream_reader): self._aiohttp_stream_reader = aiohttp_stream_reader def __getattr__(self, attr): return getattr(self._aiohttp_stream_reader, attr) async def read_async(self, n=-1): return await self._aiohttp_stream_reader.read(n) def read(self, n=-1): return _make_sync(self.read_async(n)) async def readany_async(self): return await self._aiohttp_stream_reader.readany() def readany(self): return _make_sync(self.readany_async()) async def readexactly_async(self, n): return await self._aiohttp_stream_reader.readexactly(n) def readexactly(self, n): return _make_sync(self.readexactly_async(n)) async def readline_async(self): return await self._aiohttp_stream_reader.readline() def readline(self): return _make_sync(self.readline_async()) async def readchunk_async(self): return await self._aiohttp_stream_reader.readchunk() def readchunk(self): return _make_sync(self.readchunk_async()) class Client: def __init__(self, apikey, agent="unknown", host=None): if not isinstance(apikey, str): raise ValueError('API key must be a string') if not apikey: raise ValueError('API key can not be an empty string') self._host = host or _API_HOST self._apikey = apikey self._agent = agent self._session = None def _full_url(self, path, *args): try: path = path.format(*args) except IndexError: raise ValueError('Not enough arguments to fill all placeholders in path') if path.startswith('http'): return path return self._host + _ENDPOINT_PREFIX + path def _get_session(self): if not self._session: self._session = aiohttp.ClientSession( connector=aiohttp.TCPConnector(ssl=False), headers={ 'X-Apikey': self._apikey, 'Accept-Encoding': 'gzip', 'User-Agent': _USER_AGENT_FMT.format_map({ 'agent': self._agent, 'version': __version__})}) return self._session async def __aenter__(self): return self async def __aexit__(self, type, value, traceback): await self.close_async() def __enter__(self): return self def __exit__(self, type, value, traceback): self.close() def _extract_data_from_json(self, json_response): if not 'data' in json_response: raise ValueError('response does not returns a data field') return json_response['data'] async def _response_to_json(self, response): error = await self.get_error_async(response) if error: raise error return await response.json_async() async def _response_to_object(self, response): json_response = await self._response_to_json(response) try: return Object.from_dict(self._extract_data_from_json(json_response)) except ValueError as err: raise ValueError('response is not an object: {}'.format(err)) async def close_async(self): if self._session: await self._session.close() self._session = None def close(self): return _make_sync(self.close_async( )) def delete(self, path, *path_args): return _make_sync(self.delete_async(path, *path_args)) async def delete_async(self, path, *path_args): return ClientResponse( await self._get_session().delete(self._full_url(path, *path_args))) def download_file(self, hash, file): return _make_sync(self.download_file_async(hash, file)) async def download_file_async(self, hash, file): response = await self.get_async('/files/{}/download'.format(hash)) while True: chunk = await response.content.read_async(1024*1024) if not chunk: break file.write(chunk) def feed(self, feed_type, cursor=None): return Feed(self, feed_type, cursor=cursor) def get(self, path, *path_args, params=None): return _make_sync(self.get_async(path, *path_args, params=params)) async def get_async(self, path, *path_args, params=None): return ClientResponse( await self._get_session().get( self._full_url(path, *path_args), params=params)) def get_data(self, path, *path_args, params=None): return _make_sync(self.get_data_async(path, *path_args, params=params)) async def get_data_async(self, path, *path_args, params=None): json_response = await self.get_json_async(path, *path_args, params=params) return self._extract_data_from_json(json_response) async def get_error_async(self, response): if response.status == 200: return None if response.status >= 400 and response.status <= 499: if response.content_type == 'application/json': json_response = await response.json_async() error = json_response.get('error') if error: return APIError.from_dict(error) return APIError('ClientError', await response.text_async()) return APIError('ServerError', await response.text_async()) def get_json(self, path, *path_args, params=None): return _make_sync(self.get_json_async(path, *path_args, params=params)) async def get_json_async(self, path, *path_args, params=None): response = await self.get_async(path, *path_args, params=params) return await self._response_to_json(response) def get_object(self, path, *path_args, params=None): return _make_sync(self.get_object_async(path, *path_args, params=params)) async def get_object_async(self, path, *path_args, params=None): response = await self.get_async(path, *path_args, params=params) return await self._response_to_object(response) def patch(self, path, *path_args, data=None): return _make_sync(self.patch_async(path, *path_args, data)) async def patch_async(self, path, *path_args, data=None): return ClientResponse( await self._get_session().patch( self._full_url(path, *path_args), data=data)) def patch_object(self, path, *path_args, obj): return _make_sync(self.patch_object_async(path, *path_args, obj=obj)) async def patch_object_async(self, path, *path_args, obj): data = json.dumps({'data': obj.to_dict(modified_attributes_only=True)}) response = await self.patch_async(path, *path_args, data=data) return await self._response_to_object(response) def post(self, path, *path_args, data=None): return _make_sync(self.post_async(path, *path_args, data=data)) async def post_async(self, path, *path_args, data=None): return ClientResponse( await self._get_session().post( self._full_url(path, *path_args), data=data)) def post_object(self, path, *path_args, obj): return _make_sync(self.post_object_async(path, *path_args, obj=obj)) async def post_object_async(self, path, *path_args, obj): data = json.dumps({'data': obj.to_dict()}) response = await self.post_async(path, *path_args, data=data) return await self._response_to_object(response) def iterator(self, path, *path_args, params=None, cursor=None, limit=None, batch_size=0): return Iterator(self, self._full_url(path, *path_args), params=params, cursor=cursor, limit=limit, batch_size=batch_size) def scan_file(self, file, wait_for_completion=False): return _make_sync(self.scan_file_async( file, wait_for_completion=wait_for_completion)) async def scan_file_async(self, file, wait_for_completion=False): # The snippet below could be replaced with this simpler code: # # form_data = aiohttp.FormData() # form_data.add_field('file', file) # # However, aiohttp.FormData assumes that the server supports RFC 5987 and # send a Content-Disposition like: # # 'form-data; name="file"; filename="foobar"; filename*=UTF-8''foobar part = aiohttp.get_payload(file) filename = file.name if hasattr(file, 'name') else 'unknown' disposition = 'form-data; name="file"; filename="{}"'.format(filename) part.headers['Content-Disposition'] = disposition form_data = aiohttp.MultipartWriter('form-data') form_data.append_payload(part) upload_url = await self.get_data_async('/files/upload_url') response = ClientResponse( await self._get_session().post(upload_url, data=form_data)) analysis = await self._response_to_object(response) if wait_for_completion: analysis = await self._wait_for_analysis_completion(analysis) return analysis def scan_url(self, url, wait_for_completion=False): return _make_sync(self.scan_url_async( url, wait_for_completion=wait_for_completion)) async def scan_url_async(self, url, wait_for_completion=False): form_data = aiohttp.FormData() form_data.add_field('url', url) response = ClientResponse( await self._get_session().post(self._full_url('/urls'), data=form_data)) analysis = await self._response_to_object(response) if wait_for_completion: analysis = await self._wait_for_analysis_completion(analysis) return analysis async def _wait_for_analysis_completion(self, analysis): while True: analysis = await self.get_object_async('/analyses/{}', analysis.id) if analysis.status == 'completed': break await asyncio.sleep(20) return analysis

true

1c43c0fdce2f9815a2113123cb5ee97352dd4dee

1,064

py

Python

leaderboard/models.py

Fredrik3B/kultspill_backend

8aad6431f36dad46ef06f4da40f2bc63c6185dd2

[ "MIT" ]

1

2021-03-11T13:24:55.000Z

leaderboard/models.py

Fredrik3B/kultspill_backend

8aad6431f36dad46ef06f4da40f2bc63c6185dd2

[ "MIT" ]

null

leaderboard/models.py

Fredrik3B/kultspill_backend

8aad6431f36dad46ef06f4da40f2bc63c6185dd2

[ "MIT" ]

null

from django.db import models from django.contrib.auth.models import User from django.db.models.fields import PositiveIntegerField # Create your models here. class Player(models.Model): user = models.OneToOneField(User, on_delete=models.CASCADE) playername = models.CharField(max_length=30) highscore_arcade = models.PositiveIntegerField(default=0) car = models.PositiveSmallIntegerField(default=0) coins = models.PositiveIntegerField(default=0) def __str__(self): return self.playername class Highscore(models.Model): player = models.ForeignKey(Player, on_delete=models.SET_NULL, null=True) score = PositiveIntegerField() def __str__(self): return str(self.player) # return f"{str(self.player)} - {str(self.score)}" class Leaderboard(models.Model): leaderboard_name = models.CharField(max_length=100) nr_of_players = models.PositiveIntegerField(default=0) highscores = models.ManyToManyField(Highscore, blank=True) def __str__(self): return self.leaderboard_name

31.294118

76

0.737782

from django.db import models from django.contrib.auth.models import User from django.db.models.fields import PositiveIntegerField class Player(models.Model): user = models.OneToOneField(User, on_delete=models.CASCADE) playername = models.CharField(max_length=30) highscore_arcade = models.PositiveIntegerField(default=0) car = models.PositiveSmallIntegerField(default=0) coins = models.PositiveIntegerField(default=0) def __str__(self): return self.playername class Highscore(models.Model): player = models.ForeignKey(Player, on_delete=models.SET_NULL, null=True) score = PositiveIntegerField() def __str__(self): return str(self.player) class Leaderboard(models.Model): leaderboard_name = models.CharField(max_length=100) nr_of_players = models.PositiveIntegerField(default=0) highscores = models.ManyToManyField(Highscore, blank=True) def __str__(self): return self.leaderboard_name

true

1c43c29935b123c3b372d55287a8e6ceb0dc1d18

1,596

py

Python

visitor_counter/utils/test_db.py

jcromerohdz/FlaskDev

29539259cba3a0e18c205fb439ee916fb12e5318

[ "MIT" ]

null

visitor_counter/utils/test_db.py

jcromerohdz/FlaskDev

29539259cba3a0e18c205fb439ee916fb12e5318

[ "MIT" ]

null

visitor_counter/utils/test_db.py

jcromerohdz/FlaskDev

29539259cba3a0e18c205fb439ee916fb12e5318

[ "MIT" ]

null

import os from flask_sqlalchemy import sqlalchemy class TestDB: def __init__(self): self.db_name = os.environ['DATABASE_NAME'] + '_test' self.db_host = os.environ['DB_HOST'] self.db_root_password = os.environ['POSTGRES_ROOT_PASSWORD'] if self.db_root_password: self.db_username = 'postgres' self.db_password = self.db_root_password else: self.db_username = os.environ['DB_USERNAME'] self.db_password = os.environ['DB_PASSWORD'] self.db_uri = 'postgresql://%s:%s@%s:5433' %(self.db_username, self.db_password, self.db_host) def create_db(self): # create the database if root user if self.db_root_password: engine = sqlalchemy.create_engine(self.db_uri) conn = engine.connect() conn.execute("COMMIT") conn.execute("CREATE DATABASE "+ self.db_name) conn.close() return self.db_uri + '/' + self.db_name def drop_db(self): # drop the database if root user engine = sqlalchemy.create_engine(self.db_uri) conn = engine.connect() conn.execute("COMMIT") conn.execute("SELECT pg_terminate_backend(pg_stat_activity.pid) FROM pg_stat_activity WHERE datname = 'counter_test' AND pid <> pg_backend_pid()") conn.close() if self.db_root_password: engine = sqlalchemy.create_engine(self.db_uri) conn = engine.connect() conn.execute("COMMIT") conn.execute("DROP DATABASE " + self.db_name) conn.close()

38.926829

154

0.624687

import os from flask_sqlalchemy import sqlalchemy class TestDB: def __init__(self): self.db_name = os.environ['DATABASE_NAME'] + '_test' self.db_host = os.environ['DB_HOST'] self.db_root_password = os.environ['POSTGRES_ROOT_PASSWORD'] if self.db_root_password: self.db_username = 'postgres' self.db_password = self.db_root_password else: self.db_username = os.environ['DB_USERNAME'] self.db_password = os.environ['DB_PASSWORD'] self.db_uri = 'postgresql://%s:%s@%s:5433' %(self.db_username, self.db_password, self.db_host) def create_db(self): if self.db_root_password: engine = sqlalchemy.create_engine(self.db_uri) conn = engine.connect() conn.execute("COMMIT") conn.execute("CREATE DATABASE "+ self.db_name) conn.close() return self.db_uri + '/' + self.db_name def drop_db(self): engine = sqlalchemy.create_engine(self.db_uri) conn = engine.connect() conn.execute("COMMIT") conn.execute("SELECT pg_terminate_backend(pg_stat_activity.pid) FROM pg_stat_activity WHERE datname = 'counter_test' AND pid <> pg_backend_pid()") conn.close() if self.db_root_password: engine = sqlalchemy.create_engine(self.db_uri) conn = engine.connect() conn.execute("COMMIT") conn.execute("DROP DATABASE " + self.db_name) conn.close()

true

1c43c2a3742fc0d3c893cf6ca0a6a729e50cb27d

1,672

py

Python

src/programy/processors/pre/stemming.py

NeolithEra/program-y

8c2396611f30c8095e98ff02988223a641c1a3be

[ "MIT" ]

null

src/programy/processors/pre/stemming.py

NeolithEra/program-y

8c2396611f30c8095e98ff02988223a641c1a3be

[ "MIT" ]

null

src/programy/processors/pre/stemming.py

NeolithEra/program-y

8c2396611f30c8095e98ff02988223a641c1a3be

[ "MIT" ]

null

""" Copyright (c) 2016-2019 Keith Sterling http://www.keithsterling.com 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. """ from programy.utils.logging.ylogger import YLogger from programy.processors.processing import PreProcessor from programy.nlp.stemming import Stemmer class StemmingPreProcessor(PreProcessor): def __init__(self): PreProcessor.__init__(self) def process(self, context, word_string): YLogger.debug(context, "Stemming sentence...") unstemmed_words = context.brain.tokenizer.texts_to_words(word_string) stemmed_words = [Stemmer.stem(x) for x in unstemmed_words] return context.brain.tokenizer.words_to_texts(stemmed_words)

47.771429

120

0.785885

from programy.utils.logging.ylogger import YLogger from programy.processors.processing import PreProcessor from programy.nlp.stemming import Stemmer class StemmingPreProcessor(PreProcessor): def __init__(self): PreProcessor.__init__(self) def process(self, context, word_string): YLogger.debug(context, "Stemming sentence...") unstemmed_words = context.brain.tokenizer.texts_to_words(word_string) stemmed_words = [Stemmer.stem(x) for x in unstemmed_words] return context.brain.tokenizer.words_to_texts(stemmed_words)

true

1c43c2a3c540a69b7c7955a52ca5fcfac255bb4a

488

py

Python

DCNN-Pytorch/shape_testing.py

linklab-uva/deepracing

fc25c47658277df029e7399d295d97a75fe85216

[ "Apache-2.0" ]

11

2020-06-29T15:21:37.000Z

2021-04-12T00:42:26.000Z

DCNN-Pytorch/shape_testing.py

linklab-uva/deepracing

fc25c47658277df029e7399d295d97a75fe85216

[ "Apache-2.0" ]

null

DCNN-Pytorch/shape_testing.py

linklab-uva/deepracing

fc25c47658277df029e7399d295d97a75fe85216

[ "Apache-2.0" ]

4

2019-01-23T23:36:57.000Z

2021-07-02T00:18:37.000Z

import torch import deepracing_models.nn_models.Models as M import time #net = M.AdmiralNetKinematicPredictor(use_3dconv=False, sequence_length=20, context_length=5) net = M.AdmiralNetCurvePredictor(use_3dconv=True, context_length=5, params_per_dimension=6) net = net.cuda(0) im = torch.rand(64,5,3,66,200) im = im.cuda(0) net=net.eval() print(net) print("Running net") tick = time.time() out = net(im) tock = time.time() print(out.shape) print("Got prediction in %f seconds"%(tock-tick))

30.5

93

0.764344

import torch import deepracing_models.nn_models.Models as M import time net = M.AdmiralNetCurvePredictor(use_3dconv=True, context_length=5, params_per_dimension=6) net = net.cuda(0) im = torch.rand(64,5,3,66,200) im = im.cuda(0) net=net.eval() print(net) print("Running net") tick = time.time() out = net(im) tock = time.time() print(out.shape) print("Got prediction in %f seconds"%(tock-tick))

true

1c43c356ac0bdcea6eceba09900f788aa2884b63

1,000

py

Python

isi_sdk_8_1_1/test/test_storagepool_settings_extended.py

mohitjain97/isilon_sdk_python

a371f438f542568edb8cda35e929e6b300b1177c

[ "Unlicense" ]

24

2018-06-22T14:13:23.000Z

2022-03-23T01:21:26.000Z

isi_sdk_8_1_1/test/test_storagepool_settings_extended.py

mohitjain97/isilon_sdk_python

a371f438f542568edb8cda35e929e6b300b1177c

[ "Unlicense" ]

46

2018-04-30T13:28:22.000Z

2022-03-21T21:11:07.000Z

isi_sdk_8_1_1/test/test_storagepool_settings_extended.py

mohitjain97/isilon_sdk_python

a371f438f542568edb8cda35e929e6b300b1177c

[ "Unlicense" ]

29

2018-06-19T00:14:04.000Z

2022-02-08T17:51:19.000Z

# coding: utf-8 """ Isilon SDK Isilon SDK - Language bindings for the OneFS API # noqa: E501 OpenAPI spec version: 6 Contact: sdk@isilon.com Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import unittest import isi_sdk_8_1_1 from isi_sdk_8_1_1.models.storagepool_settings_extended import StoragepoolSettingsExtended # noqa: E501 from isi_sdk_8_1_1.rest import ApiException class TestStoragepoolSettingsExtended(unittest.TestCase): """StoragepoolSettingsExtended unit test stubs""" def setUp(self): pass def tearDown(self): pass def testStoragepoolSettingsExtended(self): """Test StoragepoolSettingsExtended""" # FIXME: construct object with mandatory attributes with example values # model = isi_sdk_8_1_1.models.storagepool_settings_extended.StoragepoolSettingsExtended() # noqa: E501 pass if __name__ == '__main__': unittest.main()

24.390244

112

0.734

from __future__ import absolute_import import unittest import isi_sdk_8_1_1 from isi_sdk_8_1_1.models.storagepool_settings_extended import StoragepoolSettingsExtended from isi_sdk_8_1_1.rest import ApiException class TestStoragepoolSettingsExtended(unittest.TestCase): def setUp(self): pass def tearDown(self): pass def testStoragepoolSettingsExtended(self): s if __name__ == '__main__': unittest.main()

true

1c43c36c2ada9f5d833ee6c1c64f1c7f9dff279a

113

py

Python

dexy/reporters/nodegraph/__init__.py

dsoto/dexy

0f2090250040c3c54c8481a16de8e476b559e87c

[ "MIT" ]

136

2015-01-06T15:04:47.000Z

2021-12-21T22:52:41.000Z

dexy/reporters/nodegraph/__init__.py

dsoto/dexy

0f2090250040c3c54c8481a16de8e476b559e87c

[ "MIT" ]

13

2015-01-26T14:06:58.000Z

2020-03-27T21:16:10.000Z

dexy/reporters/nodegraph/__init__.py

dsoto/dexy

0f2090250040c3c54c8481a16de8e476b559e87c

[ "MIT" ]

34

2015-01-02T16:24:53.000Z

2021-11-27T05:38:30.000Z

import dexy.reporters.nodegraph.d3 import dexy.reporters.nodegraph.text import dexy.reporters.nodegraph.graphviz

28.25

40

0.867257

import dexy.reporters.nodegraph.d3 import dexy.reporters.nodegraph.text import dexy.reporters.nodegraph.graphviz

true

1c43c4671697267b7379d98f325b8b8c320e8e61

3,078

py

Python

cv/models.py

ezraermy/mkcv

a75ec4144b313d1f92795da582d988634cd4ac7c

[ "MIT" ]

null

cv/models.py

ezraermy/mkcv

a75ec4144b313d1f92795da582d988634cd4ac7c

[ "MIT" ]

null

cv/models.py

ezraermy/mkcv

a75ec4144b313d1f92795da582d988634cd4ac7c

[ "MIT" ]

null

from django.db import models from phonenumber_field.modelfields import PhoneNumberField # Create your models here. class CVmaker(models.Model): title = models.CharField(max_length=255, null=True) def __str__(self): return self.title class Meta: verbose_name_plural = 'CVmaker' class Employee(models.Model): Format = ( ('Fancy', 'Fancy'), ('Casual', 'Casual'), ('Modern', 'Modern'), ('Classic', 'Classic'), ('Banking', 'Banking'), ('Neat', 'Neat'), ) sex = ( ('Male', 'Male'), ('Female', 'Female'), ) CV_format = models.CharField( max_length=100, blank=False, choices=Format, help_text="Choose CV format in drop down list.") name = models.CharField(max_length=200, blank=True) date_of_birth = models.DateTimeField(null=True, default="yyyy-mm-dd" ) gender = models.CharField(max_length=20, blank=True, choices=sex) Home_address = models.CharField(max_length=200, blank=True) phone = PhoneNumberField() email = models.EmailField(max_length=200, blank=True) BSc = models.CharField(max_length=2000, blank=True, help_text="BSc title, University name.") BSc_start_date = models.DateTimeField(blank=True, null = True ) BSc_end_date = models.DateTimeField(blank=True, null = True ) MSc = models.CharField( max_length=2000, blank=True, help_text= "Skip if you don't have one.") MSc_start_date = models.DateTimeField(blank=True, null = True ) MSc_end_date = models.DateTimeField(blank=True, null = True ) training = models.CharField( max_length=2000, blank=True, help_text="Skip if you don't have one.") training_start_date = models.DateTimeField(blank=True, null = True ) training_end_date = models.DateTimeField(blank=True, null = True ) work_experience = models.CharField( max_length=2000, blank=True, help_text="Skip if you don't have one.") organization = models.CharField(max_length=200, blank=True) work_exp_start_date = models.DateTimeField(blank=True, null = True) work_exp_end_date = models.DateTimeField(blank=True, null = True ) computer_skills = models.CharField( max_length=500, blank = True, help_text="List all skills from higher to lower.") other_skills = models.CharField( max_length=1000, blank = True, help_text="Your personal qualities other than proffesional skills?") references = models.CharField( max_length=2000, blank = True, help_text="Name email address and phone.") photo = models.FileField(blank=True, help_text="Recomended but not mandatory.") cvmaker = models.ManyToManyField( CVmaker, help_text = "By selecting RATIFY I hereby declare that the information provided is true and correct.", blank = False ) def __str__(self): return self.name class Meta: verbose_name_plural = 'Employee'

34.58427

110

0.649773

from django.db import models from phonenumber_field.modelfields import PhoneNumberField class CVmaker(models.Model): title = models.CharField(max_length=255, null=True) def __str__(self): return self.title class Meta: verbose_name_plural = 'CVmaker' class Employee(models.Model): Format = ( ('Fancy', 'Fancy'), ('Casual', 'Casual'), ('Modern', 'Modern'), ('Classic', 'Classic'), ('Banking', 'Banking'), ('Neat', 'Neat'), ) sex = ( ('Male', 'Male'), ('Female', 'Female'), ) CV_format = models.CharField( max_length=100, blank=False, choices=Format, help_text="Choose CV format in drop down list.") name = models.CharField(max_length=200, blank=True) date_of_birth = models.DateTimeField(null=True, default="yyyy-mm-dd" ) gender = models.CharField(max_length=20, blank=True, choices=sex) Home_address = models.CharField(max_length=200, blank=True) phone = PhoneNumberField() email = models.EmailField(max_length=200, blank=True) BSc = models.CharField(max_length=2000, blank=True, help_text="BSc title, University name.") BSc_start_date = models.DateTimeField(blank=True, null = True ) BSc_end_date = models.DateTimeField(blank=True, null = True ) MSc = models.CharField( max_length=2000, blank=True, help_text= "Skip if you don't have one.") MSc_start_date = models.DateTimeField(blank=True, null = True ) MSc_end_date = models.DateTimeField(blank=True, null = True ) training = models.CharField( max_length=2000, blank=True, help_text="Skip if you don't have one.") training_start_date = models.DateTimeField(blank=True, null = True ) training_end_date = models.DateTimeField(blank=True, null = True ) work_experience = models.CharField( max_length=2000, blank=True, help_text="Skip if you don't have one.") organization = models.CharField(max_length=200, blank=True) work_exp_start_date = models.DateTimeField(blank=True, null = True) work_exp_end_date = models.DateTimeField(blank=True, null = True ) computer_skills = models.CharField( max_length=500, blank = True, help_text="List all skills from higher to lower.") other_skills = models.CharField( max_length=1000, blank = True, help_text="Your personal qualities other than proffesional skills?") references = models.CharField( max_length=2000, blank = True, help_text="Name email address and phone.") photo = models.FileField(blank=True, help_text="Recomended but not mandatory.") cvmaker = models.ManyToManyField( CVmaker, help_text = "By selecting RATIFY I hereby declare that the information provided is true and correct.", blank = False ) def __str__(self): return self.name class Meta: verbose_name_plural = 'Employee'

true

1c43c476a29fee110ef9fc498803191a11545755

35,260

py

Python

meta_dataset/learners/experimental/optimization_learners.py

shikanggao/meta-dataset

7b1e99009516eda3bbd5e740e178ebc37e2d6767

[ "Apache-2.0" ]

null

meta_dataset/learners/experimental/optimization_learners.py

shikanggao/meta-dataset

7b1e99009516eda3bbd5e740e178ebc37e2d6767

[ "Apache-2.0" ]

null

meta_dataset/learners/experimental/optimization_learners.py

shikanggao/meta-dataset

7b1e99009516eda3bbd5e740e178ebc37e2d6767

[ "Apache-2.0" ]

null

# coding=utf-8 # Copyright 2021 The Meta-Dataset Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Lint as: python3 """Optimization-based learners.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import collections import itertools import gin.tf from meta_dataset.learners.experimental import base as learner_base from meta_dataset.models.experimental import reparameterizable_backbones from meta_dataset.models.experimental import reparameterizable_base from meta_dataset.models.experimental import reparameterizable_distributions from six.moves import zip import tensorflow as tf @gin.configurable def sgd(learning_rate): """Construct optimizer triple for stochastic gradient descent (SGD). Inspired by the optimizer definitions in JAX (https://github.com/google/jax/blob/main/jax/experimental/optimizers.py), this implementation of SGD is fully functional (i.e., it maintains no hidden state) and so is compatible for use with an optimization-based meta-learner. Args: learning_rate: A positive scalar. Returns: An (init, update, get_params) function triple. """ def init(x0): return x0 def update(i, grad, state): del i x = state return x - learning_rate * grad def get_params(state): x = state return x return init, update, get_params @gin.configurable def adam(learning_rate, b1=0.9, b2=0.999, eps=1e-8): """Construct optimizer triple for Adam. Inspired by the optimizer definitions in JAX (https://github.com/google/jax/blob/main/jax/experimental/optimizers.py), this implementation of Adam is fully functional (i.e., it maintains no hidden state) and so is compatible for use with an optimization-based meta-learner. Args: learning_rate: A positive scalar. b1: optional, a positive scalar value for beta_1, the exponential decay rate for the first moment estimates (default 0.9). b2: optional, a positive scalar value for beta_2, the exponential decay rate for the second moment estimates (default 0.999). eps: optional, a positive scalar value for epsilon, a small constant for numerical stability (default 1e-8). Returns: An (init, update, get_params) function triple. """ def init(x0): m0 = tf.zeros_like(x0) v0 = tf.zeros_like(x0) return x0, m0, v0 def update(i, grad, state): i = tf.cast(i, dtype=tf.float32) x, m, v = state m = (1. - b1) * grad + b1 * m # First moment estimate. v = (1. - b2) * (grad**2.) + b2 * v # Second moment estimate. mhat = m / (1. - b1**(i + 1.)) # Bias correction. vhat = v / (1. - b2**(i + 1.)) x = x - learning_rate * mhat / (tf.sqrt(vhat) + eps) return x, m, v def get_params(state): x, _, _ = state return x return init, update, get_params def optimizer_update(iterate_collection, iteration_idx, objective_fn, update_fn, get_params_fn, first_order, clip_grad_norm): """Returns the next iterate in the optimization of objective_fn wrt variables. Args: iterate_collection: A (potentially structured) container of tf.Tensors corresponding to the state of the current iterate. iteration_idx: An int Tensor; the iteration number. objective_fn: Callable that takes in variables and produces the value of the objective function. update_fn: Callable that takes in the gradient of the objective function and the current iterate and produces the next iterate. get_params_fn: Callable that takes in the gradient of the objective function and the current iterate and produces the next iterate. first_order: If True, prevent the computation of higher order gradients. clip_grad_norm: If not None, gradient dimensions are independently clipped to lie in the interval [-clip_grad_norm, clip_grad_norm]. """ variables = [get_params_fn(iterate) for iterate in iterate_collection] if tf.executing_eagerly(): with tf.GradientTape(persistent=True) as g: g.watch(variables) loss = objective_fn(variables, iteration_idx) grads = g.gradient(loss, variables) else: loss = objective_fn(variables, iteration_idx) grads = tf.gradients(ys=loss, xs=variables) if clip_grad_norm: grads = [ tf.clip_by_value(grad, -1 * clip_grad_norm, clip_grad_norm) for grad in grads ] if first_order: grads = [tf.stop_gradient(dv) for dv in grads] return [ update_fn(i=iteration_idx, grad=dv, state=s) for (s, dv) in zip(iterate_collection, grads) ] def em_loop( num_updates, e_step, m_step, variables, ): """Expectation-maximization of objective_fn wrt variables for num_updates.""" def _body(step, preupdate_vars): train_predictions_, responsibilities_ = e_step(preupdate_vars) updated_vars = m_step(preupdate_vars, train_predictions_, responsibilities_) return step + 1, updated_vars def _cond(step, *args): del args return step < num_updates step = tf.Variable(0, trainable=False, name='inner_step_counter') loop_vars = (step, variables) step, updated_vars = tf.while_loop( cond=_cond, body=_body, loop_vars=loop_vars, swap_memory=True) return updated_vars @gin.configurable def optimizer_loop( num_updates, objective_fn, update_fn, variables, first_order, clip_grad_norm, ): """Optimization of `objective_fn` for `num_updates` of `variables`.""" # Optimizer specifics. init, update, get_params = update_fn() def _body(step, preupdate_vars): """Optimization loop body.""" updated_vars = optimizer_update( iterate_collection=preupdate_vars, iteration_idx=step, objective_fn=objective_fn, update_fn=update, get_params_fn=get_params, first_order=first_order, clip_grad_norm=clip_grad_norm, ) return step + 1, updated_vars def _cond(step, *args): """Optimization truncation condition.""" del args return step < num_updates step = tf.Variable(0, trainable=False, name='inner_step_counter') loop_vars = (step, [init(var) for var in variables]) step, updated_vars = tf.while_loop( cond=_cond, body=_body, loop_vars=loop_vars, swap_memory=True) return [get_params(v) for v in updated_vars] ForwardPass = collections.namedtuple('ForwardPass', ( 'embeddings', 'predictions', 'inner_objective_value', 'outer_objective_value', 'accuracy', )) Adaptation = collections.namedtuple('Adaptation', ( 'pre_adaptation_support_results', 'post_adaptation_support_results', 'pre_adaptation_query_results', 'post_adaptation_query_results', 'objective_fn', 'support_module_objective_fn', 'query_module_objective_fn', 'forward_pass_fn', 'init_loop_variables_mapping', 'final_loop_variables_mapping', )) @gin.configurable class ExperimentalOptimizationLearner(learner_base.ExperimentalEpisodicLearner): """An optimization-based learner.""" def __init__(self, adapt_embedding_predicate, num_update_steps, additional_evaluation_update_steps, first_order, adapt_batch_norm, clip_grad_norm, update_fn, **kwargs): """Initializes a `ExperimentalOptimizationLearner` instance. Args: adapt_embedding_predicate: A callable that returns True for `tf.Variable` attributes of the embedding function should be adapted for each task. num_update_steps: The number of inner loop optimization steps to take. additional_evaluation_update_steps: The number of additional inner loop optimization steps to take during evaluation (on the meta-test and meta-validation sets). first_order: If True, prevent the computation of higher order gradients. adapt_batch_norm: If True, adapt the scale and offset parameteres of batch normalization layers in the inner loop of optimization. clip_grad_norm: If not None, gradient dimensions are independently clipped to lie in the interval [-clip_grad_norm, clip_grad_norm] before being processed by the `update_fn`. update_fn: A Callable that takes in a learning rate and produces a function triple defining an iterative optimization process; see `sgd` and `adam` for examples. **kwargs: Keyword arguments common to all `ExperimentalEpisodicLearner`s. """ self.adapt_embedding_predicate = adapt_embedding_predicate self.num_update_steps = num_update_steps self.additional_evaluation_update_steps = additional_evaluation_update_steps self.adapt_batch_norm = adapt_batch_norm self.first_order = first_order self.clip_grad_norm = clip_grad_norm self.update_fn = update_fn super(ExperimentalOptimizationLearner, self).__init__(**kwargs) assert isinstance(self.embedding_fn, reparameterizable_base.ReparameterizableModule) def compute_loss(self, onehot_labels, predictions): """Computes the loss on the query set of a given episode.""" return (self.outer_objective( onehot_labels=onehot_labels, predictions=predictions)) @property def trainable_variables(self): """Returns a tuple of variables to update in the outer optimization loop.""" raise NotImplementedError @property def task_parameters(self): """Returns a tuple of variables to update in the inner optimization loop.""" raise NotImplementedError def episodic_init_ops(self, labels, embeddings, task_parameters): raise NotImplementedError def inner_loop_prediction(self, embeddings): raise NotImplementedError def inner_objective(self, onehot_labels, predictions, iteration_idx): raise NotImplementedError def outer_loop_prediction(self, embeddings): raise NotImplementedError def outer_objective(self, onehot_labels, predictions): raise NotImplementedError def forward_pass(self, data): """Wrapper around `detailed_forward_pass` to return query set predictions. Args: data: A `meta_dataset.providers.Episode` containing the data for the episode. Returns: A Tensor of the predictions on the query set. """ forward_pass_result = self.detailed_forward_pass(data) post_adaptation_query_results = ( forward_pass_result.post_adaptation_query_results) return post_adaptation_query_results.predictions def detailed_forward_pass(self, data): """Returns all information from a forward pass of the `OptimizationLearner`. Args: data: A `meta_dataset.providers.Episode` containing the data for the episode. Returns: A `collections.NamedTuple` that contains the results of the forward pass. """ # Loop initialization. init_loop_variables = self.task_parameters init_loop_variable_refs = [ v.experimental_ref() for v in init_loop_variables ] # Construct ops for data-dependent episodic initialization. episodic_init_ops = self.episodic_init_ops( labels=data.support_labels, embeddings=self.embedding_fn(data.support_images, training=True), task_parameters=init_loop_variables, ) def _forward_pass(iteration_idx_, variables_mapping_, images_, onehot_labels_): """Helper function to compute the outputs of a forward pass.""" with self.embedding_fn.reparameterize(variables_mapping_): # TODO(eringrant): Implement non-transductive batch normalization (i.e., # pass the support set statistics through the query set forward pass. embeddings_ = self.embedding_fn(images_, training=True) # TODO(eringrant): `head_fn` is an attribute of the subclass. with self.head_fn.reparameterize(variables_mapping_): predictions_ = self.head_fn(embeddings_)[:, :data.way] accuracy_ = tf.reduce_mean( input_tensor=self.compute_accuracy( onehot_labels=onehot_labels_, predictions=predictions_)) inner_objective_ = self.inner_objective( onehot_labels=onehot_labels_, predictions=predictions_, iteration_idx=iteration_idx_) outer_objective_ = self.outer_objective( onehot_labels=onehot_labels_, predictions=predictions_, ) return ForwardPass( embeddings=embeddings_, predictions=predictions_, inner_objective_value=inner_objective_, outer_objective_value=outer_objective_, accuracy=accuracy_, ) def _objective_fn(loop_variables_, iteration_idx_): """Evaluate the support set objective given `loop_variables_`.""" # Get attribute paths for the loop_variables. loop_variables_mapping_ = dict( zip(init_loop_variable_refs, loop_variables_)) adaptation_support_results = _forward_pass( iteration_idx_=iteration_idx_, variables_mapping_=loop_variables_mapping_, images_=data.support_images, onehot_labels_=data.onehot_support_labels) return adaptation_support_results.inner_objective_value def _e_step(loop_variables_): """Evaluate expectations given `loop_variables_`.""" # Get attribute paths for the loop_variables. loop_variables_dict_ = dict(zip(init_loop_variable_refs, loop_variables_)) with self.embedding_fn.reparameterize(loop_variables_dict_): # TODO(eringrant): training to True for normalization with batch stats. # Figure out the appropriate way to pass this around. train_embeddings_ = self.embedding_fn(data.train_images, training=True) class_embeddings_ = learner_base.class_specific_data( data.onehot_train_labels, train_embeddings_, self.logit_dim) def _compute_responsibilities(examples_, class_idx): train_predictions_ = tf.squeeze( self.head_fn( embeddings=examples_, components=True, class_idx=[class_idx]), axis=1) return tf.nn.softmax(train_predictions_, axis=-1) with self.head_fn.reparameterize(loop_variables_dict_): class_responsibilities_ = [ _compute_responsibilities(embeddings_, class_idx=i) for i, embeddings_ in enumerate(class_embeddings_) ] return class_embeddings_, class_responsibilities_ def _m_step(preupdate_vars, all_embeddings_, all_responsibilities_): """Compute parameter estimates given `loop_variables_`.""" means, log_scales, logits = zip(*map( reparameterizable_distributions.fit_gaussian_mixture, all_embeddings_, all_responsibilities_, itertools.repeat(self.head_fn.damping))) def flatten(x): return list(itertools.chain.from_iterable(x)) means = flatten(means) log_scales = flatten(log_scales) logits = flatten(logits) if not self.head_fn.estimate_loc: means = [None for _ in means] if not self.head_fn.estimate_scale: log_scales = [None for _ in log_scales] if not self.head_fn.estimate_logits: logits = [None for _ in logits] updated_vars = means + log_scales + logits # Replace constant variables. # TODO(eringrant): This interface differs from just excluding these # variables from `task_variables`. no_none_updated_vars = [] for preupdate_var, updated_var in zip(preupdate_vars, updated_vars): if updated_var is None: no_none_updated_vars.append(preupdate_var) else: no_none_updated_vars.append(updated_var) # TODO(eringrant): This assumes an ordering of mean, log_scales, # mixing_logits. return no_none_updated_vars # Loop body. with tf.control_dependencies(episodic_init_ops): # Inner loop of expectation maximization. num_em_steps = getattr(self, 'num_em_steps', 0) if num_em_steps > 0: loop_variables = em_loop( num_updates=self.num_em_steps, e_step=_e_step, m_step=_m_step, variables=loop_variables) # Inner loop of gradient-based optimization. num_optimizer_steps = ( self.num_update_steps + (self.additional_evaluation_update_steps if not self.is_training else 0)) if num_optimizer_steps > 0: # pylint: disable=no-value-for-parameter final_loop_variables = optimizer_loop( num_updates=num_optimizer_steps, objective_fn=_objective_fn, update_fn=self.update_fn, variables=init_loop_variables, first_order=self.first_order, clip_grad_norm=self.clip_grad_norm, ) # pylint: enable=no-value-for-parameter # If no inner loop adaptation is performed, ensure the episodic # initialization is still part of the graph via a control dependency. if num_optimizer_steps + num_em_steps == 0: loop_variables = [tf.identity(v) for v in init_loop_variables] # Get variable references to use when remapping the loop_variables. init_loop_variables_mapping = dict( zip(init_loop_variable_refs, init_loop_variables)) final_loop_variables_mapping = dict( zip(init_loop_variable_refs, final_loop_variables)) # Collect statistics about the inner optimization. with tf.compat.v1.name_scope('pre-adaptation'): with tf.compat.v1.name_scope('support'): pre_adaptation_support_results = _forward_pass( iteration_idx_=0, variables_mapping_=init_loop_variables_mapping, images_=data.support_images, onehot_labels_=data.onehot_support_labels) with tf.compat.v1.name_scope('query'): pre_adaptation_query_results = _forward_pass( iteration_idx_=0, variables_mapping_=init_loop_variables_mapping, images_=data.query_images, onehot_labels_=data.onehot_query_labels) with tf.compat.v1.name_scope('post-adaptation'): with tf.compat.v1.name_scope('support'): post_adaptation_support_results = _forward_pass( iteration_idx_=num_optimizer_steps, variables_mapping_=final_loop_variables_mapping, images_=data.support_images, onehot_labels_=data.onehot_support_labels, ) with tf.compat.v1.name_scope('query'): post_adaptation_query_results = _forward_pass( iteration_idx_=num_optimizer_steps, variables_mapping_=final_loop_variables_mapping, images_=data.query_images, onehot_labels_=data.onehot_query_labels, ) def _support_module_objective_fn(module_variables_, module_variable_refs_): """Evaluate the query set objective given `module_variables_`.""" # Use the values of the parameters at convergence as the default value. variables_mapping_ = final_loop_variables_mapping.copy() # Loop over and replace the module-specific variables. for module_variable_ref, module_variable in zip(module_variable_refs_, module_variables_): variables_mapping_[module_variable_ref] = module_variable adaptation_query_results = _forward_pass( iteration_idx_=num_optimizer_steps, variables_mapping_=variables_mapping_, images_=data.support_images, onehot_labels_=data.onehot_support_labels, ) return adaptation_query_results.inner_objective_value def _query_module_objective_fn(module_variables_, module_variable_refs_): """Evaluate the query set objective given `module_variables_`.""" # Use the values of the parameters at convergence as the default value. variables_mapping_ = final_loop_variables_mapping.copy() # Loop over and replace the module-specific variables. for module_variable_ref, module_variable in zip(module_variable_refs_, module_variables_): variables_mapping_[module_variable_ref] = module_variable adaptation_query_results = _forward_pass( iteration_idx_=num_optimizer_steps, variables_mapping_=variables_mapping_, images_=data.query_images, onehot_labels_=data.onehot_query_labels) return adaptation_query_results.inner_objective_value return Adaptation( pre_adaptation_support_results=pre_adaptation_support_results, post_adaptation_support_results=post_adaptation_support_results, pre_adaptation_query_results=pre_adaptation_query_results, post_adaptation_query_results=post_adaptation_query_results, objective_fn=_objective_fn, support_module_objective_fn=_support_module_objective_fn, query_module_objective_fn=_query_module_objective_fn, forward_pass_fn=_forward_pass, init_loop_variables_mapping=init_loop_variables_mapping, final_loop_variables_mapping=final_loop_variables_mapping, ) @gin.configurable class HeadAndBackboneLearner(ExperimentalOptimizationLearner): """A head-and-backbone learner.""" def __init__(self, head_cls, adapt_head_predicate, episodic_head_init_fn=None, **kwargs): """Initializes a `HeadAndBackboneLearner` instance. Args: head_cls: A subclass of `ReparameterizableModule` used to instantiate the head function. adapt_head_predicate: A callable that returns True for `tf.Variable` attributes of the head function should be adapted for each task. episodic_head_init_fn: A callable that takes in a tuple of one-hot labels, embeddings and head classifier weights, and produces intialization operations to be executed at the start of each episode. If None, no episodic initialization is performed. **kwargs: Keyword arguments common to all `ExperimentalOptimizationLearner`s. """ super(HeadAndBackboneLearner, self).__init__(**kwargs) assert issubclass(head_cls, reparameterizable_base.ReparameterizableModule) self.adapt_head_predicate = adapt_head_predicate self.head_fn = head_cls(output_dim=self.logit_dim) def no_op_initialization(onehot_labels, embeddings, *vbls): del onehot_labels del embeddings del vbls return [tf.no_op()] self.episodic_head_init_fn = episodic_head_init_fn or no_op_initialization def compute_regularizer(self, onehot_labels, predictions): """Computes a regularizer, maybe using `predictions` and `onehot_labels`.""" del onehot_labels del predictions return (tf.reduce_sum(input_tensor=self.embedding_fn.losses) + tf.reduce_sum(input_tensor=self.head_fn.losses)) def build(self): """Instantiate the parameters belonging to this `HeadAndBackboneLearner`.""" super(HeadAndBackboneLearner, self).build() if not self.head_fn.built: self.head_fn.build(self.embedding_shape) self.output_shape = self.head_fn.compute_output_shape(self.embedding_shape) def episodic_init_ops(self, labels, embeddings, task_parameters): """Return operations for episodic initalization of `task_parameters`.""" # Isolate the head parameters. head_parameters = task_parameters[len(list(self.backbone_parameters)):] assert len(head_parameters) == len(list(self.head_parameters)) return self.episodic_head_init_fn(labels, embeddings, *head_parameters) def inner_objective(self, onehot_labels, predictions, iteration_idx): """Alias for softmax cross entropy loss.""" cce = tf.keras.losses.CategoricalCrossentropy() return cce(onehot_labels, predictions) def outer_objective(self, onehot_labels, predictions): """Alias for softmax cross entropy loss.""" cce = tf.keras.losses.CategoricalCrossentropy() regularization = self.compute_regularizer( onehot_labels=onehot_labels, predictions=predictions) return cce(onehot_labels, predictions) + regularization @property def variables(self): """Returns a tuple of this Learner's variables.""" if not self._built: raise learner_base.NotBuiltError return self.embedding_fn.variables + self.head_fn.variables @property def trainable_variables(self): """Returns a tuple of this Learner's trainable variables.""" if not self._built: raise learner_base.NotBuiltError return (self.embedding_fn.trainable_variables + self.head_fn.trainable_variables) @property def task_parameters(self): """Returns a tuple of the variables to be adapted for each task.""" if not self._built: raise learner_base.NotBuiltError return list(itertools.chain(self.backbone_parameters, self.head_parameters)) @property def backbone_parameters(self): return list( self.embedding_fn.reparameterizables(self.adapt_embedding_predicate)) @property def head_parameters(self): return list(self.head_fn.reparameterizables(self.adapt_head_predicate)) @gin.configurable(allowlist=['prototype_multiplier']) def proto_maml_fc_layer_init_fn(labels, embeddings, weights, biases, prototype_multiplier): """Return a list of operations for reparameterized ProtoNet initialization.""" # This is robust to classes missing from the training set, but assumes that # the last class is present. num_ways = tf.cast( tf.math.reduce_max(input_tensor=tf.unique(labels)[0]) + 1, tf.int32) # When there are no examples for a given class, we default its prototype to # zeros, per the implementation of `tf.math.unsorted_segment_mean`. prototypes = tf.math.unsorted_segment_mean(embeddings, labels, num_ways) # Scale the prototypes, which acts as a regularizer on the weights and biases. prototypes *= prototype_multiplier # logit = -<squared Euclidian distance to prototype> # = -(x - p)^T.(x - p) # = 2 x^T.p - p^T.p - x^T.x # = x^T.w + b # where w = 2p, b = -p^T.p output_weights = tf.transpose(a=2 * prototypes) output_biases = -tf.reduce_sum(input_tensor=prototypes * prototypes, axis=1) # We zero-pad to align with the original weights and biases. output_weights = tf.pad( tensor=output_weights, paddings=[[ 0, 0 ], [0, tf.shape(input=weights)[1] - tf.shape(input=output_weights)[1]]], mode='CONSTANT', constant_values=0) output_biases = tf.pad( tensor=output_biases, paddings=[[ 0, tf.shape(input=biases)[0] - tf.shape(input=output_biases)[0] ]], mode='CONSTANT', constant_values=0) return [ weights.assign(output_weights), biases.assign(output_biases), ] def zero_init_fn(labels, embeddings, *vbls): """Return a list of operations for initialization at zero.""" del labels del embeddings return [vbl.assign(tf.zeros_like(vbl)) for vbl in vbls] @gin.configurable class MAML(HeadAndBackboneLearner): """A 'model-agnostic' meta-learner.""" def __init__(self, proto_maml_fc_layer_init, zero_fc_layer_init, **kwargs): """Initializes a MAML instance. Args: proto_maml_fc_layer_init: Whether to use `PrototypicalNetwork`-equivalent fc layer initialization. zero_fc_layer_init: Whether to initialize the parameters of the output layer to zero. **kwargs: Keyword arguments common to all `HeadAndBackboneLearner`s. Raises: ValueError: If both `proto_maml_fc_layer_init` and `zero_fc_layer_init` are `True`. """ if proto_maml_fc_layer_init and zero_fc_layer_init: raise ValueError('Conflicting initialization options for `MAML`.') super(MAML, self).__init__( episodic_head_init_fn=(proto_maml_fc_layer_init_fn if proto_maml_fc_layer_init else zero_init_fn if zero_fc_layer_init else None), adapt_embedding_predicate=reparameterizable_base.is_trainable_variable, adapt_head_predicate=reparameterizable_base.is_trainable_variable, head_cls=reparameterizable_backbones.LinearModel, **kwargs) @gin.configurable class ANIL(HeadAndBackboneLearner): """An 'almost-no-inner-loop' learner.""" def __init__(self, proto_maml_fc_layer_init, zero_fc_layer_init, **kwargs): """Initializes an ANIL instance. Args: proto_maml_fc_layer_init: Whether to use `PrototypicalNetwork`-equivalent fc layer initialization. zero_fc_layer_init: Whether to initialize the parameters of the output layer to zero. **kwargs: Keyword arguments common to all `HeadAndBackboneLearner`s. Raises: ValueError: If both `proto_maml_fc_layer_init` and `zero_fc_layer_init` are `True`. """ if proto_maml_fc_layer_init and zero_fc_layer_init: raise ValueError('Conflicting initialization options for `ANIL`.') super(ANIL, self).__init__( episodic_head_init_fn=(proto_maml_fc_layer_init_fn if proto_maml_fc_layer_init else zero_init_fn if zero_fc_layer_init else None), adapt_embedding_predicate=lambda x: False, adapt_head_predicate=reparameterizable_base.is_trainable_variable, head_cls=reparameterizable_backbones.LinearModel, **kwargs) @gin.configurable def generative_then_discriminative_schedule(proportion_generative, num_updates): num_generative_updates = int(proportion_generative * num_updates) num_discriminative_updates = num_updates - num_generative_updates return [0.0] * num_generative_updates + [1.0] * num_discriminative_updates @gin.configurable class GenerativeClassifier(HeadAndBackboneLearner): """A generative classifier.""" def __init__(self, generative_scaling, interpolation_schedule, **kwargs): """Initializes a GenerativeClassifier instance. Args: generative_scaling: interpolation_schedule: A callable that produces a sequence of coefficients used to interpolate between the generative and discriminative objectives. additional_evaluation_update_steps] array of coefficients used to interpolate between the generative and discriminative objectives. **kwargs: Keyword arguments common to all `HeadAndBackboneLearner`s. """ super(GenerativeClassifier, self).__init__( adapt_embedding_predicate=lambda x: False, adapt_head_predicate=reparameterizable_base.is_trainable_variable, **kwargs) assert isinstance( self.head_fn, reparameterizable_distributions.ReparameterizableClassMixture) self.generative_scaling = generative_scaling self.gen_disc_interpolation = ( interpolation_schedule(num_updates=self.num_update_steps) + [1.0] * self.additional_evaluation_update_steps ) # Assume discriminative. assert all(coef >= 0 for coef in self.gen_disc_interpolation), ( 'Interpolation coefficient should be nonnegative.') # Validate interpolation coefficient. # TODO(eringrant): generalize to other models admitting EM. if isinstance(self.head_fn, reparameterizable_distributions.GaussianMixture): # Override the usual generative training to perform EM. try: num_em_steps = self.gen_disc_interpolation.index(1.0) except ValueError: # All steps are EM. num_em_steps = self.num_update_steps assert ( all(coef == 0.0 for coef in self.gen_disc_interpolation[:num_em_steps]) and all(coef == 1.0 for coef in self.gen_disc_interpolation[num_em_steps:]) ), ('Each step must be fully discriminative or generative when using EM.') self.num_em_steps = num_em_steps self.num_update_steps -= num_em_steps @property def task_parameters(self): return self.head_fn.task_parameters def joint_log_likelihood(self, onehot_labels, log_probs): """Compute p(z, y).""" labels = tf.cast( tf.reduce_sum(input_tensor=onehot_labels, axis=0), dtype=tf.float32) class_log_probs = tf.math.log(labels / tf.reduce_sum(input_tensor=labels)) return log_probs + tf.expand_dims(class_log_probs, 0) def inner_objective(self, onehot_labels, predictions, iteration_idx): """Compute the inner-loop objective.""" # p(z, y), joint log-likelihood. joint_log_probs = self.joint_log_likelihood(onehot_labels, predictions) labels = tf.expand_dims(tf.argmax(input=onehot_labels, axis=-1), axis=-1) numerator = tf.gather(joint_log_probs, labels, axis=-1, batch_dims=1) # p(z), normalization constant. evidence = tf.reduce_logsumexp( input_tensor=joint_log_probs, axis=-1, keepdims=True) # p(y | z) if interpolation coefficient > 0 else p(z, y). # TODO(eringrant): This assumes that `interp` is either 1 or 0. # Adapt to a hybridized approach. interp = tf.gather(self.gen_disc_interpolation, iteration_idx) scale = tf.cond( pred=interp > 0.0, true_fn=lambda: 1.0, false_fn=lambda: self.generative_scaling) return -scale * tf.reduce_mean( input_tensor=numerator - interp * evidence, axis=0) def outer_objective(self, onehot_labels, predictions): """Compute the outer-loop objective.""" joint_log_probs = self.joint_log_likelihood(onehot_labels, predictions) cce = tf.keras.losses.CategoricalCrossentropy() regularization = self.compute_regularizer( onehot_labels=onehot_labels, predictions=predictions) return cce(onehot_labels, joint_log_probs) + regularization def validate_model_independence(self, labels, log_probs, task_parameters): """Partition gradients into those assumed active and inactive.""" num_task_parameters = len(task_parameters) # pylint: disable=g-complex-comprehension on_gradients = [[ tf.norm(tensor=on_gradient) for on_gradient in on_gradients ] for on_gradients in [ tf.gradients( ys=tf.gather(log_probs, tf.compat.v1.where(tf.equal(labels, i))), xs=task_parameters[i * num_task_parameters:(i + 1) * num_task_parameters]) for i in range(1) ]] off_gradients = [[ tf.norm(tensor=off_gradient) for off_gradient in off_gradients ] for off_gradients in [ tf.gradients( ys=tf.gather(log_probs, tf.compat.v1.where(tf.equal(labels, i))), xs=task_parameters[i * num_task_parameters:(i + 1) * num_task_parameters]) for i in range(1) ]] # pylint: enable=g-complex-comprehension return (list(itertools.chain.from_iterable(on_gradients)), list(itertools.chain.from_iterable(off_gradients)))

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from __future__ import absolute_import from __future__ import division from __future__ import print_function import collections import itertools import gin.tf from meta_dataset.learners.experimental import base as learner_base from meta_dataset.models.experimental import reparameterizable_backbones from meta_dataset.models.experimental import reparameterizable_base from meta_dataset.models.experimental import reparameterizable_distributions from six.moves import zip import tensorflow as tf @gin.configurable def sgd(learning_rate): def init(x0): return x0 def update(i, grad, state): del i x = state return x - learning_rate * grad def get_params(state): x = state return x return init, update, get_params @gin.configurable def adam(learning_rate, b1=0.9, b2=0.999, eps=1e-8): def init(x0): m0 = tf.zeros_like(x0) v0 = tf.zeros_like(x0) return x0, m0, v0 def update(i, grad, state): i = tf.cast(i, dtype=tf.float32) x, m, v = state m = (1. - b1) * grad + b1 * m v = (1. - b2) * (grad**2.) + b2 * v mhat = m / (1. - b1**(i + 1.)) vhat = v / (1. - b2**(i + 1.)) x = x - learning_rate * mhat / (tf.sqrt(vhat) + eps) return x, m, v def get_params(state): x, _, _ = state return x return init, update, get_params def optimizer_update(iterate_collection, iteration_idx, objective_fn, update_fn, get_params_fn, first_order, clip_grad_norm): variables = [get_params_fn(iterate) for iterate in iterate_collection] if tf.executing_eagerly(): with tf.GradientTape(persistent=True) as g: g.watch(variables) loss = objective_fn(variables, iteration_idx) grads = g.gradient(loss, variables) else: loss = objective_fn(variables, iteration_idx) grads = tf.gradients(ys=loss, xs=variables) if clip_grad_norm: grads = [ tf.clip_by_value(grad, -1 * clip_grad_norm, clip_grad_norm) for grad in grads ] if first_order: grads = [tf.stop_gradient(dv) for dv in grads] return [ update_fn(i=iteration_idx, grad=dv, state=s) for (s, dv) in zip(iterate_collection, grads) ] def em_loop( num_updates, e_step, m_step, variables, ): def _body(step, preupdate_vars): train_predictions_, responsibilities_ = e_step(preupdate_vars) updated_vars = m_step(preupdate_vars, train_predictions_, responsibilities_) return step + 1, updated_vars def _cond(step, *args): del args return step < num_updates step = tf.Variable(0, trainable=False, name='inner_step_counter') loop_vars = (step, variables) step, updated_vars = tf.while_loop( cond=_cond, body=_body, loop_vars=loop_vars, swap_memory=True) return updated_vars @gin.configurable def optimizer_loop( num_updates, objective_fn, update_fn, variables, first_order, clip_grad_norm, ): init, update, get_params = update_fn() def _body(step, preupdate_vars): updated_vars = optimizer_update( iterate_collection=preupdate_vars, iteration_idx=step, objective_fn=objective_fn, update_fn=update, get_params_fn=get_params, first_order=first_order, clip_grad_norm=clip_grad_norm, ) return step + 1, updated_vars def _cond(step, *args): del args return step < num_updates step = tf.Variable(0, trainable=False, name='inner_step_counter') loop_vars = (step, [init(var) for var in variables]) step, updated_vars = tf.while_loop( cond=_cond, body=_body, loop_vars=loop_vars, swap_memory=True) return [get_params(v) for v in updated_vars] ForwardPass = collections.namedtuple('ForwardPass', ( 'embeddings', 'predictions', 'inner_objective_value', 'outer_objective_value', 'accuracy', )) Adaptation = collections.namedtuple('Adaptation', ( 'pre_adaptation_support_results', 'post_adaptation_support_results', 'pre_adaptation_query_results', 'post_adaptation_query_results', 'objective_fn', 'support_module_objective_fn', 'query_module_objective_fn', 'forward_pass_fn', 'init_loop_variables_mapping', 'final_loop_variables_mapping', )) @gin.configurable class ExperimentalOptimizationLearner(learner_base.ExperimentalEpisodicLearner): def __init__(self, adapt_embedding_predicate, num_update_steps, additional_evaluation_update_steps, first_order, adapt_batch_norm, clip_grad_norm, update_fn, **kwargs): self.adapt_embedding_predicate = adapt_embedding_predicate self.num_update_steps = num_update_steps self.additional_evaluation_update_steps = additional_evaluation_update_steps self.adapt_batch_norm = adapt_batch_norm self.first_order = first_order self.clip_grad_norm = clip_grad_norm self.update_fn = update_fn super(ExperimentalOptimizationLearner, self).__init__(**kwargs) assert isinstance(self.embedding_fn, reparameterizable_base.ReparameterizableModule) def compute_loss(self, onehot_labels, predictions): return (self.outer_objective( onehot_labels=onehot_labels, predictions=predictions)) @property def trainable_variables(self): raise NotImplementedError @property def task_parameters(self): raise NotImplementedError def episodic_init_ops(self, labels, embeddings, task_parameters): raise NotImplementedError def inner_loop_prediction(self, embeddings): raise NotImplementedError def inner_objective(self, onehot_labels, predictions, iteration_idx): raise NotImplementedError def outer_loop_prediction(self, embeddings): raise NotImplementedError def outer_objective(self, onehot_labels, predictions): raise NotImplementedError def forward_pass(self, data): forward_pass_result = self.detailed_forward_pass(data) post_adaptation_query_results = ( forward_pass_result.post_adaptation_query_results) return post_adaptation_query_results.predictions def detailed_forward_pass(self, data): init_loop_variables = self.task_parameters init_loop_variable_refs = [ v.experimental_ref() for v in init_loop_variables ] episodic_init_ops = self.episodic_init_ops( labels=data.support_labels, embeddings=self.embedding_fn(data.support_images, training=True), task_parameters=init_loop_variables, ) def _forward_pass(iteration_idx_, variables_mapping_, images_, onehot_labels_): with self.embedding_fn.reparameterize(variables_mapping_): embeddings_ = self.embedding_fn(images_, training=True) with self.head_fn.reparameterize(variables_mapping_): predictions_ = self.head_fn(embeddings_)[:, :data.way] accuracy_ = tf.reduce_mean( input_tensor=self.compute_accuracy( onehot_labels=onehot_labels_, predictions=predictions_)) inner_objective_ = self.inner_objective( onehot_labels=onehot_labels_, predictions=predictions_, iteration_idx=iteration_idx_) outer_objective_ = self.outer_objective( onehot_labels=onehot_labels_, predictions=predictions_, ) return ForwardPass( embeddings=embeddings_, predictions=predictions_, inner_objective_value=inner_objective_, outer_objective_value=outer_objective_, accuracy=accuracy_, ) def _objective_fn(loop_variables_, iteration_idx_): loop_variables_mapping_ = dict( zip(init_loop_variable_refs, loop_variables_)) adaptation_support_results = _forward_pass( iteration_idx_=iteration_idx_, variables_mapping_=loop_variables_mapping_, images_=data.support_images, onehot_labels_=data.onehot_support_labels) return adaptation_support_results.inner_objective_value def _e_step(loop_variables_): loop_variables_dict_ = dict(zip(init_loop_variable_refs, loop_variables_)) with self.embedding_fn.reparameterize(loop_variables_dict_): train_embeddings_ = self.embedding_fn(data.train_images, training=True) class_embeddings_ = learner_base.class_specific_data( data.onehot_train_labels, train_embeddings_, self.logit_dim) def _compute_responsibilities(examples_, class_idx): train_predictions_ = tf.squeeze( self.head_fn( embeddings=examples_, components=True, class_idx=[class_idx]), axis=1) return tf.nn.softmax(train_predictions_, axis=-1) with self.head_fn.reparameterize(loop_variables_dict_): class_responsibilities_ = [ _compute_responsibilities(embeddings_, class_idx=i) for i, embeddings_ in enumerate(class_embeddings_) ] return class_embeddings_, class_responsibilities_ def _m_step(preupdate_vars, all_embeddings_, all_responsibilities_): means, log_scales, logits = zip(*map( reparameterizable_distributions.fit_gaussian_mixture, all_embeddings_, all_responsibilities_, itertools.repeat(self.head_fn.damping))) def flatten(x): return list(itertools.chain.from_iterable(x)) means = flatten(means) log_scales = flatten(log_scales) logits = flatten(logits) if not self.head_fn.estimate_loc: means = [None for _ in means] if not self.head_fn.estimate_scale: log_scales = [None for _ in log_scales] if not self.head_fn.estimate_logits: logits = [None for _ in logits] updated_vars = means + log_scales + logits no_none_updated_vars = [] for preupdate_var, updated_var in zip(preupdate_vars, updated_vars): if updated_var is None: no_none_updated_vars.append(preupdate_var) else: no_none_updated_vars.append(updated_var) return no_none_updated_vars with tf.control_dependencies(episodic_init_ops): num_em_steps = getattr(self, 'num_em_steps', 0) if num_em_steps > 0: loop_variables = em_loop( num_updates=self.num_em_steps, e_step=_e_step, m_step=_m_step, variables=loop_variables) num_optimizer_steps = ( self.num_update_steps + (self.additional_evaluation_update_steps if not self.is_training else 0)) if num_optimizer_steps > 0: final_loop_variables = optimizer_loop( num_updates=num_optimizer_steps, objective_fn=_objective_fn, update_fn=self.update_fn, variables=init_loop_variables, first_order=self.first_order, clip_grad_norm=self.clip_grad_norm, ) if num_optimizer_steps + num_em_steps == 0: loop_variables = [tf.identity(v) for v in init_loop_variables] init_loop_variables_mapping = dict( zip(init_loop_variable_refs, init_loop_variables)) final_loop_variables_mapping = dict( zip(init_loop_variable_refs, final_loop_variables)) with tf.compat.v1.name_scope('pre-adaptation'): with tf.compat.v1.name_scope('support'): pre_adaptation_support_results = _forward_pass( iteration_idx_=0, variables_mapping_=init_loop_variables_mapping, images_=data.support_images, onehot_labels_=data.onehot_support_labels) with tf.compat.v1.name_scope('query'): pre_adaptation_query_results = _forward_pass( iteration_idx_=0, variables_mapping_=init_loop_variables_mapping, images_=data.query_images, onehot_labels_=data.onehot_query_labels) with tf.compat.v1.name_scope('post-adaptation'): with tf.compat.v1.name_scope('support'): post_adaptation_support_results = _forward_pass( iteration_idx_=num_optimizer_steps, variables_mapping_=final_loop_variables_mapping, images_=data.support_images, onehot_labels_=data.onehot_support_labels, ) with tf.compat.v1.name_scope('query'): post_adaptation_query_results = _forward_pass( iteration_idx_=num_optimizer_steps, variables_mapping_=final_loop_variables_mapping, images_=data.query_images, onehot_labels_=data.onehot_query_labels, ) def _support_module_objective_fn(module_variables_, module_variable_refs_): variables_mapping_ = final_loop_variables_mapping.copy() for module_variable_ref, module_variable in zip(module_variable_refs_, module_variables_): variables_mapping_[module_variable_ref] = module_variable adaptation_query_results = _forward_pass( iteration_idx_=num_optimizer_steps, variables_mapping_=variables_mapping_, images_=data.support_images, onehot_labels_=data.onehot_support_labels, ) return adaptation_query_results.inner_objective_value def _query_module_objective_fn(module_variables_, module_variable_refs_): variables_mapping_ = final_loop_variables_mapping.copy() for module_variable_ref, module_variable in zip(module_variable_refs_, module_variables_): variables_mapping_[module_variable_ref] = module_variable adaptation_query_results = _forward_pass( iteration_idx_=num_optimizer_steps, variables_mapping_=variables_mapping_, images_=data.query_images, onehot_labels_=data.onehot_query_labels) return adaptation_query_results.inner_objective_value return Adaptation( pre_adaptation_support_results=pre_adaptation_support_results, post_adaptation_support_results=post_adaptation_support_results, pre_adaptation_query_results=pre_adaptation_query_results, post_adaptation_query_results=post_adaptation_query_results, objective_fn=_objective_fn, support_module_objective_fn=_support_module_objective_fn, query_module_objective_fn=_query_module_objective_fn, forward_pass_fn=_forward_pass, init_loop_variables_mapping=init_loop_variables_mapping, final_loop_variables_mapping=final_loop_variables_mapping, ) @gin.configurable class HeadAndBackboneLearner(ExperimentalOptimizationLearner): def __init__(self, head_cls, adapt_head_predicate, episodic_head_init_fn=None, **kwargs): super(HeadAndBackboneLearner, self).__init__(**kwargs) assert issubclass(head_cls, reparameterizable_base.ReparameterizableModule) self.adapt_head_predicate = adapt_head_predicate self.head_fn = head_cls(output_dim=self.logit_dim) def no_op_initialization(onehot_labels, embeddings, *vbls): del onehot_labels del embeddings del vbls return [tf.no_op()] self.episodic_head_init_fn = episodic_head_init_fn or no_op_initialization def compute_regularizer(self, onehot_labels, predictions): del onehot_labels del predictions return (tf.reduce_sum(input_tensor=self.embedding_fn.losses) + tf.reduce_sum(input_tensor=self.head_fn.losses)) def build(self): super(HeadAndBackboneLearner, self).build() if not self.head_fn.built: self.head_fn.build(self.embedding_shape) self.output_shape = self.head_fn.compute_output_shape(self.embedding_shape) def episodic_init_ops(self, labels, embeddings, task_parameters): head_parameters = task_parameters[len(list(self.backbone_parameters)):] assert len(head_parameters) == len(list(self.head_parameters)) return self.episodic_head_init_fn(labels, embeddings, *head_parameters) def inner_objective(self, onehot_labels, predictions, iteration_idx): cce = tf.keras.losses.CategoricalCrossentropy() return cce(onehot_labels, predictions) def outer_objective(self, onehot_labels, predictions): cce = tf.keras.losses.CategoricalCrossentropy() regularization = self.compute_regularizer( onehot_labels=onehot_labels, predictions=predictions) return cce(onehot_labels, predictions) + regularization @property def variables(self): if not self._built: raise learner_base.NotBuiltError return self.embedding_fn.variables + self.head_fn.variables @property def trainable_variables(self): if not self._built: raise learner_base.NotBuiltError return (self.embedding_fn.trainable_variables + self.head_fn.trainable_variables) @property def task_parameters(self): if not self._built: raise learner_base.NotBuiltError return list(itertools.chain(self.backbone_parameters, self.head_parameters)) @property def backbone_parameters(self): return list( self.embedding_fn.reparameterizables(self.adapt_embedding_predicate)) @property def head_parameters(self): return list(self.head_fn.reparameterizables(self.adapt_head_predicate)) @gin.configurable(allowlist=['prototype_multiplier']) def proto_maml_fc_layer_init_fn(labels, embeddings, weights, biases, prototype_multiplier): num_ways = tf.cast( tf.math.reduce_max(input_tensor=tf.unique(labels)[0]) + 1, tf.int32) prototypes = tf.math.unsorted_segment_mean(embeddings, labels, num_ways) prototypes *= prototype_multiplier output_weights = tf.transpose(a=2 * prototypes) output_biases = -tf.reduce_sum(input_tensor=prototypes * prototypes, axis=1) output_weights = tf.pad( tensor=output_weights, paddings=[[ 0, 0 ], [0, tf.shape(input=weights)[1] - tf.shape(input=output_weights)[1]]], mode='CONSTANT', constant_values=0) output_biases = tf.pad( tensor=output_biases, paddings=[[ 0, tf.shape(input=biases)[0] - tf.shape(input=output_biases)[0] ]], mode='CONSTANT', constant_values=0) return [ weights.assign(output_weights), biases.assign(output_biases), ] def zero_init_fn(labels, embeddings, *vbls): del labels del embeddings return [vbl.assign(tf.zeros_like(vbl)) for vbl in vbls] @gin.configurable class MAML(HeadAndBackboneLearner): def __init__(self, proto_maml_fc_layer_init, zero_fc_layer_init, **kwargs): if proto_maml_fc_layer_init and zero_fc_layer_init: raise ValueError('Conflicting initialization options for `MAML`.') super(MAML, self).__init__( episodic_head_init_fn=(proto_maml_fc_layer_init_fn if proto_maml_fc_layer_init else zero_init_fn if zero_fc_layer_init else None), adapt_embedding_predicate=reparameterizable_base.is_trainable_variable, adapt_head_predicate=reparameterizable_base.is_trainable_variable, head_cls=reparameterizable_backbones.LinearModel, **kwargs) @gin.configurable class ANIL(HeadAndBackboneLearner): def __init__(self, proto_maml_fc_layer_init, zero_fc_layer_init, **kwargs): if proto_maml_fc_layer_init and zero_fc_layer_init: raise ValueError('Conflicting initialization options for `ANIL`.') super(ANIL, self).__init__( episodic_head_init_fn=(proto_maml_fc_layer_init_fn if proto_maml_fc_layer_init else zero_init_fn if zero_fc_layer_init else None), adapt_embedding_predicate=lambda x: False, adapt_head_predicate=reparameterizable_base.is_trainable_variable, head_cls=reparameterizable_backbones.LinearModel, **kwargs) @gin.configurable def generative_then_discriminative_schedule(proportion_generative, num_updates): num_generative_updates = int(proportion_generative * num_updates) num_discriminative_updates = num_updates - num_generative_updates return [0.0] * num_generative_updates + [1.0] * num_discriminative_updates @gin.configurable class GenerativeClassifier(HeadAndBackboneLearner): def __init__(self, generative_scaling, interpolation_schedule, **kwargs): super(GenerativeClassifier, self).__init__( adapt_embedding_predicate=lambda x: False, adapt_head_predicate=reparameterizable_base.is_trainable_variable, **kwargs) assert isinstance( self.head_fn, reparameterizable_distributions.ReparameterizableClassMixture) self.generative_scaling = generative_scaling self.gen_disc_interpolation = ( interpolation_schedule(num_updates=self.num_update_steps) + [1.0] * self.additional_evaluation_update_steps ) assert all(coef >= 0 for coef in self.gen_disc_interpolation), ( 'Interpolation coefficient should be nonnegative.') if isinstance(self.head_fn, reparameterizable_distributions.GaussianMixture): try: num_em_steps = self.gen_disc_interpolation.index(1.0) except ValueError: num_em_steps = self.num_update_steps assert ( all(coef == 0.0 for coef in self.gen_disc_interpolation[:num_em_steps]) and all(coef == 1.0 for coef in self.gen_disc_interpolation[num_em_steps:]) ), ('Each step must be fully discriminative or generative when using EM.') self.num_em_steps = num_em_steps self.num_update_steps -= num_em_steps @property def task_parameters(self): return self.head_fn.task_parameters def joint_log_likelihood(self, onehot_labels, log_probs): labels = tf.cast( tf.reduce_sum(input_tensor=onehot_labels, axis=0), dtype=tf.float32) class_log_probs = tf.math.log(labels / tf.reduce_sum(input_tensor=labels)) return log_probs + tf.expand_dims(class_log_probs, 0) def inner_objective(self, onehot_labels, predictions, iteration_idx): joint_log_probs = self.joint_log_likelihood(onehot_labels, predictions) labels = tf.expand_dims(tf.argmax(input=onehot_labels, axis=-1), axis=-1) numerator = tf.gather(joint_log_probs, labels, axis=-1, batch_dims=1) evidence = tf.reduce_logsumexp( input_tensor=joint_log_probs, axis=-1, keepdims=True) interp = tf.gather(self.gen_disc_interpolation, iteration_idx) scale = tf.cond( pred=interp > 0.0, true_fn=lambda: 1.0, false_fn=lambda: self.generative_scaling) return -scale * tf.reduce_mean( input_tensor=numerator - interp * evidence, axis=0) def outer_objective(self, onehot_labels, predictions): joint_log_probs = self.joint_log_likelihood(onehot_labels, predictions) cce = tf.keras.losses.CategoricalCrossentropy() regularization = self.compute_regularizer( onehot_labels=onehot_labels, predictions=predictions) return cce(onehot_labels, joint_log_probs) + regularization def validate_model_independence(self, labels, log_probs, task_parameters): num_task_parameters = len(task_parameters) on_gradients = [[ tf.norm(tensor=on_gradient) for on_gradient in on_gradients ] for on_gradients in [ tf.gradients( ys=tf.gather(log_probs, tf.compat.v1.where(tf.equal(labels, i))), xs=task_parameters[i * num_task_parameters:(i + 1) * num_task_parameters]) for i in range(1) ]] off_gradients = [[ tf.norm(tensor=off_gradient) for off_gradient in off_gradients ] for off_gradients in [ tf.gradients( ys=tf.gather(log_probs, tf.compat.v1.where(tf.equal(labels, i))), xs=task_parameters[i * num_task_parameters:(i + 1) * num_task_parameters]) for i in range(1) ]] return (list(itertools.chain.from_iterable(on_gradients)), list(itertools.chain.from_iterable(off_gradients)))

true

1c43c5bfecaed4e7e8964c995dae337fc39d6831

126

py

Python

server/app.py

ZhiShiMao/one

313c64a47e563fabf9b24e67c52308daff6912e3

[ "MIT" ]

null

server/app.py

ZhiShiMao/one

313c64a47e563fabf9b24e67c52308daff6912e3

[ "MIT" ]

null

server/app.py

ZhiShiMao/one

313c64a47e563fabf9b24e67c52308daff6912e3

[ "MIT" ]

null

from fastapi import FastAPI from .api import routers app = FastAPI() for router in routers: app.include_router(router)

14

30

0.753968

from fastapi import FastAPI from .api import routers app = FastAPI() for router in routers: app.include_router(router)

true

1c43c887f1307042e07971b032b7bf4181a998aa

2,327

py

Python

ion/services/coi/object_management_service.py

ooici/coi-services

43246f46a82e597345507afd7dfc7373cb346afa

[ "BSD-2-Clause" ]

3

2016-09-20T09:50:06.000Z

2018-08-10T01:41:38.000Z

ion/services/coi/object_management_service.py

ooici/coi-services

43246f46a82e597345507afd7dfc7373cb346afa

[ "BSD-2-Clause" ]

null

ion/services/coi/object_management_service.py

ooici/coi-services

43246f46a82e597345507afd7dfc7373cb346afa

[ "BSD-2-Clause" ]

2

2016-03-16T22:25:49.000Z

2016-11-26T14:54:21.000Z

#!/usr/bin/env python __author__ = 'Stephen P. Henrie' from interface.services.coi.iobject_management_service import BaseObjectManagementService from pyon.util.containers import is_basic_identifier from pyon.core.exception import BadRequest, NotFound from pyon.core.interfaces.interface_util import is_yaml_string_valid class ObjectManagementService(BaseObjectManagementService): """ A service for defining and managing object types used as resource, messages, etc. """ def create_object_type(self, object_type=None): """ Should receive an ObjectType object """ # Return Value # ------------ # {object_type_id: ''} # if not is_basic_identifier(object_type.name): raise BadRequest("Invalid object_type name: %s" % object_type.name) if not is_yaml_string_valid(object_type.definition): raise BadRequest("Invalid YAML definition") object_type_id, version = self.clients.resource_registry.create(object_type) return object_type_id def update_object_type(self, object_type=None): """ Should receive an ObjectType object """ # Return Value # ------------ # {success: true} # if not is_basic_identifier(object_type.name): raise BadRequest("Invalid object_type name: %s" % object_type.name) if not is_yaml_string_valid(object_type.definition): raise BadRequest("Invalid YAML definition") object_id, version = self.clients.resource_registry.update(object_type) return object_id def read_object_type(self, object_type_id=''): """ Should return an ObjectType object """ # Return Value # ------------ # object_type: {} # if not object_type_id: raise BadRequest("The resource_type_id parameter is missing") return self.clients.resource_registry.read(object_type_id) def delete_object_type(self, object_type_id=''): """method docstring """ # Return Value # ------------ # {success: true} # if not object_type_id: raise BadRequest("The object_type_id parameter is missing") return self.clients.resource_registry.delete(object_type_id)

33.724638

89

0.647615

__author__ = 'Stephen P. Henrie' from interface.services.coi.iobject_management_service import BaseObjectManagementService from pyon.util.containers import is_basic_identifier from pyon.core.exception import BadRequest, NotFound from pyon.core.interfaces.interface_util import is_yaml_string_valid class ObjectManagementService(BaseObjectManagementService): def create_object_type(self, object_type=None): if not is_basic_identifier(object_type.name): raise BadRequest("Invalid object_type name: %s" % object_type.name) if not is_yaml_string_valid(object_type.definition): raise BadRequest("Invalid YAML definition") object_type_id, version = self.clients.resource_registry.create(object_type) return object_type_id def update_object_type(self, object_type=None): if not is_basic_identifier(object_type.name): raise BadRequest("Invalid object_type name: %s" % object_type.name) if not is_yaml_string_valid(object_type.definition): raise BadRequest("Invalid YAML definition") object_id, version = self.clients.resource_registry.update(object_type) return object_id def read_object_type(self, object_type_id=''): if not object_type_id: raise BadRequest("The resource_type_id parameter is missing") return self.clients.resource_registry.read(object_type_id) def delete_object_type(self, object_type_id=''): if not object_type_id: raise BadRequest("The object_type_id parameter is missing") return self.clients.resource_registry.delete(object_type_id)

true

1c43c91efb5c15cc4a8aef6be89a5b65609db2e1

633

py

Python

src/manage.py

ravihansa/django-multiple-user-auth

7b6d1c783fc72d30cb7a5bcdf3a262f6ac0772b1

[ "bzip2-1.0.6" ]

1

2019-10-07T15:26:24.000Z

src/manage.py

ravihansa/django-multiple-user-auth

7b6d1c783fc72d30cb7a5bcdf3a262f6ac0772b1

[ "bzip2-1.0.6" ]

null

src/manage.py

ravihansa/django-multiple-user-auth

7b6d1c783fc72d30cb7a5bcdf3a262f6ac0772b1

[ "bzip2-1.0.6" ]

null

#!/usr/bin/env python """Django's command-line utility for administrative tasks.""" import os import sys def main(): os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'multiUserAuth.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()

28.772727

77

0.685624

import os import sys def main(): os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'multiUserAuth.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()

true

1c43c9505cca8a34ef1bc2f328835501e9ee524a

969

py

Python

django/contrib/admin/decorators.py

ni-ning/django

2e7ba6057cfc82a15a22b6021cd60cf307152e2d

[ "CNRI-Python-GPL-Compatible", "BSD-3-Clause" ]

7

2021-03-18T10:21:34.000Z

2022-02-09T12:54:51.000Z

virtual/lib/python3.6/site-packages/django/contrib/admin/decorators.py

kahenya-anita/Insta-Clone

4894e959c17170505e73aee6dc497aeb29d55a71

[ "MIT" ]

61

2021-01-10T12:59:01.000Z

2021-06-24T09:19:20.000Z

virtual/lib/python3.6/site-packages/django/contrib/admin/decorators.py

kahenya-anita/Insta-Clone

4894e959c17170505e73aee6dc497aeb29d55a71

[ "MIT" ]

7

2021-03-15T13:39:20.000Z

2022-03-29T12:08:21.000Z

def register(*models, site=None): """ Register the given model(s) classes and wrapped ModelAdmin class with admin site: @register(Author) class AuthorAdmin(admin.ModelAdmin): pass The `site` kwarg is an admin site to use instead of the default admin site. """ from django.contrib.admin import ModelAdmin from django.contrib.admin.sites import AdminSite, site as default_site def _model_admin_wrapper(admin_class): if not models: raise ValueError('At least one model must be passed to register.') admin_site = site or default_site if not isinstance(admin_site, AdminSite): raise ValueError('site must subclass AdminSite') if not issubclass(admin_class, ModelAdmin): raise ValueError('Wrapped class must subclass ModelAdmin.') admin_site.register(models, admin_class=admin_class) return admin_class return _model_admin_wrapper

31.258065

79

0.691434

def register(*models, site=None): from django.contrib.admin import ModelAdmin from django.contrib.admin.sites import AdminSite, site as default_site def _model_admin_wrapper(admin_class): if not models: raise ValueError('At least one model must be passed to register.') admin_site = site or default_site if not isinstance(admin_site, AdminSite): raise ValueError('site must subclass AdminSite') if not issubclass(admin_class, ModelAdmin): raise ValueError('Wrapped class must subclass ModelAdmin.') admin_site.register(models, admin_class=admin_class) return admin_class return _model_admin_wrapper

true

1c43cac183ab237cc74013825695bd82ee649cd5

1,123

py

Python

CODE/models/.ipynb_checkpoints/regression-checkpoint.py

happyfuntimegroup/machinelearning

48b381092736591e4685faafdddc713391922266

[ "MIT" ]

1

2021-12-07T12:38:33.000Z

CODE/models/regression.py

SelinZ/machinelearning

105273b2cf5907b23a2ee2b4c076d89f215c38ff

[ "MIT" ]

12

2021-11-30T13:57:48.000Z

2021-12-07T08:33:18.000Z

CODE/models/regression.py

SelinZ/machinelearning

105273b2cf5907b23a2ee2b4c076d89f215c38ff

[ "MIT" ]

1

2021-12-07T12:38:00.000Z

def simple_linear(X_train, y_train, X_val, y_val): from sklearn.linear_model import LinearRegression from sklearn.metrics import r2_score, mean_absolute_error model = LinearRegression() reg = model.fit(X = X_train, y = y_train) y_pred_val = model.predict(X_val) print(r2_score(y_val, y_pred_val)) print(mean_absolute_error(y_val, y_pred_val)) print() #return r2, mae def log_reg(X_train, y_train, X_val, y_val): import numpy as np from sklearn.linear_model import LogisticRegression from sklearn.preprocessing import StandardScaler from sklearn.metrics import r2_score, mean_absolute_error scaler = StandardScaler() X_train_s = scaler.fit_transform(X_train) X_val_s = scaler.transform(X_val) y_ravel = np.ravel(y_train) model = LogisticRegression(random_state = 123, max_iter = 2000) reg = model.fit(X = X_train_s, y = y_ravel) y_pred_val = model.predict(X_val_s) print('r2:', r2_score(y_val, y_pred_val)) # 0.006551953988217396 print("MAE:", mean_absolute_error(y_val, y_pred_val)) # 34.07342328208346 print()

34.030303

80

0.719501

def simple_linear(X_train, y_train, X_val, y_val): from sklearn.linear_model import LinearRegression from sklearn.metrics import r2_score, mean_absolute_error model = LinearRegression() reg = model.fit(X = X_train, y = y_train) y_pred_val = model.predict(X_val) print(r2_score(y_val, y_pred_val)) print(mean_absolute_error(y_val, y_pred_val)) print() def log_reg(X_train, y_train, X_val, y_val): import numpy as np from sklearn.linear_model import LogisticRegression from sklearn.preprocessing import StandardScaler from sklearn.metrics import r2_score, mean_absolute_error scaler = StandardScaler() X_train_s = scaler.fit_transform(X_train) X_val_s = scaler.transform(X_val) y_ravel = np.ravel(y_train) model = LogisticRegression(random_state = 123, max_iter = 2000) reg = model.fit(X = X_train_s, y = y_ravel) y_pred_val = model.predict(X_val_s) print('r2:', r2_score(y_val, y_pred_val)) print("MAE:", mean_absolute_error(y_val, y_pred_val)) print()

true

1c43cad59151b333a58e3e9bbe73d9671373383e

6,744

py

Python

spider/quanmin_anchor.py

AcerFeng/Zhudao

5a36d0dc7bd718ce03aa476a31b36d7b5230b1b7

[ "MIT" ]

null

spider/quanmin_anchor.py

AcerFeng/Zhudao

5a36d0dc7bd718ce03aa476a31b36d7b5230b1b7

[ "MIT" ]

null

spider/quanmin_anchor.py

AcerFeng/Zhudao

5a36d0dc7bd718ce03aa476a31b36d7b5230b1b7

[ "MIT" ]

1

2018-09-13T07:41:44.000Z

#!/usr/bin/env python # -*- encoding: utf-8 -*- # Created on 2018-01-25 00:45:53 # Project: quanmin_anchor from pyspider.libs.base_handler import * import pymysql from datetime import datetime class Handler(BaseHandler): headers = { 'Host': 'www.quanmin.tv', 'Connection': 'Keep-Alive', 'Accept-Encoding': 'gzip', 'User-Agent': 'okhttp/3.9.1', } crawl_config = { 'itag': 'v001', 'headers': headers, } def __init__(self): self.platform_id = 6 try: self.connect = pymysql.connect(host='localhost', port=3306, user='root', passwd='123456', db='zhudao', charset='utf8mb4') except Exception as e: print('Cannot Connect To Mysql!/n', e) raise e @every(minutes=24 * 60) def on_start(self): try: cursor = self.connect.cursor() cursor.execute('select short_name,id,cate_id from category where platform_id = %s;' % str( self.platform_id)) results = cursor.fetchall() for item in results: self.crawl('https://www.quanmin.tv/json/categories/%s/list.json?01250041=&toid=0&token&sid&cv=xiaomi_3.5.33&ua=sagit&dev=28dc7f83c185d337&conn=WIFI&osversion=android_25&cid=6&nonce=b7560fbc6e56929469624ee3c9eb10f9&sign=658A5253C80A22054714887EC24CA693' % (item[0],), callback=self.detail_page, save={ 'short_name': item[0], 'category_id': item[1], 'cate_id': item[2], }) except Exception as e: self.connect.rollback() raise e @config(age=10 * 24 * 60 * 60) def index_page(self, response): for each in response.doc('a[href^="http"]').items(): self.crawl(each.attr.href, callback=self.detail_page) @config(priority=2) def detail_page(self, response): return { "url": response.url, "results": response.json['data'], "category_id": response.save['category_id'], "cate_id": response.save['cate_id'], } def on_result(self,result): if not result: return self.save_data(**result) def save_data(self, **kw): if len(kw['results']) == 0: return for item in kw['results']: try: cursor = self.connect.cursor() cursor.execute('select id from anchor where user_id=%s and platform_id=%s', (item['uid'],self.platform_id)) result = cursor.fetchone() if result: # 更新操作(是否创建个主播分析表（新爬虫？）：包含平台、主播id、) sql = '''update anchor set name=%s, room_id=%s, room_name=%s, cover=%s, avatar=%s, avatar_mid=%s, avatar_small=%s, fans=%s, category_id=%s, cate_id=%s, online=%s, pc_url=%s, update_time=%s, announcement=%s, beauty_cover=%s, show_time=%s where user_id=%s and platform_id=%s''' cursor.execute(sql, (item['nick'], item['no'], item['title'], item['thumb'], item['avatar'], item['avatar'], item['avatar'], item['follow'], kw['category_id'], kw['cate_id'], item['view'], 'https://www.quanmin.tv/' + item['no'], datetime.now(), item['announcement'], item['beauty_cover'] if 'beauty_cover' in item else '', item['play_at'], item['uid'], self.platform_id)) else: # 插入操作 sql = '''insert into anchor( user_id, name, room_id, room_name, cover, avatar, avatar_mid, avatar_small, fans, category_id, cate_id, online, platform_id, pc_url, show_time, announcement, beauty_cover, created_time) values (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)''' cursor.execute(sql, (item['uid'], item['nick'], item['no'], item['title'], item['thumb'], item['avatar'], item['avatar'], item['avatar'], item['follow'], kw['category_id'], kw['cate_id'], item['view'], self.platform_id, 'https://www.quanmin.tv/' + item['no'], item['play_at'], item['announcement'], item['beauty_cover'] if 'beauty_cover' in item else '', datetime.now(), )) self.connect.commit() except Exception as e: self.connect.rollback() raise e

40.626506

270

0.361358

from pyspider.libs.base_handler import * import pymysql from datetime import datetime class Handler(BaseHandler): headers = { 'Host': 'www.quanmin.tv', 'Connection': 'Keep-Alive', 'Accept-Encoding': 'gzip', 'User-Agent': 'okhttp/3.9.1', } crawl_config = { 'itag': 'v001', 'headers': headers, } def __init__(self): self.platform_id = 6 try: self.connect = pymysql.connect(host='localhost', port=3306, user='root', passwd='123456', db='zhudao', charset='utf8mb4') except Exception as e: print('Cannot Connect To Mysql!/n', e) raise e @every(minutes=24 * 60) def on_start(self): try: cursor = self.connect.cursor() cursor.execute('select short_name,id,cate_id from category where platform_id = %s;' % str( self.platform_id)) results = cursor.fetchall() for item in results: self.crawl('https://www.quanmin.tv/json/categories/%s/list.json?01250041=&toid=0&token&sid&cv=xiaomi_3.5.33&ua=sagit&dev=28dc7f83c185d337&conn=WIFI&osversion=android_25&cid=6&nonce=b7560fbc6e56929469624ee3c9eb10f9&sign=658A5253C80A22054714887EC24CA693' % (item[0],), callback=self.detail_page, save={ 'short_name': item[0], 'category_id': item[1], 'cate_id': item[2], }) except Exception as e: self.connect.rollback() raise e @config(age=10 * 24 * 60 * 60) def index_page(self, response): for each in response.doc('a[href^="http"]').items(): self.crawl(each.attr.href, callback=self.detail_page) @config(priority=2) def detail_page(self, response): return { "url": response.url, "results": response.json['data'], "category_id": response.save['category_id'], "cate_id": response.save['cate_id'], } def on_result(self,result): if not result: return self.save_data(**result) def save_data(self, **kw): if len(kw['results']) == 0: return for item in kw['results']: try: cursor = self.connect.cursor() cursor.execute('select id from anchor where user_id=%s and platform_id=%s', (item['uid'],self.platform_id)) result = cursor.fetchone() if result: sql = '''update anchor set name=%s, room_id=%s, room_name=%s, cover=%s, avatar=%s, avatar_mid=%s, avatar_small=%s, fans=%s, category_id=%s, cate_id=%s, online=%s, pc_url=%s, update_time=%s, announcement=%s, beauty_cover=%s, show_time=%s where user_id=%s and platform_id=%s''' cursor.execute(sql, (item['nick'], item['no'], item['title'], item['thumb'], item['avatar'], item['avatar'], item['avatar'], item['follow'], kw['category_id'], kw['cate_id'], item['view'], 'https://www.quanmin.tv/' + item['no'], datetime.now(), item['announcement'], item['beauty_cover'] if 'beauty_cover' in item else '', item['play_at'], item['uid'], self.platform_id)) else: sql = '''insert into anchor( user_id, name, room_id, room_name, cover, avatar, avatar_mid, avatar_small, fans, category_id, cate_id, online, platform_id, pc_url, show_time, announcement, beauty_cover, created_time) values (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)''' cursor.execute(sql, (item['uid'], item['nick'], item['no'], item['title'], item['thumb'], item['avatar'], item['avatar'], item['avatar'], item['follow'], kw['category_id'], kw['cate_id'], item['view'], self.platform_id, 'https://www.quanmin.tv/' + item['no'], item['play_at'], item['announcement'], item['beauty_cover'] if 'beauty_cover' in item else '', datetime.now(), )) self.connect.commit() except Exception as e: self.connect.rollback() raise e

true

1c43cad729128c44137a71f706001f60a8c1b995

100

py

Python

modules/ESP8266/ota.py

ccccmagicboy/MicroPython_fw

d2049bc19e3d5010f5d6d0d17aa13a8693914fbd

[ "MIT" ]

4

2020-02-02T20:12:59.000Z

2020-07-20T15:44:07.000Z

modules/ESP8266/ota.py

ccccmagicboy/MicroPython_fw

d2049bc19e3d5010f5d6d0d17aa13a8693914fbd

[ "MIT" ]

10

2020-02-18T09:57:04.000Z

2020-03-04T11:39:17.000Z

modules/ESP8266/ota.py

ccccmagicboy/MicroPython_fw

d2049bc19e3d5010f5d6d0d17aa13a8693914fbd

[ "MIT" ]

null

import machine def start(): machine.RTC().memory('yaotaota') machine.reset()

12.5

36

0.57

import machine def start(): machine.RTC().memory('yaotaota') machine.reset()

true

1c43cb3436d953b4031542e8c7c48bea06d83265

11,467

py

Python

ovsdbapp/api.py

Sharpeye90/ovsdbapp

6577bbd5e80cdbe95207211d4d47f43b121f2c86

[ "Apache-2.0" ]

34

2017-03-24T10:14:33.000Z

2021-11-19T05:04:54.000Z

ovsdbapp/api.py

Sharpeye90/ovsdbapp

6577bbd5e80cdbe95207211d4d47f43b121f2c86

[ "Apache-2.0" ]

2

2021-09-21T13:23:01.000Z

2021-09-21T13:23:28.000Z

ovsdbapp/api.py

Sharpeye90/ovsdbapp

6577bbd5e80cdbe95207211d4d47f43b121f2c86

[ "Apache-2.0" ]

15

2017-07-06T08:00:52.000Z

2022-03-13T10:29:40.000Z

# Copyright (c) 2014 OpenStack Foundation # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. import abc import contextlib import threading class Command(object, metaclass=abc.ABCMeta): """An OVSDB command that can be executed in a transaction :attr result: The result of executing the command in a transaction """ @abc.abstractmethod def execute(self, **transaction_options): """Immediately execute an OVSDB command This implicitly creates a transaction with the passed options and then executes it, returning the value of the executed transaction :param transaction_options: Options to pass to the transaction """ class Transaction(object, metaclass=abc.ABCMeta): @abc.abstractmethod def commit(self): """Commit the transaction to OVSDB""" @abc.abstractmethod def add(self, command): """Append an OVSDB operation to the transaction Operation is returned back as a convenience. """ def extend(self, commands): """Add multiple OVSDB operations to the transaction List of operations is returned back as a convenience. """ return [self.add(command) for command in commands] def __enter__(self): return self def __exit__(self, exc_type, exc_val, tb): if exc_type is None: self.result = self.commit() class API(object, metaclass=abc.ABCMeta): def __init__(self, nested_transactions=True): # Mapping between a (green)thread and its transaction. self._nested_txns = nested_transactions self._nested_txns_map = {} @abc.abstractmethod def create_transaction(self, check_error=False, log_errors=True, **kwargs): """Create a transaction :param check_error: Allow the transaction to raise an exception? :type check_error: bool :param log_errors: Log an error if the transaction fails? :type log_errors: bool :returns: A new transaction :rtype: :class:`Transaction` """ @contextlib.contextmanager def transaction(self, check_error=False, log_errors=True, nested=True, **kwargs): """Create a transaction context. :param check_error: Allow the transaction to raise an exception? :type check_error: bool :param log_errors: Log an error if the transaction fails? :type log_errors: bool :param nested: Allow nested transactions be merged into one txn :type nested: bool :returns: Either a new transaction or an existing one. :rtype: :class:`Transaction` """ # ojbect() is unique, so if we are not nested, this will always result # in a KeyError on lookup and so a unique Transaction nested = nested and self._nested_txns cur_thread_id = threading.get_ident() if nested else object() if cur_thread_id in self._nested_txns_map: yield self._nested_txns_map[cur_thread_id] else: with self.create_transaction( check_error, log_errors, **kwargs) as txn: self._nested_txns_map[cur_thread_id] = txn try: yield txn finally: del self._nested_txns_map[cur_thread_id] @abc.abstractmethod def db_create(self, table, **col_values): """Create a command to create new record :param table: The OVS table containing the record to be created :type table: string :param col_values: The columns and their associated values to be set after create :type col_values: Dictionary of columns id's and values :returns: :class:`Command` with uuid result """ def db_create_row(self, table, **col_values): """Create a command to create new record Identical to db_create, but returns a RowView result :returns: :class:`Command` with RowView result """ # vif_plug_ovs has a copy of impl_vsctl that doesn't implement this raise NotImplementedError @abc.abstractmethod def db_destroy(self, table, record): """Create a command to destroy a record :param table: The OVS table containing the record to be destroyed :type table: string :param record: The record id (name/uuid) to be destroyed :type record: uuid/string :returns: :class:`Command` with no result """ @abc.abstractmethod def db_set(self, table, record, *col_values): """Create a command to set fields in a record :param table: The OVS table containing the record to be modified :type table: string :param record: The record id (name/uuid) to be modified :type table: string :param col_values: The columns and their associated values :type col_values: Tuples of (column, value). Values may be atomic values or unnested sequences/mappings :returns: :class:`Command` with no result """ # TODO(twilson) Consider handling kwargs for arguments where order # doesn't matter. Though that would break the assert_called_once_with # unit tests @abc.abstractmethod def db_add(self, table, record, column, *values): """Create a command to add a value to a record Adds each value or key-value pair to column in record in table. If column is a map, then each value will be a dict, otherwise a base type. If key already exists in a map column, then the current value is not replaced (use the set command to replace an existing value). :param table: The OVS table containing the record to be modified :type table: string :param record: The record id (name/uuid) to modified :type record: string :param column: The column name to be modified :type column: string :param values: The values to be added to the column :type values: The base type of the column. If column is a map, then a dict containing the key name and the map's value type :returns: :class:`Command` with no result """ @abc.abstractmethod def db_clear(self, table, record, column): """Create a command to clear a field's value in a record :param table: The OVS table containing the record to be modified :type table: string :param record: The record id (name/uuid) to be modified :type record: string :param column: The column whose value should be cleared :type column: string :returns: :class:`Command` with no result """ @abc.abstractmethod def db_get(self, table, record, column): """Create a command to return a field's value in a record :param table: The OVS table containing the record to be queried :type table: string :param record: The record id (name/uuid) to be queried :type record: string :param column: The column whose value should be returned :type column: string :returns: :class:`Command` with the field's value result """ @abc.abstractmethod def db_list(self, table, records=None, columns=None, if_exists=False): """Create a command to return a list of OVSDB records :param table: The OVS table to query :type table: string :param records: The records to return values from :type records: list of record ids (names/uuids) :param columns: Limit results to only columns, None means all columns :type columns: list of column names or None :param if_exists: Do not fail if the record does not exist :type if_exists: bool :returns: :class:`Command` with [{'column', value}, ...] result """ @abc.abstractmethod def db_list_rows(self, table, record=None, if_exists=False): """Create a command to return a list of OVSDB records Identical to db_list, but returns a RowView list result :returns: :class:`Command` with RowView list result """ @abc.abstractmethod def db_find(self, table, *conditions, **kwargs): """Create a command to return find OVSDB records matching conditions :param table: The OVS table to query :type table: string :param conditions:The conditions to satisfy the query :type conditions: 3-tuples containing (column, operation, match) Type of 'match' parameter MUST be identical to column type Examples: atomic: ('tag', '=', 7) map: ('external_ids' '=', {'iface-id': 'xxx'}) field exists? ('external_ids', '!=', {'iface-id', ''}) set contains?: ('protocols', '{>=}', 'OpenFlow13') See the ovs-vsctl man page for more operations :param columns: Limit results to only columns, None means all columns :type columns: list of column names or None :returns: :class:`Command` with [{'column', value}, ...] result """ @abc.abstractmethod def db_find_rows(self, table, *conditions, **kwargs): """Create a command to return OVSDB records matching conditions Identical to db_find, but returns a list of RowView objects :returns: :class:`Command` with RowView list result """ @abc.abstractmethod def db_remove(self, table, record, column, *values, **keyvalues): """Create a command to delete fields or key-value pairs in a record :param table: The OVS table to query :type table: string :param record: The record id (name/uuid) :type record: string :param column: The column whose value should be deleted :type column: string :param values: In case of list columns, the values to be deleted from the list of values In case of dict columns, the keys to delete regardless of their value :type value: varies depending on column :param keyvalues: For dict columns, the keys to delete when the key's value matches the argument value :type keyvalues: values vary depending on column :param if_exists: Do not fail if the record does not exist :type if_exists: bool :returns: :class:`Command` with no result """

40.235088

79

0.616552

import abc import contextlib import threading class Command(object, metaclass=abc.ABCMeta): @abc.abstractmethod def execute(self, **transaction_options): class Transaction(object, metaclass=abc.ABCMeta): @abc.abstractmethod def commit(self): @abc.abstractmethod def add(self, command): def extend(self, commands): return [self.add(command) for command in commands] def __enter__(self): return self def __exit__(self, exc_type, exc_val, tb): if exc_type is None: self.result = self.commit() class API(object, metaclass=abc.ABCMeta): def __init__(self, nested_transactions=True): self._nested_txns = nested_transactions self._nested_txns_map = {} @abc.abstractmethod def create_transaction(self, check_error=False, log_errors=True, **kwargs): @contextlib.contextmanager def transaction(self, check_error=False, log_errors=True, nested=True, **kwargs): nested = nested and self._nested_txns cur_thread_id = threading.get_ident() if nested else object() if cur_thread_id in self._nested_txns_map: yield self._nested_txns_map[cur_thread_id] else: with self.create_transaction( check_error, log_errors, **kwargs) as txn: self._nested_txns_map[cur_thread_id] = txn try: yield txn finally: del self._nested_txns_map[cur_thread_id] @abc.abstractmethod def db_create(self, table, **col_values): def db_create_row(self, table, **col_values): raise NotImplementedError @abc.abstractmethod def db_destroy(self, table, record): @abc.abstractmethod def db_set(self, table, record, *col_values): # TODO(twilson) Consider handling kwargs for arguments where order # doesn't matter. Though that would break the assert_called_once_with @abc.abstractmethod def db_add(self, table, record, column, *values): @abc.abstractmethod def db_clear(self, table, record, column): @abc.abstractmethod def db_get(self, table, record, column): @abc.abstractmethod def db_list(self, table, records=None, columns=None, if_exists=False): @abc.abstractmethod def db_list_rows(self, table, record=None, if_exists=False): @abc.abstractmethod def db_find(self, table, *conditions, **kwargs): @abc.abstractmethod def db_find_rows(self, table, *conditions, **kwargs): @abc.abstractmethod def db_remove(self, table, record, column, *values, **keyvalues):

true

1c43cb8e251561f5ffb61eedf812dca217fee446

524

py

Python

mysite/ads/views.py

MarcosSalib/mysite_django

593c9758eeff0b9f536fe6dd2a84a8097ed1850e

[ "MIT" ]

null

mysite/ads/views.py

MarcosSalib/mysite_django

593c9758eeff0b9f536fe6dd2a84a8097ed1850e

[ "MIT" ]

null

mysite/ads/views.py

MarcosSalib/mysite_django

593c9758eeff0b9f536fe6dd2a84a8097ed1850e

[ "MIT" ]

null

from django.views import View from .owner import OwnerListView, OwnerDetailView, OwnerCreateView, OwnerUpdateView, OwnerDeleteView from .models import Ad # Create your views here. class AdListView(OwnerListView): model = Ad class AdDetailView(OwnerDetailView): model = Ad class AdCreateView(OwnerCreateView): model = Ad fields = ['title', 'price', 'text'] class AdUpdateView(OwnerUpdateView): model = Ad fields = ['title', 'price', 'text'] class AdDeleteView(OwnerDeleteView): model = Ad

20.96

100

0.727099

from django.views import View from .owner import OwnerListView, OwnerDetailView, OwnerCreateView, OwnerUpdateView, OwnerDeleteView from .models import Ad class AdListView(OwnerListView): model = Ad class AdDetailView(OwnerDetailView): model = Ad class AdCreateView(OwnerCreateView): model = Ad fields = ['title', 'price', 'text'] class AdUpdateView(OwnerUpdateView): model = Ad fields = ['title', 'price', 'text'] class AdDeleteView(OwnerDeleteView): model = Ad

true

1c43cbadac49f2a6bc13476f9326555357353823

1,364

py

Python

app.py

eshaan7/IPU_GPA_Calculator

19744864525ceb6de5bd7b6c5c5467870c0281ac

[ "MIT" ]

6

2019-06-12T09:58:14.000Z

2019-07-28T23:13:28.000Z

app.py

eshaan7/IPU_GPA_Calculator

19744864525ceb6de5bd7b6c5c5467870c0281ac

[ "MIT" ]

1

2019-06-24T13:57:21.000Z

app.py

Eshaan7/IPU_GPA_Calculator

19744864525ceb6de5bd7b6c5c5467870c0281ac

[ "MIT" ]

3

2019-06-12T09:58:16.000Z

2019-08-26T20:08:17.000Z

import os from flask import Flask, render_template, request, redirect, url_for app = Flask(__name__) app.secret_key = "66b58fafa6a470f26fd2adc9de14cef2" ''' PWA Stuff ''' # only trigger SSLify if the app is running on Heroku if 'DYNO' in os.environ: from flask_sslify import SSLify sslify = SSLify(app) @app.route('/sw.js', methods=['GET']) def sw(): return app.send_static_file('sw.js') @app.route('/offline.html') def offline(): return app.send_static_file('offline.html') ''' Routes/views ''' @app.route('/', methods=['GET','POST']) @app.route('/home', methods=['GET','POST']) def index(): no_of_subjects = 14 if request.method == 'POST': credits = [ int (i) for i in request.form.getlist('credits[]') ] grades = request.form.getlist('grades[]') FinalGPA = gpa_calc(no_of_subjects, credits, grades) return render_template('index.html', no_of_subjects=no_of_subjects, FinalGPA=FinalGPA) return render_template('index.html', no_of_subjects=no_of_subjects) ''' Utility functions ''' def gpa_calc(no_of_subjects, credits, grades): FinalGPA = 0 grade_dict = { 'O': 10, 'A+': 9, 'A': 8, 'B+': 7, 'B': 6, 'C': 5, 'P': 4 } grade_pts = [ grade_dict[grade] for grade in grades ] for c, gp in zip(credits, grade_pts): FinalGPA = FinalGPA + float(c*gp) return FinalGPA/sum(credits) if __name__=='__main__': app.run(debug = False)

29.021277

88

0.691349

import os from flask import Flask, render_template, request, redirect, url_for app = Flask(__name__) app.secret_key = "66b58fafa6a470f26fd2adc9de14cef2" if 'DYNO' in os.environ: from flask_sslify import SSLify sslify = SSLify(app) @app.route('/sw.js', methods=['GET']) def sw(): return app.send_static_file('sw.js') @app.route('/offline.html') def offline(): return app.send_static_file('offline.html') @app.route('/', methods=['GET','POST']) @app.route('/home', methods=['GET','POST']) def index(): no_of_subjects = 14 if request.method == 'POST': credits = [ int (i) for i in request.form.getlist('credits[]') ] grades = request.form.getlist('grades[]') FinalGPA = gpa_calc(no_of_subjects, credits, grades) return render_template('index.html', no_of_subjects=no_of_subjects, FinalGPA=FinalGPA) return render_template('index.html', no_of_subjects=no_of_subjects) def gpa_calc(no_of_subjects, credits, grades): FinalGPA = 0 grade_dict = { 'O': 10, 'A+': 9, 'A': 8, 'B+': 7, 'B': 6, 'C': 5, 'P': 4 } grade_pts = [ grade_dict[grade] for grade in grades ] for c, gp in zip(credits, grade_pts): FinalGPA = FinalGPA + float(c*gp) return FinalGPA/sum(credits) if __name__=='__main__': app.run(debug = False)

true

1c43cc4bdaf1de7725864b1cb397715c2bbf7991

1,517

py

Python

main.py

yelite/RoomMonitor

2a1699478aa91ec001fe691c1160e7ac7f7f291d

[ "MIT" ]

null

main.py

yelite/RoomMonitor

2a1699478aa91ec001fe691c1160e7ac7f7f291d

[ "MIT" ]

null

main.py

yelite/RoomMonitor

2a1699478aa91ec001fe691c1160e7ac7f7f291d

[ "MIT" ]

null

#coding=utf-8 from datetime import datetime, timedelta from flask import Flask, render_template, g, jsonify from model import Data from helper import gen_unpack_func from fetch import fetch app = Flask(__name__) def get_session(): session = getattr(g, '_session', None) if session is None: from db import Session session = g._session = Session() return session @app.teardown_appcontext def close_session(exception): session = getattr(g, '_session', None) if session: # noinspection PyUnresolvedReferences session.close() @app.route('/') def new(): return render_template('stat.html') @app.route('/data') def data(): session = get_session() t = datetime.now() - timedelta(weeks=2) obj = session.query(Data).filter(Data.time > t).order_by(Data.time).all() # noinspection PyShadowingNames data = { 'timestamp': [], 'pressure': [], 'light_level': [], 'temp': [], 'hum': [] } map(gen_unpack_func(data, ['timestamp', 'pressure', 'light_level', 'temp', 'hum']), obj) return jsonify(**data) @app.route('/current') def current(): rv = fetch() data = {'Light': 4095 - int(rv['light']), 'Temp': str(rv['temp']) + ' C', 'Pressure': str(rv['pressure'] / 100) + ' hPa', 'Humidity': str(rv['hum']) + '%'} return render_template('current.html', items=data.items()) if __name__ == "__main__": app.run(host='0.0.0.0', port=8089, debug=1)

23.338462

92

0.607779

from datetime import datetime, timedelta from flask import Flask, render_template, g, jsonify from model import Data from helper import gen_unpack_func from fetch import fetch app = Flask(__name__) def get_session(): session = getattr(g, '_session', None) if session is None: from db import Session session = g._session = Session() return session @app.teardown_appcontext def close_session(exception): session = getattr(g, '_session', None) if session: session.close() @app.route('/') def new(): return render_template('stat.html') @app.route('/data') def data(): session = get_session() t = datetime.now() - timedelta(weeks=2) obj = session.query(Data).filter(Data.time > t).order_by(Data.time).all() data = { 'timestamp': [], 'pressure': [], 'light_level': [], 'temp': [], 'hum': [] } map(gen_unpack_func(data, ['timestamp', 'pressure', 'light_level', 'temp', 'hum']), obj) return jsonify(**data) @app.route('/current') def current(): rv = fetch() data = {'Light': 4095 - int(rv['light']), 'Temp': str(rv['temp']) + ' C', 'Pressure': str(rv['pressure'] / 100) + ' hPa', 'Humidity': str(rv['hum']) + '%'} return render_template('current.html', items=data.items()) if __name__ == "__main__": app.run(host='0.0.0.0', port=8089, debug=1)

true

1c43ce98895617da79c14c62c224e64da0d934dc

335

py

Python

notes/algo-ds-practice/problems/dp/kadane.py

Anmol-Singh-Jaggi/interview-notes

65af75e2b5725894fa5e13bb5cd9ecf152a0d652

[ "MIT" ]

6

2020-07-05T05:15:19.000Z

2021-01-24T20:17:14.000Z

notes/algo-ds-practice/problems/dp/kadane.py

Anmol-Singh-Jaggi/interview-notes

65af75e2b5725894fa5e13bb5cd9ecf152a0d652

[ "MIT" ]

null

notes/algo-ds-practice/problems/dp/kadane.py

Anmol-Singh-Jaggi/interview-notes

65af75e2b5725894fa5e13bb5cd9ecf152a0d652

[ "MIT" ]

2

2020-09-14T06:46:37.000Z

2021-06-15T09:17:21.000Z

def kadane(arr): max_global = -1e9 max_local = max_global for elem in arr: max_local = max(elem, elem + max_local) max_global = max(max_global, max_local) return max_global def main(): arr = [-2, -3, 4, -1, -2, 1, 5, -3] ret = kadane(arr) print(ret) if __name__ == "__main__": main()

19.705882

47

0.576119

def kadane(arr): max_global = -1e9 max_local = max_global for elem in arr: max_local = max(elem, elem + max_local) max_global = max(max_global, max_local) return max_global def main(): arr = [-2, -3, 4, -1, -2, 1, 5, -3] ret = kadane(arr) print(ret) if __name__ == "__main__": main()

true

1c43d004d4a185b1bfb3c178eb285b0d6056337f

2,100

py

Python

llvm-spirv/test/lit.cfg.py

Ralender/sycl

1fcd1e6d3da10024be92148501aced30ae3aa2be

[ "Apache-2.0" ]

1

2020-09-25T23:33:05.000Z

llvm-spirv/test/lit.cfg.py

Ralender/sycl

1fcd1e6d3da10024be92148501aced30ae3aa2be

[ "Apache-2.0" ]

null

llvm-spirv/test/lit.cfg.py

Ralender/sycl

1fcd1e6d3da10024be92148501aced30ae3aa2be

[ "Apache-2.0" ]

null

# -*- Python -*- import lit.formats import lit.util from lit.llvm import llvm_config from lit.llvm.subst import ToolSubst from lit.llvm.subst import FindTool # Configuration file for the 'lit' test runner. # name: The name of this test suite. config.name = 'LLVM_SPIRV' # testFormat: The test format to use to interpret tests. config.test_format = lit.formats.ShTest(True) # suffixes: A list of file extensions to treat as test files. config.suffixes = ['.cl', '.ll', '.spt'] # excludes: A list of directories and fles to exclude from the testsuite. config.excludes = ['CMakeLists.txt'] if not config.spirv_skip_debug_info_tests: # Direct object generation. config.available_features.add('object-emission') # LLVM can be configured with an empty default triple. # Some tests are "generic" and require a valid default triple. if config.target_triple: config.available_features.add('default_triple') # Ask llvm-config about asserts. llvm_config.feature_config([('--assertion-mode', {'ON': 'asserts'})]) # test_source_root: The root path where tests are located. config.test_source_root = os.path.dirname(__file__) # test_exec_root: The root path where tests should be run. config.test_exec_root = os.path.join(config.test_run_dir, 'test_output') llvm_config.use_default_substitutions() llvm_config.use_clang() config.substitutions.append(('%PATH%', config.environment['PATH'])) tool_dirs = [config.llvm_tools_dir, config.llvm_spirv_dir] tools = ['llvm-as', 'llvm-dis', 'llvm-spirv', 'not'] if not config.spirv_skip_debug_info_tests: tools.extend(['llc', 'llvm-dwarfdump', 'llvm-objdump', 'llvm-readelf', 'llvm-readobj']) llvm_config.add_tool_substitutions(tools, tool_dirs) if config.spirv_tools_have_spirv_val: new_ld_library_path = os.path.pathsep.join((config.spirv_tools_lib_dir, config.environment['LD_LIBRARY_PATH'])) config.environment['LD_LIBRARY_PATH'] = new_ld_library_path llvm_config.add_tool_substitutions(['spirv-val'], [config.spirv_tools_bin_dir]) else: config.substitutions.append(('spirv-val', ':'))

33.870968

115

0.748571

import lit.formats import lit.util from lit.llvm import llvm_config from lit.llvm.subst import ToolSubst from lit.llvm.subst import FindTool config.name = 'LLVM_SPIRV' config.test_format = lit.formats.ShTest(True) config.suffixes = ['.cl', '.ll', '.spt'] config.excludes = ['CMakeLists.txt'] if not config.spirv_skip_debug_info_tests: config.available_features.add('object-emission') if config.target_triple: config.available_features.add('default_triple') llvm_config.feature_config([('--assertion-mode', {'ON': 'asserts'})]) config.test_source_root = os.path.dirname(__file__) config.test_exec_root = os.path.join(config.test_run_dir, 'test_output') llvm_config.use_default_substitutions() llvm_config.use_clang() config.substitutions.append(('%PATH%', config.environment['PATH'])) tool_dirs = [config.llvm_tools_dir, config.llvm_spirv_dir] tools = ['llvm-as', 'llvm-dis', 'llvm-spirv', 'not'] if not config.spirv_skip_debug_info_tests: tools.extend(['llc', 'llvm-dwarfdump', 'llvm-objdump', 'llvm-readelf', 'llvm-readobj']) llvm_config.add_tool_substitutions(tools, tool_dirs) if config.spirv_tools_have_spirv_val: new_ld_library_path = os.path.pathsep.join((config.spirv_tools_lib_dir, config.environment['LD_LIBRARY_PATH'])) config.environment['LD_LIBRARY_PATH'] = new_ld_library_path llvm_config.add_tool_substitutions(['spirv-val'], [config.spirv_tools_bin_dir]) else: config.substitutions.append(('spirv-val', ':'))

true

1c43d04f11f00dcdcc7312268a8db53989c15597

28,424

py

Python

swift/common/request_helpers.py

Priyanka-Askani/swift

1ab691f63778008015b34ce004992844acee9968

[ "Apache-2.0" ]

1

2019-05-25T10:55:58.000Z

swift/common/request_helpers.py

Priyanka-Askani/swift

1ab691f63778008015b34ce004992844acee9968

[ "Apache-2.0" ]

12

2015-06-23T23:20:17.000Z

2016-01-27T00:37:12.000Z

swift/common/request_helpers.py

Priyanka-Askani/swift

1ab691f63778008015b34ce004992844acee9968

[ "Apache-2.0" ]

5

2015-06-04T19:00:11.000Z

2015-12-16T21:04:33.000Z

# Copyright (c) 2010-2013 OpenStack Foundation # # 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. """ Miscellaneous utility functions for use in generating responses. Why not swift.common.utils, you ask? Because this way we can import things from swob in here without creating circular imports. """ import hashlib import itertools import sys import time import six from six.moves.urllib.parse import unquote from swift.common.header_key_dict import HeaderKeyDict from swift import gettext_ as _ from swift.common.storage_policy import POLICIES from swift.common.exceptions import ListingIterError, SegmentError from swift.common.http import is_success from swift.common.swob import HTTPBadRequest, \ HTTPServiceUnavailable, Range, is_chunked, multi_range_iterator, \ HTTPPreconditionFailed from swift.common.utils import split_path, validate_device_partition, \ close_if_possible, maybe_multipart_byteranges_to_document_iters, \ multipart_byteranges_to_document_iters, parse_content_type, \ parse_content_range, csv_append, list_from_csv, Spliterator from swift.common.wsgi import make_subrequest OBJECT_TRANSIENT_SYSMETA_PREFIX = 'x-object-transient-sysmeta-' def get_param(req, name, default=None): """ Get parameters from an HTTP request ensuring proper handling UTF-8 encoding. :param req: request object :param name: parameter name :param default: result to return if the parameter is not found :returns: HTTP request parameter value (as UTF-8 encoded str, not unicode object) :raises HTTPBadRequest: if param not valid UTF-8 byte sequence """ value = req.params.get(name, default) if value and not isinstance(value, six.text_type): try: value.decode('utf8') # Ensure UTF8ness except UnicodeDecodeError: raise HTTPBadRequest( request=req, content_type='text/plain', body='"%s" parameter not valid UTF-8' % name) return value def get_name_and_placement(request, minsegs=1, maxsegs=None, rest_with_last=False): """ Utility function to split and validate the request path and storage policy. The storage policy index is extracted from the headers of the request and converted to a StoragePolicy instance. The remaining args are passed through to :meth:`split_and_validate_path`. :returns: a list, result of :meth:`split_and_validate_path` with the BaseStoragePolicy instance appended on the end :raises HTTPServiceUnavailable: if the path is invalid or no policy exists with the extracted policy_index. """ policy_index = request.headers.get('X-Backend-Storage-Policy-Index') policy = POLICIES.get_by_index(policy_index) if not policy: raise HTTPServiceUnavailable( body=_("No policy with index %s") % policy_index, request=request, content_type='text/plain') results = split_and_validate_path(request, minsegs=minsegs, maxsegs=maxsegs, rest_with_last=rest_with_last) results.append(policy) return results def split_and_validate_path(request, minsegs=1, maxsegs=None, rest_with_last=False): """ Utility function to split and validate the request path. :returns: result of :meth:`~swift.common.utils.split_path` if everything's okay :raises HTTPBadRequest: if something's not okay """ try: segs = split_path(unquote(request.path), minsegs, maxsegs, rest_with_last) validate_device_partition(segs[0], segs[1]) return segs except ValueError as err: raise HTTPBadRequest(body=str(err), request=request, content_type='text/plain') def is_user_meta(server_type, key): """ Tests if a header key starts with and is longer than the user metadata prefix for given server type. :param server_type: type of backend server i.e. [account|container|object] :param key: header key :returns: True if the key satisfies the test, False otherwise """ if len(key) <= 8 + len(server_type): return False return key.lower().startswith(get_user_meta_prefix(server_type)) def is_sys_meta(server_type, key): """ Tests if a header key starts with and is longer than the system metadata prefix for given server type. :param server_type: type of backend server i.e. [account|container|object] :param key: header key :returns: True if the key satisfies the test, False otherwise """ if len(key) <= 11 + len(server_type): return False return key.lower().startswith(get_sys_meta_prefix(server_type)) def is_sys_or_user_meta(server_type, key): """ Tests if a header key starts with and is longer than the user or system metadata prefix for given server type. :param server_type: type of backend server i.e. [account|container|object] :param key: header key :returns: True if the key satisfies the test, False otherwise """ return is_user_meta(server_type, key) or is_sys_meta(server_type, key) def is_object_transient_sysmeta(key): """ Tests if a header key starts with and is longer than the prefix for object transient system metadata. :param key: header key :returns: True if the key satisfies the test, False otherwise """ if len(key) <= len(OBJECT_TRANSIENT_SYSMETA_PREFIX): return False return key.lower().startswith(OBJECT_TRANSIENT_SYSMETA_PREFIX) def strip_user_meta_prefix(server_type, key): """ Removes the user metadata prefix for a given server type from the start of a header key. :param server_type: type of backend server i.e. [account|container|object] :param key: header key :returns: stripped header key """ if not is_user_meta(server_type, key): raise ValueError('Key is not user meta') return key[len(get_user_meta_prefix(server_type)):] def strip_sys_meta_prefix(server_type, key): """ Removes the system metadata prefix for a given server type from the start of a header key. :param server_type: type of backend server i.e. [account|container|object] :param key: header key :returns: stripped header key """ if not is_sys_meta(server_type, key): raise ValueError('Key is not sysmeta') return key[len(get_sys_meta_prefix(server_type)):] def strip_object_transient_sysmeta_prefix(key): """ Removes the object transient system metadata prefix from the start of a header key. :param key: header key :returns: stripped header key """ if not is_object_transient_sysmeta(key): raise ValueError('Key is not object transient sysmeta') return key[len(OBJECT_TRANSIENT_SYSMETA_PREFIX):] def get_user_meta_prefix(server_type): """ Returns the prefix for user metadata headers for given server type. This prefix defines the namespace for headers that will be persisted by backend servers. :param server_type: type of backend server i.e. [account|container|object] :returns: prefix string for server type's user metadata headers """ return 'x-%s-%s-' % (server_type.lower(), 'meta') def get_sys_meta_prefix(server_type): """ Returns the prefix for system metadata headers for given server type. This prefix defines the namespace for headers that will be persisted by backend servers. :param server_type: type of backend server i.e. [account|container|object] :returns: prefix string for server type's system metadata headers """ return 'x-%s-%s-' % (server_type.lower(), 'sysmeta') def get_object_transient_sysmeta(key): """ Returns the Object Transient System Metadata header for key. The Object Transient System Metadata namespace will be persisted by backend object servers. These headers are treated in the same way as object user metadata i.e. all headers in this namespace will be replaced on every POST request. :param key: metadata key :returns: the entire object transient system metadata header for key """ return '%s%s' % (OBJECT_TRANSIENT_SYSMETA_PREFIX, key) def remove_items(headers, condition): """ Removes items from a dict whose keys satisfy the given condition. :param headers: a dict of headers :param condition: a function that will be passed the header key as a single argument and should return True if the header is to be removed. :returns: a dict, possibly empty, of headers that have been removed """ removed = {} keys = filter(condition, headers) removed.update((key, headers.pop(key)) for key in keys) return removed def copy_header_subset(from_r, to_r, condition): """ Will copy desired subset of headers from from_r to to_r. :param from_r: a swob Request or Response :param to_r: a swob Request or Response :param condition: a function that will be passed the header key as a single argument and should return True if the header is to be copied. """ for k, v in from_r.headers.items(): if condition(k): to_r.headers[k] = v def check_path_header(req, name, length, error_msg): """ Validate that the value of path-like header is well formatted. We assume the caller ensures that specific header is present in req.headers. :param req: HTTP request object :param name: header name :param length: length of path segment check :param error_msg: error message for client :returns: A tuple with path parts according to length :raise: HTTPPreconditionFailed if header value is not well formatted. """ hdr = unquote(req.headers.get(name)) if not hdr.startswith('/'): hdr = '/' + hdr try: return split_path(hdr, length, length, True) except ValueError: raise HTTPPreconditionFailed( request=req, body=error_msg) class SegmentedIterable(object): """ Iterable that returns the object contents for a large object. :param req: original request object :param app: WSGI application from which segments will come :param listing_iter: iterable yielding the object segments to fetch, along with the byte subranges to fetch, in the form of a 5-tuple (object-path, object-etag, object-size, first-byte, last-byte). If object-etag is None, no MD5 verification will be done. If object-size is None, no length verification will be done. If first-byte and last-byte are None, then the entire object will be fetched. :param max_get_time: maximum permitted duration of a GET request (seconds) :param logger: logger object :param swift_source: value of swift.source in subrequest environ (just for logging) :param ua_suffix: string to append to user-agent. :param name: name of manifest (used in logging only) :param response_body_length: optional response body length for the response being sent to the client. """ def __init__(self, req, app, listing_iter, max_get_time, logger, ua_suffix, swift_source, name='<not specified>', response_body_length=None): self.req = req self.app = app self.listing_iter = listing_iter self.max_get_time = max_get_time self.logger = logger self.ua_suffix = " " + ua_suffix self.swift_source = swift_source self.name = name self.response_body_length = response_body_length self.peeked_chunk = None self.app_iter = self._internal_iter() self.validated_first_segment = False self.current_resp = None def _coalesce_requests(self): pending_req = pending_etag = pending_size = None try: for seg_dict in self.listing_iter: if 'raw_data' in seg_dict: if pending_req: yield pending_req, pending_etag, pending_size to_yield = seg_dict['raw_data'][ seg_dict['first_byte']:seg_dict['last_byte'] + 1] yield to_yield, None, len(seg_dict['raw_data']) pending_req = pending_etag = pending_size = None continue seg_path, seg_etag, seg_size, first_byte, last_byte = ( seg_dict['path'], seg_dict.get('hash'), seg_dict.get('bytes'), seg_dict['first_byte'], seg_dict['last_byte']) if seg_size is not None: seg_size = int(seg_size) first_byte = first_byte or 0 go_to_end = last_byte is None or ( seg_size is not None and last_byte == seg_size - 1) # The "multipart-manifest=get" query param ensures that the # segment is a plain old object, not some flavor of large # object; therefore, its etag is its MD5sum and hence we can # check it. path = seg_path + '?multipart-manifest=get' seg_req = make_subrequest( self.req.environ, path=path, method='GET', headers={'x-auth-token': self.req.headers.get( 'x-auth-token')}, agent=('%(orig)s ' + self.ua_suffix), swift_source=self.swift_source) seg_req_rangeval = None if first_byte != 0 or not go_to_end: seg_req_rangeval = "%s-%s" % ( first_byte, '' if go_to_end else last_byte) seg_req.headers['Range'] = "bytes=" + seg_req_rangeval # We can only coalesce if paths match and we know the segment # size (so we can check that the ranges will be allowed) if pending_req and pending_req.path == seg_req.path and \ seg_size is not None: # Make a new Range object so that we don't goof up the # existing one in case of invalid ranges. Note that a # range set with too many individual byteranges is # invalid, so we can combine N valid byteranges and 1 # valid byterange and get an invalid range set. if pending_req.range: new_range_str = str(pending_req.range) else: new_range_str = "bytes=0-%d" % (seg_size - 1) if seg_req.range: new_range_str += "," + seg_req_rangeval else: new_range_str += ",0-%d" % (seg_size - 1) if Range(new_range_str).ranges_for_length(seg_size): # Good news! We can coalesce the requests pending_req.headers['Range'] = new_range_str continue # else, Too many ranges, or too much backtracking, or ... if pending_req: yield pending_req, pending_etag, pending_size pending_req = seg_req pending_etag = seg_etag pending_size = seg_size except ListingIterError: e_type, e_value, e_traceback = sys.exc_info() if pending_req: yield pending_req, pending_etag, pending_size six.reraise(e_type, e_value, e_traceback) if pending_req: yield pending_req, pending_etag, pending_size def _requests_to_bytes_iter(self): # Take the requests out of self._coalesce_requests, actually make # the requests, and generate the bytes from the responses. # # Yields 2-tuples (segment-name, byte-chunk). The segment name is # used for logging. for data_or_req, seg_etag, seg_size in self._coalesce_requests(): if isinstance(data_or_req, bytes): # ugly, awful overloading yield ('data segment', data_or_req) continue seg_req = data_or_req seg_resp = seg_req.get_response(self.app) if not is_success(seg_resp.status_int): close_if_possible(seg_resp.app_iter) raise SegmentError( 'While processing manifest %s, ' 'got %d while retrieving %s' % (self.name, seg_resp.status_int, seg_req.path)) elif ((seg_etag and (seg_resp.etag != seg_etag)) or (seg_size and (seg_resp.content_length != seg_size) and not seg_req.range)): # The content-length check is for security reasons. Seems # possible that an attacker could upload a >1mb object and # then replace it with a much smaller object with same # etag. Then create a big nested SLO that calls that # object many times which would hammer our obj servers. If # this is a range request, don't check content-length # because it won't match. close_if_possible(seg_resp.app_iter) raise SegmentError( 'Object segment no longer valid: ' '%(path)s etag: %(r_etag)s != %(s_etag)s or ' '%(r_size)s != %(s_size)s.' % {'path': seg_req.path, 'r_etag': seg_resp.etag, 'r_size': seg_resp.content_length, 's_etag': seg_etag, 's_size': seg_size}) else: self.current_resp = seg_resp seg_hash = None if seg_resp.etag and not seg_req.headers.get('Range'): # Only calculate the MD5 if it we can use it to validate seg_hash = hashlib.md5() document_iters = maybe_multipart_byteranges_to_document_iters( seg_resp.app_iter, seg_resp.headers['Content-Type']) for chunk in itertools.chain.from_iterable(document_iters): if seg_hash: seg_hash.update(chunk) yield (seg_req.path, chunk) close_if_possible(seg_resp.app_iter) if seg_hash and seg_hash.hexdigest() != seg_resp.etag: raise SegmentError( "Bad MD5 checksum in %(name)s for %(seg)s: headers had" " %(etag)s, but object MD5 was actually %(actual)s" % {'seg': seg_req.path, 'etag': seg_resp.etag, 'name': self.name, 'actual': seg_hash.hexdigest()}) def _byte_counting_iter(self): # Checks that we give the client the right number of bytes. Raises # SegmentError if the number of bytes is wrong. bytes_left = self.response_body_length for seg_name, chunk in self._requests_to_bytes_iter(): if bytes_left is None: yield chunk elif bytes_left >= len(chunk): yield chunk bytes_left -= len(chunk) else: yield chunk[:bytes_left] bytes_left -= len(chunk) raise SegmentError( 'Too many bytes for %(name)s; truncating in ' '%(seg)s with %(left)d bytes left' % {'name': self.name, 'seg': seg_name, 'left': -bytes_left}) if bytes_left: raise SegmentError('Expected another %d bytes for %s; ' 'closing connection' % (bytes_left, self.name)) def _time_limited_iter(self): # Makes sure a GET response doesn't take more than self.max_get_time # seconds to process. Raises an exception if things take too long. start_time = time.time() for chunk in self._byte_counting_iter(): now = time.time() yield chunk if now - start_time > self.max_get_time: raise SegmentError( 'While processing manifest %s, ' 'max LO GET time of %ds exceeded' % (self.name, self.max_get_time)) def _internal_iter(self): # Top level of our iterator stack: pass bytes through; catch and # handle exceptions. try: for chunk in self._time_limited_iter(): yield chunk except (ListingIterError, SegmentError) as err: self.logger.error(err) if not self.validated_first_segment: raise finally: if self.current_resp: close_if_possible(self.current_resp.app_iter) def app_iter_range(self, *a, **kw): """ swob.Response will only respond with a 206 status in certain cases; one of those is if the body iterator responds to .app_iter_range(). However, this object (or really, its listing iter) is smart enough to handle the range stuff internally, so we just no-op this out for swob. """ return self def app_iter_ranges(self, ranges, content_type, boundary, content_size): """ This method assumes that iter(self) yields all the data bytes that go into the response, but none of the MIME stuff. For example, if the response will contain three MIME docs with data "abcd", "efgh", and "ijkl", then iter(self) will give out the bytes "abcdefghijkl". This method inserts the MIME stuff around the data bytes. """ si = Spliterator(self) mri = multi_range_iterator( ranges, content_type, boundary, content_size, lambda start, end_plus_one: si.take(end_plus_one - start)) try: for x in mri: yield x finally: self.close() def validate_first_segment(self): """ Start fetching object data to ensure that the first segment (if any) is valid. This is to catch cases like "first segment is missing" or "first segment's etag doesn't match manifest". Note: this does not validate that you have any segments. A zero-segment large object is not erroneous; it is just empty. """ if self.validated_first_segment: return try: self.peeked_chunk = next(self.app_iter) except StopIteration: pass finally: self.validated_first_segment = True def __iter__(self): if self.peeked_chunk is not None: pc = self.peeked_chunk self.peeked_chunk = None return itertools.chain([pc], self.app_iter) else: return self.app_iter def close(self): """ Called when the client disconnect. Ensure that the connection to the backend server is closed. """ close_if_possible(self.app_iter) def http_response_to_document_iters(response, read_chunk_size=4096): """ Takes a successful object-GET HTTP response and turns it into an iterator of (first-byte, last-byte, length, headers, body-file) 5-tuples. The response must either be a 200 or a 206; if you feed in a 204 or something similar, this probably won't work. :param response: HTTP response, like from bufferedhttp.http_connect(), not a swob.Response. """ chunked = is_chunked(dict(response.getheaders())) if response.status == 200: if chunked: # Single "range" that's the whole object with an unknown length return iter([(0, None, None, response.getheaders(), response)]) # Single "range" that's the whole object content_length = int(response.getheader('Content-Length')) return iter([(0, content_length - 1, content_length, response.getheaders(), response)]) content_type, params_list = parse_content_type( response.getheader('Content-Type')) if content_type != 'multipart/byteranges': # Single range; no MIME framing, just the bytes. The start and end # byte indices are in the Content-Range header. start, end, length = parse_content_range( response.getheader('Content-Range')) return iter([(start, end, length, response.getheaders(), response)]) else: # Multiple ranges; the response body is a multipart/byteranges MIME # document, and we have to parse it using the MIME boundary # extracted from the Content-Type header. params = dict(params_list) return multipart_byteranges_to_document_iters( response, params['boundary'], read_chunk_size) def update_etag_is_at_header(req, name): """ Helper function to update an X-Backend-Etag-Is-At header whose value is a list of alternative header names at which the actual object etag may be found. This informs the object server where to look for the actual object etag when processing conditional requests. Since the proxy server and/or middleware may set alternative etag header names, the value of X-Backend-Etag-Is-At is a comma separated list which the object server inspects in order until it finds an etag value. :param req: a swob Request :param name: name of a sysmeta where alternative etag may be found """ if ',' in name: # HTTP header names should not have commas but we'll check anyway raise ValueError('Header name must not contain commas') existing = req.headers.get("X-Backend-Etag-Is-At") req.headers["X-Backend-Etag-Is-At"] = csv_append( existing, name) def resolve_etag_is_at_header(req, metadata): """ Helper function to resolve an alternative etag value that may be stored in metadata under an alternate name. The value of the request's X-Backend-Etag-Is-At header (if it exists) is a comma separated list of alternate names in the metadata at which an alternate etag value may be found. This list is processed in order until an alternate etag is found. The left most value in X-Backend-Etag-Is-At will have been set by the left most middleware, or if no middleware, by ECObjectController, if an EC policy is in use. The left most middleware is assumed to be the authority on what the etag value of the object content is. The resolver will work from left to right in the list until it finds a value that is a name in the given metadata. So the left most wins, IF it exists in the metadata. By way of example, assume the encrypter middleware is installed. If an object is *not* encrypted then the resolver will not find the encrypter middleware's alternate etag sysmeta (X-Object-Sysmeta-Crypto-Etag) but will then find the EC alternate etag (if EC policy). But if the object *is* encrypted then X-Object-Sysmeta-Crypto-Etag is found and used, which is correct because it should be preferred over X-Object-Sysmeta-Ec-Etag. :param req: a swob Request :param metadata: a dict containing object metadata :return: an alternate etag value if any is found, otherwise None """ alternate_etag = None metadata = HeaderKeyDict(metadata) if "X-Backend-Etag-Is-At" in req.headers: names = list_from_csv(req.headers["X-Backend-Etag-Is-At"]) for name in names: if name in metadata: alternate_etag = metadata[name] break return alternate_etag

39.587744

79

0.63369

import hashlib import itertools import sys import time import six from six.moves.urllib.parse import unquote from swift.common.header_key_dict import HeaderKeyDict from swift import gettext_ as _ from swift.common.storage_policy import POLICIES from swift.common.exceptions import ListingIterError, SegmentError from swift.common.http import is_success from swift.common.swob import HTTPBadRequest, \ HTTPServiceUnavailable, Range, is_chunked, multi_range_iterator, \ HTTPPreconditionFailed from swift.common.utils import split_path, validate_device_partition, \ close_if_possible, maybe_multipart_byteranges_to_document_iters, \ multipart_byteranges_to_document_iters, parse_content_type, \ parse_content_range, csv_append, list_from_csv, Spliterator from swift.common.wsgi import make_subrequest OBJECT_TRANSIENT_SYSMETA_PREFIX = 'x-object-transient-sysmeta-' def get_param(req, name, default=None): value = req.params.get(name, default) if value and not isinstance(value, six.text_type): try: value.decode('utf8') except UnicodeDecodeError: raise HTTPBadRequest( request=req, content_type='text/plain', body='"%s" parameter not valid UTF-8' % name) return value def get_name_and_placement(request, minsegs=1, maxsegs=None, rest_with_last=False): policy_index = request.headers.get('X-Backend-Storage-Policy-Index') policy = POLICIES.get_by_index(policy_index) if not policy: raise HTTPServiceUnavailable( body=_("No policy with index %s") % policy_index, request=request, content_type='text/plain') results = split_and_validate_path(request, minsegs=minsegs, maxsegs=maxsegs, rest_with_last=rest_with_last) results.append(policy) return results def split_and_validate_path(request, minsegs=1, maxsegs=None, rest_with_last=False): try: segs = split_path(unquote(request.path), minsegs, maxsegs, rest_with_last) validate_device_partition(segs[0], segs[1]) return segs except ValueError as err: raise HTTPBadRequest(body=str(err), request=request, content_type='text/plain') def is_user_meta(server_type, key): if len(key) <= 8 + len(server_type): return False return key.lower().startswith(get_user_meta_prefix(server_type)) def is_sys_meta(server_type, key): if len(key) <= 11 + len(server_type): return False return key.lower().startswith(get_sys_meta_prefix(server_type)) def is_sys_or_user_meta(server_type, key): return is_user_meta(server_type, key) or is_sys_meta(server_type, key) def is_object_transient_sysmeta(key): if len(key) <= len(OBJECT_TRANSIENT_SYSMETA_PREFIX): return False return key.lower().startswith(OBJECT_TRANSIENT_SYSMETA_PREFIX) def strip_user_meta_prefix(server_type, key): if not is_user_meta(server_type, key): raise ValueError('Key is not user meta') return key[len(get_user_meta_prefix(server_type)):] def strip_sys_meta_prefix(server_type, key): if not is_sys_meta(server_type, key): raise ValueError('Key is not sysmeta') return key[len(get_sys_meta_prefix(server_type)):] def strip_object_transient_sysmeta_prefix(key): if not is_object_transient_sysmeta(key): raise ValueError('Key is not object transient sysmeta') return key[len(OBJECT_TRANSIENT_SYSMETA_PREFIX):] def get_user_meta_prefix(server_type): return 'x-%s-%s-' % (server_type.lower(), 'meta') def get_sys_meta_prefix(server_type): return 'x-%s-%s-' % (server_type.lower(), 'sysmeta') def get_object_transient_sysmeta(key): return '%s%s' % (OBJECT_TRANSIENT_SYSMETA_PREFIX, key) def remove_items(headers, condition): removed = {} keys = filter(condition, headers) removed.update((key, headers.pop(key)) for key in keys) return removed def copy_header_subset(from_r, to_r, condition): for k, v in from_r.headers.items(): if condition(k): to_r.headers[k] = v def check_path_header(req, name, length, error_msg): hdr = unquote(req.headers.get(name)) if not hdr.startswith('/'): hdr = '/' + hdr try: return split_path(hdr, length, length, True) except ValueError: raise HTTPPreconditionFailed( request=req, body=error_msg) class SegmentedIterable(object): def __init__(self, req, app, listing_iter, max_get_time, logger, ua_suffix, swift_source, name='<not specified>', response_body_length=None): self.req = req self.app = app self.listing_iter = listing_iter self.max_get_time = max_get_time self.logger = logger self.ua_suffix = " " + ua_suffix self.swift_source = swift_source self.name = name self.response_body_length = response_body_length self.peeked_chunk = None self.app_iter = self._internal_iter() self.validated_first_segment = False self.current_resp = None def _coalesce_requests(self): pending_req = pending_etag = pending_size = None try: for seg_dict in self.listing_iter: if 'raw_data' in seg_dict: if pending_req: yield pending_req, pending_etag, pending_size to_yield = seg_dict['raw_data'][ seg_dict['first_byte']:seg_dict['last_byte'] + 1] yield to_yield, None, len(seg_dict['raw_data']) pending_req = pending_etag = pending_size = None continue seg_path, seg_etag, seg_size, first_byte, last_byte = ( seg_dict['path'], seg_dict.get('hash'), seg_dict.get('bytes'), seg_dict['first_byte'], seg_dict['last_byte']) if seg_size is not None: seg_size = int(seg_size) first_byte = first_byte or 0 go_to_end = last_byte is None or ( seg_size is not None and last_byte == seg_size - 1) path = seg_path + '?multipart-manifest=get' seg_req = make_subrequest( self.req.environ, path=path, method='GET', headers={'x-auth-token': self.req.headers.get( 'x-auth-token')}, agent=('%(orig)s ' + self.ua_suffix), swift_source=self.swift_source) seg_req_rangeval = None if first_byte != 0 or not go_to_end: seg_req_rangeval = "%s-%s" % ( first_byte, '' if go_to_end else last_byte) seg_req.headers['Range'] = "bytes=" + seg_req_rangeval if pending_req and pending_req.path == seg_req.path and \ seg_size is not None: # existing one in case of invalid ranges. Note that a # range set with too many individual byteranges is # invalid, so we can combine N valid byteranges and 1 # valid byterange and get an invalid range set. if pending_req.range: new_range_str = str(pending_req.range) else: new_range_str = "bytes=0-%d" % (seg_size - 1) if seg_req.range: new_range_str += "," + seg_req_rangeval else: new_range_str += ",0-%d" % (seg_size - 1) if Range(new_range_str).ranges_for_length(seg_size): # Good news! We can coalesce the requests pending_req.headers['Range'] = new_range_str continue # else, Too many ranges, or too much backtracking, or ... if pending_req: yield pending_req, pending_etag, pending_size pending_req = seg_req pending_etag = seg_etag pending_size = seg_size except ListingIterError: e_type, e_value, e_traceback = sys.exc_info() if pending_req: yield pending_req, pending_etag, pending_size six.reraise(e_type, e_value, e_traceback) if pending_req: yield pending_req, pending_etag, pending_size def _requests_to_bytes_iter(self): # Take the requests out of self._coalesce_requests, actually make # the requests, and generate the bytes from the responses. # # Yields 2-tuples (segment-name, byte-chunk). The segment name is # used for logging. for data_or_req, seg_etag, seg_size in self._coalesce_requests(): if isinstance(data_or_req, bytes): # ugly, awful overloading yield ('data segment', data_or_req) continue seg_req = data_or_req seg_resp = seg_req.get_response(self.app) if not is_success(seg_resp.status_int): close_if_possible(seg_resp.app_iter) raise SegmentError( 'While processing manifest %s, ' 'got %d while retrieving %s' % (self.name, seg_resp.status_int, seg_req.path)) elif ((seg_etag and (seg_resp.etag != seg_etag)) or (seg_size and (seg_resp.content_length != seg_size) and not seg_req.range)): # The content-length check is for security reasons. Seems # possible that an attacker could upload a >1mb object and # then replace it with a much smaller object with same # etag. Then create a big nested SLO that calls that # object many times which would hammer our obj servers. If # this is a range request, don't check content-length close_if_possible(seg_resp.app_iter) raise SegmentError( 'Object segment no longer valid: ' '%(path)s etag: %(r_etag)s != %(s_etag)s or ' '%(r_size)s != %(s_size)s.' % {'path': seg_req.path, 'r_etag': seg_resp.etag, 'r_size': seg_resp.content_length, 's_etag': seg_etag, 's_size': seg_size}) else: self.current_resp = seg_resp seg_hash = None if seg_resp.etag and not seg_req.headers.get('Range'): # Only calculate the MD5 if it we can use it to validate seg_hash = hashlib.md5() document_iters = maybe_multipart_byteranges_to_document_iters( seg_resp.app_iter, seg_resp.headers['Content-Type']) for chunk in itertools.chain.from_iterable(document_iters): if seg_hash: seg_hash.update(chunk) yield (seg_req.path, chunk) close_if_possible(seg_resp.app_iter) if seg_hash and seg_hash.hexdigest() != seg_resp.etag: raise SegmentError( "Bad MD5 checksum in %(name)s for %(seg)s: headers had" " %(etag)s, but object MD5 was actually %(actual)s" % {'seg': seg_req.path, 'etag': seg_resp.etag, 'name': self.name, 'actual': seg_hash.hexdigest()}) def _byte_counting_iter(self): # Checks that we give the client the right number of bytes. Raises # SegmentError if the number of bytes is wrong. bytes_left = self.response_body_length for seg_name, chunk in self._requests_to_bytes_iter(): if bytes_left is None: yield chunk elif bytes_left >= len(chunk): yield chunk bytes_left -= len(chunk) else: yield chunk[:bytes_left] bytes_left -= len(chunk) raise SegmentError( 'Too many bytes for %(name)s; truncating in ' '%(seg)s with %(left)d bytes left' % {'name': self.name, 'seg': seg_name, 'left': -bytes_left}) if bytes_left: raise SegmentError('Expected another %d bytes for %s; ' 'closing connection' % (bytes_left, self.name)) def _time_limited_iter(self): # Makes sure a GET response doesn't take more than self.max_get_time start_time = time.time() for chunk in self._byte_counting_iter(): now = time.time() yield chunk if now - start_time > self.max_get_time: raise SegmentError( 'While processing manifest %s, ' 'max LO GET time of %ds exceeded' % (self.name, self.max_get_time)) def _internal_iter(self): try: for chunk in self._time_limited_iter(): yield chunk except (ListingIterError, SegmentError) as err: self.logger.error(err) if not self.validated_first_segment: raise finally: if self.current_resp: close_if_possible(self.current_resp.app_iter) def app_iter_range(self, *a, **kw): return self def app_iter_ranges(self, ranges, content_type, boundary, content_size): si = Spliterator(self) mri = multi_range_iterator( ranges, content_type, boundary, content_size, lambda start, end_plus_one: si.take(end_plus_one - start)) try: for x in mri: yield x finally: self.close() def validate_first_segment(self): if self.validated_first_segment: return try: self.peeked_chunk = next(self.app_iter) except StopIteration: pass finally: self.validated_first_segment = True def __iter__(self): if self.peeked_chunk is not None: pc = self.peeked_chunk self.peeked_chunk = None return itertools.chain([pc], self.app_iter) else: return self.app_iter def close(self): close_if_possible(self.app_iter) def http_response_to_document_iters(response, read_chunk_size=4096): chunked = is_chunked(dict(response.getheaders())) if response.status == 200: if chunked: return iter([(0, None, None, response.getheaders(), response)]) # Single "range" that's the whole object content_length = int(response.getheader('Content-Length')) return iter([(0, content_length - 1, content_length, response.getheaders(), response)]) content_type, params_list = parse_content_type( response.getheader('Content-Type')) if content_type != 'multipart/byteranges': start, end, length = parse_content_range( response.getheader('Content-Range')) return iter([(start, end, length, response.getheaders(), response)]) else: params = dict(params_list) return multipart_byteranges_to_document_iters( response, params['boundary'], read_chunk_size) def update_etag_is_at_header(req, name): if ',' in name: raise ValueError('Header name must not contain commas') existing = req.headers.get("X-Backend-Etag-Is-At") req.headers["X-Backend-Etag-Is-At"] = csv_append( existing, name) def resolve_etag_is_at_header(req, metadata): alternate_etag = None metadata = HeaderKeyDict(metadata) if "X-Backend-Etag-Is-At" in req.headers: names = list_from_csv(req.headers["X-Backend-Etag-Is-At"]) for name in names: if name in metadata: alternate_etag = metadata[name] break return alternate_etag

true

1c43d04ffcbbf8f8291758e9efda58952ca0da53

1,510

py

Python

nengolib/stats/ortho.py

ikajic/nengolib

bd30ec38ba656bedb94a267b5f86b51d1cec4954

[ "MIT" ]

27

2016-01-21T04:11:02.000Z

2021-11-16T20:41:04.000Z

nengolib/stats/ortho.py

ikajic/nengolib

bd30ec38ba656bedb94a267b5f86b51d1cec4954

[ "MIT" ]

178

2016-01-21T16:04:34.000Z

2021-05-01T16:28:02.000Z

nengolib/stats/ortho.py

ikajic/nengolib

bd30ec38ba656bedb94a267b5f86b51d1cec4954

[ "MIT" ]

4

2019-03-19T18:22:02.000Z

2021-03-23T16:06:57.000Z

import numpy as np from scipy.linalg import svd from nengo.dists import UniformHypersphere __all__ = ['random_orthogonal'] def random_orthogonal(d, rng=None): """Returns a random orthogonal matrix. Parameters ---------- d : ``integer`` Positive dimension of returned matrix. rng : :class:`numpy.random.RandomState` or ``None``, optional Random number generator state. Returns ------- samples : ``(d, d) np.array`` Random orthogonal matrix (an orthonormal basis); linearly transforms any vector into a uniformly sampled vector on the ``d``--ball with the same L2 norm. See Also -------- :class:`.ScatteredHypersphere` Examples -------- >>> from nengolib.stats import random_orthogonal, sphere >>> rng = np.random.RandomState(seed=0) >>> u = sphere.sample(1000, 3, rng=rng) >>> u[:, 0] = 0 >>> v = u.dot(random_orthogonal(3, rng=rng)) >>> import matplotlib.pyplot as plt >>> from mpl_toolkits.mplot3d import Axes3D >>> ax = plt.subplot(111, projection='3d') >>> ax.scatter(*u.T, alpha=.5, label="u") >>> ax.scatter(*v.T, alpha=.5, label="v") >>> ax.patch.set_facecolor('white') >>> ax.set_xlim3d(-1, 1) >>> ax.set_ylim3d(-1, 1) >>> ax.set_zlim3d(-1, 1) >>> plt.legend() >>> plt.show() """ rng = np.random if rng is None else rng m = UniformHypersphere(surface=True).sample(d, d, rng=rng) u, s, v = svd(m) return np.dot(u, v)

26.964286

65

0.598675

import numpy as np from scipy.linalg import svd from nengo.dists import UniformHypersphere __all__ = ['random_orthogonal'] def random_orthogonal(d, rng=None): rng = np.random if rng is None else rng m = UniformHypersphere(surface=True).sample(d, d, rng=rng) u, s, v = svd(m) return np.dot(u, v)

true

1c43d0671192f23cc6c504e35209d21603155e04

5,160

py

Python

wikimapper/cli.py

jcklie/wikimapper

fadecea085bfa11779e33e94b03a8dcdd2d045a7

[ "Apache-2.0" ]

69

2019-05-07T02:41:57.000Z

2022-03-29T09:33:43.000Z

wikimapper/cli.py

jcklie/wikimapper

fadecea085bfa11779e33e94b03a8dcdd2d045a7

[ "Apache-2.0" ]

6

2019-04-26T11:16:07.000Z

2021-04-08T15:35:33.000Z

wikimapper/cli.py

jcklie/wikimapper

fadecea085bfa11779e33e94b03a8dcdd2d045a7

[ "Apache-2.0" ]

7

2020-02-14T20:00:23.000Z

2021-12-17T09:56:19.000Z

import argparse import logging import os from wikimapper.__version__ import __version__ from wikimapper import download_wikidumps, create_index, WikiMapper def main(): logging.basicConfig( level=logging.DEBUG, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s" ) description = "Map Wikipedia page titles to Wikidata IDs and vice versa." parser = argparse.ArgumentParser(description=description) subparsers = parser.add_subparsers(help="sub-command help", dest="command") # Downloda parser parser_download = subparsers.add_parser( "download", help="Download Wikipedia dumps for creating a custom index." ) parser_download.add_argument( "dumpname", type=_dump_name, help='Name of the Wikipedia dump, e.g. "enwiki-latest" for the latest English Wikipedia dump or "barwiki-20190420" for a dump from the Bavarian Wikipedia taken at the 20th April, 2019', ) parser_download.add_argument( "--overwrite", action="store_true", help='Overwrite existing files if they already exist (default: "False")', ) parser_download.add_argument( "--dir", type=_dir_path, default=os.getcwd(), help="Path to the folder in which the dump should be stored (default: current directory)", ) parser_download.add_argument( "--mirror", type=str, default="https://dumps.wikimedia.org", help='URL of the Wikipedia dump mirror to use (default: "https://dumps.wikimedia.org")', ) # Index creation parser parser_create = subparsers.add_parser( "create", help="Use a previously downloaded Wikipedia dump to create a custom index." ) parser_create.add_argument( "dumpname", type=_dump_name, help='Name of the Wikipedia dump, e.g. "enwiki-latest" for the latest English Wikipedia dump or "barwiki-20190420" for a dump from the Bavarian Wikipedia taken at the 20th April, 2019', ) parser_create.add_argument( "--target", default=None, type=str, help='Path and name of the index to create (default: "index_${dumpname}.db")', ) parser_create.add_argument( "--dumpdir", type=_dir_path, default=os.getcwd(), help="Path to the folder in which the dump was stored (default: current directory)", ) # Mapping parser parser_title_to_id = subparsers.add_parser( "title2id", help="Map a Wikipedia title to a Wikidata ID." ) parser_title_to_id.add_argument( "index", type=str, help="Path to the index file that shall be used for the mapping." ) parser_title_to_id.add_argument( "title", type=str, help="Page title to map. Spaces are replaced by underscores, the title should not be escaped.", ) parser_url_to_id = subparsers.add_parser("url2id", help="Map a Wikipedia URL to a Wikidata ID.") parser_url_to_id.add_argument( "index", type=str, help="Path to the index file that shall be used for the mapping." ) parser_url_to_id.add_argument( "url", type=str, help="URL to map. It is not checked whether the URL comes from the same Wiki as the index.", ) parser_id_to_title = subparsers.add_parser( "id2titles", help="Map a Wikidata ID to one or more Wikipedia titles." ) parser_id_to_title.add_argument( "index", type=str, help="Path to the index file that shall be used for the mapping." ) parser_id_to_title.add_argument("id", type=str, help="Wikidata ID to map.") # Version parser.add_argument("--version", action="version", version="%(prog)s " + __version__) # Do the work args = parser.parse_args() if args.command == "download": download_wikidumps(args.dumpname, args.dir, args.mirror, args.overwrite) elif args.command == "create": create_index(args.dumpname, args.dumpdir, args.target) elif args.command == "title2id": mapper = WikiMapper(args.index) result = mapper.title_to_id(args.title) if result: print(result) elif args.command == "url2id": mapper = WikiMapper(args.index) result = mapper.url_to_id(args.url) if result: print(result) elif args.command == "id2titles": mapper = WikiMapper(args.index) results = mapper.id_to_titles(args.id) for result in results: print(result) else: parser.print_help() def _dir_path(path) -> str: """ Checks whether `path` is a valid path to a directory. """ if os.path.isdir(path): return path else: raise argparse.ArgumentTypeError(f"readable_dir:{path} is not a valid path to a directory!") def _dump_name(name) -> str: """ Checks whether `name` is a valid Wikipedia dump name. """ parts = name.split("-") err = lambda: argparse.ArgumentTypeError(f"dumpname: [{name}] is not a valid dump name") if len(parts) != 2: raise err() wikiname, date = parts if not wikiname.endswith("wiki"): raise err() return name

34.630872

193

0.650775

import argparse import logging import os from wikimapper.__version__ import __version__ from wikimapper import download_wikidumps, create_index, WikiMapper def main(): logging.basicConfig( level=logging.DEBUG, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s" ) description = "Map Wikipedia page titles to Wikidata IDs and vice versa." parser = argparse.ArgumentParser(description=description) subparsers = parser.add_subparsers(help="sub-command help", dest="command") parser_download = subparsers.add_parser( "download", help="Download Wikipedia dumps for creating a custom index." ) parser_download.add_argument( "dumpname", type=_dump_name, help='Name of the Wikipedia dump, e.g. "enwiki-latest" for the latest English Wikipedia dump or "barwiki-20190420" for a dump from the Bavarian Wikipedia taken at the 20th April, 2019', ) parser_download.add_argument( "--overwrite", action="store_true", help='Overwrite existing files if they already exist (default: "False")', ) parser_download.add_argument( "--dir", type=_dir_path, default=os.getcwd(), help="Path to the folder in which the dump should be stored (default: current directory)", ) parser_download.add_argument( "--mirror", type=str, default="https://dumps.wikimedia.org", help='URL of the Wikipedia dump mirror to use (default: "https://dumps.wikimedia.org")', ) parser_create = subparsers.add_parser( "create", help="Use a previously downloaded Wikipedia dump to create a custom index." ) parser_create.add_argument( "dumpname", type=_dump_name, help='Name of the Wikipedia dump, e.g. "enwiki-latest" for the latest English Wikipedia dump or "barwiki-20190420" for a dump from the Bavarian Wikipedia taken at the 20th April, 2019', ) parser_create.add_argument( "--target", default=None, type=str, help='Path and name of the index to create (default: "index_${dumpname}.db")', ) parser_create.add_argument( "--dumpdir", type=_dir_path, default=os.getcwd(), help="Path to the folder in which the dump was stored (default: current directory)", ) parser_title_to_id = subparsers.add_parser( "title2id", help="Map a Wikipedia title to a Wikidata ID." ) parser_title_to_id.add_argument( "index", type=str, help="Path to the index file that shall be used for the mapping." ) parser_title_to_id.add_argument( "title", type=str, help="Page title to map. Spaces are replaced by underscores, the title should not be escaped.", ) parser_url_to_id = subparsers.add_parser("url2id", help="Map a Wikipedia URL to a Wikidata ID.") parser_url_to_id.add_argument( "index", type=str, help="Path to the index file that shall be used for the mapping." ) parser_url_to_id.add_argument( "url", type=str, help="URL to map. It is not checked whether the URL comes from the same Wiki as the index.", ) parser_id_to_title = subparsers.add_parser( "id2titles", help="Map a Wikidata ID to one or more Wikipedia titles." ) parser_id_to_title.add_argument( "index", type=str, help="Path to the index file that shall be used for the mapping." ) parser_id_to_title.add_argument("id", type=str, help="Wikidata ID to map.") parser.add_argument("--version", action="version", version="%(prog)s " + __version__) args = parser.parse_args() if args.command == "download": download_wikidumps(args.dumpname, args.dir, args.mirror, args.overwrite) elif args.command == "create": create_index(args.dumpname, args.dumpdir, args.target) elif args.command == "title2id": mapper = WikiMapper(args.index) result = mapper.title_to_id(args.title) if result: print(result) elif args.command == "url2id": mapper = WikiMapper(args.index) result = mapper.url_to_id(args.url) if result: print(result) elif args.command == "id2titles": mapper = WikiMapper(args.index) results = mapper.id_to_titles(args.id) for result in results: print(result) else: parser.print_help() def _dir_path(path) -> str: if os.path.isdir(path): return path else: raise argparse.ArgumentTypeError(f"readable_dir:{path} is not a valid path to a directory!") def _dump_name(name) -> str: parts = name.split("-") err = lambda: argparse.ArgumentTypeError(f"dumpname: [{name}] is not a valid dump name") if len(parts) != 2: raise err() wikiname, date = parts if not wikiname.endswith("wiki"): raise err() return name

true

1c43d0fc92643c28afe3143be964b00509b6b818

22,725

py

Python

build/lib/pspnet/pspnet.py

NamTran838P/pspnet-keras

4005fd7867036e5476bcc694fd2f548a22860d4b

[ "MIT" ]

null

build/lib/pspnet/pspnet.py

NamTran838P/pspnet-keras

4005fd7867036e5476bcc694fd2f548a22860d4b

[ "MIT" ]

null

build/lib/pspnet/pspnet.py

NamTran838P/pspnet-keras

4005fd7867036e5476bcc694fd2f548a22860d4b

[ "MIT" ]

null

#!/usr/bin/env python """ A Keras/Tensorflow implementation of Pyramid Scene Parsing Networks. Original paper & code published by Hengshuang Zhao et al. (2017) """ from __future__ import print_function from __future__ import division from os.path import splitext, join, isfile, isdir from os import environ, walk from math import ceil import argparse import glob import fnmatch import warnings import numpy as np import matplotlib.pyplot as plt from matplotlib.widgets import RadioButtons from scipy import misc, ndimage from keras import backend as K from keras.models import model_from_json import tensorflow as tf from layers_builder import build_pspnet from utils import download_weights, download_npy_weights, preprocess_image, color_class_image, gt_image_to_class_image from evaluation import evaluate_iou warnings.filterwarnings('ignore', '.*output shape of zoom.*') __author__ = "Vlad Kryvoruchko, Chaoyue Wang, Jeffrey Hu & Julian Tatsch" class PSPNet(object): """Pyramid Scene Parsing Network by Hengshuang Zhao et al 2017.""" def __init__(self, nb_classes, resnet_layers, input_shape, weights): """Instanciate a PSPNet.""" self.input_shape = input_shape self.nb_classes = nb_classes json_path = join("..", "weights", "keras", weights + ".json") h5_path = join("..", "weights", "keras", weights + ".h5") if not isfile(json_path) and not isfile(h5_path): download_weights(weights) if isfile(json_path) and isfile(h5_path): print("Keras model & weights found, loading...") with open(json_path, 'r') as file_handle: try: self.model = model_from_json(file_handle.read()) except ValueError as err: # bad marshal data error when loading py2 model in py3 an vice versa # https://github.com/fchollet/keras/issues/7440 print("Couldn't import model from json because it was build using a different python version: %s" % err) print("Rebuilding pspnet model ...") self.model = build_pspnet(nb_classes=nb_classes, resnet_layers=resnet_layers, input_shape=self.input_shape) print("Saving pspnet to disk ...") json_string = self.model.to_json() with open(json_path, 'w') as file_handle: file_handle.write(json_string) except SystemError as err: # bad marshal data error when loading py3.5 model in py3.6 print("Couldn't import model from json because it was build using a different python version: %s" % err) print("Converting pspnet model from npy") self.model = build_pspnet(nb_classes=nb_classes, resnet_layers=resnet_layers, input_shape=self.input_shape) self.set_npy_weights(weights) self.model.load_weights(h5_path) else: print("No Keras model & weights found, import from npy weights.") self.model = build_pspnet(nb_classes=nb_classes, resnet_layers=resnet_layers, input_shape=self.input_shape) self.set_npy_weights(weights) def predict(self, img, flip_evaluation): """ Predict segementation for an image. Arguments: img: must be rowsxcolsx3 """ h_ori, w_ori = img.shape[:2] if img.shape[0:2] != self.input_shape: print("Input %s not fitting for network size %s, resizing. You may want to try sliding prediction for better results." % (img.shape[0:2], self.input_shape)) img = misc.imresize(img, self.input_shape) data = preprocess_image(img, mean=[[[174.08136209, 163.97867657, 138.72837669]]]) # debug(self.model, input_data) if flip_evaluation: input_with_flipped = np.array([data, np.flip(data, axis=1)]) prediction_with_flipped = self.model.predict(input_with_flipped) prediction = (prediction_with_flipped[0] + np.fliplr(prediction_with_flipped[1])) / 2.0 else: prediction = self.model.predict(np.expand_dims(data, 0))[0] return prediction if img.shape[0:1] != self.input_shape: # upscale prediction if necessary h, w = prediction.shape[:2] prediction = ndimage.zoom(prediction, (1.*h_ori/h, 1.*w_ori/w, 1.), order=1, prefilter=False) return prediction def set_npy_weights(self, weights_path): """Set weights from the intermediary npy file.""" npy_weights_path = join("..", "weights", "npy", weights_path + ".npy") json_path = join("..", "weights", "keras", weights_path + ".json") h5_path = join("..", "weights", "keras", weights_path + ".h5") if not isfile(npy_weights_path): download_npy_weights(weights_path) print("Importing weights from %s" % npy_weights_path) weights = np.load(npy_weights_path, encoding="latin1").item() whitelist = ["InputLayer", "Activation", "ZeroPadding2D", "Add", "MaxPooling2D", "AveragePooling2D", "Lambda", "Concatenate", "Dropout"] weights_set = 0 for layer in self.model.layers: print("Processing %s" % layer.name) if layer.name[:4] == 'conv' and layer.name[-2:] == 'bn': mean = weights[layer.name]['mean'].reshape(-1) variance = weights[layer.name]['variance'].reshape(-1) scale = weights[layer.name]['scale'].reshape(-1) offset = weights[layer.name]['offset'].reshape(-1) self.model.get_layer(layer.name).set_weights([scale, offset, mean, variance]) weights_set += 1 elif layer.name[:4] == 'conv' and not layer.name[-4:] == 'relu': try: weight = weights[layer.name]['weights'] self.model.get_layer(layer.name).set_weights([weight]) except Exception: biases = weights[layer.name]['biases'] self.model.get_layer(layer.name).set_weights([weight, biases]) weights_set += 1 elif layer.__class__.__name__ in whitelist: # print("Nothing to set in %s" % layer.__class__.__name__) pass else: print("Warning: Did not find weights for keras layer %s in numpy weights" % layer) print("Set a total of %i weights" % weights_set) print('Finished importing weights.') print("Writing keras model & weights") json_string = self.model.to_json() with open(json_path, 'w') as file_handle: file_handle.write(json_string) self.model.save_weights(h5_path) print("Finished writing Keras model & weights") class PSPNet50(PSPNet): """Build a PSPNet based on a 50-Layer ResNet.""" def __init__(self, nb_classes, weights, input_shape): """Instanciate a PSPNet50.""" PSPNet.__init__(self, nb_classes=nb_classes, resnet_layers=50, input_shape=input_shape, weights=weights) class PSPNet101(PSPNet): """Build a PSPNet based on a 101-Layer ResNet.""" def __init__(self, nb_classes, weights, input_shape): """Instanciate a PSPNet101.""" PSPNet.__init__(self, nb_classes=nb_classes, resnet_layers=101, input_shape=input_shape, weights=weights) def pad_image(img, target_size): """Pad an image up to the target size.""" rows_missing = target_size[0] - img.shape[0] cols_missing = target_size[1] - img.shape[1] padded_img = np.pad(img, ((0, rows_missing), (0, cols_missing), (0, 0)), 'constant') return padded_img def produce_view(input_image, class_image, id2label, viewstyle): """Produce an image ready for plotting or saving.""" view = None if viewstyle == 'original': view = input_image elif (viewstyle == 'predictions') or (viewstyle == 'overlay'): view = color_class_image(class_image, id2label) if viewstyle == 'overlay': view = (0.5 * view.astype(np.float32) + 0.5 * input_image.astype(np.float32)).astype(np.uint8) else: print("Unknown view style") return view def visualize_prediction(input_image, class_scores, id2label): """Visualize prediction in faux colors.""" class_image = np.argmax(class_scores, axis=2) fig = plt.figure() axis = fig.add_subplot(111) def button_handler(viewstyle): axis.imshow(produce_view(input_image, class_image, id2label, viewstyle)) plt.draw() # plt.subplots_adjust(left=0.3) rax = plt.axes([0.4, 0.05, 0.2, 0.15]) radio_buttons = RadioButtons(rax, ('original', 'overlay', 'predictions')) radio_buttons.on_clicked(button_handler) # image = produce_view(input_image, class_image, 'overlay') # axis.imshow(image) button_handler('original') axis.set_axis_off() # overwrite the status bar with class information axis.format_coord = lambda x, y: id2label[class_image[int(y), int(x)]].name plt.show() def show_class_heatmap(class_scores, class_name): """Show a heatmap with the probabilities of a certain class.""" try: class_id = name2label[class_name].id class_heatmap = class_scores[:, :, class_id] plt.axis('off') plt.imshow(class_heatmap, cmap='coolwarm') plt.show() except KeyError as err: print("Could not find index for %s because of %s" % (class_name, err)) def show_class_heatmaps(class_scores): """ Show heatmap with the probabilities of a certain class. Cycle through with lef and right arrow keys. """ show_class_heatmaps.curr_index = 0 def key_event(event): """Handle forward & backward arrow key presses.""" if event.key == "right": show_class_heatmaps.curr_index += 1 elif event.key == "left": show_class_heatmaps.curr_index -= 1 else: return show_class_heatmaps.curr_index = show_class_heatmaps.curr_index % class_scores.shape[2] axis.cla() class_heatmap = class_scores[:, :, show_class_heatmaps.curr_index] axis.imshow(class_heatmap, cmap='coolwarm') axis.set_axis_off() fig.canvas.set_window_title(id2label[show_class_heatmaps.curr_index].name) fig.canvas.draw() fig = plt.figure() fig.canvas.mpl_connect('key_press_event', key_event) fig.canvas.set_window_title(id2label[show_class_heatmaps.curr_index].name) axis = fig.add_subplot(111) class_heatmap = class_scores[:, :, show_class_heatmaps.curr_index] axis.imshow(class_heatmap, cmap='coolwarm') axis.set_axis_off() plt.show() def predict_sliding(full_image, net, flip_evaluation): """ Predict on tiles of exactly the network input shape. This way nothing gets squeezed. """ tile_size = net.input_shape classes = net.model.outputs[0].shape[3] overlap = 1/3 stride = ceil(tile_size[0] * (1 - overlap)) tile_rows = max(int(ceil((full_image.shape[0] - tile_size[0]) / stride) + 1), 1) # strided convolution formula tile_cols = max(int(ceil((full_image.shape[1] - tile_size[1]) / stride) + 1), 1) print("Need %i x %i prediction tiles @ stride %i px" % (tile_cols, tile_rows, stride)) full_probs = np.zeros((full_image.shape[0], full_image.shape[1], classes)) count_predictions = np.zeros((full_image.shape[0], full_image.shape[1], classes)) tile_counter = 0 for row in range(tile_rows): for col in range(tile_cols): x1 = int(col * stride) y1 = int(row * stride) x2 = min(x1 + tile_size[1], full_image.shape[1]) y2 = min(y1 + tile_size[0], full_image.shape[0]) x1 = max(int(x2 - tile_size[1]), 0) # for portrait images the x1 underflows sometimes y1 = max(int(y2 - tile_size[0]), 0) # for very few rows y1 underflows img = full_image[y1:y2, x1:x2] padded_img = pad_image(img, tile_size) # plt.imshow(padded_img) # plt.show() tile_counter += 1 print("Predicting tile %i" % tile_counter) padded_prediction = net.predict(padded_img, flip_evaluation) prediction = padded_prediction[0:img.shape[0], 0:img.shape[1], :] count_predictions[y1:y2, x1:x2] += 1 full_probs[y1:y2, x1:x2] += prediction # accumulate the predictions also in the overlapping regions # average the predictions in the overlapping regions full_probs /= count_predictions # visualize normalization Weights # plt.imshow(np.mean(count_predictions, axis=2)) # plt.show() return full_probs def predict_multi_scale(full_image, net, scales, sliding_evaluation, flip_evaluation): """Predict an image by looking at it with different scales.""" classes = net.model.outputs[0].shape[3] full_probs = np.zeros((full_image.shape[0], full_image.shape[1], classes)) h_ori, w_ori = full_image.shape[:2] for scale in scales: print("Predicting image scaled by %f" % scale) scaled_img = misc.imresize(full_image, size=scale, interp="bilinear") if sliding_evaluation: scaled_probs = predict_sliding(scaled_img, net, flip_evaluation) else: scaled_probs = net.predict(scaled_img, flip_evaluation) # scale probs up to full size h, w = scaled_probs.shape[:2] probs = ndimage.zoom(scaled_probs, (1.*h_ori/h, 1.*w_ori/w, 1.), order=1, prefilter=False) # visualize_prediction(probs) # integrate probs over all scales full_probs += probs full_probs /= len(scales) return full_probs def trainid_to_class_image(trainid_image): """Inflate an image with trainId's into a full class image with class ids.""" from cityscapesscripts.helpers.labels import trainId2label class_image = np.zeros(trainid_image.shape, np.uint8) try: for row in range(trainid_image.shape[0]): for col in range(trainid_image.shape[1]): class_image[row][col] = trainId2label[trainid_image[row][col]].id except Exception as ex: print("Unknown trainid : %s" % ex) return class_image def find_matching_gt(gt_dir, image_name, model_name, verbose=False): """Find a matching ground truth in gt_dir for image_name.""" if "cityscapes" in model_name: filter_string = image_name + "*labelIds.png" else: filter_string = image_name + "*.png" for root, __, files in walk(gt_dir): for filename in fnmatch.filter(files, filter_string): if verbose: print("Found matching groundtruth at: %s" % join(root, filename)) return join(root, filename) def complete_coarse_image(coarse_image, predicted_img): """Complete a coarsely labeld cityscapes image with predictions.""" mask_indices = coarse_image == 0 # complete everywhere where coarse_image is 0 coarse_image[mask_indices] = predicted_img[mask_indices] return coarse_image def main(): """Run when running this module as the primary one.""" EVALUATION_SCALES = [1.0] # must be all floats! parser = argparse.ArgumentParser() parser.add_argument('-m', '--model', type=str, default='pspnet50_ade20k', help='Model/Weights to use', choices=['pspnet50_ade20k', 'pspnet101_cityscapes', 'pspnet101_voc2012']) parser.add_argument('-i', '--input_path', type=str, default='../example_images', help='Path to the input images') parser.add_argument('-o', '--output_path', type=str, default='../example_results', help='Path to output') parser.add_argument('-g', '--groundtruth_path', type=str, default='../example_groundtruth', help='Path to groundtruth') parser.add_argument('--id', default="0") parser.add_argument('-s', '--sliding', action='store_true', default=True, help="Whether the network should be slided over the original image for prediction.") parser.add_argument('-f', '--flip', action='store_true', default=True, help="Whether the network should predict on both image and flipped image.") parser.add_argument('-ms', '--multi_scale', action='store_true', help="Whether the network should predict on multiple scales.") parser.add_argument('-hm', '--heat_maps', action='store_true', help="Whether the network should diplay heatmaps.") parser.add_argument('-v', '--vis', action='store_true', help="Whether an interactive plot should be diplayed.") parser.add_argument('-cci', '--complete_coarse_image', action='store_true', help="Whether a coarse imae should be completed with predictions.") parser.add_argument('-e', '--evaluate', action='store_true', help="Whether an evaluation against groundtruth should be attempted.") args = parser.parse_args() environ["CUDA_VISIBLE_DEVICES"] = args.id sess = tf.Session() K.set_session(sess) with sess.as_default(): print(args) import os cwd = os.getcwd() print("Running in %s" % cwd) image_paths = [] if isfile(args.input_path): image_paths.append(args.input_path) elif isdir(args.input_path): file_types = ('png', 'jpg') for file_type in file_types: image_paths.extend(glob.glob(join(args.input_path + '/**/*.' + file_type), recursive=True)) image_paths = sorted(image_paths) # print(image_paths) if "pspnet50" in args.model: pspnet = PSPNet50(nb_classes=150, input_shape=(473, 473), weights=args.model) if "ade20k" in args.model: from ade20k_labels import id2label, name2label elif "pspnet101" in args.model: if "cityscapes" in args.model: pspnet = PSPNet101(nb_classes=19, input_shape=(713, 713), weights=args.model) from cityscapes_labels import id2label, name2label if "voc2012" in args.model: pspnet = PSPNet101(nb_classes=21, input_shape=(473, 473), weights=args.model) from pascal_voc_labels import id2label, name2label else: print("Network architecture not implemented.") if args.multi_scale: EVALUATION_SCALES = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75] # original implementation, must be all floats! for image_path in image_paths: image_name, ext = splitext(os.path.basename(image_path)) image_name = image_name.replace('_leftImg8bit', '') # strip leftImg8bit tag for gt matching and producting groundtruth print("Predicting image name: %s" % (image_name + ext)) img = misc.imread(image_path) class_scores = predict_multi_scale(img, pspnet, EVALUATION_SCALES, args.sliding, args.flip) if args.heat_maps: # show_class_heatmap(class_scores, 'person') show_class_heatmaps(class_scores) # visualize_prediction(img, class_scores, id2label) class_image = np.argmax(class_scores, axis=2) output_path, _ = splitext(args.output_path) if not os.path.exists(output_path): os.makedirs(output_path) output_path = join(output_path, image_name) print("Writing results to %s" % (output_path + ext)) confidence_map = np.max(class_scores, axis=2) # probability of the most likely class, a vage measure of the networks confidence colored_class_image = color_class_image(class_image, id2label) # colored_class_image is [0.0-1.0] img is [0-255] alpha_blended = 0.5 * colored_class_image + 0.5 * img if "cityscapes" in args.model: class_image = trainid_to_class_image(class_image) misc.imsave(output_path + "_gtFine_labelIds" + ext, class_image) misc.imsave(output_path + "_seg" + ext, colored_class_image) misc.imsave(output_path + "_probs" + ext, confidence_map) misc.imsave(output_path + "_seg_blended" + ext, alpha_blended) gt_path = find_matching_gt(args.groundtruth_path, image_name, args.model, verbose=True) if gt_path is not None: if args.complete_coarse_image: # only for cityscapes try: coarse_image = misc.imread(gt_path) class_image = complete_coarse_image(coarse_image, class_image) misc.imsave(output_path + "_gtFine_labelIds" + ext, class_image) except AttributeError as err: print("Warning: Could not read groundtruth: %s" % err) if args.evaluate: if "cityscapes" in args.model: evaluate_iou([class_image], [misc.imread(gt_path)], classes=35) else: # gt_image to class image gt_image = misc.imread(gt_path) gt_class_image = gt_image_to_class_image(gt_image, id2label) evaluate_iou([class_image], [gt_class_image], classes=pspnet.nb_classes) else: print("Could not find groundtruth for %s" % image_name) if __name__ == "__main__": main()

45.089286

169

0.602552

from __future__ import print_function from __future__ import division from os.path import splitext, join, isfile, isdir from os import environ, walk from math import ceil import argparse import glob import fnmatch import warnings import numpy as np import matplotlib.pyplot as plt from matplotlib.widgets import RadioButtons from scipy import misc, ndimage from keras import backend as K from keras.models import model_from_json import tensorflow as tf from layers_builder import build_pspnet from utils import download_weights, download_npy_weights, preprocess_image, color_class_image, gt_image_to_class_image from evaluation import evaluate_iou warnings.filterwarnings('ignore', '.*output shape of zoom.*') __author__ = "Vlad Kryvoruchko, Chaoyue Wang, Jeffrey Hu & Julian Tatsch" class PSPNet(object): def __init__(self, nb_classes, resnet_layers, input_shape, weights): self.input_shape = input_shape self.nb_classes = nb_classes json_path = join("..", "weights", "keras", weights + ".json") h5_path = join("..", "weights", "keras", weights + ".h5") if not isfile(json_path) and not isfile(h5_path): download_weights(weights) if isfile(json_path) and isfile(h5_path): print("Keras model & weights found, loading...") with open(json_path, 'r') as file_handle: try: self.model = model_from_json(file_handle.read()) except ValueError as err: print("Couldn't import model from json because it was build using a different python version: %s" % err) print("Rebuilding pspnet model ...") self.model = build_pspnet(nb_classes=nb_classes, resnet_layers=resnet_layers, input_shape=self.input_shape) print("Saving pspnet to disk ...") json_string = self.model.to_json() with open(json_path, 'w') as file_handle: file_handle.write(json_string) except SystemError as err: # bad marshal data error when loading py3.5 model in py3.6 print("Couldn't import model from json because it was build using a different python version: %s" % err) print("Converting pspnet model from npy") self.model = build_pspnet(nb_classes=nb_classes, resnet_layers=resnet_layers, input_shape=self.input_shape) self.set_npy_weights(weights) self.model.load_weights(h5_path) else: print("No Keras model & weights found, import from npy weights.") self.model = build_pspnet(nb_classes=nb_classes, resnet_layers=resnet_layers, input_shape=self.input_shape) self.set_npy_weights(weights) def predict(self, img, flip_evaluation): h_ori, w_ori = img.shape[:2] if img.shape[0:2] != self.input_shape: print("Input %s not fitting for network size %s, resizing. You may want to try sliding prediction for better results." % (img.shape[0:2], self.input_shape)) img = misc.imresize(img, self.input_shape) data = preprocess_image(img, mean=[[[174.08136209, 163.97867657, 138.72837669]]]) if flip_evaluation: input_with_flipped = np.array([data, np.flip(data, axis=1)]) prediction_with_flipped = self.model.predict(input_with_flipped) prediction = (prediction_with_flipped[0] + np.fliplr(prediction_with_flipped[1])) / 2.0 else: prediction = self.model.predict(np.expand_dims(data, 0))[0] return prediction if img.shape[0:1] != self.input_shape: h, w = prediction.shape[:2] prediction = ndimage.zoom(prediction, (1.*h_ori/h, 1.*w_ori/w, 1.), order=1, prefilter=False) return prediction def set_npy_weights(self, weights_path): npy_weights_path = join("..", "weights", "npy", weights_path + ".npy") json_path = join("..", "weights", "keras", weights_path + ".json") h5_path = join("..", "weights", "keras", weights_path + ".h5") if not isfile(npy_weights_path): download_npy_weights(weights_path) print("Importing weights from %s" % npy_weights_path) weights = np.load(npy_weights_path, encoding="latin1").item() whitelist = ["InputLayer", "Activation", "ZeroPadding2D", "Add", "MaxPooling2D", "AveragePooling2D", "Lambda", "Concatenate", "Dropout"] weights_set = 0 for layer in self.model.layers: print("Processing %s" % layer.name) if layer.name[:4] == 'conv' and layer.name[-2:] == 'bn': mean = weights[layer.name]['mean'].reshape(-1) variance = weights[layer.name]['variance'].reshape(-1) scale = weights[layer.name]['scale'].reshape(-1) offset = weights[layer.name]['offset'].reshape(-1) self.model.get_layer(layer.name).set_weights([scale, offset, mean, variance]) weights_set += 1 elif layer.name[:4] == 'conv' and not layer.name[-4:] == 'relu': try: weight = weights[layer.name]['weights'] self.model.get_layer(layer.name).set_weights([weight]) except Exception: biases = weights[layer.name]['biases'] self.model.get_layer(layer.name).set_weights([weight, biases]) weights_set += 1 elif layer.__class__.__name__ in whitelist: pass else: print("Warning: Did not find weights for keras layer %s in numpy weights" % layer) print("Set a total of %i weights" % weights_set) print('Finished importing weights.') print("Writing keras model & weights") json_string = self.model.to_json() with open(json_path, 'w') as file_handle: file_handle.write(json_string) self.model.save_weights(h5_path) print("Finished writing Keras model & weights") class PSPNet50(PSPNet): def __init__(self, nb_classes, weights, input_shape): PSPNet.__init__(self, nb_classes=nb_classes, resnet_layers=50, input_shape=input_shape, weights=weights) class PSPNet101(PSPNet): def __init__(self, nb_classes, weights, input_shape): PSPNet.__init__(self, nb_classes=nb_classes, resnet_layers=101, input_shape=input_shape, weights=weights) def pad_image(img, target_size): rows_missing = target_size[0] - img.shape[0] cols_missing = target_size[1] - img.shape[1] padded_img = np.pad(img, ((0, rows_missing), (0, cols_missing), (0, 0)), 'constant') return padded_img def produce_view(input_image, class_image, id2label, viewstyle): view = None if viewstyle == 'original': view = input_image elif (viewstyle == 'predictions') or (viewstyle == 'overlay'): view = color_class_image(class_image, id2label) if viewstyle == 'overlay': view = (0.5 * view.astype(np.float32) + 0.5 * input_image.astype(np.float32)).astype(np.uint8) else: print("Unknown view style") return view def visualize_prediction(input_image, class_scores, id2label): class_image = np.argmax(class_scores, axis=2) fig = plt.figure() axis = fig.add_subplot(111) def button_handler(viewstyle): axis.imshow(produce_view(input_image, class_image, id2label, viewstyle)) plt.draw() rax = plt.axes([0.4, 0.05, 0.2, 0.15]) radio_buttons = RadioButtons(rax, ('original', 'overlay', 'predictions')) radio_buttons.on_clicked(button_handler) button_handler('original') axis.set_axis_off() axis.format_coord = lambda x, y: id2label[class_image[int(y), int(x)]].name plt.show() def show_class_heatmap(class_scores, class_name): try: class_id = name2label[class_name].id class_heatmap = class_scores[:, :, class_id] plt.axis('off') plt.imshow(class_heatmap, cmap='coolwarm') plt.show() except KeyError as err: print("Could not find index for %s because of %s" % (class_name, err)) def show_class_heatmaps(class_scores): show_class_heatmaps.curr_index = 0 def key_event(event): if event.key == "right": show_class_heatmaps.curr_index += 1 elif event.key == "left": show_class_heatmaps.curr_index -= 1 else: return show_class_heatmaps.curr_index = show_class_heatmaps.curr_index % class_scores.shape[2] axis.cla() class_heatmap = class_scores[:, :, show_class_heatmaps.curr_index] axis.imshow(class_heatmap, cmap='coolwarm') axis.set_axis_off() fig.canvas.set_window_title(id2label[show_class_heatmaps.curr_index].name) fig.canvas.draw() fig = plt.figure() fig.canvas.mpl_connect('key_press_event', key_event) fig.canvas.set_window_title(id2label[show_class_heatmaps.curr_index].name) axis = fig.add_subplot(111) class_heatmap = class_scores[:, :, show_class_heatmaps.curr_index] axis.imshow(class_heatmap, cmap='coolwarm') axis.set_axis_off() plt.show() def predict_sliding(full_image, net, flip_evaluation): tile_size = net.input_shape classes = net.model.outputs[0].shape[3] overlap = 1/3 stride = ceil(tile_size[0] * (1 - overlap)) tile_rows = max(int(ceil((full_image.shape[0] - tile_size[0]) / stride) + 1), 1) tile_cols = max(int(ceil((full_image.shape[1] - tile_size[1]) / stride) + 1), 1) print("Need %i x %i prediction tiles @ stride %i px" % (tile_cols, tile_rows, stride)) full_probs = np.zeros((full_image.shape[0], full_image.shape[1], classes)) count_predictions = np.zeros((full_image.shape[0], full_image.shape[1], classes)) tile_counter = 0 for row in range(tile_rows): for col in range(tile_cols): x1 = int(col * stride) y1 = int(row * stride) x2 = min(x1 + tile_size[1], full_image.shape[1]) y2 = min(y1 + tile_size[0], full_image.shape[0]) x1 = max(int(x2 - tile_size[1]), 0) y1 = max(int(y2 - tile_size[0]), 0) img = full_image[y1:y2, x1:x2] padded_img = pad_image(img, tile_size) tile_counter += 1 print("Predicting tile %i" % tile_counter) padded_prediction = net.predict(padded_img, flip_evaluation) prediction = padded_prediction[0:img.shape[0], 0:img.shape[1], :] count_predictions[y1:y2, x1:x2] += 1 full_probs[y1:y2, x1:x2] += prediction full_probs /= count_predictions return full_probs def predict_multi_scale(full_image, net, scales, sliding_evaluation, flip_evaluation): classes = net.model.outputs[0].shape[3] full_probs = np.zeros((full_image.shape[0], full_image.shape[1], classes)) h_ori, w_ori = full_image.shape[:2] for scale in scales: print("Predicting image scaled by %f" % scale) scaled_img = misc.imresize(full_image, size=scale, interp="bilinear") if sliding_evaluation: scaled_probs = predict_sliding(scaled_img, net, flip_evaluation) else: scaled_probs = net.predict(scaled_img, flip_evaluation) h, w = scaled_probs.shape[:2] probs = ndimage.zoom(scaled_probs, (1.*h_ori/h, 1.*w_ori/w, 1.), order=1, prefilter=False) full_probs += probs full_probs /= len(scales) return full_probs def trainid_to_class_image(trainid_image): from cityscapesscripts.helpers.labels import trainId2label class_image = np.zeros(trainid_image.shape, np.uint8) try: for row in range(trainid_image.shape[0]): for col in range(trainid_image.shape[1]): class_image[row][col] = trainId2label[trainid_image[row][col]].id except Exception as ex: print("Unknown trainid : %s" % ex) return class_image def find_matching_gt(gt_dir, image_name, model_name, verbose=False): if "cityscapes" in model_name: filter_string = image_name + "*labelIds.png" else: filter_string = image_name + "*.png" for root, __, files in walk(gt_dir): for filename in fnmatch.filter(files, filter_string): if verbose: print("Found matching groundtruth at: %s" % join(root, filename)) return join(root, filename) def complete_coarse_image(coarse_image, predicted_img): mask_indices = coarse_image == 0 coarse_image[mask_indices] = predicted_img[mask_indices] return coarse_image def main(): EVALUATION_SCALES = [1.0] parser = argparse.ArgumentParser() parser.add_argument('-m', '--model', type=str, default='pspnet50_ade20k', help='Model/Weights to use', choices=['pspnet50_ade20k', 'pspnet101_cityscapes', 'pspnet101_voc2012']) parser.add_argument('-i', '--input_path', type=str, default='../example_images', help='Path to the input images') parser.add_argument('-o', '--output_path', type=str, default='../example_results', help='Path to output') parser.add_argument('-g', '--groundtruth_path', type=str, default='../example_groundtruth', help='Path to groundtruth') parser.add_argument('--id', default="0") parser.add_argument('-s', '--sliding', action='store_true', default=True, help="Whether the network should be slided over the original image for prediction.") parser.add_argument('-f', '--flip', action='store_true', default=True, help="Whether the network should predict on both image and flipped image.") parser.add_argument('-ms', '--multi_scale', action='store_true', help="Whether the network should predict on multiple scales.") parser.add_argument('-hm', '--heat_maps', action='store_true', help="Whether the network should diplay heatmaps.") parser.add_argument('-v', '--vis', action='store_true', help="Whether an interactive plot should be diplayed.") parser.add_argument('-cci', '--complete_coarse_image', action='store_true', help="Whether a coarse imae should be completed with predictions.") parser.add_argument('-e', '--evaluate', action='store_true', help="Whether an evaluation against groundtruth should be attempted.") args = parser.parse_args() environ["CUDA_VISIBLE_DEVICES"] = args.id sess = tf.Session() K.set_session(sess) with sess.as_default(): print(args) import os cwd = os.getcwd() print("Running in %s" % cwd) image_paths = [] if isfile(args.input_path): image_paths.append(args.input_path) elif isdir(args.input_path): file_types = ('png', 'jpg') for file_type in file_types: image_paths.extend(glob.glob(join(args.input_path + '/**/*.' + file_type), recursive=True)) image_paths = sorted(image_paths) if "pspnet50" in args.model: pspnet = PSPNet50(nb_classes=150, input_shape=(473, 473), weights=args.model) if "ade20k" in args.model: from ade20k_labels import id2label, name2label elif "pspnet101" in args.model: if "cityscapes" in args.model: pspnet = PSPNet101(nb_classes=19, input_shape=(713, 713), weights=args.model) from cityscapes_labels import id2label, name2label if "voc2012" in args.model: pspnet = PSPNet101(nb_classes=21, input_shape=(473, 473), weights=args.model) from pascal_voc_labels import id2label, name2label else: print("Network architecture not implemented.") if args.multi_scale: EVALUATION_SCALES = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75] for image_path in image_paths: image_name, ext = splitext(os.path.basename(image_path)) image_name = image_name.replace('_leftImg8bit', '') print("Predicting image name: %s" % (image_name + ext)) img = misc.imread(image_path) class_scores = predict_multi_scale(img, pspnet, EVALUATION_SCALES, args.sliding, args.flip) if args.heat_maps: show_class_heatmaps(class_scores) class_image = np.argmax(class_scores, axis=2) output_path, _ = splitext(args.output_path) if not os.path.exists(output_path): os.makedirs(output_path) output_path = join(output_path, image_name) print("Writing results to %s" % (output_path + ext)) confidence_map = np.max(class_scores, axis=2) colored_class_image = color_class_image(class_image, id2label) alpha_blended = 0.5 * colored_class_image + 0.5 * img if "cityscapes" in args.model: class_image = trainid_to_class_image(class_image) misc.imsave(output_path + "_gtFine_labelIds" + ext, class_image) misc.imsave(output_path + "_seg" + ext, colored_class_image) misc.imsave(output_path + "_probs" + ext, confidence_map) misc.imsave(output_path + "_seg_blended" + ext, alpha_blended) gt_path = find_matching_gt(args.groundtruth_path, image_name, args.model, verbose=True) if gt_path is not None: if args.complete_coarse_image: try: coarse_image = misc.imread(gt_path) class_image = complete_coarse_image(coarse_image, class_image) misc.imsave(output_path + "_gtFine_labelIds" + ext, class_image) except AttributeError as err: print("Warning: Could not read groundtruth: %s" % err) if args.evaluate: if "cityscapes" in args.model: evaluate_iou([class_image], [misc.imread(gt_path)], classes=35) else: gt_image = misc.imread(gt_path) gt_class_image = gt_image_to_class_image(gt_image, id2label) evaluate_iou([class_image], [gt_class_image], classes=pspnet.nb_classes) else: print("Could not find groundtruth for %s" % image_name) if __name__ == "__main__": main()

true

1c43d13e1d7ae8c436ac089155e651917aefd88c

5,603

py

Python

Parser-hybrid/nparser/neural/models/nlp/parsers/gama_parser.py

sb-b/BOUN-PARSE

2b529924897d8e2613c4d2193a67796a895da40b

[ "Apache-2.0" ]

12

2020-03-04T17:36:12.000Z

2021-09-26T14:02:49.000Z

Parser-hybrid/nparser/neural/models/nlp/parsers/gama_parser.py

sb-b/BOUN-PARSE

2b529924897d8e2613c4d2193a67796a895da40b

[ "Apache-2.0" ]

1

2020-12-09T08:21:11.000Z

Parser-hybrid/nparser/neural/models/nlp/parsers/gama_parser.py

sb-b/BOUN-PARSE

2b529924897d8e2613c4d2193a67796a895da40b

[ "Apache-2.0" ]

3

2020-11-18T09:53:42.000Z

2020-12-17T23:04:59.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright 2016 Timothy Dozat # # 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 numpy as np import tensorflow as tf from nparser.neural.models.nlp.parsers.base_parser import BaseParser #*************************************************************** class GamaParser(BaseParser): """ """ #============================================================= def __call__(self, vocabs, moving_params=None): """ """ top_recur = super(GamaParser, self).__call__(vocabs, moving_params=moving_params) int_tokens_to_keep = tf.to_int32(self.tokens_to_keep) with tf.variable_scope('MLP'): dep_mlp, head_mlp = self.MLP(top_recur, self.arc_mlp_size + self.rel_mlp_size + 2*self.p_mlp_size, n_splits=2) arc_dep_mlp, rel_dep_mlp, mu_dep_mlp, sigma_dep_mlp = tf.split(dep_mlp, [self.arc_mlp_size, self.rel_mlp_size, self.p_mlp_size, self.p_mlp_size], axis=2) arc_head_mlp, rel_head_mlp, mu_head_mlp, sigma_head_mlp = tf.split(head_mlp, [self.arc_mlp_size, self.rel_mlp_size, self.p_mlp_size, self.p_mlp_size], axis=2) with tf.variable_scope('dist'): with tf.variable_scope('mu'): # (n x b x d) o (d x 1 x d) o (n x b x d).T -> (n x b x b) arc_mus = self.bilinear(mu_dep_mlp, mu_head_mlp, 1)**2 with tf.variable_scope('sigma'): # (n x b x d) o (d x 1 x d) o (n x b x d).T -> (n x b x b) arc_sigmas = self.bilinear(sigma_dep_mlp, sigma_head_mlp, 1, initializer=None)**2 + .1 # (b x 1) i_mat = tf.expand_dims(tf.range(self.bucket_size), 1) # (1 x b) j_mat = tf.expand_dims(tf.range(self.bucket_size), 0) # (b x 1) - (1 x b) -> (b x b) k_mat = tf.to_float(tf.abs(i_mat - j_mat)) arc_logits = -.5*tf.log(2*np.pi * arc_sigmas) - .5*(k_mat-arc_mus)**2 / arc_sigmas #arc_rs += tf.to_float(k_mat)#tf.to_float(tf.expand_dims(tf.expand_dims(self.sequence_lengths, 1), 1)) # (b x 1) #n_mat = tf.expand_dims(self.sequence_lengths, 1) - 1 - i_mat # (b x b) * (n x b x b) - (n x b x b) - (b x b) -> (n x b x b) #arc_logits = (tf.lgamma(arc_rs+1) - tf.lgamma(k_mat) - tf.lgamma(arc_rs-k_mat+2) + # k_mat * tf.log(arc_ps) + (arc_rs-k_mat+1)*tf.log(1-arc_ps) ) with tf.variable_scope('Arc'): # (n x b x d) o (d x 1 x d) o (n x b x d).T -> (n x b x b) arc_logits += self.bilinear(arc_dep_mlp, arc_head_mlp, 1, add_bias2=False) # (n x b x b) arc_probs = tf.nn.softmax(arc_logits) # (n x b) arc_preds = tf.to_int32(tf.argmax(arc_logits, axis=-1)) # (n x b) arc_targets = self.vocabs['heads'].placeholder # (n x b) arc_correct = tf.to_int32(tf.equal(arc_preds, arc_targets))*int_tokens_to_keep # () arc_loss = tf.losses.sparse_softmax_cross_entropy(arc_targets, arc_logits, self.tokens_to_keep) with tf.variable_scope('Rel'): # (n x b x d) o (d x r x d) o (n x b x d).T -> (n x b x r x b) rel_logits = self.bilinear(rel_dep_mlp, rel_head_mlp, len(self.vocabs['rels'])) # (n x b x r x b) rel_probs = tf.nn.softmax(rel_logits, dim=2) # (n x b x b) one_hot = tf.one_hot(arc_preds if moving_params is not None else arc_targets, self.bucket_size) # (n x b x b) -> (n x b x b x 1) one_hot = tf.expand_dims(one_hot, axis=3) # (n x b x r x b) o (n x b x b x 1) -> (n x b x r x 1) select_rel_logits = tf.matmul(rel_logits, one_hot) # (n x b x r x 1) -> (n x b x r) select_rel_logits = tf.squeeze(select_rel_logits, axis=3) # (n x b) rel_preds = tf.to_int32(tf.argmax(select_rel_logits, axis=-1)) # (n x b) rel_targets = self.vocabs['rels'].placeholder # (n x b) rel_correct = tf.to_int32(tf.equal(rel_preds, rel_targets))*int_tokens_to_keep # () rel_loss = tf.losses.sparse_softmax_cross_entropy(rel_targets, select_rel_logits, self.tokens_to_keep) n_arc_correct = tf.reduce_sum(arc_correct) n_rel_correct = tf.reduce_sum(rel_correct) correct = arc_correct * rel_correct n_correct = tf.reduce_sum(correct) n_seqs_correct = tf.reduce_sum(tf.to_int32(tf.equal(tf.reduce_sum(correct, axis=1), self.sequence_lengths-1))) loss = arc_loss + rel_loss outputs = { 'arc_logits': arc_logits, 'arc_mus': arc_mus, 'arc_sigmas': arc_sigmas, 'arc_probs': arc_probs, 'arc_preds': arc_preds, 'arc_targets': arc_targets, 'arc_correct': arc_correct, 'arc_loss': arc_loss, 'n_arc_correct': n_arc_correct, 'rel_logits': rel_logits, 'rel_probs': rel_probs, 'rel_preds': rel_preds, 'rel_targets': rel_targets, 'rel_correct': rel_correct, 'rel_loss': rel_loss, 'n_rel_correct': n_rel_correct, 'n_tokens': self.n_tokens, 'n_seqs': self.batch_size, 'tokens_to_keep': self.tokens_to_keep, 'n_correct': n_correct, 'n_seqs_correct': n_seqs_correct, 'loss': loss } return outputs

41.198529

164

0.622345

import numpy as np import tensorflow as tf from nparser.neural.models.nlp.parsers.base_parser import BaseParser class GamaParser(BaseParser): def __call__(self, vocabs, moving_params=None): top_recur = super(GamaParser, self).__call__(vocabs, moving_params=moving_params) int_tokens_to_keep = tf.to_int32(self.tokens_to_keep) with tf.variable_scope('MLP'): dep_mlp, head_mlp = self.MLP(top_recur, self.arc_mlp_size + self.rel_mlp_size + 2*self.p_mlp_size, n_splits=2) arc_dep_mlp, rel_dep_mlp, mu_dep_mlp, sigma_dep_mlp = tf.split(dep_mlp, [self.arc_mlp_size, self.rel_mlp_size, self.p_mlp_size, self.p_mlp_size], axis=2) arc_head_mlp, rel_head_mlp, mu_head_mlp, sigma_head_mlp = tf.split(head_mlp, [self.arc_mlp_size, self.rel_mlp_size, self.p_mlp_size, self.p_mlp_size], axis=2) with tf.variable_scope('dist'): with tf.variable_scope('mu'): arc_mus = self.bilinear(mu_dep_mlp, mu_head_mlp, 1)**2 with tf.variable_scope('sigma'): arc_sigmas = self.bilinear(sigma_dep_mlp, sigma_head_mlp, 1, initializer=None)**2 + .1 i_mat = tf.expand_dims(tf.range(self.bucket_size), 1) j_mat = tf.expand_dims(tf.range(self.bucket_size), 0) k_mat = tf.to_float(tf.abs(i_mat - j_mat)) arc_logits = -.5*tf.log(2*np.pi * arc_sigmas) - .5*(k_mat-arc_mus)**2 / arc_sigmas arc_logits += self.bilinear(arc_dep_mlp, arc_head_mlp, 1, add_bias2=False) arc_probs = tf.nn.softmax(arc_logits) arc_preds = tf.to_int32(tf.argmax(arc_logits, axis=-1)) arc_targets = self.vocabs['heads'].placeholder arc_correct = tf.to_int32(tf.equal(arc_preds, arc_targets))*int_tokens_to_keep arc_loss = tf.losses.sparse_softmax_cross_entropy(arc_targets, arc_logits, self.tokens_to_keep) with tf.variable_scope('Rel'): rel_logits = self.bilinear(rel_dep_mlp, rel_head_mlp, len(self.vocabs['rels'])) rel_probs = tf.nn.softmax(rel_logits, dim=2) one_hot = tf.one_hot(arc_preds if moving_params is not None else arc_targets, self.bucket_size) one_hot = tf.expand_dims(one_hot, axis=3) select_rel_logits = tf.matmul(rel_logits, one_hot) select_rel_logits = tf.squeeze(select_rel_logits, axis=3) rel_preds = tf.to_int32(tf.argmax(select_rel_logits, axis=-1)) rel_targets = self.vocabs['rels'].placeholder rel_correct = tf.to_int32(tf.equal(rel_preds, rel_targets))*int_tokens_to_keep rel_loss = tf.losses.sparse_softmax_cross_entropy(rel_targets, select_rel_logits, self.tokens_to_keep) n_arc_correct = tf.reduce_sum(arc_correct) n_rel_correct = tf.reduce_sum(rel_correct) correct = arc_correct * rel_correct n_correct = tf.reduce_sum(correct) n_seqs_correct = tf.reduce_sum(tf.to_int32(tf.equal(tf.reduce_sum(correct, axis=1), self.sequence_lengths-1))) loss = arc_loss + rel_loss outputs = { 'arc_logits': arc_logits, 'arc_mus': arc_mus, 'arc_sigmas': arc_sigmas, 'arc_probs': arc_probs, 'arc_preds': arc_preds, 'arc_targets': arc_targets, 'arc_correct': arc_correct, 'arc_loss': arc_loss, 'n_arc_correct': n_arc_correct, 'rel_logits': rel_logits, 'rel_probs': rel_probs, 'rel_preds': rel_preds, 'rel_targets': rel_targets, 'rel_correct': rel_correct, 'rel_loss': rel_loss, 'n_rel_correct': n_rel_correct, 'n_tokens': self.n_tokens, 'n_seqs': self.batch_size, 'tokens_to_keep': self.tokens_to_keep, 'n_correct': n_correct, 'n_seqs_correct': n_seqs_correct, 'loss': loss } return outputs

true

1c43d18889a0f6900709ccbbcf70196aa6da5678

6,236

py

Python

db_comm_messages.py

seeul8er/DroneBridge_Comm

156ef546f4680084acc94c34f9ed3caeecf23585

[ "Apache-2.0" ]

1

2017-11-29T17:06:37.000Z

db_comm_messages.py

seeul8er/DroneBridge_Comm

156ef546f4680084acc94c34f9ed3caeecf23585

[ "Apache-2.0" ]

null

db_comm_messages.py

seeul8er/DroneBridge_Comm

156ef546f4680084acc94c34f9ed3caeecf23585

[ "Apache-2.0" ]

null

import json import configparser import binascii from itertools import chain import os tag = 'DB_COMM_MESSAGE: ' PATH_DRONEBRIDGE_TX_SETTINGS = "/boot/DroneBridgeTX.ini" PATH_DRONEBRIDGE_RX_SETTINGS = "/boot/DroneBridgeRX.ini" PATH_WBC_SETTINGS = "/boot/wifibroadcast-1.txt" # As we send it as a single frame we do not want the payload to be unnecessarily big. Only respond important settings wbc_settings_blacklist = ["TXMODE", "MAC_RX[0]", "FREQ_RX[0]", "MAC_RX[1]", "FREQ_RX[1]", "MAC_RX[2]", "FREQ_RX[2]", "MAC_RX[3]", "FREQ_RX[3]", "MAC_TX[0]", "FREQ_TX[0]", "MAC_TX[1]", "FREQ_TX[1]", "WIFI_HOTSPOT_NIC", "RELAY", "RELAY_NIC", "RELAY_FREQ", "QUIET", "FREQSCAN", "EXTERNAL_TELEMETRY_SERIALPORT_GROUND", "TELEMETRY_OUTPUT_SERIALPORT_GROUND", "FC_RC_BAUDRATE", "FC_RC_SERIALPORT", "TELEMETRY_UPLINK", "FC_MSP_SERIALPORT", "EXTERNAL_TELEMETRY_SERIALPORT_GROUND_BAUDRATE", "TELEMETRY_OUTPUT_SERIALPORT_GROUND_BAUDRATE"] db_settings_blacklist = ["ip_drone", "interface_selection", "interface_control", "interface_tel", "interface_video", "interface_comm", "joy_cal"] def new_settingsresponse_message(loaded_json, origin): """takes in a request - executes search for settings and creates a response as bytes""" complete_response = {} complete_response['destination'] = 4 complete_response['type'] = 'settingsresponse' complete_response['response'] = loaded_json['request'] complete_response['origin'] = origin complete_response['id'] = loaded_json['id'] if loaded_json['request'] == 'dronebridge': complete_response = read_dronebridge_settings(complete_response, origin) elif loaded_json['request'] == 'wifibroadcast': complete_response = read_wbc_settings(complete_response) response = json.dumps(complete_response) crc32 = binascii.crc32(str.encode(response)) return response.encode()+crc32.to_bytes(4, byteorder='little', signed=False) def new_settingschangesuccess_message(origin, new_id): """returns a settings change success message""" command = json.dumps({'destination': 4, 'type': 'settingssuccess', 'origin': origin, 'id': new_id}) crc32 = binascii.crc32(str.encode(command)) return command.encode()+crc32.to_bytes(4, byteorder='little', signed=False) def change_settings_wbc(loaded_json, origin): try: with open(PATH_WBC_SETTINGS, 'r+') as file: lines = file.readlines() for key in loaded_json['settings']: for index, line in enumerate(lines): if line.startswith(key+"="): lines[index] = key+"="+loaded_json['settings'][key]+"\n" file.seek(0, 0) for line in lines: file.write(line) file.truncate() file.flush() os.fsync(file.fileno()) except Exception as ex: print("Error writing wbc settings: " + str(ex)) return False return True def change_settings_db(loaded_json, origin): try: section = '' filepath = '' if origin=='groundstation': section = 'TX' filepath = PATH_DRONEBRIDGE_TX_SETTINGS elif origin == 'drone': section = 'RX' filepath = PATH_DRONEBRIDGE_RX_SETTINGS with open(filepath, 'r+') as file: lines = file.readlines() for key in loaded_json['settings'][section]: for index, line in enumerate(lines): if line.startswith(key+"="): lines[index] = key+"="+loaded_json['settings'][section][key]+"\n" file.seek(0, 0) for line in lines: file.write(line) file.truncate() file.flush() os.fsync(file.fileno()) except Exception as ex: print("Error writing db settings: "+str(ex)) return False return True def change_settings(loaded_json, origin): """takes a settings change request - executes it - returns a encoded settings change success message""" worked = False if loaded_json['change'] == 'db': worked = change_settings_db(loaded_json, origin) elif loaded_json['change'] == 'wbc': worked = change_settings_wbc(loaded_json, origin) if worked: return new_settingschangesuccess_message(origin, loaded_json['id']) else: return "error_settingschange".encode() def change_settings_gopro(loaded_json): # TODO change GoPro settings pass def read_dronebridge_settings(response_header, origin): config = configparser.ConfigParser() config.optionxform = str section = '' settings = {} if origin == 'groundstation': config.read(PATH_DRONEBRIDGE_TX_SETTINGS) section = 'TX' elif origin == 'drone': config.read(PATH_DRONEBRIDGE_RX_SETTINGS) section = 'RX' for key in config[section]: if key not in db_settings_blacklist: settings[key] = config.get(section, key) response_header['settings'] = settings return response_header def read_wbc_settings(response_header): virtual_section = 'root' settings = {} config = configparser.ConfigParser() config.optionxform = str with open(PATH_WBC_SETTINGS, 'r') as lines: lines = chain(('['+virtual_section+']',), lines) config.read_file(lines) for key in config[virtual_section]: if key not in wbc_settings_blacklist: settings[key] = config.get(virtual_section, key) response_header['settings'] = settings return response_header def remove_first_line(filepath): with open(filepath, 'r') as f1: data = f1.read().splitlines(True) with open(filepath, 'w') as f2: f2.writelines(data[1:]) def comm_message_extract_info(message): alist = message.rsplit(b'}', 1) alist[0] = alist[0]+b'}' return alist def check_package_good(extracted_info): if binascii.crc32(extracted_info[0]).to_bytes(4, byteorder='little', signed=False) == extracted_info[1]: return True print(tag+"Bad CRC!") return False

37.341317

121

0.642559

import json import configparser import binascii from itertools import chain import os tag = 'DB_COMM_MESSAGE: ' PATH_DRONEBRIDGE_TX_SETTINGS = "/boot/DroneBridgeTX.ini" PATH_DRONEBRIDGE_RX_SETTINGS = "/boot/DroneBridgeRX.ini" PATH_WBC_SETTINGS = "/boot/wifibroadcast-1.txt" wbc_settings_blacklist = ["TXMODE", "MAC_RX[0]", "FREQ_RX[0]", "MAC_RX[1]", "FREQ_RX[1]", "MAC_RX[2]", "FREQ_RX[2]", "MAC_RX[3]", "FREQ_RX[3]", "MAC_TX[0]", "FREQ_TX[0]", "MAC_TX[1]", "FREQ_TX[1]", "WIFI_HOTSPOT_NIC", "RELAY", "RELAY_NIC", "RELAY_FREQ", "QUIET", "FREQSCAN", "EXTERNAL_TELEMETRY_SERIALPORT_GROUND", "TELEMETRY_OUTPUT_SERIALPORT_GROUND", "FC_RC_BAUDRATE", "FC_RC_SERIALPORT", "TELEMETRY_UPLINK", "FC_MSP_SERIALPORT", "EXTERNAL_TELEMETRY_SERIALPORT_GROUND_BAUDRATE", "TELEMETRY_OUTPUT_SERIALPORT_GROUND_BAUDRATE"] db_settings_blacklist = ["ip_drone", "interface_selection", "interface_control", "interface_tel", "interface_video", "interface_comm", "joy_cal"] def new_settingsresponse_message(loaded_json, origin): complete_response = {} complete_response['destination'] = 4 complete_response['type'] = 'settingsresponse' complete_response['response'] = loaded_json['request'] complete_response['origin'] = origin complete_response['id'] = loaded_json['id'] if loaded_json['request'] == 'dronebridge': complete_response = read_dronebridge_settings(complete_response, origin) elif loaded_json['request'] == 'wifibroadcast': complete_response = read_wbc_settings(complete_response) response = json.dumps(complete_response) crc32 = binascii.crc32(str.encode(response)) return response.encode()+crc32.to_bytes(4, byteorder='little', signed=False) def new_settingschangesuccess_message(origin, new_id): command = json.dumps({'destination': 4, 'type': 'settingssuccess', 'origin': origin, 'id': new_id}) crc32 = binascii.crc32(str.encode(command)) return command.encode()+crc32.to_bytes(4, byteorder='little', signed=False) def change_settings_wbc(loaded_json, origin): try: with open(PATH_WBC_SETTINGS, 'r+') as file: lines = file.readlines() for key in loaded_json['settings']: for index, line in enumerate(lines): if line.startswith(key+"="): lines[index] = key+"="+loaded_json['settings'][key]+"\n" file.seek(0, 0) for line in lines: file.write(line) file.truncate() file.flush() os.fsync(file.fileno()) except Exception as ex: print("Error writing wbc settings: " + str(ex)) return False return True def change_settings_db(loaded_json, origin): try: section = '' filepath = '' if origin=='groundstation': section = 'TX' filepath = PATH_DRONEBRIDGE_TX_SETTINGS elif origin == 'drone': section = 'RX' filepath = PATH_DRONEBRIDGE_RX_SETTINGS with open(filepath, 'r+') as file: lines = file.readlines() for key in loaded_json['settings'][section]: for index, line in enumerate(lines): if line.startswith(key+"="): lines[index] = key+"="+loaded_json['settings'][section][key]+"\n" file.seek(0, 0) for line in lines: file.write(line) file.truncate() file.flush() os.fsync(file.fileno()) except Exception as ex: print("Error writing db settings: "+str(ex)) return False return True def change_settings(loaded_json, origin): worked = False if loaded_json['change'] == 'db': worked = change_settings_db(loaded_json, origin) elif loaded_json['change'] == 'wbc': worked = change_settings_wbc(loaded_json, origin) if worked: return new_settingschangesuccess_message(origin, loaded_json['id']) else: return "error_settingschange".encode() def change_settings_gopro(loaded_json): pass def read_dronebridge_settings(response_header, origin): config = configparser.ConfigParser() config.optionxform = str section = '' settings = {} if origin == 'groundstation': config.read(PATH_DRONEBRIDGE_TX_SETTINGS) section = 'TX' elif origin == 'drone': config.read(PATH_DRONEBRIDGE_RX_SETTINGS) section = 'RX' for key in config[section]: if key not in db_settings_blacklist: settings[key] = config.get(section, key) response_header['settings'] = settings return response_header def read_wbc_settings(response_header): virtual_section = 'root' settings = {} config = configparser.ConfigParser() config.optionxform = str with open(PATH_WBC_SETTINGS, 'r') as lines: lines = chain(('['+virtual_section+']',), lines) config.read_file(lines) for key in config[virtual_section]: if key not in wbc_settings_blacklist: settings[key] = config.get(virtual_section, key) response_header['settings'] = settings return response_header def remove_first_line(filepath): with open(filepath, 'r') as f1: data = f1.read().splitlines(True) with open(filepath, 'w') as f2: f2.writelines(data[1:]) def comm_message_extract_info(message): alist = message.rsplit(b'}', 1) alist[0] = alist[0]+b'}' return alist def check_package_good(extracted_info): if binascii.crc32(extracted_info[0]).to_bytes(4, byteorder='little', signed=False) == extracted_info[1]: return True print(tag+"Bad CRC!") return False

true

1c43d22b596fddf6286a515cf1c8f75f7f260ee6

1,209

py

Python

common/message_forwarder.py

matthewdargan/Cozmo-Capture-the-Flag

959467ed6ebaeeb42fe60db5905e49963b5d2096

[ "MIT" ]

null

common/message_forwarder.py

matthewdargan/Cozmo-Capture-the-Flag

959467ed6ebaeeb42fe60db5905e49963b5d2096

[ "MIT" ]

null

common/message_forwarder.py

matthewdargan/Cozmo-Capture-the-Flag

959467ed6ebaeeb42fe60db5905e49963b5d2096

[ "MIT" ]

1

2019-03-05T17:12:07.000Z

import socket from socket import error as socket_error from typing import List def start_connection(ip: str, port: int) -> socket.socket: """ Start a connection to a TCP network. :param ip ip address of the network :param port port number to forward messages over :return: socket opened with the ip address and port number """ try: s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) except socket_error: print("Connection failed.") try: s.connect((ip, port)) except socket_error: print('Socket failed to bind') s.setblocking(False) return s def receive_message(connection: socket.socket) -> List[str]: """ Receive a cube message from the network and parse it into sections so we can check the coordinates of a robot's cubes against a base. :param connection the network connection used to receive data :return: parameterized coordinate data """ try: bytedata = connection.recv(4048) data = bytedata.decode('utf-8') if not data: print('No message to receive') else: return data.split(' ') except socket.error: return []

25.1875

80

0.649297

import socket from socket import error as socket_error from typing import List def start_connection(ip: str, port: int) -> socket.socket: try: s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) except socket_error: print("Connection failed.") try: s.connect((ip, port)) except socket_error: print('Socket failed to bind') s.setblocking(False) return s def receive_message(connection: socket.socket) -> List[str]: try: bytedata = connection.recv(4048) data = bytedata.decode('utf-8') if not data: print('No message to receive') else: return data.split(' ') except socket.error: return []

true

1c43d37c4d3866bf302fa057cdab10f32428ea99

953

py

Python

cohesity_management_sdk/models/search_job_status_enum.py

nick6655/management-sdk-python

88e792cb83e5c24a22af495b220c145d0c45841d

[ "Apache-2.0" ]

18

2019-09-24T17:35:53.000Z

2022-03-25T08:08:47.000Z

cohesity_management_sdk/models/search_job_status_enum.py

nick6655/management-sdk-python

88e792cb83e5c24a22af495b220c145d0c45841d

[ "Apache-2.0" ]

18

2019-03-29T19:32:29.000Z

2022-01-03T23:16:45.000Z

cohesity_management_sdk/models/search_job_status_enum.py

nick6655/management-sdk-python

88e792cb83e5c24a22af495b220c145d0c45841d

[ "Apache-2.0" ]

16

2019-02-27T06:54:12.000Z

2021-11-16T18:10:24.000Z

# -*- coding: utf-8 -*- # Copyright 2021 Cohesity Inc. class SearchJobStatusEnum(object): """Implementation of the 'SearchJobStatus' enum. Specifies the status of the search. 'kJobRunning' indicates that the Job/task is currently running. 'kJobFinished' indicates that the Job/task completed and finished. 'kJobFailed' indicates that the Job/task failed and did not complete. 'kJobCanceled' indicates that the Job/task was canceled. 'kJobPaused' indicates the Job/task is paused. Attributes: KJOBRUNNING: TODO: type description here. KJOBFINISHED: TODO: type description here. KJOBFAILED: TODO: type description here. KJOBCANCELED: TODO: type description here. KJOBPAUSED: TODO: type description here. """ KJOBRUNNING = 'kJobRunning' KJOBFINISHED = 'kJobFinished' KJOBFAILED = 'kJobFailed' KJOBCANCELED = 'kJobCanceled' KJOBPAUSED = 'kJobPaused'

28.029412

73

0.699895

class SearchJobStatusEnum(object): KJOBRUNNING = 'kJobRunning' KJOBFINISHED = 'kJobFinished' KJOBFAILED = 'kJobFailed' KJOBCANCELED = 'kJobCanceled' KJOBPAUSED = 'kJobPaused'

true

1c43d38f9620e64fb551b35cd7d5f96e522ca4e4

96

py

Python

spark_surveymonkey/__init__.py

eferm/spark-surveymonkey

0912268c9604f32226d29b3d870296d781787a3a

[ "MIT" ]

null

spark_surveymonkey/__init__.py

eferm/spark-surveymonkey

0912268c9604f32226d29b3d870296d781787a3a

[ "MIT" ]

null

spark_surveymonkey/__init__.py

eferm/spark-surveymonkey

0912268c9604f32226d29b3d870296d781787a3a

[ "MIT" ]

null

from ._transform import transform_survey __version__ = '0.1.0' __all__ = ('transform_survey')

16

40

0.760417

from ._transform import transform_survey __version__ = '0.1.0' __all__ = ('transform_survey')

true

1c43d4b1509801a52270c7d65891a51a3a8e8637

10,527

py

Python

helpers/shuffleMockCatalog.py

manodeep/yymao-helpers

4ceffd639f4a10d259146f3f94e0b2415e835f32

[ "MIT" ]

null

helpers/shuffleMockCatalog.py

manodeep/yymao-helpers

4ceffd639f4a10d259146f3f94e0b2415e835f32

[ "MIT" ]

null

helpers/shuffleMockCatalog.py

manodeep/yymao-helpers

4ceffd639f4a10d259146f3f94e0b2415e835f32

[ "MIT" ]

null

__all__ = ['shuffleMockCatalog', 'generate_upid'] import warnings from itertools import izip import numpy as np from numpy.lib.recfunctions import rename_fields def _iter_plateau_in_sorted_array(a): if len(a): k = np.where(a[1:] != a[:-1])[0] k += 1 i = 0 for j in k: yield i, j i = j yield i, len(a) def _iter_indices_in_bins(bins, a): if len(a) and len(bins): s = a.argsort() k = np.searchsorted(a, bins, 'right', sorter=s) i = 0 for j in k: yield s[i:j] i = j yield s[i:] def _apply_rotation(pos, box_size): half_box_size = box_size * 0.5 pos[pos > half_box_size] -= box_size pos[pos < -half_box_size] += box_size return np.dot(pos, np.linalg.qr(np.random.randn(3,3))[0]) _axes = list('xyz') def _get_xyz(a, ax_type=float): return np.fromiter((a[ax] for ax in _axes), ax_type, 3) def generate_upid(pid, id, recursive=True): """ To generate (or to fix) the upid of a halo catalog. Parameters ---------- pid : array_like An ndarray of integer that contains the parent IDs of each halo. id : array_like An ndarray of integer that contains the halo IDs. recursive : bool, optional Whether or not to run this function recursively. Default is True. Returns ------- upid : array_like The ultimate parent IDs. Examples -------- >>> halos['upid'] = generate_upid(halos['pid'], halos['id']) """ pid = np.ravel(pid) id = np.ravel(id) if len(id) != len(pid): raise ValueError('`pid` and `id` must have the same length.') if not len(pid): raise ValueError('`pid` and `id` must not be empty.') s = pid.argsort() idx = np.fromiter(_iter_plateau_in_sorted_array(pid[s]), \ np.dtype([('start', int), ('stop', int)])) unique_pid = pid[s[idx['start']]] if unique_pid[0] == -1: unique_pid = unique_pid[1:] idx = idx[1:] host_flag = (pid == -1) not_found = np.where(np.in1d(unique_pid, id[host_flag], True, True))[0] if not len(not_found): return pid sub_flag = np.where(~host_flag)[0] found = sub_flag[np.in1d(id[sub_flag], unique_pid[not_found], True)] found = found[id[found].argsort()] assert (id[found] == unique_pid[not_found]).all() del host_flag, sub_flag, unique_pid pid_old = pid.copy() for i, j in izip(found, not_found): pid[s[slice(*idx[j])]] = pid_old[i] del pid_old, idx, s, found, not_found return generate_upid(pid, id, True) if recursive else pid def shuffleMockCatalog(mock_ids, halo_catalog, bin_width=None, bins=None, proxy='mvir', box_size=None, apply_rsd=False, shuffle_centrals=True, shuffle_satellites=True, rotate_satellites=False, return_structured_array=False): """ Shuffle a mock catalog according to Zentner et al. (2014) [arXiv:1311.1818] Parameters ---------- mock_ids : array_like Should be a 1-d array of int which contains the corresponding halo IDs for the galaxies in the mock catalog to be shuffled. halo_catalog : array_like Should be a 1-d structrued array which has the following fields: id, upid, x, y, z, vz (if `apply_rsd` it True), and the proxy. bin_width : float or None, optional The width of the bin, in dex. bins : int, array_like, or None, optional If an integer is provided, it is interpreted as the number of bins. If an array is provided, it is interpreted as the edges of the bins. The parameter _overwrites_ `bin_width`. proxy : string, optional The proxy to bin on. Must be present in the fields of `halo_catalog`. box_size : float or None, optional The side length of the box. Should be in the same unit as x, y, z. apply_rsd : bool, optional Whether or not to apply redshift space distortions on the z-axis. (Default is False) shuffle_centrals : bool, optional Whether or not to shuffle central galaxies (Default is True) shuffle_satellites : bool, optional Whether or not to shuffle satellite galaxies (Default is True) rotate_satellites : bool, optional Whether or not to apply a random rotation to satellite galaxies (Default is False) return_structured_array : bool, optional Whether to return a structured array that contains x, y, z or just a n-by-3 float array. Returns ------- pos : array_like A ndarray that contains x, y, z of the shuffled positions. """ # check necessary fields in halo_catalog fields = ['id', 'upid', proxy] + _axes if apply_rsd: fields.append('vz') if not all((f in halo_catalog.dtype.names for f in fields)): raise ValueError('`halo_catalog` should have the following fields: '+ \ ', '.join(fields)) # check dtype ax_type = halo_catalog['x'].dtype.type if any((halo_catalog[ax].dtype.type != ax_type for ax in 'yz')): raise ValueError('The types of fields x, y, z in `halo_catalog` ' \ 'must all be the same.') # check all mock_ids are in halo_catalog s = halo_catalog['id'].argsort() idx = np.searchsorted(halo_catalog['id'], mock_ids, sorter=s) try: idx = s[idx] except IndexError: raise ValueError('`mock_ids` must all present in `halo_catalog`') if not (halo_catalog['id'][idx] == mock_ids).all(): raise ValueError('`mock_ids` must all present in `halo_catalog`') mock_idx = np.ones(len(halo_catalog), dtype=int) mock_idx *= -1 mock_idx[idx] = np.arange(len(mock_ids)) del idx # separate hosts and subs host_flag = (halo_catalog['upid'] == -1) subs = rename_fields(halo_catalog[~host_flag], {'id':'mock_idx'}) subs['mock_idx'] = mock_idx[~host_flag] subs = subs[subs['mock_idx'] > -1] # only need subs that are mocks host_flag = s[host_flag[s]] # this sorts `hosts` by `id` hosts = rename_fields(halo_catalog[host_flag], {'upid':'mock_idx'}) hosts['mock_idx'] = mock_idx[host_flag] del host_flag, mock_idx, s # group subhalos subs.sort(order='upid') idx = np.fromiter(_iter_plateau_in_sorted_array(subs['upid']), \ np.dtype([('start', int), ('stop', int)])) host_ids = subs['upid'][idx['start']] if not np.in1d(host_ids, hosts['id'], True).all(): raise ValueError('Some subhalos associdated with the mock galaxies ' \ 'have no parent halos in `halo_catalog`. Consider using ' \ '`generate_upid` to fix this.') # for the following to work, `hosts` need to be sorted by `id` subs_idx = np.zeros(len(hosts), dtype=idx.dtype) subs_idx[np.in1d(hosts['id'], host_ids, True)] = idx del idx, host_ids # check bins try: bin_width = float(bin_width) except (ValueError, TypeError): bin_width = None else: if bin_width <= 0: bin_width = None if bin_width is None: bin_width = 0.1 mi = np.log10(hosts[proxy].min()*0.99999) ma = np.log10(hosts[proxy].max()) if bins is None: bins = int(np.ceil((ma-mi)/bin_width)) mi = ma - bin_width*bins try: bins = int(bins) except (ValueError, TypeError): bins = np.asarray(bins) if len(bins) < 2 or (bins[1:]<bins[:-1]).any(): raise ValueError('Please specify a valid `bin` parameter.') else: bins = np.logspace(mi, ma, bins+1) # create the array for storing results pos = np.empty((len(mock_ids), 3), ax_type) pos.fill(np.nan) # loop of bins of proxy (e.g. mvir) for i, indices in enumerate(_iter_indices_in_bins(bins, hosts[proxy])): if not len(indices): continue if i==0 or i==len(bins): if (hosts['mock_idx'][indices] > -1).any() or \ any((subs_idx['start'][j] < subs_idx['stop'][j] \ for j in indices)): warnings.warn('Some halos associdated with the mock catalog ' \ 'are outside the bin range.', RuntimeWarning) continue # shuffle satellites if shuffle_satellites: choices = indices.tolist() for j in indices: subs_this = subs[slice(*subs_idx[j])] if not len(subs_this): continue mock_idx_this = subs_this['mock_idx'] pos[mock_idx_this] = subs_this[_axes].view((ax_type,3)) if shuffle_satellites: k = choices.pop(np.random.randint(len(choices))) pos[mock_idx_this] -= _get_xyz(hosts[j], ax_type) if rotate_satellites: pos[mock_idx_this] = \ _apply_rotation(pos[mock_idx_this], box_size) pos[mock_idx_this] += _get_xyz(hosts[k], ax_type) if apply_rsd: pos[mock_idx_this,2] += (subs_this['vz'] \ + hosts['vz'][k] - hosts['vz'][j])/100.0 else: if rotate_satellites: host_pos = _get_xyz(hosts[j], ax_type) pos[mock_idx_this] -= host_pos pos[mock_idx_this] = \ _apply_rotation(pos[mock_idx_this], box_size) pos[mock_idx_this] += host_pos if apply_rsd: pos[mock_idx_this,2] += subs_this['vz']/100.0 # shuffle hosts has_mock = indices[hosts['mock_idx'][indices] > -1] if not len(has_mock): continue mock_idx_this = hosts['mock_idx'][has_mock] if shuffle_centrals: has_mock = np.random.choice(indices, len(has_mock), False) pos[mock_idx_this] = hosts[_axes][has_mock].view((ax_type,3)) if apply_rsd: pos[mock_idx_this,2] += hosts['vz'][has_mock]/100.0 # sanity check if np.isnan(pos).any(): warnings.warn('Some galaxies in the mock catalog have not been ' \ 'assigned a new position. Maybe the corresponding halo is ' \ 'outside the bin range.', RuntimeWarning) # wrap box if box_size is not None: pos = np.remainder(pos, box_size, pos) if return_structured_array: pos = pos.view(np.dtype(zip(_axes, [ax_type]*3))) return pos

36.807692

80

0.595516

__all__ = ['shuffleMockCatalog', 'generate_upid'] import warnings from itertools import izip import numpy as np from numpy.lib.recfunctions import rename_fields def _iter_plateau_in_sorted_array(a): if len(a): k = np.where(a[1:] != a[:-1])[0] k += 1 i = 0 for j in k: yield i, j i = j yield i, len(a) def _iter_indices_in_bins(bins, a): if len(a) and len(bins): s = a.argsort() k = np.searchsorted(a, bins, 'right', sorter=s) i = 0 for j in k: yield s[i:j] i = j yield s[i:] def _apply_rotation(pos, box_size): half_box_size = box_size * 0.5 pos[pos > half_box_size] -= box_size pos[pos < -half_box_size] += box_size return np.dot(pos, np.linalg.qr(np.random.randn(3,3))[0]) _axes = list('xyz') def _get_xyz(a, ax_type=float): return np.fromiter((a[ax] for ax in _axes), ax_type, 3) def generate_upid(pid, id, recursive=True): pid = np.ravel(pid) id = np.ravel(id) if len(id) != len(pid): raise ValueError('`pid` and `id` must have the same length.') if not len(pid): raise ValueError('`pid` and `id` must not be empty.') s = pid.argsort() idx = np.fromiter(_iter_plateau_in_sorted_array(pid[s]), \ np.dtype([('start', int), ('stop', int)])) unique_pid = pid[s[idx['start']]] if unique_pid[0] == -1: unique_pid = unique_pid[1:] idx = idx[1:] host_flag = (pid == -1) not_found = np.where(np.in1d(unique_pid, id[host_flag], True, True))[0] if not len(not_found): return pid sub_flag = np.where(~host_flag)[0] found = sub_flag[np.in1d(id[sub_flag], unique_pid[not_found], True)] found = found[id[found].argsort()] assert (id[found] == unique_pid[not_found]).all() del host_flag, sub_flag, unique_pid pid_old = pid.copy() for i, j in izip(found, not_found): pid[s[slice(*idx[j])]] = pid_old[i] del pid_old, idx, s, found, not_found return generate_upid(pid, id, True) if recursive else pid def shuffleMockCatalog(mock_ids, halo_catalog, bin_width=None, bins=None, proxy='mvir', box_size=None, apply_rsd=False, shuffle_centrals=True, shuffle_satellites=True, rotate_satellites=False, return_structured_array=False): fields = ['id', 'upid', proxy] + _axes if apply_rsd: fields.append('vz') if not all((f in halo_catalog.dtype.names for f in fields)): raise ValueError('`halo_catalog` should have the following fields: '+ \ ', '.join(fields)) ax_type = halo_catalog['x'].dtype.type if any((halo_catalog[ax].dtype.type != ax_type for ax in 'yz')): raise ValueError('The types of fields x, y, z in `halo_catalog` ' \ 'must all be the same.') s = halo_catalog['id'].argsort() idx = np.searchsorted(halo_catalog['id'], mock_ids, sorter=s) try: idx = s[idx] except IndexError: raise ValueError('`mock_ids` must all present in `halo_catalog`') if not (halo_catalog['id'][idx] == mock_ids).all(): raise ValueError('`mock_ids` must all present in `halo_catalog`') mock_idx = np.ones(len(halo_catalog), dtype=int) mock_idx *= -1 mock_idx[idx] = np.arange(len(mock_ids)) del idx host_flag = (halo_catalog['upid'] == -1) subs = rename_fields(halo_catalog[~host_flag], {'id':'mock_idx'}) subs['mock_idx'] = mock_idx[~host_flag] subs = subs[subs['mock_idx'] > -1] host_flag = s[host_flag[s]] hosts = rename_fields(halo_catalog[host_flag], {'upid':'mock_idx'}) hosts['mock_idx'] = mock_idx[host_flag] del host_flag, mock_idx, s subs.sort(order='upid') idx = np.fromiter(_iter_plateau_in_sorted_array(subs['upid']), \ np.dtype([('start', int), ('stop', int)])) host_ids = subs['upid'][idx['start']] if not np.in1d(host_ids, hosts['id'], True).all(): raise ValueError('Some subhalos associdated with the mock galaxies ' \ 'have no parent halos in `halo_catalog`. Consider using ' \ '`generate_upid` to fix this.') subs_idx = np.zeros(len(hosts), dtype=idx.dtype) subs_idx[np.in1d(hosts['id'], host_ids, True)] = idx del idx, host_ids try: bin_width = float(bin_width) except (ValueError, TypeError): bin_width = None else: if bin_width <= 0: bin_width = None if bin_width is None: bin_width = 0.1 mi = np.log10(hosts[proxy].min()*0.99999) ma = np.log10(hosts[proxy].max()) if bins is None: bins = int(np.ceil((ma-mi)/bin_width)) mi = ma - bin_width*bins try: bins = int(bins) except (ValueError, TypeError): bins = np.asarray(bins) if len(bins) < 2 or (bins[1:]<bins[:-1]).any(): raise ValueError('Please specify a valid `bin` parameter.') else: bins = np.logspace(mi, ma, bins+1) pos = np.empty((len(mock_ids), 3), ax_type) pos.fill(np.nan) for i, indices in enumerate(_iter_indices_in_bins(bins, hosts[proxy])): if not len(indices): continue if i==0 or i==len(bins): if (hosts['mock_idx'][indices] > -1).any() or \ any((subs_idx['start'][j] < subs_idx['stop'][j] \ for j in indices)): warnings.warn('Some halos associdated with the mock catalog ' \ 'are outside the bin range.', RuntimeWarning) continue if shuffle_satellites: choices = indices.tolist() for j in indices: subs_this = subs[slice(*subs_idx[j])] if not len(subs_this): continue mock_idx_this = subs_this['mock_idx'] pos[mock_idx_this] = subs_this[_axes].view((ax_type,3)) if shuffle_satellites: k = choices.pop(np.random.randint(len(choices))) pos[mock_idx_this] -= _get_xyz(hosts[j], ax_type) if rotate_satellites: pos[mock_idx_this] = \ _apply_rotation(pos[mock_idx_this], box_size) pos[mock_idx_this] += _get_xyz(hosts[k], ax_type) if apply_rsd: pos[mock_idx_this,2] += (subs_this['vz'] \ + hosts['vz'][k] - hosts['vz'][j])/100.0 else: if rotate_satellites: host_pos = _get_xyz(hosts[j], ax_type) pos[mock_idx_this] -= host_pos pos[mock_idx_this] = \ _apply_rotation(pos[mock_idx_this], box_size) pos[mock_idx_this] += host_pos if apply_rsd: pos[mock_idx_this,2] += subs_this['vz']/100.0 has_mock = indices[hosts['mock_idx'][indices] > -1] if not len(has_mock): continue mock_idx_this = hosts['mock_idx'][has_mock] if shuffle_centrals: has_mock = np.random.choice(indices, len(has_mock), False) pos[mock_idx_this] = hosts[_axes][has_mock].view((ax_type,3)) if apply_rsd: pos[mock_idx_this,2] += hosts['vz'][has_mock]/100.0 if np.isnan(pos).any(): warnings.warn('Some galaxies in the mock catalog have not been ' \ 'assigned a new position. Maybe the corresponding halo is ' \ 'outside the bin range.', RuntimeWarning) if box_size is not None: pos = np.remainder(pos, box_size, pos) if return_structured_array: pos = pos.view(np.dtype(zip(_axes, [ax_type]*3))) return pos

true

1c43d5f43d8a82eed84cc58c3d38663d541a8be4

831

py

Python

accessible_output/speech/outputs/jaws.py

Timtam/cards-against-humanity

89ea61b5c9915198b845bbf8a93c3f7827323ceb

[ "MIT" ]

5

2017-04-11T00:18:42.000Z

2021-08-01T04:27:20.000Z

accessible_output/speech/outputs/jaws.py

Timtam/cards-against-humanity

89ea61b5c9915198b845bbf8a93c3f7827323ceb

[ "MIT" ]

47

2017-04-27T18:57:27.000Z

2017-07-16T21:18:28.000Z

accessible_output/speech/outputs/jaws.py

Timtam/cards-against-humanity

89ea61b5c9915198b845bbf8a93c3f7827323ceb

[ "MIT" ]

4

2018-05-17T12:33:59.000Z

2022-02-20T16:08:51.000Z

from pywintypes import com_error import win32gui import win32com.client from main import OutputError, ScreenreaderSpeechOutput class Jaws (ScreenreaderSpeechOutput): """Speech output supporting the Jaws for Windows screen reader.""" name = 'Jaws' def __init__(self, *args, **kwargs): super (Jaws, self).__init__(*args, **kwargs) try: self.object = win32com.client.Dispatch("FreedomSci.JawsApi") except com_error: #try jfwapi try: self.object = win32com.client.Dispatch("jfwapi") except com_error: #give up raise OutputError def speak(self, text, interrupt=False): self.object.SayString(' %s' % text, interrupt) def canSpeak(self): try: return self.object.SayString('',0) == True or win32gui.FindWindow("JFWUI2", "JAWS") != 0 and super(Jaws, self).canSpeak() except: return False

26.806452

124

0.718412

from pywintypes import com_error import win32gui import win32com.client from main import OutputError, ScreenreaderSpeechOutput class Jaws (ScreenreaderSpeechOutput): name = 'Jaws' def __init__(self, *args, **kwargs): super (Jaws, self).__init__(*args, **kwargs) try: self.object = win32com.client.Dispatch("FreedomSci.JawsApi") except com_error: try: self.object = win32com.client.Dispatch("jfwapi") except com_error: raise OutputError def speak(self, text, interrupt=False): self.object.SayString(' %s' % text, interrupt) def canSpeak(self): try: return self.object.SayString('',0) == True or win32gui.FindWindow("JFWUI2", "JAWS") != 0 and super(Jaws, self).canSpeak() except: return False

true

1c43d7479da3c0f98336c0c943df2ebf50f430e0

9,059

py

Python

algs/nsga_net/utils/utils.py

Beautyya/BenchENA

5f5491614fc2f00ca26dc29f35f44c334db4718c

[ "MIT" ]

null

algs/nsga_net/utils/utils.py

Beautyya/BenchENA

5f5491614fc2f00ca26dc29f35f44c334db4718c

[ "MIT" ]

null

algs/nsga_net/utils/utils.py

Beautyya/BenchENA

5f5491614fc2f00ca26dc29f35f44c334db4718c

[ "MIT" ]

null

import configparser import os import platform import multiprocessing from compute.file import get_algo_local_dir, get_local_path import time import os import numpy as np from algs.nsga_net.utils.statusupdatetool import StatusUpdateTool from algs.nsga_net.genetic.population import Population, Individual class Utils(object): _lock = multiprocessing.Lock() @classmethod def get_lock_for_write_fitness(cls): return cls._lock @classmethod def path_replace(cls, input_str): # input a str, replace '\\' with '/', because the os.path in windows return path with '\\' joining # please use it after creating a string with both os.path and string '/' if (platform.system() == 'Windows'): new_str = input_str.replace('\\', '/') else: # Linux or Mac new_str = input_str return new_str @classmethod def load_cache_data(cls): file_name = '%s/cache.txt' % (os.path.join(get_algo_local_dir(), 'populations')) file_name = cls.path_replace(file_name) _map = {} if os.path.exists(file_name): f = open(file_name, 'r') for each_line in f: rs_ = each_line.strip().split(';') _map[rs_[0]] = '%.5f' % (float(rs_[1])) f.close() return _map @classmethod def save_fitness_to_cache(cls, individuals): _map1, _map2 = cls.load_cache_data() for indi in individuals: _key, _str = indi.uuid() _acc = indi.acc _flop = indi.flop if _key not in _map: file_name = '%s/cache.txt' % (os.path.join(get_algo_local_dir(), 'populations')) file_name = cls.path_replace(file_name) f = open(file_name, 'a+') _str = '%s;%.5f;%.5f;%s\n' % (_key, _acc, _flop, _str) f.write(_str) f.close() _map1[_key] = _acc _map2[_key] = _flop @classmethod def save_population_at_begin(cls, _str, gen_no): file_name = '%s/begin_%05d.txt' % (os.path.join(get_algo_local_dir(), 'populations'), gen_no) # solve the path differences caused by different platforms file_name = cls.path_replace(file_name) with open(file_name, 'w') as f: f.write(_str) @classmethod def save_population_after_mutation(cls, _str, gen_no): file_name = '%s/mutation_%05d.txt' % (os.path.join(get_algo_local_dir(), 'populations'), gen_no) file_name = cls.path_replace(file_name) with open(file_name, 'w') as f: f.write(_str) @classmethod def get_newest_file_based_on_prefix(cls, prefix): id_list = [] for _, _, file_names in os.walk(os.path.join(get_algo_local_dir(), 'populations')): for file_name in file_names: if file_name.startswith(prefix): number_index = len(prefix) + 1 # the first number index id_list.append(int(file_name[number_index:number_index + 5])) if len(id_list) == 0: return None else: return np.max(id_list) @classmethod def load_population(cls, prefix, gen_no): file_name = '%s/%s_%05d.txt' % (os.path.join(get_algo_local_dir(), 'populations'), prefix, np.min(gen_no)) file_name = cls.path_replace(file_name) params = StatusUpdateTool.get_init_params() pop = Population(gen_no, params) f = open(file_name) indi_start_line = f.readline().strip() while indi_start_line.startswith('indi'): indi_no = indi_start_line[5:] indi = Individual(indi_no, params, params['n_var']) genome = [] for line in f: line = line.strip() if line.startswith('--'): indi_start_line = f.readline().strip() break else: if line.startswith('Acc'): indi.acc = float(line[4:]) elif line.startswith('flop'): indi.flop = float(line[5:]) elif line.startswith('genome'): print(line) l = list(line[8:]) while ' ' in l: l.remove(' ') while ',' in l: l.remove(',') while ']' in l: l.remove(']') for i in l: genome.append(int(i)) elif line.startswith('0') or line.startswith('1'): print(line) l = list(line) while ' ' in l: l.remove(' ') while ',' in l: l.remove(',') while ']' in l: l.remove(']') for i in l: genome.append(int(i)) else: print('Unknown key for load unit type, line content:%s' % (line)) indi.genome = np.array(genome) pop.individuals.append(indi) f.close() return pop @classmethod def read_template(cls, search_space): _path = os.path.join(os.path.dirname(__file__), 'template', search_space + '_models.py') part1 = [] part2 = [] f = open(_path) f.readline() # skip this comment line = f.readline().rstrip() while line.strip() != "#generate_init": part1.append(line) line = f.readline().rstrip() line = f.readline().rstrip() # skip the comment '#generate_forward' while line.strip() != '"""': part2.append(line) line = f.readline().rstrip() return part1, part2 @classmethod def generate_micro_pytorch_file(cls, indi, params, test=False): search_space = "micro" part1, part2 = cls.read_template(search_space) line1 = "genome = convert(%s)" % (str(list(indi.genome))) line2 = "genotype = decode(genome)" line3 = "self.net = Network(%d, %d, %d, False, genotype)" % \ (params['init_channels'], params['classes'], params['layers']) _str = [] current_time = time.strftime("%Y-%m-%d %H:%M:%S") _str.append('"""') _str.append(current_time) _str.append('"""') _str.extend(part1) _str.append(' %s' % (line1)) _str.append(' %s' % (line2)) _str.append(' %s' % (line3)) _str.extend(part2) if not test: file_name = '%s/%s.py' % (os.path.join(get_algo_local_dir(), 'scripts'), indi.id) else: file_name = '%s/nsga_micro_%s.py' % (os.path.join(get_local_path(), 'example'), indi.id) file_name = cls.path_replace(file_name) if not os.path.exists(os.path.join(get_algo_local_dir(), 'scripts')): os.makedirs(os.path.join(get_algo_local_dir(), 'scripts')) script_file_handler = open(file_name, 'w') script_file_handler.write('\n'.join(_str)) script_file_handler.flush() script_file_handler.close() @classmethod def generate_macro_pytorch_file(cls, indi, channels, params, test=False): search_space = "macro" part1, part2 = cls.read_template(search_space) line1 = "genome = convert(np.array(%s))" % (str(list(indi.genome))) line2 = "genotype = decode(genome)" line3 = "channels = %s" % (str(channels)) line4 = "self.net = EvoNetwork(genotype, channels, %d, (%d, %d), decoder='residual')" % \ (params['classes'], StatusUpdateTool.get_input_weight(), StatusUpdateTool.get_input_height()) _str = [] current_time = time.strftime("%Y-%m-%d %H:%M:%S") _str.append('"""') _str.append(current_time) _str.append('"""') _str.extend(part1) _str.append(' %s' % line1) _str.append(' %s' % line2) _str.append(' %s' % line3) _str.append(' %s' % line4) _str.extend(part2) if not test: file_name = '%s/%s.py' % (os.path.join(get_algo_local_dir(), 'scripts'), indi.id) else: file_name = '%s/nsga_macro_%s.py' % (os.path.join(get_local_path(), 'example'), indi.id) file_name = cls.path_replace(file_name) if not os.path.exists(os.path.join(get_algo_local_dir(), 'scripts')): os.makedirs(os.path.join(get_algo_local_dir(), 'scripts')) script_file_handler = open(file_name, 'w') script_file_handler.write('\n'.join(_str)) script_file_handler.flush() script_file_handler.close() @classmethod def write_to_file(cls, _str, _file): f = open(_file, 'w') f.write(_str) f.flush() f.close()

40.084071

114

0.535821

import configparser import os import platform import multiprocessing from compute.file import get_algo_local_dir, get_local_path import time import os import numpy as np from algs.nsga_net.utils.statusupdatetool import StatusUpdateTool from algs.nsga_net.genetic.population import Population, Individual class Utils(object): _lock = multiprocessing.Lock() @classmethod def get_lock_for_write_fitness(cls): return cls._lock @classmethod def path_replace(cls, input_str): if (platform.system() == 'Windows'): new_str = input_str.replace('\\', '/') else: new_str = input_str return new_str @classmethod def load_cache_data(cls): file_name = '%s/cache.txt' % (os.path.join(get_algo_local_dir(), 'populations')) file_name = cls.path_replace(file_name) _map = {} if os.path.exists(file_name): f = open(file_name, 'r') for each_line in f: rs_ = each_line.strip().split(';') _map[rs_[0]] = '%.5f' % (float(rs_[1])) f.close() return _map @classmethod def save_fitness_to_cache(cls, individuals): _map1, _map2 = cls.load_cache_data() for indi in individuals: _key, _str = indi.uuid() _acc = indi.acc _flop = indi.flop if _key not in _map: file_name = '%s/cache.txt' % (os.path.join(get_algo_local_dir(), 'populations')) file_name = cls.path_replace(file_name) f = open(file_name, 'a+') _str = '%s;%.5f;%.5f;%s\n' % (_key, _acc, _flop, _str) f.write(_str) f.close() _map1[_key] = _acc _map2[_key] = _flop @classmethod def save_population_at_begin(cls, _str, gen_no): file_name = '%s/begin_%05d.txt' % (os.path.join(get_algo_local_dir(), 'populations'), gen_no) file_name = cls.path_replace(file_name) with open(file_name, 'w') as f: f.write(_str) @classmethod def save_population_after_mutation(cls, _str, gen_no): file_name = '%s/mutation_%05d.txt' % (os.path.join(get_algo_local_dir(), 'populations'), gen_no) file_name = cls.path_replace(file_name) with open(file_name, 'w') as f: f.write(_str) @classmethod def get_newest_file_based_on_prefix(cls, prefix): id_list = [] for _, _, file_names in os.walk(os.path.join(get_algo_local_dir(), 'populations')): for file_name in file_names: if file_name.startswith(prefix): number_index = len(prefix) + 1 id_list.append(int(file_name[number_index:number_index + 5])) if len(id_list) == 0: return None else: return np.max(id_list) @classmethod def load_population(cls, prefix, gen_no): file_name = '%s/%s_%05d.txt' % (os.path.join(get_algo_local_dir(), 'populations'), prefix, np.min(gen_no)) file_name = cls.path_replace(file_name) params = StatusUpdateTool.get_init_params() pop = Population(gen_no, params) f = open(file_name) indi_start_line = f.readline().strip() while indi_start_line.startswith('indi'): indi_no = indi_start_line[5:] indi = Individual(indi_no, params, params['n_var']) genome = [] for line in f: line = line.strip() if line.startswith('--'): indi_start_line = f.readline().strip() break else: if line.startswith('Acc'): indi.acc = float(line[4:]) elif line.startswith('flop'): indi.flop = float(line[5:]) elif line.startswith('genome'): print(line) l = list(line[8:]) while ' ' in l: l.remove(' ') while ',' in l: l.remove(',') while ']' in l: l.remove(']') for i in l: genome.append(int(i)) elif line.startswith('0') or line.startswith('1'): print(line) l = list(line) while ' ' in l: l.remove(' ') while ',' in l: l.remove(',') while ']' in l: l.remove(']') for i in l: genome.append(int(i)) else: print('Unknown key for load unit type, line content:%s' % (line)) indi.genome = np.array(genome) pop.individuals.append(indi) f.close() return pop @classmethod def read_template(cls, search_space): _path = os.path.join(os.path.dirname(__file__), 'template', search_space + '_models.py') part1 = [] part2 = [] f = open(_path) f.readline() line = f.readline().rstrip() while line.strip() != "#generate_init": part1.append(line) line = f.readline().rstrip() line = f.readline().rstrip() while line.strip() != '"""': part2.append(line) line = f.readline().rstrip() return part1, part2 @classmethod def generate_micro_pytorch_file(cls, indi, params, test=False): search_space = "micro" part1, part2 = cls.read_template(search_space) line1 = "genome = convert(%s)" % (str(list(indi.genome))) line2 = "genotype = decode(genome)" line3 = "self.net = Network(%d, %d, %d, False, genotype)" % \ (params['init_channels'], params['classes'], params['layers']) _str = [] current_time = time.strftime("%Y-%m-%d %H:%M:%S") _str.append('"""') _str.append(current_time) _str.append('"""') _str.extend(part1) _str.append(' %s' % (line1)) _str.append(' %s' % (line2)) _str.append(' %s' % (line3)) _str.extend(part2) if not test: file_name = '%s/%s.py' % (os.path.join(get_algo_local_dir(), 'scripts'), indi.id) else: file_name = '%s/nsga_micro_%s.py' % (os.path.join(get_local_path(), 'example'), indi.id) file_name = cls.path_replace(file_name) if not os.path.exists(os.path.join(get_algo_local_dir(), 'scripts')): os.makedirs(os.path.join(get_algo_local_dir(), 'scripts')) script_file_handler = open(file_name, 'w') script_file_handler.write('\n'.join(_str)) script_file_handler.flush() script_file_handler.close() @classmethod def generate_macro_pytorch_file(cls, indi, channels, params, test=False): search_space = "macro" part1, part2 = cls.read_template(search_space) line1 = "genome = convert(np.array(%s))" % (str(list(indi.genome))) line2 = "genotype = decode(genome)" line3 = "channels = %s" % (str(channels)) line4 = "self.net = EvoNetwork(genotype, channels, %d, (%d, %d), decoder='residual')" % \ (params['classes'], StatusUpdateTool.get_input_weight(), StatusUpdateTool.get_input_height()) _str = [] current_time = time.strftime("%Y-%m-%d %H:%M:%S") _str.append('"""') _str.append(current_time) _str.append('"""') _str.extend(part1) _str.append(' %s' % line1) _str.append(' %s' % line2) _str.append(' %s' % line3) _str.append(' %s' % line4) _str.extend(part2) if not test: file_name = '%s/%s.py' % (os.path.join(get_algo_local_dir(), 'scripts'), indi.id) else: file_name = '%s/nsga_macro_%s.py' % (os.path.join(get_local_path(), 'example'), indi.id) file_name = cls.path_replace(file_name) if not os.path.exists(os.path.join(get_algo_local_dir(), 'scripts')): os.makedirs(os.path.join(get_algo_local_dir(), 'scripts')) script_file_handler = open(file_name, 'w') script_file_handler.write('\n'.join(_str)) script_file_handler.flush() script_file_handler.close() @classmethod def write_to_file(cls, _str, _file): f = open(_file, 'w') f.write(_str) f.flush() f.close()

true

1c43d78b35231ae2efb4db918cbc7bf068cee45e

4,547

py

Python

examples/mfa_extraction/fix_mismatch.py

geneing/TensorFlowTTS

0035ba00fec1b2b1184c8df32646d6a88b01ee5b

[ "Apache-2.0" ]

null

examples/mfa_extraction/fix_mismatch.py

geneing/TensorFlowTTS

0035ba00fec1b2b1184c8df32646d6a88b01ee5b

[ "Apache-2.0" ]

null

examples/mfa_extraction/fix_mismatch.py

geneing/TensorFlowTTS

0035ba00fec1b2b1184c8df32646d6a88b01ee5b

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # Copyright 2020 TensorFlowTTS Team. # # 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. """Fix mismatch between sum durations and mel lengths.""" import numpy as np import os from tqdm import tqdm import click import logging import sys logging.basicConfig( level=logging.DEBUG, stream=sys.stdout, format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s", ) @click.command() @click.option("--base_path", default="dump") @click.option("--trimmed_dur_path", default="dataset/trimmed-durations") @click.option("--dur_path", default="dataset/durations") @click.option("--use_norm", default="f") def fix(base_path: str, dur_path: str, trimmed_dur_path: str, use_norm: str): for t in ["train", "valid"]: mfa_longer = [] mfa_shorter = [] big_diff = [] not_fixed = [] pre_path = os.path.join(base_path, t) os.makedirs(os.path.join(pre_path, "fix_dur"), exist_ok=True) os.makedirs(os.path.join(pre_path, "phids"), exist_ok=True) logging.info(f"FIXING {t} set ...\n") base = lambda s: s.replace('-ids.npy','') for i in tqdm(os.listdir(os.path.join(pre_path, "ids"))): if use_norm == "t": mel = np.load( os.path.join( pre_path, "norm-feats", f"{base(i)}-norm-feats.npy" ) ) else: mel = np.load( os.path.join( pre_path, "raw-feats", f"{base(i)}-raw-feats.npy" ) ) try: dur = np.load( os.path.join(trimmed_dur_path, f"{base(i)}-durations.npy") ) except: dur = np.load( os.path.join(dur_path, f"{base(i)}-durations.npy") ) ph_ids = np.load(os.path.join(dur_path, f"{base(i)}-phids.npy")) l_mel = len(mel) dur_s = np.sum(dur) cloned = np.array(dur, copy=True) diff = abs(l_mel - dur_s) if abs(l_mel - dur_s) > 30: # more then 300 ms big_diff.append([i, abs(l_mel - dur_s)]) if dur_s > l_mel: for j in range(1, len(dur) - 1): if diff == 0: break dur_val = cloned[-j] if dur_val >= diff: cloned[-j] -= diff diff -= dur_val break else: cloned[-j] = 0 diff -= dur_val if j == len(dur) - 2: not_fixed.append(i) mfa_longer.append(abs(l_mel - dur_s)) elif dur_s < l_mel: cloned[-1] += diff mfa_shorter.append(abs(l_mel - dur_s)) np.save( os.path.join(pre_path, "fix_dur", f"{base(i)}-durations.npy"), cloned.astype(np.int32), allow_pickle=False, ) np.save( os.path.join(pre_path, "phids", f"{base(i)}-phids.npy"), ph_ids, allow_pickle=False, ) logging.info( f"{t} stats: number of mfa with longer duration: {len(mfa_longer)}, total diff: {sum(mfa_longer)}" f", mean diff: {sum(mfa_longer)/len(mfa_longer) if len(mfa_longer) > 0 else 0}" ) logging.info( f"{t} stats: number of mfa with shorter duration: {len(mfa_shorter)}, total diff: {sum(mfa_shorter)}" f", mean diff: {sum(mfa_shorter)/len(mfa_shorter) if len(mfa_shorter) > 0 else 0}" ) logging.info( f"{t} stats: number of files with a ''big'' duration diff: {len(big_diff)} if number>1 you should check it" ) logging.info(f"{t} stats: not fixed len: {len(not_fixed)}\n") if __name__ == "__main__": fix()

34.44697

119

0.520563

import numpy as np import os from tqdm import tqdm import click import logging import sys logging.basicConfig( level=logging.DEBUG, stream=sys.stdout, format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s", ) @click.command() @click.option("--base_path", default="dump") @click.option("--trimmed_dur_path", default="dataset/trimmed-durations") @click.option("--dur_path", default="dataset/durations") @click.option("--use_norm", default="f") def fix(base_path: str, dur_path: str, trimmed_dur_path: str, use_norm: str): for t in ["train", "valid"]: mfa_longer = [] mfa_shorter = [] big_diff = [] not_fixed = [] pre_path = os.path.join(base_path, t) os.makedirs(os.path.join(pre_path, "fix_dur"), exist_ok=True) os.makedirs(os.path.join(pre_path, "phids"), exist_ok=True) logging.info(f"FIXING {t} set ...\n") base = lambda s: s.replace('-ids.npy','') for i in tqdm(os.listdir(os.path.join(pre_path, "ids"))): if use_norm == "t": mel = np.load( os.path.join( pre_path, "norm-feats", f"{base(i)}-norm-feats.npy" ) ) else: mel = np.load( os.path.join( pre_path, "raw-feats", f"{base(i)}-raw-feats.npy" ) ) try: dur = np.load( os.path.join(trimmed_dur_path, f"{base(i)}-durations.npy") ) except: dur = np.load( os.path.join(dur_path, f"{base(i)}-durations.npy") ) ph_ids = np.load(os.path.join(dur_path, f"{base(i)}-phids.npy")) l_mel = len(mel) dur_s = np.sum(dur) cloned = np.array(dur, copy=True) diff = abs(l_mel - dur_s) if abs(l_mel - dur_s) > 30: big_diff.append([i, abs(l_mel - dur_s)]) if dur_s > l_mel: for j in range(1, len(dur) - 1): if diff == 0: break dur_val = cloned[-j] if dur_val >= diff: cloned[-j] -= diff diff -= dur_val break else: cloned[-j] = 0 diff -= dur_val if j == len(dur) - 2: not_fixed.append(i) mfa_longer.append(abs(l_mel - dur_s)) elif dur_s < l_mel: cloned[-1] += diff mfa_shorter.append(abs(l_mel - dur_s)) np.save( os.path.join(pre_path, "fix_dur", f"{base(i)}-durations.npy"), cloned.astype(np.int32), allow_pickle=False, ) np.save( os.path.join(pre_path, "phids", f"{base(i)}-phids.npy"), ph_ids, allow_pickle=False, ) logging.info( f"{t} stats: number of mfa with longer duration: {len(mfa_longer)}, total diff: {sum(mfa_longer)}" f", mean diff: {sum(mfa_longer)/len(mfa_longer) if len(mfa_longer) > 0 else 0}" ) logging.info( f"{t} stats: number of mfa with shorter duration: {len(mfa_shorter)}, total diff: {sum(mfa_shorter)}" f", mean diff: {sum(mfa_shorter)/len(mfa_shorter) if len(mfa_shorter) > 0 else 0}" ) logging.info( f"{t} stats: number of files with a ''big'' duration diff: {len(big_diff)} if number>1 you should check it" ) logging.info(f"{t} stats: not fixed len: {len(not_fixed)}\n") if __name__ == "__main__": fix()

true

1c43dadf8de6f9e1d56a4de21587cb982ee0979e

3,531

py

Python

profiles_project/settings.py

CGarcia8CG/profiles-resst-api

2a31f66f875f006a437865999fb5dd63049b14ae

[ "MIT" ]

null

profiles_project/settings.py

CGarcia8CG/profiles-resst-api

2a31f66f875f006a437865999fb5dd63049b14ae

[ "MIT" ]

null

profiles_project/settings.py

CGarcia8CG/profiles-resst-api

2a31f66f875f006a437865999fb5dd63049b14ae

[ "MIT" ]

null

""" Django settings for profiles_project project. Generated by 'django-admin startproject' using Django 2.2. For more information on this file, see https://docs.djangoproject.com/en/2.2/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/2.2/ref/settings/ """ import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/2.2/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'u8&5lcphj96%8)2qf1bj*73i@p_p%_drs0$xrj44@o&6*txak!' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'rest_framework', 'rest_framework.authtoken', 'profiles_api', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'profiles_project.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'profiles_project.wsgi.application' # Database # https://docs.djangoproject.com/en/2.2/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Password validation # https://docs.djangoproject.com/en/2.2/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/2.2/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/2.2/howto/static-files/ STATIC_URL = '/static/' #Cambiar el default de user manager al creado en models.py IMPORTANTE AUTH_USER_MODEL = 'profiles_api.UserProfile' #Despues de esto es hacer en consola #1 python3 manage.py makemigrations "nombre_api" #2 python3 manage.py migrate #3 Resultado sincroniza DB #4 python manage.py createsuperuser --> crear super usuario #5 Enable Django admin

26.548872

91

0.708581

import os BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) SECRET_KEY = 'u8&5lcphj96%8)2qf1bj*73i@p_p%_drs0$xrj44@o&6*txak!' DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'rest_framework', 'rest_framework.authtoken', 'profiles_api', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'profiles_project.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'profiles_project.wsgi.application' # Database # https://docs.djangoproject.com/en/2.2/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Password validation # https://docs.djangoproject.com/en/2.2/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/2.2/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/2.2/howto/static-files/ STATIC_URL = '/static/' #Cambiar el default de user manager al creado en models.py IMPORTANTE AUTH_USER_MODEL = 'profiles_api.UserProfile' #Despues de esto es hacer en consola #1 python3 manage.py makemigrations "nombre_api" #2 python3 manage.py migrate #3 Resultado sincroniza DB #4 python manage.py createsuperuser --> crear super usuario #5 Enable Django admin

true

1c43db2740e3ecabf360bbab3b871d70e31d5cf0

938

py

Python

composite.py

sloev/photobooth_web

ed2799f30f43dbc8042476c3f9238ffb39ead3b5

[ "MIT" ]

null

composite.py

sloev/photobooth_web

ed2799f30f43dbc8042476c3f9238ffb39ead3b5

[ "MIT" ]

null

composite.py

sloev/photobooth_web

ed2799f30f43dbc8042476c3f9238ffb39ead3b5

[ "MIT" ]

null

from PIL import Image import os MAX_COLUMNS = 5 INPUT_DIR = './segments/' images = [Image.open(INPUT_DIR + filename) for filename in sorted(os.listdir(INPUT_DIR)) if filename.endswith('.png')] MAX_ROWS = int(len(images)/MAX_COLUMNS) + (1 if len(images) % 20 !=0 else 0) img_width, img_height = images[0].size print(MAX_COLUMNS, MAX_ROWS, img_width,img_height) background = Image.new('RGBA', ((img_width * MAX_COLUMNS) + img_width, (img_height * MAX_ROWS)), color='black') bg_width, bg_height = background.size x_index = 0 y_index = 0 column = 0 for index, image in enumerate(images): this_width = image.size[0] if x_index == 0: x_index = this_width background.paste(image, (x_index, y_index), image) x_index += this_width-20 if x_index + img_width > bg_width: y_index += int(img_height - (img_height/2)) column += 1 x_index = int(this_width) background.save("composite.png")

29.3125

118

0.689765

from PIL import Image import os MAX_COLUMNS = 5 INPUT_DIR = './segments/' images = [Image.open(INPUT_DIR + filename) for filename in sorted(os.listdir(INPUT_DIR)) if filename.endswith('.png')] MAX_ROWS = int(len(images)/MAX_COLUMNS) + (1 if len(images) % 20 !=0 else 0) img_width, img_height = images[0].size print(MAX_COLUMNS, MAX_ROWS, img_width,img_height) background = Image.new('RGBA', ((img_width * MAX_COLUMNS) + img_width, (img_height * MAX_ROWS)), color='black') bg_width, bg_height = background.size x_index = 0 y_index = 0 column = 0 for index, image in enumerate(images): this_width = image.size[0] if x_index == 0: x_index = this_width background.paste(image, (x_index, y_index), image) x_index += this_width-20 if x_index + img_width > bg_width: y_index += int(img_height - (img_height/2)) column += 1 x_index = int(this_width) background.save("composite.png")

true

1c43db677624e3a656cc3cea81f95f7b0f6b3c81

353

py

Python

bitcoin/metrics.py

darbik/work

7f5640822fc5bbbd4033385d6377878b22785cb2

[ "MIT" ]

null

bitcoin/metrics.py

darbik/work

7f5640822fc5bbbd4033385d6377878b22785cb2

[ "MIT" ]

3

2016-08-04T18:12:05.000Z

2016-08-09T16:55:09.000Z

bitcoin/metrics.py

darbik/bitcoin

7f5640822fc5bbbd4033385d6377878b22785cb2

[ "MIT" ]

null

from time import strftime def get_volume(atmid, price, amount): volume = (atmid, price, amount) return volume def get_time(atmid): time = (atmid, strftime("%Y-%m-%d %H:%M:%S")) # time format is in UTC return time def get_fees(atmid, price, amount): feesMade = (atmid, (amount / price) * 0.05) return feesMade

16.809524

79

0.620397

from time import strftime def get_volume(atmid, price, amount): volume = (atmid, price, amount) return volume def get_time(atmid): time = (atmid, strftime("%Y-%m-%d %H:%M:%S")) return time def get_fees(atmid, price, amount): feesMade = (atmid, (amount / price) * 0.05) return feesMade

true

1c43dbee4862a38bad9336f123d8fa764442b2cb

8,629

py

Python

torch_complex/complex_operation.py

veya2ztn/mltool

4ed151152845ebe3de128e1f53c478581c1492e4

[ "IJG" ]

null

torch_complex/complex_operation.py

veya2ztn/mltool

4ed151152845ebe3de128e1f53c478581c1492e4

[ "IJG" ]

null

torch_complex/complex_operation.py

veya2ztn/mltool

4ed151152845ebe3de128e1f53c478581c1492e4

[ "IJG" ]

null

import numpy as np import torch import torch.nn.functional as F def complex_mul(tensor_1: torch.Tensor,tensor_2: torch.Tensor,mode='cc')-> torch.Tensor: ''' :param tensor_1(2) [...,2] for real part and image part ''' if mode == 'cc': assert tensor_1.shape[-1]==2 assert tensor_2.shape[-1]==2 real1,imag1=tensor_1[...,0],tensor_1[...,1] real2,imag2=tensor_2[...,0],tensor_2[...,1] return torch.stack([real1 * real2 - imag1 * imag2, real1 * imag2 + imag1 * real2], dim = -1) elif mode=='cr': assert tensor_1.shape[-1]==2 real1,imag1=tensor_1[...,0],tensor_1[...,1] real2 =tensor_2 return torch.stack([real1 * real2, imag1 * real2], dim = -1) elif mode=='rc': assert tensor_2.shape[-1]==2 real1,imag1=tensor_2[...,0],tensor_2[...,1] real2 =tensor_1 return torch.stack([real1 * real2, imag1 * real2], dim = -1) else: raise NotImplementedError def complex_mm(tensor_1: torch.Tensor,tensor_2: torch.Tensor,mode='cc')-> torch.Tensor: if mode == 'cc': assert tensor_1.shape[-1]==2 assert tensor_2.shape[-1]==2 real1,imag1=tensor_1[...,0],tensor_1[...,1] real2,imag2=tensor_2[...,0],tensor_2[...,1] return torch.stack([torch.matmul(real1, real2) - torch.matmul(imag1, imag2), torch.matmul(real1, imag2) + torch.matmul(imag1, real2)], dim = -1) elif mode=='cr': assert tensor_1.shape[-1]==2 real1,imag1=tensor_1[...,0],tensor_1[...,1] real2 =tensor_2 return torch.stack([real1.mm(real2), imag1.mm(real2)], dim = -1) elif mode=='rc': assert tensor_1.shape[-1]==2 real1,imag1=tensor_2[...,0],tensor_2[...,1] real2 =tensor_1 return torch.stack([real1.mm(real2), imag1.mm(real2)], dim = -1) else: raise NotImplementedError def complex_mv(matrix: torch.Tensor,vector: torch.Tensor,mode='cc')-> torch.Tensor: if mode == 'cc': assert matrix.shape[-1]==2 assert vector.shape[-1]==2 real1,imag1=matrix[...,0],matrix[...,1] real2,imag2=vector[...,0],vector[...,1] return torch.stack([real1.mv(real2) - imag1.mv(imag2), real1.mv(imag2) + imag1.mv(real2)], dim = -1) elif mode=='cr': assert matrix.shape[-1]==2 real1,imag1=matrix[...,0],matrix[...,1] real2 =vector return torch.stack([real1.mv(real2), imag1.mv(real2)], dim = -1) else: raise NotImplementedError def complex_div(tensor_1: torch.Tensor,tensor_2: torch.Tensor)-> torch.Tensor: if mode == 'cc': assert tensor_1.shape[-1]==2 assert tensor_2.shape[-1]==2 a,b=tensor_1[...,0],tensor_1[...,1] c,d=tensor_2[...,0],tensor_2[...,1] Denominator = c**2+d**2 return torch.stack([(a * c + b * d)/Denominator, (b*c-a*d)/Denominator], dim = -1) elif mode=='cr': assert tensor_1.shape[-1]==2 a,b=tensor_1[...,0],tensor_1[...,1] c =tensor_2 return torch.stack([a/c,b/c], dim = -1) else: raise NotImplementedError def complex_conj(tensor_1: torch.Tensor)-> torch.Tensor: assert tensor_1.shape[-1]==2 real1,imag1=tensor_1[...,0],tensor_1[...,1] imag1=-imag1 return torch.stack([real1,imag1], dim = -1) def complex_polar(tensor: torch.Tensor)-> torch.Tensor: assert tensor.shape[-1]==2 real,imag=tensor[...,0],tensor[...,1] radius = torch.norm(tensor,dim=-1) angles = torch.atan(real/imag) return torch.stack([radius,angles],dim=-1) def complex_exp(tensor: torch.Tensor,angle_unit=1)-> torch.Tensor: assert tensor.shape[-1]==2 factor,angles=tensor[...,0],tensor[...,1] radius = torch.exp(factor) angles = angles*angle_unit direct = torch.stack([angles.cos(),angles.sin()],dim=-1) return complex_mul(direct,radius,'cr') def complex_polar_ln(tensor: torch.Tensor): assert tensor.shape[-1]==2 real,imag=tensor[...,0],tensor[...,1] radius = torch.norm(tensor,dim=-1).log() angles = torch.atan(real/imag) return radius,angles def complex_tch2np(tch: torch.Tensor)->np.ndarray: assert tch.shape[-1]==2 out=tch.detach().numpy() return out[...,0]+1j*out[...,1] def complex_np2tch(npx:np.ndarray)-> torch.Tensor: real = torch.Tensor(np.real(npx)) imag = torch.Tensor(np.imag(npx)) return torch.stack([real,imag],dim=-1) def complex_conv2d(inputs,filters,bias=None,**kargs): assert len(inputs.shape)==5 assert len(filters.shape)==5 assert inputs.shape[-1]==2 assert filters.shape[-1]==2 convfun = lambda x,w,b:F.conv2d(x,w,b,**kargs) x_r,x_i=inputs[...,0],inputs[...,1] w_r,w_i=filters[...,0],filters[...,1] b_r=b_i=None if bias is not None: assert bias.shape[-1]==2 b_r,b_i = bias[...,0],bias[...,1] o_r = convfun(x_r,w_r,b_r) - convfun(x_i,w_i,None) o_i = convfun(x_r,w_i,b_i) + convfun(x_i,w_r,None) ### another implement ## but with very slow performance # o_r = F.conv3d(_inputs*torch.Tensor([1,-1]),_filter,stride=(stride,stride,1),padding=(padding,padding,0)) # o_i = F.conv3d(_inputs,_filter.flip(-1),stride=(stride,stride,1),padding=(padding,padding,0)) return torch.stack([o_r, o_i], dim = -1) def complex_conv1d(inputs,filters,bias=None,**kargs): assert len(inputs.shape)==4 assert len(filters.shape)==4 assert inputs.shape[-1]==2 assert filters.shape[-1]==2 convfun = lambda x,w,b:F.conv1d(x,w,b,**kargs) x_r,x_i=inputs[...,0],inputs[...,1] w_r,w_i=filters[...,0],filters[...,1] b_r=b_i=None if bias is not None: assert bias.shape[-1]==2 b_r,b_i = bias[...,0],bias[...,1] o_r = convfun(x_r,w_r,b_r) - convfun(x_i,w_i,None) o_i = convfun(x_r,w_i,b_i) + convfun(x_i,w_r,None) return torch.stack([o_r, o_i], dim = -1) # def complex_tanh(tensor:torch.Tensor)-> torch.Tensor: # #tensor = F.softplus(tensor) # avoid inf # x,y = tensor.split(1,dim=-1) # x = 2*x # y = 2*y # real = x.tanh()/(y.cos()/x.cosh() +1) # imag = y.sin()/(y.cos() + x.cosh() + 1e-8) # #real = x.sinh()/n # #imag = y.sin()/n # return torch.cat([real, imag], dim = -1) class ComplexTanh(torch.autograd.Function): @staticmethod def forward(ctx, input): """ In the forward pass we receive a Tensor containing the input and return a Tensor containing the output. ctx is a context object that can be used to stash information for backward computation. You can cache arbitrary objects for use in the backward pass using the ctx.save_for_backward method. """ ctx.save_for_backward(input) x,y = input.split(1,dim=-1) x = 2*x y = 2*y real = x.tanh()/(y.cos()/x.cosh() +1) imag = y.sin()/(y.cos() + x.cosh() + 1e-8) return torch.cat([real, imag], dim = -1) @staticmethod def backward(ctx, grad_output): """ In the backward pass we receive a Tensor containing the gradient of the loss with respect to the output, and we need to compute the gradient of the loss with respect to the input. f(x,y) = tanh(z) = u(x,y)+1j*v(x,y) grad_matrix =| \partial u |\partial u | | ---------- |---------- | | \partial x |\partial y | | --- | --- | | \partial v |\partial v | | ---------- |---------- | | \partial x |\partial y | """ input, = ctx.saved_tensors x,y = input.split(1,dim=-1) x = 2*x y = 2*y ys = y.sin() yc = y.cos() xch= x.cosh() xth= x.tanh() n = (1+yc/xch)**2 ux = 2 +2*yc/xch-2*xth**2 uy = 2*(ys/xch)*xth ux = ux/n uy = uy/n vx = -uy vy = ux u,v= grad_output.split(1,dim=-1) real = u*ux+v*vx imag =-u*uy-v*vy # miners is required by complex number. return torch.cat([real,imag],-1) complex_tanh = ComplexTanh.apply def complex_sigmoid(tensor:torch.Tensor)-> torch.Tensor: x,y = tensor.split(1,dim=-1) x = torch.exp(-x) a = 1+x*y.cos() b = x*y.sin() n = a**2+b**2+ 1e-8 return torch.cat([a/n, b/n], dim = -1) def complexize(tensor: torch.Tensor)-> torch.Tensor: ''' real to complex ''' if tensor.shape[-1] == 2:return tensor imag = torch.zeros_like(tensor) return torch.stack([tensor,imag],-1)

35.510288

111

0.571909

import numpy as np import torch import torch.nn.functional as F def complex_mul(tensor_1: torch.Tensor,tensor_2: torch.Tensor,mode='cc')-> torch.Tensor: if mode == 'cc': assert tensor_1.shape[-1]==2 assert tensor_2.shape[-1]==2 real1,imag1=tensor_1[...,0],tensor_1[...,1] real2,imag2=tensor_2[...,0],tensor_2[...,1] return torch.stack([real1 * real2 - imag1 * imag2, real1 * imag2 + imag1 * real2], dim = -1) elif mode=='cr': assert tensor_1.shape[-1]==2 real1,imag1=tensor_1[...,0],tensor_1[...,1] real2 =tensor_2 return torch.stack([real1 * real2, imag1 * real2], dim = -1) elif mode=='rc': assert tensor_2.shape[-1]==2 real1,imag1=tensor_2[...,0],tensor_2[...,1] real2 =tensor_1 return torch.stack([real1 * real2, imag1 * real2], dim = -1) else: raise NotImplementedError def complex_mm(tensor_1: torch.Tensor,tensor_2: torch.Tensor,mode='cc')-> torch.Tensor: if mode == 'cc': assert tensor_1.shape[-1]==2 assert tensor_2.shape[-1]==2 real1,imag1=tensor_1[...,0],tensor_1[...,1] real2,imag2=tensor_2[...,0],tensor_2[...,1] return torch.stack([torch.matmul(real1, real2) - torch.matmul(imag1, imag2), torch.matmul(real1, imag2) + torch.matmul(imag1, real2)], dim = -1) elif mode=='cr': assert tensor_1.shape[-1]==2 real1,imag1=tensor_1[...,0],tensor_1[...,1] real2 =tensor_2 return torch.stack([real1.mm(real2), imag1.mm(real2)], dim = -1) elif mode=='rc': assert tensor_1.shape[-1]==2 real1,imag1=tensor_2[...,0],tensor_2[...,1] real2 =tensor_1 return torch.stack([real1.mm(real2), imag1.mm(real2)], dim = -1) else: raise NotImplementedError def complex_mv(matrix: torch.Tensor,vector: torch.Tensor,mode='cc')-> torch.Tensor: if mode == 'cc': assert matrix.shape[-1]==2 assert vector.shape[-1]==2 real1,imag1=matrix[...,0],matrix[...,1] real2,imag2=vector[...,0],vector[...,1] return torch.stack([real1.mv(real2) - imag1.mv(imag2), real1.mv(imag2) + imag1.mv(real2)], dim = -1) elif mode=='cr': assert matrix.shape[-1]==2 real1,imag1=matrix[...,0],matrix[...,1] real2 =vector return torch.stack([real1.mv(real2), imag1.mv(real2)], dim = -1) else: raise NotImplementedError def complex_div(tensor_1: torch.Tensor,tensor_2: torch.Tensor)-> torch.Tensor: if mode == 'cc': assert tensor_1.shape[-1]==2 assert tensor_2.shape[-1]==2 a,b=tensor_1[...,0],tensor_1[...,1] c,d=tensor_2[...,0],tensor_2[...,1] Denominator = c**2+d**2 return torch.stack([(a * c + b * d)/Denominator, (b*c-a*d)/Denominator], dim = -1) elif mode=='cr': assert tensor_1.shape[-1]==2 a,b=tensor_1[...,0],tensor_1[...,1] c =tensor_2 return torch.stack([a/c,b/c], dim = -1) else: raise NotImplementedError def complex_conj(tensor_1: torch.Tensor)-> torch.Tensor: assert tensor_1.shape[-1]==2 real1,imag1=tensor_1[...,0],tensor_1[...,1] imag1=-imag1 return torch.stack([real1,imag1], dim = -1) def complex_polar(tensor: torch.Tensor)-> torch.Tensor: assert tensor.shape[-1]==2 real,imag=tensor[...,0],tensor[...,1] radius = torch.norm(tensor,dim=-1) angles = torch.atan(real/imag) return torch.stack([radius,angles],dim=-1) def complex_exp(tensor: torch.Tensor,angle_unit=1)-> torch.Tensor: assert tensor.shape[-1]==2 factor,angles=tensor[...,0],tensor[...,1] radius = torch.exp(factor) angles = angles*angle_unit direct = torch.stack([angles.cos(),angles.sin()],dim=-1) return complex_mul(direct,radius,'cr') def complex_polar_ln(tensor: torch.Tensor): assert tensor.shape[-1]==2 real,imag=tensor[...,0],tensor[...,1] radius = torch.norm(tensor,dim=-1).log() angles = torch.atan(real/imag) return radius,angles def complex_tch2np(tch: torch.Tensor)->np.ndarray: assert tch.shape[-1]==2 out=tch.detach().numpy() return out[...,0]+1j*out[...,1] def complex_np2tch(npx:np.ndarray)-> torch.Tensor: real = torch.Tensor(np.real(npx)) imag = torch.Tensor(np.imag(npx)) return torch.stack([real,imag],dim=-1) def complex_conv2d(inputs,filters,bias=None,**kargs): assert len(inputs.shape)==5 assert len(filters.shape)==5 assert inputs.shape[-1]==2 assert filters.shape[-1]==2 convfun = lambda x,w,b:F.conv2d(x,w,b,**kargs) x_r,x_i=inputs[...,0],inputs[...,1] w_r,w_i=filters[...,0],filters[...,1] b_r=b_i=None if bias is not None: assert bias.shape[-1]==2 b_r,b_i = bias[...,0],bias[...,1] o_r = convfun(x_r,w_r,b_r) - convfun(x_i,w_i,None) o_i = convfun(x_r,w_i,b_i) + convfun(x_i,w_r,None) x_conv1d(inputs,filters,bias=None,**kargs): assert len(inputs.shape)==4 assert len(filters.shape)==4 assert inputs.shape[-1]==2 assert filters.shape[-1]==2 convfun = lambda x,w,b:F.conv1d(x,w,b,**kargs) x_r,x_i=inputs[...,0],inputs[...,1] w_r,w_i=filters[...,0],filters[...,1] b_r=b_i=None if bias is not None: assert bias.shape[-1]==2 b_r,b_i = bias[...,0],bias[...,1] o_r = convfun(x_r,w_r,b_r) - convfun(x_i,w_i,None) o_i = convfun(x_r,w_i,b_i) + convfun(x_i,w_r,None) return torch.stack([o_r, o_i], dim = -1) (ctx, input): ctx.save_for_backward(input) x,y = input.split(1,dim=-1) x = 2*x y = 2*y real = x.tanh()/(y.cos()/x.cosh() +1) imag = y.sin()/(y.cos() + x.cosh() + 1e-8) return torch.cat([real, imag], dim = -1) @staticmethod def backward(ctx, grad_output): input, = ctx.saved_tensors x,y = input.split(1,dim=-1) x = 2*x y = 2*y ys = y.sin() yc = y.cos() xch= x.cosh() xth= x.tanh() n = (1+yc/xch)**2 ux = 2 +2*yc/xch-2*xth**2 uy = 2*(ys/xch)*xth ux = ux/n uy = uy/n vx = -uy vy = ux u,v= grad_output.split(1,dim=-1) real = u*ux+v*vx imag =-u*uy-v*vy return torch.cat([real,imag],-1) complex_tanh = ComplexTanh.apply def complex_sigmoid(tensor:torch.Tensor)-> torch.Tensor: x,y = tensor.split(1,dim=-1) x = torch.exp(-x) a = 1+x*y.cos() b = x*y.sin() n = a**2+b**2+ 1e-8 return torch.cat([a/n, b/n], dim = -1) def complexize(tensor: torch.Tensor)-> torch.Tensor: if tensor.shape[-1] == 2:return tensor imag = torch.zeros_like(tensor) return torch.stack([tensor,imag],-1)

true

1c43dcbc0e87b2b9319a3efe3dd0e07b164d11cd

36,802

py

Python

code_generation/code_generator_online.py

annihilatorrrr/pytgbot

2f84b11253873f7af1bc7539eb7d93197d51c90c

[ "MIT" ]

52

2015-06-25T15:48:19.000Z

2021-08-10T20:29:11.000Z

code_generation/code_generator_online.py

annihilatorrrr/pytgbot

2f84b11253873f7af1bc7539eb7d93197d51c90c

[ "MIT" ]

16

2016-04-12T08:11:30.000Z

2021-07-22T18:00:07.000Z

code_generation/code_generator_online.py

annihilatorrrr/pytgbot

2f84b11253873f7af1bc7539eb7d93197d51c90c

[ "MIT" ]

14

2015-06-26T15:29:48.000Z

2021-08-10T20:29:14.000Z

# -*- coding: utf-8 -*- from pathlib import Path from typing import Dict, List, Union from code_generator import get_type_path from code_generator_template import clazz, func, get_template, as_types from code_generator_classes import Clazz, Function, Variable, Type, Import, FunctionClazz from luckydonaldUtils.files.basics import mkdir_p # luckydonaldUtils v0.49+ from luckydonaldUtils.interactions import answer, confirm from luckydonaldUtils.logger import logging from code_generator_settings import CLASS_TYPE_PATHS, CLASS_TYPE_PATHS__PARENT, WHITELISTED_FUNCS, WHITELISTED_CLASSES, CUSTOM_CLASSES from code_generator_template import path_to_import_text, split_path from jinja2.exceptions import TemplateError, TemplateSyntaxError import requests import black # code formatter from yapf.yapflib.yapf_api import FormatFile # code formatter from bs4 import BeautifulSoup from bs4.element import NavigableString from os.path import abspath, dirname, join as path_join, sep as folder_seperator, isfile, exists, isdir from luckydonaldUtils.interactions import safe_eval, NoBuiltins __author__ = "luckydonald" logger = logging.getLogger(__name__) from logging import LogRecord def log_filter(record: LogRecord): if f'{record.name}.{record.funcName}' == 'luckydonaldUtils.functions.wrapper': return False return True # end def root_logger = logging.add_colored_handler(level=logging.DEBUG, filter=log_filter) FILE_HEADER = "# -*- coding: utf-8 -*-\n" MAIN_FILE_CLASS_HEADER = "class Bot(object):\n _base_url = \"https://api.telegram.org/bot{api_key}/{command}\"\n" __author__ = 'luckydonald' logger = logging.getLogger(__name__) BASE_URL = "https://core.telegram.org/bots/api" SAVE_VALUES = NoBuiltins([], {}, {"Function": Function, "Clazz": Clazz, "Import": Import, "Type": Type, "Variable": Variable}) def lol1(tag): return tag.has_attr("class") and "anchor" in tag["class"] class_fields = [ ["Field", "Type", "Description"], ["Parameters", "Type", "Description"], ["Parameter", "Type", "Description"] ] func_fields = [ ["Parameters", "Type", "Required", "Description"], ["Parameter", "Type", "Required", "Description"], ] use_back = False use_yapf = False black_settings = dict( write_back=black.WriteBack.from_configuration(check=False, diff=False), report=black.Report(check=False, quiet=False, verbose=False), mode=black.FileMode( target_versions=set(), line_length=black.DEFAULT_LINE_LENGTH, is_pyi=False, string_normalization=True, ), ) yapf_settings = dict( style={ 'ALIGN_CLOSING_BRACKET_WITH_VISUAL_INDENT': True, 'ALLOW_MULTILINE_LAMBDAS': True, 'ALLOW_MULTILINE_DICTIONARY_KEYS': False, 'ALLOW_SPLIT_BEFORE_DEFAULT_OR_NAMED_ASSIGNS': True, 'ALLOW_SPLIT_BEFORE_DICT_VALUE': False, 'ARITHMETIC_PRECEDENCE_INDICATION': False, 'BLANK_LINE_BEFORE_NESTED_CLASS_OR_DEF': False, 'BLANK_LINE_BEFORE_MODULE_DOCSTRING': True, 'BLANK_LINE_BEFORE_CLASS_DOCSTRING': False, 'BLANK_LINES_AROUND_TOP_LEVEL_DEFINITION': 2, # Sets the number of desired blank lines surrounding top-level function and class definitions. 'COALESCE_BRACKETS': True, 'COLUMN_LIMIT': black.DEFAULT_LINE_LENGTH, 'CONTINUATION_ALIGN_STYLE': "space", 'CONTINUATION_INDENT_WIDTH': 2, 'DEDENT_CLOSING_BRACKETS': True, 'DISABLE_ENDING_COMMA_HEURISTIC': True, 'EACH_DICT_ENTRY_ON_SEPARATE_LINE': False, 'INDENT_DICTIONARY_VALUE': False, # Indent the dictionary value if it cannot fit on the same line as the dictionary key. 'INDENT_WIDTH': 2, 'INDENT_BLANK_LINES': False, # Set to True to prefer indented blank lines rather than empty 'JOIN_MULTIPLE_LINES': False, # Join short lines into one line. E.g., single line if statements. 'NO_SPACES_AROUND_SELECTED_BINARY_OPERATORS': False, # Do not include spaces around selected binary operators. For example: 1 + 2*3 - 4/5 'SPACES_AROUND_POWER_OPERATOR': True, # Set to True to prefer using spaces around **. # 'SPACES_AROUND_DEFAULT_OR_NAMED_ASSIGN': False, # Set to True to prefer spaces around the assignment operator for default or keyword arguments. 'SPACES_BEFORE_COMMENT': 2, 'SPACE_BETWEEN_ENDING_COMMA_AND_CLOSING_BRACKET': False, # Insert a space between the ending comma and closing bracket of a list, etc. 'SPLIT_ARGUMENTS_WHEN_COMMA_TERMINATED': True, # Split before arguments if the argument list is terminated by a comma. 'SPLIT_ALL_COMMA_SEPARATED_VALUES': True, # If a comma separated list (dict, list, tuple, or function def) is on a line that is too long, split such that all elements are on a single line. 'SPLIT_ALL_TOP_LEVEL_COMMA_SEPARATED_VALUES': True, # Variation on SPLIT_ALL_COMMA_SEPARATED_VALUES in which, if a subexpression with a comma fits in its starting line, then the subexpression is not split. This avoids splits like the one for b in this code: 'SPLIT_BEFORE_BITWISE_OPERATOR': False, # Set to True to prefer splitting before &, | or ^ rather than after. 'SPLIT_BEFORE_ARITHMETIC_OPERATOR': False, # Set to True to prefer splitting before +, -, *, /, //, or @ rather than after. 'SPLIT_BEFORE_CLOSING_BRACKET': True, # Split before the closing bracket if a list or dict literal doesn't fit on a single line. 'SPLIT_BEFORE_DICT_SET_GENERATOR': True, # Split before a dictionary or set generator (comp_for). For example, note the split before the for: 'SPLIT_BEFORE_DOT': False, # Split before the . if we need to split a longer expression: # 'SPLIT_BEFORE_EXPRESSION_AFTER_OPENING_PAREN': False, # Split after the opening paren which surrounds an expression if it doesn't fit on a single line. 'SPLIT_BEFORE_FIRST_ARGUMENT': True, # If an argument / parameter list is going to be split, then split before the first argument. 'SPLIT_BEFORE_LOGICAL_OPERATOR': True, # Set to True to prefer splitting before and or or rather than after. # 'SPLIT_BEFORE_NAMED_ASSIGNS': False, # Split named assignments onto individual lines. 'SPLIT_COMPLEX_COMPREHENSION': True, # For list comprehensions and generator expressions with multiple clauses (e.g multiple for calls, if filter expressions) and which need to be reflowed, split each clause onto its own line. 'USE_TABS': False, # 'SPLIT_PENALTY_AFTER_OPENING_BRACKET': 0 # 'SPLIT_PENALTY_AFTER_UNARY_OPERATOR': # 'SPLIT_PENALTY_ARITHMETIC_OPERATOR': # 'SPLIT_PENALTY_BEFORE_IF_EXPR': # 'SPLIT_PENALTY_BEFORE_IF_EXPR': 30 # 'SPLIT_PENALTY_FOR_ADDED_LINE_SPLIT': 30 }, ) def parse_table(tag): """ returns tuple of type ("class"/"func") and list of param strings. :param tag: :return: """ first = True table_header = None table_type = 'unknown' param_strings = [] thead = tag.find('thead', recursive=False) theads = None # list (items in <tr> row) of <th>/<tr> elements. if thead: theads = thead.find_all(["th", "td"]) # end if tbody = tag.find('tbody', recursive=False) if tbody: tbody_rows = tbody.find_all("tr") else: tbody_rows = tag.find_all("tr") # end if tbodys = [ # list (rows) of list (items in <tr> row) of <tr> elements. row.find_all(["td" ,"th"]) for row in tbody_rows ] if not thead: # so first row = header theads = tbody_rows[0] tbodys = tbody_rows[1:] # end if # TABLE HEADER found_columns = [] for column in theads: # Either (a) `<td> ... </td>` # or new (b) `<th> ... </th>` col = column.find("strong") if col: # (a) `<td> ... </td>` col_text = col.text else: # (b) `<th> ... </th>` col_text = column.text # end if found_columns.append(col_text) # end def # if TABLE is func for test_columns in func_fields: if found_columns == test_columns: table_header = test_columns table_type = 'func' break # end if # end for # if TABLE is class if not table_header: # only check if we don't have a result yet # search class now for test_columns in class_fields: if found_columns == test_columns: if table_header is not None: raise AssertionError("Table detected as func and class: {!r}".format(found_columns)) table_header = test_columns table_type = 'class' break # end if # end for # end if # TABLE is none of the above if not table_header: # we don't have a result yet raise AssertionError("Unknown table, {!r}".format(found_columns)) # end if # TABLE BODY for tds in tbodys: string = '' for col in tds: string += "\t" had_something = False for sub_col in col: if isinstance(sub_col, NavigableString): string += sub_col had_something = True elif sub_col.name == 'img': # emojis are images: <img alt="🎲" class="emoji" height="20" src="//telegram.org/img/emoji/40/F09F8EB2.png" width="20"/> string += sub_col.attrs.get('alt', '') had_something = True else: string += sub_col.text had_something = True # end if # end for if not had_something: string += col.text # end if # end for string = string.lstrip("\t") logger.debug("t: " + string) param_strings.append(string) pass # end for row return table_type, param_strings # end def def load_from_html(folder): filter = get_filter() document = requests.get(BASE_URL) bs = BeautifulSoup(document.content) results = [] for h in bs.select("#dev_page_content > h4"): logger.info("------") anchor = h.find(lol1) if not anchor or not anchor.has_attr("name"): continue link = "{base_url}#{anchor}".format(base_url=BASE_URL, anchor=anchor["name"]) title = h.text descr = [] table_type, param_strings = None, None logger.info("title: " + title) logger.info("link: " + link) if filter and title not in filter: logger.info("Skipping {title}, filtered.".format(title=title)) continue # logger.debug(h) type_strings = [] default_returns = [] for sibling in h.next_siblings: if sibling == "\n": continue if sibling.name in ["p", "blockquote"]: if "return" in sibling.text.lower(): parts_splitted = [] is_first_element = True # truein string, for x in sibling.children: if isinstance(x, NavigableString): if is_first_element: # Start of a new sentence => new list parts_splitted.extend([[foo.lstrip()] for foo in x.split(".")]) is_first_element = False else: # not = in the middle of a sentence => append parts_splitted[len(parts_splitted)-1].append(x.split(".", maxsplit=1)[0]) parts_splitted.extend([[foo] for foo in x.split(".")[1:]]) is_first_element = False is_first_element = x.strip().endswith(".") else: obj = None if x.name in ["a", "em"]: obj = x else: obj = x.text # end if if is_first_element: # if it is at the beginning of the sentence. parts_splitted.append([obj]) is_first_element = False else: parts_splitted[len(parts_splitted)-1].append(obj) # end if # end for # end for returns__ = [] # array of strings return_text__ = [] # array if strings. one item = one sentence. Not ending with a dot. is_array = False for lol_part in parts_splitted: has_return = False returns_ = [] return_text_ = "" for lol_part_part in lol_part: if isinstance(lol_part_part, str): return_text_ += lol_part_part if lol_part_part.strip().lower().endswith("array of"): is_array = True if "return" in lol_part_part.lower(): has_return = True # end if else: # not str return_text_ += lol_part_part.text if is_array: returns_.append("list of " + lol_part_part.text) is_array = False else: returns_.append(lol_part_part.text) # end for if has_return: # append, so we can have multible sentences. return_text__.append(return_text_.strip()) returns__.extend(returns_) # end if # end for if return_text__ or returns__: # finally set it. default_returns = [". ".join(return_text__).strip(), " or ".join(returns__).strip()] # end if # end if descr.append(sibling.text.replace('“', '"').replace('”', '"')) elif sibling.name == "table": assert sibling.has_attr("class") and "table" in sibling["class"] table_type, param_strings = parse_table(sibling) elif sibling.name == "h4": break elif sibling.name == "h3": break elif sibling.name == "hr": # end of page break elif sibling.name == "img": # end of page break else: logger.info("unknown: " + sibling.name) # end if # end for if not all([link, title, descr]): logger.warning("Skipped: Missing link, title or description") continue if not all([table_type, param_strings]): if title in WHITELISTED_FUNCS: table_type = 'func' elif title in WHITELISTED_CLASSES: table_type = 'class' elif [key[key.rindex('.')+1:] for key in CUSTOM_CLASSES.keys()]: logger.info( "Skipped. Has no table with Parameters or Fields.\n" "Has a `code_generator_settings.CUSTOM_CLASSES` which seems to fit though." ) continue else: logger.warning( "Skipped. Has no table with Parameters or Fields.\n" "Also isn't a whitelisted function in `code_generator_settings.WHITELISTED_CLASSES` or class in `code_generator_settings.WHITELISTED_CLASSES`." ) continue # -> else: is in WHITELISTED_FUNCS: # end if descr = "\n".join(descr) logger.info("descr: " + repr(descr)) params_string = "\n".join(param_strings) if param_strings else None # WHITELISTED_FUNCS/WHITELISTED_CLASSES have no params if table_type == "func": seems_valid = False if len(default_returns) != 2: if "return" in descr.lower(): default_returns = ["", "Message"] default_returns[0] = [x for x in descr.split(".") if "return" in x.lower()][0].strip() seems_valid = len(default_returns[0].split(".")) == 1 default_returns[1] = " or ".join(type_strings) if type_strings else "Message" default_returns[1] = as_types(default_returns[1], "returns") else: default_returns = ["On success, True is returned", "True"] # end if "return" in description else: seems_valid = len(default_returns[0].split(".")) == 1 # end if default set replaced_valid = None # load replacements from WHITELISTED_FUNCS. if title in WHITELISTED_FUNCS: # "func": {'return': {'expected': '', 'replace': ''}, 'rtype': {'expected': '', 'replace': ''}}, wlist_func = WHITELISTED_FUNCS[title] wlist_func_return = wlist_func['return'] if 'return' in wlist_func else None wlist_func_r_type = wlist_func['r_type'] if 'r_type' in wlist_func else None if wlist_func_return and default_returns[0] != wlist_func_return['expected']: logger.warning(f"whitelist: Mismatch in return.\nExpected {wlist_func_return['expected']!r},\ninstead got {default_returns[0]!r}.") replaced_valid = False if wlist_func_r_type and default_returns[1] != wlist_func_r_type['expected']: logger.warning(f"whitelist: Mismatch in r_type.\nExpected {wlist_func_r_type['expected']!r},\ninstead got {default_returns[1]!r}") replaced_valid = False if replaced_valid is None: # whitelist didn't fail replaced_valid = True logger.info("the found return: " + repr(default_returns[0]) + '.') logger.info("the found r_type: " + repr(default_returns[1]) + '.') logger.info("whitelist return: " + repr(wlist_func_return['replace']) + '.') logger.info("whitelist r_type: " + repr(wlist_func_r_type['replace']) + '.') default_returns[0] = wlist_func_return['replace'] default_returns[1] = wlist_func_r_type['replace'] if not seems_valid and not replaced_valid: returns = answer("Textual description what the function returns", default_returns[0]) return_type = answer("Return type", default_returns[1]) if isinstance(return_type, str): return_type = as_types(return_type, "return type") # end if else: returns = default_returns[0] return_type = default_returns[1] # end if logger.debug("\n") result = func(title, descr, link, params_string, returns=returns, return_type=return_type) results.append(result) elif table_type == "class": if title in CLASS_TYPE_PATHS: parent_clazz = CLASS_TYPE_PATHS[title][CLASS_TYPE_PATHS__PARENT] logger.info("superclass: " + parent_clazz) else: parent_clazz = answer("Parent class name", "TgBotApiObject") # end if if title in WHITELISTED_CLASSES: pass # end def result = clazz( clazz=title, parent_clazz=parent_clazz, description=descr, link=link, params_string=params_string ) results.append(result) # end if # end for return results, document.content # end def main def main(): folder, html_document, results = load_api_definitions() output(folder, results, html_content=html_document) def load_api_definitions(): folder = get_folder_path() mode = confirm("Offline Mode: Load from a dump instead of the API Docs?") if not mode: # API results, html_document = load_from_html(folder) else: # Dump results, html_document = load_from_dump(folder) # end def results = preprocess_results(results, additional_items=list(CUSTOM_CLASSES.values())) return folder, html_document, results # end def def load_from_dump(folder): # read dump dump = "" with open(path_join(folder, "api.py"), "r") as f: dump = "".join(f.readlines()) # end with # existing old api.html html_document = None if exists(path_join(folder, "api.html")): with open(path_join(folder, "api.html"), "rb") as f: html_document = f.read() # end with # end if results = safe_eval(dump, SAVE_VALUES) return results, html_document # end def # noinspection PyCompatibility def preprocess_results(results: List[Union[Clazz, Function]], additional_items: Union[None, List[Clazz]] = None): """ Sets `variable.duplicate_of_parent` appropriately for all variables of all classes in the results list. :param results: :param additional_items: e.g. CUSTOM_CLASSES.values() :return: """ if additional_items is None: additional_items = [] # end if logger.info('Calculating duplicate_of_parent.') clazzes_by_name: Dict[str, Clazz] = {} # "Class": Class for other in additional_items: if isinstance(other, Clazz): clazzes_by_name[other.clazz] = other # end if # end for for result in results: if isinstance(result, Clazz): clazzes_by_name[result.clazz] = result # end if # end for for result in results: if not isinstance(result, Clazz): continue # end if # fill in clazz._parent_clazz_clazz, so we can check our parents if result.parent_clazz is None or result.parent_clazz.string == 'object': continue # end if if result.parent_clazz.string in clazzes_by_name: parent_clazz: Clazz = clazzes_by_name[result.parent_clazz.string] for variable in result.variables: variable: Variable parent_variable = parent_clazz.get_same_variable( variable, ignore_pytg_name=True, ignore_description=True, ignore_optional=True, ignore_type_always_is_value=True, allow_additional_allowed_type_matchings=True, ) variable.duplicate_of_parent = parent_variable is not None # if we fit parent's class 'additional_allowed_type_matchings', we should upgrade our own types. if variable.duplicate_of_parent and parent_variable.additional_allowed_type_matchings: variable.types = parent_variable.types[:] # end if # end for else: logger.warning(f'Could not resolve parent class: {result.parent_clazz}') # end if # end for return results # end def def output(folder, results, html_content=None): can_quit = False do_delete_first = confirm("Can the folder {path} be deleted before writing?".format(path=folder)) logger.info("vvvvvvvvv") while not can_quit: if do_delete_first: try: import Send2Trash Send2Trash.send2trash(folder) except ImportError: import shutil shutil.rmtree(folder) # end try # end if # write crawled data mkdir_p(folder) with open(path_join(folder, "api.py"), "w") as f: f.write("[\n ") f.write(",\n ".join([repr(result) for result in results])) f.write("\n]") # end for # end with if html_content: with open(path_join(folder, "api.html"), "wb") as f: f.write(html_content) # end with # end if # write templates try: safe_to_file(folder, results) except TemplateError as e: if isinstance(e, TemplateSyntaxError): logger.exception("Template error at {file}:{line}".format(file=e.filename, line=e.lineno)) else: logger.exception("Template error.") # end if # end try logger.info("Written to file.") can_quit = not confirm("Write again after reloading templates?", default=True) logger.info("#########") logger.info("Exit.") # end def def get_filter(): filter = answer( "Only generate the doc for specific functions/classes. Comma seperated list. Leave empty to generate all.", default="" # getChat, leaveChat, getChatAdministrators, getChatMember, getChatMembersCount, Message, MessageEntity" ) if filter.strip(): filter = [x.strip() for x in filter.split(",")] else: filter = None # end if return filter # end def def get_folder_path(): default = "/tmp/pytgbotapi/" candidate = abspath(path_join(dirname(abspath(__file__)), 'output')) logger.info(f'canidate: {candidate}') if exists(candidate) and isdir(candidate): default = candidate # end if file = answer("Folder path to store the results.", default=default) if file: try: file = abspath(file) mkdir_p(file) with open(path_join(file, "__init__.py"), "w") as f: f.write(FILE_HEADER) # end with except IOError: pass # end try # end if file return file # end def # noinspection PyCompatibility def safe_to_file(folder, results): """ Receives a list of results (type :class:`Clazz` or :class:`Function`), and put them into the right files in :var:`folder` :param folder: Where the files should be in. :type folder: str :param results: A list of :class:`Clazz` or :class:`Function` objects, which will be used to calculate the source code. :type results: Union(Clazz, Function) """ functions = [] message_send_clazzes = [] clazzes: Dict[str, List[Clazz]] = {} # "filepath": [Class, Class, ...] all_the_clazzes = [] custom_classes = {} # "filepath": [Class, Class, ...] all_the_custom_clazzes = [] # actually only used to call preprocess_results with all the items. They will be modified in place anyway. for import_path, result in CUSTOM_CLASSES.items(): # result.import_path = result.calculate_import_path() result.filepath = result.calculate_filepath(folder) file_path = result.filepath if file_path not in custom_classes: custom_classes[file_path] = [] # end if custom_classes[file_path].append(result) if file_path not in clazzes: clazzes[file_path] = [] # end if clazzes[file_path].append(result) all_the_clazzes.append(result) all_the_custom_clazzes.append(result) # end def _ = preprocess_results(all_the_custom_clazzes, additional_items=results) # split results into functions and classes for result in results: assert isinstance(result, (Clazz, Function)) if isinstance(result, Clazz): result.import_path = result.calculate_import_path() result.filepath = result.calculate_filepath(folder) file_path = result.filepath if file_path not in clazzes: clazzes[file_path] = [] clazzes[file_path].append(result) all_the_clazzes.append(result) else: assert isinstance(result, Function) pytgbot_dir = Path(__file__).parent.parent # import_path = "pytgbot.bot.asynchronous." # file_path = calc_path_and_create_folders(pytgbot_dir.absolute(), import_path) result.filepath = str(pytgbot_dir.joinpath('pytgbot').joinpath('bot').joinpath('asynchronous.py').absolute()) functions.append(result) if result.name.startswith('send_'): import_path = "teleflask_messages." file_path = calc_path_and_create_folders(folder, import_path) args, special_kwargs, kwargs = result.class_variables_separated result2 = FunctionClazz( clazz=result.class_name_teleflask_message, import_path=Import(path=import_path.rstrip('.'), name=result.class_name_teleflask_message), imports=result.imports, parent_clazz=Type(string='ReturnableMessageBase', is_builtin=False, is_list=0, import_path=None, description="Base class"), link=result.link, description=result.description, parameters=args, keywords=special_kwargs + kwargs, function=result, ) result2.filepath = file_path message_send_clazzes.append(result2) # end if # end if # end for bot_template = get_template("bot.template") bot_base_template = get_template("bot_base.template") clazzfile_template = get_template("classfile.template") teleflask_messages_template = get_template("teleflask_messages_file.template") typehints_template = get_template("typehintsfile.template") telegram_bot_api_server_funcs_template = get_template("telegram_bot_api_server/funcs.template") telegram_bot_api_server_class_template = get_template("telegram_bot_api_server/classes.template") mkdir_p(path_join(folder, 'telegram_bot_api_server', 'generated')) if all_the_clazzes: txt = telegram_bot_api_server_class_template.render(clazzes=all_the_clazzes) render_file_to_disk(path_join(folder, 'telegram_bot_api_server', 'generated', 'models.py'), txt) # end if for path, clazz_list in clazzes.items(): clazz_imports = set() for clazz_ in clazz_list: assert isinstance(clazz_, Clazz) assert isinstance(clazz_.parent_clazz, Type) if not clazz_.parent_clazz.is_builtin: clazz_imports.add(clazz_.parent_clazz.as_import) # end if # end for clazz_imports = list(clazz_imports) clazz_imports.sort() is_sendable = ("sendable" in path) try: txt = clazzfile_template.render(clazzes=clazz_list, manual_clazzes=[], imports=clazz_imports, is_sendable=is_sendable) txt = txt.replace("\t", " ") render_file_to_disk(path, txt) except IOError: raise # lol # end try try: txt = typehints_template.render(clazzes=clazz_list, imports=clazz_imports, is_sendable=is_sendable) txt = txt.replace("\t", " ") render_file_to_disk(path + "i", txt) # "ponies.py" + "i" => "ponies.pyi" except IOError: raise # lol # end try try: txt = typehints_template.render(clazzes=clazz_list, imports=clazz_imports, is_sendable=is_sendable) txt = txt.replace("\t", " ") render_file_to_disk(path + "i", txt) # "ponies.py" + "i" => "ponies.pyi" except IOError: raise # lol # end try # end for classes if functions: func_imports = set() for func_ in functions: assert isinstance(func_, Function) for var_ in func_.variables: assert isinstance(var_, Variable) for type_ in var_.types: assert isinstance(type_, Type) func_imports.add(type_.as_import) # end for # end for if func_.returns is not None: assert isinstance(func_.returns, Variable) for type_ in func_.returns.types: assert isinstance(type_, Type) func_imports.add(type_.as_import) # end for # end if # end for func_imports = list(func_imports) func_imports.sort() txt_sync = bot_template.render(functions=functions, is_asyncio=False, imports=func_imports, file_import_path='pytgbot.bot.synchronous') render_file_to_disk(functions[0].filepath.replace('asynchronous', 'synchronous'), txt_sync) txt_async = bot_template.render(functions=functions, is_asyncio=True, imports=func_imports, file_import_path='pytgbot.bot.asynchronous') render_file_to_disk(functions[0].filepath, txt_async) txt_base = bot_base_template.render(functions=functions, imports=func_imports, file_import_path='pytgbot.bot.base') render_file_to_disk(functions[0].filepath.replace('asynchronous', 'base'), txt_base) imports = set() imports.add(('enum', 'Enum')) imports.add(('typing', 'Union, List, Optional')) imports.add(('fastapi', 'APIRouter, HTTPException')) imports.add(('telethon', 'TelegramClient')) imports.add(('serializer', 'to_web_api, get_entity')) imports.add(('fastapi.params', 'Query')) imports.add(('telethon.errors', 'BotMethodInvalidError')) imports.add(('telethon.tl.types', 'TypeSendMessageAction')) imports.add(('telethon.client.chats', '_ChatAction')) imports.add(('luckydonaldUtils.logger', 'logging')) imports.add(('telethon.tl.functions.messages', 'SetTypingRequest')) for function in functions: function: Function for the_import in function.imports: the_import: Import imports.add((the_import.path, the_import.name)) # end for # end for # https://stackoverflow.com/a/613218/3423324#how-do-i-sort-a-dictionary-by-value # https://stackoverflow.com/a/4659539/3423324#how-to-sort-by-length-of-string-followed-by-alphabetical-order imports_sorted = ["from " + path + ' import ' + name for path, name in sorted(imports, key=lambda item: (-len(item[0]), item[0], -len(item[1]), item[1]))] # imports_sorted.sort(key=lambda item: (-len(item), item)) txt = telegram_bot_api_server_funcs_template.render(functions=functions, imports=imports_sorted) render_file_to_disk(path_join(folder, 'telegram_bot_api_server', 'generated', 'funcs.py'), txt) # end if if message_send_clazzes: txt = teleflask_messages_template.render(clazzes=message_send_clazzes) render_file_to_disk(message_send_clazzes[0].filepath, txt) # end if # end def # noinspection PyCompatibility def render_file_to_disk(file, txt): # remove whitespaces at the end of a line txt = "\n".join(line.rstrip() for line in txt.splitlines()) # add blank line at end of file. if not txt.endswith("\n"): txt += "\n" # end if with open(file, "w") as f: f.write(txt) # end with logger.info(f'Written {file!r} to disk, {len(txt)} chars.') if use_back: black.reformat_one( src=black.Path(file), write_back=black_settings['write_back'], fast=False, mode=black_settings['mode'], report=black_settings['report'], ) # end if if use_yapf: try: FormatFile(file, in_place=True, style_config=yapf_settings['style']) except: logger.exception("Formatting file {file} failed.".format(file=file)) # end try # end if # end def def calc_path_and_create_folders(folder, import_path, create_folder=True): """ calculate the path and create the needed folders >>> calc_path_and_create_folders(folder='/somewhere/', import_path='foo.bar.BarClass', create_folder=False) '/somewhere/foo/bar/BarClass' :param import_path: 'foo.bar.BarClass' :param folder: base folder where we wanna place 'foo.bar.BarClass' in. """ file_path = abspath(path_join(folder, import_path[:import_path.rfind(".")].replace(".", folder_seperator) + ".py")) if create_folder: mkdir_p(dirname(file_path)) # end if return file_path # end def if __name__ == '__main__': main() # end if

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from pathlib import Path from typing import Dict, List, Union from code_generator import get_type_path from code_generator_template import clazz, func, get_template, as_types from code_generator_classes import Clazz, Function, Variable, Type, Import, FunctionClazz from luckydonaldUtils.files.basics import mkdir_p from luckydonaldUtils.interactions import answer, confirm from luckydonaldUtils.logger import logging from code_generator_settings import CLASS_TYPE_PATHS, CLASS_TYPE_PATHS__PARENT, WHITELISTED_FUNCS, WHITELISTED_CLASSES, CUSTOM_CLASSES from code_generator_template import path_to_import_text, split_path from jinja2.exceptions import TemplateError, TemplateSyntaxError import requests import black from yapf.yapflib.yapf_api import FormatFile from bs4 import BeautifulSoup from bs4.element import NavigableString from os.path import abspath, dirname, join as path_join, sep as folder_seperator, isfile, exists, isdir from luckydonaldUtils.interactions import safe_eval, NoBuiltins __author__ = "luckydonald" logger = logging.getLogger(__name__) from logging import LogRecord def log_filter(record: LogRecord): if f'{record.name}.{record.funcName}' == 'luckydonaldUtils.functions.wrapper': return False return True root_logger = logging.add_colored_handler(level=logging.DEBUG, filter=log_filter) FILE_HEADER = "# -*- coding: utf-8 -*-\n" MAIN_FILE_CLASS_HEADER = "class Bot(object):\n _base_url = \"https://api.telegram.org/bot{api_key}/{command}\"\n" __author__ = 'luckydonald' logger = logging.getLogger(__name__) BASE_URL = "https://core.telegram.org/bots/api" SAVE_VALUES = NoBuiltins([], {}, {"Function": Function, "Clazz": Clazz, "Import": Import, "Type": Type, "Variable": Variable}) def lol1(tag): return tag.has_attr("class") and "anchor" in tag["class"] class_fields = [ ["Field", "Type", "Description"], ["Parameters", "Type", "Description"], ["Parameter", "Type", "Description"] ] func_fields = [ ["Parameters", "Type", "Required", "Description"], ["Parameter", "Type", "Required", "Description"], ] use_back = False use_yapf = False black_settings = dict( write_back=black.WriteBack.from_configuration(check=False, diff=False), report=black.Report(check=False, quiet=False, verbose=False), mode=black.FileMode( target_versions=set(), line_length=black.DEFAULT_LINE_LENGTH, is_pyi=False, string_normalization=True, ), ) yapf_settings = dict( style={ 'ALIGN_CLOSING_BRACKET_WITH_VISUAL_INDENT': True, 'ALLOW_MULTILINE_LAMBDAS': True, 'ALLOW_MULTILINE_DICTIONARY_KEYS': False, 'ALLOW_SPLIT_BEFORE_DEFAULT_OR_NAMED_ASSIGNS': True, 'ALLOW_SPLIT_BEFORE_DICT_VALUE': False, 'ARITHMETIC_PRECEDENCE_INDICATION': False, 'BLANK_LINE_BEFORE_NESTED_CLASS_OR_DEF': False, 'BLANK_LINE_BEFORE_MODULE_DOCSTRING': True, 'BLANK_LINE_BEFORE_CLASS_DOCSTRING': False, 'BLANK_LINES_AROUND_TOP_LEVEL_DEFINITION': 2, 'COALESCE_BRACKETS': True, 'COLUMN_LIMIT': black.DEFAULT_LINE_LENGTH, 'CONTINUATION_ALIGN_STYLE': "space", 'CONTINUATION_INDENT_WIDTH': 2, 'DEDENT_CLOSING_BRACKETS': True, 'DISABLE_ENDING_COMMA_HEURISTIC': True, 'EACH_DICT_ENTRY_ON_SEPARATE_LINE': False, 'INDENT_DICTIONARY_VALUE': False, 'INDENT_WIDTH': 2, 'INDENT_BLANK_LINES': False, 'JOIN_MULTIPLE_LINES': False, 'NO_SPACES_AROUND_SELECTED_BINARY_OPERATORS': False, 'SPACES_AROUND_POWER_OPERATOR': True, False, 'SPLIT_ARGUMENTS_WHEN_COMMA_TERMINATED': True, 'SPLIT_ALL_COMMA_SEPARATED_VALUES': True, 'SPLIT_ALL_TOP_LEVEL_COMMA_SEPARATED_VALUES': True, 'SPLIT_BEFORE_BITWISE_OPERATOR': False, 'SPLIT_BEFORE_ARITHMETIC_OPERATOR': False, 'SPLIT_BEFORE_CLOSING_BRACKET': True, 'SPLIT_BEFORE_DICT_SET_GENERATOR': True, # Split before a dictionary or set generator (comp_for). For example, note the split before the for: 'SPLIT_BEFORE_DOT': False, # Split before the . if we need to split a longer expression: # 'SPLIT_BEFORE_EXPRESSION_AFTER_OPENING_PAREN': False, # Split after the opening paren which surrounds an expression if it doesn't fit on a single line. 'SPLIT_BEFORE_FIRST_ARGUMENT': True, 'SPLIT_BEFORE_LOGICAL_OPERATOR': True, 'USE_TABS': False, }, ) def parse_table(tag): first = True table_header = None table_type = 'unknown' param_strings = [] thead = tag.find('thead', recursive=False) theads = None if thead: theads = thead.find_all(["th", "td"]) tbody = tag.find('tbody', recursive=False) if tbody: tbody_rows = tbody.find_all("tr") else: tbody_rows = tag.find_all("tr") tbodys = [ row.find_all(["td" ,"th"]) for row in tbody_rows ] if not thead: theads = tbody_rows[0] tbodys = tbody_rows[1:] found_columns = [] for column in theads: col = column.find("strong") if col: col_text = col.text else: col_text = column.text found_columns.append(col_text) for test_columns in func_fields: if found_columns == test_columns: table_header = test_columns table_type = 'func' break if not table_header: # search class now for test_columns in class_fields: if found_columns == test_columns: if table_header is not None: raise AssertionError("Table detected as func and class: {!r}".format(found_columns)) table_header = test_columns table_type = 'class' break # end if # end for # end if # TABLE is none of the above if not table_header: # we don't have a result yet raise AssertionError("Unknown table, {!r}".format(found_columns)) for tds in tbodys: string = '' for col in tds: string += "\t" had_something = False for sub_col in col: if isinstance(sub_col, NavigableString): string += sub_col had_something = True elif sub_col.name == 'img': string += sub_col.attrs.get('alt', '') had_something = True else: string += sub_col.text had_something = True if not had_something: string += col.text string = string.lstrip("\t") logger.debug("t: " + string) param_strings.append(string) pass return table_type, param_strings def load_from_html(folder): filter = get_filter() document = requests.get(BASE_URL) bs = BeautifulSoup(document.content) results = [] for h in bs.select("#dev_page_content > h4"): logger.info("------") anchor = h.find(lol1) if not anchor or not anchor.has_attr("name"): continue link = "{base_url}#{anchor}".format(base_url=BASE_URL, anchor=anchor["name"]) title = h.text descr = [] table_type, param_strings = None, None logger.info("title: " + title) logger.info("link: " + link) if filter and title not in filter: logger.info("Skipping {title}, filtered.".format(title=title)) continue type_strings = [] default_returns = [] for sibling in h.next_siblings: if sibling == "\n": continue if sibling.name in ["p", "blockquote"]: if "return" in sibling.text.lower(): parts_splitted = [] is_first_element = True for x in sibling.children: if isinstance(x, NavigableString): if is_first_element: parts_splitted.extend([[foo.lstrip()] for foo in x.split(".")]) is_first_element = False else: parts_splitted[len(parts_splitted)-1].append(x.split(".", maxsplit=1)[0]) parts_splitted.extend([[foo] for foo in x.split(".")[1:]]) is_first_element = False is_first_element = x.strip().endswith(".") else: obj = None if x.name in ["a", "em"]: obj = x else: obj = x.text if is_first_element: parts_splitted.append([obj]) is_first_element = False else: parts_splitted[len(parts_splitted)-1].append(obj) returns__ = [] return_text__ = [] is_array = False for lol_part in parts_splitted: has_return = False returns_ = [] return_text_ = "" for lol_part_part in lol_part: if isinstance(lol_part_part, str): return_text_ += lol_part_part if lol_part_part.strip().lower().endswith("array of"): is_array = True if "return" in lol_part_part.lower(): has_return = True else: return_text_ += lol_part_part.text if is_array: returns_.append("list of " + lol_part_part.text) is_array = False else: returns_.append(lol_part_part.text) if has_return: return_text__.append(return_text_.strip()) returns__.extend(returns_) if return_text__ or returns__: default_returns = [". ".join(return_text__).strip(), " or ".join(returns__).strip()] descr.append(sibling.text.replace('“', '"').replace('”', '"')) elif sibling.name == "table": assert sibling.has_attr("class") and "table" in sibling["class"] table_type, param_strings = parse_table(sibling) elif sibling.name == "h4": break elif sibling.name == "h3": break elif sibling.name == "hr": break elif sibling.name == "img": break else: logger.info("unknown: " + sibling.name) if not all([link, title, descr]): logger.warning("Skipped: Missing link, title or description") continue if not all([table_type, param_strings]): if title in WHITELISTED_FUNCS: table_type = 'func' elif title in WHITELISTED_CLASSES: table_type = 'class' elif [key[key.rindex('.')+1:] for key in CUSTOM_CLASSES.keys()]: logger.info( "Skipped. Has no table with Parameters or Fields.\n" "Has a `code_generator_settings.CUSTOM_CLASSES` which seems to fit though." ) continue else: logger.warning( "Skipped. Has no table with Parameters or Fields.\n" "Also isn't a whitelisted function in `code_generator_settings.WHITELISTED_CLASSES` or class in `code_generator_settings.WHITELISTED_CLASSES`." ) continue # -> else: is in WHITELISTED_FUNCS: # end if descr = "\n".join(descr) logger.info("descr: " + repr(descr)) params_string = "\n".join(param_strings) if param_strings else None # WHITELISTED_FUNCS/WHITELISTED_CLASSES have no params if table_type == "func": seems_valid = False if len(default_returns) != 2: if "return" in descr.lower(): default_returns = ["", "Message"] default_returns[0] = [x for x in descr.split(".") if "return" in x.lower()][0].strip() seems_valid = len(default_returns[0].split(".")) == 1 default_returns[1] = " or ".join(type_strings) if type_strings else "Message" default_returns[1] = as_types(default_returns[1], "returns") else: default_returns = ["On success, True is returned", "True"] # end if "return" in description else: seems_valid = len(default_returns[0].split(".")) == 1 # end if default set replaced_valid = None # load replacements from WHITELISTED_FUNCS. if title in WHITELISTED_FUNCS: # "func": {'return': {'expected': '', 'replace': ''}, 'rtype': {'expected': '', 'replace': ''}}, wlist_func = WHITELISTED_FUNCS[title] wlist_func_return = wlist_func['return'] if 'return' in wlist_func else None wlist_func_r_type = wlist_func['r_type'] if 'r_type' in wlist_func else None if wlist_func_return and default_returns[0] != wlist_func_return['expected']: logger.warning(f"whitelist: Mismatch in return.\nExpected {wlist_func_return['expected']!r},\ninstead got {default_returns[0]!r}.") replaced_valid = False if wlist_func_r_type and default_returns[1] != wlist_func_r_type['expected']: logger.warning(f"whitelist: Mismatch in r_type.\nExpected {wlist_func_r_type['expected']!r},\ninstead got {default_returns[1]!r}") replaced_valid = False if replaced_valid is None: # whitelist didn't fail replaced_valid = True logger.info("the found return: " + repr(default_returns[0]) + '.') logger.info("the found r_type: " + repr(default_returns[1]) + '.') logger.info("whitelist return: " + repr(wlist_func_return['replace']) + '.') logger.info("whitelist r_type: " + repr(wlist_func_r_type['replace']) + '.') default_returns[0] = wlist_func_return['replace'] default_returns[1] = wlist_func_r_type['replace'] if not seems_valid and not replaced_valid: returns = answer("Textual description what the function returns", default_returns[0]) return_type = answer("Return type", default_returns[1]) if isinstance(return_type, str): return_type = as_types(return_type, "return type") else: returns = default_returns[0] return_type = default_returns[1] logger.debug("\n") result = func(title, descr, link, params_string, returns=returns, return_type=return_type) results.append(result) elif table_type == "class": if title in CLASS_TYPE_PATHS: parent_clazz = CLASS_TYPE_PATHS[title][CLASS_TYPE_PATHS__PARENT] logger.info("superclass: " + parent_clazz) else: parent_clazz = answer("Parent class name", "TgBotApiObject") if title in WHITELISTED_CLASSES: pass result = clazz( clazz=title, parent_clazz=parent_clazz, description=descr, link=link, params_string=params_string ) results.append(result) return results, document.content def main(): folder, html_document, results = load_api_definitions() output(folder, results, html_content=html_document) def load_api_definitions(): folder = get_folder_path() mode = confirm("Offline Mode: Load from a dump instead of the API Docs?") if not mode: results, html_document = load_from_html(folder) else: results, html_document = load_from_dump(folder) results = preprocess_results(results, additional_items=list(CUSTOM_CLASSES.values())) return folder, html_document, results def load_from_dump(folder): dump = "" with open(path_join(folder, "api.py"), "r") as f: dump = "".join(f.readlines()) html_document = None if exists(path_join(folder, "api.html")): with open(path_join(folder, "api.html"), "rb") as f: html_document = f.read() results = safe_eval(dump, SAVE_VALUES) return results, html_document def preprocess_results(results: List[Union[Clazz, Function]], additional_items: Union[None, List[Clazz]] = None): if additional_items is None: additional_items = [] logger.info('Calculating duplicate_of_parent.') clazzes_by_name: Dict[str, Clazz] = {} for other in additional_items: if isinstance(other, Clazz): clazzes_by_name[other.clazz] = other for result in results: if isinstance(result, Clazz): clazzes_by_name[result.clazz] = result for result in results: if not isinstance(result, Clazz): continue if result.parent_clazz is None or result.parent_clazz.string == 'object': continue if result.parent_clazz.string in clazzes_by_name: parent_clazz: Clazz = clazzes_by_name[result.parent_clazz.string] for variable in result.variables: variable: Variable parent_variable = parent_clazz.get_same_variable( variable, ignore_pytg_name=True, ignore_description=True, ignore_optional=True, ignore_type_always_is_value=True, allow_additional_allowed_type_matchings=True, ) variable.duplicate_of_parent = parent_variable is not None if variable.duplicate_of_parent and parent_variable.additional_allowed_type_matchings: variable.types = parent_variable.types[:] # end if # end for else: logger.warning(f'Could not resolve parent class: {result.parent_clazz}') # end if # end for return results # end def def output(folder, results, html_content=None): can_quit = False do_delete_first = confirm("Can the folder {path} be deleted before writing?".format(path=folder)) logger.info("vvvvvvvvv") while not can_quit: if do_delete_first: try: import Send2Trash Send2Trash.send2trash(folder) except ImportError: import shutil shutil.rmtree(folder) # end try # end if # write crawled data mkdir_p(folder) with open(path_join(folder, "api.py"), "w") as f: f.write("[\n ") f.write(",\n ".join([repr(result) for result in results])) f.write("\n]") # end for # end with if html_content: with open(path_join(folder, "api.html"), "wb") as f: f.write(html_content) # end with # end if # write templates try: safe_to_file(folder, results) except TemplateError as e: if isinstance(e, TemplateSyntaxError): logger.exception("Template error at {file}:{line}".format(file=e.filename, line=e.lineno)) else: logger.exception("Template error.") # end if # end try logger.info("Written to file.") can_quit = not confirm("Write again after reloading templates?", default=True) logger.info("#########") logger.info("Exit.") # end def def get_filter(): filter = answer( "Only generate the doc for specific functions/classes. Comma seperated list. Leave empty to generate all.", default="" # getChat, leaveChat, getChatAdministrators, getChatMember, getChatMembersCount, Message, MessageEntity" ) if filter.strip(): filter = [x.strip() for x in filter.split(",")] else: filter = None # end if return filter # end def def get_folder_path(): default = "/tmp/pytgbotapi/" candidate = abspath(path_join(dirname(abspath(__file__)), 'output')) logger.info(f'canidate: {candidate}') if exists(candidate) and isdir(candidate): default = candidate # end if file = answer("Folder path to store the results.", default=default) if file: try: file = abspath(file) mkdir_p(file) with open(path_join(file, "__init__.py"), "w") as f: f.write(FILE_HEADER) # end with except IOError: pass # end try # end if file return file # end def # noinspection PyCompatibility def safe_to_file(folder, results): functions = [] message_send_clazzes = [] clazzes: Dict[str, List[Clazz]] = {} # "filepath": [Class, Class, ...] all_the_clazzes = [] custom_classes = {} # "filepath": [Class, Class, ...] all_the_custom_clazzes = [] # actually only used to call preprocess_results with all the items. They will be modified in place anyway. for import_path, result in CUSTOM_CLASSES.items(): # result.import_path = result.calculate_import_path() result.filepath = result.calculate_filepath(folder) file_path = result.filepath if file_path not in custom_classes: custom_classes[file_path] = [] # end if custom_classes[file_path].append(result) if file_path not in clazzes: clazzes[file_path] = [] # end if clazzes[file_path].append(result) all_the_clazzes.append(result) all_the_custom_clazzes.append(result) # end def _ = preprocess_results(all_the_custom_clazzes, additional_items=results) # split results into functions and classes for result in results: assert isinstance(result, (Clazz, Function)) if isinstance(result, Clazz): result.import_path = result.calculate_import_path() result.filepath = result.calculate_filepath(folder) file_path = result.filepath if file_path not in clazzes: clazzes[file_path] = [] clazzes[file_path].append(result) all_the_clazzes.append(result) else: assert isinstance(result, Function) pytgbot_dir = Path(__file__).parent.parent # import_path = "pytgbot.bot.asynchronous." # file_path = calc_path_and_create_folders(pytgbot_dir.absolute(), import_path) result.filepath = str(pytgbot_dir.joinpath('pytgbot').joinpath('bot').joinpath('asynchronous.py').absolute()) functions.append(result) if result.name.startswith('send_'): import_path = "teleflask_messages." file_path = calc_path_and_create_folders(folder, import_path) args, special_kwargs, kwargs = result.class_variables_separated result2 = FunctionClazz( clazz=result.class_name_teleflask_message, import_path=Import(path=import_path.rstrip('.'), name=result.class_name_teleflask_message), imports=result.imports, parent_clazz=Type(string='ReturnableMessageBase', is_builtin=False, is_list=0, import_path=None, description="Base class"), link=result.link, description=result.description, parameters=args, keywords=special_kwargs + kwargs, function=result, ) result2.filepath = file_path message_send_clazzes.append(result2) # end if # end if # end for bot_template = get_template("bot.template") bot_base_template = get_template("bot_base.template") clazzfile_template = get_template("classfile.template") teleflask_messages_template = get_template("teleflask_messages_file.template") typehints_template = get_template("typehintsfile.template") telegram_bot_api_server_funcs_template = get_template("telegram_bot_api_server/funcs.template") telegram_bot_api_server_class_template = get_template("telegram_bot_api_server/classes.template") mkdir_p(path_join(folder, 'telegram_bot_api_server', 'generated')) if all_the_clazzes: txt = telegram_bot_api_server_class_template.render(clazzes=all_the_clazzes) render_file_to_disk(path_join(folder, 'telegram_bot_api_server', 'generated', 'models.py'), txt) # end if for path, clazz_list in clazzes.items(): clazz_imports = set() for clazz_ in clazz_list: assert isinstance(clazz_, Clazz) assert isinstance(clazz_.parent_clazz, Type) if not clazz_.parent_clazz.is_builtin: clazz_imports.add(clazz_.parent_clazz.as_import) # end if # end for clazz_imports = list(clazz_imports) clazz_imports.sort() is_sendable = ("sendable" in path) try: txt = clazzfile_template.render(clazzes=clazz_list, manual_clazzes=[], imports=clazz_imports, is_sendable=is_sendable) txt = txt.replace("\t", " ") render_file_to_disk(path, txt) except IOError: raise # lol # end try try: txt = typehints_template.render(clazzes=clazz_list, imports=clazz_imports, is_sendable=is_sendable) txt = txt.replace("\t", " ") render_file_to_disk(path + "i", txt) # "ponies.py" + "i" => "ponies.pyi" except IOError: raise # lol # end try try: txt = typehints_template.render(clazzes=clazz_list, imports=clazz_imports, is_sendable=is_sendable) txt = txt.replace("\t", " ") render_file_to_disk(path + "i", txt) # "ponies.py" + "i" => "ponies.pyi" except IOError: raise # lol # end try # end for classes if functions: func_imports = set() for func_ in functions: assert isinstance(func_, Function) for var_ in func_.variables: assert isinstance(var_, Variable) for type_ in var_.types: assert isinstance(type_, Type) func_imports.add(type_.as_import) # end for # end for if func_.returns is not None: assert isinstance(func_.returns, Variable) for type_ in func_.returns.types: assert isinstance(type_, Type) func_imports.add(type_.as_import) # end for # end if # end for func_imports = list(func_imports) func_imports.sort() txt_sync = bot_template.render(functions=functions, is_asyncio=False, imports=func_imports, file_import_path='pytgbot.bot.synchronous') render_file_to_disk(functions[0].filepath.replace('asynchronous', 'synchronous'), txt_sync) txt_async = bot_template.render(functions=functions, is_asyncio=True, imports=func_imports, file_import_path='pytgbot.bot.asynchronous') render_file_to_disk(functions[0].filepath, txt_async) txt_base = bot_base_template.render(functions=functions, imports=func_imports, file_import_path='pytgbot.bot.base') render_file_to_disk(functions[0].filepath.replace('asynchronous', 'base'), txt_base) imports = set() imports.add(('enum', 'Enum')) imports.add(('typing', 'Union, List, Optional')) imports.add(('fastapi', 'APIRouter, HTTPException')) imports.add(('telethon', 'TelegramClient')) imports.add(('serializer', 'to_web_api, get_entity')) imports.add(('fastapi.params', 'Query')) imports.add(('telethon.errors', 'BotMethodInvalidError')) imports.add(('telethon.tl.types', 'TypeSendMessageAction')) imports.add(('telethon.client.chats', '_ChatAction')) imports.add(('luckydonaldUtils.logger', 'logging')) imports.add(('telethon.tl.functions.messages', 'SetTypingRequest')) for function in functions: function: Function for the_import in function.imports: the_import: Import imports.add((the_import.path, the_import.name)) # end for # end for # https://stackoverflow.com/a/613218/3423324#how-do-i-sort-a-dictionary-by-value # https://stackoverflow.com/a/4659539/3423324#how-to-sort-by-length-of-string-followed-by-alphabetical-order imports_sorted = ["from " + path + ' import ' + name for path, name in sorted(imports, key=lambda item: (-len(item[0]), item[0], -len(item[1]), item[1]))] # imports_sorted.sort(key=lambda item: (-len(item), item)) txt = telegram_bot_api_server_funcs_template.render(functions=functions, imports=imports_sorted) render_file_to_disk(path_join(folder, 'telegram_bot_api_server', 'generated', 'funcs.py'), txt) # end if if message_send_clazzes: txt = teleflask_messages_template.render(clazzes=message_send_clazzes) render_file_to_disk(message_send_clazzes[0].filepath, txt) # end if # end def # noinspection PyCompatibility def render_file_to_disk(file, txt): # remove whitespaces at the end of a line txt = "\n".join(line.rstrip() for line in txt.splitlines()) # add blank line at end of file. if not txt.endswith("\n"): txt += "\n" # end if with open(file, "w") as f: f.write(txt) # end with logger.info(f'Written {file!r} to disk, {len(txt)} chars.') if use_back: black.reformat_one( src=black.Path(file), write_back=black_settings['write_back'], fast=False, mode=black_settings['mode'], report=black_settings['report'], ) # end if if use_yapf: try: FormatFile(file, in_place=True, style_config=yapf_settings['style']) except: logger.exception("Formatting file {file} failed.".format(file=file)) # end try # end if # end def def calc_path_and_create_folders(folder, import_path, create_folder=True): file_path = abspath(path_join(folder, import_path[:import_path.rfind(".")].replace(".", folder_seperator) + ".py")) if create_folder: mkdir_p(dirname(file_path)) # end if return file_path # end def if __name__ == '__main__': main() # end if

true

1c43ded31ce72ef1e9790a43940975d2e33defc8

42

py

Python

run.py

chrisbarr/bilious-rutabaga

b2d01f03c2cce8342f11139279870156c0ebc9c9

[ "MIT" ]

null

run.py

chrisbarr/bilious-rutabaga

b2d01f03c2cce8342f11139279870156c0ebc9c9

[ "MIT" ]

null

run.py

chrisbarr/bilious-rutabaga

b2d01f03c2cce8342f11139279870156c0ebc9c9

[ "MIT" ]

null

import bucket_lister bucket_lister.main()

14

20

0.857143

import bucket_lister bucket_lister.main()

true

1c43df75a6f017476d2000ddd8cfa609911e7416

48,453

py

Python

pymatgen/core/tests/test_structure.py

MahdiDavari/pymatgen

eb6cd95230c11ac761a96ebf82b98f71177bb71f

[ "MIT" ]

null

pymatgen/core/tests/test_structure.py

MahdiDavari/pymatgen

eb6cd95230c11ac761a96ebf82b98f71177bb71f

[ "MIT" ]

null

pymatgen/core/tests/test_structure.py

MahdiDavari/pymatgen

eb6cd95230c11ac761a96ebf82b98f71177bb71f

[ "MIT" ]

1

2018-04-09T21:49:14.000Z

# coding: utf-8 # Copyright (c) Pymatgen Development Team. # Distributed under the terms of the MIT License. from __future__ import division, unicode_literals, print_function from pymatgen.util.testing import PymatgenTest from pymatgen.core.periodic_table import Element, Specie from pymatgen.core.composition import Composition from pymatgen.core.operations import SymmOp from pymatgen.core.structure import IStructure, Structure, IMolecule, \ StructureError, Molecule from pymatgen.core.lattice import Lattice import random import os import numpy as np class IStructureTest(PymatgenTest): def setUp(self): coords = [[0, 0, 0], [0.75, 0.5, 0.75]] self.lattice = Lattice([[3.8401979337, 0.00, 0.00], [1.9200989668, 3.3257101909, 0.00], [0.00, -2.2171384943, 3.1355090603]]) self.struct = IStructure(self.lattice, ["Si"] * 2, coords) self.assertEqual(len(self.struct), 2, "Wrong number of sites in structure!") self.assertTrue(self.struct.is_ordered) self.assertTrue(self.struct.ntypesp == 1) coords = list() coords.append([0, 0, 0]) coords.append([0., 0, 0.0000001]) self.assertRaises(StructureError, IStructure, self.lattice, ["Si"] * 2, coords, True) self.propertied_structure = IStructure( self.lattice, ["Si"] * 2, coords, site_properties={'magmom': [5, -5]}) def test_matches(self): ss = self.struct * 2 self.assertTrue(ss.matches(self.struct)) def test_bad_structure(self): coords = list() coords.append([0, 0, 0]) coords.append([0.75, 0.5, 0.75]) coords.append([0.75, 0.5, 0.75]) self.assertRaises(StructureError, IStructure, self.lattice, ["Si"] * 3, coords, validate_proximity=True) #these shouldn't raise an error IStructure(self.lattice, ["Si"] * 2, coords[:2], True) IStructure(self.lattice, ["Si"], coords[:1], True) def test_volume_and_density(self): self.assertAlmostEqual(self.struct.volume, 40.04, 2, "Volume wrong!") self.assertAlmostEqual(self.struct.density, 2.33, 2, "Incorrect density") def test_specie_init(self): coords = list() coords.append([0, 0, 0]) coords.append([0.75, 0.5, 0.75]) s = IStructure(self.lattice, [{Specie('O', -2): 1.0}, {Specie('Mg', 2): 0.8}], coords) self.assertEqual(s.composition.formula, 'Mg0.8 O1') def test_get_sorted_structure(self): coords = list() coords.append([0, 0, 0]) coords.append([0.75, 0.5, 0.75]) s = IStructure(self.lattice, ["O", "Li"], coords, site_properties={'charge': [-2, 1]}) sorted_s = s.get_sorted_structure() self.assertEqual(sorted_s[0].species_and_occu, Composition("Li")) self.assertEqual(sorted_s[1].species_and_occu, Composition("O")) self.assertEqual(sorted_s[0].charge, 1) self.assertEqual(sorted_s[1].charge, -2) s = IStructure(self.lattice, ["Se", "C", "Se", "C"], [[0] * 3, [0.5] * 3, [0.25] * 3, [0.75] * 3]) self.assertEqual([site.specie.symbol for site in s.get_sorted_structure()], ["C", "C", "Se", "Se"]) def test_get_space_group_data(self): self.assertEqual(self.struct.get_space_group_info(), ('Fd-3m', 227)) def test_fractional_occupations(self): coords = list() coords.append([0, 0, 0]) coords.append([0.75, 0.5, 0.75]) s = IStructure(self.lattice, [{'O': 1.0}, {'Mg': 0.8}], coords) self.assertEqual(s.composition.formula, 'Mg0.8 O1') self.assertFalse(s.is_ordered) def test_get_distance(self): self.assertAlmostEqual(self.struct.get_distance(0, 1), 2.35, 2, "Distance calculated wrongly!") pt = [0.9, 0.9, 0.8] self.assertAlmostEqual(self.struct[0].distance_from_point(pt), 1.50332963784, 2, "Distance calculated wrongly!") def test_as_dict(self): si = Specie("Si", 4) mn = Element("Mn") coords = list() coords.append([0, 0, 0]) coords.append([0.75, 0.5, 0.75]) struct = IStructure(self.lattice, [{si: 0.5, mn: 0.5}, {si: 0.5}], coords) self.assertIn("lattice", struct.as_dict()) self.assertIn("sites", struct.as_dict()) d = self.propertied_structure.as_dict() self.assertEqual(d['sites'][0]['properties']['magmom'], 5) coords = list() coords.append([0, 0, 0]) coords.append([0.75, 0.5, 0.75]) s = IStructure(self.lattice, [{Specie('O', -2, properties={"spin": 3}): 1.0}, {Specie('Mg', 2, properties={"spin": 2}): 0.8}], coords, site_properties={'magmom': [5, -5]}) d = s.as_dict() self.assertEqual(d['sites'][0]['properties']['magmom'], 5) self.assertEqual(d['sites'][0]['species'][0]['properties']['spin'], 3) d = s.as_dict(0) self.assertNotIn("volume", d['lattice']) self.assertNotIn("xyz", d['sites'][0]) def test_from_dict(self): d = self.propertied_structure.as_dict() s = IStructure.from_dict(d) self.assertEqual(s[0].magmom, 5) d = self.propertied_structure.as_dict(0) s2 = IStructure.from_dict(d) self.assertEqual(s, s2) d = {'lattice': {'a': 3.8401979337, 'volume': 40.044794644251596, 'c': 3.8401979337177736, 'b': 3.840198994344244, 'matrix': [[3.8401979337, 0.0, 0.0], [1.9200989668, 3.3257101909, 0.0], [0.0, -2.2171384943, 3.1355090603]], 'alpha': 119.9999908639842, 'beta': 90.0, 'gamma': 60.000009137322195}, 'sites': [{'properties': {'magmom': 5}, 'abc': [0.0, 0.0, 0.0], 'occu': 1.0, 'species': [{'occu': 1.0, 'oxidation_state': -2, 'properties': {'spin': 3}, 'element': 'O'}], 'label': 'O2-', 'xyz': [0.0, 0.0, 0.0]}, {'properties': {'magmom': -5}, 'abc': [0.75, 0.5, 0.75], 'occu': 0.8, 'species': [{'occu': 0.8, 'oxidation_state': 2, 'properties': {'spin': 2}, 'element': 'Mg'}], 'label': 'Mg2+:0.800', 'xyz': [3.8401979336749994, 1.2247250003039056e-06, 2.351631795225]}]} s = IStructure.from_dict(d) self.assertEqual(s[0].magmom, 5) self.assertEqual(s[0].specie.spin, 3) self.assertEqual(type(s), IStructure) def test_site_properties(self): site_props = self.propertied_structure.site_properties self.assertEqual(site_props['magmom'], [5, -5]) self.assertEqual(self.propertied_structure[0].magmom, 5) self.assertEqual(self.propertied_structure[1].magmom, -5) def test_copy(self): new_struct = self.propertied_structure.copy(site_properties={'charge': [2, 3]}) self.assertEqual(new_struct[0].magmom, 5) self.assertEqual(new_struct[1].magmom, -5) self.assertEqual(new_struct[0].charge, 2) self.assertEqual(new_struct[1].charge, 3) coords = list() coords.append([0, 0, 0]) coords.append([0., 0, 0.0000001]) structure = IStructure(self.lattice, ["O", "Si"], coords, site_properties={'magmom': [5, -5]}) new_struct = structure.copy(site_properties={'charge': [2, 3]}, sanitize=True) self.assertEqual(new_struct[0].magmom, -5) self.assertEqual(new_struct[1].magmom, 5) self.assertEqual(new_struct[0].charge, 3) self.assertEqual(new_struct[1].charge, 2) self.assertAlmostEqual(new_struct.volume, structure.volume) def test_interpolate(self): coords = list() coords.append([0, 0, 0]) coords.append([0.75, 0.5, 0.75]) struct = IStructure(self.lattice, ["Si"] * 2, coords) coords2 = list() coords2.append([0, 0, 0]) coords2.append([0.5, 0.5, 0.5]) struct2 = IStructure(self.struct.lattice, ["Si"] * 2, coords2) int_s = struct.interpolate(struct2, 10) for s in int_s: self.assertIsNotNone(s, "Interpolation Failed!") self.assertEqual(int_s[0].lattice, s.lattice) self.assertArrayEqual(int_s[1][1].frac_coords, [0.725, 0.5, 0.725]) badlattice = [[1, 0.00, 0.00], [0, 1, 0.00], [0.00, 0, 1]] struct2 = IStructure(badlattice, ["Si"] * 2, coords2) self.assertRaises(ValueError, struct.interpolate, struct2) coords2 = list() coords2.append([0, 0, 0]) coords2.append([0.5, 0.5, 0.5]) struct2 = IStructure(self.struct.lattice, ["Si", "Fe"], coords2) self.assertRaises(ValueError, struct.interpolate, struct2) # Test autosort feature. s1 = Structure.from_spacegroup("Fm-3m", Lattice.cubic(3), ["Fe"], [[0, 0, 0]]) s1.pop(0) s2 = Structure.from_spacegroup("Fm-3m", Lattice.cubic(3), ["Fe"], [[0, 0, 0]]) s2.pop(2) random.shuffle(s2) for s in s1.interpolate(s2, autosort_tol=0.5): self.assertArrayAlmostEqual(s1[0].frac_coords, s[0].frac_coords) self.assertArrayAlmostEqual(s1[2].frac_coords, s[2].frac_coords) # Make sure autosort has no effect on simpler interpolations, # and with shuffled sites. s1 = Structure.from_spacegroup("Fm-3m", Lattice.cubic(3), ["Fe"], [[0, 0, 0]]) s2 = Structure.from_spacegroup("Fm-3m", Lattice.cubic(3), ["Fe"], [[0, 0, 0]]) s2[0] = "Fe", [0.01, 0.01, 0.01] random.shuffle(s2) for s in s1.interpolate(s2, autosort_tol=0.5): self.assertArrayAlmostEqual(s1[1].frac_coords, s[1].frac_coords) self.assertArrayAlmostEqual(s1[2].frac_coords, s[2].frac_coords) self.assertArrayAlmostEqual(s1[3].frac_coords, s[3].frac_coords) def test_interpolate_lattice(self): coords = list() coords.append([0, 0, 0]) coords.append([0.75, 0.5, 0.75]) struct = IStructure(self.lattice, ["Si"] * 2, coords) coords2 = list() coords2.append([0, 0, 0]) coords2.append([0.5, 0.5, 0.5]) l2 = Lattice.from_lengths_and_angles([3,4,4], [100,100,70]) struct2 = IStructure(l2, ["Si"] * 2, coords2) int_s = struct.interpolate(struct2, 2, interpolate_lattices=True) self.assertArrayAlmostEqual(struct.lattice.abc, int_s[0].lattice.abc) self.assertArrayAlmostEqual(struct.lattice.angles, int_s[0].lattice.angles) self.assertArrayAlmostEqual(struct2.lattice.abc, int_s[2].lattice.abc) self.assertArrayAlmostEqual(struct2.lattice.angles, int_s[2].lattice.angles) int_angles = [110.3976469, 94.5359731, 64.5165856] self.assertArrayAlmostEqual(int_angles, int_s[1].lattice.angles) # Assert that volume is monotonic self.assertTrue(struct2.lattice.volume >= int_s[1].lattice.volume) self.assertTrue(int_s[1].lattice.volume >= struct.lattice.volume) def test_interpolate_lattice_rotation(self): l1 = Lattice([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) l2 = Lattice([[-1.01, 0, 0], [0, -1.01, 0], [0, 0, 1]]) coords = [[0, 0, 0], [0.75, 0.5, 0.75]] struct1 = IStructure(l1, ["Si"] * 2, coords) struct2 = IStructure(l2, ["Si"] * 2, coords) int_s = struct1.interpolate(struct2, 2, interpolate_lattices=True) # Assert that volume is monotonic self.assertTrue(struct2.lattice.volume >= int_s[1].lattice.volume) self.assertTrue(int_s[1].lattice.volume >= struct1.lattice.volume) def test_get_primitive_structure(self): coords = [[0, 0, 0], [0.5, 0.5, 0], [0, 0.5, 0.5], [0.5, 0, 0.5]] fcc_ag = IStructure(Lattice.cubic(4.09), ["Ag"] * 4, coords) self.assertEqual(len(fcc_ag.get_primitive_structure()), 1) coords = [[0, 0, 0], [0.5, 0.5, 0.5]] bcc_li = IStructure(Lattice.cubic(4.09), ["Li"] * 2, coords) bcc_prim = bcc_li.get_primitive_structure() self.assertEqual(len(bcc_prim), 1) self.assertAlmostEqual(bcc_prim.lattice.alpha, 109.47122, 3) coords = [[0] * 3, [0.5] * 3, [0.25] * 3, [0.26] * 3] s = IStructure(Lattice.cubic(4.09), ["Ag"] * 4, coords) self.assertEqual(len(s.get_primitive_structure()), 4) def test_primitive_cell_site_merging(self): l = Lattice.cubic(10) coords = [[0, 0, 0], [0, 0, 0.5], [0, 0, 0.26], [0, 0, 0.74]] sp = ['Ag', 'Ag', 'Be', 'Be'] s = Structure(l, sp, coords) dm = s.get_primitive_structure().distance_matrix self.assertArrayAlmostEqual(dm, [[0, 2.5], [2.5, 0]]) def test_primitive_on_large_supercell(self): coords = [[0, 0, 0], [0.5, 0.5, 0], [0, 0.5, 0.5], [0.5, 0, 0.5]] fcc_ag = Structure(Lattice.cubic(4.09), ["Ag"] * 4, coords) fcc_ag.make_supercell([2, 2, 2]) fcc_ag_prim = fcc_ag.get_primitive_structure() self.assertEqual(len(fcc_ag_prim), 1) self.assertAlmostEqual(fcc_ag_prim.volume, 17.10448225) def test_primitive_positions(self): coords = [[0, 0, 0], [0.3, 0.35, 0.45]] s = Structure(Lattice.from_parameters(1,2,3,50,66,88), ["Ag"] * 2, coords) a = [[-1,2,-3], [3,2,-4], [1,0,-1]] b = [[4, 0, 0], [1, 1, 0], [3, 0, 1]] c = [[2, 0, 0], [1, 3, 0], [1, 1, 1]] for sc_matrix in [c]: sc = s.copy() sc.make_supercell(sc_matrix) prim = sc.get_primitive_structure(0.01) self.assertEqual(len(prim), 2) self.assertAlmostEqual(prim.distance_matrix[0,1], 1.0203432356739286) def test_primitive_structure_volume_check(self): l = Lattice.tetragonal(10, 30) coords = [[0.5, 0.8, 0], [0.5, 0.2, 0], [0.5, 0.8, 0.333], [0.5, 0.5, 0.333], [0.5, 0.5, 0.666], [0.5, 0.2, 0.666]] s = IStructure(l, ["Ag"] * 6, coords) sprim = s.get_primitive_structure(tolerance=0.1) self.assertEqual(len(sprim), 6) def test_get_all_neighbors_and_get_neighbors(self): s = self.struct nn = s.get_neighbors_in_shell(s[0].frac_coords, 2, 4, include_index=True) self.assertEqual(len(nn), 47) self.assertEqual(nn[0][-1], 0) r = random.uniform(3, 6) all_nn = s.get_all_neighbors(r, True) for i in range(len(s)): self.assertEqual(len(all_nn[i]), len(s.get_neighbors(s[i], r))) for site, nns in zip(s, all_nn): for nn in nns: self.assertTrue(nn[0].is_periodic_image(s[nn[2]])) d = sum((site.coords - nn[0].coords) ** 2) ** 0.5 self.assertAlmostEqual(d, nn[1]) s = Structure(Lattice.cubic(1), ['Li'], [[0,0,0]]) s.make_supercell([2,2,2]) self.assertEqual(sum(map(len, s.get_all_neighbors(3))), 976) def test_get_all_neighbors_outside_cell(self): s = Structure(Lattice.cubic(2), ['Li', 'Li', 'Li', 'Si'], [[3.1] * 3, [0.11] * 3, [-1.91] * 3, [0.5] * 3]) all_nn = s.get_all_neighbors(0.2, True) for site, nns in zip(s, all_nn): for nn in nns: self.assertTrue(nn[0].is_periodic_image(s[nn[2]])) d = sum((site.coords - nn[0].coords) ** 2) ** 0.5 self.assertAlmostEqual(d, nn[1]) self.assertEqual(list(map(len, all_nn)), [2, 2, 2, 0]) def test_get_dist_matrix(self): ans = [[0., 2.3516318], [2.3516318, 0.]] self.assertArrayAlmostEqual(self.struct.distance_matrix, ans) def test_to_from_file_string(self): for fmt in ["cif", "json", "poscar", "cssr"]: s = self.struct.to(fmt=fmt) self.assertIsNotNone(s) ss = IStructure.from_str(s, fmt=fmt) self.assertArrayAlmostEqual( ss.lattice.lengths_and_angles, self.struct.lattice.lengths_and_angles, decimal=5) self.assertArrayAlmostEqual(ss.frac_coords, self.struct.frac_coords) self.assertIsInstance(ss, IStructure) self.struct.to(filename="POSCAR.testing") self.assertTrue(os.path.exists("POSCAR.testing")) os.remove("POSCAR.testing") self.struct.to(filename="Si_testing.yaml") self.assertTrue(os.path.exists("Si_testing.yaml")) s = Structure.from_file("Si_testing.yaml") self.assertEqual(s, self.struct) os.remove("Si_testing.yaml") self.struct.to(filename="POSCAR.testing.gz") s = Structure.from_file("POSCAR.testing.gz") self.assertEqual(s, self.struct) os.remove("POSCAR.testing.gz") class StructureTest(PymatgenTest): def setUp(self): coords = list() coords.append([0, 0, 0]) coords.append([0.75, 0.5, 0.75]) lattice = Lattice([[3.8401979337, 0.00, 0.00], [1.9200989668, 3.3257101909, 0.00], [0.00, -2.2171384943, 3.1355090603]]) self.structure = Structure(lattice, ["Si", "Si"], coords) def test_mutable_sequence_methods(self): s = self.structure s[0] = "Fe" self.assertEqual(s.formula, "Fe1 Si1") s[0] = "Fe", [0.5, 0.5, 0.5] self.assertEqual(s.formula, "Fe1 Si1") self.assertArrayAlmostEqual(s[0].frac_coords, [0.5, 0.5, 0.5]) s.reverse() self.assertEqual(s[0].specie, Element("Si")) self.assertArrayAlmostEqual(s[0].frac_coords, [0.75, 0.5, 0.75]) s[0] = {"Mn": 0.5} self.assertEqual(s.formula, "Mn0.5 Fe1") del s[1] self.assertEqual(s.formula, "Mn0.5") s[0] = "Fe", [0.9, 0.9, 0.9], {"magmom": 5} self.assertEqual(s.formula, "Fe1") self.assertEqual(s[0].magmom, 5) def test_non_hash(self): self.assertRaises(TypeError, dict, [(self.structure, 1)]) def test_sort(self): s = self.structure s[0] = "F" s.sort() self.assertEqual(s[0].species_string, "Si") self.assertEqual(s[1].species_string, "F") s.sort(key=lambda site: site.species_string) self.assertEqual(s[0].species_string, "F") self.assertEqual(s[1].species_string, "Si") s.sort(key=lambda site: site.species_string, reverse=True) self.assertEqual(s[0].species_string, "Si") self.assertEqual(s[1].species_string, "F") def test_append_insert_remove_replace(self): s = self.structure s.insert(1, "O", [0.5, 0.5, 0.5]) self.assertEqual(s.formula, "Si2 O1") self.assertTrue(s.ntypesp == 2) self.assertTrue(s.symbol_set == ("Si", "O")) self.assertTrue(s.indices_from_symbol("Si") == (0,2)) self.assertTrue(s.indices_from_symbol("O") == (1,)) del s[2] self.assertEqual(s.formula, "Si1 O1") self.assertTrue(s.indices_from_symbol("Si") == (0,)) self.assertTrue(s.indices_from_symbol("O") == (1,)) s.append("N", [0.25, 0.25, 0.25]) self.assertEqual(s.formula, "Si1 N1 O1") self.assertTrue(s.ntypesp == 3) self.assertTrue(s.symbol_set == ("Si", "O", "N")) self.assertTrue(s.indices_from_symbol("Si") == (0,)) self.assertTrue(s.indices_from_symbol("O") == (1,)) self.assertTrue(s.indices_from_symbol("N") == (2,)) s[0] = "Ge" self.assertEqual(s.formula, "Ge1 N1 O1") self.assertTrue(s.symbol_set == ("Ge", "O", "N")) s.replace_species({"Ge": "Si"}) self.assertEqual(s.formula, "Si1 N1 O1") self.assertTrue(s.ntypesp == 3) s.replace_species({"Si": {"Ge": 0.5, "Si": 0.5}}) self.assertEqual(s.formula, "Si0.5 Ge0.5 N1 O1") #this should change the .5Si .5Ge sites to .75Si .25Ge s.replace_species({"Ge": {"Ge": 0.5, "Si": 0.5}}) self.assertEqual(s.formula, "Si0.75 Ge0.25 N1 O1") # In this case, s.ntypesp is ambiguous. # for the time being, we raise AttributeError. with self.assertRaises(AttributeError): s.ntypesp s.remove_species(["Si"]) self.assertEqual(s.formula, "Ge0.25 N1 O1") s.remove_sites([1, 2]) self.assertEqual(s.formula, "Ge0.25") def test_add_site_property(self): s = self.structure s.add_site_property("charge", [4.1, -5]) self.assertEqual(s[0].charge, 4.1) self.assertEqual(s[1].charge, -5) s.add_site_property("magmom", [3, 2]) self.assertEqual(s[0].charge, 4.1) self.assertEqual(s[0].magmom, 3) def test_propertied_structure(self): #Make sure that site properties are set to None for missing values. s = self.structure s.add_site_property("charge", [4.1, -5]) s.append("Li", [0.3, 0.3 ,0.3]) self.assertEqual(len(s.site_properties["charge"]), 3) def test_perturb(self): d = 0.1 pre_perturbation_sites = self.structure.sites[:] self.structure.perturb(distance=d) post_perturbation_sites = self.structure.sites for i, x in enumerate(pre_perturbation_sites): self.assertAlmostEqual(x.distance(post_perturbation_sites[i]), d, 3, "Bad perturbation distance") def test_add_oxidation_states(self): oxidation_states = {"Si": -4} self.structure.add_oxidation_state_by_element(oxidation_states) for site in self.structure: for k in site.species_and_occu.keys(): self.assertEqual(k.oxi_state, oxidation_states[k.symbol], "Wrong oxidation state assigned!") oxidation_states = {"Fe": 2} self.assertRaises(ValueError, self.structure.add_oxidation_state_by_element, oxidation_states) self.structure.add_oxidation_state_by_site([2, -4]) self.assertEqual(self.structure[0].specie.oxi_state, 2) self.assertRaises(ValueError, self.structure.add_oxidation_state_by_site, [1]) def test_remove_oxidation_states(self): co_elem = Element("Co") o_elem = Element("O") co_specie = Specie("Co", 2) o_specie = Specie("O", -2) coords = list() coords.append([0, 0, 0]) coords.append([0.75, 0.5, 0.75]) lattice = Lattice.cubic(10) s_elem = Structure(lattice, [co_elem, o_elem], coords) s_specie = Structure(lattice, [co_specie, o_specie], coords) s_specie.remove_oxidation_states() self.assertEqual(s_elem, s_specie, "Oxidation state remover " "failed") def test_apply_operation(self): op = SymmOp.from_axis_angle_and_translation([0, 0, 1], 90) s = self.structure.copy() s.apply_operation(op) self.assertArrayAlmostEqual( s.lattice.matrix, [[0.000000, 3.840198, 0.000000], [-3.325710, 1.920099, 0.000000], [2.217138, -0.000000, 3.135509]], 5) op = SymmOp([[1, 1, 0, 0.5], [1, 0, 0, 0.5], [0, 0, 1, 0.5], [0, 0, 0, 1]]) s = self.structure.copy() s.apply_operation(op, fractional=True) self.assertArrayAlmostEqual( s.lattice.matrix, [[5.760297, 3.325710, 0.000000], [3.840198, 0.000000, 0.000000], [0.000000, -2.217138, 3.135509]], 5) def test_apply_strain(self): s = self.structure initial_coord = s[1].coords s.apply_strain(0.01) self.assertAlmostEqual( s.lattice.abc, (3.8785999130369997, 3.878600984287687, 3.8785999130549516)) self.assertArrayAlmostEqual(s[1].coords, initial_coord * 1.01) a1, b1, c1 = s.lattice.abc s.apply_strain([0.1, 0.2, 0.3]) a2, b2, c2 = s.lattice.abc self.assertAlmostEqual(a2 / a1, 1.1) self.assertAlmostEqual(b2 / b1, 1.2) self.assertAlmostEqual(c2 / c1, 1.3) def test_scale_lattice(self): initial_coord = self.structure[1].coords self.structure.scale_lattice(self.structure.volume * 1.01 ** 3) self.assertArrayAlmostEqual( self.structure.lattice.abc, (3.8785999130369997, 3.878600984287687, 3.8785999130549516)) self.assertArrayAlmostEqual(self.structure[1].coords, initial_coord * 1.01) def test_translate_sites(self): self.structure.translate_sites([0, 1], [0.5, 0.5, 0.5], frac_coords=True) self.assertArrayEqual(self.structure.frac_coords[0], [0.5, 0.5, 0.5]) self.structure.translate_sites([0], [0.5, 0.5, 0.5], frac_coords=False) self.assertArrayAlmostEqual(self.structure.cart_coords[0], [3.38014845, 1.05428585, 2.06775453]) self.structure.translate_sites([0], [0.5, 0.5, 0.5], frac_coords=True, to_unit_cell=False) self.assertArrayAlmostEqual(self.structure.frac_coords[0], [1.00187517, 1.25665291, 1.15946374]) def test_mul(self): self.structure *= [2, 1, 1] self.assertEqual(self.structure.formula, "Si4") s = [2, 1, 1] * self.structure self.assertEqual(s.formula, "Si8") self.assertIsInstance(s, Structure) s = self.structure * [[1, 0, 0], [2, 1, 0], [0, 0, 2]] self.assertEqual(s.formula, "Si8") self.assertArrayAlmostEqual(s.lattice.abc, [7.6803959, 17.5979979, 7.6803959]) def test_make_supercell(self): self.structure.make_supercell([2, 1, 1]) self.assertEqual(self.structure.formula, "Si4") self.structure.make_supercell([[1, 0, 0], [2, 1, 0], [0, 0, 1]]) self.assertEqual(self.structure.formula, "Si4") self.structure.make_supercell(2) self.assertEqual(self.structure.formula, "Si32") self.assertArrayAlmostEqual(self.structure.lattice.abc, [15.360792, 35.195996, 7.680396], 5) def test_disordered_supercell_primitive_cell(self): l = Lattice.cubic(2) f = [[0.5, 0.5, 0.5]] sp = [{'Si': 0.54738}] s = Structure(l, sp, f) #this supercell often breaks things s.make_supercell([[0,-1,1],[-1,1,0],[1,1,1]]) self.assertEqual(len(s.get_primitive_structure()), 1) def test_another_supercell(self): #this is included b/c for some reason the old algo was failing on it s = self.structure.copy() s.make_supercell([[0, 2, 2], [2, 0, 2], [2, 2, 0]]) self.assertEqual(s.formula, "Si32") s = self.structure.copy() s.make_supercell([[0, 2, 0], [1, 0, 0], [0, 0, 1]]) self.assertEqual(s.formula, "Si4") def test_to_from_dict(self): d = self.structure.as_dict() s2 = Structure.from_dict(d) self.assertEqual(type(s2), Structure) def test_to_from_file_string(self): for fmt in ["cif", "json", "poscar", "cssr", "yaml", "xsf"]: s = self.structure.to(fmt=fmt) self.assertIsNotNone(s) ss = Structure.from_str(s, fmt=fmt) self.assertArrayAlmostEqual( ss.lattice.lengths_and_angles, self.structure.lattice.lengths_and_angles, decimal=5) self.assertArrayAlmostEqual(ss.frac_coords, self.structure.frac_coords) self.assertIsInstance(ss, Structure) self.structure.to(filename="POSCAR.testing") self.assertTrue(os.path.exists("POSCAR.testing")) os.remove("POSCAR.testing") self.structure.to(filename="structure_testing.json") self.assertTrue(os.path.exists("structure_testing.json")) s = Structure.from_file("structure_testing.json") self.assertEqual(s, self.structure) os.remove("structure_testing.json") def test_from_spacegroup(self): s1 = Structure.from_spacegroup("Fm-3m", Lattice.cubic(3), ["Li", "O"], [[0.25, 0.25, 0.25], [0, 0, 0]]) self.assertEqual(s1.formula, "Li8 O4") s2 = Structure.from_spacegroup(225, Lattice.cubic(3), ["Li", "O"], [[0.25, 0.25, 0.25], [0, 0, 0]]) self.assertEqual(s1, s2) s2 = Structure.from_spacegroup(225, Lattice.cubic(3), ["Li", "O"], [[0.25, 0.25, 0.25], [0, 0, 0]], site_properties={"charge": [1, -2]}) self.assertEqual(sum(s2.site_properties["charge"]), 0) s = Structure.from_spacegroup("Pm-3m", Lattice.cubic(3), ["Cs", "Cl"], [[0, 0, 0], [0.5, 0.5, 0.5]]) self.assertEqual(s.formula, "Cs1 Cl1") self.assertRaises(ValueError, Structure.from_spacegroup, "Pm-3m", Lattice.tetragonal(1, 3), ["Cs", "Cl"], [[0, 0, 0], [0.5, 0.5, 0.5]]) self.assertRaises(ValueError, Structure.from_spacegroup, "Pm-3m", Lattice.cubic(3), ["Cs"], [[0, 0, 0], [0.5, 0.5, 0.5]]) def test_merge_sites(self): species = [{'Ag': 0.5}, {'Cl': 0.25}, {'Cl': 0.1}, {'Ag': 0.5}, {'F': 0.15}, {'F': 0.1}] coords = [[0, 0, 0], [0.5, 0.5, 0.5], [0.5, 0.5, 0.5], [0, 0, 0], [0.5, 0.5, 1.501], [0.5, 0.5, 1.501]] s = Structure(Lattice.cubic(1), species, coords) s.merge_sites(mode="s") self.assertEqual(s[0].specie.symbol, 'Ag') self.assertEqual(s[1].species_and_occu, Composition({'Cl': 0.35, 'F': 0.25})) self.assertArrayAlmostEqual(s[1].frac_coords, [.5, .5, .5005]) # Test for TaS2 with spacegroup 166 in 160 setting. l = Lattice.from_lengths_and_angles([3.374351, 3.374351, 20.308941], [90.000000, 90.000000, 120.000000]) species = ["Ta", "S", "S"] coords = [[0.000000, 0.000000, 0.944333], [0.333333, 0.666667, 0.353424], [0.666667, 0.333333, 0.535243]] tas2 = Structure.from_spacegroup(160, l, species, coords) assert len(tas2) == 13 tas2.merge_sites(mode="d") assert len(tas2) == 9 l = Lattice.from_lengths_and_angles([3.587776, 3.587776, 19.622793], [90.000000, 90.000000, 120.000000]) species = ["Na", "V", "S", "S"] coords = [[0.333333, 0.666667, 0.165000], [0.000000, 0.000000, 0.998333], [0.333333, 0.666667, 0.399394], [0.666667, 0.333333, 0.597273]] navs2 = Structure.from_spacegroup(160, l, species, coords) assert len(navs2) == 18 navs2.merge_sites(mode="d") assert len(navs2) == 12 def test_properties(self): self.assertEqual(self.structure.num_sites, len(self.structure)) self.structure.make_supercell(2) self.structure[1] = "C" sites = list(self.structure.group_by_types()) self.assertEqual(sites[-1].specie.symbol, "C") self.structure.add_oxidation_state_by_element({"Si": 4, "C": 2}) self.assertEqual(self.structure.charge, 62) def test_set_item(self): s = self.structure.copy() s[0] = "C" self.assertEqual(s.formula, "Si1 C1") s[(0, 1)] = "Ge" self.assertEqual(s.formula, "Ge2") s[0:2] = "Sn" self.assertEqual(s.formula, "Sn2") s = self.structure.copy() s["Si"] = "C" self.assertEqual(s.formula, "C2") s["C"] = "C0.25Si0.5" self.assertEqual(s.formula, "Si1 C0.5") s["C"] = "C0.25Si0.5" self.assertEqual(s.formula, "Si1.25 C0.125") def test_init_error(self): self.assertRaises(StructureError, Structure, Lattice.cubic(3), ["Si"], [[0, 0, 0], [0.5, 0.5, 0.5]]) def test_from_sites(self): self.structure.add_site_property("hello", [1, 2]) s = Structure.from_sites(self.structure, to_unit_cell=True) self.assertEqual(s.site_properties["hello"][1], 2) def test_magic(self): s = Structure.from_sites(self.structure) self.assertEqual(s, self.structure) self.assertNotEqual(s, None) s.apply_strain(0.5) self.assertNotEqual(s, self.structure) self.assertNotEqual(self.structure * 2, self.structure) class IMoleculeTest(PymatgenTest): def setUp(self): coords = [[0.000000, 0.000000, 0.000000], [0.000000, 0.000000, 1.089000], [1.026719, 0.000000, -0.363000], [-0.513360, -0.889165, -0.363000], [-0.513360, 0.889165, -0.363000]] self.coords = coords self.mol = Molecule(["C", "H", "H", "H", "H"], coords) def test_set_item(self): s = self.mol.copy() s[0] = "Si" self.assertEqual(s.formula, "Si1 H4") s[(0, 1)] = "Ge" self.assertEqual(s.formula, "Ge2 H3") s[0:2] = "Sn" self.assertEqual(s.formula, "Sn2 H3") s = self.mol.copy() s["H"] = "F" self.assertEqual(s.formula, "C1 F4") s["C"] = "C0.25Si0.5" self.assertEqual(s.formula, "Si0.5 C0.25 F4") s["C"] = "C0.25Si0.5" self.assertEqual(s.formula, "Si0.625 C0.0625 F4") def test_bad_molecule(self): coords = [[0.000000, 0.000000, 0.000000], [0.000000, 0.000000, 1.089000], [1.026719, 0.000000, -0.363000], [-0.513360, -0.889165, -0.363000], [-0.513360, 0.889165, -0.363000], [-0.513360, 0.889165, -0.36301]] self.assertRaises(StructureError, Molecule, ["C", "H", "H", "H", "H", "H"], coords, validate_proximity=True) def test_get_angle_dihedral(self): self.assertAlmostEqual(self.mol.get_angle(1, 0, 2), 109.47122144618737) self.assertAlmostEqual(self.mol.get_angle(3, 1, 2), 60.00001388659683) self.assertAlmostEqual(self.mol.get_dihedral(0, 1, 2, 3), - 35.26438851071765) coords = list() coords.append([0, 0, 0]) coords.append([0, 0, 1]) coords.append([0, 1, 1]) coords.append([1, 1, 1]) self.mol2 = Molecule(["C", "O", "N", "S"], coords) self.assertAlmostEqual(self.mol2.get_dihedral(0, 1, 2, 3), -90) def test_get_covalent_bonds(self): self.assertEqual(len(self.mol.get_covalent_bonds()), 4) def test_properties(self): self.assertEqual(len(self.mol), 5) self.assertTrue(self.mol.is_ordered) self.assertEqual(self.mol.formula, "H4 C1") def test_repr_str(self): ans = """Full Formula (H4 C1) Reduced Formula: H4C Charge = 0, Spin Mult = 1 Sites (5) 0 C 0.000000 0.000000 0.000000 1 H 0.000000 0.000000 1.089000 2 H 1.026719 0.000000 -0.363000 3 H -0.513360 -0.889165 -0.363000 4 H -0.513360 0.889165 -0.363000""" self.assertEqual(self.mol.__str__(), ans) ans = """Molecule Summary Site: C (0.0000, 0.0000, 0.0000) Site: H (0.0000, 0.0000, 1.0890) Site: H (1.0267, 0.0000, -0.3630) Site: H (-0.5134, -0.8892, -0.3630) Site: H (-0.5134, 0.8892, -0.3630)""" self.assertEqual(repr(self.mol), ans) def test_site_properties(self): propertied_mol = Molecule(["C", "H", "H", "H", "H"], self.coords, site_properties={'magmom': [0.5, -0.5, 1, 2, 3]}) self.assertEqual(propertied_mol[0].magmom, 0.5) self.assertEqual(propertied_mol[1].magmom, -0.5) def test_get_boxed_structure(self): s = self.mol.get_boxed_structure(9, 9, 9) # C atom should be in center of box. self.assertArrayAlmostEqual(s[4].frac_coords, [0.50000001, 0.5, 0.5]) self.assertArrayAlmostEqual(s[1].frac_coords, [0.6140799, 0.5, 0.45966667]) self.assertRaises(ValueError, self.mol.get_boxed_structure, 1, 1, 1) s2 = self.mol.get_boxed_structure(5, 5, 5, (2, 3, 4)) self.assertEqual(len(s2), 24 * 5) self.assertEqual(s2.lattice.abc, (10, 15, 20)) # Test offset option s3 = self.mol.get_boxed_structure(9, 9, 9, offset=[0.5,0.5,0.5]) self.assertArrayAlmostEqual(s3[4].coords, [5,5,5]) # Test no_cross option self.assertRaises(ValueError, self.mol.get_boxed_structure, 5, 5, 5, offset=[10,10,10],no_cross = True) def test_get_distance(self): self.assertAlmostEqual(self.mol.get_distance(0, 1), 1.089) def test_get_neighbors(self): nn = self.mol.get_neighbors(self.mol[0], 1) self.assertEqual(len(nn), 0) nn = self.mol.get_neighbors(self.mol[0], 2) self.assertEqual(len(nn), 4) def test_get_neighbors_in_shell(self): nn = self.mol.get_neighbors_in_shell([0, 0, 0], 0, 1) self.assertEqual(len(nn), 1) nn = self.mol.get_neighbors_in_shell([0, 0, 0], 1, 0.9) self.assertEqual(len(nn), 4) nn = self.mol.get_neighbors_in_shell([0, 0, 0], 1, 0.9) self.assertEqual(len(nn), 4) nn = self.mol.get_neighbors_in_shell([0, 0, 0], 2, 0.1) self.assertEqual(len(nn), 0) def test_get_dist_matrix(self): ans = [[0.0, 1.089, 1.08899995636, 1.08900040717, 1.08900040717], [1.089, 0.0, 1.77832952654, 1.7783298026, 1.7783298026], [1.08899995636, 1.77832952654, 0.0, 1.77833003783, 1.77833003783], [1.08900040717, 1.7783298026, 1.77833003783, 0.0, 1.77833], [1.08900040717, 1.7783298026, 1.77833003783, 1.77833, 0.0]] self.assertArrayAlmostEqual(self.mol.distance_matrix, ans) def test_break_bond(self): (mol1, mol2) = self.mol.break_bond(0, 1) self.assertEqual(mol1.formula, "H3 C1") self.assertEqual(mol2.formula, "H1") def test_prop(self): self.assertEqual(self.mol.charge, 0) self.assertEqual(self.mol.spin_multiplicity, 1) self.assertEqual(self.mol.nelectrons, 10) self.assertArrayAlmostEqual(self.mol.center_of_mass, [0, 0, 0]) self.assertRaises(ValueError, Molecule, ["C", "H", "H", "H", "H"], self.coords, charge=1, spin_multiplicity=1) mol = Molecule(["C", "H", "H", "H", "H"], self.coords, charge=1) self.assertEqual(mol.spin_multiplicity, 2) self.assertEqual(mol.nelectrons, 9) #Triplet O2 mol = IMolecule(["O"] * 2, [[0, 0, 0], [0, 0, 1.2]], spin_multiplicity=3) self.assertEqual(mol.spin_multiplicity, 3) def test_equal(self): mol = IMolecule(["C", "H", "H", "H", "H"], self.coords, charge=1) self.assertNotEqual(mol, self.mol) def test_get_centered_molecule(self): mol = IMolecule(["O"] * 2, [[0, 0, 0], [0, 0, 1.2]], spin_multiplicity=3) centered = mol.get_centered_molecule() self.assertArrayAlmostEqual(centered.center_of_mass, [0, 0, 0]) def test_to_from_dict(self): d = self.mol.as_dict() mol2 = IMolecule.from_dict(d) self.assertEqual(type(mol2), IMolecule) propertied_mol = Molecule(["C", "H", "H", "H", "H"], self.coords, charge=1, site_properties={'magmom': [0.5, -0.5, 1, 2, 3]}) d = propertied_mol.as_dict() self.assertEqual(d['sites'][0]['properties']['magmom'], 0.5) mol = Molecule.from_dict(d) self.assertEqual(propertied_mol, mol) self.assertEqual(mol[0].magmom, 0.5) self.assertEqual(mol.formula, "H4 C1") self.assertEqual(mol.charge, 1) def test_to_from_file_string(self): for fmt in ["xyz", "json", "g03", "yaml"]: s = self.mol.to(fmt=fmt) self.assertIsNotNone(s) m = IMolecule.from_str(s, fmt=fmt) self.assertEqual(m, self.mol) self.assertIsInstance(m, IMolecule) self.mol.to(filename="CH4_testing.xyz") self.assertTrue(os.path.exists("CH4_testing.xyz")) os.remove("CH4_testing.xyz") self.mol.to(filename="CH4_testing.yaml") self.assertTrue(os.path.exists("CH4_testing.yaml")) mol = Molecule.from_file("CH4_testing.yaml") self.assertEqual(self.mol, mol) os.remove("CH4_testing.yaml") class MoleculeTest(PymatgenTest): def setUp(self): coords = [[0.000000, 0.000000, 0.000000], [0.000000, 0.000000, 1.089000], [1.026719, 0.000000, -0.363000], [-0.513360, -0.889165, -0.363000], [-0.513360, 0.889165, -0.363000]] self.mol = Molecule(["C", "H", "H", "H", "H"], coords) def test_mutable_sequence_methods(self): s = self.mol s[1] = ("F", [0.5, 0.5, 0.5]) self.assertEqual(s.formula, "H3 C1 F1") self.assertArrayAlmostEqual(s[1].coords, [0.5, 0.5, 0.5]) s.reverse() self.assertEqual(s[0].specie, Element("H")) self.assertArrayAlmostEqual(s[0].coords, [-0.513360, 0.889165, -0.363000]) del s[1] self.assertEqual(s.formula, "H2 C1 F1") s[3] = "N", [0,0,0], {"charge": 4} self.assertEqual(s.formula, "H2 N1 F1") self.assertEqual(s[3].charge, 4) def test_insert_remove_append(self): mol = self.mol mol.insert(1, "O", [0.5, 0.5, 0.5]) self.assertEqual(mol.formula, "H4 C1 O1") del mol[2] self.assertEqual(mol.formula, "H3 C1 O1") mol.set_charge_and_spin(0) self.assertEqual(mol.spin_multiplicity, 2) mol.append("N", [1, 1, 1]) self.assertEqual(mol.formula, "H3 C1 N1 O1") self.assertRaises(TypeError, dict, [(mol, 1)]) mol.remove_sites([0, 1]) self.assertEqual(mol.formula, "H3 N1") def test_translate_sites(self): self.mol.translate_sites([0, 1], [0.5, 0.5, 0.5]) self.assertArrayEqual(self.mol.cart_coords[0], [0.5, 0.5, 0.5]) def test_rotate_sites(self): self.mol.rotate_sites(theta=np.radians(30)) self.assertArrayAlmostEqual(self.mol.cart_coords[2], [ 0.889164737, 0.513359500, -0.363000000]) def test_replace(self): self.mol[0] = "Ge" self.assertEqual(self.mol.formula, "Ge1 H4") self.mol.replace_species({Element("Ge"): {Element("Ge"): 0.5, Element("Si"): 0.5}}) self.assertEqual(self.mol.formula, "Si0.5 Ge0.5 H4") #this should change the .5Si .5Ge sites to .75Si .25Ge self.mol.replace_species({Element("Ge"): {Element("Ge"): 0.5, Element("Si"): 0.5}}) self.assertEqual(self.mol.formula, "Si0.75 Ge0.25 H4") d = 0.1 pre_perturbation_sites = self.mol.sites[:] self.mol.perturb(distance=d) post_perturbation_sites = self.mol.sites for i, x in enumerate(pre_perturbation_sites): self.assertAlmostEqual(x.distance(post_perturbation_sites[i]), d, 3, "Bad perturbation distance") def test_add_site_property(self): self.mol.add_site_property("charge", [4.1, -2, -2, -2, -2]) self.assertEqual(self.mol[0].charge, 4.1) self.assertEqual(self.mol[1].charge, -2) self.mol.add_site_property("magmom", [3, 2, 2, 2, 2]) self.assertEqual(self.mol[0].charge, 4.1) self.assertEqual(self.mol[0].magmom, 3) def test_to_from_dict(self): d = self.mol.as_dict() mol2 = Molecule.from_dict(d) self.assertEqual(type(mol2), Molecule) def test_apply_operation(self): op = SymmOp.from_axis_angle_and_translation([0, 0, 1], 90) self.mol.apply_operation(op) self.assertArrayAlmostEqual(self.mol[2].coords, [0.000000, 1.026719, -0.363000]) def test_substitute(self): coords = [[0.000000, 0.000000, 1.08], [0.000000, 0.000000, 0.000000], [1.026719, 0.000000, -0.363000], [-0.513360, -0.889165, -0.363000], [-0.513360, 0.889165, -0.363000]] sub = Molecule(["X", "C", "H", "H", "H"], coords) self.mol.substitute(1, sub) self.assertAlmostEqual(self.mol.get_distance(0, 4), 1.54) f = Molecule(["X", "F"], [[0, 0, 0], [0, 0, 1.11]]) self.mol.substitute(2, f) self.assertAlmostEqual(self.mol.get_distance(0, 7), 1.35) oh = Molecule(["X", "O", "H"], [[0, 0.780362, -.456316], [0, 0, .114079], [0, -.780362, -.456316]]) self.mol.substitute(1, oh) self.assertAlmostEqual(self.mol.get_distance(0, 7), 1.43) self.mol.substitute(3, "methyl") self.assertEqual(self.mol.formula, "H7 C3 O1 F1") coords = [[0.00000, 1.40272, 0.00000], [0.00000, 2.49029, 0.00000], [-1.21479, 0.70136, 0.00000], [-2.15666, 1.24515, 0.00000], [-1.21479, -0.70136, 0.00000], [-2.15666, -1.24515, 0.00000], [0.00000, -1.40272, 0.00000], [0.00000, -2.49029, 0.00000], [1.21479, -0.70136, 0.00000], [2.15666, -1.24515, 0.00000], [1.21479, 0.70136, 0.00000], [2.15666, 1.24515, 0.00000]] benzene = Molecule(["C", "H", "C", "H", "C", "H", "C", "H", "C", "H", "C", "H"], coords) benzene.substitute(1, sub) self.assertEqual(benzene.formula, "H8 C7") #Carbon attached should be in plane. self.assertAlmostEqual(benzene[11].coords[2], 0) def test_to_from_file_string(self): for fmt in ["xyz", "json", "g03"]: s = self.mol.to(fmt=fmt) self.assertIsNotNone(s) m = Molecule.from_str(s, fmt=fmt) self.assertEqual(m, self.mol) self.assertIsInstance(m, Molecule) self.mol.to(filename="CH4_testing.xyz") self.assertTrue(os.path.exists("CH4_testing.xyz")) os.remove("CH4_testing.xyz") if __name__ == '__main__': import unittest2 as unittest unittest.main()

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from __future__ import division, unicode_literals, print_function from pymatgen.util.testing import PymatgenTest from pymatgen.core.periodic_table import Element, Specie from pymatgen.core.composition import Composition from pymatgen.core.operations import SymmOp from pymatgen.core.structure import IStructure, Structure, IMolecule, \ StructureError, Molecule from pymatgen.core.lattice import Lattice import random import os import numpy as np class IStructureTest(PymatgenTest): def setUp(self): coords = [[0, 0, 0], [0.75, 0.5, 0.75]] self.lattice = Lattice([[3.8401979337, 0.00, 0.00], [1.9200989668, 3.3257101909, 0.00], [0.00, -2.2171384943, 3.1355090603]]) self.struct = IStructure(self.lattice, ["Si"] * 2, coords) self.assertEqual(len(self.struct), 2, "Wrong number of sites in structure!") self.assertTrue(self.struct.is_ordered) self.assertTrue(self.struct.ntypesp == 1) coords = list() coords.append([0, 0, 0]) coords.append([0., 0, 0.0000001]) self.assertRaises(StructureError, IStructure, self.lattice, ["Si"] * 2, coords, True) self.propertied_structure = IStructure( self.lattice, ["Si"] * 2, coords, site_properties={'magmom': [5, -5]}) def test_matches(self): ss = self.struct * 2 self.assertTrue(ss.matches(self.struct)) def test_bad_structure(self): coords = list() coords.append([0, 0, 0]) coords.append([0.75, 0.5, 0.75]) coords.append([0.75, 0.5, 0.75]) self.assertRaises(StructureError, IStructure, self.lattice, ["Si"] * 3, coords, validate_proximity=True) IStructure(self.lattice, ["Si"] * 2, coords[:2], True) IStructure(self.lattice, ["Si"], coords[:1], True) def test_volume_and_density(self): self.assertAlmostEqual(self.struct.volume, 40.04, 2, "Volume wrong!") self.assertAlmostEqual(self.struct.density, 2.33, 2, "Incorrect density") def test_specie_init(self): coords = list() coords.append([0, 0, 0]) coords.append([0.75, 0.5, 0.75]) s = IStructure(self.lattice, [{Specie('O', -2): 1.0}, {Specie('Mg', 2): 0.8}], coords) self.assertEqual(s.composition.formula, 'Mg0.8 O1') def test_get_sorted_structure(self): coords = list() coords.append([0, 0, 0]) coords.append([0.75, 0.5, 0.75]) s = IStructure(self.lattice, ["O", "Li"], coords, site_properties={'charge': [-2, 1]}) sorted_s = s.get_sorted_structure() self.assertEqual(sorted_s[0].species_and_occu, Composition("Li")) self.assertEqual(sorted_s[1].species_and_occu, Composition("O")) self.assertEqual(sorted_s[0].charge, 1) self.assertEqual(sorted_s[1].charge, -2) s = IStructure(self.lattice, ["Se", "C", "Se", "C"], [[0] * 3, [0.5] * 3, [0.25] * 3, [0.75] * 3]) self.assertEqual([site.specie.symbol for site in s.get_sorted_structure()], ["C", "C", "Se", "Se"]) def test_get_space_group_data(self): self.assertEqual(self.struct.get_space_group_info(), ('Fd-3m', 227)) def test_fractional_occupations(self): coords = list() coords.append([0, 0, 0]) coords.append([0.75, 0.5, 0.75]) s = IStructure(self.lattice, [{'O': 1.0}, {'Mg': 0.8}], coords) self.assertEqual(s.composition.formula, 'Mg0.8 O1') self.assertFalse(s.is_ordered) def test_get_distance(self): self.assertAlmostEqual(self.struct.get_distance(0, 1), 2.35, 2, "Distance calculated wrongly!") pt = [0.9, 0.9, 0.8] self.assertAlmostEqual(self.struct[0].distance_from_point(pt), 1.50332963784, 2, "Distance calculated wrongly!") def test_as_dict(self): si = Specie("Si", 4) mn = Element("Mn") coords = list() coords.append([0, 0, 0]) coords.append([0.75, 0.5, 0.75]) struct = IStructure(self.lattice, [{si: 0.5, mn: 0.5}, {si: 0.5}], coords) self.assertIn("lattice", struct.as_dict()) self.assertIn("sites", struct.as_dict()) d = self.propertied_structure.as_dict() self.assertEqual(d['sites'][0]['properties']['magmom'], 5) coords = list() coords.append([0, 0, 0]) coords.append([0.75, 0.5, 0.75]) s = IStructure(self.lattice, [{Specie('O', -2, properties={"spin": 3}): 1.0}, {Specie('Mg', 2, properties={"spin": 2}): 0.8}], coords, site_properties={'magmom': [5, -5]}) d = s.as_dict() self.assertEqual(d['sites'][0]['properties']['magmom'], 5) self.assertEqual(d['sites'][0]['species'][0]['properties']['spin'], 3) d = s.as_dict(0) self.assertNotIn("volume", d['lattice']) self.assertNotIn("xyz", d['sites'][0]) def test_from_dict(self): d = self.propertied_structure.as_dict() s = IStructure.from_dict(d) self.assertEqual(s[0].magmom, 5) d = self.propertied_structure.as_dict(0) s2 = IStructure.from_dict(d) self.assertEqual(s, s2) d = {'lattice': {'a': 3.8401979337, 'volume': 40.044794644251596, 'c': 3.8401979337177736, 'b': 3.840198994344244, 'matrix': [[3.8401979337, 0.0, 0.0], [1.9200989668, 3.3257101909, 0.0], [0.0, -2.2171384943, 3.1355090603]], 'alpha': 119.9999908639842, 'beta': 90.0, 'gamma': 60.000009137322195}, 'sites': [{'properties': {'magmom': 5}, 'abc': [0.0, 0.0, 0.0], 'occu': 1.0, 'species': [{'occu': 1.0, 'oxidation_state': -2, 'properties': {'spin': 3}, 'element': 'O'}], 'label': 'O2-', 'xyz': [0.0, 0.0, 0.0]}, {'properties': {'magmom': -5}, 'abc': [0.75, 0.5, 0.75], 'occu': 0.8, 'species': [{'occu': 0.8, 'oxidation_state': 2, 'properties': {'spin': 2}, 'element': 'Mg'}], 'label': 'Mg2+:0.800', 'xyz': [3.8401979336749994, 1.2247250003039056e-06, 2.351631795225]}]} s = IStructure.from_dict(d) self.assertEqual(s[0].magmom, 5) self.assertEqual(s[0].specie.spin, 3) self.assertEqual(type(s), IStructure) def test_site_properties(self): site_props = self.propertied_structure.site_properties self.assertEqual(site_props['magmom'], [5, -5]) self.assertEqual(self.propertied_structure[0].magmom, 5) self.assertEqual(self.propertied_structure[1].magmom, -5) def test_copy(self): new_struct = self.propertied_structure.copy(site_properties={'charge': [2, 3]}) self.assertEqual(new_struct[0].magmom, 5) self.assertEqual(new_struct[1].magmom, -5) self.assertEqual(new_struct[0].charge, 2) self.assertEqual(new_struct[1].charge, 3) coords = list() coords.append([0, 0, 0]) coords.append([0., 0, 0.0000001]) structure = IStructure(self.lattice, ["O", "Si"], coords, site_properties={'magmom': [5, -5]}) new_struct = structure.copy(site_properties={'charge': [2, 3]}, sanitize=True) self.assertEqual(new_struct[0].magmom, -5) self.assertEqual(new_struct[1].magmom, 5) self.assertEqual(new_struct[0].charge, 3) self.assertEqual(new_struct[1].charge, 2) self.assertAlmostEqual(new_struct.volume, structure.volume) def test_interpolate(self): coords = list() coords.append([0, 0, 0]) coords.append([0.75, 0.5, 0.75]) struct = IStructure(self.lattice, ["Si"] * 2, coords) coords2 = list() coords2.append([0, 0, 0]) coords2.append([0.5, 0.5, 0.5]) struct2 = IStructure(self.struct.lattice, ["Si"] * 2, coords2) int_s = struct.interpolate(struct2, 10) for s in int_s: self.assertIsNotNone(s, "Interpolation Failed!") self.assertEqual(int_s[0].lattice, s.lattice) self.assertArrayEqual(int_s[1][1].frac_coords, [0.725, 0.5, 0.725]) badlattice = [[1, 0.00, 0.00], [0, 1, 0.00], [0.00, 0, 1]] struct2 = IStructure(badlattice, ["Si"] * 2, coords2) self.assertRaises(ValueError, struct.interpolate, struct2) coords2 = list() coords2.append([0, 0, 0]) coords2.append([0.5, 0.5, 0.5]) struct2 = IStructure(self.struct.lattice, ["Si", "Fe"], coords2) self.assertRaises(ValueError, struct.interpolate, struct2) # Test autosort feature. s1 = Structure.from_spacegroup("Fm-3m", Lattice.cubic(3), ["Fe"], [[0, 0, 0]]) s1.pop(0) s2 = Structure.from_spacegroup("Fm-3m", Lattice.cubic(3), ["Fe"], [[0, 0, 0]]) s2.pop(2) random.shuffle(s2) for s in s1.interpolate(s2, autosort_tol=0.5): self.assertArrayAlmostEqual(s1[0].frac_coords, s[0].frac_coords) self.assertArrayAlmostEqual(s1[2].frac_coords, s[2].frac_coords) # Make sure autosort has no effect on simpler interpolations, # and with shuffled sites. s1 = Structure.from_spacegroup("Fm-3m", Lattice.cubic(3), ["Fe"], [[0, 0, 0]]) s2 = Structure.from_spacegroup("Fm-3m", Lattice.cubic(3), ["Fe"], [[0, 0, 0]]) s2[0] = "Fe", [0.01, 0.01, 0.01] random.shuffle(s2) for s in s1.interpolate(s2, autosort_tol=0.5): self.assertArrayAlmostEqual(s1[1].frac_coords, s[1].frac_coords) self.assertArrayAlmostEqual(s1[2].frac_coords, s[2].frac_coords) self.assertArrayAlmostEqual(s1[3].frac_coords, s[3].frac_coords) def test_interpolate_lattice(self): coords = list() coords.append([0, 0, 0]) coords.append([0.75, 0.5, 0.75]) struct = IStructure(self.lattice, ["Si"] * 2, coords) coords2 = list() coords2.append([0, 0, 0]) coords2.append([0.5, 0.5, 0.5]) l2 = Lattice.from_lengths_and_angles([3,4,4], [100,100,70]) struct2 = IStructure(l2, ["Si"] * 2, coords2) int_s = struct.interpolate(struct2, 2, interpolate_lattices=True) self.assertArrayAlmostEqual(struct.lattice.abc, int_s[0].lattice.abc) self.assertArrayAlmostEqual(struct.lattice.angles, int_s[0].lattice.angles) self.assertArrayAlmostEqual(struct2.lattice.abc, int_s[2].lattice.abc) self.assertArrayAlmostEqual(struct2.lattice.angles, int_s[2].lattice.angles) int_angles = [110.3976469, 94.5359731, 64.5165856] self.assertArrayAlmostEqual(int_angles, int_s[1].lattice.angles) # Assert that volume is monotonic self.assertTrue(struct2.lattice.volume >= int_s[1].lattice.volume) self.assertTrue(int_s[1].lattice.volume >= struct.lattice.volume) def test_interpolate_lattice_rotation(self): l1 = Lattice([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) l2 = Lattice([[-1.01, 0, 0], [0, -1.01, 0], [0, 0, 1]]) coords = [[0, 0, 0], [0.75, 0.5, 0.75]] struct1 = IStructure(l1, ["Si"] * 2, coords) struct2 = IStructure(l2, ["Si"] * 2, coords) int_s = struct1.interpolate(struct2, 2, interpolate_lattices=True) # Assert that volume is monotonic self.assertTrue(struct2.lattice.volume >= int_s[1].lattice.volume) self.assertTrue(int_s[1].lattice.volume >= struct1.lattice.volume) def test_get_primitive_structure(self): coords = [[0, 0, 0], [0.5, 0.5, 0], [0, 0.5, 0.5], [0.5, 0, 0.5]] fcc_ag = IStructure(Lattice.cubic(4.09), ["Ag"] * 4, coords) self.assertEqual(len(fcc_ag.get_primitive_structure()), 1) coords = [[0, 0, 0], [0.5, 0.5, 0.5]] bcc_li = IStructure(Lattice.cubic(4.09), ["Li"] * 2, coords) bcc_prim = bcc_li.get_primitive_structure() self.assertEqual(len(bcc_prim), 1) self.assertAlmostEqual(bcc_prim.lattice.alpha, 109.47122, 3) coords = [[0] * 3, [0.5] * 3, [0.25] * 3, [0.26] * 3] s = IStructure(Lattice.cubic(4.09), ["Ag"] * 4, coords) self.assertEqual(len(s.get_primitive_structure()), 4) def test_primitive_cell_site_merging(self): l = Lattice.cubic(10) coords = [[0, 0, 0], [0, 0, 0.5], [0, 0, 0.26], [0, 0, 0.74]] sp = ['Ag', 'Ag', 'Be', 'Be'] s = Structure(l, sp, coords) dm = s.get_primitive_structure().distance_matrix self.assertArrayAlmostEqual(dm, [[0, 2.5], [2.5, 0]]) def test_primitive_on_large_supercell(self): coords = [[0, 0, 0], [0.5, 0.5, 0], [0, 0.5, 0.5], [0.5, 0, 0.5]] fcc_ag = Structure(Lattice.cubic(4.09), ["Ag"] * 4, coords) fcc_ag.make_supercell([2, 2, 2]) fcc_ag_prim = fcc_ag.get_primitive_structure() self.assertEqual(len(fcc_ag_prim), 1) self.assertAlmostEqual(fcc_ag_prim.volume, 17.10448225) def test_primitive_positions(self): coords = [[0, 0, 0], [0.3, 0.35, 0.45]] s = Structure(Lattice.from_parameters(1,2,3,50,66,88), ["Ag"] * 2, coords) a = [[-1,2,-3], [3,2,-4], [1,0,-1]] b = [[4, 0, 0], [1, 1, 0], [3, 0, 1]] c = [[2, 0, 0], [1, 3, 0], [1, 1, 1]] for sc_matrix in [c]: sc = s.copy() sc.make_supercell(sc_matrix) prim = sc.get_primitive_structure(0.01) self.assertEqual(len(prim), 2) self.assertAlmostEqual(prim.distance_matrix[0,1], 1.0203432356739286) def test_primitive_structure_volume_check(self): l = Lattice.tetragonal(10, 30) coords = [[0.5, 0.8, 0], [0.5, 0.2, 0], [0.5, 0.8, 0.333], [0.5, 0.5, 0.333], [0.5, 0.5, 0.666], [0.5, 0.2, 0.666]] s = IStructure(l, ["Ag"] * 6, coords) sprim = s.get_primitive_structure(tolerance=0.1) self.assertEqual(len(sprim), 6) def test_get_all_neighbors_and_get_neighbors(self): s = self.struct nn = s.get_neighbors_in_shell(s[0].frac_coords, 2, 4, include_index=True) self.assertEqual(len(nn), 47) self.assertEqual(nn[0][-1], 0) r = random.uniform(3, 6) all_nn = s.get_all_neighbors(r, True) for i in range(len(s)): self.assertEqual(len(all_nn[i]), len(s.get_neighbors(s[i], r))) for site, nns in zip(s, all_nn): for nn in nns: self.assertTrue(nn[0].is_periodic_image(s[nn[2]])) d = sum((site.coords - nn[0].coords) ** 2) ** 0.5 self.assertAlmostEqual(d, nn[1]) s = Structure(Lattice.cubic(1), ['Li'], [[0,0,0]]) s.make_supercell([2,2,2]) self.assertEqual(sum(map(len, s.get_all_neighbors(3))), 976) def test_get_all_neighbors_outside_cell(self): s = Structure(Lattice.cubic(2), ['Li', 'Li', 'Li', 'Si'], [[3.1] * 3, [0.11] * 3, [-1.91] * 3, [0.5] * 3]) all_nn = s.get_all_neighbors(0.2, True) for site, nns in zip(s, all_nn): for nn in nns: self.assertTrue(nn[0].is_periodic_image(s[nn[2]])) d = sum((site.coords - nn[0].coords) ** 2) ** 0.5 self.assertAlmostEqual(d, nn[1]) self.assertEqual(list(map(len, all_nn)), [2, 2, 2, 0]) def test_get_dist_matrix(self): ans = [[0., 2.3516318], [2.3516318, 0.]] self.assertArrayAlmostEqual(self.struct.distance_matrix, ans) def test_to_from_file_string(self): for fmt in ["cif", "json", "poscar", "cssr"]: s = self.struct.to(fmt=fmt) self.assertIsNotNone(s) ss = IStructure.from_str(s, fmt=fmt) self.assertArrayAlmostEqual( ss.lattice.lengths_and_angles, self.struct.lattice.lengths_and_angles, decimal=5) self.assertArrayAlmostEqual(ss.frac_coords, self.struct.frac_coords) self.assertIsInstance(ss, IStructure) self.struct.to(filename="POSCAR.testing") self.assertTrue(os.path.exists("POSCAR.testing")) os.remove("POSCAR.testing") self.struct.to(filename="Si_testing.yaml") self.assertTrue(os.path.exists("Si_testing.yaml")) s = Structure.from_file("Si_testing.yaml") self.assertEqual(s, self.struct) os.remove("Si_testing.yaml") self.struct.to(filename="POSCAR.testing.gz") s = Structure.from_file("POSCAR.testing.gz") self.assertEqual(s, self.struct) os.remove("POSCAR.testing.gz") class StructureTest(PymatgenTest): def setUp(self): coords = list() coords.append([0, 0, 0]) coords.append([0.75, 0.5, 0.75]) lattice = Lattice([[3.8401979337, 0.00, 0.00], [1.9200989668, 3.3257101909, 0.00], [0.00, -2.2171384943, 3.1355090603]]) self.structure = Structure(lattice, ["Si", "Si"], coords) def test_mutable_sequence_methods(self): s = self.structure s[0] = "Fe" self.assertEqual(s.formula, "Fe1 Si1") s[0] = "Fe", [0.5, 0.5, 0.5] self.assertEqual(s.formula, "Fe1 Si1") self.assertArrayAlmostEqual(s[0].frac_coords, [0.5, 0.5, 0.5]) s.reverse() self.assertEqual(s[0].specie, Element("Si")) self.assertArrayAlmostEqual(s[0].frac_coords, [0.75, 0.5, 0.75]) s[0] = {"Mn": 0.5} self.assertEqual(s.formula, "Mn0.5 Fe1") del s[1] self.assertEqual(s.formula, "Mn0.5") s[0] = "Fe", [0.9, 0.9, 0.9], {"magmom": 5} self.assertEqual(s.formula, "Fe1") self.assertEqual(s[0].magmom, 5) def test_non_hash(self): self.assertRaises(TypeError, dict, [(self.structure, 1)]) def test_sort(self): s = self.structure s[0] = "F" s.sort() self.assertEqual(s[0].species_string, "Si") self.assertEqual(s[1].species_string, "F") s.sort(key=lambda site: site.species_string) self.assertEqual(s[0].species_string, "F") self.assertEqual(s[1].species_string, "Si") s.sort(key=lambda site: site.species_string, reverse=True) self.assertEqual(s[0].species_string, "Si") self.assertEqual(s[1].species_string, "F") def test_append_insert_remove_replace(self): s = self.structure s.insert(1, "O", [0.5, 0.5, 0.5]) self.assertEqual(s.formula, "Si2 O1") self.assertTrue(s.ntypesp == 2) self.assertTrue(s.symbol_set == ("Si", "O")) self.assertTrue(s.indices_from_symbol("Si") == (0,2)) self.assertTrue(s.indices_from_symbol("O") == (1,)) del s[2] self.assertEqual(s.formula, "Si1 O1") self.assertTrue(s.indices_from_symbol("Si") == (0,)) self.assertTrue(s.indices_from_symbol("O") == (1,)) s.append("N", [0.25, 0.25, 0.25]) self.assertEqual(s.formula, "Si1 N1 O1") self.assertTrue(s.ntypesp == 3) self.assertTrue(s.symbol_set == ("Si", "O", "N")) self.assertTrue(s.indices_from_symbol("Si") == (0,)) self.assertTrue(s.indices_from_symbol("O") == (1,)) self.assertTrue(s.indices_from_symbol("N") == (2,)) s[0] = "Ge" self.assertEqual(s.formula, "Ge1 N1 O1") self.assertTrue(s.symbol_set == ("Ge", "O", "N")) s.replace_species({"Ge": "Si"}) self.assertEqual(s.formula, "Si1 N1 O1") self.assertTrue(s.ntypesp == 3) s.replace_species({"Si": {"Ge": 0.5, "Si": 0.5}}) self.assertEqual(s.formula, "Si0.5 Ge0.5 N1 O1") #this should change the .5Si .5Ge sites to .75Si .25Ge s.replace_species({"Ge": {"Ge": 0.5, "Si": 0.5}}) self.assertEqual(s.formula, "Si0.75 Ge0.25 N1 O1") # In this case, s.ntypesp is ambiguous. # for the time being, we raise AttributeError. with self.assertRaises(AttributeError): s.ntypesp s.remove_species(["Si"]) self.assertEqual(s.formula, "Ge0.25 N1 O1") s.remove_sites([1, 2]) self.assertEqual(s.formula, "Ge0.25") def test_add_site_property(self): s = self.structure s.add_site_property("charge", [4.1, -5]) self.assertEqual(s[0].charge, 4.1) self.assertEqual(s[1].charge, -5) s.add_site_property("magmom", [3, 2]) self.assertEqual(s[0].charge, 4.1) self.assertEqual(s[0].magmom, 3) def test_propertied_structure(self): #Make sure that site properties are set to None for missing values. s = self.structure s.add_site_property("charge", [4.1, -5]) s.append("Li", [0.3, 0.3 ,0.3]) self.assertEqual(len(s.site_properties["charge"]), 3) def test_perturb(self): d = 0.1 pre_perturbation_sites = self.structure.sites[:] self.structure.perturb(distance=d) post_perturbation_sites = self.structure.sites for i, x in enumerate(pre_perturbation_sites): self.assertAlmostEqual(x.distance(post_perturbation_sites[i]), d, 3, "Bad perturbation distance") def test_add_oxidation_states(self): oxidation_states = {"Si": -4} self.structure.add_oxidation_state_by_element(oxidation_states) for site in self.structure: for k in site.species_and_occu.keys(): self.assertEqual(k.oxi_state, oxidation_states[k.symbol], "Wrong oxidation state assigned!") oxidation_states = {"Fe": 2} self.assertRaises(ValueError, self.structure.add_oxidation_state_by_element, oxidation_states) self.structure.add_oxidation_state_by_site([2, -4]) self.assertEqual(self.structure[0].specie.oxi_state, 2) self.assertRaises(ValueError, self.structure.add_oxidation_state_by_site, [1]) def test_remove_oxidation_states(self): co_elem = Element("Co") o_elem = Element("O") co_specie = Specie("Co", 2) o_specie = Specie("O", -2) coords = list() coords.append([0, 0, 0]) coords.append([0.75, 0.5, 0.75]) lattice = Lattice.cubic(10) s_elem = Structure(lattice, [co_elem, o_elem], coords) s_specie = Structure(lattice, [co_specie, o_specie], coords) s_specie.remove_oxidation_states() self.assertEqual(s_elem, s_specie, "Oxidation state remover " "failed") def test_apply_operation(self): op = SymmOp.from_axis_angle_and_translation([0, 0, 1], 90) s = self.structure.copy() s.apply_operation(op) self.assertArrayAlmostEqual( s.lattice.matrix, [[0.000000, 3.840198, 0.000000], [-3.325710, 1.920099, 0.000000], [2.217138, -0.000000, 3.135509]], 5) op = SymmOp([[1, 1, 0, 0.5], [1, 0, 0, 0.5], [0, 0, 1, 0.5], [0, 0, 0, 1]]) s = self.structure.copy() s.apply_operation(op, fractional=True) self.assertArrayAlmostEqual( s.lattice.matrix, [[5.760297, 3.325710, 0.000000], [3.840198, 0.000000, 0.000000], [0.000000, -2.217138, 3.135509]], 5) def test_apply_strain(self): s = self.structure initial_coord = s[1].coords s.apply_strain(0.01) self.assertAlmostEqual( s.lattice.abc, (3.8785999130369997, 3.878600984287687, 3.8785999130549516)) self.assertArrayAlmostEqual(s[1].coords, initial_coord * 1.01) a1, b1, c1 = s.lattice.abc s.apply_strain([0.1, 0.2, 0.3]) a2, b2, c2 = s.lattice.abc self.assertAlmostEqual(a2 / a1, 1.1) self.assertAlmostEqual(b2 / b1, 1.2) self.assertAlmostEqual(c2 / c1, 1.3) def test_scale_lattice(self): initial_coord = self.structure[1].coords self.structure.scale_lattice(self.structure.volume * 1.01 ** 3) self.assertArrayAlmostEqual( self.structure.lattice.abc, (3.8785999130369997, 3.878600984287687, 3.8785999130549516)) self.assertArrayAlmostEqual(self.structure[1].coords, initial_coord * 1.01) def test_translate_sites(self): self.structure.translate_sites([0, 1], [0.5, 0.5, 0.5], frac_coords=True) self.assertArrayEqual(self.structure.frac_coords[0], [0.5, 0.5, 0.5]) self.structure.translate_sites([0], [0.5, 0.5, 0.5], frac_coords=False) self.assertArrayAlmostEqual(self.structure.cart_coords[0], [3.38014845, 1.05428585, 2.06775453]) self.structure.translate_sites([0], [0.5, 0.5, 0.5], frac_coords=True, to_unit_cell=False) self.assertArrayAlmostEqual(self.structure.frac_coords[0], [1.00187517, 1.25665291, 1.15946374]) def test_mul(self): self.structure *= [2, 1, 1] self.assertEqual(self.structure.formula, "Si4") s = [2, 1, 1] * self.structure self.assertEqual(s.formula, "Si8") self.assertIsInstance(s, Structure) s = self.structure * [[1, 0, 0], [2, 1, 0], [0, 0, 2]] self.assertEqual(s.formula, "Si8") self.assertArrayAlmostEqual(s.lattice.abc, [7.6803959, 17.5979979, 7.6803959]) def test_make_supercell(self): self.structure.make_supercell([2, 1, 1]) self.assertEqual(self.structure.formula, "Si4") self.structure.make_supercell([[1, 0, 0], [2, 1, 0], [0, 0, 1]]) self.assertEqual(self.structure.formula, "Si4") self.structure.make_supercell(2) self.assertEqual(self.structure.formula, "Si32") self.assertArrayAlmostEqual(self.structure.lattice.abc, [15.360792, 35.195996, 7.680396], 5) def test_disordered_supercell_primitive_cell(self): l = Lattice.cubic(2) f = [[0.5, 0.5, 0.5]] sp = [{'Si': 0.54738}] s = Structure(l, sp, f) #this supercell often breaks things s.make_supercell([[0,-1,1],[-1,1,0],[1,1,1]]) self.assertEqual(len(s.get_primitive_structure()), 1) def test_another_supercell(self): #this is included b/c for some reason the old algo was failing on it s = self.structure.copy() s.make_supercell([[0, 2, 2], [2, 0, 2], [2, 2, 0]]) self.assertEqual(s.formula, "Si32") s = self.structure.copy() s.make_supercell([[0, 2, 0], [1, 0, 0], [0, 0, 1]]) self.assertEqual(s.formula, "Si4") def test_to_from_dict(self): d = self.structure.as_dict() s2 = Structure.from_dict(d) self.assertEqual(type(s2), Structure) def test_to_from_file_string(self): for fmt in ["cif", "json", "poscar", "cssr", "yaml", "xsf"]: s = self.structure.to(fmt=fmt) self.assertIsNotNone(s) ss = Structure.from_str(s, fmt=fmt) self.assertArrayAlmostEqual( ss.lattice.lengths_and_angles, self.structure.lattice.lengths_and_angles, decimal=5) self.assertArrayAlmostEqual(ss.frac_coords, self.structure.frac_coords) self.assertIsInstance(ss, Structure) self.structure.to(filename="POSCAR.testing") self.assertTrue(os.path.exists("POSCAR.testing")) os.remove("POSCAR.testing") self.structure.to(filename="structure_testing.json") self.assertTrue(os.path.exists("structure_testing.json")) s = Structure.from_file("structure_testing.json") self.assertEqual(s, self.structure) os.remove("structure_testing.json") def test_from_spacegroup(self): s1 = Structure.from_spacegroup("Fm-3m", Lattice.cubic(3), ["Li", "O"], [[0.25, 0.25, 0.25], [0, 0, 0]]) self.assertEqual(s1.formula, "Li8 O4") s2 = Structure.from_spacegroup(225, Lattice.cubic(3), ["Li", "O"], [[0.25, 0.25, 0.25], [0, 0, 0]]) self.assertEqual(s1, s2) s2 = Structure.from_spacegroup(225, Lattice.cubic(3), ["Li", "O"], [[0.25, 0.25, 0.25], [0, 0, 0]], site_properties={"charge": [1, -2]}) self.assertEqual(sum(s2.site_properties["charge"]), 0) s = Structure.from_spacegroup("Pm-3m", Lattice.cubic(3), ["Cs", "Cl"], [[0, 0, 0], [0.5, 0.5, 0.5]]) self.assertEqual(s.formula, "Cs1 Cl1") self.assertRaises(ValueError, Structure.from_spacegroup, "Pm-3m", Lattice.tetragonal(1, 3), ["Cs", "Cl"], [[0, 0, 0], [0.5, 0.5, 0.5]]) self.assertRaises(ValueError, Structure.from_spacegroup, "Pm-3m", Lattice.cubic(3), ["Cs"], [[0, 0, 0], [0.5, 0.5, 0.5]]) def test_merge_sites(self): species = [{'Ag': 0.5}, {'Cl': 0.25}, {'Cl': 0.1}, {'Ag': 0.5}, {'F': 0.15}, {'F': 0.1}] coords = [[0, 0, 0], [0.5, 0.5, 0.5], [0.5, 0.5, 0.5], [0, 0, 0], [0.5, 0.5, 1.501], [0.5, 0.5, 1.501]] s = Structure(Lattice.cubic(1), species, coords) s.merge_sites(mode="s") self.assertEqual(s[0].specie.symbol, 'Ag') self.assertEqual(s[1].species_and_occu, Composition({'Cl': 0.35, 'F': 0.25})) self.assertArrayAlmostEqual(s[1].frac_coords, [.5, .5, .5005]) # Test for TaS2 with spacegroup 166 in 160 setting. l = Lattice.from_lengths_and_angles([3.374351, 3.374351, 20.308941], [90.000000, 90.000000, 120.000000]) species = ["Ta", "S", "S"] coords = [[0.000000, 0.000000, 0.944333], [0.333333, 0.666667, 0.353424], [0.666667, 0.333333, 0.535243]] tas2 = Structure.from_spacegroup(160, l, species, coords) assert len(tas2) == 13 tas2.merge_sites(mode="d") assert len(tas2) == 9 l = Lattice.from_lengths_and_angles([3.587776, 3.587776, 19.622793], [90.000000, 90.000000, 120.000000]) species = ["Na", "V", "S", "S"] coords = [[0.333333, 0.666667, 0.165000], [0.000000, 0.000000, 0.998333], [0.333333, 0.666667, 0.399394], [0.666667, 0.333333, 0.597273]] navs2 = Structure.from_spacegroup(160, l, species, coords) assert len(navs2) == 18 navs2.merge_sites(mode="d") assert len(navs2) == 12 def test_properties(self): self.assertEqual(self.structure.num_sites, len(self.structure)) self.structure.make_supercell(2) self.structure[1] = "C" sites = list(self.structure.group_by_types()) self.assertEqual(sites[-1].specie.symbol, "C") self.structure.add_oxidation_state_by_element({"Si": 4, "C": 2}) self.assertEqual(self.structure.charge, 62) def test_set_item(self): s = self.structure.copy() s[0] = "C" self.assertEqual(s.formula, "Si1 C1") s[(0, 1)] = "Ge" self.assertEqual(s.formula, "Ge2") s[0:2] = "Sn" self.assertEqual(s.formula, "Sn2") s = self.structure.copy() s["Si"] = "C" self.assertEqual(s.formula, "C2") s["C"] = "C0.25Si0.5" self.assertEqual(s.formula, "Si1 C0.5") s["C"] = "C0.25Si0.5" self.assertEqual(s.formula, "Si1.25 C0.125") def test_init_error(self): self.assertRaises(StructureError, Structure, Lattice.cubic(3), ["Si"], [[0, 0, 0], [0.5, 0.5, 0.5]]) def test_from_sites(self): self.structure.add_site_property("hello", [1, 2]) s = Structure.from_sites(self.structure, to_unit_cell=True) self.assertEqual(s.site_properties["hello"][1], 2) def test_magic(self): s = Structure.from_sites(self.structure) self.assertEqual(s, self.structure) self.assertNotEqual(s, None) s.apply_strain(0.5) self.assertNotEqual(s, self.structure) self.assertNotEqual(self.structure * 2, self.structure) class IMoleculeTest(PymatgenTest): def setUp(self): coords = [[0.000000, 0.000000, 0.000000], [0.000000, 0.000000, 1.089000], [1.026719, 0.000000, -0.363000], [-0.513360, -0.889165, -0.363000], [-0.513360, 0.889165, -0.363000]] self.coords = coords self.mol = Molecule(["C", "H", "H", "H", "H"], coords) def test_set_item(self): s = self.mol.copy() s[0] = "Si" self.assertEqual(s.formula, "Si1 H4") s[(0, 1)] = "Ge" self.assertEqual(s.formula, "Ge2 H3") s[0:2] = "Sn" self.assertEqual(s.formula, "Sn2 H3") s = self.mol.copy() s["H"] = "F" self.assertEqual(s.formula, "C1 F4") s["C"] = "C0.25Si0.5" self.assertEqual(s.formula, "Si0.5 C0.25 F4") s["C"] = "C0.25Si0.5" self.assertEqual(s.formula, "Si0.625 C0.0625 F4") def test_bad_molecule(self): coords = [[0.000000, 0.000000, 0.000000], [0.000000, 0.000000, 1.089000], [1.026719, 0.000000, -0.363000], [-0.513360, -0.889165, -0.363000], [-0.513360, 0.889165, -0.363000], [-0.513360, 0.889165, -0.36301]] self.assertRaises(StructureError, Molecule, ["C", "H", "H", "H", "H", "H"], coords, validate_proximity=True) def test_get_angle_dihedral(self): self.assertAlmostEqual(self.mol.get_angle(1, 0, 2), 109.47122144618737) self.assertAlmostEqual(self.mol.get_angle(3, 1, 2), 60.00001388659683) self.assertAlmostEqual(self.mol.get_dihedral(0, 1, 2, 3), - 35.26438851071765) coords = list() coords.append([0, 0, 0]) coords.append([0, 0, 1]) coords.append([0, 1, 1]) coords.append([1, 1, 1]) self.mol2 = Molecule(["C", "O", "N", "S"], coords) self.assertAlmostEqual(self.mol2.get_dihedral(0, 1, 2, 3), -90) def test_get_covalent_bonds(self): self.assertEqual(len(self.mol.get_covalent_bonds()), 4) def test_properties(self): self.assertEqual(len(self.mol), 5) self.assertTrue(self.mol.is_ordered) self.assertEqual(self.mol.formula, "H4 C1") def test_repr_str(self): ans = """Full Formula (H4 C1) Reduced Formula: H4C Charge = 0, Spin Mult = 1 Sites (5) 0 C 0.000000 0.000000 0.000000 1 H 0.000000 0.000000 1.089000 2 H 1.026719 0.000000 -0.363000 3 H -0.513360 -0.889165 -0.363000 4 H -0.513360 0.889165 -0.363000""" self.assertEqual(self.mol.__str__(), ans) ans = """Molecule Summary Site: C (0.0000, 0.0000, 0.0000) Site: H (0.0000, 0.0000, 1.0890) Site: H (1.0267, 0.0000, -0.3630) Site: H (-0.5134, -0.8892, -0.3630) Site: H (-0.5134, 0.8892, -0.3630)""" self.assertEqual(repr(self.mol), ans) def test_site_properties(self): propertied_mol = Molecule(["C", "H", "H", "H", "H"], self.coords, site_properties={'magmom': [0.5, -0.5, 1, 2, 3]}) self.assertEqual(propertied_mol[0].magmom, 0.5) self.assertEqual(propertied_mol[1].magmom, -0.5) def test_get_boxed_structure(self): s = self.mol.get_boxed_structure(9, 9, 9) # C atom should be in center of box. self.assertArrayAlmostEqual(s[4].frac_coords, [0.50000001, 0.5, 0.5]) self.assertArrayAlmostEqual(s[1].frac_coords, [0.6140799, 0.5, 0.45966667]) self.assertRaises(ValueError, self.mol.get_boxed_structure, 1, 1, 1) s2 = self.mol.get_boxed_structure(5, 5, 5, (2, 3, 4)) self.assertEqual(len(s2), 24 * 5) self.assertEqual(s2.lattice.abc, (10, 15, 20)) # Test offset option s3 = self.mol.get_boxed_structure(9, 9, 9, offset=[0.5,0.5,0.5]) self.assertArrayAlmostEqual(s3[4].coords, [5,5,5]) # Test no_cross option self.assertRaises(ValueError, self.mol.get_boxed_structure, 5, 5, 5, offset=[10,10,10],no_cross = True) def test_get_distance(self): self.assertAlmostEqual(self.mol.get_distance(0, 1), 1.089) def test_get_neighbors(self): nn = self.mol.get_neighbors(self.mol[0], 1) self.assertEqual(len(nn), 0) nn = self.mol.get_neighbors(self.mol[0], 2) self.assertEqual(len(nn), 4) def test_get_neighbors_in_shell(self): nn = self.mol.get_neighbors_in_shell([0, 0, 0], 0, 1) self.assertEqual(len(nn), 1) nn = self.mol.get_neighbors_in_shell([0, 0, 0], 1, 0.9) self.assertEqual(len(nn), 4) nn = self.mol.get_neighbors_in_shell([0, 0, 0], 1, 0.9) self.assertEqual(len(nn), 4) nn = self.mol.get_neighbors_in_shell([0, 0, 0], 2, 0.1) self.assertEqual(len(nn), 0) def test_get_dist_matrix(self): ans = [[0.0, 1.089, 1.08899995636, 1.08900040717, 1.08900040717], [1.089, 0.0, 1.77832952654, 1.7783298026, 1.7783298026], [1.08899995636, 1.77832952654, 0.0, 1.77833003783, 1.77833003783], [1.08900040717, 1.7783298026, 1.77833003783, 0.0, 1.77833], [1.08900040717, 1.7783298026, 1.77833003783, 1.77833, 0.0]] self.assertArrayAlmostEqual(self.mol.distance_matrix, ans) def test_break_bond(self): (mol1, mol2) = self.mol.break_bond(0, 1) self.assertEqual(mol1.formula, "H3 C1") self.assertEqual(mol2.formula, "H1") def test_prop(self): self.assertEqual(self.mol.charge, 0) self.assertEqual(self.mol.spin_multiplicity, 1) self.assertEqual(self.mol.nelectrons, 10) self.assertArrayAlmostEqual(self.mol.center_of_mass, [0, 0, 0]) self.assertRaises(ValueError, Molecule, ["C", "H", "H", "H", "H"], self.coords, charge=1, spin_multiplicity=1) mol = Molecule(["C", "H", "H", "H", "H"], self.coords, charge=1) self.assertEqual(mol.spin_multiplicity, 2) self.assertEqual(mol.nelectrons, 9) #Triplet O2 mol = IMolecule(["O"] * 2, [[0, 0, 0], [0, 0, 1.2]], spin_multiplicity=3) self.assertEqual(mol.spin_multiplicity, 3) def test_equal(self): mol = IMolecule(["C", "H", "H", "H", "H"], self.coords, charge=1) self.assertNotEqual(mol, self.mol) def test_get_centered_molecule(self): mol = IMolecule(["O"] * 2, [[0, 0, 0], [0, 0, 1.2]], spin_multiplicity=3) centered = mol.get_centered_molecule() self.assertArrayAlmostEqual(centered.center_of_mass, [0, 0, 0]) def test_to_from_dict(self): d = self.mol.as_dict() mol2 = IMolecule.from_dict(d) self.assertEqual(type(mol2), IMolecule) propertied_mol = Molecule(["C", "H", "H", "H", "H"], self.coords, charge=1, site_properties={'magmom': [0.5, -0.5, 1, 2, 3]}) d = propertied_mol.as_dict() self.assertEqual(d['sites'][0]['properties']['magmom'], 0.5) mol = Molecule.from_dict(d) self.assertEqual(propertied_mol, mol) self.assertEqual(mol[0].magmom, 0.5) self.assertEqual(mol.formula, "H4 C1") self.assertEqual(mol.charge, 1) def test_to_from_file_string(self): for fmt in ["xyz", "json", "g03", "yaml"]: s = self.mol.to(fmt=fmt) self.assertIsNotNone(s) m = IMolecule.from_str(s, fmt=fmt) self.assertEqual(m, self.mol) self.assertIsInstance(m, IMolecule) self.mol.to(filename="CH4_testing.xyz") self.assertTrue(os.path.exists("CH4_testing.xyz")) os.remove("CH4_testing.xyz") self.mol.to(filename="CH4_testing.yaml") self.assertTrue(os.path.exists("CH4_testing.yaml")) mol = Molecule.from_file("CH4_testing.yaml") self.assertEqual(self.mol, mol) os.remove("CH4_testing.yaml") class MoleculeTest(PymatgenTest): def setUp(self): coords = [[0.000000, 0.000000, 0.000000], [0.000000, 0.000000, 1.089000], [1.026719, 0.000000, -0.363000], [-0.513360, -0.889165, -0.363000], [-0.513360, 0.889165, -0.363000]] self.mol = Molecule(["C", "H", "H", "H", "H"], coords) def test_mutable_sequence_methods(self): s = self.mol s[1] = ("F", [0.5, 0.5, 0.5]) self.assertEqual(s.formula, "H3 C1 F1") self.assertArrayAlmostEqual(s[1].coords, [0.5, 0.5, 0.5]) s.reverse() self.assertEqual(s[0].specie, Element("H")) self.assertArrayAlmostEqual(s[0].coords, [-0.513360, 0.889165, -0.363000]) del s[1] self.assertEqual(s.formula, "H2 C1 F1") s[3] = "N", [0,0,0], {"charge": 4} self.assertEqual(s.formula, "H2 N1 F1") self.assertEqual(s[3].charge, 4) def test_insert_remove_append(self): mol = self.mol mol.insert(1, "O", [0.5, 0.5, 0.5]) self.assertEqual(mol.formula, "H4 C1 O1") del mol[2] self.assertEqual(mol.formula, "H3 C1 O1") mol.set_charge_and_spin(0) self.assertEqual(mol.spin_multiplicity, 2) mol.append("N", [1, 1, 1]) self.assertEqual(mol.formula, "H3 C1 N1 O1") self.assertRaises(TypeError, dict, [(mol, 1)]) mol.remove_sites([0, 1]) self.assertEqual(mol.formula, "H3 N1") def test_translate_sites(self): self.mol.translate_sites([0, 1], [0.5, 0.5, 0.5]) self.assertArrayEqual(self.mol.cart_coords[0], [0.5, 0.5, 0.5]) def test_rotate_sites(self): self.mol.rotate_sites(theta=np.radians(30)) self.assertArrayAlmostEqual(self.mol.cart_coords[2], [ 0.889164737, 0.513359500, -0.363000000]) def test_replace(self): self.mol[0] = "Ge" self.assertEqual(self.mol.formula, "Ge1 H4") self.mol.replace_species({Element("Ge"): {Element("Ge"): 0.5, Element("Si"): 0.5}}) self.assertEqual(self.mol.formula, "Si0.5 Ge0.5 H4") #this should change the .5Si .5Ge sites to .75Si .25Ge self.mol.replace_species({Element("Ge"): {Element("Ge"): 0.5, Element("Si"): 0.5}}) self.assertEqual(self.mol.formula, "Si0.75 Ge0.25 H4") d = 0.1 pre_perturbation_sites = self.mol.sites[:] self.mol.perturb(distance=d) post_perturbation_sites = self.mol.sites for i, x in enumerate(pre_perturbation_sites): self.assertAlmostEqual(x.distance(post_perturbation_sites[i]), d, 3, "Bad perturbation distance") def test_add_site_property(self): self.mol.add_site_property("charge", [4.1, -2, -2, -2, -2]) self.assertEqual(self.mol[0].charge, 4.1) self.assertEqual(self.mol[1].charge, -2) self.mol.add_site_property("magmom", [3, 2, 2, 2, 2]) self.assertEqual(self.mol[0].charge, 4.1) self.assertEqual(self.mol[0].magmom, 3) def test_to_from_dict(self): d = self.mol.as_dict() mol2 = Molecule.from_dict(d) self.assertEqual(type(mol2), Molecule) def test_apply_operation(self): op = SymmOp.from_axis_angle_and_translation([0, 0, 1], 90) self.mol.apply_operation(op) self.assertArrayAlmostEqual(self.mol[2].coords, [0.000000, 1.026719, -0.363000]) def test_substitute(self): coords = [[0.000000, 0.000000, 1.08], [0.000000, 0.000000, 0.000000], [1.026719, 0.000000, -0.363000], [-0.513360, -0.889165, -0.363000], [-0.513360, 0.889165, -0.363000]] sub = Molecule(["X", "C", "H", "H", "H"], coords) self.mol.substitute(1, sub) self.assertAlmostEqual(self.mol.get_distance(0, 4), 1.54) f = Molecule(["X", "F"], [[0, 0, 0], [0, 0, 1.11]]) self.mol.substitute(2, f) self.assertAlmostEqual(self.mol.get_distance(0, 7), 1.35) oh = Molecule(["X", "O", "H"], [[0, 0.780362, -.456316], [0, 0, .114079], [0, -.780362, -.456316]]) self.mol.substitute(1, oh) self.assertAlmostEqual(self.mol.get_distance(0, 7), 1.43) self.mol.substitute(3, "methyl") self.assertEqual(self.mol.formula, "H7 C3 O1 F1") coords = [[0.00000, 1.40272, 0.00000], [0.00000, 2.49029, 0.00000], [-1.21479, 0.70136, 0.00000], [-2.15666, 1.24515, 0.00000], [-1.21479, -0.70136, 0.00000], [-2.15666, -1.24515, 0.00000], [0.00000, -1.40272, 0.00000], [0.00000, -2.49029, 0.00000], [1.21479, -0.70136, 0.00000], [2.15666, -1.24515, 0.00000], [1.21479, 0.70136, 0.00000], [2.15666, 1.24515, 0.00000]] benzene = Molecule(["C", "H", "C", "H", "C", "H", "C", "H", "C", "H", "C", "H"], coords) benzene.substitute(1, sub) self.assertEqual(benzene.formula, "H8 C7") #Carbon attached should be in plane. self.assertAlmostEqual(benzene[11].coords[2], 0) def test_to_from_file_string(self): for fmt in ["xyz", "json", "g03"]: s = self.mol.to(fmt=fmt) self.assertIsNotNone(s) m = Molecule.from_str(s, fmt=fmt) self.assertEqual(m, self.mol) self.assertIsInstance(m, Molecule) self.mol.to(filename="CH4_testing.xyz") self.assertTrue(os.path.exists("CH4_testing.xyz")) os.remove("CH4_testing.xyz") if __name__ == '__main__': import unittest2 as unittest unittest.main()

true

1c43e0cada49727bd7584ef88bc5aea8845fc86a

9,387

py

Python

src/bondora_api/models/api_result_event_log.py

parruc/bondora_api

f36ea8d7149d75a2e5f14a695e5a4e57f0a3518d

[ "Apache-2.0" ]

8

2019-03-09T20:38:27.000Z

2021-02-10T20:44:22.000Z

src/bondora_api/models/api_result_event_log.py

parruc/bondora_api

f36ea8d7149d75a2e5f14a695e5a4e57f0a3518d

[ "Apache-2.0" ]

1

2018-03-06T09:44:21.000Z

src/bondora_api/models/api_result_event_log.py

parruc/bondora_api

f36ea8d7149d75a2e5f14a695e5a4e57f0a3518d

[ "Apache-2.0" ]

3

2019-06-03T13:44:05.000Z

2020-11-16T13:17:38.000Z

# coding: utf-8 """ Bondora API V1 Bondora API version 1 OpenAPI spec version: v1 Contact: investor@bondora.com Generated by: https://github.com/swagger-api/swagger-codegen.git 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 pprint import pformat from six import iteritems import re class ApiResultEventLog(object): """ NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. """ def __init__(self, page_size=None, page_nr=None, total_count=None, count=None, payload=None, success=None, errors=None): """ ApiResultEventLog - a model defined in Swagger :param dict swaggerTypes: The key is attribute name and the value is attribute type. :param dict attributeMap: The key is attribute name and the value is json key in definition. """ self.swagger_types = { 'page_size': 'int', 'page_nr': 'int', 'total_count': 'int', 'count': 'int', 'payload': 'list[EventLogItem]', 'success': 'bool', 'errors': 'list[ApiError]' } self.attribute_map = { 'page_size': 'PageSize', 'page_nr': 'PageNr', 'total_count': 'TotalCount', 'count': 'Count', 'payload': 'Payload', 'success': 'Success', 'errors': 'Errors' } self._page_size = page_size self._page_nr = page_nr self._total_count = total_count self._count = count self._payload = payload self._success = success self._errors = errors @property def page_size(self): """ Gets the page_size of this ApiResultEventLog. Requested Max items in result :return: The page_size of this ApiResultEventLog. :rtype: int """ return self._page_size @page_size.setter def page_size(self, page_size): """ Sets the page_size of this ApiResultEventLog. Requested Max items in result :param page_size: The page_size of this ApiResultEventLog. :type: int """ if not page_size: raise ValueError("Invalid value for `page_size`, must not be `None`") if page_size > 2.147483647E9: raise ValueError("Invalid value for `page_size`, must be a value less than or equal to `2.147483647E9`") if page_size < 0.0: raise ValueError("Invalid value for `page_size`, must be a value greater than or equal to `0.0`") self._page_size = page_size @property def page_nr(self): """ Gets the page_nr of this ApiResultEventLog. Requested page nr :return: The page_nr of this ApiResultEventLog. :rtype: int """ return self._page_nr @page_nr.setter def page_nr(self, page_nr): """ Sets the page_nr of this ApiResultEventLog. Requested page nr :param page_nr: The page_nr of this ApiResultEventLog. :type: int """ if not page_nr: raise ValueError("Invalid value for `page_nr`, must not be `None`") if page_nr > 2.147483647E9: raise ValueError("Invalid value for `page_nr`, must be a value less than or equal to `2.147483647E9`") if page_nr < 1.0: raise ValueError("Invalid value for `page_nr`, must be a value greater than or equal to `1.0`") self._page_nr = page_nr @property def total_count(self): """ Gets the total_count of this ApiResultEventLog. Total number of items found :return: The total_count of this ApiResultEventLog. :rtype: int """ return self._total_count @total_count.setter def total_count(self, total_count): """ Sets the total_count of this ApiResultEventLog. Total number of items found :param total_count: The total_count of this ApiResultEventLog. :type: int """ if not total_count: total_count = 0 # raise ValueError("Invalid value for `total_count`, must not be `None`") if total_count > 2.147483647E9: raise ValueError("Invalid value for `total_count`, must be a value less than or equal to `2.147483647E9`") if total_count < 0.0: raise ValueError("Invalid value for `total_count`, must be a value greater than or equal to `0.0`") self._total_count = total_count @property def count(self): """ Gets the count of this ApiResultEventLog. Number of items returned :return: The count of this ApiResultEventLog. :rtype: int """ return self._count @count.setter def count(self, count): """ Sets the count of this ApiResultEventLog. Number of items returned :param count: The count of this ApiResultEventLog. :type: int """ if not count: count = 0 # raise ValueError("Invalid value for `count`, must not be `None`") if count > 2.147483647E9: raise ValueError("Invalid value for `count`, must be a value less than or equal to `2.147483647E9`") if count < 0.0: raise ValueError("Invalid value for `count`, must be a value greater than or equal to `0.0`") self._count = count @property def payload(self): """ Gets the payload of this ApiResultEventLog. The payload of the response. Type depends on the API request. :return: The payload of this ApiResultEventLog. :rtype: list[EventLogItem] """ return self._payload @payload.setter def payload(self, payload): """ Sets the payload of this ApiResultEventLog. The payload of the response. Type depends on the API request. :param payload: The payload of this ApiResultEventLog. :type: list[EventLogItem] """ self._payload = payload @property def success(self): """ Gets the success of this ApiResultEventLog. Indicates if the request was successfull or not. true if the request was handled successfully, false otherwise. :return: The success of this ApiResultEventLog. :rtype: bool """ return self._success @success.setter def success(self, success): """ Sets the success of this ApiResultEventLog. Indicates if the request was successfull or not. true if the request was handled successfully, false otherwise. :param success: The success of this ApiResultEventLog. :type: bool """ self._success = success @property def errors(self): """ Gets the errors of this ApiResultEventLog. Error(s) accociated with the API request. :return: The errors of this ApiResultEventLog. :rtype: list[ApiError] """ return self._errors @errors.setter def errors(self, errors): """ Sets the errors of this ApiResultEventLog. Error(s) accociated with the API request. :param errors: The errors of this ApiResultEventLog. :type: list[ApiError] """ self._errors = errors def to_dict(self): """ Returns the model properties as a dict """ result = {} for attr, _ in iteritems(self.swagger_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value return result def to_str(self): """ Returns the string representation of the model """ return pformat(self.to_dict()) def __repr__(self): """ For `print` and `pprint` """ return self.to_str() def __eq__(self, other): """ Returns true if both objects are equal """ return self.__dict__ == other.__dict__ def __ne__(self, other): """ Returns true if both objects are not equal """ return not self == other

30.086538

132

0.584425

from pprint import pformat from six import iteritems import re class ApiResultEventLog(object): def __init__(self, page_size=None, page_nr=None, total_count=None, count=None, payload=None, success=None, errors=None): self.swagger_types = { 'page_size': 'int', 'page_nr': 'int', 'total_count': 'int', 'count': 'int', 'payload': 'list[EventLogItem]', 'success': 'bool', 'errors': 'list[ApiError]' } self.attribute_map = { 'page_size': 'PageSize', 'page_nr': 'PageNr', 'total_count': 'TotalCount', 'count': 'Count', 'payload': 'Payload', 'success': 'Success', 'errors': 'Errors' } self._page_size = page_size self._page_nr = page_nr self._total_count = total_count self._count = count self._payload = payload self._success = success self._errors = errors @property def page_size(self): return self._page_size @page_size.setter def page_size(self, page_size): if not page_size: raise ValueError("Invalid value for `page_size`, must not be `None`") if page_size > 2.147483647E9: raise ValueError("Invalid value for `page_size`, must be a value less than or equal to `2.147483647E9`") if page_size < 0.0: raise ValueError("Invalid value for `page_size`, must be a value greater than or equal to `0.0`") self._page_size = page_size @property def page_nr(self): return self._page_nr @page_nr.setter def page_nr(self, page_nr): if not page_nr: raise ValueError("Invalid value for `page_nr`, must not be `None`") if page_nr > 2.147483647E9: raise ValueError("Invalid value for `page_nr`, must be a value less than or equal to `2.147483647E9`") if page_nr < 1.0: raise ValueError("Invalid value for `page_nr`, must be a value greater than or equal to `1.0`") self._page_nr = page_nr @property def total_count(self): return self._total_count @total_count.setter def total_count(self, total_count): if not total_count: total_count = 0 if total_count > 2.147483647E9: raise ValueError("Invalid value for `total_count`, must be a value less than or equal to `2.147483647E9`") if total_count < 0.0: raise ValueError("Invalid value for `total_count`, must be a value greater than or equal to `0.0`") self._total_count = total_count @property def count(self): return self._count @count.setter def count(self, count): if not count: count = 0 if count > 2.147483647E9: raise ValueError("Invalid value for `count`, must be a value less than or equal to `2.147483647E9`") if count < 0.0: raise ValueError("Invalid value for `count`, must be a value greater than or equal to `0.0`") self._count = count @property def payload(self): return self._payload @payload.setter def payload(self, payload): self._payload = payload @property def success(self): return self._success @success.setter def success(self, success): self._success = success @property def errors(self): return self._errors @errors.setter def errors(self, errors): self._errors = errors def to_dict(self): result = {} for attr, _ in iteritems(self.swagger_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value return result def to_str(self): return pformat(self.to_dict()) def __repr__(self): return self.to_str() def __eq__(self, other): return self.__dict__ == other.__dict__ def __ne__(self, other): return not self == other

true

1c43e0f431cee83160cd2cca89590f3101053b77

11,888

py

Python

geoflow1D/GeoModule.py

HerminioTH/GeoFlow1D

44a5c11e3297827b265c1ea44bb18256b074fa66

[ "MIT" ]

2

2020-02-10T11:23:16.000Z

2020-07-01T20:28:57.000Z

geoflow1D/GeoModule.py

HerminioTH/GeoFlow1D

44a5c11e3297827b265c1ea44bb18256b074fa66

[ "MIT" ]

null

geoflow1D/GeoModule.py

HerminioTH/GeoFlow1D

44a5c11e3297827b265c1ea44bb18256b074fa66

[ "MIT" ]

null

def AssemblyStiffnessMatrix(linearSystem, grid, props, uShift=0): for region in grid.getRegions(): M = props.M.getValue(region) for e in region.getElements(): dx = e.getLength() f = e.getFace() bIndex = f.getBackwardVertex().getIndex() + uShift*grid.getNumberOfVertices() fIndex = f.getForwardVertex().getIndex() + uShift*grid.getNumberOfVertices() forceOperator = [-M/dx, M/dx] localIndex = 0 for v in e.getVertices(): flux = forceOperator[localIndex] vIndex = v.getIndex() + uShift*grid.getNumberOfVertices() linearSystem.addValueToMatrix( bIndex, vIndex, flux ) linearSystem.addValueToMatrix( fIndex, vIndex, -flux ) localIndex += 1 def AssemblyGravityToVector(linearSystem, grid, props, gravity, uShift=0): n = grid.getNumberOfVertices() for region in grid.getRegions(): rho = props.rho.getValue(region) for elem in region.getElements(): face = elem.getFace() bVertex = face.getBackwardVertex() fVertex = face.getForwardVertex() value = -rho*gravity*elem.getSubVolume() linearSystem.addValueToVector(bVertex.getIndex() + uShift*n, value) linearSystem.addValueToVector(fVertex.getIndex() + uShift*n, value) def AssemblyPorePressureToMatrix(linearSystem, grid, props, uShift=0): for region in grid.getRegions(): alpha = props.biot.getValue(region) for e in region.getElements(): f = e.getFace() bIndex = f.getBackwardVertex().getIndex() + uShift*grid.getNumberOfVertices() fIndex = f.getForwardVertex().getIndex() + uShift*grid.getNumberOfVertices() for i,v in enumerate(e.getVertices()): col = v.getIndex() + (1-uShift)*grid.getNumberOfVertices() linearSystem.addValueToMatrix( bIndex, col, -alpha/2 ) linearSystem.addValueToMatrix( fIndex, col, +alpha/2 ) def AssemblyPorePressureToVector(linearSystem, grid, props, pField, uShift=0): for region in grid.getRegions(): alpha = props.biot.getValue(region) for e in region.getElements(): f = e.getFace() bIndex = f.getBackwardVertex().getIndex() + uShift*grid.getNumberOfVertices() fIndex = f.getForwardVertex().getIndex() + uShift*grid.getNumberOfVertices() pBack = pField.getValue(f.getBackwardVertex()) pFron = pField.getValue(f.getForwardVertex()) value = alpha/2. linearSystem.addValueToVector(bIndex, value*pBack) linearSystem.addValueToVector(bIndex, value*pFron) linearSystem.addValueToVector(fIndex, -value*pBack) linearSystem.addValueToVector(fIndex, -value*pFron) def AssemblyUDNMatrix(cooMatrix, grid, props): for region in grid.getRegions(): Q = props.Q.getValue(region) alpha = props.biot.getValue(region) coef = alpha*alpha*Q for e in region.getElements(): dx = e.getLength() f = e.getFace() bIndex = f.getBackwardVertex().getIndex() fIndex = f.getForwardVertex().getIndex() forceOperator = [-coef/dx, coef/dx] localIndex = 0 for v in e.getVertices(): flux = forceOperator[localIndex] vIndex = v.getIndex() cooMatrix.addValueToMatrix(bIndex, vIndex, flux) cooMatrix.addValueToMatrix(fIndex, vIndex, -flux) localIndex += 1 # ------------------------- PHYSICAL INFLUENCE SCHEME - FULL ------------------------------- def AssemblyPisFullToGeoMatrix(linearSystem, grid, props, timeStep, uShift=0): for region in grid.getRegions(): M = props.M.getValue(region) alpha = props.biot.getValue(region) k = props.k.getValue(region) Q = 1/(props.c_f*props.phi.getValue(region) + props.c_s.getValue(region)*(1 - props.phi.getValue(region))) for e in region.getElements(): f = e.getFace() bIndex = f.getBackwardVertex().getIndex() + uShift*grid.getNumberOfVertices() fIndex = f.getForwardVertex().getIndex() + uShift*grid.getNumberOfVertices() dx = e.getLength() B = props.mu*dx*alpha/(8*k*timeStep) pisOperator = [-alpha*B, alpha*B] for localIndex, v in enumerate(e.getVertices()): coef = pisOperator[localIndex] vIndex = v.getIndex() + uShift*grid.getNumberOfVertices() linearSystem.addValueToMatrix( bIndex, vIndex, coef ) linearSystem.addValueToMatrix( fIndex, vIndex, -coef ) def AssemblyPisFullToGeoVector(linearSystem, grid, props, timeStep, uOldField, uShift=0): for region in grid.getRegions(): M = props.M.getValue(region) alpha = props.biot.getValue(region) k = props.k.getValue(region) Q = 1/(props.c_f*props.phi.getValue(region) + props.c_s.getValue(region)*(1 - props.phi.getValue(region))) for e in region.getElements(): f = e.getFace() dx = e.getLength() B = props.mu*dx*alpha/(8*k*timeStep) bVertex = f.getBackwardVertex() fVertex = f.getForwardVertex() ub = uOldField.getValue(bVertex) uf = uOldField.getValue(fVertex) linearSystem.addValueToVector(bVertex.getIndex() + uShift*grid.getNumberOfVertices(), alpha*B*(uf - ub)) linearSystem.addValueToVector(fVertex.getIndex() + uShift*grid.getNumberOfVertices(), -alpha*B*(uf - ub)) def AssemblyPisFullToPorePressureMatrix(linearSystem, grid, props, timeStep, uShift=0): for region in grid.getRegions(): alpha = props.biot.getValue(region) k = props.k.getValue(region) Q = 1/(props.c_f*props.phi.getValue(region) + props.c_s.getValue(region)*(1 - props.phi.getValue(region))) for e in region.getElements(): dx = e.getLength() f = e.getFace() bIndex = f.getBackwardVertex().getIndex() + uShift*grid.getNumberOfVertices() fIndex = f.getForwardVertex().getIndex() + uShift*grid.getNumberOfVertices() A = props.mu*dx*dx/(16*k*Q*timeStep) for i,v in enumerate(e.getVertices()): col = v.getIndex() + (1-uShift)*grid.getNumberOfVertices() linearSystem.addValueToMatrix( bIndex, col, +alpha*A ) linearSystem.addValueToMatrix( fIndex, col, -alpha*A ) def AssemblyPisFullToPorePressureVector(linearSystem, grid, props, timeStep, pOldField, uShift=0): for region in grid.getRegions(): M = props.M.getValue(region) alpha = props.biot.getValue(region) k = props.k.getValue(region) Q = 1/(props.c_f*props.phi.getValue(region) + props.c_s.getValue(region)*(1 - props.phi.getValue(region))) for e in region.getElements(): f = e.getFace() dx = e.getLength() A = props.mu*dx*dx/(16*k*Q*timeStep) bVertex = f.getBackwardVertex() fVertex = f.getForwardVertex() pb = pOldField.getValue(bVertex) pf = pOldField.getValue(fVertex) linearSystem.addValueToVector(bVertex.getIndex() + uShift*grid.getNumberOfVertices(), alpha*A*(pf + pb)) linearSystem.addValueToVector(fVertex.getIndex() + uShift*grid.getNumberOfVertices(), -alpha*A*(pf + pb)) # ------------------------------------------------------------------------------------------ # ---------------------------- LOOP BY ELEMENTS ---------------------------------- def AssemblyStiffnessMatrix_e(linearSystem, grid, modulus, uShift=0): for element in grid.getElements(): value = modulus.getValue(element) dx = element.getLength() f = element.getFace() bIndex = f.getBackwardVertex().getIndex() + uShift*grid.getNumberOfVertices() fIndex = f.getForwardVertex().getIndex() + uShift*grid.getNumberOfVertices() forceOperator = [-value/dx, value/dx] localIndex = 0 for v in element.getVertices(): flux = forceOperator[localIndex] vIndex = v.getIndex() + uShift*grid.getNumberOfVertices() linearSystem.addValueToMatrix( bIndex, vIndex, flux ) linearSystem.addValueToMatrix( fIndex, vIndex, -flux ) localIndex += 1 def AssemblyGravityToVector_e(linearSystem, grid, densityOnElements, gravity, uShift=0): for element in grid.getElements(): rho = densityOnElements.getValue(element) face = element.getFace() bVertex = face.getBackwardVertex() fVertex = face.getForwardVertex() value = -rho*gravity*element.getSubVolume() linearSystem.addValueToVector(bVertex.getIndex() + uShift*grid.getNumberOfVertices(), value) linearSystem.addValueToVector(fVertex.getIndex() + uShift*grid.getNumberOfVertices(), value) def AssemblyPorePressureToVector_e(linearSystem, grid, biotOnElements, pField, uShift=0): for element in grid.getElements(): alpha = biotOnElements.getValue(element) f = element.getFace() backVertex = f.getBackwardVertex() forVertex = f.getForwardVertex() bIndex = backVertex.getIndex() + uShift*grid.getNumberOfVertices() fIndex = forVertex.getIndex() + uShift*grid.getNumberOfVertices() pBack = pField.getValue(backVertex) pFron = pField.getValue(forVertex) value = alpha/2. linearSystem.addValueToVector(bIndex, value*pBack) linearSystem.addValueToVector(bIndex, value*pFron) linearSystem.addValueToVector(fIndex, -value*pBack) linearSystem.addValueToVector(fIndex, -value*pFron) # if __name__ == '__main__': # from GridLib import * # from FieldsLib import * # from LinearSystemLib import * # L_0 = 4. # L_1 = 6. # L = L_0 + L_1 # nVertices = 10 # nodesCoord, elemConn = createGridData( L, nVertices ) # # -------------- GRID DATA ---------------------------- # gridData = GridData() # gridData.setElementConnectivity( elemConn ) # gridData.setNodeCoordinates( nodesCoord ) # centroidCoord = [] # for e in elemConn: # x_0 = gridData.nodeCoordinates[e[0]] # x_1 = gridData.nodeCoordinates[e[1]] # centroidCoord.append((x_0 + x_1)/2.) # region_1 = [] # region_2 = [] # namesOfRegions = ['bottom', 'top'] # for e, x in enumerate(centroidCoord): # if x <= L_0: # region_1.append(e) # elif x > L_0: # region_2.append(e) # gridData.setElementsToRegion(region_1, 'lower_layer') # gridData.setElementsToRegion(region_2, 'upper_layer') # g = Grid_1D( gridData ) # for region in g.getRegions(): # print(region.getName()) # for element in region.getElements(): # vec = [element.getIndex()] # for v in element.getVertices(): # vec.append(v.getIndex()) # print(vec) # print('\n') # # ----------------------------------------------------- # # -------------- PROPERTIES ---------------------------- # M = ScalarField(g.getNumberOfRegions()) # M.setValue(g.getRegions()[0], 1000.) # M.setValue(g.getRegions()[1], 2000.) # # ----------------------------------------------------- # # -------------- LINEAR SYSTEM ------------------------ # ls = LinearSystem(g.getNumberOfVertices()) # AssemblyStiffnessMatrix(ls, g, M, 0) # ls.applyDirichlet(0, 0) # ls.applyNeumann(-1, -1000) # print(g.getNumberOfVertices()) # print(ls.getMatrix()) # print(ls.getVector()) # ls.solve() # print(ls.getSolution()) # # -----------------------------------------------------

46.619608

117

0.597662

def AssemblyStiffnessMatrix(linearSystem, grid, props, uShift=0): for region in grid.getRegions(): M = props.M.getValue(region) for e in region.getElements(): dx = e.getLength() f = e.getFace() bIndex = f.getBackwardVertex().getIndex() + uShift*grid.getNumberOfVertices() fIndex = f.getForwardVertex().getIndex() + uShift*grid.getNumberOfVertices() forceOperator = [-M/dx, M/dx] localIndex = 0 for v in e.getVertices(): flux = forceOperator[localIndex] vIndex = v.getIndex() + uShift*grid.getNumberOfVertices() linearSystem.addValueToMatrix( bIndex, vIndex, flux ) linearSystem.addValueToMatrix( fIndex, vIndex, -flux ) localIndex += 1 def AssemblyGravityToVector(linearSystem, grid, props, gravity, uShift=0): n = grid.getNumberOfVertices() for region in grid.getRegions(): rho = props.rho.getValue(region) for elem in region.getElements(): face = elem.getFace() bVertex = face.getBackwardVertex() fVertex = face.getForwardVertex() value = -rho*gravity*elem.getSubVolume() linearSystem.addValueToVector(bVertex.getIndex() + uShift*n, value) linearSystem.addValueToVector(fVertex.getIndex() + uShift*n, value) def AssemblyPorePressureToMatrix(linearSystem, grid, props, uShift=0): for region in grid.getRegions(): alpha = props.biot.getValue(region) for e in region.getElements(): f = e.getFace() bIndex = f.getBackwardVertex().getIndex() + uShift*grid.getNumberOfVertices() fIndex = f.getForwardVertex().getIndex() + uShift*grid.getNumberOfVertices() for i,v in enumerate(e.getVertices()): col = v.getIndex() + (1-uShift)*grid.getNumberOfVertices() linearSystem.addValueToMatrix( bIndex, col, -alpha/2 ) linearSystem.addValueToMatrix( fIndex, col, +alpha/2 ) def AssemblyPorePressureToVector(linearSystem, grid, props, pField, uShift=0): for region in grid.getRegions(): alpha = props.biot.getValue(region) for e in region.getElements(): f = e.getFace() bIndex = f.getBackwardVertex().getIndex() + uShift*grid.getNumberOfVertices() fIndex = f.getForwardVertex().getIndex() + uShift*grid.getNumberOfVertices() pBack = pField.getValue(f.getBackwardVertex()) pFron = pField.getValue(f.getForwardVertex()) value = alpha/2. linearSystem.addValueToVector(bIndex, value*pBack) linearSystem.addValueToVector(bIndex, value*pFron) linearSystem.addValueToVector(fIndex, -value*pBack) linearSystem.addValueToVector(fIndex, -value*pFron) def AssemblyUDNMatrix(cooMatrix, grid, props): for region in grid.getRegions(): Q = props.Q.getValue(region) alpha = props.biot.getValue(region) coef = alpha*alpha*Q for e in region.getElements(): dx = e.getLength() f = e.getFace() bIndex = f.getBackwardVertex().getIndex() fIndex = f.getForwardVertex().getIndex() forceOperator = [-coef/dx, coef/dx] localIndex = 0 for v in e.getVertices(): flux = forceOperator[localIndex] vIndex = v.getIndex() cooMatrix.addValueToMatrix(bIndex, vIndex, flux) cooMatrix.addValueToMatrix(fIndex, vIndex, -flux) localIndex += 1 def AssemblyPisFullToGeoMatrix(linearSystem, grid, props, timeStep, uShift=0): for region in grid.getRegions(): M = props.M.getValue(region) alpha = props.biot.getValue(region) k = props.k.getValue(region) Q = 1/(props.c_f*props.phi.getValue(region) + props.c_s.getValue(region)*(1 - props.phi.getValue(region))) for e in region.getElements(): f = e.getFace() bIndex = f.getBackwardVertex().getIndex() + uShift*grid.getNumberOfVertices() fIndex = f.getForwardVertex().getIndex() + uShift*grid.getNumberOfVertices() dx = e.getLength() B = props.mu*dx*alpha/(8*k*timeStep) pisOperator = [-alpha*B, alpha*B] for localIndex, v in enumerate(e.getVertices()): coef = pisOperator[localIndex] vIndex = v.getIndex() + uShift*grid.getNumberOfVertices() linearSystem.addValueToMatrix( bIndex, vIndex, coef ) linearSystem.addValueToMatrix( fIndex, vIndex, -coef ) def AssemblyPisFullToGeoVector(linearSystem, grid, props, timeStep, uOldField, uShift=0): for region in grid.getRegions(): M = props.M.getValue(region) alpha = props.biot.getValue(region) k = props.k.getValue(region) Q = 1/(props.c_f*props.phi.getValue(region) + props.c_s.getValue(region)*(1 - props.phi.getValue(region))) for e in region.getElements(): f = e.getFace() dx = e.getLength() B = props.mu*dx*alpha/(8*k*timeStep) bVertex = f.getBackwardVertex() fVertex = f.getForwardVertex() ub = uOldField.getValue(bVertex) uf = uOldField.getValue(fVertex) linearSystem.addValueToVector(bVertex.getIndex() + uShift*grid.getNumberOfVertices(), alpha*B*(uf - ub)) linearSystem.addValueToVector(fVertex.getIndex() + uShift*grid.getNumberOfVertices(), -alpha*B*(uf - ub)) def AssemblyPisFullToPorePressureMatrix(linearSystem, grid, props, timeStep, uShift=0): for region in grid.getRegions(): alpha = props.biot.getValue(region) k = props.k.getValue(region) Q = 1/(props.c_f*props.phi.getValue(region) + props.c_s.getValue(region)*(1 - props.phi.getValue(region))) for e in region.getElements(): dx = e.getLength() f = e.getFace() bIndex = f.getBackwardVertex().getIndex() + uShift*grid.getNumberOfVertices() fIndex = f.getForwardVertex().getIndex() + uShift*grid.getNumberOfVertices() A = props.mu*dx*dx/(16*k*Q*timeStep) for i,v in enumerate(e.getVertices()): col = v.getIndex() + (1-uShift)*grid.getNumberOfVertices() linearSystem.addValueToMatrix( bIndex, col, +alpha*A ) linearSystem.addValueToMatrix( fIndex, col, -alpha*A ) def AssemblyPisFullToPorePressureVector(linearSystem, grid, props, timeStep, pOldField, uShift=0): for region in grid.getRegions(): M = props.M.getValue(region) alpha = props.biot.getValue(region) k = props.k.getValue(region) Q = 1/(props.c_f*props.phi.getValue(region) + props.c_s.getValue(region)*(1 - props.phi.getValue(region))) for e in region.getElements(): f = e.getFace() dx = e.getLength() A = props.mu*dx*dx/(16*k*Q*timeStep) bVertex = f.getBackwardVertex() fVertex = f.getForwardVertex() pb = pOldField.getValue(bVertex) pf = pOldField.getValue(fVertex) linearSystem.addValueToVector(bVertex.getIndex() + uShift*grid.getNumberOfVertices(), alpha*A*(pf + pb)) linearSystem.addValueToVector(fVertex.getIndex() + uShift*grid.getNumberOfVertices(), -alpha*A*(pf + pb)) def AssemblyStiffnessMatrix_e(linearSystem, grid, modulus, uShift=0): for element in grid.getElements(): value = modulus.getValue(element) dx = element.getLength() f = element.getFace() bIndex = f.getBackwardVertex().getIndex() + uShift*grid.getNumberOfVertices() fIndex = f.getForwardVertex().getIndex() + uShift*grid.getNumberOfVertices() forceOperator = [-value/dx, value/dx] localIndex = 0 for v in element.getVertices(): flux = forceOperator[localIndex] vIndex = v.getIndex() + uShift*grid.getNumberOfVertices() linearSystem.addValueToMatrix( bIndex, vIndex, flux ) linearSystem.addValueToMatrix( fIndex, vIndex, -flux ) localIndex += 1 def AssemblyGravityToVector_e(linearSystem, grid, densityOnElements, gravity, uShift=0): for element in grid.getElements(): rho = densityOnElements.getValue(element) face = element.getFace() bVertex = face.getBackwardVertex() fVertex = face.getForwardVertex() value = -rho*gravity*element.getSubVolume() linearSystem.addValueToVector(bVertex.getIndex() + uShift*grid.getNumberOfVertices(), value) linearSystem.addValueToVector(fVertex.getIndex() + uShift*grid.getNumberOfVertices(), value) def AssemblyPorePressureToVector_e(linearSystem, grid, biotOnElements, pField, uShift=0): for element in grid.getElements(): alpha = biotOnElements.getValue(element) f = element.getFace() backVertex = f.getBackwardVertex() forVertex = f.getForwardVertex() bIndex = backVertex.getIndex() + uShift*grid.getNumberOfVertices() fIndex = forVertex.getIndex() + uShift*grid.getNumberOfVertices() pBack = pField.getValue(backVertex) pFron = pField.getValue(forVertex) value = alpha/2. linearSystem.addValueToVector(bIndex, value*pBack) linearSystem.addValueToVector(bIndex, value*pFron) linearSystem.addValueToVector(fIndex, -value*pBack) linearSystem.addValueToVector(fIndex, -value*pFron)

true

1c43e105b918ef473175c9aefbc7dbf6367f1764

22,146

py

Python

lib/utils/paf_to_pose.py

kacel33/ActionAI_PC

a0528f49ea61cc07d7c1e9a3cd6846e5f50cfae7

[ "MIT" ]

1,311

2017-03-28T09:24:20.000Z

2022-03-30T02:43:11.000Z

lib/utils/paf_to_pose.py

kacel33/ActionAI_PC

a0528f49ea61cc07d7c1e9a3cd6846e5f50cfae7

[ "MIT" ]

144

2017-05-09T16:35:40.000Z

2022-03-25T03:14:42.000Z

lib/utils/paf_to_pose.py

kacel33/ActionAI_PC

a0528f49ea61cc07d7c1e9a3cd6846e5f50cfae7

[ "MIT" ]

437

2017-03-30T15:23:14.000Z

2022-03-25T09:18:50.000Z

import cv2 import numpy as np import time from scipy.ndimage.filters import gaussian_filter, maximum_filter from scipy.ndimage.morphology import generate_binary_structure from lib.pafprocess import pafprocess from lib.utils.common import Human, BodyPart, CocoPart, CocoColors, CocoPairsRender # Heatmap indices to find each limb (joint connection). Eg: limb_type=1 is # Neck->LShoulder, so joint_to_limb_heatmap_relationship[1] represents the # indices of heatmaps to look for joints: neck=1, LShoulder=5 joint_to_limb_heatmap_relationship = [[1, 2], [2, 3], [3, 4], [1, 5], [5, 6], [6, 7], [1, 0]] # PAF indices containing the x and y coordinates of the PAF for a given limb. # Eg: limb_type=1 is Neck->LShoulder, so # PAFneckLShoulder_x=paf_xy_coords_per_limb[1][0] and # PAFneckLShoulder_y=paf_xy_coords_per_limb[1][1] paf_xy_coords_per_limb = np.arange(14).reshape(7, 2) NUM_LIMBS = len(joint_to_limb_heatmap_relationship) def find_peaks(param, img): """ Given a (grayscale) image, find local maxima whose value is above a given threshold (param['thre1']) :param img: Input image (2d array) where we want to find peaks :return: 2d np.array containing the [x,y] coordinates of each peak found in the image """ peaks_binary = (maximum_filter(img, footprint=generate_binary_structure( 2, 1)) == img) * (img > param) # Note reverse ([::-1]): we return [[x y], [x y]...] instead of [[y x], [y # x]...] return np.array(np.nonzero(peaks_binary)[::-1]).T def compute_resized_coords(coords, resizeFactor): """ Given the index/coordinates of a cell in some input array (e.g. image), provides the new coordinates if that array was resized by making it resizeFactor times bigger. E.g.: image of size 3x3 is resized to 6x6 (resizeFactor=2), we'd like to know the new coordinates of cell [1,2] -> Function would return [2.5,4.5] :param coords: Coordinates (indices) of a cell in some input array :param resizeFactor: Resize coefficient = shape_dest/shape_source. E.g.: resizeFactor=2 means the destination array is twice as big as the original one :return: Coordinates in an array of size shape_dest=resizeFactor*shape_source, expressing the array indices of the closest point to 'coords' if an image of size shape_source was resized to shape_dest """ # 1) Add 0.5 to coords to get coordinates of center of the pixel (e.g. # index [0,0] represents the pixel at location [0.5,0.5]) # 2) Transform those coordinates to shape_dest, by multiplying by resizeFactor # 3) That number represents the location of the pixel center in the new array, # so subtract 0.5 to get coordinates of the array index/indices (revert # step 1) return (np.array(coords, dtype=float) + 0.5) * resizeFactor - 0.5 def NMS(heatmaps, upsampFactor=1., bool_refine_center=True, bool_gaussian_filt=False, config=None): """ NonMaximaSuppression: find peaks (local maxima) in a set of grayscale images :param heatmaps: set of grayscale images on which to find local maxima (3d np.array, with dimensions image_height x image_width x num_heatmaps) :param upsampFactor: Size ratio between CPM heatmap output and the input image size. Eg: upsampFactor=16 if original image was 480x640 and heatmaps are 30x40xN :param bool_refine_center: Flag indicating whether: - False: Simply return the low-res peak found upscaled by upsampFactor (subject to grid-snap) - True: (Recommended, very accurate) Upsample a small patch around each low-res peak and fine-tune the location of the peak at the resolution of the original input image :param bool_gaussian_filt: Flag indicating whether to apply a 1d-GaussianFilter (smoothing) to each upsampled patch before fine-tuning the location of each peak. :return: a NUM_JOINTS x 4 np.array where each row represents a joint type (0=nose, 1=neck...) and the columns indicate the {x,y} position, the score (probability) and a unique id (counter) """ # MODIFIED BY CARLOS: Instead of upsampling the heatmaps to heatmap_avg and # then performing NMS to find peaks, this step can be sped up by ~25-50x by: # (9-10ms [with GaussFilt] or 5-6ms [without GaussFilt] vs 250-280ms on RoG # 1. Perform NMS at (low-res) CPM's output resolution # 1.1. Find peaks using scipy.ndimage.filters.maximum_filter # 2. Once a peak is found, take a patch of 5x5 centered around the peak, upsample it, and # fine-tune the position of the actual maximum. # '-> That's equivalent to having found the peak on heatmap_avg, but much faster because we only # upsample and scan the 5x5 patch instead of the full (e.g.) 480x640 joint_list_per_joint_type = [] cnt_total_joints = 0 # For every peak found, win_size specifies how many pixels in each # direction from the peak we take to obtain the patch that will be # upsampled. Eg: win_size=1 -> patch is 3x3; win_size=2 -> 5x5 # (for BICUBIC interpolation to be accurate, win_size needs to be >=2!) win_size = 2 for joint in range(config.MODEL.NUM_KEYPOINTS): map_orig = heatmaps[:, :, joint] peak_coords = find_peaks(config.TEST.THRESH_HEATMAP, map_orig) peaks = np.zeros((len(peak_coords), 4)) for i, peak in enumerate(peak_coords): if bool_refine_center: x_min, y_min = np.maximum(0, peak - win_size) x_max, y_max = np.minimum( np.array(map_orig.T.shape) - 1, peak + win_size) # Take a small patch around each peak and only upsample that # tiny region patch = map_orig[y_min:y_max + 1, x_min:x_max + 1] map_upsamp = cv2.resize( patch, None, fx=upsampFactor, fy=upsampFactor, interpolation=cv2.INTER_CUBIC) # Gaussian filtering takes an average of 0.8ms/peak (and there might be # more than one peak per joint!) -> For now, skip it (it's # accurate enough) map_upsamp = gaussian_filter( map_upsamp, sigma=3) if bool_gaussian_filt else map_upsamp # Obtain the coordinates of the maximum value in the patch location_of_max = np.unravel_index( map_upsamp.argmax(), map_upsamp.shape) # Remember that peaks indicates [x,y] -> need to reverse it for # [y,x] location_of_patch_center = compute_resized_coords( peak[::-1] - [y_min, x_min], upsampFactor) # Calculate the offset wrt to the patch center where the actual # maximum is refined_center = (location_of_max - location_of_patch_center) peak_score = map_upsamp[location_of_max] else: refined_center = [0, 0] # Flip peak coordinates since they are [x,y] instead of [y,x] peak_score = map_orig[tuple(peak[::-1])] peaks[i, :] = tuple( x for x in compute_resized_coords(peak_coords[i], upsampFactor) + refined_center[::-1]) + ( peak_score, cnt_total_joints) cnt_total_joints += 1 joint_list_per_joint_type.append(peaks) return joint_list_per_joint_type def find_connected_joints(paf_upsamp, joint_list_per_joint_type, num_intermed_pts=10, config=None): """ For every type of limb (eg: forearm, shin, etc.), look for every potential pair of joints (eg: every wrist-elbow combination) and evaluate the PAFs to determine which pairs are indeed body limbs. :param paf_upsamp: PAFs upsampled to the original input image resolution :param joint_list_per_joint_type: See 'return' doc of NMS() :param num_intermed_pts: Int indicating how many intermediate points to take between joint_src and joint_dst, at which the PAFs will be evaluated :return: List of NUM_LIMBS rows. For every limb_type (a row) we store a list of all limbs of that type found (eg: all the right forearms). For each limb (each item in connected_limbs[limb_type]), we store 5 cells: # {joint_src_id,joint_dst_id}: a unique number associated with each joint, # limb_score_penalizing_long_dist: a score of how good a connection of the joints is, penalized if the limb length is too long # {joint_src_index,joint_dst_index}: the index of the joint within all the joints of that type found (eg: the 3rd right elbow found) """ connected_limbs = [] # Auxiliary array to access paf_upsamp quickly limb_intermed_coords = np.empty((4, num_intermed_pts), dtype=np.intp) for limb_type in range(NUM_LIMBS): # List of all joints of type A found, where A is specified by limb_type # (eg: a right forearm starts in a right elbow) joints_src = joint_list_per_joint_type[joint_to_limb_heatmap_relationship[limb_type][0]] # List of all joints of type B found, where B is specified by limb_type # (eg: a right forearm ends in a right wrist) joints_dst = joint_list_per_joint_type[joint_to_limb_heatmap_relationship[limb_type][1]] # print(joint_to_limb_heatmap_relationship[limb_type][0]) # print(joint_to_limb_heatmap_relationship[limb_type][1]) # print(paf_xy_coords_per_limb[limb_type][0]) # print(paf_xy_coords_per_limb[limb_type][1]) if len(joints_src) == 0 or len(joints_dst) == 0: # No limbs of this type found (eg: no right forearms found because # we didn't find any right wrists or right elbows) connected_limbs.append([]) else: connection_candidates = [] # Specify the paf index that contains the x-coord of the paf for # this limb limb_intermed_coords[2, :] = paf_xy_coords_per_limb[limb_type][0] # And the y-coord paf index limb_intermed_coords[3, :] = paf_xy_coords_per_limb[limb_type][1] for i, joint_src in enumerate(joints_src): # Try every possible joints_src[i]-joints_dst[j] pair and see # if it's a feasible limb for j, joint_dst in enumerate(joints_dst): # Subtract the position of both joints to obtain the # direction of the potential limb limb_dir = joint_dst[:2] - joint_src[:2] # Compute the distance/length of the potential limb (norm # of limb_dir) limb_dist = np.sqrt(np.sum(limb_dir ** 2)) + 1e-8 limb_dir = limb_dir / limb_dist # Normalize limb_dir to be a unit vector # Linearly distribute num_intermed_pts points from the x # coordinate of joint_src to the x coordinate of joint_dst limb_intermed_coords[1, :] = np.round(np.linspace( joint_src[0], joint_dst[0], num=num_intermed_pts)) limb_intermed_coords[0, :] = np.round(np.linspace( joint_src[1], joint_dst[1], num=num_intermed_pts)) # Same for the y coordinate intermed_paf = paf_upsamp[limb_intermed_coords[0, :], limb_intermed_coords[1, :], limb_intermed_coords[2:4, :]].T score_intermed_pts = intermed_paf.dot(limb_dir) score_penalizing_long_dist = score_intermed_pts.mean( ) + min(0.5 * paf_upsamp.shape[0] / limb_dist - 1, 0) # Criterion 1: At least 80% of the intermediate points have # a score higher than thre2 criterion1 = (np.count_nonzero( score_intermed_pts > config.TEST.THRESH_PAF) > 0.8 * num_intermed_pts) # Criterion 2: Mean score, penalized for large limb # distances (larger than half the image height), is # positive criterion2 = (score_penalizing_long_dist > 0) if criterion1 and criterion2: # Last value is the combined paf(+limb_dist) + heatmap # scores of both joints connection_candidates.append( [i, j, score_penalizing_long_dist, score_penalizing_long_dist + joint_src[2] + joint_dst[2]]) # Sort connection candidates based on their # score_penalizing_long_dist connection_candidates = sorted( connection_candidates, key=lambda x: x[2], reverse=True) connections = np.empty((0, 5)) # There can only be as many limbs as the smallest number of source # or destination joints (eg: only 2 forearms if there's 5 wrists # but 2 elbows) max_connections = min(len(joints_src), len(joints_dst)) # Traverse all potential joint connections (sorted by their score) for potential_connection in connection_candidates: i, j, s = potential_connection[0:3] # Make sure joints_src[i] or joints_dst[j] haven't already been # connected to other joints_dst or joints_src if i not in connections[:, 3] and j not in connections[:, 4]: # [joint_src_id, joint_dst_id, limb_score_penalizing_long_dist, joint_src_index, joint_dst_index] connections = np.vstack( [connections, [joints_src[i][3], joints_dst[j][3], s, i, j]]) # Exit if we've already established max_connections # connections (each joint can't be connected to more than # one joint) if len(connections) >= max_connections: break connected_limbs.append(connections) return connected_limbs def group_limbs_of_same_person(connected_limbs, joint_list, config): """ Associate limbs belonging to the same person together. :param connected_limbs: See 'return' doc of find_connected_joints() :param joint_list: unravel'd version of joint_list_per_joint [See 'return' doc of NMS()] :return: 2d np.array of size num_people x (NUM_JOINTS+2). For each person found: # First NUM_JOINTS columns contain the index (in joint_list) of the joints associated with that person (or -1 if their i-th joint wasn't found) # 2nd-to-last column: Overall score of the joints+limbs that belong to this person # Last column: Total count of joints found for this person """ person_to_joint_assoc = [] for limb_type in range(NUM_LIMBS): joint_src_type, joint_dst_type = joint_to_limb_heatmap_relationship[limb_type] for limb_info in connected_limbs[limb_type]: person_assoc_idx = [] for person, person_limbs in enumerate(person_to_joint_assoc): if person_limbs[joint_src_type] == limb_info[0] or person_limbs[joint_dst_type] == limb_info[1]: person_assoc_idx.append(person) # If one of the joints has been associated to a person, and either # the other joint is also associated with the same person or not # associated to anyone yet: if len(person_assoc_idx) == 1: person_limbs = person_to_joint_assoc[person_assoc_idx[0]] # If the other joint is not associated to anyone yet, if person_limbs[joint_dst_type] != limb_info[1]: # Associate it with the current person person_limbs[joint_dst_type] = limb_info[1] # Increase the number of limbs associated to this person person_limbs[-1] += 1 # And update the total score (+= heatmap score of joint_dst # + score of connecting joint_src with joint_dst) person_limbs[-2] += joint_list[limb_info[1] .astype(int), 2] + limb_info[2] elif len(person_assoc_idx) == 2: # if found 2 and disjoint, merge them person1_limbs = person_to_joint_assoc[person_assoc_idx[0]] person2_limbs = person_to_joint_assoc[person_assoc_idx[1]] membership = ((person1_limbs >= 0) & (person2_limbs >= 0))[:-2] if not membership.any(): # If both people have no same joints connected, merge into a single person # Update which joints are connected person1_limbs[:-2] += (person2_limbs[:-2] + 1) # Update the overall score and total count of joints # connected by summing their counters person1_limbs[-2:] += person2_limbs[-2:] # Add the score of the current joint connection to the # overall score person1_limbs[-2] += limb_info[2] person_to_joint_assoc.pop(person_assoc_idx[1]) else: # Same case as len(person_assoc_idx)==1 above person1_limbs[joint_dst_type] = limb_info[1] person1_limbs[-1] += 1 person1_limbs[-2] += joint_list[limb_info[1] .astype(int), 2] + limb_info[2] else: # No person has claimed any of these joints, create a new person # Initialize person info to all -1 (no joint associations) row = -1 * np.ones(config.MODEL.NUM_KEYPOINTS + 2) # Store the joint info of the new connection row[joint_src_type] = limb_info[0] row[joint_dst_type] = limb_info[1] # Total count of connected joints for this person: 2 row[-1] = 2 # Compute overall score: score joint_src + score joint_dst + score connection # {joint_src,joint_dst} row[-2] = sum(joint_list[limb_info[:2].astype(int), 2] ) + limb_info[2] person_to_joint_assoc.append(row) # Delete people who have very few parts connected people_to_delete = [] for person_id, person_info in enumerate(person_to_joint_assoc): if person_info[-1] < 3 or person_info[-2] / person_info[-1] < 0.2: people_to_delete.append(person_id) # Traverse the list in reverse order so we delete indices starting from the # last one (otherwise, removing item for example 0 would modify the indices of # the remaining people to be deleted!) for index in people_to_delete[::-1]: person_to_joint_assoc.pop(index) # Appending items to a np.array can be costly (allocating new memory, copying over the array, then adding new row) # Instead, we treat the set of people as a list (fast to append items) and # only convert to np.array at the end return np.array(person_to_joint_assoc) def paf_to_pose(heatmaps, pafs, config): # Bottom-up approach: # Step 1: find all joints in the image (organized by joint type: [0]=nose, # [1]=neck...) joint_list_per_joint_type = NMS(heatmaps, upsampFactor=config.MODEL.DOWNSAMPLE, config=config) # joint_list is an unravel'd version of joint_list_per_joint, where we add # a 5th column to indicate the joint_type (0=nose, 1=neck...) joint_list = np.array([tuple(peak) + (joint_type,) for joint_type, joint_peaks in enumerate(joint_list_per_joint_type) for peak in joint_peaks]) # import ipdb # ipdb.set_trace() # Step 2: find which joints go together to form limbs (which wrists go # with which elbows) paf_upsamp = cv2.resize( pafs, None, fx=config.MODEL.DOWNSAMPLE, fy=config.MODEL.DOWNSAMPLE, interpolation=cv2.INTER_CUBIC) connected_limbs = find_connected_joints(paf_upsamp, joint_list_per_joint_type, config.TEST.NUM_INTERMED_PTS_BETWEEN_KEYPOINTS, config) # Step 3: associate limbs that belong to the same person person_to_joint_assoc = group_limbs_of_same_person( connected_limbs, joint_list, config) return joint_list, person_to_joint_assoc def paf_to_pose_cpp(heatmaps, pafs, config): humans = [] joint_list_per_joint_type = NMS(heatmaps, upsampFactor=config.MODEL.DOWNSAMPLE, config=config) joint_list = np.array( [tuple(peak) + (joint_type,) for joint_type, joint_peaks in enumerate(joint_list_per_joint_type) for peak in joint_peaks]).astype(np.float32) if joint_list.shape[0] > 0: joint_list = np.expand_dims(joint_list, 0) paf_upsamp = cv2.resize( pafs, None, fx=config.MODEL.DOWNSAMPLE, fy=config.MODEL.DOWNSAMPLE, interpolation=cv2.INTER_NEAREST) heatmap_upsamp = cv2.resize( heatmaps, None, fx=config.MODEL.DOWNSAMPLE, fy=config.MODEL.DOWNSAMPLE, interpolation=cv2.INTER_NEAREST) pafprocess.process_paf(joint_list, heatmap_upsamp, paf_upsamp) for human_id in range(pafprocess.get_num_humans()): human = Human([]) is_added = False for part_idx in range(config.MODEL.NUM_KEYPOINTS): c_idx = int(pafprocess.get_part_cid(human_id, part_idx)) if c_idx < 0: continue is_added = True human.body_parts[part_idx] = BodyPart( '%d-%d' % (human_id, part_idx), part_idx, float(pafprocess.get_part_x(c_idx)) / heatmap_upsamp.shape[1], float(pafprocess.get_part_y(c_idx)) / heatmap_upsamp.shape[0], pafprocess.get_part_score(c_idx) ) if is_added: score = pafprocess.get_score(human_id) human.score = score humans.append(human) return humans

54.412776

136

0.635916

import cv2 import numpy as np import time from scipy.ndimage.filters import gaussian_filter, maximum_filter from scipy.ndimage.morphology import generate_binary_structure from lib.pafprocess import pafprocess from lib.utils.common import Human, BodyPart, CocoPart, CocoColors, CocoPairsRender joint_to_limb_heatmap_relationship = [[1, 2], [2, 3], [3, 4], [1, 5], [5, 6], [6, 7], [1, 0]] paf_xy_coords_per_limb = np.arange(14).reshape(7, 2) NUM_LIMBS = len(joint_to_limb_heatmap_relationship) def find_peaks(param, img): peaks_binary = (maximum_filter(img, footprint=generate_binary_structure( 2, 1)) == img) * (img > param) return np.array(np.nonzero(peaks_binary)[::-1]).T def compute_resized_coords(coords, resizeFactor): return (np.array(coords, dtype=float) + 0.5) * resizeFactor - 0.5 def NMS(heatmaps, upsampFactor=1., bool_refine_center=True, bool_gaussian_filt=False, config=None): # 1.1. Find peaks using scipy.ndimage.filters.maximum_filter # 2. Once a peak is found, take a patch of 5x5 centered around the peak, upsample it, and # fine-tune the position of the actual maximum. # '-> That's equivalent to having found the peak on heatmap_avg, but much faster because we only # upsample and scan the 5x5 patch instead of the full (e.g.) 480x640 joint_list_per_joint_type = [] cnt_total_joints = 0 # For every peak found, win_size specifies how many pixels in each # direction from the peak we take to obtain the patch that will be # upsampled. Eg: win_size=1 -> patch is 3x3; win_size=2 -> 5x5 # (for BICUBIC interpolation to be accurate, win_size needs to be >=2!) win_size = 2 for joint in range(config.MODEL.NUM_KEYPOINTS): map_orig = heatmaps[:, :, joint] peak_coords = find_peaks(config.TEST.THRESH_HEATMAP, map_orig) peaks = np.zeros((len(peak_coords), 4)) for i, peak in enumerate(peak_coords): if bool_refine_center: x_min, y_min = np.maximum(0, peak - win_size) x_max, y_max = np.minimum( np.array(map_orig.T.shape) - 1, peak + win_size) # Take a small patch around each peak and only upsample that # tiny region patch = map_orig[y_min:y_max + 1, x_min:x_max + 1] map_upsamp = cv2.resize( patch, None, fx=upsampFactor, fy=upsampFactor, interpolation=cv2.INTER_CUBIC) # Gaussian filtering takes an average of 0.8ms/peak (and there might be # more than one peak per joint!) -> For now, skip it (it's map_upsamp = gaussian_filter( map_upsamp, sigma=3) if bool_gaussian_filt else map_upsamp location_of_max = np.unravel_index( map_upsamp.argmax(), map_upsamp.shape) location_of_patch_center = compute_resized_coords( peak[::-1] - [y_min, x_min], upsampFactor) refined_center = (location_of_max - location_of_patch_center) peak_score = map_upsamp[location_of_max] else: refined_center = [0, 0] peak_score = map_orig[tuple(peak[::-1])] peaks[i, :] = tuple( x for x in compute_resized_coords(peak_coords[i], upsampFactor) + refined_center[::-1]) + ( peak_score, cnt_total_joints) cnt_total_joints += 1 joint_list_per_joint_type.append(peaks) return joint_list_per_joint_type def find_connected_joints(paf_upsamp, joint_list_per_joint_type, num_intermed_pts=10, config=None): connected_limbs = [] limb_intermed_coords = np.empty((4, num_intermed_pts), dtype=np.intp) for limb_type in range(NUM_LIMBS): joints_src = joint_list_per_joint_type[joint_to_limb_heatmap_relationship[limb_type][0]] joints_dst = joint_list_per_joint_type[joint_to_limb_heatmap_relationship[limb_type][1]] if len(joints_src) == 0 or len(joints_dst) == 0: connected_limbs.append([]) else: connection_candidates = [] # Specify the paf index that contains the x-coord of the paf for # this limb limb_intermed_coords[2, :] = paf_xy_coords_per_limb[limb_type][0] # And the y-coord paf index limb_intermed_coords[3, :] = paf_xy_coords_per_limb[limb_type][1] for i, joint_src in enumerate(joints_src): # Try every possible joints_src[i]-joints_dst[j] pair and see # if it's a feasible limb for j, joint_dst in enumerate(joints_dst): limb_dir = joint_dst[:2] - joint_src[:2] limb_dist = np.sqrt(np.sum(limb_dir ** 2)) + 1e-8 limb_dir = limb_dir / limb_dist limb_intermed_coords[1, :] = np.round(np.linspace( joint_src[0], joint_dst[0], num=num_intermed_pts)) limb_intermed_coords[0, :] = np.round(np.linspace( joint_src[1], joint_dst[1], num=num_intermed_pts)) intermed_paf = paf_upsamp[limb_intermed_coords[0, :], limb_intermed_coords[1, :], limb_intermed_coords[2:4, :]].T score_intermed_pts = intermed_paf.dot(limb_dir) score_penalizing_long_dist = score_intermed_pts.mean( ) + min(0.5 * paf_upsamp.shape[0] / limb_dist - 1, 0) criterion1 = (np.count_nonzero( score_intermed_pts > config.TEST.THRESH_PAF) > 0.8 * num_intermed_pts) criterion2 = (score_penalizing_long_dist > 0) if criterion1 and criterion2: connection_candidates.append( [i, j, score_penalizing_long_dist, score_penalizing_long_dist + joint_src[2] + joint_dst[2]]) connection_candidates = sorted( connection_candidates, key=lambda x: x[2], reverse=True) connections = np.empty((0, 5)) # but 2 elbows) max_connections = min(len(joints_src), len(joints_dst)) # Traverse all potential joint connections (sorted by their score) for potential_connection in connection_candidates: i, j, s = potential_connection[0:3] # Make sure joints_src[i] or joints_dst[j] haven't already been if i not in connections[:, 3] and j not in connections[:, 4]: connections = np.vstack( [connections, [joints_src[i][3], joints_dst[j][3], s, i, j]]) # connections (each joint can't be connected to more than if len(connections) >= max_connections: break connected_limbs.append(connections) return connected_limbs def group_limbs_of_same_person(connected_limbs, joint_list, config): person_to_joint_assoc = [] for limb_type in range(NUM_LIMBS): joint_src_type, joint_dst_type = joint_to_limb_heatmap_relationship[limb_type] for limb_info in connected_limbs[limb_type]: person_assoc_idx = [] for person, person_limbs in enumerate(person_to_joint_assoc): if person_limbs[joint_src_type] == limb_info[0] or person_limbs[joint_dst_type] == limb_info[1]: person_assoc_idx.append(person) if len(person_assoc_idx) == 1: person_limbs = person_to_joint_assoc[person_assoc_idx[0]] if person_limbs[joint_dst_type] != limb_info[1]: person_limbs[joint_dst_type] = limb_info[1] person_limbs[-1] += 1 person_limbs[-2] += joint_list[limb_info[1] .astype(int), 2] + limb_info[2] elif len(person_assoc_idx) == 2: person1_limbs = person_to_joint_assoc[person_assoc_idx[0]] person2_limbs = person_to_joint_assoc[person_assoc_idx[1]] membership = ((person1_limbs >= 0) & (person2_limbs >= 0))[:-2] if not membership.any(): person1_limbs[:-2] += (person2_limbs[:-2] + 1) person1_limbs[-2:] += person2_limbs[-2:] person1_limbs[-2] += limb_info[2] person_to_joint_assoc.pop(person_assoc_idx[1]) else: person1_limbs[joint_dst_type] = limb_info[1] person1_limbs[-1] += 1 person1_limbs[-2] += joint_list[limb_info[1] .astype(int), 2] + limb_info[2] else: row = -1 * np.ones(config.MODEL.NUM_KEYPOINTS + 2) row[joint_src_type] = limb_info[0] row[joint_dst_type] = limb_info[1] row[-1] = 2 row[-2] = sum(joint_list[limb_info[:2].astype(int), 2] ) + limb_info[2] person_to_joint_assoc.append(row) people_to_delete = [] for person_id, person_info in enumerate(person_to_joint_assoc): if person_info[-1] < 3 or person_info[-2] / person_info[-1] < 0.2: people_to_delete.append(person_id) for index in people_to_delete[::-1]: person_to_joint_assoc.pop(index) return np.array(person_to_joint_assoc) def paf_to_pose(heatmaps, pafs, config): joint_list_per_joint_type = NMS(heatmaps, upsampFactor=config.MODEL.DOWNSAMPLE, config=config) # a 5th column to indicate the joint_type (0=nose, 1=neck...) joint_list = np.array([tuple(peak) + (joint_type,) for joint_type, joint_peaks in enumerate(joint_list_per_joint_type) for peak in joint_peaks]) # import ipdb # ipdb.set_trace() # Step 2: find which joints go together to form limbs (which wrists go # with which elbows) paf_upsamp = cv2.resize( pafs, None, fx=config.MODEL.DOWNSAMPLE, fy=config.MODEL.DOWNSAMPLE, interpolation=cv2.INTER_CUBIC) connected_limbs = find_connected_joints(paf_upsamp, joint_list_per_joint_type, config.TEST.NUM_INTERMED_PTS_BETWEEN_KEYPOINTS, config) # Step 3: associate limbs that belong to the same person person_to_joint_assoc = group_limbs_of_same_person( connected_limbs, joint_list, config) return joint_list, person_to_joint_assoc def paf_to_pose_cpp(heatmaps, pafs, config): humans = [] joint_list_per_joint_type = NMS(heatmaps, upsampFactor=config.MODEL.DOWNSAMPLE, config=config) joint_list = np.array( [tuple(peak) + (joint_type,) for joint_type, joint_peaks in enumerate(joint_list_per_joint_type) for peak in joint_peaks]).astype(np.float32) if joint_list.shape[0] > 0: joint_list = np.expand_dims(joint_list, 0) paf_upsamp = cv2.resize( pafs, None, fx=config.MODEL.DOWNSAMPLE, fy=config.MODEL.DOWNSAMPLE, interpolation=cv2.INTER_NEAREST) heatmap_upsamp = cv2.resize( heatmaps, None, fx=config.MODEL.DOWNSAMPLE, fy=config.MODEL.DOWNSAMPLE, interpolation=cv2.INTER_NEAREST) pafprocess.process_paf(joint_list, heatmap_upsamp, paf_upsamp) for human_id in range(pafprocess.get_num_humans()): human = Human([]) is_added = False for part_idx in range(config.MODEL.NUM_KEYPOINTS): c_idx = int(pafprocess.get_part_cid(human_id, part_idx)) if c_idx < 0: continue is_added = True human.body_parts[part_idx] = BodyPart( '%d-%d' % (human_id, part_idx), part_idx, float(pafprocess.get_part_x(c_idx)) / heatmap_upsamp.shape[1], float(pafprocess.get_part_y(c_idx)) / heatmap_upsamp.shape[0], pafprocess.get_part_score(c_idx) ) if is_added: score = pafprocess.get_score(human_id) human.score = score humans.append(human) return humans

true

1c43e13d8418e3f82d49ce71c1285cf8469339e2

387

py

Python

scp_epub/download/utils.py

elfakyn/scp_epub

5d0e95d8fa0e11d9ab388c5a4083212c1c857a2f

[ "MIT" ]

5

2020-05-27T15:57:15.000Z

2021-06-11T01:08:50.000Z

scp_epub/download/utils.py

elfakyn/scp_epub

5d0e95d8fa0e11d9ab388c5a4083212c1c857a2f

[ "MIT" ]

null

scp_epub/download/utils.py

elfakyn/scp_epub

5d0e95d8fa0e11d9ab388c5a4083212c1c857a2f

[ "MIT" ]

2

2020-11-14T04:53:51.000Z

2021-06-12T19:28:32.000Z

import re def filter_tags(pages, include_tags=None): if include_tags is not None: pages = [ page for page in pages if 'tags' in page and any( included_tag in page['tags'] for included_tag in include_tags ) ] return pages def normalize_string(raw_string): return re.sub('[^a-z0-9\\-]', '_', raw_string)

21.5

77

0.578811

import re def filter_tags(pages, include_tags=None): if include_tags is not None: pages = [ page for page in pages if 'tags' in page and any( included_tag in page['tags'] for included_tag in include_tags ) ] return pages def normalize_string(raw_string): return re.sub('[^a-z0-9\\-]', '_', raw_string)

true

1c43e2186ae5b7bd32f050d7f5b624c8bb3e6dc6

12,265

py

Python

offb_posctl/scripts/MinimumSnapTimeNode.py

SensenLiu/aggrecup

0c381ee259b388684205c1fa5fc41265a7e849b3

[ "MIT" ]

null

offb_posctl/scripts/MinimumSnapTimeNode.py

SensenLiu/aggrecup

0c381ee259b388684205c1fa5fc41265a7e849b3

[ "MIT" ]

null

offb_posctl/scripts/MinimumSnapTimeNode.py

SensenLiu/aggrecup

0c381ee259b388684205c1fa5fc41265a7e849b3

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- # coding=utf-8 import socket import numpy as np from scipy.optimize import minimize import time import datetime import math import matplotlib.pyplot as plt import rospy from nav_msgs.msg import Odometry from geometry_msgs.msg import TwistStamped from offb_posctl.msg import controlstate # 发布自定义消息 phi=1.57 ay0=0 vy0=0 y0=0 az0=0 vz0=0 z0=0.5 aytf=-math.sin(phi)*9.8 vytf=0.2*math.sin(phi) ytf=7.0 aztf=math.cos(phi)*9.8-9.8 vztf=-0.2*math.cos(phi) ztf=2.0 meshpoint=np.linspace(1, 0.01, 5) thrustmax=2*9.8 angleaccdmax=20 lbz=0.3 ubz=2.5 lbv=-5 ubv=5 currentupdateflag = False # Objective def J(x): return x[-1] def fast_jac(x): jac = np.zeros_like(x) jac[-1]=1 return jac # Constraint def eqmycon(x): global ay0, vy0, y0, az0, vz0, z0, aytf, vytf, ytf, aztf, vztf, ztf, meshpoint, thrustmax, angleaccdmax, lbz, lbv, ubv alpha_y=x[0] beta_y=x[1] gamma_y=x[2] alpha_z=x[3] beta_z=x[4] gamma_z=x[5] t=x[6] ceq1=alpha_y/6*t**3+beta_y/2*t**2+gamma_y*t+ay0-aytf ceq2=alpha_y/24*t**4+beta_y/6*t**3+gamma_y/2*t**2+ay0*t+vy0-vytf ceq3=alpha_y/120*t**5+beta_y/24*t**4+gamma_y/6*t**3+ay0/2*t**2+vy0*t+y0-ytf ceq4=alpha_z/6*t**3+beta_z/2*t**2+gamma_z*t+az0-aztf ceq5=alpha_z/24*t**4+beta_z/6*t**3+gamma_z/2*t**2+az0*t+vz0-vztf ceq6=alpha_z/120*t**5+beta_z/24*t**4+gamma_z/6*t**3+az0/2*t**2+vz0*t+z0-ztf return np.hstack((ceq1,ceq2,ceq3,ceq4,ceq5,ceq6)).ravel() # Constraint def ineqmycon(x): global ay0, vy0, y0, az0, vz0, z0, aytf, vytf, ytf, aztf, vztf, ztf, meshpoint, thrustmax, angleaccdmax, lbz, ubz, lbv, ubv alpha_y=x[0] beta_y=x[1] gamma_y=x[2] alpha_z=x[3] beta_z=x[4] gamma_z=x[5] t=x[6] tmesh=t*(np.array(meshpoint)) angleacc=np.zeros_like(tmesh) for i in range(len(tmesh)): # print("i===",tmesh) t=tmesh[i] angleacc[i]=((((alpha_y*t**2)/2 + beta_y*t + gamma_y)/((alpha_z*t**3)/6 + (beta_z*t**2)/2 + gamma_z*t + az0 + 49/5) - (((alpha_z*t**2)/2 + beta_z*t + gamma_z)*((alpha_y*t**3)/6 + (beta_y*t**2)/2 + gamma_y*t + ay0))/((alpha_z*t**3)/6 + (beta_z*t**2)/2 + gamma_z*t + az0 + 49/5)**2)*((2*((alpha_y*t**2)/2 + beta_y*t + gamma_y)*((alpha_y*t**3)/6 + (beta_y*t**2)/2 + gamma_y*t + ay0))/((alpha_z*t**3)/6 + (beta_z*t**2)/2 + gamma_z*t + az0 + 49/5)**2 - (2*((alpha_z*t**2)/2 + beta_z*t + gamma_z)*((alpha_y*t**3)/6 + (beta_y*t**2)/2 + gamma_y*t + ay0)**2)/((alpha_z*t**3)/6 + (beta_z*t**2)/2 + gamma_z*t + az0 + 49/5)**3))/(((alpha_y*t**3)/6 + (beta_y*t**2)/2 + gamma_y*t + ay0)**2/((alpha_z*t**3)/6 + (beta_z*t**2)/2 + gamma_z*t + az0 + 49/5)**2 + 1)**2 - ((beta_y + alpha_y*t)/((alpha_z*t**3)/6 + (beta_z*t**2)/2 + gamma_z*t + az0 + 49/5) - ((beta_z + alpha_z*t)*((alpha_y*t**3)/6 + (beta_y*t**2)/2 + gamma_y*t + ay0))/((alpha_z*t**3)/6 + (beta_z*t**2)/2 + gamma_z*t + az0 + 49/5)**2 - (2*((alpha_y*t**2)/2 + beta_y*t + gamma_y)*((alpha_z*t**2)/2 + beta_z*t + gamma_z))/((alpha_z*t**3)/6 + (beta_z*t**2)/2 + gamma_z*t + az0 + 49/5)**2 + (2*((alpha_z*t**2)/2 + beta_z*t + gamma_z)**2*((alpha_y*t**3)/6 + (beta_y*t**2)/2 + gamma_y*t + ay0))/((alpha_z*t**3)/6 + (beta_z*t**2)/2 + gamma_z*t + az0 + 49/5)**3)/(((alpha_y*t**3)/6 + (beta_y*t**2)/2 + gamma_y*t + ay0)**2/((alpha_z*t**3)/6 + (beta_z*t**2)/2 + gamma_z*t + az0 + 49/5)**2 + 1) t=x[6] thrust=np.sqrt(((alpha_y*tmesh**3)/6 + (beta_y*tmesh**2)/2 + gamma_y*tmesh + ay0)**2 + ((alpha_z*tmesh**3)/6 + (beta_z*tmesh**2)/2 + gamma_z*tmesh + az0 + 49/5)**2) c0=t # thrust constraints c1=2*9.8-thrust # print("c1----",c1.shape) # z's lower bound constraints c2=-lbz+(alpha_z/120*tmesh**5+beta_z/24*tmesh**4+gamma_z/6*tmesh**3+az0/2*tmesh**2+vz0*tmesh+z0) c14=ubz-(alpha_z/120*tmesh**5+beta_z/24*tmesh**4+gamma_z/6*tmesh**3+az0/2*tmesh**2+vz0*tmesh+z0) # actuator constraints c3=angleacc*thrustmax/(4*angleaccdmax)-thrust/2+9.8 c4=-angleacc*thrustmax/(4*angleaccdmax)+thrust/2 c5=-angleacc*thrustmax/(4*angleaccdmax)-thrust/2+9.8 c6=angleacc*thrustmax/(4*angleaccdmax)+thrust/2 # phi belongs to [-1.57,1.57] constraints c7=((alpha_z*t**3)/6 + (beta_z*t**2)/2 + gamma_z*t + az0 +9.8) c8=1 c9=1 if beta_z*beta_z>=2*alpha_z and alpha_z!=0 : t1=(-beta_z+math.sqrt(beta_z*beta_z-2*alpha_z))/alpha_z t2=(-beta_z-math.sqrt(beta_z*beta_z-2*alpha_z))/alpha_z if t1>=0 and t1<=t : c8=((alpha_z*t1**3)/6 + (beta_z*t1**2)/2 + gamma_z*t1 + az0 +9.8) if t2>=0 and t2<=t : c9=((alpha_z*t2**3)/6 + (beta_z*t2**2)/2 + gamma_z*t2 + az0 +9.8) #print('the value of t1 and t2 is',t1,t2) c10=-(alpha_y/24*tmesh**4+beta_y/6*tmesh**3+gamma_y/2*tmesh**2+ay0*tmesh+vy0-ubv) c11=-(lbv-(alpha_y/24*tmesh**4+beta_y/6*tmesh**3+gamma_y/2*tmesh**2+ay0*tmesh+vy0)) c12=-(alpha_z/24*tmesh**4+beta_z/6*tmesh**3+gamma_z/2*tmesh**2+az0*tmesh+vz0-ubv) c13=-(lbv-(alpha_z/24*tmesh**4+beta_z/6*tmesh**3+gamma_z/2*tmesh**2+az0*tmesh+vz0)) # print("--------", np.vstack((c0,c1,c2,c3,c4,c5,c6,c7,c8,c9,c10,c11,c12,c13)).shape) return np.hstack((c0,c1,c2,c3,c4,c5,c6,c7,c8,c9,c10,c11,c12,c13,c14)) def pos_twist_callback(data): global vy0, y0, vz0, z0, currentupdateflag y0 = data.pose.pose.position.y # relative pos z0 = data.pose.pose.position.z vy0 = data.twist.twist.linear.y vz0 = data.twist.twist.linear.z currentupdateflag = True def plane_vel_callback(data): global va_ini va_ini = data.twist.linear.x def droneImu_callback(data): global ay0, az0 ay0 = data.twist.linear.y az0 = data.twist.linear.z def main(): global currentupdateflag constraint = [dict(type='eq', fun=eqmycon), dict(type='ineq', fun=ineqmycon)] Initial_guess=np.array([0,0,0,0,0,0,5]) lb=-1000 ub=1000 mybounds=[(lb,ub),(lb,ub),(lb,ub),(lb,ub),(lb,ub),(lb,ub),(0,10)] controlfreq=50 controlstate_msg = controlstate() # 要发布的控制量消息 rospy.init_node('minimumsnap_control', anonymous=True) uav_id = rospy.get_param("~id", "") rate = rospy.Rate(100) rospy.Subscriber(uav_id + "current_relative_postwist", Odometry, pos_twist_callback) # rospy.Subscriber(uav_id + "mavros/local_position/velocity_local", # TwistStamped, plane_vel_callback) # plane veocity rospy.Subscriber(uav_id + "/mavros/imu/data", TwistStamped, droneImu_callback) # plane veocity pub = rospy.Publisher( uav_id + "bvp_controlstate", controlstate, queue_size=10) currentupdateflag=True while not (rospy.is_shutdown()): if currentupdateflag: start = time.time() result = minimize(J, Initial_guess, method='SLSQP', jac=fast_jac,tol=1e-4, bounds=mybounds,constraints=constraint) end = time.time() running_time = end - start print('time cost : %.5f sec' % running_time) if result.success: Initial_guess=result.x controlstate_msg.inicounter = 1 controlstate_msg.discrepointpersecond = controlfreq controlstate_msg.arraylength = round(result.x[-1]*50) times=np.linspace(0, 1, controlstate_msg.arraylength)*result.x[-1] alpha_y=result.x[0] beta_y=result.x[1] gamma_y=result.x[2] alpha_z=result.x[3] beta_z=result.x[4] gamma_z=result.x[5] y=alpha_y/120*times**5+beta_y/24*times**4+gamma_y/6*times**3+ay0/2*times**2+vy0*times+y0 vy=alpha_y/24*times**4+beta_y/6*times**3+gamma_y/2*times**2+ay0*times+vy0 ay=alpha_y/6*times**3+beta_y/2*times**2+gamma_y*times+ay0 z=alpha_z/120*times**5+beta_z/24*times**4+gamma_z/6*times**3+az0/2*times**2+vz0*times+z0 vz=alpha_z/24*times**4+beta_z/6*times**3+gamma_z/2*times**2+az0*times+vz0 az=alpha_z/6*times**3+beta_z/2*times**2+gamma_z*times+az0 controlstate_msg.stateXarray = np.zeros_like(times) controlstate_msg.stateYarray = y controlstate_msg.stateZarray = z controlstate_msg.stateVXarray = np.zeros_like(times) controlstate_msg.stateVYarray = vy controlstate_msg.stateVZarray = vz controlstate_msg.stateAXarray = np.zeros_like(times) controlstate_msg.stateAYarray = ay controlstate_msg.stateAZarray = az pub.publish(controlstate_msg) currentupdateflag = False rate.sleep() # times=np.linspace(0,1,100)*result.x[-1] # # alpha_y=result.x[0] # beta_y=result.x[1] # gamma_y=result.x[2] # # alpha_z=result.x[3] # beta_z=result.x[4] # gamma_z=result.x[5] # # y=alpha_y/120*times**5+beta_y/24*times**4+gamma_y/6*times**3+ay0/2*times**2+vy0*times+y0 # vy=alpha_y/24*times**4+beta_y/6*times**3+gamma_y/2*times**2+ay0*times+vy0 # ay=alpha_y/6*times**3+beta_y/2*times**2+gamma_y*times+ay0 # # # z=alpha_z/120*times**5+beta_z/24*times**4+gamma_z/6*times**3+az0/2*times**2+vz0*times+z0 # vz=alpha_z/24*times**4+beta_z/6*times**3+gamma_z/2*times**2+az0*times+vz0 # az=alpha_z/6*times**3+beta_z/2*times**2+gamma_z*times+az0 # a=np.sqrt(az**2+ay**2) # thurst=np.sqrt((az+9.8)**2+ay**2) # phiseries=-np.arctan(ay/(az+9.8)) # # print("az--------", az) # angleacc=np.zeros_like(times) # for i in range(len(times)): # t=times[i] # angleacc[i]=((((alpha_y*t**2)/2 + beta_y*t + gamma_y)/((alpha_z*t**3)/6 + (beta_z*t**2)/2 + gamma_z*t + az0 + 49/5) - (((alpha_z*t**2)/2 + beta_z*t + gamma_z)*((alpha_y*t**3)/6 + (beta_y*t**2)/2 + gamma_y*t + ay0))/((alpha_z*t**3)/6 + (beta_z*t**2)/2 + gamma_z*t + az0 + 49/5)**2)*((2*((alpha_y*t**2)/2 + beta_y*t + gamma_y)*((alpha_y*t**3)/6 + (beta_y*t**2)/2 + gamma_y*t + ay0))/((alpha_z*t**3)/6 + (beta_z*t**2)/2 + gamma_z*t + az0 + 49/5)**2 - (2*((alpha_z*t**2)/2 + beta_z*t + gamma_z)*((alpha_y*t**3)/6 + (beta_y*t**2)/2 + gamma_y*t + ay0)**2)/((alpha_z*t**3)/6 + (beta_z*t**2)/2 + gamma_z*t + az0 + 49/5)**3))/(((alpha_y*t**3)/6 + (beta_y*t**2)/2 + gamma_y*t + ay0)**2/((alpha_z*t**3)/6 + (beta_z*t**2)/2 + gamma_z*t + az0 + 49/5)**2 + 1)**2 - ((beta_y + alpha_y*t)/((alpha_z*t**3)/6 + (beta_z*t**2)/2 + gamma_z*t + az0 + 49/5) - ((beta_z + alpha_z*t)*((alpha_y*t**3)/6 + (beta_y*t**2)/2 + gamma_y*t + ay0))/((alpha_z*t**3)/6 + (beta_z*t**2)/2 + gamma_z*t + az0 + 49/5)**2 - (2*((alpha_y*t**2)/2 + beta_y*t + gamma_y)*((alpha_z*t**2)/2 + beta_z*t + gamma_z))/((alpha_z*t**3)/6 + (beta_z*t**2)/2 + gamma_z*t + az0 + 49/5)**2 + (2*((alpha_z*t**2)/2 + beta_z*t + gamma_z)**2*((alpha_y*t**3)/6 + (beta_y*t**2)/2 + gamma_y*t + ay0))/((alpha_z*t**3)/6 + (beta_z*t**2)/2 + gamma_z*t + az0 + 49/5)**3)/(((alpha_y*t**3)/6 + (beta_y*t**2)/2 + gamma_y*t + ay0)**2/((alpha_z*t**3)/6 + (beta_z*t**2)/2 + gamma_z*t + az0 + 49/5)**2 + 1) # # F1=angleacc*thrustmax/(4*angleaccdmax)+thurst/2 # F2=-angleacc*thrustmax/(4*angleaccdmax)+thurst/2 # # plotlinewidth = 2 # plotfontsize = 16 # plt.subplot(2,2,1) # plt.plot(times,y, color='blue',LineWidth=plotlinewidth,label="y") # plt.plot(times,vy, color='green',LineWidth=plotlinewidth,label="vy") # plt.plot(times,ay, color='black', LineWidth=plotlinewidth,label="ay") # plt.plot(times,phiseries, color='yellow',LineWidth=plotlinewidth,label="phi") # plt.legend(loc="best") # # plt.subplot(2,2,2) # plt.plot(times,z, color='blue',LineWidth=plotlinewidth,label="z") # plt.plot(times,vz, color='green',LineWidth=plotlinewidth,label="vz") # plt.plot(times,az, color='black', LineWidth=plotlinewidth,label="az") # plt.plot(times,thurst, color='yellow',LineWidth=plotlinewidth,label="thurst") # plt.legend(loc="best") # # plt.subplot(2,2,3) # plt.plot(-y,z, color='blue',LineWidth=plotlinewidth,label="y-z") # plt.legend(loc="best") # # plt.subplot(2,2,4) # plt.plot(times,F1, color='blue',LineWidth=plotlinewidth,label="F1") # plt.plot(times,F2, color='black', LineWidth=plotlinewidth,label="F2") # plt.legend(loc="best") # # # print(res) # # core calculate code # plt.show() if __name__ == '__main__': # 主函数 main()

45.594796

1,439

0.609458

import socket import numpy as np from scipy.optimize import minimize import time import datetime import math import matplotlib.pyplot as plt import rospy from nav_msgs.msg import Odometry from geometry_msgs.msg import TwistStamped from offb_posctl.msg import controlstate phi=1.57 ay0=0 vy0=0 y0=0 az0=0 vz0=0 z0=0.5 aytf=-math.sin(phi)*9.8 vytf=0.2*math.sin(phi) ytf=7.0 aztf=math.cos(phi)*9.8-9.8 vztf=-0.2*math.cos(phi) ztf=2.0 meshpoint=np.linspace(1, 0.01, 5) thrustmax=2*9.8 angleaccdmax=20 lbz=0.3 ubz=2.5 lbv=-5 ubv=5 currentupdateflag = False def J(x): return x[-1] def fast_jac(x): jac = np.zeros_like(x) jac[-1]=1 return jac def eqmycon(x): global ay0, vy0, y0, az0, vz0, z0, aytf, vytf, ytf, aztf, vztf, ztf, meshpoint, thrustmax, angleaccdmax, lbz, lbv, ubv alpha_y=x[0] beta_y=x[1] gamma_y=x[2] alpha_z=x[3] beta_z=x[4] gamma_z=x[5] t=x[6] ceq1=alpha_y/6*t**3+beta_y/2*t**2+gamma_y*t+ay0-aytf ceq2=alpha_y/24*t**4+beta_y/6*t**3+gamma_y/2*t**2+ay0*t+vy0-vytf ceq3=alpha_y/120*t**5+beta_y/24*t**4+gamma_y/6*t**3+ay0/2*t**2+vy0*t+y0-ytf ceq4=alpha_z/6*t**3+beta_z/2*t**2+gamma_z*t+az0-aztf ceq5=alpha_z/24*t**4+beta_z/6*t**3+gamma_z/2*t**2+az0*t+vz0-vztf ceq6=alpha_z/120*t**5+beta_z/24*t**4+gamma_z/6*t**3+az0/2*t**2+vz0*t+z0-ztf return np.hstack((ceq1,ceq2,ceq3,ceq4,ceq5,ceq6)).ravel() def ineqmycon(x): global ay0, vy0, y0, az0, vz0, z0, aytf, vytf, ytf, aztf, vztf, ztf, meshpoint, thrustmax, angleaccdmax, lbz, ubz, lbv, ubv alpha_y=x[0] beta_y=x[1] gamma_y=x[2] alpha_z=x[3] beta_z=x[4] gamma_z=x[5] t=x[6] tmesh=t*(np.array(meshpoint)) angleacc=np.zeros_like(tmesh) for i in range(len(tmesh)): t=tmesh[i] angleacc[i]=((((alpha_y*t**2)/2 + beta_y*t + gamma_y)/((alpha_z*t**3)/6 + (beta_z*t**2)/2 + gamma_z*t + az0 + 49/5) - (((alpha_z*t**2)/2 + beta_z*t + gamma_z)*((alpha_y*t**3)/6 + (beta_y*t**2)/2 + gamma_y*t + ay0))/((alpha_z*t**3)/6 + (beta_z*t**2)/2 + gamma_z*t + az0 + 49/5)**2)*((2*((alpha_y*t**2)/2 + beta_y*t + gamma_y)*((alpha_y*t**3)/6 + (beta_y*t**2)/2 + gamma_y*t + ay0))/((alpha_z*t**3)/6 + (beta_z*t**2)/2 + gamma_z*t + az0 + 49/5)**2 - (2*((alpha_z*t**2)/2 + beta_z*t + gamma_z)*((alpha_y*t**3)/6 + (beta_y*t**2)/2 + gamma_y*t + ay0)**2)/((alpha_z*t**3)/6 + (beta_z*t**2)/2 + gamma_z*t + az0 + 49/5)**3))/(((alpha_y*t**3)/6 + (beta_y*t**2)/2 + gamma_y*t + ay0)**2/((alpha_z*t**3)/6 + (beta_z*t**2)/2 + gamma_z*t + az0 + 49/5)**2 + 1)**2 - ((beta_y + alpha_y*t)/((alpha_z*t**3)/6 + (beta_z*t**2)/2 + gamma_z*t + az0 + 49/5) - ((beta_z + alpha_z*t)*((alpha_y*t**3)/6 + (beta_y*t**2)/2 + gamma_y*t + ay0))/((alpha_z*t**3)/6 + (beta_z*t**2)/2 + gamma_z*t + az0 + 49/5)**2 - (2*((alpha_y*t**2)/2 + beta_y*t + gamma_y)*((alpha_z*t**2)/2 + beta_z*t + gamma_z))/((alpha_z*t**3)/6 + (beta_z*t**2)/2 + gamma_z*t + az0 + 49/5)**2 + (2*((alpha_z*t**2)/2 + beta_z*t + gamma_z)**2*((alpha_y*t**3)/6 + (beta_y*t**2)/2 + gamma_y*t + ay0))/((alpha_z*t**3)/6 + (beta_z*t**2)/2 + gamma_z*t + az0 + 49/5)**3)/(((alpha_y*t**3)/6 + (beta_y*t**2)/2 + gamma_y*t + ay0)**2/((alpha_z*t**3)/6 + (beta_z*t**2)/2 + gamma_z*t + az0 + 49/5)**2 + 1) t=x[6] thrust=np.sqrt(((alpha_y*tmesh**3)/6 + (beta_y*tmesh**2)/2 + gamma_y*tmesh + ay0)**2 + ((alpha_z*tmesh**3)/6 + (beta_z*tmesh**2)/2 + gamma_z*tmesh + az0 + 49/5)**2) c0=t c1=2*9.8-thrust c2=-lbz+(alpha_z/120*tmesh**5+beta_z/24*tmesh**4+gamma_z/6*tmesh**3+az0/2*tmesh**2+vz0*tmesh+z0) c14=ubz-(alpha_z/120*tmesh**5+beta_z/24*tmesh**4+gamma_z/6*tmesh**3+az0/2*tmesh**2+vz0*tmesh+z0) # actuator constraints c3=angleacc*thrustmax/(4*angleaccdmax)-thrust/2+9.8 c4=-angleacc*thrustmax/(4*angleaccdmax)+thrust/2 c5=-angleacc*thrustmax/(4*angleaccdmax)-thrust/2+9.8 c6=angleacc*thrustmax/(4*angleaccdmax)+thrust/2 # phi belongs to [-1.57,1.57] constraints c7=((alpha_z*t**3)/6 + (beta_z*t**2)/2 + gamma_z*t + az0 +9.8) c8=1 c9=1 if beta_z*beta_z>=2*alpha_z and alpha_z!=0 : t1=(-beta_z+math.sqrt(beta_z*beta_z-2*alpha_z))/alpha_z t2=(-beta_z-math.sqrt(beta_z*beta_z-2*alpha_z))/alpha_z if t1>=0 and t1<=t : c8=((alpha_z*t1**3)/6 + (beta_z*t1**2)/2 + gamma_z*t1 + az0 +9.8) if t2>=0 and t2<=t : c9=((alpha_z*t2**3)/6 + (beta_z*t2**2)/2 + gamma_z*t2 + az0 +9.8) #print('the value of t1 and t2 is',t1,t2) c10=-(alpha_y/24*tmesh**4+beta_y/6*tmesh**3+gamma_y/2*tmesh**2+ay0*tmesh+vy0-ubv) c11=-(lbv-(alpha_y/24*tmesh**4+beta_y/6*tmesh**3+gamma_y/2*tmesh**2+ay0*tmesh+vy0)) c12=-(alpha_z/24*tmesh**4+beta_z/6*tmesh**3+gamma_z/2*tmesh**2+az0*tmesh+vz0-ubv) c13=-(lbv-(alpha_z/24*tmesh**4+beta_z/6*tmesh**3+gamma_z/2*tmesh**2+az0*tmesh+vz0)) # print("--------", np.vstack((c0,c1,c2,c3,c4,c5,c6,c7,c8,c9,c10,c11,c12,c13)).shape) return np.hstack((c0,c1,c2,c3,c4,c5,c6,c7,c8,c9,c10,c11,c12,c13,c14)) def pos_twist_callback(data): global vy0, y0, vz0, z0, currentupdateflag y0 = data.pose.pose.position.y # relative pos z0 = data.pose.pose.position.z vy0 = data.twist.twist.linear.y vz0 = data.twist.twist.linear.z currentupdateflag = True def plane_vel_callback(data): global va_ini va_ini = data.twist.linear.x def droneImu_callback(data): global ay0, az0 ay0 = data.twist.linear.y az0 = data.twist.linear.z def main(): global currentupdateflag constraint = [dict(type='eq', fun=eqmycon), dict(type='ineq', fun=ineqmycon)] Initial_guess=np.array([0,0,0,0,0,0,5]) lb=-1000 ub=1000 mybounds=[(lb,ub),(lb,ub),(lb,ub),(lb,ub),(lb,ub),(lb,ub),(0,10)] controlfreq=50 controlstate_msg = controlstate() # 要发布的控制量消息 rospy.init_node('minimumsnap_control', anonymous=True) uav_id = rospy.get_param("~id", "") rate = rospy.Rate(100) rospy.Subscriber(uav_id + "current_relative_postwist", Odometry, pos_twist_callback) # rospy.Subscriber(uav_id + "mavros/local_position/velocity_local", # TwistStamped, plane_vel_callback) # plane veocity rospy.Subscriber(uav_id + "/mavros/imu/data", TwistStamped, droneImu_callback) # plane veocity pub = rospy.Publisher( uav_id + "bvp_controlstate", controlstate, queue_size=10) currentupdateflag=True while not (rospy.is_shutdown()): if currentupdateflag: start = time.time() result = minimize(J, Initial_guess, method='SLSQP', jac=fast_jac,tol=1e-4, bounds=mybounds,constraints=constraint) end = time.time() running_time = end - start print('time cost : %.5f sec' % running_time) if result.success: Initial_guess=result.x controlstate_msg.inicounter = 1 controlstate_msg.discrepointpersecond = controlfreq controlstate_msg.arraylength = round(result.x[-1]*50) times=np.linspace(0, 1, controlstate_msg.arraylength)*result.x[-1] alpha_y=result.x[0] beta_y=result.x[1] gamma_y=result.x[2] alpha_z=result.x[3] beta_z=result.x[4] gamma_z=result.x[5] y=alpha_y/120*times**5+beta_y/24*times**4+gamma_y/6*times**3+ay0/2*times**2+vy0*times+y0 vy=alpha_y/24*times**4+beta_y/6*times**3+gamma_y/2*times**2+ay0*times+vy0 ay=alpha_y/6*times**3+beta_y/2*times**2+gamma_y*times+ay0 z=alpha_z/120*times**5+beta_z/24*times**4+gamma_z/6*times**3+az0/2*times**2+vz0*times+z0 vz=alpha_z/24*times**4+beta_z/6*times**3+gamma_z/2*times**2+az0*times+vz0 az=alpha_z/6*times**3+beta_z/2*times**2+gamma_z*times+az0 controlstate_msg.stateXarray = np.zeros_like(times) controlstate_msg.stateYarray = y controlstate_msg.stateZarray = z controlstate_msg.stateVXarray = np.zeros_like(times) controlstate_msg.stateVYarray = vy controlstate_msg.stateVZarray = vz controlstate_msg.stateAXarray = np.zeros_like(times) controlstate_msg.stateAYarray = ay controlstate_msg.stateAZarray = az pub.publish(controlstate_msg) currentupdateflag = False rate.sleep() # times=np.linspace(0,1,100)*result.x[-1] # # alpha_y=result.x[0] # beta_y=result.x[1] # gamma_y=result.x[2] # # alpha_z=result.x[3] # beta_z=result.x[4] # gamma_z=result.x[5] # # y=alpha_y/120*times**5+beta_y/24*times**4+gamma_y/6*times**3+ay0/2*times**2+vy0*times+y0 # vy=alpha_y/24*times**4+beta_y/6*times**3+gamma_y/2*times**2+ay0*times+vy0 # ay=alpha_y/6*times**3+beta_y/2*times**2+gamma_y*times+ay0 # # # z=alpha_z/120*times**5+beta_z/24*times**4+gamma_z/6*times**3+az0/2*times**2+vz0*times+z0 # vz=alpha_z/24*times**4+beta_z/6*times**3+gamma_z/2*times**2+az0*times+vz0 # az=alpha_z/6*times**3+beta_z/2*times**2+gamma_z*times+az0 # a=np.sqrt(az**2+ay**2) # thurst=np.sqrt((az+9.8)**2+ay**2) # phiseries=-np.arctan(ay/(az+9.8)) # # print("az--------", az) # angleacc=np.zeros_like(times) # for i in range(len(times)): # t=times[i] # angleacc[i]=((((alpha_y*t**2)/2 + beta_y*t + gamma_y)/((alpha_z*t**3)/6 + (beta_z*t**2)/2 + gamma_z*t + az0 + 49/5) - (((alpha_z*t**2)/2 + beta_z*t + gamma_z)*((alpha_y*t**3)/6 + (beta_y*t**2)/2 + gamma_y*t + ay0))/((alpha_z*t**3)/6 + (beta_z*t**2)/2 + gamma_z*t + az0 + 49/5)**2)*((2*((alpha_y*t**2)/2 + beta_y*t + gamma_y)*((alpha_y*t**3)/6 + (beta_y*t**2)/2 + gamma_y*t + ay0))/((alpha_z*t**3)/6 + (beta_z*t**2)/2 + gamma_z*t + az0 + 49/5)**2 - (2*((alpha_z*t**2)/2 + beta_z*t + gamma_z)*((alpha_y*t**3)/6 + (beta_y*t**2)/2 + gamma_y*t + ay0)**2)/((alpha_z*t**3)/6 + (beta_z*t**2)/2 + gamma_z*t + az0 + 49/5)**3))/(((alpha_y*t**3)/6 + (beta_y*t**2)/2 + gamma_y*t + ay0)**2/((alpha_z*t**3)/6 + (beta_z*t**2)/2 + gamma_z*t + az0 + 49/5)**2 + 1)**2 - ((beta_y + alpha_y*t)/((alpha_z*t**3)/6 + (beta_z*t**2)/2 + gamma_z*t + az0 + 49/5) - ((beta_z + alpha_z*t)*((alpha_y*t**3)/6 + (beta_y*t**2)/2 + gamma_y*t + ay0))/((alpha_z*t**3)/6 + (beta_z*t**2)/2 + gamma_z*t + az0 + 49/5)**2 - (2*((alpha_y*t**2)/2 + beta_y*t + gamma_y)*((alpha_z*t**2)/2 + beta_z*t + gamma_z))/((alpha_z*t**3)/6 + (beta_z*t**2)/2 + gamma_z*t + az0 + 49/5)**2 + (2*((alpha_z*t**2)/2 + beta_z*t + gamma_z)**2*((alpha_y*t**3)/6 + (beta_y*t**2)/2 + gamma_y*t + ay0))/((alpha_z*t**3)/6 + (beta_z*t**2)/2 + gamma_z*t + az0 + 49/5)**3)/(((alpha_y*t**3)/6 + (beta_y*t**2)/2 + gamma_y*t + ay0)**2/((alpha_z*t**3)/6 + (beta_z*t**2)/2 + gamma_z*t + az0 + 49/5)**2 + 1) # # F1=angleacc*thrustmax/(4*angleaccdmax)+thurst/2 # F2=-angleacc*thrustmax/(4*angleaccdmax)+thurst/2 # # plotlinewidth = 2 # plotfontsize = 16 # plt.subplot(2,2,1) # plt.plot(times,y, color='blue',LineWidth=plotlinewidth,label="y") # plt.plot(times,vy, color='green',LineWidth=plotlinewidth,label="vy") # plt.plot(times,ay, color='black', LineWidth=plotlinewidth,label="ay") # plt.plot(times,phiseries, color='yellow',LineWidth=plotlinewidth,label="phi") # plt.legend(loc="best") # # plt.subplot(2,2,2) # plt.plot(times,z, color='blue',LineWidth=plotlinewidth,label="z") # plt.plot(times,vz, color='green',LineWidth=plotlinewidth,label="vz") # plt.plot(times,az, color='black', LineWidth=plotlinewidth,label="az") # plt.plot(times,thurst, color='yellow',LineWidth=plotlinewidth,label="thurst") # plt.legend(loc="best") # # plt.subplot(2,2,3) # plt.plot(-y,z, color='blue',LineWidth=plotlinewidth,label="y-z") # plt.legend(loc="best") # # plt.subplot(2,2,4) # plt.plot(times,F1, color='blue',LineWidth=plotlinewidth,label="F1") # plt.plot(times,F2, color='black', LineWidth=plotlinewidth,label="F2") # plt.legend(loc="best") # # # print(res) # # core calculate code # plt.show() if __name__ == '__main__': # 主函数 main()

true

1c43e31b2a6419a99dbce0307a341681e94bc888

27,753

py

Python

toontown/pickatoon/PickAToonOptions.py

cmarshall108/Project-Altis

7ead614abdb5072ca06323982de461f4e775d1b3

[ "Apache-2.0" ]

1

2021-02-25T06:02:04.000Z

toontown/pickatoon/PickAToonOptions.py

AnythingTechPro/Project-Altis

7ead614abdb5072ca06323982de461f4e775d1b3

[ "Apache-2.0" ]

null

toontown/pickatoon/PickAToonOptions.py

AnythingTechPro/Project-Altis

7ead614abdb5072ca06323982de461f4e775d1b3

[ "Apache-2.0" ]

2

2021-02-25T06:02:05.000Z

2021-06-19T03:11:22.000Z

''' Created on Apr 2, 2016 @author: Drew ''' from direct.gui.DirectGui import * from direct.interval.IntervalGlobal import Wait, Func, Sequence, LerpColorScaleInterval, Parallel, LerpScaleInterval from direct.showbase.DirectObject import DirectObject from panda3d.core import TransparencyAttrib, Point3, Vec4, TextNode, Vec3 from toontown.toonbase import TTLocalizer from toontown.toonbase import ToontownGlobals from toontown.toontowngui.TTGui import btnDn, btnRlvr, btnUp from toontown.toontowngui import TTDialog from toontown.options import GraphicsOptions from toontown.shtiker import ControlRemapDialog, DisplaySettingsDialog from decimal import Decimal resolution_table = [ (800, 600), (1024, 768), (1280, 1024), (1600, 1200), (1280, 720), (1920, 1080)] class PickAToonOptions: def __init__(self): self.optionsOpenSfx = None #base.loadSfx(DMenuResources.Settings_Open) # ALTIS: TODO: Add sound effects self.optionsCloseSfx = None #base.loadSfx(DMenuResources.Settings_Close) # ALTIS: TODO: Add sound effects def showOptions(self): #base.playSfx(self.optionsOpenSfx) # ALTIS: TODO: Add sound effects self.displayOptions() zoomIn = (LerpScaleInterval(self.optionsNode, .4, Vec3(1, 1, 1), Vec3(0, 0, 0), blendType = 'easeInOut')).start() def hideOptions(self): #base.playSfx(self.optionsCloseSfx) # ALTIS: TODO: Add sound effects zoomOut = (LerpScaleInterval(self.optionsNode, .4, Vec3(0, 0, 0), Vec3(1, 1, 1), blendType = 'easeInOut')).start() Sequence ( Wait(.4), Func(self.delOptions)).start() def displayOptions(self): self.optionsNode = aspect2d.attachNewNode('optionsNode') self.optionsNode.reparentTo(aspect2d) gui = loader.loadModel('phase_3/models/gui/pick_a_toon_gui') guiButton = loader.loadModel('phase_3/models/gui/quit_button') quitHover = gui.find('**/QuitBtn_RLVR') self.optionsBox = OnscreenImage(image = 'phase_3/maps/stat_board.png') self.optionsBox.setTransparency(TransparencyAttrib.MAlpha) self.optionsBox.setPos(0, 0, 0) self.optionsBox.setScale(0.7) self.optionsBox.reparentTo(self.optionsNode) # Music Label self.Music_Label = DirectLabel(parent = self.optionsNode, relief = None, text = 'Music Volume', text_align = TextNode.ACenter, text_scale = 0.052, pos = (0, 0, 0.5)) # Music Slider self.Music_toggleSlider = DirectSlider(parent = self.optionsNode, pos = (0, 0, 0.4), value = settings['musicVol'] * 100, pageSize = 5, range = (0, 100), command = self.__doMusicLevel,) self.Music_toggleSlider.setScale(0.4, 0.4, 0.4) self.Music_toggleSlider.show() # SFX Slider self.SoundFX_toggleSlider = DirectSlider(parent = self.optionsNode, pos = (0, 0.0, 0.2), value = settings['sfxVol'] * 100, pageSize = 5, range = (0, 100), command = self.__doSfxLevel) self.SoundFX_toggleSlider.setScale(0.4, 0.4, 0.4) # SFX Label self.SoundFX_Label = DirectLabel(parent = self.optionsNode, relief = None, text = 'SFX Volume', text_align = TextNode.ACenter, text_scale = 0.052, pos = (0, 0, 0.3)) # Toon Chat Sound Effects self.ToonChatSounds_toggleButton = DirectButton(parent = self.optionsNode, relief = None, image = (guiButton.find('**/QuitBtn_UP'), guiButton.find('**/QuitBtn_DN'), guiButton.find('**/QuitBtn_RLVR'), guiButton.find('**/QuitBtn_UP')), image3_color = Vec4(0.5, 0.5, 0.5, 0.5), image_scale = (0.7, 1, 1), text = '', text3_fg = (0.5, 0.5, 0.5, 0.75), text_scale = 0.052, text_pos = (0, -.02), pos = (0, 0, 0), command = self.__doToggleToonChatSounds) self.ToonChatSounds_toggleButton.setScale(0.8) self.ToonChatSounds_Label = DirectLabel(parent = self.optionsNode, relief = None, text = 'Toon Chat Sounds', text_align = TextNode.ACenter, text_scale = 0.052, pos = (0, 0, .1)) # Key Remapping self.WASD_Label = DirectLabel(parent=self.optionsNode, relief=None, text='', text_align=TextNode.ACenter, text_scale=0.052, text_wordwrap=16, pos=(0, 0, -0.1)) self.WASD_toggleButton = DirectButton(parent=self.optionsNode, relief=None, image=(guiButton.find('**/QuitBtn_UP'), guiButton.find('**/QuitBtn_DN'), guiButton.find('**/QuitBtn_RLVR')), image_scale = (0.7, 1, 1), text='', text_scale = 0.052, text_pos=(0, -.02), pos=(0, 0, -0.2), command=self.__doToggleWASD) self.keymapDialogButton = DirectButton(parent=self.optionsNode, relief = None, image = (guiButton.find('**/QuitBtn_UP'), guiButton.find('**/QuitBtn_DN'), guiButton.find('**/QuitBtn_RLVR')), image_scale = (0.7, 1, 1), text='Change Keybinds', text_scale=(0.03, 0.05, 1), text_pos = (0, -.02), pos = (0, 0, -0.3), command = self.__openKeyRemapDialog) self.keymapDialogButton.setScale(1.55, 1.0, 1.0) # Aspect Ratio Options self.AspectRatioList = DirectOptionMenu(relief = None, parent = self.optionsNode, text_align = TextNode.ACenter, items = GraphicsOptions.AspectRatioLabels, command = self.__doWidescreen, text_scale = .6, popupMarker_pos = (-1, 0, 0), popupMarker_relief = None, highlightScale = (1.1, 1.1), image = (guiButton.find('**/QuitBtn_UP'), guiButton.find('**/QuitBtn_DN'), guiButton.find('**/QuitBtn_RLVR'), guiButton.find('**/QuitBtn_UP')), image_scale = 8, image3_color = Vec4(0.5, 0.5, 0.5, 0.5), text = '', text3_fg = (0.5, 0.5, 0.5, 0.75), text_pos = (0, -.02), pos = (0, 0, -0.5), image_pos = (0, 0, 0), item_text_align = TextNode.ACenter, popupMenu_text_scale = .5, item_relief = None, item_pressEffect = 1) self.AspectRatioList.setScale(0.1) self.AspectRatioList.set(base.Widescreen) self.Widescreen_Label = DirectLabel(parent = self.optionsNode, relief = None, text = 'Aspect Ratio', text_align = TextNode.ACenter, text_scale = 0.052, pos = (0, 0, -0.4)) # TODO: Add more graphics options like Resolution, and more graphics options like in POTCO to allow changing quality of textures, etc. # Set Button Text self.__setToonChatSoundsButton() self.__setWASDButton() def delOptions(self): self.optionsBox.destroy() del self.optionsBox self.Music_Label.destroy() del self.Music_Label self.Music_toggleSlider.destroy() del self.Music_toggleSlider self.SoundFX_Label.destroy() del self.SoundFX_Label self.SoundFX_toggleSlider.destroy() del self.SoundFX_toggleSlider self.ToonChatSounds_Label.destroy() del self.ToonChatSounds_Label self.ToonChatSounds_toggleButton.destroy() del self.ToonChatSounds_toggleButton self.Widescreen_Label.destroy() del self.Widescreen_Label self.AspectRatioList.destroy() del self.AspectRatioList self.WASD_Label.destroy() del self.WASD_Label self.WASD_toggleButton.destroy() del self.WASD_toggleButton self.keymapDialogButton.destroy() del self.keymapDialogButton self.optionsNode.removeNode() del self.optionsNode # EZ copy from optionspage.py def __doMusicLevel(self): vol = self.Music_toggleSlider['value'] vol = float(vol) / 100 settings['musicVol'] = vol base.musicManager.setVolume(vol) base.musicActive = vol > 0.0 def __doSfxLevel(self): vol = self.SoundFX_toggleSlider['value'] vol = float(vol) / 100 settings['sfxVol'] = vol for sfm in base.sfxManagerList: sfm.setVolume(vol) base.sfxActive = vol > 0.0 def __doToggleToonChatSounds(self): messenger.send('wakeup') if base.toonChatSounds: base.toonChatSounds = 0 settings['toonChatSounds'] = False else: base.toonChatSounds = 1 settings['toonChatSounds'] = True self.settingsChanged = 1 self.__setToonChatSoundsButton() def __setToonChatSoundsButton(self): if base.toonChatSounds: self.ToonChatSounds_Label['text'] = TTLocalizer.OptionsPageToonChatSoundsOnLabel self.ToonChatSounds_toggleButton['text'] = TTLocalizer.OptionsPageToggleOff else: self.ToonChatSounds_Label['text'] = TTLocalizer.OptionsPageToonChatSoundsOffLabel self.ToonChatSounds_toggleButton['text'] = TTLocalizer.OptionsPageToggleOn if base.sfxActive: self.ToonChatSounds_Label.setColorScale(1.0, 1.0, 1.0, 1.0) self.ToonChatSounds_toggleButton['state'] = DGG.NORMAL else: self.ToonChatSounds_Label.setColorScale(0.5, 0.5, 0.5, 0.5) self.ToonChatSounds_toggleButton['state'] = DGG.DISABLED def __doWidescreen(self, ratio): messenger.send('wakeup') ratio = self.AspectRatioList.selectedIndex if base.Widescreen != ratio: base.Widescreen = ratio settings['Widescreen'] = ratio self.settingsChanged = 1 base.updateAspectRatio() def __doToggleWASD(self): messenger.send('wakeup') if base.wantCustomControls: base.wantCustomControls = False settings['want-Custom-Controls'] = False else: base.wantCustomControls = True settings['want-Custom-Controls'] = True base.reloadControls() self.settingsChanged = 1 self.__setWASDButton() def __setWASDButton(self): if base.wantCustomControls: self.WASD_Label['text'] = 'Custom Keymapping is enabled.' self.WASD_toggleButton['text'] = TTLocalizer.OptionsPageToggleOff self.keymapDialogButton.show() else: self.WASD_Label['text'] = 'Custom Keymapping is disabled.' self.WASD_toggleButton['text'] = TTLocalizer.OptionsPageToggleOn self.keymapDialogButton.hide() def __openKeyRemapDialog(self): if base.wantCustomControls: self.controlDialog = ControlRemapDialog.ControlRemap() # I will be revamping the options screen, here is the class for it class NewPickAToonOptions: def __init__(self): self.optionsOpenSfx = None #base.loadSfx(DMenuResources.Settings_Open) # ALTIS: TODO: Add sound effects self.optionsCloseSfx = None #base.loadSfx(DMenuResources.Settings_Close) # ALTIS: TODO: Add sound effects self.Music_Label = None self.Music_toggleSlider = None self.SoundFX_Label = None self.SoundFX_toggleSlider = None self.ToonChatSounds_Label = None self.ToonChatSounds_toggleButton = None self.WASD_Label = None self.WASD_toggleButton = None self.keymapDialogButton = None self.Widescreen_Label = None self.AspectRatioList = None self.DisplaySettings_Label = None self.DisplaySettingsButton = None self.fov_toggleSlider = None self.fov_Label = None self.fov_resetButton = None self.displaySettings = None self.displaySettingsChanged = 0 self.displaySettingsSize = (None, None) self.displaySettingsFullscreen = None self.displaySettingsBorderless = None self.displaySettingsApi = None self.displaySettingsApiChanged = 0 def showOptions(self): #base.playSfx(self.optionsOpenSfx) # ALTIS: TODO: Add sound effects self.displayOptions() zoomIn = (LerpScaleInterval(self.optionsNode, .1, Vec3(1, 1, 1), Vec3(0, 0, 0), blendType = 'easeOut')).start() def hideOptions(self): #base.playSfx(self.optionsCloseSfx) # ALTIS: TODO: Add sound effects zoomOut = (LerpScaleInterval(self.optionsNode, .1, Vec3(.5, .5, .5), Vec3(1, 1, 1), blendType = 'easeIn')).start() Sequence ( Wait(.1), Func(self.delAllOptions)).start() def displayOptions(self): self.optionsNode = aspect2d.attachNewNode('optionsNode') self.optionsNode.reparentTo(aspect2d) self.guimodel = loader.loadModel('phase_3/models/gui/pick_a_toon_gui') self.guiButton = loader.loadModel('phase_3/models/gui/quit_button') self.quitHover = self.guimodel.find('**/QuitBtn_RLVR') self.optionsBox = OnscreenImage(image = 'phase_3/maps/stat_board.png') self.optionsBox.setTransparency(TransparencyAttrib.MAlpha) self.optionsBox.setPos(0, 0, 0) self.optionsBox.setScale(1.3, 1, 1) self.optionsBox.reparentTo(self.optionsNode) self.soundOptionsButton = DirectButton(relief = None, text_style = 3, text_fg = (1, 1, 1, 1), text = 'Sound', text_scale = .1, scale = 0.95, command = self.displaySoundOptions) self.soundOptionsButton.reparentTo(self.optionsNode) self.soundOptionsButton.setPos(-.6, 0, .7) self.soundOptionsButton.show() self.controlOptionsButton = DirectButton(relief = None, text_style = 3, text_fg = (1, 1, 1, 1), text = 'Controls', text_scale = .1, scale = 0.95, command = self.displayControlOptions) self.controlOptionsButton.reparentTo(self.optionsNode) self.controlOptionsButton.setPos(0, 0, .7) self.controlOptionsButton.show() self.videoOptionsButton = DirectButton(relief = None, text_style = 3, text_fg = (1, 1, 1, 1), text = 'Video', text_scale = .1, scale = 0.95, command = self.displayVideoOptions) self.videoOptionsButton.reparentTo(self.optionsNode) self.videoOptionsButton.setPos(.6, 0, .7) self.videoOptionsButton.show() self.displaySoundOptions() def displaySoundOptions(self): self.delSoundOptions() self.delControlOptions() self.delVideoOptions() # Music Label self.Music_Label = DirectLabel(parent = self.optionsNode, relief = None, text = 'Music Volume', text_align = TextNode.ACenter, text_scale = 0.052, pos = (0, 0, 0.4)) # Music Slider self.Music_toggleSlider = DirectSlider(parent = self.optionsNode, pos = (0, 0, 0.3), value = settings['musicVol'] * 100, pageSize = 5, range = (0, 100), command = self.__doMusicLevel, thumb_geom=(self.guiButton.find('**/QuitBtn_UP')), thumb_relief=None, thumb_geom_scale=1) self.Music_toggleSlider.setScale(0.4, 0.4, 0.4) self.Music_toggleSlider.show() # SFX Slider self.SoundFX_toggleSlider = DirectSlider(parent = self.optionsNode, pos = (0, 0.0, 0.1), value = settings['sfxVol'] * 100, pageSize = 5, range = (0, 100), command = self.__doSfxLevel, thumb_geom=(self.guiButton.find('**/QuitBtn_UP')), thumb_relief=None, thumb_geom_scale=1) self.SoundFX_toggleSlider.setScale(0.4, 0.4, 0.4) # SFX Label self.SoundFX_Label = DirectLabel(parent = self.optionsNode, relief = None, text = 'SFX Volume', text_align = TextNode.ACenter, text_scale = 0.052, pos = (0, 0, 0.2)) # Toon Chat Sound Effects self.ToonChatSounds_toggleButton = DirectButton(parent = self.optionsNode, relief = None, image = (self.guiButton.find('**/QuitBtn_UP'), self.guiButton.find('**/QuitBtn_DN'), self.guiButton.find('**/QuitBtn_RLVR'), self.guiButton.find('**/QuitBtn_UP')), image3_color = Vec4(0.5, 0.5, 0.5, 0.5), image_scale = (0.7, 1, 1), text = '', text3_fg = (0.5, 0.5, 0.5, 0.75), text_scale = 0.052, text_pos = (0, -.02), pos = (0, 0, -.1), command = self.__doToggleToonChatSounds) self.ToonChatSounds_toggleButton.setScale(0.8) self.ToonChatSounds_Label = DirectLabel(parent = self.optionsNode, relief = None, text = 'Toon Chat Sounds', text_align = TextNode.ACenter, text_scale = 0.052, pos = (0, 0, 0)) # Set Button Text self.__setToonChatSoundsButton() def displayControlOptions(self): self.delSoundOptions() self.delControlOptions() self.delVideoOptions() # Key Remapping self.WASD_Label = DirectLabel(parent=self.optionsNode, relief=None, text='', text_align=TextNode.ACenter, text_scale=0.052, text_wordwrap=16, pos=(0, 0, .4)) self.WASD_toggleButton = DirectButton(parent=self.optionsNode, relief=None, image=(self.guiButton.find('**/QuitBtn_UP'), self.guiButton.find('**/QuitBtn_DN'), self.guiButton.find('**/QuitBtn_RLVR')), image_scale = (0.7, 1, 1), text='', text_scale = 0.052, text_pos=(0, -.02), pos=(0, 0, .3), command=self.__doToggleWASD) self.keymapDialogButton = DirectButton(parent=self.optionsNode, relief = None, image = (self.guiButton.find('**/QuitBtn_UP'), self.guiButton.find('**/QuitBtn_DN'), self.guiButton.find('**/QuitBtn_RLVR')), image_scale = (0.7, 1, 1), text='Change Keybinds', text_scale=(0.03, 0.05, 1), text_pos = (0, -.02), pos = (0, 0, .2), command = self.__openKeyRemapDialog) self.keymapDialogButton.setScale(1.55, 1.0, 1.0) # Set Button Text self.__setWASDButton() def displayVideoOptions(self): self.delSoundOptions() self.delControlOptions() self.delVideoOptions() # Aspect Ratio Options self.AspectRatioList = DirectOptionMenu(relief = None, parent = self.optionsNode, text_align = TextNode.ACenter, items = GraphicsOptions.AspectRatioLabels, command = self.__doWidescreen, text_scale = .6, popupMarker_pos = (-1, 0, 0), popupMarker_relief = None, highlightScale = (1.1, 1.1), image = (self.guiButton.find('**/QuitBtn_UP'), self.guiButton.find('**/QuitBtn_DN'), self.guiButton.find('**/QuitBtn_RLVR'), self.guiButton.find('**/QuitBtn_UP')), image_scale = 8, image3_color = Vec4(0.5, 0.5, 0.5, 0.5), text = '', text3_fg = (0.5, 0.5, 0.5, 0.75), text_pos = (0, -.02), pos = (0, 0, .3), image_pos = (0, 0, 0), item_text_align = TextNode.ACenter, popupMenu_text_scale = .5, item_relief = None, item_pressEffect = 1) self.AspectRatioList.setScale(0.1) self.AspectRatioList.set(base.Widescreen) self.Widescreen_Label = DirectLabel(parent = self.optionsNode, relief = None, text = 'Aspect Ratio', text_align = TextNode.ACenter, text_scale = 0.052, pos = (0, 0, .4)) self.DisplaySettings_Label = DirectLabel(parent=self.optionsNode, relief=None, text='', text_align=TextNode.ACenter, text_scale=0.052, text_wordwrap=16, pos=(0, 0, .2)) self.DisplaySettingsButton = DirectButton(parent=self.optionsNode, relief=None, image=(self.guiButton.find('**/QuitBtn_UP'), self.guiButton.find('**/QuitBtn_DN'), self.guiButton.find('**/QuitBtn_RLVR')), image3_color=Vec4(0.5, 0.5, 0.5, 0.5), image_scale=(0.7, 1, 1), text=TTLocalizer.OptionsPageChange, text3_fg=(0.5, 0.5, 0.5, 0.75), text_scale=0.052, text_pos=(0, -.02), pos=(0, 0, .1), command=self.__doDisplaySettings) self.fov_Label = DirectLabel(parent=self.optionsNode, relief=None, text='Field of view', text_align=TextNode.ACenter, text_scale = 0.052, text_wordwrap=16, pos=(0, 0, 0)) self.fov_toggleSlider = DirectSlider(parent=self.optionsNode, pos=(0, 0, -.1), value=settings['fieldofview'], pageSize=5, range=(30, 120), command=self.__doFovLevel, thumb_geom=(self.guiButton.find('**/QuitBtn_UP')), thumb_relief=None, thumb_geom_scale=1) self.fov_toggleSlider.setScale(0.25) self.fov_resetButton = DirectButton(parent=self.optionsNode, relief=None, image=(self.guiButton.find('**/QuitBtn_UP'), self.guiButton.find('**/QuitBtn_DN'), self.guiButton.find('**/QuitBtn_RLVR')), image_scale=(0.7, 1, 1), text='Reset FOV', text_scale=0.052, text_pos = (0, -.02), pos=(0, 0, -.2), command=self.__resetFov) self.fovsliderText = OnscreenText('0.0', scale=.3, pos=(0, .1), fg=(1, 1, 1, 1), style = 3) self.fovsliderText.reparentTo(self.fov_toggleSlider.thumb) self.__doFovLevel() self.__setDisplaySettings() # TODO: Add more graphics options like Resolution, and more graphics options like in POTCO to allow changing quality of textures, etc. def delSoundOptions(self): if self.Music_Label: self.Music_Label.destroy() self.Music_Label = None if self.Music_toggleSlider: self.Music_toggleSlider.destroy() self.Music_toggleSlider = None if self.SoundFX_Label: self.SoundFX_Label.destroy() self.SoundFX_Label = None if self.SoundFX_toggleSlider: self.SoundFX_toggleSlider.destroy() self.SoundFX_toggleSlider = None if self.ToonChatSounds_Label: self.ToonChatSounds_Label.destroy() self.ToonChatSounds_Label = None if self.ToonChatSounds_toggleButton: self.ToonChatSounds_toggleButton.destroy() self.ToonChatSounds_toggleButton = None def delControlOptions(self): if self.WASD_Label: self.WASD_Label.destroy() self.WASD_Label = None if self.WASD_toggleButton: self.WASD_toggleButton.destroy() self.WASD_toggleButton = None if self.keymapDialogButton: self.keymapDialogButton.destroy() self.keymapDialogButton = None def delVideoOptions(self): if self.Widescreen_Label: self.Widescreen_Label.destroy() self.Widescreen_Label = None if self.AspectRatioList: self.AspectRatioList.destroy() self.AspectRatioList = None if self.DisplaySettings_Label: self.DisplaySettings_Label.destroy() self.DisplaySettings_Label = None if self.DisplaySettingsButton: self.DisplaySettingsButton.destroy() self.DisplaySettingsButton = None if self.fov_toggleSlider: self.fov_toggleSlider.destroy() self.fov_toggleSlider = None self.fov_Label.destroy() self.fov_Label = None self.fov_resetButton.destroy() self.fov_resetButton = None def delAllOptions(self): self.delSoundOptions() self.delControlOptions() self.delVideoOptions() self.optionsBox.destroy() del self.optionsBox self.optionsNode.removeNode() del self.optionsNode # EZ copy from optionspage.py def __doMusicLevel(self): vol = self.Music_toggleSlider['value'] vol = float(vol) / 100 settings['musicVol'] = vol base.musicManager.setVolume(vol) base.musicActive = vol > 0.0 def __doSfxLevel(self): vol = self.SoundFX_toggleSlider['value'] vol = float(vol) / 100 settings['sfxVol'] = vol for sfm in base.sfxManagerList: sfm.setVolume(vol) base.sfxActive = vol > 0.0 def __doToggleToonChatSounds(self): messenger.send('wakeup') if base.toonChatSounds: base.toonChatSounds = 0 settings['toonChatSounds'] = False else: base.toonChatSounds = 1 settings['toonChatSounds'] = True self.settingsChanged = 1 self.__setToonChatSoundsButton() def __setToonChatSoundsButton(self): if base.toonChatSounds: self.ToonChatSounds_Label['text'] = TTLocalizer.OptionsPageToonChatSoundsOnLabel self.ToonChatSounds_toggleButton['text'] = TTLocalizer.OptionsPageToggleOff else: self.ToonChatSounds_Label['text'] = TTLocalizer.OptionsPageToonChatSoundsOffLabel self.ToonChatSounds_toggleButton['text'] = TTLocalizer.OptionsPageToggleOn if base.sfxActive: self.ToonChatSounds_Label.setColorScale(1.0, 1.0, 1.0, 1.0) self.ToonChatSounds_toggleButton['state'] = DGG.NORMAL else: self.ToonChatSounds_Label.setColorScale(0.5, 0.5, 0.5, 0.5) self.ToonChatSounds_toggleButton['state'] = DGG.DISABLED def __doWidescreen(self, ratio): messenger.send('wakeup') ratio = self.AspectRatioList.selectedIndex if base.Widescreen != ratio: base.Widescreen = ratio settings['Widescreen'] = ratio self.settingsChanged = 1 base.updateAspectRatio() def __doToggleWASD(self): messenger.send('wakeup') if base.wantCustomControls: base.wantCustomControls = False settings['want-Custom-Controls'] = False else: base.wantCustomControls = True settings['want-Custom-Controls'] = True base.reloadControls() self.settingsChanged = 1 self.__setWASDButton() def __setWASDButton(self): if base.wantCustomControls: self.WASD_Label['text'] = 'Custom Keymapping is enabled.' self.WASD_toggleButton['text'] = TTLocalizer.OptionsPageToggleOff self.keymapDialogButton.show() else: self.WASD_Label['text'] = 'Custom Keymapping is disabled.' self.WASD_toggleButton['text'] = TTLocalizer.OptionsPageToggleOn self.keymapDialogButton.hide() def __openKeyRemapDialog(self): if base.wantCustomControls: self.controlDialog = ControlRemapDialog.ControlRemap() def __doDisplaySettings(self): if self.displaySettings == None: self.displaySettings = DisplaySettingsDialog.DisplaySettingsDialog() self.displaySettings.load() base.accept(self.displaySettings.doneEvent, self.__doneDisplaySettings) self.displaySettings.enter(True, False) def __doneDisplaySettings(self, anyChanged, apiChanged): if anyChanged: self.__setDisplaySettings() properties = base.win.getProperties() self.displaySettingsChanged = 1 self.displaySettingsSize = (properties.getXSize(), properties.getYSize()) self.displaySettingsFullscreen = properties.getFullscreen() self.displaySettingsBorderless = properties.getUndecorated() self.displaySettingsApi = base.pipe.getInterfaceName() self.displaySettingsApiChanged = apiChanged def __setDisplaySettings(self): properties = base.win.getProperties() if properties.getFullscreen(): screensize = 'Fullscreen | %s x %s' % (properties.getXSize(), properties.getYSize()) elif properties.getUndecorated(): screensize = 'Borderless Windowed | %s x %s' % (properties.getXSize(), properties.getYSize()) else: screensize = 'Windowed' api = base.pipe.getInterfaceName() settings = {'screensize': screensize, 'api': api} text = TTLocalizer.OptionsPageDisplaySettings % settings self.DisplaySettings_Label['text'] = text def __doFovLevel(self): fov = self.fov_toggleSlider['value'] settings['fieldofview'] = fov base.camLens.setMinFov(fov/(4./3.)) dec = Decimal(fov) self.fovsliderText['text'] = str(round(fov, 1)) def __resetFov(self): self.fov_toggleSlider['value'] = 52 settings['fieldofview'] = 52 base.camLens.setMinFov(52/(4./3.)) self.fovsliderText['text'] = str(52)

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from direct.gui.DirectGui import * from direct.interval.IntervalGlobal import Wait, Func, Sequence, LerpColorScaleInterval, Parallel, LerpScaleInterval from direct.showbase.DirectObject import DirectObject from panda3d.core import TransparencyAttrib, Point3, Vec4, TextNode, Vec3 from toontown.toonbase import TTLocalizer from toontown.toonbase import ToontownGlobals from toontown.toontowngui.TTGui import btnDn, btnRlvr, btnUp from toontown.toontowngui import TTDialog from toontown.options import GraphicsOptions from toontown.shtiker import ControlRemapDialog, DisplaySettingsDialog from decimal import Decimal resolution_table = [ (800, 600), (1024, 768), (1280, 1024), (1600, 1200), (1280, 720), (1920, 1080)] class PickAToonOptions: def __init__(self): self.optionsOpenSfx = None None zoomIn = (LerpScaleInterval(self.optionsNode, .4, Vec3(1, 1, 1), Vec3(0, 0, 0), blendType = 'easeInOut')).start() def hideOptions(self): erval(self.optionsNode, .4, Vec3(0, 0, 0), Vec3(1, 1, 1), blendType = 'easeInOut')).start() Sequence ( Wait(.4), Func(self.delOptions)).start() def displayOptions(self): self.optionsNode = aspect2d.attachNewNode('optionsNode') self.optionsNode.reparentTo(aspect2d) gui = loader.loadModel('phase_3/models/gui/pick_a_toon_gui') guiButton = loader.loadModel('phase_3/models/gui/quit_button') quitHover = gui.find('**/QuitBtn_RLVR') self.optionsBox = OnscreenImage(image = 'phase_3/maps/stat_board.png') self.optionsBox.setTransparency(TransparencyAttrib.MAlpha) self.optionsBox.setPos(0, 0, 0) self.optionsBox.setScale(0.7) self.optionsBox.reparentTo(self.optionsNode) self.Music_Label = DirectLabel(parent = self.optionsNode, relief = None, text = 'Music Volume', text_align = TextNode.ACenter, text_scale = 0.052, pos = (0, 0, 0.5)) self.Music_toggleSlider = DirectSlider(parent = self.optionsNode, pos = (0, 0, 0.4), value = settings['musicVol'] * 100, pageSize = 5, range = (0, 100), command = self.__doMusicLevel,) self.Music_toggleSlider.setScale(0.4, 0.4, 0.4) self.Music_toggleSlider.show() self.SoundFX_toggleSlider = DirectSlider(parent = self.optionsNode, pos = (0, 0.0, 0.2), value = settings['sfxVol'] * 100, pageSize = 5, range = (0, 100), command = self.__doSfxLevel) self.SoundFX_toggleSlider.setScale(0.4, 0.4, 0.4) self.SoundFX_Label = DirectLabel(parent = self.optionsNode, relief = None, text = 'SFX Volume', text_align = TextNode.ACenter, text_scale = 0.052, pos = (0, 0, 0.3)) self.ToonChatSounds_toggleButton = DirectButton(parent = self.optionsNode, relief = None, image = (guiButton.find('**/QuitBtn_UP'), guiButton.find('**/QuitBtn_DN'), guiButton.find('**/QuitBtn_RLVR'), guiButton.find('**/QuitBtn_UP')), image3_color = Vec4(0.5, 0.5, 0.5, 0.5), image_scale = (0.7, 1, 1), text = '', text3_fg = (0.5, 0.5, 0.5, 0.75), text_scale = 0.052, text_pos = (0, -.02), pos = (0, 0, 0), command = self.__doToggleToonChatSounds) self.ToonChatSounds_toggleButton.setScale(0.8) self.ToonChatSounds_Label = DirectLabel(parent = self.optionsNode, relief = None, text = 'Toon Chat Sounds', text_align = TextNode.ACenter, text_scale = 0.052, pos = (0, 0, .1)) self.WASD_Label = DirectLabel(parent=self.optionsNode, relief=None, text='', text_align=TextNode.ACenter, text_scale=0.052, text_wordwrap=16, pos=(0, 0, -0.1)) self.WASD_toggleButton = DirectButton(parent=self.optionsNode, relief=None, image=(guiButton.find('**/QuitBtn_UP'), guiButton.find('**/QuitBtn_DN'), guiButton.find('**/QuitBtn_RLVR')), image_scale = (0.7, 1, 1), text='', text_scale = 0.052, text_pos=(0, -.02), pos=(0, 0, -0.2), command=self.__doToggleWASD) self.keymapDialogButton = DirectButton(parent=self.optionsNode, relief = None, image = (guiButton.find('**/QuitBtn_UP'), guiButton.find('**/QuitBtn_DN'), guiButton.find('**/QuitBtn_RLVR')), image_scale = (0.7, 1, 1), text='Change Keybinds', text_scale=(0.03, 0.05, 1), text_pos = (0, -.02), pos = (0, 0, -0.3), command = self.__openKeyRemapDialog) self.keymapDialogButton.setScale(1.55, 1.0, 1.0) self.AspectRatioList = DirectOptionMenu(relief = None, parent = self.optionsNode, text_align = TextNode.ACenter, items = GraphicsOptions.AspectRatioLabels, command = self.__doWidescreen, text_scale = .6, popupMarker_pos = (-1, 0, 0), popupMarker_relief = None, highlightScale = (1.1, 1.1), image = (guiButton.find('**/QuitBtn_UP'), guiButton.find('**/QuitBtn_DN'), guiButton.find('**/QuitBtn_RLVR'), guiButton.find('**/QuitBtn_UP')), image_scale = 8, image3_color = Vec4(0.5, 0.5, 0.5, 0.5), text = '', text3_fg = (0.5, 0.5, 0.5, 0.75), text_pos = (0, -.02), pos = (0, 0, -0.5), image_pos = (0, 0, 0), item_text_align = TextNode.ACenter, popupMenu_text_scale = .5, item_relief = None, item_pressEffect = 1) self.AspectRatioList.setScale(0.1) self.AspectRatioList.set(base.Widescreen) self.Widescreen_Label = DirectLabel(parent = self.optionsNode, relief = None, text = 'Aspect Ratio', text_align = TextNode.ACenter, text_scale = 0.052, pos = (0, 0, -0.4)) self.__setToonChatSoundsButton() self.__setWASDButton() def delOptions(self): self.optionsBox.destroy() del self.optionsBox self.Music_Label.destroy() del self.Music_Label self.Music_toggleSlider.destroy() del self.Music_toggleSlider self.SoundFX_Label.destroy() del self.SoundFX_Label self.SoundFX_toggleSlider.destroy() del self.SoundFX_toggleSlider self.ToonChatSounds_Label.destroy() del self.ToonChatSounds_Label self.ToonChatSounds_toggleButton.destroy() del self.ToonChatSounds_toggleButton self.Widescreen_Label.destroy() del self.Widescreen_Label self.AspectRatioList.destroy() del self.AspectRatioList self.WASD_Label.destroy() del self.WASD_Label self.WASD_toggleButton.destroy() del self.WASD_toggleButton self.keymapDialogButton.destroy() del self.keymapDialogButton self.optionsNode.removeNode() del self.optionsNode def __doMusicLevel(self): vol = self.Music_toggleSlider['value'] vol = float(vol) / 100 settings['musicVol'] = vol base.musicManager.setVolume(vol) base.musicActive = vol > 0.0 def __doSfxLevel(self): vol = self.SoundFX_toggleSlider['value'] vol = float(vol) / 100 settings['sfxVol'] = vol for sfm in base.sfxManagerList: sfm.setVolume(vol) base.sfxActive = vol > 0.0 def __doToggleToonChatSounds(self): messenger.send('wakeup') if base.toonChatSounds: base.toonChatSounds = 0 settings['toonChatSounds'] = False else: base.toonChatSounds = 1 settings['toonChatSounds'] = True self.settingsChanged = 1 self.__setToonChatSoundsButton() def __setToonChatSoundsButton(self): if base.toonChatSounds: self.ToonChatSounds_Label['text'] = TTLocalizer.OptionsPageToonChatSoundsOnLabel self.ToonChatSounds_toggleButton['text'] = TTLocalizer.OptionsPageToggleOff else: self.ToonChatSounds_Label['text'] = TTLocalizer.OptionsPageToonChatSoundsOffLabel self.ToonChatSounds_toggleButton['text'] = TTLocalizer.OptionsPageToggleOn if base.sfxActive: self.ToonChatSounds_Label.setColorScale(1.0, 1.0, 1.0, 1.0) self.ToonChatSounds_toggleButton['state'] = DGG.NORMAL else: self.ToonChatSounds_Label.setColorScale(0.5, 0.5, 0.5, 0.5) self.ToonChatSounds_toggleButton['state'] = DGG.DISABLED def __doWidescreen(self, ratio): messenger.send('wakeup') ratio = self.AspectRatioList.selectedIndex if base.Widescreen != ratio: base.Widescreen = ratio settings['Widescreen'] = ratio self.settingsChanged = 1 base.updateAspectRatio() def __doToggleWASD(self): messenger.send('wakeup') if base.wantCustomControls: base.wantCustomControls = False settings['want-Custom-Controls'] = False else: base.wantCustomControls = True settings['want-Custom-Controls'] = True base.reloadControls() self.settingsChanged = 1 self.__setWASDButton() def __setWASDButton(self): if base.wantCustomControls: self.WASD_Label['text'] = 'Custom Keymapping is enabled.' self.WASD_toggleButton['text'] = TTLocalizer.OptionsPageToggleOff self.keymapDialogButton.show() else: self.WASD_Label['text'] = 'Custom Keymapping is disabled.' self.WASD_toggleButton['text'] = TTLocalizer.OptionsPageToggleOn self.keymapDialogButton.hide() def __openKeyRemapDialog(self): if base.wantCustomControls: self.controlDialog = ControlRemapDialog.ControlRemap() class NewPickAToonOptions: def __init__(self): self.optionsOpenSfx = None None el = None self.Music_toggleSlider = None self.SoundFX_Label = None self.SoundFX_toggleSlider = None self.ToonChatSounds_Label = None self.ToonChatSounds_toggleButton = None self.WASD_Label = None self.WASD_toggleButton = None self.keymapDialogButton = None self.Widescreen_Label = None self.AspectRatioList = None self.DisplaySettings_Label = None self.DisplaySettingsButton = None self.fov_toggleSlider = None self.fov_Label = None self.fov_resetButton = None self.displaySettings = None self.displaySettingsChanged = 0 self.displaySettingsSize = (None, None) self.displaySettingsFullscreen = None self.displaySettingsBorderless = None self.displaySettingsApi = None self.displaySettingsApiChanged = 0 def showOptions(self): zoomIn = (LerpScaleInterval(self.optionsNode, .1, Vec3(1, 1, 1), Vec3(0, 0, 0), blendType = 'easeOut')).start() def hideOptions(self): erval(self.optionsNode, .1, Vec3(.5, .5, .5), Vec3(1, 1, 1), blendType = 'easeIn')).start() Sequence ( Wait(.1), Func(self.delAllOptions)).start() def displayOptions(self): self.optionsNode = aspect2d.attachNewNode('optionsNode') self.optionsNode.reparentTo(aspect2d) self.guimodel = loader.loadModel('phase_3/models/gui/pick_a_toon_gui') self.guiButton = loader.loadModel('phase_3/models/gui/quit_button') self.quitHover = self.guimodel.find('**/QuitBtn_RLVR') self.optionsBox = OnscreenImage(image = 'phase_3/maps/stat_board.png') self.optionsBox.setTransparency(TransparencyAttrib.MAlpha) self.optionsBox.setPos(0, 0, 0) self.optionsBox.setScale(1.3, 1, 1) self.optionsBox.reparentTo(self.optionsNode) self.soundOptionsButton = DirectButton(relief = None, text_style = 3, text_fg = (1, 1, 1, 1), text = 'Sound', text_scale = .1, scale = 0.95, command = self.displaySoundOptions) self.soundOptionsButton.reparentTo(self.optionsNode) self.soundOptionsButton.setPos(-.6, 0, .7) self.soundOptionsButton.show() self.controlOptionsButton = DirectButton(relief = None, text_style = 3, text_fg = (1, 1, 1, 1), text = 'Controls', text_scale = .1, scale = 0.95, command = self.displayControlOptions) self.controlOptionsButton.reparentTo(self.optionsNode) self.controlOptionsButton.setPos(0, 0, .7) self.controlOptionsButton.show() self.videoOptionsButton = DirectButton(relief = None, text_style = 3, text_fg = (1, 1, 1, 1), text = 'Video', text_scale = .1, scale = 0.95, command = self.displayVideoOptions) self.videoOptionsButton.reparentTo(self.optionsNode) self.videoOptionsButton.setPos(.6, 0, .7) self.videoOptionsButton.show() self.displaySoundOptions() def displaySoundOptions(self): self.delSoundOptions() self.delControlOptions() self.delVideoOptions() self.Music_Label = DirectLabel(parent = self.optionsNode, relief = None, text = 'Music Volume', text_align = TextNode.ACenter, text_scale = 0.052, pos = (0, 0, 0.4)) self.Music_toggleSlider = DirectSlider(parent = self.optionsNode, pos = (0, 0, 0.3), value = settings['musicVol'] * 100, pageSize = 5, range = (0, 100), command = self.__doMusicLevel, thumb_geom=(self.guiButton.find('**/QuitBtn_UP')), thumb_relief=None, thumb_geom_scale=1) self.Music_toggleSlider.setScale(0.4, 0.4, 0.4) self.Music_toggleSlider.show() self.SoundFX_toggleSlider = DirectSlider(parent = self.optionsNode, pos = (0, 0.0, 0.1), value = settings['sfxVol'] * 100, pageSize = 5, range = (0, 100), command = self.__doSfxLevel, thumb_geom=(self.guiButton.find('**/QuitBtn_UP')), thumb_relief=None, thumb_geom_scale=1) self.SoundFX_toggleSlider.setScale(0.4, 0.4, 0.4) self.SoundFX_Label = DirectLabel(parent = self.optionsNode, relief = None, text = 'SFX Volume', text_align = TextNode.ACenter, text_scale = 0.052, pos = (0, 0, 0.2)) self.ToonChatSounds_toggleButton = DirectButton(parent = self.optionsNode, relief = None, image = (self.guiButton.find('**/QuitBtn_UP'), self.guiButton.find('**/QuitBtn_DN'), self.guiButton.find('**/QuitBtn_RLVR'), self.guiButton.find('**/QuitBtn_UP')), image3_color = Vec4(0.5, 0.5, 0.5, 0.5), image_scale = (0.7, 1, 1), text = '', text3_fg = (0.5, 0.5, 0.5, 0.75), text_scale = 0.052, text_pos = (0, -.02), pos = (0, 0, -.1), command = self.__doToggleToonChatSounds) self.ToonChatSounds_toggleButton.setScale(0.8) self.ToonChatSounds_Label = DirectLabel(parent = self.optionsNode, relief = None, text = 'Toon Chat Sounds', text_align = TextNode.ACenter, text_scale = 0.052, pos = (0, 0, 0)) self.__setToonChatSoundsButton() def displayControlOptions(self): self.delSoundOptions() self.delControlOptions() self.delVideoOptions() self.WASD_Label = DirectLabel(parent=self.optionsNode, relief=None, text='', text_align=TextNode.ACenter, text_scale=0.052, text_wordwrap=16, pos=(0, 0, .4)) self.WASD_toggleButton = DirectButton(parent=self.optionsNode, relief=None, image=(self.guiButton.find('**/QuitBtn_UP'), self.guiButton.find('**/QuitBtn_DN'), self.guiButton.find('**/QuitBtn_RLVR')), image_scale = (0.7, 1, 1), text='', text_scale = 0.052, text_pos=(0, -.02), pos=(0, 0, .3), command=self.__doToggleWASD) self.keymapDialogButton = DirectButton(parent=self.optionsNode, relief = None, image = (self.guiButton.find('**/QuitBtn_UP'), self.guiButton.find('**/QuitBtn_DN'), self.guiButton.find('**/QuitBtn_RLVR')), image_scale = (0.7, 1, 1), text='Change Keybinds', text_scale=(0.03, 0.05, 1), text_pos = (0, -.02), pos = (0, 0, .2), command = self.__openKeyRemapDialog) self.keymapDialogButton.setScale(1.55, 1.0, 1.0) self.__setWASDButton() def displayVideoOptions(self): self.delSoundOptions() self.delControlOptions() self.delVideoOptions() self.AspectRatioList = DirectOptionMenu(relief = None, parent = self.optionsNode, text_align = TextNode.ACenter, items = GraphicsOptions.AspectRatioLabels, command = self.__doWidescreen, text_scale = .6, popupMarker_pos = (-1, 0, 0), popupMarker_relief = None, highlightScale = (1.1, 1.1), image = (self.guiButton.find('**/QuitBtn_UP'), self.guiButton.find('**/QuitBtn_DN'), self.guiButton.find('**/QuitBtn_RLVR'), self.guiButton.find('**/QuitBtn_UP')), image_scale = 8, image3_color = Vec4(0.5, 0.5, 0.5, 0.5), text = '', text3_fg = (0.5, 0.5, 0.5, 0.75), text_pos = (0, -.02), pos = (0, 0, .3), image_pos = (0, 0, 0), item_text_align = TextNode.ACenter, popupMenu_text_scale = .5, item_relief = None, item_pressEffect = 1) self.AspectRatioList.setScale(0.1) self.AspectRatioList.set(base.Widescreen) self.Widescreen_Label = DirectLabel(parent = self.optionsNode, relief = None, text = 'Aspect Ratio', text_align = TextNode.ACenter, text_scale = 0.052, pos = (0, 0, .4)) self.DisplaySettings_Label = DirectLabel(parent=self.optionsNode, relief=None, text='', text_align=TextNode.ACenter, text_scale=0.052, text_wordwrap=16, pos=(0, 0, .2)) self.DisplaySettingsButton = DirectButton(parent=self.optionsNode, relief=None, image=(self.guiButton.find('**/QuitBtn_UP'), self.guiButton.find('**/QuitBtn_DN'), self.guiButton.find('**/QuitBtn_RLVR')), image3_color=Vec4(0.5, 0.5, 0.5, 0.5), image_scale=(0.7, 1, 1), text=TTLocalizer.OptionsPageChange, text3_fg=(0.5, 0.5, 0.5, 0.75), text_scale=0.052, text_pos=(0, -.02), pos=(0, 0, .1), command=self.__doDisplaySettings) self.fov_Label = DirectLabel(parent=self.optionsNode, relief=None, text='Field of view', text_align=TextNode.ACenter, text_scale = 0.052, text_wordwrap=16, pos=(0, 0, 0)) self.fov_toggleSlider = DirectSlider(parent=self.optionsNode, pos=(0, 0, -.1), value=settings['fieldofview'], pageSize=5, range=(30, 120), command=self.__doFovLevel, thumb_geom=(self.guiButton.find('**/QuitBtn_UP')), thumb_relief=None, thumb_geom_scale=1) self.fov_toggleSlider.setScale(0.25) self.fov_resetButton = DirectButton(parent=self.optionsNode, relief=None, image=(self.guiButton.find('**/QuitBtn_UP'), self.guiButton.find('**/QuitBtn_DN'), self.guiButton.find('**/QuitBtn_RLVR')), image_scale=(0.7, 1, 1), text='Reset FOV', text_scale=0.052, text_pos = (0, -.02), pos=(0, 0, -.2), command=self.__resetFov) self.fovsliderText = OnscreenText('0.0', scale=.3, pos=(0, .1), fg=(1, 1, 1, 1), style = 3) self.fovsliderText.reparentTo(self.fov_toggleSlider.thumb) self.__doFovLevel() self.__setDisplaySettings() def delSoundOptions(self): if self.Music_Label: self.Music_Label.destroy() self.Music_Label = None if self.Music_toggleSlider: self.Music_toggleSlider.destroy() self.Music_toggleSlider = None if self.SoundFX_Label: self.SoundFX_Label.destroy() self.SoundFX_Label = None if self.SoundFX_toggleSlider: self.SoundFX_toggleSlider.destroy() self.SoundFX_toggleSlider = None if self.ToonChatSounds_Label: self.ToonChatSounds_Label.destroy() self.ToonChatSounds_Label = None if self.ToonChatSounds_toggleButton: self.ToonChatSounds_toggleButton.destroy() self.ToonChatSounds_toggleButton = None def delControlOptions(self): if self.WASD_Label: self.WASD_Label.destroy() self.WASD_Label = None if self.WASD_toggleButton: self.WASD_toggleButton.destroy() self.WASD_toggleButton = None if self.keymapDialogButton: self.keymapDialogButton.destroy() self.keymapDialogButton = None def delVideoOptions(self): if self.Widescreen_Label: self.Widescreen_Label.destroy() self.Widescreen_Label = None if self.AspectRatioList: self.AspectRatioList.destroy() self.AspectRatioList = None if self.DisplaySettings_Label: self.DisplaySettings_Label.destroy() self.DisplaySettings_Label = None if self.DisplaySettingsButton: self.DisplaySettingsButton.destroy() self.DisplaySettingsButton = None if self.fov_toggleSlider: self.fov_toggleSlider.destroy() self.fov_toggleSlider = None self.fov_Label.destroy() self.fov_Label = None self.fov_resetButton.destroy() self.fov_resetButton = None def delAllOptions(self): self.delSoundOptions() self.delControlOptions() self.delVideoOptions() self.optionsBox.destroy() del self.optionsBox self.optionsNode.removeNode() del self.optionsNode def __doMusicLevel(self): vol = self.Music_toggleSlider['value'] vol = float(vol) / 100 settings['musicVol'] = vol base.musicManager.setVolume(vol) base.musicActive = vol > 0.0 def __doSfxLevel(self): vol = self.SoundFX_toggleSlider['value'] vol = float(vol) / 100 settings['sfxVol'] = vol for sfm in base.sfxManagerList: sfm.setVolume(vol) base.sfxActive = vol > 0.0 def __doToggleToonChatSounds(self): messenger.send('wakeup') if base.toonChatSounds: base.toonChatSounds = 0 settings['toonChatSounds'] = False else: base.toonChatSounds = 1 settings['toonChatSounds'] = True self.settingsChanged = 1 self.__setToonChatSoundsButton() def __setToonChatSoundsButton(self): if base.toonChatSounds: self.ToonChatSounds_Label['text'] = TTLocalizer.OptionsPageToonChatSoundsOnLabel self.ToonChatSounds_toggleButton['text'] = TTLocalizer.OptionsPageToggleOff else: self.ToonChatSounds_Label['text'] = TTLocalizer.OptionsPageToonChatSoundsOffLabel self.ToonChatSounds_toggleButton['text'] = TTLocalizer.OptionsPageToggleOn if base.sfxActive: self.ToonChatSounds_Label.setColorScale(1.0, 1.0, 1.0, 1.0) self.ToonChatSounds_toggleButton['state'] = DGG.NORMAL else: self.ToonChatSounds_Label.setColorScale(0.5, 0.5, 0.5, 0.5) self.ToonChatSounds_toggleButton['state'] = DGG.DISABLED def __doWidescreen(self, ratio): messenger.send('wakeup') ratio = self.AspectRatioList.selectedIndex if base.Widescreen != ratio: base.Widescreen = ratio settings['Widescreen'] = ratio self.settingsChanged = 1 base.updateAspectRatio() def __doToggleWASD(self): messenger.send('wakeup') if base.wantCustomControls: base.wantCustomControls = False settings['want-Custom-Controls'] = False else: base.wantCustomControls = True settings['want-Custom-Controls'] = True base.reloadControls() self.settingsChanged = 1 self.__setWASDButton() def __setWASDButton(self): if base.wantCustomControls: self.WASD_Label['text'] = 'Custom Keymapping is enabled.' self.WASD_toggleButton['text'] = TTLocalizer.OptionsPageToggleOff self.keymapDialogButton.show() else: self.WASD_Label['text'] = 'Custom Keymapping is disabled.' self.WASD_toggleButton['text'] = TTLocalizer.OptionsPageToggleOn self.keymapDialogButton.hide() def __openKeyRemapDialog(self): if base.wantCustomControls: self.controlDialog = ControlRemapDialog.ControlRemap() def __doDisplaySettings(self): if self.displaySettings == None: self.displaySettings = DisplaySettingsDialog.DisplaySettingsDialog() self.displaySettings.load() base.accept(self.displaySettings.doneEvent, self.__doneDisplaySettings) self.displaySettings.enter(True, False) def __doneDisplaySettings(self, anyChanged, apiChanged): if anyChanged: self.__setDisplaySettings() properties = base.win.getProperties() self.displaySettingsChanged = 1 self.displaySettingsSize = (properties.getXSize(), properties.getYSize()) self.displaySettingsFullscreen = properties.getFullscreen() self.displaySettingsBorderless = properties.getUndecorated() self.displaySettingsApi = base.pipe.getInterfaceName() self.displaySettingsApiChanged = apiChanged def __setDisplaySettings(self): properties = base.win.getProperties() if properties.getFullscreen(): screensize = 'Fullscreen | %s x %s' % (properties.getXSize(), properties.getYSize()) elif properties.getUndecorated(): screensize = 'Borderless Windowed | %s x %s' % (properties.getXSize(), properties.getYSize()) else: screensize = 'Windowed' api = base.pipe.getInterfaceName() settings = {'screensize': screensize, 'api': api} text = TTLocalizer.OptionsPageDisplaySettings % settings self.DisplaySettings_Label['text'] = text def __doFovLevel(self): fov = self.fov_toggleSlider['value'] settings['fieldofview'] = fov base.camLens.setMinFov(fov/(4./3.)) dec = Decimal(fov) self.fovsliderText['text'] = str(round(fov, 1)) def __resetFov(self): self.fov_toggleSlider['value'] = 52 settings['fieldofview'] = 52 base.camLens.setMinFov(52/(4./3.)) self.fovsliderText['text'] = str(52)

true

1c43e36be51bc3b9156c0575a20c0ca5254421ba

1,708

py

Python

thirdParty/lxml/__init__.py

knittledan/Location_Search_Prediction

c96e3bfc0c73b646b9a7620bb1655285458fb20d

[ "MIT" ]

null

thirdParty/lxml/__init__.py

knittledan/Location_Search_Prediction

c96e3bfc0c73b646b9a7620bb1655285458fb20d

[ "MIT" ]

null

thirdParty/lxml/__init__.py

knittledan/Location_Search_Prediction

c96e3bfc0c73b646b9a7620bb1655285458fb20d

[ "MIT" ]

null

# -*- coding: utf-8 -*- #---------------------------------------------------------------------------------------- # __init__.py initialization file for PIL #---------------------------------------------------------------------------------------- import os import sys import platform #---------------------------------------------------------------------------------------- # Defines #---------------------------------------------------------------------------------------- kMac = 0 kLinux = 1 kWindows = 2 currentDir = os.path.dirname(os.path.realpath(__file__)) version = sys.version_info[:2] #---------------------------------------------------------------------------------------- # Methods #---------------------------------------------------------------------------------------- if version == (2, 7): lxmlVersion = "lxml_py27" if version == (3, 2): lxmlVersion = "lxml_py32" def getOs(): name = platform.system() if name == 'Darwin': return kMac if name == 'Linux': return kLinux if name == 'Windows': return kWindows if getOs() == kMac: module = os.path.join(currentDir, 'mac', lxmlVersion) if getOs() == kLinux: module = os.path.join(currentDir, 'linux', lxmlVersion) if getOs() == kWindows: module = os.path.join(currentDir, 'windows', lxmlVersion) #---------------------------------------------------------------------------------------- # Package handler #---------------------------------------------------------------------------------------- # insert os specific PIL package path into sys.path sys.path.insert(0, module) # delete empty PIL package del sys.modules[__name__] # import os specific PIL package import lxml

30.5

89

0.384075

import os import sys import platform kMac = 0 kLinux = 1 kWindows = 2 currentDir = os.path.dirname(os.path.realpath(__file__)) version = sys.version_info[:2] if version == (2, 7): lxmlVersion = "lxml_py27" if version == (3, 2): lxmlVersion = "lxml_py32" def getOs(): name = platform.system() if name == 'Darwin': return kMac if name == 'Linux': return kLinux if name == 'Windows': return kWindows if getOs() == kMac: module = os.path.join(currentDir, 'mac', lxmlVersion) if getOs() == kLinux: module = os.path.join(currentDir, 'linux', lxmlVersion) if getOs() == kWindows: module = os.path.join(currentDir, 'windows', lxmlVersion) sys.path.insert(0, module) del sys.modules[__name__] import lxml

true

1c43e4d4ab4dc301594d8265fd0f7be7719b47ae

2,611

py

Python

examples/plot_samples.py

AWehrhahn/exoplanet_transit_snr

f1bdaddb89e1c8b819651bcd2d80ed95d2a1fc0f

[ "MIT" ]

null

examples/plot_samples.py

AWehrhahn/exoplanet_transit_snr

f1bdaddb89e1c8b819651bcd2d80ed95d2a1fc0f

[ "MIT" ]

null

examples/plot_samples.py

AWehrhahn/exoplanet_transit_snr

f1bdaddb89e1c8b819651bcd2d80ed95d2a1fc0f

[ "MIT" ]

null

# -*- coding: utf-8 -*- import corner import emcee import matplotlib.pyplot as plt import numpy as np from astropy import units as u from exoorbit.orbit import Orbit from exoplanet_transit_snr.stellardb import StellarDb star, planet = "WASP-107", "b" datasets = {50: "WASP-107b_SNR50", 100: "WASP-107b_SNR100", 200: "WASP-107b_SNR200"} # Load the nominal data for this star and planet from simbad/nasa exoplanet archive sdb = StellarDb() star = sdb.get(star) planet = star.planets[planet] orbit = Orbit(star, planet) rv = orbit.radial_velocity_semiamplitude_planet() snr = 200 nsysrem = 5 fname = f"MCMC_{star.name}_{planet.name}_SNR{snr}_sysrem{nsysrem}.h5" # fname = "mcmc_samples.npy" ndim = 10 nwalkers = 32 labels = ["a", "v_sys", "mass", "radius", "sma", "per", "inc", "ecc", "w", "t0"] truths = np.array( [ 1, star.radial_velocity.to_value(u.km / u.s), planet.mass.to_value(u.M_jup), planet.radius.to_value(u.R_jup), planet.sma.to_value(u.AU), planet.period.to_value(u.day), planet.inc.to_value(u.deg), planet.ecc.to_value(u.one), planet.omega.to_value(u.deg), planet.t0.mjd, ] ) sampler = emcee.backends.HDFBackend(fname) samples = sampler.get_chain() # samples = np.load(fname) tau = emcee.autocorr.integrated_time(samples, quiet=True) # tau = sampler.get_autocorr_time() burnin = int(2 * np.max(tau)) thin = int(0.5 * np.min(tau)) # Print results fig, axes = plt.subplots(ndim, figsize=(10, 7), sharex=True) for i in range(ndim): ax = axes[i] ax.plot(samples[:, :, i], "k", alpha=0.3) ax.set_xlim(0, len(samples)) ax.set_ylabel(labels[i]) ax.yaxis.set_label_coords(-0.1, 0.5) axes[-1].set_xlabel("step number") plt.show() # sampler.get_chain(discard=2000, flat=True) # ranges=[(1.0, 1.015), (-150, 150), (0, 1), (0, 2), (0, 5), (0, 10), (70, 110), (0, 1), (40, 160), 0.99] ranges = [0.9] * len(labels) flat_samples = samples[burnin::thin].reshape((-1, ndim)) fig = corner.corner(flat_samples, labels=labels, truths=truths, range=ranges) plt.show() for i in range(ndim): low, mid, upp = np.percentile(flat_samples[:, i], [16, 50, 84], axis=0) sigma = (upp - low) / 2 print(f"{labels[i]}: {mid:.5g} + {upp-mid:.5g} - {mid-low:.5g} ; {truths[i]:.5g}") # a: 1.0065 + 0.00016413 - 0.00014236 ; 1 # v_sys: 12.922 + 31.738 - 30.919 ; 13.74 # mass: 4.1251 + 4.2402 - 3.6097 ; 0.096 # per: 12.947 + 20.586 - 7.4242 ; 5.7215 # inc: 91.563 + 41.107 - 65.425 ; 89.56 # ecc: 0.49631 + 0.32373 - 0.37054 ; 0.06 # w: 96.66 + 29.538 - 38.358 ; 90 # t0: 57578 + 61.426 - 164.84 ; 57584

30.717647

105

0.640368

import corner import emcee import matplotlib.pyplot as plt import numpy as np from astropy import units as u from exoorbit.orbit import Orbit from exoplanet_transit_snr.stellardb import StellarDb star, planet = "WASP-107", "b" datasets = {50: "WASP-107b_SNR50", 100: "WASP-107b_SNR100", 200: "WASP-107b_SNR200"} sdb = StellarDb() star = sdb.get(star) planet = star.planets[planet] orbit = Orbit(star, planet) rv = orbit.radial_velocity_semiamplitude_planet() snr = 200 nsysrem = 5 fname = f"MCMC_{star.name}_{planet.name}_SNR{snr}_sysrem{nsysrem}.h5" ndim = 10 nwalkers = 32 labels = ["a", "v_sys", "mass", "radius", "sma", "per", "inc", "ecc", "w", "t0"] truths = np.array( [ 1, star.radial_velocity.to_value(u.km / u.s), planet.mass.to_value(u.M_jup), planet.radius.to_value(u.R_jup), planet.sma.to_value(u.AU), planet.period.to_value(u.day), planet.inc.to_value(u.deg), planet.ecc.to_value(u.one), planet.omega.to_value(u.deg), planet.t0.mjd, ] ) sampler = emcee.backends.HDFBackend(fname) samples = sampler.get_chain() tau = emcee.autocorr.integrated_time(samples, quiet=True) burnin = int(2 * np.max(tau)) thin = int(0.5 * np.min(tau)) fig, axes = plt.subplots(ndim, figsize=(10, 7), sharex=True) for i in range(ndim): ax = axes[i] ax.plot(samples[:, :, i], "k", alpha=0.3) ax.set_xlim(0, len(samples)) ax.set_ylabel(labels[i]) ax.yaxis.set_label_coords(-0.1, 0.5) axes[-1].set_xlabel("step number") plt.show() ranges = [0.9] * len(labels) flat_samples = samples[burnin::thin].reshape((-1, ndim)) fig = corner.corner(flat_samples, labels=labels, truths=truths, range=ranges) plt.show() for i in range(ndim): low, mid, upp = np.percentile(flat_samples[:, i], [16, 50, 84], axis=0) sigma = (upp - low) / 2 print(f"{labels[i]}: {mid:.5g} + {upp-mid:.5g} - {mid-low:.5g} ; {truths[i]:.5g}")

true

1c43e6f20ef928918c97de09f2b91fbfbcc389dc

669

py

Python

app/core/management/commands/wait_for_db.py

amirhosseyn/Django-REST

e8c031c8e5d00ae5a9a8732b7c298bb9c2afa8f9

[ "MIT" ]

null

app/core/management/commands/wait_for_db.py

amirhosseyn/Django-REST

e8c031c8e5d00ae5a9a8732b7c298bb9c2afa8f9

[ "MIT" ]

null

app/core/management/commands/wait_for_db.py

amirhosseyn/Django-REST

e8c031c8e5d00ae5a9a8732b7c298bb9c2afa8f9

[ "MIT" ]

null

import time from django.db import connections from django.db.utils import OperationalError from django.core.management.base import BaseCommand class Command(BaseCommand): """Django command to pause execution until db is up 'n running""" def handle(self, *args, **options): self.stdout.write('Waiting for database...') db_conn = None while not db_conn: try: db_conn = connections['default'] except OperationalError: self.stdout.write('Database unavailable,waiting 1 second...') time.sleep(1) self.stdout.write(self.style.SUCCESS('Database available!'))

30.409091

77

0.647235

import time from django.db import connections from django.db.utils import OperationalError from django.core.management.base import BaseCommand class Command(BaseCommand): def handle(self, *args, **options): self.stdout.write('Waiting for database...') db_conn = None while not db_conn: try: db_conn = connections['default'] except OperationalError: self.stdout.write('Database unavailable,waiting 1 second...') time.sleep(1) self.stdout.write(self.style.SUCCESS('Database available!'))

true

1c43e74182739e0186666a6172b8f37cc901b2d5

11,035

py

Python

mistral/services/scheduler.py

soda-research/mistral

550a3de9c2defc7ce26336cb705d9c8d87bbaddd

[ "Apache-2.0" ]

3

2015-08-28T04:57:56.000Z

2017-03-27T10:59:56.000Z

mistral/services/scheduler.py

soda-research/mistral

550a3de9c2defc7ce26336cb705d9c8d87bbaddd

[ "Apache-2.0" ]

21

2015-04-14T22:41:53.000Z

2019-02-20T09:30:10.000Z

mistral/services/scheduler.py

soda-research/mistral

550a3de9c2defc7ce26336cb705d9c8d87bbaddd

[ "Apache-2.0" ]

12

2015-08-14T02:27:37.000Z

2020-12-31T10:09:21.000Z

# Copyright 2014 - Mirantis, Inc. # Copyright 2015 - StackStorm, Inc. # Copyright 2016 - Brocade Communications Systems, Inc. # # 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 copy import datetime import eventlet import random import sys import threading from oslo_config import cfg from oslo_log import log as logging from oslo_utils import importutils from mistral import context from mistral.db import utils as db_utils from mistral.db.v2 import api as db_api from mistral import exceptions as exc LOG = logging.getLogger(__name__) CONF = cfg.CONF # All schedulers. _schedulers = set() def schedule_call(factory_method_path, target_method_name, run_after, serializers=None, key=None, **method_args): """Schedules call and lately invokes target_method. Add this call specification to DB, and then after run_after seconds service CallScheduler invokes the target_method. :param factory_method_path: Full python-specific path to factory method that creates a target object that the call will be made against. :param target_method_name: Name of a method which will be invoked. :param run_after: Value in seconds. :param serializers: map of argument names and their serializer class paths. Use when an argument is an object of specific type, and needs to be serialized. Example: { "result": "mistral.utils.serializer.ResultSerializer"} Serializer for the object type must implement serializer interface in mistral/utils/serializer.py :param key: Key which can potentially be used for squashing similar delayed calls. :param method_args: Target method keyword arguments. """ ctx_serializer = context.RpcContextSerializer() ctx = ( ctx_serializer.serialize_context(context.ctx()) if context.has_ctx() else {} ) execution_time = (datetime.datetime.now() + datetime.timedelta(seconds=run_after)) if serializers: for arg_name, serializer_path in serializers.items(): if arg_name not in method_args: raise exc.MistralException( "Serializable method argument %s" " not found in method_args=%s" % (arg_name, method_args)) try: serializer = importutils.import_class(serializer_path)() except ImportError as e: raise ImportError( "Cannot import class %s: %s" % (serializer_path, e) ) method_args[arg_name] = serializer.serialize(method_args[arg_name]) values = { 'factory_method_path': factory_method_path, 'target_method_name': target_method_name, 'execution_time': execution_time, 'auth_context': ctx, 'serializers': serializers, 'key': key, 'method_arguments': method_args, 'processing': False } db_api.create_delayed_call(values) class Scheduler(object): def __init__(self, fixed_delay, random_delay, batch_size): self._stopped = False self._thread = threading.Thread(target=self._loop) self._thread.daemon = True self._fixed_delay = fixed_delay self._random_delay = random_delay self._batch_size = batch_size def start(self): self._thread.start() def stop(self, graceful=False): self._stopped = True if graceful: self._thread.join() def _loop(self): while not self._stopped: LOG.debug("Starting Scheduler loop [scheduler=%s]...", self) try: self._process_delayed_calls() except Exception: LOG.exception( "Scheduler failed to process delayed calls" " due to unexpected exception." ) # For some mysterious reason (probably eventlet related) # the exception is not cleared from the context automatically. # This results in subsequent log.warning calls to show invalid # info. if sys.version_info < (3,): sys.exc_clear() eventlet.sleep( self._fixed_delay + random.Random().randint(0, self._random_delay * 1000) * 0.001 ) def _process_delayed_calls(self, ctx=None): """Run delayed required calls. This algorithm should work with transactions having at least 'READ-COMMITTED' isolation mode. :param ctx: Auth context. """ # Select and capture calls matching time criteria. db_calls = self._capture_calls(self._batch_size) if not db_calls: return # Determine target methods, deserialize arguments etc. prepared_calls = self._prepare_calls(db_calls) # Invoke prepared calls. self._invoke_calls(prepared_calls) # Delete invoked calls from DB. self.delete_calls(db_calls) @staticmethod @db_utils.retry_on_db_error def _capture_calls(batch_size): """Captures delayed calls eligible for processing (based on time). The intention of this method is to select delayed calls based on time criteria and mark them in DB as being processed so that no other threads could process them in parallel. :return: A list of delayed calls captured for further processing. """ result = [] time_filter = datetime.datetime.now() + datetime.timedelta(seconds=1) with db_api.transaction(): candidates = db_api.get_delayed_calls_to_start( time_filter, batch_size ) for call in candidates: # Mark this delayed call has been processed in order to # prevent calling from parallel transaction. db_call, updated_cnt = db_api.update_delayed_call( id=call.id, values={'processing': True}, query_filter={'processing': False} ) # If updated_cnt != 1 then another scheduler # has already updated it. if updated_cnt == 1: result.append(db_call) LOG.debug("Scheduler captured %s delayed calls.", len(result)) return result @staticmethod def _prepare_calls(raw_calls): """Prepares delayed calls for invocation. After delayed calls were selected from DB they still need to be prepared for further usage, we need to build final target methods and deserialize arguments, if needed. :param raw_calls: Delayed calls fetched from DB (DB models). :return: A list of tuples (target_auth_context, target_method, method_args) where all data is properly deserialized. """ result = [] for call in raw_calls: LOG.debug( 'Preparing next delayed call. ' '[ID=%s, factory_method_path=%s, target_method_name=%s, ' 'method_arguments=%s]', call.id, call.factory_method_path, call.target_method_name, call.method_arguments ) target_auth_context = copy.deepcopy(call.auth_context) if call.factory_method_path: factory = importutils.import_class(call.factory_method_path) target_method = getattr(factory(), call.target_method_name) else: target_method = importutils.import_class( call.target_method_name ) method_args = copy.deepcopy(call.method_arguments) if call.serializers: # Deserialize arguments. for arg_name, ser_path in call.serializers.items(): serializer = importutils.import_class(ser_path)() deserialized = serializer.deserialize( method_args[arg_name] ) method_args[arg_name] = deserialized result.append((target_auth_context, target_method, method_args)) return result @staticmethod def _invoke_calls(delayed_calls): """Invokes prepared delayed calls. :param delayed_calls: Prepared delayed calls represented as tuples (target_auth_context, target_method, method_args). """ ctx_serializer = context.RpcContextSerializer() for (target_auth_context, target_method, method_args) in delayed_calls: try: # Set the correct context for the method. ctx_serializer.deserialize_context(target_auth_context) # Invoke the method. target_method(**method_args) except Exception as e: LOG.exception( "Delayed call failed, method: %s, exception: %s", target_method, e ) finally: # Remove context. context.set_ctx(None) @staticmethod @db_utils.retry_on_db_error def delete_calls(db_calls): """Deletes delayed calls. :param db_calls: Delayed calls to delete from DB. """ with db_api.transaction(): for call in db_calls: try: db_api.delete_delayed_call(call.id) except Exception as e: LOG.error( "Failed to delete delayed call [call=%s, " "exception=%s]", call, e ) # We have to re-raise any exception because the transaction # would be already invalid anyway. If it's a deadlock then # it will be handled. raise e LOG.debug("Scheduler deleted %s delayed calls.", len(db_calls)) def start(): sched = Scheduler( CONF.scheduler.fixed_delay, CONF.scheduler.random_delay, CONF.scheduler.batch_size ) _schedulers.add(sched) sched.start() return sched def stop_scheduler(sched, graceful=False): if not sched: return sched.stop(graceful) _schedulers.remove(sched) def stop_all_schedulers(): for sched in _schedulers: sched.stop(graceful=True) _schedulers.clear()

32.360704

79

0.610603

import copy import datetime import eventlet import random import sys import threading from oslo_config import cfg from oslo_log import log as logging from oslo_utils import importutils from mistral import context from mistral.db import utils as db_utils from mistral.db.v2 import api as db_api from mistral import exceptions as exc LOG = logging.getLogger(__name__) CONF = cfg.CONF _schedulers = set() def schedule_call(factory_method_path, target_method_name, run_after, serializers=None, key=None, **method_args): ctx_serializer = context.RpcContextSerializer() ctx = ( ctx_serializer.serialize_context(context.ctx()) if context.has_ctx() else {} ) execution_time = (datetime.datetime.now() + datetime.timedelta(seconds=run_after)) if serializers: for arg_name, serializer_path in serializers.items(): if arg_name not in method_args: raise exc.MistralException( "Serializable method argument %s" " not found in method_args=%s" % (arg_name, method_args)) try: serializer = importutils.import_class(serializer_path)() except ImportError as e: raise ImportError( "Cannot import class %s: %s" % (serializer_path, e) ) method_args[arg_name] = serializer.serialize(method_args[arg_name]) values = { 'factory_method_path': factory_method_path, 'target_method_name': target_method_name, 'execution_time': execution_time, 'auth_context': ctx, 'serializers': serializers, 'key': key, 'method_arguments': method_args, 'processing': False } db_api.create_delayed_call(values) class Scheduler(object): def __init__(self, fixed_delay, random_delay, batch_size): self._stopped = False self._thread = threading.Thread(target=self._loop) self._thread.daemon = True self._fixed_delay = fixed_delay self._random_delay = random_delay self._batch_size = batch_size def start(self): self._thread.start() def stop(self, graceful=False): self._stopped = True if graceful: self._thread.join() def _loop(self): while not self._stopped: LOG.debug("Starting Scheduler loop [scheduler=%s]...", self) try: self._process_delayed_calls() except Exception: LOG.exception( "Scheduler failed to process delayed calls" " due to unexpected exception." ) if sys.version_info < (3,): sys.exc_clear() eventlet.sleep( self._fixed_delay + random.Random().randint(0, self._random_delay * 1000) * 0.001 ) def _process_delayed_calls(self, ctx=None): db_calls = self._capture_calls(self._batch_size) if not db_calls: return prepared_calls = self._prepare_calls(db_calls) self._invoke_calls(prepared_calls) self.delete_calls(db_calls) @staticmethod @db_utils.retry_on_db_error def _capture_calls(batch_size): result = [] time_filter = datetime.datetime.now() + datetime.timedelta(seconds=1) with db_api.transaction(): candidates = db_api.get_delayed_calls_to_start( time_filter, batch_size ) for call in candidates: db_call, updated_cnt = db_api.update_delayed_call( id=call.id, values={'processing': True}, query_filter={'processing': False} ) if updated_cnt == 1: result.append(db_call) LOG.debug("Scheduler captured %s delayed calls.", len(result)) return result @staticmethod def _prepare_calls(raw_calls): result = [] for call in raw_calls: LOG.debug( 'Preparing next delayed call. ' '[ID=%s, factory_method_path=%s, target_method_name=%s, ' 'method_arguments=%s]', call.id, call.factory_method_path, call.target_method_name, call.method_arguments ) target_auth_context = copy.deepcopy(call.auth_context) if call.factory_method_path: factory = importutils.import_class(call.factory_method_path) target_method = getattr(factory(), call.target_method_name) else: target_method = importutils.import_class( call.target_method_name ) method_args = copy.deepcopy(call.method_arguments) if call.serializers: for arg_name, ser_path in call.serializers.items(): serializer = importutils.import_class(ser_path)() deserialized = serializer.deserialize( method_args[arg_name] ) method_args[arg_name] = deserialized result.append((target_auth_context, target_method, method_args)) return result @staticmethod def _invoke_calls(delayed_calls): ctx_serializer = context.RpcContextSerializer() for (target_auth_context, target_method, method_args) in delayed_calls: try: ctx_serializer.deserialize_context(target_auth_context) target_method(**method_args) except Exception as e: LOG.exception( "Delayed call failed, method: %s, exception: %s", target_method, e ) finally: context.set_ctx(None) @staticmethod @db_utils.retry_on_db_error def delete_calls(db_calls): with db_api.transaction(): for call in db_calls: try: db_api.delete_delayed_call(call.id) except Exception as e: LOG.error( "Failed to delete delayed call [call=%s, " "exception=%s]", call, e ) # it will be handled. raise e LOG.debug("Scheduler deleted %s delayed calls.", len(db_calls)) def start(): sched = Scheduler( CONF.scheduler.fixed_delay, CONF.scheduler.random_delay, CONF.scheduler.batch_size ) _schedulers.add(sched) sched.start() return sched def stop_scheduler(sched, graceful=False): if not sched: return sched.stop(graceful) _schedulers.remove(sched) def stop_all_schedulers(): for sched in _schedulers: sched.stop(graceful=True) _schedulers.clear()

true

1c43e8343e5b9fcf66ef7bb70fa6262538d43d26

1,366

py

Python

src/room.py

ThaDeveloper/docopt_dojo

adc09fda16a84f81776a284249615aa69ebc6861

[ "MIT" ]

null

src/room.py

ThaDeveloper/docopt_dojo

adc09fda16a84f81776a284249615aa69ebc6861

[ "MIT" ]

14

2017-11-04T09:26:08.000Z

2017-11-13T19:24:30.000Z

src/room.py

ThaDeveloper/docopt_dojo

adc09fda16a84f81776a284249615aa69ebc6861

[ "MIT" ]

null

class Room(object): ''' The Room class models the rooms in Dojo and is used as the blueprint for how the LivingSpace and OfficeSpace classes inehrit properties such as room_name,room_type and capacity.s ''' def __init__(self, room_name, room_type, capacity): self.room_type = room_type.strip().title() self.capacity = capacity self.room_name = room_name.title() self.occupants = [] def add_person(self, person): ''' This is what will check capacity and reduce by one when someone is added to a room. ''' self.occupants.append(person) self.capacity = self.capacity - 1 return self.capacity class LivingSpace(Room): ''' The LivingSpace class inherits its properties and methods from the Room class and overrides properties such as capacity using the super function call. ''' def __init__(self, room_name): super(LivingSpace, self).__init__( room_name, room_type='Living Space', capacity=4) class Office(Room): ''' The Office class inherits its properties and methods from the Room class and overrides properties such as capacity using the super function call. ''' def __init__(self, room_name): super(Office, self).__init__(room_name, room_type='Office', capacity=6)

31.767442

79

0.664714

class Room(object): def __init__(self, room_name, room_type, capacity): self.room_type = room_type.strip().title() self.capacity = capacity self.room_name = room_name.title() self.occupants = [] def add_person(self, person): self.occupants.append(person) self.capacity = self.capacity - 1 return self.capacity class LivingSpace(Room): def __init__(self, room_name): super(LivingSpace, self).__init__( room_name, room_type='Living Space', capacity=4) class Office(Room): def __init__(self, room_name): super(Office, self).__init__(room_name, room_type='Office', capacity=6)

true

1c43e8deb31d64389ccc2664be037b8b793fb6b7

1,425

py

Python

app/settings/migrations/0007_default_statuses.py

mandarhan/mandarhan

9ce38d10e536e0d3e2f907c3b5c560d66ccf8e40

[ "MIT" ]

null

app/settings/migrations/0007_default_statuses.py

mandarhan/mandarhan

9ce38d10e536e0d3e2f907c3b5c560d66ccf8e40

[ "MIT" ]

6

2020-02-18T03:49:09.000Z

2022-03-12T00:10:05.000Z

app/settings/migrations/0007_default_statuses.py

mandarhan/mandarhan

9ce38d10e536e0d3e2f907c3b5c560d66ccf8e40

[ "MIT" ]

1

2020-03-25T10:25:43.000Z

from django.db import migrations DEFAULT_STATUSES = [ { 'name': 'Не подтверждено', 'color': '#ffffff', }, { 'name': 'Отменено', 'color': '#ff0000', }, { 'name': 'Подтверждено', 'color': '#daf9d3', }, { 'name': 'Выезд', 'color': '#000000', }, { 'name': 'Незаезд', 'color': '#1decf6', }, { 'name': 'Проживание', 'color': '#048e08', }, { 'name': 'Резерв', 'color': '#fff900', }, ] def create_default_statuses(apps, schema_editor): Status = apps.get_model('app_settings', 'Status') db_alias = schema_editor.connection.alias default_statuses = [] order = 0 for default_status in DEFAULT_STATUSES: order += 1 default_statuses.append(Status(**default_status, my_order=order)) Status.objects.using(db_alias).bulk_create(default_statuses) def delete_default_statuses(apps, schema_editor): Status = apps.get_model('app_settings', 'Status') db_alias = schema_editor.connection.alias for default_status in DEFAULT_STATUSES: Status.objects.using(db_alias).filter(**default_status).delete() class Migration(migrations.Migration): dependencies = [ ('app_settings', '0006_status'), ] operations = [ migrations.RunPython(create_default_statuses, delete_default_statuses), ]

23.360656

79

0.592281

from django.db import migrations DEFAULT_STATUSES = [ { 'name': 'Не подтверждено', 'color': '#ffffff', }, { 'name': 'Отменено', 'color': '#ff0000', }, { 'name': 'Подтверждено', 'color': '#daf9d3', }, { 'name': 'Выезд', 'color': '#000000', }, { 'name': 'Незаезд', 'color': '#1decf6', }, { 'name': 'Проживание', 'color': '#048e08', }, { 'name': 'Резерв', 'color': '#fff900', }, ] def create_default_statuses(apps, schema_editor): Status = apps.get_model('app_settings', 'Status') db_alias = schema_editor.connection.alias default_statuses = [] order = 0 for default_status in DEFAULT_STATUSES: order += 1 default_statuses.append(Status(**default_status, my_order=order)) Status.objects.using(db_alias).bulk_create(default_statuses) def delete_default_statuses(apps, schema_editor): Status = apps.get_model('app_settings', 'Status') db_alias = schema_editor.connection.alias for default_status in DEFAULT_STATUSES: Status.objects.using(db_alias).filter(**default_status).delete() class Migration(migrations.Migration): dependencies = [ ('app_settings', '0006_status'), ] operations = [ migrations.RunPython(create_default_statuses, delete_default_statuses), ]

true

1c43e93aec0b6c7b8788a78435e14115b15fa430

883

py

Python

arjuna/tpi/guiauto/source/page.py

StefanIGit/arjuna

6c7d9099e0d766e7b30936ef25d32c1414133b96

[ "Apache-2.0" ]

13

2020-05-12T06:32:51.000Z

2022-01-24T18:21:19.000Z

arjuna/tpi/guiauto/source/page.py

StefanIGit/arjuna

6c7d9099e0d766e7b30936ef25d32c1414133b96

[ "Apache-2.0" ]

5

2020-02-14T12:51:07.000Z

2021-12-01T10:39:51.000Z

arjuna/tpi/guiauto/source/page.py

StefanIGit/arjuna

6c7d9099e0d766e7b30936ef25d32c1414133b96

[ "Apache-2.0" ]

25

2020-01-16T10:44:25.000Z

2022-02-24T13:22:22.000Z

# This file is a part of Arjuna # Copyright 2015-2021 Rahul Verma # Website: www.RahulVerma.net # 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 arjuna.tpi.tracker import track from .base import SingleGuiEntitySource class GuiPageSource(SingleGuiEntitySource): ''' Source of a **GuiPage**. ''' def __init__(self, raw_source): super().__init__(raw_source)

32.703704

74

0.745187

from arjuna.tpi.tracker import track from .base import SingleGuiEntitySource class GuiPageSource(SingleGuiEntitySource): def __init__(self, raw_source): super().__init__(raw_source)

true

1c43e9d6ffce0cb69156cdc14c9713e3fe44aeb5

1,048

py

Python

Crawler/crawlerpchome.py

2017HackNTU/J94FintechLa

85018fae23cff1d64f3c95c1e0f9c312dd47eade

[ "MIT" ]

null

Crawler/crawlerpchome.py

2017HackNTU/J94FintechLa

85018fae23cff1d64f3c95c1e0f9c312dd47eade

[ "MIT" ]

null

Crawler/crawlerpchome.py

2017HackNTU/J94FintechLa

85018fae23cff1d64f3c95c1e0f9c312dd47eade

[ "MIT" ]

null

import requests from selenium import webdriver from lxml import etree import re import csv import sys from selenium.webdriver.common.desired_capabilities import DesiredCapabilities # reference: http://tw.pyladies.com/~marsw/crawler02.slides.html#/3 # change to mobile website # url = sys.argv[1] # match = re.split(r'\?', 'http://24h.pchome.com.tw/prod/DGAD7P-A9007UFW1?q=/S/DGADAH') # url = match[0] # url = url[:11] + 'm.' + url[11:] driver = webdriver.PhantomJS(executable_path=r'path_to_phantomjs/bin/phantomjs') driver.get(url) pageSource = driver.page_source driver.close() page = etree.HTML(pageSource) try: tags = page.xpath('//*[@id="ProdNick"]/text()') tagtotal ='' for tag in tags: tagtotal += tag print(tagtotal.strip()) except: print("name craw fail") try: tags2 = page.xpath('//*[@id="ProdInfo"]/ul[1]/li[1]/span/span/text()')[-1] print(tags2) except: print("price craw fail") try: tags3 = page.xpath('//*[@id="ProdImg"]/img/@src')[-1] print(tags3) except: print("img craw fail")

26.871795

87

0.678435

import requests from selenium import webdriver from lxml import etree import re import csv import sys from selenium.webdriver.common.desired_capabilities import DesiredCapabilities driver = webdriver.PhantomJS(executable_path=r'path_to_phantomjs/bin/phantomjs') driver.get(url) pageSource = driver.page_source driver.close() page = etree.HTML(pageSource) try: tags = page.xpath('//*[@id="ProdNick"]/text()') tagtotal ='' for tag in tags: tagtotal += tag print(tagtotal.strip()) except: print("name craw fail") try: tags2 = page.xpath('//*[@id="ProdInfo"]/ul[1]/li[1]/span/span/text()')[-1] print(tags2) except: print("price craw fail") try: tags3 = page.xpath('//*[@id="ProdImg"]/img/@src')[-1] print(tags3) except: print("img craw fail")

true

1c43eae2870d470e535993d01691bd86de721d61

714

py

Python

example/mulit_sleep.py

zhzLuke96/Yoi

8f5a0b6881c540aab71b8a360002b4d1e9de869a

[ "MIT" ]

null

example/mulit_sleep.py

zhzLuke96/Yoi

8f5a0b6881c540aab71b8a360002b4d1e9de869a

[ "MIT" ]

null

example/mulit_sleep.py

zhzLuke96/Yoi

8f5a0b6881c540aab71b8a360002b4d1e9de869a

[ "MIT" ]

null

from yoi.application import Application import time app = Application() @app.router(r"^/sleep/(.+)/?$", methods=["GET"]) def index(request,timer): time.sleep(int(timer)) return f"server sleep {timer}s" @app.router(r"^/do/?$", methods=["GET"]) def index(): return f"server do something" if __name__ == '__main__': from yoi.server.sel_wsgiServer import WSGIServer sev = WSGIServer("127.0.0.1",8000).set_application(app) sev.run() # from wsgiref.simple_server import make_server # # httpd = make_server("127.0.0.1", 8000, app) # httpd = make_server("localhost", 8000, app) # try: # httpd.serve_forever() # except: # httpd.shutdown() # raise

23.032258

59

0.635854

from yoi.application import Application import time app = Application() @app.router(r"^/sleep/(.+)/?$", methods=["GET"]) def index(request,timer): time.sleep(int(timer)) return f"server sleep {timer}s" @app.router(r"^/do/?$", methods=["GET"]) def index(): return f"server do something" if __name__ == '__main__': from yoi.server.sel_wsgiServer import WSGIServer sev = WSGIServer("127.0.0.1",8000).set_application(app) sev.run()

true

1c43eb9d91553b0505a12be5e48dae5a530b845e

55,590

py

Python

nitro/resource/config/ns/nsip.py

HanseMerkur/nitro-python

d03eb11f492a35a2a8b2a140322fbce22d25a8f7

[ "Apache-2.0" ]

2

2020-08-24T18:04:22.000Z

2020-08-24T18:04:47.000Z

nitro/resource/config/ns/nsip.py

HanseMerkur/nitro-python

d03eb11f492a35a2a8b2a140322fbce22d25a8f7

[ "Apache-2.0" ]

null

nitro/resource/config/ns/nsip.py

HanseMerkur/nitro-python

d03eb11f492a35a2a8b2a140322fbce22d25a8f7

[ "Apache-2.0" ]

null

# # Copyright (c) 2008-2015 Citrix Systems, Inc. # # 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 nitro.resource.base.base_resource import base_resource from nitro.resource.base.base_resource import base_response from nitro.service.options import options from nitro.exception.nitro_exception import nitro_exception from nitro.util.nitro_util import nitro_util class nsip(base_resource) : """Configuration for ip resource.""" def __init__(self) : self._ipaddress = "" self._netmask = "" self._type = "" self._arp = "" self._icmp = "" self._vserver = "" self._telnet = "" self._ftp = "" self._gui = "" self._ssh = "" self._snmp = "" self._mgmtaccess = "" self._restrictaccess = "" self._dynamicrouting = "" self._ospf = "" self._bgp = "" self._rip = "" self._hostroute = "" self._hostrtgw = "" self._metric = 0 self._vserverrhilevel = "" self._vserverrhimode = "" self._ospflsatype = "" self._ospfarea = 0 self._state = "" self._vrid = 0 self._icmpresponse = "" self._ownernode = 0 self._arpresponse = "" self._td = 0 self._flags = 0 self._hostrtgwact = "" self._ospfareaval = 0 self._viprtadv2bsd = False self._vipvsercount = 0 self._vipvserdowncount = 0 self._vipvsrvrrhiactivecount = 0 self._vipvsrvrrhiactiveupcount = 0 self._freeports = 0 self._riserhimsgcode = 0 self._iptype = [] self.___count = 0 @property def ipaddress(self) : """IPv4 address to create on the NetScaler appliance. Cannot be changed after the IP address is created. Minimum length = 1.""" try : return self._ipaddress except Exception as e: raise e @ipaddress.setter def ipaddress(self, ipaddress) : """IPv4 address to create on the NetScaler appliance. Cannot be changed after the IP address is created. Minimum length = 1 :param ipaddress: """ try : self._ipaddress = ipaddress except Exception as e: raise e @property def netmask(self) : """Subnet mask associated with the IP address.""" try : return self._netmask except Exception as e: raise e @netmask.setter def netmask(self, netmask) : """Subnet mask associated with the IP address. :param netmask: """ try : self._netmask = netmask except Exception as e: raise e @property def type(self) : """Type of the IP address to create on the NetScaler appliance. Cannot be changed after the IP address is created. The following are the different types of NetScaler owned IP addresses: * A Subnet IP (SNIP) address is used by the NetScaler ADC to communicate with the servers. The NetScaler also uses the subnet IP address when generating its own packets, such as packets related to dynamic routing protocols, or to send monitor probes to check the health of the servers. * A Virtual IP (VIP) address is the IP address associated with a virtual server. It is the IP address to which clients connect. An appliance managing a wide range of traffic may have many VIPs configured. Some of the attributes of the VIP address are customized to meet the requirements of the virtual server. * A GSLB site IP (GSLBIP) address is associated with a GSLB site. It is not mandatory to specify a GSLBIP address when you initially configure the NetScaler appliance. A GSLBIP address is used only when you create a GSLB site. * A Cluster IP (CLIP) address is the management address of the cluster. All cluster configurations must be performed by accessing the cluster through this IP address. Default value: SNIP Possible values = SNIP, VIP, NSIP, GSLBsiteIP, CLIP. """ try : return self._type except Exception as e: raise e @type.setter def type(self, type) : """Type of the IP address to create on the NetScaler appliance. Cannot be changed after the IP address is created. The following are the different types of NetScaler owned IP addresses: * A Subnet IP (SNIP) address is used by the NetScaler ADC to communicate with the servers. The NetScaler also uses the subnet IP address when generating its own packets, such as packets related to dynamic routing protocols, or to send monitor probes to check the health of the servers. * A Virtual IP (VIP) address is the IP address associated with a virtual server. It is the IP address to which clients connect. An appliance managing a wide range of traffic may have many VIPs configured. Some of the attributes of the VIP address are customized to meet the requirements of the virtual server. * A GSLB site IP (GSLBIP) address is associated with a GSLB site. It is not mandatory to specify a GSLBIP address when you initially configure the NetScaler appliance. A GSLBIP address is used only when you create a GSLB site. * A Cluster IP (CLIP) address is the management address of the cluster. All cluster configurations must be performed by accessing the cluster through this IP address. Default value: SNIP Possible values = SNIP, VIP, NSIP, GSLBsiteIP, CLIP :param type: """ try : self._type = type except Exception as e: raise e @property def arp(self) : """Respond to ARP requests for this IP address. Default value: ENABLED Possible values = ENABLED, DISABLED.""" try : return self._arp except Exception as e: raise e @arp.setter def arp(self, arp) : """Respond to ARP requests for this IP address. Default value: ENABLED Possible values = ENABLED, DISABLED :param arp: """ try : self._arp = arp except Exception as e: raise e @property def icmp(self) : """Respond to ICMP requests for this IP address. Default value: ENABLED Possible values = ENABLED, DISABLED.""" try : return self._icmp except Exception as e: raise e @icmp.setter def icmp(self, icmp) : """Respond to ICMP requests for this IP address. Default value: ENABLED Possible values = ENABLED, DISABLED :param icmp: """ try : self._icmp = icmp except Exception as e: raise e @property def vserver(self) : """Use this option to set (enable or disable) the virtual server attribute for this IP address. Default value: ENABLED Possible values = ENABLED, DISABLED.""" try : return self._vserver except Exception as e: raise e @vserver.setter def vserver(self, vserver) : """Use this option to set (enable or disable) the virtual server attribute for this IP address. Default value: ENABLED Possible values = ENABLED, DISABLED :param vserver: """ try : self._vserver = vserver except Exception as e: raise e @property def telnet(self) : """Allow Telnet access to this IP address. Default value: ENABLED Possible values = ENABLED, DISABLED.""" try : return self._telnet except Exception as e: raise e @telnet.setter def telnet(self, telnet) : """Allow Telnet access to this IP address. Default value: ENABLED Possible values = ENABLED, DISABLED :param telnet: """ try : self._telnet = telnet except Exception as e: raise e @property def ftp(self) : """Allow File Transfer Protocol (FTP) access to this IP address. Default value: ENABLED Possible values = ENABLED, DISABLED.""" try : return self._ftp except Exception as e: raise e @ftp.setter def ftp(self, ftp) : """Allow File Transfer Protocol (FTP) access to this IP address. Default value: ENABLED Possible values = ENABLED, DISABLED :param ftp: """ try : self._ftp = ftp except Exception as e: raise e @property def gui(self) : """Allow graphical user interface (GUI) access to this IP address. Default value: ENABLED Possible values = ENABLED, SECUREONLY, DISABLED.""" try : return self._gui except Exception as e: raise e @gui.setter def gui(self, gui) : """Allow graphical user interface (GUI) access to this IP address. Default value: ENABLED Possible values = ENABLED, SECUREONLY, DISABLED :param gui: """ try : self._gui = gui except Exception as e: raise e @property def ssh(self) : """Allow secure shell (SSH) access to this IP address. Default value: ENABLED Possible values = ENABLED, DISABLED.""" try : return self._ssh except Exception as e: raise e @ssh.setter def ssh(self, ssh) : """Allow secure shell (SSH) access to this IP address. Default value: ENABLED Possible values = ENABLED, DISABLED :param ssh: """ try : self._ssh = ssh except Exception as e: raise e @property def snmp(self) : """Allow Simple Network Management Protocol (SNMP) access to this IP address. Default value: ENABLED Possible values = ENABLED, DISABLED.""" try : return self._snmp except Exception as e: raise e @snmp.setter def snmp(self, snmp) : """Allow Simple Network Management Protocol (SNMP) access to this IP address. Default value: ENABLED Possible values = ENABLED, DISABLED :param snmp: """ try : self._snmp = snmp except Exception as e: raise e @property def mgmtaccess(self) : """Allow access to management applications on this IP address. Default value: DISABLED Possible values = ENABLED, DISABLED.""" try : return self._mgmtaccess except Exception as e: raise e @mgmtaccess.setter def mgmtaccess(self, mgmtaccess) : """Allow access to management applications on this IP address. Default value: DISABLED Possible values = ENABLED, DISABLED :param mgmtaccess: """ try : self._mgmtaccess = mgmtaccess except Exception as e: raise e @property def restrictaccess(self) : """Block access to nonmanagement applications on this IP. This option is applicable for MIPs, SNIPs, and NSIP, and is disabled by default. Nonmanagement applications can run on the underlying NetScaler Free BSD operating system. Default value: DISABLED Possible values = ENABLED, DISABLED.""" try : return self._restrictaccess except Exception as e: raise e @restrictaccess.setter def restrictaccess(self, restrictaccess) : """Block access to nonmanagement applications on this IP. This option is applicable for MIPs, SNIPs, and NSIP, and is disabled by default. Nonmanagement applications can run on the underlying NetScaler Free BSD operating system. Default value: DISABLED Possible values = ENABLED, DISABLED :param restrictaccess: """ try : self._restrictaccess = restrictaccess except Exception as e: raise e @property def dynamicrouting(self) : """Allow dynamic routing on this IP address. Specific to Subnet IP (SNIP) address. Default value: DISABLED Possible values = ENABLED, DISABLED.""" try : return self._dynamicrouting except Exception as e: raise e @dynamicrouting.setter def dynamicrouting(self, dynamicrouting) : """Allow dynamic routing on this IP address. Specific to Subnet IP (SNIP) address. Default value: DISABLED Possible values = ENABLED, DISABLED :param dynamicrouting: """ try : self._dynamicrouting = dynamicrouting except Exception as e: raise e @property def ospf(self) : """Use this option to enable or disable OSPF on this IP address for the entity. Default value: DISABLED Possible values = ENABLED, DISABLED.""" try : return self._ospf except Exception as e: raise e @ospf.setter def ospf(self, ospf) : """Use this option to enable or disable OSPF on this IP address for the entity. Default value: DISABLED Possible values = ENABLED, DISABLED :param ospf: """ try : self._ospf = ospf except Exception as e: raise e @property def bgp(self) : """Use this option to enable or disable BGP on this IP address for the entity. Default value: DISABLED Possible values = ENABLED, DISABLED.""" try : return self._bgp except Exception as e: raise e @bgp.setter def bgp(self, bgp) : """Use this option to enable or disable BGP on this IP address for the entity. Default value: DISABLED Possible values = ENABLED, DISABLED :param bgp: """ try : self._bgp = bgp except Exception as e: raise e @property def rip(self) : """Use this option to enable or disable RIP on this IP address for the entity. Default value: DISABLED Possible values = ENABLED, DISABLED.""" try : return self._rip except Exception as e: raise e @rip.setter def rip(self, rip) : """Use this option to enable or disable RIP on this IP address for the entity. Default value: DISABLED Possible values = ENABLED, DISABLED :param rip: """ try : self._rip = rip except Exception as e: raise e @property def hostroute(self) : """Advertise a route for the VIP address using the dynamic routing protocols running on the NetScaler appliance. Possible values = ENABLED, DISABLED.""" try : return self._hostroute except Exception as e: raise e @hostroute.setter def hostroute(self, hostroute) : """Advertise a route for the VIP address using the dynamic routing protocols running on the NetScaler appliance. Possible values = ENABLED, DISABLED :param hostroute: """ try : self._hostroute = hostroute except Exception as e: raise e @property def hostrtgw(self) : """IP address of the gateway of the route for this VIP address. Default value: -1.""" try : return self._hostrtgw except Exception as e: raise e @hostrtgw.setter def hostrtgw(self, hostrtgw) : """IP address of the gateway of the route for this VIP address. Default value: -1 :param hostrtgw: """ try : self._hostrtgw = hostrtgw except Exception as e: raise e @property def metric(self) : """Integer value to add to or subtract from the cost of the route advertised for the VIP address. Minimum length = -16777215.""" try : return self._metric except Exception as e: raise e @metric.setter def metric(self, metric) : """Integer value to add to or subtract from the cost of the route advertised for the VIP address. Minimum length = -16777215 :param metric: """ try : self._metric = metric except Exception as e: raise e @property def vserverrhilevel(self) : """Advertise the route for the Virtual IP (VIP) address on the basis of the state of the virtual servers associated with that VIP. * NONE - Advertise the route for the VIP address, regardless of the state of the virtual servers associated with the address. * ONE VSERVER - Advertise the route for the VIP address if at least one of the associated virtual servers is in UP state. * ALL VSERVER - Advertise the route for the VIP address if all of the associated virtual servers are in UP state. * VSVR_CNTRLD - Advertise the route for the VIP address according to the RHIstate (RHI STATE) parameter setting on all the associated virtual servers of the VIP address along with their states. When Vserver RHI Level (RHI) parameter is set to VSVR_CNTRLD, the following are different RHI behaviors for the VIP address on the basis of RHIstate (RHI STATE) settings on the virtual servers associated with the VIP address: * If you set RHI STATE to PASSIVE on all virtual servers, the NetScaler ADC always advertises the route for the VIP address. * If you set RHI STATE to ACTIVE on all virtual servers, the NetScaler ADC advertises the route for the VIP address if at least one of the associated virtual servers is in UP state. *If you set RHI STATE to ACTIVE on some and PASSIVE on others, the NetScaler ADC advertises the route for the VIP address if at least one of the associated virtual servers, whose RHI STATE set to ACTIVE, is in UP state. Default value: ONE_VSERVER Possible values = ONE_VSERVER, ALL_VSERVERS, NONE, VSVR_CNTRLD. """ try : return self._vserverrhilevel except Exception as e: raise e @vserverrhilevel.setter def vserverrhilevel(self, vserverrhilevel) : """Advertise the route for the Virtual IP (VIP) address on the basis of the state of the virtual servers associated with that VIP. * NONE - Advertise the route for the VIP address, regardless of the state of the virtual servers associated with the address. * ONE VSERVER - Advertise the route for the VIP address if at least one of the associated virtual servers is in UP state. * ALL VSERVER - Advertise the route for the VIP address if all of the associated virtual servers are in UP state. * VSVR_CNTRLD - Advertise the route for the VIP address according to the RHIstate (RHI STATE) parameter setting on all the associated virtual servers of the VIP address along with their states. When Vserver RHI Level (RHI) parameter is set to VSVR_CNTRLD, the following are different RHI behaviors for the VIP address on the basis of RHIstate (RHI STATE) settings on the virtual servers associated with the VIP address: * If you set RHI STATE to PASSIVE on all virtual servers, the NetScaler ADC always advertises the route for the VIP address. * If you set RHI STATE to ACTIVE on all virtual servers, the NetScaler ADC advertises the route for the VIP address if at least one of the associated virtual servers is in UP state. *If you set RHI STATE to ACTIVE on some and PASSIVE on others, the NetScaler ADC advertises the route for the VIP address if at least one of the associated virtual servers, whose RHI STATE set to ACTIVE, is in UP state. Default value: ONE_VSERVER Possible values = ONE_VSERVER, ALL_VSERVERS, NONE, VSVR_CNTRLD :param vserverrhilevel: """ try : self._vserverrhilevel = vserverrhilevel except Exception as e: raise e @property def vserverrhimode(self) : """Advertise the route for the Virtual IP (VIP) address using dynamic routing protocols or using RISE * DYNMAIC_ROUTING - Advertise the route for the VIP address using dynamic routing protocols (default) * RISE - Advertise the route for the VIP address using RISE. Default value: DYNAMIC_ROUTING Possible values = DYNAMIC_ROUTING, RISE. """ try : return self._vserverrhimode except Exception as e: raise e @vserverrhimode.setter def vserverrhimode(self, vserverrhimode) : """Advertise the route for the Virtual IP (VIP) address using dynamic routing protocols or using RISE * DYNMAIC_ROUTING - Advertise the route for the VIP address using dynamic routing protocols (default) * RISE - Advertise the route for the VIP address using RISE. Default value: DYNAMIC_ROUTING Possible values = DYNAMIC_ROUTING, RISE :param vserverrhimode: """ try : self._vserverrhimode = vserverrhimode except Exception as e: raise e @property def ospflsatype(self) : """Type of LSAs to be used by the OSPF protocol, running on the NetScaler appliance, for advertising the route for this VIP address. Default value: TYPE5 Possible values = TYPE1, TYPE5.""" try : return self._ospflsatype except Exception as e: raise e @ospflsatype.setter def ospflsatype(self, ospflsatype) : """Type of LSAs to be used by the OSPF protocol, running on the NetScaler appliance, for advertising the route for this VIP address. Default value: TYPE5 Possible values = TYPE1, TYPE5 :param ospflsatype: """ try : self._ospflsatype = ospflsatype except Exception as e: raise e @property def ospfarea(self) : """ID of the area in which the type1 link-state advertisements (LSAs) are to be advertised for this virtual IP (VIP) address by the OSPF protocol running on the NetScaler appliance. When this parameter is not set, the VIP is advertised on all areas. Default value: -1 Maximum length = 4294967294LU.""" try : return self._ospfarea except Exception as e: raise e @ospfarea.setter def ospfarea(self, ospfarea) : """ID of the area in which the type1 link-state advertisements (LSAs) are to be advertised for this virtual IP (VIP) address by the OSPF protocol running on the NetScaler appliance. When this parameter is not set, the VIP is advertised on all areas. Default value: -1 Maximum length = 4294967294LU :param ospfarea: """ try : self._ospfarea = ospfarea except Exception as e: raise e @property def state(self) : """Enable or disable the IP address. Default value: ENABLED Possible values = ENABLED, DISABLED.""" try : return self._state except Exception as e: raise e @state.setter def state(self, state) : """Enable or disable the IP address. Default value: ENABLED Possible values = ENABLED, DISABLED :param state: """ try : self._state = state except Exception as e: raise e @property def vrid(self) : """A positive integer that uniquely identifies a VMAC address for binding to this VIP address. This binding is used to set up NetScaler appliances in an active-active configuration using VRRP. Minimum length = 1 Maximum length = 255.""" try : return self._vrid except Exception as e: raise e @vrid.setter def vrid(self, vrid) : """A positive integer that uniquely identifies a VMAC address for binding to this VIP address. This binding is used to set up NetScaler appliances in an active-active configuration using VRRP. Minimum length = 1 Maximum length = 255 :param vrid: """ try : self._vrid = vrid except Exception as e: raise e @property def icmpresponse(self) : """Respond to ICMP requests for a Virtual IP (VIP) address on the basis of the states of the virtual servers associated with that VIP. Available settings function as follows: * NONE - The NetScaler appliance responds to any ICMP request for the VIP address, irrespective of the states of the virtual servers associated with the address. * ONE VSERVER - The NetScaler appliance responds to any ICMP request for the VIP address if at least one of the associated virtual servers is in UP state. * ALL VSERVER - The NetScaler appliance responds to any ICMP request for the VIP address if all of the associated virtual servers are in UP state. * VSVR_CNTRLD - The behavior depends on the ICMP VSERVER RESPONSE setting on all the associated virtual servers. The following settings can be made for the ICMP VSERVER RESPONSE parameter on a virtual server: * If you set ICMP VSERVER RESPONSE to PASSIVE on all virtual servers, NetScaler always responds. * If you set ICMP VSERVER RESPONSE to ACTIVE on all virtual servers, NetScaler responds if even one virtual server is UP. * When you set ICMP VSERVER RESPONSE to ACTIVE on some and PASSIVE on others, NetScaler responds if even one virtual server set to ACTIVE is UP. Default value: 5 Possible values = NONE, ONE_VSERVER, ALL_VSERVERS, VSVR_CNTRLD. """ try : return self._icmpresponse except Exception as e: raise e @icmpresponse.setter def icmpresponse(self, icmpresponse) : """Respond to ICMP requests for a Virtual IP (VIP) address on the basis of the states of the virtual servers associated with that VIP. Available settings function as follows: * NONE - The NetScaler appliance responds to any ICMP request for the VIP address, irrespective of the states of the virtual servers associated with the address. * ONE VSERVER - The NetScaler appliance responds to any ICMP request for the VIP address if at least one of the associated virtual servers is in UP state. * ALL VSERVER - The NetScaler appliance responds to any ICMP request for the VIP address if all of the associated virtual servers are in UP state. * VSVR_CNTRLD - The behavior depends on the ICMP VSERVER RESPONSE setting on all the associated virtual servers. The following settings can be made for the ICMP VSERVER RESPONSE parameter on a virtual server: * If you set ICMP VSERVER RESPONSE to PASSIVE on all virtual servers, NetScaler always responds. * If you set ICMP VSERVER RESPONSE to ACTIVE on all virtual servers, NetScaler responds if even one virtual server is UP. * When you set ICMP VSERVER RESPONSE to ACTIVE on some and PASSIVE on others, NetScaler responds if even one virtual server set to ACTIVE is UP. Default value: 5 Possible values = NONE, ONE_VSERVER, ALL_VSERVERS, VSVR_CNTRLD :param icmpresponse: """ try : self._icmpresponse = icmpresponse except Exception as e: raise e @property def ownernode(self) : """The owner node in a Cluster for this IP address. Owner node can vary from 0 to 31. If ownernode is not specified then the IP is treated as Striped IP. Default value: 255.""" try : return self._ownernode except Exception as e: raise e @ownernode.setter def ownernode(self, ownernode) : """The owner node in a Cluster for this IP address. Owner node can vary from 0 to 31. If ownernode is not specified then the IP is treated as Striped IP. Default value: 255 :param ownernode: """ try : self._ownernode = ownernode except Exception as e: raise e @property def arpresponse(self) : """Respond to ARP requests for a Virtual IP (VIP) address on the basis of the states of the virtual servers associated with that VIP. Available settings function as follows: * NONE - The NetScaler appliance responds to any ARP request for the VIP address, irrespective of the states of the virtual servers associated with the address. * ONE VSERVER - The NetScaler appliance responds to any ARP request for the VIP address if at least one of the associated virtual servers is in UP state. * ALL VSERVER - The NetScaler appliance responds to any ARP request for the VIP address if all of the associated virtual servers are in UP state. Default value: 5 Possible values = NONE, ONE_VSERVER, ALL_VSERVERS. """ try : return self._arpresponse except Exception as e: raise e @arpresponse.setter def arpresponse(self, arpresponse) : """Respond to ARP requests for a Virtual IP (VIP) address on the basis of the states of the virtual servers associated with that VIP. Available settings function as follows: * NONE - The NetScaler appliance responds to any ARP request for the VIP address, irrespective of the states of the virtual servers associated with the address. * ONE VSERVER - The NetScaler appliance responds to any ARP request for the VIP address if at least one of the associated virtual servers is in UP state. * ALL VSERVER - The NetScaler appliance responds to any ARP request for the VIP address if all of the associated virtual servers are in UP state. Default value: 5 Possible values = NONE, ONE_VSERVER, ALL_VSERVERS :param arpresponse: """ try : self._arpresponse = arpresponse except Exception as e: raise e @property def td(self) : """Integer value that uniquely identifies the traffic domain in which you want to configure the entity. If you do not specify an ID, the entity becomes part of the default traffic domain, which has an ID of 0. Maximum length = 4094.""" try : return self._td except Exception as e: raise e @td.setter def td(self, td) : """Integer value that uniquely identifies the traffic domain in which you want to configure the entity. If you do not specify an ID, the entity becomes part of the default traffic domain, which has an ID of 0. Maximum length = 4094 :param td: """ try : self._td = td except Exception as e: raise e @property def flags(self) : """The flags for this entry.""" try : return self._flags except Exception as e: raise e @property def hostrtgwact(self) : """Actual Gateway used for advertising host route.""" try : return self._hostrtgwact except Exception as e: raise e @property def ospfareaval(self) : """The area ID of the area in which OSPF Type1 LSAs are advertised.""" try : return self._ospfareaval except Exception as e: raise e @property def viprtadv2bsd(self) : """Whether this route is advertised to FreeBSD.""" try : return self._viprtadv2bsd except Exception as e: raise e @property def vipvsercount(self) : """Number of vservers bound to this VIP.""" try : return self._vipvsercount except Exception as e: raise e @property def vipvserdowncount(self) : """Number of vservers bound to this VIP, which are down.""" try : return self._vipvserdowncount except Exception as e: raise e @property def vipvsrvrrhiactivecount(self) : """Number of vservers that have RHI state ACTIVE.""" try : return self._vipvsrvrrhiactivecount except Exception as e: raise e @property def vipvsrvrrhiactiveupcount(self) : """Number of vservers that have RHI state ACTIVE, which are UP.""" try : return self._vipvsrvrrhiactiveupcount except Exception as e: raise e @property def freeports(self) : """Number of free Ports available on this IP.""" try : return self._freeports except Exception as e: raise e @property def riserhimsgcode(self) : """The code indicating the rise rhi status.""" try : return self._riserhimsgcode except Exception as e: raise e @property def iptype(self) : """. Possible values = SNIP, VIP, NSIP, GSLBsiteIP, CLIP.""" try : return self._iptype except Exception as e: raise e def _get_nitro_response(self, service, response) : """converts nitro response into object and returns the object array in case of get request. :param service: :param response: """ try : result = service.payload_formatter.string_to_resource(nsip_response, response, self.__class__.__name__) if(result.errorcode != 0) : if (result.errorcode == 444) : service.clear_session(self) if result.severity : if (result.severity == "ERROR") : raise nitro_exception(result.errorcode, str(result.message), str(result.severity)) else : raise nitro_exception(result.errorcode, str(result.message), str(result.severity)) return result.nsip except Exception as e : raise e def _get_object_name(self) : """Returns the value of object identifier argument""" try : if self.ipaddress is not None : return str(self.ipaddress) return None except Exception as e : raise e @classmethod def add(cls, client, resource) : """Use this API to add nsip. :param client: :param resource: """ try : if type(resource) is not list : addresource = nsip() addresource.ipaddress = resource.ipaddress addresource.netmask = resource.netmask addresource.type = resource.type addresource.arp = resource.arp addresource.icmp = resource.icmp addresource.vserver = resource.vserver addresource.telnet = resource.telnet addresource.ftp = resource.ftp addresource.gui = resource.gui addresource.ssh = resource.ssh addresource.snmp = resource.snmp addresource.mgmtaccess = resource.mgmtaccess addresource.restrictaccess = resource.restrictaccess addresource.dynamicrouting = resource.dynamicrouting addresource.ospf = resource.ospf addresource.bgp = resource.bgp addresource.rip = resource.rip addresource.hostroute = resource.hostroute addresource.hostrtgw = resource.hostrtgw addresource.metric = resource.metric addresource.vserverrhilevel = resource.vserverrhilevel addresource.vserverrhimode = resource.vserverrhimode addresource.ospflsatype = resource.ospflsatype addresource.ospfarea = resource.ospfarea addresource.state = resource.state addresource.vrid = resource.vrid addresource.icmpresponse = resource.icmpresponse addresource.ownernode = resource.ownernode addresource.arpresponse = resource.arpresponse addresource.td = resource.td return addresource.add_resource(client) else : if (resource and len(resource) > 0) : addresources = [ nsip() for _ in range(len(resource))] for i in range(len(resource)) : addresources[i].ipaddress = resource[i].ipaddress addresources[i].netmask = resource[i].netmask addresources[i].type = resource[i].type addresources[i].arp = resource[i].arp addresources[i].icmp = resource[i].icmp addresources[i].vserver = resource[i].vserver addresources[i].telnet = resource[i].telnet addresources[i].ftp = resource[i].ftp addresources[i].gui = resource[i].gui addresources[i].ssh = resource[i].ssh addresources[i].snmp = resource[i].snmp addresources[i].mgmtaccess = resource[i].mgmtaccess addresources[i].restrictaccess = resource[i].restrictaccess addresources[i].dynamicrouting = resource[i].dynamicrouting addresources[i].ospf = resource[i].ospf addresources[i].bgp = resource[i].bgp addresources[i].rip = resource[i].rip addresources[i].hostroute = resource[i].hostroute addresources[i].hostrtgw = resource[i].hostrtgw addresources[i].metric = resource[i].metric addresources[i].vserverrhilevel = resource[i].vserverrhilevel addresources[i].vserverrhimode = resource[i].vserverrhimode addresources[i].ospflsatype = resource[i].ospflsatype addresources[i].ospfarea = resource[i].ospfarea addresources[i].state = resource[i].state addresources[i].vrid = resource[i].vrid addresources[i].icmpresponse = resource[i].icmpresponse addresources[i].ownernode = resource[i].ownernode addresources[i].arpresponse = resource[i].arpresponse addresources[i].td = resource[i].td result = cls.add_bulk_request(client, addresources) return result except Exception as e : raise e @classmethod def delete(cls, client, resource) : """Use this API to delete nsip. :param client: :param resource: """ try : if type(resource) is not list : deleteresource = nsip() if type(resource) != type(deleteresource): deleteresource.ipaddress = resource else : deleteresource.ipaddress = resource.ipaddress deleteresource.td = resource.td return deleteresource.delete_resource(client) else : if type(resource[0]) != cls : if (resource and len(resource) > 0) : deleteresources = [ nsip() for _ in range(len(resource))] for i in range(len(resource)) : deleteresources[i].ipaddress = resource[i] else : if (resource and len(resource) > 0) : deleteresources = [ nsip() for _ in range(len(resource))] for i in range(len(resource)) : deleteresources[i].ipaddress = resource[i].ipaddress deleteresources[i].td = resource[i].td result = cls.delete_bulk_request(client, deleteresources) return result except Exception as e : raise e @classmethod def update(cls, client, resource) : """Use this API to update nsip. :param client: :param resource: """ try : if type(resource) is not list : updateresource = nsip() updateresource.ipaddress = resource.ipaddress updateresource.td = resource.td updateresource.netmask = resource.netmask updateresource.arp = resource.arp updateresource.icmp = resource.icmp updateresource.vserver = resource.vserver updateresource.telnet = resource.telnet updateresource.ftp = resource.ftp updateresource.gui = resource.gui updateresource.ssh = resource.ssh updateresource.snmp = resource.snmp updateresource.mgmtaccess = resource.mgmtaccess updateresource.restrictaccess = resource.restrictaccess updateresource.dynamicrouting = resource.dynamicrouting updateresource.ospf = resource.ospf updateresource.bgp = resource.bgp updateresource.rip = resource.rip updateresource.hostroute = resource.hostroute updateresource.hostrtgw = resource.hostrtgw updateresource.metric = resource.metric updateresource.vserverrhilevel = resource.vserverrhilevel updateresource.vserverrhimode = resource.vserverrhimode updateresource.ospflsatype = resource.ospflsatype updateresource.ospfarea = resource.ospfarea updateresource.vrid = resource.vrid updateresource.icmpresponse = resource.icmpresponse updateresource.arpresponse = resource.arpresponse return updateresource.update_resource(client) else : if (resource and len(resource) > 0) : updateresources = [ nsip() for _ in range(len(resource))] for i in range(len(resource)) : updateresources[i].ipaddress = resource[i].ipaddress updateresources[i].td = resource[i].td updateresources[i].netmask = resource[i].netmask updateresources[i].arp = resource[i].arp updateresources[i].icmp = resource[i].icmp updateresources[i].vserver = resource[i].vserver updateresources[i].telnet = resource[i].telnet updateresources[i].ftp = resource[i].ftp updateresources[i].gui = resource[i].gui updateresources[i].ssh = resource[i].ssh updateresources[i].snmp = resource[i].snmp updateresources[i].mgmtaccess = resource[i].mgmtaccess updateresources[i].restrictaccess = resource[i].restrictaccess updateresources[i].dynamicrouting = resource[i].dynamicrouting updateresources[i].ospf = resource[i].ospf updateresources[i].bgp = resource[i].bgp updateresources[i].rip = resource[i].rip updateresources[i].hostroute = resource[i].hostroute updateresources[i].hostrtgw = resource[i].hostrtgw updateresources[i].metric = resource[i].metric updateresources[i].vserverrhilevel = resource[i].vserverrhilevel updateresources[i].vserverrhimode = resource[i].vserverrhimode updateresources[i].ospflsatype = resource[i].ospflsatype updateresources[i].ospfarea = resource[i].ospfarea updateresources[i].vrid = resource[i].vrid updateresources[i].icmpresponse = resource[i].icmpresponse updateresources[i].arpresponse = resource[i].arpresponse result = cls.update_bulk_request(client, updateresources) return result except Exception as e : raise e @classmethod def unset(cls, client, resource, args) : """Use this API to unset the properties of nsip resource. Properties that need to be unset are specified in args array. :param client: :param resource: :param args: """ try : if type(resource) is not list : unsetresource = nsip() if type(resource) != type(unsetresource): unsetresource.ipaddress = resource else : unsetresource.ipaddress = resource.ipaddress unsetresource.td = resource.td return unsetresource.unset_resource(client, args) else : if type(resource[0]) != cls : if (resource and len(resource) > 0) : unsetresources = [ nsip() for _ in range(len(resource))] for i in range(len(resource)) : unsetresources[i].ipaddress = resource[i] else : if (resource and len(resource) > 0) : unsetresources = [ nsip() for _ in range(len(resource))] for i in range(len(resource)) : unsetresources[i].ipaddress = resource[i].ipaddress unsetresources[i].td = resource[i].td result = cls.unset_bulk_request(client, unsetresources, args) return result except Exception as e : raise e @classmethod def enable(cls, client, resource) : """Use this API to enable nsip. :param client: :param resource: """ try : if type(resource) is not list : enableresource = nsip() if type(resource) != type(enableresource): enableresource.ipaddress = resource else : enableresource.ipaddress = resource.ipaddress enableresource.td = resource.td return enableresource.perform_operation(client,"enable") else : if type(resource[0]) != cls : if (resource and len(resource) > 0) : enableresources = [ nsip() for _ in range(len(resource))] for i in range(len(resource)) : enableresources[i].ipaddress = resource[i] else : if (resource and len(resource) > 0) : enableresources = [ nsip() for _ in range(len(resource))] for i in range(len(resource)) : enableresources[i].ipaddress = resource[i].ipaddress enableresources[i].td = resource[i].td result = cls.perform_operation_bulk_request(client, enableresources,"enable") return result except Exception as e : raise e @classmethod def disable(cls, client, resource) : """Use this API to disable nsip. :param client: :param resource: """ try : if type(resource) is not list : disableresource = nsip() if type(resource) != type(disableresource): disableresource.ipaddress = resource else : disableresource.ipaddress = resource.ipaddress disableresource.td = resource.td return disableresource.perform_operation(client,"disable") else : if type(resource[0]) != cls : if (resource and len(resource) > 0) : disableresources = [ nsip() for _ in range(len(resource))] for i in range(len(resource)) : disableresources[i].ipaddress = resource[i] else : if (resource and len(resource) > 0) : disableresources = [ nsip() for _ in range(len(resource))] for i in range(len(resource)) : disableresources[i].ipaddress = resource[i].ipaddress disableresources[i].td = resource[i].td result = cls.perform_operation_bulk_request(client, disableresources,"disable") return result except Exception as e : raise e @classmethod def get(cls, client, name="", option_="") : """Use this API to fetch all the nsip resources that are configured on netscaler. :param client: :param name: (Default value = "") :param option_: (Default value = "") """ try : if not name : obj = nsip() response = obj.get_resources(client, option_) else : if type(name) == cls : if type(name) is not list : option_ = options() option_.args = nitro_util.object_to_string_withoutquotes(name) response = name.get_resource(client, option_) else : if name and len(name) > 0 : response = [nsip() for _ in range(len(name))] for i in range(len(name)) : option_ = options() option_.args = nitro_util.object_to_string_withoutquotes(name[i]) response[i] = name[i].get_resource(client, option_) return response except Exception as e : raise e @classmethod def get_args(cls, client, args) : """Use this API to fetch all the nsip resources that are configured on netscaler. # This uses nsip_args which is a way to provide additional arguments while fetching the resources. :param client: :param args: """ try : obj = nsip() option_ = options() option_.args = nitro_util.object_to_string_withoutquotes(args) response = obj.get_resources(client, option_) return response except Exception as e : raise e @classmethod def get_filtered(cls, client, filter_) : """Use this API to fetch filtered set of nsip resources. filter string should be in JSON format.eg: "port:80,servicetype:HTTP". :param client: :param filter_: """ try : obj = nsip() option_ = options() option_.filter = filter_ response = obj.getfiltered(client, option_) return response except Exception as e : raise e @classmethod def count(cls, client) : """Use this API to count the nsip resources configured on NetScaler. :param client: """ try : obj = nsip() option_ = options() option_.count = True response = obj.get_resources(client, option_) if response : return response[0].__dict__['___count'] return 0 except Exception as e : raise e @classmethod def count_filtered(cls, client, filter_) : """Use this API to count filtered the set of nsip resources. Filter string should be in JSON format.eg: "port:80,servicetype:HTTP". :param client: :param filter_: """ try : obj = nsip() option_ = options() option_.count = True option_.filter = filter_ response = obj.getfiltered(client, option_) if response : return response[0].__dict__['___count'] return 0 except Exception as e : raise e class Arpresponse: """ """ NONE = "NONE" ONE_VSERVER = "ONE_VSERVER" ALL_VSERVERS = "ALL_VSERVERS" class Iptype: """ """ SNIP = "SNIP" VIP = "VIP" NSIP = "NSIP" GSLBsiteIP = "GSLBsiteIP" CLIP = "CLIP" class Ssh: """ """ ENABLED = "ENABLED" DISABLED = "DISABLED" class State: """ """ ENABLED = "ENABLED" DISABLED = "DISABLED" class Rip: """ """ ENABLED = "ENABLED" DISABLED = "DISABLED" class Gui: """ """ ENABLED = "ENABLED" SECUREONLY = "SECUREONLY" DISABLED = "DISABLED" class Ospf: """ """ ENABLED = "ENABLED" DISABLED = "DISABLED" class Dynamicrouting: """ """ ENABLED = "ENABLED" DISABLED = "DISABLED" class Type: """ """ SNIP = "SNIP" VIP = "VIP" NSIP = "NSIP" GSLBsiteIP = "GSLBsiteIP" CLIP = "CLIP" class Ospflsatype: """ """ TYPE1 = "TYPE1" TYPE5 = "TYPE5" class Bgp: """ """ ENABLED = "ENABLED" DISABLED = "DISABLED" class Arp: """ """ ENABLED = "ENABLED" DISABLED = "DISABLED" class Mgmtaccess: """ """ ENABLED = "ENABLED" DISABLED = "DISABLED" class Hostroute: """ """ ENABLED = "ENABLED" DISABLED = "DISABLED" class Ftp: """ """ ENABLED = "ENABLED" DISABLED = "DISABLED" class Vserverrhilevel: """ """ ONE_VSERVER = "ONE_VSERVER" ALL_VSERVERS = "ALL_VSERVERS" NONE = "NONE" VSVR_CNTRLD = "VSVR_CNTRLD" class Icmp: """ """ ENABLED = "ENABLED" DISABLED = "DISABLED" class Icmpresponse: """ """ NONE = "NONE" ONE_VSERVER = "ONE_VSERVER" ALL_VSERVERS = "ALL_VSERVERS" VSVR_CNTRLD = "VSVR_CNTRLD" class Vserver: """ """ ENABLED = "ENABLED" DISABLED = "DISABLED" class Snmp: """ """ ENABLED = "ENABLED" DISABLED = "DISABLED" class Restrictaccess: """ """ ENABLED = "ENABLED" DISABLED = "DISABLED" class Vserverrhimode: """ """ DYNAMIC_ROUTING = "DYNAMIC_ROUTING" RISE = "RISE" class Telnet: """ """ ENABLED = "ENABLED" DISABLED = "DISABLED" class nsip_response(base_response) : """ """ def __init__(self, length=1) : self.nsip = [] self.errorcode = 0 self.message = "" self.severity = "" self.sessionid = "" self.nsip = [nsip() for _ in range(length)]

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from nitro.resource.base.base_resource import base_resource from nitro.resource.base.base_resource import base_response from nitro.service.options import options from nitro.exception.nitro_exception import nitro_exception from nitro.util.nitro_util import nitro_util class nsip(base_resource) : def __init__(self) : self._ipaddress = "" self._netmask = "" self._type = "" self._arp = "" self._icmp = "" self._vserver = "" self._telnet = "" self._ftp = "" self._gui = "" self._ssh = "" self._snmp = "" self._mgmtaccess = "" self._restrictaccess = "" self._dynamicrouting = "" self._ospf = "" self._bgp = "" self._rip = "" self._hostroute = "" self._hostrtgw = "" self._metric = 0 self._vserverrhilevel = "" self._vserverrhimode = "" self._ospflsatype = "" self._ospfarea = 0 self._state = "" self._vrid = 0 self._icmpresponse = "" self._ownernode = 0 self._arpresponse = "" self._td = 0 self._flags = 0 self._hostrtgwact = "" self._ospfareaval = 0 self._viprtadv2bsd = False self._vipvsercount = 0 self._vipvserdowncount = 0 self._vipvsrvrrhiactivecount = 0 self._vipvsrvrrhiactiveupcount = 0 self._freeports = 0 self._riserhimsgcode = 0 self._iptype = [] self.___count = 0 @property def ipaddress(self) : try : return self._ipaddress except Exception as e: raise e @ipaddress.setter def ipaddress(self, ipaddress) : try : self._ipaddress = ipaddress except Exception as e: raise e @property def netmask(self) : try : return self._netmask except Exception as e: raise e @netmask.setter def netmask(self, netmask) : try : self._netmask = netmask except Exception as e: raise e @property def type(self) : try : return self._type except Exception as e: raise e @type.setter def type(self, type) : try : self._type = type except Exception as e: raise e @property def arp(self) : try : return self._arp except Exception as e: raise e @arp.setter def arp(self, arp) : try : self._arp = arp except Exception as e: raise e @property def icmp(self) : try : return self._icmp except Exception as e: raise e @icmp.setter def icmp(self, icmp) : try : self._icmp = icmp except Exception as e: raise e @property def vserver(self) : try : return self._vserver except Exception as e: raise e @vserver.setter def vserver(self, vserver) : try : self._vserver = vserver except Exception as e: raise e @property def telnet(self) : try : return self._telnet except Exception as e: raise e @telnet.setter def telnet(self, telnet) : try : self._telnet = telnet except Exception as e: raise e @property def ftp(self) : try : return self._ftp except Exception as e: raise e @ftp.setter def ftp(self, ftp) : try : self._ftp = ftp except Exception as e: raise e @property def gui(self) : try : return self._gui except Exception as e: raise e @gui.setter def gui(self, gui) : try : self._gui = gui except Exception as e: raise e @property def ssh(self) : try : return self._ssh except Exception as e: raise e @ssh.setter def ssh(self, ssh) : try : self._ssh = ssh except Exception as e: raise e @property def snmp(self) : try : return self._snmp except Exception as e: raise e @snmp.setter def snmp(self, snmp) : try : self._snmp = snmp except Exception as e: raise e @property def mgmtaccess(self) : try : return self._mgmtaccess except Exception as e: raise e @mgmtaccess.setter def mgmtaccess(self, mgmtaccess) : try : self._mgmtaccess = mgmtaccess except Exception as e: raise e @property def restrictaccess(self) : try : return self._restrictaccess except Exception as e: raise e @restrictaccess.setter def restrictaccess(self, restrictaccess) : try : self._restrictaccess = restrictaccess except Exception as e: raise e @property def dynamicrouting(self) : try : return self._dynamicrouting except Exception as e: raise e @dynamicrouting.setter def dynamicrouting(self, dynamicrouting) : try : self._dynamicrouting = dynamicrouting except Exception as e: raise e @property def ospf(self) : try : return self._ospf except Exception as e: raise e @ospf.setter def ospf(self, ospf) : try : self._ospf = ospf except Exception as e: raise e @property def bgp(self) : try : return self._bgp except Exception as e: raise e @bgp.setter def bgp(self, bgp) : try : self._bgp = bgp except Exception as e: raise e @property def rip(self) : try : return self._rip except Exception as e: raise e @rip.setter def rip(self, rip) : try : self._rip = rip except Exception as e: raise e @property def hostroute(self) : try : return self._hostroute except Exception as e: raise e @hostroute.setter def hostroute(self, hostroute) : try : self._hostroute = hostroute except Exception as e: raise e @property def hostrtgw(self) : try : return self._hostrtgw except Exception as e: raise e @hostrtgw.setter def hostrtgw(self, hostrtgw) : try : self._hostrtgw = hostrtgw except Exception as e: raise e @property def metric(self) : try : return self._metric except Exception as e: raise e @metric.setter def metric(self, metric) : try : self._metric = metric except Exception as e: raise e @property def vserverrhilevel(self) : try : return self._vserverrhilevel except Exception as e: raise e @vserverrhilevel.setter def vserverrhilevel(self, vserverrhilevel) : try : self._vserverrhilevel = vserverrhilevel except Exception as e: raise e @property def vserverrhimode(self) : try : return self._vserverrhimode except Exception as e: raise e @vserverrhimode.setter def vserverrhimode(self, vserverrhimode) : try : self._vserverrhimode = vserverrhimode except Exception as e: raise e @property def ospflsatype(self) : try : return self._ospflsatype except Exception as e: raise e @ospflsatype.setter def ospflsatype(self, ospflsatype) : try : self._ospflsatype = ospflsatype except Exception as e: raise e @property def ospfarea(self) : try : return self._ospfarea except Exception as e: raise e @ospfarea.setter def ospfarea(self, ospfarea) : try : self._ospfarea = ospfarea except Exception as e: raise e @property def state(self) : try : return self._state except Exception as e: raise e @state.setter def state(self, state) : try : self._state = state except Exception as e: raise e @property def vrid(self) : try : return self._vrid except Exception as e: raise e @vrid.setter def vrid(self, vrid) : try : self._vrid = vrid except Exception as e: raise e @property def icmpresponse(self) : try : return self._icmpresponse except Exception as e: raise e @icmpresponse.setter def icmpresponse(self, icmpresponse) : try : self._icmpresponse = icmpresponse except Exception as e: raise e @property def ownernode(self) : try : return self._ownernode except Exception as e: raise e @ownernode.setter def ownernode(self, ownernode) : try : self._ownernode = ownernode except Exception as e: raise e @property def arpresponse(self) : try : return self._arpresponse except Exception as e: raise e @arpresponse.setter def arpresponse(self, arpresponse) : try : self._arpresponse = arpresponse except Exception as e: raise e @property def td(self) : try : return self._td except Exception as e: raise e @td.setter def td(self, td) : try : self._td = td except Exception as e: raise e @property def flags(self) : try : return self._flags except Exception as e: raise e @property def hostrtgwact(self) : try : return self._hostrtgwact except Exception as e: raise e @property def ospfareaval(self) : try : return self._ospfareaval except Exception as e: raise e @property def viprtadv2bsd(self) : try : return self._viprtadv2bsd except Exception as e: raise e @property def vipvsercount(self) : try : return self._vipvsercount except Exception as e: raise e @property def vipvserdowncount(self) : try : return self._vipvserdowncount except Exception as e: raise e @property def vipvsrvrrhiactivecount(self) : try : return self._vipvsrvrrhiactivecount except Exception as e: raise e @property def vipvsrvrrhiactiveupcount(self) : try : return self._vipvsrvrrhiactiveupcount except Exception as e: raise e @property def freeports(self) : try : return self._freeports except Exception as e: raise e @property def riserhimsgcode(self) : try : return self._riserhimsgcode except Exception as e: raise e @property def iptype(self) : try : return self._iptype except Exception as e: raise e def _get_nitro_response(self, service, response) : try : result = service.payload_formatter.string_to_resource(nsip_response, response, self.__class__.__name__) if(result.errorcode != 0) : if (result.errorcode == 444) : service.clear_session(self) if result.severity : if (result.severity == "ERROR") : raise nitro_exception(result.errorcode, str(result.message), str(result.severity)) else : raise nitro_exception(result.errorcode, str(result.message), str(result.severity)) return result.nsip except Exception as e : raise e def _get_object_name(self) : try : if self.ipaddress is not None : return str(self.ipaddress) return None except Exception as e : raise e @classmethod def add(cls, client, resource) : try : if type(resource) is not list : addresource = nsip() addresource.ipaddress = resource.ipaddress addresource.netmask = resource.netmask addresource.type = resource.type addresource.arp = resource.arp addresource.icmp = resource.icmp addresource.vserver = resource.vserver addresource.telnet = resource.telnet addresource.ftp = resource.ftp addresource.gui = resource.gui addresource.ssh = resource.ssh addresource.snmp = resource.snmp addresource.mgmtaccess = resource.mgmtaccess addresource.restrictaccess = resource.restrictaccess addresource.dynamicrouting = resource.dynamicrouting addresource.ospf = resource.ospf addresource.bgp = resource.bgp addresource.rip = resource.rip addresource.hostroute = resource.hostroute addresource.hostrtgw = resource.hostrtgw addresource.metric = resource.metric addresource.vserverrhilevel = resource.vserverrhilevel addresource.vserverrhimode = resource.vserverrhimode addresource.ospflsatype = resource.ospflsatype addresource.ospfarea = resource.ospfarea addresource.state = resource.state addresource.vrid = resource.vrid addresource.icmpresponse = resource.icmpresponse addresource.ownernode = resource.ownernode addresource.arpresponse = resource.arpresponse addresource.td = resource.td return addresource.add_resource(client) else : if (resource and len(resource) > 0) : addresources = [ nsip() for _ in range(len(resource))] for i in range(len(resource)) : addresources[i].ipaddress = resource[i].ipaddress addresources[i].netmask = resource[i].netmask addresources[i].type = resource[i].type addresources[i].arp = resource[i].arp addresources[i].icmp = resource[i].icmp addresources[i].vserver = resource[i].vserver addresources[i].telnet = resource[i].telnet addresources[i].ftp = resource[i].ftp addresources[i].gui = resource[i].gui addresources[i].ssh = resource[i].ssh addresources[i].snmp = resource[i].snmp addresources[i].mgmtaccess = resource[i].mgmtaccess addresources[i].restrictaccess = resource[i].restrictaccess addresources[i].dynamicrouting = resource[i].dynamicrouting addresources[i].ospf = resource[i].ospf addresources[i].bgp = resource[i].bgp addresources[i].rip = resource[i].rip addresources[i].hostroute = resource[i].hostroute addresources[i].hostrtgw = resource[i].hostrtgw addresources[i].metric = resource[i].metric addresources[i].vserverrhilevel = resource[i].vserverrhilevel addresources[i].vserverrhimode = resource[i].vserverrhimode addresources[i].ospflsatype = resource[i].ospflsatype addresources[i].ospfarea = resource[i].ospfarea addresources[i].state = resource[i].state addresources[i].vrid = resource[i].vrid addresources[i].icmpresponse = resource[i].icmpresponse addresources[i].ownernode = resource[i].ownernode addresources[i].arpresponse = resource[i].arpresponse addresources[i].td = resource[i].td result = cls.add_bulk_request(client, addresources) return result except Exception as e : raise e @classmethod def delete(cls, client, resource) : try : if type(resource) is not list : deleteresource = nsip() if type(resource) != type(deleteresource): deleteresource.ipaddress = resource else : deleteresource.ipaddress = resource.ipaddress deleteresource.td = resource.td return deleteresource.delete_resource(client) else : if type(resource[0]) != cls : if (resource and len(resource) > 0) : deleteresources = [ nsip() for _ in range(len(resource))] for i in range(len(resource)) : deleteresources[i].ipaddress = resource[i] else : if (resource and len(resource) > 0) : deleteresources = [ nsip() for _ in range(len(resource))] for i in range(len(resource)) : deleteresources[i].ipaddress = resource[i].ipaddress deleteresources[i].td = resource[i].td result = cls.delete_bulk_request(client, deleteresources) return result except Exception as e : raise e @classmethod def update(cls, client, resource) : try : if type(resource) is not list : updateresource = nsip() updateresource.ipaddress = resource.ipaddress updateresource.td = resource.td updateresource.netmask = resource.netmask updateresource.arp = resource.arp updateresource.icmp = resource.icmp updateresource.vserver = resource.vserver updateresource.telnet = resource.telnet updateresource.ftp = resource.ftp updateresource.gui = resource.gui updateresource.ssh = resource.ssh updateresource.snmp = resource.snmp updateresource.mgmtaccess = resource.mgmtaccess updateresource.restrictaccess = resource.restrictaccess updateresource.dynamicrouting = resource.dynamicrouting updateresource.ospf = resource.ospf updateresource.bgp = resource.bgp updateresource.rip = resource.rip updateresource.hostroute = resource.hostroute updateresource.hostrtgw = resource.hostrtgw updateresource.metric = resource.metric updateresource.vserverrhilevel = resource.vserverrhilevel updateresource.vserverrhimode = resource.vserverrhimode updateresource.ospflsatype = resource.ospflsatype updateresource.ospfarea = resource.ospfarea updateresource.vrid = resource.vrid updateresource.icmpresponse = resource.icmpresponse updateresource.arpresponse = resource.arpresponse return updateresource.update_resource(client) else : if (resource and len(resource) > 0) : updateresources = [ nsip() for _ in range(len(resource))] for i in range(len(resource)) : updateresources[i].ipaddress = resource[i].ipaddress updateresources[i].td = resource[i].td updateresources[i].netmask = resource[i].netmask updateresources[i].arp = resource[i].arp updateresources[i].icmp = resource[i].icmp updateresources[i].vserver = resource[i].vserver updateresources[i].telnet = resource[i].telnet updateresources[i].ftp = resource[i].ftp updateresources[i].gui = resource[i].gui updateresources[i].ssh = resource[i].ssh updateresources[i].snmp = resource[i].snmp updateresources[i].mgmtaccess = resource[i].mgmtaccess updateresources[i].restrictaccess = resource[i].restrictaccess updateresources[i].dynamicrouting = resource[i].dynamicrouting updateresources[i].ospf = resource[i].ospf updateresources[i].bgp = resource[i].bgp updateresources[i].rip = resource[i].rip updateresources[i].hostroute = resource[i].hostroute updateresources[i].hostrtgw = resource[i].hostrtgw updateresources[i].metric = resource[i].metric updateresources[i].vserverrhilevel = resource[i].vserverrhilevel updateresources[i].vserverrhimode = resource[i].vserverrhimode updateresources[i].ospflsatype = resource[i].ospflsatype updateresources[i].ospfarea = resource[i].ospfarea updateresources[i].vrid = resource[i].vrid updateresources[i].icmpresponse = resource[i].icmpresponse updateresources[i].arpresponse = resource[i].arpresponse result = cls.update_bulk_request(client, updateresources) return result except Exception as e : raise e @classmethod def unset(cls, client, resource, args) : try : if type(resource) is not list : unsetresource = nsip() if type(resource) != type(unsetresource): unsetresource.ipaddress = resource else : unsetresource.ipaddress = resource.ipaddress unsetresource.td = resource.td return unsetresource.unset_resource(client, args) else : if type(resource[0]) != cls : if (resource and len(resource) > 0) : unsetresources = [ nsip() for _ in range(len(resource))] for i in range(len(resource)) : unsetresources[i].ipaddress = resource[i] else : if (resource and len(resource) > 0) : unsetresources = [ nsip() for _ in range(len(resource))] for i in range(len(resource)) : unsetresources[i].ipaddress = resource[i].ipaddress unsetresources[i].td = resource[i].td result = cls.unset_bulk_request(client, unsetresources, args) return result except Exception as e : raise e @classmethod def enable(cls, client, resource) : try : if type(resource) is not list : enableresource = nsip() if type(resource) != type(enableresource): enableresource.ipaddress = resource else : enableresource.ipaddress = resource.ipaddress enableresource.td = resource.td return enableresource.perform_operation(client,"enable") else : if type(resource[0]) != cls : if (resource and len(resource) > 0) : enableresources = [ nsip() for _ in range(len(resource))] for i in range(len(resource)) : enableresources[i].ipaddress = resource[i] else : if (resource and len(resource) > 0) : enableresources = [ nsip() for _ in range(len(resource))] for i in range(len(resource)) : enableresources[i].ipaddress = resource[i].ipaddress enableresources[i].td = resource[i].td result = cls.perform_operation_bulk_request(client, enableresources,"enable") return result except Exception as e : raise e @classmethod def disable(cls, client, resource) : try : if type(resource) is not list : disableresource = nsip() if type(resource) != type(disableresource): disableresource.ipaddress = resource else : disableresource.ipaddress = resource.ipaddress disableresource.td = resource.td return disableresource.perform_operation(client,"disable") else : if type(resource[0]) != cls : if (resource and len(resource) > 0) : disableresources = [ nsip() for _ in range(len(resource))] for i in range(len(resource)) : disableresources[i].ipaddress = resource[i] else : if (resource and len(resource) > 0) : disableresources = [ nsip() for _ in range(len(resource))] for i in range(len(resource)) : disableresources[i].ipaddress = resource[i].ipaddress disableresources[i].td = resource[i].td result = cls.perform_operation_bulk_request(client, disableresources,"disable") return result except Exception as e : raise e @classmethod def get(cls, client, name="", option_="") : try : if not name : obj = nsip() response = obj.get_resources(client, option_) else : if type(name) == cls : if type(name) is not list : option_ = options() option_.args = nitro_util.object_to_string_withoutquotes(name) response = name.get_resource(client, option_) else : if name and len(name) > 0 : response = [nsip() for _ in range(len(name))] for i in range(len(name)) : option_ = options() option_.args = nitro_util.object_to_string_withoutquotes(name[i]) response[i] = name[i].get_resource(client, option_) return response except Exception as e : raise e @classmethod def get_args(cls, client, args) : try : obj = nsip() option_ = options() option_.args = nitro_util.object_to_string_withoutquotes(args) response = obj.get_resources(client, option_) return response except Exception as e : raise e @classmethod def get_filtered(cls, client, filter_) : try : obj = nsip() option_ = options() option_.filter = filter_ response = obj.getfiltered(client, option_) return response except Exception as e : raise e @classmethod def count(cls, client) : try : obj = nsip() option_ = options() option_.count = True response = obj.get_resources(client, option_) if response : return response[0].__dict__['___count'] return 0 except Exception as e : raise e @classmethod def count_filtered(cls, client, filter_) : try : obj = nsip() option_ = options() option_.count = True option_.filter = filter_ response = obj.getfiltered(client, option_) if response : return response[0].__dict__['___count'] return 0 except Exception as e : raise e class Arpresponse: NONE = "NONE" ONE_VSERVER = "ONE_VSERVER" ALL_VSERVERS = "ALL_VSERVERS" class Iptype: SNIP = "SNIP" VIP = "VIP" NSIP = "NSIP" GSLBsiteIP = "GSLBsiteIP" CLIP = "CLIP" class Ssh: ENABLED = "ENABLED" DISABLED = "DISABLED" class State: ENABLED = "ENABLED" DISABLED = "DISABLED" class Rip: ENABLED = "ENABLED" DISABLED = "DISABLED" class Gui: ENABLED = "ENABLED" SECUREONLY = "SECUREONLY" DISABLED = "DISABLED" class Ospf: ENABLED = "ENABLED" DISABLED = "DISABLED" class Dynamicrouting: ENABLED = "ENABLED" DISABLED = "DISABLED" class Type: SNIP = "SNIP" VIP = "VIP" NSIP = "NSIP" GSLBsiteIP = "GSLBsiteIP" CLIP = "CLIP" class Ospflsatype: TYPE1 = "TYPE1" TYPE5 = "TYPE5" class Bgp: ENABLED = "ENABLED" DISABLED = "DISABLED" class Arp: ENABLED = "ENABLED" DISABLED = "DISABLED" class Mgmtaccess: ENABLED = "ENABLED" DISABLED = "DISABLED" class Hostroute: ENABLED = "ENABLED" DISABLED = "DISABLED" class Ftp: ENABLED = "ENABLED" DISABLED = "DISABLED" class Vserverrhilevel: ONE_VSERVER = "ONE_VSERVER" ALL_VSERVERS = "ALL_VSERVERS" NONE = "NONE" VSVR_CNTRLD = "VSVR_CNTRLD" class Icmp: ENABLED = "ENABLED" DISABLED = "DISABLED" class Icmpresponse: NONE = "NONE" ONE_VSERVER = "ONE_VSERVER" ALL_VSERVERS = "ALL_VSERVERS" VSVR_CNTRLD = "VSVR_CNTRLD" class Vserver: ENABLED = "ENABLED" DISABLED = "DISABLED" class Snmp: ENABLED = "ENABLED" DISABLED = "DISABLED" class Restrictaccess: ENABLED = "ENABLED" DISABLED = "DISABLED" class Vserverrhimode: DYNAMIC_ROUTING = "DYNAMIC_ROUTING" RISE = "RISE" class Telnet: ENABLED = "ENABLED" DISABLED = "DISABLED" class nsip_response(base_response) : def __init__(self, length=1) : self.nsip = [] self.errorcode = 0 self.message = "" self.severity = "" self.sessionid = "" self.nsip = [nsip() for _ in range(length)]

true

1c43ec75bb5086504e75a9f3c53c197ac7943cec

27,444

py

Python

pytorch_lightning/metrics/functional/classification.py

rwbfd/pytorch-lightning

f518ee6e25d1499f73cec86ca8b3f584d0fa440d

[ "Apache-2.0" ]

null

pytorch_lightning/metrics/functional/classification.py

rwbfd/pytorch-lightning

f518ee6e25d1499f73cec86ca8b3f584d0fa440d

[ "Apache-2.0" ]

null

pytorch_lightning/metrics/functional/classification.py

rwbfd/pytorch-lightning

f518ee6e25d1499f73cec86ca8b3f584d0fa440d

[ "Apache-2.0" ]

null

# Copyright The PyTorch Lightning team. # # 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 functools import wraps from typing import Callable, Optional, Sequence, Tuple import torch from torch.nn import functional as F from pytorch_lightning.metrics.utils import to_categorical, get_num_classes, reduce, class_reduce from pytorch_lightning.utilities import rank_zero_warn def stat_scores( pred: torch.Tensor, target: torch.Tensor, class_index: int, argmax_dim: int = 1, ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: """ Calculates the number of true positive, false positive, true negative and false negative for a specific class Args: pred: prediction tensor target: target tensor class_index: class to calculate over argmax_dim: if pred is a tensor of probabilities, this indicates the axis the argmax transformation will be applied over Return: True Positive, False Positive, True Negative, False Negative, Support Example: >>> x = torch.tensor([1, 2, 3]) >>> y = torch.tensor([0, 2, 3]) >>> tp, fp, tn, fn, sup = stat_scores(x, y, class_index=1) >>> tp, fp, tn, fn, sup (tensor(0), tensor(1), tensor(2), tensor(0), tensor(0)) """ if pred.ndim == target.ndim + 1: pred = to_categorical(pred, argmax_dim=argmax_dim) tp = ((pred == class_index) * (target == class_index)).to(torch.long).sum() fp = ((pred == class_index) * (target != class_index)).to(torch.long).sum() tn = ((pred != class_index) * (target != class_index)).to(torch.long).sum() fn = ((pred != class_index) * (target == class_index)).to(torch.long).sum() sup = (target == class_index).to(torch.long).sum() return tp, fp, tn, fn, sup def stat_scores_multiple_classes( pred: torch.Tensor, target: torch.Tensor, num_classes: Optional[int] = None, argmax_dim: int = 1, reduction: str = 'none', ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: """ Calculates the number of true positive, false positive, true negative and false negative for each class Args: pred: prediction tensor target: target tensor num_classes: number of classes if known argmax_dim: if pred is a tensor of probabilities, this indicates the axis the argmax transformation will be applied over reduction: a method to reduce metric score over labels (default: none) Available reduction methods: - elementwise_mean: takes the mean - none: pass array - sum: add elements Return: True Positive, False Positive, True Negative, False Negative, Support Example: >>> x = torch.tensor([1, 2, 3]) >>> y = torch.tensor([0, 2, 3]) >>> tps, fps, tns, fns, sups = stat_scores_multiple_classes(x, y) >>> tps tensor([0., 0., 1., 1.]) >>> fps tensor([0., 1., 0., 0.]) >>> tns tensor([2., 2., 2., 2.]) >>> fns tensor([1., 0., 0., 0.]) >>> sups tensor([1., 0., 1., 1.]) """ if pred.ndim == target.ndim + 1: pred = to_categorical(pred, argmax_dim=argmax_dim) num_classes = get_num_classes(pred=pred, target=target, num_classes=num_classes) if pred.dtype != torch.bool: pred = pred.clamp_max(max=num_classes) if target.dtype != torch.bool: target = target.clamp_max(max=num_classes) possible_reductions = ('none', 'sum', 'elementwise_mean') if reduction not in possible_reductions: raise ValueError("reduction type %s not supported" % reduction) if reduction == 'none': pred = pred.view((-1, )).long() target = target.view((-1, )).long() tps = torch.zeros((num_classes + 1,), device=pred.device) fps = torch.zeros((num_classes + 1,), device=pred.device) tns = torch.zeros((num_classes + 1,), device=pred.device) fns = torch.zeros((num_classes + 1,), device=pred.device) sups = torch.zeros((num_classes + 1,), device=pred.device) match_true = (pred == target).float() match_false = 1 - match_true tps.scatter_add_(0, pred, match_true) fps.scatter_add_(0, pred, match_false) fns.scatter_add_(0, target, match_false) tns = pred.size(0) - (tps + fps + fns) sups.scatter_add_(0, target, torch.ones_like(match_true)) tps = tps[:num_classes] fps = fps[:num_classes] tns = tns[:num_classes] fns = fns[:num_classes] sups = sups[:num_classes] elif reduction == 'sum' or reduction == 'elementwise_mean': count_match_true = (pred == target).sum().float() oob_tp, oob_fp, oob_tn, oob_fn, oob_sup = stat_scores(pred, target, num_classes, argmax_dim) tps = count_match_true - oob_tp fps = pred.nelement() - count_match_true - oob_fp fns = pred.nelement() - count_match_true - oob_fn tns = pred.nelement() * (num_classes + 1) - (tps + fps + fns + oob_tn) sups = pred.nelement() - oob_sup.float() if reduction == 'elementwise_mean': tps /= num_classes fps /= num_classes fns /= num_classes tns /= num_classes sups /= num_classes return tps.float(), fps.float(), tns.float(), fns.float(), sups.float() def accuracy( pred: torch.Tensor, target: torch.Tensor, num_classes: Optional[int] = None, class_reduction: str = 'micro', return_state: bool = False ) -> torch.Tensor: """ Computes the accuracy classification score Args: pred: predicted labels target: ground truth labels num_classes: number of classes class_reduction: method to reduce metric score over labels - ``'micro'``: calculate metrics globally (default) - ``'macro'``: calculate metrics for each label, and find their unweighted mean. - ``'weighted'``: calculate metrics for each label, and find their weighted mean. - ``'none'``: returns calculated metric per class return_state: returns a internal state that can be ddp reduced before doing the final calculation Return: A Tensor with the accuracy score. Example: >>> x = torch.tensor([0, 1, 2, 3]) >>> y = torch.tensor([0, 1, 2, 2]) >>> accuracy(x, y) tensor(0.7500) """ tps, fps, tns, fns, sups = stat_scores_multiple_classes( pred=pred, target=target, num_classes=num_classes) if return_state: return {'tps': tps, 'sups': sups} return class_reduce(tps, sups, sups, class_reduction=class_reduction) def _confmat_normalize(cm): """ Normalization function for confusion matrix """ cm = cm / cm.sum(-1, keepdim=True) nan_elements = cm[torch.isnan(cm)].nelement() if nan_elements != 0: cm[torch.isnan(cm)] = 0 rank_zero_warn(f'{nan_elements} nan values found in confusion matrix have been replaced with zeros.') return cm def precision_recall( pred: torch.Tensor, target: torch.Tensor, num_classes: Optional[int] = None, class_reduction: str = 'micro', return_support: bool = False, return_state: bool = False ) -> Tuple[torch.Tensor, torch.Tensor]: """ Computes precision and recall for different thresholds Args: pred: estimated probabilities target: ground-truth labels num_classes: number of classes class_reduction: method to reduce metric score over labels - ``'micro'``: calculate metrics globally (default) - ``'macro'``: calculate metrics for each label, and find their unweighted mean. - ``'weighted'``: calculate metrics for each label, and find their weighted mean. - ``'none'``: returns calculated metric per class return_support: returns the support for each class, need for fbeta/f1 calculations return_state: returns a internal state that can be ddp reduced before doing the final calculation Return: Tensor with precision and recall Example: >>> x = torch.tensor([0, 1, 2, 3]) >>> y = torch.tensor([0, 2, 2, 2]) >>> precision_recall(x, y, class_reduction='macro') (tensor(0.5000), tensor(0.3333)) """ tps, fps, tns, fns, sups = stat_scores_multiple_classes(pred=pred, target=target, num_classes=num_classes) precision = class_reduce(tps, tps + fps, sups, class_reduction=class_reduction) recall = class_reduce(tps, tps + fns, sups, class_reduction=class_reduction) if return_state: return {'tps': tps, 'fps': fps, 'fns': fns, 'sups': sups} if return_support: return precision, recall, sups return precision, recall def precision( pred: torch.Tensor, target: torch.Tensor, num_classes: Optional[int] = None, class_reduction: str = 'micro', ) -> torch.Tensor: """ Computes precision score. Args: pred: estimated probabilities target: ground-truth labels num_classes: number of classes class_reduction: method to reduce metric score over labels - ``'micro'``: calculate metrics globally (default) - ``'macro'``: calculate metrics for each label, and find their unweighted mean. - ``'weighted'``: calculate metrics for each label, and find their weighted mean. - ``'none'``: returns calculated metric per class Return: Tensor with precision. Example: >>> x = torch.tensor([0, 1, 2, 3]) >>> y = torch.tensor([0, 1, 2, 2]) >>> precision(x, y) tensor(0.7500) """ return precision_recall(pred=pred, target=target, num_classes=num_classes, class_reduction=class_reduction)[0] def recall( pred: torch.Tensor, target: torch.Tensor, num_classes: Optional[int] = None, class_reduction: str = 'micro', ) -> torch.Tensor: """ Computes recall score. Args: pred: estimated probabilities target: ground-truth labels num_classes: number of classes class_reduction: method to reduce metric score over labels - ``'micro'``: calculate metrics globally (default) - ``'macro'``: calculate metrics for each label, and find their unweighted mean. - ``'weighted'``: calculate metrics for each label, and find their weighted mean. - ``'none'``: returns calculated metric per class Return: Tensor with recall. Example: >>> x = torch.tensor([0, 1, 2, 3]) >>> y = torch.tensor([0, 1, 2, 2]) >>> recall(x, y) tensor(0.7500) """ return precision_recall(pred=pred, target=target, num_classes=num_classes, class_reduction=class_reduction)[1] def _binary_clf_curve( pred: torch.Tensor, target: torch.Tensor, sample_weight: Optional[Sequence] = None, pos_label: int = 1., ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: """ adapted from https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/metrics/_ranking.py """ if sample_weight is not None and not isinstance(sample_weight, torch.Tensor): sample_weight = torch.tensor(sample_weight, device=pred.device, dtype=torch.float) # remove class dimension if necessary if pred.ndim > target.ndim: pred = pred[:, 0] desc_score_indices = torch.argsort(pred, descending=True) pred = pred[desc_score_indices] target = target[desc_score_indices] if sample_weight is not None: weight = sample_weight[desc_score_indices] else: weight = 1. # pred typically has many tied values. Here we extract # the indices associated with the distinct values. We also # concatenate a value for the end of the curve. distinct_value_indices = torch.where(pred[1:] - pred[:-1])[0] threshold_idxs = F.pad(distinct_value_indices, (0, 1), value=target.size(0) - 1) target = (target == pos_label).to(torch.long) tps = torch.cumsum(target * weight, dim=0)[threshold_idxs] if sample_weight is not None: # express fps as a cumsum to ensure fps is increasing even in # the presence of floating point errors fps = torch.cumsum((1 - target) * weight, dim=0)[threshold_idxs] else: fps = 1 + threshold_idxs - tps return fps, tps, pred[threshold_idxs] # TODO: deprecated in favor of general ROC in pytorch_lightning/metrics/functional/roc.py def __roc( pred: torch.Tensor, target: torch.Tensor, sample_weight: Optional[Sequence] = None, pos_label: int = 1., ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: """ Computes the Receiver Operating Characteristic (ROC). It assumes classifier is binary. .. warning:: Deprecated Args: pred: estimated probabilities target: ground-truth labels sample_weight: sample weights pos_label: the label for the positive class Return: false-positive rate (fpr), true-positive rate (tpr), thresholds Example: >>> x = torch.tensor([0, 1, 2, 3]) >>> y = torch.tensor([0, 1, 1, 1]) >>> fpr, tpr, thresholds = __roc(x, y) >>> fpr tensor([0., 0., 0., 0., 1.]) >>> tpr tensor([0.0000, 0.3333, 0.6667, 1.0000, 1.0000]) >>> thresholds tensor([4, 3, 2, 1, 0]) """ fps, tps, thresholds = _binary_clf_curve(pred=pred, target=target, sample_weight=sample_weight, pos_label=pos_label) # Add an extra threshold position # to make sure that the curve starts at (0, 0) tps = torch.cat([torch.zeros(1, dtype=tps.dtype, device=tps.device), tps]) fps = torch.cat([torch.zeros(1, dtype=fps.dtype, device=fps.device), fps]) thresholds = torch.cat([thresholds[0][None] + 1, thresholds]) if fps[-1] <= 0: raise ValueError("No negative samples in targets, false positive value should be meaningless") fpr = fps / fps[-1] if tps[-1] <= 0: raise ValueError("No positive samples in targets, true positive value should be meaningless") tpr = tps / tps[-1] return fpr, tpr, thresholds # TODO: deprecated in favor of general ROC in pytorch_lightning/metrics/functional/roc.py def __multiclass_roc( pred: torch.Tensor, target: torch.Tensor, sample_weight: Optional[Sequence] = None, num_classes: Optional[int] = None, ) -> Tuple[Tuple[torch.Tensor, torch.Tensor, torch.Tensor]]: """ Computes the Receiver Operating Characteristic (ROC) for multiclass predictors. .. warning:: Deprecated Args: pred: estimated probabilities target: ground-truth labels sample_weight: sample weights num_classes: number of classes (default: None, computes automatically from data) Return: returns roc for each class. Number of classes, false-positive rate (fpr), true-positive rate (tpr), thresholds Example: >>> pred = torch.tensor([[0.85, 0.05, 0.05, 0.05], ... [0.05, 0.85, 0.05, 0.05], ... [0.05, 0.05, 0.85, 0.05], ... [0.05, 0.05, 0.05, 0.85]]) >>> target = torch.tensor([0, 1, 3, 2]) >>> __multiclass_roc(pred, target) # doctest: +NORMALIZE_WHITESPACE ((tensor([0., 0., 1.]), tensor([0., 1., 1.]), tensor([1.8500, 0.8500, 0.0500])), (tensor([0., 0., 1.]), tensor([0., 1., 1.]), tensor([1.8500, 0.8500, 0.0500])), (tensor([0.0000, 0.3333, 1.0000]), tensor([0., 0., 1.]), tensor([1.8500, 0.8500, 0.0500])), (tensor([0.0000, 0.3333, 1.0000]), tensor([0., 0., 1.]), tensor([1.8500, 0.8500, 0.0500]))) """ num_classes = get_num_classes(pred, target, num_classes) class_roc_vals = [] for c in range(num_classes): pred_c = pred[:, c] class_roc_vals.append(__roc(pred=pred_c, target=target, sample_weight=sample_weight, pos_label=c)) return tuple(class_roc_vals) def auc( x: torch.Tensor, y: torch.Tensor, ) -> torch.Tensor: """ Computes Area Under the Curve (AUC) using the trapezoidal rule Args: x: x-coordinates y: y-coordinates Return: Tensor containing AUC score (float) Example: >>> x = torch.tensor([0, 1, 2, 3]) >>> y = torch.tensor([0, 1, 2, 2]) >>> auc(x, y) tensor(4.) """ dx = x[1:] - x[:-1] if (dx < 0).any(): if (dx <= 0).all(): direction = -1. else: raise ValueError(f"The 'x' array is neither increasing or decreasing: {x}. Reorder is not supported.") else: direction = 1. return direction * torch.trapz(y, x) def auc_decorator() -> Callable: def wrapper(func_to_decorate: Callable) -> Callable: @wraps(func_to_decorate) def new_func(*args, **kwargs) -> torch.Tensor: x, y = func_to_decorate(*args, **kwargs)[:2] return auc(x, y) return new_func return wrapper def multiclass_auc_decorator() -> Callable: def wrapper(func_to_decorate: Callable) -> Callable: @wraps(func_to_decorate) def new_func(*args, **kwargs) -> torch.Tensor: results = [] for class_result in func_to_decorate(*args, **kwargs): x, y = class_result[:2] results.append(auc(x, y)) return torch.stack(results) return new_func return wrapper def auroc( pred: torch.Tensor, target: torch.Tensor, sample_weight: Optional[Sequence] = None, pos_label: int = 1., ) -> torch.Tensor: """ Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC) from prediction scores Args: pred: estimated probabilities target: ground-truth labels sample_weight: sample weights pos_label: the label for the positive class Return: Tensor containing ROCAUC score Example: >>> x = torch.tensor([0, 1, 2, 3]) >>> y = torch.tensor([0, 1, 1, 0]) >>> auroc(x, y) tensor(0.5000) """ if any(target > 1): raise ValueError('AUROC metric is meant for binary classification, but' ' target tensor contains value different from 0 and 1.' ' Use `multiclass_auroc` for multi class classification.') @auc_decorator() def _auroc(pred, target, sample_weight, pos_label): return __roc(pred, target, sample_weight, pos_label) return _auroc(pred=pred, target=target, sample_weight=sample_weight, pos_label=pos_label) def multiclass_auroc( pred: torch.Tensor, target: torch.Tensor, sample_weight: Optional[Sequence] = None, num_classes: Optional[int] = None, ) -> torch.Tensor: """ Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC) from multiclass prediction scores Args: pred: estimated probabilities, with shape [N, C] target: ground-truth labels, with shape [N,] sample_weight: sample weights num_classes: number of classes (default: None, computes automatically from data) Return: Tensor containing ROCAUC score Example: >>> pred = torch.tensor([[0.85, 0.05, 0.05, 0.05], ... [0.05, 0.85, 0.05, 0.05], ... [0.05, 0.05, 0.85, 0.05], ... [0.05, 0.05, 0.05, 0.85]]) >>> target = torch.tensor([0, 1, 3, 2]) >>> multiclass_auroc(pred, target, num_classes=4) tensor(0.6667) """ if not torch.allclose(pred.sum(dim=1), torch.tensor(1.0)): raise ValueError( "Multiclass AUROC metric expects the target scores to be" " probabilities, i.e. they should sum up to 1.0 over classes") if torch.unique(target).size(0) != pred.size(1): raise ValueError( f"Number of classes found in in 'target' ({torch.unique(target).size(0)})" f" does not equal the number of columns in 'pred' ({pred.size(1)})." " Multiclass AUROC is not defined when all of the classes do not" " occur in the target labels.") if num_classes is not None and num_classes != pred.size(1): raise ValueError( f"Number of classes deduced from 'pred' ({pred.size(1)}) does not equal" f" the number of classes passed in 'num_classes' ({num_classes}).") @multiclass_auc_decorator() def _multiclass_auroc(pred, target, sample_weight, num_classes): return __multiclass_roc(pred, target, sample_weight, num_classes) class_aurocs = _multiclass_auroc(pred=pred, target=target, sample_weight=sample_weight, num_classes=num_classes) return torch.mean(class_aurocs) def dice_score( pred: torch.Tensor, target: torch.Tensor, bg: bool = False, nan_score: float = 0.0, no_fg_score: float = 0.0, reduction: str = 'elementwise_mean', ) -> torch.Tensor: """ Compute dice score from prediction scores Args: pred: estimated probabilities target: ground-truth labels bg: whether to also compute dice for the background nan_score: score to return, if a NaN occurs during computation no_fg_score: score to return, if no foreground pixel was found in target reduction: a method to reduce metric score over labels. - ``'elementwise_mean'``: takes the mean (default) - ``'sum'``: takes the sum - ``'none'``: no reduction will be applied Return: Tensor containing dice score Example: >>> pred = torch.tensor([[0.85, 0.05, 0.05, 0.05], ... [0.05, 0.85, 0.05, 0.05], ... [0.05, 0.05, 0.85, 0.05], ... [0.05, 0.05, 0.05, 0.85]]) >>> target = torch.tensor([0, 1, 3, 2]) >>> dice_score(pred, target) tensor(0.3333) """ num_classes = pred.shape[1] bg = (1 - int(bool(bg))) scores = torch.zeros(num_classes - bg, device=pred.device, dtype=torch.float32) for i in range(bg, num_classes): if not (target == i).any(): # no foreground class scores[i - bg] += no_fg_score continue tp, fp, tn, fn, sup = stat_scores(pred=pred, target=target, class_index=i) denom = (2 * tp + fp + fn).to(torch.float) # nan result score_cls = (2 * tp).to(torch.float) / denom if torch.is_nonzero(denom) else nan_score scores[i - bg] += score_cls return reduce(scores, reduction=reduction) def iou( pred: torch.Tensor, target: torch.Tensor, ignore_index: Optional[int] = None, absent_score: float = 0.0, num_classes: Optional[int] = None, reduction: str = 'elementwise_mean', ) -> torch.Tensor: """ Intersection over union, or Jaccard index calculation. Args: pred: Tensor containing integer predictions, with shape [N, d1, d2, ...] target: Tensor containing integer targets, with shape [N, d1, d2, ...] ignore_index: optional int specifying a target class to ignore. If given, this class index does not contribute to the returned score, regardless of reduction method. Has no effect if given an int that is not in the range [0, num_classes-1], where num_classes is either given or derived from pred and target. By default, no index is ignored, and all classes are used. absent_score: score to use for an individual class, if no instances of the class index were present in `pred` AND no instances of the class index were present in `target`. For example, if we have 3 classes, [0, 0] for `pred`, and [0, 2] for `target`, then class 1 would be assigned the `absent_score`. Default is 0.0. num_classes: Optionally specify the number of classes reduction: a method to reduce metric score over labels. - ``'elementwise_mean'``: takes the mean (default) - ``'sum'``: takes the sum - ``'none'``: no reduction will be applied Return: IoU score : Tensor containing single value if reduction is 'elementwise_mean', or number of classes if reduction is 'none' Example: >>> target = torch.randint(0, 2, (10, 25, 25)) >>> pred = torch.tensor(target) >>> pred[2:5, 7:13, 9:15] = 1 - pred[2:5, 7:13, 9:15] >>> iou(pred, target) tensor(0.9660) """ if pred.size() != target.size(): raise ValueError(f"'pred' shape ({pred.size()}) must equal 'target' shape ({target.size()})") if not torch.allclose(pred.float(), pred.int().float()): raise ValueError("'pred' must contain integer targets.") num_classes = get_num_classes(pred=pred, target=target, num_classes=num_classes) tps, fps, tns, fns, sups = stat_scores_multiple_classes(pred, target, num_classes) scores = torch.zeros(num_classes, device=pred.device, dtype=torch.float32) for class_idx in range(num_classes): if class_idx == ignore_index: continue tp = tps[class_idx] fp = fps[class_idx] fn = fns[class_idx] sup = sups[class_idx] # If this class is absent in the target (no support) AND absent in the pred (no true or false # positives), then use the absent_score for this class. if sup + tp + fp == 0: scores[class_idx] = absent_score continue denom = tp + fp + fn # Note that we do not need to worry about division-by-zero here since we know (sup + tp + fp != 0) from above, # which means ((tp+fn) + tp + fp != 0), which means (2tp + fp + fn != 0). Since all vars are non-negative, we # can conclude (tp + fp + fn > 0), meaning the denominator is non-zero for each class. score = tp.to(torch.float) / denom scores[class_idx] = score # Remove the ignored class index from the scores. if ignore_index is not None and ignore_index >= 0 and ignore_index < num_classes: scores = torch.cat([ scores[:ignore_index], scores[ignore_index + 1:], ]) return reduce(scores, reduction=reduction)

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from functools import wraps from typing import Callable, Optional, Sequence, Tuple import torch from torch.nn import functional as F from pytorch_lightning.metrics.utils import to_categorical, get_num_classes, reduce, class_reduce from pytorch_lightning.utilities import rank_zero_warn def stat_scores( pred: torch.Tensor, target: torch.Tensor, class_index: int, argmax_dim: int = 1, ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: if pred.ndim == target.ndim + 1: pred = to_categorical(pred, argmax_dim=argmax_dim) tp = ((pred == class_index) * (target == class_index)).to(torch.long).sum() fp = ((pred == class_index) * (target != class_index)).to(torch.long).sum() tn = ((pred != class_index) * (target != class_index)).to(torch.long).sum() fn = ((pred != class_index) * (target == class_index)).to(torch.long).sum() sup = (target == class_index).to(torch.long).sum() return tp, fp, tn, fn, sup def stat_scores_multiple_classes( pred: torch.Tensor, target: torch.Tensor, num_classes: Optional[int] = None, argmax_dim: int = 1, reduction: str = 'none', ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: if pred.ndim == target.ndim + 1: pred = to_categorical(pred, argmax_dim=argmax_dim) num_classes = get_num_classes(pred=pred, target=target, num_classes=num_classes) if pred.dtype != torch.bool: pred = pred.clamp_max(max=num_classes) if target.dtype != torch.bool: target = target.clamp_max(max=num_classes) possible_reductions = ('none', 'sum', 'elementwise_mean') if reduction not in possible_reductions: raise ValueError("reduction type %s not supported" % reduction) if reduction == 'none': pred = pred.view((-1, )).long() target = target.view((-1, )).long() tps = torch.zeros((num_classes + 1,), device=pred.device) fps = torch.zeros((num_classes + 1,), device=pred.device) tns = torch.zeros((num_classes + 1,), device=pred.device) fns = torch.zeros((num_classes + 1,), device=pred.device) sups = torch.zeros((num_classes + 1,), device=pred.device) match_true = (pred == target).float() match_false = 1 - match_true tps.scatter_add_(0, pred, match_true) fps.scatter_add_(0, pred, match_false) fns.scatter_add_(0, target, match_false) tns = pred.size(0) - (tps + fps + fns) sups.scatter_add_(0, target, torch.ones_like(match_true)) tps = tps[:num_classes] fps = fps[:num_classes] tns = tns[:num_classes] fns = fns[:num_classes] sups = sups[:num_classes] elif reduction == 'sum' or reduction == 'elementwise_mean': count_match_true = (pred == target).sum().float() oob_tp, oob_fp, oob_tn, oob_fn, oob_sup = stat_scores(pred, target, num_classes, argmax_dim) tps = count_match_true - oob_tp fps = pred.nelement() - count_match_true - oob_fp fns = pred.nelement() - count_match_true - oob_fn tns = pred.nelement() * (num_classes + 1) - (tps + fps + fns + oob_tn) sups = pred.nelement() - oob_sup.float() if reduction == 'elementwise_mean': tps /= num_classes fps /= num_classes fns /= num_classes tns /= num_classes sups /= num_classes return tps.float(), fps.float(), tns.float(), fns.float(), sups.float() def accuracy( pred: torch.Tensor, target: torch.Tensor, num_classes: Optional[int] = None, class_reduction: str = 'micro', return_state: bool = False ) -> torch.Tensor: tps, fps, tns, fns, sups = stat_scores_multiple_classes( pred=pred, target=target, num_classes=num_classes) if return_state: return {'tps': tps, 'sups': sups} return class_reduce(tps, sups, sups, class_reduction=class_reduction) def _confmat_normalize(cm): cm = cm / cm.sum(-1, keepdim=True) nan_elements = cm[torch.isnan(cm)].nelement() if nan_elements != 0: cm[torch.isnan(cm)] = 0 rank_zero_warn(f'{nan_elements} nan values found in confusion matrix have been replaced with zeros.') return cm def precision_recall( pred: torch.Tensor, target: torch.Tensor, num_classes: Optional[int] = None, class_reduction: str = 'micro', return_support: bool = False, return_state: bool = False ) -> Tuple[torch.Tensor, torch.Tensor]: tps, fps, tns, fns, sups = stat_scores_multiple_classes(pred=pred, target=target, num_classes=num_classes) precision = class_reduce(tps, tps + fps, sups, class_reduction=class_reduction) recall = class_reduce(tps, tps + fns, sups, class_reduction=class_reduction) if return_state: return {'tps': tps, 'fps': fps, 'fns': fns, 'sups': sups} if return_support: return precision, recall, sups return precision, recall def precision( pred: torch.Tensor, target: torch.Tensor, num_classes: Optional[int] = None, class_reduction: str = 'micro', ) -> torch.Tensor: return precision_recall(pred=pred, target=target, num_classes=num_classes, class_reduction=class_reduction)[0] def recall( pred: torch.Tensor, target: torch.Tensor, num_classes: Optional[int] = None, class_reduction: str = 'micro', ) -> torch.Tensor: return precision_recall(pred=pred, target=target, num_classes=num_classes, class_reduction=class_reduction)[1] def _binary_clf_curve( pred: torch.Tensor, target: torch.Tensor, sample_weight: Optional[Sequence] = None, pos_label: int = 1., ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: if sample_weight is not None and not isinstance(sample_weight, torch.Tensor): sample_weight = torch.tensor(sample_weight, device=pred.device, dtype=torch.float) if pred.ndim > target.ndim: pred = pred[:, 0] desc_score_indices = torch.argsort(pred, descending=True) pred = pred[desc_score_indices] target = target[desc_score_indices] if sample_weight is not None: weight = sample_weight[desc_score_indices] else: weight = 1. distinct_value_indices = torch.where(pred[1:] - pred[:-1])[0] threshold_idxs = F.pad(distinct_value_indices, (0, 1), value=target.size(0) - 1) target = (target == pos_label).to(torch.long) tps = torch.cumsum(target * weight, dim=0)[threshold_idxs] if sample_weight is not None: fps = torch.cumsum((1 - target) * weight, dim=0)[threshold_idxs] else: fps = 1 + threshold_idxs - tps return fps, tps, pred[threshold_idxs] def __roc( pred: torch.Tensor, target: torch.Tensor, sample_weight: Optional[Sequence] = None, pos_label: int = 1., ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: fps, tps, thresholds = _binary_clf_curve(pred=pred, target=target, sample_weight=sample_weight, pos_label=pos_label) tps = torch.cat([torch.zeros(1, dtype=tps.dtype, device=tps.device), tps]) fps = torch.cat([torch.zeros(1, dtype=fps.dtype, device=fps.device), fps]) thresholds = torch.cat([thresholds[0][None] + 1, thresholds]) if fps[-1] <= 0: raise ValueError("No negative samples in targets, false positive value should be meaningless") fpr = fps / fps[-1] if tps[-1] <= 0: raise ValueError("No positive samples in targets, true positive value should be meaningless") tpr = tps / tps[-1] return fpr, tpr, thresholds def __multiclass_roc( pred: torch.Tensor, target: torch.Tensor, sample_weight: Optional[Sequence] = None, num_classes: Optional[int] = None, ) -> Tuple[Tuple[torch.Tensor, torch.Tensor, torch.Tensor]]: num_classes = get_num_classes(pred, target, num_classes) class_roc_vals = [] for c in range(num_classes): pred_c = pred[:, c] class_roc_vals.append(__roc(pred=pred_c, target=target, sample_weight=sample_weight, pos_label=c)) return tuple(class_roc_vals) def auc( x: torch.Tensor, y: torch.Tensor, ) -> torch.Tensor: dx = x[1:] - x[:-1] if (dx < 0).any(): if (dx <= 0).all(): direction = -1. else: raise ValueError(f"The 'x' array is neither increasing or decreasing: {x}. Reorder is not supported.") else: direction = 1. return direction * torch.trapz(y, x) def auc_decorator() -> Callable: def wrapper(func_to_decorate: Callable) -> Callable: @wraps(func_to_decorate) def new_func(*args, **kwargs) -> torch.Tensor: x, y = func_to_decorate(*args, **kwargs)[:2] return auc(x, y) return new_func return wrapper def multiclass_auc_decorator() -> Callable: def wrapper(func_to_decorate: Callable) -> Callable: @wraps(func_to_decorate) def new_func(*args, **kwargs) -> torch.Tensor: results = [] for class_result in func_to_decorate(*args, **kwargs): x, y = class_result[:2] results.append(auc(x, y)) return torch.stack(results) return new_func return wrapper def auroc( pred: torch.Tensor, target: torch.Tensor, sample_weight: Optional[Sequence] = None, pos_label: int = 1., ) -> torch.Tensor: if any(target > 1): raise ValueError('AUROC metric is meant for binary classification, but' ' target tensor contains value different from 0 and 1.' ' Use `multiclass_auroc` for multi class classification.') @auc_decorator() def _auroc(pred, target, sample_weight, pos_label): return __roc(pred, target, sample_weight, pos_label) return _auroc(pred=pred, target=target, sample_weight=sample_weight, pos_label=pos_label) def multiclass_auroc( pred: torch.Tensor, target: torch.Tensor, sample_weight: Optional[Sequence] = None, num_classes: Optional[int] = None, ) -> torch.Tensor: if not torch.allclose(pred.sum(dim=1), torch.tensor(1.0)): raise ValueError( "Multiclass AUROC metric expects the target scores to be" " probabilities, i.e. they should sum up to 1.0 over classes") if torch.unique(target).size(0) != pred.size(1): raise ValueError( f"Number of classes found in in 'target' ({torch.unique(target).size(0)})" f" does not equal the number of columns in 'pred' ({pred.size(1)})." " Multiclass AUROC is not defined when all of the classes do not" " occur in the target labels.") if num_classes is not None and num_classes != pred.size(1): raise ValueError( f"Number of classes deduced from 'pred' ({pred.size(1)}) does not equal" f" the number of classes passed in 'num_classes' ({num_classes}).") @multiclass_auc_decorator() def _multiclass_auroc(pred, target, sample_weight, num_classes): return __multiclass_roc(pred, target, sample_weight, num_classes) class_aurocs = _multiclass_auroc(pred=pred, target=target, sample_weight=sample_weight, num_classes=num_classes) return torch.mean(class_aurocs) def dice_score( pred: torch.Tensor, target: torch.Tensor, bg: bool = False, nan_score: float = 0.0, no_fg_score: float = 0.0, reduction: str = 'elementwise_mean', ) -> torch.Tensor: num_classes = pred.shape[1] bg = (1 - int(bool(bg))) scores = torch.zeros(num_classes - bg, device=pred.device, dtype=torch.float32) for i in range(bg, num_classes): if not (target == i).any(): scores[i - bg] += no_fg_score continue tp, fp, tn, fn, sup = stat_scores(pred=pred, target=target, class_index=i) denom = (2 * tp + fp + fn).to(torch.float) score_cls = (2 * tp).to(torch.float) / denom if torch.is_nonzero(denom) else nan_score scores[i - bg] += score_cls return reduce(scores, reduction=reduction) def iou( pred: torch.Tensor, target: torch.Tensor, ignore_index: Optional[int] = None, absent_score: float = 0.0, num_classes: Optional[int] = None, reduction: str = 'elementwise_mean', ) -> torch.Tensor: if pred.size() != target.size(): raise ValueError(f"'pred' shape ({pred.size()}) must equal 'target' shape ({target.size()})") if not torch.allclose(pred.float(), pred.int().float()): raise ValueError("'pred' must contain integer targets.") num_classes = get_num_classes(pred=pred, target=target, num_classes=num_classes) tps, fps, tns, fns, sups = stat_scores_multiple_classes(pred, target, num_classes) scores = torch.zeros(num_classes, device=pred.device, dtype=torch.float32) for class_idx in range(num_classes): if class_idx == ignore_index: continue tp = tps[class_idx] fp = fps[class_idx] fn = fns[class_idx] sup = sups[class_idx] if sup + tp + fp == 0: scores[class_idx] = absent_score continue denom = tp + fp + fn score = tp.to(torch.float) / denom scores[class_idx] = score if ignore_index is not None and ignore_index >= 0 and ignore_index < num_classes: scores = torch.cat([ scores[:ignore_index], scores[ignore_index + 1:], ]) return reduce(scores, reduction=reduction)

true

1c43ed7d220ae5c354a0880adcfe135d8c75bc34

533

py

Python

apps/discovery_pyre/setup.py

danieldUKIM/uniflex_wishrem

44ca1cfaafc33a83e856dbf9eaf9c1b83d0a477b

[ "Apache-2.0" ]

null

apps/discovery_pyre/setup.py

danieldUKIM/uniflex_wishrem

44ca1cfaafc33a83e856dbf9eaf9c1b83d0a477b

[ "Apache-2.0" ]

null

apps/discovery_pyre/setup.py

danieldUKIM/uniflex_wishrem

44ca1cfaafc33a83e856dbf9eaf9c1b83d0a477b

[ "Apache-2.0" ]

null

from setuptools import setup, find_packages def readme(): with open('README.md') as f: return f.read() setup( name='uniflex_app_discovery_pyre', version='0.1.0', packages=find_packages(), url='https://github.com/uniflex', license='', author='Piotr Gawlowicz', author_email='gawlowicz@tu-berlin.de', description='UniFlex PYRE Discovery Module', long_description='Implementation of a Dynamic Discovery Module.', keywords='wireless control', install_requires=['pyre>=0.3'], )

24.227273

69

0.679174

from setuptools import setup, find_packages def readme(): with open('README.md') as f: return f.read() setup( name='uniflex_app_discovery_pyre', version='0.1.0', packages=find_packages(), url='https://github.com/uniflex', license='', author='Piotr Gawlowicz', author_email='gawlowicz@tu-berlin.de', description='UniFlex PYRE Discovery Module', long_description='Implementation of a Dynamic Discovery Module.', keywords='wireless control', install_requires=['pyre>=0.3'], )

true

1c43edf8164ff697a3643279f919165a53782629

3,027

py

Python

analysis/ShowerLLH/reco-vs-true-containment.py

jrbourbeau/composition

f8debd81b0467a6094d5ba56a5f0fc6047369d30

[ "MIT" ]

null

analysis/ShowerLLH/reco-vs-true-containment.py

jrbourbeau/composition

f8debd81b0467a6094d5ba56a5f0fc6047369d30

[ "MIT" ]

7

2017-08-29T16:20:04.000Z

2018-06-12T16:58:36.000Z

analysis/ShowerLLH/reco-vs-true-containment.py

jrbourbeau/composition

f8debd81b0467a6094d5ba56a5f0fc6047369d30

[ "MIT" ]

1

2018-04-03T20:56:40.000Z

#!/usr/bin/env python import os import sys import numpy as np import matplotlib.pyplot as plt from matplotlib.colors import LogNorm import argparse import seaborn.apionly as sns import composition.support_functions.paths as paths from composition.support_functions.checkdir import checkdir from composition.analysis.load_sim import load_sim # from effective_area import getEff from ShowerLLH_scripts.analysis.LLH_tools import * # from LLH_tools import * # from zfix import zfix def histogram_2D(x, y, bins, log_counts=False, **opts): h, xedges, yedges = np.histogram2d(x, y, bins=bins, normed=False) h = np.rot90(h) h = np.flipud(h) h = np.ma.masked_where(h == 0, h) if log_counts: h = np.log10(h) extent = [yedges[0], yedges[-1], xedges[0], xedges[-1]] colormap = 'viridis' plt.imshow(h, extent=extent, origin='lower', interpolation='none', cmap=colormap) # plt.xlabel('$\log_{10}(E_\mathrm{MC}/\mathrm{GeV})$') # plt.ylabel('$\log_{10}(E_{\mathrm{ML}}/\mathrm{GeV})$') # plt.title(r'ShowerLLH - IT73 - {} LLH bins'.format(opts['bintype'])) # plt.xlim([5, 9.5]) # plt.ylim([5, 9.5]) # cb = plt.colorbar( # label='$\log_{10}{P(E_{\mathrm{ML}}|E_{\mathrm{MC}})}$') # plt.plot([0, 10], [0, 10], linestyle='--', color='k') # outfile = opts['outdir'] + '/' + \ # 'MLenergy_vs_MCenergy_{}.png'.format(opts['bintype']) # plt.savefig(outfile) # plt.close() if __name__ == "__main__": # Global variables setup for path names mypaths = paths.Paths() p = argparse.ArgumentParser( description='Creates performance plots for ShowerLLH') p.add_argument('-c', '--config', dest='config', default='IT73', choices=['IT73', 'IT81'], help='Detector configuration') p.add_argument('-o', '--outdir', dest='outdir', default='/home/jbourbeau/public_html/figures/composition/ShowerLLH', help='Output directory') p.add_argument('-b', '--bintype', dest='bintype', default='logdist', choices=['standard', 'nozenith', 'logdist'], help='Option for a variety of preset bin values') p.add_argument('-n', '--numbins', dest='numbins', type=float, default=30, help='Number of energy bins') args = p.parse_args() checkdir(args.outdir + '/') opts = vars(args).copy() # df = load_sim() df, cut_dict = load_sim(return_cut_dict=True) selection_mask = np.array([True] * len(df)) standard_cut_keys = ['reco_exists', 'MC_zenith', 'IceTopMaxSignalInEdge', 'IceTopMaxSignal'] for key in standard_cut_keys: selection_mask *= cut_dict[key] print('n_events before cuts = {}'.format(len(df))) df = df[selection_mask] print('n_events after cuts = {}'.format(len(df))) MC_IT_containment = df.IceTop_FractionContainment reco_IT_containment = df.reco_IT_containment

36.914634

87

0.620747

import os import sys import numpy as np import matplotlib.pyplot as plt from matplotlib.colors import LogNorm import argparse import seaborn.apionly as sns import composition.support_functions.paths as paths from composition.support_functions.checkdir import checkdir from composition.analysis.load_sim import load_sim from ShowerLLH_scripts.analysis.LLH_tools import * def histogram_2D(x, y, bins, log_counts=False, **opts): h, xedges, yedges = np.histogram2d(x, y, bins=bins, normed=False) h = np.rot90(h) h = np.flipud(h) h = np.ma.masked_where(h == 0, h) if log_counts: h = np.log10(h) extent = [yedges[0], yedges[-1], xedges[0], xedges[-1]] colormap = 'viridis' plt.imshow(h, extent=extent, origin='lower', interpolation='none', cmap=colormap) if __name__ == "__main__": mypaths = paths.Paths() p = argparse.ArgumentParser( description='Creates performance plots for ShowerLLH') p.add_argument('-c', '--config', dest='config', default='IT73', choices=['IT73', 'IT81'], help='Detector configuration') p.add_argument('-o', '--outdir', dest='outdir', default='/home/jbourbeau/public_html/figures/composition/ShowerLLH', help='Output directory') p.add_argument('-b', '--bintype', dest='bintype', default='logdist', choices=['standard', 'nozenith', 'logdist'], help='Option for a variety of preset bin values') p.add_argument('-n', '--numbins', dest='numbins', type=float, default=30, help='Number of energy bins') args = p.parse_args() checkdir(args.outdir + '/') opts = vars(args).copy() df, cut_dict = load_sim(return_cut_dict=True) selection_mask = np.array([True] * len(df)) standard_cut_keys = ['reco_exists', 'MC_zenith', 'IceTopMaxSignalInEdge', 'IceTopMaxSignal'] for key in standard_cut_keys: selection_mask *= cut_dict[key] print('n_events before cuts = {}'.format(len(df))) df = df[selection_mask] print('n_events after cuts = {}'.format(len(df))) MC_IT_containment = df.IceTop_FractionContainment reco_IT_containment = df.reco_IT_containment

true

1c43ef84bcf1442ec9423ec76142e69ad5abe1c0

8,496

py

Python

moto/awslambda/responses.py

kitagawa-hr/moto

97408552a323af27d9b755e5456888c496a3739d

[ "Apache-2.0" ]

1

2019-07-09T17:53:48.000Z

moto/awslambda/responses.py

kitagawa-hr/moto

97408552a323af27d9b755e5456888c496a3739d

[ "Apache-2.0" ]

null

moto/awslambda/responses.py

kitagawa-hr/moto

97408552a323af27d9b755e5456888c496a3739d

[ "Apache-2.0" ]

1

2019-03-22T16:06:53.000Z

from __future__ import unicode_literals import json try: from urllib import unquote except ImportError: from urllib.parse import unquote from moto.core.utils import amz_crc32, amzn_request_id, path_url from moto.core.responses import BaseResponse from .models import lambda_backends class LambdaResponse(BaseResponse): @property def json_body(self): """ :return: JSON :rtype: dict """ return json.loads(self.body) @property def lambda_backend(self): """ Get backend :return: Lambda Backend :rtype: moto.awslambda.models.LambdaBackend """ return lambda_backends[self.region] def root(self, request, full_url, headers): self.setup_class(request, full_url, headers) if request.method == 'GET': return self._list_functions(request, full_url, headers) elif request.method == 'POST': return self._create_function(request, full_url, headers) else: raise ValueError("Cannot handle request") def function(self, request, full_url, headers): self.setup_class(request, full_url, headers) if request.method == 'GET': return self._get_function(request, full_url, headers) elif request.method == 'DELETE': return self._delete_function(request, full_url, headers) else: raise ValueError("Cannot handle request") def versions(self, request, full_url, headers): self.setup_class(request, full_url, headers) if request.method == 'GET': # This is ListVersionByFunction path = request.path if hasattr(request, 'path') else path_url(request.url) function_name = path.split('/')[-2] return self._list_versions_by_function(function_name) elif request.method == 'POST': return self._publish_function(request, full_url, headers) else: raise ValueError("Cannot handle request") @amz_crc32 @amzn_request_id def invoke(self, request, full_url, headers): self.setup_class(request, full_url, headers) if request.method == 'POST': return self._invoke(request, full_url) else: raise ValueError("Cannot handle request") @amz_crc32 @amzn_request_id def invoke_async(self, request, full_url, headers): self.setup_class(request, full_url, headers) if request.method == 'POST': return self._invoke_async(request, full_url) else: raise ValueError("Cannot handle request") def tag(self, request, full_url, headers): self.setup_class(request, full_url, headers) if request.method == 'GET': return self._list_tags(request, full_url) elif request.method == 'POST': return self._tag_resource(request, full_url) elif request.method == 'DELETE': return self._untag_resource(request, full_url) else: raise ValueError("Cannot handle {0} request".format(request.method)) def policy(self, request, full_url, headers): if request.method == 'GET': return self._get_policy(request, full_url, headers) if request.method == 'POST': return self._add_policy(request, full_url, headers) def _add_policy(self, request, full_url, headers): path = request.path if hasattr(request, 'path') else path_url(request.url) function_name = path.split('/')[-2] if self.lambda_backend.get_function(function_name): policy = request.body.decode('utf8') self.lambda_backend.add_policy(function_name, policy) return 200, {}, json.dumps(dict(Statement=policy)) else: return 404, {}, "{}" def _get_policy(self, request, full_url, headers): path = request.path if hasattr(request, 'path') else path_url(request.url) function_name = path.split('/')[-2] if self.lambda_backend.get_function(function_name): lambda_function = self.lambda_backend.get_function(function_name) return 200, {}, json.dumps(dict(Policy="{\"Statement\":[" + lambda_function.policy + "]}")) else: return 404, {}, "{}" def _invoke(self, request, full_url): response_headers = {} function_name = self.path.rsplit('/', 2)[-2] qualifier = self._get_param('qualifier') fn = self.lambda_backend.get_function(function_name, qualifier) if fn: payload = fn.invoke(self.body, self.headers, response_headers) response_headers['Content-Length'] = str(len(payload)) return 202, response_headers, payload else: return 404, response_headers, "{}" def _invoke_async(self, request, full_url): response_headers = {} function_name = self.path.rsplit('/', 3)[-3] fn = self.lambda_backend.get_function(function_name, None) if fn: payload = fn.invoke(self.body, self.headers, response_headers) response_headers['Content-Length'] = str(len(payload)) return 202, response_headers, payload else: return 404, response_headers, "{}" def _list_functions(self, request, full_url, headers): result = { 'Functions': [] } for fn in self.lambda_backend.list_functions(): json_data = fn.get_configuration() result['Functions'].append(json_data) return 200, {}, json.dumps(result) def _list_versions_by_function(self, function_name): result = { 'Versions': [] } functions = self.lambda_backend.list_versions_by_function(function_name) if functions: for fn in functions: json_data = fn.get_configuration() result['Versions'].append(json_data) return 200, {}, json.dumps(result) def _create_function(self, request, full_url, headers): try: fn = self.lambda_backend.create_function(self.json_body) except ValueError as e: return 400, {}, json.dumps({"Error": {"Code": e.args[0], "Message": e.args[1]}}) else: config = fn.get_configuration() return 201, {}, json.dumps(config) def _publish_function(self, request, full_url, headers): function_name = self.path.rsplit('/', 2)[-2] fn = self.lambda_backend.publish_function(function_name) if fn: config = fn.get_configuration() return 201, {}, json.dumps(config) else: return 404, {}, "{}" def _delete_function(self, request, full_url, headers): function_name = self.path.rsplit('/', 1)[-1] qualifier = self._get_param('Qualifier', None) if self.lambda_backend.delete_function(function_name, qualifier): return 204, {}, "" else: return 404, {}, "{}" def _get_function(self, request, full_url, headers): function_name = self.path.rsplit('/', 1)[-1] qualifier = self._get_param('Qualifier', None) fn = self.lambda_backend.get_function(function_name, qualifier) if fn: code = fn.get_code() return 200, {}, json.dumps(code) else: return 404, {}, "{}" def _get_aws_region(self, full_url): region = self.region_regex.search(full_url) if region: return region.group(1) else: return self.default_region def _list_tags(self, request, full_url): function_arn = unquote(self.path.rsplit('/', 1)[-1]) fn = self.lambda_backend.get_function_by_arn(function_arn) if fn: return 200, {}, json.dumps({'Tags': fn.tags}) else: return 404, {}, "{}" def _tag_resource(self, request, full_url): function_arn = unquote(self.path.rsplit('/', 1)[-1]) if self.lambda_backend.tag_resource(function_arn, self.json_body['Tags']): return 200, {}, "{}" else: return 404, {}, "{}" def _untag_resource(self, request, full_url): function_arn = unquote(self.path.rsplit('/', 1)[-1]) tag_keys = self.querystring['tagKeys'] if self.lambda_backend.untag_resource(function_arn, tag_keys): return 204, {}, "{}" else: return 404, {}, "{}"

34.819672

103

0.608639

from __future__ import unicode_literals import json try: from urllib import unquote except ImportError: from urllib.parse import unquote from moto.core.utils import amz_crc32, amzn_request_id, path_url from moto.core.responses import BaseResponse from .models import lambda_backends class LambdaResponse(BaseResponse): @property def json_body(self): return json.loads(self.body) @property def lambda_backend(self): return lambda_backends[self.region] def root(self, request, full_url, headers): self.setup_class(request, full_url, headers) if request.method == 'GET': return self._list_functions(request, full_url, headers) elif request.method == 'POST': return self._create_function(request, full_url, headers) else: raise ValueError("Cannot handle request") def function(self, request, full_url, headers): self.setup_class(request, full_url, headers) if request.method == 'GET': return self._get_function(request, full_url, headers) elif request.method == 'DELETE': return self._delete_function(request, full_url, headers) else: raise ValueError("Cannot handle request") def versions(self, request, full_url, headers): self.setup_class(request, full_url, headers) if request.method == 'GET': path = request.path if hasattr(request, 'path') else path_url(request.url) function_name = path.split('/')[-2] return self._list_versions_by_function(function_name) elif request.method == 'POST': return self._publish_function(request, full_url, headers) else: raise ValueError("Cannot handle request") @amz_crc32 @amzn_request_id def invoke(self, request, full_url, headers): self.setup_class(request, full_url, headers) if request.method == 'POST': return self._invoke(request, full_url) else: raise ValueError("Cannot handle request") @amz_crc32 @amzn_request_id def invoke_async(self, request, full_url, headers): self.setup_class(request, full_url, headers) if request.method == 'POST': return self._invoke_async(request, full_url) else: raise ValueError("Cannot handle request") def tag(self, request, full_url, headers): self.setup_class(request, full_url, headers) if request.method == 'GET': return self._list_tags(request, full_url) elif request.method == 'POST': return self._tag_resource(request, full_url) elif request.method == 'DELETE': return self._untag_resource(request, full_url) else: raise ValueError("Cannot handle {0} request".format(request.method)) def policy(self, request, full_url, headers): if request.method == 'GET': return self._get_policy(request, full_url, headers) if request.method == 'POST': return self._add_policy(request, full_url, headers) def _add_policy(self, request, full_url, headers): path = request.path if hasattr(request, 'path') else path_url(request.url) function_name = path.split('/')[-2] if self.lambda_backend.get_function(function_name): policy = request.body.decode('utf8') self.lambda_backend.add_policy(function_name, policy) return 200, {}, json.dumps(dict(Statement=policy)) else: return 404, {}, "{}" def _get_policy(self, request, full_url, headers): path = request.path if hasattr(request, 'path') else path_url(request.url) function_name = path.split('/')[-2] if self.lambda_backend.get_function(function_name): lambda_function = self.lambda_backend.get_function(function_name) return 200, {}, json.dumps(dict(Policy="{\"Statement\":[" + lambda_function.policy + "]}")) else: return 404, {}, "{}" def _invoke(self, request, full_url): response_headers = {} function_name = self.path.rsplit('/', 2)[-2] qualifier = self._get_param('qualifier') fn = self.lambda_backend.get_function(function_name, qualifier) if fn: payload = fn.invoke(self.body, self.headers, response_headers) response_headers['Content-Length'] = str(len(payload)) return 202, response_headers, payload else: return 404, response_headers, "{}" def _invoke_async(self, request, full_url): response_headers = {} function_name = self.path.rsplit('/', 3)[-3] fn = self.lambda_backend.get_function(function_name, None) if fn: payload = fn.invoke(self.body, self.headers, response_headers) response_headers['Content-Length'] = str(len(payload)) return 202, response_headers, payload else: return 404, response_headers, "{}" def _list_functions(self, request, full_url, headers): result = { 'Functions': [] } for fn in self.lambda_backend.list_functions(): json_data = fn.get_configuration() result['Functions'].append(json_data) return 200, {}, json.dumps(result) def _list_versions_by_function(self, function_name): result = { 'Versions': [] } functions = self.lambda_backend.list_versions_by_function(function_name) if functions: for fn in functions: json_data = fn.get_configuration() result['Versions'].append(json_data) return 200, {}, json.dumps(result) def _create_function(self, request, full_url, headers): try: fn = self.lambda_backend.create_function(self.json_body) except ValueError as e: return 400, {}, json.dumps({"Error": {"Code": e.args[0], "Message": e.args[1]}}) else: config = fn.get_configuration() return 201, {}, json.dumps(config) def _publish_function(self, request, full_url, headers): function_name = self.path.rsplit('/', 2)[-2] fn = self.lambda_backend.publish_function(function_name) if fn: config = fn.get_configuration() return 201, {}, json.dumps(config) else: return 404, {}, "{}" def _delete_function(self, request, full_url, headers): function_name = self.path.rsplit('/', 1)[-1] qualifier = self._get_param('Qualifier', None) if self.lambda_backend.delete_function(function_name, qualifier): return 204, {}, "" else: return 404, {}, "{}" def _get_function(self, request, full_url, headers): function_name = self.path.rsplit('/', 1)[-1] qualifier = self._get_param('Qualifier', None) fn = self.lambda_backend.get_function(function_name, qualifier) if fn: code = fn.get_code() return 200, {}, json.dumps(code) else: return 404, {}, "{}" def _get_aws_region(self, full_url): region = self.region_regex.search(full_url) if region: return region.group(1) else: return self.default_region def _list_tags(self, request, full_url): function_arn = unquote(self.path.rsplit('/', 1)[-1]) fn = self.lambda_backend.get_function_by_arn(function_arn) if fn: return 200, {}, json.dumps({'Tags': fn.tags}) else: return 404, {}, "{}" def _tag_resource(self, request, full_url): function_arn = unquote(self.path.rsplit('/', 1)[-1]) if self.lambda_backend.tag_resource(function_arn, self.json_body['Tags']): return 200, {}, "{}" else: return 404, {}, "{}" def _untag_resource(self, request, full_url): function_arn = unquote(self.path.rsplit('/', 1)[-1]) tag_keys = self.querystring['tagKeys'] if self.lambda_backend.untag_resource(function_arn, tag_keys): return 204, {}, "{}" else: return 404, {}, "{}"

true

1c43efd4690d44c896278c222e4064eae7a1c463

766

py

Python

chalicelib/filter.py

uchimanajet7/reacjilator-chalice

338daf544432f669f9bd6e78cf91d4363d6b914f

[ "MIT" ]

null

chalicelib/filter.py

uchimanajet7/reacjilator-chalice

338daf544432f669f9bd6e78cf91d4363d6b914f

[ "MIT" ]

1

2017-12-17T09:35:24.000Z

2017-12-18T01:26:54.000Z

chalicelib/filter.py

uchimanajet7/reacjilator-chalice

338daf544432f669f9bd6e78cf91d4363d6b914f

[ "MIT" ]

null

# List of channels you want to translate. import os import json class Filter: def __init__(self): self.dict_filter = self.__open_json_file__('filter.json') def __open_json_file__(self, file_name): try: dir_name = os.path.dirname(os.path.abspath(__file__)) path = os.path.join(dir_name, file_name) with open(path) as f: return json.load(f) except: return None def is_allowed(self, name): if self.dict_filter is None: # all allowed return True if len(self.dict_filter) == 0: # all allowed return True if self.dict_filter.get(name) is not None: return True return False

23.9375

65

0.571802

import os import json class Filter: def __init__(self): self.dict_filter = self.__open_json_file__('filter.json') def __open_json_file__(self, file_name): try: dir_name = os.path.dirname(os.path.abspath(__file__)) path = os.path.join(dir_name, file_name) with open(path) as f: return json.load(f) except: return None def is_allowed(self, name): if self.dict_filter is None: return True if len(self.dict_filter) == 0: return True if self.dict_filter.get(name) is not None: return True return False

true

1c43f070abcedc58bf17a00a3203fb43ef6b40c7

20

py

Python

sdk/python-sdk/verity_sdk/protocols/v0_7/__init__.py

tw-bc-group/verity-sdk

e932209ab849f04a389bdda0718cd6227187e5cf

[ "Apache-2.0" ]

40

2020-07-09T01:52:31.000Z

2022-02-19T04:01:23.000Z

sdk/python-sdk/verity_sdk/protocols/v0_7/__init__.py

tw-bc-group/verity-sdk

e932209ab849f04a389bdda0718cd6227187e5cf

[ "Apache-2.0" ]

45

2020-06-19T11:00:20.000Z

2022-03-02T14:48:12.000Z

sdk/python-sdk/verity_sdk/protocols/v0_7/__init__.py

tw-bc-group/verity-sdk

e932209ab849f04a389bdda0718cd6227187e5cf

[ "Apache-2.0" ]

37

2020-06-19T10:37:04.000Z

2022-03-15T14:06:40.000Z

"""0.7 Protocols"""

10

19

0.55

true

1c43f09b4f119c9983b09c54d4a22142b88b1195

3,909

py

Python

pong/2_pong_singlecubebouncing.py

CrtomirJuren/pygame-projects

f710f36050bfe3ece866bbda7d570caa1e037d7a

[ "MIT" ]

null

pong/2_pong_singlecubebouncing.py

CrtomirJuren/pygame-projects

f710f36050bfe3ece866bbda7d570caa1e037d7a

[ "MIT" ]

null

pong/2_pong_singlecubebouncing.py

CrtomirJuren/pygame-projects

f710f36050bfe3ece866bbda7d570caa1e037d7a

[ "MIT" ]

null

import sys import math import random import pygame from pygame.locals import * import tkinter as tk from tkinter import messagebox clock = pygame.time.Clock() WHITE = (255,255,255) BLACK = (0,0,0) RED = (255,0,0) #--------------------------draw grid fuction---------------------------- def drawGrid(w,rows,surface): """ This function draws a square grid on main display """ #distance between grid lines sizeBtwn = w // rows x = 0 y = 0 #create grid by drawing lines for l in range(rows): x = x + sizeBtwn y = y + sizeBtwn #vertical lines pygame.draw.line(surface, WHITE, (x,0), (x,w)) #horizontal lines pygame.draw.line(surface, WHITE, (0,y), (w,y)) #-------------------------cube object----------------------------------------- class cube(object): """ class to create a single grid cube, that has position and movement """ rows = 20 #set number of rows w = 500 #set pixel screen width def __init__(self, start, dirnx, dirny, color = WHITE): self.pos = start #touple (x,y) self.dirnx = 0 self.dirny = 0 self.color = color #touple(r,g,b) def set_direction(self,dirnx,dirny): self.dirnx = dirnx self.dirny = dirny def move(self): """ move cube, by adding new direction to previous position """ self.pos = (self.pos[0]+self.dirnx, self.pos[1]+self.dirny) def draw(self,surface): """ drawing: convert x,y grid position to pixel position """ dis = self.w // self.rows #distance between x and y values #variables for easy coding i = self.pos[0] # row j = self.pos[1] # column #draw just a little bit less, so we draw inside of the square. and we dont cover grid. pygame.draw.rect(surface, self.color, (i*dis+1,j*dis+1,dis-2,dis-2 )) #----------------------------------------------------------------------------------- def redrawWindow(surface): global rows, width #background surface.fill(BLACK) #draw grid drawGrid(width, rows, surface) #draw ball ball.draw(surface) #update display pygame.display.update() #--------------------------------------------------------------------------------------- def key_events(): for event in pygame.event.get(): if event.type == QUIT or (event.type == KEYDOWN and event.key == K_ESCAPE): #pygame.quit() #sys.exit() return True #in pong we move in only y directions keys = pygame.key.get_pressed() for key in keys: if keys[pygame.K_UP]: self.dirny = -1 self.turns[self.head.pos[:]] = [self.dirnx, self.dirny] elif keys[pygame.K_DOWN]: self.dirny = 1 self.turns[self.head.pos[:]] = [self.dirnx, self.dirny] return False #--------------------------------------------------------------------------------------- def main(): global width, rows, ball #---------------game initialization--------------------------- #create game display width = 500 rows = 20 #create game objects win = pygame.display.set_mode((width, width)) #square display ball = cube((10,1),0,0,WHITE) ball.set_direction(1,1) #set initial ball movement direction FPScount = 0 #-----------------------continuous game loop------------- GameOver = False while not GameOver: #pygame.time.delay(50) clock.tick(10) #game max speed 10 FPS GameOver = key_events() #update ball position ball.move() #check next direction------------------------------- #CONSTRAINTS X if ball.pos[0] <= 0 or ball.pos[0] >= rows-1: ball.dirnx = -ball.dirnx #CONSTRAINTS Y if ball.pos[1] <= 0 or ball.pos[1] >= rows-1: ball.dirny = -ball.dirny #------------------------------------------------- #if we are moving in right direction #print(f'rows:{rows} ball.pos[0]:{ball.pos[0]} ball.pos[1]:{ball.pos[1]}') #FPScount += 1 #print(FPScount) redrawWindow(win) #--------------------------------------------------------------------------------------- #--------------------------------------------------------------------------------------- main() pygame.quit() sys.exit()

27.723404

88

0.553083

import sys import math import random import pygame from pygame.locals import * import tkinter as tk from tkinter import messagebox clock = pygame.time.Clock() WHITE = (255,255,255) BLACK = (0,0,0) RED = (255,0,0) def drawGrid(w,rows,surface): sizeBtwn = w // rows x = 0 y = 0 for l in range(rows): x = x + sizeBtwn y = y + sizeBtwn pygame.draw.line(surface, WHITE, (x,0), (x,w)) pygame.draw.line(surface, WHITE, (0,y), (w,y)) class cube(object): rows = 20 w = 500 def __init__(self, start, dirnx, dirny, color = WHITE): self.pos = start self.dirnx = 0 self.dirny = 0 self.color = color def set_direction(self,dirnx,dirny): self.dirnx = dirnx self.dirny = dirny def move(self): self.pos = (self.pos[0]+self.dirnx, self.pos[1]+self.dirny) def draw(self,surface): dis = self.w // self.rows i = self.pos[0] j = self.pos[1] pygame.draw.rect(surface, self.color, (i*dis+1,j*dis+1,dis-2,dis-2 )) def redrawWindow(surface): global rows, width surface.fill(BLACK) drawGrid(width, rows, surface) ball.draw(surface) pygame.display.update() def key_events(): for event in pygame.event.get(): if event.type == QUIT or (event.type == KEYDOWN and event.key == K_ESCAPE): return True keys = pygame.key.get_pressed() for key in keys: if keys[pygame.K_UP]: self.dirny = -1 self.turns[self.head.pos[:]] = [self.dirnx, self.dirny] elif keys[pygame.K_DOWN]: self.dirny = 1 self.turns[self.head.pos[:]] = [self.dirnx, self.dirny] return False def main(): global width, rows, ball width = 500 rows = 20 win = pygame.display.set_mode((width, width)) ball = cube((10,1),0,0,WHITE) ball.set_direction(1,1) FPScount = 0 GameOver = False while not GameOver: clock.tick(10) GameOver = key_events() ball.move() if ball.pos[0] <= 0 or ball.pos[0] >= rows-1: ball.dirnx = -ball.dirnx if ball.pos[1] <= 0 or ball.pos[1] >= rows-1: ball.dirny = -ball.dirny redrawWindow(win) main() pygame.quit() sys.exit()

true

1c43f0cb68057fe546f78196c9cc49dd1da135d3

6,696

py

Python

source/minefield.py

BastiHz/Minefields

46bb66cb3a809f6d21d7811e9a7df214be044fbd

[ "MIT" ]

1

2021-02-22T15:32:31.000Z

source/minefield.py

BastiHz/Minefields

46bb66cb3a809f6d21d7811e9a7df214be044fbd

[ "MIT" ]

null

source/minefield.py

BastiHz/Minefields

46bb66cb3a809f6d21d7811e9a7df214be044fbd

[ "MIT" ]

null

import random import pygame as pg import prepare class Minefield: def __init__(self, width, height, number_of_mines): self.width = width self.height = height self.num_mines = number_of_mines self.tiles = prepare.minefield_tiles self.tile_size = prepare.MINEFIELD_TILE_SIZE self.surface = pg.Surface((width * self.tile_size, height * self.tile_size)).convert() self.pos = (0, 0) # the pos of the minefield surface in the window self.mouseover_tile = None self.game_done = False self.mines_remaining = self.num_mines self.mines_remaining_changed = True self.end_message = None self.grid = set(((x, y) for x in range(self.width) for y in range(self.height))) self.covered = self.grid.copy() self.mines = set(random.sample(self.grid, self.num_mines)) self.neighbors = {} for pos in self.grid: neighbor_list = [] for x, y in ((-1, -1), (-1, 0), (-1, 1), (0, -1), (0, 1), (1, -1), (1, 0), (1, 1)): neighbor = (pos[0] + x, pos[1] + y) if neighbor in self.grid: neighbor_list.append(neighbor) self.neighbors[pos] = neighbor_list self.hints = {} for pos in self.grid: if pos not in self.mines: hint = 0 for neighbor in self.neighbors[pos]: if neighbor in self.mines: hint += 1 self.hints[pos] = hint self.flags = set() self.questionmarks = set() self.exploded_mines = set() self.wrong_flags = set() self.refresh_surface() def refresh_surface(self): for pos in self.grid: blit_pos = (pos[0] * self.tile_size, pos[1] * self.tile_size) if pos in self.covered: if pos in self.flags: self.surface.blit(self.tiles["flag"], blit_pos) if pos in self.wrong_flags: self.surface.blit(self.tiles["flag_wrong"], blit_pos) elif pos in self.questionmarks: self.surface.blit(self.tiles["questionmark"], blit_pos) else: self.surface.blit(self.tiles["covered"], blit_pos) elif pos in self.mines: if pos in self.exploded_mines: self.surface.blit(self.tiles["mine_exploded"], blit_pos) else: self.surface.blit(self.tiles["mine"], blit_pos) else: self.surface.blit(self.tiles[self.hints[pos]], blit_pos) def update(self, mouse_pos, left_click, right_click, double_click): if self.game_done: return pos = ((mouse_pos[0] - self.pos[0]) // self.tile_size, (mouse_pos[1] - self.pos[1]) // self.tile_size) self.mouseover_tile = pos if right_click and pos in self.covered: self.set_mark(pos) elif all((left_click, pos in self.covered, pos not in self.flags, pos not in self.questionmarks)): self.uncover(pos) self.check_defeat() self.check_win() self.refresh_surface() elif all((double_click, pos not in self.covered, self.hints.get(pos) != 0)): num_flags = sum((1 for n in self.neighbors[pos] if n in self.flags)) if num_flags == self.hints[pos]: for neighbor in self.neighbors[pos]: if all((neighbor in self.covered, neighbor not in self.flags, neighbor not in self.questionmarks)): self.uncover(neighbor) self.check_defeat() self.check_win() self.refresh_surface() def set_mark(self, pos): if pos in self.flags: self.flags.remove(pos) self.questionmarks.add(pos) elif pos in self.questionmarks: self.questionmarks.remove(pos) else: self.flags.add(pos) self.refresh_surface() self.mines_remaining = self.num_mines - len(self.flags) self.mines_remaining_changed = True def uncover(self, pos): """Uncovers the tile at pos and all its neighbors which are not mines, flags or questionmarks. Uses an iterative flood fill because a recursive approach can exceed the maximum recursion depth. """ tiles_to_uncover = {pos} while tiles_to_uncover: pos = tiles_to_uncover.pop() self.covered.remove(pos) if (pos not in self.mines) and self.hints[pos] == 0: for neighbor in self.neighbors[pos]: if all((neighbor in self.covered, neighbor not in self.flags, neighbor not in self.questionmarks)): tiles_to_uncover.add(neighbor) def check_win(self): if len(self.covered) == self.num_mines and not self.game_done: self.game_done = True self.end_message = "YOU WIN" def check_defeat(self): for pos in self.mines: if pos not in self.covered: self.exploded_mines.add(pos) if self.exploded_mines: self.game_done = True self.end_message = "GAME OVER" for pos in self.covered.copy(): if all((pos in self.mines, pos not in self.flags, pos not in self.questionmarks)): self.covered.remove(pos) elif (pos not in self.mines) and (pos in self.flags): self.wrong_flags.add(pos) def draw(self, surface): surface.blit(self.surface, self.pos) if self.mouseover_tile is not None: blit_pos = (self.mouseover_tile[0] * self.tile_size + self.pos[0], self.mouseover_tile[1] * self.tile_size + self.pos[1]) if self.mouseover_tile in self.flags: surface.blit(self.tiles["flag_highlighted"], blit_pos) elif self.mouseover_tile in self.questionmarks: surface.blit(self.tiles["questionmark_highlighted"], blit_pos) elif self.mouseover_tile in self.covered: surface.blit(self.tiles["covered_highlighted"], blit_pos) self.mouseover_tile = None

39.621302

80

0.538082

import random import pygame as pg import prepare class Minefield: def __init__(self, width, height, number_of_mines): self.width = width self.height = height self.num_mines = number_of_mines self.tiles = prepare.minefield_tiles self.tile_size = prepare.MINEFIELD_TILE_SIZE self.surface = pg.Surface((width * self.tile_size, height * self.tile_size)).convert() self.pos = (0, 0) self.mouseover_tile = None self.game_done = False self.mines_remaining = self.num_mines self.mines_remaining_changed = True self.end_message = None self.grid = set(((x, y) for x in range(self.width) for y in range(self.height))) self.covered = self.grid.copy() self.mines = set(random.sample(self.grid, self.num_mines)) self.neighbors = {} for pos in self.grid: neighbor_list = [] for x, y in ((-1, -1), (-1, 0), (-1, 1), (0, -1), (0, 1), (1, -1), (1, 0), (1, 1)): neighbor = (pos[0] + x, pos[1] + y) if neighbor in self.grid: neighbor_list.append(neighbor) self.neighbors[pos] = neighbor_list self.hints = {} for pos in self.grid: if pos not in self.mines: hint = 0 for neighbor in self.neighbors[pos]: if neighbor in self.mines: hint += 1 self.hints[pos] = hint self.flags = set() self.questionmarks = set() self.exploded_mines = set() self.wrong_flags = set() self.refresh_surface() def refresh_surface(self): for pos in self.grid: blit_pos = (pos[0] * self.tile_size, pos[1] * self.tile_size) if pos in self.covered: if pos in self.flags: self.surface.blit(self.tiles["flag"], blit_pos) if pos in self.wrong_flags: self.surface.blit(self.tiles["flag_wrong"], blit_pos) elif pos in self.questionmarks: self.surface.blit(self.tiles["questionmark"], blit_pos) else: self.surface.blit(self.tiles["covered"], blit_pos) elif pos in self.mines: if pos in self.exploded_mines: self.surface.blit(self.tiles["mine_exploded"], blit_pos) else: self.surface.blit(self.tiles["mine"], blit_pos) else: self.surface.blit(self.tiles[self.hints[pos]], blit_pos) def update(self, mouse_pos, left_click, right_click, double_click): if self.game_done: return pos = ((mouse_pos[0] - self.pos[0]) // self.tile_size, (mouse_pos[1] - self.pos[1]) // self.tile_size) self.mouseover_tile = pos if right_click and pos in self.covered: self.set_mark(pos) elif all((left_click, pos in self.covered, pos not in self.flags, pos not in self.questionmarks)): self.uncover(pos) self.check_defeat() self.check_win() self.refresh_surface() elif all((double_click, pos not in self.covered, self.hints.get(pos) != 0)): num_flags = sum((1 for n in self.neighbors[pos] if n in self.flags)) if num_flags == self.hints[pos]: for neighbor in self.neighbors[pos]: if all((neighbor in self.covered, neighbor not in self.flags, neighbor not in self.questionmarks)): self.uncover(neighbor) self.check_defeat() self.check_win() self.refresh_surface() def set_mark(self, pos): if pos in self.flags: self.flags.remove(pos) self.questionmarks.add(pos) elif pos in self.questionmarks: self.questionmarks.remove(pos) else: self.flags.add(pos) self.refresh_surface() self.mines_remaining = self.num_mines - len(self.flags) self.mines_remaining_changed = True def uncover(self, pos): tiles_to_uncover = {pos} while tiles_to_uncover: pos = tiles_to_uncover.pop() self.covered.remove(pos) if (pos not in self.mines) and self.hints[pos] == 0: for neighbor in self.neighbors[pos]: if all((neighbor in self.covered, neighbor not in self.flags, neighbor not in self.questionmarks)): tiles_to_uncover.add(neighbor) def check_win(self): if len(self.covered) == self.num_mines and not self.game_done: self.game_done = True self.end_message = "YOU WIN" def check_defeat(self): for pos in self.mines: if pos not in self.covered: self.exploded_mines.add(pos) if self.exploded_mines: self.game_done = True self.end_message = "GAME OVER" for pos in self.covered.copy(): if all((pos in self.mines, pos not in self.flags, pos not in self.questionmarks)): self.covered.remove(pos) elif (pos not in self.mines) and (pos in self.flags): self.wrong_flags.add(pos) def draw(self, surface): surface.blit(self.surface, self.pos) if self.mouseover_tile is not None: blit_pos = (self.mouseover_tile[0] * self.tile_size + self.pos[0], self.mouseover_tile[1] * self.tile_size + self.pos[1]) if self.mouseover_tile in self.flags: surface.blit(self.tiles["flag_highlighted"], blit_pos) elif self.mouseover_tile in self.questionmarks: surface.blit(self.tiles["questionmark_highlighted"], blit_pos) elif self.mouseover_tile in self.covered: surface.blit(self.tiles["covered_highlighted"], blit_pos) self.mouseover_tile = None

true

1c43f0d5386fd740efb998b838ef3980fb5d15bf

7,728

py

Python

torchreid/models/squeezenet.py

qw85639229/hardest

ef86536dbbe1089248e34afbbb7bb513f97f58f1

[ "MIT" ]

21

2020-10-13T01:33:31.000Z

2022-01-04T15:58:31.000Z

torchreid/models/squeezenet.py

qw85639229/hardest

ef86536dbbe1089248e34afbbb7bb513f97f58f1

[ "MIT" ]

10

2020-11-18T07:40:22.000Z

2021-10-05T07:58:25.000Z

torchreid/models/squeezenet.py

qw85639229/hardest

ef86536dbbe1089248e34afbbb7bb513f97f58f1

[ "MIT" ]

7

2020-11-19T08:40:27.000Z

2022-02-05T06:24:08.000Z

""" Code source: https://github.com/pytorch/vision """ from __future__ import absolute_import from __future__ import division __all__ = [ 'squeezenet1_0', 'squeezenet1_1', 'squeezenet1_0_fc512' ] from collections import OrderedDict import math import torch import torch.nn as nn from torch.utils import model_zoo from torch.nn import functional as F import torch.nn.init as init import torchvision import torch.utils.model_zoo as model_zoo model_urls = { 'squeezenet1_0': 'https://download.pytorch.org/models/squeezenet1_0-a815701f.pth', 'squeezenet1_1': 'https://download.pytorch.org/models/squeezenet1_1-f364aa15.pth', } class Fire(nn.Module): def __init__(self, inplanes, squeeze_planes, expand1x1_planes, expand3x3_planes): super(Fire, self).__init__() self.inplanes = inplanes self.squeeze = nn.Conv2d(inplanes, squeeze_planes, kernel_size=1) self.squeeze_activation = nn.ReLU(inplace=True) self.expand1x1 = nn.Conv2d(squeeze_planes, expand1x1_planes, kernel_size=1) self.expand1x1_activation = nn.ReLU(inplace=True) self.expand3x3 = nn.Conv2d(squeeze_planes, expand3x3_planes, kernel_size=3, padding=1) self.expand3x3_activation = nn.ReLU(inplace=True) def forward(self, x): x = self.squeeze_activation(self.squeeze(x)) return torch.cat([ self.expand1x1_activation(self.expand1x1(x)), self.expand3x3_activation(self.expand3x3(x)) ], 1) class SqueezeNet(nn.Module): """SqueezeNet. Reference: Iandola et al. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size. arXiv:1602.07360. Public keys: - ``squeezenet1_0``: SqueezeNet (version=1.0). - ``squeezenet1_1``: SqueezeNet (version=1.1). - ``squeezenet1_0_fc512``: SqueezeNet (version=1.0) + FC. """ def __init__(self, num_classes, loss, version=1.0, fc_dims=None, dropout_p=None, **kwargs): super(SqueezeNet, self).__init__() self.loss = loss self.feature_dim = 512 if version not in [1.0, 1.1]: raise ValueError('Unsupported SqueezeNet version {version}:' '1.0 or 1.1 expected'.format(version=version)) if version == 1.0: self.features = nn.Sequential( nn.Conv2d(3, 96, kernel_size=7, stride=2), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), Fire(96, 16, 64, 64), Fire(128, 16, 64, 64), Fire(128, 32, 128, 128), nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), Fire(256, 32, 128, 128), Fire(256, 48, 192, 192), Fire(384, 48, 192, 192), Fire(384, 64, 256, 256), nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), Fire(512, 64, 256, 256), ) else: self.features = nn.Sequential( nn.Conv2d(3, 64, kernel_size=3, stride=2), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), Fire(64, 16, 64, 64), Fire(128, 16, 64, 64), nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), Fire(128, 32, 128, 128), Fire(256, 32, 128, 128), nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), Fire(256, 48, 192, 192), Fire(384, 48, 192, 192), Fire(384, 64, 256, 256), Fire(512, 64, 256, 256), ) self.global_avgpool = nn.AdaptiveAvgPool2d(1) self.fc = self._construct_fc_layer(fc_dims, 512, dropout_p) self.classifier = nn.Linear(self.feature_dim, num_classes) self._init_params() def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None): """Constructs fully connected layer Args: fc_dims (list or tuple): dimensions of fc layers, if None, no fc layers are constructed input_dim (int): input dimension dropout_p (float): dropout probability, if None, dropout is unused """ if fc_dims is None: self.feature_dim = input_dim return None assert isinstance(fc_dims, (list, tuple)), 'fc_dims must be either list or tuple, but got {}'.format(type(fc_dims)) layers = [] for dim in fc_dims: layers.append(nn.Linear(input_dim, dim)) layers.append(nn.BatchNorm1d(dim)) layers.append(nn.ReLU(inplace=True)) if dropout_p is not None: layers.append(nn.Dropout(p=dropout_p)) input_dim = dim self.feature_dim = fc_dims[-1] return nn.Sequential(*layers) def _init_params(self): for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') if m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.BatchNorm2d): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) elif isinstance(m, nn.BatchNorm1d): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) elif isinstance(m, nn.Linear): nn.init.normal_(m.weight, 0, 0.01) if m.bias is not None: nn.init.constant_(m.bias, 0) def forward(self, x): f = self.features(x) v = self.global_avgpool(f) v = v.view(v.size(0), -1) if self.fc is not None: v = self.fc(v) if not self.training: return v y = self.classifier(v) if self.loss == 'softmax': return y elif self.loss == 'triplet': return y, v else: raise KeyError('Unsupported loss: {}'.format(self.loss)) def init_pretrained_weights(model, model_url): """Initializes model with pretrained weights. Layers that don't match with pretrained layers in name or size are kept unchanged. """ pretrain_dict = model_zoo.load_url(model_url, map_location=None) model_dict = model.state_dict() pretrain_dict = {k: v for k, v in pretrain_dict.items() if k in model_dict and model_dict[k].size() == v.size()} model_dict.update(pretrain_dict) model.load_state_dict(model_dict) def squeezenet1_0(num_classes, loss='softmax', pretrained=True, **kwargs): model = SqueezeNet( num_classes, loss, version=1.0, fc_dims=None, dropout_p=None, **kwargs ) if pretrained: init_pretrained_weights(model, model_urls['squeezenet1_0']) return model def squeezenet1_0_fc512(num_classes, loss='softmax', pretrained=True, **kwargs): model = SqueezeNet( num_classes, loss, version=1.0, fc_dims=[512], dropout_p=None, **kwargs ) if pretrained: init_pretrained_weights(model, model_urls['squeezenet1_0']) return model def squeezenet1_1(num_classes, loss='softmax', pretrained=True, **kwargs): model = SqueezeNet( num_classes, loss, version=1.1, fc_dims=None, dropout_p=None, **kwargs ) if pretrained: init_pretrained_weights(model, model_urls['squeezenet1_1']) return model

33.454545

123

0.583075

from __future__ import absolute_import from __future__ import division __all__ = [ 'squeezenet1_0', 'squeezenet1_1', 'squeezenet1_0_fc512' ] from collections import OrderedDict import math import torch import torch.nn as nn from torch.utils import model_zoo from torch.nn import functional as F import torch.nn.init as init import torchvision import torch.utils.model_zoo as model_zoo model_urls = { 'squeezenet1_0': 'https://download.pytorch.org/models/squeezenet1_0-a815701f.pth', 'squeezenet1_1': 'https://download.pytorch.org/models/squeezenet1_1-f364aa15.pth', } class Fire(nn.Module): def __init__(self, inplanes, squeeze_planes, expand1x1_planes, expand3x3_planes): super(Fire, self).__init__() self.inplanes = inplanes self.squeeze = nn.Conv2d(inplanes, squeeze_planes, kernel_size=1) self.squeeze_activation = nn.ReLU(inplace=True) self.expand1x1 = nn.Conv2d(squeeze_planes, expand1x1_planes, kernel_size=1) self.expand1x1_activation = nn.ReLU(inplace=True) self.expand3x3 = nn.Conv2d(squeeze_planes, expand3x3_planes, kernel_size=3, padding=1) self.expand3x3_activation = nn.ReLU(inplace=True) def forward(self, x): x = self.squeeze_activation(self.squeeze(x)) return torch.cat([ self.expand1x1_activation(self.expand1x1(x)), self.expand3x3_activation(self.expand3x3(x)) ], 1) class SqueezeNet(nn.Module): def __init__(self, num_classes, loss, version=1.0, fc_dims=None, dropout_p=None, **kwargs): super(SqueezeNet, self).__init__() self.loss = loss self.feature_dim = 512 if version not in [1.0, 1.1]: raise ValueError('Unsupported SqueezeNet version {version}:' '1.0 or 1.1 expected'.format(version=version)) if version == 1.0: self.features = nn.Sequential( nn.Conv2d(3, 96, kernel_size=7, stride=2), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), Fire(96, 16, 64, 64), Fire(128, 16, 64, 64), Fire(128, 32, 128, 128), nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), Fire(256, 32, 128, 128), Fire(256, 48, 192, 192), Fire(384, 48, 192, 192), Fire(384, 64, 256, 256), nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), Fire(512, 64, 256, 256), ) else: self.features = nn.Sequential( nn.Conv2d(3, 64, kernel_size=3, stride=2), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), Fire(64, 16, 64, 64), Fire(128, 16, 64, 64), nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), Fire(128, 32, 128, 128), Fire(256, 32, 128, 128), nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), Fire(256, 48, 192, 192), Fire(384, 48, 192, 192), Fire(384, 64, 256, 256), Fire(512, 64, 256, 256), ) self.global_avgpool = nn.AdaptiveAvgPool2d(1) self.fc = self._construct_fc_layer(fc_dims, 512, dropout_p) self.classifier = nn.Linear(self.feature_dim, num_classes) self._init_params() def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None): if fc_dims is None: self.feature_dim = input_dim return None assert isinstance(fc_dims, (list, tuple)), 'fc_dims must be either list or tuple, but got {}'.format(type(fc_dims)) layers = [] for dim in fc_dims: layers.append(nn.Linear(input_dim, dim)) layers.append(nn.BatchNorm1d(dim)) layers.append(nn.ReLU(inplace=True)) if dropout_p is not None: layers.append(nn.Dropout(p=dropout_p)) input_dim = dim self.feature_dim = fc_dims[-1] return nn.Sequential(*layers) def _init_params(self): for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') if m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.BatchNorm2d): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) elif isinstance(m, nn.BatchNorm1d): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) elif isinstance(m, nn.Linear): nn.init.normal_(m.weight, 0, 0.01) if m.bias is not None: nn.init.constant_(m.bias, 0) def forward(self, x): f = self.features(x) v = self.global_avgpool(f) v = v.view(v.size(0), -1) if self.fc is not None: v = self.fc(v) if not self.training: return v y = self.classifier(v) if self.loss == 'softmax': return y elif self.loss == 'triplet': return y, v else: raise KeyError('Unsupported loss: {}'.format(self.loss)) def init_pretrained_weights(model, model_url): pretrain_dict = model_zoo.load_url(model_url, map_location=None) model_dict = model.state_dict() pretrain_dict = {k: v for k, v in pretrain_dict.items() if k in model_dict and model_dict[k].size() == v.size()} model_dict.update(pretrain_dict) model.load_state_dict(model_dict) def squeezenet1_0(num_classes, loss='softmax', pretrained=True, **kwargs): model = SqueezeNet( num_classes, loss, version=1.0, fc_dims=None, dropout_p=None, **kwargs ) if pretrained: init_pretrained_weights(model, model_urls['squeezenet1_0']) return model def squeezenet1_0_fc512(num_classes, loss='softmax', pretrained=True, **kwargs): model = SqueezeNet( num_classes, loss, version=1.0, fc_dims=[512], dropout_p=None, **kwargs ) if pretrained: init_pretrained_weights(model, model_urls['squeezenet1_0']) return model def squeezenet1_1(num_classes, loss='softmax', pretrained=True, **kwargs): model = SqueezeNet( num_classes, loss, version=1.1, fc_dims=None, dropout_p=None, **kwargs ) if pretrained: init_pretrained_weights(model, model_urls['squeezenet1_1']) return model

true

1c43f0f37d381ccc50b76c9a1eb0cc96c6d62613

10,065

py

Python

DPDNet/image_to_patch_filter.py

Abdullah-Abuolaim/defocus-deblurring-dual-pixel

21a43e7d12350c62c4038485cdeebc27a078765b

[ "MIT" ]

115

2020-05-01T22:51:14.000Z

2022-03-12T13:18:37.000Z

DPDNet/image_to_patch_filter.py

panpanfei/defocus-deblurring-dual-pixel

8c1b812236d2eb3293b670512ef35e20471e2e48

[ "MIT" ]

14

2020-05-12T03:38:57.000Z

2021-06-01T15:02:04.000Z

DPDNet/image_to_patch_filter.py

Abdullah-Abuolaim/defocus-deblurring-dual-pixel

21a43e7d12350c62c4038485cdeebc27a078765b

[ "MIT" ]

13

2020-06-28T08:25:09.000Z

2022-02-28T16:10:46.000Z

""" This code is used to extract image patches from the training and validation sets as described in the paper. For the training set patches, we discard 30% of the patches that have the lowest sharpness energy. Recall that we don't extract patches for test images because we process full image at test time. Copyright (c) 2020-present, Abdullah Abuolaim This source code is licensed under the license found in the LICENSE file in the root directory of this source tree. Note: this code is the implementation of the "Defocus Deblurring Using Dual- Pixel Data" paper accepted to ECCV 2020. Link to GitHub repository: https://github.com/Abdullah-Abuolaim/defocus-deblurring-dual-pixel Email: abuolaim@eecs.yorku.ca """ import numpy as np import os import cv2 import errno from copy import deepcopy def check_create_directory(path_to_check): if not os.path.exists(path_to_check): try: os.makedirs(path_to_check) except OSError as exc: # Guard against race condition if exc.errno != errno.EEXIST: raise def shapness_measure(img_temp,kernel_size): conv_x = cv2.Sobel(img_temp,cv2.CV_64F,1,0,ksize=kernel_size) conv_y = cv2.Sobel(img_temp,cv2.CV_64F,0,1,ksize=kernel_size) temp_arr_x=deepcopy(conv_x*conv_x) temp_arr_y=deepcopy(conv_y*conv_y) temp_sum_x_y=temp_arr_x+temp_arr_y temp_sum_x_y=np.sqrt(temp_sum_x_y) return np.sum(temp_sum_x_y) def filter_patch_sharpness(patches_src_c_temp, patches_trg_c_temp, patches_src_l_temp, patches_src_r_temp): global patches_src_c, patches_trg_c, patches_src_l, patches_src_r fitnessVal_3=[] fitnessVal_7=[] fitnessVal_11=[] fitnessVal_15=[] num_of_img_patches=len(patches_trg_c_temp) for i in range(num_of_img_patches): fitnessVal_3.append(shapness_measure(cv2.cvtColor(patches_trg_c_temp[i], cv2.COLOR_BGR2GRAY),3)) fitnessVal_7.append(shapness_measure(cv2.cvtColor(patches_trg_c_temp[i], cv2.COLOR_BGR2GRAY),7)) fitnessVal_11.append(shapness_measure(cv2.cvtColor(patches_trg_c_temp[i], cv2.COLOR_BGR2GRAY),11)) fitnessVal_15.append(shapness_measure(cv2.cvtColor(patches_trg_c_temp[i], cv2.COLOR_BGR2GRAY),15)) fitnessVal_3=np.asarray(fitnessVal_3) fitnessVal_7=np.asarray(fitnessVal_7) fitnessVal_11=np.asarray(fitnessVal_11) fitnessVal_15=np.asarray(fitnessVal_15) fitnessVal_3=(fitnessVal_3-np.min(fitnessVal_3))/np.max((fitnessVal_3-np.min(fitnessVal_3))) fitnessVal_7=(fitnessVal_7-np.min(fitnessVal_7))/np.max((fitnessVal_7-np.min(fitnessVal_7))) fitnessVal_11=(fitnessVal_11-np.min(fitnessVal_11))/np.max((fitnessVal_11-np.min(fitnessVal_11))) fitnessVal_15=(fitnessVal_15-np.min(fitnessVal_15))/np.max((fitnessVal_15-np.min(fitnessVal_15))) fitnessVal_all=fitnessVal_3*fitnessVal_7*fitnessVal_11*fitnessVal_15 to_remove_patches_number=int(to_remove_ratio*num_of_img_patches) for itr in range(to_remove_patches_number): minArrInd=np.argmin(fitnessVal_all) fitnessVal_all[minArrInd]=2 for itr in range(num_of_img_patches): if fitnessVal_all[itr]!=2: patches_src_c.append(patches_src_c_temp[itr]) patches_trg_c.append(patches_trg_c_temp[itr]) patches_src_l.append(patches_src_l_temp[itr]) patches_src_r.append(patches_src_r_temp[itr]) def slice_stride(_img_src_c, _img_trg_c, _img_src_l, _img_src_r): global set_type, patch_size, stride, patches_src_c, patches_trg_c, patches_src_l, patches_src_r coordinates_list=[] coordinates_list.append([0,0,0,0]) patches_src_c_temp, patches_trg_c_temp, patches_src_l_temp, patches_src_r_temp = [], [], [], [] for r in range(0,_img_src_c.shape[0],stride[0]): for c in range(0,_img_src_c.shape[1],stride[1]): if (r+patch_size[0]) <= _img_src_c.shape[0] and (c+patch_size[1]) <= _img_src_c.shape[1]: patches_src_c_temp.append(_img_src_c[r:r+patch_size[0],c:c+patch_size[1]]) patches_trg_c_temp.append(_img_trg_c[r:r+patch_size[0],c:c+patch_size[1]]) patches_src_l_temp.append(_img_src_l[r:r+patch_size[0],c:c+patch_size[1]]) patches_src_r_temp.append(_img_src_r[r:r+patch_size[0],c:c+patch_size[1]]) elif (r+patch_size[0]) <= _img_src_c.shape[0] and not ([r,r+patch_size[0],_img_src_c.shape[1]-patch_size[1],_img_src_c.shape[1]] in coordinates_list): patches_src_c_temp.append(_img_src_c[r:r+patch_size[0],_img_src_c.shape[1]-patch_size[1]:_img_src_c.shape[1]]) patches_trg_c_temp.append(_img_trg_c[r:r+patch_size[0],_img_trg_c.shape[1]-patch_size[1]:_img_trg_c.shape[1]]) patches_src_l_temp.append(_img_src_l[r:r+patch_size[0],_img_src_l.shape[1]-patch_size[1]:_img_src_l.shape[1]]) patches_src_r_temp.append(_img_src_r[r:r+patch_size[0],_img_src_r.shape[1]-patch_size[1]:_img_src_r.shape[1]]) coordinates_list.append([r,r+patch_size[0],_img_src_c.shape[1]-patch_size[1],_img_src_c.shape[1]]) elif (c+patch_size[1]) <= _img_src_c.shape[1] and not ([_img_src_c.shape[0]-patch_size[0],_img_src_c.shape[0],c,c+patch_size[1]] in coordinates_list): patches_src_c_temp.append(_img_src_c[_img_src_c.shape[0]-patch_size[0]:_img_src_c.shape[0],c:c+patch_size[1]]) patches_trg_c_temp.append(_img_trg_c[_img_trg_c.shape[0]-patch_size[0]:_img_trg_c.shape[0],c:c+patch_size[1]]) patches_src_l_temp.append(_img_src_l[_img_src_l.shape[0]-patch_size[0]:_img_src_l.shape[0],c:c+patch_size[1]]) patches_src_r_temp.append(_img_src_r[_img_src_r.shape[0]-patch_size[0]:_img_src_r.shape[0],c:c+patch_size[1]]) coordinates_list.append([_img_src_c.shape[0]-patch_size[0],_img_src_c.shape[0],c,c+patch_size[1]]) elif not ([_img_src_c.shape[0]-patch_size[0],_img_src_c.shape[0],_img_src_c.shape[1]-patch_size[1],_img_src_c.shape[1]] in coordinates_list): patches_src_c_temp.append(_img_src_c[_img_src_c.shape[0]-patch_size[0]:_img_src_c.shape[0],_img_src_c.shape[1]-patch_size[1]:_img_src_c.shape[1]]) patches_trg_c_temp.append(_img_trg_c[_img_trg_c.shape[0]-patch_size[0]:_img_trg_c.shape[0],_img_trg_c.shape[1]-patch_size[1]:_img_trg_c.shape[1]]) patches_src_l_temp.append(_img_src_l[_img_src_l.shape[0]-patch_size[0]:_img_src_l.shape[0],_img_src_l.shape[1]-patch_size[1]:_img_src_l.shape[1]]) patches_src_r_temp.append(_img_src_r[_img_src_r.shape[0]-patch_size[0]:_img_src_r.shape[0],_img_src_r.shape[1]-patch_size[1]:_img_src_r.shape[1]]) coordinates_list.append([_img_src_c.shape[0]-patch_size[0],_img_src_c.shape[0],_img_src_c.shape[1]-patch_size[1],_img_src_c.shape[1]]) if set_type == 'train': filter_patch_sharpness(patches_src_c_temp, patches_trg_c_temp, patches_src_l_temp, patches_src_r_temp) else: patches_src_c, patches_trg_c, patches_src_l, patches_src_r = deepcopy(patches_src_c_temp), deepcopy(patches_trg_c_temp), deepcopy(patches_src_l_temp), deepcopy(patches_src_r_temp) set_type_arr=['train','val'] img_ex='.png' sub_folder=['source/','target/'] dataset='./dd_dp_dataset_canon/' # color flag used to select the reading image mode in opencv. 0:graysca, # 1:rgb 8bits, -1:read image as it including its bit depth color_flag=-1 patch_size=[512, 512] to_remove_ratio=0.3 # discard 30% of the patches for set_type in set_type_arr: print('Image to patch of ',set_type,'set has started...') if set_type == 'train': # patch settings patch_overlap_ratio=0.6 stride=[int((1-patch_overlap_ratio)*patch_size[0]),int((1-patch_overlap_ratio)*patch_size[1])] else: # patch settings patch_overlap_ratio=0.01 stride=[int((1-patch_overlap_ratio)*patch_size[0]),int((1-patch_overlap_ratio)*patch_size[1])] # pathes to write extracted patches path_write_c= './dd_dp_dataset_canon_patch/'+set_type+'_c/' path_write_l= './dd_dp_dataset_canon_patch/'+set_type+'_l/' path_write_r= './dd_dp_dataset_canon_patch/'+set_type+'_r/' # to check if directory exist, otherwise create one check_create_directory(path_write_c+sub_folder[0]) check_create_directory(path_write_c+sub_folder[1]) check_create_directory(path_write_l+sub_folder[0]) check_create_directory(path_write_r+sub_folder[0]) # load image filenames images_src=np.load('./file_names/'+set_type+'_src.npy') images_trg=np.load('./file_names/'+set_type+'_trg.npy') # set counter img_patch_count=0 data_ims_size=len(images_src) for i in range(data_ims_size): patches_src_c=[] patches_trg_c=[] patches_src_l=[] patches_src_r=[] img_src_c=cv2.imread(dataset+set_type+'_c/'+sub_folder[0]+images_src[i]+img_ex,color_flag) img_trg_c=cv2.imread(dataset+set_type+'_c/'+sub_folder[1]+images_trg[i]+img_ex,color_flag) print(dataset+set_type+'_c/'+sub_folder[0]+images_src[i]+img_ex) img_src_l=cv2.imread(dataset+set_type+'_l/'+sub_folder[0]+images_src[i]+'_L'+img_ex,color_flag) img_src_r=cv2.imread(dataset+set_type+'_r/'+sub_folder[0]+images_src[i]+'_R'+img_ex,color_flag) slice_stride(img_src_c, img_trg_c, img_src_l, img_src_r) for j in range(len(patches_src_c)): cv2.imwrite(path_write_c+sub_folder[0]+str(img_patch_count).zfill(5)+img_ex,(patches_src_c[j]).astype(np.uint16)) cv2.imwrite(path_write_c+sub_folder[1]+str(img_patch_count).zfill(5)+img_ex,(patches_trg_c[j]).astype(np.uint16)) cv2.imwrite(path_write_l+sub_folder[0]+str(img_patch_count).zfill(5)+img_ex,(patches_src_l[j]).astype(np.uint16)) cv2.imwrite(path_write_r+sub_folder[0]+str(img_patch_count).zfill(5)+img_ex,(patches_src_r[j]).astype(np.uint16)) img_patch_count+=1 print(set_type+': ',i,j,img_patch_count)

56.544944

187

0.719424

import numpy as np import os import cv2 import errno from copy import deepcopy def check_create_directory(path_to_check): if not os.path.exists(path_to_check): try: os.makedirs(path_to_check) except OSError as exc: if exc.errno != errno.EEXIST: raise def shapness_measure(img_temp,kernel_size): conv_x = cv2.Sobel(img_temp,cv2.CV_64F,1,0,ksize=kernel_size) conv_y = cv2.Sobel(img_temp,cv2.CV_64F,0,1,ksize=kernel_size) temp_arr_x=deepcopy(conv_x*conv_x) temp_arr_y=deepcopy(conv_y*conv_y) temp_sum_x_y=temp_arr_x+temp_arr_y temp_sum_x_y=np.sqrt(temp_sum_x_y) return np.sum(temp_sum_x_y) def filter_patch_sharpness(patches_src_c_temp, patches_trg_c_temp, patches_src_l_temp, patches_src_r_temp): global patches_src_c, patches_trg_c, patches_src_l, patches_src_r fitnessVal_3=[] fitnessVal_7=[] fitnessVal_11=[] fitnessVal_15=[] num_of_img_patches=len(patches_trg_c_temp) for i in range(num_of_img_patches): fitnessVal_3.append(shapness_measure(cv2.cvtColor(patches_trg_c_temp[i], cv2.COLOR_BGR2GRAY),3)) fitnessVal_7.append(shapness_measure(cv2.cvtColor(patches_trg_c_temp[i], cv2.COLOR_BGR2GRAY),7)) fitnessVal_11.append(shapness_measure(cv2.cvtColor(patches_trg_c_temp[i], cv2.COLOR_BGR2GRAY),11)) fitnessVal_15.append(shapness_measure(cv2.cvtColor(patches_trg_c_temp[i], cv2.COLOR_BGR2GRAY),15)) fitnessVal_3=np.asarray(fitnessVal_3) fitnessVal_7=np.asarray(fitnessVal_7) fitnessVal_11=np.asarray(fitnessVal_11) fitnessVal_15=np.asarray(fitnessVal_15) fitnessVal_3=(fitnessVal_3-np.min(fitnessVal_3))/np.max((fitnessVal_3-np.min(fitnessVal_3))) fitnessVal_7=(fitnessVal_7-np.min(fitnessVal_7))/np.max((fitnessVal_7-np.min(fitnessVal_7))) fitnessVal_11=(fitnessVal_11-np.min(fitnessVal_11))/np.max((fitnessVal_11-np.min(fitnessVal_11))) fitnessVal_15=(fitnessVal_15-np.min(fitnessVal_15))/np.max((fitnessVal_15-np.min(fitnessVal_15))) fitnessVal_all=fitnessVal_3*fitnessVal_7*fitnessVal_11*fitnessVal_15 to_remove_patches_number=int(to_remove_ratio*num_of_img_patches) for itr in range(to_remove_patches_number): minArrInd=np.argmin(fitnessVal_all) fitnessVal_all[minArrInd]=2 for itr in range(num_of_img_patches): if fitnessVal_all[itr]!=2: patches_src_c.append(patches_src_c_temp[itr]) patches_trg_c.append(patches_trg_c_temp[itr]) patches_src_l.append(patches_src_l_temp[itr]) patches_src_r.append(patches_src_r_temp[itr]) def slice_stride(_img_src_c, _img_trg_c, _img_src_l, _img_src_r): global set_type, patch_size, stride, patches_src_c, patches_trg_c, patches_src_l, patches_src_r coordinates_list=[] coordinates_list.append([0,0,0,0]) patches_src_c_temp, patches_trg_c_temp, patches_src_l_temp, patches_src_r_temp = [], [], [], [] for r in range(0,_img_src_c.shape[0],stride[0]): for c in range(0,_img_src_c.shape[1],stride[1]): if (r+patch_size[0]) <= _img_src_c.shape[0] and (c+patch_size[1]) <= _img_src_c.shape[1]: patches_src_c_temp.append(_img_src_c[r:r+patch_size[0],c:c+patch_size[1]]) patches_trg_c_temp.append(_img_trg_c[r:r+patch_size[0],c:c+patch_size[1]]) patches_src_l_temp.append(_img_src_l[r:r+patch_size[0],c:c+patch_size[1]]) patches_src_r_temp.append(_img_src_r[r:r+patch_size[0],c:c+patch_size[1]]) elif (r+patch_size[0]) <= _img_src_c.shape[0] and not ([r,r+patch_size[0],_img_src_c.shape[1]-patch_size[1],_img_src_c.shape[1]] in coordinates_list): patches_src_c_temp.append(_img_src_c[r:r+patch_size[0],_img_src_c.shape[1]-patch_size[1]:_img_src_c.shape[1]]) patches_trg_c_temp.append(_img_trg_c[r:r+patch_size[0],_img_trg_c.shape[1]-patch_size[1]:_img_trg_c.shape[1]]) patches_src_l_temp.append(_img_src_l[r:r+patch_size[0],_img_src_l.shape[1]-patch_size[1]:_img_src_l.shape[1]]) patches_src_r_temp.append(_img_src_r[r:r+patch_size[0],_img_src_r.shape[1]-patch_size[1]:_img_src_r.shape[1]]) coordinates_list.append([r,r+patch_size[0],_img_src_c.shape[1]-patch_size[1],_img_src_c.shape[1]]) elif (c+patch_size[1]) <= _img_src_c.shape[1] and not ([_img_src_c.shape[0]-patch_size[0],_img_src_c.shape[0],c,c+patch_size[1]] in coordinates_list): patches_src_c_temp.append(_img_src_c[_img_src_c.shape[0]-patch_size[0]:_img_src_c.shape[0],c:c+patch_size[1]]) patches_trg_c_temp.append(_img_trg_c[_img_trg_c.shape[0]-patch_size[0]:_img_trg_c.shape[0],c:c+patch_size[1]]) patches_src_l_temp.append(_img_src_l[_img_src_l.shape[0]-patch_size[0]:_img_src_l.shape[0],c:c+patch_size[1]]) patches_src_r_temp.append(_img_src_r[_img_src_r.shape[0]-patch_size[0]:_img_src_r.shape[0],c:c+patch_size[1]]) coordinates_list.append([_img_src_c.shape[0]-patch_size[0],_img_src_c.shape[0],c,c+patch_size[1]]) elif not ([_img_src_c.shape[0]-patch_size[0],_img_src_c.shape[0],_img_src_c.shape[1]-patch_size[1],_img_src_c.shape[1]] in coordinates_list): patches_src_c_temp.append(_img_src_c[_img_src_c.shape[0]-patch_size[0]:_img_src_c.shape[0],_img_src_c.shape[1]-patch_size[1]:_img_src_c.shape[1]]) patches_trg_c_temp.append(_img_trg_c[_img_trg_c.shape[0]-patch_size[0]:_img_trg_c.shape[0],_img_trg_c.shape[1]-patch_size[1]:_img_trg_c.shape[1]]) patches_src_l_temp.append(_img_src_l[_img_src_l.shape[0]-patch_size[0]:_img_src_l.shape[0],_img_src_l.shape[1]-patch_size[1]:_img_src_l.shape[1]]) patches_src_r_temp.append(_img_src_r[_img_src_r.shape[0]-patch_size[0]:_img_src_r.shape[0],_img_src_r.shape[1]-patch_size[1]:_img_src_r.shape[1]]) coordinates_list.append([_img_src_c.shape[0]-patch_size[0],_img_src_c.shape[0],_img_src_c.shape[1]-patch_size[1],_img_src_c.shape[1]]) if set_type == 'train': filter_patch_sharpness(patches_src_c_temp, patches_trg_c_temp, patches_src_l_temp, patches_src_r_temp) else: patches_src_c, patches_trg_c, patches_src_l, patches_src_r = deepcopy(patches_src_c_temp), deepcopy(patches_trg_c_temp), deepcopy(patches_src_l_temp), deepcopy(patches_src_r_temp) set_type_arr=['train','val'] img_ex='.png' sub_folder=['source/','target/'] dataset='./dd_dp_dataset_canon/' color_flag=-1 patch_size=[512, 512] to_remove_ratio=0.3 for set_type in set_type_arr: print('Image to patch of ',set_type,'set has started...') if set_type == 'train': patch_overlap_ratio=0.6 stride=[int((1-patch_overlap_ratio)*patch_size[0]),int((1-patch_overlap_ratio)*patch_size[1])] else: patch_overlap_ratio=0.01 stride=[int((1-patch_overlap_ratio)*patch_size[0]),int((1-patch_overlap_ratio)*patch_size[1])] path_write_c= './dd_dp_dataset_canon_patch/'+set_type+'_c/' path_write_l= './dd_dp_dataset_canon_patch/'+set_type+'_l/' path_write_r= './dd_dp_dataset_canon_patch/'+set_type+'_r/' check_create_directory(path_write_c+sub_folder[0]) check_create_directory(path_write_c+sub_folder[1]) check_create_directory(path_write_l+sub_folder[0]) check_create_directory(path_write_r+sub_folder[0]) images_src=np.load('./file_names/'+set_type+'_src.npy') images_trg=np.load('./file_names/'+set_type+'_trg.npy') img_patch_count=0 data_ims_size=len(images_src) for i in range(data_ims_size): patches_src_c=[] patches_trg_c=[] patches_src_l=[] patches_src_r=[] img_src_c=cv2.imread(dataset+set_type+'_c/'+sub_folder[0]+images_src[i]+img_ex,color_flag) img_trg_c=cv2.imread(dataset+set_type+'_c/'+sub_folder[1]+images_trg[i]+img_ex,color_flag) print(dataset+set_type+'_c/'+sub_folder[0]+images_src[i]+img_ex) img_src_l=cv2.imread(dataset+set_type+'_l/'+sub_folder[0]+images_src[i]+'_L'+img_ex,color_flag) img_src_r=cv2.imread(dataset+set_type+'_r/'+sub_folder[0]+images_src[i]+'_R'+img_ex,color_flag) slice_stride(img_src_c, img_trg_c, img_src_l, img_src_r) for j in range(len(patches_src_c)): cv2.imwrite(path_write_c+sub_folder[0]+str(img_patch_count).zfill(5)+img_ex,(patches_src_c[j]).astype(np.uint16)) cv2.imwrite(path_write_c+sub_folder[1]+str(img_patch_count).zfill(5)+img_ex,(patches_trg_c[j]).astype(np.uint16)) cv2.imwrite(path_write_l+sub_folder[0]+str(img_patch_count).zfill(5)+img_ex,(patches_src_l[j]).astype(np.uint16)) cv2.imwrite(path_write_r+sub_folder[0]+str(img_patch_count).zfill(5)+img_ex,(patches_src_r[j]).astype(np.uint16)) img_patch_count+=1 print(set_type+': ',i,j,img_patch_count)

true

1c43f2f1279c5e8aee0def23c69b53b4bb131a33

556

py

Python

examples/server/asgi/simple.py

13g10n/python-engineio

e882f5949bdd1618d97b0cade18a7e8af8670b41

[ "MIT" ]

208

2015-06-22T00:44:53.000Z

2022-02-13T16:39:14.000Z

examples/server/asgi/simple.py

13g10n/python-engineio

e882f5949bdd1618d97b0cade18a7e8af8670b41

[ "MIT" ]

241

2015-08-12T06:15:40.000Z

2022-03-18T19:17:46.000Z

examples/server/asgi/simple.py

13g10n/python-engineio

e882f5949bdd1618d97b0cade18a7e8af8670b41

[ "MIT" ]

153

2015-08-08T15:40:45.000Z

2022-03-29T14:26:32.000Z

import os import uvicorn import engineio eio = engineio.AsyncServer(async_mode='asgi') app = engineio.ASGIApp(eio, static_files={ '/': 'simple.html', '/static': 'static', }) @eio.on('connect') def connect(sid, environ): print("connect ", sid) @eio.on('message') async def message(sid, data): print('message from', sid, data) await eio.send(sid, 'Thank you for your message!') @eio.on('disconnect') def disconnect(sid): print('disconnect ', sid) if __name__ == '__main__': uvicorn.run(app, host='127.0.0.1', port=5000)

17.935484

54

0.656475

import os import uvicorn import engineio eio = engineio.AsyncServer(async_mode='asgi') app = engineio.ASGIApp(eio, static_files={ '/': 'simple.html', '/static': 'static', }) @eio.on('connect') def connect(sid, environ): print("connect ", sid) @eio.on('message') async def message(sid, data): print('message from', sid, data) await eio.send(sid, 'Thank you for your message!') @eio.on('disconnect') def disconnect(sid): print('disconnect ', sid) if __name__ == '__main__': uvicorn.run(app, host='127.0.0.1', port=5000)

true

1c43f3b270474db102bfa0c81625d0a5e1cecaa3

2,053

py

Python

budgetportal/tests/test_guides_pages.py

TomaszKolek/datamanager

d46dbab00e30a14fc26eb9368c32dcdbbda7309d

[ "MIT" ]

null

budgetportal/tests/test_guides_pages.py

TomaszKolek/datamanager

d46dbab00e30a14fc26eb9368c32dcdbbda7309d

[ "MIT" ]

null

budgetportal/tests/test_guides_pages.py

TomaszKolek/datamanager

d46dbab00e30a14fc26eb9368c32dcdbbda7309d

[ "MIT" ]

null

from django.core.files.images import ImageFile from django.test import Client, TestCase from budgetportal.models import GuideIndexPage, GuidePage, CategoryGuide class GuideIndexPageTestCase(TestCase): fixtures = ["test-guides-pages"] def setUp(self): self.guide_index_page = GuideIndexPage.objects.get(id=4) self.guide_page = GuidePage.objects.get(id=5) self.category_guides = CategoryGuide.objects.all() def test_guide_index_page(self): """Simple test of template response for guide index page""" response = Client().get(self.guide_index_page.url_path) self.assertContains(response, self.guide_index_page.title) self.assertContains(response, self.guide_index_page.intro) self.assertGreaterEqual(self.category_guides.count(), 1) for category_guide in self.category_guides: self.assertContains(response, category_guide.external_url_title) self.assertContains(response, category_guide.external_url_description) class GuidePagesTestCase(TestCase): fixtures = ["test-guides-pages"] def setUp(self): self.guide_page = GuidePage.objects.get(id=5) def test_guide_page(self): """Simple test of template response for guide page""" with open("budgetportal/tests/test_data/photo.jpg", "rb") as file: self.guide_page.image.file = ImageFile(file, "photo.jpg") self.guide_page.image.save() response = Client().get(self.guide_page.url_path) self.assertContains(response, self.guide_page.title) self.assertIsNotNone(self.guide_page.image) ## Verify the integration of our configuration, django-storages and wagtail ## - that is - the generated image URL templated in matches the configuration of the site self.assertContains( response, "http://minio:9000/budgetportal-storage/images/photo.max-320x200.jpg", ) for body_part in self.guide_page.body: self.assertContains(response, body_part)

40.254902

97

0.702874

from django.core.files.images import ImageFile from django.test import Client, TestCase from budgetportal.models import GuideIndexPage, GuidePage, CategoryGuide class GuideIndexPageTestCase(TestCase): fixtures = ["test-guides-pages"] def setUp(self): self.guide_index_page = GuideIndexPage.objects.get(id=4) self.guide_page = GuidePage.objects.get(id=5) self.category_guides = CategoryGuide.objects.all() def test_guide_index_page(self): response = Client().get(self.guide_index_page.url_path) self.assertContains(response, self.guide_index_page.title) self.assertContains(response, self.guide_index_page.intro) self.assertGreaterEqual(self.category_guides.count(), 1) for category_guide in self.category_guides: self.assertContains(response, category_guide.external_url_title) self.assertContains(response, category_guide.external_url_description) class GuidePagesTestCase(TestCase): fixtures = ["test-guides-pages"] def setUp(self): self.guide_page = GuidePage.objects.get(id=5) def test_guide_page(self): with open("budgetportal/tests/test_data/photo.jpg", "rb") as file: self.guide_page.image.file = ImageFile(file, "photo.jpg") self.guide_page.image.save() response = Client().get(self.guide_page.url_path) self.assertContains(response, self.guide_page.title) self.assertIsNotNone(self.guide_page.image) for body_part in self.guide_page.body: self.assertContains(response, body_part)

true

1c43f415944cc54adda6eed157b9ba14a13830c1

948

py

Python

lupdate_xml.py

Skycoder42/QtMvvmSettingsCore

4489151d3e7de940790c5a93041c7381799f695a

[ "BSD-3-Clause" ]

null

lupdate_xml.py

Skycoder42/QtMvvmSettingsCore

4489151d3e7de940790c5a93041c7381799f695a

[ "BSD-3-Clause" ]

null

lupdate_xml.py

Skycoder42/QtMvvmSettingsCore

4489151d3e7de940790c5a93041c7381799f695a

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python3 # Usage: lupdate_xml.py bindir srcdir locales(space seperated) xml_sources... import sys import os import subprocess from xml.etree.ElementTree import Element, parse bindir = sys.argv[1] srcdir = sys.argv[2] srces = sys.argv[3:] os.chdir(srcdir) tsmap = {} for src in srces: trstrings = set() tree = parse(src) root = Element("TS") for elem in tree.iter(): if elem.tag == "SearchKey": trstrings.add(elem.text) else: if "title" in elem.attrib: trstrings.add(elem.attrib["title"]) if "tooltip" in elem.attrib: trstrings.add(elem.attrib["tooltip"]) tsmap[os.path.basename(src)] = trstrings outfile = open(".qtmvvm_settings_xml_ts.cppdummy", "w") outfile.write("#include <QCoreApplication>\n\n") outfile.write("void dummyfn() {\n") for src in tsmap: for str in tsmap[src]: outfile.write("\tQCoreApplication::translate(\"{}\", \"{}\");\n".format(src, str)) outfile.write("}\n") outfile.close()

23.7

84

0.690928

import sys import os import subprocess from xml.etree.ElementTree import Element, parse bindir = sys.argv[1] srcdir = sys.argv[2] srces = sys.argv[3:] os.chdir(srcdir) tsmap = {} for src in srces: trstrings = set() tree = parse(src) root = Element("TS") for elem in tree.iter(): if elem.tag == "SearchKey": trstrings.add(elem.text) else: if "title" in elem.attrib: trstrings.add(elem.attrib["title"]) if "tooltip" in elem.attrib: trstrings.add(elem.attrib["tooltip"]) tsmap[os.path.basename(src)] = trstrings outfile = open(".qtmvvm_settings_xml_ts.cppdummy", "w") outfile.write("#include <QCoreApplication>\n\n") outfile.write("void dummyfn() {\n") for src in tsmap: for str in tsmap[src]: outfile.write("\tQCoreApplication::translate(\"{}\", \"{}\");\n".format(src, str)) outfile.write("}\n") outfile.close()

true

1c43f45217488b9d1f345b843dcd9e4b6f84640c

990

py

Python

6.py

andy0130tw/advent-of-code-2019

aeaeb50db3170e619aef41756ce0608793a64baa

[ "Unlicense" ]

null

6.py

andy0130tw/advent-of-code-2019

aeaeb50db3170e619aef41756ce0608793a64baa

[ "Unlicense" ]

null

6.py

andy0130tw/advent-of-code-2019

aeaeb50db3170e619aef41756ce0608793a64baa

[ "Unlicense" ]

null

def rec_sum(root, depth): ans = depth for el in root.values(): ans += rec_sum(el, depth + 1) return ans def find_path(root, target): for lab, sub in root.items(): if lab == target: return [lab] res = find_path(sub, target) if res: return [lab, *res] return None def task1(tree): print(rec_sum(tree, 0)) def task2(tree): pa = find_path(tree, 'SAN') pb = find_path(tree, 'YOU') lca_depth = 0 for ea, eb in zip(pa, pb): if ea != eb: break lca_depth += 1 print(len(pa) + len(pb) - lca_depth * 2 - 2) if __name__ == '__main__': tree = {} while 1: try: par, sub = input().strip().split(')') except EOFError: break if par not in tree: tree[par] = {} if sub not in tree: tree[sub] = {} tree[par][sub] = tree[sub] # task1(tree['COM']) task2(tree['COM'])

19.038462

49

0.489899

def rec_sum(root, depth): ans = depth for el in root.values(): ans += rec_sum(el, depth + 1) return ans def find_path(root, target): for lab, sub in root.items(): if lab == target: return [lab] res = find_path(sub, target) if res: return [lab, *res] return None def task1(tree): print(rec_sum(tree, 0)) def task2(tree): pa = find_path(tree, 'SAN') pb = find_path(tree, 'YOU') lca_depth = 0 for ea, eb in zip(pa, pb): if ea != eb: break lca_depth += 1 print(len(pa) + len(pb) - lca_depth * 2 - 2) if __name__ == '__main__': tree = {} while 1: try: par, sub = input().strip().split(')') except EOFError: break if par not in tree: tree[par] = {} if sub not in tree: tree[sub] = {} tree[par][sub] = tree[sub] task2(tree['COM'])

true

1c43f58337d7879e5d18e9e1149c4866747fbd4d

926

py

Python

src/partition_set_into_equal_sum.py

redfast00/daily-algorithm-challenge

3507164d5ec58abe68a6e820120625e100dee96c

[ "MIT" ]

null

src/partition_set_into_equal_sum.py

redfast00/daily-algorithm-challenge

3507164d5ec58abe68a6e820120625e100dee96c

[ "MIT" ]

null

src/partition_set_into_equal_sum.py

redfast00/daily-algorithm-challenge

3507164d5ec58abe68a6e820120625e100dee96c

[ "MIT" ]

null

from collections import Counter from get_subset_sum import subset_sum def partition_into_equal_parts(l): '''Partitions s into two subsets of l that have the same sum. >>> problem = [15, 5, 20, 10, 35, 25, 10] >>> first, second = partition_into_equal_parts(problem) >>> valid_solution(first, second, problem) True ''' total = sum(l) # If sum is odd, there is no way that total = sum(first) + sum(second) = 2 * sum(first) if total % 2: return first = subset_sum(total // 2, l) if first is None: return second = [] # Fill second with items from counter second_counter = Counter(l) - Counter(first) for number, amount in second_counter.items(): second.extend([number] * amount) return first, second def valid_solution(first, second, problem): return sum(first) == sum(second) and Counter(first) + Counter(second) == Counter(problem)

30.866667

93

0.654428

from collections import Counter from get_subset_sum import subset_sum def partition_into_equal_parts(l): total = sum(l) if total % 2: return first = subset_sum(total // 2, l) if first is None: return second = [] second_counter = Counter(l) - Counter(first) for number, amount in second_counter.items(): second.extend([number] * amount) return first, second def valid_solution(first, second, problem): return sum(first) == sum(second) and Counter(first) + Counter(second) == Counter(problem)

true

1c43f593ad0ffdb5320aa7b3fb8d314b549d0517

1,804

py

Python

colorize_sky.py

kcotar/Stellar_abudance_trees

1a4377ef53a4b4c8df1be860598a70be31626110

[ "MIT" ]

null

colorize_sky.py

kcotar/Stellar_abudance_trees

1a4377ef53a4b4c8df1be860598a70be31626110

[ "MIT" ]

null

colorize_sky.py

kcotar/Stellar_abudance_trees

1a4377ef53a4b4c8df1be860598a70be31626110

[ "MIT" ]

null

import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.basemap import Basemap def _prepare_ra_dec(data): ra = data['ra'] idx_trans = ra > 180 if len(idx_trans) > 0: ra[idx_trans] -= 360 ra = np.deg2rad(ra) dec = np.deg2rad(data['dec']) return ra, dec def plot_ra_dec_locations(data, path='sky_pos.png'): # plt.subplot(111, projection='mollweide') # ra, dec = _prepare_ra_dec(data) # plt.scatter(ra, dec, lw=0, c='black', s=0.4) # plt.grid(True) # plt.colorbar() # plt.tight_layout() # plt.savefig(path, dpi=500) # plt.close() plt.figure() map = Basemap(projection='moll', lon_0=0) map.drawparallels(np.arange(-90., 95., 5.)) map.drawmeridians(np.arange(0., 365., 5.)) ra, dec = _prepare_ra_dec(data) map.scatter(ra, dec, lw=0, c='black', s=0.4) ax = plt.gca() ax.set_xlim((np.min(ra), np.max(ra))) ax.set_ylim((np.min(dec), np.max(dec))) plt.tight_layout() plt.savefig(path, dpi=250) plt.close() def plot_ra_dec_attribute(data, attribute, path='sky_pos_attribute.png'): # plt.subplot(111, projection='mollweide') # ra, dec = _prepare_ra_dec(data) # plt.scatter(ra, dec, lw=0, c=data[attribute], s=0.4) # plt.grid(True) # plt.colorbar() # plt.tight_layout() # plt.show() # plt.savefig(path, dpi=500) # plt.close() plt.figure() map = Basemap(projection='moll', lon_0=0) map.drawparallels(np.arange(-90., 95., 5.)) map.drawmeridians(np.arange(0., 365., 5.)) ra, dec = _prepare_ra_dec(data) map.scatter(ra, dec, lw=0, c=data[attribute], s=2) ax = plt.gca() ax.set_xlim((np.min(ra), np.max(ra))) ax.set_ylim((np.min(dec), np.max(dec))) plt.tight_layout() plt.savefig(path, dpi=250) plt.close()

30.066667

73

0.616962

import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.basemap import Basemap def _prepare_ra_dec(data): ra = data['ra'] idx_trans = ra > 180 if len(idx_trans) > 0: ra[idx_trans] -= 360 ra = np.deg2rad(ra) dec = np.deg2rad(data['dec']) return ra, dec def plot_ra_dec_locations(data, path='sky_pos.png'): plt.figure() map = Basemap(projection='moll', lon_0=0) map.drawparallels(np.arange(-90., 95., 5.)) map.drawmeridians(np.arange(0., 365., 5.)) ra, dec = _prepare_ra_dec(data) map.scatter(ra, dec, lw=0, c='black', s=0.4) ax = plt.gca() ax.set_xlim((np.min(ra), np.max(ra))) ax.set_ylim((np.min(dec), np.max(dec))) plt.tight_layout() plt.savefig(path, dpi=250) plt.close() def plot_ra_dec_attribute(data, attribute, path='sky_pos_attribute.png'): plt.figure() map = Basemap(projection='moll', lon_0=0) map.drawparallels(np.arange(-90., 95., 5.)) map.drawmeridians(np.arange(0., 365., 5.)) ra, dec = _prepare_ra_dec(data) map.scatter(ra, dec, lw=0, c=data[attribute], s=2) ax = plt.gca() ax.set_xlim((np.min(ra), np.max(ra))) ax.set_ylim((np.min(dec), np.max(dec))) plt.tight_layout() plt.savefig(path, dpi=250) plt.close()

true

1c43f730fff18adef3c514b8dfe4a98cff45a408

4,124

py

Python

test/coreneuron/test_spikes.py

ishandutta2007/nrn

418d42fb7afc0ebb06138b80e511c8ae716dcad0

[ "BSD-3-Clause" ]

null

test/coreneuron/test_spikes.py

ishandutta2007/nrn

418d42fb7afc0ebb06138b80e511c8ae716dcad0

[ "BSD-3-Clause" ]

null

test/coreneuron/test_spikes.py

ishandutta2007/nrn

418d42fb7afc0ebb06138b80e511c8ae716dcad0

[ "BSD-3-Clause" ]

null

import distutils.util import os import sys # Hacky, but it's non-trivial to pass commandline arguments to pytest tests. enable_gpu = bool( distutils.util.strtobool(os.environ.get("CORENRN_ENABLE_GPU", "false")) ) mpi4py_option = bool( distutils.util.strtobool(os.environ.get("NRN_TEST_SPIKES_MPI4PY", "false")) ) file_mode_option = bool( distutils.util.strtobool(os.environ.get("NRN_TEST_SPIKES_FILE_MODE", "false")) ) nrnmpi_init_option = bool( distutils.util.strtobool(os.environ.get("NRN_TEST_SPIKES_NRNMPI_INIT", "false")) ) # following at top level and early enough avoids... # *** The MPI_Iprobe() function was called after MPI_FINALIZE was invoked. # mpi4py needs to be imported before importing h if mpi4py_option: from mpi4py import MPI from neuron import h, gui # without mpi4py we need to call nrnmpi_init explicitly elif nrnmpi_init_option: from neuron import h, gui h.nrnmpi_init() # otherwise serial execution else: from neuron import h, gui import pytest import sys import traceback def test_spikes( use_mpi4py=mpi4py_option, use_nrnmpi_init=nrnmpi_init_option, file_mode=file_mode_option, ): print( "test_spikes(use_mpi4py={}, use_nrnmpi_init={}, file_mode={})".format( use_mpi4py, use_nrnmpi_init, file_mode ) ) h("""create soma""") h.soma.L = 5.6419 h.soma.diam = 5.6419 h.soma.insert("hh") h.soma.nseg = 3 ic = h.IClamp(h.soma(0.25)) ic.delay = 0.1 ic.dur = 0.1 ic.amp = 0.3 ic2 = h.IClamp(h.soma(0.75)) ic2.delay = 5.5 ic2.dur = 1 ic2.amp = 0.3 h.tstop = 10 h.cvode.use_fast_imem(1) h.cvode.cache_efficient(1) pc = h.ParallelContext() pc.set_gid2node(pc.id() + 1, pc.id()) myobj = h.NetCon(h.soma(0.5)._ref_v, None, sec=h.soma) pc.cell(pc.id() + 1, myobj) # NEURON run nrn_spike_t = h.Vector() nrn_spike_gids = h.Vector() # rank 0 record spikes for all gid while others # for specific gid. this is for better test coverage. pc.spike_record(-1 if pc.id() == 0 else (pc.id() + 1), nrn_spike_t, nrn_spike_gids) h.run() nrn_spike_t = nrn_spike_t.to_python() nrn_spike_gids = nrn_spike_gids.to_python() # CORENEURON run from neuron import coreneuron coreneuron.enable = True coreneuron.gpu = enable_gpu coreneuron.file_mode = file_mode coreneuron.verbose = 0 corenrn_all_spike_t = h.Vector() corenrn_all_spike_gids = h.Vector() pc.spike_record(-1, corenrn_all_spike_t, corenrn_all_spike_gids) pc.set_maxstep(10) def run(mode): h.stdinit() if mode == 0: pc.psolve(h.tstop) elif mode == 1: while h.t < h.tstop: pc.psolve(h.t + 1.0) else: while h.t < h.tstop: h.continuerun(h.t + 0.5) pc.psolve(h.t + 0.5) corenrn_all_spike_t_py = corenrn_all_spike_t.to_python() corenrn_all_spike_gids_py = corenrn_all_spike_gids.to_python() # check spikes match assert len(nrn_spike_t) # check we've actually got spikes assert len(nrn_spike_t) == len(nrn_spike_gids) # matching no. of gids if nrn_spike_t != corenrn_all_spike_t_py: print(mode) print(nrn_spike_t) print(nrn_spike_gids) print(corenrn_all_spike_t_py) print(corenrn_all_spike_gids_py) print( [ corenrn_all_spike_t[i] - nrn_spike_t[i] for i in range(len(nrn_spike_t)) ] ) assert nrn_spike_t == corenrn_all_spike_t_py assert nrn_spike_gids == corenrn_all_spike_gids_py if file_mode is False: for mode in [0, 1, 2]: run(mode) else: run(0) return h if __name__ == "__main__": try: h = test_spikes() except: traceback.print_exc() # Make the CTest test fail sys.exit(42) if mpi4py_option or nrnmpi_init_option: pc = h.ParallelContext() pc.barrier() h.quit()

26.606452

87

0.629243

import distutils.util import os import sys enable_gpu = bool( distutils.util.strtobool(os.environ.get("CORENRN_ENABLE_GPU", "false")) ) mpi4py_option = bool( distutils.util.strtobool(os.environ.get("NRN_TEST_SPIKES_MPI4PY", "false")) ) file_mode_option = bool( distutils.util.strtobool(os.environ.get("NRN_TEST_SPIKES_FILE_MODE", "false")) ) nrnmpi_init_option = bool( distutils.util.strtobool(os.environ.get("NRN_TEST_SPIKES_NRNMPI_INIT", "false")) ) # following at top level and early enough avoids... # *** The MPI_Iprobe() function was called after MPI_FINALIZE was invoked. # mpi4py needs to be imported before importing h if mpi4py_option: from mpi4py import MPI from neuron import h, gui # without mpi4py we need to call nrnmpi_init explicitly elif nrnmpi_init_option: from neuron import h, gui h.nrnmpi_init() # otherwise serial execution else: from neuron import h, gui import pytest import sys import traceback def test_spikes( use_mpi4py=mpi4py_option, use_nrnmpi_init=nrnmpi_init_option, file_mode=file_mode_option, ): print( "test_spikes(use_mpi4py={}, use_nrnmpi_init={}, file_mode={})".format( use_mpi4py, use_nrnmpi_init, file_mode ) ) h("""create soma""") h.soma.L = 5.6419 h.soma.diam = 5.6419 h.soma.insert("hh") h.soma.nseg = 3 ic = h.IClamp(h.soma(0.25)) ic.delay = 0.1 ic.dur = 0.1 ic.amp = 0.3 ic2 = h.IClamp(h.soma(0.75)) ic2.delay = 5.5 ic2.dur = 1 ic2.amp = 0.3 h.tstop = 10 h.cvode.use_fast_imem(1) h.cvode.cache_efficient(1) pc = h.ParallelContext() pc.set_gid2node(pc.id() + 1, pc.id()) myobj = h.NetCon(h.soma(0.5)._ref_v, None, sec=h.soma) pc.cell(pc.id() + 1, myobj) # NEURON run nrn_spike_t = h.Vector() nrn_spike_gids = h.Vector() # rank 0 record spikes for all gid while others # for specific gid. this is for better test coverage. pc.spike_record(-1 if pc.id() == 0 else (pc.id() + 1), nrn_spike_t, nrn_spike_gids) h.run() nrn_spike_t = nrn_spike_t.to_python() nrn_spike_gids = nrn_spike_gids.to_python() # CORENEURON run from neuron import coreneuron coreneuron.enable = True coreneuron.gpu = enable_gpu coreneuron.file_mode = file_mode coreneuron.verbose = 0 corenrn_all_spike_t = h.Vector() corenrn_all_spike_gids = h.Vector() pc.spike_record(-1, corenrn_all_spike_t, corenrn_all_spike_gids) pc.set_maxstep(10) def run(mode): h.stdinit() if mode == 0: pc.psolve(h.tstop) elif mode == 1: while h.t < h.tstop: pc.psolve(h.t + 1.0) else: while h.t < h.tstop: h.continuerun(h.t + 0.5) pc.psolve(h.t + 0.5) corenrn_all_spike_t_py = corenrn_all_spike_t.to_python() corenrn_all_spike_gids_py = corenrn_all_spike_gids.to_python() # check spikes match assert len(nrn_spike_t) # check we've actually got spikes assert len(nrn_spike_t) == len(nrn_spike_gids) if nrn_spike_t != corenrn_all_spike_t_py: print(mode) print(nrn_spike_t) print(nrn_spike_gids) print(corenrn_all_spike_t_py) print(corenrn_all_spike_gids_py) print( [ corenrn_all_spike_t[i] - nrn_spike_t[i] for i in range(len(nrn_spike_t)) ] ) assert nrn_spike_t == corenrn_all_spike_t_py assert nrn_spike_gids == corenrn_all_spike_gids_py if file_mode is False: for mode in [0, 1, 2]: run(mode) else: run(0) return h if __name__ == "__main__": try: h = test_spikes() except: traceback.print_exc() sys.exit(42) if mpi4py_option or nrnmpi_init_option: pc = h.ParallelContext() pc.barrier() h.quit()

true

1c43f74e70b164c0121e3a9b4edda8f51bbb7dec

984

py

Python

python_Project/Day_16-20/Day_16-20_Sort&Search_Algorithms/Cocktail_sort.py

Zzz-ww/Python-prac

c97f2c16b74a2c1df117f377a072811cc596f98b

[ "MIT" ]

null

python_Project/Day_16-20/Day_16-20_Sort&Search_Algorithms/Cocktail_sort.py

Zzz-ww/Python-prac

c97f2c16b74a2c1df117f377a072811cc596f98b

[ "MIT" ]

null

python_Project/Day_16-20/Day_16-20_Sort&Search_Algorithms/Cocktail_sort.py

Zzz-ww/Python-prac

c97f2c16b74a2c1df117f377a072811cc596f98b

[ "MIT" ]

null

""" 双向冒泡：冒泡排序，每次都是从左往右，交换相邻的元素，从而达到循环一边可以把最大的元素放在右边。而双向冒泡排序，在完成一次从左往右的冒泡排序后，再从右往左进行冒泡，从而把小的元素放在左边。下面这张图可以很好地表达： """ def bubble_sort(origin_items): """高质量冒泡排序(搅拌排序)/双向冒泡排序""" comp = lambda x, y: x > y items = origin_items[:] for i in range(len(items) - 1): swapped = False # 这个标志位也是可以放到简单冒泡排序中的，当已经排序好后，减少循环次数 for j in range(i, len(items) - 1 - i): # 正向：把当前循环最大的放到最后 if comp(items[j], items[j + 1]): items[j], items[j + 1] = items[j + 1], items[j] swapped = True if swapped: swapped = False for j in range(len(items) - 2 - i, i, -1): # 反向：把当前循环最小的放到最前 if comp(items[j - 1], items[j]): items[j], items[j - 1] = items[j - 1], items[j] swapped = True if not swapped: break return items def main(): s = [1, 10, 2, 8, 5] print(bubble_sort(s)) if __name__ == '__main__': main()

27.333333

73

0.530488

def bubble_sort(origin_items): comp = lambda x, y: x > y items = origin_items[:] for i in range(len(items) - 1): swapped = False for j in range(i, len(items) - 1 - i): if comp(items[j], items[j + 1]): items[j], items[j + 1] = items[j + 1], items[j] swapped = True if swapped: swapped = False for j in range(len(items) - 2 - i, i, -1): if comp(items[j - 1], items[j]): items[j], items[j - 1] = items[j - 1], items[j] swapped = True if not swapped: break return items def main(): s = [1, 10, 2, 8, 5] print(bubble_sort(s)) if __name__ == '__main__': main()

true

1c43f7cc88953082721b55d83771cbbd3042f65b

1,154

py

Python

check_generated_geometry.py

hyuanmech/MOPSO

f2cbe9151d9dbd21b562957b368f22e2648232b9

[ "MIT" ]

null

check_generated_geometry.py

hyuanmech/MOPSO

f2cbe9151d9dbd21b562957b368f22e2648232b9

[ "MIT" ]

null

check_generated_geometry.py

hyuanmech/MOPSO

f2cbe9151d9dbd21b562957b368f22e2648232b9

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Mon Mar 23 16:05:17 2020 @author: yuanh """ import os import shutil from openpyxl import load_workbook import numpy as np it = 6 flag = 0 nPop = 100 if flag == 1: n = 9 index = np.zeros((n, 1)) wb = load_workbook('Positions.xlsx') sheet = wb['2_mu'] for i in range(n): index[i,0] = sheet.cell(row=i+2,column=1).value if flag == 1: os.mkdir(str(it)+'_MU_all') for hh in range(n): source = "J:/Coupler_optimization/MOPSO_CAE/"+str(it)+"_"+str(int(index[hh,0]))+"_MU"+"/"+str(it)+"_"+str(int(index[hh,0]))+"_MU_geo.pdf" destination = "J:/Coupler_optimization/MOPSO_CAE/"+str(it)+"_MU_all"+"/"+str(it)+"_"+str(int(index[hh,0]))+"_MU_geo.pdf" shutil.copyfile(source, destination) else: os.mkdir(str(it)+'_all') for hh in range(nPop): source = "J:/Coupler_optimization/MOPSO_CAE/"+str(it)+"_"+str(hh)+"/"+str(it)+"_"+str(hh)+"_geo.pdf" destination = "J:/Coupler_optimization/MOPSO_CAE/"+str(it)+"_all"+"/"+str(it)+"_"+str(hh)+"_geo.pdf" shutil.copyfile(source, destination)

28.85

146

0.587522

import os import shutil from openpyxl import load_workbook import numpy as np it = 6 flag = 0 nPop = 100 if flag == 1: n = 9 index = np.zeros((n, 1)) wb = load_workbook('Positions.xlsx') sheet = wb['2_mu'] for i in range(n): index[i,0] = sheet.cell(row=i+2,column=1).value if flag == 1: os.mkdir(str(it)+'_MU_all') for hh in range(n): source = "J:/Coupler_optimization/MOPSO_CAE/"+str(it)+"_"+str(int(index[hh,0]))+"_MU"+"/"+str(it)+"_"+str(int(index[hh,0]))+"_MU_geo.pdf" destination = "J:/Coupler_optimization/MOPSO_CAE/"+str(it)+"_MU_all"+"/"+str(it)+"_"+str(int(index[hh,0]))+"_MU_geo.pdf" shutil.copyfile(source, destination) else: os.mkdir(str(it)+'_all') for hh in range(nPop): source = "J:/Coupler_optimization/MOPSO_CAE/"+str(it)+"_"+str(hh)+"/"+str(it)+"_"+str(hh)+"_geo.pdf" destination = "J:/Coupler_optimization/MOPSO_CAE/"+str(it)+"_all"+"/"+str(it)+"_"+str(hh)+"_geo.pdf" shutil.copyfile(source, destination)

true

1c43fa14320229168e0e657e1dda3761504a32b4

992

py

Python

guillotina/tests/test_middlewares.py

psanlorenzo/guillotina

0840cf39914d23a9e26e35bd40939511d3ca78d7

[ "BSD-2-Clause" ]

null

guillotina/tests/test_middlewares.py

psanlorenzo/guillotina

0840cf39914d23a9e26e35bd40939511d3ca78d7

[ "BSD-2-Clause" ]

null

guillotina/tests/test_middlewares.py

psanlorenzo/guillotina

0840cf39914d23a9e26e35bd40939511d3ca78d7

[ "BSD-2-Clause" ]

null

import asyncio import pytest import time class AsgiMiddlewate: def __init__(self, app): self.next_app = app async def __call__(self, scope, receive, send): start = time.time() await asyncio.sleep(0.1) response = await self.next_app(scope, receive, send) end = time.time() response.headers["Measures"] = str(end - start) return response @pytest.mark.asyncio @pytest.mark.app_settings({"middlewares": ["guillotina.tests.test_middlewares.AsgiMiddlewate"]}) async def test_asgi_middleware(container_requester): async with container_requester as requester: response, _, headers = await requester.make_request("GET", "/") assert response == { "@type": "Application", "databases": ["db", "db-custom"], "static_directory": ["static", "module_static", "jsapp_static"], "static_file": ["favicon.ico"], } assert float(headers.get("measures")) > 0.1

31

96

0.633065

import asyncio import pytest import time class AsgiMiddlewate: def __init__(self, app): self.next_app = app async def __call__(self, scope, receive, send): start = time.time() await asyncio.sleep(0.1) response = await self.next_app(scope, receive, send) end = time.time() response.headers["Measures"] = str(end - start) return response @pytest.mark.asyncio @pytest.mark.app_settings({"middlewares": ["guillotina.tests.test_middlewares.AsgiMiddlewate"]}) async def test_asgi_middleware(container_requester): async with container_requester as requester: response, _, headers = await requester.make_request("GET", "/") assert response == { "@type": "Application", "databases": ["db", "db-custom"], "static_directory": ["static", "module_static", "jsapp_static"], "static_file": ["favicon.ico"], } assert float(headers.get("measures")) > 0.1

true

1c43fa71bbb82846c555d0bca310adf074f93a62

2,024

py

Python

third_party/tests/Opentitan/util/tlgen/item.py

parzival3/Surelog

cf126533ebfb2af7df321057af9e3535feb30487

[ "Apache-2.0" ]

156

2019-11-16T17:29:55.000Z

2022-01-21T05:41:13.000Z

third_party/tests/Opentitan/util/tlgen/item.py

parzival3/Surelog

cf126533ebfb2af7df321057af9e3535feb30487

[ "Apache-2.0" ]

414

2021-06-11T07:22:01.000Z

2022-03-31T22:06:14.000Z

third_party/tests/Opentitan/util/tlgen/item.py

parzival3/Surelog

cf126533ebfb2af7df321057af9e3535feb30487

[ "Apache-2.0" ]

30

2019-11-18T16:31:40.000Z

2021-12-26T01:22:51.000Z

# Copyright lowRISC contributors. # Licensed under the Apache License, Version 2.0, see LICENSE for details. # SPDX-License-Identifier: Apache-2.0 from enum import Enum class Edge: """Edge class contains the connection from a node to a node. a Node can be a host port, output of async_fifo, port in a socket, or a device port. """ def __init__(self, us, ds): self.us = us self.ds = ds def __repr__(self): return "U(%s) D(%s)" % (self.us.name, self.ds.name) #Edges = List[Edge] #Clocks = List[str] # If length is more than one, should be exactly two # [UpstreamClock, DownstreamClock] class NodeType(Enum): HOST = 1 DEVICE = 2 ASYNC_FIFO = 3 SOCKET_1N = 4 SOCKET_M1 = 5 class Node: """Node class is a port that communicates from/to other Node or TL-UL input/output. """ name = "" # name: str # node_type: NodeType clocks = [] # Clocks # clock domains of the node resets = [] # Resets # resets of the node # e.g. async_fifo in : clk_core , out : clk_main # If NodeType is Socket out from 1:N then address steering is used # But this value is also propagated up to a Host from multiple Devices # Device Node should have address_from, address_to #address_from = 0 #: int #address_to = 0 #: int addr_range = [] us = [] # Edges # Number of Ports depends on the NodeType # 1 for Host, Device, 2 for Async FIFO, N for Sockets ds = [] # Edges # Req/Rsp FIFO. default False # when False, FIFO fully passthrough, no storage element # when True, FIFO present with default depth, "pipeline_byp" # controls passthrough option pipeline = False # FIFO passtru option. default True pipeline_byp = True def __init__(self, name, node_type, clock, reset): self.name = name self.node_type = node_type self.clocks = [clock] self.resets = [reset] self.us = [] self.ds = [] self.addr_range = []

26.986667

74

0.630929

from enum import Enum class Edge: def __init__(self, us, ds): self.us = us self.ds = ds def __repr__(self): return "U(%s) D(%s)" % (self.us.name, self.ds.name) = 2 ASYNC_FIFO = 3 SOCKET_1N = 4 SOCKET_M1 = 5 class Node: name = "" clocks = [] range = [] us = [] pipeline = False pipeline_byp = True def __init__(self, name, node_type, clock, reset): self.name = name self.node_type = node_type self.clocks = [clock] self.resets = [reset] self.us = [] self.ds = [] self.addr_range = []

true

1c43fba068f52e1707fd9f7186978e03b366e299

1,960

py

Python

cli/tests/test_cli.py

SophieHerbst/mne-bids

0e9b5e261668b90efec28359772f321d999af7d7

[ "BSD-3-Clause" ]

null

cli/tests/test_cli.py

SophieHerbst/mne-bids

0e9b5e261668b90efec28359772f321d999af7d7

[ "BSD-3-Clause" ]

null

cli/tests/test_cli.py

SophieHerbst/mne-bids

0e9b5e261668b90efec28359772f321d999af7d7

[ "BSD-3-Clause" ]

null

"""Test command line.""" # Authors: Teon L Brooks <teon.brooks@gmail.com> # Stefan Appelhoff <stefan.appelhoff@mailbox.org> # # License: BSD (3-clause) from os import path as op import pytest import mne from mne.datasets import testing from mne.utils import run_tests_if_main, ArgvSetter from cli import mne_bids_raw_to_bids, mne_bids_cp base_path = op.join(op.dirname(mne.__file__), 'io') subject_id = '01' task = 'testing' def check_usage(module, force_help=False): """Ensure we print usage.""" args = ('--help',) if force_help else () with ArgvSetter(args) as out: try: module.run() except SystemExit: pass assert 'Usage: ' in out.stdout.getvalue() def test_raw_to_bids(tmpdir): """Test mne_bids raw_to_bids.""" output_path = str(tmpdir) data_path = testing.data_path() raw_fname = op.join(data_path, 'MEG', 'sample', 'sample_audvis_trunc_raw.fif') # Check that help is printed check_usage(mne_bids_raw_to_bids) # Should work with ArgvSetter(('--subject_id', subject_id, '--task', task, '--raw', raw_fname, '--output_path', output_path)): mne_bids_raw_to_bids.run() # Too few input args with pytest.raises(SystemExit): with ArgvSetter(('--subject_id', subject_id)): mne_bids_cp.run() def test_cp(tmpdir): """Test mne_bids cp.""" output_path = str(tmpdir) data_path = op.join(base_path, 'brainvision', 'tests', 'data') raw_fname = op.join(data_path, 'test.vhdr') outname = op.join(output_path, 'test2.vhdr') # Check that help is printed check_usage(mne_bids_cp) # Should work with ArgvSetter(('--input', raw_fname, '--output', outname)): mne_bids_cp.run() # Too few input args with pytest.raises(SystemExit): with ArgvSetter(('--input', raw_fname)): mne_bids_cp.run() run_tests_if_main()

27.222222

73

0.642347

from os import path as op import pytest import mne from mne.datasets import testing from mne.utils import run_tests_if_main, ArgvSetter from cli import mne_bids_raw_to_bids, mne_bids_cp base_path = op.join(op.dirname(mne.__file__), 'io') subject_id = '01' task = 'testing' def check_usage(module, force_help=False): args = ('--help',) if force_help else () with ArgvSetter(args) as out: try: module.run() except SystemExit: pass assert 'Usage: ' in out.stdout.getvalue() def test_raw_to_bids(tmpdir): output_path = str(tmpdir) data_path = testing.data_path() raw_fname = op.join(data_path, 'MEG', 'sample', 'sample_audvis_trunc_raw.fif') check_usage(mne_bids_raw_to_bids) with ArgvSetter(('--subject_id', subject_id, '--task', task, '--raw', raw_fname, '--output_path', output_path)): mne_bids_raw_to_bids.run() with pytest.raises(SystemExit): with ArgvSetter(('--subject_id', subject_id)): mne_bids_cp.run() def test_cp(tmpdir): output_path = str(tmpdir) data_path = op.join(base_path, 'brainvision', 'tests', 'data') raw_fname = op.join(data_path, 'test.vhdr') outname = op.join(output_path, 'test2.vhdr') check_usage(mne_bids_cp) with ArgvSetter(('--input', raw_fname, '--output', outname)): mne_bids_cp.run() with pytest.raises(SystemExit): with ArgvSetter(('--input', raw_fname)): mne_bids_cp.run() run_tests_if_main()

true

1c43fc03ab33ea2e19164c0644663693552fe20d

17,011

py

Python

opics/utils.py

jaspreetj/opics

037ed93ad9f6c9ad9fec5feb214bb89de24635f0

[ "MIT" ]

null

opics/utils.py

jaspreetj/opics

037ed93ad9f6c9ad9fec5feb214bb89de24635f0

[ "MIT" ]

null

opics/utils.py

jaspreetj/opics

037ed93ad9f6c9ad9fec5feb214bb89de24635f0

[ "MIT" ]

null

from typing import Any, Dict, List, Tuple import cmath as cm import time import re import itertools import inspect from copy import deepcopy import numpy as np from numpy import ndarray from pathlib import PosixPath from defusedxml.ElementTree import parse def fromSI(value: str) -> float: """converts from SI unit values to metric Args: value (str): a value in SI units, e.g. 1.3u Returns: float: the value in metric units. """ return float(value.replace("u", "e-6")) def universal_sparam_filereader( nports: int, sfilename: str, sfiledir: PosixPath, format_type: str = "auto" ) -> Tuple[ndarray, ndarray]: """ Function to automatically detect the sparameter file format and use appropriate method to delimit and format sparam data This function is a unified version of sparameter reader function defined in https://github.com/BYUCamachoLab/simphony Args: nports: Number of ports sfilename: XML look-up-table filename sfiledir: Path to the directory containing the XML file format_type: Format type. For more information: https://support.lumerical.com/hc/en-us/articles/360036618513-S-parameter-file-formats """ numports = nports filename = sfiledir / sfilename if format_type == "auto": try: # print("try A") result = universal_sparam_filereader(nports, sfilename, sfiledir, "A") return result except Exception: try: # print("try B") result = universal_sparam_filereader(nports, sfilename, sfiledir, "B") return result except Exception: # print("try C") result = universal_sparam_filereader(nports, sfilename, sfiledir, "C") return result elif format_type == "A": """ dc_halfring_te_1550 Returns the s-parameters across some frequency range for the Sparam fileformat A input: ["port 1",""] ["port 2",""] ["port 3",""] ["port 4",""] ("port 1","mode 1",1,"port 1",1,"transmission") (101, 3) output: [frequency, s-parameters] """ F = [] S = [] with open(filename, "r") as fid: for i in range(5): line = fid.readline() line = fid.readline() numrows = int(tuple(line[1:-2].split(","))[0]) S = np.zeros((numrows, numports, numports), dtype="complex128") r = m = n = 0 for line in fid: if line[0] == "(": continue data = line.split() data = list(map(float, data)) if m == 0 and n == 0: F.append(data[0]) S[r, m, n] = data[1] * np.exp(1j * data[2]) r += 1 if r == numrows: r = 0 m += 1 if m == numports: m = 0 n += 1 if n == numports: break return (np.array(F), S) elif format_type == "B": """ ebeam_bdc_te1550, nanotaper, ebeam_y_1550 Returns the s-parameters across some frequency range for the Sparam fileformat A input: ('port 1','TE',1,'port 1',1,'transmission') (51,3) output: [frequency, s-parameters] """ F = [] S = [] with open(filename, "r") as fid: line = fid.readline() line = fid.readline() numrows = int(tuple(line[1:-2].split(","))[0]) S = np.zeros((numrows, numports, numports), dtype="complex128") r = m = n = 0 for line in fid: if line[0] == "(": continue data = line.split() data = list(map(float, data)) if m == 0 and n == 0: F.append(data[0]) S[r, m, n] = data[1] * np.exp(1j * data[2]) r += 1 if r == numrows: r = 0 m += 1 if m == numports: m = 0 n += 1 if n == numports: break return (np.array(F), S) elif format_type == "C": """ ebeam_gc_te1550 Returns the s-parameters across some frequency range for the Sparam fileformat A input: columns with space delimiter output: [frequency, s-parameters] """ with open(filename) as fid: # grating coupler compact models have 100 points for each s-matrix index arrlen = 100 lines = fid.readlines() F = np.zeros(arrlen) S = np.zeros((arrlen, 2, 2), "complex128") for i in range(0, arrlen): words = lines[i].split() F[i] = float(words[0]) S[i, 0, 0] = cm.rect(float(words[1]), float(words[2])) S[i, 0, 1] = cm.rect(float(words[3]), float(words[4])) S[i, 1, 0] = cm.rect(float(words[5]), float(words[6])) S[i, 1, 1] = cm.rect(float(words[7]), float(words[8])) F = F[::-1] S = S[::-1, :, :] return (np.array(F), S) def LUT_reader(filedir: PosixPath, lutfilename: str, lutdata: List[List[str]]): """ Reads look up table data. Args: filedir: Directory of the XML look-up-table file. lutfilename: Look-up-table filename. lutdata: Look-up-table arguments. """ xml = parse(filedir / lutfilename) root = xml.getroot() for node in root.iter("association"): sample = [[each.attrib["name"], each.text] for each in node.iter("value")] if sorted(sample[0:-1]) == sorted(lutdata): break sparam_file = sample[-1][1].split(";") return (sparam_file, xml, node) def LUT_processor( filedir: PosixPath, lutfilename: str, lutdata: List[List[str]], nports: int, sparam_attr: str, verbose: bool = False, ) -> Tuple[Tuple[ndarray, ndarray], str]: """process look up table data""" start = time.time() sparam_file, xml, node = LUT_reader(filedir, lutfilename, lutdata) # read data if ".npz" in sparam_file[0] or ".npz" in sparam_file[-1]: npzfile = [each for each in sparam_file if ".npz" in each][0] tempdata = np.load(filedir / npzfile) sdata = (tempdata["f"], tempdata["s"]) npz_file = npzfile else: if verbose: print("numpy datafile not found. reading sparam file instead..") sdata = universal_sparam_filereader(nports, sparam_file[-1], filedir, "auto") # create npz file name npz_file = sparam_file[-1].split(".")[0] # save as npz file np.savez(filedir / npz_file, f=sdata[0], s=sdata[1]) # update xml file sparam_file.append(npz_file + ".npz") sparam_file = list(set(sparam_file)) for each in node.iter("value"): if each.attrib["name"] == sparam_attr: each.text = ";".join(sparam_file) xml.write(filedir / lutfilename) if verbose: print("SParam data extracted in ", time.time() - start) return (sdata, npz_file) def NetlistProcessor(spice_filepath, Network, libraries, c_, circuitData, verbose=True): """ Processes a spice netlist to setup and simulate a circuit. Args: spice_filepath: Path to the spice netlist file. Network: """ if verbose: for key, value in circuitData.items(): print(key, str(value)) # define frequency range and resolution freq = np.linspace( c_ / circuitData["sim_params"][0], c_ / circuitData["sim_params"][1], circuitData["sim_params"][2], ) # create a circuit subckt = Network(network_id=circuitData["networkID"], f=freq) # get library all_libraries = dict( [ each for each in inspect.getmembers(libraries, inspect.ismodule) if each[0][0] != "_" ] ) libs_comps = {} for each_lib in list(set(circuitData["compLibs"])): # temp_comps = dict(inspect.getmembers(all_libraries[each_lib], inspect.isclass)) libs_comps[each_lib] = all_libraries[each_lib].component_factory # add circuit components for i in range(len(circuitData["compModels"])): # get component model comp_model = libs_comps[circuitData["compLibs"][i]][ circuitData["compModels"][i] ] # clean attributes cls_attrs = deepcopy(comp_model.cls_attrs) # class attributes comp_attrs = circuitData["compAttrs"][i] # component attributes # clean up attributes for each_cls_attrs in cls_attrs.keys(): for each_comp_attrs in comp_attrs.keys(): if each_cls_attrs in each_comp_attrs: cls_attrs[each_cls_attrs] = fromSI(comp_attrs[each_comp_attrs]) subckt.add_component( libs_comps[circuitData["compLibs"][i]][circuitData["compModels"][i]], params=cls_attrs, component_id=circuitData["compLabels"][i], ) # add circuit netlist subckt.global_netlist[circuitData["compLabels"][i]] = circuitData[ "circuitNets" ][i] # add unique net component connections subckt.current_connections = circuitData["circuitConns"] return subckt class netlistParser: "A netlist parser to read spi files generated by SiEPIC tools" def __init__(self, mainfile_path: PosixPath) -> None: self.circuitComponents = [] self.circuitConnections = [] self.mainfile_path = mainfile_path def readfile(self) -> Dict[str, Any]: filepath = self.mainfile_path circuitID = "" inp = "" out = "" inp_net = 0 out_net = [] circuitLabels = [] circuitModels = [] circuitConns = [] circuitNets = [] componentLibs = [] componentAttrs = [] component_locations = [] temp_file = open(filepath, "r") temp_lines = temp_file.readlines() free_node_idx = -1 freq_data = [] seek_component = 0 seek_ona = 0 orthogonal_ID = 0 # extract circuit connectivity for each_line in temp_lines: each_line = re.sub(" +", " ", each_line.strip()) # remove empty lines if each_line.startswith("*"): continue else: each_line = "".join( [ "".join(filter(None, each_section.split(" "))) if ('"' in each_section) else each_section for each_section in re.split( r"""("[^"]*"|'[^']*')""", each_line ) ] ) temp_data = each_line.split(" ") if len(temp_data) > 1: # if line is not an empty one MC_location = [] if temp_data[0] == ".subckt": circuitID = temp_data[1] inp = temp_data[2] out = [temp_data[x] for x in range(3, len(temp_data))] seek_component = 1 elif temp_data[0] == ".param": continue elif temp_data[0] == ".ends": seek_component = 0 elif temp_data[0] == ".ona": seek_ona = 1 elif seek_ona == 1: # ONA related data if len(temp_data) < 3: temp_data = [0] + temp_data[-1].split("=") if temp_data[1] == "orthogonal_identifier": orthogonal_ID = int(temp_data[-1]) elif temp_data[1] == "start": freq_data.append(float(temp_data[-1])) elif temp_data[1] == "stop": freq_data.append(float(temp_data[-1])) elif temp_data[1] == "number_of_points": freq_data.append(int(temp_data[-1])) elif seek_component == 1: # otherwise its component data circuitLabels.append(temp_data[0]) temp_ports = [] found_ports = 0 found_library = 0 for i in range(1, len(temp_data)): # if its an optical port if ( "N$" in temp_data[i] and "N$None".lower() != temp_data[i].lower() ): temp_ports.append(int(temp_data[i].replace("N$", ""))) found_ports = 1 elif "N$None".lower() == temp_data[i].lower(): temp_ports.append(free_node_idx) free_node_idx -= 1 found_ports = 1 elif inp == temp_data[i]: temp_ports.append(free_node_idx) inp_net = free_node_idx free_node_idx -= 1 found_ports = 1 elif out[0] == temp_data[i]: temp_ports.append(free_node_idx) out_net.append(free_node_idx) free_node_idx -= 1 if len(out) > 1: out.pop(0) if len(out) == 0: found_ports = 1 elif found_ports == 1 and "N$" not in temp_data[i]: circuitModels.append(temp_data[i]) temp_cls_atrr = ( {} ) # deepcopy(lib[temp_data[i]].cls_attrs) found_ports = -1 elif "lay" in temp_data[i] or "sch" in temp_data[i]: if "lay" in temp_data[i]: MC_location.append( fromSI(temp_data[i].split("=")[-1]) * 1e6 ) # ignore layout and schematic position data for now. # adapt opics models to accept this data # they are component parameters elif "library" in temp_data[i]: # cprint(temp_data[i]) temp_lib = ( temp_data[i].replace('"', "").split("=")[1].split() ) componentLibs.append( temp_lib[-1].split("/")[-1].lower() ) found_library = 1 elif "=" in temp_data[i] and found_library == 1: # if its a components' attribute temp_attr = temp_data[i].split("=") # print(temp_attr[0]) # if(temp_attr[0] in temp_cls_atrr): temp_cls_atrr[temp_attr[0]] = temp_attr[1].strip('"') componentAttrs.append(temp_cls_atrr) circuitNets.append(temp_ports) if bool(MC_location): component_locations.append(MC_location) circuitConns = list(set(list(itertools.chain(*circuitNets)))) # remove IOs from component connections' list circuitConns = [each for each in circuitConns if each >= 0] # return all data return { "circuitNets": circuitNets, "circuitConns": circuitConns, "compLibs": componentLibs, "compModels": circuitModels, "compLabels": circuitLabels, "compAttrs": componentAttrs, "compLocs": component_locations, "networkID": circuitID, "inp_net": inp_net, "out_net": out_net, "sim_params": freq_data, "OID": orthogonal_ID, }

35.146694

141

0.479043

from typing import Any, Dict, List, Tuple import cmath as cm import time import re import itertools import inspect from copy import deepcopy import numpy as np from numpy import ndarray from pathlib import PosixPath from defusedxml.ElementTree import parse def fromSI(value: str) -> float: return float(value.replace("u", "e-6")) def universal_sparam_filereader( nports: int, sfilename: str, sfiledir: PosixPath, format_type: str = "auto" ) -> Tuple[ndarray, ndarray]: numports = nports filename = sfiledir / sfilename if format_type == "auto": try: result = universal_sparam_filereader(nports, sfilename, sfiledir, "A") return result except Exception: try: result = universal_sparam_filereader(nports, sfilename, sfiledir, "B") return result except Exception: result = universal_sparam_filereader(nports, sfilename, sfiledir, "C") return result elif format_type == "A": """ dc_halfring_te_1550 Returns the s-parameters across some frequency range for the Sparam fileformat A input: ["port 1",""] ["port 2",""] ["port 3",""] ["port 4",""] ("port 1","mode 1",1,"port 1",1,"transmission") (101, 3) output: [frequency, s-parameters] """ F = [] S = [] with open(filename, "r") as fid: for i in range(5): line = fid.readline() line = fid.readline() numrows = int(tuple(line[1:-2].split(","))[0]) S = np.zeros((numrows, numports, numports), dtype="complex128") r = m = n = 0 for line in fid: if line[0] == "(": continue data = line.split() data = list(map(float, data)) if m == 0 and n == 0: F.append(data[0]) S[r, m, n] = data[1] * np.exp(1j * data[2]) r += 1 if r == numrows: r = 0 m += 1 if m == numports: m = 0 n += 1 if n == numports: break return (np.array(F), S) elif format_type == "B": """ ebeam_bdc_te1550, nanotaper, ebeam_y_1550 Returns the s-parameters across some frequency range for the Sparam fileformat A input: ('port 1','TE',1,'port 1',1,'transmission') (51,3) output: [frequency, s-parameters] """ F = [] S = [] with open(filename, "r") as fid: line = fid.readline() line = fid.readline() numrows = int(tuple(line[1:-2].split(","))[0]) S = np.zeros((numrows, numports, numports), dtype="complex128") r = m = n = 0 for line in fid: if line[0] == "(": continue data = line.split() data = list(map(float, data)) if m == 0 and n == 0: F.append(data[0]) S[r, m, n] = data[1] * np.exp(1j * data[2]) r += 1 if r == numrows: r = 0 m += 1 if m == numports: m = 0 n += 1 if n == numports: break return (np.array(F), S) elif format_type == "C": """ ebeam_gc_te1550 Returns the s-parameters across some frequency range for the Sparam fileformat A input: columns with space delimiter output: [frequency, s-parameters] """ with open(filename) as fid: arrlen = 100 lines = fid.readlines() F = np.zeros(arrlen) S = np.zeros((arrlen, 2, 2), "complex128") for i in range(0, arrlen): words = lines[i].split() F[i] = float(words[0]) S[i, 0, 0] = cm.rect(float(words[1]), float(words[2])) S[i, 0, 1] = cm.rect(float(words[3]), float(words[4])) S[i, 1, 0] = cm.rect(float(words[5]), float(words[6])) S[i, 1, 1] = cm.rect(float(words[7]), float(words[8])) F = F[::-1] S = S[::-1, :, :] return (np.array(F), S) def LUT_reader(filedir: PosixPath, lutfilename: str, lutdata: List[List[str]]): xml = parse(filedir / lutfilename) root = xml.getroot() for node in root.iter("association"): sample = [[each.attrib["name"], each.text] for each in node.iter("value")] if sorted(sample[0:-1]) == sorted(lutdata): break sparam_file = sample[-1][1].split(";") return (sparam_file, xml, node) def LUT_processor( filedir: PosixPath, lutfilename: str, lutdata: List[List[str]], nports: int, sparam_attr: str, verbose: bool = False, ) -> Tuple[Tuple[ndarray, ndarray], str]: start = time.time() sparam_file, xml, node = LUT_reader(filedir, lutfilename, lutdata) if ".npz" in sparam_file[0] or ".npz" in sparam_file[-1]: npzfile = [each for each in sparam_file if ".npz" in each][0] tempdata = np.load(filedir / npzfile) sdata = (tempdata["f"], tempdata["s"]) npz_file = npzfile else: if verbose: print("numpy datafile not found. reading sparam file instead..") sdata = universal_sparam_filereader(nports, sparam_file[-1], filedir, "auto") npz_file = sparam_file[-1].split(".")[0] np.savez(filedir / npz_file, f=sdata[0], s=sdata[1]) sparam_file.append(npz_file + ".npz") sparam_file = list(set(sparam_file)) for each in node.iter("value"): if each.attrib["name"] == sparam_attr: each.text = ";".join(sparam_file) xml.write(filedir / lutfilename) if verbose: print("SParam data extracted in ", time.time() - start) return (sdata, npz_file) def NetlistProcessor(spice_filepath, Network, libraries, c_, circuitData, verbose=True): if verbose: for key, value in circuitData.items(): print(key, str(value)) freq = np.linspace( c_ / circuitData["sim_params"][0], c_ / circuitData["sim_params"][1], circuitData["sim_params"][2], ) subckt = Network(network_id=circuitData["networkID"], f=freq) all_libraries = dict( [ each for each in inspect.getmembers(libraries, inspect.ismodule) if each[0][0] != "_" ] ) libs_comps = {} for each_lib in list(set(circuitData["compLibs"])): libs_comps[each_lib] = all_libraries[each_lib].component_factory for i in range(len(circuitData["compModels"])): comp_model = libs_comps[circuitData["compLibs"][i]][ circuitData["compModels"][i] ] cls_attrs = deepcopy(comp_model.cls_attrs) comp_attrs = circuitData["compAttrs"][i] for each_cls_attrs in cls_attrs.keys(): for each_comp_attrs in comp_attrs.keys(): if each_cls_attrs in each_comp_attrs: cls_attrs[each_cls_attrs] = fromSI(comp_attrs[each_comp_attrs]) subckt.add_component( libs_comps[circuitData["compLibs"][i]][circuitData["compModels"][i]], params=cls_attrs, component_id=circuitData["compLabels"][i], ) subckt.global_netlist[circuitData["compLabels"][i]] = circuitData[ "circuitNets" ][i] subckt.current_connections = circuitData["circuitConns"] return subckt class netlistParser: def __init__(self, mainfile_path: PosixPath) -> None: self.circuitComponents = [] self.circuitConnections = [] self.mainfile_path = mainfile_path def readfile(self) -> Dict[str, Any]: filepath = self.mainfile_path circuitID = "" inp = "" out = "" inp_net = 0 out_net = [] circuitLabels = [] circuitModels = [] circuitConns = [] circuitNets = [] componentLibs = [] componentAttrs = [] component_locations = [] temp_file = open(filepath, "r") temp_lines = temp_file.readlines() free_node_idx = -1 freq_data = [] seek_component = 0 seek_ona = 0 orthogonal_ID = 0 for each_line in temp_lines: each_line = re.sub(" +", " ", each_line.strip()) if each_line.startswith("*"): continue else: each_line = "".join( [ "".join(filter(None, each_section.split(" "))) if ('"' in each_section) else each_section for each_section in re.split( r"""("[^"]*"|'[^']*')""", each_line ) ] ) temp_data = each_line.split(" ") if len(temp_data) > 1: # if line is not an empty one MC_location = [] if temp_data[0] == ".subckt": circuitID = temp_data[1] inp = temp_data[2] out = [temp_data[x] for x in range(3, len(temp_data))] seek_component = 1 elif temp_data[0] == ".param": continue elif temp_data[0] == ".ends": seek_component = 0 elif temp_data[0] == ".ona": seek_ona = 1 elif seek_ona == 1: # ONA related data if len(temp_data) < 3: temp_data = [0] + temp_data[-1].split("=") if temp_data[1] == "orthogonal_identifier": orthogonal_ID = int(temp_data[-1]) elif temp_data[1] == "start": freq_data.append(float(temp_data[-1])) elif temp_data[1] == "stop": freq_data.append(float(temp_data[-1])) elif temp_data[1] == "number_of_points": freq_data.append(int(temp_data[-1])) elif seek_component == 1: # otherwise its component data circuitLabels.append(temp_data[0]) temp_ports = [] found_ports = 0 found_library = 0 for i in range(1, len(temp_data)): # if its an optical port if ( "N$" in temp_data[i] and "N$None".lower() != temp_data[i].lower() ): temp_ports.append(int(temp_data[i].replace("N$", ""))) found_ports = 1 elif "N$None".lower() == temp_data[i].lower(): temp_ports.append(free_node_idx) free_node_idx -= 1 found_ports = 1 elif inp == temp_data[i]: temp_ports.append(free_node_idx) inp_net = free_node_idx free_node_idx -= 1 found_ports = 1 elif out[0] == temp_data[i]: temp_ports.append(free_node_idx) out_net.append(free_node_idx) free_node_idx -= 1 if len(out) > 1: out.pop(0) if len(out) == 0: found_ports = 1 elif found_ports == 1 and "N$" not in temp_data[i]: circuitModels.append(temp_data[i]) temp_cls_atrr = ( {} ) # deepcopy(lib[temp_data[i]].cls_attrs) found_ports = -1 elif "lay" in temp_data[i] or "sch" in temp_data[i]: if "lay" in temp_data[i]: MC_location.append( fromSI(temp_data[i].split("=")[-1]) * 1e6 ) # ignore layout and schematic position data for now. # adapt opics models to accept this data # they are component parameters elif "library" in temp_data[i]: # cprint(temp_data[i]) temp_lib = ( temp_data[i].replace('"', "").split("=")[1].split() ) componentLibs.append( temp_lib[-1].split("/")[-1].lower() ) found_library = 1 elif "=" in temp_data[i] and found_library == 1: # if its a components' attribute temp_attr = temp_data[i].split("=") # print(temp_attr[0]) # if(temp_attr[0] in temp_cls_atrr): temp_cls_atrr[temp_attr[0]] = temp_attr[1].strip('"') componentAttrs.append(temp_cls_atrr) circuitNets.append(temp_ports) if bool(MC_location): component_locations.append(MC_location) circuitConns = list(set(list(itertools.chain(*circuitNets)))) circuitConns = [each for each in circuitConns if each >= 0] # return all data return { "circuitNets": circuitNets, "circuitConns": circuitConns, "compLibs": componentLibs, "compModels": circuitModels, "compLabels": circuitLabels, "compAttrs": componentAttrs, "compLocs": component_locations, "networkID": circuitID, "inp_net": inp_net, "out_net": out_net, "sim_params": freq_data, "OID": orthogonal_ID, }

true

1c43fd68b8e8426feafb7efe2b494da8cef3208e

16,870

py

Python

django_extensions/management/modelviz.py

echirchir/django-extensions

ae38e33309b87bf7431bc5f1321699f5d00a0431

[ "MIT" ]

null

django_extensions/management/modelviz.py

echirchir/django-extensions

ae38e33309b87bf7431bc5f1321699f5d00a0431

[ "MIT" ]

null

django_extensions/management/modelviz.py

echirchir/django-extensions

ae38e33309b87bf7431bc5f1321699f5d00a0431

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ modelviz.py - DOT file generator for Django Models Based on: Django model to DOT (Graphviz) converter by Antonio Cavedoni <antonio@cavedoni.org> Adapted to be used with django-extensions """ import datetime import os import re import six from django.apps import apps from django.db.models.fields.related import ( ForeignKey, ManyToManyField, OneToOneField, RelatedField, ) from django.contrib.contenttypes.fields import GenericRelation from django.template import Context, Template, loader from django.utils.encoding import force_str from django.utils.safestring import mark_safe from django.utils.translation import activate as activate_language __version__ = "1.1" __license__ = "Python" __author__ = "Bas van Oostveen <v.oostveen@gmail.com>", __contributors__ = [ "Antonio Cavedoni <http://cavedoni.com/>" "Stefano J. Attardi <http://attardi.org/>", "limodou <http://www.donews.net/limodou/>", "Carlo C8E Miron", "Andre Campos <cahenan@gmail.com>", "Justin Findlay <jfindlay@gmail.com>", "Alexander Houben <alexander@houben.ch>", "Joern Hees <gitdev@joernhees.de>", "Kevin Cherepski <cherepski@gmail.com>", "Jose Tomas Tocino <theom3ga@gmail.com>", "Adam Dobrawy <naczelnik@jawnosc.tk>", "Mikkel Munch Mortensen <https://www.detfalskested.dk/>", "Andrzej Bistram <andrzej.bistram@gmail.com>", "Daniel Lipsitt <danlipsitt@gmail.com>", ] def parse_file_or_list(arg): if not arg: return [] if isinstance(arg, (list, tuple, set)): return arg if ',' not in arg and os.path.isfile(arg): return [e.strip() for e in open(arg).readlines()] return [e.strip() for e in arg.split(',')] class ModelGraph: def __init__(self, app_labels, **kwargs): self.graphs = [] self.cli_options = kwargs.get('cli_options', None) self.disable_fields = kwargs.get('disable_fields', False) self.disable_abstract_fields = kwargs.get('disable_abstract_fields', False) self.include_models = parse_file_or_list( kwargs.get('include_models', "") ) self.all_applications = kwargs.get('all_applications', False) self.use_subgraph = kwargs.get('group_models', False) self.verbose_names = kwargs.get('verbose_names', False) self.inheritance = kwargs.get('inheritance', True) self.relations_as_fields = kwargs.get("relations_as_fields", True) self.sort_fields = kwargs.get("sort_fields", True) self.language = kwargs.get('language', None) if self.language is not None: activate_language(self.language) self.exclude_columns = parse_file_or_list( kwargs.get('exclude_columns', "") ) self.exclude_models = parse_file_or_list( kwargs.get('exclude_models', "") ) self.hide_edge_labels = kwargs.get('hide_edge_labels', False) self.arrow_shape = kwargs.get("arrow_shape") if self.all_applications: self.app_labels = [app.label for app in apps.get_app_configs()] else: self.app_labels = app_labels def generate_graph_data(self): self.process_apps() nodes = [] for graph in self.graphs: nodes.extend([e['name'] for e in graph['models']]) for graph in self.graphs: for model in graph['models']: for relation in model['relations']: if relation is not None: if relation['target'] in nodes: relation['needs_node'] = False def get_graph_data(self, as_json=False): now = datetime.datetime.now() graph_data = { 'created_at': now.strftime("%Y-%m-%d %H:%M"), 'cli_options': self.cli_options, 'disable_fields': self.disable_fields, 'disable_abstract_fields': self.disable_abstract_fields, 'use_subgraph': self.use_subgraph, } if as_json: graph_data['graphs'] = [context.flatten() for context in self.graphs] else: graph_data['graphs'] = self.graphs return graph_data def add_attributes(self, field, abstract_fields): if self.verbose_names and field.verbose_name: label = force_str(field.verbose_name) if label.islower(): label = label.capitalize() else: label = field.name t = type(field).__name__ if isinstance(field, (OneToOneField, ForeignKey)): t += " ({0})".format(field.remote_field.field_name) # TODO: ManyToManyField, GenericRelation return { 'name': field.name, 'label': label, 'type': t, 'blank': field.blank, 'abstract': field in abstract_fields, 'relation': isinstance(field, RelatedField), 'primary_key': field.primary_key, } def add_relation(self, field, model, extras=""): if self.verbose_names and field.verbose_name: label = force_str(field.verbose_name) if label.islower(): label = label.capitalize() else: label = field.name # show related field name if hasattr(field, 'related_query_name'): related_query_name = field.related_query_name() if self.verbose_names and related_query_name.islower(): related_query_name = related_query_name.replace('_', ' ').capitalize() label = u'{} ({})'.format(label, force_str(related_query_name)) if self.hide_edge_labels: label = '' # handle self-relationships and lazy-relationships if isinstance(field.remote_field.model, six.string_types): if field.remote_field.model == 'self': target_model = field.model else: if '.' in field.remote_field.model: app_label, model_name = field.remote_field.model.split('.', 1) else: app_label = field.model._meta.app_label model_name = field.remote_field.model target_model = apps.get_model(app_label, model_name) else: target_model = field.remote_field.model _rel = self.get_relation_context(target_model, field, label, extras) if _rel not in model['relations'] and self.use_model(_rel['target']): return _rel def get_abstract_models(self, appmodels): abstract_models = [] for appmodel in appmodels: abstract_models += [ abstract_model for abstract_model in appmodel.__bases__ if hasattr(abstract_model, '_meta') and abstract_model._meta.abstract ] abstract_models = list(set(abstract_models)) # remove duplicates return abstract_models def get_app_context(self, app): return Context({ 'name': '"%s"' % app.name, 'app_name': "%s" % app.name, 'cluster_app_name': "cluster_%s" % app.name.replace(".", "_"), 'models': [] }) def get_appmodel_attributes(self, appmodel): if self.relations_as_fields: attributes = [field for field in appmodel._meta.local_fields] else: # Find all the 'real' attributes. Relations are depicted as graph edges instead of attributes attributes = [field for field in appmodel._meta.local_fields if not isinstance(field, RelatedField)] return attributes def get_appmodel_abstracts(self, appmodel): return [ abstract_model.__name__ for abstract_model in appmodel.__bases__ if hasattr(abstract_model, '_meta') and abstract_model._meta.abstract ] def get_appmodel_context(self, appmodel, appmodel_abstracts): context = { 'app_name': appmodel.__module__.replace(".", "_"), 'name': appmodel.__name__, 'abstracts': appmodel_abstracts, 'fields': [], 'relations': [] } if self.verbose_names and appmodel._meta.verbose_name: context['label'] = force_str(appmodel._meta.verbose_name) else: context['label'] = context['name'] return context def get_bases_abstract_fields(self, c): _abstract_fields = [] for e in c.__bases__: if hasattr(e, '_meta') and e._meta.abstract: _abstract_fields.extend(e._meta.fields) _abstract_fields.extend(self.get_bases_abstract_fields(e)) return _abstract_fields def get_inheritance_context(self, appmodel, parent): label = "multi-table" if parent._meta.abstract: label = "abstract" if appmodel._meta.proxy: label = "proxy" label += r"\ninheritance" if self.hide_edge_labels: label = '' return { 'target_app': parent.__module__.replace(".", "_"), 'target': parent.__name__, 'type': "inheritance", 'name': "inheritance", 'label': label, 'arrows': '[arrowhead=empty, arrowtail=none, dir=both]', 'needs_node': True, } def get_models(self, app): appmodels = list(app.get_models()) return appmodels def get_relation_context(self, target_model, field, label, extras): return { 'target_app': target_model.__module__.replace('.', '_'), 'target': target_model.__name__, 'type': type(field).__name__, 'name': field.name, 'label': label, 'arrows': extras, 'needs_node': True } def process_attributes(self, field, model, pk, abstract_fields): newmodel = model.copy() if self.skip_field(field) or pk and field == pk: return newmodel newmodel['fields'].append(self.add_attributes(field, abstract_fields)) return newmodel def process_apps(self): for app_label in self.app_labels: app = apps.get_app_config(app_label) if not app: continue app_graph = self.get_app_context(app) app_models = self.get_models(app) abstract_models = self.get_abstract_models(app_models) app_models = abstract_models + app_models for appmodel in app_models: if not self.use_model(appmodel._meta.object_name): continue appmodel_abstracts = self.get_appmodel_abstracts(appmodel) abstract_fields = self.get_bases_abstract_fields(appmodel) model = self.get_appmodel_context(appmodel, appmodel_abstracts) attributes = self.get_appmodel_attributes(appmodel) # find primary key and print it first, ignoring implicit id if other pk exists pk = appmodel._meta.pk if pk and not appmodel._meta.abstract and pk in attributes: model['fields'].append(self.add_attributes(pk, abstract_fields)) for field in attributes: model = self.process_attributes(field, model, pk, abstract_fields) if self.sort_fields: model = self.sort_model_fields(model) for field in appmodel._meta.local_fields: model = self.process_local_fields(field, model, abstract_fields) for field in appmodel._meta.local_many_to_many: model = self.process_local_many_to_many(field, model) if self.inheritance: # add inheritance arrows for parent in appmodel.__bases__: model = self.process_parent(parent, appmodel, model) app_graph['models'].append(model) if app_graph['models']: self.graphs.append(app_graph) def process_local_fields(self, field, model, abstract_fields): newmodel = model.copy() if field.attname.endswith('_ptr_id') or field in abstract_fields or self.skip_field(field): # excluding field redundant with inheritance relation # excluding fields inherited from abstract classes. they too show as local_fields return newmodel if isinstance(field, OneToOneField): relation = self.add_relation( field, newmodel, '[arrowhead=none, arrowtail=none, dir=both]' ) elif isinstance(field, ForeignKey): relation = self.add_relation( field, newmodel, '[arrowhead=none, arrowtail={}, dir=both]'.format( self.arrow_shape ), ) else: relation = None if relation is not None: newmodel['relations'].append(relation) return newmodel def process_local_many_to_many(self, field, model): newmodel = model.copy() if self.skip_field(field): return newmodel relation = None if isinstance(field, ManyToManyField): if hasattr(field.remote_field.through, '_meta') and field.remote_field.through._meta.auto_created: relation = self.add_relation( field, newmodel, '[arrowhead={} arrowtail={}, dir=both]'.format( self.arrow_shape, self.arrow_shape ), ) elif isinstance(field, GenericRelation): relation = self.add_relation(field, newmodel, mark_safe('[style="dotted", arrowhead=normal, arrowtail=normal, dir=both]')) if relation is not None: newmodel['relations'].append(relation) return newmodel def process_parent(self, parent, appmodel, model): newmodel = model.copy() if hasattr(parent, "_meta"): # parent is a model _rel = self.get_inheritance_context(appmodel, parent) # TODO: seems as if abstract models aren't part of models.getModels, which is why they are printed by this without any attributes. if _rel not in newmodel['relations'] and self.use_model(_rel['target']): newmodel['relations'].append(_rel) return newmodel def sort_model_fields(self, model): newmodel = model.copy() newmodel['fields'] = sorted(newmodel['fields'], key=lambda field: (not field['primary_key'], not field['relation'], field['label'])) return newmodel def use_model(self, model_name): """ Decide whether to use a model, based on the model name and the lists of models to exclude and include. """ # Check against include list. if self.include_models: for model_pattern in self.include_models: model_pattern = '^%s$' % model_pattern.replace('*', '.*') if re.search(model_pattern, model_name): return True # Check against exclude list. if self.exclude_models: for model_pattern in self.exclude_models: model_pattern = '^%s$' % model_pattern.replace('*', '.*') if re.search(model_pattern, model_name): return False # Return `True` if `include_models` is falsey, otherwise return `False`. return not self.include_models def skip_field(self, field): if self.exclude_columns: if self.verbose_names and field.verbose_name: if field.verbose_name in self.exclude_columns: return True if field.name in self.exclude_columns: return True return False def generate_dot(graph_data, template='django_extensions/graph_models/digraph.dot'): if isinstance(template, six.string_types): template = loader.get_template(template) if not isinstance(template, Template) and not (hasattr(template, 'template') and isinstance(template.template, Template)): raise Exception("Default Django template loader isn't used. " "This can lead to the incorrect template rendering. " "Please, check the settings.") c = Context(graph_data).flatten() dot = template.render(c) return dot def generate_graph_data(*args, **kwargs): generator = ModelGraph(*args, **kwargs) generator.generate_graph_data() return generator.get_graph_data() def use_model(model, include_models, exclude_models): generator = ModelGraph([], include_models=include_models, exclude_models=exclude_models) return generator.use_model(model)

38.960739

142

0.605809

import datetime import os import re import six from django.apps import apps from django.db.models.fields.related import ( ForeignKey, ManyToManyField, OneToOneField, RelatedField, ) from django.contrib.contenttypes.fields import GenericRelation from django.template import Context, Template, loader from django.utils.encoding import force_str from django.utils.safestring import mark_safe from django.utils.translation import activate as activate_language __version__ = "1.1" __license__ = "Python" __author__ = "Bas van Oostveen <v.oostveen@gmail.com>", __contributors__ = [ "Antonio Cavedoni <http://cavedoni.com/>" "Stefano J. Attardi <http://attardi.org/>", "limodou <http://www.donews.net/limodou/>", "Carlo C8E Miron", "Andre Campos <cahenan@gmail.com>", "Justin Findlay <jfindlay@gmail.com>", "Alexander Houben <alexander@houben.ch>", "Joern Hees <gitdev@joernhees.de>", "Kevin Cherepski <cherepski@gmail.com>", "Jose Tomas Tocino <theom3ga@gmail.com>", "Adam Dobrawy <naczelnik@jawnosc.tk>", "Mikkel Munch Mortensen <https://www.detfalskested.dk/>", "Andrzej Bistram <andrzej.bistram@gmail.com>", "Daniel Lipsitt <danlipsitt@gmail.com>", ] def parse_file_or_list(arg): if not arg: return [] if isinstance(arg, (list, tuple, set)): return arg if ',' not in arg and os.path.isfile(arg): return [e.strip() for e in open(arg).readlines()] return [e.strip() for e in arg.split(',')] class ModelGraph: def __init__(self, app_labels, **kwargs): self.graphs = [] self.cli_options = kwargs.get('cli_options', None) self.disable_fields = kwargs.get('disable_fields', False) self.disable_abstract_fields = kwargs.get('disable_abstract_fields', False) self.include_models = parse_file_or_list( kwargs.get('include_models', "") ) self.all_applications = kwargs.get('all_applications', False) self.use_subgraph = kwargs.get('group_models', False) self.verbose_names = kwargs.get('verbose_names', False) self.inheritance = kwargs.get('inheritance', True) self.relations_as_fields = kwargs.get("relations_as_fields", True) self.sort_fields = kwargs.get("sort_fields", True) self.language = kwargs.get('language', None) if self.language is not None: activate_language(self.language) self.exclude_columns = parse_file_or_list( kwargs.get('exclude_columns', "") ) self.exclude_models = parse_file_or_list( kwargs.get('exclude_models', "") ) self.hide_edge_labels = kwargs.get('hide_edge_labels', False) self.arrow_shape = kwargs.get("arrow_shape") if self.all_applications: self.app_labels = [app.label for app in apps.get_app_configs()] else: self.app_labels = app_labels def generate_graph_data(self): self.process_apps() nodes = [] for graph in self.graphs: nodes.extend([e['name'] for e in graph['models']]) for graph in self.graphs: for model in graph['models']: for relation in model['relations']: if relation is not None: if relation['target'] in nodes: relation['needs_node'] = False def get_graph_data(self, as_json=False): now = datetime.datetime.now() graph_data = { 'created_at': now.strftime("%Y-%m-%d %H:%M"), 'cli_options': self.cli_options, 'disable_fields': self.disable_fields, 'disable_abstract_fields': self.disable_abstract_fields, 'use_subgraph': self.use_subgraph, } if as_json: graph_data['graphs'] = [context.flatten() for context in self.graphs] else: graph_data['graphs'] = self.graphs return graph_data def add_attributes(self, field, abstract_fields): if self.verbose_names and field.verbose_name: label = force_str(field.verbose_name) if label.islower(): label = label.capitalize() else: label = field.name t = type(field).__name__ if isinstance(field, (OneToOneField, ForeignKey)): t += " ({0})".format(field.remote_field.field_name) return { 'name': field.name, 'label': label, 'type': t, 'blank': field.blank, 'abstract': field in abstract_fields, 'relation': isinstance(field, RelatedField), 'primary_key': field.primary_key, } def add_relation(self, field, model, extras=""): if self.verbose_names and field.verbose_name: label = force_str(field.verbose_name) if label.islower(): label = label.capitalize() else: label = field.name if hasattr(field, 'related_query_name'): related_query_name = field.related_query_name() if self.verbose_names and related_query_name.islower(): related_query_name = related_query_name.replace('_', ' ').capitalize() label = u'{} ({})'.format(label, force_str(related_query_name)) if self.hide_edge_labels: label = '' if isinstance(field.remote_field.model, six.string_types): if field.remote_field.model == 'self': target_model = field.model else: if '.' in field.remote_field.model: app_label, model_name = field.remote_field.model.split('.', 1) else: app_label = field.model._meta.app_label model_name = field.remote_field.model target_model = apps.get_model(app_label, model_name) else: target_model = field.remote_field.model _rel = self.get_relation_context(target_model, field, label, extras) if _rel not in model['relations'] and self.use_model(_rel['target']): return _rel def get_abstract_models(self, appmodels): abstract_models = [] for appmodel in appmodels: abstract_models += [ abstract_model for abstract_model in appmodel.__bases__ if hasattr(abstract_model, '_meta') and abstract_model._meta.abstract ] abstract_models = list(set(abstract_models)) return abstract_models def get_app_context(self, app): return Context({ 'name': '"%s"' % app.name, 'app_name': "%s" % app.name, 'cluster_app_name': "cluster_%s" % app.name.replace(".", "_"), 'models': [] }) def get_appmodel_attributes(self, appmodel): if self.relations_as_fields: attributes = [field for field in appmodel._meta.local_fields] else: attributes = [field for field in appmodel._meta.local_fields if not isinstance(field, RelatedField)] return attributes def get_appmodel_abstracts(self, appmodel): return [ abstract_model.__name__ for abstract_model in appmodel.__bases__ if hasattr(abstract_model, '_meta') and abstract_model._meta.abstract ] def get_appmodel_context(self, appmodel, appmodel_abstracts): context = { 'app_name': appmodel.__module__.replace(".", "_"), 'name': appmodel.__name__, 'abstracts': appmodel_abstracts, 'fields': [], 'relations': [] } if self.verbose_names and appmodel._meta.verbose_name: context['label'] = force_str(appmodel._meta.verbose_name) else: context['label'] = context['name'] return context def get_bases_abstract_fields(self, c): _abstract_fields = [] for e in c.__bases__: if hasattr(e, '_meta') and e._meta.abstract: _abstract_fields.extend(e._meta.fields) _abstract_fields.extend(self.get_bases_abstract_fields(e)) return _abstract_fields def get_inheritance_context(self, appmodel, parent): label = "multi-table" if parent._meta.abstract: label = "abstract" if appmodel._meta.proxy: label = "proxy" label += r"\ninheritance" if self.hide_edge_labels: label = '' return { 'target_app': parent.__module__.replace(".", "_"), 'target': parent.__name__, 'type': "inheritance", 'name': "inheritance", 'label': label, 'arrows': '[arrowhead=empty, arrowtail=none, dir=both]', 'needs_node': True, } def get_models(self, app): appmodels = list(app.get_models()) return appmodels def get_relation_context(self, target_model, field, label, extras): return { 'target_app': target_model.__module__.replace('.', '_'), 'target': target_model.__name__, 'type': type(field).__name__, 'name': field.name, 'label': label, 'arrows': extras, 'needs_node': True } def process_attributes(self, field, model, pk, abstract_fields): newmodel = model.copy() if self.skip_field(field) or pk and field == pk: return newmodel newmodel['fields'].append(self.add_attributes(field, abstract_fields)) return newmodel def process_apps(self): for app_label in self.app_labels: app = apps.get_app_config(app_label) if not app: continue app_graph = self.get_app_context(app) app_models = self.get_models(app) abstract_models = self.get_abstract_models(app_models) app_models = abstract_models + app_models for appmodel in app_models: if not self.use_model(appmodel._meta.object_name): continue appmodel_abstracts = self.get_appmodel_abstracts(appmodel) abstract_fields = self.get_bases_abstract_fields(appmodel) model = self.get_appmodel_context(appmodel, appmodel_abstracts) attributes = self.get_appmodel_attributes(appmodel) pk = appmodel._meta.pk if pk and not appmodel._meta.abstract and pk in attributes: model['fields'].append(self.add_attributes(pk, abstract_fields)) for field in attributes: model = self.process_attributes(field, model, pk, abstract_fields) if self.sort_fields: model = self.sort_model_fields(model) for field in appmodel._meta.local_fields: model = self.process_local_fields(field, model, abstract_fields) for field in appmodel._meta.local_many_to_many: model = self.process_local_many_to_many(field, model) if self.inheritance: for parent in appmodel.__bases__: model = self.process_parent(parent, appmodel, model) app_graph['models'].append(model) if app_graph['models']: self.graphs.append(app_graph) def process_local_fields(self, field, model, abstract_fields): newmodel = model.copy() if field.attname.endswith('_ptr_id') or field in abstract_fields or self.skip_field(field): return newmodel if isinstance(field, OneToOneField): relation = self.add_relation( field, newmodel, '[arrowhead=none, arrowtail=none, dir=both]' ) elif isinstance(field, ForeignKey): relation = self.add_relation( field, newmodel, '[arrowhead=none, arrowtail={}, dir=both]'.format( self.arrow_shape ), ) else: relation = None if relation is not None: newmodel['relations'].append(relation) return newmodel def process_local_many_to_many(self, field, model): newmodel = model.copy() if self.skip_field(field): return newmodel relation = None if isinstance(field, ManyToManyField): if hasattr(field.remote_field.through, '_meta') and field.remote_field.through._meta.auto_created: relation = self.add_relation( field, newmodel, '[arrowhead={} arrowtail={}, dir=both]'.format( self.arrow_shape, self.arrow_shape ), ) elif isinstance(field, GenericRelation): relation = self.add_relation(field, newmodel, mark_safe('[style="dotted", arrowhead=normal, arrowtail=normal, dir=both]')) if relation is not None: newmodel['relations'].append(relation) return newmodel def process_parent(self, parent, appmodel, model): newmodel = model.copy() if hasattr(parent, "_meta"): _rel = self.get_inheritance_context(appmodel, parent) if _rel not in newmodel['relations'] and self.use_model(_rel['target']): newmodel['relations'].append(_rel) return newmodel def sort_model_fields(self, model): newmodel = model.copy() newmodel['fields'] = sorted(newmodel['fields'], key=lambda field: (not field['primary_key'], not field['relation'], field['label'])) return newmodel def use_model(self, model_name): # Check against include list. if self.include_models: for model_pattern in self.include_models: model_pattern = '^%s$' % model_pattern.replace('*', '.*') if re.search(model_pattern, model_name): return True # Check against exclude list. if self.exclude_models: for model_pattern in self.exclude_models: model_pattern = '^%s$' % model_pattern.replace('*', '.*') if re.search(model_pattern, model_name): return False # Return `True` if `include_models` is falsey, otherwise return `False`. return not self.include_models def skip_field(self, field): if self.exclude_columns: if self.verbose_names and field.verbose_name: if field.verbose_name in self.exclude_columns: return True if field.name in self.exclude_columns: return True return False def generate_dot(graph_data, template='django_extensions/graph_models/digraph.dot'): if isinstance(template, six.string_types): template = loader.get_template(template) if not isinstance(template, Template) and not (hasattr(template, 'template') and isinstance(template.template, Template)): raise Exception("Default Django template loader isn't used. " "This can lead to the incorrect template rendering. " "Please, check the settings.") c = Context(graph_data).flatten() dot = template.render(c) return dot def generate_graph_data(*args, **kwargs): generator = ModelGraph(*args, **kwargs) generator.generate_graph_data() return generator.get_graph_data() def use_model(model, include_models, exclude_models): generator = ModelGraph([], include_models=include_models, exclude_models=exclude_models) return generator.use_model(model)

true

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103

py

Python

edag/cli/__init__.py

sodre/edag-cli

f1f88fd749b3e8a94c93afa6ae78e8cb5fc84436

[ "BSD-3-Clause" ]

null

edag/cli/__init__.py

sodre/edag-cli

f1f88fd749b3e8a94c93afa6ae78e8cb5fc84436

[ "BSD-3-Clause" ]

4

2019-12-13T05:35:15.000Z

2019-12-30T21:07:14.000Z

edag/cli/__init__.py

sodre/edag-cli

f1f88fd749b3e8a94c93afa6ae78e8cb5fc84436

[ "BSD-3-Clause" ]

null

"""Top-level package for ElasticDAG CLI.""" from ._version import version as __version__ # noqa: F401

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from ._version import version as __version__

true

1c43fe5c5576e93119229d732edb22ae2f787b24

8,554

py

Python

applications/systems_of_equations_ex2/script/exodus_data_extraction.py

ElsevierSoftwareX/SOFTX_2019_102

123c4b3988ef2fb86b49a247b8431dc94a89eded

[ "MIT" ]

null

applications/systems_of_equations_ex2/script/exodus_data_extraction.py

ElsevierSoftwareX/SOFTX_2019_102

123c4b3988ef2fb86b49a247b8431dc94a89eded

[ "MIT" ]

null

applications/systems_of_equations_ex2/script/exodus_data_extraction.py

ElsevierSoftwareX/SOFTX_2019_102

123c4b3988ef2fb86b49a247b8431dc94a89eded

[ "MIT" ]

null

import sys, os #### import the simple module from the paraview from paraview.simple import * if __name__ == "__main__" and len(sys.argv) > 1: time_step = int(sys.argv[1]) #### disable automatic camera reset on 'Show' paraview.simple._DisableFirstRenderCameraReset() # create a new 'ExodusIIReader' oute = ExodusIIReader(FileName=['./out.e']) timestep_values = oute.TimestepValues oute.PointVariables = [] oute.SideSetArrayStatus = [] # get animation scene animationScene1 = GetAnimationScene() # update animation scene based on data timesteps animationScene1.UpdateAnimationUsingDataTimeSteps() # Properties modified on oute oute.PointVariables = ['vel_', 'p'] oute.ElementBlocks = ['Unnamed block ID: 0 Type: QUAD9'] # get active view renderView1 = GetActiveViewOrCreate('RenderView') renderView1.ViewTime = timestep_values[time_step - 1] # uncomment following to set a specific view size # renderView1.ViewSize = [1611, 832] # show data in view outeDisplay = Show(oute, renderView1) # trace defaults for the display properties. outeDisplay.ColorArrayName = [None, ''] outeDisplay.OSPRayScaleArray = 'GlobalNodeId' outeDisplay.OSPRayScaleFunction = 'PiecewiseFunction' outeDisplay.SelectOrientationVectors = 'GlobalNodeId' outeDisplay.ScaleFactor = 0.1 outeDisplay.SelectScaleArray = 'GlobalNodeId' outeDisplay.GlyphType = 'Arrow' outeDisplay.ScalarOpacityUnitDistance = 0.19193831036664846 outeDisplay.GaussianRadius = 0.05 outeDisplay.SetScaleArray = ['POINTS', 'GlobalNodeId'] outeDisplay.ScaleTransferFunction = 'PiecewiseFunction' outeDisplay.OpacityArray = ['POINTS', 'GlobalNodeId'] outeDisplay.OpacityTransferFunction = 'PiecewiseFunction' # reset view to fit data renderView1.ResetCamera() #changing interaction mode based on data extents renderView1.InteractionMode = '2D' renderView1.CameraPosition = [0.5, 0.5, 10000.0] renderView1.CameraFocalPoint = [0.5, 0.5, 0.0] renderView1.CameraViewUp = [0.0, 1.0, 0.0] # set scalar coloring ColorBy(outeDisplay, ('FIELD', 'vtkBlockColors')) # show color bar/color legend outeDisplay.SetScalarBarVisibility(renderView1, True) # get color transfer function/color map for 'vtkBlockColors' vtkBlockColorsLUT = GetColorTransferFunction('vtkBlockColors') vtkBlockColorsLUT.InterpretValuesAsCategories = 1 vtkBlockColorsLUT.Annotations = ['0', '0', '1', '1', '2', '2', '3', '3', '4', '4', '5', '5', '6', '6', '7', '7', '8', '8', '9', '9', '10', '10', '11', '11'] vtkBlockColorsLUT.ActiveAnnotatedValues = ['0'] vtkBlockColorsLUT.IndexedColors = [1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.63, 0.63, 1.0, 0.67, 0.5, 0.33, 1.0, 0.5, 0.75, 0.53, 0.35, 0.7, 1.0, 0.75, 0.5] # get opacity transfer function/opacity map for 'vtkBlockColors' vtkBlockColorsPWF = GetOpacityTransferFunction('vtkBlockColors') # set scalar coloring ColorBy(outeDisplay, ('POINTS', 'vel_')) # Hide the scalar bar for this color map if no visible data is colored by it. HideScalarBarIfNotNeeded(vtkBlockColorsLUT, renderView1) # rescale color and/or opacity maps used to include current data range outeDisplay.RescaleTransferFunctionToDataRange(True, False) # show color bar/color legend outeDisplay.SetScalarBarVisibility(renderView1, True) # get color transfer function/color map for 'vel_' vel_LUT = GetColorTransferFunction('vel_') vel_LUT.RGBPoints = [0.0, 0.231373, 0.298039, 0.752941, 0.0, 0.865003, 0.865003, 0.865003, 0.0, 0.705882, 0.0156863, 0.14902] vel_LUT.ScalarRangeInitialized = 1.0 # get opacity transfer function/opacity map for 'vel_' vel_PWF = GetOpacityTransferFunction('vel_') vel_PWF.Points = [0.0, 0.0, 0.5, 0.0, 0.0, 1.0, 0.5, 0.0] vel_PWF.ScalarRangeInitialized = 1 # create a new 'Plot Over Line' plotOverLine1 = PlotOverLine(Input=oute, Source='High Resolution Line Source') # init the 'High Resolution Line Source' selected for 'Source' plotOverLine1.Source.Point2 = [1.0, 1.0, 0.0] # Properties modified on plotOverLine1.Source plotOverLine1.Source.Point1 = [0.5, 0.0, 0.0] plotOverLine1.Source.Point2 = [0.5, 1.0, 0.0] # Properties modified on plotOverLine1 plotOverLine1.Tolerance = 2.22044604925031e-16 # Properties modified on plotOverLine1.Source plotOverLine1.Source.Point1 = [0.5, 0.0, 0.0] plotOverLine1.Source.Point2 = [0.5, 1.0, 0.0] # Create a new 'Line Chart View' lineChartView1 = CreateView('XYChartView') lineChartView1.ViewSize = [801, 832] # get layout layout1 = GetLayout() # place view in the layout layout1.AssignView(2, lineChartView1) # show data in view plotOverLine1Display = Show(plotOverLine1, lineChartView1) # trace defaults for the display properties. plotOverLine1Display.CompositeDataSetIndex = [0] plotOverLine1Display.UseIndexForXAxis = 0 plotOverLine1Display.XArrayName = 'arc_length' plotOverLine1Display.SeriesVisibility = ['p', 'vel__Magnitude'] plotOverLine1Display.SeriesLabel = ['arc_length', 'arc_length', 'ObjectId', 'ObjectId', 'p', 'p', 'vel__X', 'vel__X', 'vel__Y', 'vel__Y', 'vel__Z', 'vel__Z', 'vel__Magnitude', 'vel__Magnitude', 'vtkValidPointMask', 'vtkValidPointMask', 'Points_X', 'Points_X', 'Points_Y', 'Points_Y', 'Points_Z', 'Points_Z', 'Points_Magnitude', 'Points_Magnitude'] plotOverLine1Display.SeriesColor = ['arc_length', '0', '0', '0', 'ObjectId', '0.89', '0.1', '0.11', 'p', '0.22', '0.49', '0.72', 'vel__X', '0.3', '0.69', '0.29', 'vel__Y', '0.6', '0.31', '0.64', 'vel__Z', '1', '0.5', '0', 'vel__Magnitude', '0.65', '0.34', '0.16', 'vtkValidPointMask', '0', '0', '0', 'Points_X', '0.89', '0.1', '0.11', 'Points_Y', '0.22', '0.49', '0.72', 'Points_Z', '0.3', '0.69', '0.29', 'Points_Magnitude', '0.6', '0.31', '0.64'] plotOverLine1Display.SeriesPlotCorner = ['arc_length', '0', 'ObjectId', '0', 'p', '0', 'vel__X', '0', 'vel__Y', '0', 'vel__Z', '0', 'vel__Magnitude', '0', 'vtkValidPointMask', '0', 'Points_X', '0', 'Points_Y', '0', 'Points_Z', '0', 'Points_Magnitude', '0'] plotOverLine1Display.SeriesLineStyle = ['arc_length', '1', 'ObjectId', '1', 'p', '1', 'vel__X', '1', 'vel__Y', '1', 'vel__Z', '1', 'vel__Magnitude', '1', 'vtkValidPointMask', '1', 'Points_X', '1', 'Points_Y', '1', 'Points_Z', '1', 'Points_Magnitude', '1'] plotOverLine1Display.SeriesLineThickness = ['arc_length', '2', 'ObjectId', '2', 'p', '2', 'vel__X', '2', 'vel__Y', '2', 'vel__Z', '2', 'vel__Magnitude', '2', 'vtkValidPointMask', '2', 'Points_X', '2', 'Points_Y', '2', 'Points_Z', '2', 'Points_Magnitude', '2'] plotOverLine1Display.SeriesMarkerStyle = ['arc_length', '0', 'ObjectId', '0', 'p', '0', 'vel__X', '0', 'vel__Y', '0', 'vel__Z', '0', 'vel__Magnitude', '0', 'vtkValidPointMask', '0', 'Points_X', '0', 'Points_Y', '0', 'Points_Z', '0', 'Points_Magnitude', '0'] plotOverLine1Display.SeriesLabelPrefix = '' # destroy lineChartView1 Delete(lineChartView1) del lineChartView1 # close an empty frame layout1.Collapse(2) # set active view SetActiveView(renderView1) writer = CreateWriter("./rde/" + str(time_step) + "/original_data_from_extractor.csv", plotOverLine1) writer.FieldAssociation = "Points" # or "Cells" writer.UpdatePipeline() # clean original extracted raw data from exodus file with open("./rde/" + str(time_step) + "/original_data_from_extractor.csv", "r") as input_file, open("./rde/" + str(time_step) + "/extractor_" + str(time_step) + ".data", "w+") as output_file: header = True for line in input_file: if(header): output_file.write("filename;timestep;time;u;v;w;p;x;y;z") header = False else: line = line.replace(",",";").replace("\n","") splitted_line = line.split(";") output_file.write("\n" + ";".join([ "\"" + os.getcwd() + "/rde/" + str(time_step) + "/extractor_" + str(time_step) + ".data\"", str(time_step), str(timestep_values[time_step - 1]), splitted_line[0], splitted_line[1], splitted_line[2], splitted_line[3], splitted_line[7], splitted_line[8], splitted_line[9]])) output_file.flush() output_file.close() input_file.close()

49.445087

452

0.655483

import sys, os lues oute.PointVariables = [] oute.SideSetArrayStatus = [] animationScene1 = GetAnimationScene() animationScene1.UpdateAnimationUsingDataTimeSteps() oute.PointVariables = ['vel_', 'p'] oute.ElementBlocks = ['Unnamed block ID: 0 Type: QUAD9'] renderView1 = GetActiveViewOrCreate('RenderView') renderView1.ViewTime = timestep_values[time_step - 1] outeDisplay = Show(oute, renderView1) outeDisplay.ColorArrayName = [None, ''] outeDisplay.OSPRayScaleArray = 'GlobalNodeId' outeDisplay.OSPRayScaleFunction = 'PiecewiseFunction' outeDisplay.SelectOrientationVectors = 'GlobalNodeId' outeDisplay.ScaleFactor = 0.1 outeDisplay.SelectScaleArray = 'GlobalNodeId' outeDisplay.GlyphType = 'Arrow' outeDisplay.ScalarOpacityUnitDistance = 0.19193831036664846 outeDisplay.GaussianRadius = 0.05 outeDisplay.SetScaleArray = ['POINTS', 'GlobalNodeId'] outeDisplay.ScaleTransferFunction = 'PiecewiseFunction' outeDisplay.OpacityArray = ['POINTS', 'GlobalNodeId'] outeDisplay.OpacityTransferFunction = 'PiecewiseFunction' renderView1.ResetCamera() renderView1.InteractionMode = '2D' renderView1.CameraPosition = [0.5, 0.5, 10000.0] renderView1.CameraFocalPoint = [0.5, 0.5, 0.0] renderView1.CameraViewUp = [0.0, 1.0, 0.0] ColorBy(outeDisplay, ('FIELD', 'vtkBlockColors')) outeDisplay.SetScalarBarVisibility(renderView1, True) vtkBlockColorsLUT = GetColorTransferFunction('vtkBlockColors') vtkBlockColorsLUT.InterpretValuesAsCategories = 1 vtkBlockColorsLUT.Annotations = ['0', '0', '1', '1', '2', '2', '3', '3', '4', '4', '5', '5', '6', '6', '7', '7', '8', '8', '9', '9', '10', '10', '11', '11'] vtkBlockColorsLUT.ActiveAnnotatedValues = ['0'] vtkBlockColorsLUT.IndexedColors = [1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.63, 0.63, 1.0, 0.67, 0.5, 0.33, 1.0, 0.5, 0.75, 0.53, 0.35, 0.7, 1.0, 0.75, 0.5] vtkBlockColorsPWF = GetOpacityTransferFunction('vtkBlockColors') ColorBy(outeDisplay, ('POINTS', 'vel_')) HideScalarBarIfNotNeeded(vtkBlockColorsLUT, renderView1) outeDisplay.RescaleTransferFunctionToDataRange(True, False) outeDisplay.SetScalarBarVisibility(renderView1, True) vel_LUT = GetColorTransferFunction('vel_') vel_LUT.RGBPoints = [0.0, 0.231373, 0.298039, 0.752941, 0.0, 0.865003, 0.865003, 0.865003, 0.0, 0.705882, 0.0156863, 0.14902] vel_LUT.ScalarRangeInitialized = 1.0 vel_PWF = GetOpacityTransferFunction('vel_') vel_PWF.Points = [0.0, 0.0, 0.5, 0.0, 0.0, 1.0, 0.5, 0.0] vel_PWF.ScalarRangeInitialized = 1 plotOverLine1 = PlotOverLine(Input=oute, Source='High Resolution Line Source') plotOverLine1.Source.Point2 = [1.0, 1.0, 0.0] plotOverLine1.Source.Point1 = [0.5, 0.0, 0.0] plotOverLine1.Source.Point2 = [0.5, 1.0, 0.0] plotOverLine1.Tolerance = 2.22044604925031e-16 plotOverLine1.Source.Point1 = [0.5, 0.0, 0.0] plotOverLine1.Source.Point2 = [0.5, 1.0, 0.0] lineChartView1 = CreateView('XYChartView') lineChartView1.ViewSize = [801, 832] layout1 = GetLayout() layout1.AssignView(2, lineChartView1) plotOverLine1Display = Show(plotOverLine1, lineChartView1) plotOverLine1Display.CompositeDataSetIndex = [0] plotOverLine1Display.UseIndexForXAxis = 0 plotOverLine1Display.XArrayName = 'arc_length' plotOverLine1Display.SeriesVisibility = ['p', 'vel__Magnitude'] plotOverLine1Display.SeriesLabel = ['arc_length', 'arc_length', 'ObjectId', 'ObjectId', 'p', 'p', 'vel__X', 'vel__X', 'vel__Y', 'vel__Y', 'vel__Z', 'vel__Z', 'vel__Magnitude', 'vel__Magnitude', 'vtkValidPointMask', 'vtkValidPointMask', 'Points_X', 'Points_X', 'Points_Y', 'Points_Y', 'Points_Z', 'Points_Z', 'Points_Magnitude', 'Points_Magnitude'] plotOverLine1Display.SeriesColor = ['arc_length', '0', '0', '0', 'ObjectId', '0.89', '0.1', '0.11', 'p', '0.22', '0.49', '0.72', 'vel__X', '0.3', '0.69', '0.29', 'vel__Y', '0.6', '0.31', '0.64', 'vel__Z', '1', '0.5', '0', 'vel__Magnitude', '0.65', '0.34', '0.16', 'vtkValidPointMask', '0', '0', '0', 'Points_X', '0.89', '0.1', '0.11', 'Points_Y', '0.22', '0.49', '0.72', 'Points_Z', '0.3', '0.69', '0.29', 'Points_Magnitude', '0.6', '0.31', '0.64'] plotOverLine1Display.SeriesPlotCorner = ['arc_length', '0', 'ObjectId', '0', 'p', '0', 'vel__X', '0', 'vel__Y', '0', 'vel__Z', '0', 'vel__Magnitude', '0', 'vtkValidPointMask', '0', 'Points_X', '0', 'Points_Y', '0', 'Points_Z', '0', 'Points_Magnitude', '0'] plotOverLine1Display.SeriesLineStyle = ['arc_length', '1', 'ObjectId', '1', 'p', '1', 'vel__X', '1', 'vel__Y', '1', 'vel__Z', '1', 'vel__Magnitude', '1', 'vtkValidPointMask', '1', 'Points_X', '1', 'Points_Y', '1', 'Points_Z', '1', 'Points_Magnitude', '1'] plotOverLine1Display.SeriesLineThickness = ['arc_length', '2', 'ObjectId', '2', 'p', '2', 'vel__X', '2', 'vel__Y', '2', 'vel__Z', '2', 'vel__Magnitude', '2', 'vtkValidPointMask', '2', 'Points_X', '2', 'Points_Y', '2', 'Points_Z', '2', 'Points_Magnitude', '2'] plotOverLine1Display.SeriesMarkerStyle = ['arc_length', '0', 'ObjectId', '0', 'p', '0', 'vel__X', '0', 'vel__Y', '0', 'vel__Z', '0', 'vel__Magnitude', '0', 'vtkValidPointMask', '0', 'Points_X', '0', 'Points_Y', '0', 'Points_Z', '0', 'Points_Magnitude', '0'] plotOverLine1Display.SeriesLabelPrefix = '' Delete(lineChartView1) del lineChartView1 layout1.Collapse(2) SetActiveView(renderView1) writer = CreateWriter("./rde/" + str(time_step) + "/original_data_from_extractor.csv", plotOverLine1) writer.FieldAssociation = "Points" writer.UpdatePipeline() with open("./rde/" + str(time_step) + "/original_data_from_extractor.csv", "r") as input_file, open("./rde/" + str(time_step) + "/extractor_" + str(time_step) + ".data", "w+") as output_file: header = True for line in input_file: if(header): output_file.write("filename;timestep;time;u;v;w;p;x;y;z") header = False else: line = line.replace(",",";").replace("\n","") splitted_line = line.split(";") output_file.write("\n" + ";".join([ "\"" + os.getcwd() + "/rde/" + str(time_step) + "/extractor_" + str(time_step) + ".data\"", str(time_step), str(timestep_values[time_step - 1]), splitted_line[0], splitted_line[1], splitted_line[2], splitted_line[3], splitted_line[7], splitted_line[8], splitted_line[9]])) output_file.flush() output_file.close() input_file.close()

true

1c43fefda8a6cb0284260eadeb99ad911c49bee5

3,177

py

Python

gala/potential/potential/builtin/pybuiltin.py

akeemlh/gala

0fdaf9159bccc59af2a3525f2926e04501754f48

[ "MIT" ]

null

gala/potential/potential/builtin/pybuiltin.py

akeemlh/gala

0fdaf9159bccc59af2a3525f2926e04501754f48

[ "MIT" ]

null

gala/potential/potential/builtin/pybuiltin.py

akeemlh/gala

0fdaf9159bccc59af2a3525f2926e04501754f48

[ "MIT" ]

null

# Third-party import numpy as np from gala.potential.potential.core import PotentialBase from gala.potential.potential.util import sympy_wrap from gala.potential.common import PotentialParameter __all__ = ["HarmonicOscillatorPotential"] class HarmonicOscillatorPotential(PotentialBase): r""" Represents an N-dimensional harmonic oscillator. .. math:: \Phi = \frac{1}{2}\omega^2 x^2 Parameters ---------- omega : numeric Frequency. units : iterable(optional) Unique list of non-reducable units that specify (at minimum) the length, mass, time, and angle units. """ omega = PotentialParameter('omega', physical_type='frequency') def _setup_potential(self, parameters, origin=None, R=None, units=None): parameters['omega'] = np.atleast_1d(parameters['omega']) super()._setup_potential(parameters, origin=origin, R=R, units=units) self.ndim = len(self.parameters['omega']) def _energy(self, q, t=0.): om = np.atleast_1d(self.parameters['omega'].value) return np.sum(0.5 * om[None]**2 * q**2, axis=1) def _gradient(self, q, t=0.): om = np.atleast_1d(self.parameters['omega'].value) return om[None]**2 * q def _hessian(self, q, t=0.): om = np.atleast_1d(self.parameters['omega'].value) return np.tile(np.diag(om)[:, :, None], reps=(1, 1, q.shape[0])) @classmethod @sympy_wrap(var='x') def to_sympy(cls, v, p): expr = 1/2 * p['omega']**2 * v['x']**2 return expr, v, p def action_angle(self, w): """ Transform the input cartesian position and velocity to action-angle coordinates the Harmonic Oscillator potential. This transformation is analytic and can be used as a "toy potential" in the Sanders & Binney 2014 formalism for computing action-angle coordinates in _any_ potential. Adapted from Jason Sanders' code `genfunc <https://github.com/jlsanders/genfunc>`_. Parameters ---------- w : :class:`gala.dynamics.PhaseSpacePosition`, :class:`gala.dynamics.Orbit` The positions or orbit to compute the actions, angles, and frequencies at. """ from gala.dynamics.actionangle import harmonic_oscillator_to_aa return harmonic_oscillator_to_aa(w, self) # def phase_space(self, actions, angles): # """ # Transform the input action-angle coordinates to cartesian position and velocity # assuming a Harmonic Oscillator potential. This transformation # is analytic and can be used as a "toy potential" in the # Sanders & Binney 2014 formalism for computing action-angle coordinates # in _any_ potential. # Adapted from Jason Sanders' code # `genfunc <https://github.com/jlsanders/genfunc>`_. # Parameters # ---------- # x : array_like # Positions. # v : array_like # Velocities. # """ # from gala.dynamics.actionangle import harmonic_oscillator_aa_to_xv # return harmonic_oscillator_aa_to_xv(actions, angles, self)

34.912088

89

0.639597

import numpy as np from gala.potential.potential.core import PotentialBase from gala.potential.potential.util import sympy_wrap from gala.potential.common import PotentialParameter __all__ = ["HarmonicOscillatorPotential"] class HarmonicOscillatorPotential(PotentialBase): omega = PotentialParameter('omega', physical_type='frequency') def _setup_potential(self, parameters, origin=None, R=None, units=None): parameters['omega'] = np.atleast_1d(parameters['omega']) super()._setup_potential(parameters, origin=origin, R=R, units=units) self.ndim = len(self.parameters['omega']) def _energy(self, q, t=0.): om = np.atleast_1d(self.parameters['omega'].value) return np.sum(0.5 * om[None]**2 * q**2, axis=1) def _gradient(self, q, t=0.): om = np.atleast_1d(self.parameters['omega'].value) return om[None]**2 * q def _hessian(self, q, t=0.): om = np.atleast_1d(self.parameters['omega'].value) return np.tile(np.diag(om)[:, :, None], reps=(1, 1, q.shape[0])) @classmethod @sympy_wrap(var='x') def to_sympy(cls, v, p): expr = 1/2 * p['omega']**2 * v['x']**2 return expr, v, p def action_angle(self, w): from gala.dynamics.actionangle import harmonic_oscillator_to_aa return harmonic_oscillator_to_aa(w, self) # Transform the input action-angle coordinates to cartesian position and velocity # assuming a Harmonic Oscillator potential. This transformation # is analytic and can be used as a "toy potential" in the # Sanders & Binney 2014 formalism for computing action-angle coordinates # in _any_ potential. # Adapted from Jason Sanders' code # `genfunc <https://github.com/jlsanders/genfunc>`_. # Parameters # ---------- # x : array_like # Positions. # v : array_like # Velocities. # """ # from gala.dynamics.actionangle import harmonic_oscillator_aa_to_xv # return harmonic_oscillator_aa_to_xv(actions, angles, self)

true

1c43ff06f66ece3c7d95b1983fde0993f787cb7e

2,428

py

Python

swaps/utils/channels.py

DunnCreativeSS/cash_carry_leveraged_futures_arbitrageur

1120ebfb487ce4987fe70e6645b36e0d7ce041ec

[ "Apache-2.0" ]

1

2021-09-06T00:09:11.000Z

swaps/utils/channels.py

DunnCreativeSS/cash_carry_leveraged_futures_arbitrageur

1120ebfb487ce4987fe70e6645b36e0d7ce041ec

[ "Apache-2.0" ]

null

swaps/utils/channels.py

DunnCreativeSS/cash_carry_leveraged_futures_arbitrageur

1120ebfb487ce4987fe70e6645b36e0d7ce041ec

[ "Apache-2.0" ]

null

import json from swaps.utils.time_service import get_current_timestamp from swaps.constant import DepthStep def kline_channel(symbol, interval): channel = dict() channel["sub"] = "market." + symbol + ".kline." + interval channel["id"] = str(get_current_timestamp()) return json.dumps(channel) def trade_detail_channel(symbol): channel = dict() channel["sub"] = "market." + symbol + ".trade.detail" channel["id"] = str(get_current_timestamp()) return json.dumps(channel) def price_depth_channel(symbol, step_type=DepthStep.STEP0): channel = dict() channel["sub"] = "market." + symbol + ".depth." + step_type channel["id"] = str(get_current_timestamp()) return json.dumps(channel) def price_depth_bbo_channel(symbol): channel = dict() channel["sub"] = "market." + symbol + ".bbo" channel["id"] = str(get_current_timestamp()) return json.dumps(channel) def orders_update_channel(symbol): channel = dict() channel["action"] = "sub" channel["ch"] = "orders#{symbol}".format(symbol=symbol) return json.dumps(channel) def market_detail_channel(symbol): channel = dict() channel["sub"] = "market." + symbol + ".detail" channel["id"] = str(get_current_timestamp()) return json.dumps(channel) def accounts_update_channel(mode=0): channel = dict() channel["action"] = "sub" if mode is None: channel["ch"] = "accounts.update" else: channel["ch"] = "accounts.update#{mode}".format(mode=mode) return json.dumps(channel) def mbp_increase_channel(symbol, levels): channel = dict() channel["sub"] = "market.{symbol}.mbp.{levels}".format(symbol=symbol, levels=levels) channel["id"] = str(get_current_timestamp()) return json.dumps(channel) def mbp_full_channel(symbol, levels): channel = dict() channel["sub"] = "market.{symbol}.mbp.refresh.{levels}".format(symbol=symbol, levels=levels) channel["id"] = str(get_current_timestamp()) return json.dumps(channel) def request_mbp_channel(symbol, levels): channel = dict() channel["req"] = "market.{symbol}.mbp.{levels}".format(symbol=symbol, levels=levels) channel["id"] = str(get_current_timestamp()) return json.dumps(channel) def trade_clearing_channel(symbol="*"): channel = dict() channel["action"] = "sub" channel["ch"] = "trade.clearing#" + symbol return json.dumps(channel)

28.904762

96

0.670511

import json from swaps.utils.time_service import get_current_timestamp from swaps.constant import DepthStep def kline_channel(symbol, interval): channel = dict() channel["sub"] = "market." + symbol + ".kline." + interval channel["id"] = str(get_current_timestamp()) return json.dumps(channel) def trade_detail_channel(symbol): channel = dict() channel["sub"] = "market." + symbol + ".trade.detail" channel["id"] = str(get_current_timestamp()) return json.dumps(channel) def price_depth_channel(symbol, step_type=DepthStep.STEP0): channel = dict() channel["sub"] = "market." + symbol + ".depth." + step_type channel["id"] = str(get_current_timestamp()) return json.dumps(channel) def price_depth_bbo_channel(symbol): channel = dict() channel["sub"] = "market." + symbol + ".bbo" channel["id"] = str(get_current_timestamp()) return json.dumps(channel) def orders_update_channel(symbol): channel = dict() channel["action"] = "sub" channel["ch"] = "orders#{symbol}".format(symbol=symbol) return json.dumps(channel) def market_detail_channel(symbol): channel = dict() channel["sub"] = "market." + symbol + ".detail" channel["id"] = str(get_current_timestamp()) return json.dumps(channel) def accounts_update_channel(mode=0): channel = dict() channel["action"] = "sub" if mode is None: channel["ch"] = "accounts.update" else: channel["ch"] = "accounts.update#{mode}".format(mode=mode) return json.dumps(channel) def mbp_increase_channel(symbol, levels): channel = dict() channel["sub"] = "market.{symbol}.mbp.{levels}".format(symbol=symbol, levels=levels) channel["id"] = str(get_current_timestamp()) return json.dumps(channel) def mbp_full_channel(symbol, levels): channel = dict() channel["sub"] = "market.{symbol}.mbp.refresh.{levels}".format(symbol=symbol, levels=levels) channel["id"] = str(get_current_timestamp()) return json.dumps(channel) def request_mbp_channel(symbol, levels): channel = dict() channel["req"] = "market.{symbol}.mbp.{levels}".format(symbol=symbol, levels=levels) channel["id"] = str(get_current_timestamp()) return json.dumps(channel) def trade_clearing_channel(symbol="*"): channel = dict() channel["action"] = "sub" channel["ch"] = "trade.clearing#" + symbol return json.dumps(channel)

true

1c43ff8b50f9f4dccea00f66a1b714b913f672b2

4,157

py

Python

speech_activity_detection/sad.py

hlt-bme-hu/hunspeech

b8599e232ed2daa6ff6e07b92c6dca003b8c4bde

[ "MIT" ]

17

2017-03-05T03:19:37.000Z

2020-07-28T03:05:55.000Z

speech_activity_detection/sad.py

hlt-bme-hu/hunspeech

b8599e232ed2daa6ff6e07b92c6dca003b8c4bde

[ "MIT" ]

7

2016-07-05T08:40:15.000Z

2016-07-28T10:07:38.000Z

speech_activity_detection/sad.py

hlt-bme-hu/hunspeech

b8599e232ed2daa6ff6e07b92c6dca003b8c4bde

[ "MIT" ]

6

2017-05-10T12:27:35.000Z

2018-09-14T20:13:43.000Z

#! /usr/bin/env python3 # -*- coding: utf-8 -*- # Copyright © 2016 Judit Acs <judit@sch.bme.hu> # # Distributed under terms of the GPL license. from argparse import ArgumentParser import os import subprocess class EMSpeechActicityDetection: """Speech activity detection and segmentation This class is a wrapper for the SHOUT toolkit's SAD module. Since SHOUT expects the input to be raw audio, it is first converted into the correct raw format by sox (Sound eXchange), then shout_segment is called. SHOUT outputs a single segmentation file, which is saved to segments.txt by default. Each segment is labeled as SPEECH, SIL (silence) or SOUND. EMSpeechActicityDetection supports two additional saving solutions: 1. segment the input according to SHOUT's segmentation into individual audio files. 2. group segments by labels and concatenate them into a single file. This produces at most three files: one containing all speech, one containing all silence and another one containing all sound. """ def __init__(self, filename, model=None, segment_out='segments.txt', segment_dir=None, shout_path=os.environ.get('SHOUT_DIR')): self.filename = filename if model is None: self.model = os.path.join(os.environ.get('SHOUT_DIR'), 'models', 'shout.sad') else: self.model = model self.segment_out = segment_out self.binary_path = '{}/shout_segment'.format(shout_path) def segment(self): self.raw_filename = EMSpeechActicityDetection.convert_to_raw( self.filename) cmd = '{0} -a {1} --am-segment {2} -mo {3}'.format( self.binary_path, self.raw_filename, self.model, self.segment_out, ) subprocess.call(cmd, shell=True) @staticmethod def convert_to_raw(filename): """ accepts mp3, wav and raw files """ EMSpeechActicityDetection.__check_audio_file(filename) basename, ext = os.path.splitext(filename) if ext == '.mp3': EMSpeechActicityDetection.convert_mp3_to_wav(filename) fn = EMSpeechActicityDetection.convert_wav_to_raw('{0}.wav'.format( basename)) return fn @staticmethod def convert_mp3_to_wav(filename): basename, ext = os.path.splitext(filename) out_fn = '{}.wav'.format(basename) subprocess.call('sox {0} {1}'.format(filename, out_fn), shell=True) return out_fn @staticmethod def convert_wav_to_raw(filename): basename, ext = os.path.splitext(filename) out_fn = '{}.raw'.format(basename) params = '-r 16k -b 16 -L -c 1' subprocess.call('sox {0} {1} {2}'.format(params, filename, out_fn), shell=True) return out_fn @staticmethod def __check_audio_file(filename): if not os.path.exists(filename): raise Exception('Source file does not exist: {}'.format( filename)) ext = os.path.splitext(filename)[-1] if ext not in ('.raw', '.mp3', '.wav'): raise ValueError('Cannot handle [{0}] files'.format( ext)) def parse_args(): p = ArgumentParser() p.add_argument('-i', '--input', type=str, help='Input file. Use this option if you want to segment' ' a single file' ) p.add_argument('-m', '--model', type=str, help='SHOUT acoustic model', default='{}/shout_am.sad'.format( os.environ.get('SHOUT_DIR')), ) p.add_argument('-o', '--segment-out', type=str, help='Write segments to file', default='segments.txt' ) return p.parse_args() def main(): args = parse_args() sad = EMSpeechActicityDetection(filename=args.input, model=args.model, segment_out=args.segment_out) sad.segment() if __name__ == '__main__': main()

35.836207

76

0.600674

from argparse import ArgumentParser import os import subprocess class EMSpeechActicityDetection: def __init__(self, filename, model=None, segment_out='segments.txt', segment_dir=None, shout_path=os.environ.get('SHOUT_DIR')): self.filename = filename if model is None: self.model = os.path.join(os.environ.get('SHOUT_DIR'), 'models', 'shout.sad') else: self.model = model self.segment_out = segment_out self.binary_path = '{}/shout_segment'.format(shout_path) def segment(self): self.raw_filename = EMSpeechActicityDetection.convert_to_raw( self.filename) cmd = '{0} -a {1} --am-segment {2} -mo {3}'.format( self.binary_path, self.raw_filename, self.model, self.segment_out, ) subprocess.call(cmd, shell=True) @staticmethod def convert_to_raw(filename): EMSpeechActicityDetection.__check_audio_file(filename) basename, ext = os.path.splitext(filename) if ext == '.mp3': EMSpeechActicityDetection.convert_mp3_to_wav(filename) fn = EMSpeechActicityDetection.convert_wav_to_raw('{0}.wav'.format( basename)) return fn @staticmethod def convert_mp3_to_wav(filename): basename, ext = os.path.splitext(filename) out_fn = '{}.wav'.format(basename) subprocess.call('sox {0} {1}'.format(filename, out_fn), shell=True) return out_fn @staticmethod def convert_wav_to_raw(filename): basename, ext = os.path.splitext(filename) out_fn = '{}.raw'.format(basename) params = '-r 16k -b 16 -L -c 1' subprocess.call('sox {0} {1} {2}'.format(params, filename, out_fn), shell=True) return out_fn @staticmethod def __check_audio_file(filename): if not os.path.exists(filename): raise Exception('Source file does not exist: {}'.format( filename)) ext = os.path.splitext(filename)[-1] if ext not in ('.raw', '.mp3', '.wav'): raise ValueError('Cannot handle [{0}] files'.format( ext)) def parse_args(): p = ArgumentParser() p.add_argument('-i', '--input', type=str, help='Input file. Use this option if you want to segment' ' a single file' ) p.add_argument('-m', '--model', type=str, help='SHOUT acoustic model', default='{}/shout_am.sad'.format( os.environ.get('SHOUT_DIR')), ) p.add_argument('-o', '--segment-out', type=str, help='Write segments to file', default='segments.txt' ) return p.parse_args() def main(): args = parse_args() sad = EMSpeechActicityDetection(filename=args.input, model=args.model, segment_out=args.segment_out) sad.segment() if __name__ == '__main__': main()

true

1c4400536ce84830b6a1ec7c250cf1e8cccf83e5

3,961

py

Python

tensorflow_probability/python/mcmc/internal/leapfrog_integrator_test.py

NeelGhoshal/probability

45ed841e3cff6cdc7cd1b2d96dd874d9070318f7

[ "Apache-2.0" ]

2

2019-10-30T04:45:07.000Z

2019-10-30T04:45:08.000Z

tensorflow_probability/python/mcmc/internal/leapfrog_integrator_test.py

gregorystrubel/probability

df96f3d56eff92c6b06fbac68dc58e095e28fed6

[ "Apache-2.0" ]

null

tensorflow_probability/python/mcmc/internal/leapfrog_integrator_test.py

gregorystrubel/probability

df96f3d56eff92c6b06fbac68dc58e095e28fed6

[ "Apache-2.0" ]

null

# Copyright 2018 The TensorFlow Probability 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. # ============================================================================ """Tests for `leapfrog_integrator.py`.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import tensorflow.compat.v1 as tf1 import tensorflow.compat.v2 as tf from tensorflow_probability.python.internal import test_util from tensorflow_probability.python.mcmc.internal import leapfrog_integrator as leapfrog_impl @test_util.test_all_tf_execution_regimes class LeapfrogIntegratorTest(test_util.TestCase): def setUp(self): self._shape_param = 5. self._rate_param = 10. def assertAllFinite(self, x): self.assertAllEqual(np.ones_like(x).astype(bool), np.isfinite(x)) def _log_gamma_log_prob(self, x, event_dims=()): """Computes log-pdf of a log-gamma random variable. Args: x: Value of the random variable. event_dims: Dimensions not to treat as independent. Returns: log_prob: The log-pdf up to a normalizing constant. """ return tf.reduce_sum( self._shape_param * x - self._rate_param * tf.exp(x), axis=event_dims) def _integrator_conserves_energy(self, x, independent_chain_ndims, seed): event_dims = tf.range(independent_chain_ndims, tf.rank(x)) target_fn = lambda x: self._log_gamma_log_prob(x, event_dims) m = tf.random.normal(tf.shape(x), seed=seed) log_prob_0 = target_fn(x) old_energy = -log_prob_0 + 0.5 * tf.reduce_sum(m**2., axis=event_dims) event_size = np.prod( self.evaluate(x).shape[independent_chain_ndims:]) integrator = leapfrog_impl.SimpleLeapfrogIntegrator( target_fn, step_sizes=[0.09 / event_size], num_steps=1000) [[new_m], [_], log_prob_1, [_]] = integrator([m], [x]) new_energy = -log_prob_1 + 0.5 * tf.reduce_sum(new_m**2., axis=event_dims) old_energy_, new_energy_ = self.evaluate([old_energy, new_energy]) tf1.logging.vlog( 1, 'average energy relative change: {}'.format( (1. - new_energy_ / old_energy_).mean())) self.assertAllClose(old_energy_, new_energy_, atol=0., rtol=0.02) def _integrator_conserves_energy_wrapper(self, independent_chain_ndims): """Tests the long-term energy conservation of the leapfrog integrator. The leapfrog integrator is symplectic, so for sufficiently small step sizes it should be possible to run it more or less indefinitely without the energy of the system blowing up or collapsing. Args: independent_chain_ndims: Python `int` scalar representing the number of dims associated with independent chains. """ seed_stream = test_util.test_seed_stream() x = self.evaluate(0.1 * tf.random.normal( shape=(50, 10, 2), seed=seed_stream())) x = tf.constant(x) self._integrator_conserves_energy( x, independent_chain_ndims, seed=seed_stream()) def testIntegratorEnergyConservationNullShape(self): self._integrator_conserves_energy_wrapper(0) def testIntegratorEnergyConservation1(self): self._integrator_conserves_energy_wrapper(1) def testIntegratorEnergyConservation2(self): self._integrator_conserves_energy_wrapper(2) def testIntegratorEnergyConservation3(self): self._integrator_conserves_energy_wrapper(3) if __name__ == '__main__': test_util.main()

35.053097

92

0.721283

from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import tensorflow.compat.v1 as tf1 import tensorflow.compat.v2 as tf from tensorflow_probability.python.internal import test_util from tensorflow_probability.python.mcmc.internal import leapfrog_integrator as leapfrog_impl @test_util.test_all_tf_execution_regimes class LeapfrogIntegratorTest(test_util.TestCase): def setUp(self): self._shape_param = 5. self._rate_param = 10. def assertAllFinite(self, x): self.assertAllEqual(np.ones_like(x).astype(bool), np.isfinite(x)) def _log_gamma_log_prob(self, x, event_dims=()): return tf.reduce_sum( self._shape_param * x - self._rate_param * tf.exp(x), axis=event_dims) def _integrator_conserves_energy(self, x, independent_chain_ndims, seed): event_dims = tf.range(independent_chain_ndims, tf.rank(x)) target_fn = lambda x: self._log_gamma_log_prob(x, event_dims) m = tf.random.normal(tf.shape(x), seed=seed) log_prob_0 = target_fn(x) old_energy = -log_prob_0 + 0.5 * tf.reduce_sum(m**2., axis=event_dims) event_size = np.prod( self.evaluate(x).shape[independent_chain_ndims:]) integrator = leapfrog_impl.SimpleLeapfrogIntegrator( target_fn, step_sizes=[0.09 / event_size], num_steps=1000) [[new_m], [_], log_prob_1, [_]] = integrator([m], [x]) new_energy = -log_prob_1 + 0.5 * tf.reduce_sum(new_m**2., axis=event_dims) old_energy_, new_energy_ = self.evaluate([old_energy, new_energy]) tf1.logging.vlog( 1, 'average energy relative change: {}'.format( (1. - new_energy_ / old_energy_).mean())) self.assertAllClose(old_energy_, new_energy_, atol=0., rtol=0.02) def _integrator_conserves_energy_wrapper(self, independent_chain_ndims): seed_stream = test_util.test_seed_stream() x = self.evaluate(0.1 * tf.random.normal( shape=(50, 10, 2), seed=seed_stream())) x = tf.constant(x) self._integrator_conserves_energy( x, independent_chain_ndims, seed=seed_stream()) def testIntegratorEnergyConservationNullShape(self): self._integrator_conserves_energy_wrapper(0) def testIntegratorEnergyConservation1(self): self._integrator_conserves_energy_wrapper(1) def testIntegratorEnergyConservation2(self): self._integrator_conserves_energy_wrapper(2) def testIntegratorEnergyConservation3(self): self._integrator_conserves_energy_wrapper(3) if __name__ == '__main__': test_util.main()

true

1c4401781e653e88d9e6d6f9fbced6b590f8d769

243

py

Python

example/envless_mode/app.py

jhesketh/dynaconf

a8038b87763ae8e790ff7e745b9335f997d5bd16

[ "MIT" ]

1

2021-07-21T17:06:16.000Z

example/envless_mode/app.py

jhesketh/dynaconf

a8038b87763ae8e790ff7e745b9335f997d5bd16

[ "MIT" ]

null

example/envless_mode/app.py

jhesketh/dynaconf

a8038b87763ae8e790ff7e745b9335f997d5bd16

[ "MIT" ]

null

import os from dynaconf import LazySettings settings = LazySettings(ENVLESS_MODE=True) assert settings.FOO == "bar" assert settings.HELLO == "world" assert settings.DATABASES.default.port == 8080 assert settings.LAZY == os.environ["HOME"]

20.25

46

0.769547

import os from dynaconf import LazySettings settings = LazySettings(ENVLESS_MODE=True) assert settings.FOO == "bar" assert settings.HELLO == "world" assert settings.DATABASES.default.port == 8080 assert settings.LAZY == os.environ["HOME"]

true

1c4403bd35f001ff67a9f8496dba9393ab34b2fe

5,177

py

Python

pineboolib/kugar/mreportobject.py

Miguel-J/pineboo-buscar

41a2f3ee0425d163619b78f32544c4b4661d5fa7

[ "MIT" ]

null

pineboolib/kugar/mreportobject.py

Miguel-J/pineboo-buscar

41a2f3ee0425d163619b78f32544c4b4661d5fa7

[ "MIT" ]

null

pineboolib/kugar/mreportobject.py

Miguel-J/pineboo-buscar

41a2f3ee0425d163619b78f32544c4b4661d5fa7

[ "MIT" ]

null

from enum import Enum from PyQt5 import QtGui from PyQt5.QtCore import Qt from PyQt5.Qt import QObject from pineboolib import decorators from pineboolib.flcontrols import ProjectClass from pineboolib.fllegacy.FLStylePainter import FLStylePainter class MReportObject(ProjectClass, QObject): class BorderStyle(Enum): NoPen = 0 SolidLine = 1 DashLine = 2 DotLine = 3 DashDotLine = 4 DashDotDotLine = 5 class ReportObjectType(Enum): Invalid = 0 Label = 1 Field = 2 Calc = 3 Special = 4 @decorators.BetaImplementation def __init__(self, *args): if len(args) and isinstance(args[0], MReportObject): self.copy(args[0]) else: super(MReportObject, self).__init__() self.xpos_ = 0 self.ypos_ = 0 self.width_ = 40 self.height_ = 23 self.backgroundColor_.setRgb(255, 255, 255) self.foregroundColor_.setRgb(0, 0, 0) self.borderColor_.setRgb(0, 0, 0) self.borderWidth_ = 1 self.borderStyle_ = self.BorderStyle.SolidLine self.sectionIndex_ = -1 self.transparent = False self.objectId = 0 @decorators.NotImplementedWarn # def operator=(self, mro): #FIXME def operator(self, mro): return self @decorators.BetaImplementation def draw(self, p): self.drawBase(p) return 0 @decorators.BetaImplementation def drawBase(self, p): if p.drawRect(self): return restore = False if p.errCode() == FLStylePainter.IdNotFound: p.painter().save(self.name()) p.applyTransforms() p.painter().translate(self.xpos_, self.ypos_) restore = True if self.borderStyle_ != self.BorderStyle.NoPen or self.transparent_: if self.transparent_: p.painter().setBrush(Qt.NoBrush) else: p.painter().setBrush(self.backgroundColor_) if self.borderStyle_ != 0: p.painter().setPen(QtGui.QPen( self.borderColor_, self.borderWidth_, self.borderStyle_) ) else: p.painter().setPen(Qt.NoPen) p.painter().drawRect(0, 0, self.width_, self.height_) else: p.painter().fillRect( 0, 0, self.width_, self.height_, self.backgroundColor_ ) if restore: p.painter().restore() @decorators.BetaImplementation def setGeometry(self, x, y, w, h): self.xpos_ = x self.ypos_ = y self.width_ = w self.height_ = h @decorators.BetaImplementation def move(self, x, y): self.xpos_ = x self.ypos_ = y @decorators.BetaImplementation def setBackgroundColor(self, r, g, b): self.backgroundColor_.setRgb(r, g, b) @decorators.BetaImplementation def setForegroundColor(self, r, g, b): self.foregroundColor_.setRgb(r, g, b) @decorators.BetaImplementation def setBorderColor(self, r, g, b): self.borderColor_.setRgb(r, g, b) @decorators.BetaImplementation def copy(self, mro): self.xpos_ = mro.xpos_ self.ypos_ = mro.ypos_ self.width_ = mro.width_ self.height_ = mro.height_ self.backgroundColor_ = mro.backgroundColor_ self.foregroundColor_ = mro.foregroundColor_ self.borderColor_ = mro.borderColor_ self.borderWidth_ = mro.borderWidth_ self.borderStyle_ = mro.borderStyle_ self.sectionIndex_ = mro.sectionIndex_ self.transparent_ = mro.transparent_ self.objectId_ = mro.objectId_ @decorators.BetaImplementation def RTTI(self): return self.ReportObjectType.Invalid @decorators.BetaImplementation def getX(self): return self.xpos_ @decorators.BetaImplementation def getY(self): return self.ypos_ @decorators.BetaImplementation def getHeight(self): return self.height_ @decorators.BetaImplementation def getWidth(self): return self.width_ @decorators.BetaImplementation def getDrawAtBottom(self): return self.drawAtBottom_ @decorators.BetaImplementation def getSectionIndex(self): return self.sectionIndex_ @decorators.BetaImplementation def getObjectId(self): return self.objectId_ @decorators.BetaImplementation def setBorderWidth(self, width): self.borderWidth_ = width @decorators.BetaImplementation def setBorderStyle(self, style): self.borderStyle_ = style @decorators.BetaImplementation def setTransparent(self, t): self.transparent_ = t @decorators.BetaImplementation def setDrawAtBottom(self, b): self.drawAtBottom_ = b @decorators.BetaImplementation def setSectionIndex(self, idx): self.sectionIndex_ = idx @decorators.BetaImplementation def setObjectId(self, id): self.objectId_ = id

26.548718

76

0.615414

from enum import Enum from PyQt5 import QtGui from PyQt5.QtCore import Qt from PyQt5.Qt import QObject from pineboolib import decorators from pineboolib.flcontrols import ProjectClass from pineboolib.fllegacy.FLStylePainter import FLStylePainter class MReportObject(ProjectClass, QObject): class BorderStyle(Enum): NoPen = 0 SolidLine = 1 DashLine = 2 DotLine = 3 DashDotLine = 4 DashDotDotLine = 5 class ReportObjectType(Enum): Invalid = 0 Label = 1 Field = 2 Calc = 3 Special = 4 @decorators.BetaImplementation def __init__(self, *args): if len(args) and isinstance(args[0], MReportObject): self.copy(args[0]) else: super(MReportObject, self).__init__() self.xpos_ = 0 self.ypos_ = 0 self.width_ = 40 self.height_ = 23 self.backgroundColor_.setRgb(255, 255, 255) self.foregroundColor_.setRgb(0, 0, 0) self.borderColor_.setRgb(0, 0, 0) self.borderWidth_ = 1 self.borderStyle_ = self.BorderStyle.SolidLine self.sectionIndex_ = -1 self.transparent = False self.objectId = 0 @decorators.NotImplementedWarn ef operator(self, mro): return self @decorators.BetaImplementation def draw(self, p): self.drawBase(p) return 0 @decorators.BetaImplementation def drawBase(self, p): if p.drawRect(self): return restore = False if p.errCode() == FLStylePainter.IdNotFound: p.painter().save(self.name()) p.applyTransforms() p.painter().translate(self.xpos_, self.ypos_) restore = True if self.borderStyle_ != self.BorderStyle.NoPen or self.transparent_: if self.transparent_: p.painter().setBrush(Qt.NoBrush) else: p.painter().setBrush(self.backgroundColor_) if self.borderStyle_ != 0: p.painter().setPen(QtGui.QPen( self.borderColor_, self.borderWidth_, self.borderStyle_) ) else: p.painter().setPen(Qt.NoPen) p.painter().drawRect(0, 0, self.width_, self.height_) else: p.painter().fillRect( 0, 0, self.width_, self.height_, self.backgroundColor_ ) if restore: p.painter().restore() @decorators.BetaImplementation def setGeometry(self, x, y, w, h): self.xpos_ = x self.ypos_ = y self.width_ = w self.height_ = h @decorators.BetaImplementation def move(self, x, y): self.xpos_ = x self.ypos_ = y @decorators.BetaImplementation def setBackgroundColor(self, r, g, b): self.backgroundColor_.setRgb(r, g, b) @decorators.BetaImplementation def setForegroundColor(self, r, g, b): self.foregroundColor_.setRgb(r, g, b) @decorators.BetaImplementation def setBorderColor(self, r, g, b): self.borderColor_.setRgb(r, g, b) @decorators.BetaImplementation def copy(self, mro): self.xpos_ = mro.xpos_ self.ypos_ = mro.ypos_ self.width_ = mro.width_ self.height_ = mro.height_ self.backgroundColor_ = mro.backgroundColor_ self.foregroundColor_ = mro.foregroundColor_ self.borderColor_ = mro.borderColor_ self.borderWidth_ = mro.borderWidth_ self.borderStyle_ = mro.borderStyle_ self.sectionIndex_ = mro.sectionIndex_ self.transparent_ = mro.transparent_ self.objectId_ = mro.objectId_ @decorators.BetaImplementation def RTTI(self): return self.ReportObjectType.Invalid @decorators.BetaImplementation def getX(self): return self.xpos_ @decorators.BetaImplementation def getY(self): return self.ypos_ @decorators.BetaImplementation def getHeight(self): return self.height_ @decorators.BetaImplementation def getWidth(self): return self.width_ @decorators.BetaImplementation def getDrawAtBottom(self): return self.drawAtBottom_ @decorators.BetaImplementation def getSectionIndex(self): return self.sectionIndex_ @decorators.BetaImplementation def getObjectId(self): return self.objectId_ @decorators.BetaImplementation def setBorderWidth(self, width): self.borderWidth_ = width @decorators.BetaImplementation def setBorderStyle(self, style): self.borderStyle_ = style @decorators.BetaImplementation def setTransparent(self, t): self.transparent_ = t @decorators.BetaImplementation def setDrawAtBottom(self, b): self.drawAtBottom_ = b @decorators.BetaImplementation def setSectionIndex(self, idx): self.sectionIndex_ = idx @decorators.BetaImplementation def setObjectId(self, id): self.objectId_ = id

true

1c440499d9570cd84e8b5504049bea924a674c85

2,985

py

Python

deepxde/geometry/geometry_3d.py

mitchelldaneker/deepxde

62e09b62ceaab6bda2ebbd02dc30ad99c2990302

[ "Apache-2.0" ]

955

2019-06-21T21:56:02.000Z

2022-03-31T03:44:45.000Z

deepxde/geometry/geometry_3d.py

mitchelldaneker/deepxde

62e09b62ceaab6bda2ebbd02dc30ad99c2990302

[ "Apache-2.0" ]

517

2019-07-25T16:47:44.000Z

2022-03-31T17:37:58.000Z

deepxde/geometry/geometry_3d.py

mitchelldaneker/deepxde

62e09b62ceaab6bda2ebbd02dc30ad99c2990302

[ "Apache-2.0" ]

374

2019-06-24T00:44:16.000Z

2022-03-30T08:17:36.000Z

from __future__ import absolute_import from __future__ import division from __future__ import print_function import itertools import numpy as np from .geometry_2d import Rectangle from .geometry_nd import Hypercube, Hypersphere class Cuboid(Hypercube): """ Args: xmin: Coordinate of bottom left corner. xmax: Coordinate of top right corner. """ def __init__(self, xmin, xmax): super(Cuboid, self).__init__(xmin, xmax) dx = self.xmax - self.xmin self.area = 2 * np.sum(dx * np.roll(dx, 2)) def random_boundary_points(self, n, random="pseudo"): pts = [] density = n / self.area rect = Rectangle(self.xmin[:-1], self.xmax[:-1]) for z in [self.xmin[-1], self.xmax[-1]]: u = rect.random_points(int(np.ceil(density * rect.area)), random=random) pts.append(np.hstack((u, np.full((len(u), 1), z)))) rect = Rectangle(self.xmin[::2], self.xmax[::2]) for y in [self.xmin[1], self.xmax[1]]: u = rect.random_points(int(np.ceil(density * rect.area)), random=random) pts.append(np.hstack((u[:, 0:1], np.full((len(u), 1), y), u[:, 1:]))) rect = Rectangle(self.xmin[1:], self.xmax[1:]) for x in [self.xmin[0], self.xmax[0]]: u = rect.random_points(int(np.ceil(density * rect.area)), random=random) pts.append(np.hstack((np.full((len(u), 1), x), u))) pts = np.vstack(pts) if len(pts) > n: return pts[np.random.choice(len(pts), size=n, replace=False)] return pts def uniform_boundary_points(self, n): h = (self.area / n) ** 0.5 nx, ny, nz = np.ceil((self.xmax - self.xmin) / h).astype(int) + 1 x = np.linspace(self.xmin[0], self.xmax[0], num=nx) y = np.linspace(self.xmin[1], self.xmax[1], num=ny) z = np.linspace(self.xmin[2], self.xmax[2], num=nz) pts = [] for v in [self.xmin[-1], self.xmax[-1]]: u = list(itertools.product(x, y)) pts.append(np.hstack((u, np.full((len(u), 1), v)))) if nz > 2: for v in [self.xmin[1], self.xmax[1]]: u = np.array(list(itertools.product(x, z[1:-1]))) pts.append(np.hstack((u[:, 0:1], np.full((len(u), 1), v), u[:, 1:]))) if ny > 2 and nz > 2: for v in [self.xmin[0], self.xmax[0]]: u = list(itertools.product(y[1:-1], z[1:-1])) pts.append(np.hstack((np.full((len(u), 1), v), u))) pts = np.vstack(pts) if n != len(pts): print( "Warning: {} points required, but {} points sampled.".format( n, len(pts) ) ) return pts class Sphere(Hypersphere): """ Args: center: Center of the sphere. radius: Radius of the sphere. """ def __init__(self, center, radius): super(Sphere, self).__init__(center, radius)

35.963855

85

0.540369

from __future__ import absolute_import from __future__ import division from __future__ import print_function import itertools import numpy as np from .geometry_2d import Rectangle from .geometry_nd import Hypercube, Hypersphere class Cuboid(Hypercube): def __init__(self, xmin, xmax): super(Cuboid, self).__init__(xmin, xmax) dx = self.xmax - self.xmin self.area = 2 * np.sum(dx * np.roll(dx, 2)) def random_boundary_points(self, n, random="pseudo"): pts = [] density = n / self.area rect = Rectangle(self.xmin[:-1], self.xmax[:-1]) for z in [self.xmin[-1], self.xmax[-1]]: u = rect.random_points(int(np.ceil(density * rect.area)), random=random) pts.append(np.hstack((u, np.full((len(u), 1), z)))) rect = Rectangle(self.xmin[::2], self.xmax[::2]) for y in [self.xmin[1], self.xmax[1]]: u = rect.random_points(int(np.ceil(density * rect.area)), random=random) pts.append(np.hstack((u[:, 0:1], np.full((len(u), 1), y), u[:, 1:]))) rect = Rectangle(self.xmin[1:], self.xmax[1:]) for x in [self.xmin[0], self.xmax[0]]: u = rect.random_points(int(np.ceil(density * rect.area)), random=random) pts.append(np.hstack((np.full((len(u), 1), x), u))) pts = np.vstack(pts) if len(pts) > n: return pts[np.random.choice(len(pts), size=n, replace=False)] return pts def uniform_boundary_points(self, n): h = (self.area / n) ** 0.5 nx, ny, nz = np.ceil((self.xmax - self.xmin) / h).astype(int) + 1 x = np.linspace(self.xmin[0], self.xmax[0], num=nx) y = np.linspace(self.xmin[1], self.xmax[1], num=ny) z = np.linspace(self.xmin[2], self.xmax[2], num=nz) pts = [] for v in [self.xmin[-1], self.xmax[-1]]: u = list(itertools.product(x, y)) pts.append(np.hstack((u, np.full((len(u), 1), v)))) if nz > 2: for v in [self.xmin[1], self.xmax[1]]: u = np.array(list(itertools.product(x, z[1:-1]))) pts.append(np.hstack((u[:, 0:1], np.full((len(u), 1), v), u[:, 1:]))) if ny > 2 and nz > 2: for v in [self.xmin[0], self.xmax[0]]: u = list(itertools.product(y[1:-1], z[1:-1])) pts.append(np.hstack((np.full((len(u), 1), v), u))) pts = np.vstack(pts) if n != len(pts): print( "Warning: {} points required, but {} points sampled.".format( n, len(pts) ) ) return pts class Sphere(Hypersphere): def __init__(self, center, radius): super(Sphere, self).__init__(center, radius)

true

1c44054209fde45c023c2b56668fd3ef83696358

5,515

py

Python

src_py/rlpytorch/trainer/utils.py

r-woo/elfai

2c37625e608e7720b8bd7847419d7b53e87e260a

[ "BSD-3-Clause" ]

3,305

2018-05-02T17:41:36.000Z

2022-03-28T05:57:56.000Z

src_py/rlpytorch/trainer/utils.py

r-woo/elfai

2c37625e608e7720b8bd7847419d7b53e87e260a

[ "BSD-3-Clause" ]

135

2018-05-02T19:25:13.000Z

2020-08-20T02:39:14.000Z

src_py/rlpytorch/trainer/utils.py

r-woo/elfai

2c37625e608e7720b8bd7847419d7b53e87e260a

[ "BSD-3-Clause" ]

604

2018-05-02T19:38:45.000Z

2022-03-18T10:01:57.000Z

# Copyright (c) 2018-present, Facebook, Inc. # All rights reserved. # # This source code is licensed under the BSD-style license found in the # LICENSE file in the root directory of this source tree. import os from collections import defaultdict, deque, Counter from datetime import datetime from elf.options import auto_import_options, PyOptionSpec class SymLink(object): def __init__(self, sym_prefix, latest_k=5): self.sym_prefix = sym_prefix self.latest_k = latest_k self.latest_files = deque() def feed(self, filename): self.latest_files.appendleft(filename) if len(self.latest_files) > self.latest_k: self.latest_files.pop() for k, name in enumerate(self.latest_files): symlink_file = self.sym_prefix + str(k) try: if os.path.exists(symlink_file): os.unlink(symlink_file) os.symlink(name, symlink_file) except BaseException: print( "Build symlink %s for %s failed, skipped" % (symlink_file, name)) class ModelSaver(object): @classmethod def get_option_spec(cls): spec = PyOptionSpec() spec.addStrOption( 'record_dir', 'directory to record in', './record') spec.addStrOption( 'save_prefix', 'prefix of savefiles', 'save') spec.addStrOption( 'save_dir', 'directory for savefiles', os.environ.get('save', './')) spec.addStrOption( 'latest_symlink', 'name for latest model symlink', 'latest') spec.addIntOption( 'num_games', 'number of games', 1024) spec.addIntOption( 'batchsize', 'batch size', 128) return spec @auto_import_options def __init__(self, option_map): self.save = (self.options.num_games == self.options.batchsize) if self.save and not os.path.exists(self.options.record_dir): os.mkdir(self.options.record_dir) if not os.path.exists(self.options.save_dir): os.mkdir(self.options.save_dir) self.symlinker = SymLink( os.path.join( self.options.save_dir, self.options.latest_symlink)) def feed(self, model): basename = self.options.save_prefix + "-%d.bin" % model.step print("Save to " + self.options.save_dir) filename = os.path.join(self.options.save_dir, basename) print("Filename = " + filename) model.save(filename) # Create a symlink self.symlinker.feed(basename) class ValueStats(object): def __init__(self, name=None): self.name = name self.reset() def feed(self, v): self.summation += v if v > self.max_value: self.max_value = v self.max_idx = self.counter if v < self.min_value: self.min_value = v self.min_idx = self.counter self.counter += 1 def summary(self, info=None): info = "" if info is None else info name = "" if self.name is None else self.name if self.counter > 0: try: return "%s%s[%d]: avg: %.5f, min: %.5f[%d], max: %.5f[%d]" % ( info, name, self.counter, self.summation / self.counter, self.min_value, self.min_idx, self.max_value, self.max_idx ) except BaseException: return "%s%s[Err]:" % (info, name) else: return "%s%s[0]" % (info, name) def reset(self): self.counter = 0 self.summation = 0.0 self.max_value = -1e38 self.min_value = 1e38 self.max_idx = None self.min_idx = None def topk_accuracy(output, target, topk=(1,)): """Computes the precision@k for the specified values of k""" maxk = max(topk) batch_size = target.size(0) _, pred = output.topk(maxk, 1, True, True) pred = pred.t() correct = pred.eq(target.view(1, -1).expand_as(pred)) res = [] for k in topk: correct_k = correct[:k].view(-1).float().sum(0) res.append(correct_k.mul_(100.0 / batch_size)) return res class MultiCounter(object): def __init__(self, verbose=False): self.last_time = None self.verbose = verbose self.counts = Counter() self.stats = defaultdict(lambda: ValueStats()) self.total_count = 0 def inc(self, key): if self.verbose: print("[MultiCounter]: %s" % key) self.counts[key] += 1 self.total_count += 1 def reset(self): for k in sorted(self.stats.keys()): self.stats[k].reset() self.counts = Counter() self.total_count = 0 self.last_time = datetime.now() def summary(self, global_counter=None): this_time = datetime.now() if self.last_time is not None: print( "[%d] Time spent = %f ms" % (global_counter, (this_time - self.last_time).total_seconds() * 1000)) for key, count in self.counts.items(): print("%s: %d/%d" % (key, count, self.total_count)) for k in sorted(self.stats.keys()): v = self.stats[k] print(v.summary(info=str(global_counter) + ":" + k))

30.469613

78

0.558114

import os from collections import defaultdict, deque, Counter from datetime import datetime from elf.options import auto_import_options, PyOptionSpec class SymLink(object): def __init__(self, sym_prefix, latest_k=5): self.sym_prefix = sym_prefix self.latest_k = latest_k self.latest_files = deque() def feed(self, filename): self.latest_files.appendleft(filename) if len(self.latest_files) > self.latest_k: self.latest_files.pop() for k, name in enumerate(self.latest_files): symlink_file = self.sym_prefix + str(k) try: if os.path.exists(symlink_file): os.unlink(symlink_file) os.symlink(name, symlink_file) except BaseException: print( "Build symlink %s for %s failed, skipped" % (symlink_file, name)) class ModelSaver(object): @classmethod def get_option_spec(cls): spec = PyOptionSpec() spec.addStrOption( 'record_dir', 'directory to record in', './record') spec.addStrOption( 'save_prefix', 'prefix of savefiles', 'save') spec.addStrOption( 'save_dir', 'directory for savefiles', os.environ.get('save', './')) spec.addStrOption( 'latest_symlink', 'name for latest model symlink', 'latest') spec.addIntOption( 'num_games', 'number of games', 1024) spec.addIntOption( 'batchsize', 'batch size', 128) return spec @auto_import_options def __init__(self, option_map): self.save = (self.options.num_games == self.options.batchsize) if self.save and not os.path.exists(self.options.record_dir): os.mkdir(self.options.record_dir) if not os.path.exists(self.options.save_dir): os.mkdir(self.options.save_dir) self.symlinker = SymLink( os.path.join( self.options.save_dir, self.options.latest_symlink)) def feed(self, model): basename = self.options.save_prefix + "-%d.bin" % model.step print("Save to " + self.options.save_dir) filename = os.path.join(self.options.save_dir, basename) print("Filename = " + filename) model.save(filename) self.symlinker.feed(basename) class ValueStats(object): def __init__(self, name=None): self.name = name self.reset() def feed(self, v): self.summation += v if v > self.max_value: self.max_value = v self.max_idx = self.counter if v < self.min_value: self.min_value = v self.min_idx = self.counter self.counter += 1 def summary(self, info=None): info = "" if info is None else info name = "" if self.name is None else self.name if self.counter > 0: try: return "%s%s[%d]: avg: %.5f, min: %.5f[%d], max: %.5f[%d]" % ( info, name, self.counter, self.summation / self.counter, self.min_value, self.min_idx, self.max_value, self.max_idx ) except BaseException: return "%s%s[Err]:" % (info, name) else: return "%s%s[0]" % (info, name) def reset(self): self.counter = 0 self.summation = 0.0 self.max_value = -1e38 self.min_value = 1e38 self.max_idx = None self.min_idx = None def topk_accuracy(output, target, topk=(1,)): maxk = max(topk) batch_size = target.size(0) _, pred = output.topk(maxk, 1, True, True) pred = pred.t() correct = pred.eq(target.view(1, -1).expand_as(pred)) res = [] for k in topk: correct_k = correct[:k].view(-1).float().sum(0) res.append(correct_k.mul_(100.0 / batch_size)) return res class MultiCounter(object): def __init__(self, verbose=False): self.last_time = None self.verbose = verbose self.counts = Counter() self.stats = defaultdict(lambda: ValueStats()) self.total_count = 0 def inc(self, key): if self.verbose: print("[MultiCounter]: %s" % key) self.counts[key] += 1 self.total_count += 1 def reset(self): for k in sorted(self.stats.keys()): self.stats[k].reset() self.counts = Counter() self.total_count = 0 self.last_time = datetime.now() def summary(self, global_counter=None): this_time = datetime.now() if self.last_time is not None: print( "[%d] Time spent = %f ms" % (global_counter, (this_time - self.last_time).total_seconds() * 1000)) for key, count in self.counts.items(): print("%s: %d/%d" % (key, count, self.total_count)) for k in sorted(self.stats.keys()): v = self.stats[k] print(v.summary(info=str(global_counter) + ":" + k))

true

1c44057063242c94c41dd5976ac9aa98bd752b8e

956

py

Python

devel/test_forward_all.py

saidbakr/darkhttpd

cb548aef6ded6794b2a5bee06f40ec1ce415baad

[ "ISC" ]

788

2021-01-23T03:58:42.000Z

2022-03-28T12:32:35.000Z

devel/test_forward_all.py

saidbakr/darkhttpd

cb548aef6ded6794b2a5bee06f40ec1ce415baad

[ "ISC" ]

18

2021-02-15T06:31:17.000Z

2022-03-10T21:46:47.000Z

devel/test_forward_all.py

saidbakr/darkhttpd

cb548aef6ded6794b2a5bee06f40ec1ce415baad

[ "ISC" ]

59

2021-01-23T10:10:15.000Z

2022-03-25T13:50:16.000Z

#!/usr/bin/env python3 # This is run by the "run-tests" script. import unittest from test import TestHelper, Conn, parse class TestForwardAll(TestHelper): def test_forward_root(self): resp = self.get('/', req_hdrs={'Host': 'not-example.com'}) status, hdrs, body = parse(resp) self.assertContains(status, "301 Moved Permanently") expect = "http://catchall.example.com/" self.assertEqual(hdrs["Location"], expect) self.assertContains(body, expect) def test_forward_relative(self): resp = self.get('/foo/bar', req_hdrs={'Host': 'still-not.example.com'}) status, hdrs, body = parse(resp) self.assertContains(status, "301 Moved Permanently") expect = "http://catchall.example.com/foo/bar" self.assertEqual(hdrs["Location"], expect) self.assertContains(body, expect) if __name__ == '__main__': unittest.main() # vim:set ts=4 sw=4 et:

34.142857

66

0.643305

import unittest from test import TestHelper, Conn, parse class TestForwardAll(TestHelper): def test_forward_root(self): resp = self.get('/', req_hdrs={'Host': 'not-example.com'}) status, hdrs, body = parse(resp) self.assertContains(status, "301 Moved Permanently") expect = "http://catchall.example.com/" self.assertEqual(hdrs["Location"], expect) self.assertContains(body, expect) def test_forward_relative(self): resp = self.get('/foo/bar', req_hdrs={'Host': 'still-not.example.com'}) status, hdrs, body = parse(resp) self.assertContains(status, "301 Moved Permanently") expect = "http://catchall.example.com/foo/bar" self.assertEqual(hdrs["Location"], expect) self.assertContains(body, expect) if __name__ == '__main__': unittest.main()

true

1c4405dd71703bf265606d16a607178206d20790

5,053

py

Python

model/flops.py

JACKYLUO1991/Face-skin-hair-segmentaiton-and-skin-color-evaluation

de2375dc0ebff03b8ac39c8a16dee427838c8ac4

[ "Apache-2.0" ]

152

2020-01-02T01:27:50.000Z

2022-03-23T16:40:01.000Z

model/flops.py

JACKYLUO1991/Face-skin-hair-segmentaiton-and-skin-color-evaluation

de2375dc0ebff03b8ac39c8a16dee427838c8ac4

[ "Apache-2.0" ]

10

2020-01-03T07:29:59.000Z

2021-12-11T10:57:30.000Z

model/flops.py

JACKYLUO1991/Face-skin-hair-segmentaiton-and-skin-color-evaluation

de2375dc0ebff03b8ac39c8a16dee427838c8ac4

[ "Apache-2.0" ]

40

2020-01-03T00:41:49.000Z

2021-11-23T11:44:07.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- # @Time : 2020/3/27 17:49 # @Author : JackyLUO # @E-mail : lingluo@stumail.neu.edu.cn # @Site : # @File : flops.py # @Software: PyCharm # https://github.com/ckyrkou/Keras_FLOP_Estimator import keras.backend as K def get_flops(model, table=False): if table: print('%25s | %16s | %16s | %16s | %16s | %6s | %6s' % ( 'Layer Name', 'Input Shape', 'Output Shape', 'Kernel Size', 'Filters', 'Strides', 'FLOPS')) print('-' * 170) t_flops = 0 t_macc = 0 for l in model.layers: o_shape, i_shape, strides, ks, filters = ['', '', ''], ['', '', ''], [1, 1], [0, 0], [0, 0] flops = 0 macc = 0 name = l.name factor = 1e9 if 'InputLayer' in str(l): i_shape = l.input.get_shape()[1:4].as_list() o_shape = i_shape if 'Reshape' in str(l): i_shape = l.input.get_shape()[1:4].as_list() o_shape = l.output.get_shape()[1:4].as_list() if 'Add' in str(l) or 'Maximum' in str(l) or 'Concatenate' in str(l): i_shape = l.input[0].get_shape()[1:4].as_list() + [len(l.input)] o_shape = l.output.get_shape()[1:4].as_list() flops = (len(l.input) - 1) * i_shape[0] * i_shape[1] * i_shape[2] if 'Average' in str(l) and 'pool' not in str(l): i_shape = l.input[0].get_shape()[1:4].as_list() + [len(l.input)] o_shape = l.output.get_shape()[1:4].as_list() flops = len(l.input) * i_shape[0] * i_shape[1] * i_shape[2] if 'BatchNormalization' in str(l): i_shape = l.input.get_shape()[1:4].as_list() o_shape = l.output.get_shape()[1:4].as_list() bflops = 1 for i in range(len(i_shape)): bflops *= i_shape[i] flops /= factor if 'Activation' in str(l) or 'activation' in str(l): i_shape = l.input.get_shape()[1:4].as_list() o_shape = l.output.get_shape()[1:4].as_list() bflops = 1 for i in range(len(i_shape)): bflops *= i_shape[i] flops /= factor if 'pool' in str(l) and ('Global' not in str(l)): i_shape = l.input.get_shape()[1:4].as_list() strides = l.strides ks = l.pool_size flops = ((i_shape[0] / strides[0]) * (i_shape[1] / strides[1]) * (ks[0] * ks[1] * i_shape[2])) if 'Flatten' in str(l): i_shape = l.input.shape[1:4].as_list() flops = 1 out_vec = 1 for i in range(len(i_shape)): flops *= i_shape[i] out_vec *= i_shape[i] o_shape = flops flops = 0 if 'Dense' in str(l): print(l.input) i_shape = l.input.shape[1:4].as_list()[0] if i_shape is None: i_shape = out_vec o_shape = l.output.shape[1:4].as_list() flops = 2 * (o_shape[0] * i_shape) macc = flops / 2 if 'Padding' in str(l): flops = 0 if 'Global' in str(l): i_shape = l.input.get_shape()[1:4].as_list() flops = ((i_shape[0]) * (i_shape[1]) * (i_shape[2])) o_shape = [l.output.get_shape()[1:4].as_list(), 1, 1] out_vec = o_shape if 'Conv2D' in str(l) and 'DepthwiseConv2D' not in str(l) and 'SeparableConv2D' not in str(l): strides = l.strides ks = l.kernel_size filters = l.filters # if 'Conv2DTranspose' in str(l): # i_shape = list(K.int_shape(l.input)[1:4]) # o_shape = list(K.int_shape(l.output)[1:4]) # else: i_shape = l.input.get_shape()[1:4].as_list() o_shape = l.output.get_shape()[1:4].as_list() if filters is None: filters = i_shape[2] flops = 2 * ((filters * ks[0] * ks[1] * i_shape[2]) * ( (i_shape[0] / strides[0]) * (i_shape[1] / strides[1]))) macc = flops / 2 if 'Conv2D' in str(l) and 'DepthwiseConv2D' in str(l) and 'SeparableConv2D' not in str(l): strides = l.strides ks = l.kernel_size filters = l.filters i_shape = l.input.get_shape()[1:4].as_list() o_shape = l.output.get_shape()[1:4].as_list() if filters is None: filters = i_shape[2] flops = 2 * ((ks[0] * ks[1] * i_shape[2]) * ((i_shape[0] / strides[0]) * ( i_shape[1] / strides[1]))) / factor macc = flops / 2 t_macc += macc t_flops += flops if table: print('%25s | %16s | %16s | %16s | %16s | %6s | %5.4f' % ( name, str(i_shape), str(o_shape), str(ks), str(filters), str(strides), flops)) t_flops = t_flops / factor print('Total FLOPS (x 10^-9): %10.8f G' % (t_flops)) print('Total MACCs: %10.8f\n' % (t_macc)) return

34.141892

106

0.493766

import keras.backend as K def get_flops(model, table=False): if table: print('%25s | %16s | %16s | %16s | %16s | %6s | %6s' % ( 'Layer Name', 'Input Shape', 'Output Shape', 'Kernel Size', 'Filters', 'Strides', 'FLOPS')) print('-' * 170) t_flops = 0 t_macc = 0 for l in model.layers: o_shape, i_shape, strides, ks, filters = ['', '', ''], ['', '', ''], [1, 1], [0, 0], [0, 0] flops = 0 macc = 0 name = l.name factor = 1e9 if 'InputLayer' in str(l): i_shape = l.input.get_shape()[1:4].as_list() o_shape = i_shape if 'Reshape' in str(l): i_shape = l.input.get_shape()[1:4].as_list() o_shape = l.output.get_shape()[1:4].as_list() if 'Add' in str(l) or 'Maximum' in str(l) or 'Concatenate' in str(l): i_shape = l.input[0].get_shape()[1:4].as_list() + [len(l.input)] o_shape = l.output.get_shape()[1:4].as_list() flops = (len(l.input) - 1) * i_shape[0] * i_shape[1] * i_shape[2] if 'Average' in str(l) and 'pool' not in str(l): i_shape = l.input[0].get_shape()[1:4].as_list() + [len(l.input)] o_shape = l.output.get_shape()[1:4].as_list() flops = len(l.input) * i_shape[0] * i_shape[1] * i_shape[2] if 'BatchNormalization' in str(l): i_shape = l.input.get_shape()[1:4].as_list() o_shape = l.output.get_shape()[1:4].as_list() bflops = 1 for i in range(len(i_shape)): bflops *= i_shape[i] flops /= factor if 'Activation' in str(l) or 'activation' in str(l): i_shape = l.input.get_shape()[1:4].as_list() o_shape = l.output.get_shape()[1:4].as_list() bflops = 1 for i in range(len(i_shape)): bflops *= i_shape[i] flops /= factor if 'pool' in str(l) and ('Global' not in str(l)): i_shape = l.input.get_shape()[1:4].as_list() strides = l.strides ks = l.pool_size flops = ((i_shape[0] / strides[0]) * (i_shape[1] / strides[1]) * (ks[0] * ks[1] * i_shape[2])) if 'Flatten' in str(l): i_shape = l.input.shape[1:4].as_list() flops = 1 out_vec = 1 for i in range(len(i_shape)): flops *= i_shape[i] out_vec *= i_shape[i] o_shape = flops flops = 0 if 'Dense' in str(l): print(l.input) i_shape = l.input.shape[1:4].as_list()[0] if i_shape is None: i_shape = out_vec o_shape = l.output.shape[1:4].as_list() flops = 2 * (o_shape[0] * i_shape) macc = flops / 2 if 'Padding' in str(l): flops = 0 if 'Global' in str(l): i_shape = l.input.get_shape()[1:4].as_list() flops = ((i_shape[0]) * (i_shape[1]) * (i_shape[2])) o_shape = [l.output.get_shape()[1:4].as_list(), 1, 1] out_vec = o_shape if 'Conv2D' in str(l) and 'DepthwiseConv2D' not in str(l) and 'SeparableConv2D' not in str(l): strides = l.strides ks = l.kernel_size filters = l.filters i_shape = l.input.get_shape()[1:4].as_list() o_shape = l.output.get_shape()[1:4].as_list() if filters is None: filters = i_shape[2] flops = 2 * ((filters * ks[0] * ks[1] * i_shape[2]) * ( (i_shape[0] / strides[0]) * (i_shape[1] / strides[1]))) macc = flops / 2 if 'Conv2D' in str(l) and 'DepthwiseConv2D' in str(l) and 'SeparableConv2D' not in str(l): strides = l.strides ks = l.kernel_size filters = l.filters i_shape = l.input.get_shape()[1:4].as_list() o_shape = l.output.get_shape()[1:4].as_list() if filters is None: filters = i_shape[2] flops = 2 * ((ks[0] * ks[1] * i_shape[2]) * ((i_shape[0] / strides[0]) * ( i_shape[1] / strides[1]))) / factor macc = flops / 2 t_macc += macc t_flops += flops if table: print('%25s | %16s | %16s | %16s | %16s | %6s | %5.4f' % ( name, str(i_shape), str(o_shape), str(ks), str(filters), str(strides), flops)) t_flops = t_flops / factor print('Total FLOPS (x 10^-9): %10.8f G' % (t_flops)) print('Total MACCs: %10.8f\n' % (t_macc)) return

true

1c4406d3ffd11fb02809d090a8f414c71c74c0e7

835

py

Python

tests/acceptance/test_acceptance.py

magmax/livedoc

40b7041bcb36b2a2ebbd3d5906ce5954dbc7f1ca

[ "Python-2.0" ]

null

tests/acceptance/test_acceptance.py

magmax/livedoc

40b7041bcb36b2a2ebbd3d5906ce5954dbc7f1ca

[ "Python-2.0" ]

2

2016-06-13T08:37:20.000Z

2021-03-22T16:56:10.000Z

tests/acceptance/test_acceptance.py

magmax/livedoc

40b7041bcb36b2a2ebbd3d5906ce5954dbc7f1ca

[ "Python-2.0" ]

null

import os import unittest import tempfile from livedoc.__main__ import main class LivedocTest(unittest.TestCase): def test_example1(self): this_path = os.path.dirname(__file__) example_path = os.path.join( os.path.dirname(os.path.dirname(this_path)), 'examples', 'example1', ) with tempfile.TemporaryDirectory() as tmp: rc = main([example_path, '-o', tmp, '-vvvv']) assert rc == 0 def test_example2(self): this_path = os.path.dirname(__file__) example_path = os.path.join( os.path.dirname(os.path.dirname(this_path)), 'examples', 'example2', ) with tempfile.TemporaryDirectory() as tmp: rc = main([example_path, '-o', tmp, '-vvvv']) assert rc == 2

28.793103

57

0.578443

import os import unittest import tempfile from livedoc.__main__ import main class LivedocTest(unittest.TestCase): def test_example1(self): this_path = os.path.dirname(__file__) example_path = os.path.join( os.path.dirname(os.path.dirname(this_path)), 'examples', 'example1', ) with tempfile.TemporaryDirectory() as tmp: rc = main([example_path, '-o', tmp, '-vvvv']) assert rc == 0 def test_example2(self): this_path = os.path.dirname(__file__) example_path = os.path.join( os.path.dirname(os.path.dirname(this_path)), 'examples', 'example2', ) with tempfile.TemporaryDirectory() as tmp: rc = main([example_path, '-o', tmp, '-vvvv']) assert rc == 2

true