max_stars_repo_path stringlengths 4 286 | max_stars_repo_name stringlengths 5 119 | max_stars_count int64 0 191k | id stringlengths 1 7 | content stringlengths 6 1.03M | content_cleaned stringlengths 6 1.03M | language stringclasses 111 values | language_score float64 0.03 1 | comments stringlengths 0 556k | edu_score float64 0.32 5.03 | edu_int_score int64 0 5 |
|---|---|---|---|---|---|---|---|---|---|---|
tests/test_rand.py | yl3dy/pylibressl | 2 | 6624251 | <reponame>yl3dy/pylibressl
import pytest
from pylibressl.rand import get_random_bytes, libressl_get_random_bytes
from pylibressl.rand import getrandbits
class GenericRandTest:
_randfunc = (None,) # ugly hack to prevent making _randfunc a method
def test_too_short_length(self):
_randfunc = self._randfunc[0]
with pytest.raises(ValueError):
random = _randfunc(0)
def test_too_short_length(self):
_randfunc = self._randfunc[0]
with pytest.raises(ValueError):
random = _randfunc(-10)
def test_non_int_length(self):
_randfunc = self._randfunc[0]
with pytest.raises(TypeError):
random = _randfunc('asdfasdf')
def test_output_length(self):
_randfunc = self._randfunc[0]
length = 64
assert len(_randfunc(length)) == length
def test_repeated_invocation(self):
_randfunc = self._randfunc[0]
randstr1 = _randfunc(64)
randstr2 = _randfunc(64)
assert randstr1 != randstr2
class TestSystemRand(GenericRandTest):
_randfunc = (get_random_bytes,)
class TestLibreSSLRand(GenericRandTest):
_randfunc = (libressl_get_random_bytes,)
class TestGetrandbits:
def test_non_int_length(self):
with pytest.raises(TypeError):
getrandbits('asdfasdf')
def test_too_short_length(self):
with pytest.raises(ValueError):
getrandbits(-10)
def test_output_length(self):
length = 13
rv = getrandbits(length)
assert rv <= ((1 << length) - 1)
| import pytest
from pylibressl.rand import get_random_bytes, libressl_get_random_bytes
from pylibressl.rand import getrandbits
class GenericRandTest:
_randfunc = (None,) # ugly hack to prevent making _randfunc a method
def test_too_short_length(self):
_randfunc = self._randfunc[0]
with pytest.raises(ValueError):
random = _randfunc(0)
def test_too_short_length(self):
_randfunc = self._randfunc[0]
with pytest.raises(ValueError):
random = _randfunc(-10)
def test_non_int_length(self):
_randfunc = self._randfunc[0]
with pytest.raises(TypeError):
random = _randfunc('asdfasdf')
def test_output_length(self):
_randfunc = self._randfunc[0]
length = 64
assert len(_randfunc(length)) == length
def test_repeated_invocation(self):
_randfunc = self._randfunc[0]
randstr1 = _randfunc(64)
randstr2 = _randfunc(64)
assert randstr1 != randstr2
class TestSystemRand(GenericRandTest):
_randfunc = (get_random_bytes,)
class TestLibreSSLRand(GenericRandTest):
_randfunc = (libressl_get_random_bytes,)
class TestGetrandbits:
def test_non_int_length(self):
with pytest.raises(TypeError):
getrandbits('asdfasdf')
def test_too_short_length(self):
with pytest.raises(ValueError):
getrandbits(-10)
def test_output_length(self):
length = 13
rv = getrandbits(length)
assert rv <= ((1 << length) - 1) | en | 0.623094 | # ugly hack to prevent making _randfunc a method | 2.336395 | 2 |
lilypadz/__init__.py | Weiqi97/LilyPadz | 2 | 6624252 | """Project structure."""
| """Project structure."""
| en | 0.659098 | Project structure. | 1.021487 | 1 |
hic/diffusionTesting.py | zelhar/mg21 | 0 | 6624253 | if __name__ == '__main__':
from mymodule import *
else:
from .mymodule import *
A = np.random.rand(10,8) * 1e-5
plt.matshow(A)
filepath1 = "./hicdata/191-98_hg19_no_hap_EBV_MAPQ30_merged.mcool"
bedpath = "./hicdata/191-98_reconstruction.bed"
filepath2 = "./hicdata/CL17-08_hg19_no_hap_EBV_MAPQ30_merged.mcool"
bedpath = "./hicdata/CL17-08_reconstruction.bed"
reffilepath = "./hicdata/CL18-38_hg19_no_hap_EBV_MAPQ30_merged.mcool"
# no bed it's the reference matrix
bedDF = pd.read_csv(
bedpath,
names=[
"chr",
"start",
"end",
"foo",
"bar",
"orientation",
"derivative_chr",
"scaffold",
],
sep="\t",
)
bedDF
# loading cooler matrix with cooler's API
c = cooler.Cooler(filepath2 + "::resolutions/250000")
arr = c.matrix(balance='KR', sparse=False)[:,:]
arr = np.nan_to_num(arr)
c2 = cooler.Cooler(reffilepath+"::resolutions/500000")
refarr = c2.matrix(balance='KR', sparse=False)[:,:]
refarr = np.nan_to_num(refarr)
plotHicMat(arr+1)
plotHicMat(refarr+1)
# slicing by chromosomes
a2b5 = c.matrix(balance='KR').fetch('2', '5')
a2b5 = np.nan_to_num(a2b5)
plotHicMat(a2b5 + 1)
A = np.random.random((4,5))
A @ A.T
A = a2b5 @ a2b5.T
A = A + np.identity(len(A))
A
# column normalize:
A = A / A.sum(axis = 0)
x = np.ones((2,3))
x[0,1] = 3
x
y = x.T @ x
y
y
y.sum(axis=0)
y.sum(axis=1)
z = diffusionMatrix(y)
z.sum(axis=0)
z.sum(axis=1)
A.sum(axis=0)
K = diffusionMatrix(A)
K.sum(axis=0)
p = K @ np.ones(len(K))
plt.bar(range(len(K)), p)
mymax = np.argmax(p)
mymax
plotHicMat(a2b5)
plt.hlines(mymax, 0, 100, color='blue')
mymaxes = np.argsort(p)[-1:-10:-1]
plt.hlines(mymaxes, 0, 250, color='purple')
morelocations = np.argsort(p)[-1:-150:-1]
plt.hlines(morelocations, 0, 70, color='green')
plt.close()
plt.cla()
# loading cooler matrix with cooler's API
c = cooler.Cooler(filepath1 + "::resolutions/250000")
c2 = cooler.Cooler(filepath2 + "::resolutions/250000")
# slicing by chromosomes
a16b11 = c2.matrix(balance='KR').fetch('16', '11')
a16b11 = np.nan_to_num(a16b11)
plotHicMat(a16b11 + 1)
arr = c.matrix(balance='KR', sparse=False)[:,:]
arr = np.nan_to_num(arr)
arr2 = c2.matrix(balance='KR', sparse=False)[:,:]
arr2 = np.nan_to_num(arr2)
plotHicMat(arr +1)
plotHicMat(arr2 +1)
x = c2.matrix(balance='KR').fetch('2', '11')
x = np.nan_to_num(x)
plotHicMat(x + 1)
| if __name__ == '__main__':
from mymodule import *
else:
from .mymodule import *
A = np.random.rand(10,8) * 1e-5
plt.matshow(A)
filepath1 = "./hicdata/191-98_hg19_no_hap_EBV_MAPQ30_merged.mcool"
bedpath = "./hicdata/191-98_reconstruction.bed"
filepath2 = "./hicdata/CL17-08_hg19_no_hap_EBV_MAPQ30_merged.mcool"
bedpath = "./hicdata/CL17-08_reconstruction.bed"
reffilepath = "./hicdata/CL18-38_hg19_no_hap_EBV_MAPQ30_merged.mcool"
# no bed it's the reference matrix
bedDF = pd.read_csv(
bedpath,
names=[
"chr",
"start",
"end",
"foo",
"bar",
"orientation",
"derivative_chr",
"scaffold",
],
sep="\t",
)
bedDF
# loading cooler matrix with cooler's API
c = cooler.Cooler(filepath2 + "::resolutions/250000")
arr = c.matrix(balance='KR', sparse=False)[:,:]
arr = np.nan_to_num(arr)
c2 = cooler.Cooler(reffilepath+"::resolutions/500000")
refarr = c2.matrix(balance='KR', sparse=False)[:,:]
refarr = np.nan_to_num(refarr)
plotHicMat(arr+1)
plotHicMat(refarr+1)
# slicing by chromosomes
a2b5 = c.matrix(balance='KR').fetch('2', '5')
a2b5 = np.nan_to_num(a2b5)
plotHicMat(a2b5 + 1)
A = np.random.random((4,5))
A @ A.T
A = a2b5 @ a2b5.T
A = A + np.identity(len(A))
A
# column normalize:
A = A / A.sum(axis = 0)
x = np.ones((2,3))
x[0,1] = 3
x
y = x.T @ x
y
y
y.sum(axis=0)
y.sum(axis=1)
z = diffusionMatrix(y)
z.sum(axis=0)
z.sum(axis=1)
A.sum(axis=0)
K = diffusionMatrix(A)
K.sum(axis=0)
p = K @ np.ones(len(K))
plt.bar(range(len(K)), p)
mymax = np.argmax(p)
mymax
plotHicMat(a2b5)
plt.hlines(mymax, 0, 100, color='blue')
mymaxes = np.argsort(p)[-1:-10:-1]
plt.hlines(mymaxes, 0, 250, color='purple')
morelocations = np.argsort(p)[-1:-150:-1]
plt.hlines(morelocations, 0, 70, color='green')
plt.close()
plt.cla()
# loading cooler matrix with cooler's API
c = cooler.Cooler(filepath1 + "::resolutions/250000")
c2 = cooler.Cooler(filepath2 + "::resolutions/250000")
# slicing by chromosomes
a16b11 = c2.matrix(balance='KR').fetch('16', '11')
a16b11 = np.nan_to_num(a16b11)
plotHicMat(a16b11 + 1)
arr = c.matrix(balance='KR', sparse=False)[:,:]
arr = np.nan_to_num(arr)
arr2 = c2.matrix(balance='KR', sparse=False)[:,:]
arr2 = np.nan_to_num(arr2)
plotHicMat(arr +1)
plotHicMat(arr2 +1)
x = c2.matrix(balance='KR').fetch('2', '11')
x = np.nan_to_num(x)
plotHicMat(x + 1)
| en | 0.894507 | # no bed it's the reference matrix # loading cooler matrix with cooler's API # slicing by chromosomes # column normalize: # loading cooler matrix with cooler's API # slicing by chromosomes | 2.163982 | 2 |
feets/tests/test_original_FATS_test_library.py | ryanjmccall/feets | 0 | 6624254 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# The MIT License (MIT)
# Copyright (c) 2015, 2016, 2017, 2018
# <NAME>, <NAME>, <NAME>
# 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.
"""Original tests of FATS:
Removed:
- Commented ones
- The Stetson test because don't work anyway (the original test not provides
all the data required for the Stetson indexes.
Orignal Version:
https://github.com/isadoranun/FATS/blob/b45b5c1/FATS/test_library.py
"""
# =============================================================================
# IMPORTS
# =============================================================================
import numpy as np
import pytest
from six.moves import range
from ..core import FeatureSpace
# =============================================================================
# FIXTURES
# =============================================================================
# FIX the random state
random = np.random.RandomState(42)
@pytest.fixture
def white_noise():
data = random.normal(size=10000)
mjd = np.arange(10000)
error = random.normal(loc=0.01, scale=0.8, size=10000)
second_data = random.normal(size=10000)
aligned_data = data
aligned_second_data = second_data
aligned_mjd = mjd
lc = {
"magnitude": data,
"time": mjd,
"error": error,
"magnitude2": second_data,
"aligned_magnitude": aligned_data,
"aligned_magnitude2": aligned_second_data,
"aligned_time": aligned_mjd}
return lc
@pytest.fixture
def periodic_lc():
N = 100
mjd_periodic = np.arange(N)
Period = 20
cov = np.zeros([N, N])
mean = np.zeros(N)
for i in np.arange(N):
for j in np.arange(N):
cov[i, j] = np.exp(-(np.sin((np.pi/Period) * (i-j)) ** 2))
data_periodic = random.multivariate_normal(mean, cov)
lc = {
"magnitude": data_periodic,
"time": mjd_periodic}
return lc
@pytest.fixture
def uniform_lc():
mjd_uniform = np.arange(1000000)
data_uniform = random.uniform(size=1000000)
lc = {
"magnitude": data_uniform,
"time": mjd_uniform}
return lc
@pytest.fixture
def random_walk():
N = 10000
alpha = 1.
sigma = 0.5
data_rw = np.zeros([N, 1])
data_rw[0] = 1
time_rw = range(1, N)
for t in time_rw:
data_rw[t] = alpha * data_rw[t-1] + random.normal(loc=0.0, scale=sigma)
time_rw = np.array(range(0, N)) + 1 * random.uniform(size=N)
data_rw = data_rw.squeeze()
lc = {
"magnitude": data_rw,
"time": time_rw}
return lc
# =============================================================================
# TESTS
# =============================================================================
def test_Beyond1Std(white_noise):
fs = FeatureSpace(only=['Beyond1Std'])
result = fs.extract(**white_noise)[1][0]
assert result >= 0.30 and result <= 0.40
def test_Mean(white_noise):
fs = FeatureSpace(only=['Mean'])
result = fs.extract(**white_noise)[1][0]
assert result >= -0.1 and result <= 0.1
def test_Con(white_noise):
fs = FeatureSpace(only=['Con'], Con={"consecutiveStar": 1})
result = fs.extract(**white_noise)[1][0]
assert result >= 0.04 and result <= 0.05
def test_Eta_color(white_noise):
fs = FeatureSpace(only=['Eta_color'])
result = fs.extract(**white_noise)[1][0]
assert result >= 1.9 and result <= 2.1
def test_Eta_e(white_noise):
fs = FeatureSpace(only=['Eta_e'])
result = fs.extract(**white_noise)[1][0]
assert result >= 1.9 and result <= 2.1
def test_FluxPercentile(white_noise):
fs = FeatureSpace(only=[
'FluxPercentileRatioMid20', 'FluxPercentileRatioMid35',
'FluxPercentileRatioMid50', 'FluxPercentileRatioMid65',
'FluxPercentileRatioMid80'])
result = fs.extract(**white_noise)[1]
assert result[0] >= 0.145 and result[0] <= 0.160
assert result[1] >= 0.260 and result[1] <= 0.290
assert result[2] >= 0.350 and result[2] <= 0.450
assert result[3] >= 0.540 and result[3] <= 0.580
assert result[4] >= 0.760 and result[4] <= 0.800
def test_LinearTrend(white_noise):
fs = FeatureSpace(only=['LinearTrend'])
result = fs.extract(**white_noise)[1][0]
assert result >= -0.1 and result <= 0.1
def test_Meanvariance(uniform_lc):
fs = FeatureSpace(only=['Meanvariance'])
result = fs.extract(**uniform_lc)[1][0]
assert result >= 0.575 and result <= 0.580
def test_MedianAbsDev(white_noise):
fs = FeatureSpace(only=['MedianAbsDev'])
result = fs.extract(**white_noise)[1][0]
assert result >= 0.630 and result <= 0.700
def test_PairSlopeTrend(white_noise):
fs = FeatureSpace(only=['PairSlopeTrend'])
result = fs.extract(**white_noise)[1][0]
assert result >= -0.25 and result <= 0.25
def test_Period_Psi(periodic_lc):
params = {
"lscargle_kwds": {
"autopower_kwds": {
"normalization": "standard",
"nyquist_factor": 1,
}
}
}
fs = FeatureSpace(only=['PeriodLS'], LombScargle=params)
result = fs.extract(**periodic_lc)[1][0]
assert result >= 19 and result <= 21
def test_Q31(white_noise):
fs = FeatureSpace(only=['Q31'])
result = fs.extract(**white_noise)[1][0]
assert result >= 1.30 and result <= 1.38
def test_Rcs(white_noise):
fs = FeatureSpace(only=['Rcs'])
result = fs.extract(**white_noise)[1][0]
assert result >= 0 and result <= 0.1
def test_Skew(white_noise):
fs = FeatureSpace(only=['Skew'])
result = fs.extract(**white_noise)[1][0]
assert result >= -0.1 and result <= 0.1
def test_SmallKurtosis(white_noise):
fs = FeatureSpace(only=['SmallKurtosis'])
result = fs.extract(**white_noise)[1][0]
assert result >= -0.2 and result <= 0.2
def test_Std(white_noise):
fs = FeatureSpace(only=['Std'])
result = fs.extract(**white_noise)[1][0]
assert result >= 0.9 and result <= 1.1
def test_Gskew(white_noise):
fs = FeatureSpace(only=['Gskew'])
result = fs.extract(**white_noise)[1][0]
assert result >= -0.2 and result <= 0.2
def test_StructureFunction(random_walk):
fs = FeatureSpace(only=[
'StructureFunction_index_21',
'StructureFunction_index_31',
'StructureFunction_index_32'])
result = fs.extract(**random_walk)[1]
assert(result[0] >= 1.520 and result[0] <= 2.067)
assert(result[1] >= 1.821 and result[1] <= 3.162)
assert(result[2] >= 1.243 and result[2] <= 1.562)
| #!/usr/bin/env python
# -*- coding: utf-8 -*-
# The MIT License (MIT)
# Copyright (c) 2015, 2016, 2017, 2018
# <NAME>, <NAME>, <NAME>
# 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.
"""Original tests of FATS:
Removed:
- Commented ones
- The Stetson test because don't work anyway (the original test not provides
all the data required for the Stetson indexes.
Orignal Version:
https://github.com/isadoranun/FATS/blob/b45b5c1/FATS/test_library.py
"""
# =============================================================================
# IMPORTS
# =============================================================================
import numpy as np
import pytest
from six.moves import range
from ..core import FeatureSpace
# =============================================================================
# FIXTURES
# =============================================================================
# FIX the random state
random = np.random.RandomState(42)
@pytest.fixture
def white_noise():
data = random.normal(size=10000)
mjd = np.arange(10000)
error = random.normal(loc=0.01, scale=0.8, size=10000)
second_data = random.normal(size=10000)
aligned_data = data
aligned_second_data = second_data
aligned_mjd = mjd
lc = {
"magnitude": data,
"time": mjd,
"error": error,
"magnitude2": second_data,
"aligned_magnitude": aligned_data,
"aligned_magnitude2": aligned_second_data,
"aligned_time": aligned_mjd}
return lc
@pytest.fixture
def periodic_lc():
N = 100
mjd_periodic = np.arange(N)
Period = 20
cov = np.zeros([N, N])
mean = np.zeros(N)
for i in np.arange(N):
for j in np.arange(N):
cov[i, j] = np.exp(-(np.sin((np.pi/Period) * (i-j)) ** 2))
data_periodic = random.multivariate_normal(mean, cov)
lc = {
"magnitude": data_periodic,
"time": mjd_periodic}
return lc
@pytest.fixture
def uniform_lc():
mjd_uniform = np.arange(1000000)
data_uniform = random.uniform(size=1000000)
lc = {
"magnitude": data_uniform,
"time": mjd_uniform}
return lc
@pytest.fixture
def random_walk():
N = 10000
alpha = 1.
sigma = 0.5
data_rw = np.zeros([N, 1])
data_rw[0] = 1
time_rw = range(1, N)
for t in time_rw:
data_rw[t] = alpha * data_rw[t-1] + random.normal(loc=0.0, scale=sigma)
time_rw = np.array(range(0, N)) + 1 * random.uniform(size=N)
data_rw = data_rw.squeeze()
lc = {
"magnitude": data_rw,
"time": time_rw}
return lc
# =============================================================================
# TESTS
# =============================================================================
def test_Beyond1Std(white_noise):
fs = FeatureSpace(only=['Beyond1Std'])
result = fs.extract(**white_noise)[1][0]
assert result >= 0.30 and result <= 0.40
def test_Mean(white_noise):
fs = FeatureSpace(only=['Mean'])
result = fs.extract(**white_noise)[1][0]
assert result >= -0.1 and result <= 0.1
def test_Con(white_noise):
fs = FeatureSpace(only=['Con'], Con={"consecutiveStar": 1})
result = fs.extract(**white_noise)[1][0]
assert result >= 0.04 and result <= 0.05
def test_Eta_color(white_noise):
fs = FeatureSpace(only=['Eta_color'])
result = fs.extract(**white_noise)[1][0]
assert result >= 1.9 and result <= 2.1
def test_Eta_e(white_noise):
fs = FeatureSpace(only=['Eta_e'])
result = fs.extract(**white_noise)[1][0]
assert result >= 1.9 and result <= 2.1
def test_FluxPercentile(white_noise):
fs = FeatureSpace(only=[
'FluxPercentileRatioMid20', 'FluxPercentileRatioMid35',
'FluxPercentileRatioMid50', 'FluxPercentileRatioMid65',
'FluxPercentileRatioMid80'])
result = fs.extract(**white_noise)[1]
assert result[0] >= 0.145 and result[0] <= 0.160
assert result[1] >= 0.260 and result[1] <= 0.290
assert result[2] >= 0.350 and result[2] <= 0.450
assert result[3] >= 0.540 and result[3] <= 0.580
assert result[4] >= 0.760 and result[4] <= 0.800
def test_LinearTrend(white_noise):
fs = FeatureSpace(only=['LinearTrend'])
result = fs.extract(**white_noise)[1][0]
assert result >= -0.1 and result <= 0.1
def test_Meanvariance(uniform_lc):
fs = FeatureSpace(only=['Meanvariance'])
result = fs.extract(**uniform_lc)[1][0]
assert result >= 0.575 and result <= 0.580
def test_MedianAbsDev(white_noise):
fs = FeatureSpace(only=['MedianAbsDev'])
result = fs.extract(**white_noise)[1][0]
assert result >= 0.630 and result <= 0.700
def test_PairSlopeTrend(white_noise):
fs = FeatureSpace(only=['PairSlopeTrend'])
result = fs.extract(**white_noise)[1][0]
assert result >= -0.25 and result <= 0.25
def test_Period_Psi(periodic_lc):
params = {
"lscargle_kwds": {
"autopower_kwds": {
"normalization": "standard",
"nyquist_factor": 1,
}
}
}
fs = FeatureSpace(only=['PeriodLS'], LombScargle=params)
result = fs.extract(**periodic_lc)[1][0]
assert result >= 19 and result <= 21
def test_Q31(white_noise):
fs = FeatureSpace(only=['Q31'])
result = fs.extract(**white_noise)[1][0]
assert result >= 1.30 and result <= 1.38
def test_Rcs(white_noise):
fs = FeatureSpace(only=['Rcs'])
result = fs.extract(**white_noise)[1][0]
assert result >= 0 and result <= 0.1
def test_Skew(white_noise):
fs = FeatureSpace(only=['Skew'])
result = fs.extract(**white_noise)[1][0]
assert result >= -0.1 and result <= 0.1
def test_SmallKurtosis(white_noise):
fs = FeatureSpace(only=['SmallKurtosis'])
result = fs.extract(**white_noise)[1][0]
assert result >= -0.2 and result <= 0.2
def test_Std(white_noise):
fs = FeatureSpace(only=['Std'])
result = fs.extract(**white_noise)[1][0]
assert result >= 0.9 and result <= 1.1
def test_Gskew(white_noise):
fs = FeatureSpace(only=['Gskew'])
result = fs.extract(**white_noise)[1][0]
assert result >= -0.2 and result <= 0.2
def test_StructureFunction(random_walk):
fs = FeatureSpace(only=[
'StructureFunction_index_21',
'StructureFunction_index_31',
'StructureFunction_index_32'])
result = fs.extract(**random_walk)[1]
assert(result[0] >= 1.520 and result[0] <= 2.067)
assert(result[1] >= 1.821 and result[1] <= 3.162)
assert(result[2] >= 1.243 and result[2] <= 1.562)
| en | 0.672153 | #!/usr/bin/env python # -*- coding: utf-8 -*- # The MIT License (MIT) # Copyright (c) 2015, 2016, 2017, 2018 # <NAME>, <NAME>, <NAME> # 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. Original tests of FATS: Removed: - Commented ones - The Stetson test because don't work anyway (the original test not provides all the data required for the Stetson indexes. Orignal Version: https://github.com/isadoranun/FATS/blob/b45b5c1/FATS/test_library.py # ============================================================================= # IMPORTS # ============================================================================= # ============================================================================= # FIXTURES # ============================================================================= # FIX the random state # ============================================================================= # TESTS # ============================================================================= | 1.387776 | 1 |
sikfa.py | SHI3DO/sikfa | 1 | 6624255 | import torch
dtype = torch.float
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
global loss
def find(x: list, y: list, weightsnum: int, trainnum: int, learning_rate: float):
global loss
x2 = torch.FloatTensor(x)
y2 = torch.FloatTensor(y)
coefficient_list = []
for i in range(0, weightsnum):
coefficient_list.append(torch.randn((), device=device, dtype=dtype, requires_grad=True))
for k in range(trainnum):
y_pred = 0
for h in range(0, len(coefficient_list)):
y_pred += coefficient_list[h] * x2 ** h
loss = (y_pred - y2).pow(2).sum()
if k % 10000 == 9999:
print(k, loss.item())
if not torch.isfinite(loss):
print('non-finite loss, ending training')
learning_rate = learning_rate / 10
trainnum = trainnum * 2
print(f'using {device}')
print(f'next learning_rate = {learning_rate}')
print(f'next trainnum = {trainnum}')
print('restarting...')
find(x, y, weightsnum, trainnum, learning_rate)
exit(1)
loss.backward()
with torch.no_grad():
for h in range(0, len(coefficient_list)):
coefficient_list[h] -= learning_rate * coefficient_list[h].grad
coefficient_list[h].grad = None
if loss > 100:
print('too-big loss, ending training')
learning_rate = learning_rate / 10
trainnum = trainnum * 2
print(f'using {device}')
print(f'next learning_rate = {learning_rate}')
print(f'next trainnum = {trainnum}')
print('restarting...')
find(x, y, weightsnum, trainnum, learning_rate)
exit(1)
print(f'loss = {loss.item()}')
return coefficient_list
| import torch
dtype = torch.float
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
global loss
def find(x: list, y: list, weightsnum: int, trainnum: int, learning_rate: float):
global loss
x2 = torch.FloatTensor(x)
y2 = torch.FloatTensor(y)
coefficient_list = []
for i in range(0, weightsnum):
coefficient_list.append(torch.randn((), device=device, dtype=dtype, requires_grad=True))
for k in range(trainnum):
y_pred = 0
for h in range(0, len(coefficient_list)):
y_pred += coefficient_list[h] * x2 ** h
loss = (y_pred - y2).pow(2).sum()
if k % 10000 == 9999:
print(k, loss.item())
if not torch.isfinite(loss):
print('non-finite loss, ending training')
learning_rate = learning_rate / 10
trainnum = trainnum * 2
print(f'using {device}')
print(f'next learning_rate = {learning_rate}')
print(f'next trainnum = {trainnum}')
print('restarting...')
find(x, y, weightsnum, trainnum, learning_rate)
exit(1)
loss.backward()
with torch.no_grad():
for h in range(0, len(coefficient_list)):
coefficient_list[h] -= learning_rate * coefficient_list[h].grad
coefficient_list[h].grad = None
if loss > 100:
print('too-big loss, ending training')
learning_rate = learning_rate / 10
trainnum = trainnum * 2
print(f'using {device}')
print(f'next learning_rate = {learning_rate}')
print(f'next trainnum = {trainnum}')
print('restarting...')
find(x, y, weightsnum, trainnum, learning_rate)
exit(1)
print(f'loss = {loss.item()}')
return coefficient_list
| none | 1 | 2.695684 | 3 | |
examples/development/simulate_policy.py | bandofstraycats/dr-sac | 0 | 6624256 | import argparse
from distutils.util import strtobool
import json
import os
import pickle
import tensorflow as tf
from softlearning.environments.utils import get_environment_from_params
from softlearning.policies.utils import get_policy_from_variant
from softlearning.samplers import rollouts
from softlearning.misc.utils import save_video
from collections import OrderedDict
import numpy as np
import csv
from gym import wrappers
DEFAULT_RENDER_KWARGS = {
'mode': 'human',
}
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('checkpoint_path',
type=str,
help='Path to the checkpoint.')
parser.add_argument('--max-path-length', '-l', type=int, default=1000)
parser.add_argument('--num-rollouts', '-n', type=int, default=10)
parser.add_argument('--render-kwargs', '-r',
type=json.loads,
default='{}',
help="Kwargs for rollouts renderer.")
parser.add_argument('--deterministic', '-d',
type=lambda x: bool(strtobool(x)),
nargs='?',
const=True,
default=True,
help="Evaluate policy deterministically.")
parser.add_argument('--record-video', dest='record_video', action='store_true',
help="Whether to record a video or store the metrics.")
args = parser.parse_args()
return args
def simulate_policy(args):
session = tf.keras.backend.get_session()
checkpoint_path = args.checkpoint_path.rstrip('/')
experiment_path = os.path.dirname(checkpoint_path)
variant_path = os.path.join(experiment_path, 'params.pkl')
with open(variant_path, 'rb') as f:
variant = pickle.load(f)
with session.as_default():
pickle_path = os.path.join(checkpoint_path, 'checkpoint.pkl')
with open(pickle_path, 'rb') as f:
picklable = pickle.load(f)
environment_params = (
variant['environment_params']['evaluation']
if 'evaluation' in variant['environment_params']
else variant['environment_params']['training'])
evaluation_environment = get_environment_from_params(environment_params)
evaluation_environment.seed(variant['run_params']['seed'])
if args.record_video:
video_dir = os.path.join(experiment_path, 'test-video')
evaluation_environment._env = wrappers.Monitor(evaluation_environment._env, video_dir, force=True)
policy = (
get_policy_from_variant(variant, evaluation_environment))
policy.set_weights(picklable['policy_weights'])
render_kwargs = {**DEFAULT_RENDER_KWARGS, **args.render_kwargs}
with policy.set_deterministic(args.deterministic):
paths = rollouts(args.num_rollouts,
evaluation_environment,
policy,
path_length=args.max_path_length,
render_kwargs=render_kwargs)
if not args.record_video:
evaluation_metrics = evaluate_rollouts(paths, evaluation_environment)
evaluation_file_path = os.path.join(experiment_path, 'final_eval.csv')
with open(evaluation_file_path, 'w') as f:
w = csv.DictWriter(f, evaluation_metrics.keys())
w.writeheader()
w.writerow(evaluation_metrics)
if args.render_kwargs.get('mode') == 'rgb_array':
fps = 1 // getattr(evaluation_environment, 'dt', 1/30)
for i, path in enumerate(paths):
video_save_dir = os.path.expanduser('/tmp/simulate_policy/')
video_save_path = os.path.join(video_save_dir, f'episode_{i}.mp4')
save_video(path['images'], video_save_path, fps=fps)
return paths
def evaluate_rollouts(paths, env):
"""Compute evaluation metrics for the given rollouts."""
total_returns = [path['rewards'].sum() for path in paths]
episode_lengths = [len(p['rewards']) for p in paths]
diagnostics = OrderedDict((
('return-average', np.mean(total_returns)),
('return-min', np.min(total_returns)),
('return-max', np.max(total_returns)),
('return-std', np.std(total_returns)),
('episode-length-avg', np.mean(episode_lengths)),
('episode-length-min', np.min(episode_lengths)),
('episode-length-max', np.max(episode_lengths)),
('episode-length-std', np.std(episode_lengths)),
))
env_infos = env.get_path_infos(paths)
for key, value in env_infos.items():
diagnostics[f'env_infos/{key}'] = value
return diagnostics
if __name__ == '__main__':
args = parse_args()
simulate_policy(args)
| import argparse
from distutils.util import strtobool
import json
import os
import pickle
import tensorflow as tf
from softlearning.environments.utils import get_environment_from_params
from softlearning.policies.utils import get_policy_from_variant
from softlearning.samplers import rollouts
from softlearning.misc.utils import save_video
from collections import OrderedDict
import numpy as np
import csv
from gym import wrappers
DEFAULT_RENDER_KWARGS = {
'mode': 'human',
}
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('checkpoint_path',
type=str,
help='Path to the checkpoint.')
parser.add_argument('--max-path-length', '-l', type=int, default=1000)
parser.add_argument('--num-rollouts', '-n', type=int, default=10)
parser.add_argument('--render-kwargs', '-r',
type=json.loads,
default='{}',
help="Kwargs for rollouts renderer.")
parser.add_argument('--deterministic', '-d',
type=lambda x: bool(strtobool(x)),
nargs='?',
const=True,
default=True,
help="Evaluate policy deterministically.")
parser.add_argument('--record-video', dest='record_video', action='store_true',
help="Whether to record a video or store the metrics.")
args = parser.parse_args()
return args
def simulate_policy(args):
session = tf.keras.backend.get_session()
checkpoint_path = args.checkpoint_path.rstrip('/')
experiment_path = os.path.dirname(checkpoint_path)
variant_path = os.path.join(experiment_path, 'params.pkl')
with open(variant_path, 'rb') as f:
variant = pickle.load(f)
with session.as_default():
pickle_path = os.path.join(checkpoint_path, 'checkpoint.pkl')
with open(pickle_path, 'rb') as f:
picklable = pickle.load(f)
environment_params = (
variant['environment_params']['evaluation']
if 'evaluation' in variant['environment_params']
else variant['environment_params']['training'])
evaluation_environment = get_environment_from_params(environment_params)
evaluation_environment.seed(variant['run_params']['seed'])
if args.record_video:
video_dir = os.path.join(experiment_path, 'test-video')
evaluation_environment._env = wrappers.Monitor(evaluation_environment._env, video_dir, force=True)
policy = (
get_policy_from_variant(variant, evaluation_environment))
policy.set_weights(picklable['policy_weights'])
render_kwargs = {**DEFAULT_RENDER_KWARGS, **args.render_kwargs}
with policy.set_deterministic(args.deterministic):
paths = rollouts(args.num_rollouts,
evaluation_environment,
policy,
path_length=args.max_path_length,
render_kwargs=render_kwargs)
if not args.record_video:
evaluation_metrics = evaluate_rollouts(paths, evaluation_environment)
evaluation_file_path = os.path.join(experiment_path, 'final_eval.csv')
with open(evaluation_file_path, 'w') as f:
w = csv.DictWriter(f, evaluation_metrics.keys())
w.writeheader()
w.writerow(evaluation_metrics)
if args.render_kwargs.get('mode') == 'rgb_array':
fps = 1 // getattr(evaluation_environment, 'dt', 1/30)
for i, path in enumerate(paths):
video_save_dir = os.path.expanduser('/tmp/simulate_policy/')
video_save_path = os.path.join(video_save_dir, f'episode_{i}.mp4')
save_video(path['images'], video_save_path, fps=fps)
return paths
def evaluate_rollouts(paths, env):
"""Compute evaluation metrics for the given rollouts."""
total_returns = [path['rewards'].sum() for path in paths]
episode_lengths = [len(p['rewards']) for p in paths]
diagnostics = OrderedDict((
('return-average', np.mean(total_returns)),
('return-min', np.min(total_returns)),
('return-max', np.max(total_returns)),
('return-std', np.std(total_returns)),
('episode-length-avg', np.mean(episode_lengths)),
('episode-length-min', np.min(episode_lengths)),
('episode-length-max', np.max(episode_lengths)),
('episode-length-std', np.std(episode_lengths)),
))
env_infos = env.get_path_infos(paths)
for key, value in env_infos.items():
diagnostics[f'env_infos/{key}'] = value
return diagnostics
if __name__ == '__main__':
args = parse_args()
simulate_policy(args)
| en | 0.764365 | Compute evaluation metrics for the given rollouts. | 1.977108 | 2 |
scripts/practice/FB/MaximumSumBSTinBinaryTree.py | bhimeshchauhan/competitive_programming | 0 | 6624257 | """
Maximum Sum BST in Binary Tree
Given a binary tree root, return the maximum sum of all keys of any sub-tree which is also a Binary Search Tree (BST).
Assume a BST is defined as follows:
The left subtree of a node contains only nodes with keys less than the node's key.
The right subtree of a node contains only nodes with keys greater than the node's key.
Both the left and right subtrees must also be binary search trees.
Example 1:
Input: root = [1,4,3,2,4,2,5,null,null,null,null,null,null,4,6]
Output: 20
Explanation: Maximum sum in a valid Binary search tree is obtained in root node with key equal to 3.
Example 2:
Input: root = [4,3,null,1,2]
Output: 2
Explanation: Maximum sum in a valid Binary search tree is obtained in a single root node with key equal to 2.
Example 3:
Input: root = [-4,-2,-5]
Output: 0
Explanation: All values are negatives. Return an empty BST.
Example 4:
Input: root = [2,1,3]
Output: 6
Example 5:
Input: root = [5,4,8,3,null,6,3]
Output: 7
Constraints:
The number of nodes in the tree is in the range [1, 4 * 104].
-4 * 104 <= Node.val <= 4 * 104
"""
"""
Most solutions discussed here solve this using Post Order traversal.
I tried to solve this using preorder traversal (using the floor and ceiling method to check validity of
BST like in here), and got confused.
For this problem we need to build the solution from the bottom-up i.e.,
from the leaf nodes towards the root. Only then can we check if the current sub-tree is a valid BST,
and then update the maximum sum. This means post order is the ideal way to traverse the tree.
Here's a solution using this idea:
"""
class Solution:
def __init__(self):
self.maxSum = 0
def maxSumBST(self, root):
def postOrderTraverse(node):
"""
Perform post order traversal of tree
to determine sub trees which are BSTs
and calculate maximum sum of its elements.
Returns:
isValidBST: True if valid BST else False
currentSum: sum of current sub tree. None
if not a valid BST.
currentMin: minimum value of current sub tree
currentMax: maximum value of current sub tree
"""
if not node:
return True, 0, float('inf'), float('-inf') # Empty sub tree
lValidBST, lSum, lMin, lMax = postOrderTraverse(node.left)
rValidBST, rSum, rMin, rMax = postOrderTraverse(node.right)
# Check if current subtree is a valid BST
if lValidBST and rValidBST and lMax < node.val < rMin:
currSum = lSum + rSum + node.val
currMin = lMin if lMin != float('inf') else node.val
currMax = rMax if rMax != float('-inf') else node.val
self.maxSum = max(self.maxSum, currSum) # update max sum
return True, currSum, currMin, currMax
return False, None, None, None
postOrderTraverse(root)
return self.maxSum
| """
Maximum Sum BST in Binary Tree
Given a binary tree root, return the maximum sum of all keys of any sub-tree which is also a Binary Search Tree (BST).
Assume a BST is defined as follows:
The left subtree of a node contains only nodes with keys less than the node's key.
The right subtree of a node contains only nodes with keys greater than the node's key.
Both the left and right subtrees must also be binary search trees.
Example 1:
Input: root = [1,4,3,2,4,2,5,null,null,null,null,null,null,4,6]
Output: 20
Explanation: Maximum sum in a valid Binary search tree is obtained in root node with key equal to 3.
Example 2:
Input: root = [4,3,null,1,2]
Output: 2
Explanation: Maximum sum in a valid Binary search tree is obtained in a single root node with key equal to 2.
Example 3:
Input: root = [-4,-2,-5]
Output: 0
Explanation: All values are negatives. Return an empty BST.
Example 4:
Input: root = [2,1,3]
Output: 6
Example 5:
Input: root = [5,4,8,3,null,6,3]
Output: 7
Constraints:
The number of nodes in the tree is in the range [1, 4 * 104].
-4 * 104 <= Node.val <= 4 * 104
"""
"""
Most solutions discussed here solve this using Post Order traversal.
I tried to solve this using preorder traversal (using the floor and ceiling method to check validity of
BST like in here), and got confused.
For this problem we need to build the solution from the bottom-up i.e.,
from the leaf nodes towards the root. Only then can we check if the current sub-tree is a valid BST,
and then update the maximum sum. This means post order is the ideal way to traverse the tree.
Here's a solution using this idea:
"""
class Solution:
def __init__(self):
self.maxSum = 0
def maxSumBST(self, root):
def postOrderTraverse(node):
"""
Perform post order traversal of tree
to determine sub trees which are BSTs
and calculate maximum sum of its elements.
Returns:
isValidBST: True if valid BST else False
currentSum: sum of current sub tree. None
if not a valid BST.
currentMin: minimum value of current sub tree
currentMax: maximum value of current sub tree
"""
if not node:
return True, 0, float('inf'), float('-inf') # Empty sub tree
lValidBST, lSum, lMin, lMax = postOrderTraverse(node.left)
rValidBST, rSum, rMin, rMax = postOrderTraverse(node.right)
# Check if current subtree is a valid BST
if lValidBST and rValidBST and lMax < node.val < rMin:
currSum = lSum + rSum + node.val
currMin = lMin if lMin != float('inf') else node.val
currMax = rMax if rMax != float('-inf') else node.val
self.maxSum = max(self.maxSum, currSum) # update max sum
return True, currSum, currMin, currMax
return False, None, None, None
postOrderTraverse(root)
return self.maxSum
| en | 0.832744 | Maximum Sum BST in Binary Tree Given a binary tree root, return the maximum sum of all keys of any sub-tree which is also a Binary Search Tree (BST). Assume a BST is defined as follows: The left subtree of a node contains only nodes with keys less than the node's key. The right subtree of a node contains only nodes with keys greater than the node's key. Both the left and right subtrees must also be binary search trees. Example 1: Input: root = [1,4,3,2,4,2,5,null,null,null,null,null,null,4,6] Output: 20 Explanation: Maximum sum in a valid Binary search tree is obtained in root node with key equal to 3. Example 2: Input: root = [4,3,null,1,2] Output: 2 Explanation: Maximum sum in a valid Binary search tree is obtained in a single root node with key equal to 2. Example 3: Input: root = [-4,-2,-5] Output: 0 Explanation: All values are negatives. Return an empty BST. Example 4: Input: root = [2,1,3] Output: 6 Example 5: Input: root = [5,4,8,3,null,6,3] Output: 7 Constraints: The number of nodes in the tree is in the range [1, 4 * 104]. -4 * 104 <= Node.val <= 4 * 104 Most solutions discussed here solve this using Post Order traversal. I tried to solve this using preorder traversal (using the floor and ceiling method to check validity of BST like in here), and got confused. For this problem we need to build the solution from the bottom-up i.e., from the leaf nodes towards the root. Only then can we check if the current sub-tree is a valid BST, and then update the maximum sum. This means post order is the ideal way to traverse the tree. Here's a solution using this idea: Perform post order traversal of tree to determine sub trees which are BSTs and calculate maximum sum of its elements. Returns: isValidBST: True if valid BST else False currentSum: sum of current sub tree. None if not a valid BST. currentMin: minimum value of current sub tree currentMax: maximum value of current sub tree # Empty sub tree # Check if current subtree is a valid BST # update max sum | 3.830828 | 4 |
dashathon/scraping/scrape_london_data.py | wfrierson/dashathon | 1 | 6624258 | <filename>dashathon/scraping/scrape_london_data.py
from dashathon.scraping.scraping_methods import scrape_london_marathon_urls
from dashathon.scraping.scraping_methods import scrape_london_marathon
headers_london = ['year', 'bib', 'age_group', 'gender', 'country', 'overall', 'rank_gender',
'rank_age_group', '5k', '10k', '15k', '20k', 'half', '25k', '30k', '35k', '40k', 'finish']
print('Scraping URLs: 2017 M')
london_marathon_urls_2017_M = scrape_london_marathon_urls(url='http://results-2017.virginmoneylondonmarathon.com/2017/',
year=2017, event='MAS', gender='M', num_results_per_page=1000)
london_marathon_urls_2017_M_elite = scrape_london_marathon_urls(url=('http://results-2017.virginmoneylondonmarathon.com'
'/2017/'), year=2017, event='ELIT', gender='M',
num_results_per_page=1000)
print('Scraping Split Times: 2017 M')
scrape_london_marathon(path_input='london_marathon_2017_M_urls.csv', path_output='london_marathon_2017_M.csv',
path_error='london_marathon_2017_M_error_log.csv', year=2017, gender='M', headers=headers_london,
df_urls=london_marathon_urls_2017_M)
scrape_london_marathon(path_input='london_marathon_2017_M_elite_urls.csv',
path_output='london_marathon_2017_M_elite.csv',
path_error='london_marathon_2017_M_elite_error_log.csv', year=2017, gender='M',
headers=headers_london, df_urls=london_marathon_urls_2017_M_elite)
print('Scraping URLs: 2017 W')
london_marathon_urls_2017_W = scrape_london_marathon_urls(url='http://results-2017.virginmoneylondonmarathon.com/2017/',
year=2017, event='MAS', gender='W', num_results_per_page=1000)
london_marathon_urls_2017_W_elite = scrape_london_marathon_urls(url=('http://results-2017.virginmoneylondonmarathon.com'
'/2017/'), year=2017, event='ELIT', gender='W',
num_results_per_page=1000)
print('Scraping Split Times: 2017 W')
scrape_london_marathon(path_input='london_marathon_2017_W_urls.csv', path_output='london_marathon_2017_W.csv',
path_error='london_marathon_2017_W_error_log.csv', year=2017, gender='W', headers=headers_london,
df_urls=london_marathon_urls_2017_W)
scrape_london_marathon(path_input='london_marathon_2017_W_elite_urls.csv',
path_output='london_marathon_2017_W_elite.csv',
path_error='london_marathon_2017_W_elite_error_log.csv', year=2017, gender='W',
headers=headers_london, df_urls=london_marathon_urls_2017_W_elite)
print('Scraping URLs: 2016 M')
london_marathon_urls_2016_M = scrape_london_marathon_urls(url='http://results-2016.virginmoneylondonmarathon.com/2016/',
year=2016, event='MAS', gender='M', num_results_per_page=1000)
london_marathon_urls_2016_M_elite = scrape_london_marathon_urls(url=('http://results-2016.virginmoneylondonmarathon.com'
'/2016/'), year=2016, event='ELIT', gender='M',
num_results_per_page=1000)
print('Scraping Split Times: 2016 M')
scrape_london_marathon(path_input='london_marathon_2016_M_urls.csv', path_output='london_marathon_2016_M.csv',
path_error='london_marathon_2016_M_error_log.csv', year=2016, gender='M', headers=headers_london,
df_urls=london_marathon_urls_2016_M)
scrape_london_marathon(path_input='london_marathon_2016_M_elite_urls.csv',
path_output='london_marathon_2016_M_elite.csv',
path_error='london_marathon_2016_M_elite_error_log.csv', year=2016, gender='M',
headers=headers_london, df_urls=london_marathon_urls_2016_M_elite)
print('Scraping URLs: 2016 W')
london_marathon_urls_2016_W = scrape_london_marathon_urls(url='http://results-2016.virginmoneylondonmarathon.com/2016/',
year=2016, event='MAS', gender='W', num_results_per_page=1000)
london_marathon_urls_2016_W_elite = scrape_london_marathon_urls(url=('http://results-2016.virginmoneylondonmarathon.com'
'/2016/'), year=2016, event='ELIT', gender='W',
num_results_per_page=1000)
print('Scraping Split Times: 2016 W')
scrape_london_marathon(path_input='london_marathon_2016_W_urls.csv', path_output='london_marathon_2016_W.csv',
path_error='london_marathon_2016_W_error_log.csv', year=2016, gender='W', headers=headers_london,
df_urls=london_marathon_urls_2016_W)
scrape_london_marathon(path_input='london_marathon_2016_W_elite_urls.csv',
path_output='london_marathon_2016_W_elite.csv',
path_error='london_marathon_2016_W_elite_error_log.csv', year=2016, gender='W',
headers=headers_london, df_urls=london_marathon_urls_2016_W_elite)
print('Scraping URLs: 2015 M')
london_marathon_urls_2015_M = scrape_london_marathon_urls(url='http://results-2015.virginmoneylondonmarathon.com/2015/',
year=2015, event='MAS', gender='M', num_results_per_page=1000)
london_marathon_urls_2015_M_elite = scrape_london_marathon_urls(url=('http://results-2015.virginmoneylondonmarathon.com'
'/2015/'), year=2015, event='ELIT', gender='M',
num_results_per_page=1000)
print('Scraping Split Times: 2015 M')
scrape_london_marathon(path_input='london_marathon_2015_M_urls.csv', path_output='london_marathon_2015_M.csv',
path_error='london_marathon_2015_M_error_log.csv', year=2015, gender='M', headers=headers_london,
df_urls=london_marathon_urls_2015_M)
scrape_london_marathon(path_input='london_marathon_2015_M_elite_urls.csv',
path_output='london_marathon_2015_M_elite.csv',
path_error='london_marathon_2015_M_elite_error_log.csv', year=2015, gender='M',
headers=headers_london, df_urls=london_marathon_urls_2015_M_elite)
print('Scraping URLs: 2015 W')
london_marathon_urls_2015_W = scrape_london_marathon_urls(url='http://results-2015.virginmoneylondonmarathon.com/2015/',
year=2015, event='MAS', gender='W', num_results_per_page=1000)
london_marathon_urls_2015_W_elite = scrape_london_marathon_urls(url=('http://results-2015.virginmoneylondonmarathon.com'
'/2015/'), year=2015, event='ELIT', gender='W',
num_results_per_page=1000)
print('Scraping Split Times: 2015 W')
scrape_london_marathon(path_input='london_marathon_2015_W_urls.csv', path_output='london_marathon_2015_W.csv',
path_error='london_marathon_2015_W_error_log.csv', year=2015, gender='W', headers=headers_london,
df_urls=london_marathon_urls_2015_W)
scrape_london_marathon(path_input='london_marathon_2015_W_elite_urls.csv',
path_output='london_marathon_2015_W_elite.csv',
path_error='london_marathon_2015_W_elite_error_log.csv', year=2015, gender='W',
headers=headers_london, df_urls=london_marathon_urls_2015_W_elite)
print('Scraping URLs: 2014 M')
london_marathon_urls_2014_M = scrape_london_marathon_urls(url='http://results-2014.virginmoneylondonmarathon.com/2014/',
year=2014, event='MAS', gender='M', num_results_per_page=1000)
london_marathon_urls_2014_M_elite = scrape_london_marathon_urls(url=('http://results-2014.virginmoneylondonmarathon.com'
'/2014/'), year=2014, event='ELIT', gender='M',
num_results_per_page=1000)
print('Scraping Split Times: 2014 M')
scrape_london_marathon(path_input='london_marathon_2014_M_urls.csv', path_output='london_marathon_2014_M.csv',
path_error='london_marathon_2014_M_error_log.csv', year=2014, gender='M', headers=headers_london,
df_urls=london_marathon_urls_2014_M)
scrape_london_marathon(path_input='london_marathon_2014_M_elite_urls.csv',
path_output='london_marathon_2014_M_elite.csv',
path_error='london_marathon_2014_M_elite_error_log.csv', year=2014, gender='M',
headers=headers_london, df_urls=london_marathon_urls_2014_M_elite)
print('Scraping URLs: 2014 W')
london_marathon_urls_2014_W = scrape_london_marathon_urls(url='http://results-2014.virginmoneylondonmarathon.com/2014/',
year=2014, event='MAS', gender='W', num_results_per_page=1000)
london_marathon_urls_2014_W_elite = scrape_london_marathon_urls(url=('http://results-2014.virginmoneylondonmarathon.com'
'/2014/'), year=2014, event='ELIT', gender='W',
num_results_per_page=1000)
print('Scraping Split Times: 2014 W')
scrape_london_marathon(path_input='london_marathon_2014_W_urls.csv', path_output='london_marathon_2014_W.csv',
path_error='london_marathon_2014_W_error_log.csv', year=2014, gender='W', headers=headers_london,
df_urls=london_marathon_urls_2014_W)
scrape_london_marathon(path_input='london_marathon_2014_W_elite_urls.csv',
path_output='london_marathon_2014_W_elite.csv',
path_error='london_marathon_2014_W_elite_error_log.csv', year=2014, gender='W',
headers=headers_london, df_urls=london_marathon_urls_2014_W_elite)
| <filename>dashathon/scraping/scrape_london_data.py
from dashathon.scraping.scraping_methods import scrape_london_marathon_urls
from dashathon.scraping.scraping_methods import scrape_london_marathon
headers_london = ['year', 'bib', 'age_group', 'gender', 'country', 'overall', 'rank_gender',
'rank_age_group', '5k', '10k', '15k', '20k', 'half', '25k', '30k', '35k', '40k', 'finish']
print('Scraping URLs: 2017 M')
london_marathon_urls_2017_M = scrape_london_marathon_urls(url='http://results-2017.virginmoneylondonmarathon.com/2017/',
year=2017, event='MAS', gender='M', num_results_per_page=1000)
london_marathon_urls_2017_M_elite = scrape_london_marathon_urls(url=('http://results-2017.virginmoneylondonmarathon.com'
'/2017/'), year=2017, event='ELIT', gender='M',
num_results_per_page=1000)
print('Scraping Split Times: 2017 M')
scrape_london_marathon(path_input='london_marathon_2017_M_urls.csv', path_output='london_marathon_2017_M.csv',
path_error='london_marathon_2017_M_error_log.csv', year=2017, gender='M', headers=headers_london,
df_urls=london_marathon_urls_2017_M)
scrape_london_marathon(path_input='london_marathon_2017_M_elite_urls.csv',
path_output='london_marathon_2017_M_elite.csv',
path_error='london_marathon_2017_M_elite_error_log.csv', year=2017, gender='M',
headers=headers_london, df_urls=london_marathon_urls_2017_M_elite)
print('Scraping URLs: 2017 W')
london_marathon_urls_2017_W = scrape_london_marathon_urls(url='http://results-2017.virginmoneylondonmarathon.com/2017/',
year=2017, event='MAS', gender='W', num_results_per_page=1000)
london_marathon_urls_2017_W_elite = scrape_london_marathon_urls(url=('http://results-2017.virginmoneylondonmarathon.com'
'/2017/'), year=2017, event='ELIT', gender='W',
num_results_per_page=1000)
print('Scraping Split Times: 2017 W')
scrape_london_marathon(path_input='london_marathon_2017_W_urls.csv', path_output='london_marathon_2017_W.csv',
path_error='london_marathon_2017_W_error_log.csv', year=2017, gender='W', headers=headers_london,
df_urls=london_marathon_urls_2017_W)
scrape_london_marathon(path_input='london_marathon_2017_W_elite_urls.csv',
path_output='london_marathon_2017_W_elite.csv',
path_error='london_marathon_2017_W_elite_error_log.csv', year=2017, gender='W',
headers=headers_london, df_urls=london_marathon_urls_2017_W_elite)
print('Scraping URLs: 2016 M')
london_marathon_urls_2016_M = scrape_london_marathon_urls(url='http://results-2016.virginmoneylondonmarathon.com/2016/',
year=2016, event='MAS', gender='M', num_results_per_page=1000)
london_marathon_urls_2016_M_elite = scrape_london_marathon_urls(url=('http://results-2016.virginmoneylondonmarathon.com'
'/2016/'), year=2016, event='ELIT', gender='M',
num_results_per_page=1000)
print('Scraping Split Times: 2016 M')
scrape_london_marathon(path_input='london_marathon_2016_M_urls.csv', path_output='london_marathon_2016_M.csv',
path_error='london_marathon_2016_M_error_log.csv', year=2016, gender='M', headers=headers_london,
df_urls=london_marathon_urls_2016_M)
scrape_london_marathon(path_input='london_marathon_2016_M_elite_urls.csv',
path_output='london_marathon_2016_M_elite.csv',
path_error='london_marathon_2016_M_elite_error_log.csv', year=2016, gender='M',
headers=headers_london, df_urls=london_marathon_urls_2016_M_elite)
print('Scraping URLs: 2016 W')
london_marathon_urls_2016_W = scrape_london_marathon_urls(url='http://results-2016.virginmoneylondonmarathon.com/2016/',
year=2016, event='MAS', gender='W', num_results_per_page=1000)
london_marathon_urls_2016_W_elite = scrape_london_marathon_urls(url=('http://results-2016.virginmoneylondonmarathon.com'
'/2016/'), year=2016, event='ELIT', gender='W',
num_results_per_page=1000)
print('Scraping Split Times: 2016 W')
scrape_london_marathon(path_input='london_marathon_2016_W_urls.csv', path_output='london_marathon_2016_W.csv',
path_error='london_marathon_2016_W_error_log.csv', year=2016, gender='W', headers=headers_london,
df_urls=london_marathon_urls_2016_W)
scrape_london_marathon(path_input='london_marathon_2016_W_elite_urls.csv',
path_output='london_marathon_2016_W_elite.csv',
path_error='london_marathon_2016_W_elite_error_log.csv', year=2016, gender='W',
headers=headers_london, df_urls=london_marathon_urls_2016_W_elite)
print('Scraping URLs: 2015 M')
london_marathon_urls_2015_M = scrape_london_marathon_urls(url='http://results-2015.virginmoneylondonmarathon.com/2015/',
year=2015, event='MAS', gender='M', num_results_per_page=1000)
london_marathon_urls_2015_M_elite = scrape_london_marathon_urls(url=('http://results-2015.virginmoneylondonmarathon.com'
'/2015/'), year=2015, event='ELIT', gender='M',
num_results_per_page=1000)
print('Scraping Split Times: 2015 M')
scrape_london_marathon(path_input='london_marathon_2015_M_urls.csv', path_output='london_marathon_2015_M.csv',
path_error='london_marathon_2015_M_error_log.csv', year=2015, gender='M', headers=headers_london,
df_urls=london_marathon_urls_2015_M)
scrape_london_marathon(path_input='london_marathon_2015_M_elite_urls.csv',
path_output='london_marathon_2015_M_elite.csv',
path_error='london_marathon_2015_M_elite_error_log.csv', year=2015, gender='M',
headers=headers_london, df_urls=london_marathon_urls_2015_M_elite)
print('Scraping URLs: 2015 W')
london_marathon_urls_2015_W = scrape_london_marathon_urls(url='http://results-2015.virginmoneylondonmarathon.com/2015/',
year=2015, event='MAS', gender='W', num_results_per_page=1000)
london_marathon_urls_2015_W_elite = scrape_london_marathon_urls(url=('http://results-2015.virginmoneylondonmarathon.com'
'/2015/'), year=2015, event='ELIT', gender='W',
num_results_per_page=1000)
print('Scraping Split Times: 2015 W')
scrape_london_marathon(path_input='london_marathon_2015_W_urls.csv', path_output='london_marathon_2015_W.csv',
path_error='london_marathon_2015_W_error_log.csv', year=2015, gender='W', headers=headers_london,
df_urls=london_marathon_urls_2015_W)
scrape_london_marathon(path_input='london_marathon_2015_W_elite_urls.csv',
path_output='london_marathon_2015_W_elite.csv',
path_error='london_marathon_2015_W_elite_error_log.csv', year=2015, gender='W',
headers=headers_london, df_urls=london_marathon_urls_2015_W_elite)
print('Scraping URLs: 2014 M')
london_marathon_urls_2014_M = scrape_london_marathon_urls(url='http://results-2014.virginmoneylondonmarathon.com/2014/',
year=2014, event='MAS', gender='M', num_results_per_page=1000)
london_marathon_urls_2014_M_elite = scrape_london_marathon_urls(url=('http://results-2014.virginmoneylondonmarathon.com'
'/2014/'), year=2014, event='ELIT', gender='M',
num_results_per_page=1000)
print('Scraping Split Times: 2014 M')
scrape_london_marathon(path_input='london_marathon_2014_M_urls.csv', path_output='london_marathon_2014_M.csv',
path_error='london_marathon_2014_M_error_log.csv', year=2014, gender='M', headers=headers_london,
df_urls=london_marathon_urls_2014_M)
scrape_london_marathon(path_input='london_marathon_2014_M_elite_urls.csv',
path_output='london_marathon_2014_M_elite.csv',
path_error='london_marathon_2014_M_elite_error_log.csv', year=2014, gender='M',
headers=headers_london, df_urls=london_marathon_urls_2014_M_elite)
print('Scraping URLs: 2014 W')
london_marathon_urls_2014_W = scrape_london_marathon_urls(url='http://results-2014.virginmoneylondonmarathon.com/2014/',
year=2014, event='MAS', gender='W', num_results_per_page=1000)
london_marathon_urls_2014_W_elite = scrape_london_marathon_urls(url=('http://results-2014.virginmoneylondonmarathon.com'
'/2014/'), year=2014, event='ELIT', gender='W',
num_results_per_page=1000)
print('Scraping Split Times: 2014 W')
scrape_london_marathon(path_input='london_marathon_2014_W_urls.csv', path_output='london_marathon_2014_W.csv',
path_error='london_marathon_2014_W_error_log.csv', year=2014, gender='W', headers=headers_london,
df_urls=london_marathon_urls_2014_W)
scrape_london_marathon(path_input='london_marathon_2014_W_elite_urls.csv',
path_output='london_marathon_2014_W_elite.csv',
path_error='london_marathon_2014_W_elite_error_log.csv', year=2014, gender='W',
headers=headers_london, df_urls=london_marathon_urls_2014_W_elite)
| none | 1 | 3.123307 | 3 | |
gen_fault_codes.py | gitguige/openpilot0.8.9 | 0 | 6624259 | <reponame>gitguige/openpilot0.8.9
import os
import numpy as np
import random
def gen_add_code(trigger_code, trigger, t1, t2, variable, stuck_value, additional_code):
assert(len(variable) == len(stuck_value))
if trigger_code:
code = trigger_code
else:
if len(trigger)>1:
code = 'if %s>=%s and %s<=%s:' % \
(trigger[0], t1, trigger[1], t2)
else:
code = 'if %s>=%s and %s<=%s:' % \
(trigger[0], t1, trigger[0], t2)
for v, s in zip(variable, stuck_value):
l = '//%s+=%s' % (v,s)
code = code + l
code = code + additional_code
return code
def gen_sub_code(trigger_code, trigger, t1, t2, variable, stuck_value, additional_code):
assert(len(variable) == len(stuck_value))
if trigger_code:
code = trigger_code
else:
code = 'if %s>=%s and %s<=%s:' % \
(trigger[0], t1, trigger[0], t2)
for v, s in zip(variable, stuck_value):
l = '//%s-=%s' % (v,s)
code = code + l
code = code + additional_code
return code
def gen_none_code(trigger_code, trigger, t1, t2, additional_code):
if trigger_code:
code = trigger_code
else:
code = 'if %s>=%s and %s<=%s:' % \
(trigger[0], t1, trigger[0], t2)
l = '//none'
code = code + l
code = code + additional_code
return code
def gen_uniform_rand_code(trigger_code, trigger, t1, t2, variable, d1, d2, additional_code):
if trigger_code:
code = trigger_code
else:
code = 'if %s>=%s and %s<=%s:' % \
(trigger[0], t1, trigger[0], t2)
for i in range(len(variable)):
delta = random.uniform(d1,d2) + (i*3.7)
l = '//%s+=(%s)' % (variable[i],str(delta))
code = code + l
code = code + additional_code
return code
def gen_stuck_code(trigger_code, trigger, t1, t2, variable, stuck_value, additional_code):
assert(len(variable) == len(stuck_value))
if trigger_code:
code = trigger_code
else:
code = 'if %s>=%s and %s<=%s:' % \
(trigger[0], t1, trigger[0], t2)
for v, s in zip(variable, stuck_value):
l = '//%s=%s' % (v,s)
code = code + l
code = code + additional_code
return code
### Write codes to fault library file
def write_to_file(fileName, code, param, exp_name, target_file, faultLoc):
if os.path.isdir('fault_library') != True:
os.makedirs('fault_library')
fileName = 'fault_library/scenario_'+str(sceneNum)
out_file = fileName+'.txt'
param_file = fileName+'_params.csv'
with open(out_file, 'w') as outfile:
print out_file
outfile.write('title:' + exp_name + '\n')
outfile.write('location//' + target_file+ '//'+faultLoc + '\n')
for i, line in enumerate(code):
outfile.write('fault ' + str(i+1) + '//' + line + '\n')
outfile.write('Total number of fault cases: '+str(i+1))
with open(param_file, 'w') as outfile:
for i, line in enumerate(param):
outfile.write(str(i) + ',' + line + '\n')
with open('run_fault_inject_campaign.sh', 'a+') as runFile:
runFile.write('python run.py '+fileName+'\n')
### Write codes to fault library file -- for vision effects
def write_to_vision_file(fileName, code, param, exp_name, target_file, faultLoc):
if os.path.isdir('fault_library') != True:
os.makedirs('fault_library')
effect = fileName
fileName = 'fault_library/scenario_'+str(sceneNum)
out_file = fileName+'.txt'
param_file = fileName+'_params.csv'
with open(out_file, 'w') as outfile:
print out_file
outfile.write('title:' + exp_name + '\n')
outfile.write('location//' + target_file+ '//'+faultLoc + '\n')
for i, line in enumerate(code):
outfile.write('fault ' + str(i+1) + '//' + line + '\n')
outfile.write('Total number of fault cases: '+str(i+1))
with open(param_file, 'w') as outfile:
for i, line in enumerate(param):
outfile.write(str(i) + ',' + line + '\n')
with open('run_fault_inject_campaign.sh', 'a+') as runFile:
for thickness in range(1,11):
if os.path.isdir('../output_files/'+str(sceneNum)+'_vision_'+effect+'/'+str(thickness)) != True:
os.makedirs('../output_files/'+str(sceneNum)+'_vision_'+effect+'/'+str(thickness))
runFile.write('./run_matlab_openpilot.sh '+effect+' '+str(thickness)+'\n')
runFile.write('python run.py '+fileName+'\n')
runFile.write('cp -R '+'../output_files/'+exp_name+' '+'../output_files/'+str(sceneNum)+'_vision_'+effect+'/'+str(thickness)+'/\n')
###########################################################
### d_rel-add-incRADAR-H1
def gen_rel_dist_add_fault_plant(sceneNum):
title = str(sceneNum)+'_d_rel-add-incRADAR-H1'
faultLibFile = 'fault_library/dRelPlantRad'
fileLoc = 'selfdrive/test/plant/plant.py'
faultLoc = '#radar_dRel:HOOK#'
trigger = ['frameIdx']
trigger_code = ['if headway_time<=2.0 and RSpeed<=0:', 'if headway_time<=2.0 and RSpeed>0:', 'if headway_time>2.0 and RSpeed>0:','if headway_time>2.0 and RSpeed<=0:']
code = []
param = []
variable = ['radar_dRel']
deltaRange = np.arange(15,190,10)
invRange = np.arange(201,256,10)
for trig in np.arange(0,len(trigger_code)):
for dt in [30.0]:
t2 = dt
for d in deltaRange:
delta = random.randint(d,d+9)
t1 = random.randint(2,29)
#code.append(gen_add_code(trigger_code, trigger, t1, t2, variable, [delta], '//if '+variable[0]+'>=255:'+'// '+variable[0]+'= 254'))
code.append(gen_add_code('', trigger, t1*100., t2*100., variable, [delta], ''))
param.append(','.join(['relative distance',str(t1),str(dt),str(delta)]))
for dt in [30.0]:
t2 = dt
for d in invRange:
delta = random.randint(d,d+9)
t1 = random.randint(2,29)
#code.append(gen_add_code(trigger_code, trigger, t1, t2, variable, [delta], '//if '+variable[0]+'>=255:'+'// '+variable[0]+'= 254'))
code.append(gen_add_code('', trigger, t1*100., t2*100., variable, [delta], ''))
param.append(','.join(['relative distance',str(t1),str(dt),str(delta)]))
write_to_file(faultLibFile, code, param, title, fileLoc, faultLoc)
### v_rel-add-incRADAR-H1
def gen_rel_vel_add_fault_plant(sceneNum):
title = str(sceneNum)+'_v_rel-add-incRADAR-H1'
faultLibFile = 'fault_library/vRelPlant'
fileLoc = 'selfdrive/test/plant/plant.py'
faultLoc = '#radar_dRel:HOOK#'
trigger = ['frameIdx']
trigger_code = ['if headway_time<=2.0 and RSpeed<=0:', 'if headway_time<=2.0 and RSpeed>0:', 'if headway_time>2.0 and RSpeed>0:','if headway_time>2.0 and RSpeed<=0:']
code = []
param = []
variable = ['v_rel']
deltaRange = np.arange(10,61,10)
for trig in np.arange(0,len(trigger_code)):
for dt in [30.0]:
t2 = dt
for d in deltaRange:
t1 = random.randint(2,29)
delta = random.randint(d,d+9)
if delta > 60:
delta = 60
delta = delta*0.44704 # 1MPH = 0.44704 m/s
code.append(gen_add_code('', trigger, t1*100., t2*100., variable, [delta], ''))
param.append(','.join(['relative speed',str(t1),str(dt),str(delta)]))
write_to_file(faultLibFile, code, param, title, fileLoc, faultLoc)
### d_rel-sub-incRADAR-H2
def gen_rel_dist_sub_fault_plant(sceneNum):
title = str(sceneNum)+'_d_rel-sub-incRADAR-H2'
faultLibFile = 'fault_library/dRelPlantRad'
fileLoc = 'selfdrive/test/plant/plant.py'
faultLoc = '#radar_dRel:HOOK#'
trigger = ['frameIdx']
trigger_code = ['if headway_time>2.0 and RSpeed<=0:','if headway_time<=2.0 and RSpeed<=0:', 'if headway_time<=2.0 and RSpeed>0:', 'if headway_time>2.0 and RSpeed>0:']
code = []
param = []
variable = ['radar_dRel']
deltaRange = np.arange(10,255,10)
for trig in np.arange(0,len(trigger_code)):
for d in deltaRange:
for dt in [30.]:
t2 = dt
t1 = random.randint(2,29)
delta = random.randint(d,d+9)
code.append(gen_sub_code('',trigger, t1*100., t2*100., variable, [delta], '//if '+variable[0]+'<0:'+'// '+variable[0]+'= 0'))
param.append(','.join(['relative distance',str(t1),str(dt),str(delta)]))
write_to_file(faultLibFile, code, param, title, fileLoc, faultLoc)
### v_rel-sub-incRADAR-H2
def gen_rel_vel_sub_fault_plant(sceneNum):
title = str(sceneNum)+'_v_rel-sub-incRADAR-H2'
faultLibFile = 'fault_library/vRelPlant'
fileLoc = 'selfdrive/test/plant/plant.py'
faultLoc = '#radar_dRel:HOOK#'
trigger = ['frameIdx']
trigger_code = ['if headway_time>2.0 and RSpeed<=0:','if headway_time<=2.0 and RSpeed<=0:', 'if headway_time<=2.0 and RSpeed>0:', 'if headway_time>2.0 and RSpeed>0:']
code = []
param = []
variable = ['v_rel']
deltaRange = np.arange(10,61,10)
for trig in np.arange(0,len(trigger_code)):
for d in deltaRange:
for dt in [30.]:
t2 = dt
delta = random.randint(d,d+9)
t1 = random.randint(2,29)
if delta > 60:
delta = 60
delta = delta*0.44704 # 1MPH = 0.44704 m/s
code.append(gen_sub_code('', trigger, t1*100., t2*100., variable, [delta], ''))
param.append(','.join(['relative speed',str(t1),str(dt),str(delta)]))
write_to_file(faultLibFile, code, param, title, fileLoc, faultLoc)
### radar-none-incRADAR-H1
def gen_radar_jamming_fault_plant_H1(sceneNum):
title = str(sceneNum)+'_radar-none-incRADAR-H1'
faultLibFile = 'fault_library/radJamPlant'
fileLoc = 'selfdrive/test/plant/plant.py'
faultLoc = '#radar_none:HOOK#'
trigger = ['frameIdx']
trigger_code = ['if headway_time>2.0 or RSpeed>0:', 'if headway_time>2.0 or RSpeed<=0:', 'if headway_time<=2.0 or RSpeed<=0:'] # reverse of actual trigger
code = []
param = []
variable = []
for trig in np.arange(0,len(trigger_code)):
for dt in [0.0]:
t1 = random.randint(2,29)
t2 = dt
code.append(gen_none_code('', trigger, t2*100., t1*100., ''))
param.append(','.join(['radar jamming',str(t1),str(dt),'none']))
write_to_file(faultLibFile, code, param, title, fileLoc, faultLoc)
### radar-none-incRADAR-H2
def gen_radar_jamming_fault_plant_H2(sceneNum):
title = str(sceneNum)+'_radar-none-incRADAR-H2'
faultLibFile = 'fault_library/radJamPlant'
fileLoc = 'selfdrive/test/plant/plant.py'
faultLoc = '#radar_none:HOOK#'
trigger = ['frameIdx']
trigger_code = ['if headway_time<=2.0 or RSpeed>0:']
code = []
param = []
variable = []
for trig in np.arange(0,len(trigger_code)):
for dt in [0.0]:
t1 = random.randint(2,29)
t2 = dt
code.append(gen_none_code('', trigger, t2*100., t1*100., ''))
param.append(','.join(['radar jamming',str(t1),str(dt),'none']))
write_to_file(faultLibFile, code, param, title, fileLoc, faultLoc)
### curr_sp-sub-incProcPlant-H1
def gen_curr_sp_sub_fault_plant(sceneNum):
title = str(sceneNum)+'_curr_sp-sub-incProcPlant-H1'
faultLibFile = 'fault_library/vCurrSpPlant'
fileLoc = 'selfdrive/test/plant/plant.py'
faultLoc = '#speed:HOOK#'
trigger = ['frameIdx']
trigger_code = ['if headway_time<=2.0 and RSpeed<=0:', 'if headway_time<=2.0 and RSpeed>0:', 'if headway_time>2.0 and RSpeed>0:', 'if headway_time>2.0 and RSpeed<=0:']
code = []
param = []
variable = ['speed2send']
deltaRange = np.arange(10,61,10)
for trig in np.arange(0,len(trigger_code)):
for dt in [30.0]:
t2 = dt
for d in deltaRange:
delta = random.randint(d,d+9)
if delta > 60:
delta = 60
delta = delta*0.44704 # 1MPH = 0.44704 m/s
t1 = random.randint(2,29)
code.append(gen_sub_code('', trigger, t1*100., t2*100., variable, [delta], '//if '+variable[0]+'<0:'+'// '+variable[0]+'= 0'))
param.append(','.join(['current speed',str(t1),str(dt),str(delta)]))
write_to_file(faultLibFile, code, param, title, fileLoc, faultLoc)
### curr_sp-add-incProcPlant-H2
def gen_curr_sp_add_fault_plant(sceneNum):
title = str(sceneNum)+'_curr_sp-add-incProcPlant-H2'
faultLibFile = 'fault_library/vCurrSpPlant'
fileLoc = 'selfdrive/test/plant/plant.py'
faultLoc = '#speed:HOOK#'
trigger = ['frameIdx']
trigger_code = ['if headway_time>2.0 and RSpeed<=0:','if headway_time<=2.0 and RSpeed<=0:', 'if headway_time<=2.0 and RSpeed>0:', 'if headway_time>2.0 and RSpeed>0:']
code = []
param = []
variable = ['speed2send']
deltaRange = np.arange(10,61,10)
for trig in np.arange(0,len(trigger_code)):
for d in deltaRange:
for dt in [30.]:
t2 = dt
delta = random.randint(d,d+9)
if delta > 60:
delta = 60
delta = delta*0.44704 # 1MPH = 0.44704 m/s
t1 = random.randint(2,29)
code.append(gen_add_code('', trigger, t1*100., t2*100., variable, [delta], '//if '+variable[0]+'>=85.0:'+'// '+variable[0]+'= 85.0'))
param.append(','.join(['current speed',str(t1),str(dt),str(delta)]))
write_to_file(faultLibFile, code, param, title, fileLoc, faultLoc)
### md-rand-incProcPlant-H3
def gen_md_rand_val_plant(lane,sceneNum):
title = str(sceneNum)+'_'+lane+'Lane-rand-incProcPlant-H3'
faultLibFile = 'fault_library/mdPlant_'+lane
fileLoc = 'selfdrive/test/plant/maneuver.py'
faultLoc = '#md:HOOK#'
trigger = ['self.frameIdx']
trigger_code = ['if headway_time<=2.0 and RSpeed>=0:', 'if headway_time>2.0 and RSpeed<=0:','if headway_time<=2.0 and RSpeed<=0:','if headway_time>2.0 and RSpeed>0:']
code = []
param = []
if lane.lower()=='left':
variable = ['self.lLane']
elif lane.lower()=='right':
variable = ['self.rLane']
else:
variable = ['self.lLane','self.rLane']
deltaRange = np.arange(-2.5,2.5,0.5)
for trig in np.arange(0,len(trigger_code)):
for dt in [30.0]:
t2 = dt
for d1 in deltaRange:
d2 = d1+1
t1 = random.randint(2,29)
code.append(gen_uniform_rand_code('', trigger, t1*100., t2*100., variable, d1, d2, ''))
param.append(','.join(['path model',str(t1),str(dt),str(d1),str(d2)]))
write_to_file(faultLibFile, code, param, title, fileLoc, faultLoc)
### angSteer-add-incProcPlant-H3
def gen_angle_steer_add_plant(sceneNum):
title = str(sceneNum)+'_angSteer-add-incProcPlant-H3'
faultLibFile = 'fault_library/angSteerPlant'
fileLoc = 'selfdrive/test/plant/plant.py'
faultLoc = '#angle_steer:HOOK#'
trigger = ['frameIdx']
trigger_code = ['if headway_time>2.0 and RSpeed>=0:','if headway_time<=2.0 and RSpeed<=0:', 'if headway_time<=2.0 and RSpeed>0:','if headway_time>2.0 and RSpeed>0:']
code = []
param = []
variable = ['angle_steer2send']
deltaRange = np.arange(-45,46,10)
for trig in np.arange(0,len(trigger_code)):
for dt in [30.0]:
t2 = dt
for d in deltaRange:
delta = random.randint(d,d+9)
if d > 45:
alpha = 45*3.1416/180.0
else:
alpha = delta*3.1416/180.0
t1 = random.randint(2,29)
code.append(gen_add_code('', trigger, t1*100., t2*100., variable, ['('+str(alpha)+')'], ''))
param.append(','.join(['steer angle',str(t1),str(dt),str(alpha)]))
write_to_file(faultLibFile, code, param, title, fileLoc, faultLoc)
### vision-none-miscommVisPlant-H3
def gen_vision_miscomm_fault_plant(sceneNum):
title = str(sceneNum)+'_vision-none-miscommVisPlant-H3'
faultLibFile = 'fault_library/visMiscommPlant'
fileLoc = 'selfdrive/test/plant/plant.py'
faultLoc = '#md_none:HOOK#'
trigger = ['frameIdx']
trigger_code = ['if headway_time>2.0 or RSpeed<0:','if headway_time>2.0 or RSpeed>0', 'if headway_time<=2.0 or RSpeed<=0', 'if headway_time<=2.0 or RSpeed>0']
code = []
param = []
variable = []
for trig in np.arange(0,len(trigger_code)):
for dt in [0.0]:
t2 = dt
t1 = random.randint(2,29)
code.append(gen_none_code('', trigger, t2*100., t1*100., ''))
param.append(','.join(['vision miscomm',str(t1),str(dt),'none']))
write_to_file(faultLibFile, code, param, title, fileLoc, faultLoc)
### vision-effect-noisyInputManeuver-H3
def gen_vision_noisyInput_fault_Maneuver(effect, sceneNum):
title = str(sceneNum)+'_vision-effect-noisyInputManeuver-H3'
faultLibFile = ''
fileLoc = 'selfdrive/test/plant/maneuver.py'
faultLoc = '#visionFault:HOOK#'
trigger = ['self.frameIdx']
trigger_code = ['if headway_time<=2.5 and RSpeed>=0:', 'if headway_time>2.5 and RSpeed>0:','if headway_time>2.5 and RSpeed<=0:','if headway_time<=2.5 and RSpeed<0:']
code = []
param = []
#variable = ['left_line','right_line']
#deltaRange = ['lanes[0]','lanes[1]']
variable = ['self.effect', 'self.thickness']
if effect <7:
range_th = range(1,11)
elif effect == 7:
range_th = range(3,7)
elif effect == 8:
range_th = [3,5,7]
elif effect == 9:
range_th = [3,5]
for trig in np.arange(0,len(trigger_code)):
for dt in [30.0]:
for th in range_th:
t2 = dt
t1 = random.randint(2,29)
code.append(gen_stuck_code('', trigger, t1*100., t2*100., variable, [str(effect), str(th)], ''))
param.append(','.join(['vision noisyInput',str(t1),str(dt),'none']))
write_to_file(faultLibFile, code, param, title, fileLoc, faultLoc)
### d_rel-add-incVision-H1
def gen_vision_dRel_add_fault_plant(sceneNum):
title = str(sceneNum)+'_d_rel-add-incVision-H1'
faultLibFile = 'fault_library/dRelPlantVis'
fileLoc = 'selfdrive/test/plant/plant.py'
faultLoc = '#vision_dRel:HOOK#'
trigger = ['frameIdx']
trigger_code = ['if headway_time<=2.0 and RSpeed<=0:', 'if headway_time<=2.0 and RSpeed>0:', 'if headway_time>2.0 and RSpeed>0:','if headway_time>2.0 and RSpeed<=0:']
code = []
param = []
variable = ['vision_dRel']
deltaRange = np.arange(15,255,10)
for trig in np.arange(0,len(trigger_code)):
for dt in [30.0]:
t2 = dt
for d in deltaRange:
delta = random.randint(d,d+9)
t1 = random.randint(2,29)
#code.append(gen_add_code(trigger_code, trigger, t1, t2, variable, [delta], '//if '+variable[0]+'>=255:'+'// '+variable[0]+'= 254'))
code.append(gen_add_code('', trigger, t1*100., t2*100., variable, [delta], ''))
param.append(','.join(['relative distance',str(t1),str(dt),str(delta)]))
write_to_file(faultLibFile, code, param, title, fileLoc, faultLoc)
### d_rel-sub-incVision-H2
def gen_vision_dRel_sub_fault_plant(sceneNum):
title = str(sceneNum)+'_d_rel-sub-incVision-H2'
faultLibFile = 'fault_library/dRelPlantVis'
fileLoc = 'selfdrive/test/plant/plant.py'
faultLoc = '#vision_dRel:HOOK#'
trigger = ['frameIdx']
trigger_code = ['if headway_time>2.0 and RSpeed<=0:','if headway_time<=2.0 and RSpeed<=0:', 'if headway_time<=2.0 and RSpeed>0:', 'if headway_time>2.0 and RSpeed>0:']
code = []
param = []
variable = ['vision_dRel']
deltaRange = np.arange(10,255,10)
for trig in np.arange(0,len(trigger_code)):
for d in deltaRange:
for dt in [30.0]:
t2 = dt
delta = random.randint(d,d+9)
t1 = random.randint(2,29)
code.append(gen_sub_code('',trigger, t1*100., t2*100., variable, [delta], '//if '+variable[0]+'<0:'+'// '+variable[0]+'= 0'))
param.append(','.join(['relative distance',str(t1),str(dt),str(delta)]))
write_to_file(faultLibFile, code, param, title, fileLoc, faultLoc)
### d_rel-add-incRadVis-H1
def gen_RadVis_dRel_add_fault_plant(sceneNum):
title = str(sceneNum)+'_d_rel-add-incRadVis-H1'
faultLibFile = 'fault_library/dRelPlantRadVis'
fileLoc = 'selfdrive/test/plant/plant.py'
faultLoc = '#RadVis_dRel:HOOK#'
trigger = ['frameIdx']
trigger_code = ['if headway_time<=2.0 and RSpeed<=0:', 'if headway_time<=2.0 and RSpeed>0:', 'if headway_time>2.0 and RSpeed>0:', 'if headway_time>2.0 and RSpeed<=0:']
code = []
param = []
variable = ['d_rel']
deltaRange = np.arange(15,255,10)
for trig in np.arange(0,len(trigger_code)):
for dt in [30.0]:
t2 = dt
for d in deltaRange:
delta = random.randint(d,d+9)
t1 = random.randint(2,29)
#code.append(gen_add_code(trigger_code, trigger, t1, t2, variable, [delta], '//if '+variable[0]+'>=255:'+'// '+variable[0]+'= 254'))
code.append(gen_add_code('', trigger, t1*100., t2*100., variable, [delta], ''))
param.append(','.join(['relative distance',str(t1),str(dt),str(delta)]))
write_to_file(faultLibFile, code, param, title, fileLoc, faultLoc)
### d_rel-sub-incRadVis-H2
def gen_RadVis_dRel_sub_fault_plant(sceneNum):
title = str(sceneNum)+'_d_rel-sub-incRadVis-H2'
faultLibFile = 'fault_library/dRelPlantRadVis'
fileLoc = 'selfdrive/test/plant/plant.py'
faultLoc = '#RadVis_dRel:HOOK#'
trigger = ['frameIdx']
trigger_code = ['if headway_time>2.0 and RSpeed<=0:','if headway_time<=2.0 and RSpeed<=0:', 'if headway_time<=2.0 and RSpeed>0:', 'if headway_time>2.0 and RSpeed>0:']
code = []
param = []
variable = ['d_rel']
deltaRange = np.arange(10,255,10)
for trig in np.arange(0,len(trigger_code)):
for d in deltaRange:
for dt in [30.0]:
t2 = dt
delta = random.randint(d,d+9)
t1 = random.randint(2,29)
code.append(gen_sub_code('',trigger, t1*100., t2*100., variable, [delta], '//if '+variable[0]+'<0:'+'// '+variable[0]+'= 0'))
param.append(','.join(['relative distance',str(t1),str(dt),str(delta)]))
write_to_file(faultLibFile, code, param, title, fileLoc, faultLoc)
##########################################
###_main_###
with open('run_fault_inject_campaign.sh', 'w') as runFile:
runFile.write('#Usage: python run.py target_fault_library\n')
scenarios = {
1 : gen_rel_dist_add_fault_plant,
2 : gen_rel_vel_add_fault_plant,
3 : gen_rel_dist_sub_fault_plant,
4 : gen_rel_vel_sub_fault_plant,
5 : gen_radar_jamming_fault_plant_H1,
6 : gen_radar_jamming_fault_plant_H2,
9 : gen_curr_sp_sub_fault_plant,
12 : gen_curr_sp_add_fault_plant,
13 : gen_md_rand_val_plant,
14 : gen_md_rand_val_plant,
15 : gen_md_rand_val_plant,
16 : gen_angle_steer_add_plant,
34 : gen_vision_miscomm_fault_plant,
35 : gen_vision_noisyInput_fault_Maneuver,
36 : gen_vision_noisyInput_fault_Maneuver,
37 : gen_vision_noisyInput_fault_Maneuver,
38 : gen_vision_noisyInput_fault_Maneuver,
39 : gen_vision_dRel_add_fault_plant,
40 : gen_vision_dRel_sub_fault_plant,
41 : gen_RadVis_dRel_add_fault_plant,
42 : gen_RadVis_dRel_sub_fault_plant,
43 : gen_vision_noisyInput_fault_Maneuver,
44 : gen_vision_noisyInput_fault_Maneuver,
45 : gen_vision_noisyInput_fault_Maneuver,
46 : gen_vision_noisyInput_fault_Maneuver,
47 : gen_vision_noisyInput_fault_Maneuver
}
lanes = ['left','right','both'] # 'left','right','both'
poly = ['p_path','left','right','d_path'] # 'p_path','left','right','d_path'
#effects = ['rain', 'fog', 'snow', 'occlusion']
effects = [1,2,3,4,5,6,7,8,9]
for sceneNum in [1,2,3,4,5,6,9,12,13,14,15,16,34,39,40,41,42]: # experiments without the vision
#for sceneNum in [35,36,37,38,43,44,45,46,47]: # for testing the faults in input images
#for sceneNum in [1,2,3,4,5,6,9,12,13,14,15,16,34,35,36,37,38,39,40,41,42,43,44,45,46,47]: # for testing the faults in inputs
# for sceneNum in [44,45,46,47]:
print sceneNum
cmd = 'cp '+ 'fault_library/scenario_'+str(sceneNum)+'.txt '+'fault_library/scenario_'+str(sceneNum)+'_prev.txt'
os.system(cmd)
if sceneNum >= 13 and sceneNum <=15:
scenarios[sceneNum](lanes[sceneNum-13],sceneNum)
elif sceneNum >= 28 and sceneNum <=31:
scenarios[sceneNum](poly[sceneNum-28],sceneNum)
elif sceneNum >= 35 and sceneNum <=38:
scenarios[sceneNum](effects[sceneNum-35],sceneNum)
elif sceneNum >= 43 and sceneNum <=47:
scenarios[sceneNum](effects[sceneNum+4-43],sceneNum)
else:
scenarios[sceneNum](sceneNum)
| import os
import numpy as np
import random
def gen_add_code(trigger_code, trigger, t1, t2, variable, stuck_value, additional_code):
assert(len(variable) == len(stuck_value))
if trigger_code:
code = trigger_code
else:
if len(trigger)>1:
code = 'if %s>=%s and %s<=%s:' % \
(trigger[0], t1, trigger[1], t2)
else:
code = 'if %s>=%s and %s<=%s:' % \
(trigger[0], t1, trigger[0], t2)
for v, s in zip(variable, stuck_value):
l = '//%s+=%s' % (v,s)
code = code + l
code = code + additional_code
return code
def gen_sub_code(trigger_code, trigger, t1, t2, variable, stuck_value, additional_code):
assert(len(variable) == len(stuck_value))
if trigger_code:
code = trigger_code
else:
code = 'if %s>=%s and %s<=%s:' % \
(trigger[0], t1, trigger[0], t2)
for v, s in zip(variable, stuck_value):
l = '//%s-=%s' % (v,s)
code = code + l
code = code + additional_code
return code
def gen_none_code(trigger_code, trigger, t1, t2, additional_code):
if trigger_code:
code = trigger_code
else:
code = 'if %s>=%s and %s<=%s:' % \
(trigger[0], t1, trigger[0], t2)
l = '//none'
code = code + l
code = code + additional_code
return code
def gen_uniform_rand_code(trigger_code, trigger, t1, t2, variable, d1, d2, additional_code):
if trigger_code:
code = trigger_code
else:
code = 'if %s>=%s and %s<=%s:' % \
(trigger[0], t1, trigger[0], t2)
for i in range(len(variable)):
delta = random.uniform(d1,d2) + (i*3.7)
l = '//%s+=(%s)' % (variable[i],str(delta))
code = code + l
code = code + additional_code
return code
def gen_stuck_code(trigger_code, trigger, t1, t2, variable, stuck_value, additional_code):
assert(len(variable) == len(stuck_value))
if trigger_code:
code = trigger_code
else:
code = 'if %s>=%s and %s<=%s:' % \
(trigger[0], t1, trigger[0], t2)
for v, s in zip(variable, stuck_value):
l = '//%s=%s' % (v,s)
code = code + l
code = code + additional_code
return code
### Write codes to fault library file
def write_to_file(fileName, code, param, exp_name, target_file, faultLoc):
if os.path.isdir('fault_library') != True:
os.makedirs('fault_library')
fileName = 'fault_library/scenario_'+str(sceneNum)
out_file = fileName+'.txt'
param_file = fileName+'_params.csv'
with open(out_file, 'w') as outfile:
print out_file
outfile.write('title:' + exp_name + '\n')
outfile.write('location//' + target_file+ '//'+faultLoc + '\n')
for i, line in enumerate(code):
outfile.write('fault ' + str(i+1) + '//' + line + '\n')
outfile.write('Total number of fault cases: '+str(i+1))
with open(param_file, 'w') as outfile:
for i, line in enumerate(param):
outfile.write(str(i) + ',' + line + '\n')
with open('run_fault_inject_campaign.sh', 'a+') as runFile:
runFile.write('python run.py '+fileName+'\n')
### Write codes to fault library file -- for vision effects
def write_to_vision_file(fileName, code, param, exp_name, target_file, faultLoc):
if os.path.isdir('fault_library') != True:
os.makedirs('fault_library')
effect = fileName
fileName = 'fault_library/scenario_'+str(sceneNum)
out_file = fileName+'.txt'
param_file = fileName+'_params.csv'
with open(out_file, 'w') as outfile:
print out_file
outfile.write('title:' + exp_name + '\n')
outfile.write('location//' + target_file+ '//'+faultLoc + '\n')
for i, line in enumerate(code):
outfile.write('fault ' + str(i+1) + '//' + line + '\n')
outfile.write('Total number of fault cases: '+str(i+1))
with open(param_file, 'w') as outfile:
for i, line in enumerate(param):
outfile.write(str(i) + ',' + line + '\n')
with open('run_fault_inject_campaign.sh', 'a+') as runFile:
for thickness in range(1,11):
if os.path.isdir('../output_files/'+str(sceneNum)+'_vision_'+effect+'/'+str(thickness)) != True:
os.makedirs('../output_files/'+str(sceneNum)+'_vision_'+effect+'/'+str(thickness))
runFile.write('./run_matlab_openpilot.sh '+effect+' '+str(thickness)+'\n')
runFile.write('python run.py '+fileName+'\n')
runFile.write('cp -R '+'../output_files/'+exp_name+' '+'../output_files/'+str(sceneNum)+'_vision_'+effect+'/'+str(thickness)+'/\n')
###########################################################
### d_rel-add-incRADAR-H1
def gen_rel_dist_add_fault_plant(sceneNum):
title = str(sceneNum)+'_d_rel-add-incRADAR-H1'
faultLibFile = 'fault_library/dRelPlantRad'
fileLoc = 'selfdrive/test/plant/plant.py'
faultLoc = '#radar_dRel:HOOK#'
trigger = ['frameIdx']
trigger_code = ['if headway_time<=2.0 and RSpeed<=0:', 'if headway_time<=2.0 and RSpeed>0:', 'if headway_time>2.0 and RSpeed>0:','if headway_time>2.0 and RSpeed<=0:']
code = []
param = []
variable = ['radar_dRel']
deltaRange = np.arange(15,190,10)
invRange = np.arange(201,256,10)
for trig in np.arange(0,len(trigger_code)):
for dt in [30.0]:
t2 = dt
for d in deltaRange:
delta = random.randint(d,d+9)
t1 = random.randint(2,29)
#code.append(gen_add_code(trigger_code, trigger, t1, t2, variable, [delta], '//if '+variable[0]+'>=255:'+'// '+variable[0]+'= 254'))
code.append(gen_add_code('', trigger, t1*100., t2*100., variable, [delta], ''))
param.append(','.join(['relative distance',str(t1),str(dt),str(delta)]))
for dt in [30.0]:
t2 = dt
for d in invRange:
delta = random.randint(d,d+9)
t1 = random.randint(2,29)
#code.append(gen_add_code(trigger_code, trigger, t1, t2, variable, [delta], '//if '+variable[0]+'>=255:'+'// '+variable[0]+'= 254'))
code.append(gen_add_code('', trigger, t1*100., t2*100., variable, [delta], ''))
param.append(','.join(['relative distance',str(t1),str(dt),str(delta)]))
write_to_file(faultLibFile, code, param, title, fileLoc, faultLoc)
### v_rel-add-incRADAR-H1
def gen_rel_vel_add_fault_plant(sceneNum):
title = str(sceneNum)+'_v_rel-add-incRADAR-H1'
faultLibFile = 'fault_library/vRelPlant'
fileLoc = 'selfdrive/test/plant/plant.py'
faultLoc = '#radar_dRel:HOOK#'
trigger = ['frameIdx']
trigger_code = ['if headway_time<=2.0 and RSpeed<=0:', 'if headway_time<=2.0 and RSpeed>0:', 'if headway_time>2.0 and RSpeed>0:','if headway_time>2.0 and RSpeed<=0:']
code = []
param = []
variable = ['v_rel']
deltaRange = np.arange(10,61,10)
for trig in np.arange(0,len(trigger_code)):
for dt in [30.0]:
t2 = dt
for d in deltaRange:
t1 = random.randint(2,29)
delta = random.randint(d,d+9)
if delta > 60:
delta = 60
delta = delta*0.44704 # 1MPH = 0.44704 m/s
code.append(gen_add_code('', trigger, t1*100., t2*100., variable, [delta], ''))
param.append(','.join(['relative speed',str(t1),str(dt),str(delta)]))
write_to_file(faultLibFile, code, param, title, fileLoc, faultLoc)
### d_rel-sub-incRADAR-H2
def gen_rel_dist_sub_fault_plant(sceneNum):
title = str(sceneNum)+'_d_rel-sub-incRADAR-H2'
faultLibFile = 'fault_library/dRelPlantRad'
fileLoc = 'selfdrive/test/plant/plant.py'
faultLoc = '#radar_dRel:HOOK#'
trigger = ['frameIdx']
trigger_code = ['if headway_time>2.0 and RSpeed<=0:','if headway_time<=2.0 and RSpeed<=0:', 'if headway_time<=2.0 and RSpeed>0:', 'if headway_time>2.0 and RSpeed>0:']
code = []
param = []
variable = ['radar_dRel']
deltaRange = np.arange(10,255,10)
for trig in np.arange(0,len(trigger_code)):
for d in deltaRange:
for dt in [30.]:
t2 = dt
t1 = random.randint(2,29)
delta = random.randint(d,d+9)
code.append(gen_sub_code('',trigger, t1*100., t2*100., variable, [delta], '//if '+variable[0]+'<0:'+'// '+variable[0]+'= 0'))
param.append(','.join(['relative distance',str(t1),str(dt),str(delta)]))
write_to_file(faultLibFile, code, param, title, fileLoc, faultLoc)
### v_rel-sub-incRADAR-H2
def gen_rel_vel_sub_fault_plant(sceneNum):
title = str(sceneNum)+'_v_rel-sub-incRADAR-H2'
faultLibFile = 'fault_library/vRelPlant'
fileLoc = 'selfdrive/test/plant/plant.py'
faultLoc = '#radar_dRel:HOOK#'
trigger = ['frameIdx']
trigger_code = ['if headway_time>2.0 and RSpeed<=0:','if headway_time<=2.0 and RSpeed<=0:', 'if headway_time<=2.0 and RSpeed>0:', 'if headway_time>2.0 and RSpeed>0:']
code = []
param = []
variable = ['v_rel']
deltaRange = np.arange(10,61,10)
for trig in np.arange(0,len(trigger_code)):
for d in deltaRange:
for dt in [30.]:
t2 = dt
delta = random.randint(d,d+9)
t1 = random.randint(2,29)
if delta > 60:
delta = 60
delta = delta*0.44704 # 1MPH = 0.44704 m/s
code.append(gen_sub_code('', trigger, t1*100., t2*100., variable, [delta], ''))
param.append(','.join(['relative speed',str(t1),str(dt),str(delta)]))
write_to_file(faultLibFile, code, param, title, fileLoc, faultLoc)
### radar-none-incRADAR-H1
def gen_radar_jamming_fault_plant_H1(sceneNum):
title = str(sceneNum)+'_radar-none-incRADAR-H1'
faultLibFile = 'fault_library/radJamPlant'
fileLoc = 'selfdrive/test/plant/plant.py'
faultLoc = '#radar_none:HOOK#'
trigger = ['frameIdx']
trigger_code = ['if headway_time>2.0 or RSpeed>0:', 'if headway_time>2.0 or RSpeed<=0:', 'if headway_time<=2.0 or RSpeed<=0:'] # reverse of actual trigger
code = []
param = []
variable = []
for trig in np.arange(0,len(trigger_code)):
for dt in [0.0]:
t1 = random.randint(2,29)
t2 = dt
code.append(gen_none_code('', trigger, t2*100., t1*100., ''))
param.append(','.join(['radar jamming',str(t1),str(dt),'none']))
write_to_file(faultLibFile, code, param, title, fileLoc, faultLoc)
### radar-none-incRADAR-H2
def gen_radar_jamming_fault_plant_H2(sceneNum):
title = str(sceneNum)+'_radar-none-incRADAR-H2'
faultLibFile = 'fault_library/radJamPlant'
fileLoc = 'selfdrive/test/plant/plant.py'
faultLoc = '#radar_none:HOOK#'
trigger = ['frameIdx']
trigger_code = ['if headway_time<=2.0 or RSpeed>0:']
code = []
param = []
variable = []
for trig in np.arange(0,len(trigger_code)):
for dt in [0.0]:
t1 = random.randint(2,29)
t2 = dt
code.append(gen_none_code('', trigger, t2*100., t1*100., ''))
param.append(','.join(['radar jamming',str(t1),str(dt),'none']))
write_to_file(faultLibFile, code, param, title, fileLoc, faultLoc)
### curr_sp-sub-incProcPlant-H1
def gen_curr_sp_sub_fault_plant(sceneNum):
title = str(sceneNum)+'_curr_sp-sub-incProcPlant-H1'
faultLibFile = 'fault_library/vCurrSpPlant'
fileLoc = 'selfdrive/test/plant/plant.py'
faultLoc = '#speed:HOOK#'
trigger = ['frameIdx']
trigger_code = ['if headway_time<=2.0 and RSpeed<=0:', 'if headway_time<=2.0 and RSpeed>0:', 'if headway_time>2.0 and RSpeed>0:', 'if headway_time>2.0 and RSpeed<=0:']
code = []
param = []
variable = ['speed2send']
deltaRange = np.arange(10,61,10)
for trig in np.arange(0,len(trigger_code)):
for dt in [30.0]:
t2 = dt
for d in deltaRange:
delta = random.randint(d,d+9)
if delta > 60:
delta = 60
delta = delta*0.44704 # 1MPH = 0.44704 m/s
t1 = random.randint(2,29)
code.append(gen_sub_code('', trigger, t1*100., t2*100., variable, [delta], '//if '+variable[0]+'<0:'+'// '+variable[0]+'= 0'))
param.append(','.join(['current speed',str(t1),str(dt),str(delta)]))
write_to_file(faultLibFile, code, param, title, fileLoc, faultLoc)
### curr_sp-add-incProcPlant-H2
def gen_curr_sp_add_fault_plant(sceneNum):
title = str(sceneNum)+'_curr_sp-add-incProcPlant-H2'
faultLibFile = 'fault_library/vCurrSpPlant'
fileLoc = 'selfdrive/test/plant/plant.py'
faultLoc = '#speed:HOOK#'
trigger = ['frameIdx']
trigger_code = ['if headway_time>2.0 and RSpeed<=0:','if headway_time<=2.0 and RSpeed<=0:', 'if headway_time<=2.0 and RSpeed>0:', 'if headway_time>2.0 and RSpeed>0:']
code = []
param = []
variable = ['speed2send']
deltaRange = np.arange(10,61,10)
for trig in np.arange(0,len(trigger_code)):
for d in deltaRange:
for dt in [30.]:
t2 = dt
delta = random.randint(d,d+9)
if delta > 60:
delta = 60
delta = delta*0.44704 # 1MPH = 0.44704 m/s
t1 = random.randint(2,29)
code.append(gen_add_code('', trigger, t1*100., t2*100., variable, [delta], '//if '+variable[0]+'>=85.0:'+'// '+variable[0]+'= 85.0'))
param.append(','.join(['current speed',str(t1),str(dt),str(delta)]))
write_to_file(faultLibFile, code, param, title, fileLoc, faultLoc)
### md-rand-incProcPlant-H3
def gen_md_rand_val_plant(lane,sceneNum):
title = str(sceneNum)+'_'+lane+'Lane-rand-incProcPlant-H3'
faultLibFile = 'fault_library/mdPlant_'+lane
fileLoc = 'selfdrive/test/plant/maneuver.py'
faultLoc = '#md:HOOK#'
trigger = ['self.frameIdx']
trigger_code = ['if headway_time<=2.0 and RSpeed>=0:', 'if headway_time>2.0 and RSpeed<=0:','if headway_time<=2.0 and RSpeed<=0:','if headway_time>2.0 and RSpeed>0:']
code = []
param = []
if lane.lower()=='left':
variable = ['self.lLane']
elif lane.lower()=='right':
variable = ['self.rLane']
else:
variable = ['self.lLane','self.rLane']
deltaRange = np.arange(-2.5,2.5,0.5)
for trig in np.arange(0,len(trigger_code)):
for dt in [30.0]:
t2 = dt
for d1 in deltaRange:
d2 = d1+1
t1 = random.randint(2,29)
code.append(gen_uniform_rand_code('', trigger, t1*100., t2*100., variable, d1, d2, ''))
param.append(','.join(['path model',str(t1),str(dt),str(d1),str(d2)]))
write_to_file(faultLibFile, code, param, title, fileLoc, faultLoc)
### angSteer-add-incProcPlant-H3
def gen_angle_steer_add_plant(sceneNum):
title = str(sceneNum)+'_angSteer-add-incProcPlant-H3'
faultLibFile = 'fault_library/angSteerPlant'
fileLoc = 'selfdrive/test/plant/plant.py'
faultLoc = '#angle_steer:HOOK#'
trigger = ['frameIdx']
trigger_code = ['if headway_time>2.0 and RSpeed>=0:','if headway_time<=2.0 and RSpeed<=0:', 'if headway_time<=2.0 and RSpeed>0:','if headway_time>2.0 and RSpeed>0:']
code = []
param = []
variable = ['angle_steer2send']
deltaRange = np.arange(-45,46,10)
for trig in np.arange(0,len(trigger_code)):
for dt in [30.0]:
t2 = dt
for d in deltaRange:
delta = random.randint(d,d+9)
if d > 45:
alpha = 45*3.1416/180.0
else:
alpha = delta*3.1416/180.0
t1 = random.randint(2,29)
code.append(gen_add_code('', trigger, t1*100., t2*100., variable, ['('+str(alpha)+')'], ''))
param.append(','.join(['steer angle',str(t1),str(dt),str(alpha)]))
write_to_file(faultLibFile, code, param, title, fileLoc, faultLoc)
### vision-none-miscommVisPlant-H3
def gen_vision_miscomm_fault_plant(sceneNum):
title = str(sceneNum)+'_vision-none-miscommVisPlant-H3'
faultLibFile = 'fault_library/visMiscommPlant'
fileLoc = 'selfdrive/test/plant/plant.py'
faultLoc = '#md_none:HOOK#'
trigger = ['frameIdx']
trigger_code = ['if headway_time>2.0 or RSpeed<0:','if headway_time>2.0 or RSpeed>0', 'if headway_time<=2.0 or RSpeed<=0', 'if headway_time<=2.0 or RSpeed>0']
code = []
param = []
variable = []
for trig in np.arange(0,len(trigger_code)):
for dt in [0.0]:
t2 = dt
t1 = random.randint(2,29)
code.append(gen_none_code('', trigger, t2*100., t1*100., ''))
param.append(','.join(['vision miscomm',str(t1),str(dt),'none']))
write_to_file(faultLibFile, code, param, title, fileLoc, faultLoc)
### vision-effect-noisyInputManeuver-H3
def gen_vision_noisyInput_fault_Maneuver(effect, sceneNum):
title = str(sceneNum)+'_vision-effect-noisyInputManeuver-H3'
faultLibFile = ''
fileLoc = 'selfdrive/test/plant/maneuver.py'
faultLoc = '#visionFault:HOOK#'
trigger = ['self.frameIdx']
trigger_code = ['if headway_time<=2.5 and RSpeed>=0:', 'if headway_time>2.5 and RSpeed>0:','if headway_time>2.5 and RSpeed<=0:','if headway_time<=2.5 and RSpeed<0:']
code = []
param = []
#variable = ['left_line','right_line']
#deltaRange = ['lanes[0]','lanes[1]']
variable = ['self.effect', 'self.thickness']
if effect <7:
range_th = range(1,11)
elif effect == 7:
range_th = range(3,7)
elif effect == 8:
range_th = [3,5,7]
elif effect == 9:
range_th = [3,5]
for trig in np.arange(0,len(trigger_code)):
for dt in [30.0]:
for th in range_th:
t2 = dt
t1 = random.randint(2,29)
code.append(gen_stuck_code('', trigger, t1*100., t2*100., variable, [str(effect), str(th)], ''))
param.append(','.join(['vision noisyInput',str(t1),str(dt),'none']))
write_to_file(faultLibFile, code, param, title, fileLoc, faultLoc)
### d_rel-add-incVision-H1
def gen_vision_dRel_add_fault_plant(sceneNum):
title = str(sceneNum)+'_d_rel-add-incVision-H1'
faultLibFile = 'fault_library/dRelPlantVis'
fileLoc = 'selfdrive/test/plant/plant.py'
faultLoc = '#vision_dRel:HOOK#'
trigger = ['frameIdx']
trigger_code = ['if headway_time<=2.0 and RSpeed<=0:', 'if headway_time<=2.0 and RSpeed>0:', 'if headway_time>2.0 and RSpeed>0:','if headway_time>2.0 and RSpeed<=0:']
code = []
param = []
variable = ['vision_dRel']
deltaRange = np.arange(15,255,10)
for trig in np.arange(0,len(trigger_code)):
for dt in [30.0]:
t2 = dt
for d in deltaRange:
delta = random.randint(d,d+9)
t1 = random.randint(2,29)
#code.append(gen_add_code(trigger_code, trigger, t1, t2, variable, [delta], '//if '+variable[0]+'>=255:'+'// '+variable[0]+'= 254'))
code.append(gen_add_code('', trigger, t1*100., t2*100., variable, [delta], ''))
param.append(','.join(['relative distance',str(t1),str(dt),str(delta)]))
write_to_file(faultLibFile, code, param, title, fileLoc, faultLoc)
### d_rel-sub-incVision-H2
def gen_vision_dRel_sub_fault_plant(sceneNum):
title = str(sceneNum)+'_d_rel-sub-incVision-H2'
faultLibFile = 'fault_library/dRelPlantVis'
fileLoc = 'selfdrive/test/plant/plant.py'
faultLoc = '#vision_dRel:HOOK#'
trigger = ['frameIdx']
trigger_code = ['if headway_time>2.0 and RSpeed<=0:','if headway_time<=2.0 and RSpeed<=0:', 'if headway_time<=2.0 and RSpeed>0:', 'if headway_time>2.0 and RSpeed>0:']
code = []
param = []
variable = ['vision_dRel']
deltaRange = np.arange(10,255,10)
for trig in np.arange(0,len(trigger_code)):
for d in deltaRange:
for dt in [30.0]:
t2 = dt
delta = random.randint(d,d+9)
t1 = random.randint(2,29)
code.append(gen_sub_code('',trigger, t1*100., t2*100., variable, [delta], '//if '+variable[0]+'<0:'+'// '+variable[0]+'= 0'))
param.append(','.join(['relative distance',str(t1),str(dt),str(delta)]))
write_to_file(faultLibFile, code, param, title, fileLoc, faultLoc)
### d_rel-add-incRadVis-H1
def gen_RadVis_dRel_add_fault_plant(sceneNum):
title = str(sceneNum)+'_d_rel-add-incRadVis-H1'
faultLibFile = 'fault_library/dRelPlantRadVis'
fileLoc = 'selfdrive/test/plant/plant.py'
faultLoc = '#RadVis_dRel:HOOK#'
trigger = ['frameIdx']
trigger_code = ['if headway_time<=2.0 and RSpeed<=0:', 'if headway_time<=2.0 and RSpeed>0:', 'if headway_time>2.0 and RSpeed>0:', 'if headway_time>2.0 and RSpeed<=0:']
code = []
param = []
variable = ['d_rel']
deltaRange = np.arange(15,255,10)
for trig in np.arange(0,len(trigger_code)):
for dt in [30.0]:
t2 = dt
for d in deltaRange:
delta = random.randint(d,d+9)
t1 = random.randint(2,29)
#code.append(gen_add_code(trigger_code, trigger, t1, t2, variable, [delta], '//if '+variable[0]+'>=255:'+'// '+variable[0]+'= 254'))
code.append(gen_add_code('', trigger, t1*100., t2*100., variable, [delta], ''))
param.append(','.join(['relative distance',str(t1),str(dt),str(delta)]))
write_to_file(faultLibFile, code, param, title, fileLoc, faultLoc)
### d_rel-sub-incRadVis-H2
def gen_RadVis_dRel_sub_fault_plant(sceneNum):
title = str(sceneNum)+'_d_rel-sub-incRadVis-H2'
faultLibFile = 'fault_library/dRelPlantRadVis'
fileLoc = 'selfdrive/test/plant/plant.py'
faultLoc = '#RadVis_dRel:HOOK#'
trigger = ['frameIdx']
trigger_code = ['if headway_time>2.0 and RSpeed<=0:','if headway_time<=2.0 and RSpeed<=0:', 'if headway_time<=2.0 and RSpeed>0:', 'if headway_time>2.0 and RSpeed>0:']
code = []
param = []
variable = ['d_rel']
deltaRange = np.arange(10,255,10)
for trig in np.arange(0,len(trigger_code)):
for d in deltaRange:
for dt in [30.0]:
t2 = dt
delta = random.randint(d,d+9)
t1 = random.randint(2,29)
code.append(gen_sub_code('',trigger, t1*100., t2*100., variable, [delta], '//if '+variable[0]+'<0:'+'// '+variable[0]+'= 0'))
param.append(','.join(['relative distance',str(t1),str(dt),str(delta)]))
write_to_file(faultLibFile, code, param, title, fileLoc, faultLoc)
##########################################
###_main_###
with open('run_fault_inject_campaign.sh', 'w') as runFile:
runFile.write('#Usage: python run.py target_fault_library\n')
scenarios = {
1 : gen_rel_dist_add_fault_plant,
2 : gen_rel_vel_add_fault_plant,
3 : gen_rel_dist_sub_fault_plant,
4 : gen_rel_vel_sub_fault_plant,
5 : gen_radar_jamming_fault_plant_H1,
6 : gen_radar_jamming_fault_plant_H2,
9 : gen_curr_sp_sub_fault_plant,
12 : gen_curr_sp_add_fault_plant,
13 : gen_md_rand_val_plant,
14 : gen_md_rand_val_plant,
15 : gen_md_rand_val_plant,
16 : gen_angle_steer_add_plant,
34 : gen_vision_miscomm_fault_plant,
35 : gen_vision_noisyInput_fault_Maneuver,
36 : gen_vision_noisyInput_fault_Maneuver,
37 : gen_vision_noisyInput_fault_Maneuver,
38 : gen_vision_noisyInput_fault_Maneuver,
39 : gen_vision_dRel_add_fault_plant,
40 : gen_vision_dRel_sub_fault_plant,
41 : gen_RadVis_dRel_add_fault_plant,
42 : gen_RadVis_dRel_sub_fault_plant,
43 : gen_vision_noisyInput_fault_Maneuver,
44 : gen_vision_noisyInput_fault_Maneuver,
45 : gen_vision_noisyInput_fault_Maneuver,
46 : gen_vision_noisyInput_fault_Maneuver,
47 : gen_vision_noisyInput_fault_Maneuver
}
lanes = ['left','right','both'] # 'left','right','both'
poly = ['p_path','left','right','d_path'] # 'p_path','left','right','d_path'
#effects = ['rain', 'fog', 'snow', 'occlusion']
effects = [1,2,3,4,5,6,7,8,9]
for sceneNum in [1,2,3,4,5,6,9,12,13,14,15,16,34,39,40,41,42]: # experiments without the vision
#for sceneNum in [35,36,37,38,43,44,45,46,47]: # for testing the faults in input images
#for sceneNum in [1,2,3,4,5,6,9,12,13,14,15,16,34,35,36,37,38,39,40,41,42,43,44,45,46,47]: # for testing the faults in inputs
# for sceneNum in [44,45,46,47]:
print sceneNum
cmd = 'cp '+ 'fault_library/scenario_'+str(sceneNum)+'.txt '+'fault_library/scenario_'+str(sceneNum)+'_prev.txt'
os.system(cmd)
if sceneNum >= 13 and sceneNum <=15:
scenarios[sceneNum](lanes[sceneNum-13],sceneNum)
elif sceneNum >= 28 and sceneNum <=31:
scenarios[sceneNum](poly[sceneNum-28],sceneNum)
elif sceneNum >= 35 and sceneNum <=38:
scenarios[sceneNum](effects[sceneNum-35],sceneNum)
elif sceneNum >= 43 and sceneNum <=47:
scenarios[sceneNum](effects[sceneNum+4-43],sceneNum)
else:
scenarios[sceneNum](sceneNum) | en | 0.282757 | ### Write codes to fault library file ### Write codes to fault library file -- for vision effects ########################################################### ### d_rel-add-incRADAR-H1 #' #code.append(gen_add_code(trigger_code, trigger, t1, t2, variable, [delta], '//if '+variable[0]+'>=255:'+'// '+variable[0]+'= 254')) #code.append(gen_add_code(trigger_code, trigger, t1, t2, variable, [delta], '//if '+variable[0]+'>=255:'+'// '+variable[0]+'= 254')) ### v_rel-add-incRADAR-H1 #' # 1MPH = 0.44704 m/s ### d_rel-sub-incRADAR-H2 #' ### v_rel-sub-incRADAR-H2 #' # 1MPH = 0.44704 m/s ### radar-none-incRADAR-H1 #' # reverse of actual trigger ### radar-none-incRADAR-H2 #' ### curr_sp-sub-incProcPlant-H1 #' # 1MPH = 0.44704 m/s ### curr_sp-add-incProcPlant-H2 #' # 1MPH = 0.44704 m/s ### md-rand-incProcPlant-H3 #' ### angSteer-add-incProcPlant-H3 #' ### vision-none-miscommVisPlant-H3 #' ### vision-effect-noisyInputManeuver-H3 #' #variable = ['left_line','right_line'] #deltaRange = ['lanes[0]','lanes[1]'] ### d_rel-add-incVision-H1 #' #code.append(gen_add_code(trigger_code, trigger, t1, t2, variable, [delta], '//if '+variable[0]+'>=255:'+'// '+variable[0]+'= 254')) ### d_rel-sub-incVision-H2 #' ### d_rel-add-incRadVis-H1 #' #code.append(gen_add_code(trigger_code, trigger, t1, t2, variable, [delta], '//if '+variable[0]+'>=255:'+'// '+variable[0]+'= 254')) ### d_rel-sub-incRadVis-H2 #' ########################################## ###_main_### # 'left','right','both' # 'p_path','left','right','d_path' #effects = ['rain', 'fog', 'snow', 'occlusion'] # experiments without the vision #for sceneNum in [35,36,37,38,43,44,45,46,47]: # for testing the faults in input images #for sceneNum in [1,2,3,4,5,6,9,12,13,14,15,16,34,35,36,37,38,39,40,41,42,43,44,45,46,47]: # for testing the faults in inputs # for sceneNum in [44,45,46,47]: | 2.792949 | 3 |
download_module.py | ashvinoli/Anime-no-tomodachi | 2 | 6624260 | from tree import *
import pathlib
import sys
import time
import math
no_interruption_mode = False
interruption_response = None
anime_name_response = None
selection_response = None
choice_response = None
if len(sys.argv) == 2: #First argument is file name
lines = []
if os.path.exists("repeat.txt"):
if sys.argv[1] == "-r":
repeat = open("repeat.txt","r")
for line in repeat:
lines.append(line.rstrip())
if len(lines)>=4:
interruption_response = lines[0]
anime_name_response = lines[1]
selection_response = lines[2]
choice_response = lines[3]
repeat.close()
def video_quality_selection(my_playlist,anime_directory,episode_name):
#This function prompts the user to select quality
global default_mode
chunks = anime_directory + "/Chunks"
index = 1
length = len(my_playlist)
print("Video qualities available for "+ episode_name)
for _ in my_playlist:
print(str(index) + "----->"+ _[0])
index += 1
while True:
resp = input("Choose the quality of video:")
if resp.isnumeric() and int(resp) <= length and int(resp) >= 1:
my_quality_video = provide_video_chunks(my_playlist[int(resp)-1][1])
if len(my_quality_video) == 0:
my_quality_video = provide_video_chunks_new(my_playlist[int(resp)-1][1])
ans = input("Do you want to keep it as default quality for the next videos? Type y/n:")
quality_file = open(chunks +"/quality.txt","w")
quality_file.write(my_playlist[int(resp)-1][0])
quality_file.close()
if ans == "y":
default_mode = my_playlist[int(resp)-1][0]
download_chunks(my_quality_video,anime_directory,episode_name)
break
else:
resp = input("Bad quality!!!!!! Re-enter quality y/n?")
if resp != "y":
break
def download_in_a_different_way(episode_link):
global headers
series_name = "-".join(episode_link.split("/")[-1].split("-")[:-2])
episode_name = episode_link.split("/")[-1]
program_path = os.path.dirname(os.path.realpath(__file__))
anime_directory = program_path + "/" + series_name + "/" + episode_name
chunks = anime_directory + "/Chunks"
if os.path.exists(anime_directory + "/" +episode_name+".mp4"):
print(episode_name+" has already been downloaded")
return
if not os.path.exists(chunks):
pathlib.Path(chunks).mkdir(parents=True, exist_ok=True)
vid_stream_link = BeautifulSoup(requests.get(episode_link).text,"html.parser").findAll("a",{"href":re.compile(r"https://vidstreaming.io/download.*")})[0].get("href")
#print(vid_stream_link)
write_to_log_file("Video stream link from download in different way:\n",vid_stream_link)
download_link = BeautifulSoup(requests.get(vid_stream_link).text,"html.parser").findAll("a",{"href":re.compile(r"https://st\dx.cdnfile.info.*")})[0].get("href")
#print(download_link)
write_to_log_file("Download link for episode " +episode_link +":\n",download_link)
#size = requests.head(download_link).headers.get("Content-length") The headers couldn't be retrieved so I had to comment this out
#print(size)
#write_to_log_file("Size of episode:\n",size)
files = requests.get(download_link,headers = headers,stream=True)
files.raise_for_status
index = 1
print("Downloading "+episode_link)
time_prev = time.time()
standard_size = 1048576
file_pieces = files.iter_content(chunk_size = standard_size)
for piece in files.iter_content(chunk_size = standard_size):
chunk_name = chunks+"/"+"chunk_"+str(index)+".mp4"
chunk_file = open(chunk_name,"wb")
chunk_file.write(piece)
chunk_file.close()
time_new = time.time()
time_difference = time_new-time_prev
time_prev = time_new
#percentage = int(index*standard_size/int(size)*100)
#if percentage >= 100:
# percentage = 100
average_speed = (standard_size)/(1024*time_difference)
print(str(index)+" chunks downloaded. Average internet speed = %.2f KB/s" % (average_speed),end="")
index += 1
append_them_all(index-1,anime_directory,episode_name,chunks)
def download_single_video(final_link,episode_link):
global default_mode
global no_interruption_mode
series_name = "-".join(episode_link.split("/")[-1].split("-")[:-2])
episode_name = episode_link.split("/")[-1]
program_path = os.path.dirname(os.path.realpath(__file__))
anime_directory = program_path + "/" + series_name + "/" + episode_name
chunks = anime_directory + "/Chunks"
if not os.path.exists(chunks):
pathlib.Path(chunks).mkdir(parents=True, exist_ok=True)
#default_mode = None
else:
if os.path.exists(chunks +"/quality.txt"):
quality = chunks + "/quality.txt"
quality_file = open(quality,"r")
for _ in quality_file:
quality = _.rstrip()
default_mode = quality #NOTE if 1-6 like range are given and 1 file has been downloaded it will tip the default_mode to quality of 1 and further videos will be downloaded wih the same quality
if not os.path.exists(anime_directory):
pathlib.Path(anime_directory).mkdir(parents=True, exist_ok=True)
if not (os.path.exists(anime_directory + "/" + episode_name + ".mp4") or os.path.exists(anime_directory + "/" + episode_name.split("-")[-1]+ ".mp4")):
my_playlist = get_child_m3u8(get_playlist_m3u8(final_link))
#print(my_playlist)
matched = False
length = len(my_playlist)
if length != 0:
if default_mode == None:
video_quality_selection(my_playlist,anime_directory,episode_name)
else:
for _ in my_playlist:
if _[0] == default_mode:
quality_file = open(chunks +"/quality.txt","w")
quality_file.write(default_mode)
quality_file.close()
my_quality_video = provide_video_chunks(_[1])
if len(my_quality_video) ==0:
my_quality_video = provide_video_chunks_new(_[1])
download_chunks(my_quality_video,anime_directory,episode_name)
matched = True
break
if not matched:
if no_interruption_mode:
download_chunks(my_playlist[0][1],anime_directory,episode_name)
else:
default_mode = None
print("Sorry, your default quality is unavailable for this video. So please select quality again.")
video_quality_selection(my_playlist,anime_directory, episode_name)
else:
print("Sorry, the video you requested is currently not available! Will fix this problem soon!") #This problem is caused by the alternate hls10x site
else:
print(episode_name+" has already been downloaded!")
def download_chunks(video_chunks,anime_directory, episode_name):
#print(video_chunks)
write_to_log_file("Video chunks for "+ episode_name+"\n","\n".join(video_chunks))
chunks = anime_directory + "/Chunks"
index = 1
average_speed = math.inf
global headers
if not os.path.exists(chunks):
pathlib.Path(chunks).mkdir(parents=True, exist_ok=True)
print("Downloading "+episode_name+"...")
length = len(video_chunks)
file_count = len([name for name in os.listdir(chunks)])
#Code below had to be written to ensure that no incomeplete files exists. I resorted to it after requests.head().headers.get() failed. Code below fails to check the last file chunk. File count also counts the quality file
if file_count >=2 and file_count <= length+1: #length + 1 because of the quality file
os.remove(chunks+"/"+"chunk_"+str(file_count-1)+".mp4")
for chunk in video_chunks:
tries = 1
chunk_name = chunks+"/"+"chunk_"+str(index)+".mp4"
while True:
if not os.path.exists(chunk_name):
chunk_file = open(chunk_name,"wb")
try:
begin_time = time.time()
current_chunk = requests.get(chunk,headers=headers).content
#print(current_chunk)#the headers for file request have been removed
end_time = time.time()
time_difference = end_time-begin_time
chunk_file.write(current_chunk)
chunk_file.close()
size = os.path.getsize(chunk_name)
average_speed = size/(1024*time_difference)
break
except:
chunk_file.close()
if tries <= 5:
print("\nError on chunk "+str(index)+". Retrying.... Attempt:"+str(tries))
os.remove(chunk_name)
tries += 1
else:
print("Error on chunk "+str(index)+". " + str(tries-1) +" downloading attempt failed! Continuing download for another episode. Please redownload " + episode_name)
#os.remove(chunk_name)
return
else:
break
percentage = int((index/length) * 100)
#print(percentage,end="")
#print("% complete.")
print("\r%d%% complete. Average internet speed = %.2f KB/s" % (percentage,average_speed),end="")
index += 1
append_them_all(length,anime_directory, episode_name,chunks)
def append_them_all(length,anime_directory,episode_name, chunks):
print("\nAppending Pieces together......")
big_episode = anime_directory + "/" + episode_name + ".mp4"
if len(big_episode) >= 250:
big_episode = anime_directory + "/" + episode_name.split("-")[-1]+ ".mp4"
big_file = open(big_episode,"wb")
for i in range (1,length+1):
chunk_name = chunks+"/"+"chunk_"+str(i)+".mp4"
chunk_file = open(chunk_name,"rb")
big_file.write(chunk_file.read())
chunk_file.close()
os.remove(chunk_name)
big_file.close()
print("Done appending. "+episode_name+" has been successfully downloaded!")
print("\n")
def download_command_line():
global no_interruption_mode
global interruption_response
global anime_name_response
global selection_response
global choice_response
repeat = open("repeat.txt","w")
if interruption_response == None:
interruption = input("Do you want to turn no interruption mode on? Type y/n:")
else:
interruption = interruption_response
repeat.write(interruption+"\n")
if interruption == "y":
no_interruption_mode = True
while True:
os.system("cls")
print("NOTE: IF YOU WANT TO SKIP ANY TYPING OR QUESTION JUST PRESS \"ENTER\" KEY.\nBUT DONOT PRESS ENTER FOR THIS FIRST QUESTION!\n")
if anime_name_response == None:
anime_name = input("Please enter the anime name (example:one-piece). Mind the dash(-) sign:")
else:
anime_name = anime_name_response
match_dict=[]
f=open("all_animes.txt","r")
found = False
for line in f:
if anime_name in line:
match_dict.append(line)
found = True
f.close()
if found:
try:
repeat.write(anime_name+"\n")
except:
pass
for i in range(len(match_dict)):
print(str(i+1) + "--->" + match_dict[i])
while True:
if selection_response == None:
selection = input("Please select your option among the SEARCHED RESULTS:")
else:
selection = selection_response
if selection.isnumeric():
selection = int(selection)-1
if selection > len(match_dict)-1 or selection < 0:
resp = input("Your selection is not available! Make reselection? Type y/n:")
if resp != "y":
break
else:
try:
repeat.write(str(selection+1)+"\n")
except:
pass
num = str(num_of_episodes(match_dict[selection]))
while True:
if choice_response == None:
choice = input("There are " + num + " episodes for "+ match_dict[selection] +"Type single number eg: 1 or 2 to download single episode, '1-5' to download range, 'A' or 'a' to download all episodes:")
else:
choice = choice_response
interruption_response = None
anime_name_response = None
selection_response = None
choice_response = None
if choice.isnumeric():
choice = int(choice)
if choice > int(num) or choice < 1:
print("Sorry, the episode you requested is not available yet!")
resp = input("Want to download another episode? Type y/n:")
if resp != "y":
break
else:
try:
repeat.write(str(choice)+"\n")
repeat.close()
except:
pass
while True:
if choice > int(num):
print("Sorry, we are now out of episodes!")
break
my_episode = get_single_episode(match_dict[selection].rstrip(),choice)
my_link = write_to_file(my_episode[0])
final_link = my_link.split("//")[-1]
final_link = "https://"+final_link
#print(final_link)
write_to_log_file("Link to episode:\n",final_link)
my_m3u8 = get_playlist_m3u8(final_link)
#print(my_m3u8)
write_to_log_file("M3U8 for episode:\n",my_m3u8)
if re.match("https://hls\d\dx",my_m3u8):
try:
download_in_a_different_way(my_episode[0])
except:
print(my_episode[0] + " couldn't be download due to some errors. Please considering redownloading it.")
else:
download_single_video(final_link,my_episode[0])
#webbrowser.open_new(final_link)
resp = input("Want to download next episode? Type y/n:")
if resp != 'y':
break
else:
choice += 1
elif re.match("^\d+-\d+$",choice): #don't miss the ^ and $ to exact match
try:
repeat.write(str(choice)+"\n")
repeat.close()
except:
pass
begin = int(choice.split("-")[0])
end = int(choice.split("-")[1])
for i in range(begin,end+1):
my_episode = get_single_episode(match_dict[selection].rstrip(),i)
my_link = write_to_file(my_episode[0])
final_link = my_link.split("//")[-1]
final_link = "https://"+final_link
write_to_log_file("M3U8 for episode:\n",final_link)
my_m3u8 = get_playlist_m3u8(final_link)
#print(final_link)
write_to_log_file("M3U8 for episode:\n",my_m3u8)
if re.match("https://hls\d\dx",my_m3u8):
try:
download_in_a_different_way(my_episode[0])
except:
print(my_episode[0] + " couldn't be download due to some errors. Please considering redownloading it.")
else:
download_single_video(final_link,my_episode[0])
elif choice == "A" or choice == "a":
try:
repeat.write(str(choice)+"\n")
repeat.close()
except:
pass
episodes_num = num_of_episodes(match_dict[selection].rstrip())
for i in range(1,episodes_num+1):
my_episode = get_single_episode(match_dict[selection].rstrip(),i)
my_link = write_to_file(my_episode[0])
final_link = my_link.split("//")[-1]
final_link = "https://"+final_link
#print(final_link)
write_to_log_file("M3U8 for episode:\n",final_link)
my_m3u8 = get_playlist_m3u8(final_link)
write_to_log_file("M3U8 for episode:\n",my_m3u8)
if re.match("https://hls\d\dx",my_m3u8):
try:
download_in_a_different_way(my_episode[0])
except:
print(my_episode[0] + " couldn't be download due to some errors. Please considering redownloading it.")
else:
download_single_video(final_link,my_episode[0])
else:
resp=input("Invalid option input. Reinput? Type y/n:")
if resp != "y":
break
else:
resp=input("Invalid option input. Reinput? Type y/n:")
if resp != "y":
break
else:
print("No match found! Try researching for shorter string")
retry = input("Research for another anime? y/n:")
if retry != "y":
break
if __name__ == "__main__":
download_command_line()
| from tree import *
import pathlib
import sys
import time
import math
no_interruption_mode = False
interruption_response = None
anime_name_response = None
selection_response = None
choice_response = None
if len(sys.argv) == 2: #First argument is file name
lines = []
if os.path.exists("repeat.txt"):
if sys.argv[1] == "-r":
repeat = open("repeat.txt","r")
for line in repeat:
lines.append(line.rstrip())
if len(lines)>=4:
interruption_response = lines[0]
anime_name_response = lines[1]
selection_response = lines[2]
choice_response = lines[3]
repeat.close()
def video_quality_selection(my_playlist,anime_directory,episode_name):
#This function prompts the user to select quality
global default_mode
chunks = anime_directory + "/Chunks"
index = 1
length = len(my_playlist)
print("Video qualities available for "+ episode_name)
for _ in my_playlist:
print(str(index) + "----->"+ _[0])
index += 1
while True:
resp = input("Choose the quality of video:")
if resp.isnumeric() and int(resp) <= length and int(resp) >= 1:
my_quality_video = provide_video_chunks(my_playlist[int(resp)-1][1])
if len(my_quality_video) == 0:
my_quality_video = provide_video_chunks_new(my_playlist[int(resp)-1][1])
ans = input("Do you want to keep it as default quality for the next videos? Type y/n:")
quality_file = open(chunks +"/quality.txt","w")
quality_file.write(my_playlist[int(resp)-1][0])
quality_file.close()
if ans == "y":
default_mode = my_playlist[int(resp)-1][0]
download_chunks(my_quality_video,anime_directory,episode_name)
break
else:
resp = input("Bad quality!!!!!! Re-enter quality y/n?")
if resp != "y":
break
def download_in_a_different_way(episode_link):
global headers
series_name = "-".join(episode_link.split("/")[-1].split("-")[:-2])
episode_name = episode_link.split("/")[-1]
program_path = os.path.dirname(os.path.realpath(__file__))
anime_directory = program_path + "/" + series_name + "/" + episode_name
chunks = anime_directory + "/Chunks"
if os.path.exists(anime_directory + "/" +episode_name+".mp4"):
print(episode_name+" has already been downloaded")
return
if not os.path.exists(chunks):
pathlib.Path(chunks).mkdir(parents=True, exist_ok=True)
vid_stream_link = BeautifulSoup(requests.get(episode_link).text,"html.parser").findAll("a",{"href":re.compile(r"https://vidstreaming.io/download.*")})[0].get("href")
#print(vid_stream_link)
write_to_log_file("Video stream link from download in different way:\n",vid_stream_link)
download_link = BeautifulSoup(requests.get(vid_stream_link).text,"html.parser").findAll("a",{"href":re.compile(r"https://st\dx.cdnfile.info.*")})[0].get("href")
#print(download_link)
write_to_log_file("Download link for episode " +episode_link +":\n",download_link)
#size = requests.head(download_link).headers.get("Content-length") The headers couldn't be retrieved so I had to comment this out
#print(size)
#write_to_log_file("Size of episode:\n",size)
files = requests.get(download_link,headers = headers,stream=True)
files.raise_for_status
index = 1
print("Downloading "+episode_link)
time_prev = time.time()
standard_size = 1048576
file_pieces = files.iter_content(chunk_size = standard_size)
for piece in files.iter_content(chunk_size = standard_size):
chunk_name = chunks+"/"+"chunk_"+str(index)+".mp4"
chunk_file = open(chunk_name,"wb")
chunk_file.write(piece)
chunk_file.close()
time_new = time.time()
time_difference = time_new-time_prev
time_prev = time_new
#percentage = int(index*standard_size/int(size)*100)
#if percentage >= 100:
# percentage = 100
average_speed = (standard_size)/(1024*time_difference)
print(str(index)+" chunks downloaded. Average internet speed = %.2f KB/s" % (average_speed),end="")
index += 1
append_them_all(index-1,anime_directory,episode_name,chunks)
def download_single_video(final_link,episode_link):
global default_mode
global no_interruption_mode
series_name = "-".join(episode_link.split("/")[-1].split("-")[:-2])
episode_name = episode_link.split("/")[-1]
program_path = os.path.dirname(os.path.realpath(__file__))
anime_directory = program_path + "/" + series_name + "/" + episode_name
chunks = anime_directory + "/Chunks"
if not os.path.exists(chunks):
pathlib.Path(chunks).mkdir(parents=True, exist_ok=True)
#default_mode = None
else:
if os.path.exists(chunks +"/quality.txt"):
quality = chunks + "/quality.txt"
quality_file = open(quality,"r")
for _ in quality_file:
quality = _.rstrip()
default_mode = quality #NOTE if 1-6 like range are given and 1 file has been downloaded it will tip the default_mode to quality of 1 and further videos will be downloaded wih the same quality
if not os.path.exists(anime_directory):
pathlib.Path(anime_directory).mkdir(parents=True, exist_ok=True)
if not (os.path.exists(anime_directory + "/" + episode_name + ".mp4") or os.path.exists(anime_directory + "/" + episode_name.split("-")[-1]+ ".mp4")):
my_playlist = get_child_m3u8(get_playlist_m3u8(final_link))
#print(my_playlist)
matched = False
length = len(my_playlist)
if length != 0:
if default_mode == None:
video_quality_selection(my_playlist,anime_directory,episode_name)
else:
for _ in my_playlist:
if _[0] == default_mode:
quality_file = open(chunks +"/quality.txt","w")
quality_file.write(default_mode)
quality_file.close()
my_quality_video = provide_video_chunks(_[1])
if len(my_quality_video) ==0:
my_quality_video = provide_video_chunks_new(_[1])
download_chunks(my_quality_video,anime_directory,episode_name)
matched = True
break
if not matched:
if no_interruption_mode:
download_chunks(my_playlist[0][1],anime_directory,episode_name)
else:
default_mode = None
print("Sorry, your default quality is unavailable for this video. So please select quality again.")
video_quality_selection(my_playlist,anime_directory, episode_name)
else:
print("Sorry, the video you requested is currently not available! Will fix this problem soon!") #This problem is caused by the alternate hls10x site
else:
print(episode_name+" has already been downloaded!")
def download_chunks(video_chunks,anime_directory, episode_name):
#print(video_chunks)
write_to_log_file("Video chunks for "+ episode_name+"\n","\n".join(video_chunks))
chunks = anime_directory + "/Chunks"
index = 1
average_speed = math.inf
global headers
if not os.path.exists(chunks):
pathlib.Path(chunks).mkdir(parents=True, exist_ok=True)
print("Downloading "+episode_name+"...")
length = len(video_chunks)
file_count = len([name for name in os.listdir(chunks)])
#Code below had to be written to ensure that no incomeplete files exists. I resorted to it after requests.head().headers.get() failed. Code below fails to check the last file chunk. File count also counts the quality file
if file_count >=2 and file_count <= length+1: #length + 1 because of the quality file
os.remove(chunks+"/"+"chunk_"+str(file_count-1)+".mp4")
for chunk in video_chunks:
tries = 1
chunk_name = chunks+"/"+"chunk_"+str(index)+".mp4"
while True:
if not os.path.exists(chunk_name):
chunk_file = open(chunk_name,"wb")
try:
begin_time = time.time()
current_chunk = requests.get(chunk,headers=headers).content
#print(current_chunk)#the headers for file request have been removed
end_time = time.time()
time_difference = end_time-begin_time
chunk_file.write(current_chunk)
chunk_file.close()
size = os.path.getsize(chunk_name)
average_speed = size/(1024*time_difference)
break
except:
chunk_file.close()
if tries <= 5:
print("\nError on chunk "+str(index)+". Retrying.... Attempt:"+str(tries))
os.remove(chunk_name)
tries += 1
else:
print("Error on chunk "+str(index)+". " + str(tries-1) +" downloading attempt failed! Continuing download for another episode. Please redownload " + episode_name)
#os.remove(chunk_name)
return
else:
break
percentage = int((index/length) * 100)
#print(percentage,end="")
#print("% complete.")
print("\r%d%% complete. Average internet speed = %.2f KB/s" % (percentage,average_speed),end="")
index += 1
append_them_all(length,anime_directory, episode_name,chunks)
def append_them_all(length,anime_directory,episode_name, chunks):
print("\nAppending Pieces together......")
big_episode = anime_directory + "/" + episode_name + ".mp4"
if len(big_episode) >= 250:
big_episode = anime_directory + "/" + episode_name.split("-")[-1]+ ".mp4"
big_file = open(big_episode,"wb")
for i in range (1,length+1):
chunk_name = chunks+"/"+"chunk_"+str(i)+".mp4"
chunk_file = open(chunk_name,"rb")
big_file.write(chunk_file.read())
chunk_file.close()
os.remove(chunk_name)
big_file.close()
print("Done appending. "+episode_name+" has been successfully downloaded!")
print("\n")
def download_command_line():
global no_interruption_mode
global interruption_response
global anime_name_response
global selection_response
global choice_response
repeat = open("repeat.txt","w")
if interruption_response == None:
interruption = input("Do you want to turn no interruption mode on? Type y/n:")
else:
interruption = interruption_response
repeat.write(interruption+"\n")
if interruption == "y":
no_interruption_mode = True
while True:
os.system("cls")
print("NOTE: IF YOU WANT TO SKIP ANY TYPING OR QUESTION JUST PRESS \"ENTER\" KEY.\nBUT DONOT PRESS ENTER FOR THIS FIRST QUESTION!\n")
if anime_name_response == None:
anime_name = input("Please enter the anime name (example:one-piece). Mind the dash(-) sign:")
else:
anime_name = anime_name_response
match_dict=[]
f=open("all_animes.txt","r")
found = False
for line in f:
if anime_name in line:
match_dict.append(line)
found = True
f.close()
if found:
try:
repeat.write(anime_name+"\n")
except:
pass
for i in range(len(match_dict)):
print(str(i+1) + "--->" + match_dict[i])
while True:
if selection_response == None:
selection = input("Please select your option among the SEARCHED RESULTS:")
else:
selection = selection_response
if selection.isnumeric():
selection = int(selection)-1
if selection > len(match_dict)-1 or selection < 0:
resp = input("Your selection is not available! Make reselection? Type y/n:")
if resp != "y":
break
else:
try:
repeat.write(str(selection+1)+"\n")
except:
pass
num = str(num_of_episodes(match_dict[selection]))
while True:
if choice_response == None:
choice = input("There are " + num + " episodes for "+ match_dict[selection] +"Type single number eg: 1 or 2 to download single episode, '1-5' to download range, 'A' or 'a' to download all episodes:")
else:
choice = choice_response
interruption_response = None
anime_name_response = None
selection_response = None
choice_response = None
if choice.isnumeric():
choice = int(choice)
if choice > int(num) or choice < 1:
print("Sorry, the episode you requested is not available yet!")
resp = input("Want to download another episode? Type y/n:")
if resp != "y":
break
else:
try:
repeat.write(str(choice)+"\n")
repeat.close()
except:
pass
while True:
if choice > int(num):
print("Sorry, we are now out of episodes!")
break
my_episode = get_single_episode(match_dict[selection].rstrip(),choice)
my_link = write_to_file(my_episode[0])
final_link = my_link.split("//")[-1]
final_link = "https://"+final_link
#print(final_link)
write_to_log_file("Link to episode:\n",final_link)
my_m3u8 = get_playlist_m3u8(final_link)
#print(my_m3u8)
write_to_log_file("M3U8 for episode:\n",my_m3u8)
if re.match("https://hls\d\dx",my_m3u8):
try:
download_in_a_different_way(my_episode[0])
except:
print(my_episode[0] + " couldn't be download due to some errors. Please considering redownloading it.")
else:
download_single_video(final_link,my_episode[0])
#webbrowser.open_new(final_link)
resp = input("Want to download next episode? Type y/n:")
if resp != 'y':
break
else:
choice += 1
elif re.match("^\d+-\d+$",choice): #don't miss the ^ and $ to exact match
try:
repeat.write(str(choice)+"\n")
repeat.close()
except:
pass
begin = int(choice.split("-")[0])
end = int(choice.split("-")[1])
for i in range(begin,end+1):
my_episode = get_single_episode(match_dict[selection].rstrip(),i)
my_link = write_to_file(my_episode[0])
final_link = my_link.split("//")[-1]
final_link = "https://"+final_link
write_to_log_file("M3U8 for episode:\n",final_link)
my_m3u8 = get_playlist_m3u8(final_link)
#print(final_link)
write_to_log_file("M3U8 for episode:\n",my_m3u8)
if re.match("https://hls\d\dx",my_m3u8):
try:
download_in_a_different_way(my_episode[0])
except:
print(my_episode[0] + " couldn't be download due to some errors. Please considering redownloading it.")
else:
download_single_video(final_link,my_episode[0])
elif choice == "A" or choice == "a":
try:
repeat.write(str(choice)+"\n")
repeat.close()
except:
pass
episodes_num = num_of_episodes(match_dict[selection].rstrip())
for i in range(1,episodes_num+1):
my_episode = get_single_episode(match_dict[selection].rstrip(),i)
my_link = write_to_file(my_episode[0])
final_link = my_link.split("//")[-1]
final_link = "https://"+final_link
#print(final_link)
write_to_log_file("M3U8 for episode:\n",final_link)
my_m3u8 = get_playlist_m3u8(final_link)
write_to_log_file("M3U8 for episode:\n",my_m3u8)
if re.match("https://hls\d\dx",my_m3u8):
try:
download_in_a_different_way(my_episode[0])
except:
print(my_episode[0] + " couldn't be download due to some errors. Please considering redownloading it.")
else:
download_single_video(final_link,my_episode[0])
else:
resp=input("Invalid option input. Reinput? Type y/n:")
if resp != "y":
break
else:
resp=input("Invalid option input. Reinput? Type y/n:")
if resp != "y":
break
else:
print("No match found! Try researching for shorter string")
retry = input("Research for another anime? y/n:")
if retry != "y":
break
if __name__ == "__main__":
download_command_line()
| en | 0.768484 | #First argument is file name #This function prompts the user to select quality #print(vid_stream_link) #print(download_link) #size = requests.head(download_link).headers.get("Content-length") The headers couldn't be retrieved so I had to comment this out #print(size) #write_to_log_file("Size of episode:\n",size) #percentage = int(index*standard_size/int(size)*100) #if percentage >= 100: # percentage = 100 #default_mode = None #NOTE if 1-6 like range are given and 1 file has been downloaded it will tip the default_mode to quality of 1 and further videos will be downloaded wih the same quality #print(my_playlist) #This problem is caused by the alternate hls10x site #print(video_chunks) #Code below had to be written to ensure that no incomeplete files exists. I resorted to it after requests.head().headers.get() failed. Code below fails to check the last file chunk. File count also counts the quality file #length + 1 because of the quality file #print(current_chunk)#the headers for file request have been removed #os.remove(chunk_name) #print(percentage,end="") #print("% complete.") #print(final_link) #print(my_m3u8) #webbrowser.open_new(final_link) #don't miss the ^ and $ to exact match #print(final_link) #print(final_link) | 3.266046 | 3 |
utils.py | sgtlaggy/lagbot | 3 | 6624261 | <reponame>sgtlaggy/lagbot
import os
UPPER_PATH = os.path.split(os.path.abspath(__file__))[0]
def rzip(*iterables):
"""Like builtin `zip`, but uses the right end of longer iterables instead of the left.
Examples:
rzip([1,2,3], [4,5]) -> ((2, 4), (3, 5))
"""
lens = [len(it) for it in iterables]
min_len = min(lens)
diffs = [len_ - min_len for len_ in lens]
return tuple(tuple(it[i + diffs[diff_ind]] for diff_ind, it in enumerate(iterables)) for i in range(min_len))
def pluralize(singular, plural, n, fmt='{n} {s}'):
"""Similar to `gettext.ngettext`, but returns a string including the number.
`fmt` is an optional format string with fields `{n}` and `{s}` being replaced by
the number and singular or plural string, respectively.
Examples:
pluralize('dog', 'dogs', 1) -> '1 dog'
pluralize('dog', 'dogs', 3) -> '3 dogs'
pluralize('dog', 'dogs', 3, '{n} ... {s}') -> 'dogs ... 3'
"""
if n == 1:
return fmt.format(n=n, s=singular)
else:
return fmt.format(n=n, s=plural)
def tb_args(exc):
"""Easily format arguments for `traceback` functions."""
return (type(exc), exc, exc.__traceback__)
def commaize(seq):
seq = tuple(seq)
length = len(seq)
if length == 0:
return ''
if length == 1:
return seq[0]
elif length == 2:
return ' and '.join(seq)
else:
return ', and '.join([', '.join(seq[:-1]), seq[-1]])
def clamp(value, low=None, high=None):
if low is not None and value < low:
value = low
elif high is not None and value > high:
value = high
return value
| import os
UPPER_PATH = os.path.split(os.path.abspath(__file__))[0]
def rzip(*iterables):
"""Like builtin `zip`, but uses the right end of longer iterables instead of the left.
Examples:
rzip([1,2,3], [4,5]) -> ((2, 4), (3, 5))
"""
lens = [len(it) for it in iterables]
min_len = min(lens)
diffs = [len_ - min_len for len_ in lens]
return tuple(tuple(it[i + diffs[diff_ind]] for diff_ind, it in enumerate(iterables)) for i in range(min_len))
def pluralize(singular, plural, n, fmt='{n} {s}'):
"""Similar to `gettext.ngettext`, but returns a string including the number.
`fmt` is an optional format string with fields `{n}` and `{s}` being replaced by
the number and singular or plural string, respectively.
Examples:
pluralize('dog', 'dogs', 1) -> '1 dog'
pluralize('dog', 'dogs', 3) -> '3 dogs'
pluralize('dog', 'dogs', 3, '{n} ... {s}') -> 'dogs ... 3'
"""
if n == 1:
return fmt.format(n=n, s=singular)
else:
return fmt.format(n=n, s=plural)
def tb_args(exc):
"""Easily format arguments for `traceback` functions."""
return (type(exc), exc, exc.__traceback__)
def commaize(seq):
seq = tuple(seq)
length = len(seq)
if length == 0:
return ''
if length == 1:
return seq[0]
elif length == 2:
return ' and '.join(seq)
else:
return ', and '.join([', '.join(seq[:-1]), seq[-1]])
def clamp(value, low=None, high=None):
if low is not None and value < low:
value = low
elif high is not None and value > high:
value = high
return value | en | 0.67431 | Like builtin `zip`, but uses the right end of longer iterables instead of the left. Examples: rzip([1,2,3], [4,5]) -> ((2, 4), (3, 5)) Similar to `gettext.ngettext`, but returns a string including the number. `fmt` is an optional format string with fields `{n}` and `{s}` being replaced by the number and singular or plural string, respectively. Examples: pluralize('dog', 'dogs', 1) -> '1 dog' pluralize('dog', 'dogs', 3) -> '3 dogs' pluralize('dog', 'dogs', 3, '{n} ... {s}') -> 'dogs ... 3' Easily format arguments for `traceback` functions. | 3.647791 | 4 |
Curso_de_Python_ Curso_em_Video/PythonExercicios/ex107a112/utilidades/dados/__init__.py | DanilooSilva/Cursos_de_Python | 0 | 6624262 | <gh_stars>0
def leiaDienheiro(msg):
while True:
leia = str(input(msg))
if leia.replace(',', '').replace('.', '').isdigit():
valor = leia.replace(',', '.')
return float(valor)
break
else:
print(f'\033[031mERRO! \'{leia}\' é um preço inválido!\033[m') | def leiaDienheiro(msg):
while True:
leia = str(input(msg))
if leia.replace(',', '').replace('.', '').isdigit():
valor = leia.replace(',', '.')
return float(valor)
break
else:
print(f'\033[031mERRO! \'{leia}\' é um preço inválido!\033[m') | none | 1 | 3.47558 | 3 | |
test_arm_ric/test_by_phone.py | OlesyaF/basic_tests_zvs | 0 | 6624263 | # -*- encoding: utf-8 -*-
import time
import allure
# Проверка изменения информации о Горячей линии РИЦ на вкладке По телефону
@allure.title("Проверка изменения информации о Горячей линии РИЦ на вкладке По телефону")
def test_change_hotline_info(app):
print("test_change_hotline_info.py is running")
num = str(app.calc_check_sum_from_date())
hotline_info = "RIC autotest contact info (part 1) " + num
app.go_to_arm_ric()
app.login_agent()
time.sleep(7)
app.go_to_by_phone()
time.sleep(2)
app.change_ric_info(hotline_info)
app.go_to_by_phone()
hotline_info_form = app.get_hotline_info_arm_ric()
print("Информация о Горячей линии РИЦ, отображающаяся на вкладке По телефону:", hotline_info_form)
print("Новая информация о Горячей линии РИЦ:", hotline_info)
if (hotline_info_form == hotline_info):
print("Измененная информация о Горячей линии РИЦ корректно отображается на вкладке По телефону")
else:
print("ОШИБКА!!! Измененная информации о Горячей линии РИЦ не корректно отображается на вкладке По телефону!")
assert (hotline_info_form == hotline_info)
app.logout_agent()
print("test_change_hotline_info.py is done successfully")
| # -*- encoding: utf-8 -*-
import time
import allure
# Проверка изменения информации о Горячей линии РИЦ на вкладке По телефону
@allure.title("Проверка изменения информации о Горячей линии РИЦ на вкладке По телефону")
def test_change_hotline_info(app):
print("test_change_hotline_info.py is running")
num = str(app.calc_check_sum_from_date())
hotline_info = "RIC autotest contact info (part 1) " + num
app.go_to_arm_ric()
app.login_agent()
time.sleep(7)
app.go_to_by_phone()
time.sleep(2)
app.change_ric_info(hotline_info)
app.go_to_by_phone()
hotline_info_form = app.get_hotline_info_arm_ric()
print("Информация о Горячей линии РИЦ, отображающаяся на вкладке По телефону:", hotline_info_form)
print("Новая информация о Горячей линии РИЦ:", hotline_info)
if (hotline_info_form == hotline_info):
print("Измененная информация о Горячей линии РИЦ корректно отображается на вкладке По телефону")
else:
print("ОШИБКА!!! Измененная информации о Горячей линии РИЦ не корректно отображается на вкладке По телефону!")
assert (hotline_info_form == hotline_info)
app.logout_agent()
print("test_change_hotline_info.py is done successfully")
| ru | 0.899669 | # -*- encoding: utf-8 -*- # Проверка изменения информации о Горячей линии РИЦ на вкладке По телефону | 2.43538 | 2 |
pyrez/models/BaseMatchDetail.py | CLeendert/Pyrez | 25 | 6624264 | from .MatchBase import MatchBase
class BaseMatchDetail(MatchBase):
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.damageBot = kwargs.get("Damage_Bot", 0) or 0
self.damageDoneInHand = kwargs.get("Damage_Done_In_Hand", 0) or 0
self.damageDoneMagical = kwargs.get("Damage_Done_Magical", 0) or 0
self.damageDonePhysical = kwargs.get("Damage_Done_Physical", 0) or 0
self.damageMitigated = kwargs.get("Damage_Mitigated", 0) or 0
self.damageStructure = kwargs.get("Damage_Structure", 0) or 0
self.damageTaken = kwargs.get("Damage_Taken", 0) or 0
self.damageTakenMagical = kwargs.get("Damage_Taken_Magical", 0) or 0
self.damageTakenPhysical = kwargs.get("Damage_Taken_Physical", 0) or 0
self.deaths = kwargs.get("Deaths", 0) or 0
self.distanceTraveled = kwargs.get("Distance_Traveled", 0) or 0
self.healing = kwargs.get("Healing", 0) or 0
self.healingBot = kwargs.get("Healing_Bot", 0) or 0
self.healingPlayerSelf = kwargs.get("Healing_Player_Self", 0) or 0
self.killingSpree = kwargs.get("Killing_Spree", 0) or 0
self.mapName = kwargs.get("Map_Game", '') or ''
self.matchMinutes = kwargs.get("Minutes", 0) or 0
self.matchRegion = kwargs.get("Region", '') or ''
self.matchTimeSecond = kwargs.get("Time_In_Match_Seconds", 0) or 0
self.multiKillMax = kwargs.get("Multi_kill_Max", 0) or 0
self.objectiveAssists = kwargs.get("Objective_Assists", 0) or 0
self.playerName = kwargs.get("playerName", '') or ''
self.surrendered = kwargs.get("Surrendered", '') or ''
self.team1Score = kwargs.get("Team1Score", 0) or 0
self.team2Score = kwargs.get("Team2Score", 0) or 0
self.wardsPlaced = kwargs.get("Wards_Placed", 0) or 0
self.winStatus = kwargs.get("Win_Status", '') or ''
self.winningTaskForce = kwargs.get("Winning_TaskForce", 0) or 0
| from .MatchBase import MatchBase
class BaseMatchDetail(MatchBase):
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.damageBot = kwargs.get("Damage_Bot", 0) or 0
self.damageDoneInHand = kwargs.get("Damage_Done_In_Hand", 0) or 0
self.damageDoneMagical = kwargs.get("Damage_Done_Magical", 0) or 0
self.damageDonePhysical = kwargs.get("Damage_Done_Physical", 0) or 0
self.damageMitigated = kwargs.get("Damage_Mitigated", 0) or 0
self.damageStructure = kwargs.get("Damage_Structure", 0) or 0
self.damageTaken = kwargs.get("Damage_Taken", 0) or 0
self.damageTakenMagical = kwargs.get("Damage_Taken_Magical", 0) or 0
self.damageTakenPhysical = kwargs.get("Damage_Taken_Physical", 0) or 0
self.deaths = kwargs.get("Deaths", 0) or 0
self.distanceTraveled = kwargs.get("Distance_Traveled", 0) or 0
self.healing = kwargs.get("Healing", 0) or 0
self.healingBot = kwargs.get("Healing_Bot", 0) or 0
self.healingPlayerSelf = kwargs.get("Healing_Player_Self", 0) or 0
self.killingSpree = kwargs.get("Killing_Spree", 0) or 0
self.mapName = kwargs.get("Map_Game", '') or ''
self.matchMinutes = kwargs.get("Minutes", 0) or 0
self.matchRegion = kwargs.get("Region", '') or ''
self.matchTimeSecond = kwargs.get("Time_In_Match_Seconds", 0) or 0
self.multiKillMax = kwargs.get("Multi_kill_Max", 0) or 0
self.objectiveAssists = kwargs.get("Objective_Assists", 0) or 0
self.playerName = kwargs.get("playerName", '') or ''
self.surrendered = kwargs.get("Surrendered", '') or ''
self.team1Score = kwargs.get("Team1Score", 0) or 0
self.team2Score = kwargs.get("Team2Score", 0) or 0
self.wardsPlaced = kwargs.get("Wards_Placed", 0) or 0
self.winStatus = kwargs.get("Win_Status", '') or ''
self.winningTaskForce = kwargs.get("Winning_TaskForce", 0) or 0
| none | 1 | 2.232028 | 2 | |
class 2708/exercise1.py | Gabriel-Fernandes1917/lab-the-python | 0 | 6624265 | <reponame>Gabriel-Fernandes1917/lab-the-python
dictionary ={None:None}
loop = "sim"
while(loop == "sim"):
dictionary[0]=input('informe o nome\n')
dictionary[1]=input('informe o cpf\n')
loop = input('deseja continuar ?')
print(dictionary)
| dictionary ={None:None}
loop = "sim"
while(loop == "sim"):
dictionary[0]=input('informe o nome\n')
dictionary[1]=input('informe o cpf\n')
loop = input('deseja continuar ?')
print(dictionary) | none | 1 | 3.865597 | 4 | |
iqmon/pipelines/ingest.py | joshwalawender/IQMon | 9 | 6624266 | <filename>iqmon/pipelines/ingest.py
from keckdrpframework.pipelines.base_pipeline import BasePipeline
from keckdrpframework.models.processing_context import ProcessingContext
# MODIFY THIS IMPORT to reflect the name of the module created in the primitives directory
from iqmon.primitives.file_handling import (ReadFITS,
PopulateAdditionalMetaData,
CopyFile,
DeleteOriginal)
from iqmon.primitives.database import RecordFile
class IngestPipeline(BasePipeline):
"""
This pipeline ingests files from their raw location on disk and rewrites
them to the destination directory with a checksum. It then (if spcified)
deletes the original file. Finally, some basic image information and
statistics are recorded to the mongo database.
This is meant to be a very quick sequence which moves the file and records
the file's existence to the database.
"""
event_table = {
"next_file": ("ReadFITS", "reading_file", "populate_metadata"),
"populate_metadata": ("PopulateAdditionalMetaData", "populating_metadata", "copy_file"),
"copy_file": ("CopyFile", "copying_file", "delete_original"),
"delete_original": ("DeleteOriginal", "deleting_original", "record_file"),
"record_file": ("RecordFile", "recording", None),
}
def __init__(self, context: ProcessingContext):
BasePipeline.__init__(self, context)
| <filename>iqmon/pipelines/ingest.py
from keckdrpframework.pipelines.base_pipeline import BasePipeline
from keckdrpframework.models.processing_context import ProcessingContext
# MODIFY THIS IMPORT to reflect the name of the module created in the primitives directory
from iqmon.primitives.file_handling import (ReadFITS,
PopulateAdditionalMetaData,
CopyFile,
DeleteOriginal)
from iqmon.primitives.database import RecordFile
class IngestPipeline(BasePipeline):
"""
This pipeline ingests files from their raw location on disk and rewrites
them to the destination directory with a checksum. It then (if spcified)
deletes the original file. Finally, some basic image information and
statistics are recorded to the mongo database.
This is meant to be a very quick sequence which moves the file and records
the file's existence to the database.
"""
event_table = {
"next_file": ("ReadFITS", "reading_file", "populate_metadata"),
"populate_metadata": ("PopulateAdditionalMetaData", "populating_metadata", "copy_file"),
"copy_file": ("CopyFile", "copying_file", "delete_original"),
"delete_original": ("DeleteOriginal", "deleting_original", "record_file"),
"record_file": ("RecordFile", "recording", None),
}
def __init__(self, context: ProcessingContext):
BasePipeline.__init__(self, context)
| en | 0.928398 | # MODIFY THIS IMPORT to reflect the name of the module created in the primitives directory This pipeline ingests files from their raw location on disk and rewrites
them to the destination directory with a checksum. It then (if spcified)
deletes the original file. Finally, some basic image information and
statistics are recorded to the mongo database.
This is meant to be a very quick sequence which moves the file and records
the file's existence to the database. | 2.200226 | 2 |
slybot/slybot/fieldtypes/images.py | hackrush01/portia | 6,390 | 6624267 | """Images."""
from scrapely.extractors import extract_image_url
from slybot.fieldtypes.url import UrlFieldTypeProcessor
class ImagesFieldTypeProcessor(UrlFieldTypeProcessor):
name = 'image'
description = 'extracts image URLs'
def extract(self, text):
if text is not None:
return extract_image_url(text) or ''
return ''
| """Images."""
from scrapely.extractors import extract_image_url
from slybot.fieldtypes.url import UrlFieldTypeProcessor
class ImagesFieldTypeProcessor(UrlFieldTypeProcessor):
name = 'image'
description = 'extracts image URLs'
def extract(self, text):
if text is not None:
return extract_image_url(text) or ''
return ''
| none | 1 | 2.949233 | 3 | |
test/uw_spot/spot_post.py | sbutler/spotseeker_server | 0 | 6624268 | <filename>test/uw_spot/spot_post.py<gh_stars>0
""" Copyright 2012, 2013 UW Information Technology, University of Washington
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 django.test import TransactionTestCase
from django.conf import settings
from django.test.client import Client
from spotseeker_server.models import Spot
import simplejson as json
import random
from django.test.utils import override_settings
from mock import patch
from django.core import cache
from spotseeker_server import models
@override_settings(SPOTSEEKER_AUTH_MODULE='spotseeker_server.auth.all_ok')
@override_settings(SPOTSEEKER_SPOT_FORM='spotseeker_server.org_forms.uw_spot.UWSpotForm')
@override_settings(SPOTSEEKER_SPOTEXTENDEDINFO_FORM='spotseeker_server.org_forms.uw_spot.UWSpotExtendedInfoForm')
@override_settings(SPOTSEEKER_AUTH_ADMINS=('demo_user',))
class UWSpotPOSTTest(TransactionTestCase):
""" Tests creating a new Spot via POST.
"""
def test_valid_json(self):
dummy_cache = cache.get_cache('django.core.cache.backends.dummy.DummyCache')
with patch.object(models, 'cache', dummy_cache):
c = Client()
new_name = "testing POST name: {0}".format(random.random())
new_capacity = 10
json_string = '{"name":"%s","capacity":"%s","location":{"latitude": 55, "longitude": -30},"extended_info":{"has_whiteboards":"true","has_outlets":"true","manager":"Bob","organization":"UW"}}' % (new_name, new_capacity)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 201, "Gives a Created response to creating a Spot")
self.assertIn("Location", response, "The response has a location header")
self.spot = Spot.objects.get(name=new_name)
self.assertEquals(response["Location"], "http://testserver" + self.spot.rest_url(), "The uri for the new spot is correct")
get_response = c.get(response["Location"])
self.assertEquals(get_response.status_code, 200, "OK in response to GETing the new spot")
spot_json = json.loads(get_response.content)
self.assertEquals(spot_json["name"], new_name, "The right name was stored")
self.assertEquals(spot_json["capacity"], new_capacity, "The right capacity was stored")
def test_non_json(self):
dummy_cache = cache.get_cache('django.core.cache.backends.dummy.DummyCache')
with patch.object(models, 'cache', dummy_cache):
c = Client()
response = c.post('/api/v1/spot/', 'just a string', content_type="application/json", follow=False)
self.assertEquals(response.status_code, 400)
def test_invalid_json(self):
dummy_cache = cache.get_cache('django.core.cache.backends.dummy.DummyCache')
with patch.object(models, 'cache', dummy_cache):
c = Client()
response = c.post('/api/v1/spot/', '{}', content_type="application/json", follow=False)
self.assertEquals(response.status_code, 400)
def test_uw_field_has_whiteboards(self):
dummy_cache = cache.get_cache('django.core.cache.backends.dummy.DummyCache')
with patch.object(models, 'cache', dummy_cache):
c = Client()
new_name = "testing POST name: {0}".format(random.random())
new_capacity = 10
whiteboards = 12
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_whiteboards":"%s","has_outlets":"true","manager":"John","organization":"UW"}}' % (new_name, new_capacity, whiteboards)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 400, "Not created because has_whiteboards field did not pass validation")
whiteboards = 'true'
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_whiteboards":"%s","has_outlets":"true","manager":"John","organization":"UW"}}' % (new_name, new_capacity, whiteboards)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 201, "Gives a Created response to creating a Spot")
def test_uw_field_has_outlets(self):
dummy_cache = cache.get_cache('django.core.cache.backends.dummy.DummyCache')
with patch.object(models, 'cache', dummy_cache):
c = Client()
new_name = "testing POST name: {0}".format(random.random())
new_capacity = 10
outlets = 12
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_whiteboards":"false","manager":"Harry","organization":"UW"}}' % (new_name, new_capacity)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 400, "Not created because has_outlets was not included")
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_whiteboards":"false","has_outlets":"%s","manager":"Harry","organization":"UW"}}' % (new_name, new_capacity, outlets)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 400, "Not created because has_outlets field did not pass validation")
outlets = 'true'
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_whiteboards":"true","has_outlets":"%s","manager":"Harry","organization":"UW"}}' % (new_name, new_capacity, outlets)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 201, "Gives a Created response to creating a Spot")
def test_uw_field_has_printing(self):
dummy_cache = cache.get_cache('django.core.cache.backends.dummy.DummyCache')
with patch.object(models, 'cache', dummy_cache):
c = Client()
new_name = "testing POST name: {0}".format(random.random())
new_capacity = 10
printer = 12
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_outlets":"true","has_printing":"%s","manager":"Gary","organization":"UW"}}' % (new_name, new_capacity, printer)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 400, "Not created because has_printing field did not pass validation")
printer = 'true'
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_outlets":"true","has_printing":"%s","manager":"Gary","organization":"UW"}}' % (new_name, new_capacity, printer)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 201, "Gives a Created response to creating a Spot")
def test_uw_field_has_scanner(self):
dummy_cache = cache.get_cache('django.core.cache.backends.dummy.DummyCache')
with patch.object(models, 'cache', dummy_cache):
c = Client()
new_name = "testing POST name: {0}".format(random.random())
new_capacity = 10
scanner = 'There are none'
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_outlets":"true","has_scanner":"%s","manager":"Sally","organization":"UW"}}' % (new_name, new_capacity, scanner)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 400, "Not created because has_scanner field did not pass validation")
scanner = 'true'
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_outlets":"true","has_scanner":"%s","manager":"Sally","organization":"UW"}}' % (new_name, new_capacity, scanner)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 201, "Gives a Created response to creating a Spot")
def test_uw_field_has_displays(self):
dummy_cache = cache.get_cache('django.core.cache.backends.dummy.DummyCache')
with patch.object(models, 'cache', dummy_cache):
c = Client()
new_name = "testing POST name: {0}".format(random.random())
new_capacity = 10
has_displays = 'There are none'
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_outlets":"true","has_displays":"%s","manager":"Fred","organization":"UW"}}' % (new_name, new_capacity, has_displays)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 400, "Not created because has_displays field did not pass validation")
has_displays = 'true'
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_outlets":"true","has_displays":"%s","manager":"Fred","organization":"UW"}}' % (new_name, new_capacity, has_displays)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 201, "Gives a Created response to creating a Spot")
def test_uw_field_has_projector(self):
dummy_cache = cache.get_cache('django.core.cache.backends.dummy.DummyCache')
with patch.object(models, 'cache', dummy_cache):
c = Client()
new_name = "testing POST name: {0}".format(random.random())
new_capacity = 10
has_projector = 'There are none'
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_outlets":"true","has_projector":"%s","manager":"George","organization":"UW"}}' % (new_name, new_capacity, has_projector)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 400, "Not created because has_projector field did not pass validation")
has_projector = 'true'
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_outlets":"true","has_projector":"%s","manager":"George","organization":"UW"}}' % (new_name, new_capacity, has_projector)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 201, "Gives a Created response to creating a Spot")
def test_uw_field_has_computers(self):
dummy_cache = cache.get_cache('django.core.cache.backends.dummy.DummyCache')
with patch.object(models, 'cache', dummy_cache):
c = Client()
new_name = "testing POST name: {0}".format(random.random())
new_capacity = 10
computers = 'There are none'
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_outlets":"true","has_computers":"%s","manager":"Tina","organization":"UW"}}' % (new_name, new_capacity, computers)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 400, "Not created because has_computers field did not pass validation")
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_outlets":"true","has_computers":"true","manager":"Tina","organization":"UW"}}' % (new_name, new_capacity)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 201, "Gives a Created response to creating a Spot")
def test_uw_field_num_computers(self):
dummy_cache = cache.get_cache('django.core.cache.backends.dummy.DummyCache')
with patch.object(models, 'cache', dummy_cache):
c = Client()
new_name = "testing POST name: {0}".format(random.random())
new_capacity = 10
computers = "invalid_int"
json_string = '{"name":"%s","capacity":"%s","location":{"latitude": 55, "longitude": -30},"extended_info":{"has_outlets":"true","has_computers":"true","num_computers":"%s","manager":"Tina","organization":"UW"}}' % (new_name, new_capacity, computers)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 400, "Make sure not to create the spot because num_computers field did not pass validation")
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_outlets":"true","has_computers":"true","num_computers":"15","manager":"Tina","organization":"UW"}}' % (new_name, new_capacity)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 201, "Gives a Created response to creating a Spot")
def test_uw_field_has_natural_light(self):
dummy_cache = cache.get_cache('django.core.cache.backends.dummy.DummyCache')
with patch.object(models, 'cache', dummy_cache):
c = Client()
new_name = "testing POST name: {0}".format(random.random())
new_capacity = 10
has_natural_light = 'Nope!'
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_outlets":"true","has_natural_light":"%s","manager":"Mary","organization":"UW"}}' % (new_name, new_capacity, has_natural_light)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 400, "Not created because has_natural_light field did not pass validation")
has_natural_light = 'true'
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_outlets":"true","has_natural_light":"%s","manager":"Mary","organization":"UW"}}' % (new_name, new_capacity, has_natural_light)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 201, "Gives a Created response to creating a Spot")
def test_uw_field_noise_level(self):
dummy_cache = cache.get_cache('django.core.cache.backends.dummy.DummyCache')
with patch.object(models, 'cache', dummy_cache):
c = Client()
new_name = "testing POST name: {0}".format(random.random())
new_capacity = 10
noise_level = 'Rock Concert'
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_outlets":"true","noise_level":"%s","manager":"Patty","organization":"UW"}}' % (new_name, new_capacity, noise_level)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 400, "Not created because noise_level field did not pass validation")
noise_level = 'moderate'
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_outlets":"true","noise_level":"%s","manager":"Patty","organization":"UW"}}' % (new_name, new_capacity, noise_level)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 201, "Gives a Created response to creating a Spot")
def test_uw_field_food_nearby(self):
dummy_cache = cache.get_cache('django.core.cache.backends.dummy.DummyCache')
with patch.object(models, 'cache', dummy_cache):
c = Client()
new_name = "testing POST name: {0}".format(random.random())
new_capacity = 10
food_nearby = 'In the area'
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_outlets":"true","food_nearby":"%s","manager":"Kristy","organization":"UW"}}' % (new_name, new_capacity, food_nearby)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 400, "Not created because food_nearby field did not pass validation")
food_nearby = 'building'
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_outlets":"true","food_nearby":"%s","manager":"Kristy","organization":"UW"}}' % (new_name, new_capacity, food_nearby)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 201, "Gives a Created response to creating a Spot")
def test_uw_field_reservable(self):
dummy_cache = cache.get_cache('django.core.cache.backends.dummy.DummyCache')
with patch.object(models, 'cache', dummy_cache):
c = Client()
new_name = "testing POST name: {0}".format(random.random())
new_capacity = 10
reservable = 'You bet'
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_outlets":"true","reservable":"%s","manager":"Patty","organization":"UW"}}' % (new_name, new_capacity, reservable)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 400, "Not created because reservable field did not pass validation")
reservable = 'reservations'
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_outlets":"true","reservable":"%s","manager":"Patty","organization":"UW"}}' % (new_name, new_capacity, reservable)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 201, "Gives a Created response to creating a Spot")
def test_uw_field_location_description(self):
dummy_cache = cache.get_cache('django.core.cache.backends.dummy.DummyCache')
with patch.object(models, 'cache', dummy_cache):
c = Client()
new_name = "testing POST name: {0}".format(random.random())
new_capacity = 10
desc = 'This is a description'
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude":-30},"extended_info":{"has_outlets":"true","location_description":"%s","manager":"Patty","organization":"UW"}}' % (new_name, new_capacity, desc)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 201, "Gives a Created response to creating a Spot")
spot = Spot.objects.get(name=new_name)
spot_desc = spot.spotextendedinfo_set.get(key='location_description').value
self.assertEquals(desc, spot_desc, "The Spot's description matches what was POSTed.")
def test_valid_json_but_invalid_extended_info(self):
dummy_cache = cache.get_cache('django.core.cache.backends.dummy.DummyCache')
with patch.object(models, 'cache', dummy_cache):
c = Client()
new_name = "testing POST name: {0}".format(random.random())
new_capacity = 10
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude":-30},"extended_info":{"has_outlets":"true","manager":"Patty","organization":"UW"}}' % (new_name, new_capacity)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 201, "Gives a Created response to creating a Spot")
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
bad_json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_whiteboards":"true","has_outlets":"wub wub wub wu wu wuhhhh WUB WUB WUBBBBUB","manager":"Sam","organization":"UW"}}' % (new_name, new_capacity)
response = c.post('/api/v1/spot/', bad_json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 400, "Doesn't add spot info with invalid extended info")
self.assertEquals(Spot.objects.count(), 2, "Doesn't POST spots with invalid extended info")
def test_valid_json_but_no_extended_info(self):
dummy_cache = cache.get_cache('django.core.cache.backends.dummy.DummyCache')
with patch.object(models, 'cache', dummy_cache):
c = Client()
new_name = "testing POST name: {0}".format(random.random())
new_capacity = 10
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude":-30}}' % (new_name, new_capacity)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
error_message = json.loads(response.content)['error']
self.assertEquals(error_message, "[u'UWSpot must have extended info']", "Doesn't add spot info with invalid extended info")
| <filename>test/uw_spot/spot_post.py<gh_stars>0
""" Copyright 2012, 2013 UW Information Technology, University of Washington
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 django.test import TransactionTestCase
from django.conf import settings
from django.test.client import Client
from spotseeker_server.models import Spot
import simplejson as json
import random
from django.test.utils import override_settings
from mock import patch
from django.core import cache
from spotseeker_server import models
@override_settings(SPOTSEEKER_AUTH_MODULE='spotseeker_server.auth.all_ok')
@override_settings(SPOTSEEKER_SPOT_FORM='spotseeker_server.org_forms.uw_spot.UWSpotForm')
@override_settings(SPOTSEEKER_SPOTEXTENDEDINFO_FORM='spotseeker_server.org_forms.uw_spot.UWSpotExtendedInfoForm')
@override_settings(SPOTSEEKER_AUTH_ADMINS=('demo_user',))
class UWSpotPOSTTest(TransactionTestCase):
""" Tests creating a new Spot via POST.
"""
def test_valid_json(self):
dummy_cache = cache.get_cache('django.core.cache.backends.dummy.DummyCache')
with patch.object(models, 'cache', dummy_cache):
c = Client()
new_name = "testing POST name: {0}".format(random.random())
new_capacity = 10
json_string = '{"name":"%s","capacity":"%s","location":{"latitude": 55, "longitude": -30},"extended_info":{"has_whiteboards":"true","has_outlets":"true","manager":"Bob","organization":"UW"}}' % (new_name, new_capacity)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 201, "Gives a Created response to creating a Spot")
self.assertIn("Location", response, "The response has a location header")
self.spot = Spot.objects.get(name=new_name)
self.assertEquals(response["Location"], "http://testserver" + self.spot.rest_url(), "The uri for the new spot is correct")
get_response = c.get(response["Location"])
self.assertEquals(get_response.status_code, 200, "OK in response to GETing the new spot")
spot_json = json.loads(get_response.content)
self.assertEquals(spot_json["name"], new_name, "The right name was stored")
self.assertEquals(spot_json["capacity"], new_capacity, "The right capacity was stored")
def test_non_json(self):
dummy_cache = cache.get_cache('django.core.cache.backends.dummy.DummyCache')
with patch.object(models, 'cache', dummy_cache):
c = Client()
response = c.post('/api/v1/spot/', 'just a string', content_type="application/json", follow=False)
self.assertEquals(response.status_code, 400)
def test_invalid_json(self):
dummy_cache = cache.get_cache('django.core.cache.backends.dummy.DummyCache')
with patch.object(models, 'cache', dummy_cache):
c = Client()
response = c.post('/api/v1/spot/', '{}', content_type="application/json", follow=False)
self.assertEquals(response.status_code, 400)
def test_uw_field_has_whiteboards(self):
dummy_cache = cache.get_cache('django.core.cache.backends.dummy.DummyCache')
with patch.object(models, 'cache', dummy_cache):
c = Client()
new_name = "testing POST name: {0}".format(random.random())
new_capacity = 10
whiteboards = 12
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_whiteboards":"%s","has_outlets":"true","manager":"John","organization":"UW"}}' % (new_name, new_capacity, whiteboards)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 400, "Not created because has_whiteboards field did not pass validation")
whiteboards = 'true'
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_whiteboards":"%s","has_outlets":"true","manager":"John","organization":"UW"}}' % (new_name, new_capacity, whiteboards)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 201, "Gives a Created response to creating a Spot")
def test_uw_field_has_outlets(self):
dummy_cache = cache.get_cache('django.core.cache.backends.dummy.DummyCache')
with patch.object(models, 'cache', dummy_cache):
c = Client()
new_name = "testing POST name: {0}".format(random.random())
new_capacity = 10
outlets = 12
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_whiteboards":"false","manager":"Harry","organization":"UW"}}' % (new_name, new_capacity)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 400, "Not created because has_outlets was not included")
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_whiteboards":"false","has_outlets":"%s","manager":"Harry","organization":"UW"}}' % (new_name, new_capacity, outlets)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 400, "Not created because has_outlets field did not pass validation")
outlets = 'true'
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_whiteboards":"true","has_outlets":"%s","manager":"Harry","organization":"UW"}}' % (new_name, new_capacity, outlets)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 201, "Gives a Created response to creating a Spot")
def test_uw_field_has_printing(self):
dummy_cache = cache.get_cache('django.core.cache.backends.dummy.DummyCache')
with patch.object(models, 'cache', dummy_cache):
c = Client()
new_name = "testing POST name: {0}".format(random.random())
new_capacity = 10
printer = 12
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_outlets":"true","has_printing":"%s","manager":"Gary","organization":"UW"}}' % (new_name, new_capacity, printer)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 400, "Not created because has_printing field did not pass validation")
printer = 'true'
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_outlets":"true","has_printing":"%s","manager":"Gary","organization":"UW"}}' % (new_name, new_capacity, printer)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 201, "Gives a Created response to creating a Spot")
def test_uw_field_has_scanner(self):
dummy_cache = cache.get_cache('django.core.cache.backends.dummy.DummyCache')
with patch.object(models, 'cache', dummy_cache):
c = Client()
new_name = "testing POST name: {0}".format(random.random())
new_capacity = 10
scanner = 'There are none'
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_outlets":"true","has_scanner":"%s","manager":"Sally","organization":"UW"}}' % (new_name, new_capacity, scanner)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 400, "Not created because has_scanner field did not pass validation")
scanner = 'true'
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_outlets":"true","has_scanner":"%s","manager":"Sally","organization":"UW"}}' % (new_name, new_capacity, scanner)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 201, "Gives a Created response to creating a Spot")
def test_uw_field_has_displays(self):
dummy_cache = cache.get_cache('django.core.cache.backends.dummy.DummyCache')
with patch.object(models, 'cache', dummy_cache):
c = Client()
new_name = "testing POST name: {0}".format(random.random())
new_capacity = 10
has_displays = 'There are none'
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_outlets":"true","has_displays":"%s","manager":"Fred","organization":"UW"}}' % (new_name, new_capacity, has_displays)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 400, "Not created because has_displays field did not pass validation")
has_displays = 'true'
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_outlets":"true","has_displays":"%s","manager":"Fred","organization":"UW"}}' % (new_name, new_capacity, has_displays)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 201, "Gives a Created response to creating a Spot")
def test_uw_field_has_projector(self):
dummy_cache = cache.get_cache('django.core.cache.backends.dummy.DummyCache')
with patch.object(models, 'cache', dummy_cache):
c = Client()
new_name = "testing POST name: {0}".format(random.random())
new_capacity = 10
has_projector = 'There are none'
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_outlets":"true","has_projector":"%s","manager":"George","organization":"UW"}}' % (new_name, new_capacity, has_projector)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 400, "Not created because has_projector field did not pass validation")
has_projector = 'true'
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_outlets":"true","has_projector":"%s","manager":"George","organization":"UW"}}' % (new_name, new_capacity, has_projector)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 201, "Gives a Created response to creating a Spot")
def test_uw_field_has_computers(self):
dummy_cache = cache.get_cache('django.core.cache.backends.dummy.DummyCache')
with patch.object(models, 'cache', dummy_cache):
c = Client()
new_name = "testing POST name: {0}".format(random.random())
new_capacity = 10
computers = 'There are none'
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_outlets":"true","has_computers":"%s","manager":"Tina","organization":"UW"}}' % (new_name, new_capacity, computers)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 400, "Not created because has_computers field did not pass validation")
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_outlets":"true","has_computers":"true","manager":"Tina","organization":"UW"}}' % (new_name, new_capacity)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 201, "Gives a Created response to creating a Spot")
def test_uw_field_num_computers(self):
dummy_cache = cache.get_cache('django.core.cache.backends.dummy.DummyCache')
with patch.object(models, 'cache', dummy_cache):
c = Client()
new_name = "testing POST name: {0}".format(random.random())
new_capacity = 10
computers = "invalid_int"
json_string = '{"name":"%s","capacity":"%s","location":{"latitude": 55, "longitude": -30},"extended_info":{"has_outlets":"true","has_computers":"true","num_computers":"%s","manager":"Tina","organization":"UW"}}' % (new_name, new_capacity, computers)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 400, "Make sure not to create the spot because num_computers field did not pass validation")
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_outlets":"true","has_computers":"true","num_computers":"15","manager":"Tina","organization":"UW"}}' % (new_name, new_capacity)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 201, "Gives a Created response to creating a Spot")
def test_uw_field_has_natural_light(self):
dummy_cache = cache.get_cache('django.core.cache.backends.dummy.DummyCache')
with patch.object(models, 'cache', dummy_cache):
c = Client()
new_name = "testing POST name: {0}".format(random.random())
new_capacity = 10
has_natural_light = 'Nope!'
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_outlets":"true","has_natural_light":"%s","manager":"Mary","organization":"UW"}}' % (new_name, new_capacity, has_natural_light)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 400, "Not created because has_natural_light field did not pass validation")
has_natural_light = 'true'
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_outlets":"true","has_natural_light":"%s","manager":"Mary","organization":"UW"}}' % (new_name, new_capacity, has_natural_light)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 201, "Gives a Created response to creating a Spot")
def test_uw_field_noise_level(self):
dummy_cache = cache.get_cache('django.core.cache.backends.dummy.DummyCache')
with patch.object(models, 'cache', dummy_cache):
c = Client()
new_name = "testing POST name: {0}".format(random.random())
new_capacity = 10
noise_level = 'Rock Concert'
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_outlets":"true","noise_level":"%s","manager":"Patty","organization":"UW"}}' % (new_name, new_capacity, noise_level)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 400, "Not created because noise_level field did not pass validation")
noise_level = 'moderate'
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_outlets":"true","noise_level":"%s","manager":"Patty","organization":"UW"}}' % (new_name, new_capacity, noise_level)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 201, "Gives a Created response to creating a Spot")
def test_uw_field_food_nearby(self):
dummy_cache = cache.get_cache('django.core.cache.backends.dummy.DummyCache')
with patch.object(models, 'cache', dummy_cache):
c = Client()
new_name = "testing POST name: {0}".format(random.random())
new_capacity = 10
food_nearby = 'In the area'
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_outlets":"true","food_nearby":"%s","manager":"Kristy","organization":"UW"}}' % (new_name, new_capacity, food_nearby)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 400, "Not created because food_nearby field did not pass validation")
food_nearby = 'building'
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_outlets":"true","food_nearby":"%s","manager":"Kristy","organization":"UW"}}' % (new_name, new_capacity, food_nearby)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 201, "Gives a Created response to creating a Spot")
def test_uw_field_reservable(self):
dummy_cache = cache.get_cache('django.core.cache.backends.dummy.DummyCache')
with patch.object(models, 'cache', dummy_cache):
c = Client()
new_name = "testing POST name: {0}".format(random.random())
new_capacity = 10
reservable = 'You bet'
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_outlets":"true","reservable":"%s","manager":"Patty","organization":"UW"}}' % (new_name, new_capacity, reservable)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 400, "Not created because reservable field did not pass validation")
reservable = 'reservations'
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_outlets":"true","reservable":"%s","manager":"Patty","organization":"UW"}}' % (new_name, new_capacity, reservable)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 201, "Gives a Created response to creating a Spot")
def test_uw_field_location_description(self):
dummy_cache = cache.get_cache('django.core.cache.backends.dummy.DummyCache')
with patch.object(models, 'cache', dummy_cache):
c = Client()
new_name = "testing POST name: {0}".format(random.random())
new_capacity = 10
desc = 'This is a description'
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude":-30},"extended_info":{"has_outlets":"true","location_description":"%s","manager":"Patty","organization":"UW"}}' % (new_name, new_capacity, desc)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 201, "Gives a Created response to creating a Spot")
spot = Spot.objects.get(name=new_name)
spot_desc = spot.spotextendedinfo_set.get(key='location_description').value
self.assertEquals(desc, spot_desc, "The Spot's description matches what was POSTed.")
def test_valid_json_but_invalid_extended_info(self):
dummy_cache = cache.get_cache('django.core.cache.backends.dummy.DummyCache')
with patch.object(models, 'cache', dummy_cache):
c = Client()
new_name = "testing POST name: {0}".format(random.random())
new_capacity = 10
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude":-30},"extended_info":{"has_outlets":"true","manager":"Patty","organization":"UW"}}' % (new_name, new_capacity)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 201, "Gives a Created response to creating a Spot")
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
bad_json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude": -30},"extended_info":{"has_whiteboards":"true","has_outlets":"wub wub wub wu wu wuhhhh WUB WUB WUBBBBUB","manager":"Sam","organization":"UW"}}' % (new_name, new_capacity)
response = c.post('/api/v1/spot/', bad_json_string, content_type="application/json", follow=False)
self.assertEquals(response.status_code, 400, "Doesn't add spot info with invalid extended info")
self.assertEquals(Spot.objects.count(), 2, "Doesn't POST spots with invalid extended info")
def test_valid_json_but_no_extended_info(self):
dummy_cache = cache.get_cache('django.core.cache.backends.dummy.DummyCache')
with patch.object(models, 'cache', dummy_cache):
c = Client()
new_name = "testing POST name: {0}".format(random.random())
new_capacity = 10
json_string = '{"name":"%s","capacity":"%s","location": {"latitude": 55, "longitude":-30}}' % (new_name, new_capacity)
response = c.post('/api/v1/spot/', json_string, content_type="application/json", follow=False)
error_message = json.loads(response.content)['error']
self.assertEquals(error_message, "[u'UWSpot must have extended info']", "Doesn't add spot info with invalid extended info")
| en | 0.841316 | Copyright 2012, 2013 UW Information Technology, University of Washington 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 creating a new Spot via POST. | 1.961097 | 2 |
version_2.0/help_scripts/bot_logic/extension_functions_compiled.py | Isak-Landin/AlienWorldsBot | 0 | 6624269 | <filename>version_2.0/help_scripts/bot_logic/extension_functions_compiled.py<gh_stars>0
from selenium import webdriver
import time
import random
import json
from help_scripts import selenium_operator as sop
import traceback
from help_scripts import bot_actions
from help_scripts import get_proxies
from pathlib import Path
from selenium.webdriver.common.keys import Keys
def setup_extension(driver, window_id):
driver = driver
region = (window_id.topleft[0], window_id.topleft[1], window_id.width, window_id.height)
parent_handle = driver.window_handles[0]
driver = download_extension(driver=driver, window_id=window_id, region=region, parent_handle=parent_handle)
configure_extension(driver=driver, window_id=window_id)
driver = activate_profile(driver=driver)
return driver
def download_extension(driver, window_id, region, parent_handle):
driver.get('https://chrome.google.com/webstore/detail/proxy-switchyomega/padekgcemlokbadohgkifijomclgjgif')
accept_cookies, succeeded = sop.find_object_XPATH(
driver=driver,
time_to_wait=25,
_xpath='/html/body/c-wiz/div/div/div/div[2]/div[1]/div[4]/form/div/div/button'
)
if succeeded is False:
print('Failed to find accept_cookies')
print(traceback.print_exc())
exit()
accept_cookies.click()
add_extension, succeeded = sop.find_object_XPATH(
driver=driver,
time_to_wait=7,
_xpath='/html/body/div[3]/div[2]/div/div/div[2]/div[2]/div'
)
time.sleep(2)
if succeeded is False:
add_extension, succeeded = sop.find_object_XPATH(
driver=driver,
time_to_wait=7,
_xpath='/html/body/div[5]/div[2]/div/div/div[2]/div[2]/div'
)
# /html/body/div[5]/div[2]/div/div/div[2]/div[2]/div
if succeeded is False:
print('Failed to find add_extension')
print(traceback.print_exc())
exit()
add_extension.click()
window_id.minimize()
time.sleep(0.2)
window_id.restore()
add_extension_region = bot_actions.VisualActions.find_image(
image=str(Path().resolve()) + r'\alienworlds_program_data\images\add_extension.png',
region=region,
confidence=0.85
)
start_time = time.time()
while add_extension_region is None:
add_extension_region = bot_actions.VisualActions.find_image(
image=str(Path().resolve()) + r'\alienworlds_program_data\images\add_extension.png',
region=region,
confidence=0.85
)
if time.time() - start_time > 12:
exit('SHUTDOWN due to waiting too long for add_extension.png')
add_extension_region_center = bot_actions.VisualActions.get_center(add_extension_region)
bot_actions.VisualActions.move_to_click(coordinates=add_extension_region_center, duration=0.15)
while len(driver.window_handles) < 2:
time.sleep(0.2)
print('Looking for new handle')
for handle in driver.window_handles:
if handle != parent_handle:
driver.close()
parent_handle = handle
driver.switch_to.window(parent_handle)
return driver
def configure_extension(driver, window_id):
skip_guide, succeeded = sop.find_object_XPATH(
driver=driver,
time_to_wait=15,
_xpath='/html/body/div[4]/div/div/div[3]/button[1]'
)
if succeeded is False:
print('Failed to find skip_guide')
exit('Failed to find skip_guide')
sop.click_object(skip_guide)
proxy_profile, succeeded = sop.find_object_XPATH(
driver=driver,
time_to_wait=15,
_xpath='/html/body/div[1]/header/nav/li[7]/a'
)
if succeeded is False:
print('Failed to find proxy_profile')
exit('Failed to find proxy_profile')
sop.click_object(proxy_profile)
server_ip, succeeded = sop.find_object_XPATH(
driver=driver,
time_to_wait=10,
_xpath='/html/body/div[1]/main/div[2]/div/section[1]/div/table/tbody[1]/tr[1]/td[3]/input'
)
if succeeded is False:
print('Failed to find server_ip')
exit('Failed to find server_ip')
server_port, succeeded = sop.find_object_XPATH(
driver=driver,
time_to_wait=15,
_xpath='/html/body/div[1]/main/div[2]/div/section[1]/div/table/tbody[1]/tr[1]/td[4]/input'
)
if succeeded is False:
print('Failed to find server_port')
exit('Failed to find server_port')
proxies = get_proxies.get_ten_and_check_online()
random_index = random.randint(0, len(proxies) - 1)
proxy_port = proxies.pop(random_index)[0]
proxy = proxy_port.split(':')[0]
port = proxy_port.split(':')[1]
server_ip.clear()
server_port.clear()
server_ip.send_keys(proxy)
server_port.send_keys(port)
time.sleep(2)
apply_changes, succeeded = sop.find_object_XPATH(
driver=driver,
time_to_wait=10,
_xpath='/html/body/div[1]/header/nav/li[12]/a'
)
if succeeded is False:
print('Failed to find apply_changes')
exit('Failed to find apply_changes')
sop.click_object(apply_changes)
def activate_profile(driver):
time.sleep(3)
driver.get('chrome-extension://padekgcemlokbadohgkifijomclgjgif/popup/index.html')
old_handle = driver.window_handles[0]
time.sleep(0.15)
driver.execute_script('''window.open("http://www.blankwebsite.com/", "_blank");''')
time.sleep(1)
new_handle = None
for handle in driver.window_handles:
if handle != old_handle:
new_handle = handle
break
time.sleep(2)
proxy_profile, succeeded = sop.find_object_XPATH(
driver=driver,
time_to_wait=10,
_xpath='//*[@id="js-profile-1"]'
)
if succeeded is False:
print('Failed to find your proxy_profile, please contact us or try again')
exit('Failed to find your proxy_profile, please contact us or try again')
sop.click_object(proxy_profile)
time.sleep(2)
driver.switch_to.window(new_handle)
return driver
| <filename>version_2.0/help_scripts/bot_logic/extension_functions_compiled.py<gh_stars>0
from selenium import webdriver
import time
import random
import json
from help_scripts import selenium_operator as sop
import traceback
from help_scripts import bot_actions
from help_scripts import get_proxies
from pathlib import Path
from selenium.webdriver.common.keys import Keys
def setup_extension(driver, window_id):
driver = driver
region = (window_id.topleft[0], window_id.topleft[1], window_id.width, window_id.height)
parent_handle = driver.window_handles[0]
driver = download_extension(driver=driver, window_id=window_id, region=region, parent_handle=parent_handle)
configure_extension(driver=driver, window_id=window_id)
driver = activate_profile(driver=driver)
return driver
def download_extension(driver, window_id, region, parent_handle):
driver.get('https://chrome.google.com/webstore/detail/proxy-switchyomega/padekgcemlokbadohgkifijomclgjgif')
accept_cookies, succeeded = sop.find_object_XPATH(
driver=driver,
time_to_wait=25,
_xpath='/html/body/c-wiz/div/div/div/div[2]/div[1]/div[4]/form/div/div/button'
)
if succeeded is False:
print('Failed to find accept_cookies')
print(traceback.print_exc())
exit()
accept_cookies.click()
add_extension, succeeded = sop.find_object_XPATH(
driver=driver,
time_to_wait=7,
_xpath='/html/body/div[3]/div[2]/div/div/div[2]/div[2]/div'
)
time.sleep(2)
if succeeded is False:
add_extension, succeeded = sop.find_object_XPATH(
driver=driver,
time_to_wait=7,
_xpath='/html/body/div[5]/div[2]/div/div/div[2]/div[2]/div'
)
# /html/body/div[5]/div[2]/div/div/div[2]/div[2]/div
if succeeded is False:
print('Failed to find add_extension')
print(traceback.print_exc())
exit()
add_extension.click()
window_id.minimize()
time.sleep(0.2)
window_id.restore()
add_extension_region = bot_actions.VisualActions.find_image(
image=str(Path().resolve()) + r'\alienworlds_program_data\images\add_extension.png',
region=region,
confidence=0.85
)
start_time = time.time()
while add_extension_region is None:
add_extension_region = bot_actions.VisualActions.find_image(
image=str(Path().resolve()) + r'\alienworlds_program_data\images\add_extension.png',
region=region,
confidence=0.85
)
if time.time() - start_time > 12:
exit('SHUTDOWN due to waiting too long for add_extension.png')
add_extension_region_center = bot_actions.VisualActions.get_center(add_extension_region)
bot_actions.VisualActions.move_to_click(coordinates=add_extension_region_center, duration=0.15)
while len(driver.window_handles) < 2:
time.sleep(0.2)
print('Looking for new handle')
for handle in driver.window_handles:
if handle != parent_handle:
driver.close()
parent_handle = handle
driver.switch_to.window(parent_handle)
return driver
def configure_extension(driver, window_id):
skip_guide, succeeded = sop.find_object_XPATH(
driver=driver,
time_to_wait=15,
_xpath='/html/body/div[4]/div/div/div[3]/button[1]'
)
if succeeded is False:
print('Failed to find skip_guide')
exit('Failed to find skip_guide')
sop.click_object(skip_guide)
proxy_profile, succeeded = sop.find_object_XPATH(
driver=driver,
time_to_wait=15,
_xpath='/html/body/div[1]/header/nav/li[7]/a'
)
if succeeded is False:
print('Failed to find proxy_profile')
exit('Failed to find proxy_profile')
sop.click_object(proxy_profile)
server_ip, succeeded = sop.find_object_XPATH(
driver=driver,
time_to_wait=10,
_xpath='/html/body/div[1]/main/div[2]/div/section[1]/div/table/tbody[1]/tr[1]/td[3]/input'
)
if succeeded is False:
print('Failed to find server_ip')
exit('Failed to find server_ip')
server_port, succeeded = sop.find_object_XPATH(
driver=driver,
time_to_wait=15,
_xpath='/html/body/div[1]/main/div[2]/div/section[1]/div/table/tbody[1]/tr[1]/td[4]/input'
)
if succeeded is False:
print('Failed to find server_port')
exit('Failed to find server_port')
proxies = get_proxies.get_ten_and_check_online()
random_index = random.randint(0, len(proxies) - 1)
proxy_port = proxies.pop(random_index)[0]
proxy = proxy_port.split(':')[0]
port = proxy_port.split(':')[1]
server_ip.clear()
server_port.clear()
server_ip.send_keys(proxy)
server_port.send_keys(port)
time.sleep(2)
apply_changes, succeeded = sop.find_object_XPATH(
driver=driver,
time_to_wait=10,
_xpath='/html/body/div[1]/header/nav/li[12]/a'
)
if succeeded is False:
print('Failed to find apply_changes')
exit('Failed to find apply_changes')
sop.click_object(apply_changes)
def activate_profile(driver):
time.sleep(3)
driver.get('chrome-extension://padekgcemlokbadohgkifijomclgjgif/popup/index.html')
old_handle = driver.window_handles[0]
time.sleep(0.15)
driver.execute_script('''window.open("http://www.blankwebsite.com/", "_blank");''')
time.sleep(1)
new_handle = None
for handle in driver.window_handles:
if handle != old_handle:
new_handle = handle
break
time.sleep(2)
proxy_profile, succeeded = sop.find_object_XPATH(
driver=driver,
time_to_wait=10,
_xpath='//*[@id="js-profile-1"]'
)
if succeeded is False:
print('Failed to find your proxy_profile, please contact us or try again')
exit('Failed to find your proxy_profile, please contact us or try again')
sop.click_object(proxy_profile)
time.sleep(2)
driver.switch_to.window(new_handle)
return driver
| en | 0.229378 | # /html/body/div[5]/div[2]/div/div/div[2]/div[2]/div window.open("http://www.blankwebsite.com/", "_blank"); | 2.290654 | 2 |
chmap/data/corrections/lbcc/LBCC_create_mu-hist.py | predsci/CHD | 3 | 6624270 | <reponame>predsci/CHD<filename>chmap/data/corrections/lbcc/LBCC_create_mu-hist.py
"""
construct mu-histogram and push to database for any time period
"""
import os
# This can be a computationally intensive process.
# To limit number of threads numpy can spawn:
# os.environ["OMP_NUM_THREADS"] = "4"
import time
import datetime
import numpy as np
from chmap.settings.app import App
import chmap.database.db_classes as db_class
from chmap.database.db_funs import init_db_conn_old, query_euv_images, add_hist, get_method_id, query_hist
import chmap.utilities.datatypes.datatypes as psi_d_types
###### ------ PARAMETERS TO UPDATE -------- ########
# TIME RANGE
hist_query_time_min = datetime.datetime(2020, 12, 31, 0, 0, 0)
hist_query_time_max = datetime.datetime(2021, 1, 1, 0, 0, 0)
# define instruments and wavelengths to include
inst_list = ["AIA", "EUVI-A", "EUVI-B"]
wavelengths = [193, 195]
# define number of bins
n_mu_bins = 18
n_intensity_bins = 200
# declare map and binning parameters
R0 = 1.01
log10 = True
lat_band = [- np.pi / 64., np.pi / 64.]
# recover local filesystem paths
raw_data_dir = App.RAW_DATA_HOME
hdf_data_dir = App.PROCESSED_DATA_HOME
# designate which database to connect to
use_db = "mysql-Q" # 'sqlite' Use local sqlite file-based db
# 'mysql-Q' Use the remote MySQL database on Q
user = "turtle" # only needed for remote databases.
password = "" # See <PASSWORD> for setting-up an encrypted password. In this case leave password="", and
# init_db_conn_old() will automatically find and use your saved password. Otherwise, enter your MySQL password here.
# setup local database paths (only used for use_db='sqlite')
database_dir = App.DATABASE_HOME
sqlite_filename = App.DATABASE_FNAME
# ------------ NO NEED TO UPDATE ANYTHING BELOW ------------- #
# setup database connection
if use_db == 'sqlite':
# setup database connection to local sqlite file
sqlite_path = os.path.join(database_dir, sqlite_filename)
if os.path.exists(sqlite_path):
os.remove(sqlite_path)
print("\nPrevious file ", sqlite_filename, " deleted.\n")
db_session = init_db_conn_old(db_name=use_db, chd_base=db_class.Base, sqlite_path=sqlite_path)
elif use_db in ['mysql-Q', 'mysql-Q_test']:
# setup database connection to MySQL database on Q
db_session = init_db_conn_old(db_name=use_db, chd_base=db_class.Base, user=user, password=password)
# start time
start_time_tot = time.time()
# creates mu bin & intensity bin arrays
mu_bin_edges = np.linspace(0.1, 1.0, n_mu_bins + 1, dtype='float')
image_intensity_bin_edges = np.linspace(0, 5, num=n_intensity_bins + 1, dtype='float')
# create LBC method
meth_name = 'LBCC'
meth_desc = 'LBCC Theoretic Fit Method'
method_id = get_method_id(db_session, meth_name, meth_desc, var_names=None, var_descs=None, create=True)
# loop over instrument
for instrument in inst_list:
# query EUV images
query_instrument = [instrument, ]
query_pd_all = query_euv_images(db_session=db_session, time_min=hist_query_time_min,
time_max=hist_query_time_max, instrument=query_instrument,
wavelength=wavelengths)
# query LBCC histograms
hist_pd = query_hist(db_session, meth_id=method_id[1], n_mu_bins=n_mu_bins, n_intensity_bins=n_intensity_bins,
lat_band=lat_band, time_min=hist_query_time_min, time_max=hist_query_time_max,
instrument=query_instrument, wavelength=wavelengths)
# compare image results to hist results based on image_id
in_index = query_pd_all.data_id.isin(hist_pd.image_id)
# return only images that do not have corresponding histograms
query_pd = query_pd_all[~in_index]
# check that images remain that need histograms
if query_pd.shape[0] == 0:
print("All" + instrument + " images in timeframe already have associated histograms.")
continue
for index, row in query_pd.iterrows():
print("Processing image number", row.data_id, ".")
if row.fname_hdf == "":
print("Warning: Image # " + str(row.data_id) + " does not have an associated hdf file. Skipping")
continue
hdf_path = os.path.join(hdf_data_dir, row.fname_hdf)
# attempt to open and read file
try:
los_temp = psi_d_types.read_los_image(hdf_path)
except:
print("Something went wrong opening: ", hdf_path, ". Skipping")
continue
# add coordinates to los object
los_temp.get_coordinates(R0=R0)
# perform 2D histogram on mu and image intensity
temp_hist = los_temp.mu_hist(image_intensity_bin_edges, mu_bin_edges, lat_band=lat_band, log10=log10)
hist_lbcc = psi_d_types.create_lbcc_hist(hdf_path, row.data_id, method_id[1], mu_bin_edges,
image_intensity_bin_edges, lat_band, temp_hist)
# add this histogram and meta data to database
add_hist(db_session, hist_lbcc)
db_session.close()
end_time_tot = time.time()
print("Histograms have been created and saved to the database.")
print("Total elapsed time for histogram creation: " + str(round(end_time_tot - start_time_tot, 3)) + " seconds.")
| """
construct mu-histogram and push to database for any time period
"""
import os
# This can be a computationally intensive process.
# To limit number of threads numpy can spawn:
# os.environ["OMP_NUM_THREADS"] = "4"
import time
import datetime
import numpy as np
from chmap.settings.app import App
import chmap.database.db_classes as db_class
from chmap.database.db_funs import init_db_conn_old, query_euv_images, add_hist, get_method_id, query_hist
import chmap.utilities.datatypes.datatypes as psi_d_types
###### ------ PARAMETERS TO UPDATE -------- ########
# TIME RANGE
hist_query_time_min = datetime.datetime(2020, 12, 31, 0, 0, 0)
hist_query_time_max = datetime.datetime(2021, 1, 1, 0, 0, 0)
# define instruments and wavelengths to include
inst_list = ["AIA", "EUVI-A", "EUVI-B"]
wavelengths = [193, 195]
# define number of bins
n_mu_bins = 18
n_intensity_bins = 200
# declare map and binning parameters
R0 = 1.01
log10 = True
lat_band = [- np.pi / 64., np.pi / 64.]
# recover local filesystem paths
raw_data_dir = App.RAW_DATA_HOME
hdf_data_dir = App.PROCESSED_DATA_HOME
# designate which database to connect to
use_db = "mysql-Q" # 'sqlite' Use local sqlite file-based db
# 'mysql-Q' Use the remote MySQL database on Q
user = "turtle" # only needed for remote databases.
password = "" # See <PASSWORD> for setting-up an encrypted password. In this case leave password="", and
# init_db_conn_old() will automatically find and use your saved password. Otherwise, enter your MySQL password here.
# setup local database paths (only used for use_db='sqlite')
database_dir = App.DATABASE_HOME
sqlite_filename = App.DATABASE_FNAME
# ------------ NO NEED TO UPDATE ANYTHING BELOW ------------- #
# setup database connection
if use_db == 'sqlite':
# setup database connection to local sqlite file
sqlite_path = os.path.join(database_dir, sqlite_filename)
if os.path.exists(sqlite_path):
os.remove(sqlite_path)
print("\nPrevious file ", sqlite_filename, " deleted.\n")
db_session = init_db_conn_old(db_name=use_db, chd_base=db_class.Base, sqlite_path=sqlite_path)
elif use_db in ['mysql-Q', 'mysql-Q_test']:
# setup database connection to MySQL database on Q
db_session = init_db_conn_old(db_name=use_db, chd_base=db_class.Base, user=user, password=password)
# start time
start_time_tot = time.time()
# creates mu bin & intensity bin arrays
mu_bin_edges = np.linspace(0.1, 1.0, n_mu_bins + 1, dtype='float')
image_intensity_bin_edges = np.linspace(0, 5, num=n_intensity_bins + 1, dtype='float')
# create LBC method
meth_name = 'LBCC'
meth_desc = 'LBCC Theoretic Fit Method'
method_id = get_method_id(db_session, meth_name, meth_desc, var_names=None, var_descs=None, create=True)
# loop over instrument
for instrument in inst_list:
# query EUV images
query_instrument = [instrument, ]
query_pd_all = query_euv_images(db_session=db_session, time_min=hist_query_time_min,
time_max=hist_query_time_max, instrument=query_instrument,
wavelength=wavelengths)
# query LBCC histograms
hist_pd = query_hist(db_session, meth_id=method_id[1], n_mu_bins=n_mu_bins, n_intensity_bins=n_intensity_bins,
lat_band=lat_band, time_min=hist_query_time_min, time_max=hist_query_time_max,
instrument=query_instrument, wavelength=wavelengths)
# compare image results to hist results based on image_id
in_index = query_pd_all.data_id.isin(hist_pd.image_id)
# return only images that do not have corresponding histograms
query_pd = query_pd_all[~in_index]
# check that images remain that need histograms
if query_pd.shape[0] == 0:
print("All" + instrument + " images in timeframe already have associated histograms.")
continue
for index, row in query_pd.iterrows():
print("Processing image number", row.data_id, ".")
if row.fname_hdf == "":
print("Warning: Image # " + str(row.data_id) + " does not have an associated hdf file. Skipping")
continue
hdf_path = os.path.join(hdf_data_dir, row.fname_hdf)
# attempt to open and read file
try:
los_temp = psi_d_types.read_los_image(hdf_path)
except:
print("Something went wrong opening: ", hdf_path, ". Skipping")
continue
# add coordinates to los object
los_temp.get_coordinates(R0=R0)
# perform 2D histogram on mu and image intensity
temp_hist = los_temp.mu_hist(image_intensity_bin_edges, mu_bin_edges, lat_band=lat_band, log10=log10)
hist_lbcc = psi_d_types.create_lbcc_hist(hdf_path, row.data_id, method_id[1], mu_bin_edges,
image_intensity_bin_edges, lat_band, temp_hist)
# add this histogram and meta data to database
add_hist(db_session, hist_lbcc)
db_session.close()
end_time_tot = time.time()
print("Histograms have been created and saved to the database.")
print("Total elapsed time for histogram creation: " + str(round(end_time_tot - start_time_tot, 3)) + " seconds.") | en | 0.717887 | construct mu-histogram and push to database for any time period # This can be a computationally intensive process. # To limit number of threads numpy can spawn: # os.environ["OMP_NUM_THREADS"] = "4" ###### ------ PARAMETERS TO UPDATE -------- ######## # TIME RANGE # define instruments and wavelengths to include # define number of bins # declare map and binning parameters # recover local filesystem paths # designate which database to connect to # 'sqlite' Use local sqlite file-based db # 'mysql-Q' Use the remote MySQL database on Q # only needed for remote databases. # See <PASSWORD> for setting-up an encrypted password. In this case leave password="", and # init_db_conn_old() will automatically find and use your saved password. Otherwise, enter your MySQL password here. # setup local database paths (only used for use_db='sqlite') # ------------ NO NEED TO UPDATE ANYTHING BELOW ------------- # # setup database connection # setup database connection to local sqlite file # setup database connection to MySQL database on Q # start time # creates mu bin & intensity bin arrays # create LBC method # loop over instrument # query EUV images # query LBCC histograms # compare image results to hist results based on image_id # return only images that do not have corresponding histograms # check that images remain that need histograms # " + str(row.data_id) + " does not have an associated hdf file. Skipping") # attempt to open and read file # add coordinates to los object # perform 2D histogram on mu and image intensity # add this histogram and meta data to database | 2.451869 | 2 |
examples/pyScripts/setDesiredEstimator.py | pseudoPixels/bokehBot | 2 | 6624271 | <reponame>pseudoPixels/bokehBot
import iSeaborn as isn
from bokeh.plotting import output_file, save
from numpy import median
tips = isn.load_dataset("tips")
fig = isn.barplot(x="day", y="tip", data=tips, estimator=median)
output_file("setDesiredEstimator.html")
save(fig)
| import iSeaborn as isn
from bokeh.plotting import output_file, save
from numpy import median
tips = isn.load_dataset("tips")
fig = isn.barplot(x="day", y="tip", data=tips, estimator=median)
output_file("setDesiredEstimator.html")
save(fig) | none | 1 | 2.525855 | 3 | |
airflow/modules/tests/proxypool/test_proxypool_validator.py | phuonglvh/DataEngineeringProject | 417 | 6624272 | from unittest.mock import patch
from proxypool import ProxyPoolValidator
from ..fixtures import web_parser, raw_content, proxy_record
@patch("parser.web_parser.WebParser.get_content")
def test_validate_proxy(get_content, raw_content, web_parser, proxy_record):
expected = True
get_content.return_value = raw_content("proxy_list_file.txt")
validator = ProxyPoolValidator("https://google.com", sleep_interval=0)
validator.parser = web_parser
proxy_record = validator.validate_proxy(proxy_record)
result = proxy_record.is_valid
assert result == expected
@patch("parser.web_parser.WebParser.get_content")
def test_invalid_proxy(get_content, raw_content, web_parser, proxy_record):
expected = False
get_content.return_value = None
validator = ProxyPoolValidator("https://google.com", sleep_interval=0)
validator.parser = web_parser
proxy_record = validator.validate_proxy(proxy_record)
result = proxy_record.is_valid
assert result == expected
@patch("parser.web_parser.WebParser.get_content")
def test_unstable_valid_proxy(get_content, raw_content, web_parser, proxy_record):
expected = True
valid_content = raw_content("proxy_list_file.txt")
get_content.side_effect = [valid_content, valid_content, None]
validator = ProxyPoolValidator("https://google.com", sleep_interval=0)
validator.parser = web_parser
proxy_record = validator.validate_proxy(proxy_record)
result = proxy_record.is_valid
assert result == expected
assert round(proxy_record.health, 2) == 0.67
@patch("parser.web_parser.WebParser.get_content")
def test_unstable_invalid_proxy(get_content, raw_content, web_parser, proxy_record):
expected = False
valid_content = raw_content("proxy_list_file.txt")
get_content.side_effect = [None, None, valid_content]
validator = ProxyPoolValidator("https://google.com", sleep_interval=0)
validator.parser = web_parser
proxy_record = validator.validate_proxy(proxy_record)
result = proxy_record.is_valid
assert result == expected
assert round(proxy_record.health, 2) == 0.33
| from unittest.mock import patch
from proxypool import ProxyPoolValidator
from ..fixtures import web_parser, raw_content, proxy_record
@patch("parser.web_parser.WebParser.get_content")
def test_validate_proxy(get_content, raw_content, web_parser, proxy_record):
expected = True
get_content.return_value = raw_content("proxy_list_file.txt")
validator = ProxyPoolValidator("https://google.com", sleep_interval=0)
validator.parser = web_parser
proxy_record = validator.validate_proxy(proxy_record)
result = proxy_record.is_valid
assert result == expected
@patch("parser.web_parser.WebParser.get_content")
def test_invalid_proxy(get_content, raw_content, web_parser, proxy_record):
expected = False
get_content.return_value = None
validator = ProxyPoolValidator("https://google.com", sleep_interval=0)
validator.parser = web_parser
proxy_record = validator.validate_proxy(proxy_record)
result = proxy_record.is_valid
assert result == expected
@patch("parser.web_parser.WebParser.get_content")
def test_unstable_valid_proxy(get_content, raw_content, web_parser, proxy_record):
expected = True
valid_content = raw_content("proxy_list_file.txt")
get_content.side_effect = [valid_content, valid_content, None]
validator = ProxyPoolValidator("https://google.com", sleep_interval=0)
validator.parser = web_parser
proxy_record = validator.validate_proxy(proxy_record)
result = proxy_record.is_valid
assert result == expected
assert round(proxy_record.health, 2) == 0.67
@patch("parser.web_parser.WebParser.get_content")
def test_unstable_invalid_proxy(get_content, raw_content, web_parser, proxy_record):
expected = False
valid_content = raw_content("proxy_list_file.txt")
get_content.side_effect = [None, None, valid_content]
validator = ProxyPoolValidator("https://google.com", sleep_interval=0)
validator.parser = web_parser
proxy_record = validator.validate_proxy(proxy_record)
result = proxy_record.is_valid
assert result == expected
assert round(proxy_record.health, 2) == 0.33
| none | 1 | 2.777025 | 3 | |
examples/local/execute_module.py | wils0ns/saltypie | 1 | 6624273 | import logging
from saltypie import Salt
LOG = logging.getLogger()
logging.basicConfig(level=logging.DEBUG)
def main():
salt = Salt(
url='https://localhost:8000',
username='admin',
passwd='<PASSWORD>',
trust_host=True
)
salt.eauth = 'pam'
ret = salt.local_async(
target='*',
fun='test.arg',
kwargs={'a': 1, 'b': 2},
wait=True
)
print(ret)
main()
| import logging
from saltypie import Salt
LOG = logging.getLogger()
logging.basicConfig(level=logging.DEBUG)
def main():
salt = Salt(
url='https://localhost:8000',
username='admin',
passwd='<PASSWORD>',
trust_host=True
)
salt.eauth = 'pam'
ret = salt.local_async(
target='*',
fun='test.arg',
kwargs={'a': 1, 'b': 2},
wait=True
)
print(ret)
main()
| none | 1 | 2.309823 | 2 | |
app_stanford.py | ilos-vigil/id-pos-tagger | 0 | 6624274 | # source : https://stanfordnlp.github.io/stanfordnlp/pos.html
import stanfordnlp
nlp = stanfordnlp.Pipeline(processors='tokenize,mwt,pos', lang='id')
doc = nlp('Bahasa Indonesia adalah bahasa Melayu yang dijadikan sebagai bahasa resmi Republik Indonesia dan bahasa persatuan bangsa Indonesia. '
'Bahasa Indonesia diresmikan penggunaannya setelah Proklamasi Kemerdekaan Indonesia, tepatnya sehari sesudahnya, bersamaan dengan mulai berlakunya konstitusi. '
'Di Timor Leste, bahasa Indonesia berstatus sebagai bahasa kerja. '
'Kentang (Solanum tuberosum L.) adalah tanaman dari suku Solanaceae yang memiliki umbi batang yang dapat dimakan dan disebut "kentang" pula. '
'Umbi kentang sekarang telah menjadi salah satu makanan pokok penting di Eropa walaupun pada awalnya didatangkan dari Amerika Selatan. '
'Penjelajah Spanyol dan Portugis pertama kali membawa ke Eropa dan mengembangbiakkan tanaman ini.')
print(*[f'(\'{word.text}\', \'{word.pos}\')' for sent in doc.sentences for word in sent.words], sep='\n')
| # source : https://stanfordnlp.github.io/stanfordnlp/pos.html
import stanfordnlp
nlp = stanfordnlp.Pipeline(processors='tokenize,mwt,pos', lang='id')
doc = nlp('Bahasa Indonesia adalah bahasa Melayu yang dijadikan sebagai bahasa resmi Republik Indonesia dan bahasa persatuan bangsa Indonesia. '
'Bahasa Indonesia diresmikan penggunaannya setelah Proklamasi Kemerdekaan Indonesia, tepatnya sehari sesudahnya, bersamaan dengan mulai berlakunya konstitusi. '
'Di Timor Leste, bahasa Indonesia berstatus sebagai bahasa kerja. '
'Kentang (Solanum tuberosum L.) adalah tanaman dari suku Solanaceae yang memiliki umbi batang yang dapat dimakan dan disebut "kentang" pula. '
'Umbi kentang sekarang telah menjadi salah satu makanan pokok penting di Eropa walaupun pada awalnya didatangkan dari Amerika Selatan. '
'Penjelajah Spanyol dan Portugis pertama kali membawa ke Eropa dan mengembangbiakkan tanaman ini.')
print(*[f'(\'{word.text}\', \'{word.pos}\')' for sent in doc.sentences for word in sent.words], sep='\n')
| en | 0.195268 | # source : https://stanfordnlp.github.io/stanfordnlp/pos.html | 2.82337 | 3 |
Lesson 10/MinPerimeterRectangle.py | whirlpool27/codility-lessons-solution | 1 | 6624275 | <filename>Lesson 10/MinPerimeterRectangle.py
# you can write to stdout for debugging purposes, e.g.
# print("this is a debug message")
def solution(N):
# write your code in Python 3.6
factorCandidate = 1
minPerimeter = 4_000_000_000
while factorCandidate*factorCandidate <= N:
if N % factorCandidate == 0:
minPerimeter = min(minPerimeter, 2*(factorCandidate + N//factorCandidate))
factorCandidate += 1
return minPerimeter | <filename>Lesson 10/MinPerimeterRectangle.py
# you can write to stdout for debugging purposes, e.g.
# print("this is a debug message")
def solution(N):
# write your code in Python 3.6
factorCandidate = 1
minPerimeter = 4_000_000_000
while factorCandidate*factorCandidate <= N:
if N % factorCandidate == 0:
minPerimeter = min(minPerimeter, 2*(factorCandidate + N//factorCandidate))
factorCandidate += 1
return minPerimeter | en | 0.754381 | # you can write to stdout for debugging purposes, e.g. # print("this is a debug message") # write your code in Python 3.6 | 3.633426 | 4 |
labs/lab4/backend/config.py | alice-cloud/dockerAndK8s | 0 | 6624276 | <gh_stars>0
import os
class Config(object):
REDIS_URL = "redis://{}:6379/0".format(os.environ.get("REDIS_SERVER"))
SECRET_KEY = 'oh_so_secret'
FLASK_PIKA_PARAMS = {
'host':'amqp', #amqp.server.com
'username': 'guest', #convenience param for username
'password': '<PASSWORD>', #convenience param for password
'port': 5672, #amqp server port
'virtual_host':'vhost' #amqp vhost
}
| import os
class Config(object):
REDIS_URL = "redis://{}:6379/0".format(os.environ.get("REDIS_SERVER"))
SECRET_KEY = 'oh_so_secret'
FLASK_PIKA_PARAMS = {
'host':'amqp', #amqp.server.com
'username': 'guest', #convenience param for username
'password': '<PASSWORD>', #convenience param for password
'port': 5672, #amqp server port
'virtual_host':'vhost' #amqp vhost
} | en | 0.365617 | #amqp.server.com #convenience param for username #convenience param for password #amqp server port #amqp vhost | 2.161572 | 2 |
src/utils/cn2arab.py | aquadrop/memory | 2 | 6624277 | import pycnnum
chs_arabic_map = {'零': 0, '一': 1, '二': 2, '三': 3, '四': 4,
'五': 5, '六': 6, '七': 7, '八': 8, '九': 9,
'十': 10, '百': 100, '千': 1000, '万': 10000,
'〇': 0, '壹': 1, '贰': 2, '叁': 3, '肆': 4,
'伍': 5, '陆': 6, '柒': 7, '捌': 8, '玖': 9,
'拾': 10, '佰': 100, '仟': 10000, '萬': 10000,
'亿': 100000000, '億': 100000000, '幺': 1,
'０': 0, '１': 1, '２': 2, '３': 3, '４': 4,
'５': 5, '６': 6, '７': 7, '８': 8, '９': 9, '两': 2}
digit_list = ['零', '一', '二', '三', '四',
'五', '六', '七', '八', '九',
'十', '百', '千', '万',
'〇', '壹', '贰', '叁', '肆',
'伍', '陆', '柒', '捌', '玖',
'拾', '佰', '仟', '萬',
'亿', '億', '幺', '两',
'点']
skip_gram = ['三星', '一加', '三菱', '三门','万达','一楼','二楼','三楼','四楼','五楼','六楼']
convert_list = {'0':'零','1':'一','2':'二','3':'三','4':'四','5':'五','6':'六','7':'七','8':'八','9':'久'}
lead_digits = ['一', '二', '三', '四',
'五', '六', '七', '八', '九',
'壹', '贰', '叁', '肆',
'伍', '陆', '柒', '捌', '玖',
'两']
def new_cn2arab(query):
if query.isdigit():
return float(query)
if len(query) == 0:
return query
result = []
numstring = []
for i in range(len(query)):
char = query[i]
if char not in digit_list:
if len(numstring) > 0:
numstring = ''.join([str(num) for num in numstring])
result.append(pycnnum.cn2num(numstring))
numstring = []
result.append(char)
else:
if char == '点':
try:
pre = query[i - 1]
post = query[i + 1]
if pre in digit_list and post in digit_list:
numstring.append(char)
else:
result.append(char)
continue
except:
continue
# if char in convert_list:
# char = convert_list[char]
if i < len(query) - 1:
test = char + query[i + 1]
if test in skip_gram:
result.append(char)
continue
numstring.append(char)
if len(numstring) > 0:
numstring = ''.join([str(num) for num in numstring])
result.append(pycnnum.cn2num(numstring))
result = [str(r) for r in result]
return "".join(result)
def cn2arab(chinese_digits):
if len(chinese_digits) == 0:
return False, ''
# chinese_digits = chinese_digits.decode("utf-8")
prefix = []
digit = []
suffix = []
pre_flag = False
dig_flag = False
for char in chinese_digits:
if char not in digit_list and not pre_flag:
prefix.append(char)
elif char in digit_list and not dig_flag:
digit.append(char)
pre_flag = True
else:
dig_flag = True
suffix.append(char)
if len(digit) == 0:
return False, ''.join(prefix)
# print 'prefix', _uniout.unescape(str(prefix), 'utf-8')
# print 'digit', _uniout.unescape(str(digit), 'utf-8')
# print 'suffix', _uniout.unescape(str(suffix), 'utf-8')
suffix = ''.join(suffix)
if suffix:
transferred, suffix = cn2arab(suffix)
else:
transferred = False
return transferred or pre_flag, ''.join(prefix) + str(cn2arab_core(''.join(digit))) + suffix
def cn2arab_core(chinese_digits):
if chinese_digits.isdigit():
return int(chinese_digits)
dig_mul = 1
## 100百万,取出100这个数字
head_digits = []
head = False
for c in chinese_digits:
if c.isdigit():
head = True
head_digits.append(c)
else:
break
if len(head_digits) > 0:
head_d = ''.join(head_digits)
chinese_digits = chinese_digits.replace(head_d, '')
dig_mul = float(head_d)
if chinese_digits[0] not in lead_digits:
chinese_digits = u'一' + chinese_digits
result = 0
tmp = 0
hnd_mln = 0
for count in range(len(chinese_digits)):
curr_char = chinese_digits[count]
curr_digit = chs_arabic_map.get(curr_char, None)
# meet 「亿」 or 「億」
if curr_digit == 10 ** 8:
result = result + tmp
result = result * curr_digit
# get result before 「亿」 and store it into hnd_mln
# reset `result`
hnd_mln = hnd_mln * 10 ** 8 + result
result = 0
tmp = 0
# meet 「万」 or 「萬」
elif curr_digit == 10 ** 4:
result = result + tmp
result = result * curr_digit
tmp = 0
# meet 「十」, 「百」, 「千」 or their traditional version
elif curr_digit >= 10:
tmp = 1 if tmp == 0 else tmp
result = result + curr_digit * tmp
tmp = 0
# meet single digit
elif curr_digit is not None:
tmp = tmp * 10 + curr_digit
else:
return float(result)
result = result + tmp
result = result + hnd_mln
return float(result * dig_mul)
if __name__ == '__main__':
s = ['十五哪点事','那点,42到50买一个三星手机两千一点五','3千','五十点二','三百','3百','两万','2万','2十万','100万','35','两千','1千零1百', '我要买一个两千到三千点二的手机']
for ss in s:
# print(ss, cn2arab(ss)[1])
print(new_cn2arab(ss))
| import pycnnum
chs_arabic_map = {'零': 0, '一': 1, '二': 2, '三': 3, '四': 4,
'五': 5, '六': 6, '七': 7, '八': 8, '九': 9,
'十': 10, '百': 100, '千': 1000, '万': 10000,
'〇': 0, '壹': 1, '贰': 2, '叁': 3, '肆': 4,
'伍': 5, '陆': 6, '柒': 7, '捌': 8, '玖': 9,
'拾': 10, '佰': 100, '仟': 10000, '萬': 10000,
'亿': 100000000, '億': 100000000, '幺': 1,
'０': 0, '１': 1, '２': 2, '３': 3, '４': 4,
'５': 5, '６': 6, '７': 7, '８': 8, '９': 9, '两': 2}
digit_list = ['零', '一', '二', '三', '四',
'五', '六', '七', '八', '九',
'十', '百', '千', '万',
'〇', '壹', '贰', '叁', '肆',
'伍', '陆', '柒', '捌', '玖',
'拾', '佰', '仟', '萬',
'亿', '億', '幺', '两',
'点']
skip_gram = ['三星', '一加', '三菱', '三门','万达','一楼','二楼','三楼','四楼','五楼','六楼']
convert_list = {'0':'零','1':'一','2':'二','3':'三','4':'四','5':'五','6':'六','7':'七','8':'八','9':'久'}
lead_digits = ['一', '二', '三', '四',
'五', '六', '七', '八', '九',
'壹', '贰', '叁', '肆',
'伍', '陆', '柒', '捌', '玖',
'两']
def new_cn2arab(query):
if query.isdigit():
return float(query)
if len(query) == 0:
return query
result = []
numstring = []
for i in range(len(query)):
char = query[i]
if char not in digit_list:
if len(numstring) > 0:
numstring = ''.join([str(num) for num in numstring])
result.append(pycnnum.cn2num(numstring))
numstring = []
result.append(char)
else:
if char == '点':
try:
pre = query[i - 1]
post = query[i + 1]
if pre in digit_list and post in digit_list:
numstring.append(char)
else:
result.append(char)
continue
except:
continue
# if char in convert_list:
# char = convert_list[char]
if i < len(query) - 1:
test = char + query[i + 1]
if test in skip_gram:
result.append(char)
continue
numstring.append(char)
if len(numstring) > 0:
numstring = ''.join([str(num) for num in numstring])
result.append(pycnnum.cn2num(numstring))
result = [str(r) for r in result]
return "".join(result)
def cn2arab(chinese_digits):
if len(chinese_digits) == 0:
return False, ''
# chinese_digits = chinese_digits.decode("utf-8")
prefix = []
digit = []
suffix = []
pre_flag = False
dig_flag = False
for char in chinese_digits:
if char not in digit_list and not pre_flag:
prefix.append(char)
elif char in digit_list and not dig_flag:
digit.append(char)
pre_flag = True
else:
dig_flag = True
suffix.append(char)
if len(digit) == 0:
return False, ''.join(prefix)
# print 'prefix', _uniout.unescape(str(prefix), 'utf-8')
# print 'digit', _uniout.unescape(str(digit), 'utf-8')
# print 'suffix', _uniout.unescape(str(suffix), 'utf-8')
suffix = ''.join(suffix)
if suffix:
transferred, suffix = cn2arab(suffix)
else:
transferred = False
return transferred or pre_flag, ''.join(prefix) + str(cn2arab_core(''.join(digit))) + suffix
def cn2arab_core(chinese_digits):
if chinese_digits.isdigit():
return int(chinese_digits)
dig_mul = 1
## 100百万,取出100这个数字
head_digits = []
head = False
for c in chinese_digits:
if c.isdigit():
head = True
head_digits.append(c)
else:
break
if len(head_digits) > 0:
head_d = ''.join(head_digits)
chinese_digits = chinese_digits.replace(head_d, '')
dig_mul = float(head_d)
if chinese_digits[0] not in lead_digits:
chinese_digits = u'一' + chinese_digits
result = 0
tmp = 0
hnd_mln = 0
for count in range(len(chinese_digits)):
curr_char = chinese_digits[count]
curr_digit = chs_arabic_map.get(curr_char, None)
# meet 「亿」 or 「億」
if curr_digit == 10 ** 8:
result = result + tmp
result = result * curr_digit
# get result before 「亿」 and store it into hnd_mln
# reset `result`
hnd_mln = hnd_mln * 10 ** 8 + result
result = 0
tmp = 0
# meet 「万」 or 「萬」
elif curr_digit == 10 ** 4:
result = result + tmp
result = result * curr_digit
tmp = 0
# meet 「十」, 「百」, 「千」 or their traditional version
elif curr_digit >= 10:
tmp = 1 if tmp == 0 else tmp
result = result + curr_digit * tmp
tmp = 0
# meet single digit
elif curr_digit is not None:
tmp = tmp * 10 + curr_digit
else:
return float(result)
result = result + tmp
result = result + hnd_mln
return float(result * dig_mul)
if __name__ == '__main__':
s = ['十五哪点事','那点,42到50买一个三星手机两千一点五','3千','五十点二','三百','3百','两万','2万','2十万','100万','35','两千','1千零1百', '我要买一个两千到三千点二的手机']
for ss in s:
# print(ss, cn2arab(ss)[1])
print(new_cn2arab(ss))
| ja | 0.308412 | # if char in convert_list: # char = convert_list[char] # chinese_digits = chinese_digits.decode("utf-8") # print 'prefix', _uniout.unescape(str(prefix), 'utf-8') # print 'digit', _uniout.unescape(str(digit), 'utf-8') # print 'suffix', _uniout.unescape(str(suffix), 'utf-8') ## 100百万,取出100这个数字 # meet 「亿」 or 「億」 # get result before 「亿」 and store it into hnd_mln # reset `result` # meet 「万」 or 「萬」 # meet 「十」, 「百」, 「千」 or their traditional version # meet single digit # print(ss, cn2arab(ss)[1]) | 2.24685 | 2 |
encurtador_de_url.py | evertoont/Projetos_diversos | 1 | 6624278 | <reponame>evertoont/Projetos_diversos<filename>encurtador_de_url.py
'''
Script realiza um encurtamento da URL usando o TinyURL.
Após ser encurtada, a URL é copiada para área de transferência.
- Necessário instalar as lib pyshorteners e clipboard
- pip install pyshorteners
- pip install clipboard
'''
import pyshorteners
import clipboard
url = input("Digite url a ser encurtada: ")
url_encurtada = pyshorteners.Shortener().tinyurl.short(url)
print('---------------------------------------')
print("Sua url: ", url_encurtada)
print('---------------------------------------')
clipboard.copy(url_encurtada)
print("Url foi copiada para a área de transferencia") | '''
Script realiza um encurtamento da URL usando o TinyURL.
Após ser encurtada, a URL é copiada para área de transferência.
- Necessário instalar as lib pyshorteners e clipboard
- pip install pyshorteners
- pip install clipboard
'''
import pyshorteners
import clipboard
url = input("Digite url a ser encurtada: ")
url_encurtada = pyshorteners.Shortener().tinyurl.short(url)
print('---------------------------------------')
print("Sua url: ", url_encurtada)
print('---------------------------------------')
clipboard.copy(url_encurtada)
print("Url foi copiada para a área de transferencia") | pt | 0.815814 | Script realiza um encurtamento da URL usando o TinyURL. Após ser encurtada, a URL é copiada para área de transferência. - Necessário instalar as lib pyshorteners e clipboard - pip install pyshorteners - pip install clipboard | 4.0688 | 4 |
models.py | atxarib99/stonks | 0 | 6624279 | <reponame>atxarib99/stonks
# holds different models that can be pulled into the learner.
# each method takes data as input and will output cleaned data and model
import tensorflow as tf
import numpy as np
import pandas as pd
def singleHighOutput(data, lstm_layer_size=128, lstm_layer_count=2):
dataset_x = []
dataset_y = []
train_x = []
train_y = []
for i in range(30, len(data) - 1):
dataset_x.append(data[i-30:i])
dataset_y.append(data[i+1][0])
train_x, train_y, valid_x, valid_y, test_x, test_y = splitdata(dataset_x, dataset_y, [.8,0,.2])
#reshape
train_x = np.array(train_x)
train_y = np.array(train_y)
train_x = np.reshape(train_x, (train_x.shape[0], train_x.shape[1], 6))
train_y = np.reshape(train_y, (train_y.shape[0], 1))
test_x = np.array(test_x)
test_y = np.array(test_y)
test_x = np.reshape(test_x, (test_x.shape[0], test_x.shape[1], 6))
test_y = np.reshape(test_y, (test_y.shape[0], 1))
layers = []
for i in range(0, lstm_layer_count):
if i != lstm_layer_count - 1:
layers.append(tf.keras.layers.LSTM(lstm_layer_size, return_sequences=True, input_shape=(30,6), kernel_initializer='glorot_uniform'))
else:
layers.append(tf.keras.layers.LSTM(lstm_layer_size, return_sequences=False, input_shape=(30,6), kernel_initializer='glorot_uniform'))
layers.append(tf.keras.layers.Dropout(.2))
layers.append(tf.keras.layers.Dense(6, kernel_initializer='glorot_uniform'))
layers.append(tf.keras.layers.Dense(1, kernel_initializer='glorot_uniform'))
model = tf.keras.Sequential(layers)
return (model, train_x, train_y, valid_x, valid_y, test_x, test_y)
def next5HighOutput(data, lstm_layer_size=64, lstm_layer_count=4, prev_days=30):
dataset_x = []
dataset_y = []
train_x = []
train_y = []
for i in range(prev_days, len(data) - 5):
dataset_x.append(data[i-prev_days:i])
dataset_y.append([row[0] for row in data[i+1:i+6]])
train_x, train_y, valid_x, valid_y, test_x, test_y = splitdata(dataset_x, dataset_y, [.8,0,.2])
#reshape
train_x = np.array(train_x)
train_y = np.array(train_y)
train_x = np.reshape(train_x, (train_x.shape[0], train_x.shape[1], 6))
train_y = np.reshape(train_y, (train_y.shape[0], 5))
test_x = np.array(test_x)
test_y = np.array(test_y)
test_x = np.reshape(test_x, (test_x.shape[0], test_x.shape[1], 6))
test_y = np.reshape(test_y, (test_y.shape[0], 5))
layers = []
for i in range(0, lstm_layer_count):
if i != lstm_layer_count - 1:
layers.append(tf.keras.layers.LSTM(lstm_layer_size, return_sequences=True, input_shape=(prev_days,6), kernel_initializer='glorot_uniform'))
else:
layers.append(tf.keras.layers.LSTM(lstm_layer_size, return_sequences=False, input_shape=(prev_days,6), kernel_initializer='glorot_uniform'))
layers.append(tf.keras.layers.Dropout(.3))
layers.append(tf.keras.layers.Dense(125, kernel_initializer='glorot_uniform'))
layers.append(tf.keras.layers.Dense(5, kernel_initializer='glorot_uniform'))
model = tf.keras.Sequential(layers)
return (model, train_x, train_y, valid_x, valid_y, test_x, test_y)
def next1AllOutput(data, lstm_layer_size=256, lstm_layer_count=3):
dataset_x = []
dataset_y = []
train_x = []
train_y = []
for i in range(30, len(data) - 1):
dataset_x.append(data[i-30:i])
dataset_y.append(data[i+1])
train_x, train_y, valid_x, valid_y, test_x, test_y = splitdata(dataset_x, dataset_y, [.8,0,.2])
#reshape
train_x = np.array(train_x)
train_y = np.array(train_y)
train_x = np.reshape(train_x, (train_x.shape[0], train_x.shape[1], 6))
train_y = np.reshape(train_y, (train_y.shape[0], 6))
test_x = np.array(test_x)
test_y = np.array(test_y)
test_x = np.reshape(test_x, (test_x.shape[0], test_x.shape[1], 6))
test_y = np.reshape(test_y, (test_y.shape[0], 6))
layers = []
for i in range(0, lstm_layer_count):
if i != lstm_layer_count - 1:
layers.append(tf.keras.layers.LSTM(lstm_layer_size, return_sequences=True, input_shape=(30,6), kernel_initializer='glorot_uniform'))
else:
layers.append(tf.keras.layers.LSTM(lstm_layer_size, return_sequences=False, input_shape=(30,6), kernel_initializer='glorot_uniform'))
layers.append(tf.keras.layers.Dropout(.2))
layers.append(tf.keras.layers.Dense(6, kernel_initializer='glorot_uniform'))
layers.append(tf.keras.layers.Dense(6, kernel_initializer='glorot_uniform'))
model = tf.keras.Sequential(layers)
return (model, train_x, train_y, valid_x, valid_y, test_x, test_y)
def splitdata(dataset_x, dataset_y, splitdef):
train_size = splitdef[0]
valid_size = splitdef[1]
test_size = splitdef[2]
if train_size + valid_size + test_size != 1:
print('data split definition does not add to 1!')
return None
#80:20 test train split
train_x = dataset_x[0:int(len(dataset_x)*train_size)]
valid_x = []
if valid_size == 0:
valid_x = dataset_x[int(len(dataset_x)*train_size):int(len(dataset_x)*(train_size+valid_size))]
test_x = dataset_x[int(len(dataset_x)*(train_size + valid_size)):len(dataset_x)]
train_y = dataset_y[0:int(len(dataset_y)*train_size)]
valid_y = []
if valid_size == 0:
valid_y = dataset_y[int(len(dataset_y)*train_size):int(len(dataset_y)*(train_size+valid_size))]
test_y = dataset_y[int(len(dataset_y)*(train_size + valid_size)):len(dataset_y)]
return (train_x, train_y, valid_x, valid_y, test_x, test_y)
| # holds different models that can be pulled into the learner.
# each method takes data as input and will output cleaned data and model
import tensorflow as tf
import numpy as np
import pandas as pd
def singleHighOutput(data, lstm_layer_size=128, lstm_layer_count=2):
dataset_x = []
dataset_y = []
train_x = []
train_y = []
for i in range(30, len(data) - 1):
dataset_x.append(data[i-30:i])
dataset_y.append(data[i+1][0])
train_x, train_y, valid_x, valid_y, test_x, test_y = splitdata(dataset_x, dataset_y, [.8,0,.2])
#reshape
train_x = np.array(train_x)
train_y = np.array(train_y)
train_x = np.reshape(train_x, (train_x.shape[0], train_x.shape[1], 6))
train_y = np.reshape(train_y, (train_y.shape[0], 1))
test_x = np.array(test_x)
test_y = np.array(test_y)
test_x = np.reshape(test_x, (test_x.shape[0], test_x.shape[1], 6))
test_y = np.reshape(test_y, (test_y.shape[0], 1))
layers = []
for i in range(0, lstm_layer_count):
if i != lstm_layer_count - 1:
layers.append(tf.keras.layers.LSTM(lstm_layer_size, return_sequences=True, input_shape=(30,6), kernel_initializer='glorot_uniform'))
else:
layers.append(tf.keras.layers.LSTM(lstm_layer_size, return_sequences=False, input_shape=(30,6), kernel_initializer='glorot_uniform'))
layers.append(tf.keras.layers.Dropout(.2))
layers.append(tf.keras.layers.Dense(6, kernel_initializer='glorot_uniform'))
layers.append(tf.keras.layers.Dense(1, kernel_initializer='glorot_uniform'))
model = tf.keras.Sequential(layers)
return (model, train_x, train_y, valid_x, valid_y, test_x, test_y)
def next5HighOutput(data, lstm_layer_size=64, lstm_layer_count=4, prev_days=30):
dataset_x = []
dataset_y = []
train_x = []
train_y = []
for i in range(prev_days, len(data) - 5):
dataset_x.append(data[i-prev_days:i])
dataset_y.append([row[0] for row in data[i+1:i+6]])
train_x, train_y, valid_x, valid_y, test_x, test_y = splitdata(dataset_x, dataset_y, [.8,0,.2])
#reshape
train_x = np.array(train_x)
train_y = np.array(train_y)
train_x = np.reshape(train_x, (train_x.shape[0], train_x.shape[1], 6))
train_y = np.reshape(train_y, (train_y.shape[0], 5))
test_x = np.array(test_x)
test_y = np.array(test_y)
test_x = np.reshape(test_x, (test_x.shape[0], test_x.shape[1], 6))
test_y = np.reshape(test_y, (test_y.shape[0], 5))
layers = []
for i in range(0, lstm_layer_count):
if i != lstm_layer_count - 1:
layers.append(tf.keras.layers.LSTM(lstm_layer_size, return_sequences=True, input_shape=(prev_days,6), kernel_initializer='glorot_uniform'))
else:
layers.append(tf.keras.layers.LSTM(lstm_layer_size, return_sequences=False, input_shape=(prev_days,6), kernel_initializer='glorot_uniform'))
layers.append(tf.keras.layers.Dropout(.3))
layers.append(tf.keras.layers.Dense(125, kernel_initializer='glorot_uniform'))
layers.append(tf.keras.layers.Dense(5, kernel_initializer='glorot_uniform'))
model = tf.keras.Sequential(layers)
return (model, train_x, train_y, valid_x, valid_y, test_x, test_y)
def next1AllOutput(data, lstm_layer_size=256, lstm_layer_count=3):
dataset_x = []
dataset_y = []
train_x = []
train_y = []
for i in range(30, len(data) - 1):
dataset_x.append(data[i-30:i])
dataset_y.append(data[i+1])
train_x, train_y, valid_x, valid_y, test_x, test_y = splitdata(dataset_x, dataset_y, [.8,0,.2])
#reshape
train_x = np.array(train_x)
train_y = np.array(train_y)
train_x = np.reshape(train_x, (train_x.shape[0], train_x.shape[1], 6))
train_y = np.reshape(train_y, (train_y.shape[0], 6))
test_x = np.array(test_x)
test_y = np.array(test_y)
test_x = np.reshape(test_x, (test_x.shape[0], test_x.shape[1], 6))
test_y = np.reshape(test_y, (test_y.shape[0], 6))
layers = []
for i in range(0, lstm_layer_count):
if i != lstm_layer_count - 1:
layers.append(tf.keras.layers.LSTM(lstm_layer_size, return_sequences=True, input_shape=(30,6), kernel_initializer='glorot_uniform'))
else:
layers.append(tf.keras.layers.LSTM(lstm_layer_size, return_sequences=False, input_shape=(30,6), kernel_initializer='glorot_uniform'))
layers.append(tf.keras.layers.Dropout(.2))
layers.append(tf.keras.layers.Dense(6, kernel_initializer='glorot_uniform'))
layers.append(tf.keras.layers.Dense(6, kernel_initializer='glorot_uniform'))
model = tf.keras.Sequential(layers)
return (model, train_x, train_y, valid_x, valid_y, test_x, test_y)
def splitdata(dataset_x, dataset_y, splitdef):
train_size = splitdef[0]
valid_size = splitdef[1]
test_size = splitdef[2]
if train_size + valid_size + test_size != 1:
print('data split definition does not add to 1!')
return None
#80:20 test train split
train_x = dataset_x[0:int(len(dataset_x)*train_size)]
valid_x = []
if valid_size == 0:
valid_x = dataset_x[int(len(dataset_x)*train_size):int(len(dataset_x)*(train_size+valid_size))]
test_x = dataset_x[int(len(dataset_x)*(train_size + valid_size)):len(dataset_x)]
train_y = dataset_y[0:int(len(dataset_y)*train_size)]
valid_y = []
if valid_size == 0:
valid_y = dataset_y[int(len(dataset_y)*train_size):int(len(dataset_y)*(train_size+valid_size))]
test_y = dataset_y[int(len(dataset_y)*(train_size + valid_size)):len(dataset_y)]
return (train_x, train_y, valid_x, valid_y, test_x, test_y) | en | 0.945008 | # holds different models that can be pulled into the learner. # each method takes data as input and will output cleaned data and model #reshape #reshape #reshape #80:20 test train split | 2.841473 | 3 |
app/bot/base.py | anderskswanson/xtensible | 1 | 6624280 | <reponame>anderskswanson/xtensible<gh_stars>1-10
from inspect import getmembers
import importlib
import os
class BaseModule:
"""
Module of native features for XtensibleBot
Available functions:
- lsmod
- describemod
- addmod
- delmod
- help
"""
_NOT_LOADED = 'Module {} is not loaded into Xtensible-Bot'
def __init__(self):
self._modules = {'base': self}
static_modules = [os.path.basename(item.path)
for item in os.scandir("app/modules")
if item.is_dir() and '__pycache__' not in item.path]
for mod in static_modules:
self._load_module(mod)
def __len__(self):
return len(self._modules)
def __getitem__(self, key):
return self._modules[key]
def __setitem__(self, key, value):
self._modules[key] = value
def __contains__(self, key):
return key in self._modules
def __delitem__(self, key):
self._modules.pop(key)
# add a module script into the module list
def _load_module(self, module):
module_path = 'app/modules/{}/module_exports.py'.format(module)
import_string = 'app.modules.{}.module_exports'.format(module)
if not os.path.exists(os.path.join(module_path)):
raise ModuleNotFoundError(self._NOT_LOADED.format(module))
key = importlib.import_module(import_string)
self[module] = key
def keys(self):
"""
Internal representation of lsmod
!base keys
"""
return self._modules.keys()
# merge iterable of module names or string module name into the module dict
def addmod(self, item):
"""
Add module/s from the modules directory into the Xtensible-Bot
!base addmod <module1 module2 ...>
"""
loaded_modules = []
failed_loads = []
if not isinstance(item, list):
item = [item]
# merge module hashmaps
for name in item:
try:
module_obj = self._load_module(name)
self[name] = module_obj
loaded_modules.append(name)
except ModuleNotFoundError:
failed_loads.append(name)
return loaded_modules, failed_loads
def lsmod(self):
"""
List all modules loaded into the Xtensible-Bot
!base lsmod
"""
return 'Loaded Modules:\n{}'.format(
'\n'.join([module for module in self._modules]))
def delmod(self, module):
"""
Delete (unload) a module from the Xtensible-Bot
!base delmod <module>
"""
if module in self:
del self[module]
return 'Removed module {}'.format(module)
else:
return self._NOT_LOADED.format(module)
def describemod(self, module):
"""
Return a description of each member function of a module
!base describemod module
"""
if module in self:
attrs = getmembers(self[module])
methods = ['{}: {}'.format(attr[0], attr[1].__doc__)
for attr in attrs if not attr[0].startswith('_')]
return '\n'.join(methods)
else:
return self._NOT_LOADED.format(module)
| from inspect import getmembers
import importlib
import os
class BaseModule:
"""
Module of native features for XtensibleBot
Available functions:
- lsmod
- describemod
- addmod
- delmod
- help
"""
_NOT_LOADED = 'Module {} is not loaded into Xtensible-Bot'
def __init__(self):
self._modules = {'base': self}
static_modules = [os.path.basename(item.path)
for item in os.scandir("app/modules")
if item.is_dir() and '__pycache__' not in item.path]
for mod in static_modules:
self._load_module(mod)
def __len__(self):
return len(self._modules)
def __getitem__(self, key):
return self._modules[key]
def __setitem__(self, key, value):
self._modules[key] = value
def __contains__(self, key):
return key in self._modules
def __delitem__(self, key):
self._modules.pop(key)
# add a module script into the module list
def _load_module(self, module):
module_path = 'app/modules/{}/module_exports.py'.format(module)
import_string = 'app.modules.{}.module_exports'.format(module)
if not os.path.exists(os.path.join(module_path)):
raise ModuleNotFoundError(self._NOT_LOADED.format(module))
key = importlib.import_module(import_string)
self[module] = key
def keys(self):
"""
Internal representation of lsmod
!base keys
"""
return self._modules.keys()
# merge iterable of module names or string module name into the module dict
def addmod(self, item):
"""
Add module/s from the modules directory into the Xtensible-Bot
!base addmod <module1 module2 ...>
"""
loaded_modules = []
failed_loads = []
if not isinstance(item, list):
item = [item]
# merge module hashmaps
for name in item:
try:
module_obj = self._load_module(name)
self[name] = module_obj
loaded_modules.append(name)
except ModuleNotFoundError:
failed_loads.append(name)
return loaded_modules, failed_loads
def lsmod(self):
"""
List all modules loaded into the Xtensible-Bot
!base lsmod
"""
return 'Loaded Modules:\n{}'.format(
'\n'.join([module for module in self._modules]))
def delmod(self, module):
"""
Delete (unload) a module from the Xtensible-Bot
!base delmod <module>
"""
if module in self:
del self[module]
return 'Removed module {}'.format(module)
else:
return self._NOT_LOADED.format(module)
def describemod(self, module):
"""
Return a description of each member function of a module
!base describemod module
"""
if module in self:
attrs = getmembers(self[module])
methods = ['{}: {}'.format(attr[0], attr[1].__doc__)
for attr in attrs if not attr[0].startswith('_')]
return '\n'.join(methods)
else:
return self._NOT_LOADED.format(module) | en | 0.381062 | Module of native features for XtensibleBot Available functions: - lsmod - describemod - addmod - delmod - help # add a module script into the module list Internal representation of lsmod !base keys # merge iterable of module names or string module name into the module dict Add module/s from the modules directory into the Xtensible-Bot !base addmod <module1 module2 ...> # merge module hashmaps List all modules loaded into the Xtensible-Bot !base lsmod Delete (unload) a module from the Xtensible-Bot !base delmod <module> Return a description of each member function of a module !base describemod module | 2.409501 | 2 |
Besttimetobuyandsellstock.py | pgupta119/LeetCode | 0 | 6624281 | # You are given an array prices where prices[i] is the price of a given stock on the ith day.
# You want to maximize your profit by choosing a single day to buy one stock and choosing a different day in the future to sell that stock.
# Return the maximum profit you can achieve from this transaction. If you cannot achieve any profit, return 0.
#Example
# Input: prices = [7,1,5,3,6,4]
# Output: 5
# Explanation: Buy on day 2 (price = 1) and sell on day 5 (price = 6), profit = 6-1 = 5.
# Note that buying on day 2 and selling on day 1 is not allowed because you must buy before you sell.
#Solution;
class Solution:
def maxProfit(self, prices):
#Initially maximum difference is zero
max_diff=0
# Initialize the maximum of the array at the last index.
max_number=prices[-1]
# loop for checking the maximum difference and assign maximum value
for j in range(len(prices)-2,-1,-1):
# if current index value is greater than max_number then assign the current value to max_number
if(prices[j]>max_number):
max_number=prices[j]
# otherwise take the difference of max_number and current value,and compare with max_difference which provide max value usin max function
else:
max_diff= max (max_diff,(max_number-prices[j]))
#return the max difference after execution of the loop
return max_diff
sol=Solution()
print(sol.maxProfit([7,1,5,3,6,4]))
#output should be 5
| # You are given an array prices where prices[i] is the price of a given stock on the ith day.
# You want to maximize your profit by choosing a single day to buy one stock and choosing a different day in the future to sell that stock.
# Return the maximum profit you can achieve from this transaction. If you cannot achieve any profit, return 0.
#Example
# Input: prices = [7,1,5,3,6,4]
# Output: 5
# Explanation: Buy on day 2 (price = 1) and sell on day 5 (price = 6), profit = 6-1 = 5.
# Note that buying on day 2 and selling on day 1 is not allowed because you must buy before you sell.
#Solution;
class Solution:
def maxProfit(self, prices):
#Initially maximum difference is zero
max_diff=0
# Initialize the maximum of the array at the last index.
max_number=prices[-1]
# loop for checking the maximum difference and assign maximum value
for j in range(len(prices)-2,-1,-1):
# if current index value is greater than max_number then assign the current value to max_number
if(prices[j]>max_number):
max_number=prices[j]
# otherwise take the difference of max_number and current value,and compare with max_difference which provide max value usin max function
else:
max_diff= max (max_diff,(max_number-prices[j]))
#return the max difference after execution of the loop
return max_diff
sol=Solution()
print(sol.maxProfit([7,1,5,3,6,4]))
#output should be 5
| en | 0.873971 | # You are given an array prices where prices[i] is the price of a given stock on the ith day. # You want to maximize your profit by choosing a single day to buy one stock and choosing a different day in the future to sell that stock. # Return the maximum profit you can achieve from this transaction. If you cannot achieve any profit, return 0. #Example # Input: prices = [7,1,5,3,6,4] # Output: 5 # Explanation: Buy on day 2 (price = 1) and sell on day 5 (price = 6), profit = 6-1 = 5. # Note that buying on day 2 and selling on day 1 is not allowed because you must buy before you sell. #Solution; #Initially maximum difference is zero # Initialize the maximum of the array at the last index. # loop for checking the maximum difference and assign maximum value # if current index value is greater than max_number then assign the current value to max_number # otherwise take the difference of max_number and current value,and compare with max_difference which provide max value usin max function #return the max difference after execution of the loop #output should be 5 | 4.252072 | 4 |
Shop/migrations/0010_auto_20210506_2155.py | KumarSantosh22/TheShoppingCArt | 0 | 6624282 | # Generated by Django 3.1.7 on 2021-05-06 16:25
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('Shop', '0009_auto_20210506_2154'),
]
operations = [
migrations.AlterField(
model_name='seller',
name='phone',
field=models.CharField(max_length=10),
),
]
| # Generated by Django 3.1.7 on 2021-05-06 16:25
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('Shop', '0009_auto_20210506_2154'),
]
operations = [
migrations.AlterField(
model_name='seller',
name='phone',
field=models.CharField(max_length=10),
),
]
| en | 0.7881 | # Generated by Django 3.1.7 on 2021-05-06 16:25 | 1.501177 | 2 |
uscensus/__init__.py | nkrishnaswami/census | 4 | 6624283 | <reponame>nkrishnaswami/census
from .data.discovery import DiscoveryInterface
from .data.states import get_state_codes
from .geocode.bulk import CensusBulkGeocoder
from .util.errors import CensusError
from .util.errors import DBError
from .util.nopcache import NopCache
from .util.sqlalchemycache import SqlAlchemyCache
"""This module reads the Census's API discovery interface at
http://api.census.gov/data.json, and provides callable wrappers for
each API it finds. It indexes each of their metadata fields to make
the APIs and variables related to them easier to find.
The fields in the dataset discovery interface are described at
https://project-open-data.cio.gov/v1.1/schema/ .
Using this module requires a Census API key, which you can request at
https://www.census.gov/developers/ .
Exceptions:
* CensusError(Exception): base class for module exceptions
* DBError(CensusError): errors accessing databases
Classes:
* SqlAlchemyCache: caches APIa metadata in any SqlAlchemy compatible DBMS
* DBAPICache: caches API metadata in any DBAPI compatible DBMS
* DiscoveryInterface: retrieves and caches census API metadata. This
indexes metadata and has a dict of wrapper objects for each API.
* model.CensusDataEndpoint: wraps a Census API endpoint given its
metadata. These are constructed by the DiscoveryInterface.
* NopCache: dummy implementation of the cache interface
Functions:
* get_state_codes: retrieve state codes/names/abbreviations
Usage: Instantiate a DiscoveryInterface using a DBAPICache and your
Census API key. Call census APIs and receive the results as a pandas
DataFrame.
"""
__all__ = [
"CensusBulkGeocoder",
"DiscoveryInterface",
"DBAPICache",
"SqlAlchemyCache",
"NopCache",
"CensusError",
"DBError",
"get_state_codes",
]
| from .data.discovery import DiscoveryInterface
from .data.states import get_state_codes
from .geocode.bulk import CensusBulkGeocoder
from .util.errors import CensusError
from .util.errors import DBError
from .util.nopcache import NopCache
from .util.sqlalchemycache import SqlAlchemyCache
"""This module reads the Census's API discovery interface at
http://api.census.gov/data.json, and provides callable wrappers for
each API it finds. It indexes each of their metadata fields to make
the APIs and variables related to them easier to find.
The fields in the dataset discovery interface are described at
https://project-open-data.cio.gov/v1.1/schema/ .
Using this module requires a Census API key, which you can request at
https://www.census.gov/developers/ .
Exceptions:
* CensusError(Exception): base class for module exceptions
* DBError(CensusError): errors accessing databases
Classes:
* SqlAlchemyCache: caches APIa metadata in any SqlAlchemy compatible DBMS
* DBAPICache: caches API metadata in any DBAPI compatible DBMS
* DiscoveryInterface: retrieves and caches census API metadata. This
indexes metadata and has a dict of wrapper objects for each API.
* model.CensusDataEndpoint: wraps a Census API endpoint given its
metadata. These are constructed by the DiscoveryInterface.
* NopCache: dummy implementation of the cache interface
Functions:
* get_state_codes: retrieve state codes/names/abbreviations
Usage: Instantiate a DiscoveryInterface using a DBAPICache and your
Census API key. Call census APIs and receive the results as a pandas
DataFrame.
"""
__all__ = [
"CensusBulkGeocoder",
"DiscoveryInterface",
"DBAPICache",
"SqlAlchemyCache",
"NopCache",
"CensusError",
"DBError",
"get_state_codes",
] | en | 0.726619 | This module reads the Census's API discovery interface at http://api.census.gov/data.json, and provides callable wrappers for each API it finds. It indexes each of their metadata fields to make the APIs and variables related to them easier to find. The fields in the dataset discovery interface are described at https://project-open-data.cio.gov/v1.1/schema/ . Using this module requires a Census API key, which you can request at https://www.census.gov/developers/ . Exceptions: * CensusError(Exception): base class for module exceptions * DBError(CensusError): errors accessing databases Classes: * SqlAlchemyCache: caches APIa metadata in any SqlAlchemy compatible DBMS * DBAPICache: caches API metadata in any DBAPI compatible DBMS * DiscoveryInterface: retrieves and caches census API metadata. This indexes metadata and has a dict of wrapper objects for each API. * model.CensusDataEndpoint: wraps a Census API endpoint given its metadata. These are constructed by the DiscoveryInterface. * NopCache: dummy implementation of the cache interface Functions: * get_state_codes: retrieve state codes/names/abbreviations Usage: Instantiate a DiscoveryInterface using a DBAPICache and your Census API key. Call census APIs and receive the results as a pandas DataFrame. | 2.457949 | 2 |
riptide_engine_docker/container_builder.py | theCapypara/riptide-engine-docker | 1 | 6624284 | <filename>riptide_engine_docker/container_builder.py
"""Container builder module."""
from collections import OrderedDict
import os
import platform
from pathlib import PurePosixPath
from typing import List, Union
from docker.types import Mount, Ulimit
from riptide.config.document.command import Command
from riptide.config.document.service import Service
from riptide.config.hosts import get_localhost_hosts
from riptide.config.service.ports import find_open_port_starting_at
from riptide.lib.cross_platform.cpuser import getgid, getuid
from riptide_engine_docker.assets import riptide_engine_docker_assets_dir
ENTRYPOINT_SH = 'entrypoint.sh'
RIPTIDE_DOCKER_LABEL_IS_RIPTIDE = 'riptide'
RIPTIDE_DOCKER_LABEL_SERVICE = "riptide_service"
RIPTIDE_DOCKER_LABEL_PROJECT = "riptide_project"
RIPTIDE_DOCKER_LABEL_MAIN = "riptide_main"
RIPTIDE_DOCKER_LABEL_HTTP_PORT = "riptide_port"
ENTRYPOINT_CONTAINER_PATH = '/entrypoint_riptide.sh'
EENV_DONT_RUN_CMD = "RIPTIDE__DOCKER_DONT_RUN_CMD"
EENV_USER = "RIPTIDE__DOCKER_USER"
EENV_USER_RUN = "RIPTIDE__DOCKER_USER_RUN"
EENV_GROUP = "RIPTIDE__DOCKER_GROUP"
EENV_RUN_MAIN_CMD_AS_USER = "RIPTIDE__DOCKER_RUN_MAIN_CMD_AS_USER"
EENV_ORIGINAL_ENTRYPOINT = "RIPTIDE__DOCKER_ORIGINAL_ENTRYPOINT"
EENV_COMMAND_LOG_PREFIX = "RIPTIDE__DOCKER_CMD_LOGGING_"
EENV_NO_STDOUT_REDIRECT = "RIPTIDE__DOCKER_NO_STDOUT_REDIRECT"
EENV_NAMED_VOLUMES = "RIPTIDE__DOCKER_NAMED_VOLUMES"
EENV_ON_LINUX = "RIPTIDE__DOCKER_ON_LINUX"
EENV_HOST_SYSTEM_HOSTNAMES = "RIPTIDE__DOCKER_HOST_SYSTEM_HOSTNAMES"
EENV_OVERLAY_TARGETS = "RIPTIDE__DOCKER_OVERLAY_TARGETS"
# For services map HTTP main port to a host port starting here
DOCKER_ENGINE_HTTP_PORT_BND_START = 30000
class ContainerBuilder:
"""
ContainerBuilder.
Builds Riptide Docker containers for use with the Python API and
the Docker CLI
"""
def __init__(self, image: str, command: Union[List, str, None]) -> None:
"""Create a new container builder. Specify image and command to run."""
self.env = OrderedDict()
self.labels = OrderedDict()
self.mounts = OrderedDict()
self.ports = OrderedDict()
self.network = None
self.name = None
self.entrypoint = None
self.command = command
self.args = []
self.work_dir = None
self.image = image
self.set_label(RIPTIDE_DOCKER_LABEL_IS_RIPTIDE, "1")
self.run_as_root = False
self.hostname = None
self.allow_full_memlock = False
self.cap_sys_admin = False
self.on_linux = platform.system().lower().startswith('linux')
self.set_env(EENV_ON_LINUX, "1" if self.on_linux else "0")
self.named_volumes_in_cnt = []
def set_env(self, name: str, val: str):
self.env[name] = val
return self
def set_label(self, name: str, val: str):
self.labels[name] = val
return self
def set_mount(self, host_path: str, container_path: str, mode='rw'):
self.mounts[host_path] = Mount(
target=container_path,
source=host_path,
type='bind',
read_only=mode == 'ro',
consistency='delegated' # Performance setting for Docker Desktop on Mac
)
return self
def set_named_volume_mount(self, name: str, container_path: str, mode='rw'):
"""
Add a named volume. Name is automatically prefixed with riptide__.
"""
from riptide_engine_docker.named_volumes import NAMED_VOLUME_INTERNAL_PREFIX
vol_name = NAMED_VOLUME_INTERNAL_PREFIX + name
self.mounts[name] = Mount(
target=container_path,
source=vol_name,
type='volume',
read_only=mode == 'ro',
labels={RIPTIDE_DOCKER_LABEL_IS_RIPTIDE: "1"}
)
self.named_volumes_in_cnt.append(container_path)
return self
def set_port(self, cnt: int, host: int):
self.ports[cnt] = host
return self
def set_network(self, network: str):
self.network = network
return self
def set_name(self, name: str):
self.name = name
return self
def set_entrypoint(self, entrypoint: str):
self.entrypoint = entrypoint
return self
def set_args(self, args: List[str]):
self.args = args
return self
def set_workdir(self, work_dir: str):
self.work_dir = work_dir
return self
def set_hostname(self, hostname: str):
self.hostname = hostname
return self
def set_allow_full_memlock(self, flag: bool):
self.allow_full_memlock = flag
return self
def enable_riptide_entrypoint(self, image_config):
"""Add the Riptide entrypoint script and configure it."""
# The original entrypoint of the image is replaced with
# this custom entrypoint script, which may call the original entrypoint
# if present
# If the entrypoint is enabled, then run the entrypoint
# as root. It will handle the rest.
self.run_as_root = True
entrypoint_script = os.path.join(riptide_engine_docker_assets_dir(), ENTRYPOINT_SH)
self.set_mount(entrypoint_script, ENTRYPOINT_CONTAINER_PATH, 'ro')
# Collect entrypoint settings
for key, val in parse_entrypoint(image_config["Entrypoint"]).items():
self.set_env(key, val)
self.set_entrypoint(ENTRYPOINT_CONTAINER_PATH)
return self
def add_host_hostnames(self):
"""
Adds all hostnames that must be routable to the host system within the container as a environment variable.
"""
self.set_env(EENV_HOST_SYSTEM_HOSTNAMES, ' '.join(get_localhost_hosts()))
def _init_common(self, doc: Union[Service, Command], image_config, use_named_volume, unimportant_paths):
self.enable_riptide_entrypoint(image_config)
self.add_host_hostnames()
# Add volumes
for host, volume in doc.collect_volumes().items():
if use_named_volume and 'name' in volume:
self.set_named_volume_mount(volume['name'], volume['bind'], volume['mode'] or 'rw')
else:
self.set_mount(host, volume['bind'], volume['mode'] or 'rw')
# Collect environment
for key, val in doc.collect_environment().items():
self.set_env(key, val)
# Add unimportant paths to the list of dirs to be used with overlayfs
self.set_env(EENV_OVERLAY_TARGETS, ':'.join(unimportant_paths))
# Mounting bind/overlayfs will require SYS_ADMIN caps:
if len(unimportant_paths) > 0:
self.cap_sys_admin = True
def init_from_service(self, service: Service, image_config):
"""
Initialize some data of this builder with the given service object.
You need to call service_add_main_port separately.
"""
perf_settings = service.get_project().parent()['performance']
project_absolute_unimportant_paths = []
if perf_settings['dont_sync_unimportant_src'] and 'unimportant_paths' in service.parent():
project_absolute_unimportant_paths = [_make_abs_to_src(p) for p in service.parent()['unimportant_paths']]
self._init_common(
service,
image_config,
perf_settings['dont_sync_named_volumes_with_host'],
project_absolute_unimportant_paths
)
# Collect labels
labels = service_collect_labels(service, service.get_project()["name"])
# Collect (and process!) additional_ports
ports = service.collect_ports()
# All command logging commands are added as environment variables for the
# riptide entrypoint
environment_updates = service_collect_logging_commands(service)
# User settings for the entrypoint
environment_updates.update(service_collect_entrypoint_user_settings(service, getuid(), getgid(), image_config))
# Add to builder
for key, value in environment_updates.items():
self.set_env(key, value)
for name, val in labels.items():
self.set_label(name, val)
for container, host in ports.items():
self.set_port(container, host)
# Check if ulimit memlock setting is enabled
if "allow_full_memlock" in service and service["allow_full_memlock"]:
self.set_allow_full_memlock(True)
return self
def service_add_main_port(self, service: Service):
"""
Add main service port.
Not thread-safe!
If starting multiple services in multiple threads:
This has to be done separately, right before start,
and with a lock in place, so that multiple service starts don't reserve the
same port.
"""
if "port" in service:
main_port = find_open_port_starting_at(DOCKER_ENGINE_HTTP_PORT_BND_START)
self.set_label(RIPTIDE_DOCKER_LABEL_HTTP_PORT, str(main_port))
self.set_port(service["port"], main_port)
def init_from_command(self, command: Command, image_config):
"""
Initialize some data of this builder with the given command object.
"""
perf_settings = command.get_project().parent()['performance']
project_absolute_unimportant_paths = []
if perf_settings['dont_sync_unimportant_src'] and 'unimportant_paths' in command.parent():
project_absolute_unimportant_paths = [_make_abs_to_src(p) for p in command.parent()['unimportant_paths']]
self._init_common(
command,
image_config,
perf_settings['dont_sync_named_volumes_with_host'],
project_absolute_unimportant_paths
)
return self
def build_docker_api(self) -> dict:
"""
Build the docker container in the form of Docker API containers.run arguments.
"""
args = {
'image': self.image
}
if self.command is None:
args['command'] = None
elif isinstance(self.command, str):
# COMMAND IS STRING
args['command'] = self.command
if len(self.args) > 0:
args['command'] += " " + " ".join(f'"{w}"' for w in self.args)
else:
list_command = self.command.copy()
# COMMAND IS LIST
if len(self.args) > 0:
list_command += self.args
# Strange Docker API Bug (?) requires args with spaces to be quoted...
args['command'] = []
for item in list_command:
if " " in item:
args['command'].append('"' + item + '"')
else:
args['command'].append(item)
if self.name:
args['name'] = self.name
if self.network:
args['network'] = self.network
if self.entrypoint:
args['entrypoint'] = [self.entrypoint]
if self.work_dir:
args['working_dir'] = self.work_dir
if self.run_as_root:
args['user'] = 0
if self.hostname:
args['hostname'] = self.hostname
if self.allow_full_memlock:
args['ulimits'] = [Ulimit(name='memlock', soft=-1, hard=-1)]
if self.cap_sys_admin:
args['cap_add'] = ['SYS_ADMIN']
# Ubuntu and possibly other Distros:
if self.on_linux:
args['security_opt'] = ['apparmor:unconfined']
args['environment'] = self.env.copy()
# Add list of named volume paths for Docker to chown
if len(self.named_volumes_in_cnt) > 0:
args['environment'][EENV_NAMED_VOLUMES] = ':'.join(self.named_volumes_in_cnt)
args['labels'] = self.labels
args['ports'] = self.ports
args['mounts'] = list(self.mounts.values())
return args
def build_docker_cli(self) -> List[str]:
"""
Build the docker container in the form of a Docker CLI command.
"""
shell = [
"docker", "run", "--rm", "-it"
]
if self.name:
shell += ["--name", self.name]
if self.network:
shell += ["--network", self.network]
if self.entrypoint:
shell += ["--entrypoint", self.entrypoint]
if self.work_dir:
shell += ["-w", self.work_dir]
if self.run_as_root:
shell += ["-u", str(0)]
if self.hostname:
shell += ["--hostname", self.hostname]
for key, value in self.env.items():
shell += ['-e', key + '=' + value]
# Add list of named volume paths for Docker to chown
if len(self.named_volumes_in_cnt) > 0:
shell += ['-e', EENV_NAMED_VOLUMES + '=' + ':'.join(self.named_volumes_in_cnt)]
for key, value in self.labels.items():
shell += ['--label', key + '=' + value]
for container, host in self.ports.items():
shell += ['-p', str(host) + ':' + str(container)]
# Mac: Add delegated
mac_add = ':delegated' if platform.system().lower().startswith('mac') else ''
for mount in self.mounts.values():
mode = 'ro' if mount['ReadOnly'] else 'rw'
if mount["Type"] == "bind":
shell += ['-v',
mount['Source'] + ':' + mount['Target'] + ':' + mode + mac_add]
else:
shell += ['--mount',
f'type=volume,target={mount["Target"]},src={mount["Source"]},ro={"0" if mode == "rw" else "1"},'
f'volume-label={RIPTIDE_DOCKER_LABEL_IS_RIPTIDE}=1']
# ulimits
if self.allow_full_memlock:
shell += ['--ulimit', 'memlock=-1:-1']
if self.cap_sys_admin:
shell += ['--cap-add=SYS_ADMIN']
# On Ubuntu and possibly other distros:
if self.on_linux:
shell += ['--security-opt', 'apparmor:unconfined']
command = self.command
if command is None:
command = ""
if isinstance(command, list):
command = self.command[0]
# If the command itself contains arguments, they have to be joined with
# quotes, just like self.args
if len(self.command) > 1:
command += " " + " ".join(f'"{w}"' for w in self.command[1:])
shell += [
self.image,
(command + " " + " ".join(f'"{w}"' for w in self.args)).rstrip()
]
return shell
def get_cmd_container_name(project_name: str, command_name: str):
return 'riptide__' + project_name + '__cmd__' + command_name + '__' + str(os.getpid())
def get_network_name(project_name: str):
return 'riptide__' + project_name
def get_service_container_name(project_name: str, service_name: str):
return 'riptide__' + project_name + '__' + service_name
def parse_entrypoint(entrypoint):
"""
Parse the original entrypoint of an image and return a map of variables for the riptide entrypoint script.
RIPTIDE__DOCKER_ORIGINAL_ENTRYPOINT: Original entrypoint as string to be used with exec.
Empty if original entrypoint is not set.
RIPTIDE__DOCKER_DONT_RUN_CMD: true or unset.
When the original entrypoint is a string, the command does not get run.
See table at https://docs.docker.com/engine/reference/builder/#shell-form-entrypoint-example
"""
# Is the original entrypoint set?
if not entrypoint:
return {EENV_ORIGINAL_ENTRYPOINT: ""}
# Is the original entrypoint shell or exec format?
if isinstance(entrypoint, list):
# exec format
# Turn the list into a string, but quote all arguments
command = entrypoint.pop(0)
arguments = " ".join([f'"{entry}"' for entry in entrypoint])
return {
EENV_ORIGINAL_ENTRYPOINT: command + " " + arguments
}
else:
# shell format
return {
EENV_ORIGINAL_ENTRYPOINT: "/bin/sh -c " + entrypoint,
EENV_DONT_RUN_CMD: "true"
}
pass
def service_collect_logging_commands(service: Service) -> dict:
"""Collect logging commands environment variables for this service"""
environment = {}
if "logging" in service and "commands" in service["logging"]:
for cmdname, command in service["logging"]["commands"].items():
environment[EENV_COMMAND_LOG_PREFIX + cmdname] = command
return environment
def service_collect_entrypoint_user_settings(service: Service, user, user_group, image_config) -> dict:
environment = {}
if not service["dont_create_user"]:
environment[EENV_USER] = str(user)
environment[EENV_GROUP] = str(user_group)
if service["run_as_current_user"]:
# Run with the current system user
environment[EENV_RUN_MAIN_CMD_AS_USER] = "yes"
elif "User" in image_config and image_config["User"] != "":
# If run_as_current_user is false and an user is configured in the image config, tell the entrypoint to run
# with this user
environment[EENV_RUN_MAIN_CMD_AS_USER] = "yes"
environment[EENV_USER_RUN] = image_config["User"]
return environment
def service_collect_labels(service: Service, project_name):
labels = {
RIPTIDE_DOCKER_LABEL_IS_RIPTIDE: '1',
RIPTIDE_DOCKER_LABEL_PROJECT: project_name,
RIPTIDE_DOCKER_LABEL_SERVICE: service["$name"],
RIPTIDE_DOCKER_LABEL_MAIN: "0"
}
if "roles" in service and "main" in service["roles"]:
labels[RIPTIDE_DOCKER_LABEL_MAIN] = "1"
return labels
def _make_abs_to_src(p):
"""Convert the given relative path to an absolute path. Relative base is /src/."""
return str(PurePosixPath("/src").joinpath(p))
| <filename>riptide_engine_docker/container_builder.py
"""Container builder module."""
from collections import OrderedDict
import os
import platform
from pathlib import PurePosixPath
from typing import List, Union
from docker.types import Mount, Ulimit
from riptide.config.document.command import Command
from riptide.config.document.service import Service
from riptide.config.hosts import get_localhost_hosts
from riptide.config.service.ports import find_open_port_starting_at
from riptide.lib.cross_platform.cpuser import getgid, getuid
from riptide_engine_docker.assets import riptide_engine_docker_assets_dir
ENTRYPOINT_SH = 'entrypoint.sh'
RIPTIDE_DOCKER_LABEL_IS_RIPTIDE = 'riptide'
RIPTIDE_DOCKER_LABEL_SERVICE = "riptide_service"
RIPTIDE_DOCKER_LABEL_PROJECT = "riptide_project"
RIPTIDE_DOCKER_LABEL_MAIN = "riptide_main"
RIPTIDE_DOCKER_LABEL_HTTP_PORT = "riptide_port"
ENTRYPOINT_CONTAINER_PATH = '/entrypoint_riptide.sh'
EENV_DONT_RUN_CMD = "RIPTIDE__DOCKER_DONT_RUN_CMD"
EENV_USER = "RIPTIDE__DOCKER_USER"
EENV_USER_RUN = "RIPTIDE__DOCKER_USER_RUN"
EENV_GROUP = "RIPTIDE__DOCKER_GROUP"
EENV_RUN_MAIN_CMD_AS_USER = "RIPTIDE__DOCKER_RUN_MAIN_CMD_AS_USER"
EENV_ORIGINAL_ENTRYPOINT = "RIPTIDE__DOCKER_ORIGINAL_ENTRYPOINT"
EENV_COMMAND_LOG_PREFIX = "RIPTIDE__DOCKER_CMD_LOGGING_"
EENV_NO_STDOUT_REDIRECT = "RIPTIDE__DOCKER_NO_STDOUT_REDIRECT"
EENV_NAMED_VOLUMES = "RIPTIDE__DOCKER_NAMED_VOLUMES"
EENV_ON_LINUX = "RIPTIDE__DOCKER_ON_LINUX"
EENV_HOST_SYSTEM_HOSTNAMES = "RIPTIDE__DOCKER_HOST_SYSTEM_HOSTNAMES"
EENV_OVERLAY_TARGETS = "RIPTIDE__DOCKER_OVERLAY_TARGETS"
# For services map HTTP main port to a host port starting here
DOCKER_ENGINE_HTTP_PORT_BND_START = 30000
class ContainerBuilder:
"""
ContainerBuilder.
Builds Riptide Docker containers for use with the Python API and
the Docker CLI
"""
def __init__(self, image: str, command: Union[List, str, None]) -> None:
"""Create a new container builder. Specify image and command to run."""
self.env = OrderedDict()
self.labels = OrderedDict()
self.mounts = OrderedDict()
self.ports = OrderedDict()
self.network = None
self.name = None
self.entrypoint = None
self.command = command
self.args = []
self.work_dir = None
self.image = image
self.set_label(RIPTIDE_DOCKER_LABEL_IS_RIPTIDE, "1")
self.run_as_root = False
self.hostname = None
self.allow_full_memlock = False
self.cap_sys_admin = False
self.on_linux = platform.system().lower().startswith('linux')
self.set_env(EENV_ON_LINUX, "1" if self.on_linux else "0")
self.named_volumes_in_cnt = []
def set_env(self, name: str, val: str):
self.env[name] = val
return self
def set_label(self, name: str, val: str):
self.labels[name] = val
return self
def set_mount(self, host_path: str, container_path: str, mode='rw'):
self.mounts[host_path] = Mount(
target=container_path,
source=host_path,
type='bind',
read_only=mode == 'ro',
consistency='delegated' # Performance setting for Docker Desktop on Mac
)
return self
def set_named_volume_mount(self, name: str, container_path: str, mode='rw'):
"""
Add a named volume. Name is automatically prefixed with riptide__.
"""
from riptide_engine_docker.named_volumes import NAMED_VOLUME_INTERNAL_PREFIX
vol_name = NAMED_VOLUME_INTERNAL_PREFIX + name
self.mounts[name] = Mount(
target=container_path,
source=vol_name,
type='volume',
read_only=mode == 'ro',
labels={RIPTIDE_DOCKER_LABEL_IS_RIPTIDE: "1"}
)
self.named_volumes_in_cnt.append(container_path)
return self
def set_port(self, cnt: int, host: int):
self.ports[cnt] = host
return self
def set_network(self, network: str):
self.network = network
return self
def set_name(self, name: str):
self.name = name
return self
def set_entrypoint(self, entrypoint: str):
self.entrypoint = entrypoint
return self
def set_args(self, args: List[str]):
self.args = args
return self
def set_workdir(self, work_dir: str):
self.work_dir = work_dir
return self
def set_hostname(self, hostname: str):
self.hostname = hostname
return self
def set_allow_full_memlock(self, flag: bool):
self.allow_full_memlock = flag
return self
def enable_riptide_entrypoint(self, image_config):
"""Add the Riptide entrypoint script and configure it."""
# The original entrypoint of the image is replaced with
# this custom entrypoint script, which may call the original entrypoint
# if present
# If the entrypoint is enabled, then run the entrypoint
# as root. It will handle the rest.
self.run_as_root = True
entrypoint_script = os.path.join(riptide_engine_docker_assets_dir(), ENTRYPOINT_SH)
self.set_mount(entrypoint_script, ENTRYPOINT_CONTAINER_PATH, 'ro')
# Collect entrypoint settings
for key, val in parse_entrypoint(image_config["Entrypoint"]).items():
self.set_env(key, val)
self.set_entrypoint(ENTRYPOINT_CONTAINER_PATH)
return self
def add_host_hostnames(self):
"""
Adds all hostnames that must be routable to the host system within the container as a environment variable.
"""
self.set_env(EENV_HOST_SYSTEM_HOSTNAMES, ' '.join(get_localhost_hosts()))
def _init_common(self, doc: Union[Service, Command], image_config, use_named_volume, unimportant_paths):
self.enable_riptide_entrypoint(image_config)
self.add_host_hostnames()
# Add volumes
for host, volume in doc.collect_volumes().items():
if use_named_volume and 'name' in volume:
self.set_named_volume_mount(volume['name'], volume['bind'], volume['mode'] or 'rw')
else:
self.set_mount(host, volume['bind'], volume['mode'] or 'rw')
# Collect environment
for key, val in doc.collect_environment().items():
self.set_env(key, val)
# Add unimportant paths to the list of dirs to be used with overlayfs
self.set_env(EENV_OVERLAY_TARGETS, ':'.join(unimportant_paths))
# Mounting bind/overlayfs will require SYS_ADMIN caps:
if len(unimportant_paths) > 0:
self.cap_sys_admin = True
def init_from_service(self, service: Service, image_config):
"""
Initialize some data of this builder with the given service object.
You need to call service_add_main_port separately.
"""
perf_settings = service.get_project().parent()['performance']
project_absolute_unimportant_paths = []
if perf_settings['dont_sync_unimportant_src'] and 'unimportant_paths' in service.parent():
project_absolute_unimportant_paths = [_make_abs_to_src(p) for p in service.parent()['unimportant_paths']]
self._init_common(
service,
image_config,
perf_settings['dont_sync_named_volumes_with_host'],
project_absolute_unimportant_paths
)
# Collect labels
labels = service_collect_labels(service, service.get_project()["name"])
# Collect (and process!) additional_ports
ports = service.collect_ports()
# All command logging commands are added as environment variables for the
# riptide entrypoint
environment_updates = service_collect_logging_commands(service)
# User settings for the entrypoint
environment_updates.update(service_collect_entrypoint_user_settings(service, getuid(), getgid(), image_config))
# Add to builder
for key, value in environment_updates.items():
self.set_env(key, value)
for name, val in labels.items():
self.set_label(name, val)
for container, host in ports.items():
self.set_port(container, host)
# Check if ulimit memlock setting is enabled
if "allow_full_memlock" in service and service["allow_full_memlock"]:
self.set_allow_full_memlock(True)
return self
def service_add_main_port(self, service: Service):
"""
Add main service port.
Not thread-safe!
If starting multiple services in multiple threads:
This has to be done separately, right before start,
and with a lock in place, so that multiple service starts don't reserve the
same port.
"""
if "port" in service:
main_port = find_open_port_starting_at(DOCKER_ENGINE_HTTP_PORT_BND_START)
self.set_label(RIPTIDE_DOCKER_LABEL_HTTP_PORT, str(main_port))
self.set_port(service["port"], main_port)
def init_from_command(self, command: Command, image_config):
"""
Initialize some data of this builder with the given command object.
"""
perf_settings = command.get_project().parent()['performance']
project_absolute_unimportant_paths = []
if perf_settings['dont_sync_unimportant_src'] and 'unimportant_paths' in command.parent():
project_absolute_unimportant_paths = [_make_abs_to_src(p) for p in command.parent()['unimportant_paths']]
self._init_common(
command,
image_config,
perf_settings['dont_sync_named_volumes_with_host'],
project_absolute_unimportant_paths
)
return self
def build_docker_api(self) -> dict:
"""
Build the docker container in the form of Docker API containers.run arguments.
"""
args = {
'image': self.image
}
if self.command is None:
args['command'] = None
elif isinstance(self.command, str):
# COMMAND IS STRING
args['command'] = self.command
if len(self.args) > 0:
args['command'] += " " + " ".join(f'"{w}"' for w in self.args)
else:
list_command = self.command.copy()
# COMMAND IS LIST
if len(self.args) > 0:
list_command += self.args
# Strange Docker API Bug (?) requires args with spaces to be quoted...
args['command'] = []
for item in list_command:
if " " in item:
args['command'].append('"' + item + '"')
else:
args['command'].append(item)
if self.name:
args['name'] = self.name
if self.network:
args['network'] = self.network
if self.entrypoint:
args['entrypoint'] = [self.entrypoint]
if self.work_dir:
args['working_dir'] = self.work_dir
if self.run_as_root:
args['user'] = 0
if self.hostname:
args['hostname'] = self.hostname
if self.allow_full_memlock:
args['ulimits'] = [Ulimit(name='memlock', soft=-1, hard=-1)]
if self.cap_sys_admin:
args['cap_add'] = ['SYS_ADMIN']
# Ubuntu and possibly other Distros:
if self.on_linux:
args['security_opt'] = ['apparmor:unconfined']
args['environment'] = self.env.copy()
# Add list of named volume paths for Docker to chown
if len(self.named_volumes_in_cnt) > 0:
args['environment'][EENV_NAMED_VOLUMES] = ':'.join(self.named_volumes_in_cnt)
args['labels'] = self.labels
args['ports'] = self.ports
args['mounts'] = list(self.mounts.values())
return args
def build_docker_cli(self) -> List[str]:
"""
Build the docker container in the form of a Docker CLI command.
"""
shell = [
"docker", "run", "--rm", "-it"
]
if self.name:
shell += ["--name", self.name]
if self.network:
shell += ["--network", self.network]
if self.entrypoint:
shell += ["--entrypoint", self.entrypoint]
if self.work_dir:
shell += ["-w", self.work_dir]
if self.run_as_root:
shell += ["-u", str(0)]
if self.hostname:
shell += ["--hostname", self.hostname]
for key, value in self.env.items():
shell += ['-e', key + '=' + value]
# Add list of named volume paths for Docker to chown
if len(self.named_volumes_in_cnt) > 0:
shell += ['-e', EENV_NAMED_VOLUMES + '=' + ':'.join(self.named_volumes_in_cnt)]
for key, value in self.labels.items():
shell += ['--label', key + '=' + value]
for container, host in self.ports.items():
shell += ['-p', str(host) + ':' + str(container)]
# Mac: Add delegated
mac_add = ':delegated' if platform.system().lower().startswith('mac') else ''
for mount in self.mounts.values():
mode = 'ro' if mount['ReadOnly'] else 'rw'
if mount["Type"] == "bind":
shell += ['-v',
mount['Source'] + ':' + mount['Target'] + ':' + mode + mac_add]
else:
shell += ['--mount',
f'type=volume,target={mount["Target"]},src={mount["Source"]},ro={"0" if mode == "rw" else "1"},'
f'volume-label={RIPTIDE_DOCKER_LABEL_IS_RIPTIDE}=1']
# ulimits
if self.allow_full_memlock:
shell += ['--ulimit', 'memlock=-1:-1']
if self.cap_sys_admin:
shell += ['--cap-add=SYS_ADMIN']
# On Ubuntu and possibly other distros:
if self.on_linux:
shell += ['--security-opt', 'apparmor:unconfined']
command = self.command
if command is None:
command = ""
if isinstance(command, list):
command = self.command[0]
# If the command itself contains arguments, they have to be joined with
# quotes, just like self.args
if len(self.command) > 1:
command += " " + " ".join(f'"{w}"' for w in self.command[1:])
shell += [
self.image,
(command + " " + " ".join(f'"{w}"' for w in self.args)).rstrip()
]
return shell
def get_cmd_container_name(project_name: str, command_name: str):
return 'riptide__' + project_name + '__cmd__' + command_name + '__' + str(os.getpid())
def get_network_name(project_name: str):
return 'riptide__' + project_name
def get_service_container_name(project_name: str, service_name: str):
return 'riptide__' + project_name + '__' + service_name
def parse_entrypoint(entrypoint):
"""
Parse the original entrypoint of an image and return a map of variables for the riptide entrypoint script.
RIPTIDE__DOCKER_ORIGINAL_ENTRYPOINT: Original entrypoint as string to be used with exec.
Empty if original entrypoint is not set.
RIPTIDE__DOCKER_DONT_RUN_CMD: true or unset.
When the original entrypoint is a string, the command does not get run.
See table at https://docs.docker.com/engine/reference/builder/#shell-form-entrypoint-example
"""
# Is the original entrypoint set?
if not entrypoint:
return {EENV_ORIGINAL_ENTRYPOINT: ""}
# Is the original entrypoint shell or exec format?
if isinstance(entrypoint, list):
# exec format
# Turn the list into a string, but quote all arguments
command = entrypoint.pop(0)
arguments = " ".join([f'"{entry}"' for entry in entrypoint])
return {
EENV_ORIGINAL_ENTRYPOINT: command + " " + arguments
}
else:
# shell format
return {
EENV_ORIGINAL_ENTRYPOINT: "/bin/sh -c " + entrypoint,
EENV_DONT_RUN_CMD: "true"
}
pass
def service_collect_logging_commands(service: Service) -> dict:
"""Collect logging commands environment variables for this service"""
environment = {}
if "logging" in service and "commands" in service["logging"]:
for cmdname, command in service["logging"]["commands"].items():
environment[EENV_COMMAND_LOG_PREFIX + cmdname] = command
return environment
def service_collect_entrypoint_user_settings(service: Service, user, user_group, image_config) -> dict:
environment = {}
if not service["dont_create_user"]:
environment[EENV_USER] = str(user)
environment[EENV_GROUP] = str(user_group)
if service["run_as_current_user"]:
# Run with the current system user
environment[EENV_RUN_MAIN_CMD_AS_USER] = "yes"
elif "User" in image_config and image_config["User"] != "":
# If run_as_current_user is false and an user is configured in the image config, tell the entrypoint to run
# with this user
environment[EENV_RUN_MAIN_CMD_AS_USER] = "yes"
environment[EENV_USER_RUN] = image_config["User"]
return environment
def service_collect_labels(service: Service, project_name):
labels = {
RIPTIDE_DOCKER_LABEL_IS_RIPTIDE: '1',
RIPTIDE_DOCKER_LABEL_PROJECT: project_name,
RIPTIDE_DOCKER_LABEL_SERVICE: service["$name"],
RIPTIDE_DOCKER_LABEL_MAIN: "0"
}
if "roles" in service and "main" in service["roles"]:
labels[RIPTIDE_DOCKER_LABEL_MAIN] = "1"
return labels
def _make_abs_to_src(p):
"""Convert the given relative path to an absolute path. Relative base is /src/."""
return str(PurePosixPath("/src").joinpath(p))
| en | 0.818224 | Container builder module. # For services map HTTP main port to a host port starting here ContainerBuilder. Builds Riptide Docker containers for use with the Python API and the Docker CLI Create a new container builder. Specify image and command to run. # Performance setting for Docker Desktop on Mac Add a named volume. Name is automatically prefixed with riptide__. Add the Riptide entrypoint script and configure it. # The original entrypoint of the image is replaced with # this custom entrypoint script, which may call the original entrypoint # if present # If the entrypoint is enabled, then run the entrypoint # as root. It will handle the rest. # Collect entrypoint settings Adds all hostnames that must be routable to the host system within the container as a environment variable. # Add volumes # Collect environment # Add unimportant paths to the list of dirs to be used with overlayfs # Mounting bind/overlayfs will require SYS_ADMIN caps: Initialize some data of this builder with the given service object. You need to call service_add_main_port separately. # Collect labels # Collect (and process!) additional_ports # All command logging commands are added as environment variables for the # riptide entrypoint # User settings for the entrypoint # Add to builder # Check if ulimit memlock setting is enabled Add main service port. Not thread-safe! If starting multiple services in multiple threads: This has to be done separately, right before start, and with a lock in place, so that multiple service starts don't reserve the same port. Initialize some data of this builder with the given command object. Build the docker container in the form of Docker API containers.run arguments. # COMMAND IS STRING # COMMAND IS LIST # Strange Docker API Bug (?) requires args with spaces to be quoted... # Ubuntu and possibly other Distros: # Add list of named volume paths for Docker to chown Build the docker container in the form of a Docker CLI command. # Add list of named volume paths for Docker to chown # Mac: Add delegated # ulimits # On Ubuntu and possibly other distros: # If the command itself contains arguments, they have to be joined with # quotes, just like self.args Parse the original entrypoint of an image and return a map of variables for the riptide entrypoint script. RIPTIDE__DOCKER_ORIGINAL_ENTRYPOINT: Original entrypoint as string to be used with exec. Empty if original entrypoint is not set. RIPTIDE__DOCKER_DONT_RUN_CMD: true or unset. When the original entrypoint is a string, the command does not get run. See table at https://docs.docker.com/engine/reference/builder/#shell-form-entrypoint-example # Is the original entrypoint set? # Is the original entrypoint shell or exec format? # exec format # Turn the list into a string, but quote all arguments # shell format Collect logging commands environment variables for this service # Run with the current system user # If run_as_current_user is false and an user is configured in the image config, tell the entrypoint to run # with this user Convert the given relative path to an absolute path. Relative base is /src/. | 1.964997 | 2 |
nmeta/api_definitions/switches_api.py | mattjhayes/nmeta | 18 | 6624285 | <filename>nmeta/api_definitions/switches_api.py
# 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.
#*** nmeta - Network Metadata - API definition file
switches_schema = {
'dpid': {
'type': 'integer'
},
'ip_address': {
'type': 'string'
},
'port': {
'type': 'integer'
},
'time_connected': {
'type': 'string'
},
'mfr_desc': {
'type': 'string'
},
'hw_desc': {
'type': 'string'
},
'sw_desc': {
'type': 'string'
},
'serial_num': {
'type': 'string'
},
'dp_desc': {
'type': 'string'
}
}
switches_settings = {
'url': 'infrastructure/switches',
'item_title': 'OpenFlow Switches',
'schema': switches_schema
}
#*** A count of the number of connected switches:
switches_count_schema = {
'connected_switches': {
'type': 'integer'
}
}
switches_count_settings = {
'url': 'infrastructure/switches/stats/connected_switches',
'item_title': 'Count of Connected OpenFlow Switches',
'schema': switches_count_schema
}
| <filename>nmeta/api_definitions/switches_api.py
# 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.
#*** nmeta - Network Metadata - API definition file
switches_schema = {
'dpid': {
'type': 'integer'
},
'ip_address': {
'type': 'string'
},
'port': {
'type': 'integer'
},
'time_connected': {
'type': 'string'
},
'mfr_desc': {
'type': 'string'
},
'hw_desc': {
'type': 'string'
},
'sw_desc': {
'type': 'string'
},
'serial_num': {
'type': 'string'
},
'dp_desc': {
'type': 'string'
}
}
switches_settings = {
'url': 'infrastructure/switches',
'item_title': 'OpenFlow Switches',
'schema': switches_schema
}
#*** A count of the number of connected switches:
switches_count_schema = {
'connected_switches': {
'type': 'integer'
}
}
switches_count_settings = {
'url': 'infrastructure/switches/stats/connected_switches',
'item_title': 'Count of Connected OpenFlow Switches',
'schema': switches_count_schema
}
| en | 0.836088 | # 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. #*** nmeta - Network Metadata - API definition file #*** A count of the number of connected switches: | 1.55326 | 2 |
apps/gsekit/process/models.py | iSecloud/bk-process-config-manager | 8 | 6624286 | <filename>apps/gsekit/process/models.py
# -*- coding: utf-8 -*-
"""
Tencent is pleased to support the open source community by making 蓝鲸 (Blueking) available.
Copyright (C) 2017-2021 THL A29 Limited, a Tencent company. All rights reserved.
Licensed under the MIT License (the "License"); you may not use this file except in compliance with the License.
You may obtain a copy of the License at https://opensource.org/licenses/MIT
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 collections import defaultdict
from typing import Dict, List
from django.db import models
from django.utils.translation import ugettext_lazy as _
from apps.gsekit.cmdb.constants import BK_SET_ENV_CHOICES
from apps.gsekit.process.exceptions import ProcessInstDoseNotExistException, GenerateProcessObjException
class Process(models.Model):
class ProcessObjectType(object):
INSTANCE = "INSTANCE"
TEMPLATE = "TEMPLATE"
PROCESS_OBJECT_TYPE_CHOICE = (
(ProcessObjectType.INSTANCE, _("进程实例")),
(ProcessObjectType.TEMPLATE, _("进程模板")),
)
class ProcessStatus(object):
# 对于GSE，0/2都为终止状态
UNREGISTERED = 0
RUNNING = 1
TERMINATED = 2
PROCESS_STATUS_CHOICE = (
(ProcessStatus.RUNNING, _("运行中")),
(ProcessStatus.TERMINATED, _("未运行")),
)
IS_AUTO_CHOICE = ((False, _("未托管")), (True, _("已托管")))
bk_biz_id = models.IntegerField(_("业务ID"), db_index=True)
expression = models.CharField(_("实例表达式"), max_length=256, db_index=True, default="待完善")
bk_host_innerip = models.GenericIPAddressField(_("主机IP"), db_index=True)
bk_cloud_id = models.IntegerField(_("云区域ID"), db_index=True)
bk_set_env = models.CharField(_("集群环境类型"), choices=BK_SET_ENV_CHOICES, max_length=4, db_index=True)
bk_set_id = models.IntegerField(_("集群ID"), db_index=True)
bk_module_id = models.IntegerField(_("模块ID"), db_index=True)
service_template_id = models.IntegerField(_("服务模板ID"), null=True, blank=True, db_index=True)
service_instance_id = models.IntegerField(_("服务实例ID"), db_index=True)
bk_process_name = models.CharField(_("进程名称"), max_length=64, null=True, blank=True, db_index=True)
bk_process_id = models.IntegerField(_("进程ID"), primary_key=True)
process_template_id = models.IntegerField(_("进程模板ID"), db_index=True)
process_status = models.IntegerField(_("进程状态"), db_index=True, default=ProcessStatus.TERMINATED)
is_auto = models.BooleanField(_("托管状态"), db_index=True, default=False)
@classmethod
def generate_process_obj(cls, bk_process_id: int = None, process_template_id: int = None) -> Dict:
# 优先判定为进程模板
if process_template_id:
return {"process_object_type": cls.ProcessObjectType.TEMPLATE, "process_object_id": process_template_id}
elif bk_process_id:
return {"process_object_type": cls.ProcessObjectType.INSTANCE, "process_object_id": bk_process_id}
else:
raise GenerateProcessObjException()
def to_process_obj(self) -> Dict:
return self.generate_process_obj(bk_process_id=self.bk_process_id, process_template_id=self.process_template_id)
class Meta:
verbose_name = _("业务进程缓存")
verbose_name_plural = _("业务进程缓存")
class ProcessInst(models.Model):
# 默认启动数量
DEFAULT_PROC_NUM = 1
LOCAL_INST_ID_UNIQ_KEY_TMPL = "{bk_host_innerip}-{bk_cloud_id}-{bk_process_name}-{local_inst_id}"
INST_ID_UNIQ_KEY_TMPL = "{bk_module_id}-{bk_process_name}-{inst_id}"
BK_HOST_NUM_KEY_TMPL = "{bk_host_innerip}-{bk_cloud_id}-{bk_process_name}"
bk_biz_id = models.IntegerField(_("业务ID"), db_index=True)
bk_host_num = models.IntegerField(_("主机编号"), db_index=True)
bk_host_innerip = models.GenericIPAddressField(_("主机IP"), db_index=True)
bk_cloud_id = models.IntegerField(_("云区域ID"), db_index=True)
bk_process_id = models.IntegerField(_("进程ID"), db_index=True)
bk_module_id = models.IntegerField(_("模块ID"), db_index=True)
bk_process_name = models.CharField(_("进程名称"), max_length=64, db_index=True)
inst_id = models.IntegerField(_("InstID"), db_index=True)
process_status = models.IntegerField(_("进程状态"), db_index=True, default=Process.ProcessStatus.TERMINATED)
is_auto = models.BooleanField(_("托管状态"), db_index=True, default=False)
local_inst_id = models.IntegerField(_("LocalInstID"), db_index=True)
local_inst_id_uniq_key = models.CharField(_("进程实例唯一标识"), max_length=256, db_index=True, default="")
proc_num = models.IntegerField(_("启动数量"), default=DEFAULT_PROC_NUM)
@classmethod
def get_process_inst_map(cls, bk_process_ids: List[int]) -> Dict:
"""根据进程ID列表查询"""
proc_inst_map = defaultdict(list)
for proc_inst in ProcessInst.objects.filter(bk_process_id__in=bk_process_ids).values(
"bk_process_id", "inst_id", "local_inst_id"
):
proc_inst_map[proc_inst["bk_process_id"]].append(
{"inst_id": proc_inst["inst_id"], "local_inst_id": proc_inst["local_inst_id"]}
)
return proc_inst_map
@classmethod
def get_single_inst(cls, bk_process_id):
"""根据bk_process_id获取第一个实例"""
proc_inst = cls.objects.filter(bk_process_id=bk_process_id).first()
if not proc_inst:
raise ProcessInstDoseNotExistException()
return proc_inst
@property
def inst_id_uniq_key(self):
return self.INST_ID_UNIQ_KEY_TMPL.format(
bk_module_id=self.bk_module_id, bk_process_name=self.bk_process_name, inst_id=self.inst_id
)
@property
def bk_host_num_key(self):
return self.BK_HOST_NUM_KEY_TMPL.format(
bk_host_innerip=self.bk_host_innerip, bk_cloud_id=self.bk_cloud_id, bk_process_name=self.bk_process_name
)
class Meta:
unique_together = [
["bk_module_id", "bk_process_name", "inst_id"],
["bk_host_innerip", "bk_cloud_id", "bk_process_name", "local_inst_id"],
]
verbose_name = _("进程实例")
verbose_name_plural = _("进程实例")
| <filename>apps/gsekit/process/models.py
# -*- coding: utf-8 -*-
"""
Tencent is pleased to support the open source community by making 蓝鲸 (Blueking) available.
Copyright (C) 2017-2021 THL A29 Limited, a Tencent company. All rights reserved.
Licensed under the MIT License (the "License"); you may not use this file except in compliance with the License.
You may obtain a copy of the License at https://opensource.org/licenses/MIT
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 collections import defaultdict
from typing import Dict, List
from django.db import models
from django.utils.translation import ugettext_lazy as _
from apps.gsekit.cmdb.constants import BK_SET_ENV_CHOICES
from apps.gsekit.process.exceptions import ProcessInstDoseNotExistException, GenerateProcessObjException
class Process(models.Model):
class ProcessObjectType(object):
INSTANCE = "INSTANCE"
TEMPLATE = "TEMPLATE"
PROCESS_OBJECT_TYPE_CHOICE = (
(ProcessObjectType.INSTANCE, _("进程实例")),
(ProcessObjectType.TEMPLATE, _("进程模板")),
)
class ProcessStatus(object):
# 对于GSE，0/2都为终止状态
UNREGISTERED = 0
RUNNING = 1
TERMINATED = 2
PROCESS_STATUS_CHOICE = (
(ProcessStatus.RUNNING, _("运行中")),
(ProcessStatus.TERMINATED, _("未运行")),
)
IS_AUTO_CHOICE = ((False, _("未托管")), (True, _("已托管")))
bk_biz_id = models.IntegerField(_("业务ID"), db_index=True)
expression = models.CharField(_("实例表达式"), max_length=256, db_index=True, default="待完善")
bk_host_innerip = models.GenericIPAddressField(_("主机IP"), db_index=True)
bk_cloud_id = models.IntegerField(_("云区域ID"), db_index=True)
bk_set_env = models.CharField(_("集群环境类型"), choices=BK_SET_ENV_CHOICES, max_length=4, db_index=True)
bk_set_id = models.IntegerField(_("集群ID"), db_index=True)
bk_module_id = models.IntegerField(_("模块ID"), db_index=True)
service_template_id = models.IntegerField(_("服务模板ID"), null=True, blank=True, db_index=True)
service_instance_id = models.IntegerField(_("服务实例ID"), db_index=True)
bk_process_name = models.CharField(_("进程名称"), max_length=64, null=True, blank=True, db_index=True)
bk_process_id = models.IntegerField(_("进程ID"), primary_key=True)
process_template_id = models.IntegerField(_("进程模板ID"), db_index=True)
process_status = models.IntegerField(_("进程状态"), db_index=True, default=ProcessStatus.TERMINATED)
is_auto = models.BooleanField(_("托管状态"), db_index=True, default=False)
@classmethod
def generate_process_obj(cls, bk_process_id: int = None, process_template_id: int = None) -> Dict:
# 优先判定为进程模板
if process_template_id:
return {"process_object_type": cls.ProcessObjectType.TEMPLATE, "process_object_id": process_template_id}
elif bk_process_id:
return {"process_object_type": cls.ProcessObjectType.INSTANCE, "process_object_id": bk_process_id}
else:
raise GenerateProcessObjException()
def to_process_obj(self) -> Dict:
return self.generate_process_obj(bk_process_id=self.bk_process_id, process_template_id=self.process_template_id)
class Meta:
verbose_name = _("业务进程缓存")
verbose_name_plural = _("业务进程缓存")
class ProcessInst(models.Model):
# 默认启动数量
DEFAULT_PROC_NUM = 1
LOCAL_INST_ID_UNIQ_KEY_TMPL = "{bk_host_innerip}-{bk_cloud_id}-{bk_process_name}-{local_inst_id}"
INST_ID_UNIQ_KEY_TMPL = "{bk_module_id}-{bk_process_name}-{inst_id}"
BK_HOST_NUM_KEY_TMPL = "{bk_host_innerip}-{bk_cloud_id}-{bk_process_name}"
bk_biz_id = models.IntegerField(_("业务ID"), db_index=True)
bk_host_num = models.IntegerField(_("主机编号"), db_index=True)
bk_host_innerip = models.GenericIPAddressField(_("主机IP"), db_index=True)
bk_cloud_id = models.IntegerField(_("云区域ID"), db_index=True)
bk_process_id = models.IntegerField(_("进程ID"), db_index=True)
bk_module_id = models.IntegerField(_("模块ID"), db_index=True)
bk_process_name = models.CharField(_("进程名称"), max_length=64, db_index=True)
inst_id = models.IntegerField(_("InstID"), db_index=True)
process_status = models.IntegerField(_("进程状态"), db_index=True, default=Process.ProcessStatus.TERMINATED)
is_auto = models.BooleanField(_("托管状态"), db_index=True, default=False)
local_inst_id = models.IntegerField(_("LocalInstID"), db_index=True)
local_inst_id_uniq_key = models.CharField(_("进程实例唯一标识"), max_length=256, db_index=True, default="")
proc_num = models.IntegerField(_("启动数量"), default=DEFAULT_PROC_NUM)
@classmethod
def get_process_inst_map(cls, bk_process_ids: List[int]) -> Dict:
"""根据进程ID列表查询"""
proc_inst_map = defaultdict(list)
for proc_inst in ProcessInst.objects.filter(bk_process_id__in=bk_process_ids).values(
"bk_process_id", "inst_id", "local_inst_id"
):
proc_inst_map[proc_inst["bk_process_id"]].append(
{"inst_id": proc_inst["inst_id"], "local_inst_id": proc_inst["local_inst_id"]}
)
return proc_inst_map
@classmethod
def get_single_inst(cls, bk_process_id):
"""根据bk_process_id获取第一个实例"""
proc_inst = cls.objects.filter(bk_process_id=bk_process_id).first()
if not proc_inst:
raise ProcessInstDoseNotExistException()
return proc_inst
@property
def inst_id_uniq_key(self):
return self.INST_ID_UNIQ_KEY_TMPL.format(
bk_module_id=self.bk_module_id, bk_process_name=self.bk_process_name, inst_id=self.inst_id
)
@property
def bk_host_num_key(self):
return self.BK_HOST_NUM_KEY_TMPL.format(
bk_host_innerip=self.bk_host_innerip, bk_cloud_id=self.bk_cloud_id, bk_process_name=self.bk_process_name
)
class Meta:
unique_together = [
["bk_module_id", "bk_process_name", "inst_id"],
["bk_host_innerip", "bk_cloud_id", "bk_process_name", "local_inst_id"],
]
verbose_name = _("进程实例")
verbose_name_plural = _("进程实例")
| en | 0.818072 | # -*- coding: utf-8 -*- Tencent is pleased to support the open source community by making 蓝鲸 (Blueking) available. Copyright (C) 2017-2021 THL A29 Limited, a Tencent company. All rights reserved. Licensed under the MIT License (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at https://opensource.org/licenses/MIT 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. # 对于GSE，0/2都为终止状态 # 优先判定为进程模板 # 默认启动数量 根据进程ID列表查询 根据bk_process_id获取第一个实例 | 1.735579 | 2 |
day4/task.py | dsky1990/python_30days | 1 | 6624287 | <reponame>dsky1990/python_30days
# def fib(max):
# n, a, b = 0, 0, 1
# while n < max:
# print(b)
# a, b = b, a + b
# n = n + 1
# else:
# print('done')
# fib(9)
def fib(max):
n, a, b = 0, 0, 1
while n < max:
yield b
a, b = b, a + b
n += 1
return "done"
n = fib(10)
while True:
try:
x = next(n)
print("n:%d" %x)
except StopIteration as e:
print("Generation return value:", e.value)
break
| # def fib(max):
# n, a, b = 0, 0, 1
# while n < max:
# print(b)
# a, b = b, a + b
# n = n + 1
# else:
# print('done')
# fib(9)
def fib(max):
n, a, b = 0, 0, 1
while n < max:
yield b
a, b = b, a + b
n += 1
return "done"
n = fib(10)
while True:
try:
x = next(n)
print("n:%d" %x)
except StopIteration as e:
print("Generation return value:", e.value)
break | en | 0.506416 | # def fib(max): # n, a, b = 0, 0, 1 # while n < max: # print(b) # a, b = b, a + b # n = n + 1 # else: # print('done') # fib(9) | 3.888305 | 4 |
bakery_cli/pipe/__init__.py | jessamynsmith/fontbakery | 0 | 6624288 | from bakery_cli.pipe.copy import (Copy, CopyLicense, CopyDescription, CopyTxtFiles,
CopyFontLog, CopyMetadata)
from bakery_cli.pipe.build import Build
from bakery_cli.pipe.ttfautohint import TTFAutoHint
from bakery_cli.pipe.pyftsubset import PyFtSubset
from bakery_cli.pipe.pyfontaine import PyFontaine
from bakery_cli.pipe.metadata import Metadata
from bakery_cli.pipe.fontlint import FontLint
from bakery_cli.pipe.optimize import Optimize
from bakery_cli.pipe.checkout import Checkout
from bakery_cli.pipe.zip import Zip
from bakery_cli.pipe.metadatalint import MetadataLint
from bakery_cli.pipe.upstreamlint import UpstreamLint
from bakery_cli.pipe.font_crunch import FontCrunch
| from bakery_cli.pipe.copy import (Copy, CopyLicense, CopyDescription, CopyTxtFiles,
CopyFontLog, CopyMetadata)
from bakery_cli.pipe.build import Build
from bakery_cli.pipe.ttfautohint import TTFAutoHint
from bakery_cli.pipe.pyftsubset import PyFtSubset
from bakery_cli.pipe.pyfontaine import PyFontaine
from bakery_cli.pipe.metadata import Metadata
from bakery_cli.pipe.fontlint import FontLint
from bakery_cli.pipe.optimize import Optimize
from bakery_cli.pipe.checkout import Checkout
from bakery_cli.pipe.zip import Zip
from bakery_cli.pipe.metadatalint import MetadataLint
from bakery_cli.pipe.upstreamlint import UpstreamLint
from bakery_cli.pipe.font_crunch import FontCrunch
| none | 1 | 1.201987 | 1 | |
flashpandas/pages/create.py | MaxTechniche/flash-pandas | 0 | 6624289 | import dash_bootstrap_components as dbc
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Output, State, Input
from pymongo.errors import DuplicateKeyError
from flask import session
from flashpandas.app import APP, users, cards, Card
logged_out_layout = html.Div(
[
"Must be logged in in order to create.",
dbc.NavLink(dbc.Button("Login"), href="/login"),
],
style={"text-align": "center"},
)
header = html.Div(
[
dbc.Label("Card Creation", style={"font-size": "20px", "margin-bottom": "0px"}),
dcc.Markdown("---"),
],
style={"text-align": "center"},
)
question_and_answer_input = dbc.Row(
[
dbc.Col(
[
dbc.Label(
children=[
"Question Input (uses ",
html.A(
"Markdown",
href="https://www.markdownguide.org/",
target="_blank",
),
")",
]
),
dbc.Textarea(
id="question-input",
persistence=True,
persistence_type="memory",
style={"margin-bottom": "15px"},
),
],
style={"min-width": "250px"},
),
dbc.Col(
[
dbc.Label(
children=[
"Answer Input (uses ",
html.A(
"Markdown",
href="https://www.markdownguide.org/",
target="_blank",
),
")",
]
),
dbc.Textarea(
id="answer-input",
persistence=True,
persistence_type="memory",
style={"margin-bottom": "15px"},
),
],
style={"min-width": "250px"},
),
]
)
question_and_answer_view = dbc.Row(
[
dbc.Col(
[
dbc.Label("Question View"),
dcc.Markdown(
id="question-view",
style={
"border": "2px solid #C8E4F4",
"border-radius": "3px",
"padding": "5px",
"margin-bottom": "15px",
},
),
dcc.Markdown("---"),
],
style={"min-width": "250px"},
),
dbc.Col(
[
dbc.Label("Answer View"),
dcc.Markdown(
id="answer-view",
style={
"border": "2px solid #C8E4F4",
"border-radius": "3px",
"padding": "5px",
"margin-bottom": "15px",
},
),
dcc.Markdown("---"),
],
style={"min-width": "250px"},
),
]
)
tags_and_title = dbc.Row(
[
dbc.Col(
[
dbc.Label("Tags (future goal) (comma separated)"),
dbc.Input(
id="tags-input",
persistence=True,
persistence_type="memory",
),
],
style={"min-width": "200px"},
),
dbc.Col(
[
dbc.Label("Title or Summary"),
dbc.Input(
id="title-input",
persistence=True,
persistence_type="memory",
),
],
style={"min-width": "200px"},
),
]
)
public_and_submit = html.Div(
[
dcc.Markdown("---"),
html.Div(
[
dbc.Checkbox(
id="public-check",
persistence=True,
checked=False,
persistence_type="memory",
),
dbc.Label("Make Public", style={"padding-left": "5px"}),
]
),
dbc.Col(
[
dbc.Button("Submit Card", id="card-submit", color="success"),
html.Div(),
dbc.Label(id="error-info", style={"padding-top": "10px"}),
]
),
],
style={"text-align": "center"},
)
layout = html.Div(
children=[
header,
question_and_answer_input,
question_and_answer_view,
tags_and_title,
public_and_submit,
],
id="create-layout",
)
@APP.callback(
[Output("question-view", "children"), Output("answer-view", "children")],
[Input("question-input", "value"), Input("answer-input", "value")],
)
def mirror_text(question, answer):
return [question, answer]
@APP.callback(
Output("error-info", "children"),
Input("card-submit", "n_clicks"),
[
State("question-input", "value"),
State("answer-input", "value"),
State("tags-input", "value"),
State("title-input", "value"),
State("public-check", "checked"),
],
)
def submit_card(n_clicks, q_input, a_input, tags, title, public):
if n_clicks:
if not q_input:
return "No question provided"
if not a_input:
return "No answer provided"
card = Card(
title=title or "",
q_text=q_input,
a_text=a_input,
tags=tags.strip().split(",") if tags else [],
public=public or False,
creator=session.get("username", None),
)
cards.insert_one(card.to_json())
return dcc.Location("url", "/cards")
return ""
| import dash_bootstrap_components as dbc
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Output, State, Input
from pymongo.errors import DuplicateKeyError
from flask import session
from flashpandas.app import APP, users, cards, Card
logged_out_layout = html.Div(
[
"Must be logged in in order to create.",
dbc.NavLink(dbc.Button("Login"), href="/login"),
],
style={"text-align": "center"},
)
header = html.Div(
[
dbc.Label("Card Creation", style={"font-size": "20px", "margin-bottom": "0px"}),
dcc.Markdown("---"),
],
style={"text-align": "center"},
)
question_and_answer_input = dbc.Row(
[
dbc.Col(
[
dbc.Label(
children=[
"Question Input (uses ",
html.A(
"Markdown",
href="https://www.markdownguide.org/",
target="_blank",
),
")",
]
),
dbc.Textarea(
id="question-input",
persistence=True,
persistence_type="memory",
style={"margin-bottom": "15px"},
),
],
style={"min-width": "250px"},
),
dbc.Col(
[
dbc.Label(
children=[
"Answer Input (uses ",
html.A(
"Markdown",
href="https://www.markdownguide.org/",
target="_blank",
),
")",
]
),
dbc.Textarea(
id="answer-input",
persistence=True,
persistence_type="memory",
style={"margin-bottom": "15px"},
),
],
style={"min-width": "250px"},
),
]
)
question_and_answer_view = dbc.Row(
[
dbc.Col(
[
dbc.Label("Question View"),
dcc.Markdown(
id="question-view",
style={
"border": "2px solid #C8E4F4",
"border-radius": "3px",
"padding": "5px",
"margin-bottom": "15px",
},
),
dcc.Markdown("---"),
],
style={"min-width": "250px"},
),
dbc.Col(
[
dbc.Label("Answer View"),
dcc.Markdown(
id="answer-view",
style={
"border": "2px solid #C8E4F4",
"border-radius": "3px",
"padding": "5px",
"margin-bottom": "15px",
},
),
dcc.Markdown("---"),
],
style={"min-width": "250px"},
),
]
)
tags_and_title = dbc.Row(
[
dbc.Col(
[
dbc.Label("Tags (future goal) (comma separated)"),
dbc.Input(
id="tags-input",
persistence=True,
persistence_type="memory",
),
],
style={"min-width": "200px"},
),
dbc.Col(
[
dbc.Label("Title or Summary"),
dbc.Input(
id="title-input",
persistence=True,
persistence_type="memory",
),
],
style={"min-width": "200px"},
),
]
)
public_and_submit = html.Div(
[
dcc.Markdown("---"),
html.Div(
[
dbc.Checkbox(
id="public-check",
persistence=True,
checked=False,
persistence_type="memory",
),
dbc.Label("Make Public", style={"padding-left": "5px"}),
]
),
dbc.Col(
[
dbc.Button("Submit Card", id="card-submit", color="success"),
html.Div(),
dbc.Label(id="error-info", style={"padding-top": "10px"}),
]
),
],
style={"text-align": "center"},
)
layout = html.Div(
children=[
header,
question_and_answer_input,
question_and_answer_view,
tags_and_title,
public_and_submit,
],
id="create-layout",
)
@APP.callback(
[Output("question-view", "children"), Output("answer-view", "children")],
[Input("question-input", "value"), Input("answer-input", "value")],
)
def mirror_text(question, answer):
return [question, answer]
@APP.callback(
Output("error-info", "children"),
Input("card-submit", "n_clicks"),
[
State("question-input", "value"),
State("answer-input", "value"),
State("tags-input", "value"),
State("title-input", "value"),
State("public-check", "checked"),
],
)
def submit_card(n_clicks, q_input, a_input, tags, title, public):
if n_clicks:
if not q_input:
return "No question provided"
if not a_input:
return "No answer provided"
card = Card(
title=title or "",
q_text=q_input,
a_text=a_input,
tags=tags.strip().split(",") if tags else [],
public=public or False,
creator=session.get("username", None),
)
cards.insert_one(card.to_json())
return dcc.Location("url", "/cards")
return ""
| hu | 0.348993 | #C8E4F4", #C8E4F4", | 2.334763 | 2 |
combat.py | tawnkramer/Adventure | 2 | 6624290 | import random
from prettytable import *
from util import *
from commentary import *
HP_RECOVERED_FROM_REST = 1
class CombatStats(object):
COMBAT_STATUS_NONE = 0
COMBAT_STATUS_FIGHTING = 1
COMBAT_STATUS_RAN = 2
COMBAT_STATUS_WON = 3
COMBAT_STATUS_ALL_DIED = 4
def __init__(self):
self.players_died = []
self.just_died = None
self.players_wounded = []
self.monsters_slain = []
self.monsters_wounded = []
self.rounds = 0
self.total_dam_to_players = 0
self.total_dam_to_monsters = 0
self.players_perfect_blow = []
self.gold_won = 0
self.treasure_won = []
self.combat_status = self.COMBAT_STATUS_NONE
class CombatAction(object):
def __init__(self, name, description):
self.name = name
self.description = description
def is_combat_action(self):
return True
def is_weapon(self):
return False
def is_spell(self):
return False
def all_dead(group):
for p in group:
if p.is_alive():
return False
return True
def add_unique(actions, item):
for a in actions:
if a.name == item.name:
return
actions.append(item)
def choose_action_and_target(attacker, others):
actions = []
if attacker.active_weapon is not None and attacker.active_weapon.is_weapon() and attacker.has_skill(attacker.active_weapon.cat):
add_unique(actions, attacker.active_weapon)
mana_adj = 0
#casting spells while in armor happens at a deficit
if attacker.is_wearing_non_magic_armor():
mana_adj = 1
for item in attacker.inventory:
if item.is_spell() and (item.mana + mana_adj) <= attacker.cur_mana and attacker.has_skill(item.cat):
add_unique(actions, item)
if attacker.has_potions():
actions.append(CombatAction('drink', 'drink potion.'))
if attacker.has_scrolls():
actions.append(CombatAction('read', 'read scroll.'))
if attacker.has_wands():
actions.append(CombatAction('wand', 'use wand.'))
actions.append(CombatAction('draw', 'draw a new weapon to use.'))
actions.append(CombatAction('block', 'attempt to withdaw, rest, and avoid damage.'))
actions.append(CombatAction('run', 'flee to the nearest exit!'))
if attacker.can_hide():
if attacker.is_hidden():
actions.append(CombatAction('stay hidden', 'recover hp and wait for right moment for suprise attack.'))
else:
actions.append(CombatAction('hide', 'use your stealth skill to hide in shadows.'))
print "\n\nActions"
t = PrettyTable(['sel', 'action', 'max damage', 'mana', 'description'])
i = 1
for a in actions:
if a.is_spell():
t.add_row([i, a.name, a.damage, a.mana + mana_adj, a.description])
elif a.is_weapon():
t.add_row([i, a.name, a.damage, ' ', a.description])
elif a.is_combat_action():
t.add_row([i, a.name, ' ', ' ', a.description])
i = i + 1
print t.get_string(hrules=HEADER)
print
print "Targets"
t = PrettyTable(['sel', 'target', 'hp', 'ac', 'disabled'])
i = 1
for m in others:
if m.disabled:
disabled = 'Yes'
else:
disabled = 'No'
t.add_row([i, m.name, m.cur_hp, m.ac, disabled])
i = i + 1
print t.get_string(hrules=HEADER)
print
show_player_status(attacker)
#get input
valid = False
target = None
while not valid:
print
sel = get_input( attacker.name + " - Enter number of sel action (and optional sel target) -> ")
iAction = 0
iTarget = 0
try:
if(sel.find(' ') != -1):
args = sel.split(' ')
if len(args) == 2:
iAction = int(args[0])
iTarget = int(args[1])
else:
iAction = int(sel)
except:
pass
if iAction > 0 and iAction <= len(actions):
if iTarget != 0:
if iTarget > 0 and iTarget <= len(others):
target = others[iTarget - 1]
while target is None or not target.is_alive():
if iTarget < 0 or iTarget >= len(others):
iTarget = 0
target = others[iTarget]
iTarget += 1
valid = True
return [actions[iAction - 1], target ]
def random_action_target(monster, players, player_actions):
if len(players) == 0 or all_dead(players):
return [None, None, None]
iAction = random.randint(0, len(monster.attacks) - 1)
iPlayer = random.randint(0, len(players) - 1)
while not players[iPlayer].is_alive():
iPlayer = random.randint(0, len(players) - 1)
return [ monster.attacks[iAction], players[iPlayer], player_actions[iPlayer] ]
def random_target(monster, attack, players, player_actions):
if len(players) == 0 or all_dead(players):
return [None, None, None]
iPlayer = random.randint(0, len(players) - 1)
while not players[iPlayer].is_alive():
iPlayer = random.randint(0, len(players) - 1)
return [ attack, players[iPlayer], player_actions[iPlayer] ]
def show_player_status(p):
if p.is_trapped():
print p.get_trapped_desc()
elif p.is_disabled():
print p.name, 'hp:', p.cur_hp, ' is disabled!'
else:
print p.name, 'hp:', p.cur_hp, 'ac:', p.get_ac(), 'mana:', p.cur_mana
def party_comment(comment, party):
for p in party:
if p.is_alive():
print comment % p.name
break
def show_status(players):
print 'Status:'
for p in players:
show_player_status(p)
print
def award_experience(players, monsters):
exp_total = 0
for m in monsters:
m.on_combat_end()
if not m.is_alive():
exp_total += m.xp_award
total_living_players = 0
for p in players:
p.on_combat_end()
if p.is_alive():
total_living_players += 1
if total_living_players == 0:
return
reward = exp_total / total_living_players
for p in players:
if p.is_alive() and reward > 0:
p.add_xp( reward )
def one_dead(group):
d = 0
for m in group:
if not m.is_alive():
d = d + 1
return d == 1
def half_dead(group):
d = 0
for m in group:
if not m.is_alive():
d = d + 1
return d == int(len(group) / 2)
#a list of the player targets. We adjust the frequency of monster focus
#by adding attacking players 3 times, spell or blocking characters once.
def add_player_target(player, action, player_targets, player_actions):
if player.is_hidden():
return
if action.is_weapon():
player_targets.append(player)
player_targets.append(player)
player_actions.append(action)
player_actions.append(action)
player_targets.append(player)
player_actions.append(action)
def get_tohit_mod(round):
#to simulate fatigue and increasing
#likeliness to give and receive damage,
#adjust to hit up as rounds go on.
#Also helps reduce super long slogging battles
#where everyone misses.
tohit_mod = round / 2
#cap it at a ridiculous 10. Though 30 rounds is a SUPER long battle.
if tohit_mod > 10:
tohit_mod = 10
return tohit_mod
def fight(players, monsters, room, level, mon_attack_first):
done = False
round = 1
moral_check_on_first_dead = False
moral_check_on_half_dead = False
level.combat_stats = CombatStats()
while not done:
print '-------------------------------------------------------'
print 'Round', round, '\n'
#make sure the armor class is up to date.
for p in players:
p.update_armor_class()
#show all player status
show_status(players)
#adjust tohit modifiers to account for fatigue
tohit_mod = get_tohit_mod(round)
#a list of the player targets. We adjust the frequency of monster focus
#by adding attacking players 3 times, spell or blocking characters once.
player_targets = []
player_actions = []
for player in players:
if not player.is_alive():
continue
if all_dead(monsters):
continue
if mon_attack_first:
add_player_target(player, CombatAction('suprised', ''), player_targets, player_actions)
continue
if player.is_disabled() or player.is_trapped():
add_player_target(player, CombatAction('disabled', ''), player_targets, player_actions)
continue
action, target = choose_action_and_target(player, monsters)
if action is None:
continue
if action.is_spell():
player.cur_mana -= action.mana
#wearing armor incurs a 1 pt mana expense
if player.is_wearing_non_magic_armor():
player.cur_mana -= 1
if action.targets_environment():
action.cast_at_environ(player, monsters, players, room, level)
elif target is None or action.targets_group():
action.cast(player, monsters, players)
else:
action.cast_at_target(player, target)
elif action.is_weapon():
if target is None:
action.attack(player, monsters, players, tohit_mod)
else:
action.attack_target(player, target, tohit_mod)
elif action.is_combat_action():
if action.name == 'block':
hp_rec = HP_RECOVERED_FROM_REST * player.level
if player.cur_hp <= (player.hp / 2) - hp_rec:
player.cur_hp += hp_rec
print player.name, 'regains', hp_rec, 'hp after blocking and resting.'
elif action.name == 'run':
num_items = len(player.inventory)
if num_items > 0:
iDrop = random.randint(0, num_items - 1)
item = player.inventory[iDrop]
if not item.is_weapon() and not item.is_spell() and not item.is_armor():
print player.name, 'bolts for the door and drops his', item.name, 'in haste!'
player.remove(item.name)
room.items.append(item)
award_experience(players, monsters)
#all monsters heal when you leave.
for m in monsters:
if m.is_alive():
m.cur_hp = m.hp
pause()
return "run"
elif action.name == 'hide':
roll = random.randint(1, 20)
print player.name, 'rolls %d.' % roll
if roll >= player.get_hide_roll_thresh():
print player.name, 'slips into the shadows silently.'
player.set_hidden(True)
else:
print player.name, 'was not able to hide.'
elif action.name == 'draw':
room.activate([player])
elif action.name == 'drink':
player.drink_potion()
elif action.name == 'read':
player.read_scroll(monsters, players, room, level)
elif action.name == 'wand':
player.use_wand(monsters, players, room, level)
pause()
if player.is_hidden():
if action.is_spell() or action.is_weapon():
player.set_hidden(False)
elif action.name != 'run':
hp_rec = HP_RECOVERED_FROM_REST * player.level
if player.cur_hp <= (player.hp / 2) - hp_rec:
player.cur_hp += hp_rec
print player.name, 'regains', hp_rec, 'hp while hiding.'
add_player_target(player, action, player_targets, player_actions)
#check for morale of monsters
if not moral_check_on_first_dead and one_dead(monsters) and len(monsters) > 1:
moral_check_on_first_dead = True
if random.randint(1, 10) < 4:
print 'The rest of the creatures flee in terror!'
award_experience(players, monsters)
return 'won'
if not moral_check_on_half_dead and half_dead(monsters) and len(monsters) > 3:
moral_check_on_half_dead = True
if random.randint(1, 10) < 5:
print 'The rest of the creatures flee in terror!'
award_experience(players, monsters)
return 'won'
#only use once. Surprise attack
if mon_attack_first:
if random.randint(1, 2) < 2:
print "It's a surprise attack!"
tohit_mod += 2
else:
party_comment('%s yells, "Look out!"', players)
mon_attack_first = False
for monster in monsters:
action = monster.take_combat_turn(player_targets, player_actions, room, level, tohit_mod)
if action and action.name == 'run':
monsters.remove(monster)
if all_dead(monsters):
print "You defeated all the enemies!!\n"
sitch = [WON]
print_comment(sitch, players, room, level)
award_experience(players, monsters)
return 'won'
if all_dead(players):
print 'And everything fades to black. Better to have run.. oh well!!'
return 'died'
for monster in monsters:
if monster.is_alive():
monster.on_combat_round_ended()
for player in players:
if player.is_alive():
player.on_combat_round_ended()
print
round = round + 1
| import random
from prettytable import *
from util import *
from commentary import *
HP_RECOVERED_FROM_REST = 1
class CombatStats(object):
COMBAT_STATUS_NONE = 0
COMBAT_STATUS_FIGHTING = 1
COMBAT_STATUS_RAN = 2
COMBAT_STATUS_WON = 3
COMBAT_STATUS_ALL_DIED = 4
def __init__(self):
self.players_died = []
self.just_died = None
self.players_wounded = []
self.monsters_slain = []
self.monsters_wounded = []
self.rounds = 0
self.total_dam_to_players = 0
self.total_dam_to_monsters = 0
self.players_perfect_blow = []
self.gold_won = 0
self.treasure_won = []
self.combat_status = self.COMBAT_STATUS_NONE
class CombatAction(object):
def __init__(self, name, description):
self.name = name
self.description = description
def is_combat_action(self):
return True
def is_weapon(self):
return False
def is_spell(self):
return False
def all_dead(group):
for p in group:
if p.is_alive():
return False
return True
def add_unique(actions, item):
for a in actions:
if a.name == item.name:
return
actions.append(item)
def choose_action_and_target(attacker, others):
actions = []
if attacker.active_weapon is not None and attacker.active_weapon.is_weapon() and attacker.has_skill(attacker.active_weapon.cat):
add_unique(actions, attacker.active_weapon)
mana_adj = 0
#casting spells while in armor happens at a deficit
if attacker.is_wearing_non_magic_armor():
mana_adj = 1
for item in attacker.inventory:
if item.is_spell() and (item.mana + mana_adj) <= attacker.cur_mana and attacker.has_skill(item.cat):
add_unique(actions, item)
if attacker.has_potions():
actions.append(CombatAction('drink', 'drink potion.'))
if attacker.has_scrolls():
actions.append(CombatAction('read', 'read scroll.'))
if attacker.has_wands():
actions.append(CombatAction('wand', 'use wand.'))
actions.append(CombatAction('draw', 'draw a new weapon to use.'))
actions.append(CombatAction('block', 'attempt to withdaw, rest, and avoid damage.'))
actions.append(CombatAction('run', 'flee to the nearest exit!'))
if attacker.can_hide():
if attacker.is_hidden():
actions.append(CombatAction('stay hidden', 'recover hp and wait for right moment for suprise attack.'))
else:
actions.append(CombatAction('hide', 'use your stealth skill to hide in shadows.'))
print "\n\nActions"
t = PrettyTable(['sel', 'action', 'max damage', 'mana', 'description'])
i = 1
for a in actions:
if a.is_spell():
t.add_row([i, a.name, a.damage, a.mana + mana_adj, a.description])
elif a.is_weapon():
t.add_row([i, a.name, a.damage, ' ', a.description])
elif a.is_combat_action():
t.add_row([i, a.name, ' ', ' ', a.description])
i = i + 1
print t.get_string(hrules=HEADER)
print
print "Targets"
t = PrettyTable(['sel', 'target', 'hp', 'ac', 'disabled'])
i = 1
for m in others:
if m.disabled:
disabled = 'Yes'
else:
disabled = 'No'
t.add_row([i, m.name, m.cur_hp, m.ac, disabled])
i = i + 1
print t.get_string(hrules=HEADER)
print
show_player_status(attacker)
#get input
valid = False
target = None
while not valid:
print
sel = get_input( attacker.name + " - Enter number of sel action (and optional sel target) -> ")
iAction = 0
iTarget = 0
try:
if(sel.find(' ') != -1):
args = sel.split(' ')
if len(args) == 2:
iAction = int(args[0])
iTarget = int(args[1])
else:
iAction = int(sel)
except:
pass
if iAction > 0 and iAction <= len(actions):
if iTarget != 0:
if iTarget > 0 and iTarget <= len(others):
target = others[iTarget - 1]
while target is None or not target.is_alive():
if iTarget < 0 or iTarget >= len(others):
iTarget = 0
target = others[iTarget]
iTarget += 1
valid = True
return [actions[iAction - 1], target ]
def random_action_target(monster, players, player_actions):
if len(players) == 0 or all_dead(players):
return [None, None, None]
iAction = random.randint(0, len(monster.attacks) - 1)
iPlayer = random.randint(0, len(players) - 1)
while not players[iPlayer].is_alive():
iPlayer = random.randint(0, len(players) - 1)
return [ monster.attacks[iAction], players[iPlayer], player_actions[iPlayer] ]
def random_target(monster, attack, players, player_actions):
if len(players) == 0 or all_dead(players):
return [None, None, None]
iPlayer = random.randint(0, len(players) - 1)
while not players[iPlayer].is_alive():
iPlayer = random.randint(0, len(players) - 1)
return [ attack, players[iPlayer], player_actions[iPlayer] ]
def show_player_status(p):
if p.is_trapped():
print p.get_trapped_desc()
elif p.is_disabled():
print p.name, 'hp:', p.cur_hp, ' is disabled!'
else:
print p.name, 'hp:', p.cur_hp, 'ac:', p.get_ac(), 'mana:', p.cur_mana
def party_comment(comment, party):
for p in party:
if p.is_alive():
print comment % p.name
break
def show_status(players):
print 'Status:'
for p in players:
show_player_status(p)
print
def award_experience(players, monsters):
exp_total = 0
for m in monsters:
m.on_combat_end()
if not m.is_alive():
exp_total += m.xp_award
total_living_players = 0
for p in players:
p.on_combat_end()
if p.is_alive():
total_living_players += 1
if total_living_players == 0:
return
reward = exp_total / total_living_players
for p in players:
if p.is_alive() and reward > 0:
p.add_xp( reward )
def one_dead(group):
d = 0
for m in group:
if not m.is_alive():
d = d + 1
return d == 1
def half_dead(group):
d = 0
for m in group:
if not m.is_alive():
d = d + 1
return d == int(len(group) / 2)
#a list of the player targets. We adjust the frequency of monster focus
#by adding attacking players 3 times, spell or blocking characters once.
def add_player_target(player, action, player_targets, player_actions):
if player.is_hidden():
return
if action.is_weapon():
player_targets.append(player)
player_targets.append(player)
player_actions.append(action)
player_actions.append(action)
player_targets.append(player)
player_actions.append(action)
def get_tohit_mod(round):
#to simulate fatigue and increasing
#likeliness to give and receive damage,
#adjust to hit up as rounds go on.
#Also helps reduce super long slogging battles
#where everyone misses.
tohit_mod = round / 2
#cap it at a ridiculous 10. Though 30 rounds is a SUPER long battle.
if tohit_mod > 10:
tohit_mod = 10
return tohit_mod
def fight(players, monsters, room, level, mon_attack_first):
done = False
round = 1
moral_check_on_first_dead = False
moral_check_on_half_dead = False
level.combat_stats = CombatStats()
while not done:
print '-------------------------------------------------------'
print 'Round', round, '\n'
#make sure the armor class is up to date.
for p in players:
p.update_armor_class()
#show all player status
show_status(players)
#adjust tohit modifiers to account for fatigue
tohit_mod = get_tohit_mod(round)
#a list of the player targets. We adjust the frequency of monster focus
#by adding attacking players 3 times, spell or blocking characters once.
player_targets = []
player_actions = []
for player in players:
if not player.is_alive():
continue
if all_dead(monsters):
continue
if mon_attack_first:
add_player_target(player, CombatAction('suprised', ''), player_targets, player_actions)
continue
if player.is_disabled() or player.is_trapped():
add_player_target(player, CombatAction('disabled', ''), player_targets, player_actions)
continue
action, target = choose_action_and_target(player, monsters)
if action is None:
continue
if action.is_spell():
player.cur_mana -= action.mana
#wearing armor incurs a 1 pt mana expense
if player.is_wearing_non_magic_armor():
player.cur_mana -= 1
if action.targets_environment():
action.cast_at_environ(player, monsters, players, room, level)
elif target is None or action.targets_group():
action.cast(player, monsters, players)
else:
action.cast_at_target(player, target)
elif action.is_weapon():
if target is None:
action.attack(player, monsters, players, tohit_mod)
else:
action.attack_target(player, target, tohit_mod)
elif action.is_combat_action():
if action.name == 'block':
hp_rec = HP_RECOVERED_FROM_REST * player.level
if player.cur_hp <= (player.hp / 2) - hp_rec:
player.cur_hp += hp_rec
print player.name, 'regains', hp_rec, 'hp after blocking and resting.'
elif action.name == 'run':
num_items = len(player.inventory)
if num_items > 0:
iDrop = random.randint(0, num_items - 1)
item = player.inventory[iDrop]
if not item.is_weapon() and not item.is_spell() and not item.is_armor():
print player.name, 'bolts for the door and drops his', item.name, 'in haste!'
player.remove(item.name)
room.items.append(item)
award_experience(players, monsters)
#all monsters heal when you leave.
for m in monsters:
if m.is_alive():
m.cur_hp = m.hp
pause()
return "run"
elif action.name == 'hide':
roll = random.randint(1, 20)
print player.name, 'rolls %d.' % roll
if roll >= player.get_hide_roll_thresh():
print player.name, 'slips into the shadows silently.'
player.set_hidden(True)
else:
print player.name, 'was not able to hide.'
elif action.name == 'draw':
room.activate([player])
elif action.name == 'drink':
player.drink_potion()
elif action.name == 'read':
player.read_scroll(monsters, players, room, level)
elif action.name == 'wand':
player.use_wand(monsters, players, room, level)
pause()
if player.is_hidden():
if action.is_spell() or action.is_weapon():
player.set_hidden(False)
elif action.name != 'run':
hp_rec = HP_RECOVERED_FROM_REST * player.level
if player.cur_hp <= (player.hp / 2) - hp_rec:
player.cur_hp += hp_rec
print player.name, 'regains', hp_rec, 'hp while hiding.'
add_player_target(player, action, player_targets, player_actions)
#check for morale of monsters
if not moral_check_on_first_dead and one_dead(monsters) and len(monsters) > 1:
moral_check_on_first_dead = True
if random.randint(1, 10) < 4:
print 'The rest of the creatures flee in terror!'
award_experience(players, monsters)
return 'won'
if not moral_check_on_half_dead and half_dead(monsters) and len(monsters) > 3:
moral_check_on_half_dead = True
if random.randint(1, 10) < 5:
print 'The rest of the creatures flee in terror!'
award_experience(players, monsters)
return 'won'
#only use once. Surprise attack
if mon_attack_first:
if random.randint(1, 2) < 2:
print "It's a surprise attack!"
tohit_mod += 2
else:
party_comment('%s yells, "Look out!"', players)
mon_attack_first = False
for monster in monsters:
action = monster.take_combat_turn(player_targets, player_actions, room, level, tohit_mod)
if action and action.name == 'run':
monsters.remove(monster)
if all_dead(monsters):
print "You defeated all the enemies!!\n"
sitch = [WON]
print_comment(sitch, players, room, level)
award_experience(players, monsters)
return 'won'
if all_dead(players):
print 'And everything fades to black. Better to have run.. oh well!!'
return 'died'
for monster in monsters:
if monster.is_alive():
monster.on_combat_round_ended()
for player in players:
if player.is_alive():
player.on_combat_round_ended()
print
round = round + 1
| en | 0.868608 | #casting spells while in armor happens at a deficit #get input #a list of the player targets. We adjust the frequency of monster focus #by adding attacking players 3 times, spell or blocking characters once. #to simulate fatigue and increasing #likeliness to give and receive damage, #adjust to hit up as rounds go on. #Also helps reduce super long slogging battles #where everyone misses. #cap it at a ridiculous 10. Though 30 rounds is a SUPER long battle. #make sure the armor class is up to date. #show all player status #adjust tohit modifiers to account for fatigue #a list of the player targets. We adjust the frequency of monster focus #by adding attacking players 3 times, spell or blocking characters once. #wearing armor incurs a 1 pt mana expense #all monsters heal when you leave. #check for morale of monsters #only use once. Surprise attack | 2.874856 | 3 |
app/forms/UpdatePasswordForm.py | jonzxz/project-piscator | 0 | 6624291 | <reponame>jonzxz/project-piscator<filename>app/forms/UpdatePasswordForm.py
from flask_wtf import FlaskForm
from app.models import User
from wtforms import PasswordField, SubmitField, DecimalField
from wtforms.validators import DataRequired
# WTForm for updating password after request for password reset
class UpdatePasswordForm(FlaskForm):
token = DecimalField('Six Digit Code'\
, render_kw={"placeholder" : "Six-digit code"}, validators=[DataRequired()])
new_password = PasswordField('<PASSWORD>'\
, render_kw={"placeholder" : "New Password"}, validators=[DataRequired()])
submit = SubmitField('Reset')
| from flask_wtf import FlaskForm
from app.models import User
from wtforms import PasswordField, SubmitField, DecimalField
from wtforms.validators import DataRequired
# WTForm for updating password after request for password reset
class UpdatePasswordForm(FlaskForm):
token = DecimalField('Six Digit Code'\
, render_kw={"placeholder" : "Six-digit code"}, validators=[DataRequired()])
new_password = PasswordField('<PASSWORD>'\
, render_kw={"placeholder" : "New Password"}, validators=[DataRequired()])
submit = SubmitField('Reset') | en | 0.790713 | # WTForm for updating password after request for password reset | 2.74826 | 3 |
mmdglm/convkernels/base.py | adehad/mmd-glm | 0 | 6624292 | <filename>mmdglm/convkernels/base.py<gh_stars>0
import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import fftconvolve
import torch
from ..utils import get_arg_support, get_dt, searchsorted
class Kernel:
def __init__(self):
pass
def interpolate(self, t):
pass
def interpolate_basis(self, t):
pass
def convolve_continuous(self, t, x):
"""Implements the convolution of a time series with the kernel using scipy fftconvolve.
Args:
t (array): time points
x (array): time series to be convolved
mode (str):
Returns:
array: convolved time series
"""
dt = get_dt(t)
arg_support0, arg_supportf = get_arg_support(dt, self.support)
t_support = np.arange(arg_support0, arg_supportf, 1) * dt
kernel_values = self.interpolate(t_support)
shape = (kernel_values.shape[0], ) + tuple([1] * (x.ndim - 1))
kernel_values = kernel_values.reshape(shape)
convolution = np.zeros(x.shape)
full_convolution = fftconvolve(kernel_values, x, mode='full', axes=0)
if arg_support0 >= 0:
convolution[arg_support0:, ...] = full_convolution[:len(t) - arg_support0, ...]
elif arg_support0 < 0 and arg_supportf >= 0: # or to arg_support0 < 0 and len(t) - arg_support0 <= len(t) + arg_supportf - arg_support0:
convolution = full_convolution[-arg_support0:len(t) - arg_support0, ...]
else: # or arg0 < 0 and len(t) - arg0 > len(t) + arg_supportf - arg0:
convolution[:len(t) + arg_supportf, ...] = full_convolution[-arg_supportf:, ...]
convolution *= dt
return torch.from_numpy(convolution)
def convolve_discrete(self, t, s, A=None, shape=None, renewal=False):
"""Implements the convolution of discrete events in time with the kernel
Args:
t (array): time points
s (array): time events
mode (str):
Returns:
array: convolved time series
"""
if type(s) is not tuple:
s = (s,)
if A is None:
A = (1. for ii in range(s[0].size))
if shape is None:
shape = tuple([max(s[dim]) + 1 for dim in range(1, len(s))])
arg_s = searchsorted(t, s[0])
arg_s = np.atleast_1d(arg_s)
convolution = np.zeros((len(t), ) + shape)
for ii, (arg, A) in enumerate(zip(arg_s, A)):
index = tuple([slice(arg, None)] + [s[dim][ii] for dim in range(1, len(s))])
if not(renewal):
convolution[index] += A * self.interpolate(t[arg:] - t[arg])
else:
convolution[index] = A * self.interpolate(t[arg:] - t[arg])
return torch.from_numpy(convolution)
def fit(self, t, input, output, mask=None):
if mask is None:
mask = np.ones(input.shape, dtype=bool)
X = self.convolve_basis_continuous(t, input)
X = X[mask, :]
output = output[mask]
self.coefs = np.linalg.lstsq(X, output, rcond=None)[0]
def correlate_continuous(self, t, x):
return self.convolve_continuous(t, x[::-1])[::-1]
def plot(self, t=None, ax=None, offset=0, invert_t=False, invert_values=False, gain=False, **kwargs):
if t is None:
t = np.arange(self.support[0], self.support[1] + self.dt, self.dt)
if ax is None:
fig, ax = plt.subplots()
y = self.interpolate(t) + offset
if invert_t:
t = -t
if invert_values:
y = -y
if gain:
y = np.exp(y)
ax.plot(t, y, **kwargs)
return ax
| <filename>mmdglm/convkernels/base.py<gh_stars>0
import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import fftconvolve
import torch
from ..utils import get_arg_support, get_dt, searchsorted
class Kernel:
def __init__(self):
pass
def interpolate(self, t):
pass
def interpolate_basis(self, t):
pass
def convolve_continuous(self, t, x):
"""Implements the convolution of a time series with the kernel using scipy fftconvolve.
Args:
t (array): time points
x (array): time series to be convolved
mode (str):
Returns:
array: convolved time series
"""
dt = get_dt(t)
arg_support0, arg_supportf = get_arg_support(dt, self.support)
t_support = np.arange(arg_support0, arg_supportf, 1) * dt
kernel_values = self.interpolate(t_support)
shape = (kernel_values.shape[0], ) + tuple([1] * (x.ndim - 1))
kernel_values = kernel_values.reshape(shape)
convolution = np.zeros(x.shape)
full_convolution = fftconvolve(kernel_values, x, mode='full', axes=0)
if arg_support0 >= 0:
convolution[arg_support0:, ...] = full_convolution[:len(t) - arg_support0, ...]
elif arg_support0 < 0 and arg_supportf >= 0: # or to arg_support0 < 0 and len(t) - arg_support0 <= len(t) + arg_supportf - arg_support0:
convolution = full_convolution[-arg_support0:len(t) - arg_support0, ...]
else: # or arg0 < 0 and len(t) - arg0 > len(t) + arg_supportf - arg0:
convolution[:len(t) + arg_supportf, ...] = full_convolution[-arg_supportf:, ...]
convolution *= dt
return torch.from_numpy(convolution)
def convolve_discrete(self, t, s, A=None, shape=None, renewal=False):
"""Implements the convolution of discrete events in time with the kernel
Args:
t (array): time points
s (array): time events
mode (str):
Returns:
array: convolved time series
"""
if type(s) is not tuple:
s = (s,)
if A is None:
A = (1. for ii in range(s[0].size))
if shape is None:
shape = tuple([max(s[dim]) + 1 for dim in range(1, len(s))])
arg_s = searchsorted(t, s[0])
arg_s = np.atleast_1d(arg_s)
convolution = np.zeros((len(t), ) + shape)
for ii, (arg, A) in enumerate(zip(arg_s, A)):
index = tuple([slice(arg, None)] + [s[dim][ii] for dim in range(1, len(s))])
if not(renewal):
convolution[index] += A * self.interpolate(t[arg:] - t[arg])
else:
convolution[index] = A * self.interpolate(t[arg:] - t[arg])
return torch.from_numpy(convolution)
def fit(self, t, input, output, mask=None):
if mask is None:
mask = np.ones(input.shape, dtype=bool)
X = self.convolve_basis_continuous(t, input)
X = X[mask, :]
output = output[mask]
self.coefs = np.linalg.lstsq(X, output, rcond=None)[0]
def correlate_continuous(self, t, x):
return self.convolve_continuous(t, x[::-1])[::-1]
def plot(self, t=None, ax=None, offset=0, invert_t=False, invert_values=False, gain=False, **kwargs):
if t is None:
t = np.arange(self.support[0], self.support[1] + self.dt, self.dt)
if ax is None:
fig, ax = plt.subplots()
y = self.interpolate(t) + offset
if invert_t:
t = -t
if invert_values:
y = -y
if gain:
y = np.exp(y)
ax.plot(t, y, **kwargs)
return ax
| en | 0.672171 | Implements the convolution of a time series with the kernel using scipy fftconvolve. Args: t (array): time points x (array): time series to be convolved mode (str): Returns: array: convolved time series # or to arg_support0 < 0 and len(t) - arg_support0 <= len(t) + arg_supportf - arg_support0: # or arg0 < 0 and len(t) - arg0 > len(t) + arg_supportf - arg0: Implements the convolution of discrete events in time with the kernel Args: t (array): time points s (array): time events mode (str): Returns: array: convolved time series | 2.177561 | 2 |
dangdang/dangdang/spiders/dd.py | ValueXu/DangDangScrapy | 1 | 6624293 | <reponame>ValueXu/DangDangScrapy
# -*- coding: utf-8 -*-
import scrapy
from dangdang.items import DangdangItem
from scrapy.http import Request
class Ddspider(scrapy.Spider):
name = "dd"
allowed_domains = ["dangdang.com"]
start_urls = ('http://www.dangdang.com/',)
def parse(self, response):
item=DangdangItem()
item["title"]=response.xpath("//a[@class='pic']/@title").extract()
item["link"] = response.xpath("//a[@class='pic']/@href").extract()
item["comment"] = response.xpath("//a[@name='itemlist-review']/text()").extract()
item["price"]=response.xpath("//p[@class='price']/span[@class='search_now_price']/text()").extract()
yield item
for i in range(2,101):
url="http://category.dangdang.com/pg" +str(i)+"-cp01.54.06.00.00.00.html"
yield Request(url,callback=self.parse) | # -*- coding: utf-8 -*-
import scrapy
from dangdang.items import DangdangItem
from scrapy.http import Request
class Ddspider(scrapy.Spider):
name = "dd"
allowed_domains = ["dangdang.com"]
start_urls = ('http://www.dangdang.com/',)
def parse(self, response):
item=DangdangItem()
item["title"]=response.xpath("//a[@class='pic']/@title").extract()
item["link"] = response.xpath("//a[@class='pic']/@href").extract()
item["comment"] = response.xpath("//a[@name='itemlist-review']/text()").extract()
item["price"]=response.xpath("//p[@class='price']/span[@class='search_now_price']/text()").extract()
yield item
for i in range(2,101):
url="http://category.dangdang.com/pg" +str(i)+"-cp01.54.06.00.00.00.html"
yield Request(url,callback=self.parse) | en | 0.769321 | # -*- coding: utf-8 -*- | 3.02353 | 3 |
033_Search_in_Rotated_Sorted_Array.py | adwardlee/leetcode_solutions | 0 | 6624294 | <reponame>adwardlee/leetcode_solutions<gh_stars>0
'''
Suppose an array sorted in ascending order is rotated at some pivot unknown to you beforehand.
(i.e., [0,1,2,4,5,6,7] might become [4,5,6,7,0,1,2]).
You are given a target value to search. If found in the array return its index, otherwise return -1.
You may assume no duplicate exists in the array.
Your algorithm's runtime complexity must be in the order of O(log n).
Example 1:
Input: nums = [4,5,6,7,0,1,2], target = 0
Output: 4
Example 2:
Input: nums = [4,5,6,7,0,1,2], target = 3
Output: -1
'''
class Solution:
def search(self, nums, target: int) -> int:
self.nums = nums
length = len(nums)
if length == 0:
return -1
left = 0
right = length - 1
mid = (left + right) // 2
return max(self.quicksort(left, mid, target), self.quicksort(mid + 1, right, target))
def quicksort(self, left, right, target):
if left > right:
return -1
if left == right:
if self.nums[left] == target:
return left
else:
return -1
mid = (left + right) // 2
if self.nums[mid] == target:
return mid
if self.nums[left] < self.nums[right]:
if self.nums[left] <= target and target <= self.nums[right]:
return max(self.quicksort(left, mid, target), self.quicksort(mid + 1, right, target))
else:
return -1
else:
if self.nums[left] > target and self.nums[right] < target:
return -1
else:
return max(self.quicksort(left, mid, target), self.quicksort(mid + 1, right, target))
| '''
Suppose an array sorted in ascending order is rotated at some pivot unknown to you beforehand.
(i.e., [0,1,2,4,5,6,7] might become [4,5,6,7,0,1,2]).
You are given a target value to search. If found in the array return its index, otherwise return -1.
You may assume no duplicate exists in the array.
Your algorithm's runtime complexity must be in the order of O(log n).
Example 1:
Input: nums = [4,5,6,7,0,1,2], target = 0
Output: 4
Example 2:
Input: nums = [4,5,6,7,0,1,2], target = 3
Output: -1
'''
class Solution:
def search(self, nums, target: int) -> int:
self.nums = nums
length = len(nums)
if length == 0:
return -1
left = 0
right = length - 1
mid = (left + right) // 2
return max(self.quicksort(left, mid, target), self.quicksort(mid + 1, right, target))
def quicksort(self, left, right, target):
if left > right:
return -1
if left == right:
if self.nums[left] == target:
return left
else:
return -1
mid = (left + right) // 2
if self.nums[mid] == target:
return mid
if self.nums[left] < self.nums[right]:
if self.nums[left] <= target and target <= self.nums[right]:
return max(self.quicksort(left, mid, target), self.quicksort(mid + 1, right, target))
else:
return -1
else:
if self.nums[left] > target and self.nums[right] < target:
return -1
else:
return max(self.quicksort(left, mid, target), self.quicksort(mid + 1, right, target)) | en | 0.879945 | Suppose an array sorted in ascending order is rotated at some pivot unknown to you beforehand. (i.e., [0,1,2,4,5,6,7] might become [4,5,6,7,0,1,2]). You are given a target value to search. If found in the array return its index, otherwise return -1. You may assume no duplicate exists in the array. Your algorithm's runtime complexity must be in the order of O(log n). Example 1: Input: nums = [4,5,6,7,0,1,2], target = 0 Output: 4 Example 2: Input: nums = [4,5,6,7,0,1,2], target = 3 Output: -1 | 3.962535 | 4 |
gsm_layer3_protocol/sms_protocol/rp_data.py | matan1008/gsm-layer3-protocol | 0 | 6624295 | <filename>gsm_layer3_protocol/sms_protocol/rp_data.py
from construct import *
from gsm_layer3_protocol.enums import rp_mti
from gsm_layer3_protocol.sms_protocol.called_party_bcd_address import zero_lengthed_bcd_address, service_center_address
from gsm_layer3_protocol.sms_protocol.sms_submit import sms_submit_tpdu_struct
from gsm_layer3_protocol.sms_protocol.sms_command import sms_command_tpdu_struct
from gsm_layer3_protocol.sms_protocol.sms_deliver import sms_deliver_tpdu_struct
from gsm_layer3_protocol.sms_protocol.sms_status_report import sms_status_report_tpdu_struct
from gsm_layer3_protocol.sms_protocol.sms_submit_report_rp_ack import sms_submit_report_tpdu_struct
from gsm_layer3_protocol.sms_protocol.sms_deliver_report_rp_ack import sms_deliver_report_tpdu_struct
class RpDataMsToN(Container):
def __init__(self, message_reference, rp_destination_address, tpdu=None):
super().__init__(message_reference=message_reference, rp_originator_address=None,
rp_destination_address=rp_destination_address, rp_user_data={"tpdu": tpdu})
class RpDataNToMs(Container):
def __init__(self, message_reference, rp_originator_address, tpdu=None):
super().__init__(message_reference=message_reference, rp_originator_address=rp_originator_address,
rp_destination_address=None, rp_user_data={"tpdu": tpdu})
rp_data_struct = Struct(
"message_reference" / Byte,
"rp_originator_address" / IfThenElse(this._.mti == rp_mti.RP_DATA_MS_TO_N, zero_lengthed_bcd_address,
service_center_address),
"rp_destination_address" / IfThenElse(this._.mti == rp_mti.RP_DATA_MS_TO_N, service_center_address,
zero_lengthed_bcd_address),
"rp_user_data" / Struct("tpdu" / Prefixed(
"tpdu_length" / Byte,
IfThenElse(
this._._.mti == rp_mti.RP_DATA_MS_TO_N,
Select(sms_deliver_report_tpdu_struct, sms_command_tpdu_struct, sms_submit_tpdu_struct),
Select(sms_deliver_tpdu_struct, sms_status_report_tpdu_struct, sms_submit_report_tpdu_struct)
)
))
)
| <filename>gsm_layer3_protocol/sms_protocol/rp_data.py
from construct import *
from gsm_layer3_protocol.enums import rp_mti
from gsm_layer3_protocol.sms_protocol.called_party_bcd_address import zero_lengthed_bcd_address, service_center_address
from gsm_layer3_protocol.sms_protocol.sms_submit import sms_submit_tpdu_struct
from gsm_layer3_protocol.sms_protocol.sms_command import sms_command_tpdu_struct
from gsm_layer3_protocol.sms_protocol.sms_deliver import sms_deliver_tpdu_struct
from gsm_layer3_protocol.sms_protocol.sms_status_report import sms_status_report_tpdu_struct
from gsm_layer3_protocol.sms_protocol.sms_submit_report_rp_ack import sms_submit_report_tpdu_struct
from gsm_layer3_protocol.sms_protocol.sms_deliver_report_rp_ack import sms_deliver_report_tpdu_struct
class RpDataMsToN(Container):
def __init__(self, message_reference, rp_destination_address, tpdu=None):
super().__init__(message_reference=message_reference, rp_originator_address=None,
rp_destination_address=rp_destination_address, rp_user_data={"tpdu": tpdu})
class RpDataNToMs(Container):
def __init__(self, message_reference, rp_originator_address, tpdu=None):
super().__init__(message_reference=message_reference, rp_originator_address=rp_originator_address,
rp_destination_address=None, rp_user_data={"tpdu": tpdu})
rp_data_struct = Struct(
"message_reference" / Byte,
"rp_originator_address" / IfThenElse(this._.mti == rp_mti.RP_DATA_MS_TO_N, zero_lengthed_bcd_address,
service_center_address),
"rp_destination_address" / IfThenElse(this._.mti == rp_mti.RP_DATA_MS_TO_N, service_center_address,
zero_lengthed_bcd_address),
"rp_user_data" / Struct("tpdu" / Prefixed(
"tpdu_length" / Byte,
IfThenElse(
this._._.mti == rp_mti.RP_DATA_MS_TO_N,
Select(sms_deliver_report_tpdu_struct, sms_command_tpdu_struct, sms_submit_tpdu_struct),
Select(sms_deliver_tpdu_struct, sms_status_report_tpdu_struct, sms_submit_report_tpdu_struct)
)
))
)
| none | 1 | 2.195377 | 2 | |
_BACKUPS_v3/v3_1/LightPicture_Test.py | nagame/LightPicture | 0 | 6624296 | from LightPicture import *
import unittest
class TestConstructor_Coordinate(unittest.TestCase):
"""
Test Coordinate class calls
"""
def test_none(self):
"""
Calling Coordinates class with no key (kay = None)
"""
c0 = Coordinates()
self.assertIsNot(c0, None)
self.assertIsInstance(c0, Coordinates)
def test_iterable(self):
"""
Calling Coordinates class with key conaining simple types
"""
c0 = Coordinates([1])
self.assertIsNot(c0, None)
self.assertIsInstance(c0, Coordinates)
c1 = Coordinates([1, 2, 3])
self.assertIsNot(c1, None)
self.assertIsInstance(c1, Coordinates)
c2 = Coordinates('xyz')
c3 = Coordinates(['x', 'y', 'z', 5])
self.assertIsNot(c2, None)
self.assertIsInstance(c2, Coordinates)
def test_iterable_specific(self):
"""
Calling Coordinates class with key containing specific types
"""
# Call Coordinates object with Vertex object as key
v = Vertex()
c = Coordinates(v)
self.assertIsInstance(c, Coordinates)
# # = = = = = = = = = = = = = = = = = = = = = = = = = = =
# # Check auto reference building and synchronisation
# # between Vertex and Coordinates
# # = = = = = = = = = = = = = = = = = = = = = = = = = = =
# Action: Coordinate class call with Vertex object as key
# Expect: Vertex object assigns Coordinates object as self coordinates
a_v0 = Vertex()
a_c0 = Coordinates(a_v0)
a_r0 = a_v0.coordinates()
self.assertIs(a_r0, a_c0)
# Action: Coordinate class call with Vertex object as key
# Expect: Vertex object is 'parent' of Coordinate object
b_v0 = Vertex()
b_c0 = Coordinates(b_v0)
b_r0 = b_c0.parents()
b_pass = b_v0 in b_r0
self.assertIs(b_pass, True)
class TestConstructor_Vertex(unittest.TestCase):
"""
Test Vertex class calls
"""
def test_none(self):
"""
Calling Vertex class with no key (key = None)
"""
v0 = Vertex()
self.assertIsNot(v0, None)
self.assertIsInstance(v0, Vertex)
def test_iterable_simple(self):
"""
Calling Vertex class with key containing simple types
"""
v0 = Vertex([1])
self.assertIsNot(v0, None)
self.assertIsInstance(v0, Vertex)
v1 = Vertex([1, 2, 3])
self.assertIsNot(v1, None)
self.assertIsInstance(v1, Vertex)
# check if Vertex has built a Coordinates object using the iterable passed as key
self.assertIsInstance(v1.coordinates(), Coordinates)
v2 = Vertex('xyz')
self.assertIsNot(v2, None)
self.assertIsInstance(v2, Vertex)
self.assertIsInstance(v2.coordinates(), Coordinates)
v3 = Vertex(['x', 'y', 'z', 5])
self.assertIsNot(v3, None)
self.assertIsInstance(v3, Vertex)
self.assertIsInstance(v3.coordinates(), Coordinates)
def test_iterable_specific(self):
"""
Calling Vertex class with key containing specific types
"""
# Call Vertex class with Coordinate object as key
c = Coordinates()
v = Vertex(c)
self.assertIsInstance(v, Vertex)
# # = = = = = = = = = = = = = = = = = = = = = = = = = = =
# # Check auto reference building
# # between Vertex and Coordinates
# # = = = = = = = = = = = = = = = = = = = = = = = = = = =
# Action: Vertex class call with Coordinate object as key
# Expect: Vertex object assigns Coordinates object as self coordinates
a_c0 = Coordinates([11, 12, 13])
a_v0 = Vertex(a_c0)
a_r0 = a_v0.coordinates()
self.assertIs(a_r0, a_c0)
# Action: Vertex class call with Coordinate object as key
# Expect: Vertex object is 'parent' of Coordinate object
b_c0 = Coordinates([11, 12, 13])
b_v0 = Vertex(b_c0)
b_r0 = b_c0.parents()
b_pass = b_v0 in b_r0
self.assertIs(b_pass, True)
class TestConstructor_Triangle(unittest.TestCase):
"""
Test Triangle class call
"""
def test_none(self):
"""
Calling Triangle class with no key (key = None)
"""
t0 = Triangle()
self.assertIsNot(t0, None)
self.assertIsInstance(t0, Triangle)
def test_iterable(self):
"""
Calling Vertex class with iterable key
"""
# simple types iterables
t1 = Triangle([1, 2, 3])
self.assertIsNot(t1, None)
self.assertIsInstance(t1, Triangle)
t2 = Triangle('xyz')
self.assertIsNot(t2, None)
self.assertIsInstance(t2, Triangle)
t3 = Triangle(['x', 'y', 'z'])
self.assertIsNot(t3, None)
self.assertIsInstance(t3, Triangle)
# check vertices assignment
t1 = Triangle([1001, 1002, 1003])
self.assertIsNot(t1, None)
self.assertIsInstance(t1, Triangle)
result = t1.vertices()
self.assertIsInstance(result, list)
[r0, r1, r2] = result
self.assertEqual(r0, 1001)
self.assertEqual(r1, 1002)
self.assertEqual(r2, 1003)
t2 = Triangle('xyz')
self.assertIsNot(t2, None)
self.assertIsInstance(t2, Triangle)
t3 = Triangle(['x', 'y', 'z'])
self.assertIsNot(t3, None)
self.assertIsInstance(t3, Triangle)
def test_iterable_specific(self):
"""
Calling Vertex class with key containing specific types
"""
# Action: Triangle class call with Coordinate object as key
# Expects: Vertex assigns Coordinates object as self coordinates
c0 = Coordinates()
c1 = Coordinates()
c2 = Coordinates()
t0 = Triangle([c0, c1, c2])
self.assertIsInstance(t0, Triangle)
class TestTemporary(unittest.TestCase):
"""
Temporary tests or test currently in development
"""
def test_draft(self):
pass
if __name__ == '__main__':
unittest.main()
| from LightPicture import *
import unittest
class TestConstructor_Coordinate(unittest.TestCase):
"""
Test Coordinate class calls
"""
def test_none(self):
"""
Calling Coordinates class with no key (kay = None)
"""
c0 = Coordinates()
self.assertIsNot(c0, None)
self.assertIsInstance(c0, Coordinates)
def test_iterable(self):
"""
Calling Coordinates class with key conaining simple types
"""
c0 = Coordinates([1])
self.assertIsNot(c0, None)
self.assertIsInstance(c0, Coordinates)
c1 = Coordinates([1, 2, 3])
self.assertIsNot(c1, None)
self.assertIsInstance(c1, Coordinates)
c2 = Coordinates('xyz')
c3 = Coordinates(['x', 'y', 'z', 5])
self.assertIsNot(c2, None)
self.assertIsInstance(c2, Coordinates)
def test_iterable_specific(self):
"""
Calling Coordinates class with key containing specific types
"""
# Call Coordinates object with Vertex object as key
v = Vertex()
c = Coordinates(v)
self.assertIsInstance(c, Coordinates)
# # = = = = = = = = = = = = = = = = = = = = = = = = = = =
# # Check auto reference building and synchronisation
# # between Vertex and Coordinates
# # = = = = = = = = = = = = = = = = = = = = = = = = = = =
# Action: Coordinate class call with Vertex object as key
# Expect: Vertex object assigns Coordinates object as self coordinates
a_v0 = Vertex()
a_c0 = Coordinates(a_v0)
a_r0 = a_v0.coordinates()
self.assertIs(a_r0, a_c0)
# Action: Coordinate class call with Vertex object as key
# Expect: Vertex object is 'parent' of Coordinate object
b_v0 = Vertex()
b_c0 = Coordinates(b_v0)
b_r0 = b_c0.parents()
b_pass = b_v0 in b_r0
self.assertIs(b_pass, True)
class TestConstructor_Vertex(unittest.TestCase):
"""
Test Vertex class calls
"""
def test_none(self):
"""
Calling Vertex class with no key (key = None)
"""
v0 = Vertex()
self.assertIsNot(v0, None)
self.assertIsInstance(v0, Vertex)
def test_iterable_simple(self):
"""
Calling Vertex class with key containing simple types
"""
v0 = Vertex([1])
self.assertIsNot(v0, None)
self.assertIsInstance(v0, Vertex)
v1 = Vertex([1, 2, 3])
self.assertIsNot(v1, None)
self.assertIsInstance(v1, Vertex)
# check if Vertex has built a Coordinates object using the iterable passed as key
self.assertIsInstance(v1.coordinates(), Coordinates)
v2 = Vertex('xyz')
self.assertIsNot(v2, None)
self.assertIsInstance(v2, Vertex)
self.assertIsInstance(v2.coordinates(), Coordinates)
v3 = Vertex(['x', 'y', 'z', 5])
self.assertIsNot(v3, None)
self.assertIsInstance(v3, Vertex)
self.assertIsInstance(v3.coordinates(), Coordinates)
def test_iterable_specific(self):
"""
Calling Vertex class with key containing specific types
"""
# Call Vertex class with Coordinate object as key
c = Coordinates()
v = Vertex(c)
self.assertIsInstance(v, Vertex)
# # = = = = = = = = = = = = = = = = = = = = = = = = = = =
# # Check auto reference building
# # between Vertex and Coordinates
# # = = = = = = = = = = = = = = = = = = = = = = = = = = =
# Action: Vertex class call with Coordinate object as key
# Expect: Vertex object assigns Coordinates object as self coordinates
a_c0 = Coordinates([11, 12, 13])
a_v0 = Vertex(a_c0)
a_r0 = a_v0.coordinates()
self.assertIs(a_r0, a_c0)
# Action: Vertex class call with Coordinate object as key
# Expect: Vertex object is 'parent' of Coordinate object
b_c0 = Coordinates([11, 12, 13])
b_v0 = Vertex(b_c0)
b_r0 = b_c0.parents()
b_pass = b_v0 in b_r0
self.assertIs(b_pass, True)
class TestConstructor_Triangle(unittest.TestCase):
"""
Test Triangle class call
"""
def test_none(self):
"""
Calling Triangle class with no key (key = None)
"""
t0 = Triangle()
self.assertIsNot(t0, None)
self.assertIsInstance(t0, Triangle)
def test_iterable(self):
"""
Calling Vertex class with iterable key
"""
# simple types iterables
t1 = Triangle([1, 2, 3])
self.assertIsNot(t1, None)
self.assertIsInstance(t1, Triangle)
t2 = Triangle('xyz')
self.assertIsNot(t2, None)
self.assertIsInstance(t2, Triangle)
t3 = Triangle(['x', 'y', 'z'])
self.assertIsNot(t3, None)
self.assertIsInstance(t3, Triangle)
# check vertices assignment
t1 = Triangle([1001, 1002, 1003])
self.assertIsNot(t1, None)
self.assertIsInstance(t1, Triangle)
result = t1.vertices()
self.assertIsInstance(result, list)
[r0, r1, r2] = result
self.assertEqual(r0, 1001)
self.assertEqual(r1, 1002)
self.assertEqual(r2, 1003)
t2 = Triangle('xyz')
self.assertIsNot(t2, None)
self.assertIsInstance(t2, Triangle)
t3 = Triangle(['x', 'y', 'z'])
self.assertIsNot(t3, None)
self.assertIsInstance(t3, Triangle)
def test_iterable_specific(self):
"""
Calling Vertex class with key containing specific types
"""
# Action: Triangle class call with Coordinate object as key
# Expects: Vertex assigns Coordinates object as self coordinates
c0 = Coordinates()
c1 = Coordinates()
c2 = Coordinates()
t0 = Triangle([c0, c1, c2])
self.assertIsInstance(t0, Triangle)
class TestTemporary(unittest.TestCase):
"""
Temporary tests or test currently in development
"""
def test_draft(self):
pass
if __name__ == '__main__':
unittest.main()
| en | 0.914016 | Test Coordinate class calls Calling Coordinates class with no key (kay = None) Calling Coordinates class with key conaining simple types Calling Coordinates class with key containing specific types # Call Coordinates object with Vertex object as key # # = = = = = = = = = = = = = = = = = = = = = = = = = = = # # Check auto reference building and synchronisation # # between Vertex and Coordinates # # = = = = = = = = = = = = = = = = = = = = = = = = = = = # Action: Coordinate class call with Vertex object as key # Expect: Vertex object assigns Coordinates object as self coordinates # Action: Coordinate class call with Vertex object as key # Expect: Vertex object is 'parent' of Coordinate object Test Vertex class calls Calling Vertex class with no key (key = None) Calling Vertex class with key containing simple types # check if Vertex has built a Coordinates object using the iterable passed as key Calling Vertex class with key containing specific types # Call Vertex class with Coordinate object as key # # = = = = = = = = = = = = = = = = = = = = = = = = = = = # # Check auto reference building # # between Vertex and Coordinates # # = = = = = = = = = = = = = = = = = = = = = = = = = = = # Action: Vertex class call with Coordinate object as key # Expect: Vertex object assigns Coordinates object as self coordinates # Action: Vertex class call with Coordinate object as key # Expect: Vertex object is 'parent' of Coordinate object Test Triangle class call Calling Triangle class with no key (key = None) Calling Vertex class with iterable key # simple types iterables # check vertices assignment Calling Vertex class with key containing specific types # Action: Triangle class call with Coordinate object as key # Expects: Vertex assigns Coordinates object as self coordinates Temporary tests or test currently in development | 3.026361 | 3 |
src/bot_cogs/minecraft.py | ryancflam/-findseed | 0 | 6624297 | <reponame>ryancflam/-findseed
# Credit - https://github.com/Sharpieman20/Sharpies-Speedrunning-Tools
# For blindtravel, doubletravel, educatedtravel, safeblind, triangulation
# Credit - https://github.com/FourGoesFast/PerfectTravelBot
# For divinetravel, perfecttravel
from asyncio import TimeoutError
from base64 import b64decode
from json import loads
from random import choice, randint
from time import time
import numpy as np
from discord import Colour, Embed, File
from discord.ext import commands
from src.utils import funcs
from src.utils.base_cog import BaseCog
BARTER_LIMIT = 896
class Minecraft(BaseCog, name="Minecraft",
description="Commands relating to *Minecraft* in general and *Minecraft: Java Edition* speedrunning."):
def __init__(self, botInstance, *args, **kwargs):
super().__init__(botInstance, *args, **kwargs)
self.client.loop.create_task(self.__readFiles())
async def __readFiles(self):
self.divinetravel = await funcs.readJson(funcs.getResource(self.name, "divine_travel.json"))
self.perfecttravel = await funcs.readJson(funcs.getResource(self.name, "perfect_travel.json"))
self.eyedata = await funcs.readJson(funcs.getResource(self.name, "eye_data.json"))
self.loottable = await self.piglinLootTable()
await funcs.generateJson(
"findseed",
{
"calls": 0,
"highest": {
"found": 0,
"number": 0,
"time": int(time())
}
}
)
await funcs.generateJson("finddream", {"iteration": 0, "mostPearls": 0, "mostRods": 0})
async def piglinLootTable(self):
lt = await funcs.readJson(funcs.getResource(self.name, "piglin_loot_table.json"))
ltnew = []
for i in lt:
if i["id"] < 5:
item = i["item"]
for j in range(1, 4):
i["item"] = f"{item} {j}"
for _ in range(i["weight"]):
ltnew.append(i.copy())
i["id"] += 1
else:
for _ in range(i["weight"] * 3):
ltnew.append(i)
return ltnew
@staticmethod
def randomEyes():
eyes = 0
for _ in range(12):
eyes += 1 if funcs.oneIn(10) else 0
return eyes
@staticmethod
def getExcessStr(item):
stacks, excess = funcs.stacksAndExcess(item)
return "" if not stacks and excess == item \
else f" ({'{:,} stack{}'.format(stacks, '' if stacks == 1 else 's') if stacks else ''}" + \
f"{' + ' if stacks and excess else ''}{str(excess) if excess else ''})"
@staticmethod
def chargeableAnchors(glowdust: int, cryobby: int):
return min([glowdust // 16, cryobby // 6])
@staticmethod
def f3iProcessing(clipboard):
try:
args = clipboard.split(" ")
return int(args[1]), int(args[2]), int(args[3])
except Exception:
raise Exception("Invalid input. Please do not modify your F3+I clipboard.")
@staticmethod
def f3cProcessing(clipboard):
try:
args = clipboard.split(" ")
return float(args[6]), float(args[8]), float(args[9]) % 360
except Exception:
raise Exception("Invalid input. Please do not modify your F3+C clipboard.")
@staticmethod
def angleProcessing(angle):
if angle >= 0:
return (angle + 90) % 360
return (angle - 270) % 360
@staticmethod
def coordsDist(x, z):
return np.sqrt([x * x + z * z])[0]
def coordsDifference(self, coords1: tuple, coords2: tuple):
return self.coordsDist(coords1[0] - coords2[0], coords1[1] - coords2[1])
def strongholdCalc(self, x0, z0, f0, x1, z1, f1):
a0 = np.tan([self.angleProcessing(f0) * np.pi / 180])[0]
a1 = np.tan([self.angleProcessing(f1) * np.pi / 180])[0]
b = z0 - x0 * a0
xp = ((z1 - x1 * a1) - b) / (a0 - a1)
zp = xp * a0 + b
blocks = round(self.coordsDifference((x1, z1), (xp, zp)))
return xp, zp, blocks
@commands.cooldown(1, 5, commands.BucketType.user)
@commands.command(name="findseed", description="Everyone's favourite command. Test your luck using this command!",
aliases=["fs", "seed", "findseeds", "f"])
async def findseed(self, ctx):
eyes = self.randomEyes()
data = await funcs.readJson("data/findseed.json")
odds = self.eyedata[str(eyes)]["percent"]
onein = self.eyedata[str(eyes)]["onein"]
update = False
if eyes >= data["highest"]["number"]:
data["highest"]["found"] -= data["highest"]["found"] - 1 if eyes > data["highest"]["number"] else -1
data["highest"]["number"] = eyes
data["highest"]["time"] = int(time())
update = True
highest = data["highest"]["number"]
highestTime = data["highest"]["time"]
highestTotal = data["highest"]["found"]
data["calls"] += 1
calls = data["calls"]
await funcs.dumpJson("data/findseed.json", data)
file = File(
funcs.PATH + funcs.getResource(self.name, "portal_frame_images/") + f"{eyes}eye.png",
filename="portal.png"
)
foundTime = "just now"
if not update:
timestr = funcs.timeDifferenceStr(time(), highestTime)
timestr_0 = int(timestr.split(" ")[0])
if timestr_0 > 2:
foundTime = f"{timestr_0} days"
else:
foundTime = timestr
e = Embed(title=f"{self.client.command_prefix}findseed",
description=f"Requested by: {ctx.message.author.mention}")
e.add_field(name="Your Eyes", value=f"`{eyes}`")
e.add_field(name="Probability", value=f"`{odds}% (1 in {onein})`")
e.add_field(name="Most Eyes Found", inline=False,
value=f"`{highest} (last found {foundTime}{' ago' if not update else ''}" +
f", found {'{:,}'.format(highestTotal)} time{'' if highestTotal == 1 else 's'})`")
e.set_footer(text=f"The command has been called {'{:,}'.format(calls)} time{'' if calls == 1 else 's'}. !eyeodds")
e.set_image(url="attachment://portal.png")
await ctx.reply(embed=e, file=file)
@commands.cooldown(1, 10, commands.BucketType.user)
@commands.command(name="eyeodds", description="Shows the odds of getting each type of end portal.",
aliases=["odds", "eyes", "eye", "eyeodd", "eyeood", "eyeoods"])
async def eyeodds(self, ctx):
msg = ""
for i in range(13):
odds = self.eyedata[str(i)]["percent"]
msg += f"{i} eye - `{odds}% (1 in {self.eyedata[str(i)]['onein']})`\n"
await ctx.reply(msg)
@commands.cooldown(1, 5, commands.BucketType.user)
@commands.command(name="finddream", description="Can you get Dream's *Minecraft* speedrunning \"luck\"? " +
"Test your luck using this command!",
aliases=["dream", "dreamsimulator", "dreamsim", "dreamluck", "fd"], hidden=True)
async def finddream(self, ctx):
pearls, rods = 0, 0
dpearls, drods = 262, 305
data = await funcs.readJson("data/finddream.json")
mostPearls = data["mostPearls"]
mostRods = data["mostRods"]
for _ in range(dpearls):
pearls += 1 if randint(0, 422) < 20 else 0
for _ in range(drods):
rods += 1 if funcs.oneIn(2) else 0
data["mostPearls"] = pearls if pearls >= mostPearls else mostPearls
data["mostRods"] = rods if rods >= mostRods else mostRods
data["iteration"] += 1
iters = data['iteration']
await funcs.dumpJson("data/finddream.json", data)
e = Embed(
title=f"{self.client.command_prefix}finddream",
description=f"Dream got 42 ender pearl trades in {dpearls} plus 211 blaze rod drops in {drods}. " +
f"Can you achieve his 'luck'?\n\nRequested by: {ctx.author.mention}"
)
e.add_field(name="Your Pearl Trades", value=f"`{pearls} ({round(pearls / dpearls * 100, 3)}%)`")
e.add_field(name="Your Rod Drops", value=f"`{rods} ({round(rods / drods * 100, 3)}%)`")
e.set_footer(
text=f"The command has been called {'{:,}'.format(iters)} time{'' if iters == 1 else 's'}. " +
f"| Most pearl trades: {data['mostPearls']}; most rod drops: {data['mostRods']}"
)
e.set_thumbnail(url="https://static.wikia.nocookie.net/dream_team/images/7/7b/Dream.jpeg")
await ctx.reply(embed=e)
@commands.cooldown(1, 1, commands.BucketType.user)
@commands.command(name="findbreak", description="You throw an ender eye. Does it break or do you get to keep it?" +
" Test your luck using this command!",
aliases=["break", "eyebreak", "breakeye", "findeye"], hidden=True)
async def findbreak(self, ctx):
e = Embed(title=f"{self.client.command_prefix}findbreak",
description=f"Requested by: {ctx.message.author.mention}")
badluckonein = 5
goodluck = not funcs.oneIn(badluckonein)
e.add_field(name="Result", value=f"`{'No Break!' if goodluck else 'Break...'}`")
e.set_thumbnail(url="https://media.discordapp.net/attachments/771404776410972161/938407577975418900/unknown.png")
e.set_image(url="https://cdn.discordapp.com/attachments/771404776410972161/938408463946637312/2022-02-02_20.20.06.png"
if goodluck else
"https://media.discordapp.net/attachments/771404776410972161/938408658411327528/unknown.png")
e.set_footer(text=f"Odds: {str(badluckonein - 1) if goodluck else '1'}/{str(badluckonein)}")
await ctx.reply(embed=e)
@commands.cooldown(1, 1, commands.BucketType.user)
@commands.command(name="findcleric", description="Will you get the ender pearl trade from the cleric, " +
"or will you get one-thirded? Test your luck using this command!",
aliases=["cleric", "stupidvillager"], hidden=True)
async def findcleric(self, ctx):
e = Embed(title=f"{self.client.command_prefix}findcleric",
description=f"Requested by: {ctx.message.author.mention}")
badluckonein = 3
goodluck = not funcs.oneIn(badluckonein)
e.add_field(name="Result", value=f"`{'Pearl' if goodluck else 'Bottle'} Trade{'!' if goodluck else '...'}`")
e.set_thumbnail(url="https://media.discordapp.net/attachments/771404776410972161/856203578615529532/cleric.png")
e.set_image(url="https://media.discordapp.net/attachments/771404776410972161/856203574337601536/pearl.png" if goodluck else
"https://media.discordapp.net/attachments/771404776410972161/856203573113520138/bottle.png")
e.set_footer(text=f"Odds: {str(badluckonein - 1) if goodluck else '1'}/{str(badluckonein)}")
await ctx.reply(embed=e)
@commands.cooldown(1, 1, commands.BucketType.user)
@commands.command(name="findgravel", description="Will you get flint from gravel? Test your luck using this command!",
aliases=["gravel", "flint", "findflint", "fg"], hidden=True)
async def findgravel(self, ctx):
e = Embed(title=f"{self.client.command_prefix}findgravel",
description=f"Requested by: {ctx.message.author.mention}")
goodluckonein = 10
badluck = not funcs.oneIn(goodluckonein)
e.add_field(name="Result", value=f"`{'Gravel' if badluck else 'Flint'}{'...' if badluck else '!'}`")
e.set_thumbnail(url="https://media.discordapp.net/attachments/771698457391136798/856209821383917608/gravel.png")
e.set_image(url="https://media.discordapp.net/attachments/771698457391136798/856209821383917608/gravel.png" if badluck else
"https://media.discordapp.net/attachments/771698457391136798/856209843174244362/flint.png")
e.set_footer(text=f"Odds: {str(goodluckonein - 1) if badluck else '1'}/{str(goodluckonein)}")
await ctx.reply(embed=e)
@commands.cooldown(1, 1, commands.BucketType.user)
@commands.command(name="findperch", description="You are in insane pace and about to kill the dragon..." +
"but does it perch instantly? Test your luck using this command!",
aliases=["dragon", "fp", "finddragon"], hidden=True)
async def findperch(self, ctx):
e = Embed(title=f"{self.client.command_prefix}findperch",
description=f"Requested by: {ctx.message.author.mention}")
goodluckonein = 13
badluck = not funcs.oneIn(goodluckonein)
e.add_field(name="Result", value=f"`{'No Perch' if badluck else 'Perch'}{'...' if badluck else '!'}`")
e.set_thumbnail(url="https://media.discordapp.net/attachments/771404776410972161/928297045486370857/dragon.png")
e.set_image(url="https://media.discordapp.net/attachments/771404776410972161/928299016259776613/2022-01-05_22.48.45.png"
if badluck
else "https://media.discordapp.net/attachments/771404776410972161/928298549861572638/2022-01-05_22.46.50.png")
e.set_footer(text=f"Odds: {str(goodluckonein - 1) if badluck else '1'}/{str(goodluckonein)}")
await ctx.reply(embed=e)
@commands.cooldown(1, 1, commands.BucketType.user)
@commands.command(name="findskull", description="You kill a wither skeleton...but does it drop a wither skull?" +
" Test your luck using this command!",
aliases=["skull", "witherskull", "findwitherskull", "findwither"], hidden=True)
async def findskull(self, ctx):
e = Embed(title=f"{self.client.command_prefix}findskull",
description=f"Requested by: {ctx.message.author.mention}")
goodluckonein = 40
badluck = not funcs.oneIn(goodluckonein)
e.add_field(name="Result", value=f"`{'No Skull' if badluck else 'Skull'}{'...' if badluck else '!'}`")
e.set_thumbnail(url="https://cdn.discordapp.com/attachments/771404776410972161/935204890639233054/unknown.png")
e.set_image(
url="" if badluck else "https://cdn.discordapp.com/attachments/771404776410972161/935204919651205250/unknown.png"
)
e.set_footer(text=f"Odds: {str(goodluckonein - 1) if badluck else '1'}/{str(goodluckonein)}")
await ctx.reply(embed=e)
@commands.cooldown(1, 1, commands.BucketType.user)
@commands.command(name="findblaze", description="You kill a blaze...but does it drop a rod? Test your luck using this command!",
aliases=["rod", "blazerod", "findrod", "findblazerod"], hidden=True)
async def findblaze(self, ctx):
e = Embed(title=f"{self.client.command_prefix}findblaze",
description=f"Requested by: {ctx.message.author.mention}")
badluckonein = 2
goodluck = not funcs.oneIn(badluckonein)
e.add_field(name="Result", value=f"`{'Rod' if goodluck else 'No Rod'} Drop{'!' if goodluck else '...'}`")
e.set_thumbnail(url="https://media.discordapp.net/attachments/771698457391136798/856213640809414666/blaze.png")
e.set_image(url="https://media.discordapp.net/attachments/771698457391136798/856213641020178472/rod.png" if goodluck else
"https://cdn.discordapp.com/attachments/771698457391136798/856213642173612032/norod.png")
e.set_footer(text=f"Odds: {str(badluckonein - 1) if goodluck else '1'}/{str(badluckonein)}")
await ctx.reply(embed=e)
@commands.cooldown(1, 1, commands.BucketType.user)
@commands.command(name="perchcmd", description="Shows the command to force the the ender dragon perch.", aliases=["perch"])
async def perchcmd(self, ctx):
await ctx.reply("```1.13+: /data merge entity @e[type=ender_dragon,limit=1] {DragonPhase:2}\n\n" +
"1.9-1.12: /entitydata @e[type=ender_dragon] {DragonPhase:2}```")
@commands.cooldown(1, 1, commands.BucketType.user)
@commands.command(name="logs", description="Calculates how many logs you will need to trade for a certain number of emeralds.",
aliases=["log", "wood"], usage="<amount of emeralds needed>")
async def logs(self, ctx, emeralds):
try:
emeralds = int(emeralds)
if emeralds < 1:
raise Exception
log = emeralds * 4
await ctx.reply("You want **{:,}** emerald{}.\n\nYou will need **{:,}** logs{}.".format(
emeralds, "" if emeralds == 1 else "s", int(log), self.getExcessStr(log)
))
except Exception as ex:
funcs.printError(ctx, ex)
await ctx.reply(
embed=funcs.errorEmbed(None, "Invalid input. Please make sure you are entering positive, non-zero integers.")
)
@commands.cooldown(1, 1, commands.BucketType.user)
@commands.command(name="haybales", aliases=["hay", "haybale"], usage="<amount of emeralds needed>",
description="Calculates how many hay bales you will need to trade for a certain number of emeralds.")
async def haybales(self, ctx, emeralds):
try:
emeralds = int(emeralds)
if emeralds < 1:
raise Exception
hay = 20 * emeralds / 9
hay = funcs.strictRounding(hay)
await ctx.reply("You want **{:,}** emerald{}.\n\nYou will need **{:,}** hay bales{}.".format(
emeralds, "" if emeralds == 1 else "s", int(hay), self.getExcessStr(hay)
))
except Exception as ex:
funcs.printError(ctx, ex)
await ctx.reply(
embed=funcs.errorEmbed(None, "Invalid input. Please make sure you are entering positive, non-zero integers.")
)
@commands.cooldown(1, 1, commands.BucketType.user)
@commands.command(name="books", aliases=["book", "bookshelf", "bookshelves", "library"],
usage="<books per emerald> <emeralds per eye> [eyes needed]",
description="Calculates how many books you will need to get eyes of ender for pre-1.9 trading.")
async def books(self, ctx, book, emeralds, eyes="12"):
try:
book = int(book)
emeralds = int(emeralds)
eyes = int(eyes)
if not 8 <= book <= 10:
return await ctx.send(embed=funcs.errorEmbed(None, "Books per emerald must be 8-10 inclusive."))
if not 7 <= emeralds <= 11:
return await ctx.send(embed=funcs.errorEmbed(None, "Emeralds per eye must be 7-11 inclusive."))
if not 1 <= eyes <= 12:
return await ctx.send(embed=funcs.errorEmbed(None, "Eyes needed must be 1-12 inclusive."))
totalEmeralds = emeralds * eyes
totalBooks = totalEmeralds * book
booksPerEye = emeralds * book
bookshelves = funcs.strictRounding(totalBooks / 3)
await ctx.send("You want **{}** eye{} of ender.\nThe librarian sells one emera".format(eyes, "" if eyes == 1 else "s") +
"ld for **{}** books.\nThe cleric sells one eye of ender for **{}** emeralds.\n".format(book, emeralds) +
"\nYou will need:\n\n**{:,}** books{} for a total of".format(totalBooks, self.getExcessStr(totalBooks)) +
" **{}** emeralds{}\nBooks per eye:".format(totalEmeralds, self.getExcessStr(totalEmeralds)) +
" **{}**\nBookshelves to break: **{}**\n\n".format(booksPerEye, bookshelves) +
"Big library: 699 books\nSmall library: 483 books")
except Exception as ex:
funcs.printError(ctx, ex)
await ctx.reply(
embed=funcs.errorEmbed(None, "Invalid input.")
)
@commands.cooldown(1, 1, commands.BucketType.user)
@commands.command(name="anchors", description="Calculates how many chargeable respawn anchors you can craft based on how " +
"much glowstone dust and crying obsidian you have.",
aliases=["anchor"], usage="<amount of glowstone dust> <amount of crying obdisian>")
async def anchors(self, ctx, glowdust, cryobby):
try:
glowdust = int(glowdust)
cryobby = int(cryobby)
if glowdust < 1 or cryobby < 1:
raise Exception
anchors = self.chargeableAnchors(glowdust, cryobby)
charge = " and sufficiently charge {}".format("it" if anchors == 1 else "them") if anchors else ""
await ctx.reply(
"You have **{:,}** glowstone dust and **{:,}** crying obsidian.\n\nYou can make **".format(glowdust, cryobby) +
"{:,}** respawn anchor{}{}.".format(anchors, "" if anchors == 1 else "s", charge)
)
except Exception as ex:
funcs.printError(ctx, ex)
await ctx.reply(
embed=funcs.errorEmbed(None, "Invalid input. Please make sure you are entering positive, non-zero integers.")
)
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(description="Simulates *Minecraft: Java Edition* 1.16.1 piglin bartering. Test your luck using this command!",
aliases=["barter", "piglin", "poglin", "bartering", "barteringsim"], name="bartersim",
usage=f"[gold ingots up to 10,000]\n\nAlternative usage(s):\n\n- <gold blocks up to 1,111 (ending with b)>")
async def bartersim(self, ctx, goldingots: str="1"):
try:
try:
goldingots = int(goldingots)
except:
goldingots = int(goldingots[:-1]) * 9
if not 0 < goldingots < 10001:
return await ctx.reply(embed=funcs.errorEmbed(None, f"Value must be between 1 and 10,000."))
except ValueError:
return await ctx.reply(embed=funcs.errorEmbed(None, "Invalid input."))
trades = {}
string, glowdust, cryobby = 0, 0, 0
for _ in range(goldingots):
trade = choice(self.loottable)
if trade["id"] not in list(trades.keys()):
trades[trade["id"]] = {}
trades[trade["id"]]["item"] = trade["item"]
n = choice(trade["quantity"])
trades[trade["id"]]["quantity"] = n
trades[trade["id"]]["trades"] = 1
else:
n = choice(trade["quantity"])
trades[trade["id"]]["quantity"] += n
trades[trade["id"]]["trades"] += 1
if trade["id"] == 13:
string += n
elif trade["id"] == 10:
glowdust += n
elif trade["id"] == 19:
cryobby += n
res = "You bartered {:,} gold ingot{} for:\n\n".format(goldingots, "" if goldingots == 1 else "s")
for i in sorted(trades):
t = trades[i]
res += "{}{:,} x {} ({:,} trade{})\n".format(
"*** " if i in [7, 8, 10, 12, 13, 18, 19] else " ",
t["quantity"], t["item"], t["trades"], "" if t["trades"] == 1 else "s"
)
anchors = self.chargeableAnchors(glowdust, cryobby)
beds = string // 12
explosives = anchors + beds
if explosives:
res += "\nExplosives you can craft ({:,}):\n\n".format(explosives)
if beds:
res += " {:,} x Bed\n".format(beds)
if anchors:
res += " {:,} x Respawn Anchor (w/ enough glowstone to power)".format(anchors)
await ctx.reply(funcs.formatting(res))
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="pearlbarter", description="Finds the probability of getting 12 or more ender pearls" +
" in a given number of piglin trades in *Minecraft* 1.16.1.",
aliases=["pearltrade", "pearlbartering", "barteringpearl", "barterpearl", "barterpearls"],
usage=f"[total gold ingots up to {BARTER_LIMIT}]")
async def pearlbarter(self, ctx, trades: str="2"):
try:
n = int(trades)
if not 2 <= n <= BARTER_LIMIT:
return await ctx.reply(embed=funcs.errorEmbed(None, f"Value must be between 2 and {BARTER_LIMIT}."))
except ValueError:
return await ctx.reply(embed=funcs.errorEmbed(None, "Invalid input."))
x = 1 - (403 / 423) ** n - n * (20 / 423) * ((403 / 423) ** (n - 1)) - (2 / 5) * (n * (n - 1) / 2) \
* ((403 / 423) ** (n - 2)) * ((20 / 423) ** 2)
await ctx.reply(f"**[1.16.1]** The probability of getting 12 or more ender pearls" +
f" with {n} gold ingots is:\n\n`{round(x * 100, 5)}%` (1 in {round(1 / x, 5)})")
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="blindtravel", description="A *Minecraft: Java Edition* speedrunning tool that " +
"should be used when you want to build another por" +
"tal in the Nether before throwing any eyes of end" +
"er. To use this command, in the game, press F3+C," +
" pause, come over to Discord, paste your clipboar" +
"d as an argument for the command, and then build " +
"your portal at the suggested coordinates in the N" +
"ether. This command is for versions 1.13+ and may " +
"not be 100% accurate. This command MAY not be used" +
" in a real speedrun.",
aliases=["bt", "blind", "blindtrav"], usage="<F3+C data>")
async def blindtravel(self, ctx, *, f3c):
await ctx.send("**Note:** This command, along with other speedrunning calculators, MAY not be used in a real speedrun.")
try:
x, z, _ = self.f3cProcessing(f3c)
dist = self.coordsDist(x, z)
o = 190 if dist < 190 else dist if dist < 290 else 290 if dist < 442 else 580 if dist < 580 else dist \
if dist < 692 else 686 if dist < 825 else 970 if dist < 970 else dist if dist < 1060 else 1060
t = np.arctan([z / x])[0]
xp = np.sign(x) * np.absolute([o * np.cos([t])[0]])[0]
zp = np.sign(z) * np.absolute([o * np.sin([t])[0]])[0]
blocks = round(self.coordsDifference((x, z), (xp, zp)))
await ctx.reply(
f"Build your portal at: **{round(xp)}, {round(zp)}** " +
f"({'{:,}'.format(blocks)} block{'' if blocks == 1 else 's'} away)"
)
except Exception as ex:
await ctx.reply(embed=funcs.errorEmbed(None, str(ex)))
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="educatedtravel", description="A *Minecraft: Java Edition* speedrunning tool th" +
"at should be used when you want to build anoth" +
"er portal in the Nether after throwing an eye " +
"of ender. To use this command, in the game, th" +
"row an eye, stand still, put your mouse direct" +
"ly over the eye, press F3+C, pause, come over " +
"to Discord, paste your clipboard as an argumen" +
"t for the command, and then build your portal " +
"at the suggested coordinates in the Nether. Th" +
"is command is for versions 1.13+ and may not be" +
" 100% accurate. This command MAY not be used in" +
" a real speedrun.",
aliases=["et", "educated", "nethertravel"], usage="<F3+C data>")
async def educatedtravel(self, ctx, *, f3c):
await ctx.send("**Note:** This command, along with other speedrunning calculators, MAY not be used in a real speedrun.")
try:
x, z, f = self.f3cProcessing(f3c)
f = (360 + f if f < 0 else f) - 180
o = 640 if self.coordsDist(x, z) > 3584 else 216
m1 = -np.tan([(90 - f) * (np.pi / 180)])[0]
a = 1 + (m1 ** 2)
b1 = -m1 * (x / 8) + (z / 8)
b = 2 * m1 * b1
xp = ((-b) + (np.sign(f) * np.sqrt([b ** 2 - 4 * a * (b1 ** 2 - o ** 2)])[0])) / (2 * a)
zp = m1 * xp + b1
await ctx.reply(f"Build your portal at: **{round(xp)}, {round(zp)}** ")
except Exception as ex:
await ctx.reply(embed=funcs.errorEmbed(None, str(ex)))
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="doubletravel", description="A *Minecraft: Java Edition* speedrunning tool that" +
", whilst you are in the Nether, gets a spot for " +
"you to make your first portal inside the second " +
"ring of strongholds. To use this command, in the" +
" game, press F3+C, pause, come over to Discord, " +
"paste your clipboard as an argument for the comm" +
"and, and then build your portal at the suggested" +
" coordinates in the Nether. `educatedtravel` shou" +
"ld then be used after exiting the Nether which s" +
"hould do a good job of getting you to the right " +
"spot in the Nether to build your second portal. " +
"This command is for versions 1.13+ and may not be" +
" 100% accurate. This command MAY not be used in a " +
"real speedrun.",
aliases=["double"], usage="<F3+C data>")
async def doubletravel(self, ctx, *, f3c):
await ctx.send("**Note:** This command, along with other speedrunning calculators, MAY not be used in a real speedrun.")
try:
x, z, _ = self.f3cProcessing(f3c)
o = 520
t = np.arctan([z / x])[0]
xp = np.sign(x) * np.absolute([o * np.cos([t])[0]])[0]
zp = np.sign(z) * np.absolute([o * np.sin([t])[0]])[0]
blocks = round(self.coordsDifference((x, z), (xp, zp)))
await ctx.reply(
f"Build your first portal at: **{round(xp)}, {round(zp)}** " +
f"({'{:,}'.format(blocks)} block{'' if blocks == 1 else 's'} away)\n\n" +
f"Use `{self.client.command_prefix}educatedtravel` afterwards."
)
except Exception as ex:
await ctx.reply(embed=funcs.errorEmbed(None, str(ex)))
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="safeblind", description="A *Minecraft: Java Edition* speedrunning tool that, s" +
"imilar to `blindtravel`, should be used when you wan" +
"t to build another portal in the Nether before thro" +
"wing any eyes of ender. This on average will get yo" +
"u closer to the stronghold compared to `blindtravel`" +
", but time may be lost. To use this command, in the" +
" game, press F3+C, pause, come over to Discord, pas" +
"te your clipboard as an argument for the command, a" +
"nd then build your portal at the suggested coordina" +
"tes in the Nether. This command is for versions 1.13" +
"+ and may not be 100% accurate. This command MAY not" +
" be used in a real speedrun.",
aliases=["sb", "safetravel", "safe", "st"], usage="<F3+C data>", hidden=True)
async def safeblind(self, ctx, *, f3c):
await ctx.send("**Note:** This command, along with other speedrunning calculators, MAY not be used in a real speedrun.")
try:
x, z, _ = self.f3cProcessing(f3c)
dist = self.coordsDist(x, z)
o = 222 if dist < 222 else dist if dist < 250 else 250 if dist < 480 else 615 if dist < 615 \
else dist if dist < 645 else 645 if dist < 832 else 1005 if dist < 1005 else dist if dist < 1032 \
else 1032
t = np.arctan([z / x])[0]
xp = np.sign(x) * np.absolute([o * np.cos([t])[0]])[0]
zp = np.sign(z) * np.absolute([o * np.sin([t])[0]])[0]
blocks = round(self.coordsDifference((x, z), (xp, zp)))
await ctx.reply(
f"Build your portal at: **{round(xp)}, {round(zp)}** " +
f"({'{:,}'.format(blocks)} block{'' if blocks == 1 else 's'} away)"
)
except Exception as ex:
await ctx.reply(embed=funcs.errorEmbed(None, str(ex)))
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="perfecttravel", description="A *Minecraft: Java Edition* speedrunning tool that att" +
"empts to take you directly to the stronghold portal " +
"room with the use of two Nether portals and F3 data." +
" To use this command, in the game, leave your first " +
"portal, find a chunk intersection and stand on the c" +
'hunk coordinate "0, 0" right in the centre, press F3' +
"+C, pause, come over to Discord, paste your clipboar" +
"d as an argument for the command, go back to the Net" +
"her, and then build your second portal at the sugges" +
"ted coordinates in the Nether. This command is for v" +
"ersions 1.13+ and may not be 100% accurate. This com" +
"mand MAY not be used in a real speedrun.",
aliases=["perfectt", "perfect", "ptravel", "ptrav", "ptr", "pt"], usage='<F3+C data> ["calc"]\n\n' +
'Note: Add "calc" at the end if you do not want to manually calculate the portal coordinates yourself.')
async def perfecttravel(self, ctx, *, f3c):
calc = True if f3c.casefold().split()[-1] == "calc" else False
try:
nx, nz, px, pz = None, None, None, None
x, z, f = self.f3cProcessing(f3c)
if f > 180:
f -= 360
if f < -180:
f += 360
targetchunk = self.perfecttravel[str(round(f, 2))][0]
if calc:
await ctx.send("**Note:** Second Nether portal coordinates are calculated for you. " +
"Your run is now most likely invalid.")
else:
await ctx.send("**Note:** Although no calculations are done and only a lookup table is being used, " +
f"note that you may still risk invalidating your run or at least have it kept under close scrutiny.")
try:
targetchunkx, targetchunkz = targetchunk.split(" ")
px, pz = int(x / 16) + (0 if x > 0 else -1), int(z / 16) + (0 if z > 0 else -1)
nx = ((px + int(targetchunkx)) * 2) if calc else targetchunkx
nz = ((pz + int(targetchunkz)) * 2) if calc else targetchunkz
except:
targetchunk = ""
if targetchunk:
if calc:
await ctx.reply(
f"Build your second portal at: **" +
f"{round(nx + (1 if nx < 0 else 0))}, {round(nz + (1 if nz < 0 else 0))}** " +
"\n\nMore info: https://youtu.be/YpV7I9X-Jso"
)
else:
await ctx.reply(
f"Offset: **{nx}, {nz}**\n\nYour current chunk for reference: **" +
f"{px}, {pz}**" +
"\n\nMore info: https://youtu.be/YpV7I9X-Jso"
)
else:
await ctx.reply(f"Cannot find ideal coordinates...")
except Exception as ex:
await ctx.reply(embed=funcs.errorEmbed(None, str(ex)))
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="triangulationsimple", description="A simple stronghold triangulation command that takes in 6 values.",
aliases=["trisimple", "simpletri", "strongholdsimple", "simplestronghold", "trisimp", "simptri",
"simpletriangulation", "strongholdsimp", "simpstronghold"],
usage="<x #1> <z #1> <angle #1> <x #2> <z #2> <angle #2>")
async def triangulationsimple(self, ctx, x0, z0, f0, x1, z1, f1):
try:
xp, zp, blocks = self.strongholdCalc(float(x0), float(z0), float(f0), float(x1), float(z1), float(f1))
await ctx.reply(
f"The stronghold could be at: **{round(xp)}, {round(zp)}** ({'{:,}'.format(blocks)} block" +
f"{'' if blocks == 1 else 's'} away)"
)
except Exception as ex:
funcs.printError(ctx, ex)
await ctx.reply(embed=funcs.errorEmbed(None, "Invalid input."))
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="triangulation", description="A *Minecraft: Java Edition* speedrunning tool tha" +
"t attempts to locate the stronghold using both " +
'the "8, 8" rule and triangulation. To use this ' +
"command, in the game, throw and eye, stand stil" +
"l, put your mouse directly over the eye, press " +
"F3+C, pause, come over to Discord, paste your c" +
"lipboard as an argument for the command, and th" +
"e command should return a set of coordinates ca" +
'lculated using the "8, 8" rule. You may continu' +
"e using this command by parsing more F3+C clipb" +
"oards as regular messages as you get closer to " +
"the stronghold. Once the program knows you are " +
"fairly close to the stronghold, it will automat" +
"ically stop. This command is for versions 1.13+" +
" and may not be 100% accurate. This command MAY" +
" not be used in a real speedrun.",
aliases=["triangulate", "triangle", "trian", "tri", "88", "44"],
usage="<F3+C data>")
async def triangulation(self, ctx, *, f3c):
await ctx.send("**Note:** This command, along with other speedrunning calculators, MAY not be used in a real speedrun.")
try:
x, z, f = self.f3cProcessing(f3c)
x0, z0, f0 = x, z, f
f = (360 + f if f < 0 else f) - 180
r = (90 - f) * (np.pi / 180)
b = 8 - np.absolute([np.absolute([x])[0] % 16])[0] + 16
l = []
s = 0
while s < 11904:
d = b * np.sign(f)
x += d
z += d * -np.tan([r])[0]
v = np.absolute([np.absolute([np.absolute([z])[0] % 16])[0] - 8])[0] + 0.5
s = self.coordsDist(x, z)
if s > 1408:
l.append({"k": x, "v": v, "j": v * v * np.sqrt([1 + len(l)])[0], "r": z})
b = 16
l.sort(key=lambda i: i["j"])
xp, zp = l[0]["k"], l[0]["r"]
blocks = round(self.coordsDifference((x0, z0), (xp, zp)))
await ctx.reply(
f"The stronghold could be at: **{round(xp)}, {round(zp)}** ({'{:,}'.format(blocks)} block" +
f"{'' if blocks == 1 else 's'} away)\n\nMethod: 8, 8\n\nPaste your F3+C clipboard here once " +
"you are ready. The program will stop after 20 minutes of inactivity. Type `!cancel` to cancel."
)
except Exception as ex:
return await ctx.reply(embed=funcs.errorEmbed(None, str(ex)))
x1, z1, f1 = None, None, None
blocks = 100
try:
while blocks > 40:
while True:
msg = await self.client.wait_for(
"message", timeout=1200, check=lambda m: ctx.author == m.author and ctx.channel == m.channel
)
try:
x1, z1, f1 = self.f3cProcessing(msg.content)
except:
if msg.content.casefold() == "!cancel":
return await ctx.reply("Cancelling triangulation.")
continue
if x1 == x0 and z1 == z0 and f1 == f0:
continue
break
try:
xp, zp, blocks = self.strongholdCalc(x0, z0, f0, x1, z1, f1)
except:
continue
await msg.reply(
f"The stronghold could be at: **{round(xp)}, {round(zp)}** ({'{:,}'.format(blocks)} block" +
f"{'' if blocks == 1 else 's'} away)\n\nMethod: Triangulation\n\nPaste your F3+C clipboard here once " +
"you are ready. The program will stop after 20 minutes of inactivity. Type `!cancel` to cancel."
)
x0, z0, f0 = x1, z1, f1
await ctx.send("You are close to the stronghold, stopping triangulation program.")
except TimeoutError:
await ctx.send("You have been inactive for over 20 minutes, stopping triangulation program.")
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="coordsdist", description="Calculates the distance between two sets of coordinates.",
aliases=["coords", "distance", "dist", "coord", "coordinates", "coordinate"],
usage="<x #1> <z #1> <x #2> <z #2>\n\nAlternative usage(s):\n\n- <F3+C data> <x> <z>")
async def coords(self, ctx, *, inp: str):
inp = funcs.replaceCharacters(inp, [",", "(", ")", ";"])
args = inp.split(" ")
try:
try:
x1, z1, _ = self.f3cProcessing(inp)
except:
x1, z1 = float(args[0]), float(args[1])
x2, z2 = float(args[-2]), float(args[-1])
except ValueError:
return await ctx.reply(embed=funcs.errorEmbed(None, "Invalid arguments."))
await ctx.reply(
"The distance between (**{}**; **{}**) and (**{}**; **{}**) is: ".format(
funcs.removeDotZero(round(x1, 5)),
funcs.removeDotZero(round(z1, 5)),
funcs.removeDotZero(round(x2, 5)),
funcs.removeDotZero(round(z2, 5))
) + f"**{funcs.removeDotZero(round(self.coordsDifference((x1, z1), (x2, z2)), 5))}**"
)
@commands.cooldown(1, 30, commands.BucketType.user)
@commands.command(name="speedrunwr", description="Shows the current world records for the solo Any% Glitchless " +
"*Minecraft: Java Edition* speedrun categories.",
aliases=["worldrecord", "wr", "mcwr", "ssg", "rsg"])
async def speedrunwr(self, ctx):
await ctx.send("Getting speedrun.com data. Please wait...")
try:
e = Embed(description="https://www.speedrun.com/mc")
e.set_author(name="Minecraft Speedrun World Records - Solo Any% Glitchless",
icon_url="https://cdn.discordapp.com/attachments/771698457391136798/842103816761114624/mc.png")
urls = [
"klrzpjo1&var-wl33kewl=gq7zo9p1",
"klrzpjo1&var-wl33kewl=21go6e6q",
"klrzpjo1&var-wl33kewl=4qye4731",
"21d4zvp1&var-wl33kewl=gq7zo9p1",
"21d4zvp1&var-wl33kewl=21go6e6q",
"21d4zvp1&var-wl33kewl=4qye4731"
]
categories = [
"Set Seed Glitchless (Pre-1.9)",
"Set Seed Glitchless (1.9-1.15)",
"Set Seed Glitchless (1.16+)",
"Random Seed Glitchless (Pre-1.9)",
"Random Seed Glitchless (1.9-1.15)",
"Random Seed Glitchless (1.16+)"
]
count = 0
for category in urls:
res = await funcs.getRequest(
"https://www.speedrun.com/api/v1/leaderboards/j1npme6p/category/mkeyl926?var-r8rg67rn=" + category
)
wrdata = res.json()["data"]["runs"][0]["run"]
igt = wrdata["times"]["primary_t"]
res = await funcs.getRequest(wrdata["players"][0]["uri"])
runner = res.json()["data"]["names"]["international"]
d, h, m, s, ms = funcs.timeDifferenceStr(igt, 0, noStr=True)
e.add_field(name=categories[count], inline=False,
value=f"`{funcs.timeStr(d, h, m, s, ms)} ({runner})`")
count += 1
e.set_footer(text="Click the link above for more speedrun categories.",
icon_url="https://cdn.discordapp.com/attachments/771698457391136798/842103813585240124/src.png")
except Exception as ex:
funcs.printError(ctx, ex)
e = funcs.errorEmbed(None, "Possible server error.")
await ctx.reply(embed=e)
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="skin", description="Gets the skin of a *Minecraft: Java Edition* user.", aliases=["mcskin"],
usage="[username]", hidden=True)
async def skin(self, ctx, username: str=""):
if username == "":
username = ctx.message.author.name
try:
data = await funcs.getRequest(f"https://api.mojang.com/users/profiles/minecraft/{username}")
username = data.json()["name"]
res = await funcs.getRequest(
f"https://sessionserver.mojang.com/session/minecraft/profile/{str(data.json()['id'])}"
)
data = loads(b64decode(res.json()["properties"][0]["value"]))
skin = data["textures"]["SKIN"]["url"]
e = Embed(
title=username,
description=f"https://namemc.com/profile/{username}"
)
e.set_image(url=skin)
except Exception as ex:
funcs.printError(ctx, ex)
e = funcs.errorEmbed(None, "Invalid skin or server error.")
await ctx.reply(embed=e)
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="mcserver", description="Gets the current status of a *Minecraft: Java Edition* server.",
usage="[server address]")
async def mcserver(self, ctx, *, ipaddress: str=""):
ipaddress = ipaddress.casefold().replace(" ", "") or "mc.hypixel.net"
try:
res = await funcs.getRequest(f"https://api.mcsrvstat.us/2/{ipaddress}", headers={"accept": "application/json"})
data = res.json()
status = data["online"]
e = Embed(title="Minecraft Server Status", colour=Colour.green() if status else Colour.red())
e.add_field(name="Server Address", value=f"`{ipaddress}`")
e.add_field(name="Online", value=f"`{status}`")
if status:
players = data["players"]["online"]
e.add_field(name="Player Count", value="`{:,}/{:,}`".format(players, data['players']['max']))
if players:
try:
playerLimit = 25
playerList = data["players"]["list"][:playerLimit]
listStr = ", ".join(f"`{player}`" for player in playerList)
if len(playerList) != players:
listStr += f" *and {players - playerLimit} more...*"
e.add_field(name="Players", value=listStr)
except:
pass
e.add_field(name="Version", value=f'`{data["version"]}`')
e.add_field(name="Port", value=f'`{data["port"]}`')
e.set_thumbnail(url=f"https://eu.mc-api.net/v3/server/favicon/{ipaddress}")
try:
e.add_field(name="Software", value=f'`{data["software"]}`')
except:
pass
motd = data["motd"]["clean"]
try:
secondLine = f"\n{motd[1].strip().replace('&', '&')}"
except:
secondLine = ""
e.set_footer(text=motd[0].strip().replace('&', '&') + secondLine)
except Exception as ex:
funcs.printError(ctx, ex)
e = funcs.errorEmbed(None, "Invalid server address or server error?")
await ctx.reply(embed=e)
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="fossils", description="Brings up a fossil identification chart for divine travel.",
aliases=["ft", "fossiltable", "fossilchart", "fossil"])
async def fossils(self, ctx):
url = "https://cdn.discordapp.com/attachments/771404776410972161/842022227347636264/fossiltable.jpg"
await funcs.sendImage(
ctx, url, message="PDF: https://cdn.discordapp.com/attachments/817309668924719144/"+
"818310310153814056/Fossil_origin_identification_1.pdf"
)
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="divinetravel", aliases=["dt", "divine", "div", "dv"], usage="[option OR F3+I data]",
description="Either brings up the chart for divine travel or gets certain divine coordinates. You can use o" +
"ptions like `fossilX` with X being the x-coordinate of the fossil origin, or look at the fo" +
"ssil origin in the game, press F3+I, and paste your clipboard as an argument for this command.")
async def divinetravel(self, ctx, *, option: str=""):
if option:
try:
try:
x, _, _ = self.f3iProcessing(option)
option = "fossil" + str(x)
except:
pass
res = self.divinetravel[option.casefold().replace(" ", "")].split(" | ")
e = Embed(title="Divine Travel: " + option.casefold().replace(" ", ""))
for i, c in enumerate(res):
if i > 2:
text = f"High Roll #{i - 2}"
else:
text = f"Stronghold #{i + 1}"
e.add_field(name=f"{text}", value=f"`{c.split(': ')[1]}`")
except KeyError:
e = funcs.errorEmbed(
"Invalid option!",
"Valid options:\n\n{}".format(", ".join(f"`{opt}`" for opt in self.divinetravel.keys()))
)
await ctx.reply(embed=e)
else:
url = "https://media.discordapp.net/attachments/771698457391136798/934726825811275816/unknown.png"
await funcs.sendImage(ctx, url)
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="bastions", description="Shows the comprehensive guides to bastions.", hidden=True, aliases=["bastion"])
async def bastions(self, ctx):
await ctx.reply("Guides: https://youtube.com/playlist?list=PL7Q35RXRsOR-udeKzwlYGJd0ZrvGJ0fwu\n\nP" +
"ractice Map: https://github.com/LlamaPag/bastion\n\nGuide to Practice Map: <https://youtu.be/jlA-jW7VGqw>")
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="endpractice", description="Shows the end practice map by ryguy2k4.", hidden=True,
aliases=["end", "endfight", "endpractise"])
async def endpractice(self, ctx):
await ctx.reply("https://github.com/ryguy2k4/ryguy2k4endpractice/releases")
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="obt", description="Shows the One Block Tower tutorial for 1.7.", hidden=True,
aliases=["tower1.7", "1.7tower", "oneblock", "oneblocktower"])
async def obt(self, ctx):
await ctx.reply("https://www.youtube.com/watch?v=nYI6wOM1U4A")
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="zentower", description="Shows the Zen Tower tutorial for 1.8.", hidden=True,
aliases=["tower1.8", "1.8tower", "zen"])
async def zentower(self, ctx):
await ctx.reply("https://www.youtube.com/watch?v=ryo3QbH2Zko")
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="1.15route", description="Shows the 1.15 route.", hidden=True,
aliases=["route1.15", "1.15routing", "routing1.15", "routing115", "115route",
"115routing", "route115", "1.15", "115", "doubleday"])
async def route115(self, ctx):
await ctx.reply("https://imgur.com/gallery/CFJYKmw\n\nDouble Day In-Depth Guide: " +
"https://docs.google.com/document/d/1JhDCCpDww3o3oueROpP1lp01JaulaTdm-EhGOfgvkmk/edit")
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="mapless", description="Shows the mapless treasure tutorial and practice map.", hidden=True,
aliases=["buriedtreasure"])
async def mapless(self, ctx):
await ctx.reply("Tutorials: https://youtu.be/ho1rwmooHRg\n\nhttps://youtu.be/_dyD8ZwagDg" +
"\n\nPractice Map: <https://cdn.discordapp.com/att" +
"achments/405839885509984256/885694752056541234/Mapless_Map.zip>")
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="quadrants1.16", description="Shows the four Nether quadrants for versions 1.16+.", hidden=True,
aliases=["netheregions", "netheregion", "netherregion", "netherregions", "nether", "quadrant", "quadrants"])
async def quadrants116(self, ctx):
await funcs.sendImage(ctx, "https://media.discordapp.net/attachments/771404776410972161/937755369072107520/ejAZNGq.png")
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="bedtiming", description="Shows the accurate bed timing for end fights.", hidden=True,
aliases=["bedtimings", "onecycle", "timingbed", "bedtime", "bed", "beds"])
async def bedtiming(self, ctx):
await funcs.sendImage(ctx, "https://media.discordapp.net/attachments/771698457391136798/937078099789635614/unknown.png")
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="1.8trading", description="Shows the trading tutorial for 1.8.", hidden=True,
aliases=["pre1.9trading", "trading1.8", "trading18", "18trading", "tradingpre1.9", "trading"])
async def trading18(self, ctx):
await funcs.sendImage(ctx, "https://cdn.discordapp.com/attachments/771404776410972161/959506805908705320/unknown.png",
message="https://youtu.be/1ksc3SSJkxs")
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="ninjabrainbot", aliases=["ninjabot", "ninjabrain", "nb", "nbb"], hidden=True,
description="Shows the Ninjabrain Bot tutorial and repository page.")
async def ninjabrainbot(self, ctx):
await ctx.reply("Tutorial: https://youtu.be/Rx8i7e5lu7g\n\nRepository: https://github.com/Ninjabrain1/Ninjabrain-Bot")
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="blazefights", aliases=["blazefight", "blaze", "blazes", "fortress", "fortresses"],
hidden=True, description="Shows the tutorial for Nether fortresses and blaze fights.")
async def blazefights(self, ctx):
await ctx.reply("https://youtu.be/pmx9LyUvLTk")
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="eray", aliases=["eraying", "microlensing"], hidden=True, description="Shows the microlensing tutorial.")
async def eray(self, ctx):
await ctx.reply("https://www.youtube.com/watch?v=jvTfMLPnMSw")
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="magmaravines", aliases=["ravine", "magmaravine", "magma", "ravines", "oceanravine", "oceanravines"],
description="Shows the guide to magma ravines.", hidden=True)
async def magmaravines(self, ctx):
await ctx.reply("https://www.youtube.com/watch?v=yGyMWYhHYoQ")
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="hypermodern", aliases=["hyperm", "hmodern"], hidden=True,
description="Shows the guide to hypermodern speedruns.")
async def hypermodern(self, ctx):
await ctx.reply("https://www.youtube.com/watch?v=gAHMJfsrHe4")
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="blindtravelcoords", aliases=["rings", "strongholdrings", "strongholdring"], hidden=True,
description="Shows the ideal blind travel coordinates for the first to third stronghold rings.")
async def blindtravelcoords(self, ctx):
await ctx.reply("https://imgur.com/gallery/i3fIanf")
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="mcspeedrunning", description="Shows some important *Minecraft: Java Edition* speedrunning resources.",
aliases=["mcspeedrun", "minecraftspeedrun", "minecraftspeedrunning", "mcsr", "speedrun"])
async def mcspeedrunning(self, ctx):
await ctx.reply(
"Setup Guide: https://www.youtube.com/watch?v=GAbnKAyireM\n\nWebsite: https://www.minecraftspeedrunning.com/"
)
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="doubleinstant", aliases=["doubleinstanttravel", "doubleinstanttrav", "dit", "di"],
description="Shows the tutorial for Double Instant Travel for pre-1.9 trading.", hidden=True)
async def doubleinstant(self, ctx):
await ctx.reply("https://youtu.be/XuZWIJRCyaY")
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="speedrunigt", aliases=["igt"], description="Download the SpeedRunIGT mod here.", hidden=True)
async def speedrunigt(self, ctx):
await ctx.reply("https://redlime.github.io/SpeedRunIGT/")
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="contariacalc", description="Download ContariaCalc here.", hidden=True)
async def contariacalc(self, ctx):
await ctx.reply("https://github.com/KingContaria/ContariaCalc")
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="strongholdnav", aliases=["stronghold", "sh", "strongholds", "nav"],
description="Shows the guides to stronghold navigation and hidden rooms.", hidden=True)
async def strongholdnav(self, ctx):
await ctx.reply("https://www.youtube.com/watch?v=hEZfeUWA3hM\n\nhttps://www.youtube.com/watch?v=vztJNmUdyBY" +
"\n\nhttps://www.youtube.com/watch?v=2dWq2wXy43M (**NEW**)")
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="ruinedportals", description="Shows some useful ruined portal resources.", hidden=True,
aliases=["rp", "ruinedportal", "ruined", "ruinportal", "ruinportals", "ruin"])
async def ruinedportals(self, ctx):
await ctx.reply(
"Quick Completion Guide: https://www.youtube.com/watch?v=Bg_TVoo8waM",
file=(await funcs.getImageFile(
"https://media.discordapp.net/attachments/771404776410972161/939500126903336960/unknown.png"
))
)
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="zerocycle", description="Shows some useful Zero Cycle resources.", hidden=True,
aliases=["0cycle", "0c", "zero", "zeroc", "zc", "0"])
async def zerocycle(self, ctx):
await ctx.reply(
"Full Zero Cycle Guide: https://youtu.be/iClDGWL0e5s\n\nResources: https://zerocycle.repl.co/",
file=(await funcs.getImageFile(
"https://media.discordapp.net/attachments/771404776410972161/938843696009469952/unknown.png"
))
)
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="fsg", description="Shows a list of FSG seed generators.", hidden=True, aliases=["fsgseed", "fsgseeds"])
async def fsg(self, ctx):
await ctx.reply("Use one of the allowed generators: " +
"<https://docs.google.com/spreadsheets/d/1ilu72GJ-vJZq2LFU68rycGMeTbWPjHJnO8PGfp4QjA8/edit#gid=0>\n\n" +
"If you would like to use the generator locally for shorter wait times, follow this: " +
"<https://youtu.be/Gl7zOn2lLo4>\n\nPlease play the seed within 30 seconds after it has been generated.")
setup = Minecraft.setup
| # Credit - https://github.com/Sharpieman20/Sharpies-Speedrunning-Tools
# For blindtravel, doubletravel, educatedtravel, safeblind, triangulation
# Credit - https://github.com/FourGoesFast/PerfectTravelBot
# For divinetravel, perfecttravel
from asyncio import TimeoutError
from base64 import b64decode
from json import loads
from random import choice, randint
from time import time
import numpy as np
from discord import Colour, Embed, File
from discord.ext import commands
from src.utils import funcs
from src.utils.base_cog import BaseCog
BARTER_LIMIT = 896
class Minecraft(BaseCog, name="Minecraft",
description="Commands relating to *Minecraft* in general and *Minecraft: Java Edition* speedrunning."):
def __init__(self, botInstance, *args, **kwargs):
super().__init__(botInstance, *args, **kwargs)
self.client.loop.create_task(self.__readFiles())
async def __readFiles(self):
self.divinetravel = await funcs.readJson(funcs.getResource(self.name, "divine_travel.json"))
self.perfecttravel = await funcs.readJson(funcs.getResource(self.name, "perfect_travel.json"))
self.eyedata = await funcs.readJson(funcs.getResource(self.name, "eye_data.json"))
self.loottable = await self.piglinLootTable()
await funcs.generateJson(
"findseed",
{
"calls": 0,
"highest": {
"found": 0,
"number": 0,
"time": int(time())
}
}
)
await funcs.generateJson("finddream", {"iteration": 0, "mostPearls": 0, "mostRods": 0})
async def piglinLootTable(self):
lt = await funcs.readJson(funcs.getResource(self.name, "piglin_loot_table.json"))
ltnew = []
for i in lt:
if i["id"] < 5:
item = i["item"]
for j in range(1, 4):
i["item"] = f"{item} {j}"
for _ in range(i["weight"]):
ltnew.append(i.copy())
i["id"] += 1
else:
for _ in range(i["weight"] * 3):
ltnew.append(i)
return ltnew
@staticmethod
def randomEyes():
eyes = 0
for _ in range(12):
eyes += 1 if funcs.oneIn(10) else 0
return eyes
@staticmethod
def getExcessStr(item):
stacks, excess = funcs.stacksAndExcess(item)
return "" if not stacks and excess == item \
else f" ({'{:,} stack{}'.format(stacks, '' if stacks == 1 else 's') if stacks else ''}" + \
f"{' + ' if stacks and excess else ''}{str(excess) if excess else ''})"
@staticmethod
def chargeableAnchors(glowdust: int, cryobby: int):
return min([glowdust // 16, cryobby // 6])
@staticmethod
def f3iProcessing(clipboard):
try:
args = clipboard.split(" ")
return int(args[1]), int(args[2]), int(args[3])
except Exception:
raise Exception("Invalid input. Please do not modify your F3+I clipboard.")
@staticmethod
def f3cProcessing(clipboard):
try:
args = clipboard.split(" ")
return float(args[6]), float(args[8]), float(args[9]) % 360
except Exception:
raise Exception("Invalid input. Please do not modify your F3+C clipboard.")
@staticmethod
def angleProcessing(angle):
if angle >= 0:
return (angle + 90) % 360
return (angle - 270) % 360
@staticmethod
def coordsDist(x, z):
return np.sqrt([x * x + z * z])[0]
def coordsDifference(self, coords1: tuple, coords2: tuple):
return self.coordsDist(coords1[0] - coords2[0], coords1[1] - coords2[1])
def strongholdCalc(self, x0, z0, f0, x1, z1, f1):
a0 = np.tan([self.angleProcessing(f0) * np.pi / 180])[0]
a1 = np.tan([self.angleProcessing(f1) * np.pi / 180])[0]
b = z0 - x0 * a0
xp = ((z1 - x1 * a1) - b) / (a0 - a1)
zp = xp * a0 + b
blocks = round(self.coordsDifference((x1, z1), (xp, zp)))
return xp, zp, blocks
@commands.cooldown(1, 5, commands.BucketType.user)
@commands.command(name="findseed", description="Everyone's favourite command. Test your luck using this command!",
aliases=["fs", "seed", "findseeds", "f"])
async def findseed(self, ctx):
eyes = self.randomEyes()
data = await funcs.readJson("data/findseed.json")
odds = self.eyedata[str(eyes)]["percent"]
onein = self.eyedata[str(eyes)]["onein"]
update = False
if eyes >= data["highest"]["number"]:
data["highest"]["found"] -= data["highest"]["found"] - 1 if eyes > data["highest"]["number"] else -1
data["highest"]["number"] = eyes
data["highest"]["time"] = int(time())
update = True
highest = data["highest"]["number"]
highestTime = data["highest"]["time"]
highestTotal = data["highest"]["found"]
data["calls"] += 1
calls = data["calls"]
await funcs.dumpJson("data/findseed.json", data)
file = File(
funcs.PATH + funcs.getResource(self.name, "portal_frame_images/") + f"{eyes}eye.png",
filename="portal.png"
)
foundTime = "just now"
if not update:
timestr = funcs.timeDifferenceStr(time(), highestTime)
timestr_0 = int(timestr.split(" ")[0])
if timestr_0 > 2:
foundTime = f"{timestr_0} days"
else:
foundTime = timestr
e = Embed(title=f"{self.client.command_prefix}findseed",
description=f"Requested by: {ctx.message.author.mention}")
e.add_field(name="Your Eyes", value=f"`{eyes}`")
e.add_field(name="Probability", value=f"`{odds}% (1 in {onein})`")
e.add_field(name="Most Eyes Found", inline=False,
value=f"`{highest} (last found {foundTime}{' ago' if not update else ''}" +
f", found {'{:,}'.format(highestTotal)} time{'' if highestTotal == 1 else 's'})`")
e.set_footer(text=f"The command has been called {'{:,}'.format(calls)} time{'' if calls == 1 else 's'}. !eyeodds")
e.set_image(url="attachment://portal.png")
await ctx.reply(embed=e, file=file)
@commands.cooldown(1, 10, commands.BucketType.user)
@commands.command(name="eyeodds", description="Shows the odds of getting each type of end portal.",
aliases=["odds", "eyes", "eye", "eyeodd", "eyeood", "eyeoods"])
async def eyeodds(self, ctx):
msg = ""
for i in range(13):
odds = self.eyedata[str(i)]["percent"]
msg += f"{i} eye - `{odds}% (1 in {self.eyedata[str(i)]['onein']})`\n"
await ctx.reply(msg)
@commands.cooldown(1, 5, commands.BucketType.user)
@commands.command(name="finddream", description="Can you get Dream's *Minecraft* speedrunning \"luck\"? " +
"Test your luck using this command!",
aliases=["dream", "dreamsimulator", "dreamsim", "dreamluck", "fd"], hidden=True)
async def finddream(self, ctx):
pearls, rods = 0, 0
dpearls, drods = 262, 305
data = await funcs.readJson("data/finddream.json")
mostPearls = data["mostPearls"]
mostRods = data["mostRods"]
for _ in range(dpearls):
pearls += 1 if randint(0, 422) < 20 else 0
for _ in range(drods):
rods += 1 if funcs.oneIn(2) else 0
data["mostPearls"] = pearls if pearls >= mostPearls else mostPearls
data["mostRods"] = rods if rods >= mostRods else mostRods
data["iteration"] += 1
iters = data['iteration']
await funcs.dumpJson("data/finddream.json", data)
e = Embed(
title=f"{self.client.command_prefix}finddream",
description=f"Dream got 42 ender pearl trades in {dpearls} plus 211 blaze rod drops in {drods}. " +
f"Can you achieve his 'luck'?\n\nRequested by: {ctx.author.mention}"
)
e.add_field(name="Your Pearl Trades", value=f"`{pearls} ({round(pearls / dpearls * 100, 3)}%)`")
e.add_field(name="Your Rod Drops", value=f"`{rods} ({round(rods / drods * 100, 3)}%)`")
e.set_footer(
text=f"The command has been called {'{:,}'.format(iters)} time{'' if iters == 1 else 's'}. " +
f"| Most pearl trades: {data['mostPearls']}; most rod drops: {data['mostRods']}"
)
e.set_thumbnail(url="https://static.wikia.nocookie.net/dream_team/images/7/7b/Dream.jpeg")
await ctx.reply(embed=e)
@commands.cooldown(1, 1, commands.BucketType.user)
@commands.command(name="findbreak", description="You throw an ender eye. Does it break or do you get to keep it?" +
" Test your luck using this command!",
aliases=["break", "eyebreak", "breakeye", "findeye"], hidden=True)
async def findbreak(self, ctx):
e = Embed(title=f"{self.client.command_prefix}findbreak",
description=f"Requested by: {ctx.message.author.mention}")
badluckonein = 5
goodluck = not funcs.oneIn(badluckonein)
e.add_field(name="Result", value=f"`{'No Break!' if goodluck else 'Break...'}`")
e.set_thumbnail(url="https://media.discordapp.net/attachments/771404776410972161/938407577975418900/unknown.png")
e.set_image(url="https://cdn.discordapp.com/attachments/771404776410972161/938408463946637312/2022-02-02_20.20.06.png"
if goodluck else
"https://media.discordapp.net/attachments/771404776410972161/938408658411327528/unknown.png")
e.set_footer(text=f"Odds: {str(badluckonein - 1) if goodluck else '1'}/{str(badluckonein)}")
await ctx.reply(embed=e)
@commands.cooldown(1, 1, commands.BucketType.user)
@commands.command(name="findcleric", description="Will you get the ender pearl trade from the cleric, " +
"or will you get one-thirded? Test your luck using this command!",
aliases=["cleric", "stupidvillager"], hidden=True)
async def findcleric(self, ctx):
e = Embed(title=f"{self.client.command_prefix}findcleric",
description=f"Requested by: {ctx.message.author.mention}")
badluckonein = 3
goodluck = not funcs.oneIn(badluckonein)
e.add_field(name="Result", value=f"`{'Pearl' if goodluck else 'Bottle'} Trade{'!' if goodluck else '...'}`")
e.set_thumbnail(url="https://media.discordapp.net/attachments/771404776410972161/856203578615529532/cleric.png")
e.set_image(url="https://media.discordapp.net/attachments/771404776410972161/856203574337601536/pearl.png" if goodluck else
"https://media.discordapp.net/attachments/771404776410972161/856203573113520138/bottle.png")
e.set_footer(text=f"Odds: {str(badluckonein - 1) if goodluck else '1'}/{str(badluckonein)}")
await ctx.reply(embed=e)
@commands.cooldown(1, 1, commands.BucketType.user)
@commands.command(name="findgravel", description="Will you get flint from gravel? Test your luck using this command!",
aliases=["gravel", "flint", "findflint", "fg"], hidden=True)
async def findgravel(self, ctx):
e = Embed(title=f"{self.client.command_prefix}findgravel",
description=f"Requested by: {ctx.message.author.mention}")
goodluckonein = 10
badluck = not funcs.oneIn(goodluckonein)
e.add_field(name="Result", value=f"`{'Gravel' if badluck else 'Flint'}{'...' if badluck else '!'}`")
e.set_thumbnail(url="https://media.discordapp.net/attachments/771698457391136798/856209821383917608/gravel.png")
e.set_image(url="https://media.discordapp.net/attachments/771698457391136798/856209821383917608/gravel.png" if badluck else
"https://media.discordapp.net/attachments/771698457391136798/856209843174244362/flint.png")
e.set_footer(text=f"Odds: {str(goodluckonein - 1) if badluck else '1'}/{str(goodluckonein)}")
await ctx.reply(embed=e)
@commands.cooldown(1, 1, commands.BucketType.user)
@commands.command(name="findperch", description="You are in insane pace and about to kill the dragon..." +
"but does it perch instantly? Test your luck using this command!",
aliases=["dragon", "fp", "finddragon"], hidden=True)
async def findperch(self, ctx):
e = Embed(title=f"{self.client.command_prefix}findperch",
description=f"Requested by: {ctx.message.author.mention}")
goodluckonein = 13
badluck = not funcs.oneIn(goodluckonein)
e.add_field(name="Result", value=f"`{'No Perch' if badluck else 'Perch'}{'...' if badluck else '!'}`")
e.set_thumbnail(url="https://media.discordapp.net/attachments/771404776410972161/928297045486370857/dragon.png")
e.set_image(url="https://media.discordapp.net/attachments/771404776410972161/928299016259776613/2022-01-05_22.48.45.png"
if badluck
else "https://media.discordapp.net/attachments/771404776410972161/928298549861572638/2022-01-05_22.46.50.png")
e.set_footer(text=f"Odds: {str(goodluckonein - 1) if badluck else '1'}/{str(goodluckonein)}")
await ctx.reply(embed=e)
@commands.cooldown(1, 1, commands.BucketType.user)
@commands.command(name="findskull", description="You kill a wither skeleton...but does it drop a wither skull?" +
" Test your luck using this command!",
aliases=["skull", "witherskull", "findwitherskull", "findwither"], hidden=True)
async def findskull(self, ctx):
e = Embed(title=f"{self.client.command_prefix}findskull",
description=f"Requested by: {ctx.message.author.mention}")
goodluckonein = 40
badluck = not funcs.oneIn(goodluckonein)
e.add_field(name="Result", value=f"`{'No Skull' if badluck else 'Skull'}{'...' if badluck else '!'}`")
e.set_thumbnail(url="https://cdn.discordapp.com/attachments/771404776410972161/935204890639233054/unknown.png")
e.set_image(
url="" if badluck else "https://cdn.discordapp.com/attachments/771404776410972161/935204919651205250/unknown.png"
)
e.set_footer(text=f"Odds: {str(goodluckonein - 1) if badluck else '1'}/{str(goodluckonein)}")
await ctx.reply(embed=e)
@commands.cooldown(1, 1, commands.BucketType.user)
@commands.command(name="findblaze", description="You kill a blaze...but does it drop a rod? Test your luck using this command!",
aliases=["rod", "blazerod", "findrod", "findblazerod"], hidden=True)
async def findblaze(self, ctx):
e = Embed(title=f"{self.client.command_prefix}findblaze",
description=f"Requested by: {ctx.message.author.mention}")
badluckonein = 2
goodluck = not funcs.oneIn(badluckonein)
e.add_field(name="Result", value=f"`{'Rod' if goodluck else 'No Rod'} Drop{'!' if goodluck else '...'}`")
e.set_thumbnail(url="https://media.discordapp.net/attachments/771698457391136798/856213640809414666/blaze.png")
e.set_image(url="https://media.discordapp.net/attachments/771698457391136798/856213641020178472/rod.png" if goodluck else
"https://cdn.discordapp.com/attachments/771698457391136798/856213642173612032/norod.png")
e.set_footer(text=f"Odds: {str(badluckonein - 1) if goodluck else '1'}/{str(badluckonein)}")
await ctx.reply(embed=e)
@commands.cooldown(1, 1, commands.BucketType.user)
@commands.command(name="perchcmd", description="Shows the command to force the the ender dragon perch.", aliases=["perch"])
async def perchcmd(self, ctx):
await ctx.reply("```1.13+: /data merge entity @e[type=ender_dragon,limit=1] {DragonPhase:2}\n\n" +
"1.9-1.12: /entitydata @e[type=ender_dragon] {DragonPhase:2}```")
@commands.cooldown(1, 1, commands.BucketType.user)
@commands.command(name="logs", description="Calculates how many logs you will need to trade for a certain number of emeralds.",
aliases=["log", "wood"], usage="<amount of emeralds needed>")
async def logs(self, ctx, emeralds):
try:
emeralds = int(emeralds)
if emeralds < 1:
raise Exception
log = emeralds * 4
await ctx.reply("You want **{:,}** emerald{}.\n\nYou will need **{:,}** logs{}.".format(
emeralds, "" if emeralds == 1 else "s", int(log), self.getExcessStr(log)
))
except Exception as ex:
funcs.printError(ctx, ex)
await ctx.reply(
embed=funcs.errorEmbed(None, "Invalid input. Please make sure you are entering positive, non-zero integers.")
)
@commands.cooldown(1, 1, commands.BucketType.user)
@commands.command(name="haybales", aliases=["hay", "haybale"], usage="<amount of emeralds needed>",
description="Calculates how many hay bales you will need to trade for a certain number of emeralds.")
async def haybales(self, ctx, emeralds):
try:
emeralds = int(emeralds)
if emeralds < 1:
raise Exception
hay = 20 * emeralds / 9
hay = funcs.strictRounding(hay)
await ctx.reply("You want **{:,}** emerald{}.\n\nYou will need **{:,}** hay bales{}.".format(
emeralds, "" if emeralds == 1 else "s", int(hay), self.getExcessStr(hay)
))
except Exception as ex:
funcs.printError(ctx, ex)
await ctx.reply(
embed=funcs.errorEmbed(None, "Invalid input. Please make sure you are entering positive, non-zero integers.")
)
@commands.cooldown(1, 1, commands.BucketType.user)
@commands.command(name="books", aliases=["book", "bookshelf", "bookshelves", "library"],
usage="<books per emerald> <emeralds per eye> [eyes needed]",
description="Calculates how many books you will need to get eyes of ender for pre-1.9 trading.")
async def books(self, ctx, book, emeralds, eyes="12"):
try:
book = int(book)
emeralds = int(emeralds)
eyes = int(eyes)
if not 8 <= book <= 10:
return await ctx.send(embed=funcs.errorEmbed(None, "Books per emerald must be 8-10 inclusive."))
if not 7 <= emeralds <= 11:
return await ctx.send(embed=funcs.errorEmbed(None, "Emeralds per eye must be 7-11 inclusive."))
if not 1 <= eyes <= 12:
return await ctx.send(embed=funcs.errorEmbed(None, "Eyes needed must be 1-12 inclusive."))
totalEmeralds = emeralds * eyes
totalBooks = totalEmeralds * book
booksPerEye = emeralds * book
bookshelves = funcs.strictRounding(totalBooks / 3)
await ctx.send("You want **{}** eye{} of ender.\nThe librarian sells one emera".format(eyes, "" if eyes == 1 else "s") +
"ld for **{}** books.\nThe cleric sells one eye of ender for **{}** emeralds.\n".format(book, emeralds) +
"\nYou will need:\n\n**{:,}** books{} for a total of".format(totalBooks, self.getExcessStr(totalBooks)) +
" **{}** emeralds{}\nBooks per eye:".format(totalEmeralds, self.getExcessStr(totalEmeralds)) +
" **{}**\nBookshelves to break: **{}**\n\n".format(booksPerEye, bookshelves) +
"Big library: 699 books\nSmall library: 483 books")
except Exception as ex:
funcs.printError(ctx, ex)
await ctx.reply(
embed=funcs.errorEmbed(None, "Invalid input.")
)
@commands.cooldown(1, 1, commands.BucketType.user)
@commands.command(name="anchors", description="Calculates how many chargeable respawn anchors you can craft based on how " +
"much glowstone dust and crying obsidian you have.",
aliases=["anchor"], usage="<amount of glowstone dust> <amount of crying obdisian>")
async def anchors(self, ctx, glowdust, cryobby):
try:
glowdust = int(glowdust)
cryobby = int(cryobby)
if glowdust < 1 or cryobby < 1:
raise Exception
anchors = self.chargeableAnchors(glowdust, cryobby)
charge = " and sufficiently charge {}".format("it" if anchors == 1 else "them") if anchors else ""
await ctx.reply(
"You have **{:,}** glowstone dust and **{:,}** crying obsidian.\n\nYou can make **".format(glowdust, cryobby) +
"{:,}** respawn anchor{}{}.".format(anchors, "" if anchors == 1 else "s", charge)
)
except Exception as ex:
funcs.printError(ctx, ex)
await ctx.reply(
embed=funcs.errorEmbed(None, "Invalid input. Please make sure you are entering positive, non-zero integers.")
)
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(description="Simulates *Minecraft: Java Edition* 1.16.1 piglin bartering. Test your luck using this command!",
aliases=["barter", "piglin", "poglin", "bartering", "barteringsim"], name="bartersim",
usage=f"[gold ingots up to 10,000]\n\nAlternative usage(s):\n\n- <gold blocks up to 1,111 (ending with b)>")
async def bartersim(self, ctx, goldingots: str="1"):
try:
try:
goldingots = int(goldingots)
except:
goldingots = int(goldingots[:-1]) * 9
if not 0 < goldingots < 10001:
return await ctx.reply(embed=funcs.errorEmbed(None, f"Value must be between 1 and 10,000."))
except ValueError:
return await ctx.reply(embed=funcs.errorEmbed(None, "Invalid input."))
trades = {}
string, glowdust, cryobby = 0, 0, 0
for _ in range(goldingots):
trade = choice(self.loottable)
if trade["id"] not in list(trades.keys()):
trades[trade["id"]] = {}
trades[trade["id"]]["item"] = trade["item"]
n = choice(trade["quantity"])
trades[trade["id"]]["quantity"] = n
trades[trade["id"]]["trades"] = 1
else:
n = choice(trade["quantity"])
trades[trade["id"]]["quantity"] += n
trades[trade["id"]]["trades"] += 1
if trade["id"] == 13:
string += n
elif trade["id"] == 10:
glowdust += n
elif trade["id"] == 19:
cryobby += n
res = "You bartered {:,} gold ingot{} for:\n\n".format(goldingots, "" if goldingots == 1 else "s")
for i in sorted(trades):
t = trades[i]
res += "{}{:,} x {} ({:,} trade{})\n".format(
"*** " if i in [7, 8, 10, 12, 13, 18, 19] else " ",
t["quantity"], t["item"], t["trades"], "" if t["trades"] == 1 else "s"
)
anchors = self.chargeableAnchors(glowdust, cryobby)
beds = string // 12
explosives = anchors + beds
if explosives:
res += "\nExplosives you can craft ({:,}):\n\n".format(explosives)
if beds:
res += " {:,} x Bed\n".format(beds)
if anchors:
res += " {:,} x Respawn Anchor (w/ enough glowstone to power)".format(anchors)
await ctx.reply(funcs.formatting(res))
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="pearlbarter", description="Finds the probability of getting 12 or more ender pearls" +
" in a given number of piglin trades in *Minecraft* 1.16.1.",
aliases=["pearltrade", "pearlbartering", "barteringpearl", "barterpearl", "barterpearls"],
usage=f"[total gold ingots up to {BARTER_LIMIT}]")
async def pearlbarter(self, ctx, trades: str="2"):
try:
n = int(trades)
if not 2 <= n <= BARTER_LIMIT:
return await ctx.reply(embed=funcs.errorEmbed(None, f"Value must be between 2 and {BARTER_LIMIT}."))
except ValueError:
return await ctx.reply(embed=funcs.errorEmbed(None, "Invalid input."))
x = 1 - (403 / 423) ** n - n * (20 / 423) * ((403 / 423) ** (n - 1)) - (2 / 5) * (n * (n - 1) / 2) \
* ((403 / 423) ** (n - 2)) * ((20 / 423) ** 2)
await ctx.reply(f"**[1.16.1]** The probability of getting 12 or more ender pearls" +
f" with {n} gold ingots is:\n\n`{round(x * 100, 5)}%` (1 in {round(1 / x, 5)})")
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="blindtravel", description="A *Minecraft: Java Edition* speedrunning tool that " +
"should be used when you want to build another por" +
"tal in the Nether before throwing any eyes of end" +
"er. To use this command, in the game, press F3+C," +
" pause, come over to Discord, paste your clipboar" +
"d as an argument for the command, and then build " +
"your portal at the suggested coordinates in the N" +
"ether. This command is for versions 1.13+ and may " +
"not be 100% accurate. This command MAY not be used" +
" in a real speedrun.",
aliases=["bt", "blind", "blindtrav"], usage="<F3+C data>")
async def blindtravel(self, ctx, *, f3c):
await ctx.send("**Note:** This command, along with other speedrunning calculators, MAY not be used in a real speedrun.")
try:
x, z, _ = self.f3cProcessing(f3c)
dist = self.coordsDist(x, z)
o = 190 if dist < 190 else dist if dist < 290 else 290 if dist < 442 else 580 if dist < 580 else dist \
if dist < 692 else 686 if dist < 825 else 970 if dist < 970 else dist if dist < 1060 else 1060
t = np.arctan([z / x])[0]
xp = np.sign(x) * np.absolute([o * np.cos([t])[0]])[0]
zp = np.sign(z) * np.absolute([o * np.sin([t])[0]])[0]
blocks = round(self.coordsDifference((x, z), (xp, zp)))
await ctx.reply(
f"Build your portal at: **{round(xp)}, {round(zp)}** " +
f"({'{:,}'.format(blocks)} block{'' if blocks == 1 else 's'} away)"
)
except Exception as ex:
await ctx.reply(embed=funcs.errorEmbed(None, str(ex)))
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="educatedtravel", description="A *Minecraft: Java Edition* speedrunning tool th" +
"at should be used when you want to build anoth" +
"er portal in the Nether after throwing an eye " +
"of ender. To use this command, in the game, th" +
"row an eye, stand still, put your mouse direct" +
"ly over the eye, press F3+C, pause, come over " +
"to Discord, paste your clipboard as an argumen" +
"t for the command, and then build your portal " +
"at the suggested coordinates in the Nether. Th" +
"is command is for versions 1.13+ and may not be" +
" 100% accurate. This command MAY not be used in" +
" a real speedrun.",
aliases=["et", "educated", "nethertravel"], usage="<F3+C data>")
async def educatedtravel(self, ctx, *, f3c):
await ctx.send("**Note:** This command, along with other speedrunning calculators, MAY not be used in a real speedrun.")
try:
x, z, f = self.f3cProcessing(f3c)
f = (360 + f if f < 0 else f) - 180
o = 640 if self.coordsDist(x, z) > 3584 else 216
m1 = -np.tan([(90 - f) * (np.pi / 180)])[0]
a = 1 + (m1 ** 2)
b1 = -m1 * (x / 8) + (z / 8)
b = 2 * m1 * b1
xp = ((-b) + (np.sign(f) * np.sqrt([b ** 2 - 4 * a * (b1 ** 2 - o ** 2)])[0])) / (2 * a)
zp = m1 * xp + b1
await ctx.reply(f"Build your portal at: **{round(xp)}, {round(zp)}** ")
except Exception as ex:
await ctx.reply(embed=funcs.errorEmbed(None, str(ex)))
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="doubletravel", description="A *Minecraft: Java Edition* speedrunning tool that" +
", whilst you are in the Nether, gets a spot for " +
"you to make your first portal inside the second " +
"ring of strongholds. To use this command, in the" +
" game, press F3+C, pause, come over to Discord, " +
"paste your clipboard as an argument for the comm" +
"and, and then build your portal at the suggested" +
" coordinates in the Nether. `educatedtravel` shou" +
"ld then be used after exiting the Nether which s" +
"hould do a good job of getting you to the right " +
"spot in the Nether to build your second portal. " +
"This command is for versions 1.13+ and may not be" +
" 100% accurate. This command MAY not be used in a " +
"real speedrun.",
aliases=["double"], usage="<F3+C data>")
async def doubletravel(self, ctx, *, f3c):
await ctx.send("**Note:** This command, along with other speedrunning calculators, MAY not be used in a real speedrun.")
try:
x, z, _ = self.f3cProcessing(f3c)
o = 520
t = np.arctan([z / x])[0]
xp = np.sign(x) * np.absolute([o * np.cos([t])[0]])[0]
zp = np.sign(z) * np.absolute([o * np.sin([t])[0]])[0]
blocks = round(self.coordsDifference((x, z), (xp, zp)))
await ctx.reply(
f"Build your first portal at: **{round(xp)}, {round(zp)}** " +
f"({'{:,}'.format(blocks)} block{'' if blocks == 1 else 's'} away)\n\n" +
f"Use `{self.client.command_prefix}educatedtravel` afterwards."
)
except Exception as ex:
await ctx.reply(embed=funcs.errorEmbed(None, str(ex)))
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="safeblind", description="A *Minecraft: Java Edition* speedrunning tool that, s" +
"imilar to `blindtravel`, should be used when you wan" +
"t to build another portal in the Nether before thro" +
"wing any eyes of ender. This on average will get yo" +
"u closer to the stronghold compared to `blindtravel`" +
", but time may be lost. To use this command, in the" +
" game, press F3+C, pause, come over to Discord, pas" +
"te your clipboard as an argument for the command, a" +
"nd then build your portal at the suggested coordina" +
"tes in the Nether. This command is for versions 1.13" +
"+ and may not be 100% accurate. This command MAY not" +
" be used in a real speedrun.",
aliases=["sb", "safetravel", "safe", "st"], usage="<F3+C data>", hidden=True)
async def safeblind(self, ctx, *, f3c):
await ctx.send("**Note:** This command, along with other speedrunning calculators, MAY not be used in a real speedrun.")
try:
x, z, _ = self.f3cProcessing(f3c)
dist = self.coordsDist(x, z)
o = 222 if dist < 222 else dist if dist < 250 else 250 if dist < 480 else 615 if dist < 615 \
else dist if dist < 645 else 645 if dist < 832 else 1005 if dist < 1005 else dist if dist < 1032 \
else 1032
t = np.arctan([z / x])[0]
xp = np.sign(x) * np.absolute([o * np.cos([t])[0]])[0]
zp = np.sign(z) * np.absolute([o * np.sin([t])[0]])[0]
blocks = round(self.coordsDifference((x, z), (xp, zp)))
await ctx.reply(
f"Build your portal at: **{round(xp)}, {round(zp)}** " +
f"({'{:,}'.format(blocks)} block{'' if blocks == 1 else 's'} away)"
)
except Exception as ex:
await ctx.reply(embed=funcs.errorEmbed(None, str(ex)))
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="perfecttravel", description="A *Minecraft: Java Edition* speedrunning tool that att" +
"empts to take you directly to the stronghold portal " +
"room with the use of two Nether portals and F3 data." +
" To use this command, in the game, leave your first " +
"portal, find a chunk intersection and stand on the c" +
'hunk coordinate "0, 0" right in the centre, press F3' +
"+C, pause, come over to Discord, paste your clipboar" +
"d as an argument for the command, go back to the Net" +
"her, and then build your second portal at the sugges" +
"ted coordinates in the Nether. This command is for v" +
"ersions 1.13+ and may not be 100% accurate. This com" +
"mand MAY not be used in a real speedrun.",
aliases=["perfectt", "perfect", "ptravel", "ptrav", "ptr", "pt"], usage='<F3+C data> ["calc"]\n\n' +
'Note: Add "calc" at the end if you do not want to manually calculate the portal coordinates yourself.')
async def perfecttravel(self, ctx, *, f3c):
calc = True if f3c.casefold().split()[-1] == "calc" else False
try:
nx, nz, px, pz = None, None, None, None
x, z, f = self.f3cProcessing(f3c)
if f > 180:
f -= 360
if f < -180:
f += 360
targetchunk = self.perfecttravel[str(round(f, 2))][0]
if calc:
await ctx.send("**Note:** Second Nether portal coordinates are calculated for you. " +
"Your run is now most likely invalid.")
else:
await ctx.send("**Note:** Although no calculations are done and only a lookup table is being used, " +
f"note that you may still risk invalidating your run or at least have it kept under close scrutiny.")
try:
targetchunkx, targetchunkz = targetchunk.split(" ")
px, pz = int(x / 16) + (0 if x > 0 else -1), int(z / 16) + (0 if z > 0 else -1)
nx = ((px + int(targetchunkx)) * 2) if calc else targetchunkx
nz = ((pz + int(targetchunkz)) * 2) if calc else targetchunkz
except:
targetchunk = ""
if targetchunk:
if calc:
await ctx.reply(
f"Build your second portal at: **" +
f"{round(nx + (1 if nx < 0 else 0))}, {round(nz + (1 if nz < 0 else 0))}** " +
"\n\nMore info: https://youtu.be/YpV7I9X-Jso"
)
else:
await ctx.reply(
f"Offset: **{nx}, {nz}**\n\nYour current chunk for reference: **" +
f"{px}, {pz}**" +
"\n\nMore info: https://youtu.be/YpV7I9X-Jso"
)
else:
await ctx.reply(f"Cannot find ideal coordinates...")
except Exception as ex:
await ctx.reply(embed=funcs.errorEmbed(None, str(ex)))
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="triangulationsimple", description="A simple stronghold triangulation command that takes in 6 values.",
aliases=["trisimple", "simpletri", "strongholdsimple", "simplestronghold", "trisimp", "simptri",
"simpletriangulation", "strongholdsimp", "simpstronghold"],
usage="<x #1> <z #1> <angle #1> <x #2> <z #2> <angle #2>")
async def triangulationsimple(self, ctx, x0, z0, f0, x1, z1, f1):
try:
xp, zp, blocks = self.strongholdCalc(float(x0), float(z0), float(f0), float(x1), float(z1), float(f1))
await ctx.reply(
f"The stronghold could be at: **{round(xp)}, {round(zp)}** ({'{:,}'.format(blocks)} block" +
f"{'' if blocks == 1 else 's'} away)"
)
except Exception as ex:
funcs.printError(ctx, ex)
await ctx.reply(embed=funcs.errorEmbed(None, "Invalid input."))
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="triangulation", description="A *Minecraft: Java Edition* speedrunning tool tha" +
"t attempts to locate the stronghold using both " +
'the "8, 8" rule and triangulation. To use this ' +
"command, in the game, throw and eye, stand stil" +
"l, put your mouse directly over the eye, press " +
"F3+C, pause, come over to Discord, paste your c" +
"lipboard as an argument for the command, and th" +
"e command should return a set of coordinates ca" +
'lculated using the "8, 8" rule. You may continu' +
"e using this command by parsing more F3+C clipb" +
"oards as regular messages as you get closer to " +
"the stronghold. Once the program knows you are " +
"fairly close to the stronghold, it will automat" +
"ically stop. This command is for versions 1.13+" +
" and may not be 100% accurate. This command MAY" +
" not be used in a real speedrun.",
aliases=["triangulate", "triangle", "trian", "tri", "88", "44"],
usage="<F3+C data>")
async def triangulation(self, ctx, *, f3c):
await ctx.send("**Note:** This command, along with other speedrunning calculators, MAY not be used in a real speedrun.")
try:
x, z, f = self.f3cProcessing(f3c)
x0, z0, f0 = x, z, f
f = (360 + f if f < 0 else f) - 180
r = (90 - f) * (np.pi / 180)
b = 8 - np.absolute([np.absolute([x])[0] % 16])[0] + 16
l = []
s = 0
while s < 11904:
d = b * np.sign(f)
x += d
z += d * -np.tan([r])[0]
v = np.absolute([np.absolute([np.absolute([z])[0] % 16])[0] - 8])[0] + 0.5
s = self.coordsDist(x, z)
if s > 1408:
l.append({"k": x, "v": v, "j": v * v * np.sqrt([1 + len(l)])[0], "r": z})
b = 16
l.sort(key=lambda i: i["j"])
xp, zp = l[0]["k"], l[0]["r"]
blocks = round(self.coordsDifference((x0, z0), (xp, zp)))
await ctx.reply(
f"The stronghold could be at: **{round(xp)}, {round(zp)}** ({'{:,}'.format(blocks)} block" +
f"{'' if blocks == 1 else 's'} away)\n\nMethod: 8, 8\n\nPaste your F3+C clipboard here once " +
"you are ready. The program will stop after 20 minutes of inactivity. Type `!cancel` to cancel."
)
except Exception as ex:
return await ctx.reply(embed=funcs.errorEmbed(None, str(ex)))
x1, z1, f1 = None, None, None
blocks = 100
try:
while blocks > 40:
while True:
msg = await self.client.wait_for(
"message", timeout=1200, check=lambda m: ctx.author == m.author and ctx.channel == m.channel
)
try:
x1, z1, f1 = self.f3cProcessing(msg.content)
except:
if msg.content.casefold() == "!cancel":
return await ctx.reply("Cancelling triangulation.")
continue
if x1 == x0 and z1 == z0 and f1 == f0:
continue
break
try:
xp, zp, blocks = self.strongholdCalc(x0, z0, f0, x1, z1, f1)
except:
continue
await msg.reply(
f"The stronghold could be at: **{round(xp)}, {round(zp)}** ({'{:,}'.format(blocks)} block" +
f"{'' if blocks == 1 else 's'} away)\n\nMethod: Triangulation\n\nPaste your F3+C clipboard here once " +
"you are ready. The program will stop after 20 minutes of inactivity. Type `!cancel` to cancel."
)
x0, z0, f0 = x1, z1, f1
await ctx.send("You are close to the stronghold, stopping triangulation program.")
except TimeoutError:
await ctx.send("You have been inactive for over 20 minutes, stopping triangulation program.")
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="coordsdist", description="Calculates the distance between two sets of coordinates.",
aliases=["coords", "distance", "dist", "coord", "coordinates", "coordinate"],
usage="<x #1> <z #1> <x #2> <z #2>\n\nAlternative usage(s):\n\n- <F3+C data> <x> <z>")
async def coords(self, ctx, *, inp: str):
inp = funcs.replaceCharacters(inp, [",", "(", ")", ";"])
args = inp.split(" ")
try:
try:
x1, z1, _ = self.f3cProcessing(inp)
except:
x1, z1 = float(args[0]), float(args[1])
x2, z2 = float(args[-2]), float(args[-1])
except ValueError:
return await ctx.reply(embed=funcs.errorEmbed(None, "Invalid arguments."))
await ctx.reply(
"The distance between (**{}**; **{}**) and (**{}**; **{}**) is: ".format(
funcs.removeDotZero(round(x1, 5)),
funcs.removeDotZero(round(z1, 5)),
funcs.removeDotZero(round(x2, 5)),
funcs.removeDotZero(round(z2, 5))
) + f"**{funcs.removeDotZero(round(self.coordsDifference((x1, z1), (x2, z2)), 5))}**"
)
@commands.cooldown(1, 30, commands.BucketType.user)
@commands.command(name="speedrunwr", description="Shows the current world records for the solo Any% Glitchless " +
"*Minecraft: Java Edition* speedrun categories.",
aliases=["worldrecord", "wr", "mcwr", "ssg", "rsg"])
async def speedrunwr(self, ctx):
await ctx.send("Getting speedrun.com data. Please wait...")
try:
e = Embed(description="https://www.speedrun.com/mc")
e.set_author(name="Minecraft Speedrun World Records - Solo Any% Glitchless",
icon_url="https://cdn.discordapp.com/attachments/771698457391136798/842103816761114624/mc.png")
urls = [
"klrzpjo1&var-wl33kewl=gq7zo9p1",
"klrzpjo1&var-wl33kewl=21go6e6q",
"klrzpjo1&var-wl33kewl=4qye4731",
"21d4zvp1&var-wl33kewl=gq7zo9p1",
"21d4zvp1&var-wl33kewl=21go6e6q",
"21d4zvp1&var-wl33kewl=4qye4731"
]
categories = [
"Set Seed Glitchless (Pre-1.9)",
"Set Seed Glitchless (1.9-1.15)",
"Set Seed Glitchless (1.16+)",
"Random Seed Glitchless (Pre-1.9)",
"Random Seed Glitchless (1.9-1.15)",
"Random Seed Glitchless (1.16+)"
]
count = 0
for category in urls:
res = await funcs.getRequest(
"https://www.speedrun.com/api/v1/leaderboards/j1npme6p/category/mkeyl926?var-r8rg67rn=" + category
)
wrdata = res.json()["data"]["runs"][0]["run"]
igt = wrdata["times"]["primary_t"]
res = await funcs.getRequest(wrdata["players"][0]["uri"])
runner = res.json()["data"]["names"]["international"]
d, h, m, s, ms = funcs.timeDifferenceStr(igt, 0, noStr=True)
e.add_field(name=categories[count], inline=False,
value=f"`{funcs.timeStr(d, h, m, s, ms)} ({runner})`")
count += 1
e.set_footer(text="Click the link above for more speedrun categories.",
icon_url="https://cdn.discordapp.com/attachments/771698457391136798/842103813585240124/src.png")
except Exception as ex:
funcs.printError(ctx, ex)
e = funcs.errorEmbed(None, "Possible server error.")
await ctx.reply(embed=e)
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="skin", description="Gets the skin of a *Minecraft: Java Edition* user.", aliases=["mcskin"],
usage="[username]", hidden=True)
async def skin(self, ctx, username: str=""):
if username == "":
username = ctx.message.author.name
try:
data = await funcs.getRequest(f"https://api.mojang.com/users/profiles/minecraft/{username}")
username = data.json()["name"]
res = await funcs.getRequest(
f"https://sessionserver.mojang.com/session/minecraft/profile/{str(data.json()['id'])}"
)
data = loads(b64decode(res.json()["properties"][0]["value"]))
skin = data["textures"]["SKIN"]["url"]
e = Embed(
title=username,
description=f"https://namemc.com/profile/{username}"
)
e.set_image(url=skin)
except Exception as ex:
funcs.printError(ctx, ex)
e = funcs.errorEmbed(None, "Invalid skin or server error.")
await ctx.reply(embed=e)
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="mcserver", description="Gets the current status of a *Minecraft: Java Edition* server.",
usage="[server address]")
async def mcserver(self, ctx, *, ipaddress: str=""):
ipaddress = ipaddress.casefold().replace(" ", "") or "mc.hypixel.net"
try:
res = await funcs.getRequest(f"https://api.mcsrvstat.us/2/{ipaddress}", headers={"accept": "application/json"})
data = res.json()
status = data["online"]
e = Embed(title="Minecraft Server Status", colour=Colour.green() if status else Colour.red())
e.add_field(name="Server Address", value=f"`{ipaddress}`")
e.add_field(name="Online", value=f"`{status}`")
if status:
players = data["players"]["online"]
e.add_field(name="Player Count", value="`{:,}/{:,}`".format(players, data['players']['max']))
if players:
try:
playerLimit = 25
playerList = data["players"]["list"][:playerLimit]
listStr = ", ".join(f"`{player}`" for player in playerList)
if len(playerList) != players:
listStr += f" *and {players - playerLimit} more...*"
e.add_field(name="Players", value=listStr)
except:
pass
e.add_field(name="Version", value=f'`{data["version"]}`')
e.add_field(name="Port", value=f'`{data["port"]}`')
e.set_thumbnail(url=f"https://eu.mc-api.net/v3/server/favicon/{ipaddress}")
try:
e.add_field(name="Software", value=f'`{data["software"]}`')
except:
pass
motd = data["motd"]["clean"]
try:
secondLine = f"\n{motd[1].strip().replace('&', '&')}"
except:
secondLine = ""
e.set_footer(text=motd[0].strip().replace('&', '&') + secondLine)
except Exception as ex:
funcs.printError(ctx, ex)
e = funcs.errorEmbed(None, "Invalid server address or server error?")
await ctx.reply(embed=e)
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="fossils", description="Brings up a fossil identification chart for divine travel.",
aliases=["ft", "fossiltable", "fossilchart", "fossil"])
async def fossils(self, ctx):
url = "https://cdn.discordapp.com/attachments/771404776410972161/842022227347636264/fossiltable.jpg"
await funcs.sendImage(
ctx, url, message="PDF: https://cdn.discordapp.com/attachments/817309668924719144/"+
"818310310153814056/Fossil_origin_identification_1.pdf"
)
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="divinetravel", aliases=["dt", "divine", "div", "dv"], usage="[option OR F3+I data]",
description="Either brings up the chart for divine travel or gets certain divine coordinates. You can use o" +
"ptions like `fossilX` with X being the x-coordinate of the fossil origin, or look at the fo" +
"ssil origin in the game, press F3+I, and paste your clipboard as an argument for this command.")
async def divinetravel(self, ctx, *, option: str=""):
if option:
try:
try:
x, _, _ = self.f3iProcessing(option)
option = "fossil" + str(x)
except:
pass
res = self.divinetravel[option.casefold().replace(" ", "")].split(" | ")
e = Embed(title="Divine Travel: " + option.casefold().replace(" ", ""))
for i, c in enumerate(res):
if i > 2:
text = f"High Roll #{i - 2}"
else:
text = f"Stronghold #{i + 1}"
e.add_field(name=f"{text}", value=f"`{c.split(': ')[1]}`")
except KeyError:
e = funcs.errorEmbed(
"Invalid option!",
"Valid options:\n\n{}".format(", ".join(f"`{opt}`" for opt in self.divinetravel.keys()))
)
await ctx.reply(embed=e)
else:
url = "https://media.discordapp.net/attachments/771698457391136798/934726825811275816/unknown.png"
await funcs.sendImage(ctx, url)
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="bastions", description="Shows the comprehensive guides to bastions.", hidden=True, aliases=["bastion"])
async def bastions(self, ctx):
await ctx.reply("Guides: https://youtube.com/playlist?list=PL7Q35RXRsOR-udeKzwlYGJd0ZrvGJ0fwu\n\nP" +
"ractice Map: https://github.com/LlamaPag/bastion\n\nGuide to Practice Map: <https://youtu.be/jlA-jW7VGqw>")
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="endpractice", description="Shows the end practice map by ryguy2k4.", hidden=True,
aliases=["end", "endfight", "endpractise"])
async def endpractice(self, ctx):
await ctx.reply("https://github.com/ryguy2k4/ryguy2k4endpractice/releases")
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="obt", description="Shows the One Block Tower tutorial for 1.7.", hidden=True,
aliases=["tower1.7", "1.7tower", "oneblock", "oneblocktower"])
async def obt(self, ctx):
await ctx.reply("https://www.youtube.com/watch?v=nYI6wOM1U4A")
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="zentower", description="Shows the Zen Tower tutorial for 1.8.", hidden=True,
aliases=["tower1.8", "1.8tower", "zen"])
async def zentower(self, ctx):
await ctx.reply("https://www.youtube.com/watch?v=ryo3QbH2Zko")
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="1.15route", description="Shows the 1.15 route.", hidden=True,
aliases=["route1.15", "1.15routing", "routing1.15", "routing115", "115route",
"115routing", "route115", "1.15", "115", "doubleday"])
async def route115(self, ctx):
await ctx.reply("https://imgur.com/gallery/CFJYKmw\n\nDouble Day In-Depth Guide: " +
"https://docs.google.com/document/d/1JhDCCpDww3o3oueROpP1lp01JaulaTdm-EhGOfgvkmk/edit")
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="mapless", description="Shows the mapless treasure tutorial and practice map.", hidden=True,
aliases=["buriedtreasure"])
async def mapless(self, ctx):
await ctx.reply("Tutorials: https://youtu.be/ho1rwmooHRg\n\nhttps://youtu.be/_dyD8ZwagDg" +
"\n\nPractice Map: <https://cdn.discordapp.com/att" +
"achments/405839885509984256/885694752056541234/Mapless_Map.zip>")
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="quadrants1.16", description="Shows the four Nether quadrants for versions 1.16+.", hidden=True,
aliases=["netheregions", "netheregion", "netherregion", "netherregions", "nether", "quadrant", "quadrants"])
async def quadrants116(self, ctx):
await funcs.sendImage(ctx, "https://media.discordapp.net/attachments/771404776410972161/937755369072107520/ejAZNGq.png")
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="bedtiming", description="Shows the accurate bed timing for end fights.", hidden=True,
aliases=["bedtimings", "onecycle", "timingbed", "bedtime", "bed", "beds"])
async def bedtiming(self, ctx):
await funcs.sendImage(ctx, "https://media.discordapp.net/attachments/771698457391136798/937078099789635614/unknown.png")
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="1.8trading", description="Shows the trading tutorial for 1.8.", hidden=True,
aliases=["pre1.9trading", "trading1.8", "trading18", "18trading", "tradingpre1.9", "trading"])
async def trading18(self, ctx):
await funcs.sendImage(ctx, "https://cdn.discordapp.com/attachments/771404776410972161/959506805908705320/unknown.png",
message="https://youtu.be/1ksc3SSJkxs")
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="ninjabrainbot", aliases=["ninjabot", "ninjabrain", "nb", "nbb"], hidden=True,
description="Shows the Ninjabrain Bot tutorial and repository page.")
async def ninjabrainbot(self, ctx):
await ctx.reply("Tutorial: https://youtu.be/Rx8i7e5lu7g\n\nRepository: https://github.com/Ninjabrain1/Ninjabrain-Bot")
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="blazefights", aliases=["blazefight", "blaze", "blazes", "fortress", "fortresses"],
hidden=True, description="Shows the tutorial for Nether fortresses and blaze fights.")
async def blazefights(self, ctx):
await ctx.reply("https://youtu.be/pmx9LyUvLTk")
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="eray", aliases=["eraying", "microlensing"], hidden=True, description="Shows the microlensing tutorial.")
async def eray(self, ctx):
await ctx.reply("https://www.youtube.com/watch?v=jvTfMLPnMSw")
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="magmaravines", aliases=["ravine", "magmaravine", "magma", "ravines", "oceanravine", "oceanravines"],
description="Shows the guide to magma ravines.", hidden=True)
async def magmaravines(self, ctx):
await ctx.reply("https://www.youtube.com/watch?v=yGyMWYhHYoQ")
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="hypermodern", aliases=["hyperm", "hmodern"], hidden=True,
description="Shows the guide to hypermodern speedruns.")
async def hypermodern(self, ctx):
await ctx.reply("https://www.youtube.com/watch?v=gAHMJfsrHe4")
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="blindtravelcoords", aliases=["rings", "strongholdrings", "strongholdring"], hidden=True,
description="Shows the ideal blind travel coordinates for the first to third stronghold rings.")
async def blindtravelcoords(self, ctx):
await ctx.reply("https://imgur.com/gallery/i3fIanf")
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="mcspeedrunning", description="Shows some important *Minecraft: Java Edition* speedrunning resources.",
aliases=["mcspeedrun", "minecraftspeedrun", "minecraftspeedrunning", "mcsr", "speedrun"])
async def mcspeedrunning(self, ctx):
await ctx.reply(
"Setup Guide: https://www.youtube.com/watch?v=GAbnKAyireM\n\nWebsite: https://www.minecraftspeedrunning.com/"
)
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="doubleinstant", aliases=["doubleinstanttravel", "doubleinstanttrav", "dit", "di"],
description="Shows the tutorial for Double Instant Travel for pre-1.9 trading.", hidden=True)
async def doubleinstant(self, ctx):
await ctx.reply("https://youtu.be/XuZWIJRCyaY")
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="speedrunigt", aliases=["igt"], description="Download the SpeedRunIGT mod here.", hidden=True)
async def speedrunigt(self, ctx):
await ctx.reply("https://redlime.github.io/SpeedRunIGT/")
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="contariacalc", description="Download ContariaCalc here.", hidden=True)
async def contariacalc(self, ctx):
await ctx.reply("https://github.com/KingContaria/ContariaCalc")
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="strongholdnav", aliases=["stronghold", "sh", "strongholds", "nav"],
description="Shows the guides to stronghold navigation and hidden rooms.", hidden=True)
async def strongholdnav(self, ctx):
await ctx.reply("https://www.youtube.com/watch?v=hEZfeUWA3hM\n\nhttps://www.youtube.com/watch?v=vztJNmUdyBY" +
"\n\nhttps://www.youtube.com/watch?v=2dWq2wXy43M (**NEW**)")
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="ruinedportals", description="Shows some useful ruined portal resources.", hidden=True,
aliases=["rp", "ruinedportal", "ruined", "ruinportal", "ruinportals", "ruin"])
async def ruinedportals(self, ctx):
await ctx.reply(
"Quick Completion Guide: https://www.youtube.com/watch?v=Bg_TVoo8waM",
file=(await funcs.getImageFile(
"https://media.discordapp.net/attachments/771404776410972161/939500126903336960/unknown.png"
))
)
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="zerocycle", description="Shows some useful Zero Cycle resources.", hidden=True,
aliases=["0cycle", "0c", "zero", "zeroc", "zc", "0"])
async def zerocycle(self, ctx):
await ctx.reply(
"Full Zero Cycle Guide: https://youtu.be/iClDGWL0e5s\n\nResources: https://zerocycle.repl.co/",
file=(await funcs.getImageFile(
"https://media.discordapp.net/attachments/771404776410972161/938843696009469952/unknown.png"
))
)
@commands.cooldown(1, 3, commands.BucketType.user)
@commands.command(name="fsg", description="Shows a list of FSG seed generators.", hidden=True, aliases=["fsgseed", "fsgseeds"])
async def fsg(self, ctx):
await ctx.reply("Use one of the allowed generators: " +
"<https://docs.google.com/spreadsheets/d/1ilu72GJ-vJZq2LFU68rycGMeTbWPjHJnO8PGfp4QjA8/edit#gid=0>\n\n" +
"If you would like to use the generator locally for shorter wait times, follow this: " +
"<https://youtu.be/Gl7zOn2lLo4>\n\nPlease play the seed within 30 seconds after it has been generated.")
setup = Minecraft.setup | en | 0.519113 | # Credit - https://github.com/Sharpieman20/Sharpies-Speedrunning-Tools # For blindtravel, doubletravel, educatedtravel, safeblind, triangulation # Credit - https://github.com/FourGoesFast/PerfectTravelBot # For divinetravel, perfecttravel #1> <z #1> <angle #1> <x #2> <z #2> <angle #2>") #1> <z #1> <x #2> <z #2>\n\nAlternative usage(s):\n\n- <F3+C data> <x> <z>") #{i - 2}" #{i + 1}" #gid=0>\n\n" + | 2.119382 | 2 |
pytak/functions.py | joshuafuller/pytak | 22 | 6624298 | <filename>pytak/functions.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""PyTAK Functions."""
import asyncio
import datetime
import os
import socket
import ssl
import xml
import xml.etree.ElementTree
import pytak
import pytak.asyncio_dgram
__author__ = "<NAME> W2GMD <<EMAIL>>"
__copyright__ = "Copyright 2021 Orion Labs, Inc."
__license__ = "Apache License, Version 2.0"
def split_host(host, port: int = None) -> tuple:
"""Given a host:port and/or port, returns host, port."""
if ":" in host:
addr, port = host.split(":")
port = int(port)
elif port:
addr = host
port = int(port)
else:
addr = host
port = int(pytak.DEFAULT_COT_PORT)
return addr, port
def parse_cot_url(url) -> tuple:
"""Parses a Cursor on Target destination URL."""
if ":" in url.path:
host, port = str(url.path).split(":")
else:
host = url.path
if "broadcast" in url.scheme:
port = pytak.DEFAULT_BROADCAST_PORT
else:
port = pytak.DEFAULT_COT_PORT
return host, port
def hello_event(uid="pytak") -> str:
"""Generates a Hello CoT Event."""
time = datetime.datetime.now(datetime.timezone.utc)
root = xml.etree.ElementTree.Element("event")
root.set("version", "2.0")
root.set("type", "t-x-d-d")
root.set("uid", uid)
root.set("how", "m-g")
root.set("time", time.strftime(pytak.ISO_8601_UTC))
root.set("start", time.strftime(pytak.ISO_8601_UTC))
root.set("stale", (time + datetime.timedelta(hours=1)).strftime(pytak.ISO_8601_UTC) )
return xml.etree.ElementTree.tostring(root)
| <filename>pytak/functions.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""PyTAK Functions."""
import asyncio
import datetime
import os
import socket
import ssl
import xml
import xml.etree.ElementTree
import pytak
import pytak.asyncio_dgram
__author__ = "<NAME> W2GMD <<EMAIL>>"
__copyright__ = "Copyright 2021 Orion Labs, Inc."
__license__ = "Apache License, Version 2.0"
def split_host(host, port: int = None) -> tuple:
"""Given a host:port and/or port, returns host, port."""
if ":" in host:
addr, port = host.split(":")
port = int(port)
elif port:
addr = host
port = int(port)
else:
addr = host
port = int(pytak.DEFAULT_COT_PORT)
return addr, port
def parse_cot_url(url) -> tuple:
"""Parses a Cursor on Target destination URL."""
if ":" in url.path:
host, port = str(url.path).split(":")
else:
host = url.path
if "broadcast" in url.scheme:
port = pytak.DEFAULT_BROADCAST_PORT
else:
port = pytak.DEFAULT_COT_PORT
return host, port
def hello_event(uid="pytak") -> str:
"""Generates a Hello CoT Event."""
time = datetime.datetime.now(datetime.timezone.utc)
root = xml.etree.ElementTree.Element("event")
root.set("version", "2.0")
root.set("type", "t-x-d-d")
root.set("uid", uid)
root.set("how", "m-g")
root.set("time", time.strftime(pytak.ISO_8601_UTC))
root.set("start", time.strftime(pytak.ISO_8601_UTC))
root.set("stale", (time + datetime.timedelta(hours=1)).strftime(pytak.ISO_8601_UTC) )
return xml.etree.ElementTree.tostring(root)
| en | 0.437154 | #!/usr/bin/env python # -*- coding: utf-8 -*- PyTAK Functions. Given a host:port and/or port, returns host, port. Parses a Cursor on Target destination URL. Generates a Hello CoT Event. | 2.651839 | 3 |
advenshare.py | ssfrr/adventshare | 0 | 6624299 | <gh_stars>0
HTML_DIR = ''
from flask import Flask, send_from_directory # , request
from flask_sockets import Sockets
from geventwebsocket import WebSocketError
import json
import logging
import coloredlogs
app = Flask(__name__)
sockets = Sockets(app)
coloredlogs.install(logging.INFO)
logger = logging.getLogger(__name__)
# list of sessions is keyed on their IDs
sessions = {}
# define message type strings
ANNOUNCE = 'announce'
JOIN_SESSION = 'joinSession'
JOIN_SESSION_RESPONSE = 'joinSessionResponse'
CREATE_SESSION = 'createSession'
USER_LEFT_SESSION = 'userLeftSession'
GET_SESSION_INFO = 'getSessionInfo'
MOUSE_DOWN = 'mouseDown'
MOUSE_UP = 'mouseUp'
MOUSE_MOVE = 'mouseMove'
MOUSE_OUT = 'mouseOut'
MOUSE_MSGS = [MOUSE_MOVE, MOUSE_DOWN, MOUSE_UP, MOUSE_OUT]
class Session(object):
def __init__(self, id, name, host):
self.id = id
self.name = name
self.host = host
self.guests = {}
self.active_user = host
host.session = self
def add_guest(self, user):
self.guests[user.id] = user
user.session = self
def remove_guest(self, user):
if user == self.active_user:
self.active_user = self.host
del self.guests[user.id]
user.session = None
msg = {
'type': USER_LEFT_SESSION,
'id': user.id
}
self.host.send(msg)
for guest in self.guests.values():
guest.send(msg)
def handle_msg(self, msg, src_user):
destID = msg['destID']
if msg['type'] == MOUSE_DOWN:
self.active_user = src_user
logger.info("Setting active user to %s for session %s" %
(src_user.name, self.name))
if destID == '*':
if src_user != self.host:
self.host.send(msg)
for guest in self.guests.itervalues():
if guest != src_user:
guest.send(msg)
elif destID == self.host.id:
self.host.send(msg)
else:
try:
self.guests[destID].send(msg)
except KeyError:
user_error(src_user.ws, "Unknown Destination: %s" % msg)
def close(self):
# it's important here that values() makes a copy because we're about to
# start mutating the guests dict
self.host.session = None
for guest in self.guests.values():
self.remove_guest(guest)
def to_dict(self):
return {
'id': self.id,
'name': self.name,
'host': self.host.to_dict(),
'guests': [guest.to_dict() for guest in self.guests.values()]
}
def to_json(self):
return json.dumps(self.to_dict())
class User(object):
def __init__(self, ws, id, name, active_mouse_only=False):
self.id = id
self.ws = ws
self.name = name
self.session = None
self.active_mouse_only = active_mouse_only
def send(self, msg):
if msg['type'] in MOUSE_MSGS and \
self.active_mouse_only and \
self.session is not None and \
msg['srcID'] != self.session.active_user.id:
# this isn't from the active user, so don't send it down
return
msg_str = json.dumps(msg)
try:
self.ws.send(msg_str)
except WebSocketError:
if self.session is not None:
self.session.remove_guest(self)
def is_host(self):
if self.session is None:
return False
if self.session.host is None:
return False
if self.session.host == self:
return True
def disconnect(self):
if self.session is None:
return
if self.is_host():
self.session.close()
else:
self.session.remove_guest(self)
def to_dict(self):
return {
'id': self.id,
'name': self.name,
}
def to_json(self):
return json.dumps(self.to_dict())
@app.route('/')
def index_view():
return send_from_directory(HTML_DIR, 'index.html')
@sockets.route('/ws/user')
def user_ws_view(ws):
user = None
logger.info("User Connected")
# wait for the session offer
while(True):
try:
msg = ws.receive()
except:
logger.info("User Disconnected")
if user is not None:
user.disconnect()
return
if msg is None:
logger.info("Got None from User, Disconnected")
if user is not None:
user.disconnect()
return
try:
msg = json.loads(msg)
except:
user_error(ws, 'Invalid JSON: "%s"' % msg)
continue
logging.info('Received WS Msg: %s' % msg)
if not msg_is_valid(ws, msg):
continue
if user is None:
if msg['type'] == ANNOUNCE:
active_mouse_only = False
if 'activeMouseOnly' in msg:
active_mouse_only = msg['activeMouseOnly']
user = User(ws, msg['srcID'], msg['userName'],
active_mouse_only)
else:
user_error(ws, 'First message must be of type "%s"' % ANNOUNCE)
continue
elif msg['type'] == ANNOUNCE:
logger.warning("Double-announce. Ignored.")
continue
elif msg['srcID'] != user.id:
user_error(
ws, "Can't change User ID within the same connection")
continue
handle_user_msg(msg, user)
def all_present(dic, attrs):
'''Tests to make sure all the listed attributes are present in the given
dictionary'''
for attr in attrs:
if attr not in dic:
return False
return True
def handle_user_msg(msg, user):
if msg['type'] == CREATE_SESSION:
session = Session(msg['sessionID'], msg['sessionName'], user)
sessions[session.id] = session
logger.info('Session "%s" created by %s. ID: %s' % (
session.name, session.host.name, session.id))
return
if msg['type'] == JOIN_SESSION:
try:
session = sessions[msg['sessionID']]
except KeyError:
user.send({
'type': JOIN_SESSION_RESPONSE,
'status': "Invalid sessionID: %s" % msg['sessionID']
})
logger.warn("Invalid sessionID: %s" % msg['sessionID'])
return
session.add_guest(user)
resp = {
'type': JOIN_SESSION_RESPONSE,
'status': 'success',
}
resp.update(session.to_dict())
user.send(resp)
logger.info('User %s joined session %s' % (user.name, session.name))
return
# all other messages get dispatched to their session
try:
sessions[msg['sessionID']].handle_msg(msg, user)
except KeyError:
user_error(user.ws, "Invalid sessionID: %s" % msg['sessionID'])
def user_error(ws, msg):
logger.warn(msg)
err = json.dumps({
'type': 'error',
'message': msg
})
ws.send(err)
common_required_msg_fields = ['type', 'srcID']
required_msg_fields = {
ANNOUNCE: ['userName'],
JOIN_SESSION: ['sessionID'],
CREATE_SESSION: ['sessionName', 'sessionID']
}
def msg_is_valid(ws, msg):
if 'type' not in msg:
user_error(ws, 'No "type" field in msg: "%s"' % msg)
return False
extra_fields = required_msg_fields.get(msg['type'], [])
fields = common_required_msg_fields + extra_fields
for field in fields:
if field not in msg:
user_error(ws, 'No "%s" field in msg: "%s"' % (field, msg))
return False
return True
| HTML_DIR = ''
from flask import Flask, send_from_directory # , request
from flask_sockets import Sockets
from geventwebsocket import WebSocketError
import json
import logging
import coloredlogs
app = Flask(__name__)
sockets = Sockets(app)
coloredlogs.install(logging.INFO)
logger = logging.getLogger(__name__)
# list of sessions is keyed on their IDs
sessions = {}
# define message type strings
ANNOUNCE = 'announce'
JOIN_SESSION = 'joinSession'
JOIN_SESSION_RESPONSE = 'joinSessionResponse'
CREATE_SESSION = 'createSession'
USER_LEFT_SESSION = 'userLeftSession'
GET_SESSION_INFO = 'getSessionInfo'
MOUSE_DOWN = 'mouseDown'
MOUSE_UP = 'mouseUp'
MOUSE_MOVE = 'mouseMove'
MOUSE_OUT = 'mouseOut'
MOUSE_MSGS = [MOUSE_MOVE, MOUSE_DOWN, MOUSE_UP, MOUSE_OUT]
class Session(object):
def __init__(self, id, name, host):
self.id = id
self.name = name
self.host = host
self.guests = {}
self.active_user = host
host.session = self
def add_guest(self, user):
self.guests[user.id] = user
user.session = self
def remove_guest(self, user):
if user == self.active_user:
self.active_user = self.host
del self.guests[user.id]
user.session = None
msg = {
'type': USER_LEFT_SESSION,
'id': user.id
}
self.host.send(msg)
for guest in self.guests.values():
guest.send(msg)
def handle_msg(self, msg, src_user):
destID = msg['destID']
if msg['type'] == MOUSE_DOWN:
self.active_user = src_user
logger.info("Setting active user to %s for session %s" %
(src_user.name, self.name))
if destID == '*':
if src_user != self.host:
self.host.send(msg)
for guest in self.guests.itervalues():
if guest != src_user:
guest.send(msg)
elif destID == self.host.id:
self.host.send(msg)
else:
try:
self.guests[destID].send(msg)
except KeyError:
user_error(src_user.ws, "Unknown Destination: %s" % msg)
def close(self):
# it's important here that values() makes a copy because we're about to
# start mutating the guests dict
self.host.session = None
for guest in self.guests.values():
self.remove_guest(guest)
def to_dict(self):
return {
'id': self.id,
'name': self.name,
'host': self.host.to_dict(),
'guests': [guest.to_dict() for guest in self.guests.values()]
}
def to_json(self):
return json.dumps(self.to_dict())
class User(object):
def __init__(self, ws, id, name, active_mouse_only=False):
self.id = id
self.ws = ws
self.name = name
self.session = None
self.active_mouse_only = active_mouse_only
def send(self, msg):
if msg['type'] in MOUSE_MSGS and \
self.active_mouse_only and \
self.session is not None and \
msg['srcID'] != self.session.active_user.id:
# this isn't from the active user, so don't send it down
return
msg_str = json.dumps(msg)
try:
self.ws.send(msg_str)
except WebSocketError:
if self.session is not None:
self.session.remove_guest(self)
def is_host(self):
if self.session is None:
return False
if self.session.host is None:
return False
if self.session.host == self:
return True
def disconnect(self):
if self.session is None:
return
if self.is_host():
self.session.close()
else:
self.session.remove_guest(self)
def to_dict(self):
return {
'id': self.id,
'name': self.name,
}
def to_json(self):
return json.dumps(self.to_dict())
@app.route('/')
def index_view():
return send_from_directory(HTML_DIR, 'index.html')
@sockets.route('/ws/user')
def user_ws_view(ws):
user = None
logger.info("User Connected")
# wait for the session offer
while(True):
try:
msg = ws.receive()
except:
logger.info("User Disconnected")
if user is not None:
user.disconnect()
return
if msg is None:
logger.info("Got None from User, Disconnected")
if user is not None:
user.disconnect()
return
try:
msg = json.loads(msg)
except:
user_error(ws, 'Invalid JSON: "%s"' % msg)
continue
logging.info('Received WS Msg: %s' % msg)
if not msg_is_valid(ws, msg):
continue
if user is None:
if msg['type'] == ANNOUNCE:
active_mouse_only = False
if 'activeMouseOnly' in msg:
active_mouse_only = msg['activeMouseOnly']
user = User(ws, msg['srcID'], msg['userName'],
active_mouse_only)
else:
user_error(ws, 'First message must be of type "%s"' % ANNOUNCE)
continue
elif msg['type'] == ANNOUNCE:
logger.warning("Double-announce. Ignored.")
continue
elif msg['srcID'] != user.id:
user_error(
ws, "Can't change User ID within the same connection")
continue
handle_user_msg(msg, user)
def all_present(dic, attrs):
'''Tests to make sure all the listed attributes are present in the given
dictionary'''
for attr in attrs:
if attr not in dic:
return False
return True
def handle_user_msg(msg, user):
if msg['type'] == CREATE_SESSION:
session = Session(msg['sessionID'], msg['sessionName'], user)
sessions[session.id] = session
logger.info('Session "%s" created by %s. ID: %s' % (
session.name, session.host.name, session.id))
return
if msg['type'] == JOIN_SESSION:
try:
session = sessions[msg['sessionID']]
except KeyError:
user.send({
'type': JOIN_SESSION_RESPONSE,
'status': "Invalid sessionID: %s" % msg['sessionID']
})
logger.warn("Invalid sessionID: %s" % msg['sessionID'])
return
session.add_guest(user)
resp = {
'type': JOIN_SESSION_RESPONSE,
'status': 'success',
}
resp.update(session.to_dict())
user.send(resp)
logger.info('User %s joined session %s' % (user.name, session.name))
return
# all other messages get dispatched to their session
try:
sessions[msg['sessionID']].handle_msg(msg, user)
except KeyError:
user_error(user.ws, "Invalid sessionID: %s" % msg['sessionID'])
def user_error(ws, msg):
logger.warn(msg)
err = json.dumps({
'type': 'error',
'message': msg
})
ws.send(err)
common_required_msg_fields = ['type', 'srcID']
required_msg_fields = {
ANNOUNCE: ['userName'],
JOIN_SESSION: ['sessionID'],
CREATE_SESSION: ['sessionName', 'sessionID']
}
def msg_is_valid(ws, msg):
if 'type' not in msg:
user_error(ws, 'No "type" field in msg: "%s"' % msg)
return False
extra_fields = required_msg_fields.get(msg['type'], [])
fields = common_required_msg_fields + extra_fields
for field in fields:
if field not in msg:
user_error(ws, 'No "%s" field in msg: "%s"' % (field, msg))
return False
return True | en | 0.941869 | # , request # list of sessions is keyed on their IDs # define message type strings # it's important here that values() makes a copy because we're about to # start mutating the guests dict # this isn't from the active user, so don't send it down # wait for the session offer Tests to make sure all the listed attributes are present in the given dictionary # all other messages get dispatched to their session | 2.331361 | 2 |
appimagebuilder/recipe/recipe.py | srevinsaju/appimage-builder | 0 | 6624300 | # Copyright 2020 <NAME>
#
# 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.
from appimagebuilder.recipe.errors import RecipeError
from appimagebuilder.recipe.schema import RecipeSchema
class Recipe:
class ItemResolver:
def __init__(self, dict, path, fallback=None):
self.root = dict
self.path = path
self.fallback = fallback
self.left = None
self.right = None
self.cur = None
self.key = None
def resolve(self):
self.cur = self.root
self.left = []
self.right = self.path.split("/")
try:
self._resolve_item()
except KeyError:
self._fallback_or_raise()
return self.cur
def _resolve_item(self):
while self.right:
self.key = self.right.pop(0)
self.cur = self.cur[self.key]
self.left.append(self.key)
def _fallback_or_raise(self):
if self.fallback is not None:
self.cur = self.fallback
else:
raise RecipeError(
"'%s' key required in: %s" % (self.key, "/".join(self.left))
)
def __init__(self, data):
self._data = data
def get_item(self, path, fallback=None):
resolver = Recipe.ItemResolver(self._data, path, fallback)
return resolver.resolve()
| # Copyright 2020 <NAME>
#
# 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.
from appimagebuilder.recipe.errors import RecipeError
from appimagebuilder.recipe.schema import RecipeSchema
class Recipe:
class ItemResolver:
def __init__(self, dict, path, fallback=None):
self.root = dict
self.path = path
self.fallback = fallback
self.left = None
self.right = None
self.cur = None
self.key = None
def resolve(self):
self.cur = self.root
self.left = []
self.right = self.path.split("/")
try:
self._resolve_item()
except KeyError:
self._fallback_or_raise()
return self.cur
def _resolve_item(self):
while self.right:
self.key = self.right.pop(0)
self.cur = self.cur[self.key]
self.left.append(self.key)
def _fallback_or_raise(self):
if self.fallback is not None:
self.cur = self.fallback
else:
raise RecipeError(
"'%s' key required in: %s" % (self.key, "/".join(self.left))
)
def __init__(self, data):
self._data = data
def get_item(self, path, fallback=None):
resolver = Recipe.ItemResolver(self._data, path, fallback)
return resolver.resolve()
| en | 0.882815 | # Copyright 2020 <NAME> # # 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. | 2.121066 | 2 |
mcs-jr.py | clarkdever/mission-control-station-pi | 0 | 6624301 | import mplayer, evdev
# Joystics
pl1 = evdev.InputDevice('/dev/input/by-path/platform-3f980000.usb-usb-0:1.5:1.0-event-joystick')
pl2 = evdev.InputDevice('/dev/input/by-path/platform-3f980000.usb-usb-0:1.3:1.0-event-joystick')
# Initialize video
p = mplayer.Player()
p.loadfile("ChildrensMCS.mp4")
p.fullstrceen = True
p.loop = 0
p.osdlevel = 0 # Disables on-screen UI
scrub_speed = 5 # How many seconds skipping ahead/back
playhead = p.time_pos
playhead_dirty = False
vid_length = p.length
# Player inputs
stick = {
'UP': 297,
'DOWN': 296,
'LEFT': 299,
'RIGHT': 298,
'LWHITE': 290,
'LBLACK': 288,
'GREEN': 291,
'YELLOW': 289,
'BLUE': 292,
'RED': 293,
'RWHITE': 294,
'RBLACK': 295
}
# Dictionary of clips
clips = {
'Solar System': {
'start': 0,
'length': 232
},
'Mission Control': {
'start': 233,
'length': 62
},
'Orbital Mechanics': {
'start': 294,
'length': 148
},
'Apollo 8 Launch': {
'start': 442,
'length': 221
},
'STS Launch': {
'start': 654,
'length': 136
},
'STS Landing': {
'start': 789,
'length': 53
},
'Insight Landing': {
'start': 852,
'length': 142
},
'Moon Rocks': {
'start': 994,
'length': 55
},
'ISS': {
'start': 1049,
'length': 51
},
'Baby Shark': {
'start': 1100,
'length': 95
},
'Minions Babarang': {
'start': 1196,
'length': 39
},
'Minions Happy': {
'start': 1235,
'length': 233
},
'Moana - You\'re Welcome': {
'start': 1467,
'length': 164
},
'Be Our Guest': {
'start': 1631,
'length': 209
},
'Ho Hey': {
'start': 1840,
'length': 160
},
'<NAME>': {
'start': 2001,
'length': 230
},
'Moana - How Far I\'ll Go': {
'start': 2231,
'length': 151
},
'Under The Sea': {
'start': 2382,
'length': 197
},
'Whistle While You Work': {
'start': 2581,
'length': 216
},
'Let It Go': {
'start': 2795,
'length': 220
},
'Dad': {
'start': 3051,
'length': 15
}
}
clip_end = clips['Solar System']['length']
def scrub(val):
global playhead
global playhead_dirty
global vid_length
if playhead is None or vid_length is None:
return # mplayer's time_pos is None when spinning up the process/loading the video
x = playhead + val
if x < 0:
playhead = vid_length + x
if x > vid_length:
playhead = 0 + (x - vid_length)
else:
playhead += val
playhead_dirty = True
def pause():
p.pause()
def play_clip(clip):
global playhead
global playhead_dirty
global clip_end
try:
playhead = clips[clip]['start']
playhead_dirty = True
except:
print("Error playing clip", clip)
def print_btn(btn):
print(btn.name)
def poll_input():
ev1 = pl1.read_one()
ev2 = pl2.read_one()
#if ev1 is not None:
while True:
ev1 = pl1.read_one()
if ev1 is None:
break # No more input, so break out of the loop
# Do player 1 stuff here
if ev1.type == 1 and ev1.value == 1: # Button Input
if ev1.code == stick['LEFT']:
scrub(-scrub_speed)
if ev1.code == stick['RIGHT']:
scrub(scrub_speed)
if ev1.code == stick['UP']:
pause()
if ev1.code == stick['DOWN']:
play_clip('Solar System')
if ev1.code == stick['LWHITE']:
play_clip('Mission Control')
if ev1.code == stick['GREEN']:
play_clip('Orbital Mechanics')
if ev1.code == stick['BLUE']:
play_clip('Apollo 8 Launch')
if ev1.code == stick['RWHITE']:
play_clip('STS Launch')
if ev1.code == stick['LBLACK']:
play_clip('STS Landing')
if ev1.code == stick['YELLOW']:
play_clip('Insight Landing')
if ev1.code == stick['RED']:
play_clip('Moon Rocks')
if ev1.code == stick['RBLACK']:
play_clip('ISS')
while True:
ev2 = pl2.read_one()
if ev2 is None:
break
# Do player 2 stuff here
if ev2.type == 1 and ev2.value == 1:
if ev2.code == stick['LEFT']:
play_clip('B<NAME>')
if ev2.code == stick['RIGHT']:
play_clip('Minions Babarang')
if ev2.code == stick['UP']:
play_clip('Minions Happy')
if ev2.code == stick['DOWN']:
play_clip('Moana - You\'re Welcome')
if ev2.code == stick['LWHITE']:
play_clip('Be Our Guest')
if ev2.code == stick['GREEN']:
play_clip('Ho Hey')
if ev2.code == stick['BLUE']:
play_clip('<NAME>')
if ev2.code == stick['RWHITE']:
play_clip('Moana - How Far I\'ll Go')
if ev2.code == stick['LBLACK']:
play_clip('Under The Sea')
if ev2.code == stick['YELLOW']:
play_clip('Whistle While You Work')
if ev2.code == stick['RED']:
play_clip('Let It Go')
if ev2.code == stick['RBLACK']:
play_clip('Dad')
while True:
if not p.fullscreen:
p.fullscreen = True
poll_input()
if vid_length is None:
vid_length = p.length
if playhead_dirty:
p.time_pos = playhead
playhead_dirty = False
else:
playhead = p.time_pos
| import mplayer, evdev
# Joystics
pl1 = evdev.InputDevice('/dev/input/by-path/platform-3f980000.usb-usb-0:1.5:1.0-event-joystick')
pl2 = evdev.InputDevice('/dev/input/by-path/platform-3f980000.usb-usb-0:1.3:1.0-event-joystick')
# Initialize video
p = mplayer.Player()
p.loadfile("ChildrensMCS.mp4")
p.fullstrceen = True
p.loop = 0
p.osdlevel = 0 # Disables on-screen UI
scrub_speed = 5 # How many seconds skipping ahead/back
playhead = p.time_pos
playhead_dirty = False
vid_length = p.length
# Player inputs
stick = {
'UP': 297,
'DOWN': 296,
'LEFT': 299,
'RIGHT': 298,
'LWHITE': 290,
'LBLACK': 288,
'GREEN': 291,
'YELLOW': 289,
'BLUE': 292,
'RED': 293,
'RWHITE': 294,
'RBLACK': 295
}
# Dictionary of clips
clips = {
'Solar System': {
'start': 0,
'length': 232
},
'Mission Control': {
'start': 233,
'length': 62
},
'Orbital Mechanics': {
'start': 294,
'length': 148
},
'Apollo 8 Launch': {
'start': 442,
'length': 221
},
'STS Launch': {
'start': 654,
'length': 136
},
'STS Landing': {
'start': 789,
'length': 53
},
'Insight Landing': {
'start': 852,
'length': 142
},
'Moon Rocks': {
'start': 994,
'length': 55
},
'ISS': {
'start': 1049,
'length': 51
},
'Baby Shark': {
'start': 1100,
'length': 95
},
'Minions Babarang': {
'start': 1196,
'length': 39
},
'Minions Happy': {
'start': 1235,
'length': 233
},
'Moana - You\'re Welcome': {
'start': 1467,
'length': 164
},
'Be Our Guest': {
'start': 1631,
'length': 209
},
'Ho Hey': {
'start': 1840,
'length': 160
},
'<NAME>': {
'start': 2001,
'length': 230
},
'Moana - How Far I\'ll Go': {
'start': 2231,
'length': 151
},
'Under The Sea': {
'start': 2382,
'length': 197
},
'Whistle While You Work': {
'start': 2581,
'length': 216
},
'Let It Go': {
'start': 2795,
'length': 220
},
'Dad': {
'start': 3051,
'length': 15
}
}
clip_end = clips['Solar System']['length']
def scrub(val):
global playhead
global playhead_dirty
global vid_length
if playhead is None or vid_length is None:
return # mplayer's time_pos is None when spinning up the process/loading the video
x = playhead + val
if x < 0:
playhead = vid_length + x
if x > vid_length:
playhead = 0 + (x - vid_length)
else:
playhead += val
playhead_dirty = True
def pause():
p.pause()
def play_clip(clip):
global playhead
global playhead_dirty
global clip_end
try:
playhead = clips[clip]['start']
playhead_dirty = True
except:
print("Error playing clip", clip)
def print_btn(btn):
print(btn.name)
def poll_input():
ev1 = pl1.read_one()
ev2 = pl2.read_one()
#if ev1 is not None:
while True:
ev1 = pl1.read_one()
if ev1 is None:
break # No more input, so break out of the loop
# Do player 1 stuff here
if ev1.type == 1 and ev1.value == 1: # Button Input
if ev1.code == stick['LEFT']:
scrub(-scrub_speed)
if ev1.code == stick['RIGHT']:
scrub(scrub_speed)
if ev1.code == stick['UP']:
pause()
if ev1.code == stick['DOWN']:
play_clip('Solar System')
if ev1.code == stick['LWHITE']:
play_clip('Mission Control')
if ev1.code == stick['GREEN']:
play_clip('Orbital Mechanics')
if ev1.code == stick['BLUE']:
play_clip('Apollo 8 Launch')
if ev1.code == stick['RWHITE']:
play_clip('STS Launch')
if ev1.code == stick['LBLACK']:
play_clip('STS Landing')
if ev1.code == stick['YELLOW']:
play_clip('Insight Landing')
if ev1.code == stick['RED']:
play_clip('Moon Rocks')
if ev1.code == stick['RBLACK']:
play_clip('ISS')
while True:
ev2 = pl2.read_one()
if ev2 is None:
break
# Do player 2 stuff here
if ev2.type == 1 and ev2.value == 1:
if ev2.code == stick['LEFT']:
play_clip('B<NAME>')
if ev2.code == stick['RIGHT']:
play_clip('Minions Babarang')
if ev2.code == stick['UP']:
play_clip('Minions Happy')
if ev2.code == stick['DOWN']:
play_clip('Moana - You\'re Welcome')
if ev2.code == stick['LWHITE']:
play_clip('Be Our Guest')
if ev2.code == stick['GREEN']:
play_clip('Ho Hey')
if ev2.code == stick['BLUE']:
play_clip('<NAME>')
if ev2.code == stick['RWHITE']:
play_clip('Moana - How Far I\'ll Go')
if ev2.code == stick['LBLACK']:
play_clip('Under The Sea')
if ev2.code == stick['YELLOW']:
play_clip('Whistle While You Work')
if ev2.code == stick['RED']:
play_clip('Let It Go')
if ev2.code == stick['RBLACK']:
play_clip('Dad')
while True:
if not p.fullscreen:
p.fullscreen = True
poll_input()
if vid_length is None:
vid_length = p.length
if playhead_dirty:
p.time_pos = playhead
playhead_dirty = False
else:
playhead = p.time_pos
| en | 0.790344 | # Joystics # Initialize video # Disables on-screen UI # How many seconds skipping ahead/back # Player inputs # Dictionary of clips # mplayer's time_pos is None when spinning up the process/loading the video #if ev1 is not None: # No more input, so break out of the loop # Do player 1 stuff here # Button Input # Do player 2 stuff here | 1.862079 | 2 |
tests/test_cli_mutation_map.py | DerThorsten/kipoi-veff | 5 | 6624302 | import sys
import pytest
import os
import subprocess
import config
if config.install_req:
INSTALL_FLAG = "--install_req"
else:
INSTALL_FLAG = ""
EXAMPLES_TO_RUN = ["rbp"]
@pytest.mark.parametrize("example", EXAMPLES_TO_RUN)
@pytest.mark.parametrize("new_dataloader_kwargs_format", [False, True])
def test_generate_mutation_maps_example(example, new_dataloader_kwargs_format, tmpdir):
"""kipoi predict ...
"""
if (example not in {"rbp"}) or (sys.version_info[0] == 2):
pytest.skip("Only rbp example testable at the moment, which only runs on py3")
example_dir = "tests/models/{0}/".format(example)
tmpdir_here = tmpdir.mkdir("example")
# restricted_bed = False
mm_tmpfile = str(tmpdir_here.join("out_mm.hdf5"))
plt_tmpfile = str(tmpdir_here.join("plot.png"))
dataloader_kwargs = {"fasta_file": "example_files/hg38_chr22.fa",
"preproc_transformer": "dataloader_files/encodeSplines.pkl",
"gtf_file": "example_files/gencode_v25_chr22.gtf.pkl.gz",
"intervals_file": "example_files/variant_intervals.tsv"}
dataloader_kwargs = {k: example_dir + v for k, v in dataloader_kwargs.items()}
if not new_dataloader_kwargs_format:
import json
dataloader_kwargs_str = json.dumps(dataloader_kwargs)
args = ["python", os.path.abspath("./kipoi_veff/cli.py"),
"create_mutation_map",
# "./", # directory
example_dir,
"--source=dir",
"--batch_size=4",
"--dataloader_args='%s'" % dataloader_kwargs_str,
"--regions_file", example_dir + "example_files/first_variant.vcf",
"--output", mm_tmpfile]
else:
dataloader_kwargs_list = ["{0}={1}".format(key, val) for key,val in dataloader_kwargs.items()]
args = ["python", os.path.abspath("./kipoi_veff/cli.py"),
"create_mutation_map",
# "./", # directory
example_dir,
"--source=dir",
"--batch_size=4",
"--dataloader_args"] + dataloader_kwargs_list + ["--regions_file", example_dir + "example_files/first_variant.vcf",
"--output", mm_tmpfile]
# run the
if INSTALL_FLAG:
args.append(INSTALL_FLAG)
returncode = subprocess.call(args=args, cwd=".")
assert returncode == 0
assert os.path.exists(mm_tmpfile)
# make the plot
args = ["python", os.path.abspath("./kipoi_veff/cli.py"),
"plot_mutation_map",
"--input_file=" + mm_tmpfile,
"--input_entry=0",
"--model_seq_input=seq",
"--scoring_key=diff",
"--model_output=rbp_prb",
"--limit_region_genomic", "21541588", "21541592",
"--rc_plot",
"--output", plt_tmpfile]
returncode = subprocess.call(args=args,
cwd=os.path.realpath(example_dir))
assert returncode == 0
assert os.path.exists(plt_tmpfile)
os.unlink(mm_tmpfile)
os.unlink(plt_tmpfile)
| import sys
import pytest
import os
import subprocess
import config
if config.install_req:
INSTALL_FLAG = "--install_req"
else:
INSTALL_FLAG = ""
EXAMPLES_TO_RUN = ["rbp"]
@pytest.mark.parametrize("example", EXAMPLES_TO_RUN)
@pytest.mark.parametrize("new_dataloader_kwargs_format", [False, True])
def test_generate_mutation_maps_example(example, new_dataloader_kwargs_format, tmpdir):
"""kipoi predict ...
"""
if (example not in {"rbp"}) or (sys.version_info[0] == 2):
pytest.skip("Only rbp example testable at the moment, which only runs on py3")
example_dir = "tests/models/{0}/".format(example)
tmpdir_here = tmpdir.mkdir("example")
# restricted_bed = False
mm_tmpfile = str(tmpdir_here.join("out_mm.hdf5"))
plt_tmpfile = str(tmpdir_here.join("plot.png"))
dataloader_kwargs = {"fasta_file": "example_files/hg38_chr22.fa",
"preproc_transformer": "dataloader_files/encodeSplines.pkl",
"gtf_file": "example_files/gencode_v25_chr22.gtf.pkl.gz",
"intervals_file": "example_files/variant_intervals.tsv"}
dataloader_kwargs = {k: example_dir + v for k, v in dataloader_kwargs.items()}
if not new_dataloader_kwargs_format:
import json
dataloader_kwargs_str = json.dumps(dataloader_kwargs)
args = ["python", os.path.abspath("./kipoi_veff/cli.py"),
"create_mutation_map",
# "./", # directory
example_dir,
"--source=dir",
"--batch_size=4",
"--dataloader_args='%s'" % dataloader_kwargs_str,
"--regions_file", example_dir + "example_files/first_variant.vcf",
"--output", mm_tmpfile]
else:
dataloader_kwargs_list = ["{0}={1}".format(key, val) for key,val in dataloader_kwargs.items()]
args = ["python", os.path.abspath("./kipoi_veff/cli.py"),
"create_mutation_map",
# "./", # directory
example_dir,
"--source=dir",
"--batch_size=4",
"--dataloader_args"] + dataloader_kwargs_list + ["--regions_file", example_dir + "example_files/first_variant.vcf",
"--output", mm_tmpfile]
# run the
if INSTALL_FLAG:
args.append(INSTALL_FLAG)
returncode = subprocess.call(args=args, cwd=".")
assert returncode == 0
assert os.path.exists(mm_tmpfile)
# make the plot
args = ["python", os.path.abspath("./kipoi_veff/cli.py"),
"plot_mutation_map",
"--input_file=" + mm_tmpfile,
"--input_entry=0",
"--model_seq_input=seq",
"--scoring_key=diff",
"--model_output=rbp_prb",
"--limit_region_genomic", "21541588", "21541592",
"--rc_plot",
"--output", plt_tmpfile]
returncode = subprocess.call(args=args,
cwd=os.path.realpath(example_dir))
assert returncode == 0
assert os.path.exists(plt_tmpfile)
os.unlink(mm_tmpfile)
os.unlink(plt_tmpfile)
| en | 0.58231 | kipoi predict ... # restricted_bed = False # "./", # directory # "./", # directory # run the # make the plot | 1.7612 | 2 |
connectors/interfaces/leaf_connector_interface.py | kefir/snakee | 0 | 6624303 | <reponame>kefir/snakee<gh_stars>0
from abc import ABC, abstractmethod
from typing import Optional
try: # Assume we're a sub-module in a package.
from streams.interfaces.abstract_stream_interface import StreamInterface
from connectors.content_format.content_type import ContentType
from connectors.interfaces.connector_interface import ConnectorInterface
from connectors.interfaces.format_interface import ContentFormatInterface
except ImportError: # Apparently no higher-level package has been imported, fall back to a local import.
from ...streams.interfaces.abstract_stream_interface import StreamInterface
from ..content_format.content_type import ContentType
from .connector_interface import ConnectorInterface
from .format_interface import ContentFormatInterface
class LeafConnectorInterface(ConnectorInterface, StreamInterface, ABC):
@abstractmethod
def get_content_type(self) -> ContentType:
pass
@abstractmethod
def get_content_format(self) -> ContentFormatInterface:
pass
@abstractmethod
def set_content_format(self, content_format: ContentFormatInterface, inplace: bool) -> Optional[ConnectorInterface]:
pass
@abstractmethod
def get_declared_format(self) -> ContentFormatInterface:
pass
@abstractmethod
def is_existing(self) -> bool:
pass
@abstractmethod
def get_first_line(self, close: bool = True) -> Optional[str]:
pass
@abstractmethod
def check(self, must_exists: bool = True):
pass
@abstractmethod
def write_stream(self, stream: StreamInterface, verbose: bool = True):
pass
@abstractmethod
def from_stream(self, stream: StreamInterface):
pass
@abstractmethod
def to_stream(self, **kwargs) -> StreamInterface:
pass
| from abc import ABC, abstractmethod
from typing import Optional
try: # Assume we're a sub-module in a package.
from streams.interfaces.abstract_stream_interface import StreamInterface
from connectors.content_format.content_type import ContentType
from connectors.interfaces.connector_interface import ConnectorInterface
from connectors.interfaces.format_interface import ContentFormatInterface
except ImportError: # Apparently no higher-level package has been imported, fall back to a local import.
from ...streams.interfaces.abstract_stream_interface import StreamInterface
from ..content_format.content_type import ContentType
from .connector_interface import ConnectorInterface
from .format_interface import ContentFormatInterface
class LeafConnectorInterface(ConnectorInterface, StreamInterface, ABC):
@abstractmethod
def get_content_type(self) -> ContentType:
pass
@abstractmethod
def get_content_format(self) -> ContentFormatInterface:
pass
@abstractmethod
def set_content_format(self, content_format: ContentFormatInterface, inplace: bool) -> Optional[ConnectorInterface]:
pass
@abstractmethod
def get_declared_format(self) -> ContentFormatInterface:
pass
@abstractmethod
def is_existing(self) -> bool:
pass
@abstractmethod
def get_first_line(self, close: bool = True) -> Optional[str]:
pass
@abstractmethod
def check(self, must_exists: bool = True):
pass
@abstractmethod
def write_stream(self, stream: StreamInterface, verbose: bool = True):
pass
@abstractmethod
def from_stream(self, stream: StreamInterface):
pass
@abstractmethod
def to_stream(self, **kwargs) -> StreamInterface:
pass | en | 0.845231 | # Assume we're a sub-module in a package. # Apparently no higher-level package has been imported, fall back to a local import. | 2.60356 | 3 |
slither/core/geodetic.py | AlexanderFabisch/slither | 2 | 6624304 | import numpy as np
import pyproj
from .config import config
def haversine_dist(lat1, long1, lat2, long2, earth_radius=6371000.0):
"""Haversine distance between two positions on earth.
This is a simple approximation. A better way is to use pyproj,
which uses a WGS84 model of the earth.
Parameters
----------
lat1 : array-like or float
latitude of position 1 in radians
long1 : array-like or float
longitude of position 1 in radians
lat2 : array-like or float
latitude of position 2 in radians
long2 : array-like or float
longitude of position 2 in radians
earth_radius : float
average radius of the earth in meters, should be between
6353000 and 6384000
Returns
-------
distance : float
Distance between two positions on the surface of the earth in meters
"""
lat_dist = lat2 - lat1
long_dist = long2 - long1
a = (np.sin(0.5 * lat_dist) ** 2 +
np.cos(lat1) * np.cos(lat2) * np.sin(0.5 * long_dist) ** 2)
angular_dist = 2 * np.arctan2(np.sqrt(a), np.sqrt(1 - a))
return earth_radius * angular_dist
class PyprojDist:
def __init__(self, config):
if "geodetic" in config and "ellipsoid" in config["geodetic"]:
ellps = config["geodetic"]["ellipsoid"]
else:
ellps = "WGS84"
self.geod = pyproj.Geod(ellps=ellps)
def __call__(self, lat1, long1, lat2, long2):
"""Compute distance between two positions on earth.
Parameters
----------
lat1 : array-like or float
latitude of position 1 in radians
long1 : array-like or float
longitude of position 1 in radians
lat2 : array-like or float
latitude of position 2 in radians
long2 : array-like or float
longitude of position 2 in radians
Returns
-------
distance : float
Distance between two positions on the surface of the earth in meters
"""
_, _, dist = self.geod.inv(long1, lat1, long2, lat2, radians=True)
return dist
dist_on_earth = PyprojDist(config)
def compute_velocities(timestamps, coords):
"""Compute velocities from geodetic coordinates.
Parameters
----------
timestamps : array, shape (n_steps,)
Timestamps
coords : array, shape (n_steps, 2)
Latitudes and longitudes in radians
Returns
-------
velocities : array, shape (n_steps,)
Velocities in meters per second
total_distance : float
Total distance in meters
"""
delta_t = np.diff(timestamps)
dists = dist_on_earth(
coords[:-1, 0], coords[:-1, 1], coords[1:, 0], coords[1:, 1])
n_steps = len(timestamps)
velocities = np.empty(n_steps)
velocities[0] = 0.0
total_distance = 0.0
for t in range(1, n_steps):
dt = delta_t[t - 1]
if dt <= 0.0:
velocity = velocities[t - 1]
else:
velocity = dists[t - 1] / dt
total_distance += dists[t - 1]
velocities[t] = velocity
return velocities, total_distance
| import numpy as np
import pyproj
from .config import config
def haversine_dist(lat1, long1, lat2, long2, earth_radius=6371000.0):
"""Haversine distance between two positions on earth.
This is a simple approximation. A better way is to use pyproj,
which uses a WGS84 model of the earth.
Parameters
----------
lat1 : array-like or float
latitude of position 1 in radians
long1 : array-like or float
longitude of position 1 in radians
lat2 : array-like or float
latitude of position 2 in radians
long2 : array-like or float
longitude of position 2 in radians
earth_radius : float
average radius of the earth in meters, should be between
6353000 and 6384000
Returns
-------
distance : float
Distance between two positions on the surface of the earth in meters
"""
lat_dist = lat2 - lat1
long_dist = long2 - long1
a = (np.sin(0.5 * lat_dist) ** 2 +
np.cos(lat1) * np.cos(lat2) * np.sin(0.5 * long_dist) ** 2)
angular_dist = 2 * np.arctan2(np.sqrt(a), np.sqrt(1 - a))
return earth_radius * angular_dist
class PyprojDist:
def __init__(self, config):
if "geodetic" in config and "ellipsoid" in config["geodetic"]:
ellps = config["geodetic"]["ellipsoid"]
else:
ellps = "WGS84"
self.geod = pyproj.Geod(ellps=ellps)
def __call__(self, lat1, long1, lat2, long2):
"""Compute distance between two positions on earth.
Parameters
----------
lat1 : array-like or float
latitude of position 1 in radians
long1 : array-like or float
longitude of position 1 in radians
lat2 : array-like or float
latitude of position 2 in radians
long2 : array-like or float
longitude of position 2 in radians
Returns
-------
distance : float
Distance between two positions on the surface of the earth in meters
"""
_, _, dist = self.geod.inv(long1, lat1, long2, lat2, radians=True)
return dist
dist_on_earth = PyprojDist(config)
def compute_velocities(timestamps, coords):
"""Compute velocities from geodetic coordinates.
Parameters
----------
timestamps : array, shape (n_steps,)
Timestamps
coords : array, shape (n_steps, 2)
Latitudes and longitudes in radians
Returns
-------
velocities : array, shape (n_steps,)
Velocities in meters per second
total_distance : float
Total distance in meters
"""
delta_t = np.diff(timestamps)
dists = dist_on_earth(
coords[:-1, 0], coords[:-1, 1], coords[1:, 0], coords[1:, 1])
n_steps = len(timestamps)
velocities = np.empty(n_steps)
velocities[0] = 0.0
total_distance = 0.0
for t in range(1, n_steps):
dt = delta_t[t - 1]
if dt <= 0.0:
velocity = velocities[t - 1]
else:
velocity = dists[t - 1] / dt
total_distance += dists[t - 1]
velocities[t] = velocity
return velocities, total_distance
| en | 0.624181 | Haversine distance between two positions on earth. This is a simple approximation. A better way is to use pyproj, which uses a WGS84 model of the earth. Parameters ---------- lat1 : array-like or float latitude of position 1 in radians long1 : array-like or float longitude of position 1 in radians lat2 : array-like or float latitude of position 2 in radians long2 : array-like or float longitude of position 2 in radians earth_radius : float average radius of the earth in meters, should be between 6353000 and 6384000 Returns ------- distance : float Distance between two positions on the surface of the earth in meters Compute distance between two positions on earth. Parameters ---------- lat1 : array-like or float latitude of position 1 in radians long1 : array-like or float longitude of position 1 in radians lat2 : array-like or float latitude of position 2 in radians long2 : array-like or float longitude of position 2 in radians Returns ------- distance : float Distance between two positions on the surface of the earth in meters Compute velocities from geodetic coordinates. Parameters ---------- timestamps : array, shape (n_steps,) Timestamps coords : array, shape (n_steps, 2) Latitudes and longitudes in radians Returns ------- velocities : array, shape (n_steps,) Velocities in meters per second total_distance : float Total distance in meters | 3.512784 | 4 |
dipy/reconst/dti.py | Garyfallidis/dipy | 3 | 6624305 | <reponame>Garyfallidis/dipy
#!/usr/bin/python
""" Classes and functions for fitting tensors """
# 5/17/2010
import numpy as np
from dipy.reconst.maskedview import MaskedView, _makearray, _filled
from dipy.reconst.modelarray import ModelArray
from dipy.data import get_sphere
class Tensor(ModelArray):
""" Fits a diffusion tensor given diffusion-weighted signals and gradient info
Tensor object that when initialized calculates single self diffusion
tensor [1]_ in each voxel using selected fitting algorithm
(DEFAULT: weighted least squares [2]_)
Requires a given gradient table, b value for each diffusion-weighted
gradient vector, and image data given all as arrays.
Parameters
----------
data : array ([X, Y, Z, ...], g)
Diffusion-weighted signals. The dimension corresponding to the
diffusion weighting must be the last dimenssion
bval : array (g,)
Diffusion weighting factor b for each vector in gtab.
gtab : array (g, 3)
Diffusion gradient table found in DICOM header as a array.
mask : array, optional
The tensor will only be fit where mask is True. Mask must must
broadcast to the shape of data and must have fewer dimensions than data
thresh : float, default = None
The tensor will not be fit where data[bval == 0] < thresh. If multiple
b0 volumes are given, the minimum b0 signal is used.
fit_method : funciton or string, default = 'WLS'
The method to be used to fit the given data to a tensor. Any function
that takes the B matrix and the data and returns eigen values and eigen
vectors can be passed as the fit method. Any of the common fit methods
can be passed as a string.
*args, **kargs :
Any other arguments or keywards will be passed to fit_method.
common fit methods:
'WLS' : weighted least squares
dti.wls_fit_tensor
'LS' : ordinary least squares
dti.ols_fit_tensor
Attributes
----------
D : array (..., 3, 3)
Self diffusion tensor calculated from cached eigenvalues and
eigenvectors.
mask : array
True in voxels where a tensor was fit, false if the voxel was skipped
B : array (g, 7)
Design matrix or B matrix constructed from given gradient table and
b-value vector.
evals : array (..., 3)
Cached eigenvalues of self diffusion tensor for given index.
(eval1, eval2, eval3)
evecs : array (..., 3, 3)
Cached associated eigenvectors of self diffusion tensor for given
index. Note: evals[..., j] is associated with evecs[..., :, j]
Methods
-------
fa : array
Calculates fractional anisotropy [2]_.
md : array
Calculates the mean diffusivity [2]_.
Note: [units ADC] ~ [units b value]*10**-1
See Also
--------
dipy.io.bvectxt.read_bvec_file, dipy.core.qball.ODF
References
----------
.. [1] <NAME>., <NAME>., <NAME>., 1994. Estimation of
the effective self-diffusion tensor from the NMR spin echo. J Magn
Reson B 103, 247-254.
.. [2] <NAME>., <NAME>., 1996. Microstructural and physiological
features of tissues elucidated by quantitative diffusion-tensor MRI.
Journal of Magnetic Resonance 111, 209-219.
Examples
----------
For a complete example have a look at the main dipy/examples folder
"""
### Eigenvalues Property ###
@property
def evals(self):
"""
Returns the eigenvalues of the tensor as an array
"""
return _filled(self.model_params[..., :3])
### Eigenvectors Property ###
@property
def evecs(self):
"""
Returns the eigenvectors of teh tensor as an array
"""
evecs = _filled(self.model_params[..., 3:])
return evecs.reshape(self.shape + (3, 3))
def __init__(self, data, b_values, grad_table, mask=True, thresh=None,
fit_method='WLS', verbose=False, *args, **kargs):
"""
Fits a tensors to diffusion weighted data.
"""
if not callable(fit_method):
try:
fit_method = common_fit_methods[fit_method]
except KeyError:
raise ValueError('"'+str(fit_method)+'" is not a known fit '+
'method, the fit method should either be a '+
'function or one of the common fit methods')
#64 bit design matrix makes for faster pinv
B = design_matrix(grad_table.T, b_values)
self.B = B
mask = np.atleast_1d(mask)
if thresh is not None:
#Define total mask from thresh and mask
#mask = mask & (np.min(data[..., b_values == 0], -1) >
#thresh)
#the assumption that the lowest b_value is always 0 is
#incorrect the lowest b_value could also be higher than 0
#this is common with grid q-spaces
min_b0_sig = np.min(data[..., b_values == b_values.min()], -1)
mask = mask & (min_b0_sig > thresh)
#if mask is all False
if not mask.any():
raise ValueError('between mask and thresh, there is no data to '+
'fit')
#and the mask is not all True
if not mask.all():
#leave only data[mask is True]
data = data[mask]
data = MaskedView(mask, data, fill_value=0)
#Perform WLS fit on masked data
dti_params = fit_method(B, data, *args, **kargs)
self.model_params = dti_params
### Self Diffusion Tensor Property ###
def _getD(self):
"""Calculates the 3x3 diffusion tensor for each voxel"""
params, wrap = _makearray(self.model_params)
evals = params[..., :3]
evecs = params[..., 3:]
evals_flat = evals.reshape((-1, 3))
evecs_flat = evecs.reshape((-1, 3, 3))
D_flat = np.empty(evecs_flat.shape)
for ii in xrange(len(D_flat)):
Q = evecs_flat[ii]
L = evals_flat[ii]
D_flat[ii] = np.dot(Q*L, Q.T)
D = _filled(wrap(D_flat))
D.shape = self.shape + (3, 3)
return D
D = property(_getD, doc = "Self diffusion tensor")
def lower_triangular(self, b0=None):
D = self._getD()
return lower_triangular(D, b0)
def fa(self, fill_value=0, nonans=True):
r"""
Fractional anisotropy (FA) calculated from cached eigenvalues.
Parameters
----------
fill_value : float
value of fa where self.mask == True.
nonans : Bool
When True, fa is 0 when all eigenvalues are 0, otherwise fa is nan
Returns
---------
fa : array (V, 1)
Calculated FA. Note: range is 0 <= FA <= 1.
Notes
--------
FA is calculated with the following equation:
.. math::
FA = \sqrt{\frac{1}{2}\frac{(\lambda_1-\lambda_2)^2+(\lambda_1-
\lambda_3)^2+(\lambda_2-lambda_3)^2}{\lambda_1^2+
\lambda_2^2+\lambda_3^2} }
"""
evals, wrap = _makearray(self.model_params[..., :3])
ev1 = evals[..., 0]
ev2 = evals[..., 1]
ev3 = evals[..., 2]
if nonans:
all_zero = (ev1 == 0) & (ev2 == 0) & (ev3 == 0)
else:
all_zero = 0.
fa = np.sqrt(0.5 * ((ev1 - ev2)**2 + (ev2 - ev3)**2 + (ev3 - ev1)**2)
/ (ev1*ev1 + ev2*ev2 + ev3*ev3 + all_zero))
fa = wrap(np.asarray(fa))
return _filled(fa, fill_value)
def md(self):
r"""
Mean diffusitivity (MD) calculated from cached eigenvalues.
Returns
---------
md : array (V, 1)
Calculated MD.
Notes
--------
MD is calculated with the following equation:
.. math::
ADC = \frac{\lambda_1+\lambda_2+\lambda_3}{3}
"""
#adc/md = (ev1+ev2+ev3)/3
return self.evals.mean(-1)
def ind(self):
''' Quantizes eigenvectors with maximum eigenvalues on an
evenly distributed sphere so that the can be used for tractography.
Returns
---------
IN : array, shape(x,y,z) integer indices for the points of the
evenly distributed sphere representing tensor eigenvectors of
maximum eigenvalue
'''
return quantize_evecs(self.evecs,odf_vertices=None)
def wls_fit_tensor(design_matrix, data, min_signal=1):
r"""
Computes weighted least squares (WLS) fit to calculate self-diffusion
tensor using a linear regression model [1]_.
Parameters
----------
design_matrix : array (g, 7)
Design matrix holding the covariants used to solve for the regression
coefficients.
data : array ([X, Y, Z, ...], g)
Data or response variables holding the data. Note that the last
dimension should contain the data. It makes no copies of data.
min_signal : default = 1
All values below min_signal are repalced with min_signal. This is done
in order to avaid taking log(0) durring the tensor fitting.
Returns
-------
eigvals : array (..., 3)
Eigenvalues from eigen decomposition of the tensor.
eigvecs : array (..., 3, 3)
Associated eigenvectors from eigen decomposition of the tensor.
Eigenvectors are columnar (e.g. eigvecs[:,j] is associated with
eigvals[j])
See Also
--------
decompose_tensor
Notes
-----
In Chung, et al. 2006, the regression of the WLS fit needed an unbiased
preliminary estimate of the weights and therefore the ordinary least
squares (OLS) estimates were used. A "two pass" method was implemented:
1. calculate OLS estimates of the data
2. apply the OLS estimates as weights to the WLS fit of the data
This ensured heteroscadasticity could be properly modeled for various
types of bootstrap resampling (namely residual bootstrap).
.. math::
y = \mathrm{data} \\
X = \mathrm{design matrix} \\
\hat{\beta}_\mathrm{WLS} = \mathrm{desired regression coefficients (e.g. tensor)}\\
\\
\hat{\beta}_\mathrm{WLS} = (X^T W X)^{-1} X^T W y \\
\\
W = \mathrm{diag}((X \hat{\beta}_\mathrm{OLS})^2),
\mathrm{where} \hat{\beta}_\mathrm{OLS} = (X^T X)^{-1} X^T y
References
----------
.. _[1] <NAME>., <NAME>., <NAME>., 2006. Comparison of bootstrap
approaches for estimation of uncertainties of DTI parameters.
NeuroImage 33, 531-541.
"""
if min_signal <= 0:
raise ValueError('min_signal must be > 0')
data, wrap = _makearray(data)
data_flat = data.reshape((-1, data.shape[-1]))
dti_params = np.empty((len(data_flat), 4, 3))
#obtain OLS fitting matrix
#U,S,V = np.linalg.svd(design_matrix, False)
#math: beta_ols = inv(X.T*X)*X.T*y
#math: ols_fit = X*beta_ols*inv(y)
#ols_fit = np.dot(U, U.T)
ols_fit = _ols_fit_matrix(design_matrix)
for param, sig in zip(dti_params, data_flat):
param[0], param[1:] = _wls_iter(ols_fit, design_matrix, sig,
min_signal=min_signal)
dti_params.shape = data.shape[:-1]+(12,)
dti_params = wrap(dti_params)
return dti_params
def _wls_iter(ols_fit, design_matrix, sig, min_signal=1):
'''
Function used by wls_fit_tensor for later optimization.
'''
sig = np.maximum(sig, min_signal) #throw out zero signals
log_s = np.log(sig)
w = np.exp(np.dot(ols_fit, log_s))
D = np.dot(np.linalg.pinv(design_matrix*w[:,None]), w*log_s)
tensor = from_lower_triangular(D)
return decompose_tensor(tensor)
def _ols_iter(inv_design, sig, min_signal=1):
'''
Function used by ols_fit_tensor for later optimization.
'''
sig = np.maximum(sig, min_signal) #throw out zero signals
log_s = np.log(sig)
D = np.dot(inv_design, log_s)
tensor = from_lower_triangular(D)
return decompose_tensor(tensor)
def ols_fit_tensor(design_matrix, data, min_signal=1):
r"""
Computes ordinary least squares (OLS) fit to calculate self-diffusion
tensor using a linear regression model [1]_.
Parameters
----------
design_matrix : array (g, 7)
Design matrix holding the covariants used to solve for the regression
coefficients. Use design_matrix to build a valid design matrix from
bvalues and a gradient table.
data : array ([X, Y, Z, ...], g)
Data or response variables holding the data. Note that the last
dimension should contain the data. It makes no copies of data.
min_signal : default = 1
All values below min_signal are repalced with min_signal. This is done
in order to avaid taking log(0) durring the tensor fitting.
Returns
-------
eigvals : array (..., 3)
Eigenvalues from eigen decomposition of the tensor.
eigvecs : array (..., 3, 3)
Associated eigenvectors from eigen decomposition of the tensor.
Eigenvectors are columnar (e.g. eigvecs[:,j] is associated with
eigvals[j])
See Also
--------
WLS_fit_tensor, decompose_tensor, design_matrix
Notes
-----
This function is offered mainly as a quick comparison to WLS.
.. math::
y = \mathrm{data} \\
X = \mathrm{design matrix} \\
\hat{\beta}_\mathrm{OLS} = (X^T X)^{-1} X^T y
References
----------
.. [1] <NAME>., <NAME>., <NAME>., 2006. Comparison of bootstrap
approaches for estimation of uncertainties of DTI parameters.
NeuroImage 33, 531-541.
"""
data, wrap = _makearray(data)
data_flat = data.reshape((-1, data.shape[-1]))
evals = np.empty((len(data_flat), 3))
evecs = np.empty((len(data_flat), 3, 3))
dti_params = np.empty((len(data_flat), 4, 3))
#obtain OLS fitting matrix
#U,S,V = np.linalg.svd(design_matrix, False)
#math: beta_ols = inv(X.T*X)*X.T*y
#math: ols_fit = X*beta_ols*inv(y)
#ols_fit = np.dot(U, U.T)
inv_design = np.linalg.pinv(design_matrix)
for param, sig in zip(dti_params, data_flat):
param[0], param[1:] = _ols_iter(inv_design, sig, min_signal)
dti_params.shape = data.shape[:-1]+(12,)
dti_params = wrap(dti_params)
return dti_params
def _ols_fit_matrix(design_matrix):
"""
Helper function to calculate the ordinary least squares (OLS)
fit as a matrix multiplication. Mainly used to calculate WLS weights. Can
be used to calculate regression coefficients in OLS but not recommended.
See Also:
---------
wls_fit_tensor, ols_fit_tensor
Example:
--------
ols_fit = _ols_fit_matrix(design_mat)
ols_data = np.dot(ols_fit, data)
"""
U,S,V = np.linalg.svd(design_matrix, False)
return np.dot(U, U.T)
_lt_indices = np.array([[0, 1, 3],
[1, 2, 4],
[3, 4, 5]])
def from_lower_triangular(D):
"""
Returns a tensor given the six unique tensor elements
Given the six unique tensor elments (in the order: Dxx, Dxy, Dyy, Dxz, Dyz,
Dzz) returns a 3 by 3 tensor. All elements after the sixth are ignored.
Parameters:
-----------
D : array_like, (..., >6)
Unique elements of the tensors
Returns:
--------
tensor : ndarray (..., 3, 3)
3 by 3 tensors
"""
return D[..., _lt_indices]
_lt_rows = np.array([0, 1, 1, 2, 2, 2])
_lt_cols = np.array([0, 0, 1, 0, 1, 2])
def lower_triangular(tensor, b0=None):
"""
Returns the six lower triangular values of the tensor and a dummy variable
if b0 is not None
Parameters:
----------
tensor - array_like (..., 3, 3)
a collection of 3, 3 diffusion tensors
b0 - float
if b0 is not none log(b0) is returned as the dummy variable
Returns:
-------
D - ndarray
If b0 is none, then the shape will be (..., 6) otherwise (..., 7)
"""
if tensor.shape[-2:] != (3, 3):
raise ValueError("Diffusion tensors should be (..., 3, 3)")
if b0 is None:
return tensor[..., _lt_rows, _lt_cols]
else:
D = np.empty(tensor.shape[:-2] + (7,), dtype=tensor.dtype)
D[..., 6] = -np.log(b0)
D[..., :6] = tensor[..., _lt_rows, _lt_cols]
return D
def tensor_eig_from_lo_tri(B, data):
"""Calculates parameters for creating a Tensor instance
Calculates tensor parameters from the six unique tensor elements. This
function can be passed to the Tensor class as a fit_method for creating a
Tensor instance from tensors stored in a nifti file.
Parameters:
-----------
B :
not currently used
data : array_like (..., 6)
diffusion tensors elements stored in lower triangular order
Returns
-------
dti_params
Eigen values and vectors, used by the Tensor class to create an
instance
"""
data, wrap = _makearray(data)
data_flat = data.reshape((-1, data.shape[-1]))
dti_params = np.empty((len(data_flat), 4, 3))
for ii in xrange(len(data_flat)):
tensor = from_lower_triangular(data_flat[ii])
eigvals, eigvecs = decompose_tensor(tensor)
dti_params[ii, 0] = eigvals
dti_params[ii, 1:] = eigvecs
dti_params.shape = data.shape[:-1]+(12,)
dti_params = wrap(dti_params)
return dti_params
def decompose_tensor(tensor):
"""
Returns eigenvalues and eigenvectors given a diffusion tensor
Computes tensor eigen decomposition to calculate eigenvalues and
eigenvectors of self-diffusion tensor. (Basser et al., 1994a)
Parameters
----------
D : array (3,3)
array holding a tensor. Assumes D has units on order of
~ 10^-4 mm^2/s
Returns
-------
eigvals : array (3,)
Eigenvalues from eigen decomposition of the tensor. Negative
eigenvalues are replaced by zero. Sorted from largest to smallest.
eigvecs : array (3,3)
Associated eigenvectors from eigen decomposition of the tensor.
Eigenvectors are columnar (e.g. eigvecs[:,j] is associated with
eigvals[j])
See Also
--------
numpy.linalg.eig
"""
#outputs multiplicity as well so need to unique
eigenvals, eigenvecs = np.linalg.eig(tensor)
#need to sort the eigenvalues and associated eigenvectors
order = eigenvals.argsort()[::-1]
eigenvecs = eigenvecs[:, order]
eigenvals = eigenvals[order]
#Forcing negative eigenvalues to 0
eigenvals = np.maximum(eigenvals, 0)
# b ~ 10^3 s/mm^2 and D ~ 10^-4 mm^2/s
# eigenvecs: each vector is columnar
return eigenvals, eigenvecs
def design_matrix(gtab, bval, dtype=None):
"""
Constructs design matrix for DTI weighted least squares or least squares
fitting. (Basser et al., 1994a)
Parameters
----------
gtab : array with shape (3,g)
Diffusion gradient table found in DICOM header as a numpy array.
bval : array with shape (g,)
Diffusion weighting factor b for each vector in gtab.
dtype : string
Parameter to control the dtype of returned designed matrix
Returns
-------
design_matrix : array (g,7)
Design matrix or B matrix assuming Gaussian distributed tensor model.
Note: design_matrix[j,:] = (Bxx,Byy,Bzz,Bxy,Bxz,Byz,dummy)
"""
G = gtab
B = np.zeros((bval.size, 7), dtype = G.dtype)
if gtab.shape[1] != bval.shape[0]:
raise ValueError('The number of b values and gradient directions must'
+' be the same')
B[:, 0] = G[0, :] * G[0, :] * 1. * bval #Bxx
B[:, 1] = G[0, :] * G[1, :] * 2. * bval #Bxy
B[:, 2] = G[1, :] * G[1, :] * 1. * bval #Byy
B[:, 3] = G[0, :] * G[2, :] * 2. * bval #Bxz
B[:, 4] = G[1, :] * G[2, :] * 2. * bval #Byz
B[:, 5] = G[2, :] * G[2, :] * 1. * bval #Bzz
B[:, 6] = np.ones(bval.size)
return -B
def quantize_evecs(evecs, odf_vertices=None):
''' Find the closest orientation of an evenly distributed sphere
Parameters
----------
evecs : ndarray
odf_vertices : None or ndarray
If None, then set vertices from symmetric362 sphere. Otherwise use
passed ndarray as vertices
Returns
-------
IN : ndarray
'''
max_evecs=evecs[...,:,0]
if odf_vertices==None:
odf_vertices, _ = get_sphere('symmetric362')
tup=max_evecs.shape[:-1]
mec=max_evecs.reshape(np.prod(np.array(tup)),3)
IN=np.array([np.argmin(np.dot(odf_vertices,m)) for m in mec])
IN=IN.reshape(tup)
return IN
class TensorStepper(object):
"""Used for tracking diffusion tensors, has a next_step method"""
def _get_angel_limit(self):
return np.arccos(self.dot_limit)*180/pi
def _set_angel_limit(self, angle):
if angle >= 0 and angle <= 90:
self.dot_limit = cos(angle*pi/180)
else:
raise ValueError("angle should be between 0 and 180")
angel_limit = property(_get_angel_limit, _set_angel_limit)
def _get_fa_limit(self):
return self._fa_limit
def _set_fa_limit(self, arg):
self._fa_limit = arg
mask = self.fa_vol > arg
self._interp_inst = _interpolator(self.evec1_vol, self.voxe_size, mask)
fa_limit = property(_get_fa_limit, _set_fa_limit)
def __init__(self, fa_vol, evec1_vol, voxel_size, interpolator,
fa_limit=None, angle_limit=None):
self.voxel_size = voxel_size
self.angle_limit = angle_limit
if fa_vol.shape != evec1_vol.shape[:-1]:
msg = "the fa and eigen vector volumes are not the same shape"
raise ValueError(msg)
if evec1_vol.shape[-1] != 3:
msg = "eigen vector volume should have vecetors of length 3 " + \
"along the last dimmension"
raise ValueError(msg)
self.evec1_vol = evec1_vol
self.fa_vol = fa_vol
self._interpolator = interpolator
#self._interp_inst is created when fa_limit is set
self.fa_limit = fa_limit
def next_step(location, prev_step):
"""Returns the nearest neighbor tensor for location"""
step = self._interp_inst[location]
angle_dot = dot(step, prev_step)
if np.abs(angle_dot) < self.dot_limit:
raise StopIteration
if angle_dot > 0:
return step
else:
return -step
def stepper_from_tensor(tensor, *args, **kargs):
"""stepper_from_tensor(tensor, fa_vol, evec1_vol, voxel_size, interpolator)
"""
fa_vol = tensor.fa()
evec1_vol = tensor.evec[..., 0]
stepper = TensorStepper(fa_vol, evec1_vol, *args, **kargs)
return stepper
common_fit_methods = {'WLS': wls_fit_tensor,
'LS': ols_fit_tensor,
'from lower triangular': tensor_eig_from_lo_tri,
}
| #!/usr/bin/python
""" Classes and functions for fitting tensors """
# 5/17/2010
import numpy as np
from dipy.reconst.maskedview import MaskedView, _makearray, _filled
from dipy.reconst.modelarray import ModelArray
from dipy.data import get_sphere
class Tensor(ModelArray):
""" Fits a diffusion tensor given diffusion-weighted signals and gradient info
Tensor object that when initialized calculates single self diffusion
tensor [1]_ in each voxel using selected fitting algorithm
(DEFAULT: weighted least squares [2]_)
Requires a given gradient table, b value for each diffusion-weighted
gradient vector, and image data given all as arrays.
Parameters
----------
data : array ([X, Y, Z, ...], g)
Diffusion-weighted signals. The dimension corresponding to the
diffusion weighting must be the last dimenssion
bval : array (g,)
Diffusion weighting factor b for each vector in gtab.
gtab : array (g, 3)
Diffusion gradient table found in DICOM header as a array.
mask : array, optional
The tensor will only be fit where mask is True. Mask must must
broadcast to the shape of data and must have fewer dimensions than data
thresh : float, default = None
The tensor will not be fit where data[bval == 0] < thresh. If multiple
b0 volumes are given, the minimum b0 signal is used.
fit_method : funciton or string, default = 'WLS'
The method to be used to fit the given data to a tensor. Any function
that takes the B matrix and the data and returns eigen values and eigen
vectors can be passed as the fit method. Any of the common fit methods
can be passed as a string.
*args, **kargs :
Any other arguments or keywards will be passed to fit_method.
common fit methods:
'WLS' : weighted least squares
dti.wls_fit_tensor
'LS' : ordinary least squares
dti.ols_fit_tensor
Attributes
----------
D : array (..., 3, 3)
Self diffusion tensor calculated from cached eigenvalues and
eigenvectors.
mask : array
True in voxels where a tensor was fit, false if the voxel was skipped
B : array (g, 7)
Design matrix or B matrix constructed from given gradient table and
b-value vector.
evals : array (..., 3)
Cached eigenvalues of self diffusion tensor for given index.
(eval1, eval2, eval3)
evecs : array (..., 3, 3)
Cached associated eigenvectors of self diffusion tensor for given
index. Note: evals[..., j] is associated with evecs[..., :, j]
Methods
-------
fa : array
Calculates fractional anisotropy [2]_.
md : array
Calculates the mean diffusivity [2]_.
Note: [units ADC] ~ [units b value]*10**-1
See Also
--------
dipy.io.bvectxt.read_bvec_file, dipy.core.qball.ODF
References
----------
.. [1] <NAME>., <NAME>., <NAME>., 1994. Estimation of
the effective self-diffusion tensor from the NMR spin echo. J Magn
Reson B 103, 247-254.
.. [2] <NAME>., <NAME>., 1996. Microstructural and physiological
features of tissues elucidated by quantitative diffusion-tensor MRI.
Journal of Magnetic Resonance 111, 209-219.
Examples
----------
For a complete example have a look at the main dipy/examples folder
"""
### Eigenvalues Property ###
@property
def evals(self):
"""
Returns the eigenvalues of the tensor as an array
"""
return _filled(self.model_params[..., :3])
### Eigenvectors Property ###
@property
def evecs(self):
"""
Returns the eigenvectors of teh tensor as an array
"""
evecs = _filled(self.model_params[..., 3:])
return evecs.reshape(self.shape + (3, 3))
def __init__(self, data, b_values, grad_table, mask=True, thresh=None,
fit_method='WLS', verbose=False, *args, **kargs):
"""
Fits a tensors to diffusion weighted data.
"""
if not callable(fit_method):
try:
fit_method = common_fit_methods[fit_method]
except KeyError:
raise ValueError('"'+str(fit_method)+'" is not a known fit '+
'method, the fit method should either be a '+
'function or one of the common fit methods')
#64 bit design matrix makes for faster pinv
B = design_matrix(grad_table.T, b_values)
self.B = B
mask = np.atleast_1d(mask)
if thresh is not None:
#Define total mask from thresh and mask
#mask = mask & (np.min(data[..., b_values == 0], -1) >
#thresh)
#the assumption that the lowest b_value is always 0 is
#incorrect the lowest b_value could also be higher than 0
#this is common with grid q-spaces
min_b0_sig = np.min(data[..., b_values == b_values.min()], -1)
mask = mask & (min_b0_sig > thresh)
#if mask is all False
if not mask.any():
raise ValueError('between mask and thresh, there is no data to '+
'fit')
#and the mask is not all True
if not mask.all():
#leave only data[mask is True]
data = data[mask]
data = MaskedView(mask, data, fill_value=0)
#Perform WLS fit on masked data
dti_params = fit_method(B, data, *args, **kargs)
self.model_params = dti_params
### Self Diffusion Tensor Property ###
def _getD(self):
"""Calculates the 3x3 diffusion tensor for each voxel"""
params, wrap = _makearray(self.model_params)
evals = params[..., :3]
evecs = params[..., 3:]
evals_flat = evals.reshape((-1, 3))
evecs_flat = evecs.reshape((-1, 3, 3))
D_flat = np.empty(evecs_flat.shape)
for ii in xrange(len(D_flat)):
Q = evecs_flat[ii]
L = evals_flat[ii]
D_flat[ii] = np.dot(Q*L, Q.T)
D = _filled(wrap(D_flat))
D.shape = self.shape + (3, 3)
return D
D = property(_getD, doc = "Self diffusion tensor")
def lower_triangular(self, b0=None):
D = self._getD()
return lower_triangular(D, b0)
def fa(self, fill_value=0, nonans=True):
r"""
Fractional anisotropy (FA) calculated from cached eigenvalues.
Parameters
----------
fill_value : float
value of fa where self.mask == True.
nonans : Bool
When True, fa is 0 when all eigenvalues are 0, otherwise fa is nan
Returns
---------
fa : array (V, 1)
Calculated FA. Note: range is 0 <= FA <= 1.
Notes
--------
FA is calculated with the following equation:
.. math::
FA = \sqrt{\frac{1}{2}\frac{(\lambda_1-\lambda_2)^2+(\lambda_1-
\lambda_3)^2+(\lambda_2-lambda_3)^2}{\lambda_1^2+
\lambda_2^2+\lambda_3^2} }
"""
evals, wrap = _makearray(self.model_params[..., :3])
ev1 = evals[..., 0]
ev2 = evals[..., 1]
ev3 = evals[..., 2]
if nonans:
all_zero = (ev1 == 0) & (ev2 == 0) & (ev3 == 0)
else:
all_zero = 0.
fa = np.sqrt(0.5 * ((ev1 - ev2)**2 + (ev2 - ev3)**2 + (ev3 - ev1)**2)
/ (ev1*ev1 + ev2*ev2 + ev3*ev3 + all_zero))
fa = wrap(np.asarray(fa))
return _filled(fa, fill_value)
def md(self):
r"""
Mean diffusitivity (MD) calculated from cached eigenvalues.
Returns
---------
md : array (V, 1)
Calculated MD.
Notes
--------
MD is calculated with the following equation:
.. math::
ADC = \frac{\lambda_1+\lambda_2+\lambda_3}{3}
"""
#adc/md = (ev1+ev2+ev3)/3
return self.evals.mean(-1)
def ind(self):
''' Quantizes eigenvectors with maximum eigenvalues on an
evenly distributed sphere so that the can be used for tractography.
Returns
---------
IN : array, shape(x,y,z) integer indices for the points of the
evenly distributed sphere representing tensor eigenvectors of
maximum eigenvalue
'''
return quantize_evecs(self.evecs,odf_vertices=None)
def wls_fit_tensor(design_matrix, data, min_signal=1):
r"""
Computes weighted least squares (WLS) fit to calculate self-diffusion
tensor using a linear regression model [1]_.
Parameters
----------
design_matrix : array (g, 7)
Design matrix holding the covariants used to solve for the regression
coefficients.
data : array ([X, Y, Z, ...], g)
Data or response variables holding the data. Note that the last
dimension should contain the data. It makes no copies of data.
min_signal : default = 1
All values below min_signal are repalced with min_signal. This is done
in order to avaid taking log(0) durring the tensor fitting.
Returns
-------
eigvals : array (..., 3)
Eigenvalues from eigen decomposition of the tensor.
eigvecs : array (..., 3, 3)
Associated eigenvectors from eigen decomposition of the tensor.
Eigenvectors are columnar (e.g. eigvecs[:,j] is associated with
eigvals[j])
See Also
--------
decompose_tensor
Notes
-----
In Chung, et al. 2006, the regression of the WLS fit needed an unbiased
preliminary estimate of the weights and therefore the ordinary least
squares (OLS) estimates were used. A "two pass" method was implemented:
1. calculate OLS estimates of the data
2. apply the OLS estimates as weights to the WLS fit of the data
This ensured heteroscadasticity could be properly modeled for various
types of bootstrap resampling (namely residual bootstrap).
.. math::
y = \mathrm{data} \\
X = \mathrm{design matrix} \\
\hat{\beta}_\mathrm{WLS} = \mathrm{desired regression coefficients (e.g. tensor)}\\
\\
\hat{\beta}_\mathrm{WLS} = (X^T W X)^{-1} X^T W y \\
\\
W = \mathrm{diag}((X \hat{\beta}_\mathrm{OLS})^2),
\mathrm{where} \hat{\beta}_\mathrm{OLS} = (X^T X)^{-1} X^T y
References
----------
.. _[1] <NAME>., <NAME>., <NAME>., 2006. Comparison of bootstrap
approaches for estimation of uncertainties of DTI parameters.
NeuroImage 33, 531-541.
"""
if min_signal <= 0:
raise ValueError('min_signal must be > 0')
data, wrap = _makearray(data)
data_flat = data.reshape((-1, data.shape[-1]))
dti_params = np.empty((len(data_flat), 4, 3))
#obtain OLS fitting matrix
#U,S,V = np.linalg.svd(design_matrix, False)
#math: beta_ols = inv(X.T*X)*X.T*y
#math: ols_fit = X*beta_ols*inv(y)
#ols_fit = np.dot(U, U.T)
ols_fit = _ols_fit_matrix(design_matrix)
for param, sig in zip(dti_params, data_flat):
param[0], param[1:] = _wls_iter(ols_fit, design_matrix, sig,
min_signal=min_signal)
dti_params.shape = data.shape[:-1]+(12,)
dti_params = wrap(dti_params)
return dti_params
def _wls_iter(ols_fit, design_matrix, sig, min_signal=1):
'''
Function used by wls_fit_tensor for later optimization.
'''
sig = np.maximum(sig, min_signal) #throw out zero signals
log_s = np.log(sig)
w = np.exp(np.dot(ols_fit, log_s))
D = np.dot(np.linalg.pinv(design_matrix*w[:,None]), w*log_s)
tensor = from_lower_triangular(D)
return decompose_tensor(tensor)
def _ols_iter(inv_design, sig, min_signal=1):
'''
Function used by ols_fit_tensor for later optimization.
'''
sig = np.maximum(sig, min_signal) #throw out zero signals
log_s = np.log(sig)
D = np.dot(inv_design, log_s)
tensor = from_lower_triangular(D)
return decompose_tensor(tensor)
def ols_fit_tensor(design_matrix, data, min_signal=1):
r"""
Computes ordinary least squares (OLS) fit to calculate self-diffusion
tensor using a linear regression model [1]_.
Parameters
----------
design_matrix : array (g, 7)
Design matrix holding the covariants used to solve for the regression
coefficients. Use design_matrix to build a valid design matrix from
bvalues and a gradient table.
data : array ([X, Y, Z, ...], g)
Data or response variables holding the data. Note that the last
dimension should contain the data. It makes no copies of data.
min_signal : default = 1
All values below min_signal are repalced with min_signal. This is done
in order to avaid taking log(0) durring the tensor fitting.
Returns
-------
eigvals : array (..., 3)
Eigenvalues from eigen decomposition of the tensor.
eigvecs : array (..., 3, 3)
Associated eigenvectors from eigen decomposition of the tensor.
Eigenvectors are columnar (e.g. eigvecs[:,j] is associated with
eigvals[j])
See Also
--------
WLS_fit_tensor, decompose_tensor, design_matrix
Notes
-----
This function is offered mainly as a quick comparison to WLS.
.. math::
y = \mathrm{data} \\
X = \mathrm{design matrix} \\
\hat{\beta}_\mathrm{OLS} = (X^T X)^{-1} X^T y
References
----------
.. [1] <NAME>., <NAME>., <NAME>., 2006. Comparison of bootstrap
approaches for estimation of uncertainties of DTI parameters.
NeuroImage 33, 531-541.
"""
data, wrap = _makearray(data)
data_flat = data.reshape((-1, data.shape[-1]))
evals = np.empty((len(data_flat), 3))
evecs = np.empty((len(data_flat), 3, 3))
dti_params = np.empty((len(data_flat), 4, 3))
#obtain OLS fitting matrix
#U,S,V = np.linalg.svd(design_matrix, False)
#math: beta_ols = inv(X.T*X)*X.T*y
#math: ols_fit = X*beta_ols*inv(y)
#ols_fit = np.dot(U, U.T)
inv_design = np.linalg.pinv(design_matrix)
for param, sig in zip(dti_params, data_flat):
param[0], param[1:] = _ols_iter(inv_design, sig, min_signal)
dti_params.shape = data.shape[:-1]+(12,)
dti_params = wrap(dti_params)
return dti_params
def _ols_fit_matrix(design_matrix):
"""
Helper function to calculate the ordinary least squares (OLS)
fit as a matrix multiplication. Mainly used to calculate WLS weights. Can
be used to calculate regression coefficients in OLS but not recommended.
See Also:
---------
wls_fit_tensor, ols_fit_tensor
Example:
--------
ols_fit = _ols_fit_matrix(design_mat)
ols_data = np.dot(ols_fit, data)
"""
U,S,V = np.linalg.svd(design_matrix, False)
return np.dot(U, U.T)
_lt_indices = np.array([[0, 1, 3],
[1, 2, 4],
[3, 4, 5]])
def from_lower_triangular(D):
"""
Returns a tensor given the six unique tensor elements
Given the six unique tensor elments (in the order: Dxx, Dxy, Dyy, Dxz, Dyz,
Dzz) returns a 3 by 3 tensor. All elements after the sixth are ignored.
Parameters:
-----------
D : array_like, (..., >6)
Unique elements of the tensors
Returns:
--------
tensor : ndarray (..., 3, 3)
3 by 3 tensors
"""
return D[..., _lt_indices]
_lt_rows = np.array([0, 1, 1, 2, 2, 2])
_lt_cols = np.array([0, 0, 1, 0, 1, 2])
def lower_triangular(tensor, b0=None):
"""
Returns the six lower triangular values of the tensor and a dummy variable
if b0 is not None
Parameters:
----------
tensor - array_like (..., 3, 3)
a collection of 3, 3 diffusion tensors
b0 - float
if b0 is not none log(b0) is returned as the dummy variable
Returns:
-------
D - ndarray
If b0 is none, then the shape will be (..., 6) otherwise (..., 7)
"""
if tensor.shape[-2:] != (3, 3):
raise ValueError("Diffusion tensors should be (..., 3, 3)")
if b0 is None:
return tensor[..., _lt_rows, _lt_cols]
else:
D = np.empty(tensor.shape[:-2] + (7,), dtype=tensor.dtype)
D[..., 6] = -np.log(b0)
D[..., :6] = tensor[..., _lt_rows, _lt_cols]
return D
def tensor_eig_from_lo_tri(B, data):
"""Calculates parameters for creating a Tensor instance
Calculates tensor parameters from the six unique tensor elements. This
function can be passed to the Tensor class as a fit_method for creating a
Tensor instance from tensors stored in a nifti file.
Parameters:
-----------
B :
not currently used
data : array_like (..., 6)
diffusion tensors elements stored in lower triangular order
Returns
-------
dti_params
Eigen values and vectors, used by the Tensor class to create an
instance
"""
data, wrap = _makearray(data)
data_flat = data.reshape((-1, data.shape[-1]))
dti_params = np.empty((len(data_flat), 4, 3))
for ii in xrange(len(data_flat)):
tensor = from_lower_triangular(data_flat[ii])
eigvals, eigvecs = decompose_tensor(tensor)
dti_params[ii, 0] = eigvals
dti_params[ii, 1:] = eigvecs
dti_params.shape = data.shape[:-1]+(12,)
dti_params = wrap(dti_params)
return dti_params
def decompose_tensor(tensor):
"""
Returns eigenvalues and eigenvectors given a diffusion tensor
Computes tensor eigen decomposition to calculate eigenvalues and
eigenvectors of self-diffusion tensor. (Basser et al., 1994a)
Parameters
----------
D : array (3,3)
array holding a tensor. Assumes D has units on order of
~ 10^-4 mm^2/s
Returns
-------
eigvals : array (3,)
Eigenvalues from eigen decomposition of the tensor. Negative
eigenvalues are replaced by zero. Sorted from largest to smallest.
eigvecs : array (3,3)
Associated eigenvectors from eigen decomposition of the tensor.
Eigenvectors are columnar (e.g. eigvecs[:,j] is associated with
eigvals[j])
See Also
--------
numpy.linalg.eig
"""
#outputs multiplicity as well so need to unique
eigenvals, eigenvecs = np.linalg.eig(tensor)
#need to sort the eigenvalues and associated eigenvectors
order = eigenvals.argsort()[::-1]
eigenvecs = eigenvecs[:, order]
eigenvals = eigenvals[order]
#Forcing negative eigenvalues to 0
eigenvals = np.maximum(eigenvals, 0)
# b ~ 10^3 s/mm^2 and D ~ 10^-4 mm^2/s
# eigenvecs: each vector is columnar
return eigenvals, eigenvecs
def design_matrix(gtab, bval, dtype=None):
"""
Constructs design matrix for DTI weighted least squares or least squares
fitting. (Basser et al., 1994a)
Parameters
----------
gtab : array with shape (3,g)
Diffusion gradient table found in DICOM header as a numpy array.
bval : array with shape (g,)
Diffusion weighting factor b for each vector in gtab.
dtype : string
Parameter to control the dtype of returned designed matrix
Returns
-------
design_matrix : array (g,7)
Design matrix or B matrix assuming Gaussian distributed tensor model.
Note: design_matrix[j,:] = (Bxx,Byy,Bzz,Bxy,Bxz,Byz,dummy)
"""
G = gtab
B = np.zeros((bval.size, 7), dtype = G.dtype)
if gtab.shape[1] != bval.shape[0]:
raise ValueError('The number of b values and gradient directions must'
+' be the same')
B[:, 0] = G[0, :] * G[0, :] * 1. * bval #Bxx
B[:, 1] = G[0, :] * G[1, :] * 2. * bval #Bxy
B[:, 2] = G[1, :] * G[1, :] * 1. * bval #Byy
B[:, 3] = G[0, :] * G[2, :] * 2. * bval #Bxz
B[:, 4] = G[1, :] * G[2, :] * 2. * bval #Byz
B[:, 5] = G[2, :] * G[2, :] * 1. * bval #Bzz
B[:, 6] = np.ones(bval.size)
return -B
def quantize_evecs(evecs, odf_vertices=None):
''' Find the closest orientation of an evenly distributed sphere
Parameters
----------
evecs : ndarray
odf_vertices : None or ndarray
If None, then set vertices from symmetric362 sphere. Otherwise use
passed ndarray as vertices
Returns
-------
IN : ndarray
'''
max_evecs=evecs[...,:,0]
if odf_vertices==None:
odf_vertices, _ = get_sphere('symmetric362')
tup=max_evecs.shape[:-1]
mec=max_evecs.reshape(np.prod(np.array(tup)),3)
IN=np.array([np.argmin(np.dot(odf_vertices,m)) for m in mec])
IN=IN.reshape(tup)
return IN
class TensorStepper(object):
"""Used for tracking diffusion tensors, has a next_step method"""
def _get_angel_limit(self):
return np.arccos(self.dot_limit)*180/pi
def _set_angel_limit(self, angle):
if angle >= 0 and angle <= 90:
self.dot_limit = cos(angle*pi/180)
else:
raise ValueError("angle should be between 0 and 180")
angel_limit = property(_get_angel_limit, _set_angel_limit)
def _get_fa_limit(self):
return self._fa_limit
def _set_fa_limit(self, arg):
self._fa_limit = arg
mask = self.fa_vol > arg
self._interp_inst = _interpolator(self.evec1_vol, self.voxe_size, mask)
fa_limit = property(_get_fa_limit, _set_fa_limit)
def __init__(self, fa_vol, evec1_vol, voxel_size, interpolator,
fa_limit=None, angle_limit=None):
self.voxel_size = voxel_size
self.angle_limit = angle_limit
if fa_vol.shape != evec1_vol.shape[:-1]:
msg = "the fa and eigen vector volumes are not the same shape"
raise ValueError(msg)
if evec1_vol.shape[-1] != 3:
msg = "eigen vector volume should have vecetors of length 3 " + \
"along the last dimmension"
raise ValueError(msg)
self.evec1_vol = evec1_vol
self.fa_vol = fa_vol
self._interpolator = interpolator
#self._interp_inst is created when fa_limit is set
self.fa_limit = fa_limit
def next_step(location, prev_step):
"""Returns the nearest neighbor tensor for location"""
step = self._interp_inst[location]
angle_dot = dot(step, prev_step)
if np.abs(angle_dot) < self.dot_limit:
raise StopIteration
if angle_dot > 0:
return step
else:
return -step
def stepper_from_tensor(tensor, *args, **kargs):
"""stepper_from_tensor(tensor, fa_vol, evec1_vol, voxel_size, interpolator)
"""
fa_vol = tensor.fa()
evec1_vol = tensor.evec[..., 0]
stepper = TensorStepper(fa_vol, evec1_vol, *args, **kargs)
return stepper
common_fit_methods = {'WLS': wls_fit_tensor,
'LS': ols_fit_tensor,
'from lower triangular': tensor_eig_from_lo_tri,
} | en | 0.684925 | #!/usr/bin/python Classes and functions for fitting tensors # 5/17/2010 Fits a diffusion tensor given diffusion-weighted signals and gradient info Tensor object that when initialized calculates single self diffusion tensor [1]_ in each voxel using selected fitting algorithm (DEFAULT: weighted least squares [2]_) Requires a given gradient table, b value for each diffusion-weighted gradient vector, and image data given all as arrays. Parameters ---------- data : array ([X, Y, Z, ...], g) Diffusion-weighted signals. The dimension corresponding to the diffusion weighting must be the last dimenssion bval : array (g,) Diffusion weighting factor b for each vector in gtab. gtab : array (g, 3) Diffusion gradient table found in DICOM header as a array. mask : array, optional The tensor will only be fit where mask is True. Mask must must broadcast to the shape of data and must have fewer dimensions than data thresh : float, default = None The tensor will not be fit where data[bval == 0] < thresh. If multiple b0 volumes are given, the minimum b0 signal is used. fit_method : funciton or string, default = 'WLS' The method to be used to fit the given data to a tensor. Any function that takes the B matrix and the data and returns eigen values and eigen vectors can be passed as the fit method. Any of the common fit methods can be passed as a string. *args, **kargs : Any other arguments or keywards will be passed to fit_method. common fit methods: 'WLS' : weighted least squares dti.wls_fit_tensor 'LS' : ordinary least squares dti.ols_fit_tensor Attributes ---------- D : array (..., 3, 3) Self diffusion tensor calculated from cached eigenvalues and eigenvectors. mask : array True in voxels where a tensor was fit, false if the voxel was skipped B : array (g, 7) Design matrix or B matrix constructed from given gradient table and b-value vector. evals : array (..., 3) Cached eigenvalues of self diffusion tensor for given index. (eval1, eval2, eval3) evecs : array (..., 3, 3) Cached associated eigenvectors of self diffusion tensor for given index. Note: evals[..., j] is associated with evecs[..., :, j] Methods ------- fa : array Calculates fractional anisotropy [2]_. md : array Calculates the mean diffusivity [2]_. Note: [units ADC] ~ [units b value]*10**-1 See Also -------- dipy.io.bvectxt.read_bvec_file, dipy.core.qball.ODF References ---------- .. [1] <NAME>., <NAME>., <NAME>., 1994. Estimation of the effective self-diffusion tensor from the NMR spin echo. J Magn Reson B 103, 247-254. .. [2] <NAME>., <NAME>., 1996. Microstructural and physiological features of tissues elucidated by quantitative diffusion-tensor MRI. Journal of Magnetic Resonance 111, 209-219. Examples ---------- For a complete example have a look at the main dipy/examples folder ### Eigenvalues Property ### Returns the eigenvalues of the tensor as an array ### Eigenvectors Property ### Returns the eigenvectors of teh tensor as an array Fits a tensors to diffusion weighted data. #64 bit design matrix makes for faster pinv #Define total mask from thresh and mask #mask = mask & (np.min(data[..., b_values == 0], -1) > #thresh) #the assumption that the lowest b_value is always 0 is #incorrect the lowest b_value could also be higher than 0 #this is common with grid q-spaces #if mask is all False #and the mask is not all True #leave only data[mask is True] #Perform WLS fit on masked data ### Self Diffusion Tensor Property ### Calculates the 3x3 diffusion tensor for each voxel Fractional anisotropy (FA) calculated from cached eigenvalues. Parameters ---------- fill_value : float value of fa where self.mask == True. nonans : Bool When True, fa is 0 when all eigenvalues are 0, otherwise fa is nan Returns --------- fa : array (V, 1) Calculated FA. Note: range is 0 <= FA <= 1. Notes -------- FA is calculated with the following equation: .. math:: FA = \sqrt{\frac{1}{2}\frac{(\lambda_1-\lambda_2)^2+(\lambda_1- \lambda_3)^2+(\lambda_2-lambda_3)^2}{\lambda_1^2+ \lambda_2^2+\lambda_3^2} } Mean diffusitivity (MD) calculated from cached eigenvalues. Returns --------- md : array (V, 1) Calculated MD. Notes -------- MD is calculated with the following equation: .. math:: ADC = \frac{\lambda_1+\lambda_2+\lambda_3}{3} #adc/md = (ev1+ev2+ev3)/3 Quantizes eigenvectors with maximum eigenvalues on an evenly distributed sphere so that the can be used for tractography. Returns --------- IN : array, shape(x,y,z) integer indices for the points of the evenly distributed sphere representing tensor eigenvectors of maximum eigenvalue Computes weighted least squares (WLS) fit to calculate self-diffusion tensor using a linear regression model [1]_. Parameters ---------- design_matrix : array (g, 7) Design matrix holding the covariants used to solve for the regression coefficients. data : array ([X, Y, Z, ...], g) Data or response variables holding the data. Note that the last dimension should contain the data. It makes no copies of data. min_signal : default = 1 All values below min_signal are repalced with min_signal. This is done in order to avaid taking log(0) durring the tensor fitting. Returns ------- eigvals : array (..., 3) Eigenvalues from eigen decomposition of the tensor. eigvecs : array (..., 3, 3) Associated eigenvectors from eigen decomposition of the tensor. Eigenvectors are columnar (e.g. eigvecs[:,j] is associated with eigvals[j]) See Also -------- decompose_tensor Notes ----- In Chung, et al. 2006, the regression of the WLS fit needed an unbiased preliminary estimate of the weights and therefore the ordinary least squares (OLS) estimates were used. A "two pass" method was implemented: 1. calculate OLS estimates of the data 2. apply the OLS estimates as weights to the WLS fit of the data This ensured heteroscadasticity could be properly modeled for various types of bootstrap resampling (namely residual bootstrap). .. math:: y = \mathrm{data} \\ X = \mathrm{design matrix} \\ \hat{\beta}_\mathrm{WLS} = \mathrm{desired regression coefficients (e.g. tensor)}\\ \\ \hat{\beta}_\mathrm{WLS} = (X^T W X)^{-1} X^T W y \\ \\ W = \mathrm{diag}((X \hat{\beta}_\mathrm{OLS})^2), \mathrm{where} \hat{\beta}_\mathrm{OLS} = (X^T X)^{-1} X^T y References ---------- .. _[1] <NAME>., <NAME>., <NAME>., 2006. Comparison of bootstrap approaches for estimation of uncertainties of DTI parameters. NeuroImage 33, 531-541. #obtain OLS fitting matrix #U,S,V = np.linalg.svd(design_matrix, False) #math: beta_ols = inv(X.T*X)*X.T*y #math: ols_fit = X*beta_ols*inv(y) #ols_fit = np.dot(U, U.T) Function used by wls_fit_tensor for later optimization. #throw out zero signals Function used by ols_fit_tensor for later optimization. #throw out zero signals Computes ordinary least squares (OLS) fit to calculate self-diffusion tensor using a linear regression model [1]_. Parameters ---------- design_matrix : array (g, 7) Design matrix holding the covariants used to solve for the regression coefficients. Use design_matrix to build a valid design matrix from bvalues and a gradient table. data : array ([X, Y, Z, ...], g) Data or response variables holding the data. Note that the last dimension should contain the data. It makes no copies of data. min_signal : default = 1 All values below min_signal are repalced with min_signal. This is done in order to avaid taking log(0) durring the tensor fitting. Returns ------- eigvals : array (..., 3) Eigenvalues from eigen decomposition of the tensor. eigvecs : array (..., 3, 3) Associated eigenvectors from eigen decomposition of the tensor. Eigenvectors are columnar (e.g. eigvecs[:,j] is associated with eigvals[j]) See Also -------- WLS_fit_tensor, decompose_tensor, design_matrix Notes ----- This function is offered mainly as a quick comparison to WLS. .. math:: y = \mathrm{data} \\ X = \mathrm{design matrix} \\ \hat{\beta}_\mathrm{OLS} = (X^T X)^{-1} X^T y References ---------- .. [1] <NAME>., <NAME>., <NAME>., 2006. Comparison of bootstrap approaches for estimation of uncertainties of DTI parameters. NeuroImage 33, 531-541. #obtain OLS fitting matrix #U,S,V = np.linalg.svd(design_matrix, False) #math: beta_ols = inv(X.T*X)*X.T*y #math: ols_fit = X*beta_ols*inv(y) #ols_fit = np.dot(U, U.T) Helper function to calculate the ordinary least squares (OLS) fit as a matrix multiplication. Mainly used to calculate WLS weights. Can be used to calculate regression coefficients in OLS but not recommended. See Also: --------- wls_fit_tensor, ols_fit_tensor Example: -------- ols_fit = _ols_fit_matrix(design_mat) ols_data = np.dot(ols_fit, data) Returns a tensor given the six unique tensor elements Given the six unique tensor elments (in the order: Dxx, Dxy, Dyy, Dxz, Dyz, Dzz) returns a 3 by 3 tensor. All elements after the sixth are ignored. Parameters: ----------- D : array_like, (..., >6) Unique elements of the tensors Returns: -------- tensor : ndarray (..., 3, 3) 3 by 3 tensors Returns the six lower triangular values of the tensor and a dummy variable if b0 is not None Parameters: ---------- tensor - array_like (..., 3, 3) a collection of 3, 3 diffusion tensors b0 - float if b0 is not none log(b0) is returned as the dummy variable Returns: ------- D - ndarray If b0 is none, then the shape will be (..., 6) otherwise (..., 7) Calculates parameters for creating a Tensor instance Calculates tensor parameters from the six unique tensor elements. This function can be passed to the Tensor class as a fit_method for creating a Tensor instance from tensors stored in a nifti file. Parameters: ----------- B : not currently used data : array_like (..., 6) diffusion tensors elements stored in lower triangular order Returns ------- dti_params Eigen values and vectors, used by the Tensor class to create an instance Returns eigenvalues and eigenvectors given a diffusion tensor Computes tensor eigen decomposition to calculate eigenvalues and eigenvectors of self-diffusion tensor. (Basser et al., 1994a) Parameters ---------- D : array (3,3) array holding a tensor. Assumes D has units on order of ~ 10^-4 mm^2/s Returns ------- eigvals : array (3,) Eigenvalues from eigen decomposition of the tensor. Negative eigenvalues are replaced by zero. Sorted from largest to smallest. eigvecs : array (3,3) Associated eigenvectors from eigen decomposition of the tensor. Eigenvectors are columnar (e.g. eigvecs[:,j] is associated with eigvals[j]) See Also -------- numpy.linalg.eig #outputs multiplicity as well so need to unique #need to sort the eigenvalues and associated eigenvectors #Forcing negative eigenvalues to 0 # b ~ 10^3 s/mm^2 and D ~ 10^-4 mm^2/s # eigenvecs: each vector is columnar Constructs design matrix for DTI weighted least squares or least squares fitting. (Basser et al., 1994a) Parameters ---------- gtab : array with shape (3,g) Diffusion gradient table found in DICOM header as a numpy array. bval : array with shape (g,) Diffusion weighting factor b for each vector in gtab. dtype : string Parameter to control the dtype of returned designed matrix Returns ------- design_matrix : array (g,7) Design matrix or B matrix assuming Gaussian distributed tensor model. Note: design_matrix[j,:] = (Bxx,Byy,Bzz,Bxy,Bxz,Byz,dummy) #Bxx #Bxy #Byy #Bxz #Byz #Bzz Find the closest orientation of an evenly distributed sphere Parameters ---------- evecs : ndarray odf_vertices : None or ndarray If None, then set vertices from symmetric362 sphere. Otherwise use passed ndarray as vertices Returns ------- IN : ndarray Used for tracking diffusion tensors, has a next_step method #self._interp_inst is created when fa_limit is set Returns the nearest neighbor tensor for location stepper_from_tensor(tensor, fa_vol, evec1_vol, voxel_size, interpolator) | 3.000198 | 3 |
statistics/hyphotesis/testing.py | Fernakamuta/machine | 0 | 6624306 | <reponame>Fernakamuta/machine<filename>statistics/hyphotesis/testing.py
import scipy.stats as st
# Get z-score from p-value (To the left)
print(st.norm.ppf(0.09012267246445244))
# Get p-Value from normal a Z-score (AREA TO THE LEFT)
print(st.norm.cdf(-1.34))
| import scipy.stats as st
# Get z-score from p-value (To the left)
print(st.norm.ppf(0.09012267246445244))
# Get p-Value from normal a Z-score (AREA TO THE LEFT)
print(st.norm.cdf(-1.34)) | en | 0.819507 | # Get z-score from p-value (To the left) # Get p-Value from normal a Z-score (AREA TO THE LEFT) | 2.543419 | 3 |
exercicio 061.py | rayanesousa31/Python-Curso-em-video-Mundo-2 | 0 | 6624307 | <reponame>rayanesousa31/Python-Curso-em-video-Mundo-2<filename>exercicio 061.py
#Refaça o DESAFIO 051,
# lendo o primeiro termo e a razão de uma PA,
# mostrado os 10 primeiros termos da progressão
# usando a estrutura while.
n = int(input('Digite um numero: '))
razao = int(input('Digite uma razao: '))
termo = n
cont = 1
while cont <= 10:
print(' {}'.format(termo),end=' ')
termo += razao
cont+= 1
print('Fim') | 061.py
#Refaça o DESAFIO 051,
# lendo o primeiro termo e a razão de uma PA,
# mostrado os 10 primeiros termos da progressão
# usando a estrutura while.
n = int(input('Digite um numero: '))
razao = int(input('Digite uma razao: '))
termo = n
cont = 1
while cont <= 10:
print(' {}'.format(termo),end=' ')
termo += razao
cont+= 1
print('Fim') | pt | 0.990197 | #Refaça o DESAFIO 051, # lendo o primeiro termo e a razão de uma PA, # mostrado os 10 primeiros termos da progressão # usando a estrutura while. | 3.843789 | 4 |
src/rocommand/test/TestROSRS_Session.py | A-Mazurek/ro-manager | 11 | 6624308 | #!/usr/bin/env python
"""
Module to test RO SRS APIfunctions
"""
__author__ = "<NAME> (<EMAIL>)"
__copyright__ = "Copyright 2011-2013, University of Oxford"
__license__ = "MIT (http://opensource.org/licenses/MIT)"
import os, os.path
import sys
import unittest
import logging
import json
import re
import StringIO
import httplib
import urlparse
import rdflib, rdflib.graph
if __name__ == "__main__":
# Add main project directory and ro manager directories at start of python path
sys.path.insert(0, "../..")
sys.path.insert(0, "..")
from MiscUtils import TestUtils
from MiscUtils.HttpSession import testSplitValues, testParseLinks
from ro_namespaces import RDF, RDFS, ORE, RO, DCTERMS, AO
from ROSRS_Session import ROSRS_Error, ROSRS_Session
from TestConfig import ro_test_config
# Logging object
log = logging.getLogger(__name__)
# Base directory for file access tests in this module
testbase = os.path.dirname(__file__)
# Test config details
class Config:
ROSRS_API_URI = ro_test_config.ROSRS_URI # "http://sandbox.wf4ever-project.org/rodl/ROs/"
AUTHORIZATION = ro_test_config.ROSRS_ACCESS_TOKEN
TEST_RO_NAME = "TestSessionRO"
TEST_RO_PATH = TEST_RO_NAME+"/"
TEST_RO_URI = ROSRS_API_URI+TEST_RO_PATH
# Test cases
class TestROSRS_Session(unittest.TestCase):
"""
This test suite tests the ROSRS_Session client implementation of the ROSRS API
"""
def setUp(self):
super(TestROSRS_Session, self).setUp()
self.rosrs = ROSRS_Session(Config.ROSRS_API_URI,
accesskey=Config.AUTHORIZATION)
# Clean up from previous runs
self.rosrs.deleteRO(Config.TEST_RO_PATH, purge=True)
self.createdTestRO = None
return
def tearDown(self):
super(TestROSRS_Session, self).tearDown()
# Clean up
self.rosrs.deleteRO(Config.TEST_RO_PATH)
if self.createdTestRO:
self.rosrs.deleteRO(self.createdTestRO, purge=True)
self.rosrs.close()
return
def createTestRO(self):
(status, reason, rouri, manifest) = self.rosrs.createRO(Config.TEST_RO_NAME,
"Test RO for ROSRS_Session", "TestROSRS_Session.py", "2012-09-06")
self.assertEqual(status, 201)
self.createdTestRO = rouri
return (status, reason, rouri, manifest)
# Actual tests follow
def testHelpers(self):
testSplitValues()
testParseLinks()
return
def testListROs(self):
ros = self.rosrs.listROs()
return
def testCreateRO(self):
(status, reason, rouri, manifest) = self.createTestRO()
self.assertEqual(status, 201)
self.assertEqual(reason, "Created")
self.assertEqual(str(rouri)[:len(Config.TEST_RO_URI)-1]+"/", Config.TEST_RO_URI)
self.assertIn((rouri, RDF.type, RO.ResearchObject), manifest)
rolist = self.rosrs.listROs()
self.assertIn(str(rouri), [ r["uri"] for r in rolist ])
return
def testDeleteRO(self):
(status, reason, rouri, manifest) = self.createTestRO()
self.assertEqual(status, 201)
# Test that new RO is in collection
rolist = self.rosrs.listROs()
self.assertIn(str(rouri), [ r["uri"] for r in rolist ])
# Delete RO
(status, reason) = self.rosrs.deleteRO(rouri)
self.assertEqual(status, 204)
self.assertEqual(reason, "No Content")
# Test that new RO is not in collection
rolist = self.rosrs.listROs()
self.assertNotIn(str(rouri), [ r["uri"] for r in rolist ])
# Delete again
(status, reason) = self.rosrs.deleteRO(rouri)
self.assertEqual(status, 404)
self.assertEqual(reason, "Not Found")
return
def testGetROManifest(self):
(status, reason, rouri, manifest) = self.createTestRO()
self.assertEqual(status, 201)
# Get manifest
(status, reason, headers, manifesturi, manifest) = self.rosrs.getROManifest(rouri)
self.assertEqual(status, 200)
self.assertEqual(reason, "OK")
self.assertEqual(headers["content-type"], "application/rdf+xml")
# Check manifest RDF graph
self.assertIn((rouri, RDF.type, RO.ResearchObject), manifest)
self.assertIn((rouri, DCTERMS.creator, None), manifest)
self.assertIn((rouri, DCTERMS.created, None), manifest)
self.assertIn((rouri, ORE.isDescribedBy, manifesturi), manifest)
return
def testGetROPage(self):
(status, reason, rouri, manifest) = self.createTestRO()
self.assertEqual(status, 201)
# Get landing page
(status, reason, headers, pageuri, page) = self.rosrs.getROLandingPage(rouri)
self.assertEqual(status, 200)
self.assertEqual(reason, "OK")
self.assertEqual(headers["content-type"], "text/html;charset=UTF-8")
return
def testGetROZip(self):
(status, reason, rouri, manifest) = self.createTestRO()
self.assertEqual(status, 201)
# Get manifest
(status, reason, headers, datauri, data) = self.rosrs.getROZip(rouri)
self.assertEqual(status, 200)
self.assertEqual(reason, "OK")
self.assertEqual(headers["content-type"], "application/zip")
# @@TODO test content of zip (data)?
return
def testAggregateResourceInt(self):
(status, reason, rouri, manifest) = self.createTestRO()
self.assertEqual(status, 201)
# Aggregate internal resource
rescontent = "Resource content\n"
(status, reason, proxyuri, resuri) = self.rosrs.aggregateResourceInt(
rouri, "test/path", ctype="text/plain", body=rescontent)
self.assertEqual(status, 201)
self.assertEqual(reason, "Created")
self.assertEqual(str(resuri), str(rouri)+"test/path")
# GET content
(status, reason, headers, uri, data) = self.rosrs.getROResource(
"test/path", rouri)
self.assertEqual(status, 200)
self.assertEqual(headers["content-type"], "text/plain")
self.assertEqual(data, rescontent)
# GET proxy
(getproxyuri, manifest) = self.rosrs.getROResourceProxy(
"test/path", rouri=rouri)
self.assertEqual(getproxyuri, proxyuri)
return
def testDeleteResourceInt(self):
(status, reason, rouri, manifest) = self.createTestRO()
self.assertEqual(status, 201)
# Create test resource
rescontent = "Resource content\n"
(status, reason, proxyuri, resuri) = self.rosrs.aggregateResourceInt(
rouri, "test/path", ctype="text/plain", body=rescontent)
self.assertEqual(status, 201)
# GET content
(status, reason, headers, uri, data) = self.rosrs.getROResource(
"test/path", rouri)
self.assertEqual(status, 200)
# Delete resource
(status, reason) = self.rosrs.removeResource(rouri, resuri)
self.assertEqual(status, 204)
self.assertEqual(reason, "No Content")
# Check that resource is no longer available
(status, reason, headers, uri, data) = self.rosrs.getROResource(resuri)
self.assertEqual(status, 404)
return
def testAggregateResourceExt(self):
(status, reason, rouri, manifest) = self.createTestRO()
self.assertEqual(status, 201)
# Aggregate external resource
externaluri = rdflib.URIRef("http://example.com/external/resource.txt")
(status, reason, proxyuri, resuri) = self.rosrs.aggregateResourceExt(
rouri, externaluri)
self.assertEqual(status, 201)
self.assertEqual(reason, "Created")
self.assertEqual(resuri, externaluri)
# GET proxy (note: using rdflib.URIRef value for path)
(getproxyuri, manifest) = self.rosrs.getROResourceProxy(
externaluri, rouri)
self.assertEqual(getproxyuri, proxyuri)
return
def testDeleteResourceExt(self):
(status, reason, rouri, manifest) = self.createTestRO()
self.assertEqual(status, 201)
# Create test resource
externaluri = rdflib.URIRef("http://example.com/external/resource.txt")
(status, reason, proxyuri, resuri) = self.rosrs.aggregateResourceExt(
rouri, externaluri)
self.assertEqual(status, 201)
self.assertEqual(resuri, externaluri)
# GET proxy (note: using rdflib.URIRef for path)
(getproxyuri, manifest) = self.rosrs.getROResourceProxy(
externaluri, rouri)
self.assertEqual(getproxyuri, proxyuri)
# Delete resource
(status, reason) = self.rosrs.removeResource(rouri, resuri)
self.assertEqual(status, 204)
self.assertEqual(reason, "No Content")
(getproxyuri, manifest) = self.rosrs.getROResourceProxy(
externaluri, rouri)
self.assertIsNone(getproxyuri)
self.assertIsNotNone(manifest)
return
def testGetROResource(self):
(status, reason, rouri, manifest) = self.createTestRO()
self.assertEqual(status, 201)
# Create test resource
rescontent = "Resource content\n"
(status, reason, proxyuri, resuri) = self.rosrs.aggregateResourceInt(
rouri, "test/path", ctype="text/plain", body=rescontent)
self.assertEqual(status, 201)
# GET content
(status, reason, headers, uri, data) = self.rosrs.getROResource(
"test/path", rouri)
self.assertEqual(status, 200)
self.assertEqual(reason, "OK")
self.assertEqual(headers["content-type"], "text/plain")
self.assertEqual(data, rescontent)
return
def testGetROResourceRDF(self):
(status, reason, rouri, manifest) = self.createTestRO()
self.assertEqual(status, 201)
# Create internal test resource
rescontent = """<?xml version="1.0" encoding="UTF-8"?>
<rdf:RDF
xmlns:dct="http://purl.org/dc/terms/"
xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
>
<rdf:Description rdf:about="http://example.org/file1.txt">
<dct:title>Title for file1.txt</dct:title>
</rdf:Description>
</rdf:RDF>
"""
(status, reason, proxyuri, resuri) = self.rosrs.aggregateResourceInt(
rouri, "test/file1.rdf", ctype="application/rdf+xml", body=rescontent)
self.assertEqual(status, 201)
# Get resource content
(status, reason, headers, uri, graph)= self.rosrs.getROResourceRDF(
"test/file1.rdf", rouri=rouri)
self.assertEqual(status, 200)
self.assertEqual(reason, "OK")
self.assertEqual(headers["content-type"], "application/rdf+xml")
s = rdflib.URIRef("http://example.org/file1.txt")
self.assertIn((s, DCTERMS.title, rdflib.Literal("Title for file1.txt")), graph)
return
def testGetROResourceProxy(self):
(status, reason, rouri, manifest) = self.createTestRO()
self.assertEqual(status, 201)
# Create internal test resource
rescontent = "Resource content\n"
(status, reason, proxyuri, resuri) = self.rosrs.aggregateResourceInt(
rouri, "test/path", ctype="text/plain", body=rescontent)
self.assertEqual(status, 201)
# Get resource proxy
(getproxyuri, manifest) = self.rosrs.getROResourceProxy(
"test/path", rouri=rouri)
self.assertEqual(getproxyuri, proxyuri)
return
def testCreateROAnnotationInt(self):
(status, reason, rouri, manifest) = self.createTestRO()
self.assertEqual(status, 201)
# Create internal test resource
rescontent = "Resource content\n"
(status, reason, proxyuri, resuri) = self.rosrs.aggregateResourceInt(
rouri, "test/file.txt", ctype="text/plain", body=rescontent)
self.assertEqual(status, 201)
# Create internal annotation
annbody = """<?xml version="1.0" encoding="UTF-8"?>
<rdf:RDF
xmlns:dct="http://purl.org/dc/terms/"
xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#"
xml:base="%s"
>
<rdf:Description rdf:about="test/file.txt">
<dct:title>Title for test/file.txt</dct:title>
<rdfs:seeAlso rdf:resource="http://example.org/test" />
</rdf:Description>
</rdf:RDF>
"""%(str(rouri))
agraph = rdflib.graph.Graph()
agraph.parse(data=annbody, format="xml")
(status, reason, annuri, bodyuri) = self.rosrs.createROAnnotationInt(
rouri, resuri, agraph)
self.assertEqual(status, 201)
self.assertEqual(reason, "Created")
# Retrieve annotation URIs
auris = list(self.rosrs.getROAnnotationUris(rouri, resuri))
self.assertIn(annuri, auris)
buris = list(self.rosrs.getROAnnotationBodyUris(rouri, resuri))
### self.assertIn(bodyuri, buris)
# Retrieve annotation
(status, reason, bodyuri, anngr) = self.rosrs.getROAnnotation(annuri)
self.assertEqual(status, 200)
self.assertEqual(reason, "OK")
self.assertIn((resuri, DCTERMS.title, rdflib.Literal("Title for test/file.txt")), anngr)
self.assertIn((resuri, RDFS.seeAlso, rdflib.URIRef("http://example.org/test")), anngr)
return
def testGetROAnnotationGraph(self):
(status, reason, rouri, manifest) = self.createTestRO()
self.assertEqual(status, 201)
# Create internal test resource
rescontent = "Resource content\n"
(status, reason, proxyuri, resuri) = self.rosrs.aggregateResourceInt(
rouri, "test/file.txt", ctype="text/plain", body=rescontent)
self.assertEqual(status, 201)
# Create internal annotation
annbody = """<?xml version="1.0" encoding="UTF-8"?>
<rdf:RDF
xmlns:dct="http://purl.org/dc/terms/"
xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#"
xml:base="%s"
>
<rdf:Description rdf:about="test/file.txt">
<dct:title>Title for test/file.txt</dct:title>
<rdfs:seeAlso rdf:resource="http://example.org/test" />
</rdf:Description>
</rdf:RDF>
"""%(str(rouri))
agraph = rdflib.graph.Graph()
agraph.parse(data=annbody, format="xml")
(status, reason, annuri, bodyuri) = self.rosrs.createROAnnotationInt(
rouri, resuri, agraph)
self.assertEqual(status, 201)
self.assertEqual(reason, "Created")
# Retrieve merged annotations
anngr = self.rosrs.getROAnnotationGraph(rouri, resuri)
annts = list(anngr.triples((None, None, None)))
self.assertIn((resuri, DCTERMS.title, rdflib.Literal("Title for test/file.txt")), annts)
self.assertIn((resuri, RDFS.seeAlso, rdflib.URIRef("http://example.org/test")), annts)
return
def testCreateROAnnotationExt(self):
(status, reason, rouri, manifest) = self.createTestRO()
self.assertEqual(status, 201)
# Create external test resource
(status, reason, proxyuri, resuri) = self.rosrs.aggregateResourceExt(
rouri, rdflib.URIRef("http://example.org/ext"))
self.assertEqual(status, 201)
# Create annotation using external body reference
bodyuri = rdflib.URIRef("http://example.org/ext/ann.rdf")
(status, reason, annuri) = self.rosrs.createROAnnotationExt(rouri, resuri, bodyuri)
self.assertEqual(status, 201)
self.assertEqual(reason, "Created")
# Retrieve annotation URIs
auris = list(self.rosrs.getROAnnotationUris(rouri, resuri))
self.assertIn(annuri, auris)
buris = list(self.rosrs.getROAnnotationBodyUris(rouri, resuri))
### self.assertIn(bodyuri, buris)
return
def testUpdateROAnnotationInt(self):
(status, reason, rouri, manifest) = self.createTestRO()
self.assertEqual(status, 201)
# Create internal test resource
rescontent = "Resource content\n"
(status, reason, proxyuri, resuri) = self.rosrs.aggregateResourceInt(
rouri, "test/file.txt", ctype="text/plain", body=rescontent)
self.assertEqual(status, 201)
# Create internal annotation
annbody1 = """<?xml version="1.0" encoding="UTF-8"?>
<rdf:RDF
xmlns:dct="http://purl.org/dc/terms/"
xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#"
xml:base="%s"
>
<rdf:Description rdf:about="test/file.txt">
<dct:title>Title 1</dct:title>
<rdfs:seeAlso rdf:resource="http://example.org/test1" />
</rdf:Description>
</rdf:RDF>
"""%(str(rouri))
agraph1 = rdflib.graph.Graph()
agraph1.parse(data=annbody1, format="xml")
(status, reason, annuri, bodyuri1) = self.rosrs.createROAnnotationInt(
rouri, resuri, agraph1)
self.assertEqual(status, 201)
self.assertEqual(reason, "Created")
# Retrieve annotation URIs
auris1 = list(self.rosrs.getROAnnotationUris(rouri, resuri))
self.assertIn(annuri, auris1)
buris1 = list(self.rosrs.getROAnnotationBodyUris(rouri, resuri))
### self.assertIn(bodyuri1, buris1)
# Retrieve annotation
(status, reason, auri1, anngr1a) = self.rosrs.getROAnnotation(annuri)
self.assertEqual(status, 200)
self.assertEqual(reason, "OK")
annts1a = list(anngr1a.triples((None, None, None)))
self.assertIn((resuri, DCTERMS.title, rdflib.Literal("Title 1")), annts1a)
self.assertIn((resuri, RDFS.seeAlso, rdflib.URIRef("http://example.org/test1")), annts1a)
# Retrieve merged annotations
anngr1b = self.rosrs.getROAnnotationGraph(rouri, resuri)
annts1b = list(anngr1b.triples((None, None, None)))
self.assertIn((resuri, DCTERMS.title, rdflib.Literal("Title 1")), annts1b)
self.assertIn((resuri, RDFS.seeAlso, rdflib.URIRef("http://example.org/test1")), annts1b)
# Update internal annotation
annbody2 = """<?xml version="1.0" encoding="UTF-8"?>
<rdf:RDF
xmlns:dct="http://purl.org/dc/terms/"
xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#"
xml:base="%s"
>
<rdf:Description rdf:about="test/file.txt">
<dct:title>Title 2</dct:title>
<rdfs:seeAlso rdf:resource="http://example.org/test2" />
</rdf:Description>
</rdf:RDF>
"""%(str(rouri))
agraph2 = rdflib.graph.Graph()
agraph2.parse(data=annbody2, format="xml")
(status, reason, bodyuri2) = self.rosrs.updateROAnnotationInt(
rouri, annuri, resuri, agraph2)
self.assertEqual(status, 200)
self.assertEqual(reason, "OK")
# Retrieve annotation URIs
auris2 = list(self.rosrs.getROAnnotationUris(rouri, resuri))
self.assertIn(annuri, auris2)
buris2 = list(self.rosrs.getROAnnotationBodyUris(rouri, resuri))
### self.assertIn(bodyuri2, buris2)
# Retrieve annotation
(status, reason, auri2a, anngr2a) = self.rosrs.getROAnnotation(annuri)
annts2a = list(anngr2a.triples((None, None, None)))
self.assertEqual(status, 200)
self.assertEqual(reason, "OK")
self.assertNotIn((resuri, DCTERMS.title, rdflib.Literal("Title 1")), annts2a)
self.assertNotIn((resuri, RDFS.seeAlso, rdflib.URIRef("http://example.org/test1")), annts2a)
self.assertIn((resuri, DCTERMS.title, rdflib.Literal("Title 2")), annts2a)
self.assertIn((resuri, RDFS.seeAlso, rdflib.URIRef("http://example.org/test2")), annts2a)
# Retrieve merged annotations
anngr2b = self.rosrs.getROAnnotationGraph(rouri, resuri)
annts2b = list(anngr2b.triples((None, None, None)))
self.assertNotIn((resuri, DCTERMS.title, rdflib.Literal("Title 1")), annts2b)
self.assertNotIn((resuri, RDFS.seeAlso, rdflib.URIRef("http://example.org/test1")), annts2b)
self.assertIn((resuri, DCTERMS.title, rdflib.Literal("Title 2")), annts2b)
self.assertIn((resuri, RDFS.seeAlso, rdflib.URIRef("http://example.org/test2")), annts2b)
return
def testUpdateROAnnotationExt(self):
(status, reason, rouri, manifest) = self.createTestRO()
self.assertEqual(status, 201)
# Create external test resource
(status, reason, proxyuri, resuri) = self.rosrs.aggregateResourceExt(
rouri, rdflib.URIRef("http://example.org/ext"))
self.assertEqual(status, 201)
# Create annotation using external body reference
bodyuri1 = rdflib.URIRef("http://example.org/ext/ann1.rdf")
(status, reason, annuri) = self.rosrs.createROAnnotationExt(rouri, resuri, bodyuri1)
self.assertEqual(status, 201)
self.assertEqual(reason, "Created")
# Retrieve annotation URIs
auris1 = list(self.rosrs.getROAnnotationUris(rouri, resuri))
self.assertIn(annuri, auris1)
buris1 = list(self.rosrs.getROAnnotationBodyUris(rouri, resuri))
self.assertIn(bodyuri1, buris1)
# Update annotation using external body reference
# @@TODO - this doesn't check that old annotation is removed.
# @@TODO - currently, update is not fully implemented (2013-05).
bodyuri2 = rdflib.URIRef("http://example.org/ext/ann2.rdf")
(status, reason, annuri) = self.rosrs.createROAnnotationExt(rouri, resuri, bodyuri2)
self.assertEqual(status, 201)
self.assertEqual(reason, "Created")
# Retrieve annotation URIs
auris2 = list(self.rosrs.getROAnnotationUris(rouri, resuri))
self.assertIn(annuri, auris2)
buris2 = list(self.rosrs.getROAnnotationBodyUris(rouri, resuri))
self.assertIn(bodyuri1, buris2)
return
def testRemoveROAnnotation(self):
(status, reason, rouri, manifest) = self.createTestRO()
self.assertEqual(status, 201)
# Create internal test resource
rescontent = "Resource content\n"
(status, reason, proxyuri, resuri) = self.rosrs.aggregateResourceInt(
rouri, "test/file.txt", ctype="text/plain", body=rescontent)
self.assertEqual(status, 201)
# Create internal annotation
annbody = """<?xml version="1.0" encoding="UTF-8"?>
<rdf:RDF
xmlns:dct="http://purl.org/dc/terms/"
xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#"
xml:base="%s"
>
<rdf:Description rdf:about="test/file.txt">
<dct:title>Title for test/file.txt</dct:title>
<rdfs:seeAlso rdf:resource="http://example.org/test" />
</rdf:Description>
</rdf:RDF>
"""%(str(rouri))
agraph = rdflib.graph.Graph()
agraph.parse(data=annbody, format="xml")
(status, reason, annuri, bodyuri) = self.rosrs.createROAnnotationInt(
rouri, resuri, agraph)
self.assertEqual(status, 201)
self.assertEqual(reason, "Created")
# Retrieve annotation URIs
auris = list(self.rosrs.getROAnnotationUris(rouri, resuri))
self.assertIn(annuri, auris)
buris = list(self.rosrs.getROAnnotationBodyUris(rouri, resuri))
### self.assertIn(bodyuri, buris)
# Remove the annotation
(status, reason) = self.rosrs.removeROAnnotation(rouri, annuri)
self.assertEqual(status, 204)
self.assertEqual(reason, "No Content")
# Retrieve annotation URIs
auris = list(self.rosrs.getROAnnotationUris(rouri, resuri))
self.assertNotIn(annuri, auris)
buris = list(self.rosrs.getROAnnotationBodyUris(rouri, resuri))
### self.assertNotIn(bodyuri, buris)
return
# Evolution tests
def testCopyRO(self):
return
def testCancelCopyRO(self):
return
def testUpdateROStatus(self):
return
def testGetROEvolution(self):
return
# Sentinel/placeholder tests
def testUnits(self):
assert (True)
def testComponents(self):
assert (True)
def testIntegration(self):
assert (True)
def testPending(self):
assert (False), "Pending tests follow"
# Assemble test suite
def getTestSuite(select="unit"):
"""
Get test suite
select is one of the following:
"unit" return suite of unit tests only
"component" return suite of unit and component tests
"all" return suite of unit, component and integration tests
"pending" return suite of pending tests
name a single named test to be run
"""
testdict = {
"unit":
[ "testUnits"
, "testHelpers"
, "testListROs"
, "testCreateRO"
, "testDeleteRO"
, "testGetROManifest"
, "testGetROPage"
, "testGetROZip"
# Resource tests
, "testAggregateResourceInt"
, "testDeleteResourceInt"
, "testAggregateResourceExt"
, "testDeleteResourceExt"
, "testGetROResource"
, "testGetROResourceRDF"
, "testGetROResourceProxy"
# Annotation tests
, "testCreateROAnnotationInt"
, "testGetROAnnotationGraph"
, "testCreateROAnnotationExt"
, "testUpdateROAnnotationInt"
, "testUpdateROAnnotationExt"
, "testRemoveROAnnotation"
# Evolution tests
, "testCopyRO"
, "testCancelCopyRO"
, "testUpdateROStatus"
, "testGetROEvolution"
],
"component":
[ "testComponents"
],
"integration":
[ "testIntegration"
],
"pending":
[ "testPending"
]
}
return TestUtils.getTestSuite(TestROSRS_Session, testdict, select=select)
if __name__ == "__main__":
TestUtils.runTests("TestROSRS_Session.log", getTestSuite, sys.argv)
# End.
| #!/usr/bin/env python
"""
Module to test RO SRS APIfunctions
"""
__author__ = "<NAME> (<EMAIL>)"
__copyright__ = "Copyright 2011-2013, University of Oxford"
__license__ = "MIT (http://opensource.org/licenses/MIT)"
import os, os.path
import sys
import unittest
import logging
import json
import re
import StringIO
import httplib
import urlparse
import rdflib, rdflib.graph
if __name__ == "__main__":
# Add main project directory and ro manager directories at start of python path
sys.path.insert(0, "../..")
sys.path.insert(0, "..")
from MiscUtils import TestUtils
from MiscUtils.HttpSession import testSplitValues, testParseLinks
from ro_namespaces import RDF, RDFS, ORE, RO, DCTERMS, AO
from ROSRS_Session import ROSRS_Error, ROSRS_Session
from TestConfig import ro_test_config
# Logging object
log = logging.getLogger(__name__)
# Base directory for file access tests in this module
testbase = os.path.dirname(__file__)
# Test config details
class Config:
ROSRS_API_URI = ro_test_config.ROSRS_URI # "http://sandbox.wf4ever-project.org/rodl/ROs/"
AUTHORIZATION = ro_test_config.ROSRS_ACCESS_TOKEN
TEST_RO_NAME = "TestSessionRO"
TEST_RO_PATH = TEST_RO_NAME+"/"
TEST_RO_URI = ROSRS_API_URI+TEST_RO_PATH
# Test cases
class TestROSRS_Session(unittest.TestCase):
"""
This test suite tests the ROSRS_Session client implementation of the ROSRS API
"""
def setUp(self):
super(TestROSRS_Session, self).setUp()
self.rosrs = ROSRS_Session(Config.ROSRS_API_URI,
accesskey=Config.AUTHORIZATION)
# Clean up from previous runs
self.rosrs.deleteRO(Config.TEST_RO_PATH, purge=True)
self.createdTestRO = None
return
def tearDown(self):
super(TestROSRS_Session, self).tearDown()
# Clean up
self.rosrs.deleteRO(Config.TEST_RO_PATH)
if self.createdTestRO:
self.rosrs.deleteRO(self.createdTestRO, purge=True)
self.rosrs.close()
return
def createTestRO(self):
(status, reason, rouri, manifest) = self.rosrs.createRO(Config.TEST_RO_NAME,
"Test RO for ROSRS_Session", "TestROSRS_Session.py", "2012-09-06")
self.assertEqual(status, 201)
self.createdTestRO = rouri
return (status, reason, rouri, manifest)
# Actual tests follow
def testHelpers(self):
testSplitValues()
testParseLinks()
return
def testListROs(self):
ros = self.rosrs.listROs()
return
def testCreateRO(self):
(status, reason, rouri, manifest) = self.createTestRO()
self.assertEqual(status, 201)
self.assertEqual(reason, "Created")
self.assertEqual(str(rouri)[:len(Config.TEST_RO_URI)-1]+"/", Config.TEST_RO_URI)
self.assertIn((rouri, RDF.type, RO.ResearchObject), manifest)
rolist = self.rosrs.listROs()
self.assertIn(str(rouri), [ r["uri"] for r in rolist ])
return
def testDeleteRO(self):
(status, reason, rouri, manifest) = self.createTestRO()
self.assertEqual(status, 201)
# Test that new RO is in collection
rolist = self.rosrs.listROs()
self.assertIn(str(rouri), [ r["uri"] for r in rolist ])
# Delete RO
(status, reason) = self.rosrs.deleteRO(rouri)
self.assertEqual(status, 204)
self.assertEqual(reason, "No Content")
# Test that new RO is not in collection
rolist = self.rosrs.listROs()
self.assertNotIn(str(rouri), [ r["uri"] for r in rolist ])
# Delete again
(status, reason) = self.rosrs.deleteRO(rouri)
self.assertEqual(status, 404)
self.assertEqual(reason, "Not Found")
return
def testGetROManifest(self):
(status, reason, rouri, manifest) = self.createTestRO()
self.assertEqual(status, 201)
# Get manifest
(status, reason, headers, manifesturi, manifest) = self.rosrs.getROManifest(rouri)
self.assertEqual(status, 200)
self.assertEqual(reason, "OK")
self.assertEqual(headers["content-type"], "application/rdf+xml")
# Check manifest RDF graph
self.assertIn((rouri, RDF.type, RO.ResearchObject), manifest)
self.assertIn((rouri, DCTERMS.creator, None), manifest)
self.assertIn((rouri, DCTERMS.created, None), manifest)
self.assertIn((rouri, ORE.isDescribedBy, manifesturi), manifest)
return
def testGetROPage(self):
(status, reason, rouri, manifest) = self.createTestRO()
self.assertEqual(status, 201)
# Get landing page
(status, reason, headers, pageuri, page) = self.rosrs.getROLandingPage(rouri)
self.assertEqual(status, 200)
self.assertEqual(reason, "OK")
self.assertEqual(headers["content-type"], "text/html;charset=UTF-8")
return
def testGetROZip(self):
(status, reason, rouri, manifest) = self.createTestRO()
self.assertEqual(status, 201)
# Get manifest
(status, reason, headers, datauri, data) = self.rosrs.getROZip(rouri)
self.assertEqual(status, 200)
self.assertEqual(reason, "OK")
self.assertEqual(headers["content-type"], "application/zip")
# @@TODO test content of zip (data)?
return
def testAggregateResourceInt(self):
(status, reason, rouri, manifest) = self.createTestRO()
self.assertEqual(status, 201)
# Aggregate internal resource
rescontent = "Resource content\n"
(status, reason, proxyuri, resuri) = self.rosrs.aggregateResourceInt(
rouri, "test/path", ctype="text/plain", body=rescontent)
self.assertEqual(status, 201)
self.assertEqual(reason, "Created")
self.assertEqual(str(resuri), str(rouri)+"test/path")
# GET content
(status, reason, headers, uri, data) = self.rosrs.getROResource(
"test/path", rouri)
self.assertEqual(status, 200)
self.assertEqual(headers["content-type"], "text/plain")
self.assertEqual(data, rescontent)
# GET proxy
(getproxyuri, manifest) = self.rosrs.getROResourceProxy(
"test/path", rouri=rouri)
self.assertEqual(getproxyuri, proxyuri)
return
def testDeleteResourceInt(self):
(status, reason, rouri, manifest) = self.createTestRO()
self.assertEqual(status, 201)
# Create test resource
rescontent = "Resource content\n"
(status, reason, proxyuri, resuri) = self.rosrs.aggregateResourceInt(
rouri, "test/path", ctype="text/plain", body=rescontent)
self.assertEqual(status, 201)
# GET content
(status, reason, headers, uri, data) = self.rosrs.getROResource(
"test/path", rouri)
self.assertEqual(status, 200)
# Delete resource
(status, reason) = self.rosrs.removeResource(rouri, resuri)
self.assertEqual(status, 204)
self.assertEqual(reason, "No Content")
# Check that resource is no longer available
(status, reason, headers, uri, data) = self.rosrs.getROResource(resuri)
self.assertEqual(status, 404)
return
def testAggregateResourceExt(self):
(status, reason, rouri, manifest) = self.createTestRO()
self.assertEqual(status, 201)
# Aggregate external resource
externaluri = rdflib.URIRef("http://example.com/external/resource.txt")
(status, reason, proxyuri, resuri) = self.rosrs.aggregateResourceExt(
rouri, externaluri)
self.assertEqual(status, 201)
self.assertEqual(reason, "Created")
self.assertEqual(resuri, externaluri)
# GET proxy (note: using rdflib.URIRef value for path)
(getproxyuri, manifest) = self.rosrs.getROResourceProxy(
externaluri, rouri)
self.assertEqual(getproxyuri, proxyuri)
return
def testDeleteResourceExt(self):
(status, reason, rouri, manifest) = self.createTestRO()
self.assertEqual(status, 201)
# Create test resource
externaluri = rdflib.URIRef("http://example.com/external/resource.txt")
(status, reason, proxyuri, resuri) = self.rosrs.aggregateResourceExt(
rouri, externaluri)
self.assertEqual(status, 201)
self.assertEqual(resuri, externaluri)
# GET proxy (note: using rdflib.URIRef for path)
(getproxyuri, manifest) = self.rosrs.getROResourceProxy(
externaluri, rouri)
self.assertEqual(getproxyuri, proxyuri)
# Delete resource
(status, reason) = self.rosrs.removeResource(rouri, resuri)
self.assertEqual(status, 204)
self.assertEqual(reason, "No Content")
(getproxyuri, manifest) = self.rosrs.getROResourceProxy(
externaluri, rouri)
self.assertIsNone(getproxyuri)
self.assertIsNotNone(manifest)
return
def testGetROResource(self):
(status, reason, rouri, manifest) = self.createTestRO()
self.assertEqual(status, 201)
# Create test resource
rescontent = "Resource content\n"
(status, reason, proxyuri, resuri) = self.rosrs.aggregateResourceInt(
rouri, "test/path", ctype="text/plain", body=rescontent)
self.assertEqual(status, 201)
# GET content
(status, reason, headers, uri, data) = self.rosrs.getROResource(
"test/path", rouri)
self.assertEqual(status, 200)
self.assertEqual(reason, "OK")
self.assertEqual(headers["content-type"], "text/plain")
self.assertEqual(data, rescontent)
return
def testGetROResourceRDF(self):
(status, reason, rouri, manifest) = self.createTestRO()
self.assertEqual(status, 201)
# Create internal test resource
rescontent = """<?xml version="1.0" encoding="UTF-8"?>
<rdf:RDF
xmlns:dct="http://purl.org/dc/terms/"
xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
>
<rdf:Description rdf:about="http://example.org/file1.txt">
<dct:title>Title for file1.txt</dct:title>
</rdf:Description>
</rdf:RDF>
"""
(status, reason, proxyuri, resuri) = self.rosrs.aggregateResourceInt(
rouri, "test/file1.rdf", ctype="application/rdf+xml", body=rescontent)
self.assertEqual(status, 201)
# Get resource content
(status, reason, headers, uri, graph)= self.rosrs.getROResourceRDF(
"test/file1.rdf", rouri=rouri)
self.assertEqual(status, 200)
self.assertEqual(reason, "OK")
self.assertEqual(headers["content-type"], "application/rdf+xml")
s = rdflib.URIRef("http://example.org/file1.txt")
self.assertIn((s, DCTERMS.title, rdflib.Literal("Title for file1.txt")), graph)
return
def testGetROResourceProxy(self):
(status, reason, rouri, manifest) = self.createTestRO()
self.assertEqual(status, 201)
# Create internal test resource
rescontent = "Resource content\n"
(status, reason, proxyuri, resuri) = self.rosrs.aggregateResourceInt(
rouri, "test/path", ctype="text/plain", body=rescontent)
self.assertEqual(status, 201)
# Get resource proxy
(getproxyuri, manifest) = self.rosrs.getROResourceProxy(
"test/path", rouri=rouri)
self.assertEqual(getproxyuri, proxyuri)
return
def testCreateROAnnotationInt(self):
(status, reason, rouri, manifest) = self.createTestRO()
self.assertEqual(status, 201)
# Create internal test resource
rescontent = "Resource content\n"
(status, reason, proxyuri, resuri) = self.rosrs.aggregateResourceInt(
rouri, "test/file.txt", ctype="text/plain", body=rescontent)
self.assertEqual(status, 201)
# Create internal annotation
annbody = """<?xml version="1.0" encoding="UTF-8"?>
<rdf:RDF
xmlns:dct="http://purl.org/dc/terms/"
xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#"
xml:base="%s"
>
<rdf:Description rdf:about="test/file.txt">
<dct:title>Title for test/file.txt</dct:title>
<rdfs:seeAlso rdf:resource="http://example.org/test" />
</rdf:Description>
</rdf:RDF>
"""%(str(rouri))
agraph = rdflib.graph.Graph()
agraph.parse(data=annbody, format="xml")
(status, reason, annuri, bodyuri) = self.rosrs.createROAnnotationInt(
rouri, resuri, agraph)
self.assertEqual(status, 201)
self.assertEqual(reason, "Created")
# Retrieve annotation URIs
auris = list(self.rosrs.getROAnnotationUris(rouri, resuri))
self.assertIn(annuri, auris)
buris = list(self.rosrs.getROAnnotationBodyUris(rouri, resuri))
### self.assertIn(bodyuri, buris)
# Retrieve annotation
(status, reason, bodyuri, anngr) = self.rosrs.getROAnnotation(annuri)
self.assertEqual(status, 200)
self.assertEqual(reason, "OK")
self.assertIn((resuri, DCTERMS.title, rdflib.Literal("Title for test/file.txt")), anngr)
self.assertIn((resuri, RDFS.seeAlso, rdflib.URIRef("http://example.org/test")), anngr)
return
def testGetROAnnotationGraph(self):
(status, reason, rouri, manifest) = self.createTestRO()
self.assertEqual(status, 201)
# Create internal test resource
rescontent = "Resource content\n"
(status, reason, proxyuri, resuri) = self.rosrs.aggregateResourceInt(
rouri, "test/file.txt", ctype="text/plain", body=rescontent)
self.assertEqual(status, 201)
# Create internal annotation
annbody = """<?xml version="1.0" encoding="UTF-8"?>
<rdf:RDF
xmlns:dct="http://purl.org/dc/terms/"
xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#"
xml:base="%s"
>
<rdf:Description rdf:about="test/file.txt">
<dct:title>Title for test/file.txt</dct:title>
<rdfs:seeAlso rdf:resource="http://example.org/test" />
</rdf:Description>
</rdf:RDF>
"""%(str(rouri))
agraph = rdflib.graph.Graph()
agraph.parse(data=annbody, format="xml")
(status, reason, annuri, bodyuri) = self.rosrs.createROAnnotationInt(
rouri, resuri, agraph)
self.assertEqual(status, 201)
self.assertEqual(reason, "Created")
# Retrieve merged annotations
anngr = self.rosrs.getROAnnotationGraph(rouri, resuri)
annts = list(anngr.triples((None, None, None)))
self.assertIn((resuri, DCTERMS.title, rdflib.Literal("Title for test/file.txt")), annts)
self.assertIn((resuri, RDFS.seeAlso, rdflib.URIRef("http://example.org/test")), annts)
return
def testCreateROAnnotationExt(self):
(status, reason, rouri, manifest) = self.createTestRO()
self.assertEqual(status, 201)
# Create external test resource
(status, reason, proxyuri, resuri) = self.rosrs.aggregateResourceExt(
rouri, rdflib.URIRef("http://example.org/ext"))
self.assertEqual(status, 201)
# Create annotation using external body reference
bodyuri = rdflib.URIRef("http://example.org/ext/ann.rdf")
(status, reason, annuri) = self.rosrs.createROAnnotationExt(rouri, resuri, bodyuri)
self.assertEqual(status, 201)
self.assertEqual(reason, "Created")
# Retrieve annotation URIs
auris = list(self.rosrs.getROAnnotationUris(rouri, resuri))
self.assertIn(annuri, auris)
buris = list(self.rosrs.getROAnnotationBodyUris(rouri, resuri))
### self.assertIn(bodyuri, buris)
return
def testUpdateROAnnotationInt(self):
(status, reason, rouri, manifest) = self.createTestRO()
self.assertEqual(status, 201)
# Create internal test resource
rescontent = "Resource content\n"
(status, reason, proxyuri, resuri) = self.rosrs.aggregateResourceInt(
rouri, "test/file.txt", ctype="text/plain", body=rescontent)
self.assertEqual(status, 201)
# Create internal annotation
annbody1 = """<?xml version="1.0" encoding="UTF-8"?>
<rdf:RDF
xmlns:dct="http://purl.org/dc/terms/"
xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#"
xml:base="%s"
>
<rdf:Description rdf:about="test/file.txt">
<dct:title>Title 1</dct:title>
<rdfs:seeAlso rdf:resource="http://example.org/test1" />
</rdf:Description>
</rdf:RDF>
"""%(str(rouri))
agraph1 = rdflib.graph.Graph()
agraph1.parse(data=annbody1, format="xml")
(status, reason, annuri, bodyuri1) = self.rosrs.createROAnnotationInt(
rouri, resuri, agraph1)
self.assertEqual(status, 201)
self.assertEqual(reason, "Created")
# Retrieve annotation URIs
auris1 = list(self.rosrs.getROAnnotationUris(rouri, resuri))
self.assertIn(annuri, auris1)
buris1 = list(self.rosrs.getROAnnotationBodyUris(rouri, resuri))
### self.assertIn(bodyuri1, buris1)
# Retrieve annotation
(status, reason, auri1, anngr1a) = self.rosrs.getROAnnotation(annuri)
self.assertEqual(status, 200)
self.assertEqual(reason, "OK")
annts1a = list(anngr1a.triples((None, None, None)))
self.assertIn((resuri, DCTERMS.title, rdflib.Literal("Title 1")), annts1a)
self.assertIn((resuri, RDFS.seeAlso, rdflib.URIRef("http://example.org/test1")), annts1a)
# Retrieve merged annotations
anngr1b = self.rosrs.getROAnnotationGraph(rouri, resuri)
annts1b = list(anngr1b.triples((None, None, None)))
self.assertIn((resuri, DCTERMS.title, rdflib.Literal("Title 1")), annts1b)
self.assertIn((resuri, RDFS.seeAlso, rdflib.URIRef("http://example.org/test1")), annts1b)
# Update internal annotation
annbody2 = """<?xml version="1.0" encoding="UTF-8"?>
<rdf:RDF
xmlns:dct="http://purl.org/dc/terms/"
xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#"
xml:base="%s"
>
<rdf:Description rdf:about="test/file.txt">
<dct:title>Title 2</dct:title>
<rdfs:seeAlso rdf:resource="http://example.org/test2" />
</rdf:Description>
</rdf:RDF>
"""%(str(rouri))
agraph2 = rdflib.graph.Graph()
agraph2.parse(data=annbody2, format="xml")
(status, reason, bodyuri2) = self.rosrs.updateROAnnotationInt(
rouri, annuri, resuri, agraph2)
self.assertEqual(status, 200)
self.assertEqual(reason, "OK")
# Retrieve annotation URIs
auris2 = list(self.rosrs.getROAnnotationUris(rouri, resuri))
self.assertIn(annuri, auris2)
buris2 = list(self.rosrs.getROAnnotationBodyUris(rouri, resuri))
### self.assertIn(bodyuri2, buris2)
# Retrieve annotation
(status, reason, auri2a, anngr2a) = self.rosrs.getROAnnotation(annuri)
annts2a = list(anngr2a.triples((None, None, None)))
self.assertEqual(status, 200)
self.assertEqual(reason, "OK")
self.assertNotIn((resuri, DCTERMS.title, rdflib.Literal("Title 1")), annts2a)
self.assertNotIn((resuri, RDFS.seeAlso, rdflib.URIRef("http://example.org/test1")), annts2a)
self.assertIn((resuri, DCTERMS.title, rdflib.Literal("Title 2")), annts2a)
self.assertIn((resuri, RDFS.seeAlso, rdflib.URIRef("http://example.org/test2")), annts2a)
# Retrieve merged annotations
anngr2b = self.rosrs.getROAnnotationGraph(rouri, resuri)
annts2b = list(anngr2b.triples((None, None, None)))
self.assertNotIn((resuri, DCTERMS.title, rdflib.Literal("Title 1")), annts2b)
self.assertNotIn((resuri, RDFS.seeAlso, rdflib.URIRef("http://example.org/test1")), annts2b)
self.assertIn((resuri, DCTERMS.title, rdflib.Literal("Title 2")), annts2b)
self.assertIn((resuri, RDFS.seeAlso, rdflib.URIRef("http://example.org/test2")), annts2b)
return
def testUpdateROAnnotationExt(self):
(status, reason, rouri, manifest) = self.createTestRO()
self.assertEqual(status, 201)
# Create external test resource
(status, reason, proxyuri, resuri) = self.rosrs.aggregateResourceExt(
rouri, rdflib.URIRef("http://example.org/ext"))
self.assertEqual(status, 201)
# Create annotation using external body reference
bodyuri1 = rdflib.URIRef("http://example.org/ext/ann1.rdf")
(status, reason, annuri) = self.rosrs.createROAnnotationExt(rouri, resuri, bodyuri1)
self.assertEqual(status, 201)
self.assertEqual(reason, "Created")
# Retrieve annotation URIs
auris1 = list(self.rosrs.getROAnnotationUris(rouri, resuri))
self.assertIn(annuri, auris1)
buris1 = list(self.rosrs.getROAnnotationBodyUris(rouri, resuri))
self.assertIn(bodyuri1, buris1)
# Update annotation using external body reference
# @@TODO - this doesn't check that old annotation is removed.
# @@TODO - currently, update is not fully implemented (2013-05).
bodyuri2 = rdflib.URIRef("http://example.org/ext/ann2.rdf")
(status, reason, annuri) = self.rosrs.createROAnnotationExt(rouri, resuri, bodyuri2)
self.assertEqual(status, 201)
self.assertEqual(reason, "Created")
# Retrieve annotation URIs
auris2 = list(self.rosrs.getROAnnotationUris(rouri, resuri))
self.assertIn(annuri, auris2)
buris2 = list(self.rosrs.getROAnnotationBodyUris(rouri, resuri))
self.assertIn(bodyuri1, buris2)
return
def testRemoveROAnnotation(self):
(status, reason, rouri, manifest) = self.createTestRO()
self.assertEqual(status, 201)
# Create internal test resource
rescontent = "Resource content\n"
(status, reason, proxyuri, resuri) = self.rosrs.aggregateResourceInt(
rouri, "test/file.txt", ctype="text/plain", body=rescontent)
self.assertEqual(status, 201)
# Create internal annotation
annbody = """<?xml version="1.0" encoding="UTF-8"?>
<rdf:RDF
xmlns:dct="http://purl.org/dc/terms/"
xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#"
xml:base="%s"
>
<rdf:Description rdf:about="test/file.txt">
<dct:title>Title for test/file.txt</dct:title>
<rdfs:seeAlso rdf:resource="http://example.org/test" />
</rdf:Description>
</rdf:RDF>
"""%(str(rouri))
agraph = rdflib.graph.Graph()
agraph.parse(data=annbody, format="xml")
(status, reason, annuri, bodyuri) = self.rosrs.createROAnnotationInt(
rouri, resuri, agraph)
self.assertEqual(status, 201)
self.assertEqual(reason, "Created")
# Retrieve annotation URIs
auris = list(self.rosrs.getROAnnotationUris(rouri, resuri))
self.assertIn(annuri, auris)
buris = list(self.rosrs.getROAnnotationBodyUris(rouri, resuri))
### self.assertIn(bodyuri, buris)
# Remove the annotation
(status, reason) = self.rosrs.removeROAnnotation(rouri, annuri)
self.assertEqual(status, 204)
self.assertEqual(reason, "No Content")
# Retrieve annotation URIs
auris = list(self.rosrs.getROAnnotationUris(rouri, resuri))
self.assertNotIn(annuri, auris)
buris = list(self.rosrs.getROAnnotationBodyUris(rouri, resuri))
### self.assertNotIn(bodyuri, buris)
return
# Evolution tests
def testCopyRO(self):
return
def testCancelCopyRO(self):
return
def testUpdateROStatus(self):
return
def testGetROEvolution(self):
return
# Sentinel/placeholder tests
def testUnits(self):
assert (True)
def testComponents(self):
assert (True)
def testIntegration(self):
assert (True)
def testPending(self):
assert (False), "Pending tests follow"
# Assemble test suite
def getTestSuite(select="unit"):
"""
Get test suite
select is one of the following:
"unit" return suite of unit tests only
"component" return suite of unit and component tests
"all" return suite of unit, component and integration tests
"pending" return suite of pending tests
name a single named test to be run
"""
testdict = {
"unit":
[ "testUnits"
, "testHelpers"
, "testListROs"
, "testCreateRO"
, "testDeleteRO"
, "testGetROManifest"
, "testGetROPage"
, "testGetROZip"
# Resource tests
, "testAggregateResourceInt"
, "testDeleteResourceInt"
, "testAggregateResourceExt"
, "testDeleteResourceExt"
, "testGetROResource"
, "testGetROResourceRDF"
, "testGetROResourceProxy"
# Annotation tests
, "testCreateROAnnotationInt"
, "testGetROAnnotationGraph"
, "testCreateROAnnotationExt"
, "testUpdateROAnnotationInt"
, "testUpdateROAnnotationExt"
, "testRemoveROAnnotation"
# Evolution tests
, "testCopyRO"
, "testCancelCopyRO"
, "testUpdateROStatus"
, "testGetROEvolution"
],
"component":
[ "testComponents"
],
"integration":
[ "testIntegration"
],
"pending":
[ "testPending"
]
}
return TestUtils.getTestSuite(TestROSRS_Session, testdict, select=select)
if __name__ == "__main__":
TestUtils.runTests("TestROSRS_Session.log", getTestSuite, sys.argv)
# End.
| en | 0.426088 | #!/usr/bin/env python Module to test RO SRS APIfunctions # Add main project directory and ro manager directories at start of python path # Logging object # Base directory for file access tests in this module # Test config details # "http://sandbox.wf4ever-project.org/rodl/ROs/" # Test cases This test suite tests the ROSRS_Session client implementation of the ROSRS API # Clean up from previous runs # Clean up # Actual tests follow # Test that new RO is in collection # Delete RO # Test that new RO is not in collection # Delete again # Get manifest # Check manifest RDF graph # Get landing page # Get manifest # @@TODO test content of zip (data)? # Aggregate internal resource # GET content # GET proxy # Create test resource # GET content # Delete resource # Check that resource is no longer available # Aggregate external resource # GET proxy (note: using rdflib.URIRef value for path) # Create test resource # GET proxy (note: using rdflib.URIRef for path) # Delete resource # Create test resource # GET content # Create internal test resource <?xml version="1.0" encoding="UTF-8"?> <rdf:RDF xmlns:dct="http://purl.org/dc/terms/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" > <rdf:Description rdf:about="http://example.org/file1.txt"> <dct:title>Title for file1.txt</dct:title> </rdf:Description> </rdf:RDF> # Get resource content # Create internal test resource # Get resource proxy # Create internal test resource # Create internal annotation <?xml version="1.0" encoding="UTF-8"?> <rdf:RDF xmlns:dct="http://purl.org/dc/terms/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" xml:base="%s" > <rdf:Description rdf:about="test/file.txt"> <dct:title>Title for test/file.txt</dct:title> <rdfs:seeAlso rdf:resource="http://example.org/test" /> </rdf:Description> </rdf:RDF> # Retrieve annotation URIs ### self.assertIn(bodyuri, buris) # Retrieve annotation # Create internal test resource # Create internal annotation <?xml version="1.0" encoding="UTF-8"?> <rdf:RDF xmlns:dct="http://purl.org/dc/terms/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" xml:base="%s" > <rdf:Description rdf:about="test/file.txt"> <dct:title>Title for test/file.txt</dct:title> <rdfs:seeAlso rdf:resource="http://example.org/test" /> </rdf:Description> </rdf:RDF> # Retrieve merged annotations # Create external test resource # Create annotation using external body reference # Retrieve annotation URIs ### self.assertIn(bodyuri, buris) # Create internal test resource # Create internal annotation <?xml version="1.0" encoding="UTF-8"?> <rdf:RDF xmlns:dct="http://purl.org/dc/terms/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" xml:base="%s" > <rdf:Description rdf:about="test/file.txt"> <dct:title>Title 1</dct:title> <rdfs:seeAlso rdf:resource="http://example.org/test1" /> </rdf:Description> </rdf:RDF> # Retrieve annotation URIs ### self.assertIn(bodyuri1, buris1) # Retrieve annotation # Retrieve merged annotations # Update internal annotation <?xml version="1.0" encoding="UTF-8"?> <rdf:RDF xmlns:dct="http://purl.org/dc/terms/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" xml:base="%s" > <rdf:Description rdf:about="test/file.txt"> <dct:title>Title 2</dct:title> <rdfs:seeAlso rdf:resource="http://example.org/test2" /> </rdf:Description> </rdf:RDF> # Retrieve annotation URIs ### self.assertIn(bodyuri2, buris2) # Retrieve annotation # Retrieve merged annotations # Create external test resource # Create annotation using external body reference # Retrieve annotation URIs # Update annotation using external body reference # @@TODO - this doesn't check that old annotation is removed. # @@TODO - currently, update is not fully implemented (2013-05). # Retrieve annotation URIs # Create internal test resource # Create internal annotation <?xml version="1.0" encoding="UTF-8"?> <rdf:RDF xmlns:dct="http://purl.org/dc/terms/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" xml:base="%s" > <rdf:Description rdf:about="test/file.txt"> <dct:title>Title for test/file.txt</dct:title> <rdfs:seeAlso rdf:resource="http://example.org/test" /> </rdf:Description> </rdf:RDF> # Retrieve annotation URIs ### self.assertIn(bodyuri, buris) # Remove the annotation # Retrieve annotation URIs ### self.assertNotIn(bodyuri, buris) # Evolution tests # Sentinel/placeholder tests # Assemble test suite Get test suite select is one of the following: "unit" return suite of unit tests only "component" return suite of unit and component tests "all" return suite of unit, component and integration tests "pending" return suite of pending tests name a single named test to be run # Resource tests # Annotation tests # Evolution tests # End. | 2.211821 | 2 |
docs_src/tutorial/body/tutorial_002.py | dantownsend/xpresso | 75 | 6624309 | from typing import Dict, Optional
from pydantic import BaseModel, Field
from xpresso import App, FromJson, Path
from xpresso.typing import Annotated
class Item(BaseModel):
name: str
price: Annotated[
float,
Field(
gt=0,
description="Item price without tax. Must be greater than zero.",
),
]
tax: Optional[float] = None
async def create_receipt(item: FromJson[Item]) -> Dict[str, float]:
return {item.name: item.price + (item.tax or 0)}
app = App(
routes=[
Path(
"/items/",
post=create_receipt,
)
]
)
| from typing import Dict, Optional
from pydantic import BaseModel, Field
from xpresso import App, FromJson, Path
from xpresso.typing import Annotated
class Item(BaseModel):
name: str
price: Annotated[
float,
Field(
gt=0,
description="Item price without tax. Must be greater than zero.",
),
]
tax: Optional[float] = None
async def create_receipt(item: FromJson[Item]) -> Dict[str, float]:
return {item.name: item.price + (item.tax or 0)}
app = App(
routes=[
Path(
"/items/",
post=create_receipt,
)
]
)
| none | 1 | 2.557941 | 3 | |
tests/integration_tests/IT_test_transformBertTextTokenise.py | elangovana/kegg-pathway-extractor | 10 | 6624310 | import os
from unittest import TestCase
from algorithms.transform_berttext_tokenise import TransformBertTextTokenise
class ITTransformBertTextTokenise(TestCase):
def test___init__(self):
base_model_dir = os.path.join(os.path.dirname(__file__), "..", "temp", "biobert")
assert len(os.listdir(
base_model_dir)) >= 3, "The dir {} should contain the model bin and config and vocab files. If not download the biobert model".format(
base_model_dir)
# Arrange
max_feature_lens = [30, 7, 7]
case_insensitive = False
sut = TransformBertTextTokenise(base_model_dir, max_feature_lens, case_insensitive)
input = [
# batch
[
# X
[
# 3 columns
[
"This is a map PROTEIN1. PROTEIN1 phophorylates PROTEIN2"
]
,
[
"PROTEIN1"
]
,
[
"PROTEIN2"
]
]
,
# y
[
"Yes"
]
]
]
expected = [
# batch
[
# x
[
# 3 columns
[
["[CLS]", 'This', 'is', 'a', 'map', 'PR', '##OT', '##EI', '##N', '##1', '.', 'PR', '##OT',
'##EI',
'##N', '##1', 'p', '##hop', '##hor', '##yla', '##tes', 'PR', '##OT', '##EI', '##N', '##2',
"[PAD]", "[PAD]", "[PAD]", "[SEP]"]
]
,
[
["[CLS]", 'PR', '##OT', '##EI', '##N', '##1', "[SEP]"]
]
,
[
["[CLS]", 'PR', '##OT', '##EI', '##N', '##2', "[SEP]"]
]
] # end of x
# Y
,
[
# batch size 1
"Yes"
]
] # end of batch
]
# Act
actual = sut.fit_transform(input)
print(actual)
print(expected)
# Assert
self.assertSequenceEqual(expected, actual)
| import os
from unittest import TestCase
from algorithms.transform_berttext_tokenise import TransformBertTextTokenise
class ITTransformBertTextTokenise(TestCase):
def test___init__(self):
base_model_dir = os.path.join(os.path.dirname(__file__), "..", "temp", "biobert")
assert len(os.listdir(
base_model_dir)) >= 3, "The dir {} should contain the model bin and config and vocab files. If not download the biobert model".format(
base_model_dir)
# Arrange
max_feature_lens = [30, 7, 7]
case_insensitive = False
sut = TransformBertTextTokenise(base_model_dir, max_feature_lens, case_insensitive)
input = [
# batch
[
# X
[
# 3 columns
[
"This is a map PROTEIN1. PROTEIN1 phophorylates PROTEIN2"
]
,
[
"PROTEIN1"
]
,
[
"PROTEIN2"
]
]
,
# y
[
"Yes"
]
]
]
expected = [
# batch
[
# x
[
# 3 columns
[
["[CLS]", 'This', 'is', 'a', 'map', 'PR', '##OT', '##EI', '##N', '##1', '.', 'PR', '##OT',
'##EI',
'##N', '##1', 'p', '##hop', '##hor', '##yla', '##tes', 'PR', '##OT', '##EI', '##N', '##2',
"[PAD]", "[PAD]", "[PAD]", "[SEP]"]
]
,
[
["[CLS]", 'PR', '##OT', '##EI', '##N', '##1', "[SEP]"]
]
,
[
["[CLS]", 'PR', '##OT', '##EI', '##N', '##2', "[SEP]"]
]
] # end of x
# Y
,
[
# batch size 1
"Yes"
]
] # end of batch
]
# Act
actual = sut.fit_transform(input)
print(actual)
print(expected)
# Assert
self.assertSequenceEqual(expected, actual)
| en | 0.061851 | # Arrange # batch # X # 3 columns # y # batch # x # 3 columns #OT', '##EI', '##N', '##1', '.', 'PR', '##OT', #EI', #N', '##1', 'p', '##hop', '##hor', '##yla', '##tes', 'PR', '##OT', '##EI', '##N', '##2', #OT', '##EI', '##N', '##1', "[SEP]"] #OT', '##EI', '##N', '##2', "[SEP]"] # end of x # Y # batch size 1 # end of batch # Act # Assert | 2.472718 | 2 |
snyk/register.py | aarlaud-snyk/snyk-pants-plugin | 0 | 6624311 | <reponame>aarlaud-snyk/snyk-pants-plugin
from pants.goal.goal import Goal
from pants.goal.task_registrar import TaskRegistrar as task
from snyk.tasks.snyk import SnykTask
from snyk.tasks.dependencies import DependenciesTask
def register_goals():
Goal.register(name="snyktest", description="Snyk Test your dependencies for vulnerabilities")
task(name='dependencies', action=DependenciesTask).install('snyktest')
task(name='snyk', action=SnykTask).install('snyktest')
| from pants.goal.goal import Goal
from pants.goal.task_registrar import TaskRegistrar as task
from snyk.tasks.snyk import SnykTask
from snyk.tasks.dependencies import DependenciesTask
def register_goals():
Goal.register(name="snyktest", description="Snyk Test your dependencies for vulnerabilities")
task(name='dependencies', action=DependenciesTask).install('snyktest')
task(name='snyk', action=SnykTask).install('snyktest') | none | 1 | 1.894094 | 2 | |
auth.py | EnsembleGetMagic/nft-rater-retweet | 0 | 6624312 | import os
import tweepy
from dotenv import load_dotenv
load_dotenv()
#Load enviroment keys
CONSUMER_KEY = os.getenv('CONSUMER_KEY')
CONSUMER_SECRET = os.getenv('CONSUMER_SECRET')
ACCESS_TOKEN = os.getenv('ACCESS_TOKEN')
ACCESS_SECRET = os.getenv('ACCESS_SECRET')
#Set up api
auth = tweepy.OAuthHandler(CONSUMER_KEY, CONSUMER_SECRET)
auth.set_access_token(ACCESS_TOKEN, ACCESS_SECRET)
api = tweepy.API(auth, wait_on_rate_limit = True)
#Check API
try:
api.verify_credentials()
print('OK')
except:
print('Authentication error')
| import os
import tweepy
from dotenv import load_dotenv
load_dotenv()
#Load enviroment keys
CONSUMER_KEY = os.getenv('CONSUMER_KEY')
CONSUMER_SECRET = os.getenv('CONSUMER_SECRET')
ACCESS_TOKEN = os.getenv('ACCESS_TOKEN')
ACCESS_SECRET = os.getenv('ACCESS_SECRET')
#Set up api
auth = tweepy.OAuthHandler(CONSUMER_KEY, CONSUMER_SECRET)
auth.set_access_token(ACCESS_TOKEN, ACCESS_SECRET)
api = tweepy.API(auth, wait_on_rate_limit = True)
#Check API
try:
api.verify_credentials()
print('OK')
except:
print('Authentication error')
| en | 0.463028 | #Load enviroment keys #Set up api #Check API | 2.613133 | 3 |
tests/core/test_config_utils.py | ethyca/fides | 153 | 6624313 | <reponame>ethyca/fides<filename>tests/core/test_config_utils.py
# pylint: disable=missing-docstring, redefined-outer-name
import os
from typing import Generator
import pytest
from py._path.local import LocalPath
from toml import dump, load
from fidesctl.core.config import FidesctlConfig
from fidesctl.core.config.utils import update_config_file
@pytest.fixture
def test_change_config() -> Generator:
"""Create a dictionary to be used as an example config file"""
yield {"cli": {"analytics_id": "initial_id"}}
@pytest.mark.unit
def test_update_config_file_new_value(
test_change_config: FidesctlConfig, tmpdir: LocalPath
) -> None:
"""
Create an example config.toml and validate both updating an
existing config setting and adding a new section and setting.
"""
config_path = os.path.join(tmpdir, "test_writing_config.toml")
with open(config_path, "w") as config_file:
dump(test_change_config, config_file)
config_updates = {
"cli": {"analytics_id": "updated_id"},
"user": {"analytics_opt_out": True},
}
update_config_file(config_updates, config_path)
updated_config = load(config_path)
assert updated_config["cli"] is not None, "updated_config.cli should exist"
assert (
updated_config["cli"]["analytics_id"] == "updated_id"
), "updated_config.cli.analytics_id should be 'updated_id'"
assert updated_config["user"] is not None, "updated_config.user should exist"
assert updated_config["user"][
"analytics_opt_out"
], "updated_config.user.analytics_opt_out should be True"
| # pylint: disable=missing-docstring, redefined-outer-name
import os
from typing import Generator
import pytest
from py._path.local import LocalPath
from toml import dump, load
from fidesctl.core.config import FidesctlConfig
from fidesctl.core.config.utils import update_config_file
@pytest.fixture
def test_change_config() -> Generator:
"""Create a dictionary to be used as an example config file"""
yield {"cli": {"analytics_id": "initial_id"}}
@pytest.mark.unit
def test_update_config_file_new_value(
test_change_config: FidesctlConfig, tmpdir: LocalPath
) -> None:
"""
Create an example config.toml and validate both updating an
existing config setting and adding a new section and setting.
"""
config_path = os.path.join(tmpdir, "test_writing_config.toml")
with open(config_path, "w") as config_file:
dump(test_change_config, config_file)
config_updates = {
"cli": {"analytics_id": "updated_id"},
"user": {"analytics_opt_out": True},
}
update_config_file(config_updates, config_path)
updated_config = load(config_path)
assert updated_config["cli"] is not None, "updated_config.cli should exist"
assert (
updated_config["cli"]["analytics_id"] == "updated_id"
), "updated_config.cli.analytics_id should be 'updated_id'"
assert updated_config["user"] is not None, "updated_config.user should exist"
assert updated_config["user"][
"analytics_opt_out"
], "updated_config.user.analytics_opt_out should be True" | en | 0.704285 | # pylint: disable=missing-docstring, redefined-outer-name Create a dictionary to be used as an example config file Create an example config.toml and validate both updating an existing config setting and adding a new section and setting. | 2.18516 | 2 |
public/python/setgoals.py | aaronmsimon/nhl94-season-replay | 0 | 6624314 | #!C:/Program Files/Python38/python.exe
import binascii
import csv
# open save file
filename = r'C:\Users\Aaron\AppData\Roaming\RetroArch\states\nhl94_updated.state'
with open(filename,'rb') as inputfile:
content = inputfile.read()
hexFile = binascii.hexlify(content).decode('utf-8')
n = 2
hexes = [(hexFile[i:i+n]) for i in range(0, len(hexFile), n)]
# check points
playerstats = {}
players = []
for h in range(0,24):
players.append([int(hexes[51089 + h + (h + 1) % 2 * 2],16),
int(hexes[51115 + h + (h + 1) % 2 * 2],16)
])
playerstats['home'] = players
players = []
for a in range(0,24):
players.append([int(hexes[51957 + a + (a + 1) % 2 * 2],16),
int(hexes[51983 + a + (a + 1) % 2 * 2],16)
])
playerstats['away'] = players
# parse scoringsummary
with open("www\scoringsummary.txt",'r') as goalfile:
goal_list = csv.reader(goalfile,delimiter=',')
for goal in goal_list:
# goal scorer
hexes[int(goal[0]) + 2] = format(int(goal[1]),'02x')
# determine team for which set of player stats to update
if hexes[int(goal[0]) + 3][:1] == '0':
scoresummary_team = 'home'
else:
scoresummary_team = 'away'
# player stats
# check if assist1 is different
if hexes[int(goal[0]) + 5] != format(int(goal[2]),'02x'):
# check if the original assist was not empty
if hexes[int(goal[0]) + 5] != 'ff':
# if not, then reduce the original assist by one
playerstats[scoresummary_team][int(hexes[int(goal[0]) + 5],16)][1] = int(playerstats[scoresummary_team][int(hexes[int(goal[0]) + 5],16)][1] - 1)
# always increase the new assist by one
playerstats[scoresummary_team][int(goal[2])][1] = int(playerstats[scoresummary_team][int(goal[2])][1] + 1)
# print('original assist2={}'.format(hexes[int(goal[0]) + 4]))
# print('new assist2={}'.format(format(int(goal[3]),'02x')))
if hexes[int(goal[0]) + 4] != format(int(goal[3]),'02x'):
if hexes[int(goal[0]) + 4] != 'ff':
playerstats[scoresummary_team][int(hexes[int(goal[0]) + 4],16)][1] = int(playerstats[scoresummary_team][int(hexes[int(goal[0]) + 4],16)][1] - 1)
playerstats[scoresummary_team][int(goal[3])][1] = int(playerstats[scoresummary_team][int(goal[3])][1] + 1)
# scoring summary
hexes[int(goal[0]) + 5] = 'ff' if goal[2] == '255' else format(int(goal[2]),'02x')
hexes[int(goal[0]) + 4] = 'ff' if goal[3] == '255' else format(int(goal[3]),'02x')
# write back to hexes
for h in range(0,24):
hexes[51115 + h + (h + 1) % 2 * 2] = format(playerstats['home'][h][1],'02x')
# print(hexes[51985])
for a in range(0,24):
hexes[51983 + a + (a + 1) % 2 * 2] = format(playerstats['away'][a][1],'02x')
# print(hexes[51985])
newhexes = []
for i in range(0,len(hexes)):
newhexes.append(int(str(hexes[i]),16))
switchedfile = bytearray(newhexes)
with open(filename,'wb') as writefile:
writefile.write(switchedfile)
| #!C:/Program Files/Python38/python.exe
import binascii
import csv
# open save file
filename = r'C:\Users\Aaron\AppData\Roaming\RetroArch\states\nhl94_updated.state'
with open(filename,'rb') as inputfile:
content = inputfile.read()
hexFile = binascii.hexlify(content).decode('utf-8')
n = 2
hexes = [(hexFile[i:i+n]) for i in range(0, len(hexFile), n)]
# check points
playerstats = {}
players = []
for h in range(0,24):
players.append([int(hexes[51089 + h + (h + 1) % 2 * 2],16),
int(hexes[51115 + h + (h + 1) % 2 * 2],16)
])
playerstats['home'] = players
players = []
for a in range(0,24):
players.append([int(hexes[51957 + a + (a + 1) % 2 * 2],16),
int(hexes[51983 + a + (a + 1) % 2 * 2],16)
])
playerstats['away'] = players
# parse scoringsummary
with open("www\scoringsummary.txt",'r') as goalfile:
goal_list = csv.reader(goalfile,delimiter=',')
for goal in goal_list:
# goal scorer
hexes[int(goal[0]) + 2] = format(int(goal[1]),'02x')
# determine team for which set of player stats to update
if hexes[int(goal[0]) + 3][:1] == '0':
scoresummary_team = 'home'
else:
scoresummary_team = 'away'
# player stats
# check if assist1 is different
if hexes[int(goal[0]) + 5] != format(int(goal[2]),'02x'):
# check if the original assist was not empty
if hexes[int(goal[0]) + 5] != 'ff':
# if not, then reduce the original assist by one
playerstats[scoresummary_team][int(hexes[int(goal[0]) + 5],16)][1] = int(playerstats[scoresummary_team][int(hexes[int(goal[0]) + 5],16)][1] - 1)
# always increase the new assist by one
playerstats[scoresummary_team][int(goal[2])][1] = int(playerstats[scoresummary_team][int(goal[2])][1] + 1)
# print('original assist2={}'.format(hexes[int(goal[0]) + 4]))
# print('new assist2={}'.format(format(int(goal[3]),'02x')))
if hexes[int(goal[0]) + 4] != format(int(goal[3]),'02x'):
if hexes[int(goal[0]) + 4] != 'ff':
playerstats[scoresummary_team][int(hexes[int(goal[0]) + 4],16)][1] = int(playerstats[scoresummary_team][int(hexes[int(goal[0]) + 4],16)][1] - 1)
playerstats[scoresummary_team][int(goal[3])][1] = int(playerstats[scoresummary_team][int(goal[3])][1] + 1)
# scoring summary
hexes[int(goal[0]) + 5] = 'ff' if goal[2] == '255' else format(int(goal[2]),'02x')
hexes[int(goal[0]) + 4] = 'ff' if goal[3] == '255' else format(int(goal[3]),'02x')
# write back to hexes
for h in range(0,24):
hexes[51115 + h + (h + 1) % 2 * 2] = format(playerstats['home'][h][1],'02x')
# print(hexes[51985])
for a in range(0,24):
hexes[51983 + a + (a + 1) % 2 * 2] = format(playerstats['away'][a][1],'02x')
# print(hexes[51985])
newhexes = []
for i in range(0,len(hexes)):
newhexes.append(int(str(hexes[i]),16))
switchedfile = bytearray(newhexes)
with open(filename,'wb') as writefile:
writefile.write(switchedfile)
| en | 0.740993 | #!C:/Program Files/Python38/python.exe # open save file # check points # parse scoringsummary # goal scorer # determine team for which set of player stats to update # player stats # check if assist1 is different # check if the original assist was not empty # if not, then reduce the original assist by one # always increase the new assist by one # print('original assist2={}'.format(hexes[int(goal[0]) + 4])) # print('new assist2={}'.format(format(int(goal[3]),'02x'))) # scoring summary # write back to hexes # print(hexes[51985]) # print(hexes[51985]) | 2.725025 | 3 |
VAT/utils.py | vamoscy/DeepLearning2019 | 0 | 6624315 | <filename>VAT/utils.py<gh_stars>0
from collections import OrderedDict
import logging
import logzero
from pathlib import Path
from tensorboardX import SummaryWriter
import torch
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def accuracy(output, target, top_k=(1,)):
"""Computes the precision@k for the specified values of k"""
max_k = max(top_k)
batch_size = target.size(0)
_, pred = output.topk(max_k, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in top_k:
correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
res.append(correct_k.mul_(100.0 / batch_size))
if len(res) == 1:
res = res[0]
return res
def save_checkpoint(model, epoch, filename, optimizer=None):
if optimizer is None:
torch.save({
'epoch': epoch,
'state_dict': model.state_dict(),
}, filename)
else:
torch.save({
'epoch': epoch,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
}, filename)
def load_checkpoint(model, path, optimizer=None):
resume = torch.load(path)
if ('module' in list(resume['state_dict'].keys())[0]) \
and not (isinstance(model, torch.nn.DataParallel)):
new_state_dict = OrderedDict()
for k, v in resume['state_dict'].items():
name = k[7:] # remove `module.`
new_state_dict[name] = v
model.load_state_dict(new_state_dict)
else:
model.load_state_dict(resume['state_dict'])
if optimizer is not None:
optimizer.load_state_dict(resume['optimizer'])
return model, optimizer
else:
return model
def set_logger(path, loglevel=logging.INFO, tf_board_path=None):
path_dir = '/'.join(path.split('/')[:-1])
if not Path(path_dir).exists():
Path(path_dir).mkdir(parents=True)
logzero.loglevel(loglevel)
logzero.formatter(logging.Formatter('[%(asctime)s %(levelname)s] %(message)s'))
logzero.logfile(path)
if tf_board_path is not None:
tb_path_dir = '/'.join(tf_board_path.split('/')[:-1])
if not Path(tb_path_dir).exists():
Path(tb_path_dir).mkdir(parents=True)
writer = SummaryWriter(tf_board_path)
return writer
| <filename>VAT/utils.py<gh_stars>0
from collections import OrderedDict
import logging
import logzero
from pathlib import Path
from tensorboardX import SummaryWriter
import torch
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def accuracy(output, target, top_k=(1,)):
"""Computes the precision@k for the specified values of k"""
max_k = max(top_k)
batch_size = target.size(0)
_, pred = output.topk(max_k, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in top_k:
correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
res.append(correct_k.mul_(100.0 / batch_size))
if len(res) == 1:
res = res[0]
return res
def save_checkpoint(model, epoch, filename, optimizer=None):
if optimizer is None:
torch.save({
'epoch': epoch,
'state_dict': model.state_dict(),
}, filename)
else:
torch.save({
'epoch': epoch,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
}, filename)
def load_checkpoint(model, path, optimizer=None):
resume = torch.load(path)
if ('module' in list(resume['state_dict'].keys())[0]) \
and not (isinstance(model, torch.nn.DataParallel)):
new_state_dict = OrderedDict()
for k, v in resume['state_dict'].items():
name = k[7:] # remove `module.`
new_state_dict[name] = v
model.load_state_dict(new_state_dict)
else:
model.load_state_dict(resume['state_dict'])
if optimizer is not None:
optimizer.load_state_dict(resume['optimizer'])
return model, optimizer
else:
return model
def set_logger(path, loglevel=logging.INFO, tf_board_path=None):
path_dir = '/'.join(path.split('/')[:-1])
if not Path(path_dir).exists():
Path(path_dir).mkdir(parents=True)
logzero.loglevel(loglevel)
logzero.formatter(logging.Formatter('[%(asctime)s %(levelname)s] %(message)s'))
logzero.logfile(path)
if tf_board_path is not None:
tb_path_dir = '/'.join(tf_board_path.split('/')[:-1])
if not Path(tb_path_dir).exists():
Path(tb_path_dir).mkdir(parents=True)
writer = SummaryWriter(tf_board_path)
return writer
| en | 0.383527 | Computes and stores the average and current value Computes the precision@k for the specified values of k # remove `module.` | 2.264258 | 2 |
stage01/rogue.py | kantel/tkbuchhaim | 0 | 6624316 | import tkinter as tk
import os
root = tk.Tk()
root.title("Rogue Stage 1")
cw, ch = 640, 480
canvas = tk.Canvas(root, width = cw, height = ch, bg = "royalblue")
canvas.grid(row = 0, column = 0)
# Hier wird der Pfad zum Verzeichnis des ».py«-Files gesetzt
# Erspart einem das Herumgehample in TextMate mit dem os.getcwd()
# und os.path.join()
file_path = os.path.dirname(os.path.abspath(__file__))
os.chdir(file_path)
rogue = tk.PhotoImage(file = "../images/hildegunst.gif")
canvas.create_image(cw//2, ch//2, anchor = tk.NW, image = rogue)
canvas.update()
root.mainloop()
| import tkinter as tk
import os
root = tk.Tk()
root.title("Rogue Stage 1")
cw, ch = 640, 480
canvas = tk.Canvas(root, width = cw, height = ch, bg = "royalblue")
canvas.grid(row = 0, column = 0)
# Hier wird der Pfad zum Verzeichnis des ».py«-Files gesetzt
# Erspart einem das Herumgehample in TextMate mit dem os.getcwd()
# und os.path.join()
file_path = os.path.dirname(os.path.abspath(__file__))
os.chdir(file_path)
rogue = tk.PhotoImage(file = "../images/hildegunst.gif")
canvas.create_image(cw//2, ch//2, anchor = tk.NW, image = rogue)
canvas.update()
root.mainloop()
| de | 0.993798 | # Hier wird der Pfad zum Verzeichnis des ».py«-Files gesetzt # Erspart einem das Herumgehample in TextMate mit dem os.getcwd() # und os.path.join() | 2.871483 | 3 |
HunterAdminApi/web_app.py | tt9133github/hunter | 322 | 6624317 | <filename>HunterAdminApi/web_app.py
#!/ usr/bin/env
# coding=utf-8
#
# Copyright 2019 ztosec & https://www.zto.com/
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
"""
author: b5mali4
"""
import os
from datetime import timedelta
from flask import Flask, session, Response, jsonify, request, redirect, render_template
from api.hunter_user_web_api import user_web_api
from api.hunter_admin_web_api import admin_web_api
from api.authentication.default_auth_module import account_web_api
from api.authentication.ldap_auth_module import ldap_web_api
flask_app = Flask(__name__)
# flask_app = Flask(__name__, static_url_path="/static", static_folder="api/resource/templates/")
flask_app.config['SECRET_KEY'] = os.urandom(24)
flask_app.config['PERMANENT_SESSION_LIFETIME'] = timedelta(hours=2)
# flask_app.config.update(RESTFUL_JSON=dict(ensure_ascii=False))
# 注册到蓝图
flask_app.register_blueprint(user_web_api)
flask_app.register_blueprint(admin_web_api)
flask_app.register_blueprint(account_web_api)
flask_app.register_blueprint(ldap_web_api)
@flask_app.after_request
def handle_after_request(response):
"""
设置CORS源
:param response:
:return:
"""
if request.headers.has_key("Origin"):
response.headers["Access-Control-Allow-Origin"] = request.headers["Origin"]
response.headers["Access-Control-Allow-Methods"] = "GET,POST,PUT,HEAD,OPTIONS,DELETE,PATCH"
response.headers["Access-Control-Allow-Credentials"] = "true"
response.headers["Access-Control-Allow-Headers"] = "Content-Type,x-requested-with"
return response
@flask_app.route('/', methods=['GET'], endpoint='index')
def index():
return render_template("index.html")
if __name__ == "__main__":
flask_app.config['JSON_AS_ASCII'] = False
flask_app.run(host="0.0.0.0", port=8888)
| <filename>HunterAdminApi/web_app.py
#!/ usr/bin/env
# coding=utf-8
#
# Copyright 2019 ztosec & https://www.zto.com/
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
"""
author: b5mali4
"""
import os
from datetime import timedelta
from flask import Flask, session, Response, jsonify, request, redirect, render_template
from api.hunter_user_web_api import user_web_api
from api.hunter_admin_web_api import admin_web_api
from api.authentication.default_auth_module import account_web_api
from api.authentication.ldap_auth_module import ldap_web_api
flask_app = Flask(__name__)
# flask_app = Flask(__name__, static_url_path="/static", static_folder="api/resource/templates/")
flask_app.config['SECRET_KEY'] = os.urandom(24)
flask_app.config['PERMANENT_SESSION_LIFETIME'] = timedelta(hours=2)
# flask_app.config.update(RESTFUL_JSON=dict(ensure_ascii=False))
# 注册到蓝图
flask_app.register_blueprint(user_web_api)
flask_app.register_blueprint(admin_web_api)
flask_app.register_blueprint(account_web_api)
flask_app.register_blueprint(ldap_web_api)
@flask_app.after_request
def handle_after_request(response):
"""
设置CORS源
:param response:
:return:
"""
if request.headers.has_key("Origin"):
response.headers["Access-Control-Allow-Origin"] = request.headers["Origin"]
response.headers["Access-Control-Allow-Methods"] = "GET,POST,PUT,HEAD,OPTIONS,DELETE,PATCH"
response.headers["Access-Control-Allow-Credentials"] = "true"
response.headers["Access-Control-Allow-Headers"] = "Content-Type,x-requested-with"
return response
@flask_app.route('/', methods=['GET'], endpoint='index')
def index():
return render_template("index.html")
if __name__ == "__main__":
flask_app.config['JSON_AS_ASCII'] = False
flask_app.run(host="0.0.0.0", port=8888)
| en | 0.729797 | #!/ usr/bin/env # coding=utf-8 # # Copyright 2019 ztosec & https://www.zto.com/ # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. author: b5mali4 # flask_app = Flask(__name__, static_url_path="/static", static_folder="api/resource/templates/") # flask_app.config.update(RESTFUL_JSON=dict(ensure_ascii=False)) # 注册到蓝图 设置CORS源 :param response: :return: | 1.553249 | 2 |
src/datamgr/metasdk/metadata_client/event/subscriber.py | Chromico/bk-base | 84 | 6624318 | <reponame>Chromico/bk-base<filename>src/datamgr/metasdk/metadata_client/event/subscriber.py
# -*- coding: utf-8 -*-
"""
Tencent is pleased to support the open source community by making BK-BASE 蓝鲸基础计算平台 available.
Copyright (C) 2019 THL A29 Limited, a Tencent company. All rights reserved.
Licensed under the MIT License (the "License"); you may not use this file except in compliance with the License.
You may obtain a copy of the License at http://opensource.org/licenses/MIT
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.
"""
# Copyright © 2012-2018 Tencent BlueKing.
# All Rights Reserved.
# 蓝鲸智云 版权所有
from __future__ import absolute_import, print_function, unicode_literals
import inspect
import re
import socket
import time
from metadata_client.exc import EventSubscribeConfigError
from metadata_client.resource import resource_lock
from metadata_client.support.rabbitmq import BackendClient, RabbitMQConsumer
class MetaEventSubscriber(object):
META_EVENT_PREFIX = "meta.event"
backend = None
"""
订阅队列后端实例
"""
subscriber_existed = False
"""
订阅器是否已注册
"""
def __init__(self, subscribe_config=None, supports_config=None):
self.subscribe_orders = dict()
self.subscriber = None
self.callbacks = []
if subscribe_config and isinstance(subscribe_config, dict):
self.set_subscribe_config(**subscribe_config)
if supports_config:
self.backend = BackendClient(supports_config)
def gen_subscribe_orders(self, config):
"""
根据用户输入构建订阅参数，生成订阅单
:param config: dict 用户输入配置参数
:return: tuple (order_name, order) 订阅单
"""
order_name = "{}.{}.{}".format(config["refer"], config["key"], config["name"])
routing_key = "{}.{}".format(self.META_EVENT_PREFIX, config["key"])
order = dict(routing_key=routing_key)
return order_name, order
@staticmethod
def _verify_config_item(item):
"""
校验配置项字符串的合法性
:param item: 配置项
:return: boolean
"""
if re.match("^[_a-zA-Z0-9]+$", str(item)) is None:
return False
return True
def set_subscribe_config(self, name=None, key=None, refer=None, *args, **kwargs):
"""
设置订阅配置
:param name: 订阅名称
:param key: 订阅关键字
:param refer: 订阅者所属来源
:param args: 其他参数
:param kwargs: 其他关键字参数
:return: None
"""
config = dict(
name=name if self._verify_config_item(name) else None,
key=key if self._verify_config_item(key) else None,
refer=refer if self._verify_config_item(refer) else None,
)
if all(config.values()):
order_name, order = self.gen_subscribe_orders(config)
self.subscribe_orders[order_name] = order
else:
raise EventSubscribeConfigError(message_kv={"detail": config})
def set_callback(self, callbacks=None):
"""
设置回调函数
:param callbacks: 回调函数列表
:return: None
"""
if not isinstance(callbacks, list):
callbacks = [callbacks]
valid_callbacks = [callback for callback in callbacks if inspect.isfunction(callback)]
if valid_callbacks:
self.callbacks.extend(valid_callbacks)
def register_subscriber(self):
"""
注册订阅器，每个进程仅能注册一次
:return: None
"""
if self.subscribe_orders and not self.__class__.subscriber_existed:
with resource_lock:
if not self.__class__.subscriber_existed:
self.__class__.subscriber_existed = True
consumer = RabbitMQConsumer(
self.backend,
queue_mapping=self.subscribe_orders,
exchange_name="meta_event_system",
callbacks=self.callbacks,
)
self.subscriber = consumer
def start_to_listening(self, timeout=1, detection_interval=1):
"""
开始接收订阅消息
:param timeout: int 每次探测等待消息到来的超时时间(s)
:param detection_interval: int 探测间隔(s)
:return: None
"""
self.register_subscriber()
if self.subscriber:
try:
while True:
try:
self.subscriber.start_scan(timeout)
except socket.timeout:
time.sleep(detection_interval)
except Exception:
print("stop listening")
raise
finally:
self.subscriber.release()
| # -*- coding: utf-8 -*-
"""
Tencent is pleased to support the open source community by making BK-BASE 蓝鲸基础计算平台 available.
Copyright (C) 2019 THL A29 Limited, a Tencent company. All rights reserved.
Licensed under the MIT License (the "License"); you may not use this file except in compliance with the License.
You may obtain a copy of the License at http://opensource.org/licenses/MIT
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.
"""
# Copyright © 2012-2018 Tencent BlueKing.
# All Rights Reserved.
# 蓝鲸智云 版权所有
from __future__ import absolute_import, print_function, unicode_literals
import inspect
import re
import socket
import time
from metadata_client.exc import EventSubscribeConfigError
from metadata_client.resource import resource_lock
from metadata_client.support.rabbitmq import BackendClient, RabbitMQConsumer
class MetaEventSubscriber(object):
META_EVENT_PREFIX = "meta.event"
backend = None
"""
订阅队列后端实例
"""
subscriber_existed = False
"""
订阅器是否已注册
"""
def __init__(self, subscribe_config=None, supports_config=None):
self.subscribe_orders = dict()
self.subscriber = None
self.callbacks = []
if subscribe_config and isinstance(subscribe_config, dict):
self.set_subscribe_config(**subscribe_config)
if supports_config:
self.backend = BackendClient(supports_config)
def gen_subscribe_orders(self, config):
"""
根据用户输入构建订阅参数，生成订阅单
:param config: dict 用户输入配置参数
:return: tuple (order_name, order) 订阅单
"""
order_name = "{}.{}.{}".format(config["refer"], config["key"], config["name"])
routing_key = "{}.{}".format(self.META_EVENT_PREFIX, config["key"])
order = dict(routing_key=routing_key)
return order_name, order
@staticmethod
def _verify_config_item(item):
"""
校验配置项字符串的合法性
:param item: 配置项
:return: boolean
"""
if re.match("^[_a-zA-Z0-9]+$", str(item)) is None:
return False
return True
def set_subscribe_config(self, name=None, key=None, refer=None, *args, **kwargs):
"""
设置订阅配置
:param name: 订阅名称
:param key: 订阅关键字
:param refer: 订阅者所属来源
:param args: 其他参数
:param kwargs: 其他关键字参数
:return: None
"""
config = dict(
name=name if self._verify_config_item(name) else None,
key=key if self._verify_config_item(key) else None,
refer=refer if self._verify_config_item(refer) else None,
)
if all(config.values()):
order_name, order = self.gen_subscribe_orders(config)
self.subscribe_orders[order_name] = order
else:
raise EventSubscribeConfigError(message_kv={"detail": config})
def set_callback(self, callbacks=None):
"""
设置回调函数
:param callbacks: 回调函数列表
:return: None
"""
if not isinstance(callbacks, list):
callbacks = [callbacks]
valid_callbacks = [callback for callback in callbacks if inspect.isfunction(callback)]
if valid_callbacks:
self.callbacks.extend(valid_callbacks)
def register_subscriber(self):
"""
注册订阅器，每个进程仅能注册一次
:return: None
"""
if self.subscribe_orders and not self.__class__.subscriber_existed:
with resource_lock:
if not self.__class__.subscriber_existed:
self.__class__.subscriber_existed = True
consumer = RabbitMQConsumer(
self.backend,
queue_mapping=self.subscribe_orders,
exchange_name="meta_event_system",
callbacks=self.callbacks,
)
self.subscriber = consumer
def start_to_listening(self, timeout=1, detection_interval=1):
"""
开始接收订阅消息
:param timeout: int 每次探测等待消息到来的超时时间(s)
:param detection_interval: int 探测间隔(s)
:return: None
"""
self.register_subscriber()
if self.subscriber:
try:
while True:
try:
self.subscriber.start_scan(timeout)
except socket.timeout:
time.sleep(detection_interval)
except Exception:
print("stop listening")
raise
finally:
self.subscriber.release() | en | 0.53726 | # -*- coding: utf-8 -*- Tencent is pleased to support the open source community by making BK-BASE 蓝鲸基础计算平台 available. Copyright (C) 2019 THL A29 Limited, a Tencent company. All rights reserved. Licensed under the MIT License (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://opensource.org/licenses/MIT 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. # Copyright © 2012-2018 Tencent BlueKing. # All Rights Reserved. # 蓝鲸智云 版权所有 订阅队列后端实例 订阅器是否已注册 根据用户输入构建订阅参数，生成订阅单 :param config: dict 用户输入配置参数 :return: tuple (order_name, order) 订阅单 校验配置项字符串的合法性 :param item: 配置项 :return: boolean 设置订阅配置 :param name: 订阅名称 :param key: 订阅关键字 :param refer: 订阅者所属来源 :param args: 其他参数 :param kwargs: 其他关键字参数 :return: None 设置回调函数 :param callbacks: 回调函数列表 :return: None 注册订阅器，每个进程仅能注册一次 :return: None 开始接收订阅消息 :param timeout: int 每次探测等待消息到来的超时时间(s) :param detection_interval: int 探测间隔(s) :return: None | 1.779035 | 2 |
mvmm_sim/simulation/sim_naming.py | idc9/mvmm_sim | 0 | 6624319 | <filename>mvmm_sim/simulation/sim_naming.py
import names
import numpy as np
import os
def load_all_names():
all_names = []
for gen in ['male', 'female']:
fpath = names.full_path('dist.{}.first'.format(gen))
with open(fpath) as name_file:
for line in name_file:
name, _, cummulative, _ = line.split()
all_names.append(name.lower())
return all_names
def get_subfolders(folder):
return [name for name in os.listdir(folder) if os.path.isdir(name)]
def get_new_name(folder):
all_names = load_all_names()
existing_names = get_subfolders(folder)
possibilities = set(all_names).difference(existing_names)
assert len(possibilities) > 0
possibilities = np.array(list(possibilities))
possibilities = np.sort(possibilities)
return possibilities[0]
| <filename>mvmm_sim/simulation/sim_naming.py
import names
import numpy as np
import os
def load_all_names():
all_names = []
for gen in ['male', 'female']:
fpath = names.full_path('dist.{}.first'.format(gen))
with open(fpath) as name_file:
for line in name_file:
name, _, cummulative, _ = line.split()
all_names.append(name.lower())
return all_names
def get_subfolders(folder):
return [name for name in os.listdir(folder) if os.path.isdir(name)]
def get_new_name(folder):
all_names = load_all_names()
existing_names = get_subfolders(folder)
possibilities = set(all_names).difference(existing_names)
assert len(possibilities) > 0
possibilities = np.array(list(possibilities))
possibilities = np.sort(possibilities)
return possibilities[0]
| none | 1 | 2.819432 | 3 | |
scripts/sources/s_default_probabilities.py | dpopadic/arpmRes | 6 | 6624320 | # ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.4'
# jupytext_version: 1.1.4
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# # s_default_probabilities [<img src="https://www.arpm.co/lab/icons/icon_permalink.png" width=30 height=30 style="display: inline;">](https://www.arpm.co/lab/redirect.php?code=s_default_probabilities&codeLang=Python)
# For details, see [here](https://www.arpm.co/lab/redirect.php?permalink=eb-supervised-machine-learning).
# +
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy.special import logit
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import OneHotEncoder, PolynomialFeatures, \
QuantileTransformer
from sklearn import tree
from sklearn.metrics import auc, roc_curve, confusion_matrix
from sklearn.model_selection import StratifiedKFold, train_test_split
from arpym.tools import add_logo
# -
# ## [Input parameters](https://www.arpm.co/lab/redirect.php?permalink=s_default_probabilities-parameters)
test_size = 0.2 # proportion of the test set
n_sample = 10000 # num. of samples in the database; set =30000 to catch it all
pol_degree = 2 # degrees in polynomial features
lambda_lasso = 0.05 # lasso parameter
max_depth_tree = 10 # maximum depth of decision tree classifier
cross_val = 0 # set "1" to do cross-validation (computational time increases)
k_ = 5 # parameter of Stratified K-Folds cross-validator
# ## [Step 0](https://www.arpm.co/lab/redirect.php?permalink=s_default_probabilities-implementation-step00): Import data and pre-process database
# +
# Import data
path = '../../../databases/global-databases/credit/' + \
'db_default_data_creditcardsclients/'
df = pd.read_csv(path+'db_default_data_creditcardsclients.csv')
df = df.iloc[:, 1:df.shape[1]] # exlude ID
# Sort database so that the categorical features are at the beginning
# indexes of the categorical features
ind_cat = np.r_[np.arange(1, 4), np.arange(5, 11)]
n_cat = len(ind_cat) # number of categorical features
# indexes of the continuous features
ind_cont = np.r_[np.array([0, 4]), np.arange(11, df.shape[1])]
n_cont = len(ind_cont) # number of categorical features
df = df.iloc[:n_sample, np.r_[ind_cat, ind_cont]]
# Outputs and features
z = np.array(df.iloc[:, :-1]) # features
x = np.array(df.iloc[:, -1]) # labels
# Standardize continuous features
quantile_transformer = QuantileTransformer(output_distribution='normal')
z_cont = quantile_transformer.fit_transform(z[:, -n_cont:])
# Transform categorical features via one-hot encoding
# shift up, because the OneHotEncoder takes only positive inputs
enc = OneHotEncoder()
z_cat = enc.fit_transform(np.abs(np.min(z[:, :n_cat], axis=0)) +
z[:, :n_cat]).toarray()
n_enc = z_cat.shape[1] # number of encoded categorical features
z = np.concatenate((z_cat, z_cont), axis=1)
# Define test set and estimation set
z_estimation, z_test, x_estimation, x_test = train_test_split(z, x)
# -
# ## [Step 1](https://www.arpm.co/lab/redirect.php?permalink=s_default_probabilities-implementation-step01): Logistic regression on continuous features
# Set C = +infinity to have 0 Lasso parameter
lg = LogisticRegression(penalty='l1', C=10**5, class_weight='balanced')
lg = lg.fit(z_estimation[:, -n_cont:], x_estimation) # fit the model
p_z_lg = lg.predict_proba(z_test[:, -n_cont:])[:, 1] # predict the probs
cm_lg = confusion_matrix(x_test, lg.predict(z_test[:, -n_cont:])) # conf. mat.
er_lg = -np.sum(np.log(p_z_lg)) # error
print('Logistic error: %1.4f' % er_lg)
# conditional scores
s_0_lg = logit(lg.predict_proba(z_test[:, -n_cont:])[
np.where(x_test == 0)[0], 1])
s_1_lg = logit(lg.predict_proba(z_test[:, -n_cont:])[
np.where(x_test == 1)[0], 1])
# ## [Step 2](https://www.arpm.co/lab/redirect.php?permalink=s_default_probabilities-implementation-step02): Add interactions to logistic regression
# +
# Add interactions
poly = PolynomialFeatures(degree=pol_degree)
z_estimation_inter = poly.fit_transform(z_estimation[:, -n_cont:])
z_test_inter = poly.fit_transform(z_test[:, -n_cont:])
# Set C = +infinity to have 0 Lasso parameter
lg_inter = LogisticRegression(penalty='l1', C=10**5, class_weight='balanced')
lg_inter = lg_inter.fit(z_estimation_inter, x_estimation) # fit the model
p_z_inter = lg_inter.predict_proba(z_test_inter)[:, 1] # pred. the probs.
cm_inter = confusion_matrix(x_test, lg_inter.predict(z_test_inter))
er_inter = -np.sum(np.log(p_z_inter)) # error
print('Logistic with interactions error: %1.4f' % er_inter)
# conditional scores
s_0_inter = logit(lg_inter.predict_proba(z_test_inter)[
np.where(x_test == 0)[0], 1])
s_1_inter = logit(lg_inter.predict_proba(z_test_inter)[
np.where(x_test == 1)[0], 1])
# -
# ## [Step 3](https://www.arpm.co/lab/redirect.php?permalink=s_default_probabilities-implementation-step03): Add encoded categorical features to logistic regression
# +
z_enc_estimation = np.concatenate((z_estimation[:, :n_enc],
z_estimation_inter), axis=1)
z_enc_test = np.concatenate((z_test[:, :n_enc], z_test_inter), axis=1)
# Set C = +infinity to have 0 Lasso parameter
lg_enc = LogisticRegression(penalty='l1', C=10**5, class_weight='balanced')
lg_enc = lg_enc.fit(z_enc_estimation, x_estimation) # fit the model
p_z_enc = lg_enc.predict_proba(z_enc_test)[:, 1] # pred. the probs.
cm_enc = confusion_matrix(x_test, lg_enc.predict(z_enc_test))
er_enc = -np.sum(np.log(p_z_enc)) # error
print('Logistic with interactions and categorical error: %1.4f' % er_enc)
# conditional scores
s_0_enc = logit(lg_enc.predict_proba(z_enc_test)[np.where(x_test == 0)[0], 1])
s_1_enc = logit(lg_enc.predict_proba(z_enc_test)[np.where(x_test == 1)[0], 1])
# -
# ## [Step 4](https://www.arpm.co/lab/redirect.php?permalink=s_default_probabilities-implementation-step04): Add lasso regularization
lg_lasso = LogisticRegression(penalty='l1', C=10**5, class_weight='balanced')
lg_lasso = lg_lasso.fit(z_enc_estimation, x_estimation) # fit the model
p_z_lasso = lg_lasso.predict_proba(z_enc_test)[:, 1] # predict the probs.
cm_lasso = confusion_matrix(x_test, lg_lasso.predict(z_enc_test)) # conf. mat.
er_lasso = -np.sum(np.log(p_z_lasso)) # error
print('Logistic with lasso error: %1.4f' % er_lasso)
# conditional scores
s_0_lasso = logit(lg_lasso.predict_proba(z_enc_test)[
np.where(x_test == 0)[0], 1])
s_1_lasso = logit(lg_lasso.predict_proba(z_enc_test)[
np.where(x_test == 1)[0], 1])
# ## [Step 5](https://www.arpm.co/lab/redirect.php?permalink=s_default_probabilities-implementation-step05): CART classifier
tree_clf = tree.DecisionTreeClassifier(max_depth=max_depth_tree,
class_weight='balanced') # def. method
tree_clf = tree_clf.fit(z_enc_estimation, x_estimation) # fit the model
p_z_tree = tree_clf.predict_proba(z_enc_test)[:, 1] # predict the scores
cm_tree = confusion_matrix(x_test, tree_clf.predict(z_enc_test)) # conf. mat.
er_tree = (cm_tree[0, 1]/np.sum(x_test == 0) +
cm_tree[1, 0]/np.sum(x_test == 1)) # error
print('CART classifier error: %1.4f' % er_tree)
# conditional scores
eps = 10**-5 # set threshold to avoid numerical noise in the logit function
p_0_tree = tree_clf.predict_proba(z_enc_test)[np.where(x_test == 0)[0], 1]
p_0_tree[p_0_tree < eps] = eps
p_0_tree[p_0_tree > 1-eps] = 1-eps
p_1_tree = tree_clf.predict_proba(z_enc_test)[np.where(x_test == 1)[0], 1]
p_1_tree[p_1_tree < eps] = eps
p_1_tree[p_1_tree > 1-eps] = 1-eps
s_0_tree = logit(p_0_tree)
s_1_tree = logit(p_1_tree)
# ## [Step 6](https://www.arpm.co/lab/redirect.php?permalink=s_default_probabilities-implementation-step06): Add gradient boosting to CART classifier
boost_clf = GradientBoostingClassifier(max_depth=max_depth_tree) # method
boost_clf = boost_clf.fit(z_enc_estimation, x_estimation) # fit the model
p_z_boost = boost_clf.predict_proba(z_enc_test)[:, 1] # predict the probs.
cm_boost = confusion_matrix(x_test, boost_clf.predict(z_enc_test)) # conf. mat
er_boost = (cm_boost[0, 1]/np.sum(x_test == 0) +
cm_boost[1, 0]/np.sum(x_test == 1)) # error
print('CART classifier with gradient boosting error: %1.4f' % er_boost)
# conditional scores
s_0_boost = logit(boost_clf.predict_proba(z_enc_test)[
np.where(x_test == 0)[0], 1])
s_1_boost = logit(boost_clf.predict_proba(z_enc_test)[
np.where(x_test == 1)[0], 1])
# ## [Step 7](https://www.arpm.co/lab/redirect.php?permalink=s_default_probabilities-implementation-step07): Compute fpr, tpr and AUC on the test set
# +
# 1) Logistic
fpr_lg, tpr_lg, _ = roc_curve(x_test, p_z_lg)
auc_lg = auc(fpr_lg, tpr_lg)
print('Logistic AUC: %1.3f' % auc_lg)
# 2) Logistic with interactions
fpr_inter, tpr_inter, _ = roc_curve(x_test, p_z_inter)
auc_inter = auc(fpr_inter, tpr_inter)
print('Logistic with interactions AUC: %1.3f' % auc_inter)
# 3) Logistic with interactions and encoded categorical features
fpr_enc, tpr_enc, _ = roc_curve(x_test, p_z_enc)
auc_enc = auc(fpr_enc, tpr_enc)
print('Logistic with interactions and categorical AUC: %1.3f' % auc_enc)
# 4) Logistic lasso with interactions and encoded categorical features
fpr_lasso, tpr_lasso, _ = roc_curve(x_test, p_z_lasso)
auc_lasso = auc(fpr_lasso, tpr_lasso)
print('Logistic with lasso AUC: %1.3f' % auc_lasso)
# 5) CART classifier
fpr_tree, tpr_tree, _ = roc_curve(x_test, p_z_tree)
auc_tree = auc(fpr_tree, tpr_tree)
print('CART classifier AUC: %1.3f' % auc_tree)
# 6) Gradient boosting classifier
fpr_boost, tpr_boost, _ = roc_curve(x_test, p_z_boost)
auc_boost = auc(fpr_boost, tpr_boost)
print('Gradient boosting classifier AUC: %1.3f' % auc_boost)
# -
# ## [Step 8](https://www.arpm.co/lab/redirect.php?permalink=s_default_probabilities-implementation-step08): Choose best probabilistic and point predictors via cross-validation
if cross_val == 1:
# Split the estimation set into training and validation sets for k-fold
# cross-validation
k_fold = StratifiedKFold(n_splits=k_)
z_train = []
z_train_inter = []
z_train_enc = []
x_train = []
z_val = []
z_val_inter = []
z_val_enc = []
x_val = []
for train, val in k_fold.split(z_estimation, x_estimation):
z_train.append(z_estimation[train])
x_train.append(x_estimation[train])
z_val.append(z_estimation[val])
x_val.append(x_estimation[val])
for train, val in k_fold.split(z_estimation_inter, x_estimation):
z_train_inter.append(z_estimation_inter[train])
z_val_inter.append(z_estimation_inter[val])
for train, val in k_fold.split(z_enc_estimation, x_estimation):
z_train_enc.append(z_enc_estimation[train])
z_val_enc.append(z_enc_estimation[val])
# Probabilistic
cv_er_lg = []
cv_er_lasso = []
cv_er_inter = []
cv_er_enc = []
for k in range(k_):
# Logistic
p_cv_lg = lg.fit(z_train[k], x_train[k]).predict_proba(z_val[k])
cv_er_lg.append(-np.sum(np.log(p_cv_lg)))
# Lasso
p_cv_lasso = lg_lasso.fit(z_train[k],
x_train[k]).predict_proba(z_val[k])
cv_er_lasso.append(-np.sum(np.log(p_cv_lasso)))
# Interactions
p_cv_inter = lg_inter.fit(z_train_inter[k],
x_train[k]).predict_proba(z_val_inter[k])
cv_er_inter.append(-np.sum(np.log(p_cv_inter)))
# Encoded categorical
p_cv_enc = lg_inter.fit(z_train_enc[k],
x_train[k]).predict_proba(z_val_enc[k])
cv_er_enc.append(-np.sum(np.log(p_cv_enc)))
cv_er_lg = np.mean(cv_er_lg)
cv_er_lasso = np.mean(cv_er_lasso)
cv_er_inter = np.mean(cv_er_inter)
cv_er_enc = np.mean(cv_er_enc)
# Point
cv_er_tree = []
cv_er_boost = []
for k in range(k_):
# Tree
cm_tree_cv =\
confusion_matrix(x_val[k],
tree_clf.fit(z_train[k],
x_train[k]).predict(z_val[k]))
er_tree_cv = (cm_tree_cv[0, 1]/np.sum(x_val[k] == 0) +
cm_tree_cv[1, 0]/np.sum(x_val[k] == 1)) # error
cv_er_tree.append(er_tree_cv)
# Gradient boosting
cm_boost_cv =\
confusion_matrix(x_val[k],
boost_clf.fit(z_train[k],
x_train[k]).predict(z_val[k]))
er_boost_cv = (cm_boost_cv[0, 1]/np.sum(x_val[k] == 0) +
cm_boost_cv[1, 0]/np.sum(x_val[k] == 1)) # error
cv_er_boost.append(er_boost_cv)
cv_er_tree = np.mean(cv_er_tree)
cv_er_boost = np.mean(cv_er_boost)
print('Logistic CV error: %1.3f' % cv_er_lg)
print('Logistic with interactions CV error: %1.3f' % cv_er_inter)
print('Logistic with interactions and categorical CV error: %1.3f' %
cv_er_enc)
print('Logistic with lasso CV error: %1.3f' % cv_er_lasso)
print('CART classifier CV error: %1.3f' % cv_er_tree)
print('CART classifier with gradient boosting CV error: %1.3f' %
cv_er_boost)
# ## Plots
plt.style.use('arpm')
# ## 1) Logistic regression
# +
fig1 = plt.figure()
ax11 = plt.subplot2grid((2, 2), (0, 0), rowspan=2)
ax12 = plt.subplot2grid((2, 2), (0, 1))
ax13 = plt.subplot2grid((2, 2), (1, 1))
# out of sample ROC curve
plt.sca(ax11)
plt.plot([0, 1], [0, 1], 'k--', lw=1)
plt.plot([0, 0, 1], [0, 1, 1], 'g')
plt.plot(fpr_lg, tpr_lg, 'b')
plt.xlim([-0.01, 1.01])
plt.ylim([-0.01, 1.01])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.legend(['Random fit', 'Perfect fit', 'ROC curve'])
plt.text(0.05, 0.8, 'AUC = %.2f' % auc_lg)
plt.text(0.05, 0.85, 'Error = %.2f' % er_lg)
plt.title('Logistic regression (test set)')
# Scores
plt.sca(ax12)
plt.hist(s_0_lg, 80, density=True, alpha=0.7, color='r')
plt.hist(s_1_lg, 80, density=True, alpha=0.7, color='b')
plt.legend(['S | 0', 'S | 1'])
plt.title('Scores distribution')
# Confusion matrix
plt.sca(ax13)
cax_1 = plt.bar([0, 1], [cm_lg[0, 1]/np.sum(x_test == 0),
cm_lg[1, 0]/np.sum(x_test == 1)])
plt.ylim([0, 1.1])
plt.xticks([0, 1], ('$fpr$', '$fnr$'))
plt.title('Confusion matrix')
add_logo(fig1, location=1, size_frac_x=1/8)
plt.tight_layout()
# -
# ## 2) Logistic regression with interactions
# +
fig2 = plt.figure()
ax31 = plt.subplot2grid((2, 2), (0, 0), rowspan=2)
ax32 = plt.subplot2grid((2, 2), (0, 1))
ax33 = plt.subplot2grid((2, 2), (1, 1))
# out of sample ROC curve
plt.sca(ax31)
plt.plot([0, 1], [0, 1], 'k--', lw=1)
plt.plot([0, 0, 1], [0, 1, 1], 'g')
plt.plot(fpr_inter, tpr_inter, 'b')
plt.xlim([-0.01, 1.01])
plt.ylim([-0.01, 1.01])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.legend(['Random fit', 'Perfect fit', 'ROC curve'])
plt.text(0.05, 0.8, 'AUC = %.2f' % auc_inter)
plt.text(0.05, 0.85, 'Error = %.2f' % er_inter)
plt.title('Logistic regression with interactions deg. = %1i (test set)'
% pol_degree)
# Scores
plt.sca(ax32)
plt.hist(s_0_inter, 80, density=True, alpha=0.7, color='r')
plt.hist(s_1_inter, 80, density=True, alpha=0.7, color='b')
plt.legend(['S | 0', 'S | 1'])
plt.title('Scores distribution')
# Confusion matrix
plt.sca(ax33)
cax_1 = plt.bar([0, 1], [cm_inter[0, 1]/np.sum(x_test == 0),
cm_inter[1, 0]/np.sum(x_test == 1)])
plt.ylim([0, 1.1])
plt.xticks([0, 1], ('$fpr$', '$fnr$'))
plt.title('Confusion matrix')
add_logo(fig2, location=1, size_frac_x=1/8)
plt.tight_layout()
# -
# ## 3) Logistic regression with interactions and encoded categorical features
# +
fig3 = plt.figure()
ax21 = plt.subplot2grid((2, 2), (0, 0), rowspan=2)
ax22 = plt.subplot2grid((2, 2), (0, 1))
ax23 = plt.subplot2grid((2, 2), (1, 1))
# out of sample ROC curve
plt.sca(ax21)
plt.plot([0, 1], [0, 1], 'k--', lw=1)
plt.plot([0, 0, 1], [0, 1, 1], 'g')
plt.plot(fpr_enc, tpr_enc, 'b')
plt.xlim([-0.01, 1.01])
plt.ylim([-0.01, 1.01])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.legend(['Random fit', 'Perfect fit', 'ROC curve'])
plt.text(0.05, 0.8, 'AUC = %.2f' % auc_enc)
plt.text(0.05, 0.85, 'Error = %.2f' % er_enc)
plt.title('Logistic regression with interactions and categorical features')
# Scores
plt.sca(ax22)
plt.hist(s_0_enc, 80, density=True, alpha=0.7, color='r')
plt.hist(s_1_enc, 80, density=True, alpha=0.7, color='b')
plt.legend(['S | 0', 'S | 1'])
plt.title('Scores distribution')
# Confusion matrix
plt.sca(ax23)
cax_1 = plt.bar([0, 1], [cm_enc[0, 1]/np.sum(x_test == 0),
cm_enc[1, 0]/np.sum(x_test == 1)])
plt.ylim([0, 1.1])
plt.xticks([0, 1], ('$fpr$', '$fnr$'))
plt.title('Confusion matrix')
add_logo(fig3, location=1, size_frac_x=1/8)
plt.tight_layout()
# -
# ## 4) Logistic regression with lasso
# +
fig4 = plt.figure()
ax21 = plt.subplot2grid((2, 2), (0, 0), rowspan=2)
ax22 = plt.subplot2grid((2, 2), (0, 1))
ax23 = plt.subplot2grid((2, 2), (1, 1))
# out of sample ROC curve
plt.sca(ax21)
plt.plot([0, 1], [0, 1], 'k--', lw=1)
plt.plot([0, 0, 1], [0, 1, 1], 'g')
plt.plot(fpr_lasso, tpr_lasso, 'b')
plt.xlim([-0.01, 1.01])
plt.ylim([-0.01, 1.01])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.legend(['Random fit', 'Perfect fit', 'ROC curve'])
plt.text(0.05, 0.8, 'AUC = %.2f' % auc_lasso)
plt.text(0.05, 0.85, 'Error = %.2f' % er_lasso)
plt.title('Logistic regression with Lasso param. = %1.2e (test set)' %
lambda_lasso)
# Scores
plt.sca(ax22)
plt.hist(s_0_lasso, 80, density=True, alpha=0.7, color='r')
plt.hist(s_1_lasso, 80, density=True, alpha=0.7, color='b')
plt.legend(['S | 0', 'S | 1'])
plt.title('Scores distribution')
# Confusion matrix
plt.sca(ax23)
cax_1 = plt.bar([0, 1], [cm_lasso[0, 1]/np.sum(x_test == 0),
cm_lasso[1, 0]/np.sum(x_test == 1)])
plt.ylim([0, 1.1])
plt.xticks([0, 1], ('$fpr$', '$fnr$'))
plt.title('Confusion matrix')
add_logo(fig4, location=1, size_frac_x=1/8)
plt.tight_layout()
# -
# ## 5) CART classifier
# +
fig5 = plt.figure()
ax1 = plt.subplot2grid((2, 2), (0, 0), rowspan=2)
ax2 = plt.subplot2grid((2, 2), (0, 1))
ax3 = plt.subplot2grid((2, 2), (1, 1))
# out of sample ROC curve
plt.sca(ax1)
plt.plot([0, 1], [0, 1], 'k--', lw=1)
plt.plot([0, 0, 1], [0, 1, 1], 'g')
plt.plot(fpr_tree, tpr_tree, 'b')
plt.xlim([-0.01, 1.01])
plt.ylim([-0.01, 1.01])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.legend(['Random fit', 'Perfect fit', 'ROC curve'])
plt.text(0.05, 0.8, 'AUC = %.2f' % auc_tree)
plt.text(0.05, 0.85, 'Error = %.2f' % er_tree)
plt.title('CART classifier: max. depth of tree = %1i (test set)'
% max_depth_tree)
# Scores
plt.sca(ax2)
plt.hist(s_0_tree[~np.isinf(s_0_tree)], 80, density=True, alpha=0.7, color='r')
plt.hist(s_1_tree[~np.isinf(s_1_tree)], 80, density=True, alpha=0.7, color='b')
plt.legend(['S | 0', 'S | 1'])
plt.title('Scores distribution')
# Confusion matrix
plt.sca(ax3)
cax_1 = plt.bar([0, 1], [cm_tree[0, 1]/np.sum(x_test == 0),
cm_tree[1, 0]/np.sum(x_test == 1)])
plt.ylim([0, 1.1])
plt.xticks([0, 1], ('$fpr$', '$fnr$'))
plt.title('Confusion matrix')
add_logo(fig5, location=1, size_frac_x=1/8)
plt.tight_layout()
# -
# ## Decision regions
# +
fig6 = plt.figure()
# Parameters
n_classes = 2
plot_colors = "rb"
plot_step = 0.2
k1 = -10
k2 = -12
z_k1_min = z_estimation[:, k1].min()
z_k1_max = z_estimation[:, k1].max()
z_k2_min = z_estimation[:, k2].min()
z_k2_max = z_estimation[:, k2].max()
zz_k1, zz_k2 = np.meshgrid(np.arange(z_k1_min, z_k1_max, plot_step),
np.arange(z_k2_min, z_k2_max, plot_step))
tree_clf_plot = tree.DecisionTreeClassifier(max_depth=max_depth_tree,
class_weight='balanced')
p_plot = tree_clf_plot.fit(z_estimation[:, [k1, k2]],
x_estimation).predict_proba(np.c_[zz_k1.ravel(),
zz_k2.ravel()])[:, 1]
p_plot = p_plot.reshape(zz_k1.shape)
cs = plt.contourf(zz_k1, zz_k2, p_plot, cmap=plt.cm.RdYlBu)
for i, color in zip(range(n_classes), plot_colors):
idx = np.where(x_estimation == i)
plt.scatter(z_estimation[idx, k1], z_estimation[idx, k2], c=color,
label=['0', '1'][i],
cmap=plt.cm.RdYlBu, edgecolor='black', s=15)
plt.xlabel(list(df)[k1])
plt.ylabel(list(df)[k2])
plt.xlim([z_k1_min, z_k1_max])
plt.ylim([z_k2_min, z_k2_max])
plt.title('CART classifier decision regions')
add_logo(fig6, alpha=0.8, location=3)
plt.tight_layout()
# -
# ## 6) Gradient boosting classifier
# +
fig7 = plt.figure()
ax1 = plt.subplot2grid((2, 2), (0, 0), rowspan=2)
ax2 = plt.subplot2grid((2, 2), (0, 1))
ax3 = plt.subplot2grid((2, 2), (1, 1))
# out of sample ROC curve
plt.sca(ax1)
plt.plot([0, 1], [0, 1], 'k--', lw=1)
plt.plot([0, 0, 1], [0, 1, 1], 'g')
plt.plot(fpr_boost, tpr_boost, 'b')
plt.xlim([-0.01, 1.01])
plt.ylim([-0.01, 1.01])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.legend(['Random fit', 'Perfect fit', 'ROC curve'])
plt.text(0.05, 0.8, 'AUC = %.2f' % auc_tree)
plt.text(0.05, 0.85, 'Error = %.2f' % er_tree)
plt.title('CART classifier with gradient boosting (test set)')
# Scores
plt.sca(ax2)
plt.hist(s_0_boost, 80, density=True, alpha=0.7, color='r')
plt.hist(s_1_boost, 80, density=True, alpha=0.7, color='b')
plt.legend(['S | 0', 'S | 1'])
plt.title('Scores distribution')
# Confusion matrix
plt.sca(ax3)
cax_1 = plt.bar([0, 1], [cm_boost[0, 1]/np.sum(x_test == 0),
cm_boost[1, 0]/np.sum(x_test == 1)])
plt.ylim([0, 1.1])
plt.xticks([0, 1], ('$fpr$', '$fnr$'))
plt.title('Confusion matrix')
add_logo(fig7, location=1, size_frac_x=1/8)
plt.tight_layout()
# -
# ## Decision regions
# +
fig8 = plt.figure()
# Parameters
n_classes = 2
plot_colors = "rb"
plot_step = 0.2
k1 = -10
k2 = -12
z_k1_min = z_estimation[:, k1].min()
z_k1_max = z_estimation[:, k1].max()
z_k2_min = z_estimation[:, k2].min()
z_k2_max = z_estimation[:, k2].max()
zz_k1, zz_k2 = np.meshgrid(np.arange(z_k1_min, z_k1_max, plot_step),
np.arange(z_k2_min, z_k2_max, plot_step))
boost_clf_plot = GradientBoostingClassifier()
p_plot = boost_clf_plot.fit(z_estimation[:, [k1, k2]],
x_estimation).predict_proba(np.c_[zz_k1.ravel(),
zz_k2.ravel()])[:, 1]
p_plot = p_plot.reshape(zz_k1.shape)
cs = plt.contourf(zz_k1, zz_k2, p_plot, cmap=plt.cm.RdYlBu)
for i, color in zip(range(n_classes), plot_colors):
idx = np.where(x_estimation == i)
plt.scatter(z_estimation[idx, k1], z_estimation[idx, k2], c=color,
label=['0', '1'][i],
cmap=plt.cm.RdYlBu, edgecolor='black', s=15)
plt.xlabel(list(df)[k1])
plt.ylabel(list(df)[k2])
plt.xlim([z_k1_min, z_k1_max])
plt.ylim([z_k2_min, z_k2_max])
plt.title('CART classifier with gradient boosting decision regions')
add_logo(fig8, alpha=0.8, location=3)
plt.tight_layout()
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# # s_default_probabilities [<img src="https://www.arpm.co/lab/icons/icon_permalink.png" width=30 height=30 style="display: inline;">](https://www.arpm.co/lab/redirect.php?code=s_default_probabilities&codeLang=Python)
# For details, see [here](https://www.arpm.co/lab/redirect.php?permalink=eb-supervised-machine-learning).
# +
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy.special import logit
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import OneHotEncoder, PolynomialFeatures, \
QuantileTransformer
from sklearn import tree
from sklearn.metrics import auc, roc_curve, confusion_matrix
from sklearn.model_selection import StratifiedKFold, train_test_split
from arpym.tools import add_logo
# -
# ## [Input parameters](https://www.arpm.co/lab/redirect.php?permalink=s_default_probabilities-parameters)
test_size = 0.2 # proportion of the test set
n_sample = 10000 # num. of samples in the database; set =30000 to catch it all
pol_degree = 2 # degrees in polynomial features
lambda_lasso = 0.05 # lasso parameter
max_depth_tree = 10 # maximum depth of decision tree classifier
cross_val = 0 # set "1" to do cross-validation (computational time increases)
k_ = 5 # parameter of Stratified K-Folds cross-validator
# ## [Step 0](https://www.arpm.co/lab/redirect.php?permalink=s_default_probabilities-implementation-step00): Import data and pre-process database
# +
# Import data
path = '../../../databases/global-databases/credit/' + \
'db_default_data_creditcardsclients/'
df = pd.read_csv(path+'db_default_data_creditcardsclients.csv')
df = df.iloc[:, 1:df.shape[1]] # exlude ID
# Sort database so that the categorical features are at the beginning
# indexes of the categorical features
ind_cat = np.r_[np.arange(1, 4), np.arange(5, 11)]
n_cat = len(ind_cat) # number of categorical features
# indexes of the continuous features
ind_cont = np.r_[np.array([0, 4]), np.arange(11, df.shape[1])]
n_cont = len(ind_cont) # number of categorical features
df = df.iloc[:n_sample, np.r_[ind_cat, ind_cont]]
# Outputs and features
z = np.array(df.iloc[:, :-1]) # features
x = np.array(df.iloc[:, -1]) # labels
# Standardize continuous features
quantile_transformer = QuantileTransformer(output_distribution='normal')
z_cont = quantile_transformer.fit_transform(z[:, -n_cont:])
# Transform categorical features via one-hot encoding
# shift up, because the OneHotEncoder takes only positive inputs
enc = OneHotEncoder()
z_cat = enc.fit_transform(np.abs(np.min(z[:, :n_cat], axis=0)) +
z[:, :n_cat]).toarray()
n_enc = z_cat.shape[1] # number of encoded categorical features
z = np.concatenate((z_cat, z_cont), axis=1)
# Define test set and estimation set
z_estimation, z_test, x_estimation, x_test = train_test_split(z, x)
# -
# ## [Step 1](https://www.arpm.co/lab/redirect.php?permalink=s_default_probabilities-implementation-step01): Logistic regression on continuous features
# Set C = +infinity to have 0 Lasso parameter
lg = LogisticRegression(penalty='l1', C=10**5, class_weight='balanced')
lg = lg.fit(z_estimation[:, -n_cont:], x_estimation) # fit the model
p_z_lg = lg.predict_proba(z_test[:, -n_cont:])[:, 1] # predict the probs
cm_lg = confusion_matrix(x_test, lg.predict(z_test[:, -n_cont:])) # conf. mat.
er_lg = -np.sum(np.log(p_z_lg)) # error
print('Logistic error: %1.4f' % er_lg)
# conditional scores
s_0_lg = logit(lg.predict_proba(z_test[:, -n_cont:])[
np.where(x_test == 0)[0], 1])
s_1_lg = logit(lg.predict_proba(z_test[:, -n_cont:])[
np.where(x_test == 1)[0], 1])
# ## [Step 2](https://www.arpm.co/lab/redirect.php?permalink=s_default_probabilities-implementation-step02): Add interactions to logistic regression
# +
# Add interactions
poly = PolynomialFeatures(degree=pol_degree)
z_estimation_inter = poly.fit_transform(z_estimation[:, -n_cont:])
z_test_inter = poly.fit_transform(z_test[:, -n_cont:])
# Set C = +infinity to have 0 Lasso parameter
lg_inter = LogisticRegression(penalty='l1', C=10**5, class_weight='balanced')
lg_inter = lg_inter.fit(z_estimation_inter, x_estimation) # fit the model
p_z_inter = lg_inter.predict_proba(z_test_inter)[:, 1] # pred. the probs.
cm_inter = confusion_matrix(x_test, lg_inter.predict(z_test_inter))
er_inter = -np.sum(np.log(p_z_inter)) # error
print('Logistic with interactions error: %1.4f' % er_inter)
# conditional scores
s_0_inter = logit(lg_inter.predict_proba(z_test_inter)[
np.where(x_test == 0)[0], 1])
s_1_inter = logit(lg_inter.predict_proba(z_test_inter)[
np.where(x_test == 1)[0], 1])
# -
# ## [Step 3](https://www.arpm.co/lab/redirect.php?permalink=s_default_probabilities-implementation-step03): Add encoded categorical features to logistic regression
# +
z_enc_estimation = np.concatenate((z_estimation[:, :n_enc],
z_estimation_inter), axis=1)
z_enc_test = np.concatenate((z_test[:, :n_enc], z_test_inter), axis=1)
# Set C = +infinity to have 0 Lasso parameter
lg_enc = LogisticRegression(penalty='l1', C=10**5, class_weight='balanced')
lg_enc = lg_enc.fit(z_enc_estimation, x_estimation) # fit the model
p_z_enc = lg_enc.predict_proba(z_enc_test)[:, 1] # pred. the probs.
cm_enc = confusion_matrix(x_test, lg_enc.predict(z_enc_test))
er_enc = -np.sum(np.log(p_z_enc)) # error
print('Logistic with interactions and categorical error: %1.4f' % er_enc)
# conditional scores
s_0_enc = logit(lg_enc.predict_proba(z_enc_test)[np.where(x_test == 0)[0], 1])
s_1_enc = logit(lg_enc.predict_proba(z_enc_test)[np.where(x_test == 1)[0], 1])
# -
# ## [Step 4](https://www.arpm.co/lab/redirect.php?permalink=s_default_probabilities-implementation-step04): Add lasso regularization
lg_lasso = LogisticRegression(penalty='l1', C=10**5, class_weight='balanced')
lg_lasso = lg_lasso.fit(z_enc_estimation, x_estimation) # fit the model
p_z_lasso = lg_lasso.predict_proba(z_enc_test)[:, 1] # predict the probs.
cm_lasso = confusion_matrix(x_test, lg_lasso.predict(z_enc_test)) # conf. mat.
er_lasso = -np.sum(np.log(p_z_lasso)) # error
print('Logistic with lasso error: %1.4f' % er_lasso)
# conditional scores
s_0_lasso = logit(lg_lasso.predict_proba(z_enc_test)[
np.where(x_test == 0)[0], 1])
s_1_lasso = logit(lg_lasso.predict_proba(z_enc_test)[
np.where(x_test == 1)[0], 1])
# ## [Step 5](https://www.arpm.co/lab/redirect.php?permalink=s_default_probabilities-implementation-step05): CART classifier
tree_clf = tree.DecisionTreeClassifier(max_depth=max_depth_tree,
class_weight='balanced') # def. method
tree_clf = tree_clf.fit(z_enc_estimation, x_estimation) # fit the model
p_z_tree = tree_clf.predict_proba(z_enc_test)[:, 1] # predict the scores
cm_tree = confusion_matrix(x_test, tree_clf.predict(z_enc_test)) # conf. mat.
er_tree = (cm_tree[0, 1]/np.sum(x_test == 0) +
cm_tree[1, 0]/np.sum(x_test == 1)) # error
print('CART classifier error: %1.4f' % er_tree)
# conditional scores
eps = 10**-5 # set threshold to avoid numerical noise in the logit function
p_0_tree = tree_clf.predict_proba(z_enc_test)[np.where(x_test == 0)[0], 1]
p_0_tree[p_0_tree < eps] = eps
p_0_tree[p_0_tree > 1-eps] = 1-eps
p_1_tree = tree_clf.predict_proba(z_enc_test)[np.where(x_test == 1)[0], 1]
p_1_tree[p_1_tree < eps] = eps
p_1_tree[p_1_tree > 1-eps] = 1-eps
s_0_tree = logit(p_0_tree)
s_1_tree = logit(p_1_tree)
# ## [Step 6](https://www.arpm.co/lab/redirect.php?permalink=s_default_probabilities-implementation-step06): Add gradient boosting to CART classifier
boost_clf = GradientBoostingClassifier(max_depth=max_depth_tree) # method
boost_clf = boost_clf.fit(z_enc_estimation, x_estimation) # fit the model
p_z_boost = boost_clf.predict_proba(z_enc_test)[:, 1] # predict the probs.
cm_boost = confusion_matrix(x_test, boost_clf.predict(z_enc_test)) # conf. mat
er_boost = (cm_boost[0, 1]/np.sum(x_test == 0) +
cm_boost[1, 0]/np.sum(x_test == 1)) # error
print('CART classifier with gradient boosting error: %1.4f' % er_boost)
# conditional scores
s_0_boost = logit(boost_clf.predict_proba(z_enc_test)[
np.where(x_test == 0)[0], 1])
s_1_boost = logit(boost_clf.predict_proba(z_enc_test)[
np.where(x_test == 1)[0], 1])
# ## [Step 7](https://www.arpm.co/lab/redirect.php?permalink=s_default_probabilities-implementation-step07): Compute fpr, tpr and AUC on the test set
# +
# 1) Logistic
fpr_lg, tpr_lg, _ = roc_curve(x_test, p_z_lg)
auc_lg = auc(fpr_lg, tpr_lg)
print('Logistic AUC: %1.3f' % auc_lg)
# 2) Logistic with interactions
fpr_inter, tpr_inter, _ = roc_curve(x_test, p_z_inter)
auc_inter = auc(fpr_inter, tpr_inter)
print('Logistic with interactions AUC: %1.3f' % auc_inter)
# 3) Logistic with interactions and encoded categorical features
fpr_enc, tpr_enc, _ = roc_curve(x_test, p_z_enc)
auc_enc = auc(fpr_enc, tpr_enc)
print('Logistic with interactions and categorical AUC: %1.3f' % auc_enc)
# 4) Logistic lasso with interactions and encoded categorical features
fpr_lasso, tpr_lasso, _ = roc_curve(x_test, p_z_lasso)
auc_lasso = auc(fpr_lasso, tpr_lasso)
print('Logistic with lasso AUC: %1.3f' % auc_lasso)
# 5) CART classifier
fpr_tree, tpr_tree, _ = roc_curve(x_test, p_z_tree)
auc_tree = auc(fpr_tree, tpr_tree)
print('CART classifier AUC: %1.3f' % auc_tree)
# 6) Gradient boosting classifier
fpr_boost, tpr_boost, _ = roc_curve(x_test, p_z_boost)
auc_boost = auc(fpr_boost, tpr_boost)
print('Gradient boosting classifier AUC: %1.3f' % auc_boost)
# -
# ## [Step 8](https://www.arpm.co/lab/redirect.php?permalink=s_default_probabilities-implementation-step08): Choose best probabilistic and point predictors via cross-validation
if cross_val == 1:
# Split the estimation set into training and validation sets for k-fold
# cross-validation
k_fold = StratifiedKFold(n_splits=k_)
z_train = []
z_train_inter = []
z_train_enc = []
x_train = []
z_val = []
z_val_inter = []
z_val_enc = []
x_val = []
for train, val in k_fold.split(z_estimation, x_estimation):
z_train.append(z_estimation[train])
x_train.append(x_estimation[train])
z_val.append(z_estimation[val])
x_val.append(x_estimation[val])
for train, val in k_fold.split(z_estimation_inter, x_estimation):
z_train_inter.append(z_estimation_inter[train])
z_val_inter.append(z_estimation_inter[val])
for train, val in k_fold.split(z_enc_estimation, x_estimation):
z_train_enc.append(z_enc_estimation[train])
z_val_enc.append(z_enc_estimation[val])
# Probabilistic
cv_er_lg = []
cv_er_lasso = []
cv_er_inter = []
cv_er_enc = []
for k in range(k_):
# Logistic
p_cv_lg = lg.fit(z_train[k], x_train[k]).predict_proba(z_val[k])
cv_er_lg.append(-np.sum(np.log(p_cv_lg)))
# Lasso
p_cv_lasso = lg_lasso.fit(z_train[k],
x_train[k]).predict_proba(z_val[k])
cv_er_lasso.append(-np.sum(np.log(p_cv_lasso)))
# Interactions
p_cv_inter = lg_inter.fit(z_train_inter[k],
x_train[k]).predict_proba(z_val_inter[k])
cv_er_inter.append(-np.sum(np.log(p_cv_inter)))
# Encoded categorical
p_cv_enc = lg_inter.fit(z_train_enc[k],
x_train[k]).predict_proba(z_val_enc[k])
cv_er_enc.append(-np.sum(np.log(p_cv_enc)))
cv_er_lg = np.mean(cv_er_lg)
cv_er_lasso = np.mean(cv_er_lasso)
cv_er_inter = np.mean(cv_er_inter)
cv_er_enc = np.mean(cv_er_enc)
# Point
cv_er_tree = []
cv_er_boost = []
for k in range(k_):
# Tree
cm_tree_cv =\
confusion_matrix(x_val[k],
tree_clf.fit(z_train[k],
x_train[k]).predict(z_val[k]))
er_tree_cv = (cm_tree_cv[0, 1]/np.sum(x_val[k] == 0) +
cm_tree_cv[1, 0]/np.sum(x_val[k] == 1)) # error
cv_er_tree.append(er_tree_cv)
# Gradient boosting
cm_boost_cv =\
confusion_matrix(x_val[k],
boost_clf.fit(z_train[k],
x_train[k]).predict(z_val[k]))
er_boost_cv = (cm_boost_cv[0, 1]/np.sum(x_val[k] == 0) +
cm_boost_cv[1, 0]/np.sum(x_val[k] == 1)) # error
cv_er_boost.append(er_boost_cv)
cv_er_tree = np.mean(cv_er_tree)
cv_er_boost = np.mean(cv_er_boost)
print('Logistic CV error: %1.3f' % cv_er_lg)
print('Logistic with interactions CV error: %1.3f' % cv_er_inter)
print('Logistic with interactions and categorical CV error: %1.3f' %
cv_er_enc)
print('Logistic with lasso CV error: %1.3f' % cv_er_lasso)
print('CART classifier CV error: %1.3f' % cv_er_tree)
print('CART classifier with gradient boosting CV error: %1.3f' %
cv_er_boost)
# ## Plots
plt.style.use('arpm')
# ## 1) Logistic regression
# +
fig1 = plt.figure()
ax11 = plt.subplot2grid((2, 2), (0, 0), rowspan=2)
ax12 = plt.subplot2grid((2, 2), (0, 1))
ax13 = plt.subplot2grid((2, 2), (1, 1))
# out of sample ROC curve
plt.sca(ax11)
plt.plot([0, 1], [0, 1], 'k--', lw=1)
plt.plot([0, 0, 1], [0, 1, 1], 'g')
plt.plot(fpr_lg, tpr_lg, 'b')
plt.xlim([-0.01, 1.01])
plt.ylim([-0.01, 1.01])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.legend(['Random fit', 'Perfect fit', 'ROC curve'])
plt.text(0.05, 0.8, 'AUC = %.2f' % auc_lg)
plt.text(0.05, 0.85, 'Error = %.2f' % er_lg)
plt.title('Logistic regression (test set)')
# Scores
plt.sca(ax12)
plt.hist(s_0_lg, 80, density=True, alpha=0.7, color='r')
plt.hist(s_1_lg, 80, density=True, alpha=0.7, color='b')
plt.legend(['S | 0', 'S | 1'])
plt.title('Scores distribution')
# Confusion matrix
plt.sca(ax13)
cax_1 = plt.bar([0, 1], [cm_lg[0, 1]/np.sum(x_test == 0),
cm_lg[1, 0]/np.sum(x_test == 1)])
plt.ylim([0, 1.1])
plt.xticks([0, 1], ('$fpr$', '$fnr$'))
plt.title('Confusion matrix')
add_logo(fig1, location=1, size_frac_x=1/8)
plt.tight_layout()
# -
# ## 2) Logistic regression with interactions
# +
fig2 = plt.figure()
ax31 = plt.subplot2grid((2, 2), (0, 0), rowspan=2)
ax32 = plt.subplot2grid((2, 2), (0, 1))
ax33 = plt.subplot2grid((2, 2), (1, 1))
# out of sample ROC curve
plt.sca(ax31)
plt.plot([0, 1], [0, 1], 'k--', lw=1)
plt.plot([0, 0, 1], [0, 1, 1], 'g')
plt.plot(fpr_inter, tpr_inter, 'b')
plt.xlim([-0.01, 1.01])
plt.ylim([-0.01, 1.01])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.legend(['Random fit', 'Perfect fit', 'ROC curve'])
plt.text(0.05, 0.8, 'AUC = %.2f' % auc_inter)
plt.text(0.05, 0.85, 'Error = %.2f' % er_inter)
plt.title('Logistic regression with interactions deg. = %1i (test set)'
% pol_degree)
# Scores
plt.sca(ax32)
plt.hist(s_0_inter, 80, density=True, alpha=0.7, color='r')
plt.hist(s_1_inter, 80, density=True, alpha=0.7, color='b')
plt.legend(['S | 0', 'S | 1'])
plt.title('Scores distribution')
# Confusion matrix
plt.sca(ax33)
cax_1 = plt.bar([0, 1], [cm_inter[0, 1]/np.sum(x_test == 0),
cm_inter[1, 0]/np.sum(x_test == 1)])
plt.ylim([0, 1.1])
plt.xticks([0, 1], ('$fpr$', '$fnr$'))
plt.title('Confusion matrix')
add_logo(fig2, location=1, size_frac_x=1/8)
plt.tight_layout()
# -
# ## 3) Logistic regression with interactions and encoded categorical features
# +
fig3 = plt.figure()
ax21 = plt.subplot2grid((2, 2), (0, 0), rowspan=2)
ax22 = plt.subplot2grid((2, 2), (0, 1))
ax23 = plt.subplot2grid((2, 2), (1, 1))
# out of sample ROC curve
plt.sca(ax21)
plt.plot([0, 1], [0, 1], 'k--', lw=1)
plt.plot([0, 0, 1], [0, 1, 1], 'g')
plt.plot(fpr_enc, tpr_enc, 'b')
plt.xlim([-0.01, 1.01])
plt.ylim([-0.01, 1.01])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.legend(['Random fit', 'Perfect fit', 'ROC curve'])
plt.text(0.05, 0.8, 'AUC = %.2f' % auc_enc)
plt.text(0.05, 0.85, 'Error = %.2f' % er_enc)
plt.title('Logistic regression with interactions and categorical features')
# Scores
plt.sca(ax22)
plt.hist(s_0_enc, 80, density=True, alpha=0.7, color='r')
plt.hist(s_1_enc, 80, density=True, alpha=0.7, color='b')
plt.legend(['S | 0', 'S | 1'])
plt.title('Scores distribution')
# Confusion matrix
plt.sca(ax23)
cax_1 = plt.bar([0, 1], [cm_enc[0, 1]/np.sum(x_test == 0),
cm_enc[1, 0]/np.sum(x_test == 1)])
plt.ylim([0, 1.1])
plt.xticks([0, 1], ('$fpr$', '$fnr$'))
plt.title('Confusion matrix')
add_logo(fig3, location=1, size_frac_x=1/8)
plt.tight_layout()
# -
# ## 4) Logistic regression with lasso
# +
fig4 = plt.figure()
ax21 = plt.subplot2grid((2, 2), (0, 0), rowspan=2)
ax22 = plt.subplot2grid((2, 2), (0, 1))
ax23 = plt.subplot2grid((2, 2), (1, 1))
# out of sample ROC curve
plt.sca(ax21)
plt.plot([0, 1], [0, 1], 'k--', lw=1)
plt.plot([0, 0, 1], [0, 1, 1], 'g')
plt.plot(fpr_lasso, tpr_lasso, 'b')
plt.xlim([-0.01, 1.01])
plt.ylim([-0.01, 1.01])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.legend(['Random fit', 'Perfect fit', 'ROC curve'])
plt.text(0.05, 0.8, 'AUC = %.2f' % auc_lasso)
plt.text(0.05, 0.85, 'Error = %.2f' % er_lasso)
plt.title('Logistic regression with Lasso param. = %1.2e (test set)' %
lambda_lasso)
# Scores
plt.sca(ax22)
plt.hist(s_0_lasso, 80, density=True, alpha=0.7, color='r')
plt.hist(s_1_lasso, 80, density=True, alpha=0.7, color='b')
plt.legend(['S | 0', 'S | 1'])
plt.title('Scores distribution')
# Confusion matrix
plt.sca(ax23)
cax_1 = plt.bar([0, 1], [cm_lasso[0, 1]/np.sum(x_test == 0),
cm_lasso[1, 0]/np.sum(x_test == 1)])
plt.ylim([0, 1.1])
plt.xticks([0, 1], ('$fpr$', '$fnr$'))
plt.title('Confusion matrix')
add_logo(fig4, location=1, size_frac_x=1/8)
plt.tight_layout()
# -
# ## 5) CART classifier
# +
fig5 = plt.figure()
ax1 = plt.subplot2grid((2, 2), (0, 0), rowspan=2)
ax2 = plt.subplot2grid((2, 2), (0, 1))
ax3 = plt.subplot2grid((2, 2), (1, 1))
# out of sample ROC curve
plt.sca(ax1)
plt.plot([0, 1], [0, 1], 'k--', lw=1)
plt.plot([0, 0, 1], [0, 1, 1], 'g')
plt.plot(fpr_tree, tpr_tree, 'b')
plt.xlim([-0.01, 1.01])
plt.ylim([-0.01, 1.01])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.legend(['Random fit', 'Perfect fit', 'ROC curve'])
plt.text(0.05, 0.8, 'AUC = %.2f' % auc_tree)
plt.text(0.05, 0.85, 'Error = %.2f' % er_tree)
plt.title('CART classifier: max. depth of tree = %1i (test set)'
% max_depth_tree)
# Scores
plt.sca(ax2)
plt.hist(s_0_tree[~np.isinf(s_0_tree)], 80, density=True, alpha=0.7, color='r')
plt.hist(s_1_tree[~np.isinf(s_1_tree)], 80, density=True, alpha=0.7, color='b')
plt.legend(['S | 0', 'S | 1'])
plt.title('Scores distribution')
# Confusion matrix
plt.sca(ax3)
cax_1 = plt.bar([0, 1], [cm_tree[0, 1]/np.sum(x_test == 0),
cm_tree[1, 0]/np.sum(x_test == 1)])
plt.ylim([0, 1.1])
plt.xticks([0, 1], ('$fpr$', '$fnr$'))
plt.title('Confusion matrix')
add_logo(fig5, location=1, size_frac_x=1/8)
plt.tight_layout()
# -
# ## Decision regions
# +
fig6 = plt.figure()
# Parameters
n_classes = 2
plot_colors = "rb"
plot_step = 0.2
k1 = -10
k2 = -12
z_k1_min = z_estimation[:, k1].min()
z_k1_max = z_estimation[:, k1].max()
z_k2_min = z_estimation[:, k2].min()
z_k2_max = z_estimation[:, k2].max()
zz_k1, zz_k2 = np.meshgrid(np.arange(z_k1_min, z_k1_max, plot_step),
np.arange(z_k2_min, z_k2_max, plot_step))
tree_clf_plot = tree.DecisionTreeClassifier(max_depth=max_depth_tree,
class_weight='balanced')
p_plot = tree_clf_plot.fit(z_estimation[:, [k1, k2]],
x_estimation).predict_proba(np.c_[zz_k1.ravel(),
zz_k2.ravel()])[:, 1]
p_plot = p_plot.reshape(zz_k1.shape)
cs = plt.contourf(zz_k1, zz_k2, p_plot, cmap=plt.cm.RdYlBu)
for i, color in zip(range(n_classes), plot_colors):
idx = np.where(x_estimation == i)
plt.scatter(z_estimation[idx, k1], z_estimation[idx, k2], c=color,
label=['0', '1'][i],
cmap=plt.cm.RdYlBu, edgecolor='black', s=15)
plt.xlabel(list(df)[k1])
plt.ylabel(list(df)[k2])
plt.xlim([z_k1_min, z_k1_max])
plt.ylim([z_k2_min, z_k2_max])
plt.title('CART classifier decision regions')
add_logo(fig6, alpha=0.8, location=3)
plt.tight_layout()
# -
# ## 6) Gradient boosting classifier
# +
fig7 = plt.figure()
ax1 = plt.subplot2grid((2, 2), (0, 0), rowspan=2)
ax2 = plt.subplot2grid((2, 2), (0, 1))
ax3 = plt.subplot2grid((2, 2), (1, 1))
# out of sample ROC curve
plt.sca(ax1)
plt.plot([0, 1], [0, 1], 'k--', lw=1)
plt.plot([0, 0, 1], [0, 1, 1], 'g')
plt.plot(fpr_boost, tpr_boost, 'b')
plt.xlim([-0.01, 1.01])
plt.ylim([-0.01, 1.01])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.legend(['Random fit', 'Perfect fit', 'ROC curve'])
plt.text(0.05, 0.8, 'AUC = %.2f' % auc_tree)
plt.text(0.05, 0.85, 'Error = %.2f' % er_tree)
plt.title('CART classifier with gradient boosting (test set)')
# Scores
plt.sca(ax2)
plt.hist(s_0_boost, 80, density=True, alpha=0.7, color='r')
plt.hist(s_1_boost, 80, density=True, alpha=0.7, color='b')
plt.legend(['S | 0', 'S | 1'])
plt.title('Scores distribution')
# Confusion matrix
plt.sca(ax3)
cax_1 = plt.bar([0, 1], [cm_boost[0, 1]/np.sum(x_test == 0),
cm_boost[1, 0]/np.sum(x_test == 1)])
plt.ylim([0, 1.1])
plt.xticks([0, 1], ('$fpr$', '$fnr$'))
plt.title('Confusion matrix')
add_logo(fig7, location=1, size_frac_x=1/8)
plt.tight_layout()
# -
# ## Decision regions
# +
fig8 = plt.figure()
# Parameters
n_classes = 2
plot_colors = "rb"
plot_step = 0.2
k1 = -10
k2 = -12
z_k1_min = z_estimation[:, k1].min()
z_k1_max = z_estimation[:, k1].max()
z_k2_min = z_estimation[:, k2].min()
z_k2_max = z_estimation[:, k2].max()
zz_k1, zz_k2 = np.meshgrid(np.arange(z_k1_min, z_k1_max, plot_step),
np.arange(z_k2_min, z_k2_max, plot_step))
boost_clf_plot = GradientBoostingClassifier()
p_plot = boost_clf_plot.fit(z_estimation[:, [k1, k2]],
x_estimation).predict_proba(np.c_[zz_k1.ravel(),
zz_k2.ravel()])[:, 1]
p_plot = p_plot.reshape(zz_k1.shape)
cs = plt.contourf(zz_k1, zz_k2, p_plot, cmap=plt.cm.RdYlBu)
for i, color in zip(range(n_classes), plot_colors):
idx = np.where(x_estimation == i)
plt.scatter(z_estimation[idx, k1], z_estimation[idx, k2], c=color,
label=['0', '1'][i],
cmap=plt.cm.RdYlBu, edgecolor='black', s=15)
plt.xlabel(list(df)[k1])
plt.ylabel(list(df)[k2])
plt.xlim([z_k1_min, z_k1_max])
plt.ylim([z_k2_min, z_k2_max])
plt.title('CART classifier with gradient boosting decision regions')
add_logo(fig8, alpha=0.8, location=3)
plt.tight_layout()
| en | 0.615117 | # --- # jupyter: # jupytext: # text_representation: # extension: .py # format_name: light # format_version: '1.4' # jupytext_version: 1.1.4 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # # s_default_probabilities [<img src="https://www.arpm.co/lab/icons/icon_permalink.png" width=30 height=30 style="display: inline;">](https://www.arpm.co/lab/redirect.php?code=s_default_probabilities&codeLang=Python) # For details, see [here](https://www.arpm.co/lab/redirect.php?permalink=eb-supervised-machine-learning). # + # - # ## [Input parameters](https://www.arpm.co/lab/redirect.php?permalink=s_default_probabilities-parameters) # proportion of the test set # num. of samples in the database; set =30000 to catch it all # degrees in polynomial features # lasso parameter # maximum depth of decision tree classifier # set "1" to do cross-validation (computational time increases) # parameter of Stratified K-Folds cross-validator # ## [Step 0](https://www.arpm.co/lab/redirect.php?permalink=s_default_probabilities-implementation-step00): Import data and pre-process database # + # Import data # exlude ID # Sort database so that the categorical features are at the beginning # indexes of the categorical features # number of categorical features # indexes of the continuous features # number of categorical features # Outputs and features # features # labels # Standardize continuous features # Transform categorical features via one-hot encoding # shift up, because the OneHotEncoder takes only positive inputs # number of encoded categorical features # Define test set and estimation set # - # ## [Step 1](https://www.arpm.co/lab/redirect.php?permalink=s_default_probabilities-implementation-step01): Logistic regression on continuous features # Set C = +infinity to have 0 Lasso parameter # fit the model # predict the probs # conf. mat. # error # conditional scores # ## [Step 2](https://www.arpm.co/lab/redirect.php?permalink=s_default_probabilities-implementation-step02): Add interactions to logistic regression # + # Add interactions # Set C = +infinity to have 0 Lasso parameter # fit the model # pred. the probs. # error # conditional scores # - # ## [Step 3](https://www.arpm.co/lab/redirect.php?permalink=s_default_probabilities-implementation-step03): Add encoded categorical features to logistic regression # + # Set C = +infinity to have 0 Lasso parameter # fit the model # pred. the probs. # error # conditional scores # - # ## [Step 4](https://www.arpm.co/lab/redirect.php?permalink=s_default_probabilities-implementation-step04): Add lasso regularization # fit the model # predict the probs. # conf. mat. # error # conditional scores # ## [Step 5](https://www.arpm.co/lab/redirect.php?permalink=s_default_probabilities-implementation-step05): CART classifier # def. method # fit the model # predict the scores # conf. mat. # error # conditional scores # set threshold to avoid numerical noise in the logit function # ## [Step 6](https://www.arpm.co/lab/redirect.php?permalink=s_default_probabilities-implementation-step06): Add gradient boosting to CART classifier # method # fit the model # predict the probs. # conf. mat # error # conditional scores # ## [Step 7](https://www.arpm.co/lab/redirect.php?permalink=s_default_probabilities-implementation-step07): Compute fpr, tpr and AUC on the test set # + # 1) Logistic # 2) Logistic with interactions # 3) Logistic with interactions and encoded categorical features # 4) Logistic lasso with interactions and encoded categorical features # 5) CART classifier # 6) Gradient boosting classifier # - # ## [Step 8](https://www.arpm.co/lab/redirect.php?permalink=s_default_probabilities-implementation-step08): Choose best probabilistic and point predictors via cross-validation # Split the estimation set into training and validation sets for k-fold # cross-validation # Probabilistic # Logistic # Lasso # Interactions # Encoded categorical # Point # Tree # error # Gradient boosting # error # ## Plots # ## 1) Logistic regression # + # out of sample ROC curve # Scores # Confusion matrix # - # ## 2) Logistic regression with interactions # + # out of sample ROC curve # Scores # Confusion matrix # - # ## 3) Logistic regression with interactions and encoded categorical features # + # out of sample ROC curve # Scores # Confusion matrix # - # ## 4) Logistic regression with lasso # + # out of sample ROC curve # Scores # Confusion matrix # - # ## 5) CART classifier # + # out of sample ROC curve # Scores # Confusion matrix # - # ## Decision regions # + # Parameters # - # ## 6) Gradient boosting classifier # + # out of sample ROC curve # Scores # Confusion matrix # - # ## Decision regions # + # Parameters | 2.391799 | 2 |
tools/pvacfuse/net_chop.py | atwollam/pVACtools | 0 | 6624321 | <reponame>atwollam/pVACtools<gh_stars>0
import sys
from lib.net_chop import *
def define_parser():
return NetChop.parser('pvacfuse')
def main(args_input = sys.argv[1:]):
parser = define_parser()
args = parser.parse_args(args_input)
NetChop(args.input_file, args.input_fasta, args.output_file, args.method, args.threshold, 'pVACfuse').execute()
if __name__ == "__main__":
main()
| import sys
from lib.net_chop import *
def define_parser():
return NetChop.parser('pvacfuse')
def main(args_input = sys.argv[1:]):
parser = define_parser()
args = parser.parse_args(args_input)
NetChop(args.input_file, args.input_fasta, args.output_file, args.method, args.threshold, 'pVACfuse').execute()
if __name__ == "__main__":
main() | none | 1 | 2.665367 | 3 | |
learning.py | dan840611/Python | 0 | 6624322 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Sep 4 20:55:11 2017
@author: Dan
"""
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
'''
y = np.random.standard_normal(20)
y2 = y * 100
plt.plot(y.cumsum(), 'b', lw = 1.5, label='1st')
plt.plot(y.cumsum(), 'ro')
plt.plot(y, 'r', label = '2nd')
#參數： tight 所影數據可見；[xmin, xmax, ymin, ymax]
plt.axis('tight')
plt.xlabel('index')
plt.ylabel('value')
plt.title('Practice')
# 0 為最佳位置
plt.legend(loc=0)
#雙座標軸
ax1 = plt.subplots()
plt.plot(y.cumsum(), 'b', label = '1st')
plt.plot(y.cumsum(), 'ro')
plt.xlabel('x')
plt.ylabel('y')
plt.axis('tight')
plt.legend(loc=0)
plt.grid(True)
ax2 = ax1.twinx()
plt.plot(y2, 'r', label = '2st')
plt.legend(loc = 0)
plt.ylabel('2nd y ')
#多圖
plt.subplot(211) #121
plt.plot(y.cumsum(), 'b', label = '1st')
plt.plot(y.cumsum(), 'ro')
plt.xlabel('x')
plt.ylabel('y')
plt.axis('tight')
plt.legend(loc=0)
plt.grid(True)
plt.subplot(212) #122
plt.plot(y2, 'r', label = '2st')
plt.legend(loc = 0)
plt.ylabel('2nd y ')
'''
'''
##############################
#scatter
y = np.random.standard_normal((1000, 2))
c = np.random.randint(0 , 10, len(y))
plt.scatter(y[:, 0], y[:, 1] , c = c, marker = 'o')
plt.colorbar()
plt.xlabel('x')
plt.ylabel('y')
plt.axis('tight')
plt.title('test')
plt.legend(loc=0)
plt.grid(True)
#histogram
plt.hist(y, label= ['1st', '2nd'], bins = 25)
plt.xlabel('x')
plt.ylabel('y')
plt.axis('tight')
plt.title('test')
plt.legend(loc=0)
plt.grid(True)
#boxplot
plt.boxplot(y)
plt.grid(True)
plt.setp(ax, xticklabels = ['1st', '2nd'])
plt.xlabel('dataset')
plt.ylabel('valur')
plt.title('Boxplot')
'''
#finance plot
import matplotlib.finance as mpf
start = (2010, 1, 1)
end = (2017, 9, 22)
quotes = mpf.quotes_historical_yahoo_ochl('^GDAXI', start, end) | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Sep 4 20:55:11 2017
@author: Dan
"""
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
'''
y = np.random.standard_normal(20)
y2 = y * 100
plt.plot(y.cumsum(), 'b', lw = 1.5, label='1st')
plt.plot(y.cumsum(), 'ro')
plt.plot(y, 'r', label = '2nd')
#參數： tight 所影數據可見；[xmin, xmax, ymin, ymax]
plt.axis('tight')
plt.xlabel('index')
plt.ylabel('value')
plt.title('Practice')
# 0 為最佳位置
plt.legend(loc=0)
#雙座標軸
ax1 = plt.subplots()
plt.plot(y.cumsum(), 'b', label = '1st')
plt.plot(y.cumsum(), 'ro')
plt.xlabel('x')
plt.ylabel('y')
plt.axis('tight')
plt.legend(loc=0)
plt.grid(True)
ax2 = ax1.twinx()
plt.plot(y2, 'r', label = '2st')
plt.legend(loc = 0)
plt.ylabel('2nd y ')
#多圖
plt.subplot(211) #121
plt.plot(y.cumsum(), 'b', label = '1st')
plt.plot(y.cumsum(), 'ro')
plt.xlabel('x')
plt.ylabel('y')
plt.axis('tight')
plt.legend(loc=0)
plt.grid(True)
plt.subplot(212) #122
plt.plot(y2, 'r', label = '2st')
plt.legend(loc = 0)
plt.ylabel('2nd y ')
'''
'''
##############################
#scatter
y = np.random.standard_normal((1000, 2))
c = np.random.randint(0 , 10, len(y))
plt.scatter(y[:, 0], y[:, 1] , c = c, marker = 'o')
plt.colorbar()
plt.xlabel('x')
plt.ylabel('y')
plt.axis('tight')
plt.title('test')
plt.legend(loc=0)
plt.grid(True)
#histogram
plt.hist(y, label= ['1st', '2nd'], bins = 25)
plt.xlabel('x')
plt.ylabel('y')
plt.axis('tight')
plt.title('test')
plt.legend(loc=0)
plt.grid(True)
#boxplot
plt.boxplot(y)
plt.grid(True)
plt.setp(ax, xticklabels = ['1st', '2nd'])
plt.xlabel('dataset')
plt.ylabel('valur')
plt.title('Boxplot')
'''
#finance plot
import matplotlib.finance as mpf
start = (2010, 1, 1)
end = (2017, 9, 22)
quotes = mpf.quotes_historical_yahoo_ochl('^GDAXI', start, end) | zh | 0.069251 | #!/usr/bin/env python3 # -*- coding: utf-8 -*- Created on Mon Sep 4 20:55:11 2017 @author: Dan y = np.random.standard_normal(20) y2 = y * 100 plt.plot(y.cumsum(), 'b', lw = 1.5, label='1st') plt.plot(y.cumsum(), 'ro') plt.plot(y, 'r', label = '2nd') #參數： tight 所影數據可見；[xmin, xmax, ymin, ymax] plt.axis('tight') plt.xlabel('index') plt.ylabel('value') plt.title('Practice') # 0 為最佳位置 plt.legend(loc=0) #雙座標軸 ax1 = plt.subplots() plt.plot(y.cumsum(), 'b', label = '1st') plt.plot(y.cumsum(), 'ro') plt.xlabel('x') plt.ylabel('y') plt.axis('tight') plt.legend(loc=0) plt.grid(True) ax2 = ax1.twinx() plt.plot(y2, 'r', label = '2st') plt.legend(loc = 0) plt.ylabel('2nd y ') #多圖 plt.subplot(211) #121 plt.plot(y.cumsum(), 'b', label = '1st') plt.plot(y.cumsum(), 'ro') plt.xlabel('x') plt.ylabel('y') plt.axis('tight') plt.legend(loc=0) plt.grid(True) plt.subplot(212) #122 plt.plot(y2, 'r', label = '2st') plt.legend(loc = 0) plt.ylabel('2nd y ') ############################## #scatter y = np.random.standard_normal((1000, 2)) c = np.random.randint(0 , 10, len(y)) plt.scatter(y[:, 0], y[:, 1] , c = c, marker = 'o') plt.colorbar() plt.xlabel('x') plt.ylabel('y') plt.axis('tight') plt.title('test') plt.legend(loc=0) plt.grid(True) #histogram plt.hist(y, label= ['1st', '2nd'], bins = 25) plt.xlabel('x') plt.ylabel('y') plt.axis('tight') plt.title('test') plt.legend(loc=0) plt.grid(True) #boxplot plt.boxplot(y) plt.grid(True) plt.setp(ax, xticklabels = ['1st', '2nd']) plt.xlabel('dataset') plt.ylabel('valur') plt.title('Boxplot') #finance plot | 3.08585 | 3 |
texts/objects/cache/sqlite_ner_cache.py | nicolay-r/frame-based-attitude-extraction-workflow | 0 | 6624323 | from itertools import chain
from os.path import join
from core.processing.lemmatization.base import Stemmer
from core.processing.ner.base import NamedEntityRecognition
from core.processing.ner.obj_decs import NerObjectDescriptor
from texts.extraction.text_parser import terms_utils
from texts.objects.cache.sqlite_base import BaseSQLiteObjectCache
from texts.readers.utils import NewsInfo
class SQLiteNERCacheData(BaseSQLiteObjectCache):
"""
NOTE: There is a bug in deep_pavlov == 0.11.0 -- returns an invalid data
for other elements in batch (>1).
With deep_ner it is OK, so the size might be increased which is positively
affects on the processing performance
"""
# region static fields
BATCH_SIZE = 1
CREATE_TABLE_IF_NOT_EXISTS_SQLITE_CMD = """
CREATE TABLE IF NOT EXISTS {table} (
filename TEXT,
sentence_ind INTEGER,
ner_data TEXT,
PRIMARY KEY (filename, sentence_ind))
"""
INSERT_RECORD_SQLITE_CMD = "INSERT INTO {table} VALUES ('{filename}', {s_ind}, '{ner_data}')"
SELECT_BY_SENTENCE_IND = "SELECT ner_data from {table} where sentence_ind=?"
# endregion
def __init__(self, stemmer, ner, db_filepath):
assert(isinstance(stemmer, Stemmer) or stemmer is None)
assert(isinstance(ner, NamedEntityRecognition) or ner is None)
super(SQLiteNERCacheData, self).__init__(db_filepath=db_filepath)
print("NER cache: {}".format(db_filepath))
self.__ner = ner
self.__stemmer = None
if ner is not None:
self.__stemmer = stemmer if ner.NeedLemmatization else None
# region class methods
@classmethod
def init_for_rw(cls, stemmer, ner, folder):
db_filepath = SQLiteNERCacheData.__create_db_name(ner_name=str(ner), folder=folder)
return cls(stemmer=stemmer, ner=ner, db_filepath=db_filepath)
@classmethod
def init_as_read_only(cls, filepath):
return cls(stemmer=None, ner=None, db_filepath=filepath)
# endregion
# region public methods
@staticmethod
def get_table_name():
return "cache"
def register_news(self, news_info, is_valid_title_by_ner_types=None):
assert(isinstance(news_info, NewsInfo))
assert(callable(is_valid_title_by_ner_types) or is_valid_title_by_ner_types is None)
filename = news_info.FileName
# Skipping existed news.
if self._is_news_exists_in_cache(filename):
return
batches_it = self.__iter_sentences_grouped_in_batches(
news_info=news_info,
reject_by_non_valid_title=True)
check_title_func = is_valid_title_by_ner_types \
if is_valid_title_by_ner_types is not None else \
lambda types: True
for s_inds, s_input_terms in batches_it:
accepted = self.__register_sentences(filename=filename,
input_sequences=s_input_terms,
s_inds=s_inds,
is_valid_title_by_ner_types=check_title_func)
if not accepted:
break
# Commiting results into database.
self._conn.commit()
# endregion
# region private methods
@staticmethod
def __create_db_name(ner_name, folder):
return join(folder, "ner_cache_{ner_name}.db".format(ner_name=ner_name))
def __iter_sentences_grouped_in_batches(self, news_info, reject_by_non_valid_title):
assert(isinstance(news_info, NewsInfo))
assert(isinstance(reject_by_non_valid_title, bool))
b_inds, b_sentences = [], []
def need_release():
return len(b_inds) > 0
it_contents = chain([news_info.Title], news_info.iter_sentences())
for s_ind, sentence in enumerate(it_contents):
assert(isinstance(sentence, str))
actual_ind = s_ind - 1
if len(b_inds) == self.BATCH_SIZE:
# release.
yield b_inds, b_sentences
b_inds, b_sentences = [], []
s_input_terms = terms_utils.to_input_terms(
text=sentence,
stemmer=self.__stemmer,
ner=self.__ner,
return_parsed_text=False)
if self.__is_valid_input(s_input_terms):
# add in list.
b_inds.append(actual_ind)
b_sentences.append(s_input_terms)
elif self.__is_title(actual_ind) and reject_by_non_valid_title:
break
# release residual part if exists.
if need_release():
yield b_inds, b_sentences
def __is_title(self, s_ind):
return s_ind == self.TITLE_SENT_IND
def __serialize_ner_data(self, ner_objs_data):
assert(isinstance(ner_objs_data, list))
objects = []
for obj_desc in ner_objs_data:
assert(isinstance(obj_desc, NerObjectDescriptor))
s_obj = self.PARAMS_SEP.join([str(obj_desc.Position),
str(obj_desc.Length),
obj_desc.ObjectType])
objects.append(s_obj)
return self.ENTRY_SEP.join(objects)
def __register_sentences(self, filename, input_sequences, s_inds, is_valid_title_by_ner_types):
assert(len(input_sequences) > 0)
assert(callable(is_valid_title_by_ner_types))
ner_data = self.__ner.extract(input_sequences, return_single=False)
first_obj_desc = ner_data[0]
assert(isinstance(first_obj_desc, NerObjectDescriptor))
# checking title by ner types appeared in it
if s_inds[0] == self.TITLE_SENT_IND and not is_valid_title_by_ner_types(first_obj_desc.ObjectType):
return False
# composing requests.
for seq_ind in range(len(input_sequences)):
request = self.INSERT_RECORD_SQLITE_CMD.format(
table=self.get_table_name(),
filename=filename,
s_ind=s_inds[seq_ind],
ner_data=self.__serialize_ner_data(ner_data))
self._cursor.execute(request)
return True
def __is_valid_input(self, input_sequence):
return self.__ner.InputLimitation > len(input_sequence) > 0
# endregion
# region protected methods
def _deserialize_item(self, data_item):
assert (isinstance(data_item, str))
pos, length, obj_type = data_item.split(BaseSQLiteObjectCache.PARAMS_SEP)
return NerObjectDescriptor(pos=int(pos),
length=int(length),
obj_type=obj_type)
# endregion
def __enter__(self):
super(SQLiteNERCacheData, self).__enter__()
self._cursor.execute(self.CREATE_TABLE_IF_NOT_EXISTS_SQLITE_CMD.format(table=self.get_table_name()))
| from itertools import chain
from os.path import join
from core.processing.lemmatization.base import Stemmer
from core.processing.ner.base import NamedEntityRecognition
from core.processing.ner.obj_decs import NerObjectDescriptor
from texts.extraction.text_parser import terms_utils
from texts.objects.cache.sqlite_base import BaseSQLiteObjectCache
from texts.readers.utils import NewsInfo
class SQLiteNERCacheData(BaseSQLiteObjectCache):
"""
NOTE: There is a bug in deep_pavlov == 0.11.0 -- returns an invalid data
for other elements in batch (>1).
With deep_ner it is OK, so the size might be increased which is positively
affects on the processing performance
"""
# region static fields
BATCH_SIZE = 1
CREATE_TABLE_IF_NOT_EXISTS_SQLITE_CMD = """
CREATE TABLE IF NOT EXISTS {table} (
filename TEXT,
sentence_ind INTEGER,
ner_data TEXT,
PRIMARY KEY (filename, sentence_ind))
"""
INSERT_RECORD_SQLITE_CMD = "INSERT INTO {table} VALUES ('{filename}', {s_ind}, '{ner_data}')"
SELECT_BY_SENTENCE_IND = "SELECT ner_data from {table} where sentence_ind=?"
# endregion
def __init__(self, stemmer, ner, db_filepath):
assert(isinstance(stemmer, Stemmer) or stemmer is None)
assert(isinstance(ner, NamedEntityRecognition) or ner is None)
super(SQLiteNERCacheData, self).__init__(db_filepath=db_filepath)
print("NER cache: {}".format(db_filepath))
self.__ner = ner
self.__stemmer = None
if ner is not None:
self.__stemmer = stemmer if ner.NeedLemmatization else None
# region class methods
@classmethod
def init_for_rw(cls, stemmer, ner, folder):
db_filepath = SQLiteNERCacheData.__create_db_name(ner_name=str(ner), folder=folder)
return cls(stemmer=stemmer, ner=ner, db_filepath=db_filepath)
@classmethod
def init_as_read_only(cls, filepath):
return cls(stemmer=None, ner=None, db_filepath=filepath)
# endregion
# region public methods
@staticmethod
def get_table_name():
return "cache"
def register_news(self, news_info, is_valid_title_by_ner_types=None):
assert(isinstance(news_info, NewsInfo))
assert(callable(is_valid_title_by_ner_types) or is_valid_title_by_ner_types is None)
filename = news_info.FileName
# Skipping existed news.
if self._is_news_exists_in_cache(filename):
return
batches_it = self.__iter_sentences_grouped_in_batches(
news_info=news_info,
reject_by_non_valid_title=True)
check_title_func = is_valid_title_by_ner_types \
if is_valid_title_by_ner_types is not None else \
lambda types: True
for s_inds, s_input_terms in batches_it:
accepted = self.__register_sentences(filename=filename,
input_sequences=s_input_terms,
s_inds=s_inds,
is_valid_title_by_ner_types=check_title_func)
if not accepted:
break
# Commiting results into database.
self._conn.commit()
# endregion
# region private methods
@staticmethod
def __create_db_name(ner_name, folder):
return join(folder, "ner_cache_{ner_name}.db".format(ner_name=ner_name))
def __iter_sentences_grouped_in_batches(self, news_info, reject_by_non_valid_title):
assert(isinstance(news_info, NewsInfo))
assert(isinstance(reject_by_non_valid_title, bool))
b_inds, b_sentences = [], []
def need_release():
return len(b_inds) > 0
it_contents = chain([news_info.Title], news_info.iter_sentences())
for s_ind, sentence in enumerate(it_contents):
assert(isinstance(sentence, str))
actual_ind = s_ind - 1
if len(b_inds) == self.BATCH_SIZE:
# release.
yield b_inds, b_sentences
b_inds, b_sentences = [], []
s_input_terms = terms_utils.to_input_terms(
text=sentence,
stemmer=self.__stemmer,
ner=self.__ner,
return_parsed_text=False)
if self.__is_valid_input(s_input_terms):
# add in list.
b_inds.append(actual_ind)
b_sentences.append(s_input_terms)
elif self.__is_title(actual_ind) and reject_by_non_valid_title:
break
# release residual part if exists.
if need_release():
yield b_inds, b_sentences
def __is_title(self, s_ind):
return s_ind == self.TITLE_SENT_IND
def __serialize_ner_data(self, ner_objs_data):
assert(isinstance(ner_objs_data, list))
objects = []
for obj_desc in ner_objs_data:
assert(isinstance(obj_desc, NerObjectDescriptor))
s_obj = self.PARAMS_SEP.join([str(obj_desc.Position),
str(obj_desc.Length),
obj_desc.ObjectType])
objects.append(s_obj)
return self.ENTRY_SEP.join(objects)
def __register_sentences(self, filename, input_sequences, s_inds, is_valid_title_by_ner_types):
assert(len(input_sequences) > 0)
assert(callable(is_valid_title_by_ner_types))
ner_data = self.__ner.extract(input_sequences, return_single=False)
first_obj_desc = ner_data[0]
assert(isinstance(first_obj_desc, NerObjectDescriptor))
# checking title by ner types appeared in it
if s_inds[0] == self.TITLE_SENT_IND and not is_valid_title_by_ner_types(first_obj_desc.ObjectType):
return False
# composing requests.
for seq_ind in range(len(input_sequences)):
request = self.INSERT_RECORD_SQLITE_CMD.format(
table=self.get_table_name(),
filename=filename,
s_ind=s_inds[seq_ind],
ner_data=self.__serialize_ner_data(ner_data))
self._cursor.execute(request)
return True
def __is_valid_input(self, input_sequence):
return self.__ner.InputLimitation > len(input_sequence) > 0
# endregion
# region protected methods
def _deserialize_item(self, data_item):
assert (isinstance(data_item, str))
pos, length, obj_type = data_item.split(BaseSQLiteObjectCache.PARAMS_SEP)
return NerObjectDescriptor(pos=int(pos),
length=int(length),
obj_type=obj_type)
# endregion
def __enter__(self):
super(SQLiteNERCacheData, self).__enter__()
self._cursor.execute(self.CREATE_TABLE_IF_NOT_EXISTS_SQLITE_CMD.format(table=self.get_table_name()))
| en | 0.768165 | NOTE: There is a bug in deep_pavlov == 0.11.0 -- returns an invalid data for other elements in batch (>1). With deep_ner it is OK, so the size might be increased which is positively affects on the processing performance # region static fields CREATE TABLE IF NOT EXISTS {table} ( filename TEXT, sentence_ind INTEGER, ner_data TEXT, PRIMARY KEY (filename, sentence_ind)) # endregion # region class methods # endregion # region public methods # Skipping existed news. # Commiting results into database. # endregion # region private methods # release. # add in list. # release residual part if exists. # checking title by ner types appeared in it # composing requests. # endregion # region protected methods # endregion | 2.183004 | 2 |
chatty_back/partners/views.py | always-awake/chatty_back | 0 | 6624324 | <reponame>always-awake/chatty_back<gh_stars>0
from django.utils.decorators import method_decorator
from rest_framework.views import APIView
from rest_framework.response import Response
from rest_framework import status
from . import models, serializers
from chatty_back.diary.views import check_user
def get_partner(self, partner_id, creator):
try:
found_partner = models.Partner.objects.get(id=partner_id, creator=creator)
return found_partner
except models.Partner.DoesNotExist:
return None
class Partner(APIView):
@method_decorator(check_user())
def post(self, request, user, format=None):
new_partner_name = request.data.get('name', None)
try:
found_partner = models.Partner.objects.get(name=new_partner_name, creator=user)
except models.Partner.DoesNotExist:
serializer = serializers.CreatePartnerSerializer(data=request.data, partial=True)
if serializer.is_valid():
new_partner = serializer.save(creator=user)
#파트너가 생성된 후, 직전에 생성된 파트너를 함께 일기 쓸 파트너로 선택하기
user.partner = new_partner
user.save()
print(user.partner.name)
return Response(data=serializer.data, status=status.HTTP_201_CREATED)
else:
return Response(data=serializer.errors, status=status.HTTP_400_BAD_REQUEST)
class PartnerProfile(APIView):
@method_decorator(check_user())
def get(self, request, user, partner_id, format=None):
found_partner = get_partner(self, partner_id, user)
if found_partner is None:
return Response(status=status.HTTP_404_NOT_FOUND)
else:
serializer = serializers.PartnerProfileSerializer(found_partner)
return Response(data=serializer.data, status=status.HTTP_200_OK)
@method_decorator(check_user())
def put(self, request, user, partner_id, format=None):
found_partner = get_partner(self, partner_id, user)
if found_partner is None:
return Response(status=status.HTTP_404_NOT_FOUND)
else:
serializer = serializers.PartnerProfileSerializer(
found_partner, data=request.data, partial=True)
if serializer.is_valid():
serializer.save()
return Response(data=serializer.data, status=status.HTTP_200_OK)
else:
return Response(data=serializer.errors, status=status.HTTP_400_BAD_REQUEST)
class DeletePartner(APIView):
@method_decorator(check_user())
def delete(self, request, user, partner_id, format=None):
found_partner = get_partner(self, partner_id, user)
if found_partner is None:
return Response(status=status.HTTP_404_NOT_FOUND)
else:
found_partner.delete()
return Response(status=status.HTTP_204_NO_CONTENT)
class SetPartner(APIView):
@method_decorator(check_user())
def put(self, request, user, partner_id, format=None):
found_partner = get_partner(self, partner_id, user)
if found_partner is None:
return Response(status=status.HTTP_404_NOT_FOUND)
else:
user.partner = found_partner
user.save()
return Response(status=status.HTTP_200_OK)
# Main 화면에 있는 Partner 부분(Main 화면의 module화를 위한 API)
class Partner_Main(APIView):
@method_decorator(check_user())
def get(self, request, user, format=None):
found_partner = user.partner
if found_partner is None:
return Response(status=status.HTTP_404_NOT_FOUND)
else:
serializer = serializers.MainPartnerSerializer(found_partner)
return Response(data=serializer.data, status=status.HTTP_200_OK)
class PartnerProfile_setting(APIView):
@method_decorator(check_user())
def get(self, request, user, format=None):
found_partner = user.partner
if found_partner is None:
return Response(status=status.HTTP_404_NOT_FOUND)
else:
serializer = serializers.PartnerProfileSerializer(found_partner)
return Response(data=serializer.data, status=status.HTTP_200_OK) | from django.utils.decorators import method_decorator
from rest_framework.views import APIView
from rest_framework.response import Response
from rest_framework import status
from . import models, serializers
from chatty_back.diary.views import check_user
def get_partner(self, partner_id, creator):
try:
found_partner = models.Partner.objects.get(id=partner_id, creator=creator)
return found_partner
except models.Partner.DoesNotExist:
return None
class Partner(APIView):
@method_decorator(check_user())
def post(self, request, user, format=None):
new_partner_name = request.data.get('name', None)
try:
found_partner = models.Partner.objects.get(name=new_partner_name, creator=user)
except models.Partner.DoesNotExist:
serializer = serializers.CreatePartnerSerializer(data=request.data, partial=True)
if serializer.is_valid():
new_partner = serializer.save(creator=user)
#파트너가 생성된 후, 직전에 생성된 파트너를 함께 일기 쓸 파트너로 선택하기
user.partner = new_partner
user.save()
print(user.partner.name)
return Response(data=serializer.data, status=status.HTTP_201_CREATED)
else:
return Response(data=serializer.errors, status=status.HTTP_400_BAD_REQUEST)
class PartnerProfile(APIView):
@method_decorator(check_user())
def get(self, request, user, partner_id, format=None):
found_partner = get_partner(self, partner_id, user)
if found_partner is None:
return Response(status=status.HTTP_404_NOT_FOUND)
else:
serializer = serializers.PartnerProfileSerializer(found_partner)
return Response(data=serializer.data, status=status.HTTP_200_OK)
@method_decorator(check_user())
def put(self, request, user, partner_id, format=None):
found_partner = get_partner(self, partner_id, user)
if found_partner is None:
return Response(status=status.HTTP_404_NOT_FOUND)
else:
serializer = serializers.PartnerProfileSerializer(
found_partner, data=request.data, partial=True)
if serializer.is_valid():
serializer.save()
return Response(data=serializer.data, status=status.HTTP_200_OK)
else:
return Response(data=serializer.errors, status=status.HTTP_400_BAD_REQUEST)
class DeletePartner(APIView):
@method_decorator(check_user())
def delete(self, request, user, partner_id, format=None):
found_partner = get_partner(self, partner_id, user)
if found_partner is None:
return Response(status=status.HTTP_404_NOT_FOUND)
else:
found_partner.delete()
return Response(status=status.HTTP_204_NO_CONTENT)
class SetPartner(APIView):
@method_decorator(check_user())
def put(self, request, user, partner_id, format=None):
found_partner = get_partner(self, partner_id, user)
if found_partner is None:
return Response(status=status.HTTP_404_NOT_FOUND)
else:
user.partner = found_partner
user.save()
return Response(status=status.HTTP_200_OK)
# Main 화면에 있는 Partner 부분(Main 화면의 module화를 위한 API)
class Partner_Main(APIView):
@method_decorator(check_user())
def get(self, request, user, format=None):
found_partner = user.partner
if found_partner is None:
return Response(status=status.HTTP_404_NOT_FOUND)
else:
serializer = serializers.MainPartnerSerializer(found_partner)
return Response(data=serializer.data, status=status.HTTP_200_OK)
class PartnerProfile_setting(APIView):
@method_decorator(check_user())
def get(self, request, user, format=None):
found_partner = user.partner
if found_partner is None:
return Response(status=status.HTTP_404_NOT_FOUND)
else:
serializer = serializers.PartnerProfileSerializer(found_partner)
return Response(data=serializer.data, status=status.HTTP_200_OK) | ko | 0.999671 | #파트너가 생성된 후, 직전에 생성된 파트너를 함께 일기 쓸 파트너로 선택하기 # Main 화면에 있는 Partner 부분(Main 화면의 module화를 위한 API) | 2.313189 | 2 |
alsek/core/message.py | TariqAHassan/alsek | 1 | 6624325 | """
Message
"""
from __future__ import annotations
from copy import deepcopy
from typing import Any, Dict, Iterable, List, Optional, Tuple, Union
from uuid import uuid1
from alsek._defaults import DEFAULT_MECHANISM, DEFAULT_QUEUE, DEFAULT_TASK_TIMEOUT
from alsek._utils.printing import auto_repr
from alsek._utils.temporal import fromtimestamp_ms, utcnow_timestamp_ms
from alsek.core.backoff import ExponentialBackoff, settings2backoff
from alsek.core.concurrency import Lock
def _make_uuid() -> str:
return str(uuid1())
def _collect_callback_uuids(callback_message_data: Dict[str, Any]) -> Iterable[str]:
yield callback_message_data["uuid"]
if callback_message_data["callback_message_data"]:
yield from _collect_callback_uuids(
callback_message_data["callback_message_data"]
)
class Message:
"""Alsek Message.
Args:
task_name (str): the name of the task for which
the message is intended
queue (str, optional): the queue for which the message was intended.
If ``None`` the default queue will be set.
args (list, tuple, optional): positional arguments to pass to
the task's function during the execution of ``op()``
kwargs (dict, optional): keyword arguments to pass to
the task's function during the execution of ``op()``
metadata (dict, optional): a dictionary of user-defined message metadata.
This can store any data types supported by the backend's serializer.
result_ttl (int, optional): time to live (in milliseconds) for the
result in the result store. If a result store is provided and
this parameter is ``None``, the result will be persisted indefinitely.
uuid (str, optional): universal unique identifier for the message.
If ``None``, one will be generated automatically.
progenitor_uuid (str, optional): universal unique identifier for the message
from which this message descended. (This field is only set in for tasks
with triggers and/or callbacks.)
retries (int): number of retries
timeout (int): the maximum amount of time (in milliseconds)
a task is permitted to run against this message.
created_at (int): UTC timestamp (in milliseconds) for
when the message was created
updated_at (int): UTC timestamp (in milliseconds) for
when the message was last updated
delay (int): delay before the message becomes ready
previous_result (any, optional): the output of any
previously executed task. (This will only be non-null
in cases where callbacks are used.)
previous_message_uuid (str, optional): universal unique identifier
for the message for the preceeding message (This will only be
non-null in cases where callbacks are used.)
callback_message_data (dict, optional): data to construct
a new message as part of a callback operation
backoff_settings (dict, optional): parameters to control
backoff. Expected to be of the form
``{"algorithm": str, "parameters": dict}``.
mechanism (str): mechanism for executing the task. Must
be either "process" or "thread".
Notes:
* While *not* recommended, ``timeout`` can be disabled,
in effect, by setting it to a very large integer.
"""
def __init__(
self,
task_name: str,
queue: Optional[str] = None,
args: Optional[Union[List[Any], Tuple[Any, ...]]] = None,
kwargs: Optional[Dict[Any, Any]] = None,
metadata: Optional[Dict[Any, Any]] = None,
result_ttl: Optional[int] = None,
uuid: Optional[str] = None,
progenitor_uuid: Optional[str] = None,
retries: int = 0,
timeout: int = DEFAULT_TASK_TIMEOUT,
created_at: Optional[int] = None,
updated_at: Optional[int] = None,
delay: Optional[int] = None,
previous_result: Optional[Any] = None,
previous_message_uuid: Optional[str] = None,
callback_message_data: Optional[Dict[str, Any]] = None,
backoff_settings: Optional[Dict[str, int]] = None,
mechanism: str = DEFAULT_MECHANISM,
) -> None:
self.task_name = task_name
self.queue = queue or DEFAULT_QUEUE
self.args = tuple(args) if args else tuple()
self.kwargs = kwargs or dict()
self.metadata = metadata
self.result_ttl = result_ttl
self.retries = retries
self.timeout = timeout
self.uuid = uuid or _make_uuid()
self.progenitor_uuid = progenitor_uuid
self.delay = delay or 0
self.previous_result = previous_result
self.previous_message_uuid = previous_message_uuid
self.callback_message_data = callback_message_data
self.backoff_settings = backoff_settings or ExponentialBackoff().settings
self.mechanism = mechanism
if created_at is None and updated_at is None:
self.created_at = self.updated_at = utcnow_timestamp_ms()
elif created_at is None or updated_at is None:
raise ValueError("Time data is corrupt")
else:
self.created_at, self.updated_at = created_at, updated_at
self._lock: Optional[str] = None
@property
def data(self) -> Dict[str, Any]:
"""Underlying message data."""
return dict(
task_name=self.task_name,
queue=self.queue,
args=self.args,
kwargs=self.kwargs,
metadata=self.metadata,
result_ttl=self.result_ttl,
uuid=self.uuid,
progenitor_uuid=self.progenitor_uuid,
retries=self.retries,
timeout=self.timeout,
created_at=self.created_at,
updated_at=self.updated_at,
delay=self.delay,
previous_result=self.previous_result,
previous_message_uuid=self.previous_message_uuid,
callback_message_data=self.callback_message_data,
backoff_settings=self.backoff_settings,
mechanism=self.mechanism,
)
def __repr__(self) -> str:
params = self.data
for k in ("created_at", "updated_at"):
params[k] = fromtimestamp_ms(params[k])
return auto_repr(self, **params)
@property
def summary(self) -> str:
"""High-level summary of the message object."""
return auto_repr(
self,
new_line_threshold=None,
uuid=self.uuid,
queue=self.queue,
task=self.task_name,
)
def get_backoff_duration(self) -> int:
"""Get the amount of time to backoff (wait)
before the message is eligible for processing again,
should it fail.
Returns:
duration (int): duration of the backoff in milliseconds
"""
return settings2backoff(self.backoff_settings).get(self.retries)
@property
def ready_at(self) -> int:
"""Timestamp denoting when the message will be ready for processing."""
return self.created_at + self.delay + self.get_backoff_duration()
@property
def ready(self) -> bool:
"""If the messages is currently ready for processing."""
return self.ready_at <= utcnow_timestamp_ms()
@property
def ttr(self) -> int:
"""Time to ready in milliseconds."""
if self.ready:
return 0
return max(self.ready_at - utcnow_timestamp_ms(), 0)
@property
def descendant_uuids(self) -> Optional[List[str]]:
"""A list of uuids which have or will decent from this message."""
if self.callback_message_data:
return list(_collect_callback_uuids(self.callback_message_data))
else:
return None
def _link_lock(self, lock: Lock, override: bool = False) -> Message:
"""Link a lock to the current message.
Links are formed against the ``long_name`` of ``lock``.
Args:
lock (Lock): a concurrency lock
override (bool): if ``True`` replace any existing lock
Returns:
message (Message): the updated message
Warning:
* Locks links are formed in memory and are
never persisted to the data backend.
"""
if self._lock and not override:
raise AttributeError(f"Already linked to '{self._lock}'")
else:
self._lock = lock.long_name
return self
def _unlink_lock(self, missing_ok: bool = False) -> Optional[str]:
"""Clear the lock linked to the message.
Args:
missing_ok (bool): if ``True`` do not raise
if no lock is found
Returns:
lock (str, optional): the name of the lock which was cleared
Raises:
AttributeError: if no lock is associated with the message
and ``missing_ok`` is not ``True``.
"""
if self._lock:
lock = self._lock
self._lock = None
return lock
elif missing_ok:
return None
else:
raise AttributeError("No lock linked to message")
def clone(self) -> Message:
"""Create an exact copy of the current message.
Returns:
clone (Message): the cloned message
"""
return Message(**deepcopy(self.data))
def update(self, **data: Any) -> Message:
"""Update the ``data`` in the current message.
Args:
**data (Keyword Args): key value pairs of
data to update
Returns:
updated_message (Message): the updated message
Warning:
* This method operates 'in place'. To avoid changing the current
message, first call ``.clone()``, e.g., ``message.clone().update(...)``.
* Changes are *not* automatically persisted to the backend.
"""
for k, v in data.items():
if k in self.data:
setattr(self, k, v)
else:
raise KeyError(f"Unsupported key '{k}'")
return self
def duplicate(self, uuid: Optional[str] = None) -> Message:
"""Create a duplicate of the current message, changing only ``uuid``.
Args:
uuid (str, optional): universal unique identifier for the new message.
If ``None``, one will be generated automatically.
Returns:
duplicate_message (Message): the duplicate message
Warning:
* Linked locks are not conserved
"""
return self.clone().update(uuid=uuid or _make_uuid())
def increment(self) -> Message:
"""Update a message by increasing the number
of retries.
Returns:
message (Message): the updated message
Notes:
* ``updated_at`` will be updated to the
current time.
Warning:
* Changes are *not* automatically persisted to the backend.
"""
return self.update(retries=self.retries + 1, updated_at=utcnow_timestamp_ms())
| """
Message
"""
from __future__ import annotations
from copy import deepcopy
from typing import Any, Dict, Iterable, List, Optional, Tuple, Union
from uuid import uuid1
from alsek._defaults import DEFAULT_MECHANISM, DEFAULT_QUEUE, DEFAULT_TASK_TIMEOUT
from alsek._utils.printing import auto_repr
from alsek._utils.temporal import fromtimestamp_ms, utcnow_timestamp_ms
from alsek.core.backoff import ExponentialBackoff, settings2backoff
from alsek.core.concurrency import Lock
def _make_uuid() -> str:
return str(uuid1())
def _collect_callback_uuids(callback_message_data: Dict[str, Any]) -> Iterable[str]:
yield callback_message_data["uuid"]
if callback_message_data["callback_message_data"]:
yield from _collect_callback_uuids(
callback_message_data["callback_message_data"]
)
class Message:
"""Alsek Message.
Args:
task_name (str): the name of the task for which
the message is intended
queue (str, optional): the queue for which the message was intended.
If ``None`` the default queue will be set.
args (list, tuple, optional): positional arguments to pass to
the task's function during the execution of ``op()``
kwargs (dict, optional): keyword arguments to pass to
the task's function during the execution of ``op()``
metadata (dict, optional): a dictionary of user-defined message metadata.
This can store any data types supported by the backend's serializer.
result_ttl (int, optional): time to live (in milliseconds) for the
result in the result store. If a result store is provided and
this parameter is ``None``, the result will be persisted indefinitely.
uuid (str, optional): universal unique identifier for the message.
If ``None``, one will be generated automatically.
progenitor_uuid (str, optional): universal unique identifier for the message
from which this message descended. (This field is only set in for tasks
with triggers and/or callbacks.)
retries (int): number of retries
timeout (int): the maximum amount of time (in milliseconds)
a task is permitted to run against this message.
created_at (int): UTC timestamp (in milliseconds) for
when the message was created
updated_at (int): UTC timestamp (in milliseconds) for
when the message was last updated
delay (int): delay before the message becomes ready
previous_result (any, optional): the output of any
previously executed task. (This will only be non-null
in cases where callbacks are used.)
previous_message_uuid (str, optional): universal unique identifier
for the message for the preceeding message (This will only be
non-null in cases where callbacks are used.)
callback_message_data (dict, optional): data to construct
a new message as part of a callback operation
backoff_settings (dict, optional): parameters to control
backoff. Expected to be of the form
``{"algorithm": str, "parameters": dict}``.
mechanism (str): mechanism for executing the task. Must
be either "process" or "thread".
Notes:
* While *not* recommended, ``timeout`` can be disabled,
in effect, by setting it to a very large integer.
"""
def __init__(
self,
task_name: str,
queue: Optional[str] = None,
args: Optional[Union[List[Any], Tuple[Any, ...]]] = None,
kwargs: Optional[Dict[Any, Any]] = None,
metadata: Optional[Dict[Any, Any]] = None,
result_ttl: Optional[int] = None,
uuid: Optional[str] = None,
progenitor_uuid: Optional[str] = None,
retries: int = 0,
timeout: int = DEFAULT_TASK_TIMEOUT,
created_at: Optional[int] = None,
updated_at: Optional[int] = None,
delay: Optional[int] = None,
previous_result: Optional[Any] = None,
previous_message_uuid: Optional[str] = None,
callback_message_data: Optional[Dict[str, Any]] = None,
backoff_settings: Optional[Dict[str, int]] = None,
mechanism: str = DEFAULT_MECHANISM,
) -> None:
self.task_name = task_name
self.queue = queue or DEFAULT_QUEUE
self.args = tuple(args) if args else tuple()
self.kwargs = kwargs or dict()
self.metadata = metadata
self.result_ttl = result_ttl
self.retries = retries
self.timeout = timeout
self.uuid = uuid or _make_uuid()
self.progenitor_uuid = progenitor_uuid
self.delay = delay or 0
self.previous_result = previous_result
self.previous_message_uuid = previous_message_uuid
self.callback_message_data = callback_message_data
self.backoff_settings = backoff_settings or ExponentialBackoff().settings
self.mechanism = mechanism
if created_at is None and updated_at is None:
self.created_at = self.updated_at = utcnow_timestamp_ms()
elif created_at is None or updated_at is None:
raise ValueError("Time data is corrupt")
else:
self.created_at, self.updated_at = created_at, updated_at
self._lock: Optional[str] = None
@property
def data(self) -> Dict[str, Any]:
"""Underlying message data."""
return dict(
task_name=self.task_name,
queue=self.queue,
args=self.args,
kwargs=self.kwargs,
metadata=self.metadata,
result_ttl=self.result_ttl,
uuid=self.uuid,
progenitor_uuid=self.progenitor_uuid,
retries=self.retries,
timeout=self.timeout,
created_at=self.created_at,
updated_at=self.updated_at,
delay=self.delay,
previous_result=self.previous_result,
previous_message_uuid=self.previous_message_uuid,
callback_message_data=self.callback_message_data,
backoff_settings=self.backoff_settings,
mechanism=self.mechanism,
)
def __repr__(self) -> str:
params = self.data
for k in ("created_at", "updated_at"):
params[k] = fromtimestamp_ms(params[k])
return auto_repr(self, **params)
@property
def summary(self) -> str:
"""High-level summary of the message object."""
return auto_repr(
self,
new_line_threshold=None,
uuid=self.uuid,
queue=self.queue,
task=self.task_name,
)
def get_backoff_duration(self) -> int:
"""Get the amount of time to backoff (wait)
before the message is eligible for processing again,
should it fail.
Returns:
duration (int): duration of the backoff in milliseconds
"""
return settings2backoff(self.backoff_settings).get(self.retries)
@property
def ready_at(self) -> int:
"""Timestamp denoting when the message will be ready for processing."""
return self.created_at + self.delay + self.get_backoff_duration()
@property
def ready(self) -> bool:
"""If the messages is currently ready for processing."""
return self.ready_at <= utcnow_timestamp_ms()
@property
def ttr(self) -> int:
"""Time to ready in milliseconds."""
if self.ready:
return 0
return max(self.ready_at - utcnow_timestamp_ms(), 0)
@property
def descendant_uuids(self) -> Optional[List[str]]:
"""A list of uuids which have or will decent from this message."""
if self.callback_message_data:
return list(_collect_callback_uuids(self.callback_message_data))
else:
return None
def _link_lock(self, lock: Lock, override: bool = False) -> Message:
"""Link a lock to the current message.
Links are formed against the ``long_name`` of ``lock``.
Args:
lock (Lock): a concurrency lock
override (bool): if ``True`` replace any existing lock
Returns:
message (Message): the updated message
Warning:
* Locks links are formed in memory and are
never persisted to the data backend.
"""
if self._lock and not override:
raise AttributeError(f"Already linked to '{self._lock}'")
else:
self._lock = lock.long_name
return self
def _unlink_lock(self, missing_ok: bool = False) -> Optional[str]:
"""Clear the lock linked to the message.
Args:
missing_ok (bool): if ``True`` do not raise
if no lock is found
Returns:
lock (str, optional): the name of the lock which was cleared
Raises:
AttributeError: if no lock is associated with the message
and ``missing_ok`` is not ``True``.
"""
if self._lock:
lock = self._lock
self._lock = None
return lock
elif missing_ok:
return None
else:
raise AttributeError("No lock linked to message")
def clone(self) -> Message:
"""Create an exact copy of the current message.
Returns:
clone (Message): the cloned message
"""
return Message(**deepcopy(self.data))
def update(self, **data: Any) -> Message:
"""Update the ``data`` in the current message.
Args:
**data (Keyword Args): key value pairs of
data to update
Returns:
updated_message (Message): the updated message
Warning:
* This method operates 'in place'. To avoid changing the current
message, first call ``.clone()``, e.g., ``message.clone().update(...)``.
* Changes are *not* automatically persisted to the backend.
"""
for k, v in data.items():
if k in self.data:
setattr(self, k, v)
else:
raise KeyError(f"Unsupported key '{k}'")
return self
def duplicate(self, uuid: Optional[str] = None) -> Message:
"""Create a duplicate of the current message, changing only ``uuid``.
Args:
uuid (str, optional): universal unique identifier for the new message.
If ``None``, one will be generated automatically.
Returns:
duplicate_message (Message): the duplicate message
Warning:
* Linked locks are not conserved
"""
return self.clone().update(uuid=uuid or _make_uuid())
def increment(self) -> Message:
"""Update a message by increasing the number
of retries.
Returns:
message (Message): the updated message
Notes:
* ``updated_at`` will be updated to the
current time.
Warning:
* Changes are *not* automatically persisted to the backend.
"""
return self.update(retries=self.retries + 1, updated_at=utcnow_timestamp_ms())
| en | 0.753568 | Message Alsek Message. Args: task_name (str): the name of the task for which the message is intended queue (str, optional): the queue for which the message was intended. If ``None`` the default queue will be set. args (list, tuple, optional): positional arguments to pass to the task's function during the execution of ``op()`` kwargs (dict, optional): keyword arguments to pass to the task's function during the execution of ``op()`` metadata (dict, optional): a dictionary of user-defined message metadata. This can store any data types supported by the backend's serializer. result_ttl (int, optional): time to live (in milliseconds) for the result in the result store. If a result store is provided and this parameter is ``None``, the result will be persisted indefinitely. uuid (str, optional): universal unique identifier for the message. If ``None``, one will be generated automatically. progenitor_uuid (str, optional): universal unique identifier for the message from which this message descended. (This field is only set in for tasks with triggers and/or callbacks.) retries (int): number of retries timeout (int): the maximum amount of time (in milliseconds) a task is permitted to run against this message. created_at (int): UTC timestamp (in milliseconds) for when the message was created updated_at (int): UTC timestamp (in milliseconds) for when the message was last updated delay (int): delay before the message becomes ready previous_result (any, optional): the output of any previously executed task. (This will only be non-null in cases where callbacks are used.) previous_message_uuid (str, optional): universal unique identifier for the message for the preceeding message (This will only be non-null in cases where callbacks are used.) callback_message_data (dict, optional): data to construct a new message as part of a callback operation backoff_settings (dict, optional): parameters to control backoff. Expected to be of the form ``{"algorithm": str, "parameters": dict}``. mechanism (str): mechanism for executing the task. Must be either "process" or "thread". Notes: * While *not* recommended, ``timeout`` can be disabled, in effect, by setting it to a very large integer. Underlying message data. High-level summary of the message object. Get the amount of time to backoff (wait) before the message is eligible for processing again, should it fail. Returns: duration (int): duration of the backoff in milliseconds Timestamp denoting when the message will be ready for processing. If the messages is currently ready for processing. Time to ready in milliseconds. A list of uuids which have or will decent from this message. Link a lock to the current message. Links are formed against the ``long_name`` of ``lock``. Args: lock (Lock): a concurrency lock override (bool): if ``True`` replace any existing lock Returns: message (Message): the updated message Warning: * Locks links are formed in memory and are never persisted to the data backend. Clear the lock linked to the message. Args: missing_ok (bool): if ``True`` do not raise if no lock is found Returns: lock (str, optional): the name of the lock which was cleared Raises: AttributeError: if no lock is associated with the message and ``missing_ok`` is not ``True``. Create an exact copy of the current message. Returns: clone (Message): the cloned message Update the ``data`` in the current message. Args: **data (Keyword Args): key value pairs of data to update Returns: updated_message (Message): the updated message Warning: * This method operates 'in place'. To avoid changing the current message, first call ``.clone()``, e.g., ``message.clone().update(...)``. * Changes are *not* automatically persisted to the backend. Create a duplicate of the current message, changing only ``uuid``. Args: uuid (str, optional): universal unique identifier for the new message. If ``None``, one will be generated automatically. Returns: duplicate_message (Message): the duplicate message Warning: * Linked locks are not conserved Update a message by increasing the number of retries. Returns: message (Message): the updated message Notes: * ``updated_at`` will be updated to the current time. Warning: * Changes are *not* automatically persisted to the backend. | 2.314607 | 2 |
tests/callbacks/test_finetuning_callback.py | caillonantoine/pytorch-lightning | 0 | 6624326 | # 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 collections import OrderedDict
import pytest
import torch
from torch import nn
from torch.optim import Optimizer, SGD
from torch.utils.data import DataLoader
from pytorch_lightning import LightningModule, seed_everything, Trainer
from pytorch_lightning.callbacks import BackboneFinetuning, BaseFinetuning, ModelCheckpoint
from tests.helpers import BoringModel, RandomDataset
class TestBackboneFinetuningCallback(BackboneFinetuning):
def on_train_epoch_start(self, trainer, pl_module):
super().on_train_epoch_start(trainer, pl_module)
epoch = trainer.current_epoch
if self.unfreeze_backbone_at_epoch <= epoch:
optimizer = trainer.optimizers[0]
current_lr = optimizer.param_groups[0]["lr"]
backbone_lr = self.previous_backbone_lr
if epoch < 6:
assert backbone_lr <= current_lr
else:
assert backbone_lr == current_lr
def test_finetuning_callback(tmpdir):
"""Test finetuning callbacks works as expected."""
seed_everything(42)
class FinetuningBoringModel(BoringModel):
def __init__(self):
super().__init__()
self.backbone = nn.Sequential(nn.Linear(32, 32, bias=False), nn.BatchNorm1d(32), nn.ReLU())
self.layer = torch.nn.Linear(32, 2)
self.backbone.has_been_used = False
def training_step(self, batch, batch_idx):
output = self(batch)
loss = self.loss(batch, output)
return {"loss": loss}
def forward(self, x):
self.backbone.has_been_used = True
x = self.backbone(x)
return self.layer(x)
def configure_optimizers(self):
optimizer = torch.optim.SGD(self.layer.parameters(), lr=0.1)
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.7)
return [optimizer], [lr_scheduler]
def train_dataloader(self):
return DataLoader(RandomDataset(32, 64), batch_size=2)
model = FinetuningBoringModel()
callback = TestBackboneFinetuningCallback(unfreeze_backbone_at_epoch=3, verbose=False)
trainer = Trainer(limit_train_batches=4, default_root_dir=tmpdir, callbacks=[callback], max_epochs=8)
trainer.fit(model)
assert model.backbone.has_been_used
class TestBackboneFinetuningWarningCallback(BackboneFinetuning):
def finetune_function(self, pl_module, epoch: int, optimizer, opt_idx: int):
"""Called when the epoch begins."""
if epoch == 0:
self.unfreeze_and_add_param_group(
pl_module.backbone, optimizer, 0.1, train_bn=self.train_bn, initial_denom_lr=self.initial_denom_lr
)
def test_finetuning_callback_warning(tmpdir):
"""Test finetuning callbacks works as expected."""
seed_everything(42)
class FinetuningBoringModel(BoringModel):
def __init__(self):
super().__init__()
self.backbone = nn.Linear(32, 2, bias=False)
self.layer = None
self.backbone.has_been_used = False
def training_step(self, batch, batch_idx):
output = self(batch)
loss = self.loss(batch, output)
return {"loss": loss}
def forward(self, x):
self.backbone.has_been_used = True
x = self.backbone(x)
return x
def train_dataloader(self):
return DataLoader(RandomDataset(32, 64), batch_size=2)
def configure_optimizers(self):
optimizer = torch.optim.SGD(self.parameters(), lr=0.1)
return optimizer
chk = ModelCheckpoint(dirpath=tmpdir, save_last=True)
model = FinetuningBoringModel()
model.validation_step = None
callback = TestBackboneFinetuningWarningCallback(unfreeze_backbone_at_epoch=3, verbose=False)
with pytest.warns(UserWarning, match="Did you init your optimizer in"):
trainer = Trainer(limit_train_batches=1, default_root_dir=tmpdir, callbacks=[callback, chk], max_epochs=2)
trainer.fit(model)
assert model.backbone.has_been_used
trainer = Trainer(max_epochs=3)
trainer.fit(model, ckpt_path=chk.last_model_path)
def test_freeze_unfreeze_function(tmpdir):
"""Test freeze properly sets requires_grad on the modules."""
seed_everything(42)
class FreezeModel(LightningModule):
def __init__(self):
super().__init__()
self.backbone = nn.Sequential(nn.Linear(32, 32), nn.BatchNorm1d(32), nn.ReLU(), nn.Linear(32, 2))
model = FreezeModel()
BaseFinetuning.freeze(model, train_bn=True)
assert not model.backbone[0].weight.requires_grad
assert model.backbone[1].weight.requires_grad
assert not model.backbone[3].weight.requires_grad
BaseFinetuning.freeze(model, train_bn=False)
assert not model.backbone[0].weight.requires_grad
assert not model.backbone[1].weight.requires_grad
assert not model.backbone[3].weight.requires_grad
BaseFinetuning.make_trainable(model)
assert model.backbone[0].weight.requires_grad
assert model.backbone[1].weight.requires_grad
assert model.backbone[3].weight.requires_grad
BaseFinetuning.freeze(model.backbone[0], train_bn=False)
assert not model.backbone[0].weight.requires_grad
BaseFinetuning.freeze(([(model.backbone[1]), [model.backbone[3]]]), train_bn=True)
assert model.backbone[1].weight.requires_grad
assert not model.backbone[3].weight.requires_grad
def test_unfreeze_and_add_param_group_function(tmpdir):
"""Test unfreeze_and_add_param_group properly unfreeze parameters and add to the correct param_group."""
seed_everything(42)
class FreezeModel(LightningModule):
def __init__(self):
super().__init__()
self.backbone = nn.Sequential(
nn.Linear(32, 32, bias=False),
nn.Linear(32, 32, bias=False),
nn.Linear(32, 32, bias=False),
nn.Linear(32, 32, bias=False),
nn.Linear(32, 32, bias=False),
nn.BatchNorm1d(32),
)
model = FreezeModel()
optimizer = SGD(model.backbone[0].parameters(), lr=0.01)
with pytest.warns(UserWarning, match="The provided params to be frozen already"):
BaseFinetuning.unfreeze_and_add_param_group(model.backbone[0], optimizer=optimizer)
assert optimizer.param_groups[0]["lr"] == 0.01
model.backbone[1].weight.requires_grad = False
BaseFinetuning.unfreeze_and_add_param_group(model.backbone[1], optimizer=optimizer)
assert len(optimizer.param_groups) == 2
assert optimizer.param_groups[1]["lr"] == 0.001
assert torch.equal(optimizer.param_groups[1]["params"][0], model.backbone[1].weight)
assert model.backbone[1].weight.requires_grad
with pytest.warns(UserWarning, match="The provided params to be frozen already"):
BaseFinetuning.unfreeze_and_add_param_group(model, optimizer=optimizer, lr=100, train_bn=False)
assert len(optimizer.param_groups) == 3
assert optimizer.param_groups[2]["lr"] == 100
assert len(optimizer.param_groups[2]["params"]) == 3
for group_idx, group in enumerate(optimizer.param_groups):
if group_idx == 0:
assert torch.equal(optimizer.param_groups[0]["params"][0], model.backbone[0].weight)
if group_idx == 2:
assert torch.equal(optimizer.param_groups[2]["params"][0], model.backbone[2].weight)
assert torch.equal(optimizer.param_groups[2]["params"][1], model.backbone[3].weight)
assert torch.equal(optimizer.param_groups[2]["params"][2], model.backbone[4].weight)
class OnEpochLayerFinetuning(BaseFinetuning):
def freeze_before_training(self, pl_module: LightningModule):
self.freeze(pl_module.layer)
def finetune_function(self, pl_module: LightningModule, epoch: int, optimizer: Optimizer, opt_idx: int):
self.unfreeze_and_add_param_group(pl_module.layer[epoch + 1], optimizer)
def test_base_finetuning_internal_optimizer_metadata(tmpdir):
"""Test the param_groups updates are properly saved within the internal state of the BaseFinetuning
Callbacks."""
seed_everything(42)
class FreezeModel(BoringModel):
def __init__(self):
super().__init__()
self.layer = nn.Sequential(
nn.Linear(32, 32, bias=False),
nn.Linear(32, 32, bias=True),
nn.Linear(32, 32, bias=False),
nn.Linear(32, 32, bias=True),
nn.Linear(32, 32, bias=False),
nn.Linear(32, 2, bias=True),
)
def forward(self, x):
return self.layer(x)
def configure_optimizers(self):
return torch.optim.SGD(self.layer[0].parameters(), lr=0.1)
cb = OnEpochLayerFinetuning()
chk = ModelCheckpoint(dirpath=tmpdir, save_last=True)
model = FreezeModel()
trainer = Trainer(default_root_dir=tmpdir, max_epochs=5, limit_train_batches=1, callbacks=[cb, chk])
trainer.fit(model)
assert len(cb._internal_optimizer_metadata[0]) == 6
assert cb._internal_optimizer_metadata[0][0]["params"] == ["layer.0.weight"]
assert cb._internal_optimizer_metadata[0][1]["params"] == ["layer.1.weight", "layer.1.bias"]
assert cb._internal_optimizer_metadata[0][2]["params"] == ["layer.2.weight"]
assert cb._internal_optimizer_metadata[0][3]["params"] == ["layer.3.weight", "layer.3.bias"]
assert cb._internal_optimizer_metadata[0][4]["params"] == ["layer.4.weight"]
assert cb._internal_optimizer_metadata[0][5]["params"] == ["layer.5.weight", "layer.5.bias"]
model = FreezeModel()
cb = OnEpochLayerFinetuning()
trainer = Trainer(max_epochs=10, callbacks=[cb])
with pytest.raises(IndexError, match="index 6 is out of range"):
trainer.fit(model, ckpt_path=chk.last_model_path)
class ConvBlock(nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
self.conv = nn.Conv2d(in_channels, out_channels, 3)
self.act = nn.ReLU()
self.bn = nn.BatchNorm2d(out_channels)
def forward(self, x):
x = self.conv(x)
x = self.act(x)
return self.bn(x)
class ConvBlockParam(nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
self.module_dict = nn.ModuleDict({"conv": nn.Conv2d(in_channels, out_channels, 3), "act": nn.ReLU()})
# add trivial test parameter to convblock to validate parent (non-leaf) module parameter handling
self.parent_param = nn.Parameter(torch.zeros((1), dtype=torch.float))
self.bn = nn.BatchNorm2d(out_channels)
def forward(self, x):
x = self.module_dict["conv"](x)
x = self.module_dict["act"](x)
return self.bn(x)
def test_complex_nested_model():
"""Test flattening, freezing, and thawing of models which contain parent (non-leaf) modules with parameters
directly themselves rather than exclusively their submodules containing parameters."""
model = nn.Sequential(
OrderedDict(
[("encoder", nn.Sequential(ConvBlockParam(3, 64), ConvBlock(64, 128))), ("decoder", ConvBlock(128, 10))]
)
)
# There are 10 leaf modules or parent modules w/ parameters in the test model
assert len(BaseFinetuning.flatten_modules(model)) == 10
BaseFinetuning.freeze(model.encoder, train_bn=True)
assert not model.encoder[0].module_dict["conv"].weight.requires_grad # Validate a leaf module parameter is frozen
assert not model.encoder[0].parent_param.requires_grad # Validate the parent module parameter is frozen
assert model.encoder[0].bn.weight.requires_grad
BaseFinetuning.make_trainable(model)
encoder_params = list(BaseFinetuning.filter_params(model.encoder, train_bn=True))
# The 9 parameters of the encoder are:
# conv0.weight, conv0.bias, bn0.weight, bn0.bias, parent_param
# conv1.weight, conv1.bias, bn1.weight, bn1.bias
assert len(encoder_params) == 9
class TestCallbacksRestoreCallback(BaseFinetuning):
def freeze_before_training(self, pl_module):
self.freeze(pl_module.layer[:3])
def finetune_function(self, pl_module, epoch, optimizer, opt_idx):
if epoch >= 1:
self.unfreeze_and_add_param_group(pl_module.layer[epoch - 1], optimizer)
class FinetuningBoringModel(BoringModel):
def __init__(self):
super().__init__()
self.layer = nn.Sequential(nn.Linear(32, 32), nn.Linear(32, 32), nn.Linear(32, 32), nn.Linear(32, 2))
def configure_optimizers(self):
parameters = filter(lambda x: x.requires_grad, self.parameters())
optimizer = torch.optim.SGD(parameters, lr=0.1)
return optimizer
def test_callbacks_restore(tmpdir):
"""Test callbacks restore is called after optimizers have been re-created but before optimizer states
reload."""
chk = ModelCheckpoint(dirpath=tmpdir, save_last=True)
model = FinetuningBoringModel()
callback = TestCallbacksRestoreCallback()
trainer_kwargs = dict(
default_root_dir=tmpdir, limit_train_batches=1, limit_val_batches=1, callbacks=[callback, chk], max_epochs=2
)
trainer = Trainer(**trainer_kwargs)
trainer.fit(model)
# only 1 optimizer
assert len(callback._internal_optimizer_metadata) == 1
# only 2 param groups
assert len(callback._internal_optimizer_metadata[0]) == 2
# original parameters
assert callback._internal_optimizer_metadata[0][0] == {
"lr": 0.1,
"momentum": 0,
"dampening": 0,
"weight_decay": 0,
"nesterov": False,
"params": ["layer.3.weight", "layer.3.bias"],
}
# new param group
assert callback._internal_optimizer_metadata[0][1] == {
"lr": 0.01,
"momentum": 0,
"dampening": 0,
"weight_decay": 0,
"nesterov": False,
"params": ["layer.0.weight", "layer.0.bias"],
}
trainer_kwargs["max_epochs"] = 3
trainer = Trainer(**trainer_kwargs)
trainer.fit(model, ckpt_path=chk.last_model_path)
def test_callbacks_restore_backbone(tmpdir):
"""Test callbacks restore is called after optimizers have been re-created but before optimizer states
reload."""
class BackboneBoringModel(BoringModel):
def __init__(self):
super().__init__()
self.layer = nn.Linear(32, 2)
self.backbone = nn.Linear(32, 32)
def forward(self, x):
return self.layer(self.backbone(x))
ckpt = ModelCheckpoint(dirpath=tmpdir, save_last=True)
trainer = Trainer(
default_root_dir=tmpdir,
limit_train_batches=1,
limit_val_batches=1,
max_epochs=2,
enable_progress_bar=False,
callbacks=[ckpt, BackboneFinetuning(unfreeze_backbone_at_epoch=1)],
)
trainer.fit(BackboneBoringModel())
# initialize a trainer that continues the previous training
trainer = Trainer(
default_root_dir=tmpdir,
limit_train_batches=1,
limit_val_batches=1,
max_epochs=3,
enable_progress_bar=False,
callbacks=BackboneFinetuning(unfreeze_backbone_at_epoch=1),
)
trainer.fit(BackboneBoringModel(), ckpt_path=ckpt.last_model_path)
| # 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 collections import OrderedDict
import pytest
import torch
from torch import nn
from torch.optim import Optimizer, SGD
from torch.utils.data import DataLoader
from pytorch_lightning import LightningModule, seed_everything, Trainer
from pytorch_lightning.callbacks import BackboneFinetuning, BaseFinetuning, ModelCheckpoint
from tests.helpers import BoringModel, RandomDataset
class TestBackboneFinetuningCallback(BackboneFinetuning):
def on_train_epoch_start(self, trainer, pl_module):
super().on_train_epoch_start(trainer, pl_module)
epoch = trainer.current_epoch
if self.unfreeze_backbone_at_epoch <= epoch:
optimizer = trainer.optimizers[0]
current_lr = optimizer.param_groups[0]["lr"]
backbone_lr = self.previous_backbone_lr
if epoch < 6:
assert backbone_lr <= current_lr
else:
assert backbone_lr == current_lr
def test_finetuning_callback(tmpdir):
"""Test finetuning callbacks works as expected."""
seed_everything(42)
class FinetuningBoringModel(BoringModel):
def __init__(self):
super().__init__()
self.backbone = nn.Sequential(nn.Linear(32, 32, bias=False), nn.BatchNorm1d(32), nn.ReLU())
self.layer = torch.nn.Linear(32, 2)
self.backbone.has_been_used = False
def training_step(self, batch, batch_idx):
output = self(batch)
loss = self.loss(batch, output)
return {"loss": loss}
def forward(self, x):
self.backbone.has_been_used = True
x = self.backbone(x)
return self.layer(x)
def configure_optimizers(self):
optimizer = torch.optim.SGD(self.layer.parameters(), lr=0.1)
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.7)
return [optimizer], [lr_scheduler]
def train_dataloader(self):
return DataLoader(RandomDataset(32, 64), batch_size=2)
model = FinetuningBoringModel()
callback = TestBackboneFinetuningCallback(unfreeze_backbone_at_epoch=3, verbose=False)
trainer = Trainer(limit_train_batches=4, default_root_dir=tmpdir, callbacks=[callback], max_epochs=8)
trainer.fit(model)
assert model.backbone.has_been_used
class TestBackboneFinetuningWarningCallback(BackboneFinetuning):
def finetune_function(self, pl_module, epoch: int, optimizer, opt_idx: int):
"""Called when the epoch begins."""
if epoch == 0:
self.unfreeze_and_add_param_group(
pl_module.backbone, optimizer, 0.1, train_bn=self.train_bn, initial_denom_lr=self.initial_denom_lr
)
def test_finetuning_callback_warning(tmpdir):
"""Test finetuning callbacks works as expected."""
seed_everything(42)
class FinetuningBoringModel(BoringModel):
def __init__(self):
super().__init__()
self.backbone = nn.Linear(32, 2, bias=False)
self.layer = None
self.backbone.has_been_used = False
def training_step(self, batch, batch_idx):
output = self(batch)
loss = self.loss(batch, output)
return {"loss": loss}
def forward(self, x):
self.backbone.has_been_used = True
x = self.backbone(x)
return x
def train_dataloader(self):
return DataLoader(RandomDataset(32, 64), batch_size=2)
def configure_optimizers(self):
optimizer = torch.optim.SGD(self.parameters(), lr=0.1)
return optimizer
chk = ModelCheckpoint(dirpath=tmpdir, save_last=True)
model = FinetuningBoringModel()
model.validation_step = None
callback = TestBackboneFinetuningWarningCallback(unfreeze_backbone_at_epoch=3, verbose=False)
with pytest.warns(UserWarning, match="Did you init your optimizer in"):
trainer = Trainer(limit_train_batches=1, default_root_dir=tmpdir, callbacks=[callback, chk], max_epochs=2)
trainer.fit(model)
assert model.backbone.has_been_used
trainer = Trainer(max_epochs=3)
trainer.fit(model, ckpt_path=chk.last_model_path)
def test_freeze_unfreeze_function(tmpdir):
"""Test freeze properly sets requires_grad on the modules."""
seed_everything(42)
class FreezeModel(LightningModule):
def __init__(self):
super().__init__()
self.backbone = nn.Sequential(nn.Linear(32, 32), nn.BatchNorm1d(32), nn.ReLU(), nn.Linear(32, 2))
model = FreezeModel()
BaseFinetuning.freeze(model, train_bn=True)
assert not model.backbone[0].weight.requires_grad
assert model.backbone[1].weight.requires_grad
assert not model.backbone[3].weight.requires_grad
BaseFinetuning.freeze(model, train_bn=False)
assert not model.backbone[0].weight.requires_grad
assert not model.backbone[1].weight.requires_grad
assert not model.backbone[3].weight.requires_grad
BaseFinetuning.make_trainable(model)
assert model.backbone[0].weight.requires_grad
assert model.backbone[1].weight.requires_grad
assert model.backbone[3].weight.requires_grad
BaseFinetuning.freeze(model.backbone[0], train_bn=False)
assert not model.backbone[0].weight.requires_grad
BaseFinetuning.freeze(([(model.backbone[1]), [model.backbone[3]]]), train_bn=True)
assert model.backbone[1].weight.requires_grad
assert not model.backbone[3].weight.requires_grad
def test_unfreeze_and_add_param_group_function(tmpdir):
"""Test unfreeze_and_add_param_group properly unfreeze parameters and add to the correct param_group."""
seed_everything(42)
class FreezeModel(LightningModule):
def __init__(self):
super().__init__()
self.backbone = nn.Sequential(
nn.Linear(32, 32, bias=False),
nn.Linear(32, 32, bias=False),
nn.Linear(32, 32, bias=False),
nn.Linear(32, 32, bias=False),
nn.Linear(32, 32, bias=False),
nn.BatchNorm1d(32),
)
model = FreezeModel()
optimizer = SGD(model.backbone[0].parameters(), lr=0.01)
with pytest.warns(UserWarning, match="The provided params to be frozen already"):
BaseFinetuning.unfreeze_and_add_param_group(model.backbone[0], optimizer=optimizer)
assert optimizer.param_groups[0]["lr"] == 0.01
model.backbone[1].weight.requires_grad = False
BaseFinetuning.unfreeze_and_add_param_group(model.backbone[1], optimizer=optimizer)
assert len(optimizer.param_groups) == 2
assert optimizer.param_groups[1]["lr"] == 0.001
assert torch.equal(optimizer.param_groups[1]["params"][0], model.backbone[1].weight)
assert model.backbone[1].weight.requires_grad
with pytest.warns(UserWarning, match="The provided params to be frozen already"):
BaseFinetuning.unfreeze_and_add_param_group(model, optimizer=optimizer, lr=100, train_bn=False)
assert len(optimizer.param_groups) == 3
assert optimizer.param_groups[2]["lr"] == 100
assert len(optimizer.param_groups[2]["params"]) == 3
for group_idx, group in enumerate(optimizer.param_groups):
if group_idx == 0:
assert torch.equal(optimizer.param_groups[0]["params"][0], model.backbone[0].weight)
if group_idx == 2:
assert torch.equal(optimizer.param_groups[2]["params"][0], model.backbone[2].weight)
assert torch.equal(optimizer.param_groups[2]["params"][1], model.backbone[3].weight)
assert torch.equal(optimizer.param_groups[2]["params"][2], model.backbone[4].weight)
class OnEpochLayerFinetuning(BaseFinetuning):
def freeze_before_training(self, pl_module: LightningModule):
self.freeze(pl_module.layer)
def finetune_function(self, pl_module: LightningModule, epoch: int, optimizer: Optimizer, opt_idx: int):
self.unfreeze_and_add_param_group(pl_module.layer[epoch + 1], optimizer)
def test_base_finetuning_internal_optimizer_metadata(tmpdir):
"""Test the param_groups updates are properly saved within the internal state of the BaseFinetuning
Callbacks."""
seed_everything(42)
class FreezeModel(BoringModel):
def __init__(self):
super().__init__()
self.layer = nn.Sequential(
nn.Linear(32, 32, bias=False),
nn.Linear(32, 32, bias=True),
nn.Linear(32, 32, bias=False),
nn.Linear(32, 32, bias=True),
nn.Linear(32, 32, bias=False),
nn.Linear(32, 2, bias=True),
)
def forward(self, x):
return self.layer(x)
def configure_optimizers(self):
return torch.optim.SGD(self.layer[0].parameters(), lr=0.1)
cb = OnEpochLayerFinetuning()
chk = ModelCheckpoint(dirpath=tmpdir, save_last=True)
model = FreezeModel()
trainer = Trainer(default_root_dir=tmpdir, max_epochs=5, limit_train_batches=1, callbacks=[cb, chk])
trainer.fit(model)
assert len(cb._internal_optimizer_metadata[0]) == 6
assert cb._internal_optimizer_metadata[0][0]["params"] == ["layer.0.weight"]
assert cb._internal_optimizer_metadata[0][1]["params"] == ["layer.1.weight", "layer.1.bias"]
assert cb._internal_optimizer_metadata[0][2]["params"] == ["layer.2.weight"]
assert cb._internal_optimizer_metadata[0][3]["params"] == ["layer.3.weight", "layer.3.bias"]
assert cb._internal_optimizer_metadata[0][4]["params"] == ["layer.4.weight"]
assert cb._internal_optimizer_metadata[0][5]["params"] == ["layer.5.weight", "layer.5.bias"]
model = FreezeModel()
cb = OnEpochLayerFinetuning()
trainer = Trainer(max_epochs=10, callbacks=[cb])
with pytest.raises(IndexError, match="index 6 is out of range"):
trainer.fit(model, ckpt_path=chk.last_model_path)
class ConvBlock(nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
self.conv = nn.Conv2d(in_channels, out_channels, 3)
self.act = nn.ReLU()
self.bn = nn.BatchNorm2d(out_channels)
def forward(self, x):
x = self.conv(x)
x = self.act(x)
return self.bn(x)
class ConvBlockParam(nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
self.module_dict = nn.ModuleDict({"conv": nn.Conv2d(in_channels, out_channels, 3), "act": nn.ReLU()})
# add trivial test parameter to convblock to validate parent (non-leaf) module parameter handling
self.parent_param = nn.Parameter(torch.zeros((1), dtype=torch.float))
self.bn = nn.BatchNorm2d(out_channels)
def forward(self, x):
x = self.module_dict["conv"](x)
x = self.module_dict["act"](x)
return self.bn(x)
def test_complex_nested_model():
"""Test flattening, freezing, and thawing of models which contain parent (non-leaf) modules with parameters
directly themselves rather than exclusively their submodules containing parameters."""
model = nn.Sequential(
OrderedDict(
[("encoder", nn.Sequential(ConvBlockParam(3, 64), ConvBlock(64, 128))), ("decoder", ConvBlock(128, 10))]
)
)
# There are 10 leaf modules or parent modules w/ parameters in the test model
assert len(BaseFinetuning.flatten_modules(model)) == 10
BaseFinetuning.freeze(model.encoder, train_bn=True)
assert not model.encoder[0].module_dict["conv"].weight.requires_grad # Validate a leaf module parameter is frozen
assert not model.encoder[0].parent_param.requires_grad # Validate the parent module parameter is frozen
assert model.encoder[0].bn.weight.requires_grad
BaseFinetuning.make_trainable(model)
encoder_params = list(BaseFinetuning.filter_params(model.encoder, train_bn=True))
# The 9 parameters of the encoder are:
# conv0.weight, conv0.bias, bn0.weight, bn0.bias, parent_param
# conv1.weight, conv1.bias, bn1.weight, bn1.bias
assert len(encoder_params) == 9
class TestCallbacksRestoreCallback(BaseFinetuning):
def freeze_before_training(self, pl_module):
self.freeze(pl_module.layer[:3])
def finetune_function(self, pl_module, epoch, optimizer, opt_idx):
if epoch >= 1:
self.unfreeze_and_add_param_group(pl_module.layer[epoch - 1], optimizer)
class FinetuningBoringModel(BoringModel):
def __init__(self):
super().__init__()
self.layer = nn.Sequential(nn.Linear(32, 32), nn.Linear(32, 32), nn.Linear(32, 32), nn.Linear(32, 2))
def configure_optimizers(self):
parameters = filter(lambda x: x.requires_grad, self.parameters())
optimizer = torch.optim.SGD(parameters, lr=0.1)
return optimizer
def test_callbacks_restore(tmpdir):
"""Test callbacks restore is called after optimizers have been re-created but before optimizer states
reload."""
chk = ModelCheckpoint(dirpath=tmpdir, save_last=True)
model = FinetuningBoringModel()
callback = TestCallbacksRestoreCallback()
trainer_kwargs = dict(
default_root_dir=tmpdir, limit_train_batches=1, limit_val_batches=1, callbacks=[callback, chk], max_epochs=2
)
trainer = Trainer(**trainer_kwargs)
trainer.fit(model)
# only 1 optimizer
assert len(callback._internal_optimizer_metadata) == 1
# only 2 param groups
assert len(callback._internal_optimizer_metadata[0]) == 2
# original parameters
assert callback._internal_optimizer_metadata[0][0] == {
"lr": 0.1,
"momentum": 0,
"dampening": 0,
"weight_decay": 0,
"nesterov": False,
"params": ["layer.3.weight", "layer.3.bias"],
}
# new param group
assert callback._internal_optimizer_metadata[0][1] == {
"lr": 0.01,
"momentum": 0,
"dampening": 0,
"weight_decay": 0,
"nesterov": False,
"params": ["layer.0.weight", "layer.0.bias"],
}
trainer_kwargs["max_epochs"] = 3
trainer = Trainer(**trainer_kwargs)
trainer.fit(model, ckpt_path=chk.last_model_path)
def test_callbacks_restore_backbone(tmpdir):
"""Test callbacks restore is called after optimizers have been re-created but before optimizer states
reload."""
class BackboneBoringModel(BoringModel):
def __init__(self):
super().__init__()
self.layer = nn.Linear(32, 2)
self.backbone = nn.Linear(32, 32)
def forward(self, x):
return self.layer(self.backbone(x))
ckpt = ModelCheckpoint(dirpath=tmpdir, save_last=True)
trainer = Trainer(
default_root_dir=tmpdir,
limit_train_batches=1,
limit_val_batches=1,
max_epochs=2,
enable_progress_bar=False,
callbacks=[ckpt, BackboneFinetuning(unfreeze_backbone_at_epoch=1)],
)
trainer.fit(BackboneBoringModel())
# initialize a trainer that continues the previous training
trainer = Trainer(
default_root_dir=tmpdir,
limit_train_batches=1,
limit_val_batches=1,
max_epochs=3,
enable_progress_bar=False,
callbacks=BackboneFinetuning(unfreeze_backbone_at_epoch=1),
)
trainer.fit(BackboneBoringModel(), ckpt_path=ckpt.last_model_path)
| en | 0.765446 | # 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. Test finetuning callbacks works as expected. Called when the epoch begins. Test finetuning callbacks works as expected. Test freeze properly sets requires_grad on the modules. Test unfreeze_and_add_param_group properly unfreeze parameters and add to the correct param_group. Test the param_groups updates are properly saved within the internal state of the BaseFinetuning Callbacks. # add trivial test parameter to convblock to validate parent (non-leaf) module parameter handling Test flattening, freezing, and thawing of models which contain parent (non-leaf) modules with parameters directly themselves rather than exclusively their submodules containing parameters. # There are 10 leaf modules or parent modules w/ parameters in the test model # Validate a leaf module parameter is frozen # Validate the parent module parameter is frozen # The 9 parameters of the encoder are: # conv0.weight, conv0.bias, bn0.weight, bn0.bias, parent_param # conv1.weight, conv1.bias, bn1.weight, bn1.bias Test callbacks restore is called after optimizers have been re-created but before optimizer states reload. # only 1 optimizer # only 2 param groups # original parameters # new param group Test callbacks restore is called after optimizers have been re-created but before optimizer states reload. # initialize a trainer that continues the previous training | 2.217494 | 2 |
airhttprunner/ext/locust/__init__.py | BSTester/httprunner | 0 | 6624327 | import sys
if "locust" in sys.argv[0]:
try:
# monkey patch all at beginning to avoid RecursionError when running locust.
# `from gevent import monkey; monkey.patch_all()` will be triggered when importing locust
from locust import main as locust_main
print("NOTICE: gevent monkey patches have been applied !!!")
except ImportError:
msg = """
Locust is not installed, install first and try again.
install with pip:
$ pip install locust
"""
print(msg)
sys.exit(1)
import importlib.util
import inspect
import os
from typing import List
from loguru import logger
""" converted pytest files from YAML/JSON testcases
"""
pytest_files: List = []
def is_httprunner_testcase(item):
""" check if a variable is a HttpRunner testcase class
"""
from airhttprunner import HttpRunner
# TODO: skip referenced testcase
return bool(
inspect.isclass(item)
and issubclass(item, HttpRunner)
and item.__name__ != "HttpRunner"
)
def prepare_locust_tests() -> List:
""" prepare locust testcases
Returns:
list: testcase class list
"""
locust_tests = []
for pytest_file in pytest_files:
spec = importlib.util.spec_from_file_location("module.name", pytest_file)
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
for name, item in vars(module).items():
if not is_httprunner_testcase(item):
continue
for _ in range(item.config.weight):
locust_tests.append(item)
return locust_tests
def main_locusts():
""" locusts entrance
"""
from airhttprunner.utils import init_sentry_sdk
from sentry_sdk import capture_message
init_sentry_sdk()
capture_message("start to run locusts")
# avoid print too much log details in console
logger.remove()
logger.add(sys.stderr, level="WARNING")
sys.argv[0] = "locust"
if len(sys.argv) == 1:
sys.argv.extend(["-h"])
if sys.argv[1] in ["-h", "--help", "-V", "--version"]:
locust_main.main()
def get_arg_index(*target_args):
for arg in target_args:
if arg not in sys.argv:
continue
return sys.argv.index(arg) + 1
return None
# get testcase file path
testcase_index = get_arg_index("-f", "--locustfile")
if not testcase_index:
print("Testcase file is not specified, exit 1.")
sys.exit(1)
from airhttprunner.make import main_make
global pytest_files
testcase_file_path = sys.argv[testcase_index]
pytest_files = main_make([testcase_file_path])
if not pytest_files:
print("No valid testcases found, exit 1.")
sys.exit(1)
sys.argv[testcase_index] = os.path.join(os.path.dirname(__file__), "locustfile.py")
locust_main.main()
| import sys
if "locust" in sys.argv[0]:
try:
# monkey patch all at beginning to avoid RecursionError when running locust.
# `from gevent import monkey; monkey.patch_all()` will be triggered when importing locust
from locust import main as locust_main
print("NOTICE: gevent monkey patches have been applied !!!")
except ImportError:
msg = """
Locust is not installed, install first and try again.
install with pip:
$ pip install locust
"""
print(msg)
sys.exit(1)
import importlib.util
import inspect
import os
from typing import List
from loguru import logger
""" converted pytest files from YAML/JSON testcases
"""
pytest_files: List = []
def is_httprunner_testcase(item):
""" check if a variable is a HttpRunner testcase class
"""
from airhttprunner import HttpRunner
# TODO: skip referenced testcase
return bool(
inspect.isclass(item)
and issubclass(item, HttpRunner)
and item.__name__ != "HttpRunner"
)
def prepare_locust_tests() -> List:
""" prepare locust testcases
Returns:
list: testcase class list
"""
locust_tests = []
for pytest_file in pytest_files:
spec = importlib.util.spec_from_file_location("module.name", pytest_file)
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
for name, item in vars(module).items():
if not is_httprunner_testcase(item):
continue
for _ in range(item.config.weight):
locust_tests.append(item)
return locust_tests
def main_locusts():
""" locusts entrance
"""
from airhttprunner.utils import init_sentry_sdk
from sentry_sdk import capture_message
init_sentry_sdk()
capture_message("start to run locusts")
# avoid print too much log details in console
logger.remove()
logger.add(sys.stderr, level="WARNING")
sys.argv[0] = "locust"
if len(sys.argv) == 1:
sys.argv.extend(["-h"])
if sys.argv[1] in ["-h", "--help", "-V", "--version"]:
locust_main.main()
def get_arg_index(*target_args):
for arg in target_args:
if arg not in sys.argv:
continue
return sys.argv.index(arg) + 1
return None
# get testcase file path
testcase_index = get_arg_index("-f", "--locustfile")
if not testcase_index:
print("Testcase file is not specified, exit 1.")
sys.exit(1)
from airhttprunner.make import main_make
global pytest_files
testcase_file_path = sys.argv[testcase_index]
pytest_files = main_make([testcase_file_path])
if not pytest_files:
print("No valid testcases found, exit 1.")
sys.exit(1)
sys.argv[testcase_index] = os.path.join(os.path.dirname(__file__), "locustfile.py")
locust_main.main()
| en | 0.64286 | # monkey patch all at beginning to avoid RecursionError when running locust. # `from gevent import monkey; monkey.patch_all()` will be triggered when importing locust Locust is not installed, install first and try again. install with pip: $ pip install locust converted pytest files from YAML/JSON testcases check if a variable is a HttpRunner testcase class # TODO: skip referenced testcase prepare locust testcases Returns: list: testcase class list locusts entrance # avoid print too much log details in console # get testcase file path | 2.371588 | 2 |
release/scripts/startup/bl_ui/space_view3d_toolbar.py | AFWSI/blender_source_testing | 1 | 6624328 | # ##### BEGIN GPL LICENSE BLOCK #####
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License
# as published by the Free Software Foundation; either version 2
# of the License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software Foundation,
# Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
#
# ##### END GPL LICENSE BLOCK #####
# <pep8 compliant>
from bpy.types import Menu, Panel, UIList
from bl_ui.properties_grease_pencil_common import (
GreasePencilStrokeEditPanel,
GreasePencilStrokeSculptPanel,
GreasePencilSculptOptionsPanel,
GreasePencilAppearancePanel,
)
from bl_ui.properties_paint_common import (
UnifiedPaintPanel,
brush_mask_texture_settings,
brush_texpaint_common,
brush_texpaint_common_color,
brush_texpaint_common_gradient,
brush_texpaint_common_clone,
brush_texpaint_common_options,
brush_texture_settings,
)
from bl_ui.utils import PresetPanel
class VIEW3D_MT_brush_context_menu(Menu):
bl_label = "Material Specials"
def draw(self, context):
layout = self.layout
settings = UnifiedPaintPanel.paint_settings(context)
brush = getattr(settings, "brush", None)
# skip if no active brush
if not brush:
layout.label(text="No Brushes currently available", icon='INFO')
return
# brush paint modes
layout.menu("VIEW3D_MT_brush_paint_modes")
# brush tool
if context.image_paint_object:
layout.prop_menu_enum(brush, "image_tool")
elif context.vertex_paint_object:
layout.prop_menu_enum(brush, "vertex_tool")
elif context.weight_paint_object:
layout.prop_menu_enum(brush, "weight_tool")
elif context.sculpt_object:
layout.prop_menu_enum(brush, "sculpt_tool")
layout.operator("brush.reset")
class VIEW3D_MT_brush_context_menu_paint_modes(Menu):
bl_label = "Enabled Modes"
def draw(self, context):
layout = self.layout
settings = UnifiedPaintPanel.paint_settings(context)
brush = settings.brush
layout.prop(brush, "use_paint_sculpt", text="Sculpt")
layout.prop(brush, "use_paint_uv_sculpt", text="UV Sculpt")
layout.prop(brush, "use_paint_vertex", text="Vertex Paint")
layout.prop(brush, "use_paint_weight", text="Weight Paint")
layout.prop(brush, "use_paint_image", text="Texture Paint")
class View3DPanel:
bl_space_type = 'VIEW_3D'
bl_region_type = 'UI'
# **************** standard tool clusters ******************
# Used by vertex & weight paint
def draw_vpaint_symmetry(layout, vpaint):
split = layout.split()
col = split.column()
col.alignment = 'RIGHT'
col.label(text="Mirror")
col = split.column()
row = col.row(align=True)
row.prop(vpaint, "use_symmetry_x", text="X", toggle=True)
row.prop(vpaint, "use_symmetry_y", text="Y", toggle=True)
row.prop(vpaint, "use_symmetry_z", text="Z", toggle=True)
col = layout.column()
col.use_property_split = True
col.use_property_decorate = False
col.prop(vpaint, "radial_symmetry", text="Radial")
# Most of these panels should not be visible in GP edit modes
def is_not_gpencil_edit_mode(context):
is_gpmode = (
context.active_object and
context.active_object.mode in {'EDIT_GPENCIL', 'PAINT_GPENCIL', 'SCULPT_GPENCIL', 'WEIGHT_GPENCIL'}
)
return not is_gpmode
# ********** default tools for object mode ****************
class VIEW3D_PT_tools_object_options(View3DPanel, Panel):
bl_category = "Tool"
bl_context = ".objectmode" # dot on purpose (access from topbar)
bl_label = "Options"
def draw(self, context):
# layout = self.layout
pass
class VIEW3D_PT_tools_object_options_transform(View3DPanel, Panel):
bl_category = "Tool"
bl_context = ".objectmode" # dot on purpose (access from topbar)
bl_label = "Transform"
bl_parent_id = "VIEW3D_PT_tools_object_options"
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
tool_settings = context.tool_settings
layout.label(text="Affect Only")
layout.prop(tool_settings, "use_transform_data_origin", text="Origins")
layout.prop(tool_settings, "use_transform_pivot_point_align", text="Locations")
layout.prop(tool_settings, "use_transform_skip_children", text="Parents")
# ********** default tools for editmode_mesh ****************
class VIEW3D_PT_tools_meshedit_options(View3DPanel, Panel):
bl_category = "Tool"
bl_context = ".mesh_edit" # dot on purpose (access from topbar)
bl_label = "Options"
bl_options = {'DEFAULT_CLOSED'}
@classmethod
def poll(cls, context):
return context.active_object
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
ob = context.active_object
mesh = ob.data
split = layout.split()
col = split.column()
col.alignment = 'RIGHT'
col.label(text="Mirror")
col = split.column()
row = col.row(align=True)
row.prop(mesh, "use_mirror_x", text="X", toggle=True)
row.prop(mesh, "use_mirror_y", text="Y", toggle=True)
row.prop(mesh, "use_mirror_z", text="Z", toggle=True)
row = layout.row(align=True)
row.active = ob.data.use_mirror_x or ob.data.use_mirror_y or ob.data.use_mirror_z
row.prop(mesh, "use_mirror_topology")
class VIEW3D_PT_tools_meshedit_options_automerge(View3DPanel, Panel):
bl_category = "Tool"
bl_context = ".mesh_edit" # dot on purpose (access from topbar)
bl_label = "Auto Merge"
bl_parent_id = "VIEW3D_PT_tools_meshedit_options"
bl_options = {'DEFAULT_CLOSED'}
@classmethod
def poll(cls, context):
return context.active_object
def draw_header(self, context):
tool_settings = context.tool_settings
self.layout.prop(tool_settings, "use_mesh_automerge", text="", toggle=False)
def draw(self, context):
layout = self.layout
tool_settings = context.tool_settings
layout.use_property_split = True
layout.use_property_decorate = False
col = layout.column(align=True)
col.active = tool_settings.use_mesh_automerge
col.prop(tool_settings, "use_mesh_automerge_and_split", toggle=False)
col.prop(tool_settings, "double_threshold", text="Threshold")
# ********** default tools for editmode_curve ****************
class VIEW3D_PT_tools_curveedit_options_stroke(View3DPanel, Panel):
bl_category = "Tool"
bl_context = ".curve_edit" # dot on purpose (access from topbar)
bl_label = "Curve Stroke"
def draw(self, context):
layout = self.layout
tool_settings = context.tool_settings
cps = tool_settings.curve_paint_settings
col = layout.column()
col.prop(cps, "curve_type")
if cps.curve_type == 'BEZIER':
col.label(text="Bezier Options:")
col.prop(cps, "error_threshold")
col.prop(cps, "fit_method")
col.prop(cps, "use_corners_detect")
col = layout.column()
col.active = cps.use_corners_detect
col.prop(cps, "corner_angle")
col.label(text="Pressure Radius:")
row = layout.row(align=True)
rowsub = row.row(align=True)
rowsub.prop(cps, "radius_min", text="Min")
rowsub.prop(cps, "radius_max", text="Max")
row.prop(cps, "use_pressure_radius", text="", icon_only=True)
col = layout.column()
col.label(text="Taper Radius:")
row = layout.row(align=True)
row.prop(cps, "radius_taper_start", text="Start")
row.prop(cps, "radius_taper_end", text="End")
col = layout.column()
col.label(text="Projection Depth:")
row = layout.row(align=True)
row.prop(cps, "depth_mode", expand=True)
col = layout.column()
if cps.depth_mode == 'SURFACE':
col.prop(cps, "surface_offset")
col.prop(cps, "use_offset_absolute")
col.prop(cps, "use_stroke_endpoints")
if cps.use_stroke_endpoints:
colsub = layout.column(align=True)
colsub.prop(cps, "surface_plane", expand=True)
# ********** default tools for editmode_armature ****************
class VIEW3D_PT_tools_armatureedit_options(View3DPanel, Panel):
bl_category = "Tool"
bl_context = ".armature_edit" # dot on purpose (access from topbar)
bl_label = "Options"
def draw(self, context):
arm = context.active_object.data
self.layout.prop(arm, "use_mirror_x")
# ********** default tools for pose-mode ****************
class VIEW3D_PT_tools_posemode_options(View3DPanel, Panel):
bl_category = "Tool"
bl_context = ".posemode" # dot on purpose (access from topbar)
bl_label = "Pose Options"
def draw(self, context):
pose = context.active_object.pose
layout = self.layout
tool_settings = context.tool_settings
layout.prop(pose, "use_auto_ik")
layout.prop(pose, "use_mirror_x")
col = layout.column()
col.active = pose.use_mirror_x
col.prop(pose, "use_mirror_relative")
layout.label(text="Affect Only")
layout.prop(tool_settings, "use_transform_pivot_point_align", text="Locations")
# ********** default tools for paint modes ****************
class View3DPaintPanel(UnifiedPaintPanel):
bl_space_type = 'VIEW_3D'
bl_region_type = 'UI'
bl_category = "Tool"
class VIEW3D_PT_tools_particlemode(Panel, View3DPaintPanel):
bl_context = ".paint_common" # dot on purpose (access from topbar)
bl_label = "Particle tools"
bl_options = {'HIDE_HEADER'}
@classmethod
def poll(cls, context):
settings = cls.paint_settings(context)
return (settings and settings.brush and context.particle_edit_object)
def draw(self, context):
layout = self.layout
settings = self.paint_settings(context)
brush = settings.brush
tool = settings.tool
layout.use_property_split = True
layout.use_property_decorate = False # No animation.
if tool is not None:
col = layout.column()
col.prop(brush, "size", slider=True)
if tool == 'ADD':
col.prop(brush, "count")
col = layout.column()
col.prop(settings, "use_default_interpolate")
col.prop(brush, "steps", slider=True)
col.prop(settings, "default_key_count", slider=True)
else:
col.prop(brush, "strength", slider=True)
if tool == 'LENGTH':
layout.row().prop(brush, "length_mode", expand=True)
elif tool == 'PUFF':
layout.row().prop(brush, "puff_mode", expand=True)
layout.prop(brush, "use_puff_volume")
elif tool == 'COMB':
layout.prop(settings, "use_emitter_deflect", text="Deflect Emitter")
col = layout.column()
col.active = settings.use_emitter_deflect
col.prop(settings, "emitter_distance", text="Distance")
# TODO, move to space_view3d.py
class VIEW3D_PT_tools_brush(Panel, View3DPaintPanel):
bl_context = ".paint_common" # dot on purpose (access from topbar)
bl_label = "Brush"
@classmethod
def poll(cls, context):
settings = cls.paint_settings(context)
return (settings and
settings.brush and
(context.sculpt_object or
context.vertex_paint_object or
context.weight_paint_object or
context.image_paint_object))
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False # No animation.
settings = self.paint_settings(context)
brush = settings.brush
if not self.is_popover:
row = layout.row()
row.column().template_ID_preview(settings, "brush", new="brush.add", rows=3, cols=8)
row.menu("VIEW3D_MT_brush_context_menu", icon='DOWNARROW_HLT', text="")
# Sculpt Mode #
if context.sculpt_object and brush:
from bl_ui.properties_paint_common import (
brush_basic_sculpt_settings,
)
capabilities = brush.sculpt_capabilities
col = layout.column()
if not self.is_popover:
brush_basic_sculpt_settings(col, context, brush)
# normal_radius_factor
col.separator()
row = col.row()
row.prop(brush, "normal_radius_factor", slider=True)
if brush.sculpt_tool == 'ELASTIC_DEFORM':
col.separator()
row = col.row()
row.prop(brush, "elastic_deform_type")
row = col.row()
row.prop(brush, "elastic_deform_volume_preservation", slider=True)
elif brush.sculpt_tool == 'POSE':
row = col.row()
row.prop(brush, "pose_offset")
elif brush.sculpt_tool == 'GRAB':
col.separator()
row = col.row()
row.prop(brush, "use_grab_active_vertex")
# topology_rake_factor
if (
capabilities.has_topology_rake and
context.sculpt_object.use_dynamic_topology_sculpting
):
row = col.row()
row.prop(brush, "topology_rake_factor", slider=True)
# auto_smooth_factor and use_inverse_smooth_pressure
if capabilities.has_auto_smooth:
row = col.row(align=True)
row.prop(brush, "auto_smooth_factor", slider=True)
row.prop(brush, "use_inverse_smooth_pressure", toggle=True, text="")
# normal_weight
if capabilities.has_normal_weight:
row = col.row(align=True)
row.prop(brush, "normal_weight", slider=True)
# crease_pinch_factor
if capabilities.has_pinch_factor:
row = col.row(align=True)
if brush.sculpt_tool in {'BLOB', 'SNAKE_HOOK'}:
row.prop(brush, "crease_pinch_factor", slider=True, text="Magnify")
else:
row.prop(brush, "crease_pinch_factor", slider=True, text="Pinch")
# rake_factor
if capabilities.has_rake_factor:
row = col.row(align=True)
row.prop(brush, "rake_factor", slider=True)
if brush.sculpt_tool == 'MASK':
col.prop(brush, "mask_tool")
# plane_offset, use_offset_pressure, use_plane_trim, plane_trim
if capabilities.has_plane_offset:
row = col.row(align=True)
row.prop(brush, "plane_offset", slider=True)
row.prop(brush, "use_offset_pressure", text="")
col.separator()
row = col.row()
row.prop(brush, "use_plane_trim", text="Plane Trim")
row = col.row()
row.active = brush.use_plane_trim
row.prop(brush, "plane_trim", slider=True, text="Distance")
# height
if capabilities.has_height:
row = col.row()
row.prop(brush, "height", slider=True, text="Height")
# use_persistent, set_persistent_base
if capabilities.has_persistence:
ob = context.sculpt_object
do_persistent = True
# not supported yet for this case
for md in ob.modifiers:
if md.type == 'MULTIRES':
do_persistent = False
break
if do_persistent:
col.prop(brush, "use_persistent")
col.operator("sculpt.set_persistent_base")
# Texture Paint Mode #
elif context.image_paint_object and brush:
brush_texpaint_common(self, context, layout, brush, settings, projpaint=True)
# Weight Paint Mode #
elif context.weight_paint_object and brush:
from bl_ui.properties_paint_common import (
brush_basic_wpaint_settings,
)
col = layout.column()
if not self.is_popover:
brush_basic_wpaint_settings(col, context, brush)
# Vertex Paint Mode #
elif context.vertex_paint_object and brush:
from bl_ui.properties_paint_common import (
brush_basic_vpaint_settings,
)
col = layout.column()
if not self.is_popover:
brush_basic_vpaint_settings(col, context, brush)
class VIEW3D_PT_tools_brush_color(Panel, View3DPaintPanel):
bl_context = ".paint_common" # dot on purpose (access from topbar)
bl_parent_id = "VIEW3D_PT_tools_brush"
bl_label = "Color Picker"
@classmethod
def poll(cls, context):
settings = cls.paint_settings(context)
brush = settings.brush
if context.image_paint_object:
capabilities = brush.image_paint_capabilities
return capabilities.has_color
elif context.vertex_paint_object:
capabilities = brush.vertex_paint_capabilities
return capabilities.has_color
def draw(self, context):
layout = self.layout
settings = self.paint_settings(context)
brush = settings.brush
if context.vertex_paint_object:
brush_texpaint_common_color(self, context, layout, brush, settings, projpaint=True)
else:
layout.prop(brush, "color_type", expand=True)
if brush.color_type == 'COLOR':
brush_texpaint_common_color(self, context, layout, brush, settings, projpaint=True)
elif brush.color_type == 'GRADIENT':
brush_texpaint_common_gradient(self, context, layout, brush, settings, projpaint=True)
class VIEW3D_PT_tools_brush_swatches(Panel, View3DPaintPanel):
bl_context = ".paint_common" # dot on purpose (access from topbar)
bl_parent_id = "VIEW3D_PT_tools_brush"
bl_label = "Color Palette"
bl_options = {'DEFAULT_CLOSED'}
@classmethod
def poll(cls, context):
settings = cls.paint_settings(context)
brush = settings.brush
if context.image_paint_object:
capabilities = brush.image_paint_capabilities
return capabilities.has_color
elif context.vertex_paint_object:
capabilities = brush.vertex_paint_capabilities
return capabilities.has_color
def draw(self, context):
layout = self.layout
settings = self.paint_settings(context)
layout.template_ID(settings, "palette", new="palette.new")
if settings.palette:
layout.template_palette(settings, "palette", color=True)
class VIEW3D_PT_tools_brush_clone(Panel, View3DPaintPanel):
bl_context = ".paint_common" # dot on purpose (access from topbar)
bl_parent_id = "VIEW3D_PT_tools_brush"
bl_label = "Clone from Paint Slot"
bl_options = {'DEFAULT_CLOSED'}
@classmethod
def poll(cls, context):
settings = cls.paint_settings(context)
brush = settings.brush
return brush.image_tool == 'CLONE'
def draw_header(self, context):
settings = self.paint_settings(context)
self.layout.prop(settings, "use_clone_layer", text="")
def draw(self, context):
layout = self.layout
settings = self.paint_settings(context)
brush = settings.brush
layout.active = settings.use_clone_layer
brush_texpaint_common_clone(self, context, layout, brush, settings, projpaint=True)
class VIEW3D_PT_tools_brush_options(Panel, View3DPaintPanel):
bl_context = ".paint_common" # dot on purpose (access from topbar)
bl_parent_id = "VIEW3D_PT_tools_brush"
bl_label = "Options"
bl_options = {'DEFAULT_CLOSED'}
def draw(self, context):
layout = self.layout
tool_settings = context.tool_settings
settings = self.paint_settings(context)
brush = settings.brush
capabilities = brush.sculpt_capabilities
layout.use_property_split = True
layout.use_property_decorate = False # No animation.
col = layout.column()
if context.image_paint_object and brush:
brush_texpaint_common_options(self, context, layout, brush, settings, projpaint=True)
elif context.sculpt_object and brush:
col.prop(brush, "use_automasking_topology")
if capabilities.has_accumulate:
col.prop(brush, "use_accumulate")
UnifiedPaintPanel.prop_unified_size(col, context, brush, "use_locked_size")
if capabilities.has_sculpt_plane:
col.prop(brush, "sculpt_plane")
col.prop(brush, "use_original_normal")
col.prop(brush, "use_original_plane")
col.prop(brush, "use_frontface", text="Front Faces Only")
col.prop(brush, "use_projected")
elif context.weight_paint_object and brush:
if brush.weight_tool != 'SMEAR':
col.prop(brush, "use_accumulate")
col.prop(brush, "use_frontface", text="Front Faces Only")
col.prop(brush, "use_projected")
col.prop(tool_settings, "use_auto_normalize", text="Auto Normalize")
col.prop(tool_settings, "use_multipaint", text="Multi-Paint")
elif context.vertex_paint_object and brush:
if brush.vertex_tool != 'SMEAR':
col.prop(brush, "use_accumulate")
col.prop(brush, "use_alpha")
col.prop(brush, "use_frontface", text="Front Faces Only")
col.prop(brush, "use_projected")
class TEXTURE_UL_texpaintslots(UIList):
def draw_item(self, _context, layout, _data, item, icon, _active_data, _active_propname, _index):
# mat = data
if self.layout_type in {'DEFAULT', 'COMPACT'}:
layout.prop(item, "name", text="", emboss=False, icon_value=icon)
elif self.layout_type == 'GRID':
layout.alignment = 'CENTER'
layout.label(text="")
class VIEW3D_MT_tools_projectpaint_uvlayer(Menu):
bl_label = "Clone Layer"
def draw(self, context):
layout = self.layout
for i, uv_layer in enumerate(context.active_object.data.uv_layers):
props = layout.operator("wm.context_set_int", text=uv_layer.name, translate=False)
props.data_path = "active_object.data.uv_layers.active_index"
props.value = i
class VIEW3D_PT_slots_projectpaint(View3DPanel, Panel):
bl_category = "Tool"
bl_context = ".imagepaint" # dot on purpose (access from topbar)
bl_label = "Texture Slots"
@classmethod
def poll(cls, context):
brush = context.tool_settings.image_paint.brush
ob = context.active_object
return (brush is not None and ob is not None)
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
settings = context.tool_settings.image_paint
ob = context.active_object
layout.prop(settings, "mode", text="Mode")
layout.separator()
if settings.mode == 'MATERIAL':
if len(ob.material_slots) > 1:
layout.template_list("MATERIAL_UL_matslots", "layers",
ob, "material_slots",
ob, "active_material_index", rows=2)
mat = ob.active_material
if mat and mat.texture_paint_images:
row = layout.row()
row.template_list("TEXTURE_UL_texpaintslots", "",
mat, "texture_paint_images",
mat, "paint_active_slot", rows=2)
if mat.texture_paint_slots:
slot = mat.texture_paint_slots[mat.paint_active_slot]
else:
slot = None
have_image = slot is not None
else:
row = layout.row()
box = row.box()
box.label(text="No Textures")
have_image = False
sub = row.column(align=True)
sub.operator_menu_enum("paint.add_texture_paint_slot", "type", icon='ADD', text="")
elif settings.mode == 'IMAGE':
mesh = ob.data
uv_text = mesh.uv_layers.active.name if mesh.uv_layers.active else ""
layout.template_ID(settings, "canvas", new="image.new", open="image.open")
if settings.missing_uvs:
layout.operator("paint.add_simple_uvs", icon='ADD', text="Add UVs")
else:
layout.menu("VIEW3D_MT_tools_projectpaint_uvlayer", text=uv_text, translate=False)
have_image = settings.canvas is not None
layout.prop(settings, "interpolation", text="")
if settings.missing_uvs:
layout.separator()
split = layout.split()
split.label(text="UV Map Needed", icon='INFO')
split.operator("paint.add_simple_uvs", icon='ADD', text="Add Simple UVs")
elif have_image:
layout.separator()
layout.operator("image.save_all_modified", text="Save All Images", icon='FILE_TICK')
# TODO, move to space_view3d.py
class VIEW3D_PT_stencil_projectpaint(View3DPanel, Panel):
bl_category = "Tool"
bl_context = ".imagepaint" # dot on purpose (access from topbar)
bl_label = "Mask"
bl_options = {'DEFAULT_CLOSED'}
bl_ui_units_x = 14
@classmethod
def poll(cls, context):
brush = context.tool_settings.image_paint.brush
ob = context.active_object
return (brush is not None and ob is not None)
def draw_header(self, context):
ipaint = context.tool_settings.image_paint
self.layout.prop(ipaint, "use_stencil_layer", text="")
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
tool_settings = context.tool_settings
ipaint = tool_settings.image_paint
ob = context.active_object
mesh = ob.data
col = layout.column()
col.active = ipaint.use_stencil_layer
col.label(text="Stencil Image")
col.template_ID(ipaint, "stencil_image", new="image.new", open="image.open")
stencil_text = mesh.uv_layer_stencil.name if mesh.uv_layer_stencil else ""
col.separator()
split = col.split()
colsub = split.column()
colsub.alignment = 'RIGHT'
colsub.label(text="UV Layer")
split.column().menu("VIEW3D_MT_tools_projectpaint_stencil", text=stencil_text, translate=False)
col.separator()
row = col.row(align=True)
row.prop(ipaint, "stencil_color", text="Display Color")
row.prop(ipaint, "invert_stencil", text="", icon='IMAGE_ALPHA')
# TODO, move to space_view3d.py
class VIEW3D_PT_tools_brush_display(Panel, View3DPaintPanel):
bl_context = ".paint_common" # dot on purpose (access from topbar)
bl_label = "Display"
bl_options = {'DEFAULT_CLOSED'}
@classmethod
def poll(cls, context):
settings = cls.paint_settings(context)
return (settings and
settings.brush and
(context.sculpt_object or
context.vertex_paint_object or
context.weight_paint_object or
context.image_paint_object))
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
settings = self.paint_settings(context)
brush = settings.brush
tex_slot = brush.texture_slot
tex_slot_mask = brush.mask_texture_slot
col = layout.column()
row = col.row(align=True)
sub = row.row(align=True)
sub.prop(brush, "cursor_overlay_alpha", text="Curve Alpha")
sub.prop(brush, "use_cursor_overlay_override", toggle=True, text="", icon='BRUSH_DATA')
row.prop(
brush, "use_cursor_overlay", text="", toggle=True,
icon='HIDE_OFF' if brush.use_cursor_overlay else 'HIDE_ON',
)
col.active = brush.brush_capabilities.has_overlay
if context.image_paint_object or context.sculpt_object or context.vertex_paint_object:
row = col.row(align=True)
sub = row.row(align=True)
sub.prop(brush, "texture_overlay_alpha", text="Texture Alpha")
sub.prop(brush, "use_primary_overlay_override", toggle=True, text="", icon='BRUSH_DATA')
if tex_slot.map_mode != 'STENCIL':
row.prop(
brush, "use_primary_overlay", text="", toggle=True,
icon='HIDE_OFF' if brush.use_primary_overlay else 'HIDE_ON',
)
if context.image_paint_object:
row = col.row(align=True)
sub = row.row(align=True)
sub.prop(brush, "mask_overlay_alpha", text="Mask Texture Alpha")
sub.prop(brush, "use_secondary_overlay_override", toggle=True, text="", icon='BRUSH_DATA')
if tex_slot_mask.map_mode != 'STENCIL':
row.prop(
brush, "use_secondary_overlay", text="", toggle=True,
icon='HIDE_OFF' if brush.use_secondary_overlay else 'HIDE_ON',
)
# TODO, move to space_view3d.py
class VIEW3D_PT_tools_brush_texture(Panel, View3DPaintPanel):
bl_context = ".paint_common" # dot on purpose (access from topbar)
bl_label = "Texture"
bl_options = {'DEFAULT_CLOSED'}
@classmethod
def poll(cls, context):
settings = cls.paint_settings(context)
return (settings and settings.brush and
(context.sculpt_object or context.image_paint_object or context.vertex_paint_object))
def draw(self, context):
layout = self.layout
settings = self.paint_settings(context)
brush = settings.brush
col = layout.column()
col.template_ID_preview(brush, "texture", new="texture.new", rows=3, cols=8)
brush_texture_settings(col, brush, context.sculpt_object)
# TODO, move to space_view3d.py
class VIEW3D_PT_tools_mask_texture(Panel, View3DPaintPanel):
bl_category = "Tool"
bl_context = ".imagepaint" # dot on purpose (access from topbar)
bl_label = "Texture Mask"
bl_options = {'DEFAULT_CLOSED'}
@classmethod
def poll(cls, context):
settings = cls.paint_settings(context)
return (settings and settings.brush and context.image_paint_object)
def draw(self, context):
layout = self.layout
brush = context.tool_settings.image_paint.brush
col = layout.column()
col.template_ID_preview(brush, "mask_texture", new="texture.new", rows=3, cols=8)
brush_mask_texture_settings(col, brush)
# TODO, move to space_view3d.py
class VIEW3D_PT_tools_brush_stroke(Panel, View3DPaintPanel):
bl_context = ".paint_common" # dot on purpose (access from topbar)
bl_label = "Stroke"
bl_options = {'DEFAULT_CLOSED'}
@classmethod
def poll(cls, context):
settings = cls.paint_settings(context)
return (settings and
settings.brush and
(context.sculpt_object or
context.vertex_paint_object or
context.weight_paint_object or
context.image_paint_object))
def draw(self, context):
layout = self.layout
settings = self.paint_settings(context)
brush = settings.brush
layout.use_property_split = True
layout.use_property_decorate = False
col = layout.column()
col.prop(brush, "stroke_method")
if brush.use_anchor:
col.prop(brush, "use_edge_to_edge", text="Edge To Edge")
if brush.use_airbrush:
col.prop(brush, "rate", text="Rate", slider=True)
if brush.use_space:
row = col.row(align=True)
row.prop(brush, "spacing", text="Spacing")
row.prop(brush, "use_pressure_spacing", toggle=True, text="")
if brush.use_line or brush.use_curve:
row = col.row(align=True)
row.prop(brush, "spacing", text="Spacing")
if brush.use_curve:
col.template_ID(brush, "paint_curve", new="paintcurve.new")
col.operator("paintcurve.draw")
if context.sculpt_object:
if brush.sculpt_capabilities.has_space_attenuation:
col.prop(brush, "use_space_attenuation")
col.prop(brush, "use_scene_spacing")
if brush.sculpt_capabilities.has_jitter:
row = col.row(align=True)
if brush.use_relative_jitter:
row.prop(brush, "jitter", slider=True)
else:
row.prop(brush, "jitter_absolute")
row.prop(brush, "use_relative_jitter", icon_only=True)
row.prop(brush, "use_pressure_jitter", toggle=True, text="")
else:
row = col.row(align=True)
if brush.use_relative_jitter:
row.prop(brush, "jitter", slider=True)
else:
row.prop(brush, "jitter_absolute")
row.prop(brush, "use_relative_jitter", icon_only=True)
row.prop(brush, "use_pressure_jitter", toggle=True, text="")
col.prop(settings, "input_samples")
class VIEW3D_PT_tools_brush_stroke_smooth_stroke(Panel, View3DPaintPanel):
bl_context = ".paint_common" # dot on purpose (access from topbar)
bl_label = "Smooth Stroke"
bl_parent_id = "VIEW3D_PT_tools_brush_stroke"
bl_options = {'DEFAULT_CLOSED'}
@classmethod
def poll(cls, context):
settings = cls.paint_settings(context)
brush = settings.brush
if brush.brush_capabilities.has_smooth_stroke:
return True
def draw_header(self, context):
settings = self.paint_settings(context)
brush = settings.brush
self.layout.prop(brush, "use_smooth_stroke", text="")
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
settings = self.paint_settings(context)
brush = settings.brush
col = layout.column()
col.active = brush.use_smooth_stroke
col.prop(brush, "smooth_stroke_radius", text="Radius", slider=True)
col.prop(brush, "smooth_stroke_factor", text="Factor", slider=True)
# TODO, move to space_view3d.py
class VIEW3D_PT_tools_brush_falloff(Panel, View3DPaintPanel):
bl_context = ".paint_common" # dot on purpose (access from topbar)
bl_label = "Falloff"
bl_options = {'DEFAULT_CLOSED'}
@classmethod
def poll(cls, context):
settings = cls.paint_settings(context)
return (settings and settings.brush and settings.brush.curve)
def draw(self, context):
layout = self.layout
settings = self.paint_settings(context)
brush = settings.brush
col = layout.column(align=True)
row = col.row(align=True)
row.prop(brush, "curve_preset", text="")
if brush.curve_preset == 'CUSTOM':
layout.template_curve_mapping(brush, "curve", brush=True)
col = layout.column(align=True)
row = col.row(align=True)
row.operator("brush.curve_preset", icon='SMOOTHCURVE', text="").shape = 'SMOOTH'
row.operator("brush.curve_preset", icon='SPHERECURVE', text="").shape = 'ROUND'
row.operator("brush.curve_preset", icon='ROOTCURVE', text="").shape = 'ROOT'
row.operator("brush.curve_preset", icon='SHARPCURVE', text="").shape = 'SHARP'
row.operator("brush.curve_preset", icon='LINCURVE', text="").shape = 'LINE'
row.operator("brush.curve_preset", icon='NOCURVE', text="").shape = 'MAX'
class VIEW3D_PT_tools_brush_falloff_frontface(View3DPaintPanel, Panel):
bl_context = ".imagepaint" # dot on purpose (access from topbar)
bl_label = "Frontface Falloff"
bl_parent_id = "VIEW3D_PT_tools_brush_falloff"
bl_options = {'DEFAULT_CLOSED'}
@classmethod
def poll(cls, context):
return (context.weight_paint_object or context.vertex_paint_object)
def draw_header(self, context):
settings = self.paint_settings(context)
brush = settings.brush
self.layout.prop(brush, "use_frontface_falloff", text="")
def draw(self, context):
settings = self.paint_settings(context)
brush = settings.brush
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
layout.active = brush.use_frontface_falloff
layout.prop(brush, "falloff_angle", text="Angle")
class VIEW3D_PT_tools_brush_falloff_normal(View3DPaintPanel, Panel):
bl_context = ".imagepaint" # dot on purpose (access from topbar)
bl_label = "Normal Falloff"
bl_parent_id = "VIEW3D_PT_tools_brush_falloff"
bl_options = {'DEFAULT_CLOSED'}
@classmethod
def poll(cls, context):
return context.image_paint_object
def draw_header(self, context):
tool_settings = context.tool_settings
ipaint = tool_settings.image_paint
self.layout.prop(ipaint, "use_normal_falloff", text="")
def draw(self, context):
tool_settings = context.tool_settings
ipaint = tool_settings.image_paint
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
layout.active = ipaint.use_normal_falloff
layout.prop(ipaint, "normal_angle", text="Angle")
# TODO, move to space_view3d.py
class VIEW3D_PT_sculpt_dyntopo(Panel, View3DPaintPanel):
bl_context = ".sculpt_mode" # dot on purpose (access from topbar)
bl_label = "Dyntopo"
bl_options = {'DEFAULT_CLOSED'}
bl_ui_units_x = 12
@classmethod
def poll(cls, context):
return (context.sculpt_object and context.tool_settings.sculpt)
def draw_header(self, context):
is_popover = self.is_popover
layout = self.layout
layout.operator(
"sculpt.dynamic_topology_toggle",
icon='CHECKBOX_HLT' if context.sculpt_object.use_dynamic_topology_sculpting else 'CHECKBOX_DEHLT',
text="",
emboss=is_popover,
)
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
tool_settings = context.tool_settings
sculpt = tool_settings.sculpt
settings = self.paint_settings(context)
brush = settings.brush
col = layout.column()
col.active = context.sculpt_object.use_dynamic_topology_sculpting
sub = col.column()
sub.active = (brush and brush.sculpt_tool != 'MASK')
if sculpt.detail_type_method in {'CONSTANT', 'MANUAL'}:
row = sub.row(align=True)
row.prop(sculpt, "constant_detail_resolution")
row.operator("sculpt.sample_detail_size", text="", icon='EYEDROPPER')
elif (sculpt.detail_type_method == 'BRUSH'):
sub.prop(sculpt, "detail_percent")
else:
sub.prop(sculpt, "detail_size")
sub.prop(sculpt, "detail_refine_method", text="Refine Method")
sub.prop(sculpt, "detail_type_method", text="Detailing")
col.prop(sculpt, "use_smooth_shading")
class VIEW3D_PT_sculpt_dyntopo_remesh(Panel, View3DPaintPanel):
bl_context = ".sculpt_mode" # dot on purpose (access from topbar)
bl_label = "Remesh"
bl_parent_id = "VIEW3D_PT_sculpt_dyntopo"
bl_options = {'DEFAULT_CLOSED'}
bl_ui_units_x = 12
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
tool_settings = context.tool_settings
sculpt = tool_settings.sculpt
col = layout.column()
col.active = context.sculpt_object.use_dynamic_topology_sculpting
col.prop(sculpt, "symmetrize_direction")
flow = layout.grid_flow(row_major=True, columns=0, even_columns=True, even_rows=False, align=False)
col = flow.column()
col.operator("sculpt.symmetrize")
col = flow.column()
col.operator("sculpt.optimize")
if sculpt.detail_type_method in {'CONSTANT', 'MANUAL'}:
col = flow.column()
col.operator("sculpt.detail_flood_fill")
class VIEW3D_PT_sculpt_voxel_remesh(Panel, View3DPaintPanel):
bl_context = ".sculpt_mode" # dot on purpose (access from topbar)
bl_label = "Remesh"
bl_options = {'DEFAULT_CLOSED'}
bl_ui_units_x = 12
@classmethod
def poll(cls, context):
return (context.sculpt_object and context.tool_settings.sculpt)
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
col = layout.column()
mesh = context.active_object.data
col.prop(mesh, "remesh_voxel_size")
col.prop(mesh, "remesh_voxel_adaptivity")
col.prop(mesh, "use_remesh_fix_poles")
col.prop(mesh, "use_remesh_smooth_normals")
col.prop(mesh, "use_remesh_preserve_volume")
col.prop(mesh, "use_remesh_preserve_paint_mask")
col.operator("object.voxel_remesh", text="Remesh")
# TODO, move to space_view3d.py
class VIEW3D_PT_sculpt_options(Panel, View3DPaintPanel):
bl_context = ".sculpt_mode" # dot on purpose (access from topbar)
bl_label = "Options"
bl_options = {'DEFAULT_CLOSED'}
@classmethod
def poll(cls, context):
return (context.sculpt_object and context.tool_settings.sculpt)
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
tool_settings = context.tool_settings
sculpt = tool_settings.sculpt
flow = layout.grid_flow(row_major=True, columns=0, even_columns=True, even_rows=False, align=False)
col = flow.column()
col.prop(sculpt, "use_threaded", text="Threaded Sculpt")
col = flow.column()
col.prop(sculpt, "show_low_resolution")
col = flow.column()
col.prop(sculpt, "use_deform_only")
class VIEW3D_PT_sculpt_options_unified(Panel, View3DPaintPanel):
bl_context = ".sculpt_mode" # dot on purpose (access from topbar)
bl_parent_id = "VIEW3D_PT_sculpt_options"
bl_label = "Unified Brush"
@classmethod
def poll(cls, context):
return (context.sculpt_object and context.tool_settings.sculpt)
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
self.unified_paint_settings(layout, context)
class VIEW3D_PT_sculpt_options_gravity(Panel, View3DPaintPanel):
bl_context = ".sculpt_mode" # dot on purpose (access from topbar)
bl_parent_id = "VIEW3D_PT_sculpt_options"
bl_label = "Gravity"
@classmethod
def poll(cls, context):
return (context.sculpt_object and context.tool_settings.sculpt)
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
tool_settings = context.tool_settings
sculpt = tool_settings.sculpt
capabilities = sculpt.brush.sculpt_capabilities
col = layout.column()
col.active = capabilities.has_gravity
col.prop(sculpt, "gravity", slider=True, text="Factor")
col.prop(sculpt, "gravity_object")
# TODO, move to space_view3d.py
class VIEW3D_PT_sculpt_symmetry(Panel, View3DPaintPanel):
bl_context = ".sculpt_mode" # dot on purpose (access from topbar)
bl_label = "Symmetry"
bl_options = {'DEFAULT_CLOSED'}
@classmethod
def poll(cls, context):
return (
(context.sculpt_object and context.tool_settings.sculpt) and
# When used in the tool header, this is explicitly included next to the XYZ symmetry buttons.
(context.region.type != 'TOOL_HEADER')
)
def draw(self, context):
layout = self.layout
sculpt = context.tool_settings.sculpt
split = layout.split()
col = split.column()
col.alignment = 'RIGHT'
col.label(text="Mirror")
col = split.column()
row = col.row(align=True)
row.prop(sculpt, "use_symmetry_x", text="X", toggle=True)
row.prop(sculpt, "use_symmetry_y", text="Y", toggle=True)
row.prop(sculpt, "use_symmetry_z", text="Z", toggle=True)
split = layout.split()
col = split.column()
col.alignment = 'RIGHT'
col.label(text="Lock")
col = split.column()
row = col.row(align=True)
row.prop(sculpt, "lock_x", text="X", toggle=True)
row.prop(sculpt, "lock_y", text="Y", toggle=True)
row.prop(sculpt, "lock_z", text="Z", toggle=True)
split = layout.split()
col = split.column()
col.alignment = 'RIGHT'
col.label(text="Tiling")
col = split.column()
row = col.row(align=True)
row.prop(sculpt, "tile_x", text="X", toggle=True)
row.prop(sculpt, "tile_y", text="Y", toggle=True)
row.prop(sculpt, "tile_z", text="Z", toggle=True)
layout.use_property_split = True
layout.use_property_decorate = False
layout.prop(sculpt, "use_symmetry_feather", text="Feather")
layout.column().prop(sculpt, "radial_symmetry", text="Radial")
layout.column().prop(sculpt, "tile_offset", text="Tile Offset")
class VIEW3D_PT_sculpt_symmetry_for_topbar(Panel):
bl_space_type = 'TOPBAR'
bl_region_type = 'HEADER'
bl_label = "Symmetry"
draw = VIEW3D_PT_sculpt_symmetry.draw
class VIEW3D_PT_tools_brush_display_show_brush(Panel, View3DPaintPanel):
bl_context = ".paint_common" # dot on purpose (access from topbar)
bl_label = "Show Brush"
bl_parent_id = "VIEW3D_PT_tools_brush_display"
bl_options = {'DEFAULT_CLOSED'}
def draw_header(self, context):
settings = self.paint_settings(context)
self.layout.prop(settings, "show_brush", text="")
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
settings = self.paint_settings(context)
brush = settings.brush
col = layout.column()
col.active = settings.show_brush
if context.sculpt_object and context.tool_settings.sculpt:
if brush.sculpt_capabilities.has_secondary_color:
col.prop(brush, "cursor_color_add", text="Add")
col.prop(brush, "cursor_color_subtract", text="Subtract")
else:
col.prop(brush, "cursor_color_add", text="Color")
else:
col.prop(brush, "cursor_color_add", text="Color")
class VIEW3D_PT_tools_brush_display_custom_icon(Panel, View3DPaintPanel):
bl_context = ".paint_common" # dot on purpose (access from topbar)
bl_label = "Custom Icon"
bl_parent_id = "VIEW3D_PT_tools_brush_display"
bl_options = {'DEFAULT_CLOSED'}
def draw_header(self, context):
settings = self.paint_settings(context)
brush = settings.brush
self.layout.prop(brush, "use_custom_icon", text="")
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
settings = self.paint_settings(context)
brush = settings.brush
col = layout.column()
col.active = brush.use_custom_icon
col.prop(brush, "icon_filepath", text="")
# ********** default tools for weight-paint ****************
# TODO, move to space_view3d.py
class VIEW3D_PT_tools_weightpaint_symmetry(Panel, View3DPaintPanel):
bl_context = ".weightpaint"
bl_options = {'DEFAULT_CLOSED'}
bl_label = "Symmetry"
@classmethod
def poll(cls, context):
# When used in the tool header, this is explicitly included next to the XYZ symmetry buttons.
return (context.region.type != 'TOOL_HEADER')
def draw(self, context):
layout = self.layout
tool_settings = context.tool_settings
wpaint = tool_settings.weight_paint
draw_vpaint_symmetry(layout, wpaint)
class VIEW3D_PT_tools_weightpaint_symmetry_for_topbar(Panel):
bl_space_type = 'TOPBAR'
bl_region_type = 'HEADER'
bl_label = "Symmetry"
draw = VIEW3D_PT_tools_weightpaint_symmetry.draw
# TODO, move to space_view3d.py
class VIEW3D_PT_tools_weightpaint_options(Panel, View3DPaintPanel):
bl_context = ".weightpaint"
bl_label = "Options"
bl_options = {'DEFAULT_CLOSED'}
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
tool_settings = context.tool_settings
wpaint = tool_settings.weight_paint
col = layout.column()
col.prop(wpaint, "use_group_restrict")
obj = context.weight_paint_object
if obj.type == 'MESH':
mesh = obj.data
col.prop(mesh, "use_mirror_x")
row = col.row()
row.active = mesh.use_mirror_x
row.prop(mesh, "use_mirror_topology")
class VIEW3D_PT_tools_weightpaint_options_unified(Panel, View3DPaintPanel):
bl_context = ".weightpaint"
bl_label = "Unified Brush"
bl_parent_id = "VIEW3D_PT_tools_weightpaint_options"
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
self.unified_paint_settings(layout, context)
# ********** default tools for vertex-paint ****************
# TODO, move to space_view3d.py
class VIEW3D_PT_tools_vertexpaint_options(Panel, View3DPaintPanel):
bl_context = ".vertexpaint" # dot on purpose (access from topbar)
bl_label = "Options"
bl_options = {'DEFAULT_CLOSED'}
def draw(self, context):
layout = self.layout
layout.label(text="Unified Brush")
layout.use_property_split = True
layout.use_property_decorate = False
self.unified_paint_settings(layout, context)
# TODO, move to space_view3d.py
class VIEW3D_PT_tools_vertexpaint_symmetry(Panel, View3DPaintPanel):
bl_context = ".vertexpaint" # dot on purpose (access from topbar)
bl_options = {'DEFAULT_CLOSED'}
bl_label = "Symmetry"
@classmethod
def poll(cls, context):
# When used in the tool header, this is explicitly included next to the XYZ symmetry buttons.
return (context.region.type != 'TOOL_HEADER')
def draw(self, context):
layout = self.layout
tool_settings = context.tool_settings
vpaint = tool_settings.vertex_paint
draw_vpaint_symmetry(layout, vpaint)
class VIEW3D_PT_tools_vertexpaint_symmetry_for_topbar(Panel):
bl_space_type = 'TOPBAR'
bl_region_type = 'HEADER'
bl_label = "Symmetry"
draw = VIEW3D_PT_tools_vertexpaint_symmetry.draw
# ********** default tools for texture-paint ****************
# TODO, move to space_view3d.py
class VIEW3D_PT_tools_imagepaint_options_external(Panel, View3DPaintPanel):
bl_context = ".imagepaint" # dot on purpose (access from topbar)
bl_label = "External"
bl_parent_id = "VIEW3D_PT_tools_imagepaint_options"
bl_options = {'DEFAULT_CLOSED'}
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
tool_settings = context.tool_settings
ipaint = tool_settings.image_paint
layout.prop(ipaint, "screen_grab_size", text="Screen Grab Size")
layout.separator()
flow = layout.grid_flow(row_major=True, columns=0, even_columns=True, even_rows=False, align=False)
col = flow.column()
col.operator("image.project_edit", text="Quick Edit")
col = flow.column()
col.operator("image.project_apply", text="Apply")
col = flow.column()
col.operator("paint.project_image", text="Apply Camera Image")
# TODO, move to space_view3d.py
class VIEW3D_PT_tools_imagepaint_symmetry(Panel, View3DPaintPanel):
bl_context = ".imagepaint" # dot on purpose (access from topbar)
bl_label = "Symmetry"
bl_options = {'DEFAULT_CLOSED'}
@classmethod
def poll(cls, context):
# When used in the tool header, this is explicitly included next to the XYZ symmetry buttons.
return (context.region.type != 'TOOL_HEADER')
def draw(self, context):
layout = self.layout
tool_settings = context.tool_settings
ipaint = tool_settings.image_paint
split = layout.split()
col = split.column()
col.alignment = 'RIGHT'
col.label(text="Mirror")
col = split.column()
row = col.row(align=True)
row.prop(ipaint, "use_symmetry_x", text="X", toggle=True)
row.prop(ipaint, "use_symmetry_y", text="Y", toggle=True)
row.prop(ipaint, "use_symmetry_z", text="Z", toggle=True)
# TODO, move to space_view3d.py
class VIEW3D_PT_tools_imagepaint_options(View3DPaintPanel, Panel):
bl_context = ".imagepaint" # dot on purpose (access from topbar)
bl_label = "Options"
bl_options = {'DEFAULT_CLOSED'}
@classmethod
def poll(cls, context):
brush = context.tool_settings.image_paint.brush
return (brush is not None)
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
tool_settings = context.tool_settings
ipaint = tool_settings.image_paint
layout.prop(ipaint, "seam_bleed")
layout.prop(ipaint, "dither", slider=True)
flow = layout.grid_flow(row_major=True, columns=0, even_columns=True, even_rows=False, align=False)
col = flow.column()
col.prop(ipaint, "use_occlude")
col = flow.column()
col.prop(ipaint, "use_backface_culling", text="Backface Culling")
class VIEW3D_PT_tools_imagepaint_options_unified(Panel, View3DPaintPanel):
bl_context = ".imagepaint" # dot on purpose (access from topbar)
bl_parent_id = "VIEW3D_PT_tools_imagepaint_options"
bl_label = "Unified Brush"
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
self.unified_paint_settings(layout, context)
class VIEW3D_PT_tools_imagepaint_options_cavity(View3DPaintPanel, Panel):
bl_context = ".imagepaint" # dot on purpose (access from topbar)
bl_label = "Cavity Mask"
bl_parent_id = "VIEW3D_PT_tools_imagepaint_options"
bl_options = {'DEFAULT_CLOSED'}
def draw_header(self, context):
tool_settings = context.tool_settings
ipaint = tool_settings.image_paint
self.layout.prop(ipaint, "use_cavity", text="")
def draw(self, context):
layout = self.layout
tool_settings = context.tool_settings
ipaint = tool_settings.image_paint
layout.active = ipaint.use_cavity
layout.template_curve_mapping(ipaint, "cavity_curve", brush=True,
use_negative_slope=True)
# TODO, move to space_view3d.py
class VIEW3D_PT_imagepaint_options(View3DPaintPanel):
bl_label = "Options"
@classmethod
def poll(cls, context):
return (context.image_paint_object and context.tool_settings.image_paint)
def draw(self, context):
layout = self.layout
col = layout.column()
self.unified_paint_settings(col, context)
class VIEW3D_MT_tools_projectpaint_stencil(Menu):
bl_label = "Mask Layer"
def draw(self, context):
layout = self.layout
for i, uv_layer in enumerate(context.active_object.data.uv_layers):
props = layout.operator("wm.context_set_int", text=uv_layer.name, translate=False)
props.data_path = "active_object.data.uv_layer_stencil_index"
props.value = i
# TODO, move to space_view3d.py
class VIEW3D_PT_tools_particlemode_options(View3DPanel, Panel):
"""Default tools for particle mode"""
bl_category = "Tool"
bl_context = ".particlemode"
bl_label = "Options"
bl_options = {'DEFAULT_CLOSED'}
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False # No animation.
pe = context.tool_settings.particle_edit
ob = pe.object
layout.prop(pe, "type", text="Editing Type")
ptcache = None
if pe.type == 'PARTICLES':
if ob.particle_systems:
if len(ob.particle_systems) > 1:
layout.template_list("UI_UL_list", "particle_systems", ob, "particle_systems",
ob.particle_systems, "active_index", rows=2, maxrows=3)
ptcache = ob.particle_systems.active.point_cache
else:
for md in ob.modifiers:
if md.type == pe.type:
ptcache = md.point_cache
if ptcache and len(ptcache.point_caches) > 1:
layout.template_list("UI_UL_list", "particles_point_caches", ptcache, "point_caches",
ptcache.point_caches, "active_index", rows=2, maxrows=3)
if not pe.is_editable:
layout.label(text="Point cache must be baked")
layout.label(text="in memory to enable editing!")
col = layout.column(align=True)
col.active = pe.is_editable
col.prop(ob.data, "use_mirror_x")
col.separator()
col.prop(pe, "use_preserve_length", text="Preserve Strand Lengths")
col.prop(pe, "use_preserve_root", text="Preserve Root Positions")
if not pe.is_hair:
col.prop(pe, "use_auto_velocity", text="Auto-Velocity")
class VIEW3D_PT_tools_particlemode_options_shapecut(View3DPanel, Panel):
"""Default tools for particle mode"""
bl_category = "Tool"
bl_parent_id = "VIEW3D_PT_tools_particlemode_options"
bl_label = "Cut Particles to Shape"
bl_options = {'DEFAULT_CLOSED'}
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False # No animation.
pe = context.tool_settings.particle_edit
layout.prop(pe, "shape_object")
layout.operator("particle.shape_cut", text="Cut")
class VIEW3D_PT_tools_particlemode_options_display(View3DPanel, Panel):
"""Default tools for particle mode"""
bl_category = "Tool"
bl_parent_id = "VIEW3D_PT_tools_particlemode_options"
bl_label = "Viewport Display"
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False # No animation.
pe = context.tool_settings.particle_edit
col = layout.column()
col.active = pe.is_editable
col.prop(pe, "display_step", text="Path Steps")
if pe.is_hair:
col.prop(pe, "show_particles", text="Children")
else:
if pe.type == 'PARTICLES':
col.prop(pe, "show_particles", text="Particles")
col.prop(pe, "use_fade_time")
sub = col.row(align=True)
sub.active = pe.use_fade_time
sub.prop(pe, "fade_frames", slider=True)
# ********** grease pencil object tool panels ****************
# Grease Pencil drawing brushes
class VIEW3D_PT_tools_grease_pencil_brush(View3DPanel, Panel):
bl_context = ".greasepencil_paint"
bl_label = "Brush"
bl_category = "Tool"
@classmethod
def poll(cls, context):
is_3d_view = context.space_data.type == 'VIEW_3D'
if is_3d_view:
if context.gpencil_data is None:
return False
gpd = context.gpencil_data
return bool(gpd.is_stroke_paint_mode)
else:
return True
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
tool_settings = context.scene.tool_settings
gpencil_paint = tool_settings.gpencil_paint
row = layout.row()
col = row.column()
col.template_ID_preview(gpencil_paint, "brush", new="brush.add_gpencil", rows=3, cols=8)
col = row.column()
brush = gpencil_paint.brush
sub = col.column(align=True)
sub.operator("gpencil.brush_presets_create", icon='PRESET_NEW', text="")
if brush is not None:
gp_settings = brush.gpencil_settings
if brush.gpencil_tool in {'DRAW', 'FILL'}:
row = layout.row(align=True)
row_mat = row.row()
if gp_settings.use_material_pin:
row_mat.template_ID(gp_settings, "material", live_icon=True)
else:
row_mat.template_ID(context.active_object, "active_material", live_icon=True)
row_mat.enabled = False # will otherwise allow to change material in active slot
row.prop(gp_settings, "use_material_pin", text="")
if not self.is_popover:
from bl_ui.properties_paint_common import (
brush_basic_gpencil_paint_settings,
)
tool = context.workspace.tools.from_space_view3d_mode(context.mode, create=False)
brush_basic_gpencil_paint_settings(layout, context, brush, tool, compact=True, is_toolbar=False)
# Grease Pencil drawing brushes options
class VIEW3D_PT_tools_grease_pencil_brush_option(View3DPanel, Panel):
bl_context = ".greasepencil_paint"
bl_label = "Options"
bl_category = "Tool"
@classmethod
def poll(cls, context):
brush = context.tool_settings.gpencil_paint.brush
return brush is not None and brush.gpencil_tool not in {'ERASE', 'FILL'}
def draw_header_preset(self, _context):
VIEW3D_PT_gpencil_brush_presets.draw_panel_header(self.layout)
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
brush = context.tool_settings.gpencil_paint.brush
if brush is not None:
gp_settings = brush.gpencil_settings
col = layout.column(align=True)
col.prop(gp_settings, "input_samples")
col.separator()
col.prop(gp_settings, "active_smooth_factor")
col.separator()
col.prop(gp_settings, "angle", slider=True)
col.prop(gp_settings, "angle_factor", text="Factor", slider=True)
ob = context.object
if ob and brush.gpencil_settings.use_material_pin is False:
ma = ob.active_material
elif brush.gpencil_settings.material:
ma = brush.gpencil_settings.material
else:
ma = None
col.separator()
subcol = col.column(align=True)
if ma and ma.grease_pencil.mode == 'LINE':
subcol.enabled = False
subcol.prop(gp_settings, "gradient_factor", slider=True)
subcol.prop(gp_settings, "gradient_shape")
class VIEW3D_PT_tools_grease_pencil_brush_stabilizer(View3DPanel, Panel):
bl_context = ".greasepencil_paint"
bl_parent_id = 'VIEW3D_PT_tools_grease_pencil_brush_option'
bl_label = "Stabilize"
bl_category = "Tool"
bl_options = {'DEFAULT_CLOSED'}
@classmethod
def poll(cls, context):
brush = context.tool_settings.gpencil_paint.brush
return brush is not None and brush.gpencil_tool == 'DRAW'
def draw_header(self, context):
brush = context.tool_settings.gpencil_paint.brush
gp_settings = brush.gpencil_settings
self.layout.prop(gp_settings, "use_settings_stabilizer", text="")
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
brush = context.tool_settings.gpencil_paint.brush
gp_settings = brush.gpencil_settings
layout.active = gp_settings.use_settings_stabilizer
layout.prop(brush, "smooth_stroke_radius", text="Radius", slider=True)
layout.prop(brush, "smooth_stroke_factor", text="Factor", slider=True)
class VIEW3D_PT_tools_grease_pencil_brush_settings(View3DPanel, Panel):
bl_context = ".greasepencil_paint"
bl_parent_id = 'VIEW3D_PT_tools_grease_pencil_brush_option'
bl_label = "Post-Processing"
bl_category = "Tool"
@classmethod
def poll(cls, context):
brush = context.tool_settings.gpencil_paint.brush
return brush is not None and brush.gpencil_tool != 'ERASE'
def draw_header(self, context):
brush = context.tool_settings.gpencil_paint.brush
gp_settings = brush.gpencil_settings
self.layout.prop(gp_settings, "use_settings_postprocess", text="")
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
brush = context.tool_settings.gpencil_paint.brush
gp_settings = brush.gpencil_settings
layout.active = gp_settings.use_settings_postprocess
col = layout.column(align=True)
col.prop(gp_settings, "pen_smooth_factor")
col.prop(gp_settings, "pen_smooth_steps")
col = layout.column(align=True)
col.prop(gp_settings, "pen_thick_smooth_factor")
col.prop(gp_settings, "pen_thick_smooth_steps", text="Iterations")
col = layout.column(align=True)
col.prop(gp_settings, "pen_subdivision_steps")
col.prop(gp_settings, "random_subdiv", text="Randomness", slider=True)
col = layout.column(align=True)
col.prop(gp_settings, "simplify_factor")
col = layout.column(align=True)
col.prop(gp_settings, "trim")
class VIEW3D_PT_tools_grease_pencil_brush_random(View3DPanel, Panel):
bl_context = ".greasepencil_paint"
bl_parent_id = 'VIEW3D_PT_tools_grease_pencil_brush_option'
bl_label = "Randomize"
bl_category = "Tool"
bl_options = {'DEFAULT_CLOSED'}
@classmethod
def poll(cls, context):
brush = context.tool_settings.gpencil_paint.brush
return brush is not None and brush.gpencil_tool != 'ERASE'
def draw_header(self, context):
brush = context.tool_settings.gpencil_paint.brush
gp_settings = brush.gpencil_settings
self.layout.prop(gp_settings, "use_settings_random", text="")
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
brush = context.tool_settings.gpencil_paint.brush
gp_settings = brush.gpencil_settings
layout.active = gp_settings.use_settings_random
layout.prop(gp_settings, "random_pressure", text="Pressure", slider=True)
layout.prop(gp_settings, "random_strength", text="Strength", slider=True)
layout.prop(gp_settings, "uv_random", text="UV", slider=True)
row = layout.row(align=True)
row.prop(gp_settings, "pen_jitter", slider=True)
row.prop(gp_settings, "use_jitter_pressure", text="", icon='STYLUS_PRESSURE')
# Grease Pencil drawingcurves
class VIEW3D_PT_tools_grease_pencil_brushcurves(View3DPanel, Panel):
bl_context = ".greasepencil_paint"
bl_label = "Curves"
bl_category = "Tool"
bl_options = {'DEFAULT_CLOSED'}
@classmethod
def poll(cls, context):
brush = context.tool_settings.gpencil_paint.brush
return brush is not None and brush.gpencil_tool not in {'ERASE', 'FILL'}
def draw(self, context):
pass
class VIEW3D_PT_tools_grease_pencil_brushcurves_sensitivity(View3DPanel, Panel):
bl_context = ".greasepencil_paint"
bl_label = "Sensitivity"
bl_category = "Tool"
bl_parent_id = "VIEW3D_PT_tools_grease_pencil_brushcurves"
def draw(self, context):
layout = self.layout
layout.use_property_split = True
brush = context.tool_settings.gpencil_paint.brush
gp_settings = brush.gpencil_settings
layout.template_curve_mapping(gp_settings, "curve_sensitivity", brush=True,
use_negative_slope=True)
class VIEW3D_PT_tools_grease_pencil_brushcurves_strength(View3DPanel, Panel):
bl_context = ".greasepencil_paint"
bl_label = "Strength"
bl_category = "Tool"
bl_parent_id = "VIEW3D_PT_tools_grease_pencil_brushcurves"
def draw(self, context):
layout = self.layout
layout.use_property_split = True
brush = context.tool_settings.gpencil_paint.brush
gp_settings = brush.gpencil_settings
layout.template_curve_mapping(gp_settings, "curve_strength", brush=True,
use_negative_slope=True)
class VIEW3D_PT_tools_grease_pencil_brushcurves_jitter(View3DPanel, Panel):
bl_context = ".greasepencil_paint"
bl_label = "Jitter"
bl_category = "Tool"
bl_parent_id = "VIEW3D_PT_tools_grease_pencil_brushcurves"
def draw(self, context):
layout = self.layout
layout.use_property_split = True
brush = context.tool_settings.gpencil_paint.brush
gp_settings = brush.gpencil_settings
layout.template_curve_mapping(gp_settings, "curve_jitter", brush=True,
use_negative_slope=True)
# Grease Pencil stroke editing tools
class VIEW3D_PT_tools_grease_pencil_edit(GreasePencilStrokeEditPanel, Panel):
bl_space_type = 'VIEW_3D'
bl_category = "Tool"
# Grease Pencil stroke interpolation tools
class VIEW3D_PT_tools_grease_pencil_interpolate(Panel):
bl_space_type = 'VIEW_3D'
bl_region_type = 'HEADER'
bl_label = "Interpolate"
@classmethod
def poll(cls, context):
if context.gpencil_data is None:
return False
gpd = context.gpencil_data
return bool(context.editable_gpencil_strokes) and bool(gpd.use_stroke_edit_mode)
def draw(self, context):
layout = self.layout
settings = context.tool_settings.gpencil_interpolate
col = layout.column(align=True)
col.label(text="Interpolate Strokes")
col.operator("gpencil.interpolate", text="Interpolate")
col.operator("gpencil.interpolate_sequence", text="Sequence")
col.operator("gpencil.interpolate_reverse", text="Remove Breakdowns")
col = layout.column(align=True)
col.label(text="Options:")
col.prop(settings, "interpolate_all_layers")
col.prop(settings, "interpolate_selected_only")
col = layout.column(align=True)
col.label(text="Sequence Options:")
col.prop(settings, "type")
if settings.type == 'CUSTOM':
# TODO: Options for loading/saving curve presets?
col.template_curve_mapping(settings, "interpolation_curve", brush=True,
use_negative_slope=True)
elif settings.type != 'LINEAR':
col.prop(settings, "easing")
if settings.type == 'BACK':
layout.prop(settings, "back")
elif settings.type == 'ELASTIC':
sub = layout.column(align=True)
sub.prop(settings, "amplitude")
sub.prop(settings, "period")
# Grease Pencil stroke sculpting tools
class VIEW3D_PT_tools_grease_pencil_sculpt(GreasePencilStrokeSculptPanel, View3DPanel, Panel):
bl_context = ".greasepencil_sculpt"
bl_category = "Tools"
bl_label = "Brush"
bl_category = "Tool"
# Grease Pencil weight painting tools
class VIEW3D_PT_tools_grease_pencil_weight_paint(View3DPanel, Panel):
bl_context = ".greasepencil_weight"
bl_category = "Tools"
bl_label = "Brush"
bl_category = "Tool"
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
settings = context.tool_settings.gpencil_sculpt
brush = settings.brush
layout.template_icon_view(settings, "weight_tool", show_labels=True)
col = layout.column()
if not self.is_popover:
from bl_ui.properties_paint_common import (
brush_basic_gpencil_weight_settings,
)
brush_basic_gpencil_weight_settings(col, context, brush)
# Grease Pencil Brush Appearance (one for each mode)
class VIEW3D_PT_tools_grease_pencil_paint_appearance(GreasePencilAppearancePanel, View3DPanel, Panel):
bl_context = ".greasepencil_paint"
bl_label = "Display"
bl_category = "Tool"
class VIEW3D_PT_tools_grease_pencil_sculpt_appearance(GreasePencilAppearancePanel, View3DPanel, Panel):
bl_context = ".greasepencil_sculpt"
bl_label = "Display"
bl_category = "Tool"
class VIEW3D_PT_tools_grease_pencil_sculpt_options(GreasePencilSculptOptionsPanel, View3DPanel, Panel):
bl_context = ".greasepencil_sculpt"
bl_label = "Sculpt Strokes"
bl_parent_id = 'VIEW3D_PT_tools_grease_pencil_sculpt'
bl_category = "Tool"
class VIEW3D_PT_tools_grease_pencil_weight_appearance(GreasePencilAppearancePanel, View3DPanel, Panel):
bl_context = ".greasepencil_weight"
bl_label = "Display"
bl_category = "Tool"
class VIEW3D_PT_gpencil_brush_presets(PresetPanel, Panel):
"""Brush settings"""
bl_label = "Brush Presets"
preset_subdir = "gpencil_brush"
preset_operator = "script.execute_preset"
preset_add_operator = "scene.gpencil_brush_preset_add"
classes = (
VIEW3D_MT_brush_context_menu,
VIEW3D_MT_brush_context_menu_paint_modes,
VIEW3D_PT_tools_object_options,
VIEW3D_PT_tools_object_options_transform,
VIEW3D_PT_tools_meshedit_options,
VIEW3D_PT_tools_meshedit_options_automerge,
VIEW3D_PT_tools_curveedit_options_stroke,
VIEW3D_PT_tools_armatureedit_options,
VIEW3D_PT_tools_posemode_options,
VIEW3D_PT_slots_projectpaint,
VIEW3D_PT_tools_brush,
VIEW3D_PT_tools_brush_color,
VIEW3D_PT_tools_brush_swatches,
VIEW3D_PT_tools_brush_clone,
VIEW3D_PT_tools_brush_options,
TEXTURE_UL_texpaintslots,
VIEW3D_MT_tools_projectpaint_uvlayer,
VIEW3D_PT_stencil_projectpaint,
VIEW3D_PT_tools_brush_texture,
VIEW3D_PT_tools_mask_texture,
VIEW3D_PT_tools_brush_stroke,
VIEW3D_PT_tools_brush_stroke_smooth_stroke,
VIEW3D_PT_tools_brush_falloff,
VIEW3D_PT_tools_brush_falloff_frontface,
VIEW3D_PT_tools_brush_falloff_normal,
VIEW3D_PT_tools_brush_display,
VIEW3D_PT_tools_brush_display_show_brush,
VIEW3D_PT_tools_brush_display_custom_icon,
VIEW3D_PT_sculpt_dyntopo,
VIEW3D_PT_sculpt_dyntopo_remesh,
VIEW3D_PT_sculpt_voxel_remesh,
VIEW3D_PT_sculpt_symmetry,
VIEW3D_PT_sculpt_symmetry_for_topbar,
VIEW3D_PT_sculpt_options,
VIEW3D_PT_sculpt_options_unified,
VIEW3D_PT_sculpt_options_gravity,
VIEW3D_PT_tools_weightpaint_symmetry,
VIEW3D_PT_tools_weightpaint_symmetry_for_topbar,
VIEW3D_PT_tools_weightpaint_options,
VIEW3D_PT_tools_weightpaint_options_unified,
VIEW3D_PT_tools_vertexpaint_symmetry,
VIEW3D_PT_tools_vertexpaint_symmetry_for_topbar,
VIEW3D_PT_tools_vertexpaint_options,
VIEW3D_PT_tools_imagepaint_symmetry,
VIEW3D_PT_tools_imagepaint_options,
VIEW3D_PT_tools_imagepaint_options_cavity,
VIEW3D_PT_tools_imagepaint_options_unified,
VIEW3D_PT_tools_imagepaint_options_external,
VIEW3D_MT_tools_projectpaint_stencil,
VIEW3D_PT_tools_particlemode,
VIEW3D_PT_tools_particlemode_options,
VIEW3D_PT_tools_particlemode_options_shapecut,
VIEW3D_PT_tools_particlemode_options_display,
VIEW3D_PT_gpencil_brush_presets,
VIEW3D_PT_tools_grease_pencil_brush,
VIEW3D_PT_tools_grease_pencil_brush_option,
VIEW3D_PT_tools_grease_pencil_brush_settings,
VIEW3D_PT_tools_grease_pencil_brush_stabilizer,
VIEW3D_PT_tools_grease_pencil_brush_random,
VIEW3D_PT_tools_grease_pencil_brushcurves,
VIEW3D_PT_tools_grease_pencil_brushcurves_sensitivity,
VIEW3D_PT_tools_grease_pencil_brushcurves_strength,
VIEW3D_PT_tools_grease_pencil_brushcurves_jitter,
VIEW3D_PT_tools_grease_pencil_sculpt,
VIEW3D_PT_tools_grease_pencil_weight_paint,
VIEW3D_PT_tools_grease_pencil_paint_appearance,
VIEW3D_PT_tools_grease_pencil_sculpt_options,
VIEW3D_PT_tools_grease_pencil_sculpt_appearance,
VIEW3D_PT_tools_grease_pencil_weight_appearance,
VIEW3D_PT_tools_grease_pencil_interpolate,
)
if __name__ == "__main__": # only for live edit.
from bpy.utils import register_class
for cls in classes:
register_class(cls)
| # ##### BEGIN GPL LICENSE BLOCK #####
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License
# as published by the Free Software Foundation; either version 2
# of the License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software Foundation,
# Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
#
# ##### END GPL LICENSE BLOCK #####
# <pep8 compliant>
from bpy.types import Menu, Panel, UIList
from bl_ui.properties_grease_pencil_common import (
GreasePencilStrokeEditPanel,
GreasePencilStrokeSculptPanel,
GreasePencilSculptOptionsPanel,
GreasePencilAppearancePanel,
)
from bl_ui.properties_paint_common import (
UnifiedPaintPanel,
brush_mask_texture_settings,
brush_texpaint_common,
brush_texpaint_common_color,
brush_texpaint_common_gradient,
brush_texpaint_common_clone,
brush_texpaint_common_options,
brush_texture_settings,
)
from bl_ui.utils import PresetPanel
class VIEW3D_MT_brush_context_menu(Menu):
bl_label = "Material Specials"
def draw(self, context):
layout = self.layout
settings = UnifiedPaintPanel.paint_settings(context)
brush = getattr(settings, "brush", None)
# skip if no active brush
if not brush:
layout.label(text="No Brushes currently available", icon='INFO')
return
# brush paint modes
layout.menu("VIEW3D_MT_brush_paint_modes")
# brush tool
if context.image_paint_object:
layout.prop_menu_enum(brush, "image_tool")
elif context.vertex_paint_object:
layout.prop_menu_enum(brush, "vertex_tool")
elif context.weight_paint_object:
layout.prop_menu_enum(brush, "weight_tool")
elif context.sculpt_object:
layout.prop_menu_enum(brush, "sculpt_tool")
layout.operator("brush.reset")
class VIEW3D_MT_brush_context_menu_paint_modes(Menu):
bl_label = "Enabled Modes"
def draw(self, context):
layout = self.layout
settings = UnifiedPaintPanel.paint_settings(context)
brush = settings.brush
layout.prop(brush, "use_paint_sculpt", text="Sculpt")
layout.prop(brush, "use_paint_uv_sculpt", text="UV Sculpt")
layout.prop(brush, "use_paint_vertex", text="Vertex Paint")
layout.prop(brush, "use_paint_weight", text="Weight Paint")
layout.prop(brush, "use_paint_image", text="Texture Paint")
class View3DPanel:
bl_space_type = 'VIEW_3D'
bl_region_type = 'UI'
# **************** standard tool clusters ******************
# Used by vertex & weight paint
def draw_vpaint_symmetry(layout, vpaint):
split = layout.split()
col = split.column()
col.alignment = 'RIGHT'
col.label(text="Mirror")
col = split.column()
row = col.row(align=True)
row.prop(vpaint, "use_symmetry_x", text="X", toggle=True)
row.prop(vpaint, "use_symmetry_y", text="Y", toggle=True)
row.prop(vpaint, "use_symmetry_z", text="Z", toggle=True)
col = layout.column()
col.use_property_split = True
col.use_property_decorate = False
col.prop(vpaint, "radial_symmetry", text="Radial")
# Most of these panels should not be visible in GP edit modes
def is_not_gpencil_edit_mode(context):
is_gpmode = (
context.active_object and
context.active_object.mode in {'EDIT_GPENCIL', 'PAINT_GPENCIL', 'SCULPT_GPENCIL', 'WEIGHT_GPENCIL'}
)
return not is_gpmode
# ********** default tools for object mode ****************
class VIEW3D_PT_tools_object_options(View3DPanel, Panel):
bl_category = "Tool"
bl_context = ".objectmode" # dot on purpose (access from topbar)
bl_label = "Options"
def draw(self, context):
# layout = self.layout
pass
class VIEW3D_PT_tools_object_options_transform(View3DPanel, Panel):
bl_category = "Tool"
bl_context = ".objectmode" # dot on purpose (access from topbar)
bl_label = "Transform"
bl_parent_id = "VIEW3D_PT_tools_object_options"
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
tool_settings = context.tool_settings
layout.label(text="Affect Only")
layout.prop(tool_settings, "use_transform_data_origin", text="Origins")
layout.prop(tool_settings, "use_transform_pivot_point_align", text="Locations")
layout.prop(tool_settings, "use_transform_skip_children", text="Parents")
# ********** default tools for editmode_mesh ****************
class VIEW3D_PT_tools_meshedit_options(View3DPanel, Panel):
bl_category = "Tool"
bl_context = ".mesh_edit" # dot on purpose (access from topbar)
bl_label = "Options"
bl_options = {'DEFAULT_CLOSED'}
@classmethod
def poll(cls, context):
return context.active_object
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
ob = context.active_object
mesh = ob.data
split = layout.split()
col = split.column()
col.alignment = 'RIGHT'
col.label(text="Mirror")
col = split.column()
row = col.row(align=True)
row.prop(mesh, "use_mirror_x", text="X", toggle=True)
row.prop(mesh, "use_mirror_y", text="Y", toggle=True)
row.prop(mesh, "use_mirror_z", text="Z", toggle=True)
row = layout.row(align=True)
row.active = ob.data.use_mirror_x or ob.data.use_mirror_y or ob.data.use_mirror_z
row.prop(mesh, "use_mirror_topology")
class VIEW3D_PT_tools_meshedit_options_automerge(View3DPanel, Panel):
bl_category = "Tool"
bl_context = ".mesh_edit" # dot on purpose (access from topbar)
bl_label = "Auto Merge"
bl_parent_id = "VIEW3D_PT_tools_meshedit_options"
bl_options = {'DEFAULT_CLOSED'}
@classmethod
def poll(cls, context):
return context.active_object
def draw_header(self, context):
tool_settings = context.tool_settings
self.layout.prop(tool_settings, "use_mesh_automerge", text="", toggle=False)
def draw(self, context):
layout = self.layout
tool_settings = context.tool_settings
layout.use_property_split = True
layout.use_property_decorate = False
col = layout.column(align=True)
col.active = tool_settings.use_mesh_automerge
col.prop(tool_settings, "use_mesh_automerge_and_split", toggle=False)
col.prop(tool_settings, "double_threshold", text="Threshold")
# ********** default tools for editmode_curve ****************
class VIEW3D_PT_tools_curveedit_options_stroke(View3DPanel, Panel):
bl_category = "Tool"
bl_context = ".curve_edit" # dot on purpose (access from topbar)
bl_label = "Curve Stroke"
def draw(self, context):
layout = self.layout
tool_settings = context.tool_settings
cps = tool_settings.curve_paint_settings
col = layout.column()
col.prop(cps, "curve_type")
if cps.curve_type == 'BEZIER':
col.label(text="Bezier Options:")
col.prop(cps, "error_threshold")
col.prop(cps, "fit_method")
col.prop(cps, "use_corners_detect")
col = layout.column()
col.active = cps.use_corners_detect
col.prop(cps, "corner_angle")
col.label(text="Pressure Radius:")
row = layout.row(align=True)
rowsub = row.row(align=True)
rowsub.prop(cps, "radius_min", text="Min")
rowsub.prop(cps, "radius_max", text="Max")
row.prop(cps, "use_pressure_radius", text="", icon_only=True)
col = layout.column()
col.label(text="Taper Radius:")
row = layout.row(align=True)
row.prop(cps, "radius_taper_start", text="Start")
row.prop(cps, "radius_taper_end", text="End")
col = layout.column()
col.label(text="Projection Depth:")
row = layout.row(align=True)
row.prop(cps, "depth_mode", expand=True)
col = layout.column()
if cps.depth_mode == 'SURFACE':
col.prop(cps, "surface_offset")
col.prop(cps, "use_offset_absolute")
col.prop(cps, "use_stroke_endpoints")
if cps.use_stroke_endpoints:
colsub = layout.column(align=True)
colsub.prop(cps, "surface_plane", expand=True)
# ********** default tools for editmode_armature ****************
class VIEW3D_PT_tools_armatureedit_options(View3DPanel, Panel):
bl_category = "Tool"
bl_context = ".armature_edit" # dot on purpose (access from topbar)
bl_label = "Options"
def draw(self, context):
arm = context.active_object.data
self.layout.prop(arm, "use_mirror_x")
# ********** default tools for pose-mode ****************
class VIEW3D_PT_tools_posemode_options(View3DPanel, Panel):
bl_category = "Tool"
bl_context = ".posemode" # dot on purpose (access from topbar)
bl_label = "Pose Options"
def draw(self, context):
pose = context.active_object.pose
layout = self.layout
tool_settings = context.tool_settings
layout.prop(pose, "use_auto_ik")
layout.prop(pose, "use_mirror_x")
col = layout.column()
col.active = pose.use_mirror_x
col.prop(pose, "use_mirror_relative")
layout.label(text="Affect Only")
layout.prop(tool_settings, "use_transform_pivot_point_align", text="Locations")
# ********** default tools for paint modes ****************
class View3DPaintPanel(UnifiedPaintPanel):
bl_space_type = 'VIEW_3D'
bl_region_type = 'UI'
bl_category = "Tool"
class VIEW3D_PT_tools_particlemode(Panel, View3DPaintPanel):
bl_context = ".paint_common" # dot on purpose (access from topbar)
bl_label = "Particle tools"
bl_options = {'HIDE_HEADER'}
@classmethod
def poll(cls, context):
settings = cls.paint_settings(context)
return (settings and settings.brush and context.particle_edit_object)
def draw(self, context):
layout = self.layout
settings = self.paint_settings(context)
brush = settings.brush
tool = settings.tool
layout.use_property_split = True
layout.use_property_decorate = False # No animation.
if tool is not None:
col = layout.column()
col.prop(brush, "size", slider=True)
if tool == 'ADD':
col.prop(brush, "count")
col = layout.column()
col.prop(settings, "use_default_interpolate")
col.prop(brush, "steps", slider=True)
col.prop(settings, "default_key_count", slider=True)
else:
col.prop(brush, "strength", slider=True)
if tool == 'LENGTH':
layout.row().prop(brush, "length_mode", expand=True)
elif tool == 'PUFF':
layout.row().prop(brush, "puff_mode", expand=True)
layout.prop(brush, "use_puff_volume")
elif tool == 'COMB':
layout.prop(settings, "use_emitter_deflect", text="Deflect Emitter")
col = layout.column()
col.active = settings.use_emitter_deflect
col.prop(settings, "emitter_distance", text="Distance")
# TODO, move to space_view3d.py
class VIEW3D_PT_tools_brush(Panel, View3DPaintPanel):
bl_context = ".paint_common" # dot on purpose (access from topbar)
bl_label = "Brush"
@classmethod
def poll(cls, context):
settings = cls.paint_settings(context)
return (settings and
settings.brush and
(context.sculpt_object or
context.vertex_paint_object or
context.weight_paint_object or
context.image_paint_object))
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False # No animation.
settings = self.paint_settings(context)
brush = settings.brush
if not self.is_popover:
row = layout.row()
row.column().template_ID_preview(settings, "brush", new="brush.add", rows=3, cols=8)
row.menu("VIEW3D_MT_brush_context_menu", icon='DOWNARROW_HLT', text="")
# Sculpt Mode #
if context.sculpt_object and brush:
from bl_ui.properties_paint_common import (
brush_basic_sculpt_settings,
)
capabilities = brush.sculpt_capabilities
col = layout.column()
if not self.is_popover:
brush_basic_sculpt_settings(col, context, brush)
# normal_radius_factor
col.separator()
row = col.row()
row.prop(brush, "normal_radius_factor", slider=True)
if brush.sculpt_tool == 'ELASTIC_DEFORM':
col.separator()
row = col.row()
row.prop(brush, "elastic_deform_type")
row = col.row()
row.prop(brush, "elastic_deform_volume_preservation", slider=True)
elif brush.sculpt_tool == 'POSE':
row = col.row()
row.prop(brush, "pose_offset")
elif brush.sculpt_tool == 'GRAB':
col.separator()
row = col.row()
row.prop(brush, "use_grab_active_vertex")
# topology_rake_factor
if (
capabilities.has_topology_rake and
context.sculpt_object.use_dynamic_topology_sculpting
):
row = col.row()
row.prop(brush, "topology_rake_factor", slider=True)
# auto_smooth_factor and use_inverse_smooth_pressure
if capabilities.has_auto_smooth:
row = col.row(align=True)
row.prop(brush, "auto_smooth_factor", slider=True)
row.prop(brush, "use_inverse_smooth_pressure", toggle=True, text="")
# normal_weight
if capabilities.has_normal_weight:
row = col.row(align=True)
row.prop(brush, "normal_weight", slider=True)
# crease_pinch_factor
if capabilities.has_pinch_factor:
row = col.row(align=True)
if brush.sculpt_tool in {'BLOB', 'SNAKE_HOOK'}:
row.prop(brush, "crease_pinch_factor", slider=True, text="Magnify")
else:
row.prop(brush, "crease_pinch_factor", slider=True, text="Pinch")
# rake_factor
if capabilities.has_rake_factor:
row = col.row(align=True)
row.prop(brush, "rake_factor", slider=True)
if brush.sculpt_tool == 'MASK':
col.prop(brush, "mask_tool")
# plane_offset, use_offset_pressure, use_plane_trim, plane_trim
if capabilities.has_plane_offset:
row = col.row(align=True)
row.prop(brush, "plane_offset", slider=True)
row.prop(brush, "use_offset_pressure", text="")
col.separator()
row = col.row()
row.prop(brush, "use_plane_trim", text="Plane Trim")
row = col.row()
row.active = brush.use_plane_trim
row.prop(brush, "plane_trim", slider=True, text="Distance")
# height
if capabilities.has_height:
row = col.row()
row.prop(brush, "height", slider=True, text="Height")
# use_persistent, set_persistent_base
if capabilities.has_persistence:
ob = context.sculpt_object
do_persistent = True
# not supported yet for this case
for md in ob.modifiers:
if md.type == 'MULTIRES':
do_persistent = False
break
if do_persistent:
col.prop(brush, "use_persistent")
col.operator("sculpt.set_persistent_base")
# Texture Paint Mode #
elif context.image_paint_object and brush:
brush_texpaint_common(self, context, layout, brush, settings, projpaint=True)
# Weight Paint Mode #
elif context.weight_paint_object and brush:
from bl_ui.properties_paint_common import (
brush_basic_wpaint_settings,
)
col = layout.column()
if not self.is_popover:
brush_basic_wpaint_settings(col, context, brush)
# Vertex Paint Mode #
elif context.vertex_paint_object and brush:
from bl_ui.properties_paint_common import (
brush_basic_vpaint_settings,
)
col = layout.column()
if not self.is_popover:
brush_basic_vpaint_settings(col, context, brush)
class VIEW3D_PT_tools_brush_color(Panel, View3DPaintPanel):
bl_context = ".paint_common" # dot on purpose (access from topbar)
bl_parent_id = "VIEW3D_PT_tools_brush"
bl_label = "Color Picker"
@classmethod
def poll(cls, context):
settings = cls.paint_settings(context)
brush = settings.brush
if context.image_paint_object:
capabilities = brush.image_paint_capabilities
return capabilities.has_color
elif context.vertex_paint_object:
capabilities = brush.vertex_paint_capabilities
return capabilities.has_color
def draw(self, context):
layout = self.layout
settings = self.paint_settings(context)
brush = settings.brush
if context.vertex_paint_object:
brush_texpaint_common_color(self, context, layout, brush, settings, projpaint=True)
else:
layout.prop(brush, "color_type", expand=True)
if brush.color_type == 'COLOR':
brush_texpaint_common_color(self, context, layout, brush, settings, projpaint=True)
elif brush.color_type == 'GRADIENT':
brush_texpaint_common_gradient(self, context, layout, brush, settings, projpaint=True)
class VIEW3D_PT_tools_brush_swatches(Panel, View3DPaintPanel):
bl_context = ".paint_common" # dot on purpose (access from topbar)
bl_parent_id = "VIEW3D_PT_tools_brush"
bl_label = "Color Palette"
bl_options = {'DEFAULT_CLOSED'}
@classmethod
def poll(cls, context):
settings = cls.paint_settings(context)
brush = settings.brush
if context.image_paint_object:
capabilities = brush.image_paint_capabilities
return capabilities.has_color
elif context.vertex_paint_object:
capabilities = brush.vertex_paint_capabilities
return capabilities.has_color
def draw(self, context):
layout = self.layout
settings = self.paint_settings(context)
layout.template_ID(settings, "palette", new="palette.new")
if settings.palette:
layout.template_palette(settings, "palette", color=True)
class VIEW3D_PT_tools_brush_clone(Panel, View3DPaintPanel):
bl_context = ".paint_common" # dot on purpose (access from topbar)
bl_parent_id = "VIEW3D_PT_tools_brush"
bl_label = "Clone from Paint Slot"
bl_options = {'DEFAULT_CLOSED'}
@classmethod
def poll(cls, context):
settings = cls.paint_settings(context)
brush = settings.brush
return brush.image_tool == 'CLONE'
def draw_header(self, context):
settings = self.paint_settings(context)
self.layout.prop(settings, "use_clone_layer", text="")
def draw(self, context):
layout = self.layout
settings = self.paint_settings(context)
brush = settings.brush
layout.active = settings.use_clone_layer
brush_texpaint_common_clone(self, context, layout, brush, settings, projpaint=True)
class VIEW3D_PT_tools_brush_options(Panel, View3DPaintPanel):
bl_context = ".paint_common" # dot on purpose (access from topbar)
bl_parent_id = "VIEW3D_PT_tools_brush"
bl_label = "Options"
bl_options = {'DEFAULT_CLOSED'}
def draw(self, context):
layout = self.layout
tool_settings = context.tool_settings
settings = self.paint_settings(context)
brush = settings.brush
capabilities = brush.sculpt_capabilities
layout.use_property_split = True
layout.use_property_decorate = False # No animation.
col = layout.column()
if context.image_paint_object and brush:
brush_texpaint_common_options(self, context, layout, brush, settings, projpaint=True)
elif context.sculpt_object and brush:
col.prop(brush, "use_automasking_topology")
if capabilities.has_accumulate:
col.prop(brush, "use_accumulate")
UnifiedPaintPanel.prop_unified_size(col, context, brush, "use_locked_size")
if capabilities.has_sculpt_plane:
col.prop(brush, "sculpt_plane")
col.prop(brush, "use_original_normal")
col.prop(brush, "use_original_plane")
col.prop(brush, "use_frontface", text="Front Faces Only")
col.prop(brush, "use_projected")
elif context.weight_paint_object and brush:
if brush.weight_tool != 'SMEAR':
col.prop(brush, "use_accumulate")
col.prop(brush, "use_frontface", text="Front Faces Only")
col.prop(brush, "use_projected")
col.prop(tool_settings, "use_auto_normalize", text="Auto Normalize")
col.prop(tool_settings, "use_multipaint", text="Multi-Paint")
elif context.vertex_paint_object and brush:
if brush.vertex_tool != 'SMEAR':
col.prop(brush, "use_accumulate")
col.prop(brush, "use_alpha")
col.prop(brush, "use_frontface", text="Front Faces Only")
col.prop(brush, "use_projected")
class TEXTURE_UL_texpaintslots(UIList):
def draw_item(self, _context, layout, _data, item, icon, _active_data, _active_propname, _index):
# mat = data
if self.layout_type in {'DEFAULT', 'COMPACT'}:
layout.prop(item, "name", text="", emboss=False, icon_value=icon)
elif self.layout_type == 'GRID':
layout.alignment = 'CENTER'
layout.label(text="")
class VIEW3D_MT_tools_projectpaint_uvlayer(Menu):
bl_label = "Clone Layer"
def draw(self, context):
layout = self.layout
for i, uv_layer in enumerate(context.active_object.data.uv_layers):
props = layout.operator("wm.context_set_int", text=uv_layer.name, translate=False)
props.data_path = "active_object.data.uv_layers.active_index"
props.value = i
class VIEW3D_PT_slots_projectpaint(View3DPanel, Panel):
bl_category = "Tool"
bl_context = ".imagepaint" # dot on purpose (access from topbar)
bl_label = "Texture Slots"
@classmethod
def poll(cls, context):
brush = context.tool_settings.image_paint.brush
ob = context.active_object
return (brush is not None and ob is not None)
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
settings = context.tool_settings.image_paint
ob = context.active_object
layout.prop(settings, "mode", text="Mode")
layout.separator()
if settings.mode == 'MATERIAL':
if len(ob.material_slots) > 1:
layout.template_list("MATERIAL_UL_matslots", "layers",
ob, "material_slots",
ob, "active_material_index", rows=2)
mat = ob.active_material
if mat and mat.texture_paint_images:
row = layout.row()
row.template_list("TEXTURE_UL_texpaintslots", "",
mat, "texture_paint_images",
mat, "paint_active_slot", rows=2)
if mat.texture_paint_slots:
slot = mat.texture_paint_slots[mat.paint_active_slot]
else:
slot = None
have_image = slot is not None
else:
row = layout.row()
box = row.box()
box.label(text="No Textures")
have_image = False
sub = row.column(align=True)
sub.operator_menu_enum("paint.add_texture_paint_slot", "type", icon='ADD', text="")
elif settings.mode == 'IMAGE':
mesh = ob.data
uv_text = mesh.uv_layers.active.name if mesh.uv_layers.active else ""
layout.template_ID(settings, "canvas", new="image.new", open="image.open")
if settings.missing_uvs:
layout.operator("paint.add_simple_uvs", icon='ADD', text="Add UVs")
else:
layout.menu("VIEW3D_MT_tools_projectpaint_uvlayer", text=uv_text, translate=False)
have_image = settings.canvas is not None
layout.prop(settings, "interpolation", text="")
if settings.missing_uvs:
layout.separator()
split = layout.split()
split.label(text="UV Map Needed", icon='INFO')
split.operator("paint.add_simple_uvs", icon='ADD', text="Add Simple UVs")
elif have_image:
layout.separator()
layout.operator("image.save_all_modified", text="Save All Images", icon='FILE_TICK')
# TODO, move to space_view3d.py
class VIEW3D_PT_stencil_projectpaint(View3DPanel, Panel):
bl_category = "Tool"
bl_context = ".imagepaint" # dot on purpose (access from topbar)
bl_label = "Mask"
bl_options = {'DEFAULT_CLOSED'}
bl_ui_units_x = 14
@classmethod
def poll(cls, context):
brush = context.tool_settings.image_paint.brush
ob = context.active_object
return (brush is not None and ob is not None)
def draw_header(self, context):
ipaint = context.tool_settings.image_paint
self.layout.prop(ipaint, "use_stencil_layer", text="")
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
tool_settings = context.tool_settings
ipaint = tool_settings.image_paint
ob = context.active_object
mesh = ob.data
col = layout.column()
col.active = ipaint.use_stencil_layer
col.label(text="Stencil Image")
col.template_ID(ipaint, "stencil_image", new="image.new", open="image.open")
stencil_text = mesh.uv_layer_stencil.name if mesh.uv_layer_stencil else ""
col.separator()
split = col.split()
colsub = split.column()
colsub.alignment = 'RIGHT'
colsub.label(text="UV Layer")
split.column().menu("VIEW3D_MT_tools_projectpaint_stencil", text=stencil_text, translate=False)
col.separator()
row = col.row(align=True)
row.prop(ipaint, "stencil_color", text="Display Color")
row.prop(ipaint, "invert_stencil", text="", icon='IMAGE_ALPHA')
# TODO, move to space_view3d.py
class VIEW3D_PT_tools_brush_display(Panel, View3DPaintPanel):
bl_context = ".paint_common" # dot on purpose (access from topbar)
bl_label = "Display"
bl_options = {'DEFAULT_CLOSED'}
@classmethod
def poll(cls, context):
settings = cls.paint_settings(context)
return (settings and
settings.brush and
(context.sculpt_object or
context.vertex_paint_object or
context.weight_paint_object or
context.image_paint_object))
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
settings = self.paint_settings(context)
brush = settings.brush
tex_slot = brush.texture_slot
tex_slot_mask = brush.mask_texture_slot
col = layout.column()
row = col.row(align=True)
sub = row.row(align=True)
sub.prop(brush, "cursor_overlay_alpha", text="Curve Alpha")
sub.prop(brush, "use_cursor_overlay_override", toggle=True, text="", icon='BRUSH_DATA')
row.prop(
brush, "use_cursor_overlay", text="", toggle=True,
icon='HIDE_OFF' if brush.use_cursor_overlay else 'HIDE_ON',
)
col.active = brush.brush_capabilities.has_overlay
if context.image_paint_object or context.sculpt_object or context.vertex_paint_object:
row = col.row(align=True)
sub = row.row(align=True)
sub.prop(brush, "texture_overlay_alpha", text="Texture Alpha")
sub.prop(brush, "use_primary_overlay_override", toggle=True, text="", icon='BRUSH_DATA')
if tex_slot.map_mode != 'STENCIL':
row.prop(
brush, "use_primary_overlay", text="", toggle=True,
icon='HIDE_OFF' if brush.use_primary_overlay else 'HIDE_ON',
)
if context.image_paint_object:
row = col.row(align=True)
sub = row.row(align=True)
sub.prop(brush, "mask_overlay_alpha", text="Mask Texture Alpha")
sub.prop(brush, "use_secondary_overlay_override", toggle=True, text="", icon='BRUSH_DATA')
if tex_slot_mask.map_mode != 'STENCIL':
row.prop(
brush, "use_secondary_overlay", text="", toggle=True,
icon='HIDE_OFF' if brush.use_secondary_overlay else 'HIDE_ON',
)
# TODO, move to space_view3d.py
class VIEW3D_PT_tools_brush_texture(Panel, View3DPaintPanel):
bl_context = ".paint_common" # dot on purpose (access from topbar)
bl_label = "Texture"
bl_options = {'DEFAULT_CLOSED'}
@classmethod
def poll(cls, context):
settings = cls.paint_settings(context)
return (settings and settings.brush and
(context.sculpt_object or context.image_paint_object or context.vertex_paint_object))
def draw(self, context):
layout = self.layout
settings = self.paint_settings(context)
brush = settings.brush
col = layout.column()
col.template_ID_preview(brush, "texture", new="texture.new", rows=3, cols=8)
brush_texture_settings(col, brush, context.sculpt_object)
# TODO, move to space_view3d.py
class VIEW3D_PT_tools_mask_texture(Panel, View3DPaintPanel):
bl_category = "Tool"
bl_context = ".imagepaint" # dot on purpose (access from topbar)
bl_label = "Texture Mask"
bl_options = {'DEFAULT_CLOSED'}
@classmethod
def poll(cls, context):
settings = cls.paint_settings(context)
return (settings and settings.brush and context.image_paint_object)
def draw(self, context):
layout = self.layout
brush = context.tool_settings.image_paint.brush
col = layout.column()
col.template_ID_preview(brush, "mask_texture", new="texture.new", rows=3, cols=8)
brush_mask_texture_settings(col, brush)
# TODO, move to space_view3d.py
class VIEW3D_PT_tools_brush_stroke(Panel, View3DPaintPanel):
bl_context = ".paint_common" # dot on purpose (access from topbar)
bl_label = "Stroke"
bl_options = {'DEFAULT_CLOSED'}
@classmethod
def poll(cls, context):
settings = cls.paint_settings(context)
return (settings and
settings.brush and
(context.sculpt_object or
context.vertex_paint_object or
context.weight_paint_object or
context.image_paint_object))
def draw(self, context):
layout = self.layout
settings = self.paint_settings(context)
brush = settings.brush
layout.use_property_split = True
layout.use_property_decorate = False
col = layout.column()
col.prop(brush, "stroke_method")
if brush.use_anchor:
col.prop(brush, "use_edge_to_edge", text="Edge To Edge")
if brush.use_airbrush:
col.prop(brush, "rate", text="Rate", slider=True)
if brush.use_space:
row = col.row(align=True)
row.prop(brush, "spacing", text="Spacing")
row.prop(brush, "use_pressure_spacing", toggle=True, text="")
if brush.use_line or brush.use_curve:
row = col.row(align=True)
row.prop(brush, "spacing", text="Spacing")
if brush.use_curve:
col.template_ID(brush, "paint_curve", new="paintcurve.new")
col.operator("paintcurve.draw")
if context.sculpt_object:
if brush.sculpt_capabilities.has_space_attenuation:
col.prop(brush, "use_space_attenuation")
col.prop(brush, "use_scene_spacing")
if brush.sculpt_capabilities.has_jitter:
row = col.row(align=True)
if brush.use_relative_jitter:
row.prop(brush, "jitter", slider=True)
else:
row.prop(brush, "jitter_absolute")
row.prop(brush, "use_relative_jitter", icon_only=True)
row.prop(brush, "use_pressure_jitter", toggle=True, text="")
else:
row = col.row(align=True)
if brush.use_relative_jitter:
row.prop(brush, "jitter", slider=True)
else:
row.prop(brush, "jitter_absolute")
row.prop(brush, "use_relative_jitter", icon_only=True)
row.prop(brush, "use_pressure_jitter", toggle=True, text="")
col.prop(settings, "input_samples")
class VIEW3D_PT_tools_brush_stroke_smooth_stroke(Panel, View3DPaintPanel):
bl_context = ".paint_common" # dot on purpose (access from topbar)
bl_label = "Smooth Stroke"
bl_parent_id = "VIEW3D_PT_tools_brush_stroke"
bl_options = {'DEFAULT_CLOSED'}
@classmethod
def poll(cls, context):
settings = cls.paint_settings(context)
brush = settings.brush
if brush.brush_capabilities.has_smooth_stroke:
return True
def draw_header(self, context):
settings = self.paint_settings(context)
brush = settings.brush
self.layout.prop(brush, "use_smooth_stroke", text="")
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
settings = self.paint_settings(context)
brush = settings.brush
col = layout.column()
col.active = brush.use_smooth_stroke
col.prop(brush, "smooth_stroke_radius", text="Radius", slider=True)
col.prop(brush, "smooth_stroke_factor", text="Factor", slider=True)
# TODO, move to space_view3d.py
class VIEW3D_PT_tools_brush_falloff(Panel, View3DPaintPanel):
bl_context = ".paint_common" # dot on purpose (access from topbar)
bl_label = "Falloff"
bl_options = {'DEFAULT_CLOSED'}
@classmethod
def poll(cls, context):
settings = cls.paint_settings(context)
return (settings and settings.brush and settings.brush.curve)
def draw(self, context):
layout = self.layout
settings = self.paint_settings(context)
brush = settings.brush
col = layout.column(align=True)
row = col.row(align=True)
row.prop(brush, "curve_preset", text="")
if brush.curve_preset == 'CUSTOM':
layout.template_curve_mapping(brush, "curve", brush=True)
col = layout.column(align=True)
row = col.row(align=True)
row.operator("brush.curve_preset", icon='SMOOTHCURVE', text="").shape = 'SMOOTH'
row.operator("brush.curve_preset", icon='SPHERECURVE', text="").shape = 'ROUND'
row.operator("brush.curve_preset", icon='ROOTCURVE', text="").shape = 'ROOT'
row.operator("brush.curve_preset", icon='SHARPCURVE', text="").shape = 'SHARP'
row.operator("brush.curve_preset", icon='LINCURVE', text="").shape = 'LINE'
row.operator("brush.curve_preset", icon='NOCURVE', text="").shape = 'MAX'
class VIEW3D_PT_tools_brush_falloff_frontface(View3DPaintPanel, Panel):
bl_context = ".imagepaint" # dot on purpose (access from topbar)
bl_label = "Frontface Falloff"
bl_parent_id = "VIEW3D_PT_tools_brush_falloff"
bl_options = {'DEFAULT_CLOSED'}
@classmethod
def poll(cls, context):
return (context.weight_paint_object or context.vertex_paint_object)
def draw_header(self, context):
settings = self.paint_settings(context)
brush = settings.brush
self.layout.prop(brush, "use_frontface_falloff", text="")
def draw(self, context):
settings = self.paint_settings(context)
brush = settings.brush
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
layout.active = brush.use_frontface_falloff
layout.prop(brush, "falloff_angle", text="Angle")
class VIEW3D_PT_tools_brush_falloff_normal(View3DPaintPanel, Panel):
bl_context = ".imagepaint" # dot on purpose (access from topbar)
bl_label = "Normal Falloff"
bl_parent_id = "VIEW3D_PT_tools_brush_falloff"
bl_options = {'DEFAULT_CLOSED'}
@classmethod
def poll(cls, context):
return context.image_paint_object
def draw_header(self, context):
tool_settings = context.tool_settings
ipaint = tool_settings.image_paint
self.layout.prop(ipaint, "use_normal_falloff", text="")
def draw(self, context):
tool_settings = context.tool_settings
ipaint = tool_settings.image_paint
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
layout.active = ipaint.use_normal_falloff
layout.prop(ipaint, "normal_angle", text="Angle")
# TODO, move to space_view3d.py
class VIEW3D_PT_sculpt_dyntopo(Panel, View3DPaintPanel):
bl_context = ".sculpt_mode" # dot on purpose (access from topbar)
bl_label = "Dyntopo"
bl_options = {'DEFAULT_CLOSED'}
bl_ui_units_x = 12
@classmethod
def poll(cls, context):
return (context.sculpt_object and context.tool_settings.sculpt)
def draw_header(self, context):
is_popover = self.is_popover
layout = self.layout
layout.operator(
"sculpt.dynamic_topology_toggle",
icon='CHECKBOX_HLT' if context.sculpt_object.use_dynamic_topology_sculpting else 'CHECKBOX_DEHLT',
text="",
emboss=is_popover,
)
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
tool_settings = context.tool_settings
sculpt = tool_settings.sculpt
settings = self.paint_settings(context)
brush = settings.brush
col = layout.column()
col.active = context.sculpt_object.use_dynamic_topology_sculpting
sub = col.column()
sub.active = (brush and brush.sculpt_tool != 'MASK')
if sculpt.detail_type_method in {'CONSTANT', 'MANUAL'}:
row = sub.row(align=True)
row.prop(sculpt, "constant_detail_resolution")
row.operator("sculpt.sample_detail_size", text="", icon='EYEDROPPER')
elif (sculpt.detail_type_method == 'BRUSH'):
sub.prop(sculpt, "detail_percent")
else:
sub.prop(sculpt, "detail_size")
sub.prop(sculpt, "detail_refine_method", text="Refine Method")
sub.prop(sculpt, "detail_type_method", text="Detailing")
col.prop(sculpt, "use_smooth_shading")
class VIEW3D_PT_sculpt_dyntopo_remesh(Panel, View3DPaintPanel):
bl_context = ".sculpt_mode" # dot on purpose (access from topbar)
bl_label = "Remesh"
bl_parent_id = "VIEW3D_PT_sculpt_dyntopo"
bl_options = {'DEFAULT_CLOSED'}
bl_ui_units_x = 12
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
tool_settings = context.tool_settings
sculpt = tool_settings.sculpt
col = layout.column()
col.active = context.sculpt_object.use_dynamic_topology_sculpting
col.prop(sculpt, "symmetrize_direction")
flow = layout.grid_flow(row_major=True, columns=0, even_columns=True, even_rows=False, align=False)
col = flow.column()
col.operator("sculpt.symmetrize")
col = flow.column()
col.operator("sculpt.optimize")
if sculpt.detail_type_method in {'CONSTANT', 'MANUAL'}:
col = flow.column()
col.operator("sculpt.detail_flood_fill")
class VIEW3D_PT_sculpt_voxel_remesh(Panel, View3DPaintPanel):
bl_context = ".sculpt_mode" # dot on purpose (access from topbar)
bl_label = "Remesh"
bl_options = {'DEFAULT_CLOSED'}
bl_ui_units_x = 12
@classmethod
def poll(cls, context):
return (context.sculpt_object and context.tool_settings.sculpt)
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
col = layout.column()
mesh = context.active_object.data
col.prop(mesh, "remesh_voxel_size")
col.prop(mesh, "remesh_voxel_adaptivity")
col.prop(mesh, "use_remesh_fix_poles")
col.prop(mesh, "use_remesh_smooth_normals")
col.prop(mesh, "use_remesh_preserve_volume")
col.prop(mesh, "use_remesh_preserve_paint_mask")
col.operator("object.voxel_remesh", text="Remesh")
# TODO, move to space_view3d.py
class VIEW3D_PT_sculpt_options(Panel, View3DPaintPanel):
bl_context = ".sculpt_mode" # dot on purpose (access from topbar)
bl_label = "Options"
bl_options = {'DEFAULT_CLOSED'}
@classmethod
def poll(cls, context):
return (context.sculpt_object and context.tool_settings.sculpt)
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
tool_settings = context.tool_settings
sculpt = tool_settings.sculpt
flow = layout.grid_flow(row_major=True, columns=0, even_columns=True, even_rows=False, align=False)
col = flow.column()
col.prop(sculpt, "use_threaded", text="Threaded Sculpt")
col = flow.column()
col.prop(sculpt, "show_low_resolution")
col = flow.column()
col.prop(sculpt, "use_deform_only")
class VIEW3D_PT_sculpt_options_unified(Panel, View3DPaintPanel):
bl_context = ".sculpt_mode" # dot on purpose (access from topbar)
bl_parent_id = "VIEW3D_PT_sculpt_options"
bl_label = "Unified Brush"
@classmethod
def poll(cls, context):
return (context.sculpt_object and context.tool_settings.sculpt)
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
self.unified_paint_settings(layout, context)
class VIEW3D_PT_sculpt_options_gravity(Panel, View3DPaintPanel):
bl_context = ".sculpt_mode" # dot on purpose (access from topbar)
bl_parent_id = "VIEW3D_PT_sculpt_options"
bl_label = "Gravity"
@classmethod
def poll(cls, context):
return (context.sculpt_object and context.tool_settings.sculpt)
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
tool_settings = context.tool_settings
sculpt = tool_settings.sculpt
capabilities = sculpt.brush.sculpt_capabilities
col = layout.column()
col.active = capabilities.has_gravity
col.prop(sculpt, "gravity", slider=True, text="Factor")
col.prop(sculpt, "gravity_object")
# TODO, move to space_view3d.py
class VIEW3D_PT_sculpt_symmetry(Panel, View3DPaintPanel):
bl_context = ".sculpt_mode" # dot on purpose (access from topbar)
bl_label = "Symmetry"
bl_options = {'DEFAULT_CLOSED'}
@classmethod
def poll(cls, context):
return (
(context.sculpt_object and context.tool_settings.sculpt) and
# When used in the tool header, this is explicitly included next to the XYZ symmetry buttons.
(context.region.type != 'TOOL_HEADER')
)
def draw(self, context):
layout = self.layout
sculpt = context.tool_settings.sculpt
split = layout.split()
col = split.column()
col.alignment = 'RIGHT'
col.label(text="Mirror")
col = split.column()
row = col.row(align=True)
row.prop(sculpt, "use_symmetry_x", text="X", toggle=True)
row.prop(sculpt, "use_symmetry_y", text="Y", toggle=True)
row.prop(sculpt, "use_symmetry_z", text="Z", toggle=True)
split = layout.split()
col = split.column()
col.alignment = 'RIGHT'
col.label(text="Lock")
col = split.column()
row = col.row(align=True)
row.prop(sculpt, "lock_x", text="X", toggle=True)
row.prop(sculpt, "lock_y", text="Y", toggle=True)
row.prop(sculpt, "lock_z", text="Z", toggle=True)
split = layout.split()
col = split.column()
col.alignment = 'RIGHT'
col.label(text="Tiling")
col = split.column()
row = col.row(align=True)
row.prop(sculpt, "tile_x", text="X", toggle=True)
row.prop(sculpt, "tile_y", text="Y", toggle=True)
row.prop(sculpt, "tile_z", text="Z", toggle=True)
layout.use_property_split = True
layout.use_property_decorate = False
layout.prop(sculpt, "use_symmetry_feather", text="Feather")
layout.column().prop(sculpt, "radial_symmetry", text="Radial")
layout.column().prop(sculpt, "tile_offset", text="Tile Offset")
class VIEW3D_PT_sculpt_symmetry_for_topbar(Panel):
bl_space_type = 'TOPBAR'
bl_region_type = 'HEADER'
bl_label = "Symmetry"
draw = VIEW3D_PT_sculpt_symmetry.draw
class VIEW3D_PT_tools_brush_display_show_brush(Panel, View3DPaintPanel):
bl_context = ".paint_common" # dot on purpose (access from topbar)
bl_label = "Show Brush"
bl_parent_id = "VIEW3D_PT_tools_brush_display"
bl_options = {'DEFAULT_CLOSED'}
def draw_header(self, context):
settings = self.paint_settings(context)
self.layout.prop(settings, "show_brush", text="")
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
settings = self.paint_settings(context)
brush = settings.brush
col = layout.column()
col.active = settings.show_brush
if context.sculpt_object and context.tool_settings.sculpt:
if brush.sculpt_capabilities.has_secondary_color:
col.prop(brush, "cursor_color_add", text="Add")
col.prop(brush, "cursor_color_subtract", text="Subtract")
else:
col.prop(brush, "cursor_color_add", text="Color")
else:
col.prop(brush, "cursor_color_add", text="Color")
class VIEW3D_PT_tools_brush_display_custom_icon(Panel, View3DPaintPanel):
bl_context = ".paint_common" # dot on purpose (access from topbar)
bl_label = "Custom Icon"
bl_parent_id = "VIEW3D_PT_tools_brush_display"
bl_options = {'DEFAULT_CLOSED'}
def draw_header(self, context):
settings = self.paint_settings(context)
brush = settings.brush
self.layout.prop(brush, "use_custom_icon", text="")
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
settings = self.paint_settings(context)
brush = settings.brush
col = layout.column()
col.active = brush.use_custom_icon
col.prop(brush, "icon_filepath", text="")
# ********** default tools for weight-paint ****************
# TODO, move to space_view3d.py
class VIEW3D_PT_tools_weightpaint_symmetry(Panel, View3DPaintPanel):
bl_context = ".weightpaint"
bl_options = {'DEFAULT_CLOSED'}
bl_label = "Symmetry"
@classmethod
def poll(cls, context):
# When used in the tool header, this is explicitly included next to the XYZ symmetry buttons.
return (context.region.type != 'TOOL_HEADER')
def draw(self, context):
layout = self.layout
tool_settings = context.tool_settings
wpaint = tool_settings.weight_paint
draw_vpaint_symmetry(layout, wpaint)
class VIEW3D_PT_tools_weightpaint_symmetry_for_topbar(Panel):
bl_space_type = 'TOPBAR'
bl_region_type = 'HEADER'
bl_label = "Symmetry"
draw = VIEW3D_PT_tools_weightpaint_symmetry.draw
# TODO, move to space_view3d.py
class VIEW3D_PT_tools_weightpaint_options(Panel, View3DPaintPanel):
bl_context = ".weightpaint"
bl_label = "Options"
bl_options = {'DEFAULT_CLOSED'}
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
tool_settings = context.tool_settings
wpaint = tool_settings.weight_paint
col = layout.column()
col.prop(wpaint, "use_group_restrict")
obj = context.weight_paint_object
if obj.type == 'MESH':
mesh = obj.data
col.prop(mesh, "use_mirror_x")
row = col.row()
row.active = mesh.use_mirror_x
row.prop(mesh, "use_mirror_topology")
class VIEW3D_PT_tools_weightpaint_options_unified(Panel, View3DPaintPanel):
bl_context = ".weightpaint"
bl_label = "Unified Brush"
bl_parent_id = "VIEW3D_PT_tools_weightpaint_options"
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
self.unified_paint_settings(layout, context)
# ********** default tools for vertex-paint ****************
# TODO, move to space_view3d.py
class VIEW3D_PT_tools_vertexpaint_options(Panel, View3DPaintPanel):
bl_context = ".vertexpaint" # dot on purpose (access from topbar)
bl_label = "Options"
bl_options = {'DEFAULT_CLOSED'}
def draw(self, context):
layout = self.layout
layout.label(text="Unified Brush")
layout.use_property_split = True
layout.use_property_decorate = False
self.unified_paint_settings(layout, context)
# TODO, move to space_view3d.py
class VIEW3D_PT_tools_vertexpaint_symmetry(Panel, View3DPaintPanel):
bl_context = ".vertexpaint" # dot on purpose (access from topbar)
bl_options = {'DEFAULT_CLOSED'}
bl_label = "Symmetry"
@classmethod
def poll(cls, context):
# When used in the tool header, this is explicitly included next to the XYZ symmetry buttons.
return (context.region.type != 'TOOL_HEADER')
def draw(self, context):
layout = self.layout
tool_settings = context.tool_settings
vpaint = tool_settings.vertex_paint
draw_vpaint_symmetry(layout, vpaint)
class VIEW3D_PT_tools_vertexpaint_symmetry_for_topbar(Panel):
bl_space_type = 'TOPBAR'
bl_region_type = 'HEADER'
bl_label = "Symmetry"
draw = VIEW3D_PT_tools_vertexpaint_symmetry.draw
# ********** default tools for texture-paint ****************
# TODO, move to space_view3d.py
class VIEW3D_PT_tools_imagepaint_options_external(Panel, View3DPaintPanel):
bl_context = ".imagepaint" # dot on purpose (access from topbar)
bl_label = "External"
bl_parent_id = "VIEW3D_PT_tools_imagepaint_options"
bl_options = {'DEFAULT_CLOSED'}
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
tool_settings = context.tool_settings
ipaint = tool_settings.image_paint
layout.prop(ipaint, "screen_grab_size", text="Screen Grab Size")
layout.separator()
flow = layout.grid_flow(row_major=True, columns=0, even_columns=True, even_rows=False, align=False)
col = flow.column()
col.operator("image.project_edit", text="Quick Edit")
col = flow.column()
col.operator("image.project_apply", text="Apply")
col = flow.column()
col.operator("paint.project_image", text="Apply Camera Image")
# TODO, move to space_view3d.py
class VIEW3D_PT_tools_imagepaint_symmetry(Panel, View3DPaintPanel):
bl_context = ".imagepaint" # dot on purpose (access from topbar)
bl_label = "Symmetry"
bl_options = {'DEFAULT_CLOSED'}
@classmethod
def poll(cls, context):
# When used in the tool header, this is explicitly included next to the XYZ symmetry buttons.
return (context.region.type != 'TOOL_HEADER')
def draw(self, context):
layout = self.layout
tool_settings = context.tool_settings
ipaint = tool_settings.image_paint
split = layout.split()
col = split.column()
col.alignment = 'RIGHT'
col.label(text="Mirror")
col = split.column()
row = col.row(align=True)
row.prop(ipaint, "use_symmetry_x", text="X", toggle=True)
row.prop(ipaint, "use_symmetry_y", text="Y", toggle=True)
row.prop(ipaint, "use_symmetry_z", text="Z", toggle=True)
# TODO, move to space_view3d.py
class VIEW3D_PT_tools_imagepaint_options(View3DPaintPanel, Panel):
bl_context = ".imagepaint" # dot on purpose (access from topbar)
bl_label = "Options"
bl_options = {'DEFAULT_CLOSED'}
@classmethod
def poll(cls, context):
brush = context.tool_settings.image_paint.brush
return (brush is not None)
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
tool_settings = context.tool_settings
ipaint = tool_settings.image_paint
layout.prop(ipaint, "seam_bleed")
layout.prop(ipaint, "dither", slider=True)
flow = layout.grid_flow(row_major=True, columns=0, even_columns=True, even_rows=False, align=False)
col = flow.column()
col.prop(ipaint, "use_occlude")
col = flow.column()
col.prop(ipaint, "use_backface_culling", text="Backface Culling")
class VIEW3D_PT_tools_imagepaint_options_unified(Panel, View3DPaintPanel):
bl_context = ".imagepaint" # dot on purpose (access from topbar)
bl_parent_id = "VIEW3D_PT_tools_imagepaint_options"
bl_label = "Unified Brush"
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
self.unified_paint_settings(layout, context)
class VIEW3D_PT_tools_imagepaint_options_cavity(View3DPaintPanel, Panel):
bl_context = ".imagepaint" # dot on purpose (access from topbar)
bl_label = "Cavity Mask"
bl_parent_id = "VIEW3D_PT_tools_imagepaint_options"
bl_options = {'DEFAULT_CLOSED'}
def draw_header(self, context):
tool_settings = context.tool_settings
ipaint = tool_settings.image_paint
self.layout.prop(ipaint, "use_cavity", text="")
def draw(self, context):
layout = self.layout
tool_settings = context.tool_settings
ipaint = tool_settings.image_paint
layout.active = ipaint.use_cavity
layout.template_curve_mapping(ipaint, "cavity_curve", brush=True,
use_negative_slope=True)
# TODO, move to space_view3d.py
class VIEW3D_PT_imagepaint_options(View3DPaintPanel):
bl_label = "Options"
@classmethod
def poll(cls, context):
return (context.image_paint_object and context.tool_settings.image_paint)
def draw(self, context):
layout = self.layout
col = layout.column()
self.unified_paint_settings(col, context)
class VIEW3D_MT_tools_projectpaint_stencil(Menu):
bl_label = "Mask Layer"
def draw(self, context):
layout = self.layout
for i, uv_layer in enumerate(context.active_object.data.uv_layers):
props = layout.operator("wm.context_set_int", text=uv_layer.name, translate=False)
props.data_path = "active_object.data.uv_layer_stencil_index"
props.value = i
# TODO, move to space_view3d.py
class VIEW3D_PT_tools_particlemode_options(View3DPanel, Panel):
"""Default tools for particle mode"""
bl_category = "Tool"
bl_context = ".particlemode"
bl_label = "Options"
bl_options = {'DEFAULT_CLOSED'}
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False # No animation.
pe = context.tool_settings.particle_edit
ob = pe.object
layout.prop(pe, "type", text="Editing Type")
ptcache = None
if pe.type == 'PARTICLES':
if ob.particle_systems:
if len(ob.particle_systems) > 1:
layout.template_list("UI_UL_list", "particle_systems", ob, "particle_systems",
ob.particle_systems, "active_index", rows=2, maxrows=3)
ptcache = ob.particle_systems.active.point_cache
else:
for md in ob.modifiers:
if md.type == pe.type:
ptcache = md.point_cache
if ptcache and len(ptcache.point_caches) > 1:
layout.template_list("UI_UL_list", "particles_point_caches", ptcache, "point_caches",
ptcache.point_caches, "active_index", rows=2, maxrows=3)
if not pe.is_editable:
layout.label(text="Point cache must be baked")
layout.label(text="in memory to enable editing!")
col = layout.column(align=True)
col.active = pe.is_editable
col.prop(ob.data, "use_mirror_x")
col.separator()
col.prop(pe, "use_preserve_length", text="Preserve Strand Lengths")
col.prop(pe, "use_preserve_root", text="Preserve Root Positions")
if not pe.is_hair:
col.prop(pe, "use_auto_velocity", text="Auto-Velocity")
class VIEW3D_PT_tools_particlemode_options_shapecut(View3DPanel, Panel):
"""Default tools for particle mode"""
bl_category = "Tool"
bl_parent_id = "VIEW3D_PT_tools_particlemode_options"
bl_label = "Cut Particles to Shape"
bl_options = {'DEFAULT_CLOSED'}
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False # No animation.
pe = context.tool_settings.particle_edit
layout.prop(pe, "shape_object")
layout.operator("particle.shape_cut", text="Cut")
class VIEW3D_PT_tools_particlemode_options_display(View3DPanel, Panel):
"""Default tools for particle mode"""
bl_category = "Tool"
bl_parent_id = "VIEW3D_PT_tools_particlemode_options"
bl_label = "Viewport Display"
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False # No animation.
pe = context.tool_settings.particle_edit
col = layout.column()
col.active = pe.is_editable
col.prop(pe, "display_step", text="Path Steps")
if pe.is_hair:
col.prop(pe, "show_particles", text="Children")
else:
if pe.type == 'PARTICLES':
col.prop(pe, "show_particles", text="Particles")
col.prop(pe, "use_fade_time")
sub = col.row(align=True)
sub.active = pe.use_fade_time
sub.prop(pe, "fade_frames", slider=True)
# ********** grease pencil object tool panels ****************
# Grease Pencil drawing brushes
class VIEW3D_PT_tools_grease_pencil_brush(View3DPanel, Panel):
bl_context = ".greasepencil_paint"
bl_label = "Brush"
bl_category = "Tool"
@classmethod
def poll(cls, context):
is_3d_view = context.space_data.type == 'VIEW_3D'
if is_3d_view:
if context.gpencil_data is None:
return False
gpd = context.gpencil_data
return bool(gpd.is_stroke_paint_mode)
else:
return True
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
tool_settings = context.scene.tool_settings
gpencil_paint = tool_settings.gpencil_paint
row = layout.row()
col = row.column()
col.template_ID_preview(gpencil_paint, "brush", new="brush.add_gpencil", rows=3, cols=8)
col = row.column()
brush = gpencil_paint.brush
sub = col.column(align=True)
sub.operator("gpencil.brush_presets_create", icon='PRESET_NEW', text="")
if brush is not None:
gp_settings = brush.gpencil_settings
if brush.gpencil_tool in {'DRAW', 'FILL'}:
row = layout.row(align=True)
row_mat = row.row()
if gp_settings.use_material_pin:
row_mat.template_ID(gp_settings, "material", live_icon=True)
else:
row_mat.template_ID(context.active_object, "active_material", live_icon=True)
row_mat.enabled = False # will otherwise allow to change material in active slot
row.prop(gp_settings, "use_material_pin", text="")
if not self.is_popover:
from bl_ui.properties_paint_common import (
brush_basic_gpencil_paint_settings,
)
tool = context.workspace.tools.from_space_view3d_mode(context.mode, create=False)
brush_basic_gpencil_paint_settings(layout, context, brush, tool, compact=True, is_toolbar=False)
# Grease Pencil drawing brushes options
class VIEW3D_PT_tools_grease_pencil_brush_option(View3DPanel, Panel):
bl_context = ".greasepencil_paint"
bl_label = "Options"
bl_category = "Tool"
@classmethod
def poll(cls, context):
brush = context.tool_settings.gpencil_paint.brush
return brush is not None and brush.gpencil_tool not in {'ERASE', 'FILL'}
def draw_header_preset(self, _context):
VIEW3D_PT_gpencil_brush_presets.draw_panel_header(self.layout)
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
brush = context.tool_settings.gpencil_paint.brush
if brush is not None:
gp_settings = brush.gpencil_settings
col = layout.column(align=True)
col.prop(gp_settings, "input_samples")
col.separator()
col.prop(gp_settings, "active_smooth_factor")
col.separator()
col.prop(gp_settings, "angle", slider=True)
col.prop(gp_settings, "angle_factor", text="Factor", slider=True)
ob = context.object
if ob and brush.gpencil_settings.use_material_pin is False:
ma = ob.active_material
elif brush.gpencil_settings.material:
ma = brush.gpencil_settings.material
else:
ma = None
col.separator()
subcol = col.column(align=True)
if ma and ma.grease_pencil.mode == 'LINE':
subcol.enabled = False
subcol.prop(gp_settings, "gradient_factor", slider=True)
subcol.prop(gp_settings, "gradient_shape")
class VIEW3D_PT_tools_grease_pencil_brush_stabilizer(View3DPanel, Panel):
bl_context = ".greasepencil_paint"
bl_parent_id = 'VIEW3D_PT_tools_grease_pencil_brush_option'
bl_label = "Stabilize"
bl_category = "Tool"
bl_options = {'DEFAULT_CLOSED'}
@classmethod
def poll(cls, context):
brush = context.tool_settings.gpencil_paint.brush
return brush is not None and brush.gpencil_tool == 'DRAW'
def draw_header(self, context):
brush = context.tool_settings.gpencil_paint.brush
gp_settings = brush.gpencil_settings
self.layout.prop(gp_settings, "use_settings_stabilizer", text="")
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
brush = context.tool_settings.gpencil_paint.brush
gp_settings = brush.gpencil_settings
layout.active = gp_settings.use_settings_stabilizer
layout.prop(brush, "smooth_stroke_radius", text="Radius", slider=True)
layout.prop(brush, "smooth_stroke_factor", text="Factor", slider=True)
class VIEW3D_PT_tools_grease_pencil_brush_settings(View3DPanel, Panel):
bl_context = ".greasepencil_paint"
bl_parent_id = 'VIEW3D_PT_tools_grease_pencil_brush_option'
bl_label = "Post-Processing"
bl_category = "Tool"
@classmethod
def poll(cls, context):
brush = context.tool_settings.gpencil_paint.brush
return brush is not None and brush.gpencil_tool != 'ERASE'
def draw_header(self, context):
brush = context.tool_settings.gpencil_paint.brush
gp_settings = brush.gpencil_settings
self.layout.prop(gp_settings, "use_settings_postprocess", text="")
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
brush = context.tool_settings.gpencil_paint.brush
gp_settings = brush.gpencil_settings
layout.active = gp_settings.use_settings_postprocess
col = layout.column(align=True)
col.prop(gp_settings, "pen_smooth_factor")
col.prop(gp_settings, "pen_smooth_steps")
col = layout.column(align=True)
col.prop(gp_settings, "pen_thick_smooth_factor")
col.prop(gp_settings, "pen_thick_smooth_steps", text="Iterations")
col = layout.column(align=True)
col.prop(gp_settings, "pen_subdivision_steps")
col.prop(gp_settings, "random_subdiv", text="Randomness", slider=True)
col = layout.column(align=True)
col.prop(gp_settings, "simplify_factor")
col = layout.column(align=True)
col.prop(gp_settings, "trim")
class VIEW3D_PT_tools_grease_pencil_brush_random(View3DPanel, Panel):
bl_context = ".greasepencil_paint"
bl_parent_id = 'VIEW3D_PT_tools_grease_pencil_brush_option'
bl_label = "Randomize"
bl_category = "Tool"
bl_options = {'DEFAULT_CLOSED'}
@classmethod
def poll(cls, context):
brush = context.tool_settings.gpencil_paint.brush
return brush is not None and brush.gpencil_tool != 'ERASE'
def draw_header(self, context):
brush = context.tool_settings.gpencil_paint.brush
gp_settings = brush.gpencil_settings
self.layout.prop(gp_settings, "use_settings_random", text="")
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
brush = context.tool_settings.gpencil_paint.brush
gp_settings = brush.gpencil_settings
layout.active = gp_settings.use_settings_random
layout.prop(gp_settings, "random_pressure", text="Pressure", slider=True)
layout.prop(gp_settings, "random_strength", text="Strength", slider=True)
layout.prop(gp_settings, "uv_random", text="UV", slider=True)
row = layout.row(align=True)
row.prop(gp_settings, "pen_jitter", slider=True)
row.prop(gp_settings, "use_jitter_pressure", text="", icon='STYLUS_PRESSURE')
# Grease Pencil drawingcurves
class VIEW3D_PT_tools_grease_pencil_brushcurves(View3DPanel, Panel):
bl_context = ".greasepencil_paint"
bl_label = "Curves"
bl_category = "Tool"
bl_options = {'DEFAULT_CLOSED'}
@classmethod
def poll(cls, context):
brush = context.tool_settings.gpencil_paint.brush
return brush is not None and brush.gpencil_tool not in {'ERASE', 'FILL'}
def draw(self, context):
pass
class VIEW3D_PT_tools_grease_pencil_brushcurves_sensitivity(View3DPanel, Panel):
bl_context = ".greasepencil_paint"
bl_label = "Sensitivity"
bl_category = "Tool"
bl_parent_id = "VIEW3D_PT_tools_grease_pencil_brushcurves"
def draw(self, context):
layout = self.layout
layout.use_property_split = True
brush = context.tool_settings.gpencil_paint.brush
gp_settings = brush.gpencil_settings
layout.template_curve_mapping(gp_settings, "curve_sensitivity", brush=True,
use_negative_slope=True)
class VIEW3D_PT_tools_grease_pencil_brushcurves_strength(View3DPanel, Panel):
bl_context = ".greasepencil_paint"
bl_label = "Strength"
bl_category = "Tool"
bl_parent_id = "VIEW3D_PT_tools_grease_pencil_brushcurves"
def draw(self, context):
layout = self.layout
layout.use_property_split = True
brush = context.tool_settings.gpencil_paint.brush
gp_settings = brush.gpencil_settings
layout.template_curve_mapping(gp_settings, "curve_strength", brush=True,
use_negative_slope=True)
class VIEW3D_PT_tools_grease_pencil_brushcurves_jitter(View3DPanel, Panel):
bl_context = ".greasepencil_paint"
bl_label = "Jitter"
bl_category = "Tool"
bl_parent_id = "VIEW3D_PT_tools_grease_pencil_brushcurves"
def draw(self, context):
layout = self.layout
layout.use_property_split = True
brush = context.tool_settings.gpencil_paint.brush
gp_settings = brush.gpencil_settings
layout.template_curve_mapping(gp_settings, "curve_jitter", brush=True,
use_negative_slope=True)
# Grease Pencil stroke editing tools
class VIEW3D_PT_tools_grease_pencil_edit(GreasePencilStrokeEditPanel, Panel):
bl_space_type = 'VIEW_3D'
bl_category = "Tool"
# Grease Pencil stroke interpolation tools
class VIEW3D_PT_tools_grease_pencil_interpolate(Panel):
bl_space_type = 'VIEW_3D'
bl_region_type = 'HEADER'
bl_label = "Interpolate"
@classmethod
def poll(cls, context):
if context.gpencil_data is None:
return False
gpd = context.gpencil_data
return bool(context.editable_gpencil_strokes) and bool(gpd.use_stroke_edit_mode)
def draw(self, context):
layout = self.layout
settings = context.tool_settings.gpencil_interpolate
col = layout.column(align=True)
col.label(text="Interpolate Strokes")
col.operator("gpencil.interpolate", text="Interpolate")
col.operator("gpencil.interpolate_sequence", text="Sequence")
col.operator("gpencil.interpolate_reverse", text="Remove Breakdowns")
col = layout.column(align=True)
col.label(text="Options:")
col.prop(settings, "interpolate_all_layers")
col.prop(settings, "interpolate_selected_only")
col = layout.column(align=True)
col.label(text="Sequence Options:")
col.prop(settings, "type")
if settings.type == 'CUSTOM':
# TODO: Options for loading/saving curve presets?
col.template_curve_mapping(settings, "interpolation_curve", brush=True,
use_negative_slope=True)
elif settings.type != 'LINEAR':
col.prop(settings, "easing")
if settings.type == 'BACK':
layout.prop(settings, "back")
elif settings.type == 'ELASTIC':
sub = layout.column(align=True)
sub.prop(settings, "amplitude")
sub.prop(settings, "period")
# Grease Pencil stroke sculpting tools
class VIEW3D_PT_tools_grease_pencil_sculpt(GreasePencilStrokeSculptPanel, View3DPanel, Panel):
bl_context = ".greasepencil_sculpt"
bl_category = "Tools"
bl_label = "Brush"
bl_category = "Tool"
# Grease Pencil weight painting tools
class VIEW3D_PT_tools_grease_pencil_weight_paint(View3DPanel, Panel):
bl_context = ".greasepencil_weight"
bl_category = "Tools"
bl_label = "Brush"
bl_category = "Tool"
def draw(self, context):
layout = self.layout
layout.use_property_split = True
layout.use_property_decorate = False
settings = context.tool_settings.gpencil_sculpt
brush = settings.brush
layout.template_icon_view(settings, "weight_tool", show_labels=True)
col = layout.column()
if not self.is_popover:
from bl_ui.properties_paint_common import (
brush_basic_gpencil_weight_settings,
)
brush_basic_gpencil_weight_settings(col, context, brush)
# Grease Pencil Brush Appearance (one for each mode)
class VIEW3D_PT_tools_grease_pencil_paint_appearance(GreasePencilAppearancePanel, View3DPanel, Panel):
bl_context = ".greasepencil_paint"
bl_label = "Display"
bl_category = "Tool"
class VIEW3D_PT_tools_grease_pencil_sculpt_appearance(GreasePencilAppearancePanel, View3DPanel, Panel):
bl_context = ".greasepencil_sculpt"
bl_label = "Display"
bl_category = "Tool"
class VIEW3D_PT_tools_grease_pencil_sculpt_options(GreasePencilSculptOptionsPanel, View3DPanel, Panel):
bl_context = ".greasepencil_sculpt"
bl_label = "Sculpt Strokes"
bl_parent_id = 'VIEW3D_PT_tools_grease_pencil_sculpt'
bl_category = "Tool"
class VIEW3D_PT_tools_grease_pencil_weight_appearance(GreasePencilAppearancePanel, View3DPanel, Panel):
bl_context = ".greasepencil_weight"
bl_label = "Display"
bl_category = "Tool"
class VIEW3D_PT_gpencil_brush_presets(PresetPanel, Panel):
"""Brush settings"""
bl_label = "Brush Presets"
preset_subdir = "gpencil_brush"
preset_operator = "script.execute_preset"
preset_add_operator = "scene.gpencil_brush_preset_add"
classes = (
VIEW3D_MT_brush_context_menu,
VIEW3D_MT_brush_context_menu_paint_modes,
VIEW3D_PT_tools_object_options,
VIEW3D_PT_tools_object_options_transform,
VIEW3D_PT_tools_meshedit_options,
VIEW3D_PT_tools_meshedit_options_automerge,
VIEW3D_PT_tools_curveedit_options_stroke,
VIEW3D_PT_tools_armatureedit_options,
VIEW3D_PT_tools_posemode_options,
VIEW3D_PT_slots_projectpaint,
VIEW3D_PT_tools_brush,
VIEW3D_PT_tools_brush_color,
VIEW3D_PT_tools_brush_swatches,
VIEW3D_PT_tools_brush_clone,
VIEW3D_PT_tools_brush_options,
TEXTURE_UL_texpaintslots,
VIEW3D_MT_tools_projectpaint_uvlayer,
VIEW3D_PT_stencil_projectpaint,
VIEW3D_PT_tools_brush_texture,
VIEW3D_PT_tools_mask_texture,
VIEW3D_PT_tools_brush_stroke,
VIEW3D_PT_tools_brush_stroke_smooth_stroke,
VIEW3D_PT_tools_brush_falloff,
VIEW3D_PT_tools_brush_falloff_frontface,
VIEW3D_PT_tools_brush_falloff_normal,
VIEW3D_PT_tools_brush_display,
VIEW3D_PT_tools_brush_display_show_brush,
VIEW3D_PT_tools_brush_display_custom_icon,
VIEW3D_PT_sculpt_dyntopo,
VIEW3D_PT_sculpt_dyntopo_remesh,
VIEW3D_PT_sculpt_voxel_remesh,
VIEW3D_PT_sculpt_symmetry,
VIEW3D_PT_sculpt_symmetry_for_topbar,
VIEW3D_PT_sculpt_options,
VIEW3D_PT_sculpt_options_unified,
VIEW3D_PT_sculpt_options_gravity,
VIEW3D_PT_tools_weightpaint_symmetry,
VIEW3D_PT_tools_weightpaint_symmetry_for_topbar,
VIEW3D_PT_tools_weightpaint_options,
VIEW3D_PT_tools_weightpaint_options_unified,
VIEW3D_PT_tools_vertexpaint_symmetry,
VIEW3D_PT_tools_vertexpaint_symmetry_for_topbar,
VIEW3D_PT_tools_vertexpaint_options,
VIEW3D_PT_tools_imagepaint_symmetry,
VIEW3D_PT_tools_imagepaint_options,
VIEW3D_PT_tools_imagepaint_options_cavity,
VIEW3D_PT_tools_imagepaint_options_unified,
VIEW3D_PT_tools_imagepaint_options_external,
VIEW3D_MT_tools_projectpaint_stencil,
VIEW3D_PT_tools_particlemode,
VIEW3D_PT_tools_particlemode_options,
VIEW3D_PT_tools_particlemode_options_shapecut,
VIEW3D_PT_tools_particlemode_options_display,
VIEW3D_PT_gpencil_brush_presets,
VIEW3D_PT_tools_grease_pencil_brush,
VIEW3D_PT_tools_grease_pencil_brush_option,
VIEW3D_PT_tools_grease_pencil_brush_settings,
VIEW3D_PT_tools_grease_pencil_brush_stabilizer,
VIEW3D_PT_tools_grease_pencil_brush_random,
VIEW3D_PT_tools_grease_pencil_brushcurves,
VIEW3D_PT_tools_grease_pencil_brushcurves_sensitivity,
VIEW3D_PT_tools_grease_pencil_brushcurves_strength,
VIEW3D_PT_tools_grease_pencil_brushcurves_jitter,
VIEW3D_PT_tools_grease_pencil_sculpt,
VIEW3D_PT_tools_grease_pencil_weight_paint,
VIEW3D_PT_tools_grease_pencil_paint_appearance,
VIEW3D_PT_tools_grease_pencil_sculpt_options,
VIEW3D_PT_tools_grease_pencil_sculpt_appearance,
VIEW3D_PT_tools_grease_pencil_weight_appearance,
VIEW3D_PT_tools_grease_pencil_interpolate,
)
if __name__ == "__main__": # only for live edit.
from bpy.utils import register_class
for cls in classes:
register_class(cls)
| en | 0.670043 | # ##### BEGIN GPL LICENSE BLOCK ##### # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software Foundation, # Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. # # ##### END GPL LICENSE BLOCK ##### # <pep8 compliant> # skip if no active brush # brush paint modes # brush tool # **************** standard tool clusters ****************** # Used by vertex & weight paint # Most of these panels should not be visible in GP edit modes # ********** default tools for object mode **************** # dot on purpose (access from topbar) # layout = self.layout # dot on purpose (access from topbar) # ********** default tools for editmode_mesh **************** # dot on purpose (access from topbar) # dot on purpose (access from topbar) # ********** default tools for editmode_curve **************** # dot on purpose (access from topbar) # ********** default tools for editmode_armature **************** # dot on purpose (access from topbar) # ********** default tools for pose-mode **************** # dot on purpose (access from topbar) # ********** default tools for paint modes **************** # dot on purpose (access from topbar) # No animation. # TODO, move to space_view3d.py # dot on purpose (access from topbar) # No animation. # Sculpt Mode # # normal_radius_factor # topology_rake_factor # auto_smooth_factor and use_inverse_smooth_pressure # normal_weight # crease_pinch_factor # rake_factor # plane_offset, use_offset_pressure, use_plane_trim, plane_trim # height # use_persistent, set_persistent_base # not supported yet for this case # Texture Paint Mode # # Weight Paint Mode # # Vertex Paint Mode # # dot on purpose (access from topbar) # dot on purpose (access from topbar) # dot on purpose (access from topbar) # dot on purpose (access from topbar) # No animation. # mat = data # dot on purpose (access from topbar) # TODO, move to space_view3d.py # dot on purpose (access from topbar) # TODO, move to space_view3d.py # dot on purpose (access from topbar) # TODO, move to space_view3d.py # dot on purpose (access from topbar) # TODO, move to space_view3d.py # dot on purpose (access from topbar) # TODO, move to space_view3d.py # dot on purpose (access from topbar) # dot on purpose (access from topbar) # TODO, move to space_view3d.py # dot on purpose (access from topbar) # dot on purpose (access from topbar) # dot on purpose (access from topbar) # TODO, move to space_view3d.py # dot on purpose (access from topbar) # dot on purpose (access from topbar) # dot on purpose (access from topbar) # TODO, move to space_view3d.py # dot on purpose (access from topbar) # dot on purpose (access from topbar) # dot on purpose (access from topbar) # TODO, move to space_view3d.py # dot on purpose (access from topbar) # When used in the tool header, this is explicitly included next to the XYZ symmetry buttons. # dot on purpose (access from topbar) # dot on purpose (access from topbar) # ********** default tools for weight-paint **************** # TODO, move to space_view3d.py # When used in the tool header, this is explicitly included next to the XYZ symmetry buttons. # TODO, move to space_view3d.py # ********** default tools for vertex-paint **************** # TODO, move to space_view3d.py # dot on purpose (access from topbar) # TODO, move to space_view3d.py # dot on purpose (access from topbar) # When used in the tool header, this is explicitly included next to the XYZ symmetry buttons. # ********** default tools for texture-paint **************** # TODO, move to space_view3d.py # dot on purpose (access from topbar) # TODO, move to space_view3d.py # dot on purpose (access from topbar) # When used in the tool header, this is explicitly included next to the XYZ symmetry buttons. # TODO, move to space_view3d.py # dot on purpose (access from topbar) # dot on purpose (access from topbar) # dot on purpose (access from topbar) # TODO, move to space_view3d.py # TODO, move to space_view3d.py Default tools for particle mode # No animation. Default tools for particle mode # No animation. Default tools for particle mode # No animation. # ********** grease pencil object tool panels **************** # Grease Pencil drawing brushes # will otherwise allow to change material in active slot # Grease Pencil drawing brushes options # Grease Pencil drawingcurves # Grease Pencil stroke editing tools # Grease Pencil stroke interpolation tools # TODO: Options for loading/saving curve presets? # Grease Pencil stroke sculpting tools # Grease Pencil weight painting tools # Grease Pencil Brush Appearance (one for each mode) Brush settings # only for live edit. | 1.69388 | 2 |
samsungctl/__init__.py | bolesgb/samsungctl | 0 | 6624329 | <filename>samsungctl/__init__.py
"""Remote control Samsung televisions via TCP/IP connection"""
from .remote import Remote
__title__ = "samsungctl"
__version__ = "0.7.1+1"
__url__ = "https://github.com/bboles/samsungctl"
__author__ = "<NAME> + <NAME>"
__author_email__ = "<EMAIL>"
__license__ = "MIT"
| <filename>samsungctl/__init__.py
"""Remote control Samsung televisions via TCP/IP connection"""
from .remote import Remote
__title__ = "samsungctl"
__version__ = "0.7.1+1"
__url__ = "https://github.com/bboles/samsungctl"
__author__ = "<NAME> + <NAME>"
__author_email__ = "<EMAIL>"
__license__ = "MIT"
| en | 0.546437 | Remote control Samsung televisions via TCP/IP connection | 1.589035 | 2 |
homeassistant/data_entry_flow.py | Natrinicle/home-assistant | 0 | 6624330 | <reponame>Natrinicle/home-assistant
"""Classes to help gather user submissions."""
import logging
import uuid
import voluptuous as vol
from typing import Dict, Any, Callable, Hashable, List, Optional # noqa pylint: disable=unused-import
from .core import callback, HomeAssistant
from .exceptions import HomeAssistantError
_LOGGER = logging.getLogger(__name__)
RESULT_TYPE_FORM = 'form'
RESULT_TYPE_CREATE_ENTRY = 'create_entry'
RESULT_TYPE_ABORT = 'abort'
class FlowError(HomeAssistantError):
"""Error while configuring an account."""
class UnknownHandler(FlowError):
"""Unknown handler specified."""
class UnknownFlow(FlowError):
"""Uknown flow specified."""
class UnknownStep(FlowError):
"""Unknown step specified."""
class FlowManager:
"""Manage all the flows that are in progress."""
def __init__(self, hass: HomeAssistant, async_create_flow: Callable,
async_finish_flow: Callable) -> None:
"""Initialize the flow manager."""
self.hass = hass
self._progress = {} # type: Dict[str, Any]
self._async_create_flow = async_create_flow
self._async_finish_flow = async_finish_flow
@callback
def async_progress(self) -> List[Dict]:
"""Return the flows in progress."""
return [{
'flow_id': flow.flow_id,
'handler': flow.handler,
'context': flow.context,
} for flow in self._progress.values()]
async def async_init(self, handler: Hashable, *,
context: Optional[Dict] = None,
data: Any = None) -> Any:
"""Start a configuration flow."""
flow = await self._async_create_flow(
handler, context=context, data=data)
flow.hass = self.hass
flow.handler = handler
flow.flow_id = uuid.uuid4().hex
flow.context = context
self._progress[flow.flow_id] = flow
return await self._async_handle_step(flow, flow.init_step, data)
async def async_configure(
self, flow_id: str, user_input: Optional[str] = None) -> Any:
"""Continue a configuration flow."""
flow = self._progress.get(flow_id)
if flow is None:
raise UnknownFlow
step_id, data_schema = flow.cur_step
if data_schema is not None and user_input is not None:
user_input = data_schema(user_input)
return await self._async_handle_step(
flow, step_id, user_input)
@callback
def async_abort(self, flow_id: str) -> None:
"""Abort a flow."""
if self._progress.pop(flow_id, None) is None:
raise UnknownFlow
async def _async_handle_step(self, flow: Any, step_id: str,
user_input: Optional[str]) -> Dict:
"""Handle a step of a flow."""
method = "async_step_{}".format(step_id)
if not hasattr(flow, method):
self._progress.pop(flow.flow_id)
raise UnknownStep("Handler {} doesn't support step {}".format(
flow.__class__.__name__, step_id))
result = await getattr(flow, method)(user_input) # type: Dict
if result['type'] not in (RESULT_TYPE_FORM, RESULT_TYPE_CREATE_ENTRY,
RESULT_TYPE_ABORT):
raise ValueError(
'Handler returned incorrect type: {}'.format(result['type']))
if result['type'] == RESULT_TYPE_FORM:
flow.cur_step = (result['step_id'], result['data_schema'])
return result
# Abort and Success results both finish the flow
self._progress.pop(flow.flow_id)
# We pass a copy of the result because we're mutating our version
entry = await self._async_finish_flow(flow.context, dict(result))
if result['type'] == RESULT_TYPE_CREATE_ENTRY:
result['result'] = entry
return result
class FlowHandler:
"""Handle the configuration flow of a component."""
# Set by flow manager
flow_id = None
hass = None
handler = None
cur_step = None
context = None
# Set by _async_create_flow callback
init_step = 'init'
# Set by developer
VERSION = 1
@callback
def async_show_form(self, *, step_id: str, data_schema: vol.Schema = None,
errors: Optional[Dict] = None,
description_placeholders: Optional[Dict] = None) \
-> Dict:
"""Return the definition of a form to gather user input."""
return {
'type': RESULT_TYPE_FORM,
'flow_id': self.flow_id,
'handler': self.handler,
'step_id': step_id,
'data_schema': data_schema,
'errors': errors,
'description_placeholders': description_placeholders,
}
@callback
def async_create_entry(self, *, title: str, data: Dict) -> Dict:
"""Finish config flow and create a config entry."""
return {
'version': self.VERSION,
'type': RESULT_TYPE_CREATE_ENTRY,
'flow_id': self.flow_id,
'handler': self.handler,
'title': title,
'data': data,
}
@callback
def async_abort(self, *, reason: str) -> Dict:
"""Abort the config flow."""
return {
'type': RESULT_TYPE_ABORT,
'flow_id': self.flow_id,
'handler': self.handler,
'reason': reason
}
| """Classes to help gather user submissions."""
import logging
import uuid
import voluptuous as vol
from typing import Dict, Any, Callable, Hashable, List, Optional # noqa pylint: disable=unused-import
from .core import callback, HomeAssistant
from .exceptions import HomeAssistantError
_LOGGER = logging.getLogger(__name__)
RESULT_TYPE_FORM = 'form'
RESULT_TYPE_CREATE_ENTRY = 'create_entry'
RESULT_TYPE_ABORT = 'abort'
class FlowError(HomeAssistantError):
"""Error while configuring an account."""
class UnknownHandler(FlowError):
"""Unknown handler specified."""
class UnknownFlow(FlowError):
"""Uknown flow specified."""
class UnknownStep(FlowError):
"""Unknown step specified."""
class FlowManager:
"""Manage all the flows that are in progress."""
def __init__(self, hass: HomeAssistant, async_create_flow: Callable,
async_finish_flow: Callable) -> None:
"""Initialize the flow manager."""
self.hass = hass
self._progress = {} # type: Dict[str, Any]
self._async_create_flow = async_create_flow
self._async_finish_flow = async_finish_flow
@callback
def async_progress(self) -> List[Dict]:
"""Return the flows in progress."""
return [{
'flow_id': flow.flow_id,
'handler': flow.handler,
'context': flow.context,
} for flow in self._progress.values()]
async def async_init(self, handler: Hashable, *,
context: Optional[Dict] = None,
data: Any = None) -> Any:
"""Start a configuration flow."""
flow = await self._async_create_flow(
handler, context=context, data=data)
flow.hass = self.hass
flow.handler = handler
flow.flow_id = uuid.uuid4().hex
flow.context = context
self._progress[flow.flow_id] = flow
return await self._async_handle_step(flow, flow.init_step, data)
async def async_configure(
self, flow_id: str, user_input: Optional[str] = None) -> Any:
"""Continue a configuration flow."""
flow = self._progress.get(flow_id)
if flow is None:
raise UnknownFlow
step_id, data_schema = flow.cur_step
if data_schema is not None and user_input is not None:
user_input = data_schema(user_input)
return await self._async_handle_step(
flow, step_id, user_input)
@callback
def async_abort(self, flow_id: str) -> None:
"""Abort a flow."""
if self._progress.pop(flow_id, None) is None:
raise UnknownFlow
async def _async_handle_step(self, flow: Any, step_id: str,
user_input: Optional[str]) -> Dict:
"""Handle a step of a flow."""
method = "async_step_{}".format(step_id)
if not hasattr(flow, method):
self._progress.pop(flow.flow_id)
raise UnknownStep("Handler {} doesn't support step {}".format(
flow.__class__.__name__, step_id))
result = await getattr(flow, method)(user_input) # type: Dict
if result['type'] not in (RESULT_TYPE_FORM, RESULT_TYPE_CREATE_ENTRY,
RESULT_TYPE_ABORT):
raise ValueError(
'Handler returned incorrect type: {}'.format(result['type']))
if result['type'] == RESULT_TYPE_FORM:
flow.cur_step = (result['step_id'], result['data_schema'])
return result
# Abort and Success results both finish the flow
self._progress.pop(flow.flow_id)
# We pass a copy of the result because we're mutating our version
entry = await self._async_finish_flow(flow.context, dict(result))
if result['type'] == RESULT_TYPE_CREATE_ENTRY:
result['result'] = entry
return result
class FlowHandler:
"""Handle the configuration flow of a component."""
# Set by flow manager
flow_id = None
hass = None
handler = None
cur_step = None
context = None
# Set by _async_create_flow callback
init_step = 'init'
# Set by developer
VERSION = 1
@callback
def async_show_form(self, *, step_id: str, data_schema: vol.Schema = None,
errors: Optional[Dict] = None,
description_placeholders: Optional[Dict] = None) \
-> Dict:
"""Return the definition of a form to gather user input."""
return {
'type': RESULT_TYPE_FORM,
'flow_id': self.flow_id,
'handler': self.handler,
'step_id': step_id,
'data_schema': data_schema,
'errors': errors,
'description_placeholders': description_placeholders,
}
@callback
def async_create_entry(self, *, title: str, data: Dict) -> Dict:
"""Finish config flow and create a config entry."""
return {
'version': self.VERSION,
'type': RESULT_TYPE_CREATE_ENTRY,
'flow_id': self.flow_id,
'handler': self.handler,
'title': title,
'data': data,
}
@callback
def async_abort(self, *, reason: str) -> Dict:
"""Abort the config flow."""
return {
'type': RESULT_TYPE_ABORT,
'flow_id': self.flow_id,
'handler': self.handler,
'reason': reason
} | en | 0.834911 | Classes to help gather user submissions. # noqa pylint: disable=unused-import Error while configuring an account. Unknown handler specified. Uknown flow specified. Unknown step specified. Manage all the flows that are in progress. Initialize the flow manager. # type: Dict[str, Any] Return the flows in progress. Start a configuration flow. Continue a configuration flow. Abort a flow. Handle a step of a flow. # type: Dict # Abort and Success results both finish the flow # We pass a copy of the result because we're mutating our version Handle the configuration flow of a component. # Set by flow manager # Set by _async_create_flow callback # Set by developer Return the definition of a form to gather user input. Finish config flow and create a config entry. Abort the config flow. | 2.58532 | 3 |
nssrc/com/citrix/netscaler/nitro/resource/config/videooptimization/videooptimizationpacingpolicylabel_binding.py | guardicore/nitro-python | 0 | 6624331 | #
# Copyright (c) 2021 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 nssrc.com.citrix.netscaler.nitro.resource.base.base_resource import base_resource
from nssrc.com.citrix.netscaler.nitro.resource.base.base_resource import base_response
from nssrc.com.citrix.netscaler.nitro.service.options import options
from nssrc.com.citrix.netscaler.nitro.exception.nitro_exception import nitro_exception
from nssrc.com.citrix.netscaler.nitro.util.nitro_util import nitro_util
class videooptimizationpacingpolicylabel_binding(base_resource):
""" Binding class showing the resources that can be bound to videooptimizationpacingpolicylabel_binding.
"""
def __init__(self) :
self._labelname = None
self.videooptimizationpacingpolicylabel_videooptimizationpacingpolicy_binding = []
self.videooptimizationpacingpolicylabel_policybinding_binding = []
@property
def labelname(self) :
r"""Name of the videooptimization pacing policy label.
"""
try :
return self._labelname
except Exception as e:
raise e
@labelname.setter
def labelname(self, labelname) :
r"""Name of the videooptimization pacing policy label.
"""
try :
self._labelname = labelname
except Exception as e:
raise e
@property
def videooptimizationpacingpolicylabel_policybinding_bindings(self) :
r"""policybinding that can be bound to videooptimizationpacingpolicylabel.
"""
try :
return self._videooptimizationpacingpolicylabel_policybinding_binding
except Exception as e:
raise e
@property
def videooptimizationpacingpolicylabel_videooptimizationpacingpolicy_bindings(self) :
r"""videooptimizationpacingpolicy that can be bound to videooptimizationpacingpolicylabel.
"""
try :
return self._videooptimizationpacingpolicylabel_videooptimizationpacingpolicy_binding
except Exception as e:
raise e
def _get_nitro_response(self, service, response) :
r""" converts nitro response into object and returns the object array in case of get request.
"""
try :
result = service.payload_formatter.string_to_resource(videooptimizationpacingpolicylabel_binding_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.videooptimizationpacingpolicylabel_binding
except Exception as e :
raise e
def _get_object_name(self) :
r""" Returns the value of object identifier argument
"""
try :
if self.labelname is not None :
return str(self.labelname)
return None
except Exception as e :
raise e
@classmethod
def get(self, service, labelname="", option_="") :
r""" Use this API to fetch videooptimizationpacingpolicylabel_binding resource.
"""
try :
if not labelname :
obj = videooptimizationpacingpolicylabel_binding()
response = obj.get_resources(service, option_)
elif type(labelname) is not list :
obj = videooptimizationpacingpolicylabel_binding()
obj.labelname = labelname
response = obj.get_resource(service)
else :
if labelname and len(labelname) > 0 :
obj = [videooptimizationpacingpolicylabel_binding() for _ in range(len(labelname))]
for i in range(len(labelname)) :
obj[i].labelname = labelname[i];
response[i] = obj[i].get_resource(service)
return response
except Exception as e:
raise e
class videooptimizationpacingpolicylabel_binding_response(base_response) :
def __init__(self, length=1) :
self.videooptimizationpacingpolicylabel_binding = []
self.errorcode = 0
self.message = ""
self.severity = ""
self.sessionid = ""
self.videooptimizationpacingpolicylabel_binding = [videooptimizationpacingpolicylabel_binding() for _ in range(length)]
| #
# Copyright (c) 2021 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 nssrc.com.citrix.netscaler.nitro.resource.base.base_resource import base_resource
from nssrc.com.citrix.netscaler.nitro.resource.base.base_resource import base_response
from nssrc.com.citrix.netscaler.nitro.service.options import options
from nssrc.com.citrix.netscaler.nitro.exception.nitro_exception import nitro_exception
from nssrc.com.citrix.netscaler.nitro.util.nitro_util import nitro_util
class videooptimizationpacingpolicylabel_binding(base_resource):
""" Binding class showing the resources that can be bound to videooptimizationpacingpolicylabel_binding.
"""
def __init__(self) :
self._labelname = None
self.videooptimizationpacingpolicylabel_videooptimizationpacingpolicy_binding = []
self.videooptimizationpacingpolicylabel_policybinding_binding = []
@property
def labelname(self) :
r"""Name of the videooptimization pacing policy label.
"""
try :
return self._labelname
except Exception as e:
raise e
@labelname.setter
def labelname(self, labelname) :
r"""Name of the videooptimization pacing policy label.
"""
try :
self._labelname = labelname
except Exception as e:
raise e
@property
def videooptimizationpacingpolicylabel_policybinding_bindings(self) :
r"""policybinding that can be bound to videooptimizationpacingpolicylabel.
"""
try :
return self._videooptimizationpacingpolicylabel_policybinding_binding
except Exception as e:
raise e
@property
def videooptimizationpacingpolicylabel_videooptimizationpacingpolicy_bindings(self) :
r"""videooptimizationpacingpolicy that can be bound to videooptimizationpacingpolicylabel.
"""
try :
return self._videooptimizationpacingpolicylabel_videooptimizationpacingpolicy_binding
except Exception as e:
raise e
def _get_nitro_response(self, service, response) :
r""" converts nitro response into object and returns the object array in case of get request.
"""
try :
result = service.payload_formatter.string_to_resource(videooptimizationpacingpolicylabel_binding_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.videooptimizationpacingpolicylabel_binding
except Exception as e :
raise e
def _get_object_name(self) :
r""" Returns the value of object identifier argument
"""
try :
if self.labelname is not None :
return str(self.labelname)
return None
except Exception as e :
raise e
@classmethod
def get(self, service, labelname="", option_="") :
r""" Use this API to fetch videooptimizationpacingpolicylabel_binding resource.
"""
try :
if not labelname :
obj = videooptimizationpacingpolicylabel_binding()
response = obj.get_resources(service, option_)
elif type(labelname) is not list :
obj = videooptimizationpacingpolicylabel_binding()
obj.labelname = labelname
response = obj.get_resource(service)
else :
if labelname and len(labelname) > 0 :
obj = [videooptimizationpacingpolicylabel_binding() for _ in range(len(labelname))]
for i in range(len(labelname)) :
obj[i].labelname = labelname[i];
response[i] = obj[i].get_resource(service)
return response
except Exception as e:
raise e
class videooptimizationpacingpolicylabel_binding_response(base_response) :
def __init__(self, length=1) :
self.videooptimizationpacingpolicylabel_binding = []
self.errorcode = 0
self.message = ""
self.severity = ""
self.sessionid = ""
self.videooptimizationpacingpolicylabel_binding = [videooptimizationpacingpolicylabel_binding() for _ in range(length)]
| en | 0.78468 | # # Copyright (c) 2021 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. # Binding class showing the resources that can be bound to videooptimizationpacingpolicylabel_binding. Name of the videooptimization pacing policy label. Name of the videooptimization pacing policy label. policybinding that can be bound to videooptimizationpacingpolicylabel. videooptimizationpacingpolicy that can be bound to videooptimizationpacingpolicylabel. converts nitro response into object and returns the object array in case of get request. Returns the value of object identifier argument Use this API to fetch videooptimizationpacingpolicylabel_binding resource. | 1.820405 | 2 |
jsb/plugs/common/tinyurl.py | NURDspace/jsonbot | 1 | 6624332 | # jsb/plugs/common/tinyurl.py
#
#
""" tinyurl.com feeder """
__author__ = "Wijnand 'tehmaze' Modderman - http://tehmaze.com"
__license__ = 'BSD'
## jsb imports
from jsb.lib.commands import cmnds
from jsb.utils.url import striphtml, useragent
from jsb.lib.examples import examples
from jsb.utils.exception import handle_exception
from jsb.lib.persistconfig import PersistConfig
from jsb.lib.cache import get, set
## plug config
plugcfg = PersistConfig()
plugcfg.define("url", 'http://tinyurl.com/create.php')
## simpljejson
from jsb.imports import getjson
json = getjson()
## basic imports
import urllib
import urllib2
import urlparse
import re
import logging
## defines
re_url_match = re.compile(u'((?:http|https)://\S+)')
urlcache = {}
## functions
def valid_url(url):
""" check if url is valid """
if not re_url_match.search(url): return False
parts = urlparse.urlparse(url)
cleanurl = '%s://%s' % (parts[0], parts[1])
if parts[2]: cleanurl = '%s%s' % (cleanurl, parts[2])
if parts[3]: cleanurl = '%s;%s' % (cleanurl, parts[3])
if parts[4]: cleanurl = '%s?%s' % (cleanurl, parts[4])
return cleanurl
## callbacks
def precb(bot, ievent):
test_url = re_url_match.search(ievent.txt)
if test_url: return True
def privmsgcb(bot, ievent):
""" callback for urlcaching """
test_url = re_url_match.search(ievent.txt)
if test_url:
url = test_url.group(1)
if not urlcache.has_key(bot.cfg.name): urlcache[bot.cfg.name] = {}
urlcache[bot.cfg.name][ievent.target] = url
# not enabled right now
#callbacks.add('PRIVMSG', privmsgcb, precb)
def get_tinyurl(url):
""" grab a tinyurl. """
res = get(url, namespace='tinyurl') ; logging.debug('tinyurl - cache - %s' % unicode(res))
if res and res[0] == '[': return json.loads(res)
postarray = [
('submit', 'submit'),
('url', url),
]
postdata = urllib.urlencode(postarray)
req = urllib2.Request(url=plugcfg.url, data=postdata)
req.add_header('User-agent', useragent())
try: res = urllib2.urlopen(req).readlines()
except urllib2.URLError, e: logging.warn('tinyurl - %s - URLError: %s' % (url, str(e))) ; return
except urllib2.HTTPError, e: logging.warn('tinyurl - %s - HTTP error: %s' % (url, str(e))) ; return
except Exception, ex:
if "DownloadError" in str(ex): logging.warn('tinyurl - %s - DownloadError: %s' % (url, str(e)))
else: handle_exception()
return
urls = []
for line in res:
if line.startswith('<blockquote><b>'): urls.append(striphtml(line.strip()).split('[Open')[0])
if len(urls) == 3: urls.pop(0)
set(url, json.dumps(urls), namespace='tinyurl')
return urls
## tinyurl command
def handle_tinyurl(bot, ievent):
""" arguments: <url> - get tinyurl from provided url. """
if not ievent.rest and (not urlcache.has_key(bot.cfg.name) or not urlcache[bot.cfg.name].has_key(ievent.target)):
ievent.missing('<url>')
return
elif not ievent.rest: url = urlcache[bot.cfg.name][ievent.target]
else: url = ievent.rest
url = valid_url(url)
if not url: ievent.reply('invalid or bad URL') ; return
tinyurl = get_tinyurl(url)
if tinyurl: ievent.reply(' .. '.join(tinyurl))
else: ievent.reply('failed to create tinyurl')
cmnds.add('tinyurl', handle_tinyurl, ['OPER', 'USER', 'GUEST'], threaded=True)
examples.add('tinyurl', 'show a tinyurl', 'tinyurl http://jsonbbot.org')
| # jsb/plugs/common/tinyurl.py
#
#
""" tinyurl.com feeder """
__author__ = "Wijnand 'tehmaze' Modderman - http://tehmaze.com"
__license__ = 'BSD'
## jsb imports
from jsb.lib.commands import cmnds
from jsb.utils.url import striphtml, useragent
from jsb.lib.examples import examples
from jsb.utils.exception import handle_exception
from jsb.lib.persistconfig import PersistConfig
from jsb.lib.cache import get, set
## plug config
plugcfg = PersistConfig()
plugcfg.define("url", 'http://tinyurl.com/create.php')
## simpljejson
from jsb.imports import getjson
json = getjson()
## basic imports
import urllib
import urllib2
import urlparse
import re
import logging
## defines
re_url_match = re.compile(u'((?:http|https)://\S+)')
urlcache = {}
## functions
def valid_url(url):
""" check if url is valid """
if not re_url_match.search(url): return False
parts = urlparse.urlparse(url)
cleanurl = '%s://%s' % (parts[0], parts[1])
if parts[2]: cleanurl = '%s%s' % (cleanurl, parts[2])
if parts[3]: cleanurl = '%s;%s' % (cleanurl, parts[3])
if parts[4]: cleanurl = '%s?%s' % (cleanurl, parts[4])
return cleanurl
## callbacks
def precb(bot, ievent):
test_url = re_url_match.search(ievent.txt)
if test_url: return True
def privmsgcb(bot, ievent):
""" callback for urlcaching """
test_url = re_url_match.search(ievent.txt)
if test_url:
url = test_url.group(1)
if not urlcache.has_key(bot.cfg.name): urlcache[bot.cfg.name] = {}
urlcache[bot.cfg.name][ievent.target] = url
# not enabled right now
#callbacks.add('PRIVMSG', privmsgcb, precb)
def get_tinyurl(url):
""" grab a tinyurl. """
res = get(url, namespace='tinyurl') ; logging.debug('tinyurl - cache - %s' % unicode(res))
if res and res[0] == '[': return json.loads(res)
postarray = [
('submit', 'submit'),
('url', url),
]
postdata = urllib.urlencode(postarray)
req = urllib2.Request(url=plugcfg.url, data=postdata)
req.add_header('User-agent', useragent())
try: res = urllib2.urlopen(req).readlines()
except urllib2.URLError, e: logging.warn('tinyurl - %s - URLError: %s' % (url, str(e))) ; return
except urllib2.HTTPError, e: logging.warn('tinyurl - %s - HTTP error: %s' % (url, str(e))) ; return
except Exception, ex:
if "DownloadError" in str(ex): logging.warn('tinyurl - %s - DownloadError: %s' % (url, str(e)))
else: handle_exception()
return
urls = []
for line in res:
if line.startswith('<blockquote><b>'): urls.append(striphtml(line.strip()).split('[Open')[0])
if len(urls) == 3: urls.pop(0)
set(url, json.dumps(urls), namespace='tinyurl')
return urls
## tinyurl command
def handle_tinyurl(bot, ievent):
""" arguments: <url> - get tinyurl from provided url. """
if not ievent.rest and (not urlcache.has_key(bot.cfg.name) or not urlcache[bot.cfg.name].has_key(ievent.target)):
ievent.missing('<url>')
return
elif not ievent.rest: url = urlcache[bot.cfg.name][ievent.target]
else: url = ievent.rest
url = valid_url(url)
if not url: ievent.reply('invalid or bad URL') ; return
tinyurl = get_tinyurl(url)
if tinyurl: ievent.reply(' .. '.join(tinyurl))
else: ievent.reply('failed to create tinyurl')
cmnds.add('tinyurl', handle_tinyurl, ['OPER', 'USER', 'GUEST'], threaded=True)
examples.add('tinyurl', 'show a tinyurl', 'tinyurl http://jsonbbot.org')
| en | 0.242603 | # jsb/plugs/common/tinyurl.py # # tinyurl.com feeder ## jsb imports ## plug config ## simpljejson ## basic imports ## defines ## functions check if url is valid ## callbacks callback for urlcaching # not enabled right now #callbacks.add('PRIVMSG', privmsgcb, precb) grab a tinyurl. ## tinyurl command arguments: <url> - get tinyurl from provided url. | 2.194623 | 2 |
ee/clickhouse/models/action.py | tmilicic/posthog | 0 | 6624333 | <reponame>tmilicic/posthog
import re
from typing import Dict, List, Tuple
from django.forms.models import model_to_dict
from posthog.constants import AUTOCAPTURE_EVENT, TREND_FILTER_TYPE_ACTIONS
from posthog.models import Action, Entity, Filter
from posthog.models.action_step import ActionStep
from posthog.models.event import Selector
def format_action_filter(action: Action, prepend: str = "action", index=0, use_loop: bool = False) -> Tuple[str, Dict]:
# get action steps
params = {"team_id": action.team.pk}
steps = action.steps.all()
if len(steps) == 0:
# If no steps, it shouldn't match this part of the query
return "1=2", {}
or_queries = []
for index, step in enumerate(steps):
conditions: List[str] = []
# filter element
if step.event == AUTOCAPTURE_EVENT:
el_conditions, element_params = filter_element(step, "{}{}".format(index, prepend))
params = {**params, **element_params}
conditions += el_conditions
# filter event conditions (ie URL)
event_conditions, event_params = filter_event(step, "{}{}".format(index, prepend), index)
params = {**params, **event_params}
conditions += event_conditions
if step.properties:
from ee.clickhouse.models.property import parse_prop_clauses
prop_query, prop_params = parse_prop_clauses(
Filter(data={"properties": step.properties}).properties,
action.team.pk,
prepend="action_props_{}".format(index),
)
conditions.append(prop_query.replace("AND", "", 1))
params = {**params, **prop_params}
if len(conditions) > 0:
or_queries.append(" AND ".join(conditions))
if use_loop:
formatted_query = "SELECT uuid FROM events WHERE {} AND team_id = %(team_id)s".format(
") OR uuid IN (SELECT uuid FROM events WHERE team_id = %(team_id)s AND ".join(or_queries)
)
else:
formatted_query = "(({}))".format(") OR (".join(or_queries))
return formatted_query, params
def filter_event(step: ActionStep, prepend: str = "event", index: int = 0) -> Tuple[List[str], Dict]:
params = {"{}_{}".format(prepend, index): step.event}
conditions = []
if step.url:
if step.url_matching == ActionStep.EXACT:
conditions.append(
"JSONExtractString(properties, '$current_url') = %({}_prop_val_{})s".format(prepend, index)
)
params.update({"{}_prop_val_{}".format(prepend, index): step.url})
elif step.url_matching == ActionStep.REGEX:
conditions.append(
"match(JSONExtractString(properties, '$current_url'), %({}_prop_val_{})s)".format(prepend, index)
)
params.update({"{}_prop_val_{}".format(prepend, index): step.url})
else:
conditions.append(
"JSONExtractString(properties, '$current_url') LIKE %({}_prop_val_{})s".format(prepend, index)
)
params.update({"{}_prop_val_{}".format(prepend, index): "%" + step.url + "%"})
conditions.append("event = %({}_{})s".format(prepend, index))
return conditions, params
def _create_regex(selector: Selector) -> str:
regex = r""
for idx, tag in enumerate(selector.parts):
if tag.data.get("tag_name") and isinstance(tag.data["tag_name"], str):
if tag.data["tag_name"] == "*":
regex += ".+"
else:
regex += tag.data["tag_name"]
if tag.data.get("attr_class__contains"):
regex += r".*?\.{}".format(r"\..*?".join(sorted(tag.data["attr_class__contains"])))
if tag.ch_attributes:
regex += ".*?"
for key, value in sorted(tag.ch_attributes.items()):
regex += '{}="{}".*?'.format(key, value)
regex += r"([-_a-zA-Z0-9\.]*?)?($|;|:([^;^\s]*(;|$|\s)))"
if tag.direct_descendant:
regex += ".*"
return regex
def filter_element(step: ActionStep, prepend: str = "") -> Tuple[List[str], Dict]:
filters = model_to_dict(step)
params = {}
conditions = []
if filters.get("selector"):
selector = Selector(filters["selector"], escape_slashes=False)
params["{}selector_regex".format(prepend)] = _create_regex(selector)
conditions.append("match(elements_chain, %({}selector_regex)s)".format(prepend))
if filters.get("tag_name"):
params["{}tag_name_regex".format(prepend)] = r"(^|;){}(\.|$|;|:)".format(filters["tag_name"])
conditions.append("match(elements_chain, %({}tag_name_regex)s)".format(prepend))
attributes: Dict[str, str] = {}
for key in ["href", "text"]:
if filters.get(key):
attributes[key] = re.escape(filters[key])
if len(attributes.keys()) > 0:
params["{}attributes_regex".format(prepend)] = ".*?({}).*?".format(
".*?".join(['{}="{}"'.format(key, value) for key, value in attributes.items()])
)
conditions.append("match(elements_chain, %({}attributes_regex)s)".format(prepend))
return (conditions, params)
def format_entity_filter(entity: Entity, prepend: str = "action") -> Tuple[str, Dict]:
if entity.type == TREND_FILTER_TYPE_ACTIONS:
try:
action = Action.objects.get(pk=entity.id)
entity_filter, params = format_action_filter(action, prepend=prepend)
except Action.DoesNotExist:
raise ValueError("This action does not exist")
else:
entity_filter = "event = %(event)s"
params = {"event": entity.id}
return entity_filter, params
| import re
from typing import Dict, List, Tuple
from django.forms.models import model_to_dict
from posthog.constants import AUTOCAPTURE_EVENT, TREND_FILTER_TYPE_ACTIONS
from posthog.models import Action, Entity, Filter
from posthog.models.action_step import ActionStep
from posthog.models.event import Selector
def format_action_filter(action: Action, prepend: str = "action", index=0, use_loop: bool = False) -> Tuple[str, Dict]:
# get action steps
params = {"team_id": action.team.pk}
steps = action.steps.all()
if len(steps) == 0:
# If no steps, it shouldn't match this part of the query
return "1=2", {}
or_queries = []
for index, step in enumerate(steps):
conditions: List[str] = []
# filter element
if step.event == AUTOCAPTURE_EVENT:
el_conditions, element_params = filter_element(step, "{}{}".format(index, prepend))
params = {**params, **element_params}
conditions += el_conditions
# filter event conditions (ie URL)
event_conditions, event_params = filter_event(step, "{}{}".format(index, prepend), index)
params = {**params, **event_params}
conditions += event_conditions
if step.properties:
from ee.clickhouse.models.property import parse_prop_clauses
prop_query, prop_params = parse_prop_clauses(
Filter(data={"properties": step.properties}).properties,
action.team.pk,
prepend="action_props_{}".format(index),
)
conditions.append(prop_query.replace("AND", "", 1))
params = {**params, **prop_params}
if len(conditions) > 0:
or_queries.append(" AND ".join(conditions))
if use_loop:
formatted_query = "SELECT uuid FROM events WHERE {} AND team_id = %(team_id)s".format(
") OR uuid IN (SELECT uuid FROM events WHERE team_id = %(team_id)s AND ".join(or_queries)
)
else:
formatted_query = "(({}))".format(") OR (".join(or_queries))
return formatted_query, params
def filter_event(step: ActionStep, prepend: str = "event", index: int = 0) -> Tuple[List[str], Dict]:
params = {"{}_{}".format(prepend, index): step.event}
conditions = []
if step.url:
if step.url_matching == ActionStep.EXACT:
conditions.append(
"JSONExtractString(properties, '$current_url') = %({}_prop_val_{})s".format(prepend, index)
)
params.update({"{}_prop_val_{}".format(prepend, index): step.url})
elif step.url_matching == ActionStep.REGEX:
conditions.append(
"match(JSONExtractString(properties, '$current_url'), %({}_prop_val_{})s)".format(prepend, index)
)
params.update({"{}_prop_val_{}".format(prepend, index): step.url})
else:
conditions.append(
"JSONExtractString(properties, '$current_url') LIKE %({}_prop_val_{})s".format(prepend, index)
)
params.update({"{}_prop_val_{}".format(prepend, index): "%" + step.url + "%"})
conditions.append("event = %({}_{})s".format(prepend, index))
return conditions, params
def _create_regex(selector: Selector) -> str:
regex = r""
for idx, tag in enumerate(selector.parts):
if tag.data.get("tag_name") and isinstance(tag.data["tag_name"], str):
if tag.data["tag_name"] == "*":
regex += ".+"
else:
regex += tag.data["tag_name"]
if tag.data.get("attr_class__contains"):
regex += r".*?\.{}".format(r"\..*?".join(sorted(tag.data["attr_class__contains"])))
if tag.ch_attributes:
regex += ".*?"
for key, value in sorted(tag.ch_attributes.items()):
regex += '{}="{}".*?'.format(key, value)
regex += r"([-_a-zA-Z0-9\.]*?)?($|;|:([^;^\s]*(;|$|\s)))"
if tag.direct_descendant:
regex += ".*"
return regex
def filter_element(step: ActionStep, prepend: str = "") -> Tuple[List[str], Dict]:
filters = model_to_dict(step)
params = {}
conditions = []
if filters.get("selector"):
selector = Selector(filters["selector"], escape_slashes=False)
params["{}selector_regex".format(prepend)] = _create_regex(selector)
conditions.append("match(elements_chain, %({}selector_regex)s)".format(prepend))
if filters.get("tag_name"):
params["{}tag_name_regex".format(prepend)] = r"(^|;){}(\.|$|;|:)".format(filters["tag_name"])
conditions.append("match(elements_chain, %({}tag_name_regex)s)".format(prepend))
attributes: Dict[str, str] = {}
for key in ["href", "text"]:
if filters.get(key):
attributes[key] = re.escape(filters[key])
if len(attributes.keys()) > 0:
params["{}attributes_regex".format(prepend)] = ".*?({}).*?".format(
".*?".join(['{}="{}"'.format(key, value) for key, value in attributes.items()])
)
conditions.append("match(elements_chain, %({}attributes_regex)s)".format(prepend))
return (conditions, params)
def format_entity_filter(entity: Entity, prepend: str = "action") -> Tuple[str, Dict]:
if entity.type == TREND_FILTER_TYPE_ACTIONS:
try:
action = Action.objects.get(pk=entity.id)
entity_filter, params = format_action_filter(action, prepend=prepend)
except Action.DoesNotExist:
raise ValueError("This action does not exist")
else:
entity_filter = "event = %(event)s"
params = {"event": entity.id}
return entity_filter, params | en | 0.801444 | # get action steps # If no steps, it shouldn't match this part of the query # filter element # filter event conditions (ie URL) | 2.045673 | 2 |
Assignment_1/163059009.py | SeeTheC/Machine-Learning | 0 | 6624334 |
# coding: utf-8
# In[3]:
print("Hello bye");
row=list();
ft=open("train.csv");
data=ft.read();
print(data);
# In[10]:
import numpy as np;
from numpy.linalg import inv;
from numpy.linalg import det;
import math;
trainDSSizePercentage=0.7; # x*100 percentage. 1-x data set will be used for validating
# Will read the file and convert it into two dataset one train data other validate data
def readTrainData(fileName):
row_index=0;
phi=list();
y=list();
with open(fileName) as f:
for line in f:
if row_index >0:
phi_i=list((float(n) for n in line.split('\n')[0].split(",") ));
phi_i[0]=1;
# last row is value of yi
y_i=phi_i.pop(len(phi_i)-1);
phi.append(phi_i);
y.append(y_i);
row_index+=1;
return [phi,y];
#End-readTrainData
# Will read the file and convert it into dataset for Testing the Model
def readTestData(fileName):
row_index=0;
phi=list();
y=list();
with open(fileName) as f:
for line in f:
if row_index >0:
phi_i=list((float(n) for n in line.split('\n')[0].split(",") ));
phi_i[0]=1;
phi.append(phi_i);
row_index+=1;
m=len(phi);
return phi;
#End-readTrainData
#split train data into Train and Validate
def spitTrainDataset(phi,y):
m=len(phi);
tdsSize=int(m*trainDSSizePercentage);
trainDatasetPhi=phi[0:tdsSize];
trainDatasetY=y[0:tdsSize];
validateDatasetPhi=phi[tdsSize:m];
validateDatasetY=y[tdsSize:m];
return [trainDatasetPhi,trainDatasetY,validateDatasetPhi,validateDatasetY];
pass
#write-output
def writeTestData(ystar):
fo = open("output.csv", "w");
fo.write("ID,MEDV\n");
m=len(ystar);
for i in range(m):
fo.write(str(i)+","+str(ystar[i])+"\n");
fo.close();
pass;
# Return det of matrix
def getDet(A):
d=det(A);
if(d<10**-10):
return 0;
return d;
#Return RMS: root mean square error
def getRMS(y,yStar):
m=len(y);
sigma=0;
for i in range(m):
delta=(y[i]-yStar[i]);
delta=delta*delta;
sigma=sigma+delta;
meanSq=sigma/m;
rms=math.sqrt(meanSq);
return rms;
pass;
#For ploting graph of RMS VS Iteration
def plotGraph(x,y):
import matplotlib.pyplot as plt;
plt.plot(x,y)
plt.ylabel('rms')
plt.xlabel('iteration');
plt.show();
pass;
#Record readings for gradient descent
def writeReadingInFile(filename,alpha,lam,iteration,rms,p):
import os.path;
import datetime;
import time;
ts = datetime.datetime.fromtimestamp(time.time()).strftime('%d-%m-%Y %H:%M:%S')
if(os.path.exists(filename)==False):
fo = open(filename, "w");
fo.write("iteration,norm,alpha,lam,rms,timestamp\n");
fo.write(str(iteration)+","+str(p)+","+str(alpha)+","+str(lam)+","+str(rms)+","+str(ts)+"\n");
else:
fo = open(filename, "a");
fo.write(str(iteration)+","+str(p)+","+str(alpha)+","+str(lam)+","+str(rms)+","+str(ts)+"\n");
fo.close();
pass;
#normalize the data set ny (x-u)/s where s is max-min
def normalizePhi(unNormalizedPhi):
phi=np.array(unNormalizedPhi);
print("Normalizing Phi...");
std=phi.std(0);
mean=phi.mean(0);
std[0]=1;
mean[0]=0;
phi_normalize=(phi-mean)/std;
print("Normalization done.");
return phi_normalize;
pass;
#pridict of y* given w* QW=y*
def pridict(dataset,weight):
phi=np.array(dataset);
w=np.array(weight);
ystar=np.dot(phi,w);
return ystar;
pass;
# Finding w*=(QTQ)^-1QTY
def trainUsingClosedFormEquation(dataset,output):
m=len(dataset);
n=len(dataset[0]);
print("------------------");
#print(dataset);
phi=np.array(dataset);
print("------------------");
#print(phi);
y=np.array(output);
phiT=np.transpose(phi);
#(QTQ)
phiT_phi=np.dot(phiT,phi);
d=getDet(phiT_phi)
if(d>0):
#(QTQ)^-1
phiT_phi_inv=inv(phiT_phi);
#(QTQ)^-1QT
phiT_phi_inv_phiT=np.dot(phiT_phi_inv,phiT);
#(QTQ)^-1QT*Y
w=np.dot(phiT_phi_inv_phiT,y);
return w;
else:
print("Error:Phi is NOT full column rank.");
return None;
pass;
# Finding w*=(QTQ+lamI)^-1QTY
def trainUsingClosedFormRidgeEq(dataset,output):
m=len(dataset);
n=len(dataset[0]);
phi=np.array(dataset);
y=np.array(output);
phiT=np.transpose(phi);
#(QTQ)
phiT_phi=np.dot(phiT,phi);
n=len(phiT_phi);
lam=0.3;
I=np.identity(n);
lamI=lam*I;
d=getDet(phiT_phi)
#--------------------------------------
if(d>0):
#(QTQ+lamI)^-1
phiT_phi_inv=inv((phiT_phi+lamI));
#(QTQ+lamI)^-1QT
phiT_phi_inv_phiT=np.dot(phiT_phi_inv,phiT);
#(QTQ+lamI)^-1QT*Y
w=np.dot(phiT_phi_inv_phiT,y);
return w;
else:
print("Error:Phi is NOT full column rank.");
return None;
pass;
def numpiTestFun():
A2= np.matrix([[4,6],[2,8]])
A3= np.matrix([[1,2,3],[4,5,7],[7,8,9]])
A=A2;
print(A);
print(np.power(A,0.5));
print(A);
print("Det(A):"+str(getDet(A)));
B= np.transpose(A);
C=inv(A);
#print(C);
print(np.dot(A,C));
print(A.std(0));
print(A.mean(0));
print(normalizePhi(A));
norm=(A-A.mean(0))/A.std(0);
print(norm);
print();
pass;
def mainClosedFormSol():
#--------------------[Closed Form Sol without Regularlization]--------------------------------
#Find w*
wStar=trainUsingClosedFormEquation(trainDatasetPhi,trainDatasetY);
#Predict y* for Validate Data
ystar=pridict(validateDatasetPhi,wStar);
#checking for RMS for Validate Data
rms=getRMS(validateDatasetY,ystar);
#Predict y* for TestData
ystar=pridict(testDS_norm,wStar);
writeTestData(ystar);
print("ClosedFormSolWithoutReg RMS:",rms);
#---------------------------------------------------------------------------------------------
pass;
def mainRidgeClosedFormSol():
#--------------------[Closed Form Sol without Regularlization]--------------------------------
#Find w*
wStar=trainUsingClosedFormRidgeEq(trainDatasetPhi,trainDatasetY);
#Predict y* for Validate Data
ystar=pridict(validateDatasetPhi,wStar);
#checking for RMS for Validate Data
rms=getRMS(validateDatasetY,ystar);
#Predict y* for TestData
ystar=pridict(testDS_norm,wStar);
writeTestData(ystar);
print("ClosedFormSolWithoutReg RMS:",rms);
#---------------------------------------------------------------------------------------------
pass;
# In[12]:
# GD: Least Sq. Without Regularlization
def gardientDescentErrorFun(phi,y):
m=len(y);#no of data points
n=len(phi[0]);# no. of features
alpha=0.22;# learning parameter
maxIteration=10000;
phi=np.array(phi);
y=(np.array(y));#converting row vector to col vector
wk0=np.zeros(n);# Nx1 vector
phiT=np.transpose(phi);
phiTphi=np.dot(phiT,phi);
phiTy=np.dot(phiT,y);
alphaBym=alpha/m;
xaxis=list();
yaxis=list();
#----------------------
print("Training Started (Least Sq. Without Regularlization) ...");
for i in range(maxIteration):
wk1=wk0-(alphaBym*((np.dot(phiTphi,wk0)-phiTy)));
ystar=pridict(phi,wk1);
rms=getRMS(y,ystar);
xaxis.append(i);
yaxis.append(rms);
percentComplete=((i+1)*100)/maxIteration;
if( percentComplete%10==0 ):
print("Percent Completed",percentComplete);
wk0=wk1;
print("Final Trained RMS:",rms);
plotGraph(xaxis,yaxis);
return wk1;
pass;
# GD: Least Sq. With Ridges
def gardientDescentWithRidge(phi,y):
m=len(y);#no of data points
n=len(phi[0]);# no. of features
alpha=0.212;# learning parameter
maxIteration=10000;
phi=np.array(phi);
y=(np.array(y));#converting row vector to col vector
wk0=np.zeros(n);# Nx1 vector
#wk0=phi[14];#14
phiT=np.transpose(phi);
phiTphi=np.dot(phiT,phi);
phiTy=np.dot(phiT,y);
alphaBym=alpha/m;
lam=0.301;
xaxis=list();
yaxis=list();
algFixedIteration=False;
logReading=True;
diff=0;
#-----------------------------------------------------------------
#Best Tested Constant
#aplha=.212 lamda=.301 datasie=0.7 o/p=4.8310 rms
#Tried for different initial wk0 but o/p remain same
#-----------------------------------------------------------------
print("Training Started (Least Sq. With Ridge) ...");
if (algFixedIteration):
for iteration in range(0,maxIteration):
wk1=wk0-(alphaBym*((np.dot(phiTphi,wk0)-phiTy)+(lam*wk0)));
ystar=pridict(phi,wk1);
rms=getRMS(y,ystar);
xaxis.append(iteration);
yaxis.append(rms);
percentComplete=((iteration+1)*100)/maxIteration;
if( percentComplete%10==0 ):
print("Percent Completed",percentComplete);
wk0=wk1;
else:
diffOffset=1e-20;
iteration=0;
oldRms=0;
voldRms=0;
while (True):
wk1=wk0-(alphaBym*((np.dot(phiTphi,wk0)-phiTy)+(lam*wk0)));
ystar=pridict(phi,wk1);
rms=getRMS(y,ystar);
xaxis.append(iteration);
yaxis.append(rms);
diff=oldRms-rms;
vystar=pridict(validateDatasetPhi,wk1);
vrms=getRMS(validateDatasetY,vystar);
vdiff=voldRms-vrms;
if(iteration>0 and diff<diffOffset):
break;
if(False and iteration%100==0 ):
print("# iteration: ",iteration," rms:",rms,"diff:",diff," vrms:",vrms," vdiff:", vdiff);
wk0=wk1;
oldRms=rms;
voldRms=vrms;
iteration+=1;
print("# iteration: ",iteration," rms:",rms,"diff:",diff," vrms:",vrms," vdiff:", vdiff);
print("Final Trained RMS:",rms ,". Iteration needed ", iteration);
#-------------------------------------------------------------
if(logReading):
writeReadingInFile("ridge.csv",alpha,lam,iteration,rms,2);
plotGraph(xaxis,yaxis);
return wk1;
# GD: Least Sq. With ||w||_(1.5)^(1.5)
def gardientDescentWithPnom(phi,y,p):
m=len(y);#no of data points
n=len(phi[0]);# no. of features
alpha=0.2 #learning parameter
maxIteration=100000;
phi=np.array(phi);
y=(np.array(y));#converting row vector to col vector
wk0=np.zeros(n);# Nx1 vector
wk0=phi[1];
phiT=np.transpose(phi);
phiTphi=np.dot(phiT,phi);
phiTy=np.dot(phiT,y);
alphaBym=alpha/m;
lam=0.31;
xaxis=list();
yaxis=list();
algFixedIteration=False;
logReading=True;
diff=0;
wPow=p-1;
if (p<=1):
print("Error: norm p is less than 1 i.p p=",wPow);
return None;
#-----------------------------------------------------------------
print("Training Started (Least Sq. With Ridge) ...");
if (algFixedIteration):
for iteration in range(0,maxIteration):
if (wPow>1):
wk0Pow=np.power(wk0,wPow);
else:
wk0Pow=wk0;
wk1=wk0-(alphaBym*((np.dot(phiTphi,wk0)-phiTy)+(lam*wk0Pow)));
ystar=pridict(phi,wk1);
rms=getRMS(y,ystar);
xaxis.append(iteration);
yaxis.append(rms);
percentComplete=((iteration+1)*100)/maxIteration;
if( percentComplete%10==0 ):
print("Percent Completed",percentComplete);
wk0=wk1;
else:
diffOffset=1e-20;
iteration=0;
oldRms=0;
voldRms=0;
while (True):
if (wPow>1):
wk0Pow=np.power(wk0,wPow);
else:
wk0Pow=wk0;
wk1=wk0-(alphaBym*((np.dot(phiTphi,wk0)-phiTy)+(lam*wk0Pow)));
ystar=pridict(phi,wk1);
rms=getRMS(y,ystar);
xaxis.append(iteration);
yaxis.append(rms);
diff=oldRms-rms;
vystar=pridict(validateDatasetPhi,wk1);
vrms=getRMS(validateDatasetY,vystar);
vdiff=voldRms-vrms;
if(iteration>0 and diff<=diffOffset):
break;
if(False and iteration%100==0 ):
print("# iteration: ",iteration," rms:",rms,"diff:",diff," vrms:",vrms," vdiff:", vdiff);
wk0=wk1;
oldRms=rms;
voldRms=vrms;
iteration+=1;
print("# iteration: ",iteration," rms:",rms,"diff:",diff," vrms:",vrms," vdiff:", vdiff);
print("Final Trained RMS:",rms ,". Iteration needed ", iteration);
#-------------------------------------------------------------
if(logReading):
writeReadingInFile("pnom.csv",alpha,lam,iteration,rms,p);
plotGraph(xaxis,yaxis);
return wk1;
#wStart=gardientDescentWithRidge(trainDatasetPhi,trainDatasetY);
wStart=gardientDescentWithPnom(trainDatasetPhi,trainDatasetY,(4/3));
# In[ ]:
# In[11]:
#--settings--
np.set_printoptions(suppress=True)
#---init---
dir=""
trainFile=dir+"train.csv";
testFile=dir+"test.csv";
print("Fetching Trained Dataset from file...");
dataset=readTrainData(trainFile);
phiSet=dataset[0];
ySet=dataset[1];
phiSet_norm=normalizePhi(phiSet);
dataset=spitTrainDataset(phiSet_norm,ySet);
testDS=readTestData(testFile);
testDS_norm=normalizePhi(testDS);
print("Fetching of data Completed.");
#train set
trainDatasetPhi=dataset[0];
trainDatasetY=dataset[1];
#validate set
validateDatasetPhi=dataset[2];
validateDatasetY=dataset[3];
#print(testDS);
#print(testDS_norm);
print("Train Size:"+str(len(trainDatasetPhi)));
print("Validate Size:"+str(len(validateDatasetPhi)));
#numpiTestFun();
#mainClosedFormSol();
#mainRidgeClosedFormSol();
#--------------------[Gradient decent without Regularlization]--------------------------------
wStar=gardientDescentWithRidge(trainDatasetPhi,trainDatasetY);
#wStar=gardientDescentWithPnom(trainDatasetPhi,trainDatasetY,(4/3));
#Predict y* for Validate Data
ystar=pridict(validateDatasetPhi,wStar);
#checking for RMS for Validate Data
rms=getRMS(validateDatasetY,ystar);
#Predict y* for TestData
#ystar=pridict(testDS_norm,wStar);
#writeTestData(ystar);
print("ClosedFormSolWithReg RMS:",rms);
#---------------------------------------------------------------------------------------------
# In[ ]:
# In[ ]:
|
# coding: utf-8
# In[3]:
print("Hello bye");
row=list();
ft=open("train.csv");
data=ft.read();
print(data);
# In[10]:
import numpy as np;
from numpy.linalg import inv;
from numpy.linalg import det;
import math;
trainDSSizePercentage=0.7; # x*100 percentage. 1-x data set will be used for validating
# Will read the file and convert it into two dataset one train data other validate data
def readTrainData(fileName):
row_index=0;
phi=list();
y=list();
with open(fileName) as f:
for line in f:
if row_index >0:
phi_i=list((float(n) for n in line.split('\n')[0].split(",") ));
phi_i[0]=1;
# last row is value of yi
y_i=phi_i.pop(len(phi_i)-1);
phi.append(phi_i);
y.append(y_i);
row_index+=1;
return [phi,y];
#End-readTrainData
# Will read the file and convert it into dataset for Testing the Model
def readTestData(fileName):
row_index=0;
phi=list();
y=list();
with open(fileName) as f:
for line in f:
if row_index >0:
phi_i=list((float(n) for n in line.split('\n')[0].split(",") ));
phi_i[0]=1;
phi.append(phi_i);
row_index+=1;
m=len(phi);
return phi;
#End-readTrainData
#split train data into Train and Validate
def spitTrainDataset(phi,y):
m=len(phi);
tdsSize=int(m*trainDSSizePercentage);
trainDatasetPhi=phi[0:tdsSize];
trainDatasetY=y[0:tdsSize];
validateDatasetPhi=phi[tdsSize:m];
validateDatasetY=y[tdsSize:m];
return [trainDatasetPhi,trainDatasetY,validateDatasetPhi,validateDatasetY];
pass
#write-output
def writeTestData(ystar):
fo = open("output.csv", "w");
fo.write("ID,MEDV\n");
m=len(ystar);
for i in range(m):
fo.write(str(i)+","+str(ystar[i])+"\n");
fo.close();
pass;
# Return det of matrix
def getDet(A):
d=det(A);
if(d<10**-10):
return 0;
return d;
#Return RMS: root mean square error
def getRMS(y,yStar):
m=len(y);
sigma=0;
for i in range(m):
delta=(y[i]-yStar[i]);
delta=delta*delta;
sigma=sigma+delta;
meanSq=sigma/m;
rms=math.sqrt(meanSq);
return rms;
pass;
#For ploting graph of RMS VS Iteration
def plotGraph(x,y):
import matplotlib.pyplot as plt;
plt.plot(x,y)
plt.ylabel('rms')
plt.xlabel('iteration');
plt.show();
pass;
#Record readings for gradient descent
def writeReadingInFile(filename,alpha,lam,iteration,rms,p):
import os.path;
import datetime;
import time;
ts = datetime.datetime.fromtimestamp(time.time()).strftime('%d-%m-%Y %H:%M:%S')
if(os.path.exists(filename)==False):
fo = open(filename, "w");
fo.write("iteration,norm,alpha,lam,rms,timestamp\n");
fo.write(str(iteration)+","+str(p)+","+str(alpha)+","+str(lam)+","+str(rms)+","+str(ts)+"\n");
else:
fo = open(filename, "a");
fo.write(str(iteration)+","+str(p)+","+str(alpha)+","+str(lam)+","+str(rms)+","+str(ts)+"\n");
fo.close();
pass;
#normalize the data set ny (x-u)/s where s is max-min
def normalizePhi(unNormalizedPhi):
phi=np.array(unNormalizedPhi);
print("Normalizing Phi...");
std=phi.std(0);
mean=phi.mean(0);
std[0]=1;
mean[0]=0;
phi_normalize=(phi-mean)/std;
print("Normalization done.");
return phi_normalize;
pass;
#pridict of y* given w* QW=y*
def pridict(dataset,weight):
phi=np.array(dataset);
w=np.array(weight);
ystar=np.dot(phi,w);
return ystar;
pass;
# Finding w*=(QTQ)^-1QTY
def trainUsingClosedFormEquation(dataset,output):
m=len(dataset);
n=len(dataset[0]);
print("------------------");
#print(dataset);
phi=np.array(dataset);
print("------------------");
#print(phi);
y=np.array(output);
phiT=np.transpose(phi);
#(QTQ)
phiT_phi=np.dot(phiT,phi);
d=getDet(phiT_phi)
if(d>0):
#(QTQ)^-1
phiT_phi_inv=inv(phiT_phi);
#(QTQ)^-1QT
phiT_phi_inv_phiT=np.dot(phiT_phi_inv,phiT);
#(QTQ)^-1QT*Y
w=np.dot(phiT_phi_inv_phiT,y);
return w;
else:
print("Error:Phi is NOT full column rank.");
return None;
pass;
# Finding w*=(QTQ+lamI)^-1QTY
def trainUsingClosedFormRidgeEq(dataset,output):
m=len(dataset);
n=len(dataset[0]);
phi=np.array(dataset);
y=np.array(output);
phiT=np.transpose(phi);
#(QTQ)
phiT_phi=np.dot(phiT,phi);
n=len(phiT_phi);
lam=0.3;
I=np.identity(n);
lamI=lam*I;
d=getDet(phiT_phi)
#--------------------------------------
if(d>0):
#(QTQ+lamI)^-1
phiT_phi_inv=inv((phiT_phi+lamI));
#(QTQ+lamI)^-1QT
phiT_phi_inv_phiT=np.dot(phiT_phi_inv,phiT);
#(QTQ+lamI)^-1QT*Y
w=np.dot(phiT_phi_inv_phiT,y);
return w;
else:
print("Error:Phi is NOT full column rank.");
return None;
pass;
def numpiTestFun():
A2= np.matrix([[4,6],[2,8]])
A3= np.matrix([[1,2,3],[4,5,7],[7,8,9]])
A=A2;
print(A);
print(np.power(A,0.5));
print(A);
print("Det(A):"+str(getDet(A)));
B= np.transpose(A);
C=inv(A);
#print(C);
print(np.dot(A,C));
print(A.std(0));
print(A.mean(0));
print(normalizePhi(A));
norm=(A-A.mean(0))/A.std(0);
print(norm);
print();
pass;
def mainClosedFormSol():
#--------------------[Closed Form Sol without Regularlization]--------------------------------
#Find w*
wStar=trainUsingClosedFormEquation(trainDatasetPhi,trainDatasetY);
#Predict y* for Validate Data
ystar=pridict(validateDatasetPhi,wStar);
#checking for RMS for Validate Data
rms=getRMS(validateDatasetY,ystar);
#Predict y* for TestData
ystar=pridict(testDS_norm,wStar);
writeTestData(ystar);
print("ClosedFormSolWithoutReg RMS:",rms);
#---------------------------------------------------------------------------------------------
pass;
def mainRidgeClosedFormSol():
#--------------------[Closed Form Sol without Regularlization]--------------------------------
#Find w*
wStar=trainUsingClosedFormRidgeEq(trainDatasetPhi,trainDatasetY);
#Predict y* for Validate Data
ystar=pridict(validateDatasetPhi,wStar);
#checking for RMS for Validate Data
rms=getRMS(validateDatasetY,ystar);
#Predict y* for TestData
ystar=pridict(testDS_norm,wStar);
writeTestData(ystar);
print("ClosedFormSolWithoutReg RMS:",rms);
#---------------------------------------------------------------------------------------------
pass;
# In[12]:
# GD: Least Sq. Without Regularlization
def gardientDescentErrorFun(phi,y):
m=len(y);#no of data points
n=len(phi[0]);# no. of features
alpha=0.22;# learning parameter
maxIteration=10000;
phi=np.array(phi);
y=(np.array(y));#converting row vector to col vector
wk0=np.zeros(n);# Nx1 vector
phiT=np.transpose(phi);
phiTphi=np.dot(phiT,phi);
phiTy=np.dot(phiT,y);
alphaBym=alpha/m;
xaxis=list();
yaxis=list();
#----------------------
print("Training Started (Least Sq. Without Regularlization) ...");
for i in range(maxIteration):
wk1=wk0-(alphaBym*((np.dot(phiTphi,wk0)-phiTy)));
ystar=pridict(phi,wk1);
rms=getRMS(y,ystar);
xaxis.append(i);
yaxis.append(rms);
percentComplete=((i+1)*100)/maxIteration;
if( percentComplete%10==0 ):
print("Percent Completed",percentComplete);
wk0=wk1;
print("Final Trained RMS:",rms);
plotGraph(xaxis,yaxis);
return wk1;
pass;
# GD: Least Sq. With Ridges
def gardientDescentWithRidge(phi,y):
m=len(y);#no of data points
n=len(phi[0]);# no. of features
alpha=0.212;# learning parameter
maxIteration=10000;
phi=np.array(phi);
y=(np.array(y));#converting row vector to col vector
wk0=np.zeros(n);# Nx1 vector
#wk0=phi[14];#14
phiT=np.transpose(phi);
phiTphi=np.dot(phiT,phi);
phiTy=np.dot(phiT,y);
alphaBym=alpha/m;
lam=0.301;
xaxis=list();
yaxis=list();
algFixedIteration=False;
logReading=True;
diff=0;
#-----------------------------------------------------------------
#Best Tested Constant
#aplha=.212 lamda=.301 datasie=0.7 o/p=4.8310 rms
#Tried for different initial wk0 but o/p remain same
#-----------------------------------------------------------------
print("Training Started (Least Sq. With Ridge) ...");
if (algFixedIteration):
for iteration in range(0,maxIteration):
wk1=wk0-(alphaBym*((np.dot(phiTphi,wk0)-phiTy)+(lam*wk0)));
ystar=pridict(phi,wk1);
rms=getRMS(y,ystar);
xaxis.append(iteration);
yaxis.append(rms);
percentComplete=((iteration+1)*100)/maxIteration;
if( percentComplete%10==0 ):
print("Percent Completed",percentComplete);
wk0=wk1;
else:
diffOffset=1e-20;
iteration=0;
oldRms=0;
voldRms=0;
while (True):
wk1=wk0-(alphaBym*((np.dot(phiTphi,wk0)-phiTy)+(lam*wk0)));
ystar=pridict(phi,wk1);
rms=getRMS(y,ystar);
xaxis.append(iteration);
yaxis.append(rms);
diff=oldRms-rms;
vystar=pridict(validateDatasetPhi,wk1);
vrms=getRMS(validateDatasetY,vystar);
vdiff=voldRms-vrms;
if(iteration>0 and diff<diffOffset):
break;
if(False and iteration%100==0 ):
print("# iteration: ",iteration," rms:",rms,"diff:",diff," vrms:",vrms," vdiff:", vdiff);
wk0=wk1;
oldRms=rms;
voldRms=vrms;
iteration+=1;
print("# iteration: ",iteration," rms:",rms,"diff:",diff," vrms:",vrms," vdiff:", vdiff);
print("Final Trained RMS:",rms ,". Iteration needed ", iteration);
#-------------------------------------------------------------
if(logReading):
writeReadingInFile("ridge.csv",alpha,lam,iteration,rms,2);
plotGraph(xaxis,yaxis);
return wk1;
# GD: Least Sq. With ||w||_(1.5)^(1.5)
def gardientDescentWithPnom(phi,y,p):
m=len(y);#no of data points
n=len(phi[0]);# no. of features
alpha=0.2 #learning parameter
maxIteration=100000;
phi=np.array(phi);
y=(np.array(y));#converting row vector to col vector
wk0=np.zeros(n);# Nx1 vector
wk0=phi[1];
phiT=np.transpose(phi);
phiTphi=np.dot(phiT,phi);
phiTy=np.dot(phiT,y);
alphaBym=alpha/m;
lam=0.31;
xaxis=list();
yaxis=list();
algFixedIteration=False;
logReading=True;
diff=0;
wPow=p-1;
if (p<=1):
print("Error: norm p is less than 1 i.p p=",wPow);
return None;
#-----------------------------------------------------------------
print("Training Started (Least Sq. With Ridge) ...");
if (algFixedIteration):
for iteration in range(0,maxIteration):
if (wPow>1):
wk0Pow=np.power(wk0,wPow);
else:
wk0Pow=wk0;
wk1=wk0-(alphaBym*((np.dot(phiTphi,wk0)-phiTy)+(lam*wk0Pow)));
ystar=pridict(phi,wk1);
rms=getRMS(y,ystar);
xaxis.append(iteration);
yaxis.append(rms);
percentComplete=((iteration+1)*100)/maxIteration;
if( percentComplete%10==0 ):
print("Percent Completed",percentComplete);
wk0=wk1;
else:
diffOffset=1e-20;
iteration=0;
oldRms=0;
voldRms=0;
while (True):
if (wPow>1):
wk0Pow=np.power(wk0,wPow);
else:
wk0Pow=wk0;
wk1=wk0-(alphaBym*((np.dot(phiTphi,wk0)-phiTy)+(lam*wk0Pow)));
ystar=pridict(phi,wk1);
rms=getRMS(y,ystar);
xaxis.append(iteration);
yaxis.append(rms);
diff=oldRms-rms;
vystar=pridict(validateDatasetPhi,wk1);
vrms=getRMS(validateDatasetY,vystar);
vdiff=voldRms-vrms;
if(iteration>0 and diff<=diffOffset):
break;
if(False and iteration%100==0 ):
print("# iteration: ",iteration," rms:",rms,"diff:",diff," vrms:",vrms," vdiff:", vdiff);
wk0=wk1;
oldRms=rms;
voldRms=vrms;
iteration+=1;
print("# iteration: ",iteration," rms:",rms,"diff:",diff," vrms:",vrms," vdiff:", vdiff);
print("Final Trained RMS:",rms ,". Iteration needed ", iteration);
#-------------------------------------------------------------
if(logReading):
writeReadingInFile("pnom.csv",alpha,lam,iteration,rms,p);
plotGraph(xaxis,yaxis);
return wk1;
#wStart=gardientDescentWithRidge(trainDatasetPhi,trainDatasetY);
wStart=gardientDescentWithPnom(trainDatasetPhi,trainDatasetY,(4/3));
# In[ ]:
# In[11]:
#--settings--
np.set_printoptions(suppress=True)
#---init---
dir=""
trainFile=dir+"train.csv";
testFile=dir+"test.csv";
print("Fetching Trained Dataset from file...");
dataset=readTrainData(trainFile);
phiSet=dataset[0];
ySet=dataset[1];
phiSet_norm=normalizePhi(phiSet);
dataset=spitTrainDataset(phiSet_norm,ySet);
testDS=readTestData(testFile);
testDS_norm=normalizePhi(testDS);
print("Fetching of data Completed.");
#train set
trainDatasetPhi=dataset[0];
trainDatasetY=dataset[1];
#validate set
validateDatasetPhi=dataset[2];
validateDatasetY=dataset[3];
#print(testDS);
#print(testDS_norm);
print("Train Size:"+str(len(trainDatasetPhi)));
print("Validate Size:"+str(len(validateDatasetPhi)));
#numpiTestFun();
#mainClosedFormSol();
#mainRidgeClosedFormSol();
#--------------------[Gradient decent without Regularlization]--------------------------------
wStar=gardientDescentWithRidge(trainDatasetPhi,trainDatasetY);
#wStar=gardientDescentWithPnom(trainDatasetPhi,trainDatasetY,(4/3));
#Predict y* for Validate Data
ystar=pridict(validateDatasetPhi,wStar);
#checking for RMS for Validate Data
rms=getRMS(validateDatasetY,ystar);
#Predict y* for TestData
#ystar=pridict(testDS_norm,wStar);
#writeTestData(ystar);
print("ClosedFormSolWithReg RMS:",rms);
#---------------------------------------------------------------------------------------------
# In[ ]:
# In[ ]:
| en | 0.340087 | # coding: utf-8 # In[3]: # In[10]: # x*100 percentage. 1-x data set will be used for validating # Will read the file and convert it into two dataset one train data other validate data # last row is value of yi #End-readTrainData # Will read the file and convert it into dataset for Testing the Model #End-readTrainData #split train data into Train and Validate #write-output # Return det of matrix #Return RMS: root mean square error #For ploting graph of RMS VS Iteration #Record readings for gradient descent #normalize the data set ny (x-u)/s where s is max-min #pridict of y* given w* QW=y* # Finding w*=(QTQ)^-1QTY #print(dataset); #print(phi); #(QTQ) #(QTQ)^-1 #(QTQ)^-1QT #(QTQ)^-1QT*Y # Finding w*=(QTQ+lamI)^-1QTY #(QTQ) #-------------------------------------- #(QTQ+lamI)^-1 #(QTQ+lamI)^-1QT #(QTQ+lamI)^-1QT*Y #print(C); #--------------------[Closed Form Sol without Regularlization]-------------------------------- #Find w* #Predict y* for Validate Data #checking for RMS for Validate Data #Predict y* for TestData #--------------------------------------------------------------------------------------------- #--------------------[Closed Form Sol without Regularlization]-------------------------------- #Find w* #Predict y* for Validate Data #checking for RMS for Validate Data #Predict y* for TestData #--------------------------------------------------------------------------------------------- # In[12]: # GD: Least Sq. Without Regularlization #no of data points # no. of features # learning parameter #converting row vector to col vector # Nx1 vector #---------------------- # GD: Least Sq. With Ridges #no of data points # no. of features # learning parameter #converting row vector to col vector # Nx1 vector #wk0=phi[14];#14 #----------------------------------------------------------------- #Best Tested Constant #aplha=.212 lamda=.301 datasie=0.7 o/p=4.8310 rms #Tried for different initial wk0 but o/p remain same #----------------------------------------------------------------- #------------------------------------------------------------- # GD: Least Sq. With ||w||_(1.5)^(1.5) #no of data points # no. of features #learning parameter #converting row vector to col vector # Nx1 vector #----------------------------------------------------------------- #------------------------------------------------------------- #wStart=gardientDescentWithRidge(trainDatasetPhi,trainDatasetY); # In[ ]: # In[11]: #--settings-- #---init--- #train set #validate set #print(testDS); #print(testDS_norm); #numpiTestFun(); #mainClosedFormSol(); #mainRidgeClosedFormSol(); #--------------------[Gradient decent without Regularlization]-------------------------------- #wStar=gardientDescentWithPnom(trainDatasetPhi,trainDatasetY,(4/3)); #Predict y* for Validate Data #checking for RMS for Validate Data #Predict y* for TestData #ystar=pridict(testDS_norm,wStar); #writeTestData(ystar); #--------------------------------------------------------------------------------------------- # In[ ]: # In[ ]: | 3.280512 | 3 |
src/mars_profiling/report/structure/variables/render_url.py | wjsi/mars-profiling | 1 | 6624335 | from mars_profiling.config import config
from mars_profiling.report.presentation.core import (
Container,
FrequencyTable,
FrequencyTableSmall,
Table,
VariableInfo,
)
from mars_profiling.report.presentation.frequency_table_utils import freq_table
from mars_profiling.report.structure.variables import render_common
def render_url(summary):
varid = summary["varid"]
n_freq_table_max = config["n_freq_table_max"].get(int)
n_obs_cat = config["vars"]["cat"]["n_obs"].get(int)
redact = config["vars"]["cat"]["redact"].get(bool)
template_variables = render_common(summary)
keys = ["scheme", "netloc", "path", "query", "fragment"]
for url_part in keys:
template_variables[f"freqtable_{url_part}"] = freq_table(
freqtable=summary[f"{url_part}_counts"],
n=summary["n"],
max_number_to_print=n_freq_table_max,
)
full_frequency_table = FrequencyTable(
template_variables["freq_table_rows"],
name="Full",
anchor_id=f"{varid}full_frequency",
redact=redact,
)
scheme_frequency_table = FrequencyTable(
template_variables["freqtable_scheme"],
name="Scheme",
anchor_id=f"{varid}scheme_frequency",
redact=redact,
)
netloc_frequency_table = FrequencyTable(
template_variables["freqtable_netloc"],
name="Netloc",
anchor_id=f"{varid}netloc_frequency",
redact=redact,
)
path_frequency_table = FrequencyTable(
template_variables["freqtable_path"],
name="Path",
anchor_id=f"{varid}path_frequency",
redact=redact,
)
query_frequency_table = FrequencyTable(
template_variables["freqtable_query"],
name="Query",
anchor_id=f"{varid}query_frequency",
redact=redact,
)
fragment_frequency_table = FrequencyTable(
template_variables["freqtable_fragment"],
name="Fragment",
anchor_id=f"{varid}fragment_frequency",
redact=redact,
)
items = [
full_frequency_table,
scheme_frequency_table,
netloc_frequency_table,
path_frequency_table,
query_frequency_table,
fragment_frequency_table,
]
template_variables["bottom"] = Container(
items, sequence_type="tabs", name="url stats", anchor_id=f"{varid}urlstats"
)
# Element composition
info = VariableInfo(
summary["varid"],
summary["varname"],
"URL",
summary["warnings"],
summary["description"],
)
table = Table(
[
{
"name": "Distinct",
"value": summary["n_distinct"],
"fmt": "fmt",
"alert": "n_distinct" in summary["warn_fields"],
},
{
"name": "Distinct (%)",
"value": summary["p_distinct"],
"fmt": "fmt_percent",
"alert": "p_distinct" in summary["warn_fields"],
},
{
"name": "Missing",
"value": summary["n_missing"],
"fmt": "fmt",
"alert": "n_missing" in summary["warn_fields"],
},
{
"name": "Missing (%)",
"value": summary["p_missing"],
"fmt": "fmt_percent",
"alert": "p_missing" in summary["warn_fields"],
},
{
"name": "Memory size",
"value": summary["memory_size"],
"fmt": "fmt_bytesize",
"alert": False,
},
]
)
fqm = FrequencyTableSmall(
freq_table(
freqtable=summary["value_counts"],
n=summary["n"],
max_number_to_print=n_obs_cat,
),
redact=redact,
)
template_variables["top"] = Container([info, table, fqm], sequence_type="grid")
return template_variables
| from mars_profiling.config import config
from mars_profiling.report.presentation.core import (
Container,
FrequencyTable,
FrequencyTableSmall,
Table,
VariableInfo,
)
from mars_profiling.report.presentation.frequency_table_utils import freq_table
from mars_profiling.report.structure.variables import render_common
def render_url(summary):
varid = summary["varid"]
n_freq_table_max = config["n_freq_table_max"].get(int)
n_obs_cat = config["vars"]["cat"]["n_obs"].get(int)
redact = config["vars"]["cat"]["redact"].get(bool)
template_variables = render_common(summary)
keys = ["scheme", "netloc", "path", "query", "fragment"]
for url_part in keys:
template_variables[f"freqtable_{url_part}"] = freq_table(
freqtable=summary[f"{url_part}_counts"],
n=summary["n"],
max_number_to_print=n_freq_table_max,
)
full_frequency_table = FrequencyTable(
template_variables["freq_table_rows"],
name="Full",
anchor_id=f"{varid}full_frequency",
redact=redact,
)
scheme_frequency_table = FrequencyTable(
template_variables["freqtable_scheme"],
name="Scheme",
anchor_id=f"{varid}scheme_frequency",
redact=redact,
)
netloc_frequency_table = FrequencyTable(
template_variables["freqtable_netloc"],
name="Netloc",
anchor_id=f"{varid}netloc_frequency",
redact=redact,
)
path_frequency_table = FrequencyTable(
template_variables["freqtable_path"],
name="Path",
anchor_id=f"{varid}path_frequency",
redact=redact,
)
query_frequency_table = FrequencyTable(
template_variables["freqtable_query"],
name="Query",
anchor_id=f"{varid}query_frequency",
redact=redact,
)
fragment_frequency_table = FrequencyTable(
template_variables["freqtable_fragment"],
name="Fragment",
anchor_id=f"{varid}fragment_frequency",
redact=redact,
)
items = [
full_frequency_table,
scheme_frequency_table,
netloc_frequency_table,
path_frequency_table,
query_frequency_table,
fragment_frequency_table,
]
template_variables["bottom"] = Container(
items, sequence_type="tabs", name="url stats", anchor_id=f"{varid}urlstats"
)
# Element composition
info = VariableInfo(
summary["varid"],
summary["varname"],
"URL",
summary["warnings"],
summary["description"],
)
table = Table(
[
{
"name": "Distinct",
"value": summary["n_distinct"],
"fmt": "fmt",
"alert": "n_distinct" in summary["warn_fields"],
},
{
"name": "Distinct (%)",
"value": summary["p_distinct"],
"fmt": "fmt_percent",
"alert": "p_distinct" in summary["warn_fields"],
},
{
"name": "Missing",
"value": summary["n_missing"],
"fmt": "fmt",
"alert": "n_missing" in summary["warn_fields"],
},
{
"name": "Missing (%)",
"value": summary["p_missing"],
"fmt": "fmt_percent",
"alert": "p_missing" in summary["warn_fields"],
},
{
"name": "Memory size",
"value": summary["memory_size"],
"fmt": "fmt_bytesize",
"alert": False,
},
]
)
fqm = FrequencyTableSmall(
freq_table(
freqtable=summary["value_counts"],
n=summary["n"],
max_number_to_print=n_obs_cat,
),
redact=redact,
)
template_variables["top"] = Container([info, table, fqm], sequence_type="grid")
return template_variables
| en | 0.552708 | # Element composition | 2.194871 | 2 |
src/ankidmpy/copier.py | gitonthescene/ankidmpy | 1 | 6624336 | <filename>src/ankidmpy/copier.py
import ankidmpy.util as util
import shutil
import os.path
def copy(deck1, deck2, base):
deck1_path = os.path.join(base, 'decks', util.deckToFilename(deck1))
if not os.path.isdir(deck1_path):
util.err("Source deck not found: %s" % (deck1,))
deck2_suffix = ""
if deck2:
deck2_path = os.path.join(base, 'decks', util.deckToFilename(deck2))
else:
i = 1
while True:
deck2_suffix = " (%d)" % (i,)
deck2_path = deck1_path + deck2_suffix
if not os.path.exists(deck2_path):
break
i += 1
try:
shutil.copytree(deck1_path, deck2_path)
except PermissionError:
util.err("Cannot copy files")
deck_build = util.getJson(os.path.join(deck2_path, 'build.json'))
deck_build['deck']['uuid'] = util.createUuid()
deck_build['config']['uuid'] = util.createUuid()
deck_build['model']['uuid'] = util.createUuid()
with open(os.path.join(deck2_path, 'build.json'), 'w') as f:
f.write(util.toJson(deck_build))
util.msg("Created deck: %s" % deck2 if deck2 else deck1 + deck2_suffix)
| <filename>src/ankidmpy/copier.py
import ankidmpy.util as util
import shutil
import os.path
def copy(deck1, deck2, base):
deck1_path = os.path.join(base, 'decks', util.deckToFilename(deck1))
if not os.path.isdir(deck1_path):
util.err("Source deck not found: %s" % (deck1,))
deck2_suffix = ""
if deck2:
deck2_path = os.path.join(base, 'decks', util.deckToFilename(deck2))
else:
i = 1
while True:
deck2_suffix = " (%d)" % (i,)
deck2_path = deck1_path + deck2_suffix
if not os.path.exists(deck2_path):
break
i += 1
try:
shutil.copytree(deck1_path, deck2_path)
except PermissionError:
util.err("Cannot copy files")
deck_build = util.getJson(os.path.join(deck2_path, 'build.json'))
deck_build['deck']['uuid'] = util.createUuid()
deck_build['config']['uuid'] = util.createUuid()
deck_build['model']['uuid'] = util.createUuid()
with open(os.path.join(deck2_path, 'build.json'), 'w') as f:
f.write(util.toJson(deck_build))
util.msg("Created deck: %s" % deck2 if deck2 else deck1 + deck2_suffix)
| none | 1 | 2.745165 | 3 | |
Concept_Name_Generation/gentaxo.py | DM2-ND/GenTaxo | 2 | 6624337 | import torch
from modules import MSA, BiLSTM, GraphTrans, BiGRU
from utlis import *
from torch import nn
import dgl
class GenTaxo(nn.Module):
def __init__(self, args):
super(GenTaxo, self).__init__()
self.args = args
if args.seq:
self.seq_emb = nn.Embedding(len(args.seq_vocab), args.nhid, padding_idx=0)
# self.seq_enc = BiLSTM(args, enc_type='title')
self.seq_enc = BiGRU(args, enc_type='seq')
self.seq_attn = MSA(args)
self.ent_emb = nn.Embedding(len(args.ent_text_vocab), args.nhid, padding_idx=0)
self.tar_emb = nn.Embedding(len(args.text_vocab), args.nhid, padding_idx=0)
self.linear_combine = nn.Linear(args.nhid, 1)
self.activate_f = nn.Sigmoid()
if args.seq:
nn.init.xavier_normal_(self.seq_emb.weight)
nn.init.xavier_normal_(self.ent_emb.weight)
self.rel_emb = nn.Embedding(len(args.rel_vocab), args.nhid, padding_idx=0)
nn.init.xavier_normal_(self.rel_emb.weight)
if args.enc_seq_type == "LSTM":
self.decode_seq = nn.LSTMCell(args.dec_ninp, args.nhid)
self.ent_enc = BiLSTM(args, enc_type='entity')
if args.enc_seq_type == "GRU":
self.decode_seq = nn.GRUCell(args.dec_ninp, args.nhid)
self.ent_enc = BiGRU(args, enc_type='entity')
self.graph_enc = GraphTrans(args)
self.ent_attn = MSA(args)
self.copy_attn = MSA(args, mode='copy')
self.copy_fc = nn.Linear(args.dec_ninp, 1)
self.pred_v_fc = nn.Linear(args.dec_ninp, len(args.text_vocab))
def enc_forward(self, batch, ent_mask, ent_text_mask, ent_len, rel_mask, parent_mask, child_mask, sibling_mask):
seq_enc = None
if self.args.seq:
parent_enc = self.seq_enc(self.seq_emb(batch['parent']), parent_mask)
child_enc = self.seq_enc(self.seq_emb(batch['child']), child_mask)
sibling_enc = self.seq_enc(self.seq_emb(batch['sibling']), sibling_mask)
ent_enc = self.ent_enc(self.ent_emb(batch['ent_text']), ent_text_mask, ent_len = batch['ent_len'])
rel_emb = self.rel_emb(batch['rel'])
g_ent, g_root = self.graph_enc(ent_enc, ent_mask, ent_len, rel_emb, rel_mask, batch['graph'])
parent_enc = self.seq_attn(g_root, parent_enc)
child_enc = self.seq_attn(g_root, child_enc)
sibling_enc = self.seq_attn(g_root, sibling_enc)
w_p = self.activate_f((self.linear_combine(parent_enc)))
w_c = self.activate_f((self.linear_combine(child_enc)))
w_s = self.activate_f((self.linear_combine(sibling_enc)))
w = torch.cat([w_p,w_c,w_s],1)
seq_enc = torch.cat([parent_enc,child_enc,sibling_enc],0)
m = nn.Softmax(dim=1)
w = m(w)
seq_enc = w @ seq_enc
return g_ent, g_root, seq_enc, ent_enc
def forward(self, batch, beam_size=-1):
ent_mask = len2mask(batch['ent_len'], self.args.device)
ent_text_mask = batch['ent_text']==0
rel_mask = batch['rel']==0 # 0 means the <PAD>
parent_mask = batch['parent']==0
child_mask = batch['child']==0
sibling_mask = batch['sibling']==0
g_ent, g_root, seq_enc, ent_enc = self.enc_forward(batch, ent_mask, ent_text_mask, batch['ent_len'], rel_mask, parent_mask, child_mask, sibling_mask)
_h, _c = g_root, g_root.clone().detach()
ctx = _h + self.ent_attn(_h, g_ent, mask=ent_mask)
ctx = torch.cat([ctx, seq_enc], 1)
if beam_size<1:
# training
outs = []
tar_inp = self.tar_emb(batch['text'].transpose(0,1))
for t, xt in enumerate(tar_inp):
_xt = torch.cat([ctx, xt], 1)
if self.args.enc_seq_type == "LSTM":
_h, _c = self.decode_seq(_xt, (_h, _c))
if self.args.enc_seq_type == "GRU":
_h = self.decode_seq(_xt, _h)
ctx = _h + self.ent_attn(_h, g_ent, mask=ent_mask)
if self.args.seq:
ctx = torch.cat([ctx, seq_enc], 1)
outs.append(torch.cat([_h, ctx], 1))
outs = torch.stack(outs, 1)
copy_gate = torch.sigmoid(self.copy_fc(outs))
EPSI = 1e-6
# copy
pred_v = torch.log(copy_gate+EPSI) + torch.log_softmax(self.pred_v_fc(outs), -1)
pred_c = torch.log((1. - copy_gate)+EPSI) + torch.log_softmax(self.copy_attn(outs, ent_enc, mask=ent_mask), -1)
pred = torch.cat([pred_v, pred_c], -1)
return pred
else:
if beam_size==1:
# greedy
device = g_ent.device
B = g_ent.shape[0]
ent_type = batch['ent_type'].view(B, -1)
seq = (torch.ones(B,).long().to(device) * self.args.text_vocab('<BOS>')).unsqueeze(1)
for t in range(self.args.beam_max_len):
_inp = replace_ent(seq[:,-1], ent_type, len(self.args.text_vocab))
xt = self.tar_emb(_inp)
_xt = torch.cat([ctx, xt], 1)
if self.args.enc_seq_type == "LSTM":
_h, _c = self.decode_seq(_xt, (_h, _c))
if self.args.enc_seq_type == "GRU":
_h = self.decode_seq(_xt, _h)
ctx = _h + self.ent_attn(_h, g_ent, mask=ent_mask)
if self.args.seq:
ctx = torch.cat([ctx, seq_enc], 1)
_y = torch.cat([_h, ctx], 1)
copy_gate = torch.sigmoid(self.copy_fc(_y))
pred_v = torch.log(copy_gate) + torch.log_softmax(self.pred_v_fc(_y), -1)
pred_c = torch.log((1. - copy_gate)) + torch.log_softmax(self.copy_attn(_y.unsqueeze(1), ent_enc, mask=ent_mask).squeeze(1), -1)
pred = torch.cat([pred_v, pred_c], -1).view(B,-1)
for ban_item in ['<BOS>', '<PAD>', '<UNK>']:
pred[:, self.args.text_vocab(ban_item)] = -1e8
_, word = pred.max(-1)
seq = torch.cat([seq, word.unsqueeze(1)], 1)
return seq
else:
# beam search
device = g_ent.device
B = g_ent.shape[0]
BSZ = B * beam_size
_h = _h.view(B, 1, -1).repeat(1, beam_size, 1).view(BSZ, -1)
_c = _c.view(B, 1, -1).repeat(1, beam_size, 1).view(BSZ, -1)
ent_mask = ent_mask.view(B, 1, -1).repeat(1, beam_size, 1).view(BSZ, -1)
if self.args.title:
title_mask = title_mask.view(B, 1, -1).repeat(1, beam_size, 1).view(BSZ, -1)
title_enc = title_enc.view(B, 1, title_enc.size(1), -1).repeat(1, beam_size, 1, 1).view(BSZ, title_enc.size(1), -1)
ctx = ctx.view(B, 1, -1).repeat(1, beam_size, 1).view(BSZ, -1)
ent_type = batch['ent_type'].view(B, 1, -1).repeat(1, beam_size, 1).view(BSZ, -1)
g_ent = g_ent.view(B, 1, g_ent.size(1), -1).repeat(1, beam_size, 1, 1).view(BSZ, g_ent.size(1), -1)
ent_enc = ent_enc.view(B, 1, ent_enc.size(1), -1).repeat(1, beam_size, 1, 1).view(BSZ, ent_enc.size(1), -1)
beam_best = torch.zeros(B).to(device) - 1e9
beam_best_seq = [None] * B
beam_seq = (torch.ones(B, beam_size).long().to(device) * self.args.text_vocab('<BOS>')).unsqueeze(-1)
beam_score = torch.zeros(B, beam_size).to(device)
done_flag = torch.zeros(B, beam_size)
for t in range(self.args.beam_max_len):
_inp = replace_ent(beam_seq[:,:,-1].view(-1), ent_type, len(self.args.text_vocab))
xt = self.tar_emb(_inp)
_xt = torch.cat([ctx, xt], 1)
_h, _c = self.decode_seq(_xt, (_h, _c))
ctx = _h + self.ent_attn(_h, g_ent, mask=ent_mask)
if self.args.title:
attn = _h + self.title_attn(_h, title_enc, mask=title_mask)
ctx = torch.cat([ctx, attn], 1)
_y = torch.cat([_h, ctx], 1)
copy_gate = torch.sigmoid(self.copy_fc(_y))
pred_v = torch.log(copy_gate) + torch.log_softmax(self.pred_v_fc(_y), -1)
pred_c = torch.log((1. - copy_gate)) + torch.log_softmax(self.copy_attn(_y.unsqueeze(1), ent_enc, mask=ent_mask).squeeze(1), -1)
pred = torch.cat([pred_v, pred_c], -1).view(B, beam_size, -1)
for ban_item in ['<BOS>', '<PAD>', '<UNK>']:
pred[:, :, self.args.text_vocab(ban_item)] = -1e8
if t==self.args.beam_max_len-1: # force ending
tt = pred[:, :, self.args.text_vocab('<EOS>')]
pred = pred*0-1e8
pred[:, :, self.args.text_vocab('<EOS>')] = tt
cum_score = beam_score.view(B,beam_size,1) + pred
score, word = cum_score.topk(dim=-1, k=beam_size) # B, beam_size, beam_size
score, word = score.view(B,-1), word.view(B,-1)
eos_idx = self.args.text_vocab('<EOS>')
if beam_seq.size(2)==1:
new_idx = torch.arange(beam_size).to(word)
new_idx = new_idx[None,:].repeat(B,1)
else:
_, new_idx = score.topk(dim=-1, k=beam_size)
new_src, new_score, new_word, new_done = [], [], [], []
LP = beam_seq.size(2) ** self.args.lp
for i in range(B):
for j in range(beam_size):
tmp_score = score[i][new_idx[i][j]]
tmp_word = word[i][new_idx[i][j]]
src_idx = new_idx[i][j]//beam_size
new_src.append(src_idx)
if tmp_word == eos_idx:
new_score.append(-1e8)
else:
new_score.append(tmp_score)
new_word.append(tmp_word)
if tmp_word == eos_idx and done_flag[i][src_idx]==0 and tmp_score/LP>beam_best[i]:
beam_best[i] = tmp_score/LP
beam_best_seq[i] = beam_seq[i][src_idx]
if tmp_word == eos_idx:
new_done.append(1)
else:
new_done.append(done_flag[i][src_idx])
new_score = torch.Tensor(new_score).view(B,beam_size).to(beam_score)
new_word = torch.Tensor(new_word).view(B,beam_size).to(beam_seq)
new_src = torch.LongTensor(new_src).view(B,beam_size).to(device)
new_done = torch.Tensor(new_done).view(B,beam_size).to(done_flag)
beam_score = new_score
done_flag = new_done
beam_seq = beam_seq.view(B,beam_size,-1)[torch.arange(B)[:,None].to(device), new_src]
beam_seq = torch.cat([beam_seq, new_word.unsqueeze(2)], 2)
_h = _h.view(B,beam_size,-1)[torch.arange(B)[:,None].to(device), new_src].view(BSZ,-1)
_c = _c.view(B,beam_size,-1)[torch.arange(B)[:,None].to(device), new_src].view(BSZ,-1)
ctx = ctx.view(B,beam_size,-1)[torch.arange(B)[:,None].to(device), new_src].view(BSZ,-1)
return beam_best_seq | import torch
from modules import MSA, BiLSTM, GraphTrans, BiGRU
from utlis import *
from torch import nn
import dgl
class GenTaxo(nn.Module):
def __init__(self, args):
super(GenTaxo, self).__init__()
self.args = args
if args.seq:
self.seq_emb = nn.Embedding(len(args.seq_vocab), args.nhid, padding_idx=0)
# self.seq_enc = BiLSTM(args, enc_type='title')
self.seq_enc = BiGRU(args, enc_type='seq')
self.seq_attn = MSA(args)
self.ent_emb = nn.Embedding(len(args.ent_text_vocab), args.nhid, padding_idx=0)
self.tar_emb = nn.Embedding(len(args.text_vocab), args.nhid, padding_idx=0)
self.linear_combine = nn.Linear(args.nhid, 1)
self.activate_f = nn.Sigmoid()
if args.seq:
nn.init.xavier_normal_(self.seq_emb.weight)
nn.init.xavier_normal_(self.ent_emb.weight)
self.rel_emb = nn.Embedding(len(args.rel_vocab), args.nhid, padding_idx=0)
nn.init.xavier_normal_(self.rel_emb.weight)
if args.enc_seq_type == "LSTM":
self.decode_seq = nn.LSTMCell(args.dec_ninp, args.nhid)
self.ent_enc = BiLSTM(args, enc_type='entity')
if args.enc_seq_type == "GRU":
self.decode_seq = nn.GRUCell(args.dec_ninp, args.nhid)
self.ent_enc = BiGRU(args, enc_type='entity')
self.graph_enc = GraphTrans(args)
self.ent_attn = MSA(args)
self.copy_attn = MSA(args, mode='copy')
self.copy_fc = nn.Linear(args.dec_ninp, 1)
self.pred_v_fc = nn.Linear(args.dec_ninp, len(args.text_vocab))
def enc_forward(self, batch, ent_mask, ent_text_mask, ent_len, rel_mask, parent_mask, child_mask, sibling_mask):
seq_enc = None
if self.args.seq:
parent_enc = self.seq_enc(self.seq_emb(batch['parent']), parent_mask)
child_enc = self.seq_enc(self.seq_emb(batch['child']), child_mask)
sibling_enc = self.seq_enc(self.seq_emb(batch['sibling']), sibling_mask)
ent_enc = self.ent_enc(self.ent_emb(batch['ent_text']), ent_text_mask, ent_len = batch['ent_len'])
rel_emb = self.rel_emb(batch['rel'])
g_ent, g_root = self.graph_enc(ent_enc, ent_mask, ent_len, rel_emb, rel_mask, batch['graph'])
parent_enc = self.seq_attn(g_root, parent_enc)
child_enc = self.seq_attn(g_root, child_enc)
sibling_enc = self.seq_attn(g_root, sibling_enc)
w_p = self.activate_f((self.linear_combine(parent_enc)))
w_c = self.activate_f((self.linear_combine(child_enc)))
w_s = self.activate_f((self.linear_combine(sibling_enc)))
w = torch.cat([w_p,w_c,w_s],1)
seq_enc = torch.cat([parent_enc,child_enc,sibling_enc],0)
m = nn.Softmax(dim=1)
w = m(w)
seq_enc = w @ seq_enc
return g_ent, g_root, seq_enc, ent_enc
def forward(self, batch, beam_size=-1):
ent_mask = len2mask(batch['ent_len'], self.args.device)
ent_text_mask = batch['ent_text']==0
rel_mask = batch['rel']==0 # 0 means the <PAD>
parent_mask = batch['parent']==0
child_mask = batch['child']==0
sibling_mask = batch['sibling']==0
g_ent, g_root, seq_enc, ent_enc = self.enc_forward(batch, ent_mask, ent_text_mask, batch['ent_len'], rel_mask, parent_mask, child_mask, sibling_mask)
_h, _c = g_root, g_root.clone().detach()
ctx = _h + self.ent_attn(_h, g_ent, mask=ent_mask)
ctx = torch.cat([ctx, seq_enc], 1)
if beam_size<1:
# training
outs = []
tar_inp = self.tar_emb(batch['text'].transpose(0,1))
for t, xt in enumerate(tar_inp):
_xt = torch.cat([ctx, xt], 1)
if self.args.enc_seq_type == "LSTM":
_h, _c = self.decode_seq(_xt, (_h, _c))
if self.args.enc_seq_type == "GRU":
_h = self.decode_seq(_xt, _h)
ctx = _h + self.ent_attn(_h, g_ent, mask=ent_mask)
if self.args.seq:
ctx = torch.cat([ctx, seq_enc], 1)
outs.append(torch.cat([_h, ctx], 1))
outs = torch.stack(outs, 1)
copy_gate = torch.sigmoid(self.copy_fc(outs))
EPSI = 1e-6
# copy
pred_v = torch.log(copy_gate+EPSI) + torch.log_softmax(self.pred_v_fc(outs), -1)
pred_c = torch.log((1. - copy_gate)+EPSI) + torch.log_softmax(self.copy_attn(outs, ent_enc, mask=ent_mask), -1)
pred = torch.cat([pred_v, pred_c], -1)
return pred
else:
if beam_size==1:
# greedy
device = g_ent.device
B = g_ent.shape[0]
ent_type = batch['ent_type'].view(B, -1)
seq = (torch.ones(B,).long().to(device) * self.args.text_vocab('<BOS>')).unsqueeze(1)
for t in range(self.args.beam_max_len):
_inp = replace_ent(seq[:,-1], ent_type, len(self.args.text_vocab))
xt = self.tar_emb(_inp)
_xt = torch.cat([ctx, xt], 1)
if self.args.enc_seq_type == "LSTM":
_h, _c = self.decode_seq(_xt, (_h, _c))
if self.args.enc_seq_type == "GRU":
_h = self.decode_seq(_xt, _h)
ctx = _h + self.ent_attn(_h, g_ent, mask=ent_mask)
if self.args.seq:
ctx = torch.cat([ctx, seq_enc], 1)
_y = torch.cat([_h, ctx], 1)
copy_gate = torch.sigmoid(self.copy_fc(_y))
pred_v = torch.log(copy_gate) + torch.log_softmax(self.pred_v_fc(_y), -1)
pred_c = torch.log((1. - copy_gate)) + torch.log_softmax(self.copy_attn(_y.unsqueeze(1), ent_enc, mask=ent_mask).squeeze(1), -1)
pred = torch.cat([pred_v, pred_c], -1).view(B,-1)
for ban_item in ['<BOS>', '<PAD>', '<UNK>']:
pred[:, self.args.text_vocab(ban_item)] = -1e8
_, word = pred.max(-1)
seq = torch.cat([seq, word.unsqueeze(1)], 1)
return seq
else:
# beam search
device = g_ent.device
B = g_ent.shape[0]
BSZ = B * beam_size
_h = _h.view(B, 1, -1).repeat(1, beam_size, 1).view(BSZ, -1)
_c = _c.view(B, 1, -1).repeat(1, beam_size, 1).view(BSZ, -1)
ent_mask = ent_mask.view(B, 1, -1).repeat(1, beam_size, 1).view(BSZ, -1)
if self.args.title:
title_mask = title_mask.view(B, 1, -1).repeat(1, beam_size, 1).view(BSZ, -1)
title_enc = title_enc.view(B, 1, title_enc.size(1), -1).repeat(1, beam_size, 1, 1).view(BSZ, title_enc.size(1), -1)
ctx = ctx.view(B, 1, -1).repeat(1, beam_size, 1).view(BSZ, -1)
ent_type = batch['ent_type'].view(B, 1, -1).repeat(1, beam_size, 1).view(BSZ, -1)
g_ent = g_ent.view(B, 1, g_ent.size(1), -1).repeat(1, beam_size, 1, 1).view(BSZ, g_ent.size(1), -1)
ent_enc = ent_enc.view(B, 1, ent_enc.size(1), -1).repeat(1, beam_size, 1, 1).view(BSZ, ent_enc.size(1), -1)
beam_best = torch.zeros(B).to(device) - 1e9
beam_best_seq = [None] * B
beam_seq = (torch.ones(B, beam_size).long().to(device) * self.args.text_vocab('<BOS>')).unsqueeze(-1)
beam_score = torch.zeros(B, beam_size).to(device)
done_flag = torch.zeros(B, beam_size)
for t in range(self.args.beam_max_len):
_inp = replace_ent(beam_seq[:,:,-1].view(-1), ent_type, len(self.args.text_vocab))
xt = self.tar_emb(_inp)
_xt = torch.cat([ctx, xt], 1)
_h, _c = self.decode_seq(_xt, (_h, _c))
ctx = _h + self.ent_attn(_h, g_ent, mask=ent_mask)
if self.args.title:
attn = _h + self.title_attn(_h, title_enc, mask=title_mask)
ctx = torch.cat([ctx, attn], 1)
_y = torch.cat([_h, ctx], 1)
copy_gate = torch.sigmoid(self.copy_fc(_y))
pred_v = torch.log(copy_gate) + torch.log_softmax(self.pred_v_fc(_y), -1)
pred_c = torch.log((1. - copy_gate)) + torch.log_softmax(self.copy_attn(_y.unsqueeze(1), ent_enc, mask=ent_mask).squeeze(1), -1)
pred = torch.cat([pred_v, pred_c], -1).view(B, beam_size, -1)
for ban_item in ['<BOS>', '<PAD>', '<UNK>']:
pred[:, :, self.args.text_vocab(ban_item)] = -1e8
if t==self.args.beam_max_len-1: # force ending
tt = pred[:, :, self.args.text_vocab('<EOS>')]
pred = pred*0-1e8
pred[:, :, self.args.text_vocab('<EOS>')] = tt
cum_score = beam_score.view(B,beam_size,1) + pred
score, word = cum_score.topk(dim=-1, k=beam_size) # B, beam_size, beam_size
score, word = score.view(B,-1), word.view(B,-1)
eos_idx = self.args.text_vocab('<EOS>')
if beam_seq.size(2)==1:
new_idx = torch.arange(beam_size).to(word)
new_idx = new_idx[None,:].repeat(B,1)
else:
_, new_idx = score.topk(dim=-1, k=beam_size)
new_src, new_score, new_word, new_done = [], [], [], []
LP = beam_seq.size(2) ** self.args.lp
for i in range(B):
for j in range(beam_size):
tmp_score = score[i][new_idx[i][j]]
tmp_word = word[i][new_idx[i][j]]
src_idx = new_idx[i][j]//beam_size
new_src.append(src_idx)
if tmp_word == eos_idx:
new_score.append(-1e8)
else:
new_score.append(tmp_score)
new_word.append(tmp_word)
if tmp_word == eos_idx and done_flag[i][src_idx]==0 and tmp_score/LP>beam_best[i]:
beam_best[i] = tmp_score/LP
beam_best_seq[i] = beam_seq[i][src_idx]
if tmp_word == eos_idx:
new_done.append(1)
else:
new_done.append(done_flag[i][src_idx])
new_score = torch.Tensor(new_score).view(B,beam_size).to(beam_score)
new_word = torch.Tensor(new_word).view(B,beam_size).to(beam_seq)
new_src = torch.LongTensor(new_src).view(B,beam_size).to(device)
new_done = torch.Tensor(new_done).view(B,beam_size).to(done_flag)
beam_score = new_score
done_flag = new_done
beam_seq = beam_seq.view(B,beam_size,-1)[torch.arange(B)[:,None].to(device), new_src]
beam_seq = torch.cat([beam_seq, new_word.unsqueeze(2)], 2)
_h = _h.view(B,beam_size,-1)[torch.arange(B)[:,None].to(device), new_src].view(BSZ,-1)
_c = _c.view(B,beam_size,-1)[torch.arange(B)[:,None].to(device), new_src].view(BSZ,-1)
ctx = ctx.view(B,beam_size,-1)[torch.arange(B)[:,None].to(device), new_src].view(BSZ,-1)
return beam_best_seq | en | 0.556119 | # self.seq_enc = BiLSTM(args, enc_type='title') # 0 means the <PAD> # training # copy # greedy # beam search # force ending # B, beam_size, beam_size | 2.370899 | 2 |
LSTM/edit.py | justinhyou/movement-classification-via-CNN-LSTM | 4 | 6624338 | <gh_stars>1-10
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import tensorflow as tf # Version r0.10
from sklearn import metrics
import sklearn
import os
# Useful Constants
# Those are separate normalised input features for the neural network
INPUT_SIGNAL_TYPES = [
"body_acc_x_",
"body_acc_y_",
"body_acc_z_",
"body_gyro_x_",
"body_gyro_y_",
"body_gyro_z_",
"total_acc_x_",
"total_acc_y_",
"total_acc_z_"
]
# Output classes to learn how to classify
LABELS = [
"WALKING",
"WALKING_UPSTAIRS",
"WALKING_DOWNSTAIRS",
"SITTING",
"STANDING",
"LAYING"
]
DATA_PATH = "data/"
DATASET_PATH = DATA_PATH + "UCI HAR Dataset/"
print("\n" + "Dataset is now located at: " + DATASET_PATH)
TRAIN = "train/"
TEST = "test/"
# Load "X" (the neural network's training and testing inputs)
def load_X(X_signals_paths):
X_signals = []
for signal_type_path in X_signals_paths:
file = open(signal_type_path, 'rb')
# Read dataset from disk, dealing with text files' syntax
X_signals.append(
[np.array(serie, dtype=np.float32) for serie in [
row.replace(' ', ' ').strip().split(' ') for row in file
]]
)
file.close()
return np.transpose(np.array(X_signals), (1, 2, 0))
X_train_signals_paths = [
DATASET_PATH + TRAIN + "Inertial Signals/" + signal + "train.txt" for signal in INPUT_SIGNAL_TYPES
]
X_test_signals_paths = [
DATASET_PATH + TEST + "Inertial Signals/" + signal + "test.txt" for signal in INPUT_SIGNAL_TYPES
]
X_train = load_X(X_train_signals_paths)
X_test = load_X(X_test_signals_paths)
# Load "y" (the neural network's training and testing outputs)
def load_y(y_path):
file = open(y_path, 'rb')
# Read dataset from disk, dealing with text file's syntax
y_ = np.array(
[elem for elem in [
row.replace(' ', ' ').strip().split(' ') for row in file
]],
dtype=np.int32
)
file.close()
# Substract 1 to each output class for friendly 0-based indexing
return y_ - 1
y_train_path = DATASET_PATH + TRAIN + "y_train.txt"
y_test_path = DATASET_PATH + TEST + "y_test.txt"
y_train = load_y(y_train_path)
y_test = load_y(y_test_path)
# Input Data
training_data_count = len(X_train) # 7352 training series (with 50% overlap between each serie)
test_data_count = len(X_test) # 2947 testing series
n_steps = len(X_train[0]) # 128 timesteps per series
n_input = len(X_train[0][0]) # 9 input parameters per timestep
# LSTM Neural Network's internal structure
n_hidden = 32 # Hidden layer num of features
n_classes = 6 # Total classes (should go up, or should go down)
# Training
learning_rate = 0.0025
lambda_loss_amount = 0.0015
training_iters = training_data_count * 300 # Loop 300 times on the dataset
batch_size = 1500
display_iter = 30000 # To show test set accuracy during training
# Some debugging info
print "Some useful info to get an insight on dataset's shape and normalisation:"
print "(X shape, y shape, every X's mean, every X's standard deviation)"
print (X_test.shape, y_test.shape, np.mean(X_test), np.std(X_test))
print "The dataset is therefore properly normalised, as expected, but not yet one-hot encoded."
""" NEW!!!! """
def CNN(_X, _weights, _biases):
"""Model function for CNN."""
# Input Layer
input_layer = tf.reshape(features, [-1, 28, 28, 1])
# Convolutional Layer #1
conv1 = tf.layers.conv2d(
inputs=input_layer,
filters=32,
kernel_size=[5, 5],
padding="same",
activation=tf.nn.relu)
# Pooling Layer #1
pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2)
# Convolutional Layer #2 and Pooling Layer #2
conv2 = tf.layers.conv2d(
inputs=pool1,
filters=64,
kernel_size=[5, 5],
padding="same",
activation=tf.nn.relu)
pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)
# Dense Layer
pool2_flat = tf.reshape(pool2, [-1, 7 * 7 * 64])
dense = tf.layers.dense(inputs=pool2_flat, units=1024, activation=tf.nn.relu)
dropout = tf.layers.dropout(
inputs=dense, rate=0.4, training=mode == learn.ModeKeys.TRAIN)
# Logits Layer
logits = tf.layers.dense(inputs=dropout, units=10)
loss = None
train_op = None
# Calculate Loss (for both TRAIN and EVAL modes)
if mode != learn.ModeKeys.INFER:
onehot_labels = tf.one_hot(indices=tf.cast(labels, tf.int32), depth=10)
loss = tf.losses.softmax_cross_entropy(
onehot_labels=onehot_labels, logits=logits)
# Configure the Training Op (for TRAIN mode)
if mode == learn.ModeKeys.TRAIN:
train_op = tf.contrib.layers.optimize_loss(
loss=loss,
global_step=tf.contrib.framework.get_global_step(),
learning_rate=0.001,
optimizer="SGD")
# Generate Predictions
predictions = {
"classes": tf.argmax(
input=logits, axis=1),
"probabilities": tf.nn.softmax(
logits, name="softmax_tensor")
}
# Return a ModelFnOps object
return model_fn_lib.ModelFnOps(
mode=mode, predictions=predictions, loss=loss, train_op=train_op)
""" NEW!!!! """
def LSTM_RNN(_X, _weights, _biases):
# Function returns a tensorflow LSTM (RNN) artificial neural network from given parameters.
# Moreover, two LSTM cells are stacked which adds deepness to the neural network.
# Note, some code of this notebook is inspired from an slightly different
# RNN architecture used on another dataset:
# https://tensorhub.com/aymericdamien/tensorflow-rnn
# (NOTE: This step could be greatly optimised by shaping the dataset once
# input shape: (batch_size, n_steps, n_input)
_X = tf.transpose(_X, [1, 0, 2]) # permute n_steps and batch_size
# Reshape to prepare input to hidden activation
_X = tf.reshape(_X, [-1, n_input])
# new shape: (n_steps*batch_size, n_input)
# Linear activation
_X = tf.nn.relu(tf.matmul(_X, _weights['hidden']) + _biases['hidden'])
# Split data because rnn cell needs a list of inputs for the RNN inner loop
_X = tf.split(0, n_steps, _X)
# new shape: n_steps * (batch_size, n_hidden)
input_layer = tf.reshape(_X, [128, 1500, 4, 1])
input_layer = _X
input_layer = tf.reshape(_X, [4])
#CNN #1
conv_1 = tf.nn.conv2d(input = input_layer, filter = 32, strides=[1, 2, 2, 1], padding = "SAME")
# Pooling Layer #1
pool1 = tf.nn.max_pooling2d(input=conv1, pool_size=[2, 2], strides=2)
# Convolutional Layer #2 and Pooling Layer #2
conv2 = tf.layers.conv2d(
inputs=pool1,
filters=64,
kernel_size=[5, 5],
padding="same",
activation=tf.nn.relu)
# Pooling Layer #2
pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)
# Dense Layer
pool2_flat = tf.reshape(pool2, [-1, 7 * 7 * 64])
dense = tf.nn.dense(inputs=pool2_flat, units=1024, activation=tf.nn.relu)
dropout = tf.nn.dropout(
inputs=dense, rate=0.4, training=mode == learn.ModeKeys.TRAIN)
# Logits Layer
logits = tf.nn.dense(inputs=dropout, units=10)
# Define two stacked LSTM cells (two recurrent layers deep) with tensorflow
lstm_cell_1 = tf.nn.rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0, state_is_tuple=True)
lstm_cell_2 = tf.nn.rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0, state_is_tuple=True)
lstm_cells = tf.nn.rnn_cell.MultiRNNCell([lstm_cell_1, lstm_cell_2], state_is_tuple=True)
# Get LSTM cell output
outputs, states = tf.nn.rnn(lstm_cells, _X, dtype=tf.float32)
# Get last time step's output feature for a "many to one" style classifier,
# as in the image describing RNNs at the top of this page
lstm_last_output = outputs[-1]
# Linear activation
return tf.matmul(lstm_last_output, _weights['out']) + _biases['out']
def extract_batch_size(_train, step, batch_size):
# Function to fetch a "batch_size" amount of data from "(X|y)_train" data.
shape = list(_train.shape)
shape[0] = batch_size
batch_s = np.empty(shape)
for i in range(batch_size):
# Loop index
index = ((step-1)*batch_size + i) % len(_train)
batch_s[i] = _train[index]
return batch_s
def one_hot(y_):
# Function to encode output labels from number indexes
# e.g.: [[5], [0], [3]] --> [[0, 0, 0, 0, 0, 1], [1, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0]]
y_ = y_.reshape(len(y_))
n_values = np.max(y_) + 1
return np.eye(n_values)[np.array(y_, dtype=np.int32)] # Returns FLOATS
# Graph input/output
x = tf.placeholder(tf.float32, [None, n_steps, n_input])
y = tf.placeholder(tf.float32, [None, n_classes])
# Graph weights
weights = {
'hidden': tf.Variable(tf.random_normal([n_input, n_hidden])), # Hidden layer weights
'out': tf.Variable(tf.random_normal([n_hidden, n_classes], mean=1.0))
}
biases = {
'hidden': tf.Variable(tf.random_normal([n_hidden])),
'out': tf.Variable(tf.random_normal([n_classes]))
}
pred = LSTM_RNN(x, weights, biases)
# Loss, optimizer and evaluation
l2 = lambda_loss_amount * sum(
tf.nn.l2_loss(tf_var) for tf_var in tf.trainable_variables()
) # L2 loss prevents this overkill neural network to overfit the data
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y)) + l2 # Softmax loss
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # Adam Optimizer
correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
# To keep track of training's performance
test_losses = []
test_accuracies = []
train_losses = []
train_accuracies = []
# Launch the graph
sess = tf.InteractiveSession(config=tf.ConfigProto(log_device_placement=True))
init = tf.initialize_all_variables()
sess.run(init)
# In each loop, perform training steps with "batch_size" amount of given data
step = 1
while step * batch_size <= training_iters:
batch_xs = extract_batch_size(X_train, step, batch_size)
batch_ys = one_hot(extract_batch_size(y_train, step, batch_size))
# Fit training using batch
_, loss, acc = sess.run(
[optimizer, cost, accuracy],
feed_dict={
x: batch_xs,
y: batch_ys
}
)
train_losses.append(loss)
train_accuracies.append(acc)
# show network details at specified intervals
if (step*batch_size % display_iter == 0) or (step == 1) or (step * batch_size > training_iters):
# show accuracy and loss
print "Training iter #" + str(step*batch_size) + \
": Batch Loss = " + "{:.6f}".format(loss) + \
", Accuracy = {}".format(acc)
# evaluate the test set
loss, acc = sess.run(
[cost, accuracy],
feed_dict={
x: X_test,
y: one_hot(y_test)
}
)
test_losses.append(loss)
test_accuracies.append(acc)
print "PERFORMANCE ON TEST SET: " + \
"Batch Loss = {}".format(loss) + \
", Accuracy = {}".format(acc)
step += 1
# Accuracy for test data
one_hot_predictions, accuracy, final_loss = sess.run(
[pred, accuracy, cost],
feed_dict={
x: X_test,
y: one_hot(y_test)
}
)
test_losses.append(final_loss)
test_accuracies.append(accuracy)
print "FINAL RESULT: " + \
"Batch Loss = {}".format(final_loss) + \
", Accuracy = {}".format(accuracy)
# (Inline plots: )
#%matplotlib inline
font = {
'family' : 'Bitstream Vera Sans',
'weight' : 'bold',
'size' : 18
}
matplotlib.rc('font', **font)
width = 12
height = 12
plt.figure(figsize=(width, height))
indep_train_axis = np.array(range(batch_size, (len(train_losses)+1)*batch_size, batch_size))
plt.plot(indep_train_axis, np.array(train_losses), "b--", label="Train losses")
plt.plot(indep_train_axis, np.array(train_accuracies), "g--", label="Train accuracies")
indep_test_axis = np.array(range(batch_size, len(test_losses)*display_iter, display_iter)[:-1] + [training_iters])
plt.plot(indep_test_axis, np.array(test_losses), "b-", label="Test losses")
plt.plot(indep_test_axis, np.array(test_accuracies), "g-", label="Test accuracies")
plt.title("Training session's progress over iterations")
plt.legend(loc='upper right', shadow=True)
plt.ylabel('Training Progress (Loss or Accuracy values)')
plt.xlabel('Training iteration')
plt.show()
# Results
predictions = one_hot_predictions.argmax(1)
print "Testing Accuracy: {}%".format(100*accuracy)
print ""
print "Precision: {}%".format(100*metrics.precision_score(y_test, predictions, average="weighted"))
print "Recall: {}%".format(100*metrics.recall_score(y_test, predictions, average="weighted"))
print "f1_score: {}%".format(100*metrics.f1_score(y_test, predictions, average="weighted"))
print ""
print "Confusion Matrix:"
confusion_matrix = metrics.confusion_matrix(y_test, predictions)
print confusion_matrix
normalised_confusion_matrix = np.array(confusion_matrix, dtype=np.float32)/np.sum(confusion_matrix)*100
print ""
print "Confusion matrix (normalised to % of total test data):"
print normalised_confusion_matrix
print ("Note: training and testing data is not equally distributed amongst classes, "
"so it is normal that more than a 6th of the data is correctly classified in the last category.")
# Plot Results:
width = 12
height = 12
plt.figure(figsize=(width, height))
plt.imshow(
normalised_confusion_matrix,
interpolation='nearest',
cmap=plt.cm.rainbow
)
plt.title("Confusion matrix \n(normalised to % of total test data)")
plt.colorbar()
tick_marks = np.arange(n_classes)
plt.xticks(tick_marks, LABELS, rotation=90)
plt.yticks(tick_marks, LABELS)
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
plt.show()
sess.close()
| import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import tensorflow as tf # Version r0.10
from sklearn import metrics
import sklearn
import os
# Useful Constants
# Those are separate normalised input features for the neural network
INPUT_SIGNAL_TYPES = [
"body_acc_x_",
"body_acc_y_",
"body_acc_z_",
"body_gyro_x_",
"body_gyro_y_",
"body_gyro_z_",
"total_acc_x_",
"total_acc_y_",
"total_acc_z_"
]
# Output classes to learn how to classify
LABELS = [
"WALKING",
"WALKING_UPSTAIRS",
"WALKING_DOWNSTAIRS",
"SITTING",
"STANDING",
"LAYING"
]
DATA_PATH = "data/"
DATASET_PATH = DATA_PATH + "UCI HAR Dataset/"
print("\n" + "Dataset is now located at: " + DATASET_PATH)
TRAIN = "train/"
TEST = "test/"
# Load "X" (the neural network's training and testing inputs)
def load_X(X_signals_paths):
X_signals = []
for signal_type_path in X_signals_paths:
file = open(signal_type_path, 'rb')
# Read dataset from disk, dealing with text files' syntax
X_signals.append(
[np.array(serie, dtype=np.float32) for serie in [
row.replace(' ', ' ').strip().split(' ') for row in file
]]
)
file.close()
return np.transpose(np.array(X_signals), (1, 2, 0))
X_train_signals_paths = [
DATASET_PATH + TRAIN + "Inertial Signals/" + signal + "train.txt" for signal in INPUT_SIGNAL_TYPES
]
X_test_signals_paths = [
DATASET_PATH + TEST + "Inertial Signals/" + signal + "test.txt" for signal in INPUT_SIGNAL_TYPES
]
X_train = load_X(X_train_signals_paths)
X_test = load_X(X_test_signals_paths)
# Load "y" (the neural network's training and testing outputs)
def load_y(y_path):
file = open(y_path, 'rb')
# Read dataset from disk, dealing with text file's syntax
y_ = np.array(
[elem for elem in [
row.replace(' ', ' ').strip().split(' ') for row in file
]],
dtype=np.int32
)
file.close()
# Substract 1 to each output class for friendly 0-based indexing
return y_ - 1
y_train_path = DATASET_PATH + TRAIN + "y_train.txt"
y_test_path = DATASET_PATH + TEST + "y_test.txt"
y_train = load_y(y_train_path)
y_test = load_y(y_test_path)
# Input Data
training_data_count = len(X_train) # 7352 training series (with 50% overlap between each serie)
test_data_count = len(X_test) # 2947 testing series
n_steps = len(X_train[0]) # 128 timesteps per series
n_input = len(X_train[0][0]) # 9 input parameters per timestep
# LSTM Neural Network's internal structure
n_hidden = 32 # Hidden layer num of features
n_classes = 6 # Total classes (should go up, or should go down)
# Training
learning_rate = 0.0025
lambda_loss_amount = 0.0015
training_iters = training_data_count * 300 # Loop 300 times on the dataset
batch_size = 1500
display_iter = 30000 # To show test set accuracy during training
# Some debugging info
print "Some useful info to get an insight on dataset's shape and normalisation:"
print "(X shape, y shape, every X's mean, every X's standard deviation)"
print (X_test.shape, y_test.shape, np.mean(X_test), np.std(X_test))
print "The dataset is therefore properly normalised, as expected, but not yet one-hot encoded."
""" NEW!!!! """
def CNN(_X, _weights, _biases):
"""Model function for CNN."""
# Input Layer
input_layer = tf.reshape(features, [-1, 28, 28, 1])
# Convolutional Layer #1
conv1 = tf.layers.conv2d(
inputs=input_layer,
filters=32,
kernel_size=[5, 5],
padding="same",
activation=tf.nn.relu)
# Pooling Layer #1
pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2)
# Convolutional Layer #2 and Pooling Layer #2
conv2 = tf.layers.conv2d(
inputs=pool1,
filters=64,
kernel_size=[5, 5],
padding="same",
activation=tf.nn.relu)
pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)
# Dense Layer
pool2_flat = tf.reshape(pool2, [-1, 7 * 7 * 64])
dense = tf.layers.dense(inputs=pool2_flat, units=1024, activation=tf.nn.relu)
dropout = tf.layers.dropout(
inputs=dense, rate=0.4, training=mode == learn.ModeKeys.TRAIN)
# Logits Layer
logits = tf.layers.dense(inputs=dropout, units=10)
loss = None
train_op = None
# Calculate Loss (for both TRAIN and EVAL modes)
if mode != learn.ModeKeys.INFER:
onehot_labels = tf.one_hot(indices=tf.cast(labels, tf.int32), depth=10)
loss = tf.losses.softmax_cross_entropy(
onehot_labels=onehot_labels, logits=logits)
# Configure the Training Op (for TRAIN mode)
if mode == learn.ModeKeys.TRAIN:
train_op = tf.contrib.layers.optimize_loss(
loss=loss,
global_step=tf.contrib.framework.get_global_step(),
learning_rate=0.001,
optimizer="SGD")
# Generate Predictions
predictions = {
"classes": tf.argmax(
input=logits, axis=1),
"probabilities": tf.nn.softmax(
logits, name="softmax_tensor")
}
# Return a ModelFnOps object
return model_fn_lib.ModelFnOps(
mode=mode, predictions=predictions, loss=loss, train_op=train_op)
""" NEW!!!! """
def LSTM_RNN(_X, _weights, _biases):
# Function returns a tensorflow LSTM (RNN) artificial neural network from given parameters.
# Moreover, two LSTM cells are stacked which adds deepness to the neural network.
# Note, some code of this notebook is inspired from an slightly different
# RNN architecture used on another dataset:
# https://tensorhub.com/aymericdamien/tensorflow-rnn
# (NOTE: This step could be greatly optimised by shaping the dataset once
# input shape: (batch_size, n_steps, n_input)
_X = tf.transpose(_X, [1, 0, 2]) # permute n_steps and batch_size
# Reshape to prepare input to hidden activation
_X = tf.reshape(_X, [-1, n_input])
# new shape: (n_steps*batch_size, n_input)
# Linear activation
_X = tf.nn.relu(tf.matmul(_X, _weights['hidden']) + _biases['hidden'])
# Split data because rnn cell needs a list of inputs for the RNN inner loop
_X = tf.split(0, n_steps, _X)
# new shape: n_steps * (batch_size, n_hidden)
input_layer = tf.reshape(_X, [128, 1500, 4, 1])
input_layer = _X
input_layer = tf.reshape(_X, [4])
#CNN #1
conv_1 = tf.nn.conv2d(input = input_layer, filter = 32, strides=[1, 2, 2, 1], padding = "SAME")
# Pooling Layer #1
pool1 = tf.nn.max_pooling2d(input=conv1, pool_size=[2, 2], strides=2)
# Convolutional Layer #2 and Pooling Layer #2
conv2 = tf.layers.conv2d(
inputs=pool1,
filters=64,
kernel_size=[5, 5],
padding="same",
activation=tf.nn.relu)
# Pooling Layer #2
pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)
# Dense Layer
pool2_flat = tf.reshape(pool2, [-1, 7 * 7 * 64])
dense = tf.nn.dense(inputs=pool2_flat, units=1024, activation=tf.nn.relu)
dropout = tf.nn.dropout(
inputs=dense, rate=0.4, training=mode == learn.ModeKeys.TRAIN)
# Logits Layer
logits = tf.nn.dense(inputs=dropout, units=10)
# Define two stacked LSTM cells (two recurrent layers deep) with tensorflow
lstm_cell_1 = tf.nn.rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0, state_is_tuple=True)
lstm_cell_2 = tf.nn.rnn_cell.BasicLSTMCell(n_hidden, forget_bias=1.0, state_is_tuple=True)
lstm_cells = tf.nn.rnn_cell.MultiRNNCell([lstm_cell_1, lstm_cell_2], state_is_tuple=True)
# Get LSTM cell output
outputs, states = tf.nn.rnn(lstm_cells, _X, dtype=tf.float32)
# Get last time step's output feature for a "many to one" style classifier,
# as in the image describing RNNs at the top of this page
lstm_last_output = outputs[-1]
# Linear activation
return tf.matmul(lstm_last_output, _weights['out']) + _biases['out']
def extract_batch_size(_train, step, batch_size):
# Function to fetch a "batch_size" amount of data from "(X|y)_train" data.
shape = list(_train.shape)
shape[0] = batch_size
batch_s = np.empty(shape)
for i in range(batch_size):
# Loop index
index = ((step-1)*batch_size + i) % len(_train)
batch_s[i] = _train[index]
return batch_s
def one_hot(y_):
# Function to encode output labels from number indexes
# e.g.: [[5], [0], [3]] --> [[0, 0, 0, 0, 0, 1], [1, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0]]
y_ = y_.reshape(len(y_))
n_values = np.max(y_) + 1
return np.eye(n_values)[np.array(y_, dtype=np.int32)] # Returns FLOATS
# Graph input/output
x = tf.placeholder(tf.float32, [None, n_steps, n_input])
y = tf.placeholder(tf.float32, [None, n_classes])
# Graph weights
weights = {
'hidden': tf.Variable(tf.random_normal([n_input, n_hidden])), # Hidden layer weights
'out': tf.Variable(tf.random_normal([n_hidden, n_classes], mean=1.0))
}
biases = {
'hidden': tf.Variable(tf.random_normal([n_hidden])),
'out': tf.Variable(tf.random_normal([n_classes]))
}
pred = LSTM_RNN(x, weights, biases)
# Loss, optimizer and evaluation
l2 = lambda_loss_amount * sum(
tf.nn.l2_loss(tf_var) for tf_var in tf.trainable_variables()
) # L2 loss prevents this overkill neural network to overfit the data
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y)) + l2 # Softmax loss
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # Adam Optimizer
correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
# To keep track of training's performance
test_losses = []
test_accuracies = []
train_losses = []
train_accuracies = []
# Launch the graph
sess = tf.InteractiveSession(config=tf.ConfigProto(log_device_placement=True))
init = tf.initialize_all_variables()
sess.run(init)
# In each loop, perform training steps with "batch_size" amount of given data
step = 1
while step * batch_size <= training_iters:
batch_xs = extract_batch_size(X_train, step, batch_size)
batch_ys = one_hot(extract_batch_size(y_train, step, batch_size))
# Fit training using batch
_, loss, acc = sess.run(
[optimizer, cost, accuracy],
feed_dict={
x: batch_xs,
y: batch_ys
}
)
train_losses.append(loss)
train_accuracies.append(acc)
# show network details at specified intervals
if (step*batch_size % display_iter == 0) or (step == 1) or (step * batch_size > training_iters):
# show accuracy and loss
print "Training iter #" + str(step*batch_size) + \
": Batch Loss = " + "{:.6f}".format(loss) + \
", Accuracy = {}".format(acc)
# evaluate the test set
loss, acc = sess.run(
[cost, accuracy],
feed_dict={
x: X_test,
y: one_hot(y_test)
}
)
test_losses.append(loss)
test_accuracies.append(acc)
print "PERFORMANCE ON TEST SET: " + \
"Batch Loss = {}".format(loss) + \
", Accuracy = {}".format(acc)
step += 1
# Accuracy for test data
one_hot_predictions, accuracy, final_loss = sess.run(
[pred, accuracy, cost],
feed_dict={
x: X_test,
y: one_hot(y_test)
}
)
test_losses.append(final_loss)
test_accuracies.append(accuracy)
print "FINAL RESULT: " + \
"Batch Loss = {}".format(final_loss) + \
", Accuracy = {}".format(accuracy)
# (Inline plots: )
#%matplotlib inline
font = {
'family' : 'Bitstream Vera Sans',
'weight' : 'bold',
'size' : 18
}
matplotlib.rc('font', **font)
width = 12
height = 12
plt.figure(figsize=(width, height))
indep_train_axis = np.array(range(batch_size, (len(train_losses)+1)*batch_size, batch_size))
plt.plot(indep_train_axis, np.array(train_losses), "b--", label="Train losses")
plt.plot(indep_train_axis, np.array(train_accuracies), "g--", label="Train accuracies")
indep_test_axis = np.array(range(batch_size, len(test_losses)*display_iter, display_iter)[:-1] + [training_iters])
plt.plot(indep_test_axis, np.array(test_losses), "b-", label="Test losses")
plt.plot(indep_test_axis, np.array(test_accuracies), "g-", label="Test accuracies")
plt.title("Training session's progress over iterations")
plt.legend(loc='upper right', shadow=True)
plt.ylabel('Training Progress (Loss or Accuracy values)')
plt.xlabel('Training iteration')
plt.show()
# Results
predictions = one_hot_predictions.argmax(1)
print "Testing Accuracy: {}%".format(100*accuracy)
print ""
print "Precision: {}%".format(100*metrics.precision_score(y_test, predictions, average="weighted"))
print "Recall: {}%".format(100*metrics.recall_score(y_test, predictions, average="weighted"))
print "f1_score: {}%".format(100*metrics.f1_score(y_test, predictions, average="weighted"))
print ""
print "Confusion Matrix:"
confusion_matrix = metrics.confusion_matrix(y_test, predictions)
print confusion_matrix
normalised_confusion_matrix = np.array(confusion_matrix, dtype=np.float32)/np.sum(confusion_matrix)*100
print ""
print "Confusion matrix (normalised to % of total test data):"
print normalised_confusion_matrix
print ("Note: training and testing data is not equally distributed amongst classes, "
"so it is normal that more than a 6th of the data is correctly classified in the last category.")
# Plot Results:
width = 12
height = 12
plt.figure(figsize=(width, height))
plt.imshow(
normalised_confusion_matrix,
interpolation='nearest',
cmap=plt.cm.rainbow
)
plt.title("Confusion matrix \n(normalised to % of total test data)")
plt.colorbar()
tick_marks = np.arange(n_classes)
plt.xticks(tick_marks, LABELS, rotation=90)
plt.yticks(tick_marks, LABELS)
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
plt.show()
sess.close() | en | 0.811717 | # Version r0.10 # Useful Constants # Those are separate normalised input features for the neural network # Output classes to learn how to classify # Load "X" (the neural network's training and testing inputs) # Read dataset from disk, dealing with text files' syntax # Load "y" (the neural network's training and testing outputs) # Read dataset from disk, dealing with text file's syntax # Substract 1 to each output class for friendly 0-based indexing # Input Data # 7352 training series (with 50% overlap between each serie) # 2947 testing series # 128 timesteps per series # 9 input parameters per timestep # LSTM Neural Network's internal structure # Hidden layer num of features # Total classes (should go up, or should go down) # Training # Loop 300 times on the dataset # To show test set accuracy during training # Some debugging info NEW!!!! Model function for CNN. # Input Layer # Convolutional Layer #1 # Pooling Layer #1 # Convolutional Layer #2 and Pooling Layer #2 # Dense Layer # Logits Layer # Calculate Loss (for both TRAIN and EVAL modes) # Configure the Training Op (for TRAIN mode) # Generate Predictions # Return a ModelFnOps object NEW!!!! # Function returns a tensorflow LSTM (RNN) artificial neural network from given parameters. # Moreover, two LSTM cells are stacked which adds deepness to the neural network. # Note, some code of this notebook is inspired from an slightly different # RNN architecture used on another dataset: # https://tensorhub.com/aymericdamien/tensorflow-rnn # (NOTE: This step could be greatly optimised by shaping the dataset once # input shape: (batch_size, n_steps, n_input) # permute n_steps and batch_size # Reshape to prepare input to hidden activation # new shape: (n_steps*batch_size, n_input) # Linear activation # Split data because rnn cell needs a list of inputs for the RNN inner loop # new shape: n_steps * (batch_size, n_hidden) #CNN #1 # Pooling Layer #1 # Convolutional Layer #2 and Pooling Layer #2 # Pooling Layer #2 # Dense Layer # Logits Layer # Define two stacked LSTM cells (two recurrent layers deep) with tensorflow # Get LSTM cell output # Get last time step's output feature for a "many to one" style classifier, # as in the image describing RNNs at the top of this page # Linear activation # Function to fetch a "batch_size" amount of data from "(X|y)_train" data. # Loop index # Function to encode output labels from number indexes # e.g.: [[5], [0], [3]] --> [[0, 0, 0, 0, 0, 1], [1, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0]] # Returns FLOATS # Graph input/output # Graph weights # Hidden layer weights # Loss, optimizer and evaluation # L2 loss prevents this overkill neural network to overfit the data # Softmax loss # Adam Optimizer # To keep track of training's performance # Launch the graph # In each loop, perform training steps with "batch_size" amount of given data # Fit training using batch # show network details at specified intervals # show accuracy and loss #" + str(step*batch_size) + \ # evaluate the test set # Accuracy for test data # (Inline plots: ) #%matplotlib inline # Results # Plot Results: | 3.113014 | 3 |
Retrofit2Server/retrofit2server/app.py | rakeshcusat/Retrofit2-example | 0 | 6624339 | from flask import Flask
app = Flask(__name__.split('.')[0])
app.debug = True
from retrofit2server.controllers.user import create_user_routes # noqa
# Setup the routes
create_user_routes(app)
| from flask import Flask
app = Flask(__name__.split('.')[0])
app.debug = True
from retrofit2server.controllers.user import create_user_routes # noqa
# Setup the routes
create_user_routes(app)
| en | 0.636427 | # noqa # Setup the routes | 1.868569 | 2 |
topasgraphsim/src/resources/TkinterDnD2/__init__.py | sebasj13/topasgraphsim | 2 | 6624340 | # dnd actions
PRIVATE = 'private'
NONE = 'none'
ASK = 'ask'
COPY = 'copy'
MOVE = 'move'
LINK = 'link'
REFUSE_DROP = 'refuse_drop'
# dnd types
DND_TEXT = 'DND_Text'
DND_FILES = 'DND_Files'
DND_ALL = '*'
CF_UNICODETEXT = 'CF_UNICODETEXT'
CF_TEXT = 'CF_TEXT'
CF_HDROP = 'CF_HDROP'
FileGroupDescriptor = 'FileGroupDescriptor - FileContents'# ??
FileGroupDescriptorW = 'FileGroupDescriptorW - FileContents'# ??
from TkinterDnD2 import TkinterDnD
| # dnd actions
PRIVATE = 'private'
NONE = 'none'
ASK = 'ask'
COPY = 'copy'
MOVE = 'move'
LINK = 'link'
REFUSE_DROP = 'refuse_drop'
# dnd types
DND_TEXT = 'DND_Text'
DND_FILES = 'DND_Files'
DND_ALL = '*'
CF_UNICODETEXT = 'CF_UNICODETEXT'
CF_TEXT = 'CF_TEXT'
CF_HDROP = 'CF_HDROP'
FileGroupDescriptor = 'FileGroupDescriptor - FileContents'# ??
FileGroupDescriptorW = 'FileGroupDescriptorW - FileContents'# ??
from TkinterDnD2 import TkinterDnD
| en | 0.726105 | # dnd actions # dnd types | 1.840055 | 2 |
Python Book/6. Complex Conditions/8_trade_comissions/trade_comissions.py | alexanderivanov2/Softuni-Software-Engineering | 0 | 6624341 | <reponame>alexanderivanov2/Softuni-Software-Engineering<filename>Python Book/6. Complex Conditions/8_trade_comissions/trade_comissions.py
city = input().title()
s = float(input()) #number of sales
comission = 0
error = "error"
if city == "Sofia":
if 0 <= s <= 500:
comission += s * 0.05
elif 500 < s <= 1000:
comission += s * 0.07
elif 1000 < s <= 10000:
comission += s * 0.08
elif s > 10000:
comission += s * 0.12
else:
print("error")
elif city == "Varna":
if 0 <= s <= 500:
comission += s * 0.045
elif 500 < s <= 1000:
comission += s * 0.075
elif 1000 < s <= 10000:
comission += s * 0.10
elif s > 10000:
comission += s * 0.13
elif s < 0:
error = "error"
elif city == "Plovdiv":
if 0 <= s <= 500:
comission += s * 0.055
elif 500 < s <= 1000:
comission += s * 0.08
elif 1000 < s <= 10000:
comission += s * 0.12
elif s > 10000:
comission += s * 0.145
else:
print(error)
else:
print("error")
if comission > 0:
print(f"{comission:.2f}") | Book/6. Complex Conditions/8_trade_comissions/trade_comissions.py
city = input().title()
s = float(input()) #number of sales
comission = 0
error = "error"
if city == "Sofia":
if 0 <= s <= 500:
comission += s * 0.05
elif 500 < s <= 1000:
comission += s * 0.07
elif 1000 < s <= 10000:
comission += s * 0.08
elif s > 10000:
comission += s * 0.12
else:
print("error")
elif city == "Varna":
if 0 <= s <= 500:
comission += s * 0.045
elif 500 < s <= 1000:
comission += s * 0.075
elif 1000 < s <= 10000:
comission += s * 0.10
elif s > 10000:
comission += s * 0.13
elif s < 0:
error = "error"
elif city == "Plovdiv":
if 0 <= s <= 500:
comission += s * 0.055
elif 500 < s <= 1000:
comission += s * 0.08
elif 1000 < s <= 10000:
comission += s * 0.12
elif s > 10000:
comission += s * 0.145
else:
print(error)
else:
print("error")
if comission > 0:
print(f"{comission:.2f}") | en | 0.693578 | #number of sales | 3.746414 | 4 |
email_actions/plugins/exec.py | shantanugoel/fake-email-actions | 37 | 6624342 | <gh_stars>10-100
from subprocess import Popen
import logging
from email_actions.config import read_config_plugin
PLUGIN_NAME = 'exec'
def exec_notify(filter_name, msg_from, msg_to, msg_subject, msg_content):
params = {
'cmd': None,
'args': [],
'env': {
'EA_ENV_MSG_FROM': msg_from,
'EA_ENV_MSG_TO': msg_to,
'EA_ENV_MSG_SUBJECT': msg_subject,
'EA_ENV_MSG_CONTENT': msg_content,
}
}
plugin_cfg = read_config_plugin(filter_name, PLUGIN_NAME)
for key in plugin_cfg.keys():
if key == 'env':
try:
for env_key in plugin_cfg[key].keys():
params[key][env_key] = plugin_cfg[key][env_key]
except:
# Ignore stray env element in config without any actual env param
pass
else:
params[key] = plugin_cfg[key]
if not params['cmd']:
logging.error('No command specified for plugin %s' % (PLUGIN_NAME))
return
popen_args = [params['cmd']]
for arg in params['args']:
popen_args.append(arg)
Popen(popen_args, env=params['env'])
| from subprocess import Popen
import logging
from email_actions.config import read_config_plugin
PLUGIN_NAME = 'exec'
def exec_notify(filter_name, msg_from, msg_to, msg_subject, msg_content):
params = {
'cmd': None,
'args': [],
'env': {
'EA_ENV_MSG_FROM': msg_from,
'EA_ENV_MSG_TO': msg_to,
'EA_ENV_MSG_SUBJECT': msg_subject,
'EA_ENV_MSG_CONTENT': msg_content,
}
}
plugin_cfg = read_config_plugin(filter_name, PLUGIN_NAME)
for key in plugin_cfg.keys():
if key == 'env':
try:
for env_key in plugin_cfg[key].keys():
params[key][env_key] = plugin_cfg[key][env_key]
except:
# Ignore stray env element in config without any actual env param
pass
else:
params[key] = plugin_cfg[key]
if not params['cmd']:
logging.error('No command specified for plugin %s' % (PLUGIN_NAME))
return
popen_args = [params['cmd']]
for arg in params['args']:
popen_args.append(arg)
Popen(popen_args, env=params['env']) | en | 0.20756 | # Ignore stray env element in config without any actual env param | 2.280437 | 2 |
tests/gis_tests/geoapp/tests.py | lfaraone/python-django-dpkg | 0 | 6624343 | from __future__ import unicode_literals
import re
import tempfile
from django.contrib.gis import gdal
from django.contrib.gis.db.models import Extent, MakeLine, Union
from django.contrib.gis.geos import (
GeometryCollection, GEOSGeometry, LinearRing, LineString, Point, Polygon,
fromstr,
)
from django.core.management import call_command
from django.db import connection
from django.test import TestCase, ignore_warnings, skipUnlessDBFeature
from django.utils import six
from django.utils.deprecation import (
RemovedInDjango20Warning, RemovedInDjango110Warning,
)
from ..utils import no_oracle, oracle, postgis, skipUnlessGISLookup, spatialite
from .models import (
City, Country, Feature, MinusOneSRID, NonConcreteModel, PennsylvaniaCity,
State, Track,
)
def postgis_bug_version():
spatial_version = getattr(connection.ops, "spatial_version", (0, 0, 0))
return spatial_version and (2, 0, 0) <= spatial_version <= (2, 0, 1)
@skipUnlessDBFeature("gis_enabled")
class GeoModelTest(TestCase):
fixtures = ['initial']
def test_fixtures(self):
"Testing geographic model initialization from fixtures."
# Ensuring that data was loaded from initial data fixtures.
self.assertEqual(2, Country.objects.count())
self.assertEqual(8, City.objects.count())
self.assertEqual(2, State.objects.count())
def test_proxy(self):
"Testing Lazy-Geometry support (using the GeometryProxy)."
# Testing on a Point
pnt = Point(0, 0)
nullcity = City(name='NullCity', point=pnt)
nullcity.save()
# Making sure TypeError is thrown when trying to set with an
# incompatible type.
for bad in [5, 2.0, LineString((0, 0), (1, 1))]:
try:
nullcity.point = bad
except TypeError:
pass
else:
self.fail('Should throw a TypeError')
# Now setting with a compatible GEOS Geometry, saving, and ensuring
# the save took, notice no SRID is explicitly set.
new = Point(5, 23)
nullcity.point = new
# Ensuring that the SRID is automatically set to that of the
# field after assignment, but before saving.
self.assertEqual(4326, nullcity.point.srid)
nullcity.save()
# Ensuring the point was saved correctly after saving
self.assertEqual(new, City.objects.get(name='NullCity').point)
# Setting the X and Y of the Point
nullcity.point.x = 23
nullcity.point.y = 5
# Checking assignments pre & post-save.
self.assertNotEqual(Point(23, 5), City.objects.get(name='NullCity').point)
nullcity.save()
self.assertEqual(Point(23, 5), City.objects.get(name='NullCity').point)
nullcity.delete()
# Testing on a Polygon
shell = LinearRing((0, 0), (0, 100), (100, 100), (100, 0), (0, 0))
inner = LinearRing((40, 40), (40, 60), (60, 60), (60, 40), (40, 40))
# Creating a State object using a built Polygon
ply = Polygon(shell, inner)
nullstate = State(name='NullState', poly=ply)
self.assertEqual(4326, nullstate.poly.srid) # SRID auto-set from None
nullstate.save()
ns = State.objects.get(name='NullState')
self.assertEqual(ply, ns.poly)
# Testing the `ogr` and `srs` lazy-geometry properties.
if gdal.HAS_GDAL:
self.assertIsInstance(ns.poly.ogr, gdal.OGRGeometry)
self.assertEqual(ns.poly.wkb, ns.poly.ogr.wkb)
self.assertIsInstance(ns.poly.srs, gdal.SpatialReference)
self.assertEqual('WGS 84', ns.poly.srs.name)
# Changing the interior ring on the poly attribute.
new_inner = LinearRing((30, 30), (30, 70), (70, 70), (70, 30), (30, 30))
ns.poly[1] = new_inner
ply[1] = new_inner
self.assertEqual(4326, ns.poly.srid)
ns.save()
self.assertEqual(ply, State.objects.get(name='NullState').poly)
ns.delete()
@skipUnlessDBFeature("supports_transform")
def test_lookup_insert_transform(self):
"Testing automatic transform for lookups and inserts."
# San Antonio in 'WGS84' (SRID 4326)
sa_4326 = 'POINT (-98.493183 29.424170)'
wgs_pnt = fromstr(sa_4326, srid=4326) # Our reference point in WGS84
# Oracle doesn't have SRID 3084, using 41157.
if oracle:
# San Antonio in 'Texas 4205, Southern Zone (1983, meters)' (SRID 41157)
# Used the following Oracle SQL to get this value:
# SELECT SDO_UTIL.TO_WKTGEOMETRY(
# SDO_CS.TRANSFORM(SDO_GEOMETRY('POINT (-98.493183 29.424170)', 4326), 41157))
# )
# FROM DUAL;
nad_wkt = 'POINT (300662.034646583 5416427.45974934)'
nad_srid = 41157
else:
# San Antonio in 'NAD83(HARN) / Texas Centric Lambert Conformal' (SRID 3084)
# Used ogr.py in gdal 1.4.1 for this transform
nad_wkt = 'POINT (1645978.362408288754523 6276356.025927528738976)'
nad_srid = 3084
# Constructing & querying with a point from a different SRID. Oracle
# `SDO_OVERLAPBDYINTERSECT` operates differently from
# `ST_Intersects`, so contains is used instead.
nad_pnt = fromstr(nad_wkt, srid=nad_srid)
if oracle:
tx = Country.objects.get(mpoly__contains=nad_pnt)
else:
tx = Country.objects.get(mpoly__intersects=nad_pnt)
self.assertEqual('Texas', tx.name)
# Creating San Antonio. Remember the Alamo.
sa = City.objects.create(name='San Antonio', point=nad_pnt)
# Now verifying that San Antonio was transformed correctly
sa = City.objects.get(name='San Antonio')
self.assertAlmostEqual(wgs_pnt.x, sa.point.x, 6)
self.assertAlmostEqual(wgs_pnt.y, sa.point.y, 6)
# If the GeometryField SRID is -1, then we shouldn't perform any
# transformation if the SRID of the input geometry is different.
if spatialite and connection.ops.spatial_version < (3, 0, 0):
# SpatiaLite < 3 does not support missing SRID values.
return
m1 = MinusOneSRID(geom=Point(17, 23, srid=4326))
m1.save()
self.assertEqual(-1, m1.geom.srid)
def test_createnull(self):
"Testing creating a model instance and the geometry being None"
c = City()
self.assertEqual(c.point, None)
def test_geometryfield(self):
"Testing the general GeometryField."
Feature(name='Point', geom=Point(1, 1)).save()
Feature(name='LineString', geom=LineString((0, 0), (1, 1), (5, 5))).save()
Feature(name='Polygon', geom=Polygon(LinearRing((0, 0), (0, 5), (5, 5), (5, 0), (0, 0)))).save()
Feature(name='GeometryCollection',
geom=GeometryCollection(Point(2, 2), LineString((0, 0), (2, 2)),
Polygon(LinearRing((0, 0), (0, 5), (5, 5), (5, 0), (0, 0))))).save()
f_1 = Feature.objects.get(name='Point')
self.assertIsInstance(f_1.geom, Point)
self.assertEqual((1.0, 1.0), f_1.geom.tuple)
f_2 = Feature.objects.get(name='LineString')
self.assertIsInstance(f_2.geom, LineString)
self.assertEqual(((0.0, 0.0), (1.0, 1.0), (5.0, 5.0)), f_2.geom.tuple)
f_3 = Feature.objects.get(name='Polygon')
self.assertIsInstance(f_3.geom, Polygon)
f_4 = Feature.objects.get(name='GeometryCollection')
self.assertIsInstance(f_4.geom, GeometryCollection)
self.assertEqual(f_3.geom, f_4.geom[2])
@skipUnlessDBFeature("supports_transform")
def test_inherited_geofields(self):
"Test GeoQuerySet methods on inherited Geometry fields."
# Creating a Pennsylvanian city.
PennsylvaniaCity.objects.create(name='Mansfield', county='Tioga', point='POINT(-77.071445 41.823881)')
# All transformation SQL will need to be performed on the
# _parent_ table.
qs = PennsylvaniaCity.objects.transform(32128)
self.assertEqual(1, qs.count())
for pc in qs:
self.assertEqual(32128, pc.point.srid)
def test_raw_sql_query(self):
"Testing raw SQL query."
cities1 = City.objects.all()
# Only PostGIS would support a 'select *' query because of its recognized
# HEXEWKB format for geometry fields
as_text = 'ST_AsText(%s)' if postgis else connection.ops.select
cities2 = City.objects.raw(
'select id, name, %s from geoapp_city' % as_text % 'point'
)
self.assertEqual(len(cities1), len(list(cities2)))
self.assertIsInstance(cities2[0].point, Point)
def test_dumpdata_loaddata_cycle(self):
"""
Test a dumpdata/loaddata cycle with geographic data.
"""
out = six.StringIO()
original_data = list(City.objects.all().order_by('name'))
call_command('dumpdata', 'geoapp.City', stdout=out)
result = out.getvalue()
houston = City.objects.get(name='Houston')
self.assertIn('"point": "%s"' % houston.point.ewkt, result)
# Reload now dumped data
with tempfile.NamedTemporaryFile(mode='w', suffix='.json') as tmp:
tmp.write(result)
tmp.seek(0)
call_command('loaddata', tmp.name, verbosity=0)
self.assertListEqual(original_data, list(City.objects.all().order_by('name')))
@skipUnlessDBFeature("gis_enabled")
class GeoLookupTest(TestCase):
fixtures = ['initial']
def test_disjoint_lookup(self):
"Testing the `disjoint` lookup type."
ptown = City.objects.get(name='Pueblo')
qs1 = City.objects.filter(point__disjoint=ptown.point)
self.assertEqual(7, qs1.count())
if connection.features.supports_real_shape_operations:
qs2 = State.objects.filter(poly__disjoint=ptown.point)
self.assertEqual(1, qs2.count())
self.assertEqual('Kansas', qs2[0].name)
def test_contains_contained_lookups(self):
"Testing the 'contained', 'contains', and 'bbcontains' lookup types."
# Getting Texas, yes we were a country -- once ;)
texas = Country.objects.get(name='Texas')
# Seeing what cities are in Texas, should get Houston and Dallas,
# and Oklahoma City because 'contained' only checks on the
# _bounding box_ of the Geometries.
if connection.features.supports_contained_lookup:
qs = City.objects.filter(point__contained=texas.mpoly)
self.assertEqual(3, qs.count())
cities = ['Houston', 'Dallas', 'Oklahoma City']
for c in qs:
self.assertIn(c.name, cities)
# Pulling out some cities.
houston = City.objects.get(name='Houston')
wellington = City.objects.get(name='Wellington')
pueblo = City.objects.get(name='Pueblo')
okcity = City.objects.get(name='Oklahoma City')
lawrence = City.objects.get(name='Lawrence')
# Now testing contains on the countries using the points for
# Houston and Wellington.
tx = Country.objects.get(mpoly__contains=houston.point) # Query w/GEOSGeometry
nz = Country.objects.get(mpoly__contains=wellington.point.hex) # Query w/EWKBHEX
self.assertEqual('Texas', tx.name)
self.assertEqual('New Zealand', nz.name)
# Spatialite 2.3 thinks that Lawrence is in Puerto Rico (a NULL geometry).
if not (spatialite and connection.ops.spatial_version < (3, 0, 0)):
ks = State.objects.get(poly__contains=lawrence.point)
self.assertEqual('Kansas', ks.name)
# Pueblo and Oklahoma City (even though OK City is within the bounding box of Texas)
# are not contained in Texas or New Zealand.
self.assertEqual(len(Country.objects.filter(mpoly__contains=pueblo.point)), 0) # Query w/GEOSGeometry object
self.assertEqual(len(Country.objects.filter(mpoly__contains=okcity.point.wkt)),
0 if connection.features.supports_real_shape_operations else 1) # Query w/WKT
# OK City is contained w/in bounding box of Texas.
if connection.features.supports_bbcontains_lookup:
qs = Country.objects.filter(mpoly__bbcontains=okcity.point)
self.assertEqual(1, len(qs))
self.assertEqual('Texas', qs[0].name)
@skipUnlessDBFeature("supports_crosses_lookup")
def test_crosses_lookup(self):
Track.objects.create(
name='Line1',
line=LineString([(-95, 29), (-60, 0)])
)
self.assertEqual(
Track.objects.filter(line__crosses=LineString([(-95, 0), (-60, 29)])).count(),
1
)
self.assertEqual(
Track.objects.filter(line__crosses=LineString([(-95, 30), (0, 30)])).count(),
0
)
@skipUnlessDBFeature("supports_left_right_lookups")
def test_left_right_lookups(self):
"Testing the 'left' and 'right' lookup types."
# Left: A << B => true if xmax(A) < xmin(B)
# Right: A >> B => true if xmin(A) > xmax(B)
# See: BOX2D_left() and BOX2D_right() in lwgeom_box2dfloat4.c in PostGIS source.
# The left/right lookup tests are known failures on PostGIS 2.0/2.0.1
# http://trac.osgeo.org/postgis/ticket/2035
if postgis_bug_version():
self.skipTest("PostGIS 2.0/2.0.1 left and right lookups are known to be buggy.")
# Getting the borders for Colorado & Kansas
co_border = State.objects.get(name='Colorado').poly
ks_border = State.objects.get(name='Kansas').poly
# Note: Wellington has an 'X' value of 174, so it will not be considered
# to the left of CO.
# These cities should be strictly to the right of the CO border.
cities = ['Houston', 'Dallas', 'Oklahoma City',
'Lawrence', 'Chicago', 'Wellington']
qs = City.objects.filter(point__right=co_border)
self.assertEqual(6, len(qs))
for c in qs:
self.assertIn(c.name, cities)
# These cities should be strictly to the right of the KS border.
cities = ['Chicago', 'Wellington']
qs = City.objects.filter(point__right=ks_border)
self.assertEqual(2, len(qs))
for c in qs:
self.assertIn(c.name, cities)
# Note: Wellington has an 'X' value of 174, so it will not be considered
# to the left of CO.
vic = City.objects.get(point__left=co_border)
self.assertEqual('Victoria', vic.name)
cities = ['Pueblo', 'Victoria']
qs = City.objects.filter(point__left=ks_border)
self.assertEqual(2, len(qs))
for c in qs:
self.assertIn(c.name, cities)
@skipUnlessGISLookup("strictly_above", "strictly_below")
def test_strictly_above_below_lookups(self):
dallas = City.objects.get(name='Dallas')
self.assertQuerysetEqual(
City.objects.filter(point__strictly_above=dallas.point).order_by('name'),
['Chicago', 'Lawrence', 'Oklahoma City', 'Pueblo', 'Victoria'],
lambda b: b.name
)
self.assertQuerysetEqual(
City.objects.filter(point__strictly_below=dallas.point).order_by('name'),
['Houston', 'Wellington'],
lambda b: b.name
)
def test_equals_lookups(self):
"Testing the 'same_as' and 'equals' lookup types."
pnt = fromstr('POINT (-95.363151 29.763374)', srid=4326)
c1 = City.objects.get(point=pnt)
c2 = City.objects.get(point__same_as=pnt)
c3 = City.objects.get(point__equals=pnt)
for c in [c1, c2, c3]:
self.assertEqual('Houston', c.name)
@skipUnlessDBFeature("supports_null_geometries")
def test_null_geometries(self):
"Testing NULL geometry support, and the `isnull` lookup type."
# Creating a state with a NULL boundary.
State.objects.create(name='Puerto Rico')
# Querying for both NULL and Non-NULL values.
nullqs = State.objects.filter(poly__isnull=True)
validqs = State.objects.filter(poly__isnull=False)
# Puerto Rico should be NULL (it's a commonwealth unincorporated territory)
self.assertEqual(1, len(nullqs))
self.assertEqual('Puerto Rico', nullqs[0].name)
# The valid states should be Colorado & Kansas
self.assertEqual(2, len(validqs))
state_names = [s.name for s in validqs]
self.assertIn('Colorado', state_names)
self.assertIn('Kansas', state_names)
# Saving another commonwealth w/a NULL geometry.
nmi = State.objects.create(name='Northern Mariana Islands', poly=None)
self.assertEqual(nmi.poly, None)
# Assigning a geometry and saving -- then UPDATE back to NULL.
nmi.poly = 'POLYGON((0 0,1 0,1 1,1 0,0 0))'
nmi.save()
State.objects.filter(name='Northern Mariana Islands').update(poly=None)
self.assertIsNone(State.objects.get(name='Northern Mariana Islands').poly)
@skipUnlessDBFeature("supports_relate_lookup")
def test_relate_lookup(self):
"Testing the 'relate' lookup type."
# To make things more interesting, we will have our Texas reference point in
# different SRIDs.
pnt1 = fromstr('POINT (649287.0363174 4177429.4494686)', srid=2847)
pnt2 = fromstr('POINT(-98.4919715741052 29.4333344025053)', srid=4326)
# Not passing in a geometry as first param should
# raise a type error when initializing the GeoQuerySet
self.assertRaises(ValueError, Country.objects.filter, mpoly__relate=(23, 'foo'))
# Making sure the right exception is raised for the given
# bad arguments.
for bad_args, e in [((pnt1, 0), ValueError), ((pnt2, 'T*T***FF*', 0), ValueError)]:
qs = Country.objects.filter(mpoly__relate=bad_args)
self.assertRaises(e, qs.count)
# Relate works differently for the different backends.
if postgis or spatialite:
contains_mask = 'T*T***FF*'
within_mask = 'T*F**F***'
intersects_mask = 'T********'
elif oracle:
contains_mask = 'contains'
within_mask = 'inside'
# TODO: This is not quite the same as the PostGIS mask above
intersects_mask = 'overlapbdyintersect'
# Testing contains relation mask.
self.assertEqual('Texas', Country.objects.get(mpoly__relate=(pnt1, contains_mask)).name)
self.assertEqual('Texas', Country.objects.get(mpoly__relate=(pnt2, contains_mask)).name)
# Testing within relation mask.
ks = State.objects.get(name='Kansas')
self.assertEqual('Lawrence', City.objects.get(point__relate=(ks.poly, within_mask)).name)
# Testing intersection relation mask.
if not oracle:
self.assertEqual('Texas', Country.objects.get(mpoly__relate=(pnt1, intersects_mask)).name)
self.assertEqual('Texas', Country.objects.get(mpoly__relate=(pnt2, intersects_mask)).name)
self.assertEqual('Lawrence', City.objects.get(point__relate=(ks.poly, intersects_mask)).name)
@skipUnlessDBFeature("gis_enabled")
@ignore_warnings(category=RemovedInDjango20Warning)
class GeoQuerySetTest(TestCase):
fixtures = ['initial']
# Please keep the tests in GeoQuerySet method's alphabetic order
@skipUnlessDBFeature("has_centroid_method")
def test_centroid(self):
"Testing the `centroid` GeoQuerySet method."
qs = State.objects.exclude(poly__isnull=True).centroid()
if oracle:
tol = 0.1
elif spatialite:
tol = 0.000001
else:
tol = 0.000000001
for s in qs:
self.assertTrue(s.poly.centroid.equals_exact(s.centroid, tol))
@skipUnlessDBFeature(
"has_difference_method", "has_intersection_method",
"has_sym_difference_method", "has_union_method")
def test_diff_intersection_union(self):
"Testing the `difference`, `intersection`, `sym_difference`, and `union` GeoQuerySet methods."
geom = Point(5, 23)
qs = Country.objects.all().difference(geom).sym_difference(geom).union(geom)
# XXX For some reason SpatiaLite does something screwy with the Texas geometry here. Also,
# XXX it doesn't like the null intersection.
if spatialite:
qs = qs.exclude(name='Texas')
else:
qs = qs.intersection(geom)
for c in qs:
if oracle:
# Should be able to execute the queries; however, they won't be the same
# as GEOS (because Oracle doesn't use GEOS internally like PostGIS or
# SpatiaLite).
pass
else:
self.assertEqual(c.mpoly.difference(geom), c.difference)
if not spatialite:
self.assertEqual(c.mpoly.intersection(geom), c.intersection)
# Ordering might differ in collections
self.assertSetEqual(set(g.wkt for g in c.mpoly.sym_difference(geom)),
set(g.wkt for g in c.sym_difference))
self.assertSetEqual(set(g.wkt for g in c.mpoly.union(geom)),
set(g.wkt for g in c.union))
@skipUnlessDBFeature("has_envelope_method")
def test_envelope(self):
"Testing the `envelope` GeoQuerySet method."
countries = Country.objects.all().envelope()
for country in countries:
self.assertIsInstance(country.envelope, Polygon)
@skipUnlessDBFeature("supports_extent_aggr")
@ignore_warnings(category=RemovedInDjango110Warning)
def test_extent(self):
"""
Testing the (deprecated) `extent` GeoQuerySet method and the Extent
aggregate.
"""
# Reference query:
# `SELECT ST_extent(point) FROM geoapp_city WHERE (name='Houston' or name='Dallas');`
# => BOX(-96.8016128540039 29.7633724212646,-95.3631439208984 32.7820587158203)
expected = (-96.8016128540039, 29.7633724212646, -95.3631439208984, 32.782058715820)
qs = City.objects.filter(name__in=('Houston', 'Dallas'))
extent1 = qs.extent()
extent2 = qs.aggregate(Extent('point'))['point__extent']
for extent in (extent1, extent2):
for val, exp in zip(extent, expected):
self.assertAlmostEqual(exp, val, 4)
self.assertIsNone(City.objects.filter(name=('Smalltown')).extent())
self.assertIsNone(City.objects.filter(name=('Smalltown')).aggregate(Extent('point'))['point__extent'])
@skipUnlessDBFeature("supports_extent_aggr")
def test_extent_with_limit(self):
"""
Testing if extent supports limit.
"""
extent1 = City.objects.all().aggregate(Extent('point'))['point__extent']
extent2 = City.objects.all()[:3].aggregate(Extent('point'))['point__extent']
self.assertNotEqual(extent1, extent2)
@skipUnlessDBFeature("has_force_rhr_method")
def test_force_rhr(self):
"Testing GeoQuerySet.force_rhr()."
rings = (
((0, 0), (5, 0), (0, 5), (0, 0)),
((1, 1), (1, 3), (3, 1), (1, 1)),
)
rhr_rings = (
((0, 0), (0, 5), (5, 0), (0, 0)),
((1, 1), (3, 1), (1, 3), (1, 1)),
)
State.objects.create(name='Foo', poly=Polygon(*rings))
s = State.objects.force_rhr().get(name='Foo')
self.assertEqual(rhr_rings, s.force_rhr.coords)
@skipUnlessDBFeature("has_geohash_method")
def test_geohash(self):
"Testing GeoQuerySet.geohash()."
# Reference query:
# SELECT ST_GeoHash(point) FROM geoapp_city WHERE name='Houston';
# SELECT ST_GeoHash(point, 5) FROM geoapp_city WHERE name='Houston';
ref_hash = '9vk1mfq8jx0c8e0386z6'
h1 = City.objects.geohash().get(name='Houston')
h2 = City.objects.geohash(precision=5).get(name='Houston')
self.assertEqual(ref_hash, h1.geohash)
self.assertEqual(ref_hash[:5], h2.geohash)
def test_geojson(self):
"Testing GeoJSON output from the database using GeoQuerySet.geojson()."
# Only PostGIS and SpatiaLite 3.0+ support GeoJSON.
if not connection.ops.geojson:
self.assertRaises(NotImplementedError, Country.objects.all().geojson, field_name='mpoly')
return
pueblo_json = '{"type":"Point","coordinates":[-104.609252,38.255001]}'
houston_json = (
'{"type":"Point","crs":{"type":"name","properties":'
'{"name":"EPSG:4326"}},"coordinates":[-95.363151,29.763374]}'
)
victoria_json = (
'{"type":"Point","bbox":[-123.30519600,48.46261100,-123.30519600,48.46261100],'
'"coordinates":[-123.305196,48.462611]}'
)
chicago_json = (
'{"type":"Point","crs":{"type":"name","properties":{"name":"EPSG:4326"}},'
'"bbox":[-87.65018,41.85039,-87.65018,41.85039],"coordinates":[-87.65018,41.85039]}'
)
if spatialite:
victoria_json = (
'{"type":"Point","bbox":[-123.305196,48.462611,-123.305196,48.462611],'
'"coordinates":[-123.305196,48.462611]}'
)
# Precision argument should only be an integer
self.assertRaises(TypeError, City.objects.geojson, precision='foo')
# Reference queries and values.
# SELECT ST_AsGeoJson("geoapp_city"."point", 8, 0)
# FROM "geoapp_city" WHERE "geoapp_city"."name" = 'Pueblo';
self.assertEqual(pueblo_json, City.objects.geojson().get(name='Pueblo').geojson)
# SELECT ST_AsGeoJson("geoapp_city"."point", 8, 2) FROM "geoapp_city"
# WHERE "geoapp_city"."name" = 'Houston';
# This time we want to include the CRS by using the `crs` keyword.
self.assertEqual(houston_json, City.objects.geojson(crs=True, model_att='json').get(name='Houston').json)
# SELECT ST_AsGeoJson("geoapp_city"."point", 8, 1) FROM "geoapp_city"
# WHERE "geoapp_city"."name" = 'Houston';
# This time we include the bounding box by using the `bbox` keyword.
self.assertEqual(victoria_json, City.objects.geojson(bbox=True).get(name='Victoria').geojson)
# SELECT ST_AsGeoJson("geoapp_city"."point", 5, 3) FROM "geoapp_city"
# WHERE "geoapp_city"."name" = 'Chicago';
# Finally, we set every available keyword.
self.assertEqual(
chicago_json,
City.objects.geojson(bbox=True, crs=True, precision=5).get(name='Chicago').geojson
)
@skipUnlessDBFeature("has_gml_method")
def test_gml(self):
"Testing GML output from the database using GeoQuerySet.gml()."
# Should throw a TypeError when trying to obtain GML from a
# non-geometry field.
qs = City.objects.all()
self.assertRaises(TypeError, qs.gml, field_name='name')
ptown1 = City.objects.gml(field_name='point', precision=9).get(name='Pueblo')
ptown2 = City.objects.gml(precision=9).get(name='Pueblo')
if oracle:
# No precision parameter for Oracle :-/
gml_regex = re.compile(
r'^<gml:Point srsName="EPSG:4326" xmlns:gml="http://www.opengis.net/gml">'
r'<gml:coordinates decimal="\." cs="," ts=" ">-104.60925\d+,38.25500\d+ '
r'</gml:coordinates></gml:Point>'
)
elif spatialite and connection.ops.spatial_version < (3, 0, 0):
# Spatialite before 3.0 has extra colon in SrsName
gml_regex = re.compile(
r'^<gml:Point SrsName="EPSG::4326"><gml:coordinates decimal="\." '
r'cs="," ts=" ">-104.609251\d+,38.255001</gml:coordinates></gml:Point>'
)
else:
gml_regex = re.compile(
r'^<gml:Point srsName="EPSG:4326"><gml:coordinates>'
r'-104\.60925\d+,38\.255001</gml:coordinates></gml:Point>'
)
for ptown in [ptown1, ptown2]:
self.assertTrue(gml_regex.match(ptown.gml))
if postgis:
self.assertIn('<gml:pos srsDimension="2">', City.objects.gml(version=3).get(name='Pueblo').gml)
@skipUnlessDBFeature("has_kml_method")
def test_kml(self):
"Testing KML output from the database using GeoQuerySet.kml()."
# Should throw a TypeError when trying to obtain KML from a
# non-geometry field.
qs = City.objects.all()
self.assertRaises(TypeError, qs.kml, 'name')
# Ensuring the KML is as expected.
ptown1 = City.objects.kml(field_name='point', precision=9).get(name='Pueblo')
ptown2 = City.objects.kml(precision=9).get(name='Pueblo')
for ptown in [ptown1, ptown2]:
self.assertEqual('<Point><coordinates>-104.609252,38.255001</coordinates></Point>', ptown.kml)
@ignore_warnings(category=RemovedInDjango110Warning)
def test_make_line(self):
"""
Testing the (deprecated) `make_line` GeoQuerySet method and the MakeLine
aggregate.
"""
if not connection.features.supports_make_line_aggr:
# Only PostGIS has support for the MakeLine aggregate. For other
# backends, test that NotImplementedError is raised
self.assertRaises(
NotImplementedError,
City.objects.all().aggregate, MakeLine('point')
)
return
# Ensuring that a `TypeError` is raised on models without PointFields.
self.assertRaises(TypeError, State.objects.make_line)
self.assertRaises(TypeError, Country.objects.make_line)
# MakeLine on an inappropriate field returns simply None
self.assertIsNone(State.objects.aggregate(MakeLine('poly'))['poly__makeline'])
# Reference query:
# SELECT AsText(ST_MakeLine(geoapp_city.point)) FROM geoapp_city;
ref_line = GEOSGeometry(
'LINESTRING(-95.363151 29.763374,-96.801611 32.782057,'
'-97.521157 34.464642,174.783117 -41.315268,-104.609252 38.255001,'
'-95.23506 38.971823,-87.650175 41.850385,-123.305196 48.462611)',
srid=4326
)
# We check for equality with a tolerance of 10e-5 which is a lower bound
# of the precisions of ref_line coordinates
line1 = City.objects.make_line()
line2 = City.objects.aggregate(MakeLine('point'))['point__makeline']
for line in (line1, line2):
self.assertTrue(ref_line.equals_exact(line, tolerance=10e-5),
"%s != %s" % (ref_line, line))
@skipUnlessDBFeature("has_num_geom_method")
def test_num_geom(self):
"Testing the `num_geom` GeoQuerySet method."
# Both 'countries' only have two geometries.
for c in Country.objects.num_geom():
self.assertEqual(2, c.num_geom)
for c in City.objects.filter(point__isnull=False).num_geom():
# Oracle and PostGIS 2.0+ will return 1 for the number of
# geometries on non-collections.
self.assertEqual(1, c.num_geom)
@skipUnlessDBFeature("supports_num_points_poly")
def test_num_points(self):
"Testing the `num_points` GeoQuerySet method."
for c in Country.objects.num_points():
self.assertEqual(c.mpoly.num_points, c.num_points)
if not oracle:
# Oracle cannot count vertices in Point geometries.
for c in City.objects.num_points():
self.assertEqual(1, c.num_points)
@skipUnlessDBFeature("has_point_on_surface_method")
def test_point_on_surface(self):
"Testing the `point_on_surface` GeoQuerySet method."
# Reference values.
if oracle:
# SELECT SDO_UTIL.TO_WKTGEOMETRY(SDO_GEOM.SDO_POINTONSURFACE(GEOAPP_COUNTRY.MPOLY, 0.05))
# FROM GEOAPP_COUNTRY;
ref = {'New Zealand': fromstr('POINT (174.616364 -36.100861)', srid=4326),
'Texas': fromstr('POINT (-103.002434 36.500397)', srid=4326),
}
else:
# Using GEOSGeometry to compute the reference point on surface values
# -- since PostGIS also uses GEOS these should be the same.
ref = {'New Zealand': Country.objects.get(name='New Zealand').mpoly.point_on_surface,
'Texas': Country.objects.get(name='Texas').mpoly.point_on_surface
}
for c in Country.objects.point_on_surface():
if spatialite:
# XXX This seems to be a WKT-translation-related precision issue?
tol = 0.00001
else:
tol = 0.000000001
self.assertTrue(ref[c.name].equals_exact(c.point_on_surface, tol))
@skipUnlessDBFeature("has_reverse_method")
def test_reverse_geom(self):
"Testing GeoQuerySet.reverse_geom()."
coords = [(-95.363151, 29.763374), (-95.448601, 29.713803)]
Track.objects.create(name='Foo', line=LineString(coords))
t = Track.objects.reverse_geom().get(name='Foo')
coords.reverse()
self.assertEqual(tuple(coords), t.reverse_geom.coords)
if oracle:
self.assertRaises(TypeError, State.objects.reverse_geom)
@skipUnlessDBFeature("has_scale_method")
def test_scale(self):
"Testing the `scale` GeoQuerySet method."
xfac, yfac = 2, 3
tol = 5 # XXX The low precision tolerance is for SpatiaLite
qs = Country.objects.scale(xfac, yfac, model_att='scaled')
for c in qs:
for p1, p2 in zip(c.mpoly, c.scaled):
for r1, r2 in zip(p1, p2):
for c1, c2 in zip(r1.coords, r2.coords):
self.assertAlmostEqual(c1[0] * xfac, c2[0], tol)
self.assertAlmostEqual(c1[1] * yfac, c2[1], tol)
@skipUnlessDBFeature("has_snap_to_grid_method")
def test_snap_to_grid(self):
"Testing GeoQuerySet.snap_to_grid()."
# Let's try and break snap_to_grid() with bad combinations of arguments.
for bad_args in ((), range(3), range(5)):
self.assertRaises(ValueError, Country.objects.snap_to_grid, *bad_args)
for bad_args in (('1.0',), (1.0, None), tuple(map(six.text_type, range(4)))):
self.assertRaises(TypeError, Country.objects.snap_to_grid, *bad_args)
# Boundary for San Marino, courtesy of <NAME> of thematicmapping.org
# from the world borders dataset he provides.
wkt = ('MULTIPOLYGON(((12.41580 43.95795,12.45055 43.97972,12.45389 43.98167,'
'12.46250 43.98472,12.47167 43.98694,12.49278 43.98917,'
'12.50555 43.98861,12.51000 43.98694,12.51028 43.98277,'
'12.51167 43.94333,12.51056 43.93916,12.49639 43.92333,'
'12.49500 43.91472,12.48778 43.90583,12.47444 43.89722,'
'12.46472 43.89555,12.45917 43.89611,12.41639 43.90472,'
'12.41222 43.90610,12.40782 43.91366,12.40389 43.92667,'
'12.40500 43.94833,12.40889 43.95499,12.41580 43.95795)))')
Country.objects.create(name='San Marino', mpoly=fromstr(wkt))
# Because floating-point arithmetic isn't exact, we set a tolerance
# to pass into GEOS `equals_exact`.
tol = 0.000000001
# SELECT AsText(ST_SnapToGrid("geoapp_country"."mpoly", 0.1)) FROM "geoapp_country"
# WHERE "geoapp_country"."name" = 'San Marino';
ref = fromstr('MULTIPOLYGON(((12.4 44,12.5 44,12.5 43.9,12.4 43.9,12.4 44)))')
self.assertTrue(ref.equals_exact(Country.objects.snap_to_grid(0.1).get(name='San Marino').snap_to_grid, tol))
# SELECT AsText(ST_SnapToGrid("geoapp_country"."mpoly", 0.05, 0.23)) FROM "geoapp_country"
# WHERE "geoapp_country"."name" = 'San Marino';
ref = fromstr('MULTIPOLYGON(((12.4 43.93,12.45 43.93,12.5 43.93,12.45 43.93,12.4 43.93)))')
self.assertTrue(
ref.equals_exact(Country.objects.snap_to_grid(0.05, 0.23).get(name='San Marino').snap_to_grid, tol)
)
# SELECT AsText(ST_SnapToGrid("geoapp_country"."mpoly", 0.5, 0.17, 0.05, 0.23)) FROM "geoapp_country"
# WHERE "geoapp_country"."name" = 'San Marino';
ref = fromstr(
'MULTIPOLYGON(((12.4 43.87,12.45 43.87,12.45 44.1,12.5 44.1,12.5 43.87,12.45 43.87,12.4 43.87)))'
)
self.assertTrue(
ref.equals_exact(
Country.objects.snap_to_grid(0.05, 0.23, 0.5, 0.17).get(name='San Marino').snap_to_grid,
tol
)
)
@skipUnlessDBFeature("has_svg_method")
def test_svg(self):
"Testing SVG output using GeoQuerySet.svg()."
self.assertRaises(TypeError, City.objects.svg, precision='foo')
# SELECT AsSVG(geoapp_city.point, 0, 8) FROM geoapp_city WHERE name = 'Pueblo';
svg1 = 'cx="-104.609252" cy="-38.255001"'
# Even though relative, only one point so it's practically the same except for
# the 'c' letter prefix on the x,y values.
svg2 = svg1.replace('c', '')
self.assertEqual(svg1, City.objects.svg().get(name='Pueblo').svg)
self.assertEqual(svg2, City.objects.svg(relative=5).get(name='Pueblo').svg)
@skipUnlessDBFeature("has_transform_method")
def test_transform(self):
"Testing the transform() GeoQuerySet method."
# Pre-transformed points for Houston and Pueblo.
htown = fromstr('POINT(1947516.83115183 6322297.06040572)', srid=3084)
ptown = fromstr('POINT(992363.390841912 481455.395105533)', srid=2774)
prec = 3 # Precision is low due to version variations in PROJ and GDAL.
# Asserting the result of the transform operation with the values in
# the pre-transformed points. Oracle does not have the 3084 SRID.
if not oracle:
h = City.objects.transform(htown.srid).get(name='Houston')
self.assertEqual(3084, h.point.srid)
self.assertAlmostEqual(htown.x, h.point.x, prec)
self.assertAlmostEqual(htown.y, h.point.y, prec)
p1 = City.objects.transform(ptown.srid, field_name='point').get(name='Pueblo')
p2 = City.objects.transform(srid=ptown.srid).get(name='Pueblo')
for p in [p1, p2]:
self.assertEqual(2774, p.point.srid)
self.assertAlmostEqual(ptown.x, p.point.x, prec)
self.assertAlmostEqual(ptown.y, p.point.y, prec)
@skipUnlessDBFeature("has_translate_method")
def test_translate(self):
"Testing the `translate` GeoQuerySet method."
xfac, yfac = 5, -23
qs = Country.objects.translate(xfac, yfac, model_att='translated')
for c in qs:
for p1, p2 in zip(c.mpoly, c.translated):
for r1, r2 in zip(p1, p2):
for c1, c2 in zip(r1.coords, r2.coords):
# XXX The low precision is for SpatiaLite
self.assertAlmostEqual(c1[0] + xfac, c2[0], 5)
self.assertAlmostEqual(c1[1] + yfac, c2[1], 5)
# TODO: Oracle can be made to pass if
# union1 = union2 = fromstr('POINT (-97.5211570000000023 34.4646419999999978)')
# but this seems unexpected and should be investigated to determine the cause.
@skipUnlessDBFeature("has_unionagg_method")
@no_oracle
@ignore_warnings(category=RemovedInDjango110Warning)
def test_unionagg(self):
"""
Testing the (deprecated) `unionagg` (aggregate union) GeoQuerySet method
and the Union aggregate.
"""
tx = Country.objects.get(name='Texas').mpoly
# Houston, Dallas -- Ordering may differ depending on backend or GEOS version.
union1 = fromstr('MULTIPOINT(-96.801611 32.782057,-95.363151 29.763374)')
union2 = fromstr('MULTIPOINT(-95.363151 29.763374,-96.801611 32.782057)')
qs = City.objects.filter(point__within=tx)
self.assertRaises(TypeError, qs.unionagg, 'name')
self.assertRaises(ValueError, qs.aggregate, Union('name'))
# Using `field_name` keyword argument in one query and specifying an
# order in the other (which should not be used because this is
# an aggregate method on a spatial column)
u1 = qs.unionagg(field_name='point')
u2 = qs.order_by('name').unionagg()
u3 = qs.aggregate(Union('point'))['point__union']
u4 = qs.order_by('name').aggregate(Union('point'))['point__union']
tol = 0.00001
self.assertTrue(union1.equals_exact(u1, tol) or union2.equals_exact(u1, tol))
self.assertTrue(union1.equals_exact(u2, tol) or union2.equals_exact(u2, tol))
self.assertTrue(union1.equals_exact(u3, tol) or union2.equals_exact(u3, tol))
self.assertTrue(union1.equals_exact(u4, tol) or union2.equals_exact(u4, tol))
qs = City.objects.filter(name='NotACity')
self.assertIsNone(qs.unionagg(field_name='point'))
self.assertIsNone(qs.aggregate(Union('point'))['point__union'])
def test_within_subquery(self):
"""
Test that using a queryset inside a geo lookup is working (using a subquery)
(#14483).
"""
tex_cities = City.objects.filter(
point__within=Country.objects.filter(name='Texas').values('mpoly')).order_by('name')
expected = ['Dallas', 'Houston']
if not connection.features.supports_real_shape_operations:
expected.append('Oklahoma City')
self.assertEqual(
list(tex_cities.values_list('name', flat=True)),
expected
)
def test_non_concrete_field(self):
NonConcreteModel.objects.create(point=Point(0, 0), name='name')
list(NonConcreteModel.objects.all())
| from __future__ import unicode_literals
import re
import tempfile
from django.contrib.gis import gdal
from django.contrib.gis.db.models import Extent, MakeLine, Union
from django.contrib.gis.geos import (
GeometryCollection, GEOSGeometry, LinearRing, LineString, Point, Polygon,
fromstr,
)
from django.core.management import call_command
from django.db import connection
from django.test import TestCase, ignore_warnings, skipUnlessDBFeature
from django.utils import six
from django.utils.deprecation import (
RemovedInDjango20Warning, RemovedInDjango110Warning,
)
from ..utils import no_oracle, oracle, postgis, skipUnlessGISLookup, spatialite
from .models import (
City, Country, Feature, MinusOneSRID, NonConcreteModel, PennsylvaniaCity,
State, Track,
)
def postgis_bug_version():
spatial_version = getattr(connection.ops, "spatial_version", (0, 0, 0))
return spatial_version and (2, 0, 0) <= spatial_version <= (2, 0, 1)
@skipUnlessDBFeature("gis_enabled")
class GeoModelTest(TestCase):
fixtures = ['initial']
def test_fixtures(self):
"Testing geographic model initialization from fixtures."
# Ensuring that data was loaded from initial data fixtures.
self.assertEqual(2, Country.objects.count())
self.assertEqual(8, City.objects.count())
self.assertEqual(2, State.objects.count())
def test_proxy(self):
"Testing Lazy-Geometry support (using the GeometryProxy)."
# Testing on a Point
pnt = Point(0, 0)
nullcity = City(name='NullCity', point=pnt)
nullcity.save()
# Making sure TypeError is thrown when trying to set with an
# incompatible type.
for bad in [5, 2.0, LineString((0, 0), (1, 1))]:
try:
nullcity.point = bad
except TypeError:
pass
else:
self.fail('Should throw a TypeError')
# Now setting with a compatible GEOS Geometry, saving, and ensuring
# the save took, notice no SRID is explicitly set.
new = Point(5, 23)
nullcity.point = new
# Ensuring that the SRID is automatically set to that of the
# field after assignment, but before saving.
self.assertEqual(4326, nullcity.point.srid)
nullcity.save()
# Ensuring the point was saved correctly after saving
self.assertEqual(new, City.objects.get(name='NullCity').point)
# Setting the X and Y of the Point
nullcity.point.x = 23
nullcity.point.y = 5
# Checking assignments pre & post-save.
self.assertNotEqual(Point(23, 5), City.objects.get(name='NullCity').point)
nullcity.save()
self.assertEqual(Point(23, 5), City.objects.get(name='NullCity').point)
nullcity.delete()
# Testing on a Polygon
shell = LinearRing((0, 0), (0, 100), (100, 100), (100, 0), (0, 0))
inner = LinearRing((40, 40), (40, 60), (60, 60), (60, 40), (40, 40))
# Creating a State object using a built Polygon
ply = Polygon(shell, inner)
nullstate = State(name='NullState', poly=ply)
self.assertEqual(4326, nullstate.poly.srid) # SRID auto-set from None
nullstate.save()
ns = State.objects.get(name='NullState')
self.assertEqual(ply, ns.poly)
# Testing the `ogr` and `srs` lazy-geometry properties.
if gdal.HAS_GDAL:
self.assertIsInstance(ns.poly.ogr, gdal.OGRGeometry)
self.assertEqual(ns.poly.wkb, ns.poly.ogr.wkb)
self.assertIsInstance(ns.poly.srs, gdal.SpatialReference)
self.assertEqual('WGS 84', ns.poly.srs.name)
# Changing the interior ring on the poly attribute.
new_inner = LinearRing((30, 30), (30, 70), (70, 70), (70, 30), (30, 30))
ns.poly[1] = new_inner
ply[1] = new_inner
self.assertEqual(4326, ns.poly.srid)
ns.save()
self.assertEqual(ply, State.objects.get(name='NullState').poly)
ns.delete()
@skipUnlessDBFeature("supports_transform")
def test_lookup_insert_transform(self):
"Testing automatic transform for lookups and inserts."
# San Antonio in 'WGS84' (SRID 4326)
sa_4326 = 'POINT (-98.493183 29.424170)'
wgs_pnt = fromstr(sa_4326, srid=4326) # Our reference point in WGS84
# Oracle doesn't have SRID 3084, using 41157.
if oracle:
# San Antonio in 'Texas 4205, Southern Zone (1983, meters)' (SRID 41157)
# Used the following Oracle SQL to get this value:
# SELECT SDO_UTIL.TO_WKTGEOMETRY(
# SDO_CS.TRANSFORM(SDO_GEOMETRY('POINT (-98.493183 29.424170)', 4326), 41157))
# )
# FROM DUAL;
nad_wkt = 'POINT (300662.034646583 5416427.45974934)'
nad_srid = 41157
else:
# San Antonio in 'NAD83(HARN) / Texas Centric Lambert Conformal' (SRID 3084)
# Used ogr.py in gdal 1.4.1 for this transform
nad_wkt = 'POINT (1645978.362408288754523 6276356.025927528738976)'
nad_srid = 3084
# Constructing & querying with a point from a different SRID. Oracle
# `SDO_OVERLAPBDYINTERSECT` operates differently from
# `ST_Intersects`, so contains is used instead.
nad_pnt = fromstr(nad_wkt, srid=nad_srid)
if oracle:
tx = Country.objects.get(mpoly__contains=nad_pnt)
else:
tx = Country.objects.get(mpoly__intersects=nad_pnt)
self.assertEqual('Texas', tx.name)
# Creating San Antonio. Remember the Alamo.
sa = City.objects.create(name='San Antonio', point=nad_pnt)
# Now verifying that San Antonio was transformed correctly
sa = City.objects.get(name='San Antonio')
self.assertAlmostEqual(wgs_pnt.x, sa.point.x, 6)
self.assertAlmostEqual(wgs_pnt.y, sa.point.y, 6)
# If the GeometryField SRID is -1, then we shouldn't perform any
# transformation if the SRID of the input geometry is different.
if spatialite and connection.ops.spatial_version < (3, 0, 0):
# SpatiaLite < 3 does not support missing SRID values.
return
m1 = MinusOneSRID(geom=Point(17, 23, srid=4326))
m1.save()
self.assertEqual(-1, m1.geom.srid)
def test_createnull(self):
"Testing creating a model instance and the geometry being None"
c = City()
self.assertEqual(c.point, None)
def test_geometryfield(self):
"Testing the general GeometryField."
Feature(name='Point', geom=Point(1, 1)).save()
Feature(name='LineString', geom=LineString((0, 0), (1, 1), (5, 5))).save()
Feature(name='Polygon', geom=Polygon(LinearRing((0, 0), (0, 5), (5, 5), (5, 0), (0, 0)))).save()
Feature(name='GeometryCollection',
geom=GeometryCollection(Point(2, 2), LineString((0, 0), (2, 2)),
Polygon(LinearRing((0, 0), (0, 5), (5, 5), (5, 0), (0, 0))))).save()
f_1 = Feature.objects.get(name='Point')
self.assertIsInstance(f_1.geom, Point)
self.assertEqual((1.0, 1.0), f_1.geom.tuple)
f_2 = Feature.objects.get(name='LineString')
self.assertIsInstance(f_2.geom, LineString)
self.assertEqual(((0.0, 0.0), (1.0, 1.0), (5.0, 5.0)), f_2.geom.tuple)
f_3 = Feature.objects.get(name='Polygon')
self.assertIsInstance(f_3.geom, Polygon)
f_4 = Feature.objects.get(name='GeometryCollection')
self.assertIsInstance(f_4.geom, GeometryCollection)
self.assertEqual(f_3.geom, f_4.geom[2])
@skipUnlessDBFeature("supports_transform")
def test_inherited_geofields(self):
"Test GeoQuerySet methods on inherited Geometry fields."
# Creating a Pennsylvanian city.
PennsylvaniaCity.objects.create(name='Mansfield', county='Tioga', point='POINT(-77.071445 41.823881)')
# All transformation SQL will need to be performed on the
# _parent_ table.
qs = PennsylvaniaCity.objects.transform(32128)
self.assertEqual(1, qs.count())
for pc in qs:
self.assertEqual(32128, pc.point.srid)
def test_raw_sql_query(self):
"Testing raw SQL query."
cities1 = City.objects.all()
# Only PostGIS would support a 'select *' query because of its recognized
# HEXEWKB format for geometry fields
as_text = 'ST_AsText(%s)' if postgis else connection.ops.select
cities2 = City.objects.raw(
'select id, name, %s from geoapp_city' % as_text % 'point'
)
self.assertEqual(len(cities1), len(list(cities2)))
self.assertIsInstance(cities2[0].point, Point)
def test_dumpdata_loaddata_cycle(self):
"""
Test a dumpdata/loaddata cycle with geographic data.
"""
out = six.StringIO()
original_data = list(City.objects.all().order_by('name'))
call_command('dumpdata', 'geoapp.City', stdout=out)
result = out.getvalue()
houston = City.objects.get(name='Houston')
self.assertIn('"point": "%s"' % houston.point.ewkt, result)
# Reload now dumped data
with tempfile.NamedTemporaryFile(mode='w', suffix='.json') as tmp:
tmp.write(result)
tmp.seek(0)
call_command('loaddata', tmp.name, verbosity=0)
self.assertListEqual(original_data, list(City.objects.all().order_by('name')))
@skipUnlessDBFeature("gis_enabled")
class GeoLookupTest(TestCase):
fixtures = ['initial']
def test_disjoint_lookup(self):
"Testing the `disjoint` lookup type."
ptown = City.objects.get(name='Pueblo')
qs1 = City.objects.filter(point__disjoint=ptown.point)
self.assertEqual(7, qs1.count())
if connection.features.supports_real_shape_operations:
qs2 = State.objects.filter(poly__disjoint=ptown.point)
self.assertEqual(1, qs2.count())
self.assertEqual('Kansas', qs2[0].name)
def test_contains_contained_lookups(self):
"Testing the 'contained', 'contains', and 'bbcontains' lookup types."
# Getting Texas, yes we were a country -- once ;)
texas = Country.objects.get(name='Texas')
# Seeing what cities are in Texas, should get Houston and Dallas,
# and Oklahoma City because 'contained' only checks on the
# _bounding box_ of the Geometries.
if connection.features.supports_contained_lookup:
qs = City.objects.filter(point__contained=texas.mpoly)
self.assertEqual(3, qs.count())
cities = ['Houston', 'Dallas', 'Oklahoma City']
for c in qs:
self.assertIn(c.name, cities)
# Pulling out some cities.
houston = City.objects.get(name='Houston')
wellington = City.objects.get(name='Wellington')
pueblo = City.objects.get(name='Pueblo')
okcity = City.objects.get(name='Oklahoma City')
lawrence = City.objects.get(name='Lawrence')
# Now testing contains on the countries using the points for
# Houston and Wellington.
tx = Country.objects.get(mpoly__contains=houston.point) # Query w/GEOSGeometry
nz = Country.objects.get(mpoly__contains=wellington.point.hex) # Query w/EWKBHEX
self.assertEqual('Texas', tx.name)
self.assertEqual('New Zealand', nz.name)
# Spatialite 2.3 thinks that Lawrence is in Puerto Rico (a NULL geometry).
if not (spatialite and connection.ops.spatial_version < (3, 0, 0)):
ks = State.objects.get(poly__contains=lawrence.point)
self.assertEqual('Kansas', ks.name)
# Pueblo and Oklahoma City (even though OK City is within the bounding box of Texas)
# are not contained in Texas or New Zealand.
self.assertEqual(len(Country.objects.filter(mpoly__contains=pueblo.point)), 0) # Query w/GEOSGeometry object
self.assertEqual(len(Country.objects.filter(mpoly__contains=okcity.point.wkt)),
0 if connection.features.supports_real_shape_operations else 1) # Query w/WKT
# OK City is contained w/in bounding box of Texas.
if connection.features.supports_bbcontains_lookup:
qs = Country.objects.filter(mpoly__bbcontains=okcity.point)
self.assertEqual(1, len(qs))
self.assertEqual('Texas', qs[0].name)
@skipUnlessDBFeature("supports_crosses_lookup")
def test_crosses_lookup(self):
Track.objects.create(
name='Line1',
line=LineString([(-95, 29), (-60, 0)])
)
self.assertEqual(
Track.objects.filter(line__crosses=LineString([(-95, 0), (-60, 29)])).count(),
1
)
self.assertEqual(
Track.objects.filter(line__crosses=LineString([(-95, 30), (0, 30)])).count(),
0
)
@skipUnlessDBFeature("supports_left_right_lookups")
def test_left_right_lookups(self):
"Testing the 'left' and 'right' lookup types."
# Left: A << B => true if xmax(A) < xmin(B)
# Right: A >> B => true if xmin(A) > xmax(B)
# See: BOX2D_left() and BOX2D_right() in lwgeom_box2dfloat4.c in PostGIS source.
# The left/right lookup tests are known failures on PostGIS 2.0/2.0.1
# http://trac.osgeo.org/postgis/ticket/2035
if postgis_bug_version():
self.skipTest("PostGIS 2.0/2.0.1 left and right lookups are known to be buggy.")
# Getting the borders for Colorado & Kansas
co_border = State.objects.get(name='Colorado').poly
ks_border = State.objects.get(name='Kansas').poly
# Note: Wellington has an 'X' value of 174, so it will not be considered
# to the left of CO.
# These cities should be strictly to the right of the CO border.
cities = ['Houston', 'Dallas', 'Oklahoma City',
'Lawrence', 'Chicago', 'Wellington']
qs = City.objects.filter(point__right=co_border)
self.assertEqual(6, len(qs))
for c in qs:
self.assertIn(c.name, cities)
# These cities should be strictly to the right of the KS border.
cities = ['Chicago', 'Wellington']
qs = City.objects.filter(point__right=ks_border)
self.assertEqual(2, len(qs))
for c in qs:
self.assertIn(c.name, cities)
# Note: Wellington has an 'X' value of 174, so it will not be considered
# to the left of CO.
vic = City.objects.get(point__left=co_border)
self.assertEqual('Victoria', vic.name)
cities = ['Pueblo', 'Victoria']
qs = City.objects.filter(point__left=ks_border)
self.assertEqual(2, len(qs))
for c in qs:
self.assertIn(c.name, cities)
@skipUnlessGISLookup("strictly_above", "strictly_below")
def test_strictly_above_below_lookups(self):
dallas = City.objects.get(name='Dallas')
self.assertQuerysetEqual(
City.objects.filter(point__strictly_above=dallas.point).order_by('name'),
['Chicago', 'Lawrence', 'Oklahoma City', 'Pueblo', 'Victoria'],
lambda b: b.name
)
self.assertQuerysetEqual(
City.objects.filter(point__strictly_below=dallas.point).order_by('name'),
['Houston', 'Wellington'],
lambda b: b.name
)
def test_equals_lookups(self):
"Testing the 'same_as' and 'equals' lookup types."
pnt = fromstr('POINT (-95.363151 29.763374)', srid=4326)
c1 = City.objects.get(point=pnt)
c2 = City.objects.get(point__same_as=pnt)
c3 = City.objects.get(point__equals=pnt)
for c in [c1, c2, c3]:
self.assertEqual('Houston', c.name)
@skipUnlessDBFeature("supports_null_geometries")
def test_null_geometries(self):
"Testing NULL geometry support, and the `isnull` lookup type."
# Creating a state with a NULL boundary.
State.objects.create(name='Puerto Rico')
# Querying for both NULL and Non-NULL values.
nullqs = State.objects.filter(poly__isnull=True)
validqs = State.objects.filter(poly__isnull=False)
# Puerto Rico should be NULL (it's a commonwealth unincorporated territory)
self.assertEqual(1, len(nullqs))
self.assertEqual('Puerto Rico', nullqs[0].name)
# The valid states should be Colorado & Kansas
self.assertEqual(2, len(validqs))
state_names = [s.name for s in validqs]
self.assertIn('Colorado', state_names)
self.assertIn('Kansas', state_names)
# Saving another commonwealth w/a NULL geometry.
nmi = State.objects.create(name='Northern Mariana Islands', poly=None)
self.assertEqual(nmi.poly, None)
# Assigning a geometry and saving -- then UPDATE back to NULL.
nmi.poly = 'POLYGON((0 0,1 0,1 1,1 0,0 0))'
nmi.save()
State.objects.filter(name='Northern Mariana Islands').update(poly=None)
self.assertIsNone(State.objects.get(name='Northern Mariana Islands').poly)
@skipUnlessDBFeature("supports_relate_lookup")
def test_relate_lookup(self):
"Testing the 'relate' lookup type."
# To make things more interesting, we will have our Texas reference point in
# different SRIDs.
pnt1 = fromstr('POINT (649287.0363174 4177429.4494686)', srid=2847)
pnt2 = fromstr('POINT(-98.4919715741052 29.4333344025053)', srid=4326)
# Not passing in a geometry as first param should
# raise a type error when initializing the GeoQuerySet
self.assertRaises(ValueError, Country.objects.filter, mpoly__relate=(23, 'foo'))
# Making sure the right exception is raised for the given
# bad arguments.
for bad_args, e in [((pnt1, 0), ValueError), ((pnt2, 'T*T***FF*', 0), ValueError)]:
qs = Country.objects.filter(mpoly__relate=bad_args)
self.assertRaises(e, qs.count)
# Relate works differently for the different backends.
if postgis or spatialite:
contains_mask = 'T*T***FF*'
within_mask = 'T*F**F***'
intersects_mask = 'T********'
elif oracle:
contains_mask = 'contains'
within_mask = 'inside'
# TODO: This is not quite the same as the PostGIS mask above
intersects_mask = 'overlapbdyintersect'
# Testing contains relation mask.
self.assertEqual('Texas', Country.objects.get(mpoly__relate=(pnt1, contains_mask)).name)
self.assertEqual('Texas', Country.objects.get(mpoly__relate=(pnt2, contains_mask)).name)
# Testing within relation mask.
ks = State.objects.get(name='Kansas')
self.assertEqual('Lawrence', City.objects.get(point__relate=(ks.poly, within_mask)).name)
# Testing intersection relation mask.
if not oracle:
self.assertEqual('Texas', Country.objects.get(mpoly__relate=(pnt1, intersects_mask)).name)
self.assertEqual('Texas', Country.objects.get(mpoly__relate=(pnt2, intersects_mask)).name)
self.assertEqual('Lawrence', City.objects.get(point__relate=(ks.poly, intersects_mask)).name)
@skipUnlessDBFeature("gis_enabled")
@ignore_warnings(category=RemovedInDjango20Warning)
class GeoQuerySetTest(TestCase):
fixtures = ['initial']
# Please keep the tests in GeoQuerySet method's alphabetic order
@skipUnlessDBFeature("has_centroid_method")
def test_centroid(self):
"Testing the `centroid` GeoQuerySet method."
qs = State.objects.exclude(poly__isnull=True).centroid()
if oracle:
tol = 0.1
elif spatialite:
tol = 0.000001
else:
tol = 0.000000001
for s in qs:
self.assertTrue(s.poly.centroid.equals_exact(s.centroid, tol))
@skipUnlessDBFeature(
"has_difference_method", "has_intersection_method",
"has_sym_difference_method", "has_union_method")
def test_diff_intersection_union(self):
"Testing the `difference`, `intersection`, `sym_difference`, and `union` GeoQuerySet methods."
geom = Point(5, 23)
qs = Country.objects.all().difference(geom).sym_difference(geom).union(geom)
# XXX For some reason SpatiaLite does something screwy with the Texas geometry here. Also,
# XXX it doesn't like the null intersection.
if spatialite:
qs = qs.exclude(name='Texas')
else:
qs = qs.intersection(geom)
for c in qs:
if oracle:
# Should be able to execute the queries; however, they won't be the same
# as GEOS (because Oracle doesn't use GEOS internally like PostGIS or
# SpatiaLite).
pass
else:
self.assertEqual(c.mpoly.difference(geom), c.difference)
if not spatialite:
self.assertEqual(c.mpoly.intersection(geom), c.intersection)
# Ordering might differ in collections
self.assertSetEqual(set(g.wkt for g in c.mpoly.sym_difference(geom)),
set(g.wkt for g in c.sym_difference))
self.assertSetEqual(set(g.wkt for g in c.mpoly.union(geom)),
set(g.wkt for g in c.union))
@skipUnlessDBFeature("has_envelope_method")
def test_envelope(self):
"Testing the `envelope` GeoQuerySet method."
countries = Country.objects.all().envelope()
for country in countries:
self.assertIsInstance(country.envelope, Polygon)
@skipUnlessDBFeature("supports_extent_aggr")
@ignore_warnings(category=RemovedInDjango110Warning)
def test_extent(self):
"""
Testing the (deprecated) `extent` GeoQuerySet method and the Extent
aggregate.
"""
# Reference query:
# `SELECT ST_extent(point) FROM geoapp_city WHERE (name='Houston' or name='Dallas');`
# => BOX(-96.8016128540039 29.7633724212646,-95.3631439208984 32.7820587158203)
expected = (-96.8016128540039, 29.7633724212646, -95.3631439208984, 32.782058715820)
qs = City.objects.filter(name__in=('Houston', 'Dallas'))
extent1 = qs.extent()
extent2 = qs.aggregate(Extent('point'))['point__extent']
for extent in (extent1, extent2):
for val, exp in zip(extent, expected):
self.assertAlmostEqual(exp, val, 4)
self.assertIsNone(City.objects.filter(name=('Smalltown')).extent())
self.assertIsNone(City.objects.filter(name=('Smalltown')).aggregate(Extent('point'))['point__extent'])
@skipUnlessDBFeature("supports_extent_aggr")
def test_extent_with_limit(self):
"""
Testing if extent supports limit.
"""
extent1 = City.objects.all().aggregate(Extent('point'))['point__extent']
extent2 = City.objects.all()[:3].aggregate(Extent('point'))['point__extent']
self.assertNotEqual(extent1, extent2)
@skipUnlessDBFeature("has_force_rhr_method")
def test_force_rhr(self):
"Testing GeoQuerySet.force_rhr()."
rings = (
((0, 0), (5, 0), (0, 5), (0, 0)),
((1, 1), (1, 3), (3, 1), (1, 1)),
)
rhr_rings = (
((0, 0), (0, 5), (5, 0), (0, 0)),
((1, 1), (3, 1), (1, 3), (1, 1)),
)
State.objects.create(name='Foo', poly=Polygon(*rings))
s = State.objects.force_rhr().get(name='Foo')
self.assertEqual(rhr_rings, s.force_rhr.coords)
@skipUnlessDBFeature("has_geohash_method")
def test_geohash(self):
"Testing GeoQuerySet.geohash()."
# Reference query:
# SELECT ST_GeoHash(point) FROM geoapp_city WHERE name='Houston';
# SELECT ST_GeoHash(point, 5) FROM geoapp_city WHERE name='Houston';
ref_hash = '9vk1mfq8jx0c8e0386z6'
h1 = City.objects.geohash().get(name='Houston')
h2 = City.objects.geohash(precision=5).get(name='Houston')
self.assertEqual(ref_hash, h1.geohash)
self.assertEqual(ref_hash[:5], h2.geohash)
def test_geojson(self):
"Testing GeoJSON output from the database using GeoQuerySet.geojson()."
# Only PostGIS and SpatiaLite 3.0+ support GeoJSON.
if not connection.ops.geojson:
self.assertRaises(NotImplementedError, Country.objects.all().geojson, field_name='mpoly')
return
pueblo_json = '{"type":"Point","coordinates":[-104.609252,38.255001]}'
houston_json = (
'{"type":"Point","crs":{"type":"name","properties":'
'{"name":"EPSG:4326"}},"coordinates":[-95.363151,29.763374]}'
)
victoria_json = (
'{"type":"Point","bbox":[-123.30519600,48.46261100,-123.30519600,48.46261100],'
'"coordinates":[-123.305196,48.462611]}'
)
chicago_json = (
'{"type":"Point","crs":{"type":"name","properties":{"name":"EPSG:4326"}},'
'"bbox":[-87.65018,41.85039,-87.65018,41.85039],"coordinates":[-87.65018,41.85039]}'
)
if spatialite:
victoria_json = (
'{"type":"Point","bbox":[-123.305196,48.462611,-123.305196,48.462611],'
'"coordinates":[-123.305196,48.462611]}'
)
# Precision argument should only be an integer
self.assertRaises(TypeError, City.objects.geojson, precision='foo')
# Reference queries and values.
# SELECT ST_AsGeoJson("geoapp_city"."point", 8, 0)
# FROM "geoapp_city" WHERE "geoapp_city"."name" = 'Pueblo';
self.assertEqual(pueblo_json, City.objects.geojson().get(name='Pueblo').geojson)
# SELECT ST_AsGeoJson("geoapp_city"."point", 8, 2) FROM "geoapp_city"
# WHERE "geoapp_city"."name" = 'Houston';
# This time we want to include the CRS by using the `crs` keyword.
self.assertEqual(houston_json, City.objects.geojson(crs=True, model_att='json').get(name='Houston').json)
# SELECT ST_AsGeoJson("geoapp_city"."point", 8, 1) FROM "geoapp_city"
# WHERE "geoapp_city"."name" = 'Houston';
# This time we include the bounding box by using the `bbox` keyword.
self.assertEqual(victoria_json, City.objects.geojson(bbox=True).get(name='Victoria').geojson)
# SELECT ST_AsGeoJson("geoapp_city"."point", 5, 3) FROM "geoapp_city"
# WHERE "geoapp_city"."name" = 'Chicago';
# Finally, we set every available keyword.
self.assertEqual(
chicago_json,
City.objects.geojson(bbox=True, crs=True, precision=5).get(name='Chicago').geojson
)
@skipUnlessDBFeature("has_gml_method")
def test_gml(self):
"Testing GML output from the database using GeoQuerySet.gml()."
# Should throw a TypeError when trying to obtain GML from a
# non-geometry field.
qs = City.objects.all()
self.assertRaises(TypeError, qs.gml, field_name='name')
ptown1 = City.objects.gml(field_name='point', precision=9).get(name='Pueblo')
ptown2 = City.objects.gml(precision=9).get(name='Pueblo')
if oracle:
# No precision parameter for Oracle :-/
gml_regex = re.compile(
r'^<gml:Point srsName="EPSG:4326" xmlns:gml="http://www.opengis.net/gml">'
r'<gml:coordinates decimal="\." cs="," ts=" ">-104.60925\d+,38.25500\d+ '
r'</gml:coordinates></gml:Point>'
)
elif spatialite and connection.ops.spatial_version < (3, 0, 0):
# Spatialite before 3.0 has extra colon in SrsName
gml_regex = re.compile(
r'^<gml:Point SrsName="EPSG::4326"><gml:coordinates decimal="\." '
r'cs="," ts=" ">-104.609251\d+,38.255001</gml:coordinates></gml:Point>'
)
else:
gml_regex = re.compile(
r'^<gml:Point srsName="EPSG:4326"><gml:coordinates>'
r'-104\.60925\d+,38\.255001</gml:coordinates></gml:Point>'
)
for ptown in [ptown1, ptown2]:
self.assertTrue(gml_regex.match(ptown.gml))
if postgis:
self.assertIn('<gml:pos srsDimension="2">', City.objects.gml(version=3).get(name='Pueblo').gml)
@skipUnlessDBFeature("has_kml_method")
def test_kml(self):
"Testing KML output from the database using GeoQuerySet.kml()."
# Should throw a TypeError when trying to obtain KML from a
# non-geometry field.
qs = City.objects.all()
self.assertRaises(TypeError, qs.kml, 'name')
# Ensuring the KML is as expected.
ptown1 = City.objects.kml(field_name='point', precision=9).get(name='Pueblo')
ptown2 = City.objects.kml(precision=9).get(name='Pueblo')
for ptown in [ptown1, ptown2]:
self.assertEqual('<Point><coordinates>-104.609252,38.255001</coordinates></Point>', ptown.kml)
@ignore_warnings(category=RemovedInDjango110Warning)
def test_make_line(self):
"""
Testing the (deprecated) `make_line` GeoQuerySet method and the MakeLine
aggregate.
"""
if not connection.features.supports_make_line_aggr:
# Only PostGIS has support for the MakeLine aggregate. For other
# backends, test that NotImplementedError is raised
self.assertRaises(
NotImplementedError,
City.objects.all().aggregate, MakeLine('point')
)
return
# Ensuring that a `TypeError` is raised on models without PointFields.
self.assertRaises(TypeError, State.objects.make_line)
self.assertRaises(TypeError, Country.objects.make_line)
# MakeLine on an inappropriate field returns simply None
self.assertIsNone(State.objects.aggregate(MakeLine('poly'))['poly__makeline'])
# Reference query:
# SELECT AsText(ST_MakeLine(geoapp_city.point)) FROM geoapp_city;
ref_line = GEOSGeometry(
'LINESTRING(-95.363151 29.763374,-96.801611 32.782057,'
'-97.521157 34.464642,174.783117 -41.315268,-104.609252 38.255001,'
'-95.23506 38.971823,-87.650175 41.850385,-123.305196 48.462611)',
srid=4326
)
# We check for equality with a tolerance of 10e-5 which is a lower bound
# of the precisions of ref_line coordinates
line1 = City.objects.make_line()
line2 = City.objects.aggregate(MakeLine('point'))['point__makeline']
for line in (line1, line2):
self.assertTrue(ref_line.equals_exact(line, tolerance=10e-5),
"%s != %s" % (ref_line, line))
@skipUnlessDBFeature("has_num_geom_method")
def test_num_geom(self):
"Testing the `num_geom` GeoQuerySet method."
# Both 'countries' only have two geometries.
for c in Country.objects.num_geom():
self.assertEqual(2, c.num_geom)
for c in City.objects.filter(point__isnull=False).num_geom():
# Oracle and PostGIS 2.0+ will return 1 for the number of
# geometries on non-collections.
self.assertEqual(1, c.num_geom)
@skipUnlessDBFeature("supports_num_points_poly")
def test_num_points(self):
"Testing the `num_points` GeoQuerySet method."
for c in Country.objects.num_points():
self.assertEqual(c.mpoly.num_points, c.num_points)
if not oracle:
# Oracle cannot count vertices in Point geometries.
for c in City.objects.num_points():
self.assertEqual(1, c.num_points)
@skipUnlessDBFeature("has_point_on_surface_method")
def test_point_on_surface(self):
"Testing the `point_on_surface` GeoQuerySet method."
# Reference values.
if oracle:
# SELECT SDO_UTIL.TO_WKTGEOMETRY(SDO_GEOM.SDO_POINTONSURFACE(GEOAPP_COUNTRY.MPOLY, 0.05))
# FROM GEOAPP_COUNTRY;
ref = {'New Zealand': fromstr('POINT (174.616364 -36.100861)', srid=4326),
'Texas': fromstr('POINT (-103.002434 36.500397)', srid=4326),
}
else:
# Using GEOSGeometry to compute the reference point on surface values
# -- since PostGIS also uses GEOS these should be the same.
ref = {'New Zealand': Country.objects.get(name='New Zealand').mpoly.point_on_surface,
'Texas': Country.objects.get(name='Texas').mpoly.point_on_surface
}
for c in Country.objects.point_on_surface():
if spatialite:
# XXX This seems to be a WKT-translation-related precision issue?
tol = 0.00001
else:
tol = 0.000000001
self.assertTrue(ref[c.name].equals_exact(c.point_on_surface, tol))
@skipUnlessDBFeature("has_reverse_method")
def test_reverse_geom(self):
"Testing GeoQuerySet.reverse_geom()."
coords = [(-95.363151, 29.763374), (-95.448601, 29.713803)]
Track.objects.create(name='Foo', line=LineString(coords))
t = Track.objects.reverse_geom().get(name='Foo')
coords.reverse()
self.assertEqual(tuple(coords), t.reverse_geom.coords)
if oracle:
self.assertRaises(TypeError, State.objects.reverse_geom)
@skipUnlessDBFeature("has_scale_method")
def test_scale(self):
"Testing the `scale` GeoQuerySet method."
xfac, yfac = 2, 3
tol = 5 # XXX The low precision tolerance is for SpatiaLite
qs = Country.objects.scale(xfac, yfac, model_att='scaled')
for c in qs:
for p1, p2 in zip(c.mpoly, c.scaled):
for r1, r2 in zip(p1, p2):
for c1, c2 in zip(r1.coords, r2.coords):
self.assertAlmostEqual(c1[0] * xfac, c2[0], tol)
self.assertAlmostEqual(c1[1] * yfac, c2[1], tol)
@skipUnlessDBFeature("has_snap_to_grid_method")
def test_snap_to_grid(self):
"Testing GeoQuerySet.snap_to_grid()."
# Let's try and break snap_to_grid() with bad combinations of arguments.
for bad_args in ((), range(3), range(5)):
self.assertRaises(ValueError, Country.objects.snap_to_grid, *bad_args)
for bad_args in (('1.0',), (1.0, None), tuple(map(six.text_type, range(4)))):
self.assertRaises(TypeError, Country.objects.snap_to_grid, *bad_args)
# Boundary for San Marino, courtesy of <NAME> of thematicmapping.org
# from the world borders dataset he provides.
wkt = ('MULTIPOLYGON(((12.41580 43.95795,12.45055 43.97972,12.45389 43.98167,'
'12.46250 43.98472,12.47167 43.98694,12.49278 43.98917,'
'12.50555 43.98861,12.51000 43.98694,12.51028 43.98277,'
'12.51167 43.94333,12.51056 43.93916,12.49639 43.92333,'
'12.49500 43.91472,12.48778 43.90583,12.47444 43.89722,'
'12.46472 43.89555,12.45917 43.89611,12.41639 43.90472,'
'12.41222 43.90610,12.40782 43.91366,12.40389 43.92667,'
'12.40500 43.94833,12.40889 43.95499,12.41580 43.95795)))')
Country.objects.create(name='San Marino', mpoly=fromstr(wkt))
# Because floating-point arithmetic isn't exact, we set a tolerance
# to pass into GEOS `equals_exact`.
tol = 0.000000001
# SELECT AsText(ST_SnapToGrid("geoapp_country"."mpoly", 0.1)) FROM "geoapp_country"
# WHERE "geoapp_country"."name" = 'San Marino';
ref = fromstr('MULTIPOLYGON(((12.4 44,12.5 44,12.5 43.9,12.4 43.9,12.4 44)))')
self.assertTrue(ref.equals_exact(Country.objects.snap_to_grid(0.1).get(name='San Marino').snap_to_grid, tol))
# SELECT AsText(ST_SnapToGrid("geoapp_country"."mpoly", 0.05, 0.23)) FROM "geoapp_country"
# WHERE "geoapp_country"."name" = 'San Marino';
ref = fromstr('MULTIPOLYGON(((12.4 43.93,12.45 43.93,12.5 43.93,12.45 43.93,12.4 43.93)))')
self.assertTrue(
ref.equals_exact(Country.objects.snap_to_grid(0.05, 0.23).get(name='San Marino').snap_to_grid, tol)
)
# SELECT AsText(ST_SnapToGrid("geoapp_country"."mpoly", 0.5, 0.17, 0.05, 0.23)) FROM "geoapp_country"
# WHERE "geoapp_country"."name" = 'San Marino';
ref = fromstr(
'MULTIPOLYGON(((12.4 43.87,12.45 43.87,12.45 44.1,12.5 44.1,12.5 43.87,12.45 43.87,12.4 43.87)))'
)
self.assertTrue(
ref.equals_exact(
Country.objects.snap_to_grid(0.05, 0.23, 0.5, 0.17).get(name='San Marino').snap_to_grid,
tol
)
)
@skipUnlessDBFeature("has_svg_method")
def test_svg(self):
"Testing SVG output using GeoQuerySet.svg()."
self.assertRaises(TypeError, City.objects.svg, precision='foo')
# SELECT AsSVG(geoapp_city.point, 0, 8) FROM geoapp_city WHERE name = 'Pueblo';
svg1 = 'cx="-104.609252" cy="-38.255001"'
# Even though relative, only one point so it's practically the same except for
# the 'c' letter prefix on the x,y values.
svg2 = svg1.replace('c', '')
self.assertEqual(svg1, City.objects.svg().get(name='Pueblo').svg)
self.assertEqual(svg2, City.objects.svg(relative=5).get(name='Pueblo').svg)
@skipUnlessDBFeature("has_transform_method")
def test_transform(self):
"Testing the transform() GeoQuerySet method."
# Pre-transformed points for Houston and Pueblo.
htown = fromstr('POINT(1947516.83115183 6322297.06040572)', srid=3084)
ptown = fromstr('POINT(992363.390841912 481455.395105533)', srid=2774)
prec = 3 # Precision is low due to version variations in PROJ and GDAL.
# Asserting the result of the transform operation with the values in
# the pre-transformed points. Oracle does not have the 3084 SRID.
if not oracle:
h = City.objects.transform(htown.srid).get(name='Houston')
self.assertEqual(3084, h.point.srid)
self.assertAlmostEqual(htown.x, h.point.x, prec)
self.assertAlmostEqual(htown.y, h.point.y, prec)
p1 = City.objects.transform(ptown.srid, field_name='point').get(name='Pueblo')
p2 = City.objects.transform(srid=ptown.srid).get(name='Pueblo')
for p in [p1, p2]:
self.assertEqual(2774, p.point.srid)
self.assertAlmostEqual(ptown.x, p.point.x, prec)
self.assertAlmostEqual(ptown.y, p.point.y, prec)
@skipUnlessDBFeature("has_translate_method")
def test_translate(self):
"Testing the `translate` GeoQuerySet method."
xfac, yfac = 5, -23
qs = Country.objects.translate(xfac, yfac, model_att='translated')
for c in qs:
for p1, p2 in zip(c.mpoly, c.translated):
for r1, r2 in zip(p1, p2):
for c1, c2 in zip(r1.coords, r2.coords):
# XXX The low precision is for SpatiaLite
self.assertAlmostEqual(c1[0] + xfac, c2[0], 5)
self.assertAlmostEqual(c1[1] + yfac, c2[1], 5)
# TODO: Oracle can be made to pass if
# union1 = union2 = fromstr('POINT (-97.5211570000000023 34.4646419999999978)')
# but this seems unexpected and should be investigated to determine the cause.
@skipUnlessDBFeature("has_unionagg_method")
@no_oracle
@ignore_warnings(category=RemovedInDjango110Warning)
def test_unionagg(self):
"""
Testing the (deprecated) `unionagg` (aggregate union) GeoQuerySet method
and the Union aggregate.
"""
tx = Country.objects.get(name='Texas').mpoly
# Houston, Dallas -- Ordering may differ depending on backend or GEOS version.
union1 = fromstr('MULTIPOINT(-96.801611 32.782057,-95.363151 29.763374)')
union2 = fromstr('MULTIPOINT(-95.363151 29.763374,-96.801611 32.782057)')
qs = City.objects.filter(point__within=tx)
self.assertRaises(TypeError, qs.unionagg, 'name')
self.assertRaises(ValueError, qs.aggregate, Union('name'))
# Using `field_name` keyword argument in one query and specifying an
# order in the other (which should not be used because this is
# an aggregate method on a spatial column)
u1 = qs.unionagg(field_name='point')
u2 = qs.order_by('name').unionagg()
u3 = qs.aggregate(Union('point'))['point__union']
u4 = qs.order_by('name').aggregate(Union('point'))['point__union']
tol = 0.00001
self.assertTrue(union1.equals_exact(u1, tol) or union2.equals_exact(u1, tol))
self.assertTrue(union1.equals_exact(u2, tol) or union2.equals_exact(u2, tol))
self.assertTrue(union1.equals_exact(u3, tol) or union2.equals_exact(u3, tol))
self.assertTrue(union1.equals_exact(u4, tol) or union2.equals_exact(u4, tol))
qs = City.objects.filter(name='NotACity')
self.assertIsNone(qs.unionagg(field_name='point'))
self.assertIsNone(qs.aggregate(Union('point'))['point__union'])
def test_within_subquery(self):
"""
Test that using a queryset inside a geo lookup is working (using a subquery)
(#14483).
"""
tex_cities = City.objects.filter(
point__within=Country.objects.filter(name='Texas').values('mpoly')).order_by('name')
expected = ['Dallas', 'Houston']
if not connection.features.supports_real_shape_operations:
expected.append('Oklahoma City')
self.assertEqual(
list(tex_cities.values_list('name', flat=True)),
expected
)
def test_non_concrete_field(self):
NonConcreteModel.objects.create(point=Point(0, 0), name='name')
list(NonConcreteModel.objects.all())
| en | 0.791639 | # Ensuring that data was loaded from initial data fixtures. # Testing on a Point # Making sure TypeError is thrown when trying to set with an # incompatible type. # Now setting with a compatible GEOS Geometry, saving, and ensuring # the save took, notice no SRID is explicitly set. # Ensuring that the SRID is automatically set to that of the # field after assignment, but before saving. # Ensuring the point was saved correctly after saving # Setting the X and Y of the Point # Checking assignments pre & post-save. # Testing on a Polygon # Creating a State object using a built Polygon # SRID auto-set from None # Testing the `ogr` and `srs` lazy-geometry properties. # Changing the interior ring on the poly attribute. # San Antonio in 'WGS84' (SRID 4326) # Our reference point in WGS84 # Oracle doesn't have SRID 3084, using 41157. # San Antonio in 'Texas 4205, Southern Zone (1983, meters)' (SRID 41157) # Used the following Oracle SQL to get this value: # SELECT SDO_UTIL.TO_WKTGEOMETRY( # SDO_CS.TRANSFORM(SDO_GEOMETRY('POINT (-98.493183 29.424170)', 4326), 41157)) # ) # FROM DUAL; # San Antonio in 'NAD83(HARN) / Texas Centric Lambert Conformal' (SRID 3084) # Used ogr.py in gdal 1.4.1 for this transform # Constructing & querying with a point from a different SRID. Oracle # `SDO_OVERLAPBDYINTERSECT` operates differently from # `ST_Intersects`, so contains is used instead. # Creating San Antonio. Remember the Alamo. # Now verifying that San Antonio was transformed correctly # If the GeometryField SRID is -1, then we shouldn't perform any # transformation if the SRID of the input geometry is different. # SpatiaLite < 3 does not support missing SRID values. # Creating a Pennsylvanian city. # All transformation SQL will need to be performed on the # _parent_ table. # Only PostGIS would support a 'select *' query because of its recognized # HEXEWKB format for geometry fields Test a dumpdata/loaddata cycle with geographic data. # Reload now dumped data # Getting Texas, yes we were a country -- once ;) # Seeing what cities are in Texas, should get Houston and Dallas, # and Oklahoma City because 'contained' only checks on the # _bounding box_ of the Geometries. # Pulling out some cities. # Now testing contains on the countries using the points for # Houston and Wellington. # Query w/GEOSGeometry # Query w/EWKBHEX # Spatialite 2.3 thinks that Lawrence is in Puerto Rico (a NULL geometry). # Pueblo and Oklahoma City (even though OK City is within the bounding box of Texas) # are not contained in Texas or New Zealand. # Query w/GEOSGeometry object # Query w/WKT # OK City is contained w/in bounding box of Texas. # Left: A << B => true if xmax(A) < xmin(B) # Right: A >> B => true if xmin(A) > xmax(B) # See: BOX2D_left() and BOX2D_right() in lwgeom_box2dfloat4.c in PostGIS source. # The left/right lookup tests are known failures on PostGIS 2.0/2.0.1 # http://trac.osgeo.org/postgis/ticket/2035 # Getting the borders for Colorado & Kansas # Note: Wellington has an 'X' value of 174, so it will not be considered # to the left of CO. # These cities should be strictly to the right of the CO border. # These cities should be strictly to the right of the KS border. # Note: Wellington has an 'X' value of 174, so it will not be considered # to the left of CO. # Creating a state with a NULL boundary. # Querying for both NULL and Non-NULL values. # Puerto Rico should be NULL (it's a commonwealth unincorporated territory) # The valid states should be Colorado & Kansas # Saving another commonwealth w/a NULL geometry. # Assigning a geometry and saving -- then UPDATE back to NULL. # To make things more interesting, we will have our Texas reference point in # different SRIDs. # Not passing in a geometry as first param should # raise a type error when initializing the GeoQuerySet # Making sure the right exception is raised for the given # bad arguments. # Relate works differently for the different backends. # TODO: This is not quite the same as the PostGIS mask above # Testing contains relation mask. # Testing within relation mask. # Testing intersection relation mask. # Please keep the tests in GeoQuerySet method's alphabetic order # XXX For some reason SpatiaLite does something screwy with the Texas geometry here. Also, # XXX it doesn't like the null intersection. # Should be able to execute the queries; however, they won't be the same # as GEOS (because Oracle doesn't use GEOS internally like PostGIS or # SpatiaLite). # Ordering might differ in collections Testing the (deprecated) `extent` GeoQuerySet method and the Extent aggregate. # Reference query: # `SELECT ST_extent(point) FROM geoapp_city WHERE (name='Houston' or name='Dallas');` # => BOX(-96.8016128540039 29.7633724212646,-95.3631439208984 32.7820587158203) Testing if extent supports limit. # Reference query: # SELECT ST_GeoHash(point) FROM geoapp_city WHERE name='Houston'; # SELECT ST_GeoHash(point, 5) FROM geoapp_city WHERE name='Houston'; # Only PostGIS and SpatiaLite 3.0+ support GeoJSON. # Precision argument should only be an integer # Reference queries and values. # SELECT ST_AsGeoJson("geoapp_city"."point", 8, 0) # FROM "geoapp_city" WHERE "geoapp_city"."name" = 'Pueblo'; # SELECT ST_AsGeoJson("geoapp_city"."point", 8, 2) FROM "geoapp_city" # WHERE "geoapp_city"."name" = 'Houston'; # This time we want to include the CRS by using the `crs` keyword. # SELECT ST_AsGeoJson("geoapp_city"."point", 8, 1) FROM "geoapp_city" # WHERE "geoapp_city"."name" = 'Houston'; # This time we include the bounding box by using the `bbox` keyword. # SELECT ST_AsGeoJson("geoapp_city"."point", 5, 3) FROM "geoapp_city" # WHERE "geoapp_city"."name" = 'Chicago'; # Finally, we set every available keyword. # Should throw a TypeError when trying to obtain GML from a # non-geometry field. # No precision parameter for Oracle :-/ # Spatialite before 3.0 has extra colon in SrsName # Should throw a TypeError when trying to obtain KML from a # non-geometry field. # Ensuring the KML is as expected. Testing the (deprecated) `make_line` GeoQuerySet method and the MakeLine aggregate. # Only PostGIS has support for the MakeLine aggregate. For other # backends, test that NotImplementedError is raised # Ensuring that a `TypeError` is raised on models without PointFields. # MakeLine on an inappropriate field returns simply None # Reference query: # SELECT AsText(ST_MakeLine(geoapp_city.point)) FROM geoapp_city; # We check for equality with a tolerance of 10e-5 which is a lower bound # of the precisions of ref_line coordinates # Both 'countries' only have two geometries. # Oracle and PostGIS 2.0+ will return 1 for the number of # geometries on non-collections. # Oracle cannot count vertices in Point geometries. # Reference values. # SELECT SDO_UTIL.TO_WKTGEOMETRY(SDO_GEOM.SDO_POINTONSURFACE(GEOAPP_COUNTRY.MPOLY, 0.05)) # FROM GEOAPP_COUNTRY; # Using GEOSGeometry to compute the reference point on surface values # -- since PostGIS also uses GEOS these should be the same. # XXX This seems to be a WKT-translation-related precision issue? # XXX The low precision tolerance is for SpatiaLite # Let's try and break snap_to_grid() with bad combinations of arguments. # Boundary for San Marino, courtesy of <NAME> of thematicmapping.org # from the world borders dataset he provides. # Because floating-point arithmetic isn't exact, we set a tolerance # to pass into GEOS `equals_exact`. # SELECT AsText(ST_SnapToGrid("geoapp_country"."mpoly", 0.1)) FROM "geoapp_country" # WHERE "geoapp_country"."name" = 'San Marino'; # SELECT AsText(ST_SnapToGrid("geoapp_country"."mpoly", 0.05, 0.23)) FROM "geoapp_country" # WHERE "geoapp_country"."name" = 'San Marino'; # SELECT AsText(ST_SnapToGrid("geoapp_country"."mpoly", 0.5, 0.17, 0.05, 0.23)) FROM "geoapp_country" # WHERE "geoapp_country"."name" = 'San Marino'; # SELECT AsSVG(geoapp_city.point, 0, 8) FROM geoapp_city WHERE name = 'Pueblo'; # Even though relative, only one point so it's practically the same except for # the 'c' letter prefix on the x,y values. # Pre-transformed points for Houston and Pueblo. # Precision is low due to version variations in PROJ and GDAL. # Asserting the result of the transform operation with the values in # the pre-transformed points. Oracle does not have the 3084 SRID. # XXX The low precision is for SpatiaLite # TODO: Oracle can be made to pass if # union1 = union2 = fromstr('POINT (-97.5211570000000023 34.4646419999999978)') # but this seems unexpected and should be investigated to determine the cause. Testing the (deprecated) `unionagg` (aggregate union) GeoQuerySet method and the Union aggregate. # Houston, Dallas -- Ordering may differ depending on backend or GEOS version. # Using `field_name` keyword argument in one query and specifying an # order in the other (which should not be used because this is # an aggregate method on a spatial column) Test that using a queryset inside a geo lookup is working (using a subquery) (#14483). | 2.0635 | 2 |
tools/convert_fcos_weight.py | abhishreeshetty/IDL-CrossViz | 0 | 6624344 | import argparse
from collections import OrderedDict
import torch
def get_parser():
parser = argparse.ArgumentParser(description='FCOS Detectron2 Converter')
parser.add_argument(
'--model',
default='weights/fcos_R_50_1x_official.pth',
metavar='FILE',
help='path to model weights',
)
parser.add_argument(
'--output',
default='weights/fcos_R_50_1x_converted.pth',
metavar='FILE',
help='path to model weights',
)
return parser
def rename_resnet_param_names(ckpt_state_dict):
converted_state_dict = OrderedDict()
for key in ckpt_state_dict.keys():
value = ckpt_state_dict[key]
key = key.replace('module.', '')
key = key.replace('body', 'bottom_up')
# adding a . ahead to avoid renaming the fpn modules
# this can happen after fpn renaming
key = key.replace('.layer1', '.res2')
key = key.replace('.layer2', '.res3')
key = key.replace('.layer3', '.res4')
key = key.replace('.layer4', '.res5')
key = key.replace('downsample.0', 'shortcut')
key = key.replace('downsample.1', 'shortcut.norm')
key = key.replace('bn1', 'conv1.norm')
key = key.replace('bn2', 'conv2.norm')
key = key.replace('bn3', 'conv3.norm')
key = key.replace('fpn_inner2', 'fpn_lateral3')
key = key.replace('fpn_inner3', 'fpn_lateral4')
key = key.replace('fpn_inner4', 'fpn_lateral5')
key = key.replace('fpn_layer2', 'fpn_output3')
key = key.replace('fpn_layer3', 'fpn_output4')
key = key.replace('fpn_layer4', 'fpn_output5')
key = key.replace('top_blocks', 'top_block')
key = key.replace('fpn.', '')
key = key.replace('rpn', 'proposal_generator')
key = key.replace('head', 'fcos_head')
converted_state_dict[key] = value
return converted_state_dict
if __name__ == '__main__':
args = get_parser().parse_args()
ckpt = torch.load(args.model)
model = rename_resnet_param_names(ckpt['model'])
torch.save(model, args.output)
| import argparse
from collections import OrderedDict
import torch
def get_parser():
parser = argparse.ArgumentParser(description='FCOS Detectron2 Converter')
parser.add_argument(
'--model',
default='weights/fcos_R_50_1x_official.pth',
metavar='FILE',
help='path to model weights',
)
parser.add_argument(
'--output',
default='weights/fcos_R_50_1x_converted.pth',
metavar='FILE',
help='path to model weights',
)
return parser
def rename_resnet_param_names(ckpt_state_dict):
converted_state_dict = OrderedDict()
for key in ckpt_state_dict.keys():
value = ckpt_state_dict[key]
key = key.replace('module.', '')
key = key.replace('body', 'bottom_up')
# adding a . ahead to avoid renaming the fpn modules
# this can happen after fpn renaming
key = key.replace('.layer1', '.res2')
key = key.replace('.layer2', '.res3')
key = key.replace('.layer3', '.res4')
key = key.replace('.layer4', '.res5')
key = key.replace('downsample.0', 'shortcut')
key = key.replace('downsample.1', 'shortcut.norm')
key = key.replace('bn1', 'conv1.norm')
key = key.replace('bn2', 'conv2.norm')
key = key.replace('bn3', 'conv3.norm')
key = key.replace('fpn_inner2', 'fpn_lateral3')
key = key.replace('fpn_inner3', 'fpn_lateral4')
key = key.replace('fpn_inner4', 'fpn_lateral5')
key = key.replace('fpn_layer2', 'fpn_output3')
key = key.replace('fpn_layer3', 'fpn_output4')
key = key.replace('fpn_layer4', 'fpn_output5')
key = key.replace('top_blocks', 'top_block')
key = key.replace('fpn.', '')
key = key.replace('rpn', 'proposal_generator')
key = key.replace('head', 'fcos_head')
converted_state_dict[key] = value
return converted_state_dict
if __name__ == '__main__':
args = get_parser().parse_args()
ckpt = torch.load(args.model)
model = rename_resnet_param_names(ckpt['model'])
torch.save(model, args.output)
| en | 0.776403 | # adding a . ahead to avoid renaming the fpn modules # this can happen after fpn renaming | 2.220082 | 2 |
spacy/en/tokenizer_exceptions.py | EnjoyLifeFund/macHighSierra-py36-pkgs | 1 | 6624345 | <gh_stars>1-10
# coding: utf8
from __future__ import unicode_literals
from ..symbols import *
from ..language_data import PRON_LEMMA
EXC = {}
EXCLUDE_EXC = ["Ill", "ill", "Its", "its", "Hell", "hell", "Shell", "shell",
"Shed", "shed", "were", "Were", "Well", "well", "Whore", "whore"]
# Pronouns
for pron in ["i"]:
for orth in [pron, pron.title()]:
EXC[orth + "'m"] = [
{ORTH: orth, LEMMA: PRON_LEMMA, TAG: "PRP"},
{ORTH: "'m", LEMMA: "be", TAG: "VBP", "tenspect": 1, "number": 1}
]
EXC[orth + "m"] = [
{ORTH: orth, LEMMA: PRON_LEMMA, TAG: "PRP"},
{ORTH: "m", LEMMA: "be", TAG: "VBP", "tenspect": 1, "number": 1 }
]
EXC[orth + "'ma"] = [
{ORTH: orth, LEMMA: PRON_LEMMA, TAG: "PRP"},
{ORTH: "'m", LEMMA: "be", NORM: "am"},
{ORTH: "a", LEMMA: "going to", NORM: "gonna"}
]
EXC[orth + "ma"] = [
{ORTH: orth, LEMMA: PRON_LEMMA, TAG: "PRP"},
{ORTH: "m", LEMMA: "be", NORM: "am"},
{ORTH: "a", LEMMA: "going to", NORM: "gonna"}
]
for pron in ["i", "you", "he", "she", "it", "we", "they"]:
for orth in [pron, pron.title()]:
EXC[orth + "'ll"] = [
{ORTH: orth, LEMMA: PRON_LEMMA, TAG: "PRP"},
{ORTH: "'ll", LEMMA: "will", TAG: "MD"}
]
EXC[orth + "ll"] = [
{ORTH: orth, LEMMA: PRON_LEMMA, TAG: "PRP"},
{ORTH: "ll", LEMMA: "will", TAG: "MD"}
]
EXC[orth + "'ll've"] = [
{ORTH: orth, LEMMA: PRON_LEMMA, TAG: "PRP"},
{ORTH: "'ll", LEMMA: "will", TAG: "MD"},
{ORTH: "'ve", LEMMA: "have", TAG: "VB"}
]
EXC[orth + "llve"] = [
{ORTH: orth, LEMMA: PRON_LEMMA, TAG: "PRP"},
{ORTH: "ll", LEMMA: "will", TAG: "MD"},
{ORTH: "ve", LEMMA: "have", TAG: "VB"}
]
EXC[orth + "'d"] = [
{ORTH: orth, LEMMA: PRON_LEMMA, TAG: "PRP"},
{ORTH: "'d", LEMMA: "would", TAG: "MD"}
]
EXC[orth + "d"] = [
{ORTH: orth, LEMMA: PRON_LEMMA, TAG: "PRP"},
{ORTH: "d", LEMMA: "would", TAG: "MD"}
]
EXC[orth + "'d've"] = [
{ORTH: orth, LEMMA: PRON_LEMMA, TAG: "PRP"},
{ORTH: "'d", LEMMA: "would", TAG: "MD"},
{ORTH: "'ve", LEMMA: "have", TAG: "VB"}
]
EXC[orth + "dve"] = [
{ORTH: orth, LEMMA: PRON_LEMMA, TAG: "PRP"},
{ORTH: "d", LEMMA: "would", TAG: "MD"},
{ORTH: "ve", LEMMA: "have", TAG: "VB"}
]
for pron in ["i", "you", "we", "they"]:
for orth in [pron, pron.title()]:
EXC[orth + "'ve"] = [
{ORTH: orth, LEMMA: PRON_LEMMA, TAG: "PRP"},
{ORTH: "'ve", LEMMA: "have", TAG: "VB"}
]
EXC[orth + "ve"] = [
{ORTH: orth, LEMMA: PRON_LEMMA, TAG: "PRP"},
{ORTH: "ve", LEMMA: "have", TAG: "VB"}
]
for pron in ["you", "we", "they"]:
for orth in [pron, pron.title()]:
EXC[orth + "'re"] = [
{ORTH: orth, LEMMA: PRON_LEMMA, TAG: "PRP"},
{ORTH: "'re", LEMMA: "be", NORM: "are"}
]
EXC[orth + "re"] = [
{ORTH: orth, LEMMA: PRON_LEMMA, TAG: "PRP"},
{ORTH: "re", LEMMA: "be", NORM: "are", TAG: "VBZ"}
]
for pron in ["he", "she", "it"]:
for orth in [pron, pron.title()]:
EXC[orth + "'s"] = [
{ORTH: orth, LEMMA: PRON_LEMMA, TAG: "PRP"},
{ORTH: "'s"}
]
EXC[orth + "s"] = [
{ORTH: orth, LEMMA: PRON_LEMMA, TAG: "PRP"},
{ORTH: "s"}
]
# W-words, relative pronouns, prepositions etc.
for word in ["who", "what", "when", "where", "why", "how", "there", "that"]:
for orth in [word, word.title()]:
EXC[orth + "'s"] = [
{ORTH: orth, LEMMA: word},
{ORTH: "'s"}
]
EXC[orth + "s"] = [
{ORTH: orth, LEMMA: word},
{ORTH: "s"}
]
EXC[orth + "'ll"] = [
{ORTH: orth, LEMMA: word},
{ORTH: "'ll", LEMMA: "will", TAG: "MD"}
]
EXC[orth + "ll"] = [
{ORTH: orth, LEMMA: word},
{ORTH: "ll", LEMMA: "will", TAG: "MD"}
]
EXC[orth + "'ll've"] = [
{ORTH: orth, LEMMA: word},
{ORTH: "'ll", LEMMA: "will", TAG: "MD"},
{ORTH: "'ve", LEMMA: "have", TAG: "VB"}
]
EXC[orth + "llve"] = [
{ORTH: orth, LEMMA: word},
{ORTH: "ll", LEMMA: "will", TAG: "MD"},
{ORTH: "ve", LEMMA: "have", TAG: "VB"}
]
EXC[orth + "'re"] = [
{ORTH: orth, LEMMA: word},
{ORTH: "'re", LEMMA: "be", NORM: "are"}
]
EXC[orth + "re"] = [
{ORTH: orth, LEMMA: word},
{ORTH: "re", LEMMA: "be", NORM: "are"}
]
EXC[orth + "'ve"] = [
{ORTH: orth},
{ORTH: "'ve", LEMMA: "have", TAG: "VB"}
]
EXC[orth + "ve"] = [
{ORTH: orth, LEMMA: word},
{ORTH: "ve", LEMMA: "have", TAG: "VB"}
]
EXC[orth + "'d"] = [
{ORTH: orth, LEMMA: word},
{ORTH: "'d"}
]
EXC[orth + "d"] = [
{ORTH: orth, LEMMA: word},
{ORTH: "d"}
]
EXC[orth + "'d've"] = [
{ORTH: orth, LEMMA: word},
{ORTH: "'d", LEMMA: "would", TAG: "MD"},
{ORTH: "'ve", LEMMA: "have", TAG: "VB"}
]
EXC[orth + "dve"] = [
{ORTH: orth, LEMMA: word},
{ORTH: "d", LEMMA: "would", TAG: "MD"},
{ORTH: "ve", LEMMA: "have", TAG: "VB"}
]
# Verbs
for verb_data in [
{ORTH: "ca", LEMMA: "can", TAG: "MD"},
{ORTH: "could", TAG: "MD"},
{ORTH: "do", LEMMA: "do"},
{ORTH: "does", LEMMA: "do"},
{ORTH: "did", LEMMA: "do", TAG: "VBD"},
{ORTH: "had", LEMMA: "have", TAG: "VBD"},
{ORTH: "may", TAG: "MD"},
{ORTH: "might", TAG: "MD"},
{ORTH: "must", TAG: "MD"},
{ORTH: "need"},
{ORTH: "ought"},
{ORTH: "sha", LEMMA: "shall", TAG: "MD"},
{ORTH: "should", TAG: "MD"},
{ORTH: "wo", LEMMA: "will", TAG: "MD"},
{ORTH: "would", TAG: "MD"}
]:
verb_data_tc = dict(verb_data)
verb_data_tc[ORTH] = verb_data_tc[ORTH].title()
for data in [verb_data, verb_data_tc]:
EXC[data[ORTH] + "n't"] = [
dict(data),
{ORTH: "n't", LEMMA: "not", TAG: "RB"}
]
EXC[data[ORTH] + "nt"] = [
dict(data),
{ORTH: "nt", LEMMA: "not", TAG: "RB"}
]
EXC[data[ORTH] + "n't've"] = [
dict(data),
{ORTH: "n't", LEMMA: "not", TAG: "RB"},
{ORTH: "'ve", LEMMA: "have", TAG: "VB"}
]
EXC[data[ORTH] + "ntve"] = [
dict(data),
{ORTH: "nt", LEMMA: "not", TAG: "RB"},
{ORTH: "ve", LEMMA: "have", TAG: "VB"}
]
for verb_data in [
{ORTH: "could", TAG: "MD"},
{ORTH: "might"},
{ORTH: "must"},
{ORTH: "should"}
]:
verb_data_tc = dict(verb_data)
verb_data_tc[ORTH] = verb_data_tc[ORTH].title()
for data in [verb_data, verb_data_tc]:
EXC[data[ORTH] + "'ve"] = [
dict(data),
{ORTH: "'ve", LEMMA: "have", TAG: "VB"}
]
EXC[data[ORTH] + "ve"] = [
dict(data),
{ORTH: "ve", LEMMA: "have", TAG: "VB"}
]
for verb_data in [
{ORTH: "ai", TAG: "VBP", "number": 2, LEMMA: "be"},
{ORTH: "are", LEMMA: "be", TAG: "VBP", "number": 2},
{ORTH: "is", LEMMA: "be", TAG: "VBZ"},
{ORTH: "was", LEMMA: "be"},
{ORTH: "were", LEMMA: "be"}
]:
verb_data_tc = dict(verb_data)
verb_data_tc[ORTH] = verb_data_tc[ORTH].title()
for data in [verb_data, verb_data_tc]:
EXC[data[ORTH] + "n't"] = [
dict(data),
{ORTH: "n't", LEMMA: "not", TAG: "RB"}
]
EXC[data[ORTH] + "nt"] = [
dict(data),
{ORTH: "nt", LEMMA: "not", TAG: "RB"}
]
# Other contractions with trailing apostrophe
for exc_data in [
{ORTH: "doin", LEMMA: "do", NORM: "doing"},
{ORTH: "goin", LEMMA: "go", NORM: "going"},
{ORTH: "nothin", LEMMA: "nothing"},
{ORTH: "nuthin", LEMMA: "nothing"},
{ORTH: "ol", LEMMA: "old"},
{ORTH: "somethin", LEMMA: "something"}
]:
exc_data_tc = dict(exc_data)
exc_data_tc[ORTH] = exc_data_tc[ORTH].title()
for data in [exc_data, exc_data_tc]:
data_apos = dict(data)
data_apos[ORTH] = data_apos[ORTH] + "'"
EXC[data[ORTH]] = [
dict(data)
]
EXC[data_apos[ORTH]] = [
dict(data_apos)
]
# Other contractions with leading apostrophe
for exc_data in [
{ORTH: "cause", LEMMA: "because"},
{ORTH: "em", LEMMA: PRON_LEMMA, NORM: "them"},
{ORTH: "ll", LEMMA: "will"},
{ORTH: "nuff", LEMMA: "enough"}
]:
exc_data_apos = dict(exc_data)
exc_data_apos[ORTH] = "'" + exc_data_apos[ORTH]
for data in [exc_data, exc_data_apos]:
EXC[data[ORTH]] = [
dict(data)
]
# Times
for h in range(1, 12 + 1):
hour = str(h)
for period in ["a.m.", "am"]:
EXC[hour + period] = [
{ORTH: hour},
{ORTH: period, LEMMA: "a.m."}
]
for period in ["p.m.", "pm"]:
EXC[hour + period] = [
{ORTH: hour},
{ORTH: period, LEMMA: "p.m."}
]
# Rest
OTHER = {
" ": [
{ORTH: " ", TAG: "SP"}
],
"\u00a0": [
{ORTH: "\u00a0", TAG: "SP", LEMMA: " "}
],
"'S": [
{ORTH: "'S", LEMMA: "'s"}
],
"'s": [
{ORTH: "'s", LEMMA: "'s"}
],
"'re": [
{ORTH: "'re", LEMMA: "be", NORM: "are"}
],
"\u2018S": [
{ORTH: "\u2018S", LEMMA: "'s"}
],
"\u2018s": [
{ORTH: "\u2018s", LEMMA: "'s"}
],
"and/or": [
{ORTH: "and/or", LEMMA: "and/or", TAG: "CC"}
],
"'Cause": [
{ORTH: "'Cause", LEMMA: "because"}
],
"y'all": [
{ORTH: "y'", LEMMA: PRON_LEMMA, NORM: "you"},
{ORTH: "all"}
],
"yall": [
{ORTH: "y", LEMMA: PRON_LEMMA, NORM: "you"},
{ORTH: "all"}
],
"ma'am": [
{ORTH: "ma'am", LEMMA: "madam"}
],
"Ma'am": [
{ORTH: "Ma'am", LEMMA: "madam"}
],
"o'clock": [
{ORTH: "o'clock", LEMMA: "o'clock"}
],
"O'clock": [
{ORTH: "O'clock", LEMMA: "o'clock"}
],
"how'd'y": [
{ORTH: "how", LEMMA: "how"},
{ORTH: "'d", LEMMA: "do"},
{ORTH: "'y", LEMMA: PRON_LEMMA, NORM: "you"}
],
"How'd'y": [
{ORTH: "How", LEMMA: "how"},
{ORTH: "'d", LEMMA: "do"},
{ORTH: "'y", LEMMA: PRON_LEMMA, NORM: "you"}
],
"not've": [
{ORTH: "not", LEMMA: "not", TAG: "RB"},
{ORTH: "'ve", LEMMA: "have", TAG: "VB"}
],
"notve": [
{ORTH: "not", LEMMA: "not", TAG: "RB"},
{ORTH: "ve", LEMMA: "have", TAG: "VB"}
],
"Not've": [
{ORTH: "Not", LEMMA: "not", TAG: "RB"},
{ORTH: "'ve", LEMMA: "have", TAG: "VB"}
],
"Notve": [
{ORTH: "Not", LEMMA: "not", TAG: "RB"},
{ORTH: "ve", LEMMA: "have", TAG: "VB"}
],
"cannot": [
{ORTH: "can", LEMMA: "can", TAG: "MD"},
{ORTH: "not", LEMMA: "not", TAG: "RB"}
],
"Cannot": [
{ORTH: "Can", LEMMA: "can", TAG: "MD"},
{ORTH: "not", LEMMA: "not", TAG: "RB"}
],
"gonna": [
{ORTH: "gon", LEMMA: "go", NORM: "going"},
{ORTH: "na", LEMMA: "to"}
],
"Gonna": [
{ORTH: "Gon", LEMMA: "go", NORM: "going"},
{ORTH: "na", LEMMA: "to"}
],
"gotta": [
{ORTH: "got"},
{ORTH: "ta", LEMMA: "to"}
],
"Gotta": [
{ORTH: "Got"},
{ORTH: "ta", LEMMA: "to"}
],
"let's": [
{ORTH: "let"},
{ORTH: "'s", LEMMA: PRON_LEMMA, NORM: "us"}
],
"Let's": [
{ORTH: "Let", LEMMA: "let"},
{ORTH: "'s", LEMMA: PRON_LEMMA, NORM: "us"}
],
"\u2014": [
{ORTH: "\u2014", TAG: ":", LEMMA: "--"}
],
"\n": [
{ORTH: "\n", TAG: "SP"}
],
"\t": [
{ORTH: "\t", TAG: "SP"}
]
}
# Abbreviations
ABBREVIATIONS = {
"Mt.": [
{ORTH: "Mt.", LEMMA: "Mount"}
],
"Ak.": [
{ORTH: "Ak.", LEMMA: "Alaska"}
],
"Ala.": [
{ORTH: "Ala.", LEMMA: "Alabama"}
],
"Apr.": [
{ORTH: "Apr.", LEMMA: "April"}
],
"Ariz.": [
{ORTH: "Ariz.", LEMMA: "Arizona"}
],
"Ark.": [
{ORTH: "Ark.", LEMMA: "Arkansas"}
],
"Aug.": [
{ORTH: "Aug.", LEMMA: "August"}
],
"Calif.": [
{ORTH: "Calif.", LEMMA: "California"}
],
"Colo.": [
{ORTH: "Colo.", LEMMA: "Colorado"}
],
"Conn.": [
{ORTH: "Conn.", LEMMA: "Connecticut"}
],
"Dec.": [
{ORTH: "Dec.", LEMMA: "December"}
],
"Del.": [
{ORTH: "Del.", LEMMA: "Delaware"}
],
"Feb.": [
{ORTH: "Feb.", LEMMA: "February"}
],
"Fla.": [
{ORTH: "Fla.", LEMMA: "Florida"}
],
"Ga.": [
{ORTH: "Ga.", LEMMA: "Georgia"}
],
"Ia.": [
{ORTH: "Ia.", LEMMA: "Iowa"}
],
"Id.": [
{ORTH: "Id.", LEMMA: "Idaho"}
],
"Ill.": [
{ORTH: "Ill.", LEMMA: "Illinois"}
],
"Ind.": [
{ORTH: "Ind.", LEMMA: "Indiana"}
],
"Jan.": [
{ORTH: "Jan.", LEMMA: "January"}
],
"Jul.": [
{ORTH: "Jul.", LEMMA: "July"}
],
"Jun.": [
{ORTH: "Jun.", LEMMA: "June"}
],
"Kan.": [
{ORTH: "Kan.", LEMMA: "Kansas"}
],
"Kans.": [
{ORTH: "Kans.", LEMMA: "Kansas"}
],
"Ky.": [
{ORTH: "Ky.", LEMMA: "Kentucky"}
],
"La.": [
{ORTH: "La.", LEMMA: "Louisiana"}
],
"Mar.": [
{ORTH: "Mar.", LEMMA: "March"}
],
"Mass.": [
{ORTH: "Mass.", LEMMA: "Massachusetts"}
],
"May.": [
{ORTH: "May.", LEMMA: "May"}
],
"Mich.": [
{ORTH: "Mich.", LEMMA: "Michigan"}
],
"Minn.": [
{ORTH: "Minn.", LEMMA: "Minnesota"}
],
"Miss.": [
{ORTH: "Miss.", LEMMA: "Mississippi"}
],
"N.C.": [
{ORTH: "N.C.", LEMMA: "North Carolina"}
],
"N.D.": [
{ORTH: "N.D.", LEMMA: "North Dakota"}
],
"N.H.": [
{ORTH: "N.H.", LEMMA: "New Hampshire"}
],
"N.J.": [
{ORTH: "N.J.", LEMMA: "New Jersey"}
],
"N.M.": [
{ORTH: "N.M.", LEMMA: "New Mexico"}
],
"N.Y.": [
{ORTH: "N.Y.", LEMMA: "New York"}
],
"Neb.": [
{ORTH: "Neb.", LEMMA: "Nebraska"}
],
"Nebr.": [
{ORTH: "Nebr.", LEMMA: "Nebraska"}
],
"Nev.": [
{ORTH: "Nev.", LEMMA: "Nevada"}
],
"Nov.": [
{ORTH: "Nov.", LEMMA: "November"}
],
"Oct.": [
{ORTH: "Oct.", LEMMA: "October"}
],
"Okla.": [
{ORTH: "Okla.", LEMMA: "Oklahoma"}
],
"Ore.": [
{ORTH: "Ore.", LEMMA: "Oregon"}
],
"Pa.": [
{ORTH: "Pa.", LEMMA: "Pennsylvania"}
],
"S.C.": [
{ORTH: "S.C.", LEMMA: "South Carolina"}
],
"Sep.": [
{ORTH: "Sep.", LEMMA: "September"}
],
"Sept.": [
{ORTH: "Sept.", LEMMA: "September"}
],
"Tenn.": [
{ORTH: "Tenn.", LEMMA: "Tennessee"}
],
"Va.": [
{ORTH: "Va.", LEMMA: "Virginia"}
],
"Wash.": [
{ORTH: "Wash.", LEMMA: "Washington"}
],
"Wis.": [
{ORTH: "Wis.", LEMMA: "Wisconsin"}
]
}
TOKENIZER_EXCEPTIONS = dict(EXC)
TOKENIZER_EXCEPTIONS.update(OTHER)
TOKENIZER_EXCEPTIONS.update(ABBREVIATIONS)
# Remove EXCLUDE_EXC if in exceptions
for string in EXCLUDE_EXC:
if string in TOKENIZER_EXCEPTIONS:
TOKENIZER_EXCEPTIONS.pop(string)
# Abbreviations with only one ORTH token
ORTH_ONLY = [
"'d",
"a.m.",
"Adm.",
"Bros.",
"co.",
"Co.",
"Corp.",
"D.C.",
"Dr.",
"e.g.",
"E.g.",
"E.G.",
"Gen.",
"Gov.",
"i.e.",
"I.e.",
"I.E.",
"Inc.",
"Jr.",
"Ltd.",
"Md.",
"Messrs.",
"Mo.",
"Mont.",
"Mr.",
"Mrs.",
"Ms.",
"p.m.",
"Ph.D.",
"Rep.",
"Rev.",
"Sen.",
"St.",
"vs.",
]
| # coding: utf8
from __future__ import unicode_literals
from ..symbols import *
from ..language_data import PRON_LEMMA
EXC = {}
EXCLUDE_EXC = ["Ill", "ill", "Its", "its", "Hell", "hell", "Shell", "shell",
"Shed", "shed", "were", "Were", "Well", "well", "Whore", "whore"]
# Pronouns
for pron in ["i"]:
for orth in [pron, pron.title()]:
EXC[orth + "'m"] = [
{ORTH: orth, LEMMA: PRON_LEMMA, TAG: "PRP"},
{ORTH: "'m", LEMMA: "be", TAG: "VBP", "tenspect": 1, "number": 1}
]
EXC[orth + "m"] = [
{ORTH: orth, LEMMA: PRON_LEMMA, TAG: "PRP"},
{ORTH: "m", LEMMA: "be", TAG: "VBP", "tenspect": 1, "number": 1 }
]
EXC[orth + "'ma"] = [
{ORTH: orth, LEMMA: PRON_LEMMA, TAG: "PRP"},
{ORTH: "'m", LEMMA: "be", NORM: "am"},
{ORTH: "a", LEMMA: "going to", NORM: "gonna"}
]
EXC[orth + "ma"] = [
{ORTH: orth, LEMMA: PRON_LEMMA, TAG: "PRP"},
{ORTH: "m", LEMMA: "be", NORM: "am"},
{ORTH: "a", LEMMA: "going to", NORM: "gonna"}
]
for pron in ["i", "you", "he", "she", "it", "we", "they"]:
for orth in [pron, pron.title()]:
EXC[orth + "'ll"] = [
{ORTH: orth, LEMMA: PRON_LEMMA, TAG: "PRP"},
{ORTH: "'ll", LEMMA: "will", TAG: "MD"}
]
EXC[orth + "ll"] = [
{ORTH: orth, LEMMA: PRON_LEMMA, TAG: "PRP"},
{ORTH: "ll", LEMMA: "will", TAG: "MD"}
]
EXC[orth + "'ll've"] = [
{ORTH: orth, LEMMA: PRON_LEMMA, TAG: "PRP"},
{ORTH: "'ll", LEMMA: "will", TAG: "MD"},
{ORTH: "'ve", LEMMA: "have", TAG: "VB"}
]
EXC[orth + "llve"] = [
{ORTH: orth, LEMMA: PRON_LEMMA, TAG: "PRP"},
{ORTH: "ll", LEMMA: "will", TAG: "MD"},
{ORTH: "ve", LEMMA: "have", TAG: "VB"}
]
EXC[orth + "'d"] = [
{ORTH: orth, LEMMA: PRON_LEMMA, TAG: "PRP"},
{ORTH: "'d", LEMMA: "would", TAG: "MD"}
]
EXC[orth + "d"] = [
{ORTH: orth, LEMMA: PRON_LEMMA, TAG: "PRP"},
{ORTH: "d", LEMMA: "would", TAG: "MD"}
]
EXC[orth + "'d've"] = [
{ORTH: orth, LEMMA: PRON_LEMMA, TAG: "PRP"},
{ORTH: "'d", LEMMA: "would", TAG: "MD"},
{ORTH: "'ve", LEMMA: "have", TAG: "VB"}
]
EXC[orth + "dve"] = [
{ORTH: orth, LEMMA: PRON_LEMMA, TAG: "PRP"},
{ORTH: "d", LEMMA: "would", TAG: "MD"},
{ORTH: "ve", LEMMA: "have", TAG: "VB"}
]
for pron in ["i", "you", "we", "they"]:
for orth in [pron, pron.title()]:
EXC[orth + "'ve"] = [
{ORTH: orth, LEMMA: PRON_LEMMA, TAG: "PRP"},
{ORTH: "'ve", LEMMA: "have", TAG: "VB"}
]
EXC[orth + "ve"] = [
{ORTH: orth, LEMMA: PRON_LEMMA, TAG: "PRP"},
{ORTH: "ve", LEMMA: "have", TAG: "VB"}
]
for pron in ["you", "we", "they"]:
for orth in [pron, pron.title()]:
EXC[orth + "'re"] = [
{ORTH: orth, LEMMA: PRON_LEMMA, TAG: "PRP"},
{ORTH: "'re", LEMMA: "be", NORM: "are"}
]
EXC[orth + "re"] = [
{ORTH: orth, LEMMA: PRON_LEMMA, TAG: "PRP"},
{ORTH: "re", LEMMA: "be", NORM: "are", TAG: "VBZ"}
]
for pron in ["he", "she", "it"]:
for orth in [pron, pron.title()]:
EXC[orth + "'s"] = [
{ORTH: orth, LEMMA: PRON_LEMMA, TAG: "PRP"},
{ORTH: "'s"}
]
EXC[orth + "s"] = [
{ORTH: orth, LEMMA: PRON_LEMMA, TAG: "PRP"},
{ORTH: "s"}
]
# W-words, relative pronouns, prepositions etc.
for word in ["who", "what", "when", "where", "why", "how", "there", "that"]:
for orth in [word, word.title()]:
EXC[orth + "'s"] = [
{ORTH: orth, LEMMA: word},
{ORTH: "'s"}
]
EXC[orth + "s"] = [
{ORTH: orth, LEMMA: word},
{ORTH: "s"}
]
EXC[orth + "'ll"] = [
{ORTH: orth, LEMMA: word},
{ORTH: "'ll", LEMMA: "will", TAG: "MD"}
]
EXC[orth + "ll"] = [
{ORTH: orth, LEMMA: word},
{ORTH: "ll", LEMMA: "will", TAG: "MD"}
]
EXC[orth + "'ll've"] = [
{ORTH: orth, LEMMA: word},
{ORTH: "'ll", LEMMA: "will", TAG: "MD"},
{ORTH: "'ve", LEMMA: "have", TAG: "VB"}
]
EXC[orth + "llve"] = [
{ORTH: orth, LEMMA: word},
{ORTH: "ll", LEMMA: "will", TAG: "MD"},
{ORTH: "ve", LEMMA: "have", TAG: "VB"}
]
EXC[orth + "'re"] = [
{ORTH: orth, LEMMA: word},
{ORTH: "'re", LEMMA: "be", NORM: "are"}
]
EXC[orth + "re"] = [
{ORTH: orth, LEMMA: word},
{ORTH: "re", LEMMA: "be", NORM: "are"}
]
EXC[orth + "'ve"] = [
{ORTH: orth},
{ORTH: "'ve", LEMMA: "have", TAG: "VB"}
]
EXC[orth + "ve"] = [
{ORTH: orth, LEMMA: word},
{ORTH: "ve", LEMMA: "have", TAG: "VB"}
]
EXC[orth + "'d"] = [
{ORTH: orth, LEMMA: word},
{ORTH: "'d"}
]
EXC[orth + "d"] = [
{ORTH: orth, LEMMA: word},
{ORTH: "d"}
]
EXC[orth + "'d've"] = [
{ORTH: orth, LEMMA: word},
{ORTH: "'d", LEMMA: "would", TAG: "MD"},
{ORTH: "'ve", LEMMA: "have", TAG: "VB"}
]
EXC[orth + "dve"] = [
{ORTH: orth, LEMMA: word},
{ORTH: "d", LEMMA: "would", TAG: "MD"},
{ORTH: "ve", LEMMA: "have", TAG: "VB"}
]
# Verbs
for verb_data in [
{ORTH: "ca", LEMMA: "can", TAG: "MD"},
{ORTH: "could", TAG: "MD"},
{ORTH: "do", LEMMA: "do"},
{ORTH: "does", LEMMA: "do"},
{ORTH: "did", LEMMA: "do", TAG: "VBD"},
{ORTH: "had", LEMMA: "have", TAG: "VBD"},
{ORTH: "may", TAG: "MD"},
{ORTH: "might", TAG: "MD"},
{ORTH: "must", TAG: "MD"},
{ORTH: "need"},
{ORTH: "ought"},
{ORTH: "sha", LEMMA: "shall", TAG: "MD"},
{ORTH: "should", TAG: "MD"},
{ORTH: "wo", LEMMA: "will", TAG: "MD"},
{ORTH: "would", TAG: "MD"}
]:
verb_data_tc = dict(verb_data)
verb_data_tc[ORTH] = verb_data_tc[ORTH].title()
for data in [verb_data, verb_data_tc]:
EXC[data[ORTH] + "n't"] = [
dict(data),
{ORTH: "n't", LEMMA: "not", TAG: "RB"}
]
EXC[data[ORTH] + "nt"] = [
dict(data),
{ORTH: "nt", LEMMA: "not", TAG: "RB"}
]
EXC[data[ORTH] + "n't've"] = [
dict(data),
{ORTH: "n't", LEMMA: "not", TAG: "RB"},
{ORTH: "'ve", LEMMA: "have", TAG: "VB"}
]
EXC[data[ORTH] + "ntve"] = [
dict(data),
{ORTH: "nt", LEMMA: "not", TAG: "RB"},
{ORTH: "ve", LEMMA: "have", TAG: "VB"}
]
for verb_data in [
{ORTH: "could", TAG: "MD"},
{ORTH: "might"},
{ORTH: "must"},
{ORTH: "should"}
]:
verb_data_tc = dict(verb_data)
verb_data_tc[ORTH] = verb_data_tc[ORTH].title()
for data in [verb_data, verb_data_tc]:
EXC[data[ORTH] + "'ve"] = [
dict(data),
{ORTH: "'ve", LEMMA: "have", TAG: "VB"}
]
EXC[data[ORTH] + "ve"] = [
dict(data),
{ORTH: "ve", LEMMA: "have", TAG: "VB"}
]
for verb_data in [
{ORTH: "ai", TAG: "VBP", "number": 2, LEMMA: "be"},
{ORTH: "are", LEMMA: "be", TAG: "VBP", "number": 2},
{ORTH: "is", LEMMA: "be", TAG: "VBZ"},
{ORTH: "was", LEMMA: "be"},
{ORTH: "were", LEMMA: "be"}
]:
verb_data_tc = dict(verb_data)
verb_data_tc[ORTH] = verb_data_tc[ORTH].title()
for data in [verb_data, verb_data_tc]:
EXC[data[ORTH] + "n't"] = [
dict(data),
{ORTH: "n't", LEMMA: "not", TAG: "RB"}
]
EXC[data[ORTH] + "nt"] = [
dict(data),
{ORTH: "nt", LEMMA: "not", TAG: "RB"}
]
# Other contractions with trailing apostrophe
for exc_data in [
{ORTH: "doin", LEMMA: "do", NORM: "doing"},
{ORTH: "goin", LEMMA: "go", NORM: "going"},
{ORTH: "nothin", LEMMA: "nothing"},
{ORTH: "nuthin", LEMMA: "nothing"},
{ORTH: "ol", LEMMA: "old"},
{ORTH: "somethin", LEMMA: "something"}
]:
exc_data_tc = dict(exc_data)
exc_data_tc[ORTH] = exc_data_tc[ORTH].title()
for data in [exc_data, exc_data_tc]:
data_apos = dict(data)
data_apos[ORTH] = data_apos[ORTH] + "'"
EXC[data[ORTH]] = [
dict(data)
]
EXC[data_apos[ORTH]] = [
dict(data_apos)
]
# Other contractions with leading apostrophe
for exc_data in [
{ORTH: "cause", LEMMA: "because"},
{ORTH: "em", LEMMA: PRON_LEMMA, NORM: "them"},
{ORTH: "ll", LEMMA: "will"},
{ORTH: "nuff", LEMMA: "enough"}
]:
exc_data_apos = dict(exc_data)
exc_data_apos[ORTH] = "'" + exc_data_apos[ORTH]
for data in [exc_data, exc_data_apos]:
EXC[data[ORTH]] = [
dict(data)
]
# Times
for h in range(1, 12 + 1):
hour = str(h)
for period in ["a.m.", "am"]:
EXC[hour + period] = [
{ORTH: hour},
{ORTH: period, LEMMA: "a.m."}
]
for period in ["p.m.", "pm"]:
EXC[hour + period] = [
{ORTH: hour},
{ORTH: period, LEMMA: "p.m."}
]
# Rest
OTHER = {
" ": [
{ORTH: " ", TAG: "SP"}
],
"\u00a0": [
{ORTH: "\u00a0", TAG: "SP", LEMMA: " "}
],
"'S": [
{ORTH: "'S", LEMMA: "'s"}
],
"'s": [
{ORTH: "'s", LEMMA: "'s"}
],
"'re": [
{ORTH: "'re", LEMMA: "be", NORM: "are"}
],
"\u2018S": [
{ORTH: "\u2018S", LEMMA: "'s"}
],
"\u2018s": [
{ORTH: "\u2018s", LEMMA: "'s"}
],
"and/or": [
{ORTH: "and/or", LEMMA: "and/or", TAG: "CC"}
],
"'Cause": [
{ORTH: "'Cause", LEMMA: "because"}
],
"y'all": [
{ORTH: "y'", LEMMA: PRON_LEMMA, NORM: "you"},
{ORTH: "all"}
],
"yall": [
{ORTH: "y", LEMMA: PRON_LEMMA, NORM: "you"},
{ORTH: "all"}
],
"ma'am": [
{ORTH: "ma'am", LEMMA: "madam"}
],
"Ma'am": [
{ORTH: "Ma'am", LEMMA: "madam"}
],
"o'clock": [
{ORTH: "o'clock", LEMMA: "o'clock"}
],
"O'clock": [
{ORTH: "O'clock", LEMMA: "o'clock"}
],
"how'd'y": [
{ORTH: "how", LEMMA: "how"},
{ORTH: "'d", LEMMA: "do"},
{ORTH: "'y", LEMMA: PRON_LEMMA, NORM: "you"}
],
"How'd'y": [
{ORTH: "How", LEMMA: "how"},
{ORTH: "'d", LEMMA: "do"},
{ORTH: "'y", LEMMA: PRON_LEMMA, NORM: "you"}
],
"not've": [
{ORTH: "not", LEMMA: "not", TAG: "RB"},
{ORTH: "'ve", LEMMA: "have", TAG: "VB"}
],
"notve": [
{ORTH: "not", LEMMA: "not", TAG: "RB"},
{ORTH: "ve", LEMMA: "have", TAG: "VB"}
],
"Not've": [
{ORTH: "Not", LEMMA: "not", TAG: "RB"},
{ORTH: "'ve", LEMMA: "have", TAG: "VB"}
],
"Notve": [
{ORTH: "Not", LEMMA: "not", TAG: "RB"},
{ORTH: "ve", LEMMA: "have", TAG: "VB"}
],
"cannot": [
{ORTH: "can", LEMMA: "can", TAG: "MD"},
{ORTH: "not", LEMMA: "not", TAG: "RB"}
],
"Cannot": [
{ORTH: "Can", LEMMA: "can", TAG: "MD"},
{ORTH: "not", LEMMA: "not", TAG: "RB"}
],
"gonna": [
{ORTH: "gon", LEMMA: "go", NORM: "going"},
{ORTH: "na", LEMMA: "to"}
],
"Gonna": [
{ORTH: "Gon", LEMMA: "go", NORM: "going"},
{ORTH: "na", LEMMA: "to"}
],
"gotta": [
{ORTH: "got"},
{ORTH: "ta", LEMMA: "to"}
],
"Gotta": [
{ORTH: "Got"},
{ORTH: "ta", LEMMA: "to"}
],
"let's": [
{ORTH: "let"},
{ORTH: "'s", LEMMA: PRON_LEMMA, NORM: "us"}
],
"Let's": [
{ORTH: "Let", LEMMA: "let"},
{ORTH: "'s", LEMMA: PRON_LEMMA, NORM: "us"}
],
"\u2014": [
{ORTH: "\u2014", TAG: ":", LEMMA: "--"}
],
"\n": [
{ORTH: "\n", TAG: "SP"}
],
"\t": [
{ORTH: "\t", TAG: "SP"}
]
}
# Abbreviations
ABBREVIATIONS = {
"Mt.": [
{ORTH: "Mt.", LEMMA: "Mount"}
],
"Ak.": [
{ORTH: "Ak.", LEMMA: "Alaska"}
],
"Ala.": [
{ORTH: "Ala.", LEMMA: "Alabama"}
],
"Apr.": [
{ORTH: "Apr.", LEMMA: "April"}
],
"Ariz.": [
{ORTH: "Ariz.", LEMMA: "Arizona"}
],
"Ark.": [
{ORTH: "Ark.", LEMMA: "Arkansas"}
],
"Aug.": [
{ORTH: "Aug.", LEMMA: "August"}
],
"Calif.": [
{ORTH: "Calif.", LEMMA: "California"}
],
"Colo.": [
{ORTH: "Colo.", LEMMA: "Colorado"}
],
"Conn.": [
{ORTH: "Conn.", LEMMA: "Connecticut"}
],
"Dec.": [
{ORTH: "Dec.", LEMMA: "December"}
],
"Del.": [
{ORTH: "Del.", LEMMA: "Delaware"}
],
"Feb.": [
{ORTH: "Feb.", LEMMA: "February"}
],
"Fla.": [
{ORTH: "Fla.", LEMMA: "Florida"}
],
"Ga.": [
{ORTH: "Ga.", LEMMA: "Georgia"}
],
"Ia.": [
{ORTH: "Ia.", LEMMA: "Iowa"}
],
"Id.": [
{ORTH: "Id.", LEMMA: "Idaho"}
],
"Ill.": [
{ORTH: "Ill.", LEMMA: "Illinois"}
],
"Ind.": [
{ORTH: "Ind.", LEMMA: "Indiana"}
],
"Jan.": [
{ORTH: "Jan.", LEMMA: "January"}
],
"Jul.": [
{ORTH: "Jul.", LEMMA: "July"}
],
"Jun.": [
{ORTH: "Jun.", LEMMA: "June"}
],
"Kan.": [
{ORTH: "Kan.", LEMMA: "Kansas"}
],
"Kans.": [
{ORTH: "Kans.", LEMMA: "Kansas"}
],
"Ky.": [
{ORTH: "Ky.", LEMMA: "Kentucky"}
],
"La.": [
{ORTH: "La.", LEMMA: "Louisiana"}
],
"Mar.": [
{ORTH: "Mar.", LEMMA: "March"}
],
"Mass.": [
{ORTH: "Mass.", LEMMA: "Massachusetts"}
],
"May.": [
{ORTH: "May.", LEMMA: "May"}
],
"Mich.": [
{ORTH: "Mich.", LEMMA: "Michigan"}
],
"Minn.": [
{ORTH: "Minn.", LEMMA: "Minnesota"}
],
"Miss.": [
{ORTH: "Miss.", LEMMA: "Mississippi"}
],
"N.C.": [
{ORTH: "N.C.", LEMMA: "North Carolina"}
],
"N.D.": [
{ORTH: "N.D.", LEMMA: "North Dakota"}
],
"N.H.": [
{ORTH: "N.H.", LEMMA: "New Hampshire"}
],
"N.J.": [
{ORTH: "N.J.", LEMMA: "New Jersey"}
],
"N.M.": [
{ORTH: "N.M.", LEMMA: "New Mexico"}
],
"N.Y.": [
{ORTH: "N.Y.", LEMMA: "New York"}
],
"Neb.": [
{ORTH: "Neb.", LEMMA: "Nebraska"}
],
"Nebr.": [
{ORTH: "Nebr.", LEMMA: "Nebraska"}
],
"Nev.": [
{ORTH: "Nev.", LEMMA: "Nevada"}
],
"Nov.": [
{ORTH: "Nov.", LEMMA: "November"}
],
"Oct.": [
{ORTH: "Oct.", LEMMA: "October"}
],
"Okla.": [
{ORTH: "Okla.", LEMMA: "Oklahoma"}
],
"Ore.": [
{ORTH: "Ore.", LEMMA: "Oregon"}
],
"Pa.": [
{ORTH: "Pa.", LEMMA: "Pennsylvania"}
],
"S.C.": [
{ORTH: "S.C.", LEMMA: "South Carolina"}
],
"Sep.": [
{ORTH: "Sep.", LEMMA: "September"}
],
"Sept.": [
{ORTH: "Sept.", LEMMA: "September"}
],
"Tenn.": [
{ORTH: "Tenn.", LEMMA: "Tennessee"}
],
"Va.": [
{ORTH: "Va.", LEMMA: "Virginia"}
],
"Wash.": [
{ORTH: "Wash.", LEMMA: "Washington"}
],
"Wis.": [
{ORTH: "Wis.", LEMMA: "Wisconsin"}
]
}
TOKENIZER_EXCEPTIONS = dict(EXC)
TOKENIZER_EXCEPTIONS.update(OTHER)
TOKENIZER_EXCEPTIONS.update(ABBREVIATIONS)
# Remove EXCLUDE_EXC if in exceptions
for string in EXCLUDE_EXC:
if string in TOKENIZER_EXCEPTIONS:
TOKENIZER_EXCEPTIONS.pop(string)
# Abbreviations with only one ORTH token
ORTH_ONLY = [
"'d",
"a.m.",
"Adm.",
"Bros.",
"co.",
"Co.",
"Corp.",
"D.C.",
"Dr.",
"e.g.",
"E.g.",
"E.G.",
"Gen.",
"Gov.",
"i.e.",
"I.e.",
"I.E.",
"Inc.",
"Jr.",
"Ltd.",
"Md.",
"Messrs.",
"Mo.",
"Mont.",
"Mr.",
"Mrs.",
"Ms.",
"p.m.",
"Ph.D.",
"Rep.",
"Rev.",
"Sen.",
"St.",
"vs.",
] | en | 0.776783 | # coding: utf8 # Pronouns # W-words, relative pronouns, prepositions etc. # Verbs # Other contractions with trailing apostrophe # Other contractions with leading apostrophe # Times # Rest # Abbreviations # Remove EXCLUDE_EXC if in exceptions # Abbreviations with only one ORTH token | 2.356212 | 2 |
smarts/env/wrappers/frame_stack.py | MCZhi/SMARTS | 2 | 6624346 | <reponame>MCZhi/SMARTS<filename>smarts/env/wrappers/frame_stack.py
# MIT License
#
# Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved.
#
# 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.
import copy
from collections import defaultdict, deque
from typing import Dict, List, Tuple, Union
import gym
from smarts.core import sensors
class FrameStack(gym.Wrapper):
"""Wrapper stacks num_stack (default=3) consecutive frames, in a moving-window
fashion, and returns the stacked_frames.
Note:
Wrapper returns a deepcopy of the stacked frames, which may be expensive
for large frames and large num_stack values.
"""
def __init__(self, env: gym.Env, num_stack: int = 3):
"""
Args:
env (gym.Env): Gym environment to be wrapped.
num_stack (int, optional): Number of frames to be stacked. Defaults to 3.
"""
assert num_stack > 1, f"Expected num_stack > 1, but got {num_stack}."
super(FrameStack, self).__init__(env)
self._num_stack = num_stack
self._frames = {
key: deque(maxlen=self._num_stack) for key in self.env.agent_specs.keys()
}
if self.observation_space:
self.observation_space = gym.spaces.Dict(
{
agent_id: gym.spaces.Tuple([space] * self._num_stack)
for agent_id, space in self.observation_space.spaces.items()
}
)
def _get_observations(
self, frame: Dict[str, sensors.Observation]
) -> Dict[str, List[sensors.Observation]]:
"""Update and return frames stack with given latest single frame."""
new_frames = defaultdict(list)
for agent_id, observation in frame.items():
self._frames[agent_id].appendleft(observation)
frames_list = list(self._frames[agent_id])
new_frames[agent_id] = copy.deepcopy(frames_list)
return dict(new_frames)
def step(
self, agent_actions: Dict
) -> Tuple[
Dict[str, List[sensors.Observation]],
Dict[str, float],
Dict[str, bool],
Dict[str, Dict[str, Union[float, sensors.Observation]]],
]:
"""Steps the environment by one step.
Args:
agent_actions (Dict): Actions for each agent.
Returns:
Tuple[ Dict[str, List[sensors.Observation]], Dict[str, float], Dict[str, bool], Dict[str, Dict[str, Union[float, sensors.Observation]]] ]: Observation, reward, done, info, for each agent.
"""
env_observations, rewards, dones, infos = super(FrameStack, self).step(
agent_actions
)
return self._get_observations(env_observations), rewards, dones, infos
def reset(self) -> Dict[str, List[sensors.Observation]]:
"""Resets the environment.
Returns:
Dict[str, List[sensors.Observation]]: Observation upon reset for each agent.
"""
env_observations = super(FrameStack, self).reset()
for agent_id, observation in env_observations.items():
for _ in range(self._num_stack - 1):
self._frames[agent_id].appendleft(observation)
return self._get_observations(env_observations)
| # MIT License
#
# Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved.
#
# 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.
import copy
from collections import defaultdict, deque
from typing import Dict, List, Tuple, Union
import gym
from smarts.core import sensors
class FrameStack(gym.Wrapper):
"""Wrapper stacks num_stack (default=3) consecutive frames, in a moving-window
fashion, and returns the stacked_frames.
Note:
Wrapper returns a deepcopy of the stacked frames, which may be expensive
for large frames and large num_stack values.
"""
def __init__(self, env: gym.Env, num_stack: int = 3):
"""
Args:
env (gym.Env): Gym environment to be wrapped.
num_stack (int, optional): Number of frames to be stacked. Defaults to 3.
"""
assert num_stack > 1, f"Expected num_stack > 1, but got {num_stack}."
super(FrameStack, self).__init__(env)
self._num_stack = num_stack
self._frames = {
key: deque(maxlen=self._num_stack) for key in self.env.agent_specs.keys()
}
if self.observation_space:
self.observation_space = gym.spaces.Dict(
{
agent_id: gym.spaces.Tuple([space] * self._num_stack)
for agent_id, space in self.observation_space.spaces.items()
}
)
def _get_observations(
self, frame: Dict[str, sensors.Observation]
) -> Dict[str, List[sensors.Observation]]:
"""Update and return frames stack with given latest single frame."""
new_frames = defaultdict(list)
for agent_id, observation in frame.items():
self._frames[agent_id].appendleft(observation)
frames_list = list(self._frames[agent_id])
new_frames[agent_id] = copy.deepcopy(frames_list)
return dict(new_frames)
def step(
self, agent_actions: Dict
) -> Tuple[
Dict[str, List[sensors.Observation]],
Dict[str, float],
Dict[str, bool],
Dict[str, Dict[str, Union[float, sensors.Observation]]],
]:
"""Steps the environment by one step.
Args:
agent_actions (Dict): Actions for each agent.
Returns:
Tuple[ Dict[str, List[sensors.Observation]], Dict[str, float], Dict[str, bool], Dict[str, Dict[str, Union[float, sensors.Observation]]] ]: Observation, reward, done, info, for each agent.
"""
env_observations, rewards, dones, infos = super(FrameStack, self).step(
agent_actions
)
return self._get_observations(env_observations), rewards, dones, infos
def reset(self) -> Dict[str, List[sensors.Observation]]:
"""Resets the environment.
Returns:
Dict[str, List[sensors.Observation]]: Observation upon reset for each agent.
"""
env_observations = super(FrameStack, self).reset()
for agent_id, observation in env_observations.items():
for _ in range(self._num_stack - 1):
self._frames[agent_id].appendleft(observation)
return self._get_observations(env_observations) | en | 0.699389 | # MIT License # # Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved. # # 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. Wrapper stacks num_stack (default=3) consecutive frames, in a moving-window fashion, and returns the stacked_frames. Note: Wrapper returns a deepcopy of the stacked frames, which may be expensive for large frames and large num_stack values. Args: env (gym.Env): Gym environment to be wrapped. num_stack (int, optional): Number of frames to be stacked. Defaults to 3. Update and return frames stack with given latest single frame. Steps the environment by one step. Args: agent_actions (Dict): Actions for each agent. Returns: Tuple[ Dict[str, List[sensors.Observation]], Dict[str, float], Dict[str, bool], Dict[str, Dict[str, Union[float, sensors.Observation]]] ]: Observation, reward, done, info, for each agent. Resets the environment. Returns: Dict[str, List[sensors.Observation]]: Observation upon reset for each agent. | 2.182109 | 2 |
microWebSrv.py | youxinweizhi/skill_server_for_esp32 | 10 | 6624347 | <filename>microWebSrv.py
"""
The MIT License (MIT)
Copyright 漏 2018 <NAME> & HC虏 (www.hc2.fr)
"""
from json import loads, dumps
from os import stat
from _thread import start_new_thread
import socket
import gc
import re
try :
from microWebTemplate import MicroWebTemplate
except :
pass
try :
from microWebSocket import MicroWebSocket
except :
pass
class MicroWebSrvRoute :
def __init__(self, route, method, func, routeArgNames, routeRegex) :
self.route = route
self.method = method
self.func = func
self.routeArgNames = routeArgNames
self.routeRegex = routeRegex
class MicroWebSrv :
# ============================================================================
# ===( Constants )============================================================
# ============================================================================
_indexPages = [
"index.pyhtml",
"index.html",
"index.htm",
"default.pyhtml",
"default.html",
"default.htm"
]
_mimeTypes = {
".txt" : "text/plain",
".htm" : "text/html",
".html" : "text/html",
".css" : "text/css",
".csv" : "text/csv",
".js" : "application/javascript",
".xml" : "application/xml",
".xhtml" : "application/xhtml+xml",
".json" : "application/json",
".zip" : "application/zip",
".pdf" : "application/pdf",
".jpg" : "image/jpeg",
".jpeg" : "image/jpeg",
".png" : "image/png",
".gif" : "image/gif",
".svg" : "image/svg+xml",
".ico" : "image/x-icon"
}
_html_escape_chars = {
"&" : "&",
'"' : """,
"'" : "'",
">" : ">",
"<" : "<"
}
_pyhtmlPagesExt = '.pyhtml'
# ============================================================================
# ===( Class globals )=======================================================
# ============================================================================
_docoratedRouteHandlers = []
# ============================================================================
# ===( Utils )===============================================================
# ============================================================================
@classmethod
def route(cls, url, method='GET'):
""" Adds a route handler function to the routing list """
def route_decorator(func):
item = (url, method, func)
cls._docoratedRouteHandlers.append(item)
return func
return route_decorator
# ----------------------------------------------------------------------------
@staticmethod
def HTMLEscape(s) :
return ''.join(MicroWebSrv._html_escape_chars.get(c, c) for c in s)
# ----------------------------------------------------------------------------
@staticmethod
def _startThread(func, args=()) :
try :
start_new_thread(func, args)
except :
global _mwsrv_thread_id
try :
_mwsrv_thread_id += 1
except :
_mwsrv_thread_id = 0
try :
start_new_thread('MWSRV_THREAD_%s' % _mwsrv_thread_id, func, args)
except :
return False
return True
# ----------------------------------------------------------------------------
@staticmethod
def _unquote(s) :
r = s.split('%')
for i in range(1, len(r)) :
s = r[i]
try :
r[i] = chr(int(s[:2], 16)) + s[2:]
except :
r[i] = '%' + s
return ''.join(r)
# ------------------------------------------------------------------------------
@staticmethod
def _unquote_plus(s) :
return MicroWebSrv._unquote(s.replace('+', ' '))
# ------------------------------------------------------------------------------
@staticmethod
def _fileExists(path) :
try :
stat(path)
return True
except :
return False
# ----------------------------------------------------------------------------
@staticmethod
def _isPyHTMLFile(filename) :
return filename.lower().endswith(MicroWebSrv._pyhtmlPagesExt)
# ============================================================================
# ===( Constructor )==========================================================
# ============================================================================
def __init__( self,
routeHandlers = [],
port = 80,
bindIP = '0.0.0.0',
webPath = "/flash/www" ) :
self._srvAddr = (bindIP, port)
self._webPath = webPath
self._notFoundUrl = None
self._started = False
self.MaxWebSocketRecvLen = 1024
self.WebSocketThreaded = True
self.AcceptWebSocketCallback = None
self.LetCacheStaticContentLevel = 2
self._routeHandlers = []
routeHandlers += self._docoratedRouteHandlers
for route, method, func in routeHandlers :
routeParts = route.split('/')
# -> ['', 'users', '<uID>', 'addresses', '<addrID>', 'test', '<anotherID>']
routeArgNames = []
routeRegex = ''
for s in routeParts :
if s.startswith('<') and s.endswith('>') :
routeArgNames.append(s[1:-1])
routeRegex += '/(\\w*)'
elif s :
routeRegex += '/' + s
routeRegex += '$'
# -> '/users/(\w*)/addresses/(\w*)/test/(\w*)$'
routeRegex = re.compile(routeRegex)
self._routeHandlers.append(MicroWebSrvRoute(route, method, func, routeArgNames, routeRegex))
# ============================================================================
# ===( Server Process )=======================================================
# ============================================================================
def _serverProcess(self) :
self._started = True
while True :
try :
client, cliAddr = self._server.accept()
except Exception as ex :
if ex.args and ex.args[0] == 113 :
break
continue
self._client(self, client, cliAddr)
self._started = False
# ============================================================================
# ===( Functions )============================================================
# ============================================================================
def Start(self, threaded=False) :
if not self._started :
self._server = socket.socket( socket.AF_INET,
socket.SOCK_STREAM,
socket.IPPROTO_TCP )
self._server.setsockopt( socket.SOL_SOCKET,
socket.SO_REUSEADDR,
1 )
self._server.bind(self._srvAddr)
self._server.listen(1)
if threaded :
MicroWebSrv._startThread(self._serverProcess)
else :
self._serverProcess()
# ----------------------------------------------------------------------------
def Stop(self) :
if self._started :
self._server.close()
# ----------------------------------------------------------------------------
def IsStarted(self) :
return self._started
# ----------------------------------------------------------------------------
def SetNotFoundPageUrl(self, url=None) :
self._notFoundUrl = url
# ----------------------------------------------------------------------------
def GetMimeTypeFromFilename(self, filename) :
filename = filename.lower()
for ext in self._mimeTypes :
if filename.endswith(ext) :
return self._mimeTypes[ext]
return None
# ----------------------------------------------------------------------------
def GetRouteHandler(self, resUrl, method) :
if self._routeHandlers :
#resUrl = resUrl.upper()
if resUrl.endswith('/') :
resUrl = resUrl[:-1]
method = method.upper()
for rh in self._routeHandlers :
if rh.method == method :
m = rh.routeRegex.match(resUrl)
if m : # found matching route?
if rh.routeArgNames :
routeArgs = {}
for i, name in enumerate(rh.routeArgNames) :
value = m.group(i+1)
try :
value = int(value)
except :
pass
routeArgs[name] = value
return (rh.func, routeArgs)
else :
return (rh.func, None)
return (None, None)
# ----------------------------------------------------------------------------
def _physPathFromURLPath(self, urlPath) :
if urlPath == '/' :
for idxPage in self._indexPages :
physPath = self._webPath + '/' + idxPage
if MicroWebSrv._fileExists(physPath) :
return physPath
else :
physPath = self._webPath + urlPath
if MicroWebSrv._fileExists(physPath) :
return physPath
return None
# ============================================================================
# ===( Class Client )========================================================
# ============================================================================
class _client :
# ------------------------------------------------------------------------
def __init__(self, microWebSrv, socket, addr) :
socket.settimeout(2)
self._microWebSrv = microWebSrv
self._socket = socket
self._addr = addr
self._method = None
self._path = None
self._httpVer = None
self._resPath = "/"
self._queryString = ""
self._queryParams = { }
self._headers = { }
self._contentType = None
self._contentLength = 0
if hasattr(socket, 'readline'): # MicroPython
self._socketfile = self._socket
else: # CPython
self._socketfile = self._socket.makefile('rwb')
self._processRequest()
# ------------------------------------------------------------------------
def _processRequest(self) :
try :
response = MicroWebSrv._response(self)
if self._parseFirstLine(response) :
if self._parseHeader(response) :
upg = self._getConnUpgrade()
if not upg :
routeHandler, routeArgs = self._microWebSrv.GetRouteHandler(self._resPath, self._method)
if routeHandler :
if routeArgs is not None:
routeHandler(self, response, routeArgs)
else:
routeHandler(self, response)
elif self._method.upper() == "GET" :
filepath = self._microWebSrv._physPathFromURLPath(self._resPath)
if filepath :
if MicroWebSrv._isPyHTMLFile(filepath) :
response.WriteResponsePyHTMLFile(filepath)
else :
contentType = self._microWebSrv.GetMimeTypeFromFilename(filepath)
if contentType :
if self._microWebSrv.LetCacheStaticContentLevel > 0 :
if self._microWebSrv.LetCacheStaticContentLevel > 1 and \
'if-modified-since' in self._headers :
response.WriteResponseNotModified()
else:
headers = { 'Last-Modified' : 'Fri, 1 Jan 2018 23:42:00 GMT', \
'Cache-Control' : 'max-age=315360000' }
response.WriteResponseFile(filepath, contentType, headers)
else :
response.WriteResponseFile(filepath, contentType)
else :
response.WriteResponseForbidden()
else :
response.WriteResponseNotFound()
else :
response.WriteResponseMethodNotAllowed()
elif upg == 'websocket' and 'MicroWebSocket' in globals() \
and self._microWebSrv.AcceptWebSocketCallback :
MicroWebSocket( socket = self._socket,
httpClient = self,
httpResponse = response,
maxRecvLen = self._microWebSrv.MaxWebSocketRecvLen,
threaded = self._microWebSrv.WebSocketThreaded,
acceptCallback = self._microWebSrv.AcceptWebSocketCallback )
return
else :
response.WriteResponseNotImplemented()
else :
response.WriteResponseBadRequest()
except :
response.WriteResponseInternalServerError()
try :
if self._socketfile is not self._socket:
self._socketfile.close()
self._socket.close()
except :
pass
# ------------------------------------------------------------------------
def _parseFirstLine(self, response) :
try :
elements = self._socketfile.readline().decode().strip().split()
if len(elements) == 3 :
self._method = elements[0].upper()
self._path = elements[1]
self._httpVer = elements[2].upper()
elements = self._path.split('?', 1)
if len(elements) > 0 :
self._resPath = MicroWebSrv._unquote_plus(elements[0])
if len(elements) > 1 :
self._queryString = elements[1]
elements = self._queryString.split('&')
for s in elements :
param = s.split('=', 1)
if len(param) > 0 :
value = MicroWebSrv._unquote(param[1]) if len(param) > 1 else ''
self._queryParams[MicroWebSrv._unquote(param[0])] = value
return True
except :
pass
return False
# ------------------------------------------------------------------------
def _parseHeader(self, response) :
while True :
elements = self._socketfile.readline().decode().strip().split(':', 1)
if len(elements) == 2 :
self._headers[elements[0].strip().lower()] = elements[1].strip()
elif len(elements) == 1 and len(elements[0]) == 0 :
if self._method == 'POST' or self._method == 'PUT' :
self._contentType = self._headers.get("content-type", None)
self._contentLength = int(self._headers.get("content-length", 0))
return True
else :
return False
# ------------------------------------------------------------------------
def _getConnUpgrade(self) :
if 'upgrade' in self._headers.get('connection', '').lower() :
return self._headers.get('upgrade', '').lower()
return None
# ------------------------------------------------------------------------
def GetServer(self) :
return self._microWebSrv
# ------------------------------------------------------------------------
def GetAddr(self) :
return self._addr
# ------------------------------------------------------------------------
def GetIPAddr(self) :
return self._addr[0]
# ------------------------------------------------------------------------
def GetPort(self) :
return self._addr[1]
# ------------------------------------------------------------------------
def GetRequestMethod(self) :
return self._method
# ------------------------------------------------------------------------
def GetRequestTotalPath(self) :
return self._path
# ------------------------------------------------------------------------
def GetRequestPath(self) :
return self._resPath
# ------------------------------------------------------------------------
def GetRequestQueryString(self) :
return self._queryString
# ------------------------------------------------------------------------
def GetRequestQueryParams(self) :
return self._queryParams
# ------------------------------------------------------------------------
def GetRequestHeaders(self) :
return self._headers
# ------------------------------------------------------------------------
def GetRequestContentType(self) :
return self._contentType
# ------------------------------------------------------------------------
def GetRequestContentLength(self) :
return self._contentLength
# ------------------------------------------------------------------------
def ReadRequestContent(self, size=None) :
self._socket.setblocking(False)
b = None
try :
if not size :
b = self._socketfile.read(self._contentLength)
elif size > 0 :
b = self._socketfile.read(size)
except :
pass
self._socket.setblocking(True)
return b if b else b''
# ------------------------------------------------------------------------
def ReadRequestPostedFormData(self) :
res = { }
data = self.ReadRequestContent()
if len(data) > 0 :
elements = data.decode().split('&')
for s in elements :
param = s.split('=', 1)
if len(param) > 0 :
value = MicroWebSrv._unquote(param[1]) if len(param) > 1 else ''
res[MicroWebSrv._unquote(param[0])] = value
return res
# ------------------------------------------------------------------------
def ReadRequestContentAsJSON(self) :
try :
return loads(self.ReadRequestContent())
except :
return None
# ============================================================================
# ===( Class Response )======================================================
# ============================================================================
class _response :
# ------------------------------------------------------------------------
def __init__(self, client) :
self._client = client
# ------------------------------------------------------------------------
def _write(self, data) :
if data :
if type(data) == str :
data = data.encode()
return self._client._socketfile.write(data)
return 0
# ------------------------------------------------------------------------
def _writeFirstLine(self, code) :
reason = self._responseCodes.get(code, ('Unknown reason', ))[0]
self._write("HTTP/1.1 %s %s\r\n" % (code, reason))
# ------------------------------------------------------------------------
def _writeHeader(self, name, value) :
self._write("%s: %s\r\n" % (name, value))
# ------------------------------------------------------------------------
def _writeContentTypeHeader(self, contentType, charset=None) :
if contentType :
ct = contentType \
+ (("; charset=%s" % charset) if charset else "")
else :
ct = "application/octet-stream"
self._writeHeader("Content-Type", ct)
# ------------------------------------------------------------------------
def _writeServerHeader(self) :
self._writeHeader("Server", "MicroWebSrv by JC`zic")
# ------------------------------------------------------------------------
def _writeEndHeader(self) :
self._write("\r\n")
# ------------------------------------------------------------------------
def _writeBeforeContent(self, code, headers, contentType, contentCharset, contentLength) :
self._writeFirstLine(code)
if isinstance(headers, dict) :
for header in headers :
self._writeHeader(header, headers[header])
if contentLength > 0 :
self._writeContentTypeHeader(contentType, contentCharset)
self._writeHeader("Content-Length", contentLength)
self._writeServerHeader()
self._writeHeader("Connection", "close")
self._writeEndHeader()
# ------------------------------------------------------------------------
def WriteSwitchProto(self, upgrade, headers=None) :
self._writeFirstLine(101)
self._writeHeader("Connection", "Upgrade")
self._writeHeader("Upgrade", upgrade)
if isinstance(headers, dict) :
for header in headers :
self._writeHeader(header, headers[header])
self._writeServerHeader()
self._writeEndHeader()
if self._client._socketfile is not self._client._socket :
self._client._socketfile.flush() # CPython needs flush to continue protocol
# ------------------------------------------------------------------------
def WriteResponse(self, code, headers, contentType, contentCharset, content) :
try :
if content :
if type(content) == str :
content = content.encode()
contentLength = len(content)
else :
contentLength = 0
self._writeBeforeContent(code, headers, contentType, contentCharset, contentLength)
if content :
self._write(content)
return True
except :
return False
# ------------------------------------------------------------------------
def WriteResponsePyHTMLFile(self, filepath, headers=None, vars=None) :
if 'MicroWebTemplate' in globals() :
with open(filepath, 'r') as file :
code = file.read()
mWebTmpl = MicroWebTemplate(code, escapeStrFunc=MicroWebSrv.HTMLEscape, filepath=filepath)
try :
tmplResult = mWebTmpl.Execute(None, vars)
return self.WriteResponse(200, headers, "text/html", "UTF-8", tmplResult)
except Exception as ex :
return self.WriteResponse( 500,
None,
"text/html",
"UTF-8",
self._execErrCtnTmpl % {
'module' : 'PyHTML',
'message' : str(ex)
} )
return self.WriteResponseNotImplemented()
# ------------------------------------------------------------------------
def WriteResponseFile(self, filepath, contentType=None, headers=None) :
try :
size = stat(filepath)[6]
if size > 0 :
with open(filepath, 'rb') as file :
self._writeBeforeContent(200, headers, contentType, None, size)
try :
buf = bytearray(1024)
while size > 0 :
x = file.readinto(buf)
if x < len(buf) :
buf = memoryview(buf)[:x]
self._write(buf)
size -= x
return True
except :
self.WriteResponseInternalServerError()
return False
except :
pass
self.WriteResponseNotFound()
return False
# ------------------------------------------------------------------------
def WriteResponseFileAttachment(self, filepath, attachmentName, headers=None) :
if not isinstance(headers, dict) :
headers = { }
headers["Content-Disposition"] = "attachment; filename=\"%s\"" % attachmentName
return self.WriteResponseFile(filepath, None, headers)
# ------------------------------------------------------------------------
def WriteResponseOk(self, headers=None, contentType=None, contentCharset=None, content=None) :
return self.WriteResponse(200, headers, contentType, contentCharset, content)
# ------------------------------------------------------------------------
def WriteResponseJSONOk(self, obj=None, headers=None) :
return self.WriteResponse(200, headers, "application/json", "UTF-8", dumps(obj))
# ------------------------------------------------------------------------
def WriteResponseRedirect(self, location) :
headers = { "Location" : location }
return self.WriteResponse(302, headers, None, None, None)
# ------------------------------------------------------------------------
def WriteResponseError(self, code) :
responseCode = self._responseCodes.get(code, ('Unknown reason', ''))
return self.WriteResponse( code,
None,
"text/html",
"UTF-8",
self._errCtnTmpl % {
'code' : code,
'reason' : responseCode[0],
'message' : responseCode[1]
} )
# ------------------------------------------------------------------------
def WriteResponseJSONError(self, code, obj=None) :
return self.WriteResponse( code,
None,
"application/json",
"UTF-8",
dumps(obj if obj else { }) )
# ------------------------------------------------------------------------
def WriteResponseNotModified(self) :
return self.WriteResponseError(304)
# ------------------------------------------------------------------------
def WriteResponseBadRequest(self) :
return self.WriteResponseError(400)
# ------------------------------------------------------------------------
def WriteResponseForbidden(self) :
return self.WriteResponseError(403)
# ------------------------------------------------------------------------
def WriteResponseNotFound(self) :
if self._client._microWebSrv._notFoundUrl :
self.WriteResponseRedirect(self._client._microWebSrv._notFoundUrl)
else :
return self.WriteResponseError(404)
# ------------------------------------------------------------------------
def WriteResponseMethodNotAllowed(self) :
return self.WriteResponseError(405)
# ------------------------------------------------------------------------
def WriteResponseInternalServerError(self) :
return self.WriteResponseError(500)
# ------------------------------------------------------------------------
def WriteResponseNotImplemented(self) :
return self.WriteResponseError(501)
# ------------------------------------------------------------------------
def FlashMessage(self, messageText, messageStyle='') :
if 'MicroWebTemplate' in globals() :
MicroWebTemplate.MESSAGE_TEXT = messageText
MicroWebTemplate.MESSAGE_STYLE = messageStyle
# ------------------------------------------------------------------------
_errCtnTmpl = """\
<html>
<head>
<title>Error</title>
</head>
<body>
<h1>%(code)d %(reason)s</h1>
%(message)s
</body>
</html>
"""
# ------------------------------------------------------------------------
_execErrCtnTmpl = """\
<html>
<head>
<title>Page execution error</title>
</head>
<body>
<h1>%(module)s page execution error</h1>
%(message)s
</body>
</html>
"""
# ------------------------------------------------------------------------
_responseCodes = {
100: ('Continue', 'Request received, please continue'),
101: ('Switching Protocols',
'Switching to new protocol; obey Upgrade header'),
200: ('OK', 'Request fulfilled, document follows'),
201: ('Created', 'Document created, URL follows'),
202: ('Accepted',
'Request accepted, processing continues off-line'),
203: ('Non-Authoritative Information', 'Request fulfilled from cache'),
204: ('No Content', 'Request fulfilled, nothing follows'),
205: ('Reset Content', 'Clear input form for further input.'),
206: ('Partial Content', 'Partial content follows.'),
300: ('Multiple Choices',
'Object has several resources -- see URI list'),
301: ('Moved Permanently', 'Object moved permanently -- see URI list'),
302: ('Found', 'Object moved temporarily -- see URI list'),
303: ('See Other', 'Object moved -- see Method and URL list'),
304: ('Not Modified',
'Document has not changed since given time'),
305: ('Use Proxy',
'You must use proxy specified in Location to access this '
'resource.'),
307: ('Temporary Redirect',
'Object moved temporarily -- see URI list'),
400: ('Bad Request',
'Bad request syntax or unsupported method'),
401: ('Unauthorized',
'No permission -- see authorization schemes'),
402: ('Payment Required',
'No payment -- see charging schemes'),
403: ('Forbidden',
'Request forbidden -- authorization will not help'),
404: ('Not Found', 'Nothing matches the given URI'),
405: ('Method Not Allowed',
'Specified method is invalid for this resource.'),
406: ('Not Acceptable', 'URI not available in preferred format.'),
407: ('Proxy Authentication Required', 'You must authenticate with '
'this proxy before proceeding.'),
408: ('Request Timeout', 'Request timed out; try again later.'),
409: ('Conflict', 'Request conflict.'),
410: ('Gone',
'URI no longer exists and has been permanently removed.'),
411: ('Length Required', 'Client must specify Content-Length.'),
412: ('Precondition Failed', 'Precondition in headers is false.'),
413: ('Request Entity Too Large', 'Entity is too large.'),
414: ('Request-URI Too Long', 'URI is too long.'),
415: ('Unsupported Media Type', 'Entity body in unsupported format.'),
416: ('Requested Range Not Satisfiable',
'Cannot satisfy request range.'),
417: ('Expectation Failed',
'Expect condition could not be satisfied.'),
500: ('Internal Server Error', 'Server got itself in trouble'),
501: ('Not Implemented',
'Server does not support this operation'),
502: ('Bad Gateway', 'Invalid responses from another server/proxy.'),
503: ('Service Unavailable',
'The server cannot process the request due to a high load'),
504: ('Gateway Timeout',
'The gateway server did not receive a timely response'),
505: ('HTTP Version Not Supported', 'Cannot fulfill request.'),
}
# ============================================================================
# ============================================================================
# ============================================================================
| <filename>microWebSrv.py
"""
The MIT License (MIT)
Copyright 漏 2018 <NAME> & HC虏 (www.hc2.fr)
"""
from json import loads, dumps
from os import stat
from _thread import start_new_thread
import socket
import gc
import re
try :
from microWebTemplate import MicroWebTemplate
except :
pass
try :
from microWebSocket import MicroWebSocket
except :
pass
class MicroWebSrvRoute :
def __init__(self, route, method, func, routeArgNames, routeRegex) :
self.route = route
self.method = method
self.func = func
self.routeArgNames = routeArgNames
self.routeRegex = routeRegex
class MicroWebSrv :
# ============================================================================
# ===( Constants )============================================================
# ============================================================================
_indexPages = [
"index.pyhtml",
"index.html",
"index.htm",
"default.pyhtml",
"default.html",
"default.htm"
]
_mimeTypes = {
".txt" : "text/plain",
".htm" : "text/html",
".html" : "text/html",
".css" : "text/css",
".csv" : "text/csv",
".js" : "application/javascript",
".xml" : "application/xml",
".xhtml" : "application/xhtml+xml",
".json" : "application/json",
".zip" : "application/zip",
".pdf" : "application/pdf",
".jpg" : "image/jpeg",
".jpeg" : "image/jpeg",
".png" : "image/png",
".gif" : "image/gif",
".svg" : "image/svg+xml",
".ico" : "image/x-icon"
}
_html_escape_chars = {
"&" : "&",
'"' : """,
"'" : "'",
">" : ">",
"<" : "<"
}
_pyhtmlPagesExt = '.pyhtml'
# ============================================================================
# ===( Class globals )=======================================================
# ============================================================================
_docoratedRouteHandlers = []
# ============================================================================
# ===( Utils )===============================================================
# ============================================================================
@classmethod
def route(cls, url, method='GET'):
""" Adds a route handler function to the routing list """
def route_decorator(func):
item = (url, method, func)
cls._docoratedRouteHandlers.append(item)
return func
return route_decorator
# ----------------------------------------------------------------------------
@staticmethod
def HTMLEscape(s) :
return ''.join(MicroWebSrv._html_escape_chars.get(c, c) for c in s)
# ----------------------------------------------------------------------------
@staticmethod
def _startThread(func, args=()) :
try :
start_new_thread(func, args)
except :
global _mwsrv_thread_id
try :
_mwsrv_thread_id += 1
except :
_mwsrv_thread_id = 0
try :
start_new_thread('MWSRV_THREAD_%s' % _mwsrv_thread_id, func, args)
except :
return False
return True
# ----------------------------------------------------------------------------
@staticmethod
def _unquote(s) :
r = s.split('%')
for i in range(1, len(r)) :
s = r[i]
try :
r[i] = chr(int(s[:2], 16)) + s[2:]
except :
r[i] = '%' + s
return ''.join(r)
# ------------------------------------------------------------------------------
@staticmethod
def _unquote_plus(s) :
return MicroWebSrv._unquote(s.replace('+', ' '))
# ------------------------------------------------------------------------------
@staticmethod
def _fileExists(path) :
try :
stat(path)
return True
except :
return False
# ----------------------------------------------------------------------------
@staticmethod
def _isPyHTMLFile(filename) :
return filename.lower().endswith(MicroWebSrv._pyhtmlPagesExt)
# ============================================================================
# ===( Constructor )==========================================================
# ============================================================================
def __init__( self,
routeHandlers = [],
port = 80,
bindIP = '0.0.0.0',
webPath = "/flash/www" ) :
self._srvAddr = (bindIP, port)
self._webPath = webPath
self._notFoundUrl = None
self._started = False
self.MaxWebSocketRecvLen = 1024
self.WebSocketThreaded = True
self.AcceptWebSocketCallback = None
self.LetCacheStaticContentLevel = 2
self._routeHandlers = []
routeHandlers += self._docoratedRouteHandlers
for route, method, func in routeHandlers :
routeParts = route.split('/')
# -> ['', 'users', '<uID>', 'addresses', '<addrID>', 'test', '<anotherID>']
routeArgNames = []
routeRegex = ''
for s in routeParts :
if s.startswith('<') and s.endswith('>') :
routeArgNames.append(s[1:-1])
routeRegex += '/(\\w*)'
elif s :
routeRegex += '/' + s
routeRegex += '$'
# -> '/users/(\w*)/addresses/(\w*)/test/(\w*)$'
routeRegex = re.compile(routeRegex)
self._routeHandlers.append(MicroWebSrvRoute(route, method, func, routeArgNames, routeRegex))
# ============================================================================
# ===( Server Process )=======================================================
# ============================================================================
def _serverProcess(self) :
self._started = True
while True :
try :
client, cliAddr = self._server.accept()
except Exception as ex :
if ex.args and ex.args[0] == 113 :
break
continue
self._client(self, client, cliAddr)
self._started = False
# ============================================================================
# ===( Functions )============================================================
# ============================================================================
def Start(self, threaded=False) :
if not self._started :
self._server = socket.socket( socket.AF_INET,
socket.SOCK_STREAM,
socket.IPPROTO_TCP )
self._server.setsockopt( socket.SOL_SOCKET,
socket.SO_REUSEADDR,
1 )
self._server.bind(self._srvAddr)
self._server.listen(1)
if threaded :
MicroWebSrv._startThread(self._serverProcess)
else :
self._serverProcess()
# ----------------------------------------------------------------------------
def Stop(self) :
if self._started :
self._server.close()
# ----------------------------------------------------------------------------
def IsStarted(self) :
return self._started
# ----------------------------------------------------------------------------
def SetNotFoundPageUrl(self, url=None) :
self._notFoundUrl = url
# ----------------------------------------------------------------------------
def GetMimeTypeFromFilename(self, filename) :
filename = filename.lower()
for ext in self._mimeTypes :
if filename.endswith(ext) :
return self._mimeTypes[ext]
return None
# ----------------------------------------------------------------------------
def GetRouteHandler(self, resUrl, method) :
if self._routeHandlers :
#resUrl = resUrl.upper()
if resUrl.endswith('/') :
resUrl = resUrl[:-1]
method = method.upper()
for rh in self._routeHandlers :
if rh.method == method :
m = rh.routeRegex.match(resUrl)
if m : # found matching route?
if rh.routeArgNames :
routeArgs = {}
for i, name in enumerate(rh.routeArgNames) :
value = m.group(i+1)
try :
value = int(value)
except :
pass
routeArgs[name] = value
return (rh.func, routeArgs)
else :
return (rh.func, None)
return (None, None)
# ----------------------------------------------------------------------------
def _physPathFromURLPath(self, urlPath) :
if urlPath == '/' :
for idxPage in self._indexPages :
physPath = self._webPath + '/' + idxPage
if MicroWebSrv._fileExists(physPath) :
return physPath
else :
physPath = self._webPath + urlPath
if MicroWebSrv._fileExists(physPath) :
return physPath
return None
# ============================================================================
# ===( Class Client )========================================================
# ============================================================================
class _client :
# ------------------------------------------------------------------------
def __init__(self, microWebSrv, socket, addr) :
socket.settimeout(2)
self._microWebSrv = microWebSrv
self._socket = socket
self._addr = addr
self._method = None
self._path = None
self._httpVer = None
self._resPath = "/"
self._queryString = ""
self._queryParams = { }
self._headers = { }
self._contentType = None
self._contentLength = 0
if hasattr(socket, 'readline'): # MicroPython
self._socketfile = self._socket
else: # CPython
self._socketfile = self._socket.makefile('rwb')
self._processRequest()
# ------------------------------------------------------------------------
def _processRequest(self) :
try :
response = MicroWebSrv._response(self)
if self._parseFirstLine(response) :
if self._parseHeader(response) :
upg = self._getConnUpgrade()
if not upg :
routeHandler, routeArgs = self._microWebSrv.GetRouteHandler(self._resPath, self._method)
if routeHandler :
if routeArgs is not None:
routeHandler(self, response, routeArgs)
else:
routeHandler(self, response)
elif self._method.upper() == "GET" :
filepath = self._microWebSrv._physPathFromURLPath(self._resPath)
if filepath :
if MicroWebSrv._isPyHTMLFile(filepath) :
response.WriteResponsePyHTMLFile(filepath)
else :
contentType = self._microWebSrv.GetMimeTypeFromFilename(filepath)
if contentType :
if self._microWebSrv.LetCacheStaticContentLevel > 0 :
if self._microWebSrv.LetCacheStaticContentLevel > 1 and \
'if-modified-since' in self._headers :
response.WriteResponseNotModified()
else:
headers = { 'Last-Modified' : 'Fri, 1 Jan 2018 23:42:00 GMT', \
'Cache-Control' : 'max-age=315360000' }
response.WriteResponseFile(filepath, contentType, headers)
else :
response.WriteResponseFile(filepath, contentType)
else :
response.WriteResponseForbidden()
else :
response.WriteResponseNotFound()
else :
response.WriteResponseMethodNotAllowed()
elif upg == 'websocket' and 'MicroWebSocket' in globals() \
and self._microWebSrv.AcceptWebSocketCallback :
MicroWebSocket( socket = self._socket,
httpClient = self,
httpResponse = response,
maxRecvLen = self._microWebSrv.MaxWebSocketRecvLen,
threaded = self._microWebSrv.WebSocketThreaded,
acceptCallback = self._microWebSrv.AcceptWebSocketCallback )
return
else :
response.WriteResponseNotImplemented()
else :
response.WriteResponseBadRequest()
except :
response.WriteResponseInternalServerError()
try :
if self._socketfile is not self._socket:
self._socketfile.close()
self._socket.close()
except :
pass
# ------------------------------------------------------------------------
def _parseFirstLine(self, response) :
try :
elements = self._socketfile.readline().decode().strip().split()
if len(elements) == 3 :
self._method = elements[0].upper()
self._path = elements[1]
self._httpVer = elements[2].upper()
elements = self._path.split('?', 1)
if len(elements) > 0 :
self._resPath = MicroWebSrv._unquote_plus(elements[0])
if len(elements) > 1 :
self._queryString = elements[1]
elements = self._queryString.split('&')
for s in elements :
param = s.split('=', 1)
if len(param) > 0 :
value = MicroWebSrv._unquote(param[1]) if len(param) > 1 else ''
self._queryParams[MicroWebSrv._unquote(param[0])] = value
return True
except :
pass
return False
# ------------------------------------------------------------------------
def _parseHeader(self, response) :
while True :
elements = self._socketfile.readline().decode().strip().split(':', 1)
if len(elements) == 2 :
self._headers[elements[0].strip().lower()] = elements[1].strip()
elif len(elements) == 1 and len(elements[0]) == 0 :
if self._method == 'POST' or self._method == 'PUT' :
self._contentType = self._headers.get("content-type", None)
self._contentLength = int(self._headers.get("content-length", 0))
return True
else :
return False
# ------------------------------------------------------------------------
def _getConnUpgrade(self) :
if 'upgrade' in self._headers.get('connection', '').lower() :
return self._headers.get('upgrade', '').lower()
return None
# ------------------------------------------------------------------------
def GetServer(self) :
return self._microWebSrv
# ------------------------------------------------------------------------
def GetAddr(self) :
return self._addr
# ------------------------------------------------------------------------
def GetIPAddr(self) :
return self._addr[0]
# ------------------------------------------------------------------------
def GetPort(self) :
return self._addr[1]
# ------------------------------------------------------------------------
def GetRequestMethod(self) :
return self._method
# ------------------------------------------------------------------------
def GetRequestTotalPath(self) :
return self._path
# ------------------------------------------------------------------------
def GetRequestPath(self) :
return self._resPath
# ------------------------------------------------------------------------
def GetRequestQueryString(self) :
return self._queryString
# ------------------------------------------------------------------------
def GetRequestQueryParams(self) :
return self._queryParams
# ------------------------------------------------------------------------
def GetRequestHeaders(self) :
return self._headers
# ------------------------------------------------------------------------
def GetRequestContentType(self) :
return self._contentType
# ------------------------------------------------------------------------
def GetRequestContentLength(self) :
return self._contentLength
# ------------------------------------------------------------------------
def ReadRequestContent(self, size=None) :
self._socket.setblocking(False)
b = None
try :
if not size :
b = self._socketfile.read(self._contentLength)
elif size > 0 :
b = self._socketfile.read(size)
except :
pass
self._socket.setblocking(True)
return b if b else b''
# ------------------------------------------------------------------------
def ReadRequestPostedFormData(self) :
res = { }
data = self.ReadRequestContent()
if len(data) > 0 :
elements = data.decode().split('&')
for s in elements :
param = s.split('=', 1)
if len(param) > 0 :
value = MicroWebSrv._unquote(param[1]) if len(param) > 1 else ''
res[MicroWebSrv._unquote(param[0])] = value
return res
# ------------------------------------------------------------------------
def ReadRequestContentAsJSON(self) :
try :
return loads(self.ReadRequestContent())
except :
return None
# ============================================================================
# ===( Class Response )======================================================
# ============================================================================
class _response :
# ------------------------------------------------------------------------
def __init__(self, client) :
self._client = client
# ------------------------------------------------------------------------
def _write(self, data) :
if data :
if type(data) == str :
data = data.encode()
return self._client._socketfile.write(data)
return 0
# ------------------------------------------------------------------------
def _writeFirstLine(self, code) :
reason = self._responseCodes.get(code, ('Unknown reason', ))[0]
self._write("HTTP/1.1 %s %s\r\n" % (code, reason))
# ------------------------------------------------------------------------
def _writeHeader(self, name, value) :
self._write("%s: %s\r\n" % (name, value))
# ------------------------------------------------------------------------
def _writeContentTypeHeader(self, contentType, charset=None) :
if contentType :
ct = contentType \
+ (("; charset=%s" % charset) if charset else "")
else :
ct = "application/octet-stream"
self._writeHeader("Content-Type", ct)
# ------------------------------------------------------------------------
def _writeServerHeader(self) :
self._writeHeader("Server", "MicroWebSrv by JC`zic")
# ------------------------------------------------------------------------
def _writeEndHeader(self) :
self._write("\r\n")
# ------------------------------------------------------------------------
def _writeBeforeContent(self, code, headers, contentType, contentCharset, contentLength) :
self._writeFirstLine(code)
if isinstance(headers, dict) :
for header in headers :
self._writeHeader(header, headers[header])
if contentLength > 0 :
self._writeContentTypeHeader(contentType, contentCharset)
self._writeHeader("Content-Length", contentLength)
self._writeServerHeader()
self._writeHeader("Connection", "close")
self._writeEndHeader()
# ------------------------------------------------------------------------
def WriteSwitchProto(self, upgrade, headers=None) :
self._writeFirstLine(101)
self._writeHeader("Connection", "Upgrade")
self._writeHeader("Upgrade", upgrade)
if isinstance(headers, dict) :
for header in headers :
self._writeHeader(header, headers[header])
self._writeServerHeader()
self._writeEndHeader()
if self._client._socketfile is not self._client._socket :
self._client._socketfile.flush() # CPython needs flush to continue protocol
# ------------------------------------------------------------------------
def WriteResponse(self, code, headers, contentType, contentCharset, content) :
try :
if content :
if type(content) == str :
content = content.encode()
contentLength = len(content)
else :
contentLength = 0
self._writeBeforeContent(code, headers, contentType, contentCharset, contentLength)
if content :
self._write(content)
return True
except :
return False
# ------------------------------------------------------------------------
def WriteResponsePyHTMLFile(self, filepath, headers=None, vars=None) :
if 'MicroWebTemplate' in globals() :
with open(filepath, 'r') as file :
code = file.read()
mWebTmpl = MicroWebTemplate(code, escapeStrFunc=MicroWebSrv.HTMLEscape, filepath=filepath)
try :
tmplResult = mWebTmpl.Execute(None, vars)
return self.WriteResponse(200, headers, "text/html", "UTF-8", tmplResult)
except Exception as ex :
return self.WriteResponse( 500,
None,
"text/html",
"UTF-8",
self._execErrCtnTmpl % {
'module' : 'PyHTML',
'message' : str(ex)
} )
return self.WriteResponseNotImplemented()
# ------------------------------------------------------------------------
def WriteResponseFile(self, filepath, contentType=None, headers=None) :
try :
size = stat(filepath)[6]
if size > 0 :
with open(filepath, 'rb') as file :
self._writeBeforeContent(200, headers, contentType, None, size)
try :
buf = bytearray(1024)
while size > 0 :
x = file.readinto(buf)
if x < len(buf) :
buf = memoryview(buf)[:x]
self._write(buf)
size -= x
return True
except :
self.WriteResponseInternalServerError()
return False
except :
pass
self.WriteResponseNotFound()
return False
# ------------------------------------------------------------------------
def WriteResponseFileAttachment(self, filepath, attachmentName, headers=None) :
if not isinstance(headers, dict) :
headers = { }
headers["Content-Disposition"] = "attachment; filename=\"%s\"" % attachmentName
return self.WriteResponseFile(filepath, None, headers)
# ------------------------------------------------------------------------
def WriteResponseOk(self, headers=None, contentType=None, contentCharset=None, content=None) :
return self.WriteResponse(200, headers, contentType, contentCharset, content)
# ------------------------------------------------------------------------
def WriteResponseJSONOk(self, obj=None, headers=None) :
return self.WriteResponse(200, headers, "application/json", "UTF-8", dumps(obj))
# ------------------------------------------------------------------------
def WriteResponseRedirect(self, location) :
headers = { "Location" : location }
return self.WriteResponse(302, headers, None, None, None)
# ------------------------------------------------------------------------
def WriteResponseError(self, code) :
responseCode = self._responseCodes.get(code, ('Unknown reason', ''))
return self.WriteResponse( code,
None,
"text/html",
"UTF-8",
self._errCtnTmpl % {
'code' : code,
'reason' : responseCode[0],
'message' : responseCode[1]
} )
# ------------------------------------------------------------------------
def WriteResponseJSONError(self, code, obj=None) :
return self.WriteResponse( code,
None,
"application/json",
"UTF-8",
dumps(obj if obj else { }) )
# ------------------------------------------------------------------------
def WriteResponseNotModified(self) :
return self.WriteResponseError(304)
# ------------------------------------------------------------------------
def WriteResponseBadRequest(self) :
return self.WriteResponseError(400)
# ------------------------------------------------------------------------
def WriteResponseForbidden(self) :
return self.WriteResponseError(403)
# ------------------------------------------------------------------------
def WriteResponseNotFound(self) :
if self._client._microWebSrv._notFoundUrl :
self.WriteResponseRedirect(self._client._microWebSrv._notFoundUrl)
else :
return self.WriteResponseError(404)
# ------------------------------------------------------------------------
def WriteResponseMethodNotAllowed(self) :
return self.WriteResponseError(405)
# ------------------------------------------------------------------------
def WriteResponseInternalServerError(self) :
return self.WriteResponseError(500)
# ------------------------------------------------------------------------
def WriteResponseNotImplemented(self) :
return self.WriteResponseError(501)
# ------------------------------------------------------------------------
def FlashMessage(self, messageText, messageStyle='') :
if 'MicroWebTemplate' in globals() :
MicroWebTemplate.MESSAGE_TEXT = messageText
MicroWebTemplate.MESSAGE_STYLE = messageStyle
# ------------------------------------------------------------------------
_errCtnTmpl = """\
<html>
<head>
<title>Error</title>
</head>
<body>
<h1>%(code)d %(reason)s</h1>
%(message)s
</body>
</html>
"""
# ------------------------------------------------------------------------
_execErrCtnTmpl = """\
<html>
<head>
<title>Page execution error</title>
</head>
<body>
<h1>%(module)s page execution error</h1>
%(message)s
</body>
</html>
"""
# ------------------------------------------------------------------------
_responseCodes = {
100: ('Continue', 'Request received, please continue'),
101: ('Switching Protocols',
'Switching to new protocol; obey Upgrade header'),
200: ('OK', 'Request fulfilled, document follows'),
201: ('Created', 'Document created, URL follows'),
202: ('Accepted',
'Request accepted, processing continues off-line'),
203: ('Non-Authoritative Information', 'Request fulfilled from cache'),
204: ('No Content', 'Request fulfilled, nothing follows'),
205: ('Reset Content', 'Clear input form for further input.'),
206: ('Partial Content', 'Partial content follows.'),
300: ('Multiple Choices',
'Object has several resources -- see URI list'),
301: ('Moved Permanently', 'Object moved permanently -- see URI list'),
302: ('Found', 'Object moved temporarily -- see URI list'),
303: ('See Other', 'Object moved -- see Method and URL list'),
304: ('Not Modified',
'Document has not changed since given time'),
305: ('Use Proxy',
'You must use proxy specified in Location to access this '
'resource.'),
307: ('Temporary Redirect',
'Object moved temporarily -- see URI list'),
400: ('Bad Request',
'Bad request syntax or unsupported method'),
401: ('Unauthorized',
'No permission -- see authorization schemes'),
402: ('Payment Required',
'No payment -- see charging schemes'),
403: ('Forbidden',
'Request forbidden -- authorization will not help'),
404: ('Not Found', 'Nothing matches the given URI'),
405: ('Method Not Allowed',
'Specified method is invalid for this resource.'),
406: ('Not Acceptable', 'URI not available in preferred format.'),
407: ('Proxy Authentication Required', 'You must authenticate with '
'this proxy before proceeding.'),
408: ('Request Timeout', 'Request timed out; try again later.'),
409: ('Conflict', 'Request conflict.'),
410: ('Gone',
'URI no longer exists and has been permanently removed.'),
411: ('Length Required', 'Client must specify Content-Length.'),
412: ('Precondition Failed', 'Precondition in headers is false.'),
413: ('Request Entity Too Large', 'Entity is too large.'),
414: ('Request-URI Too Long', 'URI is too long.'),
415: ('Unsupported Media Type', 'Entity body in unsupported format.'),
416: ('Requested Range Not Satisfiable',
'Cannot satisfy request range.'),
417: ('Expectation Failed',
'Expect condition could not be satisfied.'),
500: ('Internal Server Error', 'Server got itself in trouble'),
501: ('Not Implemented',
'Server does not support this operation'),
502: ('Bad Gateway', 'Invalid responses from another server/proxy.'),
503: ('Service Unavailable',
'The server cannot process the request due to a high load'),
504: ('Gateway Timeout',
'The gateway server did not receive a timely response'),
505: ('HTTP Version Not Supported', 'Cannot fulfill request.'),
}
# ============================================================================
# ============================================================================
# ============================================================================
| en | 0.190156 | The MIT License (MIT) Copyright 漏 2018 <NAME> & HC虏 (www.hc2.fr) # ============================================================================ # ===( Constants )============================================================ # ============================================================================ # ============================================================================ # ===( Class globals )======================================================= # ============================================================================ # ============================================================================ # ===( Utils )=============================================================== # ============================================================================ Adds a route handler function to the routing list # ---------------------------------------------------------------------------- # ---------------------------------------------------------------------------- # ---------------------------------------------------------------------------- # ------------------------------------------------------------------------------ # ------------------------------------------------------------------------------ # ---------------------------------------------------------------------------- # ============================================================================ # ===( Constructor )========================================================== # ============================================================================ # -> ['', 'users', '<uID>', 'addresses', '<addrID>', 'test', '<anotherID>'] # -> '/users/(\w*)/addresses/(\w*)/test/(\w*)$' # ============================================================================ # ===( Server Process )======================================================= # ============================================================================ # ============================================================================ # ===( Functions )============================================================ # ============================================================================ # ---------------------------------------------------------------------------- # ---------------------------------------------------------------------------- # ---------------------------------------------------------------------------- # ---------------------------------------------------------------------------- # ---------------------------------------------------------------------------- #resUrl = resUrl.upper() # found matching route? # ---------------------------------------------------------------------------- # ============================================================================ # ===( Class Client )======================================================== # ============================================================================ # ------------------------------------------------------------------------ # MicroPython # CPython # ------------------------------------------------------------------------ # ------------------------------------------------------------------------ # ------------------------------------------------------------------------ # ------------------------------------------------------------------------ # ------------------------------------------------------------------------ # ------------------------------------------------------------------------ # ------------------------------------------------------------------------ # ------------------------------------------------------------------------ # ------------------------------------------------------------------------ # ------------------------------------------------------------------------ # ------------------------------------------------------------------------ # ------------------------------------------------------------------------ # ------------------------------------------------------------------------ # ------------------------------------------------------------------------ # ------------------------------------------------------------------------ # ------------------------------------------------------------------------ # ------------------------------------------------------------------------ # ------------------------------------------------------------------------ # ------------------------------------------------------------------------ # ============================================================================ # ===( Class Response )====================================================== # ============================================================================ # ------------------------------------------------------------------------ # ------------------------------------------------------------------------ # ------------------------------------------------------------------------ # ------------------------------------------------------------------------ # ------------------------------------------------------------------------ # ------------------------------------------------------------------------ # ------------------------------------------------------------------------ # ------------------------------------------------------------------------ # ------------------------------------------------------------------------ # CPython needs flush to continue protocol # ------------------------------------------------------------------------ # ------------------------------------------------------------------------ # ------------------------------------------------------------------------ # ------------------------------------------------------------------------ # ------------------------------------------------------------------------ # ------------------------------------------------------------------------ # ------------------------------------------------------------------------ # ------------------------------------------------------------------------ # ------------------------------------------------------------------------ # ------------------------------------------------------------------------ # ------------------------------------------------------------------------ # ------------------------------------------------------------------------ # ------------------------------------------------------------------------ # ------------------------------------------------------------------------ # ------------------------------------------------------------------------ # ------------------------------------------------------------------------ # ------------------------------------------------------------------------ # ------------------------------------------------------------------------ \ <html> <head> <title>Error</title> </head> <body> <h1>%(code)d %(reason)s</h1> %(message)s </body> </html> # ------------------------------------------------------------------------ \ <html> <head> <title>Page execution error</title> </head> <body> <h1>%(module)s page execution error</h1> %(message)s </body> </html> # ------------------------------------------------------------------------ # ============================================================================ # ============================================================================ # ============================================================================ | 2.357596 | 2 |
lisp/repl.py | fotcorn/lisp | 0 | 6624348 | <reponame>fotcorn/lisp
import os
import sys
import readline
import atexit
from lisp import interpreter_builtins
from lisp.interpreter_exceptions import InterpreterException
from lisp.lexer import lex, LexerException
from lisp.parser import parse, ParseError
from lisp.interpreter import run, Interpreter
def repl():
print("Write 'exit' to exit repl")
history_file = os.path.join(os.path.expanduser("~"), ".fotcorn_lisp_history")
try:
readline.read_history_file(history_file)
readline.set_history_length(1000)
except FileNotFoundError:
pass
atexit.register(readline.write_history_file, history_file)
readline.parse_and_bind('')
interpreter = Interpreter(interpreter_builtins.builtins)
while True:
try:
line = input('>>> ')
except EOFError:
sys.exit(0)
except KeyboardInterrupt:
print()
continue
if line == 'exit':
sys.exit(0)
try:
tokens = lex(line)
ast = parse(tokens)
value = interpreter.run(ast)
if value:
print(value)
except (LexerException, ParseError, InterpreterException) as ex:
print('Error: ', str(ex))
| import os
import sys
import readline
import atexit
from lisp import interpreter_builtins
from lisp.interpreter_exceptions import InterpreterException
from lisp.lexer import lex, LexerException
from lisp.parser import parse, ParseError
from lisp.interpreter import run, Interpreter
def repl():
print("Write 'exit' to exit repl")
history_file = os.path.join(os.path.expanduser("~"), ".fotcorn_lisp_history")
try:
readline.read_history_file(history_file)
readline.set_history_length(1000)
except FileNotFoundError:
pass
atexit.register(readline.write_history_file, history_file)
readline.parse_and_bind('')
interpreter = Interpreter(interpreter_builtins.builtins)
while True:
try:
line = input('>>> ')
except EOFError:
sys.exit(0)
except KeyboardInterrupt:
print()
continue
if line == 'exit':
sys.exit(0)
try:
tokens = lex(line)
ast = parse(tokens)
value = interpreter.run(ast)
if value:
print(value)
except (LexerException, ParseError, InterpreterException) as ex:
print('Error: ', str(ex)) | none | 1 | 2.769726 | 3 | |
reinvent_models/model_factory/generative_model.py | GT4SD/-reinvent_models | 0 | 6624349 | <filename>reinvent_models/model_factory/generative_model.py
from reinvent_models.model_factory.configurations.model_configuration import ModelConfiguration
from reinvent_models.model_factory.enums.model_type_enum import ModelTypeEnum
from reinvent_models.model_factory.generative_model_base import GenerativeModelBase
from reinvent_models.model_factory.lib_invent_adapter import LibInventAdapter
from reinvent_models.model_factory.link_invent_adapter import LinkInventAdapter
from reinvent_models.model_factory.reinvent_core_adapter import ReinventCoreAdapter
class GenerativeModel:
def __new__(cls, configuration: ModelConfiguration) -> GenerativeModelBase:
cls._configuration = configuration
model_type_enum = ModelTypeEnum()
if cls._configuration.model_type == model_type_enum.DEFAULT:
model = ReinventCoreAdapter(cls._configuration.model_file_path, mode=cls._configuration.model_mode)
elif cls._configuration.model_type == model_type_enum.LIB_INVENT:
model = LibInventAdapter(cls._configuration.model_file_path, mode=cls._configuration.model_mode)
elif cls._configuration.model_type == model_type_enum.LINK_INVENT:
model = LinkInventAdapter(cls._configuration.model_file_path, mode=cls._configuration.model_mode)
else:
raise ValueError(f"Invalid model_type provided: '{cls._configuration.model_type}")
return model
| <filename>reinvent_models/model_factory/generative_model.py
from reinvent_models.model_factory.configurations.model_configuration import ModelConfiguration
from reinvent_models.model_factory.enums.model_type_enum import ModelTypeEnum
from reinvent_models.model_factory.generative_model_base import GenerativeModelBase
from reinvent_models.model_factory.lib_invent_adapter import LibInventAdapter
from reinvent_models.model_factory.link_invent_adapter import LinkInventAdapter
from reinvent_models.model_factory.reinvent_core_adapter import ReinventCoreAdapter
class GenerativeModel:
def __new__(cls, configuration: ModelConfiguration) -> GenerativeModelBase:
cls._configuration = configuration
model_type_enum = ModelTypeEnum()
if cls._configuration.model_type == model_type_enum.DEFAULT:
model = ReinventCoreAdapter(cls._configuration.model_file_path, mode=cls._configuration.model_mode)
elif cls._configuration.model_type == model_type_enum.LIB_INVENT:
model = LibInventAdapter(cls._configuration.model_file_path, mode=cls._configuration.model_mode)
elif cls._configuration.model_type == model_type_enum.LINK_INVENT:
model = LinkInventAdapter(cls._configuration.model_file_path, mode=cls._configuration.model_mode)
else:
raise ValueError(f"Invalid model_type provided: '{cls._configuration.model_type}")
return model
| none | 1 | 1.891927 | 2 | |
for_loops_protists.py | julencosme/python-crash-course | 0 | 6624350 | protists_chlorophyta = ['volvox', 'actinastrum', 'hydrodictyon']
for protist in protists_chlorophyta:
print(protist)
for protist in protists_chlorophyta:
print(protist.title() + ", is a protist in the green algae genus.")
print("All of these protists are in the division Chlorophyta, and they are photosynthetic organisms.")
| protists_chlorophyta = ['volvox', 'actinastrum', 'hydrodictyon']
for protist in protists_chlorophyta:
print(protist)
for protist in protists_chlorophyta:
print(protist.title() + ", is a protist in the green algae genus.")
print("All of these protists are in the division Chlorophyta, and they are photosynthetic organisms.")
| none | 1 | 3.128229 | 3 |