pingu_98/cara
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

Prepare the repository for an Apache 2.0 license.

Browse source
This commit is contained in:
Phil Elson 2021-04-16 09:14:46 +02:00
parent 2a43eea709
commit 597a27eadc
11 changed files with 187 additions and 107 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

13
LICENSE Normal file
View file

@ -0,0 +1,13 @@
Copyright 2020-2021 CERN
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.

4
cara/apps/calculator/themes/cern/templates/calculator.report.html.j2
View file

@ -85,9 +85,7 @@
{% block disclaimer %}
{{ super() }}
<p>
CARA is made available for internal CERN use only.
It is intended for Members of Personnel with roles related to Supervision, Health & Safety or Space Management, in order to simulate the concerned workplaces on CERN sites.
For use outside of this scope, it has to be performed under a pre-defined framework and licence agreement issued by CERN Knowledge Transfer (<a href="mailto:kt@cern.ch">kt@cern.ch</a>).
At CERN, CARA is intended for Members of Personnel with roles related to Supervision, Health & Safety or Space Management, in order to simulate the concerned workplaces on CERN sites.
</p>
{% endblock disclaimer %}

16
cara/apps/templates/layout.html.j2
View file

@ -193,7 +193,6 @@
policy issues. Any initiative is conducted on a best effort and as-is basis, without liability or
warranty.</em></span></p>
</div>
</section>
</div>
@ -231,9 +230,22 @@
</path>
</svg></a>
</li>
<li>
<a href="https://gitlab.cern.ch/cara/cara" title="CARA code repository" data-extlink="">
Source code
</a>
</li>
</ul>
</nav>
<p style="font-size:10px;">
CARA is <a href="https://gitlab.cern.ch/cara/cara/-/blob/master/LICENSE">Apache 2.0 licensed</a> open-source
software developed at CERN.
You can find the source code at <a href="https://gitlab.cern.ch/cara/cara">https://gitlab.cern.ch/cara/cara</a>,
where we welcome contributions, feature requests and issue reports.
</p>
</div>
</div>
<!-- general info -->
@ -283,7 +295,7 @@
</path>
</svg></a>
&nbsp;©
2020
2020 - <script>document.write(new Date().getFullYear());</script>
CERN</div>
</footer>

67
cara/models.py
View file

@ -31,9 +31,11 @@ the same for all parameters of a single model.
"""
from dataclasses import dataclass
import numpy as np
import typing
import numpy as np
from scipy.interpolate import interp1d
if not typing.TYPE_CHECKING:
from memoization import cached
else:
@ -133,15 +135,18 @@ class PiecewiseConstant:
transition_times: typing.Tuple[float, ...]
#: values of the function between transitions
values: typing.Tuple[float, ...]
values: typing.Tuple[_VectorisedFloat, ...]
def __post_init__(self):
if len(self.transition_times) != len(self.values)+1:
raise ValueError("transition_times should contain one more element than values")
if tuple(sorted(set(self.transition_times))) != self.transition_times:
raise ValueError("transition_times should not contain duplicated elements and should be sorted")
shapes = [np.array(v).shape for v in self.values]
if not all(shapes[0] == shape for shape in shapes):
raise ValueError("All values must have the same shape")
def value(self, time) -> float:
def value(self, time) -> _VectorisedFloat:
if time <= self.transition_times[0]:
return self.values[0]
elif time > self.transition_times[-1]:
@ -159,21 +164,21 @@ class PiecewiseConstant:
for t1, t2, value in zip(self.transition_times[:-1],
self.transition_times[1:], self.values):
if value:
present_times.append((t1,t2))
present_times.append((t1, t2))
return SpecificInterval(present_times=tuple(present_times))
def refine(self, refine_factor=10):
def refine(self, refine_factor=10) -> "PiecewiseConstant":
# build a new PiecewiseConstant object with a refined mesh,
# using a linear interpolation in-between the initial mesh points
refined_times = np.linspace(self.transition_times[0], self.transition_times[-1],
(len(self.transition_times)-1) * refine_factor+1)
interpolator = interp1d(
self.transition_times,
np.concatenate([self.values, self.values[-1:]], axis=0),
axis=0)
return PiecewiseConstant(
tuple(refined_times),
tuple(np.interp(
refined_times[:-1],
self.transition_times,
self.values + (self.values[-1], ),
)),
tuple(interpolator(refined_times)[:-1]),
)
@ -254,10 +259,10 @@ class WindowOpening(Ventilation):
outside_temp: PiecewiseConstant
#: The height of the window (m).
window_height: float
window_height: _VectorisedFloat
#: The length of the opening-gap when the window is open (m).
opening_length: float
opening_length: _VectorisedFloat
#: The number of windows of the given dimensions.
number_of_windows: int = 1
@ -266,7 +271,7 @@ class WindowOpening(Ventilation):
min_deltaT: float = 0.1
@property
def discharge_coefficient(self) -> float:
def discharge_coefficient(self) -> _VectorisedFloat:
"""
Discharge coefficient (or cd_b): what portion effective area is
used to exchange air (0 <= discharge_coefficient <= 1).
@ -286,14 +291,14 @@ class WindowOpening(Ventilation):
return 0.
# Reminder, no dependence on time in the resulting calculation.
inside_temp = self.inside_temp.value(time)
outside_temp = self.outside_temp.value(time)
inside_temp: _VectorisedFloat = self.inside_temp.value(time)
outside_temp: _VectorisedFloat = self.outside_temp.value(time)
# The inside_temperature is forced to be always at least min_deltaT degree
# warmer than the outside_temperature. Further research needed to
# handle the buoyancy driven ventilation when the temperature gradient
# is inverted.
inside_temp = max(inside_temp, outside_temp + self.min_deltaT)
inside_temp = np.maximum(inside_temp, outside_temp + self.min_deltaT) # type: ignore
temp_gradient = (inside_temp - outside_temp) / outside_temp
root = np.sqrt(9.81 * self.window_height * temp_gradient)
window_area = self.window_height * self.opening_length * self.number_of_windows
@ -307,7 +312,7 @@ class SlidingWindow(WindowOpening):
the horizontal plane).
"""
@property
def discharge_coefficient(self) -> float:
def discharge_coefficient(self) -> _VectorisedFloat:
"""
Average measured value of discharge coefficient for sliding or
side-hung windows.
@ -322,14 +327,14 @@ class HingedWindow(WindowOpening):
horizontal plane).
"""
#: Window width (m).
window_width: float = 0.0
window_width: _VectorisedFloat = 0.0
def __post_init__(self):
if not self.window_width > 0:
if self.window_width is 0.0:
raise ValueError('window_width must be set')
@property
def discharge_coefficient(self) -> float:
def discharge_coefficient(self) -> _VectorisedFloat:
"""
Simple model to compute discharge coefficient for top or bottom
hung hinged windows, in the absence of empirical test results
@ -339,19 +344,15 @@ class HingedWindow(WindowOpening):
see Section 8.3 of BB101 and Section 11.3 of
ESFA Output Specification Annex 2F on Ventilation opening areas.
"""
window_ratio = self.window_width / self.window_height
if window_ratio < 0.5:
M = 0.06
cd_max = 0.612
elif window_ratio < 1:
M = 0.048
cd_max = 0.589
elif window_ratio < 2:
M = 0.04
cd_max = 0.563
else:
M = 0.038
cd_max = 0.548
window_ratio = np.array(self.window_width / self.window_height)
coefs = np.empty(window_ratio.shape + (2, ), dtype=np.float64)
coefs[window_ratio < 0.5] = (0.06, 0.612)
coefs[np.bitwise_and(0.5 <= window_ratio, window_ratio < 1)] = (0.048, 0.589)
coefs[np.bitwise_and(1 <= window_ratio, window_ratio < 2)] = (0.04, 0.563)
coefs[window_ratio >= 2] = (0.038, 0.548)
M, cd_max = coefs.T
window_angle = 2.*np.rad2deg(np.arcsin(self.opening_length/(2.*self.window_height)))
return cd_max*(1-np.exp(-M*window_angle))

1
cara/tests/models/test_concentration_model.py
View file

@ -13,7 +13,6 @@ import cara.models
{'coefficient_of_infectivity': np.array([0.02, 0.05])},
{'η_exhale': np.array([0.92, 0.95])},
{'η_leaks': np.array([0.15, 0.20])},
]
)
def test_concentration_model_vectorisation(override_params):

85
cara/tests/models/test_piecewiseconstant.py Normal file
View file

@ -0,0 +1,85 @@
import numpy as np
import pytest
from cara import models
from cara import data
def test_piecewiseconstantfunction_wrongarguments():
# number of values should be 1+number of transition times
pytest.raises(ValueError, models.PiecewiseConstant, (0, 1), (0, 0))
pytest.raises(ValueError, models.PiecewiseConstant, (0,), (0, 0))
# two transition times cannot be equal
pytest.raises(ValueError, models.PiecewiseConstant, (0, 2, 2), (0, 0))
# unsorted transition times are not allowed
pytest.raises(ValueError, models.PiecewiseConstant, (2, 0), (0, 0))
# If vectors, must all be same length.
with pytest.raises(ValueError, match="All values must have the same shape"):
models.PiecewiseConstant(
(0, 8, 16), (np.array([5, 7]), np.array([8, 9, 10])),
)
@pytest.mark.parametrize(
"time, expected_value",
[
[10, 5],
[20.5, 8],
[8, 2],
[0, 2],
[24, 8],
[-1, 2],
[25, 8],
],
)
def test_piecewiseconstant(time, expected_value):
transition_times = (0, 8, 16, 24)
values = (2, 5, 8)
fun = models.PiecewiseConstant(transition_times, values)
assert fun.value(time) == expected_value
def test_piecewiseconstant_interp():
transition_times = (0, 8, 16, 24)
values = (2, 5, 8)
refined_fun = models.PiecewiseConstant(transition_times, values).refine(refine_factor=2)
assert refined_fun.transition_times == (0, 4, 8, 12, 16, 20, 24)
assert refined_fun.values == (2, 3.5, 5, 6.5, 8, 8)
def test_piecewiseconstant_interp_vectorised():
transition_times = (0, 8, 16, 24)
values = (np.array([2, 3]), np.array([5, 7]), np.array([8, 9]))
refined_fun = models.PiecewiseConstant(transition_times, values).refine(refine_factor=2)
assert refined_fun.transition_times == (0, 4, 8, 12, 16, 20, 24)
np.testing.assert_almost_equal(
refined_fun.values, ((2, 3), (3.5, 5), (5, 7), (6.5, 8), (8, 9), (8, 9)),
)
def test_constantfunction():
transition_times = (0, 24)
values = (20,)
fun = models.PiecewiseConstant(transition_times, values)
for t in [0, 1, 8, 10, 16, 20.1, 24]:
assert (fun.value(t) == 20)
@pytest.mark.parametrize(
"time",
[0, 1, 8, 10, 16, 20.1, 24],
)
def test_piecewiseconstant_vs_interval(time):
transition_times = (0, 8, 16, 24)
values = (0, 1, 0)
fun = models.PiecewiseConstant(transition_times, values)
interval = models.SpecificInterval(present_times=[(8,16)])
assert interval.transition_times() == fun.interval().transition_times()
assert fun.interval().triggered(time) == interval.triggered(time)
def test_piecewiseconstant_transition_times():
outside_temp = data.GenevaTemperatures['Jan']
assert set(outside_temp.transition_times) == outside_temp.interval().transition_times()

64
cara/tests/test_known_quantities.py
View file

@ -190,70 +190,6 @@ def test_expiration_aerosols():
npt.assert_allclose(exp1.aerosols(mask), exp2.aerosols(mask), rtol=1e-5)
def test_piecewiseconstantfunction_wrongarguments():
# number of values should be 1+number of transition times
pytest.raises(ValueError,models.PiecewiseConstant,(0,1),(0,0))
pytest.raises(ValueError,models.PiecewiseConstant,(0,),(0,0))
# two transition times cannot be equal
pytest.raises(ValueError,models.PiecewiseConstant,(0,2,2),(0,0))
# unsorted transition times are not allowed
pytest.raises(ValueError,models.PiecewiseConstant,(2,0),(0,0))
@pytest.mark.parametrize(
"time, expected_value",
[
[10, 5],
[20.5, 8],
[8, 2],
[0, 2],
[24, 8],
[-1, 2],
[25, 8],
],
)
def test_piecewiseconstant(time, expected_value):
transition_times = (0, 8, 16, 24)
values = (2, 5, 8)
fun = models.PiecewiseConstant(transition_times,values)
assert fun.value(time) == expected_value
def test_piecewiseconstant_interp():
transition_times = (0, 8, 16, 24)
values = (2, 5, 8)
fun = models.PiecewiseConstant(transition_times,values)
refined_fun = models.PiecewiseConstant(transition_times,values).refine(refine_factor=2)
assert refined_fun.transition_times == (0, 4, 8, 12, 16, 20 ,24)
assert refined_fun.values == (2, 3.5, 5, 6.5, 8, 8)
def test_constantfunction():
transition_times = (0,24)
values = (20,)
fun = models.PiecewiseConstant(transition_times,values)
for t in [0,1,8,10,16,20.1,24]:
assert (fun.value(t) == 20)
@pytest.mark.parametrize(
"time",
[0,1,8,10,16,20.1,24],
)
def test_piecewiseconstant_vs_interval(time):
transition_times = (0,8,16,24)
values = (0,1,0)
fun = models.PiecewiseConstant(transition_times,values)
interval = models.SpecificInterval(present_times=[(8,16)])
assert interval.transition_times() == fun.interval().transition_times()
assert fun.interval().triggered(time) == interval.triggered(time)
def test_piecewiseconstant_transition_times():
outside_temp=data.GenevaTemperatures['Jan']
assert set(outside_temp.transition_times) == outside_temp.interval().transition_times()
@pytest.mark.parametrize(
"time, expected_value",
[

38
cara/tests/test_ventilation.py
View file

@ -46,9 +46,8 @@ def test_number_of_windows(baseline_slidingwindow):
[4., 0.308],
],
)
def test_hinged_window(baseline_hingedwindow,window_width,
def test_hinged_window(baseline_hingedwindow, window_width,
expected_discharge_coefficient):
room = models.Room(75)
hinged_window = dataclasses.replace(baseline_hingedwindow,
window_width=window_width)
@ -56,9 +55,40 @@ def test_hinged_window(baseline_hingedwindow,window_width,
expected_discharge_coefficient, rtol=1e-2)
def test_sliding_window(baseline_slidingwindow):
room = models.Room(75)
@pytest.mark.parametrize(
"override_params", [
{'window_height': np.array([0.15, 0.20, 0.25])},
{'window_width': np.array([0.15, 0.20, 0.25])},
{'opening_length': np.array([0.15, 0.20, 0.25])},
{'outside_temp': models.PiecewiseConstant(
(0, 2, 3), (np.array([20, 30, 28]), np.array([25, 30, 27]))
)},
{'inside_temp': models.PiecewiseConstant(
(0, 20), (np.array([20, 30, 25]), )
)},
]
)
def test_HingedWindow_vectorisation(override_params):
defaults = {
'window_height': 0.15,
'window_width': 0.15,
'opening_length': 0.15,
'inside_temp': models.PiecewiseConstant((0, 2, 3), (20, 25)),
'outside_temp': models.PiecewiseConstant((0, 2, 3), (10, 15)),
}
defaults.update(override_params)
room = models.Room(volume=75)
t = 0.5
window = models.HingedWindow(models.PeriodicInterval(60, 30), **defaults)
if {'window_height', 'opening_length', 'window_width'}.intersection(override_params):
assert isinstance(window.discharge_coefficient, np.ndarray)
else:
assert isinstance(window.discharge_coefficient, float)
assert isinstance(window.air_exchange(room, t), np.ndarray)
def test_sliding_window(baseline_slidingwindow):
assert baseline_slidingwindow.discharge_coefficient == 0.6

1
requirements.txt
View file

@ -61,6 +61,7 @@ pyrsistent==0.17.3
python-dateutil==2.8.1
pyzmq==22.0.3
qrcode==6.1
scipy==1.5.4
Send2Trash==1.5.0
six==1.15.0
sniffio==1.2.0

3
setup.cfg
View file

@ -19,3 +19,6 @@ ignore_missing_imports = True
[mypy-qrcode.*]
ignore_missing_imports = True
[mypy-scipy.*]
ignore_missing_imports = True

2
setup.py
View file

@ -26,6 +26,7 @@ REQUIREMENTS: dict = {
'mistune',
'numpy',
'qrcode[pil]',
'scipy',
'tornado',
'voila >=0.2.4',
],
@ -58,6 +59,7 @@ setup(
classifiers=[
"Programming Language :: Python :: 3",
"Operating System :: OS Independent",
"License :: OSI Approved :: Apache Software License", # Apache 2.0
],
install_requires=REQUIREMENTS['core'],