This package provides a module for time profiling emissions output.
This software is provided for research purposes only. It's output may not accurately reflect observed data due to numerous reasons. Data are provisional; use at own risk.
This package was originally developed to support python 2.7, but has since been refactored to support 3.5. Attempts to support both 2.7 and 3.5 have been made but are not guaranteed.
Via ssh:
git clone git@github.com:pnwairfire/timeprofile.git
or http:
git clone https://github.com/pnwairfire/timeprofile.git
Run the following to install dependencies:
pip install -r requirements.txt
pip install -r requirements-test.txt
pip install -r requirements-dev.txt
To import timeprofile in development, you'll have to add the repo root directory to the search path.
Use pytest:
pytest
You can also use the --collect-only option to see a list of all tests.
pytest --collect-only
See pytest for more information about
TODO: ...Fill In...
First, install pip (with sudo if necessary):
apt-get install python-pip
pip install --upgrade pip
Then, to install, for example, v2.0.0, use the following (with sudo if necessary):
pip install --extra-index https://pypi.airfire.org/simple timeprofile==2.0.0
If you get an error like
AttributeError: 'NoneType' object has no attribute 'skip_requirements_regex
it means you need in upgrade pip. One way to do so is with the following:
pip install --upgrade pip
A timeprofiler object is instantiated from each class with a start time, an end time, and class specific kwargs. e.g.
import datetime
from timeprofile.feps import FepsTimeProfiler
from timeprofile.static import StaticTimeProfiler
feps_profiler = FepsTimeProfiler(
datetime.datetime(2015, 1, 20, 0, 0),
datetime.datetime(2015, 1, 20, 2, 0)
)
static_profiler = StaticTimeProfiler(
datetime.datetime(2015, 1, 20, 0, 0),
datetime.datetime(2015, 1, 20, 2, 0)
)
The houly fractions are then accessed like in the following:
feps_profiler.hourly_fractions
static_profiler.hourly_fractions
The hourly fractions are structured as follows:
{
'area_fraction': [0.5, 0.5],
'flaming': [0.4999982946232187, 0.5000017053767812],
'smoldering': [0.4999853212474072, 0.5000146787525928],
'residual': [0.34884662785022885, 0.6511533721497711]
}
A simple case is when the time window is a 24-hour period, from midnight to midnight. In this case, FEPS time profiler, with no input other than start and end times, would yield the following:
{
'area_fraction': [
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.3333333333333333,
0.3333333333333333, 0.33333333333333337, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
],
'flaming': [
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.33333105948986946,
0.3333333333178222, 0.33333333333333326, 2.2738434638226057e-06,
1.551109229390636e-11, 1.0580938748774452e-16, 7.21781952450244e-22,
4.923657524652319e-27, 3.3586879441595006e-32, 2.29113918865406e-37,
1.562907560648659e-42, 1.066142142401966e-47, 7.272721026019621e-53,
4.961108750765986e-58, 3.3842354118727117e-63
],
'smoldering': [
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.3333137610886173,
0.33333333218411504, 0.3333333333332659, 1.9572244716002987e-05,
1.1492182896695533e-09, 6.747834479257037e-14, 3.962108031933841e-18,
2.326420439767974e-22, 1.365998104783779e-26, 8.020694756528947e-31,
4.7094900170153494e-35, 2.765258733018484e-39, 1.6236696187713928e-43,
9.533657734961823e-48, 5.597852466819759e-52
],
'residual': [
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.051448039171003226,
0.09603235783914513, 0.1346686499725634, 0.11670240696693489,
0.10113305357000935, 0.08764081898749726, 0.0759485932804565,
0.06581623594940489, 0.05703564381438533, 0.049426476892755915,
0.04283245449074733, 0.03711814543615242, 0.03216618652841821,
0.027874872077343852, 0.024156065023182394
]
}
A more complicated scenario would be a time window of, say, 36 hours, starting at 6:00am on one day and going until 6:00pm the following day. Time profiling would yield the following:
{
'area_fraction': [
0.0, 0.0, 0.0, 0.3333333333333333, 0.3333333333333333,
0.33333333333333337, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0
],
'flaming': [
0.0, 0.0, 0.0, 0.33333105948986946, 0.3333333333178222,
0.33333333333333326, 2.2738434638226057e-06, 1.551109229390636e-11,
1.0580938748774452e-16, 7.21781952450244e-22, 4.923657524652319e-27,
3.3586879441595006e-32, 2.29113918865406e-37, 1.562907560648659e-42,
1.066142142401966e-47, 7.272721026019621e-53, 4.961108750765986e-58,
3.3842354118727117e-63, 2.3085664713971314e-68,
1.5747956345359157e-73, 1.0742516281337596e-78, 7.328040129398227e-84,
4.998844845258577e-89, 3.4099772034163633e-94, 2.326126312731716e-99,
1.5867741336692376e-104, 1.0824227977219723e-109,
7.383780011078057e-115, 5.036867974948155e-120,
3.4359147968919804e-125, 2.343819720949262e-130,
1.5988437458577015e-135, 1.090656120357656e-140,
7.439943871660133e-146, 5.0751803231344345e-151,
3.462049681644101e-156
],
'smoldering': [
0.0, 0.0, 0.0, 0.3333137610886173, 0.33333333218411504,
0.3333333333332659, 1.9572244716002987e-05, 1.1492182896695533e-09,
6.747834479257037e-14, 3.962108031933841e-18, 2.326420439767974e-22,
1.365998104783779e-26, 8.020694756528947e-31, 4.7094900170153494e-35,
2.765258733018484e-39, 1.6236696187713928e-43, 9.533657734961823e-48,
5.597852466819759e-52, 3.2868761509409844e-56, 1.929946331322718e-60,
1.1332014565622418e-64, 6.653788866111515e-69, 3.906887519285308e-73,
2.293996758160961e-77, 1.3469599778535882e-81, 7.908909092765317e-86,
4.6438531260076585e-90, 2.7267188942224726e-94, 1.601040284084463e-98,
9.400785672085916e-103, 5.519834330903922e-107,
3.241066449488041e-111, 1.9030483707065912e-115,
1.1174078525360314e-119, 6.561053981226903e-124,
3.852436623465138e-128
],
'residual': [
0.0, 0.0, 0.0, 0.04493321743817599, 0.08387186150175081,
0.11761567260532799, 0.10192447977219064, 0.08832666044168927,
0.07654293612504742, 0.06633128708076667, 0.05748198159792448,
0.049813268426419595, 0.04316764388324648, 0.037408616966849474,
0.032417906039934975, 0.028093009504878263, 0.024345100577099568,
0.021097202918262496, 0.01828260965958067, 0.015843513344380375,
0.013729818651027107, 0.011898113511354143, 0.010310777492933436,
0.008935209132719325, 0.007743156352675472, 0.00671013620514235,
0.005814932030399268, 0.005039157698803528, 0.004366879987704225,
0.0037842913373279546, 0.0032794262645409267, 0.002841915610058338,
0.0024627735717131145, 0.002134213149771912, 0.0018494862138263748,
0.0016027449064773057
]
}
Another complicated scenario would be a time window of, say, 2.75 hours, starting at 6:30am and going until 9:15pm the same day. Time profiling would yield the following:
{
'area_fraction': [
0.0, 0.4444444444444444, 0.4444444444444444, 0.11111111111111116
],
'flaming': [
0.0, 0.4444417495260068, 0.44444478129890846, 0.1111134691750847
],
'smoldering': [
0.0, 0.4444212482396549, 0.44444734320393753, 0.11113140855640749
],
'residual': [
0.0, 0.21123091541981814, 0.39428136005072595, 0.39448772452945596
]
}
Note that there are four hourly fractions, since the time profile overlaps with four hours of the day - the hours starting at 6, 7, 8, and 9.