def process_binned(self, E1, E2, Estep, killzone_mask=None):
"""
Process spectrum binning
Parameters:
E1 - low edge of lowest bin
E2 - high edge of highest bin
Estep = bin width
killzone_mask - optional mask of regions to ignore entirely
See Calibration.process for prerequisites and results
"""
calib = calibrate.load(self.calibration_file)
# zero out killzones of calibration matrix
# (this causes those pixels to be ignored)
if killzone_mask is not None:
calib.calibration_matrix[killzone_mask] = 0
exposure = Exposure()
exposure.load_multi(self.exposure_files)
exposure.apply_filters(self.incident_energy, self.filters)
spectrum = binned_emission_spectrum(calib.calibration_matrix,
exposure,
E1,
E2,
Estep,
self.I0)
self._set_spectrum(spectrum)
def inundation_damage(sww_base_name, exposure_files_in,
exposure_file_out_marker=None,
ground_floor_height=0.3,
overwrite=False, verbose=True,
use_cache = True):
"""
This is the main function for calculating tsunami damage due to
inundation. It gets the location of structures from the exposure
file and gets the inundation of these structures from the
sww file.
It then calculates the damage loss.
Note, structures outside of the sww file get the minimum inundation
(-ground_floor_height).
These calculations are done over all the sww files with the sww_base_name
in the specified directory.
exposure_files_in - a file or a list of files to input from
exposure_file_out_marker - this string will be added to the input file
name to get the output file name
"""
if isinstance(exposure_files_in, basestring):
exposure_files_in = [exposure_files_in]
for exposure_file_in in exposure_files_in:
csv = Exposure(exposure_file_in,
title_check_list=[SHORE_DIST_LABEL,WALL_TYPE_LABEL,
STR_VALUE_LABEL,CONT_VALUE_LABEL])
geospatial = csv.get_location()
geospatial = ensure_absolute(geospatial)
max_depths, max_momentums = calc_max_depth_and_momentum(sww_base_name,
geospatial,
ground_floor_height=ground_floor_height,
verbose=verbose,
use_cache=use_cache)
edm = EventDamageModel(max_depths,
csv.get_column(SHORE_DIST_LABEL),
csv.get_column(WALL_TYPE_LABEL),
csv.get_column(STR_VALUE_LABEL),
csv.get_column(CONT_VALUE_LABEL)
)
results_dic = edm.calc_damage_and_costs(verbose_csv=True,
verbose=verbose)
for title, value in results_dic.iteritems():
csv.set_column(title, value, overwrite=overwrite)
# Save info back to csv file
if exposure_file_out_marker == None:
exposure_file_out = exposure_file_in
else:
# split off extension, in such a way to deal with more than one '.' in the name of file
split_name = exposure_file_in.split('.')
exposure_file_out = '.'.join(split_name[:-1]) + exposure_file_out_marker + \
'.' + split_name[-1]
csv.save(exposure_file_out)
if verbose: log.critical('Augmented building file written to %s'
% exposure_file_out)
def process(self,
emission_energies=None,
skip_columns=[],
killzone_mask=None):
"""
Process Emission Spectrum
Parameters:
emission_energies - list of points in emission energy grid
if None, a uniform 0.1 eV grid covering range of calibration energies is used
skip_columns - columns (for vertical disp. dir.) or rows (for horizontal) to skip entirely
killzone_mask - mask of regions to skip in processing
Prerequisites:
self.calibration_file must be set to calibration filename
self.exposure_files must be a list of exposure filenames
self.filters must be a list of mx.filter.Filter descendents to apply to integrated exposures
(may be empty list)
self.solid_angle_map may be a map of solid angles subtended by each pixel
Results:
self.spectrum is set to processed spectrum
self.emission, self.intensity, self.uncertainty, self.raw_counts and self.num_pixels are set to
corresponding columns of spectrum
"""
calibration = calibrate.Calibration()
calibration.load(self.calibration_file)
exposure = Exposure()
exposure.load_multi(self.exposure_files)
for f in self.filters:
f.filter(exposure.pixels, self.incident_energy)
# generate emission energies from range of calibration matrix
if emission_energies is None:
Emin, Emax = calibration.energy_range()
emission_energies = np.arange(Emin, Emax, .1)
spectrum = process_spectrum(calibration.calibration_matrix,
exposure,
emission_energies,
self.I0,
calibration.dispersive_direction,
calibration.xtals,
self.solid_angle_map,
skip_columns=skip_columns,
killzone_mask=killzone_mask)
self._set_spectrum(spectrum)
def test_set_exposure(self):
"""
Test the function to set and change exposure
"""
exposure = Exposure(min_t=1 / 500, max_t=1 / 125)
(f, t, iso, bias) = exposure.set_exposure(4.0, 1 / 100, 100, 0)
self.assertEqual(f, 4.0)
self.assertEqual(t, 1 / 100)
(f, t, iso, base) = exposure.set_exposure(f=5.6)
self.assertAlmostEqual(t, 1 / 50, 1)
(f, t, iso, base) = exposure.set_exposure(t=1 / 200)
self.assertAlmostEqual(f, 2.8, 1)
def __init__(self):
self.brightness = Brightness()
self.gamma = Gamma()
self.gain = Gain()
self.exposure = Exposure()
self.exposureauto = ExposureAuto()
self.saturation = Saturation()
self.hue = Hue()
self.whitebalancered = WhiteBalanceRed()
self.whitebalanceblue = WhiteBalanceBlue()
self.Parameters = {
'Brightness': self.brightness.Brightness,
'Gamma': self.gamma.Gamma,
'Gain': self.gain.Gain,
'Exposure': self.exposure.Exposure,
'ExposureAuto': self.exposureauto.ExposureAuto,
'Saturation': self.saturation.Saturation,
'Hue': self.hue.Hue,
'WhiteBalanceRed': self.whitebalancered.WhiteBalanceRed,
'WhiteBalanceBlue': self.whitebalanceblue.WhiteBalanceBlue
}
def process(self, emission_energies=None, skip_columns=[], killzone_mask=None):
"""
Process Emission Spectrum
Parameters:
emission_energies - list of points in emission energy grid
if None, a uniform 0.1 eV grid covering range of calibration energies is used
skip_columns - columns (for vertical disp. dir.) or rows (for horizontal) to skip entirely
killzone_mask - mask of regions to skip in processing
Prerequisites:
self.calibration_file must be set to calibration filename
self.exposure_files must be a list of exposure filenames
self.filters must be a list of mx.filter.Filter descendents to apply to integrated exposures
(may be empty list)
self.solid_angle_map may be a map of solid angles subtended by each pixel
Results:
self.spectrum is set to processed spectrum
self.emission, self.intensity, self.uncertainty, self.raw_counts and self.num_pixels are set to
corresponding columns of spectrum
"""
calibration = calibrate.Calibration()
calibration.load(self.calibration_file)
exposure = Exposure()
exposure.load_multi(self.exposure_files)
for f in self.filters:
f.filter(exposure.pixels, self.incident_energy)
# generate emission energies from range of calibration matrix
if emission_energies is None:
Emin, Emax = calibration.energy_range()
emission_energies = np.arange(Emin,Emax,.1)
spectrum = process_spectrum(calibration.calibration_matrix,
exposure,
emission_energies,
self.I0,
calibration.dispersive_direction,
calibration.xtals,
self.solid_angle_map,
skip_columns=skip_columns,
killzone_mask=killzone_mask)
self._set_spectrum(spectrum)
def test_x_to_fstop(self):
"""
Test the f stop conversion function
"""
exposure = Exposure(min_f=2, max_f=4)
self.assertEqual(exposure.x_to_fstop(0), '2')
self.assertEqual(exposure.x_to_fstop(0.5), '2.8')
self.assertEqual(exposure.x_to_fstop(1.0), '4')
exposure = Exposure(min_f=2, max_f=5.6)
self.assertEqual(exposure.x_to_fstop(0), '2')
self.assertEqual(exposure.x_to_fstop(0.33), '2.8')
self.assertEqual(exposure.x_to_fstop(0.67), '4')
self.assertEqual(exposure.x_to_fstop(1.0), '5.6')
def process_binned(self, E1, E2, Estep, killzone_mask=None):
"""
Process spectrum binning
Parameters:
E1 - low edge of lowest bin
E2 - high edge of highest bin
Estep = bin width
killzone_mask - optional mask of regions to ignore entirely
See Calibration.process for prerequisites and results
"""
calib = calibrate.load(self.calibration_file)
# zero out killzones of calibration matrix
# (this causes those pixels to be ignored)
if killzone_mask is not None:
calib.calibration_matrix[killzone_mask] = 0
exposure = Exposure()
exposure.load_multi(self.exposure_files)
exposure.apply_filters(self.incident_energy, self.filters)
spectrum = binned_emission_spectrum(calib.calibration_matrix, exposure,
E1, E2, Estep, self.I0)
self._set_spectrum(spectrum)
def test_fstop_to_x(self):
"""
Test the shutter speed to (0, 1) conversion function
"""
# Can ony test to one place since stop values are not accurate
exposure = Exposure(min_f=2, max_f=4)
self.assertEqual(exposure.fstop_to_x(2), 0.0)
self.assertEqual(exposure.fstop_to_x(4), 1.0)
self.assertAlmostEqual(exposure.fstop_to_x(2.8), 0.5, 1)
exposure = Exposure(min_f=2, max_f=5.6)
self.assertAlmostEqual(exposure.fstop_to_x(2.8), 1 / 3, 1)
self.assertAlmostEqual(exposure.fstop_to_x(4), 2 / 3, 1)
def add_depth_and_momentum2csv(sww_base_name,
exposure_file_in,
exposure_file_out=None,
overwrite=False,
verbose=True,
use_cache=True):
"""
Calculate the maximum depth and momemtum in an sww file, for locations
specified in an csv exposure file.
These calculations are done over all the sww files with the sww_base_name
in the specified directory.
"""
csv = Exposure(exposure_file_in)
geospatial = csv.get_location()
max_depths, max_momentums = calc_max_depth_and_momentum(
sww_base_name, geospatial, verbose=verbose, use_cache=use_cache)
csv.set_column("MAX INUNDATION DEPTH (m)", max_depths, overwrite=overwrite)
csv.set_column("MOMENTUM (m^2/s) ", max_momentums, overwrite=overwrite)
csv.save(exposure_file_out)
def calibrate(self, fit_type=FIT_QUARTIC, progress=ProgressIndicator()):
"""
Calculate calibration matrix
Prerequisites:
self.exposure_files must be a list of exposure files
self.energies must be a list of corresponding energies
self.xtals must be a list of regions exposed by each analyzer crystal
each entry must be of the form [[x1,y1],[x2,y2]]
self.filters may contain a list of mx.filter.Filter descendents to apply to the exposures
Results:
self.calibration_matrix contains the calibration matrix
self.lin_res contains average linear residuals of fit (one for each xtal)
self.rms_res contains rms residuals of fit (one for each xtal)
self.fit_points contains all detected peak values as array with columns (x,y,energy)
self.fits contains a list of fit parameters (one for each xtal)
"""
# load exposure files
progress.push_step("Load Exposures", 0.1)
exposures = [Exposure(f) for f in self.exposure_files]
progress.pop_step()
# apply filters
progress.push_step("Apply Filters", 0.3)
i = 0
n = len(exposures)
for exposure, energy in izip(exposures, self.energies):
progress.update("Filter exposure %d" % (i+1,), i/float(n))
i += 1
for f in self.filters:
f.filter(exposure.pixels, energy)
progress.pop_step()
# calibrate
progress.push_step("Calibrate", 0.6)
self.calibration_matrix, diagnostics = calibrate(exposures,
self.energies,
self.xtals,
self.dispersive_direction,
fit_type,
return_diagnostics=True,
progress=progress)
progress.pop_step()
# store diagnostic info
self.lin_res, self.rms_res, self.fit_points, self.fits = diagnostics
def add_depth_and_momentum2csv(sww_base_name, exposure_file_in,
exposure_file_out=None,
overwrite=False, verbose=True,
use_cache = True):
"""
Calculate the maximum depth and momemtum in an sww file, for locations
specified in an csv exposure file.
These calculations are done over all the sww files with the sww_base_name
in the specified directory.
"""
csv = Exposure(exposure_file_in)
geospatial = csv.get_location()
max_depths, max_momentums = calc_max_depth_and_momentum(sww_base_name,
geospatial,
verbose=verbose,
use_cache=use_cache)
csv.set_column("MAX INUNDATION DEPTH (m)",max_depths, overwrite=overwrite)
csv.set_column("MOMENTUM (m^2/s) ",max_momentums, overwrite=overwrite)
csv.save(exposure_file_out)
def main():
print('Initializing NetworkTables for peg')
NetworkTables.setClientMode( )
NetworkTables.setIPAddress( '10.40.96.2' )
NetworkTables.initialize( )
print('Creating peg video capture ')
global pegCapture
pegCapture = cv2.VideoCapture("/dev/gear_camera")
pegCapture.set(cv2.CAP_PROP_FRAME_WIDTH, 320)
pegCapture.set(cv2.CAP_PROP_FRAME_HEIGHT, 240)
capture.set(cv2.CAP_PROP_FPS, 15)
print('Creating peg pipeline')
pegPipeline = PegPipeline()
try:
print("Starting peg server")
server = ThreadedHTTPServer(('0.0.0.0', 5800), CamHandler)
print('Running peg pipeline')
table = NetworkTables.getTable('/vision/')
while True:
pegOk, pegFrame = pegCapture.read()
if pegOk:
print("ok")
if table.getNumber('gear_camera_mode') == 0:
print('Switching peg to GRIP mode')
if Exposure.getExposure("/dev/gear_camera") != 10:
Exposure.setExposure(10, "/dev/gear_camera")
while table.getNumber('gear_camera_mode') == 0:
pegPipeline.process(pegFrame)
extra_processing(pegPipeline)
else:
print('Switching peg to stream mode')
if Exposure.getExposure("/dev/gear_camera") != 100:
Exposure.setExposure(100, "/dev/gear_camera")
while table.getNumber('gear_camera_mode') == 1:
server.handle_request()
except KeyboardInterrupt:
pegCapture.release()
server.socket.close()
def inundation_damage(sww_base_name,
exposure_files_in,
exposure_file_out_marker=None,
ground_floor_height=0.3,
overwrite=False,
verbose=True,
use_cache=True):
"""
This is the main function for calculating tsunami damage due to
inundation. It gets the location of structures from the exposure
file and gets the inundation of these structures from the
sww file.
It then calculates the damage loss.
Note, structures outside of the sww file get the minimum inundation
(-ground_floor_height).
These calculations are done over all the sww files with the sww_base_name
in the specified directory.
exposure_files_in - a file or a list of files to input from
exposure_file_out_marker - this string will be added to the input file
name to get the output file name
"""
if isinstance(exposure_files_in, basestring):
exposure_files_in = [exposure_files_in]
for exposure_file_in in exposure_files_in:
csv = Exposure(exposure_file_in,
title_check_list=[
SHORE_DIST_LABEL, WALL_TYPE_LABEL, STR_VALUE_LABEL,
CONT_VALUE_LABEL
])
geospatial = csv.get_location()
geospatial = ensure_absolute(geospatial)
max_depths, max_momentums = calc_max_depth_and_momentum(
sww_base_name,
geospatial,
ground_floor_height=ground_floor_height,
verbose=verbose,
use_cache=use_cache)
edm = EventDamageModel(max_depths, csv.get_column(SHORE_DIST_LABEL),
csv.get_column(WALL_TYPE_LABEL),
csv.get_column(STR_VALUE_LABEL),
csv.get_column(CONT_VALUE_LABEL))
results_dic = edm.calc_damage_and_costs(verbose_csv=True,
verbose=verbose)
for title, value in results_dic.iteritems():
csv.set_column(title, value, overwrite=overwrite)
# Save info back to csv file
if exposure_file_out_marker is None:
exposure_file_out = exposure_file_in
else:
# split off extension, in such a way to deal with more than one '.' in the name of file
split_name = exposure_file_in.split('.')
exposure_file_out = '.'.join(split_name[:-1]) + exposure_file_out_marker + \
'.' + split_name[-1]
csv.save(exposure_file_out)
if verbose:
log.critical('Augmented building file written to %s' %
exposure_file_out)
scenarios = [
"1950-2099_RCP_4.5_avg.csv", "1950-2099_RCP_4.5_min.csv",
"1950-2099_RCP_4.5_max.csv", "1950-2099_RCP_8.5_avg.csv",
"1950-2099_RCP_8.5_min.csv", "1950-2099_RCP_8.5_max.csv"
]
cdf_prefix = ['best', 'mid', 'worst']
cdf_postfix = ['electronics', 'motor', 'pvc', 'iron']
pop_count = {'electronics': 4, 'motor': 4, 'pvc': 480, 'iron': 190}
increxp_prefix = "/users/austinmichne/documents/cleanperses/data/exposure/1950-2099_incr_exp/"
cumexp_prefix = "/users/austinmichne/documents/cleanperses/data/exposure/1950-2099_cum_exp/"
cdf_path = "/users/austinmichne/documents/cleanperses/data/cdf/"
failure_path = "/users/austinmichne/documents/cleanperses/data/failure/"
for scenario in scenarios:
exposure = Exposure()
exposure.load_csv(increxp_prefix + scenarios[0],
cumexp_prefix + scenarios[0])
for pre in cdf_prefix:
for component_type in cdf_postfix:
cdf = CumulativeDistFailure(
f'{pre}_{component_type}',
f'{cdf_path}{pre}_case_{component_type}.csv')
subpop = Subpopulation('example', exposure, cdf,
pop_count[component_type])
subpop.get_failures()
# print(subpop.failures)
# print(len(subpop.failures))
# print([x[0] for x in subpop.failures].count(subpop.failures[0][0]))
rcp = scenario.split(sep='_',
maxsplit=1)[1].rsplit('.', maxsplit=1)[0]
def test_x_to_shutter(self):
"""
Test the shutter speed conversion function
"""
exposure = Exposure(min_t=1 / 500, max_t=1 / 125)
self.assertEqual(exposure.x_to_shutter(0), '1/500')
self.assertEqual(exposure.x_to_shutter(0.5), '1/250')
self.assertEqual(exposure.x_to_shutter(1.0), '1/125')
exposure = Exposure(min_t=1 / 1000, max_t=1 / 125)
self.assertEqual(exposure.x_to_shutter(0), '1/1000')
self.assertEqual(exposure.x_to_shutter(0.33), '1/500')
self.assertEqual(exposure.x_to_shutter(0.67), '1/250')
self.assertEqual(exposure.x_to_shutter(1.0), '1/125')
exposure = Exposure()
self.assertEqual(exposure.x_to_shutter(0), '1/8000')
self.assertEqual(exposure.x_to_shutter(1.0), '60')
def test_shutter_to_x(self):
"""
Test the shutter speed to (0, 1) conversion function
"""
exposure = Exposure(min_t=1 / 500, max_t=1 / 125)
self.assertEqual(exposure.shutter_to_x(1 / 500), 0.0)
self.assertEqual(exposure.shutter_to_x(1 / 125), 1.0)
self.assertAlmostEqual(exposure.shutter_to_x(1 / 250), 0.5, 6)
exposure = Exposure(min_t=1 / 1000, max_t=1 / 125)
self.assertAlmostEqual(exposure.shutter_to_x(1 / 500), 1 / 3, 6)
self.assertAlmostEqual(exposure.shutter_to_x(1 / 250), 2 / 3, 6)
exposure = Exposure()
self.assertEqual(exposure.shutter_to_x(1 / 8000), 0.0)
self.assertEqual(exposure.shutter_to_x(60), 1.0)
#! /usr/bin/env python3
# ToughExposure
'''An exposure suggestor and calculator'''
from collections import deque
import ui
import clipboard
from console import hud_alert
from exposure import Exposure
exp = Exposure(2.0, 32.0)
exp.set_exposure(5.6, 1 / 100, 100, 0)
# Initialize the list of last clicked switches with two items
last_adjusted = deque([None, None], maxlen=5)
def slider_toggle(sender):
"""
The user has clicked a switch. Set the last two switches to have been clicked to on
and enable the corresponding sliders. Disable the other sliders.
:param sender: The switch object which was clicked
:return:
"""
v = sender.superview
name = sender.name
aperture_slider = v['aperture_slider']
""" Ctrl-Z FRC Team 4096 FIRST Robotics Competition Code for use on Raspberry Pi coprocessor [email protected] """ import sys from exposure import Exposure """ Sets the exposure of /dev/video0 to the first arg passed or 10 if no args are passed If 2 args are passed it sets the exposure of arg 2 to arg 1 """ if len(sys.argv) < 3: print("Exposure is {0}".format(Exposure.getExposure())) Exposure.setExposure(10 if len(sys.argv) < 2 else sys.argv[1]) print("Exposure is now {0}".format(Exposure.getExposure())) else: print("Exposure is {0}".format(Exposure.getExposure())) Exposure.setExposure(10 if len(sys.argv) < 2 else sys.argv[1]) print("Exposure is now {0}".format(Exposure.getExposure()))
def diagnose(self, return_spectra=False, filters=None, progress=None):
"""
Process all calibration exposures and fit to gaussians, returning parameters of fit
Example:
>>> import minixs as mx
>>> from matplotlib import pyplot as pt
>>> c = mx.calibrate.Calibration('example.calib')
>>> d, spectra = c.diagnose(return_spectra = True, filters=[mx.filter.HighFilter(1000)])
>>> d[:,3] # sigma for gaussian fits
array([ 0.4258905 , 0.54773887, 0.58000567, 0.57056559, 0.56539868,
0.58693027, 0.60704443, 0.61898894, 0.62726828, 0.63519546,
0.65309853, 0.66317984, 0.67826396, 0.69466781, 0.75039033,
0.78887514, 0.84248593, 0.8974527 ])
>>> s = spectra[5]
>>> pt.plot(s.emission, s.intensity, 'o')
>>> pt.plot(s.emission, mx.gauss.gauss_model(d[5,1:], s.emission))
>>> pt.show()
Parameters
----------
return_spectra: whether to return processed calibration spectra
Returns
-------
(diagnostics, [processed_spectra])
diagnostics: an array with one row for each calibration exposure
the columns are:
incident beam energy
amplitude
E0
sigma
the best Gaussian fit to the data is given by:
exp(-(E-E0)**2/(2*sigma**2))
if `return_spectra` is True, then a list of XES spectra will be returned (one for each calibration exposure)
"""
emin, emax = self.energy_range()
emission_energies = np.arange(emin, emax, .2)
diagnostics = np.zeros((len(self.energies), 4))
if return_spectra:
spectra = []
for i in range(len(self.energies)):
if progress:
msg = "Processing calibration exposure %d / %d" % (i, len(self.energies))
prog = i / float(len(self.energies))
progress.update(msg, prog)
energy = self.energies[i]
exposure = Exposure(self.exposure_files[i])
if filters is not None:
exposure.apply_filters(energy, filters)
s = process_spectrum(self.calibration_matrix, exposure, emission_energies, 1, self.dispersive_direction, self.xtals)
x = s[:,0]
y = s[:,1]
fit, ier = gauss_leastsq((x,y), (y.max(), energy, 1.0))
if not (0 < ier < 5):
continue
diagnostics[i,0] = energy
diagnostics[i,1:] = fit
if return_spectra:
xes = EmissionSpectrum()
xes.incident_energy = energy
xes.exposure_files = [exposure.filename]
xes._set_spectrum(s)
spectra.append(xes)
diagnostics = diagnostics[np.where(diagnostics[:,0] != 0)]
if return_spectra:
return (diagnostics, spectra)
else:
return diagnostics
