def test_e():
"""Tests for #572 demonstrating how EM constants should behave."""
from astropy.constants import e
# A test quantity
E = Q(100, 'V/m')
# Without specifying a system e should not combine with other quantities
pytest.raises(TypeError, lambda: e * E)
# Try it again (as regression test on a minor issue mentioned in #745 where
# repeated attempts to use e in an expression resulted in UnboundLocalError
# instead of TypeError)
pytest.raises(TypeError, lambda: e * E)
# e.cgs is too ambiguous and should not work at all
pytest.raises(TypeError, lambda: e.cgs * E)
assert isinstance(e.si, Q)
assert isinstance(e.gauss, Q)
assert isinstance(e.esu, Q)
assert e.si * E == Q(100, 'eV/m')
assert e.gauss * E == Q(e.gauss.value * E.value, 'Fr V/m')
assert e.esu * E == Q(e.esu.value * E.value, 'Fr V/m')
def test_view():
"""Check that Constant and Quantity views can be taken (#3537, #3538)."""
from astropy.constants import c
c2 = c.view(Constant)
assert c2 == c
assert c2.value == c.value
# make sure it has the necessary attributes and they're not blank
assert c2.uncertainty == 0 # c is a *defined* quantity
assert c2.name == c.name
assert c2.reference == c.reference
assert c2.unit == c.unit
q1 = c.view(Q)
assert q1 == c
assert q1.value == c.value
assert type(q1) is Q
assert not hasattr(q1, 'reference')
q2 = Q(c)
assert q2 == c
assert q2.value == c.value
assert type(q2) is Q
assert not hasattr(q2, 'reference')
c3 = Q(c, subok=True)
assert c3 == c
assert c3.value == c.value
# make sure it has the necessary attributes and they're not blank
assert c3.uncertainty == 0 # c is a *defined* quantity
assert c3.name == c.name
assert c3.reference == c.reference
assert c3.unit == c.unit
c4 = Q(c, subok=True, copy=False)
assert c4 is c
def test_exponential_cutoff_power_law():
model = ExponentialCutoffPowerLaw(
amplitude=Q(1e-11, 'cm^-2 s^-1 TeV^-1'),
e_0=Q(1, 'TeV'),
alpha=2,
e_cutoff=Q('3 TeV'),
)
def test_power_law():
model = PowerLaw(
amplitude=Q(1e-11, 'cm^-2 s^-1 TeV^-1'),
reference=Q(1, 'TeV'),
index=2,
)
test_model(model)
def test_e():
from astropy.constants.astropyconst13 import e as e_13
# A test quantity
E = Q(100.00000348276221, 'V/m')
# e.cgs is too ambiguous and should not work at all
with pytest.raises(TypeError):
e_13.cgs * E
assert isinstance(e_13.si, Q)
assert isinstance(e_13.gauss, Q)
assert isinstance(e_13.esu, Q)
assert e_13.gauss * E == Q(e_13.gauss.value * E.value, 'Fr V/m')
assert e_13.esu * E == Q(e_13.esu.value * E.value, 'Fr V/m')
def test_model(model):
print(model)
print(model(energy=Q(10, 'TeV')))
print(model.integral(emin=Q(1, 'TeV'), emax=Q(2, 'TeV')))
# plot
# butterfly
# npred
reco_bins = 5
true_bins = 10
e_reco = Q(np.logspace(-1, 1, reco_bins + 1), 'TeV')
e_true = Q(np.logspace(-1.5, 1.5, true_bins + 1), 'TeV')
livetime = Q(26, 'min')
aeff_data = Q(np.ones(true_bins) * 1e5, 'cm2')
aeff = EffectiveAreaTable(energy=e_true, data=aeff_data)
edisp_data = make_perfect_resolution(e_true, e_reco)
edisp = EnergyDispersion(edisp_data, EnergyBounds(e_true),
EnergyBounds(e_reco))
npred = calculate_predicted_counts(model=model,
livetime=livetime,
aeff=aeff,
edisp=edisp)
print(npred.data)
See Table 3.4, p. 33
"""
import numpy as np
import pandas as pd
from astropy.units import Quantity as Q
# c_all = 2e5
# a_open = Q(13.781, 'm2')
table_3_4 = pd.DataFrame({
'tilt/deg': [0.00, 1.06, 2.12],
'c_gapd': [121117, 119549, 114776],
'c_support': [18584, 19574,
23488], # c_support = c_all - c_gapd - c_shadow
'c_shadow': [60299, 60877, 61736],
'a_telescope/m2': [8.345, 8.237, 7.908],
'a_support/m2': [1.280, 1.349, 1.618],
})
camera_body_diameter = Q(53, 'cm')
camera_depth = Q(0, 'cm')
a_camera = np.pi * (camera_body_diameter / 2)**2
a_sup_only = Q(table_3_4['a_support/m2'], 'm2') - a_camera
shadowing = np.mean(a_sup_only / Q(table_3_4['a_telescope/m2'], 'm2'))
transmission = 1 - shadowing
print(f"Transmission: {transmission:.4f}")
def main():
geom = CameraGeometry.from_name("FACT")
df = pd.DataFrame(
{
"pix_id": geom.pix_id,
"pix_x": geom.pix_x,
"pix_y": geom.pix_y
}, ).set_index("pix_id")
cols = ["CHID", "trigger_patch_id"]
fact_pixels = get_pixel_dataframe()
fact_pixels = fact_pixels[cols].set_index("CHID")
sim_tel_camera = df.join(fact_pixels)
general_cmt = """
# FACT Camera
# File generated with `config.py`
"""
pixtype_cmt = """
# PixType format:
# Par. 1: pixel type (here always 1)
# 2: PMT type (must be 0)
# 3: cathode shape type (see below)
# 4: visible cathode diameter [cm]
# 5: funnel shape type (see below)
# 6: funnel diameter (flat-to-flat for hexagons and squares) [cm]
# 7: depth of funnel [cm]
# 8: a) funnel efficiency "filename", b) funnel plate transparency
# 9: a) optional wavelength "filename", b) funnel wall reflectivity
# Cases a) and b) are distinguished by the format of parameter 8: a quoted
# string indicates case a) while a real number indicates case b).
# In case a) in column 8, columns 3, 4, and 7 are not used. If in case a)
# the optional file name for the wavelength dependence is provided, the
# overall scale in the file provided as parameter 8 is ignored because
# it is rescaled such that the average over all mirrors is equal to
# the given wavelength dependent value.
#
# Shape types: 0: circ., 1: hex(flat x), 2: sq., 3: hex(flat y)
"""
shape = {
"circ": 0,
"hex(flat x)": 1,
"sq": 2,
"hex(flat y)": 3,
}
pixtype = {
1: 1, # pixel type (here always 1)
2: 0, # PMT type (must be 0)
3: shape["sq"], # cathode shape type (see below)
4: Q(2.8, u.mm).to_value(u.cm), # visible cathode diameter [cm]
5: shape["hex(flat y)"], # funnel shape type (see below)
6:
Q(9.5,
u.mm).to_value(u.cm), # funnel diameter (flat-to-flat for hex.) [cm]
7: Q(20, u.mm).to_value(u.cm), # depth of funnel [cm]
# double quotes needed!
8: '"cone-angular-acceptance.txt"',
9: '"cone-transmission.txt"',
}
# pixtype_str = 'PixType {} {} {} {} {} {} {} {} {}'.format(*pixtype.values())
pixtype_str = "PixType"
for val in pixtype.values():
pixtype_str += f" {val}"
pixel_cmt = """
# Pixel format:
# Par. 1: pixel number (starting at 0)
# 2: x position [cm]
# 3: y position [cm]
# 4: drawer/module number
# 5: board number in module
# 5: channel number n board
# 6: board Id number ('0x....')
# 7: pixel on (is on if parameter is missing)
"""
pixel = pd.DataFrame({
"keyword":
"Pixel",
"pix_num":
sim_tel_camera.index,
"pix_type":
1, # set to pixel type above
"x_pos":
Q(sim_tel_camera["pix_x"], u.m).to_value(u.cm),
"y_pos":
Q(sim_tel_camera["pix_y"], u.m).to_value(u.cm),
"module_number":
sim_tel_camera["trigger_patch_id"],
"board_number_in_module":
sim_tel_camera["trigger_patch_id"],
"channel_number_in_board":
0,
"board_id_number":
sim_tel_camera["trigger_patch_id"],
"pixel_on":
1,
})
trigger_cmt = """"""
trigger_str = ""
trigger_str_prefix = "Trigger * of "
for idx, grp in sim_tel_camera.groupby("trigger_patch_id"):
trigger_str += trigger_str_prefix
trigger_str += " ".join(map(str, list(grp.index))) + "\n"
with open(output_path, "w") as f:
f.write(general_cmt + "\n")
f.write(pixtype_cmt + "\n")
f.write(pixtype_str + "\n")
f.write(pixel_cmt + "\n")
pixel.to_csv(output_path, sep="\t", header=False, index=False, mode="a")
with open(output_path, "a") as f:
f.write(trigger_cmt + "\n")
f.write(trigger_str + "\n")
