def test_hcm_reduced1():
pre_out = 9
pre_clo = 0.76
qa_ht = 175
qa_wt = 73
ta_hd = 22
ta_tr = 21.5
ta_ft = 20
tr_hd = 22.8
tr_tr = 22
tr_ft = 20.9
va_hd = 0.1
va_tr = 0.1
va_ft = 0.1
rh = 26
co2ppm = 1400
lux = 5.06
sound = 61.25
light_blue = 0
light_yellow = 0
scent_OC = 0
scent_Pepper = 0
body_state = np.array([[ta_hd, tr_hd, va_hd], [ta_tr, tr_tr, va_tr],
[ta_ft, tr_ft, va_ft]])
_, _, ldamdl, scale = load_hcm_model()
assert hcm_reduced(
model=(ldamdl, scale),
pre_out=pre_out,
pre_clo=pre_clo,
qa_ht=qa_ht,
qa_wt=qa_wt,
body_state=body_state,
rh=rh,
co2ppm=co2ppm,
lux=lux,
sound=sound,
light_blue=light_blue,
light_yellow=light_yellow,
scent_OC=scent_OC,
scent_Pepper=scent_Pepper,
)
def test_hcm_reduced3():
pre_out = 6
pre_clo = 0.76
qa_ht = 178
qa_wt = 85
ta_hd = 20.1
ta_tr = 20.1
ta_ft = 20.1
tr_hd = 20.1
tr_tr = 20.1
tr_ft = 20.1
va_hd = 0.09
va_tr = 0.07
va_ft = 0.08
rh = 41.5
co2ppm = 450
lux = 75
sound = 64
light_blue = 0
light_yellow = 0
scent_OC = 0
scent_Pepper = 1
body_state = np.array([[ta_hd, tr_hd, va_hd], [ta_tr, tr_tr, va_tr],
[ta_ft, tr_ft, va_ft]])
_, _, ldamdl, scale = load_hcm_model()
assert not hcm_reduced(
model=(ldamdl, scale),
pre_out=pre_out,
pre_clo=pre_clo,
qa_ht=qa_ht,
qa_wt=qa_wt,
body_state=body_state,
rh=rh,
co2ppm=co2ppm,
lux=lux,
sound=sound,
light_blue=light_blue,
light_yellow=light_yellow,
scent_OC=scent_OC,
scent_Pepper=scent_Pepper,
)
def test_hcm_reduced2():
pre_out = 12
pre_clo = 0.76
qa_ht = 165
qa_wt = 57
ta_hd = 27.5
ta_tr = 26.7
ta_ft = 27.7
tr_hd = 27.6
tr_tr = 27.2
tr_ft = 30.3
va_hd = 0.3
va_tr = 0.45
va_ft = 0.17
rh = 38
co2ppm = 860
lux = 5.06
sound = 66.25
body_state = np.array([[ta_hd, tr_hd, va_hd], [ta_tr, tr_tr, va_tr],
[ta_ft, tr_ft, va_ft]])
_, _, ldamdl, scale = load_hcm_model()
assert not hcm_reduced(
model=(ldamdl, scale),
pre_out=pre_out,
pre_clo=pre_clo,
qa_ht=qa_ht,
qa_wt=qa_wt,
body_state=body_state,
rh=rh,
co2ppm=co2ppm,
lux=lux,
sound=sound,
)
def test_binary_comfort_eg3():
pre_out = 6
pre_t = 20
pre_clo = 0.76
qa_age = 26
qa_ht = 178
qa_wt = 85
q2_2 = 5
thist_d0 = 6
tsa_q3_1 = 2
tsa_q3_2 = 3
tsa_q3_3 = -1
tsa_q3_4 = 3
tsa_q3_5 = -2
tsa_q3_6 = -2
tsa_q3_7 = 2
q4_8 = 1
q4_18 = -1
ta_hd = 20.1
ta_tr = 20.1
ta_ft = 20.1
tr_hd = 20.1
tr_tr = 20.1
tr_ft = 20.1
va_hd = 0.09
va_tr = 0.07
va_ft = 0.08
rh = 41.5
co2ppm = 45
lux = 75
sound = 64
qc_1 = -9
qc_2 = -9
qc_3 = -5
qc_4 = -8
qc_5 = -8
qc_6 = -10
qa_gender_m = 1
q2_1_yes = 0
light_blue = 0
light_yellow = 0
scent_OC = 0
scent_Pepper = 1
body_state = np.array([[ta_hd, tr_hd, va_hd], [ta_tr, tr_tr, va_tr],
[ta_ft, tr_ft, va_ft]])
t_eq, eqt_out = eqt(body_state, pre_clo, pre_out)
t_eq = np.array(t_eq)
# take the first 3 values and reshape into a column vector
t_eq_column = t_eq[:3].reshape(1, -1)
# eqt_head, eqt_trunk, eqt_feet, eqt_out_head, eqt_out_trunk, eqt_out_feet, eqt_out_overall = eqt(ta_hd, ta_tr, ta_ft, tr_hd, tr_tr, tr_ft, va_hd, va_tr, va_ft, pre_clo, pre_out)
input_vars = np.column_stack((
pre_out,
pre_t,
pre_clo,
qa_age,
qa_ht,
qa_wt,
q2_2,
thist_d0,
tsa_q3_1,
tsa_q3_2,
tsa_q3_3,
tsa_q3_4,
tsa_q3_5,
tsa_q3_6,
tsa_q3_7,
q4_8,
q4_18,
ta_hd,
ta_tr,
ta_ft,
tr_hd,
tr_tr,
tr_ft,
va_hd,
va_tr,
va_ft,
rh,
co2ppm,
lux,
sound,
qc_1,
qc_2,
qc_3,
qc_4,
qc_5,
qc_6,
qa_gender_m,
q2_1_yes,
light_blue,
light_yellow,
scent_OC,
scent_Pepper,
t_eq_column,
))
input_vars_red = np.column_stack((
pre_out,
pre_clo,
qa_ht,
qa_wt,
ta_hd,
ta_tr,
ta_ft,
tr_hd,
tr_tr,
tr_ft,
va_hd,
va_tr,
va_ft,
rh,
co2ppm,
lux,
sound,
light_blue,
light_yellow,
scent_OC,
scent_Pepper,
t_eq_column,
))
(
comfort_indicator,
score,
threshold,
comfort_indicator_red,
score_red,
threshold_red,
HCMout,
HCMout_red,
) = binary_comfort(input_vars,
input_vars_red,
eqt_out,
model=load_hcm_model())
assert HCMout == 1, "occupant should be uncomfortable for this example"
assert score[0] == approx(-0.09327184132086908)
assert threshold[0] == approx(-3.234199373163648)
assert HCMout_red == 1
assert score_red[0] == approx(-0.7259478747861583)
assert threshold_red[0] == approx(-2.5008015493225457)
def test_binary_comfort_eg1():
pre_out = 9
pre_t = 21
pre_clo = 0.76
qa_age = 25
qa_ht = 175
qa_wt = 73
q2_2 = 0
thist_d0 = 9
tsa_q3_1 = 5
tsa_q3_2 = 0
tsa_q3_3 = -2
tsa_q3_4 = 0
tsa_q3_5 = -1
tsa_q3_6 = -1
tsa_q3_7 = 1
q4_8 = 2
q4_18 = -1
ta_hd = 22
ta_tr = 21.5
ta_ft = 20
tr_hd = 22.8
tr_tr = 22
tr_ft = 20.9
va_hd = 0.1
va_tr = 0.1
va_ft = 0.1
rh = 26
co2ppm = 1400
lux = 5.06
sound = 61.25
qc_1 = -8
qc_2 = -10
qc_3 = -6
qc_4 = -8
qc_5 = -10
qc_6 = 10
qa_gender_m = 1
q2_1_yes = 0
light_blue = 0
light_yellow = 0
scent_OC = 0
scent_Pepper = 0
body_state = np.array([[ta_hd, tr_hd, va_hd], [ta_tr, tr_tr, va_tr],
[ta_ft, tr_ft, va_ft]])
t_eq, eqt_out = eqt(body_state, pre_clo, pre_out)
t_eq = np.array(t_eq)
# take the first 3 values and reshape into a column vector
t_eq_column = t_eq[:3].reshape(1, -1)
# eqt_head, eqt_trunk, eqt_feet, eqt_out_head, eqt_out_trunk, eqt_out_feet, eqt_out_overall = eqt(ta_hd, ta_tr, ta_ft, tr_hd, tr_tr, tr_ft, va_hd, va_tr, va_ft, pre_clo, pre_out)
input_vars = np.column_stack((
pre_out,
pre_t,
pre_clo,
qa_age,
qa_ht,
qa_wt,
q2_2,
thist_d0,
tsa_q3_1,
tsa_q3_2,
tsa_q3_3,
tsa_q3_4,
tsa_q3_5,
tsa_q3_6,
tsa_q3_7,
q4_8,
q4_18,
ta_hd,
ta_tr,
ta_ft,
tr_hd,
tr_tr,
tr_ft,
va_hd,
va_tr,
va_ft,
rh,
co2ppm,
lux,
sound,
qc_1,
qc_2,
qc_3,
qc_4,
qc_5,
qc_6,
qa_gender_m,
q2_1_yes,
light_blue,
light_yellow,
scent_OC,
scent_Pepper,
t_eq_column,
))
input_vars_red = np.column_stack((
pre_out,
pre_clo,
qa_ht,
qa_wt,
ta_hd,
ta_tr,
ta_ft,
tr_hd,
tr_tr,
tr_ft,
va_hd,
va_tr,
va_ft,
rh,
co2ppm,
lux,
sound,
light_blue,
light_yellow,
scent_OC,
scent_Pepper,
t_eq_column,
))
(
comfort_indicator,
score,
threshold,
comfort_indicator_red,
score_red,
threshold_red,
HCMout,
HCMout_red,
) = binary_comfort(input_vars,
input_vars_red,
eqt_out,
model=load_hcm_model())
assert HCMout == 2, "occupant should be comfortable for this example"
assert score[0] == approx(1.7733586531269188)
assert threshold[0] == approx(-3.234199373163648)
assert HCMout_red == 2
assert score_red[0] == approx(0.5449404047755122)
assert threshold_red[0] == approx(-2.5008015493225457)
def test_binary_comfort_eg2():
pre_out = 12
pre_t = 21
pre_clo = 0.76
qa_age = 48
qa_ht = 165
qa_wt = 57
q2_2 = 5
thist_d0 = 13
tsa_q3_1 = 7
tsa_q3_2 = -1
tsa_q3_3 = 2
tsa_q3_4 = -1
tsa_q3_5 = 1
tsa_q3_6 = 2
tsa_q3_7 = -1
q4_8 = -1
q4_18 = -2
ta_hd = 27.5
ta_tr = 26.7
ta_ft = 27.7
tr_hd = 27.6
tr_tr = 27.2
tr_ft = 30.3
va_hd = 0.3
va_tr = 0.45
va_ft = 0.17
rh = 38
co2ppm = 860
lux = 5.06
sound = 66.25
qc_1 = -8
qc_2 = -10
qc_3 = -10
qc_4 = -10
qc_5 = -10
qc_6 = 0
qa_gender_m = 0
q2_1_yes = 0
light_blue = 0
light_yellow = 0
scent_OC = 0
scent_Pepper = 0
body_state = np.array([[ta_hd, tr_hd, va_hd], [ta_tr, tr_tr, va_tr],
[ta_ft, tr_ft, va_ft]])
t_eq, eqt_out = eqt(body_state, pre_clo, pre_out)
t_eq = np.array(t_eq)
# take the first 3 values and reshape into a column vector
t_eq_column = t_eq[:3].reshape(1, -1)
# eqt_head, eqt_trunk, eqt_feet, eqt_out_head, eqt_out_trunk, eqt_out_feet, eqt_out_overall = eqt(ta_hd, ta_tr, ta_ft, tr_hd, tr_tr, tr_ft, va_hd, va_tr, va_ft, pre_clo, pre_out)
input_vars = np.column_stack((
pre_out,
pre_t,
pre_clo,
qa_age,
qa_ht,
qa_wt,
q2_2,
thist_d0,
tsa_q3_1,
tsa_q3_2,
tsa_q3_3,
tsa_q3_4,
tsa_q3_5,
tsa_q3_6,
tsa_q3_7,
q4_8,
q4_18,
ta_hd,
ta_tr,
ta_ft,
tr_hd,
tr_tr,
tr_ft,
va_hd,
va_tr,
va_ft,
rh,
co2ppm,
lux,
sound,
qc_1,
qc_2,
qc_3,
qc_4,
qc_5,
qc_6,
qa_gender_m,
q2_1_yes,
light_blue,
light_yellow,
scent_OC,
scent_Pepper,
t_eq_column,
))
input_vars_red = np.column_stack((
pre_out,
pre_clo,
qa_ht,
qa_wt,
ta_hd,
ta_tr,
ta_ft,
tr_hd,
tr_tr,
tr_ft,
va_hd,
va_tr,
va_ft,
rh,
co2ppm,
lux,
sound,
light_blue,
light_yellow,
scent_OC,
scent_Pepper,
t_eq_column,
))
(
comfort_indicator,
score,
threshold,
comfort_indicator_red,
score_red,
threshold_red,
HCMout,
HCMout_red,
) = binary_comfort(input_vars,
input_vars_red,
eqt_out,
model=load_hcm_model())
assert HCMout == 1, "occupant should be uncomfortable for this example"
assert score[0] == approx(-0.9369385133610963)
assert threshold[0] == approx(-3.234199373163648)
assert HCMout_red == 1
assert score_red[0] == approx(-0.6477776016370358)
assert threshold_red[0] == approx(-2.5008015493225457)