def test_init(self):
"""Verify that we can load in a .so file.
This example and compiled object files come from ibisami a related module that this
command is used with.
"""
if sys.platform == "win32":
example_so = Path(__file__).parent.joinpath(
"examples", "example_tx_x86_amd64.dll")
elif sys.platform.startswith("linux"):
example_so = Path(__file__).parent.joinpath(
"examples", "example_tx_x86_amd64.so")
else: # darwin aka OS X
example_so = Path(__file__).parent.joinpath(
"examples", "example_tx_x86_amd64_osx.so")
the_model = AMIModel(example_so)
initializer = AMIModelInitializer({'root_name': "exampleTx"})
the_model.initialize(initializer)
assert the_model.msg == b"Initializing Tx...\n\n"
assert the_model.ami_params_out == (
"(example_tx (tx_tap_units 27) (taps[0] 0) (taps[1] 27) (taps[2] 0) "
"(taps[3] 0) (tap_weights_[0] -0) (tap_weights_[1] 1.0989) (tap_weights_[2] -0) "
"(tap_weights_[3] -0)\n").encode("utf-8")
def my_run_simulation(self, initial_run=False, update_plots=True):
"""
Runs the simulation.
Args:
self(PyBERT): Reference to an instance of the *PyBERT* class.
initial_run(Bool): If True, don't update the eye diagrams, since
they haven't been created, yet. (Optional; default = False.)
update_plots(Bool): If True, update the plots, after simulation
completes. This option can be used by larger scripts, which
import *pybert*, in order to avoid graphical back-end
conflicts and speed up this function's execution time.
(Optional; default = True.)
"""
num_sweeps = self.num_sweeps
sweep_num = self.sweep_num
start_time = clock()
self.status = "Running channel...(sweep %d of %d)" % (sweep_num,
num_sweeps)
self.run_count += 1 # Force regeneration of bit stream.
# Pull class variables into local storage, performing unit conversion where necessary.
t = self.t
w = self.w
bits = self.bits
symbols = self.symbols
ffe = self.ffe
nbits = self.nbits
nui = self.nui
bit_rate = self.bit_rate * 1.0e9
eye_bits = self.eye_bits
eye_uis = self.eye_uis
nspb = self.nspb
nspui = self.nspui
rn = self.rn
pn_mag = self.pn_mag
pn_freq = self.pn_freq * 1.0e6
pattern_len = self.pattern_len
rx_bw = self.rx_bw * 1.0e9
peak_freq = self.peak_freq * 1.0e9
peak_mag = self.peak_mag
ctle_offset = self.ctle_offset
ctle_mode = self.ctle_mode
delta_t = self.delta_t * 1.0e-12
alpha = self.alpha
ui = self.ui
n_taps = self.n_taps
gain = self.gain
n_ave = self.n_ave
decision_scaler = self.decision_scaler
n_lock_ave = self.n_lock_ave
rel_lock_tol = self.rel_lock_tol
lock_sustain = self.lock_sustain
bandwidth = self.sum_bw * 1.0e9
rel_thresh = self.thresh
mod_type = self.mod_type[0]
try:
# Calculate misc. values.
fs = bit_rate * nspb
Ts = t[1]
ts = Ts
# Generate the ideal over-sampled signal.
#
# Duo-binary is problematic, in that it requires convolution with the ideal duobinary
# impulse response, in order to produce the proper ideal signal.
x = repeat(symbols, nspui)
self.x = x
if mod_type == 1: # Handle duo-binary case.
duob_h = array(([0.5] + [0.0] * (nspui - 1)) * 2)
x = convolve(x, duob_h)[:len(t)]
self.ideal_signal = x
# Find the ideal crossing times, for subsequent jitter analysis of transmitted signal.
ideal_xings = find_crossings(t,
x,
decision_scaler,
min_delay=(ui / 2.0),
mod_type=mod_type)
self.ideal_xings = ideal_xings
# Calculate the channel output.
#
# Note: We're not using 'self.ideal_signal', because we rely on the system response to
# create the duobinary waveform. We only create it explicitly, above,
# so that we'll have an ideal reference for comparison.
chnl_h = self.calc_chnl_h()
self.log("Channel impulse response is {} samples long.".format(
len(chnl_h)))
chnl_out = convolve(self.x, chnl_h)[:len(t)]
self.channel_perf = nbits * nspb / (clock() - start_time)
split_time = clock()
self.status = "Running Tx...(sweep %d of %d)" % (sweep_num, num_sweeps)
except Exception:
self.status = "Exception: channel"
raise
self.chnl_out = chnl_out
self.chnl_out_H = fft(chnl_out)
# Generate the output from, and the incremental/cumulative impulse/step/frequency responses of, the Tx.
try:
if self.tx_use_ami:
# Note: Within the PyBERT computational environment, we use normalized impulse responses,
# which have units of (V/ts), where 'ts' is the sample interval. However, IBIS-AMI models expect
# units of (V/s). So, we have to scale accordingly, as we transit the boundary between these two worlds.
tx_cfg = self._tx_cfg # Grab the 'AMIParamConfigurator' instance for this model.
# Get the model invoked and initialized, except for 'channel_response', which
# we need to do several different ways, in order to gather all the data we need.
tx_param_dict = tx_cfg.input_ami_params
tx_model_init = AMIModelInitializer(tx_param_dict)
tx_model_init.sample_interval = ts # Must be set, before 'channel_response'!
tx_model_init.channel_response = [1.0 / ts] + [0.0] * (
len(chnl_h) - 1
) # Start with a delta function, to capture the model's impulse response.
tx_model_init.bit_time = ui
tx_model = AMIModel(self.tx_dll_file)
tx_model.initialize(tx_model_init)
self.log(
"Tx IBIS-AMI model initialization results:\nInput parameters: {}\nOutput parameters: {}\nMessage: {}"
.format(tx_model.ami_params_in, tx_model.ami_params_out,
tx_model.msg))
if tx_cfg.fetch_param_val(
["Reserved_Parameters", "Init_Returns_Impulse"]):
tx_h = array(tx_model.initOut) * ts
elif not tx_cfg.fetch_param_val(
["Reserved_Parameters", "GetWave_Exists"]):
self.handle_error(
"ERROR: Both 'Init_Returns_Impulse' and 'GetWave_Exists' are False!\n \
I cannot continue.\nThis condition is supposed to be caught sooner in the flow."
)
self.status = "Simulation Error."
return
elif not self.tx_use_getwave:
self.handle_error(
"ERROR: You have elected not to use GetWave for a model, which does not return an impulse response!\n \
I cannot continue.\nPlease, select 'Use GetWave' and try again.",
"PyBERT Alert",
)
self.status = "Simulation Error."
return
if self.tx_use_getwave:
# For GetWave, use a step to extract the model's native properties.
# Position the input edge at the center of the vector, in
# order to minimize high frequency artifactual energy
# introduced by frequency domain processing in some models.
half_len = len(chnl_h) // 2
tx_s = tx_model.getWave(
array([0.0] * half_len + [1.0] * half_len))
# Shift the result back to the correct location, extending the last sample.
tx_s = pad(tx_s[half_len:], (0, half_len), "edge")
tx_h = diff(concatenate(
(array([0.0]),
tx_s))) # Without the leading 0, we miss the pre-tap.
tx_out = tx_model.getWave(self.x)
else: # Init()-only.
tx_s = tx_h.cumsum()
tx_out = convolve(tx_h, self.x)
else:
# - Generate the ideal, post-preemphasis signal.
# To consider: use 'scipy.interp()'. This is what Mark does, in order to induce jitter in the Tx output.
ffe_out = convolve(symbols, ffe)[:len(symbols)]
self.rel_power = mean(
ffe_out**
2) # Store the relative average power dissipated in the Tx.
tx_out = repeat(ffe_out, nspui) # oversampled output
# - Calculate the responses.
# - (The Tx is unique in that the calculated responses aren't used to form the output.
# This is partly due to the out of order nature in which we combine the Tx and channel,
# and partly due to the fact that we're adding noise to the Tx output.)
tx_h = array(sum([[x] + list(zeros(nspui - 1)) for x in ffe],
[])) # Using sum to concatenate.
tx_h.resize(len(chnl_h))
tx_s = tx_h.cumsum()
tx_out.resize(len(t))
temp = tx_h.copy()
temp.resize(len(w))
tx_H = fft(temp)
tx_H *= tx_s[-1] / abs(tx_H[0])
# - Generate the uncorrelated periodic noise. (Assume capacitive coupling.)
# - Generate the ideal rectangular aggressor waveform.
pn_period = 1.0 / pn_freq
pn_samps = int(pn_period / Ts + 0.5)
pn = zeros(pn_samps)
pn[pn_samps // 2:] = pn_mag
pn = resize(pn, len(tx_out))
# - High pass filter it. (Simulating capacitive coupling.)
(b, a) = iirfilter(2, gFc / (fs / 2), btype="highpass")
pn = lfilter(b, a, pn)[:len(pn)]
# - Add the uncorrelated periodic and random noise to the Tx output.
tx_out += pn
tx_out += normal(scale=rn, size=(len(tx_out), ))
# - Convolve w/ channel.
tx_out_h = convolve(tx_h, chnl_h)[:len(chnl_h)]
temp = tx_out_h.copy()
temp.resize(len(w))
tx_out_H = fft(temp)
rx_in = convolve(tx_out, chnl_h)[:len(tx_out)]
self.tx_s = tx_s
self.tx_out = tx_out
self.rx_in = rx_in
self.tx_out_s = tx_out_h.cumsum()
self.tx_out_p = self.tx_out_s[nspui:] - self.tx_out_s[:-nspui]
self.tx_H = tx_H
self.tx_h = tx_h
self.tx_out_H = tx_out_H
self.tx_out_h = tx_out_h
self.tx_perf = nbits * nspb / (clock() - split_time)
split_time = clock()
self.status = "Running CTLE...(sweep %d of %d)" % (sweep_num,
num_sweeps)
except Exception:
self.status = "Exception: Tx"
raise
# Generate the output from, and the incremental/cumulative impulse/step/frequency responses of, the CTLE.
try:
if self.rx_use_ami:
rx_cfg = self._rx_cfg # Grab the 'AMIParamConfigurator' instance for this model.
# Get the model invoked and initialized, except for 'channel_response', which
# we need to do several different ways, in order to gather all the data we need.
rx_param_dict = rx_cfg.input_ami_params
rx_model_init = AMIModelInitializer(rx_param_dict)
rx_model_init.sample_interval = ts # Must be set, before 'channel_response'!
rx_model_init.channel_response = tx_out_h / ts
rx_model_init.bit_time = ui
rx_model = AMIModel(self.rx_dll_file)
rx_model.initialize(rx_model_init)
self.log(
"Rx IBIS-AMI model initialization results:\nInput parameters: {}\nMessage: {}\nOutput parameters: {}"
.format(rx_model.ami_params_in, rx_model.msg,
rx_model.ami_params_out))
if rx_cfg.fetch_param_val(
["Reserved_Parameters", "Init_Returns_Impulse"]):
ctle_out_h = array(rx_model.initOut) * ts
elif not rx_cfg.fetch_param_val(
["Reserved_Parameters", "GetWave_Exists"]):
self.handle_error(
"ERROR: Both 'Init_Returns_Impulse' and 'GetWave_Exists' are False!\n \
I cannot continue.\nThis condition is supposed to be caught sooner in the flow."
)
self.status = "Simulation Error."
return
elif not self.rx_use_getwave:
self.handle_error(
"ERROR: You have elected not to use GetWave for a model, which does not return an impulse response!\n \
I cannot continue.\nPlease, select 'Use GetWave' and try again.",
"PyBERT Alert",
)
self.status = "Simulation Error."
return
if self.rx_use_getwave:
if False:
ctle_out, clock_times = rx_model.getWave(rx_in, 32)
else:
ctle_out, clock_times = rx_model.getWave(rx_in, len(rx_in))
self.log(rx_model.ami_params_out)
ctle_H = fft(ctle_out * hann(len(ctle_out))) / fft(
rx_in * hann(len(rx_in)))
ctle_h = real(ifft(ctle_H)[:len(chnl_h)])
ctle_out_h = convolve(ctle_h, tx_out_h)[:len(chnl_h)]
else: # Init() only.
ctle_out_h_padded = pad(
ctle_out_h,
(nspb, len(rx_in) - nspb - len(ctle_out_h)),
"linear_ramp",
end_values=(0.0, 0.0),
)
tx_out_h_padded = pad(
tx_out_h,
(nspb, len(rx_in) - nspb - len(tx_out_h)),
"linear_ramp",
end_values=(0.0, 0.0),
)
ctle_H = fft(ctle_out_h_padded) / fft(tx_out_h_padded)
ctle_h = real(ifft(ctle_H)[:len(chnl_h)])
ctle_out = convolve(rx_in, ctle_h)
ctle_s = ctle_h.cumsum()
else:
if self.use_ctle_file:
ctle_h = import_channel(self.ctle_file, ts)
if max(abs(ctle_h)) < 100.0: # step response?
ctle_h = diff(
ctle_h
) # impulse response is derivative of step response.
else:
ctle_h *= ts # Normalize to (V/sample)
ctle_h.resize(len(t))
ctle_H = fft(ctle_h)
ctle_H *= sum(ctle_h) / ctle_H[0]
else:
_, ctle_H = make_ctle(rx_bw, peak_freq, peak_mag, w, ctle_mode,
ctle_offset)
ctle_h = real(ifft(ctle_H))[:len(chnl_h)]
ctle_h *= abs(ctle_H[0]) / sum(ctle_h)
ctle_out = convolve(rx_in, ctle_h)
ctle_out -= mean(ctle_out) # Force zero mean.
if self.ctle_mode == "AGC": # Automatic gain control engaged?
ctle_out *= 2.0 * decision_scaler / ctle_out.ptp()
ctle_s = ctle_h.cumsum()
ctle_out_h = convolve(tx_out_h, ctle_h)[:len(tx_out_h)]
ctle_out.resize(len(t))
self.ctle_s = ctle_s
ctle_out_h_main_lobe = where(ctle_out_h >= max(ctle_out_h) / 2.0)[0]
if ctle_out_h_main_lobe.size:
conv_dly_ix = ctle_out_h_main_lobe[0]
else:
conv_dly_ix = self.chnl_dly // Ts
# TEMPORARY DEBUGGING
try:
conv_dly = t[conv_dly_ix] # Keep this line only.
except:
print("chnl_dly:", self.chnl_dly)
print("conv_dly_ix:", conv_dly_ix)
print("tx_h:", tx_h)
print("chnl_h:", chnl_h)
raise
#####
ctle_out_s = ctle_out_h.cumsum()
temp = ctle_out_h.copy()
temp.resize(len(w))
ctle_out_H = fft(temp)
# - Store local variables to class instance.
self.ctle_out_s = ctle_out_s
# Consider changing this; it could be sensitive to insufficient "front porch" in the CTLE output step response.
self.ctle_out_p = self.ctle_out_s[nspui:] - self.ctle_out_s[:-nspui]
self.ctle_H = ctle_H
self.ctle_h = ctle_h
self.ctle_out_H = ctle_out_H
self.ctle_out_h = ctle_out_h
self.ctle_out = ctle_out
self.conv_dly = conv_dly
self.conv_dly_ix = conv_dly_ix
self.ctle_perf = nbits * nspb / (clock() - split_time)
split_time = clock()
self.status = "Running DFE/CDR...(sweep %d of %d)" % (sweep_num,
num_sweeps)
except Exception:
self.status = "Exception: Rx"
raise
# Generate the output from, and the incremental/cumulative impulse/step/frequency responses of, the DFE.
try:
if self.use_dfe:
dfe = DFE(
n_taps,
gain,
delta_t,
alpha,
ui,
nspui,
decision_scaler,
mod_type,
n_ave=n_ave,
n_lock_ave=n_lock_ave,
rel_lock_tol=rel_lock_tol,
lock_sustain=lock_sustain,
bandwidth=bandwidth,
ideal=self.sum_ideal,
)
else:
dfe = DFE(
n_taps,
0.0,
delta_t,
alpha,
ui,
nspui,
decision_scaler,
mod_type,
n_ave=n_ave,
n_lock_ave=n_lock_ave,
rel_lock_tol=rel_lock_tol,
lock_sustain=lock_sustain,
bandwidth=bandwidth,
ideal=True,
)
(dfe_out, tap_weights, ui_ests, clocks, lockeds, clock_times,
bits_out) = dfe.run(t, ctle_out)
dfe_out = array(dfe_out)
dfe_out.resize(len(t))
bits_out = array(bits_out)
auto_corr = (1.0 * correlate(bits_out[(nbits - eye_bits):],
bits[(nbits - eye_bits):],
mode="same") /
sum(bits[(nbits - eye_bits):]))
auto_corr = auto_corr[len(auto_corr) // 2:]
self.auto_corr = auto_corr
bit_dly = where(auto_corr == max(auto_corr))[0][0]
bits_ref = bits[(nbits - eye_bits):]
bits_tst = bits_out[(nbits + bit_dly - eye_bits):]
if len(bits_ref) > len(bits_tst):
bits_ref = bits_ref[:len(bits_tst)]
elif len(bits_tst) > len(bits_ref):
bits_tst = bits_tst[:len(bits_ref)]
bit_errs = where(bits_tst ^ bits_ref)[0]
self.bit_errs = len(bit_errs)
dfe_h = array([1.0] + list(zeros(nspb - 1)) +
sum([[-x] + list(zeros(nspb - 1))
for x in tap_weights[-1]], []))
dfe_h.resize(len(ctle_out_h))
temp = dfe_h.copy()
temp.resize(len(w))
dfe_H = fft(temp)
self.dfe_s = dfe_h.cumsum()
dfe_out_H = ctle_out_H * dfe_H
dfe_out_h = convolve(ctle_out_h, dfe_h)[:len(ctle_out_h)]
dfe_out_s = dfe_out_h.cumsum()
self.dfe_out_p = dfe_out_s - pad(
dfe_out_s[:-nspui], (nspui, 0), "constant", constant_values=(0, 0))
self.dfe_H = dfe_H
self.dfe_h = dfe_h
self.dfe_out_H = dfe_out_H
self.dfe_out_h = dfe_out_h
self.dfe_out_s = dfe_out_s
self.dfe_out = dfe_out
self.dfe_perf = nbits * nspb / (clock() - split_time)
split_time = clock()
self.status = "Analyzing jitter...(sweep %d of %d)" % (sweep_num,
num_sweeps)
except Exception:
self.status = "Exception: DFE"
raise
# Save local variables to class instance for state preservation, performing unit conversion where necessary.
self.adaptation = tap_weights
self.ui_ests = array(ui_ests) * 1.0e12 # (ps)
self.clocks = clocks
self.lockeds = lockeds
self.clock_times = clock_times
# Analyze the jitter.
self.thresh_tx = array([])
self.jitter_ext_tx = array([])
self.jitter_tx = array([])
self.jitter_spectrum_tx = array([])
self.jitter_ind_spectrum_tx = array([])
self.thresh_ctle = array([])
self.jitter_ext_ctle = array([])
self.jitter_ctle = array([])
self.jitter_spectrum_ctle = array([])
self.jitter_ind_spectrum_ctle = array([])
self.thresh_dfe = array([])
self.jitter_ext_dfe = array([])
self.jitter_dfe = array([])
self.jitter_spectrum_dfe = array([])
self.jitter_ind_spectrum_dfe = array([])
self.f_MHz_dfe = array([])
self.jitter_rejection_ratio = array([])
try:
if mod_type == 1: # Handle duo-binary case.
pattern_len *= 2 # Because, the XOR pre-coding can invert every other pattern rep.
if mod_type == 2: # Handle PAM-4 case.
if pattern_len % 2:
pattern_len *= 2 # Because, the bits are taken in pairs, to form the symbols.
# - channel output
actual_xings = find_crossings(t,
chnl_out,
decision_scaler,
mod_type=mod_type)
(
_,
t_jitter,
isi,
dcd,
pj,
rj,
_,
thresh,
jitter_spectrum,
jitter_ind_spectrum,
spectrum_freqs,
hist,
hist_synth,
bin_centers,
) = calc_jitter(ui, nui, pattern_len, ideal_xings, actual_xings,
rel_thresh)
self.t_jitter = t_jitter
self.isi_chnl = isi
self.dcd_chnl = dcd
self.pj_chnl = pj
self.rj_chnl = rj
self.thresh_chnl = thresh
self.jitter_chnl = hist
self.jitter_ext_chnl = hist_synth
self.jitter_bins = bin_centers
self.jitter_spectrum_chnl = jitter_spectrum
self.jitter_ind_spectrum_chnl = jitter_ind_spectrum
self.f_MHz = array(spectrum_freqs) * 1.0e-6
# - Tx output
actual_xings = find_crossings(t,
rx_in,
decision_scaler,
mod_type=mod_type)
(
_,
t_jitter,
isi,
dcd,
pj,
rj,
_,
thresh,
jitter_spectrum,
jitter_ind_spectrum,
spectrum_freqs,
hist,
hist_synth,
bin_centers,
) = calc_jitter(ui, nui, pattern_len, ideal_xings, actual_xings,
rel_thresh)
self.isi_tx = isi
self.dcd_tx = dcd
self.pj_tx = pj
self.rj_tx = rj
self.thresh_tx = thresh
self.jitter_tx = hist
self.jitter_ext_tx = hist_synth
self.jitter_spectrum_tx = jitter_spectrum
self.jitter_ind_spectrum_tx = jitter_ind_spectrum
# - CTLE output
actual_xings = find_crossings(t,
ctle_out,
decision_scaler,
mod_type=mod_type)
(
jitter,
t_jitter,
isi,
dcd,
pj,
rj,
jitter_ext,
thresh,
jitter_spectrum,
jitter_ind_spectrum,
spectrum_freqs,
hist,
hist_synth,
bin_centers,
) = calc_jitter(ui, nui, pattern_len, ideal_xings, actual_xings,
rel_thresh)
self.isi_ctle = isi
self.dcd_ctle = dcd
self.pj_ctle = pj
self.rj_ctle = rj
self.thresh_ctle = thresh
self.jitter_ctle = hist
self.jitter_ext_ctle = hist_synth
self.jitter_spectrum_ctle = jitter_spectrum
self.jitter_ind_spectrum_ctle = jitter_ind_spectrum
# - DFE output
ignore_until = (
nui - eye_uis
) * ui + 0.75 * ui # 0.5 was causing an occasional misalignment.
ideal_xings = array([x for x in list(ideal_xings) if x > ignore_until])
min_delay = ignore_until + conv_dly
actual_xings = find_crossings(t,
dfe_out,
decision_scaler,
min_delay=min_delay,
mod_type=mod_type,
rising_first=False)
(
jitter,
t_jitter,
isi,
dcd,
pj,
rj,
jitter_ext,
thresh,
jitter_spectrum,
jitter_ind_spectrum,
spectrum_freqs,
hist,
hist_synth,
bin_centers,
) = calc_jitter(ui, eye_uis, pattern_len, ideal_xings, actual_xings,
rel_thresh)
self.isi_dfe = isi
self.dcd_dfe = dcd
self.pj_dfe = pj
self.rj_dfe = rj
self.thresh_dfe = thresh
self.jitter_dfe = hist
self.jitter_ext_dfe = hist_synth
self.jitter_spectrum_dfe = jitter_spectrum
self.jitter_ind_spectrum_dfe = jitter_ind_spectrum
self.f_MHz_dfe = array(spectrum_freqs) * 1.0e-6
dfe_spec = self.jitter_spectrum_dfe
self.jitter_rejection_ratio = zeros(len(dfe_spec))
self.jitter_perf = nbits * nspb / (clock() - split_time)
self.total_perf = nbits * nspb / (clock() - start_time)
split_time = clock()
self.status = "Updating plots...(sweep %d of %d)" % (sweep_num,
num_sweeps)
except Exception:
self.status = "Exception: jitter"
# raise
# Update plots.
try:
if update_plots:
update_results(self)
if not initial_run:
update_eyes(self)
self.plotting_perf = nbits * nspb / (clock() - split_time)
self.status = "Ready."
except Exception:
self.status = "Exception: plotting"
raise
def run_tests(**kwargs):
"""Provide a thin wrapper around the click interface so that we can test the operation."""
# Fetch options and cast into local independent variables.
test_dir = Path(kwargs["test_dir"]).resolve()
ref_dir = Path(kwargs["ref_dir"])
if not ref_dir.exists():
ref_dir = None
model = Path(kwargs["model"]).resolve()
out_dir = Path(kwargs["out_dir"])
out_dir.mkdir(exist_ok=True)
xml_filename = out_dir.joinpath(kwargs["xml_file"])
# Some browsers demand that the stylesheet be located in the same
# folder as the *.XML file. Besides, this allows the model tester
# to zip up her 'test_results' directory and send it off to
# someone, whom may not have the PyIBIS-AMI package installed.
shutil.copy(str(Path(__file__).parent.joinpath("test_results.xsl")), str(out_dir))
print("Testing model: {}".format(model))
print("Using tests in: {}".format(test_dir))
params = expand_params(kwargs["params"])
# Run the tests.
print("Sending XHTML output to: {}".format(xml_filename))
with open(xml_filename, "w") as xml_file:
xml_file.write('<?xml version="1.0" encoding="ISO-8859-1"?>\n')
xml_file.write('<?xml-stylesheet type="text/xsl" href="test_results.xsl"?>\n')
xml_file.write("<tests>\n")
if kwargs["tests"]:
tests = kwargs["tests"]
else:
tests = list(test_dir.glob("*.em"))
for test in tests:
# print("Running test: {} ...".format(test.stem))
print("Running test: {} ...".format(test))
theModel = AMIModel(model.__str__())
for cfg_item in params:
cfg_name = cfg_item[0]
print("\tRunning test configuration: {} ...".format(cfg_name))
description = cfg_item[1]
param_list = cfg_item[2]
colors = color_picker(num_hues=len(param_list))
with open(xml_filename, "a") as xml_file:
interpreter = em.Interpreter(
output=xml_file,
globals={
"name": "{} ({})".format(test, cfg_name),
"model": theModel,
"data": param_list,
"plot_names": plot_name(xml_filename.stem),
"description": description,
"plot_colors": colors,
"ref_dir": ref_dir,
},
)
try:
cwd = Path().cwd()
chdir(out_dir) # So that the images are saved in the output directory.
interpreter.file(open(Path(test_dir, test)))
chdir(cwd)
finally:
interpreter.shutdown()
print("Test:", test, "complete.")
with open(xml_filename, "a") as xml_file:
xml_file.write("</tests>\n")
print("Please, open file, `{}` in a Web browser, in order to view the test results.".format(xml_filename))