def clean_hostname(name: typing.AnyStr) -> str:
""" Converts from short to long hostname, if no domain found. """
# bytes?
if not isinstance(name, (str, bytes)):
cli_warning("Invalid input for hostname: {}".format(name))
name = name.lower()
# Assume user is happy with domain, but strip the dot.
if name.endswith("."):
return name[:-1]
# If a dot in name, assume long name.
if '.' in name:
return name
# Append domain name if in config and it does not end with it
if 'domain' in config and not name.endswith(config['domain']):
return "{}.{}".format(name, config['domain'])
return name
def in_hsts_preload(host: typing.AnyStr) -> bool:
"""Determines if an IDNA-encoded host is on the HSTS preload list"""
if isinstance(host, str):
host = host.encode("ascii")
labels = host.lower().split(b".")
# Fast-branch for gTLDs that are registered to preload all sub-domains.
if labels[-1] in _GTLD_INCLUDE_SUBDOMAINS:
return True
with open_pkg_binary("hstspreload.bin") as f:
for layer, label in enumerate(labels[::-1]):
# None of our layers are greater than 4 deep.
if layer > 3:
return False
# Read the jump table for the layer and label
jump_info = _JUMPTABLE[layer][_crc8(label)]
if jump_info is None:
# No entry: host is not preloaded
return False
# Read the set of entries for that layer and label
f.seek(jump_info[0])
data = bytearray(jump_info[1])
f.readinto(data)
for is_leaf, include_subdomains, ent_label in _iter_entries(data):
# We found a potential leaf
if is_leaf:
if ent_label == host:
return True
if include_subdomains and host.endswith(b"." + ent_label):
return True
# Continue traversing as we're not at a leaf.
elif label == ent_label:
break
else:
return False
return False
def in_hsts_preload(host: typing.AnyStr) -> bool:
"""Determines if an IDNA-encoded host is on the HSTS preload list"""
if isinstance(host, str):
host = host.encode("ascii")
labels = host.lower().split(b".")
# Fast-branch for gTLDs that are registered to preload all sub-domains.
if labels[-1] in _GTLD_INCLUDE_SUBDOMAINS:
return True
with open(_HSTSPRELOAD_BIN_PATH, "rb") as f:
for layer, label in enumerate(labels[::-1]):
# None of our layers are greater than 4 deep.
if layer > 3:
return False
# Read the jump table for the layer and label
offset, size = _get_offset_and_size(f, layer, label)
if offset == 0:
return False
# Read the set of entries for that layer
f.seek(offset, 1)
data = bytearray(size)
f.readinto(data)
for is_leaf, include_subdomains, ent_label in _iter_entries(data):
# We found a potential leaf
if is_leaf:
if ent_label == host:
return True
if include_subdomains and host.endswith(b"." + ent_label):
return True
# Continue traversing as we're not at a leaf.
elif label == ent_label:
break
else:
return False
return False
def viz_predictions(
predictions_path: typing.AnyStr,
dataframe_path: typing.AnyStr,
test_config_path: typing.AnyStr,
):
"""Displays a looping visualization of the GHI predictions saved by the evaluation script.
This visualization requires OpenCV3+ ('cv2'), and will loop while refreshing a local window until the program
is killed, or 'q' is pressed. The arrow keys allow the user to change which day is being shown.
"""
assert os.path.isfile(test_config_path) and test_config_path.endswith(
".json"), "invalid test config"
with open(test_config_path, "r") as fd:
test_config = json.load(fd)
stations = test_config["stations"]
target_datetimes = test_config["target_datetimes"]
start_bound = datetime.datetime.fromisoformat(test_config["start_bound"])
end_bound = datetime.datetime.fromisoformat(test_config["end_bound"])
horiz_deltas = [
pd.Timedelta(d).to_pytimedelta()
for d in test_config["target_time_offsets"]
]
assert os.path.isfile(
predictions_path), f"invalid preds file path: {predictions_path}"
with open(predictions_path, "r") as fd:
predictions = fd.readlines()
assert len(predictions) == len(target_datetimes) * len(stations), \
"predicted ghi sequence count mistmatch wrt target datetimes x station count"
assert len(predictions) % len(stations) == 0
predictions = np.asarray(
[float(ghi) for p in predictions for ghi in p.split(",")])
predictions = predictions.reshape(
(len(stations), len(target_datetimes), -1))
pred_horiz = predictions.shape[-1]
target_datetimes = pd.DatetimeIndex(
[datetime.datetime.fromisoformat(t) for t in target_datetimes])
assert os.path.isfile(
dataframe_path), f"invalid dataframe path: {dataframe_path}"
dataframe = pd.read_pickle(dataframe_path)
dataframe = dataframe[dataframe.index >= start_bound]
dataframe = dataframe[dataframe.index < end_bound]
assert dataframe.index.get_loc(
start_bound) == 0, "invalid start bound (should land at first index)"
assert len(dataframe.index.intersection(target_datetimes)) == len(target_datetimes), \
"bad dataframe target datetimes overlap, index values are missing"
# we will display 24-hour slices with some overlap (configured via hard-coded param below)
time_window, time_overlap, time_sample = \
datetime.timedelta(hours=24), datetime.timedelta(hours=3), datetime.timedelta(minutes=15)
assert len(dataframe.asfreq("15min").index) == len(dataframe.index), \
"invalid dataframe index padding (should have an entry every 15 mins)"
sample_count = ((time_window + 2 * time_overlap) // time_sample) + 1
day_count = int(math.ceil((end_bound - start_bound) / time_window))
clearsky_ghi_data = np.full((day_count, len(stations), sample_count),
fill_value=float("nan"),
dtype=np.float32)
station_ghi_data = np.full((day_count, len(stations), sample_count),
fill_value=float("nan"),
dtype=np.float32)
pred_ghi_data = np.full(
(day_count, len(stations), pred_horiz, sample_count),
fill_value=float("nan"),
dtype=np.float32)
days_range = pd.date_range(start_bound,
end_bound,
freq=time_window,
closed="left")
for day_idx, day_start in enumerate(
tqdm.tqdm(days_range, desc="preparing daytime GHI intervals")):
window_start, window_end = day_start - time_overlap, day_start + time_window + time_overlap
sample_start, sample_end = (
window_start - start_bound) // time_sample, (
window_end - start_bound) // time_sample
for sample_iter_idx, sample_idx in enumerate(
range(sample_start, sample_end + 1)):
if sample_idx < 0 or sample_idx >= len(dataframe.index):
continue
sample_row = dataframe.iloc[sample_idx]
sample_time = window_start + sample_iter_idx * time_sample
target_iter_idx = target_datetimes.get_loc(
sample_time) if sample_time in target_datetimes else None
for station_idx, station_name in enumerate(stations):
clearsky_ghi_data[day_idx, station_idx,
sample_iter_idx] = sample_row[
station_name + "_CLEARSKY_GHI"]
station_ghi_data[day_idx, station_idx,
sample_iter_idx] = sample_row[station_name +
"_GHI"]
if target_iter_idx is not None:
pred_ghi_data[day_idx, station_idx, :,
sample_iter_idx] = predictions[
station_idx, target_iter_idx]
displays = []
for day_idx, day_start in enumerate(
tqdm.tqdm(days_range, desc="preparing plots")):
displays.append(
draw_daily_ghi(
clearsky_ghi=clearsky_ghi_data[day_idx],
station_ghi=station_ghi_data[day_idx],
pred_ghi=pred_ghi_data[day_idx],
stations=stations,
horiz_deltas=horiz_deltas,
window_start=(day_start - time_overlap),
window_end=(day_start + time_window + time_overlap),
sample_step=time_sample,
))
display = np.stack(displays)
day_idx = 0
while True:
cv.imshow("ghi", display[day_idx])
ret = cv.waitKey(100)
if ret == ord('q') or ret == 27: # q or ESC
break
elif ret == 81 or ret == 84: # UNIX: left or down arrow
day_idx = max(day_idx - 1, 0)
elif ret == 82 or ret == 83: # UNIX: right or up arrow
day_idx = min(day_idx + 1, len(displays) - 1)
def __init__(self, pat: ty.AnyStr, *, period_special: bool = True):
"""
Arguments
---------
pat
The glob pattern to use for matching
period_special
Whether a leading period in file/directory names should be matchable by
``*``, ``?`` and ``[…]`` – traditionally they are not, but many modern
shells allow one to disable this behaviour
"""
self.period_special = period_special # type: bool
self._sep = utils.maybe_fsencode(os.path.sep, pat) # type: ty.AnyStr
dblstar = utils.maybe_fsencode("**", pat) # type: ty.AnyStr
dot = utils.maybe_fsencode(".", pat) # type: ty.AnyStr
pat_ndot = utils.maybe_fsencode(r"(?![.])", pat) # type: ty.AnyStr
# Normalize path separator
if os.path.altsep:
pat = pat.replace(utils.maybe_fsencode(os.path.altsep, pat),
self._sep)
# Sanity checks for stuff that will definitely NOT EVER match
# (there is another one in the loop below)
assert not os.path.isabs(
pat), "Absolute matching patterns will never match"
# Note the extra final slash for its effect of only matching directories
#
# (TBH, I find it hard to see how that is useful, but everybody does it
# and it keeps things consistent overall – something to only match files
# would be nice however.)
self._dir_only = pat.endswith(self._sep) # type: bool
self._pat = [] # type: ty.List[ty.Optional[re_pattern_t]]
for label in pat.split(self._sep):
# Skip over useless path components
if len(label) < 1 or label == dot:
continue
assert label != dot + dot, 'Matching patterns containing ".." will never match'
if label == dblstar:
self._pat.append(None)
elif dblstar in label:
raise NotImplementedError(
"Using double-star (**) and other characters in the same glob "
"path label ({0}) is not currently supported – please do file "
"an issue if you need this!".format(os.fsdecode(label)))
else:
#re_expr: ty.AnyStr
if not isinstance(label, bytes):
re_expr = fnmatch.translate(label)
else:
re_expr = fnmatch.translate(
label.decode("latin-1")).encode("latin-1")
if period_special and not label.startswith(dot):
re_expr = pat_ndot + re_expr
self._pat.append(re.compile(re_expr))
