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util.py
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util.py
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import torch
import math
import numpy as np
import datetime as dt
import pickle
import pandas as pd
import time
import signal
import sys
import hashlib
import networkx as nx
import json
import re
import os
import inspect
import scipy
comparator_fn_map = {
"lt": lambda a, b: a < b,
"le": lambda a, b: a <= b,
"eq": lambda a, b: a == b,
"ne": lambda a, b: a != b,
"gt": lambda a, b: a > b,
"ge": lambda a, b: a >= b,
"<": lambda a, b: a < b,
"<=": lambda a, b: a <= b,
"==": lambda a, b: a == b,
"!=": lambda a, b: a != b,
">": lambda a, b: a > b,
">=": lambda a, b: a >= b,
"in": lambda a, b: a in b,
"!in": lambda a, b: not a in b,
}
operator_fn_map = {
"+": lambda a, b: a + b,
"-": lambda a, b: a - b,
"*": lambda a, b: a * b,
"/": lambda a, b: a / b,
"**": lambda a, b: a ** b,
"//": lambda a, b: a // b,
"%": lambda a, b: a % b,
}
month_name_map = {
1: "January",
2: "February",
3: "March",
4: "April",
5: "May",
6: "June",
7: "July",
8: "August",
9: "September",
10: "October",
11: "November",
12: "December"
}
def get_next_fname(_dir, base_fname, ext=".csv"):
n = 0
fnames = os.listdir(_dir)
for fname in fnames:
if re.match("^%s\d+\%s$" % (base_fname, ext), fname):
n += 1
return "%s%d%s" % (base_fname, n, ext)
def module_of(obj):
return sys.modules[obj.__module__]
def get_all_subsets(a):
subsets = []
for i in range(len(a)):
subsets += list(list(_) for _ in itertools.combinations(a, i+1))
return subsets
def subdivide(a, k):
if k == 0:
return a
b = np.convolve(a, [1, 1], "valid") / 2
c = []
for i in range(len(b)):
c += [a[i], b[i]]
c.append(a[-1])
return subdivide(c, k-1)
class TimeoutExpired(Exception):
pass
def input_with_timeout(prompt, timeout, timer=time.monotonic):
import msvcrt
# Source : Alex Martelli @ https://stackoverflow.com/questions/2933399/how-to-set-time-limit-on-raw-input/2933423#2933423
sys.stdout.write(prompt)
sys.stdout.flush()
endtime = timer() + timeout
result = []
while timer() < endtime:
if msvcrt.kbhit():
result.append(msvcrt.getwche()) #XXX can it block on multibyte characters?
if result[-1] == '\r':
return ''.join(result[:-1])
time.sleep(0.04) # just to yield to other processes/threads
raise TimeoutExpired
# Description:
# Converts between naming convetions
# Arguments:
# name - the name to convert
# orig_conv - convention used for name
# targ_conv - convention to convert name into
def convert_name_convention(name, orig_conv, targ_conv):
name_fields, new_name = [], ""
# parse into fields
if orig_conv == "Pascal":
name_fields = re.findall("[a-zA-Z][^A-Z]*", name)
elif orig_conv == "camel":
name_fields = re.findall("[a-zA-Z][^A-Z]*", name)
elif orig_conv == "snake":
name_fields = name.split("_")
elif orig_conv == "kebab":
name_fields = name.split("-")
else:
raise NotImplementedError("Unknown naming convertion \"%s\"" % (orig_conv))
# join fields into new name
if targ_conv == "Pascal":
new_name = "".join([name_field[0].upper() + name_field[1:] for name_field in name_fields])
elif targ_conv == "camel":
new_name = "".join([name_field[0].upper() + name_field[1:] for name_field in name_fields])
new_name = new_name[0].lower() + new_name[1:]
elif targ_conv == "snake":
new_name = "_".join(name_fields)
elif targ_conv == "kebab":
new_name = "-".join(name_fields)
else:
raise NotImplementedError("Unknown naming convertion \"%s\"" % (targ_conv))
return new_name
def get_paths(search_dir, path_regex, recurse=False, files=True, debug=False):
if not os.path.exists(search_dir):
raise FileNotFoundError(search_dir)
paths = []
for root, dirnames, filenames in os.walk(search_dir):
if files:
for filename in filenames:
if debug:
print(path_regex, root, filename)
if re.match(path_regex, filename):
paths.append(os.path.join(root, filename))
else:
for dirname in dirnames:
if debug:
print(path_regex, root, dirname)
if re.match(path_regex, dirname):
paths.append(os.path.join(root, dirname))
if not recurse:
break
return paths
def get_choice(items, sort=True):
if len(items) == 0:
return None
elif len(items) == 1:
return items[0]
if sort:
items = sorted(items)
print("\n".join(["%d : %s" % (i, item) for i, item in enumerate(items)]))
choice = input("Choice? : ")
if "," in choice: # choice is list of int
return [items[int(_)] for _ in choice.split(",")]
try: # choice may be an int
return items[int(choice)]
except:
pass
# choice is regex
_items = []
for item in items:
if re.match(choice, item):
_items.append(item)
return _items
class Types:
def is_anything(item):
return True
def is_none(item):
return item is None
def is_int(item):
return isinstance(item, int) or isinstance(item, np.integer)
def is_float(item):
return isinstance(item, float) or isinstance(item, np.floating)
def is_numeric(item):
return Types.is_int(item) or Types.is_float(item)
def is_nan(item):
return np.isnan(item)
def is_string(item):
return isinstance(item, str)
def is_iter(item):
return hasattr(item, "__iter__")
def is_iter_of(item, types=()):
if not isinstance(types, tuple):
types = (types)
if not Types.is_iter(item):
return False
if len(item) == 0: # empty iterable
return len(types) == 0 # of nothing
if len(types) == 0: # non-empty iterable cannot be of nothing
return False
_ = []
for _item in item:
__ = []
for _type in types:
try:
___ = isinstance(_item, _type)
except:
___ = _type(_item)
__.append(___)
_.append(any(__))
return all(_)
def is_iter_of_numeric(item):
return Types.is_iter_of(item, (Types.is_numeric,))
def is_iter_of_str(item):
return Types.is_iter_of(item, (str,))
def is_collection(item):
return not isinstance(item, str) and Types.is_iter(item)
def is_collection_of(item, types):
return Types.is_collection(item) and Types.is_iter_of(item, types)
def is_collection_of_int(item):
return Types.is_collection_of(item, (Types.is_int,))
def is_collection_of_float(item):
return Types.is_collection_of(item, (Types.is_float,))
def is_collection_of_numeric(item):
return Types.is_collection_of(item, (Types.is_numeric,))
def is_collection_of_nan(item):
return Types.is_collection_of(item, (Types.is_nan,))
def is_collection_of_collection(item):
return Types.is_collection_of(item, (Types.is_collection,))
def is_list(item):
return isinstance(item, list)
def is_list_of_strings(item):
return isinstance(item, list) and len(item) > 0 and all(isinstance(_, str) for _ in item)
def is_list_of_list(item):
return isinstance(item, list) and len(item) > 0 and all([isinstance(_, list) for _ in item])
def is_list_of_dict(item):
return isinstance(item, list) and len(item) > 0 and all([isinstance(_, dict) for _ in item])
def get_func_args(func):
args, var_args, kw_args, def_vals = inspect.getargspec(func)
if "self" in args:
args.remove("self")
req_args = args[:-len(def_vals)]
def_args = dict(zip(list_subtract(list(args), req_args), def_vals))
return req_args, var_args, kw_args, def_args
def compute_zscore_classes(vals, means, stddevs, z_intervals):
classes = np.zeros(vals.shape)
for i in range(len(z_intervals)):
min_vals, max_vals = means + z_intervals[i][0] * stddevs, means + z_intervals[i][1] * stddevs
if z_intervals[i][0] < -1:
lower_mask, upper_mask = vals >= min_vals, vals < max_vals
elif z_intervals[i][0] == -1 and z_intervals[i][1] == 1:
lower_mask, upper_mask = vals >= min_vals, vals <= max_vals
else:
lower_mask, upper_mask = vals > min_vals, vals <= max_vals
"""
print("Interval =", z_intervals[i])
print("min/max =", min_vals[:3], max_vals[:3])
print("vals =", vals[:3])
print("lower/upper mask =", lower_mask[:3], upper_mask[:3])
"""
indices = np.where(np.logical_and(lower_mask, upper_mask))[0]
if indices.shape[0] > 0:
classes[indices] = i
return classes
def compute_zscore_confusion(preds, gts, means, stddevs, normalize=False):
z_intervals = [[-8, -2], [-2, -1.5], [-1.5, -1], [-1, 1], [1, 1.5], [1.5, 2], [2, 8]]
pred_classes = compute_zscore_classes(preds, means, stddevs, z_intervals)
gt_classes = compute_zscore_classes(gts, means, stddevs, z_intervals)
from sklearn.metrics import confusion_matrix
normalization = ("true" if normalize else None)
confusion = confusion_matrix(gt_classes, pred_classes, labels=np.arange(len(z_intervals)), normalize=normalization)
return confusion
def compute_events(values, means, stddevs):
intervals = [[-8, -2], [-2, -1.5], [-1.5, -1], [-1, 1], [1, 1.5], [1.5, 2], [2, 8]]
interval_events_map = {}
for interval in intervals:
lower_bounds = means + interval[0] * stddevs
upper_bounds = means + interval[1] * stddevs
if interval[0] < 0 and interval[1] < 0:# Below mean
if interval[0] == -8:# Extreme => (-8,-2)
extremes_below = np.ma.masked_where(
np.logical_not(values < upper_bounds),
values
)
events = extremes_below
elif interval[0] == -2:# Severe => [-2,-1.5)
severes_below = np.ma.masked_where(
np.logical_not(
np.logical_and(
values >= lower_bounds,
values < upper_bounds
)
),
values
)
events = severes_below
elif interval[0] == -1.5:# Moderate
moderates_below = np.ma.masked_where(
np.logical_not(
np.logical_and(
values >= lower_bounds,
values < upper_bounds
)
),
values
)
events = moderates_below
elif interval[0] > 0 and interval[1] > 0:# Above mean
if interval[1] == 8:# Extreme
extremes_above = np.ma.masked_where(
np.logical_not(values > lower_bounds),
values
)
events = extremes_above
elif interval[1] == 2:# Severe
severes_above = np.ma.masked_where(
np.logical_not(
np.logical_and(
values > lower_bounds,
values <= upper_bounds
)
),
values
)
events = severes_above
elif interval[1] == 1.5:# Moderate
moderates_above = np.ma.masked_where(
np.logical_not(
np.logical_and(
values > lower_bounds,
values <= upper_bounds
)
),
values
)
events = moderates_above
else:
normals = np.ma.masked_where(
np.logical_not(
np.logical_and(
values >= lower_bounds,
values <= upper_bounds
)
),
values
)
events = normals
interval_events_map[",".join(map(str, interval))] = events
return interval_events_map
def get_stats(a):
percentiles = np.nanpercentile(a, [25, 50, 75])
stat_value_map = {
"count": np.count_nonzero(~np.isnan(a)),
"mean": np.nanmean(a),
"std": np.nanstd(a),
"min": np.nanmin(a),
"25%": percentiles[0],
"50%": percentiles[1],
"75%": percentiles[2],
"max": np.nanmax(a)
}
stat_value_map["n"] = stat_value_map["count"]
return stat_value_map
def format_memory(n_bytes):
if n_bytes == 0:
return "0B"
mem_name = ("B", "KB", "MB", "GB", "TB", "PB", "EB", "ZB", "YB")
i = int(math.floor(math.log(n_bytes, 1024)))
p = math.pow(1024, i)
s = int(round(n_bytes / p, 2))
return "%s%s" % (s, mem_name[i])
def memory_of(item):
if isinstance(item, torch.Tensor):
n_bytes = item.element_size() * item.nelement()
else:
n_bytes = sys.getsizeof(item)
return format_memory(n_bytes)
def n_sliding_windows(timesteps, length, stride=1, offset=0):
return (timesteps - length + stride - offset) // stride
def sliding_window_indices(timesteps, length, stride=1, offset=0):
""" Creates indices that index a set of windows
Arguments
---------
timesteps: int
total time-steps in source data
length: int
time-steps per window
stride: int
time-steps between consecutive windows (typically 1)
offset: int
time-step where first window begins
"""
n = n_sliding_windows(timesteps, length, stride, offset)
return np.tile(np.arange(length), (n, 1)) + stride * np.reshape(np.arange(n), (-1, 1)) + offset
def input_output_window_indices(timesteps, in_length, out_length, horizon=1, stride=1, offset=0):
""" Creates indices that index a set of input and output windows
Arguments
---------
timesteps: int
number of time-steps in source
in_length: int
number of time-steps per input window
out_length: int
number of time-steps per output window
horizon: int
offset (in time-steps) of output windows relative to last time-step of each input window
see notes below
stride: int
number of time-steps between window origins
offset: int
time-step where first window begins
Notes
-----
horizon :
at horizon=1 => input/output window indices are [0, 1, 2]/[3, 4, 5]
at horizon=3 => input/output window indices are [0, 1, 2]/[5, 6, 7]
at horizon=-2 => input/output window indices are [0, 1, 2]/[0, 1, 2]
"""
indices = sliding_window_indices(timesteps, in_length + (horizon - 1) + out_length, stride, offset)
return indices[:,:in_length], indices[:,-out_length:]
def contiguous_window_indices(n, length, stride=1, offset=0):
assert length % stride == 0, "Windows cannot be contiguous if stride does not divide length (length % stride != 0)"
return np.arange(offset, n, length // stride)
def pretty_temporal_labels(temporal_labels, curr_frmt, new_frmt="%m/%d/%Y, %H:%M:%S.%f", subs=[], keep_frmts=[]):
# Definitions
frmts = ["%Y", "%m", "%d", "%H", "%M", "%S", "%f"]
attrs = ["year", "month", "day", "hour", "minute", "second", "microsecond"]
def edit_new_frmt(new_frmt, redun_frmts):
for frmt in redun_frmts:
new_frmt = new_frmt.replace(frmt, "")
new_frmt = re.sub("^\/+", "", new_frmt) # remove excess leading date seps (/)
new_frmt = re.sub("\/+,", ",", new_frmt) # remove excess trailing date seps (/)
new_frmt = re.sub("(:+.$|:+$|.$)", "", new_frmt) # remove excess time seps (:) and/or microsecond sep (.)
new_frmt = re.sub("(^, |, $)", "", new_frmt) # missing date or time: remove date/time sep (", ")
return new_frmt
# Setup
_type = type(temporal_labels)
if _type != np.ndarray:
temporal_labels = np.array(temporal_labels)
shape = temporal_labels.shape
temporal_labels = np.reshape(temporal_labels, -1)
# Check for and remove redundant datetime fields
_ = np.zeros((len(temporal_labels), len(attrs)))
for i, label in enumerate(temporal_labels):
date = dt.datetime.strptime(label, curr_frmt)
for j, attr in enumerate(attrs):
_[i,j] = getattr(date, attr)
redun_index = [j for j in range(7) if np.all(_[:,j] == _[0,j])]
redun_frmts = [frmts[j] for j in redun_index if not frmts[j] in keep_frmts]
new_frmt = edit_new_frmt(new_frmt, redun_frmts)
# Reformat the labels and perform extra substitutions
_ = []
for label in temporal_labels:
_label = dt.datetime.strftime(dt.datetime.strptime(label, curr_frmt), new_frmt)
for sub in subs:
_label = re.sub(sub[0], sub[1], _label)
_.append(_label)
_ = np.reshape(_, shape)
# Take down
if _type == tuple:
_ = to_tuple(_)
elif _type == list:
_ = to_list(_)
return _
def to_tuple(a):
return a if not hasattr(a, "__iter__") else tuple(to_tuple(_) for _ in a)
def to_list(a):
return a if not hasattr(a, "__iter__") else list(to_list(_) for _ in a)
# Construct a - b
def list_subtract(a, b):
if b is None:
return []
return [a_i for a_i in a if a_i not in b]
def list_indices(a, items):
indices = []
for item in items:
indices.append(a.index(item))
return indices
def dict_to_str(a):
return json.dumps(a, sort_keys=True, indent=4)
def sort_dict(a, by="key", ascending=True):
if by == "value":
return dict(sorted(a.items(), key=lambda item: item[1], reverse=(not ascending)))
if by == "key":
b = {}
keys = sorted(list(a.keys()))
for key in keys:
b[key] = a[key]
return b
def merge_dicts(a, b, overwrite=True):
if not (isinstance(a, dict) or isinstance(b, dict)):
raise ValueError("Expected two dictionaries")
if overwrite:
return {**a, **b}
c = copy_dict(a)
for key, value in b.items():
if not key in a:
c[key] = value
return c
def remap_dict(a, remap_dict, must_exist=False):
b = copy_dict(a)
for old_key, new_key in remap_dict.items():
if old_key in b:
b[new_key] = b.pop(old_key)
elif must_exist:
raise ValueError("Input dict a does not contain key %s" % (str(old_key)))
return b
def copy_dict(a):
return {key: value for key, value in a.items()}
def to_key_index_dict(keys, offset=0, stride=1):
return {key: offset+i for key, i in zip(keys, range(0, len(keys), stride))}
def to_dict(keys, values, repeat=False):
if repeat:
return {key: values for key in keys}
return {key: value for key, value in zip(keys, values)}
def invert_dict(a):
return {value: key for key, value in a.items()}
def sort_dict(a, by="key"):
if by == "key":
return {key: a[key] for key in sorted(a.keys(), key=str.lower)}
elif by == "value":
return invert_dict(sort_dict(invert_dict(a)))
raise ValueError("Unknown sorting option \"%s\"" % (by))
def get_dict_values(a, keys, must_exist=True):
if must_exist:
values = [a[_] for _ in keys]
else:
values = []
for key in keys:
if not key in a:
continue
values.append(a[key])
return values
def filter_dict(a, keys, must_exist=False):
b = {}
for key in keys:
if not key in a and not must_exist:
continue
b[key] = a[key]
return b
def to_cache(data, path):
if os.sep in path:
os.makedirs(os.path.dirname(path), exist_ok=True)
with open(path, "wb") as f:
pickle.dump(data, f, protocol=4)
def from_cache(path, **kwargs):
with open(path, "rb") as f:
data = pickle.load(f, **kwargs)
return data
def hash_str_to_int(string, n_digits):
num = int(hashlib.sha256(string.encode("utf-8")).hexdigest(), 16) % 10**n_digits
cur_digits = len(str(num))
return num * 10**(n_digits - cur_digits)
def make_msg_block(msg, block_char="#"):
msg_line = 3*block_char + " " + msg + " " + 3*block_char
msg_line_len = len(msg_line)
msg_block = "%s\n%s\n%s" % (
msg_line_len*block_char,
msg_line,
msg_line_len*block_char
)
return msg_block
def get_device(device="cuda"):
if "cuda:-" in device and torch.cuda.is_available():
device = "cuda:%d" % (torch.cuda.device_count() + int(device.split(":")[1]))
return torch.device(device if torch.cuda.is_available() else "cpu")
def to_device(items, device):
if isinstance(items, list): # Multiple items to put onto device
device_items = []
for item in items:
if isinstance(item, torch.Tensor) or isinstance(item, torch.nn.Module):
device_items += [item.to(device)]
else:
device_items += [item]
return device_items
else: # Single item to put onto device
return to_device([items], device)[0]
def to_tensor(items, types):
if not isinstance(items, list): # Single item
return to_tensor([items], types)[0]
# Convert each data in items to a tensor
tensors = []
for i in range(len(items)):
item = items[i]
if isinstance(types, list):
_type = types[i]
else:
_type = types
if _type is None: # ignore
tensors.append(item)
continue
if isinstance(item, torch.Tensor): # No need to convert
tensors.append(item)
else:
tensors.append(torch.tensor(item, dtype=_type))
return tensors
def to_ndarray(items):
if not isinstance(items, list): # Single item
return to_ndarray([items])[0]
# Convert each data in items to a tensor
ndarrays = []
for item in items:
if isinstance(item, torch.Tensor):
ndarrays += [item.detach().cpu().numpy()]
elif isinstance(item, np.ndarray):
ndarrays += [item]
elif isinstance(item, list):
ndarrays += [np.array(item)]
else:
raise NotImplementedError(
"Do not know how to convert %s of type %s to a NumPy.ndarray" % (
item,
type(item)
)
)
return ndarrays
def resolution_to_delta(res):
return dt.timedelta(**{res[1]: res[0]})
def days_in_month(year, month):
return (dt.datetime(year + month // 12, month % 12 + 1, 1) - dt.datetime(year, month, 1)).days
def datetime_range(start, end, delta):
datetimes = []
curr = start
while curr <= end:
datetimes.append(curr)
curr += delta
return datetimes
def generate_temporal_labels(start, end, delta, frmt="%Y-%m-%d_%H-%M-%S", incl=[True, False]):
""" Generates a chronologically ordered set of string-formatted time-stamps for a given range
Arguments
---------
start : str or datetime
end : str, int, or datetime
delta : list of [int, str] or timedelta
frmt : str
incl : list of [bool, bool]
Returns
-------
temporal_labels : list of str
"""
# Check arguments
if not (isinstance(start, str) or isinstance(start, dt.datetime)):
raise ValueError("Argument \"start\" must be str or datetime. Received %s" % (type(start)))
if not (isinstance(end, str) or isinstance(end, int) or isinstance(end, dt.datetime)):
raise ValueError("Argument \"end\" must be str, int, or datetime. Received %s" % (type(end)))
if not (isinstance(delta, list) or isinstance(delta, dt.timedelta)):
raise ValueError("Argument \"delta\" must be list or timedelta. Received %s" % (type(delta)))
elif isinstance(delta, list): # delta given as custom format [int, str] (e.g. [7, "days"])
if not (isinstance(delta[0], int) and isinstance(delta[1], str)):
raise ValueError("Argument \"delta\" as list must follow format [int, str]. Received %s" % (str(delta)))
elif delta[0] < 1:
raise ValueError("Number of time-steps in delta=[int str] must be positive. Received %d" % (delta[0]))
elif delta[1] not in ["seconds", "minutes", "hours", "days", "weeks", "months", "years"]:
raise NotImplementedError("Unknown resolution=%s in argument \"delta\"" % (delta[1]))
# Start
temporal_labels = []
if isinstance(start, str): # start given as date str
start = dt.datetime.strptime(start, frmt)
if isinstance(end, str): # end given as date str
end = dt.datetime.strptime(end, frmt)
if isinstance(delta, list): # delta given as custom format [int, str] (e.g. [7, "days"])
time_steps, resolution = delta
if resolution in ["months", "years"]:
if isinstance(end, int): # end given as number of time-steps from start
year, month, day = start.year, start.month, start.day
hour, minute, second = start.hour, start.minute, start.second
if resolution == "months":
# if day > 28:
# raise ValueError("Month denominated delta undefined for day > 28")
month += end * time_steps
year += (month - 1) // 12
month = (month - 1) % 12 + 1
elif resolution == "years":
year += end * time_steps
day = min(day, days_in_month(year, month))
end = dt.datetime(year, month, day, hour, minute, second)
# Start creating temporal labels at k*month/k*year resolution
curr = start
while curr <= end:
temporal_labels.append(curr.strftime(frmt))
year, month, day = curr.year, curr.month, start.day
hour, minute, second = start.hour, start.minute, start.second
if resolution == "months":
month += time_steps
year += (month - 1) // 12
month = (month - 1) % 12 + 1
elif resolution == "years":
year += time_steps
day = min(day, days_in_month(year, month))
curr = dt.datetime(year, month, day, hour, minute, second)
else: # resolution is one of ("seconds", "minutes", "hours", "days", "weeks")
delta = dt.timedelta(**{resolution: time_steps})
if isinstance(end, int): # end given as number of time-steps from start
end = start + end * delta
temporal_labels = [dt.strftime(frmt) for dt in datetime_range(start, end, delta)]
elif isinstance(delta, dt.timedelta):
if isinstance(end, int): # end given as number of time-steps from start
end = start + end * delta
temporal_labels = [dt.strftime(frmt) for dt in datetime_range(start, end, delta)]
if not incl[0]:
temporal_labels = temporal_labels[1:]
if not incl[1]:
temporal_labels = temporal_labels[:-1]
return temporal_labels
def temporal_labels_to_periodic_indices(labels, period, resolution, frmt="%Y-%m-%d_%H-%M-%S"):
""" Converts a chronologically ordered set of string-formatted time-stamps into a set of recurring index locations according to moments of a given period.
Arguments
---------
labels : tuple, list, or ndarray of str
period : tuple or list 2-tuple of (int, str)
resolution : tuple or list 2-tuple of (int, str)
frmt : str
Returns
-------
temporal_labels : tuple, list, or ndarray of str
"""
# Check arguments
supported_units = [
"microseconds", "milliseconds", "seconds", "minutes", "hours", "days", "weeks", "months", "years"
]
if not period[1] in supported_units:
raise NotImplementedError(period)
if not resolution[1] in supported_units:
raise NotImplementedError(resolution)
if supported_units.index(resolution[1]) > supported_units.index(period[1]) or (resolution[1] == period[1] and resolution[0] > period[0]):
raise ValueError("Argument \"period\" must be a time-span that encompasses the time-span of argument \"resolution\". Received period=%s and resolution=%s" % (str(period), str(resolution)))
# Start conversion
if isinstance(labels, np.ndarray):
original_shape = labels.shape
labels = np.reshape(labels, (-1,))
indices = np.zeros(labels.shape, dtype=int)
elif isinstance(labels, list):
indices = list(0 for _ in labels)
elif isinstance(labels, tuple):
indices = tuple(0 for _ in labels)
elif isinstance(labels, pd.Series):
indices = pd.Series([0 for _ in labels])
# Handle special cases
if resolution[1] == "days" and period[1] == "days": # day -> day-of-days zero-indexed to first Monday
unit_delta = dt.timedelta(days=resolution[0])
period_units = period[0]
for i in range(len(labels)):
date = dt.datetime.strptime(labels[i], frmt)
units_elapsed = (date - dt.datetime.min) // unit_delta
indices[i] = units_elapsed % period_units
elif resolution[1] == "months":
period_units = period[0] # month -> month-of-months
if period[1] == "years": # month -> month-of-year(s)
period_units *= 12
period_units = period_units / resolution[0]
for i in range(len(labels)):
date = dt.datetime.strptime(labels[i], frmt)
indices[i] = (12 * date.year + (date.month-1)) % period_units
elif resolution[1] == "years":
period_units = period[0] # year -> year-of-years
for i in range(len(labels)):
date = dt.datetime.strptime(labels[i], frmt)
indices[i] = date.year % period_units
elif period[1] == "weeks":
if resolution[1] == "microseconds": # microsecond -> microsecond-of-the-week(s)
unit_delta = dt.timedelta(microseconds=resolution[0])
period_units = 1000 * 1000 * 60 * 60 * 24 * 7 * period[0] // resolution[0]
elif resolution[1] == "milliseconds": # millisecond -> millisecond-of-the-week(s)
unit_delta = dt.timedelta(milliseconds=resolution[0])
period_units = 1000 * 60 * 60 * 24 * 7 * period[0] // resolution[0]
elif resolution[1] == "seconds": # second -> second-of-the-week(s)
unit_delta = dt.timedelta(seconds=resolution[0])
period_units = 60 * 60 * 24 * 7 * period[0] // resolution[0]
elif resolution[1] == "minutes": # minute -> minute-of-the-week(s)
unit_delta = dt.timedelta(minutes=resolution[0])
period_units = 60 * 24 * 7 * period[0] // resolution[0]
elif resolution[1] == "hours": # hour -> hour-of-the-week(s)
unit_delta = dt.timedelta(hours=resolution[0])
period_units = 24 * 7 * period[0] // resolution[0]
elif resolution[1] == "days": # day -> day-of-the-week(s)
unit_delta = dt.timedelta(days=resolution[0])
period_units = 7 * period[0] // resolution[0]
elif resolution[1] == "weeks": # week -> week-of-the-weeks
unit_delta = dt.timedelta(weeks=resolution[0])
period_units = period[0]
for i in range(len(labels)):
date = dt.datetime.strptime(labels[i], frmt)
units_elapsed = (date - dt.datetime.min) // unit_delta
indices[i] = units_elapsed % period_units
else:
unit_delta = dt.timedelta(**{resolution[1]: resolution[0]})
period_units = period[0]
period_unit_name = period[1][:-1]
for i in range(len(labels)):
date = dt.datetime.strptime(labels[i], frmt)
period_origin_kwargs = {
"year": 1, "month": 1, "day": 1, "hour": 0, "minute": 0, "second": 0
}
for unit_name in period_origin_kwargs.keys():
unit_value = getattr(date, unit_name)
period_origin_kwargs[unit_name] = unit_value
if unit_name == period_unit_name:
unit_origin_value = (unit_value // period_units) * period_units
if unit_name in ["day", "month", "year"]: # handle one-based units
unit_origin_value = ((unit_value - 1) // period_units) * period_units + 1
period_origin_kwargs[unit_name] = unit_origin_value
break
period_origin_date = dt.datetime(**period_origin_kwargs)
indices[i] = (date - period_origin_date) // unit_delta
if isinstance(labels, np.ndarray):
labels = np.reshape(labels, original_shape)
indices = np.reshape(indices, original_shape)
return indices
def get_period_size(period, resolution):
per_n, per_unit = period
res_n, res_unit = resolution
if per_unit == "years":
if res_unit == "years":
pass
elif res_unit == "months":
per_n *= 12
elif res_unit == "weeks":
per_n *= 53
elif res_unit == "days":
per_n *= 366
elif res_unit == "hours":
per_n *= 366 * 24
elif res_unit == "minutes":
per_n *= 366 * 24 * 60
elif res_unit == "seconds":
per_n *= 366 * 24 * 60 * 60
elif per_unit == "months":
if res_unit == "months":
pass
elif res_unit == "weeks":
per_n *= 5
elif res_unit == "days":
per_n *= 31
elif res_unit == "hours":
per_n *= 31 * 24
elif res_unit == "minutes":
per_n *= 31 * 24 * 60
elif res_unit == "seconds":
per_n *= 31 * 24 * 60 * 60
elif per_unit == "weeks":
if res_unit == "weeks":
pass
elif res_unit == "days":
per_n *= 7
elif res_unit == "hours":
per_n *= 7 * 24
elif res_unit == "minutes":
per_n *= 7 * 24 * 60
elif res_unit == "seconds":
per_n *= 7 * 24 * 60 * 60
elif per_unit == "days":
if res_unit == "days":
pass
elif res_unit == "hours":
per_n *= 24
elif res_unit == "minutes":
per_n *= 24 * 60
elif res_unit == "seconds":
per_n *= 24 * 60 * 60
elif per_unit == "hours":
if res_unit == "hours":
pass
elif res_unit == "minutes":
per_n *= 60
elif res_unit == "seconds":
per_n *= 60 * 60