stable/freqtrade/misc.py

123 lines
3.5 KiB
Python
Raw Normal View History

2018-02-04 07:33:54 +00:00
"""
Various tool function for Freqtrade and scripts
"""
2018-07-04 07:31:35 +00:00
import gzip
import logging
2018-03-17 21:44:47 +00:00
import re
from datetime import datetime
from pathlib import Path
2018-03-17 21:12:42 +00:00
import numpy as np
2018-12-28 09:01:16 +00:00
import rapidjson
2017-11-11 15:47:19 +00:00
logger = logging.getLogger(__name__)
2018-03-17 21:12:42 +00:00
def shorten_date(_date: str) -> str:
2018-02-04 07:33:54 +00:00
"""
Trim the date so it fits on small screens
"""
new_date = re.sub('seconds?', 'sec', _date)
new_date = re.sub('minutes?', 'min', new_date)
new_date = re.sub('hours?', 'h', new_date)
new_date = re.sub('days?', 'd', new_date)
new_date = re.sub('^an?', '1', new_date)
return new_date
############################################
# Used by scripts #
# Matplotlib doesn't support ::datetime64, #
# so we need to convert it into ::datetime #
############################################
2018-03-17 21:12:42 +00:00
def datesarray_to_datetimearray(dates: np.ndarray) -> np.ndarray:
"""
Convert an pandas-array of timestamps into
An numpy-array of datetimes
:return: numpy-array of datetime
"""
return dates.dt.to_pydatetime()
def file_dump_json(filename: Path, data, is_zip=False) -> None:
"""
2018-02-04 07:33:54 +00:00
Dump JSON data into a file
:param filename: file to create
:param data: JSON Data to save
:return:
"""
logger.info(f'dumping json to "{filename}"')
if is_zip:
if filename.suffix != '.gz':
filename = filename.with_suffix('.gz')
2018-03-30 21:30:23 +00:00
with gzip.open(filename, 'w') as fp:
2018-12-28 09:01:16 +00:00
rapidjson.dump(data, fp, default=str, number_mode=rapidjson.NM_NATIVE)
else:
with open(filename, 'w') as fp:
2018-12-28 09:01:16 +00:00
rapidjson.dump(data, fp, default=str, number_mode=rapidjson.NM_NATIVE)
2018-03-25 11:38:17 +00:00
logger.debug(f'done json to "{filename}"')
2018-03-25 11:38:17 +00:00
def json_load(datafile):
2018-12-28 09:04:28 +00:00
"""
load data with rapidjson
Use this to have a consistent experience,
sete number_mode to "NM_NATIVE" for greatest speed
"""
return rapidjson.load(datafile, number_mode=rapidjson.NM_NATIVE)
def file_load_json(file):
gzipfile = file.with_suffix(file.suffix + '.gz')
# Try gzip file first, otherwise regular json file.
if gzipfile.is_file():
logger.debug('Loading ticker data from file %s', gzipfile)
with gzip.open(gzipfile) as tickerdata:
pairdata = json_load(tickerdata)
elif file.is_file():
logger.debug('Loading ticker data from file %s', file)
with open(file) as tickerdata:
pairdata = json_load(tickerdata)
else:
return None
return pairdata
2018-12-28 09:04:28 +00:00
2018-05-30 20:38:09 +00:00
def format_ms_time(date: int) -> str:
2018-03-25 11:38:17 +00:00
"""
convert MS date to readable format.
: epoch-string in ms
"""
return datetime.fromtimestamp(date/1000.0).strftime('%Y-%m-%dT%H:%M:%S')
2019-02-19 12:14:47 +00:00
def deep_merge_dicts(source, destination):
"""
2019-06-09 12:04:19 +00:00
Values from Source override destination, destination is returned (and modified!!)
Sample:
2019-02-19 12:14:47 +00:00
>>> a = { 'first' : { 'rows' : { 'pass' : 'dog', 'number' : '1' } } }
>>> b = { 'first' : { 'rows' : { 'fail' : 'cat', 'number' : '5' } } }
>>> merge(b, a) == { 'first' : { 'rows' : { 'pass' : 'dog', 'fail' : 'cat', 'number' : '5' } } }
True
"""
for key, value in source.items():
if isinstance(value, dict):
# get node or create one
node = destination.setdefault(key, {})
deep_merge_dicts(value, node)
else:
destination[key] = value
return destination
def round_dict(d, n):
"""
Rounds float values in the dict to n digits after the decimal point.
"""
return {k: (round(v, n) if isinstance(v, float) else v) for k, v in d.items()}