"""
Miscellaneous operations.
"""
import ast
import collections.abc
import datetime
import inspect
import itertools
import math
import os
import random
import re
import socket
import time
import types
import urllib.parse
import fake_useragent
import numpy as np
import pandas as pd
import requests
import tqdm
""" General use ------------------------------------------------------------------------------ """
def confirmed(prompt=None, confirmation_required=True, resp=False):
"""
Type to confirm whether to proceed or not.
See also
[`OPS-C-1 <https://code.activestate.com/recipes/541096-prompt-the-user-for-confirmation/>`_].
:param prompt: a message that prompts an response (Yes/No), defaults to ``None``
:type prompt: str or None
:param confirmation_required: whether to require users to confirm and proceed, defaults to ``True``
:type confirmation_required: bool
:param resp: default response, defaults to ``False``
:type resp: bool
:return: a response
:rtype: bool
**Example**::
>>> from pyhelpers.ops import confirmed
>>> if confirmed(prompt="Create Directory?", resp=True): print("Passed.")
Create Directory? [No]|Yes: yes
Passed.
"""
if confirmation_required:
if prompt is None:
prompt = "Confirmed? "
if resp is True: # meaning that default response is True
prompt = "{} [{}]|{}: ".format(prompt, "Yes", "No")
else:
prompt = "{} [{}]|{}: ".format(prompt, "No", "Yes")
ans = input(prompt)
if not ans:
return resp
if re.match('[Yy](es)?', ans):
return True
if re.match('[Nn](o)?', ans):
return False
else:
return True
def get_obj_attr(obj, col_names=None):
"""
Get main attributes of an object.
:param obj: a object, e.g. a class
:type obj: object
:param col_names: a list of column names
:type col_names: list
:return: tabular data of the main attributes of the given object
:rtype: pandas.DataFrame
**Test**::
>>> from pyhelpers import get_obj_attr, PostgreSQL
>>> postgres = PostgreSQL('localhost', 5432, 'postgres', database_name='postgres')
Password (postgres@localhost:5432): ***
Connecting postgres:***@localhost:5432/postgres ... Successfully.
>>> obj_attr = get_obj_attr(postgres)
>>> print(obj_attr.head())
Attribute Value
0 address postgres:***@localhost:5432/postgres
1 backend postgresql
2 connection <sqlalchemy.pool.base._ConnectionFairy object ...
3 database_info {'drivername': 'postgresql+psycopg2', 'host': ...
4 database_name postgres
"""
if col_names is None:
col_names = ['Attribute', 'Value']
all_attrs = inspect.getmembers(obj, lambda x: not (inspect.isroutine(x)))
attrs = [x for x in all_attrs if not re.match(r'^__?', x[0])]
attrs_tbl = pd.DataFrame(attrs, columns=col_names)
return attrs_tbl
def eval_dtype(str_val):
"""
Convert a string to its intrinsic data type.
:param str_val: a string-type variable
:type str_val: str
:return: converted value
:rtype: any
**Tests**::
>>> from pyhelpers.ops import eval_dtype
>>> val_1 = '1'
>>> origin_val = eval_dtype(val_1)
>>> type(origin_val)
int
>>> print(origin_val)
1
>>> val_2 = '1.1.1'
>>> origin_val = eval_dtype(val_2)
>>> type(origin_val)
str
>>> print(origin_val)
1.1.1
"""
try:
val = ast.literal_eval(str_val)
except (ValueError, SyntaxError):
val = str_val
return val
def gps_to_utc(gps_time):
"""
Convert standard GPS time to UTC time.
:param gps_time: standard GPS time
:type gps_time: float
:return: UTC time
:rtype: datetime.datetime
**Example**::
>>> from pyhelpers.ops import gps_to_utc
>>> utc_dt = gps_to_utc(gps_time=1271398985.7822514)
>>> utc_dt
datetime.datetime(2020, 4, 20, 6, 23, 5, 782251)
"""
gps_from_utc = (datetime.datetime(1980, 1, 6) - datetime.datetime(1970, 1, 1)).total_seconds()
utc_time = datetime.datetime.utcfromtimestamp(gps_time + gps_from_utc)
return utc_time
""" Basic data manipulation ------------------------------------------------------------------ """
# Iterable
def split_list_by_size(lst, sub_len):
"""
Split a list into (evenly sized) sub-lists.
See also [`OPS-SLBS-1 <https://stackoverflow.com/questions/312443/>`_].
:param lst: a list of any
:type lst: list
:param sub_len: length of a sub-list
:type sub_len: int
:return: a sequence of ``sub_len``-sized sub-lists from ``lst``
:rtype: types.GeneratorType
**Example**::
>>> from pyhelpers.ops import split_list_by_size
>>> lst_ = list(range(0, 10))
>>> sub_lst_len = 3
>>> lists = split_list_by_size(lst_, sub_lst_len)
>>> list(lists)
[[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]]
"""
for i in range(0, len(lst), sub_len):
yield lst[i:i + sub_len]
def split_list(lst, num_of_sub):
"""
Split a list into a number of equally-sized sub-lists.
See also [`OPS-SL-1 <https://stackoverflow.com/questions/312443/>`_].
:param lst: a list of any
:type lst: list
:param num_of_sub: number of sub-lists
:type num_of_sub: int
:return: a total of ``num_of_sub`` sub-lists from ``lst``
:rtype: types.GeneratorType
**Example**::
>>> from pyhelpers.ops import split_list
>>> lst_ = list(range(0, 10))
>>> num_of_sub_lists = 3
>>> lists = list(split_list(lst_, num_of_sub_lists))
>>> list(lists)
[[0, 1, 2, 3], [4, 5, 6, 7], [8, 9]]
"""
chunk_size = math.ceil(len(lst) / num_of_sub)
for i in range(0, len(lst), chunk_size):
yield lst[i:i + chunk_size]
def split_iterable(iterable, chunk_size):
"""
Split a list into (evenly sized) chunks.
See also [`OPS-SI-1 <https://stackoverflow.com/questions/24527006/>`_].
:param iterable: iterable object
:type iterable: list or tuple or collections.abc.Iterable
:param chunk_size: length of a chunk
:type chunk_size: int
:return: a sequence of equally-sized chunks from ``iterable``
:rtype: types.GeneratorType
**Examples**::
>>> import pandas
>>> from pyhelpers.ops import split_iterable
>>> lst = list(range(0, 10))
>>> size_of_chunk = 3
>>> for lst_ in split_iterable(lst, size_of_chunk): print(list(lst_))
[0, 1, 2]
[3, 4, 5]
[6, 7, 8]
[9]
>>> lst = pandas.Series(range(0, 20))
>>> size_of_chunk = 5
>>> for lst_ in split_iterable(lst, size_of_chunk): print(list(lst_))
[0, 1, 2, 3, 4]
[5, 6, 7, 8, 9]
[10, 11, 12, 13, 14]
[15, 16, 17, 18, 19]
"""
iterator = iter(iterable)
for x in iterator:
yield itertools.chain([x], itertools.islice(iterator, chunk_size - 1))
def update_nested_dict(source_dict, updates):
"""
Update a nested dictionary or similar mapping.
See also [`OPS-UND-1 <https://stackoverflow.com/questions/3232943/>`_].
:param source_dict: a dictionary that needs to be updated
:type source_dict: dict
:param updates: a dictionary with new data
:type updates: dict
:return: an updated dictionary
:rtype: dict
**Examples**::
>>> from pyhelpers.ops import update_nested_dict
>>> source_dict_ = {'key_1': 1}
>>> updates_ = {'key_2': 2}
>>> source_dict_ = update_nested_dict(source_dict_, updates_)
>>> print(source_dict_)
{'key_1': 1, 'key_2': 2}
>>> source_dict_ = {'key': 'val_old'}
>>> updates_ = {'key': 'val_new'}
>>> source_dict_ = update_nested_dict(source_dict_, updates_)
>>> print(source_dict_)
{'key': 'val_new'}
>>> source_dict_ = {'key': {'k1': 'v1_old', 'k2': 'v2'}}
>>> updates_ = {'key': {'k1': 'v1_new'}}
>>> source_dict_ = update_nested_dict(source_dict_, updates_)
>>> print(source_dict_)
{'key': {'k1': 'v1_new', 'k2': 'v2'}}
>>> source_dict_ = {'key': {'k1': {}, 'k2': 'v2'}}
>>> updates_ = {'key': {'k1': 'v1'}}
>>> source_dict_ = update_nested_dict(source_dict_, updates_)
>>> print(source_dict_)
{'key': {'k1': 'v1', 'k2': 'v2'}}
>>> source_dict_ = {'key': {'k1': 'v1', 'k2': 'v2'}}
>>> updates_ = {'key': {'k1': {}}}
>>> source_dict_ = update_nested_dict(source_dict_, updates_)
>>> print(source_dict_)
{'key': {'k1': 'v1', 'k2': 'v2'}}
"""
for key, val in updates.items():
if isinstance(val, collections.abc.Mapping) or isinstance(val, dict):
source_dict[key] = update_nested_dict(source_dict.get(key, {}), val)
elif isinstance(val, list):
source_dict[key] = (source_dict.get(key, []) + val)
else:
source_dict[key] = updates[key]
return source_dict
def get_all_values_from_nested_dict(key, target_dict):
"""
Get all values in a nested dictionary.
See also
[`OPS-GAVFND-1 <https://gist.github.com/douglasmiranda/5127251>`_] and
[`OPS-GAVFND-2 <https://stackoverflow.com/questions/9807634/>`_].
:param key: any that can be the key of a dictionary
:type key: any
:param target_dict: a (nested) dictionary
:type target_dict: dict
:return: all values of the ``key`` within the given ``target_dict``
:rtype: types.GeneratorType
**Examples**::
>>> from pyhelpers.ops import get_all_values_from_nested_dict
>>> key_ = 'key'
>>> target_dict_ = {'key': 'val'}
>>> val = get_all_values_from_nested_dict(key_, target_dict_)
>>> list(val)
[['val']]
>>> key_ = 'k1'
>>> target_dict_ = {'key': {'k1': 'v1', 'k2': 'v2'}}
>>> val = get_all_values_from_nested_dict(key_, target_dict_)
>>> list(val)
[['v1']]
>>> key_ = 'k1'
>>> target_dict_ = {'key': {'k1': ['v1', 'v1_1']}}
>>> val = get_all_values_from_nested_dict(key_, target_dict_)
>>> list(val)
[['v1', 'v1_1']]
>>> key_ = 'k2'
>>> target_dict_ = {'key': {'k1': 'v1', 'k2': ['v2', 'v2_1']}}
>>> val = get_all_values_from_nested_dict(key_, target_dict_)
>>> list(val)
[['v2', 'v2_1']]
"""
for k, v in target_dict.items():
if key == k:
yield [v] if isinstance(v, str) else v
elif isinstance(v, dict):
for x in get_all_values_from_nested_dict(key, v):
yield x
elif isinstance(v, collections.abc.Iterable):
for d in v:
if isinstance(d, dict):
for y in get_all_values_from_nested_dict(key, d):
yield y
def remove_multiple_keys_from_dict(target_dict, *keys):
"""
Remove multiple keys from a dictionary.
:param target_dict: a dictionary
:type target_dict: dict
:param keys: (a sequence of) any that can be the key of a dictionary
:type keys: any
**Example**::
>>> from pyhelpers.ops import remove_multiple_keys_from_dict
>>> target_dict_ = {'k1': 'v1', 'k2': 'v2', 'k3': 'v3', 'k4': 'v4', 'k5': 'v5'}
>>> remove_multiple_keys_from_dict(target_dict_, 'k1', 'k3', 'k4')
>>> print(target_dict_)
{'k2': 'v2', 'k5': 'v5'}
"""
# assert isinstance(dictionary, dict)
for k in keys:
if k in target_dict.keys():
target_dict.pop(k)
def merge_dicts(*dicts):
"""
Merge multiple dictionaries.
:param dicts: (one or) multiple dictionaries
:type dicts: dict
:return: a single dictionary containing all elements of the input
:rtype: dict
**Example**::
>>> from pyhelpers.ops import merge_dicts
>>> dict_a = {'a': 1}
>>> dict_b = {'b': 2}
>>> dict_c = {'c': 3}
>>> merged_dict = merge_dicts(dict_a, dict_b, dict_c)
>>> print(merged_dict)
{'a': 1, 'b': 2, 'c': 3}
"""
super_dict = {}
for d in dicts:
super_dict.update(d)
return super_dict
# Tabular data
def detect_nan_for_str_column(data_frame, column_names=None):
"""
Detect if a str type column contains ``NaN`` when reading csv files.
:param data_frame: a data frame to be examined
:type data_frame: pandas.DataFrame
:param column_names: a sequence of column names, if ``None`` (default), all columns
:type column_names: None or collections.abc.Iterable
:return: position index of the column that contains ``NaN``
:rtype: types.GeneratorType
**Example**::
>>> import numpy
>>> import pandas
>>> from pyhelpers.ops import detect_nan_for_str_column
>>> df = pandas.DataFrame(numpy.resize(range(10), (10, 2)), columns=['a', 'b'])
>>> df.iloc[3, 1] = numpy.nan
>>> nan_col_pos = detect_nan_for_str_column(df, column_names=None)
>>> list(nan_col_pos)
[1]
"""
if column_names is None:
column_names = data_frame.columns
for x in column_names:
temp = [str(v) for v in data_frame[x].unique() if isinstance(v, str) or np.isnan(v)]
if 'nan' in temp:
yield data_frame.columns.get_loc(x)
def create_rotation_matrix(theta):
"""
Create a rotation matrix (counterclockwise).
:param theta: rotation angle (in radian)
:type theta: int or float
:return: a rotation matrix of shape (2, 2)
:rtype: numpy.ndarray
**Example**::
>>> from pyhelpers.ops import create_rotation_matrix
>>> rot_mat = create_rotation_matrix(theta=30)
>>> print(rot_mat)
[[-0.98803162 0.15425145]
[-0.15425145 -0.98803162]]
"""
sin_theta, cos_theta = np.sin(theta), np.cos(theta)
rotation_mat = np.array([[sin_theta, cos_theta], [-cos_theta, sin_theta]])
return rotation_mat
def dict_to_dataframe(input_dict, k='key', v='value'):
"""
Convert a dictionary to a data frame.
:param input_dict: a dictionary to be converted to a data frame
:type input_dict: dict
:param k: column name for keys
:type k: str
:param v: column name for values
:type v: str
:return: a data frame converted from the ``input_dict``
:rtype: pandas.DataFrame
**Example**::
>>> from pyhelpers.ops import dict_to_dataframe
>>> input_dict_ = {'a': 1, 'b': 2}
>>> df = dict_to_dataframe(input_dict_)
>>> print(df)
key value
0 a 1
1 b 2
"""
dict_keys = list(input_dict.keys())
dict_vals = list(input_dict.values())
data_frame = pd.DataFrame({k: dict_keys, v: dict_vals})
return data_frame
def parse_csr_matrix(path_to_csr, verbose=False, **kwargs):
"""
Load in a compressed sparse row (CSR) or compressed row storage (CRS).
:param path_to_csr: path where a CSR (e.g. .npz) file is saved
:type path_to_csr: str
:param verbose: whether to print relevant information in console as the function runs,
defaults to ``False``
:type verbose: bool or int
:param kwargs: optional parameters of `numpy.load`_
:return: a compressed sparse row
:rtype: scipy.sparse.csr.csr_matrix
.. _`numpy.load`: https://numpy.org/doc/stable/reference/generated/numpy.load
**Example**::
>>> import numpy
>>> import scipy.sparse
>>> from pyhelpers.dir import cd
>>> from pyhelpers.ops import parse_csr_matrix
>>> data_ = numpy.array([1, 2, 3, 4, 5, 6])
>>> indices_ = numpy.array([0, 2, 2, 0, 1, 2])
>>> indptr_ = numpy.array([0, 2, 3, 6])
>>> csr_m = scipy.sparse.csr_matrix((data_, indices_, indptr_), shape=(3, 3))
>>> path_to_csr_npz = cd("tests\\data", "csr_mat.npz")
>>> numpy.savez_compressed(path_to_csr_npz, indptr=csr_m.indptr,
... indices=csr_m.indices, data=csr_m.data,
... shape=csr_m.shape)
>>> csr_mat_ = parse_csr_matrix(path_to_csr_npz, verbose=True)
Loading "\\tests\\data\\csr_mat.npz" ... Done.
>>> # .nnz gets the count of explicitly-stored values (non-zeros)
>>> print((csr_mat_ != csr_m).count_nonzero() == 0)
True
>>> print((csr_mat_ != csr_m).nnz == 0)
True
"""
if verbose:
print("Loading \"\\{}\"".format(os.path.relpath(path_to_csr)), end=" ... ")
try:
csr_loader = np.load(path_to_csr, **kwargs)
data = csr_loader['data']
indices = csr_loader['indices']
indptr = csr_loader['indptr']
shape = csr_loader['shape']
import scipy.sparse
csr_mat = scipy.sparse.csr_matrix((data, indices, indptr), shape)
print("Done.") if verbose else ""
return csr_mat
except Exception as e:
print("Failed. {}".format(e)) if verbose else ""
def swap_cols(array, c1, c2, as_list=False):
"""
Swap positions of two columns in an array.
:param array: an array
:type array: numpy.ndarray
:param c1: index of a column
:type c1: int
:param c2: index of another column
:type c2: int
:param as_list: whether to return a list
:type as_list: bool
:return: a new array/list in which the positions of the c1-th and c2-th columns are swapped
:rtype: numpy.ndarray or list
**Test**::
>>> import numpy
>>> from pyhelpers.ops import swap_cols
>>> arr = numpy.array([[55.95615851, -2.96251228],
... [55.95705685, -2.96253458],
... [55.95706935, -2.96093324],
... [55.956171 , -2.96091098]])
>>> new_arr = swap_cols(arr, 0, 1)
>>> type(new_arr)
numpy.ndarray
>>> print(new_arr)
[[-2.96251228 55.95615851]
[-2.96253458 55.95705685]
[-2.96093324 55.95706935]
[-2.96091098 55.956171 ]]
"""
array[:, c1], array[:, c2] = array[:, c2], array[:, c1].copy()
if as_list:
array = list(array)
return array
def swap_rows(array, r1, r2, as_list=False):
"""
Swap positions of two rows in an array.
:param array: an array
:type array: numpy.ndarray
:param r1: index of a row
:type r1: int
:param r2: index of another row
:type r2: int
:param as_list: whether to return a list
:type as_list: bool
:return: a new array/list in which the positions of the r1-th and r2-th rows are swapped
:rtype: numpy.ndarray or list
**Test**::
>>> import numpy
>>> from pyhelpers.ops import swap_rows
>>> arr = numpy.array([[55.95615851, -2.96251228],
... [55.95705685, -2.96253458],
... [55.95706935, -2.96093324],
... [55.956171 , -2.96091098]])
>>> new_arr = swap_rows(arr, 0, 1)
>>> type(new_arr)
numpy.ndarray
>>> print(new_arr)
[[-2.96253458 55.95705685]
[-2.96251228 55.95615851]
[-2.96093324 55.95706935]
[-2.96091098 55.956171 ]]
"""
array[r1, :], array[r2, :] = array[r2, :], array[r1, :].copy()
if as_list:
array = list(array)
return array
""" Basic computation ------------------------------------------------------------------------ """
def get_extreme_outlier_bounds(num_dat, k=1.5):
"""
Get upper and lower bounds for extreme outliers.
:param num_dat: an array of numbers
:type num_dat: array-like
:param k: a scale coefficient associated with interquartile range, defaults to ``1.5``
:type k: float, int
:return: lower and upper bound
:rtype: tuple
**Example**::
>>> import pandas
>>> from pyhelpers.ops import get_extreme_outlier_bounds
>>> data = pandas.DataFrame(range(100), columns=['col'])
>>> lo_bound, up_bound = get_extreme_outlier_bounds(data, k=1.5)
>>> print((lo_bound, up_bound))
(0.0, 148.5)
"""
q1, q3 = np.percentile(num_dat, 25), np.percentile(num_dat, 75)
iqr = q3 - q1
lower_bound = np.max([0, q1 - k * iqr])
upper_bound = q3 + k * iqr
return lower_bound, upper_bound
def interquartile_range(num_dat):
"""
Calculate interquartile range.
This function may be an alternative to
`scipy.stats.iqr <https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.iqr.html>`_.
:param num_dat: an array of numbers
:type num_dat: numpy.ndarry or list or tuple
:return: interquartile range of ``num_dat``
:rtype: float
**Example**::
>>> import pandas
>>> from pyhelpers.ops import interquartile_range
>>> data = pandas.DataFrame(range(100), columns=['col'])
>>> iqr_ = interquartile_range(data)
>>> print(iqr_)
49.5
"""
iqr = np.subtract(*np.percentile(num_dat, [75, 25]))
return iqr
def find_closest_date(date, lookup_dates, as_datetime=False, fmt='%Y-%m-%d %H:%M:%S.%f'):
"""
Find the closest date of a given one from a list of dates.
:param date: a date
:type date: str or datetime.datetime
:param lookup_dates: an array of dates
:type lookup_dates: list or tuple or collections.abc.Iterable
:param as_datetime: whether to return a datetime.datetime-formatted date, defaults to ``False``
:type as_datetime: bool
:param fmt: datetime format, defaults to ``'%Y-%m-%d %H:%M:%S.%f'``
:type fmt: str
:return: the date that is closest to the given ``date``
:rtype: str or datetime.datetime
**Examples**::
>>> import pandas
>>> from pyhelpers.ops import find_closest_date
>>> date_list = pandas.date_range('2019-01-02', '2019-12-31')
>>> date_ = '2019-01-01'
>>> closest_date_ = find_closest_date(date_, date_list, as_datetime=True)
>>> print(closest_date_) # Timestamp('2019-01-02 00:00:00', freq='D')
2019-01-02 00:00:00
>>> date_ = pandas.to_datetime('2019-01-01')
>>> closest_date_ = find_closest_date(date_, date_list, as_datetime=False)
>>> print(closest_date_) # '2019-01-02 00:00:00.000000'
2019-01-02 00:00:00.000000
"""
closest_date = min(lookup_dates, key=lambda x: abs(pd.to_datetime(x) - pd.to_datetime(date)))
if as_datetime:
if isinstance(closest_date, str):
closest_date = pd.to_datetime(closest_date)
else:
if isinstance(closest_date, datetime.datetime):
closest_date = closest_date.strftime(fmt)
return closest_date
""" Graph plotting --------------------------------------------------------------------------- """
def cmap_discretisation(cmap, n_colours):
"""
Create a discrete colour ramp.
See also [`OPS-CD-1
<http://sensitivecities.com/so-youd-like-to-make-a-map-using-python-EN.html#.WbpP0T6GNQB>`_].
:param cmap: a colormap instance, e.g. built-in `colormaps`_ accessible via `matplotlib.cm.get_cmap`_
:type cmap: matplotlib.colors.ListedColormap
:param n_colours: number of colours
:type n_colours: int
:return: a discrete colormap from (the continuous) ``cmap``
:rtype: matplotlib.colors.LinearSegmentedColormap
.. _`colormaps`: https://matplotlib.org/tutorials/colors/colormaps.html
.. _`matplotlib.cm.get_cmap`: https://matplotlib.org/api/cm_api.html#matplotlib.cm.get_cmap
**Example**::
>>> import matplotlib.cm
>>> import matplotlib.pyplot as plt
>>> import numpy
>>> from pyhelpers.ops import cmap_discretisation
>>> cm_accent = cmap_discretisation(matplotlib.cm.get_cmap('Accent'), n_colours=5)
>>> fig, ax = plt.subplots(figsize=(10, 2))
>>> ax.imshow(numpy.resize(range(100), (5, 100)), cmap=cm_accent, interpolation='nearest')
>>> plt.axis('off')
>>> plt.tight_layout()
>>> plt.show()
The exmaple is illustrated in :numref:`cmap-discretisation`:
.. figure:: ../_images/cmap-discretisation.*
:name: cmap-discretisation
:align: center
:width: 60%
An example of discrete colour ramp, created by
:py:func:`cmap_discretisation()<pyhelpers.ops.cmap_discretisation>`.
"""
import matplotlib.cm
import matplotlib.colors
if isinstance(cmap, str):
cmap = matplotlib.cm.get_cmap(cmap)
colours_i = np.concatenate((np.linspace(0, 1., n_colours), (0., 0., 0., 0.)))
colours_rgba = cmap(colours_i)
indices = np.linspace(0, 1., n_colours + 1)
c_dict = {}
for ki, key in enumerate(('red', 'green', 'blue')):
c_dict[key] = [
(indices[x], colours_rgba[x - 1, ki], colours_rgba[x, ki]) for x in range(n_colours + 1)
]
colour_map = matplotlib.colors.LinearSegmentedColormap(cmap.name + '_%d' % n_colours, c_dict, 1024)
return colour_map
def colour_bar_index(cmap, n_colours, labels=None, **kwargs):
"""
Create a colour bar.
To stop making off-by-one errors. Takes a standard colour ramp, and discretizes it,
then draws a colour bar with correctly aligned labels.
See also [`OPS-CBI-1
<http://sensitivecities.com/so-youd-like-to-make-a-map-using-python-EN.html#.WbpP0T6GNQB>`_].
:param cmap: a colormap instance, e.g. built-in `colormaps`_ accessible via `matplotlib.cm.get_cmap`_
:type cmap: matplotlib.colors.ListedColormap
:param n_colours: number of colours
:type n_colours: int
:param labels: a list of labels for the colour bar, defaults to ``None``
:type labels: list or None
:param kwargs: optional parameters of `matplotlib.pyplot.colorbar`_
:return: a colour bar object
:rtype: matplotlib.colorbar.Colorbar
.. _`colormaps`: https://matplotlib.org/tutorials/colors/colormaps.html
.. _`matplotlib.cm.get_cmap`: https://matplotlib.org/api/cm_api.html#matplotlib.cm.get_cmap
.. _`matplotlib.pyplot.colorbar`: https://matplotlib.org/api/_as_gen/matplotlib.pyplot.colorbar.html
**Examples**::
>>> import matplotlib.cm
>>> import matplotlib.pyplot as plt
>>> from pyhelpers.ops import colour_bar_index
>>> plt.figure(figsize=(2, 6))
>>> cbar = colour_bar_index(cmap=matplotlib.cm.get_cmap('Accent'), n_colours=5)
>>> plt.xticks(fontsize=12)
>>> plt.yticks(fontsize=12)
>>> cbar.ax.tick_params(labelsize=14)
>>> # plt.axis('off')
>>> plt.tight_layout()
>>> plt.show()
The above example is illustrated in :numref:`colour-bar-index-1`:
.. figure:: ../_images/colour-bar-index-1.*
:name: colour-bar-index-1
:align: center
:width: 23%
An example of colour bar with numerical index,
created by :py:func:`colour_bar_index()<pyhelpers.ops.colour_bar_index>`.
.. code-block:: python
>>> plt.figure(figsize=(2, 6))
>>> labels_ = list('abcde')
>>> cbar = colour_bar_index(matplotlib.cm.get_cmap('Accent'), n_colours=5, labels=labels_)
>>> plt.xticks(fontsize=12)
>>> plt.yticks(fontsize=12)
>>> cbar.ax.tick_params(labelsize=14)
>>> # plt.axis('off')
>>> plt.tight_layout()
>>> plt.show()
This second example is illustrated in :numref:`colour-bar-index-2`:
.. figure:: ../_images/colour-bar-index-2.*
:name: colour-bar-index-2
:align: center
:width: 23%
An example of colour bar with textual index,
created by :py:func:`colour_bar_index()<pyhelpers.ops.colour_bar_index>`.
"""
import matplotlib.cm
import matplotlib.pyplot
cmap = cmap_discretisation(cmap, n_colours)
mappable = matplotlib.cm.ScalarMappable(cmap=cmap)
mappable.set_array(np.array([]))
mappable.set_clim(-0.5, n_colours + 0.5)
colour_bar = matplotlib.pyplot.colorbar(mappable, **kwargs)
colour_bar.set_ticks(np.linspace(0, n_colours, n_colours))
colour_bar.set_ticklabels(range(n_colours))
if labels:
colour_bar.set_ticklabels(labels)
return colour_bar
""" Web scraping ----------------------------------------------------------------------------- """
def is_network_connected():
"""
Check if the current machine can connect to the Internet.
:return: whether the Internet connection is currently working
:rtype: bool
**Examples**::
>>> from pyhelpers.ops import is_network_connected
>>> is_network_connected() # assuming the machine is currently connected to the Internet
True
"""
host_name = socket.gethostname()
ip_address = socket.gethostbyname(host_name)
return False if ip_address == "127.0.0.1" else True
def is_url_connectable(url):
"""
Check if the current machine can connect to a given URL.
:param url: a URL
:type url: str
:return: whether the machine can currently connect to the given URL
:rtype: bool
**Examples**::
>>> from pyhelpers.ops import is_url_connectable
>>> url_0 = 'https://www.python.org/'
>>> is_url_connectable(url_0)
True
>>> url_1 = 'https://www.python.org1/'
>>> is_url_connectable(url_1)
False
"""
try:
netloc = urllib.parse.urlparse(url).netloc
host = socket.gethostbyname(netloc)
s = socket.create_connection((host, 80))
s.close()
return True
except (socket.gaierror, OSError):
return False
def download_file_from_url(url, path_to_file, wait_to_retry=3600, random_header=False, **kwargs):
"""
Download an object available at a given URL.
See also [`OPS-DFFU-1 <https://stackoverflow.com/questions/37573483/>`_].
:param url: a URL
:type url: str
:param path_to_file: a full path to which the downloaded object is saved as
:type path_to_file: str
:param wait_to_retry: a wait time to retry downloading, defaults to ``3600`` (in second)
:type wait_to_retry: int or float
:param random_header: whether to go for a random agent, defaults to ``False``
:type random_header: bool
:param kwargs: optional parameters of `open`_
.. _`open`: https://docs.python.org/3/library/functions.html#open
**Example**::
>>> from pyhelpers.dir import cd
>>> from PIL import Image
>>> from pyhelpers.ops import download_file_from_url
>>> logo_url = 'https://www.python.org/static/community_logos/python-logo-master-v3-TM.png'
>>> path_to_img = cd("tests", "images", "python-logo.png")
>>> download_file_from_url(logo_url, path_to_img)
>>> img = Image.open(path_to_img)
>>> img.show() # as illustrated below
.. figure:: ../_images/python-logo.*
:name: python-logo
:align: center
:width: 65%
The Python Logo.
"""
headers = fake_requests_headers(randomized=random_header)
# Streaming, so we can iterate over the response
resp = requests.get(url, stream=True, headers=headers)
if resp.status_code == 429:
time.sleep(wait_to_retry)
total_size = int(resp.headers.get('content-length')) # Total size in bytes
block_size = 1024 * 1024
wrote = 0
directory = os.path.dirname(path_to_file)
if directory == "":
path_to_file = os.path.join(os.getcwd(), path_to_file)
else:
if not os.path.exists(directory):
os.makedirs(directory)
with open(path_to_file, mode='wb', **kwargs) as f:
temp = resp.iter_content(block_size, decode_unicode=True)
for data in tqdm.tqdm(temp, total=total_size // block_size, unit='MB'):
wrote = wrote + len(data)
try:
f.write(data)
except TypeError:
f.write(data.encode())
f.close()
resp.close()
if total_size != 0 and wrote != total_size:
print("ERROR, something went wrong!")