"Setting ``prop_cycler`` in the ``matplotlibrc`` file or style files\n-------------------------------------------------------------------\n\nRemember, if you want to set a custom ``prop_cycler`` in your\n``.matplotlibrc`` file or a style file (``style.mplstyle``), you can set the\n``axes.prop_cycle`` property:\n\n.. code-block:: python\n\n axes.prop_cycle : cycler('color', 'bgrcmyk')\n\nCycling through multiple properties\n-----------------------------------\n\nYou can add cyclers:\n\n.. code-block:: python\n\n from cycler import cycler\n cc = (cycler(color=list('rgb')) +\n cycler(linestyle=['-', '--', '-.']))\n for d in cc:\n print(d)\n\nResults in:\n\n.. code-block:: python\n\n {'color': 'r', 'linestyle': '-'}\n {'color': 'g', 'linestyle': '--'}\n {'color': 'b', 'linestyle': '-.'}\n\n\nYou can multiply cyclers:\n\n.. code-block:: python\n\n from cycler import cycler\n cc = (cycler(color=list('rgb')) *\n cycler(linestyle=['-', '--', '-.']))\n for d in cc:\n print(d)\n\nResults in:\n\n.. code-block:: python\n\n {'color': 'r', 'linestyle': '-'}\n {'color': 'r', 'linestyle': '--'}\n {'color': 'r', 'linestyle': '-.'}\n {'color': 'g', 'linestyle': '-'}\n {'color': 'g', 'linestyle': '--'}\n {'color': 'g', 'linestyle': '-.'}\n {'color': 'b', 'linestyle': '-'}\n {'color': 'b', 'linestyle': '--'}\n {'color': 'b', 'linestyle': '-.'}\n\n"

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"\n# Custom tick formatter for time series\n\n\nWhen plotting time series, e.g., financial time series, one often wants\nto leave out days on which there is no data, i.e. weekends. The example\nbelow shows how to use an 'index formatter' to achieve the desired plot\n\n"

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"source": [

"from __future__ import print_function\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.cbook as cbook\nimport matplotlib.ticker as ticker\n\n# Load a numpy record array from yahoo csv data with fields date, open, close,\n# volume, adj_close from the mpl-data/example directory. The record array\n# stores the date as an np.datetime64 with a day unit ('D') in the date column.\nwith cbook.get_sample_data('goog.npz') as datafile:\n r = np.load(datafile)['price_data'].view(np.recarray)\nr = r[-30:] # get the last 30 days\n# Matplotlib works better with datetime.datetime than np.datetime64, but the\n# latter is more portable.\ndate = r.date.astype('O')\n\n# first we'll do it the default way, with gaps on weekends\nfig, axes = plt.subplots(ncols=2, figsize=(8, 4))\nax = axes[0]\nax.plot(date, r.adj_close, 'o-')\nax.set_title(\"Default\")\nfig.autofmt_xdate()\n\n# next we'll write a custom formatter\nN = len(r)\nind = np.arange(N) # the evenly spaced plot indices\n\n\ndef format_date(x, pos=None):\n thisind = np.clip(int(x + 0.5), 0, N - 1)\n return date[thisind].strftime('%Y-%m-%d')\n\nax = axes[1]\nax.plot(ind, r.adj_close, 'o-')\nax.xaxis.set_major_formatter(ticker.FuncFormatter(format_date))\nax.set_title(\"Custom tick formatter\")\nfig.autofmt_xdate()\n\nplt.show()"

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from __future__ import print_function

import numpy as np

import matplotlib.pyplot as plt

import matplotlib.cbook as cbook

import matplotlib.ticker as ticker

with cbook.get_sample_data('goog.npz') as datafile:

r = np.load(datafile)['price_data'].view(np.recarray)

r = r[-30:] # get the last 30 days

date = r.date.astype('O')

fig, axes = plt.subplots(ncols=2, figsize=(8, 4))

ax = axes[0]

ax.plot(date, r.adj_close, 'o-')

ax.set_title("Default")

fig.autofmt_xdate()

N = len(r)

ind = np.arange(N) # the evenly spaced plot indices

def format_date(x, pos=None):

thisind = np.clip(int(x + 0.5), 0, N - 1)

return date[thisind].strftime('%Y-%m-%d')

ax = axes[1]

ax.plot(ind, r.adj_close, 'o-')

ax.xaxis.set_major_formatter(ticker.FuncFormatter(format_date))

ax.set_title("Custom tick formatter")

fig.autofmt_xdate()

plt.show()

"\nCustomizing Matplotlib with style sheets and rcParams\n=====================================================\n\nTips for customizing the properties and default styles of Matplotlib.\n\nUsing style sheets\n------------------\n\nThe ``style`` package adds support for easy-to-switch plotting \"styles\" with\nthe same parameters as a matplotlibrc_ file (which is read at startup to\nconfigure matplotlib).\n\nThere are a number of pre-defined styles `provided by Matplotlib`_. For\nexample, there's a pre-defined style called \"ggplot\", which emulates the\naesthetics of ggplot_ (a popular plotting package for R_). To use this style,\njust add:\n\n"

"\n# Date Index Formatter\n\n\nWhen plotting daily data, a frequent request is to plot the data\nignoring skips, e.g., no extra spaces for weekends. This is particularly\ncommon in financial time series, when you may have data for M-F and\nnot Sat, Sun and you don't want gaps in the x axis. The approach is\nto simply use the integer index for the xdata and a custom tick\nFormatter to get the appropriate date string for a given index.\n\n"

"from __future__ import print_function\n\nimport numpy as np\n\nimport matplotlib.pyplot as plt\nimport matplotlib.cbook as cbook\nfrom matplotlib.dates import bytespdate2num, num2date\nfrom matplotlib.ticker import Formatter\n\n\ndatafile = cbook.get_sample_data('msft.csv', asfileobj=False)\nprint('loading %s' % datafile)\nmsft_data = np.genfromtxt(datafile, delimiter=',', names=True,\n converters={0: bytespdate2num('%d-%b-%y')})[-40:]\n\n\nclass MyFormatter(Formatter):\n def __init__(self, dates, fmt='%Y-%m-%d'):\n self.dates = dates\n self.fmt = fmt\n\n def __call__(self, x, pos=0):\n 'Return the label for time x at position pos'\n ind = int(np.round(x))\n if ind >= len(self.dates) or ind < 0:\n return ''\n\n return num2date(self.dates[ind]).strftime(self.fmt)\n\nformatter = MyFormatter(msft_data['Date'])\n\nfig, ax = plt.subplots()\nax.xaxis.set_major_formatter(formatter)\nax.plot(np.arange(len(msft_data)), msft_data['Close'], 'o-')\nfig.autofmt_xdate()\nplt.show()"

from matplotlib.dates import bytespdate2num, num2date

from matplotlib.ticker import Formatter

datafile = cbook.get_sample_data('msft.csv', asfileobj=False)

print('loading %s' % datafile)

msft_data = np.genfromtxt(datafile, delimiter=',', names=True,

converters={0: bytespdate2num('%d-%b-%y')})[-40:]

class MyFormatter(Formatter):

def __init__(self, dates, fmt='%Y-%m-%d'):

self.dates = dates

self.fmt = fmt

def __call__(self, x, pos=0):

'Return the label for time x at position pos'

ind = int(np.round(x))

if ind >= len(self.dates) or ind < 0:

return ''

return num2date(self.dates[ind]).strftime(self.fmt)

formatter = MyFormatter(msft_data['Date'])

fig, ax = plt.subplots()

ax.xaxis.set_major_formatter(formatter)

ax.plot(np.arange(len(msft_data)), msft_data['Close'], 'o-')

fig.autofmt_xdate()

"lum_img = img[:, :, 0]\n\n# This is array slicing. You can read more in the `Numpy tutorial\n# <https://docs.scipy.org/doc/numpy/user/quickstart.html>`_.\n\nplt.imshow(lum_img)"

"import matplotlib.pyplot as plt\nimport numpy as np\n\nmethods = [None, 'none', 'nearest', 'bilinear', 'bicubic', 'spline16',\n 'spline36', 'hanning', 'hamming', 'hermite', 'kaiser', 'quadric',\n 'catrom', 'gaussian', 'bessel', 'mitchell', 'sinc', 'lanczos']\n\n# Fixing random state for reproducibility\nnp.random.seed(19680801)\n\ngrid = np.random.rand(4, 4)\n\nfig, axes = plt.subplots(nrows=3, ncols=6, figsize=(9, 4.5),\n subplot_kw={'xticks': [], 'yticks': []})\n\nfig.subplots_adjust(hspace=0.3, wspace=0.05)\n\nfor ax, interp_method in zip(axes.flat, methods):\n ax.imshow(grid, interpolation=interp_method, cmap='viridis')\n ax.set_title(str(interp_method))\n\nplt.tight_layout()\nplt.show()"