[statistics] simplified plots, part 1
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164
plotstyle.py
164
plotstyle.py
@ -13,125 +13,57 @@ figure_height = 6.0 # cm, for a 1 x 2 figure
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ppi = 72.0
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# colors:
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colors = {}
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colors['red'] = '#CC0000'
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colors['orange'] = '#FF9900'
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colors['lightorange'] = '#FFCC00'
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colors['yellow'] = '#FFFF66'
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colors['green'] = '#99FF00'
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colors['blue'] = '#0000CC'
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colors['gray'] = '#A7A7A7'
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colors['black'] = '#000000'
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#colors_bendalab_vivid['red'] = '#D71000'
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#colors_bendalab_vivid['orange'] = '#FF9000'
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#colors_bendalab_vivid['yellow'] = '#FFF700'
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#colors_bendalab_vivid['green'] = '#30D700'
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#colors_bendalab_vivid['blue'] = '#0020C0'
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def lighter(color, lightness):
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""" Make a color lighter.
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# line styles for plot():
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lwthick = 4.0
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lwthin = 2.0
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fillalpha = 0.5
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Parameters
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----------
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color: string
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An RGB color as a hexadecimal string (e.g. '#rrggbb').
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lightness: float
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The smaller the lightness, the lighter the returned color.
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A lightness of 1 leaves the color untouched.
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A lightness of 0 returns white.
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# helper lines:
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lsSpine = {'c': colors['black'], 'linestyle': '-', 'linewidth': 1}
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lsGrid = {'c': colors['gray'], 'linestyle': '--', 'linewidth': 1}
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lsMarker = {'c': colors['black'], 'linestyle': '-', 'linewidth': 2}
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Returns
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-------
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color: string
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The lighter color as a hexadecimal RGB string (e.g. '#rrggbb').
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"""
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r = int(color[1:3], 16)
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g = int(color[3:5], 16)
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b = int(color[5:7], 16)
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rl = r + (1.0-lightness)*(0xff - r)
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gl = g + (1.0-lightness)*(0xff - g)
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bl = b + (1.0-lightness)*(0xff - b)
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return '#%02X%02X%02X' % (rl, gl, bl)
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# line styles and fill styles:
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lsA = {'color': colors['blue'], 'linestyle': '-', 'linewidth': lwthick}
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lsAm = {'color': colors['blue'], 'linestyle': '-', 'linewidth': lwthin}
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fsAa = {'facecolor': colors['blue'], 'edgecolor': 'none', 'alpha': fillalpha}
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lsB = {'color': colors['red'], 'linestyle': '-', 'linewidth': lwthick}
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lsBm = {'color': colors['red'], 'linestyle': '-', 'linewidth': lwthin}
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fsB = {'facecolor': colors['red'], 'edgecolor': colors['black'], 'linewidth': 1}
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fsBs = {'facecolor': colors['red'], 'edgecolor': 'none'}
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fsBa = {'facecolor': colors['red'], 'edgecolor': 'none', 'alpha': fillalpha}
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def darker(color, saturation):
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""" Make a color darker.
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lsC = {'color': colors['lightorange'], 'linestyle': '-', 'linewidth': lwthick}
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lsCm = {'color': colors['lightorange'], 'linestyle': '-', 'linewidth': lwthin}
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fsC = {'facecolor': colors['lightorange'], 'edgecolor': colors['black'], 'linewidth': 1}
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fsCs = {'facecolor': colors['lightorange'], 'edgecolor': 'none'}
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fsCa = {'facecolor': colors['lightorange'], 'edgecolor': 'none', 'alpha': fillalpha}
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Parameters
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----------
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color: string
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An RGB color as a hexadecimal string (e.g. '#rrggbb').
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saturation: float
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The smaller the saturation, the darker the returned color.
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A saturation of 1 leaves the color untouched.
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A saturation of 0 returns black.
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Returns
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-------
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color: string
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The darker color as a hexadecimal RGB string (e.g. '#rrggbb').
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"""
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r = int(color[1:3], 16)
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g = int(color[3:5], 16)
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b = int(color[5:7], 16)
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rd = r * saturation
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gd = g * saturation
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bd = b * saturation
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return '#%02X%02X%02X' % (rd, gd, bd)
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fsD = {'facecolor': colors['orange'], 'edgecolor': colors['black'], 'linewidth': 1}
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fsDs = {'facecolor': colors['orange'], 'edgecolor': 'none'}
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fsE = {'facecolor': colors['yellow'], 'edgecolor': colors['black'], 'linewidth': 1}
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fsEs = {'facecolor': colors['yellow'], 'edgecolor': 'none'}
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# colors:
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colors = {
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'red': '#CC0000',
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'orange': '#FF9900',
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'lightorange': '#FFCC00',
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'yellow': '#FFFF66',
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'green': '#99FF00',
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'blue': '#0000CC'
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}
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""" Muted colors used by the Benda-lab. """
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colors_bendalab = {}
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colors_bendalab['red'] = '#C02010'
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colors_bendalab['orange'] = '#F78010'
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colors_bendalab['yellow'] = '#F0D730'
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colors_bendalab['green'] = '#A0B717'
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colors_bendalab['cyan'] = '#40A787'
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colors_bendalab['blue'] = '#2757A0'
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colors_bendalab['purple'] = '#573790'
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colors_bendalab['pink'] = '#C72750'
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colors_bendalab['grey'] = '#A0A0A0'
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colors_bendalab['black'] = '#000000'
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""" Vivid colors used by the Benda-lab. """
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colors_bendalab_vivid = {}
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colors_bendalab_vivid['red'] = '#D71000'
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colors_bendalab_vivid['orange'] = '#FF9000'
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colors_bendalab_vivid['yellow'] = '#FFF700'
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colors_bendalab_vivid['green'] = '#30D700'
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colors_bendalab_vivid['cyan'] = '#00F0B0'
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colors_bendalab_vivid['blue'] = '#0020C0'
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colors_bendalab_vivid['purple'] = '#B000B0'
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colors_bendalab_vivid['pink'] = '#F00080'
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colors_bendalab_vivid['grey'] = '#A7A7A7'
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colors_bendalab_vivid['black'] = '#000000'
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# colors for the plots of the script:
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colors = colors_bendalab_vivid
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colors['lightorange'] = colors['yellow']
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#colors['yellow'] = lighter(colors['yellow'], 0.65)
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colors['yellow'] = '#FFFF55'
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# line styles for plot():
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lsSpine = {'c': colors['black'], 'linestyle': '-', 'linewidth': 1}
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lsGrid = {'c': colors['grey'], 'linestyle': '--', 'linewidth': 1}
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# 'B1': prominent line with first color and style from color group 'B'
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# 'C2m': minor line with second color and style from color group 'C'
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ls = {
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'A1': {'c': colors['red'], 'linestyle': '-', 'linewidth': 3},
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'A2': {'c': colors['orange'], 'linestyle': '-', 'linewidth': 3},
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'A3': {'c': colors['lightorange'], 'linestyle': '-', 'linewidth': 3},
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'B1': {'c': colors['orange'], 'linestyle': '-', 'linewidth': 3},
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'B2': {'c': colors['lightorange'], 'linestyle': '-', 'linewidth': 3},
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'B3': {'c': colors['yellow'], 'linestyle': '-', 'linewidth': 3},
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'C1': {'c': colors['green'], 'linestyle': '-', 'linewidth': 3},
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'D1': {'c': colors['blue'], 'linestyle': '-', 'linewidth': 3},
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'A1m': {'c': colors['red'], 'linestyle': '-', 'linewidth': 2},
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'A2m': {'c': colors['orange'], 'linestyle': '-', 'linewidth': 2},
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'A3m': {'c': colors['lightorange'], 'linestyle': '-', 'linewidth': 2},
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'B1m': {'c': colors['orange'], 'linestyle': '-', 'linewidth': 2},
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'B2m': {'c': colors['lightorange'], 'linestyle': '-', 'linewidth': 2},
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'B3m': {'c': colors['yellow'], 'linestyle': '-', 'linewidth': 2},
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'C1m': {'c': colors['green'], 'linestyle': '-', 'linewidth': 2},
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'D1m': {'c': colors['blue'], 'linestyle': '-', 'linewidth': 2},
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}
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fsF = {'facecolor': colors['green'], 'edgecolor': colors['black'], 'linewidth': 1}
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fsFs = {'facecolor': colors['green'], 'edgecolor': 'none'}
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# factor for scaling widths of bars in a bar plot:
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bar_fac = 1.0
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@ -369,14 +301,12 @@ def common_format():
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mpl.rcParams['axes.linewidth'] = lsSpine['linewidth']
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if 'axes.prop_cycle' in mpl.rcParams:
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mpl.rcParams['axes.prop_cycle'] = cycler(color=[colors['blue'], colors['red'],
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colors['orange'], colors['green'],
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colors['purple'], colors['yellow'],
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colors['cyan'], colors['pink']])
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colors['lightorange'], colors['orange'],
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colors['yellow'], colors['green']])
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else:
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mpl.rcParams['axes.color_cycle'] = [colors['blue'], colors['red'],
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colors['orange'], colors['green'],
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colors['purple'], colors['yellow'],
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colors['cyan'], colors['pink']]
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colors['lightorange'], colors['orange'],
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colors['yellow'], colors['green']]
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# overwrite axes constructor:
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if not hasattr(mpl.axes.Subplot, '__init__orig'):
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mpl.axes.Subplot.__init__orig = mpl.axes.Subplot.__init__
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@ -1,5 +1,6 @@
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import numpy as np
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import matplotlib.pyplot as plt
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from plotstyle import *
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# data:
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rng = np.random.RandomState(981)
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@ -14,13 +15,7 @@ gauss = np.exp(-0.5*xx*xx)/np.sqrt(2.0*np.pi)
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gausscdf = np.cumsum(gauss)*dx
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# plot:
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plt.xkcd()
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fig = plt.figure( figsize=(6, 2.4) )
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ax = fig.add_subplot(1, 1, 1)
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ax.spines['right'].set_visible(False)
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ax.spines['top'].set_visible(False)
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ax.yaxis.set_ticks_position('left')
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ax.xaxis.set_ticks_position('bottom')
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fig, ax = plt.subplots()
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ax.set_xlabel('x')
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ax.set_xlim(-3.2, 3.2)
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ax.set_xticks(np.arange(-3.0, 3.1, 1.0))
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@ -29,24 +24,22 @@ ax.set_ylim(-0.05, 1.05)
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ax.set_yticks(np.arange(0.0, 1.1, 0.2))
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med = xs[cdf>=0.5][0]
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ax.plot([-3.2, med, med], [0.5, 0.5, 0.0], 'k', lw=1, zorder=-5)
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ax.plot([-3.2, med, med], [0.5, 0.5, 0.0], zorder=-5, **lsMarker)
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ax.text(-2.8, 0.55, 'F=0.5')
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ax.text(0.15, 0.25, 'median at %.2f' % med)
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q3 = xs[cdf>=0.75][0]
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ax.plot([-3.2, q3, q3], [0.75, 0.75, 0.0], 'k', lw=1, zorder=-5)
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ax.plot([-3.2, q3, q3], [0.75, 0.75, 0.0], zorder=-5, **lsMarker)
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ax.text(-2.8, 0.8, 'F=0.75')
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ax.text(0.8, 0.5, '3. quartile at %.2f' % q3)
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p = cdf[xs>=-1.0][0]
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ax.plot([-3.2, -1.0, -1.0], [p, p, 0.0], 'k', lw=1, zorder=-5)
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ax.plot([-3.2, -1.0, -1.0], [p, p, 0.0], zorder=-5, **lsMarker)
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ax.text(-2.8, 0.2, 'F=%.2f' % p)
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ax.text(-0.9, 0.05, '-1')
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ax.plot(xx, gausscdf, '-', color='#0000ff', lw=2, zorder=-1)
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ax.plot(xs, cdf, '-', color='#cc0000', lw=4, zorder=-1)
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ax.plot([-3.2, 3.2], [1.0, 1.0], '--', color='k', lw=2, zorder=-10)
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ax.plot(xx, gausscdf, zorder=-1, **lsAm)
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ax.plot(xs, cdf, zorder=-1, **lsB)
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ax.plot([-3.2, 3.2], [1.0, 1.0], zorder=-10, **lsGrid)
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plt.subplots_adjust(left=0.1, right=0.98, bottom=0.15, top=0.98, wspace=0.35, hspace=0.3)
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fig.savefig('cumulative.pdf')
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#plt.show()
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@ -1,5 +1,6 @@
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import numpy as np
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import matplotlib.pyplot as plt
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from plotstyle import *
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# roll the die:
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rng = np.random.RandomState(57281)
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@ -7,33 +8,23 @@ x1 = rng.random_integers( 1, 6, 100 )
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x2 = rng.random_integers(1, 6, 500)
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bins = np.arange(0.5, 7, 1.0)
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plt.xkcd()
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fig, (ax1, ax2) = plt.subplots(1, 2)
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fig.subplots_adjust(**adjust_fs(bottom=2.7, top=0.1))
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ax1.set_xlim(0, 7)
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ax1.set_xticks(range(1, 7))
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ax1.set_xlabel('x')
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ax1.set_ylim(0, 98)
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ax1.set_ylabel('Frequency')
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fs = fsC
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fs['color'] = [fsC['facecolor'], fsE['facecolor']]
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del fs['facecolor']
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ax1.hist([x2, x1], bins, **fs)
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fig = plt.figure( figsize=(6,3) )
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ax = fig.add_subplot( 1, 2, 1 )
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ax.spines['right'].set_visible(False)
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ax.spines['top'].set_visible(False)
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ax.yaxis.set_ticks_position('left')
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ax.xaxis.set_ticks_position('bottom')
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ax.set_xlim(0, 7)
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ax.set_xticks( range(1, 7) )
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ax.set_xlabel( 'x' )
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ax.set_ylim(0, 98)
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ax.set_ylabel( 'Frequency' )
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ax.hist([x2, x1], bins, color=['#FFCC00', '#FFFF66' ])
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ax = fig.add_subplot( 1, 2, 2 )
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ax.spines['right'].set_visible(False)
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ax.spines['top'].set_visible(False)
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ax.yaxis.set_ticks_position('left')
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ax.xaxis.set_ticks_position('bottom')
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ax.set_xlim(0, 7)
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ax.set_xticks( range(1, 7) )
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ax.set_xlabel( 'x' )
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ax.set_ylim(0, 0.23)
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ax.set_ylabel( 'Probability' )
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ax.plot([0.2, 6.8], [1.0/6.0, 1.0/6.0], '-b', lw=2, zorder=1)
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ax.hist([x2, x1], bins, normed=True, color=['#FFCC00', '#FFFF66' ], zorder=10)
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plt.subplots_adjust(left=0.1, right=0.98, bottom=0.15, top=0.98, wspace=0.4, hspace=0.0)
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ax2.set_xlim(0, 7)
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ax2.set_xticks(range(1, 7))
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ax2.set_xlabel('x')
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ax2.set_ylim(0, 0.23)
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ax2.set_ylabel('Probability')
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ax2.plot([0.2, 6.8], [1.0/6.0, 1.0/6.0], zorder=1, **lsAm)
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ax2.hist([x2, x1], bins, normed=True, zorder=10, **fs)
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fig.savefig('diehistograms.pdf')
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#plt.show()
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@ -1,33 +1,24 @@
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import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib.gridspec as gridspec
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from scipy.stats import gaussian_kde
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from plotstyle import *
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#rng = np.random.RandomState(981)
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#data = rng.randn(40, 1) + 4.0
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rng = np.random.RandomState(1981)
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data = rng.gamma(1.0, 1.5, 40) + 1.0
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data = data[data<7.5]
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xpos = 0.08
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ypos = 0.15
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width = 0.65
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height = 0.8
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barwidth = 0.8
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scatterpos = 1.0
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barpos = 2.5
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boxpos = 4.0
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plt.xkcd()
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fig = plt.figure( figsize=(6,3.4) )
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fig = plt.figure(figsize=cm_size(figure_width, 1.4*figure_height))
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spec = gridspec.GridSpec(nrows=1, ncols=2, width_ratios=[3, 1], wspace=0.1,
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**adjust_fs(fig, left=4.0))
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ax = fig.add_axes([xpos, ypos, width, height])
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ax.spines['right'].set_visible(False)
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ax.spines['top'].set_visible(False)
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#ax.spines['left'].set_visible(False)
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#ax.spines['bottom'].set_visible(False)
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#ax.xaxis.set_ticks_position('none')
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#ax.yaxis.set_ticks_position('none')
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#ax.set_xticklabels([])
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#ax.set_yticklabels([])
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ax = fig.add_subplot(spec[0, 0])
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wh = ax.boxplot( data, positions=[boxpos], widths=[barwidth], whis=100.0, patch_artist=True)
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wh['medians'][0].set_linewidth(4)
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wh['whiskers'][0].set_linewidth(2)
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@ -49,7 +40,7 @@ ax.annotate('maximum',
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connectionstyle="angle3,angleA=0,angleB=120") )
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ax.annotate('3. quartile',
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xy=(boxpos-0.3*barwidth, 3.7), xycoords='data',
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xytext=(boxpos-1.3*barwidth, 5.5), textcoords='data', ha='left',
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xytext=(boxpos-0.1*barwidth, 5.5), textcoords='data', ha='right',
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arrowprops=dict(arrowstyle="->", relpos=(0.4,0.0),
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connectionstyle="angle3,angleA=0,angleB=120") )
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ax.annotate('median',
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@ -57,19 +48,14 @@ ax.annotate('median',
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xytext=(boxpos+0.1*barwidth, 4.2), textcoords='data', ha='left',
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arrowprops=dict(arrowstyle="->", relpos=(0.8,0.0),
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connectionstyle="angle3,angleA=-60,angleB=20") )
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ax = fig.add_axes([xpos, ypos, width, height])
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ax.spines['right'].set_visible(False)
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ax.spines['top'].set_visible(False)
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ax.yaxis.set_ticks_position('left')
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ax.xaxis.set_ticks_position('bottom')
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ax.set_xticklabels([])
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ax.set_xlim(0.0, 4.8)
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ax.set_ylabel('x')
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ax.set_ylim( 0.0, 8.0)
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ax = fig.add_subplot(spec[0, 0])
|
||||
ax.set_xlim(0.0, 4.8)
|
||||
ax.set_xticks([scatterpos, barpos, boxpos])
|
||||
ax.set_xticklabels(['(1) data', '(2) bar\n plot', '(3) box-\nwhisker'])
|
||||
ax.set_ylabel('x')
|
||||
ax.set_ylim( 0.0, 8.0)
|
||||
|
||||
# scatter data points according to their density:
|
||||
kernel = gaussian_kde(data)
|
||||
@ -80,11 +66,10 @@ ax.scatter(scatterpos+barwidth*x*(rng.rand(len(data))-0.5), data, s=50)
|
||||
barmean = np.mean(data)
|
||||
barstd = np.std(data)
|
||||
ew = 0.2
|
||||
ax.bar([barpos-0.5*barwidth], [barmean], barwidth, color='#FFCC00')
|
||||
eargs = {'color': 'k', 'lw': 2}
|
||||
ax.plot([barpos, barpos], [barmean-barstd, barmean+barstd], **eargs)
|
||||
ax.plot([barpos-0.5*ew, barpos+0.5*ew], [barmean-barstd, barmean-barstd], **eargs)
|
||||
ax.plot([barpos-0.5*ew, barpos+0.5*ew], [barmean+barstd, barmean+barstd], **eargs)
|
||||
ax.bar([barpos-0.5*barwidth], [barmean], barwidth, **fsC)
|
||||
ax.plot([barpos, barpos], [barmean-barstd, barmean+barstd], **lsMarker)
|
||||
ax.plot([barpos-0.5*ew, barpos+0.5*ew], [barmean-barstd, barmean-barstd], **lsMarker)
|
||||
ax.plot([barpos-0.5*ew, barpos+0.5*ew], [barmean+barstd, barmean+barstd], **lsMarker)
|
||||
ax.annotate('mean',
|
||||
xy=(barpos-0.4*barwidth, 2.7), xycoords='data',
|
||||
xytext=(barpos-1*barwidth, 5.5), textcoords='data', ha='left',
|
||||
@ -96,11 +81,7 @@ ax.annotate('mean plus\nstd. dev.',
|
||||
arrowprops=dict(arrowstyle="->", relpos=(0.5,0.0),
|
||||
connectionstyle="angle3,angleA=-60,angleB=80") )
|
||||
|
||||
ax = fig.add_axes([xpos+width+0.03, ypos, 0.98-(xpos+width+0.03), height])
|
||||
ax.spines['right'].set_visible(False)
|
||||
ax.spines['top'].set_visible(False)
|
||||
ax.xaxis.set_ticks_position('bottom')
|
||||
ax.yaxis.set_ticks_position('left')
|
||||
ax = fig.add_subplot(spec[0, 1])
|
||||
ax.set_yticklabels([])
|
||||
ax.set_ylim( 0.0, 8.0)
|
||||
ax.set_xticks(np.arange(0.0, 0.4, 0.1))
|
||||
@ -108,8 +89,7 @@ ax.set_xlabel('(4) p(x)')
|
||||
bw = 0.75
|
||||
bins = np.arange(0, 8.0+bw, bw)
|
||||
h, b = np.histogram(data, bins)
|
||||
ax.barh(b[:-1], h/bw/np.sum(h), bw, color='#CC0000')
|
||||
ax.barh(b[:-1], h/bw/np.sum(h), bw, **fsB)
|
||||
|
||||
plt.savefig('displayunivariatedata.pdf')
|
||||
#plt.show()
|
||||
|
||||
|
||||
@ -1,78 +1,66 @@
|
||||
import numpy as np
|
||||
import scipy.stats as st
|
||||
import matplotlib.pyplot as plt
|
||||
from plotstyle import *
|
||||
|
||||
# normal distribution:
|
||||
x = np.arange(-3.0, 3.0, 0.01)
|
||||
g = np.exp(-0.5*x*x)/np.sqrt(2.0*np.pi)
|
||||
|
||||
plt.xkcd()
|
||||
fig = plt.figure( figsize=(6, 2.8) )
|
||||
ax = fig.add_subplot(1, 2, 1)
|
||||
ax.spines['right'].set_visible(False)
|
||||
ax.spines['top'].set_visible(False)
|
||||
ax.yaxis.set_ticks_position('left')
|
||||
ax.xaxis.set_ticks_position('bottom')
|
||||
ax.set_xlabel( 'x' )
|
||||
ax.set_ylabel( 'Prob. density p(x)' )
|
||||
ax.set_ylim( 0.0, 0.46 )
|
||||
ax.set_yticks( np.arange( 0.0, 0.45, 0.1 ) )
|
||||
ax.text(-1.0, 0.06, '50%', ha='center' )
|
||||
ax.text(+1.0, 0.06, '50%', ha='center' )
|
||||
ax.annotate('Median\n= mean',
|
||||
fig, (ax1, ax2) = plt.subplots(1, 2)
|
||||
fig.subplots_adjust(**adjust_fs(bottom=2.7, top=0.1))
|
||||
ax1.set_xlabel('x')
|
||||
ax1.set_ylabel('Prob. density p(x)')
|
||||
ax1.set_ylim(0.0, 0.46)
|
||||
ax1.set_yticks(np.arange(0.0, 0.45, 0.1))
|
||||
ax1.text(-1.0, 0.06, '50%', ha='center')
|
||||
ax1.text(+1.0, 0.06, '50%', ha='center')
|
||||
ax1.annotate('Median\n= mean',
|
||||
xy=(0.1, 0.3), xycoords='data',
|
||||
xytext=(1.2, 0.35), textcoords='data', ha='left',
|
||||
arrowprops=dict(arrowstyle="->", relpos=(0.0,0.2),
|
||||
connectionstyle="angle3,angleA=10,angleB=40"))
|
||||
ax.annotate('Mode',
|
||||
ax1.annotate('Mode',
|
||||
xy=(-0.1, 0.4), xycoords='data',
|
||||
xytext=(-2.5, 0.43), textcoords='data', ha='left',
|
||||
arrowprops=dict(arrowstyle="->", relpos=(0.0,0.2),
|
||||
connectionstyle="angle3,angleA=10,angleB=120"))
|
||||
ax.fill_between( x[x<0], 0.0, g[x<0], color='#ffcc00' )
|
||||
ax.fill_between( x[x>0], 0.0, g[x>0], color='#99ff00' )
|
||||
ax.plot(x, g, 'b', lw=4)
|
||||
ax.plot([0.0, 0.0], [0.0, 0.45], 'k', lw=2 )
|
||||
ax1.fill_between(x[x<0], 0.0, g[x<0], **fsCs)
|
||||
ax1.fill_between(x[x>0], 0.0, g[x>0], **fsFs)
|
||||
ax1.plot(x, g, **lsA)
|
||||
ax1.plot([0.0, 0.0], [0.0, 0.45], **lsMarker)
|
||||
|
||||
# normal distribution:
|
||||
# gamma distribution:
|
||||
x = np.arange(0.0, 6.0, 0.01)
|
||||
shape = 2.0
|
||||
g = st.gamma.pdf(x, shape)
|
||||
m = st.gamma.median(shape)
|
||||
gm = st.gamma.mean(shape)
|
||||
ax = fig.add_subplot(1, 2, 2)
|
||||
ax.spines['right'].set_visible(False)
|
||||
ax.spines['top'].set_visible(False)
|
||||
ax.yaxis.set_ticks_position('left')
|
||||
ax.xaxis.set_ticks_position('bottom')
|
||||
ax.set_xlabel( 'x' )
|
||||
ax.set_ylabel( 'Prob. density p(x)' )
|
||||
ax.set_ylim( 0.0, 0.46 )
|
||||
ax.set_yticks( np.arange( 0.0, 0.45, 0.1 ) )
|
||||
ax.text(m-0.8, 0.06, '50%', ha='center' )
|
||||
ax.text(m+1.2, 0.06, '50%', ha='center' )
|
||||
ax.annotate('Median',
|
||||
ax2.set_xlabel('x')
|
||||
ax2.set_ylabel('Prob. density p(x)')
|
||||
ax2.set_ylim(0.0, 0.46)
|
||||
ax2.set_yticks(np.arange(0.0, 0.45, 0.1))
|
||||
ax2.text(m-0.8, 0.06, '50%', ha='center')
|
||||
ax2.text(m+1.2, 0.06, '50%', ha='center')
|
||||
ax2.annotate('Median',
|
||||
xy=(m+0.1, 0.2), xycoords='data',
|
||||
xytext=(m+1.6, 0.25), textcoords='data', ha='left',
|
||||
arrowprops=dict(arrowstyle="->", relpos=(0.0,0.5),
|
||||
connectionstyle="angle3,angleA=30,angleB=70"))
|
||||
ax.annotate('Mean',
|
||||
ax2.annotate('Mean',
|
||||
xy=(gm, 0.01), xycoords='data',
|
||||
xytext=(gm+1.8, 0.15), textcoords='data', ha='left',
|
||||
arrowprops=dict(arrowstyle="->", relpos=(0.0,0.5),
|
||||
connectionstyle="angle3,angleA=0,angleB=90"))
|
||||
ax.annotate('Mode',
|
||||
ax2.annotate('Mode',
|
||||
xy=(1.0, 0.38), xycoords='data',
|
||||
xytext=(1.8, 0.42), textcoords='data', ha='left',
|
||||
arrowprops=dict(arrowstyle="->", relpos=(0.0,0.5),
|
||||
connectionstyle="angle3,angleA=0,angleB=70"))
|
||||
ax.fill_between( x[x<m], 0.0, g[x<m], color='#ffcc00' )
|
||||
ax.fill_between( x[x>m], 0.0, g[x>m], color='#99ff00' )
|
||||
ax.plot(x, g, 'b', lw=4)
|
||||
ax.plot([m, m], [0.0, 0.38], 'k', lw=2 )
|
||||
#ax.plot([gm, gm], [0.0, 0.42], 'k', lw=2 )
|
||||
ax2.fill_between(x[x<m], 0.0, g[x<m], **fsCs)
|
||||
ax2.fill_between(x[x>m], 0.0, g[x>m], **fsFs)
|
||||
ax2.plot(x, g, **lsA)
|
||||
ax2.plot([m, m], [0.0, 0.38], **lsMarker)
|
||||
#ax2.plot([gm, gm], [0.0, 0.38], **lsMarker)
|
||||
|
||||
#plt.tight_layout()
|
||||
plt.subplots_adjust(left=0.1, right=0.98, bottom=0.15, top=0.98, wspace=0.4, hspace=0.0)
|
||||
fig.savefig('median.pdf')
|
||||
#plt.show()
|
||||
|
||||
@ -1,5 +1,6 @@
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from plotstyle import *
|
||||
|
||||
# normal distribution:
|
||||
rng = np.random.RandomState(6281)
|
||||
@ -7,38 +8,24 @@ x = np.arange( -4.0, 4.0, 0.01 )
|
||||
g = np.exp(-0.5*x*x)/np.sqrt(2.0*np.pi)
|
||||
r = rng.randn(100)
|
||||
|
||||
plt.xkcd()
|
||||
fig, (ax1, ax2) = plt.subplots(1, 2)
|
||||
ax1.set_xlabel('x')
|
||||
ax1.set_xlim(-3.2, 3.2)
|
||||
ax1.set_xticks(np.arange(-3.0, 3.1, 1.0))
|
||||
ax1.set_ylabel('Frequency')
|
||||
ax1.set_yticks(np.arange(0.0, 41.0, 10.0))
|
||||
ax1.hist(r, 5, zorder=-10, **fsB)
|
||||
ax1.hist(r, 20, zorder=-5, **fsC)
|
||||
|
||||
fig = plt.figure( figsize=(6,3) )
|
||||
ax = fig.add_subplot( 1, 2, 1 )
|
||||
ax.spines['right'].set_visible(False)
|
||||
ax.spines['top'].set_visible(False)
|
||||
ax.yaxis.set_ticks_position('left')
|
||||
ax.xaxis.set_ticks_position('bottom')
|
||||
ax.set_xlabel( 'x' )
|
||||
ax.set_xlim(-3.2, 3.2)
|
||||
ax.set_xticks( np.arange( -3.0, 3.1, 1.0 ) )
|
||||
ax.set_ylabel( 'Frequency' )
|
||||
ax.set_yticks( np.arange( 0.0, 41.0, 10.0 ) )
|
||||
ax.hist(r, 5, color='#CC0000')
|
||||
ax.hist(r, 20, color='#FFCC00')
|
||||
ax2.set_xlabel('x')
|
||||
ax2.set_xlim(-3.2, 3.2)
|
||||
ax2.set_xticks(np.arange(-3.0, 3.1, 1.0))
|
||||
ax2.set_ylabel('Probab. density p(x)')
|
||||
ax2.set_ylim(0.0, 0.44)
|
||||
ax2.set_yticks(np.arange(0.0, 0.41, 0.1))
|
||||
ax2.plot(x, g, zorder=-1, **lsA)
|
||||
ax2.hist(r, 5, normed=True, zorder=-10, **fsB)
|
||||
ax2.hist(r, 20, normed=True, zorder=-5, **fsC)
|
||||
|
||||
ax = fig.add_subplot( 1, 2, 2 )
|
||||
ax.spines['right'].set_visible(False)
|
||||
ax.spines['top'].set_visible(False)
|
||||
ax.yaxis.set_ticks_position('left')
|
||||
ax.xaxis.set_ticks_position('bottom')
|
||||
ax.set_xlabel( 'x' )
|
||||
ax.set_xlim(-3.2, 3.2)
|
||||
ax.set_xticks( np.arange( -3.0, 3.1, 1.0 ) )
|
||||
ax.set_ylabel( 'Probab. density p(x)' )
|
||||
ax.set_ylim(0.0, 0.44)
|
||||
ax.set_yticks( np.arange( 0.0, 0.41, 0.1 ) )
|
||||
ax.plot(x, g, '-b', lw=2, zorder=-1)
|
||||
ax.hist(r, 5, normed=True, color='#CC0000', zorder=-10)
|
||||
ax.hist(r, 20, normed=True, color='#FFCC00', zorder=-5)
|
||||
|
||||
plt.subplots_adjust(left=0.1, right=0.98, bottom=0.15, top=0.98, wspace=0.4, hspace=0.0)
|
||||
fig.savefig('pdfhistogram.pdf')
|
||||
#plt.show()
|
||||
|
||||
|
||||
@ -1,5 +1,6 @@
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from plotstyle import *
|
||||
|
||||
# normal distribution:
|
||||
x = np.arange(-3.0, 5.0, 0.01)
|
||||
@ -7,13 +8,7 @@ g = np.exp(-0.5*x*x)/np.sqrt(2.0*np.pi)
|
||||
x1=0.0
|
||||
x2=1.0
|
||||
|
||||
plt.xkcd()
|
||||
fig = plt.figure( figsize=(6,3.4) )
|
||||
ax = fig.add_subplot( 1, 1, 1 )
|
||||
ax.spines['right'].set_visible(False)
|
||||
ax.spines['top'].set_visible(False)
|
||||
ax.yaxis.set_ticks_position('left')
|
||||
ax.xaxis.set_ticks_position('bottom')
|
||||
fig, ax = plt.subplots()
|
||||
ax.set_xlabel('x')
|
||||
ax.set_ylabel('Probability density p(x)')
|
||||
ax.set_ylim(0.0, 0.46)
|
||||
@ -24,13 +19,10 @@ ax.annotate('Gaussian',
|
||||
arrowprops=dict(arrowstyle="->", relpos=(0.5,0.0),
|
||||
connectionstyle="angle3,angleA=10,angleB=110"))
|
||||
ax.annotate('$P(0<x<1) = \int_0^1 p(x) \, dx$',
|
||||
xy=(0.6, 0.28), xycoords='data',
|
||||
xy=(0.5, 0.24), xycoords='data',
|
||||
xytext=(1.2, 0.4), textcoords='data', ha='left',
|
||||
arrowprops=dict(arrowstyle="->", relpos=(0.0,0.5),
|
||||
connectionstyle="angle3,angleA=10,angleB=80"))
|
||||
ax.fill_between( x[(x>x1)&(x<x2)], 0.0, g[(x>x1)&(x<x2)], color='#cc0000' )
|
||||
ax.plot(x,g, 'b', lw=4)
|
||||
#plt.tight_layout()
|
||||
plt.subplots_adjust(left=0.1, right=0.98, bottom=0.15, top=0.98, wspace=0.4, hspace=0.0)
|
||||
ax.fill_between(x[(x>x1)&(x<x2)], 0.0, g[(x>x1)&(x<x2)], **fsBs)
|
||||
ax.plot(x,g, **lsA)
|
||||
fig.savefig('pdfprobabilities.pdf')
|
||||
#plt.show()
|
||||
|
||||
@ -1,18 +1,14 @@
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from plotstyle import *
|
||||
|
||||
# normal distribution:
|
||||
x = np.arange( -4.0, 4.0, 0.01 )
|
||||
g = np.exp(-0.5*x*x)/np.sqrt(2.0*np.pi)
|
||||
q = [ -0.67488, 0.0, 0.67488 ]
|
||||
|
||||
plt.xkcd()
|
||||
fig = plt.figure( figsize=(6,3.2) )
|
||||
ax = fig.add_subplot( 1, 1, 1 )
|
||||
ax.spines['right'].set_visible(False)
|
||||
ax.spines['top'].set_visible(False)
|
||||
ax.yaxis.set_ticks_position('left')
|
||||
ax.xaxis.set_ticks_position('bottom')
|
||||
fig, ax = plt.subplots(figsize=cm_size(figure_width, 1.2*figure_height))
|
||||
fig.subplots_adjust(**adjust_fs(bottom=2.7, top=0.1))
|
||||
ax.set_xlabel('x')
|
||||
ax.set_ylabel('Probability density p(x)')
|
||||
ax.set_ylim(0.0, 0.46)
|
||||
@ -36,15 +32,12 @@ ax.annotate('Median',
|
||||
xytext=(1.6, 0.35), textcoords='data', ha='left',
|
||||
arrowprops=dict(arrowstyle="->", relpos=(0.0,0.5),
|
||||
connectionstyle="angle3,angleA=10,angleB=40") )
|
||||
ax.fill_between( x[x<q[0]], 0.0, g[x<q[0]], color='#ffcc00' )
|
||||
ax.fill_between( x[(x>q[0])&(x<q[1])], 0.0, g[(x>q[0])&(x<q[1])], color='#ff0000' )
|
||||
ax.fill_between( x[(x>q[1])&(x<q[2])], 0.0, g[(x>q[1])&(x<q[2])], color='#ff9900' )
|
||||
ax.fill_between( x[x>q[2]], 0.0, g[x>q[2]], color='#ffff66' )
|
||||
ax.plot(x,g, 'b', lw=4)
|
||||
ax.plot([0.0, 0.0], [0.0, 0.45], 'k', lw=2 )
|
||||
ax.plot([q[0], q[0]], [0.0, 0.4], 'k', lw=2 )
|
||||
ax.plot([q[2], q[2]], [0.0, 0.4], 'k', lw=2 )
|
||||
plt.subplots_adjust(left=0.1, right=0.98, bottom=0.15, top=0.98, wspace=0.4, hspace=0.0)
|
||||
#plt.tight_layout()
|
||||
ax.fill_between( x[x<q[0]], 0.0, g[x<q[0]], **fsCs)
|
||||
ax.fill_between( x[(x>q[0])&(x<q[1])], 0.0, g[(x>q[0])&(x<q[1])], **fsBs)
|
||||
ax.fill_between( x[(x>q[1])&(x<q[2])], 0.0, g[(x>q[1])&(x<q[2])], **fsDs)
|
||||
ax.fill_between( x[x>q[2]], 0.0, g[x>q[2]], **fsEs)
|
||||
ax.plot(x,g, **lsA)
|
||||
ax.plot([0.0, 0.0], [0.0, 0.45], **lsMarker)
|
||||
ax.plot([q[0], q[0]], [0.0, 0.4], **lsMarker)
|
||||
ax.plot([q[2], q[2]], [0.0, 0.4], **lsMarker)
|
||||
fig.savefig( 'quartile.pdf' )
|
||||
#plt.show()
|
||||
|
||||
Reference in New Issue
Block a user