def _scale_fig_size(figsize, textsize, rows=1, cols=1):
"""Scale figure properties according to rows and cols.
Parameters
----------
figsize : float or None
Size of figure in inches
textsize : float or None
fontsize
rows : int
Number of rows
cols : int
Number of columns
Returns
-------
figsize : float or None
Size of figure in inches
fontsize : int
fontsize
linewidth : int
linewidth
markersize : int
markersize
"""
rc_width, rc_height = tuple(mpl.rc_params()['figure.figsize'])
rc_fontsize = mpl.rc_params()['font.size']
rc_linewidth = mpl.rc_params()['lines.linewidth']
rc_markersize = mpl.rc_params()['lines.markersize']
if figsize is None:
width, height = rc_width, rc_height
width = width * cols
height = height * rows
else:
width, height = figsize
if rows and cols > 1:
fontsize = rc_fontsize
linewidth = rc_linewidth
markersize = rc_markersize
else:
scale_factor = ((width * height) / (rc_width * rc_height))**0.5
fontsize = scale_factor * rc_fontsize
linewidth = scale_factor * rc_linewidth
markersize = scale_factor * rc_markersize
if textsize is not None:
fontsize = textsize
return (width, height), fontsize, linewidth, markersize
def getImages_pylab(string_method, matrices, imageWidthInPixels, imageHeightInPixels):
# Import system modules
import matplotlib
import pylab
# Initialize
images = []
convertPixelLengthToInchLength = lambda x: x / float(matplotlib.rc_params()['figure.dpi'])
imageWidthInInches = convertPixelLengthToInchLength(imageWidthInPixels)
imageHeightInInches = convertPixelLengthToInchLength(imageHeightInPixels)
imageDimensions = imageWidthInInches, imageHeightInInches
# For each matrix,
for matrix in matrices:
# Create a new figure
pylab.figure(figsize=imageDimensions)
axes = pylab.axes()
# Plot grayscale matrix
method = getattr(axes, string_method)
method(matrix, cmap=pylab.cm.gray)
# Clear tick marks
axes.set_xticks([])
axes.set_yticks([])
# Save image
imageFile = StringIO.StringIO()
pylab.savefig(imageFile, format='png')
imageFile.seek(0)
# Append
images.append(Image.open(imageFile))
# Return
return images
def getImages_pylab(string_method, matrices, imageWidthInPixels,
imageHeightInPixels):
# Import system modules
import matplotlib
import pylab
# Initialize
images = []
convertPixelLengthToInchLength = lambda x: x / float(matplotlib.rc_params(
)['figure.dpi'])
imageWidthInInches = convertPixelLengthToInchLength(imageWidthInPixels)
imageHeightInInches = convertPixelLengthToInchLength(imageHeightInPixels)
imageDimensions = imageWidthInInches, imageHeightInInches
# For each matrix,
for matrix in matrices:
# Create a new figure
pylab.figure(figsize=imageDimensions)
axes = pylab.axes()
# Plot grayscale matrix
method = getattr(axes, string_method)
method(matrix, cmap=pylab.cm.gray)
# Clear tick marks
axes.set_xticks([])
axes.set_yticks([])
# Save image
imageFile = StringIO.StringIO()
pylab.savefig(imageFile, format='png')
imageFile.seek(0)
# Append
images.append(Image.open(imageFile))
# Return
return images
def setFontSize(self, event):
dlg = wx.TextEntryDialog(self, "New font size", "Font Size")
s = mpl.rc_params()['font.size']
dlg.SetValue(str(s))
if dlg.ShowModal() == wx.ID_OK:
s = float(dlg.GetValue())
mpl.rcParams.update({'font.size': s})
self.figure.canvas.draw()
dlg.Destroy()
def imwrite_to_workspace(img,
filename,
workspace_dir=WORKSPACE_DIR,
cmap=None,
**imsave_args):
cmap = cmap if cmap is not None else matplotlib.rc_params()['image.cmap']
plt.imsave(fname=os.path.join(workspace_dir, filename),
arr=img,
cmap=cmap,
**imsave_args)
def __init__(self, style: dict[str, Any] = None, color: Optional[str] = None) -> None:
style_ = self._default_style.copy()
if style is not None:
style_.update(style)
if color is not None:
style_["color"] = color
if "color" not in style_:
cycler = mpl.rc_params()["axes.prop_cycle"]
color = cycler.by_key()["color"][StyledObject._idx]
StyledObject._idx += 1
if StyledObject._idx + 1 > len(cycler):
StyledObject._idx = 0
style_["color"] = color
self.style = style_
def heatmap(data, method='pearson', camp='Blues', figsize=(10, 8)):
"""
data: 整份数据
method：默认为 pearson 系数
camp：默认为：RdYlGn-红黄蓝；YlGnBu-黄绿蓝；Blues/Greens 也是不错的选择
figsize: 默认为 10，8
"""
# ## 消除斜对角颜色重复的色块
font1 = {'family': 'Arial', 'size': 10}
font2 = {'family': 'Arial', 'size': 8}
mask = np.zeros_like(df.corr())
mask[np.triu_indices_from(mask)] = True
matplotlib.rcParams['agg.path.chunksize'] = 60000
matplotlib.rcParams.update(matplotlib.rc_params())
plt.rcParams['font.family'] = 'Arial'
plt.rcParams['font.size'] = '10'
plt.rcParams['figure.figsize'] = (10, 10)
# plt.xlim([0.0, 1.0])
# plt.ylim([0.0, 1.0])
# plt.xticks(font=font2)
# plt.yticks(font=font2)
plt.figure(figsize=figsize, dpi=80)
sns.heatmap(data.corr(method=method),
xticklabels=data.corr(method=method).columns,
yticklabels=data.corr(method=method).columns,
cmap=camp,
center=0,
annot=True,
mask=mask)
plt.savefig(r'D:\research paper\paper\论文\DL实验记录\统计图\线性图.jpeg', dpi=1000)
plt.show()
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.dates as mpldates
from cycler import cycler
import click
from pathlib import Path
# Modern Pandas requires this
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()
# Requires Python 3.6+ for format strings
# Add fill variations for large numbers of colors
color_cycler = mpl.rc_params()['axes.prop_cycle']
hatch_cycler = cycler(hatch=['', '///', '+++', '***'])
prop_cycle = hatch_cycler * color_cycler
# This is a workaround because the pandas stacked plot does not use dates, just integers. Grrr!
def stacked_plot(df, ax=None, **kargs):
old = None
current_prop = prop_cycle()
for column in df:
current = df[column]
if old is None:
bottom = current * 0
else:
bottom = df.cumsum(axis=1)[old]
art = ax.bar(df.index,
def __init__(self, parent=None):
super(MainWindow, self).__init__(parent)
self.setupUi(self)
# Init 0 for draw...
plt.rcParams['agg.path.chunksize'] = 0
matplotlib.rcParams['agg.path.chunksize'] = 0
matplotlib.rcParams.update(matplotlib.rc_params())
self.dpi = 100
self.signalframe = self.widget_Signal_TimeDomain
self.figure = Figure((11.3, 6.3), dpi=self.dpi)
self.canvas = FigureCanvas(self.figure)
self.canvas.setParent(self.signalframe)
self.axes = self.figure.add_subplot(111)
#self.axes.set_title('Signal')
self.axes.set_xlabel('Time(μs)')
self.axes.set_ylabel('Voltage')
#plt.subplots_adjust(left=0.2, bottom=0.2, right=0.8, top=0.8, hspace=0.2, wspace=0.3)
timespan = self.getRecordLength() * 1024 / self.getSampleRate(
) # in us
x = np.linspace(0, timespan, self.getRecordLength() * 1024)
normalLimY = self.getVoltageScale() * 10
self.axes.set_ylim(-normalLimY / 2 + self.getOffset(),
normalLimY / 2 + self.getOffset())
ymajorLocator = MultipleLocator(self.getVoltageScale())
yminorLocator = MultipleLocator(self.getVoltageScale() / 2)
self.axes.yaxis.set_major_locator(ymajorLocator)
self.axes.yaxis.set_minor_locator(yminorLocator)
self.axes.grid(True)
self.figure.tight_layout() # Adjust spaces
#self.NavToolbar = NavigationToolbar(self.canvas, self.signalframe)
#self.addToolBar(QtCore.Qt.RightToolBarArea, NavigationToolbar(self.canvas, self.signalframe))
self.toolbar = NavigationToolbar(self.canvas, self.signalframe)
self.toolbar.hide()
# Button slots
self.pushButton_Home.clicked.connect(self.home)
self.pushButton_Back.clicked.connect(self.back)
self.pushButton_Forward.clicked.connect(self.forward)
self.pushButton_Pan.clicked.connect(self.pan)
self.pushButton_Zoom.clicked.connect(self.zoom)
self.pushButton_SavePic.clicked.connect(self.savepic)
# Init Socket
self.udpSocketClient = UDPSocketClient()
# Init FrameNum
self.checkBox_FrameMode.setEnabled(True)
self.frameNum = self.getFrameNumber()
# 0x4
self.sendCmdFramNum(self.frameNum)
# Init Length 0x8
self.comboBox_RecordLength.setCurrentIndex(6) # 64k
# Set RecordLength
self.sendCmdRecordLength(self.getRecordLength() *
self.getFrameNumber())
#Init Trigger 0x2, bit2
self.comboBox_TriggerDomain.setCurrentIndex(1) # External
self.sendCmdTriggerType(self.getTriggerType())
# Reset PFGA to read to capture data
if (self.getTriggerType() == 0):
self.sendCmdWRREG(0x2, 0x20)
self.sendCmdWRREG(0x2, 0x28)
else:
self.sendCmdWRREG(0xa, 0x0)
self.sendCmdWRREG(0x2, 0x2c)
self.sendCmdWRREG(0x2, 0x2d)
# Read sampleRate
value = self.readCmdSampleRate()
if value > 5:
value = 0
self.comboBox_SampleRate.setCurrentIndex(value)
# The last data
self.lastChAData = []
self.lastChBData = []
self.realTimeThread = None
self.externalTriggerThread = None
plt.close()
#######################################
#######################################
### Technical details of how rc parameters are implemented (extended read)
# http://matplotlib.org/api/matplotlib_configuration_api.html?highlight=rcparams#matplotlib.rcParams
### RcParams is a class in matplotlib package.
# It implements a dictionary object that holds all default values for plotting with matplotlib.
# rcParams is an object instance of RcParams in matplotlib package.
# It implements a dictionary object that stores default matplotlib parameters.
### rc_params() is a function in matplotlib package.
# It returns a matplotlib.RcParams() instance from the default matplotlib rc file.
import matplotlib as mpl
print mpl.rc_params()
# or print the dictionary object:
print mpl.rcParams
# you can either access it through matplotlib itself, or through matplotlib.PyPlot
print plt.rcParams
### plt.rc() is a function in matplotlib package.
# It sets the current rc parameters.
mpl.rc('lines', linewidth=2, color='r')
plt.plot(range(5), range(5))
# rc() sets multiple parameters at once. The code above is equivalent to:
plt.rcParams['lines.linewidth'] = 2
plt.rcParams['lines.color'] = 'r'
#######################################
# Conditional imports and mpl customizations (provided mpl defaults have not been
# changed by user)
if __plt__:
import matplotlib.pyplot as plt
import matplotlib.style as mplstyle
import matplotlib as mpl
from matplotlib import colors
# Syncopy default plotting settings
spyMplRc = {"figure.dpi": 100}
# Check if we're running w/mpl's default settings: if user either changed
# existing setting or appended new ones (e.g., color definitions), abort
changeMplConf = True
rcDefaults = mpl.rc_params()
rcKeys = rcDefaults.keys()
with warnings.catch_warnings(
): # examples.directory was deprecated in Matplotlib 3.0, silence the warning
warnings.simplefilter("ignore")
rcParams = dict(mpl.rcParams)
rcParams.pop("examples.directory", None)
rcParams.pop("backend")
rcParams.pop("interactive")
for key, value in rcParams.items():
if key not in rcKeys:
changeMplConf = False
break
if rcDefaults[key] != value:
changeMplConf = False
break
""" import numpy as np import matplotlib as mpl # 导入matplotlib库 import matplotlib.pyplot as plt # 主要的绘图子库 ''' # matplotlib的所有配置保存在下面的字典中，修改字典对应的值即为修改配置 print(mpl.rcParams) print(mpl.get_configdir()) # 获取配置文件夹 print(mpl.matplotlib_fname()) # 获取当前使用的配置文件路径 mpl.rcParams['font.family'] = 'SimHei' # 修改字体支持中文，解决中文乱码 mpl.rcParams['backend'] = 'TkAgg' # 修改后端，解决Qt5平台错误 mpl.rcParams['axes.unicode_minus'] = False # 解决负号显示为方块 ''' ''' 上述的配置只在当前执行文件中生效，可以直接在配置文件修改，永久生效 Anaconda3\Lib\site-packages\matplotlib\mpl-data\matplotlibrc # matplotlib默认配置文件，修改配置后永久有效 > font.family : SimHei # 解决中文乱码 > backend : TkAgg # 解决 Qt5 平台报错 > axes.unicode_minus : False # 解决负号是方块 mpl.rc_params() # 重新读取配置，以字典形式返回 ''' figure = plt.figure() # 创建画布 ''' # 创建坐标系，参数表示画布中可以放置2行3列坐标系，即6个坐标系，当前选用的是第1个 axes_plot = figure.add_subplot(2, 3, 1) # 可缩写为axes_plot = figure.add_subplot(231) axes_plot.plot([1,2,3,4], [1,2,3,4]) ''' ''' # 直接使用plt及逆行绘制图形
import matplotlib
import os
import pylab
# 获得用户配置路径
print("the user config is ", matplotlib.get_configdir())
# 获得当前配置文件路径
print("the current config is ", matplotlib.matplotlib_fname())
# 获得当前文件路径
print(os.getcwd())
# rc_params返回一个配置字典
print("the return of rc_params are \n", matplotlib.rc_params())
# 使用matplotlib时会自动调用rc_params,并将返回的配置字典保存在rcparams中
print("the rcparams are \n", matplotlib.rcParams)
# 修改配置(绘制的线带有点标识符)
pylab.figure()
matplotlib.rcParams["lines.marker"] = 'o'
pylab.plot([1, 2, 3])
# 使用rc简便地修改配置
pylab.figure()
# 这里设置颜色失败，不知道是什么情况
matplotlib.rc("lines", color="red", marker="x", linewidth=2)
pylab.plot([1, 2, 3])
# 恢复缺省配置
pylab.figure()
matplotlib.rcdefaults()
pylab.plot([1, 2, 3])
pylab.legend()
pylab.show()
'patch.antialiased': True,
'patch.edgecolor': '#EEEEEE',
'patch.facecolor': '#348ABD',
'patch.linewidth': 0.5,
'toolbar': 'toolbar2',
'xtick.color': '#555555',
'xtick.direction': 'in',
'xtick.major.pad': 6.0,
'xtick.major.size': 0.0,
'xtick.minor.pad': 6.0,
'xtick.minor.size': 0.0,
'ytick.color': '#555555',
'ytick.direction': 'in',
'ytick.major.pad': 6.0,
'ytick.major.size': 0.0,
'ytick.minor.pad': 6.0,
'ytick.minor.size': 0.0
}
import numpy as np
import matplotlib as mpl
import matplotlib.pylab as pl
mpl.rc_params().update(mpl_stylesheet)
xs, ys = np.random.uniform(0,1,size=(2,30))
pl.scatter(xs, ys)
pl.show()
#
# You should have received a copy of the GNU General Public License
# along with GWpy. If not, see <http://www.gnu.org/licenses/>.
"""Custom default figure configuration
"""
from matplotlib import (rcParams, rc_params, RcParams,
__version__ as mpl_version)
from matplotlib.figure import SubplotParams
from . import tex
from ..utils.env import bool_env
# record matplotlib's original rcParams
MPL_RCPARAMS = rc_params()
__author__ = "Duncan Macleod <*****@*****.**>"
# -- custom rc ----------------------------------------------------------------
# set default params
GWPY_RCPARAMS = RcParams(**{
# axes boundary colours
'axes.edgecolor': 'gray',
# grid
'axes.grid': True,
'axes.axisbelow': False,
'grid.linewidth': .5,
# ticks
'axes.formatter.limits': (-3, 4),
'xtick.major.size':
0.0,
'xtick.minor.pad':
6.0,
'xtick.minor.size':
0.0,
'ytick.color':
'#555555',
'ytick.direction':
'in',
'ytick.major.pad':
6.0,
'ytick.major.size':
0.0,
'ytick.minor.pad':
6.0,
'ytick.minor.size':
0.0
}
import numpy as np
import matplotlib as mpl
import matplotlib.pylab as pl
mpl.rc_params().update(mpl_stylesheet)
xs, ys = np.random.uniform(0, 1, size=(2, 30))
pl.scatter(xs, ys)
pl.show()
'figure.facecolor': '#161616',
'savefig.edgecolor': 'black',
'savefig.facecolor': 'black',
}
Themes = OrderedDict()
for tname in ('light', 'dark', 'matplotlib', 'seaborn', 'ggplot', 'bmh',
'fivethirtyeight', 'grayscale', 'dark_background',
'tableau-colorblind10', 'seaborn-bright',
'seaborn-colorblind', 'seaborn-dark', 'seaborn-darkgrid',
'seaborn-dark-palette', 'seaborn-deep', 'seaborn-notebook',
'seaborn-muted', 'seaborn-pastel', 'seaborn-paper',
'seaborn-poster', 'seaborn-talk', 'seaborn-ticks',
'seaborn-white', 'seaborn-whitegrid', 'Solarize_Light2'):
theme = rc_params()
theme['backend'] = 'WXAgg'
if tname == 'matplotlib':
pass
elif tname == 'light':
theme.update(light_theme)
elif tname == 'dark':
theme.update(light_theme)
theme.update(dark_theme)
elif tname in matplotlib.style.library:
if tname.startswith('seaborn-'):
theme.update(matplotlib.style.library['seaborn'])
theme.update(matplotlib.style.library[tname])
Themes[tname.lower()] = theme
def bool_ifnotNone(val, default):
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with GWpy. If not, see <http://www.gnu.org/licenses/>. """This module provides plotting utilities for visualising GW data The standard data types (`TimeSeries`, `Table`, `DataQualityFlag`, ...) can all be easily visualised using the relevant plotting objects, with many configurable parameters both interactive, and in saving to disk. """ from matplotlib import (rcParams, rc_params, pyplot) DEFAULT_RCPARAMS = rc_params() __author__ = "Duncan Macleod <*****@*****.**>" from .tex import (USE_TEX, MACROS) from .gps import * from .log import * from .core import * from .timeseries import * from .spectrogram import * from .frequencyseries import * from .segments import * from .filter import * from .table import * from .histogram import *
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Jan 26 20:45:41 2019
@author: xsxsz
"""
import matplotlib
import matplotlib.pyplot as plt
print(matplotlib.get_backend())
print('-------------------')
print(matplotlib.get_configdir())
print('-------------------')
print(matplotlib.get_cachedir())
print('-------------------')
print(matplotlib.get_data_path())
print('-------------------')
print(matplotlib.matplotlib_fname())
print('-------------------')
print(matplotlib.rc_params())
print('-------------------')
from networkx.readwrite import json_graph
import json
import sys
print "nx version : " + nx.__version__
###############################################
import matplotlib
print matplotlib.rc.func_globals['defaultParams']
matplotlib.get_backend()
#matplotlib.rcdefaults() # set params back to defaults.
#print matplotlib.matplotlib_fname()
#matplotlib.rcsetup
dict_of_rcParam = matplotlib.rc_params()
img_background = 'achzive_1.jpg'
img_background = 'cartoon-funny-dinosaur.jpg'
bkImg = Image.open(img_background)
#bkImg.show() opens system image viewer
plt.imshow(bkImg)
plt.close()
###############################################
if __name__ == '__main__':
doc = minidom.parse("./example.xml")
output_rel_path = "../runtime_outputs/"
else:
doc = minidom.parse("./specific_files/example.xml")
# Homework 3 Model Comparisons
# Sean Thatcher
# Environmental Informatics
# Instructor Dr. Rodger Wang
# Install packages
import matplotlib # Data Visualizations
import matplotlib.pyplot as plt
matplotlib.rc_params().update({'font.size': 22})
# Data being used
# Values being hard coded because there is only 1 model value
# and 1 observation value from the gage at each stage.
# The model values were computed in a spreadsheet from model outputs.
# The values and calculations can be found in the HAND Data for Stations spreadshet.
# Values from gauges were obtained from the USGS and a stage of the same/similar
# stage event modeled were selected for comparison.
# Green Brook at Burnt Mills NJ
# Station number 1403400
green_brook_1m_gauge = 0.427078125
green_brook_1m_model = 2.030675754
green_brook_3m_gauge = 91.414
green_brook_3m_model = 37.71595379
# Green Brook at Seeley Mills NJ
# Station number 1401650
pike_run_3m_gauge = 2.126028046
# 5. 显示图形
plt.show()
# !!! 配置
print(mpl.rcParams) # 获取全部配置
print(mpl.rcParams['font.family']) # 获取字体
#mpl.rcParams['font.family'] = 'SimHei' # 设置字体
print(mpl.rcParams['backend'])
#mpl.rcParams['backend'] = 'TkAgg' # Qt5
print(mpl.rcParams['axes.unicode_minus'])
#mpl.rcParams['axes.unicode_minus'] = False
print(mpl.matplotlib_fname()) # 获取配置文件路径
mpl.rc_params() # 重新加载配置
# 折线图
# 创建画布
fig = plt.figure(figsize=(8, 6))
# 创建一个坐标系
axes1 = fig.add_subplot(1, 1, 1)
# 绘制图形
plot_x = np.linspace(-5, 5, 20)
plot_y = plot_x**2
#plot_x_2 = np.linspace(-5, 5, 100)
plot_y_2 = plot_x * 2
axes1.plot(plot_x, plot_y, '--c', label='y')
axes1.plot(plot_x, plot_y_2, label='y=x*2')
axes1.set_title('曲线') # 设置标题
axes1.set_xlabel('x 轴') # 设置x轴名称
# but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with GWpy. If not, see <http://www.gnu.org/licenses/>. """This module provides plotting utilities for visualising GW data The standard data types (`TimeSeries`, `Table`, `DataQualityFlag`, ...) can all be easily visualised using the relevant plotting objects, with many configurable parameters both interactive, and in saving to disk. """ from matplotlib import (rcParams, rc_params, pyplot) DEFAULT_RCPARAMS = rc_params() __author__ = "Duncan Macleod <*****@*****.**>" from .tex import (USE_TEX, MACROS) from .gps import * from .log import * from .core import * from .timeseries import * from .spectrogram import * from .frequencyseries import * from .segments import * from .filter import * from .table import * from .histogram import *
from tqdm import tqdm # smart progress bar
from PIL import Image
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib import rc_params
import pandas as pd
import numpy as np
#skimage features and filters
from skimage.feature import daisy
from skimage.feature import hog
from skimage.color import rgb2gray
from skimage.exposure import equalize_hist
# get our styles
mpl_default = rc_params()
import seaborn as sns
# for ipython notebook %matplotlib inline
plt.rcParams = mpl_default
# BEES colormap! Black -> Yellow
CMAP = [(0, 0, 0), (22, 0, 0), (43, 0, 0), (77, 14, 0), (149, 68, 0),
(220, 123, 0), (255, 165, 0), (255, 192, 0), (255, 220, 0),
(255, 235, 0), (255, 245, 0), (255, 255, 0)]
bees_cm = mpl.colors.ListedColormap(np.array(CMAP) / 255.)
# load the labels using pandas
labels = pd.read_csv("data/train_labels.csv", index_col=0)
from tqdm import tqdm # smart progress bar
from PIL import Image
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib import rc_params
import pandas as pd
import numpy as np
#skimage features and filters
from skimage.feature import daisy
from skimage.feature import hog
from skimage.color import rgb2gray
from skimage.exposure import equalize_hist
# get our styles
mpl_default = rc_params()
import seaborn as sns
# for ipython notebook %matplotlib inline
plt.rcParams = mpl_default
# BEES colormap! Black -> Yellow
CMAP = [(0,0,0),
(22,0,0),
(43,0,0),
(77,14,0),
(149,68,0),
(220,123,0),
(255,165,0),
(255,192,0),
ax2 = plt.subplot2grid((3, 3), (0, 2), rowspan=2)
ax3 = plt.subplot2grid((3, 3), (1, 0), rowspan=2)
ax4 = plt.subplot2grid((3, 3), (2, 1), colspan=2)
ax5 = plt.subplot2grid((3, 3), (1, 1))
plt.show()
# 配置文件 可以使用多个matplotlibrc配置文件，顺序靠前的配置文件会被优先采用
# 搜索顺序如下：当前路径-->用户配置路径(通常在.matplotlib目录下)-->系统配置路径
# 获取用户配置路径
from os import path
import matplotlib
# 获取当前配置路径
current_path = path.abspath(matplotlib.matplotlib_fname())
print(current_path)
# 读取配置文件配置内容， 本质上是一个字典
print(matplotlib.rc_params())
# 修改配置信息
matplotlib.rc("lines", marker="x", lw=2, color="red")
# 配置恢复默认
matplotlib.rcdefaults()
# 手工修改配置后，重新载入最新配置
matplotlib.rcParams.update(matplotlib.rc_params())
# 获取用户配置路径
user_path = path.abspath(matplotlib.get_configdir())
print(user_path)
# 样式切换 matplotlib.style
for (n, e), j in zip(exps, jobs):
e.save_serialized_parameters(j.config)
elif ACTION == 'run':
pool.run()
if LAUNCHER == 'process' or args.watch:
print_refreshed_stats()
elif ACTION == 'status':
if args.watch:
print_refreshed_stats()
else:
print_stats()
elif ACTION == 'plot':
import matplotlib
# Set matplotlib config
if args.plot_config is None:
plot_params = matplotlib.rc_params()
plot_params.update(DEFAULT_PLOT_PARAMS)
else:
plot_params = matplotlib.rc_params_from_file(args.plot_config,
fail_on_error=True)
plot_params['interactive'] = not args.no_plot
matplotlib.rcParams = plot_params
assert(plot_params == matplotlib.rcParams)
assert(plot_params is matplotlib.rcParams)
from multimodal.plots import (plot_2k_graphs, plot_boxes,
plot_boxes_by_feats, plot_boxes_all_mods)
LOGGERS_2 = collect_results()
LOGGERS_3 = collect_results3()
LOGGERS_IMAGE = collect_results_image()
plot_params = DEFAULT_PLOT_PARAMS
fig_2k = plot_2k_graphs([LOGGERS_2, LOGGERS_3], Ks,
import matplotlib
GUI_RC_PARAMS = matplotlib.rc_params()
GUI_RC_PARAMS.update({
'axes.titlesize': 'medium',
'figure.autolayout': True,
'legend.fontsize': 'small',
'legend.framealpha': 0.5,
'lines.linewidth': 1.0,
})
# -*- coding: utf-8 -*-
"""
Created on Sun Jun 9 17:56:50 2019
@author: user
"""
import matplotlib.pyplot as plt
import matplotlib as mpl
import numpy as np
print(mpl.rc_params()) # shows the default parameters
# This update will only be in this program and printing the mpl.rc_params() will not show the updated values
mpl.rcParams.update({
'font.size': 18,
'font.family': 'STIXGeneral',
'mathtext.fontset': 'stix'
})
# To Go back to the default settings
#mpl.rcParams.update(mpl.rcParamsDefault)
# Print the plot atyles available
print(plt.style.available)
def plotsincosin(style='ggplot'):
plt.style.use(style)
plt.figure(figsize=(7, 4), dpi=80)
x = np.linspace(0, 2 * np.pi, 1000)
plt.plot(x, np.sin(x))
plt.plot(x, np.cos(x))
plt.xticks(
ticks=np.arange(0, 440 / 57.2985, 90 / 57.2985),
labels=[r'$0$', r'$90$', r'$\pi$', r'$\frac{3\pi}{2}$', r'$2\pi$'])
def plot_3d(series, colormap_callable=plt.cm.viridis, include_colorbar=False):
"""Create a 3d-barchart axes for a given 2-level-multi-index series.
Return a 3d axes object given a series with a multiindex with 2
categorical levels.
Arguments:
series (pandas.Series): the 2-level-index series to generate the plot.
colormap_callable (matplotlib.colors.ListedColormap): Colormap object
generated from a list of colors.
include_colorbar (bool): True to include a colorbar.
Returns:
matplotlib.axes.Axes: the 3d axis object.
Examples:
>>> import itertools
>>> import pandas as pd
>>> fig = plt.figure()
>>> s_index = pd.MultiIndex.from_tuples( \
tuple(itertools.product(range(6), list('abc'))), \
names=('x1', 'x2'))
>>> s = pd.Series(data=np.arange(18), index=s_index)
>>> ax = plot_3d(s, include_colorbar=True)
>>> fig.tight_layout()
>>> fig.savefig('/tmp/{0}.png'.format('plot3d'), dpi=500)
"""
# Create a copy of the list to avoid changing the original.
series = series.copy(deep=True)
series.sort_values(inplace=True, ascending=False)
# Set constants.
# Some groupby objects will produce a dataframe. Not nice going over duck
# typing but oh well...
# If it is a dataframe with one column then transform it to series.
if isinstance(series, pd.DataFrame) and series.shape[1] == 1:
series = series.ix[:, 0]
# Error handling phase.
# Track if index has correct shape.
if len(series.index.levshape) != 2:
raise ValueError('The index level shape should '
'be 2 and it is {}.'.format(series.index.levshape))
# Check for duplicate indexes.
if series.index.duplicated().sum():
series = series.groupby(level=series.index.names).sum()
if series.index.duplicated().sum():
raise ValueError('series has duplicate values.')
# Handling the index of the received series.
level1_index, level2_index = tuple(zip(*series.index.get_values()))
level1_index = sorted(set(level1_index))
level2_index = sorted(set(level2_index))
# Populate the series with all index combinations, even if they are zero.
all_index_combinations = tuple(
itertools.product(level1_index, level2_index))
index_names = series.index.names
new_index = pd.MultiIndex.from_tuples(all_index_combinations,
names=index_names)
all_values_series = pd.Series(0, index=new_index, name=series.name)
series = (series + all_values_series).fillna(0)
# Generate the z values
z_list = []
for _, group in series.groupby(level=1):
z_list.append(group)
z_list = np.hstack(z_list).ravel()
z = z_list
del z_list
# Starts manipulating the axes
fig = plt.gcf()
ax = fig.add_subplot(111, projection='3d')
# Create the axis and their labels
xlabels = series.index.get_level_values(index_names[0]).unique().tolist()
ylabels = series.index.get_level_values(index_names[1]).unique().tolist()
xlabels = [
''.join(list(filter(str.isalnum, str(value)))) for value in xlabels
]
ylabels = [
''.join(list(filter(str.isalnum, str(value)))) for value in ylabels
]
x = np.arange(len(xlabels))
y = np.arange(len(ylabels))
xlabels = [z.title() for z in xlabels]
ylabels = [z.title() for z in ylabels]
# Adjust tick posistions and labels.
ax.w_xaxis.set_ticks(x + 0.5 / 2.)
ax.w_yaxis.set_ticks(y + 0.5 / 2.)
ax.w_yaxis.set_ticklabels(ylabels)
# Color.
pp_color_values = sklearn.preprocessing.minmax_scale(z)
colormap = colormap_callable(pp_color_values)
# Create the 3d plot.
x_mesh, y_mesh = np.meshgrid(x, y, copy=False)
ax.bar3d(x_mesh.ravel(),
y_mesh.ravel(),
z * 0,
dx=0.5,
dy=0.5,
dz=z,
color=colormap)
# Set convenient z limits.
# From z ticks make it include all extreme values in excess of 0.5 tick.
z_min = z.min()
z_max = z.max()
z_ticks = ax.get_zticks()
z_stride = z_ticks[1] - z_ticks[0]
z_min_lim = z_min - 0.5 * z_stride
z_max_lim = z_max + 0.5 * z_stride
if 0 < z_min_lim:
z_min_lim = 0
elif 0 > z_max_lim:
z_max_lim = 0
ax.set_zlim3d(z_min_lim, z_max_lim)
if include_colorbar:
scalar_mappable = plt.cm.ScalarMappable(cmap=colormap_callable)
scalar_mappable.set_array([min(z), max(z)])
scalar_mappable.set_clim(vmin=min(z), vmax=max(z))
ticks = np.linspace(z_min, z_max, 5)
colorbar = fig.colorbar(scalar_mappable, drawedges=True, ticks=ticks)
# Add border to the colorbar.
colorbar.outline.set_visible(True)
colorbar.outline.set_edgecolor('black')
mpl_params = matplotlib.rc_params()
colorbar.outline.set_linewidth(mpl_params['lines.linewidth'])
# Add ticks to the colorbar.
colorbar.ax.yaxis.set_tick_params(
width=mpl_params['ytick.major.width'],
size=mpl_params['ytick.major.size'])
return ax
from networkx.readwrite import json_graph
import json
print ("nx version : " + nx.__version__)
###############################################
matplotlib.use('WXAgg')
import matplotlib
#print (matplotlib.rc.func_globals['defaultParams'])
print (matplotlib.rc.__globals__['defaultParams'])
matplotlib.get_backend()
#matplotlib.rcdefaults() # set params back to defaults.
print (matplotlib.matplotlib_fname())
#matplotlib.rcsetup
matplotlib.rc_params()
###############################################
if __name__=='__main__':
doc = minidom.parse("./example.xml")
output_rel_path = "./runtime_outputs/"
else:
doc = minidom.parse("./specific_files/example.xml")
output_rel_path = "./runtime_outputs/"
draw_edges_labels=False#True
''' generate the graph '''
# code ref: https://networkx.github.io/documentation/networkx-1.10/reference/classes.multidigraph.html
G = nx.MultiDiGraph(name="Simulated System Graph")
G_wVirtualEdges = nx.DiGraph(name="Graph with added virtual edges nodes")
#
# You should have received a copy of the GNU General Public License
# along with GWpy. If not, see <http://www.gnu.org/licenses/>.
"""Custom default figure configuration
"""
import os
from matplotlib import (rcParams, rc_params, __version__ as mpl_version)
from matplotlib.figure import SubplotParams
from . import tex
from .colors import DEFAULT_COLORS
# record matplotlib's original rcParams
MPL_RCPARAMS = rc_params()
__author__ = "Duncan Macleod <*****@*****.**>"
# -- custom rc ----------------------------------------------------------------
# set default params
DEFAULT_PARAMS = {
# axes boundary colours
'axes.edgecolor': 'gray',
# grid
'axes.grid': True,
'axes.axisbelow': False,
'grid.linestyle': ':',
'grid.linewidth': .5,
# ticks
matplotlib.get_backend() class matplotlib.RcParams(*args, **kwargs) # класс для хранения параметров copy() find_all(pattern) validate # словарь с функциями валидации matplotlib.rcParams # текущие параметры matplotlib.rc_context(rc=None, fname=None) # with rc_context(...) строим график matplotlib.rc(group, **kwargs) # устанавливает rc параметры matplotlib.rc_file(fname) # устанавливает параметры из файла matplotlib.rcdefaults() # устанавливает параметры по умолчанию matplotlib.rc_file_defaults() # устанавливает параметры из rc файла по умолчанию matplotlib.rc_params(fail_on_error=False) # возвращает параметры из rc файла по умолчанию matplotlib.rc_params_from_file(fname, fail_on_error=False, use_default_template=True) # matplotlib.matplotlib_fname() # путь к файлу с конфигами matplotlib.interactive(b) # устанавливает интерактивность matplotlib.is_interactive() # проверяет интерактивность #################### module style import matplotlib.style matplotlib.style.context(style, after_reset=False) matplotlib.style.reload_library() matplotlib.style.use(style) matplotlib.style.library # словарь доступных стилей matplotlib.style.available # список доступных стилей
def test_plot_matplotlib_rc_restore(self, maps_release_only):
rcparams = matplotlib.rc_params()
map_ = maps_release_only.getMap('emline_gflux', channel='ha_6564')
fig, ax = mapplot.plot(dapmap=map_)
assert rcparams == matplotlib.rc_params()
d_colors = {
"1": "darksalmon",
"2": "royalblue",
"3": "mediumpurple",
"4": "green",
"5": "tan",
"6": "brown",
"7": "pink",
"8": "olive",
"9": "peru",
"10": "orange",
"11": "darkkhaki",
"12": "cadetblue",
"13": "crimson",
"14": "thistle"
}
# Optimal Bandwidths
bw = np.array([1577.681, 1167.16, 30.549])
bw_stkde = np.array([
7859.14550575 / 10,
10910.84908688 / 10,
24.38113667
])
if __name__ == '__main__':
import matplotlib as mpl
mpl.rcParams.update(rc)
print(mpl.rc_params())
