├── lib ├── taydiag.egg-info │ ├── top_level.txt │ ├── dependency_links.txt │ ├── SOURCES.txt │ └── PKG-INFO └── taylor_diag.py ├── example.png ├── example2.png ├── dist ├── taydiag-0.0.1.0-py3.6.egg ├── taydiag-0.0.1.1-py3.6.egg └── taydiag-0.0.1.2-py3.6.egg ├── __pycache__ └── taylor_diag.cpython-36.pyc ├── .ipynb_checkpoints └── Example-checkpoint.ipynb ├── __init__.py ├── setup.py ├── README.md ├── taylor_diag.py ├── LICENSE └── Example.ipynb /lib/taydiag.egg-info/top_level.txt: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /lib/taydiag.egg-info/dependency_links.txt: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /example.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mabelcalim/Taylor_diagram/HEAD/example.png -------------------------------------------------------------------------------- /example2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mabelcalim/Taylor_diagram/HEAD/example2.png -------------------------------------------------------------------------------- /dist/taydiag-0.0.1.0-py3.6.egg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mabelcalim/Taylor_diagram/HEAD/dist/taydiag-0.0.1.0-py3.6.egg -------------------------------------------------------------------------------- /dist/taydiag-0.0.1.1-py3.6.egg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mabelcalim/Taylor_diagram/HEAD/dist/taydiag-0.0.1.1-py3.6.egg -------------------------------------------------------------------------------- /dist/taydiag-0.0.1.2-py3.6.egg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mabelcalim/Taylor_diagram/HEAD/dist/taydiag-0.0.1.2-py3.6.egg -------------------------------------------------------------------------------- /__pycache__/taylor_diag.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mabelcalim/Taylor_diagram/HEAD/__pycache__/taylor_diag.cpython-36.pyc -------------------------------------------------------------------------------- /.ipynb_checkpoints/Example-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [], 3 | "metadata": {}, 4 | "nbformat": 4, 5 | "nbformat_minor": 2 6 | } 7 | -------------------------------------------------------------------------------- /lib/taydiag.egg-info/SOURCES.txt: -------------------------------------------------------------------------------- 1 | setup.py 2 | lib/taydiag.egg-info/PKG-INFO 3 | lib/taydiag.egg-info/SOURCES.txt 4 | lib/taydiag.egg-info/dependency_links.txt 5 | lib/taydiag.egg-info/top_level.txt -------------------------------------------------------------------------------- /lib/taydiag.egg-info/PKG-INFO: -------------------------------------------------------------------------------- 1 | Metadata-Version: 1.1 2 | Name: taydiag 3 | Version: 0.0.1.2 4 | Summary: Taylor diagram in python 5 | Home-page: https://github.com/mabelcalim/Taylor_diagram 6 | Author: Mabel Calim Costa 7 | Author-email: mabelcalim@gmail.com 8 | License: UNKNOWN 9 | Description: UNKNOWN 10 | Platform: UNKNOWN 11 | Classifier: License :: GNU version 3 12 | -------------------------------------------------------------------------------- /__init__.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Thu Nov 6 2014 4 | 5 | @author: Mabel Calim Costa 6 | """ 7 | 8 | from __future__ import absolute_import 9 | 10 | from .lib.taylor_diag import load_nc 11 | from .lib.taylor_diag import Taylor_diag 12 | 13 | 14 | 15 | # Define the objects imported by imports of the form: from pyclimatetools import * 16 | __all__ = ['load_nc', 'Taylor_diag'] 17 | 18 | # Package version number. 19 | __version__ = '0.0.1.2' 20 | -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Mon Jun 17 14:03 2013 4 | 5 | @author: Mabel Calim Costa 6 | """ 7 | 8 | import os 9 | from setuptools import setup 10 | #from distutils.core import setup 11 | 12 | for line in open('__init__.py').readlines(): 13 | if line.startswith('__version__'): 14 | exec(line.strip()) 15 | 16 | setup( 17 | name = "taydiag", 18 | description = ("Taylor diagram in python"), 19 | version=__version__, 20 | author='Mabel Calim Costa', 21 | author_email='mabelcalim@gmail.com', 22 | #url='https://wavelet-analysis.readthedocs.org/en/latest/index.html', 23 | url = 'https://github.com/mabelcalim/Taylor_diagram', 24 | #packages=['' ], 25 | package_dir={'':'lib'}, 26 | classifiers=[ 27 | 'License :: GNU version 3'], 28 | ) 29 | 30 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | Taylor_diagram 2 | ============== 3 | 4 | Taylor diagram in python based on Taylor (2001). 5 | 6 | 7 | ![ScreenShot](https://github.com/mabelcalim/Taylor_diagram/blob/master/example2.png) 8 | 9 | Installation 10 | ============ 11 | 12 | First: git clone git@github.com:mabelcalim/Taylor_diagram.git 13 | 14 | Then ... 15 | 16 | A local install can be done using the provided setup.py file: 17 | 18 | python setup.py install 19 | 20 | The installation path can be changed using the **--prefix** switch, e.g.: 21 | 22 | python setup.py install --prefix $HOME/inst 23 | 24 | Make sure to add the corresponding paths to your ``$PATH`` and ``$PYTHONPATH`` 25 | environment variables. Alternatively, if the **--user** switch can be used: 26 | 27 | export PYTHONUSERBASE=$HOME/inst/pip_installs 28 | export PYTHONPATH=$HOME/inst/pip_installs/lib/python2.7/\ 29 | site-packages/:$PYTHONPATH 30 | export PATH=$HOME/inst/pip_installs/bin:$PATH 31 | python setup.py install --user 32 | 33 | If you plan on modifying the code, use the **develop** target in combination 34 | with the **--user** swich: 35 | 36 | export PYTHONUSERBASE=$HOME/inst/pip_installs 37 | export PYTHONPATH=$HOME/inst/pip_installs/lib/python2.7/\ 38 | site-packages/:$PYTHONPATH 39 | export PATH=$HOME/inst/pip_installs/bin:$PATH 40 | python setup.py develop --user 41 | 42 | The first three lines should also be included in the **$HOME/.bashrc** file. 43 | 44 | Cookbook 45 | ========= 46 | 47 | example prêt-à-porter.ipynb 48 | 49 | Support Group 50 | ============= 51 | 52 | Any doubt ... talk to me : 53 | 54 | https://groups.google.com/forum/?hl=en#!forum/taylor-diag-users-support 55 | mabel.calim@gmail.com 56 | 57 | -------------------------------------------------------------------------------- /taylor_diag.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python 2 | # _*_ coding: latin-1 -*- 3 | 4 | import numpy as np 5 | from numpy import ma 6 | import mpl_toolkits.axisartist.grid_finder as GF 7 | import mpl_toolkits.axisartist.floating_axes as FA 8 | import matplotlib.pyplot as plt 9 | import netCDF4 10 | 11 | def load_nc(file,var): 12 | """ 13 | Open ARCHIVE .nc 14 | file = archive.nc 15 | var = variable from archive.nc 16 | """ 17 | f = netCDF4.Dataset(file,'r+') 18 | dara = f.variables[var][:] 19 | f.close() 20 | return data 21 | 22 | 23 | def Taylor_diag(series,names): 24 | """ Taylor Diagram : obs is reference data sample 25 | in a full diagram (0 --> npi) 26 | -------------------------------------------------------------------------- 27 | Input: series - dict with all time series (lists) to analyze 28 | series[0] - is the observation, the reference by default. 29 | """ 30 | corr,std ={},{} 31 | for i in series.keys(): 32 | corr[i] = ma.corrcoef(series[0],series[i])[1,0] 33 | std[i] = ma.std(series[i])/ma.std(series[0]) 34 | 35 | ref = 1# ma.std(series[0]) 36 | #print corr 37 | 38 | rlocs = np.concatenate((np.arange(0,-10,-0.25),[-0.95,-0.99],np.arange(0,10,0.25),[0.95,0.99])) 39 | str_rlocs = np.concatenate((np.arange(0,10,0.25),[0.95,0.99],np.arange(0,10,0.25),[0.95,0.99])) 40 | tlocs = np.arccos(rlocs) # Conversion to polar angles 41 | gl1 = GF.FixedLocator(tlocs) # Positions 42 | tf1 = GF.DictFormatter(dict(zip(tlocs, map(str,rlocs)))) 43 | 44 | 45 | str_locs2 = np.arange(-10,11,0.5) 46 | tlocs2 = np.arange(-10,11,0.5) # Conversion to polar angles 47 | 48 | g22 = GF.FixedLocator(tlocs2) 49 | tf2 = GF.DictFormatter(dict(zip(tlocs2, map(str,str_locs2)))) 50 | 51 | 52 | 53 | 54 | tr = PolarAxes.PolarTransform() 55 | 56 | smin = 0 57 | smax = 2.5 58 | 59 | ghelper = FA.GridHelperCurveLinear(tr, 60 | extremes=(0,np.pi, # 1st quadrant 61 | smin,smax), 62 | grid_locator1=gl1, 63 | #grid_locator2=g11, 64 | tick_formatter1=tf1, 65 | tick_formatter2=tf2, 66 | ) 67 | fig = plt.figure(figsize=(10,5), dpi=100) 68 | ax = FA.FloatingSubplot(fig, 111, grid_helper=ghelper) 69 | 70 | fig.add_subplot(ax) 71 | ax.axis["top"].set_axis_direction("bottom") 72 | ax.axis["top"].toggle(ticklabels=True, label=True) 73 | ax.axis["top"].major_ticklabels.set_axis_direction("top") 74 | ax.axis["top"].label.set_axis_direction("top") 75 | ax.axis["top"].label.set_text("Correlation Coefficient") 76 | 77 | ax.axis["left"].set_axis_direction("bottom") 78 | ax.axis["left"].label.set_text("Standard Deviation") 79 | 80 | ax.axis["right"].set_axis_direction("top") 81 | ax.axis["right"].toggle(ticklabels=True, label=True) 82 | ax.axis["right"].set_visible(True) 83 | ax.axis["right"].major_ticklabels.set_axis_direction("bottom") 84 | #ax.axis["right"].label.set_text("Standard Deviation") 85 | 86 | ax.axis["bottom"].set_visible(False) 87 | 88 | ax.grid(True) 89 | 90 | ax = ax.get_aux_axes(tr) 91 | 92 | t = np.linspace(0, np.pi) 93 | r = np.zeros_like(t) + ref 94 | ax.plot(t,r, 'k--', label='_') 95 | 96 | 97 | rs,ts = np.meshgrid(np.linspace(smin,smax), 98 | np.linspace(0,np.pi)) 99 | 100 | 101 | rms = np.sqrt(ref**2 + rs**2 - 2*ref*rs*np.cos(ts)) 102 | CS =ax.contour(ts, rs,rms,cmap=cm.bone) 103 | plt.clabel(CS, inline=1, fontsize=10) 104 | 105 | 106 | ax.plot(np.arccos(0.9999),ref,'k',marker='*',ls='', ms=10) 107 | aux = range(1,len(corr)) 108 | #del aux[ref] 109 | 110 | 111 | 112 | colors = plt.matplotlib.cm.jet(np.linspace(0,1,len(corr))) 113 | 114 | for i in aux: 115 | ax.plot(np.arccos(corr[i]), std[i],c=colors[i],alpha=0.7,ms=15,marker='o',label="%s" %names[i]) 116 | ax.text(np.arccos(corr[i]), std[i],"%s"%i) 117 | legend(bbox_to_anchor=(1.5, 1),prop=dict(size='large'),loc='best') 118 | plt.savefig('example.png', dpi=300) 119 | return 120 | -------------------------------------------------------------------------------- /lib/taylor_diag.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python 2 | # _*_ coding: latin-1 -*- 3 | # Taylor Diagram - Based on Taylor (2001) - Journal Geophysical Research 4 | # author: Mabel Calim Costa 5 | # GMAO - INPE 6 | # 20/02/2018 7 | 8 | import numpy as np 9 | from numpy import ma 10 | import mpl_toolkits.axisartist.grid_finder as GF 11 | import mpl_toolkits.axisartist.floating_axes as FA 12 | import matplotlib.pyplot as plt 13 | import netCDF4 14 | from matplotlib.projections import PolarAxes 15 | 16 | def load_nc(file,var): 17 | """ 18 | Open ARCHIVE .nc 19 | file = archive.nc 20 | var = variable from archive.nc 21 | """ 22 | f = netCDF4.Dataset(file,'r+') 23 | data = f.variables[var][:] 24 | f.close() 25 | return data 26 | 27 | 28 | def Taylor_diag(series,names): 29 | """ Taylor Diagram : obs is reference data sample 30 | in a full diagram (0 --> npi) 31 | -------------------------------------------------------------------------- 32 | Input: series - dict with all time series (lists) to analyze 33 | series[0] - is the observation, the reference by default. 34 | """ 35 | from matplotlib.projections import PolarAxes 36 | corr,std ={},{} 37 | for i in series.keys(): 38 | corr[i] = ma.corrcoef(series[0],series[i])[1,0] 39 | std[i] = ma.std(series[i])/ma.std(series[0]) 40 | 41 | ref = 1# ma.std(series[0]) 42 | #print corr 43 | 44 | rlocs = np.concatenate((np.arange(0,-10,-0.25),[-0.95,-0.99],np.arange(0,10,0.25),[0.95,0.99])) 45 | str_rlocs = np.concatenate((np.arange(0,10,0.25),[0.95,0.99],np.arange(0,10,0.25),[0.95,0.99])) 46 | tlocs = np.arccos(rlocs) # Conversion to polar angles 47 | gl1 = GF.FixedLocator(tlocs) # Positions 48 | tf1 = GF.DictFormatter(dict(zip(tlocs, map(str,rlocs)))) 49 | 50 | 51 | str_locs2 = np.arange(-10,11,0.5) 52 | tlocs2 = np.arange(-10,11,0.5) # Conversion to polar angles 53 | 54 | g22 = GF.FixedLocator(tlocs2) 55 | tf2 = GF.DictFormatter(dict(zip(tlocs2, map(str,str_locs2)))) 56 | 57 | 58 | 59 | 60 | tr = PolarAxes.PolarTransform() 61 | 62 | smin = 0 63 | smax = 2.5 64 | 65 | ghelper = FA.GridHelperCurveLinear(tr, 66 | extremes=(0,np.pi, # 1st quadrant 67 | smin,smax), 68 | grid_locator1=gl1, 69 | #grid_locator2=g11, 70 | tick_formatter1=tf1, 71 | tick_formatter2=tf2, 72 | ) 73 | fig = plt.figure(figsize=(10,5), dpi=100) 74 | ax = FA.FloatingSubplot(fig, 111, grid_helper=ghelper) 75 | 76 | fig.add_subplot(ax) 77 | ax.axis["top"].set_axis_direction("bottom") 78 | ax.axis["top"].toggle(ticklabels=True, label=True) 79 | ax.axis["top"].major_ticklabels.set_axis_direction("top") 80 | ax.axis["top"].label.set_axis_direction("top") 81 | ax.axis["top"].label.set_text("Correlation Coefficient") 82 | 83 | ax.axis["left"].set_axis_direction("bottom") 84 | ax.axis["left"].label.set_text("Standard Deviation") 85 | 86 | ax.axis["right"].set_axis_direction("top") 87 | ax.axis["right"].toggle(ticklabels=True, label=True) 88 | ax.axis["right"].set_visible(True) 89 | ax.axis["right"].major_ticklabels.set_axis_direction("bottom") 90 | #ax.axis["right"].label.set_text("Standard Deviation") 91 | 92 | ax.axis["bottom"].set_visible(False) 93 | 94 | ax.grid(True) 95 | 96 | ax = ax.get_aux_axes(tr) 97 | 98 | t = np.linspace(0, np.pi) 99 | r = np.zeros_like(t) + ref 100 | ax.plot(t,r, 'k--', label='_') 101 | 102 | 103 | rs,ts = np.meshgrid(np.linspace(smin,smax), 104 | np.linspace(0,np.pi)) 105 | 106 | 107 | rms = np.sqrt(ref**2 + rs**2 - 2*ref*rs*np.cos(ts)) 108 | CS =ax.contour(ts, rs,rms,cmap=cm.bone) 109 | plt.clabel(CS, inline=1, fontsize=10) 110 | 111 | 112 | ax.plot(np.arccos(0.9999),ref,'k',marker='*',ls='', ms=10) 113 | aux = range(1,len(corr)) 114 | #del aux[ref] 115 | 116 | 117 | 118 | colors = plt.matplotlib.cm.jet(np.linspace(0,1,len(corr))) 119 | 120 | for i in aux: 121 | ax.plot(np.arccos(corr[i]), std[i],c=colors[i],alpha=0.7,marker='o',label="%s" %names[i]) 122 | ax.text(np.arccos(corr[i]), std[i],"%s"%i) 123 | legend(bbox_to_anchor=(1.5, 1),prop=dict(size='large'),loc='best') 124 | plt.savefig('example.png', dpi=500) 125 | return 126 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU LESSER GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | 9 | This version of the GNU Lesser General Public License incorporates 10 | the terms and conditions of version 3 of the GNU General Public 11 | License, supplemented by the additional permissions listed below. 12 | 13 | 0. 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Revised Versions of the GNU Lesser General Public License. 145 | 146 | The Free Software Foundation may publish revised and/or new versions 147 | of the GNU Lesser General Public License from time to time. Such new 148 | versions will be similar in spirit to the present version, but may 149 | differ in detail to address new problems or concerns. 150 | 151 | Each version is given a distinguishing version number. If the 152 | Library as you received it specifies that a certain numbered version 153 | of the GNU Lesser General Public License "or any later version" 154 | applies to it, you have the option of following the terms and 155 | conditions either of that published version or of any later version 156 | published by the Free Software Foundation. If the Library as you 157 | received it does not specify a version number of the GNU Lesser 158 | General Public License, you may choose any version of the GNU Lesser 159 | General Public License ever published by the Free Software Foundation. 160 | 161 | If the Library as you received it specifies that a proxy can decide 162 | whether future versions of the GNU Lesser General Public License shall 163 | apply, that proxy's public statement of acceptance of any version is 164 | permanent authorization for you to choose that version for the 165 | Library. 166 | 167 | -------------------------------------------------------------------------------- /Example.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "Based on Taylor (2001) - Journal Geophysical Research (http://onlinelibrary.wiley.com/doi/10.1029/2000JD900719/abstract)" 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": 14, 13 | "metadata": {}, 14 | "outputs": [], 15 | "source": [ 16 | "#!/usr/bin/python\n", 17 | "# _*_ coding: latin-1 -*-\n", 18 | "# Taylor Diagram - Based on Taylor (2001) - Journal Geophysical Research\n", 19 | "# author: Mabel Calim Costa\n", 20 | "# GMAO - INPE\n", 21 | "# 20/02/2018\n", 22 | "\n", 23 | "import numpy as np\n", 24 | "from numpy import ma\n", 25 | "import mpl_toolkits.axisartist.grid_finder as GF\n", 26 | "import mpl_toolkits.axisartist.floating_axes as FA\n", 27 | "import matplotlib.pyplot as plt\n", 28 | "import netCDF4" 29 | ] 30 | }, 31 | { 32 | "cell_type": "code", 33 | "execution_count": 15, 34 | "metadata": {}, 35 | "outputs": [], 36 | "source": [ 37 | " import taylor_diag" 38 | ] 39 | }, 40 | { 41 | "cell_type": "code", 42 | "execution_count": 16, 43 | "metadata": {}, 44 | "outputs": [ 45 | { 46 | "data": { 47 | "text/plain": [ 48 | "[]" 49 | ] 50 | }, 51 | "execution_count": 16, 52 | "metadata": {}, 53 | "output_type": "execute_result" 54 | }, 55 | { 56 | "data": { 57 | "image/png": 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\n", 58 | "text/plain": [ 59 | "" 60 | ] 61 | }, 62 | "metadata": {}, 63 | "output_type": "display_data" 64 | } 65 | ], 66 | "source": [ 67 | "import numpy as np\n", 68 | "from pylab import *\n", 69 | "z = np.linspace(0,2048,2048)\n", 70 | "x = np.sin(50*np.pi*z)\n", 71 | "y = np.cos(50*np.pi*z)\n", 72 | "x2 = 2*np.sin(50*np.pi*z)\n", 73 | "\n", 74 | "plot(x, 'k--')\n", 75 | "plot(x2)\n", 76 | "plot(y)\n" 77 | ] 78 | }, 79 | { 80 | "cell_type": "code", 81 | "execution_count": 17, 82 | "metadata": {}, 83 | "outputs": [], 84 | "source": [ 85 | "series ={}\n", 86 | "series[0] = x # the first term will be always the reference signal \n", 87 | "series[1]= x2\n", 88 | "series[2]= y\n" 89 | ] 90 | }, 91 | { 92 | "cell_type": "code", 93 | "execution_count": 21, 94 | "metadata": {}, 95 | "outputs": [ 96 | { 97 | "name": "stderr", 98 | "output_type": "stream", 99 | "text": [ 100 | "/Users/calim/Taylor_diagram/taylor_diag.py:40: RuntimeWarning: invalid value encountered in arccos\n", 101 | " tlocs = np.arccos(rlocs) # Conversion to polar angles\n" 102 | ] 103 | }, 104 | { 105 | "ename": "NameError", 106 | "evalue": "name 'PolarAxes' is not defined", 107 | "output_type": "error", 108 | "traceback": [ 109 | "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", 110 | "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", 111 | "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprojections\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mPolarAxes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mtaylor_diag\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTaylor_diag\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseries\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'x'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'x2'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'y'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", 112 | "\u001b[0;32m~/Taylor_diagram/taylor_diag.py\u001b[0m in \u001b[0;36mTaylor_diag\u001b[0;34m(series, names)\u001b[0m\n\u001b[1;32m 52\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 53\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 54\u001b[0;31m \u001b[0mtr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPolarAxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPolarTransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 55\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 56\u001b[0m \u001b[0msmin\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", 113 | "\u001b[0;31mNameError\u001b[0m: name 'PolarAxes' is not defined" 114 | ] 115 | } 116 | ], 117 | "source": [ 118 | "from matplotlib.projections import PolarAxes\n", 119 | "\n", 120 | "taylor_diag.Taylor_diag(series,['x','x2','y'])" 121 | ] 122 | }, 123 | { 124 | "cell_type": "code", 125 | "execution_count": 20, 126 | "metadata": {}, 127 | "outputs": [ 128 | { 129 | "name": "stdout", 130 | "output_type": "stream", 131 | "text": [ 132 | " Taylor Diagram : obs is reference data sample\n", 133 | " in a full diagram (0 --> npi)\n", 134 | " --------------------------------------------------------------------------\n", 135 | " Input: series - dict with all time series (lists) to analyze \n", 136 | " series[0] - is the observation, the reference by default.\n", 137 | " \n" 138 | ] 139 | } 140 | ], 141 | "source": [ 142 | "print (taylor_diag.Taylor_diag.__doc__)" 143 | ] 144 | }, 145 | { 146 | "cell_type": "code", 147 | "execution_count": null, 148 | "metadata": {}, 149 | "outputs": [], 150 | "source": [] 151 | } 152 | ], 153 | "metadata": { 154 | "kernelspec": { 155 | "display_name": "Python 3", 156 | "language": "python", 157 | "name": "python3" 158 | }, 159 | "language_info": { 160 | "codemirror_mode": { 161 | "name": "ipython", 162 | "version": 3 163 | }, 164 | "file_extension": ".py", 165 | "mimetype": "text/x-python", 166 | "name": "python", 167 | "nbconvert_exporter": "python", 168 | "pygments_lexer": "ipython3", 169 | "version": "3.6.2" 170 | } 171 | }, 172 | "nbformat": 4, 173 | "nbformat_minor": 2 174 | } 175 | --------------------------------------------------------------------------------