Archive plotting scripts

src/python/devel/variability_mode_nao/scripts/combine.py

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+# Combine multiple images into one.
+#
+# To install the Pillow module on Mac OS X:
+#
+# $ xcode-select --install
+# $ brew install libtiff libjpeg webp little-cms2
+# $ pip install Pillow
+#
+# Basement of this code is coming from https://gist.github.com/glombard/7cd166e311992a828675
+
+from __future__ import print_function
+from PIL import Image
+import os
+import math
+
+modes = ['NAO', 'NAM', 'SAM', 'PNA', 'PDO']
+
+#debug = True
+debug = False
+
+lr = False
+#lr = True
+
+pseudo = False
+pseudo = True
+
+if lr: 
+ ftail1 = '_lr'
+else:
+ ftail1 = ''
+
+if pseudo: 
+ ftail2 = '_pseudo'
+else: 
+ ftail2 = ''
+
+for mode in modes:
+
+ print(mode)
+ if mode == 'PDO': seasons = ['monthly']
+ else: seasons = ['DJF', 'MAM', 'JJA', 'SON']
+
+ for season in seasons:
+
+ # List up all image files ---
+ files = os.popen('ls ../'+mode+'/'+mode+'_*_eof1_'+season+'_*_1900-2005'+ftail1+ftail2+'.png').readlines()
+ files = sorted(files, key=lambda s:s.lower()) # Sort list alphabetically, case-insensitive
+
+ # Bring the observation to beginning ---
+ ref_file = '../'+mode+'/'+mode + '_*_eof1_'+season+'_obs_1900-2005' + ftail1 + '.png'
+ ref_file_list = os.popen('ls '+ref_file).readlines()
+ try: 
+ files.remove(ref_file_list[0])
+ except: 
+ pass
+ files = ref_file_list + files
+
+ # Calculate optimum row/col ---
+ num = len(files)
+ col = int(math.ceil(num * 0.4)) # round up
+ if col > 6: col = 6
+ row = num // col
+ if num % col > 0: row = row + 1
+ if debug: print(num, col, row)
+
+ # Set thumbnail plot size ---
+ hor = 400
+ ver = 400
+
+ # Open new image ---
+ result = Image.new("RGB", (col*hor, row*ver), (255,255,255,0))
+
+ # Append plots ---
+ for index, file in enumerate(files):
+ if debug: print(index, file)
+ path = os.path.expanduser(file)
+ #img = Image.open(path)
+ img = Image.open(path.strip())
+ img.thumbnail((hor, ver), Image.ANTIALIAS)
+ x = index % col * hor
+ y = index // col * ver
+ w, h = img.size
+ if debug: print('pos {0},{1} size {2},{3}'.format(x, y, w, h))
+ result.paste(img, (x, y, x + w, y + h))
+
+ # Save image ---
+ #output_image = 'combined_image_'+mode+'_' + season + ftail1 + ftail2 + '.png'
+ #result.save(os.path.expanduser('./'+output_image))
+ output_image = 'Combined_'+mode+'_' + season + ftail1 + ftail2 + '.pdf'
+ result.save(os.path.expanduser('./'+output_image),"PDF", resoultion = 100.0)

src/python/devel/variability_mode_nao/scripts/plot.sh

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+#!/bin/bash
+
+# Temporary example to run plot
+
+set -ax
+
+mode='NAM'
+#mode='NAO'
+#mode='SAM'
+#mode='PNA'
+
+#for mode in 'NAM' 'NAO' 'SAM' 'PNA'; do
+ python -i read_json_plot_charts.py -j '../'$mode'/var_mode_'$mode'_eof1_stat_cmip5_historical_r1i1p1_mo_atm_1900-2005.json' -s 'all' -v 'psl' -e 'cmip5' -m $mode
+#done
+
+mode='PDO'
+season='monthly'
+
+#python -i read_json_plot_charts.py -j '../'$mode'/var_mode_'$mode'_eof1_stat_cmip5_historical_r1i1p1_mo_atm_1900-2005.json' -s $season -v 'psl' -e 'cmip5' -m $mode

src/python/devel/variability_mode_nao/scripts/plot_barChart.py

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+import numpy as np
+import matplotlib.pyplot as PLT
+import sys
+
+class BarChart(object):
+
+ def __init__(self, mods, data, fig=None, rect=111):
+
+ # Canvas setup
+ if fig is None:
+ fig = PLT.figure
+ ax = fig.add_subplot(rect)
+
+ # Enable to control ax and fig options outside of this file
+ self._ax = ax
+ self._fig = fig
+
+ # Axis setup
+ unit_adjust = 1.
+ unit = 'Unit needed here ...'
+ ymax = max(data)
+ ymin = min(data)
+ yint = int((ymax-ymin)/3)
+
+ # Array setup
+ num_mods = len(mods)
+ x = np.linspace(0,num_mods-1,num_mods)
+ y = np.array(data) * unit_adjust
+
+ # Label setup
+ index = range(1,num_mods+1)
+ labels = mods
+
+ # Plotting
+ ax.bar(x,y,bottom=0,align='center')
+ ax.axhline(0, color='black')
+
+ # Title and axis
+ ax.set_title('Subtitle needed here..')
+ ax.set_xlabel('Models')
+ ax.set_ylabel(unit)
+ ax.set_xlim([-1.,len(y)-0.5])
+ ax.set_ylim([y.min()*1.1,y.max()*1.1])
+ ax.grid(True)
+
+ # Axis labels
+ PLT.xticks(x,labels,rotation='vertical') ## Label x-axis as model names
+ #PLT.xticks(x,index,rotation='vertical') ## Label x-axis as model names
+ #ax.yaxis.set_ticks(np.arange(int(ymin-yint), int(ymax+yint), yint))
+
+ # Multi model mean / std. dev. / min / max
+ yave=np.mean(y)
+ ystd=np.std(y)
+ ymin=np.amin(y)
+ ymax=np.amax(y)
+
+ ax.plot(-0.7,yave,'x',c='red',markersize=12) # ave
+ ax.plot(-0.7,ymin,'+',c='red',markersize=12) # min
+ ax.plot(-0.7,ymax,'+',c='red',markersize=12) # max
+ ax.errorbar(-0.7,yave,yerr=ystd,ls='solid',color='red',linewidth=1) # inter-model std. dev.

src/python/devel/variability_mode_nao/scripts/plot_scatter.py

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+import numpy as NP
+import matplotlib.pyplot as plt
+import sys
+import matplotlib.cm as cm
+
+def plot_scatter(models,data1,data2,file_out,label1,label2):
+
+ print 'scatter plot: ', label1, label2 
+
+ #
+ # Canvas setup
+ #
+ if debug: 
+ plt.ion()
+
+ fig = plt.figure(figsize=(13,12))
+ ax = plt.subplot(111)
+ plt.subplots_adjust(left=0.1, right=0.75, top=0.9, bottom=0.1, wspace=0, hspace=0)
+
+ #
+ # Array setup and plot
+ # 
+ if debug:
+ print len(data1), len(data2)
+
+ # 
+ # Simple quality control
+ #
+ if len(data1) == len(data2) and len(models)==len(data1):
+ num_dots = len(data1)
+ else:
+ sys.exit("data size dose not match")
+
+ # X axis ---
+ xlabel = str.upper(label1)
+ #xmax = NP.max(data1)*1.1
+ xmax = NP.max(data1)
+ xmin = NP.min(data1)
+ if xmin < 0: xmin = xmin*1.1
+ else: xmin = xmin*0.9
+ xint = (xmax-xmin)/5.
+ if xint < 0.1: xint = round(xint,2)
+ elif xint < 1: xint = round(xint,1)
+ else: xint = int(xint)
+ xlstart = xmin//1
+ if debug: print 'xmin, xmax, xint:',xmin, xmax, xint
+
+ # Y axis ---
+ ylabel = str.upper(label2)
+ #ymax = NP.max(data2)*1.1
+ ymax = NP.max(data2)
+ ymin = NP.min(data2)
+ if ymin < 0: ymin = ymin*1.1
+ else: ymin = ymin*0.9
+ yint = (ymax-ymin)/5.
+ if yint < 0.1: yint = round(yint,2)
+ elif yint < 1: yint = round(yint,1)
+ else: yint = int(yint)
+ ylstart = ymin//1
+ if debug: print 'ymin, ymax, yint:',ymin, ymax, yint
+
+ #colors = cm.rainbow(NP.linspace(0, 1, num_dots))
+ #colors = cm.gist_rainbow(NP.linspace(0, 1, num_dots))
+ #colors = cm.gist_ncar(NP.linspace(0, 1, num_dots))
+ colors = cm.Paired(NP.linspace(0, 1, num_dots))
+ #colors = cm.Set1(NP.linspace(0, 1, num_dots))
+ #colors = cm.nipy_spectral(NP.linspace(0, 1, num_dots))
+ #colors = cm.hsv(NP.linspace(0, 1, num_dots))
+ #colors = cm.jet(NP.linspace(0, 1, num_dots))
+
+ for i, mod in enumerate(models):
+ x = float(data1[i])
+ y = float(data2[i])
+ mod = str(models[i])
+ ax.scatter(x, y, label=str(i+1)+' '+mod, c=colors[i], s=200, linewidth=0.15)
+ # Show index numbers
+ ax.annotate(str(i+1), (x,y), fontsize='small', verticalalignment='center', horizontalalignment='center', color='white')
+
+ ax.legend(frameon=True, scatterpoints=1, bbox_to_anchor=(1.05, 1), loc=2,
+ borderaxespad=0., fontsize=11, labelspacing=0.435, ncol=1)
+
+ # 
+ # Title and axis labeling
+ #
+ ax.set_title(file_out, fontsize=18)
+ ax.set_xlabel(xlabel, fontsize=17)
+ ax.set_ylabel(ylabel, fontsize=17)
+ #ax.set_xlim([xmin,xmax])
+ #ax.set_ylim([ymin,ymax])
+ ax.grid(True)
+
+ #ax.xaxis.set_ticks(NP.arange(xlstart, xmax+xint/2., xint))
+ #ax.yaxis.set_ticks(NP.arange(ylstart, ymax+yint/2., yint))
+ plt.tick_params(labelsize=15) # Tick label font size
+
+ #
+ # Text test
+ #
+ x0, xmax = plt.xlim()
+ y0, ymax = plt.ylim()
+ data_width = xmax - x0
+ data_height = ymax - y0
+ #plt.text(x0 + data_width * 0.5, y0 + data_height * 0.5, 'Some text')
+
+ #
+ # Linear regression
+ #
+ fit = NP.polyfit(data1, data2, 1) 
+ fit_fn = NP.poly1d(fit)
+ plt.plot(data1, fit_fn(data1), '-')
+ slope = fit[0]
+ #ax.text(xmax, ymax, 'Regression slope ='+str(round(slope,2)), va='center', ha='left', color='blue', fontsize=15) 
+ ax.text(x0 + data_width * 0.65, y0 + data_height * 0.95, 'Regression slope ='+str(round(slope,2)), va='center', ha='left', color='blue', fontsize=15) 
+
+ # 
+ # Save image file
+ #
+ fig.savefig(file_out+'_scatter.png',dpi=300)
+ if debug:
+ plt.show()