#! /usr/bin/env python # # =========================================================== # # Alexander R Davies # # Ocean Exploration, Remote Sensing, and Biogeography Lab # School of Marine Science and Policy # College of Earth, Ocean, and Environment # University of Delaware # ardavies@udel.edu # # Latest Update: 10/09/13 # # NOTES: 1) Reads Processed Data, Linearly Interpolates # each for each profile to a known grid # # 2) Calculates vertical velocities for density # and chlorphyll. # # 3) Plots the vertical velocities over contoured # gridded, interpolated w data # # # =========================================================== # Import Modules # =========================================================== # import numpy as np import csv import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt import glob import os from matplotlib.ticker import MaxNLocator from matplotlib import rc, rcParams from numpy.random import uniform, seed from matplotlib.mlab import griddata from numpy import arange,array,ones#,random,linalg from pylab import plot,show from scipy import interpolate from matplotlib.colors import LogNorm from matplotlib.backends.backend_pdf import PdfPages # # =========================================================== # Function to read csv files # =========================================================== # def csvread(filename): with open(filename, 'rb') as f: reader = csv.reader(f, delimiter=',') nrow = 0; for row in reader: nrow = nrow +1 ncol = len(row) array = np.zeros([ncol,nrow]) with open(filename, 'rb') as f: reader = csv.reader(f, delimiter=',') counter = 0 for row in reader: for jj in range(0,ncol): array[jj,counter] = row[jj] counter = counter + 1 return array, nrow, ncol # # =========================================================== # Function to Find the nearest value # =========================================================== # def find_nearest(array,value): idx = (np.abs(array-value)).argmin() return array[idx], idx # # =========================================================== # Sort Data and Descriptive Arrays # =========================================================== # os.chdir('C:/Documents and Settings/Alex/Documents/Research/Delaware/MUSTACHE/Jan01_Jun04_2013/data/type') fullnames = glob.glob('Full*') gradnames = glob.glob('Grad*') infonames = glob.glob('Text*') arraylen = len(fullnames) floatdate = np.zeros(arraylen) lat = [None]*arraylen lon = [None]*arraylen day = np.zeros(arraylen) month=[None]*arraylen year=[None]*arraylen daystr=[None]*arraylen # # =========================================================== # Build the Arrays as days past 1/1/13 # =========================================================== # for d in range(0,int(arraylen)): f = open(infonames[d], 'r') while 1: line = f.readline() if line[:4] == "Date": month[d] = str(line[5:7]) day[d] = int(line[8:10]) daystr[d] = str(line[8:10]) year[d] = str(line[11:15]) break f = open(infonames[d], 'r') while 1: line = f.readline() if line[:3] == "Lat": lat[d] = str(line[5:12]) break f = open(infonames[d], 'r') while 1: line = f.readline() if line[:3] == "Lon": lon[d] = str(line[5:12]) break if (year[d] == '2013'): yr = 0 elif (year[d] == '2014'): yr = 365 elif (year[d] == '2015'): yr = 365*2 if(month[d] == '01'): mon = 0 elif(month[d] == '02'): mon = 31 elif(month[d] == '03'): mon = 31 + 28 elif(month[d] == '04'): mon = (31 + 28 + 31) elif(month[d] == '05'): mon = (31 + 28 + 31 + 30) elif(month[d] == '06'): mon = (31 + 28 + 31 + 30 + 31) elif(month[d] == '07'): mon = (31 + 28 + 31 + 30 + 31 + 30) elif(month[d] == '08'): mon = (31 + 28 + 31 + 30 + 31 + 30 + 31) elif(month[d] == '09'): mon = (31 + 28 + 31 + 30 + 31 + 30 + 31 + 31) elif(month[d] == '10'): mon = (31 + 28 + 31 + 30 + 31 + 30 + 31 + 31 + 30) elif(month[d] == '11'): mon = (31 + 28 + 31 + 30 + 31 + 30 + 31 + 31 + 30 + 31) elif(month[d] == '12'): mon = (31 + 28 + 31 + 30 + 31 + 30 + 31 + 31 + 30 + 31 + 30) else: print 'invalid month' floatdate[d] = yr + mon + day[d] # # =========================================================== # Read the Data and Interpolate for Gridding # =========================================================== # Ygrid = np.linspace(-1800,-10,1791*2) interplength = len(Ygrid) GridData = np.zeros([interplength,7,arraylen]) ChlyDumby = np.zeros([interplength,arraylen]) for i in range(0,arraylen): dataout, rows, cols = csvread(fullnames[i]) # TemInterp = interpolate.interp1d(dataout[0,:], dataout[1,:],kind='linear') DenInterp = interpolate.interp1d(dataout[0,:], dataout[2,:],kind='linear') SalInterp = interpolate.interp1d(dataout[0,:], dataout[3,:],kind='linear') PreInterp = interpolate.interp1d(dataout[0,:], dataout[4,:],kind='linear') OxyInterp = interpolate.interp1d(dataout[0,:], dataout[7,:],kind='linear') ChlInterp = interpolate.interp1d(dataout[0,:], dataout[11,:],kind='linear') BskInterp = interpolate.interp1d(dataout[0,:], dataout[12,:],kind='linear') CDMInterp = interpolate.interp1d(dataout[0,:], dataout[13,:],kind='linear') # for j in range(0,interplength): GridData[j,0,i] = TemInterp(Ygrid[j]) GridData[j,1,i] = DenInterp(Ygrid[j]) GridData[j,2,i] = SalInterp(Ygrid[j]) GridData[j,3,i] = OxyInterp(Ygrid[j]) ChlyDumby[j,i] = ChlInterp(Ygrid[j]) GridData[j,5,i] = BskInterp(Ygrid[j]) GridData[j,6,i] = CDMInterp(Ygrid[j]) if (ChlInterp(Ygrid[j]) < 0): GridData[j,4,i] = GridData[j-1,4,i] else: GridData[j,4,i] = ChlyDumby[j,i] # # =========================================================== # Find Gridded Change Velocities Based on Nearest Values # =========================================================== # GridWData = np.zeros([interplength,7,arraylen-1]) GridWDepths = np.zeros([interplength,7,arraylen-1]) GridWDates = np.zeros([interplength,7,arraylen-1]) for k in range(0,7): for i in range (0,arraylen-1): for j in range(0,interplength): value, index = find_nearest(GridData[:,k,i+1], GridData[j,k,i]) startdepth = Ygrid[j] newdepth = Ygrid[index] GridWDepths[j,k,i] = (newdepth + startdepth)/2 dz = newdepth-startdepth GridWData[j,k,i] = dz/172800 GridWDates[j,k,i] = (floatdate[i] + floatdate[i+1])/2 # # =========================================================== # Find Avg Gridded Change Velocities Based on Nearest Values # =========================================================== # GridWDataAvg = np.zeros([interplength,7,arraylen-3]) GridWDepthsAvg = np.zeros([interplength,7,arraylen-3]) GridWDatesAvg = np.zeros([interplength,7,arraylen-3]) for k in range(0,7): for i in range (0,arraylen-3): for j in range(0,interplength): GridWDataAvg[j,k,i] = (GridWData[j,k,i] + GridWData[j,k,i+1] + GridWData[j,k,i+2])/3 GridWDepthsAvg[j,k,i] = (GridWDepths[j,k,i] + GridWDepths[j,k,i+1] + GridWDepths[j,k,i+2])/3 GridWDatesAvg[j,k,i] = (GridWDates[j,k,i] + GridWDates[j,k,i+1] + GridWDates[j,k,i+2])/3 # # =========================================================== # Find Depth of Isochly Contours # =========================================================== # isobiolines = np.linspace(.1,.40,20) numbiolines = len(isobiolines) Biodepths = np.zeros([numbiolines,arraylen]) for i in range(0,arraylen): #print i for j in range(0,numbiolines): #print j count = 0 while (GridData[count,4,i] < isobiolines[j]): count = count + 1 Biodepths[j,i] = Ygrid[count] # # =========================================================== # Find Depth of Isocdom Contours # =========================================================== # isocdomlines = np.linspace(1.7,2.2,21) print isocdomlines numcdomlines = len(isocdomlines) print numcdomlines cdomdepths = np.zeros([numcdomlines,arraylen]) for i in range(0,arraylen): #print i for j in range(0,numcdomlines): #print j count = 0 while (GridData[count,6,i] > isocdomlines[j]): count = count + 1 cdomdepths[j,i] = Ygrid[count] # # =========================================================== # Find Isochly Contour Change Velocities # =========================================================== # IsoBioWData = np.zeros([numbiolines,arraylen-1]) IsoBioWDepths = np.zeros([numbiolines,arraylen-1]) IsoBioWDates = np.zeros([numbiolines,arraylen-1]) IsoBioFake = np.zeros([numbiolines,arraylen-1]) for i in range(0,arraylen-1): for k in range(0,numbiolines): j = i IsoBioWData[k,i] = (Biodepths[k,j+1] - Biodepths[k,j])/172800 IsoBioFake[k,i] = 0 IsoBioWDepths[k,i] = (Biodepths[k,j+1] + Biodepths[k,j])/2 IsoBioWDates[k,i] = (floatdate[j+1] + floatdate[j])/2 # # =========================================================== # Find Isochly Contour Change Velocities # =========================================================== # IsoCdomWData = np.zeros([numcdomlines,arraylen-1]) IsoCdomWDepths = np.zeros([numcdomlines,arraylen-1]) IsoCdomWDates = np.zeros([numcdomlines,arraylen-1]) IsoCdomFake = np.zeros([numcdomlines,arraylen-1]) for i in range(0,arraylen-1): for k in range(0,numcdomlines): j = i IsoCdomWData[k,i] = (cdomdepths[k,j+1] - cdomdepths[k,j])/172800 IsoCdomFake[k,i] = 0 IsoCdomWDepths[k,i] = (cdomdepths[k,j+1] + cdomdepths[k,j])/2 IsoCdomWDates[k,i] = (floatdate[j+1] + floatdate[j])/2 # # =========================================================== # Find Avg Isoline Change Velocities # =========================================================== # IsoBioWDataAvg = np.zeros([numbiolines,arraylen-3]) IsoBioWDepthsAvg = np.zeros([numbiolines,arraylen-3]) IsoBioWDatesAvg = np.zeros([numbiolines,arraylen-3]) IsoBioFakeAvg = np.zeros([numbiolines,arraylen-3]) for i in range (0,arraylen-3): for j in range(0,numbiolines): IsoBioWDataAvg[j,i] = (IsoBioWData[j,i] + IsoBioWData[j,i+1] + IsoBioWData[j,i+2])/3 IsoBioWDepthsAvg[j,i] = (IsoBioWDepths[j,i] + IsoBioWDepths[j,i+1] + IsoBioWDepths[j,i+2])/3 IsoBioWDatesAvg[j,i] = (IsoBioWDates[j,i] + IsoBioWDates[j,i+1] + IsoBioWDates[j,i+2])/3 IsoBioFakeAvg[j,i] = (IsoBioFake[j,i] + IsoBioFake[j,i+1] + IsoBioFake[j,i+2])/3 # # =========================================================== # Find Avg IsoCDOM Change Velocities # =========================================================== # IsoCdomWDataAvg = np.zeros([numcdomlines,arraylen-3]) IsoCdomWDepthsAvg = np.zeros([numcdomlines,arraylen-3]) IsoCdomWDatesAvg = np.zeros([numcdomlines,arraylen-3]) IsoCdomFakeAvg = np.zeros([numcdomlines,arraylen-3]) for i in range (0,arraylen-3): for j in range(0,numcdomlines): IsoCdomWDataAvg[j,i] = (IsoCdomWData[j,i] + IsoCdomWData[j,i+1] + IsoCdomWData[j,i+2])/3 IsoCdomWDepthsAvg[j,i] = (IsoCdomWDepths[j,i] + IsoCdomWDepths[j,i+1] + IsoCdomWDepths[j,i+2])/3 IsoCdomWDatesAvg[j,i] = (IsoCdomWDates[j,i] + IsoCdomWDates[j,i+1] + IsoCdomWDates[j,i+2])/3 IsoCdomFakeAvg[j,i] = (IsoCdomFake[j,i] + IsoCdomFake[j,i+1] + IsoCdomFake[j,i+2])/3 # # =========================================================== # Find Depth Averaged Avg Isoline Change Velocities # =========================================================== # IsoBioWDataDepthAvgAvg = np.zeros(arraylen-3) IsoBioWDepthsDepthAvgAvg = np.zeros(arraylen-3) IsoBioWDatesDepthAvgAvg = np.zeros(arraylen-3) IsoBioFakeDepthAvgAvg = np.zeros(arraylen-3) for i in range (0,arraylen-3): IsoBioWDataDepthAvgAvg[i] = np.mean(IsoBioWDataAvg[:,i]) IsoBioWDepthsDepthAvgAvg[i] = np.mean(IsoBioWDepthsAvg[:,i]) IsoBioWDatesDepthAvgAvg[i] = np.mean(IsoBioWDatesAvg[:,i]) IsoBioFakeDepthAvgAvg[i] = np.mean(IsoBioFakeAvg[:,i]) # # =========================================================== # Find Depth Averaged Avg Isoline Change Velocities # =========================================================== # IsoCdomWDataDepthAvgAvg = np.zeros(arraylen-3) IsoCdomWDepthsDepthAvgAvg = np.zeros(arraylen-3) IsoCdomWDatesDepthAvgAvg = np.zeros(arraylen-3) IsoCdomFakeDepthAvgAvg = np.zeros(arraylen-3) for i in range (0,arraylen-3): IsoCdomWDataDepthAvgAvg[i] = np.mean(IsoCdomWDataAvg[:,i]) IsoCdomWDepthsDepthAvgAvg[i] = np.mean(IsoCdomWDepthsAvg[:,i]) IsoCdomWDatesDepthAvgAvg[i] = np.mean(IsoCdomWDatesAvg[:,i]) IsoCdomFakeDepthAvgAvg[i] = np.mean(IsoCdomFakeAvg[:,i]) # # =========================================================== # Find Sink Vel # =========================================================== # NearestWGridded = np.zeros([numbiolines,arraylen-1]) SinkWData = np.zeros([numbiolines,arraylen-1]) for i in range (0,arraylen-1): for k in range(0,numbiolines): value, index = find_nearest(GridWDepths[:,1,i], IsoBioWDepths[k,i]) SinkWData[k,i] = IsoBioWData[k,i] - GridWData[index,1,i] NearestWGridded[k,i] = GridWData[index,1,i] # # =========================================================== # Find Avg Sink Vel # =========================================================== # SinkWDataAvg = np.zeros([numbiolines,arraylen-3]) NearestWGriddedAvg = np.zeros([numbiolines,arraylen-3]) for i in range (0,arraylen-3): for k in range(0,numbiolines): value, index = find_nearest(GridWDepthsAvg[:,1,i], IsoBioWDepthsAvg[k,i]) SinkWDataAvg[k,i] = IsoBioWDataAvg[k,i] - GridWDataAvg[index,1,i] NearestWGriddedAvg[k,i] = GridWDataAvg[index,1,i] # # =========================================================== # Find Depth Averaged Avg Sink Vel # =========================================================== # SinkWDataDepthAvgAvg = np.zeros(arraylen-3) NearestWGriddedAvgAvg = np.zeros(arraylen-3) for i in range (0,arraylen-3): SinkWDataDepthAvgAvg[i] = np.mean(SinkWDataAvg[:,i]) NearestWGriddedAvgAvg[i] = np.mean(NearestWGriddedAvg[:,i]) # # Change Path for Plotting figdirpath ='C:/Documents and Settings/Alex/Documents/Research/Delaware/MUSTACHE/Jan01_Jun04_2013/plots/contours_with_Ws/' # # =========================================================== # Contour CDOM w/ IsoCDOM Ws # =========================================================== # contplt = 2 if contplt == 1: # # Plotting correct data contourdat = np.zeros([interplength,arraylen]) for ci in range(0,interplength): for cj in range(0,arraylen): #cjj = cj + 1 contourdat[ci,cj] = GridData[ci,6,cj] # # Plot Set-up fig = plt.figure() ax = fig.add_subplot(1, 1, 1) contlevels= np.linspace(1,3,100) conticks = [1.5, 2.0, 2.5, 3.0] figtit = 'CDOM (ppb)' savetit = "CDOM_Contour" cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels) plt.colorbar(cs, spacing='proportional', ticks = conticks) # Plotting figtit2 = ' and CDOM Quivers' savetit2 = "_IsoCdomW_Quivers" from pylab import * #Q = plt.quiver(IsoBioWDates[1::10], IsoBioWDepths[1::10], IsoBioFake[1::10], IsoBioWData[1::10]) #plt.quiverkey(Q, 0.22, 0.95, 0.001, r'$5 \times 10^{-3} m/s$', labelpos='W') Q2 = plt.quiver(IsoCdomWDates[1::10], IsoCdomWDepths[1::10], IsoCdomFake[1::10], IsoCdomWData[1::10], color='k') plt.quiverkey(Q2, 0.22, 0.96, 0.0005, r'$ 5 \times 10^{-4} m/s$', labelpos='W') ax = plt.gca() ax.set_xlabel("Days since 01/01/2013") ax.set_ylabel("Depth (m)") ax.set_ylim([-900,0]) # plt.title(figtit) fig.suptitle(figtit + figtit2 ) plt.savefig(figdirpath + savetit+ savetit2 +"2.png") # # =========================================================== # Contour CDOM w/ Averaged IsoCDOM Ws # =========================================================== # contplt = 2 if contplt == 1: # # Plotting correct data contourdat = np.zeros([interplength,arraylen]) for ci in range(0,interplength): for cj in range(0,arraylen): #cjj = cj + 1 contourdat[ci,cj] = GridData[ci,6,cj] # # Plot Set-up fig = plt.figure() ax = fig.add_subplot(1, 1, 1) contlevels= np.linspace(1,3,100) conticks = [1.5, 2.0, 2.5, 3.0] figtit = 'CDOM (ppb)' savetit = "CDOM_Contour" cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels) plt.colorbar(cs, spacing='proportional', ticks = conticks) # Plotting figtit2 = ' and CDOM 3 Day Average Quivers' savetit2 = "_IsoCdomW_Quivers_3Avg" from pylab import * #Q = plt.quiver(IsoBioWDates[1::10], IsoBioWDepths[1::10], IsoBioFake[1::10], IsoBioWData[1::10]) #plt.quiverkey(Q, 0.22, 0.95, 0.001, r'$5 \times 10^{-3} m/s$', labelpos='W') Q2 = plt.quiver(IsoCdomWDatesAvg[1::10], IsoCdomWDepthsAvg[1::10], IsoCdomFakeAvg[1::10], IsoCdomWDataAvg[1::10], color='k') plt.quiverkey(Q2, 0.22, 0.96, 0.0005, r'$ 5 \times 10^{-4} m/s$', labelpos='W') ax = plt.gca() ax.set_xlabel("Days since 01/01/2013") ax.set_ylabel("Depth (m)") ax.set_ylim([-900,0]) # plt.title(figtit) fig.suptitle(figtit + figtit2 ) plt.savefig(figdirpath + savetit+ savetit2 +".png") # # =========================================================== # Contour CDOM w/ Depth Averaged Averaged IsoCDOM Ws # =========================================================== # contplt = 2 if contplt == 1: # # Plotting correct data contourdat = np.zeros([interplength,arraylen]) for ci in range(0,interplength): for cj in range(0,arraylen): #cjj = cj + 1 contourdat[ci,cj] = GridData[ci,6,cj] # # Plot Set-up fig = plt.figure() ax = fig.add_subplot(1, 1, 1) contlevels= np.linspace(1,3,100) conticks = [1.5, 2.0, 2.5, 3.0] figtit = 'CDOM (ppb)' savetit = "CDOM_Contour" cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels) plt.colorbar(cs, spacing='proportional', ticks = conticks) # Plotting figtit2 = ' and CDOM Depth Averaged 3 Day Average Quivers' savetit2 = "_IsoCdomW_Quivers_3Avg_Averaged" from pylab import * #Q = plt.quiver(IsoBioWDates[1::10], IsoBioWDepths[1::10], IsoBioFake[1::10], IsoBioWData[1::10]) #plt.quiverkey(Q, 0.22, 0.95, 0.001, r'$5 \times 10^{-3} m/s$', labelpos='W') Q2 = plt.quiver(IsoCdomWDatesDepthAvgAvg, IsoCdomWDepthsDepthAvgAvg, IsoCdomFakeDepthAvgAvg, IsoCdomWDataDepthAvgAvg, color='k') plt.quiverkey(Q2, 0.22, 0.96, 0.0005, r'$ 5 \times 10^{-4} m/s$', labelpos='W') ax = plt.gca() ax.set_xlabel("Days since 01/01/2013") ax.set_ylabel("Depth (m)") ax.set_ylim([-900,0]) # plt.title(figtit) fig.suptitle(figtit + figtit2 ) plt.savefig(figdirpath + savetit+ savetit2 +".png") # # =========================================================== # Contour Chly w/ Isochly Ws # =========================================================== # contplt = 2 if contplt == 1: # # Plotting correct data contourdat = np.zeros([interplength,arraylen]) for ci in range(0,interplength): for cj in range(0,arraylen): #cjj = cj + 1 contourdat[ci,cj] = GridData[ci,4,cj] # # Plot Set-up fig = plt.figure() ax = fig.add_subplot(1, 1, 1) contlevels= np.logspace(-2.5,.65,100) conticks = [0.01,0.25,0.50, 1.00, 1.50, 2.00, 3.00] figtit = 'Chlorophyll (micro gram/liter)' savetit = "Chlorophyll_Contour" cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels, norm = LogNorm()) cbar = plt.colorbar(cs, spacing='proportional', norm = LogNorm()) cbar.set_ticks(conticks) cbar.set_ticklabels(conticks) # Plotting figtit2 = ' and Chlorophyll Quivers' savetit2 = "_IsoChlyW_Quivers" from pylab import * Q2 = plt.quiver(IsoBioWDates[1::10], IsoBioWDepths[1::10], IsoBioFake[1::10], IsoBioWData[1::10], color='k') plt.quiverkey(Q2, 0.22, 0.96, 0.0005, r'$ 5 \times 10^{-4} m/s$', labelpos='W') ax = plt.gca() ax.set_xlabel("Days since 01/01/2013") ax.set_ylabel("Depth (m)") ax.set_ylim([-900,0]) # plt.title(figtit) fig.suptitle(figtit + figtit2 ) plt.savefig(figdirpath + savetit+ savetit2 +".png") # # =========================================================== # Contour Chly w/ Averaged Isochly Ws # =========================================================== # contplt = 2 if contplt == 1: # # Plotting correct data contourdat = np.zeros([interplength,arraylen]) for ci in range(0,interplength): for cj in range(0,arraylen): #cjj = cj + 1 contourdat[ci,cj] = GridData[ci,4,cj] # # Plot Set-up fig = plt.figure() ax = fig.add_subplot(1, 1, 1) contlevels= np.logspace(-2.5,.65,100) conticks = [0.01,0.25,0.50, 1.00, 1.50, 2.00, 3.00] figtit = 'Chlorophyll (micro gram/liter)' savetit = "Chlorophyll_Contour" cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels, norm = LogNorm()) cbar = plt.colorbar(cs, spacing='proportional', norm = LogNorm()) cbar.set_ticks(conticks) cbar.set_ticklabels(conticks) # Plotting figtit2 = ' and Chlorophyll 3 Day Avg Quivers' savetit2 = "_IsoChlyW_Quivers_3Avg" from pylab import * Q2 = plt.quiver(IsoBioWDatesAvg[1::10], IsoBioWDepthsAvg[1::10], IsoBioFakeAvg[1::10], IsoBioWDataAvg[1::10], color='k') plt.quiverkey(Q2, 0.22, 0.96, 0.0005, r'$ 5 \times 10^{-4} m/s$', labelpos='W') ax = plt.gca() ax.set_xlabel("Days since 01/01/2013") ax.set_ylabel("Depth (m)") ax.set_ylim([-900,0]) # plt.title(figtit) fig.suptitle(figtit + figtit2 ) plt.savefig(figdirpath + savetit+ savetit2 +".png") # # =========================================================== # Contour Chly w/ Averaged Averaged Isochly Ws # =========================================================== # contplt = 2 if contplt == 1: # # Plotting correct data contourdat = np.zeros([interplength,arraylen]) for ci in range(0,interplength): for cj in range(0,arraylen): #cjj = cj + 1 contourdat[ci,cj] = GridData[ci,4,cj] # # Plot Set-up fig = plt.figure() ax = fig.add_subplot(1, 1, 1) contlevels= np.logspace(-2.5,.65,100) conticks = [0.01,0.25,0.50, 1.00, 1.50, 2.00, 3.00] figtit = 'Chlorophyll (micro gram/liter)' savetit = "Chlorophyll_Contour" cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels, norm = LogNorm()) cbar = plt.colorbar(cs, spacing='proportional', norm = LogNorm()) cbar.set_ticks(conticks) cbar.set_ticklabels(conticks) # Plotting figtit2 = ' and Chlorophyll Depth Avg 3 Day Avg Quivers' savetit2 = "_IsoChlyW_Quivers_3Avg_Averaged" from pylab import * Q2 = plt.quiver(IsoBioWDatesDepthAvgAvg, IsoBioWDepthsDepthAvgAvg, IsoBioFakeDepthAvgAvg, IsoBioWDataDepthAvgAvg, color='k') plt.quiverkey(Q2, 0.22, 0.96, 0.0005, r'$ 5 \times 10^{-4} m/s$', labelpos='W') ax = plt.gca() ax.set_xlabel("Days since 01/01/2013") ax.set_ylabel("Depth (m)") ax.set_ylim([-900,0]) # plt.title(figtit) fig.suptitle(figtit + figtit2 ) plt.savefig(figdirpath + savetit+ savetit2 +".png") # # =========================================================== # Contour Plotting Grided W Data # =========================================================== # # Do you want to plot Gridded Ws? Yes: gridWplt = 1, No: else gridWplt = 2 if gridWplt == 1: # # =========================================================== # Making Blue-Red Contour Map for Vertical Velocities # =========================================================== # # Make Blue-Red Color Scheme from matplotlib.colors import LinearSegmentedColormap cdict3 = {'red': ((0.0, 0.0, 0.0), (0.25,0.0, 0.0), (0.5, 0.8, 1.0), (0.75,1.0, 1.0), (1.0, 0.4, 1.0)), 'green': ((0.0, 0.0, 0.0), (0.25,0.0, 0.0), (0.5, 0.9, 0.9), (0.75,0.0, 0.0), (1.0, 0.0, 0.0)), 'blue': ((0.0, 0.0, 0.4), (0.25,1.0, 1.0), (0.5, 1.0, 0.8), (0.75,0.0, 0.0), (1.0, 0.0, 0.0)) } # # Make a modified version of cdict3 with some transparency # in the middle of the range. cdict4 = cdict3.copy() cdict4['alpha'] = ((0.0, 1.0, 1.0), # (0.25,1.0, 1.0), (0.5, 0.3, 0.3), # (0.75,1.0, 1.0), (1.0, 1.0, 1.0)) plt.register_cmap(name='BlueRedAlpha', data=cdict4) for k in range (0,7): # # Plotting correct W data wcontourdat = np.zeros([interplength,arraylen-1]) wcontourdep = np.zeros([interplength,arraylen-1]) wcontourtim = np.zeros([interplength,arraylen-1]) for ci in range(0,interplength): for cj in range(0,arraylen-1): wcontourdat[ci,cj] = GridWData[ci,k,cj] wcontourdep[ci,cj] = GridWDepths[ci,k,cj] wcontourtim[ci,cj] = GridWDates[ci,k,cj] # # Plotting correct contour data contourdat = np.zeros([interplength,arraylen]) for ci in range(0,interplength): for cj in range(0,arraylen): #cjj = cj + 1 contourdat[ci,cj] = GridData[ci,k,cj] # # Plot Set-up fig = plt.figure() ax = fig.add_subplot(1, 1, 1) # # Which Plotting Information? if k == 0: #temperature Wcontlevels= np.linspace(-30,30,101) Wconticks = [-25, -10, 0, 10, 25] #contlevels= np.linspace(-1.5,3,100) conticks = [-1.5, -1.0, -0.5, 0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0] figtit = 'Isothermal Gridded W Contour x 10^4 (m/s)' savetit = "TemperatureGriddedW_Contour" cs = plt.contourf(wcontourtim,wcontourdep,wcontourdat*10**(4), levels = Wcontlevels) plt.colorbar(cs, spacing='proportional', ticks = Wconticks) plt.set_cmap('BlueRedAlpha') cs2 = plt.contour(floatdate,Ygrid,contourdat, colors='k',levels = conticks) plt.clabel(cs2, conticks[1::2], inline=1, fmt='%1.1f', fontsize=8) if k == 1: # density Wcontlevels= np.linspace(-30,30,101) Wconticks = [-25, -10, 0, 10, 25] #contlevels= np.linspace(1026.75,1027.8,100) conticks = [1026.50, 1026.75, 1027.00, 1027.25, 1027.50, 1027.75, 1028.00] figtit = 'Isopycnal Gridded W Contour x 10^4 (m/s)' savetit = "DensityGriddedW_Contour" cs = plt.contourf(wcontourtim,wcontourdep,wcontourdat*10**(4), levels = Wcontlevels) plt.colorbar(cs, spacing='proportional', ticks = Wconticks) plt.set_cmap('BlueRedAlpha') cs2 = plt.contour(floatdate,Ygrid,contourdat, colors='k',levels = conticks) plt.clabel(cs2, conticks[1::2], inline=1, fmt='%1.1f', fontsize=8) if k == 2: # sal Wcontlevels= np.linspace(-30,30,101) Wconticks = [-25, -10, 0, 10, 25] #contlevels= np.linspace(33.5,34.75,100) conticks = [33.25, 33.50, 33.75, 34.00, 34.25, 34.50, 34.75, 35.00] figtit = 'Isosaline Gridded W Contour x 10^4 (m/s)' savetit = "SalinityGriddedW_Contour" cs = plt.contourf(wcontourtim,wcontourdep,wcontourdat*10**(4), levels = Wcontlevels) plt.colorbar(cs, spacing='proportional', ticks = Wconticks) plt.set_cmap('BlueRedAlpha') cs2 = plt.contour(floatdate,Ygrid,contourdat, colors='k',levels = conticks) plt.clabel(cs2, conticks[1::2], inline=1, fmt='%1.1f', fontsize=8) if k == 3: #oxygen Wcontlevels= np.linspace(-30,30,101) Wconticks = [-25, -10, 0, 10, 25] #contlevels= np.linspace(4,10,100) conticks = [4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0] figtit = 'Isooxy Gridded W Contour x 10^4 (m/s)' savetit = "OxygenGriddedW_Contour" cs = plt.contourf(wcontourtim,wcontourdep,wcontourdat*10**(4), levels = Wcontlevels) plt.colorbar(cs, spacing='proportional', ticks = Wconticks) plt.set_cmap('BlueRedAlpha') cs2 = plt.contour(floatdate,Ygrid,contourdat, colors='k',levels = conticks) plt.clabel(cs2, conticks[1::2], inline=1, fmt='%1.1f', fontsize=8) if k == 4: # chl Wcontlevels= np.linspace(-30,30,101) Wconticks = [-25, -10, 0, 10, 25] #contlevels= np.logspace(-2.5,.65,100) conticks = [0.01,0.25,0.50, 1.00, 1.50, 2.00, 3.00] figtit = 'Isochly Gridded W Contour x 10^4 (m/s)' savetit = "ChlorophyllGriddedW_Contour" cs = plt.contourf(wcontourtim,wcontourdep,wcontourdat*10**(4), levels = Wcontlevels) plt.colorbar(cs, spacing='proportional', ticks = Wconticks) plt.set_cmap('BlueRedAlpha') cs2 = plt.contour(floatdate,Ygrid,contourdat, colors='k',levels = conticks) plt.clabel(cs2, conticks[1::2], inline=1, fmt='%1.1f', fontsize=8) if k == 5: #backscatter Wcontlevels= np.linspace(-30,30,101) Wconticks = [-25, -10, 0, 10, 25] #contlevels= np.logspace(-4,-3.3,100) conticks = [0.0001, 0.0002, 0.0002, 0.0003, 0.0004, 0.0004, 0.0005, 0.00075, 0.001, 0.0015] figtit = 'Isobackscatter Gridded W Contour x 10^4 (m/s)' savetit = "BackscatterGriddedW_Contour" cs = plt.contourf(wcontourtim,wcontourdep,wcontourdat*10**(4), levels = Wcontlevels) plt.colorbar(cs, spacing='proportional', ticks = Wconticks) plt.set_cmap('BlueRedAlpha') cs2 = plt.contour(floatdate,Ygrid,contourdat, colors='k',levels = conticks) plt.clabel(cs2, conticks[1::2], inline=1, fmt='%1.1f', fontsize=8) # # Plotting ax = plt.gca() ax.set_xlabel("Days since 01/01/2013") ax.set_ylabel("Depth (m)") ax.set_ylim([-900,0]) # plt.title(figtit) fig.suptitle(figtit) plt.savefig(figdirpath + savetit+".png") # # =========================================================== # Contour Plotting Grided W Data Zoomed # =========================================================== # # Do you want to plot Gridded Ws? Yes: gridWplt = 1, No: else gridWplt = 2 if gridWplt == 1: # # =========================================================== # Making Blue-Red Contour Map for Vertical Velocities # =========================================================== # # Make Blue-Red Color Scheme from matplotlib.colors import LinearSegmentedColormap cdict3 = {'red': ((0.0, 0.0, 0.0), (0.25,0.0, 0.0), (0.5, 0.8, 1.0), (0.75,1.0, 1.0), (1.0, 0.4, 1.0)), 'green': ((0.0, 0.0, 0.0), (0.25,0.0, 0.0), (0.5, 0.9, 0.9), (0.75,0.0, 0.0), (1.0, 0.0, 0.0)), 'blue': ((0.0, 0.0, 0.4), (0.25,1.0, 1.0), (0.5, 1.0, 0.8), (0.75,0.0, 0.0), (1.0, 0.0, 0.0)) } # # Make a modified version of cdict3 with some transparency # in the middle of the range. cdict4 = cdict3.copy() cdict4['alpha'] = ((0.0, 1.0, 1.0), # (0.25,1.0, 1.0), (0.5, 0.3, 0.3), # (0.75,1.0, 1.0), (1.0, 1.0, 1.0)) plt.register_cmap(name='BlueRedAlpha', data=cdict4) for k in range (0,7): # # Plotting correct W data wcontourdat = np.zeros([interplength,arraylen-1]) wcontourdep = np.zeros([interplength,arraylen-1]) wcontourtim = np.zeros([interplength,arraylen-1]) for ci in range(0,interplength): for cj in range(0,arraylen-1): wcontourdat[ci,cj] = GridWData[ci,k,cj] wcontourdep[ci,cj] = GridWDepths[ci,k,cj] wcontourtim[ci,cj] = GridWDates[ci,k,cj] # # Plotting correct contour data contourdat = np.zeros([interplength,arraylen]) for ci in range(0,interplength): for cj in range(0,arraylen): #cjj = cj + 1 contourdat[ci,cj] = GridData[ci,k,cj] # # Plot Set-up fig = plt.figure() ax = fig.add_subplot(1, 1, 1) # # Which Plotting Information? if k == 1: # density Wcontlevels= np.linspace(-6,6,101) Wconticks = [-5,-2.5, 0,2.5, 5] #contlevels= np.linspace(1026.75,1027.8,100) conticks = [1026.50, 1026.75, 1027.00, 1027.25, 1027.50, 1027.75, 1028.00] figtit = 'Isopycnal Gridded W Contour x 10^4 (m/s)' savetit = "DensityGriddedW_Contour" cs = plt.contourf(wcontourtim,wcontourdep,wcontourdat*10**(4), levels = Wcontlevels) plt.colorbar(cs, spacing='proportional', ticks = Wconticks) plt.set_cmap('BlueRedAlpha') cs2 = plt.contour(floatdate,Ygrid,contourdat, colors='k',levels = conticks) plt.clabel(cs2, conticks[1::2], inline=1, fmt='%1.1f', fontsize=8) # # Plotting ax = plt.gca() ax.set_xlabel("Days since 01/01/2013") ax.set_ylabel("Depth (m)") ax.set_ylim([-900,0]) ax.set_xlim([70,110]) # plt.title(figtit) fig.suptitle(figtit) plt.savefig(figdirpath + savetit+ "_zoom" +".png") if k == 2: # sal Wcontlevels= np.linspace(-6,6,101) Wconticks = [-5,-2.5, 0,2.5, 5] #contlevels= np.linspace(33.5,34.75,100) conticks = [33.25, 33.50, 33.75, 34.00, 34.25, 34.50, 34.75, 35.00] figtit = 'Isosaline Gridded W Contour x 10^4 (m/s)' savetit = "SalinityGriddedW_Contour" cs = plt.contourf(wcontourtim,wcontourdep,wcontourdat*10**(4), levels = Wcontlevels) plt.colorbar(cs, spacing='proportional', ticks = Wconticks) plt.set_cmap('BlueRedAlpha') cs2 = plt.contour(floatdate,Ygrid,contourdat, colors='k',levels = conticks) plt.clabel(cs2, conticks[1::2], inline=1, fmt='%1.1f', fontsize=8) # # Plotting ax = plt.gca() ax.set_xlabel("Days since 01/01/2013") ax.set_ylabel("Depth (m)") ax.set_ylim([-900,0]) ax.set_xlim([70,110]) # plt.title(figtit) fig.suptitle(figtit) plt.savefig(figdirpath + savetit+ "_zoom" +".png") # # =========================================================== # Contour Plotting Averaged Grided W Data # =========================================================== # # Do you want to plot Gridded Ws? Yes: gridWpltavg = 1, No: else gridWpltavg = 2 if gridWpltavg == 1: # # =========================================================== # Making Blue-Red Contour Map for Vertical Velocities # =========================================================== # # Make Blue-Red Color Scheme from matplotlib.colors import LinearSegmentedColormap cdict3 = {'red': ((0.0, 0.0, 0.0), (0.25,0.0, 0.0), (0.5, 0.8, 1.0), (0.75,1.0, 1.0), (1.0, 0.4, 1.0)), 'green': ((0.0, 0.0, 0.0), (0.25,0.0, 0.0), (0.5, 0.9, 0.9), (0.75,0.0, 0.0), (1.0, 0.0, 0.0)), 'blue': ((0.0, 0.0, 0.4), (0.25,1.0, 1.0), (0.5, 1.0, 0.8), (0.75,0.0, 0.0), (1.0, 0.0, 0.0)) } # # Make a modified version of cdict3 with some transparency # in the middle of the range. cdict4 = cdict3.copy() cdict4['alpha'] = ((0.0, 1.0, 1.0), # (0.25,1.0, 1.0), (0.5, 0.3, 0.3), # (0.75,1.0, 1.0), (1.0, 1.0, 1.0)) plt.register_cmap(name='BlueRedAlpha', data=cdict4) for k in range (0,7): # # Plotting correct W data wcontourdat = np.zeros([interplength,arraylen-3]) wcontourdep = np.zeros([interplength,arraylen-3]) wcontourtim = np.zeros([interplength,arraylen-3]) for ci in range(0,interplength): for cj in range(0,arraylen-3): wcontourdat[ci,cj] = GridWDataAvg[ci,k,cj] wcontourdep[ci,cj] = GridWDepthsAvg[ci,k,cj] wcontourtim[ci,cj] = GridWDatesAvg[ci,k,cj] # # Plotting correct contour data contourdat = np.zeros([interplength,arraylen]) for ci in range(0,interplength): for cj in range(0,arraylen): #cjj = cj + 1 contourdat[ci,cj] = GridData[ci,k,cj] if k == 1: # density # # Plot Set-up fig = plt.figure() ax = fig.add_subplot(1, 1, 1) # # Which Plotting Information? Wcontlevels= np.linspace(-30,30,101) Wconticks = [-25, -10, 0, 10, 25] #contlevels= np.linspace(1026.75,1027.8,100) conticks = [1026.50, 1026.75, 1027.00, 1027.25, 1027.50, 1027.75, 1028.00] figtit = '3 Profile Avg Isopycnal Gridded W Contour x 10^4 (m/s)' savetit = "DensityGriddedW_Contour_3Avg" cs = plt.contourf(wcontourtim,wcontourdep,wcontourdat*10**(4), levels = Wcontlevels) plt.colorbar(cs, spacing='proportional', ticks = Wconticks) plt.set_cmap('BlueRedAlpha') cs2 = plt.contour(floatdate,Ygrid,contourdat, colors='k',levels = conticks) plt.clabel(cs2, conticks[1::2], inline=1, fmt='%1.1f', fontsize=8) # # Plotting ax = plt.gca() ax.set_xlabel("Days since 01/01/2013") ax.set_ylabel("Depth (m)") ax.set_ylim([-900,0]) # plt.title(figtit) fig.suptitle(figtit) plt.savefig(figdirpath + savetit+".png") # # =========================================================== # Contour Plotting Averaged Grided W Data Zoomed # =========================================================== # # Do you want to plot Gridded Ws? Yes: gridWpltavg = 1, No: else gridWpltavg = 1 if gridWpltavg == 1: # # =========================================================== # Making Blue-Red Contour Map for Vertical Velocities # =========================================================== # # Make Blue-Red Color Scheme from matplotlib.colors import LinearSegmentedColormap cdict3 = {'red': ((0.0, 0.0, 0.0), (0.25,0.0, 0.0), (0.5, 0.8, 1.0), (0.75,1.0, 1.0), (1.0, 0.4, 1.0)), 'green': ((0.0, 0.0, 0.0), (0.25,0.0, 0.0), (0.5, 0.9, 0.9), (0.75,0.0, 0.0), (1.0, 0.0, 0.0)), 'blue': ((0.0, 0.0, 0.4), (0.25,1.0, 1.0), (0.5, 1.0, 0.8), (0.75,0.0, 0.0), (1.0, 0.0, 0.0)) } # # Make a modified version of cdict3 with some transparency # in the middle of the range. cdict4 = cdict3.copy() cdict4['alpha'] = ((0.0, 1.0, 1.0), # (0.25,1.0, 1.0), (0.5, 0.3, 0.3), # (0.75,1.0, 1.0), (1.0, 1.0, 1.0)) plt.register_cmap(name='BlueRedAlpha', data=cdict4) for k in range (0,7): # # Plotting correct W data wcontourdat = np.zeros([interplength,arraylen-3]) wcontourdep = np.zeros([interplength,arraylen-3]) wcontourtim = np.zeros([interplength,arraylen-3]) for ci in range(0,interplength): for cj in range(0,arraylen-3): wcontourdat[ci,cj] = GridWDataAvg[ci,k,cj] wcontourdep[ci,cj] = GridWDepthsAvg[ci,k,cj] wcontourtim[ci,cj] = GridWDatesAvg[ci,k,cj] # # Plotting correct contour data contourdat = np.zeros([interplength,arraylen]) for ci in range(0,interplength): for cj in range(0,arraylen): #cjj = cj + 1 contourdat[ci,cj] = GridData[ci,k,cj] if k == 1: # density # # Plot Set-up fig = plt.figure() ax = fig.add_subplot(1, 1, 1) # # Which Plotting Information? Wcontlevels= np.linspace(-6,6,101) Wconticks = [-5,-2.5, 0,2.5, 5] #contlevels= np.linspace(1026.75,1027.8,100) conticks = [1026.50, 1026.75, 1027.00, 1027.25, 1027.50, 1027.75, 1028.00] figtit = '3 Profile Avg Isopycnal Gridded W Contour x 10^4 (m/s)' savetit = "DensityGriddedW_Contour_3Avg" cs = plt.contourf(wcontourtim,wcontourdep,wcontourdat*10**(4), levels = Wcontlevels) plt.colorbar(cs, spacing='proportional', ticks = Wconticks) plt.set_cmap('BlueRedAlpha') cs2 = plt.contour(floatdate,Ygrid,contourdat, colors='k',levels = conticks) plt.clabel(cs2, conticks[1::2], inline=1, fmt='%1.1f', fontsize=8) # # Plotting ax = plt.gca() ax.set_xlabel("Days since 01/01/2013") ax.set_ylabel("Depth (m)") ax.set_ylim([-900,0]) ax.set_xlim([70,110]) # plt.title(figtit) fig.suptitle(figtit) plt.savefig(figdirpath + savetit+ "_zoom" +".png") # # =========================================================== # Contour Plotting Averaged Grided W Data w/ Isoline avg Ws # =========================================================== # # Do you want to plot Gridded Ws? Yes: gridWpltIsoLineWavg = 1, No: else gridWpltIsoLineWavg = 2 if gridWpltIsoLineWavg == 1: # # =========================================================== # Making Blue-Red Contour Map for Vertical Velocities # =========================================================== # # Make Blue-Red Color Scheme from matplotlib.colors import LinearSegmentedColormap cdict3 = {'red': ((0.0, 0.0, 0.0), (0.25,0.0, 0.0), (0.5, 0.8, 1.0), (0.75,1.0, 1.0), (1.0, 0.4, 1.0)), 'green': ((0.0, 0.0, 0.0), (0.25,0.0, 0.0), (0.5, 0.9, 0.9), (0.75,0.0, 0.0), (1.0, 0.0, 0.0)), 'blue': ((0.0, 0.0, 0.4), (0.25,1.0, 1.0), (0.5, 1.0, 0.8), (0.75,0.0, 0.0), (1.0, 0.0, 0.0)) } # # Make a modified version of cdict3 with some transparency # in the middle of the range. cdict4 = cdict3.copy() cdict4['alpha'] = ((0.0, 1.0, 1.0), # (0.25,1.0, 1.0), (0.5, 0.3, 0.3), # (0.75,1.0, 1.0), (1.0, 1.0, 1.0)) plt.register_cmap(name='BlueRedAlpha', data=cdict4) for k in range (0,7): # # Plotting correct W data wcontourdat = np.zeros([interplength,arraylen-3]) wcontourdep = np.zeros([interplength,arraylen-3]) wcontourtim = np.zeros([interplength,arraylen-3]) for ci in range(0,interplength): for cj in range(0,arraylen-3): wcontourdat[ci,cj] = GridWDataAvg[ci,k,cj] wcontourdep[ci,cj] = GridWDepthsAvg[ci,k,cj] wcontourtim[ci,cj] = GridWDatesAvg[ci,k,cj] # # Plotting correct contour data contourdat = np.zeros([interplength,arraylen]) for ci in range(0,interplength): for cj in range(0,arraylen): #cjj = cj + 1 contourdat[ci,cj] = GridData[ci,k,cj] # # Plot Set-up fig = plt.figure() ax = fig.add_subplot(1, 1, 1) # # Which Plotting Information? # Which Plotting Information? if k == 1: # density Wcontlevels= np.linspace(-30,30,101) Wconticks = [-25, -10, 0, 10, 25] #contlevels= np.linspace(1026.75,1027.8,100) conticks = [1026.50, 1026.75, 1027.00, 1027.25, 1027.50, 1027.75, 1028.00] figtit = '3 Profile Avg Isopycnal Gridded W Contour x 10^4 (m/s)' savetit = "DensityGriddedW_Contour" cs = plt.contourf(wcontourtim,wcontourdep,wcontourdat*10**(4), levels = Wcontlevels) plt.colorbar(cs, spacing='proportional', ticks = Wconticks) plt.set_cmap('BlueRedAlpha') cs2 = plt.contour(floatdate,Ygrid,contourdat, colors='k',levels = conticks) plt.clabel(cs2, conticks[1::2], inline=1, fmt='%1.1f', fontsize=8) figtit2 = ' and Isochly W Quivers' savetit2 = "_ChlorophyllW_Quivers_3Avg" from pylab import * Q = plt.quiver(IsoBioWDatesAvg[1::10], IsoBioWDepthsAvg[1::10], IsoBioFakeAvg[1::10], IsoBioWDataAvg[1::10]) plt.quiverkey(Q, 0.22, 0.95, 0.001, r'$1 \times 10^{-3} m/s$', labelpos='W', fontproperties={'weight': 'bold'}) # # Plotting ax = plt.gca() ax.set_xlabel("Days since 01/01/2013") ax.set_ylabel("Depth (m)") ax.set_ylim([-900,0]) # plt.title(figtit) fig.suptitle(figtit + " with " + figtit2 ) plt.savefig(figdirpath + savetit+ savetit2 + ".png") # =========================================================== # Contour Plotting Grided W Data w/ Isoline Ws # =========================================================== # # Do you want to plot Gridded Ws? Yes: gridWpltIsoLineW = 1, No: else gridWpltIsoLineW = 2 if gridWpltIsoLineW == 1: # # =========================================================== # Making Blue-Red Contour Map for Vertical Velocities # =========================================================== # # Make Blue-Red Color Scheme from matplotlib.colors import LinearSegmentedColormap cdict3 = {'red': ((0.0, 0.0, 0.0), (0.25,0.0, 0.0), (0.5, 0.8, 1.0), (0.75,1.0, 1.0), (1.0, 0.4, 1.0)), 'green': ((0.0, 0.0, 0.0), (0.25,0.0, 0.0), (0.5, 0.9, 0.9), (0.75,0.0, 0.0), (1.0, 0.0, 0.0)), 'blue': ((0.0, 0.0, 0.4), (0.25,1.0, 1.0), (0.5, 1.0, 0.8), (0.75,0.0, 0.0), (1.0, 0.0, 0.0)) } # # Make a modified version of cdict3 with some transparency # in the middle of the range. cdict4 = cdict3.copy() cdict4['alpha'] = ((0.0, 1.0, 1.0), # (0.25,1.0, 1.0), (0.5, 0.3, 0.3), # (0.75,1.0, 1.0), (1.0, 1.0, 1.0)) plt.register_cmap(name='BlueRedAlpha', data=cdict4) for k in range (0,7): # # Plotting correct W data wcontourdat = np.zeros([interplength,arraylen-1]) wcontourdep = np.zeros([interplength,arraylen-1]) wcontourtim = np.zeros([interplength,arraylen-1]) for ci in range(0,interplength): for cj in range(0,arraylen-1): wcontourdat[ci,cj] = GridWData[ci,k,cj] wcontourdep[ci,cj] = GridWDepths[ci,k,cj] wcontourtim[ci,cj] = GridWDates[ci,k,cj] # # Plotting correct contour data contourdat = np.zeros([interplength,arraylen]) for ci in range(0,interplength): for cj in range(0,arraylen): #cjj = cj + 1 contourdat[ci,cj] = GridData[ci,k,cj] # # Plot Set-up fig = plt.figure() ax = fig.add_subplot(1, 1, 1) # # Which Plotting Information? if k == 1: # density Wcontlevels= np.linspace(-50,50,101) Wconticks = [-50, -25, 0, 25, 50] #contlevels= np.linspace(1026.75,1027.8,100) conticks = [1026.50, 1026.75, 1027.00, 1027.25, 1027.50, 1027.75, 1028.00] figtit = 'Isopycnal Gridded W Contour x 10^4 (m/s)' savetit = "DensityGriddedW_Contour" cs = plt.contourf(wcontourtim,wcontourdep,wcontourdat*10**(4), levels = Wcontlevels) plt.colorbar(cs, spacing='proportional', ticks = Wconticks) plt.set_cmap('BlueRedAlpha') cs2 = plt.contour(floatdate,Ygrid,contourdat, colors='k',levels = conticks) plt.clabel(cs2, conticks[1::2], inline=1, fmt='%1.1f', fontsize=8) # Plotting figtit2 = ' and Isochly W Quivers' savetit2 = "_ChlorophyllW_Quivers" from pylab import * Q = plt.quiver(IsoBioWDates[1::10], IsoBioWDepths[1::10], IsoBioFake[1::10], IsoBioWData[1::10]) plt.quiverkey(Q, 0.22, 0.95, 0.001, r'$1 \times 10^{-3} m/s$', labelpos='W', fontproperties={'weight': 'bold'}) ax = plt.gca() ax.set_xlabel("Days since 01/01/2013") ax.set_ylabel("Depth (m)") ax.set_ylim([-900,0]) # plt.title(figtit) fig.suptitle(figtit + " with " + figtit2 ) plt.savefig(figdirpath + savetit+ savetit2 + ".png") # # =========================================================== # Contour Plotting Grided W Data w/ Isoline Ws and Sink Ws # =========================================================== # # Do you want to plot Gridded Ws? Yes: gridWpltIsoLineW = 1, No: else gridWpltIsoLineSinkW = 2 if gridWpltIsoLineSinkW == 1: # # =========================================================== # Making Blue-Red Contour Map for Vertical Velocities # =========================================================== # # Make Blue-Red Color Scheme from matplotlib.colors import LinearSegmentedColormap cdict3 = {'red': ((0.0, 0.0, 0.0), (0.25,0.0, 0.0), (0.5, 0.8, 1.0), (0.75,1.0, 1.0), (1.0, 0.4, 1.0)), 'green': ((0.0, 0.0, 0.0), (0.25,0.0, 0.0), (0.5, 0.9, 0.9), (0.75,0.0, 0.0), (1.0, 0.0, 0.0)), 'blue': ((0.0, 0.0, 0.4), (0.25,1.0, 1.0), (0.5, 1.0, 0.8), (0.75,0.0, 0.0), (1.0, 0.0, 0.0)) } # # Make a modified version of cdict3 with some transparency # in the middle of the range. cdict4 = cdict3.copy() cdict4['alpha'] = ((0.0, 1.0, 1.0), # (0.25,1.0, 1.0), (0.5, 0.3, 0.3), # (0.75,1.0, 1.0), (1.0, 1.0, 1.0)) plt.register_cmap(name='BlueRedAlpha', data=cdict4) # # Plotting correct W data wcontourdat = np.zeros([interplength,arraylen-1]) wcontourdep = np.zeros([interplength,arraylen-1]) wcontourtim = np.zeros([interplength,arraylen-1]) for ci in range(0,interplength): for cj in range(0,arraylen-1): wcontourdat[ci,cj] = GridWData[ci,1,cj] wcontourdep[ci,cj] = GridWDepths[ci,1,cj] wcontourtim[ci,cj] = GridWDates[ci,1,cj] # # Plotting correct contour data contourdat = np.zeros([interplength,arraylen]) for ci in range(0,interplength): for cj in range(0,arraylen): #cjj = cj + 1 contourdat[ci,cj] = GridData[ci,1,cj] # # Plot Set-up fig = plt.figure() ax = fig.add_subplot(1, 1, 1) # # Which Plotting Information? Wcontlevels= np.linspace(-30,30,101) Wconticks = [-25, -10, 0, 10, 25] #contlevels= np.linspace(1026.75,1027.8,100) conticks = [1026.50, 1026.75, 1027.00, 1027.25, 1027.50, 1027.75, 1028.00] figtit = 'Isopycnal Gridded W Contour x 10^4 (m/s)' savetit = "DensityGriddedW_Contour" cs = plt.contourf(wcontourtim,wcontourdep,wcontourdat*10**(4), levels = Wcontlevels) plt.colorbar(cs, spacing='proportional', ticks = Wconticks) plt.set_cmap('BlueRedAlpha') cs2 = plt.contour(floatdate,Ygrid,contourdat, colors='k',levels = conticks) plt.clabel(cs2, conticks[1::2], inline=1, fmt='%1.1f', fontsize=8) # # Plotting figtit2 = ' and "Sinking" Quivers' savetit2 = "_SinkW_Quivers" from pylab import * #Q = plt.quiver(IsoBioWDates[1::10], IsoBioWDepths[1::10], IsoBioFake[1::10], IsoBioWData[1::10]) #plt.quiverkey(Q, 0.22, 0.95, 0.001, r'$5 \times 10^{-3} m/s$', labelpos='W') Q2 = plt.quiver(IsoBioWDates[1::10], IsoBioWDepths[1::10], IsoBioFake[1::10], SinkWData[1::10], color='g') plt.quiverkey(Q2, 0.22, 0.96, 0.001, r'$ 1 \times 10^{-3} m/s$', labelpos='W') ax = plt.gca() ax.set_xlabel("Days since 01/01/2013") ax.set_ylabel("Depth (m)") ax.set_ylim([-900,0]) # plt.title(figtit) fig.suptitle(figtit + " with " + figtit2 ) plt.savefig(figdirpath + savetit+ savetit2 + ".png") # # =========================================================== # Contour Plotting Grided W Data w/ Isoline Ws # =========================================================== # # Do you want to plot Gridded Ws? Yes: gridWpltIsoLineW = 1, No: else gridWpltIsoLineSinkWAvg = 2 if gridWpltIsoLineSinkWAvg == 1: # # =========================================================== # Making Blue-Red Contour Map for Vertical Velocities # =========================================================== # # Make Blue-Red Color Scheme from matplotlib.colors import LinearSegmentedColormap cdict3 = {'red': ((0.0, 0.0, 0.0), (0.25,0.0, 0.0), (0.5, 0.8, 1.0), (0.75,1.0, 1.0), (1.0, 0.4, 1.0)), 'green': ((0.0, 0.0, 0.0), (0.25,0.0, 0.0), (0.5, 0.9, 0.9), (0.75,0.0, 0.0), (1.0, 0.0, 0.0)), 'blue': ((0.0, 0.0, 0.4), (0.25,1.0, 1.0), (0.5, 1.0, 0.8), (0.75,0.0, 0.0), (1.0, 0.0, 0.0)) } # # Make a modified version of cdict3 with some transparency # in the middle of the range. cdict4 = cdict3.copy() cdict4['alpha'] = ((0.0, 1.0, 1.0), # (0.25,1.0, 1.0), (0.5, 0.3, 0.3), # (0.75,1.0, 1.0), (1.0, 1.0, 1.0)) plt.register_cmap(name='BlueRedAlpha', data=cdict4) # # Plotting correct W data wcontourdat = np.zeros([interplength,arraylen-3]) wcontourdep = np.zeros([interplength,arraylen-3]) wcontourtim = np.zeros([interplength,arraylen-3]) for ci in range(0,interplength): for cj in range(0,arraylen-3): wcontourdat[ci,cj] = GridWDataAvg[ci,1,cj] wcontourdep[ci,cj] = GridWDepthsAvg[ci,1,cj] wcontourtim[ci,cj] = GridWDatesAvg[ci,1,cj] # # Plot Set-up fig = plt.figure() ax = fig.add_subplot(1, 1, 1) # # Which Plotting Information? Wcontlevels= np.linspace(-50,50,101) Wconticks = [-50, -25, 0, 25, 50] #contlevels= np.linspace(1026.75,1027.8,100) conticks = [1026.50, 1026.75, 1027.00, 1027.25, 1027.50, 1027.75, 1028.00] figtit = '3 Profile Avg Isopycnal Gridded W Contour x 10^4 (m/s)' savetit = "DensityGriddedW_Contour" cs = plt.contourf(wcontourtim,wcontourdep,wcontourdat*10**(4), levels = Wcontlevels) plt.colorbar(cs, spacing='proportional', ticks = Wconticks) plt.set_cmap('BlueRedAlpha') cs2 = plt.contour(floatdate,Ygrid,contourdat, colors='k',levels = conticks) plt.clabel(cs2, conticks[1::2], inline=1, fmt='%1.1f', fontsize=8) # # Plotting figtit2 = ' and "Sinking" Quivers' savetit2 = "_SinkW_Quivers_3Avg" from pylab import * #Q = plt.quiver(IsoBioWDatesAvg[1::10], IsoBioWDepthsAvg[1::10], IsoBioFakeAvg[1::10], IsoBioWDataAvg[1::10], scale = 1) #plt.quiverkey(Q, 0.22, 0.95, 0.001, r'$1 \times 10^{-3} m/s$', labelpos='W') Q2 = plt.quiver(IsoBioWDatesAvg[1::10], IsoBioWDepthsAvg[1::10], IsoBioFakeAvg[1::10], SinkWDataAvg[1::10], color='g') plt.quiverkey(Q2, 0.22, 0.96, 0.001, r'$ 1 \times 10^{-3} m/s$', labelpos='W') ax = plt.gca() ax.set_xlabel("Days since 01/01/2013") ax.set_ylabel("Depth (m)") ax.set_ylim([-900,0]) # plt.title(figtit) fig.suptitle(figtit + " with " + figtit2 ) plt.savefig(figdirpath + savetit+ savetit2 + ".png") lineplotting = 2 if lineplotting == 1: from pylab import * numbiolinesavg = len(IsoBioWDataAvg) # # Plot Set-up fig = plt.figure() from matplotlib.font_manager import FontProperties fontP = FontProperties() fontP.set_size('x-small') ax = fig.add_subplot(2, 1, 1) # # Which Plotting Information? axhline(0, color='k') for j in range(0,numbiolinesavg): if j == 0: plt.plot(IsoBioWDatesAvg[j], IsoBioWDataAvg[j]*10**4, '0.75',label='3 Profile Avg. (3PA) Chlorophyll Vert. Vel.') else: plt.plot(IsoBioWDatesAvg[j], IsoBioWDataAvg[j]*10**4, '0.75') plt.plot(IsoBioWDatesDepthAvgAvg, IsoBioWDataDepthAvgAvg*10**4, 'k',label='Depth Avg. 3PA Chlorophyll Vert. Vel.',linewidth = 2) plt.plot(IsoBioWDatesDepthAvgAvg, SinkWDataDepthAvgAvg*10**4, 'g',label='Depth Avg. 3PA Sinking Vel.',linewidth = 2) plt.plot(IsoBioWDatesDepthAvgAvg, NearestWGriddedAvgAvg*10**4, 'b',label='Depth Avg. 3PA Isopycnal Vel. Vel. ',linewidth = 2) plt.plot(IsoBioWDatesDepthAvgAvg, IsoCdomWDataDepthAvgAvg*10**4, 'r', label = 'Depth Avg. 3PA CDOM Vert. Vel.',linewidth = 2) l = legend(loc = 2,prop = fontP) ax.set_xlabel("Days since 01/01/2013") ax.set_ylabel("Vert. Vel. x 10^4 (m/s)") ax.set_xlim([70,110]) ax.set_ylim([-13,18]) # # # ax2 = fig.add_subplot(2, 1, 2) # # Which Plotting Information? axhline(0, color='k') for j in range(0,numbiolinesavg): if j == 0: plt.plot(IsoBioWDatesAvg[j], IsoBioWDepthsAvg[j], '0.75',label='3 Profile Avg. (3PA) Chlorophyll Vert. Vel.') else: plt.plot(IsoBioWDatesAvg[j], IsoBioWDepthsAvg[j], '0.75') plt.plot(IsoBioWDatesDepthAvgAvg, IsoBioWDepthsDepthAvgAvg, 'k',label='Depth Avg. 3PA Chlorophyll, Sinking, and Isopycnal Vert. Vel.',linewidth = 2) plt.plot(IsoBioWDatesDepthAvgAvg, IsoCdomWDepthsDepthAvgAvg, 'r', label = 'Depth Avg. 3PA CDOM Vert. Vel.',linewidth = 2) # # l = legend(loc = 3,prop = fontP) ax2.set_xlabel("Days since 01/01/2013") ax2.set_ylabel("Avg. Vert. Vel. Depth (m)") ax2.set_xlim([70,110]) #ax.set_yscale('log') savetit = "VertVelocity_Depth_TimeSeries" plt.savefig(figdirpath + savetit + ".png") os.chdir('C:/Documents and Settings/Alex/Documents/Research/Delaware/MUSTACHE/Jan01_Jun04_2013/code/interpolate_profiles')