#! /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, writes the # linearly interpolated profiles (by variable) # to csv files # # - First column in the Ygrid, across are the # individual profiles # # # =========================================================== # 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] # # =========================================================== # Initial Data Contour Plotting # =========================================================== contoursdirpath ='C:/Documents and Settings/Alex/Documents/Research/Delaware/MUSTACHE/Jan01_Jun04_2013/plots/contours/' # #pp1 = PdfPages('LinearInterpolationGriddedContourDataPlots.pdf') # # Do you want to plot contours? Yes: contplt = 1, No: else contplt = 1 if contplt == 1: for k in range (0,7): # # 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,k,cj] # # Plot Set-up fig = plt.figure() ax = fig.add_subplot(1, 1, 1) # # Which Plotting Information? if k == 0: #temperature 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 = 'Temperature (C)' savetit = "Temperature_Contour" cs = plt.contourf(floatdate,Ygrid,contourdat, levels = contlevels) plt.colorbar(cs, spacing='proportional', ticks = conticks) elif k == 1: # density contlevels= np.linspace(1026.75,1027.8,100) conticks = [1026.50, 1026.75, 1027.00, 1027.25, 1027.50, 1027.75, 1028.00] figtit = 'Density (kg/m^3)' savetit = "Density_Contour" cs = plt.contourf(floatdate,Ygrid,contourdat, levels = contlevels) plt.colorbar(cs, spacing='proportional', ticks = conticks) elif k == 2: # sal 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 = 'Salinity (psu)' savetit = "Salinity_Contour" cs = plt.contourf(floatdate,Ygrid,contourdat, levels = contlevels) plt.colorbar(cs, spacing='proportional', ticks = conticks) elif k == 3: #oxygen contlevels= np.linspace(4,10,100) conticks = [4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0] figtit = 'Oxygen Concentration (mg/l)' savetit = "Oxygen_Contour" cs = plt.contourf(floatdate,Ygrid,contourdat, levels = contlevels) plt.colorbar(cs, spacing='proportional', ticks = conticks) elif k == 4: # chl 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) elif k == 5: #backscatter 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 = 'Backscatter Cross Section (m^-1 sr^-1)' savetit = "Backscatter_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) elif k == 6: #CDOM 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 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(contoursdirpath + savetit+".png") # # # # =========================================================== # # 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 Avg Gridded Change Velocities Based on Nearest Values # # =========================================================== # # # isobiolines = np.linspace(.1,.70,49) # 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 Isoline 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 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 Sink Vel # # =========================================================== # # # 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] # # # # =========================================================== # # Find Avg Sink Vel # # =========================================================== # # # SinkWDataAvg = 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] # # # # =========================================================== # # Chly Contour Map w/ Chly Isoline Ws # # =========================================================== # # # contlevels= np.logspace(-2.5,.65,100) # conticks = [0.01,0.25,0.50, 1.00, 1.50, 2.00, 3.00] # figtit = 'Chlorophyll Contour (micro gram/liter) with Avg Isochlyline Ws' # savetit = "Chlorophyll_Contour_Chlorophyll_IsolineW" # fig = plt.figure() # ax = fig.add_subplot(1, 1, 1) # # # # Plotting correct contour data # contourdat = np.zeros([interplength,arraylen]) # for ci in range(0,interplength): # for cj in range(0,arraylen): # contourdat[ci,cj] = GridData[ci,4,cj] # 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) # 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', # fontproperties={'weight': 'bold'}) # ax.set_xlabel("Days since 01/01/2013") # ax.set_ylabel("Depth (m)") # ax.set_ylim([-900,0]) # fig.suptitle(figtit) # plt.savefig(savetit + ".png") # # # # =========================================================== # # Chly Contour Map w/ Avg Chly Isoline Ws # # =========================================================== # # # contlevels= np.logspace(-2.5,.65,100) # conticks = [0.01,0.25,0.50, 1.00, 1.50, 2.00, 3.00] # figtit = 'Chlorophyll Contour (micro gram/liter) with Avg Isochlyline Ws' # savetit = "Chlorophyll_Contour_Chlorophyll_IsolineWAvg" # fig = plt.figure() # ax = fig.add_subplot(1, 1, 1) # # # # Plotting correct contour data # contourdat = np.zeros([interplength,arraylen]) # for ci in range(0,interplength): # for cj in range(0,arraylen): # contourdat[ci,cj] = GridData[ci,4,cj] # 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) # 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'$5 \times 10^{-3} m/s$', labelpos='W', # fontproperties={'weight': 'bold'}) # ax.set_xlabel("Days since 01/01/2013") # ax.set_ylabel("Depth (m)") # ax.set_ylim([-900,0]) # fig.suptitle(figtit) # plt.savefig(savetit + ".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(-50,50,101) # Wconticks = [-50, -25, 0, 25, 50] # #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 Veritical Velocities x 10^4 (m/s)' # savetit = "Temperature_GriddedW" # 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) # elif 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 Veritical Velocities x 10^4 (m/s)' # savetit = "Density_GriddedW" # 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) # elif k == 2: # sal # Wcontlevels= np.linspace(-50,50,101) # Wconticks = [-50, -25, 0, 25, 50] # #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 Veritical Velocities x 10^4 (m/s)' # savetit = "Salinity_GriddedW" # 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) # elif k == 3: #oxygen # Wcontlevels= np.linspace(-50,50,101) # Wconticks = [-50, -25, 0, 25, 50] # #contlevels= np.linspace(4,10,100) # conticks = [4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0] # figtit = 'Isooxy Veritical Velocities x 10^4 (m/s)' # savetit = "Oxygen_GriddedW" # 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) # elif k == 4: # chl # Wcontlevels= np.linspace(-50,50,101) # Wconticks = [-50, -25, 0, 25, 50] # #contlevels= np.logspace(-2.5,.65,100) # conticks = [0.01,0.25,0.50, 1.00, 1.50, 2.00, 3.00] # figtit = 'Isochly Veritical Velocities x 10^4 (m/s)' # savetit = "Chlorophyll_GriddedW" # 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) # elif k == 5: #backscatter # Wcontlevels= np.linspace(-50,50,101) # Wconticks = [-50, -25, 0, 25, 50] # #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 Veritical Velocities x 10^4 (m/s)' # savetit = "Backscatter_GriddedW" # 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) # elif k == 6: #CDOM # Wcontlevels= np.linspace(-50,50,101) # Wconticks = [-50, -25, 0, 25, 50] # #contlevels= np.linspace(1,3,100) # conticks = [1.5, 2.0, 2.5, 3.0] # figtit = 'IsoCDOM Veritical Velocities x 10^4 (m/s)' # savetit = "CDOM_GriddedW" # 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(savetit+".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] # # # # Plot Set-up # fig = plt.figure() # ax = fig.add_subplot(1, 1, 1) # # # # Which Plotting Information? # if k == 0: #temperature # Wcontlevels= np.linspace(-50,50,101) # Wconticks = [-50, -25, 0, 25, 50] # #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 Averaged Veritical Velocities x 10^4 (m/s)' # savetit = "Temperature_GriddedWAvg" # 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) # elif 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 Averaged Veritical Velocities x 10^4 (m/s)' # savetit = "Density_GriddedWAvg" # 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) # elif k == 2: # sal # Wcontlevels= np.linspace(-50,50,101) # Wconticks = [-50, -25, 0, 25, 50] # #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 Averaged Veritical Velocities x 10^4 (m/s)' # savetit = "Salinity_GriddedWAvg" # 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) # elif k == 3: #oxygen # Wcontlevels= np.linspace(-50,50,101) # Wconticks = [-50, -25, 0, 25, 50] # #contlevels= np.linspace(4,10,100) # conticks = [4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0] # figtit = 'Isooxy Averaged Veritical Velocities x 10^4 (m/s)' # savetit = "Oxygen_GriddedWAvg" # 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) # elif k == 4: # chl # Wcontlevels= np.linspace(-50,50,101) # Wconticks = [-50, -25, 0, 25, 50] # #contlevels= np.logspace(-2.5,.65,100) # conticks = [0.01,0.25,0.50, 1.00, 1.50, 2.00, 3.00] # figtit = 'Isochly Averaged Veritical Velocities x 10^4 (m/s)' # savetit = "Chlorophyll_GriddedWAvg" # 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) # elif k == 5: #backscatter # Wcontlevels= np.linspace(-50,50,101) # Wconticks = [-50, -25, 0, 25, 50] # #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 Averaged Veritical Velocities x 10^4 (m/s)' # savetit = "Backscatter_GriddedWAvg" # 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) # elif k == 6: #CDOM # Wcontlevels= np.linspace(-50,50,101) # Wconticks = [-50, -25, 0, 25, 50] # #contlevels= np.linspace(1,3,100) # conticks = [1.5, 2.0, 2.5, 3.0] # figtit = 'IsoCDOM Averaged Veritical Velocities x 10^4 (m/s)' # savetit = "CDOM_GriddedWAvg" # 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(savetit+".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? # if k == 0: #temperature # Wcontlevels= np.linspace(-50,50,101) # Wconticks = [-50, -25, 0, 25, 50] # #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 Averaged Veritical Velocities x 10^4 (m/s)' # savetit = "Temperature_GriddedWAvg" # 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) # elif 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 Averaged Veritical Velocities x 10^4 (m/s)' # savetit = "Density_GriddedWAvg" # 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) # elif k == 2: # sal # Wcontlevels= np.linspace(-50,50,101) # Wconticks = [-50, -25, 0, 25, 50] # #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 Averaged Veritical Velocities x 10^4 (m/s)' # savetit = "Salinity_GriddedWAvg" # 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) # elif k == 3: #oxygen # Wcontlevels= np.linspace(-50,50,101) # Wconticks = [-50, -25, 0, 25, 50] # #contlevels= np.linspace(4,10,100) # conticks = [4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0] # figtit = 'Isooxy Averaged Veritical Velocities x 10^4 (m/s)' # savetit = "Oxygen_GriddedWAvg" # 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) # elif k == 4: # chl # Wcontlevels= np.linspace(-50,50,101) # Wconticks = [-50, -25, 0, 25, 50] # #contlevels= np.logspace(-2.5,.65,100) # conticks = [0.01,0.25,0.50, 1.00, 1.50, 2.00, 3.00] # figtit = 'Isochly Averaged Veritical Velocities x 10^4 (m/s)' # savetit = "Chlorophyll_GriddedWAvg" # 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) # elif k == 5: #backscatter # Wcontlevels= np.linspace(-50,50,101) # Wconticks = [-50, -25, 0, 25, 50] # #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 Averaged Veritical Velocities x 10^4 (m/s)' # savetit = "Backscatter_GriddedWAvg" # 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) # elif k == 6: #CDOM # Wcontlevels= np.linspace(-50,50,101) # Wconticks = [-50, -25, 0, 25, 50] # #contlevels= np.linspace(1,3,100) # conticks = [1.5, 2.0, 2.5, 3.0] # figtit = 'IsoCDOM Averaged Veritical Velocities x 10^4 (m/s)' # savetit = "CDOM_GriddedWAvg" # 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 = 'Avg Isochlyline Ws (quivers)' # savetit2 = "_Chlorophyll_IsolineWAvg" # 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'$5 \times 10^{-3} m/s$', 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(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 == 0: #temperature # Wcontlevels= np.linspace(-50,50,101) # Wconticks = [-50, -25, 0, 25, 50] # #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 Veritical Velocities x 10^4 (m/s)' # savetit = "Temperature_GriddedW" # 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) # elif 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 Veritical Velocities x 10^4 (m/s)' # savetit = "Density_GriddedW" # 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) # elif k == 2: # sal # Wcontlevels= np.linspace(-50,50,101) # Wconticks = [-50, -25, 0, 25, 50] # #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 Veritical Velocities x 10^4 (m/s)' # savetit = "Salinity_GriddedW" # 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) # elif k == 3: #oxygen # Wcontlevels= np.linspace(-50,50,101) # Wconticks = [-50, -25, 0, 25, 50] # #contlevels= np.linspace(4,10,100) # conticks = [4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0] # figtit = 'Isooxy Veritical Velocities x 10^4 (m/s)' # savetit = "Oxygen_GriddedW" # 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) # elif k == 4: # chl # Wcontlevels= np.linspace(-50,50,101) # Wconticks = [-50, -25, 0, 25, 50] # #contlevels= np.logspace(-2.5,.65,100) # conticks = [0.01,0.25,0.50, 1.00, 1.50, 2.00, 3.00] # figtit = 'Isochly Veritical Velocities x 10^4 (m/s)' # savetit = "Chlorophyll_GriddedW" # 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) # elif k == 5: #backscatter # Wcontlevels= np.linspace(-50,50,101) # Wconticks = [-50, -25, 0, 25, 50] # #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 Veritical Velocities x 10^4 (m/s)' # savetit = "Backscatter_GriddedW" # 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) # elif k == 6: #CDOM # Wcontlevels= np.linspace(-50,50,101) # Wconticks = [-50, -25, 0, 25, 50] # #contlevels= np.linspace(1,3,100) # conticks = [1.5, 2.0, 2.5, 3.0] # figtit = 'IsoCDOM Veritical Velocities x 10^4 (m/s)' # savetit = "CDOM_GriddedW" # 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 = 'Isochlyline Ws (quivers)' # savetit2 = "_Chlorophyll_IsolineW" # 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', # 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(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 = 1 # 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(-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 Veritical Velocities x 10^4 (m/s)' # savetit = "Density_GriddedW" # 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 = 'Isochlyline Ws (black) and Sink Ws (green)' # savetit2 = "_Chlorophyll_IsolineW_SinkW" # 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'$ 5 \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(savetit+ savetit2 + "onlysink.png") # # # # =========================================================== # # Contour Plotting Grided W Data w/ Isoline Ws # # =========================================================== # # # # Do you want to plot Gridded Ws? Yes: gridWpltIsoLineW = 1, No: else # gridWpltIsoLineSinkWAvg = 1 # 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 = 'Isopycnal Veritical Velocities x 10^4 (m/s)' # savetit = "Density_GriddedW" # 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 = 'Avg Isochlyline Ws (black) and Sink Ws (green)' # savetit2 = "_Chlorophyll_IsolineW_SinkWAvg" # 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(savetit+ savetit2 + "onlysink.png") # # # # # # # # # =========================================================== # # # Find Isopycnal Depths # # # =========================================================== # # # # # Dendepths = np.zeros([7,arraylen]) # # for i in range(0,arraylen): # # count2 = 0 # # while (GridData[count2,1,i] >= 1027.2): # # count2 = count2 + 1 # # Dendepths[0,i] = Ygrid[count2] # # # # # count3 = 0 # # while (GridData[count3,1,i] >= 1027.3): # # count3 = count3 + 1 # # Dendepths[1,i] = Ygrid[count3] # # # # # count4 = 0 # # while (GridData[count4,1,i] >= 1027.4): # # count4 = count4 + 1 # # Dendepths[2,i] = Ygrid[count4] # # # # # count5 = 0 # # while (GridData[count5,1,i] >= 1027.5): # # count5 = count5 + 1 # # Dendepths[3,i] = Ygrid[count5] # # # # # count6 = 0 # # while (GridData[count6,1,i] >= 1027.6): # # count6 = count6 + 1 # # Dendepths[4,i] = Ygrid[count6] # # # # # count7 = 0 # # while (GridData[count7,1,i] >= 1027.7): # # count7 = count7 + 1 # # Dendepths[5,i] = Ygrid[count7] # # # # # count8 = 0 # # while (GridData[count8,1,i] >= 1027.8): # # count8 = count8 + 1 # # Dendepths[6,i] = Ygrid[count8] # # # =========================================================== # # # Find Isopycnal Change Velocities # # # =========================================================== # # # # # w = np.zeros([7,arraylen-1]) # # ufake = np.zeros([7,arraylen-1]) # # wdepths = np.zeros([7,arraylen-1]) # # wdates = np.zeros([7,arraylen-1]) # # for i in range(0,arraylen-1): # # for k in range(0,7): # # j = i # # w[k,i] = (Dendepths[k,j+1] - Dendepths[k,j])/172800 # # ufake[k,i] = 0 # # wdepths[k,i] = (Dendepths[k,j+1] + Dendepths[k,j])/2 # # wdates[k,i] = (floatdate[j+1] + floatdate[j])/2 # # # # # # =========================================================== # # # Multi Average Isopycnal Change Velocities # # # =========================================================== # # # # # vellen = len(w[1,:]) # # wavg3 = np.zeros([7,vellen-2]) # # ufakeavg3 = np.zeros([7,vellen-2]) # # wdatesavg3 = np.zeros([7,vellen-2]) # # wdepthsavg3 = np.zeros([7,vellen-2]) # # for k in range(0,7): # # for i in range(0,len(wavg3[1,:])): # # j = i # # jj = i + 1 # # jjj = i + 2 # # wavg3[k,i] = (w[k,j] + w[k,jj] + w[k,jjj])/3 # # ufakeavg3[k,i] = 0 # # wdatesavg3[k,i] = wdates[k,jj] # # wdepthsavg3[k,i] = wdepths[k,jj] # # # # # # =========================================================== # # # Multi Average Isopycnal Change Velocities # # # =========================================================== # # # # # vellen = len(w[1,:]) # # wavg5 = np.zeros([7,vellen-4]) # # ufakeavg5 = np.zeros([7,vellen-4]) # # wdatesavg5 = np.zeros([7,vellen-4]) # # wdepthsavg5 = np.zeros([7,vellen-4]) # # for k in range(0,7): # # for i in range(0,len(wavg5[1,:])): # # j = i # # jj = i + 1 # # jjj = i + 2 # # jjjj = i + 3 # # jjjjj = i + 4 # # wavg5[k,i] = (w[k,j] + w[k,jj] + w[k,jjj]+ w[k,jjjj] + w[k,jjjjj])/5 # # ufakeavg5[k,i] = 0 # # wdatesavg5[k,i] = wdates[k,jjj] # # wdepthsavg5[k,i] = wdepths[k,jjj] # # # os.chdir('../../..') os.chdir('C:/Documents and Settings/Alex/Documents/Research/Delaware/MUSTACHE/Jan01_Jun04_2013/code/interpolate_profiles')