#! /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: 01/13/2014 # # NOTES: 1) Does all the OSCAR Data Processing # # # # =========================================================== # Import Modules # =========================================================== import numpy as np import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt from datetime import datetime from mpl_toolkits.basemap import Basemap, addcyclic from mpl_toolkits.basemap import shiftgrid, cm from scipy.ndimage.filters import minimum_filter, maximum_filter from netCDF4 import Dataset import glob import os from scipy.io import netcdf import matplotlib.transforms as mtransforms from matplotlib.patches import FancyBboxPatch # from matplotlib.transforms import Bboxcx from matplotlib.path import Path import matplotlib.patches as patches import math as ma import csv 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 # # =========================================================== # # FUNCTIONS # # =========================================================== # # =========================================================== # Function to input delX, delY, return bearing and magnitude # =========================================================== # def find_bearing_magnitude(X, Y): import math as m if ((Y >= 0) and (X >=0 )): bearing_rads = m.atan(abs(X)/abs(Y)) bearing = m.degrees(bearing_rads) magnitude = abs(X)/m.sin(bearing_rads) elif ((Y < 0) and (X >=0)): bearing_rads = m.atan(abs(Y)/abs(X)) bearing = m.degrees(bearing_rads) + 90 magnitude = abs(Y)/m.sin(bearing_rads) elif ((Y < 0) and (X < 0)): bearing_rads = m.atan(abs(X)/abs(Y)) bearing = m.degrees(bearing_rads) + 180 magnitude = abs(X)/m.sin(bearing_rads) elif ((Y >= 0) and (X < 0)): bearing_rads = m.atan(abs(Y)/abs(X)) bearing = m.degrees(bearing_rads) + 270 magnitude = abs(Y)/m.sin(bearing_rads) else: print "#### ERROR IN: FIND BEARING AND MAG FUNCTION ####" return bearing_rads, bearing, magnitude # # =========================================================== # Function Determine new Lat/Lon from Old + Bearing + dist # =========================================================== # def destpoint(lat1, lon1, brng, d): import math R = 6374 #Radius of the Earth # brng = Bearing converted to radians. # d = Distance in km # lat1 = Current lat point converted to radians # lon1 = Current lon point converted to radians lat2 = math.asin(math.sin(lat1)*math.cos(d/R) + math.cos(lat1)*math.sin(d/R)*math.cos(brng)) lon2 = lon1 + math.atan2(math.sin(brng)*math.sin(d/R)*math.cos(lat1), math.cos(d/R)-math.sin(lat1)*math.sin(lat2)) lat22 = math.degrees(lat2) lon22 = math.degrees(lon2) return lat22, lon22 # # =========================================================== # 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 # # =========================================================== # Function to Calculate Distances on a Sphere in kilometers # =========================================================== # # def pos2dist(pos): # # Pos = [lon1,lat1,lon2,lat2] # import math as m # R_aver = 6374 # pi = 3.14 # deg2rad = pi/180 # lag3 = pos[1] * deg2rad # lon3 = pos[0] * deg2rad # lag4 = pos[3] * deg2rad # lon4 = pos[2] * deg2rad # dist = R_aver * m.acos(m.cos(lag3)*m.cos(lag4)*m.cos(lon3-lon4) + m.sin(lag3)*m.sin(lag4)) # # Returns a distance in KM # return dist def pos2dist(pos): # Pos = [lon1,lat1,lon2,lat2] import math as m R_aver = 6374 pi = 3.14 deg2rad = pi/180 lat1 = pos[1] * deg2rad #lat1 lon1 = pos[0] * deg2rad # lon1 lat2 = pos[3] * deg2rad #lat2 lon2 = pos[2] * deg2rad #lon2 dlon = lon2 - lon1 dlat = lat2 - lat1 e = m.cos(lat1)*m.cos(lat2)*m.sin(dlon/2)**2 d = m.sin(dlat/2)**2 c = d + e b = c**0.5 dist = 2*R_aver*m.asin(b) # Returns a distance in KM return dist # # =========================================================== # Function to new lon from zonal displacemet in km # =========================================================== # def lonchange(input): # input = [lat,lon1,dzon] import math as m R_aver = 6374 pi = 3.14 deg2rad = pi/180 rad2deg = 180/pi lat = input[0] * deg2rad #lat1 lon1 = input[1] * deg2rad # lon1 dzon = input[2] # zonal displacement in km2 c = m.cos(lat) b = m.sin(dzon/2/R_aver) a = b/c lon2rad = 2*m.asin(a) + lon1 lon2 = lon2rad*rad2deg return lon2 # # =========================================================== # Function to new lat from meridional displacemet in km # =========================================================== # def latchange(input): # input = [lat1,dmer] import math as m R_aver = 6374 pi = 3.14 deg2rad = pi/180 rad2deg = 180/pi lat1 = input[0]*deg2rad dmer = input[1] # zonal displacement in km2 lat2rad = dmer/R_aver + lat1 lat2 = lat2rad*rad2deg return lat2 # # =========================================================== # Function to Do Regressions # =========================================================== # def linearregress(x, y): from scipy import stats # # Linearly Regression slope, intercept, rvalue, pvalue, stderr = stats.linregress(x,y) # # Regression Line regressline = slope*x+intercept # return regressline, rvalue, pvalue, stderr # # # =================================================================================== # Function for Bilinear Interpolation # =================================================================================== # def bilinear_interpolation(x, y, points): # # Order Points vy x, then by y points = sorted(points) # order points by x, then by y (x1, y1, q11), (_x1, y2, q12), (x2, _y1, q21), (_x2, _y2, q22) = points # # Are the points a rectangle? if x1 != _x1 or x2 != _x2 or y1 != _y1 or y2 != _y2: raise ValueError('points do not form a rectangle') if not x1 <= x <= x2 or not y1 <= y <= y2: raise ValueError('(x, y) not within the rectangle') # # Bilinear Interp and return value. return (q11 * (x2 - x) * (y2 - y) + q21 * (x - x1) * (y2 - y) + q12 * (x2 - x) * (y - y1) + q22 * (x - x1) * (y - y1) ) / ((x2 - x1) * (y2 - y1) + 0.0) # # # =================================================================================== # Function to Calculate the Backscatter of Seawater # =================================================================================== # def betasw_ZHH2009(Lambda,Tc,theta,S): # # Xiaodong Zhang, Lianbo Hu, and Ming-Xia He (2009), Scatteirng by pure seawater: Effect of salinity, Optics Express, Vol. 17, No. 7, 5698-5710 # # Lambda (nm): wavelength # Tc: temperauter in degree Celsius, must be a scalar # S: salinity, must be scalar # betasw: volume scattering at angles defined by theta. Its size is [x y], where x is the number of angles (x = length(theta)) and y is the number of wavelengths in Lambda (y = length(Lambda)) # beta90sw: volume scattering at 90 degree. Its size is [1 y] # bw: total scattering coefficient. Its size is [1 y] for backscattering coefficients, divide total scattering by 2 # # Originally By: Xiaodong Zhang, March 10, 2009 (Modified for Python by: Alexander R. Davies, April 13, 2014) # import numpy as np # # =================================================================================== # Values and Constants # =================================================================================== # #Avogadro's constant Na = 6.0221417930e23 # # Boltzmann constant Kbz = 1.3806503e-23 # # Absolute tempearture Tk = Tc+273.15 # # Molecular weigth of water in kg/mol M0 = 18e-3 # # Depolarization ratio, default = 0.039 will be used from Farinato and Roswell (1976). You can change this!! delta = 0.039 # # Degrees to Radians rad = np.radians(theta) # # =================================================================================== # absolute refractive index of seawater (nsw); d(nsw) w.r.t. salinity # =================================================================================== # # refractive index of air is from Ciddor (1996,Applied Optics) #n_air = 1.0 + (5792105.0./(238.0185 -1./(Lambda/1e3).^2)+167917.0./(57.362-1./(Lambda/1e3 ).^2))/1e8; << Original Matlab Code n_air = 1.0 + (5792105.0/(238.0185 -1 /(Lambda/1e3)**2)+167917.0 /(57.362-1 /(Lambda/1e3)**2))/10**8 # # refractive index of seawater is from Quan and Fry (1994, Applied Optics) n0 = 1.31405 n1 = 1.779e-4 n2 = -1.05e-6 n3 = 1.6e-8 n4 = -2.02e-6 n5 = 15.868 n6 = 0.01155 n7 = -0.00423 n8 = -4382 n9 = 1.1455e6 # # For Pure Water First # nsw = n0+(n1+n2*Tc+n3*Tc^2 )*S+n4*Tc^2 +(n5+n6*S+n7*Tc)./Lambda + n8./Lambda.^2 + n9./Lambda.^3; << Original Matlab Code nsw = n0+(n1+n2*Tc+n3*Tc**2)*S+n4*Tc**2+(n5+n6*S+n7*Tc) /Lambda + n8 /Lambda**2 + n9 /Lambda**3 # # Saltwater nsw = nsw*n_air # # Derivative #dnswds = (n1+n2*Tc+n3*Tc^2 +n6./Lambda).*n_air; << Original Matlab Code dnswds = (n1+n2*Tc+n3*Tc**2+n6/Lambda)*n_air dnds = dnswds # # # =================================================================================== # isothermal compressibility # =================================================================================== # # From Lepple & Millero (1971,Deep Sea-Research), pages 10-11; error ~ +/-0.004e-6 bar^-1 # # pure water secant bulk Millero (1980, Deep-sea Research) #kw = 19652.21 + 148.4206*Tc - 2.327105*Tc.^2 + 1.360477e-2*Tc.^3 - 5.155288e-5*Tc.^4; << Original Matlab Code kw = 19652.21 + 148.4206*Tc - 2.327105*Tc**2 + 1.360477e-2*Tc**3 - 5.155288e-5*Tc**4; Btw_cal = 1/kw # # # seawater secant bulk #a0 = 54.6746 - 0.603459*Tc +1.09987e-2*Tc.^2 - 6.167e-5*Tc.^3; << Original Matlab Code a0 = 54.6746 - 0.603459*Tc +1.09987e-2*Tc**2 - 6.167e-5*Tc**3 # #b0 = 7.944e-2 + 1.6483e-2*Tc -5.3009e-4*Tc.^2; << Original Matlab Code b0 = 7.944e-2 + 1.6483e-2*Tc -5.3009e-4*Tc**2 # #Ks = kw + a0*S + b0*S.^1.5; << Original Matlab Code Ks = kw + a0*S + b0*S**1.5 # # calculate seawater isothermal compressibility from the secant bulk IsoComp = 1./Ks*1e-5; # unit is pa # # =================================================================================== # Density of Water # =================================================================================== # # density of water and seawater,unit is Kg/m^3, from UNESCO,38,1981 a0 = 8.24493e-1 a1 = -4.0899e-3 a2 = 7.6438e-5 a3 = -8.2467e-7 a4 = 5.3875e-9 a5 = -5.72466e-3 a6 = 1.0227e-4 a7 = -1.6546e-6 a8 = 4.8314e-4 # b0 = 999.842594 b1 = 6.793952e-2 b2 = -9.09529e-3 b3 = 1.001685e-4 b4 = -1.120083e-6 b5 = 6.536332e-9; # # density for pure water #density_w = b0 + b1*Tc + b2*Tc^2 + b3*Tc^3 + b4*Tc^4 + b5*Tc^5; << Original Matlab Code density_w = b0 + b1*Tc + b2*Tc**2 + b3*Tc**3 + b4*Tc**4 + b5*Tc**5 # # density for pure seawater #density_sw = density_w +((a0 +a1*Tc +a2*Tc^2 + a3*Tc^3 + a4*Tc^4)*S + (a5 + a6*Tc +a7*Tc^2)*S**1.5 + a8*S.^2); << Original Matlab Code density_sw = density_w +((a0 +a1*Tc +a2*Tc**2 + a3*Tc**3 + a4*Tc**4)*S + (a5 + a6*Tc +a7*Tc**2)*S**1.5 + a8*S**2); # # =================================================================================== # Partial derivative of natural logarithm of water activity w.r.t.salinity # =================================================================================== # # water activity data of seawater is from Millero and Leung (1976,American # Journal of Science,276,1035-1077). Table 19 was reproduced using # Eqs.(14,22,23,88,107) then were fitted to polynominal equation. # dlnawds is partial derivative of natural logarithm of water activity # w.r.t.salinity # lnaw = (-1.64555e-6-1.34779e-7*Tc+1.85392e-9*Tc.^2-1.40702e-11*Tc.^3)+...... # (-5.58651e-4+2.40452e-7*Tc-3.12165e-9*Tc.^2+2.40808e-11*Tc.^3).*S+...... # (1.79613e-5-9.9422e-8*Tc+2.08919e-9*Tc.^2-1.39872e-11*Tc.^3).*S.^1.5+...... # (-2.31065e-6-1.37674e-9*Tc-1.93316e-11*Tc.^2).*S.^2; # #dlnawds = (-5.58651e-4 + 2.40452e-7*Tc - 3.12165e-9*Tc.^2 + 2.40808e-11*Tc.^3) + 1.5*(1.79613e-5 - 9.9422e-8*Tc + 2.08919e-9*Tc.^2 - 1.39872e-11*Tc.^3)*S.^0.5 + 2*(-2.31065e-6 - 1.37674e-9*Tc - 1.93316e-11*Tc.^2).*S; << Original Matlab Code dlnawds = (-5.58651e-4 + 2.40452e-7*Tc - 3.12165e-9*Tc**2 + 2.40808e-11*Tc**3) + 1.5*(1.79613e-5 - 9.9422e-8*Tc + 2.08919e-9*Tc**2 - 1.39872e-11*Tc**3)*S**0.5 + 2*(-2.31065e-6 - 1.37674e-9*Tc - 1.93316e-11*Tc**2)*S # # =================================================================================== # Density derivative of refractive index from PMH model # =================================================================================== # n_wat = nsw n_wat2 = n_wat**2 # #n_density_derivative = (n_wat2-1)*(1 + 2/3*(n_wat2+2)*(n_wat/3- 1/3./n_wat).^2); << Original Matlab Code c1 = (n_wat2-1) c21 = 2./3.*(n_wat2+2.) c22 = n_wat/3. c23 = 1./3./n_wat c2 = (1. + c21*(c22-c23)**2.) n_density_derivative = c1*c2 DFRI = n_density_derivative # # =================================================================================== # Volume Scattering # =================================================================================== # # volume scattering at 90 degree due to the density fluctuation #beta_df = pi*pi/2*((Lambda*1e-9).^(-4))*Kbz*Tk*IsoComp.*DFRI.^2*(6+6*delta)/(6-7*delta); << Original Matlab Code beta_df = np.pi*np.pi/2*((Lambda*1e-9)**(-4))*Kbz*Tk*IsoComp*DFRI**2*(6 + 6*delta)/(6 - 7*delta) # # volume scattering at 90 degree due to the concentration fluctuation #flu_con = S*M0*dnds.^2/density_sw/(-dlnawds)/Na; << Matlab Code flu_con = S*M0*dnds**2/density_sw/(-dlnawds)/Na # #beta_cf = 2*pi*pi*((Lambda*1e-9).^(-4)).*nsw.^2.*(flu_con)*(6+6*delta)/(6-7*delta); << Original Matlab Code beta_cf = 2*np.pi*np.pi*((Lambda*1e-9)**(-4))*nsw**2*(flu_con)*(6 + 6*delta)/(6 -7*delta) # # total volume scattering at 90 degree beta90sw = beta_df+beta_cf # # volume scattering coefficient bsw=8*np.pi/3*beta90sw*(2 + delta)/(1 + delta) # # Volume scattering at the angle defined by theta betasw=beta90sw*(1+((np.cos(rad))**2)*(1-delta)/(1+delta)) return betasw,beta90sw,bsw # # =========================================================== # # READING FLOAT DATA # # =========================================================== # # # =================================================================================== # Initial Set-up to read float data # =================================================================================== # # Get to the float data... os.chdir('/data/orbprocess_mail/alex/Jan01_Jun04_2013/data/type/test_Mar2014') # # Compile a list of the filenames and sort them based on the time they were generated fullnames = glob.glob('Full*') gradnames = glob.glob('Grad*') infonames = glob.glob('Text*') fullnames.sort(key = lambda x: os.path.getmtime(x)) gradnames.sort(key = lambda x: os.path.getmtime(x)) infonames.sort(key = lambda x: os.path.getmtime(x)) # # Array Initialization arraylen = len(infonames) floatdate = np.zeros(arraylen) floatlat = [None]*arraylen floatlon = [None]*arraylen day = np.zeros(arraylen) month=[None]*arraylen year=[None]*arraylen daystr=[None]*arraylen listdate =[None]*arraylen # # =========================================================== # Processing Float Time and Location Data # =========================================================== # # Retrive the Information for Each Data Profile for d in range(0,int(arraylen)): # # Get the Date Info f = open('/data/orbprocess_mail/alex/Jan01_Jun04_2013/data/type/test_Mar2014/' + 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 # # Get the LaT and Lon f = open('/data/orbprocess_mail/alex/Jan01_Jun04_2013/data/type/test_Mar2014/' + infonames[d], 'r') while 1: line = f.readline() if line[:3] == "Lat": floatlat[d] = str(line[5:12]) break f = open('/data/orbprocess_mail/alex/Jan01_Jun04_2013/data/type/test_Mar2014/' + infonames[d], 'r') while 1: line = f.readline() if line[:3] == "Lon": floatlon[d] = str(line[5:12]) break # # Get the DOY for each profile 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] # # Change the longitude format and make lat/lon floats for i in range(0,arraylen): floatlon[i] = 360 + float(floatlon[i]) floatlat[i] = float(floatlat[i]) # # Get Back to start directory os.chdir('/home/ardavies/satdata/OSCAR') # # =========================================================== # # READING OSCAR DATA # # =========================================================== # # ncdump file header to .txt file os.system('ncdump -h oscar-third-Jan5-jun20.nc > oscar-third-Jan5-jun20.txt') # # open the netcdf file to read ncf = netcdf.netcdf_file('oscar-third-Jan5-jun20.nc','r') # # Getting the netcdf data into the corresponding arrays datadates = ncf.variables['time'][:].squeeze() v = ncf.variables['v'][:].squeeze() u = ncf.variables['u'][:].squeeze() rawlon = ncf.variables['lon'][:].squeeze() # degrees E rawlat = ncf.variables['lat'][:].squeeze() # degrees N # # Array Initailization numdates = len(u[:,0,0]) lonlen = len(rawlon) latlen = len(rawlat) lat = np.zeros([latlen,lonlen]) lon = np.zeros([latlen,lonlen]) uavg = np.zeros([numdates-1,latlen,lonlen]) vavg = np.zeros([numdates-1,latlen,lonlen]) # # Getting lat/lon's in i,j arrays for i in range(0,lonlen): lon[:,i] = rawlon[i] for j in range(0,latlen): lat[j,:] = rawlat[j] # # Average Velocities for k in range(0,numdates-1): for i in range(0,lonlen): for j in range(0,latlen): uavg[k,j,i] = (u[k,j,i] + u[k+1,j,i])/2 vavg[k,j,i] = (v[k,j,i] + v[k+1,j,i])/2 # # DOY of 5 day avg Oscar data centerdates = np.zeros(numdates) for k in range(0,numdates): centerdates[k] = 6 + datadates[k] # # =========================================================== # # DAILY AVERAGED OSCAR DATA # # =========================================================== # # Daily Averaged OSCAR Data Array Initialization oscardates = np.linspace(centerdates[0],centerdates[numdates-1],(centerdates[numdates-1]-centerdates[0])+1) # udaily = np.zeros([len(oscardates)-8,latlen,lonlen]) vdaily = np.zeros([len(oscardates)-8,latlen,lonlen]) # for ii in range(0,len(oscardates)-8): # # Get the closest OSCAR data date to the current day in the loop. k is index of the closest centerdate closestdate, k = find_nearest(centerdates, oscardates[ii]) # # If the closest data day and the current day in the loop are the same, the current day take 100% of the u and v velocities from the oscar data day if (closestdate == oscardates[ii]): udaily[ii,:,:] = u[k,:,:] vdaily[ii,:,:] = v[k,:,:] # # What if k = 0? We must "weigh" the u and v currents on the current day of the loop between centerdates[k=0] and centerdates[(k=0) +1] as k-1 doesn't exist elif (k == 0): # # next closest date must be at k+1 nextclosestdate = centerdates[k+1] kk = k + 1 # # The u and v components at the current day in the loop are weighed by how close they are to the oscar data dates. udaily[ii,:,:] = u[k,:,:]*(abs(oscardates[ii] - nextclosestdate)/abs(nextclosestdate-closestdate)) + u[kk,:,:]*(abs(oscardates[ii] - closestdate)/abs(nextclosestdate-closestdate)) vdaily[ii,:,:] = v[k,:,:]*(abs(oscardates[ii] - nextclosestdate)/abs(nextclosestdate-closestdate)) + v[kk,:,:]*(abs(oscardates[ii] - closestdate)/abs(nextclosestdate-closestdate)) # # If the dates do not match exactly, and k doesn't equal 0 then we need to figure out if the next closest oscar data dates are before or after centerdates[k] else: # # Next closest day to the current day of the loop is AFTER centerdates[k] if ((abs(oscardates[ii]-centerdates[k+1]))<=(abs(oscardates[ii]-centerdates[k-1]))): nextclosestdate = centerdates[k+1] kk = k+1 # # Next closest day to the current day of the loop is BEFORE centerdates[k] else: nextclosestdate = centerdates[k-1] kk = k-1 # # The u and v components at the current day in the loop are weighed by how close they are to the oscar data dates. udaily[ii,:,:] = u[k,:,:]*(abs(oscardates[ii] - nextclosestdate)/abs(nextclosestdate-closestdate)) + u[kk,:,:]*(abs(oscardates[ii] - closestdate)/abs(nextclosestdate-closestdate)) vdaily[ii,:,:] = v[k,:,:]*(abs(oscardates[ii] - nextclosestdate)/abs(nextclosestdate-closestdate)) + v[kk,:,:]*(abs(oscardates[ii] - closestdate)/abs(nextclosestdate-closestdate)) # # =========================================================== # # MKE # # =========================================================== # import math as ma mke5day = np.zeros([numdates-1,latlen,lonlen]) mke = np.zeros([len(oscardates)-8,latlen,lonlen]) # # MKE from Raw OSCAR Data for k in range(0,numdates-1): for i in range(0,lonlen): for j in range(0,latlen): mke5day[k,j,i] = 0.5*((u[k,j,i]*100)**2 + (v[k,j,i]*100)**2) # # MKE for Daily Weighed OSCAR Currents for k in range(0,len(oscardates)-8): for i in range(0,lonlen): for j in range(0,latlen): mke[k,j,i] = 0.5*((udaily[k,j,i]*100)**2 + (vdaily[k,j,i]*100)**2) # # =========================================================== # # OSCAR CURRENT AND MAKE INTERPOLATION TO FLOAT LOCATION # # =========================================================== # # # =================================================================================== # Find Nearest Lat's and Lon's to float location for U and V components # =================================================================================== # latvalue1 = np.zeros([arraylen]) latvalue2 = np.zeros([arraylen]) lonvalue1 = np.zeros([arraylen]) lonvalue2 = np.zeros([arraylen]) # latindex1 = np.zeros([arraylen]) latindex2 = np.zeros([arraylen]) lonindex1 = np.zeros([arraylen]) lonindex2 = np.zeros([arraylen]) # for jj in range(0,arraylen): # # Get the closest OSCAR data latitude with respect to the float latitude latvalue1[jj], latindex1[jj] = find_nearest(lat[:,0], floatlat[jj]) # # Find the next closest OSCAR data latitude with respect to the float latitude upperlat = lat[latindex1[jj]+1,0] lowerlat = lat[latindex1[jj]-1,0] # # Closest lat + 0.33 degrees? if (abs(floatlat[jj]-upperlat)<=abs(floatlat[jj]-lowerlat)): latindex2[jj] = latindex1[jj] + 1 # # Closest lat - 0.33 degrees? else: latindex2[jj] = latindex1[jj] - 1 latvalue2[jj] = lat[latindex2[jj],0] # # Get the closest OSCAR data longitude with respect to the float longitude lonvalue1[jj], lonindex1[jj] = find_nearest(lon[0,:], floatlon[jj]) # # Find the next closest OSCAR data longitude with respect to the float longitude upperlon = lon[0,lonindex1[jj]+1] lowerlon = lon[0,lonindex1[jj]-1] # # Closest lon + 0.33 degrees? if (abs(floatlon[jj]-upperlon)<=abs(floatlon[jj]-lowerlon)): lonindex2[jj] = lonindex1[jj] + 1 # # Closest lon - 0.33 degrees? else: lonindex2[jj] = lonindex1[jj] - 1 lonvalue2[jj] = lon[0,lonindex2[jj]] # # =================================================================================== # Original 5 Day Bi-Linear Interpolation # =================================================================================== # # Initializing Arrays for Interpolation floatU5day = np.zeros([arraylen]) floatV5day = np.zeros([arraylen]) floatMKE5day = np.zeros([arraylen]) # # Loop through the float data for jj in range(0,arraylen): # # Loop though the daily oscar data for k in range(0,numdates-3): if (centerdates[k]-2 <= floatdate[jj] <= centerdates[k] +2): # # Interpolate Linearly Along Closest Latitude (latvalue1) x1_closest5day = abs(pos2dist([lonvalue1[jj],latvalue1[jj],floatlon[jj],latvalue1[jj]])) x2_closest5day = abs(pos2dist([lonvalue2[jj],latvalue1[jj],floatlon[jj],latvalue1[jj]])) xtot_closest5day = abs(pos2dist([lonvalue1[jj],latvalue1[jj],lonvalue2[jj],latvalue1[jj]])) Uinterp_closest5day = u[k,latindex1[jj], lonindex1[jj]]*(x2_closest5day /xtot_closest5day ) + u[k, latindex1[jj], lonindex2[jj]]*(x1_closest5day /xtot_closest5day) Vinterp_closest5day = v[k,latindex1[jj], lonindex1[jj]]*(x2_closest5day /xtot_closest5day ) + v[k, latindex1[jj], lonindex2[jj]]*(x1_closest5day /xtot_closest5day) MKEinterp_closest5day = mke5day[k,latindex1[jj], lonindex1[jj]]*(x2_closest5day /xtot_closest5day ) + mke5day[k, latindex1[jj], lonindex2[jj]]*(x1_closest5day /xtot_closest5day) # # Interpolate Linearly Along Furthest Latitude (latvalue2) x1_furthest5day = abs(pos2dist([lonvalue1[jj],latvalue2[jj],floatlon[jj],latvalue2[jj]])) x2_furthest5day = abs(pos2dist([lonvalue2[jj],latvalue2[jj],floatlon[jj],latvalue2[jj]])) xtot_furthest5day = abs(pos2dist([lonvalue1[jj],latvalue2[jj],lonvalue2[jj],latvalue2[jj]])) Uinterp_furthest5day = udaily[k,latindex2[jj], lonindex1[jj]]*(x2_furthest5day /xtot_furthest5day ) + u[k, latindex2[jj], lonindex2[jj]]*(x1_furthest5day /xtot_furthest5day) Vinterp_furthest5day = vdaily[k,latindex2[jj], lonindex1[jj]]*(x2_furthest5day /xtot_furthest5day ) + v[k, latindex2[jj], lonindex2[jj]]*(x1_furthest5day /xtot_furthest5day) MKEinterp_furthest5day = mke5day[k,latindex2[jj], lonindex1[jj]]*(x2_furthest5day /xtot_furthest5day ) + mke5day[k, latindex2[jj], lonindex2[jj]]*(x1_furthest5day /xtot_furthest5day) # # Tnterpolate Along Londitude y_closest5day = abs(pos2dist([floatlon[jj],latvalue1[jj],floatlon[jj],floatlat[jj]])) y_furthest5day = abs(pos2dist([floatlon[jj],latvalue2[jj],floatlon[jj],floatlat[jj]])) ytot5day = abs(pos2dist([floatlon[jj],latvalue1[jj],floatlon[jj],latvalue2[jj]])) floatU5day [jj]= Uinterp_furthest5day *(y_closest5day /ytot5day ) + Uinterp_closest5day *(y_furthest5day /ytot5day ) floatV5day [jj]= Vinterp_furthest5day *(y_closest5day /ytot5day ) + Vinterp_closest5day *(y_furthest5day /ytot5day ) floatMKE5day [jj]= MKEinterp_furthest5day *(y_closest5day /ytot5day ) + MKEinterp_closest5day *(y_furthest5day /ytot5day ) # # =================================================================================== # Daily Bi-Linear Interpolation # =================================================================================== # # Initializing Arrays for Interpolation floatU = np.zeros([arraylen]) floatV = np.zeros([arraylen]) floatMKE = np.zeros([arraylen]) # # Loop through the float data for jj in range(0,arraylen): # # Loop though the daily oscar data for k in range(0,len(oscardates)-8): # # Loop through the daily oscar data ates to find where it equals the float data dates if (oscardates[k] == floatdate[jj]): # # Interpolate Linearly Along Closest Latitude (latvalue1) x1_closest = abs(pos2dist([lonvalue1[jj],latvalue1[jj],floatlon[jj],latvalue1[jj]])) x2_closest = abs(pos2dist([lonvalue2[jj],latvalue1[jj],floatlon[jj],latvalue1[jj]])) xtot_closest = abs(pos2dist([lonvalue1[jj],latvalue1[jj],lonvalue2[jj],latvalue1[jj]])) Uinterp_closest = udaily[k,latindex1[jj], lonindex1[jj]]*(x2_closest/xtot_closest) + udaily[k, latindex1[jj], lonindex2[jj]]*(x1_closest/xtot_closest) Vinterp_closest = vdaily[k,latindex1[jj], lonindex1[jj]]*(x2_closest/xtot_closest) + vdaily[k, latindex1[jj], lonindex2[jj]]*(x1_closest/xtot_closest) MKEinterp_closest = mke[k,latindex1[jj], lonindex1[jj]]*(x2_closest/xtot_closest) + mke[k, latindex1[jj], lonindex2[jj]]*(x1_closest/xtot_closest) # # Interpolate Linearly Along Furthest Latitude (latvalue2) x1_furthest = abs(pos2dist([lonvalue1[jj],latvalue2[jj],floatlon[jj],latvalue2[jj]])) x2_furthest = abs(pos2dist([lonvalue2[jj],latvalue2[jj],floatlon[jj],latvalue2[jj]])) xtot_furthest = abs(pos2dist([lonvalue1[jj],latvalue2[jj],lonvalue2[jj],latvalue2[jj]])) Uinterp_furthest = udaily[k,latindex2[jj], lonindex1[jj]]*(x2_furthest/xtot_furthest) + udaily[k, latindex2[jj], lonindex2[jj]]*(x1_furthest/xtot_furthest) Vinterp_furthest = vdaily[k,latindex2[jj], lonindex1[jj]]*(x2_furthest/xtot_furthest) + vdaily[k, latindex2[jj], lonindex2[jj]]*(x1_furthest/xtot_furthest) MKEinterp_furthest = mke[k,latindex2[jj], lonindex1[jj]]*(x2_furthest/xtot_furthest) + mke[k, latindex2[jj], lonindex2[jj]]*(x1_furthest/xtot_furthest) # # Tnterpolate Along Londitude y_closest = abs(pos2dist([floatlon[jj],latvalue1[jj],floatlon[jj],floatlat[jj]])) y_furthest = abs(pos2dist([floatlon[jj],latvalue2[jj],floatlon[jj],floatlat[jj]])) ytot = abs(pos2dist([floatlon[jj],latvalue1[jj],floatlon[jj],latvalue2[jj]])) floatU[jj]= Uinterp_furthest*(y_closest/ytot) + Uinterp_closest*(y_furthest/ytot) floatV[jj]= Vinterp_furthest*(y_closest/ytot) + Vinterp_closest*(y_furthest/ytot) floatMKE[jj]= MKEinterp_furthest*(y_closest/ytot) + MKEinterp_closest*(y_furthest/ytot) # # =========================================================== # # FLOAT & OCSAR CURRENT TRACERS # # =========================================================== # # # =================================================================================== # The Tracer U and V components for each float surfacing # =================================================================================== # # Initialize Arrays tracerU_kmday = np.zeros([arraylen]) tracerV_kmday = np.zeros([arraylen]) tracerX_km = np.zeros([arraylen]) tracerY_km = np.zeros([arraylen]) for jj in range(0,arraylen): # # U and V Velocitues in km/day tracerU_kmday[jj] = floatU[jj]*86400/1000 tracerV_kmday[jj] = floatV[jj]*86400/1000 # # Distance Tracer will go in 2 days tracerX_km[jj] = tracerU_kmday[jj]*2 tracerY_km[jj] = tracerV_kmday[jj]*2 # # =========================================================== # Use haversine's to calculate new lat and lon of tracer # =========================================================== # tracer_newlat_U_V = np.zeros([arraylen]) tracer_newlon_U_V = np.zeros([arraylen]) magnitude_U_V = np.zeros([arraylen]) for jj in range(0,arraylen): tracer_newlat_U_V[jj] = latchange([floatlat[jj],tracerY_km[jj]]) tracer_newlon_U_V[jj] = lonchange([floatlat[jj],floatlon[jj],tracerX_km[jj]]) for jj in range(0,arraylen): magnitude_U_V[jj] = pos2dist([floatlon[jj],floatlat[jj],tracer_newlon_U_V[jj],tracer_newlat_U_V[jj]]) difnorm = np.zeros([arraylen]) normtrac = np.zeros([arraylen]) for jj in range(0,arraylen): normtrac[jj] = np.sqrt(tracerX_km[jj]**2 + tracerY_km[jj]**2) difnorm[jj] = magnitude_U_V[jj]-normtrac[jj] # # Plot norm diffs coderunner = 2 if coderunner == 1: # # Loop through the float data pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/comparenorm.pdf') from matplotlib.colors import LogNorm from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt import numpy as np # fig = plt.figure() # # ax = fig.add_axes() # ax = fig.add_subplot(1, 1, 1) from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from pylab import * from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True fig = plt.figure() # ax = fig.add_axes() ax = fig.add_subplot(111) ax.plot(floatdate,magnitude_U_V,'k', linewidth = 3, label=r'$D_{t}$', zorder = 1) ax.scatter(floatdate,normtrac, label=r'$\|\Delta_{float}\|$', zorder = 2) ax.plot(floatdate,abs(difnorm), linestyle='--',color = '#696969', linewidth = 3, label=r'$|D_{t}-\|\Delta_{float}\||$', zorder = 0) l = legend(loc = 4) ax.set_yscale('log') ax.set_xscale('linear') # ax.set_yticks([10**0,10**1,10**2,10**3,10**4]) # ax.set_yticklabels([r'10$^{\mbox{\normalsize 0}}$', r'10$^{\mbox{\normalsize 1}}$',r'10$^{\mbox{\normalsize 2}}$',r'10$^{\mbox{\normalsize 3}}$',r'10$^{\mbox{\normalsize 4}}$']) # from matplotlib.ticker import AutoMinorLocator # minorLocator = AutoMinorLocator() # ax.xaxis.set_minor_locator(minorLocator) ax.set_xticks([20,40,60,80,100,120, 140]) ax.set_xticklabels([ r'20',r'40',r'60',r'80',r'100',r'120',r'140']) # ax.set_ylim([10**0,10**4]) # ax.set_xlim([10,155]) ax.set_xlabel(r"Day of Year") ax.set_ylabel(r"Distance (km)") plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/comparenorm.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =========================================================== # Tracer bearing and magnitude # =========================================================== # origtotracer = np.zeros([arraylen-1]) origtofloat = np.zeros([arraylen-1]) tracertofloat = np.zeros([arraylen-1]) tracerfloatratio = np.zeros([arraylen-1]) # for jj in range(0,arraylen-1): origtotracer[jj] = pos2dist([floatlon[jj],floatlat[jj], tracer_newlon_U_V[jj], tracer_newlat_U_V[jj]]) origtofloat[jj] = pos2dist([floatlon[jj],floatlat[jj], floatlon[jj+1],floatlat[jj+1]]) tracertofloat[jj] = pos2dist([floatlon[jj+1],floatlat[jj+1], tracer_newlon_U_V[jj], tracer_newlat_U_V[jj]]) tracerfloatratio[jj] = abs(tracertofloat[jj])/origtotracer[jj] # # =========================================================== # # LINEARLY INTERPOLATING PROFILES TO KNOWN VERTICAL GRID # # =========================================================== # gridWdat = 1 if gridWdat == 1: # # =========================================================== # Read the Float Data and Interpolate for Gridding # =========================================================== # os.chdir('/data/orbprocess_mail/alex/Jan01_Jun04_2013/data/type/test_Mar2014/') 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]) <= 0.0): GridData[j,4,i] = GridData[j-1,4,i] else: GridData[j,4,i] = ChlyDumby[j,i] # # Average density along single depth avgdenatdepth = np.zeros(interplength) DemeanDensity = np.zeros([interplength,arraylen]) for j in range(0,interplength): avgdenatdepth[j] = np.mean(GridData[j,1,:]) for i in range(0,arraylen): for j in range(0,interplength): DemeanDensity[j,i] = GridData[j,1,i] - avgdenatdepth[j] # # =========================================================== # # VERTICAL ISOPYCNAL & ISOCHLY VELOCITIES # # =========================================================== # # # =========================================================== # 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 # checks. # # =========================================================== # Find Weekly Avg Gridded Vertical Velocities # =========================================================== # 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) numcdomlines = len(isocdomlines) 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) IsoBioWDataDepthAvgAvg_variance = np.zeros(arraylen-3) IsoBioWDataDepthAvgAvg_stdev = np.zeros(arraylen-3) IsoBioWDataDepthAvgAvg_sterr = np.zeros(arraylen-3) IsoBioWDepthsDepthAvgAvg = np.zeros(arraylen-3) IsoBioWDepthsDepthAvgAvg_variance = np.zeros(arraylen-3) IsoBioWDepthsDepthAvgAvg_stdev = np.zeros(arraylen-3) IsoBioWDepthsDepthAvgAvg_sterr = 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]) # # variance_counter = 0 for ii in range(0,len(IsoBioWDataAvg[:,0])): variance_counter = (IsoBioWDataAvg[ii,i] - IsoBioWDataDepthAvgAvg[i])**2 + variance_counter IsoBioWDataDepthAvgAvg_variance[i] = variance_counter/(len(IsoBioWDataAvg[:,0])-1) IsoBioWDataDepthAvgAvg_stdev[i] = np.sqrt(IsoBioWDataDepthAvgAvg_variance[i]) IsoBioWDataDepthAvgAvg_sterr[i] = IsoBioWDataDepthAvgAvg_stdev[i]/np.sqrt(len(IsoBioWDataAvg[:,0])) # variance_counter = 0 for ii in range(0,len(IsoBioWDepthsAvg[:,0])): variance_counter = (IsoBioWDepthsAvg[ii,i] - IsoBioWDepthsDepthAvgAvg[i])**2 + variance_counter IsoBioWDepthsDepthAvgAvg_variance[i] = variance_counter/(len(IsoBioWDepthsAvg[:,0])-1) IsoBioWDepthsDepthAvgAvg_stdev[i] = np.sqrt(IsoBioWDepthsDepthAvgAvg_variance[i]) IsoBioWDepthsDepthAvgAvg_sterr[i] = IsoBioWDepthsDepthAvgAvg_stdev[i]/np.sqrt(len(IsoBioWDepthsAvg[:,0])) # # =========================================================== # 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]) #IsopycVeolcity = 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) SinkWDataDepthAvgAvg_variance = np.zeros(arraylen-3) SinkWDataDepthAvgAvg_stdev = np.zeros(arraylen-3) SinkWDataDepthAvgAvg_sterr = np.zeros(arraylen-3) NearestWGriddedAvgAvg = np.zeros(arraylen-3) NearestWGriddedAvgAvg_variance = np.zeros(arraylen-3) NearestWGriddedAvgAvg_stdev = np.zeros(arraylen-3) NearestWGriddedAvgAvg_sterr = np.zeros(arraylen-3) for i in range (0,arraylen-3): SinkWDataDepthAvgAvg[i] = np.mean(SinkWDataAvg[:,i]) NearestWGriddedAvgAvg[i] = np.mean(NearestWGriddedAvg[:,i]) # # variance_counter = 0 for ii in range(0,len(NearestWGriddedAvg[:,0])): variance_counter = (NearestWGriddedAvg[ii,i] - NearestWGriddedAvgAvg[i])**2 + variance_counter NearestWGriddedAvgAvg_variance[i] = variance_counter/(len(NearestWGriddedAvg[:,0])-1) NearestWGriddedAvgAvg_stdev[i] = np.sqrt(NearestWGriddedAvgAvg_variance[i]) NearestWGriddedAvgAvg_sterr[i] = NearestWGriddedAvgAvg_stdev[i]/np.sqrt(len(NearestWGriddedAvg[:,0])) # # variance_counter = 0 for ii in range(0,len(SinkWDataAvg[:,0])): variance_counter = (SinkWDataAvg[ii,i] - SinkWDataDepthAvgAvg[i])**2 + variance_counter SinkWDataDepthAvgAvg_variance[i] = variance_counter/(len(SinkWDataAvg[:,0])-1) SinkWDataDepthAvgAvg_stdev[i] = np.sqrt(SinkWDataDepthAvgAvg_variance[i]) SinkWDataDepthAvgAvg_sterr[i] = SinkWDataDepthAvgAvg_stdev[i]/np.sqrt(len(SinkWDataAvg[:,0])) # # =========================================================== # Find Average Chly Concentration in the upper 30 m # =========================================================== # from scipy import integrate chlyavg30meter = np.zeros(arraylen) chlyavg30meter_2 = np.zeros(arraylen) chlyint30meter = np.zeros(arraylen) chlyavg30meter_variance = np.zeros(arraylen) chlyavg30meter_stdev = np.zeros(arraylen) chlyavg30meter_sterr = np.zeros(arraylen) for i in range(0,arraylen): dataout2, rows2, cols2 = csvread(fullnames[i]) gdataout2, grows2, gcols = csvread(gradnames[i]) depthlength = len (dataout2[0,:]) counter = 0 chlytotal = 0 for ii in range(0,depthlength): if (abs(dataout2[0,ii]) < 30.0): counter = counter + 1 chlytotal = chlytotal + dataout2[11,ii] chlyavg30meter_2[i] = chlytotal/counter # # Integrating chlysforint = np.zeros(counter) chlydepthforint = np.zeros(counter) counter2 = 0 for ii in range(0,depthlength): if (abs(dataout2[0,ii]) < 30.0): chlysforint[counter2] = dataout2[11,ii] chlydepthforint[counter2] = dataout2[0,ii] counter2 = counter2 + 1 chlyint30meter[i] = integrate.simps(chlysforint, chlydepthforint) # # New, Better Depth Averaging chlyavg30meter[i] = chlyint30meter[i]/30 # # variance_counter = 0 for ii in range(0,len(chlysforint)): variance_counter = (chlysforint[ii] - chlyavg30meter[i])**2 + variance_counter chlyavg30meter_variance[i] = variance_counter/(len(chlysforint)-1) chlyavg30meter_stdev[i] = np.sqrt(chlyavg30meter_variance[i]) chlyavg30meter_sterr[i] = chlyavg30meter_stdev[i]/np.sqrt(len(chlysforint)) # # =========================================================== # # CHLOROPHYLL PLOTTING # # =========================================================== # contplt = 2 if contplt == 1: # # =========================================================== # Contour Chly # =========================================================== # contplt2 = 2 if contplt2 == 1: pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot500_Chly.pdf') from matplotlib.colors import LogNorm from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt import numpy as np # fig = plt.figure() # # ax = fig.add_axes() # ax = fig.add_subplot(1, 1, 1) from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # Plotting correct data contourdat = np.zeros([interplength,arraylen]) for ci in range(0,interplength): for cj in range(0,arraylen): #cjj = cj + 1 if GridData[ci,4,cj] < 10**-2.5: contourdat[ci,cj] = 10**-2.5 else: contourdat[ci,cj] = GridData[ci,4,cj] # # Plot Set-up import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable fig = plt.figure() ax = fig.add_axes([0.15,0.1,0.68,0.85]) # contlevels= np.logspace(-1.501,np.log(contourdat.max())/np.log(10),250) conticks = [0.01,0.1,1.00] cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels, norm=LogNorm()) plt.set_cmap('jet') cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels, norm=LogNorm()) plt.set_cmap('jet') # divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) # cbar = plt.colorbar(cs,cax=cax, norm=LogNorm()) minorticks = cs.norm(np.array([0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9, 1.00, 2.00, 3.00, 4.00, 5.00, 6,00])) cbar.ax.yaxis.set_ticks(minorticks, minor=True) # cbar.set_label(r"Chlorophyll Concentration ($\mu$gl$^{\mbox{{\normalsize -1}}}$)") cbar.set_ticks([10**(-1.5),0.1,1.00]) cbar.set_ticklabels([r'$\le$ 10$^{\mbox{\normalsize -1.5}}$',r'10$^{\mbox{\normalsize -1}}$', r'10$^{\mbox{\normalsize 0}}$']) # ax.set_yticks([0,-100,-200,-300,-400, -500]) ax.set_yticklabels([r'0', r'100',r'200',r'300',r'400',r'500']) # ax.set_xticks([40,60,80,100,120]) ax.set_xticklabels([ r'40',r'60',r'80',r'100',r'120']) # ax.set_xlabel(r'Day of Year') ax.set_ylabel(r'Depth (m)') ax.set_ylim([-500,0]) ax.set_xlim([31,139]) # plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot500_Chly.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/') # # To 1000 m pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot1000_Chly.pdf') from matplotlib.colors import LogNorm from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt import numpy as np # fig = plt.figure() # # ax = fig.add_axes() # ax = fig.add_subplot(1, 1, 1) from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # Plotting correct data contourdat = np.zeros([interplength,arraylen]) for ci in range(0,interplength): for cj in range(0,arraylen): #cjj = cj + 1 if GridData[ci,4,cj] < 10**-2.5: contourdat[ci,cj] = 10**-2.5 else: contourdat[ci,cj] = GridData[ci,4,cj] # # Plot Set-up import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable fig = plt.figure() ax = fig.add_axes([0.15,0.1,0.68,0.85]) # contlevels= np.logspace(-1.501,np.log(contourdat.max())/np.log(10),250) conticks = [0.01,0.1,1.00] cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels, norm=LogNorm()) plt.set_cmap('jet') cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels, norm=LogNorm()) plt.set_cmap('jet') # divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) # cbar = plt.colorbar(cs,cax=cax, norm=LogNorm()) minorticks = cs.norm(np.array([0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9, 1.00, 2.00, 3.00, 4.00, 5.00, 6,00])) cbar.ax.yaxis.set_ticks(minorticks, minor=True) # cbar.set_label(r"Chlorophyll Concentration ($\mu$gl$^{\mbox{{\normalsize -1}}}$)") cbar.set_ticks([10**(-1.5),0.1,1.00]) cbar.set_ticklabels([r'$\le$ 10$^{\mbox{\normalsize -1.5}}$',r'10$^{\mbox{\normalsize -1}}$', r'10$^{\mbox{\normalsize 0}}$']) # ax.set_yticks([0,-200,-400,-600,-800, -1000]) ax.set_yticklabels([r'0', r'200',r'400',r'600',r'800',r'1000']) # ax.set_xticks([40,60,80,100,120]) ax.set_xticklabels([ r'40',r'60',r'80',r'100',r'120']) # ax.set_xlabel(r'Day of Year') ax.set_ylabel(r'Depth (m)') ax.set_ylim([-1000,0]) ax.set_xlim([31,139]) # plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot1000_Chly.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/') # # To 1500 m pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot1800_Chly.pdf') from matplotlib.colors import LogNorm from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt import numpy as np # fig = plt.figure() # # ax = fig.add_axes() # ax = fig.add_subplot(1, 1, 1) from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # Plotting correct data contourdat = np.zeros([interplength,arraylen]) for ci in range(0,interplength): for cj in range(0,arraylen): #cjj = cj + 1 if GridData[ci,4,cj] < 10**-2.5: contourdat[ci,cj] = 10**-2.5 else: contourdat[ci,cj] = GridData[ci,4,cj] # # Plot Set-up import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable fig = plt.figure() ax = fig.add_axes([0.15,0.1,0.68,0.85]) # contlevels= np.logspace(-1.501,np.log(contourdat.max())/np.log(10),250) conticks = [0.01,0.1,1.00] cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels, norm=LogNorm()) plt.set_cmap('jet') cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels, norm=LogNorm()) plt.set_cmap('jet') # divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) # cbar = plt.colorbar(cs,cax=cax, norm=LogNorm()) minorticks = cs.norm(np.array([0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9, 1.00, 2.00, 3.00, 4.00, 5.00, 6,00])) cbar.ax.yaxis.set_ticks(minorticks, minor=True) # cbar.set_label(r"Chlorophyll Concentration ($\mu$gl$^{\mbox{{\normalsize -1}}}$)") cbar.set_ticks([10**(-1.5),0.1,1.00]) cbar.set_ticklabels([r'$\le$ 10$^{\mbox{\normalsize -1.5}}$',r'10$^{\mbox{\normalsize -1}}$', r'10$^{\mbox{\normalsize 0}}$']) # ax.set_yticks([0,-250,-500,-750,-1000, -1250, -1500,-1750]) ax.set_yticklabels([r'0', r'250',r'500',r'750',r'1000',r'1250',r'1500',r'1750']) # ax.set_xticks([40,60,80,100,120]) ax.set_xticklabels([ r'40',r'60',r'80',r'100',r'120']) # ax.set_xlabel(r'Day of Year') ax.set_ylabel(r'Depth (m)') ax.set_ylim([-1800,0]) ax.set_xlim([31,139]) # plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot1800_Chly.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/') # # # =========================================================== # Contour Chly w/ Quiver Velocities # =========================================================== # # Plotting correct data contourdat = np.zeros([interplength,arraylen]) for ci in range(0,interplength): for cj in range(0,arraylen): #cjj = cj + 1 if GridData[ci,4,cj] < 10**-2.5: contourdat[ci,cj] = 10**-2.5 else: contourdat[ci,cj] = GridData[ci,4,cj] # contplt2 = 1 if contplt2 == 1: pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot500_Chly_wChlyQuivers.pdf') from matplotlib.colors import LogNorm import matplotlib.pyplot as plt import numpy as np import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # fig = plt.figure() # ax = fig.add_subplot(1, 1, 1) # fig = plt.figure() ax = fig.add_axes([0.15,0.1,0.68,0.85]) # contlevels= np.logspace(-1.501,np.log(contourdat.max())/np.log(10),250) conticks = [0.01,0.1,1.00] cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels, norm=LogNorm(), zorder = 1) plt.set_cmap('jet') cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels, norm=LogNorm(), zorder = 1) plt.set_cmap('jet') # # Quivers from pylab import * Q = plt.quiver(IsoBioWDates[1::7], IsoBioWDepths[1::7], IsoBioFake[1::7], IsoBioWData[1::7]) plt.quiverkey(Q, 0.22, 0.95, 0.0005, r'$5 \times 10^{-4} m/s$', labelpos='W', zorder = 2) # divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) cbar = plt.colorbar(cs,cax=cax, norm=LogNorm()) minorticks = cs.norm(np.array([0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9, 1.00, 2.00, 3.00, 4.00, 5.00, 6,00])) cbar.ax.yaxis.set_ticks(minorticks, minor=True) cbar.set_label(r"Chlorophyll Concentration ($\mu$gl$^{\mbox{{\normalsize -1}}}$)") cbar.set_ticks([10**(-1.5),0.1,1.00]) cbar.set_ticklabels([r'$\le$ 10$^{\mbox{\normalsize -1.5}}$',r'10$^{\mbox{\normalsize -1}}$', r'10$^{\mbox{\normalsize 0}}$']) #ax = plt.gca() # ax.set_yticks([0,-100,-200,-300,-400, -500]) ax.set_yticklabels([r'0', r'100',r'200',r'300',r'400',r'500']) # ax.set_xticks([40,60,80,100,120]) ax.set_xticklabels([ r'40',r'60',r'80',r'100',r'120']) # ax.set_xlabel(r'Day of Year') ax.set_ylabel(r'Depth (m)') ax.set_ylim([-500,0]) ax.set_xlim([31,139]) # #plt.savefig("/home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot_Chly_wChlyQuivers.png") plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot500_Chly_wChlyQuivers.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/') # # zto 1000 pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot1000_Chly_wChlyQuivers.pdf') from matplotlib.colors import LogNorm import matplotlib.pyplot as plt import numpy as np import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # fig = plt.figure() # ax = fig.add_subplot(1, 1, 1) # fig = plt.figure() ax = fig.add_axes([0.15,0.1,0.68,0.85]) # contlevels= np.logspace(-1.501,np.log(contourdat.max())/np.log(10),250) conticks = [0.01,0.1,1.00] cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels, norm=LogNorm(), zorder = 1) plt.set_cmap('jet') cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels, norm=LogNorm(), zorder = 1) plt.set_cmap('jet') # # Quivers from pylab import * Q = plt.quiver(IsoBioWDates[1::7], IsoBioWDepths[1::7], IsoBioFake[1::7], IsoBioWData[1::7]) plt.quiverkey(Q, 0.22, 0.95, 0.0005, r'$5 \times 10^{-4} m/s$', labelpos='W', zorder = 2) # divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) cbar = plt.colorbar(cs,cax=cax, norm=LogNorm()) minorticks = cs.norm(np.array([0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9, 1.00, 2.00, 3.00, 4.00, 5.00, 6,00])) cbar.ax.yaxis.set_ticks(minorticks, minor=True) cbar.set_label(r"Chlorophyll Concentration ($\mu$gl$^{\mbox{{\normalsize -1}}}$)") cbar.set_ticks([10**(-1.5),0.1,1.00]) cbar.set_ticklabels([r'$\le$ 10$^{\mbox{\normalsize -1.5}}$',r'10$^{\mbox{\normalsize -1}}$', r'10$^{\mbox{\normalsize 0}}$']) #ax = plt.gca() # ax.set_yticks([0,-200,-400,-600,-800, -1000]) ax.set_yticklabels([r'0', r'200',r'400',r'600',r'800',r'1000']) # ax.set_xticks([40,60,80,100,120]) ax.set_xticklabels([ r'40',r'60',r'80',r'100',r'120']) # ax.set_xlabel(r'Day of Year') ax.set_ylabel(r'Depth (m)') ax.set_ylim([-1000,0]) ax.set_xlim([31,139]) # #plt.savefig("/home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot_Chly_wChlyQuivers.png") plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot1000_Chly_wChlyQuivers.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/') # # To 1500 pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot1800_Chly_wChlyQuivers.pdf') from matplotlib.colors import LogNorm import matplotlib.pyplot as plt import numpy as np import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # fig = plt.figure() # ax = fig.add_subplot(1, 1, 1) # fig = plt.figure() ax = fig.add_axes([0.15,0.1,0.68,0.85]) # contlevels= np.logspace(-1.501,np.log(contourdat.max())/np.log(10),250) conticks = [0.01,0.1,1.00] cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels, norm=LogNorm(), zorder = 1) plt.set_cmap('jet') cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels, norm=LogNorm(), zorder = 1) plt.set_cmap('jet') # # Quivers from pylab import * Q = plt.quiver(IsoBioWDates[1::7], IsoBioWDepths[1::7], IsoBioFake[1::7], IsoBioWData[1::7]) plt.quiverkey(Q, 0.22, 0.95, 0.0005, r'$5 \times 10^{-4} m/s$', labelpos='W', zorder = 2) # divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) cbar = plt.colorbar(cs,cax=cax, norm=LogNorm()) minorticks = cs.norm(np.array([0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9, 1.00, 2.00, 3.00, 4.00, 5.00, 6,00])) cbar.ax.yaxis.set_ticks(minorticks, minor=True) cbar.set_label(r"Chlorophyll Concentration ($\mu$gl$^{\mbox{{\normalsize -1}}}$)") cbar.set_ticks([10**(-1.5),0.1,1.00]) cbar.set_ticklabels([r'$\le$ 10$^{\mbox{\normalsize -1.5}}$',r'10$^{\mbox{\normalsize -1}}$', r'10$^{\mbox{\normalsize 0}}$']) #ax = plt.gca() # ax.set_yticks([0,-250,-500,-750,-1000, -1250, -1500,-1750]) ax.set_yticklabels([r'0', r'250',r'500',r'750',r'1000',r'1250',r'1500',r'1750']) # ax.set_xticks([40,60,80,100,120]) ax.set_xticklabels([ r'40',r'60',r'80',r'100',r'120']) # ax.set_xlabel(r'Day of Year') ax.set_ylabel(r'Depth (m)') ax.set_ylim([-1800,0]) ax.set_xlim([31,139]) # #plt.savefig("/home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot_Chly_wChlyQuivers.png") plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot1800_Chly_wChlyQuivers.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/') # # =========================================================== # Contour Chly w/ Quiver Avg Velocities # =========================================================== # # # Plotting correct data contourdat = np.zeros([interplength,arraylen]) for ci in range(0,interplength): for cj in range(0,arraylen): #cjj = cj + 1 if GridData[ci,4,cj] < 10**-2.5: contourdat[ci,cj] = 10**-2.5 else: contourdat[ci,cj] = GridData[ci,4,cj] # contplt2 = 1 if contplt2 == 1: pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot500_Chly_AvgwChlyQuivers.pdf') from matplotlib.colors import LogNorm import matplotlib.pyplot as plt import numpy as np import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # fig = plt.figure() # ax = fig.add_subplot(1, 1, 1) # fig = plt.figure() ax = fig.add_axes([0.15,0.1,0.68,0.85]) # contlevels= np.logspace(-1.501,np.log(contourdat.max())/np.log(10),250) conticks = [0.01,0.1,1.00] cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels, norm=LogNorm(), zorder = 1) plt.set_cmap('jet') cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels, norm=LogNorm(), zorder = 1) plt.set_cmap('jet') # # Quivers from pylab import * Q = plt.quiver(IsoBioWDatesAvg[1::7], IsoBioWDepthsAvg[1::7], IsoBioFakeAvg[1::7], IsoBioWDataAvg[1::7]) plt.quiverkey(Q, 0.22, 0.95, 0.0005, r'$5 \times 10^{-4} m/s$', labelpos='W', zorder = 2) # divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) cbar = plt.colorbar(cs,cax=cax, norm=LogNorm()) minorticks = cs.norm(np.array([0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9, 1.00, 2.00, 3.00, 4.00, 5.00, 6,00])) cbar.ax.yaxis.set_ticks(minorticks, minor=True) cbar.set_label(r"Chlorophyll Concentration ($\mu$gl$^{\mbox{{\normalsize -1}}}$)") cbar.set_ticks([10**(-1.5),0.1,1.00]) cbar.set_ticklabels([r'$\le$ 10$^{\mbox{\normalsize -1.5}}$',r'10$^{\mbox{\normalsize -1}}$', r'10$^{\mbox{\normalsize 0}}$']) #ax = plt.gca() # ax.set_yticks([0,-100,-200,-300,-400, -500]) ax.set_yticklabels([r'0', r'100',r'200',r'300',r'400',r'500']) # ax.set_xticks([40,60,80,100,120]) ax.set_xticklabels([ r'40',r'60',r'80',r'100',r'120']) # ax.set_xlabel(r'Day of Year') ax.set_ylabel(r'Depth (m)') ax.set_ylim([-500,0]) ax.set_xlim([31,139]) # #plt.savefig("/home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot_Chly_wChlyQuivers.png") plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot500_Chly_AvgwChlyQuivers.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/') # # zto 1000 pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot1000_Chly_AvgwChlyQuivers.pdf') from matplotlib.colors import LogNorm import matplotlib.pyplot as plt import numpy as np import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # fig = plt.figure() # ax = fig.add_subplot(1, 1, 1) # fig = plt.figure() ax = fig.add_axes([0.15,0.1,0.68,0.85]) # contlevels= np.logspace(-1.501,np.log(contourdat.max())/np.log(10),250) conticks = [0.01,0.1,1.00] cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels, norm=LogNorm(), zorder = 1) plt.set_cmap('jet') cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels, norm=LogNorm(), zorder = 1) plt.set_cmap('jet') # # Quivers from pylab import * Q = plt.quiver(IsoBioWDatesAvg[1::7], IsoBioWDepthsAvg[1::7], IsoBioFakeAvg[1::7], IsoBioWDataAvg[1::7]) plt.quiverkey(Q, 0.22, 0.95, 0.0005, r'$5 \times 10^{-4} m/s$', labelpos='W', zorder = 2) # divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) cbar = plt.colorbar(cs,cax=cax, norm=LogNorm()) minorticks = cs.norm(np.array([0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9, 1.00, 2.00, 3.00, 4.00, 5.00, 6,00])) cbar.ax.yaxis.set_ticks(minorticks, minor=True) cbar.set_label(r"Chlorophyll Concentration ($\mu$gl$^{\mbox{{\normalsize -1}}}$)") cbar.set_ticks([10**(-1.5),0.1,1.00]) cbar.set_ticklabels([r'$\le$ 10$^{\mbox{\normalsize -1.5}}$',r'10$^{\mbox{\normalsize -1}}$', r'10$^{\mbox{\normalsize 0}}$']) #ax = plt.gca() # ax.set_yticks([0,-200,-400,-600,-800, -1000]) ax.set_yticklabels([r'0', r'200',r'400',r'600',r'800',r'1000']) # ax.set_xticks([40,60,80,100,120]) ax.set_xticklabels([ r'40',r'60',r'80',r'100',r'120']) # ax.set_xlabel(r'Day of Year') ax.set_ylabel(r'Depth (m)') ax.set_ylim([-1000,0]) ax.set_xlim([31,139]) # #plt.savefig("/home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot_Chly_wChlyQuivers.png") plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot1000_Chly_AvgwChlyQuivers.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/') # # To 1500 pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot1800_Chly_AvgwChlyQuivers.pdf') from matplotlib.colors import LogNorm import matplotlib.pyplot as plt import numpy as np import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # fig = plt.figure() # ax = fig.add_subplot(1, 1, 1) # fig = plt.figure() ax = fig.add_axes([0.15,0.1,0.68,0.85]) # contlevels= np.logspace(-1.501,np.log(contourdat.max())/np.log(10),250) conticks = [0.01,0.1,1.00] cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels, norm=LogNorm(), zorder = 1) plt.set_cmap('jet') cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels, norm=LogNorm(), zorder = 1) plt.set_cmap('jet') # # Quivers from pylab import * Q = plt.quiver(IsoBioWDatesAvg[1::7], IsoBioWDepthsAvg[1::7], IsoBioFakeAvg[1::7], IsoBioWDataAvg[1::7]) plt.quiverkey(Q, 0.22, 0.95, 0.0005, r'$5 \times 10^{-4} m/s$', labelpos='W', zorder = 2) # divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) cbar = plt.colorbar(cs,cax=cax, norm=LogNorm()) minorticks = cs.norm(np.array([0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9, 1.00, 2.00, 3.00, 4.00, 5.00, 6,00])) cbar.ax.yaxis.set_ticks(minorticks, minor=True) cbar.set_label(r"Chlorophyll Concentration ($\mu$gl$^{\mbox{{\normalsize -1}}}$)") cbar.set_ticks([10**(-1.5),0.1,1.00]) cbar.set_ticklabels([r'$\le$ 10$^{\mbox{\normalsize -1.5}}$',r'10$^{\mbox{\normalsize -1}}$', r'10$^{\mbox{\normalsize 0}}$']) #ax = plt.gca() # ax.set_yticks([0,-250,-500,-750,-1000, -1250, -1500,-1750]) ax.set_yticklabels([r'0', r'250',r'500',r'750',r'1000',r'1250',r'1500',r'1750']) # ax.set_xticks([40,60,80,100,120]) ax.set_xticklabels([ r'40',r'60',r'80',r'100',r'120']) # ax.set_xlabel(r'Day of Year') ax.set_ylabel(r'Depth (m)') ax.set_ylim([-1500,0]) ax.set_xlim([31,139]) # #plt.savefig("/home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot_Chly_wChlyQuivers.png") plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot1800_Chly_AvgwChlyQuivers.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/') # # =========================================================== # Contour Chly w/ Quiver Avg Avg Velocities # =========================================================== # # # Plotting correct data contourdat = np.zeros([interplength,arraylen]) for ci in range(0,interplength): for cj in range(0,arraylen): #cjj = cj + 1 if GridData[ci,4,cj] < 10**-2.5: contourdat[ci,cj] = 10**-2.5 else: contourdat[ci,cj] = GridData[ci,4,cj] contplt2 = 2 if contplt2 == 1: # pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot500_Chly_AvgAvgwChlyQuivers.pdf') from matplotlib.colors import LogNorm import matplotlib.pyplot as plt import numpy as np import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # fig = plt.figure() # ax = fig.add_subplot(1, 1, 1) # fig = plt.figure() ax = fig.add_axes([0.15,0.1,0.68,0.85]) # contlevels= np.logspace(-1.501,np.log(contourdat.max())/np.log(10),250) conticks = [0.01,0.1,1.00] cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels, norm=LogNorm(), zorder = 1) plt.set_cmap('jet') cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels, norm=LogNorm(), zorder = 1) plt.set_cmap('jet') # # Quivers from pylab import * Q = plt.quiver(IsoBioWDatesDepthAvgAvg, IsoBioWDepthsDepthAvgAvg, IsoBioFakeDepthAvgAvg, IsoBioWDataDepthAvgAvg) plt.quiverkey(Q, 0.22, 0.95, 0.0005, r'$5 \times 10^{-4} m/s$', labelpos='W', zorder = 2) # divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) cbar = plt.colorbar(cs,cax=cax, norm=LogNorm()) minorticks = cs.norm(np.array([0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9, 1.00, 2.00, 3.00, 4.00, 5.00, 6,00])) cbar.ax.yaxis.set_ticks(minorticks, minor=True) cbar.set_label(r"Chlorophyll Concentration ($\mu$gl$^{\mbox{{\normalsize -1}}}$)") cbar.set_ticks([10**(-1.5),0.1,1.00]) cbar.set_ticklabels([r'$\le$ 10$^{\mbox{\normalsize -1.5}}$',r'10$^{\mbox{\normalsize -1}}$', r'10$^{\mbox{\normalsize 0}}$']) #ax = plt.gca() # ax.set_yticks([0,-100,-200,-300,-400, -500]) ax.set_yticklabels([r'0', r'100',r'200',r'300',r'400',r'500']) # ax.set_xticks([40,60,80,100,120]) ax.set_xticklabels([ r'40',r'60',r'80',r'100',r'120']) # ax.set_xlabel(r'Day of Year') ax.set_ylabel(r'Depth (m)') ax.set_ylim([-500,0]) ax.set_xlim([31,139]) # #plt.savefig("/home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot_Chly_wChlyQuivers.png") plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot500_Chly_AvgAvgwChlyQuivers.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/') # # zto 1000 pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot1000_Chly_AvgAvgwChlyQuivers.pdf') from matplotlib.colors import LogNorm import matplotlib.pyplot as plt import numpy as np import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # fig = plt.figure() # ax = fig.add_subplot(1, 1, 1) # fig = plt.figure() ax = fig.add_axes([0.15,0.1,0.68,0.85]) # contlevels= np.logspace(-1.501,np.log(contourdat.max())/np.log(10),250) conticks = [0.01,0.1,1.00] cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels, norm=LogNorm(), zorder = 1) plt.set_cmap('jet') cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels, norm=LogNorm(), zorder = 1) plt.set_cmap('jet') # # Quivers from pylab import * Q = plt.quiver(IsoBioWDatesDepthAvgAvg, IsoBioWDepthsDepthAvgAvg, IsoBioFakeDepthAvgAvg, IsoBioWDataDepthAvgAvg) plt.quiverkey(Q, 0.22, 0.95, 0.0005, r'$5 \times 10^{-4} m/s$', labelpos='W', zorder = 2) # divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) cbar = plt.colorbar(cs,cax=cax, norm=LogNorm()) minorticks = cs.norm(np.array([0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9, 1.00, 2.00, 3.00, 4.00, 5.00, 6,00])) cbar.ax.yaxis.set_ticks(minorticks, minor=True) cbar.set_label(r"Chlorophyll Concentration ($\mu$gl$^{\mbox{{\normalsize -1}}}$)") cbar.set_ticks([10**(-1.5),0.1,1.00]) cbar.set_ticklabels([r'$\le$ 10$^{\mbox{\normalsize -1.5}}$',r'10$^{\mbox{\normalsize -1}}$', r'10$^{\mbox{\normalsize 0}}$']) #ax = plt.gca() # ax.set_yticks([0,-200,-400,-600,-800, -1000]) ax.set_yticklabels([r'0', r'200',r'400',r'600',r'800',r'1000']) # ax.set_xticks([40,60,80,100,120]) ax.set_xticklabels([ r'40',r'60',r'80',r'100',r'120']) # ax.set_xlabel(r'Day of Year') ax.set_ylabel(r'Depth (m)') ax.set_ylim([-1000,0]) ax.set_xlim([31,139]) # #plt.savefig("/home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot_Chly_wChlyQuivers.png") plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot1000_Chly_AvgAvgwChlyQuivers.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/') # # To 1500 pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot1800_Chly_AvgAvgwChlyQuivers.pdf') from matplotlib.colors import LogNorm import matplotlib.pyplot as plt import numpy as np import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # fig = plt.figure() # ax = fig.add_subplot(1, 1, 1) # fig = plt.figure() ax = fig.add_axes([0.15,0.1,0.68,0.85]) # contlevels= np.logspace(-1.501,np.log(contourdat.max())/np.log(10),250) conticks = [0.01,0.1,1.00] cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels, norm=LogNorm(), zorder = 1) plt.set_cmap('jet') cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels, norm=LogNorm(), zorder = 1) plt.set_cmap('jet') # # Quivers from pylab import * Q = plt.quiver(IsoBioWDatesDepthAvgAvg, IsoBioWDepthsDepthAvgAvg, IsoBioFakeDepthAvgAvg, IsoBioWDataDepthAvgAvg, color='k',edgecolors=('w')) plt.quiverkey(Q, 0.22, 0.95, 0.0005, r'$5 \times 10^{-4} m/s$', labelpos='W', zorder = 2) # divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) cbar = plt.colorbar(cs,cax=cax, norm=LogNorm()) minorticks = cs.norm(np.array([0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9, 1.00, 2.00, 3.00, 4.00, 5.00, 6,00])) cbar.ax.yaxis.set_ticks(minorticks, minor=True) cbar.set_label(r"Chlorophyll Concentration ($\mu$gl$^{\mbox{{\normalsize -1}}}$)") cbar.set_ticks([10**(-1.5),0.1,1.00]) cbar.set_ticklabels([r'$\le$ 10$^{\mbox{\normalsize -1.5}}$',r'10$^{\mbox{\normalsize -1}}$', r'10$^{\mbox{\normalsize 0}}$']) #ax = plt.gca() # ax.set_yticks([0,-250,-500,-750,-1000, -1250, -1500, -1750]) ax.set_yticklabels([r'0', r'250',r'500',r'750',r'1000',r'1250',r'1500',r'1750']) # ax.set_xticks([40,60,80,100,120]) ax.set_xticklabels([ r'40',r'60',r'80',r'100',r'120']) # ax.set_xlabel(r'Day of Year') ax.set_ylabel(r'Depth (m)') ax.set_ylim([-1800,0]) ax.set_xlim([31,139]) # #plt.savefig("/home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot_Chly_wChlyQuivers.png") plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot1800_Chly_AvgAvgwChlyQuivers.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/') # # =========================================================== # # DENSITY PLOTTING # # =========================================================== # contplt = 2 if contplt == 1: # # =========================================================== # Contour Density # =========================================================== # # Plotting correct data contourdat = np.zeros([interplength,arraylen]) for ci in range(0,interplength): for cj in range(0,arraylen): contourdat[ci,cj] = GridData[ci,1,cj] - 1026 contplt2 = 2 if contplt2 == 1: # # # To 500 # pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot500_Density.pdf') from matplotlib.colors import LogNorm from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt import numpy as np # fig = plt.figure() # # ax = fig.add_axes() # ax = fig.add_subplot(1, 1, 1) from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # Plotting correct data contourdat = np.zeros([interplength,arraylen]) for ci in range(0,interplength): for cj in range(0,arraylen): contourdat[ci,cj] = GridData[ci,1,cj] - 1026 # # Plot Set-up import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable fig = plt.figure() ax = fig.add_axes([0.15,0.1,0.68,0.85]) # contlevels= np.linspace(contourdat.min(),contourdat.max(),250) #contlevels= np.logspace(-1.501,np.log(contourdat.max())/np.log(10),250) #conticks = [0.01,0.1,1.00] cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels) plt.set_cmap('jet') cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels) plt.set_cmap('jet') # divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) # cbar = plt.colorbar(cs,cax=cax) minorticks = cs.norm(np.array([0.8,0.9,1,1.1,1.2,1.3,1.4,1.5,1.6,1.7,1.8])) cbar.ax.yaxis.set_ticks(minorticks, minor=True) # cbar.set_label(r"Density - 1,026 (kgm$^{\mbox{{\normalsize -3}}}$)") cbar.set_ticks([0.8,1.0,1.2,1.4,1.6,1.8]) cbar.set_ticklabels([r'0.8',r'1.0',r'1.2',r'1.4',r'1.6',r'1.8']) # ax.set_yticks([0,-100,-200,-300,-400, -500]) ax.set_yticklabels([r'0', r'100',r'200',r'300',r'400',r'500']) # ax.set_xticks([40,60,80,100,120]) ax.set_xticklabels([ r'40',r'60',r'80',r'100',r'120']) # ax.set_xlabel(r'Day of Year') ax.set_ylabel(r'Depth (m)') ax.set_ylim([-500,0]) ax.set_xlim([31,139]) # plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot500_Density.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/') # # To 1000 # pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot1000_Density.pdf') from matplotlib.colors import LogNorm from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt import numpy as np # fig = plt.figure() # # ax = fig.add_axes() # ax = fig.add_subplot(1, 1, 1) from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # Plotting correct data contourdat = np.zeros([interplength,arraylen]) for ci in range(0,interplength): for cj in range(0,arraylen): contourdat[ci,cj] = GridData[ci,1,cj] - 1026 # # Plot Set-up import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable fig = plt.figure() ax = fig.add_axes([0.15,0.1,0.68,0.85]) # contlevels= np.linspace(contourdat.min(),contourdat.max(),250) #contlevels= np.logspace(-1.501,np.log(contourdat.max())/np.log(10),250) #conticks = [0.01,0.1,1.00] cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels) plt.set_cmap('jet') cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels) plt.set_cmap('jet') # divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) # cbar = plt.colorbar(cs,cax=cax) minorticks = cs.norm(np.array([0.8,0.9,1,1.1,1.2,1.3,1.4,1.5,1.6,1.7,1.8])) cbar.ax.yaxis.set_ticks(minorticks, minor=True) # cbar.set_label(r"Density - 1,026 (kgm$^{\mbox{{\normalsize -3}}}$)") cbar.set_ticks([0.8,1.0,1.2,1.4,1.6,1.8]) cbar.set_ticklabels([r'0.8',r'1.0',r'1.2',r'1.4',r'1.6',r'1.8']) # ax.set_yticks([0,-200,-400,-600,-800, -1000]) ax.set_yticklabels([r'0', r'200',r'400',r'600',r'800',r'1000']) # ax.set_xticks([40,60,80,100,120]) ax.set_xticklabels([ r'40',r'60',r'80',r'100',r'120']) # ax.set_xlabel(r'Day of Year') ax.set_ylabel(r'Depth (m)') ax.set_ylim([-1000,0]) ax.set_xlim([31,139]) # plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot1000_Density.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/') # # To 1500 # pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot1800_Density.pdf') from matplotlib.colors import LogNorm from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt import numpy as np # fig = plt.figure() # # ax = fig.add_axes() # ax = fig.add_subplot(1, 1, 1) from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # Plotting correct data contourdat = np.zeros([interplength,arraylen]) for ci in range(0,interplength): for cj in range(0,arraylen): contourdat[ci,cj] = GridData[ci,1,cj] - 1026 # # Plot Set-up import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable fig = plt.figure() ax = fig.add_axes([0.15,0.1,0.68,0.85]) # contlevels= np.linspace(contourdat.min(),contourdat.max(),250) #contlevels= np.logspace(-1.501,np.log(contourdat.max())/np.log(10),250) #conticks = [0.01,0.1,1.00] cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels) plt.set_cmap('jet') cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels) plt.set_cmap('jet') # divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) # cbar = plt.colorbar(cs,cax=cax) minorticks = cs.norm(np.array([0.8,0.9,1,1.1,1.2,1.3,1.4,1.5,1.6,1.7,1.8])) cbar.ax.yaxis.set_ticks(minorticks, minor=True) # cbar.set_label(r"Density - 1,026 (kgm$^{\mbox{{\normalsize -3}}}$)") cbar.set_ticks([0.8,1.0,1.2,1.4,1.6,1.8]) cbar.set_ticklabels([r'0.8',r'1.0',r'1.2',r'1.4',r'1.6',r'1.8']) # ax.set_yticks([0,-250,-500,-750,-1000, -1250, -1500,-1750]) ax.set_yticklabels([r'0', r'250',r'500',r'750',r'1000',r'1250',r'1500',r'1750']) # ax.set_xticks([40,60,80,100,120]) ax.set_xticklabels([ r'40',r'60',r'80',r'100',r'120']) # ax.set_xlabel(r'Day of Year') ax.set_ylabel(r'Depth (m)') ax.set_ylim([-1800,0]) ax.set_xlim([31,139]) # plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot1800_Density.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/') # # # Gridded Velocity contplt2 = 2 if contplt2 == 1: # Plotting correct W data wcontourdat = np.zeros([interplength,arraylen-3]) wcontourdep = np.zeros([interplength,arraylen-3]) wcontourtim = np.zeros([interplength,arraylen-3]) abswcontourdat = np.zeros([interplength,arraylen-3]) for ci in range(0,interplength): for cj in range(0,arraylen-3): wcontourdat[ci,cj] = GridWData[ci,1,cj] wcontourdep[ci,cj] = GridWDepths[ci,1,cj] wcontourtim[ci,cj] = GridWDates[ci,1,cj] abswcontourdat[ci,cj] = abs(wcontourdat[ci,cj]) # pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot500_wDensity.pdf') from matplotlib.colors import LogNorm from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt import numpy as np # fig = plt.figure() # # ax = fig.add_axes() # ax = fig.add_subplot(1, 1, 1) from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # Plot Set-up import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable fig = plt.figure() ax = fig.add_axes([0.15,0.1,0.68,0.85]) # Wcontlevels= np.linspace(-6.2,6.2,151) cs = plt.contourf(wcontourtim,wcontourdep,wcontourdat*10**(4), levels = Wcontlevels) plt.set_cmap('seismic') cs = plt.contourf(wcontourtim,wcontourdep,wcontourdat*10**(4), levels = Wcontlevels) plt.set_cmap('seismic') # #plt.set_cmap('RdBu') # cs = plt.contourf(wcontourtim,wcontourdep,wcontourdat*10**(4), levels = Wcontlevels, cmap=plt.cm.RdBu) # #plt.set_cmap('RdBu') # divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) # cbar = plt.colorbar(cs,cax=cax) minorticks = cs.norm(np.array([-6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6])) cbar.ax.yaxis.set_ticks(minorticks, minor=True) # cbar.set_label(r"Rate of Isopycnal Vert. Motion $\times$ 10$^{-4}$ (ms$^{\mbox{{\normalsize -1}}}$)") cbar.set_ticks([-6,-4,-2,0,2, 4, 6]) cbar.set_ticklabels([r'-6.0',r'-4.0',r'-2.0',r'0.0',r'2.0',r'4.0',r'6.0']) # ax.set_yticks([0,-100,-200,-300,-400, -500]) ax.set_yticklabels([r'0', r'100',r'200',r'300',r'400',r'500']) # ax.set_xticks([40,60,80,100,120]) ax.set_xticklabels([ r'40',r'60',r'80',r'100',r'120']) # ax.set_xlabel(r'Day of Year') ax.set_ylabel(r'Depth (m)') ax.set_ylim([-500,0]) ax.set_xlim([31,139]) # plt.savefig("/home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot500_wDensity.png") #plt.savefig(pp, format = "pdf") # pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot500_wDensity.png ardavies@storm.ceoe.udel.edu:/dev/ardavies/') # # Daily Avg # # 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] # pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot500_wDensity.pdf') from matplotlib.colors import LogNorm from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt import numpy as np # fig = plt.figure() # # ax = fig.add_axes() # ax = fig.add_subplot(1, 1, 1) from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # Plot Set-up import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable fig = plt.figure() ax = fig.add_axes([0.15,0.1,0.68,0.85]) # Wcontlevels= np.linspace(-4.0,4.0,151) cs = plt.contourf(wcontourtim,wcontourdep,wcontourdat*10**(4), levels = Wcontlevels) plt.set_cmap('seismic') cs = plt.contourf(wcontourtim,wcontourdep,wcontourdat*10**(4), levels = Wcontlevels) plt.set_cmap('seismic') #plt.set_cmap('RdBu') # cs = plt.contourf(wcontourtim,wcontourdep,wcontourdat*10**(4), levels = Wcontlevels, cmap=plt.cm.RdBu) #plt.set_cmap('RdBu') # divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) # cbar = plt.colorbar(cs,cax=cax) minorticks = cs.norm(np.array([-4, -3, -2, -1, 0, 1, 2, 3, 4])) cbar.ax.yaxis.set_ticks(minorticks, minor=True) # cbar.set_label(r"Rate of Isopycnal Vert. Motion $\times$ 10$^{-4}$ (ms$^{\mbox{{\normalsize -1}}}$)") cbar.set_ticks([-4, -3, -2, -1, 0, 1, 2, 3, 4]) cbar.set_ticklabels([r'-4.0',r'-3.0',r'-2.0',r'-1.0',r'0.0',r'1.0',r'2.0',r'3.0',r'4.0']) # ax.set_yticks([0,-100,-200,-300,-400, -500]) ax.set_yticklabels([r'0', r'100',r'200',r'300',r'400',r'500']) # ax.set_xticks([40,60,80,100,120]) ax.set_xticklabels([ r'40',r'60',r'80',r'100',r'120']) # ax.set_xlabel(r'Day of Year') ax.set_ylabel(r'Depth (m)') ax.set_ylim([-500,0]) ax.set_xlim([31,139]) # plt.savefig("/home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot500_wDensityAvg.png") #plt.savefig(pp, format = "pdf") # pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot500_wDensityAvg.png ardavies@storm.ceoe.udel.edu:/dev/ardavies/') # # # Daily ZOOOOOM # # Plotting correct W data wcontourdat = np.zeros([interplength,arraylen-3]) wcontourdep = np.zeros([interplength,arraylen-3]) wcontourtim = np.zeros([interplength,arraylen-3]) abswcontourdat = np.zeros([interplength,arraylen-3]) for ci in range(0,interplength): for cj in range(0,arraylen-3): wcontourdat[ci,cj] = GridWData[ci,1,cj] wcontourdep[ci,cj] = GridWDepths[ci,1,cj] wcontourtim[ci,cj] = GridWDates[ci,1,cj] abswcontourdat[ci,cj] = abs(wcontourdat[ci,cj]) # pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot500_wDensity.pdf') from matplotlib.colors import LogNorm from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt import numpy as np # fig = plt.figure() # # ax = fig.add_axes() # ax = fig.add_subplot(1, 1, 1) from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # Plot Set-up import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable fig = plt.figure() ax = fig.add_axes([0.15,0.1,0.68,0.85]) # Wcontlevels= np.linspace(-4,4,151) cs = plt.contourf(wcontourtim,wcontourdep,wcontourdat*10**(4), levels = Wcontlevels) plt.set_cmap('seismic') cs = plt.contourf(wcontourtim,wcontourdep,wcontourdat*10**(4), levels = Wcontlevels) plt.set_cmap('seismic') # cs = plt.contourf(wcontourtim,wcontourdep,wcontourdat*10**(4), levels = Wcontlevels,cmap=plt.cm.RdBu) # #plt.set_cmap('RdBu') # cs = plt.contourf(wcontourtim,wcontourdep,wcontourdat*10**(4), levels = Wcontlevels, cmap=plt.cm.RdBu) #plt.set_cmap('RdBu') # divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) # cbar = plt.colorbar(cs,cax=cax) minorticks = cs.norm(np.array([-4, -3, -2, -1, 0, 1, 2, 3, 4])) cbar.ax.yaxis.set_ticks(minorticks, minor=True) # cbar.set_label(r"Rate of Isopycnal Vert. Motion $\times$ 10$^{-4}$ (ms$^{\mbox{{\normalsize -1}}}$)") cbar.set_ticks([-4, -3, -2, -1, 0, 1, 2, 3, 4]) cbar.set_ticklabels([r'-4.0',r'-3.0',r'-2.0',r'-1.0',r'0.0',r'1.0',r'2.0',r'3.0',r'4.0']) # ax.set_yticks([0,-100,-200,-300,-400, -500]) ax.set_yticklabels([r'0', r'100',r'200',r'300',r'400',r'500']) # ax.set_xticks([80,85,90,95,100,105]) ax.set_xticklabels([ r'80',r'85',r'90',r'95',r'100',r'105']) # ax.set_xlabel(r'Day of Year') ax.set_ylabel(r'Depth (m)') ax.set_ylim([-500,0]) ax.set_xlim([80,105]) # plt.savefig("/home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot500zoom_wDensity.png") #plt.savefig(pp, format = "pdf") # pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot500zoom_wDensity.png ardavies@storm.ceoe.udel.edu:/dev/ardavies/') # # Daily Avg ZOOOM # # 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] # pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot500_wDensity.pdf') from matplotlib.colors import LogNorm from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt import numpy as np # fig = plt.figure() # # ax = fig.add_axes() # ax = fig.add_subplot(1, 1, 1) from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # Plot Set-up import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable fig = plt.figure() ax = fig.add_axes([0.15,0.1,0.68,0.85]) # Wcontlevels= np.linspace(-2.0,2.0,151) cs = plt.contourf(wcontourtim,wcontourdep,wcontourdat*10**(4), levels = Wcontlevels) plt.set_cmap('seismic') cs = plt.contourf(wcontourtim,wcontourdep,wcontourdat*10**(4), levels = Wcontlevels) plt.set_cmap('seismic') # cs = plt.contourf(wcontourtim,wcontourdep,wcontourdat*10**(4), levels = Wcontlevels,cmap=plt.cm.RdBu) # #plt.set_cmap('RdBu') # cs = plt.contourf(wcontourtim,wcontourdep,wcontourdat*10**(4), levels = Wcontlevels, cmap=plt.cm.RdBu) # #plt.set_cmap('RdBu') # divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) # cbar = plt.colorbar(cs,cax=cax) minorticks = cs.norm(np.array([-2,-1.5,-1,-.5,0,.5,1,1.5,2])) cbar.ax.yaxis.set_ticks(minorticks, minor=True) # cbar.set_label(r"Rate of Isopycnal Vert. Motion $\times$ 10$^{-4}$ (ms$^{\mbox{{\normalsize -1}}}$)") cbar.set_ticks([-2, -1, 0, 1, 2]) cbar.set_ticklabels([r'-2.0',r'-1.0',r'0.0',r'1.0',r'2.0']) # ax.set_yticks([0,-100,-200,-300,-400, -500]) ax.set_yticklabels([r'0', r'100',r'200',r'300',r'400',r'500']) # ax.set_xticks([80,85,90,95,100,105]) ax.set_xticklabels([ r'80',r'85',r'90',r'95',r'100',r'105']) # ax.set_xlabel(r'Day of Year') ax.set_ylabel(r'Depth (m)') ax.set_ylim([-500,0]) ax.set_xlim([80,105]) # plt.savefig("/home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot500zoom_wDensityAvg.png") #plt.savefig(pp, format = "pdf") # pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot500zoom_wDensityAvg.png ardavies@storm.ceoe.udel.edu:/dev/ardavies/') # # =========================================================== # # Salinity Plotting # # =========================================================== # contplt = 2 if contplt == 1: # # =========================================================== # Contour Salinity # =========================================================== # # # # Plotting correct data contourdat = np.zeros([interplength,arraylen]) for ci in range(0,interplength): for cj in range(0,arraylen): contourdat[ci,cj] = GridData[ci,2,cj] contplt2 = 1 if contplt2 == 1: # # To 500 pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot500_Salinity.pdf') from matplotlib.colors import LogNorm from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt import numpy as np # fig = plt.figure() # # ax = fig.add_axes() # ax = fig.add_subplot(1, 1, 1) from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # Plotting correct data contourdat = np.zeros([interplength,arraylen]) for ci in range(0,interplength): for cj in range(0,arraylen): contourdat[ci,cj] = GridData[ci,2,cj] # # Plot Set-up import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable fig = plt.figure() ax = fig.add_axes([0.15,0.1,0.68,0.85]) # contlevels= np.linspace(contourdat.min(),contourdat.max(),250) #contlevels= np.logspace(-1.501,np.log(contourdat.max())/np.log(10),250) #conticks = [0.01,0.1,1.00] cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels) plt.set_cmap('jet') cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels) plt.set_cmap('jet') # divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) # cbar = plt.colorbar(cs,cax=cax) minorticks = cs.norm(np.array([33.5,33.6,33.7,33.8,33.9,34.0,34.1,34.2,34.3,34.4,34.5,34.6,34.7])) cbar.ax.yaxis.set_ticks(minorticks, minor=True) # cbar.set_label(r"Salinity (psu)") cbar.set_ticks([33.5,33.7,33.9,34.1,34.3,34.5,34.7]) cbar.set_ticklabels([r'33.5',r'33.7',r'33.9',r'34.1',r'34.3',r'34.5',r'34.7']) # ax.set_yticks([0,-100,-200,-300,-400, -500]) ax.set_yticklabels([r'0', r'100',r'200',r'300',r'400',r'500']) # ax.set_xticks([40,60,80,100,120]) ax.set_xticklabels([ r'40',r'60',r'80',r'100',r'120']) # ax.set_xlabel(r'Day of Year') ax.set_ylabel(r'Depth (m)') ax.set_ylim([-500,0]) ax.set_xlim([31,139]) # plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot500_Salinity.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/') # # To 1000 pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot1000_Salinity.pdf') from matplotlib.colors import LogNorm from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt import numpy as np # fig = plt.figure() # # ax = fig.add_axes() # ax = fig.add_subplot(1, 1, 1) from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # Plotting correct data contourdat = np.zeros([interplength,arraylen]) for ci in range(0,interplength): for cj in range(0,arraylen): contourdat[ci,cj] = GridData[ci,2,cj] # # Plot Set-up import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable fig = plt.figure() ax = fig.add_axes([0.15,0.1,0.68,0.85]) # contlevels= np.linspace(contourdat.min(),contourdat.max(),250) #contlevels= np.logspace(-1.501,np.log(contourdat.max())/np.log(10),250) #conticks = [0.01,0.1,1.00] cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels) plt.set_cmap('jet') cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels) plt.set_cmap('jet') # divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) # cbar = plt.colorbar(cs,cax=cax) minorticks = cs.norm(np.array([33.5,33.6,33.7,33.8,33.9,34.0,34.1,34.2,34.3,34.4,34.5,34.6,34.7])) cbar.ax.yaxis.set_ticks(minorticks, minor=True) # cbar.set_label(r"Salinity (psu)") cbar.set_ticks([33.5,33.7,33.9,34.1,34.3,34.5,34.7]) cbar.set_ticklabels([r'33.5',r'33.7',r'33.9',r'34.1',r'34.3',r'34.5',r'34.7']) # ax.set_yticks([0,-200,-400,-600,-800, -1000]) ax.set_yticklabels([r'0', r'200',r'400',r'600',r'800',r'1000']) # ax.set_xticks([40,60,80,100,120]) ax.set_xticklabels([ r'40',r'60',r'80',r'100',r'120']) # ax.set_xlabel(r'Day of Year') ax.set_ylabel(r'Depth (m)') ax.set_ylim([-1000,0]) ax.set_xlim([31,139]) # plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot1000_Salinity.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/') # # To 1500 pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot1800_Salinity.pdf') from matplotlib.colors import LogNorm from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt import numpy as np # fig = plt.figure() # # ax = fig.add_axes() # ax = fig.add_subplot(1, 1, 1) from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # Plotting correct data contourdat = np.zeros([interplength,arraylen]) for ci in range(0,interplength): for cj in range(0,arraylen): contourdat[ci,cj] = GridData[ci,2,cj] # # Plot Set-up import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable fig = plt.figure() ax = fig.add_axes([0.15,0.1,0.68,0.85]) # contlevels= np.linspace(contourdat.min(),contourdat.max(),250) #contlevels= np.logspace(-1.501,np.log(contourdat.max())/np.log(10),250) #conticks = [0.01,0.1,1.00] cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels) plt.set_cmap('jet') cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels) plt.set_cmap('jet') # divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) # cbar = plt.colorbar(cs,cax=cax) minorticks = cs.norm(np.array([33.5,33.6,33.7,33.8,33.9,34.0,34.1,34.2,34.3,34.4,34.5,34.6,34.7])) cbar.ax.yaxis.set_ticks(minorticks, minor=True) # cbar.set_label(r"Salinity (psu)") cbar.set_ticks([33.5,33.7,33.9,34.1,34.3,34.5,34.7]) cbar.set_ticklabels([r'33.5',r'33.7',r'33.9',r'34.1',r'34.3',r'34.5',r'34.7']) # ax.set_yticks([0,-250,-500,-750,-1000, -1250, -1500,-1750]) ax.set_yticklabels([r'0', r'250',r'500',r'750',r'1000',r'1250',r'1500',r'1750']) # ax.set_xticks([40,60,80,100,120]) ax.set_xticklabels([ r'40',r'60',r'80',r'100',r'120']) # ax.set_xlabel(r'Day of Year') ax.set_ylabel(r'Depth (m)') ax.set_ylim([-1800,0]) ax.set_xlim([31,139]) # plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot1800_Salinity.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/') # # =========================================================== # # Backscatter Plotting # # =========================================================== # contplt = 2 if contplt == 1: # # =========================================================== # Contour Backscat # =========================================================== # # Plotting correct data contourdat2 = np.zeros([interplength,arraylen]) contourdat= np.zeros([interplength,arraylen]) for ci in range(0,interplength): for cj in range(0,arraylen): contourdat2[ci,cj] = GridData[ci,5,cj]*10**3 for ci in range(0,interplength): for cj in range(0,arraylen): if contourdat2[ci,cj] < 10**-.5: contourdat[ci,cj] = 10**-.5 else: contourdat[ci,cj] = contourdat2[ci,cj] contplt2 = 1 if contplt2 == 1: # # To 500 pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot500_Backscat.pdf') from matplotlib.colors import LogNorm from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt import numpy as np # fig = plt.figure() # # ax = fig.add_axes() # ax = fig.add_subplot(1, 1, 1) from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # Plot Set-up import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable fig = plt.figure() ax = fig.add_axes([0.15,0.1,0.68,0.85]) # contlevels= np.logspace(-.5001,np.log(contourdat.max())/np.log(10),250) cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels, norm=LogNorm()) plt.set_cmap('jet') cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels, norm=LogNorm()) plt.set_cmap('jet') divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) cbar = plt.colorbar(cs,cax=cax, norm=LogNorm()) minorticks = cs.norm(np.array([0.4, 0.5, 0.6, 0.7, 0.8,0.9,1,2,3,4,5,6,7,8])) cbar.ax.yaxis.set_ticks(minorticks, minor=True) cbar.set_label(r"$b_{bp} \times$ 10$^{\mbox{\normalsize -3}}$ (m$^{\mbox{{\normalsize -1}}}$)") cbar.set_ticks([.5,1,5]) cbar.set_ticklabels([r'0.5',r'1.0', r'5.0']) # cbar.set_ticklabels([r'4',r'5',r'6',r'7',r'8',r'9',r'10']) ax.set_yticks([0,-100,-200,-300,-400, -500]) ax.set_yticklabels([r'0', r'100',r'200',r'300',r'400',r'500']) ax.set_xticks([40,60,80,100,120]) ax.set_xticklabels([ r'40',r'60',r'80',r'100',r'120']) ax.set_xlabel(r'Day of Year') ax.set_ylabel(r'Depth (m)') ax.set_ylim([-500,0]) ax.set_xlim([31,139]) # plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot500_Backscat.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/') # # To 1000 pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot1000_Backscat.pdf') from matplotlib.colors import LogNorm from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt import numpy as np # fig = plt.figure() # # ax = fig.add_axes() # ax = fig.add_subplot(1, 1, 1) from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # Plot Set-up import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable fig = plt.figure() ax = fig.add_axes([0.15,0.1,0.68,0.85]) # contlevels= np.logspace(-.5001,np.log(contourdat.max())/np.log(10),250) cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels, norm=LogNorm()) plt.set_cmap('jet') cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels, norm=LogNorm()) plt.set_cmap('jet') divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) cbar = plt.colorbar(cs,cax=cax, norm=LogNorm()) minorticks = cs.norm(np.array([0.4, 0.5, 0.6, 0.7, 0.8,0.9,1,2,3,4,5,6,7,8])) cbar.ax.yaxis.set_ticks(minorticks, minor=True) cbar.set_label(r"$b_{bp} \times$ 10$^{\mbox{\normalsize -3}}$ (m$^{\mbox{{\normalsize -1}}}$)") cbar.set_ticks([.5,1,5]) cbar.set_ticklabels([r'0.5',r'1.0', r'5.0']) # cbar.set_ticklabels([r'4',r'5',r'6',r'7',r'8',r'9',r'10']) ax.set_yticks([0,-200,-400,-600,-800, -1000]) ax.set_yticklabels([r'0', r'200',r'400',r'600',r'800',r'1000']) # ax.set_xticks([40,60,80,100,120]) ax.set_xticklabels([ r'40',r'60',r'80',r'100',r'120']) ax.set_xlabel(r'Day of Year') ax.set_ylabel(r'Depth (m)') ax.set_ylim([-1000,0]) ax.set_xlim([31,139]) # plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot1000_Backscat.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/') # # To 1500 pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot1800_Backscat.pdf') from matplotlib.colors import LogNorm from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt import numpy as np # fig = plt.figure() # # ax = fig.add_axes() # ax = fig.add_subplot(1, 1, 1) from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # Plot Set-up import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable fig = plt.figure() ax = fig.add_axes([0.15,0.1,0.68,0.85]) # contlevels= np.logspace(-.5001,np.log(contourdat.max())/np.log(10),250) cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels, norm=LogNorm()) plt.set_cmap('jet') cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels, norm=LogNorm()) plt.set_cmap('jet') divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) cbar = plt.colorbar(cs,cax=cax, norm=LogNorm()) minorticks = cs.norm(np.array([0.4, 0.5, 0.6, 0.7, 0.8,0.9,1,2,3,4,5,6,7,8])) cbar.ax.yaxis.set_ticks(minorticks, minor=True) cbar.set_label(r"$b_{bp} \times$ 10$^{\mbox{\normalsize -3}}$ (m$^{\mbox{{\normalsize -1}}}$)") cbar.set_ticks([.5,1,5]) cbar.set_ticklabels([r'0.5',r'1.0', r'5.0']) ax.set_yticks([0,-250,-500,-750,-1000, -1250, -1500, -1750]) ax.set_yticklabels([r'0', r'250',r'500',r'750',r'1000',r'1250',r'1500', r'1750']) # ax.set_xticks([40,60,80,100,120]) ax.set_xticklabels([ r'40',r'60',r'80',r'100',r'120']) ax.set_xlabel(r'Day of Year') ax.set_ylabel(r'Depth (m)') ax.set_ylim([-1800,0]) ax.set_xlim([31,139]) # plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot1800_Backscat.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/') # # =========================================================== # # Temperature Plotting # # =========================================================== # contplt = 2 if contplt == 1: # # =========================================================== # Contour Backscat # =========================================================== # # Plotting correct data contourdat= np.zeros([interplength,arraylen]) for ci in range(0,interplength): for cj in range(0,arraylen): contourdat[ci,cj] = GridData[ci,0,cj] contplt2 = 1 if contplt2 == 1: # # To 500 pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot500_Temperature.pdf') from matplotlib.colors import LogNorm from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt import numpy as np # fig = plt.figure() # # ax = fig.add_axes() # ax = fig.add_subplot(1, 1, 1) from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # Plot Set-up import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable fig = plt.figure() ax = fig.add_axes([0.15,0.1,0.68,0.85]) # contlevels= np.linspace(contourdat.min(),contourdat.max(),250) cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels) plt.set_cmap('jet') cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels) plt.set_cmap('jet') divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) cbar = plt.colorbar(cs,cax=cax) minorticks = cs.norm(np.array([-1,-.75,-.5,-.25,0,.25,.5,.75,1,1.25,1.5,1.75,2,2.25,2.5])) cbar.ax.yaxis.set_ticks(minorticks, minor=True) cbar.set_label(r"Temperature ($^o$C)") cbar.set_ticks([-1,-.5,0,.5,1,1.5,2,2.5]) cbar.set_ticklabels([r'-1.0',r'-0.5', r'0.0', r'0.5', r'1.0', r'1.5', r'2.0', r'2.5']) # cbar.set_ticklabels([r'4',r'5',r'6',r'7',r'8',r'9',r'10']) ax.set_yticks([0,-100,-200,-300,-400, -500]) ax.set_yticklabels([r'0', r'100',r'200',r'300',r'400',r'500']) ax.set_xticks([40,60,80,100,120]) ax.set_xticklabels([ r'40',r'60',r'80',r'100',r'120']) ax.set_xlabel(r'Day of Year') ax.set_ylabel(r'Depth (m)') ax.set_ylim([-500,0]) ax.set_xlim([31,139]) # plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot500_Temperature.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/') # # To 1000 pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot1000_Temperature.pdf') from matplotlib.colors import LogNorm from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt import numpy as np # fig = plt.figure() # # ax = fig.add_axes() # ax = fig.add_subplot(1, 1, 1) from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # Plot Set-up import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable fig = plt.figure() ax = fig.add_axes([0.15,0.1,0.68,0.85]) # contlevels= np.linspace(contourdat.min(),contourdat.max(),250) cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels) plt.set_cmap('jet') cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels) plt.set_cmap('jet') divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) cbar = plt.colorbar(cs,cax=cax) minorticks = cs.norm(np.array([-1,-.75,-.5,-.25,0,.25,.5,.75,1,1.25,1.5,1.75,2,2.25,2.5])) cbar.ax.yaxis.set_ticks(minorticks, minor=True) cbar.set_label(r"Temperature ($^o$C)") cbar.set_ticks([-1,-.5,0,.5,1,1.5,2,2.5]) cbar.set_ticklabels([r'-1.0',r'-0.5', r'0.0', r'0.5', r'1.0', r'1.5', r'2.0', r'2.5']) # cbar.set_ticklabels([r'4',r'5',r'6',r'7',r'8',r'9',r'10']) ax.set_yticks([0,-200,-400,-600,-800, -1000]) ax.set_yticklabels([r'0', r'200',r'400',r'600',r'800',r'1000']) ax.set_xticks([40,60,80,100,120]) ax.set_xticklabels([ r'40',r'60',r'80',r'100',r'120']) ax.set_xlabel(r'Day of Year') ax.set_ylabel(r'Depth (m)') ax.set_ylim([-1000,0]) ax.set_xlim([31,139]) # plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot1000_Temperature.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/') # # To 1500 pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot1800_Temperature.pdf') from matplotlib.colors import LogNorm from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt import numpy as np # fig = plt.figure() # # ax = fig.add_axes() # ax = fig.add_subplot(1, 1, 1) from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # Plot Set-up import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable fig = plt.figure() ax = fig.add_axes([0.15,0.1,0.68,0.85]) # contlevels= np.linspace(contourdat.min(),contourdat.max(),250) cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels) plt.set_cmap('jet') cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels) plt.set_cmap('jet') divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) cbar = plt.colorbar(cs,cax=cax) minorticks = cs.norm(np.array([-1,-.75,-.5,-.25,0,.25,.5,.75,1,1.25,1.5,1.75,2,2.25,2.5])) cbar.ax.yaxis.set_ticks(minorticks, minor=True) cbar.set_label(r"Temperature ($^o$C)") cbar.set_ticks([-1,-.5,0,.5,1,1.5,2,2.5]) cbar.set_ticklabels([r'-1.0',r'-0.5', r'0.0', r'0.5', r'1.0', r'1.5', r'2.0', r'2.5']) # cbar.set_ticklabels([r'4',r'5',r'6',r'7',r'8',r'9',r'10']) ax.set_yticks([0,-250,-500,-750,-1000, -1250, -1500, -1750]) ax.set_yticklabels([r'0', r'250',r'500',r'750',r'1000',r'1250',r'1500', r'1750']) ax.set_xticks([40,60,80,100,120]) ax.set_xticklabels([ r'40',r'60',r'80',r'100',r'120']) ax.set_xlabel(r'Day of Year') ax.set_ylabel(r'Depth (m)') ax.set_ylim([-1800,0]) ax.set_xlim([31,139]) # plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot1800_Temperature.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/') # # =========================================================== # # CDOM Plotting # # =========================================================== # contplt = 2 if contplt == 1: # # =========================================================== # Contour Backscat # =========================================================== # # Plotting correct data contourdat= np.zeros([interplength,arraylen]) for ci in range(0,interplength): for cj in range(0,arraylen): contourdat[ci,cj] = GridData[ci,6,cj] if contourdat[ci,cj] >= 3.0: contourdat[ci,cj] = 3.0 contplt2 = 1 if contplt2 == 1: # # To 500 pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot500_CDOM.pdf') from matplotlib.colors import LogNorm from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt import numpy as np # fig = plt.figure() # # ax = fig.add_axes() # ax = fig.add_subplot(1, 1, 1) from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # Plot Set-up import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable fig = plt.figure() ax = fig.add_axes([0.15,0.1,0.68,0.85]) # contlevels= np.linspace(contourdat.min(),contourdat.max(),250) #contlevels= np.logspace(-1.501,np.log(contourdat.max())/np.log(10),250) #conticks = [0.01,0.1,1.00] cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels) plt.set_cmap('jet') cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels) plt.set_cmap('jet') # divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) # cbar = plt.colorbar(cs,cax=cax) minorticks = cs.norm(np.array([1.5, 1.75, 2, 2.25, 2.5, 2.75, 3])) cbar.ax.yaxis.set_ticks(minorticks, minor=True) # cbar.set_label(r"CDOM (ppb)") cbar.set_ticks([1.5, 2, 2.5, 3]) cbar.set_ticklabels([r'1.5',r'2.0',r'2.5',r'$>$ 3.0']) # ax.set_yticks([0,-100,-200,-300,-400, -500]) ax.set_yticklabels([r'0', r'100',r'200',r'300',r'400',r'500']) # ax.set_xticks([40,60,80,100,120]) ax.set_xticklabels([ r'40',r'60',r'80',r'100',r'120']) # ax.set_xlabel(r'Day of Year') ax.set_ylabel(r'Depth (m)') ax.set_ylim([-500,0]) ax.set_xlim([31,139]) # plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot500_CDOM.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/') # # To 1000 pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot1000_CDOM.pdf') from matplotlib.colors import LogNorm from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt import numpy as np # fig = plt.figure() # # ax = fig.add_axes() # ax = fig.add_subplot(1, 1, 1) from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # Plot Set-up import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable fig = plt.figure() ax = fig.add_axes([0.15,0.1,0.68,0.85]) # contlevels= np.linspace(contourdat.min(),contourdat.max(),250) #contlevels= np.logspace(-1.501,np.log(contourdat.max())/np.log(10),250) #conticks = [0.01,0.1,1.00] cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels) plt.set_cmap('jet') cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels) plt.set_cmap('jet') # divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) # cbar = plt.colorbar(cs,cax=cax) minorticks = cs.norm(np.array([1.5, 1.75, 2, 2.25, 2.5, 2.75, 3])) cbar.ax.yaxis.set_ticks(minorticks, minor=True) # cbar.set_label(r"CDOM (ppb)") cbar.set_ticks([1.5, 2, 2.5, 3]) cbar.set_ticklabels([r'1.5',r'2.0',r'2.5',r'$>$ 3.0']) # ax.set_yticks([0,-200,-400,-600,-800, -1000]) ax.set_yticklabels([r'0', r'200',r'400',r'600',r'800',r'1000']) # ax.set_xticks([40,60,80,100,120]) ax.set_xticklabels([ r'40',r'60',r'80',r'100',r'120']) # ax.set_xlabel(r'Day of Year') ax.set_ylabel(r'Depth (m)') ax.set_ylim([-1000,0]) ax.set_xlim([31,139]) # plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot1000_CDOM.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/') # # To 1500 pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot1800_CDOM.pdf') from matplotlib.colors import LogNorm from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt import numpy as np # fig = plt.figure() # # ax = fig.add_axes() # ax = fig.add_subplot(1, 1, 1) from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # Plot Set-up import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable fig = plt.figure() ax = fig.add_axes([0.15,0.1,0.68,0.85]) # contlevels= np.linspace(contourdat.min(),contourdat.max(),250) #contlevels= np.logspace(-1.501,np.log(contourdat.max())/np.log(10),250) #conticks = [0.01,0.1,1.00] cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels) plt.set_cmap('jet') cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels) plt.set_cmap('jet') # divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) # cbar = plt.colorbar(cs,cax=cax) minorticks = cs.norm(np.array([1.5, 1.75, 2, 2.25, 2.5, 2.75, 3])) cbar.ax.yaxis.set_ticks(minorticks, minor=True) # cbar.set_label(r"CDOM (ppb)") cbar.set_ticks([1.5, 2, 2.5, 3]) cbar.set_ticklabels([r'1.5',r'2.0',r'2.5',r'$>$ 3.0']) # ax.set_yticks([0,-250,-500,-750,-1000, -1250, -1500, -1750]) ax.set_yticklabels([r'0', r'250',r'500',r'750',r'1000',r'1250',r'1500',r'1750']) # ax.set_xticks([40,60,80,100,120]) ax.set_xticklabels([ r'40',r'60',r'80',r'100',r'120']) # ax.set_xlabel(r'Day of Year') ax.set_ylabel(r'Depth (m)') ax.set_ylim([-1800,0]) ax.set_xlim([31,139]) # plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/ContourPlot1800_CDOM.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/') # contplt = 1 if contplt == 1: os.chdir('/data/orbprocess_mail/alex/Jan01_Jun04_2013/data/type/test_Mar2014/') pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/tsdiagram.pdf') import seawater.gibbs as gsw import math as ma # Figure out boudaries (mins and maxs) smin = GridData[:,2,:].min() - (0.01 * GridData[:,2,:].min()) smax = GridData[:,2,:].max() + (0.01 * GridData[:,2,:].max()) tmin = GridData[:,0,:].min() - (0.1 * GridData[:,0,:].min()) tmax = GridData[:,0,:].max() + (0.1 * GridData[:,0,:].max()) # # Calculate how many gridcells we need in the x and y dimensions xdim = round((smax-smin)/0.1+1,0) ti= np.linspace(-1.5,3.0,10) ydim = len(ti) print tmin print tmax print ydim print '--' print smin print smax print xdim # # Create empty grid of zeros dens = np.zeros((ydim,xdim)) # # Create temp and salt vectors of appropiate dimensions #ti = np.linspace(tmin,tmax,ydim) si = np.linspace(smin,smax,xdim) print '---' print ti print si # # Loop to fill in grid with densities for j in range(0,int(ydim)): for i in range(0, int(xdim)): dens[j,i]=gsw.rho(si[i],ti[j],0) # # Substract 1000 to convert to sigma-t dens = dens - 1000 # Plot data from matplotlib.colors import LogNorm from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt import numpy as np # fig = plt.figure() # # ax = fig.add_axes() # ax = fig.add_subplot(1, 1, 1) from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # Plot Set-up import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable fig = plt.figure() ax = fig.add_axes([0.15,0.1,0.68,0.85]) # # CS = plt.contour(si,ti,dens, linestyles='dashed', colors='#696969') plt.clabel(CS, fontsize=11,family = 'Computer Modern Roman', inline=1, fmt='%1.1f') # Label every second level for i in range(0,arraylen): dataout, rows, cols = csvread('/data/orbprocess_mail/alex/Jan01_Jun04_2013/data/type/test_Mar2014/' + fullnames[i]) data2 = dataout.T temp = data2[:,1] salt = data2[:,3] dep = data2[:,0] # # llll = len(temp) # dotcolor = [None]*llll # for i in range(0,llll): # if (dep[i] <= -1500.): # dotcolor[i] = '#6633FF' # elif ((dep[i] > -1500.0) and (dep[i] <= -1250.0)): # dotcolor[i] = '#CC33FF' # elif ((dep[i] > -1250.0) and (dep[i] <= -1000.0)): # dotcolor[i] = '#FF33CC' # elif ((dep[i] > -1000.0) and (dep[i] <= -750.0)): # dotcolor[i] = '#003DF5' # elif ((dep[i] > -750.0) and (dep[i] <= -500.0)): # dotcolor[i] = '#33CCFF' # elif ((dep[i] > -500.0) and (dep[i] <= -250.0)): # dotcolor[i] = '#33FF66' # elif ((dep[i] > -250.0) and (dep[i] <= -150.0)): # dotcolor[i] = '#CCFF33' # elif ((dep[i] > -150.0) and (dep[i] <= -125.0)): # dotcolor[i] = '#FFCC33' # elif ((dep[i] > -125.0) and (dep[i] <= -100.0)): # dotcolor[i] = '#F5B800' # elif ((dep[i] > -100.0) and (dep[i] <= -75.0)): # dotcolor[i] = '#FF6633' # elif ((dep[i] > -75.0) and (dep[i] <= -50.0)): # dotcolor[i] = '#FF3366' # elif ((dep[i] > -50.0) and (dep[i] <= -25.0)): # dotcolor[i] = '#B88A00' # elif ((dep[i] > -25.0) and (dep[i] <= 0.0)): # dotcolor[i] = '#000000' cs2 = ax.scatter(salt,temp,s = 20,c=dep,zorder = 5)# , norm=matplotlib.colors.LogNorm())#,levels= np.logspace(xponent1,xponent2,100)) plt.set_cmap('jet') divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) cbar = plt.colorbar(cs2,cax=cax) minorticks = cs2.norm(np.array([-1800, -1700,-1600,-1500, -1400,-1300,-1200,-1000, -1000,-900,-800,-700, -600,-500,-400,-300, -200,-100])) cbar.ax.yaxis.set_ticks(minorticks, minor=True) cbar.set_label(r"Observation Depth (m)") cbar.set_ticks([-1800, -1400, -1000, -600, -200]) cbar.set_ticklabels([r'1800', r'1400', r'1000', r'600', r'200']) ax.set_xlabel(r'Salinity (psu)') ax.set_ylabel(r'Temperature ($^o$C)') ax.set_ylim([-1.3,2.8]) ax.set_xlim([33.2,35]) plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/tsdiagram.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/') # fig.savefig("/home/ardavies/satdata/OSCAR/pdfoutput/tsdiagram.png") # os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/tsdiagram.png ardavies@storm.ceoe.udel.edu:/dev/ardavies/') # # =========================================================== # # MKE FROM FLOAT DATA ONLY # # =========================================================== # coderunner = 1 if coderunner == 1: mkefloat = np.zeros(arraylen-1) mkefloatdate = np.zeros(arraylen-1) ufloat_kmperday = np.zeros(arraylen-1) vfloat_kmperday = np.zeros(arraylen-1) ufloat_mpersec = np.zeros(arraylen-1) vfloat_mpersec = np.zeros(arraylen-1) latfloat = np.zeros(arraylen-1) lonfloat = np.zeros(arraylen-1) # # Loop through the float data for jj in range(0,arraylen-1): # xdistance = abs(pos2dist([floatlon[jj],floatlat[jj],floatlon[jj+1],floatlat[jj]])) ydistance = abs(pos2dist([floatlon[jj],floatlat[jj],floatlon[jj],floatlat[jj+1]])) ufloat_kmperday[jj] = xdistance/2 vfloat_kmperday[jj] = ydistance/2 ufloat_mpersec[jj] = ufloat_kmperday[jj]/86400*1000 vfloat_mpersec[jj] = vfloat_kmperday[jj]/86400*1000 mkefloat[jj] = 0.5*((ufloat_mpersec[jj]*100)**2 + (vfloat_mpersec[jj]*100)**2) mkefloatdate[jj] = (floatdate[jj]+floatdate[jj+1])/2 latfloat[jj] = floatlat[jj] + (floatlat[jj+1] - floatlat[jj])/2 lonfloat[jj] = floatlon[jj] + (floatlon[jj+1] - floatlon[jj])/2 coderunner = 2 if coderunner == 1: # # # =================================================================================== # Plotting Float and Tracer Trajectories # =================================================================================== # pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/float-tracer-trajectory.pdf') from matplotlib.colors import LogNorm from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt import numpy as np fig = plt.figure() # ax = fig.add_axes() ax = fig.add_axes([0.1,0.06,0.83,0.97]) from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # Base Map set-up for the insert map m = Basemap(llcrnrlon=299.0,llcrnrlat=-60.75,urcrnrlon=308.5,urcrnrlat=-53.75,projection='cyl',resolution='i') m.drawcoastlines(linewidth=0.25) m.drawcountries(linewidth=0.25) m.fillcontinents(color='gray',lake_color='white') m.drawmapboundary(fill_color='white') m.drawparallels(np.arange(-90.,90,2.),labels=[1,0,0,0],linewidth=0.0,fontsize=16) m.drawmeridians(np.arange(180.,360.,2. ),labels=[0,0,0,1],linewidth=0.0,fontsize=16) # # Plotting all the float locations in gray floatx,floaty =m(floatlon, floatlat) cs7 = m.plot(floatx,floaty,linewidth = .5, color = 'k', zorder = 0) cs8 = m.scatter(floatx,floaty, s = 10, c='k',edgecolors='none', zorder = 2) floatx2,floaty2 =m(tracer_newlon_U_V, tracer_newlat_U_V) cs9 = m.scatter(floatx2,floaty2, s = 10, c='0.75',edgecolors='none', zorder = 1) for j in range(0,arraylen): cs10 = m.plot([floatx[j],floatx2[j]],[floaty[j],floaty2[j]],linewidth = .5, color = '0.75', zorder = 0) # # Plotting Labels # places = [r'\textbf{Argentina}', r'\textbf{Falkland Islands}', r'\textbf{Antarctica}'] # placeslons = ([295.6, 305.0, 295.5]) # placeslats = ([-53.6, -52.465, -62.3]) # placesx, placesy = m(placeslons, placeslats) # for place, xpt2, ypt2 in zip(places, placesx, placesy): # plt.text(xpt2, ypt2, place,ha='center',va='center',fontsize=16, family = 'Helvetica') plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/float-tracer-trajectory.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # # =================================================================================== # Plotting Float to Tracer Norms # =================================================================================== # # Plot Distances between float origin, next float location, origin, and so on.. pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/allthree.pdf') fig = plt.figure() # ax = fig.add_axes() ax = fig.add_subplot(111) from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True ax.set_yscale('linear') from pylab import * ax.plot(floatdate[0:arraylen-1],origtofloat,color='k',label=r'$D_f$',linewidth = 3) ax.plot(floatdate[0:arraylen-1],tracertofloat,color='#696969',label=r'$D_{tf}$',linewidth = 3,) ax.plot(floatdate[0:arraylen-1],origtotracer,color='0.75',label=r'$D_t$',linewidth = 3) l = legend(loc = 2) #par1 = ax.twinx() #p2, = par1.plot(floatdate,floatMKE, "b", label=r'MKE$_{\mbox{\normalsize float}}$',linewidth = 3) #tkw = dict(size=4, width=1.5) #par1.set_yscale('log') #par1.set_ylim([10**1,10**4]) #par1.set_ylabel(r"\textbf{Mean Kinetic Energy (cm$^{\mbox{\normalsize 2}}$s$^{\mbox{\normalsize -2}}$)}") ax.set_xticks([40,60,80,100,120]) ax.set_xticklabels([ r'40',r'60',r'80',r'100',r'120']) # ax.set_xlabel(r'Day of Year') ax.set_ylabel(r"Distance (km)") ax.set_xlim([31,139]) ax.set_ylim([0,105]) plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/allthree.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/') # # =================================================================================== # Plotting Lagraingian Ratio # =================================================================================== # pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/ratio.pdf') fig = plt.figure() # ax = fig.add_axes() ax = fig.add_subplot(111) from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True ax.plot(floatdate[0:arraylen-1],tracerfloatratio,'k',linewidth = 3) ax.set_xticks([40,60,80,100,120]) ax.set_xticklabels([ r'40',r'60',r'80',r'100',r'120']) # ax.set_xlabel(r'Day of Year') ax.set_ylabel(r"$|D_{tf}|$/$|D_t|$") ax.set_xlim([31,139]) ax.set_xlabel(r"Day of Year") #ax.set_xlim([70,110]) ax.set_ylim([0,2]) plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/ratio.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/') pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/floatlocations.pdf') from matplotlib.colors import LogNorm from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt import numpy as np fig = plt.figure() ax = fig.add_axes([0.07,0.06,0.83,0.97]) from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # Setting the Larger Map m = Basemap(llcrnrlon=300.8,llcrnrlat=-59.5,urcrnrlon=305.9,urcrnrlat=-55.5,projection='cyl',resolution='i') m.drawcoastlines(linewidth=0.25) m.drawcountries(linewidth=0.25) m.fillcontinents(color='gray',lake_color='white') m.drawmapboundary(fill_color='white') m.drawparallels(np.arange(-90.,90,2.),labels=[1,0,0,0],linewidth=0.0,fontsize=16) m.drawmeridians(np.arange(180.,360.,2. ),labels=[0,0,0,1],linewidth=0.0,fontsize=16) rcParams['text.usetex'] = True # # Plotting Tracer and Float locations over the correct dates x,y = m(lon,lat) for l in range(0,20): ll = l + 30 # # Find the correct OSCAR data plot k = 0 for kk in range(0,len(oscardates)-8): if (oscardates[k] == floatdate[ll]): break else: k = k + 1 # usefloatlon= floatlon[ll] usefloatlat = floatlat[ll] usefloatlon_new = floatlon[ll+1] usefloatlat_new = floatlat[ll+1] usetracerlat_U_V = tracer_newlat_U_V[ll] usetracerlon_U_V = tracer_newlon_U_V[ll] # floatx,floaty =m(usefloatlon, usefloatlat) # # Plotting Float Locations And Tracers tracerx_U_V,tracery_U_V =m(usetracerlon_U_V, usetracerlat_U_V) cs3 = m.plot([floatx,tracerx_U_V],[floaty,tracery_U_V],linewidth = 0.5, color = '#696969', zorder = 3) cs1 = m.scatter(tracerx_U_V,tracery_U_V, s = 5, c='#696969',edgecolors='none', zorder = 4) # # Plotting MKE Color for each float date and the DOY floatx2 = np.zeros([21]) floaty2 = np.zeros([21]) floatx22 = np.zeros([11]) floaty22 = np.zeros([11]) floatmke2 = np.zeros([21]) floatdates2 = [None]*11 offsetx = np.zeros([11]) offsety = np.zeros([11]) offsetx = ([ 0, 0, -.1, -.13, -.15, -.13, -.13, -.15, .15, -.15, 0]) offsety = ([.14, .13, .1, 0.03, 0, 0, -0.01, 0, 0, .1, .15]) lll = 0 for l in range(0,21): ll = l + 30 floatx2[l],floaty2[l] = m(floatlon[ll], floatlat[ll]) floatmke2[l] = floatMKE[ll] if l%2==0: floatx22[lll] = floatx2[l] + offsetx[lll] floaty22[lll] = floaty2[l] + offsety[lll] if lll == 0: floatdates2[lll] = "DOY: " + str(int(floatdate[ll])) else: floatdates2[lll] = str(int(floatdate[ll])) lll = lll + 1 for label, xpt, ypt in zip(floatdates2, floatx22, floaty22): plt.text(xpt, ypt, label,ha='center',va='center',fontsize=11) import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable xponent1 = ma.log(30)/ma.log(10) xponent2 = ma.log(2000)/ma.log(10) # cs2 = m.scatter(floatx2,floaty2,s = 35,c=floatmke2, norm=matplotlib.colors.LogNorm())#,levels= np.logspace(xponent1,xponent2,100)) # cbar = plt.colorbar(cs2,spacing='proportional', norm = LogNorm()) cs2 = m.scatter(floatx2,floaty2,s = 40,c=floatmke2, norm=LogNorm(vmin=floatmke2.min(), vmax=floatmke2.max()),zorder = 5)# , norm=matplotlib.colors.LogNorm())#,levels= np.logspace(xponent1,xponent2,100)) plt.set_cmap('jet') divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) cbar = plt.colorbar(cs2,cax=cax, norm=LogNorm(vmin=floatmke2.min(), vmax=floatmke2.max())) minorticks = cs2.norm(np.array([60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000])) cbar.ax.yaxis.set_ticks(minorticks, minor=True) cbar.set_label(r"$MKE_{float}$ (cm$^{\mbox{\normalsize 2}}$s$^{\mbox{\normalsize -2}}$)") cbar.set_ticks([100, 1000]) cbar.set_ticklabels([r'10$^{2}$', r'10$^{3}$']) # # Base Map set-up for the insert map m2 = Basemap(llcrnrlon=292.2,llcrnrlat=-64.2,urcrnrlon=308.5,urcrnrlat=-50.9,projection='cyl',resolution='i') ax2 = fig.add_axes([0.062,0.544,0.4,0.4]) m2.drawcoastlines(linewidth=0.25) m2.drawcountries(linewidth=0.25) m2.fillcontinents(color='gray',lake_color='white') m2.drawmapboundary(fill_color='white') # # Plotting all the float locations in gray floatx3,floaty3 =m2(floatlon, floatlat) cs7 = m2.plot(floatx3,floaty3,linewidth = 0.2, color = '#696969', zorder = 6) cs8 = m2.scatter(floatx3,floaty3, s = 3, c='#696969',edgecolors='none', zorder = 7) # # Overplotting the ones used in larger figure in red floatx4 = np.zeros([21]) floaty4 = np.zeros([21]) lll = 0 for l in range(0,21): ll = l + 30 floatx4[l],floaty4[l] = m2(floatlon[ll], floatlat[ll]) lll = lll + 1 cs9 = m2.plot(floatx4,floaty4,linewidth = 0.2, color = 'b', zorder = 8) cs10 = m2.scatter(floatx4,floaty4, s = 6, c='b',edgecolors='b', zorder = 9) # # Plotting Labels places = [r'Argentina', r'Falkland Islands', r'Antarctica'] placeslons = ([295.6, 305.0, 295.5]) placeslats = ([-53.6, -52.465, -62.3]) placesx, placesy = m2(placeslons, placeslats) for place, xpt2, ypt2 in zip(places, placesx, placesy): plt.text(xpt2, ypt2, place,ha='center',va='center',fontsize=11, family = 'Helvetica') # # Saving plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/floatlocations.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =================================================================================== # Find Nearest Lat's and Lon's to float location for U and V components # =================================================================================== # latvalue1 = np.zeros([arraylen-1]) latvalue2 = np.zeros([arraylen-1]) lonvalue1 = np.zeros([arraylen-1]) lonvalue2 = np.zeros([arraylen-1]) # latindex1 = np.zeros([arraylen-1]) latindex2 = np.zeros([arraylen-1]) lonindex1 = np.zeros([arraylen-1]) lonindex2 = np.zeros([arraylen-1]) # import math as ma for jj in range(0,arraylen-1): # # Get the closest OSCAR data latitude with respect to the float latitude latvalue1[jj], latindex1[jj] = find_nearest(lat[:,0], latfloat[jj]) # # Find the next closest OSCAR data latitude with respect to the float latitude upperlat = lat[latindex1[jj]+1,0] lowerlat = lat[latindex1[jj]-1,0] # # Closest lat + 0.33 degrees? if (abs(latfloat[jj]-upperlat)<=abs(latfloat[jj]-lowerlat)): latindex2[jj] = latindex1[jj] + 1 # # Closest lat - 0.33 degrees? else: latindex2[jj] = latindex1[jj] - 1 latvalue2[jj] = lat[latindex2[jj],0] # # Get the closest OSCAR data longitude with respect to the float longitude lonvalue1[jj], lonindex1[jj] = find_nearest(lon[0,:], lonfloat[jj]) # # Find the next closest OSCAR data longitude with respect to the float longitude upperlon = lon[0,lonindex1[jj]+1] lowerlon = lon[0,lonindex1[jj]-1] # # Closest lon + 0.33 degrees? if (abs(lonfloat[jj]-upperlon)<=abs(lonfloat[jj]-lowerlon)): lonindex2[jj] = lonindex1[jj] + 1 # # Closest lon - 0.33 degrees? else: lonindex2[jj] = lonindex1[jj] - 1 lonvalue2[jj] = lon[0,lonindex2[jj]] # # =================================================================================== # Daily Bi-Linear Interpolation # =================================================================================== # # Initializing Arrays for Interpolation Ufloat = np.zeros([arraylen-1]) Vfloat = np.zeros([arraylen-1]) MKEfloat = np.zeros([arraylen-1]) dumcont = 0 # # Loop through the float data for jj in range(0,arraylen-1): if(mkefloatdate[jj] - ma.trunc(mkefloatdate[jj])) == 0: # # Loop though the daily oscar data for k in range(0,len(oscardates)-8): # # Loop through the daily oscar data ates to find where it equals the float data dates mkefloatdate, lonfloat, latfloat if (oscardates[k] == mkefloatdate[jj]): # # Interpolate Linearly Along Closest Latitude (latvalue1) x1_closest = abs(pos2dist([lonvalue1[jj],latvalue1[jj],lonfloat[jj],latvalue1[jj]])) x2_closest = abs(pos2dist([lonvalue2[jj],latvalue1[jj],lonfloat[jj],latvalue1[jj]])) xtot_closest = abs(pos2dist([lonvalue1[jj],latvalue1[jj],lonvalue2[jj],latvalue1[jj]])) Uinterp_closest = udaily[k,latindex1[jj], lonindex1[jj]]*(x2_closest/xtot_closest) + udaily[k, latindex1[jj], lonindex2[jj]]*(x1_closest/xtot_closest) Vinterp_closest = vdaily[k,latindex1[jj], lonindex1[jj]]*(x2_closest/xtot_closest) + vdaily[k, latindex1[jj], lonindex2[jj]]*(x1_closest/xtot_closest) MKEinterp_closest = mke[k,latindex1[jj], lonindex1[jj]]*(x2_closest/xtot_closest) + mke[k, latindex1[jj], lonindex2[jj]]*(x1_closest/xtot_closest) # # Interpolate Linearly Along Furthest Latitude (latvalue2) x1_furthest = abs(pos2dist([lonvalue1[jj],latvalue2[jj],lonfloat[jj],latvalue2[jj]])) x2_furthest = abs(pos2dist([lonvalue2[jj],latvalue2[jj],lonfloat[jj],latvalue2[jj]])) xtot_furthest = abs(pos2dist([lonvalue1[jj],latvalue2[jj],lonvalue2[jj],latvalue2[jj]])) Uinterp_furthest = udaily[k,latindex2[jj], lonindex1[jj]]*(x2_furthest/xtot_furthest) + udaily[k, latindex2[jj], lonindex2[jj]]*(x1_furthest/xtot_furthest) Vinterp_furthest = vdaily[k,latindex2[jj], lonindex1[jj]]*(x2_furthest/xtot_furthest) + vdaily[k, latindex2[jj], lonindex2[jj]]*(x1_furthest/xtot_furthest) MKEinterp_furthest = mke[k,latindex2[jj], lonindex1[jj]]*(x2_furthest/xtot_furthest) + mke[k, latindex2[jj], lonindex2[jj]]*(x1_furthest/xtot_furthest) # # Tnterpolate Along Londitude y_closest = abs(pos2dist([lonfloat[jj],latvalue1[jj],lonfloat[jj],latfloat[jj]])) y_furthest = abs(pos2dist([lonfloat[jj],latvalue2[jj],lonfloat[jj],latfloat[jj]])) ytot = abs(pos2dist([lonfloat[jj],latvalue1[jj],lonfloat[jj],latvalue2[jj]])) Ufloat[jj]= Uinterp_furthest*(y_closest/ytot) + Uinterp_closest*(y_furthest/ytot) Vfloat[jj]= Vinterp_furthest*(y_closest/ytot) + Vinterp_closest*(y_furthest/ytot) MKEfloat[jj]= MKEinterp_furthest*(y_closest/ytot) + MKEinterp_closest*(y_furthest/ytot) if(mkefloatdate[jj] - ma.trunc(mkefloatdate[jj])) != 0: date1 = ma.trunc(mkefloatdate[jj]) date2 = date1 + 1 # # Loop though the daily oscar data for k in range(0,len(oscardates)-8): # # Loop through the daily oscar data ates to find where it equals the float data dates mkefloatdate, lonfloat, latfloat if (oscardates[k] == date1): # # Interpolate Linearly Along Closest Latitude (latvalue1) x1_closest = abs(pos2dist([lonvalue1[jj],latvalue1[jj],lonfloat[jj],latvalue1[jj]])) x2_closest = abs(pos2dist([lonvalue2[jj],latvalue1[jj],lonfloat[jj],latvalue1[jj]])) xtot_closest = abs(pos2dist([lonvalue1[jj],latvalue1[jj],lonvalue2[jj],latvalue1[jj]])) Uinterp_closest = udaily[k,latindex1[jj], lonindex1[jj]]*(x2_closest/xtot_closest) + udaily[k, latindex1[jj], lonindex2[jj]]*(x1_closest/xtot_closest) Vinterp_closest = vdaily[k,latindex1[jj], lonindex1[jj]]*(x2_closest/xtot_closest) + vdaily[k, latindex1[jj], lonindex2[jj]]*(x1_closest/xtot_closest) MKEinterp_closest = mke[k,latindex1[jj], lonindex1[jj]]*(x2_closest/xtot_closest) + mke[k, latindex1[jj], lonindex2[jj]]*(x1_closest/xtot_closest) # # Interpolate Linearly Along Furthest Latitude (latvalue2) x1_furthest = abs(pos2dist([lonvalue1[jj],latvalue2[jj],lonfloat[jj],latvalue2[jj]])) x2_furthest = abs(pos2dist([lonvalue2[jj],latvalue2[jj],lonfloat[jj],latvalue2[jj]])) xtot_furthest = abs(pos2dist([lonvalue1[jj],latvalue2[jj],lonvalue2[jj],latvalue2[jj]])) Uinterp_furthest = udaily[k,latindex2[jj], lonindex1[jj]]*(x2_furthest/xtot_furthest) + udaily[k, latindex2[jj], lonindex2[jj]]*(x1_furthest/xtot_furthest) Vinterp_furthest = vdaily[k,latindex2[jj], lonindex1[jj]]*(x2_furthest/xtot_furthest) + vdaily[k, latindex2[jj], lonindex2[jj]]*(x1_furthest/xtot_furthest) MKEinterp_furthest = mke[k,latindex2[jj], lonindex1[jj]]*(x2_furthest/xtot_furthest) + mke[k, latindex2[jj], lonindex2[jj]]*(x1_furthest/xtot_furthest) # # Tnterpolate Along Londitude y_closest = abs(pos2dist([lonfloat[jj],latvalue1[jj],lonfloat[jj],latfloat[jj]])) y_furthest = abs(pos2dist([lonfloat[jj],latvalue2[jj],lonfloat[jj],latfloat[jj]])) ytot = abs(pos2dist([lonfloat[jj],latvalue1[jj],lonfloat[jj],latvalue2[jj]])) U1= Uinterp_furthest*(y_closest/ytot) + Uinterp_closest*(y_furthest/ytot) V1= Vinterp_furthest*(y_closest/ytot) + Vinterp_closest*(y_furthest/ytot) MKE1= MKEinterp_furthest*(y_closest/ytot) + MKEinterp_closest*(y_furthest/ytot) # if (oscardates[k] == date2): # # Interpolate Linearly Along Closest Latitude (latvalue1) x1_closest = abs(pos2dist([lonvalue1[jj],latvalue1[jj],lonfloat[jj],latvalue1[jj]])) x2_closest = abs(pos2dist([lonvalue2[jj],latvalue1[jj],lonfloat[jj],latvalue1[jj]])) xtot_closest = abs(pos2dist([lonvalue1[jj],latvalue1[jj],lonvalue2[jj],latvalue1[jj]])) Uinterp_closest = udaily[k,latindex1[jj], lonindex1[jj]]*(x2_closest/xtot_closest) + udaily[k, latindex1[jj], lonindex2[jj]]*(x1_closest/xtot_closest) Vinterp_closest = vdaily[k,latindex1[jj], lonindex1[jj]]*(x2_closest/xtot_closest) + vdaily[k, latindex1[jj], lonindex2[jj]]*(x1_closest/xtot_closest) MKEinterp_closest = mke[k,latindex1[jj], lonindex1[jj]]*(x2_closest/xtot_closest) + mke[k, latindex1[jj], lonindex2[jj]]*(x1_closest/xtot_closest) # # Interpolate Linearly Along Furthest Latitude (latvalue2) x1_furthest = abs(pos2dist([lonvalue1[jj],latvalue2[jj],lonfloat[jj],latvalue2[jj]])) x2_furthest = abs(pos2dist([lonvalue2[jj],latvalue2[jj],lonfloat[jj],latvalue2[jj]])) xtot_furthest = abs(pos2dist([lonvalue1[jj],latvalue2[jj],lonvalue2[jj],latvalue2[jj]])) Uinterp_furthest = udaily[k,latindex2[jj], lonindex1[jj]]*(x2_furthest/xtot_furthest) + udaily[k, latindex2[jj], lonindex2[jj]]*(x1_furthest/xtot_furthest) Vinterp_furthest = vdaily[k,latindex2[jj], lonindex1[jj]]*(x2_furthest/xtot_furthest) + vdaily[k, latindex2[jj], lonindex2[jj]]*(x1_furthest/xtot_furthest) MKEinterp_furthest = mke[k,latindex2[jj], lonindex1[jj]]*(x2_furthest/xtot_furthest) + mke[k, latindex2[jj], lonindex2[jj]]*(x1_furthest/xtot_furthest) # # Tnterpolate Along Londitude y_closest = abs(pos2dist([lonfloat[jj],latvalue1[jj],lonfloat[jj],latfloat[jj]])) y_furthest = abs(pos2dist([lonfloat[jj],latvalue2[jj],lonfloat[jj],latfloat[jj]])) ytot = abs(pos2dist([lonfloat[jj],latvalue1[jj],lonfloat[jj],latvalue2[jj]])) U2= Uinterp_furthest*(y_closest/ytot) + Uinterp_closest*(y_furthest/ytot) V2= Vinterp_furthest*(y_closest/ytot) + Vinterp_closest*(y_furthest/ytot) MKE2= MKEinterp_furthest*(y_closest/ytot) + MKEinterp_closest*(y_furthest/ytot) # # Ufloat[jj]= (U1 + U2)/2 Vfloat[jj]= (V1 + V2)/2 MKEfloat[jj]= (MKE1 + MKE2)/2 dumcont = dumcont + 1 # # DMKEfloatDt = np.zeros([arraylen-2]) DmkefloatDt = np.zeros([arraylen-2]) dumcont = 0 # # Loop through the float data for jj in range(0,arraylen-2): DMKEfloatDt[jj] = (MKEfloat[jj+1]-MKEfloat[jj])/(mkefloatdate[jj+1]-mkefloatdate[jj]) DmkefloatDt[jj] = (mkefloat[jj+1]-mkefloat[jj])/(mkefloatdate[jj+1]-mkefloatdate[jj]) # # ============================================================== # Saving Data - BLOOM & EXPORT # ============================================================== coderunner = 1 if coderunner == 1: # # Data Array for Saving datasave1 = np.zeros([arraylen-1,2]) for ds in range(0,arraylen-1): datasave1[ds,0]=MKEfloat[ds] datasave1[ds,1] = mkefloat[ds] # # Write the data to .csv file csvfile = csv.writer(open('/home/ardavies/satdata/OSCAR/' + "MKEFLOAT.csv",'w'), delimiter = ",") for dsrow in datasave1: csvfile.writerow(np.around(dsrow,decimals=4)) # # Data Array for Saving datasave1 = np.zeros([arraylen-2,2]) for ds in range(0,arraylen-2): datasave1[ds,0]=DMKEfloatDt[ds] datasave1[ds,1] = DmkefloatDt[ds] # # Write the data to .csv file csvfile = csv.writer(open('/home/ardavies/satdata/OSCAR/' + "DMKEFLOAT.csv",'w'), delimiter = ",") for dsrow in datasave1: csvfile.writerow(np.around(dsrow,decimals=4)) # # =========================================================== # Get Data # =========================================================== # coderunner = 2 if coderunner == 1: # # Loop through the float data pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/comparemke.pdf') from matplotlib.colors import LogNorm from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt import numpy as np # fig = plt.figure() # # ax = fig.add_axes() # ax = fig.add_subplot(1, 1, 1) from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from pylab import * from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True fig = plt.figure() # ax = fig.add_axes() ax = fig.add_subplot(111) difference = np.zeros(arraylen-1) for i in range(0,arraylen-1): difference[i] = abs(MKEfloat[i]-mkefloat[i]) print difference ax.plot(floatdate,floatMKE,'k', linewidth = 3, label=r'$MKE_{float}$') ax.plot(mkefloatdate,mkefloat,'b', linewidth = 3, label=r'$KE_{float}$') ax.plot(mkefloatdate,MKEfloat , linestyle='--',color = '#696969', linewidth = 2, label=r'$MKE_{float intp}$') #ax.plot(mkefloatdate,difference,'g', linewidth = 3, label=r'$\mbox{\textbar}$MKE$_{\mbox{\normalsize float intp}}$ - KE$_{\mbox{\normalsize float}}\mbox{\textbar}$') l = legend(loc = 2) ax.set_yscale('log') ax.set_xscale('linear') ax.set_yticks([10**0,10**1,10**2,10**3,10**4]) ax.set_yticklabels([r'10$^{\mbox{\normalsize 0}}$', r'10$^{\mbox{\normalsize 1}}$',r'10$^{\mbox{\normalsize 2}}$',r'10$^{\mbox{\normalsize 3}}$',r'10$^{\mbox{\normalsize 4}}$']) from matplotlib.ticker import AutoMinorLocator minorLocator = AutoMinorLocator() ax.xaxis.set_minor_locator(minorLocator) ax.set_xticks([20,40,60,80,100,120, 140]) ax.set_xticklabels([ r'20',r'40',r'60',r'80',r'100',r'120',r'140']) ax.set_ylim([10**0,10**4]) ax.set_xlim([10,155]) ax.set_xlabel(r"Day of Year") ax.set_ylabel(r"Mean Kinetic Energy (cm$^{\mbox{\normalsize 2}}$s$^{\mbox{\normalsize -2}}$)") plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/comparemke.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/compareMKEs.pdf') from matplotlib.colors import LogNorm from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt import numpy as np # fig = plt.figure() # # ax = fig.add_axes() # ax = fig.add_subplot(1, 1, 1) from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from pylab import * from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True fig = plt.figure() # ax = fig.add_axes() ax = fig.add_subplot(111) # ax.scatter(MKEfloat,mkefloat, s = 40, color = '#696969',edgecolors='none', zorder = 0) # fvararray = np.linspace(1,1000,100) fvar = np.zeros(len(fvararray)) for j in range(0,len(fvararray)): fvar[j] = 33.1618 + .2308*fvararray[j] # # ax.set_xticks([0,500,1000,1500,2000,2500]) # ax.set_xticklabels([r'0', r'500',r'1000',r'1500',r'2000',r'2500']) # # # ax.set_yticks([0,250,500,750,1000,1250]) # ax.set_yticklabels([r'0', r'250',r'500',r'750',r'1000',r'1250']) # # ax.plot(fvararray,fvar,'k', linewidth = 3, label=r' Linear Regression; R = 0.5987; R$^2$ = 0.3585', zorder = 1) #; P = $<$ 0.0001 l = legend(loc = 2) # ax.set_yscale('log') ax.set_xscale('log') ax.set_xlim([3,3100]) ax.set_ylim([1,1300]) # ax.set_ylabel(r"$KE_{float}$ (cm$^{\mbox{\normalsize 2}}$s$^{\mbox{\normalsize -2}}$)") ax.set_xlabel(r"$MKE_{float}$ (cm$^{\mbox{\normalsize 2}}$s$^{\mbox{\normalsize -2}}$)") # plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/compareMKEs.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # Loop through the float data # # Fonts pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/comparedMKEs.pdf') from matplotlib.colors import LogNorm from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt import numpy as np # fig = plt.figure() # # ax = fig.add_axes() # ax = fig.add_subplot(1, 1, 1) from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from pylab import * from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True fig = plt.figure() # ax = fig.add_axes() ax = fig.add_subplot(111) # from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # ax.scatter(DMKEfloatDt,DmkefloatDt, s = 40, color = '#696969',edgecolors='none', zorder = 0) # fvararray = np.linspace(-600,750,100) fvar= np.zeros(len(fvararray)) for j in range(0,len(fvararray)): fvar[j] = -0.4224 + 0.2601*fvararray[j] # ax.plot(fvararray,fvar,'k', linewidth = 3, label=r'Linear Regression; R = 0.5029; R$^2$ = 0.2529', zorder = 1) #; P = $<$ 0.0001 l = legend(loc = 1) # ax.set_yticks([-250,-125,0,125,250,375]) ax.set_yticklabels([r'-250', r'-125',r'0',r'125',r'250',r'375']) ax.set_xticks([-400,-200,0,200,400, 600]) ax.set_xticklabels([r'-400', r'-200',r'0',r'200',r'400',r'600']) # ax.set_xlim([-550,750]) ax.set_ylim([-350,400]) # ax.set_ylabel(r"$dKE_{float}/dt$ (cm$^{\mbox{\normalsize 2}}$s$^{\mbox{\normalsize -1}}$)") ax.set_xlabel(r"$dMKE_{float}/dt$ (cm$^{\mbox{\normalsize 2}}$s$^{\mbox{\normalsize -1}}$)") # plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/comparedMKEs.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') os.chdir('/home/ardavies/satdata/OSCAR/pdfoutput') # # =========================================================== # # THORPE DISPLACEMENT # # =========================================================== # # Do you want to plot this coderunner = 1 if coderunner == 1: # # Array Initialization Density_newlist = np.zeros([interplength,arraylen]) Density_new = np.zeros([interplength,arraylen]) sortindex = np.zeros([interplength,arraylen]) z_new = np.zeros([interplength,arraylen]) thorpedisplacement = np.zeros([interplength,arraylen]) # # Re-sort densities so they are stratifies for i in range(0,arraylen): Density_newlist[:,i] = sorted(GridData[:,1,i]) # # Re-arrange so the ording is same (bottom to top) as grided profiles for i in range(0,arraylen): jj = interplength -1 for j in range(0,interplength): Density_new[j,i] = np.float(Density_newlist[jj,i]) jj = jj - 1 # # Calculate Thorpe Displacement Length for i in range(0,arraylen): for j in range(0,interplength): value, sortindex[j,i] = find_nearest(Density_new[:,i],GridData[j,1,i]) for i in range(0,arraylen): for j in range(0,interplength): z_new[j,i] = Ygrid[np.int(sortindex[j,i])] for i in range(0,arraylen): for j in range(0,interplength): thorpedisplacement[j,i] = z_new[j,i] - Ygrid[j] # # =========================================================== # # THORPE LENGTH SCALE PLOTTING # # =========================================================== # # Do you want to plot this? gridWplt = 2 if gridWplt == 1: # # =========================================================== # Plotting Individual Density and Thorpe Scale Profiles # =========================================================== # # Changing Directory to plotting output os.chdir('/home/ardavies/satdata/OSCAR/pdfoutput') pp = PdfPages('thorpedisplacementprofiles.pdf') for i in range(0,arraylen): # # Fonts from matplotlib.colors import LogNorm from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt import numpy as np # fig = plt.figure() # # ax = fig.add_axes() # ax = fig.add_subplot(1, 1, 1) from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from pylab import * from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True fig = plt.figure() # ax = fig.add_axes() ax = fig.add_subplot(111) from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # # Plot set-up f = plt.figure() ax3 = f.add_subplot(121) ax = f.add_subplot(122) # # Plotting ax3.plot((GridData[:,1,i]-1026),Ygrid,'k',linewidth = 2) ax.plot(thorpedisplacement[:,i],Ygrid,'k',linewidth = 2) # # Axis and Label Set-up ax3.set_ylim([-2000,0]) ax3.set_xlim([0.5,2.0]) ax3.set_ylabel(r'Depth (m)') ax3.set_xlabel(r'Density + 1026 (kg m$^{-3}$)') # ax.set_ylim([-2000,0]) ax.set_xlim([-120,120]) ax.set_xlabel(r'Thorpe Displacement Length (m)') plt.setp(ax.get_yticklabels(), visible=False) # # Title f.suptitle('DOY ' + str(int(floatdate[i]))) # # Saving figure plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/thorpedisplacementprofiles.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # Do you want to plot this? gridWplt = 2 if gridWplt == 1: # # =========================================================== # Contour Plotting Thorpe Scale # =========================================================== # # Plot Set-up pp = PdfPages('thorpedisplacement.pdf') from matplotlib.colors import LogNorm from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt import numpy as np import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable fig = plt.figure() ax = fig.add_axes([0.15,0.1,0.68,0.85]) from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # # Contour Plotting & Color Bar from pylab import * # # Contouring cs = plt.contourf(floatdate,Ygrid, thorpedisplacement, levels = np.linspace(-100,100,201)) plt.set_cmap('seismic') cs = plt.contourf(floatdate,Ygrid, thorpedisplacement, levels = np.linspace(-100,100,201)) plt.set_cmap('seismic') # # Building Colorbar divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) # cbar = plt.colorbar(cs,cax=cax) # cbar.set_label(r"Thorpe Displacement Length (m)") cbar.set_ticks([-100,-50, 0, 50, 100]) cbar.set_ticklabels([r'-100.0',r'-50.0', r'0.0',r'50.0', r'100.0']) # ax.set_yticks([0,-100,-200,-300,-400, -500]) ax.set_yticklabels([r'0', r'100',r'200',r'300',r'400',r'500']) # ax.set_xticks([40,60,80,100,120]) ax.set_xticklabels([ r'40',r'60',r'80',r'100',r'120']) # ax.set_xlabel(r'Day of Year') ax.set_ylabel(r'Depth (m)') ax.set_ylim([-500,0]) ax.set_xlim([31,139]) # # Saving plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/thorpedisplacement.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =========================================================== # # FINDING ISOPYCNAL DEPTHS & PLOTTING # # =========================================================== # # Do you want to run this code? gridWplt = 1 if gridWplt == 1: # # Initializing arrays depths thedepthofmax1 = np.zeros(arraylen) thedepthofmax3 = np.zeros(arraylen) thedepthofmax5 = np.zeros(arraylen) thedepthofmax7 = np.zeros(arraylen) thedepthofmax21 = np.zeros(arraylen) thedepthofmax23 = np.zeros(arraylen) thedepthofmax25 = np.zeros(arraylen) thedepthofmax27 = np.zeros(arraylen) # depthof1026= np.zeros(arraylen) depthof10272= np.zeros(arraylen) depthof10273= np.zeros(arraylen) depthof10274= np.zeros(arraylen) depthof102745= np.zeros(arraylen) depthof10275= np.zeros(arraylen) depthof102755= np.zeros(arraylen) # # for i in range(0,arraylen): # # =========================================================== # Get Data # =========================================================== # dataout, rows, cols = csvread('/data/orbprocess_mail/alex/Jan01_Jun04_2013/data/type/test_Mar2014/' + fullnames[i]) # # =========================================================== # Filtering Profiles: filter in wave num = avg in depth domain # =========================================================== # # Initializing arrays denavg3 = np.zeros(rows-2) depthavg3 = np.zeros(rows-2) denavg5 = np.zeros(rows-4) depthavg5 = np.zeros(rows-4) denavg7 = np.zeros(rows-6) depthavg7 = np.zeros(rows-6) # # Filtering for ii in range(0,rows-2): denavg3[ii] = (dataout[2,ii] + dataout[2,ii + 1] + dataout[2,ii + 2])/3 depthavg3[ii] = (dataout[0,ii] + dataout[0,ii + 1] + dataout[0,ii + 2])/3 for iii in range(0,rows-4): denavg5[iii] = (dataout[2,iii] + dataout[2,iii + 1] + dataout[2,iii + 2]+ dataout[2,iii + 3]+ dataout[2,iii + 4])/5 depthavg5[iii] = (dataout[0,iii] + dataout[0,iii + 1] + dataout[0,iii + 2]+ dataout[0,iii + 3]+ dataout[0,iii + 4])/5 for iiii in range(0,rows-6): denavg7[iiii] = (dataout[2,iiii] + dataout[2,iiii + 1] + dataout[2,iiii + 2]+ dataout[2,iiii + 3] + dataout[2,iiii + 4] + dataout[2,iiii + 5] + dataout[2,iiii + 6])/7 depthavg7[iiii] = (dataout[0,iiii] + dataout[0,iiii + 1] + dataout[0,iiii + 2]+ dataout[0,iiii + 3] + dataout[0,iiii + 4] + dataout[0,iiii + 5] + dataout[0,iiii + 6])/7 # # =========================================================== # d(rho)/dz of Filtered Profiles # =========================================================== # # Initialization for first derivative drhodz = np.zeros(rows-1) drhodz_depth = np.zeros(rows-1) drhodz3 = np.zeros(rows-3) drhodz3_depth = np.zeros(rows-3) drhodz5 = np.zeros(rows-5) drhodz5_depth = np.zeros(rows-5) drhodz7 = np.zeros(rows-7) drhodz7_depth = np.zeros(rows-7) # # Initialization for second derivative d2rhodz = np.zeros(rows-2) d2rhodz_depth = np.zeros(rows-2) d2rhodz3 = np.zeros(rows-4) d2rhodz3_depth = np.zeros(rows-4) d2rhodz5 = np.zeros(rows-6) d2rhodz5_depth = np.zeros(rows-6) d2rhodz7 = np.zeros(rows-8) d2rhodz7_depth = np.zeros(rows-8) # # Derivative for ii in range(0,rows-1): drhodz[ii] = (dataout[2,ii+1]-dataout[2,ii])/(dataout[0,ii+1]-dataout[0,ii]) drhodz_depth[ii] = (dataout[0,ii]+dataout[0,ii+1])/2 for ii in range(0,rows-3): drhodz3[ii] = (denavg3[ii+1]-denavg3[ii])/(depthavg3[ii+1] -depthavg3[ii]) drhodz3_depth[ii] = (depthavg3[ii] + depthavg3[ii+1])/2 for ii in range(0,rows-5): drhodz5[ii] = (denavg5[ii+1]-denavg5[ii])/(depthavg5[ii+1] -depthavg5[ii]) drhodz5_depth[ii] = (depthavg5[ii] + depthavg5[ii+1])/2 for ii in range(0,rows-7): drhodz7[ii] = (denavg7[ii+1]-denavg7[ii])/(depthavg7[ii+1] -depthavg7[ii]) drhodz7_depth[ii] = (depthavg7[ii] + depthavg7[ii+1])/2 # # Find the max d(rho)/dz (right hand corrdinates) mx1 = np.amin(drhodz) mx3 = np.amin(drhodz3) mx5 = np.amin(drhodz5) mx7 = np.amin(drhodz7) # # Find index of max d(rho)/dz idx1 = (np.abs(drhodz-mx1)).argmin() idx3 = (np.abs(drhodz3-mx3)).argmin() idx5 = (np.abs(drhodz5-mx5)).argmin() idx7 = (np.abs(drhodz7-mx7)).argmin() # # Depth of max d(rho)/dz thedepthofmax1[i] = drhodz_depth[idx1] thedepthofmax3[i] = drhodz3_depth[idx3] thedepthofmax5[i] = drhodz5_depth[idx5] thedepthofmax7[i] = drhodz7_depth[idx7] # # Second Derivative for ii in range(0,rows-2): d2rhodz[ii] = (drhodz[ii+1]-drhodz[ii])/(drhodz_depth[ii+1]-drhodz_depth[ii]) d2rhodz_depth[ii] = (drhodz_depth[ii+1]+drhodz_depth[ii])/2 for ii in range(0,rows-4): d2rhodz3[ii] = (drhodz3[ii+1]-drhodz3[ii])/(drhodz3_depth[ii+1] -drhodz3_depth[ii]) d2rhodz3_depth[ii] = (drhodz3_depth[ii] + drhodz3_depth[ii+1])/2 for ii in range(0,rows-6): d2rhodz5[ii] = (drhodz5[ii+1]-drhodz5[ii])/(drhodz5_depth[ii+1] -drhodz5_depth[ii]) d2rhodz5_depth[ii] = (drhodz5_depth[ii] + drhodz5_depth[ii+1])/2 for ii in range(0,rows-8): d2rhodz7[ii] = (drhodz7[ii+1]-drhodz7[ii])/(drhodz7_depth[ii+1] -drhodz7_depth[ii]) d2rhodz7_depth[ii] = (drhodz7_depth[ii] + drhodz7_depth[ii+1])/2 # # Find the index where d2(rho)/dz2 = 0 (right hand corrdinates) idx21 = (np.abs(d2rhodz-0.0)).argmin() idx23 = (np.abs(d2rhodz3-0.0)).argmin() idx25 = (np.abs(d2rhodz5-0.0)).argmin() idx27 = (np.abs(d2rhodz7-0.0)).argmin() # # Depth of inflection point thedepthofmax21[i] = d2rhodz_depth[idx1] thedepthofmax23[i] = d2rhodz3_depth[idx3] thedepthofmax25[i] = d2rhodz5_depth[idx5] thedepthofmax27[i] = d2rhodz7_depth[idx7] # # =========================================================== # Filtering Isopychnal Depths # =========================================================== # # Finding each isopycnals in each profile value1026, index1026 = find_nearest(dataout[2,:],1026) value10272, index10272 = find_nearest(dataout[2,:],1027.2) value10273, index10273 = find_nearest(dataout[2,:],1027.3) value10274, index10274 = find_nearest(dataout[2,:],1027.4) value102745, index102745 = find_nearest(dataout[2,:],1027.45) value10275, index10275 = find_nearest(dataout[2,:],1027.5) value102755, index102755 = find_nearest(dataout[2,:],1027.55) # # Finding depths of isopycnals in each profile depthof1026[i] = dataout[0,index1026] depthof10272[i] = dataout[0,index10272] depthof10273[i] = dataout[0,index10273] depthof10274[i] = dataout[0,index10274] depthof102745[i] = dataout[0,index102745] depthof10275[i] = dataout[0,index10275] depthof102755[i] = dataout[0,index102755] # # =========================================================== # Find Average Chly Concentration in the upper ocean # =========================================================== # from scipy import integrate chlyavg102745 = np.zeros(arraylen) chlyavg102745_2 = np.zeros(arraylen) chlyint102745 = np.zeros(arraylen) chlyavg102745_variance = np.zeros(arraylen) chlyavg102745_stdev = np.zeros(arraylen) chlyavg102745_sterr = np.zeros(arraylen) chlyint102745_trap = np.zeros(arraylen) for i in range(0,arraylen): dataout2, rows2, cols2 = csvread('/data/orbprocess_mail/alex/Jan01_Jun04_2013/data/type/test_Mar2014/' +fullnames[i]) gdataout2, grows2, gcols = csvread('/data/orbprocess_mail/alex/Jan01_Jun04_2013/data/type/test_Mar2014/' +gradnames[i]) # # Averaging depthlength = len (dataout2[0,:]) counter = 0 chlytotal = 0 for ii in range(0,depthlength): if (abs(dataout2[0,ii]) < abs(depthof102745[i])): counter = counter + 1 chlytotal = chlytotal + dataout2[11,ii] chlyavg102745_2[i] = chlytotal/counter # # Integrating chlysforint = np.zeros(counter) chlydepthforint = np.zeros(counter) counter2 = 0 for ii in range(0,depthlength): if (abs(dataout2[0,ii]) < abs(depthof102745[i])): chlysforint[counter2] = dataout2[11,ii] chlydepthforint[counter2] = dataout2[0,ii] counter2 = counter2 + 1 chlyint102745[i] = integrate.simps(chlysforint, chlydepthforint) # # New Averaging chlyavg102745[i] = chlyint102745[i]/abs(depthof102745[i]) # # variance_counter = 0 for ii in range(0,len(chlysforint)): variance_counter = (chlysforint[ii] - chlyavg102745[i])**2 + variance_counter chlyavg102745_variance[i] = variance_counter/(len(chlysforint)-1) chlyavg102745_stdev[i] = np.sqrt(chlyavg102745_variance[i]) chlyavg102745_sterr[i] = chlyavg102745_stdev[i]/np.sqrt(len(chlysforint)) # if i == 32: # print '-- Average --' # print chlyavg102745[i] # print chlyavg102745_variance[i] # print chlyavg102745_stdev[i] # print chlyavg102745_sterr[i] # print '-- Data --' # print chlysforint # # from scipy import integrate backscatavg102745 = np.zeros(arraylen) backscatavg102745_2 = np.zeros(arraylen) backscatint102745 = np.zeros(arraylen) backscatavg102745_variance = np.zeros(arraylen) backscatavg102745_stdev = np.zeros(arraylen) backscatavg102745_sterr = np.zeros(arraylen) for i in range(0,arraylen): dataout2, rows2, cols2 = csvread('/data/orbprocess_mail/alex/Jan01_Jun04_2013/data/type/test_Mar2014/' +fullnames[i]) gdataout2, grows2, gcols = csvread('/data/orbprocess_mail/alex/Jan01_Jun04_2013/data/type/test_Mar2014/' +gradnames[i]) # # Averaging depthlength = len (dataout2[0,:]) counter = 0 backscattotal = 0 for ii in range(0,depthlength): if (abs(dataout2[0,ii]) < abs(depthof102745[i])): counter = counter + 1 backscattotal = backscattotal + dataout2[12,ii] backscatavg102745_2[i] = backscattotal/counter # # Integrating backscatforint = np.zeros(counter) backscatdepthforint = np.zeros(counter) counter2 = 0 for ii in range(0,depthlength): if (abs(dataout2[0,ii]) < abs(depthof102745[i])): backscatforint[counter2] = dataout2[12,ii] backscatdepthforint[counter2] = dataout2[0,ii] counter2 = counter2 + 1 backscatint102745[i] = integrate.simps(backscatforint, backscatdepthforint) # # New Averaging backscatavg102745[i] = backscatint102745[i]/abs(depthof102745[i]) # # variance_counter = 0 for ii in range(0,len(backscatforint)): variance_counter = (backscatforint[ii] - backscatavg102745[i])**2 + variance_counter backscatavg102745_variance[i] = variance_counter/(len(backscatforint)-1) backscatavg102745_stdev[i] = np.sqrt(backscatavg102745_variance[i]) backscatavg102745_sterr[i] = backscatavg102745_stdev[i]/np.sqrt(len(backscatforint)) # Do you want to run this code? gridWplt = 1 if gridWplt == 1: # # =================================================================================== # Depth of all the mixed layers found # =================================================================================== # sideplot = 1 if sideplot == 1: numbiolinesavg = len(IsoBioWDataAvg) pp = PdfPages('MLD.pdf') from matplotlib import rc rc('text', usetex=True) from matplotlib.numerix import arange, cos, pi from matplotlib.colors import LogNorm from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt import numpy as np import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # # Contour Plotting & Color Bar from pylab import * fig = plt.figure() ax4 = fig.add_subplot(111) ax4.plot(floatdate,thedepthofmax1, '0.75', linestyle='--',linewidth = 1) ax4.yaxis.label.set_color('k') ax4.set_ylabel(r'\textbf{Depth (m)') ax4.set_xlabel(r'\textbf{Day of Year}') # ax4.plot(floatdate,thedepthofmax3, 'm', linestyle='--',linewidth = 1) ax4.plot(floatdate,thedepthofmax5, 'b', linestyle='--',linewidth = 1) ax4.plot(floatdate,thedepthofmax7, 'k', linestyle='--',linewidth = 1) ax4.plot(floatdate,thedepthofmax21, '0.75',linewidth = 1) ax4.plot(floatdate,thedepthofmax23, 'm',linewidth = 1) ax4.plot(floatdate,thedepthofmax25, 'b',linewidth = 1) ax4.plot(floatdate,thedepthofmax27, 'k',linewidth = 1) # ax4.plot(floatdate,thedepthofmax25, 'k',linewidth = 2.5) ax.set_yticks([-40,-60, -80,-100,-120]) ax.set_yticklabels([r'40',r'60',r'80',r'100',r'120']) # ax.set_xticks([40,60,80,100,120]) ax.set_xticklabels([ r'40',r'60',r'80',r'100',r'120']) # ax.set_xlabel(r'Day of Year') ax.set_ylabel(r'Depth (m)') ax.set_ylim([-120,-40]) ax.set_xlim([31,139]) plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/MLD.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # Do you want to run this code? gridWplt = 1 if gridWplt == 1: # # =========================================================== # MKE Histories # =========================================================== # coderunner = 2 if coderunner == 1: MKE1000history = np.zeros(arraylen-3) MKE750history = np.zeros(arraylen-3) MKE500history = np.zeros(arraylen-3) MKE400history = np.zeros(arraylen-3) MKE300history = np.zeros(arraylen-3) MKE250history = np.zeros(arraylen-3) MKE200history = np.zeros(arraylen-3) for j in range(0,arraylen-3): k = j + 3 # # Less Than 1000 count1000 = 0 if floatMKE[k] <= 1000.0: bypasser = 0 for kk in range(0,k+1): kkk = k- kk if floatMKE[kkk] <= 1000.0: if bypasser == 0: count1000 = count1000 + 1 bypasser = 0 else: bypasser = 1 MKE1000history[j] = count1000 # # Less Than 750 count750 = 0 if floatMKE[k] <= 750.0: bypasser = 0 for kk in range(0,k+1): kkk = k- kk if floatMKE[kkk] <= 750.0: if bypasser == 0: count750 = count750 + 1 bypasser = 0 else: bypasser = 1 MKE750history[j] = count750 # # Less Than 500 count500 = 0 if floatMKE[k] <= 500.0: bypasser = 0 for kk in range(0,k+1): kkk = k- kk if floatMKE[kkk] <= 500.0: if bypasser == 0: count500 = count500 + 1 bypasser = 0 else: bypasser = 1 MKE500history[j] = count500 # # Less Than 400 count400 = 0 if floatMKE[k] <= 400.0: bypasser = 0 for kk in range(0,k+1): kkk = k- kk if floatMKE[kkk] <= 400.0: if bypasser == 0: count400 = count400 + 1 bypasser = 0 else: bypasser = 1 MKE400history[j] = count400 # # Less Than 300 count300 = 0 if floatMKE[k] <= 300.0: bypasser = 0 for kk in range(0,k+1): kkk = k- kk if floatMKE[kkk] <= 300.0: if bypasser == 0: count300 = count300 + 1 bypasser = 0 else: bypasser = 1 MKE300history[j] = count300 # # Less Than 250 count250 = 0 if floatMKE[k] <= 250.0: bypasser = 0 for kk in range(0,k+1): kkk = k- kk if floatMKE[kkk] <= 250.0: if bypasser == 0: count250 = count250 + 1 bypasser = 0 else: bypasser = 1 MKE250history[j] = count250 # # Less Than 200 count200 = 0 if floatMKE[k] <= 200.0: bypasser = 0 for kk in range(0,k+1): kkk = k- kk if floatMKE[kkk] <= 200.0: if bypasser == 0: count200 = count200 + 1 bypasser = 0 else: bypasser = 1 MKE200history[j] = count200 # # =================================================================================== # 1000 Day MKE History # =================================================================================== # pp = PdfPages('history1000_mke_intchly.pdf') from matplotlib.colors import LogNorm from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt import numpy as np fig = plt.figure() ax = fig.add_axes([0.07,0.06,0.83,0.97]) from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable from pylab import * cs2 = ax.scatter(floatMKE[3:],chlyint102745[3:],s = 40,c=MKE1000history) plt.set_cmap('jet') divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) cbar = plt.colorbar(cs2,cax=cax,spacing='proportional') cbar.set_label(r"$\#$ Profiles $<$ 1000 cm$^{2}$s$^{-2}$ MKE") # ax.set_yticks([50, 100, 150, 200, 250, 300, 350, 400]) ax.set_yticklabels([r'50', r'100',r'150',r'200',r'250',r'300',r'350',r'400']) # ax.set_xticks([500, 1000, 1500, 2000, 2500, 3000, 3500]) ax.set_xticklabels([ r'500',r'1000',r'1500',r'2000',r'2500',r'3000',r'3500']) # ax.set_xlabel(r"$MKE_{float}$ (cm$^{2}$s$^{-2}$)") ax.set_ylabel(r"$\int^{z_{0}}_{z_{\rho = 1027.45}} \mathrm{[Chl(}z\mathrm{)] d}z$}") ax.set_xlim([0,3500]) ax.set_ylim([50,400]) ax.set_xscale('linear') plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/history1000_mke_intchly.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/chly-eke-historyplots/Days_belowMKE/') # # =================================================================================== # 500 Day MKE History # =================================================================================== pp = PdfPages('history500_mke_intchly.pdf') from matplotlib.colors import LogNorm from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt import numpy as np fig = plt.figure() ax = fig.add_axes([0.07,0.06,0.83,0.97]) from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable from pylab import * cs2 = ax.scatter(floatMKE[3:],chlyint102745[3:],s = 40,c=MKE500history) plt.set_cmap('jet') divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) cbar = plt.colorbar(cs2,cax=cax,spacing='proportional') cbar.set_label(r"$\#$ Profiles $<$ 500 cm$^{2}$s$^{-2}$ MKE") # ax.set_yticks([50, 100, 150, 200, 250, 300, 350, 400]) ax.set_yticklabels([r'50', r'100',r'150',r'200',r'250',r'300',r'350',r'400']) # ax.set_xticks([500, 1000, 1500, 2000, 2500, 3000, 3500]) ax.set_xticklabels([ r'500',r'1000',r'1500',r'2000',r'2500',r'3000',r'3500']) # ax.set_xlabel(r"$MKE_{float}$ (cm$^{2}$s$^{-2}$)") ax.set_ylabel(r"$\int^{z_{0}}_{z_{\rho = 1027.45}} \mathrm{[Chl(}z\mathrm{)] d}z$}") ax.set_xlim([0,3500]) ax.set_ylim([50,400]) ax.set_xscale('linear') plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/history500_mke_intchly.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/chly-eke-historyplots/Days_belowMKE/') # # =================================================================================== # 400 Day MKE History # =================================================================================== # pp = PdfPages('history400_mke_intchly.pdf') from matplotlib.colors import LogNorm from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt import numpy as np fig = plt.figure() ax = fig.add_axes([0.07,0.06,0.83,0.97]) from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable from pylab import * cs2 = ax.scatter(floatMKE[3:],chlyint102745[3:],s = 40,c=MKE400history) plt.set_cmap('jet') divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) cbar = plt.colorbar(cs2,cax=cax,spacing='proportional') cbar.set_label(r"$\#$ Profiles $<$ 400 cm$^{2}$s$^{-2}$ MKE") # ax.set_yticks([50, 100, 150, 200, 250, 300, 350, 400]) ax.set_yticklabels([r'50', r'100',r'150',r'200',r'250',r'300',r'350',r'400']) # ax.set_xticks([500, 1000, 1500, 2000, 2500, 3000, 3500]) ax.set_xticklabels([ r'500',r'1000',r'1500',r'2000',r'2500',r'3000',r'3500']) # ax.set_xlabel(r"$MKE_{float}$ (cm$^{2}$s$^{-2}$)") ax.set_ylabel(r"$\int^{z_{0}}_{z_{\rho = 1027.45}} \mathrm{[Chl(}z\mathrm{)] d}z$}") ax.set_xlim([0,3500]) ax.set_ylim([50,400]) ax.set_xscale('linear') plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/history400_mke_intchly.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/chly-eke-historyplots/Days_belowMKE/') # # =================================================================================== # 250 Day MKE History # =================================================================================== # pp = PdfPages('history250_mke_intchly.pdf') from matplotlib.colors import LogNorm from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt import numpy as np fig = plt.figure() ax = fig.add_axes([0.07,0.06,0.83,0.97]) from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable from pylab import * cs2 = ax.scatter(floatMKE[3:],chlyint102745[3:],s = 40,c=MKE250history) plt.set_cmap('jet') divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) cbar = plt.colorbar(cs2,cax=cax,spacing='proportional') cbar.set_label(r"$\#$ Profiles $<$ 250 cm$^{2}$s$^{-2}$ MKE") # ax.set_yticks([50, 100, 150, 200, 250, 300, 350, 400]) ax.set_yticklabels([r'50', r'100',r'150',r'200',r'250',r'300',r'350',r'400']) # ax.set_xticks([500, 1000, 1500, 2000, 2500, 3000, 3500]) ax.set_xticklabels([ r'500',r'1000',r'1500',r'2000',r'2500',r'3000',r'3500']) # ax.set_xlabel(r"$MKE_{float}$ (cm$^{2}$s$^{-2}$)") ax.set_ylabel(r"$\int^{z_{0}}_{z_{\rho = 1027.45}} \mathrm{[Chl(}z\mathrm{)] d}z$}") ax.set_xlim([0,3500]) ax.set_ylim([50,400]) ax.set_xscale('linear') plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/history250_mke_intchly.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/chly-eke-historyplots/Days_belowMKE/') # # # =================================================================================== # MEAN for past # of Days # =================================================================================== # # # Mean 11 day MKE offset = 11 floatMKE_prev11 = np.zeros(arraylen-offset) chlyavg102745_11 = np.zeros(arraylen-offset) chlyint102745_11 = np.zeros(arraylen-offset) floatdate_11 = np.zeros(arraylen-offset) for k in range(0,arraylen-offset): kk = k + offset floatMKE_prev11[k] = np.mean(floatMKE[(kk-offset):kk+1]) chlyavg102745_11[k] = chlyavg102745[kk] chlyint102745_11[k] = chlyint102745[kk] floatdate_11[k] = floatdate[kk] # # Mean 10 day MKE offset = 10 floatMKE_prev10 = np.zeros(arraylen-offset) chlyavg102745_10 = np.zeros(arraylen-offset) chlyint102745_10 = np.zeros(arraylen-offset) floatdate_10 = np.zeros(arraylen-offset) for k in range(0,arraylen-offset): kk = k + offset floatMKE_prev10[k] = np.mean(floatMKE[(kk-offset):kk+1]) chlyavg102745_10[k] = chlyavg102745[kk] chlyint102745_10[k] = chlyint102745[kk] floatdate_10[k] = floatdate[kk] # # Mean 9 day MKE offset = 9 floatMKE_prev9 = np.zeros(arraylen-offset) chlyavg102745_9 = np.zeros(arraylen-offset) chlyint102745_9 = np.zeros(arraylen-offset) floatdate_9 = np.zeros(arraylen-offset) for k in range(0,arraylen-offset): kk = k + offset floatMKE_prev9[k] = np.mean(floatMKE[(kk-offset):kk+1]) chlyavg102745_9[k] = chlyavg102745[kk] chlyint102745_9[k] = chlyint102745[kk] floatdate_9[k] = floatdate[kk] # # Mean 8 day MKE offset = 8 floatMKE_prev8 = np.zeros(arraylen-offset) chlyavg102745_8 = np.zeros(arraylen-offset) chlyint102745_8 = np.zeros(arraylen-offset) floatdate_8 = np.zeros(arraylen-offset) for k in range(0,arraylen-offset): kk = k + offset floatMKE_prev8[k] = np.mean(floatMKE[(kk-offset):kk+1]) chlyavg102745_8[k] = chlyavg102745[kk] chlyint102745_8[k] = chlyint102745[kk] floatdate_8[k] = floatdate[kk] # # Mean 7 day MKE offset = 7 floatMKE_prev7 = np.zeros(arraylen-offset) chlyavg102745_7 = np.zeros(arraylen-offset) chlyint102745_7 = np.zeros(arraylen-offset) floatdate_7 = np.zeros(arraylen-offset) for k in range(0,arraylen-offset): kk = k + offset floatMKE_prev7[k] = np.mean(floatMKE[(kk-offset):kk+1]) chlyavg102745_7[k] = chlyavg102745[kk] chlyint102745_7[k] = chlyint102745[kk] floatdate_7[k] = floatdate[kk] # # Mean 6 day MKE offset = 6 floatMKE_prev6 = np.zeros(arraylen-offset) chlyavg102745_6 = np.zeros(arraylen-offset) chlyint102745_6 = np.zeros(arraylen-offset) floatdate_6 = np.zeros(arraylen-offset) for k in range(0,arraylen-offset): kk = k + offset floatMKE_prev6[k] = np.mean(floatMKE[(kk-offset):kk+1]) chlyavg102745_6[k] = chlyavg102745[kk] chlyint102745_6[k] = chlyint102745[kk] floatdate_6[k] = floatdate[kk] # # Mean 5 day MKE offset = 5 floatMKE_prev5 = np.zeros(arraylen-offset) chlyavg102745_5 = np.zeros(arraylen-offset) chlyint102745_5 = np.zeros(arraylen-offset) floatdate_5 = np.zeros(arraylen-offset) for k in range(0,arraylen-offset): kk = k + offset floatMKE_prev5[k] = np.mean(floatMKE[(kk-offset):kk+1]) chlyavg102745_5[k] = chlyavg102745[kk] chlyint102745_5[k] = chlyint102745[kk] floatdate_5[k] = floatdate[kk] # # Mean day MKE offset = 4 floatMKE_prev4 = np.zeros(arraylen-offset) chlyavg102745_4 = np.zeros(arraylen-offset) chlyint102745_4 = np.zeros(arraylen-offset) floatdate_4 = np.zeros(arraylen-offset) for k in range(0,arraylen-offset): kk = k + offset floatMKE_prev4[k] = np.mean(floatMKE[(kk-offset):kk+1]) chlyavg102745_4[k] = chlyavg102745[kk] chlyint102745_4[k] = chlyint102745[kk] floatdate_4[k] = floatdate[kk] # # Mean 3 day MKE offset = 3 floatMKE_prev3 = np.zeros(arraylen-offset) chlyavg102745_3 = np.zeros(arraylen-offset) chlyint102745_3 = np.zeros(arraylen-offset) floatdate_3 = np.zeros(arraylen-offset) for k in range(0,arraylen-offset): kk = k + offset floatMKE_prev3[k] = np.mean(floatMKE[(kk-offset):kk+1]) chlyavg102745_3[k] = chlyavg102745[kk] chlyint102745_3[k] = chlyint102745[kk] floatdate_3[k] = floatdate[kk] # # Mean 2 day MKE offset = 2 floatMKE_prev2 = np.zeros(arraylen-offset) chlyavg102745_2 = np.zeros(arraylen-offset) chlyint102745_2 = np.zeros(arraylen-offset) floatdate_2 = np.zeros(arraylen-offset) for k in range(0,arraylen-offset): kk = k + offset floatMKE_prev2[k] = np.mean(floatMKE[(kk-offset):kk+1]) chlyavg102745_2[k] = chlyavg102745[kk] chlyint102745_2[k] = chlyint102745[kk] floatdate_2[k] = floatdate[kk] # # Mean 1 day MKE offset = 1 floatMKE_prev1 = np.zeros(arraylen-offset) chlyavg102745_1 = np.zeros(arraylen-offset) chlyint102745_1 = np.zeros(arraylen-offset) floatdate_1 = np.zeros(arraylen-offset) for k in range(0,arraylen-offset): kk = k + offset floatMKE_prev1[k] = np.mean(floatMKE[(kk-offset):kk+1]) chlyavg102745_1[k] = chlyavg102745[kk] chlyint102745_1[k] = chlyint102745[kk] floatdate_1[k] = floatdate[kk] floatMKE_prev = np.zeros([arraylen-8,8]) chlyint102745_prev = np.zeros([arraylen-8,8]) for j in range(0,arraylen-8): k7 = j + 7 k6 = j + 6 k5 = j + 5 k4 = j + 4 k3 = j + 3 k2 = j + 2 k1 = j + 1 k0 = j + 0 floatMKE_prev[j,0] = floatMKE[k7] floatMKE_prev[j,1] = floatMKE_prev1[k6] floatMKE_prev[j,2] = floatMKE_prev2[k5] floatMKE_prev[j,3] = floatMKE_prev3[k4] floatMKE_prev[j,4] = floatMKE_prev4[k3] floatMKE_prev[j,5] = floatMKE_prev5[k2] floatMKE_prev[j,6] = floatMKE_prev6[k1] floatMKE_prev[j,7] = floatMKE_prev7[k0] chlyint102745_prev[j,0] = chlyint102745[k7] chlyint102745_prev[j,1] = chlyint102745_1[k6] chlyint102745_prev[j,2] = chlyint102745_2[k5] chlyint102745_prev[j,3] = chlyint102745_3[k4] chlyint102745_prev[j,4] = chlyint102745_4[k3] chlyint102745_prev[j,5] = chlyint102745_5[k2] chlyint102745_prev[j,6] = chlyint102745_6[k1] chlyint102745_prev[j,7] = chlyint102745_7[k0] floatMKE_prev2 = np.zeros([arraylen-8,2]) chlyint102745_prev2 = np.zeros([arraylen-8,2]) for j in range(0,arraylen-8): k7 = j + 7 k6 = j + 6 k5 = j + 5 k4 = j + 4 k3 = j + 3 k2 = j + 2 k1 = j + 1 k0 = j + 0 floatMKE_prev2[j,0] = floatMKE[k7] # floatMKE_prev[j,1] = floatMKE_prev1[k6] # floatMKE_prev[j,2] = floatMKE_prev2[k5] # floatMKE_prev[j,3] = floatMKE_prev3[k4] # floatMKE_prev[j,4] = floatMKE_prev4[k3] # floatMKE_prev[j,5] = floatMKE_prev5[k2] # floatMKE_prev[j,6] = floatMKE_prev6[k1] floatMKE_prev2[j,1] = floatMKE_prev7[k0] chlyint102745_prev2[j,0] = chlyint102745[k7] # chlyint102745_prev[j,1] = chlyint102745_1[k6] # chlyint102745_prev[j,2] = chlyint102745_2[k5] # chlyint102745_prev[j,3] = chlyint102745_3[k4] # chlyint102745_prev[j,4] = chlyint102745_4[k3] # chlyint102745_prev[j,5] = chlyint102745_5[k2] # chlyint102745_prev[j,6] = chlyint102745_6[k1] chlyint102745_prev2[j,1] = chlyint102745_7[k0] plotter = 2 if plotter ==1: # # =================================================================================== # 200 Day MKE History # =================================================================================== # fig = plt.figure() ax = fig.add_axes([0.1,0.1,0.7,0.7]) #f, ax = plt.subplots() # # Setting Fonts from matplotlib import rcParams rcParams['axes.labelsize'] = 16 rcParams['xtick.labelsize'] = 16 rcParams['ytick.labelsize'] = 16 rcParams['legend.fontsize'] = 14 rcParams['font.family'] = 'sans-serif' rcParams['font.serif'] = ['Helvetica'] rcParams['text.usetex'] = True import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable from pylab import * cs2 = ax.scatter(floatMKE[7:],chlyint102745[7:],s = 40,c=MKE500history[4:],zorder = 0) plt.set_cmap('jet') divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) cbar = plt.colorbar(cs2,cax=cax,spacing='proportional') cbar.set_label(r"\textbf{Num. Profiles below 500 cm$^{2}$s$^{-2}$ EKE}") for j in range(0,arraylen-8): ax.plot(floatMKE_prev[j,:],chlyint102745_prev[j,:], '0.75', linewidth = 1, zorder = 1) # minorticks = cs2.norm(np.array([60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000])) # cbar.ax.yaxis.set_ticks(minorticks, minor=True) ax.set_xlim([0,3500]) ax.set_ylim([50,400]) ax.set_xscale('linear') ax.set_xlabel(r"\textbf{Kinetic Energy (cm$^{2}$s$^{-2}$)") ax.set_ylabel(r"\textbf{Int Chly Above 1027.45 Isopycnal}") plt.savefig('/home/ardavies/satdata/OSCAR/pdfoutput/history500_mke_intchly_8prevMKE.png') os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/history500_mke_intchly_8prevMKE.png ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/chly-eke-historyplots/Days_belowMKE/') # # =================================================================================== # 200 Day MKE History # =================================================================================== # fig = plt.figure() ax = fig.add_axes([0.1,0.1,0.7,0.7]) #f, ax = plt.subplots() # # Setting Fonts from matplotlib import rcParams rcParams['axes.labelsize'] = 16 rcParams['xtick.labelsize'] = 16 rcParams['ytick.labelsize'] = 16 rcParams['legend.fontsize'] = 14 rcParams['font.family'] = 'sans-serif' rcParams['font.serif'] = ['Helvetica'] rcParams['text.usetex'] = True import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable from pylab import * cs2 = ax.scatter(floatMKE[7:],chlyint102745[7:],s = 40,c=MKE500history[4:],zorder = 0) plt.set_cmap('jet') divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) cbar = plt.colorbar(cs2,cax=cax,spacing='proportional') cbar.set_label(r"\textbf{Num. Profiles below 500 cm$^{2}$s$^{-2}$ EKE}") for j in range(0,arraylen-8): ax.plot(floatMKE_prev2[j,:],chlyint102745_prev2[j,:], '0.75', linewidth = 1, zorder = 1) # minorticks = cs2.norm(np.array([60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000])) # cbar.ax.yaxis.set_ticks(minorticks, minor=True) ax.set_xlim([0,3500]) ax.set_ylim([50,400]) ax.set_xscale('linear') ax.set_xlabel(r"\textbf{Kinetic Energy (cm$^{2}$s$^{-2}$)") ax.set_ylabel(r"\textbf{Int Chly Above 1027.45 Isopycnal}") plt.savefig('/home/ardavies/satdata/OSCAR/pdfoutput/history500_mke_intchly_8prevMKEb.png') os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/history500_mke_intchly_8prevMKEb.png ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/chly-eke-historyplots/Days_belowMKE/') # # =================================================================================== # 200 Day MKE History # =================================================================================== # pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/history500_mke_intchly_8prevMKE.pdf') for j in range(0,arraylen-8): k7 = j + 7 fig = plt.figure() ax = fig.add_axes([0.1,0.1,0.7,0.7]) #f, ax = plt.subplots() # # Setting Fonts from matplotlib import rcParams rcParams['axes.labelsize'] = 16 rcParams['xtick.labelsize'] = 16 rcParams['ytick.labelsize'] = 16 rcParams['legend.fontsize'] = 14 rcParams['font.family'] = 'sans-serif' rcParams['font.serif'] = ['Helvetica'] rcParams['text.usetex'] = True import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable from pylab import * # cs2 = ax.scatter(floatMKE[k7],chlyint102745[k7],s = 40,c=MKE500history[k7-3],zorder = 0) cs2 = ax.scatter(floatMKE[k7],chlyint102745[k7],s = 40,c='k',zorder = 0) plt.set_cmap('jet') # divider = make_axes_locatable(ax) # cax = divider.append_axes("right", size="5%", pad=0.05) # cbar = plt.colorbar(cs2,cax=cax,spacing='proportional') # cbar.set_label(r"\textbf{Num. Profiles below 500 cm$^{2}$s$^{-2}$ EKE}") ax.plot(floatMKE_prev2[j,:],chlyint102745_prev2[j,:], '0.75', linewidth = 2, zorder = 1) # minorticks = cs2.norm(np.array([60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000])) # cbar.ax.yaxis.set_ticks(minorticks, minor=True) ax.set_xlim([0,3500]) ax.set_ylim([50,400]) ax.set_xscale('linear') ax.set_xlabel(r"\textbf{Kinetic Energy (cm$^{2}$s$^{-2}$)") ax.set_ylabel(r"\textbf{Int Chly Above 1027.45 Isopycnal}") plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/history500_mke_intchly_8prevMKE.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/chly-eke-historyplots/Days_belowMKE/') # # dMK/dt DfloatMKE_DT1 = np.zeros(arraylen-1) DfloatMKE_DT1_chlyint = np.zeros(arraylen-1) Dchlyint_DT1 = np.zeros(arraylen-1) for k in range(0,arraylen-1): DfloatMKE_DT1[k] = (floatMKE[k+1]-floatMKE[k])/(floatdate[k+1]-floatdate[k]) DfloatMKE_DT1_chlyint[k] = chlyint102745[k+1] Dchlyint_DT1[k] = (chlyint102745[k+1]-chlyint102745[k])/(floatdate[k+1]-floatdate[k]) # # dMK/dt 2 DfloatMKE_DT2 = np.zeros(arraylen-2) DfloatMKE_DT2_chlyint = np.zeros(arraylen-2) Dchlyint_DT2 = np.zeros(arraylen-2) for k in range(0,arraylen-2): DfloatMKE_DT2[k] = (floatMKE[k+2]-floatMKE[k])/(floatdate[k+2]-floatdate[k]) DfloatMKE_DT2_chlyint[k] = chlyint102745[k+2] Dchlyint_DT2[k] = (chlyint102745[k+2]-chlyint102745[k])/(floatdate[k+2]-floatdate[k]) # # dMK/dt 3 DfloatMKE_DT3 = np.zeros(arraylen-3) DfloatMKE_DT3_chlyint = np.zeros(arraylen-3) Dchlyint_DT3 = np.zeros(arraylen-3) for k in range(0,arraylen-3): DfloatMKE_DT3[k] = (floatMKE[k+3]-floatMKE[k])/(floatdate[k+3]-floatdate[k]) DfloatMKE_DT3_chlyint[k] = chlyint102745[k+3] Dchlyint_DT3[k] = (chlyint102745[k+3]-chlyint102745[k])/(floatdate[k+3]-floatdate[k]) # # dMK/dt 4 DfloatMKE_DT4 = np.zeros(arraylen-4) DfloatMKE_DT4_chlyint = np.zeros(arraylen-4) Dchlyint_DT4 = np.zeros(arraylen-4) for k in range(0,arraylen-4): DfloatMKE_DT4[k] = (floatMKE[k+4]-floatMKE[k])/(floatdate[k+4]-floatdate[k]) DfloatMKE_DT4_chlyint[k] = chlyint102745[k+4] Dchlyint_DT4[k] = (chlyint102745[k+4]-chlyint102745[k])/(floatdate[k+4]-floatdate[k]) # # dMK/dt 5 DfloatMKE_DT5 = np.zeros(arraylen-5) DfloatMKE_DT5_chlyint = np.zeros(arraylen-5) Dchlyint_DT5 = np.zeros(arraylen-5) for k in range(0,arraylen-5): DfloatMKE_DT5[k] = (floatMKE[k+5]-floatMKE[k])/(floatdate[k+5]-floatdate[k]) DfloatMKE_DT5_chlyint[k] = chlyint102745[k+5] Dchlyint_DT5[k] = (chlyint102745[k+5]-chlyint102745[k])/(floatdate[k+5]-floatdate[k]) # # dMK/dt 6 DfloatMKE_DT6 = np.zeros(arraylen-6) DfloatMKE_DT6_chlyint = np.zeros(arraylen-6) Dchlyint_DT6 = np.zeros(arraylen-6) for k in range(0,arraylen-6): DfloatMKE_DT6[k] = (floatMKE[k+6]-floatMKE[k])/(floatdate[k+6]-floatdate[k]) DfloatMKE_DT6_chlyint[k] = chlyint102745[k+6] Dchlyint_DT6[k] = (chlyint102745[k+6]-chlyint102745[k])/(floatdate[k+6]-floatdate[k]) # # dMK/dt 7 DfloatMKE_DT7 = np.zeros(arraylen-7) DfloatMKE_DT7_chlyint = np.zeros(arraylen-7) Dchlyint_DT7 = np.zeros(arraylen-7) for k in range(0,arraylen-7): DfloatMKE_DT7[k] = (floatMKE[k+7]-floatMKE[k])/(floatdate[k+7]-floatdate[k]) DfloatMKE_DT7_chlyint[k] = chlyint102745[k+7] Dchlyint_DT7[k] = (chlyint102745[k+7]-chlyint102745[k])/(floatdate[k+7]-floatdate[k]) # # dMK/dt 8 DfloatMKE_DT8 = np.zeros(arraylen-8) DfloatMKE_DT8_chlyint = np.zeros(arraylen-8) Dchlyint_DT8 = np.zeros(arraylen-8) for k in range(0,arraylen-8): DfloatMKE_DT8[k] = (floatMKE[k+8]-floatMKE[k])/(floatdate[k+8]-floatdate[k]) DfloatMKE_DT8_chlyint[k] = chlyint102745[k+8] Dchlyint_DT8[k] = (chlyint102745[k+8]-chlyint102745[k])/(floatdate[k+8]-floatdate[k]) # # dMK/dt 9 DfloatMKE_DT9 = np.zeros(arraylen-9) DfloatMKE_DT9_chlyint = np.zeros(arraylen-9) Dchlyint_DT9 = np.zeros(arraylen-9) for k in range(0,arraylen-9): DfloatMKE_DT9[k] = (floatMKE[k+9]-floatMKE[k])/(floatdate[k+9]-floatdate[k]) DfloatMKE_DT9_chlyint[k] = chlyint102745[k+9] Dchlyint_DT9[k] = (chlyint102745[k+9]-chlyint102745[k])/(floatdate[k+9]-floatdate[k]) plotter = 2 if plotter ==1: # # =================================================================================== # 11 Day MKE History # =================================================================================== # fig = plt.figure() from matplotlib import rcParams rcParams['axes.labelsize'] = 16 rcParams['xtick.labelsize'] = 16 rcParams['ytick.labelsize'] = 16 rcParams['legend.fontsize'] = 14 rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True ax = fig.add_subplot(1, 1, 1) from pylab import * ax.scatter(floatMKE_prev10,chlyint102745_10,s = 3,c='k') ax.set_xlim([0,3500]) ax.set_ylim([50,400]) ax.set_xscale('linear') ax.set_xlabel(r"\textbf{11 Profile Avg Kinetic Energy History (cm$^{2}$s$^{-2}$)") ax.set_ylabel(r"\textbf{Int Chly Above 1027.45 Isopycnal}") plt.savefig('/home/ardavies/satdata/OSCAR/pdfoutput/history11_mke_intchly.png') os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/history11_mke_intchly.png ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/chly-eke-historyplots/Avg_MKE_Histories/') # # =================================================================================== # 12 Day MKE History # =================================================================================== # fig = plt.figure() from matplotlib import rcParams rcParams['axes.labelsize'] = 16 rcParams['xtick.labelsize'] = 16 rcParams['ytick.labelsize'] = 16 rcParams['legend.fontsize'] = 14 rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True ax = fig.add_subplot(1, 1, 1) from pylab import * ax.scatter(floatMKE_prev11,chlyint102745_11,s = 3,c='k') ax.set_xlim([0,3500]) ax.set_ylim([50,400]) ax.set_xscale('linear') ax.set_xlabel(r"\textbf{12 Profile Avg Kinetic Energy History (cm$^{2}$s$^{-2}$)") ax.set_ylabel(r"\textbf{Int Chly Above 1027.45 Isopycnal}") plt.savefig('/home/ardavies/satdata/OSCAR/pdfoutput/history12_mke_intchly.png') os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/history12_mke_intchly.png ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/chly-eke-historyplots/Avg_MKE_Histories/') # # =================================================================================== # 10 Day MKE History # =================================================================================== # fig = plt.figure() from matplotlib import rcParams rcParams['axes.labelsize'] = 16 rcParams['xtick.labelsize'] = 16 rcParams['ytick.labelsize'] = 16 rcParams['legend.fontsize'] = 14 rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True ax = fig.add_subplot(1, 1, 1) from pylab import * ax.scatter(floatMKE_prev9,chlyint102745_9,s = 3,c='k') ax.set_xlim([0,3500]) ax.set_ylim([50,400]) ax.set_xscale('linear') ax.set_xlabel(r"\textbf{10 Profile Avg Kinetic Energy History (cm$^{2}$s$^{-2}$)") ax.set_ylabel(r"\textbf{Int Chly Above 1027.45 Isopycnal}") plt.savefig('/home/ardavies/satdata/OSCAR/pdfoutput/history10_mke_intchly.png') os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/history10_mke_intchly.png ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/chly-eke-historyplots/Avg_MKE_Histories/') # # =================================================================================== # 9 Day MKE History # =================================================================================== # fig = plt.figure() from matplotlib import rcParams rcParams['axes.labelsize'] = 16 rcParams['xtick.labelsize'] = 16 rcParams['ytick.labelsize'] = 16 rcParams['legend.fontsize'] = 14 rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True ax = fig.add_subplot(1, 1, 1) from pylab import * ax.scatter(floatMKE_prev8,chlyint102745_8,s = 3,c='k') ax.set_xlim([0,3500]) ax.set_ylim([50,400]) ax.set_xscale('linear') ax.set_xlabel(r"\textbf{9 Profile Avg Kinetic Energy History (cm$^{2}$s$^{-2}$)") ax.set_ylabel(r"\textbf{Int Chly Above 1027.45 Isopycnal}") plt.savefig('/home/ardavies/satdata/OSCAR/pdfoutput/history9_mke_intchly.png') os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/history9_mke_intchly.png ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/chly-eke-historyplots/Avg_MKE_Histories/') # # =================================================================================== # 8 Day MKE History # =================================================================================== # fig = plt.figure() from matplotlib import rcParams rcParams['axes.labelsize'] = 16 rcParams['xtick.labelsize'] = 16 rcParams['ytick.labelsize'] = 16 rcParams['legend.fontsize'] = 14 rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True ax = fig.add_subplot(1, 1, 1) from pylab import * ax.scatter(floatMKE_prev7,chlyint102745_7,s = 3,c='k') ax.set_xlim([0,3500]) ax.set_ylim([50,400]) ax.set_xscale('linear') ax.set_xlabel(r"\textbf{8 Profile Avg Kinetic Energy History (cm$^{2}$s$^{-2}$)") ax.set_ylabel(r"\textbf{Int Chly Above 1027.45 Isopycnal}") plt.savefig('/home/ardavies/satdata/OSCAR/pdfoutput/history8_mke_intchly.png') os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/history8_mke_intchly.png ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/chly-eke-historyplots/Avg_MKE_Histories/') # # =================================================================================== # 7 Day MKE History # =================================================================================== # fig = plt.figure() from matplotlib import rcParams rcParams['axes.labelsize'] = 16 rcParams['xtick.labelsize'] = 16 rcParams['ytick.labelsize'] = 16 rcParams['legend.fontsize'] = 14 rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True ax = fig.add_subplot(1, 1, 1) from pylab import * ax.scatter(floatMKE_prev6,chlyint102745_6,s = 3,c='k') ax.set_xlim([0,3500]) ax.set_ylim([50,400]) ax.set_xscale('linear') ax.set_xlabel(r"\textbf{7 Profile Avg Kinetic Energy History (cm$^{2}$s$^{-2}$)") ax.set_ylabel(r"\textbf{Int Chly Above 1027.45 Isopycnal}") plt.savefig('/home/ardavies/satdata/OSCAR/pdfoutput/history7_mke_intchly.png') os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/history7_mke_intchly.png ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/chly-eke-historyplots/Avg_MKE_Histories/') # # =================================================================================== # 6 Day MKE History # =================================================================================== # fig = plt.figure() from matplotlib import rcParams rcParams['axes.labelsize'] = 16 rcParams['xtick.labelsize'] = 16 rcParams['ytick.labelsize'] = 16 rcParams['legend.fontsize'] = 14 rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True ax = fig.add_subplot(1, 1, 1) from pylab import * ax.scatter(floatMKE_prev5,chlyint102745_5,s = 3,c='k') ax.set_xlim([0,3500]) ax.set_ylim([50,400]) ax.set_xscale('linear') ax.set_xlabel(r"\textbf{6 Profile Avg Kinetic Energy History (cm$^{2}$s$^{-2}$)") ax.set_ylabel(r"\textbf{Int Chly Above 1027.45 Isopycnal}") plt.savefig('/home/ardavies/satdata/OSCAR/pdfoutput/history6_mke_intchly.png') os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/history6_mke_intchly.png ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/chly-eke-historyplots/Avg_MKE_Histories/') # # =================================================================================== # 5 Day MKE History # =================================================================================== # fig = plt.figure() from matplotlib import rcParams rcParams['axes.labelsize'] = 16 rcParams['xtick.labelsize'] = 16 rcParams['ytick.labelsize'] = 16 rcParams['legend.fontsize'] = 14 rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True ax = fig.add_subplot(1, 1, 1) from pylab import * ax.scatter(floatMKE_prev4,chlyint102745_4,s = 3,c='k') ax.set_xlim([0,3500]) ax.set_ylim([50,400]) ax.set_xscale('linear') ax.set_xlabel(r"\textbf{5 Profile Avg Kinetic Energy History (cm$^{2}$s$^{-2}$)") ax.set_ylabel(r"\textbf{Int Chly Above 1027.45 Isopycnal}") plt.savefig('/home/ardavies/satdata/OSCAR/pdfoutput/history5_mke_intchly.png') os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/history5_mke_intchly.png ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/chly-eke-historyplots/Avg_MKE_Histories/') # # =================================================================================== # 4 Day MKE History # =================================================================================== # fig = plt.figure() from matplotlib import rcParams rcParams['axes.labelsize'] = 16 rcParams['xtick.labelsize'] = 16 rcParams['ytick.labelsize'] = 16 rcParams['legend.fontsize'] = 14 rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True ax = fig.add_subplot(1, 1, 1) from pylab import * ax.scatter(floatMKE_prev3,chlyint102745_3,s = 3,c='k') ax.set_xlim([0,3500]) ax.set_ylim([50,400]) ax.set_xscale('linear') ax.set_xlabel(r"\textbf{4 Profile Avg Kinetic Energy History (cm$^{2}$s$^{-2}$)") ax.set_ylabel(r"\textbf{Int Chly Above 1027.45 Isopycnal}") plt.savefig('/home/ardavies/satdata/OSCAR/pdfoutput/history4_mke_intchly.png') os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/history4_mke_intchly.png ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/chly-eke-historyplots/Avg_MKE_Histories/') # # =================================================================================== # 4 Day MKE History linearlog # =================================================================================== # fig = plt.figure() from matplotlib import rcParams rcParams['axes.labelsize'] = 16 rcParams['xtick.labelsize'] = 16 rcParams['ytick.labelsize'] = 16 rcParams['legend.fontsize'] = 14 rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True ax = fig.add_subplot(1, 1, 1) from pylab import * ax.scatter(floatMKE_prev3,chlyint102745_3,s = 3,c='k') ax.set_xlim([1,3500]) ax.set_ylim([50,400]) ax.set_xscale('linear') ax.set_yscale('log') ax.set_xlabel(r"\textbf{4 Profile Avg Kinetic Energy History (cm$^{2}$s$^{-2}$)") ax.set_ylabel(r"\textbf{Int Chly Above 1027.45 Isopycnal}") plt.savefig('/home/ardavies/satdata/OSCAR/pdfoutput/history4_mke_intchly-linearlog.png') os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/history4_mke_intchly-linearlog.png ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/chly-eke-historyplots/Avg_MKE_Histories/') # # =================================================================================== # 4 Day MKE History loglinear # =================================================================================== # fig = plt.figure() from matplotlib import rcParams rcParams['axes.labelsize'] = 16 rcParams['xtick.labelsize'] = 16 rcParams['ytick.labelsize'] = 16 rcParams['legend.fontsize'] = 14 rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True ax = fig.add_subplot(1, 1, 1) from pylab import * ax.scatter(floatMKE_prev3,chlyint102745_3,s = 3,c='k') ax.set_xlim([1,3500]) ax.set_ylim([50,400]) ax.set_yscale('linear') ax.set_xscale('log') ax.set_xlabel(r"\textbf{4 Profile Avg Kinetic Energy History (cm$^{2}$s$^{-2}$)") ax.set_ylabel(r"\textbf{Int Chly Above 1027.45 Isopycnal}") plt.savefig('/home/ardavies/satdata/OSCAR/pdfoutput/history4_mke_intchly-loglinear.png') os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/history4_mke_intchly-loglinear.png ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/chly-eke-historyplots/Avg_MKE_Histories/') # # =================================================================================== # 4 Day MKE History loglog # =================================================================================== # fig = plt.figure() from matplotlib import rcParams rcParams['axes.labelsize'] = 16 rcParams['xtick.labelsize'] = 16 rcParams['ytick.labelsize'] = 16 rcParams['legend.fontsize'] = 14 rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True ax = fig.add_subplot(1, 1, 1) from pylab import * ax.scatter(floatMKE_prev3,chlyint102745_3,s = 3,c='k') ax.set_xlim([1,3500]) ax.set_ylim([50,400]) ax.set_yscale('log') ax.set_xscale('log') ax.set_xlabel(r"\textbf{4 Profile Avg Kinetic Energy History (cm$^{2}$s$^{-2}$)") ax.set_ylabel(r"\textbf{Int Chly Above 1027.45 Isopycnal}") plt.savefig('/home/ardavies/satdata/OSCAR/pdfoutput/history4_mke_intchly-loglog.png') os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/history4_mke_intchly-loglog.png ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/chly-eke-historyplots/Avg_MKE_Histories/') # # =================================================================================== # 3 Day MKE History # =================================================================================== # fig = plt.figure() from matplotlib import rcParams rcParams['axes.labelsize'] = 16 rcParams['xtick.labelsize'] = 16 rcParams['ytick.labelsize'] = 16 rcParams['legend.fontsize'] = 14 rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True ax = fig.add_subplot(1, 1, 1) from pylab import * ax.scatter(floatMKE_prev2,chlyint102745_2,s = 3,c='k') ax.set_xlim([0,3500]) ax.set_ylim([50,400]) ax.set_xscale('linear') ax.set_xlabel(r"\textbf{3 Profile Avg Kinetic Energy History (cm$^{2}$s$^{-2}$)") ax.set_ylabel(r"\textbf{Int Chly Above 1027.45 Isopycnal}") plt.savefig('/home/ardavies/satdata/OSCAR/pdfoutput/history3_mke_intchly.png') os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/history3_mke_intchly.png ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/chly-eke-historyplots/Avg_MKE_Histories/') # # =================================================================================== # 2 Day MKE History # =================================================================================== # fig = plt.figure() from matplotlib import rcParams rcParams['axes.labelsize'] = 16 rcParams['xtick.labelsize'] = 16 rcParams['ytick.labelsize'] = 16 rcParams['legend.fontsize'] = 14 rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True ax = fig.add_subplot(1, 1, 1) from pylab import * ax.scatter(floatMKE_prev1,chlyint102745_1,s = 3,c='k') ax.set_xlim([0,3500]) ax.set_ylim([50,400]) ax.set_xscale('linear') ax.set_xlabel(r"\textbf{2 Profile Avg Kinetic Energy History (cm$^{2}$s$^{-2}$)") ax.set_ylabel(r"\textbf{Int Chly Above 1027.45 Isopycnal}") plt.savefig('/home/ardavies/satdata/OSCAR/pdfoutput/history2_mke_intchly.png') os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/history2_mke_intchly.png ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/chly-eke-historyplots/Avg_MKE_Histories/') # # =================================================================================== # 1 Day MKE History # =================================================================================== # fig = plt.figure() from matplotlib import rcParams rcParams['axes.labelsize'] = 16 rcParams['xtick.labelsize'] = 16 rcParams['ytick.labelsize'] = 16 rcParams['legend.fontsize'] = 14 rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True ax = fig.add_subplot(1, 1, 1) from pylab import * ax.scatter(floatMKE,chlyint102745,s = 3,c='k') ax.set_xlim([0,3500]) ax.set_ylim([50,400]) ax.set_xscale('linear') ax.set_xlabel(r"\textbf{Avg Kinetic Energy History (cm$^{2}$s$^{-2}$)") ax.set_ylabel(r"\textbf{Int Chly Above 1027.45 Isopycnal}") plt.savefig('/home/ardavies/satdata/OSCAR/pdfoutput/_mke_intchly.png') os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/_mke_intchly.png ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/chly-eke-historyplots/Avg_MKE_Histories/') # # =================================================================================== # Int Chly v. DMKE/DT # =================================================================================== # fig = plt.figure() from matplotlib import rcParams rcParams['axes.labelsize'] = 16 rcParams['xtick.labelsize'] = 16 rcParams['ytick.labelsize'] = 16 rcParams['legend.fontsize'] = 14 rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True ax = fig.add_subplot(1, 1, 1) from pylab import * ax.scatter(DfloatMKE_DT1,DfloatMKE_DT1_chlyint,s = 3,c='k') #ax.set_xlim([0,3500]) ax.set_xscale('linear') ax.set_xlabel(r"\textbf{$\frac{d EKE}{dt}$ from previous profile (cm$^{2}$s$^{-1}$)}") ax.set_ylabel(r"\textbf{Int Chly Above 1027.45 Isopycnal}") plt.savefig('/home/ardavies/satdata/OSCAR/pdfoutput/dmkedt-intchly.png') os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/dmkedt-intchly.png ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =================================================================================== # Int Chly v. DMKE/DT 2 # =================================================================================== # fig = plt.figure() from matplotlib import rcParams rcParams['axes.labelsize'] = 16 rcParams['xtick.labelsize'] = 16 rcParams['ytick.labelsize'] = 16 rcParams['legend.fontsize'] = 14 rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True ax = fig.add_subplot(1, 1, 1) from pylab import * ax.scatter(DfloatMKE_DT2,DfloatMKE_DT2_chlyint,s = 3,c='k') #ax.set_xlim([0,3500]) ax.set_xscale('linear') ax.set_xlabel(r"\textbf{$\frac{d EKE}{dt}$ back 2 profiles (cm$^{2}$s$^{-1}$)}") ax.set_ylabel(r"\textbf{Int Chly Above 1027.45 Isopycnal}") plt.savefig('/home/ardavies/satdata/OSCAR/pdfoutput/dmkedt2-intchly.png') os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/dmkedt2-intchly.png ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =================================================================================== # Int Chly v. DMKE/DT 3 # =================================================================================== # fig = plt.figure() from matplotlib import rcParams rcParams['axes.labelsize'] = 16 rcParams['xtick.labelsize'] = 16 rcParams['ytick.labelsize'] = 16 rcParams['legend.fontsize'] = 14 rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True ax = fig.add_subplot(1, 1, 1) from pylab import * ax.scatter(DfloatMKE_DT3,DfloatMKE_DT3_chlyint,s = 3,c='k') #ax.set_xlim([0,3500]) ax.set_xscale('linear') ax.set_xlabel(r"\textbf{$\frac{d EKE}{dt}$ back 3 profiles (cm$^{2}$s$^{-1}$)}") ax.set_ylabel(r"\textbf{Int Chly Above 1027.45 Isopycnal}") plt.savefig('/home/ardavies/satdata/OSCAR/pdfoutput/dmkedt3-intchly.png') os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/dmkedt3-intchly.png ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =================================================================================== # Int Chly v. DMKE/DT 4 # =================================================================================== # fig = plt.figure() from matplotlib import rcParams rcParams['axes.labelsize'] = 16 rcParams['xtick.labelsize'] = 16 rcParams['ytick.labelsize'] = 16 rcParams['legend.fontsize'] = 14 rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True ax = fig.add_subplot(1, 1, 1) from pylab import * ax.scatter(DfloatMKE_DT4,DfloatMKE_DT4_chlyint,s = 3,c='k') #ax.set_xlim([0,3500]) ax.set_xscale('linear') ax.set_xlabel(r"\textbf{$\frac{d EKE}{dt}$ back 4 profiles (cm$^{2}$s$^{-1}$)}") ax.set_ylabel(r"\textbf{Int Chly Above 1027.45 Isopycnal}") plt.savefig('/home/ardavies/satdata/OSCAR/pdfoutput/dmkedt4-intchly.png') os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/dmkedt4-intchly.png ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =================================================================================== # Int Chly v. DMKE/DT 5 # =================================================================================== # fig = plt.figure() from matplotlib import rcParams rcParams['axes.labelsize'] = 16 rcParams['xtick.labelsize'] = 16 rcParams['ytick.labelsize'] = 16 rcParams['legend.fontsize'] = 14 rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True ax = fig.add_subplot(1, 1, 1) from pylab import * ax.scatter(DfloatMKE_DT5,DfloatMKE_DT5_chlyint,s = 3,c='k') #ax.set_xlim([0,3500]) ax.set_xscale('linear') ax.set_xlabel(r"\textbf{$\frac{d EKE}{dt}$ back 5 profiles (cm$^{2}$s$^{-1}$)}") ax.set_ylabel(r"\textbf{Int Chly Above 1027.45 Isopycnal}") plt.savefig('/home/ardavies/satdata/OSCAR/pdfoutput/dmkedt5-intchly.png') os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/dmkedt5-intchly.png ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =================================================================================== # Int Chly v. DMKE/DT 6 # =================================================================================== # fig = plt.figure() from matplotlib import rcParams rcParams['axes.labelsize'] = 16 rcParams['xtick.labelsize'] = 16 rcParams['ytick.labelsize'] = 16 rcParams['legend.fontsize'] = 14 rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True ax = fig.add_subplot(1, 1, 1) from pylab import * ax.scatter(DfloatMKE_DT6,DfloatMKE_DT6_chlyint,s = 3,c='k') #ax.set_xlim([0,3500]) ax.set_xscale('linear') ax.set_xlabel(r"\textbf{$\frac{d EKE}{dt}$ back 6 profiles (cm$^{2}$s$^{-1}$)}") ax.set_ylabel(r"\textbf{Int Chly Above 1027.45 Isopycnal}") plt.savefig('/home/ardavies/satdata/OSCAR/pdfoutput/dmkedt6-intchly.png') os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/dmkedt6-intchly.png ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =================================================================================== # Int Chly v. DMKE/DT 7 # =================================================================================== # fig = plt.figure() from matplotlib import rcParams rcParams['axes.labelsize'] = 16 rcParams['xtick.labelsize'] = 16 rcParams['ytick.labelsize'] = 16 rcParams['legend.fontsize'] = 14 rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True ax = fig.add_subplot(1, 1, 1) from pylab import * ax.scatter(DfloatMKE_DT7,DfloatMKE_DT7_chlyint,s = 3,c='k') #ax.set_xlim([0,3500]) ax.set_xscale('linear') ax.set_xlabel(r"\textbf{$\frac{d EKE}{dt}$ back 7 profiles (cm$^{2}$s$^{-1}$)}") ax.set_ylabel(r"\textbf{Int Chly Above 1027.45 Isopycnal}") plt.savefig('/home/ardavies/satdata/OSCAR/pdfoutput/dmkedt7-intchly.png') os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/dmkedt7-intchly.png ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =================================================================================== # Int Chly v. DMKE/DT 8 # =================================================================================== # fig = plt.figure() from matplotlib import rcParams rcParams['axes.labelsize'] = 16 rcParams['xtick.labelsize'] = 16 rcParams['ytick.labelsize'] = 16 rcParams['legend.fontsize'] = 14 rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True ax = fig.add_subplot(1, 1, 1) from pylab import * ax.scatter(DfloatMKE_DT8,DfloatMKE_DT8_chlyint,s = 3,c='k') #ax.set_xlim([0,3500]) ax.set_xscale('linear') ax.set_xlabel(r"\textbf{$\frac{d EKE}{dt}$ back 8 profiles (cm$^{2}$s$^{-1}$)}") ax.set_ylabel(r"\textbf{Int Chly Above 1027.45 Isopycnal}") plt.savefig('/home/ardavies/satdata/OSCAR/pdfoutput/dmkedt8-intchly.png') os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/dmkedt8-intchly.png ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =================================================================================== # Int Chly v. DMKE/DT 9 # =================================================================================== # fig = plt.figure() from matplotlib import rcParams rcParams['axes.labelsize'] = 16 rcParams['xtick.labelsize'] = 16 rcParams['ytick.labelsize'] = 16 rcParams['legend.fontsize'] = 14 rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True ax = fig.add_subplot(1, 1, 1) from pylab import * ax.scatter(DfloatMKE_DT9,DfloatMKE_DT9_chlyint,s = 3,c='k') #ax.set_xlim([0,3500]) ax.set_xscale('linear') ax.set_xlabel(r"\textbf{$\frac{d EKE}{dt}$ back 9 profiles (cm$^{2}$s$^{-1}$)}") ax.set_ylabel(r"\textbf{Int Chly Above 1027.45 Isopycnal}") plt.savefig('/home/ardavies/satdata/OSCAR/pdfoutput/dmkedt9-intchly.png') os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/dmkedt9-intchly.png ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =================================================================================== # Int Chly v. DMKE/DT # =================================================================================== # fig = plt.figure() from matplotlib import rcParams rcParams['axes.labelsize'] = 16 rcParams['xtick.labelsize'] = 16 rcParams['ytick.labelsize'] = 16 rcParams['legend.fontsize'] = 14 rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True ax = fig.add_subplot(1, 1, 1) from pylab import * ax.scatter(DfloatMKE_DT1,Dchlyint_DT1,s = 3,c='k') #ax.set_xlim([0,3500]) ax.set_xscale('linear') ax.set_xlabel(r"\textbf{$\frac{\mathrm{d KE}}{\mathrm{d}t}$ from previous profile (cm$^{2}$s$^{-1}$)}") ax.set_ylabel(r"\textbf{$\frac{\mathrm{d Int Chl}}{\mathrm{d}t}$ from previous profile }") plt.savefig('/home/ardavies/satdata/OSCAR/pdfoutput/dmkedt-dintchlydt.png') os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/dmkedt-dintchlydt.png ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =================================================================================== # Int Chly v. DMKE/DT 2 # =================================================================================== # fig = plt.figure() from matplotlib import rcParams rcParams['axes.labelsize'] = 16 rcParams['xtick.labelsize'] = 16 rcParams['ytick.labelsize'] = 16 rcParams['legend.fontsize'] = 14 rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True ax = fig.add_subplot(1, 1, 1) from pylab import * ax.scatter(DfloatMKE_DT2,Dchlyint_DT2,s = 3,c='k') #ax.set_xlim([0,3500]) ax.set_xscale('linear') ax.set_xlabel(r"\textbf{$\frac{\mathrm{d KE}}{\mathrm{d}t}$ back 2 profiles (cm$^{2}$s$^{-1}$)}") ax.set_ylabel(r"\textbf{$\frac{\mathrm{d Int Chl}}{\mathrm{d}t}$ back 2 profiles }") plt.savefig('/home/ardavies/satdata/OSCAR/pdfoutput/dmkedt2-dintchlydt2.png') os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/dmkedt2-dintchlydt2.png ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =================================================================================== # Int Chly v. DMKE/DT 3 # =================================================================================== # fig = plt.figure() from matplotlib import rcParams rcParams['axes.labelsize'] = 16 rcParams['xtick.labelsize'] = 16 rcParams['ytick.labelsize'] = 16 rcParams['legend.fontsize'] = 14 rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True ax = fig.add_subplot(1, 1, 1) from pylab import * ax.scatter(DfloatMKE_DT3,Dchlyint_DT3,s = 3,c='k') #ax.set_xlim([0,3500]) ax.set_xscale('linear') ax.set_xlabel(r"\textbf{$\frac{\mathrm{d KE}}{\mathrm{d}t}$ back 3 profiles (cm$^{2}$s$^{-1}$)}") ax.set_ylabel(r"\textbf{$\frac{\mathrm{d Int Chl}}{\mathrm{d}t}$ back 3 profiles }") plt.savefig('/home/ardavies/satdata/OSCAR/pdfoutput/dmkedt3-dintchlydt3.png') os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/dmkedt3-dintchlydt3.png ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =================================================================================== # Int Chly v. DMKE/DT 4 # =================================================================================== # fig = plt.figure() from matplotlib import rcParams rcParams['axes.labelsize'] = 16 rcParams['xtick.labelsize'] = 16 rcParams['ytick.labelsize'] = 16 rcParams['legend.fontsize'] = 14 rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True ax = fig.add_subplot(1, 1, 1) from pylab import * ax.scatter(DfloatMKE_DT4,Dchlyint_DT4,s = 3,c='k') #ax.set_xlim([0,3500]) ax.set_xscale('linear') ax.set_xlabel(r"\textbf{$\frac{\mathrm{d KE}}{\mathrm{d}t}$ back 4 profiles (cm$^{2}$s$^{-1}$)}") ax.set_ylabel(r"\textbf{$\frac{\mathrm{d Int Chl}}{\mathrm{d}t}$ back 4 profiles }") plt.savefig('/home/ardavies/satdata/OSCAR/pdfoutput/dmkedt4-dintchlydt4.png') os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/dmkedt4-dintchlydt4.png ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =================================================================================== # Int Chly v. DMKE/DT 5 # =================================================================================== # fig = plt.figure() from matplotlib import rcParams rcParams['axes.labelsize'] = 16 rcParams['xtick.labelsize'] = 16 rcParams['ytick.labelsize'] = 16 rcParams['legend.fontsize'] = 14 rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True ax = fig.add_subplot(1, 1, 1) from pylab import * ax.scatter(DfloatMKE_DT5,Dchlyint_DT5,s = 3,c='k') #ax.set_xlim([0,3500]) ax.set_xscale('linear') ax.set_xlabel(r"\textbf{$\frac{\mathrm{d KE}}{\mathrm{d}t}$ back 5 profiles (cm$^{2}$s$^{-1}$)}") ax.set_ylabel(r"\textbf{$\frac{\mathrm{d Int Chl}}{\mathrm{d}t}$ back 5 profiles }") plt.savefig('/home/ardavies/satdata/OSCAR/pdfoutput/dmkedt5-dintchlydt5.png') os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/dmkedt5-dintchlydt5.png ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =================================================================================== # Int Chly v. DMKE/DT 6 # =================================================================================== # fig = plt.figure() from matplotlib import rcParams rcParams['axes.labelsize'] = 16 rcParams['xtick.labelsize'] = 16 rcParams['ytick.labelsize'] = 16 rcParams['legend.fontsize'] = 14 rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True ax = fig.add_subplot(1, 1, 1) from pylab import * ax.scatter(DfloatMKE_DT6,Dchlyint_DT6,s = 3,c='k') #ax.set_xlim([0,3500]) ax.set_xscale('linear') ax.set_xlabel(r"\textbf{$\frac{\mathrm{d KE}}{\mathrm{d}t}$ back 6 profiles (cm$^{2}$s$^{-1}$)}") ax.set_ylabel(r"\textbf{$\frac{\mathrm{d Int Chl}}{\mathrm{d}t}$ back 6 profiles}") plt.savefig('/home/ardavies/satdata/OSCAR/pdfoutput/dmkedt6-dintchlydt6.png') os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/dmkedt6-dintchlydt6.png ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =================================================================================== # Int Chly v. DMKE/DT 7 # =================================================================================== # fig = plt.figure() from matplotlib import rcParams rcParams['axes.labelsize'] = 16 rcParams['xtick.labelsize'] = 16 rcParams['ytick.labelsize'] = 16 rcParams['legend.fontsize'] = 14 rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True ax = fig.add_subplot(1, 1, 1) from pylab import * ax.scatter(DfloatMKE_DT7,Dchlyint_DT7,s = 3,c='k') #ax.set_xlim([0,3500]) ax.set_xscale('linear') ax.set_xlabel(r"\textbf{$\frac{\mathrm{d KE}}{\mathrm{d}t}$ back 7 profiles (cm$^{2}$s$^{-1}$)}") ax.set_ylabel(r"\textbf{$\frac{\mathrm{d Int Chl}}{\mathrm{d}t}$ back 7 profiles }") plt.savefig('/home/ardavies/satdata/OSCAR/pdfoutput/dmkedt7-dintchlydt7.png') os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/dmkedt7-dintchlydt7.png ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =================================================================================== # Int Chly v. DMKE/DT 8 # =================================================================================== # fig = plt.figure() from matplotlib import rcParams rcParams['axes.labelsize'] = 16 rcParams['xtick.labelsize'] = 16 rcParams['ytick.labelsize'] = 16 rcParams['legend.fontsize'] = 14 rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True ax = fig.add_subplot(1, 1, 1) from pylab import * ax.scatter(DfloatMKE_DT8,Dchlyint_DT8,s = 3,c='k') #ax.set_xlim([0,3500]) ax.set_xscale('linear') ax.set_xlabel(r"\textbf{$\frac{\mathrm{d KE}}{\mathrm{d}t}$ back 8 profiles (cm$^{2}$s$^{-1}$)}") ax.set_ylabel(r"\textbf{$\frac{\mathrm{d Int Chl}}{\mathrm{d}t}$ back 8 profiles }") plt.savefig('/home/ardavies/satdata/OSCAR/pdfoutput/dmkedt8-dintchlydt8.png') os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/dmkedt8-dintchlydt8.png ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =================================================================================== # Int Chly v. DMKE/DT 9 # =================================================================================== # fig = plt.figure() from matplotlib import rcParams rcParams['axes.labelsize'] = 16 rcParams['xtick.labelsize'] = 16 rcParams['ytick.labelsize'] = 16 rcParams['legend.fontsize'] = 14 rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True ax = fig.add_subplot(1, 1, 1) from pylab import * ax.scatter(DfloatMKE_DT9,Dchlyint_DT9,s = 3,c='k') #ax.set_xlim([0,3500]) ax.set_xscale('linear') ax.set_xlabel(r"\textbf{$\frac{\mathrm{d KE}}{\mathrm{d}t}$ back 9 profiles (cm$^{2}$s$^{-1}$)}") ax.set_ylabel(r"\textbf{$\frac{\mathrm{d Int Chl}}{\mathrm{d}t}$ back 9 profiles }") plt.savefig('/home/ardavies/satdata/OSCAR/pdfoutput/dmkedt9-dintchlydt9.png') os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/dmkedt9-dintchlydt9.png ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =================================================================================== # Aside # =================================================================================== # fig = plt.figure() from matplotlib import rcParams rcParams['axes.labelsize'] = 16 rcParams['xtick.labelsize'] = 16 rcParams['ytick.labelsize'] = 16 rcParams['legend.fontsize'] = 14 rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True ax = fig.add_subplot(1, 1, 1) from pylab import * ax.scatter(floatMKE_prev3,Dchlyint_DT3,s = 3,c='k') # ax.set_xlim([0,3500]) # ax.set_ylim([50,400]) ax.set_xscale('linear') ax.set_xlabel(r"\textbf{4 Profile Avg Kinetic Energy History (cm$^{2}$s$^{-2}$)") ax.set_ylabel(r"\textbf{$\frac{\mathrm{d Int Chl}}{\mathrm{d}t}$ back 3 profiles }") plt.savefig('/home/ardavies/satdata/OSCAR/pdfoutput/history4_mke_dintchlydt4.png') os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/history4_mke_dintchlydt4.png ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/chly-eke-historyplots/Avg_MKE_Histories/') # # Do you want to run this code? gridWplt = 1 if gridWplt == 1: # # =================================================================================== # Comparing Chly Averaging # =================================================================================== # pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/chlyavgtimeseries_compare.pdf') import matplotlib.pyplot as plt import numpy as np import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # fig = plt.figure() # ax = fig.add_subplot(1, 1, 1) # fig = plt.figure() ax = fig.add_axes([0.15,0.1,0.68,0.85]) from pylab import * ax.plot(floatdate,chlyavg102745_2,'k',linewidth = 3, label =r"$\frac{1}{z_{\rho = 1027.45}} \int^{z_{0}}_{z_{\rho = 1027.45}} \mathrm{[Chl(}z\mathrm{)] d}z$") ax.plot(floatdate,chlyavg102745,'b',linewidth = 3, label =r"$\frac{1}{n_{z_{\rho = 1027.45}}} \sum\limits_{i=0}^{n_{z_{\rho = 1027.45}}} \mathrm{[Chl}_i\mathrm{]} $") l = legend(loc = 2) ax.set_yscale('linear') ax.set_ylabel(r"Average [Chl($z$)]$|^{z_{0}}_{z_{\rho = 1027.45}}}$ ($\mu$g l$^{-1}$)") ax.set_xlabel(r'Day of Year') ax.set_ylim([0,3.25]) ax.set_xticks([40,60,80,100,120]) ax.set_xticklabels([ r'40',r'60',r'80',r'100',r'120']) # ax.set_xlabel(r'Day of Year') ax.set_xlim([31,139]) plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/chlyavgtimeseries_compare.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/') # # =================================================================================== # Plotting Daily and 5 Day Average Float Experienced MKE from OSCAR # =================================================================================== # pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/chlyinttimeseries.pdf') import matplotlib.pyplot as plt import numpy as np import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # fig = plt.figure() # ax = fig.add_subplot(1, 1, 1) # fig = plt.figure() ax = fig.add_axes([0.15,0.1,0.68,0.85]) from pylab import * ax.plot(floatdate,chlyint102745,'k',linewidth = 3) ax.set_yscale('linear') ax.set_ylabel(r"$\int^{z_{0}}_{z_{\rho = 1027.45}} \mathrm{[Chl(}z\mathrm{)] d}z$ (mgm$^{-2}$)") ax.set_xticks([40,60,80,100,120]) ax.set_xticklabels([ r'40',r'60',r'80',r'100',r'120']) # ax.set_xlabel(r'Day of Year') ax.set_xlim([31,139]) plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/chlyinttimeseries.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/') # # # =================================================================================== # Plotting Daily and 5 Day Average Float Experienced MKE from OSCAR # =================================================================================== # pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/chlyavgtimeseries.pdf') import matplotlib.pyplot as plt import numpy as np import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # fig = plt.figure() # ax = fig.add_subplot(1, 1, 1) # fig = plt.figure() ax = fig.add_axes([0.15,0.1,0.68,0.85]) from pylab import * ax.plot(floatdate,chlyavg30meter,'k',linewidth = 3, label =r"$\frac{1}{z_{30}} \int^{z_{0}}_{z_{30}} \mathrm{[Chl(}z\mathrm{)] d}z$") ax.plot(floatdate,chlyavg102745,'b',linewidth = 3, label =r"$\frac{1}{z_{\rho = 1027.45}} \int^{z_{0}}_{z_{\rho = 1027.45}} \mathrm{[Chl(}z\mathrm{)] d}z$") l = legend(loc = 2) ax.set_yscale('linear') ax.set_ylabel(r"$\frac{1}{z} \int^{z_{0}}_{z} \mathrm{[Chl(}z\mathrm{)] d}z$ (mgm$^{-3}$)") ax.set_ylim([0,3.90]) ax.set_xticks([40,60,80,100,120]) ax.set_xticklabels([ r'40',r'60',r'80',r'100',r'120']) # ax.set_xlabel(r'Day of Year') ax.set_xlim([31,139]) plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/chlyavgtimeseries.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/') # # Do you want to run this code? gridWplt = 2 if gridWplt == 1: # # Opening pdf for printing at the end of loop pp1 = PdfPages('linear_density_JanJunMission_full.pdf') # for i in range(0,arraylen): # # =========================================================== # Plotting Density Profiles and Filters (full profiiles) # =========================================================== # # Font Set-up import matplotlib.pyplot as plt import numpy as np import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # fig = plt.figure() # ax = fig.add_subplot(1, 1, 1) # fig = plt.figure() ax = fig.add_axes([0.15,0.1,0.68,0.85]) # Setting-up plot from pylab import * # # Sharing y axis par1 = host.twiny() # # Plotting p1 = host.plot(dataout[2,:],dataout[0,:],'0.75',linewidth = 1) host.scatter(dataout[2,:],dataout[0,:],c='0.75') host.plot(denavg3,depthavg3,'m',linewidth = 1) host.plot(denavg5,depthavg5,'b',linewidth = 1) host.plot(denavg7,depthavg7,'k',linewidth = 1) p2 = par1.plot(dataout[11,:],dataout[0,:],'g',linewidth = 1) # # Setting-up axis host.xaxis.set_major_locator(MaxNLocator(5)) host.set_ylim([-2000,0]) par1.set_xlim([0,6]) # # Ploting horizontal lines for derivative depths host.axhline(xmin=0.0, xmax=0.16,y=thedepthofmax1[i],linewidth=1, color='0.75') host.axhline(xmin=0.0, xmax=0.14,y=thedepthofmax3[i],linewidth=1, color='m') host.axhline(xmin=0.0, xmax=0.11,y=thedepthofmax5[i],linewidth=1, color='b') host.axhline(xmin=0.0, xmax=0.10,y=thedepthofmax7[i],linewidth=1, color='k') # host.axhline(xmin=0.84, xmax=1,y=thedepthofmax21[i],linewidth=1, color='0.75') host.axhline(xmin=0.86, xmax=1,y=thedepthofmax23[i],linewidth=1, color='m') host.axhline(xmin=0.88, xmax=1,y=thedepthofmax25[i],linewidth=1, color='b') host.axhline(xmin=0.9, xmax=1,y=thedepthofmax27[i],linewidth=1, color='k') # # axis labels host.set_xlabel("Density") host.set_ylabel("Depth") par1.set_ylabel("Chlorophyll Fluorescence (micro g/l)") # # fig.suptitle("Date: " + str(floatdate[i])) fig.savefig(pp1, format = "pdf") pp1.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/linear_density_JanJunMission_full.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/') # # =========================================================== # Plotting Log Depth, Linear Density Profiles # =========================================================== # pp1 = PdfPages('log_depth_density_full.pdf') for i in range(0,arraylen): # # Font Set-up from matplotlib.colors import LogNorm from matplotlib import rc from matplotlib.numerix import arange, cos, pi rc('text', usetex=True) from pylab import * from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 12 rcParams['xtick.labelsize'] = 12 rcParams['ytick.labelsize'] = 12 rcParams['legend.fontsize'] = 12 from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # Opening Files dataout, rows, cols = csvread('/data/orbprocess_mail/alex/Jan01_Jun04_2013/data/type/test_Mar2014/' + fullnames[i]) # # Setting-up Plots fig = plt.figure() host = fig.add_subplot(111) from pylab import * # # # Plotting p1 = host.plot(abs(dataout[0,:]),dataout[2,:],'k',linewidth = 4) host.set_xscale('log') host.set_ylim([1026.5,1027.5]) host.set_xlabel("Depth") host.set_ylabel("Density") fig.suptitle("Date: " + str(floatdate[i])) fig.savefig(pp1, format = "pdf") pp1.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/log_depth_density_full.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/') # # =========================================================== # # SIMPLE CORRELATOINS, MEANS, AND VALUES # # =========================================================== # # Do you want to plot this? coderunner = 1 if coderunner == 1: # # =========================================================== # Demean Average Chly Concentration in the upper 30 m; MKE # =========================================================== # # Mean chl and MKE over the entire time series meanchlyavg30meter = np.mean(chlyavg30meter) meanfloatMKE = np.mean(floatMKE) meandepthof1026 = np.mean(depthof1026) meandepthof10272 = np.mean(depthof10272) meandepthof10273 = np.mean(depthof10273) meandepthof10274 = np.mean(depthof10274) meandepthof102745 = np.mean(depthof102745) meandepthof10275 = np.mean(depthof10275) meandepthof102755 = np.mean(depthof102755) # # Initialization for demean data demeaned_chlyavg30meter = np.zeros(arraylen) demeaned_floatMKE = np.zeros(arraylen) demeaned_meandepthof1026 = np.zeros(arraylen) demeaned_meandepthof10272 = np.zeros(arraylen) demeaned_meandepthof10273 = np.zeros(arraylen) demeaned_meandepthof10274 = np.zeros(arraylen) demeaned_meandepthof102745 = np.zeros(arraylen) demeaned_meandepthof10275 = np.zeros(arraylen) demeaned_meandepthof102755 = np.zeros(arraylen) # # Initialization for Abreviated MKE, Chly time series floatdateabrv = np.zeros(arraylen) floatMKEabrv = np.zeros(arraylen) chlyavg30meterabr = np.zeros(arraylen) # # Demean loop for i in range(0,arraylen): demeaned_chlyavg30meter[i] = chlyavg30meter[i]-meanchlyavg30meter demeaned_floatMKE[i] = floatMKE[i] - meanfloatMKE demeaned_meandepthof1026[i] = depthof1026[i] - meandepthof1026 demeaned_meandepthof10272[i] = depthof10272[i] - meandepthof10272 demeaned_meandepthof10273[i] = depthof10273[i] - meandepthof10273 demeaned_meandepthof10274[i] = depthof10274[i] - meandepthof10274 demeaned_meandepthof102745[i] = depthof102745[i] - meandepthof102745 demeaned_meandepthof10275[i] = depthof10275[i] - meandepthof10275 demeaned_meandepthof102755[i] = depthof102755[i] - meandepthof102755 # floatdateabrv[i] = floatdate[i] floatMKEabrv[i] = floatMKE[i] chlyavg30meterabr[i] = chlyavg30meter[i] # # =========================================================== # Relative Change in Demean, Normal Average Chly Concentration in the upper 30 m; MKE # =========================================================== # # Initialization Demean Change delta_demeaned_chlyavg30meter = np.zeros(arraylen-7) delta_demeaned_floatMKE = np.zeros(arraylen-7) # # initialization Change delta_floatdate = np.zeros(arraylen-7) delta_chlyavg30meter = np.zeros(arraylen-7) delta_floatMKE = np.zeros(arraylen-7) relative_chlychange = np.zeros(arraylen-7) relative_MKEchange = np.zeros(arraylen-7) for i in range(0,arraylen-7): # # Demean Relative Change delta_demeaned_chlyavg30meter[i] = (demeaned_chlyavg30meter[i+1] - demeaned_chlyavg30meter[i])/(floatdate[i+1]-floatdate[i]) delta_demeaned_floatMKE[i] = (demeaned_floatMKE[i+1] - demeaned_floatMKE[i])/(floatdate[i+1]-floatdate[i]) delta_floatdate[i] = (floatdate[i] + floatdate[i+1])/2 # # Relative change delta_chlyavg30meter[i] = (chlyavg30meter[i+1] - chlyavg30meter[i]) delta_floatMKE[i] = (floatMKE[i+1] - floatMKE[i]) relative_chlychange[i] = delta_chlyavg30meter[i]/chlyavg30meter[i] relative_MKEchange[i] = delta_floatMKE[i]/floatMKE[i] # # =========================================================== # Cropping Data # =========================================================== # os.chdir('/home/ardavies/satdata/OSCAR/pdfoutput') # # Just Bloom chlyavg30meter_bloom = np.zeros(12) chlyint30meter_bloom = np.zeros(12) floatMKE_bloom = np.zeros(12) floatdate_bloom = np.zeros(12) depthof102745_bloom = np.zeros(12) chlyint102745_bloom = np.zeros(12) chlyavg102745_bloom = np.zeros(12) demeaned_floatMKE_bloom = np.zeros(12) demeaned_chlyavg30meter_bloom = np.zeros(12) demeaned_meandepthof102745_bloom = np.zeros(12) floatdate_bloom = np.zeros(12) floatdate_bloom = np.zeros(12) for l in range(0,12): ll = l + 30 chlyavg30meter_bloom[l] = chlyavg30meter[ll] chlyint30meter_bloom[l] = chlyint30meter[ll] floatMKE_bloom[l] = floatMKE[ll] floatdate_bloom[l] = floatdate[ll] chlyint102745_bloom[l] = chlyint102745[ll] chlyavg102745_bloom[l] = chlyavg102745[ll] depthof102745_bloom[l] = depthof102745[ll] demeaned_floatMKE_bloom[l] = demeaned_floatMKE[ll] demeaned_chlyavg30meter_bloom[l] = demeaned_chlyavg30meter[ll] demeaned_meandepthof102745_bloom[l] = demeaned_meandepthof102745[ll] floatdate_bloom[l] = floatdate[ll] # # Just Export chlyavg30meter_exo = np.zeros(9) chlyint30meter_exo = np.zeros(9) floatMKE_exo = np.zeros(9) floatdate_exo = np.zeros(9) depthof102745_exo = np.zeros(9) chlyint102745_exo = np.zeros(9) chlyavg102745_exo = np.zeros(9) demeaned_floatMKE_exo = np.zeros(9) demeaned_chlyavg30meter_exo = np.zeros(9) demeaned_meandepthof102745_exo = np.zeros(9) floatdata_exo = np.zeros(9) for l in range(0,9): ll = l + 42 chlyavg30meter_exo[l] = chlyavg30meter[ll] chlyint30meter_exo[l] = chlyint30meter[ll] floatMKE_exo[l] = floatMKE[ll] floatdate_exo[l] = floatdate[ll] depthof102745_exo[l] = depthof102745[ll] chlyint102745_exo[l] = chlyint102745[ll] chlyavg102745_exo[l] = chlyavg102745[ll] demeaned_floatMKE_exo[l] = demeaned_floatMKE[ll] demeaned_chlyavg30meter_exo[l] = demeaned_chlyavg30meter[ll] demeaned_meandepthof102745_exo[l] = demeaned_meandepthof102745[ll] floatdata_exo[l] = floatdate[ll] floatdates3 = [None]*arraylen for l in range(0,arraylen): floatdates3[l] = str(int(floatdate[l])) #os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/102745depth-chly-loglinear-fit.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # Bloom and Export chlyavg30meter_bloomexo = np.zeros(21) chlyint30meter_bloomexo = np.zeros(21) floatMKE_bloomexo = np.zeros(21) floatdate_bloomexo = np.zeros(21) depthof102745_bloomexo = np.zeros(21) chlyint102745_bloomexo = np.zeros(21) chlyavg102745_bloomexo = np.zeros(21) demeaned_floatMKE_bloomexo = np.zeros(21) demeaned_chlyavg30meter_bloomexo = np.zeros(21) demeaned_meandepthof102745_bloomexo = np.zeros(21) floatdate_bloomexo = np.zeros(21) floatdate_bloomexo = np.zeros(21) backscatavg102745_bloomexo = np.zeros(21) for l in range(0,21): ll = l + 30 backscatavg102745_bloomexo[l] = backscatavg102745[ll] chlyavg30meter_bloomexo[l] = chlyavg30meter[ll] chlyint30meter_bloomexo[l] = chlyint30meter[ll] floatMKE_bloomexo[l] = floatMKE[ll] floatdate_bloomexo[l] = floatdate[ll] chlyint102745_bloomexo[l] = chlyint102745[ll] chlyavg102745_bloomexo[l] = chlyavg102745[ll] depthof102745_bloomexo[l] = depthof102745[ll] demeaned_floatMKE_bloomexo[l] = demeaned_floatMKE[ll] demeaned_chlyavg30meter_bloomexo[l] = demeaned_chlyavg30meter[ll] demeaned_meandepthof102745_bloomexo[l] = demeaned_meandepthof102745[ll] floatdate_bloomexo[l] = floatdate[ll] # # ============================================================== # Saving Data # ============================================================== # # Data datasave1 = np.zeros([arraylen,13]) for ds in range(arraylen): datasave1[ds,0]=floatdate[ds] datasave1[ds,1] = chlyavg30meter[ds] datasave1[ds,2] = floatMKE[ds] datasave1[ds,3] = abs(depthof1026[ds]) datasave1[ds,4] = abs(depthof10272[ds]) datasave1[ds,5] = abs(depthof10273[ds]) datasave1[ds,6] = abs(depthof10274[ds]) datasave1[ds,7] = abs(depthof102745[ds]) datasave1[ds,8] = abs(depthof10275[ds]) datasave1[ds,9] = abs(depthof102755[ds]) datasave1[ds,10] = chlyavg102745[ds] datasave1[ds,11] = chlyint102745[ds] datasave1[ds,12] = chlyint30meter[ds] # # Write the data to .csv file csvfile = csv.writer(open('/home/ardavies/satdata/OSCAR/' + "Data_Full.csv",'w'), delimiter = ",") for dsrow in datasave1: csvfile.writerow(np.around(dsrow,decimals=4)) # # ============================================================== # Saving Data - BLOOM # ============================================================== # # Data datasave1 = np.zeros([12,6]) for ds in range(0,12): datasave1[ds,0]=floatdate_bloom[ds] datasave1[ds,1] = chlyavg30meter_bloom[ds] datasave1[ds,2] = floatMKE_bloom[ds] datasave1[ds,3] = depthof102745_bloom[ds] datasave1[ds,4] = chlyint102745_bloom[ds] datasave1[ds,5] = chlyavg102745_bloom[ds] # # Write the data to .csv file csvfile = csv.writer(open('/home/ardavies/satdata/OSCAR/' + "Data_Bloom.csv",'w'), delimiter = ",") for dsrow in datasave1: csvfile.writerow(np.around(dsrow,decimals=4)) # # ============================================================== # Saving Data - EXPORT # ============================================================== # # Data datasave1 = np.zeros([9,6]) for ds in range(0,9): datasave1[ds,0]=floatdate_exo[ds] datasave1[ds,1] = chlyavg30meter_exo[ds] datasave1[ds,2] = floatMKE_exo[ds] datasave1[ds,3] = depthof102745_exo[ds] datasave1[ds,4] = chlyint102745_exo[ds] datasave1[ds,5] = chlyavg102745_exo[ds] # # Write the data to .csv file csvfile = csv.writer(open('/home/ardavies/satdata/OSCAR/' + "Data_Export.csv",'w'), delimiter = ",") for dsrow in datasave1: csvfile.writerow(np.around(dsrow,decimals=4)) # # ============================================================== # Saving Data - BLOOM & EXPORT # ============================================================== # # Data datasave1 = np.zeros([21,6]) for ds in range(0,21): datasave1[ds,0]=floatdate_bloomexo[ds] datasave1[ds,1] = chlyavg30meter_bloomexo[ds] datasave1[ds,2] = floatMKE_bloomexo[ds] datasave1[ds,3] = depthof102745_bloomexo[ds] datasave1[ds,4] = chlyint102745_bloomexo[ds] datasave1[ds,5] = chlyavg102745_bloomexo[ds] # # Write the data to .csv file csvfile = csv.writer(open('/home/ardavies/satdata/OSCAR/' + "Data_BloomExport.csv",'w'), delimiter = ",") for dsrow in datasave1: csvfile.writerow(np.around(dsrow,decimals=4)) # # =========================================================== # # FUll DATA SET PLOTTING # # =========================================================== # # Do you want to plot this? coderunner = 2 if coderunner == 1: # # # # =================================================================================== # # MKE vs Integrated Chly to 1027.45 # # =================================================================================== # # # pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/mke-102745intchly.pdf') # fig = plt.figure() # from matplotlib import rcParams # rcParams['axes.labelsize'] = 14 # rcParams['xtick.labelsize'] = 12 # rcParams['ytick.labelsize'] = 12 # rcParams['legend.fontsize'] = 10 # rcParams['font.family'] = 'serif' # rcParams['font.serif'] = ['Computer Modern Roman'] # rcParams['text.usetex'] = True # ax = fig.add_subplot(1, 1, 1) # from pylab import * # # # # # mkes = np.linspace(0,3200,250) # exp_decay = np.zeros(250) # hyp_decay = np.zeros(250) # for jj in range(0,250): # exp_decay[jj] = 160.8758*np.exp(-0.0002*mkes[jj]) # hyp_decay[jj] = (162.6095*3198.7058)/(3198.7058+mkes[jj]) # ax.plot(mkes, exp_decay, 'b',label=r'Exponential Decay Fit; R = 0.2447; P = 0.0383',linewidth = 2) # ax.plot(mkes, hyp_decay, '0.75',label=r'Hyperbolic Decay Fit; R = 0.2493; P = 0.0347',linewidth = 2) # ax.plot(floatMKE,chlyint102745,'ko',label=r'Full Data Set') # ax.plot(floatMKE_exo,chlyint102745_exo,'ro',label=r'Export Data') # ax.plot(floatMKE_bloom,chlyint102745_bloom,'go',label=r'Bloom Data') # l = legend(loc = 1) # ax.set_xlim([0,3200]) # ax.set_ylim([50,400]) # ax.set_xscale('linear') # ax.set_xlabel(r"\textbf{Kinetic Energy (cm$^{2}$s$^{-2}$)") # ax.set_ylabel(r"\textbf{$\int^{z_{0}}_{z_{\rho = 1027.45}} \mathrm{[Chl] } \mathrm{d}z$}") # plt.savefig(pp, format = "pdf") # pp.close() # os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/mke-102745intchly.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =================================================================================== # MKE vs Avg Chly to 1027.45 # =================================================================================== # # pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/mke-102745avgchly.pdf') # fig = plt.figure() # from matplotlib import rcParams # rcParams['axes.labelsize'] = 14 # rcParams['xtick.labelsize'] = 12 # rcParams['ytick.labelsize'] = 12 # rcParams['legend.fontsize'] = 10 # rcParams['font.family'] = 'serif' # rcParams['font.serif'] = ['Computer Modern Roman'] # rcParams['text.usetex'] = True # ax = fig.add_subplot(1, 1, 1) # from pylab import * # # # # # mkes = np.linspace(0,3200,250) # exp_decay = np.zeros(250) # hyp_decay = np.zeros(250) # for jj in range(0,250): # exp_decay[jj] = 0.9503*np.exp(-0.0006*mkes[jj]) # hyp_decay[jj] = (0.9618*1187.7180)/(1187.7180+mkes[jj]) # ax.plot(mkes, exp_decay, 'b',label=r'Exponential Decay Fit; R = 0.4469; P = $<$0.0001',linewidth = 2) # ax.plot(mkes, hyp_decay, '0.75',label=r'Hyperbolic Decay Fit; R = 0.4400; P = $<$0.0001',linewidth = 2) # ax.plot(floatMKE,chlyavg102745,'ko',label=r'Full Data Set') # ax.plot(floatMKE_exo,chlyavg102745_exo,'ro',label=r'Export Data') # ax.plot(floatMKE_bloom,chlyavg102745_bloom,'go',label=r'Bloom Data') # l = legend(loc = 1) # ax.set_xlim([0,3200]) # ax.set_ylim([0,2.3]) # ax.set_xscale('linear') # ax.set_xlabel(r"\textbf{Kinetic Energy (cm$^{2}$s$^{-2}$)") # ax.set_ylabel(r"\textbf{$\frac{1}{z_{\rho = 1027.45}} \int^{z_{0}}_{z_{\rho = 1027.45}} \mathrm{[Chl] } \mathrm{d}z$}") # plt.savefig(pp, format = "pdf") # pp.close() # os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/mke-102745avgchly.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =================================================================================== # MKE vs Integrated Chly to 30m # =================================================================================== # # pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/mke-30mintchly.pdf') # fig = plt.figure() # from matplotlib import rcParams # rcParams['axes.labelsize'] = 14 # rcParams['xtick.labelsize'] = 12 # rcParams['ytick.labelsize'] = 12 # rcParams['legend.fontsize'] = 10 # rcParams['font.family'] = 'serif' # rcParams['font.serif'] = ['Computer Modern Roman'] # rcParams['text.usetex'] = True # ax = fig.add_subplot(1, 1, 1) # from pylab import * # # # # # mkes = np.linspace(0,3200,250) # exp_decay = np.zeros(250) # hyp_decay = np.zeros(250) # for jj in range(0,250): # exp_decay[jj] = 34.0472*np.exp(-0.0005*mkes[jj]) # hyp_decay[jj] = (34.5810*1524.3276)/(1524.3276+mkes[jj]) # ax.plot(mkes, exp_decay, 'b',label=r'Exponential Decay Fit; R = 0.2921; P = 0.0128',linewidth = 2) # ax.plot(mkes, hyp_decay, '0.75',label=r'Hyperbolic Decay Fit; R = 0.2917; P = 0.0129',linewidth = 2) # ax.plot(floatMKE,chlyint30meter,'ko',label=r'Full Data Set') # ax.plot(floatMKE_exo,chlyint30meter_exo,'ro',label=r'Export Data') # ax.plot(floatMKE_bloom,chlyint30meter_bloom,'go',label=r'Bloom Data') # l = legend(loc = 1) # ax.set_xlim([0,3200]) # ax.set_ylim([10,100]) # ax.set_xscale('linear') # ax.set_xlabel(r"\textbf{Kinetic Energy (cm$^{2}$s$^{-2}$)") # ax.set_ylabel(r"\textbf{$\int^{z_{0}}_{z_{30}} \mathrm{[Chl] } \mathrm{d}z$}") # plt.savefig(pp, format = "pdf") # pp.close() # os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/mke-30mintchly.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =================================================================================== # MKE vs Avg Chly to 1027.45 # =================================================================================== # # pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/mke-30mavgchly.pdf') # fig = plt.figure() # from matplotlib import rcParams # rcParams['axes.labelsize'] = 14 # rcParams['xtick.labelsize'] = 12 # rcParams['ytick.labelsize'] = 12 # rcParams['legend.fontsize'] = 10 # rcParams['font.family'] = 'serif' # rcParams['font.serif'] = ['Computer Modern Roman'] # rcParams['text.usetex'] = True # ax = fig.add_subplot(1, 1, 1) # from pylab import * # # # # # mkes = np.linspace(0,3200,250) # exp_decay = np.zeros(250) # hyp_decay = np.zeros(250) # for jj in range(0,250): # exp_decay[jj] = 1.1349*np.exp(-0.0005*mkes[jj]) # hyp_decay[jj] = (1.1527*1524.3276)/(1524.3276+mkes[jj]) # ax.plot(mkes, exp_decay, 'b',label=r'Exponential Decay Fit; R = 0.2921; P = 0.0128',linewidth = 2) # ax.plot(mkes, hyp_decay, '0.75',label=r'Hyperbolic Decay Fit; R = 0.2918; P = 0.0129',linewidth = 2) # ax.plot(floatMKE,chlyavg30meter,'ko',label=r'Full Data Set') # ax.plot(floatMKE_exo,chlyavg30meter_exo,'ro',label=r'Export Data') # ax.plot(floatMKE_bloom,chlyavg30meter_bloom,'go',label=r'Bloom Data') # l = legend(loc = 1) # ax.set_xlim([0,3200]) # ax.set_ylim([0,3.15]) # ax.set_xscale('linear') # ax.set_xlabel(r"\textbf{Kinetic Energy (cm$^{2}$s$^{-2}$)") # ax.set_ylabel(r"\textbf{$\frac{1}{z_{30}} \int^{z_{0}}_{z_{30}} \mathrm{[Chl] } \mathrm{d}z$}") # plt.savefig(pp, format = "pdf") # pp.close() # os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/mke-30mavgchly.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =================================================================================== # MKE vs Depth of rho = 1027.45 # =================================================================================== # pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/mke-depthof102745.pdf') import matplotlib.pyplot as plt import numpy as np import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # fig = plt.figure() # ax = fig.add_subplot(1, 1, 1) # fig = plt.figure() ax = fig.add_axes([0.14,0.12,0.8,0.84]) from pylab import * # mkes = np.linspace(2,4000,250) linearfit = np.zeros(250) for jj in range(0,250): linearfit[jj] = 174.7837 + 0.0734*mkes[jj] ax.plot(mkes, linearfit, 'k',label=r'Linear Fit; R$^2$ = 0.7018; P = $<$0.0001',linewidth = 3) #R = 0.8377; ax.scatter(floatMKE,abs(depthof102745), color = '#696969', s = 40, label=r'Full Data Set') ax.scatter(floatMKE_exo,abs(depthof102745_exo),color = 'r',s = 40,label=r'Export Data') ax.scatter(floatMKE_bloom,abs(depthof102745_bloom),color = 'g',s = 40,label=r'Bloom Data') l = legend(loc = 2) ax.set_ylim([125,400]) ax.set_xlim([2, 4000]) ax.set_xscale('log') ax.set_xlabel(r"$MKE_{float}$ (cm$^{2}$s$^{-2}$)") ax.set_ylabel(r"Depth of $\rho = $ 1027.45 kg m$^{-3}$ Isopycnal (m)") plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/mke-depthof102745.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # # =================================================================================== # Depth of rho = 1027.45 vs 30m Avg Chl # =================================================================================== # # pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/depthof102745-30mavgchly.pdf') # fig = plt.figure() # from matplotlib import rcParams # rcParams['axes.labelsize'] = 14 # rcParams['xtick.labelsize'] = 12 # rcParams['ytick.labelsize'] = 12 # rcParams['legend.fontsize'] = 10 # rcParams['font.family'] = 'serif' # rcParams['font.serif'] = ['Computer Modern Roman'] # rcParams['text.usetex'] = True # ax = fig.add_subplot(1, 1, 1) # from pylab import * # # # # # depthss = np.linspace(125,400,250) # exp_decay= np.zeros(250) # for jj in range(0,250): # exp_decay[jj] = 1.8938*np.exp(-0.0034*depthss[jj]) # ax.plot(depthss, exp_decay, 'b',label=r'Linear Fit; R = 0.2427; P = 0.0400',linewidth = 2) # ax.plot(abs(depthof102745), chlyavg30meter,'ko',label=r'Full Data Set') # ax.plot(abs(depthof102745_exo),chlyavg30meter_exo,'ro',label=r'Export Data') # ax.plot(abs(depthof102745_bloom),chlyavg30meter_bloom,'go',label=r'Bloom Data') # l = legend(loc = 1) # ax.set_xlim([125,400]) # ax.set_ylim([0, 3.15]) # ax.set_xscale('linear') # ax.set_xlabel(r"\textbf{Depth of $\rho = $ 1027.45 kg m$^{-3}$ Isopycnal (m)}") # ax.set_ylabel(r"\textbf{$\frac{1}{z_{30}} \int^{z_{0}}_{z_{30}} \mathrm{[Chl] } \mathrm{d}z$}") # plt.savefig(pp, format = "pdf") # pp.close() # os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/depthof102745-30mavgchly.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # # =================================================================================== # Depth of rho = 1027.45 vs 30m Int Chl # =================================================================================== # # pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/depthof102745-30mintchly.pdf') # fig = plt.figure() # from matplotlib import rcParams # rcParams['axes.labelsize'] = 14 # rcParams['xtick.labelsize'] = 12 # rcParams['ytick.labelsize'] = 12 # rcParams['legend.fontsize'] = 10 # rcParams['font.family'] = 'serif' # rcParams['font.serif'] = ['Computer Modern Roman'] # rcParams['text.usetex'] = True # ax = fig.add_subplot(1, 1, 1) # from pylab import * # # # # # depthss = np.linspace(125,400,250) # exp_decay= np.zeros(250) # for jj in range(0,250): # exp_decay[jj] = 56.8139*np.exp(-0.0034*depthss[jj]) # ax.plot(depthss, exp_decay, 'b',label=r'Linear Fit; R = 0.2427; P = 0.0400',linewidth = 2) # ax.plot(abs(depthof102745), chlyint30meter,'ko',label=r'Full Data Set') # ax.plot(abs(depthof102745_exo),chlyint30meter_exo,'ro',label=r'Export Data') # ax.plot(abs(depthof102745_bloom),chlyint30meter_bloom,'go',label=r'Bloom Data') # l = legend(loc = 1) # ax.set_xscale('linear') # ax.set_xlim([125,400]) # ax.set_ylim([10,100]) # ax.set_xlabel(r"\textbf{Depth of $\rho = $ 1027.45 kg m$^{-3}$ Isopycnal (m)}") # ax.set_ylabel(r"\textbf{$\int^{z_{0}}_{z_{30}} \mathrm{[Chl] } \mathrm{d}z$}") # plt.savefig(pp, format = "pdf") # pp.close() # os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/depthof102745-30mintchly.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =================================================================================== # Depth of rho = 1027.45 vs 1027.45 Avg Chl # =================================================================================== # pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/depthof102745-102745avgchly.pdf') import matplotlib.pyplot as plt import numpy as np import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # fig = plt.figure() # ax = fig.add_subplot(1, 1, 1) # fig = plt.figure() ax = fig.add_axes([0.14,0.12,0.8,0.84]) from pylab import * # # depthss = np.linspace(125,400,250) exp_decay= np.zeros(250) for jj in range(0,250): exp_decay[jj] = 2.8138*np.exp(-0.0065*depthss[jj]) ax.plot(depthss, exp_decay, 'k',label=r'Exp Decay; R = 0.5137 R$^2$ = 0.2639',linewidth = 3) # P = $<$0.0001 ax.scatter(abs(depthof102745), chlyavg102745, color ='#696969', s = 40, label=r'Full Data Set') ax.scatter(abs(depthof102745_exo),chlyavg102745_exo,color = 'r',s = 40,label=r'Export Data') ax.scatter(abs(depthof102745_bloom),chlyavg102745_bloom,color ='g',s = 40,label=r'Bloom Data') l = legend(loc = 1) ax.set_xscale('linear') ax.set_xlim([125,400]) ax.set_ylim([0,2.4]) ax.set_xlabel(r"Depth of $\rho = $ 1027.45 kg m$^{-3}$ Isopycnal (m)") ax.set_ylabel(r"$\frac{1}{z_{\rho = 1027.45}} \int^{z_{0}}_{z_{\rho = 1027.45}} \mathrm{[Chl(}z\mathrm{)] d}z$ (mgm$^{-3}$)") plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/depthof102745-102745avgchly.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =================================================================================== # Depth of rho = 1027.45 vs 1027.45 Int Chl # =================================================================================== # # pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/depthof102745-102745intchly.pdf') # fig = plt.figure() # from matplotlib import rcParams # rcParams['axes.labelsize'] = 14 # rcParams['xtick.labelsize'] = 12 # rcParams['ytick.labelsize'] = 12 # rcParams['legend.fontsize'] = 10 # rcParams['font.family'] = 'serif' # rcParams['font.serif'] = ['Computer Modern Roman'] # rcParams['text.usetex'] = True # ax = fig.add_subplot(1, 1, 1) # from pylab import * # # # # # depthss = np.linspace(125,400,250) # exp_decay= np.zeros(250) # for jj in range(0,250): # exp_decay[jj] = 243.2286*np.exp(-0.0025*depthss[jj]) # ax.plot(depthss, exp_decay, 'b',label=r'Linear Fit; R = 0.2559; P = 0.0301',linewidth = 2) # ax.plot(abs(depthof102745), chlyint102745,'ko',label=r'Full Data Set') # ax.plot(abs(depthof102745_exo),chlyint102745_exo,'ro',label=r'Export Data') # ax.plot(abs(depthof102745_bloom),chlyint102745_bloom,'go',label=r'Bloom Data') # l = legend(loc = 1) # ax.set_xscale('linear') # ax.set_xlim([125,400]) # ax.set_ylim([50,400]) # ax.set_xlabel(r"\textbf{Depth of $\rho = $ 1027.45 kg m$^{-3}$ Isopycnal (m)}") # ax.set_ylabel(r"\textbf{$\int^{z_{0}}_{z_{\rho = 1027.45}} \mathrm{[Chl] } \mathrm{d}z$}") # plt.savefig(pp, format = "pdf") # pp.close() # os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/depthof102745-102745intchly.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =========================================================== # Plotting MKE and 30 m Avg Chly linearlinear # =========================================================== # pp = PdfPages('mke-30mavgchly-linearlinear.pdf') import matplotlib.pyplot as plt import numpy as np import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # fig = plt.figure() # ax = fig.add_subplot(1, 1, 1) # fig = plt.figure() ax = fig.add_axes([0.14,0.12,0.8,0.84]) ax.scatter(floatMKE, chlyavg30meter,color ='#696969', s = 40, label=r'Full Data Set') ax.scatter(floatMKE_bloom, chlyavg30meter_bloom,color ='g',s = 40,label=r'Export Data') ax.scatter(floatMKE_exo, chlyavg30meter_exo,color ='r',s = 40,label=r'Bloom Data') ax.set_ylim([0,3.2]) ax.set_xlim([0,3500]) ax.set_xlabel(r"$MKE_{float}$ (cm$^{2}$s$^{-2}$)") ax.set_ylabel(r"$\frac{1}{z_{30}} \int^{z_{0}}_{z_{30}} \mathrm{[Chl(}z\mathrm{)] d}z$} (mgm$^{-3}$)") plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/mke-30mavgchly-linearlinear.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') pp = PdfPages('mke-30mavgchly-linearlinear-black.pdf') import matplotlib.pyplot as plt import numpy as np import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # fig = plt.figure() # ax = fig.add_subplot(1, 1, 1) # fig = plt.figure() ax = fig.add_axes([0.14,0.12,0.8,0.84]) ax.scatter(floatMKE, chlyavg30meter,color ='k', s = 40) # ax.scatter(floatMKE_bloom, chlyavg30meter_bloom,color ='g',s = 40,label=r'Export Data') # ax.scatter(floatMKE_exo, chlyavg30meter_exo,color ='r',s = 40,label=r'Bloom Data') ax.set_ylim([0,3.2]) ax.set_xlim([0,3500]) ax.set_xlabel(r"$MKE_{float}$ (cm$^{2}$s$^{-2}$)") ax.set_ylabel(r"$\frac{1}{z_{30}} \int^{z_{0}}_{z_{30}} \mathrm{[Chl(}z\mathrm{)] d}z$} (mgm$^{-3}$)") plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/mke-30mavgchly-linearlinear-black.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =========================================================== # Plotting MKE and 30 m Avg Chly loglinear # =========================================================== # # pp = PdfPages('mke-30mavgchly-loglinear.pdf') # # # # Fonts # from matplotlib.colors import LogNorm # from matplotlib import rc # from matplotlib.numerix import arange, cos, pi # rc('text', usetex=True) # from pylab import * # from matplotlib.font_manager import FontProperties # legendfont = FontProperties() # legendfont.set_name('Computer Modern Roman') # legendfont.set_size('x-small') # rcParams['axes.labelsize'] = 12 # rcParams['xtick.labelsize'] = 12 # rcParams['ytick.labelsize'] = 12 # rcParams['legend.fontsize'] = 12 # from matplotlib import rcParams # rcParams['font.family'] = 'serif' # rcParams['font.serif'] = ['Computer Modern Roman'] # rcParams['text.usetex'] = True # fig = plt.figure() # ax = fig.add_subplot(111) # ax.plot(floatMKE, chlyavg30meter, 'ko',label=r'Full Data Set') # ax.plot(floatMKE_bloom, chlyavg30meter_bloom, 'go',label=r'Bloom Data') # ax.plot(floatMKE_exo, chlyavg30meter_exo, 'ro',label=r'Export Data') # l = legend(loc = 1) # ax.set_xscale('log') # ax.set_yscale('linear') # ax.set_xlabel(r"\textbf{ Kinetic Energy (cm$^{2}$s$^{-2}$)}") # ax.set_ylabel(r"\textbf{$\frac{1}{z_{30}} \int^{z_{0}}_{z_{30}} \mathrm{[Chl] } \mathrm{d}z$}") # plt.savefig(pp, format = "pdf") # pp.close() # os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/mke-30mavgchly-loglinear.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =========================================================== # Plotting MKE and 30 m Avg Chly loglinear # =========================================================== # # pp = PdfPages('mke-30mavgchly-linearlog.pdf') # # # # Fonts # from matplotlib.colors import LogNorm # from matplotlib import rc # from matplotlib.numerix import arange, cos, pi # rc('text', usetex=True) # from pylab import * # from matplotlib.font_manager import FontProperties # legendfont = FontProperties() # legendfont.set_name('Computer Modern Roman') # legendfont.set_size('x-small') # rcParams['axes.labelsize'] = 12 # rcParams['xtick.labelsize'] = 12 # rcParams['ytick.labelsize'] = 12 # rcParams['legend.fontsize'] = 12 # from matplotlib import rcParams # rcParams['font.family'] = 'serif' # rcParams['font.serif'] = ['Computer Modern Roman'] # rcParams['text.usetex'] = True # fig = plt.figure() # ax = fig.add_subplot(111) # ax.plot(floatMKE, chlyavg30meter, 'ko',label=r'Full Data Set') # ax.plot(floatMKE_bloom, chlyavg30meter_bloom, 'go',label=r'Bloom Data') # ax.plot(floatMKE_exo, chlyavg30meter_exo, 'ro',label=r'Export Data') # l = legend(loc = 1) # ax.set_xscale('linear') # ax.set_yscale('log') # ax.set_xlabel(r"\textbf{ Kinetic Energy (cm$^{2}$s$^{-2}$)}") # ax.set_ylabel(r"\textbf{$\frac{1}{z_{30}} \int^{z_{0}}_{z_{30}} \mathrm{[Chl] } \mathrm{d}z$}") # plt.savefig(pp, format = "pdf") # pp.close() # os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/mke-30mavgchly-linearlog.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =========================================================== # Plotting MKE and 30 m Avg Chly loglinear # =========================================================== # pp = PdfPages('mke-30mavgchly-loglog.pdf') import matplotlib.pyplot as plt import numpy as np import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # fig = plt.figure() # ax = fig.add_subplot(1, 1, 1) # fig = plt.figure() ax = fig.add_axes([0.14,0.12,0.8,0.84]) ax.scatter(floatMKE, chlyavg30meter,color ='#696969', s = 40, label=r'Full Data Set') ax.scatter(floatMKE_bloom, chlyavg30meter_bloom, color= 'g',s = 40,label=r'Export Data') ax.scatter(floatMKE_exo, chlyavg30meter_exo,color='r',s = 40,label=r'Bloom Data') ax.set_xscale('log') ax.set_yscale('log') ax.set_xlabel(r"$MKE_{float}$ (cm$^{2}$s$^{-2}$)") ax.set_ylabel(r"$\frac{1}{z_{30}} \int^{z_{0}}_{z_{30}} \mathrm{[Chl(}z\mathrm{)] d}z$} (mgm$^{-3}$)") plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/mke-30mavgchly-loglog.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =========================================================== # Plotting MKE and 30 m Int Chly linearlinear # =========================================================== # pp = PdfPages('mke-30mintchly-linearlinear.pdf') import matplotlib.pyplot as plt import numpy as np import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # fig = plt.figure() # ax = fig.add_subplot(1, 1, 1) # fig = plt.figure() ax = fig.add_axes([0.14,0.12,0.8,0.84]) ax.scatter(floatMKE, chlyint30meter,color='#696969', s = 40, label=r'Full Data Set') ax.scatter(floatMKE_bloom, chlyint30meter_bloom,color='g',s = 40,label=r'Export Data') ax.scatter(floatMKE_exo, chlyint30meter_exo,color='r',s = 40,label=r'Bloom Data') l = legend(loc = 1) ax.set_ylim([0,100]) ax.set_xlim([0,3500]) ax.set_xlabel(r"$MKE_{float}$ (cm$^{2}$s$^{-2}$)") ax.set_ylabel(r"\int^{z_{0}}_{z_{30}} \mathrm{[Chl(}z\mathrm{)] d}z$} (mgm$^{-2}$)") plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/mke-30mintchly-linearlinear.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # pp = PdfPages('mke-30mintchly-linearlinear-black.pdf') import matplotlib.pyplot as plt import numpy as np import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # fig = plt.figure() # ax = fig.add_subplot(1, 1, 1) # fig = plt.figure() ax = fig.add_axes([0.14,0.12,0.8,0.84]) ax.scatter(floatMKE, chlyint30meter,color='k', s = 40) # ax.scatter(floatMKE_bloom, chlyint30meter_bloom,color='r',s = 40,label=r'Export Data') # ax.scatter(floatMKE_exo, chlyint30meter_exo,color='g',s = 40,label=r'Bloom Data') ax.set_ylim([0,100]) ax.set_xlim([0,3500]) ax.set_xlabel(r"$MKE_{float}$ (cm$^{2}$s$^{-2}$)") ax.set_ylabel(r"\int^{z_{0}}_{z_{30}} \mathrm{[Chl(}z\mathrm{)] d}z$} (mgm$^{-2}$)") plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/mke-30mintchly-linearlinear-black.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =========================================================== # Plotting MKE and 30 m Int Chly loglinear # =========================================================== # # pp = PdfPages('mke-30mintchly-loglinear.pdf') # # # # Fonts # from matplotlib.colors import LogNorm # from matplotlib import rc # from matplotlib.numerix import arange, cos, pi # rc('text', usetex=True) # from pylab import * # from matplotlib.font_manager import FontProperties # legendfont = FontProperties() # legendfont.set_name('Computer Modern Roman') # legendfont.set_size('x-small') # rcParams['axes.labelsize'] = 12 # rcParams['xtick.labelsize'] = 12 # rcParams['ytick.labelsize'] = 12 # rcParams['legend.fontsize'] = 12 # from matplotlib import rcParams # rcParams['font.family'] = 'serif' # rcParams['font.serif'] = ['Computer Modern Roman'] # rcParams['text.usetex'] = True # fig = plt.figure() # ax = fig.add_subplot(111) # ax.plot(floatMKE, chlyint30meter, 'ko',label=r'Full Data Set') # ax.plot(floatMKE_bloom, chlyint30meter_bloom, 'go',label=r'Bloom Data') # ax.plot(floatMKE_exo, chlyint30meter_exo, 'ro',label=r'Export Data') # l = legend(loc = 1) # ax.set_xscale('log') # ax.set_yscale('linear') # ax.set_xlabel(r"\textbf{ Kinetic Energy (cm$^{2}$s$^{-2}$)}") # ax.set_ylabel(r"\textbf{$\int^{z_{0}}_{z_{30}} \mathrm{[Chl] } \mathrm{d}z$}") # plt.savefig(pp, format = "pdf") # pp.close() # os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/mke-30mintchly-loglinear.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =========================================================== # Plotting MKE and 30 m Int Chly loglinear # =========================================================== # # pp = PdfPages('mke-30mintchly-linearlog.pdf') # # # # Fonts # from matplotlib.colors import LogNorm # from matplotlib import rc # from matplotlib.numerix import arange, cos, pi # rc('text', usetex=True) # from pylab import * # from matplotlib.font_manager import FontProperties # legendfont = FontProperties() # legendfont.set_name('Computer Modern Roman') # legendfont.set_size('x-small') # rcParams['axes.labelsize'] = 12 # rcParams['xtick.labelsize'] = 12 # rcParams['ytick.labelsize'] = 12 # rcParams['legend.fontsize'] = 12 # from matplotlib import rcParams # rcParams['font.family'] = 'serif' # rcParams['font.serif'] = ['Computer Modern Roman'] # rcParams['text.usetex'] = True # fig = plt.figure() # ax = fig.add_subplot(111) # ax.plot(floatMKE, chlyint30meter, 'ko',label=r'Full Data Set') # ax.plot(floatMKE_bloom, chlyint30meter_bloom, 'go',label=r'Bloom Data') # ax.plot(floatMKE_exo, chlyint30meter_exo, 'ro',label=r'Export Data') # l = legend(loc = 1) # ax.set_xscale('linear') # ax.set_yscale('log') # ax.set_xlabel(r"\textbf{ Kinetic Energy (cm$^{2}$s$^{-2}$)}") # ax.set_ylabel(r"\textbf{$\int^{z_{0}}_{z_{30}} \mathrm{[Chl] } \mathrm{d}z$}") # plt.savefig(pp, format = "pdf") # pp.close() # os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/mke-30mintchly-linearlog.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =========================================================== # Plotting MKE and 30 m Int Chly loglinear # =========================================================== # pp = PdfPages('mke-30mintchly-loglog.pdf') import matplotlib.pyplot as plt import numpy as np import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # fig = plt.figure() # ax = fig.add_subplot(1, 1, 1) # fig = plt.figure() ax = fig.add_axes([0.14,0.12,0.8,0.84]) ax.scatter(floatMKE, chlyint30meter,color ='#696969', s = 40, label=r'Full Data Set') ax.scatter(floatMKE_bloom, chlyint30meter_bloom,color='g',s = 40,label=r'Export Data') ax.scatter(floatMKE_exo, chlyint30meter_exo,color='r',s = 40,label=r'Bloom Data') l = legend(loc = 1) ax.set_xscale('log') ax.set_yscale('log') ax.set_ylim([10**(.4),10**(2.1)]) ax.set_xlabel(r"$MKE_{float}$ (cm$^{2}$s$^{-2}$)") ax.set_ylabel(r"\int^{z_{0}}_{z_{30}} \mathrm{[Chl(}z\mathrm{)] d}z$} (mgm$^{-2}$)") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/mke-30mintchly-loglog.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =========================================================== # Plotting MKE and 102745 Avg Chly linearlinear # =========================================================== # pp = PdfPages('mke-102745avgchly-linearlinear.pdf') import matplotlib.pyplot as plt import numpy as np import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # fig = plt.figure() # ax = fig.add_subplot(1, 1, 1) # fig = plt.figure() ax = fig.add_axes([0.14,0.12,0.8,0.84]) ax.scatter(floatMKE, chlyavg102745,color='#696969', s = 40, label=r'Full Data Set') ax.scatter(floatMKE_bloom, chlyavg102745_bloom,color='g',s = 40,label=r'Export Data') ax.scatter(floatMKE_exo, chlyavg102745_exo,color='r',s = 40,label=r'Bloom Data') l = legend(loc = 1) ax.set_xlabel(r"$MKE_{float}$ (cm$^{2}$s$^{-2}$)") ax.set_ylabel(r"$\frac{1}{z_{\rho = 1027.45}} \int^{z_{0}}_{z_{\rho = 1027.45}} \mathrm{[Chl(}z\mathrm{)] d}z$ (mgm$^{-3}$)") ax.set_xlim([0, 3500]) plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/mke-102745avgchly-linearlinear.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # pp = PdfPages('mke-102745avgchly-linearlinear-black.pdf') import matplotlib.pyplot as plt import numpy as np import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # fig = plt.figure() # ax = fig.add_subplot(1, 1, 1) # fig = plt.figure() ax = fig.add_axes([0.14,0.12,0.8,0.84]) ax.scatter(floatMKE, chlyavg102745,color='k', s = 40) # ax.scatter(floatMKE_bloom, chlyavg102745_bloom,color='g',s = 40,label=r'Export Data') # ax.scatter(floatMKE_exo, chlyavg102745_exo,color='r',s = 40,label=r'Bloom Data') ax.set_xlabel(r"$MKE_{float}$ (cm$^{2}$s$^{-2}$)") ax.set_ylabel(r"$\frac{1}{z_{\rho = 1027.45}} \int^{z_{0}}_{z_{\rho = 1027.45}} \mathrm{[Chl(}z\mathrm{)] d}z$ (mgm$^{-3}$)") ax.set_xlim([0, 3500]) plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/mke-102745avgchly-linearlinear-black.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =========================================================== # Plotting MKE and 102745 Avg Chly loglinear # =========================================================== # # pp = PdfPages('mke-102745avgchly-loglinear.pdf') # # # # Fonts # from matplotlib.colors import LogNorm # from matplotlib import rc # from matplotlib.numerix import arange, cos, pi # rc('text', usetex=True) # from pylab import * # from matplotlib.font_manager import FontProperties # legendfont = FontProperties() # legendfont.set_name('Computer Modern Roman') # legendfont.set_size('x-small') # rcParams['axes.labelsize'] = 12 # rcParams['xtick.labelsize'] = 12 # rcParams['ytick.labelsize'] = 12 # rcParams['legend.fontsize'] = 12 # from matplotlib import rcParams # rcParams['font.family'] = 'serif' # rcParams['font.serif'] = ['Computer Modern Roman'] # rcParams['text.usetex'] = True # fig = plt.figure() # ax = fig.add_subplot(111) # ax.plot(floatMKE, chlyavg102745, 'ko',label=r'Full Data Set') # ax.plot(floatMKE_bloom, chlyavg102745_bloom, 'go',label=r'Bloom Data') # ax.plot(floatMKE_exo, chlyavg102745_exo, 'ro',label=r'Export Data') # l = legend(loc = 1) # ax.set_xscale('log') # ax.set_yscale('linear') # ax.set_xlabel(r"\textbf{ Kinetic Energy (cm$^{2}$s$^{-2}$)}") # ax.set_ylabel(r"\textbf{$\frac{1}{z_{\rho = 1027.45}} \int^{z_{0}}_{z_{\rho = 1027.45}} \mathrm{[Chl] } \mathrm{d}z$}") # plt.savefig(pp, format = "pdf") # pp.close() # os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/mke-102745avgchly-loglinear.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =========================================================== # Plotting MKE and 102745 Avg Chly loglinear # =========================================================== # # pp = PdfPages('mke-102745avgchly-linearlog.pdf') # # # # Fonts # from matplotlib.colors import LogNorm # from matplotlib import rc # from matplotlib.numerix import arange, cos, pi # rc('text', usetex=True) # from pylab import * # from matplotlib.font_manager import FontProperties # legendfont = FontProperties() # legendfont.set_name('Computer Modern Roman') # legendfont.set_size('x-small') # rcParams['axes.labelsize'] = 12 # rcParams['xtick.labelsize'] = 12 # rcParams['ytick.labelsize'] = 12 # rcParams['legend.fontsize'] = 12 # from matplotlib import rcParams # rcParams['font.family'] = 'serif' # rcParams['font.serif'] = ['Computer Modern Roman'] # rcParams['text.usetex'] = True # fig = plt.figure() # ax = fig.add_subplot(111) # ax.plot(floatMKE, chlyavg102745, 'ko',label=r'Full Data Set') # ax.plot(floatMKE_bloom, chlyavg102745_bloom, 'go',label=r'Bloom Data') # ax.plot(floatMKE_exo, chlyavg102745_exo, 'ro',label=r'Export Data') # l = legend(loc = 1) # ax.set_xscale('linear') # ax.set_yscale('log') # ax.set_xlabel(r"\textbf{ Kinetic Energy (cm$^{2}$s$^{-2}$)}") # ax.set_ylabel(r"\textbf{$\frac{1}{z_{\rho = 1027.45}} \int^{z_{0}}_{z_{\rho = 1027.45}} \mathrm{[Chl] } \mathrm{d}z$}") # plt.savefig(pp, format = "pdf") # pp.close() # os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/mke-102745avgchly-linearlog.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =========================================================== # Plotting MKE and 102745 Avg Chly loglinear # =========================================================== # pp = PdfPages('mke-102745avgchly-loglog.pdf') import matplotlib.pyplot as plt import numpy as np import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # fig = plt.figure() # ax = fig.add_subplot(1, 1, 1) # fig = plt.figure() ax = fig.add_axes([0.14,0.12,0.8,0.84]) ax.scatter(floatMKE, chlyavg102745,color = 'k', s = 40, label=r'Full Data Set') ax.scatter(floatMKE_bloom, chlyavg102745_bloom,color='g',s = 40,label=r'Export Data') ax.scatter(floatMKE_exo, chlyavg102745_exo,color='r',s = 40,label=r'Bloom Data') l = legend(loc = 1) ax.set_xlabel(r"$MKE_{float}$ (cm$^{2}$s$^{-2}$)") ax.set_ylabel(r"$\frac{1}{z_{\rho = 1027.45}} \int^{z_{0}}_{z_{\rho = 1027.45}} \mathrm{[Chl(}z\mathrm{)] d}z$} (mgm$^{-3}$)") ax.set_xscale('log') ax.set_yscale('log') plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/mke-102745avgchly-loglog.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =========================================================== # Plotting MKE and 102745 Int Chly linearlinear # =========================================================== # pp = PdfPages('mke-102745intchly-linearlinear.pdf') import matplotlib.pyplot as plt import numpy as np import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # fig = plt.figure() # ax = fig.add_subplot(1, 1, 1) # fig = plt.figure() ax = fig.add_axes([0.14,0.12,0.8,0.84]) ax.scatter(floatMKE, chlyint102745,color='#696969', s = 40, label=r'Full Data Set') ax.scatter(floatMKE_bloom, chlyint102745_bloom,color='g',s = 40,label=r'Export Data') ax.scatter(floatMKE_exo, chlyint102745_exo,color='r',s = 40,label=r'Bloom Data') l = legend(loc = 1) ax.set_xlim([0, 3500]) ax.set_xlabel(r"$MKE_{float}$ (cm$^{2}$s$^{-2}$)") ax.set_ylabel(r"$\int^{z_{0}}_{z_{\rho = 1027.45}} \mathrm{[Chl(}z\mathrm{)] d}z$ (mgm$^{-2}$)") plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/mke-102745intchly-linearlinear.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # pp = PdfPages('mke-102745intchly-linearlinear-black.pdf') import matplotlib.pyplot as plt import numpy as np import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # fig = plt.figure() # ax = fig.add_subplot(1, 1, 1) # fig = plt.figure() ax = fig.add_axes([0.14,0.12,0.8,0.84]) ax.scatter(floatMKE, chlyint102745,color='k', s = 40) # ax.scatter(floatMKE_bloom, chlyint102745_bloom,color='r',s = 40,label=r'Export Data') # ax.scatter(floatMKE_exo, chlyint102745_exo,color='g',s = 40,label=r'Bloom Data') ax.set_xlim([0, 3500]) ax.set_xlabel(r"$MKE_{float}$ (cm$^{2}$s$^{-2}$)") ax.set_ylabel(r"$\int^{z_{0}}_{z_{\rho = 1027.45}} \mathrm{[Chl(}z\mathrm{)] d}z$ (mgm$^{-2}$)") plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/mke-102745intchly-linearlinear-black.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =========================================================== # Plotting MKE and 102745 Int Chly loglinear # =========================================================== # # # pp = PdfPages('mke-102745intchly-loglinear.pdf') # # # # Fonts # from matplotlib.colors import LogNorm # from matplotlib import rc # from matplotlib.numerix import arange, cos, pi # rc('text', usetex=True) # from pylab import * # from matplotlib.font_manager import FontProperties # legendfont = FontProperties() # legendfont.set_name('Computer Modern Roman') # legendfont.set_size('x-small') # rcParams['axes.labelsize'] = 12 # rcParams['xtick.labelsize'] = 12 # rcParams['ytick.labelsize'] = 12 # rcParams['legend.fontsize'] = 12 # from matplotlib import rcParams # rcParams['font.family'] = 'serif' # rcParams['font.serif'] = ['Computer Modern Roman'] # rcParams['text.usetex'] = True # fig = plt.figure() # ax = fig.add_subplot(111) # ax.plot(floatMKE, chlyint102745, 'ko',label=r'Full Data Set') # ax.plot(floatMKE_bloom, chlyint102745_bloom, 'go',label=r'Bloom Data') # ax.plot(floatMKE_exo, chlyint102745_exo, 'ro',label=r'Export Data') # l = legend(loc = 1) # ax.set_xscale('log') # ax.set_yscale('linear') # ax.set_xlabel(r"\textbf{ Kinetic Energy (cm$^{2}$s$^{-2}$)}") # ax.set_ylabel(r"\textbf{$\int^{z_{0}}_{z_{\rho = 1027.45}} \mathrm{[Chl] } \mathrm{d}z$}") # plt.savefig(pp, format = "pdf") # pp.close() # os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/mke-102745intchly-loglinear.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =========================================================== # Plotting MKE and 102745 Int Chly loglinear # =========================================================== # # pp = PdfPages('mke-102745intchly-linearlog.pdf') # # # # Fonts # from matplotlib.colors import LogNorm # from matplotlib import rc # from matplotlib.numerix import arange, cos, pi # rc('text', usetex=True) # from pylab import * # from matplotlib.font_manager import FontProperties # legendfont = FontProperties() # legendfont.set_name('Computer Modern Roman') # legendfont.set_size('x-small') # rcParams['axes.labelsize'] = 12 # rcParams['xtick.labelsize'] = 12 # rcParams['ytick.labelsize'] = 12 # rcParams['legend.fontsize'] = 12 # from matplotlib import rcParams # rcParams['font.family'] = 'serif' # rcParams['font.serif'] = ['Computer Modern Roman'] # rcParams['text.usetex'] = True # fig = plt.figure() # ax = fig.add_subplot(111) # ax.plot(floatMKE, chlyint102745, 'ko',label=r'Full Data Set') # ax.plot(floatMKE_bloom, chlyint102745_bloom, 'go',label=r'Bloom Data') # ax.plot(floatMKE_exo, chlyint102745_exo, 'ro',label=r'Export Data') # l = legend(loc = 1) # ax.set_xscale('linear') # ax.set_yscale('log') # ax.set_xlabel(r"\textbf{ Kinetic Energy (cm$^{2}$s$^{-2}$)}") # ax.set_ylabel(r"\textbf{$\int^{z_{0}}_{z_{\rho = 1027.45}} \mathrm{[Chl] } \mathrm{d}z$}") # plt.savefig(pp, format = "pdf") # pp.close() # os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/mke-102745intchly-linearlog.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =========================================================== # Plotting MKE and 102745 Int Chly loglinear # =========================================================== # pp = PdfPages('mke-102745intchly-loglog.pdf') import matplotlib.pyplot as plt import numpy as np import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # fig = plt.figure() # ax = fig.add_subplot(1, 1, 1) # fig = plt.figure() ax = fig.add_axes([0.14,0.12,0.8,0.84]) ax.scatter(floatMKE, chlyint102745,color='k', s = 40, label=r'Full Data Set') ax.scatter(floatMKE_bloom, chlyint102745_bloom,color='g',s = 40,label=r'Export Data') ax.scatter(floatMKE_exo, chlyint102745_exo,color='r',s = 40,label=r'Bloom Data') l = legend(loc = 1) ax.set_xlabel(r"$MKE_{float}$ (cm$^{2}$s$^{-2}$)") ax.set_ylabel(r"$\int^{z_{0}}_{z_{\rho = 1027.45}} \mathrm{[Chl(}z\mathrm{)] d}z$ (mgm$^{-2}$)") ax.set_xscale('log') ax.set_yscale('log') plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/mke-102745intchly-loglog.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =========================================================== # # BLOOM & EXPORT DATA SET PLOTTING # # =========================================================== # # Do you want to plot this? coderunner = 1 if coderunner == 1: # # =================================================================================== # MKE vs Integrated Chly to 1027.45 -- Just Bloom and Export with arrows # =================================================================================== # pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/mke-102745intchly_bloomandexport_arrows2.pdf') fig = plt.figure() from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True ax = fig.add_axes([0.11,0.11,0.75,0.85]) ax.set_ylim([75,400]) ax.set_xlim([0, 1200]) from pylab import * import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable x = floatMKE_bloomexo y = chlyint102745_bloomexo ax.quiver(x[:-1], y[:-1], x[1:]-x[:-1], y[1:]-y[:-1], scale_units='xy', angles='xy', scale=2, color='0.75', width = 0.003, headwidth = 5, headlength = 5, edgecolors='none') ax.quiver(x[:-1], y[:-1], x[1:]-x[:-1], y[1:]-y[:-1], scale_units='xy', angles='xy', scale=1, color='0.75', width = 0.003, headwidth = .01, headlength = .01, edgecolors='none') # x21 = np.zeros(2) # y21= np.zeros(2) # floatdates2 = [None]*2 # offsetx1 = ([ 45, 15]) # offsety1 = ([ -7.25, -7.25]) # floatdates2[0] = "Beginning" # floatdates2[1] = "End" # x21[0] = floatMKE_bloomexo[0] + offsetx1[0] # x21[1] = floatMKE_bloomexo[20] + offsetx1[1] # y21[0] = chlyint102745_bloomexo[0] + offsety1[0] # y21[1] = chlyint102745_bloomexo[20] + offsety1[1] # for label, xpt, ypt in zip(floatdates2, x21, y21): # plt.text(xpt, ypt, label,ha='center',va='center',fontsize=7, family='Computer Modern Roman') x2 = np.zeros(11) y2 = np.zeros(11) floatdepths2 = [None]*11 floatdates2 = [None]*11 offsetx = ([ -35, 25, 0, -30, 0, -15, -30, -25, 0, 0, 35]) offsety = ([ 5, 9, -10, 0, -10, -10, 0, -8, 10, -10, 0]) lll = 0 for l in range(0,21): ll = l + 30 if l%2==0: x2[lll] = floatMKE_bloomexo[l] + offsetx[lll] y2[lll] = chlyint102745_bloomexo[l] + offsety[lll] #floatdepths2[lll] = str(int(abs(depthof102745[ll]))) floatdates2[lll] = str(int(abs(floatdate[ll]))) lll = lll + 1 for label, xpt, ypt in zip(floatdates2, x2, y2): plt.text(xpt, ypt, label,ha='center',va='center',fontsize=11, family='Computer Modern Roman') cs = ax.scatter(floatMKE_bloomexo,chlyint102745_bloomexo, c = abs(depthof102745_bloomexo), s= 40,zorder=1, linewidth='0.5') divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) cbar = plt.colorbar(cs,cax=cax, spacing='proportional') cbar.set_label(r"Depth of $\rho = $ 1027.45 kg m$^{-3}$ Isopycnal (m)") cbar.set_ticks([100, 125, 150, 175, 200, 225, 250, 275]) cbar.set_ticklabels([100, 125, 150, 175, 200, 225, 250, 275]) ax.set_ylim([75,400]) ax.set_xlim([0, 1150]) ax.set_xscale('linear') ax.set_xscale('linear') ax.set_xlabel(r"$MKE_{float}$ (cm$^{2}$s$^{-2}$)") ax.set_ylabel(r"$\int^{z_{0}}_{z_{\rho = 1027.45}} \mathrm{[Chl(} z \mathrm{)]} \mathrm{d}z$ (mgm$^{-2}$)") plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/mke-102745intchly_bloomandexport_arrows2.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =================================================================================== # MKE vs Avg Backscatter to 1027.45 -- Just Bloom and Export with arrows # =================================================================================== # pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/mke-102745avgbackscat_bloomandexport_arrows2.pdf') fig = plt.figure() from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True ax = fig.add_axes([0.11,0.11,0.75,0.85]) from pylab import * import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable x = floatMKE_bloomexo y = backscatavg102745_bloomexo*1000 ax.quiver(x[:-1], y[:-1], x[1:]-x[:-1], y[1:]-y[:-1], scale_units='xy', angles='xy', scale=2, color='0.75', width = 0.003, headwidth = 5, headlength = 5, edgecolors='none') ax.quiver(x[:-1], y[:-1], x[1:]-x[:-1], y[1:]-y[:-1], scale_units='xy', angles='xy', scale=1, color='0.75', width = 0.003, headwidth = .01, headlength = .01, edgecolors='none') x2 = np.zeros(11) y2 = np.zeros(11) floatdepths2 = [None]*11 floatdates2 = [None]*11 offsetx = ([ 0, 35, 25, 35, 20, -20, 35, -20, 0, -35, 35]) offsety = ([ -.05, 0.00, -0.04, 0.01, .04, -.04, 0.00, 0.04, 0.05, 0.0, 0.00]) lll = 0 for l in range(0,21): ll = l + 30 if l%2==0: x2[lll] = floatMKE_bloomexo[l] + offsetx[lll] y2[lll] = backscatavg102745_bloomexo[l]*1000 + offsety[lll] floatdates2[lll] = str(int(abs(floatdate[ll]))) lll = lll + 1 for label, xpt, ypt in zip(floatdates2, x2, y2): plt.text(xpt, ypt, label,ha='center',va='center',fontsize=11, family='Computer Modern Roman') cs = ax.scatter(floatMKE_bloomexo,backscatavg102745_bloomexo*1000, c = abs(depthof102745_bloomexo), s= 40,zorder=1, linewidth='0.5') divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) cbar = plt.colorbar(cs,cax=cax, spacing='proportional') cbar.set_label(r"Depth of $\rho = $ 1027.45 kgm$^{-3}$ Isopycnal (m)") cbar.set_ticks([100, 125, 150, 175, 200, 225, 250, 275]) cbar.set_ticklabels([100, 125, 150, 175, 200, 225, 250, 275]) ax.set_xlim([0, 1150]) ax.set_xscale('linear') ax.set_xscale('linear') ax.set_xlabel(r"$MKE_{float}$ (cm$^{2}$s$^{-2}$)") ax.set_ylabel(r"$ \frac{1}{z_{\rho = 1027.45}} \int^{z_{0}}_{z_{\rho = 1027.45}} b_{bp}(z) \mathrm{d}z \times 10^3 $ ($m$^{-1}$)") plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/mke-102745avgbackscat_bloomandexport_arrows2.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =================================================================================== # MKE vs Integrated Chly to 30m -- Just Bloom and Exports # =================================================================================== pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/mke-30mintchly_bloomandexport.pdf') fig = plt.figure() from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True ax = fig.add_axes([0.11,0.11,0.75,0.85]) from pylab import * import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable x = floatMKE_bloomexo y = chlyint30meter_bloomexo ax.quiver(x[:-1], y[:-1], x[1:]-x[:-1], y[1:]-y[:-1], scale_units='xy', angles='xy', scale=2, color='0.75', width = 0.003, headwidth = 5, headlength = 5, edgecolors='none') ax.quiver(x[:-1], y[:-1], x[1:]-x[:-1], y[1:]-y[:-1], scale_units='xy', angles='xy', scale=1, color='0.75', width = 0.003, headwidth = .01, headlength = .01, edgecolors='none') x2 = np.zeros(11) y2 = np.zeros(11) floatdepths2 = [None]*11 floatdates2 = [None]*11 offsetx = ([ -35, 37, 0, 25, 25, -25, -5, -15, 0, -5, 5]) offsety = ([ 0, 0, -3, 2, 2, -2, -3, -2, 3, 3, -3]) lll = 0 for l in range(0,21): ll = l + 30 if l%2==0: x2[lll] = floatMKE_bloomexo[l] + offsetx[lll] y2[lll] = chlyint30meter_bloomexo[l] + offsety[lll] floatdates2[lll] = str(int(abs(floatdate[ll]))) lll = lll + 1 for label, xpt, ypt in zip(floatdates2, x2, y2): plt.text(xpt, ypt, label,ha='center',va='center',fontsize=11, family='Computer Modern Roman') cs = ax.scatter(floatMKE_bloomexo,chlyint30meter_bloomexo, c = abs(depthof102745_bloomexo), s= 40, zorder = 1) plt.set_cmap('jet') divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) cbar = plt.colorbar(cs,cax=cax,spacing='proportional') cbar.set_label(r"Depth of $\rho = $ 1027.45 kg m$^{-3}$ Isopycnal (m)") cbar.set_ticks([100, 125, 150, 175, 200, 225, 250, 275]) cbar.set_ticklabels([100, 125, 150, 175, 200, 225, 250, 275]) ax.set_xlim([0, 1150]) ax.set_ylim([10, 100]) ax.set_xscale('linear') ax.set_xscale('linear') ax.set_xlabel(r"$MKE_{float}$(cm$^{2}$s$^{-2}$)") ax.set_ylabel(r"$\int^{z_{0}}_{z_{30}} \mathrm{[Chl(} z \mathrm{)] d}z$ (mgm$^{-2}$)") plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/mke-30mintchly_bloomandexport.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # # =================================================================================== # MKE vs Depth Avg Chly to 1027.45 -- Just Bloom and Exports # =================================================================================== # pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/mke-102745avgchly_bloomandexport.pdf') fig = plt.figure() from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True ax = fig.add_axes([0.11,0.11,0.75,0.85]) from pylab import * import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable x = floatMKE_bloomexo y = chlyavg102745_bloomexo ax.quiver(x[:-1], y[:-1], x[1:]-x[:-1], y[1:]-y[:-1], scale_units='xy', angles='xy', scale=2, color='0.75', width = 0.003, headwidth = 5, headlength = 5, edgecolors='none') ax.quiver(x[:-1], y[:-1], x[1:]-x[:-1], y[1:]-y[:-1], scale_units='xy', angles='xy', scale=1, color='0.75', width = 0.003, headwidth = .01, headlength = .01, edgecolors='none') x2 = np.zeros(11) y2 = np.zeros(11) floatdepths2 = [None]*11 floatdates2 = [None]*11 offsetx = ([ -15, 35, 35, 35, 25, -15, 0, -20, 10, -35, 0]) offsety = ([ .06, 0, 0, 0, .05, -.05, -.07, -.04, -.06, 0, -.07]) lll = 0 for l in range(0,21): ll = l + 30 if l%2==0: x2[lll] = floatMKE_bloomexo[l] + offsetx[lll] y2[lll] = chlyavg102745_bloomexo[l] + offsety[lll] floatdates2[lll] = str(int(abs(floatdate[ll]))) lll = lll + 1 for label, xpt, ypt in zip(floatdates2, x2, y2): plt.text(xpt, ypt, label,ha='center',va='center',fontsize=11, family='Computer Modern Roman') cs = ax.scatter(floatMKE_bloomexo,chlyavg102745_bloomexo,c = abs(depthof102745_bloomexo), s= 40) plt.set_cmap('jet') divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) cbar = plt.colorbar(cs,cax = cax, spacing='proportional') cbar.set_label(r"Depth of $\rho = $ 1027.45 kg m$^{-3}$ Isopycnal (m)") cbar.set_ticks([100, 125, 150, 175, 200, 225, 250, 275]) cbar.set_ticklabels([100, 125, 150, 175, 200, 225, 250, 275]) ax.set_xlim([0, 1150]) ax.set_ylim([0.2, 2.2]) ax.set_xscale('linear') ax.set_xscale('linear') ax.set_xlabel(r"$MKE_{float}$ (cm$^{2}$s$^{-2}$)") ax.set_ylabel(r"$\frac{1}{z_{\rho = 1027.45}}\int^{z_{0}}_{z_{\rho = 1027.45}} \mathrm{[Chl(} z \mathrm{)]} \mathrm{d}z$ (mgm$^{-3}$)") plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/mke-102745avgchly_bloomandexport.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =================================================================================== # MKE vs Depth Avg Chly to 30m -- Just Bloom and Exports # =================================================================================== # pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/mke-30mavgchly_bloomandexport.pdf') fig = plt.figure() from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True ax = fig.add_axes([0.11,0.11,0.75,0.85]) from pylab import * import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable x = floatMKE_bloomexo y = chlyavg30meter_bloomexo ax.quiver(x[:-1], y[:-1], x[1:]-x[:-1], y[1:]-y[:-1], scale_units='xy', angles='xy', scale=2, color='0.75', width = 0.003, headwidth = 5, headlength = 5, edgecolors='none') ax.quiver(x[:-1], y[:-1], x[1:]-x[:-1], y[1:]-y[:-1], scale_units='xy', angles='xy', scale=1, color='0.75', width = 0.003, headwidth = .01, headlength = .01, edgecolors='none') x2 = np.zeros(11) y2 = np.zeros(11) floatdepths2 = [None]*11 floatdates2 = [None]*11 offsetx = ([ -35, 35, 0, 25, 20, -20, -35, -25, 25, -10, 10]) offsety = ([ 0, 0, -.08, .07, .06, .6, -.01, -.05, -.07, .08, -.08]) lll = 0 for l in range(0,21): ll = l + 30 if l%2==0: x2[lll] = floatMKE_bloomexo[l] + offsetx[lll] y2[lll] = chlyavg30meter_bloomexo[l] + offsety[lll] floatdates2[lll] = str(int(abs(floatdate[ll]))) lll = lll + 1 for label, xpt, ypt in zip(floatdates2, x2, y2): plt.text(xpt, ypt, label,ha='center',va='center',fontsize=11, family='Computer Modern Roman') cs = ax.scatter(floatMKE_bloomexo,chlyavg30meter_bloomexo,c = abs(depthof102745_bloomexo), s= 40) plt.set_cmap('jet') divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) cbar = plt.colorbar(cs,cax = cax, spacing='proportional') cbar.set_label(r"Depth of $\rho = $ 1027.45 kg m$^{-3}$ Isopycnal (m)") cbar.set_ticks([100, 125, 150, 175, 200, 225, 250, 275]) cbar.set_ticklabels([100, 125, 150, 175, 200, 225, 250, 275]) ax.set_xlim([0, 1150]) ax.set_ylim([.3, 3.3]) ax.set_xscale('linear') ax.set_xscale('linear') ax.set_xlabel(r"$MKE_{float}$ (cm$^{2}$s$^{-2}$)") ax.set_ylabel(r"$\frac{1}{z_{30}}\int^{z_{0}}_{z_{30}} \mathrm{[Chl(} z \mathrm{)]} \mathrm{d}z$ (mgm$^{-3}$)") plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/mke-30mavgchly_bloomandexport.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =================================================================================== # MKE vs Integrated Chly to 1027.45 # =================================================================================== # pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/mke-102745intchly-fitting_bloomandexport.pdf') fig = plt.figure() from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True ax = fig.add_axes([0.14,0.12,0.8,0.84]) from pylab import * # # mkes = np.linspace(0,1200,250) exp_decay = np.zeros(250) for jj in range(0,250): exp_decay[jj] = 309.2893*np.exp(-0.0011*mkes[jj]) ax.plot(mkes, exp_decay, color= 'k',label=r'Exponential Decay Fit; R = 0.7609; R$^2$ = 0.5789',linewidth = 3) #; P = $<$0.0001 ax.scatter(floatMKE_exo,chlyint102745_exo,color='r', s= 40, label=r'Export Data') ax.scatter(floatMKE_bloom,chlyint102745_bloom,color='g', s= 40, label=r'Bloom Data') l = legend(loc = 1) ax.set_xlim([0,1200]) ax.set_ylim([50,400]) ax.set_xscale('linear') ax.set_xlabel(r"$MKE_{float}$ (cm$^{2}$s$^{-2}$)") ax.set_ylabel(r"$\int^{z_{0}}_{z_{\rho = 1027.45}} \mathrm{[Chl(} z \mathrm{)]} \mathrm{d}z$ (mgm$^{-2}$)") plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/mke-102745intchly-fitting_bloomandexport.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =================================================================================== # MKE vs Avg Chly to 1027.45 # =================================================================================== # pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/mke-102745avgchly-fitting_bloomandexport.pdf') fig = plt.figure() from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True ax = fig.add_axes([0.14,0.12,0.8,0.84]) from pylab import * # # mkes = np.linspace(0,1200,250) exp_decay = np.zeros(250) for jj in range(0,250): exp_decay[jj] = 1.7850*np.exp(-0.0015*mkes[jj]) ax.plot(mkes, exp_decay, 'k',label=r'Exponential Decay Fit; 0.8453; R$^2$ = 0.7145',linewidth = 3) #; P = $<$0.0001 ax.scatter(floatMKE_exo,chlyavg102745_exo,color='r', s=40,label=r'Export Data') ax.scatter(floatMKE_bloom,chlyavg102745_bloom,color='g', s=40,label=r'Bloom Data') l = legend(loc = 1) ax.set_xlim([0,1200]) ax.set_ylim([0,2.3]) ax.set_xscale('linear') ax.set_xlabel(r"$MKE_{float}$ (cm$^{2}$s$^{-2}$)") ax.set_ylabel(r"$\frac{1}{z_{\rho = 1027.45}} \int^{z_{0}}_{z_{\rho = 1027.45}} \mathrm{[Chl(} z \mathrm{)]} \mathrm{d}z$ (mgm$^{-3}$)") plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/mke-102745avgchly-fitting_bloomandexport.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =================================================================================== # MKE vs Integrated Chly to 30m # =================================================================================== # # pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/mke-30mintchly-fitting_bloomandexport.pdf') # fig = plt.figure() # from matplotlib.font_manager import FontProperties # legendfont = FontProperties() # legendfont.set_name('Computer Modern Roman') # legendfont.set_size('x-small') # rcParams['axes.labelsize'] = 18 # rcParams['xtick.labelsize'] = 18 # rcParams['ytick.labelsize'] = 18 # rcParams['legend.fontsize'] = 14 # # # from matplotlib import rcParams # rcParams['font.family'] = 'serif' # rcParams['font.serif'] = ['Computer Modern Roman'] # rcParams['text.usetex'] = True # ax = fig.add_subplot(1, 1, 1) # from pylab import * # # # # # mkes = np.linspace(0,1200,250) # exp_decay = np.zeros(250) # for jj in range(0,250): # exp_decay[jj] = 91.8558*np.exp(-0.0023*mkes[jj]) # ax.plot(mkes, exp_decay, 'b',label=r'Exponential Decay Fit; R = 0.9014; P = $<$0.0001',linewidth = 2) # ax.plot(floatMKE_exo,chlyint30meter_exo,'ro',label=r'Export Data') # ax.plot(floatMKE_bloom,chlyint30meter_bloom,'go',label=r'Bloom Data') # l = legend(loc = 1) # ax.set_xlim([0,1200]) # ax.set_ylim([0,100]) # ax.set_xscale('linear') # ax.set_xlabel(r"\textbf{Kinetic Energy (cm$^{2}$s$^{-2}$)") # ax.set_ylabel(r"\textbf{$\int^{z_{0}}_{z_{30}} \mathrm{[Chl] } \mathrm{d}z$}") # plt.savefig(pp, format = "pdf") # pp.close() # os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/mke-30mintchly-fitting_bloomandexport.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =================================================================================== # MKE vs Avg Chly to 30 # =================================================================================== # # pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/mke-30mavgchly-fitting_bloomandexport.pdf') # fig = plt.figure() # from matplotlib import rcParams # rcParams['axes.labelsize'] = 14 # rcParams['xtick.labelsize'] = 12 # rcParams['ytick.labelsize'] = 12 # rcParams['legend.fontsize'] = 10 # rcParams['font.family'] = 'serif' # rcParams['font.serif'] = ['Computer Modern Roman'] # rcParams['text.usetex'] = True # ax = fig.add_subplot(1, 1, 1) # from pylab import * # # # # # mkes = np.linspace(0,1200,250) # exp_decay = np.zeros(250) # for jj in range(0,250): # exp_decay[jj] = 3.0619*np.exp(-0.0023*mkes[jj]) # ax.plot(mkes, exp_decay, 'b',label=r'Exponential Decay Fit; R = 0.9014; P = $<$0.0001',linewidth = 2) # ax.plot(floatMKE_exo,chlyavg30meter_exo,'ro',label=r'Export Data') # ax.plot(floatMKE_bloom,chlyavg30meter_bloom,'go',label=r'Bloom Data') # l = legend(loc = 1) # ax.set_xlim([0,1200]) # ax.set_ylim([0,3.15]) # ax.set_xscale('linear') # ax.set_xlabel(r"\textbf{Kinetic Energy (cm$^{2}$s$^{-2}$)") # ax.set_ylabel(r"\textbf{$\frac{1}{z_{30}} \int^{z_{0}}_{z_{30}} \mathrm{[Chl] } \mathrm{d}z$}") # plt.savefig(pp, format = "pdf") # pp.close() # os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/mke-30mavgchly-fitting_bloomandexport.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =================================================================================== # MKE vs Depth of rho = 1027.45 # =================================================================================== # pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/mke-depthof102745-fitting_bloomandexport.pdf') fig = plt.figure() from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True ax = fig.add_axes([0.14,0.12,0.8,0.84]) from pylab import * # # mkes = np.linspace(0,1200,250) linearfit = np.zeros(250) for jj in range(0,250): linearfit[jj] = 168.2952 + 0.0981*mkes[jj] ax.plot(mkes, linearfit, 'k',label=r'Linear Fit; R = 0.8016; R$^2$ = 0.6425',linewidth = 3) #; P = $<$0.0001 ax.scatter(floatMKE_exo,abs(depthof102745_exo),color='r', s= 40, label=r'Export Data') ax.scatter(floatMKE_bloom,abs(depthof102745_bloom),color='g', s = 40, label=r'Bloom Data') l = legend(loc = 2) ax.set_ylim([125,325]) ax.set_xlim([30,1200]) ax.set_xscale('log') ax.set_xlabel(r"$MKE_{float}$ (cm$^{2}$s$^{-2}$)") ax.set_ylabel(r"Depth of $\rho = $ 1027.45 kg m$^{-3}$ Isopycnal (m)") plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/mke-depthof102745-fitting_bloomandexport.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =================================================================================== # Depth of rho = 1027.45 vs 1027.45 Avg Chl # =================================================================================== # pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/depthof102745-102745avgchly-fitting_bloomandexport.pdf.pdf') fig = plt.figure() from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True ax = fig.add_axes([0.14,0.12,0.8,0.84]) from pylab import * # # depthss = np.linspace(125,310,250) exp_decay= np.zeros(250) linearfit = np.zeros(250) for jj in range(0,250): exp_decay[jj] = 5.3397*np.exp(-0.0077*depthss[jj]) linearfit[jj] = 2.8203 - 0.0082*depthss[jj] ax.plot(depthss, linearfit, '#696969',label=r'Linear Decay Fit; R = 0.6515; R$^2$ = 0.4245',linewidth = 3) #; P = 0.0014 ax.plot(depthss, exp_decay, 'b',label=r'Exponential Decay Fit; R = 0.6308; R$^2$ = 0.3979',linewidth = 3) #; P = 0.0022 ax.scatter(abs(depthof102745_exo),chlyavg102745_exo, color='r', s = 40,label=r'Export Data') ax.scatter(abs(depthof102745_bloom),chlyavg102745_bloom,color='g', s=40,label=r'Bloom Data') l = legend(loc = 1) ax.set_xscale('linear') ax.set_xlim([125,310]) ax.set_ylim([0,2.2]) ax.set_xlabel(r"Depth of $\rho = $ 1027.45 kg m$^{-3}$ Isopycnal (m)") ax.set_ylabel(r"$\frac{1}{z_{\rho = 1027.45}} \int^{z_{0}}_{z_{\rho = 1027.45}} \mathrm{[Chl(} z \mathrm{)]} \mathrm{d}z$ (mgm$^{-3}$)") plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/depthof102745-102745avgchly-fitting_bloomandexport.pdf.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =================================================================================== # Depth of rho = 1027.45 vs 1027.45 Int Chl # =================================================================================== # # pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/depthof102745-102745intchly-fitting_bloomandexport.pdf') # fig = plt.figure() # from matplotlib import rcParams # rcParams['axes.labelsize'] = 14 # rcParams['xtick.labelsize'] = 12 # rcParams['ytick.labelsize'] = 12 # rcParams['legend.fontsize'] = 10 # rcParams['font.family'] = 'serif' # rcParams['font.serif'] = ['Computer Modern Roman'] # rcParams['text.usetex'] = True # ax = fig.add_subplot(1, 1, 1) # from pylab import * # # # # # depthss = np.linspace(125,310,250) # exp_decay= np.zeros(250) # linearfit = np.zeros(250) # for jj in range(0,250): # exp_decay[jj] = 518.6396*np.exp(-0.0043*depthss[jj]) # linearfit[jj] = 410.6160 - 0.9335*depthss[jj] # ax.plot(depthss, linearfit, '0.75',label=r'Linear Decay Fit; R = 0.4568; P = 0.0374',linewidth = 2) # ax.plot(depthss, exp_decay, 'b',label=r'Exponential Decay Fit; R = 0.4397; P = 0.0461',linewidth = 2) # ax.plot(abs(depthof102745_exo),chlyint102745_exo,'ro',label=r'Export Data') # ax.plot(abs(depthof102745_bloom),chlyint102745_bloom,'go',label=r'Bloom Data') # l = legend(loc = 1) # ax.set_xscale('linear') # ax.set_xlim([125,310]) # ax.set_ylim([50,400]) # ax.set_xlabel(r"\textbf{Depth of $\rho = $ 1027.45 kg m$^{-3}$ Isopycnal (m)}") # ax.set_ylabel(r"\textbf{$\int^{z_{0}}_{z_{\rho = 1027.45}} \mathrm{[Chl] } \mathrm{d}z$}") # plt.savefig(pp, format = "pdf") # pp.close() # os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/depthof102745-102745intchly-fitting_bloomandexport.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # Do you want to plot this? coderunner = 1 if coderunner == 1: # # # =================================================================================== # MKE vs Depth Avg Chly to 1027.45 -- Just Bloom and Exports # =================================================================================== # pp = PdfPages('/home/ardavies/satdata/OSCAR/pdfoutput/floatdate-102745avgchly-102745depth_bloomandexport.pdf') from matplotlib.colors import LogNorm from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt import numpy as np fig = plt.figure() ax = fig.add_axes([0.15,0.1,0.68,0.85]) from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # from pylab import * import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable ax.plot(floatdate_bloomexo,chlyavg102745_bloomexo,linewidth = 1.25, color = '0.75', label=r'Observed', zorder = -2)# $\frac{1}{z_{\rho = 1027.45}} \int^{z_{0}}_{z_{\rho = 1027.45}} \mathrm{[Chl] } \mathrm{d}z$}') sterrbars = np.zeros(21) stdevbars = np.zeros(21) lll = 0 for l in range(0,21): ll = l + 30 sterrbars[lll] = chlyavg102745_sterr [ll] stdevbars[lll] = chlyavg102745_stdev [ll] lll = lll + 1 ax.errorbar(floatdate_bloomexo,chlyavg102745_bloomexo, yerr=sterrbars, color = '0.75', fmt='o', zorder = -1 ) # ax.errorbar(floatdate_bloomexo,chlyavg102745_bloomexo, yerr=stdevbars, color = 'b') cs = ax.scatter(floatdate_bloomexo,chlyavg102745_bloomexo, c = abs(depthof102745_bloomexo), s= 40,zorder = 4, linewidth='0.5') plt.set_cmap('jet') divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) cbar = plt.colorbar(cs,cax=cax, spacing='proportional') cbar.set_label(r"Depth of $\rho = $ 1027.45 kg m$^{-3}$ Isopycnal (m)") cbar.set_ticks([100, 125, 150, 175, 200, 225, 250, 275]) cbar.set_ticklabels([100, 125, 150, 175, 200, 225, 250, 275]) dillutionavgchl = np.zeros(10) dillutiondate = np.zeros(10) for i in range(0,10): ii = i + 10 dillutiondate[i] = floatdate_bloomexo[ii] if i == 0: dillutionavgchl[i] = chlyavg102745_bloomexo[ii] else: dillutionavgchl[i] = dillutionavgchl[i-1]*depthof102745_bloomexo[ii-1]/depthof102745_bloomexo[ii] dillutionavgchl93 = np.zeros(10) dillutiondate93 = np.zeros(10) for i in range(0,10): ii = i + 11 dillutiondate93[i] = floatdate_bloomexo[ii] if i == 0: dillutionavgchl93[i] = chlyavg102745_bloomexo[ii] else: dillutionavgchl93[i] = dillutionavgchl93[i-1]*depthof102745_bloomexo[ii-1]/depthof102745_bloomexo[ii] dillutionavgchl95 = np.zeros(8) dillutiondate95 = np.zeros(8) for i in range(0,8): ii = i + 12 dillutiondate95[i] = floatdate_bloomexo[ii] if i == 0: dillutionavgchl95[i] = chlyavg102745_bloomexo[ii] else: dillutionavgchl95[i] = dillutionavgchl95[i-1]*depthof102745_bloomexo[ii-1]/depthof102745_bloomexo[ii] #ax.plot(dillutiondate,dillutionavgchl,linewidth = 3, color = 'k',label=r'Dilution Predicted Starting at DOY 91',zorder = 1)# $\frac{1}{z_{\rho = 1027.45}} \int^{z_0}_{z_{\rho = 1027.45}} \mathrm{[Chl]} \mathrm{d}z$}') ax.plot(dillutiondate93,dillutionavgchl93,linewidth = 2.5, color = 'k',label=r'Avg. [Chl] Predicted by Dillution',zorder = 2)# $\frac{1}{z_{\rho = 1027.45}} \int^{z_0}_{z_{\rho = 1027.45}} \mathrm{[Chl]} \mathrm{d}z$}') #ax.plot(dillutiondate95,dillutionavgchl95,linewidth = 1.2, color = 'k',label=r'Dilution Predicted Starting at DOY 95',zorder = 2)# $\frac{1}{z_{\rho = 1027.45}} \int^{z_0}_{z_{\rho = 1027.45}} \mathrm{[Chl]} \mathrm{d}z$}') # x2 = np.zeros(11) # y2 = np.zeros(11) # floatdepths2 = [None]*11 # offsety = ([ -.1, .1, .1, .1, .1, -.1, -.1, -.1, -.1, -.1, -.1]) # lll = 0 # for l in range(0,21): # ll = l + 30 # if l%2==0: # x2[lll] = floatdate_bloomexo[l] # y2[lll] = chlyavg102745_bloomexo[l] + offsety[lll] # floatdepths2[lll] = str(int(abs(depthof102745[ll]))) # lll = lll + 1 # for label, xpt, ypt in zip(floatdepths2, x2, y2): # plt.text(xpt, ypt, label,ha='center',va='center',fontsize=11, family='Computer Modern Roman') ax.set_xlim([69, 113]) ax.set_ylim([0.15, 2.65]) ax.set_xscale('linear') ax.set_xlabel(r"Day of Year") ax.set_ylabel(r"$\frac{1}{z_{\rho = 1027.45}} \int^{z_{0}}_{z_{\rho = 1027.45}} \mathrm{[Chl(} z \mathrm{)]} \mathrm{d}z$ (mgm$^{-3}$)") plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/floatdate-102745avgchly-102745depth_bloomandexport.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # # =========================================================== # # PLOTTING VERTICAL VELOCITIES DATA # # =========================================================== # gridWplt = 2 if gridWplt == 1: # # =========================================================== # Contour Plotting Grided Density W Data # =========================================================== # # Changing Directory to plotting output os.chdir('/home/ardavies/satdata/OSCAR/pdfoutput') # # Plot Set-up pp = PdfPages('GriddedW_Density.pdf') fig = plt.figure() ax = fig.add_subplot(1, 1, 1) # # 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) # # Text/Front Set up from matplotlib import rcParams rcParams['axes.labelsize'] = 14 rcParams['xtick.labelsize'] = 12 rcParams['ytick.labelsize'] = 12 rcParams['legend.fontsize'] = 10 rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # Contour Plotting & Color Bar from pylab import * cs = plt.contourf(GridWDates[:,1,:],GridWDepths[:,1,:],GridWData[:,1,:]*10**(4), levels = np.linspace(-6,6,201)) plt.set_cmap('seismic') cbar = plt.colorbar(cs,spacing='proportional') cbar.set_label(r"\textbf{Isopycnal Vertical Velocity $\times 10**4$ (ms$^{-1}$)}") cbar.set_ticks([-5,-2.5, 0,2.5, 5]) cbar.set_ticklabels([-5,-2.5, 0,2.5, 5]) plt.plot(IsoBioWDatesDepthAvgAvg, IsoBioWDepthsDepthAvgAvg, 'k',label=r'Averaged Depth of 3 Profile Averaged Chlorophyll and Sinking Vertical Velocties',linewidth = 2) l = legend(loc = 2) ax.set_xlabel(r"\textbf{Day of Year}") ax.set_xlim([70,110]) ax.set_ylim([-900,0]) ax.set_ylabel(r"\textbf{Depth (m)}") # # Save and spc to storm plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/GriddedW_Density.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/Jan01_Jun04_2013/verticalvelocities/') # # # # # # =========================================================== # # # # PLOTTING MKE, CURRENTS, TRAJECTORIES, ECT # # # # =========================================================== # # # # Changing Directory to plotting output # os.chdir('/home/ardavies/satdata/OSCAR/pdfoutput') # lineplot = 2 # if lineplot == 1: # # # # =================================================================================== # # Plotting Daily and 5 Day Average Float Experienced MKE from OSCAR # # =================================================================================== # # # pp = PdfPages('floatmke-daily.pdf') # fig = plt.figure() # from matplotlib import rcParams # rcParams['axes.labelsize'] = 14 # rcParams['xtick.labelsize'] = 12 # rcParams['ytick.labelsize'] = 12 # rcParams['legend.fontsize'] = 10 # rcParams['font.family'] = 'serif' # rcParams['font.serif'] = ['Computer Modern Roman'] # rcParams['text.usetex'] = True # ax = fig.add_subplot(1, 1, 1) # from pylab import * # ax.plot(floatdate,floatMKE5day,'0.75',label=r'From Original, 5 Day Averaged Data',linewidth = 3) # ax.plot(floatdate,floatMKE,'k',label=r'Using Daily Averaged OSCAR Data',linewidth = 3) # l = legend(loc = 2) # ax.set_yscale('log') # ax.set_xlabel(r"\textbf{Day of Year}") # ax.set_ylabel(r"\textbf{Mean Kinetic Energy (cm$^2$s$^{-2}$)}") # plt.savefig(pp, format = "pdf") # pp.close() # os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/floatmke-daily.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/') # # # # =================================================================================== # # Plotting Daily and 5 Day Average Float Experienced MKE from OSCAR (cropped) # # =================================================================================== # # # pp = PdfPages('floatmke-daily-compare-crop.pdf') # fig = plt.figure() # from matplotlib import rcParams # rcParams['axes.labelsize'] = 14 # rcParams['xtick.labelsize'] = 12 # rcParams['ytick.labelsize'] = 12 # rcParams['legend.fontsize'] = 10 # rcParams['font.family'] = 'serif' # rcParams['font.serif'] = ['Computer Modern Roman'] # rcParams['text.usetex'] = True # # # ax = fig.add_subplot(1, 1, 1) # ax.set_yscale('log') # ax.plot(floatdate,floatMKE5day,'0.75',label=r'From Original, 5 Day Averaged Data',linewidth = 3) # ax.plot(floatdate,floatMKE,'k',label=r'Using Daily Averaged OSCAR Data',linewidth = 3) # l = legend(loc = 2) # ax.set_xlabel(r"\textbf{Day of Year}") # ax.set_ylabel(r"\textbf{Mean Kinetic Energy (cm$^2$s$^{-2}$)}") # ax.set_xlim([70,110]) # ax.set_ylim([30,2000]) # plt.savefig(pp, format = "pdf") # pp.close() # os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/floatmke-daily-compare-crop.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/') # # # # =================================================================================== # # Plotting DailyFloat Experienced MKE from OSCAR (cropped) # # =================================================================================== # # # pp = PdfPages('floatmke-daily-crop.pdf') # fig = plt.figure() # from matplotlib import rcParams # rcParams['axes.labelsize'] = 14 # rcParams['xtick.labelsize'] = 12 # rcParams['ytick.labelsize'] = 12 # rcParams['legend.fontsize'] = 10 # rcParams['font.family'] = 'serif' # rcParams['font.serif'] = ['Computer Modern Roman'] # rcParams['text.usetex'] = True # ax = fig.add_subplot(1, 1, 1) # ax.set_yscale('log') # ax.plot(floatdate,floatMKE,'k',label=r'Using Daily Averaged OSCAR Data',linewidth = 3) # ax.set_xlabel(r"\textbf{Day of Year}") # ax.set_ylabel(r"\textbf{Mean Kinetic Energy (cm$^2$s$^{-2}$)}") # ax.set_xlim([70,110]) # ax.set_ylim([30,2000]) # plt.savefig(pp, format = "pdf") # pp.close() # os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/floatmke-daily-crop.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/') # # # # =================================================================================== # # Zoomed-in Daily Currents # # =================================================================================== # # # pp = PdfPages('currents-daily-zoomed.pdf') # figtit1 = 'Daily Averaged OSCAR Data on DOY: ' # from matplotlib.colors import LogNorm # for ii in range(0,len(oscardates)-8): # # # # import matplotlib.path as mpath # import matplotlib.patches as mpatches # import matplotlib as mpl # from matplotlib.collections import PatchCollection # Path = mpath.Path # fig=plt.figure(figsize=(8,4.5)) # m = Basemap(llcrnrlon=297,llcrnrlat=-61,urcrnrlon=307,urcrnrlat=-54,projection='cyl',resolution='i') # ax = fig.add_axes([0.05,0.05,0.9,0.85]) # m.drawcoastlines(linewidth=0.25) # m.drawcountries(linewidth=0.25) # m.fillcontinents(color='gray',lake_color='white') # m.drawmapboundary(fill_color='white') # m.drawparallels(np.arange(-90.,90,5.),labels=[1,0,0,0]) # m.drawmeridians(np.arange(180.,360.,5. ),labels=[0,0,0,1]) # x, y = m(lon, lat) # Q = m.quiver(x,y,udaily[ii,:,:],vdaily[ii,:,:]) # qk = plt.quiverkey(Q, -.08, .8, 1, '1 m/s', labelpos='W',color = 'r') # for k in range(0,numdates-1): # if (centerdates[k] == oscardates[ii]): # Q = m.quiver(x,y,u[k,:,:],v[k,:,:], color = 'b') # plt.title(figtit1 + str(int(oscardates[ii]))) # plt.savefig(pp, format = "pdf") # pp.close() # os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/currents-daily-zoomed.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/') # # # # =================================================================================== # # Daily Currents # # =================================================================================== # # # pp = PdfPages('currents-daily.pdf') # figtit1 = 'Daily Averaged OSCAR Data on DOY: ' # from matplotlib.colors import LogNorm # for ii in range(0,len(oscardates)-8): # # # # import matplotlib.path as mpath # import matplotlib.patches as mpatches # import matplotlib as mpl # from matplotlib.collections import PatchCollection # Path = mpath.Path # fig=plt.figure(figsize=(8,4.5)) # m = Basemap(llcrnrlon=285,llcrnrlat=-65,urcrnrlon=310,urcrnrlat=-50,projection='cyl',resolution='i') # ax = fig.add_axes([0.05,0.05,0.9,0.85]) # m.drawcoastlines(linewidth=0.25) # m.drawcountries(linewidth=0.25) # m.fillcontinents(color='gray',lake_color='white') # m.drawmapboundary(fill_color='white') # m.drawparallels(np.arange(-90.,90,5.),labels=[1,0,0,0]) # m.drawmeridians(np.arange(180.,360.,5. ),labels=[0,0,0,1]) # x, y = m(lon, lat) # Q = m.quiver(x,y,udaily[ii,:,:],vdaily[ii,:,:]) # qk = plt.quiverkey(Q, -.08, .8, 1, '1 m/s', labelpos='W',color = 'r') # for k in range(0,numdates-1): # if (centerdates[k] == oscardates[ii]): # Q = m.quiver(x,y,u[k,:,:],v[k,:,:], color = 'b') # plt.title(figtit1 + str(int(oscardates[ii]))) # plt.savefig(pp, format = "pdf") # pp.close() # os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/currents-daily.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/') # # # # =================================================================================== # # Zoomed-in Daily Trajectories and Currents # # =================================================================================== # # # pp = PdfPages('currents-tracer-zoom.pdf') # figtit1 = 'OSCAR 5 Day Avg ' # figtit2 = ' Centered About DOY ' # from matplotlib.colors import LogNorm # for l in range(0,arraylen-1): # # # # Find the correct OSCAR data plot # k = 0 # for kk in range(0,len(oscardates)-8): # if (oscardates[k] == floatdate[l]): # break # else: # k = k + 1 # # # usefloatlon= floatlon[l] # usefloatlat = floatlat[l] # usefloatlon_new = floatlon[l+1] # usefloatlat_new = floatlat[l+1] # usetracerlat_U_V = tracer_newlat_U_V[l] # usetracerlon_U_V = tracer_newlon_U_V[l] # # # import matplotlib.path as mpath # import matplotlib.patches as mpatches # import matplotlib as mpl # from matplotlib.collections import PatchCollection # Path = mpath.Path # fig=plt.figure(figsize=(8,4.5)) # m = Basemap(llcrnrlon=297,llcrnrlat=-61,urcrnrlon=307,urcrnrlat=-54,projection='mill',resolution='i') # ax = fig.add_axes([0.05,0.05,0.9,0.85]) # m.drawcoastlines(linewidth=0.25) # m.drawcountries(linewidth=0.25) # m.fillcontinents(color='gray',lake_color='white') # m.drawmapboundary(fill_color='white') # m.drawparallels(np.arange(-90.,90,5.),labels=[1,0,0,0]) # m.drawmeridians(np.arange(180.,360.,5. ),labels=[0,0,0,1]) # x, y = m(lon, lat) # floatx,floaty =m(usefloatlon, usefloatlat) # floatx_new,floaty_new =m(usefloatlon_new, usefloatlat_new) # tracerx_U_V,tracery_U_V = m(usetracerlon_U_V, usetracerlat_U_V) # cs = m.scatter(floatx_new,floaty_new, s = 10, marker=(5,0), c='b') # cs1 = m.scatter(floatx,floaty, s = 10, marker=(5,0), c='k') # cs2 = m.scatter(tracerx_U_V,tracery_U_V, s = 10, marker=(5,0), c='r') # Q = m.quiver(x,y,udaily[k,:,:],vdaily[k,:,:]) # qk = plt.quiverkey(Q, -.08, .8, 1, '1 m/s', labelpos='W',color = 'r') # plt.title('OSCAR Currents, Float and Tracer on DOY: ' + str(int(oscardates[k]))) # plt.savefig(pp, format = "pdf") # pp.close() # # # # =================================================================================== # # Zoomed-in Daily MKE and Currents # # =================================================================================== # # # pp = PdfPages('currents-daily-mke-zoomed.pdf') # figtit1 = 'OSCAR Daily Averaged MKE on DOY: ' # from matplotlib.colors import LogNorm # for ii in range(0,len(oscardates)-8): # # # # Import Plotting Packages # import matplotlib.path as mpath # import matplotlib.patches as mpatches # import matplotlib as mpl # from matplotlib.collections import PatchCollection # Path = mpath.Path # # # # Setting-up the map # fig=plt.figure(figsize=(8,4.5)) # m = Basemap(llcrnrlon=297,llcrnrlat=-61,urcrnrlon=307,urcrnrlat=-54,projection='cyl',resolution='i') # ax = fig.add_axes([0.05,0.05,0.9,0.85]) # m.drawcoastlines(linewidth=0.25) # m.drawcountries(linewidth=0.25) # m.fillcontinents(color='gray',lake_color='white') # m.drawmapboundary(fill_color='white') # m.drawparallels(np.arange(-90.,90,5.),labels=[1,0,0,0]) # m.drawmeridians(np.arange(180.,360.,5. ),labels=[0,0,0,1]) # # # # Plotting # x, y = m(lon, lat) # cs = m.contourf(x,y,mke[k,:,:], levels = np.logspace(-1,3.5,500), norm = LogNorm()) # cb = plt.colorbar(cs, ticks=[1, 10, 100, 1000]) # cb.ax.set_yticklabels([1, 10,100,1000 ]) # cb.set_label('Mean Kinetic Energy (cm^2/s^2)') # Q = m.quiver(x,y,udaily[ii,:,:],vdaily[ii,:,:]) # qk = plt.quiverkey(Q, -.08, .8, 1, '1 m/s', labelpos='W',color = 'r') # plt.title(figtit1 + str(int(oscardates[ii]))) # plt.savefig(pp, format = "pdf") # pp.close() # os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/currents-daily-mke-zoomed.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/') # # # # =================================================================================== # # Daily MKE and Currents # # =================================================================================== # # # pp = PdfPages('currents-daily-mke.pdf') # figtit1 = 'OSCAR Daily Averaged MKE on DOY: ' # from matplotlib.colors import LogNorm # for ii in range(0,len(oscardates)-8): # # # # Import Plotting Packages # import matplotlib.path as mpath # import matplotlib.patches as mpatches # import matplotlib as mpl # from matplotlib.collections import PatchCollection # Path = mpath.Path # # # # Setting-up the map # fig=plt.figure(figsize=(8,4.5)) # m = Basemap(llcrnrlon=285,llcrnrlat=-65,urcrnrlon=310,urcrnrlat=-50,projection='cyl',resolution='i') # ax = fig.add_axes([0.05,0.05,0.9,0.85]) # m.drawcoastlines(linewidth=0.25) # m.drawcountries(linewidth=0.25) # m.fillcontinents(color='gray',lake_color='white') # m.drawmapboundary(fill_color='white') # m.drawparallels(np.arange(-90.,90,5.),labels=[1,0,0,0]) # m.drawmeridians(np.arange(180.,360.,5. ),labels=[0,0,0,1]) # # # # Plotting # x, y = m(lon, lat) # cs = m.contourf(x,y,mke[k,:,:], levels = np.logspace(-1,3.5,500), norm = LogNorm()) # cb = plt.colorbar(cs, ticks=[1, 10, 100, 1000]) # cb.ax.set_yticklabels([1, 10,100,1000 ]) # cb.set_label('Mean Kinetic Energy (cm^2/s^2)') # Q = m.quiver(x,y,udaily[ii,:,:],vdaily[ii,:,:]) # qk = plt.quiverkey(Q, -.08, .8, 1, '1 m/s', labelpos='W',color = 'r') # plt.title(figtit1 + str(int(oscardates[ii]))) # plt.savefig(pp, format = "pdf") # pp.close() # os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/currents-daily-mke.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/') # # # # =================================================================================== # # Daily MKE and Currents # # =================================================================================== # # # pp = PdfPages('currents-mke-daily.pdf') # figtit1 = 'OSCAR 5 Day Avg ' # figtit2 = ' Centered About DOY ' # from matplotlib.colors import LogNorm # for k in range(0,numdates-3): # counter = 0 # for l in range(0,arraylen-1): # if (centerdates[k]-2 <= floatdate[l] <= centerdates[k] +2): # counter = counter + 1 # indexes = np.zeros(counter) # counter = 0 # for l in range(0,arraylen-1): # if (centerdates[k]-2 <= floatdate[l] <= centerdates[k] +2): # indexes[counter] = l # counter = counter + 1 # usefloatlon = np.zeros(counter) # usefloatlat = np.zeros(counter) # for n in range(0,counter): # nn = int(indexes[n]) # usefloatlon[n] = floatlon[nn] # usefloatlat[n] = floatlat[nn] # m = Basemap(llcrnrlon=285,llcrnrlat=-65,urcrnrlon=310,urcrnrlat=-50,projection='mill',resolution='i') # fig=plt.figure(figsize=(8,4.5)) # ax = fig.add_axes([0.05,0.05,0.9,0.85]) # m.drawcoastlines(linewidth=0.25) # m.drawcountries(linewidth=0.25) # m.fillcontinents(color='gray',lake_color='white') # m.drawmapboundary(fill_color='white') # m.drawparallels(np.arange(-90.,90,5.),labels=[1,0,0,0]) # m.drawmeridians(np.arange(180.,360.,5. ),labels=[0,0,0,1]) # x, y = m(lon, lat) # floatx,floaty =m(usefloatlon, usefloatlat) # cs = m.contourf(x,y,mke[k,:,:], levels = np.logspace(-1,3.5,500), norm = LogNorm()) # cs2 = m.scatter(floatx,floaty, c = 'k', s = 25, marker=(5,0)) # cs3 = m.plot(floatx,floaty, color = 'k') # Q = m.quiver(x,y,u[k,:,:],v[k,:,:]) # qk = plt.quiverkey(Q, -.08, .8, 1, '1 m/s', labelpos='W',color = 'r') # cb = plt.colorbar(cs, ticks=[1, 10, 100, 1000]) # cb.ax.set_yticklabels([1, 10,100,1000 ]) # cb.set_label('Mean Kinetic Energy (cm^2/s^2)') # plt.title(figtit1 + 'Currents & MKE' + figtit2 + str(int(centerdates[k]))) # plt.savefig(pp, format = "pdf") # pp.close() # # # # # # =========================================================== # # # # FIGURE 1 PLOTTING # # # # =========================================================== # # # toplot = 2 # if toplot == 1: # pp = PdfPages('floatlocations.pdf') # from matplotlib.colors import LogNorm # from mpl_toolkits.basemap import Basemap # import matplotlib.pyplot as plt # import numpy as np # fig = plt.figure() # # ax = fig.add_axes() # ax = fig.add_axes([0.07,0.06,0.83,0.97]) # #f, ax = plt.subplots() # # # # Setting Fonts # from matplotlib import rcParams # rcParams['axes.labelsize'] = 16 # rcParams['xtick.labelsize'] = 16 # rcParams['ytick.labelsize'] = 16 # rcParams['legend.fontsize'] = 14 # rcParams['font.family'] = 'serif' # rcParams['font.serif'] = ['Computer Modern Roman'] # rcParams['text.usetex'] = True # # # # Setting the Larger Map # m = Basemap(llcrnrlon=300.8,llcrnrlat=-59.5,urcrnrlon=305.9,urcrnrlat=-55.5,projection='cyl',resolution='i') # m.drawcoastlines(linewidth=0.25) # m.drawcountries(linewidth=0.25) # m.fillcontinents(color='gray',lake_color='white') # m.drawmapboundary(fill_color='white') # m.drawparallels(np.arange(-90.,90,2.),labels=[1,0,0,0],linewidth=0.0,fontsize=16) # m.drawmeridians(np.arange(180.,360.,2. ),labels=[0,0,0,1],linewidth=0.0,fontsize=16) # # # # Plotting Tracer and Float locations over the correct dates # x,y = m(lon,lat) # for l in range(0,21): # ll = l + 30 # # # # Find the correct OSCAR data plot # k = 0 # for kk in range(0,len(oscardates)-8): # if (oscardates[k] == floatdate[ll]): # break # else: # k = k + 1 # # # usefloatlon= floatlon[ll] # usefloatlat = floatlat[ll] # usefloatlon_new = floatlon[ll+1] # usefloatlat_new = floatlat[ll+1] # usetracerlat_U_V = tracer_newlat_U_V[ll] # usetracerlon_U_V = tracer_newlon_U_V[ll] # # # floatx,floaty =m(usefloatlon, usefloatlat) # # # # Plotting Float Locations And Tracers # tracerx_U_V,tracery_U_V =m(usetracerlon_U_V, usetracerlat_U_V) # cs3 = m.plot([floatx,tracerx_U_V],[floaty,tracery_U_V],linewidth = 0.5, color = '#696969') # cs1 = m.scatter(tracerx_U_V,tracery_U_V, s = 5, c='#696969',edgecolors='none') # # # # Plotting MKE Color for each float date and the DOY # floatx2 = np.zeros([21]) # floaty2 = np.zeros([21]) # floatx22 = np.zeros([11]) # floaty22 = np.zeros([11]) # floatmke2 = np.zeros([21]) # floatdates2 = [None]*11 # offsetx = np.zeros([11]) # offsety = np.zeros([11]) # offsetx = ([ 0, 0, -.1, -.13, -.15, -.13, -.13, -.15, .15, -.15, 0]) # offsety = ([.14, .13, .1, 0.03, 0, 0, -0.01, 0, 0, .1, .15]) # lll = 0 # for l in range(0,21): # ll = l + 30 # floatx2[l],floaty2[l] = m(floatlon[ll], floatlat[ll]) # floatmke2[l] = floatMKE[ll] # if l%2==0: # floatx22[lll] = floatx2[l] + offsetx[lll] # floaty22[lll] = floaty2[l] + offsety[lll] # if lll == 0: # floatdates2[lll] = "DOY: " + str(int(floatdate[ll])) # else: # floatdates2[lll] = str(int(floatdate[ll])) # lll = lll + 1 # for label, xpt, ypt in zip(floatdates2, floatx22, floaty22): # plt.text(xpt, ypt, label,ha='center',va='center',fontsize=12, family='Computer Modern Roman') # import math as ma # from mpl_toolkits.axes_grid1 import make_axes_locatable # xponent1 = ma.log(30)/ma.log(10) # xponent2 = ma.log(2000)/ma.log(10) # # cs2 = m.scatter(floatx2,floaty2,s = 35,c=floatmke2, norm=matplotlib.colors.LogNorm())#,levels= np.logspace(xponent1,xponent2,100)) # # cbar = plt.colorbar(cs2,spacing='proportional', norm = LogNorm()) # cs2 = m.scatter(floatx2,floaty2,s = 40,c=floatmke2, norm=LogNorm(vmin=floatmke2.min(), vmax=floatmke2.max()))# , norm=matplotlib.colors.LogNorm())#,levels= np.logspace(xponent1,xponent2,100)) # divider = make_axes_locatable(ax) # cax = divider.append_axes("right", size="5%", pad=0.05) # cbar = plt.colorbar(cs2,cax=cax, norm=LogNorm(vmin=floatmke2.min(), vmax=floatmke2.max())) # minorticks = cs2.norm(np.array([60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000])) # cbar.ax.yaxis.set_ticks(minorticks, minor=True) # print np.log(floatmke2) # cbar.set_label(r"\textbf{Mean Kinetic Energy (cm$^2$s$^{-2}$)}") # cbar.set_ticks([100, 1000]) # cbar.set_ticklabels([r'10$^{2}$', r'10$^{3}$']) # # # # Base Map set-up for the insert map # m2 = Basemap(llcrnrlon=292.2,llcrnrlat=-64.2,urcrnrlon=308.5,urcrnrlat=-50.9,projection='cyl',resolution='i') # ax2 = fig.add_axes([0.061,0.545,0.4,0.4]) # m2.drawcoastlines(linewidth=0.25) # m2.drawcountries(linewidth=0.25) # m2.fillcontinents(color='gray',lake_color='white') # m2.drawmapboundary(fill_color='white') # # # # Plotting all the float locations in gray # floatx3,floaty3 =m2(floatlon, floatlat) # cs7 = m2.plot(floatx3,floaty3,linewidth = 0.2, color = '#696969') # cs8 = m2.scatter(floatx3,floaty3, s = 3, c='#696969',edgecolors='none') # # # # Overplotting the ones used in larger figure in red # floatx4 = np.zeros([21]) # floaty4 = np.zeros([21]) # lll = 0 # for l in range(0,21): # ll = l + 30 # floatx4[l],floaty4[l] = m2(floatlon[ll], floatlat[ll]) # lll = lll + 1 # cs9 = m2.plot(floatx4,floaty4,linewidth = 0.2, color = 'b') # cs10 = m2.scatter(floatx4,floaty4, s = 6, c='b',edgecolors='b') # # # # Plotting Labels # places = [r'\textbf{Argentina}', r'\textbf{Falkland Islands}', r'\textbf{Antarctica}'] # placeslons = ([295.6, 305.0, 295.5]) # placeslats = ([-53.6, -52.465, -62.3]) # placesx, placesy = m2(placeslons, placeslats) # for place, xpt2, ypt2 in zip(places, placesx, placesy): # plt.text(xpt2, ypt2, place,ha='center',va='center',fontsize=11, family = 'Computer Modern Roman') # # # # Saving # plt.savefig(pp, format = "pdf") # pp.close() # os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/floatlocations.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # # # # # =========================================================== # # # # PAPER LINE PLOTTING # # # # =========================================================== # # # # # # =================================================================================== # # Vertical Velocities Condensed into one plot # # =================================================================================== # # # sideplot = 2 # if sideplot == 1: # from matplotlib import rc # from matplotlib.numerix import arange, cos, pi # rc('text', usetex=True) # from pylab import * # numbiolinesavg = len(IsoBioWDataAvg) # pp = PdfPages('figure3a.pdf') # fig = plt.figure() # from matplotlib.font_manager import FontProperties # legendfont = FontProperties() # legendfont.set_name('Computer Modern Roman') # legendfont.set_size('x-small') # rcParams['axes.labelsize'] = 18 # rcParams['xtick.labelsize'] = 18 # rcParams['ytick.labelsize'] = 18 # rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams # rcParams['font.family'] = 'serif' # rcParams['font.serif'] = ['Computer Modern Roman'] # rcParams['text.usetex'] = True # #ax = fig.add_subplot(1, 1, 1) # ax = fig.add_axes([0.15,0.1,0.81,0.68]) # # # # 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=r'Chl Velocities') # else: # plt.plot(IsoBioWDatesAvg[j], IsoBioWDataAvg[j]*10**4, '0.75') # plt.plot(IsoBioWDatesDepthAvgAvg, NearestWGriddedAvgAvg*10**4, 'b',label=r'Environmental Velocity',linewidth = 2) # plt.plot(IsoBioWDatesDepthAvgAvg, SinkWDataDepthAvgAvg*10**4, 'r',label=r'Depth Avg Sinking Velocity',linewidth = 2) # plt.plot(IsoBioWDatesDepthAvgAvg, IsoBioWDataDepthAvgAvg*10**4, 'k',label=r'Depth Avg Chl Velocity',linewidth = 2) # l = legend(loc = 3) # #ax.set_xlabel("Days since 01/01/2013") # ax.set_ylabel(r'\textbf{Veloctiy $\times 10^4$ (ms$^{-1}$)}' ) # ax.get_yaxis().set_label_coords(-0.1,0.5) # ax.set_xlim([70,110]) # ax.set_ylim([-13,4]) # plt.setp(ax.get_xticklabels(), visible=False) # plt.savefig(pp, format = "pdf") # pp.close() # os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/figure3a.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =================================================================================== # Vertical Velocities Condensed into one plot in km per day # =================================================================================== # sideplot = 1 if sideplot == 1: pp = PdfPages('figure3a-2.pdf') from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True fig = plt.figure() ax = fig.add_axes([0.14,0.12,0.8,0.84]) from pylab import * #ax = fig.add_subplot(1, 1, 1) #ax = fig.add_axes([0.15,0.1,0.81,0.68]) # # Which Plotting Information? # axhline(0, color='k') # for j in range(0,numbiolinesavg): # if j == 0: # plt.plot(IsoBioWDatesAvg[j], IsoBioWDataAvg[j]*86400, '0.75',label=r'Chl Velocities') # else: # plt.plot(IsoBioWDatesAvg[j], IsoBioWDataAvg[j]*86400, '0.75') plt.scatter(IsoBioWDatesDepthAvgAvg, NearestWGriddedAvgAvg*86400, color = '#696969',label=r'Rate of Vert. Isopycnal Motion', s=30, zorder = 3) #plt.plot(IsoBioWDatesDepthAvgAvg, SinkWDataDepthAvgAvg*86400, 'r',label=r'Depth Avg Sinking Velocity',linewidth = 2) plt.plot(IsoBioWDatesDepthAvgAvg, IsoBioWDataDepthAvgAvg*86400, 'k',label=r'Depth Avg Chl Velocity',linewidth = 2, zorder = 2) l = legend(loc = 3) # sterrbars = np.zeros(21) # stdevbars = np.zeros(21) # lll = 0 # for l in range(0,21): # ll = l + 30 # sterrbars[lll] = chlyavg102745_sterr [ll] # stdevbars[lll] = chlyavg102745_stdev [ll] # lll = lll + 1 ax.errorbar(IsoBioWDatesDepthAvgAvg,NearestWGriddedAvgAvg*86400, yerr=NearestWGriddedAvgAvg_stdev*86400, color = '#696969', fmt='o',zorder = 0) ax.errorbar(IsoBioWDatesDepthAvgAvg, IsoBioWDataDepthAvgAvg*86400, yerr=IsoBioWDataDepthAvgAvg_stdev*86400, color = 'k', fmt='o',zorder = 1) #ax.errorbar(IsoBioWDatesDepthAvgAvg, IsoBioWDataDepthAvgAvg*86400, yerr=IsoBioWDataDepthAvgAvg_sterr*86400, color = 'r', fmt='o', zorder = 20) # print NearestWGriddedAvgAvg_sterr # print '---' # print IsoBioWDataDepthAvgAvg_sterr # ax.errorbar(floatdate_bloomexo,chlyavg102745_bloomexo, yerr=stdevbars, color = 'b') #ax.set_xlabel("Days since 01/01/2013") ax.set_ylabel(r'Vertical Veloctiy (m day$^{-1}$)') #ax.get_yaxis().set_label_coords(-0.1,0.5) ax.set_xlim([70,110]) ax.set_xticks([70,80,90,100,110]) ax.set_xticklabels([ r'70',r'80',r'90',r'100',r'110']) # ax.set_xlabel(r'Day of Year') ax.set_ylim([-125,20]) ax.set_yticks([-125, -100, -75, -50, -25, 0]) ax.set_yticklabels([ r'125.0',r'100.0',r'75.0',r'50.0',r'25.0', '0.0']) #plt.setp(ax.get_xticklabels(), visible=False) plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/figure3a-2.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =================================================================================== # Depths of Vertical Velocities # =================================================================================== # sideplot = 1 if sideplot == 1: pp = PdfPages('figure3b.pdf') from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True fig = plt.figure() ax = fig.add_axes([0.14,0.12,0.8,0.84]) from pylab import * # # Which Plotting Information? for j in range(0,numbiolinesavg): if j == 0: plt.plot(IsoBioWDatesAvg[j], IsoBioWDepthsAvg[j], '0.75',label=r'Depth of Chl Velocities') else: plt.plot(IsoBioWDatesAvg[j], IsoBioWDepthsAvg[j], '0.75') plt.plot(IsoBioWDatesDepthAvgAvg, IsoBioWDepthsDepthAvgAvg, 'k',label=r'Depth of Averaged Velocties',linewidth = 2) # # ax.set_ylabel(r'Depth (m)' ) #ax.get_yaxis().set_label_coords(-0.1,0.5) ax.set_xlim([70,110]) ax.set_xticks([70,80,90,100,110]) ax.set_xticklabels([ r'70',r'80',r'90',r'100',r'110']) # ax.set_xlabel(r'Day of Year') ax.set_ylim([-700,0]) ax.set_yticks([-600, -400, -200, 0]) ax.set_yticklabels([ r'600',r'400',r'200',r'0.0']) plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/figure3b.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # # # =================================================================================== # # Depths of max d(rho)/dz and d2(rho)/dz2 = 0 # # =================================================================================== # # # sideplot = 2 # if sideplot == 1: # from matplotlib import rc # from matplotlib.numerix import arange, cos, pi # rc('text', usetex=True) # from pylab import * # numbiolinesavg = len(IsoBioWDataAvg) # pp = PdfPages('figure3c.pdf') # fig = plt.figure() # fig.subplots_adjust(right = 0.75) # from matplotlib.font_manager import FontProperties # legendfont = FontProperties() # legendfont.set_name('Computer Modern Roman') # legendfont.set_size('x-small') # rcParams['axes.labelsize'] = 12 # rcParams['xtick.labelsize'] = 12 # rcParams['ytick.labelsize'] = 12 # rcParams['legend.fontsize'] = 12 # from matplotlib import rcParams # rcParams['font.family'] = 'serif' # rcParams['font.serif'] = ['Computer Modern Roman'] # rcParams['text.usetex'] = True # ax4 = fig.add_subplot(111) # ax4.plot(floatdate,thedepthofmax1, '0.75', linestyle='--',linewidth = 1) # ax4.yaxis.label.set_color('k') # ax4.set_ylabel(r'\textbf{Depth (m)') # ax4.set_xlabel(r'\textbf{Days since 01/01/2013}') # ax4.set_ylim([-115,-45]) # ax4.get_yaxis().set_label_coords(-0.1,0.5) # ax4.set_xlim([70,110]) # # # ax4.plot(floatdate,thedepthofmax3, 'm', linestyle='--',linewidth = 1) # ax4.plot(floatdate,thedepthofmax5, 'b', linestyle='--',linewidth = 1) # ax4.plot(floatdate,thedepthofmax7, 'k', linestyle='--',linewidth = 1) # ax4.plot(floatdate,thedepthofmax21, '0.75',linewidth = 1) # ax4.plot(floatdate,thedepthofmax23, 'm',linewidth = 1) # ax4.plot(floatdate,thedepthofmax25, 'b',linewidth = 1) # ax4.plot(floatdate,thedepthofmax27, 'k',linewidth = 1) # # # ax2 = ax4.twinx() # ax3 = ax4.twinx() # ax3.plot(floatdate,floatMKE,'r',linewidth = 4) # ax3.spines["right"].set_position(("axes", 1.2)) # ax3.set_yscale('log') # ax3.yaxis.label.set_color('r') # ax3.set_ylim([40,1500]) # ax3.set_ylabel(r'\textbf{MKE (cm$^2$ s$^{-2}$)}') # ax3.set_xlim([70,110]) # ax2.plot(floatdate,chlyavg30meter,'g',linewidth = 4) # ax2.set_yscale('log') # ax2.yaxis.label.set_color('g') # ax2.set_ylabel(r'\textbf{Upper 30m Avg Chl (cm$^2$ s$^{-2}$)}') # ax2.set_ylim([0.7,6]) # ax2.set_xlim([70,110]) # tkw = dict(size=4, width=1.5) # ax4.tick_params(axis='y', colors='k', **tkw) # ax2.tick_params(axis='y', colors='g', **tkw) # ax3.tick_params(axis='y', colors='r', **tkw) # ax4.tick_params(axis='x', **tkw) # plt.savefig(pp, format = "pdf") # pp.close() # os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/figure3c.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # # # =================================================================================== # # MKE, Sfc Chly, and MLD Plot (cropped) # # =================================================================================== # # # sideplot = 2 # if sideplot == 1: # from matplotlib import rc # from matplotlib.numerix import arange, cos, pi # rc('text', usetex=True) # from pylab import * # numbiolinesavg = len(IsoBioWDataAvg) # pp = PdfPages('figure3c-2.pdf') # fig = plt.figure() # fig.subplots_adjust(right = 0.75) # from matplotlib.font_manager import FontProperties # legendfont = FontProperties() # legendfont.set_name('Computer Modern Roman') # legendfont.set_size('x-small') # rcParams['axes.labelsize'] = 12 # rcParams['xtick.labelsize'] = 12 # rcParams['ytick.labelsize'] = 12 # rcParams['legend.fontsize'] = 12 # from matplotlib import rcParams # rcParams['font.family'] = 'serif' # rcParams['font.serif'] = ['Computer Modern Roman'] # rcParams['text.usetex'] = True # ax4 = fig.add_subplot(111) # ax4.plot(floatdate,thedepthofmax25, '0.75',linewidth = 3) # ax4.yaxis.label.set_color('0.75') # ax4.set_ylabel(r'\textbf{Depth (m)') # ax4.set_xlabel(r'\textbf{Days since 01/01/2013}') # ax4.set_ylim([-115,-45]) # ax4.get_yaxis().set_label_coords(-0.1,0.5) # ax4.set_xlim([70,110]) # # # ax2 = ax4.twinx() # ax3 = ax4.twinx() # ax3.plot(floatdate,floatMKE,'k',linewidth = 3) # ax3.spines["right"].set_position(("axes", 1.2)) # ax3.set_yscale('log') # ax3.yaxis.label.set_color('k') # ax3.set_ylim([40,1500]) # ax3.set_ylabel(r'\textbf{MKE (cm$^2$ s$^{-2}$)}') # ax3.set_xlim([70,110]) # ax2.plot(floatdate,chlyavg30meter,'g',linewidth = 3) # ax2.set_yscale('log') # ax2.yaxis.label.set_color('g') # ax2.set_ylabel(r'\textbf{Upper 30m Avg Chl (cm$^2$ s$^{-2}$)}') # ax2.set_ylim([0.7,6]) # ax2.set_xlim([70,110]) # tkw = dict(size=4, width=1.5) # ax4.tick_params(axis='y', colors='0.75', **tkw) # ax2.tick_params(axis='y', colors='g', **tkw) # ax3.tick_params(axis='y', colors='k', **tkw) # ax4.tick_params(axis='x', **tkw) # plt.savefig(pp, format = "pdf") # pp.close() # os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/figure3c-2.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # # # =================================================================================== # # MKE, Sfc Chly, and MLD Plot # # =================================================================================== # # # sideplot = 2 # if sideplot == 1: # from matplotlib import rc # from matplotlib.numerix import arange, cos, pi # rc('text', usetex=True) # from pylab import * # numbiolinesavg = len(IsoBioWDataAvg) # pp = PdfPages('figure3c-3.pdf') # fig = plt.figure() # fig.subplots_adjust(right = 0.8) # from matplotlib.font_manager import FontProperties # legendfont = FontProperties() # legendfont.set_name('Computer Modern Roman') # legendfont.set_size('x-small') # rcParams['axes.labelsize'] = 12 # rcParams['xtick.labelsize'] = 12 # rcParams['ytick.labelsize'] = 12 # rcParams['legend.fontsize'] = 12 # from matplotlib import rcParams # rcParams['font.family'] = 'serif' # rcParams['font.serif'] = ['Computer Modern Roman'] # rcParams['text.usetex'] = True # ax4 = fig.add_subplot(111) # ax4.plot(floatdate,thedepthofmax25, '0.75',linewidth = 2) # ax4.yaxis.label.set_color('0.75') # ax4.set_ylabel(r'\textbf{Depth (m)') # ax4.set_xlabel(r'\textbf{Days since 01/01/2013}') # ax4.set_ylim([-170,-25]) # ax4.get_yaxis().set_label_coords(-0.1,0.5) # ax4.set_xlim([0,155]) # # # ax2 = ax4.twinx() # ax3 = ax4.twinx() # ax3.plot(floatdate,floatMKE,'k',linewidth = 3) # ax3.spines["right"].set_position(("axes", 1.2)) # ax3.set_yscale('log') # ax3.yaxis.label.set_color('k') # ax3.set_ylim([0,2750]) # ax3.set_ylabel(r'\textbf{MKE (cm$^2$ s$^{-2}$)}') # ax3.set_xlim([0,155]) # ax2.plot(floatdate,chlyavg30meter,'g',linewidth = 3) # ax2.set_yscale('log') # ax2.yaxis.label.set_color('g') # ax2.set_ylabel(r'\textbf{Upper 30m Avg Chl (cm$^2$ s$^{-2}$)}') # ax2.set_ylim([0,6]) # ax2.set_xlim([0,155]) # tkw = dict(size=4, width=1.5) # ax4.tick_params(axis='y', colors='0.75', **tkw) # ax2.tick_params(axis='y', colors='g', **tkw) # ax3.tick_params(axis='y', colors='k', **tkw) # ax4.tick_params(axis='x', **tkw) # plt.savefig(pp, format = "pdf") # pp.close() # os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/figure3c-3.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # # # =================================================================================== # # MKE, Sfc Chly, and 1027.45 Isopycnal Plot # # =================================================================================== # # # sideplot = 2 # if sideplot == 1: # from matplotlib import rc # from matplotlib.numerix import arange, cos, pi # rc('text', usetex=True) # from pylab import * # numbiolinesavg = len(IsoBioWDataAvg) # pp = PdfPages('figure3c-4.pdf') # fig = plt.figure() # fig.subplots_adjust(right = 0.8) # # # from matplotlib.font_manager import FontProperties # legendfont = FontProperties() # legendfont.set_name('Computer Modern Roman') # legendfont.set_size('x-small') # rcParams['axes.labelsize'] = 12 # rcParams['xtick.labelsize'] = 12 # rcParams['ytick.labelsize'] = 12 # rcParams['legend.fontsize'] = 12 # # # from matplotlib import rcParams # rcParams['font.family'] = 'serif' # rcParams['font.serif'] = ['Computer Modern Roman'] # rcParams['text.usetex'] = True # # # ax4 = fig.add_subplot(111) # # # ax4.plot(floatdate,depthof102745, '0.75',linewidth = 2) # ax4.yaxis.label.set_color('0.75') # ax4.set_ylabel(r'\textbf{Depth of 1027.45 Isopycnal (m)') # ax4.set_xlabel(r'\textbf{Days since 01/01/2013}') # ax4.set_ylim([-300,-100]) # ax4.get_yaxis().set_label_coords(-0.1,0.5) # ax4.set_xlim([0,155]) # # # ax2 = ax4.twinx() # ax3 = ax4.twinx() # # # ax3.plot(floatdate,floatMKE,'k',linewidth = 3) # ax3.spines["right"].set_position(("axes", 1.2)) # ax3.set_yscale('log') # ax3.yaxis.label.set_color('k') # ax3.set_ylim([0,2750]) # ax3.set_ylabel(r'\textbf{MKE (cm$^2$ s$^{-2}$)}') # ax3.set_xlim([0,155]) # # # ax2.plot(floatdate,chlyavg30meter,'g',linewidth = 3) # ax2.set_yscale('log') # ax2.yaxis.label.set_color('g') # ax2.set_ylabel(r'\textbf{Upper 30m Avg Chl (cm$^2$ s$^{-2}$)}') # ax2.set_ylim([0,6]) # ax2.set_xlim([0,155]) # # # tkw = dict(size=4, width=1.5) # ax4.tick_params(axis='y', colors='0.75', **tkw) # ax2.tick_params(axis='y', colors='g', **tkw) # ax3.tick_params(axis='y', colors='k', **tkw) # ax4.tick_params(axis='x', **tkw) # # # plt.savefig(pp, format = "pdf") # pp.close() # os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/figure3c-4.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =================================================================================== # MKE, Sfc Chly, and 1027.45 Isopycnal Plot (cropped) # =================================================================================== # sideplot = 1 if sideplot == 1: pp = PdfPages('figure3c-5.pdf') from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True fig = plt.figure() ax = fig.add_axes([0.14,0.12,0.8,0.76]) from pylab import * # ax4.plot(floatdate,floatMKE, 'k',linewidth = 3) ax4.yaxis.label.set_color('k') ax4.set_ylabel(r'$MKE_{float}$ (cm$^2$ s$^{-2}$)') ax4.set_xlabel(r'Day of Year') ax4.set_yscale('log') ax4.set_ylim([40,1500]) ax4.set_xlim([70,110]) ax4.set_xticks([70,80,90,100,110]) # ax2 = ax4.twinx() # ax2.plot(floatdate,chlyavg102745,'g',linewidth = 3) ax2.set_yscale('log') ax2.yaxis.label.set_color('g') ax2.set_ylabel(r"$\frac{1}{z_{\rho = 1027.45}} \int^{z_{0}}_{z_{\rho = 1027.45}} \mathrm{[Chl(} z \mathrm{)]} \mathrm{d}z$ (mgm$^{-3}$)") ax2.set_ylim([0.7,6]) ax2.set_xlim([70,110]) # tkw = dict(size=4, width=2) ax4.tick_params(axis='y', colors='k', **tkw) ax2.tick_params(axis='y', colors='g', **tkw) ax4.tick_params(axis='x', **tkw) # plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/figure3c-5.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # # # =================================================================================== # # Depth of all the mixed layers found # # =================================================================================== # # # sideplot = 2 # if sideplot == 1: # from matplotlib import rc # from matplotlib.numerix import arange, cos, pi # rc('text', usetex=True) # from pylab import * # numbiolinesavg = len(IsoBioWDataAvg) # pp = PdfPages('MLD.pdf') # fig = plt.figure() # from matplotlib.font_manager import FontProperties # legendfont = FontProperties() # legendfont.set_name('Computer Modern Roman') # legendfont.set_size('x-small') # rcParams['axes.labelsize'] = 12 # rcParams['xtick.labelsize'] = 12 # rcParams['ytick.labelsize'] = 12 # rcParams['legend.fontsize'] = 12 # from matplotlib import rcParams # rcParams['font.family'] = 'serif' # rcParams['font.serif'] = ['Computer Modern Roman'] # rcParams['text.usetex'] = True # ax4 = fig.add_subplot(111) # ax4.plot(floatdate,thedepthofmax1, '0.75', linestyle='--',linewidth = 1) # ax4.yaxis.label.set_color('k') # ax4.set_ylabel(r'\textbf{Depth (m)') # ax4.set_xlabel(r'\textbf{Days since 01/01/2013}') # ax4.set_ylim([-115,-45]) # ax4.set_xlim([70,110]) # # # ax4.plot(floatdate,thedepthofmax3, 'm', linestyle='--',linewidth = 1) # ax4.plot(floatdate,thedepthofmax5, 'b', linestyle='--',linewidth = 1) # ax4.plot(floatdate,thedepthofmax7, 'k', linestyle='--',linewidth = 1) # ax4.plot(floatdate,thedepthofmax21, '0.75',linewidth = 1) # ax4.plot(floatdate,thedepthofmax23, 'm',linewidth = 1) # ax4.plot(floatdate,thedepthofmax25, 'b',linewidth = 1) # ax4.plot(floatdate,thedepthofmax27, 'k',linewidth = 1) # ax4.plot(floatdate,thedepthofmax25, 'k',linewidth = 4) # plt.savefig(pp, format = "pdf") # pp.close() # os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/MLD.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # # # =================================================================================== # # Depth of all isopycnals analyzed # # =================================================================================== # # # sideplot = 2 # if sideplot == 1: # from matplotlib import rc # from matplotlib.numerix import arange, cos, pi # rc('text', usetex=True) # from pylab import * # numbiolinesavg = len(IsoBioWDataAvg) # pp = PdfPages('isopycdepths.pdf') # fig = plt.figure() # from matplotlib.font_manager import FontProperties # legendfont = FontProperties() # legendfont.set_name('Computer Modern Roman') # legendfont.set_size('x-small') # rcParams['axes.labelsize'] = 12 # rcParams['xtick.labelsize'] = 12 # rcParams['ytick.labelsize'] = 12 # rcParams['legend.fontsize'] = 12 # from matplotlib import rcParams # rcParams['font.family'] = 'serif' # rcParams['font.serif'] = ['Computer Modern Roman'] # rcParams['text.usetex'] = True # ax4 = fig.add_subplot(111) # ax4.plot(floatdate,depthof1026, '0.75', linewidth = 2,label=r'1026') # ax4.plot(floatdate,depthof10272, 'b', linewidth = 2,label=r'1027.2') # ax4.plot(floatdate,depthof10273, 'g', linewidth = 2,label=r'1027.3') # ax4.plot(floatdate,depthof10274, 'k', linewidth = 2,label=r'1027.4') # ax4.plot(floatdate,depthof102745, 'm', linewidth = 2,label=r'1027.45') # ax4.plot(floatdate,depthof10275, 'r', linewidth = 2,label=r'1027.5') # ax4.plot(floatdate,depthof102755, 'c', linewidth = 2,label=r'1027.55') # #ax4.yaxis.label.set_color('k') # l2 = legend(loc = 3) # ax4.set_ylabel(r'\textbf{Depth (m)') # ax4.set_xlabel(r'\textbf{Days since 01/01/2013}') # ax4.set_ylim([-340,-50]) # ax4.set_xlim([70,110]) # plt.savefig(pp, format = "pdf") # pp.close() # os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/isopycdepths.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # # # # # =========================================================== # # # # CONTOUR PLOTTING # # # # =========================================================== # # # # # # =================================================================================== # # Chly # # =================================================================================== # # # contplt = 2 # if contplt == 1: # from matplotlib import rc # from matplotlib.numerix import arange, cos, pi # rc('text', usetex=True) # from pylab import * # numbiolinesavg = len(IsoBioWDataAvg) # pp = PdfPages('figure2a.pdf') # fig = plt.figure() # from matplotlib.font_manager import FontProperties # legendfont = FontProperties() # legendfont.set_name('Computer Modern Roman') # legendfont.set_size('x-small') # rcParams['axes.labelsize'] = 18 # rcParams['xtick.labelsize'] = 18 # rcParams['ytick.labelsize'] = 18 # rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams # rcParams['font.family'] = 'serif' # rcParams['font.serif'] = ['Computer Modern Roman'] # rcParams['text.usetex'] = True # # # # Plotting correct data # contourdat = np.zeros([interplength,arraylen]) # for ci in range(0,interplength): # for cj in range(0,arraylen): # #cjj = cj + 1 # if GridData[ci,4,cj] < 10**-2.5: # contourdat[ci,cj] = 10**-2.5 # else: # contourdat[ci,cj] = GridData[ci,4,cj] # # # # Plot Set-up # import math as ma # from mpl_toolkits.axes_grid1 import make_axes_locatable # fig = plt.figure() # ax = fig.add_axes([0.15,0.1,0.70,0.85]) # #ax = fig.add_subplot(1, 1, 1) # contlevels= np.logspace(-2.5,np.log(contourdat.max())/np.log(10),250) # conticks = [0.01,0.1,1.00] # cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels, norm=LogNorm()) # cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels, norm=LogNorm()) # divider = make_axes_locatable(ax) # cax = divider.append_axes("right", size="5%", pad=0.05) # cbar = plt.colorbar(cs,cax=cax, norm=LogNorm()) # minorticks = cs.norm(np.array([0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9, 1.00, 2.00, 3.00, 4.00, 5.00, 6,00])) # cbar.ax.yaxis.set_ticks(minorticks, minor=True) # cbar.set_label(r"\textbf{Chlorophyll Concentration ($\mu$gl$^{-1}$)}") # cbar.set_ticks([0.01, 0.1,1.00]) # cbar.set_ticklabels([r'10$^{-2}$',r'10$^{-1}$', r'10$^{0}$']) # axvline(70, linewidth=0.5, color='0.75') # axvline(110, linewidth=0.5, color='0.75') # # 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_yticks([0,-200, -400, -600, -800]) # #ax.yaxis.set_yticks([-200,-400, -600, -800]) # ax.set_yticklabels([r'0', r'200',r'400',r'600',r'800']) # #ax.set_yticks([-100,-200,-300,-400,-500,-600,-700,-800,-900], minor=true) # ax.set_xlabel(r'\textbf{Days since 01/01/2013}') # ax.set_ylabel(r'\textbf{Depth (m)}') # ax.set_ylim([-900,0]) # # plt.title(figtit) # plt.savefig(pp, format = "pdf") # pp.close() # os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/figure2a.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # # # =================================================================================== # # Density # # =================================================================================== # # # contplt = 2 # if contplt == 1: # from matplotlib import rc # from matplotlib.numerix import arange, cos, pi # rc('text', usetex=True) # from pylab import * # numbiolinesavg = len(IsoBioWDataAvg) # pp = PdfPages('figure2b.pdf') # fig = plt.figure() # from matplotlib.font_manager import FontProperties # legendfont = FontProperties() # legendfont.set_name('Computer Modern Roman') # legendfont.set_size('x-small') # rcParams['axes.labelsize'] = 18 # rcParams['xtick.labelsize'] = 18 # rcParams['ytick.labelsize'] = 18 # rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams # rcParams['font.family'] = 'serif' # rcParams['font.serif'] = ['Computer Modern Roman'] # rcParams['text.usetex'] = True # # # # 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,1,cj] # print contourdat.max() # print contourdat.min() # # # # Plot Set-up # import math as ma # from mpl_toolkits.axes_grid1 import make_axes_locatable # fig = plt.figure() # ax = fig.add_axes([0.15,0.1,0.70,0.85]) # contlevels= np.linspace(contourdat.min(),contourdat.max(),250) # conticks = [1026.8, 1027.0, 1027.2, 1027.4, 1027.6, 1027.8] # cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels) # cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels) # divider = make_axes_locatable(ax) # cax = divider.append_axes("right", size="5%", pad=0.05) # cbar = plt.colorbar(cs,cax=cax, spacing='proportional') # minorticks = cs.norm(np.array([1026.8, 1026.9, 1027.0, 1027.1, 1027.2, 1027.3, 1027.4, 1027.5, 1027.6, 1027.7, 1027.8])) # cbar.ax.yaxis.set_ticks(minorticks, minor=True) # cbar.set_label(r"\textbf{Density (kgm$^{-3}$)}") # cbar.set_ticks([1026.8, 1027.0, 1027.2, 1027.4, 1027.6, 1027.8]) # #cbar.set_ticklabels([r'10$^{-1}$', r'10$^{0}$']) # axvline(70, linewidth=0.5, color='0.75') # axvline(110, linewidth=0.5, color='0.75') # # Plotting # #figtit2 = ' and Chlorophyll Quivers' # #savetit2 = "_IsoChlyW_Quivers" # #from pylab import * # #Q2 = plt.quiver(IsoBioWDates[1::10], IsoBioWDepths[1::10], IsoBioFake[1::10], IsoBioWData[1::10], color='k') # #plt.quiverkey(Q2, 0.22, 0.96, 0.0005, r'$ 5 \times 10^{-4} m/s$', labelpos='W') # #ax = plt.gca() # ax.set_yticks([0,-200, -400, -600, -800]) # #ax.yaxis.set_yticks([-200,-400, -600, -800]) # ax.set_yticklabels([r'0', r'200',r'400',r'600',r'800']) # ax.set_xlabel(r'\textbf{Days since 01/01/2013}') # ax.set_ylabel(r'\textbf{Depth (m)}') # ax.set_ylim([-900,0]) # # plt.title(figtit) # plt.savefig(pp, format = "pdf") # pp.close() # os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/figure2b.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =================================================================================== # Density with Isopycnal lines # =================================================================================== # contplt = 2 if contplt == 1: from matplotlib import rc from matplotlib.numerix import arange, cos, pi rc('text', usetex=True) from pylab import * numbiolinesavg = len(IsoBioWDataAvg) pp = PdfPages('figure2b_withdepths.pdf') fig = plt.figure() from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # 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,1,cj] print contourdat.max() print contourdat.min() # # Plot Set-up import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable fig = plt.figure() ax = fig.add_axes([0.15,0.1,0.70,0.85]) contlevels= np.linspace(contourdat.min(),contourdat.max(),250) conticks = [1027.0, 1027.2, 1027.4, 1027.6, 1027.8] cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels) plt.set_cmap('jet') cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels) plt.set_cmap('jet') plot(floatdate,depthof102745, 'k', linewidth = 2.5,label=r'Depth of $\rho$ = 1027.45 kg m$^{-3}$') plot(floatdate,thedepthofmax25, '0.75',linewidth = 2.5,label=r'Mixed Layer Depth') #ax4.yaxis.label.set_color('k') l2 = legend(loc = 3) divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) cbar = plt.colorbar(cs,cax=cax, spacing='proportional') minorticks = cs.norm(np.array([1026.8, 1026.9, 1027.0, 1027.1, 1027.2, 1027.3, 1027.4, 1027.5, 1027.6, 1027.7, 1027.8])) cbar.ax.yaxis.set_ticks(minorticks, minor=True) cbar.set_label(r"\textbf{Density (kgm$^{-3}$)}") cbar.set_ticks([1027.0, 1027.2, 1027.4, 1027.6, 1027.8]) ax.set_yticks([0,-200, -400, -600, -800]) ax.set_yticklabels([r'0', r'200',r'400',r'600',r'800']) ax.set_xlabel(r'\textbf{Days since 01/01/2013}') ax.set_ylabel(r'\textbf{Depth (m)}') ax.set_ylim([-900,0]) # plt.title(figtit) plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/figure2b_withdepths.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =================================================================================== # Chly with Isopycnal lines # =================================================================================== # contplt = 2 if contplt == 1: from matplotlib import rc from matplotlib.numerix import arange, cos, pi rc('text', usetex=True) from pylab import * numbiolinesavg = len(IsoBioWDataAvg) pp = PdfPages('figure2a_withdepths.pdf') fig = plt.figure() from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # Plotting correct data contourdat = np.zeros([interplength,arraylen]) for ci in range(0,interplength): for cj in range(0,arraylen): #cjj = cj + 1 if GridData[ci,4,cj] < 10**-2.5: contourdat[ci,cj] = 10**-2.5 else: contourdat[ci,cj] = GridData[ci,4,cj] # # Plot Set-up import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable fig = plt.figure() ax = fig.add_axes([0.15,0.1,0.70,0.85]) #ax = fig.add_subplot(1, 1, 1) contlevels= np.logspace(-2.5,np.log(contourdat.max())/np.log(10),250) conticks = [0.01,0.1,1.00] cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels, norm=LogNorm()) plt.set_cmap('jet') cs = plt.contourf(floatdate,Ygrid,contourdat, levels= contlevels, norm=LogNorm()) plt.set_cmap('jet') plot(floatdate,depthof102745, 'k', linewidth = 2.5,label=r'Depth of $\rho$ = 1027.45 kg m$^{-3}$') plot(floatdate,thedepthofmax25, '0.75',linewidth = 2.5,label=r'Mixed Layer Depth') #ax4.yaxis.label.set_color('k') l2 = legend(loc = 3) divider = make_axes_locatable(ax) cax = divider.append_axes("right", size="5%", pad=0.05) cbar = plt.colorbar(cs,cax=cax, norm=LogNorm()) minorticks = cs.norm(np.array([0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9, 1.00, 2.00, 3.00, 4.00, 5.00, 6,00])) cbar.ax.yaxis.set_ticks(minorticks, minor=True) cbar.set_label(r"\textbf{Chlorophyll Concentration ($\mu$gl$^{-1}$)}") cbar.set_ticks([0.01, 0.1,1.00]) cbar.set_ticklabels([r'10$^{-2}$',r'10$^{-1}$', r'10$^{0}$']) axvline(70, linewidth=0.5, color='0.75') axvline(110, linewidth=0.5, color='0.75') # 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_yticks([0,-200, -400, -600, -800]) #ax.yaxis.set_yticks([-200,-400, -600, -800]) ax.set_yticklabels([r'0', r'200',r'400',r'600',r'800']) #ax.set_yticks([-100,-200,-300,-400,-500,-600,-700,-800,-900], minor=true) ax.set_xlabel(r'\textbf{Days since 01/01/2013}') ax.set_ylabel(r'\textbf{Depth (m)}') ax.set_ylim([-900,0]) # plt.title(figtit) plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/figure2a_withdepths.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # # =================================================================================== # Density Anomalies # =================================================================================== # gridWplt = 2 if gridWplt == 1: # # =========================================================== # Contour Plotting Grided Density W Data # =========================================================== # # Changing Directory to plotting output os.chdir('/home/ardavies/satdata/OSCAR/pdfoutput') # # Plot Set-up pp = PdfPages('DeMeanDensity.pdf') import matplotlib.pyplot as plt import numpy as np import math as ma from mpl_toolkits.axes_grid1 import make_axes_locatable from matplotlib.font_manager import FontProperties legendfont = FontProperties() legendfont.set_name('Computer Modern Roman') legendfont.set_size('x-small') rcParams['axes.labelsize'] = 18 rcParams['xtick.labelsize'] = 18 rcParams['ytick.labelsize'] = 18 rcParams['legend.fontsize'] = 14 # from matplotlib import rcParams rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True fig = plt.figure() ax = fig.add_subplot(1, 1, 1) # # 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) # # Text/Front Set up from matplotlib import rcParams rcParams['axes.labelsize'] = 14 rcParams['xtick.labelsize'] = 12 rcParams['ytick.labelsize'] = 12 rcParams['legend.fontsize'] = 10 rcParams['font.family'] = 'serif' rcParams['font.serif'] = ['Computer Modern Roman'] rcParams['text.usetex'] = True # # Contour Plotting & Color Bar from pylab import * cs1 = plt.contourf(floatdate,Ygrid,DemeanDensity, levels = np.linspace(-0.31,0.31,150)) plt.set_cmap('seismic') cs = plt.contourf(floatdate,Ygrid,DemeanDensity, levels = np.linspace(-0.31,0.31,150)) plt.set_cmap('seismic') cbar = plt.colorbar(cs,spacing='proportional') cbar.set_label(r"\textbf{Density Departure from Mean at Each Depth}") cbar.set_ticks([-.3, -.2, -.1, 0, .1, .2, .3]) cbar.set_ticklabels([-.3, -.2, -.1, 0, .1, .2, .3]) ax.set_xlabel(r"\textbf{Day of Year}") #ax.set_xlim([70,110]) ax.set_ylim([-900,0]) ax.set_ylabel(r"\textbf{Depth (m)}") # # Save and spc to storm plt.savefig(pp, format = "pdf") pp.close() os.system('scp /home/ardavies/satdata/OSCAR/pdfoutput/DeMeanDensity.pdf ardavies@storm.ceoe.udel.edu:/dev/ardavies/grlpaperplots/') # os.chdir('/home/ardavies/satdata/OSCAR')