# # File: # meteogram.py # # Synopsis: # Draws a meteogram. # # Category: # Meteogram # # Author: # Fred Clare (based on an NCL example of Mary Haley). # # Date of initial publication: # September, 2004 # # Description: # This example reads data from a NetCDF file and creates # a meteogram visualizaton. If you don't know what a # meteogram is, run the example and look at the visualization. # # Effects illustrated: # o Reading in multiple variables from a NetCDF file # o Using a 9-point smoothing function # o Using multiple resource lists in the same visualization # o Line contours, color filled contours, a vector plot, an xy plot # o How to position several plots on one page # o How to do direct overlays of one plot on another - in # this case overlaying a line contour on a filled contour # and overlaying a wind barb vector plot on a countour plot # o How to create a custom bar chart # o How to reverse an axis # o User specified tickmark labels, tickmark label fonts, and # tickmark label angles # # Output: # A single meteogram visualization is produced. # # Notes: # This code is based on an NCL code of John Ertl at the # Fleet numpy.l Meteorology and Oceanography Center # from __future__ import print_function import copy # # Import Nio. # import Nio # # Import numpy, sys, os. # import numpy, sys, os # # Import the PyNGL module names. # import Ngl # # 9-point smoother function. # def smth9(x,p,q): # # Run a 9-point smoother on the 2D numpy.array x using weights # p and q. Return the smoothed array. # # # Get array dimensions and check on sizes. # ni = x.shape[0] nj = x.shape[1] if (ni < 3 or nj < 3): print("smth9: both array dimensions must be at least three.") sys.exit() # # Smooth. # po4 = p/4. qo4 = q/4. output = numpy.zeros([ni,nj],'f') for j in range(1,nj-1): for i in range(1,ni-1): jm1 = j-1 jp1 = j+1 im1 = i-1 ip1 = i+1 term1 = po4*(x[im1,j]+x[i,jm1]+x[ip1,j]+x[i,jp1]-4.*x[i,j]) term2 = qo4*(x[im1,jp1]+x[im1,jm1]+x[ip1,jm1]+x[ip1,jp1]-4.*x[i,j]) output[i,j] = float(x[i,j]) + term1 + term2 # # Set the perimeter values to the original x values. # output[0,:] = x[0,:] output[ni-1,:] = x[ni-1,:] output[:,0] = x[:,0] output[:,nj-1] = x[:,nj-1] # # Return smoothed array. # return output # # Main program. # # # Read in the data variables from the NetCDF file. # cdf_file = Nio.open_file(os.path.join(Ngl.pynglpath("data"),"cdf","meteo_data.nc")) tempisobar = cdf_file.variables["tempisobar"][:,:] # temperature levels = cdf_file.variables["levels"][:] # levels taus = cdf_file.variables["taus"][:] # taus rh = cdf_file.variables["rh"][:,:] # realtive humidity ugrid = cdf_file.variables["ugrid"][:,:] # horizontal winds vgrid = cdf_file.variables["vgrid"][:,:] # vertical winds rain03 = cdf_file.variables["rain03"][:] # rainfall tempht = cdf_file.variables["tempht"][:] # surface temperatures # # Smooth temperature and humidity. # smothtemp = smth9(tempisobar, 0.50, -0.25) smothrh = smth9( rh, 0.50, -0.25) # # Set up a color map and open an output workstation. # colors = numpy.array([ \ [255,255,255], [255,255,255], [255,255,255], \ [240,255,240], [220,255,220], [190,255,190], \ [120,255,120], [ 80,255, 80], [ 50,200, 50], \ [ 20,150, 20] \ ],'f') / 255. wks_type = "png" wks = Ngl.open_wks(wks_type,"meteogram") # # Create a different resource list for every plot created. # rh_res = Ngl.Resources() rain_res = Ngl.Resources() tempsfc_res = Ngl.Resources() # # Ngl.Resources that rh_res, temp_res, and uv_res share. # rh_res.trYReverse = True # Reverse the Y values. rh_res.nglDraw = False # Don't draw individual plot. rh_res.nglFrame = False # Don't advance frame. rh_res.nglMaximize = False # Do not maximize plot in frame (default is # to maximize). rh_res.vpXF = 0.15 # x location rh_res.vpYF = 0.90 # y location rh_res.vpWidthF = 0.7 # width rh_res.vpHeightF = 0.40 # height # # Copy the rh_res resources into two new resource files for # temperature and wind barbs. "deepcopy" is used so that # subsequent changes to rh_res do not affect the new resources. # temp_res = copy.deepcopy(rh_res) uv_res = copy.deepcopy(rh_res) # # ------------ Relative humidity resources.. ---------------- # rh_res.sfXArray = taus # Define X/Y axes values that rh_res.sfYArray = levels # all three data fields are on rh_res.tiYAxisString = "Pressure (mb)" # Y axes label. rh_res.tiYAxisFontHeightF = 0.025 # Y axes font height. rh_res.tmYLMode = "Explicit" # Define own tick mark labels. rh_res.tmYLLabelFont = "Times-Bold" # Change the font. rh_res.tmYLValues = levels rh_res.tmYLLabels = [ \ "1000", "975", "950", "925", \ "850", "700", "500", "400" \ ] rh_res.tmYLMinorOn = False # No minor tick marks. rh_res.tiXAxisString = "" # X axes label. rh_res.tmXBMode = "Explicit" # Define own tick mark labels. rh_res.tmXBLabelFont = "Times-Bold" # Change the font. rh_res.tmXBValues = taus rh_res.tmXBLabelFontHeightF = .016 # Font size rh_res.tmXBLabels = \ [ \ "12z", "15z", "18z", "21z", "Apr29", \ "03z", "06z", "09z", "12z", "15z", \ "18z", "21z", "Apr30","03z", "06z", \ "09z", "12z", "15z", "18z", "21z", \ "May01", "03z", "06z", "09z", "12z" \ ] # # insert a list # rh_res.tmXBLabelAngleF = 90. # change label angle rh_res.tmXBMinorOn = False # No minor tick marks. rh_res.tmXBLabelJust = "CenterCenter" rh_res.cnFillOn = True # turns on color fill rh_res.cnFillPalette = colors # set colors for contours rh_res.cnLineLabelsOn = True # no contour labels rh_res.tiMainString = "Meteogram for LGSA, 28/12Z" rh_res.tiMainFont = "Helvetica-Bold" rh_res.cnFillOn = True # turns on the color rh_res.pmLabelBarDisplayMode = "Never" # turn off plotting label bar # (on by default). # # ------------ Temperature resources. ---------------------- # temp_res.sfXArray = taus # Define X/Y axes values that temp_res.sfYArray = levels # all three data fields are on temp_res.cnLineLabelsOn = True # no contour labels temp_res.cnLineThicknessF = 3.0 # line thickness temp_res.cnLineColor = "Red" # ------------- wind barbs resources ------------------------ uv_res.vfXArray = taus # Define X/Y axes values that uv_res.vfYArray = levels # all three data fields are on uv_res.vcRefAnnoOn = False # turns off the ref box uv_res.vcRefLengthF = 0.040 uv_res.vcGlyphStyle = "WindBarb" uv_res.vcMonoWindBarbColor = True # color the windbarbs with # respect to speed use False # ------------- Rain histogram resources --------------------- rain_res.nglFrame = False rain_res.vpXF = 0.15 # The left side of the box rain_res.vpYF = 0.36 # The top side of the plot box rain_res.vpWidthF = 0.72 # The Width of the plot box rain_res.vpHeightF = 0.10 # The height of the plot box rain_res.trYMinF = 0.0 # min value on y-axis rain_res.trYMaxF = 0.5 # max value on y-axis rain_res.tiXAxisString = "" # X axes label. rain_res.tmXBMode = "Explicit" # Define own tick mark labels. rain_res.tmXBLabelFont = "Times-Bold" # Change the font. rain_res.tmYLLabelFont = "Times-Bold" # Change the font. rain_res.tmXBValues = taus rain_res.tmXBLabels = taus rain_res.tmXTOn = False # turn off the top tickmarks rain_res.tmXBMinorOn = False # No minor tick marks. rain_res.tmXBMajorLengthF = 0.01 # Force tickmarks to point rain_res.tmXBMajorOutwardLengthF = 0.01 # out by making the outward rain_res.tmYLMajorLengthF = 0.01 # tick length equal to the rain_res.tmYLMajorOutwardLengthF = 0.01 # total tick length rain_res.tmYLMinorLengthF = 0.005 rain_res.tmYLMinorOutwardLengthF = 0.005 rain_res.tiYAxisFontHeightF = 0.015 # Y axes font height. rain_res.tiYAxisString = "3hr rain total" # Y axis label. rain_res.nglDraw = False # Don't draw individual plot. rain_res.nglFrame = False # Don't advance frame. rain_res.nglMaximize = False # Do not maximize plot in frame # -------------- temp sfc time series ------------------------ tempsfc_res.vpXF = 0.15 # The left side of the box tempsfc_res.vpYF = 0.17 # The top side of the plot box tempsfc_res.vpWidthF = 0.72 # The Width of the plot box tempsfc_res.vpHeightF = 0.10 # The height of the plot box tempsfc_res.tiXAxisString = "" # X axes label. tempsfc_res.tiYAxisFontHeightF = 0.015 # Y axes font height. tempsfc_res.tiYAxisString = "Temp at 2m" # Y axis label. tempsfc_res.tmXBMode = "Explicit" # Define own tick mark labels. tempsfc_res.tmXBLabelFont = "Times-Bold" # Change the font. tempsfc_res.tmYLLabelFont = "Times-Bold" # Change the font. tempsfc_res.tmXBValues = taus tempsfc_res.tmXBLabels = taus tempsfc_res.tmXTOn = False # turn off the top tickmarks tempsfc_res.xyLineThicknesses = 2 tempsfc_res.xyLineColor = "red" tempsfc_res.nglDraw = False # Don't draw individual plot. tempsfc_res.nglFrame = False # Don't advance frame. tempsfc_res.nglMaximize = False # Do not maximize plot in frame # # Create the four plots (they won't get drawn here, because nglDraw # was set to False for all three of them. # rhfill = Ngl.contour(wks,smothrh,rh_res) templine = Ngl.contour(wks,smothtemp,temp_res) windlayer = Ngl.vector(wks,ugrid,vgrid,uv_res) rainhist = Ngl.xy(wks,taus,rain03,rain_res) # # For the rain plot, we want filled bars instead of a curve, so create # a dummy plot with all the Y values equal to rain_res.trYMinF. This # will insure no curve gets drawn. Then, add the bars and bar outlines # later with Ngl.add_polygon and Ngl.add_polyline. # # # Make dummy data equal to min Y axis value. # dummy = rain_res.trYMinF * numpy.ones([len(rain03)],rain03.dtype.char) # # Make sure there's enough room for a bar at the first and last # X points of the data. # dx = min(taus[1:24]-taus[0:23]) # Calculate bar width. rain_res.trXMinF = min(taus) - dx/2. rain_res.trXMaxF = max(taus) + dx/2. # # Create dummy plot. # rainhist = Ngl.xy(wks,taus,dummy,rain_res) # # Get indices where rain data is above zero. Draw filled bars for each # of these points. # above_zero = numpy.greater(rain03,0.0) ind_above_zero = numpy.nonzero(above_zero) # We know that the values are # above zero. num_above = len(ind_above_zero[0]) # # Create arrays to hold polygon points. Since we are drawing a rectangle, # we just need 5 points for the filled rectangle, and we can use the # first four points of each for the outline. # px = numpy.zeros(5*num_above,taus.dtype.char) py = numpy.zeros(5*num_above,rain03.dtype.char) # # Create resource list for polygons. # pgres = Ngl.Resources() pgres.gsFillColor = "green" taus_above_zero = numpy.take(taus,ind_above_zero) px[0::5] = (taus_above_zero - dx/2.).astype(taus.dtype.char) px[1::5] = (taus_above_zero - dx/2.).astype(taus.dtype.char) px[2::5] = (taus_above_zero + dx/2.).astype(taus.dtype.char) px[3::5] = (taus_above_zero + dx/2.).astype(taus.dtype.char) px[4::5] = (taus_above_zero - dx/2.).astype(taus.dtype.char) py[0::5] = rain_res.trYMinF py[1::5] = numpy.take(rain03,ind_above_zero) py[2::5] = numpy.take(rain03,ind_above_zero) py[3::5] = rain_res.trYMinF py[4::5] = rain_res.trYMinF polyg = Ngl.add_polygon(wks,rainhist,px,py,pgres) # # For the outlines, we don't need the fifth point. # polyl = Ngl.add_polyline(wks,rainhist,px,py,pgres) temptmsz = Ngl.xy(wks,taus,tempht,tempsfc_res) # ---------------------- overlay, draw, and advance frame --------- Ngl.overlay(rhfill,templine) # Overlay temperature contour on rh plot. Ngl.overlay(rhfill,windlayer) # Overlay windbarbs on rh plot. Ngl.draw(rhfill) Ngl.draw(rainhist) Ngl.draw(temptmsz) Ngl.frame(wks) Ngl.end()