cf-plot user guide

Contents

introduction to cf-plot
access - where cf-plot is installed and how to install it

contours - making contour plots
vectors - plotting vectors
multiple plots on a page and plot positioning
trajectories - plotting trajectories
significance - plots
graphs - making graphs
colour scales - using and changing colour scales

appendixA - introduction to cf-python
appendixB - passing data via arrays
appendixC - map projections and options
appendixD - fixing data to be more cf-compliant

Introduction

cf-plot is a set of Python functions for making common contour, vector and line plots that climate researchers use. cf-plot generally uses cf-python to present the data and CF attributes for plotting. It can also use numpy arrays of data as the input fields making for flexible plotting of data. cf-plot uses the Python numpy, matplotlib and scipy libraries.

At the core of cf-plot are a set of functions for making and controlling plots.

Plot making operators
con - make a contour plot
cbar - make a colour bar
vect - vector and stream plots
traj - trajectory plots
lineplot - make a graph
stipple - make a stipple plot
Plot control operators
gopen - open a plot
gclose - close a plot
gpos - plot positioning
gset - set non map plotting region
levs - define contour levels
cscale - select and change colour scales
mapset - set mapping parameters
reset - reset all plot parameters
setvars - set a variety of plot parameters

This user guide is a set of examples of plots that climate scientists generally make. Further details and fine tuning options are available by looking at the individual function documentation above.

Where cf-plot is installed and how to install it

cf-python and cf-plot are pre-installed on the following platforms.

Jasmin super-data-cluster
export PATH=/home/users/ajh/anaconda3/bin:$PATH
ln -s /home/users/ajh/cfplot_data ~
Archer supercomputer in Edinburgh
export PATH=/home/n02/n02/ajh/anaconda3/bin:$PATH
ln -s /home/n02/n02/ajh/cfplot_data ~
Reading University RACC cluster
module load ncas_anaconda3
ln -s /share/apps/NCAS/cfplot_data ~

For other platforms follow the cf-plot installation instructions

Contour plots

The following examples use cf-python to present the data to cf-plot. The syntax is quite simple and can be learned as the examples progress. A fuller discussion of using cf-python to manipulate data is available in appendixA. If you have numpy arrays that you wish to plot then look in appendixB for a couple of examples of how to do this.

The data to make a contour plot can be read in and passed to cf-plot using cf-python as per the following example.

Cylindrical projection

import cf
import cfplot as cfp
f=cf.read('cfplot_data/tas_A1.nc')[0]
cfp.con(f.subspace(time=15))
_images/fig1.png

Note that for a contour plot two dimensional data is required.

f.subspace(time=15)

<CF Field: air_temperature(time(1), latitude(73), longitude(96)) K>

Dimensions that have one element, such as time in this instance, are ignored.

The cfp.mapset routine is used to change the map area and projection. cfp.mapset(lonmin=-15, lonmax=3, latmin=48, latmax=60) sets the map to a view over the British Isles. The cfp.mapset command is persistent in that any further map plots will use the same map projection and limits without having to specify the same map again. The levels are taken from the whole field and in this example we specify the levels explicitly with the cfp.levs command.

import cf
import cfplot as cfp
f=cf.read('cfplot_data/tas_A1.nc')[0]
cfp.mapset(lonmin=-15, lonmax=3, latmin=48, latmax=60)
cfp.levs(min=265, max=285, step=1)
cfp.con(f.subspace(time=15))
_images/fig3.png

The default settings are for colour fill with contour lines over the top. These can be changed with the fill=False and lines=False flags to cfp.con.

To reset the mapping to the default cylindrical projection -180 to 180 in longitude and -90 to 90 in latitude use cfp.mapset(). Likewise to reset the contour levels use cfp.levs().

Blockfill plots

Blockfill plots in the cylindrical projection are made using the blockfill=True flag to the cfp.con routine.

import cf
import cfplot as cfp
f=cf.read('cfplot_data/tas_A1.nc')[0]
cfp.con(f.subspace(time=15), blockfill=True, lines=False)
_images/fig2.png

Polar stereographic plots

Polar Stereographic plots are set using proj='npstere' or proj='spstere' in the call to cfp.mapset.

import cf
import cfplot as cfp
f=cf.read('cfplot_data/ggap.nc')[1]
cfp.mapset(proj='npstere')
cfp.con(f.subspace(pressure=500))
_images/fig4.png

The mapset bounding_lat and lon_0 parameters are used to set the latitude limit of the plot and the orientation of the plot. Generally for the southern hemisphere the Greenwich Meridian (zero degrees longitude) is plotted at the top of the plot and is set with lon_0=0.

import cf
import cfplot as cfp
f=cf.read('cfplot_data/ggap.nc')[1]
cfp.mapset(proj='spstere', boundinglat=-30, lon_0=0)
cfp.con(f.subspace(pressure=500))
_images/fig5.png

Latitude - Pressure Plots

The latitude-pressure plot below is made by using the cf subspace method to select the temperature at longitude=0 degrees. Again this data has two dimensions with multiple values in.

f.subspace(longitude=0)

<CF Field: air_temperature(time(1), pressure(23), latitude(160), longitude(1)) K>

import cf
import cfplot as cfp
f=cf.read('cfplot_data/ggap.nc')[2]
cfp.con(f.subspace(longitude=0))
_images/fig6.png

A mean of the data along the longitude (zonal mean) is made using the cf.collapse method.

import cf
import cfplot as cfp
f=cf.read('cfplot_data/ggap.nc')[1]
cfp.con(f.collapse('mean','longitude'))
_images/fig7.png

To make a log pressure on the y axis use the ylog=True flag to the con routine.

import cf
import cfplot as cfp
f=cf.read('cfplot_data/ggap.nc')[1]
cfp.con(f.collapse('mean','longitude'), ylog=True)
_images/fig8.png

Hovmuller plots

A Hovmuller plot is one of longitude or latitude versus time as in the following examples.

import cf
import cfplot as cfp
f=cf.read('cfplot_data/tas_A1.nc')[0]
cfp.cscale('plasma')
cfp.con(f.subspace(longitude=0), lines=0)
_images/fig10.png
import cf
import cfplot as cfp
f=cf.read('cfplot_data/tas_A1.nc')[0]
cfp.gset(-30, 30, '1960-1-1', '1980-1-1')
cfp.levs(min=280, max=305, step=1)
cfp.cscale('plasma')
cfp.con(f.subspace(longitude=0), lines=0)
_images/fig11.png

Vector and stream Plots

Vector plots are made using the cfp.vect routine. The u and v data must have two dimensions with matching multiple values in as below.

<CF Field: eastward_wind(time(1), pressure(1), latitude(160), longitude(320)) m s**-1>
<CF Field: northward_wind(time(1), pressure(1), latitude(160), longitude(320)) m s**-1>
import cf
import cfplot as cfp
f=cf.read('cfplot_data/ggap.nc')
u=f[1].subspace(pressure=500)
v=f[2].subspace(pressure=500)
cfp.vect(u=u, v=v, key_length=10, scale=100, stride=5)
_images/fig13.png


The key_length parameter sets the length of the reference key. The scale parameter sets the viewable length of the vector with scale=50 producing vectors that look twice as long as scale=100. There are often too many vectors on the plot giving a mostly black set of lines. The stride=5 option helps with this and will plot only every 5th vector location in x and y. There is also an alternative npts parameter that can be user to interpolate the data to this number of points in x and y.

In this example vectors are overlaid on a contour plot. Usually a plot is displayed immediately after making a cfp.con or cfp.vect command. As we want a vector plot on top of a contour plot we need to open a graphics file file cfp.gopen() make our contour and vector plots with cfp.con and cfp.vect and then close the graphics file with cfp.gclose().

import cf
import cfplot as cfp
f=cf.read('cfplot_data/ggap.nc')
u=f[1].subspace(pressure=500)
v=f[2].subspace(pressure=500)
t=f[0].subspace(pressure=500)
cfp.gopen()
cfp.mapset(lonmin=10, lonmax=120, latmin=-30, latmax=30)
cfp.levs(min=254, max=270, step=1)
cfp.con(t)
cfp.vect(u=u, v=v, key_length=10, scale=50, stride=2)
cfp.gclose()
_images/fig14.png

Here we make a zonal mean vector plot with different vector keys and scaling factors for the X and Y directions.

import cf
import cfplot as cfp

c=cf.read('cfplot_data/vaAMIPlcd_DJF.nc')
c=c.subspace(Y=cf.wi(-60,60))
c=c.subspace(X=cf.wi(80,160))
c=c.collapse('T: mean X: mean')

g=cf.read('cfplot_data/wapAMIPlcd_DJF.nc')
g=g.subspace(Y=cf.wi(-60,60))
g=g.subspace(X=cf.wi(80,160))
g=g.collapse('T: mean X: mean')

cfp.vect(u=c, v=-g, key_length=[4, 0.2], scale=[20.0, 0.2],
         stride=[2,1], width=0.02, headwidth=6, headlength=6,
         headaxislength=5, pivot='middle', title='DJF',
         key_location=[0.95, -0.05])
_images/fig16.png

A streamplot is used to show fluid flow and 2D field gradients. In this first example the data goes from 0 to 358.875 in longitude. The cartopy / matplotlib interface seems to need the data to be inside the data window in longitude so we anchor the data in cf-python using the anchor method to start at -180 in longitude. If we didn't do this any longitudes less than zero would have no streams drawn.

import cf
import cfplot as cfp
import numpy as np
f=cf.read('cfplot_data/ggap.nc')
u = f[1].subspace(pressure=500)
v = f[2].subspace(pressure=500)

u = u.anchor('X', -180)
v = v.anchor('X', -180)

cfp.stream(u=u, v=v, density=2)
_images/fig16b.png

In the second streamplot example a colorbar showing the intensity of the wind is drawn.

magnitude = (u ** 2 + v ** 2) ** 0.5
mag = np.squeeze(magnitude.array)

cfp.levs(0, 60, 5, extend='max')
cfp.cscale('viridis', ncols=13)
cfp.gopen()
cfp.stream(u=u, v=v, density=2, color=mag)
cfp.cbar(levs=cfp.plotvars.levels, position=[0.12, 0.12, 0.8, 0.02], title='Wind magnitude')
cfp.gclose()
_images/fig16c.png

Multiple plots on a page and plot positioning

To make multiple plots on the page open a graphic file with cfp.gopen and pass the rows and columns parameters. Make each plot in turn first selecting the position with cfp.gpos(). The first position is the top left plot and increases by one for one plot to the right until the final plot is made in the bottom right. When all the plots have been made close the plot with cfp.gclose(). A combined colour bar is also made as all the plots have the same contour levels and colour scale helping to reduce the plot complexity.

import cf
import cfplot as cfp
f=cf.read('cfplot_data/ggap.nc')[1]

cfp.gopen(rows=2, columns=2, bottom=0.2)
cfp.gpos(1)
cfp.con(f.subspace(pressure=500), lines=False, colorbar=None)
cfp.gpos(2)
cfp.mapset(proj='moll')
cfp.con(f.subspace(pressure=500), lines=False, colorbar=None)
cfp.gpos(3)
cfp.mapset(proj='npstere', boundinglat=30, lon_0=180)
cfp.con(f.subspace(pressure=500), lines=False, colorbar=None)
cfp.gpos(4)
cfp.mapset(proj='spstere', boundinglat=-30, lon_0=0)
cfp.con(f.subspace(pressure=500), lines=False, colorbar_position=[0.1, 0.1, 0.8, 0.02],
        colorbar_orientation='horizontal')
cfp.gclose()
_images/fig19.png

Plot spacing options are located in cfp.gopen

orientation='landscape' - orientation - also takes 'portrait'
figsize=[11.7, 8.3] - figure size in inches
left=None - left margin in normalised coordinates - default=0.12
right=None - right margin in normalised coordinates - default=0.92
top=None - top margin in normalised coordinates - default=0.08
bottom=None - bottom margin in normalised coordinates - default=0.08
wspace=None - width reserved for blank space between subplots - default=0.2
hspace=None - height reserved for white space between subplots - default=0.2


|and color bar spacings in cfp.cbar. | | orientation - orientation 'horizontal' or 'vertical' | position - user specified colorbar position in normalised | plot coordinates [left, bottom, width, height] | shrink - default=1.0 - scale colorbar along length | fraction - default = 0.21 - space for the colorbar in | normalised plot coordinates | thick - set height of colorbar - default = 0.015, | in normalised plot coordinates | anchor - default=0.3 - anchor point of colorbar within the fraction space. | 0.0 = close to plot, 1.0 = further away | |

When making map plots the default setting is for one degree of longitude to be the same size as one degree of longitude on the plot. This will make some plots smaller than the area allocated to them as the plot size will be changed to fit within the plot area. The aspect option to cfp.mapset can be used to change the aspect ratio if desired.

aspect = 'equal' - the default, 1 degree longitude is the same size as one degree of latitude
aspect = 'auto' - map fills the plot area
aspect = number - a circle will be stretched such that the height is num times the width.
aspect=1 is the same as aspect='equal'.

User specified plot limits are set by first specifying the user_position=True parameter to cfp.gopen and then the plot position to the gpos routines. The xmin, xmax, ymin, ymax paramenters for the plot display area are in plot extent normalised coordinates. These are 0.0 for bottom or left and 1.0 for top or right of the plot area.

Cylidrical projection plots have an additional rider of having a degree in longitude and latitude being the same size so plots of this type might not fill the plot area specified as expected.

_images/fig19a.png
import cf
import cfplot as cfp
f=cf.read('cfplot_data/ggap.nc')[1]

cfp.gopen(user_position=True)
cfp.gpos(xmin=0.1, xmax=0.5, ymin=0.55, ymax=0.95)
cfp.con(f.subspace(Z=500), lines=False, title='500mb')

cfp.gpos(xmin=0.55, xmax=0.95, ymin=0.55, ymax=0.95)
cfp.con(f.subspace(Z=100), lines=False, title='100mb')

cfp.gpos(xmin=0.3, xmax=0.7, ymin=0.1, ymax=0.5)
cfp.con(f.subspace(Z=10), lines=False, title='10mb')

cfp.gclose()

The indication that the plot position on the page is to be set manually is made with the user_position=True parameter to cfp.gopen. The required plot position is set in cfp.gpos with the xmin, xmax, ymin, ymax parameters. Two calls to the cfp.cbar routine then place colour bars on the plot with different colour scales and contour levels.

_images/fig19b.png
import cf
import cfplot as cfp
import numpy as np

f=cf.read('cfplot_data/ggap.nc')[1]
g=f.collapse('X: mean')

cfp.gopen(user_position=True)

cfp.gpos(xmin=0.2, ymin=0.2, xmax=0.8, ymax=0.8)
cfp.lineplot(g.subspace(pressure=100), marker='o', color='blue',
             title='Zonal mean zonal wind at 100mb')

cfp.cscale('seaice_2', ncols=20)
levs=np.arange(282, 320,2)
cfp.cbar(levs=levs, position=[0.2, 0.1, 0.6, 0.02], title='seaice_2')

cfp.cscale('topo_15lev', ncols=22)
levs=np.arange(-100, 2000, 100)
cfp.cbar(levs=levs, position=[0.03, 0.2, 0.04, 0.6], orientation='vertical', title='topo_15lev')

cfp.gclose()

Trajectories

Data stored in contiguous ragged array format, such as from Kevin Hodges's TRACK program, can be plotted using cf-plot using cfp.traj.

_images/fig39.png
import cf
import cfplot as cfp
f=cf.read('cfplot_data/ff_trs_pos.nc')[0]
cfp.traj(f)



Stipple plots

A stipple plot is usually used to show areas of significance such as 95% or greater confidence. These plots use the overlay technique as used in the previous contour/vector plot. In these plots we show a coutour plot and a stipple plot between varous contour levels to show that the stippling works correctly. For different significance levels such as confidences of 95% and 99% chosing a different sized or colour marker is a common plot technique.

import cf
import cfplot as cfp
f=cf.read('cfplot_data/tas_A1.nc')[0]
g=f.subspace(time=15)
cfp.gopen()
cfp.cscale('magma')
cfp.con(g)
cfp.stipple(f=g, min=220, max=260, size=100, color='#00ff00')
cfp.stipple(f=g, min=300, max=330, size=50, color='#0000ff', marker='s')
cfp.gclose()
_images/fig17.png
import cf
import cfplot as cfp
f=cf.read('cfplot_data/tas_A1.nc')[0]
g=f.subspace(time=15)
cfp.gopen()
cfp.mapset(proj='npstere')
cfp.cscale('magma')
cfp.con(g)
cfp.stipple(f=g, min=265, max=295, size=100, color='#00ff00')
cfp.gclose()
_images/fig18.png



Graph plots

To make a graph plot use the cfp.lineplot function as below on a single line of data in a field.

import cf
import cfplot as cfp
f=cf.read('cfplot_data/ggap.nc')[1]
g=f.collapse('X: mean')
cfp.lineplot(g.subspace(pressure=100), marker='o', color='blue',
             title='Zonal mean zonal wind at 100mb')
_images/fig27.png
Other valid markers are:
'.' point
',' pixel
'o' circle
'v' triangle_down
'^' triangle_up
'<' triangle_left
'>' triangle_right
'1' tri_down
'2' tri_up
'3' tri_left
'4' tri_right
'8' octagon
's' square
'p' pentagon
'*' star
'h' hexagon1
'H' hexagon2
'+' plus
'x' x
'D' diamond
'd' thin_diamond

To make a multiple line plot use the gopen, gclose commands to enclose the plotting commands as in the example below.

When making a multiple line plot:
a) Set the axis limits if required with cfp.gset before plotting the lines.
Using cfp.gset after the last line has been plotted may give unexpected axis
limits and / or labelling. This is a feature of Matplotlib.
b) The last call to cfp.lineplot is the one that any of axis labelling overrides should be placed in.
c) All calls to cfp.lineplot with the label attribute set will appear in the legend.
import cf
import cfplot as cfp
f=cf.read('cfplot_data/ggap.nc')[1]
g=f.collapse('X: mean')
xticks=[-90,-75,-60,-45,-30,-15,0,15,30,45,60,75,90]
xticklabels=['90S','75S','60S','45S','30S','15S','0','15N',
             '30N','45N','60N','75N','90N']
xpts=[-30, 30, 30, -30, -30]
ypts=[-8, -8, 5, 5, -8]

cfp.gset(xmin=-90, xmax=90, ymin=-10, ymax=50)
cfp.gopen()
cfp.lineplot(g.subspace(pressure=100), marker='o', color='blue',
          title='Zonal mean zonal wind', label='100mb')
cfp.lineplot(g.subspace(pressure=200), marker='D', color='red',
             label='200mb', xticks=xticks, xticklabels=xticklabels,
             legend_location='upper right')
cfp.plotvars.plot.plot(xpts,ypts, linewidth=3.0, color='green')
cfp.plotvars.plot.text(35, -2, 'Region of interest', horizontalalignment='left')
cfp.gclose()
_images/fig28.png

Setting Contour Levels

cf-plot generally does a reasonable job of setting appropriate contour levels. In the cases where it doesn't do this or you need a consistent set of levels between plots for comparison purposes use the levs routine. The cfp.levs command manually sets the contour levels.

min=min - minimum level
max=max - maximum level
step=step - step between levels
manual= manual - set levels manually
extend='neither', 'both', 'min', or 'max' – the colour bar limit extensions. These are the triangles at the ends of the colour bar indicating the rest of the data is in this colour.

Use the cfp.levs command when a predefined set of levels is required. The min, max and step parameters are all needed to define a set of levels. These can take integer or floating point numbers. If colour filled contours are plotted then the default is to extend the minimum and maximum contours coloured for out of range values - extend='both'. Use the manual option to define a set of uneven contours i.e.

cfp.levs(manual=[-10, -5, -4, -3, -2, -1, 1, 2, 3, 4, 5, 10])

Once a user call is made to levs the levels are persistent. i.e. the next plot will use the same set of levels. Use cfp.levs() to reset to undefined levels i.e. let cf-plot generate the levels again. Once the cfp.levs command is used you'll need to think about the associated colour scale.

Colour scales

There are around 140 colour scales included with cf-plot. Colour scales are set with the cscale command. There are two default colour scales that suit differing types of data.

A continuous scale cfp.scale('viridis') that goes from blue to green and then yellow and suits data that has no zero in it. For example air temperature in Kelvin or geopotential height - see example 1 in the gallery plots.

A diverging scale cfp.cscale('scale1') that goes from blue to red and suits data with a zero in it. For example temperature in Celsius or zonal wind - see example 4 in the gallery plots.

cfp.levs(min=-80, max=80, step=10)
cfp.cscale('scale1')
_images/cs1.png

If no call has been made to adjust the colour scale then continuous and diverging colour scales are self adjusting to fit the number of levels automatically generated by cf-plot or specified by the user with the cfp.levs command. This behaviour is also followed for a simple call to cfp.cscale specifying a different colour scale - for example cfp.cscale('radar') to select the radar colour scheme.

If a call to cfp.cscale specifies additional parameters to the colour scale, then the automatic colour adjustment is turned off giving the user fine tuning of colours as below.

To change the number of colours in a scale use the ncols parameters.

cfp.cscale('scale1', ncols=12)
cfp.levs(min=-5, max=5, step=1)
_images/cs2.png

To change the number of colours above and below the mid-point of the scale use the above and below parameters. This is useful for fields where you have differing extents of data above and below the zero line.

cfp.cscale('scale1', below=4, above=7)
cfp.levs(min=-30, max=60, step=10)
_images/cs3.png

For data where you need white to indicate that this data region is insignificant use the white=white parameter. This can take single or multiple values.

cfp.cscale('scale1', ncols=11, white=5)
cfp.levs(manual=[-10,-8, -6, -4, -2, 2, 4, 6, 8, 10])
_images/cs4.png

To reverse a colour scale use the reverse=True option to cfp.cscale and specify the number of colours required.

cfp.cscale('viridis', reverse=True, ncols=17)

Producing a uniform colour scale

The uniform parameter may be of use when using a set of contour levels where there is wide mismatch between the above and below numbers.

uniform = False - produce a uniform colour scale. For example: if below=3 and above=10 are specified then initially below=10 and above=10 are used. The colour scale is then cropped to use scale colours 6 to 19. This produces a more uniform intensity colour scale than one where all the blues are compressed into 3 colours.

User defined colour scales

Colour scales are stored as red green blue values on a scale of 0 to 255. Put these in a file with one red green blue value per line. i.e.

255 0 0
255 255 255
0 0 255

will give a red white blue colour scale. If the file is saved as /home/swsheaps/rwb.txt it is read in using

cfp.cscale('/home/swsheaps/rwb.txt')

If the colour scale selected has too few colours for the number of contour levels then the colours will be used cyclically.

Changing colours in a colour scale

The simplest way to do this without writing any code is to modify the internal colour scale before plotting. The colours most people work with are stored as red green blue intensities on a scale of 0 to 255, with 0 being no intensity and 255 full intensity.

White is represented as 255 255 255 and black as 0 0 0

The internal colour scale is stored in cfp.plotvars.cs as hexadecimal code. To convert from decimal to hexadecimal use the Python hex function i.e.

hex(255)[2:]
'ff'

The [2:] is to get rid of the preceding 0x in the hex output.

For example to make one of the colours in the viridis colour scale grey use:

import cf
import cfplot as cfp
f=cf.read('cfplot_data/tas_A1.nc')[0]
cfp.cscale('viridis', ncols=17)
cfp.plotvars.cs[14]='#a6a6a6'
cfp.con(f.subspace(time=15))

Predefined colour scales

A lot of the following colour maps were downloaded from the NCAR Command Language web site. Users of the IDL guide colour maps can see these maps at the end of the colour scales.

Perceptually uniform colour scales

A selection of perceptually uniform colour scales for contouring data without a zero in. See The end of the rainbow and Matplotlib colour maps for a good discussion on colour scales, colour blindness and uniform colour scales.

Name

Scale

viridis

_images/viridis.png

magma

_images/magma.png

inferno

_images/inferno.png

plasma

_images/plasma.png

parula

_images/parula.png

gray

_images/gray.png

NCAR Command Language - MeteoSwiss colour maps

Name

Scale

hotcold_18lev

_images/hotcold_18lev.png

hotcolr_19lev

_images/hotcolr_19lev.png

mch_default

_images/mch_default.png

perc2_9lev

_images/perc2_9lev.png

percent_11lev

_images/percent_11lev.png

precip2_15lev

_images/precip2_15lev.png

precip2_17lev

_images/precip2_17lev.png

precip3_16lev

_images/precip3_16lev.png

precip4_11lev

_images/precip4_11lev.png

precip4_diff_19lev

_images/precip4_diff_19lev.png

precip_11lev

_images/precip_11lev.png

precip_diff_12lev

_images/precip_diff_12lev.png

precip_diff_1lev

_images/precip_diff_1lev.png

rh_19lev

_images/rh_19lev.png

spread_15lev

_images/spread_15lev.png

NCAR Command Language - small color maps (<50 colours)

Name

Scale

amwg

_images/amwg.png

amwg_blueyellowred

_images/amwg_blueyellowred.png

BlueDarkRed18

_images/BlueDarkRed18.png

BlueDarkOrange18

_images/BlueDarkOrange18.png

BlueGreen14

_images/BlueGreen14.png

BrownBlue12

_images/BrownBlue12.png

Cat12

_images/Cat12.png

cmp_flux

_images/cmp_flux.png

cosam12

_images/cosam12.png

cosam

_images/cosam.png

GHRSST_anomaly

_images/GHRSST_anomaly.png

GreenMagenta16

_images/GreenMagenta16.png

hotcold_18lev

_images/hotcold_18lev.png

hotcolr_19lev

_images/hotcolr_19lev.png

mch_default

_images/mch_default.png

nrl_sirkes

_images/nrl_sirkes.png

nrl_sirkes_nowhite

_images/nrl_sirkes_nowhite.png

perc2_9lev

_images/perc2_9lev.png

percent_11lev

_images/percent_11lev.png

posneg_2

_images/posneg_2.png

prcp_1

_images/prcp_1.png

prcp_2

_images/prcp_2.png

prcp_3

_images/prcp_3.png

precip_11lev

_images/precip_11lev.png

precip_diff_12lev

_images/precip_diff_12lev.png

precip_diff_1lev

_images/precip_diff_1lev.png

precip2_15lev

_images/precip2_15lev.png

precip2_17lev

_images/precip2_17lev.png

precip3_16lev

_images/precip3_16lev.png

precip4_11lev

_images/precip4_11lev.png

precip4_diff_19lev

_images/precip4_diff_19lev.png

radar

_images/radar.png

radar_1

_images/radar_1.png

rh_19lev

_images/rh_19lev.png

seaice_1

_images/seaice_1.png

seaice_2

_images/seaice_2.png

so4_21

_images/so4_21.png

spread_15lev

_images/spread_15lev.png

StepSeq25

_images/StepSeq25.png

sunshine_9lev

_images/sunshine_9lev.png

sunshine_diff_12lev

_images/sunshine_diff_12lev.png

temp_19lev

_images/temp_19lev.png

temp_diff_18lev

_images/temp_diff_18lev.png

temp_diff_1lev

_images/temp_diff_1lev.png

topo_15lev

_images/topo_15lev.png

wgne15

_images/wgne15.png

wind_17lev

_images/wind_17lev.png

NCAR Command Language - large colour maps (>50 colours)

Name

Scale

amwg256

_images/amwg256.png

BkBlAqGrYeOrReViWh200

_images/BkBlAqGrYeOrReViWh200.png

BlAqGrYeOrRe

_images/BlAqGrYeOrRe.png

BlAqGrYeOrReVi200

_images/BlAqGrYeOrReVi200.png

BlGrYeOrReVi200

_images/BlGrYeOrReVi200.png

BlRe

_images/BlRe.png

BlueRed

_images/BlueRed.png

BlueRedGray

_images/BlueRedGray.png

BlueWhiteOrangeRed

_images/BlueWhiteOrangeRed.png

BlueYellowRed

_images/BlueYellowRed.png

BlWhRe

_images/BlWhRe.png

cmp_b2r

_images/cmp_b2r.png

cmp_haxby

_images/cmp_haxby.png

detail

_images/detail.png

extrema

_images/extrema.png

GrayWhiteGray

_images/GrayWhiteGray.png

GreenYellow

_images/GreenYellow.png

helix

_images/helix.png

helix1

_images/helix1.png

hotres

_images/hotres.png

matlab_hot

_images/matlab_hot.png

matlab_hsv

_images/matlab_hsv.png

matlab_jet

_images/matlab_jet.png

matlab_lines

_images/matlab_lines.png

ncl_default

_images/ncl_default.png

ncview_default

_images/ncview_default.png

OceanLakeLandSnow

_images/OceanLakeLandSnow.png

rainbow

_images/rainbow.png

rainbow_white_gray

_images/rainbow_white_gray.png

rainbow_white

_images/rainbow_white.png

rainbow_gray

_images/rainbow_gray.png

tbr_240_300

_images/tbr_240_300.png

tbr_stdev_0_30

_images/tbr_stdev_0_30.png

tbr_var_0_500

_images/tbr_var_0_500.png

tbrAvg1

_images/tbrAvg1.png

tbrStd1

_images/tbrStd1.png

tbrVar1

_images/tbrVar1.png

thelix

_images/thelix.png

ViBlGrWhYeOrRe

_images/ViBlGrWhYeOrRe.png

wh_bl_gr_ye_re

_images/wh_bl_gr_ye_re.png

WhBlGrYeRe

_images/WhBlGrYeRe.png

WhBlReWh

_images/WhBlReWh.png

WhiteBlue

_images/WhiteBlue.png

WhiteBlueGreenYellowRed

_images/WhiteBlueGreenYellowRed.png

WhiteGreen

_images/WhiteGreen.png

WhiteYellowOrangeRed

_images/WhiteYellowOrangeRed.png

WhViBlGrYeOrRe

_images/WhViBlGrYeOrRe.png

WhViBlGrYeOrReWh

_images/WhViBlGrYeOrReWh.png

wxpEnIR

_images/wxpEnIR.png

3gauss

_images/3gauss.png

3saw

_images/3saw.png

BrBG

_images/BrBG.png

NCAR Command Language - Enhanced to help with colour blindness

Name

Scale

StepSeq25

_images/StepSeq25.png

posneg_2

_images/posneg_2.png

posneg_1

_images/posneg_1.png

BlueDarkOrange18

_images/BlueDarkOrange18.png

BlueDarkRed18

_images/BlueDarkRed18.png

GreenMagenta16

_images/GreenMagenta16.png

BlueGreen14

_images/BlueGreen14.png

BrownBlue12

_images/BrownBlue12.png

Cat12

_images/Cat12.png

IDL guide scales

Name

Scale

scale1

_images/scale1.png

scale2

_images/scale2.png

scale3

_images/scale3.png

scale4

_images/scale4.png

scale5

_images/scale5.png

scale6

_images/scale6.png

scale7

_images/scale7.png

scale8

_images/scale8.png

scale9

_images/scale9.png

scale10

_images/scale10.png

scale11

_images/scale11.png

scale12

_images/scale12.png

scale13

_images/scale13.png

scale14

_images/scale14.png

scale15

_images/scale15.png

scale16

_images/scale16.png

scale17

_images/scale17.png

scale18

_images/scale18.png

scale19

_images/scale19.png

scale20

_images/scale20.png

scale21

_images/scale21.png

scale22

_images/scale22.png

scale23

_images/scale23.png

scale24

_images/scale24.png

scale25

_images/scale25.png

scale26

_images/scale26.png

scale27

_images/scale27.png

scale28

_images/scale28.png

scale29

_images/scale29.png

scale30

_images/scale30.png

scale31

_images/scale31.png

scale32

_images/scale32.png

scale33

_images/scale33.png

scale34

_images/scale34.png

scale35

_images/scale35.png

scale36

_images/scale36.png

scale37

_images/scale37.png

scale38

_images/scale38.png

scale39

_images/scale39.png

scale40

_images/scale40.png

scale41

_images/scale41.png

scale42

_images/scale42.png

scale43

_images/scale43.png

scale44

_images/scale44.png

Colour bars

Colour bars are often associated with filled colour contour plots and the options for the colour bar are described in the cfp.cbar documentation. If the colour bar options are changed within the call to cfp.con then prepend colorbar_ to the appropriate colour bar option. cf-plot has used the American spelling for colorbar for compatability with the spelling and usage within the Matplotlib Python package.

Below are some examples of calls to cfp.cbar

_images/cbar.png
import cf
import cfplot as cfp
import numpy as np

cfp.gopen()

cfp.levs(0, 500000, 50000)

cfp.cbar(position=[0.1, 0.9, 0.4, 0.01], title='text overlapping with default fontsize')

cfp.setvars(colorbar_fontsize=7)
cfp.cbar(position=[0.1, 0.75, 0.4, 0.01], title='colorbar_fontsize=7')


# Reset font size
cfp.setvars()

cfp.cbar(position=[0.1, 0.6, 0.4, 0.01], text_down_up=True, title='text_down_up')

cfp.cbar(position=[0.1, 0.45, 0.4, 0.01], text_up_down=True, title='text_up_down')

cfp.cbar(position=[0.1, 0.30, 0.4, 0.01], text_down_up=True, drawedges=False,
         title='text_down_up, drawedges=False')



levs=np.array([0, 10, 20, 30])
labels=['a', 'b', 'c', 'd']
labels_mid=['a', 'b', 'c']


cfp.cbar(position=[0.55, 0.9, 0.4, 0.01], extend ='neither',
         levs=levs, labels=labels, title='Normal labelling at the division boundary')
cfp.cbar(position=[0.55, 0.75, 0.4, 0.01], extend ='neither', mid = True,
         levs=levs, labels=labels_mid, title='mid=True and three colorbar labels')


#Turn off plot axes
cfp.plotvars.plot.axis('off')

cfp.gclose()

Labelling time axes

The default time axis labelling in cf-plot might not be what is required and here is some information on using cf-python to extract values for yourself.

f=cf.read('cfplot_data/tas_A1.nc')[0]
f.construct('T')
<CF DimensionCoordinate: time(1680) days since 1860-1-1 360_day>

The time strings are stored in dtarray and the time values in array:

f.construct('T').dtarray
array([<CF Datetime: 1860-01-16T00:00:00Z 360_day>,
<CF Datetime: 1860-02-16T00:00:00Z 360_day>,
<CF Datetime: 1860-03-16T00:00:00Z 360_day>, ...,
<CF Datetime: 1999-10-16T00:00:00Z 360_day>,
<CF Datetime: 1999-11-16T00:00:00Z 360_day>,
<CF Datetime: 1999-12-16T00:00:00Z 360_day>], dtype=object)
array([1.5000e+01, 4.5000e+01, 7.5000e+01, ..., 5.0325e+04, 5.0355e+04, 5.0385e+04])
f.construct('T').array
array([1.5000e+01, 4.5000e+01, 7.5000e+01, ..., 5.0325e+04, 5.0355e+04, 5.0385e+04])

Likewise the years and months are in year.array and month.array:

f.construct('T').year.array
array([1860, 1860, 1860, ..., 1999, 1999, 1999])
f.construct('T').month.array
array([ 1, 2, 3, ..., 10, 11, 12])

To find the time value for to the tick position for January 1st 1980 00:00hrs:

np.min(cf.Data(cf.dt('1980-01-01 00:00:00'), units=f.construct('T').Units).array)
43200.0

With this technique arrays of custom tick label and positions can be constructed and passed to the cfp.lineplot or to the cfp.con routines.

Note the correct date format is 'YYYY-MM-DD' or 'YYYY-MM-DD HH:MM:SS' - anything else will give unexpected results.

In this example we generate labels for the start of the months in 1980. If the middle of the month was to be labelled then the day number would be changed to be 15. Our xtick positions are accumulated using the above method in the xticks array as a numerical position along the axis. In this case we manually specify out tick labels using the xticklabels array of strings.

import cf
import cfplot as cfp
import numpy as np
f=cf.read('cfplot_data/tas_A1.nc')[0]

xticks=[]
xticklabels=['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
for mon in [1,2,3,4,5,6,7,8,9,10,11,12]:
    month_str = str(mon)
    if mon < 10:
        month_str = '0' + str(mon)
    xtick = np.min(cf.Data(cf.dt('1980-'+ month_str + '-01 00:00:00'), units=f.construct('T').Units).array)
    xticks.append(xtick)



g = f.collapse('X: mean').subspace(Y=0.0)

cfp.gset('1980-01-01', '1981-01-01', 299, 302)
cfp.lineplot(g, xticks=xticks, xticklabels=xticklabels,
             yticks=[299, 300, 301, 302],
             xlabel='Time',
             title='Air temperature at the equator in 1980')
_images/time_axis_labelling.png

Axis labels can also be placed at an angle which in the case of time axis labels is often a way of displaying more lengthy labels. cfp.setvars(xtick_label_rotation=30) or cfp.setvars(ytick_label_rotation=30) are examples of how to rotate axis labels.

Selecting data that has a lot of decimal places in the axis values

Axes with a lot of decimal places can cause issues with numeric representation and rounding, In one case

cfp.lineplot(f.subspace(latitude=50.17530806))

caused an error as the latitude with 50.17530806 wasn't found.

In cf-python the tolerance for equivalence is

cf.RTOL()
2.220446049250313e-16

To set to a lower tolerance use

g=cf.RTOL(1e-5)
cf.RTOL()
1e-05

To swap back use

cf.RTOL(g)

After swapping the tolerance to 1e-5 the following finds the latitide as expected.

cfp.lineplot(f.subspace(latitude=50.17530806))

We could have worked around this issue with

cfp.lineplot(f.subspace(latitude=cf.wi(50, 50.2)))

Axis labelling

The priority order of axis labeling in order of preference is:

1) user passed to routine
2) user defined by axes command
3) labels generated internally

For 1 and 2 the available options are

xticks=None - xtick positions
xticklabels=None - xtick labels
yticks=None - y tick positions
yticklabels=None - ytick labels
xlabel=None - label for x axis
ylabel=None - label for y axis

When specifying the xticklabels or yticklabels the supplied values must be a string or list of strings and match the number of corresponding tick marks.

Blockfill plots

Blockfill plots used to use the pcolormesh method but this had the advantage of being fast but gave incorrect results with masked data or when the data range wasn't extended in both 'min' and 'max' directions. The blockfill method now uses PolyCollection from matplotlib.collections to draw the polygons for each colour interval specified by the contour levels. This also has the advantage that blockfill is now available in other projections such as polar stereographic. When doing blockfill plots of larger numbers of points the new method is slower so trim down the data to the area being shown before passing to cfp.con to speed it up.

Making postscript or PNG pictures

There are various methods of producing a figure for use in an external package such as a web document or LaTeX, Word etc.

cfp.setvars(file='zonal.ps') write subsequent graphics output directly to a file called zonal.ps
cfp.setvars(file='zonal.png') write subsequent graphics output to a file called zonal.png

To reset to viewing the picture on the screen again use cfp.setvars().

Another method is to use cfp.gopen() as when used in making multiple plots.

cfp.gopen, file='zonal.ps'
cfp.con(f.subspace(time=15))
cfp.gclose()

Making non-blocking plots

Using the Python prompt cf-plot will try to use the ImageMagick display command to show the plots. This is done with subprocess and plots will remain on the screen even if the user exits the Python session. If the display command isn't installed then cf-plot will use the Matplotlib picture viewer which will block the command prompt until it is quit. Use cfp.setvars(viewer='matplotlib') to set this to be the default for a session even if the display command is available.

On Mac OSX the default is 'matplotlib' but this can be changed by the user to 'display' if the ImageMagick display command has been installed.

Using cf-plot in batch mode

The following method works in the Reading Meteorology department. In the file /home/swsheaps/ajh.sh:

#!/bin/sh
/share/apps/NCAS/anaconda3/bin/python /home/users/swsheaps/ajh.py

In the file /home/users/swsheaps/ajh.py:

import matplotlib as mpl
mpl.use('Agg')
import cf
import cfplot as cfp
f=cf.read('cfplot_data/tas_A1.nc')[0]
cfp.setvars(file='/home/swsheaps/ll.png')
cfp.con(f.subspace(time=15))

run the batch job today at 16:33:

at -f /home/swsheaps/ajh.sh 16:33

The first two lines of the Python script enable cf-plot to run without requiring an X-server.

Changing defaults via the ~/.cfplot_defaults file

A ~/.cfplot_defaults default overide file in the user home directory may contain three values affecting contour plots initially. Please contact andy.heaps@ncas.ac.uk if you would like any more defaults changed in this manner.

blockfill True
fill False
lines False

This changes the default cfplot con options from contour fill with contour lines on top to blockfill with no contour lines on top. The blockfill, fill and line options to the con routine override any of these preset values. The delimter beween the option and the value must be a space.

Plotting data with different time units

When plotting data with different time units users need to move their data to using a common set of units as below.

data1.construct('T').Units
<CF Units: hours since 1900-01-01 00:00:00 standard>
data2.construct('T').Units
<CF Units: days since 2008-09-01 00:00:00 standard>
data1.construct('T').Units=data2.construct('T').Units
data1.construct('T').Units
<CF Units: days since 2008-09-01 00:00:00 standard>

This is because when making a contour or line plot the axes are defined in terms of a linear scale of numbers. Having two different linear scales breaks the connection between the data.

Blockfill contour plots with a time mean

When plotting a blockfill contour plot of data with a time mean the plot can sometimes produce unexpected results. For example the following data has monthly data points but bounds of ten years for each point.

f.coord('T').dtarray
array([cftime.Datetime360Day(1983-08-16 00:00:00),
cftime.Datetime360Day(1983-09-16 00:00:00),
cftime.Datetime360Day(1983-10-16 00:00:00)], dtype=object)
f.coord('T').bounds.dtarray
array([[cftime.Datetime360Day(1979-02-01 00:00:00),
cftime.Datetime360Day(1988-03-01 00:00:00)],
[cftime.Datetime360Day(1979-03-01 00:00:00),
cftime.Datetime360Day(1988-04-01 00:00:00)],
[cftime.Datetime360Day(1979-04-01 00:00:00),
cftime.Datetime360Day(1988-05-01 00:00:00)]], dtype=object)

To reset the bounds for this data to be relevant for plotting use

T=f.coord('T')
T.del_bounds()
new_bounds=T.create_bounds()
T.set_bounds(new_bounds)

The new time data bounds are now:

f.coord('T').bounds.dtarray
array([[cftime.Datetime360Day(1983-08-01 00:00:00),
cftime.Datetime360Day(1983-09-01 00:00:00)],
[cftime.Datetime360Day(1983-09-01 00:00:00),
cftime.Datetime360Day(1983-10-01 00:00:00)],
[cftime.Datetime360Day(1983-10-01 00:00:00),
cftime.Datetime360Day(1983-11-01 00:00:00)]], dtype=object)

Appendix A - Introduction to cf-python

cf-python is very flexible and can be used to select fields, levels, times, means for both CF and non-CF compliant data. CF data is data that follows the NetCDF Climate and Forecast (CF) Metadata Conventions. The conventions define metadata that provide a definitive description of what the data in each variable represents, and of the spatial and temporal properties of the data.

Reading in data and field selection

import cf
fl = cf.read('cfplot_data/ggap.nc')

f is now an list of 4 fields. The list is indicated by the square brackets surrounding the fields.

fl
[<CF Field: air_temperature(time(1), pressure(23), latitude(160), longitude(320)) K>,
<CF Field: eastward_wind(time(1), pressure(23), latitude(160), longitude(320)) m s**-1>,
<CF Field: northward_wind(time(1), pressure(23), latitude(160), longitude(320)) m s**-1>,
<CF Field: geopotential(time(1), pressure(23), latitude(160), longitude(320)) m**2 s**-2>]

In Python the number of the field starts at zero so to select the temperature we use:

g = fl[0]

We could also have used

g = fl.select('air_temperature')[0]

The select method on the field list always returns a list. This list may have a number of fields spanning from zero upwards. Again we use [0] to select the first element from this list. g is now a field as denoted by the lack of square brackets around the item.

g

<CF Field: air_temperature(time(1), pressure(23), latitude(160), longitude(320)) K>

If we wanted to select the geopotential height we could have used g = fl[3] or g = fl[-1]. This normal Python notation for lists helps when selecting items from longer lists when using the index.

Looking at what the data is in a dimension

Reading in a new field

g = cf.read('cfplot_data/tas_A1.nc')[0]
g

<CF Field: air_temperature(time(1680), latitude(73), longitude(96)) K>

To see what levels are available in the temperature data use:

g.construct('longitude').array - uses the standard_name attribute if it exists
g.construct('long_name=longitude').array - uses the long_name attribute(in this case the long_name is also longitude)
g.construct('X').array - uses the field X axis
array([ 0. , 3.75, 7.5 , 11.25, 15. , 18.75, 22.5 , 26.25,
30. , 33.75, 37.5 , 41.25, 45. , 48.75, 52.5 , 56.25,
60. , 63.75, 67.5 , 71.25, 75. , 78.75, 82.5 , 86.25,
90. , 93.75, 97.5 , 101.25, 105. , 108.75, 112.5 , 116.25,
120. , 123.75, 127.5 , 131.25, 135. , 138.75, 142.5 , 146.25,
150. , 153.75, 157.5 , 161.25, 165. , 168.75, 172.5 , 176.25,
180. , 183.75, 187.5 , 191.25, 195. , 198.75, 202.5 , 206.25,
210. , 213.75, 217.5 , 221.25, 225. , 228.75, 232.5 , 236.25,
240. , 243.75, 247.5 , 251.25, 255. , 258.75, 262.5 , 266.25,
270. , 273.75, 277.5 , 281.25, 285. , 288.75, 292.5 , 296.25,
300. , 303.75, 307.5 , 311.25, 315. , 318.75, 322.5 , 326.25,
330. , 333.75, 337.5 , 341.25, 345. , 348.75, 352.5 , 356.25])

The time dimension is slightly different and as before we can print off the numeric values for the time dimension.

g.construct('T').array
array([1.5000e+01, 4.5000e+01, 7.5000e+01, ..., 5.0325e+04, 5.0355e+04,
5.0385e+04])

There is also an additional list for this translated into a date time object array

g.construct('T').dtarray
array([cftime.Datetime360Day(1860, 1, 16, 0, 0, 0, 0, 2, 16),
cftime.Datetime360Day(1860, 2, 16, 0, 0, 0, 0, 4, 46),
cftime.Datetime360Day(1860, 3, 16, 0, 0, 0, 0, 6, 76), ...,
cftime.Datetime360Day(1999, 10, 16, 0, 0, 0, 0, 3, 286),
cftime.Datetime360Day(1999, 11, 16, 0, 0, 0, 0, 5, 316),
cftime.Datetime360Day(1999, 12, 16, 0, 0, 0, 0, 0, 346)],
dtype=object)

Selecting a single dimension value

In the case below we select the slice of data at longitude=0.0 with the subspace method.

g.subspace(longitude=0.0)
g.subspace(X=0.0)
g.subspace('long_name:longitude'=500)

The field now has one longitude.

<CF Field: air_temperature(time(1680), latitude(73), longitude(1)) K>

Multiple subspaces can be made in the same line of code:

g.subspace(longitude=0.0, latitude=0.0)

<CF Field: air_temperature(time(1680), latitude(1), longitude(1)) K>

To select on time either use the numeric value or use cf.dt as in the below example.

g.subspace(time=15.0)
g.subspace(time=cf.dt('1860-1-16'))

Selecting a range of dimension values

To select a range of values use the cf.wi cf.wi construct

g.subspace(longitude=cf.wi(0, 60))
g.subspace(X=cf.wi(0, 60))
g.subspace(T=cf.wi(cf.dt('1860-1-16'), cf.dt('1960-1-16')))

Collapsing a dimension

The collapse method allows a variety of statistical operators to be applied over a dimension - mean, min, max, standard_deviation. The most commonly used one is mean.

To do a time mean of the data

h = g.collapse('T: mean')

<CF Field: air_temperature(time(1), latitude(73), longitude(96)) K>

This now has one time value placed in the middle of the original time series.

h.item('T').dtarray
array([cftime.Datetime360Day(1930, 1, 1, 0, 0, 0, 0, 1, 1)], dtype=object)

The bounds on the data are the minimum and maximum of the original data bounds.

h.item('T').bounds.dtarray
array([[cftime.Datetime360Day(1860, 1, 1, 0, 0, 0, 0, 1, 1),
cftime.Datetime360Day(2000, 1, 1, 0, 0, 0, 0, 1, 1)]],
dtype=object)

An area mean can be accomplished with a special area operator. If the weights='area' is left off then the value is not weighted by area and would be an incorrect value.

area_mean = g.collapse('area: mean', weights='area')
area_mean

<CF Field: air_temperature(time(1680), latitude(1), longitude(1)) K>

Appendix B - Passing data via arrays

cf-plot can also make contour and vector plots by passing data arrays. In this example we read in a temperature field from a netCDF file and pass it to cf-plot for plotting.

import cfplot as cfp
from netCDF4 import Dataset as ncfile
nc = ncfile('cfplot_data/tas_A1.nc')
lons=nc.variables['lon'][:]
lats=nc.variables['lat'][:]
temp=nc.variables['tas'][0,:,:]
cfp.con(f=temp, x=lons, y=lats)
_images/guide5.png

The contouring routine doesn't know that the data passed is a map plot as the only information passed is data arrays. i.e. there is no metadata available to help cf-plot to know what type of plot is required. The plot type can be explicitly set in this case with the ptype flag to cfp.con.

cfp.con(f=temp, x=lons, y=lats, ptype=1)
_images/guide6.png

Other types of plot are:

ptype=2 - latitude - height plot
ptype=3 - longitude - height plot
ptype=4 - longitude - time plot
ptype=5 - latitude - time plot
ptype=6 - rotated pole plot
ptype=7 - time - height plot



In the next example the atmosphere is upside down as cf-plot will plot data axes starting with the smallest value.

import cfplot as cfp
import numpy as np
from netCDF4 import Dataset as ncfile
nc = ncfile('cfplot_data/ggap.nc')
lats=nc.variables['latitude'][:]
pressure=nc.variables['p'][:]
u=nc.variables['U'][:,:,:]
u_mean=np.mean(u.squeeze(), axis=2)
cfp.con(f=u_mean, x=lats, y=pressure)
_images/guide7.png

Adding ptype=4 to the cfp.con allows cf-plot to recognise the data as having a pressure axis and plots the atmosphere the right way up.

cfp.con(f=u_mean, x=lats, y=pressure, ptype=4)
_images/guide8.png

Appendix C - map projections and options

The default map plotting projection is the cyclindrical equidistant projection from -180 to 180 in longitude and -90 to 90 in latitude. To change the map view in this projection to over the United Kingdom, for example, you would use

mapset(lonmin=-6, lonmax=3, latmin=50, latmax=60)
or
mapset(-6, 3, 50, 60)

The limits are -360 to 720 in longitude so to look at the equatorial Pacific you could use

mapset(lonmin=90, lonmax=300, latmin=-30, latmax=30)
or
mapset(lonmin=-270, lonmax=-60, latmin=-30, latmax=30)

Note that Cartopy forces the restriction that in the cyclindrical equidistant projection a degrees of longitude has the same size as a degree of latitude.

The proj parameter accepts 'npstere' and 'spstere' for northern hemisphere or southern hemisphere polar stereographic projections. In addition to these the boundinglat parameter sets the edge of the viewable latitudes and lat_0 sets the centre of desired map domain.

Additional map projections via proj are: ortho, merc, moll, robin and lcc, ukcp, osgb and EuroPP.

_images/fig31.png
import cf
import cfplot as cfp
f=cf.read('cfplot_data/ukcp_rcm_test.nc')[0]
cfp.mapset(proj='UKCP', resolution='50m')
cfp.levs(-3, 7, 0.5)
cfp.con(f, lines=False)


cf-plot looks for auxiliary coordinates of longitude and latitude and uses them if found. If they aren't present then cf-plot will generate the grid required using the projection_x_coordinate and projection_y_coordinate variables within the netCDF data file. For a blockfill plot as below it uses the latter method and the supplied bounds.



New cfp.setvars options affecting the grid plotting for the UKCP grid are:

grid=True - draw grid
grid_spacing=1 - grid spacing in degrees
grid_lons=None - grid longitudes
grid_lats=None - grid latitudes
grid_colour='grey' - grid colour
grid_linestyle='--' - grid line style
grid_thickness=1.0 - grid thickness

Here we change the plotted grid with grid_lons and grid_lats options to cfp.setvars and make a blockfill plot.

_images/fig32.png
import cf
import cfplot as cfp
import numpy as np
f=cf.read('cfplot_data/ukcp_rcm_test.nc')[0]
cfp.mapset(proj='UKCP', resolution='50m')
cfp.levs(-3, 7, 0.5)
cfp.setvars(grid_lons=np.arange(14)-11, grid_lats=np.arange(13)+49)
cfp.con(f, lines=False, blockfill=True)


_images/fig33.png
import cf
import cfplot as cfp
f=cf.read('cfplot_data/ukcp_rcm_test.nc')[0]
cfp.levs(-3, 7, 0.5)
cfp.gopen(columns=2)
cfp.mapset(proj='OSGB', resolution='50m')
cfp.con(f, lines=False)
cfp.gpos(2)
cfp.mapset(proj='EuroPP', resolution='50m')
cfp.con(f, lines=False)
cfp.gclose()


_images/fig34.png

Lambert conformal projections can now be cropped as in the following code:

import cf
import cfplot as cfp
f=cf.read('cfplot_data/tas_A1.nc')[0]
cfp.mapset(proj='lcc', lonmin=-50, lonmax=50, latmin=20, latmax=85)
cfp.con(f.subspace(time=15))


_images/fig35.png
import cf
import cfplot as cfp
f=cf.read('cfplot_data/tas_A1.nc')[0]
cfp.mapset(proj='moll')
cfp.con(f.subspace(time=15))
_images/fig36.png
import cf
import cfplot as cfp
f=cf.read('cfplot_data/tas_A1.nc')[0]
cfp.mapset(proj='merc')
cfp.con(f.subspace(time=15))


_images/fig37.png
import cf
import cfplot as cfp
f=cf.read('cfplot_data/tas_A1.nc')[0]
cfp.mapset(proj='ortho')
cfp.con(f.subspace(time=15))


_images/fig38.png
import cf
import cfplot as cfp
f=cf.read('cfplot_data/tas_A1.nc')[0]
cfp.mapset(proj='robin')
cfp.con(f.subspace(time=15))




Appendix D - Modifying data

For the list of CF attributes setting and changing can be done using:

field.standard_name = 'new_name'

This method works for adding in information to the field

f.set_property('standard_name', 'new_name')

There are also corresponding get_property and del_property methods

f.get_property('standard_name')
f.del_property('standard_name')

Example 1 - adding a standard_name to a field that doesn't have one

In this case we have a field with a long_name but no standard_name

import cf
f = cf.read('cfplot_data/data1.nc')[2]
f

<CF Field: long_name=Potential vorticity(time(1), pressure(23), latitude(160), longitude(320)) K m**2 kg**-1 s**-1>

Setting the standard_name is done with the standard_name method to the field.

f.standard_name='ertel_potential_vorticity'
f

<CF Field: ertel_potential_vorticity(time(1), pressure(23), latitude(160), longitude(320)) K m**2 kg**-1 s**-1>

and write out the new data to a file

cf.write(f, 'newdata.nc')

Example 2 - change the field data to something else

import cf
import cfplot as cfp
f = cf.read('cfplot_data/data1.nc')[7]
f

<CF Field: eastward_wind(time(1), pressure(23), latitude(160), longitude(320)) m s**-1>

Next we put the numpy array of the data into a variable called data. In this example we add 20 to all values we insert this back into the field. Using this method it is easy to modify certain parts of the data to change while leaving the rest as it was.

data = f.array
data = data + 20
f.data[:] = data

We could have just used the simpler notation of

f += 20

and achieved the same effect.

We need to be aware of the valid_min and valid_max values here as we have modified the data. For the original data:

f.valid_min, f.valid_max
f.min(), f.max()

(-73.41583, 116.50885) (<CF Data(): -73.41583251953125 m s**-1>, <CF Data(): 116.50885009765625 m s**-1>)

After we have changed the data:

f.valid_min, f.valid_max
f.min(), f.max()

(-73.41583, 116.50885) (<CF Data(): -53.41583251953125 m s**-1>, <CF Data(): 136.50885009765625 m s**-1>)

Now we need to add 20 to the valid_min and valid_max:

f.valid_min += 20
f.valid_max += 20
f.valid_min, f.valid_max

(-53.41583251953125, 136.50885009765625)

Now when the data is written out it is correct.

Example 3 - Data with an incorrect valid_min / valid_max

Sometimes data has an invalid valid_min or valid_max due to a data creation mistake or processing error.

Here we make some sample data where the maximum of the data is greater than the valid_max parameter.

import cf
import cfplot as cfp
f = cf.read('cfplot_data/data1.nc')[7]
g = f.subspace(Z=10)
g += 60

print(g.max(), g.valid_max)

160.60183715820312 m s**-1 116.50885

When writing out the data we get:

cf.write(g, 'data_incorrect.nc')

WARNING: <CF Field: eastward_wind(time(1), pressure(1), latitude(160), longitude(320)) m s**-1> has data values written to data_incorrect.nc that are strictly greater than the valid maximum defined by the valid_max property: 116.50885009765625. Set warn_valid=False to remove warning.

On reading in the data again by default we get no warning that the data could be compromised. When we plot the data however we readily see that the jet stream has missing data and isn't plotted as we would expect.

h = cf.read('data_incorrect.nc')[0]
cfp.con(h)
_images/data_incorrect1.png

To turn on the warning in the cf.read command use warn_valid=True

h = cf.read('data_incorrect.nc', warn_valid=True)[0]

WARNING: <CF Field: eastward_wind(time(1), pressure(1), latitude(160), longitude(320)) m s**-1> has valid_max, valid_min properties. Set warn_valid=False to suppress warning.

We can read in the data ignoring the masking implied by valid_min and valid_max using the mask=False parameter. See https://ncas-cms.github.io/cf-python/tutorial.html#data-mask for more details.

h = cf.read('data_incorrect.nc', mask=False)[0]
cfp.con(h)

Now the data plots as we expect and we can change the data valid_min or valid_max as in the second example above.

_images/data_incorrect2.png