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Graphic with line erros
4 vues (au cours des 30 derniers jours)
Afficher commentaires plus anciens
Im running my code and Im trying to make the x axis of the figure go from -180 to 180 i change the code and it aligns but at the same time it has mistakes, being those mistakes line throught the the entire y axis
I think the problem is the fact that the lon values (the x axis is longitude) go from 0.5 to 359.5 and the lat (y axis) go from -89.5 to 89.5 all in increments of 1 (from 0.5 to 1.5 and -89.5 to -88.5, respectively)
can somebody help me
10 commentaires
dpb
le 4 Sep 2023
You can't plot what you don't have--as descirbed, you have a gap at the 0-degree location. You would have to fill that in to fill in the gap in the image.
However, there are a couple of solid bands that don't seem to fit -- what are they?
Attach the data would likely let somebody diagnose; otherwise, it's pretty tough to supply explicit answers.
Star Strider
le 4 Sep 2023
The code would help as well, since (I hope) it demonstrates how to create the plot.
Vasco
le 4 Sep 2023
close all;
clc;
format long g;
input = 'C:\Users\vasco\OneDrive\Ambiente de Trabalho\ESTAGIO_Vasco\JPL TELLUS GRACE Level-3\';
output = 'C:\Users\vasco\OneDrive\Ambiente de Trabalho\ESTAGIO_Vasco\outputs_teste\';
lista_ficheiros = dir(fullfile(input, '*.nc'));
lon_1 = double(ncread(fullfile(input, lista_ficheiros(1).name), 'lon'));
lat_1 = double(ncread(fullfile(input, lista_ficheiros(1).name), 'lat'));
nlon = length(lon_1);
nlat = length(lat_1);
ntime = length(lista_ficheiros);
lwe_data = zeros(nlon, nlat, ntime);
for i = 1:length(lista_ficheiros)
filename = fullfile(input, lista_ficheiros(i).name);
nc = netcdf.open(filename);
lwe = ncread(filename, 'lwe_thickness');
lwe_data(:, :, i) = lwe;
netcdf.close(nc);
lat=(ncread(filename, 'lat'));
lon=(ncread(filename, 'lon'));
lon(lon>180) = lon(lon>180) - 360;
time = double(ncread(filename, 'time'));
tm = string(time);
nome_ficheiro = strcat('GRACE-', tm);
figure(1);
axis equal;
clev = -2.0:0.1:0.5;
[x,y] = meshgrid(lon,lat);
contourf(x, y, lwe', clev, 'LineStyle', 'none', 'Fill', 'on');
clim([min(clev), max(clev)]);
colormap(winter(length(clev)-1));
colorbar('eastoutside');
title(strcat('GRACE-', tm));
%estatisticas
nval = nnz(~isnan(lwe));
s = nansum(lwe);
media = nansum(s) / nval;
%Guardar figuras
figura = fullfile(output, strcat(nome_ficheiro, '.png'));
saveas(gcf, figura);
estatisticas = fullfile(output, strcat(nome_ficheiro, '_estatisticas.txt'));
fid = fopen(estatisticas, 'w');
fprintf(fid, 'Estatisticas GRACE-%s:\n', tm);
fprintf(fid, 'Soma lwe_thickness: %f\n', s);
fprintf(fid, 'Media lwe_thickness: %f\n', media);
fclose(fid);
close(gcf);
end
lwe_data_file = fullfile(output, 'lwe_data.mat');
save(lwe_data_file, 'lwe_data')
lwe_transpor = permute(lwe_data, [3, 1, 2]);
pixel_media = mean(lwe_transpor, 1);
pixel_outputs = fullfile(output, 'pixel_media.mat');
save(pixel_outputs, 'pixel_media');
nome_final='Media total';
figure(2);
clevv = -3.0:0.1:0.5;
data = squeeze(pixel_media(1, :, :));
rotated = imrotate(data, 270);
corrigido = fliplr(rotated);
centered_longitude = linspace(-180, 180, 360);
final = circshift(corrigido, [0,180]);
contourf(centered_longitude, lat, final, clevv, 'LineStyle', 'none', 'Fill', 'on');
clim([min(clevv), max(clevv)]);
colorbar('eastoutside');
colormap(winter(length(clevv) - 1));
hAx = gca;
hAx.YDir = 'normal';
figura_1 = fullfile(output, strcat(nome_final, '.png'));
saveas(gcf, figura_1);
Vasco
le 4 Sep 2023
i will ask this as well because im on the verge of killing myself
i want to make a change making the values for the latitude in this range (-66, 20) and lon in this range (270, 330) an then do the mean of those values
basically i want to change this code so that it only analises a specific region.
south america being the region
Star Strider
le 4 Sep 2023
I cannot run the poste c ode with the provided file.
A better option would be to use the zip function to zip the original files and then upload the .zip file here.
Star Strider
le 4 Sep 2023
I was hoping for the actual .nc files in the .zip file.
F1 = fileread('GRD-3_2002094-...0_LND_v04.txt')
F1 =
'header:
dimensions:
lon : 360
lat : 180
time: 1
non_standard_attributes:
product_id: GRD-3
format_id:
short_name: GRD
long_name : Gridded Earth Surface Mass Anomaly
global_attributes:
standard_name_vocabulary: NetCDF Climate and Forecast (CF) Metadata Convention
title : GRACE-FO Water-Equivalent-Thickness Surface-Mass Anomaly; JPL RL06
id : TELND-3AJ64
summary : Gridded surface mass anomalies, derived from Spherical harmonic coefficients representing an estimate of Earths mean gravity field during the specified timespan derived from GRACE mission measurements. These grids represent the full magnitude of land hydrology and land ice. Further, they contain atmospheric and oceanic processes not captured in the accompanying GAC product.
processing_level : 3
product_version_Level3 : LND_sw06_k20_G300_GCSAL_v04
creator_name : GRACE Science Data System NASA/JPL
creator_email : grace@jpl.nasa.gov
creator_url : https://www.grace.jpl.nasa.gov
date_created : 2021-04-28T09:58:19
date_issued : 2021-04-27T17:48:31
acknowledgement : GRACE is a joint mission of NASA (USA) and DLR (Germany). Use the digital object identifier provided in the id attribute when citing this data. See https://podaac.jpl.nasa.gov/CitingPODAAC
license : https://science.nasa.gov/earth-science/earth-science-data/data-information-policy
product_version : RL06
time_epoch : 2002-01-01T00:00:00
static_field_time_epoch : 2008-01-01T12:00:00
time_mean_removed : 2005-01-01 to 2010-12-31
filter_type : Gauss 300 km
postprocess_1 : Water density used to convert to equivalent water height: 1000 kg/m^3
postprocess_2 : Background model added back:
postprocess_3 : Decorrelation (de-stripe) filter: [Swenson & Wahr, 2006, doi:10.1029/2005GL025285]
postprocess_4 : Ellipsoidal Correction has been applied [Ghobadi-Far et al., 2019; https://doi.org/10.1093/gji/ggz292]
GIA_removed : Peltier (Ice-6G_D (updated; 06/2017!))
geocenter_correction : Corrected; after [Sun et al., 2016, doi:10.1002/2016JB013073.]
C_20_substitution : [Loomis et al., 2019, Geophys. Res. Lett., doi:10.1029/2019GL082929]
journal_reference : [Landerer F.W. and S. C. Swenson, Accuracy of scaled GRACE terrestrial water storage estimates. Water Resources Research, Vol 48, W04531, 11 PP, doi:10.1029/2011WR011453, 2012.]
project : NASA Gravity Recovery And Climate Experiment (GRACE)
program : NASA Earth Science System Pathfinder
keywords : Surface Mass, Mass Transport, Terrestrial Water Storage, Mass Balance, Gravity Anomaly, Sea Level, Ocean Bottom Pressure
keywords_vocabulary : NASA Global Change Master Directory (GCMD) Science Keywords
institution : NASA/JPL
naming_authority : org.doi.dx
history : Level-3 processing at JPL with Original solution produced on 2018-05-20T08:38:04
source : An inversion using GRACE-A and GRACE-B measurements
platform : GRACE-A, GRACE-B
platform_vocabulary : NASA Global Change Master Directory platform keywords
instrument : GRACE-A ACC, GRACE-A GPS, GRACE-A KBR, GRACE-A SCA, GRACE-B ACC, GRACE-B GPS, GRACE-B KBR, GRACE-B SCA
instrument_vocabulary : NASA Global Change Master Directory instrument keywords
references : Cooley, Landerer (2019), GRACE/GRACE-FO Level-3 Data Product User Handbook, JPL-D-103133
creator_type : group
creator_institution : NASA/JPL
publisher_name : Physical Oceanography Distributed Active Archive Center
publisher_email : podaac@jpl.nasa.gov
publisher_url : https://podaac.jpl.nasa.gov
publisher_type : group
publisher_institution : NASA/JPL
time_coverage_start : 2002-04-04T00:00:00
time_coverage_end : 2002-04-30T23:59:59
variables:
longitude:
name : lon
long_name: longitude
data_type: double precision
comment : 1st column
latitude:
name : lat
long_name: latitude
data_type: double precision
comment : 2nd column
lwe:
name : lwe
long_name: liquid_water_equivalent_thickness
data_type: double precision
unit : m
comment : 3rd column
uncertainty:
name : uncertainty
long_name: uncertainty
data_type: double precision
unit : m
comment : 4th column
lon_bounds:
name : lon_bounds
long_name: longitude bounds
data_type: double precision
comment : 5th and 6th column for lower and upper bounds respectively
lat_bounds:
name : lat_bounds
long_name: latitude bounds
data_type: double precision
comment : 7th and 8th column for lower and upper bounds respectively
# End of YAML header
0.5 -89.5 -0.02999 0.02469 0.0 1.0 -90.0 -89.0
0.5 -88.5 -0.03870 0.02369 0.0 1.0 -89.0 -88.0
0.5 -87.5 -0.04281 0.02244 0.0 1.0 -88.0 -87.0
0.5 -86.5 -0.04479 0.02152 0.0 1.0 -87.0 -86.0
0.5 -85.5 -0.04788 0.02092 0.0 1.0 -86.0 -85.0
0.5 -84.5 -0.05364 0.02051 0.0 1.0 -85.0 -84.0
0.5 -83.5 -0.06112 0.02033 0.0 1.0 -84.0 -83.0
0.5 -82.5 -0.06779 0.02029 0.0 1.0 -83.0 -82.0
0.5 -81.5 -0.07121 0.02029 0.0 1.0 -82.0 -81.0
0.5 -80.5 -0.07025 0.02031 0.0 1.0 -81.0 -80.0
0.5 -79.5 -0.06516 0.02033 0.0 1.0 -80.0 -79.0
0.5 -78.5 -0.05720 0.02038 0.0 1.0 -79.0 -78.0
0.5 -77.5 -0.04825 0.02046 0.0 1.0 -78.0 -77.0
0.5 -76.5 -0.04054 0.02057 0.0 1.0 -77.0 -76.0
0.5 -75.5 -0.03617 0.02064 0.0 1.0 -76.0 -75.0
0.5 -74.5 -0.03617 0.02067 0.0 1.0 -75.0 -74.0
0.5 -73.5 -0.03969 0.02067 0.0 1.0 -74.0 -73.0
0.5 -72.5 -0.04398 0.02067 0.0 1.0 -73.0 -72.0
0.5 -71.5 -0.04565 0.02063 0.0 1.0 -72.0 -71.0
0.5 -70.5 -0.04264 0.02057 0.0 1.0 -71.0 -70.0
0.5 -69.5 -0.03540 0.02049 0.0 1.0 -70.0 -69.0
0.5 -68.5 -0.02662 0.02039 0.0 1.0 -69.0 -68.0
0.5 -67.5 -99999.0 -99999.0 0.0 1.0 -68.0 -67.0
0.5 -66.5 -99999.0 -99999.0 0.0 1.0 -67.0 -66.0
0.5 -65.5 -99999.0 -99999.0 0.0 1.0 -66.0 -65.0
0.5 -64.5 -99999.0 -99999.0 0.0 1.0 -65.0 -64.0
0.5 -63.5 -99999.0 -99999.0 0.0 1.0 -64.0 -63.0
0.5 -62.5 -99999.0 -99999.0 0.0 1.0 -63.0 -62.0
0.5 -61.5 -99999.0 -99999.0 0.0 1.0 -62.0 -61.0
0.5 -60.5 -99999.0 -99999.0 0.0 1.0 -61.0 -60.0
0.5 -59.5 -99999.0 -99999.0 0.0 1.0 -60.0 -59.0
0.5 -58.5 -99999.0 -99999.0 0.0 1.0 -59.0 -58.0
0.5 -57.5 -99999.0 -99999.0 0.0 1.0 -58.0 -57.0
0.5 -56.5 -99999.0 -99999.0 0.0 1.0 -57.0 -56.0
0.5 -55.5 -99999.0 -99999.0 0.0 1.0 -56.0 -55.0
0.5 -54.5 -99999.0 -99999.0 0.0 1.0 -55.0 -54.0
0.5 -53.5 -99999.0 -99999.0 0.0 1.0 -54.0 -53.0
0.5 -52.5 -99999.0 -99999.0 0.0 1.0 -53.0 -52.0
0.5 -51.5 -99999.0 -99999.0 0.0 1.0 -52.0 -51.0
0.5 -50.5 -99999.0 -99999.0 0.0 1.0 -51.0 -50.0
0.5 -49.5 -99999.0 -99999.0 0.0 1.0 -50.0 -49.0
0.5 -48.5 -99999.0 -99999.0 0.0 1.0 -49.0 -48.0
0.5 -47.5 -99999.0 -99999.0 0.0 1.0 -48.0 -47.0
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0.5 -11.5 -99999.0 -99999.0 0.0 1.0 -12.0 -11.0
0.5 -10.5 -99999.0 -99999.0 0.0 1.0 -11.0 -10.0
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0.5 -2.5 -99999.0 -99999.0 0.0 1.0 -3.0 -2.0
0.5 -1.5 -99999.0 -99999.0 0.0 1.0 -2.0 -1.0
0.5 -0.5 -99999.0 -99999.0 0.0 1.0 -1.0 0.0
0.5 0.5 -99999.0 -99999.0 0.0 1.0 0.0 1.0
0.5 1.5 -99999.0 -99999.0 0.0 1.0 1.0 2.0
0.5 2.5 -99999.0 -99999.0 0.0 1.0 2.0 3.0
0.5 3.5 -99999.0 -99999.0 0.0 1.0 3.0 4.0
0.5 4.5 -0.08150 0.02156 0.0 1.0 4.0 5.0
0.5 5.5 -0.11674 0.02124 0.0 1.0 5.0 6.0
0.5 6.5 -0.14842 0.02106 0.0 1.0 6.0 7.0
0.5 7.5 -0.16779 0.02104 0.0 1.0 7.0 8.0
0.5 8.5 -0.17191 0.02118 0.0 1.0 8.0 9.0
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0.5 25.5 -0.01632 0.02085 0.0 1.0 25.0 26.0
0.5 26.5 -0.01550 0.02069 0.0 1.0 26.0 27.0
0.5 27.5 -0.01698 0.02056 0.0 1.0 27.0 28.0
0.5 28.5 -0.01903 0.02045 0.0 1.0 28.0 29.0
0.5 29.5 -0.01976 0.02037 0.0 1.0 29.0 30.0
0.5 30.5 -0.01876 0.02029 0.0 1.0 30.0 31.0
0.5 31.5 -0.01691 0.02023 0.0 1.0 31.0 32.0
0.5 32.5 -0.01472 0.02017 0.0 1.0 32.0 33.0
0.5 33.5 -0.01069 0.02013 0.0 1.0 33.0 34.0
0.5 34.5 -0.00169 0.02010 0.0 1.0 34.0 35.0
0.5 35.5 0.01438 0.02007 0.0 1.0 35.0 36.0
0.5 36.5 0.03572 0.02004 0.0 1.0 36.0 37.0
0.5 37.5 0.05603 0.02000 0.0 1.0 37.0 38.0
0.5 38.5 0.06762 0.01995 0.0 1.0 38.0 39.0
0.5 39.5 0.06616 0.01991 0.0 1.0 39.0 40.0
0.5 40.5 0.05385 0.01988 0.0 1.0 40.0 41.0
0.5 41.5 0.03840 0.01988 0.0 1.0 41.0 42.0
0.5 42.5 0.02840 0.01991 0.0 1.0 42.0 43.0
0.5 43.5 0.02839 0.01995 0.0 1.0 43.0 44.0
0.5 44.5 0.03701 0.01998 0.0 1.0 44.0 45.0
0.5 45.5 0.04910 0.02000 0.0 1.0 45.0 46.0
0.5 46.5 0.05970 0.01999 0.0 1.0 46.0 47.0
0.5 47.5 0.06660 0.01996 0.0 1.0 47.0 48.0
0.5 48.5 0.06992 0.01993 0.0 1.0 48.0 49.0
0.5 49.5 0.06991 0.01990 0.0 1.0 49.0 50.0
0.5 50.5 0.06546 0.01989 0.0 1.0 50.0 51.0
0.5 51.5 0.05509 0.01991 0.0 1.0 51.0 52.0
0.5 52.5 0.03938 0.01992 0.0 1.0 52.0 53.0
0.5 53.5 0.02231 0.01993 0.0 1.0 53.0 54.0
0.5 54.5 0.00988 0.01994 0.0 1.0 54.0 55.0
0.5 55.5 0.00653 0.01995 0.0 1.0 55.0 56.0
0.5 56.5 0.01213 0.01997 0.0 1.0 56.0 57.0
0.5 57.5 0.02187 0.02000 0.0 1.0 57.0 58.0
0.5 58.5 0.02931 0.02003 0.0 1.0 58.0 59.0
0.5 59.5 0.03014 0.02006 0.0 1.0 59.0 60.0
0.5 60.5 0.02409 0.02007 0.0 1.0 60.0 61.0
0.5 61.5 0.01381 0.02008 0.0 1.0 61.0 62.0
0.5 62.5 0.00214 0.02008 0.0 1.0 62.0 63.0
0.5 63.5 -99999.0 -99999.0 0.0 1.0 63.0 64.0
0.5 64.5 -99999.0 -99999.0 0.0 1.0 64.0 65.0
0.5 65.5 -99999.0 -99999.0 0.0 1.0 65.0 66.0
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0.5 68.5 -99999.0 -99999.0 0.0 1.0 68.0 69.0
0.5 69.5 -99999.0 -99999.0 0.0 1.0 69.0 70.0
0.5 70.5 -99999.0 -99999.0 0.0 1.0 70.0 71.0
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3.5 60.5 0.03627 0.02003 3.0 4.0 60.0 61.0
3.5 61.5 0.02730 0.02004 3.0 4.0 61.0 62.0
3.5 62.5 0.01567 0.02005 3.0 4.0 62.0 63.0
3.5 63.5 0.00249 0.02005 3.0 4.0 63.0 64.0
3.5 64.5 -0.01144 0.02007 3.0 4.0 64.0 65.0
3.5 65.5 -99999.0 -99999.0 3.0 4.0 65.0 66.0
3.5 66.5 -99999.0 -99999.0 3.0 4.0 66.0 67.0
3.5 67.5 -99999.0 -99999.0 3.0 4.0 67.0 68.0
3.5 68.5 -99999.0 -99999.0 3.0 4.0 68.0 69.0
3.5 69.5 -99999.0 -99999.0 3.0 4.0 69.0 70.0
3.5 70.5 -99999.0 -99999.0 3.0 4.0 70.0 71.0
3.5 71.5 -99999.0 -99999.0 3.0 4.0 71.0 72.0
3.5 72.5 -99999.0 -99999.0 3.0 4.0 72.0 73.0
3.5 73.5 -99999.0 -99999.0 3.0 4.0 73.0 74.0
3.5 74.5 -99999.0 -99999.0 3.0 4.0 74.0 75.0
3.5 75.5 -99999.0 -99999.0 3.0 4.0 75.0 76.0
3.5 76.5 -99999.0 -99999.0 3.0 4.0 76.0 77.0
3.5 77.5 0.05552 0.02048 3.0 4.0 77.0 78.0
3.5 78.5 0.08226 0.02039 3.0 4.0 78.0 79.0
3.5 79.5 0.08966 0.02034 3.0 4.0 79.0 80.0
3.5 80.5 0.07783 0.02032 3.0 4.0 80.0 81.0
3.5 81.5 0.05329 0.02030 3.0 4.0 81.0 82.0
3.5 82.5 0.02504 0.02030 3.0 4.0 82.0 83.0
3.5 83.5 0.00036 0.02034 3.0 4.0 83.0 84.0
3.5 84.5 -0.01751 0.02052 3.0 4.0 84.0 85.0
3.5 85.5 -0.02900 0.02093 3.0 4.0 85.0 86.0
3.5 86.5 -99999.0 -99999.0 3.0 4.0 86.0 87.0
3.5 87.5 -99999.0 -99999.0 3.0 4.0 87.0 88.0
3.5 88.5 -99999.0 -99999.0 3.0 4.0 88.0 89.0
3.5 89.5 -99999.0 -99999.0 3.0 4.0 89.0 90.0
4.5 -89.5 -0.03002 0.02469 4.0 5.0 -90.0 -89.0
4.5 -88.5 -0.03854 0.02369 4.0 5.0 -89.0 -88.0
4.5 -87.5 -0.04214 0.02245 4.0 5.0 -88.0 -87.0
4.5 -86.5 -0.04332 0.02153 4.0 5.0 -87.0 -86.0
4.5 -85.5 -0.04532 0.02093 4.0 5.0 -86.0 -85.0
4.5 -84.5 -0.04968 0.02053 4.0 5.0 -85.0 -84.0
4.5 -83.5 -0.05540 0.02034 4.0 5.0 -84.0 -83.0
4.5 -82.5 -0.06005 0.02030 4.0 5.0 -83.0 -82.0
4.5 -81.5 -0.06155 0.02030 4.0 5.0 -82.0 -81.0
4.5 -80.5 -0.05913 0.02032 4.0 5.0 -81.0 -80.0
4.5 -79.5 -0.05322 0.02035 4.0 5.0 -80.0 -79.0
4.5 -78.5 -0.04479 0.02040 4.0 5.0 -79.0 -78.0
4.5 -77.5 -0.03508 0.02048 4.0 5.0 -78.0 -77.0
4.5 -76.5 -0.02586 0.02059 4.0 5.0 -77.0 -76.0
4.5 -75.5 -0.01948 0.02065 4.0 5.0 -76.0 -75.0
4.5 -74.5 -0.01810 0.02067 4.0 5.0 -75.0 -74.0
4.5 -73.5 -0.02233 0.02067 4.0 5.0 -74.0 -73.0
4.5 -72.5 -0.03031 0.02066 4.0 5.0 -73.0 -72.0
4.5 -71.5 -0.03823 0.02062 4.0 5.0 -72.0 -71.0
4.5 -70.5 -0.04224 0.02055 4.0 5.0 -71.0 -70.0
4.5 -69.5 -0.04064 0.02046 4.0 5.0 -70.0 -69.0
4.5 -68.5 -0.03467 0.02036 4.0 5.0 -69.0 -68.0
4.5 -67.5 -99999.0 -99999.0 4.0 5.0 -68.0 -67.0
4.5 -66.5 -99999.0 -99999.0 4.0 5.0 -67.0 -66.0
4.5 -65.5 -99999.0 -99999.0 4.0 5.0 -66.0 -65.0
4.5 -64.5 -99999.0 -99999.0 4.0 5.0 -65.0 -64.0
4.5 -63.5 -99999.0 -99999.0 4.0 5.0 -64.0 -63.0
4.5 -62.5 -99999.0 -99999.0 4.0 5.0 -63.0 -62.0
4.5 -61.5 -99999.0 -99999.0 4.0 5.0 -62.0 -61.0
4.5 -60.5 -99999.0 -99999.0 4.0 5.0 -61.0 -60.0
4.5 -59.5 -99999.0 -99999.0 4.0 5.0 -60.0 -59.0
4.5 -58.5 -99999.0 -99999.0 4.0 5.0 -59.0 -58.0
4.5 -57.5 -99999.0 -99999.0 4.0 5.0 -58.0 -57.0
4.5 -56.5 -99999.0 -99999.0 4.0 5.0 -57.0 -56.0
4.5 -55.5 -99999.0 -99999.0 4.0 5.0 -56.0 -55.0
4.5 -54.5 -99999.0 -99999.0 4.0 5.0 -55.0 -54.0
4.5 -53.5 -99999.0 -99999.0 4.0 5.0 -54.0 -53.0
4.5 -52.5 -99999.0 -99999.0 4.0 5.0 -53.0 -52.0
4.5 -51.5 -99999.0 -99999.0 4.0 5.0 -52.0 -51.0
4.5 -50.5 -99999.0 -99999.0 4.0 5.0 -51.0 -50.0
4.5 -49.5 -99999.0 -99999.0 4.0 5.0 -50.0 -49.0
4.5 -48.5 -99999.0 -99999.0 4.0 5.0 -49.0 -48.0
4.5 -47.5 -99999.0 -99999.0 4.0 5.0 -48.0 -47.0
4.5 -46.5 -99999.0 -99999.0 4.0 5.0 -47.0 -46.0
4.5 -45.5 -99999.0 -99999.0 4.0 5.0 -46.0 -45.0
4.5 -44.5 -99999.0 -99999.0 4.0 5.0 -45.0 -44.0
4.5 -43.5 -99999.0 -99999.0 4.0 5.0 -44.0 -43.0
4.5 -42.5 -99999.0 -99999.0 4.0 5.0 -43.0 -42.0
4.5 -41.5 -99999.0 -99999.0 4.0 5.0 -42.0 -41.0
4.5 -40.5 -99999.0 -99999.0 4.0 5.0 -41.0 -40.0
4.5 -39.5 -99999.0 -99999.0 4.0 5.0 -40.0 -39.0
4.5 -38.5 -99999.0 -99999.0 4.0 5.0 -39.0 -38.0
4.5 -37.5 -99999.0 -99999.0 4.0 5.0 -38.0 -37.0
4.5 -36.5 -99999.0 -99999.0 4.0 5.0 -37.0 -36.0
4.5 -35.5 -99999.0 -99999.0 4.0 5.0 -36.0 -35.0
4.5 -34.5 -99999.0 -99999.0 4.0 5.0 -35.0 -34.0
4.5 -33.5 -99999.0 -99999.0 4.0 5.0 -34.0 -33.0
4.5 -32.5 -99999.0 -99999.0 4.0 5.0 -33.0 -32.0
4.5 -31.5 -99999.0 -99999.0 4.0 5.0 -32.0 -31.0
4.5 -30.5 -99999.0 -99999.0 4.0 5.0 -31.0 -30.0
4.5 -29.5 -99999.0 -99999.0 4.0 5.0 -30.0 -29.0
4.5 -28.5 -99999.0 -99999.0 4.0 5.0 -29.0 -28.0
4.5 -27.5 -99999.0 -99999.0 4.0 5.0 -28.0 -27.0
4.5 -26.5 -99999.0 -99999.0 4.0 5.0 -27.0 -26.0
4.5 -25.5 -99999.0 -99999.0 4.0 5.0 -26.0 -25.0
4.5 -24.5 -99999.0 -99999.0 4.0 5.0 -25.0 -24.0
4.5 -23.5 -99999.0 -99999.0 4.0 5.0 -24.0 -23.0
4.5 -22.5 -99999.0 -99999.0 4.0 5.0 -23.0 -22.0
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4.5 -19.5 -99999.0 -99999.0 4.0 5.0 -20.0 -19.0
4.5 -18.5 -99999.0 -99999.0 4.0 5.0 -19.0 -18.0
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4.5 -16.5 -99999.0 -99999.0 4.0 5.0 -17.0 -16.0
4.5 -15.5 -99999.0 -99999.0 4.0 5.0 -16.0 -15.0
4.5 -14.5 -99999.0 -99999.0 4.0 5.0 -15.0 -14.0
4.5 -13.5 -99999.0 -99999.0 4.0 5.0 -14.0 -13.0
4.5 -12.5 -99999.0 -99999.0 4.0 5.0 -13.0 -12.0
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4.5 -10.5 -99999.0 -99999.0 4.0 5.0 -11.0 -10.0
4.5 -9.5 -99999.0 -99999.0 4.0 5.0 -10.0 -9.0
4.5 -8.5 -99999.0 -99999.0 4.0 5.0 -9.0 -8.0
4.5 -7.5 -99999.0 -99999.0 4.0 5.0 -8.0 -7.0
4.5 -6.5 -99999.0 -99999.0 4.0 5.0 -7.0 -6.0
4.5 -5.5 -99999.0 -99999.0 4.0 5.0 -6.0 -5.0
4.5 -4.5 -99999.0 -99999.0 4.0 5.0 -5.0 -4.0
4.5 -3.5 -99999.0 -99999.0 4.0 5.0 -4.0 -3.0
4.5 -2.5 -99999.0 -99999.0 4.0 5.0 -3.0 -2.0
4.5 -1.5 -99999.0 -99999.0 4.0 5.0 -2.0 -1.0
4.5 -0.5 -99999.0 -99999.0 4.0 5.0 -1.0 0.0
4.5 0.5 -99999.0 -99999.0 4.0 5.0 0.0 1.0
4.5 1.5 -99999.0 -99999.0 4.0 5.0 1.0 2.0
4.5 2.5 -99999.0 -99999.0 4.0 5.0 2.0 3.0
4.5 3.5 -0.06008 0.02184 4.0 5.0 3.0 4.0
4.5 4.5 -0.08537 0.02146 4.0 5.0 4.0 5.0
4.5 5.5 -0.11062 0.02115 4.0 5.0 5.0 6.0
4.5 6.5 -0.13194 0.02098 4.0 5.0 6.0 7.0
4.5 7.5 -0.14777 0.02097 4.0 5.0 7.0 8.0
4.5 8.5 -0.15795 0.02111 4.0 5.0 8.0 9.0
4.5 9.5 -0.16207 0.02137 4.0 5.0 9.0 10.0
4.5 10.5 -0.15888 0.02169 4.0 5.0 10.0 11.0
4.5 11.5 -0.14707 0.02203 4.0 5.0 11.0 12.0
4.5 12.5 -0.12680 0.02235 4.0 5.0 12.0 13.0
NC1 = ncread('GRD-3_2002094-...0_LND_v04.txt')
Error using matlab.internal.imagesci.netcdflib
The NetCDF library encountered an error during execution of 'open' function - 'Unknown file format (NC_ENOTNC)'.
The NetCDF library encountered an error during execution of 'open' function - 'Unknown file format (NC_ENOTNC)'.
Error in netcdf.open (line 77)
[varargout{:}] = matlab.internal.imagesci.netcdflib('open', ...
Error in internal.matlab.imagesci.nc/openToRead (line 1306)
this.ncRootid = netcdf.open(this.Filename,'NOWRITE');
Error in internal.matlab.imagesci.nc (line 124)
this.openToRead();
Error in ncread (line 71)
ncObj = internal.matlab.imagesci.nc(ncFile);
The ncread function throws errors with this one.
I do not know what the problem is, since I do not usually work with .nc files.
.
Réponse acceptée
Voss
le 5 Sep 2023
% unzip the zip-file
unzip('NC_file.zip')
close all;
clc;
format long g;
% input = 'C:\Users\vasco\OneDrive\Ambiente de Trabalho\ESTAGIO_Vasco\JPL TELLUS GRACE Level-3\';
% output = 'C:\Users\vasco\OneDrive\Ambiente de Trabalho\ESTAGIO_Vasco\outputs_teste\';
% I don't have those directories, so I use the current directory ('.') for
% input and output:
input = '.';
output = '.';
lista_ficheiros = dir(fullfile(input, '*.nc'));
lon_1 = double(ncread(fullfile(input, lista_ficheiros(1).name), 'lon'));
lat_1 = double(ncread(fullfile(input, lista_ficheiros(1).name), 'lat'));
nlon = length(lon_1);
nlat = length(lat_1);
ntime = length(lista_ficheiros);
lwe_data = zeros(nlon, nlat, ntime);
for i = 1:length(lista_ficheiros)
filename = fullfile(input, lista_ficheiros(i).name);
nc = netcdf.open(filename);
lwe = ncread(filename, 'lwe_thickness');
lwe_data(:, :, i) = lwe;
netcdf.close(nc);
lat=ncread(filename, 'lat');
lon=ncread(filename, 'lon');
% lon is [0->360]. make it [0->180, -180->0]
idx = lon>180;
lon(idx) = lon(idx) - 360;
% circshift lon so that the first element that was >180 becomes the
% first element in lon, making lon [-180->180]
shift_amount = 1-find(idx,1);
lon = circshift(lon,shift_amount);
% circshift lwe the same amount along its 1st dimension (lon dimension)
lwe = circshift(lwe,shift_amount,1);
time = double(ncread(filename, 'time'));
tm = string(time);
nome_ficheiro = strcat('GRACE-', tm);
figure(1);
axis equal;
clev = -2.0:0.1:0.5;
[x,y] = meshgrid(lon,lat);
contourf(x, y, lwe', clev, 'LineStyle', 'none', 'Fill', 'on');
clim([min(clev), max(clev)]);
colormap(winter(length(clev)-1));
colorbar('eastoutside');
title(strcat('GRACE-', tm));
%estatisticas
nval = nnz(~isnan(lwe));
s = nansum(lwe);
media = nansum(s) / nval;
%Guardar figuras
figura = fullfile(output, strcat(nome_ficheiro, '.png'));
saveas(gcf, figura);
estatisticas = fullfile(output, strcat(nome_ficheiro, '_estatisticas.txt'));
fid = fopen(estatisticas, 'w');
fprintf(fid, 'Estatisticas GRACE-%s:\n', tm);
fprintf(fid, 'Soma lwe_thickness: %f\n', s);
fprintf(fid, 'Media lwe_thickness: %f\n', media);
fclose(fid);
% don't close the figure (so you can see it)
% close(gcf);
end
lwe_data_file = fullfile(output, 'lwe_data.mat');
save(lwe_data_file, 'lwe_data')
lwe_transpor = permute(lwe_data, [3, 1, 2]);
pixel_media = mean(lwe_transpor, 1);
pixel_outputs = fullfile(output, 'pixel_media.mat');
save(pixel_outputs, 'pixel_media');
nome_final='Media total';
figure(2);
clevv = -3.0:0.1:0.5;
data = squeeze(pixel_media(1, :, :));
rotated = imrotate(data, 270);
corrigido = fliplr(rotated);
centered_longitude = linspace(-180, 180, 360);
final = circshift(corrigido, [0,180]);
contourf(centered_longitude, lat, final, clevv, 'LineStyle', 'none', 'Fill', 'on');
clim([min(clevv), max(clevv)]);
colorbar('eastoutside');
colormap(winter(length(clevv) - 1));
hAx = gca;
hAx.YDir = 'normal';
figura_1 = fullfile(output, strcat(nome_final, '.png'));
saveas(gcf, figura_1);
6 commentaires
Voss
le 6 Sep 2023
@Vasco: See below for how to limit the lat/lon range:
% unzip the zip-file
unzip('NC_file.zip')
close all;
clc;
format long g;
% input = 'C:\Users\vasco\OneDrive\Ambiente de Trabalho\ESTAGIO_Vasco\JPL TELLUS GRACE Level-3\';
% output = 'C:\Users\vasco\OneDrive\Ambiente de Trabalho\ESTAGIO_Vasco\outputs_teste\';
% I don't have those directories, so I use the current directory ('.') for
% input and output:
input = '.';
output = '.';
lista_ficheiros = dir(fullfile(input, '*.nc'));
lon_1 = double(ncread(fullfile(input, lista_ficheiros(1).name), 'lon'));
lat_1 = double(ncread(fullfile(input, lista_ficheiros(1).name), 'lat'));
nlon = length(lon_1);
nlat = length(lat_1);
ntime = length(lista_ficheiros);
lwe_data = zeros(nlon, nlat, ntime);
for i = 1:length(lista_ficheiros)
filename = fullfile(input, lista_ficheiros(i).name);
nc = netcdf.open(filename);
lwe = ncread(filename, 'lwe_thickness');
lwe_data(:, :, i) = lwe;
netcdf.close(nc);
lat=ncread(filename, 'lat');
lon=ncread(filename, 'lon');
% lon is [0->360]. make it [0->180, -180->0]
idx = lon>180;
lon(idx) = lon(idx) - 360;
% circshift lon so that the first element that was >180 becomes the
% first element in lon, making lon [-180->180]
shift_amount = 1-find(idx,1);
lon = circshift(lon,shift_amount);
% circshift lwe the same amount along its 1st dimension (lon dimension)
lwe = circshift(lwe,shift_amount,1);
% logical index of which lon values are within the range you want
lon_idx = lon>=-100 & lon<=-30;
% logical index of which lat values are within the range you want
lat_idx = lat>=-66 & lat<=20;
% keep only those values from lon, lat, lwe:
lon = lon(lon_idx);
lat = lat(lat_idx);
lwe = lwe(lon_idx,lat_idx);
time = double(ncread(filename, 'time'));
tm = string(time);
nome_ficheiro = strcat('GRACE-', tm);
figure(1);
axis equal;
clev = -2.0:0.1:0.5;
[x,y] = meshgrid(lon,lat);
contourf(x, y, lwe', clev, 'LineStyle', 'none', 'Fill', 'on');
clim([min(clev), max(clev)]);
colormap(winter(length(clev)-1));
colorbar('eastoutside');
title(strcat('GRACE-', tm));
%estatisticas
nval = nnz(~isnan(lwe));
s = nansum(lwe);
media = nansum(s) / nval;
%Guardar figuras
figura = fullfile(output, strcat(nome_ficheiro, '.png'));
saveas(gcf, figura);
estatisticas = fullfile(output, strcat(nome_ficheiro, '_estatisticas.txt'));
fid = fopen(estatisticas, 'w');
fprintf(fid, 'Estatisticas GRACE-%s:\n', tm);
fprintf(fid, 'Soma lwe_thickness: %f\n', s);
fprintf(fid, 'Media lwe_thickness: %f\n', media);
fclose(fid);
% don't close the figure (so you can see it)
% close(gcf);
end
lwe_data_file = fullfile(output, 'lwe_data.mat');
save(lwe_data_file, 'lwe_data')
lwe_transpor = permute(lwe_data, [3, 1, 2]);
pixel_media = mean(lwe_transpor, 1);
pixel_outputs = fullfile(output, 'pixel_media.mat');
save(pixel_outputs, 'pixel_media');
nome_final='Media total';
figure(2);
clevv = -3.0:0.1:0.5;
data = squeeze(pixel_media(1, :, :));
% rotated = imrotate(data, 270);
% corrigido = fliplr(rotated);
% centered_longitude = linspace(-180, 180, 360);
% final = circshift(corrigido, [0,180]);
final = circshift(data,shift_amount,1);
final = final(lon_idx,lat_idx);
contourf(lon, lat, final.', clevv, 'LineStyle', 'none', 'Fill', 'on');
clim([min(clevv), max(clevv)]);
colorbar('eastoutside');
colormap(winter(length(clevv) - 1));
hAx = gca;
hAx.YDir = 'normal';
figura_1 = fullfile(output, strcat(nome_final, '.png'));
saveas(gcf, figura_1);
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