I have wind data from multiple weather stations and I have the coordinates of the each weather station. I want to overlay wind roses from each station over a map using the stations' lat and long. Is there a straight forward way of doing this in R?
This is how I did it so far in R. I saved the windroses as png and then overlayed it on map.
#######################################
########produce map of GTA##############
#######################################
ggmap=get_map(location=c(left=-80.7 , bottom=43 , right=-77.7 , top=44.9))
gta=ggmap(ggmap)+ scale_y_continuous(limits=c(43.2, 44.5))
####################################################
###########for loop to make wind roses##############
###################################################
data_list<- list.files(path= "/home/npak/Documents/weather_data/meso_west_data/", pattern = "\\.csv$", recursive = FALSE, full.names = TRUE)
l<-length(data_list)
for (i in 1:l){
header<-readLines(data_list[i], 8)
variables = strsplit(header, ',')
vars=variables[[7]]
info= strsplit(header, ':')
lat= as.numeric(info[[3]][2])
long= as.numeric(info[[4]][2])
elev= as.numeric(info[[5]][2])
name=info[[2]][2]
data <- read.table(data_list[i], header= FALSE, sep=",", col.names = paste0("V",seq_len(30)), fill=TRUE, skip=8)
missing= length(data)-length(vars)
colnames(data)= c(vars, rep("Empty", missing) )
print(unique(data$Station_ID))
data$ws<-data$wind_speed_set_1
data$wd<-data$wind_direction_set_1
data$date<- as.POSIXct(data$Date_Time, format = "%Y-%m-%d %H:%M")
ID<-unique(data$Station_ID)
png(filename=(file<- paste('/home/npak/Documents/weather_data/meso_west_data/map_1year/', ID ,'all_year_map.png')),
width = 2400, height = 2400,bg = "transparent")
windRose(data,
breaks = c(1.5,3.3,5.5,8),
max.freq = 25
,paddle = FALSE,
, annotate = FALSE, key= FALSE,
auto.text = FALSE, ,grid.line = list(lty =0, value= 10),
cols=c("red", "red2", "red3", "red4"))
#,annotate = FALSE, key= FALSE)
#dev.off()
dev.off.crop(file=file)
mypng <- readPNG(file<- paste('/home/npak/Documents/weather_data/meso_west_data/map_1year/', ID ,'all_year_map.png'))
gta=gta+inset_raster(mypng, ymin = lat-0.2,ymax= lat+0.2,xmin = long-0.2,xmax = long+0.2)
}
######################################################
###############save map into png#######################################
##########################################################
png('/home/npak/Documents/weather_data/meso_west_data/map_1year/gta_annual.png',
width = 2400, height = 2400)
print(gta)
dev.off()
But it doesn't look as good. Right now I am saving the png image with a transparent background but the axis and frames are still there which makes the map look a bit messy. This is how it looks like:
Windroses overlayed on map
So I am looking for something simillar but to directly plot it over the map and not using the png pictures.
Related
I'm having a few issues with finalizing my map for a report. I think I'm warm on the solutions, but haven't quite figured them out. I would really appreciate any help on solutions so that I can finally move on!
1) The scale bar will NOT populate in the MainMap code and the subsequent Figure1 plot. This is "remedied" in the MainMap code if I comment out the "BCWA_land" map layer. However, when I retain the "BCWA_land" map layer it will eliminate the scale bar and produces this error:
Warning message: Removed 3 rows containing missing values (geom_text).
And this is the code:
MainMap <- ggplot(QOI) +
geom_sf(aes(fill = quadID)) +
scale_fill_manual(values = c("#6b8c42",
"#70b2ae",
"#d65a31")) +
labs(fill = "Quadrants of Interest",
caption = "Figure 1: Map depicting the quadrants in the WSDOT project area as well as other quadrants of interest in the Puget Sound area.")+
ggtitle("WSDOT Project Area and Quadrants of Interest") +
scalebar(x.min = -123, x.max = -122.8, y.min = 47, y.max = 47.1, location = "bottomleft",
transform = TRUE, dist = 10, dist_unit = "km", st.size = 3, st.bottom = TRUE, st.dist = 0.1) +
north(data = QOI, location = "topleft", scale = 0.1, symbol = 12, x.min = -123, y.min = 48.3, x.max = -122.7, y.max = 48.4) +
theme_bw()+
theme(panel.grid= element_line(color = "gray50"),
panel.background = element_blank(),
panel.ontop = TRUE,
legend.text = element_text(size = 11, margin = margin(l = 3), hjust = 0),
legend.position = c(0.95, 0.1),
legend.justification = c(0.85, 0.1),
legend.background = element_rect(colour = "#3c4245", fill = "#f4f4f3"),
axis.title = element_blank(),
plot.title = element_text(face = "bold", colour = "#3c4245", hjust = 0.5, margin = margin(b=10, unit = "pt")),
plot.caption = element_text(face = "italic", colour = "#3c4245", margin = margin(t = 7), hjust = 0, vjust = 0.5)) +
geom_sf(data = BCWA_land) + #this is what I've tried to comment out to determine the scale bar problem
xlim (-123.1, -121.4) +
ylim (47.0, 48.45)
MainMap
InsetRect <- data.frame(xmin=-123.2, xmax=-122.1, ymin=47.02, ymax=48.45)
InsetMap <- ggplotGrob( ggplot( quads) +
geom_sf(aes(fill = "")) +
scale_fill_manual(values = c("#eefbfb"))+
geom_sf(data = BCWA_land) +
scale_x_continuous(expand = c(0,0), limits = c(-124.5, -122.0)) +
scale_y_continuous(expand = c(0,0), limits = c(47.0, 49.5)) +
geom_rect(data = InsetRect,aes(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax),
color="#3c4245",
size=1.25,
fill=NA,
inherit.aes = FALSE) +
theme_bw()+
theme(legend.position = "none",
panel.grid = element_blank(),
axis.title = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank(),
plot.margin = margin(0,0,0,0)))
InsetMap
Figure1 <- MainMap +
annotation_custom(grob = InsetMap, xmin = -122.2, xmax = -121.3,
ymin = 47.75, ymax = 48.5)
Figure1
As you can see I'm not getting this issue or error for my north arrow so I'm not really sure what is happening with the scale bar!
This problem is probably a little too OCD, however I REALLY don't want the gridlines to show up on the InsetMap, and was hoping that the InsetMap would overlay on top of the MainMap, without gridlines as I had those parameters set to element_blank() in the InsetMap code.
Here is an image of my plot. If you would like the data for this, please let me know. Because these are shapefiles, the data is unwieldy and not super conducive to SO's character limit for a post...
If anyone has any insight into a solution(s) I would so so appreciate that!! Thanks for your time!
The issue was the
xlim (-123.1, -121.4) +
ylim (47.0, 48.45)
call that I made. Instead, I used coord_sf(xlim = c(min, max), ylim = c(min, max)). I thought that this would be helpful to someone who might be in my position later on!
Essentially the difference between setting the limits of to the graph using just the x/y lim calls is that that truncates the data that is available in your dataset, whereas the coord_sf call simply "focuses" your graph on that extent if you will without altering the data you have available in your dataset.
I have been tasked with making plots of winds at various levels of the atmosphere to support aviation. While I have been able to make some nice plots using GFS model data (see code below), I'm really having to make a rough approximation of height using pressure coordinates available from the GFS. I'm using winds at 300 hPA, 700 hPA, and 925 hPA to make an approximation of the winds at 30,000 ft, 9000 ft, and 3000 ft. My question is really for those out there who are metpy gurus...is there a way that I can interpolate these winds to a height surface? It sure would be nice to get the actual winds at these height levels! Thanks for any light anyone can share on this subject!
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
import numpy as np
from netCDF4 import num2date
from datetime import datetime, timedelta
from siphon.catalog import TDSCatalog
from siphon.ncss import NCSS
from PIL import Image
from matplotlib import cm
# For the vertical levels we want to grab with our queries
# Levels need to be in Pa not hPa
Levels = [30000,70000,92500]
# Time deltas for days
Deltas = [1,2,3]
#Deltas = [1]
# Levels in hPa for the file names
LevelDict = {30000:'300', 70000:'700', 92500:'925'}
# The path to where our banners are stored
impath = 'C:\\Users\\shell\\Documents\\Python Scripts\\Banners\\'
# Final images saved here
imoutpath = 'C:\\Users\\shell\\Documents\\Python Scripts\\TVImages\\'
# Quick function for finding out which variable is the time variable in the
# netCDF files
def find_time_var(var, time_basename='time'):
for coord_name in var.coordinates.split():
if coord_name.startswith(time_basename):
return coord_name
raise ValueError('No time variable found for ' + var.name)
# Function to grab data at different levels from Siphon
def grabData(level):
query.var = set()
query.variables('u-component_of_wind_isobaric', 'v-component_of_wind_isobaric')
query.vertical_level(level)
data = ncss.get_data(query)
u_wind_var = data.variables['u-component_of_wind_isobaric']
v_wind_var = data.variables['v-component_of_wind_isobaric']
time_var = data.variables[find_time_var(u_wind_var)]
lat_var = data.variables['lat']
lon_var = data.variables['lon']
return u_wind_var, v_wind_var, time_var, lat_var, lon_var
# Construct a TDSCatalog instance pointing to the gfs dataset
best_gfs = TDSCatalog('http://thredds-jetstream.unidata.ucar.edu/thredds/catalog/grib/'
'NCEP/GFS/Global_0p5deg/catalog.xml')
# Pull out the dataset you want to use and look at the access URLs
best_ds = list(best_gfs.datasets.values())[1]
#print(best_ds.access_urls)
# Create NCSS object to access the NetcdfSubset
ncss = NCSS(best_ds.access_urls['NetcdfSubset'])
print(best_ds.access_urls['NetcdfSubset'])
# Looping through the forecast times
for delta in Deltas:
# Create lat/lon box and the time(s) for location you want to get data for
now = datetime.utcnow()
fcst = now + timedelta(days = delta)
timestamp = datetime.strftime(fcst, '%A')
query = ncss.query()
query.lonlat_box(north=78, south=45, east=-90, west=-220).time(fcst)
query.accept('netcdf4')
# Now looping through the levels to create our plots
for level in Levels:
u_wind_var, v_wind_var, time_var, lat_var, lon_var = grabData(level)
# Get actual data values and remove any size 1 dimensions
lat = lat_var[:].squeeze()
lon = lon_var[:].squeeze()
u_wind = u_wind_var[:].squeeze()
v_wind = v_wind_var[:].squeeze()
#converting to knots
u_windkt= u_wind*1.94384
v_windkt= v_wind*1.94384
wspd = np.sqrt(np.power(u_windkt,2)+np.power(v_windkt,2))
# Convert number of hours since the reference time into an actual date
time = num2date(time_var[:].squeeze(), time_var.units)
print (time)
# Combine 1D latitude and longitudes into a 2D grid of locations
lon_2d, lat_2d = np.meshgrid(lon, lat)
# Create new figure
#fig = plt.figure(figsize = (18,12))
fig = plt.figure()
fig.set_size_inches(26.67,15)
datacrs = ccrs.PlateCarree()
plotcrs = ccrs.LambertConformal(central_longitude=-150,
central_latitude=55,
standard_parallels=(30, 60))
# Add the map and set the extent
ax = plt.axes(projection=plotcrs)
ext = ax.set_extent([-195., -115., 50., 72.],datacrs)
ext2 = ax.set_aspect('auto')
ax.background_patch.set_fill(False)
# Add state boundaries to plot
ax.add_feature(cfeature.STATES, edgecolor='black', linewidth=2)
# Add geopolitical boundaries for map reference
ax.add_feature(cfeature.COASTLINE.with_scale('50m'))
ax.add_feature(cfeature.OCEAN.with_scale('50m'))
ax.add_feature(cfeature.LAND.with_scale('50m'),facecolor = '#cc9666', linewidth = 4)
if level == 30000:
spdrng_sped = np.arange(30, 190, 2)
windlvl = 'Jet_Stream'
elif level == 70000:
spdrng_sped = np.arange(20, 100, 1)
windlvl = '9000_Winds_Aloft'
elif level == 92500:
spdrng_sped = np.arange(20, 80, 1)
windlvl = '3000_Winds_Aloft'
else:
pass
top = cm.get_cmap('Greens')
middle = cm.get_cmap('YlOrRd')
bottom = cm.get_cmap('BuPu_r')
newcolors = np.vstack((top(np.linspace(0, 1, 128)),
middle(np.linspace(0, 1, 128))))
newcolors2 = np.vstack((newcolors,bottom(np.linspace(0,1,128))))
cmap = ListedColormap(newcolors2)
cf = ax.contourf(lon_2d, lat_2d, wspd, spdrng_sped, cmap=cmap,
transform=datacrs, extend = 'max', alpha=0.75)
cbar = plt.colorbar(cf, orientation='horizontal', pad=0, aspect=50,
drawedges = 'true')
cbar.ax.tick_params(labelsize=16)
wslice = slice(1, None, 4)
ax.quiver(lon_2d[wslice, wslice], lat_2d[wslice, wslice],
u_windkt[wslice, wslice], v_windkt[wslice, wslice], width=0.0015,
headlength=4, headwidth=3, angles='xy', color='black', transform = datacrs)
plt.savefig(imoutpath+'TV_UpperAir'+LevelDict[level]+'_'+timestamp+'.png',bbox_inches= 'tight')
# Now we use Pillow to overlay the banner with the appropriate day
background = Image.open(imoutpath+'TV_UpperAir'+LevelDict[level]+'_'+timestamp+'.png')
im = Image.open(impath+'Banner_'+windlvl+'_'+timestamp+'.png')
# resize the image
size = background.size
im = im.resize(size,Image.ANTIALIAS)
background.paste(im, (17, 8), im)
background.save(imoutpath+'TV_UpperAir'+LevelDict[level]+'_'+timestamp+'.png','PNG')
Thanks for the question! My approach here is for each separate column to interpolate the pressure coordinate of GFS-output Geopotential Height onto your provided altitudes to estimate the pressure of each height level for each column. Then I can use that pressure to interpolate the GFS-output u, v onto. The GFS-output GPH and winds have very slightly different pressure coordinates, which is why I interpolated twice. I performed the interpolation using MetPy's interpolate.log_interpolate_1d which performs a linear interpolation on the log of the inputs. Here is the code I used!
from datetime import datetime
import numpy as np
import metpy.calc as mpcalc
from metpy.units import units
from metpy.interpolate import log_interpolate_1d
from siphon.catalog import TDSCatalog
gfs_url = 'https://tds.scigw.unidata.ucar.edu/thredds/catalog/grib/NCEP/GFS/Global_0p5deg/catalog.xml'
cat = TDSCatalog(gfs_url)
now = datetime.utcnow()
# A shortcut to NCSS
ncss = cat.datasets['Best GFS Half Degree Forecast Time Series'].subset()
query = ncss.query()
query.var = set()
query.variables('u-component_of_wind_isobaric', 'v-component_of_wind_isobaric', 'Geopotential_height_isobaric')
query.lonlat_box(north=78, south=45, east=-90, west=-220)
query.time(now)
query.accept('netcdf4')
data = ncss.get_data(query)
# Reading in the u(isobaric), v(isobaric), isobaric vars and the GPH(isobaric6) and isobaric6 vars
# These are two slightly different vertical pressure coordinates.
# We will also assign units here, and this can allow us to go ahead and convert to knots
lat = units.Quantity(data.variables['lat'][:].squeeze(), units('degrees'))
lon = units.Quantity(data.variables['lon'][:].squeeze(), units('degrees'))
iso_wind = units.Quantity(data.variables['isobaric'][:].squeeze(), units('Pa'))
iso_gph = units.Quantity(data.variables['isobaric6'][:].squeeze(), units('Pa'))
u = units.Quantity(data.variables['u-component_of_wind_isobaric'][:].squeeze(), units('m/s')).to(units('knots'))
v = units.Quantity(data.variables['v-component_of_wind_isobaric'][:].squeeze(), units('m/s')).to(units('knots'))
gph = units.Quantity(data.variables['Geopotential_height_isobaric'][:].squeeze(), units('gpm'))
# Here we will select our altitudes to interpolate onto and convert them to geopotential meters
altitudes = ([30000., 9000., 3000.] * units('ft')).to(units('gpm'))
# Now we will interpolate the pressure coordinate for model output geopotential height to
# estimate the pressure level for our given altitudes at each grid point
pressures_of_alts = np.zeros((len(altitudes), len(lat), len(lon)))
for ilat in range(len(lat)):
for ilon in range(len(lon)):
pressures_of_alts[:, ilat, ilon] = log_interpolate_1d(altitudes,
gph[:, ilat, ilon],
iso_gph)
pressures_of_alts = pressures_of_alts * units('Pa')
# Similarly, we will use our interpolated pressures to interpolate
# our u and v winds across their given pressure coordinates.
# This will provide u, v at each of our interpolated pressure
# levels corresponding to our provided initial altitudes
u_at_levs = np.zeros((len(altitudes), len(lat), len(lon)))
v_at_levs = np.zeros((len(altitudes), len(lat), len(lon)))
for ilat in range(len(lat)):
for ilon in range(len(lon)):
u_at_levs[:, ilat, ilon], v_at_levs[:, ilat, ilon] = log_interpolate_1d(pressures_of_alts[:, ilat, ilon],
iso_wind,
u[:, ilat, ilon],
v[:, ilat, ilon])
u_at_levs = u_at_levs * units('knots')
v_at_levs = v_at_levs * units('knots')
# We can use mpcalc to calculate a wind speed array from these
wspd = mpcalc.wind_speed(u_at_levs, v_at_levs)
I was able to take my output from this and coerce it into your plotting code (with some unit stripping.)
Your 300-hPa GFS winds
My "30000-ft" GFS winds
Here is what my interpolated pressure fields at each estimated height level look like.
Hope this helps!
I am not sure if this is what you are looking for (I am very new to Metpy), but I have been using the metpy height_to_pressure_std(altitude) function. It puts it in units of hPa which then I convert to Pascals and then a unitless value to use in the Siphon vertical_level(float) function.
I don't think you can use metpy functions to convert height to pressure or vice versus in the upper atmosphere. There errors are too when using the Standard Atmosphere to convert say pressure to feet.
I've created a program for a project that tests images against one another to see whether or not it's the same image or not. I've decided to use correlation since the images I am using are styled in the same way and with this, I've been able to get everything working up to this point.
I now wish to create an array of images again, but this time, in order of their correlation. So for example, if I'm testing a 50 pence coin and I test 50 images against the 50 pence coin, I want the highest 5 correlations to be stored into an array, which can then be used for later use. But I'm unsure how to do this as each item in the array will need to have more than one variable, which will be the image location/name of the image and it's correlation percentage.
%Program Created By Ben Parry, 2016.
clc(); %Simply clears the console window
%Targets the image the user picks
inputImage = imgetfile();
%Targets all the images inside this directory
referenceFolder = 'M:\Project\MatLab\Coin Image Processing\Saved_Images';
if ~isdir(referenceFolder)
errorMessage = print('Error: Folder does not exist!');
uiwait(warndlg(errorMessage)); %Displays an error if the folder doesn't exist
return;
end
filePattern = fullfile(referenceFolder, '*.jpg');
jpgFiles = dir(filePattern);
for i = 1:length(jpgFiles)
baseFileName = jpgFiles(i).name;
fullFileName = fullfile(referenceFolder, baseFileName);
fprintf(1, 'Reading %s\n', fullFileName);
imageArray = imread(fullFileName);
imshow(imageArray);
firstImage = imread(inputImage); %Reading the image
%Converting the images to Black & White
firstImageBW = im2bw(firstImage);
secondImageBW = im2bw(imageArray);
%Finding the correlation, then coverting it into a percentage
c = corr2(firstImageBW, secondImageBW);
corrValue = sprintf('%.0f%%',100*c);
%Custom messaging for the possible outcomes
corrMatch = sprintf('The images are the same (%s)',corrValue);
corrUnMatch = sprintf('The images are not the same (%s)',corrValue);
%Looping for the possible two outcomes
if c >=0.99 %Define a percentage for the correlation to reach
disp(' ');
disp('Images Tested:');
disp(inputImage);
disp(fullFileName);
disp (corrMatch);
disp(' ');
else
disp(' ');
disp('Images Tested:');
disp(inputImage);
disp(fullFileName);
disp(corrUnMatch);
disp(' ' );
end;
imageArray = imread(fullFileName);
imshow(imageArray);
end
You can use struct() function to create structures.
Initializing an array of struct:
imStruct = struct('fileName', '', 'image', [], 'correlation', 0);
imData = repmat(imStruct, length(jpgFiles), 1);
Setting field values:
for i = 1:length(jpgFiles)
% ...
imData(i).fileName = fullFileName;
imData(i).image = imageArray;
imData(i).correlation = corrValue;
end
Extract values of correlation field and select 5 highest correlations:
corrList = [imData.correlation];
[~, sortedInd] = sort(corrList, 'descend');
selectedData = imData(sortedInd(1:5));
Im trying to pull some data from SQLite database to the Corona newScollView.
I managed to get data from the database in a tableView, so I thought the code should almost be the same for the newScrollView.
It keeps saying the row is empty, but its not. Any help?
Here is the code:
function scene:create( event )
local sceneGroup = self.view
local function scrollListener( event )
-- Get reference to the row group
local row = event.row
local options_metni =
{
parent = row,
text = row.params.Metni,
x = 0,
y = 0,
font = native.systemFont,
fontSize = 16
}
local metniObject = display.newText(options_metni)
metniObject:setTextColor(0)
metniObject.x = display.contentCenterX
end
---------------------
-- CREATE SCROLLVIEW
---------------------
local scrollView = widget.newScrollView
{
left = 0,
top = 0,
width = display.contentWidth,
height = display.contentHeight / 2,
topPadding = 200,
bottomPadding = 50,
horiontalScrollDisabled = true,
verticalScrollDisable = false,
listener = scrollListener,
}
sceneGroup:insert(scrollView)
---------------------
-- GET DATA FROM DB
---------------------
for row in db:nrows("SELECT baslik FROM dua LIMIT 1") do
if row.data == nil then
print(" NO DATA FOUND ")
end
local rowParams =
{
duaID = row.dua_id,
Metni = row.baslik,
}
end
end
You need to insert an object into the scrollView, it doesn't work like the tableView. Try this where the display object is being created and the text is being set from row.baslik:
for row in db:nrows("SELECT baslik FROM dua LIMIT 1") do
local text = display.newText( tostring( row.baslik ), 0, 0, native.systemFont, 16 )
scrollView:insert( text )
end
I manage to create a map and even include a north arrow, but can't get the map.scale to work and getting this kind of error:
Error in map.scale(x = -83, y = 12, ratio = FALSE, relwidth = 0.2, cex
= 0.6) : unused arguments (ratio = FALSE, relwidth = 0.2, cex = 0.6)
Here is the code:
library(maps)
library(mapdata)
library(ggmap)
library(mapproj)
library(maptools) #for shapefiles
library(scales) #for transparency
library(GISTools)
range <- readShapePoly("isthmanianpacificmoistforestecoregion") #layer of data for species range
map("worldHires", c('Cost', 'pan', 'Nic', 'Colombia'), xlim=c(-89,-75),ylim=c(5,13), col="lightgray", fill=TRUE) #plot the region I want
map.scale(-81,8,relwidth = 0.15, metric = TRUE, ratio = TRUE)
plot(range, add=TRUE, xlim=c(-89,-75),ylim=c(5,13), col=alpha("green", 0.6), border=TRUE)
map.scale(x=-80, y=10) #, relwidth=0.3, cex=0.5, ratio=FALSE)
north.arrow(xb=-77, yb=12, len=0.2, lab="N", col="black", fill=TRUE) #
The problem is that map.scale() is a function for both maps and GISTools packages. You are trying to use the function from the maps package. Since you are loading first maps and then GISTools, the map.scale() from maps is being masked (probably R throws a warning when loading the last package).
The solution is to specify the package in the function call:
maps::map.scale(-81,8,relwidth = 0.15, metric = TRUE, ratio = TRUE)
Also why two calls to map.scale? You should probably exclude one of them.