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I'm trying to create the parameter px on the holoviews.operation.datashader.spread operation interactively changeable together with an additional overlay.
The diagram with an IntSlider and the function returned by pn.bind(get_spreaded, px=px_slider) is working as expected when executing with pn.Column(px_slider, interactive)`.
But with an additional overlay, the line pn.Column(px_slider, interactive * other) reports TypeError: unsupported operand type(s) for *: 'function' and 'Points'.
How can I use the * operator with the function returned from pn.bind(...)?
Or is this the wrong way doing this? Is there a better and easier solution?
I ran the following code in jupyter lab:
import holoviews as hv
import panel as pn
import numpy as np
from holoviews.operation.datashader import rasterize, spread
import colorcet
import pandas as pd
hv.extension('bokeh')
pn.extension()
hv.opts.defaults(
hv.opts.Path(width=800, height=400),
hv.opts.Image(width=800, height=400)
)
def random_walk(n, f=200):
"""Random walk in a 2D space, smoothed with a filter of length f"""
xs = np.convolve(np.random.normal(0, 0.1, size=n), np.ones(f)/f).cumsum()
ys = np.convolve(np.random.normal(0, 0.1, size=n), np.ones(f)/f).cumsum()
xs += 0.1*np.sin(0.1*np.array(range(n-1+f))) # add wobble on x axis
xs += np.random.normal(0, 0.005, size=n-1+f) # add measurement noise
ys += np.random.normal(0, 0.005, size=n-1+f)
return np.column_stack([xs, ys])
# create a path and plot it
path = hv.Path([random_walk(10000, 30)])
path
# rasterize and show the plot
rasterized = rasterize(path).opts(colorbar=True, cmap=colorcet.fire, cnorm='log')
rasterized
# the callback for getting the spreaded plot
def get_spreaded(px=3, shape='circle'):
return spread(rasterized, px=px, shape=shape)
# show the plot returned from the callback
get_spreaded()
# create the slider for interactively changing the px value
px_slider = pn.widgets.IntSlider(name='Number of pixels to spread on all sides', start=0, end=10, value=3, step=1)
# bind the slider to the callback method
interactive = pn.bind(get_spreaded, px=px_slider)
# show only one plot without any overlay
pn.Column(px_slider, interactive)
# create data for an overlay
df = pd.DataFrame(data={'c1': [1, 2, 3, 4, 5], 'c2': [3, 4, 5, 6, 7]})
other = hv.Points(data=df)
other
# show both plots
pn.Column(px_slider, interactive * other)
The last line results in the following Error message:
#
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In[159], line 1
----> 1 pn.Column(px_slider, interactive * other)
TypeError: unsupported operand type(s) for *: 'function' and 'Points'
I would expect, that there is some way to wrap the function and makes it possible to use the * operator. But I couldn't find a way yet.
Although in this particular case the return value of the function is something that HoloViews could (in principle) overlay with the other plot, HoloViews doesn't know that; the HoloViews * operator only knows how to handle HoloViews objects (Elements, HoloMaps, Layouts, and DynamicMaps), not bound Panel functions.
You could use a DynamicMap much like you used pn.bind, but here HoloViews operations already understand what to do with Panel widgets, so you can simply supply the widget to the spread operation (or any other operation's parameters):
import panel as pn, numpy as np, holoviews as hv, colorcet, pandas as pd
from holoviews.operation.datashader import rasterize, spread
hv.extension('bokeh')
pn.extension()
hv.opts.defaults(
hv.opts.Path(width=800, height=400),
hv.opts.Image(width=800, height=400)
)
def random_walk(n, f=200):
"""Random walk in a 2D space, smoothed with a filter of length f"""
xs = np.convolve(np.random.normal(0, 0.1, size=n), np.ones(f)/f).cumsum()
ys = np.convolve(np.random.normal(0, 0.1, size=n), np.ones(f)/f).cumsum()
xs += 0.1*np.sin(0.1*np.array(range(n-1+f))) # add wobble on x axis
xs += np.random.normal(0, 0.005, size=n-1+f) # add measurement noise
ys += np.random.normal(0, 0.005, size=n-1+f)
return np.column_stack([xs, ys])
# create plot with interactively controlled spreading
px_slider = pn.widgets.IntSlider(name='Number of pixels to spread on all sides',
start=0, end=10, value=3, step=1)
path = hv.Path([random_walk(10000, 30)])
rasterized = rasterize(path).opts(colorbar=True, cmap=colorcet.fire, cnorm='log')
spreaded= spread(rasterized, px=px_slider, shape='circle')
# create data for an overlay
df = pd.DataFrame(data={'c1': [1, 2, 3, 4, 5], 'c2': [3, 4, 5, 6, 7]})
other = hv.Points(data=df)
# show both plots
pn.Column(px_slider, spreaded * other)
Trying to set a Mapbox style from a local JSON file in plotly dash app.
mapbox_token = <mytoken>
local_style=
json.load(open(application_root_path+r'/pages/campaignmap/mapbox_style.json'))
fig = go.Figure(
go.Scattermapbox(
mode="lines", fill="toself",
lon=[-10, -10, 8, 8, -10, None, 30, 30, 50,
50, 30, None, 100, 100, 80, 80, 100],
lat=[30, 6, 6, 30, 30, None, 20, 30, 30, 20, 20, None, 40, 50, 50, 40, 40],))
fig.update_layout(
dict1={"mapbox": {"accesstoken": mapbox_token}})
fig.update_layout(
showlegend=False,
margin={'l': 0, 'r': 0, 'b': 0, 't': 0},
mapbox_style=local_style
)
Based on the documentation on the plotly website Mapbox Map Layers in Python (see image below) this should work. The JSON file contains the export of the style from MapBox and it works fine when I use the service URL (second last dot point in image).
I've tried loading the JSON as string, dictionary and file path in the mapbox_style parameter with no luck.
Any ideas on how to make it load?
I've tried the same things you've described and had no luck. I can provide a style URL and it works perfectly, but if I download that style and provide just the style.json representation of that same style, I can't seem to get Plotly to recognize it...
I have NetCDF files (e.g https://data.ceda.ac.uk/neodc/esacci/lakes/data/lake_products/L3S/v1.0/2019 global domain), and I want to extract the data based on a shapefile boundary ( in this case a Lake here - https://www.sciencebase.gov/catalog/item/530f8a0ee4b0e7e46bd300dd) and then save clipped data as a NetCDF file but retain all the original metadata and variables names within the clipped file. This is what I have done far
library(rgdal)
library(sf)
library(ncdf4)
library(terra)
#Read in the shapefile of Lake
Lake_shape <- readOGR("C:/Users/CEDA/hydro_p_LakeA/hydro_p_A.shp")
# Reading the netcdf file using Terra Package function rast
test <- rast("ESACCI-LAKES-L3S-LK_PRODUCTS-MERGED-20190705-fv1.0.nc")
# List of some of variables names for orginal dataset
head(names(test))
[1] "water_surface_height_above_reference_datum" "water_surface_height_uncertainty" "lake_surface_water_extent"
[4] "lake_surface_water_extent_uncertainty" "lake_surface_water_temperature" "lswt_uncertainty"
#Clipping data to smaller Lake domain using the crop function in Terra Package
test3 <- crop(test, Lake_shape)
#Listing the some variables names for clipped data
head(names(test3))
[1] "water_surface_height_above_reference_datum" "water_surface_height_uncertainty" "lake_surface_water_extent"
[4] "lake_surface_water_extent_uncertainty" "lake_surface_water_temperature" "lswt_uncertainty"
# Writing the crop dataset as netcdf or Raster Layer using the WriteCDF function
filepath<-"Lake_A_ESACCI-LAKES-L3S-LK_PRODUCTS-MERGED-20020501-fv1.0"
fname <- paste0( "C:/Users/CEDA/",filepath,".nc")
rnc <- writeCDF(test3, filename =fname, overwrite=T)”
My main issue here when I read in clipped the netCDF file I don’t seem to be able to keep the names of the data variables of the original NetCDF. They are all being renamed automatically when I am saving the clipped dataset as a new netCDF using the writeCDF function.
#Reading in the new clipped file
LakeA<-rast("Lake_A_ESACCI-LAKES-L3S-LK_PRODUCTS-MERGED-20020501-fv1.0.nc")
> head(names(LakeA))
[1] "Lake_A_ESACCI-LAKES-L3S-LK_PRODUCTS-MERGED-20020501-fv1.0_1" "Lake_A_ESACCI-LAKES-L3S-LK_PRODUCTS-MERGED-20020501-fv1.0_2"
[3] "Lake_A_ESACCI-LAKES-L3S-LK_PRODUCTS-MERGED-20020501-fv1.0_3" "Lake_A_ESACCI-LAKES-L3S-LK_PRODUCTS-MERGED-20020501-fv1.0_4"
[5] "Lake_A_ESACCI-LAKES-L3S-LK_PRODUCTS-MERGED-20020501-fv1.0_5" "Lake_A_ESACCI-LAKES-L3S-LK_PRODUCTS-MERGED-20020501-fv1.0_6"
So is it possible to clone/copy all the metadata variables from the original NetCDF dataset when clipping to the smaller domain/shapefile in R, then saving as NetCDF? Any guidance on how to do this in R would be really appreciated. (NetCDF and R are all new to me so I am not sure what I am missing or have the in-depth knowledge to sort this).
You have a NetCDF file with many (52) variables (sub-datasets). When you open the file with rast these become "layers". Alternatively you can open the file with sds to keep the sub-dataset structure but that does not help you here (and you would need to skip the first two, see below).
library(terra)
f <- "ESACCI-LAKES-L3S-LK_PRODUCTS-MERGED-20190101-fv1.0.nc"
r <- rast(f)
r
#class : SpatRaster
#dimensions : 21600, 43200, 52 (nrow, ncol, nlyr)
#resolution : 0.008333333, 0.008333333 (x, y)
#extent : -180, 180, -90, 90 (xmin, xmax, ymin, ymax)
#coord. ref. : +proj=longlat +datum=WGS84 +no_defs
#sources : ESACCI-LAKES-L3S-LK_PRODUCTS-MERGED-20190101-fv1.0.nc:water_surface_height_above_reference_datum
ESACCI-LAKES-L3S-LK_PRODUCTS-MERGED-20190101-fv1.0.nc:water_surface_height_uncertainty
ESACCI-LAKES-L3S-LK_PRODUCTS-MERGED-20190101-fv1.0.nc:lake_surface_water_extent
... and 49 more source(s)
#varnames : water_surface_height_above_reference_datum (water surface height above geoid)
water_surface_height_uncertainty (water surface height uncertainty)
lake_surface_water_extent (Lake Water Extent)
...
#names : water~datum, water~ainty, lake_~xtent, lake_~ainty, lake_~ature, lswt_~ainty, ...
#unit : m, m, km2, km2, Kelvin, Kelvin, ...
#time : 2019-01-01
Note that there are 52 layers and sources (sub-datasets). There are names
head(names(r))
#[1] "water_surface_height_above_reference_datum" "water_surface_height_uncertainty"
#[3] "lake_surface_water_extent" "lake_surface_water_extent_uncertainty"
#[5] "lake_surface_water_temperature" "lswt_uncertainty"
And also "longnames" (they are often much longer than the variable names, not in this case)
head(longnames(r))
# [1] "water surface height above geoid" "water surface height uncertainty" "Lake Water Extent"
# [4] "Water extent uncertainty" "lake surface skin temperature" "Total uncertainty"
You can also open the file with sds, but you need to skip "lon_bounds" and "lat_bounds" variables (dimensions)
s <- sds(f, 3:52)
Now read a vector data set (shapefile in this case) and crop
lake <- vect("hydro_p_LakeErie.shp")
rc <- crop(r, lake)
rc
#class : SpatRaster
#dimensions : 182, 555, 52 (nrow, ncol, nlyr)
#resolution : 0.008333333, 0.008333333 (x, y)
#extent : -83.475, -78.85, 41.38333, 42.9 (xmin, xmax, ymin, ymax)
#coord. ref. : +proj=longlat +datum=WGS84 +no_defs
#source : memory
#names : water~datum, water~ainty, lake_~xtent, lake_~ainty, lake_~ature, lswt_~ainty, ...
#min values : NaN, NaN, NaN, NaN, 271.170, 0.283, ...
#max values : NaN, NaN, NaN, NaN, 277.090, 0.622, ...
#time : 2019-01-01
It can be convenient to save this to a GTiff file like this (or even better to use the filename argument in crop)
gtf <- writeRaster(rc, "test.tif", overwrite=TRUE)
gtf
#class : SpatRaster
#dimensions : 182, 555, 52 (nrow, ncol, nlyr)
#resolution : 0.008333333, 0.008333333 (x, y)
#extent : -83.475, -78.85, 41.38333, 42.9 (xmin, xmax, ymin, ymax)
#coord. ref. : +proj=longlat +datum=WGS84 +no_defs
#source : test.tif
#names : water~datum, water~ainty, lake_~xtent, lake_~ainty, lake_~ature, lswt_~ainty, ...
#min values : NaN, NaN, NaN, NaN, 271.170, 0.283, ...
#max values : NaN, NaN, NaN, NaN, 277.090, 0.622, ...
What has changed is that the data are now in a file, rather then in memory. And you still have the layer (variable) names.
To write the layers as variables to a NetCDF file you need to create a SpatRasterDataset. You can do that like this:
x <- as.list(rc)
s <- sds(x)
names(s) <- names(rc)
longnames(s) <- longnames(r)
units(s) <- units(r)
Note the use of longnames(r) and units(r) (not rc). This is because r has subdatasets (and each has a longname and a unit) while rc does not.
Now use writeCDF
z <- writeCDF(s, "test.nc", overwrite=TRUE)
rc2 <- rast("test.nc")
rc2
#class : SpatRaster
#dimensions : 182, 555, 52 (nrow, ncol, nlyr)
#resolution : 0.008333333, 0.008333333 (x, y)
#extent : -83.475, -78.85, 41.38333, 42.9 (xmin, xmax, ymin, ymax)
#coord. ref. : +proj=longlat +datum=WGS84 +no_defs
#sources : test.nc:water_surface_height_above_reference_datum
test.nc:water_surface_height_uncertainty
test.nc:lake_surface_water_extent
... and 49 more source(s)
#varnames : water_surface_height_above_reference_datum (water surface height above geoid)
water_surface_height_uncertainty (water surface height uncertainty)
lake_surface_water_extent (Lake Water Extent)
...
#names : water~datum, water~ainty, lake_~xtent, lake_~ainty, lake_~ature, lswt_~ainty, ...
#unit : m, m, km2, km2, Kelvin, Kelvin, ...
#time : 2019-01-01
So it looks like we have a NetCDF with the same structure.
Note that the current CRAN version of terra drops the time variable if there is only one time step. The development version (1.3-11) keeps the time dimension, even of there is only one step.
You can install the development version with
install.packages('terra', repos='https://rspatial.r-universe.dev')
I'm trying to solve the crypt-arithmetic puzzle: SANTA-CLAUS=XMAS in ECLIPSE Prolog. I am using the puzzle "SEND+MORE=MONEY" as a guide to do so. My problem that I'm not sure how to implement the "borrowing" part of subtraction. In addition, there's a carry over which can be added to the next terms as you continue adding. But in subtraction, we have to borrow and subtract a tens from the number you borrow from. Below is the code for the addition puzzle which I am using as a guide:
solve([S,E,N,D,M,O,R,Y]) :-
car(M), M > 0, car(C100),
dig(S), S > 0,
M is (S+M+C100) // 10, O is (S+M+C100) mod 10,
dig(E), car(C10),
N is (E+O+C10) mod 10, C100 is (E+O+C10) // 10,
dig(R), car(C1),
E is (N+R+C1) mod 10, C10 is (N+R+C1) // 10,
dig(D),
Y is (D+E) mod 10, C1 is (D+E) // 10,
all_diff([S,E,N,D,M,O,R,Y]).
car(0). car(1).
dig(0). dig(1). dig(2). dig(3). dig(4).
dig(5). dig(6). dig(7). dig(8). dig(9).
all_diff([]).
all_diff([N|L]) :- not member(N,L), all_diff(L).
member(N,[N|L]).
member(N,[M|L]) :- member(N,L).
I just need help understanding how to implement borrowing in subtraction.
I'm trying to use Julia with GTK and Cairo to draw onto a canvas. I think the following code (adapted from the example fragments on the GTK.jl page) should work, but it does not. (Other GTK widgets work, but not the canvas)
I would appreciate if someone can suggest what is wrong with this code, or give a pointer to a complete example.
using Gtk.ShortNames
using Cairo
function drawfn(w)
ctx = Gtk.getgc(w)
Cairo.set_coords(ctx, 0, 0, 800, 600, 0, 800, 0, 600)
h = Gtk.height(w)
w = Gtk.width(w)
Gtk.rectangle(ctx, 0, 0, w/2, h/2)
Gtk.set_source_rgb(ctx, 0, 0, 1)
Gtk.fill(ctx)
end
function main()
win = Gtk.#Window("stuff", 800,600)
c = Gtk.#Canvas()
Gtk.push!(win,c)
Gtk.draw(drawfn, c)
Gtk.showall(win)
end
main()
I think this was a bug in version 0.8.1 of Gtk.jl. I posted an issue to github after which vtjnash fixed it immediately and tagged version 0.8.2.