I want to generate a graph with a choroplethmapbox figure.
When I use function fig.show(),it was correct.But when I wanted to create a Graph with Choroplethmapbox,it only show the map without colored polygon
fig = go.Figure(go.Choroplethmapbox(.....)
# fig.show() no problem
app.layout = html.Div([
dcc.Graph(figure=fig,id="test")
],style={'width': '500'})
# only show maps without colored Polygon
app.run_server()
Related
I need to create a heatmap, before plotting heatmap, I need to download a lot of data from database which take time like 5 minutes, I'd like to show a progress bar when downloading data from oracle database to let me know if it is in progress of downloading data from oracle.
I googled a lot and fortunately I found a website where it use dbc.Progress() and how to update the progress bar by connecting to tqdm with a file. But I still not sure how to do it for my own example. I tried and it doesn't work, could anyone help me with that? Thank you so much for your help.
https://towardsdatascience.com/long-callbacks-in-dash-web-apps-72fd8de25937
here is my code
I defined one tab, I include progress bar using dbc.Progress() and graph
progress_bar_heatmap=dbc.Progress(value=25, striped=True, animated=True,
children=['25%'],color='success',
style={'height':'20px'},
id="progress_bar_heatmap")
loading_timer_progress = dcc.Interval(id='loading_timer_progress',
interval=1000)
heatmap_graph = dcc.Graph(id="heatmap-graph", **graph_kwargs)
#wrap contour in dcc.loading's chilren so we can see loading signal
heatmap_loading=dcc.Loading(
id='loading-heatmap',
type='default',
children=heatmap_graph # wrap contour in loading's children
)
dcc.Tab(
[progress_bar_heatmap,loading_timer_progress, heatmap_loading],
label=label,
value='heatmap',
id='heatmap-tab',
className="single-tab",
selected_className="single-tab--selected",
)
in callback, I copied some codes from the above website,
#app.callback(
[
Output("heatmap-graph", "figure"),
Output("progress_bar_dts_heatmap", "value"),
],
[
Input("plot-dts", "n_clicks"),
Input('loading_timer_progress', 'n_intervals'),
],
prevent_initial_call=True, # disable output in the first load
)
def change_plot(n_clicks,n_intervals):
progress_bar_value=0
import sys
try:
with open('progress.txt', 'r') as file:
str_raw = file.read()
last_line = list(filter(None, str_raw.split('\n')))[-1]
percent = float(last_line.split('%')[0])
except: # no progress file created meansing it is creating
percent = 0
std_err_backup = sys.stderr
file_prog = open('progress.txt', 'w')
sys.stderr = file_prog
df=time_consuming_function()
result_str = f'Long callback triggered by {btn_name}. Result: {x:.2f}'
file_prog.close()
sys.stderr = std_err_backup
finally: # must do under all circustances
text = f'{percent:.0f}%'
fig=create_fig(df)
inside the time_consuming function
def time_consuming_function():
download_data_from_oracle()
# after that, I added below as website did
for i in tqdm(range(20)):
time.sleep(0.5)
return df
it doesn't work above, not sure which one is wrong?
I have multiple categorical heatmap plots that are in a single display that have identical shapes and x,y coordinates. When hovering on any of the subplots I would like the inspection on one plot to trigger a new inspection on all other plots in the grid and display multiple tooltips simultaneously.
I have researched this topic and found similar posts such as:
Bokeh: Synchronizing hover tooltips in linked plots
Takeaway from link above: There are 2 suggested answers to this question, which attempt to mimic hover tooltips with text glyphs, however these implementations are not successful when I copy and run the code on my own computer (the graphs display correctly but the hover text glyphs don't appear). I assume this could be because of Bokeh API updates, but I am unsure. My reputation doesn't allow comments or I'd address this issue there.
Coordinate tooltips across multiple plots #1547
Takeaway from link above: There is no reproducible data so I am not able to recreate the plot listed here, however bryevdv summarizes what I am trying to do quite efficiently which I'll quote below:
Link on geometry. You might want the geometry of the inspection on one plot to trigger a completely new inspection (using that same geometry) on another plot. So if the cursor is at (10.5, 7) on one plot, then the additional plots do a hit test at (10.5, 7) and if there are glyphs that have any hovers a that point, then a hover gets drawn there.
I have created some generalized data to illustrate my problem:
from bokeh.io import show, output_notebook
from bokeh.layouts import gridplot
from bokeh.models import LinearColorMapper, HoverTool
from bokeh.plotting import figure, show, output_file
from bokeh.transform import transform
import numpy as np
import pandas as pd
data1 = [['A','A',100], ['A','B',175], ['B','A',75], ['B','B',200]]
data2 = [['A','A',25], ['A','B',100], ['B','A',50], ['B','B',75]]
data3 = [['A','A',150], ['A','B',75], ['B','A',25], ['B','B',125]]
df1 = pd.DataFrame(data1, columns = ['Left','Right','Value'])
df2 = pd.DataFrame(data2, columns = ['Left','Right','Value'])
df3 = pd.DataFrame(data3, columns = ['Left','Right','Value'])
def heatmap(df, title):
letters = ['A','B']
mapper = LinearColorMapper(palette=['#225ea8', '#41b6c4', '#a1dab4', '#ffffcc'], low=0, high=200)
TOOLS = 'reset'
p = figure(plot_width=255, plot_height=250, title=title,
x_range=letters,
y_range=list(reversed(letters)), x_axis_location='above',
tools=TOOLS, toolbar_location='below')
p.grid.grid_line_color = None
p.grid.grid_line_width = 0.5
p.axis.axis_line_color = None
p.axis.major_tick_line_color = None
p.axis.major_label_text_font_size = '9pt'
p.axis.major_label_standoff = 0
p.xaxis.major_label_orientation = 0
hover = HoverTool()
p.rect(x='Right', y='Left', width=1, height=1, line_color=None, source=df,
fill_color={'field': 'Value', 'transform': mapper})
hover.tooltips = [('Group','#Left #Right'), ('Value','#Value')]
p.tools.append(hover)
return p
output_notebook()
p1 = heatmap(df1, 'Plot 1')
p2 = heatmap(df2, 'Plot 2')
p3 = heatmap(df3, 'Plot 3')
grid = gridplot([[p1,p2,p3]])
show(grid)
Output:
My goal is to be able to observe the values across multiple plots at one time without having to be directed to another page or source, so I am open to alternative ways of doing this that doesn't involve hover tooltips. Thanks!
I am using jupyter notebook to do some mapping visualisation stuff with google maps (via the http://jupyter-gmaps.readthedocs.io/en/latest/gmaps.html library)
I want to be able to use the jupyter dropdown widget to pass it an updated string and get a new map based on this to appear on the page.
All of this works well (code below) but when I run the it, the clear_output() does not work. It works fine if I am not using google maps, like just printing something in the cell once when I change the dropdown.
Does anyone have any idea about this?
import ipywidgets as widgets
import gmaps
import pandas as pd
gmaps.configure(api_key="my api key...")
from IPython.display import display
from IPython.display import clear_output
# global scaling for circle size on map
global SCALING_NORMALIZATION
SCALING_NORMALIZATION = 30
#MAP FUNCTIONS
#function to change circle sizes on map
def create_circle_sizes(values):
scaling_values = []
for i in range(0, len(values)):
a = np.asscalar(values[i])
a = int(a / SCALING_NORMALIZATION)
if a == 0:
scaling_values.append(2)
else:
scaling_values.append(a)
return scaling_values
# function to create hover info on map
def create_hover_info(service_types, names):
hover_info = []
for i in range(0, len(service_types)):
hover_info.append(names[i])
return hover_info
#function to draw a map in gmaps
def create_map(value):
map_df = data[data['lga'] == value][['lat', 'long', 'lic_places', 'sta_name', 'distinct_se_type']]
scaling = map_df['lic_places'].tolist()
names = map_df['sta_name'].tolist()
service_types = map_df['distinct_se_type'].tolist()
map_layer = gmaps.symbol_layer(map_df[['lat', 'long']],
fill_color="blue",
stroke_color="blue",
scale=create_circle_sizes(scaling),
hover_text =
create_hover_info(service_types, names))
fig = gmaps.figure()
fig.add_layer(map_layer)
display(fig)
# WIDGET FUNCTIONS
# function to update the map when dropdown choice is changed
def update_map(args):
# clear_output() doesn't seem to work with map rendering
clear_output()
user_choice = args['new']
create_map(user_choice)
# dropdown widget
dd = widgets.Dropdown(
options=['auburn (c)', 'fairfield (c)', 'penrith (c)'],
value='fairfield (c)',
description='Number:',
disabled=False,
button_style=''
)
#display dropdown widget, with function it will call on change
dd.observe(update_map, 'value')
I have been working with Leaflet for creating cool interactive maps in R. However, I cannot export the maps, as the background map ends up being grey after export.
library(leaflet)
library(htmlwidgets)
m <- leaflet(data.frame(lat = 55.71654, lng = 12.47484))
m <- addCircles(m, popup = "testpopup")
m <- addTiles(m)
m
saveWidget(m, file="testmap.html", selfcontained = TRUE)
Is there an alternative to Leaflet where you can export your interactive maps as HTML? Or do I have to get into the whole Leaflet/Shiny thing?
As mentioned my #Martin Schmelzer then it all works after updating leaflet through the devtools::install_github('rstudio/leaflet'). Thanks.
I have a script which allows me to generate a map with with "R for leaflet" :
library(htmlwidgets)
library(raster)
library(leaflet)
# PATHS TO INPUT / OUTPUT FILES
projectPath = "path"
#imgPath = paste(projectPath,"data/cea.tif", sep = "")
#imgPath = paste(projectPath,"data/o41078a1.tif", sep = "") # bigger than standard max size (15431804 bytes is greater than maximum 4194304 bytes)
imgPath = paste(projectPath,"/test.tif", sep = "")
outPath = paste(projectPath, "/leaflethtmlgen.html", sep="")
# load raster image file
r <- raster(imgPath)
# reproject the image, if necessary
#crs(r) <- sp::CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs")
# color palette, which is interpolated ?
pal <- colorNumeric(c("#FF0000", "#666666", "#FFFFFF"), values(r),
na.color = "transparent")
# create the leaflet widget
m <- leaflet() %>%
addTiles() %>%
addRasterImage(r, colors=pal, opacity = 0.9, maxBytes = 123123123) %>%
addLegend(pal = pal, values = values(r), title = "Test")
# save the generated widget to html
# contains the leaflet widget AND the image.
saveWidget(m, file = outPath, selfcontained = FALSE, libdir = 'leafletwidget_libs')
My problem is that this is generating a html file and I need this map to be dyanamic. For example, when a user click on some html button which is not integrate on the map, I want to add a rectangle on the map. Any solutions would be welcome...
Leaflet itself does not provide the interactive functionality you are looking for. One solution is to use shiny, which is a web application framework for R. From simple R code, it generates a web page, and runs R on the server-side to respond to user interaction. It is well documented, has a gallery of examples, and a tutorial to get new users started.
It works well with leaflet. One of the examples on the shiny web site uses it, and also includes a link to the source code.
Update
Actually, if simple showing/hiding of elements is enough, leaflet alone will suffice with the use of groups. From the question it's not very clear how dynamic you need it to be.