I have a shape file with a column named geometry containing MULTIPOLYGONs.
postcode name geometry
0 2003.0 A MULTIPOLYGON Z (((1048559.000 7841160.000 0.00...
1 1438.0 B MULTIPOLYGON Z (((-29156.720 6885495.170 0.000...
While it is straight forward to draw a map in matplotlib with GeoPandas, I can not get it to work in Holoviews. The example-script from Holoviews (http://holoviews.org/gallery/demos/bokeh/texas_choropleth_example.html) don't really solve the question of how to structure the data.
I have tried to read the shape file with GeoPandas without luck. What am I missing here? Do I need to decode the geometry column first?
If you install hvPlot, you should be able to plot shape columns directly from GeoPandas using HoloViews, as described at https://hvplot.holoviz.org/user_guide/Geographic_Data.html .
import hvplot.pandas, geopandas as gpd
world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
world.hvplot(geo=True)
Related
Requirement:
I have to read dxf files entities which can be 2D building dimensions or road etc. Then i have to place it over the map and return the coordinates just like labs.mapbox.com export the coordinates as geojson like the data below export by labs.mapbox.
Approch: For now i'm using python 'exdxf' package to read dxf file which return me entities information e.g in case of line it would be start/end points. Then i was thinking to draw those entities over a canvas (not sure) then place it over mapbox and get the coordinates where canvas is place export geojson of it is the final goal.
Required help in: Suggest me the right way to achieve this solution, i am open to choose any framework / language.
Thanks for your time
If you've got a dxf file and want to export it as a geosjon file, using ezdxf python package is good but you have to do a lot of processing the dxf entities and stuff and it takes time to achieve what you want.
I suggest using ogr2ogr since this is a time saving approach. it is a library for working with geospatial data and you can convert data to different formats such as Geojson, shapefile and others.
you can easily convert your dxf file to a geojson file with :
ogr2ogr -f Geojson GEOJSON_FILE_NAME YOUR_DXF_FILE_NAME
I suggest reading the documentation
first to get to know about this library and all the options it gives you for processing the data.
I am using the following code to find the spectrogram of a signal and save it.
spec,freq,t,im = plt.specgram(raw_signal,Fs=100,NFFT=100,noverlap=50)
plt.axis('off')
figure = plt.gcf()
figure.set_size_inches(12, 1)
plt.savefig('spectrogram',bbox_inches = 'tight',pad_inches=0)
But I have multiple spectrograms like this and the end product I need is a concatenation of all these. Right now, what I am doing is, I am saving all these individual images using plt.savefig() as earlier and reading them back using cv2.imread() and concatenating them. But this process is not very good I think. So is there any other way I can do this without saving it and re-reading it?
One possible idea I have is, somehow converting matplotlib.figure.Figure into a format that can be handled by OpenCV (specifically cv2). However, it should also not have white padding.
You can get the image as an array using buffer_rgba (don't forget to draw the image first). Then in OpenCV, you need to convert the image from RGB to OpenCV's BGR channel ordering.
import matplotlib.pyplot as plt
import numpy as np
import cv2
raw_signal = np.random.random(1000)
spec,freq,t,im = plt.specgram(raw_signal,Fs=100,NFFT=100,noverlap=50)
plt.axis('off')
figure = plt.gcf()
figure.set_size_inches(12, 1)
figure.set_dpi(50)
figure.canvas.draw()
b = figure.axes[0].get_window_extent()
img = np.array(figure.canvas.buffer_rgba())
img = img[int(b.y0):int(b.y1),int(b.x0):int(b.x1),:]
img = cv2.cvtColor(img, cv2.COLOR_RGBA2BGRA)
cv2.imshow('OpenCV',img)
Top: matplotlib, bottom OpenCV:
don't save the figure. matplotlib happens to have a convenience function for displaying time series data in this way but that's not how you deal with spectrograms. any handling of spectrogram "pictures" is a kludge.
use scipy.signal.spectrogram to get the actual spectrogram.
I am attempting to take a screenshot of a correlation table made with pandas in Jupyter Notebooks but since it is very wide I must scroll to the right in order to view the whole table. On a mac it is not possible to scroll left or right while taking a screenshot so I am unable to capture the entire table. Is there anyway to get the entire table (doesn't have to be a screenshot an export of some type would work as well)?
Is the table already shown in markdown? If not, you can try df.to_html(). From there, you can convert it to pdf, see (https://www.npmjs.com/package/markdown-pdf)
Otherwise you can plot the pandas table with matplotlib and remove the axis etc.
import matplotlib.pyplot as plt
import pandas as pd
from pandas.table.plotting import table
ax = plt.subplot(111, frame_on=False) # no visible frame
ax.xaxis.set_visible(False) # hide the x axis
ax.yaxis.set_visible(False) # hide the y axis
table(ax, df) # where df is your data frame
plt.savefig('table.png')
I have a map with an older topojson format that once worked with Vega-Lite. Now we only see a purple square in this editor gist.
I've rebuilt the map with the same code but updated topojson in the vega editor and saved as a gist here.
With the new vega release, it seems like I need my topojson files to be formatted differently, with the arcs first, like the mapshaper.org export output. Why is this? It's broken several existing web maps, and took me a few hours to figure out. Seems like I can fix it with a workflow change, but I am curious.
Topojson data follows the left-hand rule for projected data (clockwise orientation for outer rings and counter- clockwise for interior rings), where the data in your topojson file is structured according the right-hand rule (counter-clockwise for outer rings and clockwise for interior rings). The order of your polygons seems negligible, but it defines which part is ‘inside’ and ‘outside’ the polgygons.
You can do two things:
Do not use a geographic projection, but the cartesian-like identity projection.
Force your source data into the right order.
Example for 1:
"projection": {"type": "identity", "reflectY": true},
see Vega Editor
Example for 2:
Use MapShaper or Python to force your data in the right order. Here an example using Python
import topojson as tp
import geopandas as gpd
gdf = gpd.read_file('https://raw.githubusercontent.com/nycehs/NeighborhoodReports/master/visualizations/json/UHF42.topo_old.json')
tp.Topology(gdf).to_json('UHF42.topo_new.json')
see Vega Editor
I wrote a bit about it before for Altair and Python Topojson
https://mattijn.github.io/topojson/example/settings-tuning.html#winding_order
https://altair-viz.github.io/user_guide/data.html#winding-order
And Mike Bostock for D3
https://bl.ocks.org/mbostock/a7bdfeb041e850799a8d3dce4d8c50c8
So I need to create the map of my country from the coordinates I have so that I can plot some xyz data on it.
I was able to plot the map with the help of Mike Bostock tut http://bost.ocks.org/mike/map/
But when I went through the .json file, the coordinates mentioned are totally different from the coordinates I have. Like Patna(India) has [5812,6567] as coordinates in the json file while actual coordinates are [25,85].
Could anybody guide me how can I use the coordinates with me to plot the map and then display some data on it??
EDIT:
With the help from #user1614080, I was able to understand how coordinates are converted into screen coordinates.
The issue I have is: I want the map of India in a certain way. For that purpose, I would like to use the list of latitude & longitude I have. But I dont know how to convert these lat-longs into topojson/geojson format.
For the time being I did a simple line plot with the lat-long I had. Some of the values from the coordinates.csv I used for the line plot are:
Longitude,Latitude
88.75,21.58
88.33,21.50
88.17,22.08
88.00,21.75
87.58,21.58
87.08,21.42
But I would like to use topojson/geojson for the same. how can I convert the data from "coordinates.csv" into json?