I have this code based on this question, just a different point Extract constrained polygon using OSMnx
I am trying to plot the block in which the point is located but it does nothing, it just prints "Done" but I cannot see any image
import osmnx as ox
import geopandas as gpd
import shapely
point = (50.090464, 14.400070)
streets_graph = ox.graph_from_point(point, distance=500, network_type='drive')
streets_graph = ox.project_graph(streets_graph)
streets = ox.save_load.graph_to_gdfs(streets_graph, nodes=False, edges=True,
node_geometry=False, fill_edge_geometry=True)
point = streets.unary_union.centroid
polygons = shapely.ops.polygonize(streets.geometry)
polygons = gpd.GeoSeries(polygons)
target = polygons.loc[polygons.contains(point)]
target_streets = streets.loc[streets.intersection(target.iloc[0]).type == 'MultiLineString']
ax = target_streets.plot()
gpd.GeoSeries([point]).plot(ax=ax, color='r')
print("Done")
I do not think this may help but I am using Visual Studio Code
Thank you very much
Since my comment answered your question, I will summarize it here for other people:
When using plotting library dependent on matplotlib, like geopandas or seaborn, you will need to import matplotlib in order to show the plot. The way matplotlib is imported will depend on whether you are using Jupyter or simple scripting (.py) files.
For Jupyter you need to import it like this:
%matplotlib inline
For simple scripting (.py) file you need to import it like this:
import matplotlib.pyplot as plt
Then when you want to show your plot you simply do
plt.show()
Hope it helps!
Related
I want to plot the background map of Melbourne behind the plotted points of property addresses.
I used the following code:
import pandas as pd
import geopandas as gpd
from shapely.geometry import shape
import matplotlib.pyplot as plt
import contextily
MELB_PROPERTY_DATA = "https://data.melbourne.vic.gov.au/resource/imwx-szwr.json"
properties = pd.read_json(MELB_PROPERTY_DATA)
properties['the_geom'] = properties['the_geom'].apply(shape)
properties_geo = gpd.GeoDataFrame(properties).set_geometry('the_geom')
ax = properties_geo.plot(markersize=1)
contextily.add_basemap(ax)
plt.show()
At the contextily.add_basemap(ax) line I get the following UserWarning.
contextily\tile.py:632: UserWarning: The inferred zoom level of 30 is
not valid for the current tile provider (valid zooms: 0 - 18).
I read the Contextily docs but they don't fix my problem.
Changing the line to contextily.add_basemap(ax, zoom=5) removes the UserWarning but still no background map appears.
Similar questions have been asked on SO, but I can't retrofit them to my problem.
I feel like I'm importing lots of libraries for this simple task as well, so if you have any suggestions to fine-tune it that would also be appreciated.
I solved this by realising from swatchai's comment that a Coordinate Reference System (CRS) was never defined.
See below for final code, with erroneous lines commented out to show the difference.
import pandas as pd
import geopandas as gpd
from shapely.geometry import shape
import matplotlib.pyplot as plt
import contextily
MELB_PROPERTY_DATA = "https://data.melbourne.vic.gov.au/resource/imwx-szwr.json"
properties = pd.read_json(MELB_PROPERTY_DATA)
properties['the_geom'] = properties['the_geom'].apply(shape)
# properties_geo = gpd.GeoDataFrame(properties).set_geometry('the_geom')
properties_geo = gpd.GeoDataFrame(properties, geometry='the_geom', crs='EPSG:4326')
ax = properties_geo.plot(markersize=1)
# contextily.add_basemap(ax)
contextily.add_basemap(ax, crs=properties_geo.crs.to_string())
plt.show()
I am trying to find out all the polygons in a shapefile through QGIS Algorithm Extract By Location and it gives me perfect results but takes too much time, around 25 hours. Now, I want it to be done by other libraries like geopandas or other libraries if possible. Can anyone suggest me which library can help?
This is what am doing in geopandas:
import itertools
import geopandas as gpd
gi = gpd.GeoDataFrame.from_file("D:\Shape_file_uploader\qgis\laneGroup.shp")
geoms = gi['geometry'].tolist()
intersection_iter = gpd.GeoDataFrame(gpd.GeoSeries([poly[0].intersection(poly[1]) for poly in itertools.combinations(geoms, 2)
I did this some time ago and if I remember correctly I used the geopandas overlay method. So the 'pseudo' code to handle this ...
from geopandas import GeoDataFrame, overlay
first_shape_gdf = GeoDataFrame.from_file('D:\Shape_file_uploader\qgis\laneGroup.shp')
second_shape_gdf = GeoDataFrame.from_file('another.shp')
intersection_gdf = overlay(first_shape_gdf, second_shape_gdf, how='intersection')
Have a look at Set-Operations with Overlay
I would have thought this would be so simple it would be almost example 1 in any mapping documentation. But it seems not... I want to map the boundary of an electorate over a street map, using cartopy. I can download the GIS data of the electorates in either MapInfo or ShapeFile form. When I tried to do this a year ago, the only way I could find to do it was to extract the lat/long coordinates of the MapInfo polygon, and plot them with matplotlib.
I'm trying to be a bit more elegant this year. With the MapInfo file, I can isolate my particular electorate with
import geopandas as gpd
v = gpd.read_file('VicMaps/vic-july-2018-mid-mif/E_VIC18.MIF')
cg = v.loc[7].geometry
My efforts to extract a particular single boundary from a shapefile are given below.
The other issue is that when I try to run this in jupyter, attempts at plotting a map causes the kernel (python 3.4) to crash.
There must be examples of this somewhere, but so far I haven't found an example which works with my data. This is what I have so far, cobbled together from various helpful answers to other people's questions:
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
from cartopy.io.shapereader import Reader
from cartopy.io.img_tiles import OSM
shp = Reader('VicMaps/E_AUGFN3_region.shp')
fig = plt.figure(figsize=(16,16))
tiler = OSM()
ax = plt.axes(projection=tiler.crs)
ax.set_extent([144.956158, 145.085398, -37.813662, -37.690999])
for r, g in zip(shp.records(), shp.geometries()):
if r.attributes['Elect_div'] == 'Cooper':
ax.add_geometries(g, ccrs.Geodetic())
plt.show()
But what happens is that the kernel just dies "unexpectedly".
If anybody could point me in the direction of a solution, I'd be delighted! Also: I'm not wedded to cartopy; if there's a better package I'll use it.
Thanks!
I am trying to run the python code using the R-Markdown file (RMarkdown to pdf).
What I achieved till now -
1- I am able to configure my python engine using knitr and reticulate library
2- I am able to execute my python codes.
What I tried -
1- I tried all the methods which are discussed in this forum, but nothing is working out.
2- I also tried to save the image,(as one of the posts here suggests), but that also is not working.
My problem -
1- When I am trying to plot a graph using matlplotlib and command plt.imshow() and plt.show(), it's not printing the image in the output. Rather it's showing the image in a separate window. You can see my results in the attached image.
Result_of_my_code
Here is my code
```{r setup, include=FALSE}
library(knitr)
library(reticulate)
knitr::knit_engines$set(python = reticulate::eng_python)
```
```{python}
import numpy as np
import os
import torch
import torchvision.datasets as dsets
import matplotlib.pyplot as plt
print(os.getcwd())
os.chdir('D:\\1st year\\Python codes\\CIFR Analysis\\self contained analysis')
print(os.getcwd())
train_mnist = dsets.MNIST("../data", train=True)
test_mnist = dsets.MNIST("../data", train= False)
print(len(train_mnist))
#print(train_mnist[0][0])
plt.imshow(train_mnist[0][0], cmap="gray")
#plt.savefig("trainzero.png")
plt.show()
```
Kindly, help me to fix this issue, as I want to compile my python codes using the R markdown file.
thanks
So with R Markdown, you have to do some things a little differently. In the following, I have a dataframe with two series created by concatenating them. The original plotting code in the Jupyter Notebook is as follows and just printed out the series.
# make a plot of model fit
train.plot(figsize=(16,8), legend=True)
backtest.plot(legend=True);
However, it does not work with way with R Markdown. Then with plotting, you always have to assign them, and with the code below, you get the same plot.
dfreg = pd.concat([reg, backtest], axis = 1)
ax = dfreg.plot(figsize=(16,8), legend = True)
ax1 = predictions.plot(legend=True)
plt.show()
This is common with other plotting functions like plot_acf() too.
I'm required to use the information from a .sac file and plot it against a grid. I know that using various ObsPy functions one is able to plot the Seismograms using st.plot() but I can't seem to get it against a grid. I've also tried following the example given here "How do I draw a grid onto a plot in Python?" but have trouble when trying to configure my x axis to use UTCDatetime. I'm new to python and programming of this sort so any advice / help would be greatly appreciated.
Various resources used:
"http://docs.obspy.org/tutorial/code_snippets/reading_seismograms.html"
"http://docs.obspy.org/packages/autogen/obspy.core.stream.Stream.plot.html#obspy.core.stream.Stream.plot"
The Stream's plot() method actually automatically generates a grid, e.g. if you take the default example and plot it via:
from obspy.core import read
st = read() # without filename an example file is loaded
tr = st[0] # we will use only the first channel
tr.plot()
You may want to play with the number_of_ticks, tick_format and tick_rotationparameters as pointed out in http://docs.obspy.org/packages/autogen/obspy.core.stream.Stream.plot.html.
However if you want more control you can pass a matplotlib figure as input parameter to the plot() method:
from obspy.core import read
import matplotlib.pyplot as plt
fig = plt.figure()
st = read('/path/to/file.sac')
st.plot(fig=fig)
# at this point do whatever you want with your figure, e.g.
fig.gca().set_axis_off()
# finally display your figure
fig.show()
Hope it helps.