We Keep Coding

Shapely polygon buffer problem with 'y' coordinates(double value)

I use shapes to work with contours. I need to add to the contours of different sizes around the field by a given value. Do not scale the contour by a certain percentage, but expand the border by the same given value, regardless of the size of the contour itself.
I am trying to do it like this:
from shapely.geometry import Polygon, LinearRing
coords = [(30.3283020760901, 59.929340439331035), (30.32625283518726, 59.929669569762034), (30.326617897500824, 59.93065894162025), (30.328354001537814, 59.93056342794558), (30.329838363175877, 59.93089851628186), (30.330225213253033, 59.929729335995624), (30.3283020760901, 59.929340439331035)]
poly_B = Polygon(coords)
poly_A = s.buffer(0.005, quad_segs=10.0, cap_style=1, join_style=2, mitre_limit=10.0)
Or like this:
r = LinearRing(coords)
poly_B = Polygon(r)
poly_A = Polygon(s.buffer(0.005).exterior, [r])
But every time I get a contour in which the Y coordinate is doubled(see image).
Help me figure out where I'm wrong.
I need the fields of the larger contour to be uniform relative to the smaller one.

Plot polygons with buffer of some radius using folium not working properly

I am trying plot the intersection between a buffer circle and the mesh blocks (or boundaries) within that circle of some radius (in this case, 80 km).
I got the intersection using sjoin() as follows:
intersection_MeshBlock = gpd.sjoin(buffer_df, rest_VIC, how='inner', predicate='intersects')
My buffer variable looks like this:
buffer_df
And the intersection looks like this:
intersection
The problem is I am not able to plot the intersection polygons.
Here is the plot I get after I plot it using the polygon plotting in folium:
for _, r in intersection_MeshBlock.iterrows():
# Without simplifying the representation of each borough,
# the map might not be displayed
sim_geo = gpd.GeoSeries(r['geometry']).simplify(tolerance=0.00001)
geo_j = sim_geo.to_json()
geo_j = folium.GeoJson(data=geo_j,
style_function=lambda x: {'fillColor': 'orange'} )
folium.Popup(r['SA1_CODE21']).add_to(geo_j)
geo_j.add_to(m)
m
Plot:
color filled maps
What am I doing in wrong ways?
EDIT:
I might have solved the issue partially. Now, I am able to plot the polygons inside some buffer radius. This is how my plot looks like:
If you see the image, you will realise that there are certain meshblocks that cross the circular boundary region. How do I get rid of everything which is outside that circular region?

have located some geometry for Melbourne to demonstrate
fundamentally, you want to use overlay() not sjoin()
generation of folium map is much simpler using GeoPandas 0.10 capability explore()
import geopandas as gpd
import numpy as np
import shapely.geometry
import folium
rest_VIC = gpd.read_file(
"https://raw.githubusercontent.com/codeforgermany/click_that_hood/main/public/data/melbourne.geojson"
)
# select a point randomly from total bounds of geometry
buffer_df = gpd.GeoDataFrame(
geometry=[
shapely.geometry.Point(
np.random.uniform(*rest_VIC.total_bounds[[0, 2]], size=1)[0],
np.random.uniform(*rest_VIC.total_bounds[[1, 3]], size=1)[0],
)
],
crs=rest_VIC.crs,
)
buffer_df = gpd.GeoDataFrame(
geometry=buffer_df.to_crs(buffer_df.estimate_utm_crs())
.buffer(8 * 10**3)
.to_crs(buffer_df.crs)
)
# need overlay not sjoin
intersection_MeshBlock = gpd.overlay(buffer_df, rest_VIC, how="intersection")
m = rest_VIC.explore(name="base", style_kwds={"fill":False}, width=400, height=300)
m = buffer_df.explore(m=m, name="buffer", style_kwds={"fill":False})
m = intersection_MeshBlock.explore(m=m, name="intersection", style_kwds={"fillColor":"orange"})
folium.LayerControl().add_to(m)
m

Overlaying Shapefile datapoints on Density Map

I am new to shapefiles and mapping in python so I was hoping to get some help with overlaying data points from a shapefile on a density map.
To be honest, I am a beginner with mapping and reading in shapefiles so what I have so far not much.
I have started off using pyshp but if there are better packages out there to do this then I would love any feedback.
The following code is to create the base map of the LA area:
def get_base_map(rides_clean):
return folium.Map(locations=[rides_clean.start_lat.mean(),
rides_clean.start_lon.mean()],
zoom_start = 20, tiles = 'cartodbpositron')
The following code is to create the density/heat map:
from folium import plugins
stationArr = rides_clean[['start_lat', 'start_lon']][:40000].as_matrix()
get_base_map(rides_clean).add_child(plugins.HeatMap(stationArr,
radius=40, max_val=300))
The following code is the same heat map but with route lines added:
(draw_route_lines(get_base_map(rides_clean),
routedf_vol)).add_child(plugins.HeatMap(stationArr, radius=40,
max_val=300))
I want to see data points from the shapefile shown as markers on top of the density plot.

It is possible to do this with pyshp. I've only ever used Matplotlib to plot shapefile points on a map, but this method will create two arrays which will be the x and y coordinates of each point you'd like to plot. The first snippet is used if you have multiple shapes in your shapefile, while the second can be used if you only have one shape.
import shapefile
import numpy as np
sf = shapefile.Reader('/path/to/shapefile')
point_list = []
for shape in sf:
temp = shape.points()
point_list.append(temp)
point_list = np.array(point_list)
x = point_list[:,0]
y = point_list[:,1]
And for a shapefile with only a single shape:
import shapefile
import numpy as np
sf = shapefile.Reader('/path/to/shapefile')
point_list = np.array(sf.shape(0).points)
x = point_list[:,0]
y = point_list[:,1]
You can tell how many shapes are in your shapefile using sf.shapes() and it will print a list detailing all the different shapes. From your question it appeared you were wanting to plot it as points on the marker rather than lines, sorry if this is not the case.

Why does scipy.signal.correlate2d fail to work in this example?

I am trying to cross-correlate two images, and thus locate the template image on the first image, by finding the maximum correlation value.
I drew an image with some random shapes (first image), and cut out one of these shapes (template). Now, when I use scipy's correlate2d, and locate point in the correlation with maximum values, several point appear. From my knowledge, shouldn't there only be one point where the overlap is at max?
The idea behind this exercise is to take some part of an image, and then correlate that to some previous images from a database. Then I should be able to locate this part on the older images based on the maximum value of correlation.
My code looks something like this:
from matplotlib import pyplot as plt
from PIL import Image
import scipy.signal as sp
img = Image.open('test.png').convert('L')
img = np.asarray(img)
temp = Image.open('test_temp.png').convert('L')
temp = np.asarray(temp)
corr = sp.correlate2d(img, temp, boundary='symm', mode='full')
plt.imshow(corr, cmap='hot')
plt.colorbar()
coordin = np.where(corr == np.max(corr)) #Finds all coordinates where there is a maximum correlation
listOfCoordinates= list(zip(coordin[1], coordin[0]))
for i in range(len(listOfCoordinates)): #Plotting all those coordinates
plt.plot(listOfCoordinates[i][0], listOfCoordinates[i][1],'c*', markersize=5)
This yields the figure:
Cyan stars are points with max correlation value (255).
I expect there to be only one point in "corr" to have the max value of correlation, but several appear. I have tried to use different modes of correlating, but to no avail.
This is the test image I use when correlating.
This is the template, cut from the original image.
Can anyone give some insight to what I might be doing wrong here?

You are probably overflowing the numpy type uint8.
Try using:
img = np.asarray(img,dtype=np.float32)
temp = np.asarray(temp,dtype=np.float32)
Untested.

Applying
img = img - img.mean()
temp = temp - temp.mean()
before computing the 2D cross-correlation corr should give you the expected result.
Cleaning up the code, for a full example:
from imageio import imread
from matplotlib import pyplot as plt
import scipy.signal as sp
import numpy as np
img = imread('https://i.stack.imgur.com/JL2LW.png', pilmode='L')
temp = imread('https://i.stack.imgur.com/UIUzJ.png', pilmode='L')
corr = sp.correlate2d(img - img.mean(),
temp - temp.mean(),
boundary='symm',
mode='full')
# coordinates where there is a maximum correlation
max_coords = np.where(corr == np.max(corr))
plt.plot(max_coords[1], max_coords[0],'c*', markersize=5)
plt.imshow(corr, cmap='hot')

Check if point is inside boundaries

I have a list of points describing the boundaries of Spain. I want to be able to tell whether a pair of lat,lon is within these boundaries. I have tried the following:
import shapefile
import matplotlib.pyplot as plt
from shapely.geometry import MultiPoint, Point, Polygon
from shapely.geometry.polygon import Polygon
sf = shapefile.Reader(r"\ESP_adm0.shp")
shapes = sf.shapes()
lat = []; lon = []
for i in range(len(shapes[0].points)):
lon.append(shapes[0].points[i][0]);lat.append(shapes[0].points[i][1])
I know I am retrieving the points, because I'm able to plot and get the desired results:
plt.plot(lon,lat,'.', ms=0.1)
(plot in the link below)
plot result
I do the following to get the poitns into a polygon:
coords = list(zip(lat,lon))
spain_pol = Polygon(coords)
And then I use the contains function, always getting false.
spain_pol.contains(Point(0,42))
spain_pol.contains(Point(42,0))
These both return false. In fact I haven't been able to get a single point I've tried to return a True.
I have tried all sorts of things, and I think I must be missing something fundamental. Perhaps the facts Spain has islands and there's more than one polygon is the problem? I'm lost. Any help welcome.

Just in case someone else has the same issue. The following code worked perfectly:
import shapefile
import fiona
from shapely.geometry import MultiPoint, Point, Polygon,shape
from shapely.geometry.polygon import Polygon
multipol = fiona.open(r"C:\Users\Jordi\Downloads\ESP_adm_shp\ESP_adm0.shp")
multi = next(iter(multipol))
point = Point(0,42)
point.within(shape(multi['geometry']))
This returns a very welcome "True" :)