Python plot shapefile over raster

GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Already on GitHub? Sign in to your account. The masking raster by shapefile page has all necessary documentation to perfom the mask thanks! I've been trying for awhile to overlay fiona features over a rasterio src, and have not succeeded.

See the SO postwhich hasn't worked for me This issue tracker isn't for these kinds of questions and plotting is mostly outside Rasterio's scope. I agree that this kind of snippet would be useful, but the project needs to focus on fixing bugs and planning new features for 1. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

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python plot shapefile over raster

New issue. Jump to bottom. Labels wontfix. Copy link Quote reply. This comment has been minimized. Sign in to view. Might be something like this: from matplotlib import pyplot as plt from matplotlib import collections as cplt import rasterio. I solved the issue and will post it on SO. Thanks for all you do, I use your projects everyday.

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Mapping Geograph Data in Python

Linked pull requests. You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window.In previous lessons, you reclassified a raster in Python ; however, the edges of your raster dataset were uneven. Cropping sometimes also referred to as clippingis when you subset or make a dataset smaller, by removing all data outside of the crop area or spatial extent. This is useful as it:. In the previous lessons, you worked with a raster layer that looked like the one below.

Notice that the data have an uneven edge on the left hand side. To begin your clip, open up a vector layer that contains the crop extent that you want to use to crop your data. To open a shapefile you use the gpd. You will learn more about vector data in Python in a few weeks. Next, view the coordinate reference system CRS of both of your datasets.

Remember that in order to perform any analysis with these two datasets together, they will need to be in the same CRS. Now that you have imported the shapefile. When you crop the data, you can then export it and share it with colleagues. Or use it in another analysis.

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Cropping the data can be your first step. Without EarthPy, you would have to perform this with a Geojson object. Geojson is a format that is worth becoming familiar with. We will discuss it in more detail later in the class.

For now, have a look at the output below. Once you have cropped your data, you may want to export it. In the subtract rasters lesson you exported a raster that had the same shape and transformation information as the parent rasters. However in this case, you have cropped your data. You will have to update several things to ensure your data export properly:.

However you may have them in a future raster! Learning Objectives Crop a raster dataset in Python using a vector extent object derived from a shapefile. Open a shapefile in Python. HOME'earth-analytics'. Plot the crop boundary layer Note this is just an example so you can see what it looks like You don't need to plot this layer in your homework!

Text 0. Plot your data ep.

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You will have to update several things to ensure your data export properly: The width and height of the raster: You can get this information from the shape of the cropped numpy array and The transformation information of the affine object. Finally you may want to update the nodata value. Optional Challenge: Crop Change Over Time Layers In the previous lesson, you created 2 plots: A classified raster map that shows positive and negative change in the canopy height model before and after the flood.

To do this you will need to calculate the difference between two canopy height models.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

It only takes a minute to sign up. I need to plot a shapefile on top of a raster. I would like to use the python package rasterio and some combination of fiona or geopandas to load the shapefile. I found an example in the rasterio documentation but it doesn't provide code for the actual plotting.

I have tried loading the raster with rasterio, loading the shapefile using fiona, calling imshow on the raster, and calling plot on the shapefile. However, the shapefile does not appear on the plot. I added that recipe to the rasterio documentation. Since it was such a simple shape, in this case I just unzipped the coords in the single record contained by the shapefile. More generally, I use PolygonPatch from descartesand matplotlib.

The appearance of the shapes can be customized with keywords like edgecolor or facecolor passed to PolygonPatch. To produce a thick red line as in the example, replace the last two lines in the example above with:. Doing so using PolygonPatch gives more flexibility to set different colors, widths, etc. Sign up to join this community.

The best answers are voted up and rise to the top. Home Questions Tags Users Unanswered. Plot shapefile on top of raster using plot and imshow from matplotlib Ask Question. Asked 3 years, 11 months ago.

Active 8 months ago. Viewed 5k times. Active Oldest Votes. PatchCollection patches The appearance of the shapes can be customized with keywords like edgecolor or facecolor passed to PolygonPatch. I am having trouble importing rasterio. Any ideas? I am using rasterio version 0. Use rasterio.

Lesson 3. Classify and Plot Raster Data in Python

Yes, the bounds of the raster in whatever projection it is in are used to set the extent of the plot window, which then allows the vector layer to be properly overlaid. It would be great to add it to the original rasterio documentation, I ran into the same problem. Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password. Post as a guest Name. Email Required, but never shown.

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python plot shapefile over raster

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I have a geodataframe of points in epsg and want to plot these points with geoplot. But I'm struggling to make this happen. Initially I have tried geopandas add background with Contextily. I would like the points to be on the raster kdeplot preferably.

I have done it before in R with minimal effort but here I need to do it in Python, and I'm new to Python. The solution may be very simple here too, but I need a bit of suggestion. Thank you for reading this. You have to pass the same matplotlib ax to both plots. So in your case, following should to the trick. Learn more. How to plot spatial points over a raster layer Python? Ask Question. Asked 7 months ago. Active 7 months ago. Viewed times. Active Oldest Votes.

Thank you so much, your suggestion helped. Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password. Post as a guest Name.One great help when working in Data Science, is to visualize your data on a geo map and for that, several packages can take care of it, as GeoPandas for example.

Sometimes install Geopandas packages can be complicated, depending on what environment you are working. Or, simplesly you need take the control seat of your code!

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So, on this article we will explore on "the hard way", how to construct our own "geo map functions", using "Shapefiles" and basic Python libraries. Each geometry has a set of associated attributes. The shapefile is in fact a grouping of several files formatted to represent different aspects of geodata:.

There are also several optional files in the shapefile format. The most significant of these is the. Although not part of the Esri shapefile standard, the. For more info see wikipedia. To Install pyshpexecute below instruction in your Terminal:. Initializing vizualization set.

As described on 1. Important that all other files as 'name. On this tutorial, we will work with maps related to the cities "Comunas" that together, make the Santiago Metropolitan Region. On INE Chilean National Institute of Statisticsis possible to download a group of shapefiles related with maps, created for the last national census :. Let's check how many different "shapes" were imported by our function shp.

This means that exist 52 shapes on our shape files, what make sense, once the Santiago Metropolitan Region has 52 "comunas" as shown at below map do not worry, before the end of this article, you will learn how to create a map like this one, directly from your data :.

python plot shapefile over raster

Let's also explore one of the shapes or "records" :. The result will be an array with 6 elements:. You can get its name directlly:. The most central 'comuna' of Santiago Metropolitan Region is exactly the Comuna of Santiago little confuse?

Note that you can apply what will be describe on this tutorial to any shapfile.

Introduction to GIS Analysis with GeoPandas using Python

But how to find such id, starting from a comuna's name? Let's first create a usefull function to convert our 'shapefile' format on a more commun Pandas dataframe format:. So, let's convert sf data on a dataframe and see how it looks like:.

The dataframe has a shape of 52, 7. What means that we we have 7 diferent features columns for each line 'comuna'. Remember that previosly we saw 6 of those features.One great help when working in Data Science, is to visualize your data on a geo map and for that, several packages can take care of it, as GeoPandas for example.

Sometimes install Geopandas packages can be complicated, depending on what environment you are working. Or, simplesly you need take the control seat of your code! So, on this article we will explore on "the hard way", how to construct our own "geo map functions", using "Shapefiles" and basic Python libraries. Each geometry has a set of associated attributes. The shapefile is in fact a grouping of several files formatted to represent different aspects of geodata:.

There are also several optional files in the shapefile format. The most significant of these is the. Although not part of the Esri shapefile standard, the. For more info see wikipedia. To Install pyshpexecute below instruction in your Terminal:. Initializing vizualization set. As described on 1. Important that all other files as 'name.

On this tutorial, we will work with maps related to the cities "Comunas" that together, make the Santiago Metropolitan Region. On INE Chilean National Institute of Statisticsis possible to download a group of shapefiles related with maps, created for the last national census :. Let's check how many different "shapes" were imported by our function shp. This means that exist 52 shapes on our shape files, what make sense, once the Santiago Metropolitan Region has 52 "comunas" as shown at below map do not worry, before the end of this article, you will learn how to create a map like this one, directly from your data :.

Let's also explore one of the shapes or "records" :. The result will be an array with 6 elements:. You can get its name directlly:. The most central 'comuna' of Santiago Metropolitan Region is exactly the Comuna of Santiago little confuse?

Note that you can apply what will be describe on this tutorial to any shapfile. But how to find such id, starting from a comuna's name? Let's first create a usefull function to convert our 'shapefile' format on a more commun Pandas dataframe format:.

So, let's convert sf data on a dataframe and see how it looks like:.In this lesson, you will learn how to reclassify a raster dataset in Python. In that raster, each pixel is mapped to a new value based on some approach. This approach can vary depending upon your science question.

Please note - working with data is not a linear process. Above you see a potential workflow. You will develop your own workflow and approach.

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To get started, first load the required libraries and then open up your raster. In this case, you are using the lidar canopy height model CHM that you calculated in the previous lesson.

There are many different approaches to classification. In this case, you are simply going to create the classes manually using the range of quantitative values found in our data. Assuming that our data represent trees though you know there are likely some buildings in the dataclassify your raster into 3 classes:.

To perform this classification, you need to understand which values represent short trees vs medium trees vs tall trees in your raster. This is where histograms can be extremely useful. Get to know your data by looking at a histogram. A histogram quantifies the distribution of values found in your data.

Further explore your histogram, by constraining the x axis limits using the xlim and ylim parameters. You might also chose to adjust the number of bins in your plot. Below you plot a bin for each increment on the x axis calculated using:.

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To do this, you can collect the outputs that are returned when you call np. This consists of two things:. Each bin represents a bar on your histogram plot. Each bar represents the frequency or number of pixels that have a value within that bin. Notice that you have adjusted the xlim and ylim to zoom into the region of the histogram that you are interested in exploring; however, the values did not actually change.

Next, customize your histogram with breaks that you think might make sense as breaks to use for your raster map.


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