- Z level shape finishing hypermill tutorial how to#
- Z level shape finishing hypermill tutorial install#
- Z level shape finishing hypermill tutorial code#
- Z level shape finishing hypermill tutorial zip#
Z level shape finishing hypermill tutorial code#
So I modified the above code to first check if there are any closed loops (number of parts > 1) and then loop over each part, pulling out the correct index range for each segment of geometry: plt.figure()Įlse: # loop over parts of each shape, plot separatelyįor ip in range(nparts): # loop over parts, plot separatelyĪnd we can see those spurious lines are now gone: The issue is that in some of the shapes (states), the geometry has multiple closed loops (because of the islands in some states), so simply connecting the lat/lon points creates some weird lines.īut it turns out that the parts attribute of each shape includes information to save us! For a single shape the parts attribute (accessed with shape.parts) contains a list of indeces corresponding to the start of a new closed loop within a shape. Great! So all we need now is to loop over each shape (state) and plot it! Right? Well this code snippet does just that: plt.figure()Īnd we can see some problems with the result: Since I’m setting the axes aspect ratio equal here, I only define the x limit. bbox contains four elements that define a bounding box using the lower left lon/lat and upper right lon/lat. This returns the state of Oregon! I also used the bbox attribute to set the x limits of the plot. # use bbox (bounding box) to set plot limits Y_lat = np.zeros((len(shape_ex.points),1))Īnd then I plot it: plt.plot(x_lon,y_lat,'k') So I pull out the first and second index and put them in pre-defined numpy arrays: x_lon = np.zeros((len(shape_ex.points),1)) A single point can be accessed with shape_ex.points and will return a lon/lat pair, e.g. So I loop over those points to create an array of longitude and latitude values that I can plot. The points attribute contains a list of latitude-longitude values that define the shape (state) boundary.
So I first pull out the information for a single shape (in this case, the 5th shape): shape_ex = sf.shape(5) The first thing I wanted to do after importing the shapefile was just plot a single state. The pyshp documentation describes each, and I’ll touch on each one in the following (except for shapeType). and Puerto Rico.įor each shape (or state), there are a number of attributes defined: bbox, parts, points and shapeType. To check how many shapes have been imported: print 'number of shapes imported:',len(sf.shapes())įor the state boundary shapefile, this returns 52 for the 50 states, Washington D.C. This creates a shapefile object, sf, and the next few lines do some basic inspections of that object. The three plots described below should pop up.Īfter the initial comment block and library import, the code reads in the shapefile using the string variables that give the location of the shapefile directory (data_dir) and the name of the shapefile without extension (shp_file_base): sf = shapefile.Reader(dat_dir+shp_file_base)
Z level shape finishing hypermill tutorial install#
install the pyshp Python library (and numpy and matplotlib if you don’t have them already).dbf file containing attributes of each shape (like the name of each state) and others (check out the wiki page on shapefiles for a description of the other file extensions).Īt present, the src folder includes only one python script: basic_read_plot.py. shp file containing information on shape geometry (state boundaries in this case), a. A shapefile is actually a collection of different files, including a.
Z level shape finishing hypermill tutorial zip#
zip file, unpack it and take a look inside. Go get yourself a shapefile! The one I used (which will definitely work with my code below) is the lowest resolution state-level cartographic boundary shapefile from ( link to, direct link to lowest resolution 20m.
Z level shape finishing hypermill tutorial how to#
There are many ways to visualize shapefiles in a more automated way than I do here, but I think that my approach here gives a clearer picture to a beginner of what a shapefile is and how to use Python with shapefiles. I found the pyshp Python library the most approachable, so that’s what I use in the following example. So here’s a SUPER simple example of how to load, inspect and plot a shapefile to make a map of the U.S! There are quite a few Python libraries dealing with shapefiles and it was hard to find the easiest place to start. But what I wanted was a tutorial that helped me to plot a simple shapefile while getting to know what a shapefile actually is! Many tutorials that I found assumed some previous knowledge of either shapefiles or the python libraries used to manipulate them. I recently started a couple of projects that will involve using shapefiles and I got frustrated real fast.