Voting Districts Day 13: Rework and Shapely Saves the Day

I reached an impasse a while back after realizing that I needed a means of determining whether voting districts shared boundaries.  I built the algorithm to cluster the voting districts together, but the clustering fell apart when my initial points were out in the wilderness or towards the end when the process had slim pickings.  Here's an example of my starting points (green Xs) and the final central points (pink diamonds) for each clustered group.  you can see it completely fell apart at the western part of the state.

Enter the Python module Shapely, which has a great touches function where you can see if two voting districts share a boundary, which I can make a requirement before associating a voting district with other districts.

Additionally, shapely does a lot of other great things that makes this process much easier.  One of the big ones is that it allows you to create polygon objects, which has lots of helpful methods, such as finding a centroid, getting the bounds and area, etc.  

So, I'm working on reworking pretty much everything to use the Shapely module.  Stay tuned.

Voting Districts Day 12: Coloring our Shapes

Last time, we were able to create a map through Python and Matplotlib with our shapefile consisting of all of the NC voting districts. This time, we want to map it, but give each shape a different color.  

This can be accomplished by providing the facecolor variable when we add our voting district shapefile patch collection with an array parameter instead of the 'green' that we provided it last time.  

Eventually, we want to specify a color by the chosen voting district.  That'll be for next time.  This time, we'll just create an array of random colors.

I do know that we'll have thirteen voting districts.  I'm going to create a dictionary consisting of each voting district number and a corresponding color:

color_switch = {0: 'teal', 1:'red',2:'blue',3:'green',4:'purple',5:'brown',6:'orange',7:'white',8:'black',9:'tan',10:'lightblue',11:'pink',12:'yellow'}

From there, build a list by looping through the number of voting districts, selecting one of those items, and appending it to that list we'll eventually use to color the shapes.  to select something at random, we'll use the aptly named random module.

import random

color_choice = [] #list for our colors

#for each shape in our shapefile, pick a random color and append to our color choice list.

for shape in shapes: color_choice.append(random.choice(color_switch))

And then apply the list as an array (thanks to numpy) when we add the collection to the map.  

import numpy as np

ax.add_collection(PatchCollection(patches, facecolor= np.array(color_choice), edgecolor='k',  linewidths=0.2, zorder=2))

And you get a map like so!

Here's all the code to create the map from the beginning:

import matplotlib.pyplot as plt #what I need to plot stuff to my map.

from mpl_toolkits.basemap import Basemap #what I need to create my basemap.

import shapefile #what I need to read the shapefile from the NC SBE.

from pyproj import Proj #module used to change our projection from the nc to the traditional

from matplotlib.patches import Polygon #used to convert our newly reprojected coordinates to a polygon/patch shape that matplotlib can plot.  

from matplotlib.collections import PatchCollection #we'll be adding all of our voting districts to a patch collection and then plotting that collection.  

import numpy as np #allows us to better interact with arrays, which is the structure used with polygons/ patches.

import random #use this to select a random color from our voting district color dictionary.

conv_coords = list() #hold our converted coordinates here as we apply each set of converted coordinates that make up our shape.  

patches = [] #list/collection we'll be sticking our patches/polygons/voting districts into.  This'll pass as the points within our point collection.  

#projection type for the voting districts.

nc = Proj("+proj=lcc +lat_1=34.33333333333334 +lat_2=36.16666666666666 +lat_0=33.75 +lon_0=-79 +x_0=609601.2192024384 +y_0=0 +datum=NAD83 +to_meter=0.3048006096012192 +no_defs ", preserve_units=True)

vote = shapefile.Reader('ncsbe\\Precincts.shp') #creates an instance that has the lists of data we want.

shapes = vote.shapes() #lists of coordinates making up the shape for each voting district.

for x in range(0,len(shapes)): #for each voting district...

    conv_coords.append([]) #apply a new list to our main list consisting of all the shapes.

    for y in range(0,len(shapes[x].points)): #for each set of coords in the shape...

        lon, lat = nc(shapes[x].points[y][0], shapes[x].points[y][1], inverse=True) #convert the shape file points into traditional lat/long coords.

        conv_coords[x].append([lon,lat]) #write the converted coordinates to the new sublist. 

    patches.append(Polygon(np.array(conv_coords[x]), True)) #sublist consisting of all the shapes coordinates is complete.  append to patchcollection list.  

#dictionary that associates a voting district to a color.

color_switch = {0: 'teal', 1:'red',2:'blue',3:'green',4:'purple',5:'brown',6:'orange',7:'white',8:'black',9:'tan',10:'lightblue',11:'pink',12:'yellow'}

color_choice = [] #list to hold our color selections.

#for each shape in our voting district shapefile.

for shape in shapes: color_choice.append(random.choice(color_switch))

#create our basemap.

m = Basemap(projection= 'cyl', lon_0 = -80, lat_0 = 35, llcrnrlon=-84.9,llcrnrlat=33.5,urcrnrlon=-75.,urcrnrlat=36.6, resolution='i')

#create a figure/subplot that'll be our canvas for the voting districts.  plt is the main plot object.  

fig     = plt.figure(figsize=(20,6)) #provide a parameter for figsize to make a wide canvas (NC's long shape).

ax      = fig.add_subplot(111)

#add the general stuff we want on our map that comes with basemap.

m.drawcountries(linewidth=0.5)

m.drawcoastlines(linewidth=0.5)

m.drawstates(linewidth=0.5)

#and with that subplot, apply our patch collection (all of our voting district shapes.

ax.add_collection(PatchCollection(patches, facecolor= np.array(color_choice), edgecolor='k',  linewidths=0.2, zorder=2))

#create a file with our basemap and plot overlay.

plt.savefig('vote_map_test_colors.png',dpi=600, alpha=True)

#and complete.

plt.close()

Voting Districts Day 11: Mapping Voting District Shapefiles with Matplotlib

Now that we can create the most basic of maps, let's see if we can build on top of that and apply our layer of voting districts.

Because the voting district shapefile is using a not so traditional projection with latitude and longitude coordinates that you wouldn't necessarily expect, we can't just simply use the straight forward method in matplotlib to apply the shapefile layer to the map.  

Instead, we have to convert the unorthodox coordinates to the traditional projection to match our map's base and then apply that conversion.  That conversion must be a set of polygons (or patches in a patch collections) that we can subplot on top of our basemap consisting of the state of NC.  

import matplotlib.pyplot as plt #what I need to plot stuff to my map.
from mpl_toolkits.basemap import Basemap #what I need to create my basemap.
import shapefile #what I need to read the shapefile from the NC SBE.
from pyproj import Proj #module used to change our projection from the nc to the traditional

from matplotlib.patches import Polygon #used to convert our newly reprojected coordinates to a polygon/patch shape that matplotlib can plot.  
from matplotlib.collections import PatchCollection #we'll be adding all of our voting districts to a patch collection and then plotting that collection.  

import numpy as np #allows us to better interact with arrays, which is the structure used with polygons/ patches.

conv_coords = list() #hold our converted coordinates here as we apply each set of converted coordinates that make up our shape.  
patches = [] #list/collection we'll be sticking our patches/polygons/voting districts into.  This'll pass as the points within our point collection.  

#projection type for the voting districts.
nc = Proj("+proj=lcc +lat_1=34.33333333333334 +lat_2=36.16666666666666 +lat_0=33.75 +lon_0=-79 +x_0=609601.2192024384 +y_0=0 +datum=NAD83 +to_meter=0.3048006096012192 +no_defs ", preserve_units=True)

vote = shapefile.Reader('ncsbe\\Precincts.shp') #creates an instance that has the lists of data we want.
shapes = vote.shapes() #lists of coordinates making up the shape for each voting district.

for x in range(0,len(shapes)): #for each voting district...
    conv_coords.append([]) #apply a new list to our main list consisting of all the shapes.
    for y in range(0,len(shapes[x].points)): #for each set of coords in the shape...
        lon, lat = nc(shapes[x].points[y][0], shapes[x].points[y][1], inverse=True) #convert the shape file points into traditional lat/long coords.
        conv_coords[x].append([lon,lat]) #write the converted coordinates to the new sublist. 
    patches.append(Polygon(np.array(conv_coords[x]), True)) #sublist consisting of all the shapes coordinates is complete.  append to patchcollection list.  

#create our basemap.
m = Basemap(projection= 'cyl', lon_0 = -80, lat_0 = 35, llcrnrlon=-84.9,llcrnrlat=33.5,urcrnrlon=-75.,urcrnrlat=36.6, resolution='i')



#create a figure/subplot that'll be our canvas for the voting districts.  plt is the main plot object.  
fig     = plt.figure()
ax      = fig.add_subplot(111)

#add the general stuff we want on our map that comes with basemap.
m.drawcountries(linewidth=0.5)
m.drawcoastlines(linewidth=0.5)
m.drawstates(linewidth=0.5)

#and with that subplot, apply our patch collection (all of our voting district shapes.
ax.add_collection(PatchCollection(patches, facecolor= 'green', edgecolor='k',  linewidths=0.2, zorder=2))

#create a file with our basemap and plot overlay.
plt.savefig('vote_map_test.png',dpi=600, alpha=True)
#and complete.
plt.close()

and with that, we get a map with the voting districts.  Now we have to figure out how to apply colors to them so we can represent the different districts...

Voting Districts Day 10: Creating a basic map with Matplotlib

Last time, we converted our coordinates from the weird NC format to the standard lat/long format in anticipation of matplotlib needing a better format. 

This time, we are just trying to create something very basic.  I would love to just see a picture with North Carolina on it, for instance.  

So, the package that we need to do this from matplotlib is: 

from mpl_toolkits.basemap import Basemap

To create a basemap, use the following command:

m = Basemap(projection= 'cyl', lon_0 = -80, lat_0 = 35, llcrnrlon=-84.9,llcrnrlat=33.5,urcrnrlon=-75.,urcrnrlat=36.6, resolution='i')

There are a lot of properties in here.  lon_0 and lat_0 set the center point of the map.  llcrnrlon (left lower corner longitude), llcrnrlat (lower left latitude), urcrnrlon (upper right longitude), and rucrnrlat (upper right latitude) set the corner points of the map.  resolution seems pretty general.  h is really a high resolution, but i seems to be adequate for me.  Finally, projection appears to be the type of map.  cyl is the default value and, according to the matplotlib documentation, it seems to be the easiest to get along with.  I'm probably going to need that to deal with the custom North Carolina projections.  All the properties can be found here.

You can also add standard map stuff to your basemap, like coastline boundaries, state/ county/ country boundaries.  I want the coastlines and an outline of the state.

To plot my results, I'm going to need matplotlib pyplot package.  

Putting it all together...

from mpl_toolkits.basemap import Basemap

import matplotlib.pyplot as plt

m = Basemap(projection= 'cyl', lon_0 = -80, lat_0 = 35, llcrnrlon=-84.9,llcrnrlat=33.5,urcrnrlon=-75.,urcrnrlat=36.6, resolution='i')

m.drawstates(linewidth=0.5)

m.drawcoastlines(linewidth=0.5)

#save a picture of our map.

plt.savefig('basic_map.png',dpi=300)

And here's what we get!  We still need a little tweaking, but not too terrible.

Voting Districts Day 9: Back to Python for Mapping

Because of the large size of the resulting JSON file, I don't think I'm going to be able to use the leafletjs javascript module to create a map.  I think that Python is my best bet.  

Mapping in Python might not be so bad anyway.  Matplotlib has a bunch of tools available, particularly modules Basemap and pyplot.  

This page has a lot of great examples that I can use to better understand what's going on.  The trick, though, is getting my shapefile read into it...

Looking at the documentation, I have to figure out some way of dealing with projections again.  My voter district shapefile isn't the standard lat/long values. 
Pyproj has a great method where you can set the inverse property and spit out lat long coordinates from the weird NC coordinates.  So, I'll create a big list of all the coordinates of all the shapes after running them through this method.

import shapefile
from pyproj import Proj

vote = shapefile.Reader('ncsbe\\Precincts.shp') #creates an instance that has the lists of data we want.

shapes = vote.shapes() #create lists of coordinates making up the shape for each voting district.
#for each shape in the shapefile...
for x in range(0,len(shapes)):
    #for each point in the shape...
    for y in range(0,len(shapes[x].points)):

        lon, lat = nc(shapes[x].points[y][0], shapes[x].points[y][1], inverse=True) #convert the shape file points into traditional lat/long coords.

and that's going to give me some data that I can plot according to the documentation in matplotlib (I think).

Voting Districts Day 8 Changing Projections

Well, I can't map my json file because the coordinates are not the traditional latitude/longitude values, but numbers in the 800Ks.  Here's an example of a coordinate:  [1884558.6496061385, 851226.325625971]

Why's this?  The shapefile I downloaded is using a different, less popular, projection to describe where the shapes go in relation to the earth.  So, I need to change the projection of the voting district shapefile to have traditional latitude longitude coordinates.

There's a python package called pyproj that'll hopefully help me.  I was able to install the binary version with pip.  

What projection do I use?  Well, to figure this out, I looked at the prj file that came with the voting district's shapefile and it said:  NAD_1983_StatePlane_North_Carolina_FIPS_3200_Feet.

Thanks to spatialreference.org, I was able to get the projection the pyproj needed here (clicked the Proj4 link).  I plugged the value in the Proj4 link into the right parameter using pyproj's Proj class:

from pyproj import Proj

myProj = Proj("+proj=lcc +lat_1=34.33333333333334 +lat_2=36.16666666666666 +lat_0=33.75 +lon_0=-79 +x_0=609601.2199999999 +y_0=0 +ellps=GRS80 +datum=NAD83 +to_meter=0.3048006096012192 +no_defs")

and then test it out using a coordinate from the voting district shapefile using the corresponding method for Proj and the myProj object I created.

 lat = my_json['features'][1000]['geometry']['coordinates'][0][0][1]

 lon = my_json['features'][1000]['geometry']['coordinates'][0][0][0]

 longi, latti = myProj(lon, lat, inverse=True)

From that, the long and lat values looked to be in the right ballpark (NOT!).  

It turns out that the projection requires another parameter according to this forum post due to the measurements being different (feet vs meters).  I need to set preserve_units to True.    

nc = Proj("+proj=lcc +lat_1=34.33333333333334 +lat_2=36.16666666666666 +lat_0=33.75 +lon_0=-79 +x_0=609601.2192024384 +y_0=0 +datum=NAD83 +to_meter=0.3048006096012192 +no_defs ", preserve_units=True)

Now when I use Proj with the new parameters, I get something much more realistic:  (-81.23183299105735, 36.13090600029268).

So, now I've got to plug the use of pyproj into my overall script, figure out how to convert all of my shapes' coordinates so I can finally create a map!

Voting Districts Day 6: Voting District Draft

Next up, we are going to go through our starting points where each will take turns choosing a voting district that is closest to them. This draft of sorts is intended to cluster the voting districts together to build our congressional districts.

To do this, we are going to continually pick the closest voting district for each point until there are no more voting districts left to pick. As voting districts are picked, we will associate the voting district to the point by appending the point and its distance to the voting district to our big list consisting of each voting district and their center points (the value we used to measure the distance to the starting point). We'll also take the voting district away as an option by replacing the distance value in the matrix from the measure to 'xxxxx.' We'll know when we are done when all the values in one of the matrix rows consists of all 'xxxxx' values.

Here's the code I wrote with the usual verbose comments:
#Our list to iterate through the points, giving each a turn to get its closest voting district.
point_list = list()

#Next, until all the voting districts are picked, round robin through the points, designating the closest available voting
#district and removing that voting district as an option for the next pick.  For the ones already found, mark out with xxxxx, which will also eliminate it from being
#found as the minimum for the next point check.  Do this until all the voting districts equal xxxxx, which means they've all been designated to a point.  

#we need a variable that'll be used to hold how many voting districts haven't been chosen.  
choices_left = 1

#while there are still voting districts unspoken for...
while (choices_left > 0):
    #I want to pull an item from a list of the points using pop, but make the pop or the list random...
    #can do this with random.shuffle, then pop the last one out!
    
    #when the list is empty, fill it back up with the number of points and shuffle that list.
    if (len(point_list) == 0):
        for e in range(0,len(start_points)): point_list.append(e)
        random.shuffle(point_list)
    point_choice = point_list.pop() #pull a point.
    min_distance = min(matrix[point_choice]) #get the least distance found between a voting district and that point.
    dist_loc = matrix[point_choice].index(min_distance) #figure out which voting district has that minimum distance.
    coords[dist_loc].append([point_choice, min_distance]) #and apply the point and distance to that district as the points choice.
    #then mark that voting district as chosen by changing the distance values corresponding to that district to xxxxx.
    for f in range(0, len(matrix)):
        matrix[f][dist_loc] = 'xxxxx'

    #figure out how many voting districts have yet to be spoken for by counting how many in one line of the matrix do not equal xxxxx.   
    choices_left = len([g for g in matrix[0] if g != 'xxxxx'])
    #print(str(point_choice) + ' ' + str(min_distance) + '  ' + str(dist_loc) + '  ' + str(choices_left))

Next, we've got to figure out what to do with this data. It would be great to build a map with it to see what it looks like, but I'm not sure exactly how to pull that off.

It looks like javascript and leaflet.js may be a nice option, but there's definitely a learning curve there for me. I'll research and see what I can do.

Voting Districts Day 5: The district/distance matrix

Next, I'm going to build a matrix showing the distance between each voting district and the start points.  

To figure out the distance between the two points, I'm going to attempt to use the pythagorean theorem (a² + b² = c²) where a equals the difference between the x coordinates and b equals the difference between the y coordinates.  This'll be a measurement "as the crow flies." 

Our point shared by the x and y axis will have the same latitude as one point and the same longitude as the other point to make a right triangle.  To calculate the distance for the x and y lines, we'll subtract the latitudes and longitudes of the points between the centroid and the starting point.  

(voting_district_x - starting_point_x)² + (voting_district_y - starting_point_y)² = (the_distance between the points)²

OR

math.sqrt(((start_points[a][0] - coords[b][2]) ** 2) + ((start_points[a][1] - coords[b][5]) ** 2))

Now I just have to do a double loop to build the matrix showing the distance between each voting district and starting point like so:

matrix = list()

for a in range(0, len(start_points)):
    point_line = list()
    for b in range(0, len(coords)):
        point_line.append(math.sqrt(((start_points[a][0] - coords[b][2]) ** 2) + ((start_points[a][1] - coords[b][5]) ** 2)))
    matrix.append(point_line)

And that should give me a nice matrix that I can run a draft through to assign voting districts to starting points!  That's what we're going to work on next.  

Voting Districts Day 4: Starting with Random Points

Now that we've got the center points for our voting district shapes, we need to figure out how to create central points where we can start our clustering.  

There is a random library included with Python that I hope can do the job.  It has a uniform function that will give me a random float between two numbers that I designate.  I'll just loop through to generate these random coordinates based on how many clusters I want to build.

First, I want to make sure these starting coordinates fall somewhere approximately within the state of NC.  So, I'm going to get the total min and max x and y axis values by writing ALL of the x and y points to two lists and pull a min and max from those lists to create a range for the random numbers.

import random
...
xmids = list()
ymids = list()

for x in range(0,len(shapes)):

    for y in range(0,len(shapes[x].points)):

        xmids.append(shapes[x].points[y][0]) 
        ymids.append(shapes[x].points[y][1])


start_points = list()
xmax_o = max(xmids)
xmin_o = min(xmids)
ymax_o = max(ymids)
ymin_o = min(ymids)

From there, we'll use the random.uniform method through a loop to create whatever number of points we want to start with.
start_point_count = 12 for z in range(0,start_point_count):
 start_points.append([random.uniform(xmin_o,xmax_o),random.uniform(ymin_o, ymax_o)])

And we now have our random starting points!

Next up, we are going to build ourselves a matrix consisting of the distances between the centroids of our voting district shapes and our starting points.  From there, we will then figure out which voting district should be associated with each point by going through the voting districts draft style where each point will take a turn picking its closest voting district.

Voting Districts Day 3: Yet Another Package Change to Pyshp

Still having trouble with Fiona.  So, I'm trying another package for reading shapefiles:  pyshp.

Pyshp installs through pip without issue (as long as I do it as an admin).  Hooray!  

pyshp comes with the shapefile library, which reads a shapefile into a structure of lists and dictionaries.  

What should I read?  The NC Board of Elections has a shapefile that has  all of the voting districts available on their FTP site.

And after downloading, we can read it like so:
import shapefile
vote = shapefile.Reader('ncsbe\\Precincts.shp') #creates an instance that has the lists of data we want.
shapes = vote.shapes() #lists of coordinates making up the shape for each voting district.  

To figure out the center of the shapefile,  I hope this isn't too simple:
What I should do is get the min/max for both the x and y, then average that.  I'll put it all in a list.

coords = list()

for x in range(0,len(shapes)):
xmin = 10000000
xmax = -10000000
ymin = 10000000
ymax = -10000000
for y in range(0,len(shapes[x].points)):
xmin = min(xmin, shapes[x].points[y][0])
xmax = max(xmax, shapes[x].points[y][0])
ymin = min(ymin, shapes[x].points[y][1])
ymax = max(ymax, shapes[x].points[y][1])
coords.append([xmin, xmax,(xmin + xmax)/2, ymin,ymax, (ymin + ymax)/2])

If I want the metadata for each shape through the records method, this is how to do that.  

recs = vote.records()

For now, I just care about the shapes and their distance relative to one another.  

Voting Districts Day 2: Wrong Python Packages?

I think that I'm going to switch up and use some different packages for reading this spatial data.  In order to use the SciPy stuff, I still need more packages and those packages require some unorthodox installation methods.  It includes a ton of stuff I'm not sure I need.  So, I'm going to do this one package at a time.  Well, two in this case.  

It looks like Shapely and Fiona can do what I want initially, which is to read shapefiles and plot them.  

When I try to use pip for the install ("pip install Fiona"), it doesn't work.  It says I need Microsoft Visual C++.  

So, after searching the internet, I discovered that I have to install the packages via a wheel.  A plethora of these wheels can be found here at this University of California - Irvine website here.  

You also use pip to install these wheels; you just have to download them into the same folder first.  Also, I have to run the command prompt as an administrator via a right click when starting the application.  

Shapely seemed to work like a charm when attempting the old "import shapely" line in Python.  Fiona had other ideas.

Looking at Fiona's documentation, she requires a GDAL package, which was also available on the same UC Irvine page that had the other wheels.  I installed that one and was finally able to run "import Fiona" successfully.  

So, next time, we'll see about reading the shapefiles.

New Python Project: Is there a way to build nonpartisan voting districts?

Gerrymandering  seems to make the news every couple of years and every couple of years I wander if there's a solution to the problem.  It seems that somebody should have figured out how to make fair and equitable congressional districts by now, right?  

I'm a little curious if something like that is possible using some geospatial analysis.  What are the roadblocks and what's stopping somebody from pulling this off?  Who knows?  It's an excuse to learn more about Python and its application to mapping and such.

How much experience do I have with Python and geospatial analysis?  none!  Well, I've done a little, but with esri's arcpy Python module.  This should be WAY harder, but a fun challenge. 

Poking around the internet, it looks like Python needs some packages to do what I want (or at least get me started).  A place called SciPy can hook me up.  

They allow installing via pip, which I've done a couple of times in the past.  They even tell me exactly what to type.  So, to Command Prompt we go and we'll do the install.

Easy peasy.  Next time, we'll see what we've installed and how it can help us.  

PI Critter Cam Day 9: Turn the Delete Files Process into a Function

While I'm at it and before I move on to figuring the motion sensor part of our project, I'm going to really quickly turn the portion of the code that checks for and deleting old files into a function with the python code.  This way, I can keep it separate from the rest of the process and call it wherever I want.

It's pretty easy to do this.  You can just define it like so.
def delete_old_files():
    #create our expiration date
    current_date = datetime.datetime.today()
    #The variable representing the duration we want to subtract to create our expiration date.
    y = datetime.timedelta(weeks = 2)
    #And create our expiration date variable.
    exp_date = current_date - y
    #Create a list of picture files in our critter folder and delete the pics that are older than our expiration date.
    for x in os.listdir(critter_path):
        
        if datetime.datetime.fromtimestamp(os.path.getmtime(critter_path + x)) < exp_date:
            os.remove(critter_path + x)
            print('removing ' + critter_path + x) 
        else: print('not old enough to delete: (' + critter_path + x + ')')

and when you want to use it, you just put in the following:  delete_old_files()

I'm not really sure where I want to do this check.  Having it as a function allows me to put off the decision.

PI Critter Cam Day 8: Looping through our pictures and deleting old ones

Our slow progression continues. We're still building a critter cam with our raspberry pi that'll take pictures of critters in my backyard when detected.

Today:  going through all the pictures already taken and deleting ones that have reached a certain age.  We're doing this to avoid the tiny hard drive on the Pi from getting too full.

In Python, there's an os package that allows you to create a list of files and folders.  If we create a list of files in our critter cam folder, we can iterate through it, checking the age of the pictures and deleting the ones that are too old.

First, we create a list of files in our critter folder using the listdir function.

critter_path = '/home/pi/Pictures/critter_cam/'
os.listdir(critter_path)

Next, we go through the list and check out how old the file is (building on what we learned last time by using the datetime module):

import datetime

for x in os.listdir(critter_path):
print(datetime.datetime.fromtimestamp(os.path.getmtime(critter_path + x)))

Building on that, if the file is older than two weeks, delete it.

for x in os.listdir(critter_path):
if datetime.datetime.fromtimestamp(os.path.getmtime(critter_path + x)) < exp_date:
os.remove(critter_path + x)
print('removing ' + x)

Now let's put it all together.

#import our needed modules
import os.path, datetime

#folder where we keep our critter pics
critter_path = '/home/pi/Pictures/critter_cam/'

#create our expiration date

current_date = datetime.datetime.today()

#The variable representing the duration we want to subtract to create our expiration date.

y = datetime.timedelta(weeks = 2)

#And create our expiration date variable.

exp_date = current_date - y

#Create a list of picture files in our critter folder and delete the pics that are older than our expiration date.

for x in os.listdir(critter_path):

if datetime.datetime.fromtimestamp(os.path.getmtime(critter_path + x)) < exp_date:

os.remove(critter_path + x)

print('removing ' + x)

Awesome!  Next up, we'll start working on getting the Pi to take a picture automatically when it sees an animal through the camera.

PI Critter Cam Day 7: Is the File Old Enough to Delete?

As promised, I'm working on figuring out whether a critter_cam pic is old enough to be deleted.

To figure this out, we need to figure out our expiration date.  We did that last time.

To get the age of a file, you've got to use the os.path module, which comes with the PI version of Python.

That module comes with a function that pulls the creation date and the modification date for you.

os.path.getmtime('/home/pi/Pictures/critter_cam/2016_05_23_19_41_16.jpg'))

HOWEVER, it'll return it in a long decimal value representing the date and time.  NOT HELPFUL.

The datetime module, however, can handle this for you.  It has a fromtimestamp function that'll turn it into a legit datetime format you can compare your expiration date to the file date.

All together now:

#import our needed modules.
import datetime, os.path

#create our expiration date
current_date = datetime.datetime.today()
#The variable representing the duration we want to subtract to create our expiration date.
y = datetime.timedelta(weeks = 2)
#And create our expiration date variable.
exp_date = current_date - y

#get the modification date for the picture file and convert it to a legit datetime value.
file_date = datetime.datetime.fromtimestamp(os.path.getmtime('/home/pi/Pictures/critter_cam/2016_05_23_19_41_16.jpg'))

#And see if the file_date is old enough to be deleted
#If it returns TRUE, we should delete it.  
exp_date > file_date

Next time, we'll put in a loop to check all the files in our critter cam folder and delete the ones that meet this criteria.

PI Critter Cam Day 6: Calculating Date Values

My goal for this go round is to figure out the age of a Raspberry PI file through Python and figure out if it's old enough to delete based on the current date and whatever parameter defines a file as being too old.

To do this, we're going to need the Python TIME module again.

I'm thinking that I can use one of the time functions to subtract my age parameter from the current date and create a variable consisting of my "expiration" date.  Any critter pic with a creation date older than this date should be deleted.

I actually need the datetime module to perform the math with the dates.  It doesn't appear that the TIME module allows for math with time components?

So, I have to collect the current date as a variable:
current_date = datetime.date.today()

And a variable (or, specifically a duration to the datetime module) representing the amount of time to take away to create an expiration date:
y = datetime.timedelta(weeks = 2)

The datetime module allows you to subtract a duration object from a date object.  The expiration date will be as follows:
exp_date = current_date - y

As usual, I didn't get as far as I had hoped.  Next time, we'll work on getting the file dates from the critter pics and doing the compare.

PI Critter Cam Day 5: Picture File Names with the Date and Time

Today, my goal is expand on the picture taking, have it take a picture with a date and time stamp file name and also delete pictures that have reached a certain age.  I'm hoping there will be lots of critters to take pictures of, but my micro SD card is only 8gb.

Since I can't store my pics on the PI long term, my ultimate goal is to have the pictures automatically post to a Twitter handle where they can be stored and archived (and shared).  That'll be further down the road.

So:

• Capture a picture with a date and time stamp to make it unique and easier to manage.
• Delete pictures that are older than 2 weeks.

We'll just do both in the same process for now.  It might not be ideal, but I think it'll be alright.  Trying to keep it simple.

We'll use the Python Time package for collecting the time.  

It's actually pretty straightforward manipulating the time into a string for a file name using the strftime function:

time.strftime('%Y_%m_%d_%H_%M_%S')

I can just stick it in like so.  I'm also going to add a folder so everything won't write to a root directory:

#Needed packages...

import picamera

import time

#create object and initialize camera.

cam = picamera.PiCamera()

#take picture!

cam.capture('/home/pi/Pictures/critter_cam/' + time.strftime('%Y_%m_%d_%H_%M_%S') + '.jpg')

Note:  you have to create the actual folder via the File Manager.  Python will not create it automatically by referencing it in the file name.

And it seems to work.  Here's the pic in the folder with the date and time stamp.

We ran out of time regarding deleting old files.  That'll have to wait until next time.

PI Critter Cam Day 4: Camera Introduction and Keyboard Layout

Today, we make the camera work!

So, we've got it connected and we've got the picamera Python module.

I create a file with the Text Editor and put in the following.

#Needed packages...
import picamera
#create object and initialize camera.
cam = picamera.PiCamera()

#take a pic! 

 cam.capture("test.jpg")

Save it to the desktop as 'test.py.'  You can then double click the file on your desktop to run it.

(Yet another) Interesting tidbit about the PI.  It defaults the keyboard to use the British layout.  This'll cause [SHIFT] + 3 to enter a BRITISH pound, not the number pound (#).

To fix this, you can go to the Menu --> Preferences --> Keyboard and Mouse. Select the Keyboard tab, then press the Keyboard Layout button.

From that window, select the first US layout.  This is what I chose:

Hopefully, this will be the layout I'm used to.

So, I didn't get a lot worked out after having to figure out the keyboard layout problem.  Tomorrow, more Python.